CreditCard_Loan_Default.ipynb

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      "execution_count": null,
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        "id": "SsDbQ3lBemZ7"
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      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing,metrics\n",
        "from IPython.core.display import HTML\n",
        "pd.set_option(\"display.max_columns\",75)\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from sklearn import linear_model,svm\n",
        "from sklearn.metrics import average_precision_score\n",
        "from sklearn.metrics import precision_recall_curve"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('credit_train_new.csv',low_memory=False,skiprows=0)\n",
        "# df = df.set_index(['Loan ID'])\n",
        "print(df.columns)\n",
        "# print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Bi7wQyhf9Jk",
        "outputId": "a4dc4323-8217-4d1e-ea89-4540136ace8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Loan_ID', 'Customer_ID', 'Loan_Status', 'Current Loan Amount', 'Term',\n",
            "       'Credit Score', 'Annual Income', 'Years in current job',\n",
            "       'Home Ownership', 'Purpose', 'Monthly Debt', 'Years of Credit History',\n",
            "       'Months since last delinquent', 'Number of Open Accounts',\n",
            "       'Number of Credit Problems', 'Current Credit Balance',\n",
            "       'Maximum Open Credit', 'Bankruptcies', 'Tax Liens'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = df.iloc[:,2:111] #Removing Empty Columns\n",
        "di = {\"Fully Paid\":0, \"Charged Off\":1}   #converting target variable to boolean\n",
        "df = df.replace({\"Loan_Status\": di})\n",
        "df.head()\n",
        "print(\"Current shape: \", df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czfto9Z5gGRa",
        "outputId": "d65ff9ca-0604-4177-f8b5-f9bf43079d43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current shape:  (77695, 19)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data encoding\n",
        "# df['grade'] = df['grade'].map({'A':7,'B':6,'C':5,'D':4,'E':3,'F':2,'G':1})\n",
        "df[\"Home Ownership\"] = df[\"Home Ownership\"].map({\"Home Mortgage\":6,\"Rent\":5,\"Own Home\":4,\"Other\":3,\"NaN\":2,\"Any\":1})\n",
        "df[\"Years in current job\"] = df[\"Years in current job\"].replace({'years':'','year':'',' ':'','<':'','\\+':'','n/a':'0'}, regex = True)\n",
        "# df[\"Years in current job\"] = df[\"Years in current job\"].apply(lambda x:int(x))\n",
        "print(\"Current shape of dataset :\",df.shape)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
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        "id": "op1DCIbkhhUX",
        "outputId": "018767ca-ed6a-4a02-f48a-99ecf29208ce"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current shape of dataset : (77695, 19)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Loan_ID                           Customer_ID  \\\n",
              "0  e3beabbb-d91d-40e2-8b2a-bbb10ee4e90a  c219d51c-2e18-4c48-855c-f23cb17cd542   \n",
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              "\n",
              "   Loan_Status  Current Loan Amount        Term  Credit Score  Annual Income  \\\n",
              "0            0               167728  Short Term         708.0       754908.0   \n",
              "1            0               405130  Short Term         701.0       778620.0   \n",
              "2            1                22242  Short Term           NaN            NaN   \n",
              "3            0               210012  Short Term         736.0      1138385.0   \n",
              "4            0             99999999  Short Term         738.0       981768.0   \n",
              "\n",
              "  Years in current job  Home Ownership             Purpose  Monthly Debt  \\\n",
              "0                    3             5.0  Debt Consolidation       4567.22   \n",
              "1                    8             6.0  Debt Consolidation      24007.45   \n",
              "2                    2             5.0  Debt Consolidation       5841.74   \n",
              "3                   10             6.0  Debt Consolidation      14514.29   \n",
              "4                   10             6.0  Debt Consolidation      22826.22   \n",
              "\n",
              "   Years of Credit History  Months since last delinquent  \\\n",
              "0                      8.9                           NaN   \n",
              "1                     22.5                          62.0   \n",
              "2                      6.8                           NaN   \n",
              "3                      9.2                          25.0   \n",
              "4                     14.6                           NaN   \n",
              "\n",
              "   Number of Open Accounts  Number of Credit Problems  Current Credit Balance  \\\n",
              "0                        8                          0                  137674   \n",
              "1                       18                          0                 1136181   \n",
              "2                        8                          0                   75924   \n",
              "3                        6                          0                   31616   \n",
              "4                       14                          1                  175047   \n",
              "\n",
              "   Maximum Open Credit  Bankruptcies  Tax Liens  \n",
              "0             358226.0           0.0        0.0  \n",
              "1            1414600.0           0.0        0.0  \n",
              "2             226556.0           0.0        0.0  \n",
              "3              46948.0           0.0        0.0  \n",
              "4             266684.0           1.0        0.0  "
            ],
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              "      <th>Purpose</th>\n",
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              "      <th>Months since last delinquent</th>\n",
              "      <th>Number of Open Accounts</th>\n",
              "      <th>Number of Credit Problems</th>\n",
              "      <th>Current Credit Balance</th>\n",
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              "      <th>Bankruptcies</th>\n",
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    {
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      "source": [
        "#Filling the missing values\n",
        "df.fillna(df.mean(), inplace=True)\n",
        "HTML(df.tail().to_html())\n",
        "print(df.shape)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "AcWo7ilwn6zD",
        "outputId": "b4798de5-58b7-4144-de66-486d8af6460e"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "(77695, 19)\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Loan_ID  \\\n",
              "0      e3beabbb-d91d-40e2-8b2a-bbb10ee4e90a   \n",
              "1      bb8b8f76-900f-41d2-8b35-08753e84a807   \n",
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              "4      5a4a7534-2c92-412c-badd-0f97dfd62335   \n",
              "...                                     ...   \n",
              "77690  3f94c18c-ba8f-45d0-8610-88a684a410a9   \n",
              "77691  06eba04f-58fc-424a-b666-ed72aa008900   \n",
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              "77694  c63916c6-6d46-47a9-949a-51d09af4414f   \n",
              "\n",
              "                                Customer_ID  Loan_Status  Current Loan Amount  \\\n",
              "0      c219d51c-2e18-4c48-855c-f23cb17cd542            0               167728   \n",
              "1      c7e0249e-42ed-41d6-a055-efc5fd24778f            0               405130   \n",
              "2      18b7a95a-8fee-4e14-bebb-dd3c41316372            1                22242   \n",
              "3      4213db91-8e56-4ac4-9a76-1ee20c91da11            0               210012   \n",
              "4      384f209e-7bb0-4e9d-81b4-959bb3c3a76c            0             99999999   \n",
              "...                                     ...          ...                  ...   \n",
              "77690  2da51983-cfef-4b8f-a733-5dfaf69e9281            0               147070   \n",
              "77691  77f2252a-b7d1-4b07-a746-1202a8304290            0             99999999   \n",
              "77692  2ced5f10-bd60-4a11-9134-cadce4e7b0a3            0               103136   \n",
              "77693  3e45ffda-99fd-4cfc-b8b8-446f4a505f36            0               530332   \n",
              "77694  1b3014be-5c07-4d41-abe7-44573c375886            0             99999999   \n",
              "\n",
              "             Term  Credit Score  Annual Income Years in current job  \\\n",
              "0      Short Term    708.000000   7.549080e+05                    3   \n",
              "1      Short Term    701.000000   7.786200e+05                    8   \n",
              "2      Short Term   1074.354551   1.378432e+06                    2   \n",
              "3      Short Term    736.000000   1.138385e+06                   10   \n",
              "4      Short Term    738.000000   9.817680e+05                   10   \n",
              "...           ...           ...            ...                  ...   \n",
              "77690  Short Term    725.000000   4.754370e+05                    7   \n",
              "77691  Short Term    732.000000   1.289416e+06                    1   \n",
              "77692  Short Term    742.000000   1.150545e+06                    6   \n",
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              "77694  Short Term    743.000000   9.351800e+05                  NaN   \n",
              "\n",
              "       Home Ownership             Purpose  Monthly Debt  \\\n",
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              "...               ...                 ...           ...   \n",
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              "\n",
              "       Years of Credit History  Months since last delinquent  \\\n",
              "0                          8.9                     34.826609   \n",
              "1                         22.5                     62.000000   \n",
              "2                          6.8                     34.826609   \n",
              "3                          9.2                     25.000000   \n",
              "4                         14.6                     34.826609   \n",
              "...                        ...                           ...   \n",
              "77690                     22.3                     34.826609   \n",
              "77691                      9.4                     21.000000   \n",
              "77692                     18.8                     18.000000   \n",
              "77693                     15.0                     34.826609   \n",
              "77694                     13.0                     34.826609   \n",
              "\n",
              "       Number of Open Accounts  Number of Credit Problems  \\\n",
              "0                            8                          0   \n",
              "1                           18                          0   \n",
              "2                            8                          0   \n",
              "3                            6                          0   \n",
              "4                           14                          1   \n",
              "...                        ...                        ...   \n",
              "77690                        5                          0   \n",
              "77691                       22                          0   \n",
              "77692                       12                          1   \n",
              "77693                        8                          0   \n",
              "77694                        4                          1   \n",
              "\n",
              "       Current Credit Balance  Maximum Open Credit  Bankruptcies  Tax Liens  \n",
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              "77693                  404225             738254.0           0.0        0.0  \n",
              "77694                   45600              91014.0           1.0        0.0  \n",
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          },
          "metadata": {},
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Loan_ID', 'Customer_ID'], axis=1).reset_index()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "b1xV2eTHmIG8",
        "outputId": "e82a62f2-3322-4fac-eb55-6341bcff4db7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       index  Loan_Status  Current Loan Amount        Term  Credit Score  \\\n",
              "0          0            0               167728  Short Term    708.000000   \n",
              "1          1            0               405130  Short Term    701.000000   \n",
              "2          2            1                22242  Short Term   1074.354551   \n",
              "3          3            0               210012  Short Term    736.000000   \n",
              "4          4            0             99999999  Short Term    738.000000   \n",
              "...      ...          ...                  ...         ...           ...   \n",
              "77690  77690            0               147070  Short Term    725.000000   \n",
              "77691  77691            0             99999999  Short Term    732.000000   \n",
              "77692  77692            0               103136  Short Term    742.000000   \n",
              "77693  77693            0               530332  Short Term    746.000000   \n",
              "77694  77694            0             99999999  Short Term    743.000000   \n",
              "\n",
              "       Annual Income Years in current job  Home Ownership             Purpose  \\\n",
              "0       7.549080e+05                    3             5.0  Debt Consolidation   \n",
              "1       7.786200e+05                    8             6.0  Debt Consolidation   \n",
              "2       1.378432e+06                    2             5.0  Debt Consolidation   \n",
              "3       1.138385e+06                   10             6.0  Debt Consolidation   \n",
              "4       9.817680e+05                   10             6.0  Debt Consolidation   \n",
              "...              ...                  ...             ...                 ...   \n",
              "77690   4.754370e+05                    7             4.0               other   \n",
              "77691   1.289416e+06                    1             5.0  Debt Consolidation   \n",
              "77692   1.150545e+06                    6             5.0  Debt Consolidation   \n",
              "77693   1.717524e+06                    9             5.0  Debt Consolidation   \n",
              "77694   9.351800e+05                  NaN             4.0  Debt Consolidation   \n",
              "\n",
              "       Monthly Debt  Years of Credit History  Months since last delinquent  \\\n",
              "0           4567.22                      8.9                     34.826609   \n",
              "1          24007.45                     22.5                     62.000000   \n",
              "2           5841.74                      6.8                     34.826609   \n",
              "3          14514.29                      9.2                     25.000000   \n",
              "4          22826.22                     14.6                     34.826609   \n",
              "...             ...                      ...                           ...   \n",
              "77690       2202.86                     22.3                     34.826609   \n",
              "77691      13109.05                      9.4                     21.000000   \n",
              "77692       7315.57                     18.8                     18.000000   \n",
              "77693       9890.07                     15.0                     34.826609   \n",
              "77694       9118.10                     13.0                     34.826609   \n",
              "\n",
              "       Number of Open Accounts  Number of Credit Problems  \\\n",
              "0                            8                          0   \n",
              "1                           18                          0   \n",
              "2                            8                          0   \n",
              "3                            6                          0   \n",
              "4                           14                          1   \n",
              "...                        ...                        ...   \n",
              "77690                        5                          0   \n",
              "77691                       22                          0   \n",
              "77692                       12                          1   \n",
              "77693                        8                          0   \n",
              "77694                        4                          1   \n",
              "\n",
              "       Current Credit Balance  Maximum Open Credit  Bankruptcies  Tax Liens  \n",
              "0                      137674             358226.0           0.0        0.0  \n",
              "1                     1136181            1414600.0           0.0        0.0  \n",
              "2                       75924             226556.0           0.0        0.0  \n",
              "3                       31616              46948.0           0.0        0.0  \n",
              "4                      175047             266684.0           1.0        0.0  \n",
              "...                       ...                  ...           ...        ...  \n",
              "77690                   47766             658548.0           0.0        0.0  \n",
              "77691                  153045             509234.0           0.0        0.0  \n",
              "77692                  109554             537548.0           1.0        0.0  \n",
              "77693                  404225             738254.0           0.0        0.0  \n",
              "77694                   45600              91014.0           1.0        0.0  \n",
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Loan_Status</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>405130</td>\n",
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              "      <td>24007.45</td>\n",
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              "      <td>1136181</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>22242</td>\n",
              "      <td>Short Term</td>\n",
              "      <td>1074.354551</td>\n",
              "      <td>1.378432e+06</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>6.8</td>\n",
              "      <td>34.826609</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>75924</td>\n",
              "      <td>226556.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>210012</td>\n",
              "      <td>Short Term</td>\n",
              "      <td>736.000000</td>\n",
              "      <td>1.138385e+06</td>\n",
              "      <td>10</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Debt Consolidation</td>\n",
              "      <td>14514.29</td>\n",
              "      <td>9.2</td>\n",
              "      <td>25.000000</td>\n",
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              "      <td>99999999</td>\n",
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              "      <td>738.000000</td>\n",
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              "      <td>10</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>22826.22</td>\n",
              "      <td>14.6</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>9.4</td>\n",
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              "      <td>22</td>\n",
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              "      <th>77692</th>\n",
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              "      <td>742.000000</td>\n",
              "      <td>1.150545e+06</td>\n",
              "      <td>6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Debt Consolidation</td>\n",
              "      <td>7315.57</td>\n",
              "      <td>18.8</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>12</td>\n",
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              "      <th>77693</th>\n",
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              "      <td>530332</td>\n",
              "      <td>Short Term</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>743.000000</td>\n",
              "      <td>9.351800e+05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Term\"] = df[\"Term\"].map({\"Short Term\":0,\"Long Term\":1})\n",
        "df[\"Purpose\"] = df[\"Purpose\"].map({\"Home Improvements\":1,\"Debt Consolidation\":2,\"Buy House\":3,\"Business Loan\":4,\"Buy a Car\":5,\"Take a Trip\":6, \"other\":7, \"Other\":7})\n",
        "df\n"
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          "height": 540
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        "id": "-LwvUzB7E6kt",
        "outputId": "fb8f5741-fdd8-428a-9636-cd6137a19f4d"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       index  Loan_Status  Current Loan Amount  Term  Credit Score  \\\n",
              "0          0            0               167728     0    708.000000   \n",
              "1          1            0               405130     0    701.000000   \n",
              "2          2            1                22242     0   1074.354551   \n",
              "3          3            0               210012     0    736.000000   \n",
              "4          4            0             99999999     0    738.000000   \n",
              "...      ...          ...                  ...   ...           ...   \n",
              "77690  77690            0               147070     0    725.000000   \n",
              "77691  77691            0             99999999     0    732.000000   \n",
              "77692  77692            0               103136     0    742.000000   \n",
              "77693  77693            0               530332     0    746.000000   \n",
              "77694  77694            0             99999999     0    743.000000   \n",
              "\n",
              "       Annual Income Years in current job  Home Ownership  Purpose  \\\n",
              "0       7.549080e+05                    3             5.0      2.0   \n",
              "1       7.786200e+05                    8             6.0      2.0   \n",
              "2       1.378432e+06                    2             5.0      2.0   \n",
              "3       1.138385e+06                   10             6.0      2.0   \n",
              "4       9.817680e+05                   10             6.0      2.0   \n",
              "...              ...                  ...             ...      ...   \n",
              "77690   4.754370e+05                    7             4.0      7.0   \n",
              "77691   1.289416e+06                    1             5.0      2.0   \n",
              "77692   1.150545e+06                    6             5.0      2.0   \n",
              "77693   1.717524e+06                    9             5.0      2.0   \n",
              "77694   9.351800e+05                  NaN             4.0      2.0   \n",
              "\n",
              "       Monthly Debt  Years of Credit History  Months since last delinquent  \\\n",
              "0           4567.22                      8.9                     34.826609   \n",
              "1          24007.45                     22.5                     62.000000   \n",
              "2           5841.74                      6.8                     34.826609   \n",
              "3          14514.29                      9.2                     25.000000   \n",
              "4          22826.22                     14.6                     34.826609   \n",
              "...             ...                      ...                           ...   \n",
              "77690       2202.86                     22.3                     34.826609   \n",
              "77691      13109.05                      9.4                     21.000000   \n",
              "77692       7315.57                     18.8                     18.000000   \n",
              "77693       9890.07                     15.0                     34.826609   \n",
              "77694       9118.10                     13.0                     34.826609   \n",
              "\n",
              "       Number of Open Accounts  Number of Credit Problems  \\\n",
              "0                            8                          0   \n",
              "1                           18                          0   \n",
              "2                            8                          0   \n",
              "3                            6                          0   \n",
              "4                           14                          1   \n",
              "...                        ...                        ...   \n",
              "77690                        5                          0   \n",
              "77691                       22                          0   \n",
              "77692                       12                          1   \n",
              "77693                        8                          0   \n",
              "77694                        4                          1   \n",
              "\n",
              "       Current Credit Balance  Maximum Open Credit  Bankruptcies  Tax Liens  \n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scl = preprocessing.StandardScaler() #instance of preprocessing\n",
        "fields = df.columns.values[:-1]\n",
        "data_clean = pd.DataFrame(scl.fit_transform(df[fields]), columns = fields)\n",
        "data_clean['Loan_Status'] = df['Loan_Status']\n",
        "data_clean['Loan_Status'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRczjuVjjR8E",
        "outputId": "677aecf2-e9cb-4c3c-c783-f99b20e92d39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    60123\n",
              "1    17572\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loanstatus_0 = df[df[\"Loan_Status\"]==0]\n",
        "# loanstatus_1 = df[df[\"Loan_Status\"]==1]\n",
        "# subset_of_loanstatus_0 = loanstatus_0.sample(n=9500)\n",
        "# subset_of_loanstatus_1 = loanstatus_1.sample(n=9500)\n",
        "# df = pd.concat([subset_of_loanstatus_1, subset_of_loanstatus_0])\n",
        "# df = df.sample(frac=1).reset_index(drop=True)\n",
        "# print(\"Current shape of dataset :\",df.shape)\n",
        "# # df.head()"
      ],
      "metadata": {
        "id": "a5QlVkHrmAV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "1m4vVeWpHnIT",
        "outputId": "ed554982-2c2b-4233-a104-a4f1475a48e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 index  Loan_Status  Current Loan Amount  \\\n",
              "index                         1.000000     0.003867            -0.004237   \n",
              "Loan_Status                   0.003867     1.000000            -0.194706   \n",
              "Current Loan Amount          -0.004237    -0.194706             1.000000   \n",
              "Term                         -0.001934     0.111057            -0.059415   \n",
              "Credit Score                  0.004734     0.410067            -0.094906   \n",
              "Annual Income                 0.002059    -0.043382             0.011107   \n",
              "Home Ownership               -0.001990    -0.039833             0.010719   \n",
              "Purpose                       0.007705    -0.002995            -0.007699   \n",
              "Monthly Debt                  0.002366     0.010047            -0.005816   \n",
              "Years of Credit History       0.004626    -0.020032             0.018126   \n",
              "Months since last delinquent -0.002993    -0.008666             0.008564   \n",
              "Number of Open Accounts       0.005166     0.013202             0.002635   \n",
              "Number of Credit Problems     0.004847     0.006632            -0.005332   \n",
              "Current Credit Balance       -0.005861    -0.012255             0.008057   \n",
              "Maximum Open Credit          -0.002426    -0.008719            -0.001014   \n",
              "Bankruptcies                  0.006188    -0.004206            -0.004417   \n",
              "Tax Liens                     0.002819     0.013302            -0.000935   \n",
              "\n",
              "                                  Term  Credit Score  Annual Income  \\\n",
              "index                        -0.001934      0.004734       0.002059   \n",
              "Loan_Status                   0.111057      0.410067      -0.043382   \n",
              "Current Loan Amount          -0.059415     -0.094906       0.011107   \n",
              "Term                          1.000000      0.030842       0.075286   \n",
              "Credit Score                  0.030842      1.000000      -0.015624   \n",
              "Annual Income                 0.075286     -0.015624       1.000000   \n",
              "Home Ownership                0.093718     -0.015478       0.134930   \n",
              "Purpose                      -0.063624     -0.011236      -0.027134   \n",
              "Monthly Debt                  0.158692     -0.001742       0.425191   \n",
              "Years of Credit History       0.042678     -0.007076       0.139883   \n",
              "Months since last delinquent -0.013811      0.000010      -0.038607   \n",
              "Number of Open Accounts       0.081294      0.007170       0.125146   \n",
              "Number of Credit Problems    -0.024488      0.000559      -0.010840   \n",
              "Current Credit Balance        0.108787     -0.003962       0.286382   \n",
              "Maximum Open Credit           0.009492     -0.002967       0.036308   \n",
              "Bankruptcies                 -0.026817     -0.004232      -0.040042   \n",
              "Tax Liens                    -0.004201      0.007014       0.039816   \n",
              "\n",
              "                              Home Ownership   Purpose  Monthly Debt  \\\n",
              "index                              -0.001990  0.007705      0.002366   \n",
              "Loan_Status                        -0.039833 -0.002995      0.010047   \n",
              "Current Loan Amount                 0.010719 -0.007699     -0.005816   \n",
              "Term                                0.093718 -0.063624      0.158692   \n",
              "Credit Score                       -0.015478 -0.011236     -0.001742   \n",
              "Annual Income                       0.134930 -0.027134      0.425191   \n",
              "Home Ownership                      1.000000 -0.078902      0.176138   \n",
              "Purpose                            -0.078902  1.000000     -0.091401   \n",
              "Monthly Debt                        0.176138 -0.091401      1.000000   \n",
              "Years of Credit History             0.122715 -0.039695      0.198381   \n",
              "Months since last delinquent       -0.026687 -0.008108     -0.039297   \n",
              "Number of Open Accounts             0.108351 -0.100941      0.409344   \n",
              "Number of Credit Problems          -0.003075 -0.011479     -0.051895   \n",
              "Current Credit Balance              0.126203 -0.071613      0.485680   \n",
              "Maximum Open Credit                 0.014821  0.001415      0.034596   \n",
              "Bankruptcies                       -0.006801 -0.019147     -0.077391   \n",
              "Tax Liens                           0.000760  0.003076      0.022883   \n",
              "\n",
              "                              Years of Credit History  \\\n",
              "index                                        0.004626   \n",
              "Loan_Status                                 -0.020032   \n",
              "Current Loan Amount                          0.018126   \n",
              "Term                                         0.042678   \n",
              "Credit Score                                -0.007076   \n",
              "Annual Income                                0.139883   \n",
              "Home Ownership                               0.122715   \n",
              "Purpose                                     -0.039695   \n",
              "Monthly Debt                                 0.198381   \n",
              "Years of Credit History                      1.000000   \n",
              "Months since last delinquent                -0.029251   \n",
              "Number of Open Accounts                      0.133166   \n",
              "Number of Credit Problems                    0.062937   \n",
              "Current Credit Balance                       0.213201   \n",
              "Maximum Open Credit                          0.030252   \n",
              "Bankruptcies                                 0.066215   \n",
              "Tax Liens                                    0.017933   \n",
              "\n",
              "                              Months since last delinquent  \\\n",
              "index                                            -0.002993   \n",
              "Loan_Status                                      -0.008666   \n",
              "Current Loan Amount                               0.008564   \n",
              "Term                                             -0.013811   \n",
              "Credit Score                                      0.000010   \n",
              "Annual Income                                    -0.038607   \n",
              "Home Ownership                                   -0.026687   \n",
              "Purpose                                          -0.008108   \n",
              "Monthly Debt                                     -0.039297   \n",
              "Years of Credit History                          -0.029251   \n",
              "Months since last delinquent                      1.000000   \n",
              "Number of Open Accounts                          -0.022049   \n",
              "Number of Credit Problems                         0.079571   \n",
              "Current Credit Balance                           -0.017334   \n",
              "Maximum Open Credit                              -0.003208   \n",
              "Bankruptcies                                      0.087643   \n",
              "Tax Liens                                         0.013025   \n",
              "\n",
              "                              Number of Open Accounts  \\\n",
              "index                                        0.005166   \n",
              "Loan_Status                                  0.013202   \n",
              "Current Loan Amount                          0.002635   \n",
              "Term                                         0.081294   \n",
              "Credit Score                                 0.007170   \n",
              "Annual Income                                0.125146   \n",
              "Home Ownership                               0.108351   \n",
              "Purpose                                     -0.100941   \n",
              "Monthly Debt                                 0.409344   \n",
              "Years of Credit History                      0.133166   \n",
              "Months since last delinquent                -0.022049   \n",
              "Number of Open Accounts                      1.000000   \n",
              "Number of Credit Problems                   -0.013938   \n",
              "Current Credit Balance                       0.232036   \n",
              "Maximum Open Credit                          0.030032   \n",
              "Bankruptcies                                -0.024701   \n",
              "Tax Liens                                    0.006628   \n",
              "\n",
              "                              Number of Credit Problems  \\\n",
              "index                                          0.004847   \n",
              "Loan_Status                                    0.006632   \n",
              "Current Loan Amount                           -0.005332   \n",
              "Term                                          -0.024488   \n",
              "Credit Score                                   0.000559   \n",
              "Annual Income                                 -0.010840   \n",
              "Home Ownership                                -0.003075   \n",
              "Purpose                                       -0.011479   \n",
              "Monthly Debt                                  -0.051895   \n",
              "Years of Credit History                        0.062937   \n",
              "Months since last delinquent                   0.079571   \n",
              "Number of Open Accounts                       -0.013938   \n",
              "Number of Credit Problems                      1.000000   \n",
              "Current Credit Balance                        -0.114596   \n",
              "Maximum Open Credit                           -0.012273   \n",
              "Bankruptcies                                   0.749769   \n",
              "Tax Liens                                      0.588276   \n",
              "\n",
              "                              Current Credit Balance  Maximum Open Credit  \\\n",
              "index                                      -0.005861            -0.002426   \n",
              "Loan_Status                                -0.012255            -0.008719   \n",
              "Current Loan Amount                         0.008057            -0.001014   \n",
              "Term                                        0.108787             0.009492   \n",
              "Credit Score                               -0.003962            -0.002967   \n",
              "Annual Income                               0.286382             0.036308   \n",
              "Home Ownership                              0.126203             0.014821   \n",
              "Purpose                                    -0.071613             0.001415   \n",
              "Monthly Debt                                0.485680             0.034596   \n",
              "Years of Credit History                     0.213201             0.030252   \n",
              "Months since last delinquent               -0.017334            -0.003208   \n",
              "Number of Open Accounts                     0.232036             0.030032   \n",
              "Number of Credit Problems                  -0.114596            -0.012273   \n",
              "Current Credit Balance                      1.000000             0.124032   \n",
              "Maximum Open Credit                         0.124032             1.000000   \n",
              "Bankruptcies                               -0.125995            -0.013383   \n",
              "Tax Liens                                  -0.015265            -0.001439   \n",
              "\n",
              "                              Bankruptcies  Tax Liens  \n",
              "index                             0.006188   0.002819  \n",
              "Loan_Status                      -0.004206   0.013302  \n",
              "Current Loan Amount              -0.004417  -0.000935  \n",
              "Term                             -0.026817  -0.004201  \n",
              "Credit Score                     -0.004232   0.007014  \n",
              "Annual Income                    -0.040042   0.039816  \n",
              "Home Ownership                   -0.006801   0.000760  \n",
              "Purpose                          -0.019147   0.003076  \n",
              "Monthly Debt                     -0.077391   0.022883  \n",
              "Years of Credit History           0.066215   0.017933  \n",
              "Months since last delinquent      0.087643   0.013025  \n",
              "Number of Open Accounts          -0.024701   0.006628  \n",
              "Number of Credit Problems         0.749769   0.588276  \n",
              "Current Credit Balance           -0.125995  -0.015265  \n",
              "Maximum Open Credit              -0.013383  -0.001439  \n",
              "Bankruptcies                      1.000000   0.044761  \n",
              "Tax Liens                         0.044761   1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0342d1a1-a2f3-46ff-8edf-c244f79bb9cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>Current Loan Amount</th>\n",
              "      <th>Term</th>\n",
              "      <th>Credit Score</th>\n",
              "      <th>Annual Income</th>\n",
              "      <th>Home Ownership</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>Monthly Debt</th>\n",
              "      <th>Years of Credit History</th>\n",
              "      <th>Months since last delinquent</th>\n",
              "      <th>Number of Open Accounts</th>\n",
              "      <th>Number of Credit Problems</th>\n",
              "      <th>Current Credit Balance</th>\n",
              "      <th>Maximum Open Credit</th>\n",
              "      <th>Bankruptcies</th>\n",
              "      <th>Tax Liens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003867</td>\n",
              "      <td>-0.004237</td>\n",
              "      <td>-0.001934</td>\n",
              "      <td>0.004734</td>\n",
              "      <td>0.002059</td>\n",
              "      <td>-0.001990</td>\n",
              "      <td>0.007705</td>\n",
              "      <td>0.002366</td>\n",
              "      <td>0.004626</td>\n",
              "      <td>-0.002993</td>\n",
              "      <td>0.005166</td>\n",
              "      <td>0.004847</td>\n",
              "      <td>-0.005861</td>\n",
              "      <td>-0.002426</td>\n",
              "      <td>0.006188</td>\n",
              "      <td>0.002819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <td>0.003867</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.194706</td>\n",
              "      <td>0.111057</td>\n",
              "      <td>0.410067</td>\n",
              "      <td>-0.043382</td>\n",
              "      <td>-0.039833</td>\n",
              "      <td>-0.002995</td>\n",
              "      <td>0.010047</td>\n",
              "      <td>-0.020032</td>\n",
              "      <td>-0.008666</td>\n",
              "      <td>0.013202</td>\n",
              "      <td>0.006632</td>\n",
              "      <td>-0.012255</td>\n",
              "      <td>-0.008719</td>\n",
              "      <td>-0.004206</td>\n",
              "      <td>0.013302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Current Loan Amount</th>\n",
              "      <td>-0.004237</td>\n",
              "      <td>-0.194706</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.059415</td>\n",
              "      <td>-0.094906</td>\n",
              "      <td>0.011107</td>\n",
              "      <td>0.010719</td>\n",
              "      <td>-0.007699</td>\n",
              "      <td>-0.005816</td>\n",
              "      <td>0.018126</td>\n",
              "      <td>0.008564</td>\n",
              "      <td>0.002635</td>\n",
              "      <td>-0.005332</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>-0.001014</td>\n",
              "      <td>-0.004417</td>\n",
              "      <td>-0.000935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Term</th>\n",
              "      <td>-0.001934</td>\n",
              "      <td>0.111057</td>\n",
              "      <td>-0.059415</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030842</td>\n",
              "      <td>0.075286</td>\n",
              "      <td>0.093718</td>\n",
              "      <td>-0.063624</td>\n",
              "      <td>0.158692</td>\n",
              "      <td>0.042678</td>\n",
              "      <td>-0.013811</td>\n",
              "      <td>0.081294</td>\n",
              "      <td>-0.024488</td>\n",
              "      <td>0.108787</td>\n",
              "      <td>0.009492</td>\n",
              "      <td>-0.026817</td>\n",
              "      <td>-0.004201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit Score</th>\n",
              "      <td>0.004734</td>\n",
              "      <td>0.410067</td>\n",
              "      <td>-0.094906</td>\n",
              "      <td>0.030842</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.015624</td>\n",
              "      <td>-0.015478</td>\n",
              "      <td>-0.011236</td>\n",
              "      <td>-0.001742</td>\n",
              "      <td>-0.007076</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.007170</td>\n",
              "      <td>0.000559</td>\n",
              "      <td>-0.003962</td>\n",
              "      <td>-0.002967</td>\n",
              "      <td>-0.004232</td>\n",
              "      <td>0.007014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual Income</th>\n",
              "      <td>0.002059</td>\n",
              "      <td>-0.043382</td>\n",
              "      <td>0.011107</td>\n",
              "      <td>0.075286</td>\n",
              "      <td>-0.015624</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.134930</td>\n",
              "      <td>-0.027134</td>\n",
              "      <td>0.425191</td>\n",
              "      <td>0.139883</td>\n",
              "      <td>-0.038607</td>\n",
              "      <td>0.125146</td>\n",
              "      <td>-0.010840</td>\n",
              "      <td>0.286382</td>\n",
              "      <td>0.036308</td>\n",
              "      <td>-0.040042</td>\n",
              "      <td>0.039816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Home Ownership</th>\n",
              "      <td>-0.001990</td>\n",
              "      <td>-0.039833</td>\n",
              "      <td>0.010719</td>\n",
              "      <td>0.093718</td>\n",
              "      <td>-0.015478</td>\n",
              "      <td>0.134930</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.078902</td>\n",
              "      <td>0.176138</td>\n",
              "      <td>0.122715</td>\n",
              "      <td>-0.026687</td>\n",
              "      <td>0.108351</td>\n",
              "      <td>-0.003075</td>\n",
              "      <td>0.126203</td>\n",
              "      <td>0.014821</td>\n",
              "      <td>-0.006801</td>\n",
              "      <td>0.000760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Purpose</th>\n",
              "      <td>0.007705</td>\n",
              "      <td>-0.002995</td>\n",
              "      <td>-0.007699</td>\n",
              "      <td>-0.063624</td>\n",
              "      <td>-0.011236</td>\n",
              "      <td>-0.027134</td>\n",
              "      <td>-0.078902</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.091401</td>\n",
              "      <td>-0.039695</td>\n",
              "      <td>-0.008108</td>\n",
              "      <td>-0.100941</td>\n",
              "      <td>-0.011479</td>\n",
              "      <td>-0.071613</td>\n",
              "      <td>0.001415</td>\n",
              "      <td>-0.019147</td>\n",
              "      <td>0.003076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monthly Debt</th>\n",
              "      <td>0.002366</td>\n",
              "      <td>0.010047</td>\n",
              "      <td>-0.005816</td>\n",
              "      <td>0.158692</td>\n",
              "      <td>-0.001742</td>\n",
              "      <td>0.425191</td>\n",
              "      <td>0.176138</td>\n",
              "      <td>-0.091401</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.198381</td>\n",
              "      <td>-0.039297</td>\n",
              "      <td>0.409344</td>\n",
              "      <td>-0.051895</td>\n",
              "      <td>0.485680</td>\n",
              "      <td>0.034596</td>\n",
              "      <td>-0.077391</td>\n",
              "      <td>0.022883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Years of Credit History</th>\n",
              "      <td>0.004626</td>\n",
              "      <td>-0.020032</td>\n",
              "      <td>0.018126</td>\n",
              "      <td>0.042678</td>\n",
              "      <td>-0.007076</td>\n",
              "      <td>0.139883</td>\n",
              "      <td>0.122715</td>\n",
              "      <td>-0.039695</td>\n",
              "      <td>0.198381</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.029251</td>\n",
              "      <td>0.133166</td>\n",
              "      <td>0.062937</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>0.030252</td>\n",
              "      <td>0.066215</td>\n",
              "      <td>0.017933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Months since last delinquent</th>\n",
              "      <td>-0.002993</td>\n",
              "      <td>-0.008666</td>\n",
              "      <td>0.008564</td>\n",
              "      <td>-0.013811</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>-0.038607</td>\n",
              "      <td>-0.026687</td>\n",
              "      <td>-0.008108</td>\n",
              "      <td>-0.039297</td>\n",
              "      <td>-0.029251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.022049</td>\n",
              "      <td>0.079571</td>\n",
              "      <td>-0.017334</td>\n",
              "      <td>-0.003208</td>\n",
              "      <td>0.087643</td>\n",
              "      <td>0.013025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Open Accounts</th>\n",
              "      <td>0.005166</td>\n",
              "      <td>0.013202</td>\n",
              "      <td>0.002635</td>\n",
              "      <td>0.081294</td>\n",
              "      <td>0.007170</td>\n",
              "      <td>0.125146</td>\n",
              "      <td>0.108351</td>\n",
              "      <td>-0.100941</td>\n",
              "      <td>0.409344</td>\n",
              "      <td>0.133166</td>\n",
              "      <td>-0.022049</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013938</td>\n",
              "      <td>0.232036</td>\n",
              "      <td>0.030032</td>\n",
              "      <td>-0.024701</td>\n",
              "      <td>0.006628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Credit Problems</th>\n",
              "      <td>0.004847</td>\n",
              "      <td>0.006632</td>\n",
              "      <td>-0.005332</td>\n",
              "      <td>-0.024488</td>\n",
              "      <td>0.000559</td>\n",
              "      <td>-0.010840</td>\n",
              "      <td>-0.003075</td>\n",
              "      <td>-0.011479</td>\n",
              "      <td>-0.051895</td>\n",
              "      <td>0.062937</td>\n",
              "      <td>0.079571</td>\n",
              "      <td>-0.013938</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.114596</td>\n",
              "      <td>-0.012273</td>\n",
              "      <td>0.749769</td>\n",
              "      <td>0.588276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Current Credit Balance</th>\n",
              "      <td>-0.005861</td>\n",
              "      <td>-0.012255</td>\n",
              "      <td>0.008057</td>\n",
              "      <td>0.108787</td>\n",
              "      <td>-0.003962</td>\n",
              "      <td>0.286382</td>\n",
              "      <td>0.126203</td>\n",
              "      <td>-0.071613</td>\n",
              "      <td>0.485680</td>\n",
              "      <td>0.213201</td>\n",
              "      <td>-0.017334</td>\n",
              "      <td>0.232036</td>\n",
              "      <td>-0.114596</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.124032</td>\n",
              "      <td>-0.125995</td>\n",
              "      <td>-0.015265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maximum Open Credit</th>\n",
              "      <td>-0.002426</td>\n",
              "      <td>-0.008719</td>\n",
              "      <td>-0.001014</td>\n",
              "      <td>0.009492</td>\n",
              "      <td>-0.002967</td>\n",
              "      <td>0.036308</td>\n",
              "      <td>0.014821</td>\n",
              "      <td>0.001415</td>\n",
              "      <td>0.034596</td>\n",
              "      <td>0.030252</td>\n",
              "      <td>-0.003208</td>\n",
              "      <td>0.030032</td>\n",
              "      <td>-0.012273</td>\n",
              "      <td>0.124032</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013383</td>\n",
              "      <td>-0.001439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bankruptcies</th>\n",
              "      <td>0.006188</td>\n",
              "      <td>-0.004206</td>\n",
              "      <td>-0.004417</td>\n",
              "      <td>-0.026817</td>\n",
              "      <td>-0.004232</td>\n",
              "      <td>-0.040042</td>\n",
              "      <td>-0.006801</td>\n",
              "      <td>-0.019147</td>\n",
              "      <td>-0.077391</td>\n",
              "      <td>0.066215</td>\n",
              "      <td>0.087643</td>\n",
              "      <td>-0.024701</td>\n",
              "      <td>0.749769</td>\n",
              "      <td>-0.125995</td>\n",
              "      <td>-0.013383</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.044761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tax Liens</th>\n",
              "      <td>0.002819</td>\n",
              "      <td>0.013302</td>\n",
              "      <td>-0.000935</td>\n",
              "      <td>-0.004201</td>\n",
              "      <td>0.007014</td>\n",
              "      <td>0.039816</td>\n",
              "      <td>0.000760</td>\n",
              "      <td>0.003076</td>\n",
              "      <td>0.022883</td>\n",
              "      <td>0.017933</td>\n",
              "      <td>0.013025</td>\n",
              "      <td>0.006628</td>\n",
              "      <td>0.588276</td>\n",
              "      <td>-0.015265</td>\n",
              "      <td>-0.001439</td>\n",
              "      <td>0.044761</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0342d1a1-a2f3-46ff-8edf-c244f79bb9cb')\"\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-0342d1a1-a2f3-46ff-8edf-c244f79bb9cb 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-0342d1a1-a2f3-46ff-8edf-c244f79bb9cb');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
        "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "X, y = df.iloc[:,:-1].values, df.iloc[:,-1].values\n",
        "title = \"Learning Curves (Logistic Regression)\"\n",
        "# Cross validation with 100 iterations to get smoother mean test and train\n",
        "# score curves, each time with 20% data randomly selected as a validation set.\n",
        "cv = ShuffleSplit(n_splits=100, test_size=0.5, random_state=0)\n",
        "estimator = linear_model.LogisticRegression()\n",
        "plot_learning_curve(estimator, title, X, y, ylim=(0.75, 0.90), cv=cv, n_jobs=5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "KyEFXx-zHtgT",
        "outputId": "15d501b4-d0b5-4891-bfa0-b2892cc7af7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set('talk', 'whitegrid', 'dark', font_scale=1, font='Ricty',rc={\"lines.linewidth\": 2, 'grid.linestyle': '--'})\n",
        "def plotAUC(truth, pred, lab):\n",
        "    fpr, tpr, _ = metrics.roc_curve(truth,pred)\n",
        "    roc_auc = metrics.auc(fpr, tpr)\n",
        "    lw = 2\n",
        "    c = (np.random.rand(), np.random.rand(), np.random.rand())\n",
        "    plt.plot(fpr, tpr, color= c,lw=lw, label= lab +'(AUC = %0.2f)' % roc_auc)\n",
        "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.ylim([0.0, 1.0])\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title('ROC curve') #Receiver Operating Characteristic\n",
        "    plt.legend(loc=\"lower right\")"
      ],
      "metadata": {
        "id": "PwEmCFLuKBOM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "def plot_confusion_matrix(model, normalize=False): # This function prints and plots the confusion matrix.\n",
        "    cm = confusion_matrix(y_test, model, labels=[0, 1])\n",
        "    classes=[\"Will Pay\", \"Will Default\"]\n",
        "    cmap = plt.cm.Blues\n",
        "    title = \"Confusion Matrix\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        cm = np.around(cm, decimals=3)\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n"
      ],
      "metadata": {
        "id": "thHm-ZV1OdgB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_clean.iloc[:,:-1], data_clean.iloc[:,-1], test_size=0.2, random_state=42)\n",
        "bs_train, bs_test = train_test_split(data_clean, test_size = 0.2, random_state=42) #just for bootstrapping\n",
        "X_train = np.nan_to_num(X_train)\n",
        "X_test = np.nan_to_num(X_test)\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KThTYgCvO_QX",
        "outputId": "aaf086ac-6f7f-406f-d659-c86c1f603b9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.52290345,  0.        , -0.35186815, ..., -0.34717312,\n",
              "         1.42846988,  0.06304118],\n",
              "       [-1.61677396,  0.        , -0.36628219, ..., -0.34717312,\n",
              "         0.19005327,  0.02624909],\n",
              "       [-0.68671205,  0.        , -0.35239795, ..., -0.34717312,\n",
              "        -0.32908255,  0.16141841],\n",
              "       ...,\n",
              "       [ 1.69306044,  0.        , -0.36205135, ..., -0.34717312,\n",
              "         0.01520019,  0.00291474],\n",
              "       [-1.69368464,  0.        , -0.3514082 , ..., -0.34717312,\n",
              "        -0.72488775, -0.08158868],\n",
              "       [-1.0277942 ,  0.        , -0.3694382 , ..., -0.34717312,\n",
              "        -0.52385625, -0.054007  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pandas as pd\n",
        "# num_samples = data_clean.shape[0]\n",
        "\n",
        "# # Remove any features that have a different number of samples than the others\n",
        "# data_clean = data_clean[data_clean.columns[num_samples == num_samples.min()]]"
      ],
      "metadata": {
        "id": "Tqy4nWwIUJvb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Create a LabelEncoder object\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "# Fit the LabelEncoder object to the target variable\n",
        "encoder.fit(y)\n",
        "\n",
        "# Transform the target variable to a categorical variable\n",
        "y_categorical = encoder.transform(y)\n",
        "# create the RFE model and select 3 attributes\n",
        "clf_LR = linear_model.LogisticRegression(C=1e30)\n",
        "clf_LR.fit(X_train,y_categorical)\n",
        "rfe = RFE(clf_LR, 10)\n",
        "rfe = rfe.fit(data_clean.iloc[:,:-1].values, data_clean.iloc[:,-1].values)\n",
        "# summarize the selection of the attributes\n",
        "print(rfe.support_)\n",
        "print(rfe.ranking_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "Pa6B90B7PCu8",
        "outputId": "a1f479b4-30dc-4c46-e26d-8a78aac480bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-9f242495f1f6>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# create the RFE model and select 3 attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mclf_LR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mclf_LR\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_categorical\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mrfe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRFE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf_LR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mrfe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrfe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_clean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_clean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1194\u001b[0m             \u001b[0m_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1196\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m   1197\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1198\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    582\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmulti_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_numeric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    395\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    398\u001b[0m             \u001b[0;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0;34m%\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [62156, 77695]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PCA (Principal Component Analysis)\n",
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=10, whiten=True)\n",
        "X_train_pca = pca.fit_transform(X_train)\n",
        "X_test_pca = pca.transform(X_test)\n",
        "explained_variance = pca.explained_variance_ratio_\n",
        "print('Expected Variance is '+ str(explained_variance))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EwtkQuFERLWr",
        "outputId": "b24adacd-202f-4eb0-e85e-d49b7f5b7049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected Variance is [0.15264176 0.08051242 0.07583801 0.07430393 0.066746   0.06622753\n",
            " 0.06443364 0.06194539 0.05915611 0.05839308]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['index', 'Loan_Status', 'Current Loan Amount', 'Term', 'Credit Score',\n",
        "       'Annual Income', 'Years in current job', 'Home Ownership', 'Purpose',\n",
        "       'Monthly Debt', 'Years of Credit History',\n",
        "       'Months since last delinquent', 'Number of Open Accounts',\n",
        "       'Number of Credit Problems', 'Current Credit Balance',\n",
        "       'Maximum Open Credit', 'Bankruptcies', 'Tax Liens']\n",
        "X_train, X_test = X_train[features[:-1]], X_test[features[:-1]]\n",
        "data_clean = data_clean[features]\n",
        "print(X_train.shape)\n",
        "print(data_clean.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "DjxHWCf_WBDA",
        "outputId": "44fa9d0c-01a9-49ef-9e86-c3a731940f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-73eeb11828b6>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m        \u001b[0;34m'Number of Credit Problems'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Current Credit Balance'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m        'Maximum Open Credit', 'Bankruptcies', 'Tax Liens']\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mdata_clean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_clean\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "yKC9khZgWdtC",
        "outputId": "75e41136-272a-4394-ab12-3593a8618df0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       index  Loan_Status  Current Loan Amount  Term  Credit Score  \\\n",
              "0          0            0               167728     0    708.000000   \n",
              "1          1            0               405130     0    701.000000   \n",
              "2          2            1                22242     0   1074.354551   \n",
              "3          3            0               210012     0    736.000000   \n",
              "4          4            0             99999999     0    738.000000   \n",
              "...      ...          ...                  ...   ...           ...   \n",
              "77690  77690            0               147070     0    725.000000   \n",
              "77691  77691            0             99999999     0    732.000000   \n",
              "77692  77692            0               103136     0    742.000000   \n",
              "77693  77693            0               530332     0    746.000000   \n",
              "77694  77694            0             99999999     0    743.000000   \n",
              "\n",
              "       Annual Income Years in current job  Home Ownership  Purpose  \\\n",
              "0       7.549080e+05                    3             5.0      2.0   \n",
              "1       7.786200e+05                    8             6.0      2.0   \n",
              "2       1.378432e+06                    2             5.0      2.0   \n",
              "3       1.138385e+06                   10             6.0      2.0   \n",
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              "...              ...                  ...             ...      ...   \n",
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              "77693   1.717524e+06                    9             5.0      2.0   \n",
              "77694   9.351800e+05                  NaN             4.0      2.0   \n",
              "\n",
              "       Monthly Debt  Years of Credit History  Months since last delinquent  \\\n",
              "0           4567.22                      8.9                     34.826609   \n",
              "1          24007.45                     22.5                     62.000000   \n",
              "2           5841.74                      6.8                     34.826609   \n",
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              "...             ...                      ...                           ...   \n",
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              "77693       9890.07                     15.0                     34.826609   \n",
              "77694       9118.10                     13.0                     34.826609   \n",
              "\n",
              "       Number of Open Accounts  Number of Credit Problems  \\\n",
              "0                            8                          0   \n",
              "1                           18                          0   \n",
              "2                            8                          0   \n",
              "3                            6                          0   \n",
              "4                           14                          1   \n",
              "...                        ...                        ...   \n",
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              "77691                       22                          0   \n",
              "77692                       12                          1   \n",
              "77693                        8                          0   \n",
              "77694                        4                          1   \n",
              "\n",
              "       Current Credit Balance  Maximum Open Credit  Bankruptcies  Tax Liens  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data_clean"
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          "height": 540
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          index  Loan_Status  Current Loan Amount      Term  Credit Score  \\\n",
              "0     -1.732029            0            -0.365079 -0.618583 -2.767540e-01   \n",
              "1     -1.731984            0            -0.357616 -0.618583 -2.820420e-01   \n",
              "2     -1.731939            1            -0.369653 -0.618583  1.717642e-16   \n",
              "3     -1.731895            0            -0.363750 -0.618583 -2.556021e-01   \n",
              "4     -1.731850            0             2.773523 -0.618583 -2.540912e-01   \n",
              "...         ...          ...                  ...       ...           ...   \n",
              "77690  1.731850            0            -0.365729 -0.618583 -2.639118e-01   \n",
              "77691  1.731895            0             2.773523 -0.618583 -2.586238e-01   \n",
              "77692  1.731939            0            -0.367110 -0.618583 -2.510695e-01   \n",
              "77693  1.731984            0            -0.353680 -0.618583 -2.480478e-01   \n",
              "77694  1.732029            0             2.773523 -0.618583 -2.503141e-01   \n",
              "\n",
              "       Annual Income  Years in current job  Home Ownership   Purpose  \\\n",
              "0      -6.187474e-01             -0.850277       -0.600993 -0.330929   \n",
              "1      -5.952170e-01              0.577610        0.934116 -0.330929   \n",
              "2      -2.310472e-16             -1.135854       -0.600993 -0.330929   \n",
              "3      -2.382078e-01              1.148764        0.934116 -0.330929   \n",
              "4      -3.936252e-01              1.148764        0.934116 -0.330929   \n",
              "...              ...                   ...             ...       ...   \n",
              "77690  -8.960777e-01              0.292032       -2.136102  2.857282   \n",
              "77691  -8.833372e-02             -1.421432       -0.600993 -0.330929   \n",
              "77692  -2.261410e-01              0.006455       -0.600993 -0.330929   \n",
              "77693   3.364950e-01              0.863187       -0.600993 -0.330929   \n",
              "77694  -4.398563e-01                   NaN       -2.136102 -0.330929   \n",
              "\n",
              "       Monthly Debt  Years of Credit History  Months since last delinquent  \\\n",
              "0         -1.142921                -1.322310                 -4.713036e-16   \n",
              "1          0.454567                 0.609806                  1.802413e+00   \n",
              "2         -1.038188                -1.620651                 -4.713036e-16   \n",
              "3         -0.325527                -1.279690                 -6.517999e-01   \n",
              "4          0.357500                -0.512526                 -4.713036e-16   \n",
              "...             ...                      ...                           ...   \n",
              "77690     -1.337211                 0.581392                 -4.713036e-16   \n",
              "77691     -0.441002                -1.251276                 -9.171202e-01   \n",
              "77692     -0.917077                 0.084157                 -1.116110e+00   \n",
              "77693     -0.705519                -0.455699                 -4.713036e-16   \n",
              "77694     -0.768955                -0.739834                 -4.713036e-16   \n",
              "\n",
              "       Number of Open Accounts  Number of Credit Problems  \\\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "dataViz = data_clean\n",
        "sns.set_context(context='notebook')\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "corr = dataViz.corr()\n",
        "\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.zeros_like(corr, dtype=np.bool)\n",
        "mask[np.tril_indices_from(mask)] = True\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "\n",
        "sns.heatmap(corr, cmap=cmap,linewidths=1, vmin=-1, vmax=1, square=True, cbar=True, center=0, ax=ax, mask=mask)"
      ],
      "metadata": {
        "colab": {
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          "height": 1000
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        "id": "vueuhLbQXpHp",
        "outputId": "ef218aae-1787-45b7-b5ff-9f8d9597b646"
      },
      "execution_count": null,
      "outputs": [
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            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'Ricty' not found.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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nXQ3rISIiIiJ60FXU036/7N69G61atRKNqgWAiIgIRERECL936dIFgYGBePPNN3Hy5MlaHXrPR8wRVcGYMWMwbdo0fPnll5g6dSqSk5OxcOFCm/3mzZuHF198EV999RUmT56Ml19+GV9//TU6duwIANi7dy+++uorLFq0CF5eXgCAzp07Y8yYMVi0aBGuXbt2X98XEREREdGDyNPTE7m5uTbrs7Ozhb/DK3P58mWcPHkSf/vb36q0/6BBgwAAp06dqnrQamBPPNU5S5YsEZZHjBhht/fbetZ5oOze76lTp9rMIn/27Fmb/Z5++mk8/fTTdl978ODBGDx4sM361157ze5z5+83jUaDFi1aQKPRODsKEREREd1P8gerfzc0NNTm3vfc3Fykp6eLHgnnyO7duyGXy/HII4/URsRqe7DOFBHdE4VCAZ1OJ8xqT0RERETkinr06IEffvgBOTk5wrp9+/ZBLpejW7duVSpj79696NixIwIDA6u8P4Baf+Qce+KJSFBUVIT09HQEBATU6GPviIiIiIjup1GjRmHbtm2YMmUKYmNjkZaWhqVLl2LUqFGiZ8SPHTsW165dw4EDB0THnz59GhcuXMD48ePtlj979mw0bdoUERERwsR2W7ZswcMPP8xGPBHdPyUlJUhNTYWPjw8b8URERER1yQM2O72Xlxe2bt2Kt956C1OmTEG9evXw+OOP46WXXhLtZzKZUFpaanP87t27oVarMWDAALvlN2/eHLt378bmzZtRXFyMRo0a4bnnnsPkyZNr5f1YYyOeiIiIiIiIHjhhYWHYsmWLw322bdtmd/0rr7yCV155pcLjYmNjERsbey/xqo33xBMRERERERG5CPbEExERERER1XEy+YM1nP5Bxp54IhIoFAr4+flxdnoiIiIiIoliTzwRCTQaDfR6vbNjEBERERFRBdiIJyKByWRCYWEhNBoN5HIO1CEiIiKqM2T8289V8EwRkaCgoACnT59GQUGBs6MQEREREZEdbMQTERERERERuQiZ2Ww2OzsEEREREREROc/lF+c6O4JdTd5d7OwIksOeeCIiIiIiIiIXwUY8ERERERERkYvg7PREVOccv3DZ2REQHdZEWM5OuujEJHd4hYYIy2mf/D8nJikT9OTjwnJaRpbzglgJ8vUWlrNzc50X5C9eOp2wzDqyz7qOcrOynBfEis7bW1i+lZ3jvCB/8fPyFJYzc5x/zgDAx1Na5836nOXmOP+cAYDOU1rnzfqcSS2PFD6LAPHnkSTJZc5OQFXEnngiIiIiIiIiF8FGPBEJ8vPzcfr0aeTn5zs7ikCKmYiIiIiInIXD6YlIYDabkZ+fDyk9tEKKmYiIiIgeODL277oKnikiIiIiIiIiF8FGPBEREREREZGL4HB6IiIiIiKiOk7G2eldBnviiUigVqsRGhoKtVrt7CgCKWYiIiIiInIW9sQTkUCpVMLHx8fZMUSkmImIiIiIyFnYE09VEh8fj+joaGfHqLLff/8dkyZNQrdu3RAZGYk+ffpg+vTpOHHihLDPN998g48++qha5V+5cgXx8fFIS0urqciSUFxcjLS0NBQXFzs7ikCKmYiIiIgeODKZNH/IBhvx9MD59ddfMXr0aCiVSsTFxWHdunWYPHkyjEYjTp48Kez3zTff4JNPPqnWa1y9ehUJCQm4ceNGTcWWhOLiYly5ckVSDWYpZiIiIiIichYOp6cHzieffIJGjRph9erVUCgUAIAuXbpg1KhRMJlMTk5HRERERERUfeyJpxpx9uxZTJw4EVFRUYiJicH06dNx7do10T6bN2/GY489hpiYGHTp0gWxsbG4ePGiaJ85c+ZgyJAhOHbsGIYNG4aoqCg8/vjjOHXqVJWz5OTkwNfXV2jAW5PL5cLr/Pvf/8b58+dhMBhgMBgwZ84cAMDx48fx3HPPoXv37oiKisLQoUPxxRdfCGUcO3YMzzzzDADg8ccfF44HgJ07d8JgMCAjI0P0ukOHDhXKB4Dz58/j2WefRadOndC2bVsMGDAAGzZsqPJ7JCIiIiKqUXK5NH/IBnvi6Z5dv34dTz/9NIKDg7Fs2TIUFhZi5cqVePrpp7Fr1y54eHgAAFJTU/H000+jYcOGyMvLw6effopRo0Zh//798Pb2FspLT0/HwoULMXnyZOh0OqxYsQJTp07FgQMHoFKpKs3TqlUrrFmzBu+++y4effRRhIWF2ezzwgsvICMjA0lJSVi+fDkAwNfXFwBw7do1tGvXDk8++STUajV+++03zJs3D2azGcOHD0erVq2wYMECvPnmm1i8eDFCQ0Pvus6ee+45+Pv7Y9GiRfDw8MDly5eRmpp61+UQEREREVHdwkY83bMtW7agpKQEmzdvFhrj4eHhGDx4MP79739jzJgxAIBXX31VOKa0tBTdunVDly5dsH//fowcOVLYlp2dje3bt6N58+YAADc3NzzzzDM4ceIE2rdvX2meiRMn4sSJE3j//ffx/vvvw9vbG927d8eTTz4pHN+kSRP4+vri2rVriIqKEh0/ePBgYdlsNqNDhw5IS0vDjh07MHz4cHh4eKBZs2YAgObNm6NNmzZ3VV8ZGRm4cuUKXnvtNfTp0wcA0Llz57sqo7YoFAp4eXnZHcXgLFLMRERERETkLGzE0z375Zdf0KlTJ1FvelhYGFq2bIlff/1VaMT//vvveO+993D69GlkZWUJ+yYnJ4vKCwwMFBrwAIQGc1Vngvfw8MDmzZtx8uRJHDp0CL/++iv279+PvXv34q233sITTzzh8Pjs7GzEx8fj4MGDSEtLQ2lpKQCI3t+98PHxQaNGjfDOO+8gOzsbXbp0Qf369WukbAAwGo3CskKhgEajgclkQkFBgc2+7u7uAICCggJhvoCGDRuitLQUJSUlUCqVKC4utplUTi6XQ6vVwmw2Iz8/v8Jyy+cBAJVKBZVKhZKSEhQVFYm2yWQyuLm52RxnyWQymSCXy1FYWCicFwulUgm1Wo3S0lIUFhZWWC4RERER2cGZ4F0GG/F0z3JychAeHm6z3s/PD9nZ2QDKhqhPmDABrVu3RlxcHAIDA6FSqRAbG2vT4PL09BT9bhlCX36/ykRGRiIyMhIAkJKSgjFjxmD58uWVNuLnzJmD48ePY8qUKWjWrBk8PDzwySef4Ouvv76r16+ITCbDpk2bsHLlSrz55pswGo1o1aoV5s6diw4dOtxz+YmJicKyr68vQkJCUFRUJFpvERMTA6Dsi5Tbt2+Ltun1evj5+SEzMxMpKSmibZ6enmjevDlMJpPDcsvnAYDGjRsjKCgIubm5SEpKEm1zc3NDREQEAODMmTMwm82i7REREXBzc8P169dx69Yt0bb69eujUaNGMBqNOHfunGibSqUSrgUiIiIiIlfGRjzdMy8vL5sGFQDcunULer0eAPDf//4XRqMRCQkJQiO9pKREaOTXtuDgYAwcOBAffPABbt68CX9/f7v7FRYW4tChQ5gzZ44wggAAPv744yq9jkajAQCbnuucnBzR7yEhIVi1ahWKi4tx/PhxvPPOO3juuedw5MgR1KtX727emg3rL1QsQ9DVarXdL1os9Ho9TCYT8vPzkZycDL1eDy8vLwBlIwcs8xpYWCYIlMvlDsstnwe486WMTqez2Saz+ga4ZcuWACDKZKnfBg0aIDAwUHSsUln2cebu7u6wXCIiIiIiV8ZGPN2zmJgYfPbZZ8jOzhYafklJSTh79iwee+wxAGXDtWUymdDQAoCvv/4aJSUlNZ6nokZ6cnIy1Gq18CWCSqWy6d0vKiqCyWQSTaCXl5eHb7/9VrRfRaMDgoKCAJS9f8vyhQsXcP36dbtZVSoVOnbsiMmTJ+P555/HjRs3EBIScjdv14b1UHYLuVxud72FVqsV/e7m5iacK8vwd3tkMpnDcivKA5Q1uq2vh8qOc3NzE748sDTm7VEoFJVmIiIiIqJy2OnhMtiIpyorLS3Fvn37bNY/88wz2LlzJyZMmIDnn38ehYWFePfdd9GgQQMMHz4cwJ2J2+bOnYtRo0bh/Pnz+OCDD2yGzteEefPmobS0FP3794der0deXh7279+P7777DmPHjoVarQZQdt/+559/jj179qBp06bw8fFB48aN0aZNG2zYsAG+vr5QKpVYv349PDw8RI+N0+v1UCgU+Pzzz6FUKqFQKNCmTRu0bdsWDRo0wD//+U/MmjULeXl5WL9+veh++jNnzuDtt9/GI488guDgYOTl5WHdunVo1KgRmjRpUuP1QUREREREDw424qnKCgsLMWPGDJv1S5cuxbZt27B06VLMnj0bcrkc3bp1w5w5c4Rh2AaDAYsXL0ZCQgJiY2MRHh6O9957Dy+++GKN5xw9ejS++OILrFu3Dunp6dBqtWjSpAkWLVokfKkAlD3j/eTJk3jrrbeQlZWF4cOHY8mSJVixYgUWLFiAOXPmwNvbG2PGjIHRaMTmzZuFY319fbFgwQJs3LgRu3btQklJCc6ePQuVSoWEhAS88cYbmDFjBpo0aYJXX30VS5YsEY4NCAiAv78/1q1bh7S0NOh0OrRv3x7Lli3jDOxEREREROSQzFx+5igiqrOMRiMSExMRHh4umSHptZHp+IXLNVLOvYgOuzPqIjvpohOT3OEVeudWjrRP/p8Tk5QJevJxYTktI8t5QawE+XoLy9m5uc4L8hcvnU5YZh3ZZ11HuVZPRnEmndXorFvZORXveJ/4ed0ZFZeZ4/xzBgA+ntI6b9bnLDfH+ecMAHSe0jpv1udManmk8FkEiD+PpOjKvIXOjmBX44XznB1BctgTT0QCNzc3REVFCfeeS4EUMxEREREROQsb8eRSTCaT8DxzexQKBWcivwcymUxyQ/qlmImIiIiIyFnYiCeXsnr1aiQkJFS4ffHixRgxYsR9TPRgKSgoQEpKCoKDg21mrHcWKWYiIiIieuCwI8xlsBFPLuXvf/87evXqVeH2xo0b378wDyCTyYScnByHox3uNylmIiIiIiJyFjbiyaUEBQUJz18nIiIiIiKqa9iIJyIiIiIiquvkHE7vKjjdMxEREREREZGLYCOeiAQqlQrBwcFQqVTOjiKQYiYiIiIiImfhcHoiEqhUKgQGBjo7hogUMxERERE9cGTs33UVPFNEJCgpKcGtW7dQUlLi7CgCKWYiIiIiInIWNuKJSFBUVITk5GQUFRU5O4pAipmIiIiIiJxFZjabzc4OQURERERERM5z5c23nR3BrsYLXnF2BMlhTzwRERERERGRi2AjnoiIiIiIiMhFcHZ6IhIUFBQgOTkZer0eWq3W2XEASC9TbeS5fjOjRsq5Vw38fYXl5NR0JyYpo68fICznZmU5L4gVnbe3sJybk+O8IH/ReXoKy1Kso+zcXOcF+YuXTicsSyEPIM6Um5npxCRldD4+wrIUryOp/VuT5HUkgfPGf/uVs84kSTKZsxNQFbERT0QCrVaLli1bOjuGiNQySS0PEREREdUtHE5PRERERERE5CLYiCcigdFoxK+//gqj0ejsKAKpZZJaHiIiIqKaIJPJJflDtlgrRERERERERC6CjXgiIiIiIiIiF8GJ7YiIiIiIiOo6OWendxXsiSciIiIiIiJyEeyJJyKBVqtFq1atoFarnR1FILVMUstDRERERHULG/FEJJDL5dBqtc6OISK1TFLLQ0RERFQjZBxO7yo4nN6OgwcPYsKECejYsSNat26NPn36YMGCBbh48aKzo1VZTk4O4uPj8eeff1a675UrV2AwGLBv3777kOzeFRcXo1OnTmjZsiWuXbvm7Dj3bMuWLTh8+LCzYwAACgsLcfHiRRQWFjo7ikBqmaSWh4iIiIjqFjbiy1m+fDleeOEFeHh44K233sIHH3yAKVOm4M8//8RLL73k7HhVlpOTg4SEhCo14l3N999/j6ysLJjNZuzZs8fZce7Zhx9+KJlGfGlpKTIyMlBaWursKAKpZZJaHiIiIiKqWzic3srhw4exYcMGvPDCC5gxY4awvkOHDnjsscfw3Xff3fNrFBQU2B2KW9F6srVnzx54enoiODgYu3fvxuTJk50diYiIiIjItcnZv+sqeKasbN68Gf7+/njhhRfsbu/duzeAioefL1q0CH369BF+37lzJwwGA44fP47x48cjKioKS5cuxbFjx2AwGHDo0CFMnz4d7dq1E740yMnJwRtvvIHu3bujdevWGDFiBL7//nvR64wZMwaxsbHYt28fBgwYgOjoaDzzzDO4fPmykK9v374AgBkzZsBgMMBgMODKlSvVrpvCwkIsXrwY3bt3R5s2bTB06FAcOHBAtM/x48fx3HPPoXv37oiKisLQoUPxxRdfiPaxvPejR49i1qxZiI6ORu/evbFhw4Yq5TAajfj2228xYMAAjBgxAufOncPZs2dF+1jq/Y8//sCECRPQtm1bDBgwAD/88ANMJhNWrlyJrl27omvXrlixYgVMJpPo+J9//hmjRo1CZGQkOnXqhLlz5yIrK8vmPfzxxx+i41544QWMGTNG+D0+Ph7R0dE4e/YsnnzySbRt2xZDhgzBf//7X2GfPn364OrVq/joo4+E87Rz584q1QUREREREdU9bMT/paSkBL/99hs6d+4MlUpVo2XPmjULnTt3xtq1azF06FBh/fz58xEcHIzVq1djwoQJKCoqwvjx43Ho0CG8+OKLeP/99xEWFobY2FibhmpiYiI2bdqE2bNnY/Hixbh8+TL+8Y9/AAACAwORkJAAAJg5cyZ27NiBHTt2IDAwsNrvYfbs2dixYwcmTZqE1atXo1mzZpg2bRoOHjwo7HPt2jW0a9cOixYtwvvvv4/+/ftj3rx5+Pe//21T3uuvvw69Xo/Vq1ejd+/eWL58OY4cOVJpjm+++QZGoxFDhgzBoEGDoFQqKxxS/8orr6BXr15ISEhAYGAgpk6dikWLFiE1NRVvv/02nnrqKaxfvx579+4Vjjl16hTGjx+PevXq4b333sPs2bPx3Xff4dlnn63W8Oni4mLMnj0bI0aMQEJCAnx9fTF9+nRkZmYCABISEhAQEIABAwYI56lXr153/TpERERERFQ3cDj9X7KyslBUVISGDRvWeNmjRo0SDfk+duwYgLJeWEvDGwA+//xznDlzBl9++SWaNWsGAHjooYdw6dIlrFmzBu+9956wb25uLr744gv4+voCKOuhnjt3LlJTU1G/fn2Eh4cDAJo2bYqoqKh7yn/mzBn85z//QVxcHEaNGgUA6NGjB65evYrVq1cLvf6DBw8WjjGbzejQoQPS0tKwY8cODB8+XFRm//79MW3aNABAly5dcOjQIezfvx89evRwmGXPnj0ICgpCx44dIZfL0blzZ+zZswczZ86ErNyMmk8//TSeeuopAEBQUBAeffRRnDp1Cjt27ABQVrfffvst9u3bh0cffRQAsHbtWgQEBGDt2rXClzkNGjTAxIkTcfjwYdFIi6qwNOJ79uwJAAgJCUHfvn1x5MgRDB06FBEREVCr1fD397/n82RhNBqFZYVCAY1GA5PJhIKCApt93d3dAZTdzmEymVBcXAx/f38UFxejpKQESqUSxcXFKC4uFh1nmaHdbDYjPz+/wnLL5wEAlUoFlUqFkpISFBUVibbJZDK4ubmJjrPOZDKZIJfLUVhYaPOlilKphFqtRmlpqc2kc9bl2suk0WigUChQVFSEkpIS0bbydWidx2g0Cu81Pz8fZrNZdKxara6wDq3riIiIiMjpODu9y2AjvpzyDcGaUFHPavn1R48eRYsWLaDX60UNia5du2LXrl2ifVu2bCk04AEIjX5LI74m/frrrwCAgQMHitYPGjQIixcvFhoy2dnZiI+Px8GDB5GWliY0sry9vW3K7N69u7Ask8kQFhaG1NRUhzkyMjJw9OhRjBkzBvK/7tl59NFH8corr+DXX39F+/btRft369ZNWNbr9QCAzp07i/YJCQkRPXXgl19+wZAhQ0SjMbp37w5PT0/8+uuvd92Il8vl6NKli/B748aNodVqkZaWdlfl3I3ExERh2dfXFyEhISgqKhKtt4iJiQEAJCcn4/bt28L6mzdvQq/Xw8/PD5mZmUhJSREd5+npiebNm8NkMjkst3weoKwOgoKCkJubi6SkJNE2Nzc3REREACj78si6UXzz5k1ERETAzc0N169fx61bt0TH1q9fH40aNYLRaMS5c+dE21QqFSIjIyvM1KJFC+h0OqSnp9tch35+ftDr9SgsLBQdd/PmTchkMrRr1w4AcPHiRZsvNEJDQ+Hj44OMjAyb21ms64iIiIiIqKrYiP+Lt7c3NBpNrTyyzN/f3+56Pz8/0e+ZmZk4ffo0WrVqZbOvQqEQ/e7p6Sn63dLorI3HXmVnZ0OlUtk0xv39/WE2m5Gbmwt3d3fMmTMHx48fx5QpU9CsWTN4eHjgk08+wddff21Tpk6ns8mfm5vrMMfXX3+NkpIS9OzZEzk5OQCATp06Qa1WY/fu3TaNeOvXUKvVAOzXm3VvcE5Ojs15AcrOVXZ2tsN89mi1WuG1rV+zNh9PZhmFAdy5btRqtWh9eXq9HiaTCaWlpcKXMpaeax8fH3h4eIj2t3yJIpfLHZZbPg9w51rV6XQ226y/RGvZsiUAiDJpNBoAZaMjyt8eolSWfZy5u7s7LNdeJku5AQEB8PHxEW2z1KFGo0F4eLgoj/W/y5CQELs98UDZlynlr3kiIiIioupgI/4vSqUS7dq1w08//SQMI66I5Q/+8sNjLQ3LqirfsPDy8oLBYMCiRYvuqpza5uXlheLiYmRnZ8PLy0tYb+mJ1Ol0KCwsxKFDhzBnzhzR5G4ff/xxjeWw3Ps+btw4m2379u3DvHnz7nk+Ay8vL5seXgC4deuW8N4dnf/aGMlxt+wN05bL5Q6Hb1uejGA0GnHlyhWEh4cL/wYsw9/tkclklQ4Lr2i7Uql0+O/Mcpx1JsuXB5ZzYI9Coah2JrVabfOli4WlDq3zWJdjPVy/PEd1SERERCQFMrnz/46lquHEdlbGjx+P9PR0rF271u52y7O8/fz8oFKpcOHCBWFbUVERfv7553t6/a5duyIlJQWBgYFo06aNzc/dqMmeecuw3/Kz8e/btw8RERFwd3dHUVERTCaTqKGSl5eHb7/99p5fHwCuXr2K48ePY9SoUfjwww9FP5bZ461nfa+umJgYHDx4UHQ7w9GjR5GTkyPUg+V2Bevzn5GRgf/7v/+r1mvWds88ERERERE9ONgTb6Vnz56YNGkS4uPj8eeff2Lw4MHw8fHBlStX8PnnnyM3Nxc9e/aEXC5Hv3798NFHH6Fp06bw8fHB9u3bYTab76kndtiwYfj000/xzDPPYMKECdDr9cjNzcXp06dRXFyMWbNmVbmsgIAAeHp6Yu/evWjcuDHUajUMBkOFvYwAcOLECZt1/v7+aN++Pfr3748lS5agoKAAISEh2LVrF44fP441a9YAKBsa3aZNG2zYsAG+vr5QKpVYv349PDw8kJGRcfeVUY6lF37SpEkIDg4WbYuJicH69euxZ8+eu75nvbznnnsOo0aNQmxsLMaMGYObN29ixYoViIyMFCanq1+/Ptq2bYvVq1dDp9NBqVRiw4YN1R4uHRoaip9++glHjx6Fp6cnGjdubDOkm4iIiIiICGAj3sY//vEPREdH46OPPsKrr76K/Px8BAYGonv37pg4caKw3/z58zF//nwsXLgQ9erVw8SJExESEiJ65NrdUqvV+PDDDxEfH4+1a9ciPT0d3t7eiIiIEGZZryq5XI7FixfjnXfewbhx41BUVISDBw+icePGFR6zefNmm3VdunTBli1bsGzZMrzzzjvYsGEDsrKyEBoailWrVokazStWrMCCBQswZ84ceHt7Y8yYMTAajXbLvVt79uxBu3btbBrwQNmw7CFDhuCzzz4TTc5WHa1bt8bmzZvxzjvvYNq0aXB3d0efPn3wyiuviO5/Xr58OebNm4e5c+fC398fL774Ivbu3Vvpff32zJw5E2+88QamTZuG27dvY/HixRgxYsQ9vQ8iIiIiInowyczlZ2IiojorPz8fFy5cQFhYmMN7vO8nqWWqjTzXb977aJWa0MD/zhMvklPTnZikjL5+gLCcm5XlvCBWdFYTfObe5TwotUFnNVmnFOsouxpfbNY0L6tRUlLIA4gz5WZmOjFJGZ3V6C8pXkdS+7cmyetIAueN//Yr5yXxSW6vrUhwdgS7Gs6a6uwIksOeeCISuLm5oXXr1s6OISK1TFLLQ0RERER1Cye2IyIiIiIiInIRbMQTkcBoNOLEiRMwGo3OjiKQWiap5SEiIiKqETKZNH/IBhvxRCRi/Xg9qZBaJqnlISIiIqK6g414IiIiIiIiIhfBie2IiIiIiIjqOjmHrrsK9sQTERERERERuQg24olIoNFoYDAYoNFonB1FILVMUstDRERERHULh9MTkUChUMDDw8PZMUSklklqeYiIiIhqhIz9u66CZ4qIBEVFRUhJSUFRUZGzowiklklqeYiIiIiobpGZzWazs0MQERERERGR81x7b62zI9jVcMZzzo4gORxOT0REREREVMfJODu9y+BweiIiIiIiIiIXwZ54IhIYjUYkJiYiPDwc7u7uzo4DQHqZpJYHqJ1MuRkZNVLOvdD5+grLOVevOTHJHZ6NGgrLOdeuOzFJGc+GDYTl7MspTkxyh1eTYGH5arrzr6NGAXeuo6z/S3Rikju8W4ULyznp6U5MUsYzIEBYzszJdWKSO3w8dcJy9pUrTkxSxqtxY2H5Zla2E5Pc4e/tJSxnX0x2XpC/eIXoheXcrCyn5bDQeXsLy1I8Z0T3go14IhIolUoEBARAqZTOR4PUMkktDyDNTERERORiZA/ecPoLFy5g4cKFOH78OOrVq4ehQ4fixRdfhFqtdnhcnz59cPXqVZv1J0+eFD1mOC0tDQsXLsT3338PlUqFfv36Ye7cubX+JCP+xUdEArVajSZNmjg7hojUMkktDyDNTERERETOlJ2djbFjx0Kv1yM+Ph5paWlYsmQJCgoKsGDBgkqPHzBgACZMmCBaZ934Ly4uxqRJkwAAK1asQEFBAd5++23MmjUL69atq9k3Uw4b8UQkMJlMKCgogFarhVwujSkzpJZJankAaWYiIiIicqZPP/0Ut2/fRkJCArz/ur2itLQUcXFxiI2NRVBQkMPj/f39ERUVVeH2/fv34/z58/jqq68QGhoKAPD09MTEiRNx8uRJREZG1tRbscG/9ohIUFBQgMTERBQUFDg7ikBqmaSWB5BmJiIiInIxcrk0f6rpyJEj6NKli9CAB4BBgwbBZDLh6NGj91xdR44cgcFgEBrwANCtWzd4e3vj8OHD91y+I2zEExERERER0QMlKSlJ1MAGynrKAwICkJSUVOnxu3fvRuvWrREdHY1nn30WZ8+erbR8mUyGkJCQKpV/LzicnoiIiIiIiCSpb9++DrcfPHjQ7vqcnBx4enrarPfy8kJ2tuMnFvTp0weRkZFo2LAhUlJSsHbtWjz11FP44osvEBwcLJSv0+lsjq1K+feKjXgiIiIiIqK6jvPqCObNmycst2/fHt26dcOgQYOwadMmvPHGG84L9hc24olIRIoTo0ktk9TyANLMRERERHSvKuppr4ynpydyc3Nt1mdnZ8PLy+uuygoMDERMTAz+7//+T1R+Xl6e3fIbNGhw94HvAhvxRCRwd3dHdHS0s2OISC2T1PIA0sxERERE5EyhoaE296bn5uYiPT3d5l726pZ/7tw50Tqz2YyLFy+iW7du91y+I2zEU51mMBgq3Wfx4sUYMWLEfUhDREREROQkMpmzE9SoHj16YO3ataJ74/ft2we5XH7Xjey0tDT8+uuvGDp0qKj8Xbt2ITk5GXq9HgDw448/IisrCz179qyx92EPG/FUp+3YsUP0+8iRIzFmzBgMGTJEWNekSZP7Hctp8vPzhZk23dzcnB0HgPQySS0PIM1MRERERM40atQobNu2DVOmTEFsbCzS0tKwdOlSjBo1SvSM+LFjx+LatWs4cOAAAGDPnj347rvv0LNnTwQGBiIlJQXr16+HQqHA+PHjheMGDBiAdevWYdq0aZg5cyby8/OxdOlS9OrVq1afEQ+wEU91XFRUlM26Bg0a2F1/NwoKCqDVau+pDGcwm80oKCiA2Wx2dhSB1DJJLQ8gzUxEREREzuTl5YWtW7firbfewpQpU1CvXj08/vjjeOmll0T7mUwmlJaWCr83btwYN27cwD//+U/k5uZCp9Ohc+fOmD59ujAzPQCoVCps3LgRCxcuxMyZM6FUKtGvXz+8+uqrtf7e2IgnqsTOnTvxwQcfIDk5Gd7e3hgxYgSmT58OhUIhbJ87dy4+/fRTrFq1CsePH8eIESMwYMAAPPPMM9i4cSP+3//7fzhy5Ai8vLwwa9YsPProo/jwww+xefNmGI1G9OvXD6+//jrUarWT3y0RERER1UUy+YM1nB4AwsLCsGXLFof7bNu2TfR7VFSUzbqKBAUFIT4+vrrxqo2NeCIHPvjgAyxbtgxjx47FnDlzcOHCBaxcuRKlpaWYPXu2aN9Zs2Zh5MiRiI2NhZubGwoKCgAAb7zxBoYPH46///3v+Oyzz/Dyyy/jzJkzOH/+POLi4pCSkoIlS5YgODgYzz33nDPeJhERERERuQg24okqkJeXh1WrVmHSpEmYOXMmAKBbt25QqVRYsmQJJk6cCB8fH2H/UaNGYfLkycLvx44dAwAMHDgQU6dOBQBERkbiwIED2Lt3Lw4cOACVSgUA+N///od9+/axEU9ERERERA6xEU9UgePHj8NoNGLgwIEoKSkR1nft2hUFBQU4f/48OnbsKKzv1auX3XKsZ7/U6XTw9fVF+/bthQY8AOj1eqHRf6+MRqOwrFAooNFoYDKZhJEB1tzd3QGU3cNvMplQUlKCxo0bo6SkBCUlJVAqlSguLkZxcbHoOLlcDq1WC7PZjPz8/ArLLZ8HKLt/SKVSoaSkBEVFRaJtMplMmJjNcpx1JpPJBLlcjsLCQtG9SwCgVCqhVqtRWlqKwsLCCsu1l0mj0UChUKCoqEh0rgHbOrTOYzQahfean59vc0+6Wq2usA4d1REAuLm5QSaTCefGUR1aZ7Kej8FeuVqttsI6tM5EREREdYxM7uwEVEVsxBNVIDMzEwAwfPhwu9uvX78u+t3f39/ufjqdTvS7Wq0WHnNhoVKpbBq01ZWYmCgs+/r6IiQkBEVFRaL1FjExMQCA5ORk3L59W7RNr9fDz88PmZmZSElJEW3z9PRE8+bNYTKZHJZbPg9QNllIUFAQcnNzbZ7d6ebmhoiICADAmTNnbBrFERERcHNzw/Xr13Hr1i3Rtvr166NRo0YwGo02z+xUqVSiWULLZ2rRogV0Oh3S09ORmpoq2ubn5we9Xo/CwkKb42QyGdq1awcAuHjxos0XGqGhofDx8UFGRgauXLki2uaojoCy+7EUCgVSUlKQk5Mj2hYcHIzAwEBkZ2cjOTlZtK1evXpo2bJlheW2atUKWq0W165dQ0ZGRoWZiIiIiEia2IgnqoCXlxcAICEhAfXr17fZ3rhx4/sdqUrCw8OFZcvke2q1WrS+PL1eD5PJhOLiYmRlZcHb2xv16tUDAPj4+MDDw0O0v1wuF/7rqNzyeQAIIxB0Op3NNpnV80ktDVHrTBqNBkDZEwQCAwNFxyqVZR9n7u7uDsu1l8lSbkBAgOgWCeBOHWo0GoSHh4vyWI+mCAkJsdsTD5R9mVL+yxxHeYA7dRwcHGy3Jx4ou0bLZ7K8l4rKtWRq2LCh6PEqREREROQa2IgnqkB0dDTc3NyQmpqKfv36OTtOldkbEi2Xyx0OlbYefn3z5k0EBAQIjWLL0G17ZDJZpUOwK9quVCqF13B0nHUmS8PWuqFankKhqHYmtVpd4RMCLHVonce6HEfPZ3dUh47yAHD4qEJLHVpnst7fUbmO6pCIiIjqINmDNzv9g4qNeKIKeHp6Yvr06Vi2bBlSU1PRsWNHYXjzwYMHER8f77DhRkREREREVNPYiCdyYMKECQgKCsIHH3yA7du3Q6lUokmTJujVq5fDnlUiIiIiIqLawEY8kZWzZ8/arBs8eDAGDx5c4TEjRozAiBEjbNZ36tTJbnnffvutzbpp06Zh2rRpd5mWiIiIiKiGyDmc3lXwOQJEJFAoFPD29hYmc5MCqWWSWh5AmpmIiIiIqHawJ56IBBqNBmFhYc6OISK1TFLLA0gzExERERHVDjbiiUhgMplQUlICpVIpzATvbFLLJLU8gDQzERERkYuR8W8IV8EzRUSCgoIC/PHHHygoKHB2FIHUMkktDyDNTERERERUO9iIJyIiIiIiInIRHE5PRERERERUx8k4O73LYE88ERERERERkYtgI56IiIiIiIjIRXA4PREJ3NzcEB0dDZlMOsOppJZJankAaWYiIiIiF8O/I1wGG/FEJJDJZJJrCEotk9TyANLMRERERES1g8PpiUhQUFCAs2fPSupRZVLLJLU8gDQzEREREVHtkJnNZrOzQxAREREREZHzpH30mbMj2BU0+u/OjiA57IknIiIiIiIichFsxBMRERERERG5CE5sR0QCo9GIxMREhIeHw93d3dlxAEgvk9TyANLLVBt5MnNya6Sce+XjqROWs3Odn8lLdyePFOtICpmkds4A8XmTQibrPLk5OU5McofO01NYlkImqeUBxJmkdh0xj33WmSSJk+S6DPbEE5FArVajadOmUKvVzo4ikFomqeUBpJdJanmIiIiIHiTsiScigVKphL+/v7NjiEgtk9TyANLLJLU8RERERA8S9sQTkaCkpAQ3b95ESUmJs6MIpJZJankA6WWSWh4iIiKqAplMmj9kg414IhIUFRXh0qVLKCoqcnYUgdQySS0PIL1MUstDRERE9CBhI56IiIiIiIjIRfCeeCIiIiIiojpOJmf/rqvgmSIiIiIiIiJyEWzEE5FALpfDw8MDcgl9Eyu1TFLLA0gvk9TyEBERET1IOJyeiARarRYGg8HZMUSklklqeQDpZZJaHiIiIqoCzgTvMthNQlV28OBBTJgwAR07dkTr1q3Rp08fLFiwABcvXqzV123fvj3i4+OF38eMGYPY2Fjh92PHjmHt2rVVLm/Xrl14/PHHERMTg3bt2mHQoEF47bXXcOvWrRrN7YrMZjNMJhPMZrOzowiklklqeQDpZZJaHiIiIqIHCRvxVCXLly/HCy+8AA8PD7z11lv44IMPMGXKFPz555946aWX7muW119/Ha+88orw+//+9z+sW7euSsdu2LABL7/8Mtq3b4+VK1di5cqVeOyxx3Dq1CncuHGjtiK7jPz8fBw/fhz5+fnOjiKQWiap5QGkl0lqeYiIiIgeJBxOT5U6fPgwNmzYgBdeeAEzZswQ1nfo0AGPPfYYvvvuuwqPLSgogFarrdE8zZo1q/ax27Ztw/DhwzFnzhxhXc+ePTFp0iSYTKaaiOdQbdQHEREREdE9k3M4vatgTzxVavPmzfD398cLL7xgd3vv3r2FZYPBgPXr12PZsmXo1q0bunTpAqBseO2mTZswYMAAtG7dGn379sWWLVtsyvrmm28wcOBAtGnTBo8//jhOnjxps4/1cPr4+HgkJCTAaDTCYDDAYDBgzJgxFb6XnJwcBAYG2t1WfhKuL774AsOGDUObNm3QqVMnPPvss7h69aqw/ezZs5g4cSKioqIQExOD6dOn49q1a6Iy7rU+iIiIiIiIrLEnnhwqKSnBb7/9hv79+0OlUlXpmA8//BBt27bFokWLUFJSAgBYtGgR/vWvf+G5555D27Zt8dtvv2H58uXQaDR48sknAQCJiYmYPn06evTogblz5+LKlSt48cUXUVRUVOFrPfHEE0hNTcWePXuwdetWAICHh0eF+7dq1QqffvopGjdujF69eiEgIMDufhs3bsSyZcvw+OOP46WXXkJxcTF++uknZGRkoFGjRrh+/TqefvppBAcHY9myZSgsLMTKlSvx9NNPY9euXaIM1a0PIiIiIiKi8tiIJ4eysrJQVFSEhg0bVvkYLy8vJCQkQPbXDJeXL1/G9u3bERcXh5EjRwIAunbtioKCAqxevRojR46EXC7H+vXr0aBBA6xevRoKhQIAoNFo8Nprr1X4WvXr10f9+vUhl8sRFRVVabbXX38dU6dOxbx58wAAjRs3Ru/evTFu3Dg0btwYAJCbm4uEhASMHDkSb775pnDsww8/LCxv2bIFJSUl2Lx5M7y9vQEA4eHhGDx4MP7973+LRgNUtz6qy2g0CssKhQIajQYmkwkFBQU2+7q7uwMoG+ZvMpmEe5jz8/OhVquhVCpRXFyM4uJi0XFyuRxarRZms9nufc+WcsvnAQCVSgWVSoWSkhKbL2hkMhnc3NxEx1ln0mq1kMvlKCwsRGlpqehYpVIJtVqN0tJSFBYWVliuvUwajQYKhQJFRUXCFy0W5evQOo/1e83Pz7eZyM1RHTqqIwBwc3ODTCYTzo218nVonclybioq11EdOspk79xYs67D8nVkOTeVXYfl69A6DxEREdUyGQdpuwo24qlKZHfxyIkePXqI9v/hhx8AAP379xc1kLp27YoNGzbg+vXraNSoEU6cOIE+ffoIDXgAGDhwoMNG/N1q0aIF9uzZgx9//BHff/89fv75Z2zbtg07d+7ERx99hPDwcGFCrscff7zCcn755Rd06tRJaMADQFhYGFq2bIlff/1V1Iivbn1UV2JiorDs6+uLkJAQFBUVidZbxMTEAACSk5Nx+/ZtYX1ycjL0ej38/PyQmZmJlJQU0XGenp5o3rw5TCaTw3LL5wHKvjgJCgpCbm4ukpKSRNvc3NwQEREBADhz5oyoQZecnAx3d3e4ubnh+vXrNk8TqF+/Pho1agSj0Yhz586JtqlUKkRGRlaYqUWLFtDpdEhPT0dqaqpom5+fH/R6PQoLC0XHJScnQyaToV27dgCAixcv2nyhERoaCh8fH2RkZODKlStVriMAiIqKgkKhQEpKCnJyckTbgoODERgYiOzsbCQnJ4sy1atXDy1btqyw3FatWkGr1eLatWvIyMiociaNRoPWrVsDAM6fP2/zZYfBYICHhwfS0tKESSIt2QICAtCkSRMUFBTYlCuXyxEdHQ0ASEpKEjXyrfMQERERURk24skhb29vaDQam3u9HfHz8xP9npmZCbPZjM6dO9vd39JoTU9PtznWw8MDGo3m7oM7oFar0bNnT/Ts2RMA8N///hexsbFYvXo1EhISkJWVBQAV3jsPlN1bHx4ebrPez88P2dnZNuusVbU+qss6l+ULEbVabTevhV6vt+ntVavVAAAfHx+bWxQsIwXkcrnDcsvnASDclqHT6Wy2WX/ZYWmIWrNcCw0aNLA5P0pl2ceZu7u7w3LtZbKUGxAQAB8fH9E261Ehjt5rSEiI3Z54oOzLFJ1OV+Gx9sq11HFwcLDdnnigbJRH+WOtR3HYK9eSqWHDhggKCqpyJus6bN68uc3+ljoMCgqyueYt50ar1Tqsw9DQUD6WjoiIiKgSbMSTQ0qlEu3atcNPP/2EkpIS4Y9xR8o3mLy8vCCTyfDxxx/bva8+JCQEQFkDqnzval5ens3Q6Jr20EMPoWXLlrhw4QIACL3rN27cQP369e0e4+XlZfe58rdu3YJerxetq259VJe9Ichyudzh0GTL8OvCwkJcuXIFjRs3Fs61Zei2PTKZrNIhzxVtVyqVDq8ny3HWmSwNVEdf7CgUimpnUqvVQiO3PEsdWuexzmE9XL88R3XoKA8Ah08zsNRhRZkclVvZl2OOjnW0Ta1Ww2w2281T2XXoqA6JiIiolnF2epfBGx+oUuPHj0d6ejrWrl1rd/vhw4cdHm+ZkT0rKwtt2rSx+bH08kZGRuK7774T3ae7b9++SvOpVCqHk99Zu3nzps26goICXL9+Hf7+/gCA6OhouLm54fPPP6+wnJiYGPz000+iXvekpCScPXu20iHAVa0PZygtLUVWVpbNvdLOJLVMUssDSC+T1PIQERERPUjYE0+VsjxHPT4+Hn/++ScGDx4MHx8fXLlyBZ9//jlyc3OFoen2hISEYPTo0Xj55ZcxceJEtG3bFsXFxUhOTsaxY8ewZs0aAMDkyZPx+OOPY8qUKXjyySdx5coVbNq0qdIew7CwMJSUlGDr1q2Ijo6Gh4cHQkND7e776KOPonfv3ujevTsCAwORlpaG7du3IzMzE2PHjgVQNsx7ypQpWL58OcxmM/r27QuTyYRjx45h8ODBaNOmDcaNG4edO3diwoQJeP7551FYWIh3330XDRo0wPDhwx3mrWp9EBERERERlcdGPFXJP/7xD0RHR+Ojjz7Cq6++ivz8fAQGBqJ79+6YOHFipcfPmzcPISEh2LFjB1avXo169eohJCQEAwcOFPaJiIjAe++9h+XLl2Pq1Klo3rw5Vq5cWWn5vXv3xlNPPYX169fj1q1b6NChA7Zt22Z336lTp+K7777DkiVLkJGRAR8fHxgMBmzZskV0j/qzzz4LX19fbNmyBTt37kS9evUQHR0t3OvboEEDbNu2DUuXLsXs2bMhl8vRrVs3zJkzp0o96VWpDyIiIiKi++VuJrIm55KZOYsQEf3FaDQiMTER4eHhknm8l9QySS0PIL1MtZEnMye3Rsq5Vz6edyYozM51fiYvqwkTpVhHUsgktXMGiM+bFDJZ58kt9zQMZ9F5egrLUsgktTyAOJPUriPmsc/LwSS3UpD+xR5nR7ArYNgQZ0eQHN4TT0QClUqFhg0bOpyE7X6TWiap5QGkl0lqeYiIiIgeJBxOT0QClUqFBg0aODuGiNQySS0PIL1MUstDREREVSBj/66r4JkiIkFJSQmysrJQUlLi7CgCqWWSWh5AepmkloeIiIjoQcJGPBEJioqKcOHChSo/su9+kFomqeUBpJdJanmIiIiIHiQcTk9ERERERFTXyTk7vatgTzwRERERERGRi2AjnoiIiIiIiMhFcDg9EQlkMhm0Wi1kMukMp5JaJqnlAaSXSWp5iIiIqAr4/22XwUY8EQnc3NzQqlUrZ8cQkVomqeUBpJdJanmIiIiIHiQcTk9ERERERETkImRms9ns7BBERERERETkPOlf/cfZEewKeKS/syNIDnviiYiIiIiIiFwEG/FERERERERELoIT2xGRwGg0IjExEeHh4XB3d3d2HADSyyS1PID0MkktD1A7mXKuXa+Rcu6FZ8MGwnLWufNOTHKHd4vmwvLNrGwnJinj7+0lLKd99JkTk9wRNPrvwrIUzpv1OUvPdP45A4AAnzvnLev8n05MUsa7eTNhOTMn14lJ7vDx1AnLN7894sQkZfz79BCWc2/edGKSMjp/f2E5LSPLeUGsBPl6OzuCY5yd3mWwJ56IiIiIiIjIRbART0QCrVaL8PBwaLVaZ0cRSC2T1PIA0ssktTyANDMRERERVQeH0xORQC6XS2b4s4XUMkktDyC9TFLLA0gzExERkZTI5BxO7yrYE09EgqKiIly+fBlFRUXOjiKQWiap5QGkl0lqeQBpZiIiIiKqDjbiiUhQUlKC9PR0lJSUODuKQGqZpJYHkF4mqeUBpJmJiIiIqDo4nJ6IiIiIiKiuk7F/11XwTBERERERERG5CDbiiYiIiIiIiFwEh9MTkUCpVCIwMBBKpXQ+GqSWSWp5AOllkloeQJqZiIiIJIWz07sM9sQ/4P72t7/BYDDgl19+cXaUu2IwGLBp0yaH+4wZMwaxsbH3KVHdoFarERwcDLVa7ewoAqllkloeQHqZpJYHkGYmIiIioupgI/4Bdv78eZw9exYAsHv3bienIVdQWlqKvLw8lJaWOjuKQGqZpJYHkF4mqeUBpJmJiIiIqDrYiH+A7d69G3K5HJ06dcK+fftQXFzs7EgkcYWFhTh79iwKCwudHUUgtUxSywNIL5PU8gDSzERERCQpMpk0f8gGG/EPKLPZjD179qBz584YP348srKy8N///le0z7Fjx2AwGHD06FHMmjUL0dHR6N27NzZs2CDab86cORgyZAiOHTuGYcOGISoqCo8//jhOnTol7HPlyhUYDAbs27dPdOyiRYvQp08f4fcbN25g7ty56Nu3LyIjI9G/f3+88847KCoquuf3vHPnThgMBpw+fRqTJk1CVFQU+vfvjy+++MJm30OHDmHUqFFo27YtOnTogDFjxuD06dPC9qtXr2L69OmIiYlBVFQUJk6cKIxqsOjTpw/efPNNbNmyBT179kR0dDTmzJmDoqIiJCYmYtSoUUJdlT/WbDZj06ZNGDBgAFq3bo2+fftiy5Yt91wHRERERET0YGMj/gH122+/4erVqxgyZAi6d+8Ob29v7Nmzx+6+r7/+OvR6PVavXo3evXtj+fLlOHLkiGif9PR0LFy4EBMnTsS7776LwsJCTJ069a579zMzM+Ht7Y25c+di48aNmDRpEv7973/j9ddfr/Z7LW/27Nno3r07Vq9ejfDwcMyZMwcXLlwQtn/11Vd47rnn4OfnhxUrVmD58uVo164d0tLSAAB5eXlCoz4uLg7Lli1DZmYmnn76aVy/fl30WgcPHsT333+PN998E7NmzcKePXvw1ltv4eWXX8YTTzyB9957D4WFhZgxYwZMJpNw3KJFi7Bq1SoMGzYM69evx/Dhw7F8+XJ88sknNVYPRERERET04OE0vQ+oPXv2QKPRoH///lCpVBgwYAB27dqF27dvo169eqJ9+/fvj2nTpgEAunTpgkOHDmH//v3o0aOHsE92dja2b9+O5s2bAwDc3NzwzDPP4MSJE2jfvn2VcxkMBrzyyivC7+3atYObmxvmzJmDBQsWwM3N7V7eNgBg9OjRGD16NAAgOjoahw8fxv79+/HCCy/AbDbj7bffRrdu3bB69WrhmJ49ewrLO3fuxLVr17B3716EhYUBADp06IDevXtj69atmDNnjuj11qxZI0yW9b///Q+fffYZNmzYINSfyWTCc889h3PnzqFly5a4fPkytm/fjri4OIwcORIA0LVrVxQUFGD16tUYOXIk5HJ+v0ZERERE95GMf3+6CjbiH0AlJSXYt28fevbsCZ1OBwB49NFHsWPHDhw4cADDhg0T7d+9e3dhWSaTISwsDKmpqaJ9AgMDhQY8ADRr1gwAhN7rqjKbzdi6dSs+++wzXLlyRXR/akpKClq0aHFX5dlj/X7c3d3RsGFD4f0kJSUhNTVV9EVCeb/88guaN28uNOABwNvbG127dsWvv/4q2rdDhw6i2a71ej3kcjk6d+4sWgcA169fR8uWLfHDDz8AKPvypKSkRNiva9eu2LBhA65fv45GjRpV452XMRqNwrJCoYBGo4HJZEJBQYHNvu7u7gCAgoICmEwm5OfnQ6FQID8/H2q1GkqlEsXFxTYjLuRyObRaLcxmM/Lz8ysst3weAFCpVFCpVCgpKbG5jUImkwlf5FiOs86k1Wohl8tRWFhoM0GZUqmEWq1GaWmpzX3P1uXay6TRaKBQKFBUVCQ6J4BtHVrnsX6v+fn5MJvNomMd1aGjOgLKviiTyWTCubFWvg6tM1nOTUXlOqpDR5nsnRtr1nVYvo4s56ay67B8HVrnsVeHlV3f1nVYPpPl3Ni7Dh1d39aZiIiIiJyBjfgH0NGjR5GRkYHevXsjJycHANCiRQsEBARgz549No14S0PfQqVSITc3V7TO09PTZh8Adz1J1NatW/H2229j0qRJ6NSpEzw9PfHHH3/gzTffrLEJp+y9H8sf6VlZWQDKvpSoSE5ODvz9/W3W+/n54fz586J19upFq9WKGvbl6yozMxNms1nU0Ld2r434xMREYdnX1xchISHCffrlxcTEAACSk5Nx+/ZtYX1ycjL0ej38/PyQmZmJlJQU0XGenp5o3rw5TCaTw3LL5wGAxo0bIygoCLm5uUhKShJtc3NzQ0REBADgzJkzogZdcnIy3N3d4ebmhuvXr+PWrVuiY+vXr49GjRrBaDTi3Llzom0qlQqRkZEVZmrRogV0Oh3S09NtvsDy8/ODXq9HYWGh6Ljk5GTIZDK0a9cOAHDx4kWbBl9oaCh8fHyQkZGBK1euVLmOACAqKgoKhQIpKSnCv2OL4OBgBAYGIjs7G8nJyaJM9erVQ8uWLSsst1WrVtBqtbh27RoyMjKqnEmj0aB169YAyp58Uf7LDoPBAA8PD6SlpeHGjRtCHgAICAhAkyZNUFBQYFOuXC5HdHQ0gLIv2awb49Z5bt68iWvXromO9fb2RlhYGEpKSuy+1+joaMhkMly6dAl5eXmiTE2bNoW/vz+ysrJw6dIl0XEeHh4wGAwwm8025VpnIiIiInIGNuIfQJbHyc2dOxdz584VbcvMzMStW7fg5+dXo6+p0WgAwKanrHzjY9++fejTpw9mzZolrLO+X722eXt7A4DQyLDHy8sLFy9etFl/69YteHl53XMGLy8vyGQyfPzxx0ID31pISMg9lR8eHi4sKxQKAGW9jtbry9Pr9Ta9vZYvInx8fODh4SHaZhnuL5fLHZZbPg9w50sNnU5ns01mNQOppSFqzXKdNWjQwOaLGKWy7OPM3d3dYbn2MlnKDQgIgI+Pj2ibpQ41Go3D9xoSEmK3Jx4o+zKl/JdLjvIAd+o4ODjYbk88UHYtlT/W+lYMe+VaMjVs2BBBQUFVzmRdh9ajciwsdRgUFGTz+WI5N1qt1mEdhoaG2tShhb+/v82/P8u5USqVdsu1ZG7atGmF17e3t7dN77qlDmUyWaXXNxER0YNCJudM8K6CjfgHTH5+Pg4ePIiHH34YzzzzjGjbzZs3MXPmTHz11VcYM2ZMjb6un58fVCqVqEFeVFSEn3/+WbRfQUGBTcP1fj7DPjQ0FPXr18fOnTvxyCOP2N0nJiYG+/fvR1JSEkJDQwGUzQnwww8/CPew34suXboAKBsVYD1zf02xN9xXLpc7HAZsGX6dn5+PCxcuICwsTGh4WYZu2yOTySodXlzRdqVSKbyGo+OsM1kaV5YGoz0KhaLamdRqtWgUhTVLHVrnsR6i72g+B0d16CgPcOfc2GOpQ+tM1vs7KtdRHVZ2rKNtllsa7NVRZddhdeuwKtd3RefN0XVYleubiIiI6H5jI/4Bc/DgQRiNRowZMwadOnWy2b5x40bs2bOnxhvxcrkc/fr1w0cffYSmTZvCx8cH27dvh9lsFvXgde3aFR9++CG2b98OvV6PXbt22QxlrU0ymQyvvPIKZs6ciWnTpmHo0KFQq9X4/fff0aZNG/Tu3RsjRozAli1bEBsbixdffBEajQbvv/8+lEolxo4de88ZQkJCMHr0aLz88suYOHEi2rZti+LiYiQnJ+PYsWNYs2ZNDbzT6jGbzSgsLKywN9QZpJZJankA6WWSWh5AmpmIiIiIqoON+AfMnj170LBhQ7sNeAAYNmwY/vnPf+Ly5cs1/trz58/H/PnzsXDhQtSrVw8TJ05ESEgIDh48KOwzZcoUZGZmYtWqVQCAAQMGYN68eXjuuedqPE9FHnnkEWi1WqxduxYzZ86ERqNBREQE+vXrB6Dsftht27ZhyZIlmD9/PkwmE9q1a4ft27ejQYMGNZJh3rx5CAkJwY4dO7B69WrUq1cPISEhGDhwYI2UT0RERER0V2QcTu8qZGZ2SxDRX4xGIxITExEeHi6ZYcRSyyS1PID0MkktD1A7mXKuXa+Rcu6FZ8M7XyxmnTvvYM/7x7vFnTkTbmZlOzFJGX/vO3MppH30mROT3BE0+u/CshTOm/U5S890/jkDgACfO+ct6/yfTkxSxrt5M2E5MyfXwZ73j4/nnblWbn57xIlJyvj3ufNo4tybN52YpIzOapLitIws5wWxEuTr7ewIDt06/L2zI9jl17N75TvVMXwYIBEREREREZGL4HB6IhJoNBo0a9as0knP7iepZZJaHkB6maSWB5BmJiIiIkmRs3/XVbART0QChUJRI4/Rq0lSyyS1PID0MkktDyDNTERERFS7Lly4gIULF+L48eOoV68ehg4dihdffLHCpxEBZY+i3rJlC44ePYrLly9Dp9OhQ4cOmDlzJho1aiTsd+zYMZungQFl82+tXLmyVt6PBRvxRCQoLi5Geno6AgICHD4S7X6SWiap5QGkl0lqeQBpZiIiIqLak52djbFjx0Kv1yM+Ph5paWlYsmQJCgoKsGDBggqP+7//+z8cOHAAjz32GNq2bYvMzEy8//77eOKJJ7Bnzx74+vqK9l+8eLHwWGoA8PHxqbX3ZMFGPBEJiouLcf36dXh7e0umoSO1TFLLA0gvk9TyANLMREREJCkP2Oz0n376KW7fvo2EhAR4e3sDAEpLSxEXF4fY2FgEBQXZPS4mJgZff/01lMo7TeV27dqhV69e+OKLLzBhwgTR/s2bN0ebNm1q7X3YwxsfiIiIiIiI6IFy5MgRdOnSRWjAA8CgQYNgMplw9OjRCo/z9PQUNeABoH79+vD19cWNGzdqK+5dYU88ERERERERSVLfvn0dbj948KDd9UlJSXjsscdE6zw9PREQEICkpKS7ynDx4kXcunULYWFhNtsmT56MrKwsBAQEYPDgwZgxYwa0Wu1dlX+32IgnIiIiIiKq6+QP1nD6nJwceHp62qz38vJCdnZ2lcsxm81YuHAhAgMDMXjwYGG9TqfDpEmT0KFDB2g0Gvz000/YvHkzkpKSsG7duhp5DxVhI56IBAqFAr6+vlAoFM6OIpBaJqnlAaSXSWp5AGlmIiIiospV1NN+v8THx+Onn37Cxo0b4e7uLqyPiIhARESE8HuXLl0QGBiIN998EydPnkRkZGStZeI98UQk0Gg0CAkJkdSztKWWSWp5AOllkloeQJqZiIiIqPZ4enoiNzfXZn12dnaVHzv72WefYfXq1YiLi0OXLl0q3X/QoEEAgFOnTt1d2LvEnngiEphMJhQVFUGtVkMul8Z3fFLLJLU8gPQySS0PIM1MREREUiKTPVj/fwwNDbW59z03Nxfp6emiR8JV5MCBA3jjjTcwffp0PP7447UVs1rYiCcigVwur/WJOO6W1DJJLQ8gvUxSywPUTibPhg1qtLx75d2iubMj2PD3rlpPx/0SNPrvzo5gQ2rnLcBHWucMALybN3N2BBEfT52zI9jw79PD2RFEdP7+zo4gEuTr7ewI5AQ9evTA2rVrRffG79u3D3K5HN26dXN47LFjxzBz5kw88cQTmDJlSpVfc+/evQBQ64+cYyOeiIiIiIiIHiijRo3Ctm3bMGXKFMTGxiItLQ1Lly7FqFGjRM+IHzt2LK5du4YDBw4AAC5cuIApU6ZAr9dj6NCh+P3334V9fX190aRJEwDA7Nmz0bRpU0RERAgT223ZsgUPP/wwG/FERERERERUy2QP1uz0Xl5e2Lp1K9566y1MmTIF9erVw+OPP46XXnpJtJ/JZEJpaanw+4kTJ5Cbm4vc3Fw8+eSTon2HDx+OJUuWAACaN2+O3bt3Y/PmzSguLkajRo3w3HPPYfLkybX+3mRms9lc669CRC7BaDQiMTER4eHhotk3nUlqmaSWB5BeJqnlAaSXqTbyZJ05VyPl3Cvvli2E5es3M5yYpEwDf19hOev8n05Mcof18PCMn352YpIyvp07CMu5OTlOTHKHzuqxUDlpN5yYpIxnUKCwnJOe7sQkd3gGBAjL2ZcuOzFJGa+mTYRlKVxH1tdQ7s2bTkxyh9RuMyhPCp9H9lh/RlGZB2v2AiIiIiIiIqIHGIfTE5HA3d0dMTExzo4hIrVMUssDSC+T1PIA0ssktTxERESQP1jD6R9k7IknIiIiIiIichFsxBORoKCgAGfOnEFBQYGzowiklklqeQDpZZJaHkB6maSWh4iIiFwHh9MTkcBkMuH27dswmUzOjiKQWiap5QGkl0lqeQDpZZJaHiIiIsjYv+sqeKaIiIiIiIiIXAQb8UREREREREQugsPpiYiIiIiI6jrOTu8y2BNPRAK1Wg29Xg+1Wu3sKAKpZZJaHkB6maSWB5BeJqnlISIiItfBRnwNGjNmDAYNGoSioiKbbdOnT0fPnj1x+/ZtJySr2LFjx2AwGPDHH384O4ok5OTkID4+Hn/++Wel+1a37ubMmYMhQ4ZUN2KtUiqV8PPzg1IpnUE6UssktTyA9DJJLQ8gvUxSy0NERESug434GhQXF4crV65g48aNovVHjhzB/v37MX/+fNSrV89J6exr1aoVduzYgbCwMGdHkYScnBwkJCRUqRH/INZdcXExbty4geLiYmdHEUgtk9TyANLLJLU8gPQySS0PERGRTCaT5A/ZYiO+BoWGhiI2NhZr165FSkoKAKCwsBBvvfUW+vbti4cffrjWXru6zxr28PBAVFQU3N3dazhRzaro/TnzGcuuUnd3o7i4GCkpKZJqWEgtk9TyANLLJLU8gPQySS0PERERuQ424mvY5MmT0bBhQ8TFxQEA1q5di5s3b2LBggVITU3F7Nmz0alTJ0RGRmL06NE4deqU6PgvvvgCTz75JDp27IgOHTpgzJgxOHnypGif+Ph4REdH4+TJkxg5ciTatGmDjz76CACwfv169OvXD23atEHnzp0xbtw44QsFe+wNCTcYDNiwYQPi4+PRtWtXdOrUCXPnzoXRaKz0/V+4cAFTp05Fx44d0bZtW/ztb3/Dnj17AABXrlyBwWDAvn37RMcsWrQIffr0EX7fuXMnDAYDjh8/jvHjxyMqKgpLly4Vsh46dAjTp09Hu3btMGPGDABlPehvvPEGunfvjtatW2PEiBH4/vvvRa8zZswYxMbGYt++fRgwYACio6PxzDPP4PLly0K+vn37AgBmzJgBg8EAg8GAK1euVLnuCgsLsXjxYnTv3h1t2rTB0KFDceDAAbvHHz58GEOGDEGbNm0wYsQI/P7775XWLxERERER1W28Ga+GqdVqvPnmmxgzZgzWrFmDjRs3YtasWXBzc8Pw4cPh7u6O+fPnQ6fTYdu2bRg7diz+85//wM/PD0BZQ3LYsGFo0qQJioqKsHfvXowePRq7du1CSEiI8DrFxcWYNWsWxo0bh5deegne3t744osv8N5772H69OmIiopCbm4ufv3112rdh//RRx8hJiYGS5YsQXJyMpYuXQo/Pz/Mnj27wmOSk5MxcuRINGjQAK+99hoCAgJw7tw5XLt27e4rEsCsWbMwcuRIxMbGws3NTeh1nz9/Pv72t79h9erVkMvlKCoqwvjx43Hr1i28+OKLCAoKwq5duxAbGyt8IWCRmJiIjIwMzJ49G6WlpViyZAn+8Y9/YMeOHQgMDERCQgKmTp2KmTNnolOnTgCAwMDAKmeePXs2/vvf/+LFF19EaGgovvzyS0ybNg2rV68WviAAgPT0dMTFxWHatGnw9PTEhg0bMHHiRNG1QERERER038jZv+sq2IivBR07dsSIESPw3nvvoVWrVhgzZgxWr16NnJwc/Otf/xIaaV26dMGAAQOwadMmvPzyywCAqVOnCuWYTCZ069YNJ0+exL///W/MnDlT2FZcXIyXXnoJjzzyiLDus88+g8FgQGxsrLCuukP4AwICsGLFCgBAjx49cPr0aezfv99hIz4+Ph4qlQqffPIJPDw8AABdu3at1usDwKhRozB58mTh92PHjgEA+vTpg3/84x/C+s8//xxnzpzBl19+iWbNmgEAHnroIVy6dAlr1qzBe++9J+ybm5uLL774Ar6+vgAAo9GIuXPnIjU1FfXr10d4eDgAoGnTpoiKirqrvGfOnMF//vMfxMXFYdSoUQDK6u7q1as2jfisrCy8++676NKlC4Cya6Znz57YsmULZs2adVevW571iAmFQgGNRgOTyWT31gPLrQAFBQUwmUzIz88HAOTn50OtVkOpVKK4uNhmyK9cLodWq4XZbBaOsVdu+TwAoFKpoFKpUFJSYjMJpEwmg5ubm+g460xarRZyuRyFhYUoLS0VHatUKqFWq1FaWorCwsIKy7WXSaPRQKFQoKioCCUlJaJt5evQOo/1e83Pz4fZbBYd66gOHdURALi5uUEmkwnnxlr5OrTOZDk3FZXrqA4dZbJ3bqxZ12H5OrKcm8quw/J1aJ3HXh1Wdn1b12H5TJZzY+86dHR9W2eyV4dVvb4tea3Lt5wbe9dhRdf3g3Q7DxEREVUNG/G1ZPLkydi5cyfGjx8PhUKBo0ePolOnTvDy8hL+OJPL5ejQoYNoOPaFCxfwzjvv4Pjx47h165awPjk52eY1evbsKfo9IiICH3/8MRYvXox+/fqhbdu2UKlU1cpfvvEdFhaGvXv3Ojzmp59+woABA4QG/L3q1atXldYfPXoULVq0gF6vF/3h27VrV+zatUu0b8uWLYUGPACh0W9pxN+LX3/9FQAwcOBA0fpBgwZh8eLFMBqNwh/cOp1OaMBbfu/atStOnDhxTxmAstEGFr6+vggJCUFRUZFovUVMTAyAsuvLesRGcnIy9Ho9/Pz8kJmZaXNLhqenJ5o3bw6TyeSw3PJ5AKBx48YICgpCbm4ukpKSRNvc3NwQEREBoOxLEesGXXJyMtzd3eHm5obr16+L/n0AQP369dGoUSMYjUacO3dOtE2lUiEyMrLCTC1atIBOp0N6ejpSU1NF2/z8/KDX61FYWCg6Ljk5GTKZDO3atQMAXLx40abBFxoaCh8fH2RkZNjcluGojgAgKioKCoUCKSkpyMnJEW0LDg5GYGAgsrOzRZ8NycnJqFevHlq2bFlhua1atYJWq8W1a9eQkZFR5UwajQatW7cGAJw/f96mkWkwGODh4YG0tDTcuHFDyAOUfSnYpEkTFBQU2JQrl8sRHR0NAEhKShI1xq3z3Lx502ZUj7e3N8LCwlBSUmL3vUZHR0Mmk+HSpUvIy8sTZWratCn8/f2RlZWFS5cuiY7z8PCAwWCA2Wy2Kdc605UrV5CVlSXa3rBhQzRo0AB5eXm4cOGCaJtWq0WrVq0AQHhN6/MXHh4Od3d3pKamIj09XXRsYGAggoODkZ+fj7Nnz9rNQ0RERHUDG/G1xNJ4tvw3MzMTv//+u/AHnLUmTZoAAPLy8jBhwgT4+vpizpw5aNiwITQaDebNm2fTs+jm5mYz0/2IESNw+/ZtfPbZZ9iyZQt0Oh2GDRuG2bNnCz1zVeXp6Wnzfuw9Os9aVlbWXQ09r4y/v7/d9eWHm2dmZuL06dN261ahUIh+t/e+ANjUb3VkZ2dDpVLB29tbtN7f3x9msxm5ublCI976iwQLPz8/mz/6q8MymgC48/7VarVofXl6vd6mt9fy/GofHx+bL2bkfw23ksvlDsstnwe4U+c6nc5mm/UMpJaGqDWNRgMAaNCggc21ZnlUl7u7u8Ny7WWylBsQEAAfHx/RNksdajQah+81JCTEbk88UHa+dTpdhcfaK9dSx8HBwXZ74gHAy8vL5li51VA4e+VaMjVs2BBBQUFVzmRdh82bN7fZ31KHQUFBNv9GLedGq9U6rMPQ0FCbOrTw9/eHl5eXaJ3l3CiVSrvlWjI3bdq0wuvb29vbpjfbUocymcxh3saNG6NBgwaidZZz4+Hh4bAOrW/zsbB8TtevX9/m889Sh25ubpX+myMiIqoWzgTvMtiIv0+8vLzw0EMPCROxWbP8Mfn7778jNTUV69atEzVgcnNzbXqJ7T1uQS6XY+zYsRg7dizS0tKwd+9erFixAj4+PpgyZUoNvyNb3t7eQg+cPZY/8ssPiS3fy1iZ8u/dy8sLBoMBixYtuqtyapqXlxeKi4uRnZ0tamzcvHkTMplM1Igr3wMKALdu3UJAQMA957A3vFYulzscdmtpPJjNZphMJsjlcqGeLcOD7ZHJZJUO561ou1KpdPiMbMtx9jJZriV7FApFtTOp1Wrh32N5ljq0lweAaLh+eY7q0FEeAA6/gLPUYUWZHJXrqA4rO9bRNrVaDZVKZTdPZddhdeuwKtd3RXXk6Dqs7Pp2VIeVXd9ubm528wCOr8OqXN9ERET0YOPsBfdJ165dceHCBYSFhaFNmzaiH0uPjGUYqfUfqr/99huuXr16168XFBSECRMmwGAw2AxZri1dunTB/v37hWGr5fn5+UGlUol6m4uKivDzzz/f0+t27doVKSkpCAwMtKnbNm3a3FVZ99IzbxnWWn72/X379iEiIkL0h3dubi5+/PFH0e8//PAD2rZte9evW5Py8/Px+++/273P3VmklklqeQDpZZJaHkB6maSWh4iIiFwHe+Lvk3HjxmH37t14+umn8cwzz6Bhw4bIyMjAiRMnEBQUhHHjxgnPHI+Li8PkyZORlpaG+Ph4h0NerS1YsACenp6IioqCp6cnfvvtN5w5cwZPPvlkLb+7MlOnTsWhQ4fw1FNPYdKkSQgICMCFCxeQn5+PZ599FnK5HP369cNHH32Epk2bwsfHB9u3b4fZbLY7sqCqhg0bhk8//RTPPPMMJkyYAL1ej9zcXJw+fVqYxb+qAgIC4Onpib1796Jx48ZQq9UwGAwV9opZa9myJfr3748lS5agoKAAISEh2LVrF44fP441a9aI9vX29sZrr72G6dOnQ6fTYcOGDTCbzRg7duxdv38iIiIionvG4fQug434+8THxwc7duzAu+++i+XLlyMrKwt+fn5o27Yt+vXrB6Dsns/33nsPS5cuxQsvvAC9Xo+4uDhs3LixSq8RHR2Nzz77DP/617+Qn5+P4OBgzJ07F0888URtvjWBXq/Hp59+ihUrViAuLg6lpaXQ6/WiGebnz5+P+fPnY+HChahXrx4mTpyIkJAQHDx4sNqvq1ar8eGHHyI+Ph5r165Feno6vL29ERERgaeeeuquypLL5Vi8eDHeeecdjBs3DkVFRTh48CAaN25c4THWX0AsW7YM77zzDjZs2ICsrCyEhoZi1apV6NOnj+iYgIAAzJ49G0uXLsXly5fRvHlzbNq0qcJ5AIiIiIiIiABAZq5oFiEicujbb7/F888/j2+++QbBwcHOjlMjjEYjEhMThVmypUBqmaSWB5BeJqnlAaSXqTbyZJ05V/lO94F3yxbC8vWbtvN/3G8N/O9MJJp1/k8nJrnDu3kzYTnjp3u7pawm+HbuICzn3uU8NbVFZzURbU5axfPt3C+eQXcmU80p9/QIZ/G0mkcn+9JlJyYp49W0ibAshevI+hrKvXnTiUnu0Em8sybz5ClnR7DLJ7K1syNIDnviiarht99+w44dOxAQEGAzOzURERERkcuRc7o0V8FGPFE1PP/88/D19cWSJUsczkDtatzc3BAZGSmp9yS1TFLLA0gvk9TyANLLJLU8RERE5Dr41wNRNRw7dszZEWqFTCZz+Cg0Z5BaJqnlAaSXSWp5AOllkloeIiIich0cM0FEgsLCQvz555/VesRebZFaJqnlAaSXSWp5AOllkloeIiIimUwmyR+yxUY8EQlKS0uRnZ2N0tJSZ0cRSC2T1PIA0ssktTyA9DJJLQ8RERG5DjbiiYiIiIiIiFwE74knIiIiIiKq6+Qcuu4q2BNPRERERERE5CLYiCcigUqlQuPGjSU1a7bUMkktDyC9TFLLA0gvk9TyEBERkevgcHoiEqhUKgQFBTk7hojUMkktDyC9TFLLA0gvk9TyEBERQcb+XVchM5vNZmeHICIiIiIiIufJOnPO2RHs8m7ZwtkRJIdftxARERERERG5CA6nJyIiIiIiqus4O73LYCOeiARGoxGJiYkIDw+Hu7u7s+MAkF4mqeUBpJdJankA6WWqK3ku37hZY2VVV5NAf2E58+QpJya5wyeytbCc9WeSE5OU8W4WKizfzMp2YpI7/L29hOXsi8nOC/IXrxC9sJydm+u8IFa8dDphOev8n05MUsa7eTNhOTcry3lB/qLz9haWpXjOiO4Fh9MTERERERERuQj2xBORQCaTwc3NDTKZdIZTSS2T1PIA0ssktTyA9DIxDxERSQ7/H+Ay2IgnIoGbmxsiIiKcHUNEapmklgeQXiap5QGkl4l5iIiIqLo4nJ6IiIiIiIjIRbART0QCo9GI3377DUaj0dlRBFLLJLU8gPQySS0PIL1MzENERJIjk0vzh2ywVohIxGw2OzuCDallkloeQHqZpJYHkF4m5iEiIqLqYCOeiIiIiIiIyEVwYjsiIiIiIqI6Tibn7PSugj3xRERERERERC6CjXgJiY+PR3R09F1vk6ojR45g3LhxaN++Pdq2bYthw4bhww8/RElJibOj1YiqnpM+ffrgzTffvA+J7p1Wq0VERAS0Wq2zowiklklqeQDpZZJaHkB6mZiHiIiIqovD6alWbN68GW+//Tb69euHpUuXws3NDUeOHMHSpUvx008/ISEhAXJ53fgOKSEhAZ6ens6OUSVyuRxubm7OjiEitUxSywNIL5PU8gDSy8Q8REQkOTIOp3cVdaMVRffV6dOnsXz5cgwfPhwJCQno06cPunTpgldeeQVxcXE4ePAgPvroI2fHrFBRURFMJlONlRcREYHGjRvXWHm1qbCwEMnJySgsLHR2FIHUMkktDyC9TFLLA0gvE/MQERFRdbER78KysrIwd+5cdOrUCZGRkRg1ahR+/vln0T5jxoxBbGws9uzZg/79+6Nt27Z47rnnkJ2djatXr2LixImIjo7G4MGDcezYMZvX2LlzJx599FG0adMGDz30EFauXInS0lKHubZt2waZTIZp06bZbBs+fDj0ej22bt0KALh+/ToMBgN+/PFHYZ+33noLBoMBhw4dEtatWLECgwcPFn43GAzYsGED4uPj0bVrV3Tq1Alz5861ecZxamoqZs+eLdTR6NGjcerUKdE+luHuGzZsQO/evREZGYmsrCykpqZixowZ6Nq1K9q0aYM+ffrgn//8p817Onv2LJ588km0bdsWQ4YMwX//+1+75VvMmTMHQ4YMweHDhzFkyBC0adMGI0aMwO+//+6wXu+H0tJS3Lp1q9JzfD9JLZPU8gDSyyS1PID0MjEPERERVRcb8RJUUlJi81O+Z7i0tBTPPvssvvvuO8yePRvvvfce3N3dMX78eJtG6unTp/Hhhx/i5ZdfRlxcHH755RfMnz8f06dPR69evRAfHw9fX19MmzYNt2/fFo774IMPMG/ePHTv3h1r167Fs88+iw8//BArV650mP/nn3+GwWBAo0aNbLbJ5XL07t0bKSkpSEtLQ4MGDdCoUSPRlw//+9//oNFoROt+/vlntG/fXlTWRx99hOTkZCxZsgRTpkzB7t27sWbNGmF7dnY2nnrqKZw5cwbz589HfHw83NzcMHbsWNy6dUtU1n/+8x8cOnQIr732GtasWQN3d3e8/PLLOHv2LObNm4eNGzdi+vTpNuehuLgYs2fPxogRI5CQkABfX19Mnz4dmZmZDusoPT0dcXFxmDhxIt59912o1WpMnDjRJhcRERER0X0hl0vzh2zwnniJMRqNaNWqld1t7u7uwvKhQ4dw8uRJbNy4EQ899BAAoHv37ujfvz/WrVuH+Ph4Yd+8vDysXbsWvr6+AMp6jjdv3ow33ngDTz75JAAgMDAQjz76KH788Uc8/PDDyMvLw6pVqzBp0iTMnDkTANCtWzeoVCosWbIEEydOhI+Pj92caWlpMBgMFb7HBg0aACjrJQ8KCkKHDh2EBnt2djbOnz+P0aNHC+sKCgpw6tQpPP3006JyAgICsGLFCgBAjx49cPr0aezfvx+zZ88GAGzduhU5OTn417/+BT8/PwBAly5dMGDAAGzatAkvv/yyUFZxcTE2bNggquM//vgDM2fOxCOPPCKsGzZsmCiDpRHfs2dPAEBISAj69u2LI0eOYOjQoRXWQVZWFt5991106dIFANCxY0f07NkTW7ZswaxZsyo8joiIiIiI6jY24iVGq9Vi+/btNus/++wz7NmzR/j9l19+gYeHh9CABwCVSoV+/fqJ9gOAli1bCg14ANDr9QCArl272qxLTU0FABw/fhxGoxEDBw4UzSbftWtXFBQU4Pz58+jYsWP136iV9u3bY+HChSgqKsIvv/wCf39/PPHEE/j0009hNBpx4sQJFBcXo0OHDqLjrPMDQFhYGPbu3Sv8fvToUXTq1AleXl7Ce5DL5ejQoQP++OMP0bGdOnUSNeCBsnvZN2/eDIVCgW7duqFp06Y22eVyudAQB4DGjRtDq9UiLS3N4XvW6XSi43Q6Hbp27YoTJ044PK4qrG8pUCgU0Gg0MJlMKCgosNnX8p4LCgpgMpmQn58PAMjPz4darYZSqURxcTGKi4tFx8nlcmi1WpjNZuEYe+WWzwOUXacqlQolJSUoKioSbZPJZMLkWpbjrDNptVrI5XIUFhbaDPtVKpVQq9UoLS21ua/Xulx7mTQaDRQKBYqKimyenlC+Dq3zWL/X/Px8mM1m0bGO6tBRHQGAm5sbZDKZcG6sla9D60yWc1NRuY7q0FEme+fGmnUdlq8jy7mp7DosX4fWeezVYWXXt3Udls9kOTf2rkNH17d1Jnt1WNXr25LXunzLubF3HVZ0fZf/3LJ3HVrOTWV1WD6Pdfn2rsOKru/ymYiIiKhmsREvMXK5HG3atLFZb31/OADk5OQIvcvW/P39kZ2dLVpXfmZ0lUoFoKzhaKFWqwFA+OPQMhx8+PDhdnNev369wvcQFBTkcLtlW/369QGU9UIXFBTgjz/+wC+//IIOHTqgRYsWcHd3x/Hjx/Hbb78hODgYQUFBlb4v6z+aMzMz8fvvv9sd2dCkSRPR7/bqcuXKlVi5ciXeffddxMXFISQkBDNnzkT//v2FfbRarVB31jkqmxzK+ksV6wwXLlxweFxVJCYmil4nJCQERUVFovUWMTExAIDk5GTRrRTJycnQ6/Xw8/NDZmYmUlJSRMd5enqiefPmMJlMDsstnwco+6IjKCgIubm5SEpKEm1zc3NDREQEAODMmTOixkhycjLc3d3h5uaG69ev29x6UL9+fTRq1AhGoxHnzp0TbVOpVIiMjKwwU4sWLaDT6ZCeni58kWXh5+cHvV6PwsJC0XHJycmQyWRo164dAODixYs2DaDQ0FD4+PggIyMDV65cqXIdAUBUVBQUCgVSUlKQk5Mj2hYcHIzAwEBkZ2cjOTlZlKlevXpo2bJlheW2atUKWq0W165dQ0ZGRpUzaTQatG7dGgBw/vx5m0amwWCAh4cH0tLScOPGDSEPUDZqpkmTJigoKLApVy6XC49qTEpKEjXGrfPcvHkT165dEx3r7e2NsLAwlJSU2H2v0dHRkMlkuHTpEvLy8kSZmjZtCn9/f2RlZeHSpUui4zw8PGAwGGA2m23Ktc505coVZGVlibY3bNgQDRo0QF5ens2/Z61WK3weWV7T+vyFh4fD3d0dqampSE9PFx0bGBiI4OBg5Ofn4+zZs3bzAMCFCxdsPn+aNWsGLy8vpKen23w2Wz4jLP/WrPNYl1/+MwJAhZ8R5TMREZGL4Oz0LoONeBfl5eVl9/7pmzdvwsvLq0bKB8oej2ZpbFtzNNt6hw4dsHv3bly/fl0YOm9hNptx+PBhUaO8adOmCAwMxC+//IKff/4Zw4cPh1wuR0xMDP73v//h+PHjNvfDV/U9PPTQQ5gxY4bNtvINb5mdD63AwEAsXrwYJpMJp06dwvvvv4+XXnoJ+/btQ3Bw8F3nsVa+8QQAt27dQkBAwD2VC5Q1BCwUCgWAsvdrvb48vV5vt5cNAHx8fODh4SHaZnk8oFwud1hu+TyA+Euk8tusz4OlIWpNo9EAKLslIzAwULRNqSz7OHN3d3dYrr1MlnIDAgJsbhOx1KFGo3H4Xq0bQhaWOvT19RV9aVaevXItdRwcHGy3Jx4ou8bLH2v96EZ75VoyNWzY0OaLMUeZrOuwefPmNvtb6jAoKMjmSzHLudFqtQ7rMDQ01KYOLfz9/W0+2yznRqlU2i3Xkrlp06YVXt/e3t42PceWOpTJZA7zNm7c2OYzznJuPDw8HNahvVuOLCMo6tevD39/f9E2Sx26ubk5zBQWFma3Jx4ou769vb1F2yx1WK9evRr9jCAiIqLaw0a8i4qJicGmTZvw/fffo3v37gDKJsT75ptvaqQXJDo6Gm5ubkhNTUW/fv3u6tgxY8bgyy+/xKpVq7B48WLRti+//BJJSUmYP3++aH379u1x6NAhJCYmCsd06NAB+/btw7lz5/C3v/3trt9D165dsWvXLoSFhd3T8E65XI7IyEi8+OKL+Pbbb3Hp0qV7bsTn5ubixx9/FIbU5+bm4ocffsDo0aPvqVzA/lBWuVzusA4sjYfS0lIYjUa4u7sLf9xbhgfbI5PJKq3birYrlUqhYeLoOOtMlsaVpVFij0KhqHYmtVpt8wWPhaUO7dURAIfP2HZUh47yAHfOjT2WOqwok6NyHdVhZcc62qZWq6FQKOzmqew6rG4dVuX6rqiOHF2HlV3fjuqwsutbo9HYzQM4vg4ru76rW4dmsxmlpaV28wCOr8PKrm8iIiKqWWzEu6hevXohMjIS//jHPzBr1iz4+/tj27ZtuHHjBlatWnXP5Xt6emL69OlYtmwZUlNT0bFjR2Fo78GDB4WZ3u2JiIjA7Nmz8fbbbyMvLw8jRoyAVqvF999/j61bt6Jv37546qmnRMe0b98eb775Jnx8fNCsWTMAZY34pUuXCst3a9y4cdi9ezeefvppPPPMM2jYsCEyMjJw4sQJBAUFYdy4cRUem5ubi4kTJ2Lo0KEICQlBcXExtm3bBk9PT2G4973w9vbGa6+9hunTp0On02HDhg0wm80YO3bsPZd9LwoLC3Hu3DlhWK8USC2T1PIA0ssktTyA9DIxDxERSY6cw+ldBRvxLkqhUGD9+vVYunQpli1bJsxqv3nzZuG+1Xs1YcIEBAUF4YMPPsD27duhVCrRpEkT9OrVq9JelwkTJiAsLAwffPABZs+ejeLiYoSEhODll1/GU089JRryC0CYJC8mJkYYchoREQF3d3fUq1fP7qRylfHx8cGOHTvw7rvvYvny5cjKyoKfnx/atm1b6egCjUaDFi1aYNu2bbh+/Tq0Wi1at26NTZs22b2f/W4FBARg9uzZWLp0KS5fvozmzZtj06ZNNkNoiYiIiIiIrMnMFd2ASES1Ys6cOTh16pTNUwSkwGg0IjExUVK9cVLLJLU8gPQySS0PIL1MdSXP5Rs3a6ys6moSeOfL0cyTp5yY5A6fyDtftmf9meRgz/vDu1mosHwzK9vBnvePv/edOTCyLyY7L8hfvEL0wnJ2bq7zgljxspprJev8n05MUsa7eTNhObfcpJ/OoLOaA0SK50yKsstNwisVXg7m4qqr2BNPRERERERUx8lk8sp3IkngmSIigUwmg0qlsjtbv7NILZPU8gDSyyS1PID0MjEPERERVReH0xMREVGt4HB6+zicvnIcTl85Dqd3jMPp717O1WvOjmCXZ6OGzo4gORxOT0REREREVNdxNJbL4HB6IhLk5+fj5MmTyM/Pd3YUgdQySS0PIL1MUssDSC8T8xAREVF1sRFPRAKz2Yzi4mJI6S4bqWWSWh5AepmklgeQXibmISIiouricHoiIiIiIqK6Ts7h9K6CPfFERERERERELoKNeCIiIiIiIiIXweH0RCTQaDRo0aIFNBqNs6MIpJZJankA6WWSWh5AepmYh4iIJEfG/l1XwUY8EQkUCgV0EnuGqdQySS0PIL1MUssDSC8T8xAREVF1ycycipaIiIiIiKhOy0m74ewIdnkGBTo7guSwJ56IiIiIiKiu4+z0LoM3PhARERERERG5CPbEE5HAaDQiMTER4eHhcHd3d3YcANLLJLU8gPQySS0PIL1MzFO52sh0KS29Rsq5V02DAoTltIws5wX5S5Cvt7CcmZPrvCBWfDzvzNGQnOr886avf+ecSbGOrty45cQkZRoH+gnLuTk5TkxSRufpKSzfynZ+HgDw8/KsfCeiKmAjnoiIiIiIqI6TyTic3lVwOD0RCRQKBfz8/KBQKJwdRSC1TFLLA0gvk9TyANLLxDyVk2ImIiIiKeDs9ERERFQncDi9fRxOXzkOp68ch9NXTurD6XNv3nR2BLt0/v7OjiA5HE5PRAKTyYTCwkJoNBrI5dIYqCO1TFLLA0gvk9TyANLLxDyVk2ImIqIHGj9rXQbPFBEJCgoKcPr0aRQUFDg7ikBqmaSWB5BeJqnlAaSXiXkqJ8VMREREUsBGPBEREREREZGL4HB6IiIiIiKiuo6z07sM9sQTERERERERuQg24sklxMfHw2AwCD+dO3fGM888g19++cXZ0R44UnxGqNQySS0PIL1MUssDSC8T81ROipmIiIicjY+YI5cQHx+PjRs3YuvWrQCA1NRUrFmzBklJSdi5cydatGjh5IRERCR1fMScfXzEXOX4iLnK8RFzlZP8I+ayspwdwS6dt7ezI0gOe+LJZcjlckRFRSEqKgoDBw7E2rVrUVJSgk8//bTaZXLWYyIiIiKiB9OFCxcwfvx4REVFoVu3bli6dCmKiooqPc5sNmP9+vXo1asXIiMjMXLkSPz+++82+6WlpWHatGmIjo5Gx44d8dprryEvL68W3okYG/Hksho2bAhfX19cuXIFY8aMQWxsrGh7YmIiDAYDjh07JqwzGAxYv349li1bhm7duqFLly4AgDlz5mDIkCE4fPgwhgwZgjZt2mDEiBE2/1hNJhPWrFmDPn36oHXr1hg4cKDNlwipqamYMWMGunbtijZt2qBPnz745z//KdrnwoULeP755xETE4OoqChMnjwZly9frsHaqZ78/HycPn0a+fn5zo4ikFomqeUBpJdJankA6WVinspJMRMREbmO7OxsjB07FsXFxYiPj8dLL72Ezz77DEuWLKn02A0bNmDVqlUYN24c1q1bh4CAAEyYMAEpKSnCPsXFxZg0aRKSk5OxYsUKvPHGG/j+++8xa9as2nxbADg7PbmwvLw8ZGVlITAwEJcuXarycR9++CHatm2LRYsWoaSkRFifnp6OuLg4TJs2DZ6entiwYQMmTpyI//znP/DzKxsitnTpUnz44Yd4/vnnER0djUOHDuH1119HSUkJnn76aQDAyy+/jBs3bmDevHnw8/PD9evXcerUKeF1UlJSMGrUKDRv3hxLliyBTCbD2rVrMW7cOOzbtw9qtbqGaujumc1m5OfnQ0p32Ugtk9TyANLLJLU8gPQyMU/lpJiJiOiBJn+w+nc//fRT3L59GwkJCfD+a0h+aWkp4uLiEBsbi6CgILvHFRYWYt26dZgwYQLGjRsHAIiJicHAgQOxadMmvPHGGwCA/fv34/z58/jqq68QGhoKAPD09MTEiRNx8uRJREZG1tp7YyOeXIql0Z2amoq3334bpaWlGDBgANavX1/lMry8vJCQkGAzYVJWVhbeffddoXe+Y8eO6NmzJ7Zs2YJZs2YhIyMD27dvx8SJEzFt2jQAQPfu3ZGZmYnVq1fjySefhEKhwB9//IGZM2fikUceEcoeNmyYsJyQkAAvLy988MEH0Gg0AIB27dqhb9+++Ne//oXRo0dXq26IiIiIiKjMkSNH0KVLF6EBDwCDBg3C66+/jqNHj2LEiBF2j/vtt9+Ql5eHQYMGCevUajX69euHAwcOiMo3GAxCAx4AunXrBm9vbxw+fJiNeCIAMBqNaNWqlfC7l5cXFixYgIceeuiuGvE9evSwO+OxTqcTGvCW37t27YoTJ04AAE6ePIni4mIMHDhQdNygQYOwZ88eJCcnIywsDBEREdi8eTMUCgW6deuGpk2bivY/evQoHnnkESgUCuFLCU9PT0RERIh67KvLaDQKywqFAhqNBiaTye79/+7u7gDK5gYwmUzCsNX8/Hyo1WoolUoUFxejuLhYdJxcLodWqxV6yioqt3weAFCpVFCpVCgpKbG5J0kmk8HNzU10nHUmrVYLuVyOwsJClJaWio5VKpVQq9UoLS1FYWFhheXay6TRaKBQKFBUVCQanQHY1qF1Huv3aq/H0FEdOqojAHBzc4NMJhPOjbXydWidyXJuKirXUR06ymTv3FizrsPydWQ5N5Vdh+Xr0DqPvTqs7Pq2rsPymSznxt516Oj6ts5krw6ren1b8lqXbzk39q7Diq5v6zyW8spfh5ZzU1kdls9jXb6967Ci69s6U0WfEZZz46gOLeutj6/sOrR3fZevIyIici19+/Z1uP3gwYN21yclJeGxxx4TrfP09ERAQACSkpIqLM+yzbpxDgBhYWHYunUrCgoKoNVqkZSUZLOPTCZDSEiIw/JrAhvx5DK0Wi22b98OmUwGHx8fNGjQAPJqDPuxDI0vz9fX1+6+Fy5cAFB2Xw0A+Pv7i/ax/J7114yeK1euxMqVK/Huu+8iLi4OISEhmDlzJvr37w8AyMzMxNatW4WZ9q2pVKq7fj/lJSYmit5TSEgIioqKROstYmJiAADJycm4ffu2sD45ORl6vR5+fn7IzMwU3f8DlH0ANm/eHCaTyWG55fMAQOPGjREUFITc3FybDzg3NzdEREQAAM6cOSNqjCQnJ8Pd3R1ubm64fv06bt0Sz8Rbv359NGrUCEajEefOnRNtU6lUom9Dy2dq0aIFdDod0tPTkZqaKtrm5+cHvV6PwsJC0XHJycmQyWRo164dAODixYs2jZXQ0FD4+PggIyMDV65cqXIdAUBUVBQUCgVSUlKQU26W3+DgYAQGBiI7OxvJycmiTPXq1UPLli0rLLdVq1bQarW4du0aMjIyqpxJo9GgdevWAIDz58/bNDINBgM8PDyQlpaGGzduCHkAICAgAE2aNEFBQYFNuXK5HNHR0QDK/qdp3Ri3znPz5k1cu3ZNdKy3tzfCwsJQUlJi971GR0dDJpPh0qVLwiQzlkxNmzaFv78/srKybG7H8fDwgMFggNlstinXOtOVK1eEf/cWDRs2RIMGDZCXlyd8dlhotVrhi0jLa1qfv/DwcLi7uyM1NRXp6eLZuAMDAxEcHIz8/HycPXvWbh6gbL6N8l9iNWvWDF5eXkhPT8f169dF2yyfEZbzaZ3HuvzynxEAKvyMsM5U0WdEZGQkVCoVUlJShM9WC8tnhOX1rDM5+owAgIiICJvPiPJ1RERE9pkesMd65uTkwNPT9okAXl5eNv/vKX+cWq0WRsxaeHp6wmw2Izs7G1qtFjk5OdDpdDbHV1Z+TWAjnlyGXC5HmzZt7G5Tq9U2PUwV/eOp6LnD5Rs0AHDr1i0EBJQ9VsYyFOfWrVuie2hu3rwp2h4YGIjFixfDZDLh1KlTeP/99/HSSy9h3759CA4OhpeXF3r27ImnnnrK5vXq1atnN9vdCA8PF5YVCgWAsvqxXl+eXq+HyWRCSUkJjEYj3N3dhd4rHx8feHh4iPa3fHkil8sdlls+D3DniwqdTmezzfrcWBqi1pksH6YNGjRAYGCg6FilsuzjzN3d3WG59jJZyg0ICICPj49om6UONRoNwsP/P3v3HR9Ftf9//LWb7KaQHlIlJCGEEGoCIh0EBBSwXC4IqFhAikhRQAUVFMsFFQUMIJALFxQLFr6CoOIVFe8VwQLIVQERCBAhEEpIQnr5/ZHfjNs3oeVEPs/Hw4fJzszZ9545nOyZOTOTbJVHe0+A+Ph4h2fioWqg5KiTd5YH/qzjmJgYh2fioeqPhG0my3sqOCpXWx4dHe30WjBH21rWYWJiot36Wh1GREQQGBhoVUdaPXl7e7tsL40aNXJ6/XP9+vUJDAy0ek3bN56eng7L1TLHxsZSUlJilUmrh6CgILsztVrdGwwGl3kbNGhAVFSU1WvavvHz83NZh40bN+b8+fNW7UibQREZGWl3sFBbx8fHx2WmhIQEh2fioap9B9k8pkerQ19fX6655hq7dq3R+ghLWh066iM0zvoI7T1iYmKIjo62WqbVoZbVMpOjPsLRZ3XURwghhKibnJ1pv5rJIF78JURGRrJ161YqKyv1L3nffPNNjcrIy8vj22+/1afU5+XlsXXrVv0a9ZYtW2Iymfj000/1M0EAn3zyiX621pLRaKRVq1Y89NBDfPHFFxw+fJiYmBg6duzI/v37adasmf4F+lJyNHXUaDS6nFKqDR4AuyOW2tRWRwwGg9upqs6WWw7u3G1nm8n2yKglDw+PC85kNpud3ljQsg4dHdW1nK5vy1UdusoD1vvGlmUdOsrkqlxXdehuW1fLtDp0lMddO7zQOqxO+/b29naYyVU7dNe+XdWhu/bt7+/v9MCOq3born1faB2azWYiIyOdbuuqHV5MH+GqDr29vV1mupj2LYQQ4q8vICCAvLw8u9fPnTtnd2LAdruSkhKKi4ut/p7k5uZiMBj0bQMCAhw+Tu7cuXN2B/kvNRnEi7+Evn378v777/Pss89yww03sGPHDjZt2lSjMoKCgnjiiSeYOHEi/v7+pKenU1lZyT333ANUnU296667WL58OWazmZSUFLZs2cKGDRuYMWMGHh4e5OXlMXLkSG699Vbi4+MpLS3ljTfe0K95B5g4cSKDBg1i5MiR3H777dSvX59Tp07x3Xffce211zJgwIBLXj/VVVpaypkzZwgJCbkkU/svBdUyqZYH1MukWh5QL5PkcU/FTEII8VdW8Rd7GEijRo3sLt3My8sjOzvb7lp22+2g6lJJy1lfBw8eJDo6Wj+w3ahRI7tLOCsrKzl06BCdO3e+VB/Dob/WcwTEVatbt2488sgjfPHFFzz44IPs37+fWbNm1aiMsLAwZs6cybJly5g0aRLFxcUsX77calrro48+yrhx4/jggw8YO3YsX3/9NbNmzdIfL+fl5UWTJk144403eOCBB3j00UeprKxk+fLl+jX3sbGxvPfeewQFBTFr1ixGjhzJ3LlzKSwsJCkp6dJVygUoLS0lMzPT7tKE2qRaJtXygHqZVMsD6mWSPO6pmEkIIUTd0a1bN7Zu3Wp1b6FPP/0Uo9HocpDdpk0b/Pz8+OSTT/TXSktL+eyzz+jWrZtV+Xv37rW6d8u3335LTk4O3bt3v7QfxoaciRd1woQJE/THujlz//33c//991u9ZnkDKEe/27r++uu5/vrrnS43Go08+OCDPPjggw6Xm81mnnvuOZfvAVXXl86fP9/tekIIIYQQQoiaGzp0KG+88QYPPvggY8aM4cSJE7z44osMHTrU6r5A99xzD8eOHdMfH+fl5cWYMWNIS0sjJCSEJk2a8Pbbb5OTk8PIkSP17fr27cvSpUuZMGECkydPprCwkBdffJHrr7/+sj5eDmQQL4QQQgghhBBXvQonN5etqwIDA1m1ahXPPvssDz74IPXq1WPQoEE8/PDDVutVVFTYPe501KhRVFZWsmLFCs6cOUNycjLLly8nJiZGX8dkMvHPf/6T5557jsmTJ+Pp6Unv3r15/PHHL/tnk0G8EEIIIYQQQoi/nISEBFauXOlynTfeeMPuNYPBwJgxYxgzZozLbSMiIkhLS7uYiBdEBvFCAHPmzKntCErw8PAgMDDwstw1/0Kplkm1PKBeJtXygHqZJI97KmYSQgghVGCodPZQXiGEEEKIv5DDJ7JrOwIAsRFh+s8nzuTUXpD/LyIkSP/5bK7945hqQ3DAn49gzMiq/f0WF/nnPlOxjjJPnq7FJFUahIfqP+dZ3EistvhbPFb09LnazwMQGmj/qFOVnMo5V9sRHKof5PxxcFcruTu9EEJXWVlJaWkpKh3bUy2TanlAvUyq5QH1Mkke91TMJIQQQqhABvFCCF1hYSG7d++msLCwtqPoVMukWh5QL5NqeUC9TJLHPRUzCSGEECqQa+KFEEIIIYQQ4ionE5/qDjkTL4QQQgghhBBC1BEyiBdCCCGEEEIIIeoImU4vhBBCCCGEEFe5CplPX2fII+aEELrKykoqKiowGo0YDIbajgOol0m1PKBeJtXygHqZJI97lyOTPGLOMXnEnHvyiDn35BFz7qn+iDkV+iNHLPsoUUUG8UIIIYQQQghxlZNBfN0h0+mFEEIIIYQQ4ion53brDrmxnRBCCCGEEEIIUUfImXghhK6goIA9e/aQnJyMr69vbccB1MukWh5QL5NqeUC9TJLHPdUyXa48Z3fsumRlXajgNin6z6pMp7WcPnvi7fdrL8j/FzFskP5z1umztZjkT5GhwfrPx5f+qxaTVIkac5/+c96pU7WYpIp//fr6zyrcMwCs7xsgxMWQQbwQQgghhBBCXOVkOn3dIdPphRBCCCGEEEKIOkIG8UIInclkIiYmBpPJVNtRdKplUi0PqJdJtTygXibJ455qmVTLI4QQ4uol0+mFEDqTyUR4eHhtx7CiWibV8oB6mVTLA+plkjzuqZZJtTxCCHGpVchs+jpDzsQLIXRlZWWcPn2asrKy2o6iUy2TanlAvUyq5QH1Mkke91TLpFoeIYQQVy8ZxAshdCUlJWRkZFBSUlLbUXSqZVItD6iXSbU8oF4myeOeaplUyyOEEOLqJdPphRBCCCGEEOIqJ3enrzvkTLwQQgghhBBCCFFHyCBeCCGEEEIIIYSoI2QQf5VLS0sjKSmJrl27UlFRYbd86NChJCUlMW3atMvy/tu3b2fJkiUOc6WmprrdvmfPnjzzzDMXnWPatGkkJSWRlJREcnIy7dq1Y+DAgbz00kscP378gsqs7mdYu3YtH3300QW9x6VmNBqpV68eRqM6XYNqmVTLA+plUi0PqJdJ8rinWibV8gghxKVWQaWS/wl78pdIYDKZOHv2LN9//73V63/88Qe7du3C19f3sr33d999x9KlSy9b+TURExPDmjVreOutt3j55Ze54YYb+OijjxgwYABbt269bO/7f//3f2zYsOGylV8T3t7eNG3aFG9v79qOolMtk2p5QL1MquUB9TJJHvdUy6RaHiGEEFcvGcQLTCYT3bp1Y+PGjVavb9y4kcTERBo2bFhLya4sb29vUlJSSE1NpVu3bowbN47169cTFRXFww8/TH5+fm1HFEIIIYQQQlzlZBAvABgwYACbNm2itLRUf23Dhg0MGDDA4frff/89Q4cOpVWrVrRv357p06eTk5OjL8/MzCQpKYl169bxzDPP0K5dO7p06cILL7ygP2M3LS2NhQsXUlBQoE9lHz58uNX77Nu3j2HDhtG6dWsGDBjAf/7zH6ef4YsvviApKYmMjAyr18+dO0erVq148803a1grEBQUxCOPPEJOTo7VQY7KykqWL19O3759adGiBb169WLlypUOy9i9ezeDBg2iZcuW3HTTTXz55Zf6suHDh/Pdd9/x1Vdf6XWQlpZW45yXSkFBAT/++CMFBQW1lsGWaplUywPqZVItD6iXSfK4p1om1fIIIcSlVllZqeR/wp4M4gUAPXr0oKSkhG+++QaA33//nX379tGvXz+7dX/++Wfuu+8+6tWrx4IFC5g6dSpffvklo0aNory83Grd+fPnYzQamT9/PkOHDmXFihW89957AAwePJhBgwbh7e3NmjVrWLNmDU899ZS+bWlpKVOnTmXgwIEsXLiQkJAQJk6cyNmzZx1+hu7duxMREcEHH3xg9bo2Vf3mm2++oLrp0KEDnp6e7Nq1S3/t+eef59VXX+W2225j2bJl/O1vf2Pu3Lm8/fbbVtuWlpby8MMP87e//Y2FCxcSGxvL+PHj2bdvHwBPPfUUzZo1o02bNnodDB48+IJyCiGEEEIIIf765DnxAgAfHx969uzJxo0buf7669mwYQOpqanExMTYrbtkyRLCwsJYsmQJJpMJgKioKEaOHMmWLVvo2bOnvm6rVq148sknAejcuTPbt29n06ZNDBs2jMjISCIjIzEajaSkpNi9jzaI7969OwDx8fH06tWLr7/+mltvvdVufQ8PDwYOHMgHH3zAQw89hIeHBwAffPABvXv3JiAg4ILqxsvLi+DgYLKzswE4cuQIq1evZtasWQwZMgSATp06UVRUxKJFixgyZIh+46PS0lIeeOABBg0aBECXLl3o06cPS5cu5ZVXXqFx48b4+fnh6+vrsA6EEEIIIYQQwpIM4oVuwIABTJkyhaKiIj7++GO7qe2aH374gQEDBugDeKganAYEBPDjjz9aDeK7dOlitW1CQgLbtm2rVh6j0UjHjh313xs0aIC3tzcnTpxwus2gQYNYsmQJ//nPf7j++uvZu3cvv/zyC4888ki13tOZyspKDAYDgH6Tuz59+uiXBkDVQD49PZ3jx49zzTXX6K/37t1b/9nDw4MbbriBzz///KLyuGI51dPDwwMvLy8qKiooKiqyW1e7aWFRUREVFRUUFhYCUFhYiNlsxtPTk9LSUqvLLKBq33h7e1NZWalv46hc2zxQdQ8Gk8lEWVkZJSUlVssMBgM+Pj5W21lm8vb2xmg0UlxcbDfrw9PTE7PZTHl5OcXFxU7LdZTJy8sLDw8PSkpKrPYp2NehZR7Lz1pYWGg35ctVHbqqI6g6sGYwGPR9Y8m2Di0zafvGWbmu6tBVJkf7xpJlHdrWkbZv3LVD2zq0zOOoDt21b8s6tM2k7RtH7dBV+7bM5KgOq9u+tbyW5Wv7xlE7dNa+bW886qgdavvGXR3a5rEs31E7dNa+LTM56yO0feOqDrXXLbd31w4dtW/bOnLXRzirQ43t56luP2tZ7uW8YawQQlwombled8ggXui6dOmCyWRiwYIFZGZmctNNNzlcLzc3l9DQULvXQ0NDOXfunNVr/v7+Vr+bTCa7L7bOeHt7Yzab7ba3/fJlqUGDBnTu3Jn333+f66+/ng8++IAGDRrQoUOHar2nI8XFxeTk5FC/fn0Azp49S2VlpdMyLQfxJpOJwMBAq+WhoaH6Wf3LYc+ePfrPISEhxMfHU1JSYvW6pm3btgBkZGRw/vx5/fWMjAzi4uIIDQ3l7NmzHD161Gq7gIAAEhMTqaiocFmubR6o2kcRERHk5eVx8OBBq2U+Pj40a9YMgL1791p9kc7IyMDX1xcfHx+OHz/O6dOnrbaNjIzkmmuuoaCggN9++81qmclkolWrVk4zNWnSBH9/f7Kzs8nKyrJaFhoaSlxcHMXFxVbbZWRkYDAYaNOmDQCHDh2y+3LfqFEjgoODOXPmDJmZmdWuI4CUlBQ8PDw4evQoubm5VstiYmIIDw/n3LlzVveAyMjIoF69ejRt2tRpuc2bN8fb25tjx45x5syZamfy8vKiRYsWAOzfv99ukJmUlISfnx8nTpzg5MmTeh6AsLAwGjZsSFFRkV25RqNRfxTjwYMHrQZBlnlOnTrFsWPHrLYNCgoiISGBsrIyh581NTUVg8HA4cOH9RtTapliY2OpX78+OTk5HD582Go7Pz8/kpKSqKystCvXMlNmZqbVvUAAoqOjiYqKIj8/nwMHDlgt8/b2pnnz5gD6e1ruv+TkZHx9fcnKyrLrI8LDw4mJiaGwsFC/HMc2D8CBAwfs+sjGjRsTGBhIdna23SMztT5C25+29xRx1kcATvsIy0zO+ohWrVphMpk4evSo3d8NrY/Q3s8yk6s+AqBZs2Z2fYRtHbnrI/bv3293sKNJkyb67C7bOnLWRwBO+wjbTEIIIURNyCBe6EwmE3369GHlypV07NhRH7TaCgwMtBtAAZw+fdpuwFobBg8ezNSpUzlx4gQfffQRw4cP18+iX4hvv/2WsrIy/YtYYGAgBoOBt956y2o2giY+Pl7/ubS0lHPnzlnVy+nTpwkLC7vgPO4kJyfrP2tfOs1ms9XrtuLi4qioqKCiooLS0lJMJpN+Njc4OBg/Pz+r9bXLBYxGo8tybfMAep35+/vbLbPcT9pA1DKTdjYsKiqK8PBwq209Pau6M19fX5flOsqklRsWFkZwcLDVMq0Ovby8SE5Otspj+bzo+Ph4h2fioWqgZHtAy1Ue+LOOY2JiHJ6Jh6q2aJtJqwdn5WqZoqOjiYiIqHYmyzpMTEy0W1+rw4iICIKDg63qSMvk7e3tsr00atTI6Q1s6tevb9e/aPvG09PTYbla5tjYWMrKyqwyafUQFBRkd1ZUq3uDweAyb4MGDYiKirJ6Tds3fn5+buvQth1p/+YiIyPt+l+tDn18fFxmSkhIcHoWOSwsjKCgIKtlWh36+fnRqFEju3at0foIS1odOuojNM76CO3zxMTEEB0dbbVMq8OQkBC8vLysMjnqIxx9Vkd9hMZdH5GYmOiwDg0GA02aNMFgMFjVkW0f4YyjPkIIIYS4EDKIF1YGDx7M6dOnuf32252u07ZtWzZv3sy0adP0L2LffPMNubm5NT67UJMz89XVq1cvAgICmDJlCufOnWPgwIEXXNa5c+eYO3cuwcHB+k3+tCn+OTk5VpcOOPPvf/9bvya+vLyczz//nNatW+vL3c0uqClH0zSNRqPL6ZuunnusTW11xGAwuJ0W6my5p6en1YCzutuB9dRWWx4eHhecyWw2283+0LirQ8vp+rZc1aGrPOB631yuOnS3ratltVGHF9O+XdWhu/btqg7d7Rtng15wXYfu2veF1qGnp6fdASxLl6uPcFWHruoBLl8f4aoOXR2Mu5j2LYQQKqiQA411hgzihZVWrVqxePFil+uMHTuWoUOHMmbMGIYPH86pU6d4+eWXadWqlX4TuurSpsKuWrWK1NRU/WzQxTCZTNx2220sX76cLl262J0pc6aoqEi/A31eXh4///wz77zzDvn5+SxatIh69eoBVWdT7rzzTh599FFGjhxJ69atKS0tJSMjg+3bt1vVn8lk4rXXXqO4uJgGDRrw9ttvk5WVxaJFi/R1GjVqxIcffsgXX3xBWFgY4eHhLs+QXk7FxcUcO3aM6Ohot4O8K0W1TKrlAfUyqZYH1MskedxTLZNqeYQQQly9ZBAvaqxFixasWLGCV155hQkTJuDr60vPnj157LHH9GmF1dWjRw/uuOMOli1bxunTp2nXrh1vvPHGRWfs3bs3y5cv5+9//3u1tzl69ChDhgzBYDDg5+dHTEwMAwYM4K677rI7EPDkk08SHx/PmjVr9AF+fHw8N954o9V6JpOJV155hVmzZvHbb7/RoEEDXn31VatpoKNGjeLIkSM89thj5ObmMn78eCZMmHBxFXCBysvLOXPmTK0dRHBEtUyq5QH1MqmWB9TLJHncUy2TanmEEEJcvQyVcoGW+AtasGABb731Fv/5z39cTscU1goKCtizZ49+gy0VqJZJtTygXibV8oB6mSSPe6plulx5zu7YdcnKulDBbVL0n0+cyam1HJYiQoL0n0+8/X7tBfn/IoYN0n/OOn22FpP8KTL0z0tgji/9Vy0mqRI15j7957xTp2oxSRV/i3uLZJ60v5dTbWgQbn9jaJUcOOb8CVC1KSFaDp7akjPx4i/l4MGDHDp0iNWrV3PHHXfIAF4IIYQQQgjxlyKDePGX8tRTT7Fr1y66du3KmDFjajuOEEIIIYQQQlxSMogXfymX4nr6q5nJZCIqKsrlndSvNNUyqZYH1MukWh5QL5PkcU+1TKrlEUKIS03uTl93yCBeCKEzmUx2z2yubaplUi0PqJdJtTygXibJ455qmVTLI4QQ4uplrO0AQgh1lJeXc+7cOcrLy2s7ik61TKrlAfUyqZYH1MskedxTLZNqeYQQQly9ZBAvhNAVFxfz+++/U1xcXNtRdKplUi0PqJdJtTygXibJ455qmVTLI4QQl1plpZr/CXsyiBdCCCGEEEIIIeoIGcQLIYQQQgghhBB1hNzYTgghhBBCCCGucpUyd73OkDPxQgidwWDAy8sLg8FQ21F0qmVSLQ+ol0m1PKBeJsnjnmqZVMsjhBDi6iVn4oUQOh8fH1q0aFHbMayolkm1PKBeJtXygHqZJI97qmVSLY8QQoirl6FS5k0IIYQQQgghxFVtz5FjtR3BoeSG0bUdQTkynV4IIYQQQgghhKgjZBAvhBBCCCGEEELUEXJNvBBCV1BQwJ49e0hOTsbX17e24wDqZVItD6iXSbU8oF4myeOeaplUywOXJ9OpnHOXpJyLVT8oUP8557f9tZikSlCTRP3n3GPHazHJnwKio/Sfz3y/oxaTVAlp10b/Oe/MmVpMUsU/JET/WcV9piK5yrrukDPxQgghhBBCCCFEHSGDeCGEEEIIIYQQoo6Q6fRCCJ2XlxdJSUl4eXnVdhSdaplUywPqZVItD6iXSfK4p1om1fKAmpmEEHWXTKavO2QQL4TQeXh44OfnV9sxrKiWSbU8oF4m1fKAepkkj3uqZVItD6iZSQghxOUn0+mFELqSkhKOHj1KSUlJbUfRqZZJtTygXibV8oB6mSSPe6plUi0PqJlJCCHE5SeDeCGErqysjJMnT1JWVlbbUXSqZVItD6iXSbU8oF4myeOeaplUywNqZhJC1F0VlZVK/ifsySBeCCGEEEIIIYSoI2QQL4QQQgghhBBC1BFX3SB++PDh3HTTTQ6vH5s4cSLdu3fn/PnztZDs4pWUlDB9+nQ6dOhAUlISK1eudLn+5s2bGTFiBNdddx0tWrSgZ8+ezJw5k0OHDl3WnNdeey1paWn678OHD2fMmDH679u3b2fJkiXVKistLY3U1NRqLcvMzCQpKYlPP/202llXrlzJli1bqr2+EEIIIYQQdVFlZaWS/wl7V90gftasWWRmZvLPf/7T6vWvv/6aTZs2MWPGDOrVq1dL6S7OunXrWLduHY8//jhr1qyhf//+TtedO3cu48aNw8/Pj2effZZ//etfPPjgg/z+++88/PDDVzA1PPXUUzz22GP679999x1Lly695O8THh7OmjVr6NChQ7W3ef3116+qQbynpydhYWF4eqrz4ArVMqmWB9TLpFoeUC+T5HFPtUyq5QE1MwkhhLj8rrpev1GjRowZM4YlS5Zw8803ExMTQ3FxMc8++yy9evXihhtuuGzvXVRUhLe392Ur/+DBg4SHh3PLLbe4XG/Lli2kp6czbtw4Jk2apL/erl07/v73v/Pll1863fZyfIbGjRtf0vKcMZvNpKSkXJH3cuZyt4GLZTabadiwYW3HsKJaJtXygHqZVMsD6mWSPO6plkm1PKBmJiGEEJffVXcmHmD06NFER0cza9YsAJYsWcKpU6eYOXMmWVlZTJ06lfbt29OqVSvuvPNOfv75Z6vtP/zwQ4YNG8Z1111Hu3btGD58OLt377ZaR5vKvXv3boYMGULLli158803AVi2bBm9e/emZcuWdOjQgXvvvZejR4+6zPzHH38wceJE2rZtS0pKCiNHjmTfvn368p49e7JixQqOHz9OUlISSUlJZGZmOixrxYoV1K9fn3Hjxjlc3qNHD/3npKQkli1bxksvvUTnzp3p2LEjUDXdZvny5fTt25cWLVrQq1cvh9P3P//8c2688UZatmzJoEGD7OoJrKfTp6WlsXDhQgoKCvTPMXz4cJd1U12OptNv3ryZgQMHkpqayrXXXsvAgQP1M+89e/bkjz/+4M0339SzrF27FoCKigoWL15Mz549adGiBTfeeCPvvPOO1fs5awMDBw5kypQpdvleeuklunTpQnl5+SX5vBeioqKCgoICKioqai2DLdUyqZYH1MukWh5QL5PkcU+1TKrlATUzCSHqrtq+C73cnb76rroz8VB15PqZZ55h+PDhLF68mH/+859MmTIFHx8f/va3v+Hr68uMGTPw9/fnjTfe4J577uGzzz4jNDQUqBoM3nbbbTRs2JCSkhI2btzInXfeyfr164mPj9ffp7S0lClTpnDvvffy8MMPExQUxIcffsiCBQuYOHEiKSkp5OXl8eOPP7q8Dj8/P5/hw4djNBqZNWsWXl5evPbaa9x1112sX7+eqKgoFi5cSHp6Ot9//z0LFy4EqqaP2yorK2PHjh306dMHk8lUrfp6/fXXad26Nc8//7z+GJvnn3+e9957j7Fjx9K6dWt27NjB3Llz8fLyYtiwYQDs2bOHiRMn0q1bN6ZPn05mZiYPPfSQy+fZDh48mKysLDZs2MCqVasA8PPzc5vR0eN13H2pOXLkCJMmTaJ///5MmTKFiooK9u7dy7lz5wBYuHAho0ePpk2bNowYMQJAP+Px4osv8vrrr/PAAw+QmprKV199xVNPPUVZWRl33XWX/h6O2oCvry9z5swhLy8Pf39/AMrLy1m3bh1/+9vf8PDwcPt5L5eioiL27NlDcnIyvr6+tZbDkmqZVMsD6mVSLQ+ol0nyuKdaJtXygJqZhBBCXH5X5SAe4LrrrmPgwIEsWLCA5s2bM3z4cBYtWkRubi7vvfeePmDv2LEjffv2Zfny5Tz66KMAjB8/Xi+noqKCzp07s3v3bv7v//6PyZMn68tKS0t5+OGH6devn/7au+++S1JSktWN3NxN4V+7di3Hjh1j48aNJCQkAFVT33v06MGqVauYNm0azZo1o379+m6njOfk5FBSUkJ0dHS16yowMJCFCxdiMBiAqsHv6tWrmTVrFkOGDAGgU6dOFBUVsWjRIoYMGYLRaGTZsmVERUWxaNEifWDq5eXFE0884fS9IiMjiYyMxGg0Vnvqe0FBAc2bN3e4zNWXml9//ZXS0lJmzJihHyjo2rWrvrxZs2aYzWbq169vleXMmTOsXr2akSNHMmHCBAC6dOnC2bNnWbRoEcOGDdM/r6M20KBBA1544QU++ugj7rjjDqDqEofs7Gz+/ve/V+szCyGEEEIIIa5OV+0gHqqm1a9du5b77rsPDw8PvvnmG9q3b09gYKB+ZtdoNNKuXTv+97//6dsdOHCAV155hZ07d3L69Gn99YyMDLv36N69u9XvzZo146233mL27Nn07t2b1q1buz0j/sMPP5CYmKgP4AGCgoLo1KkTP/7444V8dH1AXh3dunWzWn/r1q0A9OnTx+oMeKdOnUhPT+f48eNcc801/PTTT/Ts2dPqzPKNN97ochB/Iby9vVm9erXd6++++y4bNmxwul1SUhIeHh5MnTqV22+/nXbt2ulnxl3ZvXs3paWl3HjjjVav33TTTWzYsIGMjAyrfWXbBvz8/Ljpppv44IMP9EH82rVrufbaa4mLi3P7/u4UFBToP3t4eODl5UVFRQVFRUV262oHOYqKiqioqKCwsBCAwsJCzGYznp6elJaWUlpaarWd0WjE29ubyspKfRtH5drmATCZTJhMJsrKyuxmZRgMBnx8fKy2s8zk7e2N0WikuLjY7rIDT09PzGYz5eXlFBcXOy3XUSYvLy88PDwoKSmxm9VhW4eWeSw/a2Fhod0dVF3Voas6AvDx8cFgMOj7xpJtHVpm0vaNs3Jd1aGrTI72jSXLOrStI23fuGuHtnVomcdRHbpr35Z1aJtJ2zeO2qGr9m2ZyVEdVrd9a3kty9f2jaN26Kx92x6odNQOtX3jrg5t81iW76gdOmvflpmc9RHavnFVh9rrltu7a4eO2rdtHbnrI5zVocb281S3n7Us1zaTq33jqg6dZXLVDh31EXIWXwgBIDPX646rehCvDZ61/589e5Zdu3Y5PKurTaPOz89nxIgRhISEMG3aNKKjo/Hy8uLJJ5+0+1Lg4+Njd6f7gQMHcv78ed59911WrlyJv78/t912G1OnTnV6w7Pc3Fzq169v93poaCj79++v0WcOCgrCy8uLY8eOVXsbbVaC5uzZs1RWVjq9y7s2iM/Ozrbb1s/Pz+rL0KVgNBpp2bKl3etfffWVy+3i4+NZsmQJS5cuZfz48RiNRrp06cLMmTNdzlTQptvb7hPt95ycHP01R20A4Pbbb2fo0KHs3buX8PBwvvrqK5555hmXeatrz549+s8hISHEx8dTUlJi9bqmbdu2QNUBKMtLOjIyMoiLiyM0NJSzZ8/a3bMhICCAxMREKioqXJZrmweqZiJERESQl5fHwYMHrZb5+PjQrFkzAPbu3Wv1RTojIwNfX198fHw4fvy41QE0qJrFcc0111BQUMBvv/1mtcxkMtGqVSunmZo0aYK/vz/Z2dlkZWVZLQsNDSUuLo7i4mKr7TIyMjAYDLRp0waAQ4cO2X2RbtSoEcHBwZw5c8buHhWu6gggJSUFDw8Pjh49Sm5urtWymJgYwsPDOXfunNXBw4yMDOrVq0fTpk2dltu8eXO8vb05duwYZ86cqXYmLy8vWrRoAcD+/fvtBplJSUn4+flx4sQJTp48qecBCAsLo2HDhvrUX0tGo1F/FOTBgwetBkGWeU6dOmXXbwUFBZGQkEBZWZnDz5qamorBYODw4cPk5+dbZYqNjaV+/frk5ORw+PBhq+38/PxISkqisrLSrlzLTJmZmVb/3gGio6OJiooiPz+fAwcOWC3z9vbW/75o72m5/7Qp0VlZWWRnZ1ttGx4eTkxMDIWFhVb3Q7HMA1UHmW3/FjVu3JjAwECys7M5fvy41TKtj9D2p+3BaGd9BOC0j7DM5KyPaNWqFSaTiaNHj+p9qkbrI7T3s8zkqo+AqgPltn2EbR256yP2799vN6Bu0qSJfkDato6c9RGA0z7CNpOjPiIwMJDGjRs7bd+WM8RsMznrIwCHfYRtHiGEEGq7qgfxtgIDA+natavVHds1ZrMZgF27dpGVlcXSpUv1P4IAeXl5REZGWm3j6Gy30Wjknnvu4Z577uHEiRNs3LiRl19+meDgYB588EGnuRw9u/306dMEBgbW6DN6enrSpk0btm3bRllZWbUeS2P7OQIDAzEYDLz11lsOZxFo9wUICwuzG2jl5+fbfcGsTd26daNbt27k5+fz9ddfM3v2bKZPn65fj+9IUFAQUFX/ERER+uunTp2yWg7OZzykpqaSmJjIBx98QHR0NGaz2e7M/oVKTk7Wf9a+dJrNZqvXbcXFxeln4g8fPkxsbKzetoKDg+3uS2A0GvX/uyrXNg/8edDM39/fbpllfWn/viwzaQeAoqKi7O75oLVlX19fl+U6yqSVGxYWRnBwsNUyy0tBkpOTrfJYngmLj493eCYeqgZKrmZ5OKpDrY5jYmIcnomHqn+LtpksDxo5KlfLFB0dbdV+3WWyrMPExES79bU6jIiIwNfX16qOtH3j7e3tsr00atTI6fNg69evb9ffafvG09PTYbla5tjYWM6fP2+VSasH7T4VlrS6NxgMLvM2aNCAqKgoq9e0fePn5+eyDmNjY8nIyLBqR9qB3MjISLuDhFod+vj4uMyUkJDg9CxyWFiYVf8E1nVoMBjs2rVG6yMsaXXoqI/QOOsjtM8TExNjd9BUq8N69erZZXLURzj6rI76CI27PiIxMdFhHRYXFzusI9s+whlHfYTGUR/hrn1btlPbTLZ9hKPtwHEfIYQQQn0yiLfQqVMn1q9fT0JCgtOpZdpZIsvB644dO/jjjz8cfrF1JSIighEjRrBhwwa7M5KW2rZty6ZNmzh48CCNGjUCqs4Gb926Vb8mvSbuu+8+Ro8ezZIlS6yu79ds2bLFbgq4Je0O9Tk5OfTs2dPpeq1ateLLL79k+vTp+pcRyzvDO2MymVze/O5y8PPzo1+/fuzevdtqCr7JZLI76NCyZUtMJhOffvqpfkYI4JNPPtHPyFTH4MGDee211wgNDaVfv36XbDqjo3KMRqPL8rXBg6+vr93sCW1apiMGg8FtbmfLPT09XR5E0rZzlMnVbA4PD48LzmQ2m/WBiS2tDh3lARwOfDSu6tBVHsDlIwm1OnSWyVW57mbEuNrW1TKz2UxoaKjDPO7a4YXWYXXat7e3t8NMrtqhu/btqg7dte+QkBBCQkIcLnPVDt217wutQz8/P/2MsSOu2uHF9BGu6tDf399lpgst90Lr0NfX12Wey9W+XdWhu0zV7WeFEAJweqBRqEcG8RbuvfdePvroI+666y7uvvtuoqOjOXPmDD/99BMRERHce++9pKSk4Ovry6xZsxg9ejQnTpwgLS3N5RktSzNnziQgIICUlBQCAgLYsWMHe/fu1e/o7sjAgQNZuXIlY8aM4aGHHtLvTu/p6ck999xT48/ZvXt37r//ftLS0vj999/p378/wcHBZGZm8sEHH5CXl+dyEB8fH8+dd97Jo48+ysiRI2ndujWlpaVkZGSwfft2Fi9eDFTdc2DQoEE8+OCDDBs2jMzMTJYvX+528KBNj121ahWpqan4+fnpBy8upXfeeYddu3bRtWtXwsLCyMzMZP369XTu3Flfp1GjRmzbto1vvvmGgIAAGjRoQEhICHfddRfLly/XbyS4ZcsWNmzYwIwZM6p9d/lbb72VuXPncvbsWZ5//vlL/vmEEEIIIYQQfz0yiLcQHBzMmjVrmD9/PnPnziUnJ4fQ0FBat25N7969gaopnQsWLODFF19k3LhxxMXFMWvWLP75z39W6z1SU1N59913ee+99ygsLCQmJobp06czePBgp9v4+fnxxhtvMGfOHGbMmEFFRQVt2rRh9erVdlM5q+uRRx4hNTWVN998k8cff5zCwkLCw8Pp0qULI0eOdLv9k08+SXx8PGvWrGHRokXUq1eP+Ph4qynhzZo1Y8GCBcydO5fx48eTmJjIvHnz3Jbfo0cP7rjjDpYtW8bp06dp164db7zxxgV9TleSkpL48ssvmT17Njk5OYSFhdG/f3+ryykmT57M008/zYQJEzh//jyzZ89m4MCBPProo/j7+/P++++zZMkSrrnmGmbNmsXQoUOr/f5BQUFcd911ZGVlVftO/JdbYWGhPuPD1VmjK0m1TKrlAfUyqZYH1MskedxTLZNqeUDNTEIIIS4/Q6XMmxCiVuTn59O1a1cmTJigP4e+thUUFCj3zGHVMqmWB9TLpFoeUC+T5HFPtUyq5YHLk+lUzjn3K10B9YP+vAdGzm81u4nv5RDU5M9LJnOPHXex5pUTEP3niZwz3++oxSRVQtr9eWlHns2NU2uDv8UlSyruMxX9sD+jtiM4dG1iXG1HUI6ciRfiCtPuWv3WW29hMBgYOHBgbUcSQgghhBBC1BEyiBfiCvvll1+4++67iYqK4oUXXrC7W7QQQgghhBBCOCODeCGusPbt21s951kIIYQQQojaJldZ1x1G96sIIa4WZrOZhIQEp4+3qg2qZVItD6iXSbU8oF4myeOeaplUywNqZhJCCHH5yZl4IYTO09NTuen9qmVSLQ+ol0m1PKBeJsnjnmqZVMsDamYSQghx+cmZeCGErrS0lOPHj1NaWlrbUXSqZVItD6iXSbU8oF4myeOeaplUywNqZhJC1F0VlWr+J+zJIF4IoSstLeXYsWNKfSFULZNqeUC9TKrlAfUySR73VMukWh5QM5MQQojLTwbxQgghhBBCCCFEHSHXxAshhBBCCCHEVa4SmbteV8iZeCGEEEIIIYQQoo6QQbwQQufh4UFQUBAeHh61HUWnWibV8oB6mVTLA+plkjzuqZZJtTygZiYhhBCXn6GyslLmTQghhBBCCCHEVezbvQdqO4JDHZsm1HYE5ciZeCGEEEIIIYQQAvjiiy+45ZZbaNmyJX379uWDDz5wu83u3buZPn06vXv3pnXr1vTp04eXX36ZgoICq/XS0tJISkqy++/tt9+uUUa5sZ0QQgghhBBCiKveDz/8wPjx4xk0aBCPP/4427Zt44knnqBevXrceOONTrf75JNPOHz4MPfffz9xcXH8/vvvvPrqq/z000+8/vrrVut6e3uzatUqq9diYmJqlFMG8UIIXUFBAXv27CE5ORlfX9/ajgOol0m1PKBeJtXygHqZJI97qmVSLQ+ol+ly5Tl9LveSlXWhQgMD9J/PZWbWYpI/BTZooP+sXB3l5dVikiqB/v76z+eOHK3FJH8KbFizgdqVViFXWfPaa6/RqlUrnnnmGQA6dOjA0aNHefXVV10O4keNGkVISIj+e/v27QkICGDq1Kn8/PPPtGjRQl9mNBpJSUm5qJwynV4IIYQQQgghxFWtpKSE7du32w3W+/Xrx4EDB8h0cQDPcgCvadasGQAnT568tEGRM/FCCCGEEEIIIRTVq1cvl8s3b958Sd7nyJEjlJaW0qhRI6vXExKqbqx38OBBGljMgHHnxx9/BLArr6ioiA4dOpCbm0tcXBz33nsvt99+e42yyiBeCCGEEEIIIa5yV/ts+nPnzgEQEBBg9br2u7a8Os6cOUNaWhq9evUiLi5Of71hw4ZMnTqVZs2aUVxczEcffcSMGTPIy8tj5MiR1S5fHjEnhNBVVlZSWVmJwWDAYDDUdhxAvUyq5QH1MqmWB9TLJHncUy2TanlAvUyXK49y13vLNfEOyTXx7ql+Tfx/f/29tiM41KVZ4wveNi8vr1rT2WNiYvjf//7HHXfcwZo1a6yuWT9z5gwdO3Zk7ty53HzzzW7LKi0tZdSoURw4cIAPPviA8PBwl+tPnDiRrVu38u2332IymdyWD3ImXghhQZUvgpZUy6RaHlAvk2p5QL1Mksc91TKplgfUy6RaHiGE+PTTT3nyySfdrvfxxx8TGBgIVA38LeXmVh0k05a7UllZyeOPP87u3bt566233A7gAW666SY2bdrEkSNH9Kn77sggXgihKyoq4vDhw8TGxuLt7V3bcQD1MqmWB9TLpFoeUC+T5HFPtUyq5QH1MqmWRwhRM3/FCdqDBw9m8ODB1Vq3pKQEk8nEwYMH6dq1q/76wYMHAftr2x154YUX+OSTT0hPT6dp06YXFroa5O70QghdRUUF+fn5VFRU1HYUnWqZVMsD6mVSLQ+ol0nyuKdaJtXygHqZVMsjhBA1YTabad++PZs2bbJ6/eOPPyYhIcHtTe2WLVvGypUrmTNnDh07dqz2+3788ccEBATQsGHDam8jZ+KFEEIIIYQQQlz1HnjgAe6++26efvppbrrpJrZv386GDRuYN2+e1XrNmjXjtttu4x//+AcAH330ES+//DK33HILDRo0YNeuXfq6DRs21B9BN3DgQG677TYaNWpEUVERH330EZ999hmPP/54ta+HBxnECyGEEEIIIcRVr+IvOJ2+pq699lrS0tKYP38+77//PtHR0Tz33HPcdNNNVuuVl5dbzTr65ptvAFi/fj3r16+3Wnf27NkMHDgQqBrQr1y5klOnTmEwGGjSpAkvvfQSt9xyS41y1mg6fVpaGklJSXTt2tXhVKmhQ4eSlJTEtGnTahSiurZv386SJUsc5kpNTb0s7+lOUlISy5cvr5X3dmT79u0kJSXxv//975KW+/nnn/Pmm29e8PZ79uwhKSmJ7du312g72317uT5fbVq5ciVbtmyp7RhCCCGEEEJc9Xr16sVHH33Ezz//zGeffcagQYPs1tm3bx9z5szRf58zZw779u1z+J82gAeYP38+X3zxBbt37+ann37ivffeq/EAHi7gmniTycTZs2f5/vvvrV7/448/2LVrF76+vjUOUV3fffcdS5cuvWzlX4g1a9ZU61EDdd3nn3/O22+/XdsxaN68OWvWrKn2nRvrgtdff12ZQbzZbCY2Nhaz2VzbUXSqZVItD6iXSbU8oF4myeOeaplUywPqZVItjxBC/FXVeDq9yWSiY8eObNy4kfbt2+uvb9y4kcTERIzGq+teeZbPEBSXn5+fn9T5ZeTp6Un9+vVrO4YV1TKplgfUy6RaHlAvk+RxT7VMquUB9TKplkcIUTN/xbvT/1Vd0Ih7wIABbNq0idLSUv21DRs2MGDAAIfrf//99wwdOpRWrVrRvn17pk+fTk5Ojr48MzOTpKQk1q1bxzPPPEO7du3o0qULL7zwAmVlZUDVtOqFCxdSUFBAUlISSUlJDB8+3Op99u3bx7Bhw2jdujUDBgzgP//5j9XyzZs3M3DgQFJTU7n22msZOHCg2zOg77//Pv3799ezDxs2jN27d+vLbafTDx8+nDFjxvDpp5/St29fUlNTufvuuzly5IhVuSUlJcybN49evXrRokULunXrZncZws6dO7n77rtJSUmhbdu2TJkyhdOnT7vM68iKFSv4+9//Ttu2benYsSNjxozh0KFDVuvs37+fUaNG0b59e1q3bk3fvn1JT08HYNq0afzf//0f+/fv1+ve3SUTixcvpnPnzqSmpjJ+/HiHuSsrK1m+fDl9+/alRYsW9OrVi5UrV7os19F0+qSkJNLT00lLS6NTp056GysoKLDadseOHQwcOJCWLVsyYMAAtmzZwq233mr1WbT9Z8nRpQDVya5dCuCqXfbs2ZM//viDN998U6/btWvXuqyDy6msrIxTp07p/+5UoFom1fKAeplUywPqZZI87qmWSbU8oF4m1fIIIcRf1QXd2K5Hjx488cQTfPPNN1x//fX8/vvv7Nu3j0WLFvHxxx9brfvzzz9z33330b59exYsWMCpU6d4+eWX+f3333nnnXfw8PDQ150/fz69evVi/vz57Ny5k7S0NBo2bMiwYcMYPHgwWVlZbNiwgVWrVgFVZ2U1paWlTJ06lbvvvptx48aRnp7OxIkT+eKLLwgODubIkSNMmjSJ/v37M2XKFCoqKti7dy/nzp1z+jm///57nnjiCUaMGEH37t0pKipi9+7d5OXluayfPXv2cObMGaZOnUp5eTlz5szhkUceYc2aNfo6EyZMYNu2bYwZM4aUlBTOnDnDZ599pi/fuXMnw4cPp3v37sybN4/CwkLmz5/PuHHjrMqpjqysLO666y6io6PJz8/nnXfeYejQoWzatImgoCAAxo4dS/369Xn++efx8/PjyJEjZGVlATBu3DjOnDnDwYMHmTt3LoB+h0VHVq9ezYIFCxgxYgSdOnVi69atPPHEE3brPf/887z33nuMHTuW1q1bs2PHDubOnYuXlxfDhg2r0Wd88803adu2LXPmzCEjI4MXX3yR0NBQpk6dCkB2djYjR44kKSmJ+fPnk5uby6xZsygoKCA5OblG71WT7O7a5cKFCxk9ejRt2rRhxIgRADV6vMSlVlJSwuHDh/H19cXTU437XqqWSbU8oF4m1fKAepkkj3uqZVItD6iXSbU8QgjxV3VBPayPjw89e/Zk48aNXH/99WzYsIHU1FRiYmLs1l2yZAlhYWEsWbJEv21+VFQUI0eOZMuWLfTs2VNft1WrVjz55JMAdO7cme3bt7Np0yaGDRtGZGQkkZGRGI1Gh9OptcFS9+7dAYiPj6dXr158/fXX3Hrrrfz666+UlpYyY8YMffDftWtXl59z9+7dBAUF8dhjj+mvXX/99W7rJy8vjw8//FAf6BYUFDB9+nSysrKIjIzkm2++4auvvuLll1+2mr1g+fPLL79MixYtWLhwIQaDAYAmTZroZ5C1z1kdjz/+uP5zeXk5nTt3pmPHjmzatIkhQ4Zw5swZMjMzeeKJJ/T90aFDB30b7bEIx44dczuVvby8nKVLl3Lrrbfq9da1a1dOnz7NunXr9PWOHDnC6tWrmTVrFkOGDAGgU6dOFBUVsWjRIoYMGVKjSzPCwsJ4+eWXAejWrRu//vormzZt0gfxq1atwmAwkJ6ejr+/PwCRkZHce++91X6PC8nurl02a9YMs9lM/fr1L9llApYzEDw8PPDy8qKiooKioiK7dbV7WBQVFVFRUUFhYSEAhYWFmM1mPD09KS0ttZp1A2A0GvH29qayslLfxlG5tnmg6pIck8lEWVkZJSUlVssMBgM+Pj5W21lm8vb2xmg0UlxcTHl5udW2np6emM1mysvLKS4udlquo0xeXl54eHhQUlJidwbJtg4t81h+1sLCQrtpaK7q0FUdQVU/azAY9H1jybYOLTNp+8ZZua7q0FUmR/vGkmUd2taRtm/ctUPbOrTM46gO3bVvyzq0zaTtG0ft0FX7tszkqA6r2761vJbla/vGUTt01r5t70PjqB1q+8ZdHdrmsSzfUTt01r4tMznrI7R946oOtdctt3fXDh21b9s6ctdHOKtDje3nqW4/a1mubSZX+8ZVHTrL5KodOuojLPNUVFQ47SMs942jOtQ4qgdX/ayjPuJy3mNJCOFYhcymrzMu+DDpgAEDmDJlCkVFRXz88cd2U9s1P/zwAwMGDLB67l2XLl0ICAjgxx9/tBrEd+nSxWrbhIQEtm3bVq08RqORjh076r83aNAAb29vTpw4AVRNufbw8GDq1KncfvvttGvXTh/MOdOsWTNycnKYNm0aN998M23atLH6o+lM06ZNrc5UN27cGEAfxH/77bf4+PjQv39/h9sXFhayY8cOHn30Uas/oHFxcURFRfG///2vRoP4Xbt2sWDBAn799VeryxgyMjIACA4O5pprruGVV17h3LlzdOzYkcjIyGqXbykrK4uTJ0/Su3dvq9f79u1rNYjfunUrAH369LH6Y96pUyfS09M5fvw411xzTbXft1OnTla/JyQksHHjRv33n376ifbt21vt844dO+ozEWqiJtndtcvLYc+ePfrPISEhxMfHU1JSYvW6pm3btkBVWzh//rz+ekZGBnFxcYSGhnL27FmOHj1qtV1AQACJiYlUVFS4LNc2D1TVQUREBHl5eRw8eNBqmY+PD82aNQNg7969Vl+kMzIy8PX1xcfHh+PHj9tdohEZGck111xDQUEBv/32m9Uyk8lEq1atnGZq0qQJ/v7+ZGdn6zNQNKGhocTFxVFcXGy1XUZGBgaDgTZt2gBw6NAhuy/SjRo1Ijg4WD9QVt06gqr7bXh4eHD06FFyc3OtlsXExBAeHs65c+f0f8dapnr16tG0aVOn5TZv3hxvb2+OHTvGmTNnqp3Jy8uLFi1aAFWX39h+CU9KSsLPz48TJ05w8uRJPQ9UHWRr2LAhRUVFduUajUb9CRQHDx60+vJvmefUqVMcO3bMatugoCASEhIoKytz+FlTU1MxGAwcPnyY/Px8q0yxsbHUr1+fnJwcDh8+bLWdn58fSUlJVFZW2pVrmSkzM9OqTwWIjo4mKiqK/Px8Dhw4YLXM29ub5s2bA+jvabn/kpOT8fX1JSsri+zsbKttw8PDiYmJobCwkH379jnMA3DgwAG7wVXjxo0JDAwkOzub48ePWy3T+ghtf1rmsSzfto8AnPYRlpmc9RGtWrXCZDJx9OhRuxlxWh+hvZ9lJld9BFT93bbtI2zryF0fsX//frsBdZMmTfSZg7Z15KyPAJz2EbaZHPURgYGBNG7c2Gn7tjzwa5vJWR8BOOwjLPOUlJQ47SOioqL0WX2///671TIvLy8aNWoEVB3stj0A4KiP0DjqI2zrSAghxJ8ueBDfpUsXTCYTCxYsIDMz0+7ZeZrc3FxCQ0PtXg8NDbX7w207qDaZTHZHkJ3x9va2uxuqyWTSv8zEx8ezZMkSli5dyvjx4zEajXTp0oWZM2cSHR3tsMyOHTvy4osv8vrrrzNy5Ei8vLzo27cvjz/+uMvBX0BAgF0OQM+Sk5NDWFiY1VFrS7m5uZSXlzN79mxmz55tt9z2S5grx44dY8SIEbRo0YJZs2YRHh6OyWRizJgxeh6DwcDy5cuZN28ezzzzDAUFBTRv3pzp06fTrl27ar8XoH/xtJ1ub3ujm7Nnz1JZWWl1xt9STQfxjurcsu1kZ2cTGxtrt52rywKcqUl2d+3ycrC8PED70mk2m11eNhAXF6efidcG8IGBgUDVQR7LS1cAfaaB0Wh0ezmC7XLt34O/v7/dMst/E9qXTMtM2tmwqKgowsPDrbbVpm76+vq6LNdRJq3csLAwgoODrZZpdejl5UVycrJVHsuDevHx8Q7PxENVO3N10NBRHWp1HBMT4/BMPFR9ybfNVK9ePZflapmio6OJiIiodibLOkxMTLRbX6vDiIgIfH19repI2zfe3t4u20ujRo2c3lSnfv36epvUaPvG09PTYbla5tjYWM6fP2+VSauHoKAguzN+Wt0bDAaXeRs0aEBUVJTVa9q+8fPzc1mHsbGxHDp0yKodaWdHIyMj7fpMrQ59fHxcZkpISHB6FjksLMzub5dlHQJ27Vqj9RGWtDp01EdonPUR2vvFxMTY/Q3W6lBry5aZHPURllz1ERp3fURiYqLDOtT6bds6su0jnHHUR2gc9RHu2rflbDXbTLZ9hLPtatpHuGvf2udr2LCh3p41ln2E7ffC6vYRQgghqlzwIN5kMtGnTx9WrlxJx44dnd6NNDAw0OFNzU6fPm33hexy69atG926dSM/P5+vv/6a2bNnM336dP0ae0duvfVWbr31Vs6cOcPmzZuZPXs2np6e/OMf/7jgHEFBQWRnZ1NZWelwIO/v74/BYGDMmDHccMMNdsttBxmu/Oc//6GgoICFCxfqA92ysjK7Ayjx8fG8+uqrlJaWsnPnTl555RXGjh3L119/bTUocCcsLAzA7uj9qVOnrH4PDAzEYDDw1ltvWc3SsMxzKYWFhTlsh7Y5zWaz3RkY27q60tlrytEURKPR6HJqovZly2g04ufnR7169fQvVdr0SUcMBoPbKY/Olnt6erq8ZlLbzjKT9uXTcmqrLQ8PjwvOZDabnT4aSatDyzyWX1JdzdJxVYeu8gB2X4QtaXXoLJOrcl3VobttXS0zm83Uq1fPYR537fBC67C67dtRJlft0F37dlWH1WnfjvKA63born1faB16eno6zQOu2+HF9BGu6tBkMrnMdKHlXmgdOvt3Zrn8cuwbV3XoLlN1+1nbMuHC6rCoqAg/Pz98fX2dtpnq9LNCiNohd6evOy7qriODBw/m9OnT3H777U7Xadu2LZs3b2batGn6H5JvvvmG3NzcGk+VqsmZeVf8/Pzo168fu3fvZsOGDdXaJiQkhMGDB/P111/bTQGuKW3a9SeffEK/fv3slvv6+pKSksLBgwdp2bLlRb1XUVERBoPB6o/4J5984vTOsSaTieuuu47Ro0fzwAMPcPLkSeLj46t99jgyMpKwsDD+/e9/W02p37Rpk9V62hTznJwcq0sqLpdWrVrx9ttvk5eXp5/t+Pbbb+2mwkZGRrJ161arAyzffPPNZc1+uc/M14S3tzdJSUm1HcOKaplUywPqZVItD6iXSfK4p1om1fKAeplUyyOEEH9VFzWIb9WqFYsXL3a5ztixYxk6dChjxoxh+PDh+t3pW7VqVaPrugH92sdVq1aRmpqKn5+ffv2VO++88w67du2ia9euhIWFkZmZyfr16+ncubPTbV599VVycnK47rrrCA0N5bfffuM///nPBd0MzVKnTp3o3r07jz/+OEeOHKF169bk5OSwadMm5s+fD8Cjjz7KPffcw0MPPUT//v0JCAggKyuLrVu3MnDgQNq3b1+t99KmfE+fPp2hQ4eyf/9+/vWvf1lNP9+7dy8vvPAC/fr1IyYmhvz8fJYuXco111yj3yk9ISGBDz74gA0bNhAbG0twcDANGjSwez8PDw9Gjx7N888/T2hoKJ07d+abb76xejwbVJ2tvvPOO3n00UcZOXIkrVu3prS0lIyMDLZv3+62XdXUPffcw1tvvcWoUaMYNWoUubm5pKWl2U0t7du3L++//z7PPvssN9xwAzt27LA7AHGpszdq1Iht27bxzTffEBAQQIMGDWo02+JSqqys1A9gOLvc40pTLZNqeUC9TKrlAfUySR73VMukWh5QL5NqeYQQ4q/qgp4TXxMtWrRgxYoVnD9/ngkTJvDSSy9x/fXXk56ebvV4uero0aMHd9xxB8uWLeP222/nqaeeqva2SUlJnD17ltmzZzNixAjS0tLo37+/yzJatmzJwYMHmTVrFiNGjGDlypWMHDmS8ePH1yi3I2lpaQwfPpw1a9YwatQo5syZYzWFrE2bNrz11lv6ne1Hjx7N4sWL8fb2dnhttzNJSUnMnj2bX375hTFjxrBx40YWLFhgde1dWFgY9evXZ+nSpYwaNYqZM2cSFRXFihUr9H00aNAgbrzxRp599lkGDRrEwoULnb7n8OHDmTBhAuvWrWP8+PFkZGTw3HPP2a335JNP8tBDD/Hxxx8zevRoHn30UT755BOuu+66an++6goPDyc9PZ2ioiImTZpEeno6M2fOtLuBX7du3XjkkUf44osvePDBB9m/fz+zZs26rNknT55MZGQkEyZMYNCgQXz55ZcX/DkvVmFhITt37nR4J+Taolom1fKAeplUywPqZZI87qmWSbU8oF4m1fIIIWpGOxCn2n/CnqFSakZcxW699VaSk5OZM2dObUdRQkFBAXv27NHvkq0C1TKplgfUy6RaHlAvk+RxT7VMquUB9TJdrjynz+W6X+kyCw38cwbjOZsnCdSWQIsZkcrVUV5eLSapEmhxwurckaMu1rxyAhvaP45bJZ/vsn8ShgpuSJEbXtq67GfihRBCCCGEEEIIcWlc1DXxQgghhBBCCCHqvgpkgnZdIYN4cVVbt25dbUcQQgghhBBCiGqTa+KFELqKigrKysr0Z4+rQLVMquUB9TKplgfUyyR53FMtk2p5QL1MlyuPctd7yzXxDsk18e6pfk38Z7t+re0IDvVJaVbbEZQjZ+KFEDqj0YjZbK7tGFZUy6RaHlAvk2p5QL1Mksc91TKplgfUy6RaHiFEzcip3bqj9g/bCiGUUVxczIEDByguLq7tKDrVMqmWB9TLpFoeUC+T5HFPtUyq5QH1MqmWRwgh/qpkEC+E0JWXl5OTk0N5eXltR9Gplkm1PKBeJtXygHqZJI97qmVSLQ+ol0m1PEII8Vcl0+mFEEIIIYQQ4iont0qrO+RMvBBCCCGEEEIIUUfIIF4IIYQQQgghhKgjZDq9EEJnMpmIjo7GZDLVdhSdaplUywPqZVItD6iXSfK4p1om1fKAeplUyyOEqJkKmU5fZ8hz4oUQQgghhBDiKvfxjz/XdgSH+rVtUdsRlCPT6YUQQgghhBBCiDpCptMLIYQQQgghxFVOJmjXHTKIF0LoCgoK2LNnD8nJyfj6+tZ2HEC9TKrlAfUyqZYH1MskedxTLZNqeUC9TKrlgcuT6Wxu3iUp52IFB/jrP5/Z9n0tJqkS0qGd/nPemTO1mKSKf0iI/vOpnHO1mORP9YMCazuC+IuQ6fRCCCGEEEIIIUQdIWfihRBCCCGEEOIqVyGz6esMORMvhNAZDAa8vb0xGAy1HUWnWibV8oB6mVTLA+plkjzuqZZJtTygXibV8oCamYQQ4mLJI+aEEEIIIYSoJrkm3jG5Jt491a+J/+j7/9V2BIdubteytiMoR6bTCyGEEEIIIcRVTs7t1h0ynV4IoSsoKGDnzp0UFBTUdhSdaplUywPqZVItD6iXSfK4p1om1fKAeplUywNqZhJCiIslg3ghhJWKiorajmBHtUyq5QH1MqmWB9TLJHncUy2TanlAvUyq5QE1MwkhxMWQ6fRCCCGEEEIIcZWT6fR1R62ciU9LSyMpKYk777zTbtnzzz9Pz549r2ie4cOHM2bMmCv6njVRUlLC9OnT6dChA0lJSaxcudLl+uvXr2fo0KGkpqaSmprKkCFD+PDDD69I1otx5swZmjdvTmpqKkVFRbUd55LIzc0lLS2N33//vbajCCGEEEIIIf4CanU6/Q8//MD27dtrM0KdsG7dOtatW8fjjz/OmjVr6N+/v9N1n332WR599FESEhJYsGABr776KomJiUybNo1nn332CqauuY8//piysjIKCgr44osvajvOJZGbm8vChQtlEC+EEEIIIYS4JGptOr2vry+NGzdm8eLFtG/fvrZiXBFFRUV4e3tf8PYHDx4kPDycW265xeV6mzdvZvXq1YwfP54JEybor3ft2pXw8HAWLVpE586dr/hMh+rasGEDCQkJ5Ofns379evr161fbka463t7eJCcnX1R7vdRUy6RaHlAvk2p5QL1Mksc91TKplgfUy6RaHlAzkxCqqpDp9HVGrZ6JHzduHNu2bWPHjh1O11m7di1JSUmcsXne5K233sq0adP036dNm8aAAQPYunUrN998M61ateKuu+4iMzOTnJwcJk2aRJs2bbjhhhv4+OOPHb7Xhx9+yA033ECrVq0YPnw4Bw8etFpeWVnJ8uXL6du3Ly1atKBXr152U9vT0tJITU1l9+7dDBkyhJYtW/Lmm286/Xx//PEHEydOpG3btqSkpDBy5Ej27dunL+/ZsycrVqzg+PHjJCUlkZSURGZmpsOyVq1aRWBgICNGjLBbNnLkSAIDA1m1apVdnW3ZsoUBAwbQsmVLBg4cyK5du+y2X7t2LTfffDMtW7aka9euzJs3j/LycqvlSUlJ/Prrr9x///2kpKTQp0+fak/jP3r0KDt37uTmm2+mf//+/Pe//yUnJ8duva+++oqhQ4fSunVr2rVrx/Dhw/n111/15bm5uTz77LN069aNFi1a0LNnT15++WWrMt555x19H/bs2ZPFixdb3fRG24e2rr32WtLS0vTftcswPv30U/r27Utqaip33303R44cASAzM5NevXoBMGnSJLv9t2zZMnr37k3Lli3p0KED9957L0ePHq1WfV0uRqMRX19fjEZ17nmpWibV8oB6mVTLA+plkjzuqZZJtTygXibV8oCamYQQ4mLVao/Wo0cPmjVrxqJFiy5JednZ2cyZM4cHHniAuXPncuTIEaZOncrDDz9MkyZNSEtLo3nz5jzyyCP88ccfVtv+8ssvLF26lClTpvDCCy9w8uRJ7r//fkpKSvR1nn/+eV599VVuu+02li1bxt/+9jfmzp3L22+/bVVWaWkpU6ZM4ZZbbiE9PZ3OnTs7zJufn68PQmfNmsVLL73E2bNnueuuuzh+/DgACxcupF+/foSFhbFmzRrWrFlDeHi4XVllZWXs3LmT9u3bU69ePbvl9erVo3379uzcuZOysjKrOps1axYjR45k/vz5mM1mRo4cyenTp/V1/vWvf/Hkk0/SpUsXlixZwqhRo3j99deZN2+e3ftMnTqVLl26sGjRIpKTk5k2bRoHDhxw+PktbdiwAYABAwYwYMAASktL+fTTT63W+fjjjxk7diyhoaG8/PLLzJ07lzZt2nDixAmg6t4B99xzDx999BEjR44kPT2dCRMmcPbsWb2MN954g6eeeoquXbuyZMkS/va3v7Fw4UJeeukltxkd2bNnD8uXL2fq1KnMnj2bI0eO8MgjjwAQHh7OwoULAZg8ebLV/vvwww9ZsGABgwYN4p///CfPPfccycnJnD9//oJyXColJSUcOXLEqt3XNtUyqZYH1MukWh5QL5PkcU+1TKrlAfUyqZYH1MwkhBAXq9bvTv/AAw8wYcIEdu/eTatWrS6qrHPnzrF69WoSExMBOHnyJM8++yyjRo3iwQcfBKBly5b8+9//5vPPP+eee+7Rtz19+jSrV68mLi4OgGbNmnHjjTeydu1ahg4dypEjR1i9ejWzZs1iyJAhAHTq1ImioiIWLVrEkCFD9KO8paWlPPzww26ng69du5Zjx46xceNGEhISAGjXrh09evRg1apVTJs2jWbNmlG/fn3MZjMpKSlOyzp79iwlJSVERUU5XScqKori4mJycnKoX78+ADk5OcyfP5+OHTsCcN1119G9e3dWrlzJlClTyM/P59VXX+X+++9n8uTJAHTu3BmTycScOXMYOXIkwcHB+nvceeed+g0LU1NT2bJlC5s2bWLcuHEu62Ljxo2kpKQQExMDQKNGjfjoo48YOnQoUDUL4oUXXqBz585WB326d++u//zhhx/y66+/8s4771idSf/b3/4GQHl5OYsWLaJ///48+eSTAHTp0oXS0lJWrFjB6NGjrT5LdeTl5fHhhx8SEhICVD2Pdvr06WRlZREZGUlycjIAsbGxVvtv9+7dJCUlWd1Q8YYbbqjRe18OZWVlZGdn621OBaplUi0PqJdJtTygXibJ455qmVTLA+plUi0PqJlJCFXJbPq6o9YH8b1796ZJkyYsWrSIpUuXXlRZ4eHh+gAe0AfknTp10l8LCAggJCSErKwsq20TExP19aFq0NW0aVN++uknhg4dytatWwHo06eP1ZnsTp06kZ6ezvHjx7nmmmv01y0Hl8788MMPJCYm6gN4gKCgIDp16sSPP/5YvQ99kfz9/fUBvPZ7p06d+OmnnwDYuXMnBQUF3HjjjXafu6ioiP3793Pdddfpr3fp0kX/2dfXl+joaLu6trV3717279+vD6wB+vfvz8KFCzl27BjR0dEcPHiQrKwsHnvsMaflfPvttyQkJDicCg9V9xY4e/YsN954o9Xr/fr1Y+nSpezevbta+81S06ZN9QE8QOPGjQH0QbwzzZo146233mL27Nn07t2b1q1bYzKZavTezhQUFOg/e3h44OXlRUVFhcM7/vv6+gJV922oqKigsLAQgMLCQsxmM56enpSWllJaWmq1ndFoxNvbm8rKSn0bR+Xa5gEwmUyYTCbKysrszowYDAZ8fHystrPM5O3tjdFopLi42OpyDgBPT0/MZjPl5eUUFxc7LddRJi8vLzw8PCgpKbFq52Bfh5Z5LD9rYWGh3aNZXNWhqzoC8PHxwWAw6PvGkm0dWmbS9o2zcl3VoatMjvaNJcs6tK0jbd+4a4e2dWiZx1EdumvflnVom0nbN47aoav2bZnJUR1Wt31reS3L1/aNo3borH1b5tHKs22H2r5xV4e2eSzLd9QOnbVvy0zO+ght37iqQ+11y+3dtUNH7du2jtz1Ec7qUGP7earbz1qWa5vJ1b5xVYfOMrlqh476CMs8FRUVTvsIy33jqA41jurBVT/rqI+wrSPtc9akn/Xw8LDa1parfta2j3CURwghakutD+INBgNjx45l8uTJ/PLLLxdVVkBAgNXv2qDI39/f6nWz2Wz3xyc0NNSuvNDQULKzs4GqM92VlZV06NDB4XtbDuJ9fHwcTmm3lZubq58Rt33f/fv3u93eUnBwMGazWZ+G7yyjl5cXQUFB+muWA1DL99emwGtT0bWz2Y7KtGRb1yaTye0UtvXr12M0GunSpQu5ublA1UGQtLQ0NmzYwOjRo/Xr4x1dSqDJyclxufzcuXOA/b7WfteW14SzNmfbvmwNHDiQ8+fP8+6777Jy5Ur8/f257bbbmDp16kXffGfPnj36zyEhIcTHx1NSUmL1uqZt27YAZGRkWE3lz8jIIC4ujtDQUM6ePWt3rX5AQACJiYlUVFS4LNc2D0CDBg2IiIggLy/P7r4TPj4+NGvWDKg6uGP5ZS0jIwNfX198fHw4fvy41SUfAJGRkVxzzTUUFBTw22+/WS0zmUxWM31sMzVp0gR/f3+ys7PtDjqFhoYSFxdHcXGx1XYZGRkYDAbatGkDwKFDh+y+JDZq1Ijg4GDOnDljdy8LV3UEkJKSgoeHB0ePHtX/XWhiYmIIDw/n3LlzZGRkWGWqV68eTZs2dVpu8+bN8fb25tixY3b3GnGVycvLixYtWgCwf/9+uy/hSUlJ+Pn5ceLECU6ePKnnAQgLC6Nhw4YUFRXZlWs0GvUDbwcPHrT68m+Z59SpUxw7dsxq26CgIBISEigrK3P4WVNTUzEYDBw+fJj8/HyrTLGxsdSvX5+cnBwOHz5stZ2fnx9JSUlUVlbalWuZSbvniqXo6GiioqLIz8+3u5TI29ub5s2bA+jvabn/kpOT8fX1JSsrS//bowkPDycmJobCwkKr+6ZY5gE4cOCAXf/TuHFjAgMDyc7OtuuztT5C25+WeSzLt+0jAKd9hGUmZ31Eq1atMJlMHD161K7v1foI7f0sM7nqI6DqAKltH2FbR+76iP3799sNBps0aaIPCG3ryFkfATjtI2wzOeojAgMDady4sdP2bTm7yzaTsz4CcNhHWOYpKSlx2kdERUURHR1Nfn6+3VNXvLy8aNSoEQBHjhyxOwDgqI/QOOojbOsI7PsIgISEBIKCgpz2EdoMRdt6AMd9hMa2j3CURwghakutD+IBbrrpJtLS0li8eDHR0dFWy7Sj37Z/UG2/1F4s2wGB9pr2hy4wMBCDwcBbb73l8IxpfHy8/rPl0WhXAgMDOXTokMP3DQwMrG50oOoodmpqKt999x0FBQV2R4wLCgr47rvvSE1NxdPzz91u+wdae/+wsDA9I1Rdm+/ozHKDBg1qlNNWZWUlH3/8MRUVFXZnyAE++ugjRo8erR94sP3DbykoKMjqy62j5WD/mbV9r31WLy8vu/ZWWlrq8KzPhTIajdxzzz3cc889nDhxgo0bN/Lyyy8THBysX/pxobQp/PDnWQiz2Wz1uq24uDj9TLw2gNfqIzg4GD8/P7v82v9dlWubB6wPrtkus/y3o/3bs8yk9QdRUVF2B2y0du3r6+uyXEeZtHLDwsLsLqnQ6tDLy4vk5GSrPJZnwuLj4x2eIYKqgZLtAS5XeeDPOo6JiXF4Jh6q2qxtJssDiI7K1TJFR0cTERFR7UyWdWg540mj1WFERAS+vr5WdaTtG+0u0c40atTIrg419evXt+sXtX3j6enpsFwtc2xsLOfPn7fKpNVDUFCQXX+p1b3BYHCZt0GDBnaXMGn7xs/Pz2UdxsbGcujQIat2pB3Ai4yMtDvAq9Whj4+Py0wJCQlOzyKHhYVZHcQF6zoE7Nq1RusjLGl16KiP0DjrI7T3i4mJsfu7r9Wh1pYtMznqIyy56iM07vqIxMREh3WoHRyxrSPbPsIZR32ExlEf4a59W96ozTaTbR/hbLua9hHu2rf2+Ro2bGh3QNqyj7A9mH4xfYSW11kfoR1McNS2LfsIZ+3bUR8hxF9VBTKfvq5QYhBvNBoZO3Ys06ZNs5qaDeh/QA4ePKj/fODAAZdnnC/E/v37OXz4MLGxsUDVWZK9e/fq179rU85zcnIu2SPa2rZty6ZNmzh48KB+9PrcuXNs3bpVf9+auOeeexg3bhwrVqxg/PjxVstWrFhBTk6O1X0AoOqa7m+//Vb/fHl5eWzdutXqunYfHx+ysrLo3bv3hXxMl3744QeOHz/OhAkTaNeundWy//znP6Snp7Nv3z6aNGlCZGQka9eudXqvgU6dOvHxxx/z008/0bp1a7vl8fHxhISE8Omnn1p9lk8++cTqLExERASlpaUcOXKEhg0bArBt2za7swrVUZ0z8xEREYwYMYINGzbYnZm+EI6+bGh353VG+7Ll6elJeHg4/v7++pcqbfqkIwaDwe2XG2fLPT09rQ4oOdvOMpP25dNyaqstDw+PC85kNpudXjOp1aFlHst1HQ18NK7q0FUewOXMDK0OnWVyVa6rOnS3ratlZrMZf39/h3nctcMLrcPqtG+j0egwk6t26K59u6pDd+3bz8/PYR5w3Q7dte8LrUOz2ew0D7huhxfTR7iqQy8vL5eZLrTcC61DZ//ONJerfbuqQ3eZqtvPWrqYfrakpITw8HD8/PyctuHq9LPOXEgdapmc1RFUr58VQgiVKNMr3XzzzSxatIjt27dbXVveunVroqKi+Mc//qHfaG3ZsmV2ZxMuVmhoKGPHjmXixIkALFiwgIiICAYOHAhUDQDvvPNOHn30UUaOHEnr1q0pLS0lIyOD7du3s3jx4hq/58CBA1m5ciVjxozhoYcewsvLi9deew1PT0+7wXZ19OrVi7vuuouFCxeSlZWln9n+7LPPePfdd7nrrrvsDkAEBQXxxBNPMHHiRPz9/UlPT6eyslJ//4CAACZOnMhLL71EVlYW1113nT7Fd/PmzaSlpbn8o+rORx99hK+vL/fdd5/dJQiJiYmsXLmSDRs2MGXKFB577DEmT57MhAkTuPXWWzGbzezatYuWLVvSo0cPbr31Vt566y1Gjx7N+PHjSUxM5MSJE/zwww88++yzeHh4MG7cOJ577jlCQkLo3r07u3btIj09nXvuuUc/A9utWzd8fX158sknGTVqFFlZWbz++utuBz2OhIWFERAQwMaNG2nQoAFms5mkpCSee+45AgICSElJISAggB07drB3716GDRt2wXV5KZjNZv3mgqpQLZNqeUC9TKrlAfUySR73VMukWh5QL5NqeUDNTEIIcbGUGcR7eHgwevRoq5ubQdWR1YULF/L0008zadIkGjZsyOOPP86cOXMu6fs3b96cPn368NJLL5GdnU3r1q2ZNWuW1VHbJ598kvj4eNasWcOiRYuoV68e8fHxDqeBV4efnx9vvPEGc+bMYcaMGVRUVNCmTRtWr17t8i7zrsyYMYPWrVvz1ltvMWHCBKDqWr45c+Zw22232a0fFhbG1KlTefHFFzly5AiJiYksX77cairniBEjiIiI4F//+herV6/G09OThg0bcv3111/UzdhKS0vZtGkTN9xwg8N7CGgD7Q0bNjB58mT69euHt7c3S5YsYfLkyXh5edGsWTP9rLrZbGblypXMmzePpUuXkpOTQ2RkJP3799fLHD58OJ6enqxcuZK3336bsLAwxo8fz9ixY/V1goODefXVV3nhhRd48MEHSU5O5sUXX2T48OE1/oxGo5HZs2fzyiuvcO+991JSUsLmzZtJTU3l3Xff5b333qOwsJCYmBimT5/O4MGDL6AmL53y8nIKCwvx8fGxuiFQbVItk2p5QL1MquUB9TJJHvdUy6RaHlAvk2p5QM1MQqjK2SU/Qj2GStlbV61p06bx888/689oF6KgoIA9e/boN9hSgWqZVMsD6mVSLQ+ol0nyuKdaJtXygHqZVMsDlyfT2dy8S1LOxQoO+PM+Cme2fV+LSaqEdPjzssg8B/dcutL8LW7efCqn5jcvvhzqB9XsnldX2ntbd9R2BIcGd2pT2xGUY3S/ihBCCCGEEEIIIVSgzHR6IYQQQgghhBC1QyZo1x0yiL+KXer7CgghhBBCCCGEuLxkOr0QwoqKj9JRLZNqeUC9TKrlAfUySR73VMukWh5QL5NqeUDNTEIIcTHkxnZCCCGEEEJUk9zYzjG5sZ17qt/Y7p3//ljbERwa2qVtbUdQjpyJF0IIIYQQQggh6ggZxAshdIWFhfz8888UFhbWdhSdaplUywPqZVItD6iXSfK4p1om1fKAeplUywNqZhJCiIslFwkJIXSVlZUUFxcrdXdS1TKplgfUy6RaHlAvk+RxT7VMquUB9TKplgfUzCSEquTfSd0hZ+KFEEIIIYQQQog6QgbxQgghhBBCCCFEHSHT6YUQQgghhBDiKifT6esOecScEEIIIYQQQlzl3vq69h9V6Mgd3dq5X+kqI9PphRBCCCGEEEKIOkKm0wshhBBCCCHEVa5CJmjXGTKIF0LoCgoK2LNnD8nJyfj6+tZ2HEC9TKrlAfUyqZYH1MskedxTLZNqeUC9TKrlAfUyXa48B46duGRlXaiE6Aj957O5ebWYpEpwgL/+8+EHJtdikj/FvvZKbUcQfxEynV4IIYQQQgghhKgj5Ey8EEIIIYQQQlzlZDZ93SFn4oUQQgghhBBCiDpCBvFCCJ2HhwchISF4eHjUdhSdaplUywPqZVItD6iXSfK4p1om1fKAeplUywPqZVItjxCibpLnxAshhBBCCFGHyY3t7MmN7Wruja++q+0IDg2//rrajqAcORMvhNBVVFRQVFRERUVFbUfRqZZJtTygXibV8oB6mSSPe6plUi0PqJdJtTygXibV8ghhqaKyUsn/hD0ZxAshdEVFRfzyyy8UFRXVdhSdaplUywPqZVItD6iXSfK4p1om1fKAeplUywPqZVItjxCibpJBvBBCCCGEEEIIUUfII+aEEEIIIYQQ4ipXiUxdrysu65n4tLQ0kpKSuPPOO+2WPf/88/Ts2fNyvr2d4cOHM2bMmCv6njVRUlLC9OnT6dChA0lJSaxcudLl+ps3b2bEiBFcd911tGjRgp49ezJz5kwOHTp0WXNee+21pKWl6b/b1uv27dtZsmRJtcrS2oj2X4cOHbj77rv54YcfLknW7du3k5SUxP/+9z+3OVJTUy/JewohhBBCCCHE5XJFzsT/8MMPbN++nfbt21+Jt6uz1q1bx7p165gzZw4NGzbkmmuucbru3LlzSU9Pp2/fvjz77LOEhIRw5MgRPvjgAx5++GE+/PDDK5b7qaeewmj883jQd999x4oVKxg7dmy1tvf29mbVqlUAZGVlsXjxYu69917Wrl1LkyZNLktmIYQQQgghhKiLLvsg3tfXl8aNG7N48eK//CC+qKgIb2/vC97+4MGDhIeHc8stt7hcb8uWLaSnpzNu3DgmTZqkv96uXTv+/ve/8+WXX162jI40btz4orY3Go2kpKTov7dq1YqePXvyzjvvMHPmTLv1KysrKS0txWw2X9T7Cnu+vr60bdu2tmNYUS2TanlAvUyq5QH1Mkke91TLpFoeUC+TanlAvUyq5RHCkjx5vO64Ije2GzduHNu2bWPHjh1O11m7di1JSUmcOXPG6vVbb72VadOm6b9PmzaNAQMGsHXrVm6++WZatWrFXXfdRWZmJjk5OUyaNIk2bdpwww038PHHHzt8rw8//JAbbriBVq1aMXz4cA4ePGi1vLKykuXLl9O3b19atGhBr1697Ka2a9Ovd+/ezZAhQ2jZsiVvvvmm08/3xx9/MHHiRNq2bUtKSgojR45k3759+vKePXuyYsUKjh8/rk8tz8zMdFjWihUrqF+/PuPGjXO4vEePHvrPSUlJLFu2jJdeeonOnTvTsWPHan9GgM8//5wbb7yRli1bMmjQIHbv3m23juV0+rS0NBYuXEhBQYH+OYYPH+60XhyJjo4mJCRE//zaPt+yZQu33HILLVu25IsvvgDgs88+49Zbb6Vly5Z06dKF2bNnU1xcbFfmmTNnGD9+PCkpKXTp0qVa0/1zc3N5+umn6dKlCy1atGDgwIH897//dfjZN2zYQJ8+fWjdujVjx47l3Llz/PHHH4wcOZLU1FT69+/P9u3brbbdvHkzAwcOJDU1lWuvvZaBAweyZcuWGtWVEEIIIYQQ4upyRabT9+jRg2bNmrFo0SKWL19+0eVlZ2czZ84cHnjgATw9PXnuueeYOnUqPj4+XHvttdx+++28++67PPLII7Ru3dpqWvovv/zCkSNHmDJlCgDz58/n/vvv59NPP9XP7D7//PO89957jB07ltatW7Njxw7mzp2Ll5cXw4YN08sqLS1lypQp3HvvvTz88MMEBQU5zJufn8/w4cMxGo3MmjULLy8vXnvtNe666y7Wr19PVFQUCxcuJD09ne+//56FCxcCEB4ebldWWVkZO3bsoE+fPphMpmrV1+uvv07r1q15/vnnKSsrq/Zn3LNnDxMnTqRbt25Mnz6dzMxMHnroIUpKSpy+1+DBg8nKymLDhg36FHk/P79q5bSsr5ycHKvPf/LkSZ577jkeeOABoqKiiI6OZvPmzUycOJH+/fszZcoUDh48yLx58zh+/DivvvqqVZkzZsygf//+pKWlsXXrVubNm0dgYKDV/rRUUlLCfffdx+nTp3nooYeIiIhg/fr1jBkzRj/gpPn11185e/Ysjz76KPn5+Tz33HPMmDGDP/74g9tuu4377ruPpUuXMmHCBL788kvq1avHkSNHmDRpkp69oqKCvXv3cu7cuRrV1aVWVFRERkYGcXFxl3zGxoVSLZNqeUC9TKrlAfUySR73VMukWh5QL5NqeUC9TKrlEULUTVfs7vQPPPAAEyZMYPfu3bRq1eqiyjp37hyrV68mMTERqBrgPfvss4waNYoHH3wQgJYtW/Lvf/+bzz//nHvuuUff9vTp06xevZq4uDgAmjVrxo033sjatWsZOnQoR44cYfXq1cyaNYshQ4YA0KlTJ4qKili0aBFDhgzRr/8uLS3l4Ycfpl+/fi7zrl27lmPHjrFx40YSEhKAqqnvPXr0YNWqVUybNo1mzZpRv359zGaz1dRyWzk5OZSUlBAdHV3t+goMDGThwoUYDAaAan/GZcuWERUVxaJFi/Dw8ADAy8uLJ554wul7RUZGEhkZaTdF3h3t4EJWVhYvvPAC5eXl9O3bV19+7tw50tPTad26tf7aQw89REpKCi+//DIA3bp1w8fHh5kzZ7Jv3z6rgXaHDh147LHHAOjatSunT5/mtddes9qflj766CP27t3LunXr9MsFunbtyuHDh1m8eDELFizQ183Pz2fJkiWEhIQAsG/fPlasWMHTTz+tHyQIDw/n5ptv5ttvv+WGG27g119/pbS0lBkzZugHObp27Vrt+nKloKBA/9nDwwMvLy8qKiocPpPW19cXqPpSUVFRQWFhIefPn+f8+fN4enri6elJaWkppaWlVtsZjUa8vb2prKyksLDQabm2eQBMJhMmk4mysjK7A0IGgwEfHx+r7Swzmc1mjEYjxcXFlJeXW23r6emJ2WymvLzcbjaGZbmOMnl5eeHh4UFJSYneFjW2dWiZp6KiQv+shYWFdtPQzGaz0zp0VUcAPj4+GAwGfd+4qkPLTID+xdBRud7e3k7r0FUmR/vGkmUdalm0OtL2jbt2aFuHlnkc1aG79m1Zh7aZtH3jqB26at+WmRzVYU3at2Ue+HPfOGqHztq3ZR5HdQh/7htXdVhWVmaXx7J8R+3QWfu2zOSsj9D2jas6LC0ttcvkrh06at+2deSuj3BWhxUVFQ7rqLr9rGW5tplc7RtXdegsk6t2qLVv+LMOLfNUVFQ47SO0cp3VYWVlJefPn6egoMCuvbjqZx31EbZ1BDXvZz08PJzWkVaHzvpZ2z7CMo92aaKzfeOqn9X+rZWWltq9Z3Xbt1aHjupIiItRIbPp64wrNojv3bs3TZo0YdGiRSxduvSiygoPD9cH8IA+IO/UqZP+WkBAACEhIWRlZVltm5iYqK8PEBsbS9OmTfnpp58YOnQoW7duBaBPnz5Wf2Q6depEeno6x48ftzqz3717d7d5f/jhBxITE/UBPEBQUBCdOnXixx9/rN6HtqENyKujW7duVutX9zP+9NNP9OzZUx/AA9x4440uB/EXoqCggObNm+u/BwYGMnPmTKtBbVBQkNUA/vz58+zZs0cfmGv69evHzJkz+fHHH60G8b1797Zar2/fvqxbt46srCyHB0S++eYbmjRpQlxcnF0drV+/3mrdpk2b6gN4cNwetde09piUlISHhwdTp07l9ttvp127dvj7+zuuoBras2eP/nNISAjx8fGUlJRYva7RrsvLyMjQB4Da73FxcYSGhnL27FmOHj1qtV1AQACJiYlUVFS4LNc2D0CDBg2IiIggLy/P7lIWHx8fmjVrBsDevXutvqxlZGTg6+uLj48Px48f5/Tp01bbRkZGcs0111BQUMBvv/1mtcxkMlkdPLTN1KRJE/z9/cnOzrbrM0JDQ4mLi6O4uNhqu4yMDAwGA23atAHg0KFDdl/mGjVqRHBwMGfOnLG7PMZVHQGkpKTg4eHB0aNHyc3NtVoWExNDeHg4586dIyMjwypTvXr1aNq0qdNymzdvjre3N8eOHbO7fMlVJi8vL1q0aAHA/v377b6EJyUl4efnx4kTJzh58qSeByAsLIyGDRtSVFRkV67RaNSfDHHw4EGrQZBlnlOnTnHs2DGrbYOCgkhISKCsrMzhZ01NTcVgMHD48GHy8/OtMsXGxlK/fn1ycnI4fPiw1XZ+fn4kJSVRWVlpV65lJu0yLkvR0dFERUWRn5/PgQMHrJZ5e3vrfZ32npb7Lzk5GV9fX7KyssjOzrbaNjw8nJiYGAoLC60uxbK9tvbAgQN2g6vGjRsTGBhIdnY2x48ft1qm9RHa/rTMY1m+bR8BOO0jLDM56yNatWqFyWTi6NGjdjOQtD5Cez/LTK76CKg6MG/bR9jWkbs+Yv/+/XaDwSZNmuh/C23ryFkfATjtI2wzOeojAgMDady4sdP2bXmg3DaTsz4CcNhHWOYpKSlx2kdoM+Hy8/P5/fffrZZ5eXnRqFEjoOpkge3g1VEfoXHURzi6bty2jwBISEggKCjIaR8RFRXlsI7AcR+hse0jLPMcPnzYaR8BVSeSzGazwz4iLCwMqGqHtvvcso/Yt2+f3SDfto+Qa+uFuHpdsUG8wWBg7NixTJ48mV9++eWiygoICLD6XZtWbjsIMpvNdl9mQkND7coLDQ3VvzCdPXuWyspKOnTo4PC9LQfxPj4+1KtXz23e3Nxc6tev7/B99+/f73Z7S0FBQXh5edn9oXLF9jNX9zNmZ2fbbevn54eXl1eNMrvj7e3N6tWrMRgMBAcHExUVZXd23Lb+8vLyqKystMvn7++P2Wy2+1JoOci2LC87O9vhIP7s2bP8+uuvVgcXNJYHNaB67VG7VENrj/Hx8SxZsoSlS5cyfvx4jEYjXbp0YebMmTWaZeFIcnKyXVaz2Wz1uq24uDj9TLw2gA8MDAQgODjY7pIIbf8YjUaX5drmAev6sV1mebBJ+5JpmUlre1FRUXaXm3h6VnVnvr6+Lst1lEkrNywsjODgYKtllrNQkpOTrfJYnt2Pj493eIYIqtqfq4M0jupQq+OYmBiHZ+Kh6ku+bSbLPslRuVqm6OhoIiIiqp3Jsg4tD6JqtDqMiIjA19fXqo60fePt7e2yvTRq1MjpTXXq16+vt0mNtm88PT0dlqtljo2N5fz581aZtHoICgqyO5ul1b3BYHCZt0GDBvoAQaPtGz8/P5d1GBsby6FDh6zakXZ2NDIy0q7P0+rQx8fHZaaEhASHZ5Ghqn3bXvZlWYeAXbvWaH2EJa0OHfURGmd9hPZ+MTExdn2eVodaW7bM5KiPsOSqj9C46yMSExMd1qHWf9vWkW0f4YyjPkLjqI9w174t/07aZrLtI5xtV9M+wl371j5fw4YN7aaKW/YRtn+7L6aP0PI66yO0gwmO2rZlH+GsfTvqI2JjY/XtXbVvR31EWVkZ2dnZbtuh5YkIjas+Qghxdblig3iAm266ibS0NBYvXmz3R1vr3G2PftuegbpYtmfvtNe0LwOBgYEYDAbeeusth9ecx8fH6z9X92x4YGCgw2e3nz592u4Pjjuenp60adOGbdu2UVZWpv+hcMU2Z3U/Y1hYmF195efnO7xx3MUwGo20bNnS5Tq2n8Hf3x+DwWB3liAvL4+SkhK7erVd79SpU8CfR8RtBQYGkpSUxPPPP1+tz3AhunXrRrdu3cjPz+frr79m9uzZTJ8+Xb+XwIVyNL3OaDS6nHZn+2XLcuClTZ90xGAwuJ3O52y5Nl2/utv5+PjoXz5dHUjy8PC44Exms9npUw9s69DHx8fud2dc1aGrPGC/byzZ1qGPj4/V+q7KdXcwztW2rpaZzWa9LmzryF07vNA6rE771r6g22Zy1Q7dtW9XdeiufWv7yTYPuG6H7tr3xdShszyWeWta7sXUoTaIdZbpYsq90Dp0ledyte/q9LPOMtW0nwUuqp/VpoB7e3tfkn7W1oXUoZbJWR1peZ1xVIfa+hfSvrU87tqhu35WntAjLge5O33dcUXuTq+/mdHI2LFj2bx5s9V0QEA/2ms5vfbAgQN2U/8u1v79+62mTh4+fJi9e/fqU7W1u7fn5OTQsmVLu/9qepM2qJoS9ttvv1l9tnPnzrF169YLmgp13333kZ2d7fQO6+7ucF7dz9iqVSu+/PJLqylxn376qdt8JpPJ5c3vLoV69eqRnJxsl+eTTz4B7Kfh/fvf/7b6fdOmTYSHhxMZGemw/E6dOnH06FHCw8Md1tGl5OfnR79+/ejfv7/d9NsrzWw2ExcXp9SXA9UyqZYH1MukWh5QL5PkcU+1TKrlAfUyqZYH1MukWh4hRN10Rc/EA9x8880sWrSI7du3W11b3rp1a6KiovjHP/7BlClTyM/PZ9myZU7v+H6hQkNDGTt2LBMnTgRgwYIFREREMHDgQKDqLPSdd97Jo48+ysiRI2ndujWlpaVkZGSwfft2Fi9eXOP3HDhwICtXrmTMmDE89NBD+t3pPT09rW66V13du3fn/vvvJy0tjd9//53+/fsTHBxMZmYmH3zwAXl5eS6v1a/uZxw9ejSDBg3iwQcfZNiwYWRmZrJ8+XK3Z/C0a1RXrVpFamoqfn5++nVyl9L48eN58MEHmTp1KrfccguHDh1i3rx59O3b124a2rZt23jhhRfo3Lkz33zzDevWrWPmzJkOb2oHcNttt/HOO+9w9913M2LECOLi4sjLy9NvSKc93eBCvfPOO+zatYuuXbsSFhZGZmYm69evp3PnzhdV7sXy9PR0eMlJbVItk2p5QL1MquUB9TJJHvdUy6RaHlAvk2p5QL1MquURQtRNV3wQ7+HhwejRo3nyySetXjeZTCxcuJCnn36aSZMm0bBhQx5//HHmzJlzSd+/efPm9OnTh5deeons7Gxat27NrFmzrI6IPvnkk8THx7NmzRoWLVpEvXr1iI+P58Ybb7yg9/Tz8+ONN95gzpw5zJgxg4qKCtq0acPq1avtrpWqrkceeYTU1FTefPNNHn/8cQoLCwkPD6dLly6MHDnS7fbV+YzNmjVjwYIFzJ07l/Hjx5OYmMi8efPclt+jRw/uuOMOli1bxunTp2nXrh1vvPHGBX1OV3r16sWCBQtYtGgR48aNIygoiNtvv93hAPuZZ55hzZo1vP3229SrV49JkyZx5513Oi3bbDbz+uuvk5aWxpIlS8jOziYoKIhmzZpxxx13XHT2pKQkvvzyS2bPnk1OTg5hYWH079+fSZMmXXTZF6O0tJSzZ88SHBxc7UcYXm6qZVItD6iXSbU8oF4myeOeaplUywPqZVItD6iXSbU8QliS6fRVvvjiC+bPn8+hQ4eIjo5m9OjR/P3vf3e5TWZmJr169bJ7vXXr1rz77rtWr+3YsYMXXniBPXv2EBoayrBhwxg1alSNblxuqJS9JYT4/woKCtizZ49+B1wVqJZJtTygXibV8oB6mSSPe6plUi0PqJdJtTygXqbLlefAsROXrKwLlRD9540Qz+bm1WKSKsEBf94s8vADk2sxyZ9iX3ultiO4lP7vb2o7gkOjel+5mao//PADd999N4MGDaJfv35s27aNJUuWMH/+fJcndLVB/OTJk2nfvr3+er169axuCHz48GFuu+02OnfuzJ133sm+ffuYO3cuDz/8cLVOxGqu+Jl4IYQQQgghhBBCNa+99hqtWrXimWeeAaBDhw4cPXqUV199tVqzsmNjY60eAWpr+fLlBAcH88orr2A2m+nYsSNnzpxhyZIlDB8+vNr3y7iiN7YTQgghhBBCCKGeispKJf+7UkpKSti+fbvdYL1fv34cOHCAzMzMi36Pr7/+ml69elkN4vIVrgAA7UlJREFU1vv160dubi47d+6sdjkyiBdCCCGEEEIIcVU7cuQIpaWldjfkTkhIAKyfoubM008/TXJyMh07duTJJ58kJydHX1ZQUMDx48ftym/UqBEGg6Fa5WtkOr0QQmc0GgkICHB61/7aoFom1fKAeplUywPqZZI87qmWSbU8oF4m1fKAeplUyyNEXeDohnGWNm/efEne59y5cwAEBARYva79ri13xGw2M2zYMLp06UJAQAA//fQTS5Ys4eeff+a9997DZDKRl5fnsHyz2YyPj4/L8m3JIF4IofP29ra6+YYKVMukWh5QL5NqeUC9TJLHPdUyqZYH1MukWh5QL5NqeYSwdCWnrl8peXl5nDx50u16MTExF/U+4eHhPP300/rv1113HYmJiYwZM4Z///vf9OvX76LKtyWDeCGErrKykoqKCoxGY40ec3E5qZZJtTygXibV8oB6mSSPe6plUi0PqJdJtTygXibV8ghRF1zMmfZPP/3U7tHmjnz88ccEBgYC6GfMNbm5uQD68urq3r07vr6+/PLLL/Tr1w9/f3+H5ZeUlFBYWFij8mUujxBCV1hYyK5duygsLKztKDrVMqmWB9TLpFoeUC+T5HFPtUyq5QH1MqmWB9TLpFoeIf7qBg8ezL59+9z+l5CQQMOGDTGZTHbXpmu/217LXlO+vr5ERUXZlX/o0CEqKytrVL4M4oUQQgghhBDiKldZWankf1eK2Wymffv2bNq0yer1jz/+mISEBBo0aFCj8r788ksKCgpo2bKl/lq3bt3YvHkzpaWlVuUHBASQmppa7bJlOr0QQgghhBBCiKveAw88wN13383TTz/NTTfdxPbt29mwYQPz5s2zWq9Zs2bcdttt/OMf/wBgzpw5GAwGUlJSCAgIYPfu3SxdupQWLVpwww036NuNHDmSjz76iClTpjBs2DB+++03li9fzsMPP1ztZ8SDDOKFEEIIIYQQQgiuvfZa0tLSmD9/Pu+//z7R0dE899xz3HTTTVbrlZeXU1FRof+ekJDA22+/zbvvvktRUREREREMGjSIiRMn4un555A7NjaW5cuXM2fOHEaPHk1ISAgTJ05kxIgRNcopg3ghhBBCCCGEuMpV/PVuTn9BevXq5faxdvv27bP6ffDgwQwePLha5bdp04Z33333gvMBGCqv5IUGQgghhBBCCCGUs/CTr2s7gkPjb+pW2xGUIze2E0IIIYQQQggh6giZTi+EEEIIIYQQVzmZoF13yCBeCKErKChgz549JCcn4+vrW9txAPUyqZYH1MukWh5QL5PkcU+1TKrlAfUyqZYH1MukWh64PJlyfj/ofqXLLKjxn8/c/i0zqxaT/KlJg8jajiD+ImQ6vRBCCCGEEEIIUUfImXghhBBCCCGEuMrJdPq6Q87ECyGEEEIIIYQQdYQM4oUQOpPJRIMGDTCZTLUdRadaJtXygHqZVMsD6mWSPO6plkm1PKBeJtXygHqZVMsDamYSQrgmz4kXQgghhBBCXDJyYzvHVL+x3fwNX9Z2BIceGtCjtiMoR87ECyF0ZWVlnD17lrKystqOolMtk2p5QL1MquUB9TJJHvdUy6RaHlAvk2p5QL1MquUBNTMJIVyTQbwQQldSUsLBgwcpKSmp7Sg61TKplgfUy6RaHlAvk+RxT7VMquUB9TKplgfUy6RaHlAzkxDCNbk7vRBCCCGEEEJc5eQi67rjqjkTv3nzZkaMGMF1111HixYt6NmzJzNnzuTQoUO1Ha3acnNzSUtL4/fff6/2NpmZmcyYMYMePXrQokULrrvuOkaOHMmnn356GZPC888/T8+ePfXf165dS1JSEmfOnAFq9lkyMzNJSkrS/2vatCldu3ZlypQp/PHHHzXOZptFCCGEEEIIIeqKq+JM/Ny5c0lPT6dv3748++yzhISEcOTIET744AMefvhhPvzww9qOWC25ubksXLiQxMREGjdu7Hb9Xbt2cf/99xMSEsKoUaNo3Lgx+fn5bNmyhalTpxIXF0fTpk2vQHK4/vrrWbNmDQEBAUDNPwvA5MmTad++PRUVFRw5coRXX32V0aNHs379ejw8PC5nfCGEEEIIIYRQwl9+EL9lyxbS09MZN24ckyZN0l9v164df//73/nyy4u/C2NRURHe3t7Vfv1KKC4u5qGHHiIyMpJ33nkHPz8/fVnPnj0ZNmyYPqC2VVlZSWlpKWaz+ZLlCQkJISQk5KLKiI2NJSUlBYA2bdrg5+fHgw8+yKFDh6p9IEC4ZjAY8PHxwWAw1HYUnWqZVMsD6mVSLQ+ol0nyuKdaJtXygHqZVMsD6mVSLQ+omUnUjgqZT19n/OWn069YsYL69eszbtw4h8t79Kh6ZIE2Zdt2mrmzaeE7d+7kvvvuIyUlhRdffJHt27eTlJTEV199xcSJE2nTpo1+0CA3N5enn36aLl260KJFCwYOHMh///tfq/cZPnw4Y8aM4dNPP6Vv376kpqZy9913c+TIET1fr169AJg0aZI+tTwzM9Ph5/rkk084fvw4kydPthrAa5o2bUp0dDQA06ZNY8CAAWzZsoVbbrmFli1b8sUXXwCwc+dO7r77blJSUmjbti1Tpkzh9OnTVmWdOHGCsWPH0rp1a7p27Up6errd+1lOYa/pZ3GmXr16AFZ3U/3qq6+477776NixI23atGHw4MF8/fXXbsuaO3cuN998M6mpqXTt2pXJkydz8uRJq3Xc7SNNSUkJ8+bNo1evXrRo0YJu3boxbdo0q3WqU6+1wcfHh2bNmuHj41PbUXSqZVItD6iXSbU8oF4myeOeaplUywPqZVItD6iXSbU8oGYmIYRrf+kz8WVlZezYsYM+ffpgMpkuadlTpkxhyJAhjBkzBh8fH4qKigCYMWMGt9xyC4sWLcJoNFJSUsJ9993H6dOneeihh4iIiGD9+vWMGTNGH9hq9uzZw5kzZ5g6dSrl5eXMmTOHRx55hDVr1hAeHs7ChQsZP368Pq0cIDw83GG+77//Hg8PDzp16lStz3Py5Emee+45HnjgAaKiooiOjmbnzp0MHz6c7t27M2/ePAoLC5k/fz7jxo1jzZo1+rbjxo3jxIkTPP300/j7+5Oens7x48fx9HTcvGr6WTQVFRWUlZVRUVHB0aNHWbhwIY0aNSIxMVFfJzMzkx49ejBixAiMRiNff/01o0ePZtWqVfr7OHL69GnGjBlDeHg4Z86c4V//+hfDhw9n48aNVp/D1T7STJgwgW3btjFmzBhSUlI4c+YMn332mb68uvUqhBBCCCGEELb+0oP4nJwcSkpK9DPOl9LQoUMZPXq0/vv27duBqqnqjzzyiP76Bx98wN69e1m3bp0+5btr164cPnyYxYsXs2DBAn3dvLw8PvzwQ33aeUFBAdOnTycrK4vIyEiSk5MB62nlzpw4cYKQkJBqT+c/d+4c6enptG7dWn/tiSeeoEWLFixcuFCfYtWkSRP9rH337t35+uuv+fnnn1m5ciUdO3YEoH379nTv3p2goCCH72U2m2v0WTQPP/yw1e/R0dGkp6dbXQ9/11136T9XVFTQvn17fv/9d959912Xg/jZs2frP5eXl5Oamkq3bt3Ytm0bXbp00Ze520fffPMNX331FS+//DIDBgzQt7P8+eWXX3Zbr7WloKCAvXv30rRpU3x9fWsthyXVMqmWB9TLpFoeUC+T5HFPtUyq5QH1MqmWB9TLpFoeUDOTqB2VyHT6uuIvPYjXXI5rfK6//vpqvf7NN9/QpEkT4uLirKZ9d+rUifXr11ut27RpU6vrxrVBvzZAvJyCgoKsBvCFhYXs2LGDRx99lPLycv31uLg4oqKi+N///kf37t3ZvXs3/v7++gAewN/fn06dOvHrr79e0oxTp06lQ4cOVFZWcvLkSdLT07n//vtZs2YNERERQFVdzZs3j61bt5KdnU3l/7+2p3nz5i7L3rJlC6+99hr79+8nPz9ffz0jI8NqEO9uH3377bf4+PjQv39/h+9T3Xq9GAUFBfrPHh4eeHl5UVFRoc8WsaT9sS4qKqKiooLCwkIqKyspLCzEbDbj6elJaWkppaWlVtsZjUa8vb31dZ2Va5sHwGQyYTKZKCsrs3smrXZdnuV2lpm8vb0xGo0UFxdb1R+Ap6cnZrOZ8vJyiouLnZbrKJOXlxceHh6UlJRY/TsF+zq0zGP5WbXXLbmqQ1d1BOjXJ2r7xpJtHVpm0vaNs3Jd1aGrTI72jSXLOrStI23fuGuHtnVomcdRHbpr35Z1aJtJ2zeO2qGr9m2ZyVEdVrd9FxUV2ZWv7RtH7dBZ+7b9wu2oHWr7xl0duvq8jtqhs/ZtmclZH6HtG1d1WF5ebre9u3boqH3b1pG7PsJZHTr7PNXtZy3Ltc3kat+4qkNnmVy1Q0d9hGWeiooKp32E5b5xVIdaHkf14KqfddRHOBpM1rSf1Q7yu2uHrtq3VoeWebR7Hjkr11U/W1ZWRmVlJeXl5TXuZ237CMtMJSUlTv8Gguu/Vdq/tdLSUrv3rcn3iNq6D5QQV6O/9CA+KCgILy8vjh07dsnLrl+/vsPXQ0NDrX4/e/Ysv/76q8NBpO0d1W1vNKddAuCoM3YnIiKCb7/9luLiYv3Lhyu2nyc3N5fy8nJmz55tdZZac/z4caBqGr6jG9bZ1sOlEBMTQ8uWLfXf27RpQ+fOnVm5ciWPPfYYFRUVPPDAA+Tl5TFx4kRiY2Px8fHh1Vdf1fM6snv3bsaNG0evXr0YNWoUoaGhGAwGbr/9dru6d7ePcnJyCAsLc3rgqLr1ejH27Nmj/xwSEkJ8fDwlJSVWr2vatm0LVB2sOH/+vP56RkYGcXFxhIaGcvbsWY4ePWq1XUBAAImJiVRUVLgs1zYPQIMGDYiIiCAvL4+DBw9aLdOuywPYu3ev1ReNjIwMfH198fHx4fjx43b3EIiMjOSaa66hoKCA3377zWqZyWSiVatWTjM1adIEf39/srOzycrKsloWGhpKXFwcxcXFVttlZGRgMBho06YNAIcOHbL7MteoUSOCg4P1e0FUt44AUlJS8PDw4OjRo+Tm5loti4mJITw8nHPnzpGRkWGVqV69evpTJxyV27x5c7y9vTl27JjdYxZdZfLy8qJFixYA7N+/3+5LeFJSEn5+fpw4cUK/n4SWLSwsjIYNG1JUVGRXrtFoJDU1FYCDBw9afUm0zHPq1Cm7vjwoKIiEhATKysocftbU1FQMBgOHDx/WD85pmWJjY6lfvz45OTkcPnzYajs/Pz+SkpKorKy0K9cyU2ZmJjk5OVbLo6OjiYqKIj8/nwMHDlgt8/b21v8WaO9puf+Sk5Px9fUlKyuL7Oxsq23Dw8OJiYmhsLCQffv2OcwDcODAAbt+q3HjxgQGBpKdnW3Xx2h9hLY/LfNYlm/bRwBO+wjLTM76iFatWmEymTh69Cjnzp2zWqb1Edr7WWZy1UcA+nW9ln2EbR256yP2799vNxhs0qSJ/jfbto6c9RGA0z7CNpOjPiIwMJDGjRs7bd+Ws9hsMznrIwCHfYRlnpKSEqd9hHa5XX5+vt0jYr28vGjUqBEAR44csRu8OuojNI76CNs6Avs+AiAhIYGgoCCnfURUVJTDOgLHfYTGto+wzHP48GGnfQRAy5YtMZvNDvuIsLAwoKod2u5zyz5i3759dgcWbPsIy0wnTpxw2EdA1UES7USNoz4iJiYGqPoOc+rUKatlNfkecaWeeCSE+IsP4j09PWnTpg3btm2jrKzM6TXa8OdRdts/3LZfnt2xHbwFBgaSlJTE888/X6NyLtZ1113H+++/z7fffut01oAl29z+/v4YDAbGjBnDDTfcYLd+cHAwgH4Nua0rcZO2kJAQgoOD2b9/P1D1R/XXX39l0aJFVpkdHTm29Pnnn+Pn58f8+fMxGqvu9Xghz5+Hqi8M2gwARwP56tbrxdAuVYA/DxRZXsLgSFxcnH4mXhvABwYG6plsb46o1ZPRaHRZrm0e+PPAh7+/v90yyzrTvgxYZtL+nUZFRdndQ0H79+3r6+uyXEeZtHLDwsLs9oFWh15eXiQnJ1vlsTy7Hx8f7/AMEVS1VX9/f5xxVIdaHcfExDg8Ew9V/YttJu2Gj87K1TJFR0frM1iqk8myDi3vQ6HR6jAiIgJfX1+rOtL2jbe3t8v20qhRI7s61NSvX19vkxpt33h6ejosV8scGxvL+fPnrTJp9RAUFGR3xk+re4PB4DJvgwYN9AGCRts3fn5+LuswNjaWQ4cOWbUj7SxWZGSk3YFVrQ59fHxcZkpISHB6FjksLMzuMifLOgTs2rVG6yMsaXXoqI/QOOsjtPeLiYmxu+RNq0OtLVtmctRHWHLVR2jc9RGJiYkO61Ab+NjWkW0f4YyjPkLjqI9w1761duook20f4Wy7mvYR7tq39vkaNmxod1bWso+wPdB/MX2EltdZH6EdTHDUti37CGft21EfERsbq2/vqn076iPKysrIzs522w4t75mkcdVHaPvqQvoIrY6CgoL0gwyamnyPEHWfsz5KqOcvPYgHuO+++xg9ejRLlixh/Pjxdsu1a5BDQ0MxmUxWZ05KSkr4/vvvL+r9O3XqxJYtWwgPD3f5hbk6anJm/sYbb2TevHm88sorXHvttXZfsPbt20dAQIDdHxeNr68vKSkpHDx40Orst62WLVuSl5fHt99+q0+pz8vLY+vWrU6via/pZ3Hm1KlTnD17Vh90aWVZ3sTwjz/+YOfOnS7/uBQVFWEymaz+eH700UcXlKlTp06kp6fzySef0K9fP7vl1a3Xi+FoCqLRaHR5nZvtly3LgZc2fdIRg8Hg9vo5Z8s9PT1dHliz3c7Hx0f/8ulqdomHh8cFZzKbzU4frWhbhz4+Pna/O+OqDl3lAft9Y8m2Dn18fKzWd1Wuuxk6rrZ1tcxsNut1YVtH7trhhdZhddq39gXdNpOrduiufbuqQ3ftW9tPtnnAdTt0174vpg6d5bHMW9NyL6YOtcGDs0wXU+6F1qGrPJerfVenn3WWqab9LHBR/aw2Fdvb2/uS9LO2LqQOtUzO6kjL64yjOtTWv5D2reVx1w7d9bO2daj9fiHtW8tkMpmcblvT7xFCiMvrLz+I7969O/fffz9paWn8/vvv9O/fn+DgYDIzM/nggw/Iy8uje/fuGI1GevfuzZtvvklsbCzBwcGsXr3a6RnV6rrtttt45513uPvuuxkxYgRxcXHk5eXx66+/UlpaypQpU6pdVlhYGAEBAWzcuJEGDRpgNptJSkpy+MfQy8uL+fPnc//99/P3v/+de++9l8aNG5Ofn89///tf3n33Xd577z2ng3iARx99lHvuuYeHHnqI/v37ExAQQFZWFlu3bmXgwIG0b9+ebt260bx5cx555BGmTp2Kv78/y5Ytc3pW5kI+i+bw4cPs2rWLyspKTpw4wfLly/Vp71B1hD4yMpKXX36ZiooKCgoKePXVV93e9b5z586sWrWKZ599lt69e7Nz507WrVvnchtnOnXqRPfu3Xn88cc5cuQIrVu3Jicnh02bNjF//nygevVaW7y9vWnWrFm1LsG4UlTLpFoeUC+TanlAvUySxz3VMqmWB9TLpFoeUC+TanlAzUxCCNf+8oN4gEceeYTU1FTefPNNHn/8cQoLCwkPD6dLly6MHDlSX2/GjBnMmDGD5557jnr16jFy5Eji4+PZvHnzBb+32Wzm9ddfJy0tjSVLlpCdnU1QUBDNmjXjjjvuqFFZRqOR2bNn88orr3DvvfdSUlLC5s2badCggcP1U1JS+L//+z+WLVvG0qVLOXXqFL6+vrRs2ZJXXnnF7bVLbdq04a233iItLY3p06dTWlpKZGQkHTp0sJpKtnjxYp566ilmzpxJQEAAw4cP59SpUy7rraafBeCVV17Rfw4ODqZp06asWrWKdu3aAVV1nZaWxjPPPMOkSZOIiorigQceYNu2bfz8889Oy+3evTtTp05l9erVrF27ljZt2rB06VL69u3rsn6cSUtLY+HChaxZs4aFCxcSGhpK586d9eXVqdfaYjQalXtOrGqZVMsD6mVSLQ+ol0nyuKdaJtXygHqZVMsD6mVSLQ+omUnUjgqZTV9nGCrl4gchxP9XXFzM8ePHiYqKUuaIvGqZVMsD6mVSLQ+ol0nyuKdaJtXygHqZVMsD6mVSLQ9cnkw5vx90v9JlFtS4kf7zb5lZLta8cpo0uLxPm7pYL3z4eW1HcOix2+zvI3W1M7pfRQhxtSgvL+f06dN2dxWuTaplUi0PqJdJtTygXibJ455qmVTLA+plUi0PqJdJtTygZiYhhGtXxXR6IYQQQgghhBDOyQTtukPOxAshhBBCCCGEEHWEDOKFEEIIIYQQQog6QqbTCyF0np6eREZGunyu8JWmWibV8oB6mVTLA+plkjzuqZZJtTygXibV8oB6mVTLA2pmErWjQqbT1xlyd3ohhBBCCCHEJSN3p3dM9bvT/2PtZ7UdwaHHB/ap7QjKken0QghdeXk5eXl5St2hVrVMquUB9TKplgfUyyR53FMtk2p5QL1MquUB9TKplgfUzCSEcE0G8UIIXXFxMb/99hvFxcW1HUWnWibV8oB6mVTLA+plkjzuqZZJtTygXibV8oB6mVTLA2pmErWjsrJSyf+EPRnECyGEEEIIIYQQdYQM4oUQQgghhBBCiDpCbkMphBBCCCGEEFc5mbled8jd6YUQQgghhBDiKvfc+5tqO4JDTw7qW9sRlCPT6YUQQgghhBBCiDpCptMLIYQQQgghxFWuQiZo1xkyiBdC6AoLC9m/fz+JiYn4+PjUdhxAvUyq5QH1MqmWB9TLJHncUy2TanlAvUyq5QH1MqmWB9TLdDnynM3NuyTlXKzgAP/ajiD+ImQ6vRBCV1lZSWlpqVLP5FQtk2p5QL1MquUB9TJJHvdUy6RaHlAvk2p5QL1MquUB9TKplkcIFcmZeCGEEEIIIYS4ysmBk7pDzsQLIYQQQgghhBB1hAzihRBCCCGEEEKIOkKeEy+E0JWXl1NQUICvry8eHh61HQdQL5NqeUC9TKrlAfUySR73VMukWh5QL5NqeUC9TKrlAfUyXY48cmO76nl6zSe1HcGhp4fcVNsRlCODeCGEEEII8f/YO++oKK73cT8L0pSi0pRYUFREbNixixoSExN7jA17wd57j8YaG3axxErsigaNxhZ7ixoTY8MOiEgV6fz+4LfzYdldwCTMjl/uc47nwMzszuPscHfee9/7XoHg/ywiiM8dIoj/eBDp9AKBQCIpKYmXL1+SlJRkaBUJpTkpzQeU56Q0H1Cek/DJGaU5Kc0HlOekNB9QnpPSfEB5TkrzEQiUiAjiBQKBREpKCqGhoaSkpBhaRUJpTkrzAeU5Kc0HlOckfHJGaU5K8wHlOSnNB5TnpDQfUJ6T0nzyE2mkK/KfQBsRxAsEAoFAIBAIBAKBQPCRkK+C+BUrVuDq6kqjRo1IS0vT2t+5c2dcXV2ZMGHCf37uFy9e4OrqSlBQ0H/+3nJz6NAhOnfujIeHBx4eHnzzzTccOHDA0FrZkpSUxObNm2nXrh0eHh5UrVqV1q1bs2LFCmJiYvLsvH/99Reurq5cvnxZ2ubq6oq/v7/0+759+zh8+HCeOQgEAoFAIBAIBIL/OxQwtIDcmJiYEBkZydWrV6lbt660/eXLl/z+++8ULFgwT87r4OBAQEAAzs7OefL+cjF79my2b99O+/bt8fX1RaVScezYMSZMmMCdO3eYOnWqoRW1SExMpG/fvvz+++907dqVESNGYGpqyl9//cXWrVuJjY1l0qRJsvkEBATg5OQk/b5//34KFixI69atZXMQCAQCgUAgEAgyI+qdfzzkyyDe09OTI0eOaATxR44coXz58hgZ5U1ygqmpKdWrV8+T95aLkydPsm3bNoYMGcLQoUOl7Y0aNcLBwYGVK1fSoEEDvLy8DGipzbJly7h27Rr+/v7Ur19f2l6vXj26dOnCjRs39L42ISEBc3Pz/9RHyfeBsbExtra2ilhiRo3SnJTmA8pzUpoPKM9J+OSM0pyU5gPKc1KaDyjPSWk+oDwnpfkIBEokX6XTq/nyyy85duwYycnJ0rbAwEC+/PJLrWMfPXrEyJEjadKkCdWqVaNVq1Zs3LhRIx1/8+bNVK5cmT///FPa9uzZMzw8PFi8eDGgO53ey8uLWbNmsXnzZpo0aYKHhwcTJkwgKSmJv/76i86dO1O9enU6dOjA33//Lb1OX2r+nDlzNALoffv24erqyp07d+jduzfVqlXD29ubCxcukJaWxpIlS6hfvz7169dn8eLFOqcYZGbLli3Y2NjQu3dvrX19+vTBxsaGLVu2SNsmTJjAl19+yZkzZ/jyyy+pUqUK7dq14/fff9d6/b59+2jdujVVqlShUaNGLFmyhNTUVK3/y59//knfvn2pXr06n376aY5p/AkJCezcuZMWLVpoBPBqzMzM8PT0BODy5cu4urpy+vRphg0bRo0aNRg+fDgAMTExzJgxg4YNG1K5cmXatWvHb7/9pvV+q1atokGDBnh4eDBkyBAiIiK0jsmcTt+9e3euXLnC6dOncXV1xdXVlRUrVmT7f8pLzMzMcHZ2xszMzGAOWVGak9J8QHlOSvMB5TkJn5xRmpPSfEB5TkrzAeU5Kc0HlOekNB+BQInkyyC+WbNmJCUlcf78eQAePnzI33//TatWrbSOff36NWXKlGH69OmsW7eOTp06sXLlSlatWiUd4+Pjg4eHB2PHjiUxMZHU1FTGjRtHqVKlNEasdXHy5El+++03Zs2axejRowkMDGT27NmMGzeOjh07smzZMhITExk+fHiOQbY+xo8fT9OmTfHz88PBwYEhQ4YwZ84cQkNDmT9/Pl26dGHdunUcOXJE73ukpKRw8+ZN6tatS6FChbT2FypUiLp163Lz5k2NaqLh4eHMnDmTPn36sHTpUkxNTenTp49GcLtp0yamTJlCw4YNWbNmDf369ePHH39kyZIlWucZM2YMDRs2ZOXKlbi5uTFhwgQePXqk1/uPP/4gPj6eRo0a5fZyMXXqVEqWLMnKlSvp3bs3SUlJ9OrVi9OnTzNixAhWr16Ni4sLAwYM0Ohc2bZtG8uWLeOrr75i+fLllCxZksmTJ2d7runTp1OpUiVq1KhBQEAAAQEBdOzYMdeu/zVpaWm8f//+H99reYHSnJTmA8pzUpoPKM9J+OSM0pyU5gPKc1KaDyjPSWk+oDwnpfnkJ9LS0xX5T6BNvkunB7CwsMDLy4sjR47QtGlTAgMD8fDwoGTJklrHenp6SiO16enp1KxZk4SEBCmtHEClUjFv3jy++uorfvjhB4oWLcrdu3fZs2cPpqamOfqsWrVKOu7KlSv89NNPrF+/nsaNGwMZjdnAgQO5f/8+FStW/OD/b7du3ejSpQsAjo6OtG7dmj/++IOAgAAgIx3+119/JSgoSO+87MjISJKSkihevLje8xQvXpzExESioqKws7MDICoqiqVLl0rXsE6dOjRp0oTNmzczevRo4uLiWL58OX379mXUqFEANGjQABMTE+bNm0efPn0oUqSIdI6uXbvStWtXADw8PDhz5gzHjh3D19dXp9Pr168lt9zi5eXF2LFjpd/37t3LvXv3OHjwIOXKlQMyrtnTp09ZtWoVy5YtIzU1lbVr1/L1118zfvx46ZiIiAgOHjyo91zlypXD0tKSggUL/mdp9vHx8dLPxsbGmJmZkZaWRkJCgtax6hoQCQkJ0pfmkydPcHZ2xsbGhgIFCpCcnKyRtQJgZGSEubk56enpvH//Xu/7ZvWBjCktJiYmpKSkaK0Bq1KpsLCw0HhdZqciRYpgZGQkdZZlpkCBApiampKamkpiYqLe99XlZGZmhrGxMUlJSVpL2mS9hpl9LCwspP/r+/fvteaSmZqa6r2G2V0jyGinVCqV9Nlkdw0zOxUqVEiaAqLrfc3NzfVew+ycdH02mcl8DWNjYzWukfqzyek+zHoNM/vouoY53d+Zr+G7d+80nNSfja77MLv7O7OTrmuY2/s7KiqK4OBgyQf+99noug/13d9Z67joug/Vn0121zA+Pp6///5bwyfz++u6D/Xd35md9LUR6s8mu2v47t077t+/r+GU032o6/7Oeo1yaiP0XcPExET++usvrWuU23Y28/tmdcrus8nuGiYkJOh0yu4+VN/f8L9rmNknLS1Nbxuhfl991zA9PZ2//vqLMmXKaE1Fy66d1dVG6KpR9KHtrLGxMampqTqvkfoa6mtns7YRmX3UU+30fTbZtbMpKSk8ePCAChUqaKWL5/b+Vl/DzE5JSUl6vwNBfzsLGX8T9+/fp1y5cpiYmGjs+5DniMyfeXx8fLafTXbtrPo7zc3NTfoMM5PbdlYg+L9MvgziISOlfvTo0SQkJHD06FG6d++u87jExETWrl3L4cOHCQkJ0WiE3r17J41Kf/LJJ0yaNInJkydToEABhg8fjqura44etWvX1gj0nZ2dMTIyol69ehrbAEJCQv5REN+gQQOt98r8/gBlypQhODj4g987J6ysrKQAXv17/fr1uXXrFgA3b94kPj6ezz77TONLvX79+iQkJPDgwQPq1KkjbW/YsKH0c8GCBXFyciI0NDRHD5VKlWvnpk2bavx+/vx5KlSogLOzs5bjoUOHAAgNDeX169e0bNlS47Xe3t7ZBvF5wV9//SX9XLRoUcqUKSNN0chKzZo1AXjy5Anv3r2TtquDHVtbWyIjI3n+/LnG66ytrSlfvjxpaWnZvm9WH4ASJUrg6OhIbGwsjx8/1thnYWFBpUqVALh3757Gg8aTJ08oWLAgFhYWhISEaE1VKFasGJ988gnx8fHcv39fY5+JiQlVq1bV61ShQgWsrKwIDw/Xup9sbW1xdnaWHuAz+6hUKmrUqAFAcHCw1oNI2bJlKVKkCG/fvuXFixe5vkaQUTvB2NiY58+fa62gULJkSRwcHIiOjubJkycaToUKFZLaCV3v6+7ujrm5Oa9eveLt27e5djIzM6Ny5coAPHjwQOsh3NXVFUtLS8LCwqTOM7Wbvb09pUqVkgKOzBgZGeHh4QHA48ePNR4SM/u8efOGV69eaby2cOHCuLi4kJKSovP/6uHhgUql4unTp8TFxWk4lS5dGjs7O6Kionj69KnG6ywtLXF1dZUCEn3X6MWLF0RFRWnsd3Jyonjx4sTFxWllCZmbm+Pu7g4gnTPz5+fm5kbBggUJDQ0lPDxc47UODg6ULFmS9+/fa2QAZfaBjClgWR/gy5Urh42NDeHh4YSEhGjsU7cR6s8zs0/m98/aRgB624jMTvraiKpVq2JiYsLz58+Jjo7W2KduI9Tny+yUXRsBUKlSJa02Ius1yqmNePDggVbAkTngynqN9LURgN42IquTrjbCxsaGcuXK6b2/M3f8ZnXS10YAOtuIzD5JSUl624jixYvj5OREXFwcDx8+1NhnZmZG2bJlgYwphVkDL11thBpdbUTWawTabQSAi4sLhQsX1ttGqDvxs14H0N1GqMnaRmT2efr0qd42AqBKlSqYmprqbCPs7e2BjPsw62eeuY34+++/tToWsrYRmZ3CwsJ0thGQ0UlSrVo1QHcboR7EioqK4s2bNxr7PuQ5IvMzqrrj5J8+R6jJro3I6TlCIPi/TL4N4hs2bIiJiQnLli3jxYsXfP755zqPW7hwIbt372bw4MFUrlwZKysrTp48yerVq0lMTNRILW/evDmzZ88mNTWVTp065crD2tpa43cTExPMzc01Ant1r6iuntXcYGVlJf2sfl9d583am5mZIkWKYGpqqvUAmJmQkBDMzMwoXLiwtK1o0aJax9na2koPt5GRkQC0bdtW73vq+7/kxtvBwUHn+2SHra2txu+RkZH8+eef0hdrZtQPdeoH7qz/X3VGgpy4ublJP6v9TE1NNbZnxdnZWedIPGR89paWlhrHqwtAGhkZZfu+WX3gf/ezlZWV1r7MnS3qh4HMTur5ccWLF5c+WzUFCmQ0ZwULFsz2fXU5qd/X3t5eI/MD/ncNzczMcHNz0xqJV1OmTBmdI0SQcV9kvXez84H/XeOSJUvqHImHjIf8rE6Z2yRd76t2cnJywtHRMddOma9h+fLltY5XX0NHR0cKFiyoNRIPGQ+o2d0vZcuW1VsZ187OTron1ag/mwIFCuh8X7Vz6dKldY7EQ8ZDftYRP/W1V6lU2fqWKFFCK8tH/dlYWlpmew1Lly6tcyQeMjqksrYd6mtoYWGRrZOLi4vOUWTIuL8zt8+geQ0BnaOV6u26RipBdxuhRl8boT5fyZIlNVbrgP9dQ/W9nHUkXo2uTu3s2gg1ObUR5cuX1zsSn9UHtNsIfehqI9ToaiNyur8zF+LVNRIP/2sj9L3uQ9uInO5v9f+vVKlSOkfiIaONyPo9+2/aCLWvvjZC3Zmg697O3Ebou791tRGlS5eWXp/d/a2rjUhJSSE8PDzH+1DXQFB2bYT6s/onbYT6GhUuXFjqZFDzIc8RmXFzc8u2jcjuOUL9nQbZtxE5PUcIPhyRuf7xkG+DeBMTEz799FM2b96Mp6en3mArKCiIb775hv79+0vbzpw5o/PYmTNnYm1tTUpKCnPnzmX+/Pl54q7+Isw6UpCX650XKFAADw8Prly5Qnx8vNYXWnx8PFeuXMHDw0P68gK0evEBIiIipC8J9Reun58fxYoV0zq2RIkS/8q7cuXKFCxYkHPnzuV6rnnWLwAbGxtcXV2ZM2eO3teo/z9Z/79Ze7TlQFcKopGRUbbLJ2Z92MoceKnT1nShUqlyXJZR3/4CBQpo3Cs5vc7CwkL60s+u2I2xsfE/djI1NdU7BSbrNcycSq/+XR/ZXcPsfED7s8lM1mtoYWGhcXx275tTwaDsXpvdPlNTU+laZL1GOd2H//Qa5ub+Vj+gZ3XK7j7M6f7O7hrmdH+rP6esPpD9fZjT/f1vrqE+n8y+H/q+/+YaqoMHfU7/5n3/6TXMziev7u/ctLP6nD60nQX+VTurTgE3Nzf/T9rZrPyTa6h20neN1L760HUN1cf/k/tb7ZPTfZhTO5v1Gqp//yf3t9rJxMRE72s/9Dki87F51UbkdH8LBP+XyZeF7dR07NiRZs2a0aNHD73HJCYmajQ8qampOgvAHTlyhKNHjzJnzhxmzZrFgQMHOHHiRJ5429raYmJiopGqmZSUxNWrV/PkfGp8fHyIiopi48aNWvs2btxIVFQUPj4+GttjY2O5ePGixu8XLlyQ0ro8PDywsLAgNDSUKlWqaP3LOir6oZibm/Ptt9/yyy+/cOnSJa39iYmJGn66qF+/Ps+fP8fBwUGnI2T0itvb2/PLL79ovPbYsWM5OpqYmPzjLIu8QIm92EpzUpoPKM9JaT6gPCfhkzNKc1KaDyjPSWk+oDwnpfmA8pyU5iMQKI183X1VtWpVjSrzuqhfvz67d++mXLlyFClShB07dmilb4eFhTFr1iw6d+4sVUFv27YtU6dOxcPDQytt7N9iZGREy5Yt2b59O6VLl6ZIkSJs27aN9PT0PG30mjdvTrdu3fDz8yM0NJTPPvsMgOPHj/PTTz/RrVs3rTXiCxcuzOTJkxk2bBhWVlasX7+e9PR0Kdi3trZm2LBhLFy4kNDQUOrUqSPNAz558iQrVqz41wVKhg8fzp07d+jfvz9du3alfv36mJiYcO/ePbZv306zZs005u1npU2bNuzatYsePXrQu3dvnJ2diY2N5c8//yQ5OZnRo0djbGxM//79mTNnDra2tjRo0IDz589z+fLlHP3Kli3LgQMH+PXXX7G3t8fBwSHbNOe8pGDBgtL8TaWgNCel+YDynJTmA8pzEj45ozQnpfmA8pyU5gPKc1KaDyjPSWk++Ql9U34EyiNfB/G5YerUqUyfPp3Zs2djYWFB27ZtadmyJVOmTJGOmTx5MtbW1lJVcoApU6Zw+fJlpk2bxsqVK/PEa+rUqXz33XcUKlSIPn36UKZMGU6ePPmfnyvreatVq8aOHTuk5fMqVKjAvHnzaNOmjdbx9vb2jBkzhgULFvDs2TPKly+Pv7+/xvSF3r174+joyKZNm9i2bRsFChSgVKlSNG3aNNsU5NxiZmaGv78/O3bs4NChQ+zcuZO0tDRKly7N119/rZU9kBVTU1N+/PFHVqxYwZo1awgPD6dw4cJUqlRJqvoPGWu+x8TEsGPHDnbu3Imnpyffffcdffv2zfb9+/Xrx7Nnzxg/fjwxMTEMGTIkx6UJBQKBQCAQCAQCQf5ElS66XAR5xIQJE/jjjz8IDAw0tIogl7x//57g4GDKlCmjmCValOakNB9QnpPSfEB5TsInZ5TmpDQfUJ6T0nxAeU5K8wHlOeWFT2RM7H/yPv+WItb6i9wqgYnbDxtaQSffd9W9BHZ+RozECwQCCfV6rUrq21Oak9J8QHlOSvMB5TkJn5xRmpPSfEB5TkrzAeU5Kc0HlOekNJ/8RJq45h8N+bqwnUAgEAgEAoFAIBAIBB8TYiRekGfMmzfP0AoCgUAgEAgEAoFA8H8KEcQLBAKBQCAQCAQCQT5HTGH4eBDp9AKBQMLU1JSyZctiampqaBUJpTkpzQeU56Q0H1Cek/DJGaU5Kc0HlOekNB9QnpPSfEB5TkrzEQiUiKhOLxAIBAKBQCAQCP7PIqrT545xWw8aWkEnC7p/bWgFxSFG4gUCgURycjJhYWEkJycbWkVCaU5K8wHlOSnNB5TnJHxyRmlOSvMB5TkpzQeU56Q0H1Cek9J88hPp6cr8J9BGBPECgUAiOTmZFy9eKOqLU2lOSvMB5TkpzQeU5yR8ckZpTkrzAeU5Kc0HlOekNB9QnpPSfAQCJSKCeIFAIBAIBAKBQCAQCD4SRHV6gUAgEAgEAoFAIMjnpInc9Y8GMRIvEAgEAoFAIBAIBALBR4KoTi8QCAQCgUAgEAgE+ZzRWw4YWkEni33aGFpBcYh0eoFAIBAIBAKBQCDI56QjxnY/FkQ6vUAgEAgEAoFAIBAIBB8JIogXCAQS8fHxXL9+nfj4eEOrSCjNSWk+oDwnpfmA8pyET84ozUlpPqA8J6X5gPKclOYDynNSmg8o00mQvxHp9AKBQCAQCAQCgUCQzxHV6T8exEi8QCAQCAQCgUAgEAgEHwkiiBcIBAKBQCAQCAQCgeAjQaTTCwQCgUAgEAgEAkE+R2TTfzyIdeIFAoFEeno6aWlpGBkZoVKpDK0DKM9JaT6gPCel+YDynIRPzijNSWk+oDwnpfmA8pyU5gPKc1KaDyjTKS8YvmmfoRV0sqxXO0MrKA4xEi8QCCRUKhXGxsaG1tBAaU5K8wHlOSnNB5TnJHxyRmlOSvMB5TkpzQeU56Q0H1Cek9J8QJlOgvyNmBMvEAgkEhISePDgAQkJCYZWkVCak9J8QHlOSvMB5TkJn5xRmpPSfEB5TkrzAeU5Kc0HlOekNB9QplNekJ6ersh/Am1EEC8QCCTS0tKIiYkhLS3N0CoSSnNSmg8oz0lpPqA8J+GTM0pzUpoPKM9JaT6gPCel+YDynJTmA8p0EuQdv/76K1999RVVqlTB29ubvXv35viaFStW4OrqqvPftGnTcjxu586dH+Qo0ukFEitWrMDPz0/63dTUlBIlStCuXTv69OmDkdF/2+fz4sULmjdvzrJly/jss8/+0/f+UC5fvszNmzcZOHDgB7/W1dWVcePG0adPnzwwEwgEAoFAIBAIBHJw7do1hgwZQocOHZg0aRKXLl1i8uTJFCpUKNt4pWPHjjRq1Ehj29WrV1m0aBGNGzfW2G5ubs6WLVs0tpUsWfKDPEUQL9Ag802VkJDA5cuXWbx4Menp6fTv39/AdnnHlStX2Lhx4z8K4gMCAnBycsoDK4FAIBAIBAKBQB7SROo6q1evpmrVqsyaNQuAevXq8fz5c5YvX55tEF+sWDGKFSumsW3Xrl3Y2NhoBfFGRkZUr179X3mKdHqBBuqbqnr16tSrV4/hw4fTvHlzjh8/bmg1CaXNR6pevToODg6G1hAIBAKBQCAQCAT/kKSkJC5fvqwVrLdq1YpHjx7x4sWLXL9XYmIiv/zyC97e3piamv7XqiKIF+RMoUKFSElJkX5ftGgRrVu3xsPDg0aNGjFq1Chev36t8Zru3bszYMAAgoKC8Pb2xsPDgx49evDs2bNsz3X37l3q1avHxIkTSUtL4/Lly7i6unL69GmGDRtGjRo1GD58OJCRxu7v76/x+s2bN+Pq6ir9rn79mTNnGDJkCNWrV6dhw4asWbNGOkY9jSA+Pl6al9K9e3dp/6NHjxgyZAh16tShWrVqfPXVVwQGBkr7dXmcPn2ajh07UrVqVerVq8f06dOJj4+X9icnJzN//nyaNm1K5cqVadiwIQMHDiQ2Njbb65PXmJiYULJkSUxMTAzqkRmlOSnNB5TnpDQfUJ6T8MkZpTkpzQeU56Q0H1Cek9J8QHlOSvMBZTrlJ5o3b57tv/+KZ8+ekZycTNmyZTW2u7i4APD48eNcv9epU6eIi4vjyy+/1NqXkJBAvXr1qFSpEq1ateKnn376YFeRTi/QQh2wq9Ppjx8/zoABA6T9ERERDBgwAAcHB96+fcumTZvo3r07R44coUCB/91Sf/31F2/fvmXMmDGkpqYyb948xo4dS0BAgM7zXr9+nQEDBtCmTRsmT56ssQ7n1KlT+eqrr1i5cuU/mps/depUvvjiC1asWMGFCxdYsmQJNjY2fPvtt3Ts2JHQ0FACAwOlqQSWlpYAPHnyhG+++YbixYszefJk7O3tuX//Pq9evdJ7rqCgIEaOHEm7du0YOnQo4eHhLF68mJiYGJYsWQLA2rVr2bVrF2PGjKF8+fJERkZy/vx5kpKSPvj/9l9iYmKiuKwCpTkpzQeU56Q0H1Cek/DJGaU5Kc0HlOekNB9QnpPSfEB5TkrzAWU65QX5vRJ8dHQ0ANbW1hrb1b+r9+eGwMBAHB0dqV27tsb2UqVKMWbMGCpVqkRiYiKHDx9m6tSpxMbGflB9LRHECzSIj4/H3d1dY1urVq005sN///330s+pqal4eHjQuHFjLl26RMOGDaV9sbGxHDhwgKJFi0rvPXHiREJDQ7XmjFy4cIHBgwfTvXt3Ro0apeXl5eXF2LFj//H/q169eowfPx6ARo0aERERwerVq/nmm2+kOSy65qesWLECExMTdu7cKQX29evX13ue9PR0FixYQKtWrZgzZ4603d7env79++Pr60v58uW5c+cODRs2pGvXrtIx3t7e//j/91+RkpJCdHQ0NjY2Gh0yhkRpTkrzAeU5Kc0HlOckfHJGaU5K8wHlOSnNB5TnpDQfUJ6T0nxAmU75iZMnT/7j18bGxmplDOviQwvLZUdMTAxnzpyhW7duWoOPX3/9tcbvTZs2JTk5mdWrV9OjR49cZ3uIu1Cggbm5Odu2bQMy5oXcvXuX5cuXM2XKFCl4P3PmDKtXr+bBgwfExcVJr33y5IlGEF+xYkUpgAcoV64cgFYQf/r0aY4cOcKQIUM0Rvwz07Rp03/1/2rZsqXG797e3hw8eJDQ0NBsi9JdunQJb29vKYDPieDgYF6+fMmkSZM0piDUqVMHIyMj/vjjD8qXL0+lSpXw9/dnxYoVNGnShMqVK/9n1f8zp+0bGxtjZmZGWlqazloCBQsWBDKyLtLS0nj//j1PnjzB2dlZ+qJKTk4mOTlZ43VGRkaYm5uTnp7O+/fv9b5vVh/I6M02MTEhJSVFK/NApVJhYWGh8brMTkWKFMHIyIjExERSU1M1XlugQAFMTU1JTU0lMTFR7/vqcjIzM8PY2JikpCSNzw20r2FmHwsLC+n/+v79e60ebFNTU73XMLtrBGBhYYFKpZI+m+yuYWanQoUKYW5urvd9zc3N9V7D7Jx0fTaZyXwNY2NjNa6R+rPJ6T7Meg0z++i6hjnd35mv4bt37zSc1J+Nrvswu/s7s5Oua5jb+zsmJkbDB/732ei6D/Xd35l9dF1D+N9nk901TEhI0PLJ/P667kN993dmJ31thPqzye4aZv1by3oNc3t/Z71GObUR+q5hUlKSzmuU23Y28/tmdcrus8nuGupzyu4+VN/f8L9rmNknLS1Nbxuhfl991zA9PZ0nT55QpkwZ6RxqsmtndbURWa8RfHg7a2xsTGpqqs5rpL6G+trZrG1EZp+EhIRsvwOza2dTUlJ48uQJFSpUwNjYWOsa5ub+Vl/DzE5JSUl6vwNBfzsLSNeoXLlyWgHEhzxHZP7M4+Pjs/1ssmtn1X/7bm5upKam/uN2NjPq65ndZ5Pdc4S6jXRzcyM5OVlvO5vTc8R/vdKTIGeCgoKYMmVKjscdPXoUGxsbAK3prTExMQDS/pw4duwYSUlJtG7dOlfHf/755xw7doxnz55Jqfs5IYJ4gQZGRkZUqVJF+r1mzZpSKnyvXr1ISEjA19eX5s2b069fP2xtbVGpVHTq1EmrwcuaiqL+Ysh63KlTp7CwsKBVq1Z6vWxtbf/V/ytzZwKAnZ0dAOHh4dkG8VFRUR+UPhUZGQnA4MGDde4PCQkBYNCgQRgZGbF//378/PwoWrQoXbt2ZfDgwRrTCP4Jf/31l/Rz0aJFKVOmDElJSRrb1dSsWRPI6IB59+6dtF39wGNra0tkZCTPnz/XeJ21tTXly5cnLS0t2/fN6gNQokQJHB0diY2N1ZpbZGFhQaVKlQC4d++expfkkydPKFiwIBYWFoSEhBAREaHx2mLFivHJJ58QHx/P/fv3NfaZmJhQtWpVvU4VKlTAysqK8PBwQkNDNfbZ2tri7OxMYmKixuuePHmCSqWiRo0aQEYHTtYHkbJly1KkSBHevn2rVQwlu2sEGQUTjY2Nef78ufTloaZkyZI4ODgQHR3NkydPNJwKFSpExYoV9b6vu7s75ubmvHr1irdv3+bayczMjMqVKwPw4MEDrYcUV1dXLC0tCQsLk3q81W729vaUKlWKhIQErfc1MjLCw8MDyJhrlvkhMbPPmzdvtKaxFC5cGBcXF1JSUnT+Xz08PFCpVDx9+lTqcFQ7lS5dGjs7O6Kionj69KnG6ywtLXF1dSU9PV3rfTM7vXjxgqioKI39Tk5OFC9enLi4OB49eqSxz9zcXMp0Up8z8+fn5uZGwYIFCQ0NJTw8XOO1Dg4OlCxZkvfv3/P333/r9IGMGh5Z29hy5cphY2NDeHi41AapUbcR6s8zs0/m98/aRgB624jMTvraiKpVq2JiYsLz58+10hPVbYT6fJmdsmsjACpVqqTVRmS9Rjm1EQ8ePNAKODIHXFmvkb42AtDbRmR10tVG2NjYUK5cOb33d+bMsaxO+toIQGcbkdknKSlJbxtRvHhxnJyciIuL4+HDhxr7zMzMpHmkz5490wqQdLURanS1EVmvEWi3EZAxV7Vw4cJ624jixYvrvEagu41Qk7WNyOzz9OlTvW0EQJUqVTA1NdXZRtjb2wMZ92HWzzxzG/H3339rdSxkbSMyO4WFhelsIyAjOK1WrRqgu41Qj0BGRUXx5s0bjX0f8hyhvq8g4976N88RarJrI3J6jsiM+hy62gg12T1HZB5919dG5OY5QlcHg5JI+z+YTd+xY0c6duyYq2OTkpIwMTHh8ePHGsvFqe+zrHPl9REYGEjZsmV13ov/FSKIF+SI+oZ9+PAh9+7dw9LSkqVLl0q9iS9fvvxX7z9hwgR++uknevbsyfbt27VS7QGdga2pqalWI5o12FGT9UFE/SWl/jLVR+HChXOVgpP5eIBp06ZpBI1q1B0CpqamDB06lKFDh/L06VP27t3LihUrKFGiBG3atMn1+XTh5uYm/ax+6DQ1NdXYnhVnZ2edI/EARYoU0cpEUH/2RkZG2b5vVh/4X2eOlZWV1r7Mn7P6YSCzk5mZGZDxIJm1c0X9BVuwYMFs31eXk/p97e3tKVKkiMY+9TU0MzPDzc1N5+ggQJkyZXSOEEHGQ5CVlRX60HUN1de4ZMmSOkfiIeMhP6tToUKFsn1ftZOTkxOOjo65dsp8DcuXL691vPoaOjo6UrBgQa2ReMh4QM3ufilbtqze+Xh2dnZaPeDqz6ZAgQI631ftXLp0aZ0j8ZDxN5t1xE997VUqVba+JUqUkAIENerPxtLSMttrWLp0aYKDg7VG4iHjQVLd0ahGfQ0tLCyydXJxcdE5QgQZ97e6jVKT+RoCOkcr1dt1jVSC7jZCjb42Qn2+kiVLanWkqq+h+l7OOhKvJnPAoCa7NkJNTm1E+fLldV5DdeCjayRefUx2n42uNkKNrjYip/s784ierpF4+F8boe91H9pG5HR/q/9/pUqV0jkSDxltRNaO+X/TRqh99bUR6s4EXfd25jZC3/2tq40oXbq09Prs7m9dbURKSgrh4eE53oeZi/Sqya6NUH9W/6SNUF+jwoULaz0XfchzRGbc3NyybSOye45Qf6dB9m1ETs8RWX0g+zYiu+eIhIQEgoODAf1tBOT8HCFQNqamptStW5djx47h4+MjbT969CguLi6UKFEix/d4/fo1V65cYciQIbk+79GjR7G2tqZUqVK5fo0I4gU58uDBAyCjEU5ISMDExESjkTx8+PC/en8LCwvWr1+Pj48PPj4+bN++XevLSRfFihXTGum6cOGCzmN/+eUXjZT6Y8eO4eDgIHUYmJiY6Cwq5+npybFjxxgzZkyuUurLli1LsWLFeP78ucZ89+woXbo0o0aNIiAg4IOqXupDVwqikZGRzu1qsj5sZQ681GlrulCpVNm+rz4fyPiyzG5eWdbXWVhYSF/62X0RGhsb/2MnU1NTvcuAZL2GmVPp1b/rI7trmJ0PaH82mcl6DS0sLDSOz+59c3qYyO612e0zNTWVrkXWa5TTffhPr2Fu7m/1A3pWp+zuw5zu7+yuYU73t/pzyuoD2d+HOd3f/+Ya6vPJ7Puh7/tvrqH6wVef07953396DbPzyav7OzftrD6nD21ngX/VzmZOWf4v2tms/JNrqHbSd43UvvrQdQ3Vx/+T+1vtk9N9mFM7m/Uaqn//J/e32snExETvaz/0OSLzsXnVRuR0f+vzyel9/00bkdP9LVA+gwYNokePHsyYMYPPP/+cy5cvExgYKBWnVlOpUiXatGnD3LlzNbYfPXqUtLQ0van07dq1o02bNpQtW5aEhAQOHz7M8ePHmTRp0getfiCCeIEGaWlp/P7770DGHL27d++yevVqypUrR61atUhKSmLLli3Mnj2bli1bcvPmTQ4ePPivz2tpacnGjRvp0aMHvXr14scff9TqycyKt7c3W7ZsoUqVKpQpU4ZDhw4RFham89hLly4xf/58GjRowPnz5zl48CDTpk2TGlR1Su6WLVvw8PDA0tKSsmXLMmTIEE6fPk2XLl3o27cv9vb2PHr0iPfv39OvXz+t86hUKiZMmMCYMWOIj4+nadOmWFhY8OrVK86cOcPIkSMpU6YMvr6+uLu7S2ldp06dIjo6mnr16v3ra/lvMDIyolChQor6olGak9J8QHlOSvMB5TkJn5xRmpPSfEB5TkrzAeU5Kc0HlOekNB9QplNekN+r0wPUqlWLFStWsHTpUvbs2YOTkxPfffcdn3/+ucZxqampWhk8kDG4WbVqVb2j6qVKlWLz5s28efMGlUpFhQoVWLhwIV999dUHeYogXqBBQkIC33zzDZDRw1msWDG++uorhgwZgomJCU2aNGHMmDFs27aNffv2UaNGDdauXfufVFa3sbFh06ZNdOvWjT59+kjLvenD19eXiIgIVq5ciUql4ptvvqFHjx7MmzdP69hZs2YREBDAzp07KVSoEMOHD9cYKW/WrBldunRh3bp1REREULt2bbZu3YqzszO7du1i8eLFzJw5k9TUVJydnTWq9Wfl888/x9ramjVr1khZCp988gmNGjWSMgxq1KjBzz//zKZNm0hNTaVMmTIsWrQo28r3cmBubq4zRdWQKM1JaT6gPCel+YDynIRPzijNSWk+oDwnpfmA8pyU5gPKc1KaDyjTSZB35Gb9+ay1J9Ts3bs329ctXbr0n2ppoEoXXS6C/8NcvnyZHj16sGfPHo2CfQKBQCAQCAQCgeB/DFz3k6EVdLKmfydDKyiO/9s5IQKB4IOIj4/n+vXrOpe2MRRKc1KaDyjPSWk+oDwn4ZMzSnNSmg8oz0lpPqA8J6X5gPKclOYDynTKC9LT0xX5T6CNCOIFAoFAIBAIBAKBQCD4SBBz4gX/p6lbt67eOSsCgUAgEAgEAoFA8LEhgniBQCAQCAQCgUAgyOekidT1jwaRTi8QCAQCgUAgEAgEAsFHgqhOLxAIJNLS0khKSsLU1FQxa6EqzUlpPqA8J6X5gPKchE/OKM1JaT6gPCel+YDynJTmA8pzUpoPKNMpL+i3ZpehFXSyfmBnQysoDpFOLxAIJIyMjDA3Nze0hgZKc1KaDyjPSWk+oDwn4ZMzSnNSmg8oz0lpPqA8J6X5gPKclOYDynTKC8TI7sfD/92uJIFA8MEkJiYSHBxMYmKioVUklOakNB9QnpPSfEB5TsInZ5TmpDQfUJ6T0nxAeU5K8wHlOSnNB5TpJMjfiCBeIBBIpKam8vbtW1JTUw2tIqE0J6X5gPKclOYDynMSPjmjNCel+YDynJTmA8pzUpoPKM9JaT6gTCdB/kak0wsEAoFAIBAIBAJBPkeUSvt4ECPxAoFAIBAIBAKBQCAQfCSI6vQCgUAgEAgEAoFAkM/ps3qnoRV04j/oW0MrKA6RTi8QCAQCgUAgEAgE+Zw0Mbb70SDS6QUCgUAgEAgEAoFAIPhIEEG8QCAQCAQCgUAgEAgEHwkinV4gEAgEAoFAIBAI8jmiVNrHgxiJFwgEAoFAIBAIBAKB4CNBBPECgUAgEAgEAoFAIBB8JIh0eoFAIBAIBAKBQCDI56SJbPqPBjESLxAIBAKBQCAQCAQCwUeCCOIFAoFAIBAIBAKBQCD4SBDp9AKBQCAQCAQCgUCQzxHV6T8exEi8QCAQCAQCgUAgEAgEHwkiiBcIBAKBQCAQCAQCgeAjQaTTCwQCgUAgEAgEAkE+R6TTfzyIkXiBQCAQCAQCgUAgEAg+EkQQLxAIBAKBQCAQCAQCwUeCCOIFAoEgGw4cOEBkZKTOfVFRURw4cEBeIYUTEhLCjRs3iI+PN7QKoDwfwceDn58fYWFhOve9fv0aPz8/WX2UmOYq2kfBf4HS/tbyM2np6Yr8J9BGBPECgUCQDRMnTuT58+c697148YKJEyfK6pOcnMy6deto164dnp6e1KhRQ+NfzZo1ZfVRExAQQKNGjfDy8qJr164EBwcDMHjwYLZs2ZLvfd6+fcuSJUvo2bMnX3zxBT179mTJkiW8fftWdhclOymJlStXZhtYrFy5UlafJk2asGTJEp49eybrebNDae3jy5cv+fvvv6Xfk5KSWL16NWPGjGHfvn2yuqg5e/YsgYGB0u8hISH06tWLxo0bM2HCBIN1MJ49e5aVK1cydepUXr16BcDVq1f13vN5idL+1gSCjwERxAsE+ZyEhIRs94eGhspkop/379/z9OlTg4xEZXfOmJgYChUqJKMNzJw5k+XLl1OiRAk6depE7969Nf716tVLVh+AzZs3M3v2bNq0aYO/v7/GNatTpw5BQUH52ufWrVt4e3uzbds2rKysqF27NlZWVmzbto2WLVty69YtWX2U6qQ0svvbDw8Px9raWkYbaN26Nfv27cPb25vu3btz4MCBHNvvvEZp7ePUqVM5ePCg9PvChQtZuXIljx8/Ztq0aWzfvl1WH4Dly5drBKizZs3i0aNHfPHFF5w7d47ly5fL6vP27Vs6d+7MgAED2Lt3L3v27JGyKfbu3cuaNWtk9QHl/a0JBB8Dojq9QJDP+frrr1mwYAHVqlXT2rd//37mzp3L1atXZfPx9/fn/fv3DBkyBIBr164xaNAg4uLiKFGiBP7+/pQqVSpPHc6cOcO5c+ek3zdu3IidnZ3GMYmJiVy6dAk3N7c8dcnKL7/8wsSJE+nataus582Obdu24evri6+vL6mpqRr7ypQpI42C51efmTNnUq5cOdavX4+lpaW0PTY2ln79+jFr1iz27t2b753UJCcns2fPHu7cuUNoaCjTpk3D2dmZo0eP4urqiouLS56dOzAwUBo1ValUzJ8/HysrK41jkpKS+OOPP6hRo0aeeehi7NixjB49mtOnT7N//36mTJnCd999R6tWrejQoQNVq1aVxUPJ7eNff/1Ft27dAEhJSeHAgQOMGTOGnj17smbNGnbt2iV72/n06VMqVqwIQFxcHOfOnWPRokV89tlnlC9fHj8/PyZMmCCbz5w5c4iMjCQwMJDSpUtTuXJlaZ+npyerV6+WxUPJf2tq4uLiSEpKomjRotK2Q4cO8ejRIzw9PalXr55BvPISkbn+8SCCeIEgn1OyZEm6du1K3759GTJkCAUKFODt27dMmzaNkydP0r17d1l9du/eTZ8+faTfv//+e8qVK0f//v1ZvXo1P/zwA0uXLs1ThydPnvDrr78CGQ8X165dw9TUVOMYExMTypcvz6hRo/LUJSsFCxakZMmSsp4zJ8LCwvDw8NC5z8TERPZ0UaX5PHz4kGXLlmkEywBWVlb069ePkSNHyuqjVCeA58+f07NnTyIjI6lUqRLXr1/n3bt3QEaq77lz5/j+++/z7PzJycnS+dLT03n//j1GRppJi6ampnz99df07ds3zzz0YWRkhJeXF15eXrx9+5ZDhw6xZ88edu/eTbly5Wjfvj3t2rXL05FLJbeP7969kwLBW7duERcXR6tWrQCoWbOmQUaZU1JSpHtI3SHeqFEjIOP7982bN7L6nDlzhtmzZ+Pi4qLVyVm8eHHZ0umV/rcGGR1nDg4OzJw5E8iYu+/n54eNjQ3r169n0aJF0v0lEMiNCOIFgnzOhg0b2LFjBwsXLuTs2bN07NiRFStWYG5uzubNm6lbt66sPqGhoZQuXRrICMbu3r3Ltm3bqFWrFqmpqcyYMSPPHXx8fPDx8QHAy8uLVatWSSMphqZXr17s2LGDBg0aYGxsbGgdAJycnLhz5w6enp5a+27duoWzs3O+9ildujQxMTE698XGxhqkU0aJTgDfffcdRYsWZffu3VhbW2uMEtauXZsffvghT8/ftm1b2rZtC0D37t2ZMWNGno78/xvevHlDSEgIERERmJiY4ODgwPLly/Hz82P+/Pk0b948T86r5PaxWLFi/P7779SuXZtffvmFcuXK4eDgAEB0dDTm5uayO5UtW5ZDhw5RrVo1AgIC8PDwkKYZhIeHU7hwYVl9UlNTKViwoM59MTExmJiYyOLxMfyt3blzh+nTpwMZHQ07duxgwIABjBw5ku+//x5/f38RxAsMhgjiBQIBXbp0oXr16nTu3JlZs2bh5ubG1q1bZZ/PCGBmZkZcXBwAFy9epGDBgtKoqpWVFbGxsbL6qEeclEKPHj14/fo1LVu2pFatWjpH3KZMmSKrU6dOnfDz86NIkSJ8+umnQMbo0+nTp/H392fEiBH52mfs2LHMmjWL4sWLU6dOHWn75cuX8fPzY+rUqbL6KNUJ4MqVKyxevJiiRYtqjRLa29sTHh4um8vWrVtlO1duiYuLIzAwkL179/LHH39Qrlw5Bg0axNdff42NjQ1xcXHMnj2bOXPm5FkQnxmltY8dOnRg2bJlBAUF8ddff2kU1rt165ZBgkRfX1+GDx/OgQMHMDY21sgGOHfuHJUqVZLVp2rVquzdu5cmTZpo7Tty5IhBUteV+LcGGR0/RYoUAeCPP/4gMjKSDh06ABkdWLt37zakXp6Qjsin/1gQQbxAIODatWuMHz8eS0tLPD09OXLkCDNmzGDatGlac9TymqpVq7Ju3TqMjIzw9/encePG0ojzs2fPcHR0lNUHMr7Iz549S2hoKImJiRr7VCoVgwcPls0lMDCQjRs3olKpuHjxotaoiUqlkj2I79OnDyEhIUybNk0atfj222+BjA4iueegKsGndevWGr/Hxsbi4+ODlZUVRYoUITIyktjYWKytrVm0aJHOB+q8ZOHChTk6LVq0CMi4pw4dOiSLl7Gxsd4iV2/evNE7gphXPHjwgDVr1nD79m3Cw8Oxt7enWrVq9O/fnwoVKsjqMmbMGE6ePAnA559/zuTJk6levbrGMZaWlnTp0kWjuFteo6T2sX///jg4OHDnzh26dOlCu3btNDw7duwom4ua5s2b8/PPP/Pnn3/i6uqqkQlUvXp1XF1dZfUZMWIEPXr0oGvXrnh7e6NSqThx4gRr167lzJkz7NixQxaPTZs20bp1a+zs7Ni0aVO2x6pUKnr27CmLV2bs7Ox4+PAhtWrV4syZM3zyySdSltL79+8pUECEUQLDoUpX4sKjAoFANhYsWMDmzZtp2rSplMp69uxZpkyZgkqlYu7cuTRo0EA2n4cPHzJgwABevnyJk5MTmzZtktLre/fujb29PfPnz5fN57fffmPYsGHEx8djbm6uM2i+cuWKbD7NmjWjevXqzJ49W2s+s6F5/vw5Fy5cIDIyEhsbGzw9PWVPXc/qc/78eaKiomT3mTBhAiqVKtfH5+U8b10o1W/QoEHExsayadMmjIyMcHd3Z9++fbi5udG7d2+KFCmS5yn1ak6fPs2QIUMoVqwYLVq0wNbWloiICE6cOEFoaCh+fn40bdpUFheAdu3a0alTJ7788sts//bfvXvH3bt3NTIs8gqltY+vXr3C3t5eZ0p4cnIy4eHhODk5yeajVG7evMnixYu5efMmqampqFQqqlevzrhx4/TWE/mvqVixIj/99BNVq1bNcTqGSqXir7/+ksUrM/Pnz+enn36iQYMGnD17VqodBLBq1SpOnTr1f240vvNS+ZdgzQ27RvgYWkFxiCBeIMjn1KpVi0mTJmmMWEDGqMWMGTOktES5iYyMlNLY1Pz999/Y29trVIrNa1q3bk3RokWZO3cun3zyiWzn1YeHhwerVq3SOd/bECQmJlK/fn0WLlyIl5eXoXUEHzmPHj3i22+/pXDhwnh5ebFlyxbatWvHgwcPePr0Kbt3787z1SnUtGrVitKlS7Ny5UqNgltpaWn4+vry7Nkzjh49KotLYmKiVAtD7gyA7FBa++jm5kZAQIDOSv1//PEHHTt2NMj32fv377l48SIhISEkJSVp7DPUKDNkLDEbHR2NtbU1FhYWBnFQMikpKaxZs4Y//viDSpUqMXDgQKmI4+DBg6lZsya9e/c2sOV/S6clmw2toJOfRvY0tILiEHkgAkE+59ChQzpHJmxsbFiyZAktW7Y0gBVaATwge9ohZIzmTpgwQREPqABNmjTh5s2bignizczMsLCwUEyRvcz8/fffOlN8AWmuvNwkJCQQExODtbW1QYpsKR0XFxf27t2Ln58fgYGBGBsbc/r0aTw9PVm0aJFsATzAixcvmDBhglbFbCMjI7p06SKNyMmBmZkZS5cuxd3dXbZz5galtY/ZjUslJSVpVdGXgytXrjB06FCio6N17jdkEG9ubi7aoWwoUKCA3r/zlStXymwjEGgigniBIJ+TOYAPCQkhJCSEihUrSnNP5a68mrkQkT7kTD2uVKkSISEhsp0vJzp06MDMmTNJSEjA09NTZ2E7uR/027Rpw549e2Sf162P+/fvM2LECIKDg3U+1BsiNfPUqVP4+fnx119/kZ6ejkqlws3NjWHDhsl23b777jt69+6Nk5MT3333XY7Hy11bQU3JkiVlnTKjD1dXV168eKFz34sXLyhfvrysPm5ubjx8+FCWNPncooT28dGjRzx69Ej6/fLly4SGhmock5iYyJEjRwyy6sKsWbNwdXVl6tSpODs7y1b9XR8TJ07k/fv3OpdqHTlyJJaWlsyePTvPPdTL7eWW2rVr55FJzjx69Ig7d+4QGhpK+/btsbe35+nTp9ja2ipuWpsg/yCCeIFAQEBAAH5+ftJ6tXv27MHd3Z3BgwdTt25devToIZuLruAqJiaGkJAQihQpInthuxkzZjB27FgcHR3x9PQ0eCEb9Xq569atY926dRrzmtXBodwBqrW1Nb///jutW7emUaNG2NnZaXjJPdI0efJkjI2NWb16tSIemk+cOMHQoUOpVq0aEyZMwM7OjvDwcIKCghg0aBDLly+nRYsWee7x66+/0qFDB5ycnHKsKm6IAon6ePHiBc+ePaNSpUqyLsc1bdo0Ro0ahYWFBS1atJBWx/jll1/YtGkTixcvls0FYNKkSYwdO5aiRYvSpEkTRaQ/K6F9/Pnnn/Hz8wMy7lt9n4u1tbXstScAXr58yaRJk2Tv9NHHhQsXGD9+vM59n376KQsWLJDFo3v37qhUKqmjVdd3WWYMNQ1iypQpHD16FCMjI9LS0mjUqBH29vYsXryYEiVKMG7cONm98hIxy/rjQcyJFwjyOZs3b2bRokX06tULT09Pevfuzd69e3F3d2fLli0EBQWxc+dOQ2vy6NEjRo0axcSJE6lXr55s5/Xw8CAlJYWUlBSMjIwwMzPT2K9Sqbh+/bpsPrkpEiX3SJ3SihJ5eHiwbNkyGjduLNs5s6NNmzaUK1dOqvaemTFjxvDw4UMOHDggv5gCmTdvHqmpqUyePBmAX375hZEjR5KSkoKNjQ3+/v4aa8f/13h4eGgED8nJyaSkpAAZqbWZfzYxMeHGjRt55qLLLTk5WVp6z9zcXKuzTM62SO1k6PYxNjaWmJgY0tPTadGiBX5+fri5uWkcY2Jigr29/QcVc/yv6NOnD15eXrKv0qGPKlWqsG7dOp1Tsi5evEj//v25c+dOnnvcu3dP+jkiIoLJkydTt25dvL29sbOz482bNwQFBXHlyhXmzJkja4FdNTNmzOCXX35h/vz51KpVi+rVq0vPR3v37mXz5s0cPnxYdq+8pOMP2a8UYCh2j+plaAXFIUbiBYJ8zrZt2/D19cXX11drXeYyZcoQHBxsIDNNXFxc6NevH99//72syyf17t3bIA9+ukhMTOT48eN89dVXOgs3GYrMD2NKoGLFirx9+9bQGhKPHz9mzJgxOvd9/fXXsi7BpXR++eUXhg0bJv3+ww8/0KRJE4YPH86CBQtYunQpGzZsyLPzK+nvPStKdFOCk5WVlbQU6smTJ7G3tzfI3Hd9zJo1i+HDh2NiYoKnp6fOZVvlzDBxdHTk9u3bOoP427dvY29vL4tH5s7fYcOG0apVK61RbS8vL+bPn8/OnTsNEsQfO3aMcePG0bBhQ63no08++YSXL1/K7iQQqBFBvECQzwkLC9O7pIyJiQnx8fEyG+nHysqKZ8+eyXrOoUOHynq+7DAzM2Pv3r0GK8r2sTBt2jTGjx+Pvb09devWNfgUCBsbG4KDg2nYsKHWvuDgYGxsbAxglf363oCshdvUZF4C7NmzZwQHB7Nw4UIqVKhA9+7d9aYB/1co6e89K0p0U5qTusBeWFgYYWFhOu9ruedWW1tb4+TkxLRp0/R2eMiZqfTFF1+wZs0aSpYsqVHz5ueff2bNmjWyTp9Tc+7cOWlKRFYaNmxokLYIID4+Xm+nxvv372W2kYc0kZ/90SCCeIEgn+Pk5MSdO3d09srfunVL9nW+o6KitLYlJyfz6NEjfvjhB8XMKzQUHh4e/P7774oqbgUZDzv79+/n+vXrREdHY2NjQ82aNWnbtq1UJFEuXFxcqF69On379lXEFIhWrVrxww8/YG5ujre3N9bW1sTGxhIUFMTSpUvp1KmTbC5qcrO+tyEenK2srIiIiADg/Pnz2NjYSOnzpqamOoMyOdBV9NNQpKenExwcLP2dlSlTxuCj4aCMa/T8+XPGjh3LrVu3AO35vYaoGTJ27Fhu3LhBr169KFOmjMFrdAwePJh79+4xatQoJk+ejIODA69fvyYhIYHGjRsbJDOoUKFCXLx4Uedo+/nz5ylUqJDsTpBR3PL48eM6O2BPnz6dp1N7BIKcEEG8QJDP6dSpE35+fhQpUkQa4U1JSeH06dP4+/szYsQIWX3q1aun84E0PT2d4sWLy76sS25GJX788UcZTDIYNmwYY8aMwdjYmCZNmmBra6t1veRMzYSMh/fu3bvz8uVLKlasiK2tLcHBwQQFBbF582Z+/PFHihcvLpvP9OnTCQwMpGXLlop4aB49ejSvXr1i6tSpTJs2TZpbnZ6ezqeffsqoUaNkd5o/fz5VqlRRzPreamrVqsXy5cuJiIjA399fo+Df48ePZb2P4H9FP8PDw1GpVBpFP+vUqYOPj4+sPtu3b2fVqlW8fftWKv5la2uLr68vXbp0kdVFjZKu0ZQpUwgLC2Pu3Lm4uLgoIq3+0qVLzJw5k6+//trQKkBGZ9jatWs5f/48ly5dIioqisKFC1O/fn2DLV3apUsX6e++efPm2NraEhERwYkTJzh48KDBMj7UUw3fv3/PZ599hkql4vbt2wQGBrJ3717Wr19vEC+BAERhO4FAQMbSU9u3b0elUpGWliati9ylSxfZK1Tv27dPKyg1MzPD0dGRatWqyZ4a7evrq+UTExPD3bt3sba2pnLlynrTAPOCzPMIlZCaCRkdC3/++Sfr1q2jbNmy0vbHjx8zcOBA3NzcWLZsmWw+NWrUYPTo0YopJKXm77//5tq1a8TExEiZCq6urgZxqV69OitXrjTIPNPsCAsLY+zYsdy5cwd3d3eWLl2KnZ0dAN988w2urq7MmjVLFhelFf0MCAhg+vTpfPHFF7Rq1Uoq/nX06FGOHj3KrFmz6Nixo2w+oLxr5OHhwfz58xU15cjb25vx48fj5eVlaBVFs23bNtatW8fr16+lqvX29vb079+f7t27G8wrKCiIBQsW8OrVK2lbsWLFmDBhAp999pnBvPKKdos2GlpBJ/vG9Da0guIQI/ECgYApU6bg4+PD+fPniYqKwsbGBk9PT9lT6QHatWsn+zmzY9WqVTq3v337Fl9fX405hXIwd+5cRaTOZubChQvMmjVLI4AHKFu2LMOHD2f69Omy+lhbWxtkPeiccHV1NVjQnhUlrO+tC0dHR72ZLf7+/rKOrCqt6OfmzZvp3r27VLlfTfPmzSlatCj+/v6yB/FKu0aOjo5SJ7RSGDZsGOvWraNmzZoGq3+hCyXVDQDo1q0bXbp0ITQ0lPDwcOzt7SlWrJjBP8/PPvuMzz77jODgYCIjI7GxscHFxcWgTgIBiCBeIBD8f0qWLEnnzp0NrYGbmxsBAQE6q6//8ccfdOzY0SDrxWalaNGi9O3bl4ULF8oayCutkwMgNTVVa965GjMzM62H+7ymV69ebNu2jfr16xusqN3du3dxcXHB3Nycu3fvZnusSqXCxsaG4sWLy/bAqoT1vbND17xvS0tLWR2UVvTzxYsXNGvWTOe+pk2bsmvXLll9QHnXaOTIkaxfv55atWrJPq1IH4cPH+bVq1c0a9YMNzc3rer0KpWK1atXy+ajxLoBaoyMjHBycpKKWyqJMmXKUKZMGUNrCAQSyvrWFggEsnD16tUPOl7OXvnsZvikpqZibGwsm0tOpKamEh4ebpBzR0dH8+DBA0JCQmjcuDE2NjYkJiZiYmIi+8hFjRo1WL16NXXq1NF4QI2NjWXNmjXUqFFDVp9nz55x//59WrZsSe3atbG2ttY6Jq+nibRv356ffvqJqlWr0r59+1xlT9jZ2bFgwYI8m5eqaw30/v37K6L4X2Yyz/tWY4h530or+mlvb8/NmzepX7++1r7ff/9dtqXBMqO0a7R//35CQ0Px8vJSRMAM8O7dO0qXLq3xuyFRYt0AgAcPHrBq1Sru3LlDaGgoAQEBuLu7s2TJEmrUqEGTJk1k8di0aROtW7fGzs6OTZuyXzNdpVLRs2dPWbzkQsyy/ngQQbxAkA/p3r27NOcMNOdWq4slZSave+XDw8N5/fq19Pvjx4+1gvXExET27t0rew+9rlFUdbX8lStXyr5ee1paGkuXLmXr1q28f/9eKiRlY2PDkCFDqFatmuxVxcePH0+3bt1o0qQJ9erVw87OjoiICC5evIiJiQlz586V1efUqVNSR8a1a9e09qtUqjwP4n/88Ucp5TI3hQ/j4uIICAjgu+++48iRI3nipIQ1vXMiICCA2bNn65z3PXv2bExMTGRLGVda0c8OHTqwatUqkpKS+Oyzz7C1teXt27f8/PPP+Pv7G6SquNKu0bt37yhVqpTG74Zm69athlbQ4Pbt24qrG3D+/HkGDBiAu7s7rVu31uhoKVCgADt37pQtiJ8/fz41a9bEzs6O+fPnZ3vs/8UgXvDxIArbCQT5kHv37kk/R0REMHnyZOrWrYu3t7f00BwUFMSVK1eYM2dOnhe/8vPzw8/PL9sAIz09HWNjY6ZPny7rklwVK1bU8lI3m9WqVWPRokWyzr9esmQJW7duZdy4cXh6euLt7S0Vktq5cye7d+9m3759svmoCQ0NZdOmTVy/fl2jcFvPnj0pVqyY7D4fIzdu3MDX15dLly4ZWsVgfP755zRs2FBr3jfAnDlzOHfuHEFBQbL5KKnoZ3p6OvPnz2fbtm0aU1SMjY3p3r0748aNM0gnjZKukSBnPvvsM8aMGaOx8oOhadeuHeXKlWPBggWkpKRQuXJl6XvtxIkTzJo1i7NnzxpaM1/QdqG/oRV0sn9sH0MrKA4RxAsE+Zxhw4ZRokQJxo0bp7Vv/vz5PH/+PM+rr798+ZKXL1+Snp6Oj48P06ZNo1y5chrHmJiY4OzsTJEiRfLUJStXrlzR2mZmZkaxYsVwdHSU1QWgcePG9O/fn27dupGamoq7u7v0sHP+/HlGjBjxwdMlBALImHP97NkzKlWqZLD5xFWqVGHt2rU6U8bPnz/PwIEDuXPnjqxOz54948KFCwYv+qkmMjKS27dvS/UCqlatKnu7mJXnz58rojCqUlFKqjjAsWPH2LhxI2vXrlVM3YAqVaqwZs0aGjRooPW9duXKFfr06SP7331+5esFGwytoJOD4/oaWkFxiHR6gSCfc+7cOb1BesOGDWVJzf7kk0+ktap//PFHKlWqJHsRK33UqVPH0AoaREVF6a2Mm5qaSkpKisxGGevEv337Fnd3d619d+/exdbWVvbR+LCwMDZv3syNGzekdZBr1qyJj4+PQTpflMa8efNITU2VRrx/+eUXRo4cSUpKCjY2Nvj7+1O5cmXZvZQ477tUqVIaKdqG4sCBAzRp0oQiRYpoBX1RUVGcPn2aNm3aGMRNKYVRc9PhLPd0IyWlioMy6wbY2NhoTKnLzJMnTwzydw9w9OhRXr16Rd++2gHkhg0b+OSTT/j8888NYCYQiCBeIMj3FCpUiIsXL+pMmT9//jyFChWS1UdpQTNkpLGeOXOG69evS6NftWrVonHjxrKnrzo7O3P+/HmdhaSuXLlC+fLlZfWBjErnpUuX1hnEBwYG8uTJE1kfCu/fv0+3bt1ITk6mQYMGVKxYkYiICHbt2sXevXvZtm2bQa6Tkvjll18YNmyY9PsPP/xAkyZNGD58OAsWLGDp0qVs2CD/iIxS5n2/e/eOo0ePcvPmTd68eYNKpcLOzo4aNWrQqlUrLCwsZPHIzMSJEwkICNA56v7ixQsmTpwoexCfm6wfOQujbtmyRWtbfHw8qampmJubY2pqKnsQv3jxYlq1aiWlimduC93c3Ni9e7esPkqsG9CiRQtWrFhBtWrVpCKAKpWK8PBw/P398fb2NojXunXr9K4IY2Fhwfr160UQLzAYIogXCPI5Xbp0Yfny5URERNC8eXNsbW2JiIjgxIkTHDx4kKFDh8rudODAAQICAnjy5InONWxv3Lghm0t0dDT9+/fn1q1bWFtbS9dn/fr1VK9enXXr1umsfv5fknkErmfPnkydOpUCBQrw2WefARnz0X///Xe2bt3K999/n6cuurh16xbffPONzn1169blwIEDsvrMnz+fkiVLsnHjRo11maOjo+nduzfz5883SICqJMLDw6Uikc+ePSM4OJiFCxdSoUIFunfvzvjx4w3iNWjQIGJiYvD392fdunXSdvW870GDBuW5w8WLFxk5ciRRUVEUKFBASjmOiopi7969/PDDDyxdulT2tbSzm/0YExMje4craBdJBWQvjJoZXZ0KKSkpXLx4kYULF7JgwQLZXNQ8ePCA0aNHA9rXxtramsjISFl9lFZoD2D06NHcuXOHr776igoVKgAwadIknj9/TpkyZWTveFHz5MkTvR2+Li4uBAcHy2yU94hJ1h8PIogXCPI5vr6+WFtbs27dOvbv3y89kNnb2zNp0iS6d+8uq8/BgweZOnUqbdu25ebNm7Rv3560tDR+/fVXrK2t+frrr2X1mT9/Ps+ePcPf318jW+H8+fOMHTuW+fPnM2fOnDx1yDwC165dO6Kjo1mxYgVr164FYPDgwVhYWDBixAhZ16xXEx8fr3edcZVKJftIz40bN1i4cKFGAA8ZKZuDBg1i7Nixsvq8evUKe3t7TExMtPalpKTw+vVr2VddsLKyIiIiAsi4l21sbKT0eVNTU52dZ3KgUqmYMGECAwYM4NatW1KRRLnmfYeGhjJ06FCKFy/O/Pnz8fT0lJbgSkpK4sKFCyxatAhfX18CAwPzfGrGmTNnOHfunPT7xo0bsbOz0zgmMTGRS5cu4ebmlqcuutDVQRcdHc1vv/3G8ePHmTlzpuxOWSlQoACNGjUiLCyMGTNmsGvXLlnPr9RUcSVhZWXFrl27OHToEBcuXKBw4cLY2NjQtWtXvv76a4Mtg2dmZia1k1kJDw/X+70nEMiBuPsEAgHdunWjS5cuhIaGEh4ejr29PcWKFZN9vXHIWKPV19eX/v3789NPP9GlSxfc3d2Ji4ujT58+so82/frrr4wdO1ZrukGDBg0YNWoUixYtyvMgPusIXK9evejUqZM039vGxgYPDw+tuY1y4eLiwokTJ2jcuLHWvpMnT1KmTBlZfYyNjUlKStK5LykpSWv5wrymefPmBAQE6FyO8N69e3Ts2FHW0UqAWrVqSRk4/v7+GpWqHz9+TPHixWX1yUqRIkVo2rSp7Ofdtm0blpaWbN++XSvDxtTUlKZNm+Lh4cHXX3/N9u3bGTVqVJ76PHnyhF9//RXI6OC4du2aVkBjYmJC+fLl89xFFxUrVtS5vW7dupibmxMQEEC9evVkttJNsWLFNFZmkQslpoqnpaVx6dIlgoODdbaVvXr1ks0lMTGR4cOH06dPH9q3b0/79u1lO3dO1K5dm3Xr1uHl5UXBggWl7fHx8WzYsEGR0/8E+QcRxAsEAgCMjIxwcnKSfUQwK0+fPqVGjRoYGxtjbGxMXFwcAJaWlvTr14+5c+fK+oDx/v17rZEvNfb29rx//142l8wUKlSIRo0aGeTcWfHx8WHChAkYGRnRvn17HBwceP36Nfv27WP37t2yrxNfv359li5dipubm0YHwpMnT1i2bJnOoml5SXZp0ElJSQYZZZo0aRJjx45l0aJFuLu7M3LkSGnfoUOHqFWrluxOat6+fcuWLVu4deuW1KlYrVo1fHx8KFq0aJ6e++LFi3Tu3DnbKTI2NjZ07tyZ48eP53ng7OPjg4+PDwBeXl6sWrVKb+CsNGrUqIG/vzKWq3r+/Dnr16+XdTlQNUpLFQ8PD6d79+48efJEYypE5lR/Ob9jzczMuHr1qiLXWx85ciSdO3emZcuWeHt7S99tx44dIzk5mR9++MHQiv85aSKf/qNBBPECgYDHjx9z/PhxQkNDtdJoVSqVrEGYpaWlNDLg6OjIw4cPqVu3LpBRfV3u+YNubm5s27aNhg0baozgpqWlsXXrVipVqiSLR2BgINevX8/xOJVKJfvDUJs2bXjz5g0rV64kICBA2m5ubs7o0aNp27atrD4TJkygW7dufPHFF5QvXx47OzsiIiK4f/8+xYsXZ+LEiXnu8OjRIx49eiT9fvnyZUJDQzWOSUxM5MiRI7IHFunp6ZiZmbF+/XrMzMy09vv7+xssffXWrVv07duXtLQ06tevj7OzMxEREWzbto1t27axceNGqlWrlmfnf/78ea6q8leuXJmNGzfmmYcu1CPyHwsnTpyQfQkzDw8PrXnnKSkpJCcnY25unufLpepCaani8+bNo3Dhwpw5c4YmTZrw008/YWdnx6FDhzhw4IBGLQq5aNCgAefPn1dM1oYaFxcX9uzZw/Llyzl+/Li00kn9+vUZMmSIlFkhEBgCEcQLBPmcAwcOMGnSJMzMzHByctKatyt39fXKlSvz999/06hRI7y8vFi5ciXp6ekUKFCAdevWUb16dVl9Ro8eTe/evWnZsiXNmzeXAsITJ07w5s0b2R7kf/zxx1wdZ4ggHqBv37507tyZmzdvSg86Hh4eBlkq0MnJicOHD7N3716uX79OTEwMzs7OtG/fnnbt2skyJePnn3+WAgaVSsXixYt1HmdtbS17McLk5GTq16/PqlWrdKasG3J5x5kzZ1KuXDnWr1+v4REbG0u/fv2YNWsWe/fuzbPzx8XF5WpaipWVlZQlJBdnz54lJiaGL7/8EshY2nHSpEk8evSI+vXrM23aNI2UXzkYOHCg1rbk5GSCg4MJCQmRvf5E7969tb6zTE1NKVasGI0bNzbYuugmJiaKSRW/evUqU6ZM0ZiL7+TkxMCBA0lPT2fWrFmyF/5s374906ZN4927dzRp0gRbW1utz1HX6idyULp0ab3tt0BgSEQQLxDkc1avXo23tzdz5841yLJJWRkwYACvXr0CYNiwYbx8+ZK5c+eSlpZGlSpVZC+UVLt2bXbu3MmaNWsIDAyUCm3VrFmTgQMHyvZg8dNPP+mcU60kLC0tFZPiX6hQIXr06EGPHj0Mcn4fHx/atm1Leno6LVq0wM/PT6vwmImJCfb29rJ3lKmDmtTUVFnPmxsePnzIsmXLtDoSrKys6Nevn0baf16QlpaW688ju2kSecGyZcs0ClfOmjWLR48e8cUXX3Do0CGWL1/OhAkTZHXSVbTSzMyM+vXr4+3tLXt7YIjVVD42YmNjKVq0KEZGRlhaWmoUblOvuCI3AwYMAGDHjh3s2LFD428wPT0dlUole90QgB49ejB9+nRcXFy09gUHBzN9+vRcd7B/LMjdrgn+OSKIFwjyOa9fv2bGjBmKCOAh4yFCPdpubW3N6tWrSUpKIikpyWAjhJUrVzZIGqaSefv2La9fv9aan3vv3j1WrVrFo0ePsLOzw8fHBy8vL1nd7t27R1hYGE2aNNHad+bMGRwdHfN8XrGVlZU0onvy5Ens7e0NlqKuiy5durB582YaNmyoM6XeUJQuXZqYmBid+2JjY2WZejBmzJgcr4khqvc/e/ZMum/j4uI4d+4cixYt4rPPPqN8+fL4+fnJHsQrcbkyyKiQf/v2baKjo6XVDbKuVpGX6Err14dKpcrVVKn/ihIlSkjV8suVK8fBgwdp1qwZYJgpEJD7TDO5uXLlit7VVeLi4rh27ZrMRgLB/xBBvECQz6lVqxb379/H09PT0CpAxnJqvr6+Gg/rpqammJqa8vLlS/z8/AyyFrpAkx9++IG7d++yf/9+advLly/p2rUrCQkJuLq68uDBA4YMGcKWLVtkXVN77ty51KhRQ2cQf/v2bW7cuMGmTZtk8/nkk08ACAsLIywsTGcAKPea4yEhIQQHB9O0aVPq1KmDnZ2dVtAxZcoUWZ0Axo4dy6xZsyhevLhG5efLly/j5+fH1KlT8/T8H1K/oWbNmnlook1KSoq0Yoh6PXT1SHfJkiV58+aNrD5KJD09nYULF7Jt2zaNquumpqZ0795dtvR+XWn9SqFp06acP3+eVq1aMWjQIAYPHoynpycFChTgzZs3jBkzRnanj7HK+82bN/O80KZAkB0iiBcI8jmjRo1i7NixmJmZ0aBBA53zQeXsmd+/fz/ffvutzhG3yMhIDhw4IHsQf+TIEYKCgggJCdFZ+O/QoUOy+iiBGzdu0KFDB41tmzdvJj4+nvXr19OwYUMSEhLo1asX69evlzVIvXfvHn379tW5r3r16mzbtk02F8goljZ27Fhu3boFaKcrGiJV9NSpU1JmwJ07d7T2q1QqgwTxCxcuJDY2Fh8fH6ysrChSpAiRkZHExsZibW3NokWLWLRokeT4X//tKbmDsGzZshw6dIhq1aoREBCAh4eHVN8hPDzcICOoH1IkUo4iqWvWrGHLli307duXzz//HDs7O968ecPPP//Mhg0bsLa2llK38xIlp/WPHj1a+rlJkybs3LmTEydOkJCQQP369XV2fuYFsbGxrFy5kqZNm+otaHfp0iVOnz7NkCFDZMvEW7t2LWvXrgUy7lkfHx+tDpmkpCRSU1Pp0qWLLE5yko5Ip/9YEEG8QJDPUY88zZgxQ+/IgSHmouni6dOnsj+o/vDDD6xbtw53d3ecnZ0NkhJtiLWNcyIsLIzy5ctrbDt16hRubm40bNgQyKhO361bNxYsWCCrW1JSEsnJyXr3yZ0KPWXKFMLCwpg7dy4uLi6KSKtXaqVzd3f3XFWHz4/4+voyfPhwDhw4gLGxMWvWrJH2nTt3TraVMjLz119/8fr1a96+fYuNjQ22trZEREQQHR1NkSJFcHR0lI6VY2R69+7dDBo0SGPZNjs7OypWrIiJiQkBAQGyBPH6CA0N5fXr1zg4OFCsWDGDeWSmSpUqVKlSRfbzbtmyhRMnTmS7TGONGjWYNm0aNjY2DBo0SBYvDw8PevfuTXp6OitXruSLL77Q+qxMTExwcXGRpiEIBIZABPECQT5n7ty5Bk/727FjBzt37gQyHvR0zUlNSkri5cuXeHt7y+q2d+9ehg0bhq+vr6znVToqlUrjvnnz5g0vXryQ1rRW4+joaJBlAQ8ePEjz5s219h08eFD2dbZv377N/Pnz+fTTT2U978fIvHnzDK2gWJo3b87PP//Mn3/+iaurK87OztK+6tWr4+rqKrvTqFGjmDFjBps3b9YYTb148SKTJ09m5MiRso3sQkZGQo0aNXTu8/DwkEZY5SYgIIDVq1cTFhYmbXNwcGDQoEF07tw5z88fFRX1QcfL0Vl+/PhxunXrlm2npqmpKV26dGH//v2yBfF16tSR0vtVKhUdO3bU6IwSCJSCCOIFgnxOu3btDK2Ag4ODNPr24MEDypQpozXXzMTEhLJly2qlcMtBXq5L/bFSpkwZLly4II26nzp1CpVKRYMGDTSOCw8Pl33e4IABAxg0aBD9+/enXbt2ODg48Pr1a/bt28dvv/3GqlWrZPVxdHSU5jIribdv37Jx40bu3LlDaGgofn5+lC9fni1btlCtWjXZl3NMTEykfv36LFy4UPZiiB8LJUuW1DnV6JtvvjGATcb0h2HDhmmlQ3t6ejJ06FAWLlwoaxD/ySefcPr0aerXr6+178yZM1J9CjlZu3YtS5Ys4euvv8bb21tK8Q8KCmLmzJlER0fneXZAvXr1PqizXo7su6dPn+Yqe8TNzY2lS5fmuY8uMmd0KDGLIi9IE9n0Hw0iiBcI8iGtW7dm8eLFVKhQgdatW2d7rBxzvlu0aEGLFi2k37MWtjMkHTp0IDAwUCs4ze90796d8ePHExMTg52dHTt37qRUqVJaD8+//fYbFSpUkNWtadOmLF68mAULFjBixAhUKhXp6ekUK1aMRYsW6VwbPS8ZOXIk69evp1atWgZbpzord+/epWfPnlhZWVG7dm2uXLkiFQILCwtj8+bNsj84m5mZYWFhgbGxsaznVTLHjx+nXr16WFtbc/z48RyPlzvbI7spTjY2Njx79kxWn549ezJjxgzevn3LZ599JqX3BwUFceTIEWbMmCGrD2RU8O/Tp49WUT0vLy9sbW3ZunVrngfxSsi4y4qRkZFG8UF9JCcnG9Td0FkUAoE+RBAvEORDKleuLC0p5+7urqgvd12Fpd6+fYuVlRUmJiay+4wYMYI5c+bQuXNnPD09sba21tivUqno2bOn7F6G5quvviIsLIxt27YRExODu7s706dPp0CB/32tREREcOrUKYMUeWrVqhWtWrXi8ePHREVFUbhwYcqWLSu7B2QUawwNDcXLyws3Nzet4pEqlYrVq1fL6vT9999TvXp1Vq1ahUql4uDBg9K+atWq8fPPP8vqo6ZNmzbs2bNH1tFbJTNs2DB++uknqlatyrBhw7I91hAFEsuVK8e6deuoXbu2VGQPMpbfWrduHeXKlZPVp3PnziQnJ7Nq1SoCAwOlDryiRYsyefJkg2QsvHv3TmdmAEDDhg3ZtWtXnjsoIeMuK2XLltXI5tLH+fPnDdZ2KyGLQiDQhwjiBYJ8SOZAWQlzUG/fvs2dO3fo2rWrxvY9e/awaNEioqOjpblx48aNk7XT4dKlS+zfv593797x+++/a+3Pr0E8QL9+/ejXr5/e/ba2tly4cEFGI20M9fCXmXfv3lGqVCmN3w3NnTt3WLFiBSYmJqSmpmrsK1q0KBEREQbxsra25vfff6d169Y0atRIa+k7Of/edu7cSevWrWWriq2LkydPYm9vL/2sNKZMmULfvn1p0qQJdevWlUa+L1++TGpqKhs2bJDdqXv37nTt2pXHjx8THR1N4cKFKVOmjMGmtDRs2JALFy7ozOY6f/68YpZ3lZvWrVuzZMkSGjdurLc6/eXLl9m5c2e2xe/yEiVkUchN1tVTBMpFBPECgcDgbNiwgYSEBI0g/tq1a0ydOpVixYrRtm1bgoOD2bx5MxUqVPigtZz/LTNnzqRy5cpMmTIFZ2dng2QDCD6cx48fc/z4cUJDQ3UuC5jXS11lZuvWrbKdK7dYWFgQFxenc9+rV68Mlvb/ww8/ABm1FB48eKC1X84gft68eVJBwg4dOhhkLevMc7gNMZ87J2rUqMHx48fZvHkzt2/f5vHjx9jb29O5c2d8fHykDgi5MTIykj0LIDN3796Vfu7QoQPTp0/n7du3NG/eXOroOHHiBJcuXWLmzJmy+129epWAgACePHmic7WOw4cP57lD165dOXnyJH369KFFixY0bNgQJycnVCoVr1694rfffuOXX36hZs2aBlvKTQlZFAKBPkQQLxAIDM4ff/zBwIEDNbbt2rWLAgUKsG3bNunhdcKECezatUvWID40NJSpU6dqLacmUC4HDhxg0qRJmJmZ4eTkpNXxoqTpI4aiYcOGrF69WmOKiEqlIiEhgR9//NFg6exKWk7x3LlzHD58mL1799KjRw9KlixJ+/btadu2rahWnQk7OzvGjBljaA0AlixZQmRkJLNmzdLaN23aNGxtbRk+fHiee7Rv316jnUlPT2f//v3s379fSvFXM3DgQFmnQZw7d44BAwbg6enJH3/8QePGjUlISODGjRsUK1aM2rVry+JhYmKCv78/S5cuZdeuXRw7dky6Zunp6RQsWJCePXsyfPhwjWlaciKyKARKRpUu8iYEAoGBqVatGuvWraNu3brStrp161KpUiU2bdokbTt9+jQTJ07k4sWLsrn16dOHZs2a0a1bN9nOKfh3eHt7U6lSJebOnSvVfjA0Dx48YNWqVVIl+ICAANzd3VmyZAk1atSQPWgOCwvj22+/JS4ujrp163LixAkaNWrEw4cPUalU/PTTT9ja2srqpGTu3bvH3r17CQwMJCYmhgYNGtC+fXu8vLzyNDvHw8Mj151OKpWK69ev55nLx0Dz5s0ZOnQobdq00dp38OBBVq5cmasCgf+WK1eufNDxcmZ5dOrUiZo1azJmzBjc3d3Zu3cv7u7uvHz5kj59+jBo0CC+/vpr2XwgY2WKP/74Qyoe5+joSOXKlbWWmpWbM2fOMH36dDw9PfVmUdjZ2UnHu7u7G9D2v+HT7+Stz5Jbjk+RZ4nBjwkxEi8QCAyOlZWVRmqvei5jzZo1NY6ztLQkISFBVreRI0cyYcIETExMqF+/vlZRMpBnTV1B7nn9+jUzZsxQTAB//vx5BgwYgLu7O61bt9YoYlegQAF27twpexDv6OjIgQMH2Lx5MxcuXKBUqVJERUXRunVrevXqZbB7+urVqzkeI9dIYWYqVqzI5MmT6du3L2PGjOHs2bOcPXuWIkWK8O2339K/f3/Mzc3/8/P27t1bI4hPTU1l9erVdOrUCQcHh//8fB9KQkICq1at4tixY4SGhuqsNi7nKPPr168pXry4zn3FihUjNDRUFg9DTL3ILY8ePWLkyJEYGRmhUql4//49kDFdY+jQoaxYsUL2IN7MzEzr+14JqOe7Z5dFARmZA4YoLCnI34ggXiAQGBw3Nzf27t1L8+bNgYz5eCqVSiuwefLkiexzLNXr0k+fPl3viJj44lYWtWrV4v79+4pJdVy8eDGtWrViwYIFpKSkaATxbm5u7N692yBe1tbWDBs2LMeq53LSvXt3rQflrH93cv+9paenc/bsWfbu3cupU6ewtramT58+tGzZkjNnzrBt2zYePHjAihUr/vNzZ13ZQR3Ef/PNN4oY9Zs5cyaBgYF8+eWXuLi4GLxmSNGiRXnw4IFGVpeaBw8eYGNjYwCr/53/+vXrREdHY2NjQ82aNQ0yTcvMzIy0tDRUKhX29vY8e/aMWrVqAVCoUCHZOjo+Bn788UdDKwgEehFBvEAgMDiDBw+mW7dueHt7Y2try40bN2jQoAFVqlTROO748eNUq1ZNVjclrq+rNHr06MH06dNxcXHR2hccHMz06dNlfRgaNWoUY8eOxczMjAYNGhg8e+LBgweMHj0a0A5Ira2tiYyMlM1F6Rw4cEBrW3R0NL/99hvHjx+XtQjYs2fP2Lt3LwcOHOD169fUr1+fRYsW0bx5c2mObvXq1alcubLBqmcbmlOnTjF+/HjFTDdq0aIFK1asoGrVqlStWlXafvv2bVauXMnnn38uu1NSUhJjx47l+PHjpKenY2pqSlJSEiqVCm9vbxYsWICpqalsPhUrViQ4OJgGDRrg6enJmjVrKFKkCAUKFGDp0qVUqFBBNhelo+SMirwiTcyy/mgQQbxAIDA41atXZ8uWLezatYuYmBh8fX3p06ePxjEREREYGxvrnOuYl+S0vq46FTE/c+XKFb3LpsXFxXHt2jVZfdSFD2fMmKGI7AkbGxtev36tc58hsktAeWnQaipWrKhze926dTE3NycgIEDvclT/NZ9++imOjo60a9eODh066K0OX7ZsWY2AMT9hbGyMs7OzoTUkRowYwY0bN/jmm29wcXHBwcGB169f8+jRI9zc3Bg5cqTsTj/88ANnzpxh5syZtGrVCktLS+Li4jh69Cjff/89S5YsYfz48bL5+Pj48OLFCyCjw3PgwIEMGpQx37hYsWL4+fnJ5iIQCP45IogXCASKoGbNmtnOibO1tdVIQ1Zz4MABmjVrJmuaZGpqqlS5+tdff+XmzZuynftj4+bNmxQtWlTWcyote0I9OlitWjVKly4NZIzIh4eH4+/vj7e3t+xOSkuDzg01atTA399ftvOtXr2aJk2a5Li+eJkyZRS5jKAcfPvttxw8eJCGDRsaWgXIqK8SEBDAgQMHuHTpElFRUVSoUAEfHx++/vprWUe81Rw5coRRo0bRqVMnaZulpSWdOnXi/fv3bNiwQdYgPvM0NUdHR/bt28fTp09JSEigbNmyBrlGSqVixYo5fpeI6XQCQyGCeIFA8NGSmprKxIkT2bNnjyxB/PXr1wkMDCQoKIioqCiKFi2q8WCWn1i7di1r164FMgJSHx8frYedpKQkUlNTZV/jN6fsCbkZPXo0d+7c4auvvpJSVSdNmsTz588pU6YMQ4YMkd1JaWnQueHEiROyToNo1qyZbOf6UJTSSWVubs7169fp3LmzxnKFalQqFT179pTVydTUlE6dOimmbY6OjqZs2bI695UtW5bo6GiZjTRRqVSKyqZQEhMmTND6W4uJieH8+fO8fv2aHj16GMgs7xDZ9B8PIogXCAQfNXm9SuaDBw8IDAzk8OHDhISEYGJiQnJyMhMnTqRr164YGxvn6fmVioeHB7179yY9PZ2VK1fyxRdfUKxYMY1jTExMcHFxUXQwJAdWVlbs2rWLQ4cOceHCBQoXLoyNjQ1du3Y12Oig0tKg1airPWcmOTmZ4OBgQkJCGDt2bJ6e/7vvvvug46dMmZJHJhnoW2Kua9euWtsNscTcokWLAHj16hW///671n65g/h79+4RFhamc7WHM2fO4OjoqHfKRl5RtmxZvdkKhw4d0hvg/9ekp6fz+PFjUlJSqFChAiqVitevX7N582YePnxI0aJF6dChg1TkTm4eP37M8ePHCQ0NJTExUWOfSqVi7ty5sjvpu3eHDh3KuHHjDN4BI8jfiCBeIBAIshAaGioF7vfv38fY2Jh69eoxbNgw6tatS7NmzXBzc8u3ATxkFPxRF/1RqVR07NgRR0dHg/noCv70oVKpdE7NyAsSExMZPnw4ffr0oX379rRv316W8+aE0tKg1eiqrWBmZkb9+vXx9vamUaNGeXr+X3/9NdfHqlSqPA/isy4xpzTu3btnaAUN5s6dS40aNXQG8bdv3+bGjRts2rRJVidfX1+GDx/Oy5cv+fTTT7GzsyMiIoJjx47x+++/s2zZsjx3ePXqFQMGDODhw4cAuLi4sGzZMvr27UtYWBhFihQhMjKSwMBAtmzZIvtybwcOHGDSpEmYmZnh5OSkNb1HiX8DX331FePGjWP48OGGVhHkU0QQLxAIBFlQjxy7u7szZcoUPv/8c2led2xsrCHVFIkh0sGzoq+wnqExMzPj6tWrsqcU54TS0qAfPnzIrl27KFSoEA4ODnh7e9OgQQPZzq/mQ4J4Oci6xJwge+7du0ffvn117qtevTrbtm2T2SijQKKfnx8rV65k/vz50pribm5u+Pn54eXllecOixcvJj4+Hj8/PwoWLMjKlSvp168fjo6O7N27l6JFi/LmzRsGDRrEypUr2bhxY547ZWb16tV4e3szd+5cLCwsZD33PyU4OJi0tDRDa/zniOr0Hw8iiBcIBIIsWFtbEx0dzfPnz/n777+pUKGC7MXZlM7AgQOZMGECzs7OOY6CyzHyreTCYg0aNOD8+fOyVVXPDUpKg7527Rq9evUiJSWFokWLEhUVxe7du5k2bRrffvutLA6C3HP37l1cXFwwNzfn7t27OR4v53r2SUlJJCcn692XNU1bDp/Tp0/j5ubGvn37iI+PJzY2FisrKwoWLCibx9WrVxkzZgzNmzcHwMHBgS+++ILJkydL3212dnb06dOHWbNmyeal5vXr18yYMUNxAbyurI3k5GQePXpEUFAQX375pQGsBHnN+fPn2bdvH7du3eL58+d07dqVadOm5eq1sbGxfP/995w4cYLk5GQaNWrElClTcHBw0Djuxo0bzJ8/n7/++gtbW1u+/fZb+vXr90FZJyKIFwgEgiycP3+e3377jcOHD3P48GF2796No6MjrVq10pmmmR959+4dqamp0s8C/bRv355p06bx7t07mjRpgq2trdYXtZyBDigrDXrFihWULVuWNWvWULx4ceLi4pg4cSJLly41eBAfHR3N2bNndc7TBWVkochN+/bt+emnn6hatSrt27fX+9CpHnGWs3q3m5sbBw8elILVzBw8eFD2+fCmpqaMHj2aDRs2ULJkSQoWLChr8K4mPDycUqVKSb+rf846BapYsWJERkbK6gZQq1Yt7t+/j6enp+znzo758+drbTM1NaVYsWL06NEDX19fA1gJ8ppz585x7949ateu/cF1D0aMGMHDhw+ZMWMGZmZmLF26lH79+rF3714KFMgIu58+fUqfPn1o0KABI0aM4O+//2bRokUYGxtrLa+cHSKIFwgEgiwUKFCApk2b0rRpU96/f88vv/xCYGAgP/74I5s2bUKlUvHzzz9TvHhxSpQoYWhdg5B55FvJo+BKYMCAAQDs2LGDHTt2aAQ9hgh0lMb9+/eZOXMmxYsXBzKW3xo/fjwtWrQgJCRE2i43v/32G8OGDSM+Ph5zc3Od83TzYxD/448/4uLiIv2sJAYMGMCgQYPo378/7dq1k9aJ37dvH7/99hurVq2S3als2bKEhITIft7MpKenayyVqP5ZV3FEQzBq1CjGjh2LmZkZDRo0wMrKSusYOVemUKOkzk65yOtiwR8D48aNY8KECQBcvnw516+7efMmv/32G/7+/lK9mTJlytCqVSuOHz9Oq1atAPD396dIkSL88MMPmJqa4unpydu3b1mzZg3du3fPdbFbEcQLBIKPFiMjI4YMGaKVpvRfYmFhwVdffcVXX31FZGQkR48e5ciRI+zcuZNdu3ZRs2ZNEcQKskVpgQ7Al19+Se3atalduza1atXK07+hnIiMjNRa2UAduEdGRhosiJ8/fz5VqlRh7ty5fPLJJwZxUCLqgpZZf1YCTZs2ZfHixSxYsIARI0agUqlIT0+nWLFiLFq0iKZNm8ruNGrUKObOnYuLiwtVqlSR/fxqLl++TGhoKABpaWmoVCouX77My5cvpWOePHliELe2bdsCMGPGDL0dCXJ3dGYuSlq7dm1Zzy0wLJk7vD6Es2fPYm1trVHPpWzZsri5uXH27FkpiD979iwtW7bUCNZbtWrF2rVruXnzJnXr1s3V+UQQLxAIFEdqaiq3bt0iNDSUpKQkrf1t2rQB5B8JK1KkCF27dqVr1668evWKw4cPExgYKNv5lYSfn98HHZ8fRywh40Hw+PHjfPXVV1StWtXQOhLNmjXj6tWr7N69m9TUVEqWLEnNmjWlwL5kyZKGVjQ4z58/Z8KECSKA/8ho1aoVrVq14vHjx0RFRVG4cGHZlnHTxaJFi4iKiqJTp04ULlwYOzs7jf0qlYpDhw7lucfixYu1ti1YsEBrmyFG4+fOnau4CvRKLUqaX9E1RSYzJ0+elMlEP48fP6ZMmTJa93LZsmV5/PgxAPHx8YSEhGi1SWXLlkWlUvH48WMRxAsEgo+Tu3fvMnToUEJCQnSmdalUKimINyROTk4MGDBASpXOb2zZskXj9+TkZBISEoCMhx/1/GFzc3NMTU3zbRBvZmbG3r17+fTTTw2tosHo0aOBjE6GGzducP36da5evcrs2bNJSEjAwcGBM2fOyObj4+OTq/XQ5VwLvVKlSgZPg85Mjx49mD59upTKnpng4GCmT58uS9aHvrXr9XHjxo08tNFP5ofkBw8eSMuGyr0Cgbu7O5UrV5b1nFlRQoCTHe3atTO0gk6UWJQ0rzk9Q5nf1c2b7ze0Qo7ExMTonApiY2PDH3/8AfxvhaOsK8KYmppiYWHxQXPwRRAvEAgUxYwZM7C0tGTLli2UK1dOax6qQBlcvXpV+vnOnTuMGDECX19fvL29sbS0JC4ujqCgIFavXs2SJUsMaGp4PDw8+P333xWXegwZnQweHh4ApKSk8P79e27fvi3rUopK7eCZMWMGY8eOxdHREU9PT6kokaG4cuWK3iKScXFxXLt2TRYPpa9dryY0NFQK3O/fv4+xsbFB0unnzZsn+zmz8rFkk0RHR/PgwQNCQkJo3LgxNjY2JCYmYmJi8o9TnP8NSixKml/5Nx1RsbGxvH79OsfjSpYsmev56EpABPECgUBRPHz4kKVLlyoy4BHoZvbs2fTp04f27dtL2ywtLenQoQOJiYnMmjWLPXv25KmDrqWA9CH3GujDhg1jzJgxGBsb630QlLto05kzZ7h27RpXr17ljz/+oGDBgtSoUYPPPvuMKVOmyPpgqqQgPusoc3JyMv3798fIyAgzMzONY+XMCsiJmzdvyrYMppLXro+JiSEoKIjDhw9z/fp1ae5337596d27N0WKFJHN5eHDh+zatYsXL17g4ODAZ599Rv369WU7/8dEWloaS5cuZevWrbx//x6VSsWePXuwsbFhyJAhVKtWzSDthChK+n+DoKAgpkyZkuNxR48e1ZnplFusra2luhOZiY6OxsbGBkAaqc/aUZ6UlMT79++l43KDCOIFAoGicHZ2FkuWfWTcu3dPb5X+kiVL8uDBgzx30LUUkD7kDuI7d+4MwMKFC6X12bMi94PggAEDMDc3p0OHDsycOZMKFSp8FKOreY3SRpnXrl3L2rVrgYz7Vte0g6SkJFJTU+nSpYshFCVCQkIICQmhYsWKsi6jlpSUxMmTJzl8+DDnzp0jOTmZsmXLMnjwYBo3bkzHjh1p3LixrAH8tWvX6NWrFykpKRQtWpSoqCh2797NtGnTDL5sohJZtmwZ27ZtY/z48Xh6euLt7S3t8/LyYvfu3QYJ4pVYlFTw4XTs2JGOHTvm+XnKli3LxYsXpQ4eNcHBwVSoUAGAggULUrx4cWmOfOZj0tPTP6h+hwjiBQKBopg4cSJz5szB1dX1X/WICuTjk08+YdeuXTRq1EhrpGLHjh04OTnluYOSlwJSYtGmrl27cvXqVbZv387x48elKvV16tTJ1393Shtl9vDwoHfv3qSnp7Ny5Uq++OILrUr+JiYmuLi40KxZM4M4BgQE4OfnR3h4uDSC6u7uzuDBg6lTpw4+Pj55en5PT0/i4+NxdHSkW7dutG7dmkqVKgHao11ysWLFCsqWLcuaNWsoXrw4cXFxTJw4kaVLl4ogXgf79+9n1KhRdO7cmdTUVI19pUqV4vnz5wbxyikjMC4uTiYTwcdA48aNWbVqFRcvXpSyboKDg/nzzz/p27evxnEnT55k7Nix0pTRo0ePYm1tLU1vyw0iiBcIBIpi9uzZhIeH07p1axwcHLSKhMhVyVeQe0aPHs3w4cP59NNPadasGba2tkRERHDq1ClevXrFsmXLDK1oUJRYtGnq1KlARvrx1atXuXbtGvv27WPOnDlYW1tTq1Ytli9fbmBLw6KEQnJ16tSRAgmVSkXHjh1xdHTM03N+CJs3b2bRokX06tULT09PevfuLe2rU6cOQUFBeR7EJyQkkJ6ejqWlJYULF/6gdNS84v79+8ycOVNaHtHS0pLx48fTokULQkJCDLZsolKJiorS23mYmppKSkqKzEYZTJ48mTlz5ujcFxUVRZ8+fdi7d6/MVoK85uXLl9y5cweA9+/f8+zZM4KCggD47LPPpOMqVapEmzZtmDt3LpDR6dqwYUMmTZrE+PHjMTMzY8mSJbi6umoUt+3Tpw+HDx9m9OjRfPvtt9y/fx9/f39Gjhz5QXPyRRAvEAgUhbu7u+JGLX/77TeOHTtGaGioVHVdjUql0qrUnt9o0aIFe/bsYd26dZw8eZLw8HDs7e2pWrUqy5cvx83NzSBeiYmJPH/+XOszA1GMSI21tTVNmjShaNGiFClSBBMTE27cuMEvv/xiaDWDo5RCcmqUVDtAzbZt2/D19cXX11drBLVMmTIEBwfnucO5c+f4+eefOXz4MEuWLGHp0qVUq1aN1q1b07Bhwzw/vy4iIyO1MibUgXtkZKRBg/hXr15hb2+vs2hsSkoKr1+/liV7KjPOzs6cP38eT09PrX1XrlyhfPnysvqo+fXXX5k7dy6TJk3S2P727Vt8fHx0rqAj+Pi5fPkyEydOlH4/d+4c586dA+Dvv/+WtqemppKWlqbx2qVLl/L9998zbdo0UlJSaNiwIVOmTNEojFq6dGn8/f2ZN28e/fv3p2jRogwbNkyjEzQ3iCBeIBAoCiVU8s3Mhg0bWLRoEZ988gkuLi46lw8RgJubm2Kq0CclJTFjxgwOHTqkFViokXMOupeXV44dU3IvAXXp0iWpsN3t27dJSEjA3t6eWrVqMW3aNFFYMgfkKiQ3cOBAJkyYgLOzMwMHDsz2WJVKxerVq/PcKTNhYWF60z9NTEyIj4/Pc4eiRYvStWtXunbtyosXLzh8+DBHjx5l9uzZGBsbo1KpuHr1KpUrV8bCwiLPfZRO8+bNCQgIoGrVqlr77t27R8eOHWWv0dGzZ0+mTp1KgQIFpJHO0NBQfv/9d7Zu3cr3338vq4+aDRs20LNnTwoWLMiIESOAjHvex8cHc3NzNm7caBAvQd7Srl27XGXQZQ7o1VhZWTF37lxpdF4fNWrU4KeffvrHjiCCeIFAIMiWHTt20K1bt1xVNhUYrrhVZlauXMn58+eZN28eY8aMYdq0aRQsWJBDhw7x7NkzKZVcLpo3b64VxMfExHDlyhUAWrZsKasPZDw0f/LJJ9SuXZvWrVtTp04dSpUqJbuH0lBaIbl3795JHVFKLPjp5OTEnTt3dI6g3rp1C2dnZ1l9SpQowaBBgxg0aBD37t3j0KFD/PzzzyxfvpwNGzbw6aefytZRrOvegYx6FJm3y73KQXajx0lJSQZZYqtdu3ZER0ezYsUK6e9v8ODBWFhYMGLECFq1aiW7E2RkbK1Zs4a+fftSqFAhWrVqhY+PD4ULF2bjxo1aa30LBHKiShe5IAKBQGHExMRw7NgxgoODSUpK0tovZ0Bdo0YNVq5cqfMhVfA/DF3cKjPe3t707duXdu3a4e7uzp49e6hcuTIA48ePx9zcnJkzZ8rmo4+kpCSpgnb37t1lPXdoaKhWuq8gI3X3ypUrUiG5Dh06ZFtIztjY2ECmysDf3x8/Pz8mTZrEp59+St26dQkICCAyMpKxY8cyYsQIunbtamhNrl27xqFDhzh27BiXL1/O8/P5+fl90PF5PVXi0aNHPHr0CMhY8nL06NGULl1a45jExESOHDnCixcvCAwMzFMffbx7944bN24QFRWFjY0NHh4eish+u3DhAgMHDsTCwoJy5cqxbt06ChUqZGgtQT5HBPECgUBRPHnyhM6dO0trZhYtWpTo6GhSUlKwsbHB0tJS1tTj0aNHU6ZMGUXOR1UKuopb7d27F3d3d7Zs2UJQUBA7d+6UzadatWr4+/tTq1YtqlWrxurVq6VKsefPn2fUqFGyPMjnhjNnzjB9+nROnz4t+7lfv34trWlbrFgxHBwcZHdQMn5+foorJKdEvvvuO7Zv345KpSItLQ0jIyMAunTporgMppSUFI25qfkFPz8/qWNBpVLpHY23trbm+++/p3nz5nLqKYrjx4/r3H7q1ClOnTrFhAkTNLLMMhcsEwjkJP+1ZAKBQNHMmzePatWqsWzZMqpXr866deuoWLEiR48eZcmSJbJXOm/fvj0zZswgMTGR+vXr60yfy+9F0pRQ3Coz9vb2xMTEABnptZcvX5aC+CdPnsjqkhORkZGyp0lv376dLVu2aC3bVLJkSXr27Gnw9caVghI67pQ2oquLKVOm4OPjw/nz56URVE9PT9lT6XNDfgzgISO1v23btqSnp9OiRQv8/Py0Co6amJhgb29vsMKyb9++ZcuWLdy6dUsqjlqtWjV8fHxkqT+hZtiwYdnunzBhgvSzSqWSvX6AQKAmf7ZmAoFAsdy+fZs5c+ZI8/KSk5MxNjamdevWREZG8t1337Fr1y7ZfNTVQtevX8/69eu11kEXX+LKKG6VmTp16nDt2jW8vLzo2LEjCxYs4PHjx5iYmHDixAm+/PJLWX10jewkJyfz6NEjtm/fTr169WTxSE1NZfjw4Zw4cQJnZ2d69uwpVckOCQnhzJkzzJo1i4sXL7Js2TJpRDW/kpaWxu7du7NdmeLEiRN56pB15Yvk5GQSEhIAMDMzk5zMzc0xNTU1WMdDyZIl6dy5s0HOLcgZKysrKS395MmT2NvbG2Tuuz5u3bpF3759SUtLo379+jg7OxMREcG2bdvYtm0bGzdupFq1arK4yF1kVCD4p4ggXiAQKIqkpCQsLS0xMjLCxsaG169fS/vKly/PvXv3ZPXJ63Wg/y+gtOJWI0eOJDIyEsgo4AYQFBREYmIi3bt3Z/DgwbL66BvZKVCgAJ9++qlsKcc7duzg9OnTzJ07l7Zt22qNuI0fP54DBw4wZcoUdu7cqYi5zIZk4cKFbNq0idq1a1O3bl2dS3LlNVevXpV+vnPnDiNGjMDX1xdvb28sLS2Ji4sjKCiI1atXy7Y6RGan3FC7du08MhHklqioKKytrTEyMqJQoULEx8dn27lauHBh+eSAmTNnUq5cOdavX4+lpaW0PTY2ln79+jFr1izZ1mP/5JNPZDmPQPBvEUG8QCBQFM7Ozrx8+ZLatWtTqVIlduzYQf369SlQoAABAQGyz9sVS23lTKdOnfDz86NIkSLS/MCUlBROnz6Nv7+/tDSPXNjb22Nvby/93rNnTymYNwS6RnbMzMywtbWVNXV13759+Pj4ZLt0Tps2bXjw4AF79uzJ90H84cOHGTp0qOydPvqYPXs2ffr0oX379tI2S0tLOnToQGJiIrNmzWLPnj157tG9e3eNedW6spMyk98zlZSAp6entKxcvXr1cmx35P7MHj58yLJlyzQCeMjIIOjXrx8jR46U1ScrYWFhhIWFaWXjgOikEhgOEcQLBAJF8cUXX0ij7cOHD6dPnz7UqVNHemhU2jryAujTpw8hISFMmzaN6dOnA/Dtt98CGcWt8nswqJSRneDgYI35nPpo1KgR27dvl8FI2SQlJVGjRg1Da0jcu3ePEiVK6NxXsmRJHjx4IIvHgQMHpJ8jIiKYPHkydevWxdvbGzs7O968eUNQUBBXrlxhzpw5sjgJsmfu3LmULFlS+tlQ8971Ubp0aamOSVZiY2Mld7l5/vw5Y8eO5datW4D28nxiOp3AkIggXiAQKIpevXpJP1evXp3AwEDOnj1LYmIi9erVo0KFCrI7HThwgICAAJ48eaKzJ/7GjRuyOykNdXGrCxcuEBkZadDiVl5eXjk+pMo57/Ho0aO8evWKvn37au3z9/fHycmJzz//PM89TExMeP/+fY7HJSQkGCR1XGm0bt2aX3/9VTHLS37yySfs2rWLRo0aaY1+79ixAycnJ1k8KlasKP08bNgwWrVqxbhx4zSO8fLyYv78+ezcuZMGDRrI4qXm8ePHHD9+XG8dg7lz58rq06NHD6ZPn46Li4vWvuDgYKZPn57n07batm0r/ZxdJo6hGDt2LLNmzaJ48eIa2W+XL1/Gz8+PqVOnGsRrypQphIWFMXfuXFxcXBRVR0AgEEG8QCBQNMWLF+ebb74x2PkPHjzI1KlTadu2LTdv3qR9+/akpaXx66+/Ym1tzddff20wNyWgrtq/cOFCvLy8DPpZqWnevLlWEB8TE8OVK1cAaNmypaw+a9eu1UiBzoy5uTnr16+XJYivXLky+/bto2nTptket2/fPqpUqZLnPkqnWrVqLF26lIiICL0rU8i5vNTo0aMZPnw4n376Kc2aNcPW1paIiAhOnTrFq1evZF+5A+DcuXN6K+g3bNhQ9kJ7Bw4cYNKkSZiZmeHk5KTVGWWIEegrV67oXYEiLi6Oa9euyWykPBYuXEhsbCw+Pj5YWVlRpEgRIiMjiY2NxdramkWLFrFo0SIg4zM8dOiQLF63b99m/vz5Yhk5gSIRQbxAIFAc8fHx7N+/n+vXrxMdHY2NjQ01a9akbdu2GuuzysGmTZvw9fWlf//+/PTTT3Tp0gV3d3fi4uLo06cPhQoVktVHaZiZmWFhYYGxsbGhVSQmT56sc3tSUhKDBw/Wm5KcVzx9+pTy5cvr3Ofi4iLbEny9evWif//+TJo0iaFDh0qV6dWEhYWxfPlyfvnlF9atWyeLk5JRjy6/evWKo0ePau2XO5W2RYsW7Nmzh3Xr1nHy5ElpGa6qVauyfPlyrSXD5KBQoUJcvHhR52j7+fPnZW8fV69ejbe3N3PnzsXCwkLWc/8Tbt68KcvyabnJTsqM3BXa3d3dqVy5sqznzA2Ojo75fpUOgXIRQbxAIFAUISEhdO/enZcvX1KxYkVsbW0JDg4mKCiIzZs38+OPP2oFH3nJ06dPqVGjBsbGxhgbGxMXFwdkFJTq168fc+fO1ZgCkB9p06YNe/bsoUmTJoZWyRZTU1O6devG9OnT6d69u2znNTMzIyIiQue+8PBw2daubty4MePGjWPx4sUcOnQIV1dXKQX71atX3L9/n7S0NMaNG0ejRo1kcVIySlxqys3NTbYq9LmhS5cuLF++nIiICJo3by5lB5w4cYKDBw8ydOhQWX1ev37NjBkzDB7Ar127lrVr1wIZnT0+Pj5aQXRSUhKpqal06dIlz32yZicdO3aMuLg46tevL31mFy5cwMrKCm9v7zz3yYpSa92MHDmS9evXU6tWLdkr9gsEOSGCeIFAoCi+//57AI4cOULZsmWl7Y8fP2bgwIHMmzdP1rRRS0tLkpKSgIxe+YcPH1K3bl0gY91t9VJm+Rlra2t+//13WrduTaNGjbCzs9N4YFSpVAatDp+ZyMhIvamteUXt2rVZt24dXl5eGpkk8fHxbNiwQdYVEHr37k29evXYunUr165dk4qhOTo60rp1a7p3726QEV0lopSChLoICQkhJCSEihUryp6dlBlfX1+sra1Zt24d+/fvlwqQ2tvbM2nSJFk7ywBq1arF/fv3DV7HwMPDg969e5Oens7KlSv54osvKFasmMYxJiYmuLi40KxZszz3yZydtGHDBooXL86GDRt0Ludma2ub5z66SEtLIyoqCshY4k4JI+D79+8nNDQULy8v3NzcsLKy0tivUqlYvXq1gewE+R1VetZSiwKBQGBAatWqxaxZs2jVqpXWviNHjjB9+nRZ5xAOGjSImjVr0rdvX7777juOHj2Kr68vBQoUYN26dZQuXZpNmzbJ5qNEMhe60oXcacfHjx/X2pacnMyjR4/Yvn07derUYcWKFbL5PHr0iM6dO2Nqaoq3tzcODg68fv2aY8eOkZyczM6dO3UWvRIYnvT0dM6cOaMxtadWrVo0btzYIPOrAwIC8PPzIzw8HJVKxZ49e3B3d2fw4MHUqVMHHx8f2Z0gIwALDQ2VUvyLFStmkCDsr7/+YuzYsXTr1o0GDRpoBV0g/xrofn5+dOzYEUdHR1nPq48mTZowffp0vLy8tPadPHmSmTNncvbsWdl8Tp8+zZYtW7h586ZUiNDMzIwaNWrg4+Nj0Ayv3HRCbd26VQYTgUAbMRIvEAgURWpqKmZmZjr3mZmZkZqaKqvPgAEDePXqFZBRifnly5fMnTuXtLQ0qlSpwqxZs2T1USLqJQGVwrBhw3RuL1CgAJ9++ilTpkyR1cfFxYU9e/awfPlyjh8/TlRUFIULF6Z+/foMGTKE0qVLy+ojyB3R0dH079+fW7duYW1tLaUdr1+/nurVq7Nu3Tqdxe7yis2bN7No0SJ69eqFp6cnvXv3lvbVqVOHoKAggwXxRkZGODk5yVYhXx/qKuwzZszQ28ki95Jgchf3y4no6GhiY2N17ouNjdW71Fte8N1337Ft2zZsbGxo3LixNFUuJCSEy5cvM3DgQLp166a3zkleIwJ0gZIRQbxAIFAUNWrUYPXq1dSpU0djFCU2NpY1a9bIvm5z9erVqV69OpCRNr569WqSkpJISkrSSEUUKAddc5nNzMywtbU12PrIpUuXZvHixQY5t+CfMX/+fJ49e4a/v79G4bbz588zduxY5s+fL+s66Nu2bcPX1xdfX1+tzswyZcrIViBRyShlDfSBAwcyYcIEnJ2dGThwYLbHyp2SXa9ePRYtWqRzObfFixdTr149WTwOHjzI9u3bGTx4MH369NGaFvL+/Xv8/f1ZtWoVVatWpXXr1rJ4CQQfCyKIFwgEimL8+PF069aNJk2aUK9ePezs7IiIiODixYsUKFCAbdu2GcwtISGBmJgYrK2t830A/+TJE0aNGsXw4cP1pjueOXOGZcuWsWzZMkqWLCmbm5LnMgs+Hn799VfGjh2rVXm9QYMGjBo1ikWLFskaxIeFheHh4aFzn4mJCfHx8bK5KBWlrIH+7t07qaNF7hocOTFr1iwGDRqkczk3Nzc3Zs6cKYvHzp076dixo97ihxYWFgwZMoTXr1+zfft2gwXxaWlpXLp0ieDgYKk+Tmbye2FbgeEQQbxAIFAUFSpU4NChQ2zatInr16/z8OFDbGxs6NSpEz169OD69etUqFBBVqdTp07h5+fHX3/9RXp6OiqVCjc3N4YNG6b4iux5xcaNGylYsGC2//8mTZqwYcMG/P39mTFjhnxyZEzLuHXrFqGhoTofvNq0aSOrj+Dj4/3799jZ2encZ29vz/v372X1cXJy4s6dOzqLtt26dQtnZ2dZfZRMdHQ0Dx48ICQkhMaNG2NjY0NiYiImJiayzNXPnIattJRsBwcH9u7dy9mzZ7l9+7bGUoWNGzeWzePvv//O1eoF3t7eBAYGymCkTXh4ON27d+fJkydS0UZAI9tDBPECQyGCeIFAoDiKFSvGxIkTtbYfO3aMcePGydojf+LECYYOHUq1atWYMGECdnZ2hIeHExQUxKBBg1i+fDktWrSQzUcpnD9/PldzPdu3b4+fn58MRv/j7t27DB06lJCQEHTVblWpVCKIF+SIm5sb27Zto2HDhhgbG0vb09LS2Lp1K5UqVZLVp1OnTvj5+VGkSBE+/fRTAFJSUjh9+jT+/v6MGDFCVh8lkpaWxtKlS9m6dSvv37+Xiv/Z2NgwZMgQqlWrprg56oaicePGsgbtWckcFCuVefPmUbhwYc6cOUOTJk346aefsLOz49ChQxw4cIB169YZWlGQjxFBvEAgEGSDn58fX3zxBYsWLdLY7uPjw5gxY/Dz88uXQXxYWFiuUuRLlChBWFiYDEb/Y8aMGVhaWrJlyxbKlSuHiYmJrOf/mDD0iKWSGT16NL1796Zly5Y0b95cmtpz4sQJ3rx5w8aNG2X16dOnDyEhIUybNo3p06cD8O233wIZ67V37dpVVh+AiRMn4uvrq7MtePnyJX5+ftKyoXKwbNkytm3bxvjx4/H09NRY89zLy4vdu3fLEsR/aMelIToWzp49y507dwgNDWXQoEE4OTlx9epVSpUqJUsl/QoVKnD8+HEaNmyY7XFBQUG4urrmuY8url69ypQpU7C3t5e2OTk5MXDgQNLT05k1axYbNmwwiJtAIIJ4gUAgyIbHjx8zZswYnfu+/vprBg8eLLORMihUqBCRkZE5HhcVFSX7OtYPHz5k6dKlsq6//rGRnp7OkiVLxIhlNtSuXZudO3eyZs0aAgMDiYmJwcbGhpo1azJw4EDc3d1ld5oyZQo+Pj5cuHCByMhIbGxs8PT0NFgq/f79+/n22291BvGRkZEcOHBA1iB+//79jBo1is6dO2sV/ytVqhTPnz+XxWPLli0avycnJ5OQkABkFNlUL6Vmbm6OqamprH9rb9++xdfXl1u3blG8eHFCQkLo3LkzTk5O7N27FwsLC6mTKC/p0qUL48ePx9HRkd69e2NhYaGxPyEhgY0bN7J3714WLFiQ5z66iI2NpWjRohgZGWFpaUlERIS0T71ChUBgKEQQLxAIBNlgY2NDcHCwztGC4OBgbGxsDGBleCpXrszRo0dp2bJltscdOXKEypUry2SVgbOzs+KKSSmNpUuXKmLEUulUrlxZ9ukgukhMTKR+/fosXLgQLy8vvvnmG0Mr5cjTp09lX5M9KioKFxcXnftSU1NJSUmRxePq1avSz3fu3GHEiBH4+vri7e2NpaUlcXFxBAUFsXr1apYsWSKLk5o5c+YQGRlJYGAgpUuX1mifPT09ZauU/9VXX3Hr1i1WrFjBjz/+SN26daUlCl+9esWVK1eIjo6ma9eufPnll7I4ZaVEiRK8fv0agHLlynHw4EGaNWsGZEy1k/v+FggyI4J4gUAgyIZWrVrxww8/YG5ujre3N9bW1sTGxhIUFMTSpUvp1KmToRUNQpcuXRg8eDAuLi4MGjRIY84wZMxNXbVqFUFBQaxcuVJWt4kTJzJnzhz+X3v3Hpfz/f8P/HGpVIuKyiFDlihZKqIDmnKmzcRmRCkSkWMzU5jjBxmRRIfFHPfRZhQxbcuGzxDFxxz7aJoSOmud9fvDr+srXR3s8+n9fl887rfbbqvr/brt/bhNXa7n+/V6PV/du3ev8wO9kEpKShAaGoqTJ0/W2WhP6LOrpTJjSY2jrq4OTU3NWr9nYti/fz8OHDgA4Pm+5kWLFkFdXb3GmLKyMjx48KDGwyEhGBkZ4ezZswqb/124cAEmJiaC5gGAVatWwcvLC66urvLXWrRogXHjxqG0tBQrV67E4cOHBcuTmJiIVatWwdjYuNbvfvv27QXd/hQYGAh7e3vs3r0bP//8s/y9sXnz5rCysoK7uzucnJwEy/Oy9957D2fPnsXIkSMxc+ZM+Pr6ws7ODqqqqnjy5Emdq/SIhMAinohEZ2Vl1aizfV/+wCGEhQsXIiMjA4GBgVi2bBlUVVVRUVGBqqoqDB06FPPnzxc8kxQ4Oztj2rRpCAkJwcGDB2FnZyefRcnMzMT58+fx5MkTeHl5Cf4hbNWqVXj8+DFcXFzQpk0btGzZssZ1mUyGo0ePCpbniy++QGxsLEaPHg1jY2NJ7NGXyoyl1ChqqFkXmUyGtWvXNmGamsaMGYPDhw+LfiJGmzZt5LO3d+7cQZcuXdC6desaY9TU1PDOO+9g3Lhxgmbz8PBAYGAgVFVVMXz4cADAw4cPkZycjK+//lrQpf3Vbt68ibffflvhtY4dO+LOnTuC5qmsrKxzi1NBQYHg70/Ozs5wdnZGZWWlfItWq1atJPHAauHChfKvHR0dceDAAZw+fRolJSWwt7cX/XeR3mws4olIdJ6eno0q4sXQvHlzbNu2Dbdu3cKlS5dq7IsVq9mOVCxatAg2NjaIiorCyZMn5bMo6urqsLa2xurVq0X5kGNubi6pn6effvoJixcvhpubm9hR5KQ4YykF3333HbS0tNCpU6cGO2cL/TOmra2N5ORkuLi4YMCAAdDX16+RQSaTwcPDo8lzDB48uEYzz7oa24lh7NixyM/Px7Zt27Bz504AgK+vLzQ1NTFv3jyMHDlS8EwdOnTAwYMHMWDAgBp/XlVVVdi/f7/84adQLCwsEBMTo/C9OS4uDtbW1oLmqaaiolLnkY5SoampCVNTU7Rq1Qo2NjZix6E3nKxK6uc7EBFJ1KVLlxAaGip4l2opqqysRF5eHgBAV1dXErMoUuHg4ID169c32IVZSN9++y0CAwMxffp0DB8+HGPGjMH27dvx8OFDbNiwAevWrROl4BHbhAkTkJKSAhMTE4wePRqjRo1Chw4dxI4FADA1Na33ukwmE3xbhiLFxcV49OgROnXqJNrDtKKiIly+fBl5eXnQ0dGBlZVVrRU5Qjl9+jTmzp0LQ0NDDBo0CHp6esjOzsZPP/2EjIwMBAcHC3rCyZUrVzBlyhRYWFhg2LBhWLduHXx8fJCamorExETs379flKaNUlFVVYWIiAj88MMPqKiowPDhwzF9+nQsXboU3333nXxc165dsXv37lqrUIiEwiKeiOhvOnnyJObNmyeJD84kXSEhIfjjjz+wceNGsaPU8NVXX2Hbtm0oLi6WzzpramrCz88PU6dOFTmdeDIyMhAXF4fY2Fjcvn0blpaWGD16NEaMGMEP7C+JjIxEcXGxvAnipUuXMHPmTDx9+hRvv/02IiMj0alTJ5FTiu/GjRvYtWsXrl69isePH8PAwAAWFhbw9vaGmZmZ4HmuXLmCTZs24cqVK6isrIRMJoOlpSU+/fRTWFlZCZ5HSiIiIrBp0yY4OztDS0sLp06dgpOTE86cOQNfX1+88847uH37NsLCwjBmzBgEBASIHZneUCziiYj+Jhbx0vXrr7/KG8lVH+f0oj179giWJSIiAvv370ebNm1gZ2cHbW3tGteFWgatiJRmLKXo7t27iI2NxYkTJ/DgwQPY2tpi8uTJ3Av7/w0fPhxeXl4YP348AMDV1RXNmzeHt7c3duzYAUNDQ2zZskXQTDk5Odi9ezdSUlLkBXOvXr3g7u7+xj+EKSsrw88//wwzMzN07NgRJSUlyM/Ph7a2dq0j3t5UI0eOxIgRIzBnzhwAQEJCAmbPno2lS5fW2BIVHR2Nffv24YcffhArKr3huCeeiIheKxEREQgKCkKHDh1gbGwselEaFBQE4PkMb3Jycq3rYhbxWlpaGDBggCj3VgZdu3bFvHnzMHPmTAQHByM6OhoaGhqCFfFpaWlYsGAB5s6dW+c9ExMTERwcjODgYMH3pj98+BCdO3cGAGRlZeH69evYu3cv+vTpg8rKSqxYsULQPCkpKZg2bRqePXsGe3t7GBkZITs7G3v37sXevXsRFRWFXr16CZrpRZmZmcjMzISpqWmdzeWaUvPmzbFw4UJERESgY8eO0NDQgIaGhuA5pOzPP/9Ev3795N/b2tqiqqqq1haDnj17IjMzU+h4RHIs4omI6LWyf/9+uLm5SWaZ482bN8WOoFBxcTHOnz+PzMzMWsfeiflgQSoqKyvx66+/4vjx40hISICKigrGjRsnaMf1qKgovPXWW/U+NHB0dERERAQiIyMFL5rV1dXx9OlTAMD58+fx1ltvyZdjt2zZEoWFhYLm+eKLL9C1a1eEh4ejRYsW8tcLCwsxffp0rFy5EjExMYJmAoBDhw4hJCQEjx8/hkwmw+HDh2Fubg5fX1/07dsX7u7ugmV55513WHzWo6ysrMaDjeqvmzdvXmOcmpqaKCfmEFVjEU9E9JJTp041apyiWVUSX15eHpydncWOIWkXLlzAnDlzkJ+fr/D6m1zEX7x4EbGxsYiPj0dZWRmcnZ0RFBSE/v37Q1VV2I9NZ8+ele83r4+rqytCQkIESFSThYUFdu3ahWbNmiEyMhIDBw6UN7W8f/8+2rZtK2ieu3fvIjg4uEYBDzx/oDB9+nRRjgSNjo5GUFAQpk6dCjs7O3h6esqv9e3bF/Hx8YIW8QsWLMDatWthbGyMd999V7D7NiQuLg7x8fHIzMystQVK6GNBFZHSiSdEAIt4IqJa/Pz8Gj2Wf7FLz6BBg5CUlKTw+DShXL9+HcbGxtDQ0MD169cbHC90N+iVK1eie/fuCAwMhJGRkSTOrpcCR0dH5ObmYuDAgVixYgWcnJygrq4uWp6srKxGLZF/++23kZWVJUCimhYvXowZM2bAx8cHhoaGNYrkEydOCN4krXPnzigoKFB4rbCwUJSj8Pbu3YtZs2Zh1qxZtWZuu3Tpgnv37gmaJygoCHl5efjoo4+gq6tb61g3MQrmL7/8Ert27YK5uTmMjIxqzXoLzd3dvdbf7ZMmTap1RCCRmFjEExG9JCEhQewI9F9wdXXFihUrUFpaCnt7+1qN5ICmL5pdXV3xzTffwMLCAq6urnU+7KmqqhLlaLAHDx7g888/f2PPg69LVlYWVFVVcfbsWZw7d67esTKZDElJSU2aR0tLC7m5uQ2Oy8vLE2WPddeuXZGQkIDc3Fy0atWqxrXFixfDwMBA0Dz+/v5YuXIl2rdvj759+8pf/+233xASEoLAwEBB8wDPf6bqepihpqaGv/76S9A85ubm6Nmzp6D3bEhMTAz8/Pwwa9YssaM0auULkRSwiCcieolUzoWmv6d6uWp4eDjCw8NrzZ4IUTTv2bMHxsbG8q+lxtraGvfu3YO9vb3YUSRFah/ge/bsiePHj2PIkCH1jouLixO1MHu5gAeA7t27C55j48aNKCwshLu7O1q2bIlWrVohNzcXhYWF0NbWRlBQkLzRpFAzzoaGhrh27ZrClUEpKSkwMjJq8gwv+sc//iHo/RpLzIaDL5LaewBRXVjEExHRa0UKRfOLs4Avfi0VK1euxNy5c6GmpgY7OzuFHfx1dXWFDyYyqX2AnzhxInx9fWFsbIyZM2fK95tXe/bsGUJDQxEfH4/t27eLkrGgoAAnT57EvXv3ajVIBCBog0kpzjJ/9NFHCAkJQatWrTB06FAAQEVFBX7++WdERkZi3rx5guS4desWdHR00K5dO4XXs7KykJeXJ8rDl3HjxiE2NhYODg6C35tIWfGceCIieqOkp6eLsjdWSgoLC7F06VKcOnWqzqX+Qi/xJ8WCgoIQEREBfX192NnZwdDQEMDz48rOnz+PJ0+ewMvLC4sWLRI8W1paGiZMmICysjIUFxejdevWyM/PR0VFBXR0dNCiRQtuTwKwevVq7Nu3DzKZDM+ePUOzZs0APH9II8RDjpMnT8Lf3x+HDx9Gt27dFI65ffs2xo8fjy+//FLwxqBVVVVYs2YN/v3vf8POzq7WFqg3udEmUV1YxBMR0WsvJycHJ06cwLFjx5CSkvLGF6g+Pj64fPkyXF1d0aVLF4WN7T788EMRkpEiiYmJiIqKwpUrV+Sz3erq6rC2toaHh4dg59a/zMfHB1VVVQgODoalpSViYmJgamqK48ePY/Pmzdi6davgM+PPnj1DXl4egOerSaoLZrGlp6fj3LlzyM3NhY6ODuzs7ARbSj9t2jR07NgRy5cvr3fcqlWrcP/+fYSHhwuSq9r58+cxe/ZsFBUVKbwuRt8QIqnjcnoiInotFRcX44cffkBsbCzOnTuHiooK9OjRA0uWLBE7muj+9a9/4YsvvsAHH3wgdhRqBEdHRzg6OqKysrJGgfry8nqhXb16FWvWrJF3Ey8vL4eKigpcXFyQm5uL1atX4+DBg4Jk+fnnn7F7925cuXJFfkRZ9YMOd3d3UR50VDfX3LhxI5ycnPDxxx8LngEArl27Bjc3twbHDRgwAIsXLxYgUU1ffPEFevbsiYCAAJ6WQdRILOKJiOqRkZEBAwMDhR8qKioq8OjRI/nyVhJfZWUlfvnlFxw7dgw//vgjSkpKoK+vj8rKSnz55ZcYOXKk2BEloW3btgr3wZO0qaioQE9PT+wYcmVlZWjRogWaNWsGHR0dPHr0SH7NxMQEN2/eFCTH6tWrsXfvXujo6GDgwIFo3749gOdbDn777Tf4+PjAzc0NS5cuFSRPNXV1dWhqaor+sOWvv/5CixYtGhzXokULwbvlA8DDhw8RGBjI0zKIXgGLeCKiejg7O+PQoUOwsLCode3mzZsYP348l/lJQFJSEmJjYxEfH4/c3Fzo6uri/fffh4uLC0xMTNCvXz/Bj7uSMj8/P+zatQu9e/eGjo6O2HFISRkZGeHBgwewsbFBjx49sH//ftjb20NVVRWHDh1CmzZtmjzD999/j3379sHX1xdeXl61jtorLi5GZGQkQkNDYWFhARcXlybP9KIxY8bg8OHDom15AAADAwOkpqaiT58+9Y67e/euKO+TvXv3xr1799jYjugVsIgnIqpHfW1DysrK5MtISVyTJk2CTCZDv379MHXqVDg4OEBV9flfcYWFhaJme/DgAZ4+fSrv+lxWVobIyEikpqbC3t4eY8eOFTzTsWPHkJGRgUGDBsHMzKzWrLxMJsOOHTsEz0XKZdSoUfLZ9rlz58LLywt9+/aFTCZDVVWVIMeZHThwAOPHj8ecOXMUXtfU1MTs2bPx6NEj7Nu3T/AiXltbG8nJyXBxccGAAQOgr69fo5mkEE3bHBwcEBUVBRcXl1oPOaoVFRUhOjoa/fv3b9IsisyfPx+fffYZ1NTUYG9vz9MyiBqBRTwR0UtSU1ORmpoq//63337Dw4cPa4wpLS1FXFzcG9/lXCq6deuG27dv4+LFi1BRUUFubi4GDx7cqCWkTS0wMBCmpqb49NNPATw/y/rAgQPo1q0b4uPjUVxcjEmTJgmaqaioCJ07d67xPdGrmjp1qvxrS0tLxMbG4syZMygtLYWtrW2dndD/l27dulVnAf+iYcOGITY2tsnzvOzLL78EADx+/Bh37typdV2IIt7Hxwfx8fGYMGECFi5cCDs7O/kD6LKyMpw/fx6bN29GTk4OZsyY0aRZFBk3bhwAYPny5Twtg6iRWMQTEb3kxIkTCAkJAfD8A9amTZsUjtPW1sa6deuEjEZ1OHr0KO7evYujR48iLi4On332GTQ0NODo6IhBgwbV+cFQCDdu3JA3laqoqMCRI0ewaNEieHh4ICwsDAcPHhS8iP/6668FvR/9PXv27MGoUaOgp6dXb38OqWjfvr3gzduqZ/2lSqi+APXp0KEDwsPDMW/ePPj4+EBFRQWtWrWCTCZDTk4OKisr0a5dO4SHh6NDhw6C51u7dq2o79FEyohHzBERvaSwsBAFBQWoqqrC4MGDERISAjMzsxpj1NTUYGBgwA8eElW9R/7kyZPIycmBTCbD4MGDMWXKFNjY2AiaxcLCApGRkbCxsUFSUhLc3NyQmJiINm3a4OLFi5g+fTqSk5MFzUTKwczMTN6T48WvxXT9+vVXGm9ubt5ESZ6bMGECunXrhpUrV9Y7LjAwEHfv3sWBAweaNI+UlZWV4fjx47h06RKysrIAPG9y2bdvXwwfPpzbw4iUCGfiiYhe0rJlS/mevISEBBgYGPDDjZLp3bs3evfujYCAAPz666+IjY1FQkICTp8+DUNDQyQkJAiWpV27dkhOToaNjQ1++OEHdO3aVd7wKz8/HxoaGoJledHvv/+OsLAwXL58GXl5edDV1UXv3r0xY8YM9OjRQ5RMVJOuri7S09NhYWEhmdlmV1fXRj28rKqqEuR874kTJ2Lx4sVo27YtPD09oampWeN6SUkJoqKiEBMTgw0bNjRplmppaWlYsGAB5s6dW2dDu8TERAQHByM4OFiwbVnNmzfHmDFjMGbMGEHuR0RNhzPxRESNkJWVhaysLPn5wy8SemaX/p6SkhKcPn0asbGxCAsLE+y+u3btwtatW9G9e3fcuHEDS5YsweTJkwEAmzZtwuXLl7Fv3z7B8gDApUuXMHXqVBgYGGDIkCHQ09NDdnY2fvjhBzx58gRRUVENdrKmprdkyRIcO3YMbdq0QWZmJvT19et8oCiTyXD69Okmz3ThwoVXGt+3b98mSvJ/Vq1ahX379kFHRwf9+vWTH/uZkZGBCxcuID8/H5MmTUJAQECTZwGAZcuW4T//+Q/27t1b77jJkyfD2NgYK1asECSXVDk5OTX4YEjIB69EyoBFPBFRPdLT0+Hv74+UlBQAtbvVCzHTRMrvyJEjuHbtGnr06IGxY8fKP7AuW7YM1tbWgs+MTZgwAVpaWti5c6e8iz8AVFZWwtvbG3/99dcbvexYKsrLy3HkyBH85z//wVdffYWRI0dCX1+/zvGff/65gOmkJSEhAbt370ZycjLKysoAPJ95trKygru7O5ycnATL4uzsjNmzZ+PDDz+sd9yRI0cQEhIiyMMXKVuzZk2tIr6goED+wGjIkCFYsmSJGNGIJItFPBFRPdzd3XH//n34+fnB2NhY4SyYqampCMlIGZSWlmL//v1wcHAQpFN3Y/Xq1Qtbt25VuNQ3MTERfn5+8gdXJA1OTk4IDQ3l+00DKisrkZubCwBo1aoVVFRUBM/Qs2dPREdHN7iapXpFzLVr1wRKplzKysrg6+uLgQMHylcvEdFz3BNPRFSPq1evYv369Rg6dKjYUUgJqaurY8uWLU3e3OtVaWpqIjs7W+G1J0+e1NpXTOL78ccfxY6gFFRUVOpdrSAELS0t+YOE+uTl5dV5bjs9X0nh5uaG5cuXs4gnekkzsQMQEUlZ27Zt0awZ3yrp7zMzM8Pdu3fFjlHDoEGDEBQUhHPnztV4/dy5c/jyyy8FXXpMjZeVlYX169fj448/xrBhw/Dxxx9jw4YN8k7jJA09e/bE8ePHGxwXFxeHnj17CpDo/2RkZKC8vFzhtYqKCmRkZAiapyG5ubkoKioSOwaR5HAmnoioHvPnz0d4eDj69OkDXV1dseOQEvr888/h7++P1q1bw9HRURKz3J999hnu3r0LLy8vtGjRAq1bt0ZOTg6ePn2Kd999F4sXLxY7Ir3k9u3bcHNzQ3l5ORwcHGBqaors7GwcPHgQMTEx2Lt3L0xMTMSOSXjeMd/X1xfGxsaYOXNmrSX9z549Q2hoKOLj47F9+3ZBszk7O9d5VOHNmzcxfvx4wfu8nDp1qtZr5eXlSE1Nxb59+2BraytoHiJlwD3xRET18PHxwY0bN1BYWAgzMzP50XPVZDIZduzYIVI6UgZWVlYoLy9HZWUlAEBDQ6NGEyeZTIakpCTBcz179gw//fQTkpKSUFBQAB0dHfTu3RvvvfceV59IkJeXF/Ly8hAVFQUdHR356/n5+fD09ESrVq0QEREhYkJ6UVBQECIiIqCvrw87Ozt5x/zMzEycP38eT548gZeXFxYtWiRoLlNTU3zzzTcKi/jLly9j6tSpgvfDqKvPg6qqKoYOHYqAgAC0bt1a0ExEUseZeCKiehQVFaFTp041vid6FZ6eno06V1tozZo1g7OzM5ydncWOQo1w+fJlbNy4sUYBDwA6OjqYOXMm/P39RUoGnDlzBteuXcPDhw8xc+ZMGBoa4uLFi+jUqRPatm3bpPfes2cPRo0aBT09PWRkZMDAwABqampNes/GWLRoEWxsbBAVFYWTJ0/KO+arq6vD2toaq1evrvMM+f+11NRUpKamyr//7bff8PDhwxpjSktLERcXJ9iZ9S9SdHycuro69PT0JPneSSQFLOKJiOrx9ddfix2BlNycOXPEjkCvARUVFXkh+LKysjJRurDn5ORg1qxZSElJQfv27ZGZmYkJEybA0NAQMTEx0NTUxPLly5s0w7p162BpaQk9Pb16l4qLwdHREY6OjqisrEReXh4AQFdXV/A/qxMnTiAkJATA85U/mzZtUjhOW1sb69atEzIaAKBDhw6C35NI2bGIJyIiegNYWVk1elZLrCX+VDd7e3ts2bIFZmZm6NKli/z1tLQ0BAcHw97eXvBMa9asQW5uLmJjY9G5c+caTdrs7OwE2Wqkq6uL9PR0WFhYQKo7RFVUVKCnpyfa/d3d3fHhhx+iqqoKgwcPRkhICMzMzGqMUVNTg4GBgagz37du3cLDhw9RWlpa6xpPiCGqiUU8EVED7ty5g9DQUPly0UOHDsHc3BybN2+GtbW1YEsiSXn98ccf+Pbbb5GWlqbwA2pYWFiTZ3h5WX9lZSV27NiBjz76CG3atGny+9N/57PPPoObmxtGjRoFExMT6OvrIzs7G7dv30b79u2xZMkSwTMlJiZi1apVMDY2lvd8qNa+fXtBuua/9957WLx4MTZt2gSZTAZfX180b95c4ViZTIbTp083eSapadmypbyfS0JCAgwMDOr8fySG27dvY968ebh3757CBzEymUzwZntEUscinoioHmfPnsWMGTNgbm4OFxeXGjNLqqqqOHDgAIt4qtfVq1cxefJkGBoaIi0tDd27d0dhYSEePHiAdu3a1ei50JReXtZfXcR//PHHkjvHnmozNDTEsWPHEBMTI29GaGRkBFdXV4wdOxZaWlqCZ6qsrKzznPOCggJB9qavXLkS1tbW+M9//oOvvvoKNjY2op8TL2XVS9ezsrKQlZWl8KGijY2NoJmWLl0KFRUV7NixA0ZGRpLoaUAkdSziiYjqsWnTJowcORIbNmxARUVFjSLezMwM//znP0VMR8pg48aNGDFiBNasWQNzc3P5vy9fvoyFCxdi+vTpYkckJaGlpYUpU6ZgypQpYkcBAFhYWCAmJkbhg8y4uDhYW1s3eQY1NTWMHz8eAHDy5El4e3vX2e2cgPT0dPj7+8s70L888y3GrPfdu3cRHByMgQMHCnpfImXGIp6IqB537tzBwoULAaDWXkFtbW3k5uaKEYuUyK1bt+Dt7S0/tq165sva2hqzZ8/Gpk2bMGDAADEjEv0t8+bNw5QpUzBp0iQMGzZMvlx9586dSExMxP79+wXN8+OPPwp6v7pItWM+AAQEBCArKwtr166FsbGxJJbVm5qaIicnR+wYREqFRTwRUT10dHTw6NEjhdfS0tJgYGAgcCJSNjKZDGpqapDJZPIP9dUzlO3atUNaWpq4AYn+JisrK+zZswebNm3C+vXrUVVVhbCwMFhaWiI6OlqUbRpZWVmIjo7G5cuXkZeXB11dXfTu3Rvu7u5NftxdNSl3zL969SrWr18vqUZxy5Ytw+LFi2FgYIB+/fpBVZXlCVFD+FtCRFSPwYMHY9u2bejVqxc6d+4M4HlR9vjxY0RGRmLYsGEiJySpMzY2Rnp6OmxtbWFpaYmoqCh069YNqqqq2LVrlyjnMr+I5zDTf8PKygp79+5FSUkJ8vPzoa2tDU1NTVGy3L59G25ubigvL4eDgwNMTU2RnZ2NgwcPIiYmBnv37oWJiUmT55Byx/y2bdvKVwVJhbGxMSwtLTFt2jQ0a9YM6urqNa7ztAyi2mRVUnt3ISKSkMLCQnh4eODWrVvo1q0bfv/9d5iamiI9PR1dunTB7t27RWkoRcrjyJEjyMjIwKxZs5CamgpPT0/56g5NTU1s3boV/fv3b/Icio6Y++uvv6CpqVnrdX5oJmXk5eWFvLw8REVFQUdHR/56fn4+PD090apVK0RERDR5jiVLluDYsWNo06YNMjMzoa+vL5mO+SdPnkRUVBR27twJXV1dwe5bnyVLliA2NhaDBg1Cly5dFG49mD17tgjJiKSLRTwRUQPKy8tx9OhRnDt3Drm5udDR0YG9vT0++OADSewnJOVSVFSE5ORklJSUyJfcCmHbtm2vNOvOD83UkCVLlqC4uBhbtmypdW3+/Plo0aIFVq1aJVgeKysrbNy4EYMHD6517fTp0/D398eVK1eaPEd5eTmOHDki75g/cuTIejvmf/75502eqZqPjw9u3LiBwsJCmJmZyY+eqyaTyWo0cBWCtbU1Fi5ciEmTJgl6XyJlxuX0RER1KC0txdy5c+Hl5QVXV1e4urqKHYleA1paWnBwcBD8vi8fMUfK5cGDB3j69Cm6d+8OACgrK0NkZCRSU1Nhb2+PsWPHCp7p3LlzWLx4scJrQ4cOxYYNGwTNo6KigrKyMoXXysrKoKKiIkgOKXfMLyoqqnGsZVFRkYhpntPW1hZ9WxGRsmERT0RUB3V1dVy8eBEeHh5iRyEll5OTg6ioKFy7dg0PHz5ESEgITExMsHv3bvTq1QuWlpZiRySJCwwMhKmpKT799FMAz48uPHDgALp164b4+HgUFxcLPpOZk5ODVq1aKbymq6uLJ0+eCJrH3t4eW7ZsgZmZGbp06SJ/PS0tDcHBwbC3txc0DyCdjvnVvv76a7Ej1DJ16lTs3bsX9vb2bGpH1Ej8TSEiqoeDgwPOnj0LW1tbsaOQkrp+/To8PDzQsmVL2NjY4MKFC/LZwupO2oqWIxO96MaNG3BzcwMAVFRU4MiRI1i0aBE8PDwQFhaGgwcPCl7Et23bFlevXoWdnV2ta1evXhX89I7PPvsMbm5uGDVqFExMTKCvr4/s7Gzcvn0b7du3x5IlSwTNU00KHfOl7P79+7h9+zaGDBkCGxsbaGtr1xoTEBAgQjIi6WIRT0RUD1dXVyxbtgxFRUVwdHSEnp5erX3FYhyjRMqj+rip0NBQyGQyfP/99/JrvXr1wokTJ0RMR8qiqKhIvn85JSUFT58+xciRIwEAvXv3RlhYmOCZRo0ahbCwMHTs2FGeBQBOnDiBsLAwTJkyRdA8hoaGOHbsGGJiYpCUlISCggIYGRnB1dUVY8eOFaUJqVQ65r/ozp07CA0Nla8MOnToEMzNzbF582ZYW1vD0dFR0Dw//fSTvGP+pUuXal2XyWQs4olewiKeiKgeM2bMAADs378f+/fvr1HAV1VVQSaT4caNG2LFIyVw7do1bNu2DWpqaqisrKxxrXXr1sjOzhYpGSmTdu3aITk5GTY2Nvjhhx/QtWtXtGnTBsDz7usaGhqCZ/L19cXNmzexYMECLF26FG3atMGjR49QUlKCgQMHwtfXV/BMWlpamDJliuAPEOqyfv16dOzYsc6O+evXrxekY361s2fPYsaMGTA3N4eLi0uNJnaqqqo4cOCA4EW81LYcECkDFvFERPXYs2eP2BFIyWlqauLp06cKr2VkZEjmmCeStnHjxiE4OBjx8fG4ceNGjaXhKSkpMDY2FjxT8+bNsXPnTpw9exb/+te/5EvF7e3tFS6xfxNdvnwZGzdurFHAA4COjg5mzpwJf39/QfNs2rQJI0eOxIYNG1BRUVGjiDczM8M///lPQfMQ0d/DIp6IqA6lpaU4deoU3n//fVhYWIgdh5RIcXExNDU1AQD9+/fHjh07YGdnJ9/rKZPJUFJSgj179gg+60XKydvbG23atMG1a9cwceLEGt3o8/Pz5d3QxeDg4CDKiQvKQCod86vduXMHCxcuBIBaW8O0tbWRm5srSI5Tp07B1tYW2traOHXqVIPjhw4dKkAqIuXBIp6IqA7q6uqIiYnhhwd6ZSNHjkRgYCCcnJzg7++PTz75BMOGDUO/fv0gk8mwZcsW3L17FzKZDPPmzRM7LklcaWkp9u/fDwcHB4wZM6bW9ZUrVwof6gVZWVnIyspCaWlprWs2NjYiJJIOqXXM19HRwaNHjxReS0tLE6wZoZ+fH7755htYWFjAz8+v3rHctkZUG4t4IqJ6WFlZITk5GX379hU7CimRd999F7NmzcKQIUMQGBiII0eOIDo6GufOnUOnTp2Ql5cHFxcXTJ06lcvpqUHq6urYsmWL5Jpopqenw9/fHykpKQCe9wl5EYsv6XXMHzx4MLZt24ZevXqhc+fOAJ7/OT1+/BiRkZEYNmyYIDkSEhLkDwwSEhIEuSfR60RW9fI7LhERySUnJ2PRokX45JNP6uxOzyKMFDlz5gxWr16N7OxszJs3D25ubrV+dogaa8KECXj//fcxceJEsaPIubu74/79+/Dz84OxsTGaN29ea4ypqakgWV5crdCtWzdB7tlYRUVFNTrm6+jooHfv3qJ0zC8sLISHhwdu3bqFbt264ffff4epqSnS09PRpUsX7N69W5Qu/kT0aljEExHV48UPoHUVYG/6TBPVraysDDt27EBkZCS6desGT09PqKrWXgTHLRvUkKtXr8Lf3x/z58+Ho6OjvOeCmKysrLB+/XrJ/Pz26tUL4eHhXDnVgPLychw9ehTnzp1Dbm4udHR0YG9vjw8++EDhg5imtnnzZsyZM0fhe2NOTg6WL1+Obdu2CZ6LSMpYxBMR1ePbb79tcPb0ww8/FCgNKasdO3YgODhY4TUuOabGsLKyQnl5ufyYQg0NjRrvTTKZDElJSYJmGj58OBYtWoTBgwcLet+6SHG1gpSUlpZi7ty58PLyklSvAisrK3Tp0gUbNmxA165d5a8nJCQgMDAQGhoaPIaO6CXcE09EVI8XO0ATvao///wTK1euxC+//IKxY8fC29sbampqYsciJeTp6Sm57Rjz589HeHg4+vTpI4ltRZ9//jn8/f3RunVryaxWkBJ1dXVcvHgRHh4eYkep4fvvv8enn34KV1dXzJ07F+PHj8eaNWtw5MgRfPjhh1i6dKnYEYkkhzPxRERE/2MVFRWIiIhAWFgY2rdvj5UrV0pq5ovof8HHxwc3btxAYWEhzMzM0LJlyxrXZTJZjXPIm5oUVytIjZ+fHzp37iw/Zk4qnj17hvDwcISEhKBZs2Zo2bIlvvjiCzg7O4sdjUiSOBNPRFQPJyenBme/2FmXXvb+++/jzz//hLe3N2bMmMHZd3otFRUVoVOnTjW+F5MUVytIjaurK5YtW4aioqI6m7WKcQpCRUUFcnNzUVFRAQ0NDaiqqnIlBVE9OBNPRFSPNWvW1PqAU1BQgAsXLgAAhgwZIvgRQSR9U6ZMwRdffFHjXGii/9Yff/yBb7/9FmlpaQrPZA8LCxMhFdVFih3zXz4t4MW/36qqqkTp0XHz5k34+/vj4cOH+Pzzz+Hk5IQVK1YgPj4en3zyCT799FNoaGgImolI6ljEExH9DWVlZfD19cXAgQMxefJkseMQ0Wvu6tWrmDx5MgwNDZGWlobu3bujsLAQDx48QLt27dCpUyfs2bNH7JiSkZmZiczMTJiamuKtt94SLYfUOuZXP4Cuj9BZe/bsCWtra/zjH/+AoaGh/PW4uDisXLkSurq6OHnypKCZiKSOy+mJiP6G5s2bw83NDcuXL2cRT0RNbuPGjRgxYgTWrFkDc3Nz+b8vX76MhQsXYvr06YLk+Oqrr+Di4gJ9fX189dVX9Y6VyWSCN1E7dOgQQkJC8PjxY8hkMhw+fBjm5ubw9fVF37594e7uLmgeMzMz3L17VxJFfGlpKU6dOoX3338fFhYWYseR8/f3V/jnMmrUKPTp0wcBAQEipCKSNhbxRER/U25uruh7QInozXDr1i14e3ujWbNmACBfTm9tbY3Zs2dj06ZNGDBgQJPnWL9+PXr37g19fX2sX7++3rFCF/HR0dEICgrC1KlTYWdnB09PT/m1vn37Ij4+XvAiXkod89XV1RETE4OhQ4eKlkGR+v5M2rZti/DwcAHTECkHFvFERPU4depUrdfKy8uRmpqKffv2wdbWVoRURPSmkclkUFNTg0wmg56eHjIyMmBtbQ0AaNeuHdLS0gTJcfPmTYVfS8HevXsxa9YszJo1S96hvlqXLl1w7949wTO5u7ujvLwc8+fPByB+x3wrKyskJydLYmWAIsXFxQr7PUjhCEMiKWERT0RUDz8/P4Wvq6qqYujQoVzmR0SCMDY2Rnp6OmxtbWFpaYmoqCh069YNqqqq2LVrFzp27Ch2RNFlZWXByspK4TU1NTX89ddfAieSXsd8Pz8/LFq0CCoqKnV2pxe6YK6qqkJoaCgOHTqEx48fKxwjdLM9IqljEU9EVA9Fx8epq6sr/OBDRNRUPvroI2RkZAAAFixYAE9PT3zwwQcAAE1NTWzdulXwTP/+979RWFgIOzs7AEB+fj42btyI1NRU2Nvbw9fXV778XwiGhoa4du2aPM+LUlJSYGRkJFiWanPmzBH8nvWZMGECgOc9FoKCghSOEbpgjo6ORnR0NKZNm4bNmzdj5syZUFFRQVxcHMrLy+Hj4yNoHiJlwCKeiKgeHTp0EDsCERHGjBkj/9rY2BjHjx9HcnIySkpKYGlpCT09PcEzrVu3DnZ2dvKiee3atTh9+jQcHBwQFRWFZs2awdfXV7A8H330EUJCQtCqVSv5vu+Kigr8/PPPiIyMxLx58wTLoogUOuavXbtWcg+gDx8+jDlz5mDSpEnYvHkzBg8eDHNzc8yaNQszZ87E/fv3xY5IJDk8Yo6I6CVpaWlYsGAB5s6dC0dHR4VjEhMTERwcjODgYC5jJaI3Ur9+/bBhwwY4OjqipKQEtra2CAwMhKurK/bt24c9e/YIfjTY6tWrsW/fPshkMjx79ky+EmDixImibX+SWsd8qbG0tER4eDhsbGzQs2dPhIeHyx8MJSYmYunSpfj1119FTkkkLcKtcSIiUhJRUVF466236izgAcDR0RFaWlqIjIwUMBkRvclycnIQFBQEd3d3DBs2DHfu3AEA7N69G8nJyYLnKSkpkXdbv3z5MsrKyuDs7AwA6N69Ox4+fCh4poCAAJw8eRLLli3DvHnzEBgYiOPHj4tWwEdHR2PVqlUYM2YMoqKi8OLcWXXH/Dedrq6uvF+BoaEhfv/9d/m13NxclJSUiBWNSLK4nJ6I6CVnz57F7NmzGxzn6uqKkJAQARIR0Zvu+vXr8PDwQMuWLWFjY4MLFy6grKwMwPOGbtHR0diyZYugmTp27IgzZ86gb9++OHbsGMzNzeVN0bKzs9GiRQtB81Tr1KkTOnXqJMq9Xya1jvlOTk4NLqdX1AumKVlbW+PatWtwdHTE6NGjERISgidPnkBVVRXffPONwh4HRG86FvFERC/Jyspq1BL5t99+G1lZWQIkIqI33bp162BpaYnQ0FDIZDJ8//338mu9evXCiRMnBM/k4eGBgIAAHD58GPn5+diwYYP82oULF9C9e3fBM5WXl+O7775DSkoKHj9+DAMDA1haWmLMmDFQU1MTPI/UOuY7OzvXKuILCgpw4cIFAMCQIUMEzQMAs2fPlv9d6uPjg4KCAsTGxqK0tBT29vYIDAwUPBOR1LGIJyJ6iZaWFnJzcxscl5eXJ1pzIiJ6s1y7dg3btm2DmpparRnd1q1bIzs7W/BM48aNQ+fOnXHt2jX06NEDtra28mu6urqYMmWKoHnu3buHadOmyZvH6enp4caNG/juu++wY8cORERE4J133hE0k9Q65i9dulTh62VlZfD19cXbb78taB4AeOedd+R/Ls2bN0dAQACPbyVqAIt4IqKX9OzZE8ePH29wRiIuLg49e/YUKBURvck0NTXx9OlThdcyMjIEP9u7mo2NDWxsbGq9LsbRasuWLYOamhri4+NrLKf/448/4OPjgxUrVmDPnj2CZpJ6x/xqzZs3h5ubG5YvX47JkyeLHYeIGsAinojoJRMnToSvry+MjY3l59W+6NmzZwgNDUV8fDy2b98uUkoiet0VFxfLG8f1798fO3bsgJ2dHbS1tQEAMpkMJSUl2LNnT72NON8UV69exYYNG2rth+/cuTP8/Pzw2WefCZ7Jy8sLmZmZWLZsGZYvXw4A+OSTTwA8/7tm0qRJgmeqS25uLoqKigS515IlSxo9ViaTYe3atU2Yhkj5sIgnInqJs7Mzpk2bhpCQEBw8eBB2dnYwNDQE8Pyc3/Pnz+PJkyfw8vKCk5OTyGmJ6HU1cuRIBAYGwsnJCf7+/vjkk08wbNgw9OvXDzKZDFu2bMHdu3chk8kkM6MrpjZt2tTZtE0mk0FfX1/gRM8FBARgypQpOHfuHPLy8qCjowM7OzvBl9IDwKlTp2q9Vl5ejtTUVOzbt6/Gloim9N1330FLSwudOnVCQ6ddS+1ceyIp4DnxRER1SExMRFRUFK5cuSLvAq2urg5ra2t4eHhw5ouImpSfnx9OnTqFIUOGIDAwEBoaGoiOjsa5c+eQm5srLwanTp0q2nJ6KTly5AjCw8MRFhZWozlpeno6fHx8MG3aNHz44YciJhSfqampwtdVVVUxdOhQBAQEoHXr1k2eY8KECUhJSYGJiQlGjx6NUaNGoUOHDk1+X6LXBYt4IqIGVFZWIi8vD8DzZk0vL68nImoqZ86cwerVq5GdnY158+bBzc2NM5Mv8PHxqfH99evXkZOTAxMTE+jp6SE7Oxt37tyBnp4eevTogbCwMMEzSqlj/oMHD2q9pq6uDj09PcF/rjIyMhAXF4fY2Fjcvn0blpaWGD16NEaMGCHIgwQiZcYinoiIiEjCysrKsGPHDkRGRqJbt27w9PSEqmrtHZHVjdPeJK/ahO3rr79uoiSKKeqYn52djZs3b6Jdu3aidMyXort37yI2NhYnTpzAgwcPYGtri8mTJ3PFG1EdWMQTERERKYEdO3YgODhY4TWZTIYbN24ImufMmTMoKCjA6NGjATzvGfL5558jNTUV9vb2WLZs2Rt/DOfkyZPx+PFj7Nq1S2HHfAMDgybvmJ+WloYFCxZg7ty5dRbFiYmJCA4ORnBwcI2tCEIrLS1FcHAwoqOj4eTkhJCQENGyEElZM7EDEBEREVHd/vzzT3h7e2Pr1q0YO3Ys4uPjkZCQUOOf06dPC55r69atyMrKkn+/cuVKpKamYtSoUfjll1+wdetWwTNJzdWrVzF//vw6O+anpKQ0eYaoqCi89dZb9c5qOzo6QktLC5GRkU2e52WVlZVITEzE4sWL4eDggJiYGIwbNw7e3t6CZyFSFuxOT0RERCRBFRUViIiIQFhYGNq3b489e/YoPJNdLH/88Ye8UdrTp0/xyy+/ICgoCMOHD4eJiQlCQkIEP9YtMzMTp0+fRmZmprwh6YsCAgIEzSOFjvlnz57F7NmzGxzn6uoq6Mz3xYsXERsbi/j4eJSVlcHZ2RlBQUHo37+/wu0iRPR/+BtCREREJEHvv/++fBZ+xowZgjdBa0hFRQWaNXu+qPPixYsAgAEDBgAAOnbsiCdPngia5/jx4/j0009RVVWF1q1b1/r/JZPJBC/ifX19ERwcDDMzs1od87dt29ao4vq/lZWV1agl8m+//XaNlRVNydHREbm5uRg4cCBWrFgBJycnqKurC3JvotcBi3giIiIiCdLX18f27dvRpUsXsaMo9M477+Do0aPo1asXDh06BCsrK2hpaQEAHj9+LPixd5s3b8bgwYOxatUqtGzZUtB7v+jljvkFBQXy1Qkvd8w/efJkkx97p6Wlhdzc3AbH5eXlCdbDICsrC6qqqjh79izOnTtX71iZTIakpCRBchEpCxbxRERERBLU1A3P/luzZs3C3LlzceTIEaioqNQ4vu2XX35Bjx49BM2Tk5ODjz/+WNQCHgCKiopqfG9kZAQjIyMAz08aaNmyJaytrRWObQo9e/bE8ePHMWTIkHrHxcXFoWfPnk2eB4AgKxCIXmcs4omIiIjolTk7O+PEiRP4/fff0b17d3mhCgCWlpbo3r27oHkGDBiA5ORk2NnZCXrflwl9jF1DJk6cCF9fXxgbG2PmzJlQUVGpcf3Zs2cIDQ1FfHw8tm/fLkgmFvFE/x0eMUdERERESi8/Px/z58/Hu+++C1tbW2hra9caY25uLkIy8QUFBSEiIgL6+vqws7ODoaEhgOeNAM+fP48nT57Ay8sLixYtEjkpETUGi3giIiIi+lvy8/Nx5swZPHz4EKWlpTWuyWQy+Pr6CpYlIyMDCxcuxJUrV2p1hK+qqoJMJsONGzcEy1NNKh3zExMTERUVhStXrshzqKurw9raGh4eHvUeQUdE0sIinoiIiIhe2a+//go/Pz/89ddf0NDQUNgN/sKFC4LlmTx5MtLT0zF9+nQYGRkp7Obft29fwfIAjeuYn5CQIGimyspK5OXlAQB0dXVrLa8nIuljEU9EREREr8zFxQWtW7fG2rVr0aFDB7HjoFevXti0aRMGDx4sdhS5IUOGwNzcXPSO+UT0emkmdgAiIiIiUj7p6enw9vaWRAEPAJ07d0ZFRYXYMWqQSsd8Inq9sIgnIiIiolfWo0cPZGZmih1DbsmSJQgLC0NqaqrYUeSqO+YTEf0vcTk9EREREb2y27dvw9/fH4sWLYKdnR1UVcU9udjFxQWPHz9GQUEB2rRpU2v2WyaT4ejRo4JmYsd8ImoKPCeeiIiIiBrFysqqRuf38vJyeHt7o1mzZlBXV68xViaTISkpSbBs5ubmtbrSi62oqAjFxcXYuXMndu3aVeOamB3ziUi5sYgnIiIiokbx9PSUXKFc7R//+IfYEWpZvHgxMjMzERgYWGfHfCKiV8Xl9ERERERETUCKHfOJSPlxJp6IiIiIXtmUKVOwfPlyGBsb17p27949LF++HHv27BEsz5IlSxocs27dOgGS/B8pdswnIuXHIp6IiIiIXtmFCxdQVFSk8NrTp09x6dIlQfMo2lteUFCAzMxMtGrVCm3bthU0D/D8wcL69ethYmKi8GEHEdHfwSKeiIiIiP6nrly5gtatWwt6zyNHjih8PTU1FQsWLMDixYsFzQMAa9euxePHj+Hi4iKZjvlEpPxYxBMRERFRo+zcuRM7d+4E8LwAdXd3r9XorqysDJWVlZg4caIYEWsxNjbG9OnTsW7dOnz//feC3luKHfOJSPmxiCciIiKiRrGysoKnpyeqqqqwfft2jBo1Cu3atasxRk1NDcbGxhg0aJBIKWtr2bIl7t+/L/h9pdgxn4iUH4t4IiIiImqUvn37om/fvgCez8SPHz9elL3miuTl5dV6rby8HKmpqfjyyy9hYmIifCgioibAI+aIiIiISOmZmpoqXLpeVVWF9u3bY/v27ejRo4egmaTYMZ+IlB9n4omIiIjolT179gz//Oc/cfLkSTx8+BClpaU1rstkMpw+fVqwPGvXrq1VxKurq6Nt27bo1asXVFWF/9grxY75RKT8WMQTERER0SvbuHEjvvrqK9jY2KBfv35QU1MTNc/YsWNFvb8iUuyYT0TKj8vpiYiIiOiV9e/fH5988gl8fX3FjqKUYmNjER4eLnjHfCJSfpyJJyIiIqJXVlZWBmtra1EzODk5NfoIN6GX9zdErI75RKT8WMQTERER0StzcXHBjz/+CDs7O9EyODs7N1jE37p1C7/99pso57WzYz4RNQUW8URERET0ynr16oUtW7YgOzsb9vb20NbWrjVm6NChTZph6dKldV67ceMGtm/fjgsXLqBTp07w9vZu0iyK2NraNtgxn4joVXFPPBERERG9MlNT03qvy2Qyhd3Zm9q1a9ewfft2JCYmwsjICD4+PnBxcUGzZs0Ez/Ltt99KrmM+ESk/FvFERERE9MoePHjQ4JgOHToIkOS55ORkhISE4OzZs+jatStmzpyJESNGiLKMnoioKbGIJyIiIiKldfHiRYSGhuL8+fPo0aMHZs6ciSFDhogdi4ioyXANDxERERH9LVVVVUhMTERSUhLy8/Oho6ODPn36YODAgYLMgE+ePBmXLl2ChYUFdu7cCUdHxya/Z0OUuWM+ESkHzsQTERER0SvLz8+Ht7c3UlJSoK2tDT09PWRnZ6OgoACWlpbYtWuXwmZ3/0vV+/I1NTUbLJxlMhmSkpKaNA8ArFmz5pU65ovRN4CIlBtn4omIiIjola1fvx73799HZGQkHBwc5K+fPXsW/v7+WL9+PdasWdOkGWbPnt2k//2/Q+od84lI+XEmnoiIiIhema2tLfz9/eHq6lrr2uHDhxEUFIR//etfIiSTHil1zCci5ceZeCIiIiJ6ZcXFxdDX11d4zcDAAMXFxQInkp6XO+Zv2rSJHfOJ6L/GIp6IiIiIXpmZmRn27t2L/v37Q0VFRf76s2fP8PXXX6NHjx4iphPXyx3zt27dyo75RPQ/w+X0RERERPTKLl68CE9PTxgYGMDZ2Rn6+vrIzs7G6dOn8eTJE0RFRaFPnz5ixxTcix3zZ82aJYmO+UT0emERT0RERER/y7///W+EhYUhKSkJBQUF0NHRQe/eveHj4wNzc3Ox44lCih3ziej1wiKeiIiIiOh/JCQk5JXGS7HDPhFJG4t4IiIiIiIiIiXBxnZERERE1ChLlixp9FiZTIa1a9c2YRoiojcTi3giIiIiapTvvvsOWlpa6NSpExpazMlj1IiImgaLeCIiIiJqFEtLS6SkpKCyshKjR4/GqFGj0KFDB7FjERG9UbgnnoiIiIgaLSMjA3FxcYiNjcXt27dhaWmJ0aNHY8SIEWjdurXY8YiIXnss4omIiIjob7l79y5iY2Nx4sQJPHjwALa2tpg8eTLPRiciakIs4omIiIjov1JaWorg4GBER0fDycnplY9ZIyKixuOeeCIiIiJ6ZZWVlfj1119x/PhxJCQkQEVFBePGjcO4cePEjkZE9FrjTDwRERERNdrFixcRGxuL+Ph4lJWVwdnZGaNHj0b//v2hqsr5ISKipsYinoiIiIgaxdHREbm5uRg4cCBGjRoFJycnqKurix2LiOiNwiKeiIiIiBrF1NQUqqqqUFNTa/AceJlMhqSkJIGSERG9ObjmiYiIiIgaZfbs2WJHICJ643EmnoiIiIiIiEhJNBM7ABERERERERE1Dot4IiIiIiIiIiXBIp6IiIiIiIhISbCIJyIiIiIiIlISLOKJiIiIiIiIlASLeCIiIiIiIiIlwSKeiIiIiIiISEmwiCciIiIiIiJSEv8P5CWAuxqZMakAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression"
      ],
      "metadata": {
        "id": "_h285RuLY6jN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_clean = data_clean.dropna()"
      ],
      "metadata": {
        "id": "7IO7B22f0s86"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "def cross_validation_best_parameters(model, param_grid):\n",
        "    grid = GridSearchCV(model, param_grid,cv=10, scoring='accuracy')\n",
        "    X=data_clean.iloc[:,:-1].values\n",
        "    y=data_clean.iloc[:,-1].values\n",
        "    grid.fit(X,y)\n",
        "    mean_scores = [result.mean_validation_score for result in grid.grid_scores_]\n",
        "    return mean_scores,grid.best_score_,grid.best_estimator_\n",
        "logreg = linear_model.LogisticRegression(random_state=0)\n",
        "c=[0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
        "param_grid = dict(C=c)\n",
        "mean_scores,Best_Accuracy, Best_classifier = cross_validation_best_parameters(logreg,param_grid)\n",
        "print(\"Best accuracy is \"+ str(Best_Accuracy))\n",
        "print(Best_classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "P6GYcdwIYP5z",
        "outputId": "38e1f3cc-2608-43ac-97dd-5b7cb69a41c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-5ee64f3f3221>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mparam_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mmean_scores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBest_Accuracy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBest_classifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validation_best_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogreg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Best accuracy is \"\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBest_Accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBest_classifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-28-5ee64f3f3221>\u001b[0m in \u001b[0;36mcross_validation_best_parameters\u001b[0;34m(model, param_grid)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_clean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_clean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mmean_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_validation_score\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid_scores_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmean_scores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    872\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1386\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1387\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1388\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    849\u001b[0m                     )\n\u001b[1;32m    850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 851\u001b[0;31m                 \u001b[0m_warn_or_raise_about_fit_failures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m                 \u001b[0;31m# For callable self.scoring, the return type is only know after\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[0;34m(results, error_score)\u001b[0m\n\u001b[1;32m    365\u001b[0m                 \u001b[0;34mf\"Below are more details about the failures:\\n{fit_errors_summary}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m             )\n\u001b[0;32m--> 367\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_fits_failed_message\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: \nAll the 70 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n70 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1204, in fit\n    check_classification_targets(y)\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/multiclass.py\", line 218, in check_classification_targets\n    raise ValueError(\"Unknown label type: %r\" % y_type)\nValueError: Unknown label type: 'continuous'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_LR = linear_model.LogisticRegression(C=Best_classifier.C)\n",
        "clf_LR.fit(X_train,y_train)\n",
        "LR_Predict = clf_LR.predict_proba(X_test)[:,1]\n",
        "LR_Predict_bin = clf_LR.predict(X_test)\n",
        "LR_Accuracy = accuracy_score(y_test,LR_Predict.round())\n",
        "print(\"Logistic regression accuracy is \",LR_Accuracy)\n",
        "plotAUC(y_test,rfPredictproba, 'Random Forest')\n",
        "plotAUC(y_test,LR_Predict,'Logistic Regression')\n",
        "plt.show()\n",
        "plt.figure(figsize=(6,6))\n",
        "plot_confusion_matrix(LR_Predict_bin, normalize=True)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "6FSYkrTTZUNQ",
        "outputId": "f54a51bb-f47a-41be-9a78-098544e16341"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-99-4cc64904ad84>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclf_LR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBest_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mclf_LR\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mLR_Predict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf_LR\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mLR_Predict_bin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf_LR\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mLR_Accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mLR_Predict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'Best_classifier' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D6_3-yG2Za1e"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}