medical_insurance_cost_prediction.ipynb

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   "source": [
    "## Problem Statement\n",
    "Given some attributes regarding the patients, we would like to predict the insurance cost using machine learning. We can use Mean Absolute Error and Root Mean Squared Error to compare the different models."
   ]
  },
  {
   "cell_type": "markdown",
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    "| Algorithm                    | Mean Absolute Error (MAE) | Root Mean Squared Error (RMSE)|\n",
    "|------------------------------|---------------------|-------------------------|\n",
    "| Linear Regression            |                     |                         |\n",
    "| Guassian Kernel Regression   |                     |                         |\n",
    "| Polynomial Kernel Regression |                     |                         |\n",
    "| Support Vector Regression    |                     |                         |\n",
    "| Decision Tree Regression     |                     |                         |\n",
    "| Random Forest Regression     |                     |                         |\n",
    "| Light GBM Regression         |                     |                         |\n",
    "| Neural Networks              |                     |                         |"
   ]
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  {
   "cell_type": "markdown",
   "id": "1fbc8524-9580-4789-a864-58deda6eb07c",
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   "source": [
    "### TODO\n",
    "- Perform K-Fold cross validation\n",
    "- Plot a graph comparing all the algorithms\n",
    "- How does the type of encoding used affects each algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4bca4954-ba0a-498e-b6cb-0702e4f4a5e3",
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   "source": [
    "import copy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.metrics import root_mean_squared_error, mean_absolute_error\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c4f89071-1d75-45cc-a788-af8ef9b005f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/insurance.csv')"
   ]
  },
  {
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   "execution_count": 101,
   "id": "681d8680-a5af-4a26-8a59-df54be426954",
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1338 entries, 0 to 1337\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1338 non-null   int64  \n",
      " 1   sex       1338 non-null   object \n",
      " 2   bmi       1338 non-null   float64\n",
      " 3   children  1338 non-null   int64  \n",
      " 4   smoker    1338 non-null   object \n",
      " 5   region    1338 non-null   object \n",
      " 6   charges   1338 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 73.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "id": "c8848247-5ad9-46cc-9968-4e1e9926fdb8",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   "source": [
    "df.head(5)"
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   "cell_type": "code",
   "execution_count": 5,
   "id": "884c1c64-e184-43c2-96cc-9b016ff81de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_df = copy.deepcopy(df)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47d89afd-fc1c-4ef7-b3a0-c2b1b445db4a",
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   "outputs": [],
   "source": [
    "processed_df['sex'] = np.where(df['sex'] == 'female', 1, 0)\n",
    "processed_df['smoker'] = np.where(df['smoker'] == 'yes', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfec92ee-74d2-440f-b382-4ff361e18307",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age  sex     bmi  children  smoker     region      charges\n",
       "0   19    1  27.900         0       1  southwest  16884.92400\n",
       "1   18    0  33.770         1       0  southeast   1725.55230\n",
       "2   28    0  33.000         3       0  southeast   4449.46200\n",
       "3   33    0  22.705         0       0  northwest  21984.47061\n",
       "4   32    0  28.880         0       0  northwest   3866.85520"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "processed_df.head(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0fa081f-c824-44f8-8ca9-cb2685a875ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_df = pd.get_dummies(processed_df, prefix='region', columns=['region'], dtype='int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f257c441-9331-43ff-b926-c30613d612dc",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "0fa6f291-94e1-4e3f-a711-6cf55c953deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(processed_df[processed_df.columns.difference(['charges'])], processed_df['charges'])\n",
    "clf = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36133b50-7a63-49ba-928b-eadf81b4a995",
   "metadata": {},
   "outputs": [
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       "  /* Arrow on the left of the label */\n",
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       "\n",
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       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da46bfee-e06a-4e51-bb04-1c902c7c1884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>53</td>\n",
       "      <td>22.880</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>36</td>\n",
       "      <td>25.840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>50</td>\n",
       "      <td>25.365</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>62</td>\n",
       "      <td>30.875</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>55</td>\n",
       "      <td>32.775</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>25.740</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>48</td>\n",
       "      <td>29.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>54</td>\n",
       "      <td>32.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>31</td>\n",
       "      <td>21.755</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>42</td>\n",
       "      <td>29.480</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1003 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     bmi  children  region_northeast  region_northwest  \\\n",
       "58     53  22.880         1                 0                 0   \n",
       "831    36  25.840         0                 0                 1   \n",
       "387    50  25.365         2                 0                 1   \n",
       "1301   62  30.875         3                 0                 1   \n",
       "849    55  32.775         0                 0                 1   \n",
       "...   ...     ...       ...               ...               ...   \n",
       "5      31  25.740         0                 0                 0   \n",
       "1003   48  29.600         0                 0                 0   \n",
       "1110   54  32.300         1                 1                 0   \n",
       "1194   31  21.755         0                 0                 1   \n",
       "596    42  29.480         2                 0                 0   \n",
       "\n",
       "      region_southeast  region_southwest  sex  smoker  \n",
       "58                   1                 0    1       1  \n",
       "831                  0                 0    1       0  \n",
       "387                  0                 0    0       0  \n",
       "1301                 0                 0    0       1  \n",
       "849                  0                 0    0       0  \n",
       "...                ...               ...  ...     ...  \n",
       "5                    1                 0    1       0  \n",
       "1003                 0                 1    0       0  \n",
       "1110                 0                 0    1       0  \n",
       "1194                 0                 0    1       0  \n",
       "596                  1                 0    1       0  \n",
       "\n",
       "[1003 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c8c0490-76e0-48d9-af34-c8486c31d0bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  246.79915771,   343.30284352,   397.93901988,   583.54965301,\n",
       "         378.57939086,  -482.40361387,  -479.72543   ,    83.47915342,\n",
       "       23598.985058  ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d6c35996-003e-4d10-ad01-cb5f9acf3d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7631639735405947"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e562d4f2-0d87-4066-89a9-9c962d0f7cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cbf5df55-dadc-4af8-9ce9-c65c72fd67ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6130.310702645122"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5103e774-beb6-405e-814a-91025e926950",
   "metadata": {},
   "source": [
    "Let us normalize all the columns and then perform regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a7e6a75-b378-45e6-9d94-2ae55833e042",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "02c837a2-71f1-4146-a46c-ed710fcce9a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7631639735405948\n",
      "6130.310702645122\n"
     ]
    }
   ],
   "source": [
    "clf = LinearRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))\n",
    "print(root_mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e0bc0e-b558-4416-8b15-5e8ff5354928",
   "metadata": {},
   "source": [
    "Scaling did not have any affect on linear regression. Lets try Kernalized Ridge Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "338b0f29-6aef-40fb-bff5-c201f1b466be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.98636292, -1.28327175, -0.08188813, ..., -0.58387421,\n",
       "         1.02524375,  1.97298832],\n",
       "       [-0.2296355 , -0.7924489 , -0.91152236, ..., -0.58387421,\n",
       "         1.02524375, -0.50684537],\n",
       "       [ 0.77177496, -0.87121271,  0.74774611, ..., -0.58387421,\n",
       "        -0.97537781, -0.50684537],\n",
       "       ...,\n",
       "       [ 1.05789223,  0.2787388 , -0.08188813, ..., -0.58387421,\n",
       "         1.02524375, -0.50684537],\n",
       "       [-0.58728209, -1.4698176 , -0.91152236, ..., -0.58387421,\n",
       "         1.02524375, -0.50684537],\n",
       "       [ 0.19954041, -0.18886945,  0.74774611, ..., -0.58387421,\n",
       "         1.02524375, -0.50684537]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11131cfa-e191-4edf-9559-db4e00978018",
   "metadata": {},
   "source": [
    "## Gaussian Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "77927c60-e5a6-4c7e-bf6f-a3035f37bad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KernelRidge(kernel=&#x27;rbf&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KernelRidge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.kernel_ridge.KernelRidge.html\">?<span>Documentation for KernelRidge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KernelRidge(kernel=&#x27;rbf&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KernelRidge(kernel='rbf')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kr = KernelRidge(kernel='rbf')\n",
    "kr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a75e803e-5a0d-47bf-bb8d-f7ca27678289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8309920747792541"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4ea60e99-52da-4fd3-92f0-9483caa7f767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5178.597275994341"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = kr.predict(X_test)\n",
    "root_mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057d70a8-ef10-4239-9c01-4519df6b4372",
   "metadata": {},
   "source": [
    "## Polynomial Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c314333b-6ec4-4f61-a842-a50d3178a828",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8175985501797193\n",
      "5155.3436997005665\n"
     ]
    }
   ],
   "source": [
    "poly_kr = KernelRidge(kernel='poly', degree=2)\n",
    "poly_kr.fit(X_train, y_train)\n",
    "\n",
    "print(poly_kr.score(X_test, y_test))\n",
    "y_pred = poly_kr.predict(X_test)\n",
    "print(root_mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a63bd1d-8f31-49ab-8f71-c17e3d7b6787",
   "metadata": {},
   "source": [
    "## Decision Tree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "30082802-183d-4894-9a65-3fd3e7e1a3d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 coefficient - 0.8544963197666267\n",
      "RMSE - 4536.274288019071\n",
      "Mean Absolute Error - 2567.2410732553776\n"
     ]
    }
   ],
   "source": [
    "dtr = DecisionTreeRegressor(min_samples_leaf=25)\n",
    "dtr.fit(X_train, y_train)\n",
    "\n",
    "print(f'R2 coefficient - {dtr.score(X_test, y_test)}')\n",
    "y_pred = dtr.predict(X_test)\n",
    "print(f'RMSE - {root_mean_squared_error(y_test, y_pred)}')\n",
    "print(f'Mean Absolute Error - {mean_absolute_error(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a424bda9-7258-404b-b81d-e40538ffdf8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23244.7902"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[58]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64a8d9a-de82-4f9e-9b74-59499a0fc623",
   "metadata": {},
   "source": [
    "\n",
    "Lets perform K-fold cross validation on decision tree and estimate the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4dad6533-b76a-4519-81bc-9606bcd60a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_estimate(n_splits=None):\n",
    "    cv = KFold(n_splits=n_splits)\n",
    "    cv_clf = DecisionTreeRegressor(min_samples_leaf=25)\n",
    "    val_scores = []\n",
    "    for train, test in cv.split(X_train, y_train):\n",
    "        cv_clf.fit(X_train[train], y_train[train])\n",
    "        val_scores.append(cv_clf.score(X_train[test], y_train[test]))\n",
    "    return val_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddcfe6b4-55f2-4fd1-9786-da9468b6b55a",
   "metadata": {},
   "source": [
    "## Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0f516ea0-f4cd-459e-a67c-9b2f9340d4f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 coefficient - 0.8581539890413217\n",
      "RMSE - 4478.894996329005\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(max_depth=4, random_state=0, min_samples_leaf=25)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print(f'R2 coefficient - {rf.score(X_test, y_test)}')\n",
    "y_pred = rf.predict(X_test)\n",
    "print(f'RMSE - {root_mean_squared_error(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd69683-46b2-4452-a728-3862d65aeaa3",
   "metadata": {},
   "source": [
    "Lets see if changing the encoding of the categorical variables affect the performance of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f513d7b1-f448-4659-9ce3-067a6ebaa925",
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "encoder = ce.BinaryEncoder(cols=['smoker', 'region', 'sex'])\n",
    "binary_X = encoder.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "70841b32-c4fe-465e-a417-09d618763027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(binary_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3bc51906-c83a-41c7-8cde-ff8188c63849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 coefficient - 0.8760673394381929\n",
      "RMSE - 4322.600933243148\n"
     ]
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "encoder = ce.BinaryEncoder(cols=['smoker', 'region', 'sex'])\n",
    "binary_X = encoder.fit_transform(df)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(binary_X[binary_X.columns.difference(['charges'])], binary_X['charges'])\n",
    "\n",
    "rf = RandomForestRegressor(max_depth=4, random_state=0, min_samples_leaf=25)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print(f'R2 coefficient - {rf.score(X_test, y_test)}')\n",
    "y_pred = rf.predict(X_test)\n",
    "print(f'RMSE - {root_mean_squared_error(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "82452584-b69d-424f-829f-b60db7e2b036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 coefficient - 0.8258189953089246\n",
      "RMSE - 4790.720851204844\n"
     ]
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "encoder = ce.CatBoostEncoder(cols=['smoker', 'region', 'sex'])\n",
    "binary_X = encoder.fit_transform(df[df.columns.difference(['charges'])], df['charges'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(binary_X[binary_X.columns.difference(['charges'])], df['charges'])\n",
    "\n",
    "rf = RandomForestRegressor(max_depth=4, random_state=0, min_samples_leaf=25)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print(f'R2 coefficient - {rf.score(X_test, y_test)}')\n",
    "y_pred = rf.predict(X_test)\n",
    "print(f'RMSE - {root_mean_squared_error(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d558a13-6937-40f9-b1f5-17c69f55dfc2",
   "metadata": {},
   "source": [
    "## Support Vector Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73cf8acd-2ba1-4d68-b559-43dc605d0975",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eb1ff0a8-f3d0-477e-a983-12011d61795c",
   "metadata": {},
   "source": [
    "## Light GBM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "09dfd13d-fe1e-4541-a6ba-26faacc535f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 321\n",
      "[LightGBM] [Info] Number of data points in the train set: 1003, number of used features: 9\n",
      "[LightGBM] [Info] Start training from score 13346.783604\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "0.855850678554744\n"
     ]
    }
   ],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "lgbr = LGBMRegressor(max_depth=5, num_leaves=20, n_estimators=80, random_state=10, reg_lambda=0.4)\n",
    "lgbr.fit(X_train, y_train)\n",
    "\n",
    "print(lgbr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71b3beae-4b79-4e8f-949f-21ef951779a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6a31d9c6-00c7-45bb-89d8-9acc5b5457bd",
   "metadata": {},
   "source": [
    "## XGBoost Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "10c3bbb4-0a5f-4a0c-bd0c-42e14db34d44",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'northwest'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/jq/94zph4td255684v2hbsq4f340000gn/T/ipykernel_32464/1744092979.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m                                 \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                                 max_features=None)\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'charges'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'charges'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mgbr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgbr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgbr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m                 skip_parameter_validation=(\n\u001b[1;32m   1471\u001b[0m                     \u001b[0mprefer_skip_nested_validation\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1472\u001b[0m                 )\n\u001b[1;32m   1473\u001b[0m             ):\n\u001b[0;32m-> 1474\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0;31m# Check input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m         \u001b[0;31m# Since check_array converts both X and y to the same dtype, but the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;31m# trees use different types for X and y, checking them separately.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    660\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coo\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         )\n\u001b[1;32m    662\u001b[0m         \u001b[0msample_weight_is_none\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    646\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"estimator\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcheck_y_params\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m                     \u001b[0mcheck_y_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdefault_check_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    649\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcheck_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ensure_2d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1259\u001b[0m         raise ValueError(\n\u001b[1;32m   1260\u001b[0m             \u001b[0;34mf\"{estimator_name} requires y to be passed, but the target y is None\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1261\u001b[0m         )\n\u001b[1;32m   1262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m     X = check_array(\n\u001b[0m\u001b[1;32m   1264\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m         \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    994\u001b[0m                         )\n\u001b[1;32m    995\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    997\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 998\u001b[0;31m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    999\u001b[0m                 raise ValueError(\n\u001b[1;32m   1000\u001b[0m                     \u001b[0;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m                 ) from complex_warning\n",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    517\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m         \u001b[0;31m# At this point array is a NumPy ndarray. We convert it to an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/opencv_env/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   2082\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2083\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2084\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2085\u001b[0m         if (\n\u001b[1;32m   2086\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2087\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'northwest'"
     ]
    }
   ],
   "source": [
    "gbr = GradientBoostingRegressor(n_estimators=100, \\\n",
    "                                criterion='squared_error', \\\n",
    "                                min_samples_leaf=25, \\\n",
    "                                max_depth=3, \\\n",
    "                                max_features=None)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[df.columns.difference(['charges'])], df['charges'])\n",
    "gbr.fit(X_train, y_train)\n",
    "\n",
    "print(gbr.score(X_test, y_test))\n",
    "y_pred = gbr.predict(X_test)\n",
    "print(root_mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2713a2d6-50b7-4819-af36-3cb23b927562",
   "metadata": {},
   "source": [
    "## Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7dd37e79-e69a-40e4-ac08-5004c97c5306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=5, out_features=10, bias=True)\n",
      "  (fc2): Linear(in_features=10, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc1 = nn.Linear(5, 10)\n",
    "        self.fc2 = nn.Linear(10, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        return x\n",
    "\n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d58c42b-9635-4d06-90ae-15bdd8b8da7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[0.1506],\n",
      "          [0.3042],\n",
      "          [0.2463],\n",
      "          [0.1184],\n",
      "          [0.3266],\n",
      "          [0.2465],\n",
      "          [0.1533],\n",
      "          [0.3597],\n",
      "          [0.1757],\n",
      "          [0.3740]]]], grad_fn=<ReluBackward0>)\n"
     ]
    }
   ],
   "source": [
    "input = torch.randn(1, 1, 10, 5)\n",
    "out = net(input)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "28ca0c71-8396-43fe-8507-ddf4bf54b144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>region_northeast</th>\n",
       "      <th>region_northwest</th>\n",
       "      <th>region_southeast</th>\n",
       "      <th>region_southwest</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>28</td>\n",
       "      <td>24.320</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>29</td>\n",
       "      <td>20.235</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>37</td>\n",
       "      <td>23.370</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>25.740</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>50</td>\n",
       "      <td>32.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age     bmi  children  region_northeast  region_northwest  \\\n",
       "468   28  24.320         1                 1                 0   \n",
       "431   29  20.235         2                 0                 1   \n",
       "119   37  23.370         2                 0                 1   \n",
       "5     31  25.740         0                 0                 0   \n",
       "338   50  32.300         1                 1                 0   \n",
       "\n",
       "     region_southeast  region_southwest  sex  smoker  \n",
       "468                 0                 0    1       0  \n",
       "431                 0                 0    1       0  \n",
       "119                 0                 0    1       0  \n",
       "5                   1                 0    1       0  \n",
       "338                 0                 0    0       1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "25a0e81b-85b4-4fcd-874f-279743701b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train['age'], y_train, c=X_train['smoker'], s=5, cmap='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "36bb75d0-299e-4e73-be8a-99b6ee09cb6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train['age'], y_train, c=X_train['sex'], s=5)\n",
    "plt.legend(X_train['sex'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5a545772-673a-403d-a8df-f94ae8b87074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nEULg5edJwtVEpDSturwDvPLRUoVCcSdCy5fgxyOTObjhCKEVSlCtcSVHm6RQ5IqKfBRTpBaLjOmKjHkIGd0Cadhj13wfzX0Hd283AFo/00xVWdwnSCmJPn/1nld9LS74l/ClTffmyvFQFHlUwmkxRUv6ClKmAxqgA6fq6IL+tGtOo9GIIc2Au5ftURRF0cFoNDKm52Q2LdqO0An6f92PJ/p3drRZCoWiiHI3z28V+SiuyHTgepdKDWSa3VPq9XrleNxH7F55gE2LtgMgNcl3A2eRlmz/50ShUCjscj7GjRuHEIKBAweaX+vbty9CiBz/mjVrZq+dinxGePQEcT05TSC83nGoPYqihyEtp1CVZtQwZt3/zQUVCkXBY3PC6a5du5g+fTp169a1+L/OnTsza9Ys87GLi5LRLWoIpwoQtAoy94NTeYST2iNW5KRJlwZUql+e0/vPAvDUu4/g5efpWKMUCsV9gU3OR3JyMs8//zwzZsxg9OjRFv/v6upKaOi9qYQppQbGsyB8EPogR5tToAh9IOgfcrQZiiKKm4cr32wdw4H1h/Hw8aBm86qONkmhUNwn2LTt0r9/fx599FE6dOhg9f/Xr19PiRIlqFq1Kq+++irR0dG5zpWRkUFiYmKOf45CSgMy7iVkTGfk1ZbI1AUOs0WhKAq4uLnQpHMDarWohhDizm9QOJSszCzOHr5AYqyqTlIUbe468rFgwQL27t3Lrl27rP5/ly5d6N69O+XKlSMiIoLhw4fTvn179uzZg6urq8X4cePGMXLkyLu3vCBIXwWG6+JYEpn4Gbh3Qwhnh5qlUCgUdyIpLpn3Wn/CucMXcHJx4pPf36d518Y2z2fMMvLvzP+IOhtNy6eaUqNplXy0VlHcuatS2wsXLtC4cWNWrVpFvXr1AGjbti3169dn8uTJVt9z5coVypUrx4IFC3j6aUvFvYyMDDIyMszHiYmJlClTxiGltjLtD2TChze9okOEHEQIlbOiUCiKNgs+/4OfPv4VqUkQEFI2mHkR39k839dvTWfp96vROelAwjdbx6hutIrbUmCltnv27CE6OppGjRrh5OSEk5MTGzZs4JtvvsHJyQmj0WjxnrCwMMqVK8fJkyetzunq6oqPj0+Ofw7DtRM43djXFl5vK8dDoVDcE2Qasm5sjUnTFow9rF+wBQAtu8Jp29+77ZpPobiZu9p2eeihhwgPD8/x2ksvvUT16tX58MMP0ev1Fu+5du0aFy5cICwszD5LCwGh84TAxWDYAzp/hHMNR5ukUCgUeeKRVzvw74w1xFyKRQhBvzG97JovrFIop/efRTNqaEaNsEoh+WSpQnGXzoe3tze1a9fO8ZqnpyeBgYHUrl2b5ORkRowYQbdu3QgLC+Ps2bMMHTqUoKAgnnrqqXw1vKAQwhVcWzjaDIVCobgrAsP8+fHIZI7tPEVIuSBKVbZvwTd0/kDGvzCFK2ei6NC7NR1faJNPlioU+dxYTq/XEx4ezpw5c4iPjycsLIx27dqxcOFCvL298/NUCoVCobgFD293Gj5UJ1/mKl0ljCnbxubLXArFrajeLgqFQqFQOICUhBQuHL9MqSphePvf+53A7+b5na+RD4VCoShqSGmErGMgPBFO5e2eLzE2iYsnrlC2eiml+KqwmYjwcwxq+ynJcSl4+LgzYc2nxaobsWosp1Ao7lukzETG9UNeewoZ0wkt6Ru75ju++zS9K7zFgBbD6F3xLc4cPJdPliqKG/PHLiE10dSoMT05nbkjf3ewRYWLcj4UCsX9S8bmm4QDgZSpSM12FeV5o34nI8WkS5SWlM78MYvttVBRgGiaxpp5G5k78ndO7Y9wtDk5kNqNjAcJFLEMiAJHOR8KhUJxF5gfEcXsYXEvMmPwXMa/MIV5oxfxdtMhnNx7xtEmmek59GncvdwAUx+lPp8842CLCheV86FQKO5fXFuBczPI3G469nwLobM9kb338Gc4sOEwaUnpuHu703OopWqzouiwavYGADSjhg4dGxdtp0rDig62ykSleuWZe2Yq545cpGz1UvgEFq+KUOV8KBQOJjUpjVP7IggtH0yJssGONue+QghnCJgFWUeyE07te/BUa1KZeWe+48Lxy5StUeq+qFC4nwkpH0xyfIpZKC2kXNH6fnn7e1G7ZXVHm+EQ1LaLQuFArl68Rr8aA3i/7ae8UPltNi3e7miT7juE0COc69jteFzHJ9CbWi2qKcfjHuCjue9SrlZp3L3ceOTVh+jycntHm6TIRkU+FPc0KQkpnNwbQVjFkCK3qskL/0xbSVxUAgDGLI2ZQ37hwW7NHGyVQnF/ULZ6Kabv/9LRZiisUGycDyklx3aeQmoa1ZtWQadTQZ97nahzV3mn2RDiohLQO+kY9ut799yD++bPoRCoz6VCoSgWFIs7nZSScb2/5t3mQxnQ8mNGPfMlmqY52iyFnfz93UoSYpIAMBo1fhr2q4MtunueeLuzOWKjd3bija9edLBFiuKANEYjM7YgjVcdbYqimFIsIh/nj15k3a9bzMdb/tzJqX0RVG1UfNTk7kf0TjdFDRA5ju8V/EP8mHl4EuePXCS4TCC+QaqlgKJgkYZ9yNgXgXTADQLmIlzqOdosRTHj3rtb24DeSZ+n14o6Mn01WnRbtOgHkWl/O9och/PkO13MUQNnVyfe+KqvYw2yERdXZyo3qFBkHY/01AyyMrMcbUaxJtOQyf51hzi286TdYlQy5QfAkH1kQKZMt9s+heJuKRaRj9JVS/LUu4/wxzf/AvDIqx2oWLecg626O6QWi4wfABgBiUwYDM4NEU6lHW2awwgI9Wfm4UlcPH6ZoNIB+AQUrzr5gkZKydR3f+KvqStwcXPm/R/fon3PVo42q9hhyMjkf+1HcHTbCcDkdPf/up8dMzrfclwsHgOKIkax6mp7JSIKqUlKVgrN13kLA5l5BHntyRyviYBfEC5NHGOQ4r5n75qDfNjpM/Oxk4sTf8b9jKu7qwOtKn5sX7qH4Y9/nuO136Nm4hfsa9N8MvMEMrYPyDgQ/ojAXxBOlfPDVEUxR3W1zYWwCiGONsF2nCqDvgIYsxtZ6ULBqWaBnzYjLYNZw37l+O7TNOpYj55Dn0Kvv/e2rPKT0wfO8vPwBWQasug19Gnqti74v4MjSIpNznGcZcgiI9VQ7J0PabwMmcfBuQZCX/ALGSeXnLdpIezbNhbOVSF4HRgvgL4MQudhr4kKxV1TbJwPKY1g2AVo4PIAQtxbly6ECwQugNQFgBHcn0PoCr6d94wP5/H3dyuRmuTQ5mO4ebryzKCuBX7eokpaSjofPDSSlIRUpJQc3HiEOSenEFQq0NGm5TuNH65HWMUQrpyJAqB9r1bFTgL6VqRhJzK2H6acCVcImINwaVCg52zwUG0efKYZmxaZBOhe+by33QJnQucBumr5YZ5CYRP31hPYRqSUyPh3IGON6QWXB8F/OkLcWyt4ofMHrzfzbb646ASObj9BqSphlKthPXfk8Jbj5u6LQic4tvNkrvPJzMNgjAaXJgjd/an+GHX2ao6IQGZ6JmcPX7TqfGSkZbB/3WG8/Dyp2bwqQojCNNVuPH09+W73eLb9vRtPXw+aPtbQ0SY5HJk8HcjMPspEpvyIcPm2QM+p1+sZvnAQV85E4erhSmCYf4Ge735DZh4H40VwaYTQ+TnaHEU2xcL5IOvkDccDwLAJsg6Dc13H2eRgLp64zNvNhpASn4oQgo/mvWs1mbB++9qcPnAWqUmkJqnzoPUtBpk8HZk80XSgKwlBSxC6gIK8BIcQVrEE/iG+Jn0RCS7uzlSsW9ZiXFpKOgNaDCMi/DwATw98lDfvwWocLz9POr7QxtFmFB2EKyAw97YVhbMFJYS4J3PVHI1MXYBM/MR0oAuEwMUIfUnHGqUAikmprfUbhFuhm2EvRqORvWsOsmvFPrtLH5d+v4q0pHTAFBmaN+p3q+P6jelJn+Hdad61MW9O6svjbz1sMUZKiUyecuMF7QqkLbXLvqKKq7srX20YRbueLXnwmaZMXDeSgFDLlejOZXvNjgfAksnLSElMtevcUktGZh5FyjS75lHYjvB+D3TZf29dIMLrHccaVIyJPBvNpiU7zNuC1sh5X4pDpi6yPk5KDmw4zI5le8hIy8hvUxVWKBaRD+FUDunxOqT+YHrBo68p6eoeQkrJyG5fsu3vXQDUa1eL8auG25z86eLuYv5Z6ASuHtZXcM4uzvT5tPudJxSuIK9/aWWhrQgdQemqJflozru3HXPz7xdMgmj2JAnKzEMmYSiZZFrBBcxHOFWweb68kp6awb7/wvHwcadu65r33NZRfiOcKkPwejBeAX1JUy7WfYxpK/USuDQuUpHMgxuP8GGnz8gyZOHkrGfcio+p36625UDhxo1IlUQI64vOSa99z/If1wJQqX55vt4yutgnVhc0xSPyAeh83kcEb0EEb0bnM9TR5tw1549eNDseAAfWHeb4zlM2z/f0wEcpU80UfnTzdKP/1y/ZPJcQAuE7GrN+gPMD4P6EzfPdDzzwSANadWsKgE4neHvKK7jl4uDlBZk0CWSK6UCLRyZPyw8zb0taSjrvNBvCJ0+M53/tRvBN/xm5jo2Limfjom2c2h9R4HY5GiFcEU7l73/HI2UO8tpTyPi3kTGPmKp8igiLvvoHY5YRMDVk/H2iddFF4TMSyP7eOdUEj54WY+KvJpgdD4DT+8+yZ9XBfLdZkZNiEfm4jtDfe11Pr2MtMuHmafvWkV+wLz8cmEj0+Rj8Q/zsejACCLfOUKIVyATQlSz2K2S9Xs8nv73P1YvXcPdyy4f26yZxORMy+7hg2bV8H2cPXTAfL/1+Nf3G9LK4lsunI+n/wEckx5mco4Hfv8ajr3UscPsUBYtM/u7GgRYPaUvA622H2XMzbp5uCCGQSIRO4OZl/V4oXFtBiW2gxWVHqizX284uTuh0Ak27IXnl5qmiHgVNsYl83OuEli/BC58+az7u9t5jdqu06vV6wiqE2O14XEfovBD6UsXe8biOEIISZYLywfEA4dUfyF5pCw+E58t2z3knbnVu9U46C80JgOU/riU18UYeyi9jFhe4bYpCQOeBacsCTFup7laHaZrG7lUH2PzHDtJTCydf4sWRzxKQXfXjH+LLS5/1yHWs0HkinEpbdTzAVNX15uSXEDrTtXbo05r67a1s4RRxpJTsXXOQTUt2kJaS7mhz7kixUji9H0iISUTTJP4lbFM3vBVDugFnV2flMNwDSGM0ZJ0G52qFsv+uaRrjX5jC2vmb0el1vDv1FasRjXmjFzH704XmwEzJyqHMPjHFYtzdIKUk05CFi+utUuCKwkJmbEHGvwUyDZzrI/xnWdUW+rzPN/z3yyYAKtYtxzfbxhRKvkRWZhYxl2IJLOmPs4v9n5OkuGQM6Zn3bCnzxJe/Y+WsdQCUq1maKTvG4W5HdNwW7ub5rSIf9xi+QT754nikJqXxQYeRPOrxPM+Xf5OI8HP5YJ2iIBH6EgjX5oWW+KfT6RgybwALL09nScxPuW6luHu539gRArsjaYe2HKN7yCs86t6Lkd0nkmnIvPObFPmOcG2JKLEdEbwREbDQquNx7Uqc2fEAOHPwHPv+O1Qo9jk5OxFavkS+OB4A3v5e96zjkXgtyex4AJw7cpFdy/c50KI7U2ycj6zMLLb+tYvNf+zAkKFuZosnLeXA+sMAXLscx+Q3VGfL62ia5mgTihQBof54+uauphtz8Ro6/Y3IWVxUvF3nG//CFBJjkwDYvHgHq35eb9d8CtsRwh2hD801Murq7oJOl/P/3L3vPRmDex2j0fKeFRkR7QBL8k6xcD40TWPoI2P59KkJjOw2kQ87jjJnShdXEmOSzDcUzagRF53gYIscz+Gtx3mu1Gt0cenBF/2mYjQW789IXmnxRBOkZsoJAWj7XEu75kuMSTKr6up0gviriXbbqCgYvPw8eXvKy+j0pr/9I691uG97HRVlrPmGJcoW7QKLYuF8nD10gX3/hZuPD20+xok9ZxxokePp+GIb88MC4Ol3H3WgNUWDsb0mExcVj6ZJVv28nrXzNzvapHuCOg/W4Mv1I3nq3UcZMO01Xv/yBbvme/KdLuaf3bzcrCrvKooOXd98mD9if2bx1Z947/vXVf4YpvwsmfYn0rDrzoPzAb9gX3NpP0BI+WCadK5XKOe2lWJRamstDOjhYz1zu7hQtVElph/4kn1rD1G2RinqtanlaJMcTlx0Qo4+NrFX4h1r0D1EnQdrUOfBGvkyV9/PelCrZXWiz8fwwCMNKFEmKF/mVRQcHt75dz+NuRzLvjXhhJQPviejKDLrIvLaUybZAQCvQQivNwr8vB//+h6b/9hJamIqLZ964LZbpUWBYuF8hFUI4eWxvfjp419BSnoP755rI7XiROmqJSldVfU5uM6jr3bgzynLQZjKTFs/08zRJhVLhBA80KVgO8UqiiZXzkTxZuPBpMSbWhG8PO55enz4pGONulvS/zYpEWcjU2YWivOhd9LTpnvzAj9PflGsSm3TktOQMn+9dMX9g6ZpbF6yg5iLsTR/ojFhFUIcbVKBEnM5lixDFiHlglWo3IFIY7SpNYG+9H39d5BSgvECCDeEvoTVMXNH/s680YvQshMofQK9WHx1VmGaaTcyZS4yaTSmEjAB+lLogtfe6W33BXfz/C4WkY/ruHvln9MRF53AsR0nKV01jDLVSuXbvArHodPpaP3MvbNysIc5I35jbnYzwQ59WjP457fv6wdfUUUmT0MmTzIduD0Kvl/mKoZ1LyOlhox/DzKWm17weh/h9brFOC9/T3O1mRACr3wQ6Ct0PJ6FjHVg2AzCC+EzztEW5SA1KY0tf+7Exc2Flk82wcnZMW5AsYp85Bfnj13i3RZDTe3odYJh8wfS5tkWjjZLocgTMZdj6Vk6541/8ubR1GpRzUEW5URKCYYtYLwMrq0R+qLVSv7QlmMc23GSmi2qUbOZ7Q0qpRaLjM65tSf85yJcm+byjnsXmbEVGdf3pleESUNEl1NXIyMtg0+fnMCe1Qfx8vdk1J8f2p1LtG9tOFdOR9GoUz1CyhVOBYiU0rT1ItwRougI5WWkZdD/gSGcO2xqm/BAlwaMXjok3xYeKvJhhcunI/lu4CykpvHGV33tilb8M20lacnZ7eg1ydxRvyvnQ3HPYMy0LCHOMmQVyrmP7z7Nid2nqdm8KpXqlbc6RiZ/AylTTQfCGwL/RDiVKRT77sSG37Yyuscks+r4yCWDafFEE9smk9Z+5wabbSva3KqtJEFafg5d3V35fOVwUhJScPN0y7UTdKYhk5+HL+TgxiPUaVWdvqN7WlXD/WX0In7+ZCEALm7OfL/vi0KJVAshQBS9xfPBjUfNjgfAzuX7iDwb7ZAtZrvie+PGjUMIwcCBA82vSSkZMWIEJUuWxN3dnbZt23L48GF77bSL1OQ0Xq71HjuW7WXn8v28Wvd9s4iRLbi4uZgVHW/Xjv5+wJBuYOZH8xjccRQLxv+pBLjuA0LKBdOhT2vzcZ3WNajdqnqBn3fT4u283fQjvnlrBm81/pBdK/dbH5j6842fZQqkLy1w2/LKshlrTD9Ik/+x/Mf/bJ5L6EsgXW/0azLqGoDL/Rf1AMClOTg3vHHs3guhz72KydPXM1fHA+CX0Yv5/cu/ObbjJIsmLWXuyN+tjxuzxPyzIT2T+TcdF0d8AnJuY+l0Ak8fD4fYYrPzsWvXLqZPn07dunVzvD5hwgS++uorvv32W3bt2kVoaCgdO3YkKcn2h729bPt7d46VnTHTyMbftto83zODHqNkZVMo2N3LjTcn9bXXxCLLzCG/8NvEv9n3Xzg/DvnFVA2iuKeRUnLtSpz5OCE6kazMgo98/DV1hdlpl5pk2fTV1gfq/LjR0EwDXdGRvA4M8zcLagmdzi45bikln/R2591HK/NBt0q80TaY9JT7U9hOCBdEwFyE/xxEwO8In0/tmu/4rlPmsnipSY7tPGl13K1RvmuX46yOKy5Ua1KZnkOeQugETi5ODPj+dXwCvR1ii03OR3JyMs8//zwzZszA3//Gl09KyeTJkxk2bBhPP/00tWvXZvbs2aSmpjJ//vx8M/puKVvDMsxWrpbtYVz/ED9mhn/F7JNTWHBpOrVbFvyq0VGEbzyaQ/viyLbjDrZIYS+XT0eyb80N0b3zxy5xaPOxAj+vXwmfmx7cAt9cbnrCdwKI7P5Frh3A/ekCty2vvDK+N5UbVEAI04287+jcu6neiasXYti5fD/H93lycJsX549FcXDDkXy0tmghhDPCtRnCpZ7dOQb129XJnhMQ0KB9HavjytfJeZ9v19M+9d28IqURmXUWqdmvziulZNfK/fw1dQWXT0faPV+/Mb1YmjyPvxPn8MgrD9k9n63YlPPRv39/Hn30UTp06MDo0aPNr0dERBAZGUmnTp3Mr7m6utKmTRu2bt3K669bZjdnZGSQkXGjDXNiYv5LKVdpUJEurzzE8pmmEGn75x+kzoP2idfonfSUrJR/iXCn9kcQFxlP7VbV87Uqx17qtqnJ6QNnkZpEapJaLe5fR6u44OXniU4n0LQbuea+wQW/P/3q+D6cOXiOC8cuU752GV4c9ZzVccKlCZTYDhgQomj1CQkM82fqzs/RNA2dzr6qFA8fD/ROOoxZN7YyfYKKXp5AUaT7/7py7UosB9Ydpk7rmjz34RNWx33214dMfHkal05eoUPv1jz8UrsCt01qycjYFyDrEOAMft8g3Gx/yP867g9mffwrYOql8+3Ozylvx+IZslMHHMxdOx8LFixg79697NplKRsbGWnyykJCciavhISEcO6c9a6p48aNY+TIkXdrxl1hzDJy5sCN8587dJ5MQ2a+dUO0l4UT/mLmR/MAKFkphCk7xuET4JhQ2K28PO55XN1dOLUvggbt6/DE250dbdJ9x4Xjl9i98gClq4bRpHPBi2v5Bvnw3vQ3+Kb/TIxZRnp//AyV61co8POGlAvmx8OTyUgz3LHzranctGg5Hjdjr+MBJifw/ZlvMfmNH8gyZNHjo6eo0bRKPlh3Zy6euMzJvRFUaVSR0lXCCuWc+cn2f/bwxzf/goQz4eeo17YWDz5tmS9TomwwE1Z/UrjGpf0OWdfzHLOQiSPscj7+/PbGVnemIYv/5m3k5XHP2zxfRloGu1cewMXNmYYd66LX555bU5DclfNx4cIFBgwYwKpVq3Bzy/3GcGtITUqZa5htyJAhDBo0yHycmJhImTL5m9keEX6e47tOmY9PHzjHyT1nqNncsrTw9IGzrPp5PT5B3jw94JECj0JIKZn96ULz8eUzUaz7dQtP9C8aD3kXV2f6jenlaDPuW07ti+DdFkPJNGSBLDxFx8792tPxxTZITRZqnb8Q4o6OR3Gi4wttaP98q0L9O+xdc5Chj4zFmGVE76Rn3IphuW5b5IVMQybrF2wlPSWdNs+2yDWHIPFaEht+24qbpxttnmthtTolryybcVO+kIRl01dbdT4cgszAlLMkTf9kxh3ecHv8SviQEJ2Alh199ivha/NchoxM3mv9CSeze5u17t6MjxcMcojGz1192vfs2UN0dDSNGjUyv2Y0Gtm4cSPffvstx4+b8gEiIyMJC7vhTUdHR1tEQ67j6uqKq2vB3ox8grxvfBaysRZmvnw6kndbDCPLkIWUkv3rDvHFGvsSo/atDef0/rPUa1uLKg0rWh3j5KInMyO7FE2aSsIU+cuBDYc5uecMdVrXpFrjSo42x8yaeRtN7bCzP5t/TV1RaHLSer0eHLPoKVT2rQ1nydfL8PTx4MVRzxU55drC/jssnrzU3NXbmGVk8eRlNjsfUkqGPz6ePasOAKYo7g8HJlpUUKQkpvJWkw+JOnsVgP9+2ci4FR/b/NDLzMjKcT/PTL+1lNeBuD8Nqb+AFgWA8B5o13Qf/NSfT54YT8ylWJo+2pCub3a685tyIXzjEbPjAbDx9+1Ejb9KaHnrirMFyV05Hw899BDh4eE5XnvppZeoXr06H374IRUrViQ0NJTVq1fToIEpfGwwGNiwYQPjx4/PP6vvkhJlgnh36qt8P+hnNE3y6ue9KVXZMtS4779wDGk36uz3rz1EemqG1ZWaTPsXmbEe4VQZPF+yKiTz74w1THr9BxCgE4JxK4fT8KGcX3IhBOVqluHYjpPm46qNrTspCttYNXs9X7w0FQQIBGOWDSmU7Y284B/ihzRmt4/X6wgM83OsQfcZF09eYUjnMWhGDaETHNpyjNknpty2jPN2pCal8fWb0zm0+Rj12tbi3e9eveciOQlXE297fDfEXIo1Ox4AUeeucmDdYQvtkwPrDpsdD4A9qw9y9eI1m5sGSnJqYxYlrUyhLwFB/0LmXtCVRDjbt5VWpWFF5p//HmOW0e7omLvXLTsWAtw8HfP5vasr8fb2pnbt2jle8/T0JDAw0Pz6wIEDGTt2LFWqVKFKlSqMHTsWDw8PevVybOi+6xudePS1Dkgpc93jKl3tRpM1nU7gF+KHq7tlYo5MX4lMGAjoTF8CYxTCd7jFuH9+WJX9BkAnWD1nvYXzoWlaji0hKSX71x6mUr2C34MvLiz9ITtEKwEdrPx5XZFxPp58pzPhm46w89+9BJcJ5P2ZbzrapPuKk3vOmFf50iiJOnuVuOgEgkoG2DTfT0Pns37hVjSjxpp5G/EL9uG1L17IT5MLnFsfYM6utj/QPH09cHJxyiFl4B/qZzHu1tecnPV4+dnedTW0XAl0eh2aUUOn1xFasfBX7rdD6LzBtU3+zSdEvmzL1WhWlZrNqnJk+wkAOvRujV+w7ds49pDvTQQGDx7MwIEDeeutt2jcuDGXLl1i1apVeHs7NoEyOSGFGYPnMv1/c3MVGKvXphZvTX6JkPLBVGlUkTHLrMvOyozNmOKkGiDBsMHqfIElA8ylhQABIX4WY3Q6HSXKBqHT3ThPWMWiFRa+1wkseZM2gxAEhBYd3QhXd1dG/zOE5RkLmHfmOyrUKedok+4rqjSsgN5JjxACnd70XfO3Y8/87OEL5qZnUpOcO3opv0wtNCrWLWf+Puj0Oira8Znz8Hbno7nv4u7tjpOznhc+fdZq0myNplV4YcSzODnrcc9+jz0NPvuN6UmVhqYFWqX65Xl5rO0JmEWda1fi+OTJCfSrOZB5ny2yK8pzev9Zs+MBsO7XzSTFJeeHmXdNsejtkmnIpHvIK6QkmNo0u3u58VvUTNzcbQs3yZR5yKRR2Ud6cO2Azn+KxbjIs9EMf/xzzh2+QN22tRi55AM8fS29/dMHzjL+xSnEXo6n65udeGHEs6rJVx6RmUchYxM4VQTXh6z+3qLPX2X4E+OJOHiO2q1qMOKPD4pMNZGi4Nm75iCLJy/Fw9udl0b3tKtE/o9v/uW7gbPMJbIDv3+NR1/rmI/WFjzJ8SlMePFbDm05Ru1W1flw9ttW70t3g5QSKeUdq4A0TUMIkW/3N6PRmC/VGoZ0A2vmbiQtOZ12PVsWqQXK4A4jObDhiNnpHTz7bTr2sS2qsmvFPoY+MjbHa7NPTsk32Yi7eX4XC+dj46JtfPbsVzleGzTzTbr0a2/TfFIaTf0nMtaAU1WEz6cInV+u4/NDE6C4cfXiNdYv2IKXvycd+rS2WhYtDQeQsT0xRaA0hNd7CK/cty3y8++QHJ+CMcuIr9JlKFZIKVkzbyNHt52g9oM1aNejpfXoqNQg/V9Tczy39qbcMEWRRErJRw9/xt414abIaJgfM8K/wruIdNR9pkQ/EmJM0Xq9k45uAx/j1Ql9bJrr2pU4ni/3hllbxsPHnSXXZuVbua1qLHcL/qGWYdYAK/uSeUUIPcL7PfB+L0/jleNxd8RFxfNGgw9IiktGapJt/+xh1J+DLcbJ9KWcCndl93pPSlfKoOWjC9HfxvnIr7/DgvF/8tPQX5ASnnr3Ed6c1FdFqooJQgg69mlzx5WnTBoPqbMAHSRPgcAldiceKgqG2Mh49mYr/kopuXY5jv1rD/Fgt2Z3eGfh0PSxRqyavR6BwJil0ahTPZvnOr3/bA5Ru9TENKLOXs1Xwcy8UiyeinVa1aTRwzf+YHVa1+SBLkUj4VBhya4V+0m8lmSWdd/29y5SElIsxh3b48q7j1Ti5/GhfPZKeeZ/XfC5MjGXY/lxiMnxAFMY/uTeM7d/k6L4kXa90ZkGZCLTVU+kooqnr4dJ3uCm9UNgKdsSkgsCv2Afk1yIlOj0OrwDbI/IBJbMuZ3k5Ky/t3q73It8vvxjfjk3jXkR3/HV+pFqpVqECSodaP5ZCFNSm5unpajduj99kQikZvpbrvil4LdAMlItBYPSktPtmvNKRBT/zljDgQ2O7f6syEd0ody4vWoIfeGvLPODvf+F8+/M/4g+f/XOg+9R3DxcGbbgPXwDvXF1d6HvZz2o2ayqo80ys2r2TQUNAtYvtL0paqV65Xntixdw9XDF29+Lj+YNsKvqyB6KxbbLdWytKVcULg3a1+b5Yd1Y9NU/ePp58uHst63qMpQoE5LteJhWBCXKFfwNvmSlUFo9/QCbl+wEoGbzqtRqYamUm1ciDp3nnaZDyMjWl3nzq748PfDRfLFV4TiE30Rk/LtgjAS3x4pUc7y88suYxfw8fAEA7t7ufLd7/D0pxZ4XWjzehBbRTe480AGUKBtE4rUkNKOGZtQILhN45zfdhu7vd6X7+13zyTrbKRYJp4r7E0NGJhNenMLWv3ZRumpJPln0v0K5ORqNRvb9d4gsQxYNO9a1Syb6h//NYcnXy8yZ7MFlApl/7vv8MjVXpJYMaYtAGsD9KYQ+uMDPqbi3eDqoL0mxpu1OoRM8P6wbL4603gxQYRun9kVwdPsJqjapnKvy8rkjFxjV/UuuRETT5pnmvP/jm4XaEuFuUAmnVkiKS2b5zP/QNEmXl9urKoX7ABdXZz5eMOjOA/MZvV5PYzuSvm7GN8jbXLev04lC6S4rpTG76+ZhQEDqPAj6F6ErGtn9juLQlmNs+WMnJSuH8sirDzms4VaR4aat6etdrQuDU/sj2PrnLkIrlKBDn9b3bcL+zuX7+LjrONPvVcAnv//Pan+acjXL8OPhyYVvYAFTLJyPTEMmQzr9j7pNjyJ0ko86LWXylqm42qjzoVDkF0+804W9/4Wz779w/Er4Mmj6GwV/UuP57HbfABK0SMjcB64PFvy5iyiHtx5nUJtP0OlMFQXnDl/g7SkvO9osh2LMNOY41jQtl5H5x6n9EbzTdAiaJtGMGif3nqH/1/0K/LyOYOkPq270p5Hwz7QVhdIc7+iOkyye9A8ubi48/3E3q61GCoNi4Xyc2neC/321hdIVTcmCXXrFcnR7OPXbNXawZcWPpLhklk1fgzHLSJeX2xeKmI/UkpGJIyBzP7i0QvgMRQhL2XxH4OruQo2mVYg6G03ZmqUtstELBF0g4AxkYb776e+9vXzT1tECU9dQ9265JnVGngln1Y+zcfd259H+g/Dwtvwdb/1rl9nxAFi/cMt97XzI9HXIzH0Il4YI17ZWxwSE+ZOWnI7UJEKYjq2RlpzG0h/WkJ6SzsMvtbMrt27zkh1mxwNgzdwN96TzcWpfBJuX7KBEuWAefqmt1SiaX7AvQieQRlPOWm7damMuxzL59R+4eOIK7Xu2os+n3W0umIi5dI0P2o8wNTIVgr1rDjLn9FS7to5tpVg4H5rhOGWr3KhSKFnewNUDhwHlfBQmWZlZvNf6E84fvYjA1Ab7x8OTcPeyXWY5L8ikcZC+FNAg7VfQB4LXOwV6zryy4qd1zB+7BIDIs1f5ot93jPt3WIGeU+h8wO9rk0MmDQjvgfecCJZp66gPZB3BtHU0H4KWm67tJmKvRND/gU9JjtchJWz56zW+2rLQIpRfqkqY2fHQ6XWUrlqS+xWZugSZ+BGgR6YYwXc8wv0pi3GDf36bT54YT3x0As0ea8wjr3awGKNpGh92/IxjO08hBPw9dQU/Hplsc/lmSLlgs+Oh0+sIsbPbakT4OUY+M5HIs1dp+2wL/vfTWwWeL3FqfwTvNLs5enOaAd+9ZjGu72fPcWLPaU7vP0uZ6qV45fPeVucb32cKBzeaFE7njvqdsIohdHzBNoXT0/vPmpPbwaRpEn0+xiGJxMXC+ShZpS5ZWXDd+ZQalK7e0LFGFUMuHL/MucMXANN6++qFaxzfdZr67Wrf/o23QcpMMJ4DXYipmZM1Mo9h0lvIfk/mSYpKofXZQ+fNUt2aUSPi4LlCOa9w64Bws3yY3DMYL2XnrIBp6+iqqYvoLav4g//9S2LsjVXn4R0Qd+UMgaVyOlsPv9SWc0cusG7BFkpVDmXw7LcL1n4HItP/yf4pu+Fe2lKrzkeNplX47coMMg1Zua6MYy7FcjS7I7eUEH81kYMbj9DqKdu2Dzr1bcvJvWf4b94mQioEM2TeuzbNc53xL37LlTPRaEaN/+Zvombzajz+1sN2zXkntv21O0f0Zu0vm606HwGh/ny/9wsM6QZc3HKPxEaEnzPPpXfScfbQeZttK1+7LE4uThgzjQidwNvfi+DSjtE0KRbOh39oZQ6vfYuQ4JkIIbkc2Yc6Herc+Y2KfCUg1A8nZz1ZWUaQJrVIe8rGpPGaSV7deBZwA//vEa4tLMYJtw7I5HBMzQCNiHzsNmkvTbo04I9v/kWn1yE1jeaPF040LikumaXfryYzI5MurzxEcGn7yvcKHV0A4AoYMG8d6SyjFSEVKgDrARBC4uYh8QqwFKPT6/W8+VVf3vyqbwEZXITQlwW2YXLIdeBUJtehQojbhuR9Ar1x9XDBkGYwC++F2hGt0Ov19PjoKSo3qEhohRKUq5m7bXnh6oVrNyIpOh0xl67ZNV9eCCl/a/Tm9pVkt3M8AJo/3oSVs9YhsrcFG3eun+tYmXkEZAo410cIy79bSLlgxv47lAXj/8TVzYWXxvR0WO6jKrUtAkhpgNRfkdpVhNtjCOfqjjapwFg1Zz1T3/0Jzajx4qjneOY92+vNtaRJkPIDppuoAKfK6IKWWYyTUoO0hcjMcIRLM3DrWqRE5nYs28OWP3dRplpJnhrwSIGHhY1ZRt5sNJizhy8ghMAv2Icfj0x2mNiQrciM9ciET7O3jgYgPHpYHbd4wqfMnxCOhxe89/2zNOxsfVxxQWoJyPj3TZEi50YIvy8ttqvuht2rDjD59R9IT82g9/BnePLtLjbPdfHkFd5q/CFpSWkA9B3Vg+c/7mbzfD8Onc+Cz/9A6AROznqmbB9HpXrlbZ4vL2iaxveDZrN6znpCypdgyLx37XKiMg2ZLJ60jCunI2n1dFOadLauzq0ljofUH00Hzg0QAXMLPbdNNZa7x9Di3oGMVZgUEfWIoL8QTtZrvu9lsjKzeK3e/7h04jIIQUCoHz8emWxza20taQKkzOJ6+Bh9eXTBq/LP4PuUC8cv0a/GwByvjV0+jCYP13eIPYo7c+VMFMt//A83TzeeeLsznj4ejjapQJgz4jd+GbPYHDnwDvBiScwsm+eTUrJp8XaunImm2WMN7Y6kFFWkloC8RSRN+H1X6FurSufDDpb/+B9LJi/Dt4QP73z7CuVqlC7Q80mpZTseEtNDVIOMjXAfOh8Xjl3iwrFL2UeSmEuxnNhte86HcO+JTFsCWiygQ3gNzC9T72sCQv1wcXMmMyPLpDEiTOFYRdEkLjqB/k0+IiUxFSklW/7cyZTtY4uM/sXx3aeZNnAWacnp9BrWjTbdm9s8l0+Qt7mkV+gEPkH29R0RQtD6GdvtuZUrEVEsn/kfru6uPN7/4ULpfGs0Gvl3xn9cPhVJy6ceoHZLa5FxPabmNPKW14ouRePTWwhEHDrPe62HM7DVx5zIpRHY4a3H+erV7zl7+ALhG48ytMsYCjowJIQO9KW48aeQ2Xuy9x+BJQNwcnEyaxcJncj1oXfu6EVmDJ7Lr+P+IC05zeoY4VQGEbQS4f8TImgVwv0Ru+w7vvs0L1TuzyMevZjy9ky7dQ3mjvydx7x681yp19i96oBdc+Unnr6efLr4A0IrBBNYMoD3Z75F2eqlHG2WIhfCNx4hKS4ZzaghNcmJ3aeJvRLnaLMAMKQbGNJ5NEe3n+DMwXOM6TmJc0cv2jzfI688RNVspU8XN2cGfPdqfplqN4nXknj7gSEsnPAXs0cs5IP2IzAajXd8n71Me+9nvnlrBn98s4xBbT7h0JZjFmOEzgvh9b8bL7i2LfK6PcUi8pEcn8ybDT8wl9K903QIv1743kJjIiL8RhaxZtSIPh9DRpoBN4+CTcgRflORCR+BFg3uPcG1fYGez1H4BHrz6aL/MXXATxizjLw89nnCKlom/0Wfv2rudyKlZNfKfXy5znozQKHzBddW+WLf6Oe+IurcVaQm+fu7ldRqUY32vWz7Ah/YcJg5I38DwJCWwahnJrI45iecXQq/nt4aD3RpwJxTUx1thlUyDZn8890qos9fpfWzLYpUky9HcHO7c6ETuHm62dXZND+Ji0ogKTbZfCw1yfmjl2yOGB/ZdoITu04jBGSkGVj6wyoatC8axQGHthwj8VqS+fj0gXNEn4uxeg/LT643kjNmaeiddGz7a5fV6IfwehXcu5oSTvUVi1RemzWKReRj+9I9ZscDTI7F5iU7LMbVa1sTJxcndHodOr2Oms2rFrjjAYBTBYTbY6ZESLf2uX5o9q0N583Gg3mt3vtsX7qn4O0qANy93PDy98TLzxN3b8tOtQD71h4iLTndvNIL33iUlITUArft6oWYHBLSF05ctmOuG1n1Upo639rb/TYvSGlApvyIljgGadhf4Oe7G1KT0vi46zieKfEyI7tPxJBusDruq1e/5/v3f+bPb5czqPVwTuw5XciWFi0qN6jAgGmvEVQqgNJVwvjs7w9zrVAI33SUqQN+4o9v/iUrM6vAbQsqHUCZ6qXM90x3LzdqNKti83x7/wtHCGGqnJGwa0XRiRje7ASCqb2DX4h1YbD8pHTVMHR606PamKVR6jaaHEIfinCqVOQdDygmkY/KDSpavFalkeVrZaqV4su1r7N8+iJ8g7x4btj7VuczpBuY+PJ3bP9nD2Vrlmb4wkF27ZnL+PcgYy2gQ6b+AoF/IpxzfoETY5P4uOvnZKZnApIR3b5g7ump91SJZHJ8CsMeG4shzXQNo575kjmnplCibM7f3c1fLqETeAd45eqo5Cu3fGG1LNu3XRp1qodvkA+JsUlITfLAIw3xCbBv/zovyIQPIf1fTJ+leRC4GOFcs8DPmxdGP/cVu1bsB2Dz4h184fwdw+YPtBi3eckOpDTdaHV6HTuW7aVqI9tzoK5ERLHq5/W4e7nR9c1OBS5qVxA89npHHnu9423HHN56nPfbfWpSajVqnD18nvd+sJTrT4xNYmyvyRzeeoLaLasxdP5Am3MX9Ho9X64bwcIJf5GeksET/R8mqKTtuhEZqRk5troL6xl6XS1XaikI924IJ8vIjU6vy3aMTPaJ7GNrnNhzmk2LdxBSLpguL7e32pU7r3w0913GvzCFS6ciadejJQ/3a2fzXEWJYuF8lK9Vhl5DH8RN/IZOQFL649RoahnKlcZLVK80kOqfZwAayGvADItxSyYvY/2CrUgpObnnDJNe/57PVwy3GGfIMDCmx2Qiws/TqGNd3v3uVYsPqynhdC05Ek4Nm+AW5yP6fAyGtBsrRWOmkcunI+8p5+PqhRgyUm+6hiwjl05FWjgftVtWp//X/Vj4xV94B3gyaPobhdLky83DNUeExT/Uz+a5/Ev4Mm3vBNbO34yXnwcdX2xrv4F5IX0NNz5LOsjYAEXE+Tiy7USO44PrD1sdV6pKGBHh580txMtUs11tNC4q3pSsmZCdrPnXLiZv+uyeWBnezPljl/h3+mrcvNx4euCjVh3Z7Uv3IMQNifhNi3ZYdT5+GjKfff8dQjNq7F0Tzqxhv/KuHbkVfkGpvPbJSZBpCI+mQDmb53LzcM3xgC+MWkwpJTKuH2QeAIRpARi0HKHPeW+9eOJyDscoIzWDuMh4i22XU/sieLfFMORNCqfW/g55JbR8CSZt/Mzm9xdVioXzkZGawKPdZuIfbAp7J8bNITWxHx4+t/QgyNgE8qbwvmEDUqYhRM6VUtS5q2ZNfs2oceVMtNXzfvDQKI5sPQ7A0h9Wo2mS9354PccYIXRIfRkwXsSkVyFBX8FirrLVSxFcJpBrl02JZt4BXlSuXz7vv4QiQKmqJQkpF8zVi6YtCS8/Tyo3sLxWgCff6cKT79iuF2AL/b/px8SXpqJpkkr1ytHJTochuHQgzw1+In+MyytO5SDrNGZH1sn679cRlCgblCOv6tYw9nU+XjiIif2mEnX2Kg/3bUebZy2F4/LKgfWHc+QkHNl6nLio+ELpKZRfXLsSx7vNh5KWkg6aZPs/u/luzwSLapcy1UrmELcqU9260xZ5Nto8TjNqRJ61fv+KuRzLl/2+49KpSDr3a0fPIU9bWTxlIWN7g/EKIJHpqyB4JUJvm8PYpHN9fh33R7bonqRFYYjuaVGmvk8ASJDxkLkL9J1zDKvRtArtnk7n6VfPk5Gu489ZdSlR1rKPzda/dpkdD4D1C7ba5XzcrxQL5yPy1DbKhN3Yb/cPNhBxYgOVGt8iXpOjykQHOj/AMtzf5tkWLJu+Bp1eh2bU6PRCW6vnPXVLVc2e1db3L4X/98iEIWCMRnj0tJCIBpMK3uT/2rH4y1loRslTAx7G0/feEoVycXVm8ubPWDxpGVmZWTz5TpdCKVWTUrL8x7Wc3GMq683tYdaxTxsadqhLfHQC5WuVsStUmnfbDMiEoZC+CpzKIfy+QdjhMAi/Kdmfpcvg/gy4FqyU9N0w4o/BDGgxlPjoRAJLBTBs4XtWx5WuEsbkTaPz5Zw3r0qFELh5ueFVCJ+5vLL1711Meu0HDOkGXhzxHE8PfNRizOEtx3JE5E4fOMe1y3EWUc8OfVpz/uhF/pu/iZKVQvlgVn+r52z+eBP2rD6Y49gagx8ayYXjprynWR8vwK+EL4+8cotuhBaFzLrIwW2epKfqaNAqGdfMcLDR+ajdqgafrxrOxt+2ElqhBE+/95hN89wVOj8QHiDTuNFo0XLbxS8wjg+nnAQ0pBTUanoInT6TW0tawyqF5HACQyva15/mfqVYiIwlRJ/FNe1hnJxNl6oZIUn3F4GlaliMlcnfI1NmgS4A4fs5wqWe1TkPbz3O7pX7KV+rDK27N7caxn2x6ttcPhVlPm7ycH3GLretaZg0xiCvPohZUAuBCFqNcLo/y3Lzk1/H/cFPw+abe6gM/vltmxsz5Tcy5WdT4zskoAfneugCFzjarAJDSklqUhoe3u6FtvWx5OtlzPtsEe5ebnwwq3+uujJSpkHmEdCXRBRCl9/UpDSeCuybI7fou93jqdIwZz7avv/CGdxxlPlY6AR/J87BzcO2PKhJr33P8p/WmrrV6gSPvNKBgd9b9h7ppH82xzZDvba1mLh2RI4xUhqY+PyTrFpgWghVrpPG5C2TcPWyPenUEciMbRhihiBIQ+/bH73XC5Zj0tcg49/K8ZoIWmNxD9Y0jekfzGXV7PWEVijBR3PfLTal7Hfz/C4W1S6+Jcpz9uKHRJ73IvKCJ6cj3rPqeAAIrzfQhexAF7w8V8cDoFaLarw48jnaPNsi15voF2tGEFo+GCdnJyo3rMDwxf+zOi5PaFe54XiAqZlWVG6jiyxSS0SmzDQ5ecaYQjnnlr92AqYERiEE25buLpTz5gVpvMyNr6HR1DDtPkYIgaePR6E5HpmGTLb+tZOk2GSiz8ewf+0hq+Ok8SryahdkbE/k1XbItH8L3LbLpyMtkppvjkhcJykuOcex1CRJsSk2n/d6Ofn1uaLPX7U6ztktZ1m4tW2yuKgUs+MBcCrcnf0bEm22zVH8NDKCx8oE82iZsnz0ZITVSqGEhAqkJuswZoExC6IveaBJyzJbnU7HG1++yJKYWXy3a/xtHQ+ZdcrUZTjzeL5ez71AsXA+AKq37EfpZnsp3XQfNVq/WSjnLFE2iLlnvmN5xq9M2z0Bd3vKdp0qg1NVTCp2wrRF5Gx7N1hHIGUmMrYXMukLZPJk5LVnkJrtN9G8Ur5WGXT668pm0m7V2uT4FBZO+ItfRi/mmp1iT8Ltepg9+6vo/oxd8ylysnvlAQ6sP2I+/mXsYouHOWCqdDBGAqYkcC1pfIHbZk2rw5pmRJlqJbnehlnoBF7+nvgG2x4VrnhLb5Nbj68zYNprZicxIMyfl8f1shjj7OqM0OV0JF0LQ54gH0mMTWLB53+Yj/evPcQeK6KAR3bE8t7jlVm5IIC/fw5iwGPlib4Qb/N5tfQtyJjHkIkfIa89jpa+1ua5ABJiEpn50Ty+eWsGZwqpO7Y9FIucj/sBIZwh4FdIWwwYwf1pi0TYIk9WBGRdr3iQoF2GzHBwbVagp31jlJH0a7Ec2+tJwzZJ9Bhg+6rbmGVkUJtPTE3ZgKU/rGLm4Uk299oQLvUgcBFkrDclGrvZl2SbnprB7xP/5uqFa7Tv1cpm6fq7Zce/e9m8ZAelqoTR7b1H7RJTS01K45fRi4m+EEP7nq1o3tX2pEPdLQ9GAVajLlcirhIcoKHXmyoskmJS8Ldjqz45PoWfhy8g6txV2vVsRfuelkJ4IWWDebBbUzYtNmkOlatZmuZdG1mMq1CnHIN/fptfxy7B3dud/t/0y7XT7Jp5G1m/cAslK4Xy4shnreaFnT10/oYStyDXFu2dXmhDw4dqE3Mplop1y1ntvurt78UbX77I9+/PRmqSTi+2pV7bWrf5zRQ9hBAWyuTCinR96aolOXfcg68Hl0HoBO5ebnZVxJE8HlORAaaTJ40DN9sEJjVNY3CHUZw9fAGA1XM38OORyZQoY5kQW1RQzsc9hNB5g2dfR5thFamlIFNmghaFcH8S4fKA5SB9EKaP3PWQpoBC2Fv3cF3P0O+v32AFiM3AszbNdfHkFXPFhgRiLsVyfOcpGnaoa7N9wrkWOOfPDfvz3t+w9e9dCCFYMWst32wdQ/UHCnb/fc/qA3z82DhzhcLlU1cYNMMyuqhpGsumr+HYzpPUebAmD/dta9UR+Lz3N+z4dy9SStYv3MKX60ZSt7Vt5cKNOtWjUad65pXsCyOes9q9979FJWnXxYWS5Q1oRpg+qiQf2pF6M/b5r9mz6gCaUWP70j34BHrTuJPlNu7HCwexZ9UBMtIMNH64fq7t1Tv2aUPHPrfPU9qxbA/jX5gCmBIdo85dZeQfgy3GeQd4odOZkuV1Ot1t1VKDSgUSVOr25fx1W9ekVotqpCWl0+KJJoWypWZINzB98FwObjhC7Qdr8PoXfWxuDe/t70X39x/n94l/A6Yt9UYdLb/PZauXYuj8AcwdtQhXDxf6f93PLhFKY2YcQoJOB5pm2pK2dSsiLiohR7QjPSWDw1uOU6KHcj4U9zkyfgAYNgPC1OwtcLHpoXoTQhcAfpOQiaOBLITX+wgn2zUB8oxzdcg6zPVVxq0CbneDf4gvTi5Opj3h7JWjtXI7R7FzxT6kJpFIdDrBntUHC9z52LVivzmZF8hVffe3L/7mxyG/oNPrWPXzejLTDXR907IaZ+9/B83VAkInOLD+sFXnQxqjTA6vzEB49LH6d3VydmLsv0OJCD+Ph7d7rlLYoZVq8Fq7apSrmk7cVVfK1Mg93ysvhG84cuMahCB801GrzodOp6NO65oYM7PsVlM+tPkYOicdWpZJH+XgxiNWx700uid71xwkPjoRnyBv+o7qYfM5DRmZfNjpM5LjktGkZFT3L5l56CvKVCvYBMu5I3/n7+9WIjXJ2cMXcPdw5dUJfWyay2g0svPfvebto1P7I4iNjLeqodT2uZa0fa6lXbZfZ/GMGjRrF0/ZKhlEnndh/dKa9LaxyMs3yBufQG/T30EzNYssW6NoJ7kWm5yP4sSR7Sf48uXvmP7BnBy9CAoKKSUYtmB6uBsBCYZtVscKt4fRldiErsQ2hEfh5DcI7yHg9pgpT8a9J3i+fuc35YJPgDfDfxtEcKlA/EN8GTT9DUpXtV5WaMwycnz3aS6evHLbOdNTMziy/QQxl2Nttus65WuWNksxa5qkQu2Cr4aqUKes2fHQ6XVUqGvdodyxzOSUXH8oX1c7vRUX9xurf6lJXN0towGm/KHnIXUepP2OjH0u1wRmnU5HpXrlb9uDo0Of1nT/3zMkxJehbK16fDj3nVzH5gWhv7H6l1LmKiX/z/ereNLvBZ7078vUAT/Z1ciyRrOq5gRWnV5HrRbVrI7b8sdO4qMTETpBfFQCW/7cafM54yLjSbyWZHrgSdPf9uxh2xvL5ZVT+yNyJM2e3Bdh81yxV+I5d+SiyWnXJBmpBgtBvIIgfGdZXm1TjW41atGvZXX2bbE9F83J2YlxK4ZRvWkVytUszYez36FSLrk8RQUV+ShgTu2PIOLgeWq1rJarqFJ+cvHEZd5v+6n5Bn9w4xGmbB9XoKFQIQTSqdJN4lYyOzm2aCB0Xgi/ifk2X/NHS9GsbRjITISn9Qft9RXhoU1HAej7WQ+eH9bNYlzM5VjebT6UqxeuoXfW88lv79PiCeu6C3nhk0X/4+s3pxN1LoYu/drTvBBEmjq+0Iaos1dZt3ALZaqVYuD31tUyK9Urx5Gtx9A0EDooX8e6Y5SWlLMHTkqilb4+xktgvClXQSab8of0tklPx0cn8N8vm7h64RqxV+I4svW4fa3Yb84fEAJ3T8uy2MTYJL59e6bpwQ38OWU5bZ5tkUvL9DvT4okmvPfD66xbuIVSlULpZyVBFEz5OYD54b1rxT6eHmCpL5IXgkoFUKpKGFfOmCrvnF2dqf5AZZvmuhuqNq7M7pU3kkKrWmmXkVf8SvjgHehFclyKqfxYiEKJGjTu1JCd/x4gNdkFKTWaPNzQrvmqNqrE11vG5JN1BU+xcT6S4pJZPvM/NE3S5eX2+AbZpyEiDfuQGRsRTpXB7RGrD/f1C7cwptdkkODs6sTEdSMLvEPnwY1HyTLcKBM7vus0acnpeHhbJqce2X6CXcv3UbZGado+l3vJcF4Qft8hE0eAMRLh0QPh2trmuYoyUhqQ13qaOhADMn05BK82bSndxI5le82OB8DsTxfy9IBHLPqK/PPdSrNqrTHLyPTBc+1yPkLLl2Dc8o9tfr8t6HQ6XhjxLC+MuH0eTb9haaRdu8aRXZ7Ub5XM8+9ZjwbcSlxUgsVrUleCtFRnXFwzEcKUJCqNZbCeMXFnlv6w2twM0Jhl0mmwx/lo82wLVsxaiz47CtXMSiJpRqrB7Hhcx94Gio+82oFHXu1w2zGV6pVn/7pDZp2PinVs3/rUO+mZuG4EC8b9QXpKBk++06VQWj5EnY02qUxnOwuR56yXC+cFZxdn2j7bkn+mrQRMjfwKw/l48p0uOLk4cWjzUWo0rcrj/YuOIGBhUCycj0xDJgNbfWxW61s+cw3TD35pNUEpfNNR/p25Bt9Ab3oN64ZPoGUPBZmxHRn3IqBDYkQYz4HXWxbjfpv4t3kFlGUw8s/3Kwvc+ahw02pSp9cRWNIfdy/LVdeBDYf54CFTm3rNqHHx+GX6fNrd5vMKp7KIgJ9sfv+tHNq8l2XfL8Tb34uew9/Bv4Sf1XEy7V9kxn8Ip4rg+SpC2Pr4ySNZ50C7aRtFJkHmUXDNn33g+xk35628/9VNIXndduBFi3Eurs5mB1oICK1gWXZy5XQC43qU4/VPL+PiJvllcghtX4ikfY+CXXVLqUHqPGTmfoRLI3DvZdVpHzDtVao0rEjU2WgefKaZ1cZ4QaUCaN29GRt/3w5AxbrlqN+u4CtF+n72HGkp6YRvPELdNjV5YaRtydfXCSoZwNtTXs4n6/JGSmJqjuhSqh1OW1JcMku/X2k+Prn3DHtXH6RJ5wb2mHhHhBB0faMTXd/oVKDnKaoUC+fj7KELnD96Q7zp8ukoTu6NsAhvnjtygQ8eGmnedz205Rjf7vjcYj6ZvhxTuoxJ9EumLUFYcT6Sbsq3kFJy7ZJ9mhB5oUbTKgz++W0WT1qKT5A3/b/pZ/XmuPH3bWbHA2DVnPV2OR955eiOk/w45BeyMo288Gl3q1UiF4+f5IOHxqJlV6Ed2NCf7w/Ms+wrkb4GmTAQkxMowXgF4Zs/sty5IXUB3FqXJ3X+3PobbvZYQ+q2qcnBDaakv76jeljtpvp4/86smbeR6PMxODnpee0L25Lm7gmca0HmPky5QQKcrG8v9BzyFD8OnQ+AT6APHXpbRtESY5M5vs+TQU/eSDKt28H279djb3Ri9ZwNXDkThd5Jx+sTLRUuAUiZgUz+EtAh05ciZKbVCjQnZycef+v2K1khBEPnD6TTC/vJSDPwwCMNba7YuBtc3V0ZOM1S0fRe4ql3H2XX8v0Ys4zo9IKnB9ouwy41adHAzmhHR2tF3igWzkdAmH+ObHyhEwSVsmz7HL7pGMasGyqix3edJj01wyILXehLokkNIUDTBDiVtpq56+SS89fr6WubFsTd0vGFNneUDw8pF2ze89XpdYRZWV3mNymJqXzY6TPSU9JBwsddxzHn1LcWpXyH1v9DVuaNx/mZQwaS467iHZDTRmnYiskJzL5RZGws4CsAocWaHJ0cr1k+9JxdnJmw5hNO7z+Lp68HpSpbLykODPPnxyOmzsch5YKKXMMzkwLjIoTOCzxeNJV7W2H/ukNs+XMnpaqE0fWNTlb74gjv95EyAzL3gnMzhJd1sb8eHz1F7QdrEH0+hgYP1cG/hK/FmAp1yuLs5kxmeqb5NXu2q/xL+DIj/Esiws8TVDow17bw0rAp+yfTZ05mbEHYUf6u1+tp+qjllkxxRkpJSkIq7l5uufZXavhQHWaEf8mJ3Weo0qiiXfLlPoHePPN+VxZ9+Q8AtR+sQaNOtpfOK/LGXTkf06ZNY9q0aZw9exaAWrVq8cknn9Cli0kYqW/fvsyePTvHe5o2bcr27dvzx1obCQzz59UJffhp2K8gJb2Hdye0vOXDtmK9G3ufOr2gRNlgq5n2m5ZXw3DVl2adEjl33I2dW2rz0ljL89ZuVYPLp6PQjCZZ7xpN7St5vHjyCgvH/4nRaKT7+4/bVcnw5LuPEBF+ni1/7qRsjVIMmlnwqq9RZ6+SlpRmPs7MyOLC8csWzkeFOqVAmLLndXoIDMnCw9dajo4zN0R6AJt3/E2kJKQw77PFxFy6Roc+bWj6iJUEMH0YmZlu6HTp2bkGOpxyaQSn1+uthttvxc3D9Y6fDSmNkDoLmRlu0lDJJdyfn0hjJPJad5DpJocrYwME/GZx3gMbDjO4wyh0elM794vHL1sNwwvhjvDNW2vw2i2rw212svROOrz8PImLjAfAxc3ZbmVNV3fXO5clO9UGw27M0Rtn2/RHFNbJSMtgeNcP2bf2Ep6+ekb+MZh6ba0nYpapVirfSnpf/+IFOvRuTVpyOjWaVimUppL5TczlWOaPXkxqUhqP9+9c4Fv89nJXzkfp0qX5/PPPqVzZtK86e/ZsnnjiCfbt20etWqa9ys6dOzNr1izze1xcCngPPg+kJafx67g/zPvIv038i8fe6GjRUbVSvXKUrBzK5VORaEZJux6trN7g9645zopZFcyRlLCKZ606H29O6gsCTu4+TZPODehmR4fGtJR0BrUeTkKMaStn61+7mHPqW3wCrK9E74SLqzMfzrGvnPBuKVUllIAwf+KjTQmE7t5uVsvBqjbvzpAZm1k85QzefhpvTuqDXm+liZbMJMcWiEy3HHMXfPbcV+z775BJ3Oq3rXy9eTQ1m+csV4yLzuSTJ8vw8rDLOLtI5n0ZwiuTMqhc365T3xGZ/C2kfGf6OX05QmrgWcBbNIZdIG+Sv888AFos6HM6i9v/2WN2PAA2LtpW4DkAUedizI4HgCE9k1N7zxBYwFEE4T0QKQ2QuRtcmiK8rHeOVdjGsmkz2b/uIiBITcziq5cnMPt04TRazM/S1GXTV7N67gZCK5Tg9YkvWo3e5SdGo5Exz35E+yfC8fDWmPn+aj6YN5OwCrmXlzuau3I+unbtmuN4zJgxTJs2je3bt5udD1dXV0JDC76k9G44f+wyCVdvNDtKjkvh3OEL1G6Vs7nc5iU7uXwq0nz82xd/0ufTZyykois3rIhxxhrAtGVRrYn11a2HtzvvW1F6tIVLJ67kyPpPiU8lIvw89drcO1LGru6uTN70GQvH/0lWppFugx6zmtArhI72/abQrm8yCJdck0iFU8WbtkD0YId4GMCBdYdziFsd3HDEwvlITUzj+D4PBj9zI7GxR1zB96cx6ahcv1aBNGxHFLTz4VT+pgMdCG/QWUagylQrmUPno0whdPAMLh2Ad4AXKfEpSGk6b9ma9vXsyQtCuCF8Pynw8xQ0MmOzKf/GuSGiCCVLJ187faN6SQqS4/NWEVWU2L50D5PfmA7A0e0nibkYa9ENOL+Jj47jvfFbCStnAAEtu8Szb99Wwio8VaDntQebcz6MRiO///47KSkpNG9+oyRt/fr1lChRAj8/P9q0acOYMWMoUSL3fIKMjAwyMjLMx4mJ+d8RsWSlENw8XclIM32QnV2dKWVFGCqvAj+PvPoQ8dEJbP17FxXqlOXNr/rabaPRaMSQnmlVDwAguEyAubQMMClrlin4kra74U7XAKbGWQN/yJvIl9DlLvsMgEcPMJ6F9JXgVBnhO+4urLWkUv3ynNwbgWbUkJqkckNL7YBSVcJo0qUBu5bvA6BKw4rUzEXM6W5IS07D1cMVnZWeEgA414XMg9xQac3d6ZTSaFL91N0+xyjTkInUZK6S3sK5DviMQib/ADpvhM8oU4+hW3i4XzsuHL/EuoVbKVO1JB/8XPDRAFd3Vyas/oTpg+diSDPQa1i3Ir3KK0rItL+QCR9gzpfy/RLh3vVObysUOvRuwZKpEaQkmh5N3d8pnCZ1sZFxLPj8T9KS03n8rYepYuW7n1eO7zqFTm+Sr9eMGsd3n85HS63jF5iJv7jhqOn1ksq1i3Z3YSHvUlIvPDyc5s2bk56ejpeXF/Pnz+eRRx4BYOHChXh5eVGuXDkiIiIYPnw4WVlZ7NmzB1dX6x+iESNGMHLkSIvXExIS8PGxT4vjZo5sO86s4QuQmuSFEc9alWtOT03nf61fJv5qEqnJenp80IpnP3o/32zIjd2rDvDZs1+SmphG2+da8NHcdy32HE8fOMsbDT7I8dqX623veZHf7F1zkJHPTCQ1MY3W3Zsz9JcBVvdNj+86xY9D52PMMtJ7+DM0aF/HAdZaJ+bSNaa99zPR52Po8vJDueolGLOMbF+6hyxDFs26NrKrQiE1KZU3Gg7myukonN2cGbN0iNXfiZTpyMQvTKtV1xYIrwFWHQGZsQMZ3x9kIrh2RPhNsho5WjZ9Nd++8yPGLI1eQ5+m72e2S2wrCp5t/+zmty/+ws3Tjde+6GNXvpcW+1J2JC0blwfRBfyYD1baj5SSa2dmsHfVKkLKh1C300iEvmDbFxiNRl6t8z6XspWInV2d+emo7U3Z9q0NZ3CHUQghEDpBk871Gf3PkPw02QIps8i88iA6GYvQSUCHLnipSYeqEElMTMTX1zdPz++7dj4MBgPnz58nPj6exYsXM3PmTDZs2EDNmpYPwStXrlCuXDkWLFjA008/bXU+a5GPMmXK5LvzkRe0tBVoce+i05nCfkK4IEIOIITtyUexkXFcPHGFinXLWW1oJaXkmRIvkxSbbI68fDjnHYvywrioeHqWef1GxY4Q/Hzim0JRTc0L3UNfIeFqgrlk7YNZ/en0YtscY1ISU+lV9g3Sk9OREpxc9Mw5PTXXyoJ7nd+//IeFE/7Ey9eD9398izoP1rAY89mzX7Fx0Q0peg9fd/6Km2PzObXo1qBFcX2LRvh8hvB4LseY+KsJPBv6So7ywqm7Ps9TcqwCpBZrUvN1qozQFXx10oXjl3il9iA0zdQIzjfIm1/OTbO5c7CW8DGkLcIURdOB+3PofC0XgMWFqxev0avsGzle+3jhINp0t11kbtPi7az9dTMh5YLp82l3m7te3w0y6xQycQLIJITnKwi3hwr8nLdyN87HXW+7uLi4mBNOGzduzK5du/j666/54YcfLMaGhYVRrlw5Tp48met8rq6uuUZFCh3DFq5HvU15pgakTEcIS6chL+xbG86wx8aRmZ6Jd4AXkzePtigJk1KSHJ+SY8snIcYyXOYf4sdHc99l6oBZGLOMvDq+t92OR0ZaBueOXKRE2SD8gm1PiLpxDaZjIQRJsckW46LOXiU18ZZql2OX7kvn49CWY0z/wOREJMYkMfyJz1kU9SNOzjm/cpdPR+Y4Tk+yL2kWmcCN3BAdaJbqoJdORlroGhzafEw5H3lAGg4g414AmQbCCwLm3nYLLD84c/C8ORdJM2rERSUQH51os5Ko8H4fabwAhn3g0hDh/V6uY6XxqknN16lKwQv4OQi/Ej74BvmQFJeMppkqE8vXsi9/6MFuzXiwW7N8sjBvCKfKiIDpdxxnSDewe9UBXNxcaNihTu5bvQWM3TofUsockYubuXbtGhcuXCAsrODbpt+JiEPnmT9mMVKT9PjoKSo3sFIeaaUfibXQdl6Z/clCsjJMFTYpCaks+vIfBs3I6WELIdA768w3F8DcGOxWmnVtTHqqAS3LSJtnW9hsF5i2GAa0/Jjo8zE4uzoz8s/BNHm4vk1zCSF4esCj/PbFX4CpZXdrK6uGUlVCCQj1Iz47+dfdy42KuTQhk8YYyFgJwhfcOiOEfR9VmXUKMo+Bcz2EUxm75soL13tdQLZuQXwqqUlpFtVJnfq25dRNTbGs5ZkAZBnSOLTqPVxdTpCRWZ+6nSeg01v5nXi8ZK6KQXiBu2XPjoAwP4vXylSz3hxPkROZPBVk9v1OpiKTf0D4f1Og56z+QGVc3F3M1Xqh5YOt/g3zitD5IwJm33GcTF+BjH8PMIK+AgQuKJRIT14wGo38NuFvDm05Rs3mVenx4ZM2l8c6uzgzdsVQvuw3jbTkdF4c+Szlahb8PcIRGDIyea/1J5zIzkNp82xzhv36XoGX7Vvjru7oQ4cOpUuXLpQpU4akpCQWLFjA+vXrWbFiBcnJyYwYMYJu3boRFhbG2bNnGTp0KEFBQTz1lGMzblMSU3m/zSekJKaBlOxauZ+5p6daVFoI96eQqXNNSYwAnm/a5e0LvS5HJahOZ/kHllJaqulZ2QgzGo0MfmgkR3eYokh/TPmXqbvG4+Jqm3P0xzfLiblk6qKaZchixgdzbXY+AF75/Hnqt6vFtctxPPBIA6tiWa7urkza9Bm/jvsDo9HIM+91tdpjR2qxyGtPgJbdryFjDcJvss22yfT/THkQaICLabXqUrDSyQ071MXTz4O0pHSkJqnXrpZFaTfAU+88wuGtx9m1fB9BJQMYs8z63nD4yv7UabQ5OyJ3kYMrddR/xLJZnvAaAC6NwXgFXFsj9JZJmKHlS9C+VyvWzt8MQM2W1WjwkPXcm31rw/njm3/x9PWg76gehJQLzvsvoYiwf90hlny9DA8fd14c+Zx9ialCd/vjuyQlMZV/pq3CkGagc792lChr+fsNKRfMxLUj+Gvqctw83Hj+427o9QWvQyETx3BdxRnjOUj9Dbxs7widnywc/xezhv8KEnYt34dm1Ojzie0KzX9+s5wzB88BsGD8n7R8qqmFuOT9QPjGI2bHA2DDb9t4dXwfh3yv78r5iIqKok+fPly5cgVfX1/q1q3LihUr6NixI2lpaYSHhzNnzhzi4+MJCwujXbt2LFy4EG9v27Qo8ouLJ66QdFM5ZGpiGueOXLTYgxc6Lwj6y6RvoPM3ZfvbwctjevJR5zFkpGbgG+TNs4OfsBij0+l4ZuBjpj4wgF8JX6tRg3OHL5odDzBJxh/fecpqHkFekNrNDo9E0zSr44xGI39PXcnJfWdo0L4OHXq3tuolCyHy1AuhZKVQ3r+ToFnGhhuOB0D6v0ht9J2rX3JBpszkhkeXhUwteOcjMMyftyb1Zf7YP/Dwceedb1+x+nvb8PtWNizcCsD5pEt8/eYMPl30P4txPj5HzVuCmgYuugMWYwCQaUjDHtAiEfqSYMX5EELw0dx3eez1TmQasqjbuobFdhCYcg2GdB5jEsnTCQ5vOc6s418XyoMvv7h06gpDOo/GmC30d2jzMWafnGLzNQivd5GGXaaePsIb4WnZViGvaJrGBw+N5NS+CIQQ/D1tJT8dmWy1/LxG0yp5EimUGeuR6SsQ+rLg+TJC2PMAvXUVZJ/kuDTsQqb8BMLdlDDtZHtDu/BNR27I+0jJwY1HbJ4rLjqB1XM2mI/PHrrAnlUHaPnkAzbPWVSx6PMlwNXDMdtpd+V8/Phj7hnR7u7urFy5Mtf/dySlq4Ti5uVGerJpP93FzTnXroU7lh3h72mb8Qv24aXRJe3q0Fi7VQ3mn59GZEQ0ZaqXyrUE9ZXxvWnQoS6xV+Jo0qWBVUEanyDvnKW2mPYqbeWJt7vw3y+biI2MR+ekp99Y6+23541axLzPFqHT61g9ewOaUePhvra1Lc8zupuzzAUIN7DnJiq8uBGCEmBjDs/dcO7oRb569Xs0o6lz6PDHP2fWsa8t9ldX/bz+xoG80e78VpISq6AZr6HTm67EYLSeZ3DtRH/OHTqAm7uRjLSVNHj8J4RLfYtxQog7Oq4ndp8xtxuQRsmVM1EkxiThH+KXY5yUkj++/pf1v22lTPWSvD7xBZvF7wASryXx09D5RJ2/SofebXjo+QdtnuvE7jNkZWZfA5Kos1eJi0qwOc9IONeE4PWm6Ki+gs0OMUDUuauc3HPGbFvC1UQObTlGi8dtk4k3Nbx8DdAj0SDrDMLPMjpmzDIyf8wSDm09Ru2W1ek19GmrWxbJWe8ya9j3XDnnTLundXR603oDOpl5Epn0BchUhNerCFfL1g4y6yIyti/mfliGXRC81uZt7ZrNqrFn1UGkNHW1rWVHubuLm7O5NPY67la6gN8P1Ghm6p7799SVCJ3gjYkv2pXvZw/ForcLkKPxV277W6f2RfDJk+NBSoROx4k9Z5hx8Eu79sN8ArzveCMWQtC4U73bjgkqGcDAaa8xdeAspFHj5XHP5yotvGPZHv6augKfQG/6jelpNZRbomwQz7zflfULtlCxfnkatK9tda7tS/cApkQ3BOxeub/gnQ+XVqbchdQ5IDwRvl/YlXsjfD5Exh4xRVP0ZQpFlfLErtPm7TRplFw+FUlSbLLFNtOtxWa5fdJqdpzGodX98fA4RWpaXep0nmB13LXze6nbLM3kpAg4vnkG1dtPteka0lMtk1/dfCxvyusWbGHaoJ8BUyl14rUkPvvrI5vOCTC6xyQOrDcJvu1eeQC/Ej406nj770duVGlYAb2T3hy9CSzpb7fapNB5g+7OUVFN08hIM+S66PAL9sHVwwVDWqb5c2BPjyVp2AzoMW+VZGywOm7+mCXMHfUbUsK+NeEIIeg9/BmLceNfO8XulUFoRsme9eBX4SxNH825GJPSYErA1eIAiYzbA0HLETkE6oCsw8CNPjxoUWCMAifbEjt7DXsao9FI+Kaj1G5Z3ar9YIp8fd5nCpdPRdK+Vyve+OpFi6iXp48H73z7ClPenolm1Ojycvtc74f5jZSStOR03L3cCiXvQgjBO1Ne4cURz6F30uHpW/ALsdwoFs7HxZORpCXfuJFmpBk4f/QSdR7M+SA4tvOUObIgjRrnDl8gPTXjtqJZhckjr3ag88vtAXLNUD594CzDn7jZgTrNj4cnW3yw18zbyPQP5gJwav9ZMjMy+WjOuxbzhVUKuZEQKa23N79bLp+OxJhlpHTVkrlu4QifIUjvwYDO7i+lcKpsWq1qsaALum3pdGxkHHFRCZSvVcau/g5VG1dEp9chNVPkI7R8MN4BlqvkTi+2ZdeK/dmGQpMu1reDXNw8qdJ6KlfORFOtahh6J+uh0nJV07meh6oZIS051uZrOL3vrMVrkaejLDQmTuw+bW7cqBk1ju04ZfM5AY5sO2Feher0Oo5sPWGz81G6aknGLBvCoq+W4uHjTr8xPQulb8ehzUf55MkJJMUm0/SxRnzy+/sW+VnuXu6MWDKYb96aTkaqgT6fPkuFOrZvRQin6sjrjgd6cLIeDQjffNRc7SSl5NCWY1bHHd5yHM14o/nk4a3HLZvgaVdBu3bTC0bIOn6LOi7gVBNTL6YsQJiim3rr95IVs9axdv4mwiqU4OXPn7e6eNM76Xn2gydo9VRTSlYOtbptCDCu9zec3HMGzajx55TlVKxbji4vW5agPvZ6R9r3akVmRqbVPLSCIOZyLB89PJpzhy9QumoYn68cXii5F9KwCy/xA2huyKyBha4Fcp1i4Xz4W9mesLZlUb1pZYROmB/cZaqXKnJJR3cqizq+63QOB+rCscukJqVZ1Jkf3XYixwMjfONRq/OlJqaZdyyEEDlKZW3hh//NZtFXSwHo0Ls1g2e/natzYY++iuVczlbzH25mzbyNfPHSVDSjRqX65flqwyg8bAy/lqtZhtH/fMRvE//GJ9CbV8f3tvq3a/NsC5LiUti8ZDtlq5em72jrYl/Hd59mcIeRpCam4Rvsw6SNo6xGvi5EBFO+SjQ6vakpn2/JJ22yH6DZ44345/tV5mO9k45SVS0r1+q1rcXiSUtNTqOAhh3sy5Wq2byqOfKhGTVqtrCvQVajjvVsdl6scenUFU7tO0vVRhUJq2j9MzXhpakkx5vyzHYs28Pymf/xRP/OFuMad6rHnFO2RaYscHsUjBfJSvoDnXMF9H7WtTvqtKrB/rXh2VpGwtTEzwq1WlZj98oD5r+D1a0NXQjoS5sSnJGAM1jJlRNOZcD/J2TKjyA8EN4DrCbzb1+6hy9fNlVrHdDriL54jXH/DrMYF3HoPO+3/ZSk2GS8/D2ZuHaE1d4spj5dNxzZy6ejLMZcx8PbHQpxu2X28AVcOHYJgMuno5j50TyG/Zp72XN+II2XkbEvcGP7ayuU2GZnbpBtFAvn4/KZaIvXLp64YnHzrly/AqP+/JC/p63Mzvnoafeqe9eKfZzad5Z67WoVSpfB6g9UNueGCJ2gdNWSVh+gNVtUu/FgEeS6Z5oSn2JO7BI6QUpCqtVxsZFxzPhwHjGXYun8Unur+/RXIqLMjgeYHvZPvtOFak0c43nfyrT3fqTN49cILpnJ5uUZrJ6zweoDIy9omsaaeRvZv/YQeicdTTo3oPNLlis9IQRd3+hE1zc63Xa+nz9ZYM5ZSopN5tdxfzD457ctxpVssIATOwbh4hKD5vwE1Vo9ZzEmrzzQuSEvj+3F4klLcfFw4cPZb+NiRdiqedfGDPllAJuXbKdU5TCezyUEnlc+XvAePw37laiz0XTo0yZfHQd72bvmIEMfGYsxy4iTixPjVw23qjKcdC3ZvAjQ6XRWdW/ym/SUdD56+AJHtvng7pXCyD+jadDe0jnqNcwk+HhoyzFqt6pOzyHWqxE/mvsus4b9ypUzUbTr2coy6gGmEviAedklyGkIjxdNic5WEK5NEa5Nb3sNR7efyCFNfmTrcavjfvlsERVrRFG9QTLH93syd9TvjFj8gcW49r1a8de3K8zyBS2ftC2fpiBIuJZk/oxomkbitaQCP6dMX415Ww5AJiMzjyNc6hb4uW+lWDgfacmWq/XUROsP0WaPNaLZY/nTGfPv71Yy5e2Z2dEUGPPvULvKWfOCb7APbh6upCWbSjxDywdbdaCuJxKaj41GizEATw14lHG9vzZHPnKTHP/kyQnm8Ob+tYcIKhVAvbY5kyJvTujKzQ5H8vLQ03TuGY1mhOffi2LTukir46TUIHOPqauuSxOr+Si7lu8zl7IaszQmv/ED7Xq0sFmKXcsy5qg9yO335ulXmloP/2bTOazR46On6PHRnUvl2/dsRfuerW47xpCRyexPFnJk23Hqta1Fn0+6W90C8Qn0ZuD3r9lsc0Hy+5d/mz/HxiwjSyYvtep8dHvvMWZ/uhAwVRi073X7301+sPT71eaKuPSUDCa/Pp3ZJ6dYjNM76enz6Z3LUr39vXj3u1fvOE7oSyJ8x9y9wVao1bJ6jkhFbknRVeqcpPvXp9CMpgjf7zOs5468OakvFeuU48qZKFo8+QDVH7Cv+WR+8tgbndj+jymnDgmPv2XbQueu0FnJdxIFr75qjWLhfLh7Wa78CyPR5t+Zps63UpPo9Dr++2VjgTsfu5bvy5HfsmvFftKS0yx+B8d3njJvuyBN+S7WaN+zFaUqh3L6wDlqtaxGuRqWX3IpJSd3n0bL9uKFEBzbecrC+ShZKZSOL7Zh9WxTItwDjzSgeh7KBwuLjs+a8iN0enBx02j9uOVKREpJ4tk38XZfB0BySm28K/5mIYKWeotSqTHTSJYhy2bno/fw7qQl7KV8lUTOHAvg2Q8sy7aLOrM/WcjvX/6N1CSHtxzD2dWZ54d1c7RZd4Wzq7M5OVRqEhd367k3vYc/Q80W1Yg6G03jh+vbVTWXV1KT0hBCIJHmRMZ7jQe6NODDOe+wbsFmwiqE5Npz6JHnE5Ca6bsqJTzS21LJF0Cv1+e6YHI0kWeiTdU62ZHqmzuqFxTCtRNS92V2CwbAqS7CyTHKxsXC+ajSsAKunq5kpJiUCV3cnO3eRz628yR7Vh2kbM3StHrqAavRhRJlg4kIz5ZGlpLgUgV/Awq65Sbn5ulq9QZZ+8HquIp5tOicwLnjbpw+lXsfg2pNKt92a0QIQa2W1Tm89ThS05BIarW03MaRUhJ9PsZ8HHMplqxMIy6ulrkQSyYv47eJf+Hl78mgGW9a3bKSUrLoq6VsXrKdcrXK8NqEPlb75+QVvUsIMusyQkh0OnDzskz+S08+ZXY8ALw8D5FweT1+pXLe4Jo+2pAy1UuZ93S7vtnJLoe3dpNzTPrrKKZHyxV0/leA8jbP5wiObDt+Ix8J03foXiPq3NUcx1dOW27pAlw8cZlZH/9KZEQ0F49f5uXPn7ea8xNx6Dw/DvmFjFQDz334ZK5Vb0t/WM0voxfh7uXGgGmvWTj2AA+/1I6/pq4wb/H0Gmq9n1amIZOfhy8kfNNR6rauwYujnrPaJ0bTNNb9uoXIs9G0eLzxbZNhjVlGjEbNZtHDm+nQu7VFb6tb8QyohEzbC2ggdHgGFI2t27vheln99e/E9mV76P6/xwv0nELnAUFLIX05CBdwe8wh6qZQTJwPqckcJYwSLFVF74IDGw7zwUMjEQg0TaPfmF5W903fntKPmEvXiDh4jgYP1aFnLjeD/MTZJeefVO+kt/rhavtEFG3bmbo41miUhnQ5a9d5P138P6Z/MJeo81d5on9nq0lsF45d4sC6w+bjMwfOcWzHSYuw9aHNR82lm7GR8XzcdRy/R860KJFbM3ejuX/KsZ2nSI5L4ZPfbe9CLPy+xnjtXXTyKrg/jXB7zGJMzMUkSt0i3nr5TDx+t+R+unu5Ua9NTS4cu4SzmzP129lXuidT5iCuN4tDItMWIFxtb3zlCOq1rcXhLcdMlRYS6j6Ye0fm+KsJxEUlULZ6qUKpTskrUWdvcT4irCcwjuk5mTMHz6EZNX6b+DcV6pazeKAaMjL54KGRpqaSmiR801FmHfvaIon11L4Ivn7T1LND6ATDnxjPoqiZuLjlXFSEli/Bj0cmc3DDEUIrlKBaY+sr2rkjf+f3iX8jpeTYzpM4uTjRd5RlhGH6B3NZPGkpOr2OeZ8t4tsd46wmda6Zt5FJr31PpiGLZ//3OC+Pe77AH2jC+z3IOgOZ+xDO9RHelsJ8RZ2Kdcqya8U+U2RcJ3JtNZHfCJ0veDi+i3WxcD4unYokPeVG/5nM9EwuHr9std5/z7IZLJuxCp8AN1747CMCSlmuMNYv2IIQwrw3uWLWWqvOR4kyQXy3a3w+XsmdubVRWUpCKhmpGZZbT5mHuK4JoNNJ0A7add5dK/azZt5GNKNGenIGjR+ub1Gi7OnnmUNuHrBafnrppvCj1CRJ15JJT063iBwc33UqR8XO0R0n7LqGdb8nMP7FEDRjMFUaGfhyneXvrUT5Wvz2eTmefdMkxbxyQTDNeliW7u1ZfZClP6wGTJ+38S9MoVnXxravDHUB3NBwEKaeN/cYfT7pTnpKBgc3HqFxp3p0G2Tp3AFs+H0b43p/jTHTSKV65fhyw6hC6Qq69tfNbFq8nZIVQ+j9yTNWt2srN6jAgfU3HOiquTzgLaosrITUr12KJSAoig8mXcHVXWPhlBJEhJ+3cD5u/k5LTZKWlEbitSSCrERS/Uv43rEb67Gdp3JsHeW25bryZ1OE7/p1bFq03cL5SElMZeLL32HMFnJbOOEvmnVtnGsFTX4hdAGIwF/ybb7o81eZO2oR6SnpPPnOI3aJlgH8M20lq+dsILRCCd6c1NdCmA+g9yfPkBSfwv51h6jdsnquQo95JfpCDPNGLSItOc20AGxlm/p1YVEsnI8y1UriE+htLn1z93ajnJWuhWf2rWbokyuzS9DSOLb7Y6YdWGLhxQeWCsiRPBkQ6leg9t8NDTvUxd3LjYw0A1KT1G9f2+pNVLg0QabNx+QNCHC1vVGdlNIs0AMmp2DN3I0WFRwe3u7o9fobyZLCuvPR4KE6uHu7kZFqQEpJ3dY18bDy8KnXthZ/TV1h6nciBA0fsi9j+9t3fjRfw8m9Z1g127LaxdXdlVa95/Dd6LloRgOdX+mJv5U+Nom3dCY2pGdiSDPY7HwI78HIrOOmHhtOlRFe79g0jyPZv+4Qf3z9L5qmcSb8HE0fbWT1Jj/13R/ND7MzB8+z4se1dHvPuqOSX+xYtodxz39tLheOOneVjxcOshg3dsUwPn1yAmf2n6Vq40p8usT6irt19+asmLUWfXaVRbOulknswaW9mLAoAk+fTISAmo3Pkojl56NO65p4+3uRkpiKlJIqDSsSEGZ7g7f67Wqzb2246UBCfStbOADBZQJJTUwzV56UKBtkMSYtKc38t7qOPVUbUkr++nYFa37ZSMmKIbk+uPMTY5aR99uNyI5qSTb/sYOfjn5tcw+grX/v4pv+MwFTify1K3F8uc6y7NnV3ZWB0+6cWC2lZMufO7l8KpKmjza02vTOaDTywUMjiYyIBinZ/MdOfjw8yWrn84SYRNbO34yLmzMd+rS2OQ/NXoqF8+Hh7c5XG0ex4PM/kJrkucFPWBWuObx5F5rR5GhI4PQhJ9KS4/HwzvlFP3f4Qo7jy2dyrx0vbELKBTNlxzjWzNmAd4AXj+dSKircHwWZgcz4D5wqIbys96iQWjwyYQhkhoNLK4TvSKs14drN21jCejXG0e0ncr4uYdtfu+n6Zk4npUSZIL7d8TmrZ6/Hy9+Lx/s/bDWM+2C3Znwwqz9b/95FmWql6D3cvuTFrJtsE4hcK0pKVwnj7W8H33auJl0aUKJskDnHpV3PVnblowinshC0CmSKSfXVQfu09jBt0M/mHkJGg5Hpg+fw9WbLKomsmx5mElkoFVEHNxwxR9GkhH1rD1kd5+LibFV34lYGTHuVKg0rEnU2mgefaUbVRpYREr3uGj7+N1Q/dTpJoF+MxTj/Er58u3McK35ai7uXO4/3f9iuNujPffgETs56jmw7Tq0W1XONQA2ZN4DRPSYRdfYq7Xu14uGXLJWNA0sG0LxrY7b9sxuAkpVD7VIH3fLnTqYO+AmAk3vOEBeVwBf/fWrzfHnh2pU400M7myyDkQPrDtvsfJzccyZHufCJbAl9W5kz4jfmfbYIoRPMGr6AKdvHUrl+zq7s8dGJOaJrmiGLYztPWTgfqUlp9H/gI6LPxSClZM28jXy5fqRdnydbKRbOB0C5GqX5cPbtV4uunqVB7AVpyqIOCsvEzdMyvH3mwLkcxwnRiRZjCgJp2IcxYSKQhd5nYK57/uVqlOblcc/fcT7h8TTC4/Z5KDJxDGSsB4yQ/gdSH4bwHpBzHiHoN7YX3w2cBUCpymFWdT7ioi0z0uOvWs9SL1u9VJ6uodOLben0Yts7jssLL499nm/fMfUvCqsUcsekt9vh7e9F2+dasOTrZbh5uNGhj+39Sa4jhMjuU1N4zPtsEQvG/4mHtxv/+6k/D+SiwJoX4iJz/q2vXrhmdVxohRI3dDEkVKhT1uq4/KRqk8rmPDCdXkf1pvYlMDo5O/H4Ww/ffpA+DHQlsysPJOACztYf3CUrhdJvjH1hefNp9fo8VUsFhvlTs1lV/Ev4Ur1JZau5N0IIPl38PzYv2UFaSgatnnrAaqQVTBopv038GzdPV/qN6UXZ6pYieSf3nDFvzWpGjeO77FPLzRO3tDgASEuxvVKobpvqzPtMQwiJEFC/rX25HMumm7ZvpSbRjBrrF2yxcD78gn0IKhVAbGQ8UtMQOh2V6pe3mOvQ5mM58pYObT5GZES01QhJQVNsnI+8kJkVRrkq6WiawNlV4+pFFwzpmRYqp4061eV8dhUDYFX1EUwP1i9emsrJPWdo0rkBA6a9apEklleklogh6gX0ugwQkHX1ZZzC1iNykSjON7JOcUOURoAxwuqwFk80Yevfu7h6PoYn3u5stX38A10aWDTHa/uc7ds9eSUxNomJL33HsZ0nadixLgO/f92qcu0T/TvTsEMdYi7FUqNZVbvUbQ+sP8xvX5g6FadkpjL62UksjvnJalVBUSV801GzVoUhLYPPnv2SxTGzbN46qtG8KjuyewUBVis2AHOFEJgcgWM7T+WpY7I9tH6mGf2/7sf6hVsoVTWM1794oUDPB9mqu4G/IJO/A5mB8HwBoS/8h0BujHv+a/b+F45m1Diw/jB+Ib5Wm97pnfS0efb23+PLpyMZ+uhYtCxTj52j204w7+x3Ft+HyLPROXLCMg2ZFDQ+QT44uzqRmZFlfi23ZN28UL/ZCT79MYK1f/oTWjqTXv/bc+c33dY+b+KiTI67ZtTwt7LNr3fSM2HNJ8z4cB4pCal0f/9xq7IIgSVzRvH1znqrXZQLg8KPtRRhGjxUh6iLPiB0xF11oVKj+lYfQG9N7scjr3YguEwg9drU4put1gV2pg6Yxe6VB4iLSmD13A0sHP+XzbbFXgrnxD49HzxTiUFPVObAVjeiTu+0eb68Ityub9voAQ3har1mfnDHUexfe4hLpyL5bsAsdq3YZzHGJ8Cb8auGU7pqGGGVQhj+26Bcm+PlJ98Pms2Of/cSF5XA2vmbmT9mca5jy1QrRYP2deyW1b968caq/rrmwr2mu5DzGkzCVbmJ8+WF9354napNKpnFo978qq/VcWVrlDYrUmpGjfK1LPe48xshBE++04XJm0fzwU/9C+2GLPSl0PmOQec3EeFc+CqTt+PojpM5kmaP7bC9NPr0gXMYM41IaVq9x0bGc+1ynMW4W6uJsgwFv+Xm5uHKxwsG4RPojYu7C31H9aBmczsSTrVIWnRJ5uMfzvHK8Mt4eNin33Grg5bbArZMtVKM+vNDvlw3MlehzEr1yvPahD64urvg5e/JkLnv2rUdbA8q8nETYRUDWHDYiLvrUaQUZLnlviXRd9RzNH20IaWrlsw1E//C0YvmL6/A1BPCVqIueDGkZyUMGSa11OEvVOCT3zwILWjFds/XEPpgZOYRhEszhJul8yGlpUDOytnrra5WG7Svw6xj3+SbeVcvXiN84xFKVS2Z62rl/LFLNxKEJVw8afvfIa80frgevsE+JGZLKDd9tKFdbeYdQaOOdfEr4UtCTCJSkzTpXN+upluBYf5M3fH5HccN/20Qk9+YTmREFA/3bU+rp28vya0oGOq2rsmOf/eacxdyUxvNC1UbVcTZzZksQxZCmLoLB5UKsBjX5JEGHL5JUt3Pzg7EeaX5440Z+ccHpCWn5xqRA1N59IH1h3F1d6HOgzWs5165dYHk6Zi6+Grgfmc12dtx8yJAp9cRaWeOYff/PV7geiJ5QTkfN5O+CndX04pdCIlzxudI2cNCPvv8sUu823woKQmpCJ1g6C8DaPtcS4vp2jzbktMHzpkT2Vo++YDNpjm5epGRdiNQlWUQuHpaZp/nN0IIk+aF+91plBRGaeS5oxd5p+kQc0ThvR9et6pm2KZ7ixyKrq2eKviHmV+wL9/vncC6X7fg6edJxxdszx9xFL5BPvT5tDvzxyzB3cuVF0f1yDXR9dzRi+xavo9SVcJo9lgjuxJiQ8uX4PMVH9v8/oLm0OajHN1xilotqtq3QgaS45OZNmg26SkZ9B31nN2RwPOHd7Djn6WUrFSO5t365ZpIeGjLMY5uP0nN5lVzLSv9cM7bzP70NyIjomn7XItct74MGZmsnb+Z9JR02vVoadVBDSkXzCe/v8+sj3/FzdON92e8YbUTbacX2vLrmCVkpBkAePLt3CXHD289zpFtJ6jRrIrdpb1fvzWDZdml8VUaVmTy5s8sIgyGjEz+1/ZTs4R9pxfb8sGs/hZzCafKEPQnpP8H+lImZyQXjmw7zuGtJ6j+QOVcnbu2z7bgr6kr0Dvp0DRJ88cb23iVRQvlfOTg1v1FIzk2ILP5Z9pK8wNPapJ5ny2y6nz0+OhJgkoFcHp/BA0eqmO1MVNeKVujNAGhfuakTU9fD6o0rGjzfPmN0Auk8cbv6ta9xYJg+cz/zDcpgAXj/7TqfDwz6DECQv04sfs09drVsrpvXRAElQosEisMWzl94CxT3p6ZnYCtY3jXcfx64QeLxMNT+yN4t/lQsgymsPqLI5+jt5Xmcpqm8cP/5hK+6QiNH65Hv9H5k0BZmKxfuIUxPSebZcxHLP7A5kWFpmn0KvcmadlS/JsWb+eXs9NslmI/s38T7zSfTKZBIOUxen1wmJfGT7IYt3HRNj579ivTNUjJJ7+/z4PdmlmMc3ZzIahUAFmZxlxLe6WUDO86jr1rwhECfp/4N9MPTLTQ5ElJSGFK/5lcvRCDlPDtuz8xftVwCyd11c/ryTTcyL3489vlPP+x5Wdp05IdjOo+EYHpGj5eOOiO+ia5EX81wex4gKnMfveqAxb3iQPrD5sdD4BVs9fz0ugeVvVWhFMl8Lp93sjWv3bx6dMTzNcwdP5A2vWwfI68OakvZWuU5vLpSFo++UCu+h1SS4bUX5AyGeH+DMKpcETLbKXY5HzsX3eIt5t+xFtNPmTXyv3WB7l2IiX1xv5yirGf1bbPrjfJlQudwDWX/AAhBB1faMMbX/W1y/EA077k5C2jefTVDnR+qT1fbxljNanTUdwa6QgqaRlSvRvmjFhIt+B+PF/+TcI3H839nNmZ6kIn8PS1Hm0RQvDQ8w/y5qS+heZ43A+cPXTB7HtrRo24qAQSrXRnXTd/M0ajZhauMndLvoUxPSezZPJSTu45w69j/2DyGz8UmO0Fxb8z/wNMD10BrPhprc1zHVh/2Ox4gGkh8+vnS2yeb/38JWRmCqQ0PdD/mXHO6rjlN10DwPJcrmHy6z8w86NfWDZjNYM7jOLQlmMWY2IuxbJ3TXj2fBB9PoYD649YjDuw/gjR52PMhSX7/gvPsZ1wHRd3F7NdQuSe37Dip1uuIbuPli04uzih0+V0glyttKSweE2Yev3YyvKf1poKe7Kv4d9criEtOZ2I8HOcPnCW80cvWh0jpUTG9UMmfwUpM5HXnkYar1odW1QoFs5H4rUkhj02jhO7z3BqbwSfPDGea1csk51O7Y+iR21/hvSswFudqjGw02XzB+Nmug3qSqkqpgoXdy833pr8UoFfA0BYhRAGTHuNQTPesFqm5iiEELz3w+s4OZtWxHXb1OSh3raXlm5asp25oxaReC2J6PMxfPDQSLM+xM08NeARqmTrJ3j5eeapA2dhI2WW1c/QvUCtltVwcXdBp9eh0+uoUKcsvkGWeSsBYf7mqJdOr8s16rX7Fqd/61+78t3mgiawpL85GVbodATaIfZ1aysEwOpWRF5JuKZHatcfopKsLOu398CSAeZr0Olzv4atf+8yJYhmV6jsWm6ZRO7p64HTLdcREOZnMe7W15xcnKwmOj76WgdzRNfZzYUBuYhwBYYFmLeUdHodgVbyR/KKp68nTw+8oXVSq0U1GjxUx2JcnQdr0KlvW9OBgFc/7213DpS46RpyW7B98dJU/p35H/vXHmLyG9PZtGSH5SAtBjL3k908BGQSGAq+IMEeisW2y5WIaAw3heezDFlcOR1p8aU7vusUr3x8icdevEZaso5xbxlIT82wkAl383AhsKQ/F45dwifAGy9/x2QLFyVaP9OcRp3qkRyXQomyQXbt+e9YtjfHsTHTSNS5qxaiP15+nkzZPpb4q4l4+3vadePOb6SUyKTxkDobhDv4TrCarFuUCS1fgkkbR/HPtFV4+rjTY8hTVnMIur71MIe2HmPrHzsJKR9sdR8cTErAqYlp5uNbmyDeC7w6vjcXjl/mxO7T1Ghahb6jbe+RUaNZVbz8PEiON1UQ6fQ6nhv8pNWxa3/dzK9jl+Dm5cbb3/Sz2ujRI7A2cD3aIQDrUYOXx/Xi3NGLHN91impNKucq612uRmmO7TxlTjgtV9OydNPD252hvwzgy1enYUjL5Plh3ay2ra/+QBXaPNuCDb9vRafT8cKn3fHwttQD8fB2Z8r2sUSfj8E3yDtXzZCXxvTk7OELHNt5kiqNKvLKbTSBUhJSiI2Mp2TlUIv+UGDK5Vj58zqzDMCRbSc4d+QiFWrn1JcRQvDBT/15aXRPnF2c7HI8AF4a3YOzh85zZPsJqjSswCvje1sdd3jLMXPCvN5Jx9Ftx3nw1iRsnS8Ib5DJmMOVTtb1caSURJ27irOrs13Os70IWcSWZYmJifj6+pKQkICPj31/3Oukp2bQt+o7xEXFA+Ad4M3sk1MstgouhM+jVPAoADQNMlL1uFcMR6fL+VCbPWIh8z5bZPobC6jRtGqu5bZ55dS+CGIj46nzYPVcv3DFhS1/7WLEUxPMx07OepalzXeICp+tyIwtyLibI2JuiJDdVrfx7heklLd1Oq9ERDGgxTDioxMJLOXP1J2fE2BFmt4RSCmZM+I31v66mdJVw3jvh9et7uXfPD4/VGavXYljwed/kJFm4Kl3uljtHBtx6Dyv1/sfUpoakHn6ef6fvfOOkqLauvivuifnBMOQhpxzzhkkiKKAIEhQARVQERUFyRkkI4JIRpEgOYMgOeecM5OYnFPX/f6omZ5Q1cPQPQP48fZavkX1u1Ohu6ruuefsszerny5U6a2c2HqYEe/OARRX5tptizJ2y89mX0Pgw2dM6DaLgHuBtOrVJEvDuLRSiWlCct8Kg40W8g4u9qz1+91s3aPsXsPJHecY22kaifFJFKvky/QDY1QZl2dPQuhW+PMMnw37axBNNXh8B9ce449xf2PrYMMXMz+22AMmO9cwptM0jm46ZdRHGrNxCPXeVZePbx5fgwOTsXNI4vGjt6nWXt1ZJoRgau9f+GflIQB6j+1K9+GWKUOnx4vM36/PUjEXYWtvw8wdOtbPCUKWJd4f4IiDs51qXEzoI8ij/FunA3snA3JyPDqbjNyK8/uupPFQBdy/rF1bzS5WT9nE4qGKSZJPMW9+OTXpP9eWmV2IhBOIqMlAMpLTIM1sQP13a/LxhA/ZOGcH9k52DP3jq/9U4AGAnLmeHQ8iTrGx/n+K503GPkW9Gfn3d9w6fZfy9Uu/NoEHKA7Jf4z7G4CA+0FM7jGXaftHmxyfU/L2nj7uDJj9SZZjHl1/apzcZVkQFRpNxLNIFTG1dtPrfDf7MQe3uOLjm0jvH9Wl5fR43jWc3nWB68cVs8a9Kw7S4cs2JgOy5+0r/TUIWRATHktYYATevnmy/Lvn4XnH/eXLxSQlKI0E9688YtuCPXT9MaMJqK2D+pmUNVzPH914yoRusxSXdJ3EsLYTWOP3u8WaQM+7hqrNKnAkpdTi4GxPiapFVGMS4xP5/q1txMeUTPme7zLn+G3K1s6Yhbp69IYx8ABYNnI1bfo0eyXP4n/sjW4mkm/jnXc//cf7MXDiU/IXOArJau+GQP+KhD2zMpKi9q51RxbqG8s1T8aIzpLoPXXFlQr/+4H8+9dRs/f3OkPIkYiwfpB8HZJvI8K/RBieao7tNvR91vkvYsXtXyhbW1vMxGAw8PuQlfQoNoDh7ScRGpD1y/Z5SIxPZPHQPxnfZQaXDl19/h9kBdtGoEtXJrJto1hZW4DIkCiunbhFTESMZef2irD/ryN803AE879dxpd1hnFim2XKjzmJB1cfo7dKEzZ7cOXRKz6jNJSvVwp7Jzsj98a3fCFNXkViQgzN3w9j/Mr79B/3FBtbNTn4RZCqbgsQ/iySXUv+NXtfZeuURG+dVvJwy+uKV0HLSOnZQVJCUgb19PQqpqlIr7icCgcXdfb5yS0/41ghC2Ij4wgLCDf73OJjE/j5k3l8VLQ/k3vMITYqTnPc6smbMvzNjt/3qcbERMQSFx2fgV8W+CBINS5R4/qTk3JfyE0Lb0bwoWGEBurParZrwYQBTfhlWAEmfOZLYOggTSnpzJ4fWm1qLwJr23QJKAE2dv8dCe70EEmXEfH7ELIJV0uDPxCPkjZKIUYlm/+S37HwH9ZO20LAgyBO77rAtE/nm70vgEENR7B6yiYOrjvOt01Gc/mImrWfXUg6NySvzUguo5FcZyC5Tbfo3K6duEX3Il/wdb2f6FFsIA8ymRv+F5DqUZGaNdy5WP0SfVWo2boKBoOMTq9DkiTqtH85WgpPbvkxvutMRrw7mUuHtO83rwKezD46nnb9WtJpcHum7R+lyV3Y/ZcnYc9SPhewcpq27UN2YWNnTeqiXAhh0Xvp6S3/DO634c8iMvB/cgs+xdMWAEIWFK2oVst19XLh3XR6ImVrl6R6q8qqcWVrl8TR1cEYBBYsnV/T6Te7WDl6Lf+sOEjgw2f8u/ooS4at0hxnbWdtzI4IIbDW+B3c8rpm4AE5uNhTuanaJ6hSo7JUalzOuP3Wx03JWyj39aK08EaUXSQrX4RDP4hdqHzg0AvJWl2rc3C2Z9LumVw5cgMXT2eTOhrPHqU5T0o6iRD/0CyPbzAYNF8WoKTcBi34jCm95mJIMiidIhqmbK87RPRviOiUCVbnA14bkXSZVjZWRRXRHUOKGqrkAtam1QSfh2snbhn/LRtki+SfIcXUKh3WTt1CxQblTIx+PiSdBzjkjJbFilFrjJomMZGxrJ6ykR9XfJUj+35ZSO0UkQ1K94SHhkdFbkDErkfEzAfJSQkGbaqoxlRpWoGJO37i6IaT+BTPx/uD2ub6eSUlJvFt09GEB0UghODsnossuT6bfEXUfk23z93n7N6L2DvZUe/dmrjlUWfRwoPt+Kx5WUpXjibwiQ3RUXnoM9v88/vq175GvkSJKkVp91lLs/cVn9moTUBifO77tjy8mtaaKukkbp6+S4P31YvFAbM/oXn3RsRFx1OxYRlNDyZ3bzdmH5vA1l93Y2tvQ6dv22ua7WX73K4/QU7JpMgGmfsmsm1f/tKH0e9PJSE2kaIVCvOuhlO5LMuEBYQbtVviYxKICY/BPZNCrJW1FVN2fcPTq8vR6e0pULGX2edvKd6I4ANA5/IdwrEnIJD0pq2Sbe1tqd5SHfWmx6MbT9Pst2WR4QZPD//7gYx4ZzIPrz6hcpNyjN4wRLO9rGnX+tRsXYWo0GjyFc37n7NLF0JgiJxLKi1DGPwRMVvQO/fOME6SbMBjNSJmGWBAcvgISadNSkplZDs425v02YiPSciwbTBYlj7MbHrn7u1m0f7+h4zoN7UHj2/4cef8fUrXLEGvsV1y/Zgi6RoicmjKlg4R1gfyHtMk/tZ8qwo136qS6+eUiuAnoYSma/lPSkjm3sWHquDj3qWH/PzJPBDKPfpTu4ms9VeTNVt/0pQtv+7mzAHlQfxqnlqc60VQu2011gYsIuJZJN5F8phcQIHiWJ0Un0jewtocjqrNK+JdJI/Ru6VmmyovpdOicNkCPLxyAxf3RAIe2VDIhESBJEkqfoQWfMsWZODcT3Pk3Oq2r8HJ7efQ6SVkg6D+u9pidTVaVWat/yLCgyJM/g7hQZEEPU5bFMsGmVtn71GwVP4M44SIQxfZlUL5UxZa4ccR7kteyZzzZpRdgJPbz/JphQl8XG4CRzZq9Em/AGq2rmK03wbFx0ML875awqPrCqfh0qHr/DXRtICQk5sjPsW8c+QmECJB6bZIspC3kILAh884tfO8sVtIC7HRMiLlK5EkuHlGOyCT9N7oXH5A5zIMyUQrmCHZwKgOU+lRbACdvT9lWzr1wfTwLVcQKVUcSCLLFOi14zdZOe5v7l16YHJM7zFpk6FbHhf6TtVufXsV6Dn6A6PIkYOLvcmWzNcZXgU8mX92KruSVjPn2ATN1XuOIzm9C7MMIhLk8Nw/bjbgVdADZ480MrveStsG/cktP2OpSsiCmIhYIoLVpc28hfOw5PosRm/4ngXnf6b9F29ZfI6OLg7kL67dopqKddO38oFPH7oX6c+kj2ZravI8vRNA4EMl8JAkiatHb5IQl6Aal9MY+WdtVl+8ytJjN/jjXBAtuueuO/KLoHLTCji5OSAbBPbO9lRradpY0MHZPsvfwS2vC96+eYxlQ72VnlLVNTL3iWfAkC7Dm3gUDK+mhPtGBB9hQRGM7jiNJ7f8eHrHn/FdZmiq62UX/umMfSRJIihdGSY9bp6+m4GgdOWYWiEwpyHkGERIR0TYx4iQ95CjZlm0v9O7L9C71Jf81G4ivUp9yd2LDzTHzfq+EMlJSiBw8agj1y+YX644vvUMx7eeARR2/y9fLSYxPlE17v2v2xm1B+yd7PnqF22RsTVTN/F1/eGsGLWGz6p8z79/HdEc1+2njvxx/1dmH5vAn48WvFYKsuXqluaP+78y68h4Vt6dp9IgeFE8ueXH5J5zGPfB9AyS0bmJ6PAYxnSaRrfCnzPpo9nEZU7F5wZsaoDkiOLKrAOrcqB7NTXuzEhOMmQoPRiSZU3n4/L1y6RxDXQSxSv7mhRyiw6P4dmTEIKfhr4UcbvI0Ch+/2GlMTjav+oIFw+oFz1Pb/unBVBCIWuGB0Xm+vm52c/C2kYJhjzzBiHFr871Y2YXf4xdR2yKwm1CbAJLR/xl9r70ej0/7xtF4w/qUatNVSbuGKbKegCgy3zf6MFE9jm38UaUXZ49DiY5nV+AIVkm8EGQ2R4KT275G2vXQghjdiMz4jKxl0P9w8063gshYR8kp3EhiJmPcPocSVK3FmcHf47/28iGjo9JYP2sbQxZOjDDGEmS8Cj8AZ0rbMfZzUBMtCsLzjfQ3F9YUASb5uzAkGzgnf5vaaZpM9eC5WQDBoN6NeXi6cyC8z/z7HEIbnldTba8/TVpY4btJcP/oumH2ufn7ZvH4va/3IAQglM7z3Pr9F0qNSmvFhl6ASTGJ/Jtk1GEP4tECMHJ7edYdnuuxZL4z8PvP/zBsc2nkQ0yB1Yfwyu/B32n9sjVY0p6b/Bch4hdC5IjkmNvJEm95hJCsG7aFqPOx4A5n6rq5TmN8KAIEmIzrv797gZQpHxGUqSnjzuzD7zNjt/WYudoxfvffajZev7w2mMG1PqRhFglUO856gN6jDLfUTU5KZktv+4m4H4QjTrV0fQUMSQZVN0iWlyO8vXL4OTuqHR0yIIiFQq/lG4XRALp/bmESOB1KWonxCVmaD9OL4RpDnyKeTPsz6+zHCNZV0A4DoCYBYCVwoHSuVl0XHPxRmQ+ipQvhHeRPEaWslcBD830ZnZR950ayAbZ2JpnytAoMyvZUaN9K8ehqmWnrPjMRPpWMiELnj3Wzhj1n/0x3y39nk7ff8ZvF6ZrkuaSEpMY1GA4q6dsYt30rQysPVSzbbTuOzUoWjFtZd/5u3dVKrOp0Ov15CuSN8te+9TfKRVW1tqpy4fXHjOw9o98kL8vy0etsXjlKGJWIgc1RA5uh0hUS1O/CNbP3MbUXr+wdcFuxnaaxt4VBzXHndp5iF7F3+M9j/cZ9c5nminwgAfPCA0IV4JnWZAQl8i9i5Zp1WQHj248MSo1yrLMo5vaQXtOQ7Iqgc5lGDrnr022O/+7+ii///AHdy884PD6k0zpOUdzXExEDKPfn0rnfH0Y12W6yfbI7CBvYS8KlkrrSLF3tqdcXXVbuUi+Q9i9yfjfjyDg/jOiH32FEOqJav+qIxlaSTfP22n2uQFM+3Q+879Zxsa5OxjceFQGgncq3L3daNsvTaunTK0SmtLk7nld6fztO9ja2+Dk4UTPMR9kWcrJKUhOX0FquKHzQHKwzN4+J9H52/ZGbxgrGz1df3jvOX+RNa4cuU6fCt/QrfDnJv2VAOU58L6E5H0BySHnBMZeFG9E5sPGzobZRyew+ZedCFnwzoDWWaqIZtWdAlCrTSUmrC/O6Z1X8C3nRZsBrTTHvdO/NasmrDdud/mhg9nXkG3YNgebRpB4CJCQXEZmqar5PHU9fSbJci1BHgCdTkejTnWz3N+Tm3743QkwbocFRnDr7D2qNsv4srJ3tGPuiYlcOnQdZ3dHTbnmF8HAOZ8y8aPZijurTuKbhZ9pjhvTaRpPbwcgG2T+GPc3xSr5mt1GLRIvIKLGKRvyM0XfJO9xJMm8R+7w+hOAkrWTJDi6+RQtezZWjZv7xQyCnlohGySObQtl5YiJ9JqQ0Z4+b2EvHJztjROn3kpHkQrqFsScRsO3E7hyGPR6gcEg0aDtSyi7oLhQr5q4AXtnOwYt+IxKjdQlwbvn7xtJ5LJB5s65+xp7gkU//snxrWeRDTJH1p8iTwFPPp/R26zzkg0yMZFxRofcpIQkY9YiPfxvn2Vo1yIYkiUkHVw56cjyO8+wss1InnT3dkOkBJs6nYSbhZmbA6tT9IYECAQbZ2+nXB11cDRofj9a9WxCfEw8lRqX0+wUuXfpIUuHK2WF+JgEpvSYy9qARapFgxACET0X4jeDvhCS6wQkvfk+VpLDB2BTDQxPwLqqxVo7OYlydUuz7OYc7l54QLFKhU2SdbODxIQkhrefTGxUHEIWzOn/O6VqFKd0DW1nXUl69XIOb0TwAUrq8pMJWbc9GgwGuuTvR8QzpRbZ5tNmDP79C9W4pPBlVK+zgRp1wWC4TXzAEBwLLVWN6z22CwVK5OPuhQdUbV6ROm9b5mybHUiSNbj/rpCIdE7qdtcUxMcmMKHrTE7uOEeBkj6M3TSEQqXVD3npGsXTxHUkKFlVu/34ytEbjO00jYjgSN7q3ZSvF/RTBXCeBTywsbMmKUExW9PpdfgU0+48srW3zbHOg6YfNqBqy0rcPHWHCg3KqGT1U+F/L8i4Mpd0klKnNheGB+k2ZBARitmTpK7VJyUm8eSmH54FPEwq2xYul+azgSRRSKueC4QG6pENSvCn0wmCH99Rn1pScgbbctkgq9L/uYEGbR6ycV6C0nVQIo7azXM/83HzzF3mDFgEKL/piHcmsy5wsUq/p2qLSqydtsVIYK5h4t57fMsvQ/bm8S3z75HgJ6EZMovJicncvfBAVfa7fcmJ5CQleycMEPTUhpBAK7wz0X7a9mvBhQNXOLbpFB4+7gxZnrE8+qLInPnL3F2WCkmSnisz7nc3bdEhZEFcdDwRzyKxy1zijN8GMb8o4wx+ED4YyXMNliA+vgDhz5zx9nV+aSUXQ7KBRzee4pHPLUsPmDwFPbNV/o+LjiP8WaRCKtUouUWHRRMTEZvhM787ASaDj9cBb0TZJbuY3H22MfAA2Ll4P4mJ6pXI/QsHSO3q1OsFMSEXNfcnSRKtejXhi5m9nxt4PL1xmFsn/iQ+Jtzs809/XMmqsMnAA2DDrO2c3HEOIQv87gQwo+8CzXFfzOxN3fY18CzgQateTfhwqHZqcMQ7kwkLjEA2CHYu3s/e5eqygIuHM6M3DqFg6fzkL5GPYasGaZZncgNuXi7UblvNZOAB0OD92kiSkgnQ63XUblfN/APa1ElHdJTAujpIbqphYUER9K34Lf0qf8eHBT/jdCbn11R89nNP6r5TA8/87jTv3pDuI7TbKKs2SgQEOr1A0kHdDmpuS2hAuFFyGhQr9IAHuW+/vWicG0FPbQCJp/ftWD4l9w0Z/TNNerGRcUSHqZU/y9TMh3dhpQyl08k06aj97DTupJRYU0t5lggMehbwwCOfm7FDwcrGimKV1d4uJapXQ2+lQ5JApwevAq54+qgnLBtba0av/56dCatZ9WgBpapbNvGUr58xoGjTx3xjxAoNyuDs4aRcq06iRNWi5CmkvoaE6OsYUoJnCQPJcZaR9M/suUjnfH3oWXwgA2sNJSYy9vl/ZCFiImLoX+MH+lX6li4F+hmzlubi3L7LfJCvLz2LD2RAzR81S9Xu3m6UrV0SSSeh0+twdHPIICb2OuKNyXwkxCVwaN0JZFmmUee6mhyC2+fVqdYwf7X/wM2LBShZFpKTQa+H0/960a6K+ed2Yfu3VKq+Fdwg4NrPOBffgbOH9so2pxDqH4ZOJ2GQBbJBJsRPW5rcxdOZMRuHZLkvWZaJDsv4QBzddJLWnzRTja35VhVqXptl9nnnJoYsG0DZ2iUJfhJCk671NU2+sgtJnw881yPi1iNJLuDwkWY5avMvO43dU0kJScwftJSa19XKUE5ujoxe//1zjzts3QJWjfqOiGcx1G7fkHrv91GNyV88H0XKF+LR9ScgSbh6OWdL48BShAR5IxsUQT4hS4QGW6bAmR1UalxOITpGxiGEoEytEpr6LVvmzCDoifL7yAaY/+0G6nRQt1q//XkrXLxcuH7iFhUalKHBe+YTf21srfl5/2iWjfiLhNhEOn/3jmYwXqCED6PWf8+ykauxd7Jj8O+fZ+ngbInwVXqM3vA9S4auwu9eIM0+bEC9d8xXfXXL48q8U5PZsWgfdo62vDugteYK/sg2W5q8JUhOVvy1ju92obEFshpz+v9uJHLeuXCfrfP30FWj/J2clMzh9SeJi46nwfu1LPLW2rFov1EwzJBk4JevFlsUpM7p/7tRYPDuxQdsnrebbsPezzBGkiQm7R7Olnm7iY2M5a1Pmr1Sx9rs4IWCj/nz5zN//nwePHgAQPny5Rk5ciRt2rQBlDTdmDFjWLhwIWFhYdSuXZt58+ZRvrz5KpY5AYPBwPfNxhhbCjfN3cmc4xNUtcm679Tk7+lbUrYkJEngrqHCWLzWJ4z++CI1mkbw8KYd+Suan96UZZky5bcZt70LxnLl1DwqtzbfJffB1cfsXX4AZw8n3v2yjWag1ax7Q7Yt3GtUxGv/uTZvxVzEmUjRvs6wtrHm/a/bPXfc8a1n+OXLxSQnJvPxhG60/rip5jjJqhiSc9YBQ3q9GCGUdK0lcHApQJ+ZWbfs6a30jN74PbM+W0hiQhL9pvbQFL/LabTt05JLB68bOQ5vfawOTnMaHvncmXvse3YvWoG9swPvfP21ZhCYEP0USUoR/heQEKcm6oLyknf1csYtj4vFluoAhcsUYOS677IcExcTz+8//MHjG0qZauWYdfz01zcWH/t5cHZ3omqLSvjcD6KsBhH2RSGEQMiyppdKKoL8CzG0a3EatA0n4LENB7eVsCj4UDpKlH9LkqTZUSKEYGznGRzfchqAvyZtYMH5n7PMkmaFzM+wQcOk7kWQmK4rxtQ1gKLJYioz/TrihYKPggULMnnyZEqUUDTkly9fzrvvvsv58+cpX748U6dOZcaMGSxbtoxSpUoxfvx4WrZsyc2bN3F2fnUurfcvP8qgZXDn/H1un71HuboZ04o9hxWkcpV7LJ7og5uHgR/mBWBto/6KytUtRdEavdmy4iS+5QrS7zPLxHwMyVIKUZOU/8w3qgt4EMSXdYaSGJ+EEIIzey8ybd9o1bhydUox/8wUzu69ROGyBanVJmfFdxycX0JnzytAVFg0YztPJzkpGQRM7/MrFeqX1u6pzwbaf96SPcv+JTQgHJ1e91xeUk4gVcTt8Q0/kGDUe1NZemN2ruuaNO/ekDyFPLmZ4mqrRV58EUSGRLFp7k4S4xNp91lLfIqq+UNCDie/ez8+/jYYEGB4iBCLVQFIxfp52LbkAZFhViBBvdba/kT7Vx1m0kdzjGq4I9d9a7G30/Nwbu8lY+ABcGDNMb6Y2VvTiXTfn4c5sOYo+Yvno+eYD8yeQEEh1679eTOSTmLF6DX8cnKS2dnAsKCIlO62WIQQHNl4kl/PTFFlP1p/0pTtC/fyy7BgJEli8CLLhP56jurMrM8VWw0XT2daf6JeKIQFhhsDD1Bcjc/9c9nsdva3Pm7KtgV7FFE1CYuf6R6jOjOj3wIQSkDYpk9zi/Z37cQt/p6+FRt7az4a0ZmCJXM/A6mFFwo+2rdvn2F7woQJzJ8/nxMnTlCuXDlmzZrFTz/9xPvvKymh5cuX4+3tzapVq/jsM+0Og5cBK40AQqeRmrSzDaJW82hqNU8vuhQPZJxI9/152NjF8ujGUxxcHPh2kZqYmh3odDruPehNmTJL0FvB/RuelG44yKx9AZzfdzkDMeziv1eJjYrTDAaKVvS1qLSQCp1Oh72zfQZdk5wOZl4G7l58wKTuswl+GkrbPs3pO7WHapIK9Q/LoBmDgKBHwWYHH6mqlDfP3MOnWF7NCRQUP5e5AxZx7fgtqrWoSP9ZH5vtpux3N4CH19IUaCOeRXLj5G1qts7936xSo3Ka3SbpIeRoxYvF4Idk1x7JTp0hMSQbGNxkFI+vPwVJ4WctvTFbLcWfeBzkdHyWxCMgh4A+o9DYhWOFEdynVJUYosL1HN6eFy3nnF1L96eco0LA3rP8QK4HH07uGbNSeisddhrZzJM7zjG5h9IirNPrCHgQpFkyjQqLZmL32Vw7epPyDcow9I+vNAPPVOM/IQuSkwwcXHfc7PfF1aM3iApN49rcvfCAZ49DVCVtj3zuLLoygxun7pCnkJfFE2O7fi0pV7cUAQ+eUb5+ac1yir2THVbWVsqCIgVueczParnndeX3y9Nz7Bpqtq5ChQZl8LsTQPNuDS0yswt+GsKQ5mMUzpckcX7fFVbem6dpoJrbMJtwajAYWL16NTExMdStW5f79+8TEBBAq1Zp6XtbW1saN27MsWPHcuRkzcWzx2oF0kANgp1Qia3YAuqH/Mrh60bCmZAFFw9csej8Krb4kVibPQRELqdow8PYOpjfDpa/RL4M245uDtg5mtbAeB5CA8IY3Hgk7V16MOq9qcRFa+sajFjzDbYpbXN136mhyfd43TG283Qe33hKTEQs66Zv5eBa9X1bsFR+ilYsbCR2eRXwoHStEhp7yz4cXR2p1ryiycADFKn+fX8exv9eINsX/sOykaY7AOKi4wh6HGxSp8TDxx0bextjYCVJksmuIzlqDnJAReTA2oh4bUt1IQSb5u7km0YjmNF3PlEahM4XgYj4DmIWQ/xORPgXiMTTqjEBD4J4ePUxsqy0xkaGRHFdQ4cCXfrrkgBb0KknWq/CZYgKt+bWBUcCH9vjVVC79ThPQS90euXZ1+mU398U4mLis/wdsotKjcpRJh0n5+3PWmkuJq4cvm48N9kgm3TJXTx0Fef2XiI2Ko6zey6y9CftMp1nfnd0ulQejIxXAfNEGQG0uu/tnLS1e2wdbClQ0ifL7/ZFULSiL3Xb1zDJ47B3smfI8oHYOdqi00l0GfIuFRqUMbk/YQhGyBFZHtPeyZ6qzSpmGXgIORI5tA9yYBXk0N4IWdugdEyn6Vw5fINQ/3DWTd/K7qXaz2F2cPfCAxLiEpFTuH6h/mEEPcx9srkWXjj4uHz5Mk5OTtja2vL555+zceNGypUrR0CAwir39s74EvP29jb+f1pISEggMjIyw385Da0VYqpPRnpIKs+HBJTMR0aUqV3SWMdT2sxM36jZhWveIuQvVRed3jIOcGJc+vMVJCclWPTym/f1Uq4eu0l8dDwntp5h1QRtf5qarauyKWwZG0OXMXbTD1kS4l5XBD18ZnSZBGWCywy9lZ4ZB8fyyYRu9BjZmV9OTbYotZ1dnNpxLsP20U2nNMcd33qGTnk/pbvvF3zbdBTxGi20ji4OjN00hIKlfPAukochywdqZm5E4umUtscEEOGIiEEmxK0OM+/rJVw5coOdi/cztvN08y4yFQnHADnlPwkS1V5Mjq7q79xG65m2qcals604d8iJi8ecuHP/G02133b9WtC0awOsrPX4FM9nsk21z+TulKldEr2VjvL1y/Dx+A81x53avpfOeXvQ3fcLBtXrY5EY2fWTtzM4Nu9cvE9Tmr5M7ZLGNmCdXkdZE2WtgPuBae3CBlnzPgf4ceVX5CvmjZW1nhY9GmmWLLILrVdQvIaUfFx0HIPqD6e77xd0yvuJye6vbB83+Q5ycHtlgo8YjhDJmuOadq3P5ogVbI9bRZ/J2uRwIQRyxE+IZ/UQQbUQMUssO7foWUomTsRC4klE5DTNcbfP3s2w/e9qbXuI7KBIhcJY2VgZF0+uXi6aXUcvAy88Q5QuXZoLFy4QHh7O+vXr6dWrFwcPprVVZv7RnidiNWnSJMaMGfOip/FCKFe3VAZHRc/87lRtXkE90KY2YAMYAAHW1ZAk9QqjYqOy2DnaGssbtdq9PiWGkEfpV+sSCTEGEmJjccjEuREiARHxI8TvA6viSG6zNY3eAu6naV8IICCLKNnK2gont5wJOiKCIzm2+TRO7k7Ue7fGS1FD1FnpISkdWczEfevk5qjJmM9NZFbLNZXNmtlvAYkpbbSXD19nz7IDvNNfzUmq3rIySzS6ajLAEJhuQ4CIAxENUsYV6c4l+zNsXz503eQub565y60zdylXtxTFKxfRHmRdHpIuoDyHMlipCevR4eqWSS2BrkuHr/N9+0BAaTvVW+1mS+RHqgWJtY21Ik39HHlqd283Zh8Zn+UYgFmf/0ZiPIDE9VMR7Fwwl47fq0sgJ7adZUa/BSTGJdJz9AeahOfMnWiJ8UnERMSqiOT1O9Ri0IJ+7P/rCAVK+NBnSnfNc2vWrSFn914yWkSYshooXrkIy2/Nfe61Zgdl65TCwcXe+M4sWMpHU15964K9Rn5eQmwi0z/9ldVPFpp9XBH+PSTfAQwQtxasK4BDV82xOp1OswPHiKQzbJr7Lyunl8fGVvD11F+p+1EHTUmDoEfPOL3rAnl981CjVWXtOTD5KUqAjXJ+srbBm95Kn4G0asrlOzvw9s3DpJ0/sXrKJmztbfh4/IfY2pufGbcELzxT2NjYGAmnNWrU4PTp08yePZsffvgBgICAAHx80lJNQUFBqmxIegwdOpTBgwcbtyMjIylUKGfVFq1trPn1zBR2LtqPkGVaf9pM8wuXrIqC51+I2PWgc0Ny1KZZr5+5zfgQCSFYPmINTbtoP8AvGzVbeuHikUxUmB4hJBq+HY69k0Y9L2YFxO8ABCTfQEQMR/JcoRpWuGxBbp1RIm8hC4q+BCXMyNAoepYYSGykslqs2qIiU/eMzPXjZmaRXzl0HV4T91iPfO4EP0lLy5pKSSfEJRqtLCRJIt4S8zbb+qDLA3IKWdOmieaLNnMWIrOcfSoOrz/B2A+mK0qzeh3jtw3VFJKT3GYjIieA4TGSQ0ckO/WKO29hL7wKeBAaEA5CYG1rTYmqRVTjTqYYFKbCkCwTcD+IwmULmrzsnEB8rIwQKQGzBHFR6uxCbFQco9//2dgdMf+bZVRqXI4SVYpmGFe1WQW8CngQ4heGEIJqLSqabKNs168l7fq1zPLcWvVqgmseF64fv0X5+qVfCtfH08edOccmsHX+HmwdbOn8XXvNBcWVIxl1PcICsy5vPBcGP5QgFkCPMDw1W2js7oVHzB+ZIsQoCcb382XdO5E4umV8JvzuBvBF9SHG91ePkZ3pOfoD1f4k+/aIxH9RChAykt27msdt3/8t1s/YBpISIHUa3F5zXHZRpWkFqjTVWHy/ZFi8TBVCkJCQQNGiRcmXLx979+6lalXlZk5MTOTgwYNMmTLF5N/b2tpia5v7kZeLhzNdhmj/uOkhWVdEclV7E6THnUx6ICH+2hoZrwIevu35dd9yDm+OxNnNQNPuHTTTzEL2Q7npDcp/8hPVGID7lzN6ftx9CR4g66ZtMT64AOf/uUxUWPRLd5lNVbvMDCEED648IikxmRJVi2a9WsohWNume1Ql7VIiwEfDO7FwyEoAPPK50aJHI7OPKencwXOjEqRKTmD/jua4rj+8x/EtZ4wtlO01Mi0Am+ftymANv33hXs3g4+7lGMZ3FQQ/tqdFzxi+/EVtd2Bja82Mg2NZOW4dSfFJdPzmbU1OQnGNgMTjJegfFChuz40zSjArZChZU9054X8/UNWWee6fS6rgw8nNkV/PTOHf1Uexd7Kj+UeNsswmZwe121ajdlsLRPTMgG+5Qgycm3XfrLdvRjKllY2FGU/79yB2Ccq7TkKya232roIC0ilAC4mkRInIMHsc3TKO27/qSAbS/4bZ200EH2+Dzg2ReBbJpjKSbRPN4/ab2oPilYvgfzeQOu2rWywe97rghYKPYcOG0aZNGwoVKkRUVBSrV6/mwIED7Nq1C0mSGDRoEBMnTqRkyZKULFmSiRMn4uDgQLduud8++DzsXXmQ+YOWIsuCfj/3pK0F7UoFS+Xn+om0GmxmNvqrhKRzwrnoKvKV2YizpwdW7toBl2TXFhH7F8ao217bYChzytfvtmn+Tk4hvTlWKmSDQWNkziLV2yMVpUxIE8/9cjFbf90NKNowozd8l+sBSKueTbh69KYxVd6sW0PNcZ2/e4fKTcvz7HEIlRqXszhgk/R5wbF3lmNK1yxOsw8bcGzLabwKeNL5W+2VmVteF+P5SzoJVxPp44ndZuF/NwDZINj+217K1SlFq15NVON8inmrHJYzo3Hneuxa8i/n910GoPe4ri9F0+TB9fTBgcT10wZqZ/patLhCpjoZ3L3dsqVB819H60+asXXBHqPp4fOyOM+D5DwErMsqdhO2zZCszVf9rNSoIp753QkNCE8RrCup6YDt6uVsNHSUdBIuXqbLJJJtAyTbrLPmOp2Olj3UPk7/dbxQ8BEYGEiPHj3w9/fH1dWVSpUqsWvXLlq2VG6QIUOGEBcXR//+/Y0iY3v27HmlGh8AwX6hTPt4npFMOOuz36javEKW3QVZocOXbfh39RGSE5UJsduwV+cMmBkxETEMqDXa6Evy/qAIvtAwvpJsaoLHGkg8DFbFwVZ7RZChrRRISLDM9nn/X0f4ZeAiDAaZTyd21+QjdP6uPVt+3WUMQkpWL4arl/kdQMF+oYzpOI075+5RqUl5Rq4djKOregJq1bspOxftQ5LA2taaJl3qqcYEPAgyBh4Ax7ec5trxW1SoryYdrxi9lrU/b8bB1YEhywZSo1Vls6+hbd8WeBbw4Nbpu1RsVDbLtGmp6sVf6upo3bQt7PvzMACPbzxlSIuxLL46SzWu75Qe3Lv0kMc3/ChSoRC9xnbR3N+zJ6HIBuVZ1el1BD1Sd6tlF3orPVP2jODxTT/sneyy5aORE8gsLKWlIOztm4caratwZtcFAPIX96Z2u9z3f3qdUbxyEeafmcLRTafJVzQvzbpZVs6WJB3YPz/jnR04uTky7/QU9q44iK29Da0/baa56Hjrk2ac2XORY5tP4+zhxJBllnns/H+FJCztA8thREZG4urqSkREBC4ulisIAtw+d4/+NX7I8NnMw+M0JwwhEiHpIujckaxMt1A+ue3PpQNXKVy2ABUalM2R88wJ7F15kKm9fjFuSxJsifojS8v5rNClQF9C/cON2+XqlmL2UbX6qki8gAj/WuEH2HdOcdPN+GCGBUXwYcF+GV7MS2/M1uy0CHz4jL0rDuLs6USbT5tb1Ic+7oPpHN5wEiELJJ3E+1+34/PpvVTjDAYD/6w8RPCTUBp2qkPhMmqjvcCHz/ioaP8Mn80+Ol4lWHfxwFW+azYaULgXdo62rA9eoun4+V/HwNpDuXk6zcBO0knsSV6rOVYIQUJcYpb34y9fLWbzL7vQ6RS/k1/PTMG3XO5zjXIS7Z0/Sku9S0o5rNcYdbCVnJTM0U2nSYxLpF6Hmi+lc+p/eDlIiEvAxs4myxKZkEMh6RZYlUDSm6/f8brgRebv/14/pBnQEuSx07CGF3IsIrQbJKf0xzsNRnL6XHOfkqT8j/QS6v0vgszXamVthZW1+XVTJzfHDMGHVosjkBJ4BAIyxK0Cmxpg/3aGMeFBEaoVYfDTUM3gw9s3Dx+ZME97Udw8fdfIRxCy4JqWHgSg1+t5q3fTLDu0vH3z0PGbt1k/U5HEb9y5bgYNhlQ8exJi/LcQiotnXHQ81h7mBR/BfqGMfm8qt8/dp2LDsoxa/53FJZX0ks2WwLdcwQzBh4OLaXVbSZKeGwh/MrEbwU9DCXoYTPv+rf5zgQcomaoNs7YbA6hGnetqjrOytqKxif/vTUXQ42DO7rlIvqJ5qdosa/5ddiASToDhEdg2QNJriwGGBUWwdupm4qLjaf9FK9OdWC+A53WRiKSriNDuSqst9uCxDMnm9emczG28EcFHcmKS+rMkDQ5Bwp60wAOQo2ahc/xEJXd+6+xdvmk4gsR4Zb+DFvSzqDaZmJDEjoX/EOwXSvNuDSxSHa33bg0adqrD4b9PoLfW881zTKieB9t0E4UkSZr8FiFEiopkamAhpQQiGVG4bAFKVivG7fP3kJDwKe6tOXHnNDITR03xM5Jjz5IQ8AW2dpFEx7fCtehMJEkduH0+vRdvf9aSpMRkipQvpDl513irMq55XIgMiULIgtrtqmmKHAkh2LloH0c2nqRgqfz0HtdVU0Dqt2+Xc+vsPYQsuHToGitGr2XA7E+y+xWosP+vI8zp/ztJicn0HtvVJE8jO+g/qzdXj93g6e0ArG2tGLnuW7P3BfDzx/M4tvk0kiQxs+9v+JYtqMosvSokJyXz18SNXDtxkwoNytL1xw6aXRufTetJ6ZolCHzwjHrv1vhPBlDZRUxkLFvn7yE+Jp7WnzQz6VQd8CCIXUv2Y+doR/svWmlmeZ7e8ad/jR+MhPPe47rS/Sfzy9oieiEiOkU/Q3IEzw1KV2M6KN5fo3l80w+AfX8eYsn12RaV6Pb9eZh9qw6TzzcPH0/4UHOhIKJ/A5HajZaAiP4VyeN3s4/5X8MbEXz4li9EzTZVOb3zPABVmlWgZPViqnEBD0NJb3hpMAgkoZZ72LXk3wws9b9nbrMo+BjWdgIX/70KwLqfN7Pw8nR8y5r3stLr9Yxc+y2hAWHYOdpZ7LHS+pNm3D57DwCBoMVHauKTJEkI+44QtwaQQLIHW/X3odfrmX5gNHuWH8SQbKBFj0Zml4NeBOXqliLw4TNj2aV8Pe2JLOpBP5xco9DpwMVhF8/uVCFvSe0J/nly6m55XFlwbir//nUURzdHWvbU7jo59PcJZn72GwBn914iNCCc4avVpmGZszfpvYpeFJGhUUztNdeYhVr4/QpqtKpkdtDr6OrIkuuzCfELw9XL2WQnTlxMPPO/Wca1Yzep0qwC/X7uqVlOO73zAkIWCAQ6vY6zey+9NsHHH+P+ZtWEDYpv0p6L6HQ6TTMvnU5HMxP6Gf+fIMsyQ1qM5fa5e0iSxNb5e1hyfZbKdC8iOJKBtYYSFRaNEILDfx9n7slJqoXAvj8OZ+wUmbXNouBDjl5sbK0VchzEbUZyHpRhTKh/eAa7gfiYBK4dv6WZkbp5+g4bZm/Hxs6G7sM7agZaZ/ZczCBz738vkEm7hqtPTso0/WosdP4/440IPnQ6HeM2/8C5fy4hy4LqLStprlbOH87HjUMFOLbLFRd3A76l4xmyWsY+02LfxdPJOBHo9DqTrP3sQJZlY+ChbAtWT9rEDyu+NHufgKbplDnIIL0rFM6DFiSX0WBTE+QgsG2lKVgGiuzwuwPMb3dLDyFkRPRciN+t1ExdR2vqUHw+ozfhQZHcPHOHqs0q8tEI7ZeZvWM0qbeFbICnNy+R14LEjFcBTzp/p92emoqrR28Yu2xkg8zlw9qS2JkD4FQZbXMQGRKtJkT6h5sdfAghWDVhAwfWHKVQmfx8Na+vpm39kmGr2L1kP7IseHTjKU6ujvQepxZ8KlKhELfP3kM2KN9J0Yra91JuwGBQt/Wmx+XD19MUgwVcOXIdyH0n0S2/7ubPCeuxd7Tl6wX9cqQckRMIfhKSpgOEIDIkisuHr9PgvYytxZcPXyciOE29+tbZewQ/CSFv4YzdIi6eGTtFLC0tXj2lZ+GYEvg/sKXJe2G83S+KorUyjnHL64KzpxMxYTHIslJyLVxWzfd69iSEb5uMIimFhH/un0ssuzVHxeO6cuS6ouQvUmTuj2iL7klO/REJR0GEgeSI5KTlJvT/F68XYSEXobfSU7ZOKcrVLWWyDGEw6Nn5pxcRIdY8vmPL0Z3umjLsnb9tb5Qu9sjnxtfz++bouWoZ4aVCCBkhnm/RHBb4kNgo0/ojifGJjO8yg3YO3fi82vf43dVuoT2795Lx35JOMuljI0l6sGsFdh1A/5JSzHHrIGYeGO5Awl5ExDDNYe55XZmyZwSbQpcz6u/vsHfSzgYd2q6ctyxDfJyORNEkt87ciPL1ShsDAZ1eR0UT5OXyDcoYy0eSBBU1yNLZRf7i3pSvn5JJkJSWVeO2Gdi74iDLR63h4bUnHNt8JgPhOT3unL9v7DgTsuBeJg2ZVIxcO5gab1WmcNkC9J3yEfU71NIcl5NITEhiTKefaWPTle5F+3Pnwn3NcRUblM3giVPORBYtJ3H73D3mDlxEqH8YfncDGdVhKglxatn8VwEXLxfsHG0zlB7zFVVnA9J7B6Xyfly81ITEtn2bU6uNoj/i7O7I9yY6RSJDovi++RjaOnTj26ajMgQ26TGiR35uX3QgMsyKLUvysHisur/C2saaybuGU7ZuaYpV8mXYqq8pWkEd8N4+e0/xRUkJioMeBfPscYhqXHxMglHPBjApaiZZlUDKsx/JcxNSnoMWtQH/F/FGZD4A/hy/nuWjViMEdBv2vqYnw4Mr6eVtFcvs+NgEHDJNVo6ujsw6Mp646DjsHO0sIuzpdDoadKzNkfWKf4W1rTXdh2uvzEXsGkX5ERmcv0Vy/Fg1Jjk5iUkffMKhTfFYWQu+XdCAFh8PUo3bMHsHh/4+gRCC+5cfMaPvAqbtH60aV7ZOSe5dfIAsC4QQlK6h3QEk4vcrpFMSwKYhuM9XcWVyGiL5JrHR1ty9akO+QonkKaidNcguSjZaxOLJw7CyCsbB6x0++CFnWvSyQqPOdfkqNJojG05QuExBeo/Xln7+fFovwoMU99kqzSrw0Ujzybg6nY4pe0aw74/DJCYk0ezDBiqp7hfBvUsPM2Rv7l58oDmuVptqXDlyw2hHX72ldutx3sJ5mLBNO5DMLWxbsIcjG0+BgODHwfz88Tx+O6/22vhoZCckncTVYzep1KjcS5HZ97+Xxp9KJS9HhUZjW+DVyGKnh52DLWM2/cDsLxYSHx3PRyM7q0TSQGmh/Xp+P1aOWYudkx1fz++nWXK1sbNhwrahxMXEY2tvY5KfteSnv7h06BqyQebKkRssHrqKwb+rmwMS4q2Q5bQSuZWt9sKjVPXizDo8LstrLVqxMHorvVGnxtndSVNp2O9OxoVcKjdQC5LOEXRvVtCRijci+PC/H8iykauN26smbqBZ94b4ZpJYds2jLp/os0hvm1pBvyiGr/6G5SPXEPDgGZ0Gt9esIwpDICJyRNp21CSwbaIiTx1fv4RDmxQSU3ISzPjiMI27f4a1TcZzffY4WJkEDIq7oalySr+fe6LT67h15h7VW1bi/W+0hY5ExI9AigZI4mGI2wIOOdOtYgrPgqrwdcNzhARao7cSDF9WiAYW6NkVrVCEvrNWZWusLMsIISz2nJEkifaft6L9562yHOfi6czE7Tk3Idva29L602YIWaC3suwaarxVhfUzt6HT6xCyoJYJ5cyKjcqm+FQYsLa1Nsm9eRUICwhHp1ME0GRZZOjwSg8rayvNltncRKXG5XD2cCImIjZlAVAcz/w54/iaE6jWvGK2PGDe/qwlb3+WPW7c84LhoMfBGczxAh9qm+O16tmYnYsV7yFJkuhigV2CTzFvxm39kTVTNmHrYEOfSd01+U2ZFXSzEiAUSbcVaQfrCkjWlhuU/pfwRgQf0eExqs+iQtW234EP1WJGL0MGZcnQVaydtgWA8/9cYsGFaSrvBmFQp/eEIUAVfMRFpb8uiaQkSE5KUAUfTbvWZ9tve42Kk60/aaZ5bnYOtgyck7UkshAipV0s9buSFAOyXMbWRZGEBdsAAoNBYvE4g0XBR3axa8l+5n65GEOygV6ju2gSDl937F72L3MGLMKQbKDHiM4ms23ZQY1WlRm7+QeObDxJgRI+dDLROTPvqyVGonZSQhILvl2umW17FWjWrQEbZm8nUZZBoCl+9yIwJCdydd9E5MQnuBfpjG9F8/fnlseVeacns3vpv9g72dP+i1ZZZlsTE5KwtrGyuIX6dUbxyr7GBgKAYiZaY79e0I8ytUrify+Qeh1qUdZEd11cdBzbfvuH+Oh4WvVuoqlcClDzrSqalgDp8e7A1uxauh9DSkfl259rB1wi4Sj3j/fnxjl7SlaOp0T9GUh22u/h/494I4KP7EKLeZ/eYj03IMsy62dvN25HBEdyaN1x3vuqbcaBmi8S9Wf1On6Ez8Sj+D9QVrMdPsuDfWbzAaBCg7LMPTGR07suULhsQRq8Z35dXZIkcPocEa0wvNF5gd3bWf9RDkDRWFGYXRISOp1lK3ghRyGi54EcgGTfQdNrISwogpn9FhjviyU/raLO29Usao9+2YgIjmRG3wXGleOykaup/XY1zXR5dlGnRRC1G94CfSKSTTKgfpYCH2XMrmVOT79KFK3oy28XpnFq53nyF89H7XZZ+54kxiea7OoBuLyzO5VqXsSQDEIc4tHleRSuaH5HnE9Rb3qP1S7JpT+nbxqN5NaZu9g52jJh+zAqNcr9lH58bAJ7lh0gPiae5h81Mml6l5O4f+WxkdSJlLlkngadToe9sz12Tnaa2k6gLJ5+fGs810/eRpIkNv2yk8XXZuKWxzxV5aIVCvPb+Z8VldYieWjStb7muDPblzK8S0lkg4QkCUatWEL97v8LPv5fQUtfwVVDb7/XuK7sXXFQcQZF0WrIzPeAdF0WCXvBqjSSywgknZtZ5yZJEg4u9kSHRiMECAHOHmqGt6RzIzjQis2LvZANEu0/Diafl1oRz8ndm1/P/ca5Xetx9vCgcgvTpY+clOGWnAaCTV2l28WmrtnfR3okJiSht9KZLG10+LIN//51hMCHz7Cy0fPZtJ4WHU+EfwWJx5V/x+8Ej9Uq0Z/osGhVQBr+TJvslq1jCgEx8xHxO0BfHMl1lGbHjpBjEVETIek82NRHcv7ObE5NdHiMMfBIRYQl1xD/LyJiEMpsICnOoW5qvkTh0gW4euymcbtY5dcrYCtYKv9zW6ifPQlhaJsJPLz6mGKVfZm08yfNzrISZRVitt4KDMkQ+vBvzeBDyKGImOUg4pEcuqoymS+C+YOXGztP4mMSGPHOZDaHq52qcxJCCIa1ncDlw9eRJIn1s7az6MqMXDeBdPFwMpbJdDodLp7ax1v045+s/XkzkgR/jF3H3BOTKFE143cc4hfKteOK8GBqx86lg9do1Ml88TffcoWeq+2ybWkyqb0DAti8COp3N/uQ/zm8Ed0u3r55jNwFnU7i4/EfUqi0upXK1dOZz6eU4aeFjxg8I4geI7TN55Ij/1BWyMm3MMRsI87/B81x2YEkSQxd+ZVRmbRR57o01YiUk5I8+ebdGqybn5f1C/Pw9duViY3VflE6ueWhUdfPqdrqg5fiuJoKyaY6kl0biwMPIQRzBi7ibYdudHDrxZGNJzXHefq4s/jaTOadnsyqRwuo1cZCdcDEkyhCaSlvhMRTqiEFSvpQqXHaarJw2QKUq1vK/GPGbUREz4LkW5CwJ4U7o4aImgZxf0PybYhdDjG/mX1IZ40Xta2j+eRgkXgC0KO8QmVIPKo5zjGTQJ2Thr/Oq0Z8bIKx1VMLi378g8c3ngLKanvp8NWa40ICHTGk2CLprUBvqw60hDBwfHUvPix5kI6+p9kw+VPN8mp2ce9Sxu6h2Kg4EyNzDs+ehHD50HUQSgdTqH8Ylw5aRvzODnqP60r+EvkAhYvxyQTteuv23/YCyqIuOcnA/r+OqMY4ezhlq2MnFYkJSSo3YnPgnKciqQ4UOh245H19bDpeBt6IzAco7bHvDngLIYRJ2dvL/26hVYeV6CTlZn1w8wtk+ZxqAn9wYQ++xcHKCvR6QXjgaRzUsUy2UbN1VTaGLiMhLtGkKNijG08JfBRHaqklLCiJO+fvU7lxefMP/Jri9K4LRvO2+JgEJn00h83hyzVbpG3tbXMsexMd44u97V10epAkQTKlVcUDnU7HpJ0/cXDdcZITk2nUuW6WMsoJcQlYWVuZJHWK5OukOguDAZJMvLiTr5CmIAsi6YbJFr7Ah88I8QulRNWimqWBGyfUAmUX9l2lQj3zXn6SdQUEqS9jPVhra1AkxGRsD42PtaxdVCRdR0T9DCIByemL57qDZoWEuARGv/8zZ3ZfxC2vK+O2/ECZWmp+QHhQRAaio6msl733QJAmAYKIMFfKNx2kGhMbfo8Jn9qQmCiBkFgw0pPKLf+hRG3zCK2NO9flWrrMkrnGmS8CZ3dHbOysSUxIMlK+vF6CeV/eQl4suTaLmIhYHF0dTPJbEjOpWwfcVxNTbe2Vjp1Zn/1GXHQ83X/qaPKdsnjYKtZM3YSVtRVfz+/LW72bmn0Nvcf15vqpJzy69gSfYvnpO7Wf2fv6L+KNyHykwsbOJsuJIujeHqysQKdXVivFy8cSH6PWyrh5MR9WKelUWYbzRyxnnuut9FmqkeYt5IWNnbXykEmK/Xv63vn/T8jcs58Yl6jZrmYwGJg/eBndfD9naOvxBPuFmtzn3YsP2LP8AE9u+Zkc8917Xhzd6crN8w7M+q4ge/9K1hxnY2dDyx6NafNpc5NGYMbsjeNHvOPak4Nrj2mOM+jKsHCMN92qleXHLsUIfqaddpdsU5VldYAwOdHuWvovPYoP4Ov6w/m82hCiwtTEX61uKq+CFtzDdm8jOf8AVhWVf7tO0Rz2Tv+3jDQlSSfRrl8Lsw8pRDwitBckHoOkM4iwfohk7bp/bFQc+/86woltZ01mNbbO38PZPYqmTURwJNP7zNccV7JGRmXkMrW0W8+9XP9Ar1dmY1f3CKTEjaoxkeG2JCboQKRNnEH+5hvLvfdVW7oP74i3bx4qNy3PrKPjzd5XdmHvZM/wNYNxz+uKvbMd/ab2oHQNyxYDIf5h7F15kMuHtcW5UiFJEk5ujlkSazO080pQoGQ+zXHVmldkxZ1fWBewiA5fttEcc+3ELVZP3oiQBUkJSUzvM1/z+couvAp4sujyDDaFL2fpjdkmSa7/X/HGZD6yA4GHsQYny+D/yAbvKurUcKFKHzPxi4vUah7Jw1u2OPl8luvn5uLpzOiNQ1j43QoMyQY+mdiNvIXUnA8hBMtGrGb9rO04uzsyZPlAk2qI147f5NTO8xQuW5CmXeu/NHZ8YkISQpZNBoK121XDq6AnwSnmbC0+aqQZmG3/7R82zFLIuiF+YUz/9Fcm7VTLGB/ecJJxnacjhMDKWs/Uf0ZRsaF6le93V2bCZ0UAZXIsVNX8tPWZPReN2ZvEuESm9PqFuu/WVJGat/x6gvW/5QEkQgKsGdszml/OauzQ8QseXA3FEH8Ja6d6+Hprkw8XfrfcqL775KYfe5YdoOM3Gcm/1hrE6sxKky8CSZLA8VMkx6y7ohp1qssvJyZx68xdytYtZRHBFUMAiPB0HyRD8h2wylhnj4uOY0DNH3hyyx9QulqG/vG1ancRwVHG1nMhC5McmL3LD2XY3rl4n7b8tyGjt5FIfqjKVHn7FqRs7fzcOPUUJHDP60ClJpYQDpPpOfg8PfsfAn1BJLcgwM2C/WUPddvXoK7/ohzZl/+9QL6oMYSY8FgA+kz+iC5DzNfb6fztOyz5SWmft3OwpWXPJmbv6+rRGxm2hSwIehxsEb9FkqTnOhnLssy+Pw7z9I4/dd+paXFw97rgf8FHOtjYuTLxC1/e+eQZEcHWLJ2Uj0U31bW9yk3KY2P/K2d2X6B0o4I07FjnpZxfdtq8zv1ziVUTNwDKpDem4zTWBy9RkTYvHrzK983HIEkSskHmyU0/eo7+ILdO3YhNc3ey4NtlyLLgo+GdNI/p4uHMgnNTObb5NE7uTtR7t4bmvh7feJpB3Cq9P0N6rJ+51dgyLRtktszfrRl8dPmhAytGK1bwrl4uNP1Qm6WeHURmyt4kJSSRGJeoCj4ObUxf45e4c1m7PLPtt3+Y0/9SSmv0MUasraNNiMsUQGoFlI5uSpo6fRu5lmHgi8CQbMDvbgAePu5ZvkxL1yxB6Zra2YIXgj4/6HwUgjMCsAUNhcjTuy4YAw+A/auO8MXM3qpOhoad6rBm6qa0bRPPdHJSxmxYcqJ2diw70Ol0TPlnCrsW7ychLpGWPRvj5GbB7xD3N8c27uPg1nzkLxJHl0Hf41B4s/n7ewX4Z+Uh4qLijdtrf95kUfDx4dD3KF2zOP73AqneqrJJ07vswNtXg+BvwuU7J7Hohz9YN30rOr2O1ZM3MuvIeM2S4H8Nb0zwIRLPKKQ9ISM5D0ayVb9comMKc2irG4e2uhk/S060Qq9RDSlTMx9lqhQGq8ImMwaxUXHMH7yM22fuUrN1VXqP62qxoNPzkF7uVwhBTEQsiXGJKkG0Q+uOo9NJRmnvvSsO5nrwEewXyrxBS4y14ZVj19GwY23NNlVXLxfafKpN+E1FnfbV2fTLTmMAYmrCcHZ3MuqZIEk4m3jB9xjZmSpNK/DsSQjVW1ZSmWO9CDJnF3Q6CRs7dcbBq1BJOJmW6jDVvrnvT2XFLRtkJEni39VHNYOPL2b0Zton85BlQeGyBWjVu4lqTGRwlEq/xpJul8iQKAY3GcXDq4+xsbdhzMYh1GilrV6aU5AkG/D4ExHzK4hEJMfeSHp1GVJLPE9Lu+fi/ivG+1KSJC4euKoaA/Dhj+/x23dpHSTdh5voJtPlAfkpqb2gkgnLgSc3/Ti25TQJsYn4litI3fbagXZ2cPaf64z5tCiSTrmQp/diGb7J7N0hDM8QkePA8AjJ/h1w+DjXs6OObg7GzJ2kk3Bys7xrplqLShbvA8DFU/0+sLfQuDM72Lsy7dnX6XUcXn/yf8HHfwVCDlfqwyi8ARH2MeQ5jKTPGMnWatcMG7uNJMYrq5lKjUtrlgZE8iNE6AcghwJW4PaLpjjM/G+WsWf5AWSDzL1Lj3D2cOKD73NXsrtmm6o4ezgRHR6DkAX136ulqcSar0heZEOaOZ5PMfNXBNlFTERsBs8DgKgwtQAcKIHbmd0XcHJ3omqzCpovveotKzN593BObD1LoTIFaPeZNofgs2k9uXfpIUGPgilUpkCW0uRaGRFzkJyUMWMmy4LE+CRVcNFj1Ecc33qZpATl3uw1Vpu171PMm+snbqcEH5isD7fs2ZgqzSoQ6h9Gscq+KtMrgPwl8lGgpA9PbysZAWcPJ4vURrf8upvH15WsU1J8EvO+WsLSG7PN3l92IVkVRHKdmOUYre9J616KCotOK7sIoSlCCNBpcHtK1SjO6V0XqPN2NcrX01allFwnIcL7g4gCm3rgoA7sE+MT+bbJKOKilZX+qPemsuT6bAqW9NHcZ0xkLFbWepPlygtHvdDpBbJBub6zh80PngFE+DeQdBYwIKKuIenzg13OmEKaQrt+LTi5/Szn/rmMg4s93y7+wuTYJ7f9uXXmLiWrFdXsXnxRJMQlkJxkMJm5S5VgSI/46HhNKYecRL6ieYkMiTJ6yuQr8v+DG/JmBB8JZ0kNPBQYEIlHkewzBgIPrz01Bh6SBA+u+CHLsqrbRcT+AXJE2r6itZXpbp25a2TGSxImPS9kWebw3ycI8Q+jwXu1LKq/e/q4K1buq4/h7OFEq16NNce9+2Ub7l1+yLHNpylctiCDF5l+yIUwgIgAyQ1JMp+jXKh0fqq1qMi5fy4DULJaMcrWUUfwMZGxDKj5o3FybN//Lb76pY/mPqu3rGzSIyQVBUvlZ+W9eUSHx+Ds7mTx6k2IeIjbCiQpBEud+iVfu21V8hXNa2TXm0qpFylfiCXXZ3Fh/xUKlPQxGfx8Nq0nIU9DuXn6LpWblKfHqM4mzy9PQU/yZNFxEBsZR8CDNNZ/VGg0zx4HP1eXwBSSEpJSyj3KxJ0aSL0OqPFWlQyBVtOu9TXFo1r2bMymX3YaU/5ZuRFXalTuueJdkm1tyHtSUf6VnDXvuSe3/I2BBygcglPbz1FwUEYLAyEE0/rMZ8/Sf5H0El/P60u7fmrNkJI1aiIbDgOg00uUrGahwFjydUjXxSSSriPlcvBha2/L5N0jiA6PwcHZ3mSm+Pz+ywxtPQFDsgG9lY4J24c99z2QFXYt2c+sLxZiSDLQvv9bfDn3U9VvVrlJeXzLFTSWd+u+U5M8Gry7nMYPywcyodss/O8G0viDurTtaz5R+3XCGxF8oNdQ3JPUN016+3ghlHRyYnyShgFS5glY+2us1aYq9y8/QpKUlW/V5trpvzkDFin96BKsGL2W3y5Ms4j5nLdwnufWSW1srflh+ZfP3ZdIvoMI/RjkQNCXAI/lSHrzzk2n0zFm0xBWjv2b5EQD3Ye/r7kyP7H1rHGyANj66276Tu5ukZeOTqfLkRWKEDIitDcknVM+iFkGXpuRJLX54Ii1g1k5Zh0uns70n6M2AUxFviJ5Tcrbp8ItjytT/xll4dkrCA+KMEo/pyLocYjZwUfbvi3YsWgf4UERSJJEr7Ev1/skKzg42zPv1CSObz2Lg7M9td/WVi4tVLoAi67M5Py+y+Qvni9HMmCSZAWS6eyDlmO2o7t61X1y+1n2LP0XAGEQzPp8IY271FPppDTqVIfPpvVk/19HKFAiH/1nmb7nsgWb+pCwJ2XDgGTzcrhtYYHhXNh/Be8ieU1m5DbM3p6u5VmwfuY2s4OP2Kg4Zn3+m7EEvfXX3TTqWIcqTStkGGfnYMvcExM5vuUMNvY21G1f46WQ9AuWys/8M1OfO04IGRKPgBwNto0V07rXGG9E8CFZV0FYVYbki8oHVmWVlUkm1GhdBQcXe+JjFKGhum/X0HRelBx7KwqYsj9gg+Q8RPO4vcd1xdnDibsXH1C1eSXe0qi/y7LMzkX7lA2hlCaObjzF+5lWP68KInIyyClBmeE+IuZXJBfzJkEhBGM7zzB6Mlw7doOZh8ep9DscXDJO5NY2Vug1ND5eBCe2neXWGSVrULmJBdoohvtpgQeA4QEkngfbehmGBT8N4fvmY4iPSUAIQcCDIKbtH/1a+G34FPemRNWi3L3wAElSjLAsEUrz9s3DkuuzuHHyNvmK5s2RFHh2IOQYiFsHIgHs30PSa5cOHV0dafFRo+fuL28hL4t0G14UPsW9KV6lCHcvPABJIVrXe6ematzlIzdUnwXcD1J1C0mSRKfB7ek0WNtb50UhuU1BRBcDwxNFPNDWfMXP7ML/fiADavxobGHtN7WHZhbK0dXBWCaTdBKOFhA/E2ITjIFHKrT8wEBpLW7WraHZx8pNiIgfIX6TsqEvBp7rX+sA5M0IPiQdeP5JfOguhJCx92yjrEoyIW8hL349M4X9q47g6uVC609NrEYlu5QVjT9gBzrtFbXeSv9cjodOp8PeyU7hQ6RAy133RXD12E12LPoHZzdHPhz2viZxUgjBtE/mcXrXBfIV9Wb8th+1MwMikjSihgA5yuzzenzTL4MZ1I1Td7h+4rZqlVm7XTWad2/Ivj8PY2WtZ/CiLzR9d7KLrfN3M2fAInR6HSvHrmP0hu+p38FMLxudO4qaZ7rMgU6dCTq//wqxkWmtupcOXiMqLDrX68PZgV6vZ9q/o9m5aB9JCcm06t3kue1+z4OzuxM1W1uoMPsCUDJQvSD5MiBB7B/gtQPJxLP4OkKn0zHz0Fh2/L6PhLhEWvVqrNm2Wb1lJdZOTetakXQSBUzwQnISkmSP5Dwo14+THv+sPERMZNq7cPXUTZrBR++xXbl69CYB94PIU9DTpMJpduDu7UbTDxvwb4r6aeGyBaiey4TpnIaQQ9MCDwDDPSULYmeZQWJu4o0IPgBWTdjK8lGrEQK6DYvj4/Efao4rUMKHHiNN19MBRMwSMKSqREYhIsciea4z67yEECrho8xkxRfB45tP+a7pKKP/yKXD15l3arJqxT3v6yXsWX4QgLDACAbU+pGVd+ap9ic5fqp4ngBgheRgvvmAg7PaJltrxaLT6fhx5Vf0n/UxNvY2mtmnF8GeFcp1ygYZSSex/68jZgcfks4DXCchIkeDSEZy/gbJWs1byV88X7q/UVZmWYnIvWw4ujjk2Ar5lcDwBJIvpWwIpSyYdBY0zABfZ9g72at0WDKjWvNKdP2xA2t/3oK1rTVDlg94ruX8y8TN03f4ddBSozpo4w/qPf+PTMDRJV23SxYaGN6+biw7mx9D/CP0dj7oXN3MPibAjyu/pHm3BsRFx1O7XTWT3+/JHef4a9IGbO1t6Du1h2VaNTkKW5TpPF3rt5S7/jqW4o1QOPW/H8iykUrgAbBq4gYeXtfWhMgWRDRpjrKyRdkAIQRJ6dQ7JYkMfe6ZERUWTWSI6eNdPnyD5CSDkRl9++y9DKS2VBzZkNEvRUt2GECyewvJcwuS6zQkrx0qo7UXgVcBT8VjR6eotHb98T2KVTJtLubi6Wxx4AGKCVUqhCw0jfteBJJ9B6S855G8L5kU1ipfrzRfzOyNRz43CpXOz7gtP2rKw/8Paoik28jB7ZEDqyNHTlBq2ZnHSOrJQauF9lXh4fUnfFb1O97z6MW8r5dk6ReTHXw6sTs7E/5ia9RKGnXULn8YDAbmDFxEB/defFFjCI9vPrXomNlBYnwi3zcfw7Xjt7h/+RHjP5zJw2vaSrPZQdt+LajURCHK2jvb8a0JInzUk1mM7HyIzqWtGd7xCJGPZ5h9TFAWPLXbVadJl/omuWV+dwMY1WEKV4/d5MK/VxnSYiyJ8eoOmFcBSeeI5DIOYz7B/gOly+o1xhvxNtSazNOnxF8Ukn0XRNx6EIrXiuRkvsKpTqejy5AO/DlhPaDU3xt30b5p/hy/nmWjVoNQ2Pj9pvZQjUnO5GUg6SRs7NUlCwdnB0JIk47X6UxzESTr0mBtfitmenT+tj1vf94S2SBbnOrPLjL7OyRrSLW/KJRMUtb8jXcHtqZS43I4ujq8FJ+NVAg5VimX6bxzoLNHhuSbIDkiWRXOoTN8zjHDB4HhLiArJnrWFSBTZ5okYjN3bZv8NWRZ5v7lR9g72WXISOUmJnw4k4dXnyAbZDbN3UnJasVo1auJRft8nknk7iX/GlV17118yOQec5l3arJFx4yPTSAqNBqvAh6a91LAg2cZFzcCTu08bzZ52c7Blp//GUVUaDQOLvYmA/ZlYy9w5l9nZFni3EFnloy6xDdLzTpktnH/8iMjN0Q2yESFRhPiF/ba2FxIDh3Bvp2ie6PRgfe64Y3IfBSpUIgab6XV8Co1LkepTB4NLwLJujSS1y4k1xlInhuR7N+z6Px6j+vKjINjGb76GxZemo57XnUrYODDZywbudpIv1g3bQv3Lz9UjcusIyHkjJmVVPQck7G01PgD89U8XwSGZAOXDlzl4r9XFTOql4DkhGTjzKTT60hKMl+VMrtISkzih1bj+KLaEHoWH8jqKZty/ZiQYm8fVBvxrBEitIfSFmzuvkQSIuxjRMi7iOAWiGhtvxNQ/Dj+XX2UmymW7hZBfkqqiZ5Ap5RYMkNfAPS+KPwbPUjOYK3uJktOSmZY24l8XvV7epX8kj/G/W35+WUDgQ+eGbsxdHqdycxiTiJ9Nlc2yEb3XXNxdu9FOuf9lG6FP+frBsOJi1Yv2LSE8ywtL0qShIunc5aZwoBHrqQmk2QZAh7l/mQraSzQLHGDzg1Ikt1/IvCANyT40Ol0jN86lKF/fMWQ5QOZvHu4Sm78RSHpfZDs30bSkHQ2BxUblqXxB/VMEhLjY9STiFY5pXqryji42KPT60CCmq2raKYRm3xQn15jPqBIhcI0/qBulmI+OQUhBKPem8rw9pMZ9d5Uvms2WiVXnRv44Pt3jatGvbWedwdqG0flJE5uP8fFf9NUMpf+tIo4jd/wReB/L5Bjm0/z7Ilp23UR+ROQkgpOOgVx680/YMIhSDyetu/oWQhZLb715LY/n5YfxMRusxhY60e2zt9t/jGBuzeUFkdDMhiSBGHhatVPSbJG8vgTHHqCQxckzzVIerW2ydk9Fzm756Jxe8XoNSY7GXISzbopxn86vQ6dTqJeB3UXS04js76KpUWoWZ8tNApr3Thxm22//aMaY+ugnnwtLWtmB027dwEkdHoAiabdc98aQktzJDkh999f/1/xRpRdAOYPXsbmX3YBSueBqVri64rCZQtS5+3qnNimSHFXalxO0yMjT0FPfj0zhX1/HMbZw8mkc+iVI9dZOfZvEPDgyiOKVvCl+3ANg6wcxOObfpzcntamev34Lc1ul5xG3fY1+P3yDO5fekjpWiUs8nfILlJJc8ZtABOchKObTnF08ykKlsxPp2/ba3b2nNlzkeFvT8KQbMDW3oZp/47WllgWcaRNO1LKtrnIHk9h95L9GUqbf03aSPsvzGfZf/u2TON3C5C3QBJHtrtSv/Mjeo5St8ZL+rxILkOz3Jes8Tu8DG7IwDndaNP1Ohie4ujdkQLlcp+Y6OnjgSSl3WaOLpZlIOJj443flaSTNBdAWqWYlyE53rJnE1y8XLh69Abl6pamztvVc/2YlZuUx7d8IR5eVTgt9Tq8HJGx/694I4KPe5cfGgMPUNTs2nzajHJ11TwGEbcdEbsCdJ5Izj9aVOeOi4nn9yEruZXi7fLRyE5mZ1wkSWL0xu85989l5GQD1VpWMqn+V6CEz3N9Wv5dfRRJAkNKanjX0v0WBR/PnoSwYPAynj0JpW3fFrT+WK2XYO+kJglm1vTILRQuU4DCZV6O/gRA7berU75eaa4euwkovjFaGaiTO84x+v2f0el1CFkQcD+Qwb+rA+M1UzYZ0/hJicmsn7Wdn1YNUo2TnAYgolIEiXReYGdaqfO5sG0M1jUg6Yyy7dgfSade1Tq4OGSYpCz9TQ3JCez6yzPFal7geuQioO5Au3vxAYt++IOE+ES6Deuo6SdT463KVG5SjosHrgHQbej7FrmQZhdS1DBKlNqNUu+7iEgsimSTu9mPtn2bs3PJPgIfPEOn19Hv554W7a/7T52Y9/USQMlmaOkUueVx5YPv32Htz1sAqNaiIlWbVVCNyw3UbluN2m21ReNyA3YOtsw9PoHjW89ia29Dnbervxa6Pf9VvBHBx4Or6prx/cuPVMGHSLqEiBiMsj7SI5Jvg9ces2+wBd8uZ9eifciy4OaZuzi6WtbeqNfrn+tqm13kKeSVwdvF20K/gFHvTeXuhQfIBpnrJ26Rt7AX1ZpXzHjMgp606tXY2OJbq21Vilcuorm/Oxfus3PRPpzdnej0bXuL3D7Dg8L5Z/FXODneJzq2Au2+nJnrrYo2ttZMPzCGW2fv4eTmYFJ46+yei0ZjPFDIepr7s7cmNaMhZGFS90Ry7AM2tcDgDzZ1kHRq/lB2oZi3rYCkq6BzQrLStvJ+Z8BbHN92huvHb+HgYs+gBf3MPiaAlY0gMUEpk0k68MrrrxqTEJfA983HEBMegxAwov0kltyYrSL2WttYM2XvSG6fvYe9sz2+ZQtadG7ZRuJhUvIsgB6RcMzi4CM+NgG9lU5TFRgUvYpFV2Zy98ID8hT0sMimAaDDl20oX780AQ+eUalRWZNGi32n9KBVrybExyRQolpRi0va2UFMRAxTev7C5SPXKV+vND+u/MoyR+Bswt7JnmYfNsjW2LjoOGzsbV7K9/FfxBvB+ajWvEIGspAkKUJWKiRdIy1lbQDDQ4vS1teP3zKmfSUkbp29Z/a+XgTn/rnEmE7TmNF3AcF+oZpj3vq4Cc4eqQ+reK7WQFYQQnD3/P10PjYStzWuNTIkiv2rjhjJn6d3XsDvboBqnP/9QAbVH8623/by16SNDGszwaJU+fltX/H+xydo9n4g7/fex8m/R5i9rxeB3kpP2dols1T8LFa5iDHw0Ol1FK9SRHNcwVL5jel0IQSFyprep2RdSWmRtiDwMO5LskKyqWwy8ABFm2H2kfGs9f+ddQGLqNDAsjJal4FpNgfuXsm07KaeRJ89CSUqNBpZVvxkkpMMPNRYZABcPXqTtT9vYd3PWwh6pHa5fREYkg0sH72GwU1G8sf4v0230FqVRiHDAhiQrLUN6LIDIQTzBy/jHeePeNelJ3tTdGu0YOdgS/l6pS0OPFJRsloxGr5f+7kOz77lClG6ZomXNtEuHb6akzvOER0Ww+ldF1g89M+XctzsIDEhiZ/ensg7Lj3plOdTLh++/qpP6bXEG5H5cMvjypQ9I/jly8XIsuCLmb3xKqAmpyWJikgGCQmBEBAVWRCPfOa3g6YngAkhiI+1jHCYHdy/8oihKZO1JElcP3mLhRenq7I3a3/eQmSIQh6UDYJFP/xJ3bfNs/OWJIlKjctz6dA1JUgQULGRegIK8QvNIKAmhCDoUbCq/fHSwWsZHCSvn7xNTESs2SubwsUUeWorK4UZ7+F5waz95Abe6t2EZ4+DObjuOL7lCjBwrraB3o2Tt9M2pEzbrwEkScLd2y1H9vV2/w+p0eQ3gp5aU7KyjjwVf1ON8fb1Ik8hT0L8lHZxa1trSlRT8yr87gbwQ6txGJINSJLEpUPXWHpztmqSFELw9/St7F99hEKl8tN/9seaBnRzv1ys+DABlw9dJyo0mi9m9FaNk9xmISLHgOGx0g1n28qcr0I5zuHrbJi1HVBKbtP7zqf+e7VeK9G6lw2/uwHpvF1kzUXMq8KWebs4tUPJYEaHxzDug+ms9V/0is/q9cMbEXwAVG1WkcVXZ2U55sjmUHb8Woy2PUKIDNWzarY7qx4nmy0O9exxxq6EB1fMF9/JLlJt1wEEggdXHhMXHa96UV06cDXDtqUP76j137FyzDpC/ENp2bMJ5eqovUIKlSlA4bIFeHzTD0mS8PBxp1QN9Yq6cKbUuLOHE/Ya6qjZxeO7ThQuEY7eChBKW562xd/LhyRJ9BjZ+bmqukUrFubGqTuKSqskmSwfCCFD/Dal7GLXEsnK/JbyVwWXAv1xztuUkgZ/sKmh2TpobWPNjINjWTJsFQlxiXT7qSNe+T1U426evktyotKRIBD43wskPCgST5+MZpMH1hxj4ZCVgKKRERUazaRdw1X7O/z3iUx/d1Q7+NDnQ3I33Zr8IogKjaZ1txC6DQokPkbHnB8LEh+jfqZlWWbxj3+y949DFCiRjyHLBr42GhQ5jWKVfDm964Jxu2hF02KFLxuprt2pCA+KfEVnoo29Kw6yfNQabOysGfhLH1V5/GXhjQk+Ah8+4+/pW5FlmY7fvK0pNuR3N4CLx5y4eCydIqYF6X4HV4cMK3i3PLnff12qejEkSUIIgU6vI1/RvJpEz0JlCmQoAzm7W1YvdXJz5IuZvbMcY2VtxczD49i2YC+GZANt+7bQXL0ZkjPKy6cGU1o4tvk0xzafplCZAnT8pp1moLh7XU0MidGUqRbLuUPO3LjagFb9s3ddrwv6Tu1BXHQ8147fomrziibJwSJyLMStAnQQ/Qt4bcqyZJJTOLHtLEc2nKRASR86ffu2SV5CdiFZlwXrrMs3/6w8xL+rjwIKmXnI8oEqIa4SVYugt9IhpxiQeeRz03wO75y7Z+TeyAaZ2+e0S6RObg4ZFIZNkVcT4hJYO3UtgQ/9aNylJTXfMp8YWb2pNXXrPEEIEDJMWPUIe291RnbvioOsnaYQPyOeRTLpo9nMOTZRc5+yrIhkOXs4PVe8LKdw//JDNs/bjZ2jLV2GvGtRpiyzQrWlmibZxYOrj9n8y05s7W3o/P27qiAWoEj5gpzelcbd0tJCeVV4dOMpUz/+BYSy8Bn57mTW+i96JVm0NyL4iI9N4Ov6PxEWGAHAwXXHWX5rDo6ZLKkfXVffwHHR8Vh7mHfzfPv754zsMBXZIGNjb8Pg3z83az8vghJVizJq/XdsmrsTFy8n+kz+SJMw+9HIzpzYdpaYiFgknWS5/XY24eLhTLdh72c55untjATDmIhY4qLiVWWXk9vPMuq9qcZOkcCHQXw1r69qfx0Hd2NE+4ckJxmwc7RlxsGszf5yCkIIxQVXcjLpuJpdOLo4MPSPr58/MG5Dyj9kIAni94BT7raVn9lzkRHvTFZ+ByHwuxtgcSt7ZEgUYYHhFCyVX7OrKywwnOWj1hi39/15mHb9WqratguVLkC3nzqxcfY2rO2UZ1Brf1WaV2TttC1GbpgpY7Fvl/RnSPOxGJINWNno+W6JdhQ7tecIDm+4h6SD3ctPM+PfoVRsZF47qK1tICJO4apJerCzTwI5DPQZF1BPbvmj0+uM1gpPbqqJugABD4L4oeVY/O4Gkr+4N1P/GYW3b85wREwh2C+Ur+sPNy7GTu44x6IrMzQ5IlsX7GHfn4fwKebNZ9N6apa/HF1SXG1lJah8Ge29oQFhfF3/J+JjEgA4sf0ci6/OVN1PHQe3Z9+fhwkNCAfgs2mWdR3lJPzvBRppjUIIEmITCQ0I/1/wkVt4eO0JIX5h6K0EkiSIeBbJ/cuPVMQ433KZUtmSZa2gtdtVZ9WjBTy97U/RioWzbPGTZZmkhCRs7S33MvG7E8Cj609w9nAiPChSU9q7YEkflt+ey41TdyhQ0oeCL8ElM7uo2qwC9k52JMQlIoSgQoMymgZ0p3ddyNApcmLbWc3go0aryiy9OYeHVx9ToloxzdVKTkNRB/0CEg8BEjgPRXLsnevHRZ83RRFUVv7T5/7vemZ3xt/h5PazFu3v0N/Hmdh9NoYkA8WrFGH6gTEqKf7kJAPVGkcxcOITbGwFK6Z5k5SoFnx6dOMpf47/GyHLSFHxTO8znz8fzFdNGDXfqsLIv7/jwJojFCyZn24/aQfIlRqW468nv/Ho+hOKlC9kkoh5etddhNAhDKDTC87tWmd28IF1DcVFW8QAAqzKgk4dzFZuUo7Vkzcat0vXUusAASz56S8CHijE24AHz1g64i9+XPGV5ticwvUTtzOIIj656Ufwk1BV0HNs82nm9P/d+DchfmFM3TtStb8WHzVif4oLLUDLno0sOj8hBDdP3yE2Mo5KjctpZlBvnLyTwZbj6W1/gh4Fq0pbnj7uLLk+i6vHlK6/IuXNk5rPDZStUxJXLxeiwqJBCAqVLYhP0dzXPdLCC+XbJk2aRM2aNXF2diZv3rx06NCBmzdvZhjTu3dvJEnK8F+dOnVy9KRfFD5F89Lxi1B++/cGC/bd5IOBwfholF26/NgB17xpL5PWHzfFysqy+MzTx51KjcplGXic3XuR9z0/5m3Hj5SXbrL5rrYXD15l4ZCVhAaE8+jGU0a0n2ySke/q5ULtttVeq8ADIG/hPMw9MZFO37zNx+M+ZMK2oZrZm6IVC2fsFDHRtguQr0hearer/lICDwASDqYEHgACETUFYZHgV/Yguc0CfUHAFuy7gl3uO9dm/h2KVbas/v7Ll4sxpBCT7118yO4l/6rGeOW3ZezyR+j0grgYHd9Me0KleuoMZWr7txBK+S7EL4ywoAjVuIS4BHb8vpdD606wY9E+7l9+ZPL83PO6Urlx+Sw7QIqUTUKnV5aYskGiSDnzFzGS3gvJcz04foLk9BWSxwokSf3qvnPufgaDm0cmzDOjw6KNInhCFkSH5b7ia+GyBYxZJZ1OwsndEXdvdUbj5uk7ijozyu918/Qdzf3tWvqv8Z0gSRK7NO6RF8GwdhP5ss4wfmg1ju5F+pOgYRhnvAYpxanazQEPHzfN/Tm6OlKrTdWXHngEPQ7m4fUnJukCLh7OzD05kQ++e4ePRnRmxsExJvWichsvFHwcPHiQAQMGcOLECfbu3UtycjKtWrUiJibjzdu6dWv8/f2N/+3YsSNHT/pF4ewWRXKigT4Ny9KnUVmiwiTcvcJU405tP09EOnLQgTXHMRjMDwSyAyEEk7rPMUbU//51hANrjpm9P787acRRIQsigiNJiE3QHBsaEMbhDSe5f8X0i/ZVwbdcIfpO7cGHQ98z6TLZpk9zuv/UkUJl8lOvQ82XIhGffWT2rZFB5O69BCBZV0CX5x90+S6jcx2LJOX+i6VW26pGSW0hy7To0dii/aXviEJCW4JfDmHVLE961ylH38ZlmPi5LxLqMkOZWiWwstErJQtJwqdYXs1Jb+v8PZzdcwmAiOBIpvexjCz60x/vUbNpFIVLxvPJT9E07GrZvSlZ+aJz/h7JqT+STtuCISkxOQN/w9Qi5r2v26HTpwQCeokOX7a16Nyyg8JlPBm2MJ5i5eIoUz2KiWvtVT5UoCg3p5KqdTodVZpqC5YlJyYZJ1ghhJFUbA4CHgRxJh15NdQ/jI0p3UXpUbBUfkasGUzxSkUoW7skk3YOz5FM9fMghODIxpOs/Xlzlo7Bq6dsorvvF/Qp/w3D2082+fv7FPXm00nd6TGqs0k7j5eBF1rW79q1K8P20qVLyZs3L2fPnqVRo7S0l62tLfnyvRz3yOzgyY07bF6clt7b+acn73x9ixKZzOUigjOykuNj4jEkGXK1d10IQXRETJpCpKQ+jxdB9ZaV0koWsqBq8wqak/fD60/4qu4wJeiRYMiygbS0cNJ42dDpdPQe15Xe47q+6lNRw7YpWFWE5BTmu+Nnmuqg/x/w9/StRIUqbdtCwO9D/qBFd/PT4J9M6Mbs/gtBKMJ0LTXcYEOfObFqVto75vA2N66cdKRypqGObg44OKeSRAV5CnlpEiwjgqMUDoFBKEH7M8s6FLxLfcy4HY3AEADWlV/Kb9+2bwt2/P4PwU9DkSSJTyZ00xxX860q/HZhGrfO3KNUzeJZCq+FBUUQ4heKb7mClpGIE47TqO0NGhnjnLsIQyCSPmPJonrLygxfM5gDa46Sr0heeozS7gLr8sN7nNlziYTYBGxsren6o/nmnpk9cQDiTCzYGnasQ8OOLzeTv2L0Wv4Y9zeSTmLpiNXMPTGRElUytpXHRMay5KdVxu1TO85xZvcFarfLfdl5c2FRTSEiQklfenhkbHE7cOAAefPmxc3NjcaNGzNhwgTy5tWuKyUkJJCQkPZDR0bmfFuSAXUa2IBaE6DuOzXwGOlOqL+SFWnTp7lmdJ6T0Ol0vDugtbGP39nDmUadzL+58xbOwy+nJrN3xUFcPJxo31/bY2PHwn+MxCkErJqw3uLgQxgCQA4Fq5JI0uvD8M5pCEMgImYJkITk0APJSn0vSZIdeP4FiedB55xjBoSvI66fyKg5EvFMXdZ4ETi6Ohg7tuwcbbG2Ub+mhEYlUZbVQfb5fVcydKdcOniN8KAIVadFix6N2DR3h/GZ6PiN5eUqyao4vIROo1R4+riz+Nosbpy6g7evFwVKmC6n+pYr9Fzb+8PrTzCh2ywMSQYKlSnArCPjzF8pq7I1epC0M5qNO9elcee6We6ubO2SrLgzl/uXH1GkQmGLyqmFShfI4Nli52BLp8Hmiy7mNLYvVHRlhCyQDTIHVh9VBR+yQVb5SaWWQl9XmB18CCEYPHgwDRo0oEKFtNRYmzZt6Ny5M76+vty/f58RI0bQrFkzzp49i62tOkU1adIkxowZY+5pZAu+5QrT9MO6/PuX4tBZ/73qlKymNuVyy+PKx+M/ZM3UTbh6OfPhUPOj6RfB59N7Ua1FJUL9w6jdrhoe+SzjJRQuU4BPJ2qvelJh55j2W0g6SbMd90UgYtcjIodhJMR5/Pn/cqUvRCIi9ENFRwMQcVshzx4knfo3kyQbsFUbov1/Q4kqRTOoOFoqc/3roKXGF+njm37sWrJfZUvgVcCTXqOqUbTIemzsZM4dq0OlRuoAzzN/ut9FAhs7G00SuW/Zgvx+eQbn/rlM/uLeJtP9rzscnO1zTLfh12+WGrk3T275sWPhP+ZnGKxrgH0PiFsJWCG5jLLY+t0jn7vF78pU/Hb+Z3Yt3U9UaAzt+rXA2e31eXd5FvAgIjgqJcCQ8dTQs3F2d6LLkHdZM3UzAOXrlaZG6yqa+zu//zKrp2zC1s6Gjyd8SNEK5vuXWQJJmClkMWDAALZv386RI0coWNB02s7f3x9fX19Wr17N+++rGeRamY9ChQoRERGBi0vO6WIIIbh15i6yLChds7hm6vXW2bsMqPmjcdu7SB5W3p33WpkHGZINCCHMFj5LRWRIFN81G839y49wdHNg4vZhmkZ72YEQAhFULYWNr0ByGY3kkHUAlBV2LNrHuulbcPFw4st5fVSR/quCSLqNCGmX4TPJfSmSbf1XdEavHsFPQxhQayih/mFIOolhf35Nky7mfx8d8/QiMiQWULxd+kz+iA++y9geLUQCIqgRQo5AES2wQpdnD5JeLTu/cuw6Vk/eiJ2jHd8t6U/d9uYp+b5p6FKgL6H+4YBCEu01tutz2+SfByFHg2SlZAb/HyMxIYn7lx7imd9dU037RXD/yiPGdPyZgAfPaPh+bYYsH2iyBHbv0kNiI2MpU7uk5hwR+PAZvUt/hSHJgKSTcHZ34s+Hv+YYdyUyMhJXV9dszd9mzWBffvklW7Zs4dChQ1kGHgA+Pj74+vpy+7a2HLStra1mRiSnIUmSpgV9eqRv3QIIfPCMuJh4HEwQHl82ts7fzfxvliHLMh+P70aXIebrVbh4OrPg/M8EPw3FLY9LDpSX0sewknZePJu4fvI2M/stAJSX3rA2E/jryW8muTdCxAO2LydI1PuA5JDO80cHesu6O4SIg6RboPexWA/kVcCrgCdLb8zm9tl7eBfJQ74ill1DnxERzPzGCiEk8hVOoFUXdU0egz+IMNJ+8qSU71AdfGRHQfZVQiTfAxEPVmU0u1hyA3Ex8QTcD1JECE2YLPaZ/BE/fzwPIQvyFPaiTZ/mFh/3/2M2NDMiQ6MYVH84j2/6odPr+GH5QJp1a2j2/opWKMyym3OzNbZYpazfRQ+uPEpT/DUoDQnPHodQsFR+s8/PXLxQ8CGE4Msvv2Tjxo0cOHCAokWfvxoNCQnh8ePH+Pi8Xu2cWgh+EqL6zKRx1EtG8NMQ5gxcZJzjF/34B3XfqWGRTbwQAkOywWh+Zy4kSQLnIYqXBQL0xcBe28rdYDBwbPNpEhOSaNyprmZ0nr5FUJYFYYERmiJjQo5RtDSSTii6B+6/K8qYuQhJ54Rw+53EZ2MQIglbr++QrMx3ShWGIETIByD7AVbgNhfJzvKX/MuGg7M9lZuUz5F9vdXlPpVqGwgOsKZkxXjs3TWk//X5ld9cDkF5KGyeq4j6PCTEJXD34kPyFvK0eLUKijNz0MNgKjQsY5IrIUdOg9iFyoZNE3Cfn+sdSvevPOK7pqOJDInCxdOZGQfHaPI/WvZoTMWGZXn2OIQS1YqaDFKEEFw6dI2E2ESqNC2f6zy51x27Fu/n8S0/QOFiLPh2uUXBR06ieJUi2NrbKCRbScLd25W8uSwwZwovFHwMGDCAVatWsXnzZpydnQkIUF4Krq6u2NvbEx0dzejRo+nYsSM+Pj48ePCAYcOG4eXlxXvvvRz+hCUoVsmXg2uPZ/jMxkKZ6JxCVGh0xuQCEBkcCaiDj+CnIRxadwJnDyeadWug2ccd/iyC75qO5uG1J9g72zNxxzAq1DffeVNy6AY2DUB+BtYVkCR1NksIQb/K3/LomqIk+/uQlfxx71dVAFKpcTnlAUmJ0EvXKK4pMkbsCkg6qfxbDkZEDEfyWm/2NWQXv35/ic2/2AP21O9wgZF/NzdbolrE/gly6uRqQERNea2Cj9CAMA6sOYaDiwPNuzewWDY9W7BtjY/vRnx8U1oFbZuqhkiSDXisQkTPBRKRHD5B0pvfYRcWGM6XdYcR+OAZeisdw1YNolGnrEmPWWHT3J3M+3oJAO7ervx6ZooqoBGG4LTAAyDxACSeBNt6Zh83O1g5Zi3R4UqJNDo8hpVj1jF8zWDNsfmK5H1uJmt6n/nsXqrobJSsXoxZh8e90QFIdHhshne1kdj/GsCrgCc/7x/N3zO2YmNnzUcjOmFj+2rmuBcKPubPV3rfmzRpkuHzpUuX0rt3b/R6PZcvX2bFihWEh4fj4+ND06ZNWbNmDc7Or66fOLto27cF62duM7q9Nu/e8LV5iAqU8sHOyY74FJVAa1srilZSE4XCAsP5vOoQIkOiEEJwYvtZRmi8WDbM2o5Pweu82zOCR3ftmD9oEfNOT7PoHCWrwoBp8tLFA1eNgQdAyNMw9q48RJtPmmUY51PUm1lHx7Nz0T6c3Z3o9G17zZKKkMMQQkKSBCAj5FCLzj87CHz4jM2/pLWcH910iusnblO+nnl8mdcZkSFRfFFtCGGBEYrWwIYTjN86NNePK7mOB+vyCIM/kl1rJGvtjIpkVRjJ7eccOea23/YajSANyTILv19pUfCxcuxa47/DgyLYs/xgNvkSlmUhs4OkxOSMGhlaOioo5GpiliMMT5TfwVb9fYQGhBkDD4DbZ+9xft/l17rFM7eR+VWV+7/oi6Fs7ZKac8LLxguXXbKCvb09u3fvtuiEXiXc8riy6Oosjm8+jYuXM/XerfmqT8mIJzf9jYEHQFJCMvcuPlJ5WZzccT6DTsihdceJWxyn0vrw9rlG72X3SE4GvQ72rs99pX1F4VBQrHw8er3gzmV7TLE0SlQpype/aNvLp+LaubIULyKwTckGH91dlkY9cvSUcx2SQzdE3CaQ/QE9kvOQV31KRpzde8noTwFwg1VDWQAAQtxJREFUcvs5IoIjs1T2zAlIkjU49jR5b+QW0rcqZmVmmB0Y0v29ENpaEpLeC+HQB2JT7NZtGoFN7ndHffjje5z/5zIJcYnY2NnQ5QftrLSIGA7xmwEdIm610sFmk5Gsa21rbfRYSYWtgzaH79y+y6ybthk7Jzs+HvehRSXjF0Hgw2cEPQrOsnSUk8j8fDi+At+U/wLeCG+XF4F7Xlfa9m3xqk9DBdc8Luh0UgZ+hns+N9U4z3Ryv5IE9s722NirszeN3knEkAyp6vEN347O6VNWoWLDsvz0exgN2jxGCDh3yIvqHRuYvb+DG8KYvrMslepG8viOLQFP5VwPPrx989DhqzZsmrMTgAYda1O2jrptO7uQ9N7gtROSb4I+v0p06VUig3S0BLb2Nha1ZEeHxzC9z3yuHb9JlaYVGPTbZy9lMsgO8hTyzLC40nq2XgTCkL31rs5lCMLhfRAJYFU2S8KpEMmAzmJSarm6pVl+5xceXHlE0YqFTberJuxDWbcbAD0i4bAq+HB2d+Lzab1Y8N1yhCxo2bOxJvfH/14gP7WdgCFZRtJJXDt2iz/uz8v1Mt7+v44wpcccZFng7ZuHOccn5Fh7rinkKeyVYTu9Zcf/kIaXQ61+gxEXHcfD609I1Fj5vAg8fdwZvOgL7J3tsXWwZcCcTzQ9WWq8VYUPvn8XK2s9Ll4ujFz3rWaXiJNnRXQpgYcQOuzdKll0ftmBJD8h4GE87Xwr0c63EhePWKHnmtn7K1yuIE/uWrPjD0+unnahyEvqVx8w6xMWXZ3JgvM/M3LttxZbkks6BySbqq9V4AFQpnZJ8hRM4SkIqPN2DYvKkL//8AfHNp8m1D+cA6uP8ceYdTl0ppbj8fWn6KzSfseA+0EW7c/J3TGD90hW3k6SVQkk6/ImgwohBHLUDERgRURQNUScWvr7ReHp4071lpWznoitSgCp7w4DkpV2t+D7g9rx+6XpzDo6nu+XDtAskd658IDkJEUmQDbIhPqHEeKntrjIaSz64Q/jgu3ZkxB2/L4v14957+IDo48NgN/dwFw/5n8R/ws+chE3Tt3mw0Kf06f8N/Qu9SWBD59ZtL+3ejdlc/hytkatpMPANppjJEmix6jOjN44hHFbfqR6S21rcBy6ITn2AX0hJLsmSC4TNIclJiSxdPhfDGs7gXXTt1rU/RP4MIzF432QDRJCllg335trJ54+/w9NoG3f5nzwXSvyFXWhVutSfJcD3i5Pbvlx8cBVEuKyJon5li1I8cpFXisNmJzGmV0XeJauA+zgumPERJhvQvb4xlNjOUOWZR7f9LP4HHMK5RuUQU5njlepsWWKtIN++wwbe2VVX65eKdr0tYBEnHQOYhYABhCxiIghCDn3zeAkt5lgUxf0hZGcvgQ7bdXP1ZM30qfCYAbVH874rjM03xElqxXF2tYKSSeh0+vwKuiJVwG1WFaOI93jKWSRISgwB0IkIhKOIBJPm6QhVGhQxliG0ul1VGhgmsgf9DiYY1tOE/DAsmD3v4j/lV1yEb//8AexUYoeRIhfGGumbOKrX9WW7y+C5012sVFxfFV3GA+vKe2qXX/owKeTumvsJ4Vf8ByOwZJhq9gweztCFpzedQFrGys6fKkd+DwPdy7FQ6ZK/vUzMuXNtAHRiSd8+u1CPh0cAUhIrjUBdYtvXHQc879ZxvWTt6nesjJ9JnfXbPHd8utu5g5U6u8FSvow5/gEi4yXwgLDObj2OI5uDjTtWt9iYbiXjcwvakmSkCzI8jTsWIfLh6+jt9JhSJap/14tS08xx1DvnZoMWTaQg+uOkb94Pov9gopWKESVphXwvxtI/Q61sDPBg8gW5MwSAEkpgn5qJdnYqDhunr5D3sJZy6tnB5I+P5LHkizHRIZGsTidp8ihdSdo1++qSmU1X5G8TN07kvUzt2HraEuv0V1eyvPgkc/NSCQWQpDPAvt4Rd34I0i6oHxg9z6S22TVuOotKzP0z6/5968jePvmMXkvXT12kyEtxpAYn4SVjRWTdv70n1XWNQf/rbfhS4IsK66Klq5qkxKSM1CdtUhnOY1jm08bAw+ANVM38dHITpoKdqd2nufEtrMULluA9l+00izPXDxw1RjFSzqJK0evmx18VGpcTkVOq2OB2qSIXQ0ilasiENFzkTT0RRYO+YPdyw4gG2QeXn2Cs7sT3Yd3VI1bNPQP47/97gTwz8pDvP91O9W47CAiOJLPq35PaGA4CKUrZvT67zXHntlzkWObT1OwlA/vDmj9yiyuM6NWm6pUb1WZs3suAorhm4MF5LkOX7bBNY8LN07epmKjcjR8//WSnm/ZszEte+aMueL4rjO5fuI2skHpnMlX1Nv867WpB/qCYEh5rm2bgU6tzRDiH8bA2kMJfhKCJEl8v3RAjl2PKRiSDKp2DlPvuQoNylKhQe7q8GSG3520koekl7h/6SGYq7mReDIt8ACI34AwDNJs8W72YQOafZg1n+3vGVuNcgKGZANrpm76X/Dx/xGyLHPj5G1kWVC2TknNiVYIwfzBy9gybxeOro4MW/W16bJFNtBjZCdGvDsFQ5IBO0db3h9k3kT2IrDNRC7VW1uh06tXq6d2nuendhPRWemQk2UC7gfx+fReqnEVGpTh7sUHCFlx+yxXx/yWUhcPZybt/IlZny/EkGzgs597avJWsg3JjrQ3n5Syrcbts3fTuhckuHvpgea49JO+QFgUBJzZfTFDp8jRjaeICotW1f7P/XOJoa3HK7+DQcbvTgAD535q9nEjgiNZPHQVQY+e0bJnE5p3N1/cyMraiok7lCyag7M93lmIEW1fuJdDfx+nYKn8fDLhQxxd1atySZKy9VIWchgiajoYniLZd0CyN1/J91Xh3sWHxntOp9dx7+IDs4MPSecEnhsgfjdIjmD3lubCaOeifUZTTCEEi4f9mevBh7u3G+37v8XWX5Uux/L1SlOtRc54y+QEytUrxeldFxRfFIPI0kIiLjqOpMRk09lO1ftFAsl8DpStg41ioIhAkiSTXUL/X/FGBB9CCCZ+OIuD6xQBsbrtazB64/cqouCpHefYOHsHoIh6jftgBhtClppNKKzZuirT94/mwoGr1O9QiyLlTbtICiGQZdmkhHgqUuupps6p3rs1qdm6Cqd3XUCS4Kt5fTQZ5Se2nTWmvwGObDipGXz0nfIRNnY23Dp7l2rNK9HhK9NZj/tXHhHqH0a5eqVNdjFUb1mZlXfnZXmN2YXk2AMRvxsMd0CyR3IZoTmuVptq3Dx9F51emeBrmAgoB875lKm9f0E2yJSoUtSiF3eGbglJccpMb+aXipPbz2X8HTaetCj4GNt5OleO3EA2yJzdewn3fG4WGY3tWryf1VM24ejqwFe/9qVsbXVnz+H1J5j1uSKWdeHfq4QGhDPq7+/MPqYIH6SsMpERiUdB54lkqx2w+N8PJDE+icJlCrxW/JsaratwZMNJJElClmWqWmj2JuncMNh2QqfTmbxOvZU+jYcgkWXwLAwBkHwLrEo/l+hsMBiyfC99OfdTWvVsTHxMAhUalHmtyos/rPiSxUNX8fSOP027NjApn7B94V7mDlyEIVmmbd/mDFrwmfp7tq4Bdh0gfhMgITl9h6Qzn7fSY2RnLuy/QohfGC6eznxsYanvv4bX5y7JRTy89sQYeAAc33qG2+fuU7pGRrvrkBQTJVCCgZiIWJISksw23Tm96zwj3pmCIdnA2p83M+vwOIpWVGvvXzxwlXFdZhAVEkWr3k0Y9Ntnmg/7jt//4ddvliIbBJ9O7EbHb9QEsMSEJIL9QlOuAZMk18JlChgnPJ1eR5EK2oGRjZ0Nfad89NxrXT9zGwu+XQ5AvqJ5mHdqCi6euSssJ+k8wGsrGJ6CzgtJp6GCCnQf0REnd0dunb1LlSYVeOtjtWImKKJy1VpUJDwogsJlC1qU+ajarAJdf+jAuhlbsXey44flX2oGgYXLZvwdfLMIULOD6ydvG1fckk7i+vFbZgcfN8/cZeZnvxn39VO7iaz1/101uVw9dtMYQMkGmStHblh0DSSeB1JJizpIOg8awceyEav5c4KiaNuwUx2Gr/7G4s6jnMKQZQPxLVuQwIfPaNKlvqbj7otg2YjVrJ66CRs7G75d9IWm5Xy7z1qw789DPLz2BCsrPQPmfKK5L5F4FhHaG0gA7MBjOZJNVdW4m2fuMvq9qYT4hdKwYx1+/OMrzXtYkiTK1DK/3Tw3EXA/iFM7zxH8JAQnNwda9GikUvSMiYxlzoBFxudmx+/7aNKlPlWbZXxuJElCcpuKMAwGycaiwAOgQAkfVtydx7PHweQp6PnaCFq+LLweT2ouw8pGHWNZa3xW5+1quHq5GBXqmnVrYJHb38qxfxtv6PiYBDbM0m6Rm/DhTCKDo5Blwa4l/3Jo3QnVmGC/UGZ9sZCE2ESSEpJY8O1yntxSdwsc23Sa+5ceGbdXTdyg2bnR/otWdPzmbfL6elHjrcoM/v1zcy8TIQS//5jGlwi4/4wdv/9j9v5e7OCRYLgHsul2Nr1ez/tft+PHFV/R+pNmWa6Q3b3dKFrR12LehSRJfDqpOzviVrEheCl13tZWfGz9aTM++O4d8vp6Ua1lJb5fOsCi45arW8pYZhOyoFy9Umbv62m6+0vIgqjQaKLC1F0W5euXyRBAZRa+e2HYVCft1SSDdTXVkNCAMGPgAXD47xNcPnzdsuPmIOwcbOk1pgtDlg2kVhv1xP4iuHrsJn9OWI8hyUBcVByTe8whLjpONc7FQzGLXHhpOmv8fqfeO9qrfBGzEEhM2UpExPyuOW5qr7mE+IchBBxaf4Kdi/ZbdB2xUXHs+/MwRzedwmAwWLSv7OLn3vMIeRqKEErpc8dC9XspMT5JJSoXG6n+flMh6fNZHHikwsbWmgIlfN64wAPekMxHgRL5yF/C20g+ylvYC9/yajMwj3zuLLjwM4f/PoGrlzONu1jmsWBta6U0d6RkQrWCIFmWjVLooExaYem4AqmIDI7KQNQECAuMULkRZj6GTq/T5HzorfR8Pr2XZqnlRSGEUIhn6XDnwgOL9/vc4yY/4uHxDzl3UFCoRAI13h2DZK/dDviq8LyVuF6vp+/UHvSdmjPqaCPXfsuSn/4i6HEwLXs0Vq3eXgQVG5XD3tmOhNhEEIKS1YvjlkctmNTw/doM/v1zDq8/QYGSPhZ3ikhuMxFRM1M4H+8i2dZXjdFSILVUlVROOAqxf4F1aXAY8NpkUUIzvQ+SE5OJiVSrFoPC0yn6XL0bGzK8mNAW+goLjEhrGdXpCA3Q1uUQQijZKREHNjUV351MiIuJZ2DtoTy+obTWN3i/FiPXfZfrpbLQgDCjzodk4hrc87rSokcj/ll5CADf8oWo3sp8rt//kD28EcHHg6uPM7Cegx4Fc+fcfUrXVIvmeOX34L2v2ubIcftO+Ygf3xpPTEQsnvnd6fqjWsZYp9PRrl9Ltvy6GyRwcLWnwfvqFkTf8gUpW7sk10/eBqBIhUKUrqU+//odaho7FCSdxIDZn+S6imAqWSohNi3DklVve07h1rFFDGqZj+QkCZDoM2oBXUa9XsHHy4aLpzODFvTLkX3lKejJ0D8HsXL0Whyc7fl+ubaAFECbT5vT5tOcMcSTdG5IrmOyHONVwJN3BrzFlnkK0bFay0oWlTbkuD0QMVDZSNgDiafBY4XZ+8suIkOi2DB7O4lxibTt11KTgF2teQW8ffMYS6i12lTF08d8lU7J+WtE4ikQYaBzQ3L+UnPcO/3f4s/x60FSZNSbmiAKi8hRELda2bCurMiwZwpALuy/Ygw8AI5sOEWIX2iOuAdnhXcHtGHFmLXKNdhYmXSX/W5Jf4pXKUpUWDQdB7WzrDX6f8gWJPE8w5aXjMjISFxdXYmIiMDFJWdkaZ/e8ad3qa8yfLbw4jRN/kVOIy46jqDHIfgU8zbpHijLMkc3niLEP4x679YkbyEvzXHxsQkcWHMMOdlAk671TbY9CiF4eicAR1cH3PO65ti1ZIV/Vx9lSs+5GJINlKtbiil7R+b6AzxvwFdsWeiHbFAmxLwFZf58lPuutg+vPWbl2L9JTkzigyEdKFfH/NJGTiMyJCpD5qNpV3XWILt49iSET8sPypD5mHtionanxeJ9GTIfji7a/JuchBCCuxcekBifSOlaJZ5L1s4KcsgHGdsokdDlu2nxOWYFg8HA51W/59F1ZVJ2dHFgyY1ZuOVRP7ORIVEcXHcceyc7mnSpZzGpU4g4hSulL4hkoktMCMHJ7ecIuB9ErbZVyV9c3VIqDMGIZxkzxJL7QiTbJhk+u3LkOt80Gmnc1ul1bAheotkVlZMQQnBqxzn87wVRs00Vk9ons/svZNuCvcD/nHktwYvM329E5qNACR+6DHmXNVM3A4reQFZS3Dml8wFg72SPb1l1iSc9dDodDTvWee6+7BxsaW2CLJkeh9efYNPcnbh4OtPv5x6aL41UyLKcI+nlpl3rU71VJSJDoslf3DtH9vm838G9QG2EvBEAnU7gmT/3jariYxP4tulookKjEUJwZvdFlt/5xaKVaHYgRLzSfpp0AWzqITl9iSSpH9+xH0zn8qHryAaZ0zvP45bXxezSy+VD14iLSjMzvHn6DuHPIlUB7eENJ5nRdwGgmNGF+IUxct23Zh3zRSBJEiWqFs2ZnekyB/y5P/EEPQrmwZXHxu2osGiuHb+lydU4uf0cG2Ztw97JjvzFvbNsGc0OJMk+RT49qzGSSa5S2iCtKUS96Chfvwwdv3mbDbO2obe24uv5/XI98ADlGp7nsBv+LMIYeIDizHtmz0WTnJn/IWfwehQ1XwL6TP6Ivx4vYNWjBQyY/Ym2RbsQ/PrNUtrafUinvJ9ydu/FV3CmluH2uXuM6zKDy4evc3zrGYa2Hq8pAxwfm8BPb0+itU1Xepf+ikfpUqLmwsXDmYIlfSwOPIQQ/D5kJW3tu9Exzyec3HFOc9z7gzpRo7VC5svrm4fBi7VFvHISAfeDiHgWqegGyIKEuETuX36kOVYIQeDDZ0SGRFl8XBE1A2JXQtJFRWrbBEnw2vFbafoSKQZe5qJAOj6RpJNw9nDC2V09YVw9egN9ii+KbJBfK+JntuE6GaRUHRMduE7K9UO6e7th72SXpiQrKfy0zLh36SFTP/6FJ7f8uXPhAcPaTSQxPlE17pVAsiNzsCF06kBckiQ+n96LzZEr2RyxIluLqJcFaxsrlZpvZr2k/yHn8cYEH6DUiY1GWRpI1fkwJMtGnQ9LvEwgpWU3MtakD0BO4/a5+0YemWyQ8bsbaJR4T4/1M7dxetd5hCzwvxfIzH4LXsr5ZQfn/rnE2mlbMCQZiA6LZnyXGSQnJavG2TnYMnH7T+xKXM3Ku/Oz1FHJKeQrmhcXT2d0eh2STsLW3kazTdlgMDC28zQ+Ktqfzt6fsnXBHssOnHSBtPZTEEmXNYeVrV3SSDCWZUHZuuaXhErXKM43v32GTzFvilcuwoTtwzTT/eXrlc7Q7fIy+D45DZ3OBZ33Uch7CV2+G+iyIC7n1DNt52DL+G1DKVKhEPlL5FPac8up76Unt/yMz7SQBTHhsUQEWx7Q5gjkUJSW3TRIBu1gHMDe0c5k+flVwdHVkc+n9TJ2OTbpWt9iXZb/4fl4I8ou2UVO63w8vvmUoa0nEPjwGSWrFWXSruG4euWuvXL5eqXQW+mNJYsi5QtpckPCAsLR6SQMsuIymZXD5JWjN7hz/j4VG5aleOUiuXj2CkIz/A5Km3JCXKLJOvfLlCO3c7Bl+oExrBi9hqSEZLr++B5e+dVtd6d2nOfIhlOAEgTM+2oxLXs2Np8HY1NfyXoAIJBstMt0I9d9a1Q4bdWriUUCYwBt+7agbd8WWY5p2LEOQ5Z2JeDWDqzsivHO1/0tOuarhE6nzX9IReDDZ/z41jie3PLHt1xBJu8ebhFpslKjciy8MD3LMeXrl8HBxZ74GGWSL1K+EJ75c7fMl23ovMGqFCTfQVH8dABry1qLcxJKafQCfncDqdWmKj7FtAXV3h/UjuYfNSQhLtEk5+5/yFn8L/hIh1Sdj8iQSISwXOfj10FLCXocDCitp6smbOCLmb1z6Gy14VuuEJN3D2f7wr04uzvRY1RnzRJT848asW3hXmPHXfsv3tLc394VB5na+xdAWdVO3j3covbN7KBmmyq4e7sSHhSBENCoU52XQmDMLtzyuuBbrhDJickmnTkT4zKmxQ3JMoZk87UNJKcBINkhki4j2dQCB+3WXFcvF4s0W8yBSH5E89aT4K1I4DCSVXHg9VFrfHLLj52L9mHvbM97X7WxiGuwaOifRov0xzf9WDZiDd8tyd1gy9PHnTnHJ7Jj4T/YO9nx/jftXps2YEnSg8dKRMwyEHFIDl2Q9KZl+F82/hy/nuWj1gBg62DLvNOTTXLwcnth+D9kxP+Cj3TIaZ2P+1ceG/vkhSy4c/5+Tpzmc1GlaYXnGhSVrV2S+Wencm7vJQqXLUDN1tqrlc2/7krbEIJdS/bnevDhlseVBed/5tC6Ezi5O1rUsZHTSE5K5ptGI/G7EwDArqX/svTGbJzcMk5otdpVo1glX+5deghAx2/etiiAkiQrcPoMyynQOQ8Rty7FZTVlO3o+ksPrEXykmq3FRccjhOD41jP8cnKS2WTyyJAoYylWyILI0JdT/vAtWzDXFy7mQtK5Izl/86pPQxObftlp/HdSQhL7/zzMx+M/fIVnlAaDwcC6aVu5fPg65eqUouuPHV4bU8mXgf8FH5mQkzofen3GF5y17ev1dRetUPi5gkTu3q5GTxQkSbMNMDfgkc+ddwe2znKSMBgMLP3pLw79fQLfcgX5ZuFneOTL3XT009v+PLmZpvwZHhTBzdN3VAaE9o52zDk+gYsHruH0f+3de5yMZf/A8c89s7tjz3axJ+R8yJnW2UNyKIciv5JKkSdRKE8iPJVDsngilaIoUURFJVFJrCK1CItyXKfsWhZ7tIeZ+/r9MUzW7GJnd2dmd7/v12ter52Zy31f65qd+c51X9f3W96H291oO25BXZsALy+a5oPKVeDPfWapYrccID05w3b/0I6jXEi45PDupL4je/DHxliUUmgGLd8ZQ+EeylcKsCZx1K21s8o7KfXArfj89W/4YOIyUBCzfhfmHDODpjzk6m45jXvM3TmBOcfMtjUxbP3qd7KdUNoeoFp9X9Csb8pGD0Xl2u7zwr9Vz7zxBJXrWFfg12tZm0dfti9FX9SUUuipr6PONkI/2xqVtSXPdusWbmTlrK+JP3aW39f/wev/np/vMbOzcoiPO5vnwtWCqBARjJe3l+2DWDNo+V5HNnmbaNWjOQ3a1nOrome3SinFR6+ssO46ChnC9rU7827oMxA8Glp/1nzQAqfme8wju+NYNXctJw6cyrdNUap8XdIuk68X/sF++bS+ubb3RvLujpk8v3A4C3bNouXdzQrZQ1Gcxn40kqDQ8gC0vbclvYZ1c22HrhH785//LCRWsDf6gGs75GTu9VW8mOi6zsRe0/njR+sOgUYd6vP6T5PznOJS2btQl7+wZv7zHYZmcDxguJR4OteL6/KlkvfiCq8ZyocH3sScY3ZetcrsbZBurZKKuoS69CyExKBpuVfJnzxwOldBs+P78l5lHxd7grFdp5J8LoXQ6pWYs3kKIbc5dl3ar7wvU74cx7ujF2PONjP41QE3zKNSku3dcsCa4RJrledpA+aw+vxiu+RLmsEfKnwOeiIYgtC0vNdJfTP/e94asch2f+Ly5+g8IO+smUXl2qy7ALpZRxVyB1vtZjWo3ayI8ouIYlUvshYrTr+HxWxxq2q7AA3a1CVm/S6Usn6Jadi+cLlbADZ9+gsXE5PpObQL5XxuvHja1dxrNIpJXOxJW+ABsO+Xvzi44wgN2uQebGU+irowEGvEoFDZO9AqfObwef0CzBgMGlff63z83WRvvgOc+oerJ15zR4HKAJUF1wUfrXo256t5662XhXSd9n3t09IDvP/iJ6ReSAPg3KkkPnl1VaEWZUZ2b8qHB+Y6/O9Limt3HaEgKyOby2mZeWZ+1DQDGG8chF1d+HfVovHLij34uHYHG0BOlpmMlMuFWkheGpw4cIpDO45Rt2WtmyZBLOk0TXO7wANgwPi+WMwW9kTvp2G7ejw26cFCHe+Zli9yeOcxwFoF+dPT77nVQv3rud+IFANvb/uKp74+Z4DrIs3sGOCaafmc3Sh12ZoN0AHDZ3Vn/H3ruZjoSZWaWTw8wT0W4bk9r45gCAb9IqDA1B3NYD9V3vKe5kxbO4Ff1+zgtvqV6TPynjwPl5WelasoX15VfoW9O7o1ITisvLXAmFK07n1HoXYEXH/p6frETsWheZdGVIgI4kL8JZRStLynmVtd93eFmO9381LvKHSLjtHDwLS1E4l0QiG1+GNnWbdoI+V8TfQZcY/dIm2wXupbOfMrNnwcTXjNUJ6b/9QNczOVZEYPI49P7l8kx4qPO2sLPAAup2WycsZXDJn+SJEcvziUieCjYuVgnpgQz0czwlDAo6PPElo9jxe0x7XBiPHKNznHp65qthrG8qMNSEmMJTCiA0ZTE4ePVZZoxgpQ4WvIXA+GACh3b75tW/dsQeue9iXXrzVg/P280mcGFrPC08uDfs/1Kuou52n3pn18/c53+JX35fHJ/Uvcm2hABX/m75rF5pXb8AnwocujhZuleHLmQF4f8q71jkaRVFS+mYBgf+bvnMWmFVvx8femy8B/lcj1NwAWswXNoBV6m+2Xb35rC8Z1i+KreesLFXzs3LCHucPfJzM9i8deeZD7nrFfhHvpXDIjWo0nPdmanO2X1b/xTswMu98l+rNtfDBxOQCnD8Uz/ZG5vLHlVYf7Vhr8fSSeqEff5O8jCXQe0J4Rbw6xWzKQ18yOh5ttcLiee/euiBz/y4Cnl87qg/tQCjatDuLIXg8aXfdeqnk1h4AZqIyPwBCMFvByod+oPHz/RXCNvCspXnX6cDxvPv0+5/++wL3Duzvtw9GdacZQ8B1cJMdq1aM5HxyYS1zsSepG1nJKEqFTB/9m/N3TbMne9m39iw/2v+E2+RluVXBYUJG9Hu8e3JlG7esT+8ufNOvckLDqeS/ULWpBoeVL/N/U4pc+ZeWsr/Aq58WYRU/Tqb/jaQD8gvzQDBrKYt2x41fe8an5zIwsJt//P7IuZ6OU4u2Ri2jQtq5dzZ19v/xlu/QJcOSPOM6dSiK0Wu61V3GxJ22763SLnm/pgrJk5uNvc3hXHLpF55v5P1CzSXV6X7dwtlKVCrTu1YLfvrWWoggKDWTABPsq6u6kTAQflapW5MPpt/H1h5XQNMW5M94sPRqSZ1vNpx+aTz+n9u+VPjP4+3ACukVn/n8+omr9yrKKvohVrh2eb0XL4vDXb0dsScUUitMHz5B2MZ2ACv5O68ONmHPM/LpmBzlZObS9LxJvP8cuLRZU5TrhdjtQxI3t2/oXy6evBqzT6TMef5tWPZs7PGZDXnuYg78f5szRs4RVr8QTrzqe9yLlfAqZ1y3qTTieaBd8RNQKsyU01DSNcr4mAivZX8K7o3tTlketxmA0oJSiVU/3yZZaHMw5Zi7EXyQ4PCjfdSlnjp611WsyehiIP2a/jABg2jcTOLTjCJfOpxDZvZnbf9EpE8FHUEggL38+hvfGLEHXFROXD3SbFLq6rnP6ULxtGlTTNE7sPyXBRwlXu0UN65oGpdAMBipVrYBfHkXZXEEpxSt9ZhLz3W4AajS+jXm/RUkJcTd18WxyrvvmbDPpKZcdDj7Cqoew+OBbpF5Iwz/Yr1AfUhWrVKB+q9oc3HEUTbMWH2z8r9vt2tVsUo0Rbw5h6eTP8CrnxbglI/IsNdCkYwOmr/svWz7bRliNUB58If9LriVdfNxZXug8mcST56kQEcT/Nk6iaj37ytxdB3Zk1RtrrwRk0KFf63yPWTfyxpWK3YmmnFXx7BalpKQQGBhIcnIyAQHOT3ebfD6FX9fsIKCiP2163+GU6HFiz9fY8cOeK+Xj4Z2YmU6poSKK1/a1O1n1xlr8gn0ZOmOg22zJPXM0gUF1RuV6bOYPL9Oiq6xJckfpyekMaz6Ws8fPAdCqZwumfTPebdauZKSksvu7ueiWDOq1f4pKt9lvQ76cdplnWo63Jehr16clk1ePdZvfwRVmDnqbn5b/gm7RMRgNtOsTyaQv7Ctz67rOD0uiiT+aQNv7Iqnfqo4LentrCvL5XSZmPm7VpXPJDG8+1lZkrefQLvznveKvk/HyZ8/z+evfcCH+Il0f7+S0wONgzBF+X/8Ht91ehY4PtCnTbwTFoU3vO2jT+w5Xd8OOT4C39Zr/NTuACpN4SxQv30Bf3o2ZyZYvtuPtV45O/du6zd+qUopylnG06bjR+oBxJ0r/Es2Qe5bvj5/25coMvO3rGJLOXChUUb6SLvNKyn+wpuq/nJb3LjyDwcA9T3R2ZtecQoKPa/y6Zkeu6q7rFm5kxJtDin062tvPu8i2XN2q2J//5IXOk0DT0C06g6Y8xMCXH3BqH4RrlK8UyLPvDGXesx+gW3QeHn8/dVrUdHW3xA0EVPC3W2ToFvSzkLXxn/uW45C9Hcp1ydXs+rVORg8D3n7unQSruPUb3Zvf1u0iJ8uMwWig/wv3ubpLTiXBxzVysnOn3tYMGkbP0lnoZ/PKrWgGDYvZupDp+482SfBRhvQe1o27n7gT3aKX+YRbZY3FYuGL2Ws58OtBGrarx/893xuj0cH3Oc0PMALXVGw2lLdr1rBdPfq/cB+fz16Dh6cHo98bVqjqwqVB43/dzgcH5nJ45zFqNavu1AXx7kCCj2skxOVeRax0RXZmDt6+JScAUUqh0uZC+kdgCEALnIVmamvXLuS2SugW65SfwWggrHreu3/KkoM7jvLagDmcP3ORHkPuYsRbQ9x+xXhheHp53ryRKHVWzPiKj15ZAQq2rYnBYtZ52MFtmZrBDwKjUMkvA9ng+yR42ufd0TSNobMe4/Ep/TF6GN0y46grhNcIJbyGc7acu5vS+87qAPdaeuug7G2QPh+4DHoi6tJIlLLYNbv/2R7cOaAdJh8TtZpWK1S6cXeXciGVSffP4qHKQ5k56O18M5xOe2gOCcfPkZOZw5p3v2fziq1O7qlwpb+PxPPVvPX8vv4P3GwdfpHas3m/reYUCvZE7y/U8TTvvmihf6CFxmLwv/EiUpO3SQIPAcjMRy49/t2FVXPW2t54wmuF4u1buOuSB3cc5die4zTqUD/PbVRgna3Y8cMeLsRfpFXPFgQVJv2zJf7aI4NKBZUJWu4pTq9yXkxcNtrx8xSz1ItpbP9mJ35BvrTu1aJQMxALnl/C9rU70S06G5f9TEjVijwxzT63QdKZC/9seTZoJJ5Kcvicwn0oPdW6LkHzBdNdaJr9TGbcvpOMbD2B7MvW+kuDpw7g0ZeKv4KzK9zeug67N+1D6dYkY/VbFX57pqbJR4komAK9o0dFRdGyZUv8/f0JCQmhb9++HDx4MFcbpRSTJ08mIiICb29v7rzzTvbvL1xk7SwJcYm5vvGknEtFL0QFzI3LfmZk6/HMGbqAp5qMYd/Wv/Jst3Dcx0zs8RqvD3mXoY2f5/yZCw6fE1NH0AKxDa2pm93Kc3eXejGN4c3HMmvwPF7pM5PZT84v1PFO/vW3LUkPCk4dOpNnu+6DrSvKNYOGVzkvOtyfd6E64R6UykJlrEZlfIrSk/Nuo6ehkvqhksehLo1AJY/Js91Py37GfM2ar6/fWV8sfXYHA195gP4v3EfD9vV4aGwfWeslXKJAwUd0dDQjRoxg+/btbNiwAbPZTPfu3UlPT7e1mTVrFnPmzGHevHnExMQQFhZGt27dSE1NLfLOF7Xzp3N/001PySAnK8fh461+81vb9KauK9Yt/NGuja7rrH5rne1+yvkUfv5iu8Pn1IwhaBW/RvMbgxYwDa38XIePVRBKT0ZPmY5+6TlU1uZCHeu3b3eRePK87f4PH20mI/Wyw8fr9IB1zYvRw5o1sUM+1W9Hzfs345aM5IlXH2bBrllUqRvh8DlF8VJKoZIeQ6WMR6VMQp2/F6Wn2zfM3gqWE//cz1yH0u2D+6DQ8rZZL4PBQPnQ0lt8ztPLkydnDGTuz9P4d9SjchlEuESBXnXfffddrvuLFy8mJCSEnTt30rFjR5RSzJ07l//+97/062dNUb5kyRJCQ0NZvnw5w4YNK7qeF4NWvVrgH+xH2qV0lK7o1L9toXYCBFTws9UpAPAPss+loGkavgE+pF5IRSnrupPC5lzQjBHgN7RQxygodXEU5Pxu/TnzOwheiebVzKFjXf/7e5XzxDOPIkm6rvPJ1C/Ysmo71RtWYdS8J/OsuvrAmHsJCivPoR1Hada5Ee36tMzzvEajkW6PdXKozyXNge2HeH/sx+Rk5fDYKw+6ZT6SG1HmY2De/c8DegIq60c07z65G2rXBxGe5FUssvfwbuzevJ9f18QQHBHEuMUji7rLoggplYVKiYLs38ErEi1gIppWtrfuljSFWnCanGyd6gwODgYgLi6OhIQEunfvbmtjMpno1KkT27Zty/MYWVlZpKSk5Lq5SsWIYF77dgKte7agx5NdGPdR4d6Anpn7hK2Saa2m1Xjkv/Y1YzRNY/wnz1LuSqrkTv3b0nlA+0Kd19mUUlcCD/3KDciOcfh4rXo0p+dTXQHw8vbixaWj8tyZ8d2Hm/h46uec2H+Kn1f9xux/5315RtM0ug7syDNzn8g38ChLLqddZsI90zjw60EO7TzK5H6ziI/Lu17ErVLmI6j0D1CZPzpnsabKsH9Mv2T/mFdr8H4Ma2ERL7TAKDSDfSE1r3JeTP1qHN9lr2D5ifl2tUmuunQumTGdJ9HL5xHGdZ9K6sW0PNuJ4qXS3oLLK8ByBC5/hkqd6+ouiQJyeL5NKcXzzz9Phw4daNSoEQAJCQkAhIbm3joUGhrKiRMn7I4B1nUkU6ZMcbQbRSop/iIv9Z5B6oU0lFJkpmUycfloh49XtV5lPj72DpfTMvH2K5fvKvCWdzfjywuLr2zrLXnRu6ZpKI8GYP4T635/BZ4NC3W8/ywYxtNzBuPp5WFXPvqqY3uOY/QwYDFbK2Ae2R3n8DnLknOnL5CR8s9lLItZ5/TBMw5v+VM5B1BJDwJmQKH5PQt+xTtzoHnWQWk+1wQhBjTTXfbtNA0t8GVUwAuAB5p24+3F+b3Wrlo0fhn7fvkL3aKzZ9N+Fr+0gmffedLB30I4LOdPbF900K+894iSxOGZj5EjR7J3714+/fRTu+eu/5BVSuX7wTthwgSSk5Ntt1OnTjnapUL77dtdpCSl2r65bVqxNd9tmbdK0zR8/L1vmg7ZaDSWyMDjKi3oXTB1A89maAHT0UyOl/y+qpyP6YYfBpF3N8NittZF0DSNVj3t8wsIe+E1QwitXgmD0YDBaMA30IfahchwqjLXYv0guJIqOsP+PaGoaVo5tOBPwasDeEaiBS1E86h6g/beNw08bsXZE+dsl1F1i07iyXP5tt0TvZ8NS6MLt4Bc5Ekzdbzyk/G6+6KkcGjmY9SoUaxZs4YtW7ZQpUoV2+NhYdbCWQkJCYSH/5OtLTEx0W425CqTyYTJ5B4ZFiuEl7f9rGkaPgHeeJokEdOt0IxhaEFvOfWcbXrfwStfvMCva2KoWq8yD4zp7dTzl1SeXp68tnYCbzz1HjnZZp6cObBQ27s1QyWU7VuoAQzOSZqked6OFvyhU851VffH72T3T/swGDR0XdF1YN4fesunr2bxS9YgzC/Il3d3zCyzyaSKhc8gNK0cKnsnmlcL8B7g6h6JAipQVVulFKNGjeLLL79k8+bN1KlTx+75iIgI/vOf/zBu3DgAsrOzCQkJYebMmbe04NSVVW2VUrw/9mO+ensdfkF+TFj2HC26NHZqH4QobhaLhREtx3Ns7wk0ICisPB/sf8PhdNdKZaMuvQBZG8BYBa38u2ie7lt5s7B2/LCHP7cfolGH+jS/K+/3h/srDCbtonX3jcFo4LFXHpQtraLUK7aqtiNGjGD58uV8/fXX+Pv729Z4BAYG4u1tvbQwevRopk+fTp06dahTpw7Tp0/Hx8eHRx55xPHfyEk0TWPY64/z1P8ec5uqkUIUtfijZwkJ3cPUmNN4eimWzArjwPbDtLy7mUPH0zQvtKC3bnh5tTSJ7N6UyO5Nb9jGP8iX9OQMlK7QdR2/oJKVa0eI4lagmY/83lgWL17M4MGDAevswZQpU3jvvfe4ePEirVu35p133rEtSr0ZV858CHGrlJ4B6NbaFiVM2sUEPFM7YfRQXE0cG5/8IZXrdXBtx0qR7z76iTlPLkDpikpVK/DBgbklek2XELeiIJ/fBQo+nEGCD+HuVPpHqNQZgALfJzH4j82zncVs4cju4/gG+lCljvtUrFTmONT5u3M9pgV9gGb6l4t6VPo81WQMJ/48bVucOuaDZ7jnic4u7pUQxasgn99SWE4UCaUUuzftY+Oyn7l0Lu9U16WBsiSiUqOw7e5IX4jKOWDXLjsrhxe6TGFkq/E8Ue9Zlr22yul9zZfxNvBogDX3xZUFop7NXd2rUuXsyX92xRiMBhJP5L8rRoiySIIPUSSWvLKSsV2mMOOxtxjaeEzp3V6oMvinJOgVun3pgN++3cW+n//JPbBk0koupzmeIr4oaZoRLfgTNP8X0fxGo1VYVSIvH7mzq5lyr25n7tCvtYt7JIR7kaT+JUj8sbOsnPU1FrOFB8bcS7Xbq9z8HzmBUorPXl9ju598LoUtn/1Kv9G9XNirYmKsBqY74Wr9Go/G4FXyZg00gx/4DnF1N0qtp+cOpm5kLc6eOEf7vq2o2aSaq7skhFuR4OM6B2OOsP6Dnwis6E//sfc5vP2wqGVmZDG6w0tcOmdNP7/1y99YcmRenvVinO1qIrWU7Jwr9WkUPoH2KaxLA03ToPy7kLUFyAFTJzTNy65dm94taNKpAXujrZdkBk8dgPeVFPqi9DMajXQfdKeruyGE25Lg4xqnD53hPx1fxmK2XqvdE32AuT+/6uJeWZ0+eIYLCZds91MvpnNs7wmadnI8jXlRGrdkJK/2n01mehZt742k68DSu3jx1MGzLJsWS06Wmf5ja1K/lX1OC08vT2b9+ApHryw4rVzbfRacCiGEq0nwcY3dm/aTk2W23d+/9S8yM7Io5+P6DKxhNUIo52si63I2KPDwMrpVyfdWPZqzOmkxmelZbjEbU1wyM7IYc+ckUpJSQSl+X/8HS4+8TXBYkF1bo9FI3TtquaCXQhSN82cukPT3BWo0vg2vcvYzfCVBUvxFzp9OcurvkJ2VQ9zeE1SICKJi5QpOOWdJI8HHNao3+qc2hMFooEJEECZv9/iD8yvvy4zvX+aDicuw5FgYNHUAFcLtP/BcydPLM8/qs6VJQlwilxL/2c2TlZHFsb0n8ww+SgOV/jEq/T3Q/NECX0XzinR1l4STbF65laiBb6FbdCrXCefNrdMIrFiy0h9s+eJXpj8yF4tZJ6J2GG9unUb5So6XErgVKRdSGd3+JU4dPIPBaODFJSO565HSOxPsKNntco1G7eszesFTVK1fmYbt6jF93X/dKmNjw3b1mLN5Km9ufU3SvrtIaPVK+Af7YTBqaAYNL2+vXEFraaKy96BSXwU9ESzHUBeHoVS2q7slnGTBC0tt24XPHE1g3cKNLu5RwS0Ys9R2GT3h2Fm+fe/HYj/ndx/8xOnD8YC1+OCCMUuK/Zwlkcx8XKfXU93o9VQ3V3dDuClv33LM3jSZjyatxJxl5uEJ91MxItjV3SoelpPX3FGgUkGlgVZKf98yQtd1vn1vA/t/PUjDdvXpPaxb3l+yrsk/qQFulo/y1lzXZ2f8DtefoiT+tzmDzHyIIqEsCegXR6EnPYjK+NzV3SlWNRpXY8rqcbz27UQadbjd1d0pPl6tQfPHWrZcA88WoJXOy0tlyao3vuWtEYvY9OlW3npmIavnfptnu6dmPYbBYA1KQquH0HNoF2d2s0g89b/HMBitH3Mh1SrRa1jxf7Hs8e+7CK9prWBsMGg89b/Hiv2cJZGkV7/+/BdS+X3dHwRU8KflPc3c6rKLO9PP3w/mvwALAFrQR2imdq7tVAlgzjHz9shFbPliO1XqRjBx+XNuVXpdmY+jLq9C0/zA51FJRlYKjOs2lT82xtruR97dlKj1L+XZNvHUec6fTqJWs+qYvF2/8N4R504nce7UeWo2re60zQPZmdkc+SOOipWDCbmtklPO6Q6KraptaZeSlMrwFmM5dyoJgHuf7s6z7wx1ca/cn1IqV+ABGpgPgAQfN7V2wQbWLdoICg7tOMr/nniXOZunuLpbNppHdTT/Ma7uhihCdZrXYPemfShdoRk0ajWtnm/bkKoVCala0XmdKwaVqlSgUhXn7jjxKudFg7b1nHrOkkYuu1xj29cxtsADYO2CH8jOlAV2N6NpGni2xPpyuvKS8mzpyi6VGPHHzmK8Mi2sW3TOHE1wcY9Eaff4lP70HtaNag2r0nt4dx6f3N/VXRJlkMx8XMMvKHc2U5OPCaOn0UW9KVm0oHmotHdAT0Tz7ovm1dTVXSoR/vVAG756ex0GowHdotP1UdmSJ4qXydskM7rC5ST4uEa7Pi3p+lhHfvx4C17eXoxbMgqj0fHgIzMji0XjP+HQjmO0vKcZj/y3X6GOd/bEOb6Y/Q0Ws4V+o3u5VZIxzRCAFjDB1d0ocRq1r88bP7/K9rU7qVqvMl1KcWZYIYS4qswsOFXZe1BpcwGF5jfyhsmSMlIv42nyKHTCrLlPv8/6hRvRdes+86fnDHa42FrW5SwG133WlmLdr7wvSw6/jV9596g9c6sO7TzK4pc+xZJj4dGXHqDpne6RHr60SIq/yHtjlnD2xDnufuIuej5Z8nYoCCFKJllweh2lJ6MuDgKVCSjUhV1QaROaMe9FSD7+RVMA7M9fD9kCD4NB4+COIw4f69RfZzj/9z9l6lOSUjm653ix13ZRliRU8guQsx9M7dECp6Npjv3/ZKReZlzXqVxOvYwC9m07yNIj80pvngwXePXB2fz522F0i86BXw9RqWoFWt7dzNXdEkKIXMrGglPLSVAZgA4oIBMsx4r9tC26NkYzaKCBriuadGzg8LFCq1fC5GNCM2homoaHlweVa4cVYW/zplKnQfZ2UJcgcz0qbYHDx0qISyQ9OQNdVyhdkZOZw8k//y66zgoO7Tpmy0ppMGgcijnq4h4JIYS9MjHzgbGGNTmSSgEUaH7gUbfYTztk+iP4BPhwdM9xWnRpQs+hXR0+ln+QH9PXTWThi5+gW3QGT33IOQWLzMf5Zwst12W9LJiI2mEEhQaSfD4VgHK+Jmo2ua1w/RO5NO3UgF0/xqKUsga8nRwPeIUQoriUnTUf5qOotPcBHc13KJpn8QcfADHf/cGRP47TtHNDGrRxzjmLkkr/EJU6A2uWSwta+bfRyt3t8PFOH47n06jVWMwWHhxz3w1zDIiCS09O5+Mpn5N4Oomuj3akXZ+8tzxfTs/kx6XR5GSZ6TLwXyWuYJgQwv0U5PO7zAQfrrDm3e95e+Qi66UXBa+tm1jirr8rpSBzHcp8AM2rDZpJdmOUdLquM7rDS/z522E0NEKqVWTh3tl4+xXNWichRNlUkM/vsrHmw0XWLbJWULyaSXDjsi0u7lHBaZqG5t0Lg/9YCTxKiTNHEvhz+2FrrTilOHv8HPu2HnR1t4QQZYgEH8Uo5LZKtqJGKEUlZ6zREOImAir62yXPq1hZdhwJIZxHgo9iNPLtIdRqVh0PTyN3dG/KwxP7ubpLQhAQ7M+ET57DP9gPb79yPD1nMDUaycJfIYTzyJoPIYQQQhSarPkQQgghhNuS4EMIIYQQTlU2koxhrSny8ZTP0XXFoy/9X4nMuSGEEEKUBmUi+Ei7lM7YLlPITMtEAXuj9/PxsXcoXynQ1V0TQgghypwycdnl7yMJZKRcttUUyUzP4tRfZ1zdLSGEEKJMKhPBR5W64fgH+2EwGjAYNHwCvKnWsIqruyWEEEKUSWXisotvgA9zoqeyfPoqdItiwPi+BAT7u7pbQgghRJkkeT6EEEIIUWiS50MIIYQQbqvAwceWLVu49957iYiIQNM0vvrqq1zPDx482FqM7JpbmzZtiqq/QgghhCjhChx8pKen07RpU+bNm5dvm3vuuYf4+Hjbbd26dYXqpBBCCCFKjwIvOO3Rowc9evS4YRuTyURYWJjDnRJCCCFE6VUsaz42b95MSEgIdevWZejQoSQmJubbNisri5SUlFw3IYQQQpReRR589OjRg2XLlvHTTz8xe/ZsYmJiuOuuu8jKysqzfVRUFIGBgbZb1apVi7pLQgghhHAjhdpqq2kaX375JX379s23TXx8PNWqVWPFihX069fP7vmsrKxcgUlKSgpVq1aVrbZCCCFECVKQrbbFnmQsPDycatWqcfjw4TyfN5lMmEym4u6GEEIIIdxEsef5SEpK4tSpU4SHhxf3qYQQQghRAhR45iMtLY0jR47Y7sfFxbF7926Cg4MJDg5m8uTJ/N///R/h4eEcP36ciRMnUrFiRe6///4i7bgQQgghSqYCBx87duygc+fOtvvPP/88AIMGDWL+/PnExsaydOlSLl26RHh4OJ07d2blypX4+0stFSGEEEJIbRchhBBCFAG3WnBaUFdjIcn3IYQQQpQcVz+3b2VOw+2Cj9TUVADJ9yGEEEKUQKmpqQQGBt6wjdtddtF1nTNnzuDv74+maa7uzg1dzUly6tQpuUTkQjIO7kHGwT3IOLiHsjgOSilSU1OJiIjAYLjxZlq3m/kwGAxUqVLF1d0okICAgDLz4nJnMg7uQcbBPcg4uIeyNg43m/G4qtjzfAghhBBCXEuCDyGEEEI4lQQfhWAymZg0aZKkh3cxGQf3IOPgHmQc3IOMw4253YJTIYQQQpRuMvMhhBBCCKeS4EMIIYQQTiXBhxBCCCGcSoIPIYQQQjiVBB83ERUVRcuWLfH39yckJIS+ffty8ODBXG2UUkyePJmIiAi8vb2588472b9/v4t6XDrNnz+fJk2a2BL2tG3blvXr19uelzFwvqioKDRNY/To0bbHZBycY/LkyWialusWFhZme17GwXn+/vtvBg4cSIUKFfDx8aFZs2bs3LnT9ryMRd4k+LiJ6OhoRowYwfbt29mwYQNms5nu3buTnp5uazNr1izmzJnDvHnziImJISwsjG7dutnq1IjCq1KlCjNmzGDHjh3s2LGDu+66iz59+tj+iGUMnCsmJob333+fJk2a5HpcxsF5GjZsSHx8vO0WGxtre07GwTkuXrxI+/bt8fT0ZP369Rw4cIDZs2dTvnx5WxsZi3woUSCJiYkKUNHR0UoppXRdV2FhYWrGjBm2NpmZmSowMFAtWLDAVd0sE4KCgtSiRYtkDJwsNTVV1alTR23YsEF16tRJPffcc0op+VtwpkmTJqmmTZvm+ZyMg/O8+OKLqkOHDvk+L2ORP5n5KKDk5GQAgoODAYiLiyMhIYHu3bvb2phMJjp16sS2bdtc0sfSzmKxsGLFCtLT02nbtq2MgZONGDGCXr160bVr11yPyzg41+HDh4mIiKBGjRoMGDCAY8eOATIOzrRmzRoiIyN58MEHCQkJoXnz5ixcuND2vIxF/iT4KAClFM8//zwdOnSgUaNGACQkJAAQGhqaq21oaKjtOVE0YmNj8fPzw2QyMXz4cL788ksaNGggY+BEK1asYNeuXURFRdk9J+PgPK1bt2bp0qV8//33LFy4kISEBNq1a0dSUpKMgxMdO3aM+fPnU6dOHb7//nuGDx/Os88+y9KlSwH5m7gRt6tq685GjhzJ3r17+eWXX+ye0zQt132llN1jonDq1avH7t27uXTpEqtWrWLQoEFER0fbnpcxKF6nTp3iueee44cffqBcuXL5tpNxKH49evSw/dy4cWPatm1LrVq1WLJkCW3atAFkHJxB13UiIyOZPn06AM2bN2f//v3Mnz+fxx9/3NZOxsKezHzcolGjRrFmzRo2bdpElSpVbI9fXWF+fRSbmJhoF+2KwvHy8qJ27dpERkYSFRVF06ZNefPNN2UMnGTnzp0kJiZyxx134OHhgYeHB9HR0bz11lt4eHjY/q9lHJzP19eXxo0bc/jwYfl7cKLw8HAaNGiQ67Hbb7+dkydPAvL5cCMSfNyEUoqRI0eyevVqfvrpJ2rUqJHr+Ro1ahAWFsaGDRtsj2VnZxMdHU27du2c3d0yRSlFVlaWjIGTdOnShdjYWHbv3m27RUZG8uijj7J7925q1qwp4+AiWVlZ/Pnnn4SHh8vfgxO1b9/eLvXCoUOHqFatGiCfDzfkurWuJcPTTz+tAgMD1ebNm1V8fLztlpGRYWszY8YMFRgYqFavXq1iY2PVww8/rMLDw1VKSooLe166TJgwQW3ZskXFxcWpvXv3qokTJyqDwaB++OEHpZSMgatcu9tFKRkHZxkzZozavHmzOnbsmNq+fbvq3bu38vf3V8ePH1dKyTg4y++//648PDzUa6+9pg4fPqyWLVumfHx81CeffGJrI2ORNwk+bgLI87Z48WJbG13X1aRJk1RYWJgymUyqY8eOKjY21nWdLoWGDBmiqlWrpry8vFSlSpVUly5dbIGHUjIGrnJ98CHj4BwPPfSQCg8PV56enioiIkL169dP7d+/3/a8jIPzfPPNN6pRo0bKZDKp+vXrq/fffz/X8zIWedOUUsqVMy9CCCGEKFtkzYcQQgghnEqCDyGEEEI4lQQfQgghhHAqCT6EEEII4VQSfAghhBDCqST4EEIIIYRTSfAhhBBCCKeS4EMIIYQQTiXBhxBCCCGcSoIPIYQQQjiVBB9CCCGEcCoJPoQQQgjhVP8PE9R8NnU8izUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train['age'], X_train['bmi'], c=X_train['smoker'], s=5)\n",
    "plt.legend(X_train['smoker'])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}