Titanic_Prediction.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3136,"databundleVersionId":26502,"sourceType":"competition"}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:51.275570Z","iopub.execute_input":"2024-05-04T13:08:51.276288Z","iopub.status.idle":"2024-05-04T13:08:51.285252Z","shell.execute_reply.started":"2024-05-04T13:08:51.276250Z","shell.execute_reply":"2024-05-04T13:08:51.283787Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:52.083488Z","iopub.execute_input":"2024-05-04T13:08:52.083872Z","iopub.status.idle":"2024-05-04T13:08:52.088387Z","shell.execute_reply.started":"2024-05-04T13:08:52.083843Z","shell.execute_reply":"2024-05-04T13:08:52.087383Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df_train = pd.read_csv('/kaggle/input/titanic/train.csv')\ndf_train.sample(5)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:52.689366Z","iopub.execute_input":"2024-05-04T13:08:52.689738Z","iopub.status.idle":"2024-05-04T13:08:52.751311Z","shell.execute_reply.started":"2024-05-04T13:08:52.689710Z","shell.execute_reply":"2024-05-04T13:08:52.749926Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"     PassengerId  Survived  Pclass                          Name     Sex  \\\n672          673         0       2   Mitchell, Mr. Henry Michael    male   \n474          475         0       3   Strandberg, Miss. Ida Sofia  female   \n758          759         0       3  Theobald, Mr. Thomas Leonard    male   \n796          797         1       1   Leader, Dr. Alice (Farnham)  female   \n76            77         0       3             Staneff, Mr. Ivan    male   \n\n      Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n672  70.0      0      0  C.A. 24580  10.5000   NaN        S  \n474  22.0      0      0        7553   9.8375   NaN        S  \n758  34.0      0      0      363294   8.0500   NaN        S  \n796  49.0      0      0       17465  25.9292   D17        S  \n76    NaN      0      0      349208   7.8958   NaN        S  ","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>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>672</th>\n      <td>673</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Mitchell, Mr. Henry Michael</td>\n      <td>male</td>\n      <td>70.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>C.A. 24580</td>\n      <td>10.5000</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>475</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Strandberg, Miss. Ida Sofia</td>\n      <td>female</td>\n      <td>22.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7553</td>\n      <td>9.8375</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>758</th>\n      <td>759</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Theobald, Mr. Thomas Leonard</td>\n      <td>male</td>\n      <td>34.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>363294</td>\n      <td>8.0500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>796</th>\n      <td>797</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Leader, Dr. Alice (Farnham)</td>\n      <td>female</td>\n      <td>49.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17465</td>\n      <td>25.9292</td>\n      <td>D17</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>77</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Staneff, Mr. Ivan</td>\n      <td>male</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>349208</td>\n      <td>7.8958</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_test = pd.read_csv('/kaggle/input/titanic/test.csv')\ndf_test.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:53.326777Z","iopub.execute_input":"2024-05-04T13:08:53.328041Z","iopub.status.idle":"2024-05-04T13:08:53.357504Z","shell.execute_reply.started":"2024-05-04T13:08:53.327966Z","shell.execute_reply":"2024-05-04T13:08:53.356103Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"   PassengerId  Pclass                                          Name     Sex  \\\n0          892       3                              Kelly, Mr. James    male   \n1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n2          894       2                     Myles, Mr. Thomas Francis    male   \n3          895       3                              Wirz, Mr. Albert    male   \n4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n\n    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n0  34.5      0      0   330911   7.8292   NaN        Q  \n1  47.0      1      0   363272   7.0000   NaN        S  \n2  62.0      0      0   240276   9.6875   NaN        Q  \n3  27.0      0      0   315154   8.6625   NaN        S  \n4  22.0      1      1  3101298  12.2875   NaN        S  ","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>PassengerId</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>892</td>\n      <td>3</td>\n      <td>Kelly, Mr. James</td>\n      <td>male</td>\n      <td>34.5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>330911</td>\n      <td>7.8292</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>893</td>\n      <td>3</td>\n      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n      <td>female</td>\n      <td>47.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>363272</td>\n      <td>7.0000</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>894</td>\n      <td>2</td>\n      <td>Myles, Mr. Thomas Francis</td>\n      <td>male</td>\n      <td>62.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>240276</td>\n      <td>9.6875</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>895</td>\n      <td>3</td>\n      <td>Wirz, Mr. Albert</td>\n      <td>male</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>315154</td>\n      <td>8.6625</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>896</td>\n      <td>3</td>\n      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n      <td>female</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3101298</td>\n      <td>12.2875</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### We are supposed to predict if the passenger survived or not given the passenger id\n\n- Target Column: Survived\n- Model : Logistic Regression","metadata":{}},{"cell_type":"code","source":"df_train['Embarked'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:54.725385Z","iopub.execute_input":"2024-05-04T13:08:54.725851Z","iopub.status.idle":"2024-05-04T13:08:54.742369Z","shell.execute_reply.started":"2024-05-04T13:08:54.725818Z","shell.execute_reply":"2024-05-04T13:08:54.741099Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"Embarked\nS    644\nC    168\nQ     77\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"sns.distplot(df_train['Age'])","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:55.485249Z","iopub.execute_input":"2024-05-04T13:08:55.485693Z","iopub.status.idle":"2024-05-04T13:08:55.964419Z","shell.execute_reply.started":"2024-05-04T13:08:55.485654Z","shell.execute_reply":"2024-05-04T13:08:55.963173Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Age', ylabel='Density'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"sns.scatterplot(y = df_train['Age'], x = df_train['Fare'], hue = df_train['Survived'], style =df_train['Sex'])","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:56.308701Z","iopub.execute_input":"2024-05-04T13:08:56.309101Z","iopub.status.idle":"2024-05-04T13:08:56.897977Z","shell.execute_reply.started":"2024-05-04T13:08:56.309070Z","shell.execute_reply":"2024-05-04T13:08:56.896772Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Fare', ylabel='Age'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADz4UlEQVR4nOzdd3hU1dbA4d/0kknvhBBC770FVARBQESQIqDSRFRUVNBrr1evqJ9iVwSVKkUQEERABek9NKUTQk2vkzZ9vj82JAxJIIGEUPb7PHlkzjlzZk+CzMrea6+lcLvdbiRJkiRJkm5AyqoegCRJkiRJ0pWSgYwkSZIkSTcsGchIkiRJknTDkoGMJEmSJEk3LBnISJIkSZJ0w5KBjCRJkiRJNywZyEiSJEmSdMNSV/UAKpvL5SIhIQFvb28UCkVVD0eSJEmSpDJwu93k5ORQrVo1lMrS511u+kAmISGByMjIqh6GJEmSJElX4PTp01SvXr3U8zd9IOPt7Q2Ib4SPj08Vj0aSJEmSpLIwm81ERkYWfo6X5qYPZM4vJ/n4+MhARpIkSZJuMJdLC5HJvpIkSZIk3bBkICNJkiRJ0g1LBjKSJEmSJN2wbvocGUmSJEm6FKfTid1ur+ph3HI0Gg0qleqq7yMDGUmSJOmW5Ha7SUpKIisrq6qHcsvy8/MjLCzsquq8yUBGkiRJuiWdD2JCQkIwGo2yaOo15Ha7yc/PJyUlBYDw8PArvpcMZCRJkqRbjtPpLAxiAgMDq3o4tySDwQBASkoKISEhV7zMJJN9JUmSpFvO+ZwYo9FYxSO5tZ3//l9NjpIMZCRJkqRbllxOqloV8f2XS0vXO7sVLJmgUIIppKpHI0mSJEnXlSqdkXE6nbzxxhtER0djMBioXbs27777Lm63u/Aat9vNm2++SXh4OAaDgW7dunH06NEqHPU14nZDRjz8+QZ8fxfMuBe2fw/mxKoemSRJkiRdN6o0kPnwww/59ttv+eqrrzh48CAffvghH330EV9++WXhNR999BFffPEFkydPZtu2bXh5edGjRw8sFksVjvwayIyHKXfC9u8g+wykHobfn4dfHoGc5KoenSRJknQdWLt2LQqFotK3kI8cOZJ+/fpV6mtcqSoNZDZv3kzfvn3p3bs3NWvWZODAgdx9991s374dELMxn332Ga+//jp9+/alWbNmzJw5k4SEBJYsWVKVQ69c9nxY9zFYsoqfO7kZUg5e8yFJkiRJpUtNTWXs2LHUqFEDnU5HWFgYPXr0YNOmTZX6uh07diQxMRFfX99KfZ3rWZUGMh07dmT16tUcOXIEgL1797Jx40Z69eoFQHx8PElJSXTr1q3wOb6+vrRv354tW7aUeE+r1YrZbPb4uuEUZMKhpaWf3/OTWHqSJEmSrgsDBgxg9+7dzJgxgyNHjrB06VLuvPNO0tPTr+h+brcbh8Nx2eu0Wu1VF5S70VVpIPPyyy8zZMgQGjRogEajoWXLljz33HM89NBDgChWBBAaGurxvNDQ0MJzF5s4cSK+vr6FX5GRkZX7JiqFAlSa0k9rjXAL/6WVJEm6nmRlZbFhwwY+/PBDunTpQlRUFO3ateOVV17hvvvu48SJEygUCvbs2ePxHIVCwdq1a4GiJaIVK1bQunVrdDodP/74IwqFgkOHDnm83qeffkrt2rU9npeVlYXZbMZgMLBixQqP6xcvXoy3tzf5+fkAnD59mgceeAA/Pz8CAgLo27cvJ06cKLze6XQyYcIE/Pz8CAwM5MUXX/TIXb3eVGkg8/PPP/PTTz8xZ84cdu3axYwZM/j444+ZMWPGFd/zlVdeITs7u/Dr9OnTFTjia8QrGFoMK/18y0uckyRJkq4pk8mEyWRiyZIlWK3Wq7rXyy+/zAcffMDBgwcZOHAgbdq04aeffvK45qeffuLBBx8s9lwfHx/uvfde5syZU+z6fv36YTQasdvt9OjRA29vbzZs2MCmTZswmUz07NkTm80GwCeffML06dP58ccf2bhxIxkZGSxevPiq3ldlqtJA5j//+U/hrEzTpk0ZNmwY48ePZ+LEiQCEhYUBkJzsmdyanJxceO5iOp0OHx8fj68bjkoD7R+DgFrFz7UcBv7R135MkiRJUonUajXTp09nxowZ+Pn50alTJ1599VX27dtX7nv997//pXv37tSuXZuAgAAeeugh5s6dW3j+yJEjxMbGFq5cXOyhhx5iyZIlhbMvZrOZ5cuXF14/f/58XC4X33//PU2bNqVhw4ZMmzaNU6dOFc4OffbZZ7zyyiv079+fhg0bMnny5Os6B6dKA5n8/HyUSs8hqFQqXC4XANHR0YSFhbF69erC82azmW3bthETE3NNx3rN+VaHEb9Bv++g9l3QsC+MXA7d3gYvWU5bkiTpejJgwAASEhJYunQpPXv2ZO3atbRq1Yrp06eX6z5t2rTxeDxkyBBOnDjB1q1bATG70qpVKxo0aFDi8++55x40Gg1Ll4o8y19++QUfH5/CXNO9e/dy7NgxvL29C2eSAgICsFgsxMXFkZ2dTWJiIu3bty+8p1qtLjau60mVFsTr06cP//vf/6hRowaNGzdm9+7dTJo0iUceeQQQFf+ee+453nvvPerWrUt0dDRvvPEG1apVu263gVUo3whoMQQa3QdKFah1VT0iSZIkqRR6vZ7u3bvTvXt33njjDR599FHeeustNmzYAOCRZ1JaSX4vLy+Px2FhYXTt2pU5c+bQoUMH5syZw9ixY0sdg1arZeDAgcyZM4chQ4YwZ84cBg8ejFotPu5zc3Np3bp1seUqgODg4HK/5+tBlc7IfPnllwwcOJAnn3yShg0b8sILL/D444/z7rvvFl7z4osvMm7cOB577DHatm1Lbm4uK1euRK/XV+HIrzGtUQYxkiRJN5hGjRqRl5dXGCAkJhYVNL0w8fdyHnroIebPn8+WLVs4fvw4Q4YMuez1K1euZP/+/axZs8ZjGapVq1YcPXqUkJAQ6tSp4/F1fpNMeHg427ZtK3yOw+EgNja2zOO95tw3uezsbDfgzs7OruqhSJIkSdeJgoIC94EDB9wFBQVXfa+0tDR3ly5d3LNmzXLv3bvXffz4cffPP//sDg0NdT/yyCNut9vt7tChg/v22293HzhwwL127Vp3u3bt3ID777//drvdbvfff//tBtyZmZnF7m82m90Gg8HdvHlz91133eVxrqTnuVwud2RkpLt58+bu2rVre1yfl5fnrlu3rvvOO+90r1+/3n38+HH333//7R43bpz79OnTbrfb7f7ggw/cAQEB7sWLF7sPHjzoHjNmjNvb29vdt2/fq/5eXexSP4eyfn7LppGSJEmSdBVMJhPt27fn008/5Y477qBJkya88cYbjBkzhq+++gqAH3/8EYfDQevWrQtTJsrK29ubPn36sHfv3lKTfC+kUCgYOnRoidcbjUbWr19PjRo1CpN5R48ejcViKdwc8/zzzzNs2DBGjBhBTEwM3t7e3H///eX4jlxbCrf7Ot4cXgHMZjO+vr5kZ2ffmDuYJEmSpApnsViIj48nOjr61kpVuM5c6udQ1s9vOSMjSZIkSdINSwYykiRJkiTdsGQgI0mSJEnSDUsGMpIkSZIk3bBkICNJkiRJ0g1LBjKSJEmSJN2wZCAjSZIkSdINSwYykiRJkiTdsGQgI0mSJEnSDUsGMpIkSZJ0g/n666+pWbMmer2e9u3bs3379qoeUpWRgYwkSZIkXSGny82WuHR+3XOWLXHpOF2V3/Vn/vz5TJgwgbfeeotdu3bRvHlzevToQUpKSqW/9vVIXdUDkCRJkqQb0cp/E3ln2QESsy2Fx8J99bzVpxE9m4RX2utOmjSJMWPGMGrUKAAmT57M8uXL+fHHH3n55Zcr7XWvV3JGpgJZ7U5Opucxf8cpPvvzCFvi0kk2Wy7/REmSJOmGsvLfRMbO3uURxAAkZVsYO3sXK/9NrJTXtdlsxMbG0q1bt8JjSqWSbt26sWXLlkp5zeudnJGpIDaHk83H0xkzYyeO81OLq49SP8zEtJHtqOZnqNoBSpIkSRXC6XLzzrIDlLSI5AYUwDvLDtC9URgqpaJCXzstLQ2n00loaKjH8dDQUA4dOlShr3WjkDMyFSTZbOXxmbFFQcw5h5Ny+fTPIxTYHVU0MkmSJKkibY/PKDYTcyE3kJhtYXt8xrUb1C1MBjIVJPZUJjanq8RzS/acJT3Xdo1HJEmSJFWGlJyypQyU9bryCAoKQqVSkZyc7HE8OTmZsLCwCn+9G4EMZCpIWo611HN2pxu7s/Iz2SVJkqTKF+Ktr9DrykOr1dK6dWtWr15deMzlcrF69WpiYmIq/PVuBDJHpoK0jvIv9VzNQCNeWtU1HI0kSZJUWdpFBxDuqycp21JinowCCPPV0y46oFJef8KECYwYMYI2bdrQrl07PvvsM/Ly8gp3Md1qZCBTQSL9jbSN8mfHycxi597s05gQn4qPzCVJkqRrT6VU8FafRoydvQsFeAQz51N73+rTqMITfc8bPHgwqampvPnmmyQlJdGiRQtWrlxZLAH4ViGXlipIkLeOLx9syejbojFoxOxL7WATM0a1o23N0mdrJEmSpBtPzybhfPtwK8J8PX9JDfPV8+3DrSq1jgzA008/zcmTJ7FarWzbto327dtX6utdz+SMTAUK8zXwUs/6jL4tGofLjUGjJLgS1kglSZKkqtezSTjdG4WxPT6DlBwLId5iOamyZmKkkslApoJp1SpZM0aSJOkWoVIqiKkdWNXDuKXJpSVJkiRJkm5YMpCRJEmSJOmGJZeWbjB2p4vMfBsKFAR6aVGWYS3W6XKTkWfF7YZALy0qlYxfJUmSpJuDDGRuIGcy85m7/TS/7jmLWqlgSNsa9G1ZjXDf0nNyErIKWLTrDAtiz+Byu+nfsjoPtI0kQubxSJIkSTcBGchUggK7A5vDjUmnrrDs9TOZ+Qz8dgtJF3TT/mDlIRbtPsOMR9qVGMwkZhXw4NStnEjPLzz2+eqj/LLrDD8/HiOTkiVJkqQbnlxjqECZeTa2xafzzNw9jPxxO1//fYzTGfm43VfXnsDpdLFg5xmPIOa8I8m5bIlLL/F5fx1M9ghizjuTWcBv+xJwuWTbBEmSJOnGJmdkKkh2gY2pG47zzdq4wmN5NgenM/IZ373eVc1+ZOTbWLY3odTzC3ae4e7GoZh0mgvGY2fRrrOlPmfx7rMMbF2dAC9ducfjdrtRKGSdBEmSJKnqyUCmgqSYrYVBTHV/A2/c24gTaXnsPZPF1PXHGdwukkh/I1668n/LlQoFmksk6GrVSlQKz/NKBWjUl3iOSomS8gUjqTkWDiXl8EvsGbQqJQ+0i6RWkNcVBUOSJEmSVBGqdGmpZs2aKBSKYl9PPfUUABaLhaeeeorAwEBMJhMDBgwo1rr8erH+SCoAPgY179/flDeW/MvEFYf4/Z8kpm0+Qa/PN7BqfxIFNke57x3gpeWhDjVKPT88JgrDRU0pvfUahsdEXeI5NfHz0pZ5DMlmC+Pm7mbYD9tZsieBn2PPMPDbLbz320HSc0vv/C1JkiRVrPXr19OnTx+qVauGQqFgyZIlVT2kKlWlgcyOHTtITEws/Przzz8BGDRoEADjx49n2bJlLFiwgHXr1pGQkED//v2rcsilcp5LNxnUOpLvNxwnJcfzw93thv8s3FfseFkoFArubhRGswifYue6NAihWXXfEp/XrmYAnUqoONkmyp/b6gaVawyrD6aw9XhGseOLdp/lUFJOue4lSZJ003A5IX4D/LNQ/NflrPSXzMvLo3nz5nz99deV/lo3gipdWgoODvZ4/MEHH1C7dm06d+5MdnY2P/zwA3PmzKFr164ATJs2jYYNG7J161Y6dOhQ4j2tVitWa1GwYDabK+8NXOCOukG8D7StGcCPm+JLvMbpcrP7VBZRgV7lvn+Yr54pw9uw+1QW83ecRq1SMKxDFA2r+ZTazynER8+ng1vw79lsZm87hcvt5qF2NWgW6UdoObpxp+dambH5RKnnp2+Op02UPzqNqtRrJEmSbjoHlsLKl8B8QQ6jTzXo+SE0uq/SXrZXr1706tWr0u5/o7lucmRsNhuzZ89mwoQJKBQKYmNjsdvtdOvWrfCaBg0aUKNGDbZs2VJqIDNx4kTeeeedazXsQmG+eh5qXwOny82lNinlWsu/tFT0GgZ6NTXQpUEwChRlChxCfPR09dHTqU4QbkB/BcGG0+0m3176uPOsTpxyB5QkSbeSA0vh5+HARf/2mRPF8QdmVmowIxW5brZfL1myhKysLEaOHAlAUlISWq0WPz8/j+tCQ0NJSkoq9T6vvPIK2dnZhV+nT5+uxFEX8TNqmdC9HlGBRmoHm0q9rm1N/6t+Lb1GXe7ZD51GdUVBDICfQUOPRmGlnu/XMgLjFSQxS5Ik3ZBcTjETc3EQA0XHVr58TZaZpOsokPnhhx/o1asX1apVu6r76HQ6fHx8PL6ulUCTjiYRvrzbrzEl1cHr0zyckHIs6VwvtGoVwzvWxN+oKXauRoCR2+qUL99GkiTphnZys+dyUjFuMJ8V10mV7roIZE6ePMlff/3Fo48+WngsLCwMm81GVlaWx7XJycmEhZU+O3A9aBnpx5KnOnF73SCMWhU1Aoy8f38T3ry3Mf7Gsu8Uup5E+htY8lQnhraNxEevJsBLyxN31GLumA6yQrAkSbeW3DLuni3rddJVuS7WA6ZNm0ZISAi9e/cuPNa6dWs0Gg2rV69mwIABABw+fJhTp04RExNTVUMtE4NWTbPqfnz9YCvybA7USiXB3jd2rRWFQkFUoBdv3deYZ7rVRYmCAJMGjUom+EqSdIsxhVbsddJVqfJAxuVyMW3aNEaMGIFaXTQcX19fRo8ezYQJEwgICMDHx4dx48YRExNTaqLv9cbHoMHHUHw55kam16gu2aRSkiTpphfVUexOMidScp6MQpyP6lgpL5+bm8uxY8cKH8fHx7Nnzx4CAgKoUaP0mmM3qyoPZP766y9OnTrFI488Uuzcp59+ilKpZMCAAVitVnr06ME333xTBaOUJEmSpHOUKrHF+ufhgALPYOZcgmTPD8R1lWDnzp106dKl8PGECRMAGDFiBNOnT6+U17yeKdxX29HwOmc2m/H19SU7O/uaJv5KkiRJ1y+LxUJ8fDzR0dHo9Ve4CaPEOjIRIoiRW6/L5FI/h7J+flf5jIwkSZIk3ZAa3QcNeovdSbnJIicmqmOlzcRIJZOBjCRJkiRdKaUKom+v6lHc0q6L7deSJEmSJElXQgYykiRJkiTdsGQgU4mcThfZBXYsl+hTJEmSJEnSlZM5MpXA5XJzJjOfn3eeYdOxNEJ9dDx6ey3qhJjwu0Er+0qSJEnS9UgGMpXgaEouA77d7NHpeuX+ZF7sUZ9hMVF462+uInmSJEmSVFXk0lIFy8q38dqSfzyCmPP+74/DpOXaqmBUkiRJknRzkoFMBcvKt2MusNM6yh+/i7pFu92w40RGFY1MkiRJkm4+cmmpAlnsThwuN31bRJCRZ2NYhygK7E7eX36QnHMzNE7XpQspp+VYOZWZz8ajafgZNdxeN5hQbx1GXeX/qLLybSRlW1h7OBWAO+sHE+arl3k9kiRJ0nVLBjIVxGJ3suFoGk/MjvUIVhqGe/PZkBaMnb0Lm9NFu5oBpd4j2Wzh2Xm72Xq8aNZGoYAPBzSjd5NwvPSV9+PKyLXx2eojzNxysvDYBysPMSImime71SXA68bu3i1JklSZUlNTefPNN1m+fDnJycn4+/vTvHlz3nzzTTp16lTVw7upyUCmgqSYLYy9KIgBOJiYw+//JNKraRhhPnqCvUsOCJxOFz/vOO0RxIBYjnpx4T5a1fCnjt5UaeP/NyHbI4g5b8aWk3RrGMrt9YIr7bUlSZJudAMGDMBmszFjxgxq1apFcnIyq1evJj09vaqHdtOTOTIVZMvxdBylLBv9ti+RJzrX5vHOtfAxlLxjKTXXyo+b4gsfKxWe55fvS6Cy5FocfLc+rtTzUzYcJ9dqr7TXlyRJupFlZWWxYcMGPvzwQ7p06UJUVBTt2rXjlVde4b777iu85tFHHyU4OBgfHx+6du3K3r17ATGbExYWxvvvv194z82bN6PValm9enWVvKcbiZyRqSBpOaXvRrI6XBi1qksuz7jcoNeoePu+ekT4GbDanXjp1Kw/msrMLSdJMlsrY9gA2JxOMvNKD1Qy8mzYHW6Qq0vSdc5qd5KaYyU114pKqSDIpCPEW4daJX9nkyqPyWTCZDKxZMkSOnTogE5X/B/LQYMGYTAYWLFiBb6+vnz33XfcddddHDlyhODgYH788Uf69evH3XffTf369Rk2bBhPP/00d911VxW8oxuLDGQqSPtapee+1A42YdReuhuqSadi0gPNeX3Jv8Sl5gEiP6ZPs2r838Bm+JYyk1MRvPUa7qwfzIFEc4nn76wfgncl5udIUkUwF9hZti+Bd387gMXuAsDPqOHzIS3pEB2ATiM7EkuVQ61WM336dMaMGcPkyZNp1aoVnTt3ZsiQITRr1oyNGzeyfft2UlJSCoOcjz/+mCVLlrBw4UIee+wx7rnnHsaMGcNDDz1EmzZt8PLyYuLEiVX8zm4M8teUChIV6EWLSL8Sz719XyOCvfWXfH6ezcn4+XsLgxgQ+TFL9yaw90wWzar7VORwPWhUSga3jcS7hJ1RPno1g9tGyt9opevegQQzry3+tzCIAVEOYfT0HZzJLKjCkUm3ggEDBpCQkMDSpUvp2bMna9eupVWrVkyfPp29e/eSm5tLYGBg4eyNyWQiPj6euLiiZf2PP/4Yh8PBggUL+Omnn0qc2ZGKk59OFSTYW8fkh1sxsmMUeo34ttYNMTF7dDta1vC/7PNPpOWRZLaUeG7BzjNYHZfetn21Iv2NLH6qI13qB6NQiNmgLvWDWfRkRyL9DZX62pJ0tcwFdj5bfaTEcw6Xm7nbT1229IEkXS29Xk/37t1544032Lx5MyNHjuStt94iNzeX8PBw9uzZ4/F1+PBh/vOf/xQ+Py4ujoSEBFwuFydOnKi6N3KDkesFFSjM18ArvRry4u3B5GFAodYR5K2DgkywqEBf+qzKibT8Us/l25wev2VWBqVSQZ0Qb74Y2pKsfDsKBfgaNGVrp1CQCQol6H3FY3sB2PLBK7BSxyxJ5xXYncSn5ZV6/lBSDjaHC8NllnglqSI1atSIJUuW0KpVK5KSklCr1dSsWbPEa202Gw8//DCDBw+mfv36PProo/zzzz+EhIRc20HfgOSMTAXTWTMwrn+X4KzdBBkQH/KxM2H/YrCUnIMCUCvYq9RzJp0awzVa3/fWa4gMMFLd31j2IGbXbPh3kXh/9gKIXwfrPoS8tMofsCQBRq2KuiGllydoGuGLVi3/uZMqR3p6Ol27dmX27Nns27eP+Ph4FixYwEcffUTfvn3p1q0bMTEx9OvXjz/++IMTJ06wefNmXnvtNXbu3AnAa6+9RnZ2Nl988QUvvfQS9erV45FHHqnid3ZjkDMyFcmWB9unwO5ZsG8ePLgAEvfCX2+J8wG1IPr2Ep8aFehFZICB0xnF1/JH3xZNiM91uFbqcsHJLfDn6+Kx2wXe4TD/IfFnvxrQ9lHQXDo/SJKulrdew/hu9dh4bEuxc1qVkkFtqqO6uKaBJFUQk8lE+/bt+fTTT4mLi8NutxMZGcmYMWN49dVXUSgU/P7777z22muMGjWqcLv1HXfcQWhoKGvXruWzzz7j77//xsdHzNzPmjWL5s2b8+233zJ27NgqfofXN4Xb7b6pF47NZjO+vr5kZ2cX/gWpVNmnYfYgSD3oebzZYLj7f2AqvbDcyfQ8npqzi3/PipkbtVLBsA5RPNWlNkGXSRauMrnJsPQ5OPK75/FqrWDIT+BTrUqGJd16cix21hxM4Y1f/8VsES1BQn10fDGkJS2j/NCq5LKSVMRisRAfH090dDR6/XX67+st4FI/h7J+fstApjLkJMG3naBBbwhpAEoNNBkIxssn/abnWsm1OiiwO9GrlYT66DFor/OJs7w0mNYLqrWEU1vAmgNPbQOdLyjVoLrOxy/dNBxOF8k5VjLO1ZEJ8NIR6qNDoZCzMZInGchcHyoikJGfMBWtIBPi18P9k2HXTNj8JXiFiETY6M7gHVrqUx0OF5n5NqasP86Go2mYdGoe7lCDuxqGUt3feA3fRDnYC+DsTqh7N3iHiZmn356DM7tEAGMMgLDmMpiRrgm1SkmEn4EIP7nTTpJuFfLTpSJZ8+DwKtB5wbyh4DxXLdecAIvGQKuR0P1tMJQ8MxOXlseAbzeTe65TNsBbSw+wan8yHw1sdv0FMy4XnNkJB5dDQDT88brIkek/BbLPwNKnQaWDR1ZBeLOqHq0kSZJ0E5Jp/BVJ5wWRbcWOHWcJJf93TYfclBKfmpln45M/DnsEMedtjkvnZHrp27OrjFIpAhhLNoQ3B5035CTCvAfh16fA5YTAOmCU27AlSZKkyiEDmQrmxg1J/5R+wcnNJR42W+ysOVRykAOw/kgqbreb6y6lybc69HwfQpvAqBWg0ogcGbcLgurCg/PBN6KqRylJkiTdpOTSUgVJybFwOCmHWiobl/zYVpe+jVqjUuJwOT2OeWlVvHVfYwwaFc/O241OreKepuFU8zMQZNISaLoOtmX7Vge7RQRwrgtmlM7XlZEkSZKkSiJnZCpAstnCM3N3M+yH7Sw7UoAzquRaMSiUENm+xFOBJi33NQ8vdvzjB5oze+tJxs3dzdK9iSyIPcOo6TuYsj6OLXHppOdWXlfsMrNb4MRGmPuAaBDlHw1ak9iaPf0eSI+7/D0kSZIk6QrIQKYCrDmUwtbjGQBM2ZFBYqd3S07o7fkBmEretWTSaRh7Zx2qX9DXqF10APsTzOw7k13s+l92nQXgWGpuBbyDq+U+9wWENYNRv4tlJp23CGy4zpbDJEmSpJuGXFq6Sum5VmZsPlH4OCPPxsjfsvm233KCz6zC7+x63D4RKNqNETMVutLLqNcM8mL26PZsPJrKjpOZ3Ns0nHeXHyz1+j8OJNM0wocWkX7o1FVY7EtjgJq3wfBl4F9TFMEzhcHI30FrFAm/kiRJklQJZCBzlZxuN/k2z7yWYyl5dJ+WR5uo9tzXuBcPtK+Fvozt2P2MGjrUDsLboEGhVPBWn0ZM3XC8cMbnPLVSwR31gqgZ6MXKf5PwM2qoHWwizEePWnUNJtqcDijIAI1RBGcKJfhFQcoBOLUVwpuKxwbfyh+LJEmSdFVGjhxJVlYWS5YsqeqhlFuVBzJnz57lpZdeYsWKFeTn51OnTh2mTZtGmzZtAHC73bz11ltMnTqVrKwsOnXqxLfffkvdunWreOSCn0FDj8ahTN0QX+zczpNZPNCmRpmDmLQcKx+sOMTCXWcKjxk0Kt7r1wS9WsXaI6kAKBTw8aDmrNyfyMp/kwuv9dKqmDqiDW1q+lduOXanAxJ2w8w+0OcLUQwvfr2oleOwFF3XehR0ee2SbRkkSZIk6WpUaY5MZmYmnTp1QqPRsGLFCg4cOMAnn3yCv39RfslHH33EF198weTJk9m2bRteXl706NEDi8VyiTtfO1q1imExNfE3Fu8UXSPAyO11g8p0H7fbzfJ/Ej2CGIACu5OXftnHiI41OV9lvVPtIA4n53gEMQB5Nicjf9xBUlYlJwBbsmDDx2JH0uLHIe0oLBjhGcQAxE6DI6sqdyySJElVyOl0snPnTlauXMnOnTtxOp2Xf5JUoao0kPnwww+JjIxk2rRptGvXjujoaO6++25q164NiA/3zz77jNdff52+ffvSrFkzZs6cSUJCQqnTX1arFbPZ7PFV2SL9DSx5qhND20bio1cT4KXliTtqMfexDoSXsVR6So6Vb9YeK/Gcw+Vm35ksOtUKJMik5ck7azN3+6kSr7U5XWyOS7vi91ImXkHQ53OIvhOiOsGBxaJuTEk2fiJ2L0mSJN1k1qxZQ58+fXjiiSd4/fXXeeKJJ+jTpw9r1qyp1Ne98847GTduHM899xz+/v6EhoYydepU8vLyGDVqFN7e3tSpU4cVK1YAItgaPXo00dHRGAwG6tevz+eff37J13C5XEycOLHwOc2bN2fhwoWV+r6uVJUGMkuXLqVNmzYMGjSIkJAQWrZsydSpUwvPx8fHk5SURLdu3QqP+fr60r59e7Zs2VLiPSdOnIivr2/hV2RkZKW/D4VCQVSgF2/0acSycbfx8+MxPNihBnq1+PZmF9iJT83lQIKZMxn52BzFI3any01KTukzKUlmC58PbckvYztSzU9PVn4JlYPPOZGed/Vv6nK8w6DXB6Jqrzmx9OvMCWIpSpIk6SayZs0aXnzxRVJSPAuZpqSk8OKLL1Z6MDNjxgyCgoLYvn0748aNY+zYsQwaNIiOHTuya9cu7r77boYNG0Z+fj4ul4vq1auzYMECDhw4wJtvvsmrr77Kzz//XOr9J06cyMyZM5k8eTL79+9n/PjxPPzww6xbt65S39eVqNLu1+c7XU6YMIFBgwaxY8cOnn32WSZPnsyIESPYvHkznTp1IiEhgfDwohorDzzwAAqFgvnz5xe7p9VqxWotCgjMZjORkZGV3v3aXGBn9aFk/rvsAJnngoyY2oH8r18T3lq6nw1HxSyJQaNi7J21eah9DY9idhm5Vob9uJ39CSXPIH0xpAX3tRCl9lJzLAyZspW41JIDlu9HtKFbw9KbU1aItKOiRoxXMDR9AP56q+TrIjvA0Hll6vwtSZJ0rVxN92un00mfPn2KBTEXCg0NZenSpagqIV/xzjvvxOl0smHDhsLx+Pr60r9/f2bOnAlAUlIS4eHhbNmyhQ4dOhS7x9NPP01SUlLhLMuFyb5Wq5WAgAD++usvYmJiCp/z6KOPkp+fz5w5cyrsvVRE9+sqnZFxuVy0atWK999/n5YtW/LYY48xZswYJk+efMX31Ol0+Pj4eHxdC3tOZzF+/t7CIAbgvubVeGxWbGEQAyLnZdKfR1i06yxOZ9FyTIBJx2v3NCzx3sHeOlrX9L/gsZ5XS7m2mq+exuGV/J7z00VDyNwU0Ryyfk+x3FSS7u/IIEaSpJvK7t27LxnEACQnJ7N79+5KG0OzZkWNeFUqFYGBgTRt2rTwWGio+GX2/Di//vprWrduTXBwMCaTiSlTpnDqVMkpCseOHSM/P5/u3btjMpkKv2bOnElc3PVX4LRKA5nw8HAaNWrkcaxhw4aF39ywsDBA/IW4UHJycuG560FGrpUPVx7yOGbSqTHp1BxLKblg3Vd/HyP5oqWkptV9mfxwK8J8iqLSDrUC+PnxDkT4eXa+blszgM+HtCDYu2hW5/a6QeXKy7lixkDoPxWqtRS1YgLrwYjfPKsW+1aHofMhtHHljkWSJOkaS0srWx5iWa+7EhqN5wYThULhcUxxbneIy+Vi3rx5vPDCC4wePZo//viDPXv2MGrUKGw2W4n3zs0Vn1vLly9nz549hV8HDhy4LvNkqnT7dadOnTh8+LDHsSNHjhAVFQVAdHQ0YWFhrF69mhYtWgBiqmnbtm2MHTv2Wg+3VFani0NJOR7HQn10l8xVyS6wF6s/463X0KOOFy2e6ojZ4kCjUmJQAQoFKWYLwd66wr+cPgYNfZpVo110ADkFDrRqJQFeWnwMxXdPVQq/GvDQQjAEiC7YIQ1h4DSw54t+S3o/8CnecqEy2BxOMvJsuNzgrVfjrb8G34OCbM8aORYz6H1EJ3D9hccveixJ0g0vKKhsu1HLel1l27RpEx07duTJJ58sPHapmZVGjRqh0+k4deoUnTt3vhZDvCpVGsiMHz+ejh078v777/PAAw+wfft2pkyZwpQpUwARUT733HO899571K1bl+joaN544w2qVatGv379qnLoHlRKBRF+Bk5l5Bcey8izEe5b+rqrTq1Er7loQiwvFcWmLwhr8SAGn9rsOpHBxJWHOZKcSzVfPU93rcPdjcMIOpdbo1QqCPc1EF5Vn5MXLydVQZfrhKwCfth4nHnbT1Ngd9K5XjAv92pArSATGnUlTTimHYXt38Mdz4MpBLLPwqbPocMTsGESdH1dJEPnJMGa/8EdL4B/VOWMRZKka65ly5aEhIRcNkemZcuW13BUpatbty4zZ85k1apVREdHM2vWLHbs2EF0dHSJ13t7e/PCCy8wfvx4XC4Xt912G9nZ2WzatAkfHx9GjBhxjd/BpVXp0lLbtm1ZvHgxc+fOpUmTJrz77rt89tlnPPTQQ4XXvPjii4wbN47HHnuMtm3bkpuby8qVK8udnFWZQrz1jOvqWYY/M9+OXq0i0Etb4nOGtI30WBaiIBs2fQmbv8D517v8+W8io2bEciRZTPElZFt4dfG/fLn6KDkFpe9YupUkZRcw7Ift/LDxBHk2Jy43/H04lfu+2lR5O7dykmF6b9g+GVa+KvKE5g6Bxv3gp0GwexYsHA15abB4LOyeCbP6QW5q5YxHkqRrTqVS8cILL1zymueff75SEn2vxOOPP07//v0ZPHgw7du3Jz093WN2piTvvvsub7zxBhMnTqRhw4b07NmT5cuXlxr8VKUq3bV0LZQ16/lqpeeKOjA/bjrB+e9oswgf3u3XlMdnxZJkLioW161hCP+7vymhPhcFYymHYHovEnr+wD1LHCVusVYo4O/n76RmkFelvZcbxcp/k3hidmyJ53o1CePjQc3x0lXwpGNBFuyaBX++Lh4rlKKGTvOhUOtOWPKEaJR5/jjA/d9Bg96iiaYkSdeFq9m1dN6aNWv4+OOPPWZmQkNDef755+natWtFDfWmVhG7lqq8RcHNItCk47lu9RjWoSbx6XmE++rx1qkJ9dGz9OlOFNgcZFkcGDUqgkw6/EuaqQlpAI/8QVZiDln5JddmcbshPj3vlg9k3G43v+1LKPX8+iOpmC32ig9kDH7QejgUpMPGT4uClYZ9RHHA3pPgt/FFx3t+CA3uvWSzUEmSbkxdu3alc+fO7N69m7S0NIKCgmjZsuV1MxNzq5CBTAXy1mvw1mvwM2rYcDSN1lH+7DuTxc4TmVTzM1Aj0Iheoyw9IdeSDcfXoTW1vuTreGnl/yQKhQJ/Y8nLdgAmvRrl+Z4OFc2aC8dWex47+BuENYMDyzyPH/pNLDvJQEaSbkoqlaqwN6BUNao0R+ZmlJVvY8bmE4ybu5tXFv2D3enGS6dm3LzdTPh5L/Fp+RxLySn+RIsZ/v0Ffn+egLSdNIkoeRrNx6Amwt9Y4rlbzeC2pVdtHhFTszApukKZE0VOTNI+UKqgbg9xvF4P+PVJOH6ummf9e8Q64IkN8MujIrdGkiRJqnAykKlgKqWisI7LuiOpvPjLPl7/9V/cbvDRa7DYnby1dD+ZeRft39foIbgBqLQEZO7j00HNCLho+UmnVjJlWBtCvSvhA/oGFBlgZHy34l3QW0f5079VdVTKSpiRUaohtIkIYh5cKOrp3P0epB2B8Bbimvu/g/snw4AfRTBTrZV4niRJklThZLJvJcix2Fm06yxvLd1feKxFpB9P3lmbZ+btxmJ3sfaFEhJ2nTZIPQKmUDAFczYznx3HU9l+Mpu6od50bRBCuJ8erVx/LZSdbyPRbGHZngTMFjv3NA2ndoiJEO9K3NWWmyoaYQbWEQFoQRZknxbtGvJSwb+mSOy15kJmPHhXA6/AyhuPJEnlVhHJvtLVk8m+1ymVUoHpoiRTvUaJzeHC6RJxY4nho0pLmlddTmXks3HrUXy9NHSuG0yDan7sOpVFfGouoYosyDoGJzeL6rk1bwOfaqC+jmZpbHnig94YKIrBOR2QdUL82Su4Ql/K16jF16ilQc9rE6QCYAoWX+cZ/MQXiPox5+lMENYUSZIkqfLIQKaC5dscrDucygsL9wKiT1JqjpWtxzPQqJR8+kALvt8Yj6+x+Lc+2Wzh2Xm72Xo8o/CYQgGv9GrI0eQcWjfToJ0zEtIuqIas0oimjDXvAHXpya/XjC1PJMIuGC527DQfKpZdZtwLTQfDXa9XeDAjSZIk3bpkjkwFy8q3s/1EBm63yNWYMqw1r/RqAEB8Wh7+Xlre69eYAC/PGRSn08X8Hac8ghgQMzfv/36QMR3CqLnnY5Rpni0dcNph3oOQU/J27WvOng//LhIDX/Ei/P6CCGLsBSIR1lFybw9JkiRJuhJyRqYCncrI55HpOxjaLpKXezYgpnYg8Wl5uIGJ/ZvSIMwbf6OWiBKaOqbmWpm26USp9/Z2ZqE7/GvJJx1WSNh1fZTB9wqGez4SdVQO/gr75ovjfjVgxLIqaWMgSZIk3bzkjEwFycizMX7+Ho6l5PLubweJS81l8JQtfLDiEC6XmzvrBdEoSEXNAENRD6C8dLCLir8uN2Rd1Hqgur+B9/o1YerwNpg0btGMsTR5lddltdwMAaLv0IUa9RM5M5IkSdJVcbvdPPbYYwQEBKBQKNizZ0+VjOPEiRNV+vrnyUCmgmTk2Yg9mVn4eEHsGSx2F0lmCx+tOkx2bh66r1tCyn5wOUU7gm87wMmNYLdg1KpoV9O/8PnRQV4iiNlwnDEzd7L1jAX8LjHjEnGdFGRyOiBxD8zu73l88xewZ44o+idJkiRdsZUrVzJ9+nR+++03EhMTadKkSVUPqUrJQKaC5NsuMVsCmDNToSATpt0jCt9N7yUaDi5+HOx5+Bm1vHpPI86XPnm+ez2e/3kvJ9NFR+1Pt+aQdtvbJd886jaxg+l6YMmCVa+KnBi/GvDcP9Cwrzi35j1xXJIk6SZgs9nYuXMn56uYuN1udu7cic1WubmAcXFxhIeH07FjR8LCwlCrb+0sERnIVBBfgwb1JQqwBYeEgzEIrGZYNAbyM8DgDyN+E0sxQL0wbxY80ZE76gaRY3WQfkHRvAOJZqaerk7qfbMh6FwROK0JOj4DA7733A5clbyCYOA0qN9b5MT41YDeH0OLh2HUcvAKqeoRSpIkXTWbzcbzzz/PE088waRJk3C5XHzyySc88cQTPP/885UWzIwcOZJx48Zx6tQpFAoFNWvWxOVyMXHiRKKjozEYDDRv3pyFCxcWPmft2rUoFApWrVpFy5YtMRgMdO3alZSUFFasWEHDhg3x8fHhwQcfJD8/v/B5K1eu5LbbbsPPz4/AwEDuvfde4uLiLjm+f//9l169emEymQgNDWXYsGGkpVVu6oMsiFdB8q0OJq44xKytJ4ude+yOaEZ2jMaal4066ziBx5dg3DcL7v0Umj4AlszC/JEcix1FfjpnCzTsTshj3vbT7DmdVXivOiEmnmnvS496Pui0WhEYlHHbtbnATla+DacbfPRqAs+X8M9LB2OA2OsNIt9G513m2jQOuxV7XhaJdi/0GhWh3jpU+angHXrBNygD9H6glLGzJElV72oK4p0PYrZu3Vo4G1O3bl2OHj0KiF5wHTp04JNPPkGrrdiyGNnZ2XzxxRdMmTKFHTt2oFKpmDJlCrNnz+azzz6jbt26rF+/nieeeIJVq1bRuXNn1q5dS5cuXejQoQMff/wxRqORBx54gIiICHQ6HR988AG5ubncf//9/Oc//+Gll14C4JdffkGhUNCsWTNyc3N58803OXHiBHv27EGpVHLixAmio6PZvXs3LVq0ICsri3r16vHoo48yfPhwCgoKeOmll3A4HKxZs6bE9yML4l1HjDo1z9xVB6NOxYzNJ7DYXejUSt7t25hAk44hU7ZyKiMftVLBPY3689KgB4n46ymo3haWPw89PyBRG0V2WgI1Y9/Hv/FDbDlsom+LanSoFcDkdccBOJaSy1urrbRtXJ9w3+K7n0pzPDWXt5buZ8NRERk3rubD+/c3oalPPsplz0L3dyCkkQhiVv8Xmg6EGh0uG8xk5eShTtqDZudUEhr8h//blMmsfgF4r34ZRb9vipa8jAFX9o2VJEm6zuzbt48tW7Z4HDsfxIBYYtqyZQv79u2r8IaSvr6+eHt7o1KpCAsLw2q18v777/PXX38RExMDQK1atdi4cSPfffcdnTt3Lnzue++9R6dOnQAYPXo0r7zyCnFxcdSqVQuAgQMH8vfffxcGMgMGDPB47R9//JHg4GAOHDhQYl7OV199RcuWLXn//fc9nhMZGcmRI0eoV69ehX4vzpOBTAUK9tbzfPf6DOsQRZ7ViY9ezbHUXIb9sL3wGofLzdJ/0/k3yYsVw6ah+208xK+D6b0wDv4V/+3/h/7IUvRHlvLW6G3c9f1RnupSh1Y1/Nl1SiQTP3lnHUJ8yv4bxJnMfAZO3kLGBUtV+xPMpKYkodjwDhz7E87sgJHLYctXsHcu7J0D42JFuf1S5FjsaKwZeM3vDw4LbRwWfrz3NXzm3gd5qbh/GYNi8Cyx3CRJknSTaN26NUOGDGHevHmlXjN06FBat25d6WM5duwY+fn5dO/e3eO4zWajZcuWHseaNWtW+OfQ0FCMRmNhEHP+2PbtRZ9XR48e5c0332Tbtm2kpaXhcrkAOHXqVImBzN69e/n7778xmUzFzsXFxclA5kahVSupfq47dVqulfd+O1jidcfT8tiXpqDt3e/CzL6Qn4HvtNsLz2fd9iazd2eQmW/nu/XHebFHff49m83o26Lp3yqiXA0R/zqQ7BHEnPfumiRaPvA6gWd3ih5BkzsVnez6hmgpcAnpuTYWbk5i9B3/xX/Ni+jjVqCPWyFO6v1w9vo/1HLLtSRJNxmFQsGECROIjY31mIk5r27duowfPx6FohIa114kNzcXgOXLlxMR4VmnS6fznFHXaDSFf1YoFB6Pzx87H6wA9OnTh6ioKKZOnUq1atVwuVw0adKk1Pyf3Nxc+vTpw4cffljsXHh4ePneWDnIhIVKVGBzcjg5p9Tza045ILiRmAm58HnNR/GbsguTNiQDkJpjpWG4D38935ln7qpTlNtSBha7g78Pp/JI+zD8jUV/ae9v4o9KoeCx3804R67wfFKzIdBmlEhGvoTUXCtfbU5hsaMjBa0f9ziXNmAhxxU1ivJuJEmSbhJut5tJkyaVGMSAmMn49NNPuRYpqI0aNUKn03Hq1Cnq1Knj8RUZGXnF901PT+fw4cO8/vrr3HXXXTRs2JDMzMxLPqdVq1bs37+fmjVrFhuLl5fXJZ97NWQgU4nUSgXeutInvSL8DCLRd93/eRw3HP2VO8Pt1DrXHVutVGDQqKgRYMSgLd8kmlqp5OkO/jzvv5Gfh0Tib9Qwqk0Qb9Y7xaz7fHm9axiKDR97PunISsg+U0pnyyJeWhVRgUa6V3dhOLTI45xp6ydEaHPLNVZJkqQbQWxs7CWXlQDmzp1LbGxspY/F29ubF154gfHjxzNjxgzi4uLYtWsXX375JTNmzLji+/r7+xMYGMiUKVM4duwYa9asYcKECZd8zlNPPUVGRgZDhw5lx44dxMXFsWrVKkaNGoXT6bzisVyODGQqUZC3juEdSy5ip1IquL+BHla9BgcWA2BtNlzMguRnUH1Jf34aFI6vQUOPxmEEemlKvM/lqK1ZtDo7B6+1b1L3j2EsH1mbp6sdxv/3x4lY0p8WXhko49eKi1sOE/kslixR7ybzxCXvHWzS8fOQSCJ/HSCWpvR+WJqPBEAftwLjn69cXxWHJUmSKkCzZs2IiYnxWDqqW7du4Z8VCgUxMTEeOSmV6d133+WNN95g4sSJNGzYkJ49e7J8+XKio6Ov+J5KpZJ58+YRGxtLkyZNGD9+PP/3f/93yedUq1aNTZs24XQ6ufvuu2natCnPPfccfn5+KCtxx6rcfl3Jks0Wxs/fzea4omaQGpWCyQ+35vZaPmgTd8Ks+3F3f5fk2gNxZZ2h2qL7ya/Vi3U1nuTLrZl8+3BrogKvcFrOYcN1ZgfKWf3AaRN5L+eq6zqju6Ls/RGKqV3gtueh7SOQmwzTekGtrtDj/cvWpzGnJ6FaPxGvw0s4e/8vrE7U01e1Ed+/X4OHf4EaMWXexi1JknStXM32ayjagr1lyxaGDh3K+PHjmTRpEvPmzSMmJqZStl7fjCpi+7UMZK6BzDwrNoeLlBwrWQV2ogK9CPPRoVWrRDfovBTQemNVe5FithLkTCahQEmKw0Skv5HqAUZwua68Bsv5YGb6PYWH3DVicA6YjtorEHKTQGcqyonJPAkaY5mL7OVnJaN2OfjthIvawd7U9XVhVNpF3RgZxEiSdB262kAGRDCzb98+WrdujUKhwO12ExsbS7NmzWQQU0ayjswNICGrgAOJZpbsPotBo+L+lhEoQAQxIIrZnau1ogMiA9VALWoDta25kH0S/pgDWSeh/j1Q8wraETitKLNOexxS5KWgdttBrQG/ixLCytlF26hy485L5vZadVj6bzpWfzvNDOkU+NbFy1dd9F4vZMuD5APitUwhokdTyn7RPdunWvnenyRJUhXQarUedWIUCkWF142RLk8GMpUoIauAsbNj2XumqFHigtgzDGxdnXFd6hAVdInlIlsu7F8ES8cVHTvwq/iQH/k7BJRx7dOaA4eWw5Jzu4qC60NGPKTHwez74eHFxQOZ8shJgkWPoTi1GUXfOTT2i6bRkW/R7/2Rgrs+JqHWvUSGh3luF7flwtG/YOFIaDwQen0AmfEwow8E1Yehc2UwI0mSJJWJDGQqicvl5tc9Zz2CmPMWxp7h7kah+Htp8TGUksSbkwLLni1+3JwAf7wO908WbQTKwn2uLkCtrtB/CqQdgVn9LrsrqUxcDty5KSicdoJ+fZCgaq3g9DYA1ObTbD2WhNbLj2p+F1QhdjqKdkX9uwAyj0PKAdFQ0pIFTvvVj0uSJEm6JchdS5UkLdfKT9tOlXr+t32JFNgvsR0tYQ8Y/Eo+d/h3yE8v20B03tCwDzy8SAQ/pmCIbCtq1wxbcnWzMQC+1bENno8rpIkIQE5vA4WCnPbPs8yrPy+vSCAlx+r5HIMftHwY7n5PPD4be65bdhQM/7XcS1uSJElX6iZPE73uVcT3X87IVKDUHAsJWRYOJ+fQpJoPBbbSA5UCuxOnq4QfYH6mSP6150G3d0SPoq3fwomNRde4XeAqx558nTfU7lpUnE6lFT2eLipWl2y2cCazgDOZ+dQIMBLhZyhTKwSVEty9PoSfBoidUfd+hiEyhh1/ZgFgLSlg05ogsoPnsaB6oK28okmSJEnnna9qm5+fj8FQ9r51UsU632374irD5SEDmQqSmFXAM/N2s+OEqHw4oFUEXRuEsCD2TInXd64XjE590YRYbgr8+Zboc3Sexgi9PwG1Ho79JY6FNgF9OXdgXVxh96LH8Wm5DP9xO6czCgqP1Q42MX1UWyIDjKXfN+sUql9GoVDroP9UsbX72F+oN33G/w2eg1KpINzvon8knA5I3A0z+4jHKq0IgI79CStegZ7viwRgSZKkSqJSqfDz8yMlJQUAo9F4TVoKSILb7SY/P5+UlBT8/PxQqUrYFFJGMpCpAPk2Bx//cbgwiAGxdPTz4zGs/DeJHKvD4/o6ISba1gzwbDXgdsP+xZ5BDIA9XyT8Dp0LcatBoRSBjVfZtkaXRWqOlTEzYz2CGIC41Fyem7+HqcPbEOBVwlZClwtSD6M4G3vuLbhxe4ehPLAEANXxtbzctQ9epoueazXDnrlFy0mjfhfv/Y/X4egq6PKqDGQkSap0YWFhAIXBjHTt+fn5Ff4crpQMZCpAeq6NpXsTPI5ZHS5eWbSPuY914PsNx/nrYAp6jZL7W0YwqE1kCbMxybDx05JfwOWAU9ug3RPQYqhYgqnQ8Vs5llJyO4HYk5lk5NlKDmSUSlHwrv+UczuXtnD+9xl355cpqNsbtSEE48VtFYwBcOcrojhf6xFiO3nLYaDSQVTMJTtuS5IkVRSFQkF4eDghISHY7XKTwbWm0WiuaibmPBnIVACL3YndWTzf5UBiDv9dtp//9m3CI7dFo1IosDic/GfBHt7t19TzYpdDFKYrhTsvFcfd76PRF2+PfiVsDhcpZgtmiwOFAh6/oxY/bTtF7kWzRyBmnEqlM0Gd7hDcAFIPiWMGf9xtH8VoCqbURSlTMNw+oWjnlcEPmg8VOTKlFP5Ly7WSkWfD6XLjZ9QQ6q1HWY4u4JeVmwz5GSL/yBgA3uGy6WVVseWBLb+oKKPTIXa0eQVV6bCkm5NKpaqQD1SpalTprqW3334bhULh8dWgQYPC8xaLhaeeeorAwEBMJhMDBgwgOTm5CkdcMi+dGt8StlEPaBVBv5bVGfbDdu77ahO9v9zIy7/8wzN31SPw4hkOpRrCSu/LkRPekQ3xeeSVEGiUV1qula/WHKXbp+u454sN9Pp8AwcSzXz1YMti70OtVOBX2hZxED2WVr1WFMQAFGSi/GW0qDFzKRdvH9d7lxjEOF1u9p/NZuiUrdz96Xp6fb6B+77cxB8HkksMvMrN6RA7p6bdA990gMmdYGpXOPqH+ECVri1bHhz9ExaOEn+HzudUTe992f5fkiTdeqp8+3Xjxo1JTEws/Nq4sWh3zvjx41m2bBkLFixg3bp1JCQk0L9//yocbclCvXU8c1cdj2NBJi3dGoXy6uJ/SM0t2n58NCWXZ+buxnHxDI5CBR3HUSJTKKrINoyeuZPMfNtVjdXudDJv+ym+WHMMi91VeHzD0TQ+XHmI//So73H90HY1CDKV0mbA5YJTW4vyerq+Cfd/J/4cvw72zgO75arGC3A2q4AHvtvC0QuWv1JzrTwxO5bDSTlXfX+yT4kPyfRjRcdyEmHuYEg7evX3l8qnIFMEMSc2wMLR4r8z+ohgeekzYtZMkiTpnCoPZNRqNWFhYYVfQUFi6jg7O5sffviBSZMm0bVrV1q3bs20adPYvHkzW7dureJRe1KplHRvGMpz3eriYxCrdQNbV2fm5pMlXp9nc7Jy/0WzFVov8K0B937mWdU2qhP2h5cwbFEynw1ugb/x6vp3pJhtTF53vMRzBxNzCPTSotco0WuUPNG5Fs/cVQejrpQVSKUSojrB7c+LreJtH4EG98KAH6DpYGjxIGiurIfJhVb8k0heKVvZP151iOyCq1jbdrmKEo8v5nbD2g9EdWTp2tH7ib9DCgWc3CiKN55PDL/vC7HsJ0mSdE6V58gcPXqUatWqodfriYmJYeLEidSoUYPY2FjsdjvdunUrvLZBgwbUqFGDLVu20KFDhxLvZ7VasVqLZkDMZnOljDvFbMHpcqNRKQny1pFVYGf3qSze7dsEg0ZFnWAj83acLvX52+IzeKRTNJrzSb9ao9hWbbeKXTtab9AYsAfWZ+jPCTQM96Fbw1C8SgsqyijP5ihcjqkdbOKpdr5E+2tIzLHzzc4ccq0Ofn/mdrRqJSHeerQXJyVfTKmG9mPFfw3+YgamVheo1bnknVWWHLFrSaEAY+Blm0raHC62nyj+G7hOraR3s3DuahBKVr4NjVJResB1KY6CwkrEJUraK5Y6ylpFWbp6OhPU6wFtx8D2KUXHhy+RieCSJBVTpTMy7du3Z/r06axcuZJvv/2W+Ph4br/9dnJyckhKSkKr1eLn5+fxnNDQUJKSSs+9mDhxIr6+voVfkZFXWbn2Ium5VhbsPE3/bzcT88EaBk/Zyh/7k/DRq1l3JJWXf/mHEEUmPif/JOLi+ikXqBtiQq26IJHUYRdNE4//JfIC/ngNlGrUmz5hWv9w/tifxKGkHBxOV6n3LAuDRoVGpeDlO0OZ3zmd/nvH0HJhR+7ZNpyZrY7RJVJJrWAT1f2Nlw9iLNnwzwLR8kDrJYKYk5vFFmrVRTMxTgekHIRFj8LnTeGrNvDHm6JVwSVoVArqhHgmOFf3NzB1eBscTjcTft5Dt0nrePGXfcSn5Za/SqRKB4F1Sz/vW0PU8JGuHadDtKzYPcvz+K/jLp93JUnSLUfhvo7qM2dlZREVFcWkSZMwGAyMGjXKY3YFoF27dnTp0oUPP/ywxHuUNCMTGRl52TbgZZFndfDlmqMlLs38977GnM3KJyZcyW3/vI46K44/O8xkzKLibQqUCvhj/B3UCbngt/y0YzDlDvHbf/sncLUehXL2/WBOwN1kAGkd3+S2bw7y54TO1LhUgbrLKLA7mL81nn7u1fiteanYeVfH51B2fhF0l6mw67DCgaUiMFFpRbsDhwXmDBK7fgbPEb9Vq87NkqQehimdiy/hBNaG4cvAN6LUlzqWksvdn67jfCHkH0a04ZVF/xRrfeBjUPPbuNvL//1JOQDfdiy599TDi6FO1/LdT7o6Wafg63bi74p/TWg9Cla/LX4+9XpB36/BK7CqRylJUiUzm834+vpe9vO7ynNkLuTn50e9evU4duwYYWFh2Gw2srKyPK5JTk6+ZPEcnU6Hj4+Px1dFScu1MnVDfInnPlp1mCHtavDr4XxS278I+ek0Cdbw5J21PTo/e2lVTBnWhgj/iz5stUZodL/487bJKL+NEQ0itSZoP5apsdk8ens03vqrW1oyaNQ82EiL36b3Sjyv3PqlaJFwOWod1OgAQXVFVd4ZvWF2fxHEhDaFai2Kghhbnsg1KSkPJT0Ozuy45EtF+BuYMrwNXloVbaL82XM6q3j/JsBc4OCnrSexO8vRvgFE7sXA6aKK8nlKNdz1tngf0rWlNUHMOBHEDP8V2o2BAT+K3Jmur4klTEmSpHOuqxmZ3NxcatSowdtvv82IESMIDg5m7ty5DBgwAIDDhw/ToEGDS+bIXKysEV1ZbI1LZ8jU0hONfxt3G+G+egpsdoxKBzanG9R6bA4X8ak5aNVKIgNNhPjo0JZUsyA3BbZNhg2fiMdqPTy+EfxrkpzvRK9R4msoSvZ1Ol2ocMOF93I6PR9fzJYnlnOWT/Ds33ShkcshMqZs980+A4seg/Bm4jfmpH9hwFTPhOXss/BtjFiKKknD+2DQjFLrxwDYnE5SzFbMBQ7e+PVfYk9mlnhd7WAv5j8eU/pOq9I4rKKOTOYJ0fwysLbI8ZG9n6pGfoYIkL3P/dJizRUzfoaAS/49kSTp5lHWz+8qTfZ94YUX6NOnD1FRUSQkJPDWW2+hUqkYOnQovr6+jB49mgkTJhAQEICPjw/jxo0jJiamzEFMRdNrL10wyUur4nRmAd9vOM7ZzALaRQcwqE0kR5Jy+Dn2NEoFDG0XhU6tJNi7hHtZzSLht8G9oq9S/6mQEAtaAzpNCBuOpNEuOoAQHz35Ngc7jqdRx19FhL+XmNHJSRI5KrW6gPGi31rz0kSQseUrUVis5u3Q8RlY+TJkXLRU5hUCBxZBdGfRKsCaK4KesCaiCu95dgtYzFC/l+jIrVCKTtsWMxiDQH0u6FKqRLJsaYGMMfCyH05alYrq/kYKvJyXnJUy6dSor6RInloHfjXEl1T1Lt6ZpDOJL0mSpItUaSBz5swZhg4dSnp6OsHBwdx2221s3bqV4GCx2+XTTz9FqVQyYMAArFYrPXr04Jtvvqmy8YZ46wj00pKeV7yWyyOdoll3NJW3lx4oPLb7dBYztpzgy6EtOZaSy5nMAtYcSuWuBiF8MKAZwd4XzBqkx8FPg8SMwH1fwm0TRMLvqS24u75BQXR/np57kM71gvnkgebsjE9j7Jw9RAd6MWtYIyJM+bDkKYj7C7q+AW1GFwUz+enw9/9g549Fr3dmh6iSev93MH+Y6OkEcPe7sOcn2PSZCEru/Qzi1sCiMSLIevBnkc/isEFmPCx+DJL+KbrviY0Q0Rr6fgOBdcTyklcItHsc/nyj5G9s6xFl/hkYtCpG3xbN2sOpJZ5/9PZa+F3lFnVJkiTpxlGlgcy8efMueV6v1/P111/z9ddfX6MRXVqYj54pw1vz0PfbPIrJ+Ro0DG0XSc/PNxR7jsXu4tM/j/Jwhyg+WCGq364+lMKR5BzPQOb8rIXbJZpE+tUoqmLqHU6BW1TXXXcklf7fbOZ0Zj5uN/gblKgyjsGC8ZB2qPD6wvwUEEs7FwYx5+Wlwa5Z0HQg7JoJGgPUugtObRbnDy6D5P1FMzbGIDFOELMtJzZ6BjHnnY2FxL0Qcq5Ks1IJTQeJWZtTWzyvveMl8KtZ/B6X0KiaD4PbRjL/ou3tdzcKpUMtWWNEkiTpVnJd5chUhorMkQFwOF0kZFlYeySFQ4k5tIryp2PtQPaeyWLs7F2F19UO9sLfqOVkRj6pOVYmP9yaN5b8S1Sgkcx8Ow3CTHw+tCXq80sq2QlgzRb5Jkn7il6w77dgySTXEMEKR2v+s7DoXItIXyb3MBE2v1fRjMp9X0Pjvp51T1b/V+Td+Fb33O7sHQb5mTBskdgaXbc7+FQHe56oyrvixaJra94OA74vylnIT4dZ94uApSRRHWHofNBf8D3PSRZbtQ/8CjofaNJfzO5cQfJmRp6NM5n5LN2bgNPppk/zakQFGj07ikuSJEk3rBsiR+ZGpFYpqRFoZHhMTY/ju09lAdAmyp8n7qzN8dQ8ks0WHmgbiValpJqfnpd61edgYg4h3jpaRPphs7tQ687nhrhx2K2oNEYuzPBwqfUoT26GpsMxKDzzaowaFQq3UzScPE9nEu0OLuRyiq3Qd/wHjq2GtRMhtDH0+hCyzojdITVvK7reoSpeAE7rJXJgznMjZo9K43IW387sHSq+om8v/XllFOClJcBLS7Pqfld9L0mSJOnGJWdkKkhcSi7P/7yHMXfU5vkFezyWniIDDEx+uDVDp2zFbBFBh06t5IcRbWlb0w+dRo096yyKRY+hPnVuJ5EpROxiUihxDprFHm1LBv6wB7cbgk26wv5Nd9T24eMYOyF/PA3ms6Ji7gOzoPZdIgEYcCXtR5n8D/z6JLR/QuSu+ETAojG4ozqh6D0JfMLF61pzxRLQojHnxhEqdvOAyJnpPUmMze2Grd/CqldK/ob0/QZaPlSx32RJkiTplnFD1pG5UTldbo6m5PDavY14+Zd9HkEMwOmMAib+fogh7Yp2xFgdLh6ftZMkswhI7FYL6iyRi5Le81tODlqFI6yFmPVI3IslPw+3G1pG+jF1eGs+6t8YgLh0O5bAxpwetBJHRHtwu3Gd3u5Rs8Wh9cEdv17Mkmz5GuL+FoGKJRvF2Z24nRckLzvOVecFiL4Txm6Gez4Wj5P3iy2xIAKmRn1FUHSxkMZQu8sVfz8lSZIkqazk0lIFSM218tHKw/ynR31yrI4Sr9kUl8bwmCgu6BxDns1JfFoefkYtL/2RxSt9fsbHfJRJcREs+f0I84dOpmbGRnZ63YFD7cPLvRpQO9iLoVO38VD7GnwxuBlNQ7XYnW7unXKAeUM/p3bGOmh8P6YLKp9O/8dGn1bPE+oG5d6f4OBSccIUQnL/RShVoRR2RfIKEkXHQhpBo/vE42YPiJyWqBjP7de+ETB8qch52TNbLD21GgENenvWkZEkSZKkSiIDmQrgdrmxO10kZJdQufb8NW6wO4uv4mXk2bA7XBxNyWHoQifNq4Wx4mAaAIPnnuTeJi05mpZGqygnWfl2Pv/rKAV2J99vjGdQ6wi2xTnpVL8aBXYnQ+aepF+zNoyu58WF8yRJZgujF6fx072j8d/7U+FxS1QXNiYp6eAvZpBSzBbScq3k21TUbDAUvUaPCUDvC436gaaERFrfCOgwFlfzISRb1KTlu7Bnugl25BPsrUOvuXTtHUmSJEm6GjKQqQBGnZqagV7UDCy9CmyglxaLo3jp/EbhPvgY1NxeN5jpm0+QkG0pPJdnczJ/VzLju9VlW3wGm+PSPZ67IPYsfkYN7eqInUQFdid/H83m2bs9f6y9m4bxaL18/Bfc73Fcv38+PXxqoFY9wZFkJ4/O2MmpDLH7SamAB9vX4Llu9USV3JKCmHPsTjd7U2Ds7O2FuTtalZLx3esypF0N/GVdF0mSJKmSyByZCuBr0PBizwbsT8imS/2QEq954s7axeqe3FEvCD+jBq1axbAOUXiVUDnYz6ihR+MwArw0Jd738Ttqedz39XsbEurjGXQ09ncSuuP/RGVdUyjpj2wjr8nDAJh2foXGmceTs2MLgxgAlxtmbz3FvO2nLttxOyG7gIe+31YYxADYnC4+XHmY7fEZl3yuJEmSJF0NGchUkAh/PW2jAni4Qw1G3xaNz7ky+jUCjHw5tCXNq/uSbBazLd46NWNuj2bi/U0J8zUAkGOx89WDrehUJxCFQsyIdKkfwueDW/Diwr08e1c9nutWFx+DuG91fwMfDWiKxe5ic1w60UFeTB3ehjvqBqNQeJbo1/uG4Oz9Kc4G93Gm30Lun5fI7rrjcLYZg2v4UvZmGTmWmlfi+5qy4XiJDRovtGp/ElZHycHOJ38cJi330s+XJEmSpCsll5YqyJ7T2fy2N4GRMVE83LYaD7arjsvpQqlUEGFwkKvwYsHjMThcLgIUueQqfQjy1hc+/2BiDjO2nODzgQ2xuBugViqwO12o3SIIOJqSi9vtZvm427E5XSgAH72aAruLAa2ro9coCbngfhfT+kfg7PMZaqcXsx914aVVoar7Kuh82LnpVKnPMxc4Sg1Sztt3ppQeSsDJ9HxyLHa0KiU+hpJnlW5J+emix9R5eemiv5DiCvpESZIk3cJkIFMB0nKt/Hkgiec7BRBwbBqGbV9AQSbUvgtXx2dxr59Bbrs38A+JIDj3FPz+Aul3fkIakYXBTIMwb6YOiOJkZhZv/XGWuNQ8lAq4u2EQnw9qTEKukzybk9iTmXy48hCJ2Ra0KiX9W0XwbLe6lwxizlN5BRLmcUQsQdUPK70Zn79Rg1596Ym7VlH+/LYvscRztYNNLN+XxLb4dN7u05jaIbLxHxnHYdmz0Pdr0YrCnAC/jhN9rkIaymBGkiSpHOTSUgWwO1w82SkCv31TMax9RwQxej/o8T+Ucweh2v8LYRteRZNxFMX0e1DEryN89bPkZKaQY7ED0DTQRbLZyrA5R4g7t8zjcsPKA2kMm7GHKD8dkf5Gnpu/h8RzCcE2p4t5O04zdnYsqTmW0oZ3WfVCvIvl1Zz3VJc6hPhcOkjq1jAUYymdwUd1qsnPO0+z4WgaAydv5vQFeTi3pLw0mD0A4tfDrH6QEQ/zHhbNPmf0hgKZUyRJklQeMpCpADanC4MtHa/YyUUHLVkQOw3XXW8DoD/2O8apHSEvFfR+pN7xHgafILz1Yrklx6nh/XWpxar6A5zJLGD3mRyW7j1b4uvvOZ3NmczSt35fTrifgbljOtAgrKgtgValZGzn2tzfMgKV8tIzBBF+BuY91oHq/obCY15aFS/cXZ9jKbmFScSZ+Xb+OJDMTV5M+tL0PtDvW1DrRMfzL1pAQqyowdP3G9AYq3qEkiRJNxS5tFQBnE43WluWqHpb8w7S7vqYfLUfBnsWwek7cbV7DOX2olJ46QMXogxpSLBP0Qd/gcNNktnCwNbVWRgrGjsatSoGtYlk1pYTbDyWjq+h9G3M+85k07JG+ZsvYi8AjYFawSZ+erQ9LnsBSfngZ9ASZNJi0F7+r4hKqaBZdT8Wje1ISo6Vs1kFuNxuft5xhr8Pp3hcu/V4OgNaR2C1u9CqlPh73WJbs1VaiGgNg2bA3CFFx3t9BLW6gObyS4SSJElSETkjUxEUoNIZSRuxgW13TOPZVZkMmvYP/92UjyWsFcr9izwu99o6CXN6Iv+ezSYzT5T81ygVfDSgGXVDTIztXBujVsXnQ1rgrVfzv/ubEuGnJ99WctVgoNSloUvKPiv6JeWmAhBINsH7ptLUu4DIAGOZgpgLhfjoqeZn4IvVRxk7e1exIObO+sGMvi2aVxf9w31fbWTEtO2s2p9Eet4ttqspLxXW/Z/nsa3fQF5KyddLkiRJpZKBTAUI8tLi9o5gXYY/g6dsY9OxdApsTl7p6IV+9r0iL0Lvh7XFSEAsM1Xf8jZaawY7TmSQa7FjdSv5z8J9TFxxCKVSwc+Px/DlmmN8teYYm46l0bdFNaIuWLq5kEGjonE13/INOicZ5g2F1e+Ir/wMWPES/P0eLBwpzl+BAC8tY++sXex4dJAXg1pH8tD32/j9nySSzVb2ncnm8VmxfPt3HOYC+xW93g0nNwXmDytaTmo/ViwzZRyHmX2LGnRKkiRJZSIDmQrgY9SSZlPzzm8HCo/lWB3EJljIq9Ub9H7YRywnLeY1rD0+BqWanCYPY1F6nauzYkOJm3Y1xdLQ138f48Hvt7LvTDZKBfRqEobJmc0zXWvRLMKzA6heo2T6qLaE+5ZzSUKthQb3ij/vngVftITzM0cN+4oP1ysUUyuQIW0jPY4Nj4li0p+HcbqK58d8vzH+1qk1ozFC+8dBqYIhc+CuN2HYr+L73XoEqK78+y5JknQrUrhv8szLsrYBv1qxJzIYMW0HMbUD0agU7DmVRaLZwhf3RdK2mo4kRTBPzI5lYBM/nu4UymmrF0/N/5cjybnMGt2O5GwL1f20TNt8mlUHin4r//rBlvhrXYT6eVE7zJ/UHAtnMgvYezqLMF89TSJ8CfXWo7nMFukSFWTC5i9hwydFx+56C9o8Aga/q/p+ZOXbSDZb2R6fjk6jommEL70+31Dq9ZMeaE7/VtVLPX9TseaKKsvGANAYwGmHnCTQeV/1912SJOlmUdbPb5nsW0G89Wo+GtiMvw+lUGB3Mu6uuqiVCl797QBqpYIpw4JIybXx1eYU5uzNwu12k5kvllM0KgUGrRq7W0niRY0nT6Tnk2lQU+1cHm+wt55gb/0lE3vzrA4OJeUQFWgkyKTD4XRxKCmHIJOOsAtnbpx2saRxofQ4cF39Mo+fUYufUUv9czuh4tNyL3m9Tn0LNZfUmcTXeSoN+EWWfn1VyEsVW8NDGomxFmRB6iEIqicCMEmSpOuEXFqqAJl5NuZsP8WTP+1iQewZftuXyCuL/mH65hN8PKg5LjcEmrS4zhXIzcizFQYxOrWSCD8jTSN8+OSPw+w7a0apgPqhIgD4v1WHySqwY8g/UzzoKEGe1cHqgykM+HYz//vtIBl5NvaeyWbAt5t5YnYsSeebUuamiJyY/YvF47Cm4r97ZsNf74gPsgrkZ9TSsoZfiefUSgVNIypvtkwqp9xU+OMN+KE7HF4hgphdM+HHHmIGL1/WupEk6fohZ2QqwIn0PKZvPlns+P4EM7tPZfLFkBZoVUq8tCrybEUdsBUK+GhAM4K9daTkWHC43CgV8H7/plTzNfDTtlOs2p9EvtWJXe0N7uLdsy9md7oKZ3UW7znLifQ8DiSasTpcZOTZcLguaDdwflWxx/vQ4kHYNgXWvi+OV/CKo79Rywf9mzFo8mbMFs/dV+/1a0KQt8wNuW64naLaMMCiRyGqE5zcJB5nnwFX6bvnJEmSrjWZI3OVXC43L/2yjwXnar8ABJm0qJVKknMsRAcaeaFHfar7Gwnw0vLr7gS2xacTGWBkWEwUkf5GvHRqjiSbOZlegMPp4vd/EtlzOovnutVDpVRwJDmH7g0CaVkzuExjysq3MW/7aT5YeajwWGSAgTmPdiAy4IKCa7kpkLAHItuJ3IyCTDi1Faq3Aa+yvVZ5uFxuzmYVsHxfIhuOpRLpb2R4TE0iAwyFhQGl60ROEix6DOLXFR1rMgh6TgRTxf/dkCRJupjMkblG3LjJsYrfULs1DGFouxokZVuwOV3UD/WmkSmXLJuD1II8gnK2MbZFfZ5oVR23dzhqVdHKns3hJjcrlV4N/GmptqKMSkBnVGIMr8/fB3Owusr+4eGlVdMm2jOHpl6INzqFk4LTeyH1IGrvUBSBtVHX7gqqc38NDP5Qr2el9fpRKhVEBhh5vHMtRnSsiVqlQKOSq5vXJbUeIjt4BjK17hDJyZfidEBButidpTtXKTr7rCj0d2GTTEmSpApyxYGMzWYjPj6e2rVro1bfuvGQSqmkX4sIjFqxM+eJ2bHYnW7CffXMHRiK35wH8K5/D0Htn0Of5oCFo3BrvXD3+w78IgrvE2200bCGFdXPgwlL+qfoBXyqMWnwPNK8yrb0Yne42Hc2m4embgNEDo7V4WL1oRTe17h4vUEiQb+NBbcL9H44H1qIKqKV2A4M16RhoUKhwFBKbybpOlCQBbtmwfoPxWO1DhxWWDoO1Aao38szWfk8pwOS9sL0e+Gej6FRP8hNgun3QJOBcPsEGcxIklThyv3rcH5+PqNHj8ZoNNK4cWNOnToFwLhx4/jggw8qfIA3ghaRfvRrEcE7yw5gd4qVuvsa+hJ05g8wn0W1YyqG9e/B+v+DhF0oTm5CkRnvcQ+jwoLq7//BhUEMgDkB1YKH8XOk4iqhBsvFcqwOFu48jdXhIirQyLr/3MlLPeoDsOZYFnkBTcArSFxsyUI1+37c5pJ7OEm3KKcVYn8Uf276AEw4CNGdxeNtk8FeSuNPSzZs+FSc//VJ2PiJCGJykmDXDLDlXZvxS5J0Syl3IPPKK6+wd+9e1q5di15ftJW3W7duzJ8/v0IHd6MINmlZtT/J49h3W5P5lS7ktH0GANW+uZCwW1Rz7fM5zrRjuC9IvFU4bRC3uuQXyDpFZmoiqTmXLxoX4KXl+bvrM7ZzbX4a3Z4wXwN9G3nz9t3VmT+kBjV+f1jkxpxnzcGVsK/8b1q6eZlCYdgS6PQc9PifmEXpPwXaPwmDZ4EppOTneQVC74+hdjfxeMMnRfVxRv4OPrdInSBJkq6pcgcyS5Ys4auvvuK2225DccEyROPGjYmLi6vQwd0o7C5Xid2n//d3MgWtHwetV9HB8BbYI9qjPruDvPyi57it5ku+hjsvDafb5XHManeSfkFFXLfbTUaelSBvHU91rU31c4m9qvxUBubNpcFfo1CkHix+86xTZXmbN7+8VNFE87zcVLHMcivyj4I7/lMUtHiHQdfXwKfapZ/nHQa9LpqZbT4UAmqBUuZDSZJU8cr9L0tqaiohIcV/I8vLy/MIbG4lOrWKdtGeRcKMWhXzh0YRuGyk55R6wi7sWyaT1fFlXHmpZOZZcWWdAYdNJFiWQh0QhfaConFWu5MdJzIYNW07CVkFuN1uDiXlMGjyFo6n5mLSFe0CUum8MB1ZXHIQA1Ct+RW975uKOQHmPgjx60Uwk5sKq16F3bNv3WDm4jyYkvJiLpYeBzPu9Ty2fQocXArWnIobmyRJ0jnlDmTatGnD8uXLCx+fD16+//57YmJiKm5kN5g76wXjrStKen4sJox6p+ajOrsDFErS7vmenDbjADDu+RFF6iEsdgfqM1tRLByFwhQMrUeWfPNaXfDxDSDIVJTwm2t1MHrGTvadNfPojB1sj89g8HdbiEvN46k5u8g411UbAFM42R1fKfnewfVRBRZv8nhLsZhh7YdwZjvMHQJHVokg5p+f4Y/X4KJ8JqkU+Rmw7Nmi5aTRfxUtMy19WnyfJUmSKli568hs3LiRXr168fDDDzN9+nQef/xxDhw4wObNm1m3bh2tW7eurLFekWvRaykpu4AJP+/h0dtr89lfR9h3Jhu1UsG6sY0IWf8K2Q0G87+DIbSJMHBf/iJcOh/mOrqiVysYGf88nN0FdXvgvutN+GcBih3fgy1XlK5vPABn55cpMFbHZCiaZbHanWyLz+CR6TtwXJAEHGTSMv/xGGoHe/72nJ2ejOrQUkyb3hcfOAolrrq9cPf6AJV/jUr5vtxQzAkwdygk7vE83v1daDVc9kAqq6zT8PNw6PM5hDaBvBRY+iy0exRq3nb57duSJEnnlPXz+4oK4sXFxfHBBx+wd+9ecnNzadWqFS+99BJNmza9qkFXhsoKZFLMohKvVqUk1+rgzo/XEuqjY1iHmtQNNeFyuwn10ZOTnsiS/Vks/jcTgP92DyfX5uKjdcn0aBzGt/pvUO5fSN4db6Jo+yhGpR2HNQ+3NRe32oBC64VN7YWXI1tsjfYKBZVYYnLkZzJ3bzZv/LofgKc7BtOvfV3qhJbchym3wIo6LwmFLRelRo/aOxiFXrYGKJSbApMagOtcBeU63aD/1PL1FrJkg9639Me3grw0MAQU5cTkpoplKRnESJJUDpVaEK927dpMnTr1igdXkg8++IBXXnmFZ599ls8++wwAi8XC888/z7x587BarfTo0YNvvvmG0NDQCn3t8kjPtbLmUAqfrz7KmcwCageb+HJoC0w6NclmKx//cbjw2pd61mfx7lSOJBc1THzzz8TCP9f3B2W66GnkteNL0mvehSWkIel2HV/8nc6q/SIJt3fjQJ5ta6Tm2nFQrxe0Go5boUS5/mO61nuYqQFGHmzmw1DtehyZVtL0rQjyvSDB+ByTQQeGqMr61tzYss/CH68XBTEAcWvg5Gax9Vjvffl7ZJ2CdR9B19dF0mtOEqz5H9zxgkievVWc395/nqwELElSJSp3jozZbC7xKycnB5vNdvkblGDHjh189913NGvWzOP4+PHjWbZsGQsWLGDdunUkJCTQv3//K3qNipBndTB1/XH+s3Bf4S6luNRcVv6bxPCY4h9Uv+w6y7AOJX+AqZUK+jXyIa9md/EPf0EmgQvuJ6fAypApW1m2LxGb04XN6WLxvlT6z0vkdOdPRS+k+PW4105Euf07IpYMYNGQcEbp1+G7/m0CFw5ElR1PZv7lt2pL5+SmwqbPYP8i8bjTcxDeQhQN/HkYpB+9/D3yUmHW/bB7FiwcLWYlFo+F3TNhVr8Kb8IpSZIkCeUOZPz8/PD39y/25efnh8FgICoqirfeeguXy3X5mwG5ubk89NBDTJ06FX//oiWR7OxsfvjhByZNmkTXrl1p3bo106ZNY/PmzWzdurW8w64QablWpm4snvjppVXRtmYAPRp7zhRl5dtoWcOfx++ohfKCDV0mnZrvH2qKL7lkNHoY54jfwSuY7F5fsnhPIul5xQPCjDwbi446cEZ3hY2fQqsRYskiN4WgaTHo1r4DQEGde1EY/PHWyd5FZWYKhjajwTscbn9e7B6782UIby6q05ZWN+VCagN0eVUs/53cCB/XheNrxLnOL11yR5okSZJ05cq9tDR9+nRee+01Ro4cSbt27QDYvn07M2bM4PXXXyc1NZWPP/4YnU7Hq6++etn7PfXUU/Tu3Ztu3brx3nvvFR6PjY3FbrfTrVu3wmMNGjSgRo0abNmyhQ4dOpR4P6vVitVaNBthNlfcTonELAtOlxuDRkWPxmGE+Og4nZFPzWAvHpu1kzG31+LHkTVINlvw1quxOVyMn7+Ht/o0YlCb6pxKycSosBESFEiowcWp7Oo8M3sfc8d0IOCJzdhdRnxzUvmgf1MsdidHknM5nGQmyWzF5nRxNl9Dbp37MO2bhiphL65RK1F+GyNmDgB3ra6oen+AyRjo0cepRE5nYa5NiY+vxIX3cJ5bornw8dXe/0IupygueOGWf6ejqG9UeYU0gEdXw47vYd0HoDXB0Hmi/olvxOWfrzNB3Z7QexL8Nr7wZ0LPD6HBvWXbuixJkiSVW7n/1Z8xYwaffPIJDzzwQOGxPn360LRpU7777jtWr15NjRo1+N///nfZQGbevHns2rWLHTt2FDuXlJSEVqvFz8/P43hoaChJSUnFrj9v4sSJvPPOO+V7U2Wk16ro1yKCe5uHs3RPAjviM6gTaiLc10DX+iF8szYOhSIOH70Gi91JdKAXXz3Ykg1H0/j0r6MEeGkY0LIa1ZRKHp17gB0ns4kK9EIJnLGbWLDzNBuPptEy0pd7W0TQrLofPgY1eVYHuVYneVYHGf73EO/XnqY++aiO/uExPkXyP2itmeB9mZyEvHRRRbjWnWK2wZoDJzZBWNOyfWiXJDcVjv8NtbuK4OLYXxDSSHTR1niJWYrwZuBzhfe/kNMBSftE8mhwA/F66XEiWTeiNai15b9nQTYcXQUbJ4nHtlxYMBJGLgN3tbL1oLLlwIGlnscOLoPGfWUgI0mSVEnKvbS0efNmWrZsWex4y5Yt2bJlCwC33XZbYQ+m0pw+fZpnn32Wn376yaPVwdV65ZVXyM7OLvw6ffp0hd071EdHm5r+jJm5k6V7E9h9OosFO8/Q/5vNDGhdnYbh3rjdkF1gx+ly8cWDLRkzM5b3lh8k9mQmfx5I4Ymf9vDGskN8cH9jagZ5MbZTOGm5Fnp/voEv1xzDbHFwW70QRvywnRE/bictx4Zeo+KlX/Yx9qdd7E22E2dW4o7fCH+9JQYWdZtYZspLhWn3iA/10uSlw6bPYdEY+P0/4vGh5TB3sKihkn0FfZdyU2DVa+Kep7eL0vSLHoO/3xcJrweWiHvPHSq2OV8Nl1MEMdN7w7RekHpIvN/pvWFmHzizQxQXLA+LGQ4sFjMpILo+GwMhP018P1MPX/r5ALnJsPgJEcwB1L+naJlp4aOQk1y+MUmSJEllUu4ZmcjISH744YdiDSJ/+OEHIiMjAUhPT/fIdylJbGwsKSkptGrVqvCY0+lk/fr1fPXVV6xatQqbzUZWVpbHrExycjJhYWGl3len06HTla1TdHk5nW7e//0gF29Yd7jcvLV0P6/3bshTc3YD8OSddViw8zSnMoo32Ft3JI3jGTXpUsefjrWDmLD4ADlWBwAWuxOVQsz+pOZYGT1jB0atmtRcK3qNEoNWxf4kO+46TUCpFl2Fe7wPOYmiQZ9f1KW3uSpVRbMuB5aIoCDjuHhsDCzqgl0eShX4nitdf2IDnN/Sffh3EWgU3j9YLAddDaUKNEbQ6EU9nB97iO9DfrpYDtKZxOPyUOshqL7o8lynO9z7GRRkiEDJFFa22RSlBqq3FYHM/d+JDtHHVsMvj0D1Nle+5CVJkiRdUrnryCxdupRBgwbRoEED2rZtC8DOnTs5ePAgv/zyC/feey/ffvstR48eZdKkSaXeJycnh5MnT3ocGzVqFA0aNOCll14iMjKS4OBg5s6dy4ABAwA4fPgwDRo0uGSOzMUqso7MzhMZDJwsZp1qBBgJ8dZxNquAxGwLAIvGdiQz38ZfB5N5uH0Uj8zYQbK55N1D9zQN44P76pFphc4fr/c4V93fwP/6NWHCz3sLE3+1KiVfPtiSWVtO8Oxdddl3MoVR9R0ovcNEsqrbDWmHQedz+X44BVmwdy6sfLnoWM07YMBUsW34SuSnw8bPYPMX0HEcqHSw4eOi87W6wP2Tr/z+F0s5JIIYS5Z4rDHAI6sgtOmV9fRx2iDtqNhBZjqXtJ12VNzXt4zNDvMzwHwW/KNF8GPNFVWBfSLKV4tGkiRJqrw6Mvfddx+HDx9m8uTJHDlyBIBevXqxZMkScnNFvZSxY8de9j7e3t40adLE45iXlxeBgYGFx0ePHs2ECRMICAjAx8eHcePGERMTU+YgpqK53dAo3IfnutXlbFYBpzPyub9lBL5GDR+uPERKjpU3f/2X3s3CQQGuS4SIThfo9EawWYqdszpcKBQK1KqivAylUmzZtjtd+OmVnMp2ku/fAJP+3O4khULki5SFUiVKyF9IZ7r8bIk5ERwWCIgWj/NSRcE3v5qAUsyIgFiqOX/Nedpz9zcnilmaU1vBvwZEdRKBl6qceS0qjecsh1It7nGl/b5UWght7HksqG757mEMIMVhZPexLPafPUvDcB9a1ahLiEHHLdOFzG4RwZzBXwRvLpcI5nTeZdv9JUmSVE5XNN9ds2bNwqUls9nM3LlzGTx4MDt37sTpdF7m2WX36aefolQqGTBggEdBvKoS4a/n+bvr8dy8PYVLQQDB3jomPdCcHIudlBwr0zadIMikpVeTMGZuOVnivfq3ikCnUeFj0NCyhh+7T2WJe5l0fDKoOa8u/odksxWdWolOo8Rc4OC5eXv4bEgL3lx2iPfuuyCIKQ9rjsiJ+fUp8dgUKvI7Dv8ulkd6f1zyB445USS/Zp2EkctFyf6/3oZ/F8Hj60WS69r3RTn/4HoiZ+bC+x9aJgKPhn3Fcsv5XT1qHTy0ECJjQF3G93M+JyYvTSwzKdVgNYuloFErihKAr7EjyTkMmbLVo8+Vn1HDvDEdaBB+C1RQtltETtCcB6Dzy9D+cVEkcNo9UPsuuOcjGcxIklThrjhhYf369YwYMYJq1arxySef0KVLl6uu77J27drCqr4Aer2er7/+moyMDPLy8li0aNEl82Mqmxt4dfE/HkEMQGqOlQ9WHCLcpyg35fPVx3i4QxShPsXzddpG+VMvVMxe+Bu1vNevCXqN+FEEe+uwOlykncuJ+X5EG+aN6UCwt45cm4PUHCu+Bg3pOQU4rAXlfxMOK5wUy2PU6gpjN0Ovj8TjlH/FEktJLFmQsv9cLk5vWPyk6AxtzwenHU6f+9nX7QEp57psR3eGhxfB3f8Tj5P/FQHQhbMvDqtIBM5NpEycDhHA5KeL3/IfWQmP/iV++7dki2Tl0t5DJUrNsfDErFjPZp1AVr6dMbN2kmwuPvN207HlwcHfREL23/+DZeNFEGM1w6nNoqu4JElSBStXjkxSUhLTp0/nhx9+wGw288ADDzB58mT27t1Lo0aNKnOcV6wic2T2ncnivq82lXp+8ZMdOZtVwIKdZziTmU+vJmH0b1WdRbvOsGp/MnqNisFtI+lUJ5DqfkY0ahG8OJwuTmXk8+PGeLYcz6BDrQDubVYNhUI0gdTas3FY8ojLhuhAI2lWJa0ifdEarnBLb16qmElp1FfkrBRkwZGVENUR/EppIOlyiUBk+j1iVue8Lq9D20fBngf7F4vdUy6n2L4c1Unk7BgDxYyPd6jY2ZSfUfz+D/4M9XqUbfxOG5zZCVqvopyYlENgPiNeswp6+hxKNNPz8w2lnv/92dtpdCvMyuSlwl/viArH55lCYdTvEFin6sYlSdINp8JzZPr06cP69evp3bs3n332GT179kSlUjF58uQKGfCNIO+imZiLZebb+L9Vh/jmodYkZhWwan8y93+ziRmj2tG7WTWcLje/7jlLryZhhUEMgFqlpFawiTf6NCI1x4rT5cblcqFVq3C7XGRm2fBSQUvNaQI2TKXGfV9eURDjtphx5KTgzk1FUbs7dpsThc2BweAHje8XyzylUSpF0mt4C7EzCUTOS7MHwOgP+EPTB+CL5iKPpsXDYMuHtqPF+ahO8H2XkoMYELMsZaXSQkQbsaR0PrE3pIHIy7nUe6hEFselK1lb7BW35HpdMwRCu8c8A5k63UU9IUmSpEpQ5kBmxYoVPPPMM4wdO5a6dcuZBHmTCPc1oFBQbPs1gEGjwt+o5e0+TRg/fw/dGoYS4qPnnfua8OlfR/HRqxnYpjo/bIznRFo+7/dvQrC3Z/2cMxkFPDZrJ3GpeYBI8+jTrBpdGoTw/M8HaRjuw7e9XqPGwhHQfwr4RZZ57K6cZPj7fTS7Zxbmp2gCamG+fybWwPr4GS8TAOSlipyYExfMOrhdYufQqJWgAGb0KVo+OP9BlnoIen0oitRdKqE3vFnp50pSUtG7KgpiAAK8tGhVSmzO4gGNWqkgyOsKivTdaFwusfw4vbfn8T2zRdPMto/K3VuSJFW4MufIbNy4kZycHFq3bk379u356quvSEsrx2/RN4FAk5YH25W89DL2ztr46NVsPZ7OkeRcvlkbR4HNwaJdZ1h3JJVl+xLBDa2j/PnzYHJhsHJeYlYBD32/zeO4240ovHcqkx6Nw9ifYObR3zJJaTwSDv5a9pwDpx12TkO5a3pRki1AxnF85t2PM+vM5e9hThRbtkEsJz2+QeSo5CRC4l6InSESOzUGeHQN9Jgorj2wGDJPiD5Gd79f8r3rdAfvy2wZv84Fm7Q8dkd0iece6VSTIFPVBVnXjCUTVr8jcmJMofDMbmg5TJzbOEnk0EiSJFWwcteRycvLY/78+fz4449s374dp9PJpEmTeOSRR/D29r78Da6xisyRAUjLsbJo91kmr4sjI89GmI+eCXfXo13NAI6l5BAV6MWnfx3l9388k1ff69eEno3DWHUgCS+tmu6NQvHSFU2IbYlLY+jUbSW+plGr4qOBzXj6XLG938Y0pUmYF3gFlm3Q2WfgmxjxAVOC1L5zUNS9iyDTJSos2wsgfgMk/QNtHhG5MMn/wPapcNebYvpo7QfQ/EHRbNGWKwIfn2pQp5vIZynIgpOb4I/XRZE8nQ+0HQPtH6u4+jJVKD3Xyop/k/h89VFSc6wEm3Q81bUOfZqFE3grBDIgAt4VL4m/E0F1xEzeug+h2RCxLCkLA0qSVEZl/fwudyBzocOHD/PDDz8wa9YssrKy6N69O0uXLr38E6+hig5kAGwOJwlZFhwuFxqVkgg/AxuPpfHd+uO8dW9DtGoVR5NzOZKSw5xtpwj31fPNQ62wOlzgtON0K/Dx0ovf0vPSQevFikOZZObb8TVoUChgzaEUlu1NEM8BJj/cmidmxwLww/BW3NUovHA8KWYLuVYHGpWSQC8tRt1FHxbpx+DL1qW+n4zb38HZ7oliS13F5CSLGjReQeJxQZbYtXSuAF9alhmzXYlaqSDAS4fJnSvqvWi9PO+Tmwy2AnHOFCL+W9EKMkWrAnu+mIXSeYNCdfngLy8N9H5FH7j5GWJJrIy9ktxuN8nnmnxqVQpCvPUolbdMFRkhP/Nc3tT5xxkiaJVBjCTdXBwW0aLGYRX/znuHV2jpi0oriHeh+vXr89FHHzFx4kSWLVvGjz/+eDW3uyGk5liYsfkE0zadIM/mxM+o4ekudbi7USgv92zA28sOsPW4SGhtVt2X//ZtwuJdZ0g2W/n8ryP8fSQVtxvqh3rzXt9GNE1dhrV6J6wOL75ac5SEbAtalZJeTcP45qFWPDdvD26gZqCRyAADpzMKCPcTgUGOxc6O+AzeXnaAUxn5qJUK7mkazks9GxDhf8HOHbVeFCgryCzxPTmDGqC83F++vDRRqbdOd4i+XSxX7V8EtjwKmo9gb7Kd15f8y7GUXJQKuKthCK/3bkRUoFfxe52vnFtZ8jNh90zx5+zTYpagYR+x06nzi6XXMslJFrMJMU9BtZZgzYYtX0NoE6h7d5mCGYVCQZhvxfUOuyFdGMSAzIuRpJuROVH01ds9SwQ03mHQ9U3RnuUa/z9/VTMyN4KKnJExF9h5e9l+Fu3ybKyoUMCKZ2/ngclbMFs8dzbp1EoWPdmRx2bGcjbLM6dFpVTw68h6JJhtPLYwvtjrNYnwYXCbGqRlZvJYTBibEuDLNUeZNqodQSYdG46mMuyH7cWeVzvYi58e7VD0gep04tzyFaq/3iz+pnwiyBj6OwHhNUt/43YL7JgqloQUShgyR9Rr+f158Api38BN9JsSW6yScZiPnkVPdqSa3zXcDu1ywZHfYd5D4nGX10Tg9NtzYmam+7tiV43momDDYhbJzDt/EHk+w36FIytg46fiB/zYWrE0IkmSdKvLS4OFoyF+bfFz934OrYZdWd++i5T18/sqO/jdWtLzbCzeXbw79G21A/n9n8RiQQyIdgPTN5+gTc3iTTSdLjf/tzGNg1klT4z9e9ZMpL+e4eFn0OWcIjGrgO9HtCHIpCMtx8p/lx0o8XlxqXkcTbmg1otKhbLFUJwxz3gu44Q1JWfwIlR+l0m01eihyQAxS+F2iQJ2vz8PQHbXj/jgrxMltmNIMlvYEV/KduvK5BUq8nLgXGG2Z8S4w1tAZHvPhOfz9D7Q6VnwrynygX68WwQxAO0eB58y9luSJEm62eUklhzEAKz5rzh/DclF63JIyi4ocet1s+p+bDqWDoiGj31bRBDqrcOoU2GxubA6nRjUKn7dk1DsubEns+ndLKLwcbBJx397RNA8CLJtSk5ZCgjc9iHOFg+TkH1HYdJogd3J0ZTcUse6NS6D2+sW1e5QeAWjuv157K1H4c7PxK31Ar0/OoMPWl0Ztgb7VIMH58PX7YuWqILqkR/dndjFpRcJXH0omb4tI0S+CoDb6Vmwzp4v2gxUFHserHkXbp8gdlGliX5gGPzh3kmw5CkYvgS0Jbymf5Qo3PZZU1HUD0R14s4vyeURSZKk85L3i//6R0PTgaLoadpR+GeBqLp+YdHUa0DOyJSDdym9jTLybIT66BjfrS7P3FWXLXFpfL32GMv/SSTC30BStgWNWslbfRoVy4MKMmnJPTeTM/6OamwZ7kPPw29QbdH9NNz4NHdr/xUfwD4RGLUq1OcKwKmUCkwXJ/VeoJrfBUsnbjekHIDjq9Hkp6INa4hOZ0B38Be0SbtKzZ3xYDGLHk0XXpt+DJUl85Jbi2sEGEU+zZkdonng5q/AfC6gK8iEPXMho/iy2hVT6cRsTPZpkeR8XkEmHF8LzQaXXs/GnChyYlwXFK87s13UwnFc+7YHkiRJ1yVTKHR7B257DuJWw6bPIScBBnwP9XqJvMxrSAYy5RBs0lHdv3i+R1K2hae61CHP5uTFhfvYdSqLZLOVvw+lMnrGDhqE+TB98wn+OZvNY7fX8nju4zFh5OXn076mP09HJ6CedjeKY3+KqbnT21HMfwiOrEIR1pSeTYq2KAd7axnWIarEcaqVCm6rG1R0IP2o6Hnzy2hI2iu6E8/sC6tegcMr4Eys6FNUGnuBaDHw23jxuE43CGsObhfBKx7jsdtKLsynUEC/ZiFwegekHhbLPH+/Bz+PENtyY2fA8gkw416xRbwiKNUiOXfpueWksKYQfYc4t/q/YsdVSS0MclJgx/cikAGxlHZ+mWl2fxEISpIkSeLfxuzTsOxZOLtLfF4dXgHzhkLLh8Uu0WtIBjLlEOqr54cRbQi8qErrk11qo1Up+X7D8WLPcbnhkz8OMywmikW7ztK+ViDqc9tx+zQNpZvPGQbWVfBpzyBUv08ouWzwxk9R2POIuCBpVqNSMbJTTTrW9lzy0KqUTBnemjCfCyJinbf4cHe7YcWLMLWrmB1R66F6e7FspClhd1Hhixmgeluxta7BvdDvW3hwHoS3RGE1c0/jEHo18awDo1IqmHRvDcK3vQcz+0D8OrHzR6EUsxxftoa/3hIXR91WcVV5lUrR0ye4gahn0+U10ZG7zl3ifdboUHIgY/CHBr1FfZwWD0GtLtB7kvgfNqKNXFqSJEk6z2kTGyMu5nLCX2+C03pNhyN3LV2BhKwCjiTncDwtjwah3lTz0xN7MovnF+wt9TnfPtyKsbN38c59jQj31lIj0IvQtK34+/nhPrfUoZjapdTnuwdOQ9Gkf7HjablWErMKiD2ZSaBJR/NIP8J8dGjVF2WM5ySJnTxndxYdGzwbAmpDUL2y1fjIPCmCgPPbl80JYtbDtzoZeTaSzRa2x2fgo1fTuroXwetewbD/XDVgvxow8ndI2AU/Dy+6Z6N+0Pvjiu/Fk3VK/E91ais4CkRgAqIfU2kcdsg+CUf/gpUvienTgdNEK4jSmmlKkiTdav75BX55pPTzT22H4PpX/TLXpI7Mraqan4FqfgbuPPdz2ngsDddl4kHVueQYnVpFvXBfDAoneYFN2GdWsXhvEh/druSSKbcXzlhknxE5HyGNCDLpCDLpaFrd79KDdlgh/6KWEjmJYAwW0XVZAhn/i5ayfIp2OwV4aQnw0tIw3Efkk5zZAQcXFF1rzRGvk37RrJX5rMihqWjnA49LBS4Xc9lFxeGVL4nHucliOe3B+RU/PkmSpBtVSb3uLqS4+q3X5SGXlq6S0+Vi7raT+Bk16NQlfzvrhZo4lZGPUgGhPnq+WRvHiWw7py16RszYxZI9CRzI1oolkZJojCj8oyHjhAhifhoEP/YU7QIuTEwtTeZJmNVP9DxS6yHoXNPPFS+KNgMJeyquD47DKoKYWf3A5QDfSLFs02o47PkJVr8trgttcm6ZaYfImTEX39F1Tdny4PjfMGeweBxYW+ymSjssmmFWZEKyJEnSjSykochHLEm1VhW7E7UMZCBzldyIPJjZW0/xWu+GxXYlGbUq3u3bhOmbT/Bct3r8uieBn3eeweGCvaeziKklSuY/vyIZx33fFs/fUCig9yfizzPvEx+qKQdEJUVLdtkCGdwiP0ath/7fw0MLRV4KlFxT5aooxOvhFgHTqBXiS20oeq3mQ2HEUrG0pVCWnBdUFdwuwA0RbcUy2Mjl5/6HvE7GJ0mSdD1QG+Hu/xU/rvOBHu+D9hoWQUXmyFSIZXsTeHbebobHRHFXw1D+2J+E2eKgmq+BuxuHEp+ah0mvZsPRNGZtPQlA3xbV6BAdgN3pZsqG42Tk2egU7cdXvfzR/DsfZUKsSDRtNRyyTsKp7SKAiV8nIuGHf4EaMWVPks08KfJkdD4iwMhPhZSDYAwSM0El1VW5Ug4bJO4RycF+kSJQST0ktj2nH4OIViInxpYnMt4Da3ssU1UZWz6cjS0aj9MhZqz0fuVbopIkSbrZZZ8V6Qm7ZoqcxJq3ifYEPtXELHwFkDky11BUoJHZj7bnbGY+3noNdzcK40xWPuG+Bkw6NVuPp/PbP4nk24pmT/JtTlRKBY2q+TI8pibVfPVE+KjJdOaz0W8EfZs/iCYjTgQepjDRKyhLBEFoTSLIKc9OH/8oUaHX6RD5MN7houaKw1KxQQyI9dPqbcVskr1A5Mf4R4t1U2OgSL61ZIsdQsENSp+ivNa0RvE/4/lpNZVaVAOuwCZokiRJNwXfCBG0hLcQu5TUBvFvZRX8e3mdfILcuNJyrfy07RRbj6fzzn2NeWzmTlJyiraeNa7mwwt31+e3fzxLNvdsHEaS2cL2rSdZGCtqqNQIMDKzfxj3VFOiyToFeSmwcAQM+BHi14ttwfHrwJIF03vDiN/KPlOQmwLL/yNyPh7+RQRD274TPZRGraiQDHMP54OYE5tg/oOiP5NflEim/WW0aC5Wpxv82B1aPwIdnizebLAqXPw/oQxiJEmSSqZQiF/4qrizvcyRuUprDqUwf8dpxnWtw8u//OMRxADsTzAza+tJhrQtKhpXJ8REg3BvWkf5s2xvUZLrqYx8xi5PJf/0HohbA0ufFsHAjqkQ0RK3wR/3o2tEu/TsMyJR1m4p20Bzk0UzxdRDMHuAKOO/7gNRTnr3T1BwiYJ4V8qWK17HYYU5D8CeOSKIsebAxk/AliN2X22fIloVSJIkSVI5yUDmKqTmWPj672PoNUr0ahVJ5pKDir8Pp9CpThBBJi1PdK7Fdw+3xuF08d9lB7A6PJNtDybmkO7bCLZ+Iw5EdYTWI3FpvJkR78Oe/GDcI5bDfV9CvZ7FuziXJqgePLxINI1MPSSq2AK0GgmdngGD7xV+Fy7BKxiG/ARhzURS8sZJIojxDoO+38D8h0HrLWaEroccGUmSJOmGI5eWroLD5SYp24K3Xk1mvh1/o4bhMTVpEuGL3enC5XbzS+xZ/j6cgrdew8eDmlM72EhegY2fd55h7J21ef7nvdicnsFMniYIHpgJSq1IPM1LxqHyokWQm6nrj/1/e/cdHlWVPnD8O30ymcyk90LoHaSDHZCiIggiIiogdlwLuvtbd1ddd1XsdRU7qIggSBXBQu+911ADpNfJZDL9/v44kDAkQdCEJHg+z5PHzL137ty5icybc97zvjxzYxsS2zQDg/nCL1ZrEAFFo2tEbwxLvOjq3KyvSNRyFoM1GbRV95M6V57dRWGpG6/XizXYSKzFiFqtEq0HDNaKOgMh8SKL/cubK57cZRzsWyBe98onRL6PnMKRJEmSfgcZyPwBQToN7RKtbE8vIiXCxLt3XMG7S9J4d0kaAGaDlnFXpdKlURglTg+5JS6ybU5axlpIjTTz7cZ03hrRgQkzKoIZlQrCvDmixsuQSfDDE3B8DXqgo97MxO4TOJwXSVx4Yy6q5JCjADZ8IoKYqBbQ70XRe+hMmwBTOPR5AZr3h5CYak/j9yscyC7h8enbOJgtum9HBOt5YXAbrk3SETL9Vhg4UbQ+UGshZzdMvzPwJCteESMyJRmw9l3RSqBZv4sLzCRJkiQJObX0h4Sa9Px9QEu8fgWzQcszs3ex5XhFd2i7y8u7S9IIMWpJCDViNenYcryInSeLeXHhPtYezmfPKRv39KqomHtru0gij8yFvv8WVWWPr6l4Qbcdy6r/0Cx/KR6v9+IutvgErJgovr/5XZj9gCiod4ajABb8RbQQOI9TRWXc/tG68iAGIL/UzaPTtrHvVKGoHjxthJhCKiuAmWPBZRPTSaN/EIXw/D748a8w4FVRR2bOgyKfRpIkSZIukgxk/qBWcRa+vrcbR/NKOVVUVuUxHy47TJnHz9frjvPd5hM8O283AL2aRNAu0cpXa49j0KoZ2y2Gv7ctIWT3VPHEwmNVns+87jX0ZTkXd6FhqTDofbjuHyJHpqyw6uOWTzxvJ+ol+7IpcVUdRL22KoeiKx6GkdNFo8rgSPF9XAdRXC6isZhmSuoOd889vdz5GlGgr4bqDkiSJEl/LnJq6Q8KNmi5unkU7/56sNpjsmxOnB4fL/eP593VKn7Yk49Rq+Ht2zuw42QRsx/phVWxEbXlHYzejuTd8C7usOZob/yMaE2ZGEk5u4S/qwSXX0PR6cDJGqTDZDj/j9KOieLUoYBC5IHvMGh0Vfc4yt5dbe8jv19h/dGCal9jX6adshsHEprYSBS/A1F8767ZIqgB0BjFUmxjqFiyN+xTUSvn93a/9jhFj6QzbeP9fjECFBT6+853qZypo1PdY0mSJOmCyBGZGtIoMrjafRajlqggFY1+fYCXTNNZODoVo07Dx0v3kRqqwev3k+g6jK7L3fiPryM/uCkDJh8m3dQO36kt+O6YUbGqJyyV4yOW8K9fMrnujeVc+/pynpmzi2N5VfdK8vr8pGWX8MSMbVzzxkr6vr2GFzM7cWrELxVdrM9mTRbTPVVQq1W0iKk+jyUhLAjdqQ2Qfziw7cCZIAYgOEI8PlN3IDjqjwUxx9eIxGFniQhicvbAug/EsvL6qvgkLH6mIjgtyYZf/i37OUmSJP0OckSmhrRPtGI2aLFXMe0yumcKKaW7IKoFpi1f0Pz4Mj4aPJVh3xwn2mJgZJd4FH9j1NNHosreTZPcAyx/+CuCf5qA5vBiOPwr/rvnof6wGycHfMGw6Rnk2d3l55+3PYOVB3OZ/+hVJIUHVulNL3Bwy//WUOYRVYXL/D6+3pjBikMmZtz4OXGzBgVebI+HISi82vc5uGMCHyw7jNdfubPF4z3Didz8NKzKFW3cqwqUalLOHpg2XOTcDFYgviNMvlGMyGh00P1hMIbU7jVcLHsufHO7uPbcg3Dnt7DgCVHj59DPcP+y2r9vkiRJlxE5IlNDytxePrqrExHBge3Nb24fx/UtYygMawsHfxIbC4/SwrWL61tEMSjJg/WTrhTZbBR0expUarQZmwmf1BbD4cXi8M6PUezy40u+mvnHtQFBzBmFDg/fbT6B96yl3A63l/8tPVQexJwtvcDBxuLQioq+KjV0HgMoYM+q9n0mhAbx2egumM+aylKr4JHrmtCzaawYCbnhv6JcdW2zJEDq9eL7eY/AR1eJIMYcA21urX9BDIi8oN7/FPf71CZ4o7kIYgCufko09pQkSZIumGwaWUPmbz+FgoLPD3qtmhKnl1iLkQ1H87EE6RjYJpbUDxLEqh2fCyKaUnLjh4R8cyPk7OPomO3M3VfMvWG7sS4eX35e+y2fMSW7KV2aRNMyXMsbKzNZvDe3ymCmRUwI0+7vToRZTNVkFZdx03uryS+tfCxA31bRfHgd6PP3id5LBxbB5s9FV+pWg6p8DoDH6yenxMnJwjKcbg+NIs1EhhgJNmhFKwRtUGAQ4fOBRlP5caXt3upLXSuK6E6tPuc8ZfnwzW2QuUNs0+hh/AYIb1zt9dc5t0NMh815oGJbn+eg6wP1M/iSJEmqAw2iaeSkSZOYNGkSx44dA6BNmzY899xzDBw4EACn08lTTz3F9OnTcblc9O/fnw8//JCYmOrrnNSVRpHB3PX5BmxlXgxaNQadCGYUBYw6NQNax0D8FXDt30CtxV+SjTl7E/R7iaLMw+wu1PFwtwgMPy2oOOmVTxCscjO2k5XDHhNGk5a/tcxlbNt4RnyXUSmYCTFq0WoqBtk0ahVmo7baQCbUoEKz+WNI+1n0bzpDf/4PU51WTUKYiYSwKppNnjstUpoLh5ZC094iH6asSPSLSugMJzeLFUyWONGK4cQGURwvrFHgORRFdOr2uURRP7VGNNDMOwzWxMDcEp8bjq8DU1T9DQpcJbBnbuC2/Yugw8j6e82SJEn1VJ1OLSUmJvLKK6+wZcsWNm/eTO/evRk8eDB79uwB4Mknn2TBggXMnDmTFStWkJGRwdChQ+vykqvl9SvYykR+jMvrx1bmLc93dXr8FNgdYunx9/fD9/ejimmN6ud/UZRxkKmuawilGPWip1EdFNNJ/hvfBI0W1bxHMC0cTxuzA/2JNVhm3k6TRaOYMSIBS1BgHHrf1Y2xBlVU5o00Gxjbq1G113xXGwOag4vOCWLMEJpcI/cEew78+m8x8rDkRVGrZse3IkF3+zcwczR8f5/oA3V0FUwdCl8NhsLjgefJ3QeTB4pGmVk7RUXgr4bAN8Pg5EbRxsEcDQldxfHzHoH9P4CrHtamsWdX5MQANB9YMc004x4oqX5aT5IkSaqsTkdkBg0KnL546aWXmDRpEuvXrycxMZHPP/+cadOm0bt3bwAmT55Mq1atWL9+PT169KiLS65SUakLzzk9k86lUyFWprjtIrdDawK/F23BIeIir2Jvjpv2MZ3RH1pMYe/XOBV6Pa31G1GrVKjS16L58kbxAa748RnDKPOq8HgrZgVvbh9H55TAWiwqlYqB7eJYvCeL9UcCl00/dE0jGhWsFKMDZ2h0opqwoYZGBdRasJxulrl1ChxbKbpft7sNotuIMsbHV8Pn/UTBPr/v9LLsc9okaI2iUabtlEjmNVrEB77WII5HDWMWie1zHob0NRDZtH7mm2j0kNRVBDI3vwNthop7MOMukayslvn3kiRJF6Pe5Mj4fD5mzpzJ6NGj2bZtG1lZWfTp04fCwkJCQ0PLj0tJSeGJJ57gySefrPI8LpcLl6uiA7XNZiMpKalWc2SO5dnZfKyQN34+WGXjSGuQjoX3tSbx0zZgjqF4xDxmHNZwX5Ni1F/ehL3daBaFjaKwzMftzVR8stPH6hMupoxsStip5ajmPFh+LiW2A747prG/1MzcbRn4URjUPp7kcFN5bsy5ckucHM4tZeHOTAxaNb1bRhNm0tFUm43u6DKRX2JNgMa9IaIpmKNq7uY4CmD9JFj5WsW2VoNh4KtwfC18f2/F9tj2cOeMqhtIFhwRozJnRizUWlFkz5oMXoe4bhD7HQWiSWYdt5avVlmhWIIdmiKCL3epeH8hcYFL1SVJkv7EGkSODMCuXbvo2bMnTqcTs9nMnDlzaN26Ndu3b0ev1wcEMQAxMTFkZVU//D5x4kReeOGFWr7qyr5Yc5Rnb27NkzO2BzSB1KhVvHRrW0Lzt5zeoCe3TAHU+DRG1CoNapeNYL2av/54ipl7zKTl2OmeGobDr8OsDSFgHZQuCK1GS9uEUNomhF7QtUWFGIkKMdKjcUTFxrIi2DgPdn4L4U3E9I09F3r/q/wQW5mHLJuT5Qdy8Xj9XNsiivjQIMLPWZl1Xiq1+LA+mz5Y5Lmc21tJawSqaR6p1oL6rJEalVqMyITEBCYAh8SKr/osKCywkrE+GGLb1d31VMVRIAIuS4LosO6yQ2kOmCIr/zwlSZLqUJ0vv27RogXbt29nw4YNPPzww4wePZq9e/f+7vM988wzFBcXl3+dOHGiBq+2aiFBOppEmZm64Tifje7C6F6NuLJpBKO6J/P56C40saoxL/mHmFYoPkHTxaO4v40f3cy7cTQbxMpGjzF+nrjOIoeoqhsdBCGZ69DPuku8iDkaVCpUJ9ajfH/fH8ulcBTClimw7EVRvC5zB5zaClsmw4pXoTSPIoebz1Ydod/bK3n5x328/vMBbn5/Nf9ZsIe8EtdvvgQggqUd38LPp4Mj8+kkbWehyImZPjLgvXFyo8iZsWUGnqcoXeTOFJ8Q9zAoTCT1nsmZ8VdeXi79AY4CUVTwg25wYr0IYtJ+gvc7we7vwWmr6yuUJEkqV+eBjF6vp2nTpnTu3JmJEyfSoUMH3n33XWJjY3G73RQVFQUcn52dTWxs9X9xGwwGLBZLwFdt02vUPNGnCXszbIydsoljeaWkhAeTZXMyc8MRorJWcHLwdLx3zxejCPZsPM5ScvtPwtm4H1tzxcKczkkh/Dg6mTeGtaFdjAFN5lZQ/HjjOnJ8+E/kD5gkgpmCw+CtPIV1wfweSF8vvm8/QixXvmqCeHxqK/h9HMkt5b2lhyo9de72DNYfucCquWdWEIGo6/LIerj27xDVCjK3iwAkrgM8uAqGfSGCmareW1mRSJLVGmD0AlE0zpJQMSXj+QP3QqrM54EDP4LfC1OHiU7s348Tv6S7v/9jv3uSJEk1rN7kyJzRu3dvkpOTeffdd4mKiuLbb79l2LBhABw4cICWLVuybt26C072vRR1ZDKKHBjTfqQo+Qa+2nCS1YfyCDFouaNrIlfH+fG6SjlBLIt3pPNoyxL8aj0PL/FwqsjNlNFXkJZXRm6JiwEtrEQ6DpMd0oZ1Rwq4NkmL9eiPHA27khHfHmdI2zCebnIKolsTltTyj120PVssAW4zVOTEOApg71xoPgC3KZanvtvOgp2ZVT61bYKFr+/tTtiFTDHZc2D3bGg7VIy8OApg33xo0gcOL4GmN4j8HJcdjiyH6FYQ0STwHH6f6NTtdUJ8Z9DqRACTtRua9K48RSX9ccUn4ZvhkHPW6Gijq2HYZ/V/6k6SpMtCg8iReeaZZxg4cCDJycmUlJQwbdo0li9fzk8//YTVamXcuHFMmDCB8PBwLBYLf/nLX+jZs2e9WrEE4PUpqAsPYbIkc1WzZG69IgGjVo0fSLM5Meos2Ms8LE0rYn+2FqfXw64MsVpo7FfbeG1Ya3qnmPloXSZjrmyDxutnQKqO91adpNTbkRXLTvHQdU1oHWdhsz8Zq1dP07wcwn15YIoQeSLncHl85Npd5NhcaDUqIs0GYixGNGqRg6IER1PcZhQniny48wuICjES024kBkMQbpenyoJ7ZxQ5PHh81a/SKnV5ybO7yLO7MGgNJLe9G4v5dC8qUzh0uBO0+or/gghGmt1Qdd8ltUbkkPj9IogBUfDOkvD7+zRJ5xcUBlfcBT/9o2Jbtwdkl3JJkuqdOg1kcnJyuOeee8jMzMRqtdK+fXt++uknbrjhBgDefvtt1Go1w4YNCyiIV9+oVOBpdD3FxkRe/m4/469vyqq0PObvOMWZlkSpkcG8fGtbnpu/h+P5jvLnNgo30MOQjvGzO7ln0Dd8uS6dJzpp0U29hfHXvsw7RxJ5ZVh7Plx+iDd/ruiw3TM1lDevMxA/5wEY9nlFqwGguMzDvG2neOnHfbhOLwsPM+l4f+QVdEsNR61SsfNkMQ9O3ULu6XwXvUbNX/o0ZVT3FMJMOvq0imZdNVNIVzeNxBKkq3Jfvt3FxyuP8MXqo+X9mOKtRj69pwut4iyo1aqK4EV7zojO+YIStSYwqfe3jpd+vzM5MT//M3D7rLFw1/eQ3FPee0mS6o16N7VU0y7F1FKOzYmjzMnwzzbTMSmU5HATn6+u3Mk4KsTAsze14rHp28u3/fpQW5r+MAzy0kBnorjvG1hXvSCmfoLCcDy4gXHfHWHdOXVgAK5uEsb7jTcSuu1DuH+pqHILrD6Ux12fbah0vFat4ucnr0GrVtHvnZU4PZVHVT648wpuah/PyUIHQz5YU2lkJkinYeFjV9E4qvJ0jqIoTF1/nGfn7am0z2LU8uPjV5NYVTVgqX4pzYNJPcW0YMpVMPQT0QYiZy9ENoOxi+UycUmSat2Ffn7XebLv5UCnUXG0yE1uiYtB7eOYsanqlVK5JS7cPn/A8uW7ph2ibPh0sCaBx4F10SMiiDFacY+ax4FibZVBDMCqw4XkJ/YWx+eK0Zoih5u3fzlY5fFev8LqQ3ks2Z9TZRAD8NYvB8krcZEYZmLmQ70Y0DaW07NR9GoSwexHepEcXnUwkmNz8X4VCcIANqeXLccLq9wn1TPBkTDmR2g9BG77XOQwjZoJLW6EUd/LIEaSpHqlzuvIXA7Cgg0cyi6hV+MwUsMN2F3e8n1J4UHc1T2FpHATapWKIJ2az25vxsQlJ9iUbiMpLIifM4O4ut/7hM8cUv48z3XPYTcl4S/ynPe1Szyno4z8NGjaG6fHz7G80iqPDdZraGKBlCAn3UfFcbhEzTvrijmcW1HK/1i+ozz/JTUymDeGt6foplYoCliCtFiDqkjwdRRAWRFhPj8PdAvj4w2F5NorL9Hel2ljcMeE874fqZ6IbIZy89tkeYIoy7Vj0IUTd8sHqIPD6/rKJEmSAshApobc3NTAHcZjlOhSsRi12JxermsexYhuSby3JI19mSWoVHBNs0ie7xPLR/1NfHMkks6pUSQrGYTPezDgfLolzxIS154Y/flXiFh0p0dWosQqpiC9hqbRZvKPBo7ixFgMTB8eR/Lmf6E5uBAUP60jm9Hjhpd5bXc4M3eK45tEBaPTVgzUmQ06zIaq82HweSF3L/wwAU5uQg/cm9yTG4e/xAM/lbI7oyTg8HYJ1t+6jVI9UVDqZuFOG+/8uoX8UjdBOg1390xh3FUmYiz1sPWDJEl/WnJqqSZ43UQfmklIWBTRq/7JQz2isRi1jO7ViL9M28a+TPGBriiw4mAet399EKcXxncLpVFQGUmLRovpIYOFwsFflU8z6aYNI96sonfzqleK9G0RTkT6TyI3JrIZINohPNWvRaVjPxwUS+rCO9AcWADK6eAnL42oObfzdAcPMRaRvPnX/i2IrKbVQSVFx0SfpJObyjep09cRP3sw/xsYUT4lBSLZuGOSXPHSEHh8PmZuPsGz8/aUd04v8/j4ZOURnpu3myJH9SvaJEmSLjUZyNQEexaaYyshfR3avd9ze8RhXh7ciq/WHS9fuXO2PLubZblmFh8qxW8MJW/gx2CJJ+PW2UzYHkv6oBkQ1ghufB314n/wci8NfVtUDOmrVNCvVRT/7QHW4z/DPfMC+hO1igvhrds7YDGKAbfkcBMpzn2iQu65FIWotf9lfI8I/n1La7o2usCpA68b1n8EnrLK+1wlRKZ9x/XNxbmaRJmZ/kBPEsKCLuzcUp3Ksbl4b0lalft+2pN93qX5kiRJl5qcWqoJzmIITxUjE6HJaIuP067VlTx7ovrk1mWHbUSY9LRK9OELbYlx7HLu+2o/ezPt+BSYPOp7NL8+CxoDsUXb+PDKpuRe15Q8uwuLzk9ERBQWpQRGTq+UfBli1HFLh3i6N46gwO7CGqQjYvXfq70WdcZmRg6LQLEkoNdqqj0ugKtYdLOuhvnUSl4b8jB5A9sSHqwnKkQu120obE4vpe7q2z4czy+labQsQihJUv0gA5maoDWCs4jCK8az3R3PextKuCXISYTZQKGj6mTdOIsBk16L2u8h6tBc1lhvYm+mSLo9kltK0Yk8IrrcB3vmwNr30OuDSWh3GwkRTWHeU/DgSgitvrqvVqMmITSIhNDToyChydVff3AkuoI0wAuhjUB9AQN1GgMEx0Dugar3m2OJwEZEdFTl+i9SvWbUnv/nH2a6iKahkiRJtUxOLdWE4EgK2ozmq+wUxs46wbYTRczYdIIRXZOqfcqo1joe7RLMeytPYou/kuZRQZgNIq7MLXEREtcYZt0L274G2ynIOwjLXoYNH4sCeMHRF3eNbYaIjtFVueIeWPUmfHyt6HV0IYwWuOqJ6ve3HQZThwaWuJcahLBgPb2aRFS5L8psIM4qk30lSao/ZCBTE4LCKAjvxLtLj5Rv2p9VQrBew03t4gIOVavgpQGJxDkOUORRE4QLfc5OQvJ38v7Ijpj0GoZ3iIA174KzqPJrnThd6E53kR8mlnjRmFF9ziBc0z4Q3RKOrwGXDZa/KpoxXoj4jtDrscrbez4KuftE+wRT1R+IUv0VatLzyrD2NIoIrBdkDdIxZWxXYmUgI0lSPSIr+/4Rfn/5NMyPuzJ45JtttI6z0DTaTKHDzYYj+Tx4bRM6JoWSXuAgWK+mW0ooEZoyThU5+WJzDv/XLJOIBaMBFfl3L+WkrhGJmiIivrwaXCVVv26720W1VZWq0i5FUVBVsR0AjwNKskWzRnuO6DydewCWvyw6HoMoPf+XbaII2oUoKxLnOrocnDaIaw8HFsHxtXD3nIAk5PPdP6n+ybY5OZZXyp5MGynhJlrGWogPNVb/+yVJklSDGkTTyAbJ64biEyJ3JWsnJPWANkOwBun49J7OpGXb2XWqmCZRZsZemcr3W07y8Yoj/Pj4VXy/5SQpEWbW5bn4x5w9dE0JoyD2KryDphGz4m+YrWG0Kj1MricUNNXUbgHQmwKCmHy7iyN5pXy36QQen5/hXZJoFmMmOuScv5x1JpGUvDMf9i0Qoz5eZ+AxGkOVAVK1gkJF8BPdBhY+CcteEsu7Q2IrnxvEGvTiE5D2CxxdAZEtoP3tYgm5rmGuanJ7fezLLCHOaiTaYkRRFNJy7ATpNCRVUwW5IYixGImxGOneWI6qSZJUf8kRmYvh80L6WpH74TsriTexFwdunsHITzdQUFqxNFWlgv/c0pZ9WcXcd1Vjer+5AoDbOieQHB7MW78cpFtqOI9e14TuUW608x5Gc3IdRaMWE3JwLpr171d9Hff+BMmiA3heiYsXFuxhwc7MgEOubBLJ2yM6EH1u8TJnCWRugy8HVX3u7g/BDf+58KaAHgccXQ3fjhABTHhjMULjtovRmDELxbYzcvbCFwMDp83UGhgxDZr2Bk3DSiR1e31sOlbI6C820j01gnfu6Eh+qYvbP15HuEnP1Pu6y/5SkiRJv4PstVQb7Fnw3T2BQQyQdeMnPDdvd0AQA2Lw4b8/7GVMr1TUqop4cdaWU3RMCsUapMXvV3B6/Sh+H6rSXPB5CJ12E5qWAwIDgDPaDYfgqPKHezKKKwUxAGsO57EqLa+K95AJx1ZDh5GV94U1gh6P/I7OxqcL7MV1FD16xv4IerO4AWfHyaV5MOehyrk/fp/orFySdZGvW/fcXoXcEhdev8Kaw3mM/mIjt3+8DluZF7vLi6uanlaSJElSzZBTSxejJBPKKteGsamtbDi6s8qnuH1+dpwoolNyKJFmPSVOL5FmA7/szeLz0V3Jt7t5bfF+iq9tQvsbJtPsl7Go8/bBzNFw45v4S/NQH/gRRR+MqvVgESxENAGgzO3ji7XHqr3cyWuPcn3LKMKDzwpMDv4Ca9+Dq58Wq5/2zQeXHRpfB2GplZOBf4vOBKnXwugFIvCyxIE5Gu5dLPadvlZxwQWQuaPq83gckH/k/MvE6yGzUcsNrWN4e0QHnpyxg72ZNgAigvXMeKAnTWS9FUmSpFolA5mL4a3cCBHA9xuzcz6/wqFcO88Nak3T6BDU7lJSLX6yvVqS9KV8dUcqEcE6dp1S44xsgylvnxi9mDma4jt+YHGjnvRJNRL9y+Nw22cVl+P3U3aewmVqIMSdC34dhMSIjYmdYcQ38P048bhZPwgKg50zIHs3PL5TjDiVFYpAxGCG0yNFaIwQHI6r1E5OmZ+DOQ4cHj+t4y2ER3Wl1OXj4L5sgrR+usVG41IFUT6pYsuo9v6V8zjOv7+eMuk1tImzolGr8J2u5BwfasQSJP/3+r18foWs4jKO5jvIs7toERNCjMUQGJRXpfiUmJ40nx61tJ0erbTEVf8cSZIaNPkv7cWwJooRC783YLNJ4ycpPIgTBVWU60ckTd731WZ8fgW9Rs1rQ5qRvO0j9G1Gog5LISp3A+pTp+iUvQ/1/lniSVoDeF2EzbyVfrd+i0bfEhw5MGscDPsMLHGYDVoGtY9j4zkNIgFCDFo+uzUe7YyR4h/xQe+LpJ21/4P8g2LV0+wHRABzRlIPMITAqS3w1WAY8iGkXAX75sGvL8DIGTgi27LisI0nZu3B5a2YNhnZLYnW8RZ+3XWS13u40Lw7Ev9NH1Ha/AaCS0/ClBtFABUSW/UUkkoFUZV7RNV3iqJwILuE2z9eV/7zdfv87Dpl48kZO3jr9irylKTz8vkVdp4sYvTkjdjKKv5fu6ZZJK8P71B908rCY/DlLZB6DfT9twi+Z90LKHDbZBnMSNJlSubIXIzgKLj2/yptTtnyGi/c0rbKxT6DO8az/kh++V/qbp+fJ2cf4GTLMcTOGUHwto9Rz3sEYtuj3j8fAFvXx8kdtxl/ZCuRM7PnK0Kdp8Sy65MbRYNJQKVS0adVDIlV9DC6oWUEkcV7UGXthIM/wbxHYMHjsH+BWHJdeFxMCZ2h0cHAV0TC7opXxYqj2feL2jKr3xbJuzNGkVFoZ/yMXQFBDMC3G0/g8vh56rpEwje9DV4X5vn3ol//nghiHAXivANfq/re9ngkIPenoShxevl5Tza2Mi+RZj0/PXkN74zoCMCmYwXk2X9jFEqqJKu4jLs+2xAQxACsTMvjoxWHcXuryDvyOODgz1B0XBSRXPwMzBwjkvNPrBcFJS/vdQ2S9KclVy1dLEcBHF8NyyaKfzSjWlA26BPydPFkl7h446cD7DxVTIzFyF3dkzHqNDw7bzfn9o68t3sszzpeRZX2EwDeFjeh7v0vfPsXsVjfn5eWZTFzZBIJB75EaTscjSVW/INsjoHELgGre04VOpiy9hiztpzEpygMahfPg9c1Jtnkg73zYP6jgS8+4FWIaAo/PCGmkBpdDb2fhcimYiTIlgnf3yfepzURBr0nzhHZgtdCn+XDtVUn5TaJMnNntyQ6R7hou+ZxtKc2VOw0hcOYRaI+TdZu+PXfkL0LLAlw7d+g8fWVekY1FIWlbqZtTGdAm1iaRJuxu7ws2ZdNQmgQHRJD0f1GyX8p0E97snjw6y1V7jPq1CyZcC0JVa0EKyuEDZ+IukhnqFRw25fQrI9IQJckqcG40M9vGcj8XqW5oqaMLogst4GvN5zk9k4JaDQqXD4FFPjn3F2sP1J52gegT6toPrJ8iW7H1wAU3rGAf20J5m/XRGEsOoTf48JUtB+rToGOIyEkDlyloNVVuUTZ7fVRUOoBFMKC9RjONH8szYfPeothdxDneWi1qLhrzwbFB/oQ0XLgjLIicfwnp0dsrrgLolvjP7WNR5wPs3h31YGM2aDl+UGtmbL2GF8N1BPxTb+Kndf+n2hpoDv9AeQoFH9Fa3QiOfj3chSI93BmNMddKr7+yDl/B7vTi9lYMVNb6vKi16hlEPM7fLbqCC8u3Fft/qVPXUvjqGqCEnsO/K+LaOQKENMW7p4t/gCQJKlBkQXxatvZ0yAeB7e2seD2K/x3/h5WpuUx7qpUtOepWntlvAbd8f3lj8Pm3sWrI+ehnPiFkJ8nBB68fSrcPfe8K3r0Wg2x1nOaM9pzYOHTFUEMiJVXcx+GW/4n8lXOVXBUFKybNVY8btpHTEHNfRh1TBuubq5m8e6qr6FNvIWCUjefDDATMWdo4M6Vr4kPlaZ9QB8MpjAgrNr3c0EcBbDpczEy1vd5ESQdXgrrPxTtGC5hTsTZQQxAsEH+r/V7tU+0Vrsv5nSz1SoVpYuRxDNBDIgE9p+fgz7PXXi1akmSGhT552IN0KrUBAVbeHz6dlaert3y/daTjO6VUmXejCVIyw3J4PV6sY1eIoIiZzHmhQ8TEtcMAEer2ym8bZaY6sk/DEv+K0ZKLpTfC4eWiERdgAGvwM3viO/Tfha5Mt5zOnMXnRBNI5e8IFZNmSJg8CT48a/ifD4317ZJIcxUueqwSgVjr0zlqgQNCcueEEGGKRzPuGUiiVhRYPZ9F97H6ULkHYBlL4qciF//DQd+hO/uFu0R1rwrlpVLDU5KRDBNq1m2/tf+LYmxVLFyyVkC26aKXmQqFQz+AK58UuzbOR0ytomWGJIkXXZkIFMTVCqyil3lNUQAihwe5mw7xZvDO5AUXpGM2zEplBn3diJ218dkDfwMtzkZ311zIa4DhYMmU1SQh6P1CA62/xve+M5itUXKlXDDC6IdwIVSa8XS6u4Pw8DXoeOdoiP1Lf+DTmOg9RAxTXW2k5vEaM11/4D4K+DWj2DTZzBqpsjLGf4lieFmvru/C52TK64lMSyIT+/uwt6MYsbOOMTR3h9CXEfKRv2AN6Y9DJ8iVpLcNQeMF/EefktkC7j+n+L7bVPFX+OKIgKnKx8XS8elBifGYuTLsV3p1zoG9ek/BMJMOl6+tR19WkZX3evJGCK6uLe8GW58E44sA51BNDC96klI6CT7eknSZUrmyNSQ6RvT+fvsXQDoNCqeuqEFX6w5SphJz+heKXRLDafY6UUN+H0emll8eHQWPKUFxIZZUDwucvxmcNsxa/woejNmo14Mk6tUIlHR7xHLo8/lLAGNpiL/xOsGTxkEWUX9Fo0Bgk/3y3EUnm4fkFDxD7urBNQ6MaqxYZLILxn8ARxZIaalgsLhuv8DlVb0QwqyUlRURIFXi8+nYAnSEWMxUuRwU+jw4FcUkg1l6CxnTb+V5oLBCtoabkFQVgjTR4nu3SDyhybs+0OJw7YyD3aXF7VKRYRZh06j+e0nlRWB0VrRp8ppE6NpF10lWTrD7vSSX+rC5fVjNmiJsRjRqKsIYs5myxCJ+Nu+Eo8HvAKtB5+/eakkSfWSbFFwiZ1ZAq3TqHjr9o4czrXzxvAOFJW5sbu8zN+eQWGpm3/N3c2wjzfx9toCNqcXs99mAKMVVUg0MVYTMVHRBAcFYd4zDU5tFsGI3izaCuz7oXJHbJcdDi4S/Y48ZeBzizowO78Vna4PLRHNLT1OEeBk7RTVfAsOiaF2Z4l4fHytGDUBkVuzaxaEpUDz/uAqFtNLRcfENE5ZIaGhoTSONNMsJqS8rkeoSU9qZDBNosyBQQyI6bOaDmLcpeK+pK+t2OZzw6/Pi8DpYk/n9bE3w8b4aVu58tWl9H1rBW/8dJDM4qrrA5UrzYPV70DOPjEi5LTBntmQvv63iwBK1TIbtaREBNM8JoT40KDfDmIc+bDug4ogBmDx3+HwMvF7LknSZUlmJNYEVylJ4SZiLAae6Nuc+Tsy+GVvNuuO5PPZPV35futJpqw9htmg5at7u/HA15u5vXMikeoS3MZzRli8bhGYLPqrGF24Zx64HfDt7aInkfFbaNZfjMD4fGKJ9Oz7QaWGkTPEiM3Xg8UHqMYIx1fBnrkwapY45ptholCYLkiMsKSvhXnjRePGsYshobNIxm3WT+S0oBK5NWEpokie1yn2d7ij7rtVZ+8VOTFnppMSu8K698U0kzkarppQ9QhWNQ7nljL4g9V4fGKQ0u7y8vHKI6xMy2XK2G5VF2JzFosPzzVvw9Yp4h6mr4UfnhQrsu5bCnHta+gNS9XylMH2abDuf+Jxl3tFraTDS0QNJUucWOJ/MZ3dJUlqEGQgUwMcrjLCtBo+vqszHyw7xPAuSaxOy+NkYRmDP1hdXkPmxnZx7DpZxAu3tCFSycenCSHees6Ho1YvcmIim0Femigmd2b2L7Y9xHUQQQyI/8a2F/2XMrfDtOHiH2pFET2OUq+B3TNFou7XQyr2RbUAaxJ83lf8FQvQpI9YFdXvZfj+XtCbRJ7Mqa2w4LGK54alQpPedR/EgAiuWg0RK7GGTwGdUSwj3/a1yJe4iCDGVubhlUX7y4OYs+3LLGFfpq3qQMZoFQ04t30lRmYm9RRFBUEEnHLZ76WhCxJ5X1u/hKY3wDVPi4B97nixP7qNDGIk6TIlc2RqgDs/nQOZRZjCYtAazTjdPmwuL8M/Wld+zKD2cTzWpxkatYpQPahVfqwhIVUnLgIUnxR1XEpPd7C2JuJ7YBWa4PDyQ3w+PxqNWhSw+6K/WIYMovjcg6tEMTtbBnzWR/wXxEjFuF/g837lFYKJ6yhGbMxRYtSnJBNspyAoQgRHBUfEccZQUYMmNAkQheA8fj+RQRq8ipoyr58gnQa9Vg0+L2guQZxszxF1ZEJOL7V2FIjppaqWlp9HZnEZV726rLwC87nu6JrEK8POM7KSfxg+6CruH0BCF7hzeoOsVtyg2XNF7pfpdE5Yab7ILbvI3wdJkuqezJG5hBSVCp1Ox8J9xfj8Cg6Pj4U7MwOO2ZpeBMDezGLunbqDZ384zM6TxZQ4PZVP6HFCzv6ATttFHR9k0UE7OTaRr+Fwe1l9OJ/cohKRkGs/q0hdWZGYdnGViA/Ys/NFHAViX1yHim0FR8Tz/X4xxWRNhPjOolv1mQAIRLuCrF3kFxbx465MHv12K28s2kfZyZ0oOXt5Y/F+3v7lAO7cwyjp68X7qG3m6IogBkQQ9zs+tFSoMJ+n9kt48Hnye5w2kRh9JogBsYzdnivL4l9KxadECw/16Z+VPUdMLakrlwuQJOnyIQOZmqC3EBYRzY2nGzjO2XaKKWuPAXBl0wiCdBpOFZUxdsomGkeaibUYWLAzg8EfrGHBjkzK3Gf1lPG6ReLtmZyY6NYU9XmdqWW9eHTGLv46cwd5JU5WHszlnV/2o87aXpETE9ZIDKErfvh2hEiEXfOOGGKPaCqWK/u9Iq+k/e3Q5T4RCLhsMPlGyD1dTdXvF1NVXw4SOTGhyRDTTpx3xij0J1Yzb/MR8kucTGjvJnjaLRi+vpl/doM7mvnQT70F1deDUU5saDDJrhFmPXf1SKl2/+CO1ax6cZbAnjmw8HTNkqQeYsVUWaGYFsw9UAtXK1Viy4DpI2H6nSJ5vTRfJPrOvh9WvVExsilJ0mVHBjI1QNEGgdbI7lM2VqXl0TklDJUKhndJZHjnJN69oyNBOg1JYSaO5TsY2jmx/LnPz99Nrt1dcTKtXiQmGiwi/2XULLRtbyXudC7NirR8hk5ax8PfbCXL5sZjCBfLo8Mbw+gFcNcsMVVktIol1i47RLUUlYHvni2q6xqtYhTj+n/AmB9FHkdIrHhNqBiaD44UOTGjfxC1ZBK6giGEEkMsR4u82N1+vBqjyE9wFmH86kZS5gwR02JaA359iKhn0wDoNGru6pFMu4TKw5f/uLEV8dZqcoK0RpFzpDVAi5tgxFTRUyo4UtxjWcvm0lCpK/KR5j0i8r92fy8eW5MazO+hJEkXT+bI1IAcWxklTi//XbiP5QdymfVwT9xeP5lFTr7ZcBy9Rs2jvZtRVOZmwowdvD68PX+btbO8g/QXo7vQu9U5SaG5B8XqoNNl1e1FuSxKK+Wv3+8pP6RjUiif3tOZKE+G+If6TAsDW4aYVopsLqY4NIbyvBaKT4qpkOBoMJ+utZJ3SHwQnznmjIKj4gMiTIxUKLYMigvzGTG7gAPZompuSoSJb4aEkzhniJi2AtAaybptLo6w1jSOqb7cfH2UY3NyMMfOz3uyCDXpualdHHFWI5ag80xPeD2QfxBMkRBy+ueYlyYCPGti9c+TalZJFsx9REwnndHvRbji7osrJilJUr0gey1dIkUON+7TK11Swk18ek8XgrRqfD6Fo/mldEoJo3NKGOkFpby0cB9unx+/Auqzknx9VcWSUc0DHqpNYQQbvQHbTHqNSMEIbxz43LOLf0U0DdxnTYRzY4vIc445Izw14KE/OJZ3lheWBzEALo8fL9rAPASVGr9Kj09peKtEoi1Goi1Grmp6EQX1tDqIaRO4LbJZzV6Y9NtUGhH8n81gEdslSbps1enU0sSJE+natSshISFER0czZMgQDhwIzClwOp2MHz+eiIgIzGYzw4YNIzs7u46uOFCe3cWri/fz+cojJBqd3NQuho1H81l2IJcRn6znf0sP8dmqozw8dSs/78nmP0PaYtCqMRu0lHlEYqhGraJ5zPmXCTvcXlYezGX8tK0ARJlFtdi1h/N5etYOcmyXIKkW0GjUXNu8YhVOjMXA9OExNPrhdrECSmsU01YeB/FzhpKqpMtkV+nSsOfAor+J/BiomGZa8JjYJgviSdJlq04DmRUrVjB+/HjWr1/PL7/8gsfjoV+/fpSWVjQWfPLJJ1mwYAEzZ85kxYoVZGRkMHTo0POc9dLZcaKIbzeeoNjhIr+ggCe/20nXRuG88fPBSscuP5hLVrGTF4e0ZebmE+Xb/9Y7iUjj+UcunB4/29KLUBTokhLGj49fzZvDxaqjwzmluH2XrhlesxgzPRuLpa3RIXpM3iKRSKkPxnX3Qk4OWyCWHLtsaGwnG0yyr9TA+Tyi0zVA/5fh4XWiqCOI5HnvpQn2JUm69OpVjkxubi7R0dGsWLGCa665huLiYqKiopg2bRq33XYbAPv376dVq1asW7eOHj16VDqHy+XC5ar48LTZbCQlJdV4joytzMN9X21m49ECZoxMQadR8/G2MixGLTO3nEStgnNLkqRGBvP6sLb83+zdJFgNPNI1hJa5iwntPLw8D6U6haVuFu7M5IY2McRYjNidHlYczKNdgoXkiODzPremnShwsOZwHt9uSKd5pJHnO7vQG4y8tFWLSqXlH921aG3HUDe6WhTWk6RLoegEHFsFLW4UOTElWbB3HrQdKuv5SFID1CBzZIqLiwEIDxdF37Zs2YLH46Fv377lx7Rs2ZLk5ORqA5mJEyfywgsv1Op15ttdFDk8FJaK1UbWIC3ZNhexFgNdU8Pp3zaWMrcPs0HLxqMFfL76KG6fnyKHm3j3cWZ0OYDRkYH5529EjZeOQ8SJS7LFEufgKFFMriRLPDaGEhZs4s42BtSaEhwuLQWlbppEmXC4fWQVO4mxGKovrneaoihk2Zzk2924vD6iQoxEmfUE6S/u1yAp3MQd4clc3zwKr1/BYNLgVVT8pa+CSadBb9BCZKOqGyb6faKsv8FSUTCvNE9MS8kVPtIfEZqEp9VQsh1+cnIL0aqMJLcehdloql//0EmSVKPqzf/ffr+fJ554giuvvJK2bdsCkJWVhV6vJzQ0NODYmJgYsrKyqjgLPPPMM0yYMKH88ZkRmZpyosDBY9O3ER1ioGujcNJy7Kw97qBPMysDw0P5z4K97M20lR/ft1U0747syGPfbqNn4wish2YTvOl/FScMayQ+wEuyRaPBjZ/AqO9FYbqpQ6H9CFF63WBGPXc8/rAU0ls/xqAvKsrpx1qMfHJPZ9rEW6ttrOf1+dl1qpgHv95CTokYsdJpVDx6fVPu7plCePDFd2mOOWtJsg4IWKBcXRCTtVO8r5HTRV+nonRRr6bvC9B8gAxmpN+tuMzNDzuy+e/CvTg9Yro11KTjvTuuoFtqOEadTPqVpMtRvakjM378eHbv3s306dP/0HkMBgMWiyXgq6bk2V2Mn7aVbelF/LovhwFtYzEbtEzaWIA2OIx/ztkdEMQA/Lovh8W7s7i9cxJjr2xEpTBjwCuihovfK7o2FxwRTSPXT4L8Q7DsJdjxrVhWemQp6i2TifFl0jK24n1l2ZyM/GQ9GUXVd2nOKHJy56cbyoMYAI9P4e1f01iVdomKhZUVwrxHxTLtL28RHban3CyWhC94XDT+k6TfaW9GCf+cu7s8iAEocni4d8omThXK3y1JulzVi0Dm0Ucf5YcffmDZsmUkJlbU3YiNjcXtdlNUVBRwfHZ2NrGxl753Sr7dxc6TYvrL51d4dfF+/nfnFdzdPZlTdj+Hc+1VPm/hzkzu6ZXC278cJK/JrWJjZHMKhkyjJKabeBwULvodaQ3wy7MQ3xGa9xf7Vr8FR5YBUNT/fd7bqWXXqeKA1yh1+1h3JL/aa19+MKd8pdS53v7lILkllyAZMjgS7pgmlot7nTDnQdHTSR8MYxaK9gKS9DvYyjy882vlJHsAr1/h243p1fbRkiSpYavTQEZRFB599FHmzJnD0qVLSU0NrFvSuXNndDodS5ZUFLg6cOAA6enp9OzZ81JfLvlnV+AF9mTYeGLGdno2CedYfvV/8Xn9Cm6vn5AgPfnGFA6M2syeWxZyx7IQ8n0GSgtzyHerRbfpbg/BwNdg9yxRzEtT0eNHaXQNO4N7Mnlz1SMoezNsVW4H2H1O4HO24wUOvFV0fT6fEqeH9AIHR/Ps5J0Z5TlTEO8MR37l5ddhKTDss8Bt1/4dYlqL6bQLUObxcqLAwZFcO1nF8i/tesdxTkBdmidab9SiMo8Pi17FJ8MaodeIf9YSw4KYemczwkw69meV4PZeutV9kiRdOnUayIwfP56pU6cybdo0QkJCyMrKIisri7Iy8eFktVoZN24cEyZMYNmyZWzZsoWxY8fSs2fPKhN9a1tkSOW8jyKHh9VpeTSPrn7lkF6jxqx2c1VjK+YgI0ddZt5dns5f+zXH4s7FOP9+DIUHUbJ2QXQrsVy07wuw8GnRyfk01bGVdCj6lcevjK7ydTokVV9F94rksGr3NYkyo9Nc+K/CsbxSHp++jWtfX8b1b6zg8RnbcGanwXf3iJwXENWFv78fcvYFBjMFR2DG3YEnXPYSZGwTHbN/Q0ZRGc/N3UPvN5fT+80VDP1wLQt3ZlBcVrsflNIFsp2CWeNE01MQ9V1++iec3FSrwYxZD69eqXD9wZeYekcjUiOD+WZoFFdtncB3dyTSPTVcdGWXJOmyU6f/Z0+aNIni4mKuu+464uLiyr9mzJhRfszbb7/NzTffzLBhw7jmmmuIjY1l9uzZdXK9kWY9XRsFBgQGrZrRvVKICNbTKq7qwnYjrogkdtMr3NRYyycrj7DrZDE/781m9aE8tFnb0RxdjvnbW1D5fbB3rmgtsOlzOLpCnKDP89ByEADWpX9nbAsX7RMDgxZLkJaujaqfmrmqaSQh1XR3frp/iyqDtKqcKirj9o/XsXR/bnl88uINcRhn3C6Wvn41WAQr0+8SpeK/vEl00Qbxl/n0Uaenk8wi4ffMNNNXQ8BZdN7Xzi1x8uDXm5m55WR5onNGsZPx07ax9lD102rSJeLIh7njxTTolIGQsxcW/R/snA5fD4HSnFp76WBvMeEzh6JLW0jHLf9g1vAoUuYPh2OraLZ8PPd3qT4RXpKkhq3Op5aq+hozZkz5MUajkQ8++ICCggJKS0uZPXt2neTHAIQHG3j3jiu4ullF+frEsCCO5DhQo/CfW9rSNaUi0NGoVdzeMYq/NC9CG92K6TsKmLnlJF0ahZMSYeL2LkmUWppCUNjpBNib4eBiWP8hdB4jOk4PeAVaDYIb34BWt+Dv9RhZ2kQOZldUKk2NDGbGAz1JCK2msSGQEBrE9Ad6kBJRUdfFpNfw3KDW9Ei98NyUdYfyAhKGAf61+ATZvd8W+T0FR+C9KyBji+jTNPhD0J1+TVMEDP1UNLMcs1AULLt7LkQ0gdu+qDiuGukFZew6VfX02Us/7iP7ElU4lqphihC/r6Zw8fv8YU+xEg/ghv+I4LW26Ez4b3oTAP2xpURM7gUlmWAIwT3wLTTB1Y9ISpLUsNWrgni1oTaaRhY53OSXurE7vcRajWw6WkCs1ci9X27iqRua0zMlhLKCDEJ1XiIOf4/W72ZPi7/wytKTbDpWyOu3tadTcigjPllPeLCeaYMtRH51Xfn5/V3uRX3Df8DtAMUHQRGgM4g2ACotLn0ouSUu8kvdGLRqws16okOMF3TtOTYnBaVu3D4/yRY1JlMweu3p3BRPmWh0WA2/X+GRaVtZvLvy0vcbWkTwWsccwuadNW104xuiYZ/urGtTFDFCYwytyIkpzRNBzG8Uz/ts1RFeXLiv/PFVTcIZ1sKAWg3z01z8a1B7UiMvbXFAqQo5e0UQc0bne6Hff8Bw/lYcf5i7FGXVW6hWvVG+yXfvz6gTu6C6wPwrSZLqjwZZEK+hCDXpCTVVJOEG6TRYjBosBi03NdZiXfY4ugM/VDwhOJKWncYSfHpqJ8So4+v1xxl3VSqtLS7M618JOL9671zo/hBEtQh84dP9YwxAYriJxPCLr5p7piki9mzYMgU6jRbLvx0FsHOGqIpaTZVhtVpFUljlQCdYr+GxHlZCVj0TuGP9h2Ll1Zmu3AAqlfjLPeAEF9agMdYqAqLwYD1f3ZZA0on5WLdNB8VPn+a3YlDFAzKQqVP2HFjxeuC2fXOh+4MQ3bJ2X7skC9X2bwI2aVa8BkM+rOhKLknSZUdmv9WATskWQvRq5o9rhWX5v8qDGGfHsWLaqDSP4GmDeLl3GE2jzNicHqasPY7b66Odfz/Gg6LRnbPD6IpppskDKhJna5o9F36YAMtehtkPiNyGdR/A4r+LXAZbRrVPHd65cnHBb+9sQquV49FmbBXTST0eqZhm+mqw+HCrAR3iTBi0aj67NZ62S+7BunYiFB6FouOYN76D7suBoky9VDdK8+DnZyumkzrfWzHNNGUgFB6vvdcuPgVTbiqfTqLbA2L74V9h/l9EBW1Jki5LMpCpAaVuPyrFx74CKGo9CjQ6Cq9/he9C7yPj1tkQFIa72U1kORTeHd6azHxbeXKuPyQONDqKrn2RVY0ewzd6kQhmWt78mzkjv5suCNoMEd8fXSFyWs4Mxze+PmDJ97niw4J47bb2nJ03+f2ufJyd7hNTRXd8C72fhbvniWCm85jznu9ixAZ5+W5sOxoVroO8tMoH2E6JUSV/1fVypFpmsECne8TPe8ArcMMLMGaRCGZa3Fi7fbd0Rmg7TAQxYxaKBPkhH4nfya731W5+jiRJdUrmyNSAfZk2Zm4+QVqOHT1e3rgxns835fG/tTk0iw7m62Fx7Mnz0jgxgWRdMZleMz6NEZ1aha3USarFR4lHBYYQIoP1YM/BrwtCbaz6ev1+BfU5KzCq2nZeLjvsmw9zH67Y1nGUSMr8jakeh9tLbomLzccLKXV56dYonORgPyalVHxo6YLA64HSLNCHiAZ+NcRny0Lz/VixRL0qUS1g9EIwyyaBdcLrFquT9OaKn3vhcRHE1HbjRkc+uEvBkghqNbhKwGmr+J2UJKlBkTkyl5BGpcLu8qHTqFmyv4g+nzkoON1QMi2nlIFfHiciWM/k283YS3PJUpnIsBUREWzg173ZZNmc9G8TS9dUE2k5dr7fWsDxfAfXt4jmyqaRJJzOSzlVWMb6I/n8ui+bVrEhPNxBA34PXx3Us/NkMc9eG0aYvwB3RCv25jhJiTARaTbg8frZn11ClNlQnmeCzw1551RCLToh2ghUlcdyFpNeS0qElpRKXbdDK77V6sCaJKYVjq+DqJZgCgO3HbL3ih5T5qrr4Zz3XuM//wiPxiCmt6S6odWDNTFw2290dq8xpojA31tDSO0nGEuSVOdkIFMDrEFaGkUEcV2LKJbuzykPYs4odHi4u2cKhwt9dD/4OQldn2RToZrHvt1efozH58fm9PD8/D3l9VkW7c4i0qznuwd7otOoGDZpHTklLqJCDDx/lQnd1KHgcdB/yGy6RZkI/eF+NDk70I6ay+er1Bj0Rp4d1JqjeaXc+el6WsVZ+OiuzsTqHLD2f7D6bfFCse0gaxccWwkLHoO+/wYFCK4+mLkgjgLY+Cksfxmu/xd0uw+OrIRZo6H1rTDw1YsLZopPwQ9PQqtb4Mjyqo/pet8fv25JkiSpwZCBTA2IsQbRt1UsG47m06dlNEv2Bya3tom30DrOyrhpW1k25mFcunBe+2lDwDEjuyXz4NdbKlX0z7O7eW7uHm7uEFdevyVIp8Gq84nRjbJCEufeSqI5RlTRVakoLczmlnYdeGjGPo7ll7I304bL66eg1I3X70dEKafLtXe4A9rdLob/Fz4plkd73aD8dpXd3+T3VSQsL3sRjiyF9HXiNUqyQLnYkvGKyIPRaKFJbzi8NHB3QldRm0aSJEn605CBTA3ZfLyAN34+wNRxPRjYLpaf9mSDonB1syhCgnT8bdYOfH6FzTYLpfl2jFpNeRPHeKuR9AIH3mqa2q0+nMfdvSqG521OD8vzw2k/5HsS5gwVIx+OAlCpyLvxc17ZH8sdvUIB2HaiCICk8CC+ua87iWEmwAS9HoOk7uB1wdSh0GEkDJ8CpkiRZ/I7pn0qMUfBDf8W32+fWpHXktxTFMALucjChtZEuHOGmAIb/CFk74FNn4mgq9M9IpCxxP3x65YkSZIaDJlMUEO0ahU/j2tOiFLCa4sPEG818mKfcFqEOHl65g5sTjHCkWP30SVWw9IHmpdX2dVr1ZS5z7/Sxn/WUI1Ooybf4aFQMQfWx9AY8IQ3Z0uGq1LvpObRIZj0ZxUFM4VDytWQf1g83vGtmAbKOwjaGkyM1Jkg9ZrAbYldA4vkXQxrIiT3EAFLs75w+5dw+9diukkGMTXHaYOS7IoVYF6XWEbvdtTtdUmSJJ1DBjI1ZEiinZhpvYk7OJXnbkhgVCsNMXNH0H7Xy3x+W0p5n5dujUJpevhL4qb345sholXBycIyWsRWn5SYGhmMw1UR6JS6vNzSWEXLlY9C7gGRnKvRg9dJ3PeDmTYkjIwi0XjTcLpR3pL9Obz4wz7y7KfbC5QVwY5vYOl/xGOtAY6vgYUTxGomZwl/mNsOab/C3AcrXgNg7Xuw8TNwFP6+86rOWp2lNcgVKTXNaYM9c+D9KyBrp+iFlb4e3mkLR5fLYEaSpHpFBjI1QCkrQrPjG3AUYFg1kQE5n9P857shL42gwz/S1uqiUYSJwW0jSC7Zjm7161CaS/ih2dzRzorXr7A1vYib2lUeUVCp4MUhbWkeYy6v3ZIaGYzGnok2e1v5dFLWyJ9PF9PLJzJ7NRm5uaREmFjx1+v4+0BRUXXJgWxKXadzX3xu2PSp+L7VYJiwH5reIB5v+Ai8ZX/8xrjLYMMkkROT3Asm7IOOd4l9274O6Owt1SNeF6x4VSxlnnITrHkPvhkmti95ETyldX2FkiRJ5WQdmRqQbXOyJ+0w156YhGb71xU7NDpyh8zg9b0WrojR0tdygqgfxoDfh7fTvRxu+xjjZh6lsNRNj8bh/Oum1mw4WsBHKw6TW+KiY1Io/zegJU1jzKhVKg7l2HntpwNsOVbAoDYR/LezA19ZMe8dS2LVMTuTbzQTmrUWV5vb+d+6fEZ1TyYx3ESRw83cbafo0TiCFrEhqM6MaBSdgI0fi3wZc7RIwF3zLvR8FKwJNXNzbJnig/DKx8TUT2kurPsQOo8WS7Cl+qngCEy5WSRXnxHVCu6aVXl5tSRJUi240M9vGcjUgIyiMm5+fzW/3N+CiI87lK/GUZr2I7ff/yjx6Ujd8iLqzZ+LJ6i1lP1lN4cdQViD9Gg1KswGLSFGHQC5JU68fgUTLqxGLRhOVyUtzcfm01Gq6NCq1UQFgcPlJqNUhcPtJcKkI9roRWeyUuL0YCvzUFzmwaDTEGLQEhViqAhiznDZK85f1eOaUOk1Smq/vkdpHqCqWIrttIkpkppIYv4z8Lph+zfwwxMV20bNEqvFZANGSZIuARnInHYpApnCUhe27GOk/DiqUul811X/hzapE5pvRwRs90e3xn/HDLThyVSpJAvmjod2w6DVIDENs/JNUbH0qifBFEFGURn/mrubZQdyUBSRD3PvlY24u2cjftyVyTu/pmE/PZXUtVEYbwzvUEURu8tQaZ6YGtGboddfQK0VOR8HFsMt75Q335Sq4XWJnJhvhoHPU7FdHyzK/8e2l8GMJEm17kI/v2WOTA0I0zhJ2vmuCGI0evJunYGjzZ0AGFa/ikZvgqiW2NuPIW/wN6DWos7Zi3bN21Un1XrKYPMU0fBu7sOwe7boKLz+A1j7PpzcTF6Jkwe/3szS/TnltWdcXj+TVhzh6/XH2Hy8sDyIAdh0rJA7P91AZnEN5L7UZ36/WOa98RNY/RaseUf0X1rwGBz8EbZPA4+zrq+yfnPbYeZoEcREtYKHVoMlQeTMzBwjEsUlSZLqCRnI1ASjFf91/8Tf6Brybp3B3zaHsDzpEZztRuEf9D88Hjf2uB4sjR3HM9vCyR3yLd7U3riv/isYq5hi0QVBl7HQpK94vOAxkTQLIn8lsQtZNhe7TtmqvJwpa44zqH18pe2niso4lG2vqXddP6nVkNKrovvxmnfhx6fF980HQoc7f//S7z8LYyjcPRcSuoicmNh2MOYHiOsId86UlZMlSapXZCBTQ7ShCTD0E0i4ghE9mhAdG4/2mgkUJl7LW3st/BQ1lvkHnVzfJoF/bw8hr8/bvLm2CLvz9ND9uX/lhsTAsE/BaK3YFtsern4KTBEcy69+5UiZx8e5qTBn7DpV/MfeaEMQHAnXPQPRrSq2BYXBkA8D6+5IVVNrRPBy54yKxN7wxnDX9xDVvG6vTZIk6RwykKkhij0X1n2AO+8Yc7eeoIUmC+2+uYQVH6BxRBCL0kqZ2MPL4JZmXuoXT+TGV3m6hxmzUScKjS1/JbCJoyMfVr4BzrMCj6ydcHAxuEqItVQ/qqBVq8rr1pyrcdSfIEfGaYN9C0TLhjPKCsW0nKOg7q6rIVFrKndB/42u6JIkSXVBBjI1oawIZc17qNe9T/ycoXzQz0zIkYWw9L+oZ9zJ0MY+XuulEPXdIIIL9mJd+ADandPQTR8hgpifnxVTR5NvFEm+bgds+0bkxAB0GQeNrxffz30YTm4mMSyIeGvVwcxN7eNYfiCn0vYQg5a2CdYqnnEZ8ftFP6cFj4nHzfpBp9Hi+9Vvwa6ZMkdGkiTpMiJ7LdWEoFC4YhTsmAqOAtQf9Spfgu1vNgBPUBRh9myR+2KKQHXdM2JFSM5eeLNFRfPEno+KSrV6E7S5FbZ+Cc0HwNUTwOcVQYxKDdGtiA0J4qtx3RgzeRMnCysSeK9uFsnT/Vvwzi8HAy4x0qznizFdibde5lVw1WqI6wCp14lcmEHviVVLGj0cWw0tb5I5MpIkSZcRufy6Bvlz01B/2K2ijkxyT/y3fQlaPRqtQaxGCgoVq0GOLodpZy3J7vWYyH8JCq3YVpIpPoBNp5MrS7IBJaDZYnaxkyybk/xSNwmhQUSFGAgP1lPscJNX6ia9wIHVqCPOaiTWaqxcR+ZyVZIFqCpyYkrzwO+BENmPSZIkqSG40M9vOSJTU5zFqI8sqxhdAVS5+9EUpIlmjGGNRDVbo1UUhNs9O/D5h36F7g8GBjLnfuhWkagaYzUSU8UUk9Wkx2rS0ySqhovbNRTndtaW+R2SJEmXJZkjUxOcNpF7seiv4nFyL9FduqwQZtwlApjds+Cjq6DwGPz8T1HbBKBZfzH1kbMXvhkOtow6exuSJEmS1NDIQKYm6IyicJhGLxowDv0Ebv1EBDOWBFGV11ksCortnQdJ3cXz+r4AQz8Vy1rVWrHkVS0HySRJkiTpQskcmZric4vKvsFRcHQVLH8ZbnxdbP/+fnCdVbzuse0iqAlNBqNFlITPT4PgaNkLSJIkSZKQOTKXnkYPMW3E94oP8g/BvEdFAOM6qw2BSkWRYiJTiWDpuhw0qlyubxlFjKU5oSZ9pdMezy/FqNMQc7puTGZRGR6/QnK46byXk2NzciS3lHVH8oixGLmyaSQxFiNG3Z+kR46zWHTePvQzeD3Q7AbR0dskq9JKkiRdTmQgUxsSOoNKBbZTlXblD/qSN1dkM23jifJtryzez71XNuLR3k0JDzaUbz+aZ2fEx+tJCTfxv1Gd8PsV7v9qM0VlHqbd36PaYCajqIxxX25iX2ZFAKVVq/jors5c1Szy8g9mHIWiBs/K1yu2Lf0PtBsB/V+Uo16SJEmXEZkjUxuCo+Cav1XeHt6YHYbOAUHMGV+sORYQeLi9Pg7nlpJrd7HpeCEPfr2Fe6dsYneGjcxiJycKHHh9/krncXp8vLckLeBcAF6/wkNTt5Bjc/3x91ff5R0MDGLO2DUDjq+59NcjSZIk1RoZyNQGowW6PwT3zIPUayGyGXQaQ8ldi/h4deUg5oxPVx3B4RYdq/VaDb2aRPDhnZ1QqWD7iSL2ZZWgUav4YnQXOqeEodVU/vHl2V3M3lp5JAhEMLPxWH7NvMf6yuuqaLBZlTXvyjYFkiRJlxE5tVRbTOHQ+DqI7yQ+XA0huF0qihwHq31KQakbt9ePTuMno6iMfLubaIuBj+/qzJS1x1h7OJ84q5HW8dZqp4e8fgV3FSM1Z+SWXOYjMj63KH5XHUeBOEaSJEm6LNTpiMzKlSsZNGgQ8fHxqFQq5s6dG7BfURSee+454uLiCAoKom/fvqSlpdXNxf5eRguYo0BnxBKk5Zrm1Rdmu6ZZFCgKi3dnctN7qxk6aS3DJq1jwnc7GNA2lqGdEjhZWMYj32wh21Z1v6BgvZYm52kM2S31Mk921ZuhxcDq9ze+Hoyhl+xyJEmSpNpVp4FMaWkpHTp04IMPPqhy/2uvvcZ7773HRx99xIYNGwgODqZ///44nQ2z6Z9Oo+GuHimYDZUHwixBWno0Dictp5THpm/H7vKW77O7vDw3bw8juyVjNmjYdKyQzccLq8yRiQox8NygNlW+fsekUJLDL/NeSyoVtBpUdSVfnQl6/UX2WpIkSbqM1Js6MiqVijlz5jBkyBBAjMbEx8fz1FNP8fTTTwNQXFxMTEwMU6ZM4Y477qjyPC6XC5erYvrEZrORlJR0SXotXQi/X+Fwrp2XFu5jRVouAL1bRHPf1amsSsvjaF4pi3ZnVfncm9vHMbZXI3aeKmZop0SsQboqj7O7vGxLL+Q/C/aSlmMnSKdhZLck7r+mMXGXe9PIM/IPw6//hv0/iLYRqdfCgIkQ2QI0ckZVkiSpvmvwdWSOHj1KVlYWffv2Ld9mtVrp3r0769atqzaQmThxIi+88MKluswLVlDqwqxToXdk00zn4dVbW+BR2oDfg1XjochvQK1WcTzfUe05DufaibYYuDEsFmuQjoJSN2aDBr02MF/GbNBydbMovhjTFYfbi06jJjrEgNlYdeBzWYpoAkMmQdlLoCiix9XZfawkSZKky0K9DWSyssSoRExMYKPEmJiY8n1VeeaZZ5gwYUL54zMjMnUpz+7ipYV7uaNDOFdseQZHXHe+c15FbFgIA6LysfkN3PtDMW0TwmgeY2Zvpq3K87SICeFgtp0dJ4sY2TWJ1346wIiuyXRKDg0IZmxlHtYezuPFhfs4WViGTqPilg7xPNWvBfGhf5IRGQCDWXxJkiRJl63Lbvm1wWDAYrEEfNWlUpeXz1YeYc62DO7+ei+b2/6Lr8t68ebKLP46L439SjIbiqyk5ThQqxTu6dkItaryedQquLtnIxbtyuK9JYdYf7QQj1fh7s83kGsPXIWz/kg+D03dysnCMgA8PoXvt55i3JebyKkmSViSJEmSGqJ6G8jExsYCkJ2dHbA9Ozu7fF9DEGzQcne3OJpEBeP2+bnz26O8uSoHgOHtw2ls28hNyV4Sw4K4vZWReG0xb4/oSERwRbuCiGA9bwzvgMfnZ8HODHo1iSAmxIA5SMv/DWgZkDycY3Py4sJ9VV7LvswSjp1n6kqSJEmSGpp6O7WUmppKbGwsS5YsoWPHjoCYJtqwYQMPP/xw3V7cRUqw72HayFSu+XAvLq9YaXRFUij/uMJN2HdjYcQ3zB/VDPPy59BHN+XqHv/Hfwa3waTXYtSpCdJpsBh13Py/1cRYjDzRtxnLd5/giW5WwkNM6M9K+nV4fKQXVB+sbDleQLfU8Fp/z5IkSZJ0KdRpIGO32zl06FD546NHj7J9+3bCw8NJTk7miSee4MUXX6RZs2akpqby7LPPEh8fX76yqaEoMjVi5ubM8iAGYE+GjYOeFK5o1Bu9SkX4lKth6KeUemHD8WK0Gi2bjxeycGcGHp9C/zYxTB3XnY9XpNHakMcV3s/QfbdEJLH2GA/N+kFIDDq1CqNOjdNTdVG8P82qJUmSJOlPoU6XXy9fvpzrr7++0vbRo0czZcoUFEXh+eef55NPPqGoqIirrrqKDz/8kObNm1/wa1zo8q3aYnd6mLEpnf8u3A/AwJahHMz3cDi3FL1GzawxrWhfvBRMkXhObmZFxEjsagsfrTjM/qzAfklRZgPfPdCF1M/bia7aZ2vWDwZ/iMsYzksL9/HVuuOVrsWgVfPLhGt/s3O2JEmSJNW1C/38rtMcmeuuuw5FUSp9TZkyBRC1Zf7zn/+QlZWF0+nk119/vaggpj4wG3Vc0zyaMJOO2ztE8GK7XL4aZKFJVDDt481EW02w6k04tgqdJZY24SpsTk+lIAYg1+5i+oZjeFKurfxCaT9D4VEMWg2PXNeULilhAbsNWjWfj+5KrNVQ+bmSJEmS1EDVm4J4taWuR2TOOFHgwKRyE5G3CYpPcip5EBqNDk32djRZO3CHNsUf0ZRQVRmP/Opi+cHcKs+TEBrE7Gsyifn5kco7e/4F+r8IQF6Ji5NFZWw/UUi02UC7xFBirAb0mqp7NEmSJElSfdLgC+JdbpLCTYAJwm4AINavsDejmAfmebEGdSS3xMXsu9RoDDr02uqbGuq1atT+qve71QaKS5xEmg1EhoivjkmhtfBuJEmSJKl+qLfLry9nPr/CnoxiRnyynsxiJ/mlbubclUrS/s8xfHsrd3eqvrHknVdEEnHguyr3ZSf256b3VnMox15bly5JkiRJ9YoMZOrImZp3cVYjsx7qSbxVj6okA0qyaGVfx8BWlZdIt4oLYWCHRNTFlRN5Szo/wvyjKnJKXPx11g4KSqsf1ZEkSZKky4XMkakjPr/CvkwbIUYtKRHBAHgLT6BZ8jyqvfPI6/c++/Vt+XafG4fbz/Uto7EYdfywM4NJg2JQjqxAv282XkMYOa1Gszg7hP/8mll+/iVPXUuTKFmeX5IkSWqYZI5MfWHLgMJjUHxSNDK0JoI5Bo1aRZt4CypVRT8CrSkMJfVa2P09kYsf5qqoljQatgCv2kCouozx3x9i9FVNySaEmGb9+MHXmeVHS/l5dgG2ssDpJJ/vso5PJUmSJAmQgUztyj0AX98KtlMV22LawsjpEJoUEMTgssOBH1EteEw8Donj1A2TGP3NXlpGB/NCuzxeHdyJfyw8Rn6pm8/6B9FLc5x1qgRsZd6Al421GLEE/Yk6XUuSJEl/WjJHpraUZMK02wODGIDs3TD/L1BWFLjd44ANHwOgpF5L0bi1bCgM4XBuKQv35PDPrSE8Mz+NlWl57MmwsdtuxnJqJeO6V+479d8hbYmxyHoxkiRJ0uVPBjK1pSRbTClV5cgyKM0L3GaOhhFfQ/dHUA39hDKC6JwcyvMDGgPw08FiVh0pBuCNmxLpUTgfrp7AvhIDTaLMmPQauqeGMeuhnvRqEhE42iNJkiRJlyk5tVRbHHnQ6Gq45mmYNRYcBSJH5pb/wYLHA1sMKAo48sESD73/CR4HcSY/npAIBgWbmbTmFDklLgBSIkz0TlIRUtoCrAncFK6lW6NwvH4/QToNoSZ9NRckSZIkSZcfOSJTW8IbQ9dx8NM/4NZPIKkbDHwNFjwG/SeCxykSgBUFcvbB5AGQe1BMMc0cC4d+pdBm56mZ28uDGIDj+Q6eXWEnz6OHjK3g8xIVYiDOGiSDGEmSJOlPRwYytcVgEbkw2Xtg8d/hysdh9gOQlwaL/gpBVph6G6Svgyk3iu3f3Q2luXB8NZ6iDBbtyWFFWgEArwyI49m+8QAs3FfIKmcjvNu+AVdxXb5LSZIkSapTso5MbfG5IX0jTL1VfH9GUBjcMx/mPAQ5eyq2B0fCmEUQFAozx0D6WvLHruW19Q66pIQzoOwHlOYD+H5/Gafsah5JOkJ4fDOIbgUyH0aSJEm6zFzo57cMZGqTxwlZu8B2UozMbP0Khn0Ose1h29fw8z9Bo4fe/4JG10DCFeC0gbuUsm3fYUvugzo0CW3eXpwhjSD/EAZfKSRfSbjOBWqdGNk5o6wY9MGgqQepTyXZ4PeIawyJqeurkSRJkhoYWRCvrtkyYfPnsOkzcBZBUne4+S3YMxu0RrAmgS4IBn8Ih36B+Cug6CSUZKIc+BFtqyEcs4cQWVrGOxvULN63DRUqbmkbzmNhRYRbdbDideh2H0Q2g7JC2DYNkrpAfOe6C2Yc+XBoKSx7CQqPilyh6/8FTa4HU+W2C5IkSZL0R8gRmdpgzxH5LunrA7er1DD8SxHg9P03oMCK1+DAIghNgZHTYMtXsPFjMIaSPm4Ht0zaSJHDE3CaKLOBOXenkji5k1i2fe9PsHc+/Pq8CJIeXAlRLS7Nez2bpwzWvi+CmHP1fQG6PyiCN0mSJEn6DRf6+S2TfWtD4dHKQQyA4ofVb0HvZ8HrAoMVuj0gPtyLjsNHV4sgBnD1+S/fbsqoFMQA5NpdLDzqw990gAia3u8sghiA1kPqbuTDngMrX6963/KJYr8kSZIk1SAZyNSGQ0ur35exDfRmCInHl7UHIlrAiKlin+IHwN9uOKfiB/Lr/txqT7N4Ty4lA94JeB6Ne0P/lyE4Cnzeap9ba0pzAxObz+Z1ito6kiRJklSDZCBTG0xh1e/TGsBZhFKcjmbDB3B8NRz8KeAQ9YmNRGhKCDFWn+diMWrR5e8P3Ji9C8ryofA4nNgA3mqCitqi+Y22CL+1X5IkSZIukgxkakPTG6pfEt3mVtj8OaqDi+HKJ0UdmY2fAKA06VM+zWRd/Dj39Uqu9iXGdY/G9MPD4kFKLzBaxYjI5BvFNNWcB04HM65qz1HjgiNFd++qhKaI/ZIkSZJUg2QgUxtCYkQrgnNFNoMu42DfAohqCbn7ILadCHo6jcbd9yXyh30PuiAUtZquCQZubFU53+X2jtG0DlNE4NL+dujxCAz9VAQz1iRwlYgAxmgRy58vFUsc3D5VTJ2dzRAi+kiFVG5wKUmSJEl/hFy1VFtcdijJgD1zoCQLEruJpchlBaAzwYaP4MCP0G44dLoHMnfA4WUU9X8Pq/MkKo0eNn1Kftx1pAe3YUGaCw0Kg5obSMxdRXjxXpEobIkTgcva96HtMHAWi4rCd8+BmHagvsSxqt8nWi8cWQ4Z2yGhE6ReIwKsS30tkiRJUoMlC+KdVheBjMPtJcfmIlFXghY/is+FaspNMORDMVqxZYooiHdGVEvIOwgjpuKLakW2PxSVs4g48sEYQnaZBpVGR/S6/8LBReAuhbBUvPctI9NlYNORXI5n5dIpzkgLfS6xy56CO74R55VVf2udz6+QXlBKiFFHpFnkAaXnO9BrVcRa5XJzSZKk30MWxKsjDreXNYfyMHsKSNr1bzi1EeWeBaiu/wfsWwhbJ8OtH0PxCTFqASKIGfQe/sNLOaltxMiZR4kO0TNpSAKqvGweWuol2+bm29uepFH6OnCX4u75BDtyFO7+YgVOj7/89RPDgpg29EuSp42AO2fIYKaW+fwKezKKueOT9QxoE8u/bm6FrczLyE/XE2s1MmlUJxnMSJIk1SIZyNSwMrePFQfyeLiDGk3GZigrRP3VIJSUq1DtXyAOOrZGJOieCWT0wShRLfAdXsmxEjVZNicZxU7u/96HSgW7MkpRq+CYXUN8RCv0ag1ZjQYz7sONAUEMwMnCMv65vIQP2ozCUnwKwlJBZ7y0N+FPxOHysvFoAQ63j9nbTlHgcLM/s4QsmxNbmYcih0cGMpIkSbVIJi3UsAizgSf6NmPyXjg55HswRUBZYUUQ02kMdB2HX6WB6NYQ2x7b1f/Gqw9FueZpuqV/zidDU1CrYHdmaXkQ8/Gd7eme9S1lCT2w37mQEzYfJS5vlYMtqw4Xkt9mDDS6UgYxtSwkSMdtnRP5vwEtAVh+IJcsm5NgvYbpD/SkWUxIHV+hJEnS5U2OyNSCsGA9A9vGsCMnh+ikK9EfmF+xs8dDuAyR7I0bQfsr7sbjLMObfxK1qwi14kVnNNI+SkWMxUhmsROA6BAjbRMszMkbwvpMhX8HhdPal8Oau6yAQp4SylvrbSw/VFT+Mk6MHLf5yS0pwKTXEGE2EGORQU1tCDXpGdwxDq+jiF5JBpweP+uz1SSEBaFRy2k9SZKk2iQDmRrm8yvsOVXIsZOn6Fc0vSKISekl2gdk7kSf3J2O+gxUK2aiUakxNr4ODu+CtF/xDv6QpxcUlAcxAFk2Jw9N28WjvZsRlncM7cHFWH5+kjCXDYB4vZm3er/Gu9YmfLkln/BgPYUODze9vxr/6VTupPAgPr27Cy1iQ1DJnJkalVVgQ5ufxsO5L6HdtAz0Zjq0u5vivHvJV5KIMMtCgJIkSbVFTi3VsCKHmzVpOdzczEhIeCw0vh5l7GJo1h/WvgeKD9WPf0M1eSBs/kJ0x55xFxQeg9a3oJn7ILe2NKFWwSfDGvHp0GTUKthxshhbmZvHO2mx/HAfnA5iAHDbCV/8CA+2VQgxaHmiTzPeW5pWHsQAnCgoY+Sn68k4K0CS/rgSpwdTyVGipw9Ae3SJaBfhsmHe/AFxP9yDvkz2l5IkSapNMpCpYRHebB70f4fh29tg82RodJXoP7TmHbH0uqwA0n6q/MSdMyAkFpXtFH2T4ONhjbk6Hq50LueTu67g732T6ReWRej2j6GaFfNR2z9g+ug25Je6WX+koNL+QoeHvRnFNfyO/9xCKCNk9YtV9phS5+4huHBfHVyVJEnSn4ecWqpJRenwRX/UtoyKbUv/iyqqJdz0lqjku3Nm9c/fMwdaDCTYncdVEVaCsvfhSb2Sbkfn0dVbQMgRL+QfrPbpusJDNDLaeXfJ4WqPScu2c0Pr3/PmpCq5SlAdWVbtbvXeedC83yW8IEmSpD+XBjEi88EHH9CoUSOMRiPdu3dn48aNdX1Jlfm8sHUqnB3EnJG7X1T5DYkLnBI6l8sGuiDUajVBX/aF+X9B9/n1WJb+Hev616A4HSJbVP/8yOZ4nXYigvXVHtIq/hJWN/4zUKlEC4bqBEdcumuRJEn6E6r3gcyMGTOYMGECzz//PFu3bqVDhw7079+fnJx6lntQVgC7zzPacvAnMSWUek31x6ReAx4X+L1V79/3A3QdB6oqfmwqFXS8k5DN7zP+usZVPj0qxEBLuRy4ZgVHQ9f7q9/ffsSluxZJkqQ/oXofyLz11lvcf//9jB07ltatW/PRRx9hMpn44osv6vrSzqECTfUjIWj0kL0bWg8GY2jl/SFxkHIlXPWkGHUJCqt8jNEKIfFw50wIjqrYboqAQe/D4WXYuk1gQLt4Hr2+CXpNxY+3RUwI0+/vQVyoLM5WozRa6DwG4q+ovK/vC2BJuOSXJEmS9GdSr3stud1uTCYTs2bNYsiQIeXbR48eTVFREfPmzav0HJfLhcvlKn9ss9lISkqq/V5LigIbP4VFf616/y3vw4pXxTREv5dEv6WDi0ClEY0jezwCGh2ENhIfjgWHYdkrsG+uGIFpOxyueRrCU8HvB3sm2HNR/D4UfTBeRY1Ha0ZljsZk0OL0+MgtcVHocGPUagg368v7AEm1oCQLcvbDvvkiCG07TAQxQda6vjJJkqQG6bLotZSXl4fP5yMmJiZge0xMDPv376/yORMnTuSFF164FJcXSKWCVjfD9m8gc3vgvuYDILknNB8I9mxw20UwM+AVsd9gAZ8LDFbQ6sS2iKYw6F244fR7CQoH/enRFLVafEhaElABKkB/+usMo05DUriJpHBTrb1l6SwhseKryXV1fSWSJEl/KvU6kPk9nnnmGSZMmFD++MyIzCVhiYeR0+HkJtj6Jah10O0+iG0P5mi46Q0xmqKuakavitwVQ7D4kiRJkiSpSvU6kImMjESj0ZCdnR2wPTs7m9jY2CqfYzAYMBjqcArFEgetb4FmNwCqyr2OqgxiJEmSJEn6Per1p6per6dz584sWbKkfJvf72fJkiX07NmzDq/sAuiCZMNGSZIkSapl9XpEBmDChAmMHj2aLl260K1bN9555x1KS0sZO3ZsXV+aJEmSJEl1rN4HMiNGjCA3N5fnnnuOrKwsOnbsyOLFiyslAEuSJEmS9OdTr5df14QLXb4lSZIkSVL9caGf3/U6R0aSJEmSJOl8ZCAjSZIkSVKDJQMZSZIkSZIaLBnISJIkSZLUYMlARpIkSZKkBksGMpIkSZIkNVgykJEkSZIkqcGq9wXx/qgzZXJsNlsdX4kkSZIkSRfqzOf2b5W7u+wDmZKSEoBL1wFbkiRJkqQaU1JSgtVqrXb/ZV/Z1+/3k5GRQUhICCqVqsbOa7PZSEpK4sSJE7JicC2Q97d2yftbu+T9rV3y/ta++nCPFUWhpKSE+Ph41OrqM2Eu+xEZtVpNYmJirZ3fYrHI/5Fqkby/tUve39ol72/tkve39tX1PT7fSMwZMtlXkiRJkqQGSwYykiRJkiQ1WDKQ+Z0MBgPPP/88BoOhri/lsiTvb+2S97d2yftbu+T9rX0N6R5f9sm+kiRJkiRdvuSIjCRJkiRJDZYMZCRJkiRJarBkICNJkiRJUoMlAxlJkiRJkhosGcj8Th988AGNGjXCaDTSvXt3Nm7cWNeX1CCsXLmSQYMGER8fj0qlYu7cuQH7FUXhueeeIy4ujqCgIPr27UtaWlrAMQUFBYwaNQqLxUJoaCjjxo3DbrdfwndRP02cOJGuXbsSEhJCdHQ0Q4YM4cCBAwHHOJ1Oxo8fT0REBGazmWHDhpGdnR1wTHp6OjfddBMmk4no6Gj++te/4vV6L+VbqZcmTZpE+/btywuE9ezZk0WLFpXvl/e2Zr3yyiuoVCqeeOKJ8m3yHv9+//73v1GpVAFfLVu2LN/foO+tIl206dOnK3q9Xvniiy+UPXv2KPfff78SGhqqZGdn1/Wl1Xs//vij8s9//lOZPXu2Aihz5swJ2P/KK68oVqtVmTt3rrJjxw7llltuUVJTU5WysrLyYwYMGKB06NBBWb9+vbJq1SqladOmysiRIy/xO6l/+vfvr0yePFnZvXu3sn37duXGG29UkpOTFbvdXn7MQw89pCQlJSlLlixRNm/erPTo0UPp1atX+X6v16u0bdtW6du3r7Jt2zblxx9/VCIjI5VnnnmmLt5SvTJ//nxl4cKFysGDB5UDBw4o//jHPxSdTqfs3r1bURR5b2vSxo0blUaNGint27dXHn/88fLt8h7/fs8//7zSpk0bJTMzs/wrNze3fH9DvrcykPkdunXrpowfP778sc/nU+Lj45WJEyfW4VU1POcGMn6/X4mNjVVef/318m1FRUWKwWBQvv32W0VRFGXv3r0KoGzatKn8mEWLFikqlUo5derUJbv2hiAnJ0cBlBUrViiKIu6lTqdTZs6cWX7Mvn37FEBZt26doigi0FSr1UpWVlb5MZMmTVIsFovicrku7RtoAMLCwpTPPvtM3tsaVFJSojRr1kz55ZdflGuvvbY8kJH3+I95/vnnlQ4dOlS5r6HfWzm1dJHcbjdbtmyhb9++5dvUajV9+/Zl3bp1dXhlDd/Ro0fJysoKuLdWq5Xu3buX39t169YRGhpKly5dyo/p27cvarWaDRs2XPJrrs+Ki4sBCA8PB2DLli14PJ6A+9uyZUuSk5MD7m+7du2IiYkpP6Z///7YbDb27NlzCa++fvP5fEyfPp3S0lJ69uwp720NGj9+PDfddFPAvQT5+1sT0tLSiI+Pp3HjxowaNYr09HSg4d/by75pZE3Ly8vD5/MF/DABYmJi2L9/fx1d1eUhKysLoMp7e2ZfVlYW0dHRAfu1Wi3h4eHlx0ii6/sTTzzBlVdeSdu2bQFx7/R6PaGhoQHHnnt/q7r/Z/b92e3atYuePXvidDoxm83MmTOH1q1bs337dnlva8D06dPZunUrmzZtqrRP/v7+Md27d2fKlCm0aNGCzMxMXnjhBa6++mp2797d4O+tDGQk6TI0fvx4du/ezerVq+v6Ui4rLVq0YPv27RQXFzNr1ixGjx7NihUr6vqyLgsnTpzg8ccf55dffsFoNNb15Vx2Bg4cWP59+/bt6d69OykpKXz33XcEBQXV4ZX9cXJq6SJFRkai0WgqZXNnZ2cTGxtbR1d1eThz/853b2NjY8nJyQnY7/V6KSgokPf/tEcffZQffviBZcuWkZiYWL49NjYWt9tNUVFRwPHn3t+q7v+ZfX92er2epk2b0rlzZyZOnEiHDh1499135b2tAVu2bCEnJ4dOnTqh1WrRarWsWLGC9957D61WS0xMjLzHNSg0NJTmzZtz6NChBv/7KwOZi6TX6+ncuTNLliwp3+b3+1myZAk9e/aswytr+FJTU4mNjQ24tzabjQ0bNpTf2549e1JUVMSWLVvKj1m6dCl+v5/u3btf8muuTxRF4dFHH2XOnDksXbqU1NTUgP2dO3dGp9MF3N8DBw6Qnp4ecH937doVECz+8ssvWCwWWrdufWneSAPi9/txuVzy3taAPn36sGvXLrZv317+1aVLF0aNGlX+vbzHNcdut3P48GHi4uIa/u9vnaYaN1DTp09XDAaDMmXKFGXv3r3KAw88oISGhgZkc0tVKykpUbZt26Zs27ZNAZS33npL2bZtm3L8+HFFUcTy69DQUGXevHnKzp07lcGDB1e5/PqKK65QNmzYoKxevVpp1qyZXH6tKMrDDz+sWK1WZfny5QFLLB0OR/kxDz30kJKcnKwsXbpU2bx5s9KzZ0+lZ8+e5fvPLLHs16+fsn37dmXx4sVKVFRUvVhiWdf+/ve/KytWrFCOHj2q7Ny5U/n73/+uqFQq5eeff1YURd7b2nD2qiVFkff4j3jqqaeU5cuXK0ePHlXWrFmj9O3bV4mMjFRycnIURWnY91YGMr/T+++/ryQnJyt6vV7p1q2bsn79+rq+pAZh2bJlClDpa/To0YqiiCXYzz77rBITE6MYDAalT58+yoEDBwLOkZ+fr4wcOVIxm82KxWJRxo4dq5SUlNTBu6lfqrqvgDJ58uTyY8rKypRHHnlECQsLU0wmk3LrrbcqmZmZAec5duyYMnDgQCUoKEiJjIxUnnrqKcXj8Vzid1P/3HvvvUpKSoqi1+uVqKgopU+fPuVBjKLIe1sbzg1k5D3+/UaMGKHExcUper1eSUhIUEaMGKEcOnSofH9DvrcqRVGUuhkLkiRJkiRJ+mNkjowkSZIkSQ2WDGQkSZIkSWqwZCAjSZIkSVKDJQMZSZIkSZIaLBnISJIkSZLUYMlARpIkSZKkBksGMpIkSZIkNVgykJEkSZIkqcGSgYwkSZIkSQ2WDGQkSao3xowZg0qlqvR16NChur40SZLqKW1dX4AkSdLZBgwYwOTJkwO2RUVFXdQ5fD4fKpUKtVr+rSZJlzv5f7kkSfWKwWAgNjY24Ovdd9+lXbt2BAcHk5SUxCOPPILdbi9/zpQpUwgNDWX+/Pm0bt0ag8FAeno6LpeLp59+moSEBIKDg+nevTvLly+vuzcnSVKNk4GMJEn1nlqt5r333mPPnj18+eWXLF26lL/97W8BxzgcDl599VU+++wz9uzZQ3R0NI8++ijr1q1j+vTp7Ny5k+HDhzNgwADS0tLq6J1IklTTZPdrSZLqjTFjxjB16lSMRmP5toEDBzJz5syA42bNmsVDDz1EXl4eIEZkxo4dy/bt2+nQoQMA6enpNG7cmPT0dOLj48uf27dvX7p168bLL798Cd6RJEm1TebISJJUr1x//fVMmjSp/HFwcDC//vorEydOZP/+/dhsNrxeL06nE4fDgclkAkCv19O+ffvy5+3atQufz0fz5s0Dzu9yuYiIiLg0b0aSpFonAxlJkuqV4OBgmjZtWv742LFj3HzzzTz88MO89NJLhIeHs3r1asaNG4fb7S4PZIKCglCpVOXPs9vtaDQatmzZgkajCXgNs9l8ad6MJEm1TgYykiTVa1u2bMHv9/Pmm2+Wr0L67rvvfvN5V1xxBT6fj5ycHK6++uravkxJkuqITPaVJKlea9q0KR6Ph/fff58jR47w9ddf89FHH/3m85o3b86oUaO45557mD17NkePHmXjxo1MnDiRhQsXXoIrlyTpUpCBjCRJ9VqHDh146623ePXVV2nbti3ffPMNEydOvKDnTp48mXvuuYennnqKFi1aMGTIEDZt2kRycnItX7UkSZeKXLUkSZIkSVKDJUdkJEmSJElqsGQgI0mSJElSgyUDGUmSJEmSGiwZyEiSJEmS1GDJQEaSJEmSpAZLBjKSJEmSJDVYMpCRJEmSJKnBkoGMJEmSJEkNlgxkJEmSJElqsGQgI0mSJElSgyUDGUmSJEmSGqz/B0sFb80rTkKqAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"df_train = df_train.drop([\"Ticket\", \"PassengerId\", \"Name\", \"Cabin\"], axis=1)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:56.988927Z","iopub.execute_input":"2024-05-04T13:08:56.989838Z","iopub.status.idle":"2024-05-04T13:08:56.996628Z","shell.execute_reply.started":"2024-05-04T13:08:56.989802Z","shell.execute_reply":"2024-05-04T13:08:56.995333Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:57.769632Z","iopub.execute_input":"2024-05-04T13:08:57.770052Z","iopub.status.idle":"2024-05-04T13:08:57.787705Z","shell.execute_reply.started":"2024-05-04T13:08:57.769988Z","shell.execute_reply":"2024-05-04T13:08:57.786331Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n0         0       3    male  22.0      1      0   7.2500        S\n1         1       1  female  38.0      1      0  71.2833        C\n2         1       3  female  26.0      0      0   7.9250        S\n3         1       1  female  35.0      1      0  53.1000        S\n4         0       3    male  35.0      0      0   8.0500        S","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>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"cols = [\"SibSp\", \"Parch\", \"Fare\", \"Age\"]\nfor x in cols:\n    df_train[x].fillna(df_train[x].median(),inplace=True)\ndf_train.isna().sum()  ","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:58.442179Z","iopub.execute_input":"2024-05-04T13:08:58.442676Z","iopub.status.idle":"2024-05-04T13:08:58.461748Z","shell.execute_reply.started":"2024-05-04T13:08:58.442638Z","shell.execute_reply":"2024-05-04T13:08:58.460237Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Survived    0\nPclass      0\nSex         0\nAge         0\nSibSp       0\nParch       0\nFare        0\nEmbarked    2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_test['Embarked'].fillna(\"S\",inplace= True)\ndf_test.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:08:59.041568Z","iopub.execute_input":"2024-05-04T13:08:59.042046Z","iopub.status.idle":"2024-05-04T13:08:59.054702Z","shell.execute_reply.started":"2024-05-04T13:08:59.041996Z","shell.execute_reply":"2024-05-04T13:08:59.053349Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nPclass           0\nName             0\nSex              0\nAge             86\nSibSp            0\nParch            0\nTicket           0\nFare             1\nCabin          327\nEmbarked         0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Applying transformations on Columns","metadata":{}},{"cell_type":"code","source":"df_train.columns","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:02.933185Z","iopub.execute_input":"2024-05-04T13:09:02.934499Z","iopub.status.idle":"2024-05-04T13:09:02.942031Z","shell.execute_reply.started":"2024-05-04T13:09:02.934450Z","shell.execute_reply":"2024-05-04T13:09:02.940654Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n       'Embarked'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"- OneHotEncoding : Sex , Embarked\n- MinMaxScaler for scaling","metadata":{}},{"cell_type":"code","source":"from sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.linear_model import LogisticRegression","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:04.345812Z","iopub.execute_input":"2024-05-04T13:09:04.347120Z","iopub.status.idle":"2024-05-04T13:09:04.836781Z","shell.execute_reply.started":"2024-05-04T13:09:04.347070Z","shell.execute_reply":"2024-05-04T13:09:04.835601Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"X = df_train.drop('Survived', axis=1)\ny = df_train['Survived']\n\n","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:04.983310Z","iopub.execute_input":"2024-05-04T13:09:04.983773Z","iopub.status.idle":"2024-05-04T13:09:04.991743Z","shell.execute_reply.started":"2024-05-04T13:09:04.983741Z","shell.execute_reply":"2024-05-04T13:09:04.990116Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"categorical_transformer = Pipeline(steps=[\n    ('imputer',SimpleImputer(strategy='most_frequent')),\n    ('onehot', OneHotEncoder(sparse=False,handle_unknown='ignore'))\n])","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:05.693925Z","iopub.execute_input":"2024-05-04T13:09:05.695162Z","iopub.status.idle":"2024-05-04T13:09:05.703382Z","shell.execute_reply.started":"2024-05-04T13:09:05.695101Z","shell.execute_reply":"2024-05-04T13:09:05.701743Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"transformer = ColumnTransformer([\n    ('imputer_age', SimpleImputer(strategy='most_frequent'), ['Age']),\n    ('imputer_embarked',categorical_transformer,['Embarked']),\n    ('ohe_sex_embarked', OneHotEncoder(sparse=False, handle_unknown='ignore'), ['Sex'])\n], remainder='passthrough')\n\n","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:06.323689Z","iopub.execute_input":"2024-05-04T13:09:06.324713Z","iopub.status.idle":"2024-05-04T13:09:06.331147Z","shell.execute_reply.started":"2024-05-04T13:09:06.324668Z","shell.execute_reply":"2024-05-04T13:09:06.329563Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_transformed = transformer.fit_transform(X)\nX_test_transformed = transformer.transform(df_test)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:07.634338Z","iopub.execute_input":"2024-05-04T13:09:07.634821Z","iopub.status.idle":"2024-05-04T13:09:07.675592Z","shell.execute_reply.started":"2024-05-04T13:09:07.634786Z","shell.execute_reply":"2024-05-04T13:09:07.674410Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"print(X_test_transformed)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:08.263441Z","iopub.execute_input":"2024-05-04T13:09:08.264310Z","iopub.status.idle":"2024-05-04T13:09:08.272368Z","shell.execute_reply.started":"2024-05-04T13:09:08.264269Z","shell.execute_reply":"2024-05-04T13:09:08.270660Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"[[34.5     0.      1.     ...  0.      0.      7.8292]\n [47.      0.      0.     ...  1.      0.      7.    ]\n [62.      0.      1.     ...  0.      0.      9.6875]\n ...\n [38.5     0.      0.     ...  0.      0.      7.25  ]\n [28.      0.      0.     ...  0.      0.      8.05  ]\n [28.      1.      0.     ...  1.      1.     22.3583]]\n","output_type":"stream"}]},{"cell_type":"code","source":"X_transformed.shape , X_test_transformed.shape","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:09.058382Z","iopub.execute_input":"2024-05-04T13:09:09.059440Z","iopub.status.idle":"2024-05-04T13:09:09.067476Z","shell.execute_reply.started":"2024-05-04T13:09:09.059401Z","shell.execute_reply":"2024-05-04T13:09:09.066093Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"((891, 10), (418, 10))"},"metadata":{}}]},{"cell_type":"code","source":"x_train = pd.DataFrame(X_transformed)\nx_train.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:09.625087Z","iopub.execute_input":"2024-05-04T13:09:09.625549Z","iopub.status.idle":"2024-05-04T13:09:09.638385Z","shell.execute_reply.started":"2024-05-04T13:09:09.625515Z","shell.execute_reply":"2024-05-04T13:09:09.635430Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"0    0\n1    0\n2    0\n3    0\n4    0\n5    0\n6    0\n7    0\n8    0\n9    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"x_test = pd.DataFrame(X_test_transformed)\nx_test.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:10.749564Z","iopub.execute_input":"2024-05-04T13:09:10.751363Z","iopub.status.idle":"2024-05-04T13:09:10.763867Z","shell.execute_reply.started":"2024-05-04T13:09:10.751288Z","shell.execute_reply":"2024-05-04T13:09:10.762035Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"0    0\n1    0\n2    0\n3    0\n4    0\n5    0\n6    0\n7    0\n8    0\n9    1\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"nan_indices = np.where(np.isnan(X_test_transformed))\n\nprint(nan_indices)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:11.729534Z","iopub.execute_input":"2024-05-04T13:09:11.730141Z","iopub.status.idle":"2024-05-04T13:09:11.739141Z","shell.execute_reply.started":"2024-05-04T13:09:11.730092Z","shell.execute_reply":"2024-05-04T13:09:11.737374Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"(array([152]), array([9]))\n","output_type":"stream"}]},{"cell_type":"code","source":"x_test.iloc[9].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:12.663755Z","iopub.execute_input":"2024-05-04T13:09:12.665269Z","iopub.status.idle":"2024-05-04T13:09:12.678192Z","shell.execute_reply.started":"2024-05-04T13:09:12.665221Z","shell.execute_reply":"2024-05-04T13:09:12.676514Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"9\n0.00     4\n1.00     2\n21.00    1\n3.00     1\n2.00     1\n24.15    1\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":" x_test.iloc[152][9] = 0","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:13.526828Z","iopub.execute_input":"2024-05-04T13:09:13.527758Z","iopub.status.idle":"2024-05-04T13:09:13.533731Z","shell.execute_reply.started":"2024-05-04T13:09:13.527711Z","shell.execute_reply":"2024-05-04T13:09:13.532497Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"x_test.iloc[9].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:14.376360Z","iopub.execute_input":"2024-05-04T13:09:14.376877Z","iopub.status.idle":"2024-05-04T13:09:14.392235Z","shell.execute_reply.started":"2024-05-04T13:09:14.376837Z","shell.execute_reply":"2024-05-04T13:09:14.388920Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"9\n0.00     4\n1.00     2\n21.00    1\n3.00     1\n2.00     1\n24.15    1\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(random_state=42)\nclf.fit(X_transformed, y)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:15.248166Z","iopub.execute_input":"2024-05-04T13:09:15.248650Z","iopub.status.idle":"2024-05-04T13:09:15.268509Z","shell.execute_reply.started":"2024-05-04T13:09:15.248617Z","shell.execute_reply":"2024-05-04T13:09:15.266643Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"DecisionTreeClassifier(random_state=42)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"clf = DecisionTreeClassifier(random_state=42)\nclf.fit(X_transformed, y)\n\ny_test_pred = clf.predict(X_test_transformed)\n\nprint(y_test_pred)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:16.335040Z","iopub.execute_input":"2024-05-04T13:09:16.335466Z","iopub.status.idle":"2024-05-04T13:09:16.347839Z","shell.execute_reply.started":"2024-05-04T13:09:16.335434Z","shell.execute_reply":"2024-05-04T13:09:16.346959Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"[0 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 0 1 1 0\n 0 0 0 0 1 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 0 0\n 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0\n 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0\n 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1\n 0 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0\n 1 1 1 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1\n 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0\n 1 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0\n 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1 1\n 0 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0\n 0 1 1 1 1 0 0 1 0 0 0]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Submission Predictions","metadata":{}},{"cell_type":"code","source":"df = pd.DataFrame({\"PassengerId\": df_test['PassengerId'].values,\n                  \"Survive\":y_test_pred\n                  })\ndf","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:09:19.434348Z","iopub.execute_input":"2024-05-04T13:09:19.434799Z","iopub.status.idle":"2024-05-04T13:09:19.454312Z","shell.execute_reply.started":"2024-05-04T13:09:19.434766Z","shell.execute_reply":"2024-05-04T13:09:19.452626Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"     PassengerId  Survive\n0            892        0\n1            893        0\n2            894        1\n3            895        1\n4            896        1\n..           ...      ...\n413         1305        0\n414         1306        1\n415         1307        0\n416         1308        0\n417         1309        0\n\n[418 rows x 2 columns]","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>PassengerId</th>\n      <th>Survive</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>892</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>893</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>894</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>895</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>896</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>413</th>\n      <td>1305</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>414</th>\n      <td>1306</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>415</th>\n      <td>1307</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>416</th>\n      <td>1308</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>417</th>\n      <td>1309</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>418 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.to_csv(\"submission.csv\",index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T13:10:54.879528Z","iopub.execute_input":"2024-05-04T13:10:54.880104Z","iopub.status.idle":"2024-05-04T13:10:54.887904Z","shell.execute_reply.started":"2024-05-04T13:10:54.880059Z","shell.execute_reply":"2024-05-04T13:10:54.886731Z"},"trusted":true},"execution_count":34,"outputs":[]}]}