user-churn-forecast-lr-xgboost-catboost.ipynb

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    "tags": []
   },
   "source": [
    "# Исследование"
   ]
  },
  {
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   "source": [
    "Импортируем библиотеки"
   ]
  },
  {
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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder, OneHotEncoder\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "pd.options.display.max_colwidth = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e742ed",
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     "start_time": "2021-11-05T07:43:34.799745",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29b79b50",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/advanced-dls-spring-2021/submission.csv\n",
      "/kaggle/input/advanced-dls-spring-2021/train.csv\n",
      "/kaggle/input/advanced-dls-spring-2021/test.csv\n"
     ]
    }
   ],
   "source": [
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "607f3f28",
   "metadata": {
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     "exception": false,
     "start_time": "2021-11-05T07:43:34.995176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5282, 20), (1761, 19))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('/kaggle/input/advanced-dls-spring-2021/train.csv')\n",
    "test = pd.read_csv('/kaggle/input/advanced-dls-spring-2021/test.csv')\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "723bda09",
   "metadata": {
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     "status": "completed"
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   "source": [
    "# Для вашего удобства списки с именами разных колонок\n",
    "\n",
    "# Числовые признаки\n",
    "num_cols = [\n",
    "    'ClientPeriod',\n",
    "    'MonthlySpending',\n",
    "    'TotalSpent'\n",
    "]\n",
    "\n",
    "# Категориальные признаки\n",
    "cat_cols = [\n",
    "    'Sex',\n",
    "    'IsSeniorCitizen',\n",
    "    'HasPartner',\n",
    "    'HasChild',\n",
    "    'HasPhoneService',\n",
    "    'HasMultiplePhoneNumbers',\n",
    "    'HasInternetService',\n",
    "    'HasOnlineSecurityService',\n",
    "    'HasOnlineBackup',\n",
    "    'HasDeviceProtection',\n",
    "    'HasTechSupportAccess',\n",
    "    'HasOnlineTV',\n",
    "    'HasMovieSubscription',\n",
    "    'HasContractPhone',\n",
    "    'IsBillingPaperless',\n",
    "    'PaymentMethod'\n",
    "]\n",
    "\n",
    "feature_cols = num_cols + cat_cols\n",
    "target_col = 'Churn'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e2f60b",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "Случайный сэмпл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67da329d",
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    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClientPeriod</th>\n",
       "      <th>MonthlySpending</th>\n",
       "      <th>TotalSpent</th>\n",
       "      <th>Sex</th>\n",
       "      <th>IsSeniorCitizen</th>\n",
       "      <th>HasPartner</th>\n",
       "      <th>HasChild</th>\n",
       "      <th>HasPhoneService</th>\n",
       "      <th>HasMultiplePhoneNumbers</th>\n",
       "      <th>HasInternetService</th>\n",
       "      <th>HasOnlineSecurityService</th>\n",
       "      <th>HasOnlineBackup</th>\n",
       "      <th>HasDeviceProtection</th>\n",
       "      <th>HasTechSupportAccess</th>\n",
       "      <th>HasOnlineTV</th>\n",
       "      <th>HasMovieSubscription</th>\n",
       "      <th>HasContractPhone</th>\n",
       "      <th>IsBillingPaperless</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4049</th>\n",
       "      <td>67</td>\n",
       "      <td>20.05</td>\n",
       "      <td>1263.05</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>57</td>\n",
       "      <td>79.75</td>\n",
       "      <td>4438.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>1</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>33</td>\n",
       "      <td>86.15</td>\n",
       "      <td>2745.7</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>46</td>\n",
       "      <td>101.10</td>\n",
       "      <td>4674.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ClientPeriod  MonthlySpending TotalSpent     Sex  IsSeniorCitizen  \\\n",
       "4049            67            20.05    1263.05  Female                1   \n",
       "4634            57            79.75     4438.2    Male                1   \n",
       "463              1            25.25      25.25  Female                0   \n",
       "1027            33            86.15     2745.7  Female                0   \n",
       "410             46           101.10     4674.4  Female                0   \n",
       "\n",
       "     HasPartner HasChild HasPhoneService HasMultiplePhoneNumbers  \\\n",
       "4049        Yes       No             Yes                      No   \n",
       "4634         No       No             Yes                     Yes   \n",
       "463          No       No              No        No phone service   \n",
       "1027        Yes      Yes             Yes                     Yes   \n",
       "410          No       No             Yes                     Yes   \n",
       "\n",
       "     HasInternetService HasOnlineSecurityService      HasOnlineBackup  \\\n",
       "4049                 No      No internet service  No internet service   \n",
       "4634                DSL                       No                  Yes   \n",
       "463                 DSL                       No                   No   \n",
       "1027        Fiber optic                       No                   No   \n",
       "410         Fiber optic                       No                   No   \n",
       "\n",
       "      HasDeviceProtection HasTechSupportAccess          HasOnlineTV  \\\n",
       "4049  No internet service  No internet service  No internet service   \n",
       "4634                  Yes                   No                  Yes   \n",
       "463                    No                   No                   No   \n",
       "1027                   No                   No                   No   \n",
       "410                   Yes                   No                  Yes   \n",
       "\n",
       "     HasMovieSubscription HasContractPhone IsBillingPaperless  \\\n",
       "4049  No internet service         Two year                Yes   \n",
       "4634                  Yes         Two year                Yes   \n",
       "463                    No   Month-to-month                 No   \n",
       "1027                  Yes   Month-to-month                 No   \n",
       "410                   Yes         One year                Yes   \n",
       "\n",
       "                  PaymentMethod  Churn  \n",
       "4049  Bank transfer (automatic)      0  \n",
       "4634  Bank transfer (automatic)      0  \n",
       "463            Electronic check      0  \n",
       "1027    Credit card (automatic)      1  \n",
       "410            Electronic check      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "train.sample(5)"
   ]
  },
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    "tags": []
   },
   "source": [
    "Информация по столбцам - null нет"
   ]
  },
  {
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    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5282 entries, 0 to 5281\n",
      "Data columns (total 20 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   ClientPeriod              5282 non-null   int64  \n",
      " 1   MonthlySpending           5282 non-null   float64\n",
      " 2   TotalSpent                5282 non-null   object \n",
      " 3   Sex                       5282 non-null   object \n",
      " 4   IsSeniorCitizen           5282 non-null   int64  \n",
      " 5   HasPartner                5282 non-null   object \n",
      " 6   HasChild                  5282 non-null   object \n",
      " 7   HasPhoneService           5282 non-null   object \n",
      " 8   HasMultiplePhoneNumbers   5282 non-null   object \n",
      " 9   HasInternetService        5282 non-null   object \n",
      " 10  HasOnlineSecurityService  5282 non-null   object \n",
      " 11  HasOnlineBackup           5282 non-null   object \n",
      " 12  HasDeviceProtection       5282 non-null   object \n",
      " 13  HasTechSupportAccess      5282 non-null   object \n",
      " 14  HasOnlineTV               5282 non-null   object \n",
      " 15  HasMovieSubscription      5282 non-null   object \n",
      " 16  HasContractPhone          5282 non-null   object \n",
      " 17  IsBillingPaperless        5282 non-null   object \n",
      " 18  PaymentMethod             5282 non-null   object \n",
      " 19  Churn                     5282 non-null   int64  \n",
      "dtypes: float64(1), int64(3), object(16)\n",
      "memory usage: 825.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "febed649",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ClientPeriod                0\n",
       "MonthlySpending             0\n",
       "TotalSpent                  0\n",
       "Sex                         0\n",
       "IsSeniorCitizen             0\n",
       "HasPartner                  0\n",
       "HasChild                    0\n",
       "HasPhoneService             0\n",
       "HasMultiplePhoneNumbers     0\n",
       "HasInternetService          0\n",
       "HasOnlineSecurityService    0\n",
       "HasOnlineBackup             0\n",
       "HasDeviceProtection         0\n",
       "HasTechSupportAccess        0\n",
       "HasOnlineTV                 0\n",
       "HasMovieSubscription        0\n",
       "HasContractPhone            0\n",
       "IsBillingPaperless          0\n",
       "PaymentMethod               0\n",
       "Churn                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train.isna() | train.isnull()).sum()"
   ]
  },
  {
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   "id": "85caaabb",
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     "start_time": "2021-11-05T07:43:36.001070",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "В столбце TotalSpent есть пропуски, заполним"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "037ed07b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:36.146104Z",
     "iopub.status.busy": "2021-11-05T07:43:36.145412Z",
     "iopub.status.idle": "2021-11-05T07:43:36.152763Z",
     "shell.execute_reply": "2021-11-05T07:43:36.153309Z",
     "shell.execute_reply.started": "2021-11-05T07:10:19.119946Z"
    },
    "papermill": {
     "duration": 0.058342,
     "end_time": "2021-11-05T07:43:36.153481",
     "exception": false,
     "start_time": "2021-11-05T07:43:36.095139",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train.TotalSpent == ' ').sum(), (test.TotalSpent == ' ').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44b367af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:36.257698Z",
     "iopub.status.busy": "2021-11-05T07:43:36.256717Z",
     "iopub.status.idle": "2021-11-05T07:43:36.263147Z",
     "shell.execute_reply": "2021-11-05T07:43:36.262480Z",
     "shell.execute_reply.started": "2021-11-05T07:10:20.303411Z"
    },
    "papermill": {
     "duration": 0.063316,
     "end_time": "2021-11-05T07:43:36.263313",
     "exception": false,
     "start_time": "2021-11-05T07:43:36.199997",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "total_spent = pd.to_numeric(train.TotalSpent, errors='coerce')\n",
    "train.TotalSpent = total_spent.fillna(0) # total_spent.mean()\n",
    "total_spent = pd.to_numeric(test.TotalSpent, errors='coerce')\n",
    "test.TotalSpent = total_spent.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b0737b",
   "metadata": {
    "papermill": {
     "duration": 0.04647,
     "end_time": "2021-11-05T07:43:36.356401",
     "exception": false,
     "start_time": "2021-11-05T07:43:36.309931",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Анализ данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832926db",
   "metadata": {
    "papermill": {
     "duration": 0.046334,
     "end_time": "2021-11-05T07:43:36.449423",
     "exception": false,
     "start_time": "2021-11-05T07:43:36.403089",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Числовые фичи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b20747b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:36.548223Z",
     "iopub.status.busy": "2021-11-05T07:43:36.547550Z",
     "iopub.status.idle": "2021-11-05T07:43:36.550079Z",
     "shell.execute_reply": "2021-11-05T07:43:36.550580Z",
     "shell.execute_reply.started": "2021-11-05T07:10:24.496607Z"
    },
    "papermill": {
     "duration": 0.054685,
     "end_time": "2021-11-05T07:43:36.550746",
     "exception": false,
     "start_time": "2021-11-05T07:43:36.496061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ClientPeriod', 'MonthlySpending', 'TotalSpent']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b62543b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:36.649123Z",
     "iopub.status.busy": "2021-11-05T07:43:36.648156Z",
     "iopub.status.idle": "2021-11-05T07:43:37.210596Z",
     "shell.execute_reply": "2021-11-05T07:43:37.211120Z",
     "shell.execute_reply.started": "2021-11-05T07:10:25.889272Z"
    },
    "papermill": {
     "duration": 0.613499,
     "end_time": "2021-11-05T07:43:37.211314",
     "exception": false,
     "start_time": "2021-11-05T07:43:36.597815",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 7))\n",
    "ax = None\n",
    "for i, column in enumerate(num_cols):\n",
    "    ax = plt.subplot(1, 3, i + 1, sharey=ax)\n",
    "    ax.set_title(column)\n",
    "    plt.hist(train[column])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cebff59",
   "metadata": {
    "papermill": {
     "duration": 0.047707,
     "end_time": "2021-11-05T07:43:37.306852",
     "exception": false,
     "start_time": "2021-11-05T07:43:37.259145",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Категориальные фичи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c289e415",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:37.408956Z",
     "iopub.status.busy": "2021-11-05T07:43:37.407940Z",
     "iopub.status.idle": "2021-11-05T07:43:37.448751Z",
     "shell.execute_reply": "2021-11-05T07:43:37.449314Z",
     "shell.execute_reply.started": "2021-11-05T07:10:29.008491Z"
    },
    "papermill": {
     "duration": 0.095078,
     "end_time": "2021-11-05T07:43:37.449483",
     "exception": false,
     "start_time": "2021-11-05T07:43:37.354405",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>num_uniq</th>\n",
       "      <th>count_uniq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2</td>\n",
       "      <td>{'Male': 2655, 'Female': 2627}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IsSeniorCitizen</td>\n",
       "      <td>2</td>\n",
       "      <td>{0: 4440, 1: 842}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HasPartner</td>\n",
       "      <td>2</td>\n",
       "      <td>{'No': 2705, 'Yes': 2577}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HasChild</td>\n",
       "      <td>2</td>\n",
       "      <td>{'No': 3676, 'Yes': 1606}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HasPhoneService</td>\n",
       "      <td>2</td>\n",
       "      <td>{'Yes': 4761, 'No': 521}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HasMultiplePhoneNumbers</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2511, 'Yes': 2250, 'No phone service': 521}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HasInternetService</td>\n",
       "      <td>3</td>\n",
       "      <td>{'Fiber optic': 2341, 'DSL': 1800, 'No': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HasOnlineSecurityService</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2612, 'Yes': 1529, 'No internet service': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HasOnlineBackup</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2332, 'Yes': 1809, 'No internet service': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HasDeviceProtection</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2317, 'Yes': 1824, 'No internet service': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HasTechSupportAccess</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2604, 'Yes': 1537, 'No internet service': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HasOnlineTV</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2099, 'Yes': 2042, 'No internet service': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HasMovieSubscription</td>\n",
       "      <td>3</td>\n",
       "      <td>{'No': 2081, 'Yes': 2060, 'No internet service': 1141}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HasContractPhone</td>\n",
       "      <td>3</td>\n",
       "      <td>{'Month-to-month': 2920, 'Two year': 1280, 'One year': 1082}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IsBillingPaperless</td>\n",
       "      <td>2</td>\n",
       "      <td>{'Yes': 3148, 'No': 2134}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>4</td>\n",
       "      <td>{'Electronic check': 1786, 'Mailed check': 1194, 'Bank transfer (automatic)': 1159, 'Credit card (automatic)': 1143}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      column  num_uniq  \\\n",
       "0                        Sex         2   \n",
       "1            IsSeniorCitizen         2   \n",
       "2                 HasPartner         2   \n",
       "3                   HasChild         2   \n",
       "4            HasPhoneService         2   \n",
       "5    HasMultiplePhoneNumbers         3   \n",
       "6         HasInternetService         3   \n",
       "7   HasOnlineSecurityService         3   \n",
       "8            HasOnlineBackup         3   \n",
       "9        HasDeviceProtection         3   \n",
       "10      HasTechSupportAccess         3   \n",
       "11               HasOnlineTV         3   \n",
       "12      HasMovieSubscription         3   \n",
       "13          HasContractPhone         3   \n",
       "14        IsBillingPaperless         2   \n",
       "15             PaymentMethod         4   \n",
       "\n",
       "                                                                                                              count_uniq  \n",
       "0                                                                                         {'Male': 2655, 'Female': 2627}  \n",
       "1                                                                                                      {0: 4440, 1: 842}  \n",
       "2                                                                                              {'No': 2705, 'Yes': 2577}  \n",
       "3                                                                                              {'No': 3676, 'Yes': 1606}  \n",
       "4                                                                                               {'Yes': 4761, 'No': 521}  \n",
       "5                                                                     {'No': 2511, 'Yes': 2250, 'No phone service': 521}  \n",
       "6                                                                         {'Fiber optic': 2341, 'DSL': 1800, 'No': 1141}  \n",
       "7                                                                 {'No': 2612, 'Yes': 1529, 'No internet service': 1141}  \n",
       "8                                                                 {'No': 2332, 'Yes': 1809, 'No internet service': 1141}  \n",
       "9                                                                 {'No': 2317, 'Yes': 1824, 'No internet service': 1141}  \n",
       "10                                                                {'No': 2604, 'Yes': 1537, 'No internet service': 1141}  \n",
       "11                                                                {'No': 2099, 'Yes': 2042, 'No internet service': 1141}  \n",
       "12                                                                {'No': 2081, 'Yes': 2060, 'No internet service': 1141}  \n",
       "13                                                          {'Month-to-month': 2920, 'Two year': 1280, 'One year': 1082}  \n",
       "14                                                                                             {'Yes': 3148, 'No': 2134}  \n",
       "15  {'Electronic check': 1786, 'Mailed check': 1194, 'Bank transfer (automatic)': 1159, 'Credit card (automatic)': 1143}  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = list()\n",
    "for column in cat_cols:\n",
    "    out.append([column, train[column].nunique(), dict(train[column].value_counts())])\n",
    "pd.DataFrame(out, columns=['column', 'num_uniq', 'count_uniq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61f33b3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:37.549845Z",
     "iopub.status.busy": "2021-11-05T07:43:37.548874Z",
     "iopub.status.idle": "2021-11-05T07:43:39.453212Z",
     "shell.execute_reply": "2021-11-05T07:43:39.453740Z",
     "shell.execute_reply.started": "2021-11-05T07:10:31.177014Z"
    },
    "papermill": {
     "duration": 1.956171,
     "end_time": "2021-11-05T07:43:39.453912",
     "exception": false,
     "start_time": "2021-11-05T07:43:37.497741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4, 4, figsize=(16,16))\n",
    "ax = ax.ravel()\n",
    "for i, column in enumerate(cat_cols):\n",
    "    serie = train[column].value_counts()\n",
    "    labels = list(serie.index)\n",
    "    values = list(serie)\n",
    "    x = np.arange(len(labels))\n",
    "    ax[i].set_title(column)\n",
    "    ax[i].bar(x, values, width=0.5)\n",
    "    ax[i].set_xticks(x)\n",
    "    ax[i].set_xticklabels(labels)\n",
    "    if i == 15:\n",
    "        ax[i].tick_params(labelrotation=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c082b7eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:39.559154Z",
     "iopub.status.busy": "2021-11-05T07:43:39.558490Z",
     "iopub.status.idle": "2021-11-05T07:43:41.178841Z",
     "shell.execute_reply": "2021-11-05T07:43:41.179453Z",
     "shell.execute_reply.started": "2021-11-05T07:10:36.737926Z"
    },
    "papermill": {
     "duration": 1.674657,
     "end_time": "2021-11-05T07:43:41.179651",
     "exception": false,
     "start_time": "2021-11-05T07:43:39.504994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5wbrAwcCBFM4BouVJYQ8ePY1to9Md51ERIiIGuNQYc3r4+xlAjTEm7iKP5wdrAIdi26nnIkM3pIAHgTuA55OJWNpxHrUcWox2gecHo4HDsQXoBo7jZMO7wK3A7clEbI7bKEr1nOcHY1lcgK7rOE5PNWF3eO8CHkkmYvMd51Gqx8LutweHt7Udx+mpZuAp4HZsG9WeR6pHRGQhMAXY2Bgzw0Ux6vnBKOAg4BBg/XytN0cmA3cDdyQTsfdch1Ht02J0OTw/KAN2BY4N/y2GM6BdtRB4ALg+mYi94jqMUl3h+UEF9gDRCdjufVFUj92gXpZMxD52HUaprvD8oA/27MphwBZ0PFBfMZsN/Bu4IZmIfeY6jCpOIlIH/BVbgP4psxgVEQ+4BTsGyXTgSGPM99lYr+cH/YF9sAXo9kRz6sfPgTuBO5OJ2Neuw6jFtBjtgOcHfYGjgFOwAyeUiveBq7GNVY/yqoLl+cFA7EGiE4FRjuPk0/+Ay4AnddAGVcjCAU5OBI4j9wObFJKXgQuTidhTroOo4hIWo6OAD4H1gGNYXIw+BtxvjLlNRI4C9jDG7NWT9Xl+sALwf9hef716FL64PA9ckEzEnnMdRGkxupRwB/dM7E7uALdpnPoJOB+4KZmINbkOo1SrcAf3j8DR2CHiS9XnwOXALdpGVSEJu+KeDRwBVLtN49QbwHlalKrOEpE6Y0yNiPwFe6nGAhYXozOAkcaYJhGpBKYYY7o1U0M468PZ2H3dUipC23oVOD+ZiD3tOkgp02I05PlBNXASdid3oOM4heQb4M/YM6V6EbhyJuxG9Afg95R2EdrW19jvrfv0TKlyyfMDDzgH2x230m2agvI6tijVHV61TBnF6CDs1EL/wu6rZ6UY9fxgAHY7egq6Hc30FvCnZCL2P9dBSlHJF6PhcNUHY/voF8tofi58DJyTTMQecR1ElRbPD6qA44E/kfv5eovZ28CZyUTsBddBVGnx/KA39izLmZT2mdDleQ27HX3edRBVmFqL0fDni7EjTd8SFqOPAvcZY/4jIkcAexpj9u7Mcj0/qAFOBU6ntHv9Lc/T2O3oh66DlJKSLkY9P9gB+DvRGBk3X94ATkwmYu+6DqKiz/ODfYB/UDxDyheCJ4HTkonY566DqOjz/GAPbHfxlR1HKSa3A79PJmIzXAdRhaVNMToc+Ba4OCxGx2DPlHZ6ACPPD3phD+b6wNCcho+ONPAf7IEjnQIxD0qyGPX8YAhwLbCf6yxFqhlbxJ+nEwurXPD8YARwDfAr11mKVCPwN+wgKo2uw6jo8fxgFeBKIOY6S5GaCZyeTMRucx1ERZPnB5thp+9b3XGUYjUXOCmZiP3bdZCoK7li1POD3YGbgOGus0TAp8CRyUTsLddBVHR4fnAUcAnalSgbPsO20TddB1HREHab/yNwFqU98Em2/A84VqeaUNkSjoHyF2yX3ChOR5hv92Lb6GzXQaKqZIpRzw/6YbsSHeU4StS0AJcC5+pUMKonwsFPbgJ2chwlalqAi4G4niVVPRGeDb0H2Mh1lohZAJwH/COZiLW4DqOKl+cHGwK3AWu5zhIxk4DD9Hrv3CiJYtTzg+2w/ew9t0ki7Qvg0GQi9o7rIKr4hNed3YaeDc2l94F9konYt66DqOITXr99M1DrOkuEPQscqNeSqq4KB+M8DXt5ho5knRsG22vrT3pgN7siXYx6flAGXIC9cFscxykFC4FjkonY7a6DqOLg+UE5to2ehbbRfJgFHKLzHqrOCrv8XQKc4DpLifgee9BID+yqTvH8YDD22tBfOo5SKiZgt6Ofug4SFZEtRj0/6IsdsW4vx1FK0SXAWdrdSC1LOOn23cD2rrOUmDT2eqK/6Lykalk8PxiH7ZarI87nVwN21Pp/ug6iCpvnB1sBdwErus5SYhZgp4C52nWQKIhkMer5wYrAY8B4x1FK2X+x3Y30gm+1lHCUvweAUa6zlLAA+LW2UdUezw92wQ7c0d91lhL2T2xRqqPWq6V4frAfcAfaLdela7Aj7kavmMqjyBWjnh9sDDwCjHSdRfEVsKd2ZVCZPD/YG7sB7e06i+JT4OfJROxH10FU4fD84CDsNdy6k+ve28DuyURsqusgqnB4fvAb4AagzHUWxR3AEclErNl1kGIVqQ+x5wf7Ay+ihWihGAe84fnBDq6DqMLg+cHxwP1oIVoo1gRe9fxgVddBVGHw/OBE9GxLIdkYeCUcbVwpPD84HTvyfKT24YvYIcCDnh/oVFfdFJkPcrgBvRvdyS00/YDA84NdXQdRbnl+cA62S0tkvnciYgx2Z3d910GUW54fnAdchQ4mVmjGYdvomq6DKLc8P7gA+IfrHGopuwNPhtNIqi6KRDddzw9Owc4hqgpXI/Ya0odcB1H55/nB34EzXOdQyzQX2COZiL3oOojKr3Dk+auB41xnUcs0A9gxmYh96DqIyq9w6pYrgRNdZ1HL9C7wC52eqWuKvhj1/OBU4DLXOVSnNAP7JROxh10HUfnj+cHfsNMrqcK3ELsh1YK0RISF6O3AQa6zqE6ZiS1IP3AdROWH5wcVwC3Ar11nUZ3yGbCzjsXQeUXdXc7zg9+ihWgxqQDu8fwg5jqIyg/PD/6AFqLFpBfwqHbZLSnXoYVoMRkMPOv5wXqug6jcC+f5vR8tRIvJGtixGMa5DlIsirYY9fzgYOxGVBWXKuABzw92dB1E5VY42t/FrnOoLusPPKUb0ujz/OBC4Leuc6guGww84fnBCq6DqJy7FdjTdQjVZWOw29FBroMUg6LsphsOhvMo9kxbQZt03VGUVfWGsjKkrJyRh19Oy4J5zHjkIprnTqWi/3CG7OVT3qtmidc1p6Yx/aG/YkwaWlrot+Ev6bf+bpjmJqY9eD4t82bQb/0Y/TawJxlnPnUVNeN3pXpE0ew/zgY2TSZiE10HUdnn+cE+wD1Auessqtu+BbZMJmJTXAdR2ef5wUnYa9AKVnvbz/mfv0LqlTtpmvkDIw67lOqRSw8E3Tx3OjOCS0nPnwMINeN3of9Gdn9+9gv/YsE371I1bGWG/PJ0AOo+eZ50/Vz6b1x0+/zvA1snE7H5roOo7PP84Cwg4TqH6pH/YS99aXEdpJAV3ZnRcAqCuyiCQrTV8IMuZNSRVzHy8MsBmPvGffTy1mOF395EL2895r5x31KvKa8ZyIhD/8GoI69ixGGXkHrjfprnzWTBt+9RveKajDzqauo+eQ6AxmnfYNLpYipEAQYCj3l+MMB1EJVdnh9sA9yJFqLFbmXgaW2j0eP5wR4UyaB/bbefVUPGMHTvP1I9eq2OX1RWzsDtj2bUb65jxK//wbz3AhpnfE+6YT6NP33NqKOuRsoraZyeJN3UwPyPnll0YLfIrA/cEV73qyIkPOlyoescqsd2Qkc/Xq6i+gLz/KAGeBiodRylR+q/epO+a9teqn3X3pH6iW8s9Rwpr0Qq7DRvpqUJwjPYUlaOaWqAlhYIT2rPefl2Bmx9aH7CZ9fqwN2eH2jREhFht7H7sN2xVfFbB93ZjRTPDzbAHiwqyv/TyiGjqRy84jKfU1EzaNHB2bLqPlQOHk3LvJmAYNLNGGNINzUgZeXMfetB+m2wO1JeNMe329oTvRwiUjw/WA170qUo26hayqmeHxzhOkQhK5oPejis9W3YSdqLhwjT7j2XKbeewrwJTwHQMn8OFTW2G3l534G0zJ/T7kub505n8i0n8uO1R1K72T5U9BtMr5XXpzk1jSn/OZ3+G+1O/cQ3qRo+lop+g/P1jrJtF/SoUSR4flAFPAAMc51FZdVuwF9ch1A95/nBYOARoK/rLJ3Szvazq5pTU2mc+g3Vo1anrLoPvcduxJRbT6a8ZiBS3ZfGKV/SZ7XNsxw87073/OAY1yFUz3l+UIu9DK2oT7qopVzv+cFmrkMUqmI6FPhH4FeuQ3TViEMuoqLfEFrmz2HqPecsdURXRDqcXbyi/1BGHXU1zfNmMv2hC+iz+paU9x3I0D3+AIBpaWbqvecy7FfnMOvZm2iZO52+a+9In1U3zfG7yrpTPT/4KJmI3eI6iOqRK4Ci+/CpTvmj5wdvJROxR10HUT1yM7Ds04oFpL3tZ6/Ra3f69enGBUx/6EIG7XgMZdV9AKjddF9qN90XgJlPXkntVocw74OnWfjt+1QO8xiwxYE5eS95cK3nBxOTidgLroOo7gl7oNyB7TWmoqUaeNDzg42Sidhk12EKTVGcGfX8oGiPzFf0GwJAed8B9Fltcxomf0l53wE0180CoLluFmV9ByxnGYOpHDKGhT98ssT9894PqFl7Bxomf0FZdV+G7HkWc99+KCfvIw+u8/xgQ9chVPeEXVCOdZ1D5YwAt3p+4LkOorrH84PjKbJROdvbfnaWaWlm+kMX0nfN7eiz+hZLPd449WuMMVQOWpH6z19h6F4+zbN/omlW0U4NWAH8OzyzporTX4GivHhZdcpI4CHPD3q5DlJoCr4Y9fxgReyRooLP2la6cSHphvpFPy/89n2qho6hz7hNmf/xswDM//hZ+oxb+mRS89wZpJsaAGhZWEfDpE+XOKvasrCOBV+9Td+1d8A0N4AIiNifi1MVcFvY1VPlmYj8QkS+EJGvRKRL84J6frAWOs1SKRiInSe4mHrUKMDzg7WBS1zn6IqOtp+dYYxh5pNXUDl4NP032bvd5ywaayHdDCZt7yzubSjAaOAq1yFU13l+cAA6J3cp2AS40XWIQlPwU7t4fvAY8EvXObqjac5PTH/wAvtLOk3fNbeldosDaFkwlxmPJGieO52K/sMYsqdPee9+NEyZSN2EJxm868ks+PZ9Zj9/86Jl9dvgl/Qb/4tFv8969ib6rLopvVZaF9PcyLQHzqdl3kxq1t+V/hvunu+3mk2JZCJ2tusQpUREyoEvgZ2BScDbwEHGmE+X99pw8KnXgY1zGlIVknOSidhfXYdQneP5QW9sm17G8LOFp6PtZ/2XrzHrmRtoWZCirLqGqmErM/yA82meN5OZT13J8P3OY+GkT5h6x1lUDvXsgVpg4DaH0Xus/Zqq//J1Gqd9w4CtDgFg9nM3s+Db96gc5jF09z+4eLvZtm8yEXvAdQjVOZ4fjAQ+x87xrErDQclE7G7XIQpFQRejnh8cjD0rqkpHC7BFMhF7y3WQUiEimwNxY8wu4e9nAxhj/ra813p+cCZwUW4TqgLTAKyfTMQ+cx1ELZ/nB9ehXehLzQxgnWQi9pPrIGr5PD+4Cyjai5VVt0wD1kgmYrNcBykEBdvdyvODIdgBUVRpKcd2110/mYgtdB2mRKwA/JDx+yQ6MRCR5werA+flKpQqWNXALZ4fbJlMxNKuw6iOeX6wC1qIlqIhwE1AUXeTKgWeH+xADgvR9MI6Zj55JY0zvgdgyG6nIJXVzHz6GkzjQipqhzFk9z8sGuBrea+tXmENZr/wLxZ88y5Vw1ZmyC9PB6Duk+dJ18+l/8ZFdVm6S8OAS4EjHOcoCIV8HeaV2C9UVXp+BlzgOoTqWDjq3y2AXohfmjYDTnYdQnXM84NK9IBuKful5we/cR1CdSwcI+OaXK5j1rM30muVDVnhmOsZddRVVA4ezcwnr2Lgtkcw6uhr6LPa5sx9s/0e3e29Nt0wn8afvmbUUVcj5ZU0Tk+Sbmpg/kfP0G8DHXupiw73/GAn1yEKQUEWo54f/BI4yHUO5dTvPT/YxHWIEvEjduCLViuG9y3LCcDSQ1SqUvJXzw9WcR1CdehkdIqIUneR5wcDXIdQHTode/A9J9IN81n4wyfUrPtzAKS8krJeNTTN+pHqcIqkXt761H/5WqdfC4JJN2OMId3UgJSVM/etB+m3we5IecF2tixk13t+UO06hGsFV4x6ftAXHZlT2c/m312HKBFvA6uKyMoiUoXtMtThfJKeHwymSKdaUlnVB/iH6xBqaZ4fDAfOdZ1DOTcIO0e7KjCeH4wBzsnlOprnTKW8T39mPnE5k/91MjOfvJJ040KqhqzEgolvAFD/+Ss0z5vR6deWVfeh99iNmHLryZTXDESq+9I45Uv6rLZ5Lt9KlI0FTnUdwrWCK0axR3OLZlJulVPbeH6g/T5yzBjTDJwIPA18BtxrjPlkGS+JAwNyn0wVgb09P9jMdQi1lAvRkTmVdZLnByu5DqGWcgX2gF7OmHQLjT99Tb/1d2PUkVcildXMfeM+Bu92CvPef4Ipt55CunEBUrb0Gc2OXgtQu+m+jDryKgbt8BtSL99O7VaHMO+Dp5n+cII5r+kAsd3wJ88PRrgO4VJBFaPhZM2RGFddZc3fwusTVQ4ZY54wxqxmjBlrjOlw2g7PD1ZDB0RRS9LRlAuI5wcbAUe6zqEKRi9Ap2IqIOFB9pyP9FPRbwjl/YZQPcr21u+z+pY0Tv2aysGjGX7A+Yw84gr6rrktFQOXroM6em2mxqlfY4yhctCK1H/+CkP38mme/RNNs5Z3lY9qox+w3NkLoqzQdvJPw06srlSrdYBDXYdQi5xPAY/CrZzYJrzOXxWGKwFxHUIVlEM8P9jAdQgFnh/0wrbRnCuvGUhF/yE0zZwEwMLvPqByyEq0zJ8DgDFpUq/dTb/xu3b6tZnmvHw7A7Y+FNLNYMKB1UUwzQ25e1PRdbjnBxu6DuFKwRSj4XVov3edQxWkv+gF3u55frA+sJ/rHKogaQ+GAhBO5aIXb6m2BB2DoVAcCeRt4LdBOx3LjMf/weRbTqRx2rf033x/5n/2Ij/e+Fsm33Qs5TWD6bvOzgA0z5vJ1Pv+vMzXtqr/8nWqRoyjot9gynrVUDVsFSbffAKmpZGqYTquXTcI4LsO4YoYY1xnAMDzg4vRLrqqY6cnE7FLXYcoZZ4fPAD8ynUOVbB+nUzEbncdopR5fvA/YEfXOVTB2iWZiP3XdYhSFR6w+wIY5zqLKkgtwCrJROx710HyrSCOZIcX7p7gOocqaGfp2VF3wik89nKdQxU07dnikOcH49FCVC3bma4DlLhfoYWo6lg5JVoLFUQxir1WNKejiqmiNwyde9alUymc7wtVmDbw/GAb1yFK2BmuA6iCt6PnB+u6DlHCtPefWp5jPD8ouXrI+c5leDH3Ua5zqKJwiusApSgc5VpH51SdcarrAKXI84PRwAGuc6iicJrrAKUoPFC3iescquANBH7tOkS+OS9GgQOBwa5DqKIwXs+8OPFboMZ1CFUU9vT8YGXXIUrQqego16pzDgwHjFT5VZLdL1W3nOL5QUmNiF4IxejxrgOoovI71wFKiecH5cBJrnOoolEGnOw6RCnx/KA/cIzrHKpoVKM9XfLK84NhwN6uc6iisQaws+sQ+eS0GPX8YB1gY5cZVNHZx/ODQa5DlJAdgdGuQ6iicoQONpZX+2MnTVeqs35bamdeHDsKqHQdQhWVkroszfWZUT06p7qqGjjcdYgS0uNBo+a+/TCT/3k8k28+numPXoxpblz02Kz/3cD3l+7b7utaFszlp7vO5vtL92XWM9ctut80NzH13nOZfPPxzHsvWHT/zKeuouGnr3oaV/XcACDmOkQJOcR1AFV0VgW2cx2iFIRF/29d51BFZ1fPD1ZzHSJfnBWjnh9UAoe6Wr8qajqqbh6Eg4v1aF7R5nkzmPvuY4w4/DJGHX0tpNPM/+wlABqmTCS9sK7D10p5FQO2PpSB2y85vtmCb9+jesU1GXnU1dR98hwAjdO+waTTVI/QUfMLhH6354HnBysC27rOoYrSPq4DlIhtAb2OXnWVUEKDu7o8M7o9MNTh+lXx2sjzg5GuQ5SA3YD+PV5KugXT3IhJt2CaGyivGYRJtzD7hVsYsF3HnSPKqnrRa8W1kIqqJe6XsnJMUwO0tICx9815+XYGbK31TwHZzfMD7Tqaewdgd1qU6qo9XAcoEbu5DqCK1q6uA+SLy2JUu3Gp7hJgd9chSkCPz0BX9BtC/0325sfrjmTS1b9GqvvQe+UNmPfe4/QZtykVNV2//LfXyuvTnJrGlP+cTv+Ndqd+4ptUDR9LRT8dILKAVKNtNB/07JbqrtGeH2zoOkQJ2MV1AFW01i2VEy8ui9GSqfhVTuhR3Rzy/KAG+GVPl9OysI76iW+ywrE3s+IJ/8Y0NVD38bPUf/4q/TbsXq0iZeUM3eMPjDrySvqsvhVz33mE/hvvzaxnb2L6QxdSP/HNnsZW2dH+xcAqK8KdlM1c51BFbU/XAaIsbKPrus6hitovXAfIByfFqOcH47AX0CvVXTt6ftDHdYgI2wbo1dOFLExOoKJ2OOV9apHyCvqstjlzXrmTpjmT+fGGY5h03VGYpgZ+vKF7M1PMez+gZu0daJj8BWXVfRmy51nMffuhnsZW2fFzzw907svc2Qvtoqt6Zi/XASJOz4qqniqJz5CrM6N6VlT1VC/g565DRNgO2VhIRf+hNE7+gnTTQowxLPzuA/pvvBejT7ydFY+7hRWPuwWprGaF393U5WW3LKxjwVdv03ftHTDNDSACIvZnVQj6olN35ZJ+/6meWsfzg1Vch4iwkigkVE7t7PmB65lPcs7VG9QLulU2aFfd3MlKMVo9anX6rL4lU249lSm3nADG0G+9jnud1E98kzkv377o90nXHcXs5/5J3UfPMumaw2mc8f2ix1Kv3kXtFvsjUkbvlTegYdKnTLn5RPqulZXoKju2dx0gwrZwHUBFgnbVzYGwgNjZdQ5V9AYBm7gOkWtijMnrCj0/6A3MIgtdAFXJ+z6ZiI1xHSJqPD8YBEzH/TzEqvj9L5mI6Q5Zlnl+sCrwpescKhIeTCZiOhBWlnl+sDHwluscKhLOSyZicdchcsnFzuYWaCGqsmMlzw+GuA4RQdujhajKji08P6ha/tNUF23pOoCKjI1cB4iokhh4RuVF5Lt7u9jhXN/BOlV06YY0+7ZzHUBFRh9KoIuRA9pFV2XLSp4f6Jzv2Rf5AkLlzcZhj7XIclGMrudgnSq6dJ607NNBZ1Q26fQj2adnRlU26UHdLAp7g2zqOoeKjHIivh11UYzqnEsqm7QYzaJw0IV1XOdQkbKm6wBR4vnBQGAN1zlUpGgxml0eoNNaqWwa5zpALuW1GA2PFulGVGWTFqPZtSq2a6VS2bKW6wARsxE6v6jKLu0Nk11jXQdQkRPpz1S+z4yuAVTmeZ0q2nQQo+zSs1gq2/QzlV2R3ilRTuiZ0ezSNqqyLdKfqXwXo3q9qMoF/Vxlj/ZcUNlW4/nBSq5DRIjnOoCKnJFh92+VHZEuHJQTkf5M5bsY1WvRVC6s6DpAhKzuOoCKJO2qmz2e6wAqklZwHSBCIl04KCdWDsf0iKR8vzH9slO5MNJ1gAjRM1gqFyI9+EKeea4DqEga5TpAhGgxqrKtmgifeMl3MapzWalc0GI0e4a5DqAiST9X2eO5DqAiSYvRLPD8QICVXedQkRTZgxxajKoo0I1o9gx3HUBFkn73Z4HnB73RNqpyQ7ej2TES6O06hIqkyPYw0mJURYGeGc0Czw8qgEGuc6hI0u/+7BjjOoCKLC1GsyOyZ6+Uc5H9bOW7GNUpOFQuaDGaHUPR+QtVbmg33ezQg0UqV7QYzQ4dd0HlSmQ/W3krRj0/qAWq8rU+VVK0GM0O7f6nckXPjGaHbkNVruh2NDsqXQdQkdXLdYBcyeeZUT0rqnKld5SHvM6jGtcBVGQNdh0gIqpdB1CR1cd1AKXUMkX2YGQ+d+B1R1flUoXrABGgBb3KFT1bkB2R3RlRzmkbzQ7jOoCKrMh+/+dz51OvRVO5pBvSntNiVOVKuesAERHZnRHlnB7QVaqwRfb7X798usWYSlqaK2hpqqC5uYKWlgpaWippaaqU5nT4c3MVTS0VtKQraW6plJaWKppMpf09XUlzS5U0marw9yqaqKTZVEqzqaI5XUEaPcDWeT+aIWUQcx2j2OkBoy7Yquyjj7YrmzDbdY5ikEYatX1mhXbT7UB/6uYOkbmzh8vsuhHMqh8lMxsHSl2Lbkc7p4mKmdpGVb6dXnHvy71p0EbaCQup+jaqbTRvxegqMrmlLwsnthZilWKLtCqaTXhfulKa09X2Z7OoQJNmU0mLqVpUsLVQKc2Ez5FKWhb9XCEtUkGLVNBCBS1lFbRIOWmpoEXKJV1Wbn8vKyddXka6rBxTVka6rAxTLov+NeVlmAqBcuzv4c9UhLdKEQR7Jk7PxhWONFztOkOx0zOjXfC9GTbw6PInVxGhr+ssRWCe6wAREdkj44sZM4C61FBJzRkus+tGyKz6kcxsGimzmofLbIZIqmwg86r6SX3v3jT2raK5VjADROgP9Hedvoh95TqAKi3j5asvT6p4eGvXOZR7eStGn6s+Iw2smq/1qZLT7DpABOiZ0S743gxf8d8tO790eMUz27jOUgSaXAeIiKLq7iyk0wOpmzNU5qTCwnLBKJnZOJJZLcNkthkqqYoBUlfZjwV9etHQt5KW1sJyADDAcfxSo9vQ7NCzfJ10SsUDk4HVXOcoIpHdjuazm+7CPK5LlR7dkPZcZL/ociXefPhWvyp/5ZN+smAt11kKXKPrABFR52rFZaRbBjF39lBJpUbIrPkjZeaCkTKrcQSz0sNlNoNlbvkAqauyhWVj3wqaBwjUijAInR+1GOj3v8ojY7Yu+0gL0a6JbBvNZzG6II/rUqWlkXiqxXWICJjlOkCxMZSVHdL4x6pHqv6vSUS77S9DZDeieZbKxkIqaG4axNw5w8IzlqNk1sIRMrNppMxKD2MOgyVVMUDmV9WwoE81jTUVtAwQ6C/CEHSatqiqdx1AlY4dyt7/qELS67rOUWQiux3NZzE6P4/rUqXlJ9cBImKG6wDF6EMzdtX/pjd8cZfyd7d1naWAOTujFzFLFaOVNDcOITVrmMyZN1xm1Y2UWQ0jZWbjCJllbGE5t6JW5lfbwrLJFpb2+sqh4U0p0O1otqRdBygGJ1U8NMd1hiKUlYORhSh/xWg8lSJeW49OrKyyb4rrABEx03WAYnVS08mbfVR29LfV0ryy6ywF6nvXAaLgssprZ2xZ9vE7fVjYt5qmfmFhWQOMCG9KdZduR7NDt6PLUUFz03ry9TqucxSh71wHyJV8j575Q57Xp0qDbkSzIJmINaKjnnZLI5XVxzWdOtcYHbyiA9+6DhAFe5e/MmOYzNmoRhauUSktK4aFqFLZoNvR7ND93OXYu/yV98uEga5zFCEtRrNEG6nKBd2IZo921e2m59IbrPeBGfuK6xwFKuk6QCTEU7OBBtcxVCTpdjQ7IlswZMux5Y/pGB/dE9keRvkuRiP7h1ROTXYdIEKmug5QzH7d6K/bYkSvvVpa0nWACNE2qnJBi9EsSCZi84jwtX091ZcFdavIlPVc5yhSkT3QocWoigLdiGbP164DFLN59K09p/moyG4weiDpOkCEfOM6gIok3Y5mj+7rduCw8v9+IKJjx3RTZD9X2k1XRYGeGc2eia4DFLu7Wnbc9Pv0sDdc5ygwes1o9nzkOoCKpEmuA0SIHtTtwBEVT1e7zlCkphNPRXb6JT0zqqJAd86y5wvXAaJg/8ZzVzZGu2qF6omnprkOESH6faeybSrxlHb/zp5PXQcoREOYM30Yc8a7zlGkIt3jKt/F6Md5Xp+KvmnEU3pEN3s+cR0gCn5i0PBrW/b80HWOAhHpjagDuh1V2fa+6wAR85nrAIXo2IrHPhPJ45SS0RLpk3n5LUbjqZ+I+B9U5d27rgNEzOdAk+sQUfD35v23mm1qJrjOUQD0Gsfs+hh0CiGVVe+5DhAxema0HfuXv6jTuXRfpA9C5vvMKIBeS6WySYvRLEomYk3YglT1mMgBjf9XawwLXSdx7HXXASIlnpqHnm1W2aXFaHZ9jh4wWoInU37oL/XruM5RxF50HSCXXBSjbzpYp4ouLUaz71XXAaLiSzN65YfSW5X6AbjnXQeIoEgfJVd5p8VoFiUTsXq0jS7hlIoHdVCn7msi4gd1tRhVxU43otkX6SNw+XZm02+3XGCqvnSdw5E64C3XISJIt6MqW2YTT+lo19n3tOsAhWS3srdGu85QxN4mnlrgOkQuuShG30WvSVPZMYN4Sq9Bzj4tRrOomYrKIxrPbDaGdFdfe9QjCxj293msfW3dovviLyxkhUvnMf76OsZfX8cTE9v/Or3ijQbWvraOta6t4/I3Ghbdf9YzC1n3ujoOe2jxtu32DxuXeE4WvUI81ZyLBZe4/7oOoCJDexflxlOuAxSK9WXiF9XSNNZ1jiL2kusAuZb/YjSeWgjoKJMqG55zHSCKkonYFHS+0ax606y55uvpNV/u6uuOGF/JU4cuPT/47zerYsKxNUw4tobdVq1c6vGPp7Vw03tNvHVMXz44ti+Pf9nMV7PSpBYa3vuphQ+Pq6GqHD6a2sKCJsO/JjRxwsZV3Xtzy6ZtNDfeAWa5DqEiQc/g5cbLwHzXIQrBqRUP/OQ6Q5HTYjRHdAdFZcOjrgNE2AuuA0TNb5rO2KjJlHdpGqJtxlQwqLd0eV2fTU+z6Qrl9KkUKsqEbcdU8OBnTZQJNLWAMYb6JkNlOfzjtUZO2qSKyvKur6cT9HrRXIin0sD/XMdQkfC46wBRlEzEGtHvP4R0esuyj1dznaOItQCvuA6Ra66K0YccrVdFRzPwhOsQEfaC6wBRU0+vvqc1HZeVI8RXv9XIutfVcdQjC5i9YOlBG9ceVsbL37cwsz5NfZPhia+a+SGVpl+1sNuqFax/w3xG1pRRWy28+WMLe/1s6bOrWTAHvaY7l7Srruqpr4indPT03HnSdQDXdix776MKSY90naOITQhHUI80V8XoG8BkR+tW0fAq8dRs1yEi7An02u6seyy9xUZfpld4rSfLOG6jKr4+uYYJx/ZlZI1w+n+XnjlmjaHlnLVlFT+/vZ5f3F7P+OFllJfZM59nblnNhGNruGSXXvzf8w38Zftq/vleI/vfV88FL2X1utGXwjN4Kje0e6XqqcB1gIgr+WL0pIqH57rOUORKYgwPN8VoPGWAh52sW0XFY64DRFkyEZsDPOM6RxQd2HjO6mkjM7v7+uE1trAsE+GYDat468eWdp939AZVvPvbGl46si8DewurDV7y6/79KS0YA6sPLuO+T5u4d78+fD07zcSZ7S+vG/RyjFyKpyYBn7mOoYqadtHNoWQi9i0lPP5CJc2N68g3Ordoz9zvOkA+uDozCvCAw3Wr4qfXi+beva4DRNEsagdf3HzAF919/ZR5i082PvRZE2sPa/9rfNp8+7zvU2ke/KyZg9dZsivu/z3fwPk7VNOUhpZwkWVAfXbOh7dQIhtRxx50HUAVrXmUwMAoBaBkR9X9VflLE8qEAa5zFLEviKciPb9oqwqH634RmAkMdphBFacviKdK9mhjHj0MNAI5GWa1lF3fsscWR1Y89c5wmbPRsp530AP1vJBsYUa9YcVL53HedtW88F0LE35qQQBvQBk3/LIXAJPnpfnNowt54hA7+u4+9y5gZr0dpOia3XoxoNfiAYoe/ryJjUaVMaqfLWTHjyhnnevqWHd4GeuNKM/GW3ySeOrHbCxILdOtwJ9ch1BF6UniqUbXIUrAU8BJrkO48Lvyx7PWzaZE3eY6QL6IMUsPfpE38dpbgCPdBVBF6hziqb+6DlEKPD94DPil6xxRtJJMnfRi1e8HitDXdZYc2JN4Snsv5EO89iVga9cxVNH5OfGUXoqRY54fVAHfA8NdZ8mnviyY93H10RUi9HadpUilgTHh5RiR57KbLsB/HK9fFZ8m4J+uQ5SQu1wHiKrvzfAVb2/Z6R3XOXJgMjowSj7d4jqAKjpfo1MD5UU4xcv1rnPk2xHlT3+ghWiPPFsqhSi4LkbjqeeBj5xmUMXmQeKpqa5DlJAHgGmuQ0TVuc1HbD3P9P7EdY4s+xfxlHbPyp/7gDrXIVRRuSkcSFLlx3XYS15KxuEVT2sh2jO3ug6QT67PjAJc5TqAKirXuQ5QSpKJWANwo+scUWUoKzu08exKYyIzjY4BbnYdoqTEU/PRwcZU5zWiZ9PzKpmITQXudp0jX4Yxe/pQUuNd5yhic4GHXIfIp0IoRm8HZrkOoYrCJ8RTJTHnUoG5Fp1zNGc+MONW+196wx7NPVpAniWe+tZ1iBKkxYXqrIeIp6a7DlGCLncdIF+Oq3j0MxGyMhJeibqXeGqB6xD55L4YtX/wm1zHUEWh5K67KATJRGwKOk1HTp3QdPJmjaYiCkWcfpe7EE+9CrzhOoYqCje4DlCKkonY+5TIVDr7lr+ks2T0TMnt67ovRq1rsPPSKdWROuDfrkOUsCtcB4iyRiqrj2s6Za4xFPN1XNOw0wEpN/7iOoAqeBPCsTqUG5Hfjq4ik7/rJwvWcp2jiD1JPPWu6xD5VhjFaDz1AyXWP1p12WXEU3NdhyhVyUTsTeBV1zmi7Nn0hut9aFZ5xXWOHkjovIUOxVNPAm+7jqEK2jmuA5S4h4Eo9IDp0CkVDyRzteyFzYZNbqpjvevrWOvaOv78/EIAvp2dZtN/1jHuynkccH89jS0dH9P9PpWm5sK5/OO1BgCmz0+z1S3zWfvaOh7+fPHVSHveXc/keelcvZVlKcmDioVRjFp/wc6ro1RbM4F/uA6hONd1gKg7tPHsdVuMFONo0T+ig4sVgvNcB1AF63XiKZ1yyaFkIpYGrnadI5d+Ufb26Fwtu7ocnju8Lx8cW8OE3/Xlqa+beWNSM2f9byG/36yar07ux8Bews3vdTzExWlPL2TXVSsW/X7Xx00cu1Elbx3Tl8vfsMdSH/uiifVHlDGqX95LpGeIp0rycovCKUbjqY8osaGMVaf9Tc+KupdMxJ4DtItXDs2jb+2fm48oxiPn5xNPLXQdouTZYqPkunipTtGzooXhZiI6aOeG8sXn1dK8Sq6WLyLUVAkATWloagEBnvu2hX3XtAXm4etV8vAX7RejD3/exMoDylhr6OLSp7JMqG+ChmYoL4PmtOHyNxs5c8vqXL2NZfmzi5UWgsIpRq3/A+pdh1AF5QfsNcWqMJztOkDU3d6y82bfp4cW09HRr1nGdC4icouITBORj/OYqZSVZDcvtUzPEU895zqEgmQiliKi29FTKx7Mea+elrRh/PV1DPv7PHZepYKxg8oY0AsqymyRumL/Mn6cu3Q33bpGw0WvNvLn7ZYsMg9ep5JHvmhm5//M549bVXPt2438et1K+lRKrt9KWw8RT72e75UWisIqRuOpycAlrmOognKennEpHOG1o/e5zhF1BzSe6xlDynWOTjqLeKp5GY/fCvwiT1lUPPUoULI7Napdela0sNwEvOk6RDYJ6fQWZZ+sluv1lJcJE46tYdJp/Xhrcgufz+jc1X3xFxr4/WZVi86stqrtJQQH9+Gd39awwchyHvuymX3XrOSYRxew7731vP7DsjZtWdNMRA9QdFZhFaPWxUAxXjOlsu8LtOt2ITobO3G6ypEpDB5xXcseH7rO0QkvEk89sKwnGGNeIqLd0grYsdgdHKUeLeUzLoUomYgZ4DgiNIvEzmXvflAu6ZH5Wt+AXsL2XgWv/9DCnIW2ey3ApLlpVui/9FnNN39s4cxnFuJdPo/L32jkwpcbuPqtJXdjzn+pgT9tXc1dHzWx1Url3LZXb+IvNuTj7dxMPPVFPlZUqAqvGI2n6ijhftNqCacRT0XmyzoqkonY18BFrnNE3cXNB2w129R84DrHMqSBU12HUO2Ipz6kBKaRUMtVB5zkOoRaWjjv6LWuc2TLSRUPz8/1OqbPTzNnoS06FzQZnvmmmTWGlrH9yuXc/6k99nbbB03suXrlUq99+ci+JE/tR/LUfpy6WRV/3LqaEzepWvT4xJktTJqbZjuvgvomQ5mACCzoeCykbJmF1jwFWIxa/wTedx1COfVv4qknXIdQHboA+Mx1iGgTObDxnP7GkJdDs91wM/HUBNchVIf+jL3mXpWus4mnvncdQnXoHOAn1yF6qoqmhrXl23VyvZ4pdYbtb5vPutfVsfFN89l5lQp+uVolF+3Ui0tfb2DclfOYucBw9Pq2GH30iybOfb5zV3n96bkG/rqDvZ70oHUque6dJja+aT6nbFq1nFf22InEUyXfG1SMKdA51uO1a2FHBXQypFU2taQNG900nxX6lfH4wX147ttmzvjvQhpbYMNR5dy8R69FF1+3ev7bZn7/9OJG9PmMNHfv25u9flbJIQ/W89HUNL9crYILd+wFwAUvNbD2sDL2+tnSR4SK0BRgLeKp2a6DqI55frAF8DKFe1ArEi6vvPrFvcpf29Z1jjZ+ANbrbBsVEQ943Bizdk5TqSXFa/fEzm0YGW23p89+08wfnllI2kBNlXDrXr0ZN2jJr6SmFsNvHlvIe1NaaE7DYetWcvbW1Uyfn2bvexYwZ6Hhgh2qF20/97y7nutivVxM7ZBNrwDbEE8V6E6eAvD84GDgDtc5euLg8v+9eWHlLZu6zlGE7iOe2t91iEJQuN+08dQn2NF1i94VbzayxhD7p04bw+EPL+DufXvz8fE1jKkVbpuwdD+A7VeuYMKxNUw4tobnDu9Ln0r4+dgKPpzaQu8K4cPjanh7cguphYYp89K8+WNLVApRgGO1EC18yUTsNSLUzahQndF07BYLTNVE1zkypIFDtY0WgXjqEeBR1zGyKXN7CnBcsJA7ftWbCcfWcPA6lVzw0tIdCe77tJmGZsNHx9Xw7m/7csO7jSTnpAttjsFsagB+o4Vo4UsmYncCRT3S8W/LA/2cdd1U4HjXIQpFoX/bXoI981K0Js1NE0xs5jcb2FP9M+sNVeWw2uByAHZepYIHPlv2OBP3f9rErqtW0KdSqCyDBc2GtDE0tdh5kc59voHztiv6E8it7gxHg1TF4WxAu4HlUDMVlUc2/aHRGDo3bGDu/ZV46qXOPllE7sKO7rq6iEwSkaNzF02140QgEgcO2m5PwV7XNbfB7gunFhpG9Vt68BIB5jfZQU4WNBmqyoX+1VJocwxm0/mlPiBKkTmBIh0UsIb6uWNk6njXOYrQ74inZrgOUSgKuxiNp9LA4diL8IvSqU8t5OKdetHaC3dIH6E5De9MtuPy3P9pMz/MXfY+5t0fN3HQ2vas5xpDyxnap4wNbpjP7qtV8NWsNGkDG4wsz+n7yJOpwMmuQ6jOSyZiddg2qgNN5dAb6bXWeiO95iuucwCvAed15QXGmIOMMSONMZXGmBWNMR3OSapyIJ76ATgCKPqzF223pwD/3L0Xu925gBUvncd/PmzC32rpQnLfNSvoWwkjL6ljpcvrOGOLKgb1lkKaYzCb3kcHmCsqyUTsc4p0fuAjy5/6SIRernMUmX+HvVZUqLCLUYB46lvgNNcxuuPxL5sY1lfYcNTiQlFEuHuf3vz+6YVsclMd/aqhfBnbvSnz0nw0Lc0uYysW3Xf5L3ox4dgaTt+imv97voHzd6jmry81sP999dz0blEeXAO7o/Qb4qmZroOorkkmYi8QkS71hezopjM2bDZlkxxGmAMcrCNcFyHb2+QfrmP0RHvbU4DL3mjkiYN7M+m0fhw5vpLTnl56wJK3fmyhvAwmn1bDt6fUcMnrjXwzO10ocwxm02xg3+XM+6sK04VA4DpEVx1W8YwWol0zCT3pspTCL0YB4qmbgAddx+iqV79v4dEvmvEun8eB9y/guW+bOfTBBWw+uoKXj+zLW8fUsM2YClYb3PF/w72fNLH3zyqobKdifeTzJjYcWUZdo+Hr2Wnu3a8P93/WRH1TUR4AjxNPPe46hOq2BPCY6xBRVk+vvqc3Hedy5MXfEU9953D9qmf+iB3Upii1tz2N3VnPB1Nb2HRFe7D2gLUree2HpY+V3PlRE78Ya7ejw/qWseXo8kW9k1o5nGMwW9LAQcRT37gOorounHv0UKBo/v+GM2vaEFLjXecoIgY4ingq5TpIoSmOYtQ6jCKb7uVvO/Vi0ml2XqO79+3NDitXcPuvejNtvu2W29BsuOjVBo7dqOOho+/6uHlRF91MTS2Lr21Z0GSviQFoSUNj8Z23uB8433UI1X3hhvRw4FvXWaLskfSWG01Mr/Cag1XfQjx1r4P1qmyxZ8sOBKa7jtId7W1PHzmwN6mF8OVMu9F75ms772BbK9WW8VzSPmd+o+GNSS38LGMQJIdzDGbT/xFPPe06hOq+ZCI2B9gHWOA4SqccX/HoZyJE4hqxPDmTeOoZ1yEKUfEUo/HUfGB37LQfRe3vrzayxjV1rHu9ve5zh5XtUd13Jrfwm0cXfwcl56T5YW6abb2l2/o1bzdy+Hr22pZ1h5dR32xY57o6NhxZzoBeRXW9y4fAETrqX/FLJmKzgf2gYOfFjIQDGs9ZPW0kn93ZP0O7FUVDPPUjcDAUzGBYPVJRJty0ey/2uXcB611fx38+bOLvO9teg5lzDJ6wSRV1jYa1rrXzEx45vpJ1hy/erjqcYzBbHgL+5jqE6rlkIjYB+K3rHJ2xT/lLQ11nKCLXEk8V9aUSuVS484x2JF67EfAS0Nt1FNVjM4GNiKeSroOo7PH84CDsvGlFdVSkmBxX/sirZ1Xes2UeVvU9sFU4CI6Kinjt74FLXcdQWfE5sAnx1DzXQVT2eH7wFwp4LIax8uN3z1b/YYzrHEXicWAvHW+hY8VzZrRVPPUOtjtgkVXRqo1mYD8tRKMnmYjdBfzedY4ou65lzy2nmdp3c7yaacDOWohGUDx1GfY6b1XcZmB3crUQjZhkInYucJfrHB35fcUDSdcZisS7wIFaiC5b8RWjAPHUfcC5rmOobjPAMcRTz7sOonIjmYhdge7s5tR+jX8ebgzzc7T4FLAL8dSXOVq+ci2eOhu4yXUM1W3zgF11PtFIOxJ41XWI9uxc9s5KrjMUge+AX4aXGaplKM5iFCCeugC42nUM1S0nEE/d6jqEyq1kInY2cIvrHFH1nRmx4h0tO76Tg0XXAzHiqQk5WLYqLMdiB5BTxWUhsEfYU0xFVDIRawD2wo6tUTA2ls8/q5bmlV3nKHBzgN2Ip1yOgF80ircYBYinTgKudR1DdckfiKeucx1C5c1vAZ3cOUfObT5yqzrT69MsLrIJ2Id4qiCPxqssi6fSwCHA/1xHUZ3WAPyKeOoF10FU7iUTsRnAtoCLUdTbdWrFA9NcZyhw9cDexFPZ3DZHWnEXo9aJgBY3xeEMHU2stCQTsRbsCLsPuM4SRWnKyg9p/GOFMWRjkvs0cCjx1FNZWJYqFvFUI7A3BdodUC2hEVuIPuk6iMqfcMqXnQHnU/cI6fRmZZ+u7jpHAZsJ7KAHi7qm+IvReMoQTx0PXOI6iuqQAU4mntL/oxKUTMSagAOA211niaIPzLjVnk2v/0oPF9OCvY5b5xItRfFUHfBzQIucwrUA22vhCddBVP4lE7F67PSGTr+jdyl754NyMSNcZihgraPPv+k6SLEp/mK0VTx1BvBn1zHUUpqxO7lXuQ6i3AnPkB4GXOk6SxSd0HTKZo2mItnNl8/BXtui1/eWsniqHtiTAh7Bs4RNBbYnnnrcdRDlTnhg9yDgRlcZTqx4uM7Vugvcx8AWxFOfuw5SjIpvntHlidf+DrgKqHQdRTET2Fe7K6hMnh+cDVzoOkfU7Fz2zoQbKy9dT6RL87t+hR3tT0fkVFa8VoCLgD+4jqIA+BQ7oFjSdRBVODw/SABn5XOdVTQ1fFF9+EIRavO53iLwMnZAsTmugxSr6JwZbRVP3QBsD+gIVm59ip2I+wXXQVRhSSZif8NeR6pHWLPomfRG4z8yK3elu+7zwKZaiKol2EtfzsSOtJuNa5FV9z2DPduSdB1EFZZkIuaT52J0//IX3tdCdCkPAz/XQrRnoleMAuFIkBsB2m/bjQDYnHjqG9dBVGFKJmL3A5sCOo9lFh3aePa6LUamduKp12M3oLNynUkVKXtgNwbMcB2lRN2I7T6fch1EFaZkInYx9vKXvBzYPaY8yMdqism12N5/C10HKXbR66abKV5bDVwDHO06Sgn5O+CHUwYotUyeH9QC/wb2cJ0lKn5d/t83zq+8dbMOHm4BTiWe0jmaVefEa0dhBx/b3nWUErEQOFPHWVCd5fnBWGwb7eh7v8f6MT/1YfUx1SL0ytU6ishc4HfEU3e7DhIV0Twz2iqeaiCe+g1wHHZIdJU7M4D9iKfO1EJUdVYyEUthJ/X+P2yhpHroPy0/3+yH9JD2eoXMwJ5p0UJUdV48NRnYCTgHbaO59gGwkRaiqiuSidjXwFZAnBx1rT+q/KmPtBAF4B1gAy1EsyvaZ0YzxWvXAW4GNnYdJYIeAo4lntKJkFW3eX6wKfAvYA3XWYrdSGb+9Fr1Sb0zru95ADhe26jqkXjtlsCdwEquo0RMGrgU+FM476tS3eL5wWbYs6Rjs7ncd6qPfX+IzF0/m8ssMmnsFJLnaBvNvtIpRgHiteXA74G/AL0dp4mC2cCJxFN3ug6iosHzg2rgPOAMoNxxnKJ2VsVdLx9X8dia2DaqR3FVdsRrBwI3YAchUz33A3CYDvanssXzgxrgcrJ0idoIZk19vfrEoSIR703ZsYnAEcRTr7kOElWlVYy2iteOA/4JbOs6ShELsPOHTnEdREWP5webYM+Sruk6S7ESzF3vVB93yuDzfpjuOouKoHjtLth5g1dzHaVIGeBW4DQdiVPlgucHewE3AUN6spzzK2558dcV/yvF/eU0dpCis8J5mJciIoKd2uWvxpgnw/v2A442xvwib0mLXGkWo9A6l9pvgYuB/o7TFJPvgD/q2VCVa+FZ0jOww9f3cxynmHwNHJ9MxP7rOoiKuHhtFba30TlAjeM0xeQl4PfEU++5DqKizfODEcAFwOFARXeW8XH1UZ/WyMJSOzD8FLYI/XB5TxSRtYH7gPWxf+P3gV8YY77ObcToKN1itFW8dgjgAyeAXpy9DDOBvwLXEk81uA6jSofnB8OAP2MPHnVrY1oipmF3Om5IJmJ6TYvKn3jtCsA/gANdRylw32BHyn3AdRBVWjw/WBU7wNGBdGHw0tXkh2//W33WyrnKVYDewbbR57vyIhG5GJgP9A3/HQOsDVQCcWPMIyKyFrbHVxX2/2AfY8zEbIYvVlqMtrLD1/8fto99peM0hWQB9tqDi3S+M+WS5werAwns6LtqsbnYKZUuTyZieZlvTql2xWu3wV7zvZ3jJIVmLvZA0ZV6MFe55PnBWthxU37VmedfU3nFC7HyN7fLaajC8BXwJ+A+4qkuF0Yi0hd4Dztzx+PAJ8aY20VkAPAW9qxpAnjDGHOHiFQB5caYBdl6A8VMi9G24rUrY48eHUJpD6DSiJ3/8c/h0P5KFQTPD7YE/gjs5jqLYwuw8ygnkonYTNdhlFokXrsZcDawOyCO07j0E7aNXkc8pW1UFQzPDzbAHiDZdVnP+7L6sGSVNHt5CeXGNGxxfiPxVFNPFiQifwHqgP2xPS1bp9kZBOyCLUj/hN23flDPii6mxWhH4rU/A04GDqW0rlf7ETtS4o3EU1Ndh1GqI54frIO9pvRAbLeXUvEddgf35mQiNst1GKU6FK9dC3vN90GUVhf7j7FTtdypZ0JVIfP8YAtsUbp928c2lU8/vaf6gqheK/o+dnCn/xBPZaVHkYjEscXoQcDBxpgv2nnOWCAGnAT8zhjzXDbWXey0GF2eeG1f7Afrd8BGjtPk0kvA1cBDxFM5mTRZqVzw/GA4cCz2mtJRjuPk0vPAVcCjyUSsxXUYpTotXjsGu/N1MDDScZpc+i9wCfGUDh6miornBxsCR2Lb6ECAOysveHGL8k+jNIruPOAu7MmWd7O98IxidBB2YNSTjDFGRNY3xrwvIqsA34b3/QOYZIy5PNs5ipEWo10Rr90AW5QeTDRGDpwGPIgdlOgj12GU6gnPD8qwR3cPwV4PU+s2UVZ8gx2l7/ZkIvax6zBK9Yid63tHbI+jvYnGdnQCcC9wL/GUjp6pilo4iv1egjn8q+pD1ysXE4UDvG8BNwJ3E0/Nz9VKMorRa7BjrWyBHajoW2PML0XEB34NNGG78B9sjNHeTWgx2j32bOlO2FPtMYrrbMxE4GHgEeB14qm02zhKZV+4QY1hDxzFKK6RslsL0PuSiVjWj94qVRDitX2wg5Edit2eFtPAgR8D92AL0C9dh1EqJ+K1w7AHjfbDDkpWLOOoGOxBov8Bt3dmehbllhaj2RCvXZ/FhekmdGHY7DxoAt4FHgMeJp761HEepfLK84Ne2COUO4a3jSisjeoC4DXgOeCpZCKmcw+q0mIP8G4F7IDt3bABhdVGpwOvAq8AT+p2VJUcOw3irsCWwObYaUsKaV/3a2zx+SzwPPHUDMd5VBdoMZpttsFuhB01a3z47zjyM6JgGvgCeDu8vQV8oAMoKLWY5we1wLbYnd91wtsKeVp9GjsA0QfYAvRV4N1kIqZtVKlW8dpaYBtsYboJ8DNgcB4TfIUtPO0tnlpqIBKlSlq8th+2bW6BLU43I7zWNA/S2B5Eb2OLz/8RT32Xp3WrHNBiNB/itTXAetjCdBVgeMZtBHYj29lidRZ2xNsfgUkZ/34FvEs8NS+r2ZUqAZ4fDGRxYboGsCK2QB0FDKHzo/W2YNvozPA2CfgM+Dz898tkIrYwq+GVKgX2QO/q2MK09eZhrw2vxY5639nt6EJs2/wau1P7dcbtm1xeV6ZUJMVrBds+18ZuN1tvIzN+HtCFJS4Evs+4fUfGdlRPskSLFqOFIF5bAQzFFqXl2K4PZdijP43hrQGYTjylE+QqlWeeH/TDjpBXyZJttPVWhy0+U8lETL9Ulcq3eG0ZtiBtLU5rsAeHGrGXqywA5gJziacaXcVUqmTFa3tji9K+2DbZhJ2Ls6nNrZl4Sg/alhAtRpVSSimllFJK5V0hXXyslFJKKaWUUqpEaDFa4ESkRUQmZNy8HK4rKSJDcrV8pZRSygWxXhGRXTPu209EnnKZSymlSp120y1wIlJnjMnLxOAikgQ2MsbokNhKKaUiRUTWxs7huz5QAbwP/MIY87XTYEopVcL0zGgREpENReRFEXlXRJ4WkZHh/S+IyGUi8o6IfCYiG4vIgyIyUUQuyHj9w+FrPxGR33awjkNF5K3wbOwNIlJIc74ppZRSXWKM+Rg75/ZZwLnA7cCfwm3d+yKyJ4CIrJWx/ftQRFZ1GFsppSJNz4wWOBFpAT4Kf/0W2B94EdjTGDNdRA4AdjHGHCUiLwBvGmPOEpFTsBvcDbFTTXwNrGeMmSkig4wxs0SkN3aepm3D+5PYOVKHAhcDvzLGNInItcAbxph/5+2NK6WUUlkmIn2B97Cj7D4OfGKMuV1EBmDn5l4fSGC3eXeISBVQbozRkeyVUioHKlwHUMu1wBgzvvWXsJvR2sAzIgJ2mokpGc9/NPz3I+xGdkr4um+A0djpJ04Wkb3D540GVg3vb7Ujtoh9O1xHb2BaVt+VUkoplWfGmPkicg92Oqb9gd1F5Izw4V7ASsDr2DOmKwIPGmMmukmrlFLRp8Vo8RFskbl5B4+3TgSczvi59fcKEdkO2AnY3BhTH55N7dXOOm4zxpydrdBKKaVUgUiHNwH2McZ80ebxz0TkTSAGPCEivzPGPJfvkEopVQr0mtHi8wUwVEQ2BxCRShFZqwuvrwVmh4Xoz4DN2nnOs8C+IjIsXMcgERnT0+BKKaVUAXkaOEnCLkAisn747yrAN8aYK4FHgHXdRVRKqWjTYrTIGGMagX2Bi0TkA2ACsEUXFvEU9gzpZ4TXxbSzjk+Bc4D/isiHwDPAyB5GV0oppQrJ+UAl8KGIfBL+Drb77sciMgF7WYyOl6CUUjmiAxgppZRSSimllMo7PTOqlFJKKaWUUirvtBhVSimllFJKKZV3WowqpZRSSimllMo7LUaVUkoppZRSSuWdFqNKKaWUUkoppfJOi1GllFJKKaWUUnmnxahSSimllFJKqbzTYlQppZRSSimlVN5pMaqUUkoppZRSKu+0GFVKKaWUUkoplXdajCqllFJKKaWUyjstRpVSSimllFJK5Z0Wo0oppZRSSiml8k6LUaWUUkoppZRSeafFqFJKKaWUUkqpvNNiVCmllFJKKaVU3mkxqpRSSimllFIq77QYVUoppZRSSimVd1qMKqWUUkoppZTKOy1GlVJKKaWUUkrlnRajSimllFJKKaXyTotRpZRSSimllFJ5p8WoUkoppZRSSqm802JUKaWUUkoppVTeaTGqlFJKKaWUUirvtBhVSimllFJKKZV3WowqpZRSSimllMo7LUaVUkoppZRSSuWdFqNKKaWUUkoppfJOi9EeEpGkiOzkOkcuiUidiKziOocqHSLiiYgRkYplPKfTn8twWeNynSkqsvH3Ukotm4hsLSJfuM6hSkPm97qIXC8i/+c6U7YUU1vSfeqlRaoYba8wFJEjROSVHi7XiMj88AP0o4hcKiLlPUvbMyKyp4hMEJG5IjJDRJ4TkZVzsS5jTI0x5ptcLFu5k+P2Mi2zaBORyvA+081lviAiv8m8LxefSxHZTkTSYVufJyJfiMiR2VxHNzMZEbm2zf2viMgRjmKpCMjxd0CnDma4PqDbXlYR+aOIfBt+D0wSkXtysW5jzMvGmNVzsWzlRg7blIjIH0RkoogsEJHvReRvIlLdneUZY441xpzfk0wZ2fLSXpalbVvq6veK7lO7FaliNMfWM8bUADsCBwPHuAoSbjj/DZwO1AIrA9cALd1YVuTP8ignZgO7Zvy+a3hfMZgctvX+wFnATSKypuNM84Ffi4jnOEeX6PeLyqVwBz2r+zEicjjwa2Cn8HtgI+DZbi5LP/8qW64EfgscBvTDblN3BO51GSqb7aUHGXrUznSf2r2SKkZFxBeRr8MzHp+KyN4Zj40TkRdFJBUeFWn3yI4x5nPgZWDtjLvHi8iH4WvvEZFeGcs9RkS+EpFZIvKoiIzKeMyIyLHhka45InKNiEjG40eJyGciMltEnhaRMa3rA741xjxrrHnGmAeMMd+HryvLeK8zReReERkUPtba1fBoEfkeeE5EnhSRE9v8rT4QkV9l5Gzt2tFbRC4Rke/C9/uKiPQOH9tMRF4L38sHIrJdF/+LVAHpYXv5D3aj2eow7Jd95vKXOHIpInERub2dHH8FtgauDo+8Xh3en/m5vFVst6NnwrwvZrSXtsurFpF/hEeWp4av6932eWHbehhbRGcWo4eEr50hIn9qs9zLRWRyeLtcwqPWYs9uThKR08WeIZ4iGWdcO5FpDnAr8OcO3tMSfztp06VY7JnlC8L2WScij4nIYBG5Q+yR4Ldl6UJ3NxH5Jnyff5eMnf5lfDe1/r+cICITgYliXRa+77ki8pGIrI0qeD38Dmh9XlzsNujf4XI+EZGNwsf+A6wEPBZ+Ls8M7+9wWxJ+lv8qIq8C9cAq0s1tqYi8FD7lg3D9BwAbA08bY74GMMb8ZIy5MWNZtSJyc9iGfwzbVXn42BEi8mr4eZ8JnB/mWTvj9UPFntka1vq9kPHYaBF5UESmi912X72896CKS3falIisChwPHGKMed0Y02yM+QTYB/iFiOwQPu/W8LMfhMt/U0TGdpDjVhG5IPy5J9unbreX8PFjws91699jg/D+JXosdJD3LBH5CfhXZltq73sl/Juc1OZv8GH49x+P7lO7ZYyJzA1IYo/OZN53BPBK+PN+wChsEX4A9mzDyPCxu4A/hY/1ArbKWIYBxoU/rwn8BBydsc63wuUOAj4Djg0f2wGYAWwAVANXAS+1We7jwABsw5kO/CJ8bE/gK2ANoAI4B3gtfGwVYCFwGbA9UNPmPZ8CvAGsGK73BuCu8DEvXO+/gb5Ab2yh8GrG69fE7vxWt/P+rwFeAFYAyoEtwnWsAMwEdgv/hjuHvw91/bnQm5P2sjYwNfxsDwx/Xhtb47W7fiAO3N7mc1oR/v4C8Js2WTM/l7cC84Btws/jFa3vo53nXgY8im2v/YDHgL+Fj20HTAp/LgP2BpqA1TMy3RS2m/WABmCN8Pl/CdvdMGAo8BpwfsZym8PnVIbtpB4Y2NlMwAhgLrB6eP8rwBFt/3bL+Pt9BYzFHvn9FPgS2An7/fJv4F9t/l7Ph3lWCp/7m/CxDr+bMl77TPja3sAuwLvYz4KErxvp+vOvt7xtM+PY7dVu2G3G34A3OsrAcrYl4Wf5e2Ct8PNXSTe3pW2zhr8fCswC/oA9y1Pe5u/zEHab2hfb1t8Cfpfxt2sGTgrX1Ru4BfhrxutPAJ4Kf96Oxd835cAH2O+Cvpl/0+W9B70Vzi0XbQo4Fviug/W9yOJtxa1hW9kk/JzcAdzd3mc9fO4FGZ/D7m6fetJe9gN+xBa0AowDxrTNuoy8F2G3970z21J7/w/A/sCbGb+vF/6tqtB9auc35wGy+mbsh68u/E9vvdWTsVPa5vkTgD3Dn/8N3Ais2M7zDHYncDbwNXABUJaxzkMznnsxcH34883AxRmP1WB3bL2M5WZuwO8F/PDnJwkL3vD3svC9jAl/3yx8/vSwEd3a2oCwBfGOGa8dGa63IqPhrJLxeD/sF2Lrsv8K3NLm/Y8LMyzAdllu+zc6C/hPm/ueBg53/bnQm5P2Mg74J/A77Ib0pvA+02b92SxGMze6NdguNqPbZJLwsz4247mbY4+Kgt2gpcO/xazwPR/YJtOKGa99K+Pxr4HdMh7bBUhmLHdB6/sJ75uGbcedydS6w3oxcE/4c1eL0T9lPH4J8GTG77sDE9r8bX+R8fvxwLPhz8v7bjLADhmP74AtZjcj/N7UW2HcyPF3gFn82fxfxmNrAgvaZMj8HljmtiT8LP+lnfV1d1u6xE5veN8hwP/CdjkTOCu8fzj2AFTvjOceBDwf/nwE8H2bZe0EfJ3x+6vAYeHP27G4bW+O3Z5XZL6+M+9Bb4Vzy0Wbwh58eKOD198N3BT+fCvwz4zHdgM+z/i97TYzs7jr1vYp/L277eVp4JQO3tcS7bKdvI1Ar4zHF7WljP+HzO+VXth9+FXD3/8BXJvxuO5TO7xFsZvuXsaYAa037E4UACJymNgLlOeIyBzsmZoh4cNnYhvdW2K7ER3VZrkbGGMGGmPGGmPOMcakMx77KePneuyOMNijX9+1PmCMqcM21BU68doxwBUZWWeF+VYIl/WGMWZ/Y8xQbBfGbbBH1Fpf+1DGaz/D7pgPz1jXDxm55gEBcGB410HYI2ptDcE26K/beWwMsF/rOsP1boVttKpw5aq9gN2wHkY7XXRzJPMzXYdtM6PaPGco0Ad4N+N9PRXe32py+PcYZIwZb4y5u80yOtXew58z1z/TGNPczms7k6nVRcAuIrJeO48tz9SMnxe083vNkk9f/PdkyfeyzO+mtq81xjwHXI09AjxNRG4Ukf7dyK9yI5ffAa3atple0vG1VZ3ZlvzQzuu6tS1tjzHmDmPMTtgzrcdiu9vuEi6rEpiSsbwbsGd8Osr2PNBHRDYV2xV+PPZsUVujsWe/mtt5rMvvQTmV7TY1g473pUaGj7fqqB0sT7e3Tz1oL6Npf3+yM6YbYxZ29snhc+8BDhV7yclB2MuJWh/XfWqHoliMtkvs9RU3AScCg8MviI+xDR9j+7kfY4wZhT2bc630fGqDydgPVGuGvsBgbLeE5fkB25VhQMattzHmtbZPNMa8DTzI4utYfwB2bfPaXsaYzPWaNou5CzhIRDbHNo7n28k0A3vEqL1rEH7AHsXJXGdfY0yiE+9VFZgstZeXsV+cw7Fn8dqaj93ItRqxjEhtP6/tGZ2RvwbbpWhym+fMwBZea2V8TmuNHXihp5Zo79jugm3X355OZzLGzAQuB9qOgtiVv2Vnjc74OfO9dOa7aYn/L2PMlcaYDbFnxVbDdulSBSyP28y2bbsz25LOfB9kLq9T29KlghnTZIy5D/gQu339AXumZ0jGsvobY9bqKJsxpgV7xuWg8PZ4uLPaXs6VOijSu/0eVOHoQZt6DhgtIpu0Wd5o7Bm9XA4Y1JXtU1fbyw+0vz8Jthhe1jZted8B7T1+G/Ys7o5AvTHm9XZfqPvUeVcyxSi2L7fBnoJH7MXZmYMK7CciK4a/zg6fm267kC66CzhSRMaLHcjkQmyf9WQnXns9cLaIrBXmqxWR/cKftxJ70few8PefAXtg+7S3vvavsniQhqEisudy1vcEdkf6L9hugEu99/C+W4BLRWSUiJSLyObhe7sd2F1Edgnv7yX2gvIV2y5HFYUetxdjjMF2/9wj/LmtCcCBYqd92QjYdxl5pmKv61iW3cK2UYUt1t4wxixxliL8DN8EXJbRflYIj+L21F3AOWF7GwKci20Xy9SNTJdirytZI+O+CcA2IrKSiNQCZ3f/bSzyBxEZGO7wnII9qgzL+G5qj4hsHJ4VqsQWzQvp+Xeryr18bTPbtu1sb0uW93ldYv1iByGKiUg/sQOX7Iq9PvVNY8wU4L/AJSLSP3x8rIhsu5wMd2KvDzwk/Lk9bwFTgISI9A3f95adfA+qOHSrTRljvsR+Bu4QO6hNefhZeADbBf5/uQq8vO1TD9vLP4EzRGRDscbJ4oG5JgAHh+/1F8Dy2lhbS+0zhMVnGnuZyqKzorpP7V7JFKPGmE+xH8DXsR/SdbDXbrTaGHhTROqwF2qfYno4D1D4BfF/2C+MKdijHwcu80WLX/sQtkve3SIyF3v0rHWqjDnYhvJRmPcpbLefi8PHrwjfw39FZB62QW26nPU1YI8E7UTHG0uAM4CPgLexXYUuwl4H9gN2kIU/Yr9of8Ce/SiZz1iUZKu9GGM+MXbUv/b8H7ZNzAbOY9mfuyuAfcWOJHllB8+5Ezva7CxgQ+zACu05CzsYyBth2/ofdoCinroAeAd7VPgj4L3wvs7odCZjzFxsWx+Ucd8z2GLxQ+xgQY937y0s4ZFwWROwXY5uDte1rO+m9vTH7szMxnb3nQn8PQv5VA7lcZv5N+xBnDkicka2tyWd+LzGgdvC9e+PHR/ij9hBkuZg29pxxpjW3h2HYQc9+RT7mb6f5XSdM8a8iT0QMwp7/Wd7z2nBHrwbF657EraA7U6bUwWoh23qRGzxdjv2mtSnsNdP75OH6MvaPnW7vYRnUf+K3XbPAx5m8XbtFGx7mIM9iPNwFzMv8b2Scf+/sX/3zAPFc9B9aqek/RMWSilVPETkVuzgBee4zqKUUkqpwiMihwG/NcZs5TqLWiySFbZSSimllFJKAYhIH+xgUjcu77kqv7QYVUoppZRSSkVSeI3rdGz36GV1m1UOaDddpZRSSimllFJ5p2dGlVJKKaWUUkrlnRajSimllFJKKaXyTotRpZRSSimllFJ5p8WoUkoppZRSSqm802JUKaWUUqoNEWkRkQkZN09EXgsf205EHnedUSmlil2F6wBKKaWUUgVogTFmfJv7tsjGgkWk3BjTko1lKaVUMdMzo0oppZRSnSAidRm/9heRQES+EJHrRaQsfM7PReR1EXlPRO4TkZrw/qSIXCQi7wH7ucivlFKFRotRpZRSSqml9c7oovtQO49vApwErAmMBX4lIkOAc4CdjDEbAO8Ap2W8ZqYxZgNjzN25Dq+UUsVAu+kqpZRSSi2tvW66md4yxnwDICJ3AVsBC7HF6asiAlAFvJ7xmntyE1Wp4iYiBrjUGHN6+PsZQI0xJu40mMo5PTOqlFI5ICJGRC7J+P0MEYk7jKSUyi7Tzu8CPGOMGR/e1jTGHJ3xnPn5i6dUUWlgce8CVUK0GFVKqdzQDatS0baJiKwcXit6APAK8AawpYiMAxCRviKymsuQShWJZuBG4PdtHwhHsn5ORD4UkWdFZKX8x1O5osWoUkrlhm5YlYq2t4Grgc+Ab4GHjDHTgSOAu0TkQ2wX3Z85S6hUcbkGOEREatvcfxVwmzFmXeAO4Mq8J1M5I8a07WWilFKqp8JRN0cBHwLrAccQXv8iIo8B9xtjbhORo4A9jDF7uUurlFJKuSMidcaYGhH5C9AELGDxNnMGMNIY0yQilcAUY4z2OooIPTOqlFI5YoyZC/wbOLnNQ5sDd4Y//wc78IlSSilV6i4Hjgb6Os6h8kSLUaWUyq3L0Q2rUkoptVzGmFnAvdjtZqvXgAPDnw8BXs53LpU7WowqpVQO6YZVKaWU6pJLgMxuuCcBR4bXYf8aOMVJKpUTes2oUkrlQOv1L+HPw7EDnFwcXv8yBvgXdmM7HTjSGPO9u7RKqZ4SkVOBG40x9eHvTwAHG2PmuMyllFKFTItRpZRSSqkeEpEksJExZobrLEopVSy0m65SSimlVDtE5DQR+Ti8nRpOy/S5iNwhIp+JyP0i0kdETsaOnv28iDwfvjbZOs+wiBwWTuX0gYj8x+V7UkqpQqJnRpVSSiml2hCRDYFbgc0AAd4EDgXeA7YyxrwqIrcAnxpj/tH2zGjr78Bw4CFgC2PMDBEZFF5LrpRSJa/CdQAFnh+UA9VAVcatMvy3GZgNzEkmYmlnIZUqYZ4fVAK9sN+ZFdj22fpzOTAf20brnYVUqsR5flAL1Ia/mnZuc5KJ2IIuLHIr4CFjzHwAEXkQ2Br4wRjzavic27FTN/1jGcvZAbivtUjVQlSVKs8P+gGDsfu3rdJAA7AQO7fo/GQipmfKSogWo3ng+UENsC4wFlgxvI3O+HloJxZjPD+Yiy1MZwOzgB+Ar8Lb58DnyUSsIetvQKmI8/ygPzAe8Gi/jXZqcm3PDxqxbXMq8BMwGfgS+Bj4KJmIfZfl6EqVBM8P+gBrAmuH/66A3XYOC/8dwpI7uB0tZx6L2+fU8PYj8BnwKfBVMhFrWc5i2u4o646zKnmeH/QF1sHu7/4M2yNgKIvb5xDsQd3lafL84Efg+/D2Q8bP3wJf6MmZaNFuulnm+UFvYENst55NgPWxRajkYfUt2MJ0AnaqiBeAT/UIk1KLeX7QC9suNw5vGwGrk582Ohf4BFucfgi8mEzEPsrDepUqGmHhuTX2zOQ62AJ0ZfIzzkUDtn2+V/fR/6bN+u+1+5nmxvVZ3E3319huulsYY14XkX8CnxljLhGRj4A9jDHfQrvddDc3xszUbrqq2Hl+MATbRtfDFp/rAquQn+3oPOAd4A1sm3wzmYj9lIf1qhzRYjQLPD/4GRADfglsie3CVyimAy9hC9MXkonYx27jKJV/nh+MB/bAttH1KaxeIVOA/wHPAM/oRlWVGs8PKrAHb3cMb5vTibOc+TD3rYeY98HTzaZpYV3V8LGPSVWvy+o/ffEu7M7whtizqb82xtSLyEnAicBkY8z2mdeQisjhwB+wB43fN8Yc4eYdKdV14eVkmwK/CG8bUliDoH6Pnb87AB5LJmIpx3lUF2gx2g3h9WPbYQvQGDDOaaCu+Ra4G7g7mYh96DqMUrmQ0Ub3BHYHVnIaqGs+Ap4Ebtezpiqqwja6K3BI+G8/t4k6pzk11Uy9+5yGFX5308XAf5KJ2FeuMymVC54fDAD2xrbPnYCBTgN1XhPwHLY3wsPJRGyq4zxqObQY7YLwDOhvgMPp5DVkBe5TFhemE12HUaqnPD/YCDgO2Bfo7zhONryPHc3zzmQipnMXqqLm+YFgu94eAuwHDHKbqOuaU1OZdv95jDr62ta7XgduA+5JJmJznAVTKgvCNrotdl93Hzp3jWchS2PPmN4P/DuZiM12nEe1Q4vR5QivAd0XOAbbPz6qXgUuAx7SC8NVMQmvLzsIW4Ru6DhOrjRhux/dCgTJRKzZbRylOi+8vuwE4EhgjOM4udIAPAbcnEzEnnIdRqmu8PxgJHAEcBTF1duvK+qxo19fmUzEPnEdRi2mxWgHwob5B+zGc4DbNHn1DXAFcEsyEatzHUapjnh+sDpwPHAYpdVGk8BFwL909GxVyDw/GAucjt3J7e02TV59iG2j93RiZF6lnPH8YGPgT9hLzgppLIVcew64Ent9qZ6AcUyL0TbCItQHfkvxd0/oiTnAjcAVyURssuMsSi0S7uD+BTiQwhpAId8mA38HbtT5TVUhCXdwzwR+RWm30W+xbfRfyURsoeswSrXy/GBD4DxsEVrKvgUuwW5Hm1yHKVVajIa0CO3QAuAq4G96PYxyyfODFYBzsd2ISukI7vJMx3axvzqZiM1zHUaVLs8P1gMuBn7uOkuB+QlblF6lO7zKJc8P1scWobu7zlJgJgJ/TCZi97sOUopKvhgNJ7s/F3s9ixahHZsNXIBuTFWehdebnY3tkqtttGPTsGej/q1zC6t8Cg8UXQgcSmmfCV2eL4HTkolY4DqIKi2eH6yBbaN7OY5S6F4H/pBMxF51HaSUlHQx6vnBr7FHcUe4zlJEdGOq8iIc1e8E7Aa0KKZ9KBCvAMfrtDAq1zw/qMZeE/pHoK/jOMXkSeD3yUTsC9dBVLSFbfRPwFkUyNy9ReJh4KxkIval6yCloCSLUc8PVsNeD7mt6yxF7CHg2GQiNs11EBU94eBE/8ROA6G6rhm4GvhzMhGb6zqMih7PD7bHbkejOvJmrjVhL4H5sw4WqHLB84NtsG10dddZilQjtkvzRToQWW6VVDHq+UEF9ujQOWh3v2yYDvwumYg95DqIioawjZ6J7Tpf7ThOFEwBTtbrYFS2eH7QC/gbcAogjuNEwTfAoclE7HXXQVQ0eH4wEHuN8lFoG82GN4HDtSdD7pRMMer5wRjgHmBT11ki6A7gRB3gSPVEOLDCLcB4x1Gi6CbglGQitsB1EFW8PD/YAPgPsKbrLBHTgr0c4S86h7DqCc8P9gauA4a7zhIxC7BjV1ypYzJkX0kUo54f7An8CxjoOkuE/QgcnUzEnnYdRBUfzw9Ow87Lp6Pk5s7HwAHJROxT10FUcfH8oBy7I3YuUOk4TpS9hT1LOtF1EFVcwjZ6EfYabpU7zwNHJhOx71wHiZJIF6OeH1RhG+epjqOUCgP8GbhAjxypzghHs/4Xdj5ClXv12G67N7sOooqD5wdDgQeArV1nKRHzgROSidhtroOo4uD5wXBszz8dByU/5gIHJhOxJ10HiYrIFqOeH3jAvcDGjqOUovux/evrXQdRhcvzg58BjwCruc5Sgu7EXu+tA6eoDnl+sA7wGDDGdZYS9A/saJ5p10FU4fL8YAvgPmCU6ywlpgU7BcxlroNEQSSLUc8PNgEeB4a6zlLCJgB7JhOx710HUYXH84NdgbuAWtdZSti7wG46IrZqj+cHe2DHA6hxnaWEPQocnEzE5rsOogqP5wcnYw9aaNd5d27EjpnS5DpIMYtcMer5wW7YM6I655l704F9konYy66DqMIRbkAvA8pcZ1F8DeySTMS+dh1EFQ7PD84GLkDbaCGYAOyRTMR+cB1EFYbw+tDrgGNcZ1EAvIDd153lOkixilQx6vnBkdijFDoISuFowvatf9B1EOWe5wfnAOe7zqGWMA2IJROxd1wHUW55flCGnd/3SNdZ1BJ+AnbXNqo8P6jGXmah4ywUlq+wbfRz10GKUWSOeoY7ubeghWihqQTu8fxgf9dBlFueH/wNLUQL0TDgBc8PfuE6iHInPNvyH7QQLUQjgGc9P9jMdRDljucHNcATaCFaiMYBL4XX2asuisSZUc8PLkNHzC10LdhBje5wHUTll+cHAlwOnOw4ilq2ZmwbvdN1EJVfnh9UALcDB7jOopZpLvDzZCL2pusgKr88P+gHPAls6TqLWqaZwE7JRGyC6yDFpOiLUc8PzgfOcZ1DdUoaOEqHrC8dYbe/G4GjXWdRndKCnYv0AddBVH6EhehdwL6us6hOSWEL0rdcB1H54flBLfA0sKnrLKpTZgE7akHaeUXdTdfzg9PRQrSYlAH/8vzgN66DqLy5GS1Ei0k5cFc4EJyKOM8PKrHzE2ohWjxqgf+GswaoiPP8oDfwFFqIFpNB2Db6M9dBikXRFqOeHxyNHdJaFRcBbvD8YG/XQVRueX4QB45wHEN1XSXwgOcHW7kOonLuJvT6s2LUWpCOdx1E5U7Ys+h2QK8VLj5Dgf95frCy6yDFoCi76Xp+sB9wN0VcTCsWAjskE7HXXQdR2ef5wWFAUXbHNukWptz2eyr6DWbYvn/mpzvOJN24AIB0fYqqkasx7FdLd8j47uI9qBw6BoCK/kMZts+5AEx/7O80Tf+O3mM3ZuC2hwMw57W7qRoyhj6rbZ6nd9Uts4GtkonYp66DqOzz/OBP2OlbVPH6Edg0mYj96DqIyj7PDy4BTnOdoyvabj8XfPcBc56/BdPSRNWIcQze9RSkrHyp10Vw+9nqK2CTZCI223WQQlZ0xZznB9tgjxQVXXa1hF7AY54fjHUdRGWX5wfbY6eHKErz3nmUysGjF/0+4pCLGXXkVYw68iqqR/2sww2gVFQtel7rhrRx2reUVVQz6qiraZwykXTDfJrrZtE4+Yti2JAOBJ7y/GAF10FUdoWjm0dqZGuTbmHyv05m2v3nLXH/rP/dwPeXtt8LuWHyF0z+10n2dsuJ1H/5GgAt9Sl+uv1MJt98PPVfLj5eOu2B82meNzN3b6LrVgCCcHAbFSGeHxxPkRWisOT205g0M4PLGLLHmYw6+loq+g+j7qNn231dBLefrcZhL31ZugJXixRVQef5wUrA/UCV6ywqKwYDj3p+0N91EJUdnh+sATyI7epZdJrnzmDBN29Ts97Pl3os3VDPwu8+oM+qnd8ISlkF6eYGjElj0s0gZaRevp3arQ7JZuxcGg3cGw5yoyIgnB7kNuwlE5HR9iASQMOUiaQX1nX4msqhYxh5+OV2B3j/vzDz6Wsw6Rbmf/oiNevvyojDLmXuO48AUP/Vm1QNX4WKfoNz+j66YT3g9nDUchUBnh/EgCtd5+iqttvP9IJ5SHkFlYPs8cxe3njqv3y108uLwPaz1S7A31yHKGRFU4yGF3E/hO2HraJjTexRo6L5LKr2eX4wBDsH2gDHUbpt9rM3MmC7oxBZer+ufuLr9BqzHmXVfdp9rWluZMptpzLl36cvOptSOWQ05b1rmXLrKfQZtwnNs6dgjKF6xLicvo8s2wK42HUI1XOeH4wBHsH2TImM9g4imXQLs1+4hQHbdTxtalllr0VdBk1zI631uZRXYJoaMC3NSFkZJt3CvHceof+m++T0ffTAHsB5y32WKnjhdcB3YweTKyptt59lvftj0i00TJkIQP0Xr9Iyd0a7r43w9rPVHzw/OMh1iEJVTEe7rwE2cB1C5cRugA9c6DqI6pFbAM91iO6q/+otyvoOoHrEOBZ+/+FSj8//9KV2z5i2WuG4W6joN4SmOT8x9a4/UjnUo3LgSAbt9NtFz5l2/3kM2uVEUq/dQ+O0b+nljaff+F/k5P1k2e89P3glmYg96DqI6p5w5Nz7gWGus2Rb606waaxfdN+89x6nz7hNqagZtMzXNkz+gplPXEHz3GkM+eVpSFk5fdfclhmP/p26D55iwLZHMO+9gL5r7UBZZUHX8Od4fvBeMhF72HUQ1T2eH/QF7gVqXGfpqva2nyLC0D3OZPZzN2FamujlbQBl7Z93iPj2s9XNnh98nkzE3ncdpNAUxdkozw+OADo+vKmi4DzPDzZ2HUJ1T3h9y+6uc/REw4+fsmDim0y67iimP3oxC7/7kBmP2QG7W+pTNE75kj5jO/6IVvQbAkDlgBH0WmkdGqd+vcTj9RPfoGrEOEzTQprmTGHoXj71X7xKumlh7t5Udv3L84OiPCStAPgLsJHrENmWuRPcqnneTOo/f5V+Gy7/K6l61OqM+s21jDzsMlJv3IdpbqSsui/D9osz8vDLqRoxjgVfvUWf1bdk5pNXMv2hC2n48bNcvqXuEuCfnh+MdB1EddsVwKquQ3RHR9vP6hXWYMQhFzPysMvoNXotKge2PwRBCWw/AXoDD4W9yFSGgi9Gw2vQrnGdQ+VcBXCn5wdFd0Sw1Hl+sBYRmGZp4LZHsOIJt7HicbcwdI8z6TVmXYbsfgZguxf1HrcxUtH+5eotC+swzU325/oUDT9+SuWQlRY9blqamRt28zPNDSy6XM+koaU5p+8ri/oD94eXTKgi4vnBtsCZrnPkQns7wVNuPp6mOZP58YZjmHTdUZimBn684ZhlLqdyyGiksjeN079b4v7Uq3dRu8X+zP/0RapXXIvBsdOY88qduXxLPTEY20NFFZlwuruinZO7o+1ny/w5AJjmJua+eT816++61GtLZPvZagxwvesQhaagu+mG1xHeDLR/kZaKmnHYi/aPch1EdY7nB9XAXdgjfpE1/7OXqN1svyXua5gykboJTzJ415NpmvEDs56+GkTAGPpvuh9VGRvTee8F1Ky9I2WVvagcujKmuYHJN59A77EbUdarqI6/rAdcAhzvOojqHM8PBgD/oQgOPnfHwG2PYOC2RwCw8PsPmfvWQwzb989LPOf7S/dlhd/dtNRrm+b8REX/oUhZOc2paTTPmkRF7eJezE2zfqRl3kx6rbQujdO+tQejpPX60oL1C88Pjk8mYte6DqI6x/ODUdg5fyNn7lsPUv/VW4Ch3/jd6D1mPaBkt5+t9vH8YL9kInaf6yCFoqDnGfX84BTgctc5VN7tl0zE7ncdQi2f5weXA6e4zqHyymDnH33NdRC1fJ4f3APs7zpHPiyrGF3pNLtJqZ/4Jo0/TWTA1odS9/FzzH3jfigvR6SM2i0OXGLKiOkPJxiwza+pHLQCLfPnMP3BC0g31FO79SH0XX3LvL63LqoH1k8mYl+6DqKWLRwF+b/ATq6zqLyaBqyZTMQKaq4oVwq2GPX8wAM+Bvo6jqLybzawejIRm+46iOpYOJ/os0RsigjVKZ9gd3abXAdRHfP84ADsyJyq9LwNbJFMxIquH2Mp8fzgVOAy1zmUE3cmE7Gim6cmFwq5286NaCFaqgaiczIVtHDeySvRQrRUrQX8wXUI1bFwZM5LXOdQzmwMnOg6hOpYONjUBa5zKGcO9vxgD9chCkFBFqOeHxwF7Ow6h3LqSM8PIjfyY4QcB6ztOoRy6hzPD8a6DqE6dA7Q/tCVqlT8WUfuLGh/Q0+6lLrrwuv6S1rBFaOeH9QCf3edQzlXBlwZXk+hCki4c6MTrKvegA6SUoDCKXhOc51DOTcAON91CLU0zw82BA5znUM5Nwo413UI1wquGMV2/Vr2LNWqVGwO/Np1CLWU87FdqZX6uecH+7gOoZZyOdD+PESq1Bzj+cE6rkOopVyOXuairOM9PxjtOoRLBVWMen4wHDjVdQ5VUC7y/KCf6xDK8vxgPeC3rnOognJeOA2XKgCeH+wGxFznUAWjHJ2VoKCEA4tt5TqHKhjVlPjZ0ULbgTgH7T+vljQC+L3rEGqRSyi87w3l1lrAfst9lsq58LKGhOscquDs4PnBnq5DKPD8oBdwkescquAc6fnBaq5DuFIwO5XhVC56xkW15+RwZEjlkOcHmwI7us6hCtKf9exoQfgloF0yVXv+z3UABcDvgDGuQ6iCU04JX99dSDsPf0GvcVHtG4weqCgEZ7kOoArWGsABrkMo/ug6gCpYG3p+oAcTHfL8oBy9FE11bD/PD9Z3HcKFgihGPT8YA+jEr2pZTvf8QA9WOOL5weqAdvNSy3Kunh11x/OD7YHNXOdQBe1M1wFK3D6A5zqEKlhCic5UUCg7DsdTOFlUYVoBHQbdpT+gbVQt28/Qs6Mu6VlRtTw/L9UzLwXidNcBVMGLhZctlpRl7lyK9YqI7Jpx334i8lS2AoQXcx+dreWpSDtTz7zkn+cHo9ApdlTnHO86QCny/GBjYCfXOVRR0LOjDnh+sBWwiescquCVYa8rLinL3LE3xhjgWOBSEeklIjXAhcAJWcxwEPaaQKWWZ1XsAB0qv05Br+dWnbNV2KVb5ddJrgOoorFfeGmUyq8zXAdQRePoUrssbblnmYwxHwOPYQcvORe4HfiTiLwlIu+LyJ4AIrJWeN8EEflQRFbtZIYTuxtelSQ9i55Hnh9UAEe6zqGKym9cByglnh/UAvu6zqGKRjl6yUteeX4wDtjddQ5VNIZiry8uGZ3t8ngecDCwK9ALeM4YswmwPfB3EemLPYN6hTFmPLARMGl5C/X8YHNgg27kVqVrN88PRrgOUUJ2xn4xKtVZh3l+UOk6RAk5GOjtOoQqKnrZRX4dho65oLrmWNcB8qlTjcMYMx+4B/gPdufUF5EJwAvY4nQl4HXgjyJyFjDGGLOgE4s+phuZVWmrQDek+aSjXKuuGoaOvJw3n1Qf+ctbKi9+8Wfy/Teus6iisWo4b7TKj/1dB1BFZxvPD9ZyHSJfunKkJh3eBNjHGDM+vK1kjPnMGHMnsAewAHhCRHZY1sLC/tB7dze4KmkHuw5QCjw/6Avs5TqHKkraVTcf4rWr9JWG3XYon7DtU9X+Kh9VH/3J2RV3vFRL3RzX0VTB04O6eeD5wXqAXkevuuMI1wHypTvdBp4GThIRARCR9cN/VwG+McZcCTwCrLuc5ewMDOjG+pUa7/nBGq5DlIA9gb6uQ6iitLPnB9q9O/cOzPylnyxY63cVwTYTqn/b+/mq37++T9lLb5eRbnEVThW0A7U7fV7odFequ/ZwHSBfulOMng9UAh+KyCfh72C7IXwcdt9dG/j3cpajAy6ontAv+NzTLrqqu8qAX7gOUQIOau9OEapXLpu6+SVV1288sfrXM/9T+bcX15FvJuY7nCpog7HjgKjc0i66qrtW8/xgNdch8qGis080xsQzfl1qDhxjTAJIdGZZ4VyROkWH6olfAHHXIaLK84OBwM9d51BFbTfsOAMqF+K1a2EP/C5TuZhhW5d/NGzr8o+Yb6o/u7dlu+lXNe+11ixqdUo1FQMedR0iqjw/2BAY6zqHKmq7A5e4DpFrrkb32gIY4mjdKho28vxggOsQEbYtXThYpVQ7fu75QbnrEBHW5YNFfaVhjSMrnt7m3erj+r9cdcqbB5Y/92YFzU25CKeKws6uA0Tcfq4DqKJXElMCuSpG9ayo6qlyYEfXISJse9cBVNEbBOiInbmzRXdfKELl6LLpmyYq/7npl9WHp+6qPP/FDeTLz7MZThWFlT0/WMV1iAjbyXUAVfS2DHuqRZqrYnQbR+tV0aLdSHNHi1GVDbu5DhBhm2djIWVihmxe/tm2D1bHf/ZZ9RFfXlBx84vDmD09G8tWRUHPjuZAOBr9eq5zqKJXQQlc2533bnjhlC4b9GQZc995hLoPngYDNevtQv+N96RlwTxmPHIRzXOnUtF/OEP28invVbPUa6feey4Nk7+g14prMmzfPy+6f/pjf6dp+nf0HrsxA7c9HIA5r91N1ZAx9FktK9t8lX26Ec0Bzw+G0Ilr0ZTqhN2Ac1yHiJx47RhghWwvtrc0rnZoxbOrHVL+bPMUBr11ffMe6btadtigiYqqbK9LFYydgBtch4igzdBLXVR27Azc6TpELrk4M7ohUN3dFzdOT1L3wdOMOOxSRh51FQu+foum2ZOZ+8Z99PLWY4Xf3kQvbz3mvnFfu6/vv8mvGPLL05Zc5rRvKauoZtRRV9M4ZSLphvk0182icfIXWogWtpU9PxjnOkQEbYedT1ipnlovPEOgsqvbXXQ7Q4SKUTJrk79U3rrZF9WHzb+/Kv7SpvLpp7lcp3Jmh3BQSZVdW7kOoCJjQ9cBcs3FF1CPqrummZOoGrk6ZZW9kLJyqkevTf2Xr1H/1Zv0XdteQth37R2pn/hGu6/v7Y2nrKr3EvdJWQXp5gaMSWPSzSBlpF6+ndqtdGaLIqDXpGWfdtFV2VKGdlXLhZwWo5nKhIEblX25zT3VF6z5RfXhX19cccOLI5n5U77Wr3JuEKDzdmefFqMqW9b0/KD38p9WvFwUoz3aiFYNGUPDpE9oWTCXdNNCFnzzDi1zZ9Ayfw4VNYMAKO87kJb5czq9zMohoynvXcuUW0+hz7hNaJ49BWMM1SP0pFsRWNd1gAjSjajKpvGuA0RQ3orRTNXSNHb/ihe3fa36pKFvVh//7tHlT7xWTeNCF1lUVq3jOkCUhKOIb+Y6h4qMciJ+UNdFf/YenRmtHDKa/pvuy7R7/g+p7EXVsFVAlqypRaTLfQwH7fTbRT9Pu/88Bu1yIqnX7qFx2rf08sbTb7zO316gdCOaRZ4fVKJHyVV2re86QKTEa/vi+CCcCOXDmbPh/1XezjkVt6c+MGPfvqR5vwEvp9fV7+PitC5wt+sQEbIesPSgJUp13wZA+10+IyCvZ0Y9PxgGjOrpcvqt93NGHnEFIw65iLJeNVQOWoHyvgNorpsFQHPdLMr6DujWsusnvkHViHGYpoU0zZnC0L186r94lXSTHvwtUHpmNLtWBSpdh1CRosVodm1CAQ2MIkLt+LKvt/5PVWKdL6sP+/ayymteWFGmT3adS3WJHkTILh0AUGVbpK8bzXc33bHZWEhrF9zmudOo//J1+q65LX3Gbcr8j58FYP7Hz9JnXNcvJTQtzcx95xH6b7oPprmBRWO4mDS0NGcjusq+FTw/GOQ6RFSsId+t1oeF813nUJGytucHBVM8RUDBdv+rkuaV9y5/dbuXq04Z8U71se8dV/7Iq71pqHedSy2XFqPZpXO3qmzr0SwkhS7fOwhZaaDTH76Q9IJ5UFbOoJ2PpaxXDf0325cZjySo+/C/VPQfxpA9fQAapkykbsKTDN71ZAB+uuNMmmZOwjQtZNI1hzN415PpvYo94DDvvYCatXekrLIXlUNXxjQ3MPnmE+g9diPK2pkmRhWMdYEXXIeIgierz17HGPq0UDZ5Nv1++saMrPsgPZZ306v1/zC9ysgpDB7uOqMqOtXYrt8fuQ4SEZ7rAMsjQtkQ5m5wVuU9nFlxz7xPzZhXLmner99z6fXXBdGRugvPGM8P+icTsbmug0SEFqMq29by/KA8mYi1uA6SC0VZjI445OKl7ivv3Z/hB1641P3VI1eleuSqy3xtq/4b77noZxFh6B5n9jCpypM10WI0W8aJIBWkRw0lNWqopNi07HMgAMAY5s6n16QfzZDZn5oxze+lV6t+Lz1u6Jdm9Gidi1Atw8poMZotRXVASIR+a8l3W91S9Q+aTPkP/01v9M3fm/dfJWlGjnadTS1hLeB11yEiQotRlW2VwDBgiusguZDvYjQr3XSVamOk6wARsswhpEXoX8PCNVeXSazOJPYufxUAY2hpovy7GQyYNjG9Qv0EM7bsvfSqAz5Mr7LCbPprN2o1wnWACCmqYjRTpbSMjpW/OXq3sjfNHGom3Nq8S90/W3YbP5/e2vXIvR6P56EW0X1dlQsj0WI0K/RokcqFYa4DRMiK3XmRCOVVtIwZxcwxo8pnsi0fLnosbWTWPPr8+L0ZlvoovXLLe2bVPu+nxw3/1oxcIU1ZedaSq0KmxWj2FG0x2koEGUjd+N9XPsCpFQ/M/9Ks+Oplzfv0fiq9yfrajdeZov9cFYJwPkj9vlO5ENkTL/kuRlfO8/pUadCNaPZk/SxmmZhBtcwftI58yzpl33IwzwFgDA0NVH471Qyc8aUZ3fBeelzFe+lVB35sVh49n979sp1DOaU7Z9kTqYNvIvRdXSZteX3VFTSbsh+fTa//1cXNB3pfmxXGuM5WYiL1uXJIT7qoXNFiNEu0u57KBS1GsyFeW0ke50YToboXTePGyLRxY5jGzuXvLnqsxZT9NJuaKUkzYt6H6VV4N71azQdm7IhJZshIPXNSlLQYzQY7x2hf1zFypULSK+xS/u4Ku5S/S8r0+ej2lp3nXN/8y3Xn0bfWdbYSoNvR7NDuzipXtBjtKc8PyoDe+VqfKil6RDc7CuZgUbmkRwxh7oghMpeNyr7kKJ4CwBjm11P9wxQzeNanZkzTe+lVq95PrzrkczN6dANVvRzHVh2L7EY0z0qmYKiV+nVOqHiE48sfWfi1GfnaFc37VD2e3mwDQ1m+p6QrFbodzQ7t1aNyJbLb0XyeGe3Dook7lcqqktlBy7GCKUY7IkLfvjT8bJxMZhyT2aPcDv5oDOlmyn+YSf+pX6dHzQ8HUOr/YXqVFaYzcIjj2ErPjGZLyX3XidBrnEzZ4qqqq7ncXDvlxfS6X/y9+YDRn5kxOkhMdpXcZytHtBhVuaLFaBboaHkqV/p6flCRTMSaXQcpcgVfjHZEhLJKWkaPYPboEeWz2ZJPFj2WNvw/e/cd3lZ1PnD8+0qy5ZHE2SQhJIKEGQhhr5ABhVJcfsyywYxSVqGsti5QEKPUZZWyyt67bBB7hU1YIQojJICz95BjS7Y1zu+PqyROYiceko7G+3kePbE17n3l6Oje955z3rO8ntLZs0z/5d8mfPEvzealkxLD+003G28Sx53pqQqFSr//U6OgEwa3JAbu4540cB/3JFaY0m8fj49fclvskJEhuvW0HVse0KHQKTBEFhTHjHteGG9JI8UljRSX6NQSlSL5O0Ujg/vK2z+iygoeQJPRrsnLhMEl9OxBpOcImcEI1wyOZAIAxhBtxvPzInou/jExOPJ1YrjnK7NFz8mJTQfrHLWU06Q/NXQoZVJ3iYz4g+cVTnO/0jTDbPTJrfFDPM/FR++gF5g6Tf9uKfC+9/wK1urBMoZGA00GaUzgaorjao7hbo7hjkbxxJvxxJpMcayRokQEbyJivCaM14TxStiUUE+JNJhSVz2lrgZK3A2mxNNAiafBlBSFKSlqoKQ4YrzeiJP8ejUBzlt520Y1GVX5Im8baQYlbAeQSSIUeYltNpjFmw12L2Yf96RVj8WNLApRPneGGVA3ObGp+TKxRfk3ZthGM8xGG+tBvlO0faaGsR1AthHB65MFe1zvupN/ee5a9HFixHfXxY4aNNkM29x2bDlG22hqrPN3FKFEoARMhZs4RcSB6FpPSl0AxmCARgONyQS4uWUC3Iwn1kxRrMkUJZooirdMgBsoIWy80rBWAlxvSj1hvEUNpsTTMgFe2QOsNRsyIm/baCbfmH5QO2CwLJp7meehH90k9MS3HaYYn0Cl7TByXdx2ANnCLaZfb+r79Zbp7OCaThVvAmAMkUaKZ80zvZc8UbxF6OGeJSXoXPh2kKi2z5RosB1ANnOL6be3e8rYvd1TCBvvD4dWjKudWWy0cGI7GONZom00JawnDCIIrEqAyWAC3NQiAV7ZAxxdKwGON1KcaKQ4ETFe85c+mzQu88Q1P2gHk/D+kq9tNJONJrrhp6iVZpt+g56Ij190V9GN/TyS0FLhG7AvXxt4wHYYuU6T0Q0QobSU5i02k/lcHJvPVo3lE/19ew8zIn1sx5blNIlKjXrbAeSKMmnaarPSr+bPLSsdazuWHDHddgB5wnoyakNnE+CNyhd/tKK4eK9MxZnj3LYDSJdMlkhvyuC+8sI7iR2337HpjvKfEwM+sR1LDtD5ol2nf8MOOqy+Ydc3Z82N9YnFv7IdS5bTz1ZqaFLfAePCEe0VbT9to6mhHS8dsGekUS+Ct1/ettFMJqONGdxX3qijW8U+zTfucX30dx8ao1fF10MPAF2nB4VO2Cge3+jdWXN2OKpuxQSM0YturWu2HUCe0GS0A8aGI0Ntx5BD9BiaGottB5BL9mmI9LMdQw7J2zaayWRUE6kuuDV+6Oh9m69fssKUfrvhZxecEP6QJlJdl7dfdOkmIJcuWTb20XkLaosT5ifb8WShpbYDyBN6HO2AAfH4ALcx82zHkSOW2Q4gT2gy2gEjm5qGY0zEdhw5Im/baCaT0VAG95WXfjaDho5qumvL1+M7v2dMYVU+3QD98k+NRbYDyHUjm5q3/Gjm7EGjGpvetx1LltE2mhraM9pBg2KxWtsx5IiFtgPIE/pd1wFFUNQ9YabZjiNH5G0bzVgyWltT2YTOG+2yOG7P6dELxp0S/XMwZlxzbMeTJfTLPzXm2w4gH5QYU/rwvAVjrly0ZKIYo59Nh/4dUkOT0Q7aNdKkQ8TbJ29PdDNMv+s6aERzU972+KVY3rbRTPaMAizI8P7y1ruJHbbfsemObj8nBn5sO5YsoF/+qeAPNaPDKVPm0PqGXd+aNTfRNxb/wnYsWUDbaGpoMtpB+4bDvWzHkCN0ZExq6N+xg8aFI8W2Y8gRmoymyC8Z3l9ec4ob3bDnddEjPzSGFbbjsUhPdFNH51elUP94vP87s+bsdHTdivcLvLiRttHUCINO0eiIXRqbNseYvK1CmUJ5e6KbYUsAYzuIXKKFxtotb9toppPRnzO8v4JwW/yQ0fs0X7+szpROSeV2//NpE9veXs+I2+u56dN1z6OXRQyHPhlm5H/r2fXueqYsdGoILWpIMPq+Bra9vZ7nf1hdE+fgJ8LMXZGW86i56dhogdJkNMUE5JIly8Y8NnfBDG8ikbK1/Ba/sZhpl0xj2sXTWPz6urlevCHOjJtnMO3Safx0xU80znYKmsfqYvz8j5+Zdsk06r6sW/X8Gf+ZQXRZ2mpYtdpGRcSIyA0tfr9IRPzpCiLn+UMG0OkZHVBiTGmp0Tlp7ZC3J7oZ5RRTXG47jFwyOBYf5DJGR05uWN62UU1G88QvZtCQHZru2uq1+C7vGdP1JTqmLIxz91dRJp5WzjdnlPPyjzGmL10zkbzmgyZGbeRm8pndeOjQUv70mnOy+/iUKGfsXMTE08q56VNnus5LU6PsMMDFoO5p+chNTcdGC5Qmo2myXXPzFh/NnD14h8bGLhc3apzdyLIJyxh22TCGXzWcFd+soGnBmheMFr20iNIhpWx+9eYMPm0w8x51/mtDn4XoPb43wy4bxuI3nCS27us6SoaUUNSrqKuhtaWtNtoEHCYifdO14zz0g+0Acs0WzVEdOrlhM2wHkEfyNmlIlwGxeK3tGHJA3rZRTUbzSBy354zo+eNOjv7l25hxze7Ktr5flGC3jd2UFQkelzB2qIdnv1+z1+S7xQn22dQNwFZ93dQuT7CgPkGRSwhHoSkGbhfEEoabPmvmL3t5uxLS+ujJWer8aDuAfOY1lDw0b+GYqxct+bwrxY2a5jZRulkpLq8LcQvlW5av0csJ0Di3kfKty539DvLSvLiZWCgGbkg0JzAxg7gEEzcseWMJ/Q5M63JvbbXRGHAXcP7aD4iIT0TeEZHJIvK2iAxJZ4A5RL/vOmhMOJLpc51cY4DvbAeRR763HUCu2bWxUZd3Wb9QsCrYpfP6bJbpL2hdfy8D3kuMGrlD053dpycGdbq40bb9XXwwM86ScIJw1PDK9BizQmv2jG6/kYtnv3em4kycE2fGcsPsOsOx2xXxwtQY+z3cwMWjvdz+eTMnjCyirEi69sbapj2jqTPZdgCF4OD6hl3enjXH9IvFOlXcyDvYS/jHMLH6GImmBCsmryC6ZM2LRSVDSlYlqOGfw0SXRIkui9Jz957UfVXHL9f9Qr+D+rH0naX03LMnLm/aDgcRYOZ6Hr8NOE5EKta6/xbgQWPMSOBR4OY0xZdrNBntoPHhyMa2Y8hytcGqoBbHSp2vbAeQa/YJR7TQ2Pp9azuAdNJkNE+toLziV83X7/mv6FEfGUPdhl+xpq37ufnrXsXs/0iYAx4JM2ojF27Xmslk9Wgvy5sMo+6o55aJzeww0IXbBRUlQuDYMr74Qzd2HOjmpR9jHLFNEae9GOGIp8J8MiultSQW4A8tT+UGC9w3tgMoFP3iiX5vz5q707GhFRM6WtyoZFAJfQ/sS+11tdTeUEvpkFJkrfbZr7If8XCc6X+fzpI3l1A6tBQE3GVufBf4GO4fTunQUuom1dFjlx7MuW8OM2+dSXh6OKXvE/gxWBVss6CHMaYOeAg4d62H9gAeS/78MDA61YHlKO116aDh0ahPjFluO44sltJ6E0qT0Y7aLdI4HGO6PMUsj+V1G81oMlpbU7kYmJXJfRa6/8YP3mt88w2hOlMW7OhrT92xmC//0I33Ty6nV6mwRZ81Py49vML9B5cy6YxuPHRICYsaDJv1WvM5V73fxCV7e3k8GGX0EDcPHlKKf0JKi4pqL0Eq+UMzgJDtMAqFgPxt6bKxj89dMNObSHSoyErvsb0ZfsVwNrt4M1zlLooHrFkd313qZvDvBzP8quEM/sNgYnUxivuv+ZyFLy6k32/7Efo0RNkWZQw+bTALn0/5dKf2tNGbgFOB8lTvPA/pBaMOEpA+8YReDG9bXp/oWqDJaAeVGVNeYoy20bbldRu1MY/iEwv7LGi1ZuAmOzTdufUr8V07VNxoYYMzLHdmKMGz38c4drs1i5ssbzQ0x50Oj3u+ijJmqIce3tW9M9OWxJldl2Ccz0M4anAJiEAktQU79Us/9Tp84UJ1zbbNzZt/NHP2JjtFGie09zWxOmeEQfOSZuq+qKPn7j3XeDzeECcRc9rwsgnLKN+yHHepe9XjTfObiC6N0m3rbiSaE5BsuonmlFe83mAbNcYsBZ7CSUhX+hg4OvnzccAHqQ4sJ/lDS9Fl0jpsh6amQl7+bEPy+kQ34/yhecB822HkmuHRqFbUbVtet1GPhX1+DBxpYb8FLY7bc1b0vHFjXN9Mvrfo+t5FEh+8odcc/lSEJWFDkRtuO7CEniXCHV841XHP2LmY7xfFqXq+EREY0c/Fvf9XusbrL3mniX/s4xQtOma7Ig55IkLNR81cOS6lhYz0BDX1vkGHRGac11DywPyFY1/sVvb5pX37+IzIeisKzbx1JvH6OOIWBp04CHe5m6XvLAWg9z69aZrXxOy7Z4NAycYlbHzKmtPmFjyzgI0O3wiAnrv3ZMbNM1gcWEz/Q/un+q21t43eAPyxxe/nAPeLyJ9xFpI/OdWB5bAvgE1tB5FL9mkId3uzvMx2GNlqku0A8tBXwIG2g8gle4cbZYo3bYUuc5khz0fEiDGZXZvXVx3YFfgsoztVa+hOQ+jZ4sunbO6au5ftWFKgP/6Qlu1PJX/F8Thz9JQli92uRUcNGlC70OPZxXYsXRQBKoJVwbQtYFqQ/BV/Bq61HUYuWe5yLdt76GAtkrKuBcGq4ADbQeQdf8VVwKW2w8gl3xcX/XTkxgOH2Y4jC30TrAqOsh1EOtkYpvs10GhhvyppBeUV+zVfv9c/o8d0qrhRFpmqiWhavGU7gELX1ylutMtxoboJGJPL35cTNRFNi89tB5BreiYSvYqMqbUdRxZ6x3YAeUqnEHXQVs3RzTBGa1asK+/baMaT0dqayijOECNl2Z3xg/Ya33xDKNSJ4kZZ4kPbAeQlf2g+Om80K1QvXT72ibkLZnW0uFEW0WH06fEJoHMgO2hoNDrHdgxZ6G3bAeSpj4CUT8DPZwLSO6GFxlqR923U1kLQ71nar1qLU9zorm0C8d0mdKS4UZbQE930edN2AMoxIlncaOcOFDfKItpG08EfagICtsPINXtGGnPtGJcJed/rYoU/tBCnRorqgO0btdDYWmLA+7aDSDdbyejzlvarWpHA5T47+qexJ0arv4sad64svZMAXl3fE0TEiMgNLX6/SET86Q4sT2gymkW8hpL75y8c+8+Fi78QY3JlaHoD0OEEWkRqRaRvGuLJN8/YDiDX7NMQWW9RsAL0S7AqqJWZ0+c52wHkmn3CkdINP6ugfB6sCuZ9gm4lGa2tqfwSmGFj36ptHyRGbrdD050VPyY2zoWreZ8lrzyuTxNwmJ7Ydsr7OH8/lUV+2xDe+Z1Zc9goFsuFOYNvBKuC+hlKn1dxCkSpdhrZ1DQcY/RvtprWB0gvTUY7aO9wZDPbMWSZgmijtnpGQRtpVqqnrMf+zdft+c/oMR9neXGjF9rxnBhwF3D+2g+IiE9E3hGRySLytogMSXmEucwfCqPDt7JS33ii31uz5u5yQqju/SwvbrTeNppsgz+IyKMi8r2IPC0iK9feOEdEvhKRoIhslXx+bxF5PtlmPxWRkcn7/SJyn4i8JyI/i8i5LfZxvIhMFJFJInKniLhbCSU3+UMNwOu2w8glRVDUPWFydf51OjxlO4C85g/9Qp4vyZFqfRKJvh5jZtqOI4s8aTuATLCZjD5rcd9qA+6MH7TnuOYb60KmbLLtWNrwdDufdxtwnIhUrHX/LcCDxpiRwKPAzakMLk88YjsA1ba/LF0+5sm582dnaXGjKO27YLQlcLsxZmugDjgref9iY8yOwH+Bi5L3XQF8nWyzFwMPtdjOVsCvgV2By0WkSES2Bo4C9jLGjALiwHFdelfZR4fqdtCI5qZlnX1t85Jmfqn5hWkXT2PaxdNY/MZiAEITQ0y7eBpTTp5C5Je2O17jDXFm3jqTH6t/ZNrfphGeHgZg/lPzmXbpNGbfNXvVc5d/vJzFry/ubKjtMR+94JgJeq7bQUOisU5PF8uzNhoMVgW/TecOsoXNZPQjYIHF/asNmGEGDN6h6a4RL8V3fy/Liht9hT/Uroprxpg6nJPWc9d6aA/gseTPDwOjUxde3ngeqLcdhGrbNs3R4R/PmD1kl0jjBDK9aPT6vRmsCi5vx/NmGWM+Sv78CKvb4coTuC8BX/Ln0STXvzXGvAP0EZEeyccCxpgmY8xiYCGwEbAvsBPwuYhMSv6eb0PAXsJJ/FOuMWbY9e56tr+jnhG313P5u2t2wp/7aiPdrml98MyScILxDzbQ7Zo6/vjK6hO/ppjhgEca2Pb2em7/vHnV/X94KcJX8zJziBkXjhR39rXiFgYcPYDNr9mczf6+GUvfXkrjnEa8g70MOWcIZVuUrff18x6bR7fturFFzRYMu2oY3oFe4uE4kRkRNr96c8QjNM5qJNGcYNkHy+izb5/OhtoeTwSrglrtNf10FGAH7d6FQmN51kYfTefGs4m1ZLS2pjKBNtKsl8DlPid67rgTon/LpuJGj234KWu4CTgVKE99KHnMGaqrV3WzXDF475u/cGzNoiVfuozZ0DzqTGlvG107gV75+8q5pnHA047ttJybuvI1gjP6YVTytqUxxt/OuHKDPxQiTWX/vW54p6qcb87oxqTTy3ntpxifzo4B8MXcOMsa2772UeIRrhrv5fr9S9a4//WfYowe4mHymeU8PNnJob+ZHyeegB0HZmYE9dhwZGhnX1vUs4hSn1NfxV3qxjvIS2xZjJJBJXgHetf72ng4TsPUBnqN6QWAy+PCXe4GARMzGGNINCcQt7D41cX0+VUfxCOdDbU9OnocVZ3hDwUBXa6kA/YNhzud4eVRGzXA4+naeLax2TMKcJ/l/at2+jCx3XY7NN1ZMTUx+KMNPzutmoAHOvICY8xSnLkxp7a4+2Pg6OTPx6FLULRFh+rmiMqG8M7vzJzj2igWm2g5lMW0fxj9EBHZI/nzsax/7eAPSA6zFZFxOEN51zev/W3gCBHpn3xNbxHpdCKSxdIyVFdE6FbsnGhFExCNO9l9PGH485uNXPurtk/syouF0UM8lKx1GaHIBeGoIRqHlf34f3+3iav2Wf9JYioNjsUHuYzp8qis5kXNNM5opHRY+4p/Ni9qxtPdw5x75jD9sunMuW8OiaYE7lI33bfvzk+X/YSnwoOrzEXk5wg9duqx4Y123rRgVTAXiqDli4KY95cqoxqbNseYLhe/y/E2+mGwKlgwc2etJqO1NZWfA1/bjEG1Xz1lPX7dfO1e/4ge97ExhCyF8T/8oSWdeN0NQMuquucAJ4vIZOAE4E+pCC4PvQ3MtR2Eap8+iUTft2bN3bXKKW5kq2roAx2oojsVOFtEvgd64cwRbYsf2CnZZmuAqvVt2BjzHXAp8EbyNW8CA9sZVy55njRVvo4nDKPuqKf/dSvYbzMPuw32cOvEZv5vCw8Du3f89GG/YR5qlyfY/d4Gzt2tmBenRtlxoItBndhWVwyIxWu78vp4ozOvbMCxA3CXtrNHNwGRGRF679Ob4VcOx+V1sehlZ5Wmfgf2Y/hVwxl4zEAWPruQ/of2Z+mEpcy8bSYLX0zLYAe9yJhZd0JWTXXKasVQ3M10rdCYttHcYrtnFOAO2wGojrk7Xrnn2OZ/r1huym0UN2r358UY063FzwuMMWUrh+kZY2YYY/Yxxow0xuxrtHpb6/yhBHCv7TBUx1y0dPmYp+bOn1uSSPyY4V0bnBOv9ooZY443xmxtjDncGBM2xviScz8xxnxhjBmX/HmpMeaQZJvd3RgzOXm/3xhz/aoAjNnWGFOb/PnJ5BDdkcaYnYwxn6bqjWYNf2gxcH86Nu12CZPO6MbsC7ozcW6c92fE+N93Mc7ZrXPTLj0u4bHDy/j69G78bhsPN33azIV7eLng9UaOeCrMi1PTMv11Hbs2Nnb6Qo2JGWbdOouee/SkYue16+K1zdPLQ1GvIsqGOXPWeuzcg8iMNcOIzIhgjME70Evd53UMOXsIzQubaZqf0msNTXSsjaqu8odm4szvVu20dVPz0s6+Ng/a6FI0Gc24R4FOV7dTdsw0Gw3esenOES/G93jPGGIZ2m0Qf8j2MOFCdAuQzUuIqFZs3Rwd9tGM2UN3zWxxo7eCVcHpGdqXWu1aSN/3cM8SYbzPw7u/xJm+NMHwm+vx3bSCcBSG39y59dhv/7yZE7cv4tPZcSq8wpNHlHLDJ80bfmEK7BOO9OrM64wxzLlvDt6BXvoe0LHlq4t6FlHUp4imec5Ja/139ZQMWnNO7cJnF7LRYRs589MSySYrkGhOaZ2hR4NVQS0emXm3ZGIns0JO8bBtbnMKj/3nU+fzNml+nN3vaWDUHfXsfFc9E+e03lH71zcb2fb2era9vZ4np6y+OHTcs2FG/reei99efSpw9ftNPP9Dei4gjQtH2lMrYB150kbvCFYFw6ncYLaznozW1lQ2APfYjkN1XAKX+9zoOeOOj178Q9S4M9GzuL4hfCpd/KFFdHCersoOxeC9d/7Csf/KXHGjdrdRY0ytMWbbdAZTMJz1DFNa7GJRQ4LlySJFkajhzZ9j7DTIxfyLulN7nnMrK4Lp53bv8LaXRQwvT4tx4vZFhKMGl4CIs59M2C3SOBxjOjxsMjwtzPKPl1P/fT3T/z6d6X+fzopvVlD3ZR0/nP8DkZ8i1P67ltrrawGILotSe2PtqtcPPG4gs+6cxbRLp9E4s5F+B/Vb9Vjdl3WU+Eoo6lWEu9xNyZASpl06DRM1lA5p35y3djA4U1ZUpvlD7wDBdO/G44Ib9i/hu7O78emp5dz2eZTvFsX5y5uNXD62mElndOPK8V7+8ua615cDP0b5an6cSWeU89nvy7n+kybqmgyTF8Qp9QiTz+zG53PjhBoN81Yk+GxOnEO2KkrL+xgbjnRq7fc8aKNNZOjCRTbp1JWHNLgFOJ/siUd1wEeJbbcd1XTXimeK/R9u5ZqVriVS5pGmoWiqXW4A/kAWXMBSHXdgQ3jn3SONS47aeMDE+R7Prmnazbe0b21RlR7/BI7HqTPUZfPqDVXPh4knIGHgyBFF/HaLtk88X5wa5Yu5ca4c7/Qk+G5aQV2ToTkOz/8Q440TytimnzN368oJTVyytxeXCL8e7uG2z8Ns998oZ+zU6VVXOqTMmPISY35sFNmiI68r36KcbR9o/fpJa8VMinoV4bvAt+r30qGlDPcPb/P1Lbcx8Oi0TG9+LVgV/C4dG1btci3J5anSZWB3FwOT14e6e4Wt+7mYU2cQgbrkSNJQIwzqvu7XxHeLEowZ4sHjEjzFMLK/m9emx9iuv4tIzJAwTvExtwsue7eJK8alr/DY0FhssBizyIj02/CzV8uDNvpYsCo4Px0bzmaSLUvT+aoDdwGn2Y5Ddc2p7lc+vtTzyAgR2j9Qv33Owx/6T4q3qTrCX/E0cLjtMFTX3NCr5/sPVHTfBZGUXcpNOipYFXwqxdvsEBG5EnjfGPOWzTis8Vc8AxxmO4xccMygjT6Y4vXubTuODNs3WBV8x3YQBctf4QGmsXrt5LSqXZ5gzP0NTDmrG3PqEvz6kTAG5+LSx6eUM7TnmteW3/gpxhUTmnjzhDLCUcOudzdw9i7FXLinl/Nea+S92hgnjCxi38083PJZM/cenOpDyJr222RQOi+eZqttg1XBb20HkWnZ1MtxBTovLefdGz9wzzHNN9UvN+XfpHCz89CCC9ngOtsBqK67cNmq4kZTU7jZb4H/pXB7bRKRNkfQGGMuK9hE1HGN7QByxd7hxrQu4pmFvtBE1DJ/KAZcv8HnpUB9s+Hwp8LcdEAJPbzCf7+I8u9flzDr/O78+9clnPriujW89h/m4cDhHva8t4FjnomwxyZu3Mks4aYDSph0Rjcu3NO7ajmmf7zfxJH/C3P3l+mZ671zY1NBzZsEXi7ERBSyKBmtramcA9xmOw7VdbNM/413bLpz2xfie6aquFEN/pBeqLDNH/oMCNgOQ3VdsrjRprunrrjRFcGqYIe2IyLlIhIQkW9EZIqIHCUiO4nIBBH5UkReF5GByee+JyI3icgXwCUiMkNEXC22M0tEikTkARE5Inn/LiLycXL7E0Wku4i4ReQ6EflcRCaLyOkpeO/Zwx/6Enjddhi5YHw4nI/L/KzPRbYDUIBTnX5GOncQjTuJ6HHbFXHY1s7Q+ge/aeawrZ3reL/bxtNmAaNLxniZdEY33jyhHGNgiz5rpgkv/BBlp4Eu6psNPy1L8NTvynj6+yjhNMz33qchnOoRdtksDvzVdhC2ZE0ymvRPYH2LmKsckcDl/lP0j+OOi148NWrcXfninQvclaq4VJf9FV0vLS8UQ/Hd8xeOvW7Rkq9cxnSluuYU4OlOvO4AYK4xZvtkIaPXcOoHHGGM2Qm4D/hHy5CNMTsbY64AJgFjk/f/FnjdGLOqrKOIFOMsNP8nY8z2wK+ACHAqEDLG7ALsApwmIpt2IvZspr2j7bBVc3QzjLG1XnamvRisCk6wHYSC5IX1C9O1eWMMp77YyNZ93Vywx+o5nYO6u5gwwzl0v/NLnM37rHv6H08YloSdqrCTF8SZvCDB/sNWD0SJxg03fdbMX/byEomunpweT0BzGs4K9nAKjaW0TG22mnLSFPeUk6acuvJ3EblIRPwWQ8qorEpGa2sql5ChIQwqMz5ObDtiVNNdvb9LDOnskiyXaK9oFvGHvkULSeWVAxrCO707c45nYCw2sZObuKijvaJJQWA/EfmXiOwNbAJsC7wpIpOAS4HBLZ7/5Fo/H5X8+ei1HgPYEphnjPkcwBhTZ4yJAfsDJya3/xnQB9i8E7FnL3/ofeA922FkOwHpnUj8ZDuODIgBf7EdhGrBH3oGSMt0go9mxXl4cpR3fokx6o56Rt1RzyvTotx9UAkXvtHI9nfUc/E7jdz1W2e+5xdz4/w+OWQ3moC97w+zzW31/OGlRh45rBSPa/Vo9ts+b6Zq+yLKioSRG7kIxwzb/beenQa66VmS+lHv3Yzp7jXm55RvOPuswKmie5iIdGw9mjyRNQWMVvJVB8qBn4CNbMeiUutk96ufXOZ5eJsOFDf6CNgbfyi7PqSFzl/RH/gRUl6kqlPiCcPOdzewcXcXLx9bxnHPhvliboIiF+y6sZs7f1tCkXvdA+XMUILfvxhhVp1BgFeOK8PX08Vxz4YJLkjw2y08XLOvUxn06veb2La/K21l7LPFjb16vn9/x4obPRusCna6qJWI9AYOxCle9w5wgDFmj1ae9x5wkTHmi+Tv3XB6ZHfE6SXd1BgTF5EHgJeBqcAdxpi91trOM8Bdxpj8Hsrqr9gK5++SvnKXeeDc/n0nvFteNnbDz8xptwWrgn+0HYRai79ia+AbIL8PKl105KABH3zvLc73QmOXTjlpyt9wRgJ1M8ZcIiIXJX/2i4gPZ6RQX2ARcLIxJhPLKWZMVvWMwqp1R8+zHYdKvfvjv9ljTPNN9ctMt/YUN4oDZ2simoX8oYXAlbbDWOk/nzWzdd/VX2XHbVfED2eXEzyznEjMcM9XrS/KfeJzEf68p5fvz+7GxNPK6V8uVtZTyyYXLFs+5uk58+e1s7hRmC58V4vIICBsjHkEpzjWbkA/Edkj+XiRiIxo7bXGmHrgc+A/wMtm3TUjpwIDRWSX5La6JwsfvQ6cKSJFyfu3EJHyzr6HrOUP/YBTFFCtxz7hSHrLgdoXAvy2g1Ct8Ie+pwDXk+yo0ZF1Cy3lmdnAjcmfbwOOE5G1L/TfAjxojBkJPArcnMH4MiLrklGA2prKJ4AXbcehUs8pbnTHds/F95qwgeJGt+EPpbIir0qtW3B6pqyaXZcgMC3G73dcvT7hgZsXISKICLsOcjO7bt0pJ98tihNLwH7J+TDdioWyIqHIRcbXU8s2W0ajm300Y/ame4QjGypudFWwKjirC7vaDpiYHDJ7OXAZcATwLxH5Bqdnb8/1vP5JnHU11x6iizGmGWcY7y3Jbb0JlAD3AN8BX4nIFJwq3fm6vvV1wFe2g8hme4cjm9mOIc3+FqwKLrYdhGqTHyi4NSU7Yp+GyADbMaTZOcGqYASc6STAQ8C5az1nD+Cx5M8PA6MzF15mZGUymnQWzlU9lWcMLtf50bPHHhO9ZGpz68WNFuCcmKps5Q9FgROB1rsdM+S81xq59lcluFqZrhKNGx6eHOWA4evmGj8uSdCzRDjsyTA73FnPn99oJJ4wbN3PTb8yFzve2cBBW3iYvjRBwsCOA90ZeDfZoxiK71qwaOwNCxd/7TKmtZOlqcANXdmHMeZ1Y8xIY8woY8wuxpgvjDGTjDFjkkWNRhhj7k4+d9zKIbotXv+0MUaMMRNa3HeSMebp5M+fG2N2T25rd2NMvTEmYYy52BiznTFmW2PMeJOvRWycZSROwXIbzWZ9Eom+njwb7tbCW8AdtoNQ6+EPraCAK6i2xzbNzcNwRsLko0eDVcHn17rvJpxCe/k3Ymc9sjYZTS71opPu89iniREjdmi6q8+3iaEfrvXQmfhD+XmCmE/8oa+Bq23t/uUfo/QvF3Ya1HqieFagkTFDPew9dN1kNJaAD2bGuH7/Ej4/rZyflyd4YJJzzm5jPbVstX84suN7M+cUD4zGPmtxdwI4NVgV1CQn2zmjS2psh5HNNonGZtuOIQ3qcNqoTnPJfg8DH9gOIlu5wNUrkZhmO440mAucs/adxpilwFM4CelKH+MU6gM4jjz8vGRtMpp0N/Cu7SBU+jRQ2q2y+Z+jr4ie8IkxhIAH8Ieesx2XardrgC82+Kw0+GhmnBenxvDdtIKjn47wzi8xjn/WmV9yxXtNLAobbvx168NrB/cQRg1ws1kvFx6XcMiWHr6at+a0w0yup5bNeiUSvd+YPXe3U5eHPsCYMHBtsCrY2erYKvOuBgpyIfX22CPSmI8XVS4MVgXztcc3vzh1MY4DltgOJVtt19Scj0s+/iFYFVzWxmM34BQrWukc4GQRmQycAPwp3cFlWlYno7U1lQb4PTpcN+/dH//NHuObb/iedcfKq2zmDAU8Ecj48jv//FUJsy/oTu153XniiFL22dTDI4eVcs9Xzbz+U4zHDy/FJa2Xm99lkJvljYZFDc580ndq42zTb3UPa6bXU8sF5y0L7f343AXvokPoc4s/1IwzXLdAP7nrt084nG9LKbwarAreYzsI1QH+0CycJKOwrnS20/iGcIntGFLs/mBVMNDyDmNMtxY/LzDGlBlj/MnfZxhj9klOa9k33yrpQpYnowC1NZU/45zsaiPNb9FaM/Dc5BwKlUucqoB/sx3GSme83MiChgR73NvAqDvquXJCE7Dmempul3D9fiXs+1CY7f5bjzFw2k6rq+Vmej21HLFi2+bm83R4bg7yhybizEVSa9mhsWlzjGmyHUeKLMJZJknlGn/oVeBftsPIRmMijZvajiGFfgbOtx1Etsm6dUbb4qsOXEMWnfCqlLuotqaySwVRlGX+isdZPa9B5Z/j8YcetR2E6iR/RQnwHs4SOqqFPYYO/rbe5Wp1GaEcEgf2D1YF37EdiOokf4UbZ73lMbZDyTajfJvMjosMth1HF0WAPYNVwUm2A8k2Wd8z2sKlONXhVP55itXrLKncdSrwte0gVFr8RxPRHOcPNQKH4Kxrp1rYuqk5H5Y/uVgT0RznD8VxLugutB1Kthkci3VlGbFscYYmoq3LmWS0tqYyARwD5MMHUq32CVCVnB+scpk/FMY52V1kORKVWi8DF9gOQqWAPzQfOBgI2w4lm4wNR4o2/Kys9niwKnhtWw+KiBGRG1r8fpGI+DMSmeoYf2geTkGjdRfILmC7RxpzvZT9TcGq4ENtPSgiH29oAyJynoiUpTasVvczSkQOTPd+WsqZZBSgtqZyMXA4Tle3yn0/AwfX1lRmvPiNShN/aCZwBLq2Yb74BjgGf0hPjPKFP/QVcBJah2GVceHIJrZj6ILPcQpUrU8TcJiI5FuxpvzkD72FMxpQJe0TjvSxHUMXvAZctL4nGGP2bMd2zgM6lIyKSGcWSR8FaDK6PrU1lZ8DR6GVAXPdcqCytqZSe9HyjT/0PvBH22GoLpsHHIQ/lK8Ljmc1EYmLyCQR+VZEvhGRC0XElXysTEQeFZGgiEwRkQ9FpFvysQ3/f/lD/0NrMKwyNBbbRIzJxWNRLXBwsCq4oQu6MeAuWimcIiI+EXlHRCaLyNsiMiQNcaqO8of+CVxnO4xssVNj43CMycWL3EHg6GBVcL05y8rvbREZJyLvicjTIvJD8nteRORcYBDwroi8m3zu/iLyiYh8JSL/a3EMqBWRf4nIV8Dvkr9fkXxeUES2Sj6vXETuE5GJIvK1iBwsIsXAlcBRyePPUen706yWc8koQG1N5UvAH2zHoTotChxeW1P5g+1AVJr4Q3cBf7Udhuq0MPB/ySUHlB0RY8woY8wIYD/gN8Dlycf+BCwwxmxnjNkWZ752x07U/KF/Af9OYbw5baN4/BfbMXTQHGDfYFVwXjuffxtwnIhUrHX/LcCDxpiRwKPAzSmMUXWFP/QX4L+2w8gGXkNJmTHTbMfRQVOBXwWrgh1dnnIHnF7QbYDNgL2MMTcDc4HxxpjxyVEOlwK/MsbsiLPee8vpNEuMMTsaY55I/r44+bz/srqX9hLgHWPMrsB4nIsfRTjLtz2ZPP482cHYOyUnk1GA2prK+9hAt7fKSjHgxNqaSi20kO/8oWuBK2yHoTosDFTiD31hOxDlMMYsxLkA+0cREWAgTjKy8vGppnPLk1wIPJaaKHPbTo1NuTT9ZyFOIvpze19gjKkDHmLdtbz3YPVn4GFgdEoiVKlyNs7/W8Hbqrk5l0Yv/IzTRjtTjGqiMWa2MSYBTAJ8rTxnd5xk9SMRmQRUAUNbPL52Evls8t8vW2xvf6A6+fr3gBLAysiInE1GAZJLgegC7LkjChxTW1P5xAafqfKDP+QH2iysobJOA3Ag/tB7tgNRazLG/Ay4gf7AfcBfk0O0rhaRzTu1UX/I4MwffS1VceaqfRvC3W3H0E5Lgf2CVcGpnXjtTTi96OUpjUilj9NGTwGesR2KbWPCjblSaGw2TiI6Z4PPbF3LC4txwNPKcwR4M9l7OcoYs40x5tQWjze0sc2W2xPg8BbbGGKM+b6TMXdJTiejALU1lVcBV9mOQ21QM/C72prKp20HojLMH/orcKvtMNQGrUxEJ9gORK2fMWYSzvCt64DewOcisnWnNuYPRXEq7BZ0D+nukcbhOD0R2awOOCBYFZzcmRcbY5biLKXW8qT1Y1avD30c8EGXIlSp5yz5cgzwiu1QbBofDm9sO4Z2WICTiNamYdsrgJUXzT4F9hKR4bBq/ucWHdze68A5ydE2iMgOrewnI3I+GQWoram8DDgHLYWdrZqAQ2trKl+wHYiy5lzgettBqDatTETftx2Iap2IbIZzVXshgDGm3hjzrDHmLOARulL90B9qBo4H/pWCUHNSd2N6eJ3e52y1EGf+2edd3M4NQMuquucAJ4vIZOAEnPnIKts4F40OB962HYotm0VjQ8W5oJKtaoGxwargj2na/l3AayLyrnEKrp0EPJ5su58AW3Vwe1fhzBGdLCLfsrpj711gm0wWMBJj8qe6u686cATOQdlrOxa1SgQ4pLam8g3bgags4K/4I/Af8uRCWJ5YBByCP7TBdc5U5ohIvTFmZXXEfjjFZT4xxlwuInsB3xljliWrH74G3G6Mebrl6zrFX3EWTlGbgmujRw4a8OH33uJsnDP5I/CbjswRVXnKX1GMM0z/ONuh2LDvJoM+X+jx7GI7jlZ8CVQGq4ILbAeSi/LqYJMcAvproKOVq1R6zAb21kRUreIP3YquFZxNvgV200Q0K5WuXNoFeAt4g9UFwYYBE0QkCHyNU0lx5ZyyMhGZ3eJ2wTpbXh9/6HbgMAqwjY6ORLLx6vzHwJ6aiCrAGcXgDx0PXG07FBt2bGwK246hFa/g9IhqItpJedUzupKvOrAd8CqQC+PL89UnOENztXGqdfkr9gBeZM3hYiqzXgWOxh+qsx2IykL+it2BlyigNhosLp527MYDOlcMKj2eBY5rxzqiqhD5K04C7gSKLUeSMa+Wl335l/59d7IdRwt3A2duaB1RtX551TO6Um1NZRDYBWfcs8q8+4FxmoiqNvlDn+AsKTDJciSF6mbgIE1EVZv8oU+BPYGfbIeSKSOam4dhTL3tOAAD/BP4nSai+UMcH4rIb1rc9zsR6Vw1a3/oAWAszvqTBWHPSGQ42dGLFgX+HKwK/iEbE1ERuVJEfmU7jvbKy57RlXzVARfO0i9/J08T7ywTBy6sran8j+1AVI7wV3iBG4GzbIdSIBqAP+EP3Ws7EJUj/BX9cCrt5syJTVfsPWTjScvd7lEWQ1gInBCsCur0ljwkItsC/wN2wFli42vgAGNM5y/6+CsGAk/jXDzKezv5Nvm52SnoZkstcHSwKviZxRgQEY8xJmYzhlTJ62R0JV91YB+cg+lGtmPJYzOAE2prKrUsvOo4f8XhwL1Ahe1Q8tgXwLH4Q9NsB6JykL/idJylZHJlPc5OOXujfu+9X1Y6ztLu3waOD1YF51vav8oAEbkW58JgefLfocC2OJVN/caYF0RkBM4os2KczpTDjTFtf3c7hY2uBC7CWY84bx0+aMBHP3qL97K0+2eBU4NVweWp2qCIlOMsuTQY5//uKmA6zoX6bsBi4CRjzDwReQ9nRNlonGkUpwCbGmMSye38gLPs193Ay8midrvgFI4sx1ndYl8gDNQA43CKvt5mjLkzVe+powqit7C2pvIdYHsKuCR2mj0MjNREVHWaP/QMzpXiibZDyUMJ4BpgT01EVaf5Q3cC25Hnx9Hx4XCZhd3GcUZw7a+JaEG4AjgW+A1QArxjjNkVGA9cl0wqzgD+Y4wZBeyMUxCybU5ho2pgd2BK+kK3b69Io41hsU3A2cGq4OGpTESTDgDmGmO2N8Zsi1Md/RbgCGPMTjjVk//R4vnFxpidjTFX4CSmY5P3/xZ43RgTXfnEZLX1J4E/GWO2xxnhEsFZazhkjNkFZ1rjaSKyaYrfV7sVRM/oSslhu3/AmYvR0240eWE+cFZtTeVztgNRecJfUQRcgDO83sZJYb6ZCZyg64eqlPFXCLCyl7TzS8hkqYVu98J9h2zcP4O7/AY43faQP5VZInIlUA8ciZOQrhxu2RtnVYgdgEuAh4Bn19srujanl/RS4G84Q4HzyiRv8dQTBg3YMoO7/BA4I1gV/DYdGxeRLXCqpT8JvAwsw6mivbKCthuYZ4zZP9kzerkxZkLytccCY4wxZ4jIczhLfL0pIg8ktzUVuMMYs9da+3waGInTQwrOqLTTjTFWpgcUVDK6kq86sBFO9/extmPJYQ8B59XWVC6zHYjKQ/4KH3AbcKDlSHJVM/Bv4Gr8oWwoyKLyjdNG7wX2sRxJyo3ybTInLpLuavz1wOXAf7KxAIpKLxHx43wGjgGONcZMbeU5w4BK4BycROGdDu3EXzEKp1dthy6Gm1XiEB/l26QJkXRfsF4C/BW4L1gVTGuyJCK9cc53TgPewZlHvEcrz3sPuMgY80Xy9244PeE74vSSbmqMibcjGX0GuMsY83q63lNHFMQw3bXV1lQuqK2pPA6nu/pH2/HkmE+BvWprKqs0EVVp4w/V4g9VAr+jgCoFpshrwHb4Q9WaiKq08YdqcY6hZ+GcVOeNwbHYzDTv4jlg62BV8EZNRAve68A5IiIAIrJD8t/NgJ+NMTcDL+D0YnWMPzQJ2BVnCHhziuK1zg3uikRiehp3YYAHgK2CVcF7M5CIDgLCxphHcEac7Ab0E5E9ko8XJecQrxuoU/37c5w5oS8bY9b+PpkKDEzOG0VEuouIB+dzd6aIFCXv3yI5PNyKgkxGV6qtqXwbp4H/HVhhOZxs9wtwdG1N5R61NZUf2w5GFQh/6GlgK+BfrB5Oolr3M3Aw/tBv8If0IptKP3/I4A/9F9gS52QoYjmilNgt0piuE/dvgYOCVcHDglXB9c8BVIXiKpzCRZNF5Nvk7+AM350iIpNwihs91Kmt+0Mx/KGrgS1witpEN/CKnLBtU/PSNG16IjAuWBU8OVgVXJymfaxtO2Bi8v/6cpxpSkcA/xKRb3B6PNdXKflJ4Pjkv2swxjQDRwG3JLf1Js6w8HuA74CvRGQKznq11oZ0F+Qw3db4qgN9gGrgbKDUcjjZZDnOxOlbamsqmyzHogqZv6I/Ths9E+fLVDlm41xNvQt/SNckVPb4KzYC/oxTfMXaVfau+qi0JHjGgP7bpXCT3+MUrflfsCqYSOF2leoYf8WmOPNJTySH55M+0b3bp//o23v3FG7yS+DyYFUwkMJtqnbSZHQtvurAAJzS2Dl9ME2BeThz9v5bW1OZritQSnWcs6baxThzK7yWo7HpZ5zS7A/iD+XNECyVB/wVfYELcS7u5txSME1C485DN3GTHMLWBVNxltt4QpNQlVX8FcNwRgUeTw4uBTPP7Z63/5CNB6ZgU18D/mBV8MUUbEt1kiajbUj2lJ4L/B4YZDmcTJqMU9zp8dqaSj3BVdnLXzEYp7DD73EqEBaK73Aqgj+OP6TzzVT28lf0Bs7DOZbm1BrCuw0d/F3Y5dqmky//GOdi7pM6J1RlNX/F5jjDQo/CGS6cM7b3bTI/ITKgEy81OEtU3Qq8mO45oWrDNBndAF91wI1Tzew0nDWhcu4KUjskcCYz31hbU/mW7WCU6hB/RSlORcLTcYo15KMG4GngPl2mReUcf0UFTqGjk3DmrmW9Ewf2f//rkpIxHXhJCGfN7TuDVcG8XudR5SFnNMMxQBWwk+Vo2uXAwQM/nVVU1JGhuouA+4G7glXBn9IUluoETUY7wFcdGAycjLNY7FDL4aTCp8ATwFO1NZXzbAejVJf5K0bitM8jyI8RDZ/glOZ/En9Ii6yp3Oev2BU4DjgayOR6nh1yb0WPj27q3XOvDTzN4LTRe3GG4mqRNZX7/BUjcJLS48ji46i/T+8Jz/ToNnYDT4sDE3CKNz0brArqiL8spMloJ/iqAwLsAvwWp9d0B0CsBtV+k3AS0Cdraypr7YaiVJr4KwSnl/SQ5G0rm+F0QAL4DHgReA5/aJ2155TKC/4KD84apQcD/wcMthvQmn4q8sw4ZPCg1i46N+IM8XsBeClYFZyf2ciUyhB/hRtn+aYqnHPdHnYDWtP7pSWTzx7Qv7Ulb+qBN3DaaCBYFVyS2chUR2kymgK+6sAgnIb6W2A82VWwYQHOArpvAW/X1lTOsByPUqsk11b7APiHMebV5H2/A041xhyQsh35K7bEOekdD+wO9EzZtrtuHk659TeAN/GHFlqOR6nM81fshNNG98NZci3dC9pv0EjfJkuNsxj9Lzi9Ky8Brwergg12I1Mqw5yLR7viJKe/wlkLs9hmSI0ikV2GDi7CWTdzKvAezoXct4NVQV39IYdoMppivuqAC2fNtZ2AnZO3UWSmMu8y4AdgCk4BhQ9rayrTuTCwUl0mItsC/8MZYeDBqW53gDEmPXM6nF7TrYA9Wty2IjPzwZfglJD/YtW//tDMDOxXqdzh9MhsgfOdMKrFv30zsPfFON9BXx0/cKOPvinxfhGsCuo0FqVa8leU4Jzf7pm87QRsTGZGCS7EOX5+dcDgQR/PKfJMzOCaoCoNNBnNgGSCuhXOwdUHbApsAgwABgL9cNZNXN/J8AqcZLPl7Rec5PMHYGptTaX2qKicJCLX4hTpKU/+OxRnoe8iwG+MeUFERuAUHygGXMDhxphpKQnAX1GE0zY3A4a1uG2CMzSpe/LfttYgNkAzEME5UM4EZrW4zQR+xB+qTUm8ShUip4L2ysR0GE6F3pbtc+WttYu/TTiJ5qK1/l3582xgEv7QrLS+B6Xylb/Ci3Ps9uGc52661s/dcc5zPbSdtIZxRgvNA+Ymb/Na/PsD/tCcdL0FZYcmo1kkORe1OHkrSv4bA5bX1lTGbMamVDqJSDnwFU5C9zLwrTHmERHpCUzEOQGtAT41xjwqIsWA2xgTyWigTo/NypPdKM78sUb8IR0SpFS2cNrpygTVBSzRAmBKZRFnhJKH1cmpG0hoOy1MmowqpbKCiFyJU3jgSJyRAisvwPQGfo2TkF4CPAQ8m7JeUaWUUkopZYXHdgBKKZWUSN4EZwju2pVkvxeRz3CKhb0iIqcbY97JdJBKKaWUUio1XLYDUEqptbwOnJOstIuI7JD8dzPgZ2PMzTgl21sr6a6UUkoppXKEJqNKqWxzFc6c6cki8m3yd3CG704RkUk4xY0eshOeUkoppZRKBZ0zqpRSSimllFIq47RnVCmllFJKKaVUxmkyqpRSSimllFIq4zQZVUoppZRSSimVcZqMKqWUUkoppZTKOE1GlVJKKaWUUkplnCajSimllFJKKaUyTpNRpZRSSimllFIZp8moUkoppZRSSqmM02RUKaWUUkoppVTGaTKqlFJKKaWUUirjNBlVSimllFJKKZVxmowqpZRSSimllMo4TUaVUkoppZRSSmWcJqNKKaWUUkoppTJOk1GllFJKKaWUUhmnyahSSimllFJKqYzTZFQppZRSSimlVMZpMqqUUkoppZRSKuM0GVVKKaWUUkoplXGajCqllFJKKaWUyjhNRpVSSimllFJKZZwmo0oppZRSSimlMk6TUaWUUkoppZRSGafJqFJKKaWUUkqpjNNkVCmllFJKKaVUxmkyqpRSSimllFIq4zQZVUoppZRSSimVcZqMKqWUUkoppZTKOE1GlVJKKaWUUkplnCajSimllFJKKaUyTpNRpZRSSimllFIZp8moUkoppZRSSqmM02RUKaWUUkoppVTGaTKqlFJKKaWUUirjNBlVSimllFJKKZVxmoxmiIgYERme/PkOEfm77ZjWp2W8SuUjEdlbRKbajiNTRORVEamyHYcqbCLynoj83nYcSqnskWvnyCq18ioZFZFaEfnVWvedJCIfdnG7IiJ/FpFpIhIRkZki8k8R8XZme8aYM4wxV3UlpmRc40QkISL1ydscEbmiq9tVqqPS2PaMiDQkP99LRORtETmqa9E6jDEfGGO27Op21opxjojcKCLuTmzHl9yWJwUx+UXkkZb3GWN+Y4x5sKvbVtkr1e1QRIa0OL7Ur/VZrxeRvVMT+Rr7PFVEfhCRFSKyQEReEZHuqd5PV7XWxlo89oCIxERkYKbjUqothXSOLCLHtfieiqx1rlwvIq+JyJWtvO5gEZmfiuOwar+8SkbT6GbgD8CJQHfgN8C+wFM2g0qaa4zpZozpBowGThWRQyzHpFQqbZ/8fG8JPADcKiKX2w1pHStj3Bc4Fjht7SfowU3lGmPMzJXHl+TnG5Kf9eTtg1TuT0TGAtcAxxhjugNbA0+mch+psL62LCLlwOFACDg+Y0EpZU/WnSMbYx5t8b31G1qcKyfvexA4XkRkrZeeADxqjIllOuZCVlDJqIhUi8hPySuu34nIoS0eGy4iE0QkJCKLReTJ5P2bA2cBxxljPjHGxIwx3+IcbA4QkX2Sz3tARG4TkUBy+5+JyLA24nhARK5O/jxORGaLyIUislBE5onIyS2e6xWR65NXmhaIM3yhtLXtGmN+AT4Gtmnx+v+IyCwRqRORL1teyRYRt4hc3OJv8qWIbNJKvKOT2xi39tXgtXtzxBmC9U8RmZjc5wsi0rs9/z8qf3Wm7a3NGLPYGPMwcCbwNxHpk3x9hYjcm2w7c0Tk6uRn2ysiy0Vk2xb76pe8Stp/Zdtr8dgmIvKsiCwSpxf21haPnSIi34vIMhF5XUSGthHjD8AHwLYt2sapIjITeEdEXCJyqYjMSLb3h0SkIvny95P/Lk9eud1jQ/sWkREi8qaILE1+P1wsIgcAFwNHJbfzTfK5q4ZHri+OFnFXJb93FovIJe36j1ZZLRXtsMXz13tsEqeHYVLyOPBT8nO50lAR+SgZxxsi0jd5/y7AJ8aYrwGMMUuNMQ8aY1Ykt7nGEF9Zq1cn+bk9V0R+Tr6H60TE1eK5H4nIrcn3+IOI7NvitYNE5MVkW5ouIqe1eMwvIk+LyCMiUgecQSttLOlwYDlwJbDGsHgR6S0i94vI3GR7fn5Dfy9p4/ttff9n4vh3sm3XiUhQWnwPKtWaznw/SA6dI6/leaAP0PKcuBfwW+Chjv3lVFcVVDIK/ITzwasArgAekdXDaK4C3gB6AYOBW5L37wvMNsZMbLkhY8ws4FNgvxZ3H53cbi9gOvCPdsY1IBnTxsCpwG3JRgFQA2wBjAKGJ59zWWsbSX4p7JWMa6XPk6/tDTwG/E9ESpKPXQAcAxwI9ABOAcJrbfMA4HHgcGPMe+18PycmtzUQiOFcNVOFrTNtry0vAB5g1+TvD+B8zoYDOwD7A783xjQBz+J8xlc6EphgjFnYcoPJk7uXgRmAD6edPZF87GCcE8/DgH44yebjrQUmItsk3+fXLe4ei9PD82vgpORtPLAZ0A1YmfSOSf7bM3n19pP17VucoYtvAa8Bg5Lv/21jzGs4vUtPJrezfSuhri+OlUbj9EbvC1wmIlu39p5VTkllO2zz2CQiu+Kc0P0Z6Inz2a5t8dpjgZOB/kAxcFHy/s+AX4vIFSKyl3RumN+hwM7AjsDBOMeilXbD+Rv0BS4HnpXVF0ufAGbjtKUjgGtWnkgnHQw8nXw/99J2G6vCaaNPAFuJyE4tHnsYKANGJN/7v2GDf68HaOX7LflYW/9n+ye3sQXO//WRwJI2/2JKOfL6HHmt+CI4Pbcntrj7SOAHY8w3rb9KpY0xJm9uOF/e9ThXJVfewsCHbTx/EnBw8ueHgLuAwWs951Lg0zZe/wRwd/LnB4B7Wjx2IM6HeuXvBhje4rlXJ38eB0QAT4vnLgR2BwRoAIa1eGwP4JcWr00k32ddch/PAsXr+RstwxlmBTB15ftv5XkG+BvOyfm2Le73A4+0+N2XfK4n+ft7QE2Lx7cBmgG37c+H3tJ3S0fbSz62qt2sdf984DhgI6AJKG3x2DHAu8mffwX81OKxj4ATkz+PwzmIrmxXi1q2wxaveRU4tcXvruR7G9oixrpk2/oJuDr5nJVtY7MWr30bOKvF71sCUZzkeo22tKF9J9/n1238fddop8n73sNJ0tsbx+AWj08Ejrb9OdPb+m/paoctnm9wTvg2dGy6E/h3G9t4D7i0xe9nAa+1+P03wEvJ2OuBG0keP1p+hpO/n9TyvSXjO2Ctbb/d4rlzAWnx+EScYXmbAHGge4vH/gk8kPzZD7y/1vtorY0NwTkmj0r+/jrwn+TPA5OP9Wrlb9Lq34sNf7+1dd6yD/AjznmEy/bnUm/ZcUvH9wNZfI7c4r5xJI/1a90/Ovk3KEn+/hFwvu3/p0K85WPP6CHGmJ4rbzgHIwBE5MTkMJjlIrIc2BbnCinAX3A+2BNF5FsRWXk1dTHOQaQ1A5OPrzS/xc9hnN6G9lhi1hyfvvK1/XCuon7ZIubXkvevNDf5XnvgXFGN4IyFB0BELhJniF8o+foKVr/nTXBOnttyHvCUMWZKO9/HSrNa/DwDKGqxT5W/Ut32WiUiRThtYClOUlYEzGux7Ttxeh0A3gXKRGQ3EfHhXD19rpXNbgLMMK3PExkK/KfF9pcm4924xXN2NMb0MsYMM8ZcaoxJtHisZXsYhNMmVpqBkwBu1MbbXd++N9R+16c9cXT2+0zZlYl2uKFj04Y+m21+towxrxpjDsIZzXMwThLZkeq7ax9/BrX4fY5JnnWu9fggYKlJDgdu8VjLNt5yu205AfjeGDMp+fujwLHJ76xNkvtY1srr2vp7bej7rdX/M2PMOzgjHW4DForIXSLSox3xq/xXaOfIbTLGfJiM75DkkOFdcUYQqgzLx2S0VeLMs7ob+CPQJ9kIp+A0Lowx840xpxljBgGnA7eLU2b6HWCT5DCaltvbBOfKzNtpDHsxTnI5osWXR4VZXUhiDcaYEE5DOigZ4944XyBH4lyN7YlTVGHlhO1ZQKtj9pN+h9NI/9Tivgacxr/SgFZe13Le6RCcHpfFrTxPFYAutL22HIwzbG0izme4Cejboo30MMaMSG47jjMU55jk7eW1TjhXmgUMkdYLk8wCTm95ADfGlBpjPm7nn6Dlye9cnBPMlYYk38uCtZ7Xnn3Pwhliu6F9tmZ9cag8lOJ2uKFj04aOLRtkjEkYY97GOQavnO/YmePP3Ba/byyyRsGSlY/PBXrLmlV7hwBzWoa0doit7PtEYDNxqnHOx+nV7YvTCzQruY+erbyurb/Xhr7f2vw/M8bcbIzZCWd00hY4Q4CValUhnCO34SGcdns88LoxRo+BFhRMMgqU4xw8FgEkJ0C3LGzyOxEZnPx1WfK5CWPMj8AdwKMisrs4hVFGAM8Abxlj3kpXwMnelbuBf4tI/2ScG4vIr1t7voh0wxmT/23yru44J5iLAI+IXIYzN3Sle4CrRGRzcYyUZFGYpLk48wH+JCJnJu+bBIwRp+R/Bc5Q3rUdLyLbiEgZThGHp5NJgSpMnWp7a29EnOIfx+Fc7f+XMWaJMWYezjyWG0SkhziFeYaJU5VzpceAo3CG9bZ11XMiMA+oEZFyESkRkb2Sj92BUzBpRDKOChH5XSf+DuDMJTtfRDZNtteV885WttMEayaY69v3y8BAETlPnCIO3UVkt+RjCwCfJIu3dDAOlZ9S0g6hXceme4GTRWTfZJvcWES22lCA4hTxOVpEeiWPSbvizLleWQdhEnCYiJQlT4RPbWUzf06+fhPgT6xZjbc/cK6IFCXb0dbAK8aZ3/Yx8M9k2x+Z3HarS7ckrdHGxCk4trJ3ZVTyti3Od86Jye+qV3FO4nslY1g5T7zVv9eGvt/a+j8TkV3EGQ1ShJPAN9LG/6VSSXl/jtyGh3Cm85xGi1GFKrMKJhk1xnwH3AB8gnMQ2Q5nfPhKuwCfiUg98CLwJ2PMz8nH/oiTuD2CM97+NZy5K4dnIPS/4kz0/lScKn5v4czvWmmQJNdNwhlW1BvnpBuc+Sqv4cwdmYFzQGo51OhGnF6jN3DmvN0LrFGFzBgzEychrRaR3xtj3sQ5uE8GvsQ5IV7bwzhj/ucDJcC5nXnjKj90se0BfJN8bDrOcL3zjTEtCxSciFME5Tucg+TTtBg2ZIz5DOeEbBDOyWBrMcZxRhQMB2biFDI5KvnYc8C/gCeSbXAKzry2zrgPp328D/yC0ybPSe4njFPQ4SNxhhztvr59J3t490vGPR+YhlOQCOB/yX+XiMhXHYlD5acUtMO1tXlsMk4xk5NxCvSEgAms2RPflmU4J4XTcI5JjwDXGWMeTT7+b5waBAtwThwfbWUbL+AcmyYBAZzj2kqfAZvj9Kj8AzjCGLOysM8xOPOl5+IM5b98AyfSa7exKuAFY0ww2Ys03xgzH/gP8FtxCiWdgDNS6AeceW/nwQb/Xuv7fmvr/6wHzkn6Mpxj/xLguvW8F1Xg8vgceb2MMbU4F6LKcd6XskDWnD6hVNeIyHs4RR3usR2LUkqpwiEiBtjcGDO9lcdOwil+NDrjgSmllGpTwfSMKqWUUkoppZTKHpqMKqWUUkoppZTKOB2mq5RSSimllFIq47RnVCmllFJKKaVUxmkyqpRSSimllFIq4zQZVUoppZRSSimVcZqMKqWUUkoppZTKOE1GlVJKKaWUUkplnCajSimllFJKKaUyTpNRpZRSSimllFIZp8moUkoppZRSSqmM02RUKaWUUnlNRIyI3NDi94tExG8xJKWUUmgyqpRSSqn81wQcJiJ9bQeilFJqNU1G85heCVYqu2kbVSpjYsBdwPlrPyAiPhF5R0Qmi8jbIjIk8+EppVRh0mQ0v+mVYKWym7ZRpTLnNuA4EalY6/5bgAeNMSOBR4GbMx6ZUkoVKE1G85teCVYqu2kbVSpDjDF1wEPAuWs9tAfwWPLnh4HRmYxLKaUKmSaj+U+vBCuV3bSNKpU5NwGnAuWW41BKZZhOjclOmozmOb0SrFR20zaqVOYYY5YCT+EkpCt9DByd/Pk44INMx6WUygidGpOFNBktDDehV4KVymY3oW1UqUy5AWh5MnoOcLKITAZOAP5kJSqlVLrp1JgspMloAdArwUplN22jSqWXMaZbi58XGGPKjDH+5O8zjDH7GGNGGmP2NcbMtBaoUirddGpMltFktHDolWClspu2UaWUUiqNdGpM9hFjjO0YlFJKKaWUUiptRKTeGNNNRHoDXwH34+RCfhFZDAw0xkRFpAiYZ4zRuaUZoD2jSimllFJKqYKgU2Oyi/aMWuarDghQgTM8rw/gBeLJW6LFv03AQmBJbU2l/qcplSEt2miv5K0HYIBoK7c6YGFtTWXCTrRKFSZfdaAnzjG0L9ANp40anONnFAgnb/XAgtqayridSJUqPL7qgAfn+Nk7+e/Kc93EWv+GcNpnfTriWNkzmvx5I+AX4Npkz+hQnJ7SvsAi4GSdP54ZmoxmgK86MBjYpsVtc6A/zge+N+DpwOaiwAJgXovbz8C3wBRgpiarSrWfrzrgAjYFtlzrNgSnffagY6NIojjtck6L20zgO2BybU3l3JQFr1QBSF4QGgpsC2yX/HcTVl/E7ehxNAbMBWa0cptcW1M5P2XBK1UAfNWBUpxz2y3W+ncTnPbZre1XtyqM0wGzEJgPTMM5z/0W+C5dyaqyQ5PRFPNVB3oD44DxwC7A1jgns5kSwhkH/yXwGfBObU3l0gzuX6ms5qsO9AfGJG+jcS4QeTMYwmKcNjoxefuwtqZyWQb3r1RW81UHNgP2A3bCST5HAN0zGMIM4FPgk+S/X9fWVDZncP9KZTVfdWBzYM8Wt23I3NQ/g3OBNwh8CLwHfFlbUxnL0P5Vimky2kW+6kB3Vief44HtAbEZ01oSwOfA68nbZzo8SRUSX3WgAjgQp32Owen1zCZxnAtHrwCv4pz46hezKhjJ4+g+wP7Ar4FhdiNaRxPwNfAm8ExtTeU3luNRKqN81YGBwG+B3+BcxO1nN6J1rAA+AiYAb9bWVH5pOR7VAZqMdkJyOMJBwDE4DTOTvSpdtRx4C3gSeFGv9qp85KsO9AAOBo7EOcEtthtRh8wHXgOeB16pramM2g1HqdTzVQcGAMfjHEv3AIrsRtQhPwHPJG+f68UjlY981YHhwBHAYcDOZFdHy4bMBJ7DKVL0ibbR7KbJaDslJ1/vj5OAHkLHx79no6XAE8ADtTWVn9sORqmu8FUHvMDhwFE4vSu5dJGoLQuBR4B7a2sqv7MdjFJd4asOFOH0rpwCHEDH5nlmq1k4J733aY+pynW+6kA3nPPc03CmmuWDGTjnuvfW1lROsx2MWpcmoxvgqw70A85K3vpbDiedvgMewDmgLrEci1Ltlhw+dDbwB7Jv6FAqTQTuAx6vramssx2MUu3lqw6MwFlC4Xjyu41OAP4DvKAVtVUu8VUHdgBOB44ls/OzM8kAbwC3AK9qG80emoy2wVcd2Aq4ADgBKLEcTiY1AHcB12vVT5XNfNWBXYE/Ab8jt4b4dVUdcBtwY21N5WLbwSjVFl91YBxwOU5dhUJSC9wK3FNbUxmyHItSrUpWqT4EqAZ2tRtNxv0E3I7TAbPcciwFT5PRtfiqA2OAv+AUPMml8fGp1gw8CPyrtqbyJ9vBKLWSrzpwAHAZzjyzQtYA3Alcp0tRqGziqw6Mx0lCx9qOxbJ6nHUL/1FbU7nAdjBKwaok9HDgUpyim4UsBNwA3FRbU7nCdjCFSpPRJF91YHvgXzhzzdRqcZwJ4JfW1lT+bDsYVbh81YG9gGtwKuKq1Rpxhu9eU1tTOcd2MKpw+aoD++IkoXvbjiXL1APX4Yw4CtsORhWmZBJ6JPB3nOWS1GqLgRrgttqaykbbwRSagk9GfdWBQcA/ceayZGqNpFzUBFyPc8KrB1OVMcmKftcCh9qOJcs1AP8AbtAq2SqTfNWB7XCGpeqFovWbizOq436dr6YyyVcd2BlnekehDcftqLnAlcDd2kYzp2CTUV91oAS4EPgbUG45nFwyG/hzbU3lE7YDUfnNVx0owzkonEthzQntqh+Bc2trKl+3HYjKb8k26gfOJz8q42bKZOAv2kZVuvmqA31wOlxORTtcOuJz4PTamsqvbQdSCAoyGfVVB3bHqRy7peVQctn7wB9rayqDtgNR+SdZ+OReYDPLoeSy54Hza2sqay3HofKQrzpwIE5Pi89yKLnsKeDM2prKpbYDUfnFVx1w4VTHvRrobTmcXBUHbgYuq62prLcdTD4rqGTUVx0oxrmK+xfAbTeavNCMM/fgeh3OoFIhucbZtcAZFHYBsVSJABfW1lT+13YgKj8kl1K6GTjCdix5Yh7w+9qayldsB6Lyg686MAR4GB02nyqzgLNraypfsh1IviqYZNRXHRiFUx12pOVQ8tG7wIm1NZWzbQeicpevOrAfcDcw1HYseegF4FRdQ1h1ha86cARwD1BhO5Y8dDdwgfbAqK7wVQeOxVmyRNto6v0Xp41qgaMUy/tkNDlUoRqnR1TnnaXPMuAPtTWVT9sOROUWX3XAjTOn5c+2Y8lzc4ETamsq37EdiMotvupAEU412D/ZjiXP/QxU1dZUfmg7EJVbfNWBCpxk6RjbseS5b4Cja2sqf7AdSD7J62TUVx3oATwK/NZ2LAXkfuAsvXKk2sNXHegLPAHsazuWApHAqYp9aW1NZdR2MCr7+aoDG+PMbdzTdiwFIgFcUltTWWM7EJUbfNWBHYDn0FFFmdIAnFNbU3m/7UDyRd4mo8nlIF4EtrYdSwH6DDhYF/lW6+OrDuwEPAsMsR1LAXobOKK2pnK57UBU9vJVB34FPAb0sx1LAXoQZ7SRLtOk2pQcOv8gUGY7lgJ0L04BMr2w20V5mYz6qgP74/S29LIdSwGbARyk1XZVa3zVgZNx5rWU2I6lgH0PVNbWVP5iOxCVfXzVgUtwllbS5SDs+RA4tLamcrHtQFR28VUHBLgcZ91aLfZnz9vA4bU1lSHbgeSyvEtGfdWB83Hmtmi1XPtWAEfV1lS+ajsQlT181YF/4szjVvYtxBnF8KntQFR2SM7hvhNnXUJl3y84F3a/tR2Iyg7J9X0fAH5nORTl+Bbnwu4M24HkqrxKRvUkNyvFgfNqaypvtR2Isit5Jfc24Ezbsag1NOJUw/6f7UCUXb7qQCnOqKL/sx2LWkMdzoXd12wHouxK1kJ5FZ3DnW3m41w0+sJ2ILkob5JRX3Xg38B5tuNQbfqbFmQoXL7qgAenuNXxtmNRrTLA6bU1lXfbDkTZ4asOdAdeRtcmzFYxnCqez9gORNnhqw70At4AdrYdi2pVA3BgbU3l+7YDyTU5n4xqb0tO+UttTeV1toNQmeWrDnhxelsOsRyKWj+DsxapVggsMMllIV4Ddrcdi1qvGHCMLqFWeJKV598Ctrcdi1qveuA3ujxTx+R0MppcQ/Ru4BTbsah2u6i2pvIG20GozEgmoi8B+9mORbVLAjiptqbyYduBqMxI9ra8BexoOxbVLpqQFhhfdWAAThsdYTsW1S4rgANqayo/th1Irsj1Knn3oIlorrk+WWRK5bnkxaJH0EQ0l7iA+33VgaNtB6LSL3mx6EU0Ec0lHuAxX3XgQNuBqPRLXix6G01Ec0l34DVfdUBHmrRTziajvurAFcDJtuNQnXKjrzpwtu0gVNr9BzjCdhCqw9zAw77qwOG2A1Hpk5zi8hAw2nYsqsOKgGd81YFxtgNR6ZO8WPQ8sI3lUFTHdQde91UHtrMdSC7IyWG6vupAFU5Za5W7EsAhtTWVL9kORKWerzpQDfzTdhyqS5qAfXSoUX7yVQeuBy60HUdHmESceQ+ej6d7H/ofcTmRGd+w/N37MPEoxQOG0+c3f0Jca67qFgstZNFz/8CYBMTjdN/pt3Tf4UBMLMrCZ68ivmIx3XeopPuOlQAsee0Wuo36Dd4Bw228xY5aAexeW1P5ne1AVGolLxY9ARxpOxbVJTOBXWtrKhfYDiSb5VzPqK86sA/OPNG8YRJx5t5/LgufvmKN+5e+dSczb2y9YykeqWP+439j5o1HsPTN/67eVizKgqcuY+69Z7Hiq8Cq+5e8dgtN86en5w10jgt43Fcd2MF2ICq1fNWBE8nDRHTtdrr4lf8w974/Mve+P7LouWtINEfWfU08yuLATcy992zm3vdHGmdOdu7PjXbqBZ73VQc2tR2ISi1fdeCP5FgiCrDiixcp6rMJAMYkWBL4N33/7y8MOvV2PD36Ux98e53XuLv1YsDx1zPo5FsYcOINhD59mtiKJUR++Qrv4G0YeMqt1H/7DgDNC3/GJBK5koiC0/vyvK860NN2ICrlrkcT0XwwBHjBVx0osR1INsupZNRXHRgBPIszRCVvtDzArtQ0bxqJxvo2XyPuYnrufTy9xq85ZTbHDrDlwIu+6sBGtgNRqZEcNnaP7TjSYe122nvf0xh0yq0MOuVW3D36seKrl9d5Tf03rwMw6NTb2Oioq1n2zr0Yk8ildtoPeDm57IfKA77qwCE4Q+hzSqxuMZGfP6fb9vsDkIisQNweinpvDECJbxThHz9a53XiLkI8zimDiUchORpMXG5MtAnicaeONLD8g0fouXfOrT61OfBoco6+ygPJi0UX2I4j3UysmXkPne9c1L3nLJZ/8CgAkRnfMO+BPzH33rNYHLgRk4iv89rGGZOZe/85q24zrj+U8I+fALDopeuYe98fWTbhwVXPX/7xE6set2A34MFkb7dqRc58eSUncb8MVNiOJZXWPsCC0wOz7L376Dmu7SmxruISSgaPQDzFa9yfgwfYwcDTvupAXl1gKETJiwqPk2cXi6D1durylgFgjMHEmoF1jzPNi2dRMnQkAO7ynrhKymmeNy3X2uk26IE0L/iqA9sCj5FDx/6Vlr19Fz3HnYKI8zF0lfbAJOI0zZsGQHjqR8TrFrf62ljdIube90fm3H4yFbsfjqd7H0o23YFYaCHzHr6QHjsfRHjaZxRvNAxP9z4Ze08pdCBwte0gVNf5qgOjgX/bjiMj3EVsdPQ1DDrlVgaefDORX76kcfb37RrxUDJ0JINOvoVBJ9/CRkdfg6vIS8mmO9C88BdcHi+DTrmV5nnTSDQ1EKtfSvPcqZRtsYeFN7nKkcAVG3xWgcqlA9LdgM92EKm29gEWYMVXL1M2fDc83Xp3eHs5eoAdDdxkOwjVecmr8o8CA2zHkg6ttVOAxYGbmH3rCUSXzqb7Tr9d53XF/TclMv0zTCJOdPl8mub/RGzF4lxsp4cCl9gOQnVecpjY40Cp7Vg6Kjx9Iq7ynmuMGhAR+v3fX1j2zt3Me+h8pLgMXK2f0nh69HNGMfzhLuqnvE28YRnictPv//7MoJNvpmzL0dR98QI9djmUpW/fzaLnriE87bNMvb1U+ZuvOvA720Goztum+pm+wJM4FZPznojgKna+jkwiBok44nK1a8RDS+GpH1Gy2U64ikoQl4dErAljEs42xUXog0eoGH1c2t9PO1zqqw78xnYQ2SgnklFfdeAPQN5VdmztABtbsYTwDx/RfaeDOrXNHD7AnuWrDqx7Nq9yxd+BfW0HkQ6ttdOV+laex+CzH6SozyaEv/9gnce7jdwPd/e+zHvwPJa9fTfejbdCxJWr7dTvqw5YvbSsuuQGYFvbQXRG05zviEz7jNn/PYVFL15L44zJLH7perwbb82A465l4In/pmSTERT12ni92/F070NR36E0zvp2jftXfB2g27b70DR3Ki5vOX0P/it1nz+XzreULvdr9c4c5a+QKd5THv530W0/rhpLXgCcWgznMPuW4ynxjaJ44BbtHvGwUsP371O+9VgAivpugru0gnkP/Imy4bsSWzYPY0y2TH8R4CFfdWCQ7UCyTdZX0/VVB7YBviAHr+ZuyLIJD9Aw5V1wuTHxZkxTBHF7wFOEuJ3ht/G6RXh6DmDj01uv2VQffIvm+dPovd+Z6zxW98ULuLzluLv1oWnO91TseRQLnriEAcfWpPV9dcECYNvamsr1f/OorJIsKvYmOXJxq6Naa6dlW+xB34MuWvWcxllTqPvsGfofcfl6tzX/4Yvo/ZtzKe47ZNV9OdZOpwOjamsqG2wHotrPVx04CGc90ZzXOHMydROfo/8RlxNvWI67vKdTGffpy+mxx1GUDt1+jefH6hbjKu2Oq8hLvLGe+Q9dQL9DL6a4nw+AeGM9i5+vof9RVxKZPpHmhb9QsedRzH/4QgaemJOjJYPATrU1lVHbgagO8Ff8FagBWGh6fvGbpn/6llDR13JUGZNorGfhc/+g969OxzRHWPbe/Zh4lBLfjkR+msigk29p9XWx+qXMu++PDD77Ief8eS0Ln76C3r/+Iw3Bt2he+AslvlF0H3VAut/OhrwD/Kq2pjK7E7AMyuqhALk8rKg9eo09iV5jTwLWPMC2NPPGI9pMRNcn3lhPZPrnqw6wiIAIJtaUitDTZSPgLuAw24Go9vFVB/qSo3PQ2qu1dtrntxcSXTaXol6DMMYQmfYZRb0Hr/PaRLQRjDPHO/LL1+Byr5GI5mA7HY5T5XHdq18qK/mqAwOB+2zHkQ51E58lPH0iYOg+6sBViWjTvGnUT3qVPr85l+iSWSx7995Vr+mx62GrElGA0EePU7HnkYi4KN10R1Z8FWDevX+k2w45O5puO5wh9X7Lcaj28lfsQIs5v/1l+c4TvWfP+330wm/eTeyw/XpemTdcJd0oGTKSyM9fUbHbYQw47lrAKcoZWzqnzdeFf/iAsi32aDURDU/7lOIBwzHRRqLL59HvkGoWPPl3ykeMw1VktbjtPsD5wI02g8gmWZ2M4lwlGmk7iGwRnvYZzfOnrSpyMvu/p2Caw5h4jPCPn9L/qKtWnejm8AH2UF914KTamsoHbAei2uVGnIsIBcawJPBvEk1hwFDUf1P67H82sGY7TYRDLHjqMkDwdO9D39+uuZpGjrbTM3zVgRdqaypfsx2IWr9k0amHgLzpYSkZMpKSIc5pQa/xp6xTUR7AO3BzvAM3B6B00x0o3fTWNrfXe9/TVv0snmI2OuqqFEdsxcW+6sAztTWVQduBqA3wV7hxKtCvcT7ulsTA+4qu6/9sYu8JF0bPGONcqcwv8XAIcblxlXQjEW2isfZreux2xBojHuo+e5oeexzV5jYavnufXmOr1rnfxGPUffEC/Y+4nNiyuawqMGgSEI9lQ5nFa3zVgddrayq/3fBT81/WDtP1VQd2AiaSxz0uqk11wPa1NZW1tgNRbUsOz123zJ0qBHNxhtQvsx2IapuvOnAmcLvtOJQVXwC719ZUrrsuhsoe/oqLgOvW95QFzrDdTZdSkbUV7jqjeeEvLA7820kQTYKyrfam517HsOzd+9YY8dBjl4OBNUc8AMRCC5j/yJ/Z+KwHEFkzVaj7/AVcJeV02+5XGGNY/NJ1RBfNoHTYzvRaz0oVGfZBbU3lGNtBZIOsTEaTV3M/BXa1HYuyJlBbU6kFjbKUrzrgxZmbtLntWJQ199XWVJ5qOwjVOl91oDcwDeh4WXaVL6prayr/ZTsI1QZ/xabAFKBsQ0+NGde8U6J/XvR+YnsdLZhfTqqtqXxww0/Lb9na6/h7NBEtdJW+6sD+G36asuQSNBEtdCf5qgM72A5CtelKNBEtdH5fdWBL20GoNt1JOxJRAI8kBj5Y9K9trvXc8V4hVdstANf6qgM9bQdhW9Ylo8mruf+0HYfKCjf6qgNu20GoNfmqA1sBf7Udh7LORaEszp5jkst7nGE7DmVdCdpGs5O/4nBgv468RATPkZ73x33iPeeLXtQtTVNkKrP6A9fYDsK2rEtGcRLRvBoXrzptBHpClY1uAIptB6GywlhfdeBQ20GodfwH0At5CuA3vurAaNtBqBb8FR66kIAMlKW7fO49q3Fv12QtUJUfTvdVB3a0HYRNWZWM+qoDI3CG6Cq10hU6hCF7+KoDuwMH2o5DZZXrfNUBvTiRJXzVgcOB8bbjUFlFR5tll1OALbqyAY8kBj1UVLN1jeeu93TYbs5z0WJpn0KUVcko8HeyLyZlVx/gMttBqFXyYt0DlVLDgHNsB6HAVx3wsIHKnKogjfZVB7J6vaiC4a8oAy7f4PPaQQTP0Z73xn3sPefznqzQyua57TfJi/0FKWsSv+Q8tN/ZjkNlpTN81YG8WScvV/mqA2OAX9mOQ2Wlv/qqA6W2g1AcA2xqOwiVlf6RXKlA2fUnYFAqNzhIlu76hffM8F6uKVNSuV2VcVfYDsCWrElGgYvJrnhU9ihFe16ywZW2A1BZqx9wku0gFH+2HYDKWjugF/zt8leUk6Y26pHExo8UXbPlPzz3TEjH9lVG7O+rDuxlOwgbsiL581UHhgHH2o5DZbWzfdWBdpVAV6nnqw7sC4y1HYfKahf6qgNZcUwpRMlhmNvZjkNltSu0d9Sqk4Be6dq4CEXHed4Z+5H3nIkV1C9P135UWhVk72i2nDj8Da38p9avD1rcyqa/2A5AZb1hwGG2gyhg2kbVhmwF7Gs7iILkr3DhDNFNu41lya5fes+o39M15dtM7E+l1L6+6sD2toPINOvJqK86sBFwou04VE64IFmgQ2WQrzqwBR1cD00VLB0maoGvOrALMM52HCon6HJpdvwW2DxTO/NIYvCjRddscZXnPh22m3vOtB1ApllPRoGTgSLbQaicMBQ4wnYQhUZInAHo0C7VHrv6qgM6nDvz9CKAaq+DfdWBgbaDKEDnZ3qHIhSd4Hlr7Ifecz/rQX0o0/tXnXacrzrQ3XYQmWQ1GU3OXTjNZgwq55xsO4CC4q/wTveecOjjRVe/v5XM/Nl2OCon6HD6DEqOLtLh0aq9PGgbzSx/xbZYHLkwWBbv9qX3zBW7yXff2YpBdUg3CmzEqNVkdJL3tLGPF101a6T8NM1mHCqn7KtXdTPqELcY3x7u78a85q3e7CvvH7451f3KJ0XEmm0HprLWob7qQLntIArIkWjNBdUxp/mqA/qZyZwTbAdQJPHBTxRfPfwKz/3v245FtUtBDdW1moz2lIaqPdzfj33R+/fNp3hP+a7a89j73WnQoQRqfdw4a+mpzFijJ7q31G//96JH9pjqrQrdV3TtBJ/Mm2UrMJW1yoFDbAdRQPT7UHXUJsCBtoMoCP4KAY62HQaACMVVnjfHvF/8p0912G7WG+GrDuxuO4hMsZeM+itKaTG0qJs0bnOG5+Uxk72nFb9e/JeP9nd9MQmMsRafymbWrzIWBH/FYNooXOQS028f96Sx7xZfOHii98wvj3W/9ZmLRDzDEarsdbztAAqBrzrgA/awHYfKSSfZDqBA7A0MsR1ES0Nci3b/0ntm3a7yvQ7bzW4FUyPFZs/o/wE91r5ThNItXbP3uqv4xlHTvCfO/HfRbe8NYvE8C/Gp7DXKVx3Y1nYQBeBENvAdIYL0l9BO1xTdt9s074kLby+6acLGLNL2qvbzVQf62w6iAGRFj4vKSfv7qgNe20EUgONsB9CaIolv8mTxVcMv8zykw3az1+G2A8gUm8noBhtokcSHHur+aNxH3nM3+tR79hcnut/4VOeqqaSs/ILPMx0axuWWxMAD3RPHfuj9U/8PvedOPMT14RdCIpGu4FRWc6OJUiboEF3VWd3Q5YDSy19RBPzOdhhtEaH4FM9rYyYUn/epTpHLSj5fdWBH20Fkgp1k1F/hBfZv79NFcA2QZTtfWfTA7lO9VSseK7p6wnbysxY9Kmy/tR1AXvNXlAO7dualIrgHy+Jdbyq+fecfvVVzbyy6fUJ/li1KcYQq+/2f7QDyma86sA0w0nYcKqfpcTS99gJ62Q5iQ4a6Fu7+lfeM5TvL1O9tx6LWURCV0m31jO4MdGp4iEtMnz3d3419yXvp5kHvqd/+xfPEB90I16U4PpX9tk0uaaDSYzQpWP+3SOKDD3N/OPYz79k93y2+4JNfuz6f1PXQVI7Yy1cdKLEdRB6rtB2AynkH2Q4gz7W708W2IokP/V/xFZtd6nlEh+1ml4IYqmsrGd07FRvpLpERZ3le3Dvo/b3nteK/frSv68tJqdiuyhm/sh1AHhufyo2JULSpa/4edxb/e9RU74m/XO25d0JPVixL5T5U1inB6RlQ6THWdgAq5w31VQe2sx1EHmu1AGC2EsH7e88rY94tPv8T7eTJGlv5qgNZVQArHXI6GV1JhLKtXLP2urf4hlE/ek+YcWPR7RMGsmR+KvehslJOfdHnmJQmoy15Jbbp8Z63x37tPb309eK/fDTG9U0wXftS1ukFozTwVQdcaKKvUkN7R9PBX9EHyMn5fpu6Fuzxlff0ZTvKjz/YjkUBKc6ZslHmk1F/hQvYM12bL5b40MPcH4792HtOv0+8Z39+vPvNTz3Eounan7JqX9sB5CV/RQ9gp3TvRoSSLV2z93qo+F/bfe89adolnkfe16uxeUeT0fQYCfS0HYTKC9pG0+NX2C0S2iXFEh/6TLF/04s9j+qwXfs0GU2DbcnAQVQE90BZtsvVRffv/qO3KvRI0TUTtpHan9K9X5VRg33Vga1sB5GH9saphpoxpdK8+WmeV8YEvb93v1h8yQe7yA9aSCE/7OirDmR9AY8cNMZ2ACpv7OirDojtIPLQONsBdJUI3j94AmPeKb7w43IiK2zHU8A0GU2DjP9RXWL6jnZPGfuK9+JhQe+p317kefIDbVh5I2297AVsH1s7FqF8pOuXvf/nvXLrKd5Tvr/A89SHpTSFbcWjuswF7GE7iDyk80VVqlQAm9kOIg/l5BDd1mzmmrfn197Tl+wg06bajqVAbe2rDvSxHUQ62UhGR1vY5yrdJTLij54X9p7iPdX9anH1h+NdX39jMx7VZVp8IfXSNl+0I7pJ49bnep4f/Z335Oj/iq94X5dzylnaRlMv76+Uq4zKm8QpK/gr3OTZ916xxHzPFl8+9K+ex3XYbuYJlnOndCuIntHWiFC2tWvm6PuLr9v+R++JtdcX3fHeAJYusB2X6rC8+sK3zl9RAWxvO4yWRKjYxTV1zEveSzf/xvv7yWe6X/iomGiT7bhUu2kbTSFfdWAToJ/tOFReSXuNgAKzFVBqO4hUE6HkTM9LY94qvujjciL1tuMpMHl9HM1sMuqv8AEbZ3Sf7VAsMd8R7vfHfeL9Y9+PvX+ceKz7rc+06FHOyOsGasFwsrjoQoWER/616Mm9fvCe1PBw0TUTNpfZtbZjUhukbTS1NrcdgMo72jOaWjvYDiCdhrvm7vm19/RF28v0H23HUkDyuj5Kpk86s/qPKYJ7kCzd9Zqi+3b70Vu1/KGif07YWmZo0aPs1t9XHehvO4g8sontANrDJab33u4pY98o/svQL7xnfF3lfl2rZmevrXzVAY/tIPKIJqMq1TQZTa28TkYBiiW26fPFlw25yPPkB7ZjKRBZnT91VaaT0YEZ3l+nucT0G+MOjn3V+7dhk72/D17geepDHZaQtbTnJXVyanFlEaSv1O1wRdGDu0/1Vi2/p+j6CUNl/mzbcak1FANb2A4ij2gyqlKtj686MMh2EHmkIL7vRCj5o+eFvd8o/vNHen6cdlvlc9VrTUbboYeEtzvX8/zoKd5TJVD8tw/HuiYFbcek1rCN7QDySE70jLbGLabfr9xfjX2v+IJBn3rP/uJI97sTXSTituNSAIywHUAeGW47AJWXNBlNnZw9jnbGFq45e33lPX3RSPlJiwymTzkw2HYQ6ZLpZDSnv+xEKB/hmjH6weJrt5vqPfGXaz13TtiIpQttx6Vy+3OVZXL+ICqCa4As2/naort3/dF74oKbi26ZMJAl823HVeC0jaaO9oyqdMjJzoIslVMjjFLBK7FNXyj+++ALPE99aDuWPJa3Pe7aM9pJXolteqRnwthPvX/s/ZH3nIlHu9/5zE08ZjuuAqVzRlMnrw6iHkkM+j/3J2M/9p7T9/3iP316kOvjL8EY23EVIG2jKZAcpqVrQqp0yJvzM6v8FeVAL9th2CBC6bme50e/XvyXj8pobLAdTx7ayHYA6aI9o10kgmdjWbJrTdE9u/3oPXHZg0U1720pM3+xHVeB0RPd1Mn5ntHWiOAZ4lq0+y3Ft+40zXvizGs9d07oQ2ix7bgKSN4eRDNsAFBiOwiVlzQZTY28PIZ2xJau2Xt97T19/gj5ZbrtWPJMX9sBpIv2jKaQW0y/se7J4173Vm/6jff3wfM8T3+gk7ozQk90U8Ff4SHP2yhAkcSHHumZMPYL75k93iq+8ON9XV9+YzumAqAXjFKjIHtcVEbkXWeBJXk7r68jvBId9nLxJYP+5H5Gh+2mjiajKTIgw/uzpkLC253neXbvKd5Tebn44g/3dk3Wokfpoye6qTEIcNsOIlNEKB7umrfnvcU3bD/VW/XTFZ7736+gfrntuPKUttHUqLAdgMpbeX8hMkP0glGSCGXnFz0z+tXi6g9LaQrbjicPaDLaZf6KPoA3Y/vLEiJ029ZVO/rh4prtpnpP/LnGc9d7/Vm2yHZceUZPdFOjYK/oeiU6rMrz5phJ3j94Xymu/mgv15RvbceUZ7SNpkZP2wGovFUwnQVp1t12ANlma9fM0V97/zBvG6n9yXYsOS5vk9FMLkRe8FfdvBLb7GjPe5sd5X4vNoe+n90cO1SeiY/ZMY5bF4TvmlJfdaCotqYyajuQHFfwc9FEKN1GZu71aPE1hI136oPx/RfeGjtkhwZKu9mOLcdpj14K/Og9sdRDbFkCVySOOxLF3RTF09xIUTRivLEI3ngDJYkVpixRTyl1pkzqKHeFTLmrjjJPyJQX11FetMKUeldQ6q03pWUNlJaG8ZaB5O0adqpdSm0HkCf0WNGKEokOCxRfHL4h9rsPb40fOtp2PDmqt+0A0iWTSVDB9Yq2RQTPYBbvdm3R3fzTc8+C9xMjf/hn7NghP5pNNrUdWw7zAJqMdk2z7QCySZk0bXmm56Utz3C/VP+NGfbBFdET+39tNt/Sdlw5Si+4pUCxxEqBXi4SvTwk8Lb8yutCKmkMBggbJBzHFYnjaoziaW6iqKmJoljEeGNhvPF6U5qop9SsoIw6U+YKUS51ptxdR1lRyJQX1Zmy4hWUeespLVlhSkvDlJQ1UVzwF7lyhLbR1NBz3TaIUHZR0f9GH+j+7MPDm6/YMYK3zHZMOSZv22gm31gkg/vKGW4xG413f7PRePc3LDflk++LHVB3T7xyhzAl5bZjyzEFM9cxjTSZb4UI3UbJT3s/572cFab027tjBy67K/7bnRrxak9C+2n7TI3idGxUBAHKBFPmIk4RcUqI0n3lYbtriW4caEggkTiuxpjTm9vYRFG00RRHk7258QZTalY4vbmmjjJXnSl31VHuCZlyTx1lRStMWfEKSktWmLKSekrKGigt11FFKaV/y9TQZHQDtnGG7U4/tPlK+d4MHWY7nhySt8dRTUazSE9pGHlB0TOc73lmxYXdt3/5te7FOhG+nRJNAwQqbYeR6zQZ3YDuEhmRbKOhE3vu/PaXZaJttF1czdo+UyLnTnRFcAM93JgebuIUE2fVIIwuDgw2hmYD4USyNzeGp7HZ6dGNNpri2Fl9N11a643rEPH2MEWLtY2mRN4mDKlUItHhrxT/rWFsv91eX1yU0KHN7ZEo+Tlf26gmo1lIhO67yU993yzpvbvtWHKFu2RBwnYMeUCH6baTCBUj3dN7TCrpsZftWHKELnGVGjqvswURigWKXSR6OsOWY6waUiSwuSf+3qySMm2j7TPNdgB5otF2ALlChPIBxTPKlnmLtY22T96e52ZyaRdNRjtgbDjisx1DjonZDiAPaM9oB4xviPSzHUMO0faZGsttB5BLxocjOt2l/bSNpoYuYdIBoyOaGnRA3rZRTUaz1IB4fIDLmHm248ghedtIM0h7Rjtg+6am4RijV8HbRy90pMZS2wHkkjHhyGa2Y8gh2kZTQ5PRDtinIaJLCrVf3rbRDK4zGmoG4hnbXx7YOBabYTuGHBEKVgX1s9V1eftFlw5FUNQ9YXRoW/toEpUa+nfsgN6JRB+PMTNtx5Ej9LOVGpqMdsA2zc3DMEancbRP3rbRTPaMgvaOdsiukaYm2zHkiIW2A8gTmox20Ijm5rw9OKSYttHU0M9bBw2JxmbZjiFHaBtNDU1GO8AFrl6JhF7UbZ+8baOajGaxfcJhrdTZPnnbQDNMh+l20NhwOC1LbeQhbaOpocloB+0eadRRM+2jbTQ1ltkOINds19RcZzuGHJG3bVST0Sy2a2PT5hijcyE3LG8baIatQOfedsi4cGSI7RhyhLbR1FhOHldUTId9w+E+tmPIEdpGU0N74jtofEO4xHYMOSJv22imk9HFGd5fTisxprTUmOm248gBedtAM8ofigE6v6oDBsfiG7uMWWA7jhygbTQV/CGD9rx0yCjnoq5OedkwbaOpMQe9YNQhYyKNm9qOIUfkbRvNdDKq48I7aIvmaN5++FJoke0A8sjPtgPINQNi8VrbMeQA/R5LnSW2A8glxVDczWihsXbQ42gqOMU69QJlB/SPx/u7jZltO44ckLfH0Uwnoz9meH85b+9wxG07hhzwi+0A8shPtgPINbs0NuryLhumbTR1am0HkGu2btJCY+2gbTR1dKhuBw2OaaGxdsjbNqrJaJYbH44Msh1DDphiO4A8osloB+0TjlTYjiEHaBtNnUm2A8g148IRj+0Yslwc+N52EHlEl+XroN0jjVpAfYA7BgAAudNJREFUcf0WB6uC820HkS6ajGa5zaNRnxiz3HYcWcwA39oOIo9MtR1Artkt0jgcY7RiZ9tCwaqgXvVOnUm2A8g1Y7XQ2Ib8FKwK6giP1NHEvoP2CUe00Nj65fV5bqaT0e9xkgfVTgLSJ57QIkZtqw1WBRtsB5FHJtsOINeUG9OtxBjtUW5bXh9ELfjGdgC5ZmgsNliM0TmRbdORC6n1te0Acs1OjY3DMUbXOm9bXrfRzCaj/tAKQBOrDtqhqUmTrbbldQPNOH+oFtA1vzpoeDSqBSvapm00taaiy6R12EbxeN7Ot0oBbaOp9ZXtAHKN11BSpoXG1iev22ime0ZBG2mH7dMQLrcdQxbL6wZqifaOdtDosI5wWw9to6nkD8XRv2mH7dzYFLYdQxbTz1Mq+UMz0arXHbZVc7OOXmhbXrdRTUZzwOhI4zDbMWQxHQ6TepqMdtD4cFgLjbVN22jqTbIdQK7ZpyGshcbapm009fRv2kFjwo1FtmPIUnHy/LxMk9Ec0DOR6FVkTK3tOLKQAd61HUQe+sx2ALlmq+bophijw5vX1YB+ntJB54120B5OobGE7Tiy0KxgVVCnT6XeF7YDyDXjw+GNbceQpb4MVgXz+vzCRjI6EdASzh00NBqdYzuGLDQ5WBVcbDuIPPQKoCdtHeACV+9EQosYrevDYFVQi1Kk3ue2A8g13Yzp7jXmZ9txZKG3bQeQp/Tv2kGbRWNDxRhdE3hd79gOIN0yn4z6Q3Vob1aH7Rlp1KUj1qVf9ungDy0GPrEdRq7ZvrEpZDuGLKRtND2+ABbaDiLXbBaNzbMdQxbSNpoeH6CFxjqsXzyuF3XXlfdt1EbPKMBzlvabs8Y3RPrZjiEL5f3VIotesh1ArhkfjmihsXXl/UHUCn8ogbbRDhsd0dygFXocTQd/qAknIU2Zxphh17vr2f6OekbcXs/l7zqF897+OcaOd9Yz6o56Rt/XwPSl6w5senRylFF31K+6ua6oY9L8OE0xwwGPNLDt7fXc/vnqQYt/eCnCV/My3weyoxYaW1sT8JHtINLNVjL6ArreaIds39Q0HGO0ZOdqMeB920HkMT3R7aAx4cimtmPIMkvRQjvp9ILtAHLNPg2RAbZjyDI/BKuCc20HkcfeSOXGvG54p6qcb87oxqTTy3ntpxifzo5xZqCRRw8rZdIZ3Th2uyKufr9pndceN7KISWd0Y9IZ3Xj40FI27SWMGuDm9Z9ijB7iYfKZ5Tw82ZlR8c38OPEE7DjQncrw22WfcKRbxnea3T4JVgXz/iqanWTUH5oPfGpl3zmqCIq6J3QNphY+DVYFV9gOIm/5Q98BOr+qA/okEn09xsyyHUcWeSdYFdS5x+nzFqC9CB2wTXPzMIyptx1HFnnTdgB5LqXJqIjQrVgAiCYgGgcBRKCuyenfCTUaBnWX9W7n8SlRjh7hFK4tckE4aojGwSS7iP7+bhNX7eNNZejttmckMhxjtLNqtYJoo7Z6RkGH6nbYiOZmndi92hO2AygA2jvaQZtEY5qMrqZtNJ38oQgFcqKSKi5w9Uok9KLuatpG08kfCgIprVQcTxhG3VFP/+tWsN9mHnYb7OGeg0o48LEIg29cwcOTo1SPXn8i+eS3UY7ZzklG9xvmoXZ5gt3vbeDc3Yp5cWqUHQe6GNTdTnpQkTAVxfCLlZ1npydtB5AJHov7fg641uL+c87YcLj409KSTr++eUkzc+6eQ6wuBkCvcb3ou39fQhNDLHx+IU3zmhh22TBKNy1t9fXxhjhz7p9D4+xGRISNT92YsuFlzH9qPismr6B0SCmD/zAYgOUfLye2IkbfX/ftdLzrEQOeSseG1RpeAv5kO4hcsnukMfZLceeXSsujNhoCAunYsFrDC8DBtoPIJds1Nde9X9Z6+9mQPGqfAL8Eq4Ifp2vjapXHgMtStTG3S5h0RjeWNxoOfTLMlIVx/v1pM68cW8pugz1c91ETF7zeyD3/1/pn8LPZMcqKhG37O0NwPS7hscPLAIjGDb9+JMwLR5dxweuNzAwlOHH7Iv5vy8wu/+lrjs770Vu8WWdem2dt9LNgVbAgCjrZ6xn1h6YD36Zj021N8l7p3Fcb6XZN60v2vPlTjJ3uqme7/9az0131vPOL84HOhkne48KRIV15vbiFAUcPYPNrNmezv2/G0reX0jinEe9gL0POGULZFmXrff28x+bRbbtubFGzBcOuGoZ3oJd4OE5kRoTNr94c8QiNsxpJNCdY9sEy+uzbpyvhrs9bwargonRtXK3yPk5SkXJdaaO1yxOU/qNuVSGGM152plNkQxvdNxzu0oc+j9roc8GqoM5xT7+X0WWYOmR8Q7jTV3TzqH2C9opmyqPp2GjPEmG8z8Or02J8syDOboOdvqWjti3i41ltH++emBLjmG1bTy5v/7yZE7cv4tPZcSq8wpNHlHLDJ5lfiXGvLqwekWdt9LF0bjyb2BymC2kaqtvWJG+AL+bGWdbY9nD0vmXCS8eUETyzGw8eUsoJzzknutkwyXtwLL6xy5gFnX19Uc8iSn3O1SB3qRvvIC+xZTFKBpXgHbj+YR3xcJyGqQ30GtMLAJfHhbvcDQImZjDGkGhOIG5h8auL6fOrPohn/fMWuiAtX+5qLf5QFHgtHZvuShsFGNbLtaoYwx2/dT7T2dBGd2hs2hxjOn301jaqOsQfWkQBVFpMpTGRxk4XGsuj9gnaRjPDH/oRZymmLlvUkGB58tgYiRre/DnG1v1chBrhxyVO/vbmT859rUkYw1PfRTm6lWR0WcTw8rQYJ25fRDhqcIkzFzUSzfz0zX3C4Y06+9o8aqNxCmSILuRpMtrWJO94wvDnNxu59ldtfyB3GOheNVZ+RD8XkaihKWayZpL3gFi8NhXbaV7UTOOMRkqHtW+4UvOiZjzdPcy5Zw7TL5vOnPvmkGhK4C5103377vx02U94Kjy4ylxEfo7QY6ceqQizNWHg+XRtXK3joXRstCtttC3Z0EaLobjcpKbQWA630fnochGZdJ/tAHJJ/3i8v9uY2V3dTg63T4DJwapgWkamqValJPGfV28Y/2ADI/9bzy53N7DfZh5+u0URdx9UwuFPRdj+jnoenhzluv2czv8Xp0a5rMWoo/dnxNmkh4vNeq176n/lhCYu2duLS4RfD/fwwcwY2/23gRNGFqci9A7Zrql5OMZ0uThbjrfRt4NVwU53PuUam3NGwR/6Cn/F58Auqd50PGHY6S5nvaWzdylmt8Ee/vNpE/+3hYeB7ZyY/cz3MXYc6MbrEfYb5uHhyVF2v7eBP+/ptTbJe5fGxsYXirpW+TreGGfmrTMZcOwA3KXt7DFKQGRGhIHHD6RsWBnzHp3HopcXsdHhG9HvwH70O9BZBnXOfXPof2h/lk5YSv2Ueko2KaH///XvUrxreT5YFdRqiJnzKs5w+hGp3nBX2ugvyxPscGc9PbzC1eO97D3UkzVtdOum5iVfdGFuN+R8G31Mq+hm1BPA9UBax4vlk8Gx2KwZRUWDO/v6HG+fAA+neoNqvR4H/gV0KbMbuZGbr09f9/zv0K2LOHTrdXs7/2/LNed7jvN5+PT3rZ/2//uA1cesEo/wxgn2ls12g7sikZgecrtHdnYb2kZzi+2eUYCadGx05STv2Rd0Z+LcOO/PiPG/72Kcs1v7vgu+XRjnr281cmdyCODKSd5fn96N323j4aZPm7lwDy8XvN7IEU+FeXFqNB1vYx37hCMVXXm9iRlm3TqLnnv0pGLn9m/K08tDUa8iyoY54+177NyDyIw1lz6KzIhgjME70Evd53UMOXsIzQubaZq/7ppXXfDvVG5MbYA/ZIAb0rHpzrbRgd2Emed14+vTu3Hj/iUc+2yEuiaTNW10bDjSpYt8Od5G48AtqdqYagd/qBG433YYuWT3SGOnh9LnePsEqAfuSeUG1Qb4QwsooCGXqbBtU+dXj8iDNjqXAivSmQ3J6HPAD+na+MpJ3u/+Emf60gTDb67Hd9MKwlEYfnPry1TOrktw6JMRHjqklGG91/0T2ZzkvVukcTjGdGpytzGGOffNwTvQS98DOlb9q6hnEUV9imia5zS4+u/qKRm0Zu/PwmcXstFhGzlj6xPJcZICieaUdZK8H6wKpmTuheqQR3G+HNOio23U6xH6lDntcqdBbob1cvHjkjU/Yzbb6LhwZJPOvjYP2ugzwapgbao2ptrtv2SgkNGsUILxDzawzW1O4bH/fOp81r6ZH2ePexvY7r/1HPR4eNWah2s75YUI/a9bwba3rzm45a9vNjLyv/Wc+NzqE79HJjdz06cpPcFbZZ9wpFO9yHnQPgHuDVYFl6dyg6pdbrQdQC4ZF450anhRnrTRW4JVwcxXjrLIfjLq9LykdImX1iZ57zTIxfyLulN7nnMrK4Lp53Zf57XLGw2Vj4Wp+ZWXvYas28Fhe5J3uTHdSozpVKnn8LQwyz9eTv339Uz/+3Sm/306K75ZQd2Xdfxw/g9EfopQ++9aaq+vBSC6LErtjbWrXj/wuIHMunMW0y6dRuPMRvod1G/VY3Vf1lHiK6GoVxHucjclQ0qYduk0TNRQOqRzZfRbkZYeOrUB/lAzcHMqN9mVNrqoIUE8eRD4eVmCaUsTa8yBsd1GfbHYJmLM4s68Vtuo6hR/6GcyMJfe44Ib9i/hu7O78emp5dz2eZTvFsX5/UsRavb1EjyzG4du5Swv0ZqTRhXx2vFrVrMMNRq+mh9n8pndKHZDcEGcSNRw/6QoZ++SnvlqOzU2DseYDg+VyIP2GQduStXGVAf4Q5OA9yxHkTPGhiNDO/O6PGij9cCdqdpYrhBjMl8pax3+iiLgZ6DTczhamrwgTtXzEeIJSBg4ckQRl41ds4hJt2vqqL/YmXz84tQoX8yNc+X4Eq5+v4l/ftjE5i16RN84oYz+5c7v57/WyMFbeRjn89AYM/zf42HmrDCcsVNxu4cAd9Uxgzb6YIrXu3dGdpY9pgJbB6uCWfCBLUD+igpgFrBudtgJXWmjz3wX5bL3mihygUvginFeDmoxLyYb2uh+mwz6fL7Hk/K58Fnug2BVcIztIAqWv2JX4LNM7vLgJ8L8cZdijvhfmOV/7Y6IMCuU4NePhPnu7NZrG9QuT/Dbx8JMOct5fEWT4aDHw7xbVcZxz0a4bKyX/30bY7uNXByyVfrWN9xt6ODvwi7XNmnbQXZ6KlgVPMp2EAXLX3EQ8KLtMHLF9r5N5idEBtiOI8NuDlYFC2599+xIRgH8Feeh8wHb5baeFR/c0aui0JLRM4JVwYK7WpRV/BU3ABfYDiMXVPfr816gW/k423Fk2CHBquALtoMoaP6KCUBGLgjULk8w5v4GppzVjQMeCfOXvYo5ZKsibvykicvfa2LF31qvNLl2Mgpw7UdNPBaMsu+mHi7as5jTXmrk5WPXvx5gV1UN7D/hq5KSsWndSfbZLVgVnGg7iILlrxDge2BL26Hkgt8MHvjp7KKi3W3HkUFxYPNgVfAX24Fkmv1huqvdDSyxHUQuGB8OD7IdQ4bNBR60HYTiJiBmO4hcsE9DuEuFxnLQt+gV/2zwz0zspL7ZcPhTYW46oIQeXuG+g0u4/fNmdrqrnhVNUOzu2Np7f9nLy6QzunHDr0v4+7tNXDneyz1fNXPk/8Jc/X565o2OCTemr9s1O72jiahlzrS0y2yHkSt2bWxKT+PPXk8WYiIK2ZSM+kMNwK22w8gFWzVHN8WYOttxZNClwapg44afptLKH5qFVgRslz0ijcMwppCWN/mzDqHPAv7Qa8Cb6dxFNO4kosdtV8RhyeUkturr5o0TyvnyD904ZjsPw3p1biH4r+fFMQa27OPif99Feep3Zfy0LMG0JZ2q2bde48PhjVO+0exlgD/bDkIB8D/gc9tB5IJ9G8I9bceQQU3AJbaDsCV7klHHzUCD7SCynQtcvROJThUxyjVTTprClJOnbLvydxG5SET8FkMqdP9Ee0c3qLsxPYoNBXGFc8pJU5hy0pRfrfxd26h155GmNmqM4dQXG9m6r5sL9lg9x3thg3PdJWEMV7/fzBk7d25u9t/fbeKqfbxEExBPXspxAeE0rMq0WTQ2VIzp9PIRuWTKSVNkyklTjlv5u7ZRi5ze0WrbYeSCXRqbNseYgjjfmHLSFO+Uk6acs/L3Qmuj2ZWM+kNLSdO6o/lm+8amkO0YMkKIYjhMRDpWo1ulhz/0LVoxtV2GRaPzbMeQAQmgGbSNZg1/6DvgtnRs+qNZcR6eHOWdX2KMuqOeUXfU88q0KI8Ho2xxSz1b3drAoO7CyaOcHtO5KxIc+Gh41euPeSbMHvc2MHVJgsE3ruDer1avXvD8D1F2HuRiUHcXPUuEUQPcbPffehrjhu0HtHPR+g7qF48XwkXdCE6vi7bRbOEPvQO8YTuMbFdqTFmpMdNtx5EBiynwNtqlxdnT5F/A74CRtgPJZuPDkfJ3y9Nb4CELvIJhLHAXcD5rDWEQER9wH9AXWAScbIyZmekgC9AVwBHAMNuBZLPRkYj53puZ6r0WPQAchbbRbOMHjgX6beB5HTJ6iAdzeSuFiTaHP+3uXefuQd1dvHLc6uPU44e3fcw6ZKuiNarnXr9/Cdd3LdwN2rGxKfxat2w8DUqpG3F6y7WNZpe/Ar8i2zqFsswWzdGF35R4t7IdR5pdgdMRV7BtNPsagT8UBU7FqSql2jAmHNnUdgxpFmf1HJfbgONEZO2iMLcADxpjRgKPkuK1MFUb/KEIcIbtMLLd+IZIvpekbwAuTf6sbTSb+EPLKeD5R+21TzjS+voz+WMhzgV+0DaaXZx1R/9rO4xst3c4kp5hEdljKnBH8ueCbaPZl4wC+ENfoAszr1efRKKvx5hZtuNIo+uDVcHvAIxTrOkh4Ny1nrMH8Fjy54eB0ZkLr8D5Q28Bj9gOI5uNaG4ehjH1tuNIo0uCVcF5oG00S90LfGU7iGy2ZyQynKxZ3y4tzg1WBVeAttGuEseHIvKbFvf9TkRe68JmLwYKYTpHp40PR/J59QiDs2xhDAq7jWZnMur4O1AI8zk6bXA0lq/J6LfA5WvddxNOj3l5xqNRbTkfXY6pTS5w9Uwk8nW+y/use3X2JrSNZg9/KAEU3OLpHVGRMBVFUGs7jjT5X7AquHb185vQNtopxrlocQZwo4iUiEg34Brg7E5v1B+q69LrC8Dm0ahPjFluO440uTVYFXxvrftuogDbaPYmo85QwNNsh5HN9mhsTEONQetiQFWwKrjG+lLGqXr4FE4jXelj4Ojkz8cBH2QkQuXwhxYDF9oOI5uNbGpebjuGNGgATl57KRdto1nIH/oQuN12GNls0+boHNsxpMFC4Ky179Q22jXGmCnASzjzPS/DGR10iYhMFJGvReRgABEZkbxvkohMFpHN29yoP/Qczv+JaoWA9Inn5UXdabRSVblQ22j2JqMA/tD/s3ff4W2V1wPHv0eyvDKUvYfIghBCEiDsDaWAadmrFMxoyx6lpXWhgCjLlFL2KnvPHyPFzDLCCDODKJBAEmLIJFvxHtL7++O9ThTHjpekK8nn8zx+Ykt3HCl6de955/vAg26HkaoOrKjMxFm3bgwVhqY389wt2AHcDS4EzhCR2cCpaCtA8gXDjwHvuh1GqjqgsjITZxm7LFQY+qGZ57SMpp5LgVluB5Gq9qyqzsRuuueECkOrm3lOy2jHXIOdHOwwIBd4zxizK3AAcLOIdMG2oN5ujJkI7AIsaeGYFwA/JyziNDeppibTlnyMAqeHCkOVzTzf6cqopPxwiaC/B/AtMNDlSFJOLdTsHBgqiGTKlJ1fA5NDhaFMbPHNXEH/SCAE5LkdSqpZ6fWuPGjY4H5uxxFH/wMOadwqqlJc0D8amA50czuUVDMrJ3veqYMGZNJsnU+HCkOntLyZai8R+QdQDpyATUgb1sLsBfwSmISdQOxx4CVjzPwWDxr0H4Rd7iW1G4lc8FqX/K/+1q/PLm7HEUe3hApDf3Y7iFSS+h96OyugztzZhGzI6dKaL7n0UAmcqoloGgqGF5KBNXXx0C8S6ec1JlO6Aa4BztRENA0Fw/OBP7gdRiraoaZ2FMY010KRbkqxrWwqsaLOjwDHGmMmOj/DjDFzjTFPA7/GrvH6uogc2OIRg+F3gWsTGXS62ruqOpOWkfuaTbPQK0fqJ6MAwfAU4Hq3w0hFY2tqm+uKk27+ECoMhdwOQrVTMPwAcL/bYaSiIfX1mbAeWBQ4OVQYytRJ0zJfMPwsdh07FSMLsrpHo5lQqVsNHBsqDK1zO5BO5C3gQhERABGZ5Pw7AvjBGHMH8CqwYyuP9w902MsWekSjPX3GlLodRxysA44JFYaq3Q4k1aRHMmpdCUxxO4hUs29Vla/lrVLenaHC0FNuB6E67ELgY7eDSDW7VVXXuh1DHFwZKgy943YQqsMuBma7HUSq2SEzJho7L1QY0qV8kutawAfMFpFv2NSyeQIwR0RmATtgu+u2zM6AfQq63MsWhtel/URjBtv7r7n5Fjq19ElGg2ED/Ba77IdyHFBRNcTtGDroPewEG00SkWktHUBELhGRhE8UIyITReTwRJ8nbQXDdcBxQLpfNOLqwMqqXm7H0EHPhwpDNzT3pJbRNBIMV2NvlDN5/ds2O6CyKsftGDrojlBh6JHmntQyGl/GmKAx5l/GmCpjzNnGmPHGmHHGmCOc54udvycaYw51ZkhtnWD4Z+x1VFvPYuxZVR1xO4YOuiJUGCpp7snOXkbTJxkFCIbLgCOBTOma2mGB+vphYky6vh8LgOMbFvxtijFmz1Yc5xKgTQVURLxt2d4xEUjri2hjcV/I215If4Xe7G60S3X1aIxJ17HQXwGnb20DLaNpJhj+DpuQputnMu72rawKuB1DB7zFVip0Qcto2gmGp2FbSKNuh5IqDqio6ut2DB3wZKgwdOPWNujsZTShyWjcb3ShYbKUI7AT3iigXySSjs3+q4AjQoWhrdYYiki58+/+IvKBiLwoIvNE5Cnn83URMAh4X0Ted7Y9REQ+FZEZIvKCszg1IlIqIjeJyAzgeOfva5ztQiKynbNdFxF5OHbtMLEzFv8DONFZO+zExL01yZOghbxnAsezaYbBTi3HkJufnhONlQJHhgpDVVvbSMtoGgqG38D2NNKbXWBQJDLAY0w6do38GjgxVBjaaquRltE0FAy/RAuVDJ3JhJqaURiTjq3FU4HftbRRZy+jCU1GE3KjCxAMf46t2dWbXWDn6pqt3iymoHXAL0KFoe/auN8kbM3Q9sAIYC9ngoBlwAHGmANEpA92prKDjTE7YVt2Yr/Q1xhjdjLGPOv8vdrZ7l6gYartK2i0dhh2XMhVwHNO15vn2hh7ykrQQt5vorNgb7RtbdpNNLYUODBUGFrWxv20jKaLYPh57Ay7OjsyMKi+/ke3Y2ijudjraLiN+2kZTRfB8O3ArW6HkQp84OsWTbtK3c+wjS41bdyv05XRhHfTTciNLkAwXAKcneDw08JBFZXptHZcGXBoqDD0dTv2/cIYs8QYE8Uu4h5oYpvdsQX4E7GTBxQCw2Oeb1ywXnL+nR5zvEOAImf/D7DriA1rR7zpJP4LeQfDD2Fr2Dq9/Sqr29NVxi0rgYNChaFF7dhXy2g6sWX0T26HkQp2ra5Jp1aXhcDBocLQqnbsq2U0vfwJeNrtIFLBuNra1o+9dd9M4LBQYag9Q5Y6XRnNStJ5rgFmALXAa9gb3TNFpAfwhYj8j003uk85zcQt37wFww8T9PcA/oVd76lT2qOqehTGRBFJ9THAlUBBqDD0RTv3j61ditD051eAd4wxJzdzjIpmjhl7vIa1wzZruRWR3doWbvowxlSIyHNsWsj7VyLSUHvW8AX1KbYiaQitXsg7fDVBfxQIJiTwNLF/ZeWQ23r1cDuM1liLvclta6+FBlpG000wfCtBvx+42u1Q3HRgRWWvl7p1dTuM1liMrSxqa6+FBlpG00kwbAj6T8O+t6e6HY6b9quszP4sL9ftMFrjW+CQUGFofTv373RlNCnJizGmApulPwH8gqYz8U+By0Xkr8BwY0zrup4Gw//GFtBOOxlDN2O6Zxva04qRTDXAUaHC0EcJOHYZ0NA6/Bmwl4iMgo194se08XhNrh3W6DyZKP4LeQMEw9dgK5vSfTa8dhtZVz9c2jKjojs2AL9M0Hq/WkZTWTAcBG5zOQpX7VpdMwpjUn3ozwpsIpqILsVaRlNVMBzBTiT3sMuRuGr/yqp0aFlfgK3QTcTQnIwto8lsSUvMjS5AMPwUduansgTEnRZG1NWl8uQLYWx3hUStU/gf4E0Red8Yswr7pf2MiMzGVnJs18bjNbd22PvA9p1g4oV4L+QNwfD92EmN0qkrXFz1Te2JxlYAB4QKQ18l6PhaRlPfpcAdbgfhljxj8vOMWeh2HFuxANgnVBhK1Lg5LaOpzK5B+jvgPrdDccuQ+shgjzE/ux3HVswE9g0VhhJ1P56xZVTsHENJOJFIENsFsBfQHbjQGGNEZJIxZqZzo7vIeexfwBJjzG1tOknQPxF4HRgYz9jTwe09/R892MO/j9txNGExNhHV9WFTXEwZvRvbSrIntsJqkTHmCBEpYlMvhBXAb9q0fhpA0L8vMAXwxy3wNHFZ394fvNm1y/5ux9GEudgymm4TuKhECPovpZMOfTllYP8PZ+fm7Ot2HE34HPhVO8eIqkwT9N/MpkloOpVfDhn0+TJfVip29X4LOK6dY0Q7PTfGGDaXiZ8AzHG67+4APN7mIwfDs4A9gHnxCDSdHFBZ1d/tGJrwNbC7JqLpIaELeTcIhj8E9sHOCtepHFBZlYpd0z4C9tJEVG1kh76cQCfsxbBPVVUqTjQ2BdtrQRNRZQXDl2Fnwu50w9MmV1en4vfSw9hZczURbaektYwmVdDfCzuDb2sWkc0IEYhMDAytxs56mgr+BxwbKgxtcDsQlYKC/uHYmsRt3Q4lWcIeCe89bEh3nO7PKeB54LR2TDuvOoOgf3fgZWCA26Eky3c+3w/HDRk4wu04YtwDXBgqDOl6sGpLQf/+wP9hexx2Cu/l5826uH/fiW7HEeOaUGEo6HYQ6S7VZ19tn2B4LXAw8IrLkSSNF7w9otEFbsfhuB04PFUTURH5h4gc7HYcnVow/COwN3YQfqfgjxq/D0rdjgM7kVQQOClVE1Eto20n1scicljMY8eLyJvtOmAw/Bl2Cacv4xRiyhtTV7cNxrR13c5EqAEuCBWGzk/VRFTLaAoIhj8AdgPaO/t52tnNrh6RCpMhlgG/SdVENN3KZ2a2jDYI+j3Y9U2DQLa7wSTeef37fvBRft7+LoawFjgzVBh61cUYABCRLJP6MyOqoN+HXbj5cpK31JRrjh004OPvc7L3djGEZdgL6FQXYwC0jCaCiOwAvIBdND0LO6HGoaYjE/ME/bnA/cBp8Ygx1R0wdPD01VnenV0MYT5wYqgwNNPFGAAto2nDLs10P9ApJoSaPHzI99UeT1tnjo2nmdgymqjJxFolk8pnZraMNgiGowTDN2Jrd13/Yk+0/Sur8lw8/SfAxHgnos501SUi8rWIzBGRE0VkZxGZKiLTReQtERnobPuBiNwmIl9h18P8UZy1V53jLBYRn4g8KiLHOY9PFpFpzvG/EJFuIuIVkZtF5EsRmS0iZ8fzNakYwXAdwfDV2C71c90OJ9H2rKp2s/bvdWBCvBNRLaOpwxgzBztE5a/AVcCT2Pf5CxGZKSJHAojIOOexWc77N7rZgwbD1QTDhcDJwJrEvwp3TaypcXPc11PATvFORLWMZrhgOEwwfBJ2dtWMH7c4qq7OzRl17wT2iGciquUz05PRBsFwCNgV20KasQO+96usCrhwWgPcCOwfKgwtTsDxDwWWGWMmGGN2AN7EfhkcZ4zZGTtw/PqY7bONMbsYY64BZgH7OY8fAbxljNn4/y8i2dj1by82xkzAdu2uAs4CwsaYycBk4Pcisk0CXptqEAx/CewE3Ir9TGWkgyor+7pw2jrgMuwEC4lY+0zLaGq5BvgNcBh2He/3jDG7AgcAN4udV+Ac4HZjzERsZe2SFo8aDD8LbI8do5axDqiszHfhtJXAWaHC0G8TNAmKltHOIBh+DNsrIqO71u9d6cocRuuAY0KFoYsSMLyl05fPjO8Wt1EwXA9cQ9A/BXgMGO9yRHHXPxLp7zVmWURkUJJOGQLOCRWGpiX4HLeIyE3Aa9gvhB2Ad8TOA+MFYtd0eq7R7ydi10w6CTsZRKxtgeXGmC8BjDEbAETkEGDHhlol7DIko4FF8XtZagvBcDVwKUH/q8CjQMDVeBJgh5raURhTiUiybng/xZbR2Qk8h5bRFGKMqRCR57AtJCcAvxKRhmUgcoFh2M/FFSIyBHjJGNO6Wv5geCVwHEH/CdgloPrEO3637VNZPTLJp3wLOD9UGErkGqdaRjuLYHgBQf9ewJXYHhIZN0TtgMrKQff1TOrqcC8Al4QKQ4laBaDTl8/Ok4w2CIZnEvTvAlyNLaipOJV7uw2ur//xJ58v0cloBbb2/dZQYSih/dWNMd+LyE7A4cB1wHvAN8aYPbYSW4MpwA0i0gvY2dm3NQS7Du5b7QxbdUQwPJWgf0fg39hFvjNGFmR1j0a/2eD1TkjwqdYBRcADocJQQluatYympKjzI8CxxpjGE5zMFZHPgQLgdRE52xjT2vceguHnCfrfx974HNfS5umkZzTaK8uYH+tFhif4VMuBP4YKQ8+1uGUHaRntZILhOuAqgv6ngLuwrWEZY7vaum0wZgMi3RN8qh+wFUXtmwSulbR8dpZuuo0Fw7UEw1dg1yTNqDUwd6uqrk3wKaYA24cKQzcnOhEFENvKW2mMeRK4GTtzXF8R2cN53ici45ra1xhTju2ucjvwmtlyBrbvgIEiMtk5VjcRycLWVJ8rIj7n8TGSOkvmdA7BcBnB8O+x3U5cnSQg3naoqV2f4FM8AWwbKgz9J9GJKGgZTXFvAReKU70uIpOcf0cAPxhj7gBeBXZs85GD4VUEw8cDxwNL4xZxChheV5fI1xPFJgjbJSMRBS2jnVYw/B3B8C+wLWYZs7a3Bzy9otFE9iSoxSaF4xKdiIKWT+iMLaOxguEvnRaYE7FdGsa6HFGHHVhZ1fOF7t0ScegQcEWoMPTfRBx8K8ZjxzlFsWPfzgXqgTtExI/9DN9G85UKz2G7WOzf+AljTK2InAjcKSJ52H70BwMPYruIznBu4lYBR8XtFanWC4ZLCPrfBH6LnXV3lMsRddgBlVU50/ITMtfYp8DfXJgpV8to6roW+97Pdia5WISt4DkBOFVE6oAVwA3tPkMw/CJB/2vA2cDfgP4djNl1e1RV1y3MTkjvxnewZXR6Ig6+FVpGO7Ng+DmC/teBK4ALATfGRcfVhOqa8Ptd4v4yDHZJyMtDhaF58T74VnT68pnZS7u0hV0G5jhsUrqDy9G0W7VI1eThQ7Jwajvi4Btsl9wXk9HKolSzgv4sNiWlyR7XFTfLvN4Vvxw2eEAcD/kFcHUyanCV2qqgPx84H/gLaTye9MvcnG/PHNh/+zge8j1sGf04jsdUqu2C/v7YIWrnAG6uwNAhL3ft8uVVfXtPjuMhp2DL6Kw4HlO1kiajjQX9AhyDnRa/7V2XUsBuw4fMrfR4OtrKOxebhL6Qqotuq04qA5LSCYGhy6POVO0dMB178SyJR0xKxU3Q3xW4CPgz0NPlaNqsDup2CgyNIJLbwUNNxZZR19f1VWozQf9AbE+GPwA5LkfTZms8ntX7Dx8Sjwqv14CgC70VVAxNRptjk9IjsUnpJJejaZNTB/b/cFZuzr7t2NVgZ+S6F3hJk1CV0mxSeio2KR3hcjRtctiQgZ8t8fl2b8euEex6ofeGCkNvxDkspeIr6O8OXIK94R3sbjBts+ewIXPKvJ729JKqA14G7g4Vhj6Mc1hKxZdNSs/FdrPv53I0bTIpMHRxvcjQduxahe3aepcmoalBk9HWCPoPA84AfoWdGj+lPeDv/skdvXrs1YZd1mCX0rg/ngv5KpUUNik9EjgNu7ZivLqoJ8zVfXp98FK3rvu3YZdl2DEeDyZoPV+lEscOg/kFcDp2XFLKX0d/N6Df1M/zcvdrecuNFgH/AR4OFYZWJigspRIj6M/BDlU7G9jH5Wha5deDB05blO3bsw27fAvcDzweKgytT0xUqj00GW2LoN+PnTnwt8C+2KmRU84Cn6/06CEDAy1sVg98gE1CX0zAIr5KJV/Q3wc4GZuY7uJyNM2ampc7+4IB/VoaBlAN/A+74PV/kzF7tVIJZ6+jJ2IT0+aWLnDdE927ffrP3j1biq8ceAN4CHhb51VQGSHoH4u9jh4PbOdyNM26oVfPD5/xd2upF+B64L/YZc4+SnxUqj00GW2voH8I8GtsLe/+pFhrzI6BoeuMSOOxOhuwF85XgTe0ZkhltKB/BLZ8Hg3sSQotZVUlUrnr8CHZ2CnWY63BjmGZArwVKgxVbLm3Uhki6B8DFGJ7HY13OZrNLM7KWnL40EFDmnhqOfbm9lXgXa3IVRkt6B+HbTE9jhSb3PPz3Jxvfjewf1NLnvyILZ9TgKlakZv6NBmNB1vTWwAcgh1fuj0uL5tz4NBBX63KypoEzAY+AkqAD0KFoUSvQ6pU6gn6+2Erj/bGLgw9FvC6GdKuw4d8V+XxjABmYMvof4FPQoWhxuuEKZX5gv4B2CUHfoGt4B3majzAhMDQlVGR7th1/D7EltEvtAVUdUpB/zbYsrmf8xNwM5xaqN05MBTsElWfY3v7TQkVhr52My7VdpqMJoLtez8em5g2/OxIYtd2MsB8YCYw864e/k/v7+mfGSoMlSXwnEqlp6A/D1smd4r52QFIyOKCjgh2AeoZwMyr+/T69KVuXWeFCkNVCTynUukp6B+M7dGwF7YCaRtgEIkdHhPBjiubAcw4t3/fTz/Oz/taK3GVakLQPwyblO4EjHN+BiX4rNXA99jZ5L86avCATxdmZ4e09TO9aTKaLEG/FxiDTUzHAH6ge8y/sT9+oAv2olsBrI75WdXE38uBOQTDmngq1V5Bvw+bkE4ChmDLYcNP90Z/+9lUuRTGlsU1bF42Y3+WAiGCYU08lWqvoD8XGI5NTEc4/zb8dMP2SGr48Tb62wA/YycDW97Mv4u0jCrVAUF/D2zvwLHAQGAA0D/mpydNl9Fq7PjOsPPvemAdtsvtAmCh8+8ygmFNXDKMJqOpys4+6CMY1vEoSqUiO4uvEAzXuR2KUkoppVQ60mRUKaWUUkoppVTSpczskkoppZRSSimlOg9NRpVSSimllFJKJZ0mo0oppZRSSimlkk6TUaWUUkoppZRSSafJqFJKKaWUUkqppNNkVCmllFJKKaVU0mkyqpRSSimllFIq6TQZVUoppZRSSimVdJqMKqWUUkoppZRKOk1GlVJKKaWUUkolnSajSimllFJKKaWSTpNRpZRSSimllFJJp8moUkoppZRSSqmk02RUKaWUUkoppVTSaTKqlFJKKaWUUirpNBlVSimllFJKKZV0mowqpZRSSimllEo6TUaVUkoppZRSSiWdJqNKKaWUUkoppZJOk1GllFJKKaWUUkmnyahSSimllFJKqaTTZFQppZRSSimlVNJpMqqUUkoppZRSKuk0GVVKKaWUUkoplXSajCqllFJKKaWUSjpNRpVSSimllFJKJZ0mo0oppZRSSimlkk6TUaWUUkoppZRSSafJqFJKKaWUUkqppNNkVCmllFJKKaVU0mkyqpRSSimllFIq6TQZVUoppZRSSimVdJqMKqWUUkoppZRKOk1GlVJKKaWUUkolnSajSimllFJKKaWSTpNRpZRSSimllFJJp8moUkoppZRSSqmk02RUKaWUUkoppVTSaTKqlFJKKaWUUirpNBmNIxE5RUTeTsJ5giLyZBLOUy4iI9q57z4i8l28Y1KqsxGRgIgYEclyOxalWkNE3hCRQuf300Xk45jnjIiMcn6/T0SudCvORBCRUhE52O04lFKbiMgHIvK7OB3rURG5Lh7HUlZKJqNNfZk3vqC187hGRFbG3tSJiM95zHTk2ADGmKeMMYe0MpYhIvJ/IrJaRMIiMkdETu9oDPFkjOlqjPmhNdvG3mA4+35kjNk2cdGpdJHA8iwicpFTdipEZImIvCAi4zsWcXwvXM0cf7PyIiL7i0jUqQAqE5HvROSMRJ1fqea0JplyPr8Vzud1tYg8IyI9Gp43xhxmjHmspXMZY84xxlwbh7Abx7RURP4tIt54HFupVOWU1yrnc/+zkyh1dTuu1mj8XRNT8Tqz0XZ9RKRWREpbedykNNio+EnJZDTB1gGHxfx9mPNYsj0BLAaGA72BU4GfXYhjC9oCo9LE7cDFwEVAL2AM8ApQkOgTJ6iMLDPGdAW6A38FHhCR7RNwHqXiYYLzeR0B9ASC7oYDbIrpIOA3wO+TdWK9bioX/cr53O8E7AL83eV4OipfRHaI+fs3wCK3glGJl5bJqIgUichCpwXhWxE5Oua5USIy1WltXC0izzXa/QngtJi/TwMeb3T8QSIyRUTWisgCEfl9zONVItIrZttJznl8TXRH2k5E3nGO852InBBzmsnAo8aYCmNMvTFmpjHmDWe//UVkSaOYGtdW54rIc857MENEJsRs+1enZrihheUg53GviFwe895NF5GhznNGRM4XkfnA/JjHGrpTPSq2S9U7zr5TRWS489yHzqm/dmrnTmz8GkRkrNPatF5EvhGRX8c896iI3C0iJc6xPxeRkahOoT3lWURGA+cDJxtj3jPG1BhjKp3eCcXONn4ReVxEVonIjyLydxHxOM+dLiIfi8i/RGSdiCwSkcOc564H9gHucj7PdzmPN1VGbheRxSKywSlP+8TE3mR5a6q8xL4fxnoFW0kWm4yeIiI/Oe/DFTHnyRGR20RkmfNzm4jkOM/tL7bF+E9ie4Asl5gWV2fffznH/dkp43kd+f9UmaG5steYMWYDMIWYz6q0smeBxHR3a8VntbeI/Ncpa1+KyHXSTO8KY8w84CNgBxEZKSLvicga53U8JTGtuGKvrX9zvnvWicgjIpIb8/wRIjLLuXZNE5EdG+37VxGZDVRIo4RURDwx329rROR5ce4fRCRXRJ50Hl/vvKb+znOni8gPzvfGIhE5paX3UiljzFLgDWC8iLzmXPvWOb8PARCR40Vkeux+InKpiLzq/P6oiNwjtqt9uYh8IiIDnOvKOhGZJyKTYvYdJLaX3yrns3pRzHNB5zP/uPNZ/kZEdnGeewIYBvzXOc9fYkJ6AiiM+bu5+/QtzisihwKXAyc6x/06ZrfhzuspE5G3RaRPzPF+7cS33vn+Ghvz3CSx99llzvdgLiqu0jIZBRZibxb9wDXAkyIy0HnuWuBtbE3tEODORvu+AuwrIj1EpKdznFcbbfMssAQYBBwH3CAiBxpjlgGfAsfGbPsb4EVjTF3sAUSkC/AO8DTQDzgJuEc2tXR8BtwtIieJyLC2vwUcCbyAbRF6GnhFbEK8LXABMNkY0w34JVDq7HMpcDJwOLb15UygMuaYRwG7sfkNcKxTsO9vH2AW8BSAMWZf5/kJTtfezW5aRMQH/Bf7/9IPuBB4yom1wUnY/8uewALg+ta9DSoDtKc8HwQsMcZ8sZXj3ukccwSwH/aCFtv1dTfgO+zn+Z/AQyIixpgrsDeyFzif5wti9jmKzcvIl8BENpXDF2JuZJssb60oLx6xCXkPIBTz1N7Ats5rvyrmYnkFsLsTxwRgVzavGR/gvA+DgbOw3zs9neeKsS3KE4FRzjZXoVTL11IAnM/SUdhrWkdt7bN6N1DhbFPI5jerjWPaHvudMhMQ4Ebs9XwsMJQtW3FPwV4rR2LLw9+d40wCHgbOxvZguh+YIk5lj+NkbG+MHsaY+kbHvRD73uznnH+d8zpw4vc78fQGzgGqnHuHO4DDnGv4ntjrrVJbJbZx4XDgB+ARbM+7YUAVcJez2RRgm9hkC9szLzbZOwFbBvoANdj73hnO3y8C/3bO58He232NLbMHAZeIyC9jjvVr7D11D+fcdwEYY04FfsJp1TXG/DNmnyeBk8RW6G4PdAU+j3mdzZ7XGPMmcAPwnHPcCTHH/Q32HqAfkA382TneGOAZ4BKgL/A6NknOFpFsbN7wBPY6/wKb5wAqHowxKfeDTZ7KgfUxP5XAx81sPws40vn9ceA/wJAmtjPYG64HsReXc4AHnMeMs81QIAJ0i9nvRmwrJsDvgPec3wXb1XZf5+/TG2IETgQ+anT++4Grnd97Ym8Ev3HONwubQALsj73RbvyeHOz8HgQ+i3nOAyzHXnxHASuBgwFfo2N81/A+NfPeHNjU++X8/ijwbMxzXZ24hzbetvFrcOJaAXhinn8GCMYc+8GY5w4H5rn9OdSf+PwkojxjE7DPtnJOL1ALbB/z2NnAB87vpwMLYp7Ldz7DA5y/PwB+1+iYW5SRJs67Dptktqa8NS4vUee9Weu8Byc5zwWc7YfEbP9FzPMLgcNjnvslUBpz3CogK+b5ldjkVbA39yNjntsDWOT2Z0Z/3PtxyuvBzZU9ZxsDbHA+rxFgHjA45vmN5YeY62LMvrHXleuc37f2WfUCdcC2Mc9d18RxNzhlcKHzvKeJ2I8CZjZ6vefE/H04sND5/V7g2kb7fwfsF7PvmU29f87vc4GDYp4b6LyOLGzl1DRgx0b7d3He12OBPLc/D/qT2j9sfn39Ebin8ecGW9m4Lubve4Hrnd/HOWUmx/n7UeCBmG0vBObG/D0eWO/8vhvwU6Nz/Q14xPk9CPwv5rntgapGsR8c83fAKcdZwP+w17Ji7PX+YDZd11pz3icbPf8B8PeYv88D3nR+vxJ4PuY5D7AU+520L7AMkJjnp+F8b+lPfH5SuWX0KGNMj4Yf7AcHABE5LabbzHpgB2yNDcBfsDdZXzhN7mc2cezHsa0kWzT9Y2sv1xpjymIe+xFb+wLwf8AeTsvNvtgbyI+aOMdwYLeGGJ04T8HW6mKMWWeMKTLGjAP6Y28+XxERaemNcSxu+MUYE8VpyTXGLMDW7gSBlSLyrIgMcjYdir1It3jMVpyzHHvTPKj5zTcaBCx24mwQ+56CTVYbVGKTXZU54l2e12Bv7JrTB/BhP2cNmv3MGWMaegi09LnbrIyIyJ9FZK7YrozrsS0dDbG3VN4aW+a8P72MMRONMc82er65MjKILV9nbLlcYzZvsWnYty82CZ8e896/6TyuVEvX0p2cspyLvbn9KKZXQHtt7bOaxeblr6nr1U7GmJ7GmJHGmL8bY6Ii0t+5Di4VkQ3YVpc+jfaLPVZs+RkO/KnRdXwom5evrV03hwMvx+w7F5u898e2tLwFPCu2e/0/RcRnjKnAVmafAywXO3xlu62cQ6mG6+twY8x52Pn97hc7PGUD8CHQQzZN6PUY8BvnfvNUbCJWE3O82PlLqpr4u+HaMxwY1Kh8XI79fDdofN3KldaNr34cW5F1MrasxGrNeZvSqmuoc6+6GHu/MAhYapws1BF7vVVxkMrJaJPEjlN8ANsVtbdzMZyDvWhijFlhjPm9MWYQtiXkHomZtdLxEfZGtj/QeMzJMqCXiHSLeWwYtpYEY8w6bNelE7FN/s82+pA2WAxMjb0BN7bLwLmNNzTGrAb+hf3Q98K2VuTHvGYvW94gDo153oPtRrXMOd7Txpi9sQXWADfFxLS1sZhNvY7mztnViXVZC/vgbDPUibPBxvdUdV4dKM/vAkPEGX/ShNXYFojhMY+15TPXXFnY+LjY8aF/wXZp6unEHm6InZbLW7wsY8vX2ZpyuRp7YzEu5jvKb+xEGKqTa+W1FGOHqDwIbIOtSEqEVUA99jrXYGgz2zZ2A7bcjjfGdAd+y6Yy2tSxYsvPYmwLUux1PN8Y80zM9lu7bi7GdreN3T/XGLPUGFNnjLnGGLM9tivuETjzWRhj3jLG/AJ7nzIP+x2pVGv9CTukYzfnM98wNKThuvoZtufQPtj72MbJXmstxvakif18dzPGHN7K/bdWdv4P2/39B2PMT208b0v3so1tdg11kvSh2PuF5cDgRg1F7Rlap7Yi7ZJRbBcWg704IXaCg40XQLGDsxsuWOucbWNb5HCSx18Bv26cSBpjFmOb4G8UO8HAjtixK7HTRD+NvWgc5/zelNeAMSJyqjOW0ycikxv66YvITSKyg4hkOYnvudhug2uA77G1RwXOeMu/AzmNjr+ziBzj1DBdgu3X/5mIbCsiBzpjWqqxN5sNr/9B4FoRGS3WjiLSu5n4m3K4iOzt9KG/FttNsqFW+Gfs2LymfI6thfqL8z7sj33/G7f8qM6nXeXZGDMf2x3pGbETn2Q75fUkESkyxkSA54HrRaSbk/ReyubleGu29nlu0A17g7wKyBKRq7BjQxtsrby15vit9QzwdxHpK3ZChqtoxet0an8fAG4VkX4AIjJYNh/vozqp1lxLne282HFYVdixanHnlOeXgKCI5Dsthae1sFuDbthujGERGQxc1sQ254tdbq0XtktgwzjuB4BzRGQ3pwx3ca7L3Zo4RlPuw34HDQdwyuiRzu8HiMh45/3bgK08a2jJPVLs2NEaJ/Yt3neltqIbtjyudz7TVzexzePY8Zt1xpj2LrP2BVAmdhKvPLFjPHcQkcmt3L/Z66DTQ+BA7NC4tp73ZyDQqAFka54HCkTkIOee+0/YsjcNO162HrjIuX89Bjsvg4qjtEtGjTHfArdgPyA/Y/uvfxKzyWTgcxEpxw6Wvtg0sVamMeYbY8w3zZzmZGzf9WXAy9hxnv+LeX4KMBpYYYz5esvdwenmewh2Yp5l2O4BN7Epqcx3jr0eewEfjh3ojTEmjO3G+CC2ZqYC2w031qvY1tl12G4Wxzg11DnYPvarnXP2w/alBzvo/Hlsy+4G4CGgLTNnPo39UlsL7IytYW4QBB5zukzEzhqMMaYWm3we5sR1D3CasTMeqk6sg+X5IuzF9G5sOVoIHI2d2ADsWJcKbPn6GPv5fbiVod0OHCd29sA7mtnmLWy31u+x3Xaq2bzL3tbKW5Bmyks7XAd8BczGTng0w3msNf6KnTDsM7Hduf6HrVFXqqVr6dfOc+uwk/EcbYxZm8B4LsB2g1+Bbcl5BnvD2JJrsEtehIESbFLb2NPYcvoDm8abYoz5Crs8zF3Y17kA23WwtW7Hvndvi0gZdpKn3ZznBmAng9mA7b471XldHmzF2TLstXY/bGW1Uq11G/Zasxr7mXuziW2ewFb8tns9TqeS6AjsmNRFzvkexJbT1rgRW5G6XkT+3MTxvzLGbDHUpRXnfcH5d42IzGjF6/gOez97p3OsX2EnVqp17l+PwZb7tdj77qa+Q1QHSNM9TJXanIg8ip2QKN3Xr1JKKZXmROQm7IRjhR08Til2sqX/tbStUplC7BJeK7HjrOe7HY/q3NKuZVQppZRSnYvYdbt3dLrL7oodPvOy23EplabOBb7URFSlgtbMaKWUUkop5aZu2K65g7Bd+m9hyzXClVItcHoDCHaZI6Vcp910lVJKKaWUUkolnXbTVUoppZRSSimVdJqMKqWUUkoppZRKOk1GlVJKKaWUUkolnSajSimllFJKKaWSTpNRpZRSSimllFJJp8moUkoppZRSSqmk02RUKaWUUkoppVTSaTKqlFJKKaWUUirpNBlVSimllFJKKZV0mowqlQFExIjIkzF/Z4nIKhF5rZ3H6yEi58X8vX9rjiUiARH5TXvOmQgicpSIbB/z9wcisoubMSmllFJKKUuTUaUyQwWwg4jkOX//AljageP1AM5raaMmBICUSUaBo4DtW9pIKaWUUkolnyajSmWO14EC5/eTgWcanhCRXiLyiojMFpHPRGRH5/GgiDzstBj+ICIXObsUAyNFZJaI3Ow81lVEXhSReSLylIhIEzEUA/s4+/1RRHJF5BERCYnITBE5oKnAReRREbnXie0HpyX2YRGZKyKPxmx3snOsOSJyU8zj5SJyvYh87Ryjv4jsCfwauNmJZ6Sz+fEi8oWIfC8i+7T1TVZKKaWUUvGhyahSmeNZ4CQRyQV2BD6Pee4aYKYxZkfgcuDxmOe2A34J7ApcLSI+oAhYaIyZaIy5zNluEnAJtqVxBLBXEzEUAR85+90KnA8YY8x4bIL8mBNfU3oCewB/BKYAtwLjgPEiMlFEBgE3AQcCE4HJInKUs28X4DNjzATgQ+D3xphpznEuc+JZ6GybZYzZ1XktVzcTi1JqK8T6WEQOi3nseBF50824lFJKpRdNRtVGzrjDW2L+/rOIBF0MSbWBMWY2tpvsydhW0lh7A084270H9BaR7s5zJcaYGmPMamAl0L+ZU3xhjFlijIkCs5xztWRv4EnnvPOAH4ExzWz7X2OMAULAz8aYkHOub5xzTQY+MMasMsbUA08B+zr71gINY1qntxDbS63cTinVDKesngP82+kB0RW4AVsBpZRSGUNEIk4Pq4afIufxds1D4VSwHx7H+AaJyIvxOl6yaTKqYtUAx4hIH7cDUe02BfgXMV10W6Em5vcIkNXa7URkt5gv51+39oROl9pZIjKrieNHG50rupWYGtQ5N8cbY9vKtjWt3E4ptRXGmDnAf4G/AldhK56ucLrBzxSRIwFEZJzz2CxnqMBoF8NWSqm2qnJ6WDX8FHfweBOBJpNREWnzfYkxZpkx5rgOxuQaTUZVrHrgP9hukptxZkl9z7mReFdEhiU/PNUKDwPXGGNCjR7/CDgF7My4wGpjzIatHKcM6NbSyYwxn8d8OU9pYr/Y844BhgHfGWOuaNivVa/K+gLYT0T6iIgX2wI8tYV9WvU6lFLtdg120rLDgFzgPacb/AHY8dpdsC2otzvlfRdgiUuxKqVUQojIISLyqYjMEJEXnN4iiMhkEZnmzGnxhYj4gX8AJzoVdCc683c8ISKfAE80d8/tzK9xh3O8H0TkOOfxgIjMcX73isi/nLk1ZovIhS69Ja2myahq7G7gFKewxLoTeMwZc/gUcEfSI1MtcrrRNvV/EwR2FpHZ2EmGCls4zhrgE+fL7OatbdvIbCDifOn+EbgH8IhICHgOON0YU7PVIzQf03LsmNT3ga+B6caYV1vY7VngMqeVZmQL2yql2sgYU4Et209gZ/Eucno8fIBNTocBnwKXi8hfgeHGmCp3olVKqXbJa9RN98TYJ50ehX8HDjbG7AR8BVwqItnY78eLnTktDsaufnAV8JxTKf+cc5jtnf1PZuv33AOxQ6COwN7PNfYH7BCkiTH7pzTZ1LNNdXYiUm6M6Soi/wDqgCqgqzEmKCKrgYHGmDpngpvlxhjtzquUUp2cM7dAOba3wm+MMd81sc1I7GzfFwJnO2PXlVIq5TXcHzfx+AfAn4EBwKNs6vWRja2Euw24zxizV6P9Tgd2McZc4PwdxA7Fv8b5u8l7brGrC7xjjHnK2a7MGNNNRALAa8aYHUTk/5xzvhO/dyCxtGVUNeU24CzsDKVKKaVUa7wFXChil30SkUnOvyOAH5xeG69iZ/tWSqlMIdgksWHY0vbGmLPaeIyKVm4X27usqSX20o4mo2oLxpi1wPPYhLTBNOAk5/dTsGMBlVJKqQbXAj5gtoh84/wNcAIwx+m+uwObLy2llFLp7jNgLxEZBSAiXZx5Mr4DBorIZOfxbs4ERS3NZ9GRe+53gLMbJkISkV5teiUu0JkkXRQoKukB9AC6Yz+UDf/mYbvJ1jb62QAsB1aUFhdEEhzeLcAFMX9fCDwiIpcBq4AzEnx+pVwXKCrJAfzOT/dGvzdcUBr/bADKSosL2jU2Vql0Y4wJxvx5dhPPF9P02KYOCxSVeLDX0Z5AL+ffntgyWo0tk+VsKp8bfy8tLqhPRExKKStQVOIFhmDLYxegq/NvHls2iNVj73GXAEtLiwtqkxhqS/Iazf7/pjGmqOEPY8wqp+vtMyKS4zz8d2PM98740jtFJA87/O1g7NwXDePrb2zifB25534Qu4TebBGpAx4A7mrD/kmnY0YTzEk4dwG2BbYBRjg/22ALZ3tEsetBLnN+5mMnjpkNfKM3wUq1XqCopC8wATt5wLbYL/HR2EkCsjtw6HJgHjA35udbYGESKpOUyhiBopI8bNfeCdglEXYEBmGTz+60v6taGbAQ+B57Hf0OmIO9jqbSjbBSKS1QVNIV2K6Jn1FAzlZ2bY7B3ucuARY7/zb8/gMwQ8to5tBkNI4CRSU+YCdg15if0SS3T3c99qI6C9us/25pccH3STy/UinLaUWZDPwS2B17YzswyWHUsqkCaSrwXmlxwfwkx6BUSgoUlXTHls1J2PI5EXsd9SYxjDpsxdEs4BPgjdLiAl2ORik29hg6ADgEGI9NOockOYwq7ARBU7Ezd3+uDTHpS5PRDgoUlQzDTq98KLZwbjHbVgpYArwLvAe8U1pcsNzleJRKmkBRSX9s+TwUu/REb3cjalJDGX0deKu0uCDscjxKJU2gqGQcdgH4w4G9sONOU81soARbRj/V3g2qMwkUlQzEzoZ9BLabaapNcFmNHbfZkJx+VlpcUO1qRKrVNBlth0BRyUjgt8BR2FrbdGKwA6NfAF4oLS5Y5nI8SsVdoKhkInbSlEOxZTSdZpyrx7bGvAQ8WVpcsNbleJSKu0BRyc7A8cBxQLqtAbwWO3Pw69hW0zUux6NUXAWKSgTYGZt8HoHt9ZdO19Ea4H/Y8ZOv6fjw1KbJaCsFikq6YC+cZwD7kF6FsjlRbGvpE8CLpcUFlS7Ho1S7BYpK8rHrHJ6N7YqbCWqA/wMeKC0u+MDlWJTqkEBRySDgPGw5HeFyOPFSB7wM3FlaXPCx28Eo1RGBopLhwEXYMprsISyJshy7BuiDpcUFP8TjgM56n68ZY15MheOkO01GWxAoKtkFOBfbypKKXXDjZS3OjFs6Nkalk0BRyY7YBPS3tH9SsHTwPbaW99HS4oJVbgejVGsFikomAZcCJ5KaXXDjZRZwN/BUaXFBlcuxKNVqgaKSycCfsD0Vkjk+O5kMtgHmQeCljkyApMlofGky2oxAUclBwN+Ag9yOJcnqsd0DbystLvjU7WCUakqgqCQLW3N7LrCHy+EkWy3wCvDP0uKC6S7HolSTnG5+vwL+COzvbjRJtxZ4GLintLhgkdvBKNUUZ0K/I7FJ6F4uh5Nsa7DrHd9fWlzwXUsbi8hpwJ+xCe1sIIJdxm0XYADwl4aE0lmO5QTsLMIvG2OubuoYxphTY5NREbkWGAqcZYzpVGPSNRmN4Vw8jwKKsDPhdnYfA3/TrkcqlQSKSo4DrscuwdKZGWwX3r+35mKqVDI43eVPBy5Gy2gU+C9wZWlxQcjtYJSCjcPOzgAuIf3Ga8dbFHgKuLq5iiMRGYftir+nMWa1iPQC/o2dxOlE7GzCU4wxo0TkEGzr8tnY4XxTgH9ik9/NjmGMWduQjAK7Ad2Ac00nTMw0GXUEikp+AdyCnaZabe51bFI62+1AVOcVKCo5ECgmc8aDxksEeAwIlhYXLHY7GNU5OZW5hdgy2t/lcFJNBNs18ErtYq/cEigqyQUuw/ZW6OlyOKmmFjtU7drS4oKfY58QkQuBAcaYK2IeexR4xxjzlPN3mTGmm4j8C5uMrnc27QrcCOQ3PkbMcSYBnxtj/pCA15UWOn0yGigqGYOt4ShwO5YUZ4BnsK0w2u1IJY0z3qwYu6aZal4NcC9wg97wqmRyZsa9C7s+qGpeGNur4/aOjFdTqq0CRSXHAv8CAi6HkuoqgJuAmxuWhtlKMvpaTNfccmNMVxG5BfjeGHN/7EGbOkbMceqxCekvjDGdcvb8TpuMBopKegJXY2f2y+QJFeKtGrgWW1Dr3A5GZa5AUclg7MXzRDJj9upkKcOW0VtKiwuibgejMlegqKQ3cAPwO8DjcjjpZCHwl9LigpfcDkRlttFFr2xXh+9O7NqgqvUWAX8qLS54Oaab7h7GmDUx3XSbSkYPwV5/DzLGlIvIYOyM230bH6NRN13BTvJ2iDGmLNkv1m2dMhkNFJUcDdwH9HM7ljQWAn5fWlzwuduBqMwTKCr5HTYR9bsdSxr7FDi9tLjge7cDUZnFmfjkbOA6oJfL4aSzD4CLdQiMirugPwe4coXpefDuNXfv5nY4aewd4IIfbzpiD2wX5wgw03lui2TU+f1ibAUdQDnwW2PMQhEpjD2GMeb0RhMYnQmcChxujOlUs3F3qmTUaQ29EzjF7VgyRBS4B7i8tLig09XkqPhz1jh7EK3FjZcq4HJst8DO82WvEiZQVDIRO1PsJJdDyRR1wFXY2bG1J4PquKB/L+AhYFuAf9Sd+unDkcM626zz8VQBnFtaXPCE24Fkqk6TjAaKSg7HDk4e5HYsGegH4ARdZkJ1RKCo5LfYNfoyea1Qt3wInBGvBb9V5+T0WLgTyHU7lgw0FTittLjgJ7cDUWkq6PdgKx+DxKwVGjHy84SaB/LKyddra8c8AlxQWlxQ6XYgmSbjk9FAUUk2cBt2PUKVOLXAZaXFBXe4HYhKL4GiEj+2hf03bseS4Sqw49TucTsQlV6c5VruBU5zO5YMtx74XWlxwf+5HYhKM0F/X+BJmpno74votlNPqL16v+QGlZG+wTa+fOt2IJkko5PRQFHJQOw6fNo9IXleAs4sLS4Iux2ISn2BopJx2HX4tnE7lk7kBexYUq3dVS0KFJVsC7wI7OB2LJ3IrdiKo3q3A1FpIOjfD3iarfT8M4boibVXzvvCjN0+eYFlrErg/NLigkfdDiRTZGwyGigq2RN7AR3odiyd0CLgKJ2UQW2Ns7bvC+gkRW6YBRypXQLV1gSKSk7EDm/p5nYsndAnwImlxQVL3Q5EpaigX7Ddcq8hpltucypMztzxNQ+NieJpcVvVKo8D55UWF1S4HUi6y8hkNFBUcg5wB7pki5s2AMeVFhe843YgKvUEikp+j+2am+V2LJ3YKuDo0uKCT9wORKWWQFGJD7t0wQVux9LJLQcOKS0umON2ICrFBP0+7CRFp7Zlt0fqfzn1mvpC7a4bP3Ox97rabbcDMi4ZDRSV3AT8xe04FGBnCfyDdmVQDQJFJYJdUPoyt2NRgF03+NTS4oIX3Q5EpYZAUUkX7HCLJseeqaRbCxyuy6ipjYL+rtief79s667GULZnzZ0Vy+k9IP6BdVprgF+UFhfMbHFL1aSMSUaddc/uZ9PaPip1BEuLC65xOwjlrkBRSR7wBHCs27GozRjgz6XFBf92OxDlLmf5sxJ0noVUU44d+vKu24EolwX9/bBldJf2HqI02v+z/Wtv3T1+QSlgHbYXw1duB5KOMiIZdWbMfRI43u1YVLPux67TlP4fONVmzk3uG4Auvp26biwtLrjc7SCUOwJFJQOAt4HxbseimlQDnFRaXPCK24EolwT9I4C3gFEdPdQlted99Up073YntKpJYeDQ0uKCz9wOJN143A6go5wp56egiWiqOxu4y+0gVPIFikq6ooloOvhboKhEk9FOyElEP0AT0VSWA7wYKCopdDsQ5YKgf1tgGnFIRAFu9t3fP5eaqngcS23kB94OFJXs7XYg6Satk1GnRfRl2tFvXrnivEBRya1uB6GSJ1BUkgO8iiai6eL6QFHJRW4HoZInUFTSH3gf2NbtWFSLvMAjWkY7maB/GPAO0D9eh/RJZOhdvju/iNfx1EbdgDcDRSX7ux1IOknbZNQZI/o4OslCurkkUFTyT7eDUIkXKCrJAp4HDnQ7FtUmtwWKSs50OwiVeIGikn7YRHQ7t2NRrSbA7YGikrPdDkQlQdDfF5uIDo33oQ/yzNhzeyldGO/jKroAJYGikoPdDiRdpG0yCtwNnOh2EKpdLgsUlQTdDkIljjNr7qPAr10ORbWdAA84a0yqDOUMcXkTGOt2LKpd7tKb3QwX9PuxY0THJOLwIvieyr6hPBHHVuQD/w0UlWjPzVZIy2Q0UFTyD+Act+NQHXJ1oKjkt24HoRLmLuAUt4NQ7eYBnggUlRzhdiAqYR4BJrkdhGq3LOCFQFGJtmpnoqA/D/gvCS6jPaV8wkXelz5O5Dk6sVzg+UBRiQ6BaEHazaYbKCr5PfAft+NQcVEDHFBaXPCp24Go+AkUlVwDXOV2HLGW3Hsmnuw88HgQj5eBhbex/uOnKP/6LTz5fgB67nsaeSMnb7afqa9lxdN/xdTXQTRK/rZ70WMfm2Ov+u/N1K36kbyRk+m5n51TZP20Z8nuM5z8MRmzMkY1sHdpccF0twNR8eNMVHW923GouPgB2K20uGC124GoOAr6XyRJy6BFjazZueZeWUf3Xsk4Xyf0LbBraXFBhduBpKq0SkYDRSV7Ae8B2W7HouJmJTC5tLjgJ7cDUR0XKCo5HHgN29UzZSy590wGFt6K10k8AdZ//BTiy8O/2zHN7meMwdRV48nOw0TqWfHUX+h10B8QXw5l0/9L78Mu4udn/07fo/9GtK6GtW/eSb/jrk7GS0qmRcBOpcUF690ORHWc09r9Ki71jGqqYqh25Q+seetuTG01Wf5+9PnVZXhy8rfYN1pdzpo37qB2tb1c9Dn8YnIGj2XdB49Q9cN0svttQ58j/gRA+TfvE63cQPfJRyb19bnkY+Cg0uKCWrcDUXEQ9F8GJHVujTnRwMdH1N6gs8AmzvOlxQU69KUZ6dNNN+gfNFKW3kEnSURNfS3LH/8jyx6+gGUPnsf6j54CoOrHr1n+6MUse+g8Vpf8GxONNHuMaE0lS+4uZO079zrHrOPn569i2UPnUTajZON2a968k5oVCxL7gprXD3jNWf5DpbFAUclQ7KRiKZWIdoSI2BtnwETrIRoBEcSTRbS+BmOi9nHxEP7oSfx7Z2TP5G2Ax5xxwCqNBYpKxgJP4fK1v//JNzDojDsZWHgbAGveuJOe+53OoLPuJn/MHmz4/P+a3G/tu/8hd8TODP79fQw68058vYcSramgdsVCBp15F+L1UbuqlGhdDRWhd+i2U0ESX5Wr9gYedDsIFQdB//7Ajck+7Tgp3Wt/z6zZyT5vJ3JCoKjkUreDSFXpkYwG/VnAc+9kX7bNkZ5PvnI7nKTw+uh/0g0MOvMuBp5xB1WLplO9ZC5rSm6lz6//wqCz7iGrez/KQ+82e4j1Hz1BztAdNv5dtWgGOUO2Z+CZd1H+zXsA1K78ARONkjMgLktXtdd44AE3A1Ad48yc+yzQ2+1YmiTCyuevYvmjF1M2682ND5fNeI1lD1/A6tdvI1Ld9DwOJhph2SMXsuTO35IbmEjOoG3x9RmKN8/P8kcvJn/UrtSvW44xxu1ylEi/Bv7sdhCq/QJFJT2xa3J3dzuWxurWLt14rcoNTKLy+2lbbBOtqaB68Td03dFOoC9eH57croBgovUYY4jW1SAeLxu+eIluO/0K8WYl82W47VRdJzjNBf2DgeewS/gklQhyn+/W/Czq65J97k7kpkBRyX5uB5GK0iMZhRuAvT1Cz9t8d+90u++uDyCN+he3Q1MtMuLxIN4sfL0GA5AbmEjl9580uX/NigVEKtaTt82mse/i8WLqaiASAefdW//Rk/TYJyXmETpJl5NIazcAe7odRHMGnHITA0+/nX7HX0PZjNeoXjyHbpMOZ/DZDzDwjDvwdu3FuveablgQj5dBZ9zJkPMepWb599SuKgWg18F/YNAZd9J912NY/9ET9Njnt4SnPceqV4o3S3gzyA2BopJ93A5CtV2gqMSLrSxyv7akiYqh7D7DqJr/GQCV8z6mvmzL4Y/163/Gm9+dNa/fxrJHLmLNG3cQra3Gk5NP3shdWP7oRXi79kRyulC7/PtMGrfdFtcGikp0Ted0FPT7gBewvcVckSt1o27MenDLmiAVL1nAc4GiksFuB5JqUj8ZtV0WNtbIi+A50jtt/09yLvqyGxVh9wJLvMYtMtkDx2CiEWqWzweg8rtPiGzY8qJtTJR17z1IzwPO2uzx3G0mUR9eyfIn/kT3XX5F5fzPye4/kqxuKdOYdWegqCQhU5irxHHGoKV0q1lWtz4AeLv0IH/MHtQs+x5vl56Ix4uIh24Tfknt8u+3egxPbldyh+1I1Q8zNnu8cv5nZA8Yhamrpm79cvoeVUTld58QratO2OtxSRbwrLM2pUovfyRF1uRuqmKo9+EXUzbzdZY/ejHR2irEs2WLpolGqF2xkG6TDmfQGXcgvhw2fPYCAP7djmPQGXfS68DfbewuX/b1W6x6pZj1055N9kt0kwd4JFBUkuN2IKrNigHXa1CO8344ebisWOJ2HND8cLXVr99uH3v4Ala9fAPR2qot943Us7rk3yx76HyWPnAO4U+fByBSGWbFk39h2UPnUfn9prkzV/7ftdSXrUnGy+qPnQXbl4yTpYvUTkaD/i7AwzQxBm2wrNl1es6568bLD/OTH1hyNG6RqVv9I31//RfWvfcAyx//I5KdD54t/wvLZpSQN3IXsrr32eJ4fX99GYPOuIP8bfdmw1ev0n3y0ax99wFWvXwDlfM/T9ZLa04+djmJTtW3Kp0FikqGAY+RwuNEo7XVRGsqN/5evWgm2X2HU1++duM2ld9/iq/P8C32jVSGiTrdd6N1NVSXzsTXe8jG502k3paj3Y7F1New8W0wUYjUJ+5FuWcQ8JSOH00fgaKSbYBr3I6jQVMVQ77eQ+l/4rUMPP12umy/H1k9BzS5n7dbH3IG2VUS8rfdi9qfF262Te3PCzHG4Os1hMp5H9P3qCLq162gbu3SxL+w1DGWFPr/Vq0Q9O8OXOJ2GAAi5D+bfd1yt+MAmhyuVrN0Hr0O+j2DzryLQWfehbd7X8pmvLbFrpXffYypr2PQWXcz8PTbKJv1JvXhn6n4dipdJx3GgNP+zYavXrXbLvic7P4jktkwswdwS7JOlg5SOxmFm7CTZzQpW+oDU7L/Pugs7+sZ3a0gtkUmZ/BYBpzyTwaediu5Q8fh67lla3/NsnmUTS9hyb1nsu79hymf8x7rPnh0s23KZpbQdYcDqVn2HZ6cLvQ58q9s+PLlJL2irdoVuMLtIFSrPQKk9HTwkcr1rHjqLyx7+AJWPH4peSMnkzdiZ9Z/8AjLHjqfZQ9fQPVPs+l50O8BqC9bw88v2BlxI+VrWfHM5c6+fyQ3MIn8UbtuPHbZjBK67nAQHl8uvr7bYOprWPbQ+WQPGOWMZ8tIBwNnuB2EarX7sRV9rmuuYihSsR6wvXrC056l28TDttjX27UnWd37ULfGNtpU//g1vj7DNttm47CTaL2tEAIQcSqKOpU/B4pKJre8mXJd0J8NPEQK3Y8PlLWTT/W+85nbcTQ3gWDDTNvGGEx9LU3XhQumrhoTjWDqaxFvFpKdj3izMHU1mEg94vFgohHKnArlJLsgUFSSskObki11l3YJ+g8A3qWVLS6fRMZNPa2uaK8ImTFjQaQyjHi8eHK7Eq2rYeXzV9J9t+PIGTgGb5cemPo6Vr54Nd33OJG84ROaPU556H/UrphPr1+cu+nY1eWsfqWYfif+g6oFX1C7chH+PU9kxRN/YuBptybj5bWkFtixtLjgO7cDUc0LFJWcip09V3U+q4BtS4sL1rkdiGpeoKjkNGzPhZRQt34Fq166zv4RjdJl+/3w73kiG756deMM7/lj9qTHfoWICPVla1jz5h30P9429NX+/ANr3rwDE6knq8cAeh9+CV6n0qfy+0+pXfkDPZwZrde99xBVi2bg6xeg768uS/6Ldd832CWZdLmXVBb0p9y63AAR41m+Y80D3SrIc7VW1UQjLH/sEurXLafbTgX03N/Wg64uuY2qH77C12co/Y67Go8vd/P9IvWsfu0Wqn/8GlNfQ88Df0+3iYcSralg9ZSbiVSup8d+p1O3+ic8Ofl0HX+wGy8vhC2jGdmNqi1SMxkN+vOBOWylVbQp60zXWb+suWnwSnr2TUxgyVO7chGrS261tbsmSv52+9Bjr5NZ9/7DVC74AjB0m3j4xjXUapbPp3zWG/Q+7KLNjtNUMrr23QfIH70bucN2xNTXsvL/riVStoaukw6j+86/SubL3Jp3SosLUmKMk9qSMzPnd0DalzXVbveWFhec53YQqmmBopK+wFxSdYZrlQzXlxYX/N3tIFQzgv4dgBlASo4fnBbZfupv6v6eErO/RqvLWfny9fQ6+Gyy+wYAm6iu/d/95AwYTdcdf7HZ9tVLvqVsZgl9Dv8j0epyVjz9V/odfw2+HpuGADQ0zPQ95grWvfsA0epyuu96NDmDxybzpf2ltLjg5mSeMBWlajJ6NRBsz64R41leWPfXNR9Hx+/Q8tYqxR1fWlzwottBqC0FikruB/7gdhzKVVFg19LiguluB6K2FCgqeRLIyIVvVavVA7uVFhfMaHFLlVxBvweYBqTs7MfGEDm2Njh/hhmznduxAKz/5BkkKwf/bsdsfKx68Rw2fP5/9Dvu6s22XfP2veQM2pauOxwIwOrXbyNvm53pMnbThPANDTN1a5chXh/52+7Fqpevp/+J1ybnBVkVwPalxQU/JfOkqSZl+qhvFPQPAv7S3t29Eh34hO/GMX/LeurDOEal3PHvQFFJF7eDUJsLFJVMBH7ndhzKdR7gnkBRSepdRzq5QFHJoWgiquwM2P/RCcdS0hmkcCIKIIL3seybIkI06sb5m55AcDB165YBdsxo1fzP8fUassW+Wd37Uv3jbLtvbTW1y77bbPLBurVLiZStcXoI1oAICM4Y1KTqAvwz2SdNNal4E3EDHZxsQYTss7NK9n0ju+jjHGozbn2FTmQooF2MUs9tpOZ3h0q+XdGKiZTiLBlwt9txqJSxM3C820GoGEF/DnB1i9ulgG5SNe5vWc987Ma5m5pAMG/kZNaU3Mqyh85n+cPnU1+xFv9eJwNQOf9z1n/0pI17pwJMXRXLHjyPFY//kS7jDya736aRf+s/fIIe+54KQJex+1E+83VWPHYp3Xb5dfJfKJwYKCrZ3Y0Tp4rU6qYb9O8EfEUcl4moNDnzDqu9seuPZsCWVScqHVQDI0qLC1JjqvFOLlBUciygXadVrDXAmNLigrUtbqkSLlBUciZ2dk6lGnwPjMvUiVJExABPGWN+6/ydBSwHPjfGHLGV/XYBTjPGXCQipwO7GGMuaMN5S519tlzwfWuC/j8C/27TPi4yhvDuNXfV/EwvXWM6caaVFhfs5XYQbkm11o1/Eef1CvOlZrv3sy/t8ivPtK/ieVyVNLlAkdtBKHC6Y97gdhwq5fQGLnU7CAWBohIv+n2ptjQGONPtIBKoAthBRPKcv38BtLi4rDHmK2PMRS1tF1dBfzfg8qSes4NE8D+Vff0Ct+PIcHsGikqOczsIt6ROMhr07w0ckIhDe4Sed/ju2ulW390fQCo1BatW+kOgqGSg20Eofo29qVGqsQsCRSV+t4NQnACMdjsIlZKuDBSVZLsdRAK9DhQ4v58MPNPwhIjsKiKfishMEZkmIts6j+8vIq81PpCI9BWR/xORL52fvZzHe4vI2yLyjYg8SPsaT/4E9GnHfq4a5Vm+Z4HnM52sLrFu6Kzju1MnGU1wba4InqO9n+z/Sc5FX3ajIpzIc6m409bR1NApF+tTreIHktvCoDbj3MSkVYuLSqohwGluB5FAzwIniUgusCPwecxz84B9jDGTsGt6ttTD53bgVmPMZOBY4EHn8auBj40x44CXgWFtijDo70Ma9yK51Xd3H52HJaFGA4e5HYQbUiMZtWstHZ6MUw2WNbtOzzl33ThZpF0O0ou2jrooUFSyB7Cn23GolHZJoKikQ5PPqQ45CtAlzdTWFDlduTOOMWY2EMC2ir7e6Gk/8IKIzAFuBca1cLiDgbtEZBYwBeguIl2BfYEnnfOVAOvaGOb5QLc27pMysiUy/FbfPZ+5HUeGa/WY5UySGsmoXcolaU3T2VIfeC37ioFneN/4NFnnVB2WC1zodhCdmLaKqpb0AgrdDqITu8LtAFTKGwmc6HYQCTQFO/fIM40evxZ43xizA/Ar7P3E1niA3Y0xE52fwcaY8g5FFvRnkQFrcx/m+WLPbeWnRW7HkcEODRSVjHQ7iGRzPxkN+odha7KSSoQuV/ue2ONJ3w1TvUQycoa5DHSms2yBSqJAUclo4Ei341Bp4ZLOOubFTc66oju7HYdKC390O4AEehi4xhgTavS4n00TGp3eiuO8TUzlt4hMdH79EPiN89hhQM82xHY0MKgN26ckEbKfzr5+vdtxZDABznM7iGRzPxm1NUVZbp18b++c/b7KOXdOH9avcisG1Wr9sV/oKrkuJTW+K1TqG8OmSURU8mjPBdVauwSKSsa6HUQiGGOWGGPuaOKpfwI3ishMWne/eRGwi4jMFpFvgXOcx68B9hWRb4BjgJ/aEN75bdg2pfWWsknneKdMczuODHZGZxvy4u46o0G/BygFhroXhBUxnuWn1hWtnRbdoaWxBMpdH5QWFyRk1mW1pUBRSW9gMZDX0rZKOV4vLS7QhDRJAkUlw4FFJHGoi0p7N5YWF+hkV8li50Vp3Fqb1qJGVk2qud8XpmsPt2PJUH8oLS54wO0gksXt1o6DSIFEFMAr0YFP+W4YXZT19Idux6K2av9AUcm2bgfRiRyDJqKqbX4RKCppS/c11TGnoomoapvfanf6pMq4bpceMX0fy74poxLsFJMxLemt4XYyerrL59+MCNnnZL227+vZRR/r9NUp7fduB9CJHO92ACrt+NDu9MmUyct1qMQYSoLWdVeNBP25wG/dDiMRJsjCvff2hDQhTYwJgaKSvd0OIlncS0aD/u6k6A3L9p6f9p6Rc/ai4bJiiduxqCYd63YAnYHTRVdvWFR7ZPKMnSkjUFSyG3ZtOqXa6lS3A+gkDiONl3PZGhHkAd8tOToJaMKc63YAyeJmy2hKd//rIjVj38/+U/4Rnk+nux2L2kIgUFSyk9tBdAJH4eLkYiqtHRgoKunjdhCdgPZcUO11bGeZJEVEHhWR41w6TkaX0TypHXNt1iOfuB1HhjosUFTidg/WpHDzRf7KxXO3ikdMrzt9d0661Xf3VHBzpifVhGPcDqATyOiLqEqoLLSMJoP2ElHt1Y0U7Z2WMYL+HOAIt8NItJO97+0yRFYtczuODNQTmOx2EMngTjIa9PuAg105dxuJ4Dna+8l+H+dc/EVXKje4HY/aSG90EyhQVNILO8GYUu11gtsBZDKnd0jA7ThUWjvJ7QASQUROc5Zl+VpEnnAe3ldEponID7GtmyJymYh86Wx/TQvHiD3HtU5LqXcroRxAhnbRjSVCl2ezr9VhbYlxiNsBJINbLaP7AN1dOne7DJHVu83IOWfNOFm0wO1YFABjA0Ul27kdRAY7Cu2iqzpm/0BRST+3g8hgWiGnOmqfTOsGKCLjgL8DBxpjJgAXO08NBPbGtlQWO9segh1zvSswEdhZRPbdyjEaznEz0Bc4wxgT2Uo4Gd8q2mCIrN71BO/7X7gdRwb6hdsBJINbX0JpuQZdttRv81r2FQPP8L7xqduxKAAOdTuADHak2wGotOdFW9cTaX+3A1Bpzw/s6HYQcXYg8IIxZjWAMWat8/grxpioMeZboL/z2CHOz0xgBrAdNjlt7hgAVwJ+Y8w5xrQ4fKvTJKMAN2Q9NDif6gq348gwuweKSjK+dd2tZPRwl87bYSJ0udr3xB5P+m6YqjOIua7TTHvtgj3dDkBlhD3cDiATBYpKfIBO4qbiYR+3A0iSmpjfJebfG40xE52fUcaYh1o4zpfYFtReW90q6B8KDG93tGkoS6KD7/Xd9pXbcWQYH51gVYPkJ6NBf19s7VNa29s7Z78vc86b04f1q9yOpRPTZDQBAkUlIwCdCVXFw+5uB5ChJpDCs9GrtJJpyeh7wPEi0hughaTxLeBMEenqbDtYRPq1cIw3sd18S0Rkay1WHaqIWxyOcsBjFWx/dznj7inn9s9sLn3Z29Vsd1c5O95bztHPVbK+uunG2fXVhuOer2S7u8oZe3c5ny62bSd/faeaHe8t57SXqzZu++TsWm77rKbJ47TVvp7Ze+0oC+fH5WCqQcZ31XWjZXRXF86ZEL2kbOJnORfU7+mZ843bsXRS/QNFJbrGXvzt5nYAKmNMDBSV5LodRAbSJF/FS0Ylo8aYb4Drgaki8jXw761s+zbwNPCpiISAF4FuLR3DGPMC8AAwRUSaqxTqUBnN8sAth+Ty7fld+eysLtz9ZR3frorwi5FZzDmvC7PP7cqYXh5u/KjpJPLiN6s5dFQW8y7oytfndGFsXy/hasOMFRFmn9uVbC+Efo5QVWd4ZFYd50/O7ki4G4mQ9WT2jdW6AkVcZfwkRpqMdlCWRAc+5bthdFHW0x+6HUsnpa2j8afJqIoXH7Cz20FkIO3+rOJlQKCoZJTbQcSTMeYxY8wOxpgJxpjTnZ8XY57vGvP77caY8c7PHsaYhU0dw3ls43GMMQ8bYw4wxlTRtA6V0YHdPOw00E7U2y1HGNvXw9INhkNGZpHlsb2Mdx/iZUlZdIt9w9WGD3+s56xJPgCyvUKPXMEjUBcBYwyVdQafF/41rZYLd83G55UtjtNe3aVy/GVZz38ctwOqMYGikozu8u1GMppxa+aIkH1O1mv7lmT/7eMcaqvdjqeT0WQ0/jKqwki5ThOn+NOWURVPGdU66jq7vmjcxnSXro8yc3mE3YZsvorMw7PqOGzUlpPeL1ofpW++cMar1Uy6v5zfTamiotbQLUc4fHQWk+6vYGBXD/4c4fOlEY7azhevUDc61/vq+N6EV8f9wJ1XRt/rajIaR+M8P+49I+ecRcPkZ11vKXnGux1AJnEmRpnkdhwqo2jiFEeBopK+wAi341AZRZPR+NoJiEu/1/Jaw7HPV3Lbobl0z9nUenn9hzVkeeCU8VsmkvVRmLE8yrm7+Jh5dle6+ITij2133r/slcOsc7pyyy9zufL9Gv5xQA4PzqjlhBcque7D+IwbBfAIPZ7Ovv67uB1QZVTvhcaSm4wG/cPJ8IlRukj12A+yL80v8Hw23e1YOolt3Q4gw0wAdIyfiidtGY0vTe5VvE1wO4AME5cK3bqITURPGe/jmLGbks5HZ9Xy2vx6njomD5Etu9cO6S4M6S7sNsS2mh63fRYzVmzenXfm8gjGwLa9PbzwbR3PH5/PwnVR5q/Z2rKpbbOtZ8leh3i+nBm3A3ZuGV0BmeyW0U4x2YxHTK+7fHdM+rfvnqk6iDvhugeKSga5HUQG0fF9Kt4GBYpK/G4HkUF0SRcVbxk9Hs0FHU4cjDGcNaWasX28XLpHzsbH31xQzz8/qWXKSXnk+5oe5zmgq4ehfg/frbaJ5buL6tm+z+a3+1e+X8O1B+ZQF4WIk6d6gMq6jka+uTt9d/bwUV8b36N2SpqMxlFGv5mxRPAc4/14v4+yL/6iK5Ub3I4nw6X9UkEpZBu3A1AZaYjbAWQQfS9VvPUOFJXkux1EBunwdfSTxRGemF3He4vqmXhfORPvK+f1+XVc8HoVZbWGXzxRycT7yjnnNTt/0rKyKIc/Vblx/zsPy+WUl6rY8d5yZq2Icvk+mxLaV+bVscsgD4O6eeiRK0wc4GX8veVURwwTBni3iKUjcqR+m3/57v00rgftnDI6f9py5HNiZfSb2ZShntW7Tc85Z9HRtf9Y9a0JjHQ7ngw1FrsumOo4rSFXiTAE0CWw4kN7gqhEGAbMczuIDNHhZHTvYVmYq7tv8fjho5uebGhQNw+vn7KpPmHiAC9f/aFrk9setZ1vs0mL/nVILv/qYLxb82vPp7vdIcf8uNAM1vuL9hsYKCrJKy0uaG725rSmLaNJkCP125RkX96/0Pum1g4lRqfo/p0kerFQiaCtefEz0O0AVEYa5nYAGUR7GMUQIfeZ7Ot0Zt2OETL4c5XsZDRj38iWiND1Gt/jezzuu3Gqh2j8RogrgH5uB5BB9IZEJYImo/GjLaMqEfS7Px6C/h5AD5ejSDn9JLzzWd7XtUGmYzK2QS/ZyejQJJ8v5ezrDe33Zc65s3X9pbjK6Bmak0wTe5UImozGQaCoxAv0dTsOlZE0GY2PTtvo0pLLs54a0Y2KsNtxpDFNRuOkV5LPl5J6S9mkz3POr9vTM0fHUMWHJqNxECgq6QnEf/VrpTQZjZcBuLM+uMp8OkQjPvR+pBleMf0fzr75a7fjSGMZO+9M8i5qQX8X9EZ3oyyJDnzKd8Pov2Q9+5HbsWSA3m4HkCG0VVQlymC3A8gQOl5UJUqn77kWJzor8VbsIt/vvZt8+63bcaSpjP3+T2YNq64z14gI2edlTdnntezLP8qhttrteNKY1kTGhyb1KlH0sxUfOl5UJUo3twPIEJqMboUInoezbxadO6VdclreJD0lMxntksRzpZUdPKX7zMg5Z9Ew+XmJ27GkqfxAUYm2undcspd6Up2Hls/40EpdlShaRuNDk9EWdJGasVdnPf6x23GkoYwto8lMRrWAbkUXqR77Qfal+QWez6a7HUua0kSq46JuB6AyVsZeRJOs3u0AVMbSMhofeq/bCqd5395pEKuXux1Hmsl2O4BESWYyqslCCzxiet3lu2PSLb57p7odSxrSz1fHaTKqEkVvdONDk1GVKFpG40N7AbaCCN2eyb7uR7fjSDMZm4wm8wa+LonnSlsieI71frTf7F4/vvFi9y4D3I4nXUSrBwEFboeR7ozbAaSDIbJq2XneV+e/3Xdl2ZddIzoxT6tIjZbPuNDraCsd4Jn5dbHvgTovUa/bsaSDWnwrtYzGRcaO64un9R7Pugf71Fff8dx17/QN1+m8H61QlZWzlOLMLKPJTEZrk3iutDcxsqbPS1lMcjuOdOHpOl9b9TpO38Nm9GH9qrOzXpt7vHdqDz8V40UYlFvX5YvpWb21jLZOudsBZAhtGW1BNnU1D/hu+Wxfz+x9RHQZnDaY73YAGUIno9yK2TnZ313Xu9fKudm+XRDZfxff+g971Oq9bmv0qK3I2OuoJqMpat/KKl04uW30Jq3jNBmN0Z3y8FlZb4R+430vvw/hCSLsG/v8oRWV467u06sGEa0Jb5mWz/jQltGtmCgLvnsm+zpPntTu53YsaUhnN42PjE0Y2qse6p/r3vWre3r48zZ4vROAbRuemznS4xmzTG89Wiljr6OajKao3tFonyxjFteL6NpfLTPoTVo8dPpuuvlUV/zW+7+vC7PeyhrEmoki7N3stsZ06ROJfrU6y7tLMmNMUzVuB5AhMvZmpCM8RCP/8t338dGej/cQydxxVQmmLXrxUeZ2AKlircez5ubePUOvd8nfNiqye1PbfDVaBp74UbIjS1sZm0clMxnVm5E2GlpXv3hRtk+T0ZatCxWGtGqt4zplzXg2dTXHeafO+p339eg2smKCCHu2dt9DKiorn/br8nytsNrtADKEVro1MlKW/vhS9tUb/FKpraEdo2U0Pjp9y+iMnOy51/futfZ7pyvu1rb9sR8jDIRFl61qjTVuB5AoyUxGw9iWF0niOdPa7lXV9YuydYK7VljldgAZImO/6BrzEqk/wvPZrHOyptRsJ4vHi7Bbe45zUlnZcE1GW0XLaHxoy2iMv2U9/dEfvK/tJMJwt2PJAJqMxkenTEbroO6Z7t2+vK+Hv1uZ1zO+1TuKyIZ8fvBX6rjRVljpdgCJkrxkNBiuJehfA+isWa10UGVl72f0Rrc1MraAJtlSbOtoRs4+KUSjB3lmhM7PmrJhgizc3iOmw91rt6mrH+4zZlGdiI7x3jpNRuNjndsBpIKBrFnxSs6VS/rL+n3cjiWDaBmNj07VTXeV17Pqn716fvN2l/yxUZFW9yqK9f1gCU+e3+lHCbVGxt7rJnttxmVoMtpqk6prRmNMLSI6BmbrMraAJlNpcUEkUFSyHBjidizxtIfnm28u9L68ejfP3G29YibE+/g7Vdf89HleriajW6etLvGx2O0A3Ham941pf896YqxH0LHa8aVlND46RQ+jL3Jzvrmhd8/wQl/LXXFb8tVoyddktFUy9l432cnocmDHJJ8zbWVDdhdjvqkQGed2LCkuYwuoC34iA5LRibLg+wuzXl6+r2f2CJ9EElp+TtxQ1u3zvNxEniITaKtLHJQWF5QHikrWAT3djiXZ/JSvfyn76rkjPcvb1fqiWqRlND5KsTPTZ9yyQrVQ+4S/+5cP9Ojeo8Ljidt1deZI7VnUShl7r+tGy6hqg7E1tWu+0hvdlmRsAXVB2ra8jJYlpRdmvfzjLz1fDcmRujHAmGScd7/Kqh0wpgwR7VPfvBVuB5BBfqKTJaO/8kz76jbf3UO8YvZwO5YMpvdn8WCHpC0BhrkdSrz87PX+XNy759x38/PGGZG94n389V2lb8TDEm80/SvCEyxj73WTnYwuTfL50t5+lVVZmoy2aKHbAWSQn9wOoC2GysqlF3hfWXCE99P+XaRmOyCQ7BiyIXtwfWTGUl9Wk1PXKwDmuR1ABikF4t7dPBXlUVP5ZPYNX+3smb9vy1urDtIyGj8LyIBkdFpe7pwbe/XcUOrLmtzRrrgt+dnP4kHrNBltQcbe6yY7GZ2b5POlvf0rq4be0rtTVYK3xxy3A8ggKd8y2o91q87J+u/cY70f9uxO5Q4iDHY7piPKK+ru76kz02/Ft24HkEG+dzuAZNjbEwo97Ptn92yJaCKaeHVk8I2uCxYCB7odRHvUQs0jPbp/+bC/e+9Kj2eHZJ13TkBqB63TcaNb8fPYeXMzdlx3spPR2Uk+X9oL1NcPFWNWGxGd+KlpEbSSI55SsmXUT/n632W9HjrZ+17X3mzYUYSUukE9vqx8jCajzVobKgz97HYQGSSjW7CyqK+7z3fbtIM8M/YWycyZvVPQfIJhXTYoftIusV+W5V1+Y6+e303Nz9vBiOyd7PN/OVp6HjJTk9GtyOhGl2Qno98BtYDODtsG/SORRSuysjQZbdrCUGGo2u0gMkjKfOF1oar8t97/fX161lvZA1g7UYSUXcahfyTSPy8anVfl8WzndiwpSCuL4itjk9Hx8sP857KvjeZLzX5ux9LJaBmNrwVuB9BaH+blfn1T756VP2VlTUZkoFtxzB0mow3US/LzknSRMvdmiZDc//RguI6gfx46o26b7FxdU1HSVctnMzK6gCZbaXHBwkBRyc9AfzfOn0Nt9QneqbPO8r7OcPl5gghxnywhUfaoql7xXpd8TUa3pF104yvjklEhGr0x66GPTvS+v7sIOW7H0wlpMhpfM90OYGtqhOoH/f6vHvV361vt8aTE+PNan+TV+JiXW4deQ5uW0fe6bkw9rV112+jAikrt/9e8jC6gLvk0mSfLor7uaM9HX72V/ZdP5uWcXnet75HdA56fdxchL5lxdNRJG8q190LT9Ds/jkqLC9aSQeNGA7J88cycs0MnZb2/nyairpnldgAZJRj+gRScQXxJlnfpef37fjB5+NDK+3r69672eLZ1O6ZYP/XN3Nli4yCj73XdaG6bAfzWhfOmrT2qqkdiTBSRjFu3Kg6mux1ABpoGHJXIEwjR6CGe6bPPy3q1bLwsGucRk/YL2O9WXb29GLPGiPR2O5YU85HbAWSg90jS0kWJdGnW8x9f6H1lgghD3Y6lk9MyGn/TgGPcDgLg3fy8WTf36lm9NMs7GRHXJ/xrzsyRHs+YZVG3w0hFdWR4pa4byegHLpwzrXUzpnu2YWGtMNLtWFJMBJjqdhAZaFqiDry3JzTngqxX1k6Wedt6xUxM1Hnc4AHPiLq6eQuzs9Oma3ESrANCbgeRgd4DznE7iPbqx7pVL+dctWiwrEn6RClqC/MIhrVFKv4+wcVktEqk8j89uk9/onu3ATUez0S34miLr0bLwBO1WqQpn4+dN7fS7SASyY1kdBawFujlwrnT1si6umVzc7I1Gd3c9FBhKOx2EBnoK+I40dgkmf/9hVkvL9vHExrlk0jSpop3w9FlFfKv3jo/W4yPQoUhreqOv/cBA4jbgbTVb73vfPaPrEdHe8Ts6nYsCoAP3Q4gQ33ixkl/zMpafH3vnj98lpc7wYik7KR/TfmxHyMMhAV0aNrm3nU7gERLfrfPYNgQ59bR6nrDrg+UM+G+csbdU87V79vJVd9bVM9O95ezwz3lFL5SRX206WmjH5tVy+g7yxl9ZzmPzaoFoKbecOiTFexwTzn3fFm7cds//LeKGcsj8Qy/Vfaqqkr6OdNAxhdQN5QWF9Rgu9O327by06K7fbdP/S7ntB9ezrl6zIHeWfv7JJLxC1ofVV6xPcYk/wsidX3gdgCZqLS4YDVp1uLcjYrwW9l/+eQ63yO7e8RoV/bUocloYswAkjbT/9v5eTMOGTroiyOGDBz8aX7efkakR7LOHTciEs5Pv2VxkiDj73XdGoP4XjwPluOF9wq78PU5XZl1dhfeXFjPtMX1FL5SxbPH5THnvK4M9wuPzarbYt+1VYZrptbw+e+68MXvunDN1BrWVRneWljP3sOymH1uF56Ybff7ekWESBR2Gpj8pc8OrKhyZXbTFJfxBdRFba7VHSY/L/ln1v1Tv80547u3coq2KfB+vl+O1I9IRHCpyh+N9ugejX7jdhwpRLvRJ05cr6OJdKjn8xkzc86u2NazRLuwpx4to4kQDNeR4MkAK0UqbunZ48Odhw9d+Kf+fXdanpW1a7rPLTJ/sGxwO4YUUwF85nYQiebWeiFxTSJEhK5Oz7i6KNRFwCuQ7YUxvW3i+IsRWdz4cS1n7bR5F7q3FtTzixFZ9MqTjdu9uaCeHrlQWWeoi4BxGlSvfL+G+47IjWforbZ9be1IjClHpKsrAaSeGlzqBtNJvA38qaWN+rN25TlZ/517rPfD3t2lagcg41s/W7J/ZdW6Kd20mAKr0Vk6E+k94BK3g9iaXGqqHsu+6ctdZd4+IunXpbgT+IZgeInbQWSwl4ED4n3QRb6sH6/r3av0i9yciYjsG+/ju+mr0ZI/eX7TvRg7qY/Gzpu7ZUtahnEnGQ2G5xH0LwBGxeuQkahh5/9UsGBtlPMnZ7PrYC/1UfhqWYRdBnl58dt6Fm/YcujS0rIoQ/2bKpKGdPewtCzK8eOyeWJ2Hbs/VMFle+Yw5bs6dhroYVA3dyqdvODtEY0uWO/1TnQlgNTzYagwlLQuMJ3QezQztrsHZet+n/X6nJO873XrRdmOIugC9TFO2lA+UJNRAF7S8aIJNRU7iVvyu+q0wm7y7bePZxfn5kh9Rt0sZ5gX3Q4gw/0fcDtxGNttwLzRJX/6Lb16mJVe7y6IDO94eKln5kjZxu0YUszbbgeQDG61jAI8B1wRr4N5PcKsc7qyvtpw9HOVfLMqyrPH5vHHt6qpqTccMjILbxu+DrI8wtPH5gNQFzH88slKXj0pn0vfquancJTTJvj49ba+eIXfKuNrasMf5afV0ouJ9KzbAWSy0uKC+kBRyRTgdIAuVJWd5n179mlZb2cPYN1EEdJqYoRkGl9bO8ZrzLKIyKD27F+7ppalDyylfkM9AD3370mfQ/pQX17P4nsXU7e6Dl8fH8POG4a3y5Z5yJwz5pA7xPbg8PX2MfwSe8+y+L7FVC+pptvEbgw4bgAAK6esJHdwLt137t6u19qCFxJxUGWVFhdsCBSVfAqk1Iy0XiL1d/ru/OQwzxd7ibh6j6FapsloIgXDywj6PwP2aO8hykXK7urpn/F8927D6kTSfgm0lqzvKn0jHpZ4o9rLCogCz7sdRDK4eaF4ljgmow165AoHBGxX2z/vmcNHZ9iX+PbCer5fs2Ul/eBuHj4ord/495INUfYPbP623PNlLadN8PHZkgj+HOG54/I48PHKpCejB1RW5nYkGW3uJjf8RZiVr6ykZnkNI68aSd42TZ8jUhFh6SNLqV5SjYgw+KzB5I/KZ8XzKyibXUbesDyG/MF+f6yftp76snr6/LJPu+PdihpsjaNKID/lLxzp/WTbs7xvyDBZOVEEHe/VSmNrahfOyc1pVzIqXmHASQPIC+QRqYqwMLiQruO6sv7j9XQd25W+R/Rl1WurWFWyigEnDNhif0+2h1HXbt7ppHpxNZ5sD6OvG82imxcRqYwQrY1StbCKfr/u174XuXWrsTO+qsR6hBRKRreTn354MTtY3VWqtbdE6ptHMDzH7SA6gf+jHcnoAp9v0XW9e/40PTdnJ0Q6VXn62c/iQeval4wur6vjb8uXszpSjwAn9OjBqT178WbZBu5evZofamt5bvhwdsht+j53QyTCVStWML+2BgGuGzCQiXl53LJqJR+VV7Bdbg7FA+2lfUo4zPpIhNN6JWxxkKlj581dmqiDpxL3BjrbL8G4TPSxqiLK+mrbx7yqzvDOD/Vs18fDygqbfNbUG276pIZzdtlyyYVfjsri7R/qWVdlWFdlePuHen45alMyuq7K8Nr8ek6b4KOyzuARELHnSbZ9K6sDHdm/4SZ39A2jGXHlCNa+u5bqpdXkDMlh2IXDyB+Tv9X9lz+9nK7juzKmeAwjrx1JzsAcIpURqn6sYvR1o5EsoXpxNdHaKOs+WkfvgxI2YWKJLumSeF/n/uF///A9tu1wz8rdRXBnsHSaOr6sPKe9+/p6+MgL2AulN89LzqAc6tfVs2HmBnrs3QOAHnv3YMOMNszz4IVobRQTNZh6Ax5Y+dJK+h2dkEQUbBddnVU48Z4Hyt0OAoy5JuuRD9/ILhrUVaq3dzsa1SraKpocra44N2Be7drlywOGDv7q6MEDAtPzcvdDpFsig0tFcwJS2/JWTcsS4S/9+vHaNiN4dvhwnl63jgU1NYzOzuGOwYPZJW/rDTo3rvyZvbt0oWSbEbwU2IYR2dmURSJ8W13NK9tsg0+E72uqqY5GeXlDmJN79mxvqK3xdCIPnkrcnnXruXgcZHm54YDHKtjx3nImP1DBL0ZkccQYHzd/UsvYu8vZ8b4KfjUmiwO3sUnmV8si/G6KXSqlV55w5b45TH6gnMkPlHPVvjkbJzMC+MfUGq7YJwePCL8clcVHP9Uz/t4KTt0x+WsJ9o9E+nuNaXctSXM3ubmDcskZuPV750hlhIrvKui5ry14niyP7SIoYOoNxhiitVHEK6x+YzW9D+6NZCVsvopOU0BdFQzX0km6iMTboRWV4zCmpqPHqV1VS/WP1eSNzKM+XI+vh+2NkeXPoj5c3+Q+0booC4ILWPiPhWyYbhPW3EG5ZHXLYuHVC+k+sTu1P9dijNn4fZAA2kU3CUqLC8qJ03W0vYbKyqXTc86ZVZj1zr5aaZVWtIwmQzBcSgtLpW3wSPj63j2n7hQY+tPf+/aevDrLuwsinXbCry9HS7szvL5ZWWyfa7+Guni8jMjJYWV9PSNzctgme+v3uWWRCF9VVXGs3y5zmi1Cd68Xj0C9sfe51VFDFsIja9dySo+e+BL331RDJ6owcns8x9PANXRwcPeO/b3MPHvLCUNuPiSXmw/ZcvtdBnl58NebbsLOnJTNmZOaTi5vPXTTtTU3S3j71C4dCbXDBtfX//STzze4o8eJvclt7fZZ3bJY+uBSqhdXkxfIY+ApA/Hmeek2oRsLr1pIl+274Mn3UPVDFf2OTFiLSxh4LVEHV1t4AjjH7SDSTb4xXfpEotNXZ3l3bu8xItURfrrrJwb8ZgDevM3HhopIs9+a296yLb6ePmpX1rLopkXkDM0hp18OA08ZuHGbH2/9kUGnD2LllJVUL66m67iu9No/bl2NfkK76CbTQ8BZbpz4Au/Ln/wp64UdROjwNUkl1ecEw7PdDqITeRjYqfGD87J9C6/r3Wvp1znZna4r7tbMHSajDdRLB3OUpXW1zK2uZsfc1tWRLamro5fXyxUrljOvpoZxubn8rV9/uni87NulK8f8WMru+fl083qZXV3FuX0SMgytwRtj581dn8gTpBJ3W0aD4YXAm67GkGZ2q6pud/eFBlu7yW1WFKp+rKLXgb0Y9Y9ReHI8rHptFQB9D+/LqGtHMfDkgRu7/q2dupaf7v6JlVNWdjTcxp4PFYY63OKkWikYngbMczuMdHRIRWW7u0+aesPiuxbTY48e+HextbRZ/izq1tsZ3uvW15HVvenrtK+nbT3N7pdNl+26UP3j5pNOb5ixgdxALtGaKLWrahl2/jA2fLWBaE3cJr69R7voJk9pccGnwNxknrM34dUfZl/82Z99L+wlgj+Z51ZxcbfbAXQyT2DXiyQK0Ze6dvliv2GDZxw/eODIr3Nz9tUl+zZX65O8Gh/zO3KMimiUi5cu5W/9+tPV27r73AiGb6urObFHT14KbEOeeHhw7RoAzurdm5cD2/DXfv25Y/UqLuzTlxfXr+ePy5Zy35rVHQm1OY8n4qCpyu1uugB3uh1AOjmwsqpDzRdN3eS2RlbPLHw9feSPtONKu+/SnaofqzbbpurHKowx5AzMYcOXGxh2/jBqV9ZSsyJuuaMBbovXwVSr3eJ2AOnopLKyQHv2M8aw9OGl5AzMoc+hm2peu0/szvqP1wOw/uP1dJ+05Qy4kYoI0TqbVNaX1VO5oJKcQZu6Jpl6w5q319D38L5EazclnxvHknZcNfBgPA6k2uShZJ3oBO/7X3yRc54Z5lm1e7LOqeJqFTr8IrmC4Q0rvd4Hgr17Td05MHTp1X1777rW692ipVRt8lNf2p3h1RnDJUuXckR3P7/o1voht/2zfPTPymKCM670kG7d+LZ688rcb6urMUAgO5u3ysq4ddBgfqqto7S2w+1EsX4AXo3nAVOd2910wbaMfg+McTuQdDC5uno0xtQh0uapfJu7yW0NXw8fvt4+apbXkDMwh/Jvy8kdtHnXh5UvrWTQ6YPsGNKoc2MrbHbT20FvhApD38brYKrVngCuBbaculU1a5u6+uE+YxbVSdvWTaucX8n6aevJGZLDgisXAND/uP70OaIPi+9ezLqP1uHr7WPoeUMBqFpUxdr31zL4zMHULKth6WNLERGMMfQ5vA+5gzeV0zXvrqHHXj3w5HjIHZqLqTXM//t8uu3YrcllYtrhmVBhaE08DqTa5HHgRiBhU7x3oars+ex/fD3O82PKzN6r2uVBgmHtXZRkBw0b/BAQcjuOdDFzpEfGLGv7vaMxhitXLGdETjant3GW275ZWQzw+VhUW8M22Tl8VlnByEbjTO9cvYprBgyg3hgi2Ptcj0B1NK5Lat86dt7cTrVGtxiT/FlhtxD0Xwjc4XYY6WK34UPmVno8Y9u6X8X3FSy6YRE5Q3I2jo3vf1x/TL1h2ZPLiJRF8OR7yBuWR+DPAerW1bH0kaUELg0AtuVz6SNLMfWG7L7ZDPndkI03sBumb6Dqpyr6H90fgOXPLqd8Tjm5Q3IZes7QOL1yDgoVht6L18FUGwT9VwDXuR1GuvndgH5TP8/L7UxjgXYKFYZmuh1EZxQoKnkeOD4Rxz7QM+Pr+3239vZJRNf+S28RYATB8E9uB9IZjX9s/NvAL9yOIx0M/9ksvPnhyMi27je9spJTF//EmOwcGuYWuqRPX+qM4fqVP7M2EqG7x8N2Obk8MHQoK+vruHLFCu4fYu9T51ZXc9WKFdQZw5BsH9cPGIjf6eb7v7Iy5tVUc0GfvgD8c+VKPqmoYExODjcPatdKbk1ZCwwbO29uRbwOmA5SJRntBiwGHXvSGqcN7PfhzNzcfd2OI8lmhgpD2q3FLUF/L+zENO7O4JVm3s7Pm/mn/n0nuR1HknwcKgzt43YQnVWgqGQH4GviOPwmm7qah3w3f7a3Z84+IikxrEd1zPMEwye6HURnNf6x8YcCb7gdR1owxjxXHNkgnS8vuGHsvLlXuB1EsqXGxSUYLgNudTuMdLFvZXVc+tOlGR236KZgeC1JHJeWKfavrBqHMSmwDmRSXOV2AJ1ZaXHBHOK47NVO8v282Tm/W7yPd85+mohmhAhwtdtBdGahwtCbwBdux5EWRCScz0K3w0iyGjrpPDqpdIH5N7R/wHJnckBlZWebRn8BLq+lpwBbYdT04paqSdmQPbg+MsftOJLgf6HCkC7n4r6rgbqOHMBDNHKb766p/5cdHJkrdaPiFJdy39MEwzozuvuK3A4gXcwfLBvcjiHJHhk7b+4Kt4NwQ+oko7Z1tNjtMNLByLr6gBizzu04kuivocKQJkFus4t3/8ftMNJNQXlFZ/jsXu52AApKiwt+AB5o7/6jZUnprJzfzz3KO20/kcRNhqSSrh4Iuh2EAqfS7m2340gHX46WfLdjSKIyOnHPhdRJRq27gaVuB5EO+kQinaL7wpzT5zDn9Dl7NfwtIn8WkaCLIXV2VwGuVIQsDkc54LEKtr+7nHH3lHP7Z5tPCHnLtBrkmg2srtxyErpZKyLs8VAF4+4pZ8d7y3luzqbGo1NeqmTHe8u5/N1NU7hf92ENr8zrUAPTRseXlWd669IrocLQl24HoTa6Fqhs2y7GXJH15IdvZ/+lX3ep2iEhUSk3PUIw/IPbQaiNigBXJmypXVPLouJFzL98PvMvn8/qt22HxPAXYeZfPp85Z8yhalFVs/uvfns1869w9n1rU2fGFc+vYP7f57PkP0s2PrZ+2vrNtmmrmSPbNhN9Ovuupvqu7b+bd4eILBSR6SLyuoi0a5UREXlURI5zfn9QRLZ3fk/ZSuPUSkaD4WrgH26HkQ52qq7pDDNtGYRa4BgRadtaNCoxguE1uFTDnuWBWw7J5dvzu/LZWV24+8s6vl0VAWyi+vYP9QzzS5P75vvg8aNy+ea8rrz523wueaua9dWG2T9HyMsSZp/blS+XRQhXG5aXRfl8aYSjtotPw9CASGRAXjSaqd3josDf3Q5CbVJaXLCCNsxOP4jVy7/IOW/G77Ne31eEztQS0VlUYSsoVIpwZhx3Za1X8QoDThrA6BtGM+LKEax9dy3VS6vJGZLDsAuHkT+m+a+A6iXVrJu6jpFXjWTUtaMo+7qMmp9riFRGqPqxitHXjUayhOrF1URro6z7aB29D+rd7ljDXaVvxJP5DVTGmMXHlpYeBHxgjBlpjNkZ+BvQv2EbEWnXUpzGmN8ZYxqWRNRktA0ews4IqLbiwMqqzjCr6bMY6rBdQ//Y+EkRCYjIeyIyW0TeFZFhyQ+xU7oHSPp6rwO7edhpoJ27q1uOMLavh6UbbOXyH9+q5p8H59J0KgpjensZ3dvuO6ibh35dhFUVUXweqKo3RI2hLgJeD1z1fg3X7J/TzJHaZ/eq6p/jesDUcV+oMPSN20GoLfwTWN/SRn/wvjbt45yL8vpJeOfEh6Rcci3B8GK3g1Bb+DtQm+yT+nr4yAvkAeDN85IzKIf6dfXkDsolZ+DWr3s1y2rIG5GHJ8eDeIUu23Zhw/QNINj15Y0hWhtFvMLqN1bT++DeSFZzV+XW+dlPxi9DdN+aNU9FocYYc1/DY8aYrwGviHwkIlOAb0XEKyI3i8iXzn3v2QBi3SUi34nI/4B+DccRkQ9EZBcRKQbyRGSWiDyV7NfYktRLRoPhCHAuLnVhSBd7VVWPIiXW5UmYamzNENju26eISOMpvu8EHjPG7Ag8ha5VmxzBcD1NVA4kU+n6KDOXR9htiJdX59UxuJuHCQNaN8n0F0sj1EZgZC8PY/t66ZvvYaf7K/jVmCwWrI0SNWxMeuPlpA3lbVt9Oz0sY1MZVSmktLhgHVtZF7gHZevey77008t9T+/pEXokL7LkikQNk+4v54inba/lReui7PZgOaPuKOPEFyupjTR/Cf0pHKXrDRv41zQ7HGBVRZS9H65gh3vKN+vCf+SzlSwrS9n16b8B/uV2EGpLocLQAuAGN2OoXVVL9Y/V5I3Ma9X2OUNyqPy+kvryeqI1Ucpml1G3pg5vnpduE7qx8KqFZPmz8OR7qPqhiu47d+9wjHMCkvSEPcmm37Vm9XJgejPP7wRcbIwZA5wFhI0xk4HJwO9FZBvgaGBbYHvgNGDPxgcxxhQBVcaYicaYUxLwOjqkw8mok5F/LCKHxTx2vIi82e6DBsOfAg92NLZM5o9Ge/ig1O04EujqUGHoRwBjzAbgceCiRtvswaalDJ4A9k5eeJ1cMPw28Jobpy6vNRz7fCW3HZpLlgdu+LiGfxzQupbM5WVRTn25ikeOzMXjrIh926G5zDqnK3/aM4cr36/h2gNzuP7DGk54oZIHpsfnOrh7dfU4MWZNXA6WOi4MFYY2znYoIkNE5FURme+Me7ldRLLdDLCTuw3YYizv0Z6Pvpyec07tCM+KPZIfUnLd/nktY/tsus356/+q+ePuOSy4qBs9c4WHZjQ/LvzSt6o5bPSmnnHPzKnjnF18fPH7Ltz2mf1e+O93dUwa4GFQt9Sr18dW6J9DMByfwe8qEW4AQm6cOFId4ae7fmLAbwbgzWtd5WvuoFz6HN6H0ptLKb2llLxheYjHXkf7Ht6XUdeOYuDJA1n50kr6Hd2PtVPX8tPdP7Fyysp2x/nlaOnZ7p1TXz3whxZalb4wxixyfj8EOE1EZgGfA72B0cC+wDPGmIgxZhnwXqICTpQOf4Ma2zp3DvBvEckVka7YAnZ+Bw/9F2B5R+PLZIG6umVux5Agn7Jlbe5t2FqhztA9OV1cCCR16vW6iE1ETxnv45ixPhaujbJonWHCfeUEbitjyQbDTvdXsKJ8y5aKDTWGgqcruf7AHHYfsuXwi1fn1bHzQA/ltYaF66I8f3w+L86to7Ku4x0QPOAZUVf3XYcPlDpeDRWGXmr4Q0QEeAl4xRgzGhgDdAWudym+Tq+0uCACnIHTFTCf6opXsq/86Nbseyd7xfTf+t7pb8mGKCXz6/ndTrY+xBjDe4siHLe9LfuFE3y88l3Tedor8+rYpoeHcX033SL5PEJlHdTU2+789VHDbZ/X8pe94tulP44eIRj+2O0gVPNChaE67H1NJJnnNfWGxXctpscePfDv0rjD2db12q8Xo64ZxYjLR+Dp4iF7wOb1jVU/VmGMIWdgDhu+3MCw84dRu7KWmhU1zRxx6+YOk9Emc5eUu37svLkzsD0YmhsqETs/jAAXOq2bE40x2xhjMmJm5rhU5xlj5gD/Bf6KnW3zSeAKEflCRGaKyJEAIjLOeWyW0995dLMHDYbX0/GENqPtVVmd1C+wJKkCTg8VhjbLJowxa7ED/s+KeXgacJLz+ynAR0mJUFl2qZeklVFjDGdNqWZsHy+X7mFvAMf397Lysm6UXmJ/hnQXZpzdhQFdN/9qq40Yjn6uktMm+Dhu+y0nJqqLbLqxrKpj49jTSBRq41TKjirLmDnHyoALGj12IFBtjHkEwBgTwXblPlNEzhORl0TkTafV9J8NO4nIISLyqYjMEJEXnMpMFSelxQXfANfu45kd+jrn96snehbu43ZMyXLJm3YcudNww5oqQ49cyHIeGNJ905jzWOW1hps+qeXqRuPGfzPex6vf1fOLJyq4fO8c7vmyllN39JHv69iYuARZia3QVynOmYn8tmSdzxjD0oeXkjMwhz6Htn1eyPoNNi+sXVPLhq820GP3Hps9v/KllfQ/pr8dQxp1ypdAtLZ9XdlrfZJX42N+u3ZObTPZVFn7HpAjIn9oeFJEdgQaf1+/BZwrIj5nmzEi0gX4EDjRGVM6EDigmXPWNeybauLZt+Qa4DfAYUAu8J4xZlfsm3Kz84adA9xujJkI7AIsaeZYVjD8MnZCI9WEAysr+7W8Vdq5PFQY+r6Z524BYr89LwTOEJHZwKnAxYkOTjUSDD/Jpq7SCfXJ4ghPzK7jvUX1TLyvnIn3lfP6/OZ7oH21LMLvptgp6p//po4Pf4zw6Ky6jfvOWrEpy7z7y1oKJ9gbyx37e6isN4y/t5ydB3rpkRufm82jyyu2xyZp6e5PocJQ4+/ucTQa8+J0r/8JyAImAicC47EXzaHODNl/Bw42xuwEfAVcmuDYO6MbH/cVl/skMtztQJLlte/r6NdF2HlQ28d+Bz+o4Y+7Z9M1e/Ny788VSn6Tz1d/6MpOA7389/t6jtvex++nVHHc85V8ujilGm/OdGY+V+nhSmBBMk5UOb+S9dPWUz63nAVXLmDBlQso+7qMDdM3MO+P86haWEXpraWU/qsUgLp1dZT+u3Tj/j/d9RPzL5/PT7f9xKDTBuHtsqmMbZi+gdxALr6ePrxdvOQOy2X+3+dj6gx5w1o3LrUpP/ZjVbt3Tk21wGlj582tg409TI8GDnaGuHwD3AisaLTfg9jJI2eIyBzgfuz19WVgvvPc49jehU35DzA7FScwknjOgSMi/wDKgROwCWnDt3Mv4JfAJOAK7Jv1kjGm5dqOoD8fO+5l+7gFmiHqoX5SYGgdIu0v5anlQ2D/UGEokydmyjxBvx87A3anudltr72GDQ5t8HrHux1HB7wYKgwd3/hBEbkI2MYY88dGj88EHgHGG2N+7zz2BrZGuAfwKJsqJbOBT40xsb0fVDwE/aOxNfGdYpjD3/5XzROz68jyQHW97aJ/9Ngs3loQYcWfu5LlET5dXE9wag1v/Xbzt2SfRypYHLatOOurDR6BfxyQywW7buqOeOlb1fx62yzmr4mS7YXjtvdxzPOVWxzLJXcRDF/odhCqbcY/Nn4PYCqQki1Xbjrmk+jHJ30YzaQ5QS4fO2/ujW4HkUriPeo+6vwIcGxMv+Zhxpi5xpingV9ju2K+LiIHtnjEYLgSm9w2vwpvJ5UFWd2j0UzpvrAWKNRENA0Fw2HgtyR53Es62q+yKp1bK0qB3zfz3Lc0GvMiIt2BYdhKydgBQxFsba4A78RcJ7bXRDRBguH52J4kncKNB+ey5FLbdf/Z4/I4cJssnjomnwO28fLit7aO/LGv6zhy2y3v+z86o8vGbv+X7J7N5fvkbJaIzl8TYcmGKPsHsqiss8mqCFSlxjRBs4DL3A5CtV2oMPQp8Ce340hF00fJILdjiKMPsUtvqRiJmgLuLeBCZ1ILRGSS8+8I4AdjzB3Aq8COrTpaMPwN2gWzSTvU1K5zO4Y4iAKnhApDpW4HotrJTpShNX0tOHlDebpeVGuA40OFofXNPP8ukC8ipwGIiBfbrf5RoLKZfT4D9hKRUc4+XURkTDyDVjGC4UeA+1rcLoPddHAu//60hlF3lLGmynDWJJuMTvmujqver27VMa54r4brD7TjSU8e7+Per+qY/EAFF+/m+sTRG4DjCYZb90JUygkVhu4kScNe0smP/djGQNjtOOJgKXDC2HlzteK+kXh30w1iu+nejR2QvSc24V1kjDlCRIqwY/vqsH2hf+NMTNM6Qf+T2IlqlOOZbl0/u6FPr93djqODrgoVhq51OwjVQUG/F5gCHO52KKlsQmDo8qidZCCdnBsqDG01kRGRocA9wHbY7/3XgT8DJwO7GGMucLZ7DfiXMeYDp3fMTUDDbDF/N8ZMSdBrUEG/D/gfdikAlTkMcBzB8EstbqlS2vjHxudjl+3Ywe1YUsl/bq+f0aOSndyOowNqgf3Hzpvb3HjOTi2uyWjCBf052AtpJvUd75BlWd7lvxw6ON1ubGO9AJzYXPdcEZlmjNliAd9G21wC/McY01wLTFyIyERgkDHm9USeJ60F/V2xsxpPdDmSlHXyoP4fzcnJSadZTe8NFYbOczsIFSdBf1/sZFHD3A5FxU0RwfBNbgeh4mP8Y+NHY8tod7djSRWXvRj5YPJ8s7/bcXTAWWPnzX040ScRkVJs5e/qRJ8rnlJypeZmBcM1wFGQkdM8t8ug+shAjzHpuh7rdFoYJ9pSIuq4BMhvy4mdboRtNRFt9du6YLgcOALbHUU14bgN5Sm7MGETXmXLZVxUOguGV2GvowmtvFNJc78mopklVBiaj+1RklJTNLvpy9HSpnu8FPPvRCSiIrLlgulpKr2SUcCZrvxwIK2y/kQaVB/50e0Y2uEH4NehwtBWJ6YSkXLn3/1F5AMReVFE5onIU2JdBAwC3heR951tm1y7UERKReQmEZkBHO/8fY2zXUhEtnO26yIiD8eukysi2cA/sMtSzBKRExP31qS5YHgpUIBdj1I1clhF5TiMad8K4Mn1KXBy4zV/VQYIhmdih7zo2KX09ga6HntGChWGXgdOx3bB7vRmjpRt3I6hnV6iFZOKichpIjJbRL4WkSdE5Fci8rlzD/o/EenvbBd0nv8EeEJEeovI2yLyjYg8yKZl0tNK+iWjAMHwAuBIQAfqA5Orq9PhxjbWEuCgUGFoWRv3m4RtBd0eGAHs5UyGtQw4wBhzQCvWLlxjjNnJGPOs8/dqZ7t7sePbwC4/tNk6udjp1q8CnnNm/nyujbF3LsHw19hZsLVmt5F8Y7r0iUTnuB1HC74HftVSZZFKY8HwK9ibXa1sSE+zgBMIhrVCIUOFCkNPARe5HUcqCHeVvvUeGq9vnepeB04aO2/uVr9jRWQc9r71QGPMBOyErR8DuxtjJgHPAn+J2WV77D3uycDVwMfGmHHY9UbTcvhFeiajAMHwNDQhBeCgisoebsfQBj9jE9HSduz7hTFmiTEmir0QB5rYZndsQf1ERGYBhWy+/mXjJLJhwofpMcc7BChy9v8Au2ZuWhZwVwXDb2KXAtGb3UZ+UVFZ4XYMW7EcOCxUGHJ1GZp2dqVXbREMPwmc63YYqs2+AQ51hkWoDBYqDN2FTTg6vZU9+MntGNrgXeDYsfPmtmbRpwOBFxrGeToTuw4B3hKRELZldVzM9lOMMQ0VxfsCTzr7lQBpucJG+iajAMHw22xat7TT2rW6ZjTGpEPt6Brg4FBh6Pt27t/UWoWNtbR2YeMkoOGYscdrcp3cdsbcuQXDj2Jn0NYW0hgnlZUNb3krV/wE7BsqDP3Qlp1E5B/ORGINf18vIheLyGUi8qXT/eiamOdfEZHpTteiP8Q8Xi4it4jI18AecXg9qiXB8H/YvPeISm0h4ACC4Z/dDkQlR6gw9A/gdrfjcNuc4ZIaq/m27GPgyLHz5naksexO4C5jzHjgbGyjSINUrsxul/RORgGC4Xfo5AlpnjH5ucYscDuOFoSBQ0KFoUR0TywDujm/x2PtwibXyW10HtVawfDT2C67tW6HkipG1NUP9xmzyO04GlmITUTb813yMNCwxqgHOAm7fNdoYFfs5F87i0jDkiJnGmN2BnYBLhKR3s7jXYDPjTETjDEft/uVqLYJhm8FLnc7DNWiWdhEdJXbgaik+yN27eZO68vR0tPtGFrhS6Bg7Ly5bUkY38POY9IbQER6AX42TQRZuJV9PwR+4+x32P+3d+dxVtflAsc/DwKCoYOyiJCCC+moAwhueF1Q0ajMJbW8efWgZWWL5c22mzd/amopltX1ppXLmBmlqaHeXHNfUSB+2gwiioh7CD9AhoGZ89w/nu/Bw8js55zfmTPP+/U6rznLb/keZg7n93yX5wF6wr/Rh/T8YBQgSu7HMnj22uyAY9etfyftNrRhKXBQnInnFOn4vwHuFpEHVfVdbB3UH0VkPpaEZbdOHu9CbI3ofBF5ITwGeBDY3RMYdUGU3IZNq++1nUYt7bW2sZymHNVjgWiXkqGp6mJgWei4ORKYC+yTd38O9jkcG3Y5K4x+PgVsn/d8M/CXLr4H1x1RcgnWA98TZtn0RnOAw0MSR9fLxJlY40x8DrZ2sFcmNarbQcZqec+yuh+YWl1ft7IzO6nqC8BFwMPhe/FnQATcLCLP0XbC1vOBg8O16megR01l3qBn1RltT1S1LzAL2DbtppTaVYO3euzKrQeXY/3VGPhknIl72sJzVwxR1RTgDmBQyi1J3b1bDJzz7W2HlUMR7/nAEXEm7laHVuigOQAYAdQChwMvqurVLbabAvwYOFJV14jIQ0Ckqg+JyGpV7fV/G6mKqo7GEmYMTLspboMHgc8QJSvSbohLX01tTQb4HZteqlTRbpjRVDdgPdVpt2MTaoEzOrhG1LVQGSOjOVHyDLAfUO6ZKgtuypqGkWm3YRP+jo2IeiDqTJQ8hAUpnc2kXHGmrGnYE9W0E5DMAg7sbiAa3AZMw0ZE7wm30/NKK40SkeHY9KPlIRDdDUs65spFlMzCPqM+Alcefgt83ANRlxNn4lp6aa3gV4dTjlPUL6iur5vugWjXVVYwChAlrwL/hl0I9Rq7rlu/I6pJ2u3IcyMwLc7E5dQmVw6s02gi8GjaTUlTf+g/sqn5hRSbcDFwbJyJC1IPVlXXYSM4f1bVZlW9F7gJeDJkBLwFW3N9N9BXROqAn2BTdV05iZInse/RRWk3pRfLAmcTJV8iSvwit8KJSHNYAvSPUPv8gLa2jzPxXVgW1g917L58ycs0vFKZK2Lm7tynnOKWJuCL1fV1nu24m8rpl1o4UbIS+BSWjapXEJAhzdlyuHBYD3wnzsSnxJm4LL9AQ+bPqWm3o1ezTJCH04s+o5ty1Or300jqtAY4Kc7EP4wzccHWaYTERfsD1+SeU9VfqGpNuE1W1UWq2qiqn1DValU9VlWnqOpDYXufolsuomQB1ml0a3ubuoJbBXyaKLki7Ya4kmkI2fvHAz8ALmlvhzgTP40lh7u/yG0rG8+OlXKZBfgmcGR1fd017W7p2lWZwShAlDQTJWcBJwIrUm5NSUxobOzUoukiWIxNy52RcjsQkVbXUqjqj1S11/znXbaiZH34jJ5CL01sdOKq1WPb36qgFmPTclvW2+0WEdkdeAl4QFUXFvLYLkVRspIoOR4r/VKWnYsVaC6wL1Hyf2k3xKVmK0K9SBEZJCIPhNHSWESOCc+PEZG656c/f/Hz058fufAHC1/OrstuVNNbs8rS3y7l7b9UThWgJcPYUa06Q5ruASZU19c9WMiDishAEXm4KzW2RWS6SHEDdREZLCJfzXs8UkRuaWefmSLS7nVO5QajOVFyCzAeeDztphTbYWsaPpLi6W8F9go9dQUTSrPcFaauPC8inxORSeED+5yI3CMi24VtHxKRK0TkWeCHIvJqGK3JHec1EeknIteLyAnh+X1E5Ilw/GdEZEsR2UxELsurj/jlQr4n10KU3IglvulUbctKMKK5ecTAbHZBiU53HTAuzsRzC31gVf2nqu6kqt8u9LFdGbDSL4dgmdFdcShwGbA/UVKfdmNcyQ0M03TrseREuSz+a4HjVHUicChwea7sHJaF/EpV3aPxzcbnlv5m6WWEzKvarCy9ein9t+3PtsdXUE5PEUm2SG35QBPwPeAT1fV1xahgcTpwq6p2JaP5dKDYo8aDgQ3BqKq+oaontLPPr7EM0G2q/GAUIEqWYF+kF2LrMCrSgWsadkrhtI3A1+NMfHyciVcU4fjTgDdC3cE9sfVmvwJOCHUKr8VSYuf0V9W9VfV8rCbbIeH5o4B7VHVD776I9Af+BHwzTI2Zio3QfQFIVHUfLBnLGSKyYxHem8uJknnAOOAKKvgzuin7N6x9s8ineBc4Ls7EpxdqfajrhWwd6QTgjym3pBK9DhxBlHyXKPF6zL1Tbprubth1zw0h6BTg4lCq7n5gFB9UjHhFVeeF+8+tfHblamCvbEP2vTdq32DzUZsz/OjhJX4bxbdwlKQxC/BV4ODq+rpLq+vrilWG5GTgr+2Mhm9I0Coi54hIFAZX9gb+EDo0BorI4SIyN+x/rYhsHvZZLCKXhO2eFZGJYVBnkYh8JWyzyfNjOR52Dvtelt+eMIgzIwwazReRb4R9HgWmtjVbEXpLMAq5abs/wpIyzE+7OcWwTTY7pK9qKWsM3Q/UxJn4yiKeIwaOEJGfishBWE3CPYH7RGQecC7w0bzt/9Tifq4e6EktXgPYFXhTVWcDqOpKVW3CaiOeGo7/NDCED+ogumKJkveJkrOxUdI0E/uU1EkrVw8p4uFnAXvGmfj2Ip7D9RZRsowo+TzWufda2s2pEDOBcUTJA2k3xJUHVX0SGAoMwwKUYcAkVZ0AvA0MCJs25u3WDPSNM/HStUvWxvTh/tUvrF6XXVd5fbuzx8oWJTxdM9ZJXlNdX/dksU4SBkd2CjW72xoN/xBVvQV4Fjg5/I0ocD3wOVWtwUoAnZm3y5Kw3aNhuxOwfA/nh9dbO//3gUWh0+Q7LZrxJWAMMEFVxwF/CG3LYkt4xrf1/ntPMJoTJU8Bk4BzgLTLKhTc6PVNpbhAeBs4Oc7ER8SZuKjrw1T1RSyJRozVJjweeCF8GCaExChH5u3yft79WcA0EdkG+53/vYOnFeAbeefYMWQGdZ0gIkNCD9o8EXlLRF7Pe9y/1R2j5Gnsdx4BFT9KsP/atXuI6nsFPuxrwL/HmfiYApVtce4DUXIXsAdwJXbh4zqvDjicKPl3oqTQn3/Xg4mVvNoMK69UBbyjqutF5FBgdEeOsfbVtT9ofL3xgld+8spr2lxZH9G5O5dsptrTwN7V9XVnV9fXFXtW0VA+yG/T1mh4R+yKjZq/GB7XAgfnvT4r/IyBp1V1laq+CzSKyOAunn8qcHUY0EE3vqZ5h3amEPe+YBQgSpqIksuB3YHbU25NQU1uaOjKXPOOygL/C+waZ+KbinieDcKC7DWqeiO2nmY/YJiITA6v9xORPTa1r1oNx9nAL4A7NzEPfwGwnYjsE461ZZhKcA9wpoj0C89/TETSXI/bI6nqslxAD1wF/DwvwG87yIySdUTJ+VhQWtHrvftAn53WN9UV6HANwAXAbnEmnlmgYzr3YVGyiij5OnAg1ivvOmY18B1gPFHS0Q5SV/lya0bnYbO4MuGa5Q/A3mLlsU4FOryeuGlV00UNLzdc9+J3X/y7ZisnsVwySIY19Snq+vXlwFeAA6rr6+YV8Tz5GvhgxLu10fAmNo7bBtA1uRH1LBuPrmexUdS2RuO7YgDtJKlscw5vxYuS14DjiKqmYqNu+6Xcom47bE3D0BurtirGoe8Gzo0z8XPFOHgbaoDLRCSLZXM8E/tA/lJEqrC/4StofVrnn4CbgSktX1DVdSLyOeBXIjIQ+7BMxZIHjAHmhKkJ72IFpl339BGR51R1koiMx9b0jlbVJSKyCPtdD8fWAQ/F/t1PU9UDiaqOxepiVqfT9OI6dtVquXzI1t09zM1YWaVXC9Ak5zomSp4gqtoXm+p1Eb6koTVZbL3t94iS19NujCsvqrrJDKqq+i9gciu77Zm33Yy8+1Py7p8HUFNb0w/4MvDf2Pdsj/bOYJaMfG+jJVqF0Ihd/11QpARFrVLV5WHd5QBaHw1/GxguIkOwTq2jsGtzsHJQW4b7C4AxIrKLqr6EVSx4uBPNae38+edo6T7gyyLyoKo2icg2eaOjHwOeb2U/AES1sobvuyWq+jQ2qjAh5ZZ02XpYP3HM9s3YH3Qh3AecF2fios2Vd5VPRCLsP8/TsC/WU4EM1pHwGDBTVSeLyB3ALapaKyKnA0er6rEARFWbhX3OBSoqodSKPn2WH7TDqK3oQkp34G/Aj+NM/ESh2+Vcp0RVfbEEcOcB26XcmnLRjK0L/bFnyXVpq6mtGQR8CyvX1O0e0LR84e7mhz8+Vw9pf8sOWYfVx764ur4utYzhInIN1mE1D7gDGITNOtkf+ISqLhaRs4BvYknPXgYWq2okIsdjHfYN2DXWAcAMbMBmNnCmqjaKyGJgb1X9l4hMD/e/Hs6/GEuERBvnvwlLNvk3bJnGnaq6Z5hVeCmWfGs98FtV/R8R2Ra4Q1X3bfO9ezDaQlQlWA/vediamB5n8g4fjVdv1qemm4d5EPhRnIkfK0SbXO+WF4zuipUBOg37T3catoh+nKp+V0T+BWwXeuT6YQmmhm50MLvgzQA/pIKC0gN2+Oj8VZv1GdfBzbPAX4CL40w8r3itcq4LoqotsFGYs7BZJr1RM/Z/3IVEyYvtbexcKdXU1myFBTXfArZJtzWdN+7lbHzun7Ldvc5dh83Euri6vi71hGwiMhE4W1VPSbsthSIiZwMrVfWaNrfzYLQNUdWR2Ad1Gragt0f4wojhDz8zcEBXeozWYlP9fu0joa6Q8oLRt7GptodjvXdPYL2Ad6nqHR0KRnMsKD0Ku+g9kh6+Bv6/hg55+I4tP9Le57YRu8D9SZyJS1Wf1LmusdkMx2Dfowel25iSWQHcAPwPUVIx6/Rc20Lil8+r6v8W+Tx/xAZKrlPVn3f3eDW1NQOxagNf5YNRsbLXr0nX3nhZc1/p2nLDt7Ag9Orq+rpSVqBoV5gRVtvFWqNlR0ROA36fS2zU6nYejHZAVLUzdsF7GraWraz9fqstn7h0yNYHdGKXBcDVQG2ciT2rnyu4vGD0FuAR4BFV/Q8R+T9s3cv4sGZiFnCzqv4+TCE5RlWPa/cEUdUY4ItY0egeOT1w/ub9F5w8csSurbw8B/vyvCnOxMtL2CznCiOqmoiNxJwAlLI0Q6k8gX2P3kyUtJmsw1UeERlDmLLY4vm+7V2Id+IcI4DHVHWXTuzT4fPX1NbsjQWlJwEDu9bK0rlhRlPdgPUdziOh2LKzq4FZ1fV1BfmduMLwYLQzoqr+2AjMZ4GjsUW+ZWdJ376vf2r7kaPa2WwZcCdwfZyJHyp6o1yvlgtGVXWGiLwGXKiqvxGR/wJOCnWpEJHRwHVsnMCo4z2XNlp6NBaYHg60XkKmDI0fs/2bWZFcML0My6R4bZyJ/5Fis5wrnKhqEPAZLGPjYfTsRIpLsQ62a4iSNhN0uMomIjOxWQALsDVza7GsrLup6sdE5HasTvoA4Beq+puw32os4/9R2Hq/Y1T1bRE5EVsu1gwkqnpwKLUxNpzjG8Ab2Lq9YcAa4AxVrReR68P59wIeV9X/7Mx7qamt2Rr4PPY5PZgy/YxeeEPTI7u+vlHJkk1ZiH1Gf1ddX/dyCZrlusCD0a6ywPTjwInAp4HBqbanhXFjtn9XRYa1eHoR8NdwezzOxBUxDcC5TbKL3iOATwGfpAeMmJ4xYtitTw0c+BpWB+yROBN7762rXFHVEOC4cDsYS5hR7uqw5B63AU8TJX4R5TYaGRWRKcBdwJ6q+kp4fRtVfS9k7p8NHKKqy0REsUR9d4jIpdj6uh+HUi7TVPV1ERmsqitajr6KyAPAV1R1oYjsB1yiqoeFYHQoFth26zqvprZmG+wa91jsmrdsRkw/83j2sZMeyR7Y4uks8CT2HTqrur7Ok4b1AB6MFkJU1QfrgZoSbgeR8qjpkduPfObNvn13waYOPQrcFWfi1sqfOFfZLDHZRCwwnYZlzC6HL9VlWB3Ve4F7iJKXUm6Pc+mIqvoB+2KjpYdha8o3T7VN5iXse/RJ4F6ixEdX3IdsIhg9T1UPzXs9wjpdwJJ6fVxVnxKRRmCAqmooNXeEqn5RRK4Cdgb+DNwaAtf8cwzCZg/l5w7YXFWrQzD6oKrWFvI91tTWbIGVvzsQy9Y6ie7Vn+yWHd7Rl2dc07wTllX2cSzx5p3V9XXvptUm1zUejBbDB8HpJGw93B7hZzFrO72FfWnOBZ6dueWgpy4aus3COBP7L9i5liyxym5YgDoR+7xOoLidSIuxZE1zN/y0WsfOuZaiqoFYQpWacBuHfY8WpZA2Nh3yVWxa3zxyAWiU+IWta9cmgtFzVPWo8NoUrJb9kaq6RkQeAiJVfUhEVqvqoLDdCcBRqjo9PN4P60A9Fbue3DLvHFsBC1T1QzN+QjB6p6reUrQ3zIbapXthHUeTsWvdnSjemvAsVtIkBp7rk9XZM3/aPLu6vu6tIp3PlYgHo6UUVQ0FdgdGASPCbdvwcwjQL9z65t2asOx8Sd5tBbZWZdGGW5S8X7o34lwFstHT0djnc7twG5F3f1vs8wmWXTt3y1mOZQt+K++We/wiUeKJh5zrrqhqNLZuLvfZHJF3v4qNvz9zt/XY9+by8DN3ex0LPhcCrxAl67vSpDDV8meq+u3w+BxgkKpGXTme63lEZAgwR1VHbyIYPQb4oqp+WkR2wzo7prUVjIrIzqq6KDw/GzgD+5vNn6b7BPBzVb1ZRAQrkfaPUgWjramprRmBBaU7h5/bY4H0oHDL3f8I1gnUgK15zb8lWOfQ4rzbkjgTd+kz6sqbB6POubIlIptVSopz51xlEpG1wJvAPqGYvAejvZCI3ISN4DcAb+cFo5sDt2PTcxdgOUbaHBkVkVuxThcBHsDKI41m42B0R+DXWEdMP2Cmql6QdjDqXGd5MOqcKwgRuQB4T1WvCI8vAt7BMtp+Flv/dZuqnhdev53Wswteja1N+ZqqPlbad+Kccx0X/s+6CAtAf5gfjIbpm9fS1QzhzjlX4Xp0kXjnXFm5Flvbgoj0wWqVvYX17u6LrcmcJCK5VOynq+okbF3YWWGaE9jUnadVdbwHos65HuJK4GQRabnu/FdYEftxWKmmX5a8Zc45V8bKsnaQc67nUdXFIrJMRPbC1lfOBfbBavPODZsNwoLTR7AANJddcPvw/DJsDclfStl255zrDlVdKSI3AGdh0zRzJmP1GgF+D1xa6rY551w582DUOVdIvwOmYwlFrgUOx2qfXZ2/UUjwMBWYnJddMJcifq2vE3XO9UBXAHOA61Juh3PO9Rg+Tdc5V0i3YXU89wHuCbfTQ000RGSUiAzHsl4uD4HobsD+aTXYOecKQVXfw+pCfiHv6SewJQsAJ2N1v12ZEZGBIvKwiGzWhX2ni8jIYrQr7xyDReSreY9HikibCYpEZKaIjC1mu5wrBA9GnXMFo6rrsMLTf1bVZlW9F7gJeFJEYuAWLK373UBfEakDfgI8lVabnXOugC7HkhXlfAM4TUTmA6cA30ylVa49pwO3dnFWznSgqMEoloF3QzCqqm+o6gnt7PNr4LvFbJRzheDZdJ1zBRMSF80BTlTVhWm3xznnnGtPqNn5eeBfwF+BrbFyKeeq6l9DVuT8sirnYDkQngeux2rWNmBrhA8AZmBL4WYDZ6pqo4gsBv4IfAKrIf8l4BJgF+AyVb0qzCLa1PlnAsdgpWHuwxJm3amqe4bR3J9is5KywG9V9Vfh+3gRMFZVm4ryD+dcAfjIqHOuIERkd+Al4AEPRJ1zzvUEItIf2ElVFwNrgeNUdSJwKHC5iEhr+4Zans8CJ6vqBECx4PRzqlqDBaRn5u2yJGz3aNjuBGyZyvnh9dbO/31gkapOUNXvtGjGl7AaphPysjajqlnsO3l85/5FnCstT2DknCsIVf0nsFPa7XDOOec6YSiwItwX4OJQgiwLjMKyw3fUrsArqvpieFwLfA1LbgUwK/yMsVq0q4BVItIoIoOB97tw/qnAVbnRz7B2OecdbArxc514D86VlAejzjnnnHOut2rgg2zuJwPDgEmquj5MrR2ATavNn004gK5pDD+zefdzj/u2cf6uGsDGpYacKzs+Tdc555xzzvVKqroc2ExEBmCZ3t8JgeChwOiw2dvAcBEZIiKbA0flHWIVlpgPbE3nGBHZJTw+BXi4E81p7fz552jpPuDLItIXQES2yXvtY9i6VufKlgejzjnnnHOuN7sXOBBbb7l3yP5+KlAPoKrrgQuAZ7Dgrz5v3+uBq0RkHjbN9zTg5nCMLHBVJ9rR2vmXAY+LyPMiclmLfX4HLAHmi8g/sERMiMi2QIOqvtWJ8ztXcp5N1znnnHPO9VoiMhE4W1VPSbsthSIiZwMrVfWatNviXFt8ZNQ555xzzvVaqjoHeDCUSakUK7AESs6VNR8Zdc4555xzzjlXcj4y6pxzzjnnnHOu5DwYdc4555xzzjlXch6MOuecc84555wrOQ9GnXPOOeecc86VnAejzjnnnHPOOedKzoNR55xzzjnnnHMl58Goc84555xzzrmS82DUOeecc84551zJeTDqnHPOOeecc67kPBh1zjnnnHPOOVdy/w+KtixJySiDKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4, 4, figsize=(16,16))\n",
    "ax = ax.ravel()\n",
    "for i, column in enumerate(cat_cols):\n",
    "    serie = train[column].value_counts()\n",
    "    labels = list(serie.index)\n",
    "    values = list(serie)\n",
    "    ax[i].set_title(column)\n",
    "    ax[i].pie(values,\n",
    "              labels=[str(s).replace(' ', '\\n') for s in labels],\n",
    "              autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "685cd8d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:41.293602Z",
     "iopub.status.busy": "2021-11-05T07:43:41.292892Z",
     "iopub.status.idle": "2021-11-05T07:43:41.362407Z",
     "shell.execute_reply": "2021-11-05T07:43:41.363021Z",
     "shell.execute_reply.started": "2021-11-05T07:17:07.509016Z"
    },
    "papermill": {
     "duration": 0.127735,
     "end_time": "2021-11-05T07:43:41.363195",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.235460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "targets = train[target_col].value_counts()\n",
    "plt.pie(\n",
    "    targets,\n",
    "    labels=list(targets.index)\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a717b50c",
   "metadata": {
    "papermill": {
     "duration": 0.05524,
     "end_time": "2021-11-05T07:43:41.473952",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.418712",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Несбалансированных (сильно) классов нет"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91d842b",
   "metadata": {
    "papermill": {
     "duration": 0.054875,
     "end_time": "2021-11-05T07:43:41.584381",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.529506",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Нормирование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44237f4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:41.698968Z",
     "iopub.status.busy": "2021-11-05T07:43:41.698296Z",
     "iopub.status.idle": "2021-11-05T07:43:41.703056Z",
     "shell.execute_reply": "2021-11-05T07:43:41.703675Z",
     "shell.execute_reply.started": "2021-11-05T07:17:12.158902Z"
    },
    "papermill": {
     "duration": 0.063765,
     "end_time": "2021-11-05T07:43:41.703849",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.640084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cb_train = train.copy()\n",
    "cb_test = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c0e15dad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:41.821314Z",
     "iopub.status.busy": "2021-11-05T07:43:41.820646Z",
     "iopub.status.idle": "2021-11-05T07:43:41.822535Z",
     "shell.execute_reply": "2021-11-05T07:43:41.822996Z",
     "shell.execute_reply.started": "2021-11-05T07:17:12.991528Z"
    },
    "papermill": {
     "duration": 0.062655,
     "end_time": "2021-11-05T07:43:41.823161",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.760506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = train[target_col]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2096e645",
   "metadata": {
    "papermill": {
     "duration": 0.056356,
     "end_time": "2021-11-05T07:43:41.935079",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.878723",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Кодирование категориальных признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff95345",
   "metadata": {
    "papermill": {
     "duration": 0.055392,
     "end_time": "2021-11-05T07:43:42.046595",
     "exception": false,
     "start_time": "2021-11-05T07:43:41.991203",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### One-hot-encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95af5029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:42.164659Z",
     "iopub.status.busy": "2021-11-05T07:43:42.163999Z",
     "iopub.status.idle": "2021-11-05T07:43:42.193877Z",
     "shell.execute_reply": "2021-11-05T07:43:42.194488Z",
     "shell.execute_reply.started": "2021-11-05T07:17:17.648299Z"
    },
    "papermill": {
     "duration": 0.08879,
     "end_time": "2021-11-05T07:43:42.194680",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.105890",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dummy_data = pd.get_dummies(train[cat_cols], drop_first=True) # drop_first=True\n",
    "train = pd.concat([train[num_cols], dummy_data], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c953658c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:42.311445Z",
     "iopub.status.busy": "2021-11-05T07:43:42.310749Z",
     "iopub.status.idle": "2021-11-05T07:43:42.328634Z",
     "shell.execute_reply": "2021-11-05T07:43:42.329221Z",
     "shell.execute_reply.started": "2021-11-05T07:17:18.225053Z"
    },
    "papermill": {
     "duration": 0.077744,
     "end_time": "2021-11-05T07:43:42.329414",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.251670",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   HasOnlineTV_Yes  HasContractPhone_Two year  \\\n",
       "0                0                          0   \n",
       "1                0                          1   \n",
       "2                0                          0   \n",
       "3                0                          0   \n",
       "4                1                          1   \n",
       "\n",
       "   HasOnlineBackup_No internet service  IsBillingPaperless_Yes  \\\n",
       "0                                    1                       0   \n",
       "1                                    1                       0   \n",
       "2                                    0                       1   \n",
       "3                                    0                       0   \n",
       "4                                    0                       0   \n",
       "\n",
       "   HasInternetService_Fiber optic  HasPhoneService_Yes  \\\n",
       "0                               0                    1   \n",
       "1                               0                    1   \n",
       "2                               1                    1   \n",
       "3                               1                    1   \n",
       "4                               1                    1   \n",
       "\n",
       "   HasOnlineSecurityService_Yes  HasMultiplePhoneNumbers_Yes  \\\n",
       "0                             0                            0   \n",
       "1                             0                            1   \n",
       "2                             0                            0   \n",
       "3                             0                            1   \n",
       "4                             1                            1   \n",
       "\n",
       "   HasMovieSubscription_No internet service  HasOnlineBackup_Yes  ...  \\\n",
       "0                                         1                    0  ...   \n",
       "1                                         1                    0  ...   \n",
       "2                                         0                    0  ...   \n",
       "3                                         0                    0  ...   \n",
       "4                                         0                    1  ...   \n",
       "\n",
       "   HasOnlineTV_No internet service  PaymentMethod_Credit card (automatic)  \\\n",
       "0                                1                                      0   \n",
       "1                                1                                      1   \n",
       "2                                0                                      0   \n",
       "3                                0                                      0   \n",
       "4                                0                                      1   \n",
       "\n",
       "   HasInternetService_No  HasTechSupportAccess_Yes  HasMovieSubscription_Yes  \\\n",
       "0                      1                         0                         0   \n",
       "1                      1                         0                         0   \n",
       "2                      0                         1                         0   \n",
       "3                      0                         0                         0   \n",
       "4                      0                         1                         1   \n",
       "\n",
       "   HasContractPhone_One year  Sex_Male  HasDeviceProtection_Yes  \\\n",
       "0                          1         1                        0   \n",
       "1                          0         1                        0   \n",
       "2                          0         1                        0   \n",
       "3                          0         0                        1   \n",
       "4                          0         0                        1   \n",
       "\n",
       "   HasOnlineSecurityService_No internet service  HasChild_Yes  \n",
       "0                                             1             1  \n",
       "1                                             1             0  \n",
       "2                                             0             0  \n",
       "3                                             0             0  \n",
       "4                                             0             1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[set(train.columns) - set(feature_cols)].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8faf9cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:42.444867Z",
     "iopub.status.busy": "2021-11-05T07:43:42.444187Z",
     "iopub.status.idle": "2021-11-05T07:43:42.466978Z",
     "shell.execute_reply": "2021-11-05T07:43:42.467542Z",
     "shell.execute_reply.started": "2021-11-05T07:17:18.847991Z"
    },
    "papermill": {
     "duration": 0.082125,
     "end_time": "2021-11-05T07:43:42.467714",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.385589",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dummy_data = pd.get_dummies(test[cat_cols], drop_first=True) # drop_first=True\n",
    "test = pd.concat([test[num_cols], dummy_data], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34c212de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:42.585093Z",
     "iopub.status.busy": "2021-11-05T07:43:42.584337Z",
     "iopub.status.idle": "2021-11-05T07:43:42.590402Z",
     "shell.execute_reply": "2021-11-05T07:43:42.590988Z",
     "shell.execute_reply.started": "2021-11-05T07:19:37.254202Z"
    },
    "papermill": {
     "duration": 0.066199,
     "end_time": "2021-11-05T07:43:42.591155",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.524956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(train, y, train_size=0.8, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56da01c",
   "metadata": {
    "papermill": {
     "duration": 0.056257,
     "end_time": "2021-11-05T07:43:42.703904",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.647647",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Нормирование числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "40c6b2bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:42.821757Z",
     "iopub.status.busy": "2021-11-05T07:43:42.821113Z",
     "iopub.status.idle": "2021-11-05T07:43:42.845525Z",
     "shell.execute_reply": "2021-11-05T07:43:42.846220Z",
     "shell.execute_reply.started": "2021-11-05T07:19:40.223225Z"
    },
    "papermill": {
     "duration": 0.084385,
     "end_time": "2021-11-05T07:43:42.846474",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.762089",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[col] = igetitem(value, i)\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[col] = igetitem(value, i)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train[num_cols] = scaler.fit_transform(X_train[num_cols])\n",
    "X_valid[num_cols] = scaler.transform(X_valid[num_cols])\n",
    "train[num_cols] = scaler.transform(train[num_cols])\n",
    "test[num_cols] = scaler.transform(test[num_cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421c68e5",
   "metadata": {
    "papermill": {
     "duration": 0.057326,
     "end_time": "2021-11-05T07:43:42.961522",
     "exception": false,
     "start_time": "2021-11-05T07:43:42.904196",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Обучение моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dd22c3f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:43.079618Z",
     "iopub.status.busy": "2021-11-05T07:43:43.078968Z",
     "iopub.status.idle": "2021-11-05T07:43:43.104342Z",
     "shell.execute_reply": "2021-11-05T07:43:43.103811Z",
     "shell.execute_reply.started": "2021-11-05T07:20:01.792648Z"
    },
    "papermill": {
     "duration": 0.085517,
     "end_time": "2021-11-05T07:43:43.104490",
     "exception": false,
     "start_time": "2021-11-05T07:43:43.018973",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5157</th>\n",
       "      <td>0.952822</td>\n",
       "      <td>1.340060</td>\n",
       "      <td>1.646489</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4225 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ClientPeriod  MonthlySpending  TotalSpent  IsSeniorCitizen  Sex_Male  \\\n",
       "246       1.442176         1.435182    2.132938                0         1   \n",
       "3847     -0.066666         0.480628    0.037505                0         0   \n",
       "471       0.504247         1.163167    0.950690                0         0   \n",
       "1102     -1.126934        -0.689201   -0.921299                0         0   \n",
       "759      -1.290052        -0.490613   -0.995810                0         1   \n",
       "...            ...              ...         ...              ...       ...   \n",
       "905       0.422688         0.647508    0.582593                1         1   \n",
       "5192     -1.290052         0.140193   -0.987512                0         1   \n",
       "3980     -0.352123         1.129791    0.049448                0         0   \n",
       "235      -0.433682         0.458934   -0.252898                0         0   \n",
       "5157      0.952822         1.340060    1.646489                0         0   \n",
       "\n",
       "      HasPartner_Yes  HasChild_Yes  HasPhoneService_Yes  \\\n",
       "246                0             0                    1   \n",
       "3847               0             0                    1   \n",
       "471                0             0                    1   \n",
       "1102               1             1                    1   \n",
       "759                0             0                    1   \n",
       "...              ...           ...                  ...   \n",
       "905                0             0                    1   \n",
       "5192               0             0                    1   \n",
       "3980               1             0                    1   \n",
       "235                1             0                    1   \n",
       "5157               1             0                    1   \n",
       "\n",
       "      HasMultiplePhoneNumbers_No phone service  HasMultiplePhoneNumbers_Yes  \\\n",
       "246                                          0                            1   \n",
       "3847                                         0                            1   \n",
       "471                                          0                            0   \n",
       "1102                                         0                            0   \n",
       "759                                          0                            0   \n",
       "...                                        ...                          ...   \n",
       "905                                          0                            1   \n",
       "5192                                         0                            0   \n",
       "3980                                         0                            0   \n",
       "235                                          0                            1   \n",
       "5157                                         0                            0   \n",
       "\n",
       "      ...  HasOnlineTV_No internet service  HasOnlineTV_Yes  \\\n",
       "246   ...                                0                1   \n",
       "3847  ...                                0                0   \n",
       "471   ...                                0                1   \n",
       "1102  ...                                0                0   \n",
       "759   ...                                0                0   \n",
       "...   ...                              ...              ...   \n",
       "905   ...                                0                1   \n",
       "5192  ...                                0                0   \n",
       "3980  ...                                0                1   \n",
       "235   ...                                0                1   \n",
       "5157  ...                                0                1   \n",
       "\n",
       "      HasMovieSubscription_No internet service  HasMovieSubscription_Yes  \\\n",
       "246                                          0                         1   \n",
       "3847                                         0                         0   \n",
       "471                                          0                         1   \n",
       "1102                                         0                         0   \n",
       "759                                          0                         0   \n",
       "...                                        ...                       ...   \n",
       "905                                          0                         0   \n",
       "5192                                         0                         0   \n",
       "3980                                         0                         1   \n",
       "235                                          0                         1   \n",
       "5157                                         0                         1   \n",
       "\n",
       "      HasContractPhone_One year  HasContractPhone_Two year  \\\n",
       "246                           1                          0   \n",
       "3847                          0                          0   \n",
       "471                           1                          0   \n",
       "1102                          0                          0   \n",
       "759                           0                          0   \n",
       "...                         ...                        ...   \n",
       "905                           0                          0   \n",
       "5192                          0                          0   \n",
       "3980                          0                          0   \n",
       "235                           0                          0   \n",
       "5157                          1                          0   \n",
       "\n",
       "      IsBillingPaperless_Yes  PaymentMethod_Credit card (automatic)  \\\n",
       "246                        0                                      0   \n",
       "3847                       1                                      1   \n",
       "471                        1                                      0   \n",
       "1102                       0                                      0   \n",
       "759                        1                                      0   \n",
       "...                      ...                                    ...   \n",
       "905                        1                                      0   \n",
       "5192                       1                                      0   \n",
       "3980                       1                                      1   \n",
       "235                        1                                      0   \n",
       "5157                       1                                      1   \n",
       "\n",
       "      PaymentMethod_Electronic check  PaymentMethod_Mailed check  \n",
       "246                                1                           0  \n",
       "3847                               0                           0  \n",
       "471                                1                           0  \n",
       "1102                               0                           1  \n",
       "759                                0                           1  \n",
       "...                              ...                         ...  \n",
       "905                                1                           0  \n",
       "5192                               1                           0  \n",
       "3980                               0                           0  \n",
       "235                                1                           0  \n",
       "5157                               0                           0  \n",
       "\n",
       "[4225 rows x 30 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c50731c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:43.229079Z",
     "iopub.status.busy": "2021-11-05T07:43:43.227975Z",
     "iopub.status.idle": "2021-11-05T07:43:43.229889Z",
     "shell.execute_reply": "2021-11-05T07:43:43.230409Z",
     "shell.execute_reply.started": "2021-11-05T07:20:22.259281Z"
    },
    "papermill": {
     "duration": 0.068143,
     "end_time": "2021-11-05T07:43:43.230580",
     "exception": false,
     "start_time": "2021-11-05T07:43:43.162437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "models_params = {\n",
    "    'logistic_regression': {\n",
    "        'model': LogisticRegression(),\n",
    "        'params': {\n",
    "            'C': [0.001, 0.01, 0.1, 1, 10, 30, 50, 100],\n",
    "        }\n",
    "    },\n",
    "    \n",
    "    'random_forest': {\n",
    "        'model': RandomForestClassifier(),\n",
    "        'params': {\n",
    "            'n_estimators' : [30, 50, 70, 100, 150, 200],\n",
    "            'min_samples_split': [5, 10, 15, 20, 25, 30]\n",
    "        }\n",
    "    },\n",
    "    \n",
    "    'svm' : {\n",
    "        'model': svm.SVC(gamma = 'auto'),\n",
    "        'params': {\n",
    "            'C': [1, 10, 20], \n",
    "            'kernel': ['rbf', 'linear']\n",
    "        }\n",
    "    },    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23e8d6d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:43:43.354822Z",
     "iopub.status.busy": "2021-11-05T07:43:43.354034Z",
     "iopub.status.idle": "2021-11-05T07:44:41.006782Z",
     "shell.execute_reply": "2021-11-05T07:44:41.005805Z",
     "shell.execute_reply.started": "2021-11-05T07:20:32.308051Z"
    },
    "papermill": {
     "duration": 57.718653,
     "end_time": "2021-11-05T07:44:41.006988",
     "exception": false,
     "start_time": "2021-11-05T07:43:43.288335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    2.8s\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    3.1s\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=-1)]: Done  38 out of  40 | elapsed:    3.5s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:    3.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83289796 0.83912777 0.84372909 0.84494343        nan        nan\n",
      "        nan 0.84512319]\n",
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    1.2s\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    4.4s\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    6.8s\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.9s\n",
      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:   10.4s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:   12.8s\n",
      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:   14.7s\n",
      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   18.2s\n",
      "[Parallel(n_jobs=-1)]: Done 105 tasks      | elapsed:   20.4s\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:   24.2s\n",
      "[Parallel(n_jobs=-1)]: Done 137 tasks      | elapsed:   26.3s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   29.9s\n",
      "[Parallel(n_jobs=-1)]: Done 173 tasks      | elapsed:   33.1s\n",
      "[Parallel(n_jobs=-1)]: Done 180 out of 180 | elapsed:   35.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82136464 0.82550327 0.82611276 0.8289353  0.82798628 0.82979234\n",
      " 0.83248012 0.83465817 0.83452024 0.8359223  0.83566062 0.8364547\n",
      " 0.83547755 0.83653293 0.83736881 0.8378318  0.83947045 0.83910046\n",
      " 0.83434663 0.84007009 0.83961334 0.83958495 0.84062542 0.84070084\n",
      " 0.83930716 0.84031654 0.84059999 0.84068515 0.84127445 0.84192543\n",
      " 0.83977067 0.84098625 0.84293219 0.842958   0.84280827 0.84235968]\n",
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] C=0.001 .........................................................\n",
      "[CV] ............................. C=0.001, score=0.824, total=   0.0s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] .............................. C=0.01, score=0.854, total=   0.0s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] .............................. C=0.01, score=0.850, total=   0.0s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................... C=0.1, score=0.835, total=   0.1s\n",
      "[CV] C=1 .............................................................\n",
      "[CV] ................................. C=1, score=0.862, total=   0.1s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] .................................. C=10, score=nan, total=   0.1s\n",
      "[CV] C=30 ............................................................\n",
      "[CV] .................................. C=30, score=nan, total=   0.1s\n",
      "[CV] C=30 ............................................................\n",
      "[CV] .................................. C=30, score=nan, total=   0.1s\n",
      "[CV] C=50 ............................................................\n",
      "[CV] .................................. C=50, score=nan, total=   0.1s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] ............................... C=100, score=0.835, total=   0.1s\n",
      "[CV] min_samples_split=5, n_estimators=30 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=30, score=0.821, total=   0.3s\n",
      "[CV] min_samples_split=5, n_estimators=50 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=50, score=0.818, total=   0.4s\n",
      "[CV] min_samples_split=5, n_estimators=70 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=70, score=0.833, total=   0.6s\n",
      "[CV] min_samples_split=5, n_estimators=70 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=70, score=0.825, total=   0.6s\n",
      "[CV] min_samples_split=5, n_estimators=100 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=100, score=0.833, total=   0.8s\n",
      "[CV] min_samples_split=5, n_estimators=150 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=150, score=0.825, total=   1.3s\n",
      "[CV] min_samples_split=5, n_estimators=200 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=200, score=0.822, total=   1.7s\n",
      "[CV] min_samples_split=10, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=30, score=0.839, total=   0.3s\n",
      "[CV] min_samples_split=10, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=30, score=0.833, total=   0.2s\n",
      "[CV] min_samples_split=10, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=30, score=0.834, total=   0.3s\n",
      "[CV] min_samples_split=10, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=50, score=0.839, total=   0.4s\n",
      "[CV] min_samples_split=10, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=50, score=0.841, total=   0.4s\n",
      "[CV] min_samples_split=10, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=70, score=0.832, total=   0.6s\n",
      "[CV] min_samples_split=10, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=100, score=0.829, total=   0.8s\n",
      "[CV] min_samples_split=10, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=150, score=0.841, total=   1.2s\n",
      "[CV] min_samples_split=10, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=150, score=0.836, total=   1.2s\n",
      "[CV] min_samples_split=10, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=200, score=0.840, total=   1.6s\n",
      "[CV] min_samples_split=15, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=50, score=0.836, total=   0.4s\n",
      "[CV] min_samples_split=15, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=70, score=0.844, total=   0.5s\n",
      "[CV] min_samples_split=15, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=70, score=0.836, total=   0.5s\n",
      "[CV] min_samples_split=15, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=100, score=0.838, total=   0.8s\n",
      "[CV] min_samples_split=15, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=150, score=0.838, total=   1.1s\n",
      "[CV] min_samples_split=15, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=200, score=0.834, total=   1.5s\n",
      "[CV] min_samples_split=20, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=30, score=0.841, total=   0.2s\n",
      "[CV] min_samples_split=20, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=30, score=0.832, total=   0.2s\n",
      "[CV] min_samples_split=20, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=30, score=0.828, total=   0.3s\n",
      "[CV] min_samples_split=20, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=50, score=0.846, total=   0.4s\n",
      "[CV] min_samples_split=20, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=50, score=0.843, total=   0.4s\n",
      "[CV] min_samples_split=20, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=70, score=0.838, total=   0.5s\n",
      "[CV] min_samples_split=20, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=100, score=0.834, total=   0.8s\n",
      "[CV] min_samples_split=20, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=100, score=0.838, total=   0.7s\n",
      "[CV] min_samples_split=20, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=150, score=0.842, total=   1.1s\n",
      "[CV] min_samples_split=20, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=200, score=0.840, total=   1.4s\n",
      "[CV] min_samples_split=25, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=30, score=0.833, total=   0.2s\n",
      "[CV] min_samples_split=25, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=30, score=0.843, total=   0.2s\n",
      "[CV] min_samples_split=25, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=50, score=0.833, total=   0.4s\n",
      "[CV] min_samples_split=25, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=50, score=0.837, total=   0.4s\n",
      "[CV] min_samples_split=25, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=70, score=0.844, total=   0.5s\n",
      "[CV] min_samples_split=25, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=100, score=0.840, total=   0.7s\n",
      "[CV] min_samples_split=25, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=150, score=0.835, total=   1.1s\n",
      "[CV] min_samples_split=25, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=200, score=0.849, total=   1.4s\n",
      "[CV] min_samples_split=25, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=200, score=0.839, total=   1.4s\n",
      "[CV] min_samples_split=30, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=70, score=0.835, total=   0.5s\n",
      "[CV] min_samples_split=30, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=100, score=0.851, total=   0.8s\n",
      "[CV] min_samples_split=30, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=100, score=0.841, total=   0.7s\n",
      "[CV] min_samples_split=30, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=150, score=0.848, total=   1.1s\n",
      "[CV] min_samples_split=30, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=200, score=0.840, total=   1.3s\n",
      "[CV] C=1, kernel=rbf ................................................."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] C=0.001 .........................................................\n",
      "[CV] ............................. C=0.001, score=0.832, total=   0.0s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] .............................. C=0.01, score=0.830, total=   0.0s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] .............................. C=0.01, score=0.829, total=   0.0s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................... C=0.1, score=0.856, total=   0.1s\n",
      "[CV] C=1 .............................................................\n",
      "[CV] ................................. C=1, score=0.834, total=   0.1s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] .................................. C=10, score=nan, total=   0.1s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] ................................ C=10, score=0.836, total=   0.1s\n",
      "[CV] C=30 ............................................................\n",
      "[CV] .................................. C=30, score=nan, total=   0.1s\n",
      "[CV] C=50 ............................................................\n",
      "[CV] .................................. C=50, score=nan, total=   0.1s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] ............................... C=100, score=0.837, total=   0.1s\n",
      "[CV] min_samples_split=5, n_estimators=30 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=30, score=0.826, total=   0.3s\n",
      "[CV] min_samples_split=5, n_estimators=50 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=50, score=0.833, total=   0.4s\n",
      "[CV] min_samples_split=5, n_estimators=50 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=50, score=0.827, total=   0.4s\n",
      "[CV] min_samples_split=5, n_estimators=70 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=70, score=0.827, total=   0.6s\n",
      "[CV] min_samples_split=5, n_estimators=100 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=100, score=0.829, total=   0.8s\n",
      "[CV] min_samples_split=5, n_estimators=150 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=150, score=0.817, total=   1.3s\n",
      "[CV] min_samples_split=5, n_estimators=200 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=200, score=0.836, total=   1.7s\n",
      "[CV] min_samples_split=5, n_estimators=200 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=200, score=0.829, total=   1.7s\n",
      "[CV] min_samples_split=10, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=70, score=0.824, total=   0.5s\n",
      "[CV] min_samples_split=10, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=100, score=0.842, total=   0.8s\n",
      "[CV] min_samples_split=10, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=100, score=0.836, total=   0.8s\n",
      "[CV] min_samples_split=10, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=150, score=0.838, total=   1.2s\n",
      "[CV] min_samples_split=10, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=200, score=0.836, total=   1.5s\n",
      "[CV] min_samples_split=15, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=30, score=0.832, total=   0.2s\n",
      "[CV] min_samples_split=15, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=30, score=0.831, total=   0.2s\n",
      "[CV] min_samples_split=15, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=50, score=0.832, total=   0.4s\n",
      "[CV] min_samples_split=15, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=50, score=0.834, total=   0.4s\n",
      "[CV] min_samples_split=15, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=70, score=0.839, total=   0.5s\n",
      "[CV] min_samples_split=15, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=100, score=0.834, total=   0.7s\n",
      "[CV] min_samples_split=15, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=150, score=0.834, total=   1.1s\n",
      "[CV] min_samples_split=15, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=200, score=0.845, total=   1.5s\n",
      "[CV] min_samples_split=15, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=200, score=0.839, total=   1.8s\n",
      "[CV] min_samples_split=20, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=70, score=0.849, total=   0.6s\n",
      "[CV] min_samples_split=20, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=70, score=0.839, total=   0.6s\n",
      "[CV] min_samples_split=20, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=100, score=0.843, total=   0.7s\n",
      "[CV] min_samples_split=20, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=150, score=0.839, total=   1.1s\n",
      "[CV] min_samples_split=20, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=200, score=0.833, total=   1.4s\n",
      "[CV] min_samples_split=25, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=30, score=0.847, total=   0.2s\n",
      "[CV] min_samples_split=25, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=30, score=0.838, total=   0.2s\n",
      "[CV] min_samples_split=25, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=30, score=0.836, total=   0.2s\n",
      "[CV] min_samples_split=25, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=50, score=0.843, total=   0.4s\n",
      "[CV] min_samples_split=25, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=70, score=0.848, total=   0.5s\n",
      "[CV] min_samples_split=25, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=70, score=0.836, total=   0.5s\n",
      "[CV] min_samples_split=25, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=100, score=0.841, total=   0.7s\n",
      "[CV] min_samples_split=25, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=150, score=0.839, total=   1.1s\n",
      "[CV] min_samples_split=25, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=200, score=0.834, total=   1.4s\n",
      "[CV] min_samples_split=30, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=30, score=0.851, total=   0.2s\n",
      "[CV] min_samples_split=30, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=30, score=0.833, total=   0.2s\n",
      "[CV] min_samples_split=30, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=30, score=0.843, total=   0.2s\n",
      "[CV] min_samples_split=30, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=50, score=0.849, total=   0.4s\n",
      "[CV] min_samples_split=30, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=50, score=0.842, total=   0.4s\n",
      "[CV] min_samples_split=30, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=70, score=0.842, total=   0.5s\n",
      "[CV] min_samples_split=30, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=100, score=0.837, total=   0.8s\n",
      "[CV] min_samples_split=30, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=150, score=0.850, total=   1.1s\n",
      "[CV] min_samples_split=30, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=150, score=0.839, total=   1.0s\n",
      "[CV] min_samples_split=30, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=200, score=0.843, total=   1.2s\n",
      "[CV] C=1, kernel=rbf .................................................\n",
      "[CV] ..................... C=1, kernel=rbf, score=0.807, total=   1.2s\n",
      "[CV] C=1, kernel=rbf ................................................."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] C=0.001 .........................................................\n",
      "[CV] ............................. C=0.001, score=0.846, total=   0.0s\n",
      "[CV] C=0.001 .........................................................\n",
      "[CV] ............................. C=0.001, score=0.823, total=   0.0s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] .............................. C=0.01, score=0.833, total=   0.0s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................... C=0.1, score=0.860, total=   0.1s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................... C=0.1, score=0.834, total=   0.1s\n",
      "[CV] C=1 .............................................................\n",
      "[CV] ................................. C=1, score=0.856, total=   0.1s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] .................................. C=10, score=nan, total=   0.1s\n",
      "[CV] C=30 ............................................................\n",
      "[CV] ................................ C=30, score=0.837, total=   0.1s\n",
      "[CV] C=50 ............................................................\n",
      "[CV] ................................ C=50, score=0.862, total=   0.1s\n",
      "[CV] C=50 ............................................................\n",
      "[CV] .................................. C=50, score=nan, total=   0.1s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] ............................... C=100, score=0.856, total=   0.1s\n",
      "[CV] min_samples_split=5, n_estimators=30 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=30, score=0.808, total=   0.3s\n",
      "[CV] min_samples_split=5, n_estimators=30 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=30, score=0.825, total=   0.3s\n",
      "[CV] min_samples_split=5, n_estimators=50 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=50, score=0.830, total=   0.4s\n",
      "[CV] min_samples_split=5, n_estimators=70 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=70, score=0.824, total=   0.6s\n",
      "[CV] min_samples_split=5, n_estimators=100 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=100, score=0.822, total=   0.8s\n",
      "[CV] min_samples_split=5, n_estimators=150 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=150, score=0.834, total=   1.3s\n",
      "[CV] min_samples_split=5, n_estimators=150 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=150, score=0.831, total=   1.2s\n",
      "[CV] min_samples_split=5, n_estimators=200 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=200, score=0.833, total=   1.8s\n",
      "[CV] min_samples_split=10, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=50, score=0.831, total=   0.4s\n",
      "[CV] min_samples_split=10, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=50, score=0.832, total=   0.4s\n",
      "[CV] min_samples_split=10, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=70, score=0.837, total=   0.5s\n",
      "[CV] min_samples_split=10, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=100, score=0.833, total=   0.8s\n",
      "[CV] min_samples_split=10, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=150, score=0.830, total=   1.2s\n",
      "[CV] min_samples_split=10, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=200, score=0.840, total=   1.6s\n",
      "[CV] min_samples_split=10, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=200, score=0.835, total=   1.6s\n",
      "[CV] min_samples_split=15, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=70, score=0.829, total=   0.5s\n",
      "[CV] min_samples_split=15, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=100, score=0.845, total=   0.7s\n",
      "[CV] min_samples_split=15, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=100, score=0.840, total=   0.8s\n",
      "[CV] min_samples_split=15, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=150, score=0.840, total=   1.1s\n",
      "[CV] min_samples_split=15, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=200, score=0.837, total=   1.5s\n",
      "[CV] min_samples_split=20, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=30, score=0.835, total=   0.2s\n",
      "[CV] min_samples_split=20, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=30, score=0.837, total=   0.3s\n",
      "[CV] min_samples_split=20, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=50, score=0.840, total=   0.4s\n",
      "[CV] min_samples_split=20, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=50, score=0.840, total=   0.5s\n",
      "[CV] min_samples_split=20, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=70, score=0.841, total=   0.6s\n",
      "[CV] min_samples_split=20, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=100, score=0.837, total=   0.8s\n",
      "[CV] min_samples_split=20, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=150, score=0.835, total=   1.1s\n",
      "[CV] min_samples_split=20, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=200, score=0.847, total=   1.4s\n",
      "[CV] min_samples_split=20, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=200, score=0.840, total=   1.5s\n",
      "[CV] min_samples_split=25, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=70, score=0.835, total=   0.5s\n",
      "[CV] min_samples_split=25, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=100, score=0.848, total=   0.7s\n",
      "[CV] min_samples_split=25, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=100, score=0.839, total=   0.8s\n",
      "[CV] min_samples_split=25, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=150, score=0.842, total=   1.1s\n",
      "[CV] min_samples_split=25, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=200, score=0.842, total=   1.4s\n",
      "[CV] min_samples_split=30, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=30, score=0.838, total=   0.2s\n",
      "[CV] min_samples_split=30, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=30, score=0.834, total=   0.2s\n",
      "[CV] min_samples_split=30, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=50, score=0.835, total=   0.4s\n",
      "[CV] min_samples_split=30, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=50, score=0.837, total=   0.4s\n",
      "[CV] min_samples_split=30, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=70, score=0.842, total=   0.5s\n",
      "[CV] min_samples_split=30, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=100, score=0.842, total=   0.7s\n",
      "[CV] min_samples_split=30, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=150, score=0.836, total=   1.1s\n",
      "[CV] min_samples_split=30, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=200, score=0.851, total=   1.4s\n",
      "[CV] min_samples_split=30, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=200, score=0.840, total=   1.0s\n",
      "[CV] C=1, kernel=rbf .................................................\n",
      "[CV] ..................... C=1, kernel=rbf, score=0.824, total=   1.2s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] .................. C=1, kernel=linear, score=0.822, total=   0.9s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] .................. C=1, kernel=linear, score=0.824, total=   0.9s\n",
      "[CV] C=10, kernel=rbf ................................................[CV] C=0.001 .........................................................\n",
      "[CV] ............................. C=0.001, score=0.840, total=   0.0s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................... C=0.1, score=0.834, total=   0.1s\n",
      "[CV] C=1 .............................................................\n",
      "[CV] ................................. C=1, score=0.837, total=   0.1s\n",
      "[CV] C=1 .............................................................\n",
      "[CV] ................................. C=1, score=0.835, total=   0.1s\n",
      "[CV] C=10 ............................................................\n",
      "[CV] .................................. C=10, score=nan, total=   0.1s\n",
      "[CV] C=30 ............................................................\n",
      "[CV] .................................. C=30, score=nan, total=   0.1s\n",
      "[CV] C=50 ............................................................\n",
      "[CV] .................................. C=50, score=nan, total=   0.1s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] ............................... C=100, score=0.862, total=   0.1s\n",
      "[CV] C=100 ...........................................................\n",
      "[CV] ............................... C=100, score=0.836, total=   0.1s\n",
      "[CV] min_samples_split=5, n_estimators=30 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=30, score=0.827, total=   0.3s\n",
      "[CV] min_samples_split=5, n_estimators=50 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=50, score=0.820, total=   0.4s\n",
      "[CV] min_samples_split=5, n_estimators=70 ............................\n",
      "[CV]  min_samples_split=5, n_estimators=70, score=0.821, total=   0.6s\n",
      "[CV] min_samples_split=5, n_estimators=100 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=100, score=0.831, total=   0.8s\n",
      "[CV] min_samples_split=5, n_estimators=100 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=100, score=0.829, total=   0.8s\n",
      "[CV] min_samples_split=5, n_estimators=150 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=150, score=0.833, total=   1.3s\n",
      "[CV] min_samples_split=5, n_estimators=200 ...........................\n",
      "[CV]  min_samples_split=5, n_estimators=200, score=0.829, total=   1.8s\n",
      "[CV] min_samples_split=10, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=30, score=0.828, total=   0.3s\n",
      "[CV] min_samples_split=10, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=30, score=0.828, total=   0.3s\n",
      "[CV] min_samples_split=10, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=50, score=0.830, total=   0.5s\n",
      "[CV] min_samples_split=10, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=70, score=0.842, total=   0.6s\n",
      "[CV] min_samples_split=10, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=10, n_estimators=70, score=0.837, total=   0.6s\n",
      "[CV] min_samples_split=10, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=100, score=0.840, total=   0.8s\n",
      "[CV] min_samples_split=10, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=150, score=0.834, total=   1.2s\n",
      "[CV] min_samples_split=10, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=10, n_estimators=200, score=0.830, total=   1.5s\n",
      "[CV] min_samples_split=15, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=30, score=0.843, total=   0.2s\n",
      "[CV] min_samples_split=15, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=30, score=0.830, total=   0.2s\n",
      "[CV] min_samples_split=15, n_estimators=30 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=30, score=0.841, total=   0.2s\n",
      "[CV] min_samples_split=15, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=50, score=0.846, total=   0.4s\n",
      "[CV] min_samples_split=15, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=50, score=0.834, total=   0.4s\n",
      "[CV] min_samples_split=15, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=15, n_estimators=70, score=0.839, total=   0.5s\n",
      "[CV] min_samples_split=15, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=100, score=0.832, total=   0.8s\n",
      "[CV] min_samples_split=15, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=150, score=0.846, total=   1.1s\n",
      "[CV] min_samples_split=15, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=150, score=0.839, total=   1.1s\n",
      "[CV] min_samples_split=15, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=15, n_estimators=200, score=0.841, total=   1.6s\n",
      "[CV] min_samples_split=20, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=50, score=0.832, total=   0.7s\n",
      "[CV] min_samples_split=20, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=20, n_estimators=70, score=0.831, total=   0.7s\n",
      "[CV] min_samples_split=20, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=100, score=0.846, total=   0.8s\n",
      "[CV] min_samples_split=20, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=150, score=0.847, total=   1.1s\n",
      "[CV] min_samples_split=20, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=150, score=0.839, total=   1.1s\n",
      "[CV] min_samples_split=20, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=20, n_estimators=200, score=0.844, total=   1.4s\n",
      "[CV] min_samples_split=25, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=50, score=0.849, total=   0.4s\n",
      "[CV] min_samples_split=25, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=50, score=0.840, total=   0.4s\n",
      "[CV] min_samples_split=25, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=25, n_estimators=70, score=0.840, total=   0.5s\n",
      "[CV] min_samples_split=25, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=100, score=0.835, total=   0.7s\n",
      "[CV] min_samples_split=25, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=150, score=0.850, total=   1.1s\n",
      "[CV] min_samples_split=25, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=150, score=0.841, total=   1.1s\n",
      "[CV] min_samples_split=25, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=25, n_estimators=200, score=0.845, total=   1.4s\n",
      "[CV] min_samples_split=30, n_estimators=50 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=50, score=0.841, total=   0.4s\n",
      "[CV] min_samples_split=30, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=70, score=0.854, total=   0.5s\n",
      "[CV] min_samples_split=30, n_estimators=70 ...........................\n",
      "[CV]  min_samples_split=30, n_estimators=70, score=0.842, total=   0.5s\n",
      "[CV] min_samples_split=30, n_estimators=100 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=100, score=0.844, total=   0.7s\n",
      "[CV] min_samples_split=30, n_estimators=150 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=150, score=0.841, total=   1.1s\n",
      "[CV] min_samples_split=30, n_estimators=200 ..........................\n",
      "[CV]  min_samples_split=30, n_estimators=200, score=0.837, total=   1.4s\n",
      "[CV] C=1, kernel=rbf .................................................\n",
      "[CV] ..................... C=1, kernel=rbf, score=0.829, total=   1.2s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] .................. C=1, kernel=linear, score=0.852, total=   1.0s\n",
      "[CV] C=1, kernel=linear ..............................................\n",
      "[CV] .................. C=1, kernel=linear, score=0.847, total=   0.9s\n",
      "[CV] C=10, kernel=rbf ................................................\n",
      "[CV] .................... C=10, kernel=rbf, score=0.809, total=   1.3s\n",
      "[CV] C=10, kernel=linear ............................................."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  30 | elapsed:   12.6s remaining:    1.4s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   14.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.81858568 0.83433244 0.80341798 0.83441522 0.79962139 0.83451523]\n"
     ]
    }
   ],
   "source": [
    "score_table = list()\n",
    "for model_name, value in models_params.items():\n",
    "    clf = GridSearchCV(\n",
    "        value['model'],\n",
    "        value['params'],\n",
    "        cv=5,\n",
    "        scoring='roc_auc',\n",
    "        n_jobs=-1,\n",
    "        verbose=10,\n",
    "    )\n",
    "    clf.fit(train, y)\n",
    "    print(clf.cv_results_['mean_test_score'])\n",
    "    score_table.append({\n",
    "        'model': model_name ,\n",
    "        'best_score': clf.best_score_ ,\n",
    "        'best_params': clf.best_params_,\n",
    "        'best_model': clf.best_estimator_\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a8e76c7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:44:41.167511Z",
     "iopub.status.busy": "2021-11-05T07:44:41.166827Z",
     "iopub.status.idle": "2021-11-05T07:44:41.171425Z",
     "shell.execute_reply": "2021-11-05T07:44:41.170765Z",
     "shell.execute_reply.started": "2021-11-05T07:26:12.725667Z"
    },
    "papermill": {
     "duration": 0.087649,
     "end_time": "2021-11-05T07:44:41.171568",
     "exception": false,
     "start_time": "2021-11-05T07:44:41.083919",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>logistic_regression</td>\n",
       "      <td>0.845123</td>\n",
       "      <td>{'C': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.842958</td>\n",
       "      <td>{'min_samples_split': 30, 'n_estimators': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.834515</td>\n",
       "      <td>{'C': 20, 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  best_score  \\\n",
       "0  logistic_regression    0.845123   \n",
       "1        random_forest    0.842958   \n",
       "2                  svm    0.834515   \n",
       "\n",
       "                                      best_params  \n",
       "0                                      {'C': 100}  \n",
       "1  {'min_samples_split': 30, 'n_estimators': 100}  \n",
       "2                   {'C': 20, 'kernel': 'linear'}  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(score_table, columns=['model', 'best_score', 'best_params'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "59c3206c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:44:41.319221Z",
     "iopub.status.busy": "2021-11-05T07:44:41.318513Z",
     "iopub.status.idle": "2021-11-05T07:44:41.329692Z",
     "shell.execute_reply": "2021-11-05T07:44:41.330697Z",
     "shell.execute_reply.started": "2021-11-05T07:26:13.212064Z"
    },
    "papermill": {
     "duration": 0.089205,
     "end_time": "2021-11-05T07:44:41.330982",
     "exception": false,
     "start_time": "2021-11-05T07:44:41.241777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = score_table[0]['best_model'].predict_proba(test)[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff06b68",
   "metadata": {
    "papermill": {
     "duration": 0.069367,
     "end_time": "2021-11-05T07:44:41.522565",
     "exception": false,
     "start_time": "2021-11-05T07:44:41.453198",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Градиентный бустинг"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbe4721",
   "metadata": {
    "papermill": {
     "duration": 0.069526,
     "end_time": "2021-11-05T07:44:41.662007",
     "exception": false,
     "start_time": "2021-11-05T07:44:41.592481",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b66b4aae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:44:41.808020Z",
     "iopub.status.busy": "2021-11-05T07:44:41.807104Z",
     "iopub.status.idle": "2021-11-05T07:44:41.908321Z",
     "shell.execute_reply": "2021-11-05T07:44:41.907702Z",
     "shell.execute_reply.started": "2021-11-05T07:26:39.15924Z"
    },
    "papermill": {
     "duration": 0.176181,
     "end_time": "2021-11-05T07:44:41.908482",
     "exception": false,
     "start_time": "2021-11-05T07:44:41.732301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "529e0d90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T07:44:42.053364Z",
     "iopub.status.busy": "2021-11-05T07:44:42.052669Z",
     "iopub.status.idle": "2021-11-05T08:04:41.273005Z",
     "shell.execute_reply": "2021-11-05T08:04:41.273590Z",
     "shell.execute_reply.started": "2021-11-05T07:26:41.422813Z"
    },
    "papermill": {
     "duration": 1199.294467,
     "end_time": "2021-11-05T08:04:41.273788",
     "exception": false,
     "start_time": "2021-11-05T07:44:41.979321",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:552: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 531, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 1417, in fit\n",
      "    for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 1041, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 777, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in __call__\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
      "    for func, args, kwargs in self.items]\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py\", line 764, in _logistic_regression_path\n",
      "    extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "  File \"/opt/conda/lib/python3.7/site-packages/sklearn/utils/optimize.py\", line 243, in _check_optimize_result\n",
      "    ).format(solver, result.status, result.message.decode(\"latin1\"))\n",
      "AttributeError: 'str' object has no attribute 'decode'\n",
      "\n",
      "  FitFailedWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   5 | elapsed: 19.0min remaining: 28.5min\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   5 | elapsed: 19.0min remaining: 12.7min\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 19.5min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 19.5min finished\n",
      "/opt/conda/lib/python3.7/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:04:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[0.8451232]\n",
      "{'booster': 'gblinear', 'n_estimators': 10000, 'updater': 'shotgun'}\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "#     'booster': ['gbtree'],\n",
    "#     'n_estimators' : [8, 10, 12],\n",
    "#     'eta': [0.07, 0.1, 0.12],\n",
    "#     'max_depth': [7, 10, 12],\n",
    "#     'min_child_weight': [6, 7, 8, 9],\n",
    "#     'gamma': [0.03, 0.05, 0.07],\n",
    "#     'colsample_bytree': [0.25, 0.3, 0.35],\n",
    "    'booster': ['gblinear'],\n",
    "    'n_estimators': [10000],\n",
    "    'updater': ['shotgun']\n",
    "}\n",
    "clf = GridSearchCV(\n",
    "    xgboost.XGBClassifier(),\n",
    "    params,\n",
    "    cv=5,\n",
    "    scoring='roc_auc',\n",
    "    n_jobs=-1,\n",
    "    verbose=10\n",
    ")\n",
    "clf.fit(train, y)\n",
    "print(clf.cv_results_['mean_test_score'])\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "de60a653",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:41.424579Z",
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     "iopub.status.idle": "2021-11-05T08:04:41.426956Z",
     "shell.execute_reply": "2021-11-05T08:04:41.427491Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.34089Z"
    },
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     "end_time": "2021-11-05T08:04:41.427672",
     "exception": false,
     "start_time": "2021-11-05T08:04:41.346123",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8451232033979081"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(clf.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7c1b1d",
   "metadata": {
    "papermill": {
     "duration": 0.072013,
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     "start_time": "2021-11-05T08:04:41.500754",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Результаты предыдущих испытаний"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2a2b3ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:41.739418Z",
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     "exception": false,
     "start_time": "2021-11-05T08:04:41.644780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>best_params</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'booster': 'gbtree', 'colsample_bytree': 0.35, 'eta': 0.07, 'gamma': 0.07, 'max_depth': 12, 'min_child_weight': 9, 'n_estimators': 10}</td>\n",
       "      <td>0.840954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'colsample_bytree': 0.3, 'eta': 0.05, 'gamma': 0.0, 'max_depth': 20, 'min_child_weight': 5, 'n_estimators': 10}</td>\n",
       "      <td>0.841446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'colsample_bytree': 0.3, 'eta': 0.1, 'gamma': 0.05, 'max_depth': 10, 'min_child_weight': 7, 'n_estimators': 10}</td>\n",
       "      <td>0.842736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'booster': 'gbtree', 'colsample_bytree': 0.35, 'eta': 0.12, 'gamma': 0.07, 'max_depth': 7, 'min_child_weight': 9, 'n_estimators': 10}</td>\n",
       "      <td>0.843688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'booster': 'gblinear', 'eta': 0.15, 'lambda': 0, 'n_estimators': 500, 'updater': 'coord_descent'}</td>\n",
       "      <td>0.844828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'booster': 'gblinear', 'n_estimators': 500}</td>\n",
       "      <td>0.844852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'booster': 'gblinear', 'n_estimators': 1000, 'updater': 'shotgun'}</td>\n",
       "      <td>0.844947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                               best_params  \\\n",
       "0  {'booster': 'gbtree', 'colsample_bytree': 0.35, 'eta': 0.07, 'gamma': 0.07, 'max_depth': 12, 'min_child_weight': 9, 'n_estimators': 10}   \n",
       "1                         {'colsample_bytree': 0.3, 'eta': 0.05, 'gamma': 0.0, 'max_depth': 20, 'min_child_weight': 5, 'n_estimators': 10}   \n",
       "2                         {'colsample_bytree': 0.3, 'eta': 0.1, 'gamma': 0.05, 'max_depth': 10, 'min_child_weight': 7, 'n_estimators': 10}   \n",
       "3   {'booster': 'gbtree', 'colsample_bytree': 0.35, 'eta': 0.12, 'gamma': 0.07, 'max_depth': 7, 'min_child_weight': 9, 'n_estimators': 10}   \n",
       "4                                       {'booster': 'gblinear', 'eta': 0.15, 'lambda': 0, 'n_estimators': 500, 'updater': 'coord_descent'}   \n",
       "5                                                                                             {'booster': 'gblinear', 'n_estimators': 500}   \n",
       "6                                                                      {'booster': 'gblinear', 'n_estimators': 1000, 'updater': 'shotgun'}   \n",
       "\n",
       "   mean_test_score  \n",
       "0         0.840954  \n",
       "1         0.841446  \n",
       "2         0.842736  \n",
       "3         0.843688  \n",
       "4         0.844828  \n",
       "5         0.844852  \n",
       "6         0.844947  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_colwidth = 200\n",
    "pd.DataFrame([\n",
    "    [{'booster': 'gbtree', 'colsample_bytree': 0.35, 'eta': 0.07, 'gamma': 0.07, 'max_depth': 12, 'min_child_weight': 9, 'n_estimators': 10}, 0.8409537799837079],\n",
    "    [{'colsample_bytree': 0.3, 'eta': 0.05, 'gamma': 0.0, 'max_depth': 20, 'min_child_weight': 5, 'n_estimators': 10}, 0.8414464698372527],\n",
    "    [{'colsample_bytree': 0.3, 'eta': 0.1, 'gamma': 0.05, 'max_depth': 10, 'min_child_weight': 7, 'n_estimators': 10}, 0.842736281878973],\n",
    "    [{'booster': 'gbtree', 'colsample_bytree': 0.35, 'eta': 0.12, 'gamma': 0.07, 'max_depth': 7, 'min_child_weight': 9, 'n_estimators': 10}, 0.8436879028707965],\n",
    "    [{'booster': 'gblinear', 'eta': 0.15, 'lambda': 0, 'n_estimators': 500, 'updater': 'coord_descent'}, 0.844827631927552],\n",
    "    [{'booster': 'gblinear', 'n_estimators': 500}, 0.8448524559481427],\n",
    "    [{'booster': 'gblinear', 'n_estimators': 1000, 'updater': 'shotgun'}, 0.8449470472449274]\n",
    "], columns=['best_params', 'mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "05a4cd39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:41.901523Z",
     "iopub.status.busy": "2021-11-05T08:04:41.900709Z",
     "iopub.status.idle": "2021-11-05T08:04:41.910374Z",
     "shell.execute_reply": "2021-11-05T08:04:41.911008Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.346289Z"
    },
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     "duration": 0.094514,
     "end_time": "2021-11-05T08:04:41.911203",
     "exception": false,
     "start_time": "2021-11-05T08:04:41.816689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = clf.best_estimator_.predict_proba(test)[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf5cc59",
   "metadata": {
    "papermill": {
     "duration": 0.072738,
     "end_time": "2021-11-05T08:04:42.060308",
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     "start_time": "2021-11-05T08:04:41.987570",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "821871aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:42.215423Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d7702aa7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:42.682703Z",
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     "shell.execute_reply": "2021-11-05T08:04:42.693400Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.350476Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    cb_train[feature_cols], y, train_size=0.8, random_state=1, stratify=y\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ad679030",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.035992\n",
      "0:\tlearn: 0.6692203\ttotal: 64.1ms\tremaining: 32s\n",
      "1:\tlearn: 0.6490468\ttotal: 74.5ms\tremaining: 18.6s\n",
      "2:\tlearn: 0.6289092\ttotal: 85ms\tremaining: 14.1s\n",
      "3:\tlearn: 0.6106647\ttotal: 95.7ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.5943398\ttotal: 106ms\tremaining: 10.5s\n",
      "5:\tlearn: 0.5800945\ttotal: 116ms\tremaining: 9.53s\n",
      "6:\tlearn: 0.5665614\ttotal: 123ms\tremaining: 8.67s\n",
      "7:\tlearn: 0.5560929\ttotal: 127ms\tremaining: 7.82s\n",
      "8:\tlearn: 0.5452717\ttotal: 135ms\tremaining: 7.36s\n",
      "9:\tlearn: 0.5360330\ttotal: 144ms\tremaining: 7.03s\n",
      "10:\tlearn: 0.5267713\ttotal: 154ms\tremaining: 6.85s\n",
      "11:\tlearn: 0.5175953\ttotal: 163ms\tremaining: 6.62s\n",
      "12:\tlearn: 0.5099213\ttotal: 172ms\tremaining: 6.45s\n",
      "13:\tlearn: 0.5030690\ttotal: 179ms\tremaining: 6.22s\n",
      "14:\tlearn: 0.4960028\ttotal: 189ms\tremaining: 6.1s\n",
      "15:\tlearn: 0.4892920\ttotal: 202ms\tremaining: 6.09s\n",
      "16:\tlearn: 0.4832267\ttotal: 211ms\tremaining: 6s\n",
      "17:\tlearn: 0.4798925\ttotal: 215ms\tremaining: 5.77s\n",
      "18:\tlearn: 0.4751820\ttotal: 226ms\tremaining: 5.71s\n",
      "19:\tlearn: 0.4705036\ttotal: 235ms\tremaining: 5.65s\n",
      "20:\tlearn: 0.4661229\ttotal: 245ms\tremaining: 5.59s\n",
      "21:\tlearn: 0.4618200\ttotal: 255ms\tremaining: 5.54s\n",
      "22:\tlearn: 0.4581076\ttotal: 265ms\tremaining: 5.5s\n",
      "23:\tlearn: 0.4564180\ttotal: 270ms\tremaining: 5.35s\n",
      "24:\tlearn: 0.4533219\ttotal: 278ms\tremaining: 5.27s\n",
      "25:\tlearn: 0.4503108\ttotal: 287ms\tremaining: 5.23s\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8951667996474704, 0.8499097472924187)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost_model = catboost.CatBoostClassifier(n_estimators=500, cat_features=cat_cols)\n",
    "boost_model.fit(X_train, y_train)\n",
    "y_train_pred = boost_model.predict_proba(X_train)[:, 1]\n",
    "y_valid_pred = boost_model.predict_proba(X_valid)[:, 1]\n",
    "roc_auc_score(y_train, y_train_pred), roc_auc_score(y_valid, y_valid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd760b9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:47.939595Z",
     "iopub.status.busy": "2021-11-05T08:04:47.938474Z",
     "iopub.status.idle": "2021-11-05T08:04:47.943033Z",
     "shell.execute_reply": "2021-11-05T08:04:47.942478Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.355824Z"
    },
    "papermill": {
     "duration": 0.089658,
     "end_time": "2021-11-05T08:04:47.943184",
     "exception": false,
     "start_time": "2021-11-05T08:04:47.853526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# boost_model.fit(cb_train[feature_cols], y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0124500",
   "metadata": {
    "papermill": {
     "duration": 0.081973,
     "end_time": "2021-11-05T08:04:48.106295",
     "exception": false,
     "start_time": "2021-11-05T08:04:48.024322",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Пробуем подобрать"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "60704273",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:04:48.282393Z",
     "iopub.status.busy": "2021-11-05T08:04:48.281691Z",
     "iopub.status.idle": "2021-11-05T08:05:06.541825Z",
     "shell.execute_reply": "2021-11-05T08:05:06.541131Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.359275Z"
    },
    "papermill": {
     "duration": 18.353199,
     "end_time": "2021-11-05T08:05:06.541968",
     "exception": false,
     "start_time": "2021-11-05T08:04:48.188769",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "bestTest = 0.4087138728\n",
      "bestIteration = 109\n",
      "\n",
      "0:\tloss: 0.4087139\tbest: 0.4087139 (0)\ttotal: 618ms\tremaining: 16.1s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "1:\tloss: 0.4091087\tbest: 0.4087139 (0)\ttotal: 1.33s\tremaining: 16.6s\n",
      "\n",
      "bestTest = 0.4100869148\n",
      "bestIteration = 58\n",
      "\n",
      "2:\tloss: 0.4100869\tbest: 0.4087139 (0)\ttotal: 1.94s\tremaining: 15.6s\n",
      "\n",
      "bestTest = 0.4086394265\n",
      "bestIteration = 110\n",
      "\n",
      "3:\tloss: 0.4086394\tbest: 0.4086394 (3)\ttotal: 2.51s\tremaining: 14.4s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "4:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 3.1s\tremaining: 13.7s\n",
      "\n",
      "bestTest = 0.4100634663\n",
      "bestIteration = 111\n",
      "\n",
      "5:\tloss: 0.4100635\tbest: 0.4086394 (3)\ttotal: 3.68s\tremaining: 12.9s\n",
      "\n",
      "bestTest = 0.4086394265\n",
      "bestIteration = 110\n",
      "\n",
      "6:\tloss: 0.4086394\tbest: 0.4086394 (3)\ttotal: 4.29s\tremaining: 12.3s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "7:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 4.9s\tremaining: 11.6s\n",
      "\n",
      "bestTest = 0.4100634663\n",
      "bestIteration = 111\n",
      "\n",
      "8:\tloss: 0.4100635\tbest: 0.4086394 (3)\ttotal: 5.58s\tremaining: 11.2s\n",
      "\n",
      "bestTest = 0.4087138728\n",
      "bestIteration = 109\n",
      "\n",
      "9:\tloss: 0.4087139\tbest: 0.4086394 (3)\ttotal: 6.08s\tremaining: 10.3s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "10:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 6.59s\tremaining: 9.58s\n",
      "\n",
      "bestTest = 0.4100869148\n",
      "bestIteration = 58\n",
      "\n",
      "11:\tloss: 0.4100869\tbest: 0.4086394 (3)\ttotal: 7.08s\tremaining: 8.85s\n",
      "\n",
      "bestTest = 0.4086394265\n",
      "bestIteration = 110\n",
      "\n",
      "12:\tloss: 0.4086394\tbest: 0.4086394 (3)\ttotal: 7.67s\tremaining: 8.26s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "13:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 8.24s\tremaining: 7.66s\n",
      "\n",
      "bestTest = 0.4100634663\n",
      "bestIteration = 111\n",
      "\n",
      "14:\tloss: 0.4100635\tbest: 0.4086394 (3)\ttotal: 8.82s\tremaining: 7.05s\n",
      "\n",
      "bestTest = 0.4086394265\n",
      "bestIteration = 110\n",
      "\n",
      "15:\tloss: 0.4086394\tbest: 0.4086394 (3)\ttotal: 9.41s\tremaining: 6.47s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "16:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 10.1s\tremaining: 5.92s\n",
      "\n",
      "bestTest = 0.4100634663\n",
      "bestIteration = 111\n",
      "\n",
      "17:\tloss: 0.4100635\tbest: 0.4086394 (3)\ttotal: 10.7s\tremaining: 5.35s\n",
      "\n",
      "bestTest = 0.4087138728\n",
      "bestIteration = 109\n",
      "\n",
      "18:\tloss: 0.4087139\tbest: 0.4086394 (3)\ttotal: 11.2s\tremaining: 4.72s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "19:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 11.7s\tremaining: 4.11s\n",
      "\n",
      "bestTest = 0.4100869148\n",
      "bestIteration = 58\n",
      "\n",
      "20:\tloss: 0.4100869\tbest: 0.4086394 (3)\ttotal: 12.3s\tremaining: 3.51s\n",
      "\n",
      "bestTest = 0.4086394265\n",
      "bestIteration = 110\n",
      "\n",
      "21:\tloss: 0.4086394\tbest: 0.4086394 (3)\ttotal: 12.9s\tremaining: 2.92s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "22:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 13.5s\tremaining: 2.34s\n",
      "\n",
      "bestTest = 0.4100634663\n",
      "bestIteration = 111\n",
      "\n",
      "23:\tloss: 0.4100635\tbest: 0.4086394 (3)\ttotal: 14s\tremaining: 1.76s\n",
      "\n",
      "bestTest = 0.4086394265\n",
      "bestIteration = 110\n",
      "\n",
      "24:\tloss: 0.4086394\tbest: 0.4086394 (3)\ttotal: 14.7s\tremaining: 1.17s\n",
      "\n",
      "bestTest = 0.4091086554\n",
      "bestIteration = 85\n",
      "\n",
      "25:\tloss: 0.4091087\tbest: 0.4086394 (3)\ttotal: 15.3s\tremaining: 589ms\n",
      "\n",
      "bestTest = 0.4100634663\n",
      "bestIteration = 111\n",
      "\n",
      "26:\tloss: 0.4100635\tbest: 0.4086394 (3)\ttotal: 16s\tremaining: 0us\n",
      "Estimating final quality...\n",
      "Training on fold [0/3]\n",
      "\n",
      "bestTest = 0.3996878295\n",
      "bestIteration = 94\n",
      "\n",
      "Training on fold [1/3]\n",
      "\n",
      "bestTest = 0.4344953153\n",
      "bestIteration = 98\n",
      "\n",
      "Training on fold [2/3]\n",
      "\n",
      "bestTest = 0.406667598\n",
      "bestIteration = 103\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "#     \"learning_rate\":[0.025, 0.03, 0.04],\n",
    "#     \"min_data_in_leaf\":[88, 90, 92],\n",
    "#     \"depth\": [3, 4, 5],\n",
    "#     \"iterations\":[187, 190, 193]\n",
    "# iter0\n",
    "#     'learning_rate': [0.01, 0.05, 0.1],\n",
    "#     'min_data_in_leaf': [10, 50, 100],\n",
    "#     'depth': [3, 5, 10],\n",
    "#     'iterations': [100, 200, 300]\n",
    "# iter1\n",
    "#     'learning_rate': [0.07, 0.1, 0.15],\n",
    "#     'min_data_in_leaf': [5, 10, 20],\n",
    "#     'depth': [4, 5, 7],\n",
    "#     'iterations': [50, 100, 150]\n",
    "# iter2\n",
    "#     'learning_rate': [0.05, 0.1, 0.15],\n",
    "#     'min_data_in_leaf': [10, 15, 20],\n",
    "#     'depth': [4, 5, 6, 7],\n",
    "#     'iterations': [100, 120, 150]\n",
    "# iter3\n",
    "    'learning_rate': [0.07, 0.1, 0.12],\n",
    "    'min_data_in_leaf': [10, 12, 14],\n",
    "    'depth': [5],\n",
    "    'iterations': [110, 120, 130]\n",
    "# iter4\n",
    "#     'learning_rate': [0.08, 0.1, 0.12],\n",
    "#     'min_data_in_leaf': [8, 9, 10, 12, 14],\n",
    "#     'depth': [4, 5, 6],\n",
    "#     'iterations': [100, 105, 110, 120, 130]\n",
    "\n",
    "}\n",
    "boost_model = catboost.CatBoostClassifier(cat_features=cat_cols, random_seed=1, verbose=False)\n",
    "result = boost_model.grid_search(params, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d9fdc88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:05:06.726200Z",
     "iopub.status.busy": "2021-11-05T08:05:06.725560Z",
     "iopub.status.idle": "2021-11-05T08:05:06.766507Z",
     "shell.execute_reply": "2021-11-05T08:05:06.765788Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.361084Z"
    },
    "papermill": {
     "duration": 0.133977,
     "end_time": "2021-11-05T08:05:06.766657",
     "exception": false,
     "start_time": "2021-11-05T08:05:06.632680",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.858785</td>\n",
       "      <td>0.854749</td>\n",
       "      <td>{'min_data_in_leaf': 10, 'depth': 5, 'iterations': 120, 'learning_rate': 0.07}</td>\n",
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       "          0         1  \\\n",
       "0  0.858785  0.854749   \n",
       "\n",
       "                                                                                2  \n",
       "0  {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 120, 'learning_rate': 0.07}  "
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[\n",
    "    roc_auc_score(y_train, boost_model.predict_proba(X_train)[:, 1]),\n",
    "    roc_auc_score(y_valid, boost_model.predict_proba(X_valid)[:, 1]),\n",
    "    result['params'],\n",
    "]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78ca535",
   "metadata": {
    "papermill": {
     "duration": 0.092752,
     "end_time": "2021-11-05T08:05:06.951799",
     "exception": false,
     "start_time": "2021-11-05T08:05:06.859047",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Некоторые старые результаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bbcd1413",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:05:07.153815Z",
     "iopub.status.busy": "2021-11-05T08:05:07.152814Z",
     "iopub.status.idle": "2021-11-05T08:05:07.156430Z",
     "shell.execute_reply": "2021-11-05T08:05:07.156955Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.363191Z"
    },
    "papermill": {
     "duration": 0.113018,
     "end_time": "2021-11-05T08:05:07.157140",
     "exception": false,
     "start_time": "2021-11-05T08:05:07.044122",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.859125</td>\n",
       "      <td>0.855956</td>\n",
       "      <td>{'min_data_in_leaf': 10, 'depth': 5, 'iterations': 100, 'learning_rate': 0.1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.855730</td>\n",
       "      <td>0.855295</td>\n",
       "      <td>{'min_data_in_leaf': 5, 'depth': 4, 'iterations': 150, 'learning_rate': 0.07}</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.859285</td>\n",
       "      <td>0.856213</td>\n",
       "      <td>{'min_data_in_leaf': 10, 'depth': 5, 'iterations': 120, 'learning_rate': 0.1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.859223</td>\n",
       "      <td>0.856096</td>\n",
       "      <td>{'min_data_in_leaf': 10, 'depth': 5, 'iterations': 110, 'learning_rate': 0.1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.859723</td>\n",
       "      <td>0.856381</td>\n",
       "      <td>{'min_data_in_leaf': 8, 'depth': 6, 'iterations': 105, 'learning_rate': 0.08}</td>\n",
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       "          0         1  \\\n",
       "0  0.859125  0.855956   \n",
       "1  0.855730  0.855295   \n",
       "2  0.859285  0.856213   \n",
       "3  0.859223  0.856096   \n",
       "4  0.859723  0.856381   \n",
       "\n",
       "                                                                               2  \n",
       "0  {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 100, 'learning_rate': 0.1}  \n",
       "1  {'min_data_in_leaf': 5, 'depth': 4, 'iterations': 150, 'learning_rate': 0.07}  \n",
       "2  {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 120, 'learning_rate': 0.1}  \n",
       "3  {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 110, 'learning_rate': 0.1}  \n",
       "4  {'min_data_in_leaf': 8, 'depth': 6, 'iterations': 105, 'learning_rate': 0.08}  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([\n",
    "    [0.859125, 0.855956, {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 100, 'learning_rate': 0.1}],\n",
    "    [0.85573, 0.855295, {'min_data_in_leaf': 5, 'depth': 4, 'iterations': 150, 'learning_rate': 0.07}],\n",
    "    [0.859285, 0.856213, {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 120, 'learning_rate': 0.1}],\n",
    "    [0.859223, 0.856096, {'min_data_in_leaf': 10, 'depth': 5, 'iterations': 110, 'learning_rate': 0.1}],\n",
    "    [0.859723, 0.856381, {'min_data_in_leaf': 8, 'depth': 6, 'iterations': 105, 'learning_rate': 0.08}],\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846101c4",
   "metadata": {
    "papermill": {
     "duration": 0.092664,
     "end_time": "2021-11-05T08:05:07.342352",
     "exception": false,
     "start_time": "2021-11-05T08:05:07.249688",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Предсказания"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc4c821",
   "metadata": {
    "papermill": {
     "duration": 0.090758,
     "end_time": "2021-11-05T08:05:07.524507",
     "exception": false,
     "start_time": "2021-11-05T08:05:07.433749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f267f29b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:05:07.720261Z",
     "iopub.status.busy": "2021-11-05T08:05:07.715342Z",
     "iopub.status.idle": "2021-11-05T08:05:07.724697Z",
     "shell.execute_reply": "2021-11-05T08:05:07.723985Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.364997Z"
    },
    "papermill": {
     "duration": 0.108764,
     "end_time": "2021-11-05T08:05:07.724840",
     "exception": false,
     "start_time": "2021-11-05T08:05:07.616076",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = boost_model.predict_proba(cb_test)[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7e2cfdd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-05T08:05:07.914077Z",
     "iopub.status.busy": "2021-11-05T08:05:07.913212Z",
     "iopub.status.idle": "2021-11-05T08:05:07.938558Z",
     "shell.execute_reply": "2021-11-05T08:05:07.937829Z",
     "shell.execute_reply.started": "2021-11-05T07:39:19.366594Z"
    },
    "papermill": {
     "duration": 0.122254,
     "end_time": "2021-11-05T08:05:07.938699",
     "exception": false,
     "start_time": "2021-11-05T08:05:07.816445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\n",
    "    '/kaggle/input/advanced-dls-spring-2021/submission.csv',\n",
    "    index_col='Id'\n",
    ")\n",
    "submission['Churn'] = y_pred\n",
    "submission.to_csv('my_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ede9715a",
   "metadata": {
    "papermill": {
     "duration": 0.091015,
     "end_time": "2021-11-05T08:05:08.121185",
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