jupyter_notebook/Machine playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22143889",
   "metadata": {},
   "source": [
    "Building machine learning model of housing prices in California using the California census data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b62c32cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt;\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelBinarizer, StandardScaler\n",
    "from sklearn.pipeline import FeatureUnion, Pipeline\n",
    "from sklearn.model_selection import StratifiedShuffleSplit                       \n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce72bf00",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression                        \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dffba0",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "let's get the data;\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = \"datasets/housing\"\n",
    "HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + \"/housing.tgz\"\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()\n",
    "fetch_housing_data()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b410df54",
   "metadata": {},
   "source": [
    "let unzip and load the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ab8a20",
   "metadata": {},
   "source": [
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f4e6f6",
   "metadata": {},
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c039725",
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"/storage/emulated/0/datasets/housing/housing.csv\"\n",
    "housing = pd.read_csv(path)\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9422d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# lets get the info f each column;\n",
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c111e928",
   "metadata": {},
   "source": [
    "there are 20640 rows of data with no null type in every of the attributes except  in total bedrooms column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea094ca",
   "metadata": {},
   "source": [
    "All attributes are numerical, except the ocean_proximity field, let's find out what categories exist and how\n",
    "many districts belong to each category by using the value_counts() method on the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e19ea604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41ef786",
   "metadata": {},
   "source": [
    "The describe() method will provide a good summary of the remaining numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "524678b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b37f7b7",
   "metadata": {},
   "source": [
    "let's get a better view of the numerical data through histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "453cc75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc110456",
   "metadata": {},
   "source": [
    "let's create test set out of the housing data,\n",
    "the test set must be representive of the data.\n",
    "how do we achieve that?\n",
    "we can easily group the data on housing median income, since it is the best predictor according to expert."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f8c82e",
   "metadata": {},
   "source": [
    "The following code creates an income\n",
    "category attribute by dividing the median income by 1.5 (to limit the number of income categories), and\n",
    "rounding up using ceil (to have discrete categories), and then merging all the categories greater than 5\n",
    "into category 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2fb0cceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)                       \n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c82877b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "2        5.0\n",
       "3        4.0\n",
       "4        3.0\n",
       "        ... \n",
       "20635    2.0\n",
       "20636    2.0\n",
       "20637    2.0\n",
       "20638    2.0\n",
       "20639    2.0\n",
       "Name: income_cat, Length: 20640, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's view the new \"income_cat\" column;\n",
    "housing[\"income_cat\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6727ceb1",
   "metadata": {},
   "source": [
    "let stratify out our test set using the new income_cat column as the point of representativeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aee82632",
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):                     \n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17c8e12",
   "metadata": {},
   "source": [
    "we have successfully stratified sampled our data, so the next thing is to compare the test to the original data through the median income category we created, so as to show if it is representative enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fce73fd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data : 3.0    0.350581\n",
      "2.0    0.318847\n",
      "4.0    0.176308\n",
      "5.0    0.114438\n",
      "1.0    0.039826\n",
      "Name: income_cat, dtype: float64\n",
      "test : 3.0    0.350533\n",
      "2.0    0.318798\n",
      "4.0    0.176357\n",
      "5.0    0.114341\n",
      "1.0    0.039971\n",
      "Name: income_cat, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "data = housing[\"income_cat\"].value_counts() / len(housing)                 \n",
    "test = strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)                 \n",
    "\n",
    "print(\"data :\", data)\n",
    "print(\"test :\", test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13eecc4",
   "metadata": {},
   "source": [
    "clearly, they are very similar, we got that right."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe23dd48",
   "metadata": {},
   "source": [
    "the income_cat is no more useful, so let just remove it like below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8eded596",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing income_cat form test and train set\n",
    "for set in (strat_train_set, strat_test_set):\n",
    "    set.drop([\"income_cat\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f02300f5",
   "metadata": {},
   "source": [
    "let explore out data more through visualisation,\n",
    "and we want to make a copy, so as not mistakenly modify the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "083adace",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5e0037",
   "metadata": {},
   "source": [
    "Since this data is geographical information, it will be a good idea to plot the dataset with latitude and longitude like below, visualizig the location of district in California"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aecf4f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cRsnmheYQQwpcKzfOnSCm5BjMVh0Wqy62ZRDE2cHO+7Axti2DkmPwws0Ot1oDvnGtSS9IsQ3Jpy9PM1/z8C2Da3sDLs6WT8w1ZDoPZY2SjEzlpwPfMnh8ucqL6x22exGOFHz80jSPL9TwnfwkcxaDf6+7+3vtiEaNTyWKwjkUFDwgPtSOYX+H7doGDy+UsQREE44Mn5iT/Njjczy1Ms1M2cEZy00cvsakZHDJNlme8jBEbqSDKM2H7ijNdj9iUUDZtQ6MZsk2KU2bB/mIfeO6P2AnUwqVaTKlEDq/5sFzJug4xUnGXi+i2Rvyb767xl5XEWvwTegMRzx3aYbLcxU2+wFIwSNzlYnGU2hoDmNcU+LbJmGc0hzGXJjyudQoMV9xMA3J6nRp4vCgeymjvddqrNMS50XJakHB+fChdgxJpojTDNuQNEoeT1+s8bXr3SPPmXbguYdXWKiXMBD0wrwBbrHu4VrGXbWN9pvKhmOnsFhzKbsWhhRsdEMWdW5Ep0s2SaZI0rxTGJEb+amShRS52N1ONyDVmvlamSjVdEYxnmUeNM5lWh90DkdpSpopBmHCl17Z4npHHXymOIVOTxO90yIDFuslsizXU7KQdxjoTGtqnkWYZIzi9GBdW/0QzzIOnNtOP2LVMiYOD7rbDv9wJ3mSqTuef68lqEXJakHB+fGhdQz7Hc7bnZDmKKbum3zyYp03trp0Q/ANSBRYFux0h6xMe3TClGculLFMeWBk9mPkcZodVAgd3sHvG8cwzRA6PyEAlFyLBWCqZLPdDXlxvUuYZDSHEYtVl7JnMeVZOIbBUt1kpmRT801Wah61Ui6hHSWKlbpHqjVrrdHBe89VHCxT0gtiXt1s8dpmMPEebA8z1lpD3tnpszyVMYpSumF6ZIcNsNEJ6IwSDJlXVdnj0JfQHOQcDsuPSy0ODPu9yFSctsOf5ICDJCFMM1zTuMPYFyWrBQXnx4fSMezvJh1TsjTtsTMMeWt3iDBMlqd9amEeS49STaoztgdJ3r8wZ2OOE8aHTwV13+LlW92D+PuTy7U7htu4poExjrWbhiROM7TS7A0iemFCyTHoBjHDKCNMFRWgHSTUPIuVqXzXbVkGlbFjsU2DJMvj7cd3xjv9iJW6R2sQo5UgPeVeDANFZxgzV/XY7oVUPOvgOpudAA04pmS14bPVDdjohKxMeSzVPXb60ZEcQpIpbnVyJ3SvoZu77fCPJ6mHYcJ6e0QUpTiOyVLNwzLlkeS/FLlkupACfSzkVlBQcP98KB3D4d2kLwSLNQ+VwZWGx7eu7hEmGcNEIbTClAZSa1640aJesVma8hFwYGSU0nRGCRen/QMD1BklVF3rxMlnvSCiOYypeRZ7gwjLkDhmnkuwTUmUZghgGKVUHRPLkFiAKeUdVUtJpkjV8UR2SqwUWkDdt7DIK6sm0RmEZFlGzTdBHD0BDKPcpZQcE9OA1UaJfpCwVPdwLIMFKY4opKI5ont0L6Gbu+3wD9+/IEl4e3tAL4hpDmLSNONN1+ah2RKGKVmq5WG+kmPwxmY/D2VNcNgFBQX3x4eywe24vtFsxUEaAsc2+dlPLlH1HJJE49j5CaJRdTEMg4ptsd4OiFJ1kEjdN2j2/vwDyzix4cw2JCv1fGd7cdrPE9mmpDnIm8IASrYkThVX94Zs9yJSpYnH61yo3W6OG4R52Ga7G+Y5jDA3/fuJbFtK0DBV9vjkaunEezFKQQGOaWDJ2w4nzRSWIY84IaX0eL7DUZnuC9N+riA7dgrAkea/e/mZxGlGOs79HN/h77/fXNmhPYiIM8UwSnltq883r+7y4maH9faQb1xr8tpml9c3+0gJK1MeFxt+3vF9SjNfQUHB2fhQnhiOV8xYhsGPPjrL3iBivuxyadrnV759E9sw0FLgmya2KXh8sYpSsDzeMe93JR/vPzhN5qJRtnPDPQ6xLE/5BLFiEKaYUlK28517ybFYmvKwjdv5jMMS2hud4KAPYlHARjdkgfxUsVBzMU2Z5wMsgx9/YonO4Do3Wwn72QZJnkexLLClAK2ZqTi0R8lBFdFi3QM4tbLoIGkszlaeCrcdJeQOQYu88qnkGLy22UPr/PHHFisTf3YA7TChUbJpDxNMKdgLUgwEgzBjECUgFJ5l4ZoG7VHCct0jybIix1BQ8AD4UDoGOFoxk6R5XN6SEi3g6ZUGpmHwwo027UGMZUo+cWEKxzDIZC7VcNjgJ6kizhTm2Mgt1byDprfjcfPjjWymFFyZLVH3LHbHJ4ftXsTSlIc/7gY+HlKRWqC5HfYpuxaLmrxS6lAituJafGyxyosq44cfW+TqTofv3uwRjwuUyo5gruqz2PDphnl/w6QqorNUFp21PDVMMq7tDtjqhURZhtCCKd8kTDSxUpQsg5pn0QkSXt/sszyVsjSuANsnz+vYhGlGrHI1VktKumHCbm9wMJt7uWrgmvkJLjp0AinkuAsK3h0fWscAtxugbo0Hw3h2Xna5N4x5anmKqZJDdxDRTzLmKy5Rpliq3d5FHxh8U9ELEzLy3MNOP2JB5jmDw3Hz/R31fNU9sjPfVxMtOSZSCHpByk4vZLVRuqNPASZ3CxuGvKM6R0rBaqPEbj9CK4EwwHEcXr3VzhvsHIvlKZd2P8YxTUZJiueYd+yoz1pZdLfyVKU013YHvLndJ80Ub+8OyDKFaeYOCQS1mRLv7A3zedVKYwjuyFU4lsFTy1Xe3hoQxIqZUt783ezHuJak5NiMwgxdhyBJSTPQGhbrLvE491H0NhQU3D8fascAJyc9LVPy8GyFbKZMFGd5bwG5/s++rpEUkiTLyzb3+jGr0x6ubR4kXlfq3oEBT5VmsxOQZBpzrKG0X0VzfA2LdY+15oh+kBwM+DkpkX235rGSbXKxUaLqmtxsj7gwVcazBP1hwo29Ptd3hmy0A+a6ERLNzz5zgdnK/esKneZEkkyx3QuRUhBFec+GBowMtjohUapwDEGq8xOVBkxTEiXqjhDQpZkyrmFwKUlpD2MWw5jdXsJczcEQeTd6kirm6x6zVZeqkxcDrLVGRW9DQcG75EPvGE7T6tk/UewN4yNicLv9iCjJ2Nk3cklKEKs7avq1gIWay2YnYL2dD+1ZbfiYMj9VHBgkdbRj2ZTioCT0JIXRszaPSZmfSG62RqxO+cSZYqps8sqtNs0wRYgUS0imyg7fW+viWCafvtRgtVE6MorzMO8mFCPGGlJJphFCoFRGmClcx2G65BxImRsS5ioe660RdW+ySKBhSh6equBIyfXWkN1KiGsaCCGIklzDyTAknVFCL0hPFCos8g4FBffGh94xnLb7VkoTptkd5aBBkqG0RojcUG33QuJUc6M5ZHkqN/z7zsWy8u7nJMsnmsmxgTupFPNw4te5S4jjrCEey5S5rpIpAc21vQHX90aUHAPHcQjDhFe2+jwy47PTi9jojdgbxnfMaYa7y0yc5jSsseig1oqtboYQYBsmJccgVpp6yWSp7mNbJnXPxDRyaXG1rwM1IWfTHMT5PIxGCQ0HCrCzZRshBJ511KGfVihQUFBwNs7dMQghDODbwC2t9c8IIS4D/xxoAM8Df0lrHZ/nGibtvvcNYJblEhWSvFs5zRRocE0D1zJ46VY336WiiZOMteaIlSnvyBxly5DY5ljXyBATDdJ5qoHmDspgtuLQGsYMogytBdO+S3OUkGYZQazY7cdsdgM+I6aR5B3PV2Zvz2m+WxPa3ZyGlIKLjRK2Ial5FpvdEKk13ShjruKw2ighBaQKlusu270QAez2I1amU9xxKfCkHb9rGTwyV+FS43Zp7q1OcOwUd6dQYSHHXVBw77wXfQx/HXjt0Nd/F/i/a60fBtrA/+Y9WMOR4TqHDWDZs1isuWx0Q/phQpJplur5AJrNbohlSEqORck18VyT2Yp9RxXN8R6EJNMTDdLhNTzoz7ZQczGkZNq3eeZCjaUpj7JnogWMYkWmcmnx7W7A7765x2Y3IMqyI30It3Mhd/YqHL5nnp07yo1OcEffgGsZzFddKq7Nw/MVrsxX+cLjc1yaKeeT7hDMlG3WOyPao4ROkNIaxqy3Rwh9O+QGd87DkFLgWMaByOGk55Zs86D3Yr8EuKCg4N44V8cghFgBfhr4h+OvBfDjwK+Mn/KPgT96nmuYxHEDWHYtFqsuS3WP1Wkf3zGZrTh52ETlpZBzFQeV5X0Ek0ZsHm4GO2+DtN8ncNgo77//6kyJT12c4Rd+5Aq+aeBIcEx4eNbDMk2GSUY/SkiVojVIEIfs+mmT0fbvWapyh7Ddj7jVDhjG6R1r2x/gU/NsSo5JECsuTOX35lKjlMtt9GK01phCMFt12O1FByKBQZwdOOmTdvynOePjm4Dj96qgoOB0zjuU9PeA/xzY72RqAB2t9b41WQeWJ71QCPELwC8ArK6uPtBFnaUctGSbXJj2mU8zumFKlGQoDUuHQkjHuRdBufvltHDOYQXTuarPX/2Jh/nnv7/Gd9baRAokOm92Gz+37ucnisPr38+FhHFypGcDlc+C2GyP8CwT25AopdnrR5Rs84hS65ESXimIozwc5Jj5Y+447GUa0A8zOqOEIM7ojGKCRLE/m2iu4pzqYO8WnitkuQsK7o9zOzEIIX4G2NFaP38/r9da/5LW+jmt9XOzs7MPdG1nCf3sV/sYUjLlWcxV3YnJ2n1O25k+qF3raXOjD5PpfHbzIFR0ooyKb+enB1OiU81c1WGu6lByrIMwzWFZj7mKczDpbn+W8yhKafVD3t7pc2N3QC9MWKi6aDgSjjrsdMMkY62ZS39sdIKDmdCWIZmvOOz24vF8a5it2Ly5PUBojWMbOIZkpx9NvGdpqhjFKfF4gNEkp5CmivX2CENw6r0qKCi4k/M8Mfwg8HNCiD8MuEAV+H8AdSGEOT41rAC3znENJ3KW3ebOuMoFKViqeSc6hdN2pg9y13pWqen9wTtxpqi7JhKHXphSsg2GsaLqmMyVPRaqLpnWhFFeedUcBDR7EbYtuNSo4o+T8a9udPjKG5u8dKs9lgIv84mL05hS0Cg7R5Ls+053oxNwqx1gGbdLePcT2QBzNZftbohlSSwzz41c2x2w2Q+RY+dScoyDz7bvuFrDiDe3+oSpYhClPDxXpubZd9zz9daIW52A0jgs6FpGUbpaUHBGzs0xaK1/EfhFACHEF4C/qbX+C0KIfwn8SfLKpP8U+NfntYa7cVLo52iidfKQmknPPV7JA0c7qOPxmNFL0yVM82yHtcPloWedn6xFPrd6px+SIkhSRdUxqLg2jyw4/MzTy9imcZDwXe8GvLre5le+dZXdrkYa8MWn5vkLn79MyTH5B7/1Br/1ZptsfH1fDOhHGcuf8Q8MtsXtpLprGXlnc6aoHFKhjdKUYZzmiqlKIYx8vGnVt4nHIbuaZ+GNp8jt9iMuTZdIUsXN9ogbewOev9Gm7lm4tkGjZLPWHPGJZeuOe+5YkrJropVmtx8xX3GK0tWCgjPyfvQx/C3gnwsh/g7wHeD//T6s4VTOsjPfN9hKnfxcuD2jOUwydvtRLnWtYeUMCepJp43TOqL316QzTT9MWa67fGKpxrdvZMRxRtUzefZig71RzGsbfQwpmPItXtlo8Q+/cpXOftFwBr/63W12ewEfWyjz24ecAsBIwzev7vIXPn+BvUGMIYcHHdz7n+mghHd8z9JMHZSmOqbEsy1W6rk4oCklGnhorkSmYBSnpCrvnL7RGrLXj0lVPpPasQy0EHSDBN82sczcEe5XT6WpYhgn1F2bmbLD3iBiEKZEfj73oihdLSi4O++JY9Bafxn48vjvV4HPvBfve7/cbWd+2GALIEkVqTn5ufvDZHb7EWhN2TVxLHlXqYbTTiKTQmCH16S0puwYBHHGMEm5MlfGFILlqdypSKHxLIlnm7RHES+807ztFPbfH3htvUeYZKg7l8cohLe2+jy2OE3FtQ46mvd37cfHkEohmKk47Pajg2qw/Sl3i3UPW0rWOwH7BV/r7RElx6Rkm+wS0R4lmEbe0JYqhRCCQZQyYzoHZa79IOHljS5b3RDbkHxsucpcxaHmWvd0Siso+KhT/KZM4LTk9PEEsG1KEBClamLZ5EItF+cbRilCCmbKzsFO+rRZBqf1FBzvhzi+JseQDKKMqbKFKfNk8kLdwzVNWsMIKQSWaaDRGEKidTZxDRIwhMaa4Ls8J5e9mB9/1v31DeOUtdaIm60RO/2IuYpzUMJbss0j5bBxmoEGW0pMM++azhQEcUaa5Q7DNOW48Q0EHPxcJHk57WrDRwvBTMnm1c0erim52PARAr53s0OcKFam/cIpFBTcAx96SYz75aTk9KQwk2VIlsdlrMcT2a5lcGk6lwd1rDy8chaphrPmEyatybYMGmWbLFUYUtAdJcxUBQqDad9FCkHNM/Iu6ThlpuLhMCA6dt2SL/Bdl+cumvze9d7ByaEk4Sc+Ps9C1SeKU9zxZ0ozxU4vxLWMg5zKRjfg0nQJaeTrPj7lrlG2We8EB2Go/XkU1lhTSoo8DzGIUpI0YxilPLFYZbnmcmmmjOeYuahemhHEGSNAjzulbVMyX5SoFhTcM4VjOAUpBSrVhFl2sKs9yWCf1tFsmpKVaT+fxBadTarhbgqrd01KI2iHCdf2Buz1Ikrj2Q1Pr9YpOSZ7/TjvJyg7/OnPXyZW8O9f2iUcv/9S1eDKfI1LjRKdIOWnHMk3r3VoxRAr+M1Xt7mxO+K5KzM8Mleh6tvMlG06o5TVhk+cpWx3Q4IkPxXsT4Dbn3J3vTXk4rSPbd0pveFIg8W6d/uzS8nTK3X2BhEr48+4MuUfqRIzEfTCBM8yKDkmwyglSPKfW0FBwb1ROIZTaA0ivnV9j1GYUvItPnlhmkbJYa7isNENEHGGMRaOO2ywoyQ72PU640qms2glHReoO+k1d0tKA8RZxiu3ulRdmzTThKnind0+z6zWeGS2wpWZvJposxPgOyb/2Y8/xhNLZV7b6BNGCYvTFabLDqtTPm/t9nmlNyTI8+YkQDOA8FafRiUPpz1Ttqn7Nr0w4epOn26YojOFMS4TfWGtzWLVxTDkHVPuJiX3D392oWF9XHq67/iOV4kJQ/DwXJm15oj2KMYQ+dfCKJLNBQX3SuEYTmAQJPzTb1znu2tt0ixPMr92sccXP7aAISWGIe7ozg2TjDc2uzx/o8XeIGambPHsxQaPL9Zwx0bspBr6k/odjr/mLElppTRvbMakY+XS+ZpLmuaOYLcf513IlkGmNNv9CGMQY1uSH3hkgarvA3psgDVv7wzoBTGJguxYFnqYweubQ6Z9j0FU4WZ7RKbgxfUuGk2j7FCWgje2B8xVHRzbQAB7/TxotT/3ef8zHw+T7X/2fQXW06rEDCGoeTafWLYORolqIRA6nxNRTHMrKDg7hWOYgFKaN7e7fPtai4pjMtJ5qeRX39rh0ozPXMXj0mz5QBdodewY3tnt85uvbvHKRo8409iGoD1MMKTg0fnqQbgpTRWxUgfhqbupmh7mtFJay5AH85k9x0RISZTE2KYkyfKOYnv8ftFYTnyxNp42l2QMA83Hl6porfndN/ZYaw9YbwVoFJ1hSHrHnYJeGHOzG9DY6vHIXJWqb+JYAsswxxLccH13kJ8WxsY5ShUl2+CNrf5Bgv3J5dqJhvu0fMvhU9a0Z/H2bp8ozTANwWzZ5UZriBi/vpDEKCg4G4VjmECmNZ0wyauMlEZrjSUlgzDlrc0eGslc1aXsWgdGWSnN9eaQq80R0hA0XIthkvL2zoCp6y0E4Nomjim5vjfKB9FIwZPLNUqOeeYBM4eNpBSCKM0Q48f3kVJwYbrEJy/W+fcvbrHVCai6No/Ol5mtOGz2QtIsnz292vBZrnv5Zx7kCeHNXsBac8B2N6I9iumPIoJMIeFIP4MEHMvkkUYJxzJItWarGzFVcnAMSZKOews0lF0zd0hpRhinrLdHSANMJNOeRWsYUz3UDHeYk/It+2M8s0yxMwj57s0OL99ss9Ya4UqD+SmPT12e4gcfmqXkmMU0t4KCM1I4hgkIDVGkcC2DXhijlCBKU1bqHuWSA1rTHMbYY+lnQwgUGq0BrZFjwTmpYJBm9IYx272Q3X7EZjfk8myJC9MltNK8fKvLZy5OH/Q7CCnQE+ZA77NvJG80hwdDa+YqDnGmcOXt3bBrGXz64gwrNZ+X1jsIAb5lkY3nLDuuiT2K2ewEXGyU0FrTCVKmx5LY2/2QjX6Es9/9nSRUXUWS5A1lUkLdz0NlP/r4Ihu9MF83ecgp1Bm+JQnihPmyw832iG/faGEb+ZQ33zJYqPm0Rwm7g4i6Y1H3LeqePbG73JCClbqHFred4FprRKYUO/2Q3351h3f2+oRxPmRpkCSojuaNWxLPkjy1VCNIMuqeSXXCexQUFNymcAwTyLTGtgx+5NE5vvzGNkGcgpZ85so0cxWXmYpLEGdEmTroprWQXJz2mS7ZvLM7oB0mCKDsGMzUbd7eGbDVC9lsjah4BpZhcGHaZxhnpGjqvsXLt7q54R6fJE4yXva4RHZ12sO2jINQ1KTdcKLg48t1hBT0g5hXNvosTWVorSnZBp1ROpa4VoRphhUJhBSU7LG2UCqQhqTh2ThVg5mKg9SaYapZKDt84uI0zVF8MCNbmhLH1nzneotruwPaQcJsyUFIuLnXpzlKAJgpuzw0V+aHHpvHEIKtQYC+CRcbJRYPzbuYlHuxrHwWd5Yp2kFCphXIvEy1F6XYhoEy8pPetb0hu8OY33hpE8MweGypwo88OsdTy1NFWKmg4AQKx3ACtmnw6cvTPLZQZq09Yrcf8thSjcszFUwpiBKV1+dLcZDcfGi2wnMXGxhSMAhSXNug4pj0RwlfeW2bUZISRIqSZ5ApqHsGhpSYCHZGCRemPdJUkShFcxAdhFaOVyvtN8a5dv7jk4aYGHrKtCbLFKZtIDQMoowky3Kxum5IkiiW6y4PN0oMyB2FYxmsTnlYloEtJcM4xUhTRkphANM+fHy1QZwKtBIsVB36YUaWaWanHaqexasbXTbaIdIU2Ibk5Y0uG/3bGQoJhEnAMMznLlyZqfDEUo1OkLCYZidqTR3OvRhCoEWewPYtC9uQaBRhlKJEPkdDZyn9SOFYBtNlm4dmqgyCjO/caFP3bB6arRQnh4KCCRSOYQKWIZmrOrSHMZ5j8fBshY8v1al5FlrnoZKVaT+PqbeCg91so2zz5HKNpy7UiKIUKQW/f63JP/m9q6x3YiRgm/DdtQ5ZBr5t8hNPzCMMwShKuNke8c7uACnAt/J8xHQpl5U4vGO2jdz8B3GKs69HNCEZm6SKzV6IFLlkRDdIaAcxG60AQ0q01rSDhH/90gZXZsuUXZNuGGMKydPLddCab6+12Ouqg+a39beG/NZbQx6fc/nZT17As00emavw2naPOMtoDRTXWyMGSYZrCCxD0BocG+YDdGOQxFxvaeq+Qy9KMeKM3UHMYnV8ItD6jnnchxPtS7X9XgfFUyt1WqOQnV7CTm/IKAIlwDXANiVxpllrD6n5FkmqCKJCabWg4CQKxzCBw7OL9/sRFusetiEPjC7kMe7Du9ndfoSUAq01o0wzHMS8st5mZxDjmoAQxIlmpxtRechgqeZydXeIKQRff2eXb7yzByIPybiWyc4g5NGFCg/NlKm6Noh8Bz1XcYjTvJYfYK7qcLFROkjG7ms4xaliqebSDhKCOOXqzoC6ZxFXNEGcobVCSIEhNN0gIUkVCmh4FktTPjv9AAfu6IgGeH0nxP7eGjXHZBSndIYJcaKo+zaGgDBKiYRgGMZEJyh/ZApGkUILQT9IeWS+wm4/pO6Z3OoEaKXvmMd9OPfiOybPrk6x0QmYKln8mJxnsery++/sMYpjNtoRoAlihRDQyaA1DFme9g46pgsKCu6kcAwn4FoGFxulO5rL9neYk2vrFXXf4nvrHUyZl426poVtyvFUMkGqMoQAxzRolF2GUcJX397l999u8spWnzBRaJVRK1m0BiFvbPVZqjpcnq2wOu0zV3VJM0XZNSl7JmGUooXAFIL1TnDgqII4Zacf8fB8LhsRJxn9MJe9TtI8pFR2LMJEUSnbKK1AGKBBmAIfg41OyDCerKME8OZWxFff2uGxxRoLNZ/ZqgPA44tVtjoBr2726McpNpOdi2WAZ0rqjonvSEZRQqrI5UNMiWlIFkWuwLpAPlb1eMe475hcmS2TjCU5Xr3VZa7q0QstWqN8WJAhNclYHkQg+MRKnQvjMGBBQcGdFI7hFE5rSJtUW5+kaqyimjuKuZLDfNXGs3LdIEQ+IrPiGVyYLZFkGQh4a6fHRncIQmMKTTeCYZSgswFzNZ/N9ohrzREyTVmaLvHjT83zyNwUe4Mon0mdKKZL9hFHtT9GM04y3PHozYpr8dBMCdcw+No7uwzDjIprILTDMM6oujZ1z+RivcQbW10W6x4l24RgUgdDbuyzLGVvGONZBq+FCWUv34l/8mKD5WmXt7YGvGZ0udaMj6i0WoBpQNWzDyTJk1hT8UxClTsjpXU+j1vngnqHR68e/zk50mB5XAjQC1MyDdMlh7XmgJJl8vh8mdWZMkt1G0Nn7PVHLFQLxdWCgkkUjuEeOJ4EXqi5bHYChlGKIQUI8CyDsmuB1vSTjOcemuH1nQHfvdkmTjWLVYeVmTLdYcpmN2Kp6qAVCCnxTZNmlJCRJ2iDOONma4BCsDcI2R4ojOt9/u3LW/yJ55b4uWcuYRh5XqE9jI84KqXzEZ6Zhn6YgIaPLVXpjBJqrsWPPT4L4+sOQsWUn+dPTEMyiBNaQcpKw2d1rsz1bodJ0aCSAak2EFpg2wZ7vYgoy1ioeUy7kut7eegnGfdaHMYz8t2+bcDuIOKxhQpL02UsKXj+epuPLyo8x2TKs+6Yx30SNc/ms1cabPUidnoBU66JHp+yHlms8r3re/zzr/dRCqYrBn/mcxf5S59/iLpvP5h/IAUFHxIKx3BGJpVNAgcGMxsP7LHHg+53+/mAmKWay8//6EO8udnNSzUzBdJk2reY9ixGieLRhSov3eoQJhHoDCeP6KAV9GKNgWaQa9GhgEEM/+zrG6zUS3xidYbFukemNDMVh+YgPmgCu9gooZTOdZ2AXpDSKNnjyWoV0kyx1rJQSjFTdhjEuXrpdi9iseZimz5SCIJY8fKNHqPxZxXAnAfTFR/fNpESwijDtSSeLUnSlG9tDNkbRlzf7bPdzTgekEoyKNkWpmkwXXZQUo7DSYq6ayEEREnGRpzx7OrUmcI+UgoeW6jx1MoIZJUk1bR6ETuDiN9+aY1X926vIuhn/H++do35isMff/ZScXIoKDhE4RjOwCTJilvtEZnSeLZByclHU95ojijbJq5lMF9xiPx8QEyQZvRGGYtTmt1uxIVxRdNc2SFMMy41cuXVr1/dZRCklDyBVALfgbATY+ncKQjyk0TJgVEEz1/d4wcfnUOKXBeoZJuUps0jCfLrnSGWFAf9Dq1hfHCiSHU+9lIDjm0y5VtU3Dzs5JgGb+/06Qcpjy1U+MITc4RBwnp3yHY3RkjB6nSJim2Rkesw3WwN8wqqTDOKIkZxShAlE/MLwsjDXTMlB4Fgqz1CZxoNNMoOq1MlhCGI4gzrHox2xbV4ZqXO7iBidxAROhn9nTav7x11TRrojDTfud7mDz65QtUsTg0FBfsUjuEMHNcnSpXmZmtEpjV1Px8h6Vr5DOIoUyTjzuWVqXxATEkKVqY8NBpnfA2lNNdbA3Z6MfNVl8891ODpCxV+45UdemFMmGrQCssIGY4CWj2FJo/LpymYEtqjmK+80eTybInnLk3fkSDvhwm32gGOJTHGQ4I0MFtx2OtHrLeDXOlUCrJMs92LeGalzs4gYhjGXN8bYEpBo+wy4zu0kTxb99jqRTw041P1HXa7Ee0oxjcNvnk1YhjmsxY2OmqittI+NVewMlXiYytV3t7us94NyJRmquRQcsb3eazaNwoTokxRtS1c9/R/slIKLs2WkVIwiDIsS3KtFU2cQqcBqzgoFBTcQeEYzsARfSIp2OoGOKbEMCRaafYG+aQyzzaPyDYcGGopDuYLlB2T5iAeG8+QRtlhFKeAZr7q8ZMfW8zHU3YCWsOEJ1cc4qRMcHWbzT6EWZ64XZm2map61H2DQZiw2QmOaA0ppdnrR1hG3mQGsNUNmK24lGwTsyoI4pTaOL6ejJPnWkCUZnznZpetbszFRl5ttNYaYQrBQr3MbNmnGyaoTOO7Bo8uTPHieoe1dpetjiI55V6aQN2DR+ar+I7khRttukHCQsVhGKdUfZObzQG7vYjpsk1nFPPm9gADmCo7/MzTS1yaKZ/689ofjrTVDVmuuzRcAwvuWNe0A09fbuDbxa9BQcFhit+IM3BYxC2OUuJUs9rIO3N3+xG9IKFkGaw2Jle5HNf6WZ3K+Pq1PebHvRE7/ZBb7XyGA8BTy1UeX6jw+lafVClMNCvTPr/10i36cYISMF/1eHS+QtmziVPFdi/kymwZZ6yXlGSKJFPM11yag1yyIk41sxWHUZSy1h6y24vYG0aYIh9PmipNmKTsDUO6wxGClDTLqDgujhFR8U0sKahXLEwDdvsjvv5Ok2anzwtr4UEO4iRsYLYkuNAoj2cxaDrDhEGU0OxHxOPmtaWaxycvTbPVFby9M6LkmsxWPKIk40svb/KXP3f5ricHYQhmqw7NITSqJVYaIbeaCfujrWsmfPGpJT5zefZA4fa0WRkFBR8lCsdwRiaNnTQNSd01GY2rknb6EQvjATv7TEpam6bENgx6Qcr1vSECQZwkjCKb9ijGMiVCCKZLNp4pGSYpvb2AZ67McnG2xHpzRG8U0R6mvL7RQwOPzVePvOdmJ2C7F2GbgrmKixCgNURxxu+8tZt/IQRBkuCZJisNn0xl/N0vvcZ31jrECRgSHl8o85NPzdMepNim4NWNHkGS8tJai997uzsxf3ASMWCZgk6QsDNI2GyPUAjCOCaIQRqKTBn0gpi3t4dkWUYrTFk1yoyiFMsSyFgwSFNsZZxqyA0h8CyT5arkxx5foOaZvHSzSZRkLNU9vvj0CgsVnyTTjKL0ju7yQkep4KNM4RjugeNjJ8M4YasfsTLlHXTmHhazO2nOwkrdY7bqsNEdEaUKKSDONK9t9Li6O8jHdZqSC3WXhXo+/lJIwXy1RBhrZqs2a3sjLCNhuuJgCM3uICRLFMqQbHVDbFOy2vDZ7ASstwOWpzzmyg4v3GhjCEHFt+gHCRvtkAsNj9Yw4nde3+D5qx20gLItCGPNm5sDHp61+cLHVkBIfufVTb7yxjbbwb3fPx+QUhIkKSXPIY5jWqOULNOYpsQSBrYp0ELSGUVINGkKzX5I3Tdo9zPq8xamEKy1Rqca8v1T3kYnoObbfOGJBX7yYwu8szekZFtcmPaZq7ikqWKjE+DZxl1nYRQUfFQoHMN9sH96CNMMLXK5BrhzjsJJQ3W0gJWp3GgLoDmIcA2D37/aIkoVjiVx0by43sF2JD92YT6fYRxnvHyzSy+M6IxSSp5BP4gJogwhI77yzi6fujR9oHRqSLgw5TOM04OZC0GaMYxTBlFKcxjRHSWUHIP2IOGd7SFK5yeFUaxRQKZhe5BiCcEgSfjSK9v0T0sinELdB8uyMVXGKMxLam0pUTIjQ+MYECWafpgQZ5qFqsNKxaYXptxsBTRKDl98bJ5OkE4U1gPuGI16qVE6SLanmSLKYLHqUCs5KKVJxrbfHOdhTpuFAXf2shQUfBgpHMN9IqXANQ1MKSdOFoPTJ48ZQmDJfJ7DWzt9skzTGoQIIRhGoDS0hgl7/Q32ugnzUwYvXO2yO4hYqLpMz/rs9iNu7A1ZrvtM+TadIOa1jR4zZZudXnhwaql5ueOSCgZRSsUx2OhGvLjWpBsooiRhqV7ClIoMiI41HVzb7vHCWpPXN1r37RQASo5BHGcIQ1N1LSxbYsiES40qt7ohgyBFa03Vt1mue8xVXFzb5JIBlmnwxFKVxSmf5jC+w9kO4/Qgl3J8NGrFtTCEYKMbMFdx2BsmaASebbJU89gZO41JP0O47QySsT5VEXIq+LBTOIZ3wUmTxQ5XI036PsCN5pDmMOL33t7jja1eLrAXZ2z2Y4TmYFra1gDebN46eE8DGMQZyzWf1iBkd5DQCVMqjok0JBXHpOaZ7Nu1JFVsdsO8F0DDypTHRifgna0eGsFizaHuu2z2QxZqPjUnoHkocVCW4Nou292Yl9bull4+meWKQbVUIs5SXEMSK41E8vB8hcWKy2ytBFmGNCQLdZfOMEUDgyhhuuQxV3VZqbps9QKs8Ulh35BDPkfaNiWmYRCnGeutEZfGxQD7I1g9y6DiWlRdK5+lUfcwTcmCFCf+DPdzRFmWK9Uu1dyJYcOCgg8ThWN4l+yHlU4KL0z6fncU8+3rTV7Z6NIbRbiWySBOaYcxw9OK/8mdxVonod3r5lPZHMFSzSNTiu/daLNUs5gr28zWPAwt2OyHkCqccbVUnGrmyjaJypgpWTSHCb0gYqsbsNEJycat3BZQdmCu6iKlQcWVhHdZ2yQ+PmfwhSfmWWnUkAj2BjGrUx6vbPQJ4oRRku/GPVNSrzpYhoHvGqhxR1/Ntyjbknd2BuwNIrSGn/rYwnisaW7I9zvNTUMSJhl7g7zrnHHIzpDiSEjPtox8ZKs4/Wd4PEckBbSDBM8x7xpyKij4IFM4hgfAaWJ7x7+vlOb63oC11pAgzsgQqDRjrx8wCs/+nn2Vl382HJNWLyBMNUGS8Ttv7vG9621qnsPFeQ/fdqiXHUxDMOXngnUv32rxnRst2t2UHuACpgkN32Su7KB6EcMU4gz6UcZc1cA2DWolGA3PvsYfeajGM6vTlF2bINbYhqTm20RZbvDnqk7uFMOY7W6IFIJ6yebazoCtXsCFaQ+A72yOUDqXyFAa/v0r2/yFz63i2ia2lEgpaIqYOM2dglaakmPijBPxK3XvxJDeaT/DwzkipTS2aRAnCqU0ipPHrxYUfNApHMN7TJIp9oYxFc/iZmtEP0zYGUREIUxUqjsBm7xrd6udIGWek/AdaPVCXmgnB13Hsw78zCdXCBdrvBi0WZryeP5aixvd29v/ECAFN9FUSiZTZU3cjXEMqPsmV2aqPLk6zXo3ZPPV5l3X5gE/+VidZx9ZwLMM2sOIfgxaxfi2ydysgxQeqcrYGySM4gzbypvzdroBZc/AHAiagxjHlHSGCZHKO79Xpn32+iHfvNbikbkKhpFLcS/UXG42h3RGMVXXYq7iYFsGwyhP9p8W8juJ4zmiKd9isxsSJNlECfCCgg8LhWN4H5Air5iZKTtkKp/FHKUZw3yuzJkwcjFXAmBf70GF0AyPZod3I/iVb63z+YdGuK7By2u7fPVad+I1O0HGQk0xW81HdH72Up0/+Ikl2qOUR+eraC3Ybgd8d3NyrsEgd1gVNz/RKKWYLrsM45RGxWSU5ONB26OExxcqfPNak94owXcNbEfSHcWEcYLjmFR8i2GU0R9EdMMY3zGxDEl3GDFI8gS2YQh0pnhrq8tUySbV+YjV/UbBwycDy5KnhvwmcTxHZEjJs6tTWKYsqpIKPtQUjuE9xjIkcxWHjY7FVMnBt01qnsFme8SNZo+NPhN1fY4TaJAaHPK8Q8rkYTgA/QyawxFGaPHSWv/EawogShWeLfjhR2Z57soM0yWPMA1BQN21+eInlnjqwpC3d3s0BwGdviJJ8zGaYZL/iQG7vZg3tnrMVj0uTvmUPItmPyJRmrKdJ4g9y0ARY0pIVS7J0Q1SsmG+y3dNScWxmCk7dMKUMMkwpUXFsRiMMt7ZGfC99TbXmyMsBJdmSnz+kQaZgrXmiJUpj8W6d6QY4F7zAXfLIRUUfBgpHMN7jJSCyzNlWoPcIA6ijEfmyrzuO1Q8i/Juj1udhCjlQL7hJPYdiCPzL07LDUeJpj/oc1pf2tPz8Md+4AqPzVUYRopYKaI046HZEtvdgNe3u/iWxaevzPGjjy/w/I0OhtC8cqvHm1sdwlThm4CGIIrY7AS0g4CWkox2ByzVPOaqDiDoxxlLNQ/fMdntRVzfG3Bp1qdestnth+z1YxKh6KUaxzL42JTDXNXDMSXb3Ziya/LOTo/XN3oYhsTzTPYGEd9d6/DFjy8QJYqluofzAMpJ78ehFBR8kCkcw/uA75h86tI01R2T1zf7WFZeo/+5hxrcbA25udvjZjciTVO+t9ajfYqHSAFUXt5qcrJz2OxGnJYnnXbhj37uCRaqPrv9lHYQYQrBm1t9Lkx5DOOMkm2wNOWRZbDeDrANyWLd5WLDpx8F7PYD2gmQwM4oY63TJVEZz67OMIgzsnGpUaNsk6Qax5JYhsF8zWVvGNIoOVimgWNKdnsRizWfim9RMg1GScbl2TKZUtiGwV4/4vVxyW2jZCOFJMhSgiQliFJc2zoIKxU7/YKCe6NwDO8TvmPy8cU6nmViGQKEYL01wrMsLs/X+dQVi1Y/ZmW6wm985xbbJzSWjTfo1Dz4+GKdl2922Jvw3GaUh50mUQb+5HMX+OyVBqYhef56Gyk1G50IrTXv7AyZq9kM44xBlDAIMpI035FfmSnx0kaHfje444STAi/dGPDEfBWEST9MWZpyERpuNkfYlqDkWGgEl2crZBp6/bxk9tGFKkt1D9sycCzBGxt91tsjOqOY6bKNa0tsIcnSmDS1qPiS5iAhrjh5N7pjcLM9QkPRjFZQcI+cm2MQQrjAV8jtkQn8itb6bwshfgL4r8g3uQPgL2ut3z6vdXw/Y5qS1UYuD50qBQieWCyz1grJlKbk2Xzu8gymhF/99i26E44DdQ8aFZsL0xWWai6KhK9cnVxTOikH4QJPrvjsdXr8j7/zGrHO6AaCqmtSLbks1kvs9EbcbGY4piSIM4SA2arLTNlGC7i2M+DNyflshhqev9nmmZU6WWYzChN816bqWYySFJVpLNNg1pR0gpRLs2VGUUaYZNim5LH5Ci+ud/EcSaY0zX5MlGicWQMhoTUKaYcxaarwbYvlKZdXbnWoOBYVz2Z5ysccN7C9n81ohZRGwQeJ8zwxRMCPa60HQggL+KoQ4teBvw/8Ea31a0KIvwr8F8BfPsd1fF9zRHdJaXpRynzVoRem9IIE25L8yGNzbLYDfvON1h0jMrcDGIQxZTvENTW/d4JTOIkQ+P31EaxPWBvwxIqPbZhUPZN6yWWm4mAIwccWKji2yRtbXf7dS7dOLaa6sRcgheSppToKgSMhSDPmKi7tIKZhm2QKHlvwmC47CA1hmnGzNWIYJ+z0A+q+jSUlJTemPYpYf6PP9daIdi93orEG3wxZb4/4vbebfGJ1is9caoCGy7NllFbvWzPaJIXd4vRS8P3MuTkGrbUmPxFA3khrMR5lDOxrRNeAjfNawweFfd0lIQRxoqh4FrYhKdsGozRDoyn7Fhbc4Rgg35V/c30I6/fmFO5GCHxnfcSsB8uNCk8s1PBdg4ptgxBkSjOIApp3UVodxlB3TWZqLrv9kO1+yHLVo+rb1DyLQZQwX3FwTANBfpIizTBkLm8OmjhVlEomtiF4e7vHTj+gH+bp9yDO70uSaoRICdIUcRMWKi5awVzVwTSM96UZ7SSF3UJKo+D7mTMNNhRCPCqE+C0hxMvjrz8hhPgvzvA6QwjxXWAH+A2t9TeAnwf+nRBiHfhLwP/thNf+ghDi20KIb+/u7p7x43xwkVKwNFZA7QUxmYbpkk2WKtaaQzbaIdb7lBEKQrjZGvDdtRav3eoRp4oLUz4Xpn2S9O6NFyn5LGbPNIgzTZrlA4MyrRnGGdu9mExrUqUZRCmDIGFzPH1tvubz+EKVvUFEcxASximGNIgjhWkYmCKfzKaAIIM0gzgFKSSvbXa53hqy3g6o+0en2yVZ3sF83tzunr6t3qp0HlYqKPh+5awTb/974BcZT0fUWr8I/Nm7vUhrnWmtnwFWgM8IIZ4E/gbwh7XWK8A/Av6bE177S1rr57TWz83Ozp5xmR9sfMfk2dUp5qouU541Vm6FzVaIQuO+T9EH24Y00XTClO1uRLMf5U1jhmSuYp/pGtv9kHaQECcZgzDlZmvIdNlCo5ir2kyXHHzbyPMXFYeFsVidFIJHF2p88kKdxZrH8rTPXM3G92xMKQgO5V00IGT+lzRNKVkGH1uscrlRojNKUEoTJhlrrRE3WyPWWiPCZNIZ7MGglD5wPumEpruCgu9XzroH9bXW3xRH/zGfWVJNa90RQvw28IeAp8cnB4BfBr501ut8FPAdkysz5XwHnSre2R1QL9mUHSuv8Y/u1t3wYJkrCTKVy2/UfYvFuouWin6cYKcZlrCYkbB3l6683UFEGCWMTIMMxe9dbXK9OcQ0DKZKBm9utim5FlMlF600SkMYp9iWgSkFD89XmS87XGsPaI0iNrsBG+0Qg7y6QQOmAZaRN8b5joU0DOarDq5jMoxSknEY517DOveTOA6TjI1OQJqpA8luy5RnluMoKHg/Oatj2BNCPMRYsEEI8SeBzdNeIISYBZKxU/CALwJ/F6gJIR7VWr85fuy1+179h5T9hiqlNK5lsDLtIcQ0y1WXTnCL3fuYnnYW6sDFRn5CGEaghY2QBsM4Y6bmcnG6hCEFrUHKV9/c45vvbPONq9tIi5Pbrse0WglxGnO9nTIapZiWpjsIeGd3wPW9kFjlndyfuFDlTz53kZJn4Nv5iWGu4nBxpoRrGcynHrNlnycWa+hMoCoKyzCouybSgN1BwoUpj7rv8uRKlfVuyFQ5L9RNMkWqFJ49ebDSJO4ncayU5sbekE4QI4RAa03NtViue1iGLJxCwfc9Z3UM/xnwS8DjQohbwDXgL97lNYvAPxZCGOQhq3+htf41IcT/FvhXQggFtIH/9f0t/cOPZUjmay4KxW4/YWWmzJ/+7CX+uy9fP5f36wKffnyB6ZLHdjNgZzjClJIok1R8m4prkeq80/rVjRb/9uUtOmcc+twF/s33doiTsWjfMVwBWsDLmz1KL63zqcsNfvLxBUxTkiqNKQRpqmgPYx6eL/HofImnV6cYhQm2aaKVZqsX8ZjSXJ4vM1tyGMQZnSChM4xxbZPtbshWL0TCwUwFKQRCM7ER7n4Tx0mWD/SpuOaBmuvuIObyLPftFIpy14L3kjM5Bq31VeAnhRAlQGqtTxbcuf2aF4FPTnj8V4FfvdeFfhSRUnCxUcI2JNO+Q2sY49s1fvkr1+8aurkfNPAPv7YF5AqpjgMXGiV+4EqDzz00S5IpOkFMP0748mt7B07BYHK11HF6p0x/CzV4AuIEdvpRLpPRGlEv2QRxdiCMtzeIMQQkWrNY8dhG8uhcGccyqLomr2/3saUgSPNQnCHAkILyeIbCooCNbsgCYEpJ3bdY7wQTTwQnjWa917JXrTWDMGKzM2Su5OH71plfC0W5a8F7z6mOQQjxfzjhcQC01hMTxwUPDtcyuNgokWmN0NAeRlRd2Lv/YWpnIgOEgI3WkO8YgqdXp6h5Nv0oI8ti+lGeYrLGzxM6Tzot+LA9uicF8QOCsbPb64/YaHu8eLPN5x9q4DsmvTBB61wKuz3KR3hOlyw+vlhlecqnNYwJU820b/PaZh/bENiWwROzFdqjlCnfIckUvm2yWHVZrHvYUrLWHmEI8Gzj4ISwfyI4bTTraViGZK7q0B7GpFHCizc7vHizzW++uk295PCnn7vAY4u1g+efdhooyl0L3g/uVpVUGf/3HPBXgOXxf/974NnzXVrBPlLmFUCmKbFNydxs+dzf0wAsaaCFIEzVQYfyXNnJu4rtfE+RAOnYKZRN+OOfXOKnn2jQsPNThwGU72FzK4D5mk83znh+rc2tbkDJMjBkLnWd7+DFWOpCIoSgNYyxTUnJMan7NgtVh6dWajxzoc5s1SPOMq41B2x0Am40h6Ra45oGQZpxqx2w3Y/yRLHSR0pJ92W3k0yPk9f6TInj/ZPebNmhH8a8ttllqe6zOlNGa/jVF9YZjfLj092qpIpy14L3g1NPDFrr/wuAEOIrwLP7ISQhxP8Z+LfnvrqCO0iVZq7icbt38JzeB4iTDCnhQt3jieUa7riRItOaP/u5K9jfXuOl9T5KQ9mFv/SDl/nslVmGScKluTKjOKPVD/i9t5sMzlgVqskls6/MShzLomSbuQBfpsjQ2JbEdxyiNGO14RNEeZjJd8yDC+wNY0xD4jsZNcdEIA7Ge2bjElKlNHv9CFMCWpNlmo32iPmad+REcL+y23nRgM92d4hnmUxV8gR4yTHpBTG9JMFV5l1PA/d7aikoeDecNfk8z1EV6Hj8WMF7jGsaLFRdGk4ujHceCMC1wLENnliq8cc/cxHXymP0QZwPrPnsQzNcmPG5sdenPUj4sSdmsU0bx5TEacYgyLixN2IUZ1ycLbF3c3imOROQV0Q1BzHPXPQJkoxRlNIo2whgqxsyjDMaZYeNdkDVtXAt48BgbvVCFioujmUQJRkbYS4x4jsmO70QIWG7FzJbcQjTjDBVXN0dorTGtw0uzZTuWM/9ym5bhqRecbFMSX+UUHLzslnPMqha1plyGMeHBRXlrgXvBWd1DP8j8E0hxH7S+I8C//hcVlRwKp5j8kOPzvHlt3YZ7Yanzle4XxZ9+KPPLfOJlQafuTSL71kHhkmrPLZvCsGVmQorNZ9Mw1zZ4bvrHQwhME2BbQhuNgd0RjGebfHEnMMrO2fzZBHQHsTMlV0qrkGmM6I0pWRZZBpKjszDSTo3nPNVl51+RJzm4Z7VmTxhn2lNEOZDf3Z6eS1Ud5QQJopX1ruEWUaYKC5MewRxylY34JWNLoMoZaHm4ZkGWnDflUBS5vfoJ56Y5z+8skVrGFHzbf7Ysyv4voVSeuJp4HiV1OFTi9B59ZZSunAOBeeG0GeMVQohngV+ePzlV7TW3zm3VR3jueee09/+9rffq7f7vqc1jPhHX3mbr721S5Cm7HYi9s6h7+3nP7fEX/sDH6fq2yilGcYpu/2IKMloDmMaZRvPMpmrOOz0IzKlaA5jwiSlM0r4+ls77A1TKp5JcxBxfbvLKM7YnVSvegwfWJ51mPIslM5wTJNPrk7TqHq40qBRc7jSKCGl5MK0fyC8t9UL8SzjwNAmmabmmbyw1qYfptjjEuDOMOZme0icahxTEqUKAXmJqZTYlqDiWMxVXTzbZLbiULLN+zLGSmn6QcwwTqk79pGqpOMVR3Xfyru0J1QgDYKEtdYQBLiWyVLdO/heUc5aMAkhxPNa6+fu9XVnOjEIIVaBPQ6VmQohVrXWa/f6hgXvnqpj8anLM3iuiSUktmWwttfmt1/d4uoJ8tf3wz/8/Q2GUcz/8aefoe7bNAcxzjjJW3ZNokSxUvfyHew4QSrIk9aumbEyU2KYDjHHxqpedmgImIkSokQxHGUnzploVAWtXsTabnRQ4fTSep9ZD3zPYsq3uDJb5yefXKJiG2z1AjQCUwqyTB90Gdd9i/Yw95pplrFc9xCQDx5yLKZLAq3grZ0+jbLLKE7JtOCdGz0W6i5TLZvlms+N5pDlusfKlH87n3FGpBTUSg610p0TMY6fBtY7wcScQ2cU8x9e2aIXxBiG5FKjRJIqHpmvEI+fV5SzFjwozvov/N9yuwLRAy4DbwAfP49FFZyOaUqeWZ0iSjKu7g1J4oxnLi7wiYtzXN3p8o/+4w16D+i9/qfv7LEwfY1f+JGHj8TDbdMgyfRBqAVgqxvgmAa5TbK4NFMmSRSbvZA0VQiZz3DWSAwjpV52sQcBW13FYf/wxIyDkrDbi4jJm9+EyPsgegnj/yV87dqIr19v8fh8FdsyqfkWq9Mlnlqu8bGZGoYQrHcCbFPy0EyZ7wYpt9oB8zWHmmtimflJaLsfESYa35b0A0V7GLHTCwmijN8fNHl0rspi3SVTeePas6tTE53D/e7a93MYSaYm5hyiJOO7a226YUKjnIsPrrdHSClYnfbZ7kdFOWvBA+WsDW5PHf56HFb6q+eyooIzUfdtfuKJBT4f5/0Ermnw1m6fONX8p1+4xK9/7yZvtx+MQNyvfW+dP/2pFeC2ftF+fHzfCM5WHG61A4TI4+RLU/n85gs1j1udEe/s9vnujQ5CQ5BqJJJRnPH0hSk+e1nSi1Ju7Q4IVN5/kCmFYYJOwbKgNyFUlgE3d0KyTPHMhRmGYcpbW316w5jVaT8PgY0NrWlInrlQ51pzSM2zaKmE+aqLb5ss1FzKdi5VsdGNeHOrS5RoBCmWYbDdDSi7FkmqcU3NWmvIw7OVXB58zINoQjupAmkYp2z2QoZRChqmyzaZ0qRKPbAmvIKCw9yXkLPW+gUhxGcf9GIK7g3TlFTNXN00TDJMQ1J3TTYMk5966gIr6y3e3OixcYaY/mnsdSK+e6vFQrVEL8yO6Bft70pLtsnKlIcQ4JgGSms8y+SR2QpX5io0Sg57g4i3tocs1hwyDUmqMJBcnK2RZZqVeoWLMyWu7vTYG0Ts9RPiNCM4pWMaASjNKE2pmhbDOGVvKPjOWpvPXZk5YmgtMw/BLNU9LjU0O/2IIMnINCzWfZr9iDDKG+Vc22AYJRhCkErBdMlkGKdEqSJRGlNKVqZ9XMs4cxPa3U4UkyqQ5ioOG92Akm0w7duM4oz1doBvS+YqDraURTlrwQPnrDmGwx3Qkry57SM/YOf7hX3DVHZMHlus4ZgSaRh88clFnr/e5PnrTb79TpMzFgXdwSCFf/Bbb/LESoMfeniOR+bL2KbENm7vmKUULNY9trohQZId7JpNU+ICo0RR81xKToxnGqRasTSXl5V+9tIUZddmpuzQi1I+c3GaThyzOl3ma2/vsN4OGIT6jpnSAIYAy5REUcr6KMWUirmKjRKa9c6IlbrP3jA+MLSLdQ9nvJNfMSRhmrHdCymXHaqexa1OQDfMwzeWIRBaUS25pErTDRIem6/g2AaGhPX2iAtTPkpp4jTDMU8W5zvrieJ430Q2fv7Hlmq8vNElzhRhqlmp+dimwUYvPEhYF+WsBQ+Ks54YKof+npLnHP7Vg19Owf1wOJwghcZ3bZJUUXMtyq7Nk8t1psomv/LN7buJoE6kbEF7qPjeepvOIOTnPrXKw3OVA8O3vxO2DTmxGUyPZyzMlR1eUYpRnCGkQaag7JjM11wEAt80mKm4ZFozilKGQcbFmRLtIMQS8LW3W3zznTbDcUNExYbH5irMV1w2eyO6QcpU2WWrG/HarT4mEtOQLNU8LFMerOlwhVWaKbZ7EasNHykEVd/myaU6N1tDmkHCIEh4aqXGTMlBCIE0BHGquNUJ2eqGtIcRwygjzRRl12Kx7mFKcWTXfq+yFkf6JhRIISg5Jp+90mAwirnVDXlopozrmKSZojNKDooAiqqkggfBWR3Dq1rrf3n4ASHEnwL+5QnPL3gPOR6bnvItbjRHvLXbZ6sXIITgZjOi4kF0j40PJQmea2KagiROeWOry69/d40/9PQFlqoemanvuhM2hMC3TZanfX7o0Rmev9ZGIbBMSd13+K/+/RtYUuLaBn/uM6s8uTzF3iDCtU0uzhgsJS5awuevzLM5GPLyjQ5lz2J51sfWBtf2BmRXM6ZKDq4t6Y7LOp9drWNouNEa8lCjjLQlYZKx2QlYbwfYpmCu6mKbgs1OwIUpn6prsNvLeGK5htZ5SOlCzedKo8x6L6A5jHAtg+1eCCheWu/mk/Z03tC21hyxMuWxWPcODPS7yQMcDi8prTEMg8W6hztOfu9fS4v8/QsKHgRndQy/yJ1OYNJjBe8Dx2PTQggWay62FAyijNc3eoyilHuR1zGB5QokWS7D0Y8SWiNNBmx2W0hpIUQes6961qk7YSkFMxWH9XbAw7NVXMNkruaQZhn/5ntbeJbJyrRPP0j5n765xl/7SYeNTkhzEPHNa3tYpsQWkj/x3Ao/8tAii9Uy7VFMa5gQ6QzTNLAtczz1TqBVzChOUUrxzRtN3trpYhqSH3m4wUy1jG1ILDPf0e8NIuaqLuutgGGcMlN2CBJF1TXJxtPXdocxrh0yVbLZ7kWgM6JUjQ2xoOJZhHGGZUoavs3SoXAVvHtZi0klrXGSIaRAHyoCuBtFr0PBWbmbuuofAv4wsCyE+G8PfavKPUxwKzh/DhsPpTS3OgElx+TxhSrrreG4iuXu1ylLsMfloUEMiYJUZQyyvF7ZkWBK+N56i6VpZzzZbQo4fSdcsk1mKja7/XA8US0vzYzTjLmaixBQK1k0ByHXtvvEmeZb15skqSJKMyxh8BuvbPPFp/K4uxj3TlhS4kgD37VQmcazTSwDpGHwm69v8x9f3eJWP6/O+u9/Z40ffmiKX/jCw+wNEzT5+3uWYLZi57H6AMqWSZBkdEcJlmEgDYA8xzBbtrHGRnW7H+JYkiRTWOOEu2HKO3buD0LW4nB4qe5bvHyrS6Y0hhQ8uVy767UK6e6Ce+FuJ4YN4NvAzwHPH3q8Tz67ueD7iIPJb+K21MKUb/PJC1NkmeLGzrW7XmOoDsnzjV3/tA1mliulenbeddsLU7ZaAdf9IZYpWKqWcG3j1N2rROCYBjNlSXMQIYTGMiQqUwgh6A4TbNPA9y36nYh+mFB3HTphzHTFpB+lBEFMK8xYnfIA8CyTimcyTFLe3OqTac1UyWG6bPNr31k/cAoWuRLs77zTxpZv8PnHltjuRoRJyos321yc8YmijKUpn8W6x42dgE6Q8NBshemSzXY/Is1yOZC9YUrJznMkc2WHSCkqdn56WKp5E430/YrxHUcpTWeUcHGcE1E6/7rqWides5DuLrhX7qau+j3ge0KIf6q1Lk4IHxCOxqUVczWPzz40y2++skZr5/TehknRplacV4VKAAWDMP+nsN4astYK+P+9kDFVcfnxJxb42WeWJxqbTOfdyKuNEkppLs+WGIYpSzWPf/XCLa7t9HEsgz//mQuULJuWGTNVyk8kVWGRKkWUaW72Ql5Z77E55eHYJlOuBYbgUqPEQtUFwDIEhgm/8s18nSYgJAiVf771TsD31rukaUyzP+L1GwHtQ2tdsGBx1ubhuSlmyg5132K7GzBXdZkpu9R9RZQovvjEPDv9CIUGDfPV03fh9yvGd/w+Kq3xzNu/usPo9HxF0etQcK/cLZT0L7TWfxr4jhDiDpuhtf7Eua2s4F1xfId6ebrETz+zzCv/4f5UTDxASujHuYNYqFts9CNSBYbQtIOU9vAGNcfiDzy1eEdn8H6cXSl9EGe3TYNnL87w+FyNThRTd2zKJZtRlLLRDbjU8Li2F5AmGf0049lLUzQHMVO+xd4wwQxiru9pPnulzuX5KqYhCeOU680RcZwwV3FY641Iue0UJOAakhdv7HG1PblBYiuBrY2Ytza26cUpjy3UsQzB44tVpBTYMu/69hyTK+64d6If0RzGtEfJuYZp7idfUUh3F9wrdwsl/fXxnz9z3gspePAc3qHatsGf/9xjvLg25EuvN+94rgtUXE4UuKv7cKHh0wliqq7FXK3MC9dbJBoc2wQBrWHK719vcmW+xJPLU3ckoOcqDhudAESGKeVBnL1cyh3CPr5j8tzFaaZ8i8VaLt3dD1OW6x5Xd4dUXJtwEHB1d0QvTNjqBPzZz5iszlSQUhKnimaQ8QOPLXCrd53Nvjo4CV2esXA9k6tbw7vevwHwrbeauELz+NIUL9/q8KlLDVzzaMisOYgxDXEQ2jnPMM395CsK6e6Ce+VuoaTN8V//qtb6bx3+nhDi7wJ/685XFXy/Uvdt/p9/8TP87tV1/tnvXiWKA2zbZ67sMV0roYFX15t8+e3+HSGl3RFYMp/B/MTiNKM0Q6HQCoQwQAtMA1xTsNmLeGxB4cjbu+YwydjpRwjynftcxblr2KXk5HpLAJnSOJakH6bs9gLe2OpTc2yWahZozW+9vsOf+YzLTi+i4ppUHRNTaP7I00t8451tWr0E0zKY8R2u793dKezTSuC332xxdS9geapEd5Ty+YcaXNxfl9YEccooyQ6Swb5lkGQKqcWR3okHVRF0P/mKB5XjKPhocNZy1S9ypxP4QxMeK/g+xzQlP/boKp++sMh6Z0RvFPOt69vsdRNcT+HZNg0bOnFuwPczEglwYwA3BjGu0+WxhTrLtTKbvRHDOMU28iE3i3Uf51hVzuHkp2dbpFkuRrdqGRMN1P7z95Vc00wxCFMMIbnU8Lm+OyRJNXbFYK7qYAjBdi9kszUk0ZJLMyXiJOOFtRZvbI/wvRIXGhZb/Zg0S8mSexuL2Uvg7e2ArX6AZYDWilGc0Ch7+I7k7d0+NdugXvaIx9Lf++qud5PSvl+O5yuOO55JjuhB5DgKPhrcLcfwV8jF8q4IIV489K0K8LXzXFjB+VL2LHRb83f+9Yu8uHM0fuQBUw4MIiYOAnpze8DPfWqFlYbHenPE27tDXFPy5IUpHp6tsFjzjpRs3mvyc9LzLVOyVPdYmfZZnvL4+19+h4pt5lpFWYZjSnzPYhCoPH9hyIM8wJRlILXAtTOyVOF7FoSnCTDdSQQEI/iNV/Z4c6fLV990eXSxznYv73x2LINnL0zx+GKFTGksKXBtkzjNePlWl4vTPp5t3rUi6H5OFvcy06Gg4Czc7cTwz4BfB/6vwP/p0ON9rXXr3FZVcO6MRgn/9b+70ylA7gzCU7QzBDDv2SwsV1CXFf0ooRekLNRdap7DUv1oyea9Jj9Pe74GZsoe/8nnLvGll7cYRAlhnPFjj8+xWCsxdBI2uiHTvsV02aLet4mzfC60FOA4No/Mm2RqwFr37M5hvwUkBN7YTXhzN+H5a30uzTrMVDzaScLza3v4jsFcNdeISrP8VZnSiPH9OM0p3k+vwfFS1Ht1RAUFk7hbjqELdIE/ByCEmCPPU5aFEOViUM8Hl83+kO+s9U/8/n4Fz6Sgi2MJyr7F3iDiwrTHbM0jzfISztUp/4gcNZysGpppnWsBHTNYk55f9y3WO8GB0Vyc8vlf/cBlOlFMexgzXc5LVT3bZKZss1TzEFLQ8B2+e7NNexRT9Sw+sVzFdywenR/xu29u873N+xuOqoGhhjd3IppBiiVN2nbCJ5Zj6r7NWnOIISWZUmRKk2VqLAQ7uVP5fnsNjp+upBBndkQFBSdxVnXVnwX+G2AJ2AEuAq9RDOr5wJJmCnWXcQ1lwHKheehQUbXhTzx3iau7ARnQC1NmKjbLdR+FZpiklDDvcA6Hk59JmucYTtsZn3Wy2ZztESaaNFOkSrPZCUgyjW0aLNY8XNPg4myJMErz+dA1nzDJ2KgFtIchVc/ijVs92lGeR/HIHeJZ09MJEIYZTslgGCaM4gRhQJxmKJ2itKZkm6y1cqd2Uqfy/fYaHD9d7b+HVhoMitLUgvvirMnnvwN8DvhNrfUnhRA/BvzF81tWwXkzV/Z5aL7E8xuTTaAJPLLsM1vzSaKYfprw6GyVP/rsKokS3OoEOKbENiXNfkw/SOiHGTPlANOQPLlco+7bR64ppQAFt844cexuk80ynXdOL9TcI8J4qw0fUwo6o4QLUz5acOBcAMquhTRG9GOFkCaX5ypMRSlCKwxT0OyFhMPbife7EalcT6rqm2x2QuZqEXtas9YOkULjWgY//vgcMxUPrTTtYYxnGViGPPjM99trMOl09eRyjc4oYRi9P6WphSbTB5+zOoZEa90UQkghhNRa/7YQ4u+d58IKzpda2eE/+aErrP3ay+yOjgaMLOBTl6rMVV0cKTFci6fqHs+uTjNfL3F9b4htGkyXLa7tjuhHMc1+zGcfajBTcRkGMd+63uRzFxuUvKNSDfezM76b0XQtg6W6R5IpKp6FHD9+cN2xgdo3oIMw4nvrPeYqHo4RM3AM6EdkOsMAqo0Slhxys3/7flRd6IZHBcIs8tyDVJAlCX2teHurx1YvoOa5LEy5aAVbw5BreyNmKx4ZsN4OSMYNfvunpXfTazCpFLXqWu+LcS40mT4cnNUxdIQQZeArwD8VQuxw9tN2wfchUgp+/GOLdMOEL720SX8UgNZMVUv4tuSJxSlc2yBJNQrFDz40S9mxiFKFQFBzDbqjlNmKg2cJUJpMQW8U88Z2nxvNAdvdkKcv1HlkvnpgHO5nZ3wWo2kZuXhdlGQHE+SSVLHRCfJ8yfg1q9M+e4MQzxIs1jx2+iGb7RGjKKPsSSzT4LVbfQ7npWc8WJmp0gliphzNejdiFEFn/JyhhmEABhllVxF1B4zCjEwr6r5NmGR0g4QwStkchJgyV2Tdzyvsn5beTa/B8VLU96M0tdBk+vBwVsfwR8gLMv4G8BeAGvBfnteiCt4bSrbJDz0yzzMrdTZ7Y71/KbkyW2KtFaA1GAieWKoyW8mTu5nWLNU8ru8N2N7o4VoGczUPISVBnLDeHLDeCRFCkirN61t9fMvk8mw5N1b3uTO+m9GMM0U8zl0AzFRspBDYpjxwQFvdkLmKQ2sUs9WNsAzJtG/y/CCkM8pwBjCMuGOY0U4AT5ckn7o8T5hkXAwTvvzadp5gOEQGvLEbYQCOMaQTJTw8V6XiGrSHMe80B3RGKctTHnGqcC2DKE3vaIb7oCaJC02mDw9ncgxa68Ong398TmspeI+RUrBU99gSAsMwaPZjGhUbU5p85mIDaQhsKY8kkiUCy5A8OtYmsoy8Xn/Gt/nOWpub7QApJQ/PVnBtSWsY0wtjWqOIqm1h28Z974wPG83DcWwgH23qmlR9izjJ5yXsfy+IUyxDkmX5CcIzDT6xUudrb+/w2mYHjWDKN0jS7IhT2O/SBig7NovV/ISx3RqeKB2y3xQYZNDuxLyRtvnESo3LMyUuN0p0SilCw24/Yr7iTDzVfFBDL4Um04eHuzW49ZlcsSgArbWunsuqCt4zXMtgpe5xvam4PFfCNvMQwN4wPhICOJ5QNMdKqVvdkCDO8ByLLz65gEKQqgzXzucUtAYRX317j8ZWH8c0+MGHZ1gYS1Pf7y7ycBw7STIcWzJKMhp2fqpxbZNEJfSCmJdujVBKI6XgYsOn4tko8mTxjO9gmQbTnk2caVKZwujO3gYDqPkWji1ZqHk8f3331PXtl/kGCqJhxo3WkM4ooezZ2JbJ3iBiEKYEXq4xdfxU80ENvRSaTB8e7tbHUDnt+wUfDrTIf6ltc3II4KSE4vGdP8AnVmq8sdVnuxfSGUbs9CM+vlRjtuKSpIqvvb3Hzz61hG3f3674cBx7sxvym69sM4xTPFPwEx9b4NJs5aCxbKsX0homGIYgyzS2GeJYBt0gYaZi8+aWxrdN0gxsrdnrp/gSRuNutv0d0WPzDivTJWZLLjf2Brx1QiXXwRrHf45vJxvtiK++ucFnH56i6jjMlG1qrsVi1eVWNzhIlh+/7x/E6p5Ck+nDwVlzDAUfYk4LAdwtoXh85//oQhXXNri+OyBt+Fzf6zNVtmkPY2YrDr0wIcgybO50DGcxhPtx7DTVfOmlTXpBipCaLBP8u5c3+TOfsfAti7pn0Q8zVscDbcIk5XpzyEzJ5lY7ZKZic2mmzFzV54UbLdZbI+q+zcVGha3ekE4/YpSAFNALNf/6O7f4/EMzmKbEtLkjv7CPR56jEECmIVMQZfCtd7r8869d4zOPzFNyTB6dr7A9iNjuRbSGMYt1D1OKg/v+Qa7u+SDnSQpyzs0xCCFc8iomZ/w+v6K1/ttCCEHeF/GnyMOxf19r/d+efKWC8+a0EMBpPQSTfvldy+DidAk0OIZkqxuSJAopBYMgwTIknnGngTurIdx3Yu1hyGY3YLbiIg1JyTLYaA+pOiZLdZ8oyVCZYhDGlByL1ijBkoKZqkumIVGa1UYJCaw2PPphjGuZXJzxWWsPeelGi//4yi6GIbFtkzTJ+O03dvljzyzimPvz4I6y6MLnHm5wbW/IS1sh4aFRqt0Mfvlbm+wEKT/75CJff6fJ0ys1Vhs+W92AteaIlSmPxXo+ma6o7jmdD+Jp6oPEeZ4YIuDHtdYDIYQFfFUI8evAE8AF4HGttRrLbBS8z5wUAriXhOLhX1bbNDANwaevTPONt5tEmWKu6vLDj8zeEUa6lzLHfSfWHYYolecYZnyLOFEYhoFvmUgpaAcxm/2QrW6I1DBTtXn2YgPPMlma8llrjvBtg26QMldxsQyDpZpLybUQSvC2NUALyWzNwzMNUp1xdXeIMCR/4tMX+Cdfv87u8LblLxnwqYdn+fSjs1ycHfHq1vU7GuRC4DvXm3xsocJczac5jFltlFhtlOgHCUt1D2cs2V1U95zMB/k09UHh3ByD1lpze3ywNf5PA38F+PNaazV+3s55raHg3pgUAjhrQvEkhc+SbfGjj81R9S2mXHtibuGsZY77jsc2JE8s1vnC4/O8tN6hOYgRWvOZy9NUPZs4zvj6O02uNMrMV126o4T2MGSx6uSJcy1YmfJYqnu5MJ/gQKZjGKWYpsHTqzV++/VdUBppCqKhwjNNhmHKdMXjz332Im9udIm14kLDp2LbNAcJV7dHlG1BowSbx1IRkjy01B3EzNXyhjyl8kyGbRoHirRFdc/JFL0S7w3nmmMQQhjA88DDwH+ntf6GEOIh4M8IIf4YsAv8Na31WxNe+wvALwCsrq6e5zIL7sLdEoqTflk7o4SVuocW3PW4fxZDOGmX+FNPLjJbcRiFCb5r8ezFaUxT0g1ikkwxXXUpKZO5istGxyRR+kAmYnG8O9/HMiSrlnHwGReqLn/wqQFfemWLbpjg2SZ/4Kk5GmUPR0pqXgVTGriWZKHmcLMZUHEVdc+k4lss1Xx2h6MjndL5TkiRCqi5Ftu9iCnfpuRYR5xtUd1zMkWvxHvDuToGrXUGPCOEqAO/KoR4kjznEGqtnxNC/HHgfwB+eMJrfwn4JYDnnnvu3iarFDxwTksonvTLqgVH5jKcdu3TDOFpu8QfeWSOWKkj/RaeYaA1rDVHuJYkTBS2KXlkpoJhyRMd1eHP6Dsmf/Yzl3h2dYpuEOPaBoNQY5mC1jA+OBHdbA35/bf3GMWK6ZLNlcerzJUdVmcrtMOUjVbMfstD2YBHluo8e6HBTMXjYdcEKVgZy5QnmTpYW1HdM5niNPXe8J5UJWmtO0KI3wb+ILAO/M/jb/0q8I/eizUUnB/3+8t6OCcxqfR131Cetku0TInJUedjmpJH5kt890aHKM2QwMeXK9j25IlxJ+E7Jp+4ME2SKW62R0CEYxqUHZNBlDBbtnnpZpuqb2GZKRmaVzfbzDw8x9MX6syUbII45WZrQM13WW74PLMyRa3ksDie2TCMUoI0ozmI74iZn9TQ91F2EsVp6r3hPKuSZsnF9zpCCI98POjfBf4X4MeAa8CPAm+e1xoK3hvu55f1pATipL6JuYpzxPHESYYay3FPItOa6VIeaorSXDspGRvWew03SCmQWozX6LE3iPL5CioPsZmGgSEy3t6NSJOMa7tgCcnD8zUqJQelBauzdT6+VOXplTqjVBElGVrkktiCvAvaMeWR09DhMFw8fuzwvbINec+OYpJz+SA6nOI0df6c54lhEfjH4zyDBP6F1vrXhBBfJRfi+xvkyemfP8c1FLxH3Msv6+HQkJS5od/oBFxqlIA7SzV3+hFzFYedfkQvjGgOYholm/VOMLEiZf8EI6Wg4tn5CUZz3+GG/euZYwmROMmIfIubu0O0Vry2kQsESKHJUsVrt7o8tTrFxyo12sOIOFV8+uI0Zd/GCRM24owozjAMyUzFYbcfYY5DbqYh6QUR11tDpMjnKoRpRtWz8Mx8ItuNvSG2Ke9JRmOSI96/1x/E6p6iV+J8Oc+qpBeBT054vAP89Hm9b8H7x1l/WQ+a1BTs9UIypYkSxWzFwbUMUqXItGY0SLAtAyHymc+5dMeQiw3/QLpjUkXK/Zxg9nfOQnNHwvzw9VSah8kWax5ZpnlqucrvvdNEaIESikbZoZ+kDEcprmniWCaXZ120FHniW0qeXZ3CMuWBo2qK+PZpKM1oDmMuTvso4GZ3xHor4GLDZ66anxS2eiEXpr0DFdm7VeVMytFsdoI8FCcFQRqztjvkzR34gYvzlEv2xOsUfHQoOp8L3hMOhyxuC98FOKaBYeQd1nv9iNmyw3dvdPj9d3bYGyW4huDpC3X+1HMXqZXsE6U7UBw5rdzLCWZ/Nx3EKc1hTKNs41nmkR30pByIFHBh2mOmZCCQWJZBnGT0QkWsUxq+RaI0jZJzMDBo0loOOzGlNI2yjWlIbnUCTASDMOXG3ohXN7qsTnnsDlIMmSvHGlLgj6up7qU4oDXMT16vbXX55W/coBXESA2fvtzgb/7UEzy2WHt3P/CCDzR3LxkpKHgXKKXpjmLe3umztjdkrTUizvLTQZxq4vFIzsW6h1Kaq80+r91q048yqrZBlGleWu/yG69tEYxLTfe1kPaT3EmqWGuNuNkasdYaESZ5a5mU4siUtJPWt9UNMSSMkgzXlAyjDEOMwyzqdiLj8PVuNAd86eUtfuvVXbSW9KKMTIFtmzy6UKUzzOhFKY2yk8tdmPLEtew7nQvTPpcaJTzLJEozskzRClIqrslmL2S3F/HO3gil8ylwrmWglaY5jE/Mt8DR4gDIx452RymjNOFffWuN5iDGxMCQgu/dbPNPvn6V0QQxwQ8SSukjfSIF90ZxYig4N8Ik49regFdu9TCkYL7iMD/eHa/UPVamPITgICQySDNu7A5pBwkSTZRlmCaAIowT1ppDLs2U2RvGByGi/dzD/TY87e+mLSHJVC6qN4pThBSoVE3ciXcGEb/8zTVMQzJTc2i0LfpBxErVZ2WmylTJ5kKjlOcV3KMT7E5K9h4Ow81VHG61RwzilCBOcG2Dad/Gqrp4JiRZvu5BmGCbBlO+RawUUp1chnvHqaRiM2xFdMIEKQWGkQ8pCuOUzjChlyT4WPf3g3+fKTqj3z2FYyg4F5TSbHYCmoMIz5J4tkk3TDBNScO3ybRmpuKw148I9nf4QuA4JoMo5pWNHs1BihDgWWBZJot1H9cxWap5BzH6d9vwtL+bzocUCcI4PUj6Tiq5VUrzxk6P1jBmruLxynqHt/dGdIOUF24OwDB5+sIUF6ZLCGC91ac9CPE9iynXpROmIMCUcqLBCpOMnX6EFLkjTTLFIEgxDUHNNTEMiY5SZnyblWmfOM7Y6kc4neDEa8LRUNj+/Osp28I0DKI4QQqNShVpqvBdg6r1wXQKRWf0g6FwDAXnQqbzo7w53olqNEIIoiQlSg1udYI8AZ0pXFuQxookyRvVLCOPq6cKDAEqg7e3e+xdmuaJBcFOP7r9i654Vw1Ph3fTvmUc5BgyzR0Ja6XyXXprmDu7XhjyxvYQoTXLUyVqLlzfG+E6gvXWkL//H9/gq2/u0onyrmcL+JGHbP7g0w/xxGKdTa252ChNbOTzbAvXNpBCsGNEKKEZRhklU+KYNo2KQ5IotvrRgcbT3Yzg4VPJQs0lTFJ+4KEGX31rl0GUYghYnfb485+7hO9/MB1D0Rn9YCgcQ8G5YIg8Ho+GmmfSGsZESUbJchBCgNZs90K+dXWPr765i2sb2IbgU5caVD2HKd/CtxVCgtaSOIFhlJ8s4jQjyRSONB5Iw9Ph3fQjE6qSIN/J39gbst4ZcW0v4EKjzIs3WoyihIprszztkSaa5mjEr39ng29db/F2Kz7yPgnwW+/E/NY7r/GZ1Qp/7rOXmKk4VNzcCE8yaq5t8smLPruDCK1y57pU93AtgzDN+yFK49efxQge1pt6eLbCX/zsJZ6+UKXdi7EswXNXZnhqefrM9+77jaIz+sFQOIaCc0HKXI8ozhQ7vQjfNlmd8lmZzo1ccxRzozngt1/fZRjGpEONyhTv7A15aNphGCekERgmmDLD8GwsCWvtIZBPPNs3kA+i4em0UlulNBudgE4QM+XbLNc9moOIZ67UuNEZ4psQxYo4zahYku/d6vLOMadwnG+u9Xlobocnlms8OlfN4/wnGLWKa1FxrTs+n2samFKe2QhOir1/bLlO1beJkwzbMliZ+mCHXIrO6AdD4RgKzo3/f3tvHhxpet/3fZ737hPdjXsGA8y1u5zZi7scLrmUsl6KInW4KOssSU5Zcg4rUmJXHFViy3FccSL/YZWccqLIUaRyqiIpLkoUy7Kok5ZEUhSP3eXe5+zsnBhgcPfd/d7vkz/ebgyAAWYADAbA7D6fKtQuut9++3ka08/veX7H9+eYOg+MFFYL1/q6SUstH9eLWKh7NN2AIEkIYpmmyEUR7cCg2U112/va1SMDEk3TQMBEOZPm89+mYdBeEktJEMXEiSRjaUxUskggY+n81NMn+ZM35lhuu+Rti0+cGeH3nr+2aT/cjXQ6Ic2Ov+3Tz26Vb+H2vveTQ/n3VRXx7TYK92Ol90GgDIPinqJpAltbHww9UsowU+sShRFBHOFHMq1NkJIgSbi42E2Nwhremu/yN+MIQ+hUOwFDeZtE7k7mYqeLQxglLLUCVtoetW4qoDeUtxgtOJwYyvPJB4f5yntLVLIGmhB84dvT2xrHhaU237hUxTR0zhwp4Zg6lq4x3qtKvlOqLWy/4nwzN5UbhnhRjLNG8vv9wmYbBZWttH3eX/8aFPcFWdvg3FSFM8dKnO71aHaDGD9KKDoG3Y1WgfTg8Mq1GittjyhOuFHrEiXJbfP3N8ML401rHrYiSSSLLZ8jJYeMqXF+rsk3Ly7TCSKGe0J4lWKGT50ZwzRM3FDywEjujuMQQCg1Ol7Ac1eqXFps0fUjpqtdZusucw2PoJeH74dx2pFui5z829Vr9PP5hWRdLUPHC5lveMzV3G19Dvc7a09MOdvA1MUtdSqKm6gTg+JAyGdMnn1wDC9KQEiabohh6MgoYqkV4W2yTjlaghvFXK92kRLytrGql7QdUbndpDL2d9orHY8vvbXAQssFIGcZjA9kKPbqFEpZi6emKlxaafNzn/wQi83XeGNhEwsHWMBDoxkc26KQsej4MdO1LqaukbON1bFdW+mkFeHtgDCOKWQMTlbylPP2tk46XT/ierVDGCdkLIPBvE29G+KGqVEYH3DIbyObabPP8X5zx6hspZ2hDIPiwMhnTD59ZhxdaMzUu0S9HXLCIq/Mdm7x03/lfA3dcDg9kuehsQKVvA2wbVG53SwOuhCEccwfvjLL1ZU2QgjiOOFbl5fIWhoPDBfI2OnXSOgC29ApVfL8w+99hD959Rpfem15tY0hwGRRMJDPYhoahd7ONZLpCSIdm5YuvL06ENMQ+HHMK1dqLHcCChmTzzw8ymMT5XVz7J8sYilxDJ0gTvjr9xa5spx+jgOOydmjRR4ZLxH0Tlr5HWQz9blf3TEqW2lnKMOgOFAKjsmHj5V5dGIAHUHLC/CCmMVmh5nW+mvbEfz1hTkuLuQwraM4lsF4KcNiy+dYJUPWMm67+93N4qBpgoyucaOWiv1lbAPT0Km2Ai4sdGj54aphWFssd3qkwA+fO8HTD4zR6vpEQiITmF7x8PyIpY7PeNnBjSTjRZvJchbb0Gl7ISudgI4XMNf0GS3avHm9TpxIhvI2hiZ4+VqdsmNxarSApqUy5RcWmlyYb5NIyWDOopgxuLTYIUpi2l7MQsNjue0xVc5RcEykYDUTyQsigihGxhJus8bfz8VjKltpZyjDoDhQ+mmt/V1o1rF4/FiZG7UOtXdX6PZcSjoQAV0XvFzIX756kW9nczxxssSDoyPYmwjr3U0Wz1ryGZNi1sBLktXdPBqUs/1AuLOaGTVSsLlRd0HAkVKWx46VyRipyB1AHCbU/ID5lstKK0QImChnOTGUJ0kkz19eoeEGIASGEMxUu3SDBMMQgCDnGCRS4sbxqnjgbK3L5cUOxYyBoWm0vJCZeoe6G6ALjZxjoAUxTTfm8nKbcs4mjNJF3TIE16suA47BcjvgkaMDlLKbq6vejTvmMLifVB+H7aMMg+LA2fiFLdoG37y0hKWBG6duln7v5K6Ed5f6NQItvnShxbkjNf75jzzGcCFzx1PAbhaHomPxidMj/Pnbcyw2fYSAB4ayPHl8CKMX2+g3GFps+QhAAqNFh2zvNLH6RTN1slmT8YEsYS8Q3A8c+2GMpgkmylksU8cNIt6daxLFMW6Y3s/uuYkyuo6Q4MUxbpDKbPSNo2loCAm2oeGGMSKEOJZUcga1bprRVcnbOJbGC1eqHK9kyTkWXhDx5myDj58YXG2TupbNTlwAUZQazH5mkx+mBYimrmGb+qaNhg7K/bRVWvNhMFyHCWUYFIeCdSJyAxl+5pnTLNZcnrtaox3dvC7Z5LUv3mjzhefe40c/9gB5x7rjKWCnNQ+GofHph8fI2RqvX6+TMTTOHitzYjCHpqWaTRvlLPoNhibNzduJbpbGC2mMxNC1VDfK0JkayvP0qUGeu1Kl3g1ww4gnp8qU8hbTtS4ykVQ7IR0/BAmOlcYoxks5KgWHN643kEhGBhwmyxlMXcfqLcoaAinBNtNlwLEMOoFPkCS3tEvtj3ntiSuMEvw45sVrqbxJwTFww4i3ZhssNF2KWZOPHR+inLXJOwZbdajb+Pns9yK933GT+8EIKcOgOJQcreT4+595iI9eWuKNmSaxjLiy3OL8YrTp9bMtHwlM9CSu95pS1uIzZ4/w1IkhlttpxzWtJ1qnaSKVeO65WZJEIoE4Xq/OGkUJQZLqQW02RlPXGCna1DoBWi8QPzbgcGqkwFQlTyeKSCJJ3Qs5P9fCMtJ2o5YpeG+hTcsLMTSNp45X+M4HhrF0jclShvmWh63pjA44iJ4R03QBIn3PMIoxdA0v6PV50Lb+/PonrjBOmKl1qbsxliFYbgW8dLXKGzM1Zqodam6EqcPXzi/xtz48wacfGQd6Heo8n6srndRAb1iID2KR3s+4iRfGzNXdtJ+5JhgpOuQs49AZCGUYFIcSXQhKWYe/8dAYQjOod3x0BOcXq5te32o2MYRA3sX36047OcPQGCo4VHL2Ldf13SxtL6TWDQmimETCeCmDqWvUuwFvzjaIk1TFdTNfvqYJpgZzWLq26ooZL2XS5kSWjmFoTFe76JrANjUsXeNGvd/AJ8tw0SaIIqSErKFjWTqPTJR5aI3Lqu/W6cdYvuP0EBcX23Tb/uq47mRY+32w454BbHkRCMmNRofrdZdGNybr9Nqy1rv8yeszPHa0yHglTxDGrLSDTbvwwa1tXe91cHs/01iTRHJtpUOtExD1GlPlMx1ODeUZ78m7HBaUYVAcSm66LeDciTJvzTQJEsmoU2XBu/X6k6OjCG3r2MKdFv2d7FQ3c0X1A88vT9fQhcA2dcoZMy2OE4I3Zxs4RiqKdztfvmPqHCtnNz1Z9BexjJmqrvbH3XJDbMtgqe3hBTFRFDFTazNazJKxDew183C09fLbUkAlYxEhtzzJbIbec3nFsaTjRdRdn0Y3IAwjEpEgSF1olmZgGDrX6i75bJpePJizNu/CB/tea7CfaaxhTzcsZ+s02wFZW6frx8httGfdb5RhUBxa+m6L8QGHh4aL1F2fpWqdP3i7se66rA7Hx3JMlG79YiWJpBNELPdcTZst+nfjTlhrcEwjdS1lenEFTaR9nt041VlyrDv78m9noIRkVap8uNfMJwhjmm5It97lhasrLDdD/Ai+eWGJjz4wyjMPDvPohpoHTRME4a0B4Z244DQtVXl1g4g3ZutowFQlz0oroOm1cf0A09AxLBgbyHCykuNIKYOlaczU3S0X4v2uNTiINNYkkUjZD9b35prsTt7lXqEMg+JQszajZbnt0g4NbFinpfSJUxWeOjm6urD1ZSC6fsTVaotryy4DOYOTQ0UMTdyy6O/WnbBxER8p2Bg9/7wmROpOSiS20FabAPVPDJv58m9noPqfQz/NNGfrhFGC0DSuLjb48qXmuns9N9MhYRFTg0LG5PRQYTXYC3vjsnFMndMjBaIkoevHdIIY29QxdMFsrUvW1HlotMgPPH6UQtbGMe4sFHgQtQb7lcZq6hojBZtq18ePEsI4YShnIeDQFdspw6A4tKxdKBGC16ZrvDi9ss4oGMBwwaZgGsw3PEYKdk8HqcPX3l3g3YUWQsBIweHZh8b4xOkhErk+KLwbd8Jmi/hiy19tNdp0/dWmP/Ntn9MjeS4utukEW/vy+wZK60lpa732omFv4TZ1gZ2zkDLhjdkWwzmTr74zxwuXm5uO8YWZNoWMwdFyJlVw1XUMXWO4YBMlCaaur1Zb384QBkFMJ4owZBrr0HuxnL5OVcGxqOTSz/DUSI7PnBnFMKHWDSlnbHIbMsVutxAfVK3BvVTnXfseU0M5zLpG3jaodyPKWzSFOmiUYVAcWtbu5Lt+yPWlOo2eVdBJhfUiYH6lhhcn2Jpgpt6l2vG5uNjgKxeWSOJUuTWJ4S/Oz3N2PE/GttYt+rtxJ2x1yjANjYlShqvVDlOVtB4hihO8MOGpqcqWvvwkkauyFkstDyEEUkpKmTRAnUhJlMBiw2W62mG57TOQ1bi42KJ7Gx24V6/XGSpk6Hgxp8aKIKHR9Wn7CYYmsEyNcsZcTbvtj6W/MC+2PL767iILDY+2H3FiKINtGRQdnTiBsWIGQxMYmoZl6mhC46GjqVJs/z79WEaSyG0tfvuxSN8r7hTLckyd44O5dZ/LYUxbVYZBcWhZu5PXNEEjvPncWo29WgRxlIChI2OJH8W8PF1HxpJi1kRKSbXro2mShhtwotcYZ+3CJYDhnLX6pRbcfiG73SkjJnUt9esF+kZD6IKsfmtAu++SiuOE+aaHLtIFO+qlwAqZdq1baqdR90Y3xA8jLsy1cYMInc3rOwAaHlxZbtD0YjphxGDeYaUd8PSpQaIkLUa7EcQ8OVleldfop1PKWPLSTI0gjNGFoOMG/LtvzOHHEXEE+azFZCXLoxMljpRzfMcDw5SzNwX+toplAIem4G0v2W4Cw/1g+JRhUBxa1u7ko0Ty+MQAf/zaEhsrGT46VaHpx5wcKbDY9vHCGF0mZCydIIgwDZ0girFNg6lyHkvXqHV83p2vM1PrMl1tE/gRC40mi/U2fgxnRwf42NljPD4xyEg+rY1YuxPsj+1G3WWp7eL5EVND+VWJ60TKVS2ija6ptbtKgLm6ixBpxbKlC6JYEscJQRjz/NUlXr++TDdKWGh4WKaJY2icHCkwvdLEMUzKOY/Fzq2fnwBMA8IYaq7PhcU2R7yYMIZqx+fUUAGhC8IwwTQ0oijh8mKLqhugI2j7EfMNj6GcxY1ml1dnqry35JPQM8xNlzfmXZ6/WuXkUI6ZmssPPTnBaDGzang3uttu1F0EYBnalvGNnRSAHZZisftZR2ozlGFQHGrW+px/4qlTzFVd/r9vzeKT/uP9oSdG+YmPnUYCtqVzpJSh7QbkHJvBXMR8K0DGCRnL5HsfHqPuR8zNVPnCC9f56oU55prJpt3WXpxr8FuvNnhkNMuzHxrixHCekYLDSDHL1FB+1VXy+kyVP3vtBl4Y41gmn3hgkIlynlLWoN4JKTgGBcdkopzOod72mV7pYJs6tmmQMdPahIyVGq/5hsdyy8cPY/7inetcnAsIgIINRcekkrd5cKRAGMa0/YTTIwWGiiZvztS53rx5bujvUzXS4HMiNUwhaPkhowWb9xbb2IaBoQtKGYswSrhe7fDtqzUkknLWIpIJ1Y5HqxPw2vQKlxc9wk0+q1pXMltz+dqFRfw44XvOjnFypICuiVtiGR0/NeurUiEb4hs7SRs+TEqve1EPcViMHCjDoLgP6B+9TV3jn3z2cX7sqWM8d7XO1KDDgyMVEikJ417KqKnxyESZn31W5z+8eoOrSy1M0+A7T1c4NzXE1ZUOl5cbfOviAktbGIW1vLnQ5crSNIYJQzmbJybL/NC5Yzx+tMwXXrnG//u1q0QkWLrO0QH46vklPnlWMl83mK42mW/4TJYdHhwvY5kaf/H2Ao6hYRsGZ8byhICta1i6Q6Mb0g0iFlsuf/7GDa63bjrM6j5EcUgYJRRtPa3yrmQ4c6SI1CSPTpR5a7bGhfkOs/U0EGPpcGI4x+nRAdwgwjENcpZOOWfT9hNAImWqBnttpcNS02Oh6WLoOo1uyEDGJE4kXhTQ9kP8LT6sGPCCGDeMabkBs3UXxzIYydvMN266xsoZE0NPl8nNXHA72XUfth363dZDHCYjB8owKO4zNE3w4Pggk0Ml5hsebhjf2htZEzwwNsB/80yWd5aa5A2dYs7Gj2KiOKEbJHS9BB023QFvJEggrwsylsE78w1ybxgkMuGv3lkiikN03aDV8Xi741EyBV/XYqptn+majxcl6BqcqFQZyNnkLJ2uL0mkx1yjw7kTg+RyFn/59nUW6h62oWEYOvOtWzsVtSPoRDFDjRYZS6PeMRgvSwZMi4Exi2dOj9CNIlqeRyw1HFNwZTHAMQXVbghIyjkbSxdMVnIcq+QwdY2GGzDX8MhZOpVcupiHcUzO1hkp2GhIOl6ehVqDTWoLATB0yFk6Oduk5Ue4XshMFDNWsKl7EV4QMeNFq7GMVIE2xthCViS959a77sPWeOdu6iEOm5EDZRgU9ynbSWvMZk0ePnLTgAjSftPTtRamCfFWq9wGDEA3DBxTpxsmLLR83ptvMVvrsNRK8AlWr13xJJfO11d/L2jpjvrtuTZjBY/xwRzVtk8kIY7g6lKNywsR3TXvVzG2NlgSeGMl4e2VGmeGG0wvLlIuZDlaKfLkx0/jOMaq7LehaTxyxGC5F3dpdEOKWYOmGzNecrDXxD80IXp59hbVToAXxUSJxDR14iBmvFjEsRuE/vrA/00EOctgopTBCyNmGy6moffaaMJy26PphkDMUD6DbaSnnpGCvSoBsrb96J123Yex8c5uU20Pm5EDZRgU9zHbye7Y+GUN4gQvjvnI8SbPX1lgsb11Rg+ACWQzkDE1vDDG9xNGChalosFSy2fz5p03aa25+ZVGRK3dQAioR2zpxqpurhO4jhh4cynhzSWftNyvxr9//hrPPlhmpJzn1ESJj04MU3RMcmsaCUkBYZTWXHT8dGc7Uc4igVonoJixSKTkyIDD1FCOMIr5q/NLdD0XXdxME+6jAUUTRgeyRHHM6zN1hosORwYyIOD8XJ0vvb3I9aUWQQwDWZNPnBrkJ546Tt4xuN5raQqpUShlTerdcN2uG1g1HGuL9AbzFgtNj8SP0DTBaNHZ1E+/Xd/9Xvj4d5NxtJWREzKd90HEHJRhULzvWftldTSdD09UGP+bGZ67MsxCrcONWpt3Zqu8fiNeXeg14FMPFTk7XuYb71WpehFRkvDwsQF+4IkJXC/Y8v1uR33z7faecL0Lv/1qDagB1xm14KkHijw4VuTEaJlTwznCMCFnGFSKGUxDx9I0pICSbVDvehhC8tBoDtPQ0AXMNH1ODOd443qAaQjCDeZMAGMlm6wtaHkxc/UOJ0bymIbGbM3ltWtVLs010fTUuEZhzCvXapwayfOfnBphsekzWcng9Lrv1bvhqiS3kNAJIqZXOnhhzHLLZ3jAxtJ0EGnm10orwDKh7cVUOx5+CEMFi4xp7Cg19iB9/Ju5oUpZk5m6e2AxB2UYFB840q5xWT776LGeXpGg5gZ85d15zl+vkUh49HiZTz44TiFj8lNPB6y4Hpam4SVQsE2WdSg6BjV/G9v7A2IhgD98qwlvNYGZdc89PGzw5LEymiHoeiGvXa9RbYIwYHjAoJzPMlKwiaKYpYbHjVqX+U1SYmPg/JLPUMdnopwjQedG1e1VRUvcUGIaGlnLRNMlnSCm44fM1jwuLrdgk3oPKSBOJJeWWrw6Xafrh9xouJQyJtlFnamhHHnH6p00JK9NN0gkNL2Q45U8UiZMVXLbSo2Fw+HjX3uyFRJm6u6BjkcZBsUHFsPQVkXsRq0MP/rkFN1HjgKQtYzV6mR7IMPgQAa4ubO0TZOPnqpw7eXFgxn8XfLWUsRbS0u3PhHD0mKEttjcdnAeYLkLntfh7EQOmaSpr90wxg8jcraeFvglEEcRTtYmn9F63egkUZRgmfqqtpSMJVeX27x0pcrbNxpcXXbpRiFjAw7ljMViK+DjpwaRpk7TC6m7EUfLWVpexI2GS80NMHWNvGNgG/qWqbGrUz4kPv7+yXYnQfh7NpZ7dWMhhCOEeEEI8ZoQ4i0hxP+y4flfEUK079X7KxQ7xTA0ilmLYtbaUmm0v7M7NVzgH3zXGb7/THmfR7k/JGzfKPRpJ/DCdIc/eG2Gl64s44cxlYLNWNEBBH4kyZoG33VmlL/xwCinRguMDzj4cUK17XN1uYsbxlxeaXN5pc180yMGhJbKhdTbASBpeSHzrdTp5wURpiGQSYIfJSAllpGeJGrdEL3nuwe2DFCv9fHf7rr94jCM516eGHzgu6SUbSGECXxdCPGnUsrnhBDngPfnN0rxvqe/sztWyfEPPn2WycGr/PrXZzcNJpd0MAzo+OkXYqtAd1mD2u2i4PcRVQ+qnsds0+fRiTIjRYdnzgwjE8jYGpOlAn4scf2IjGVwpOhwcamFodFrdBRwabGJF0bkbX1VfRQBAo28o1POmuQdk7mGz1DWIYgTyhkDL4GspWMYOiXLYGQglQC5XQrpQUhvb2Rj4Pugx3PPDIOUUgL9E4HZ+5FCCB34ZeBvAz90r95fobjXaJrgxHCB7318kmo35s9en6fZCzkI4BPHB/joqUEmylk6fsjX313iwnydlWay+sXI6HBmPM/UcJ7rS21emWlvkQ56/1F1Jd++WGVqOMNo0UZoGrqwCOKE2AvouhGPTZaIpWSpHRDHMW/O1nl7rsV8s4umaQzmLAYyBlJKKlmTSs7k8WNlTo0WOFbOcmooz3S1w3zDY7HtM1q0mazksAyNOIGcZZCrGHfMNtovVdfNMp+2CnwfhMpsHyHlnWo/7+LmqRF4CTgN/Bsp5T8WQvy3gCal/NdCiLaUMr/Fa38G+BmAycnJj1y7du2ejVOhuBuSJBXne3euztV6iziAR48NMFUustDxyRip3PVK2+Xd+RZZSydBMr3cJYhiTFMniiULTY+ZpTbVTps3Ft8v5gEcYHTAYLCYJWPpPH60yLGhAnGc6l8lSL59eYUri22uNjyETLWioighTCTjJYcjJYfRgQxTgwWOD+aYGsqtZun0VWk7YUSjGyLErb2kb8d+SVFsZgAsPZVESQPNaapqGMs9CzQLIV6SUp7b6evuafBZShkDHxZClIDfF0I8A/wY8Ow2XvsbwG8AnDt37t5ZL4XiLtE0QTln89TJET4ih9ctMLad9okIw5icbfHI0TKmLqi5IXnLYrkT8MBwDkhz9K8sd7i61CJ3aZHnrm2SBnQf4gNtP8LxAjxf49pKh2ImzSpacQNWWj4zKy6LnYB626Xjhdi9fteFnMVEOcujRwY4PlTgaDlLzjLWyXgHcVp0mEiJEILhgk3OMra1sO5lmurtDMxWmU/jA86BB5o3Y1+ykqSUdSHEV4BPkp4eLoo0kJIVQlyUUp7ej3EoFPeSzYqbNiuwm294DGRMBhyTj0xVsHvNbwBODheYHxvg8WMVki+/ywvX7//8DAn4IfhhSIzGbN0jb3c4PpTj+kqXnKlRzJt8e3qZC/PuatD74krAWE4w/NgEBcciaxvMNz2MnnyGJgRHBjIsd4J1C+5KOyBXufPStpdpqncyMFtlPsH+tzPdDvfMMAghhoGwZxQywKeBX5JSjq25pq2MguL9zsYCu9v5jguOSW7U4PhInjPjRb701gz/8x++dxDD3jEnB8CLoN5hncQHQDsGw41x9ITiUJ6hfFrAle36DBVsriy2WGl76yTVY2C2I/mT169jG/A9zlHmGm6qQlv3QBccLWU5MZRnqJAWs+1kx71XaarbMTBbVTebunbggebNuJcnhnHgN3txBg34vJTyj+7h+ykU9wV3kk3oPz9WyvLZxyZ56Z1pvnjxTuIbB48f62g6hFuEz7s+5Io6ZccgQeDoGkkCHS+i6gaEkSSjQXdDdtZ8Bz7/4nW+8d4Sum6w0OjgBRJdh6PlDD/w5CSfOjOGYxo72nHvld7SdgzM7TKN7rRZOAjuZVbS68ATd7hm08CzQqFIMXSN0ZFRuDh9YGPIAHkLGgHcTgik5cWYGrc0Uupj6zCUs5gcKnCk4tB1Iwo5C0fTuLbcwjE0gnDznN2OCxdc7xZ11yXXZan+LroGTx0fJmMZ295x71Va6HYNzEa3YhQl1Lo+ltDI2MaqZtRhQFU+KxSHmKxl8KGjRT40nOH8kruv720DT58uUs45xHHCleUOjY7LShc6aw4F/eXPi0C30kVls+I4DTANg4wFy60w1VkyDCo5kw9PDdH2JW/NVml11ueaWKRuJXOLcVZdeP7iEo8eKfPASGHL4sTN2Iu00J0YmP5pcL7h8tXziyy2PCxd49yJCo/0emUfBpRhUCgOMYah8akz49S7Ib/73FUurOyPS8kASjn48Y9PMr3s885cC9M0sS1JUYYknRhPpoFlm9Q46DpYhmBAlyxvYsPyGZ3Hj+T5yIkhcqbO5RWXOIHFdsBEKct3nRnl7FieK8st3pytUXclbpD2epASzC00OnQdEgk3Gi4PHy3teFHbiSLqVplHOzEwQRDz5fMLzDd8bEPDjxKev7zCgG1yarTw/nYlKRSKvaGUtfjpp0/y6bOjvHK9yr/9y3d4Y2m9Hz9HWlW9l2cKXRjEiSAIJX4QkTE1AlOnE0RYBhgJeDHoWlrdPZAxGRtwMA1BdqXDXCMmIt3pHxs0+cSpYR6eHKLtxehC5/RwjlonpB2mu+xzxwdZKGc5MVrg4ckK1xZaXFhss9To0golOjCWEcy7N08UFpCxNPK2TkZP261ulKreqzqFO2UebcfAJImk7gUsNT1MTfSaR6UKsNVuwPEDTlPtowyDQnHISRKJFDBRzjNRzvPsg+N8+fwMX397Fl0XfOqRSb7j9DiJlCy3XP70jRv8xzemeW1h98qvETCYt1luBZw7WabWDYkaHRYbHrYu0LMGAkEQhvgRVPIGR8tZxgeyjAw4PHJ0gIxl0PRC4ihBNwwG8xblnEnWNnD9kBv1iErWhN78Vjo+miaIpaBgWRwfKVIpZmm4AQMZnfNzbUxNp7iUZjB5EdgWHB/M8uB4mcGizVwzjUKs7eWwF3UKe5Ha2vVTkT/Xj2h5EUEUU8rZCJEarZYX9lRpDx5lGBSKQ8xmu9SBrMXf+vAJvv/RKQBMXVtdnIpZi58uZvnoySH++LVZfvf52S3bcd6Jp0+VmazkmSjleOZDw7w1azCat2n5EfVOQNv1KTkZFrsRtqFjWxbHR/I4uobQ0gXY9dtYliDnGFSyNoJ0l/zeYpswlgw4BhlLRyAwDY0okpQyJjgmU1YWQ9MoOiamoVH6hMG1lQ5hHPP2jRaGphHJiMlSEcvSMPW0h4QEpEy4vtJBaIKMqd91ncLdprZ2/YiXp2toIv17nRkv8vyVKtWOjyY0HhovcKTXh+IwoAyDQnFIud0uFVLXxWbukXzG5NzxIR49WuY/e/oE37y6yL/94wtc3qFc6rvzLT52coRESkYLNu8IjamhAsvNLpcWG8yuePgR5GzBqZEcR0sOAmgHCWfHcxQcE8cUCKlRzlsYumCx4TFWdBgq2CQy7Ydt6jpCE4wVHbTezhkg75ir82u7IfmMxROTDrGUjA7kMDSBYWhEUaqs2vQC3lts44Ux3SCinLMo2AanRgoYuoaha3T9gJYfkjONHQWp7ya1NUkkN+ouuhAUMiZRnDBadHj8WIlS1qCQMRkvZjB1/cAL2/oow6BQHFK22qV2goiVdnBb94imCTK2wfGxASZHinzfI5OcX6hR7QScn6vz+6/cYOYO7eSW2z6VrMH1mosbRggER0s2r1xbotaJiJI06NzxJbN1l6PlHAMZi0LGYChn0Qwi4hiEkEyUMoRxwo2ahxcnrLR9KlmblU5IwUnI2SaGrpEkEstIFVVFbx5tL2Su4REmCbahM17KcLScTbOAwnSBLmVMvvLuAoam0Q0ikJKFmsvJsTyd6zGPHSvh+hHvzLeo1F1MXeORowOUsta2/hZ3k9oaSwkCLFNbNSyaJnh8YgDL1NER6L1Ct8MQeAZlGBSKQ8tmu1SA5ZZ/x65ka9E0QTlv87HsKLGUfPKhI3z81Ci//VeX+PJ7tU1rEwRQzphITTA1mDYpMjXBK9erXF3q0HIjwijNCBICXD+m0QkYLoaEnYg/eGWWct4m5+g4poEbxmQtg7PjBardkDNjRepuiBfESCkoOiZuEKcyF6X0/eYbHl4QMl3rogtB04tIkpAgTnhgpLCu49l7Sy1KjknLj3lvoUWtG1CwDSYqWSDm4nyTlW7IRNmhlHXwgog3Zxt8/MTg6snhTkHq3aa26kJgaBrljEnNDXHdkESm8ieOqR+qwrY+yjAoFIeUzXapwwWbpZaP0SuG2omvu581Y+oaT58c4ex4iQsLdb761gy/9Y15+pJ9GnBi0OYzjx0l71hYhs5Ky+UvXr3C51+trjck/UNHApd7rTo1XccLI8bDmJGBLA+PZRACjg5k8JOElU4d3dAYzNsM5iwkcKycvcU1NlnJ0g0ipmtdcraOaaRZR4tNn+ODOWxTX+14ZiBwLIPr9S6WoZOzTYqOwUrHZ9zMEvRUS7uBJGPFOJZBJ/DxohhbQBglLPaF+IChLYT4dpLautnfcSBjggNHSpnVznKHIQtpI8owKBSHmI27VIAVEdy1jENfEfajx0d4cmqYn/xYhz98fZr3FloUbZMnTw3zzAOjNL2I165X+cUvvsL1xtb36yRQbURYokogDbK2znDBwTEEM7UuuqFhGTpHBjJMlFNDYRvpicfvpZcaGyp/NU0QJ5KVdkA3iLEMjaJz65KlC4Fp6owULL51KcALIrp+xPHBHLEUBJGkWDSJElZF9koZg0RK5ntZTH2lU0PXmKu7zNRcjpYzHCll9qTo7KD7K+wUZRgUikPOxl3qXoqu9e89OVLgv3r2TOqf52bP60Qm/ObX3r2tUehTT6BeBYjQiTC1OpaucWIolcq2dY3Fls9o0WGx5VN3A5ZbAaWswXSty1jRwTS01YUzSSTVTsBIwaYTRARhzLwf8eBoYZ18hKYJRgo211c6TA1l6Hg2xaxJ24uxTYlpCCYGc4wmCe/OtWh6PpYuGMxZZHqLvi4ECy2POE7I9k4KumDXWUy3+6zvB5RhUCjuM+7V7tMwNIrG+mCsF8Z0op03DYqBV264WFqd8YFM2pwokXhRjMBmtGBzcTHA0MENE+rdLleW2hwtZ1YDzLomkMDUUI6FppeeLqKE0V59wlpMQ2O8nGEwb/HOfJNqJ8Q2BB85VmYgZ2MZGlnd4LGjA3SjmMlSloW2T5JI3DCi7YZcWmkjkWQtk8lKFquSxQ3ibaek7lfDn/1AGQaF4j5kv3afJdui4hik7XZ2zlyzQ8N1+Q+vzpK3dAYyBlcreYaKFlcXuhwbyuKYOnN1l4Wmj2Nq6JpGECecGsqjCYGhCY6VszTdgKVWQK0d0HSjddlY/QBvJqvz8ZNDdLyQBDg9XCCSct0J68RQHkvXqC40ePVanVAmXJpvUsnbDBdsZCKpdgO6fogQ2rbcdHvZ8OcwoAyDQqHYknzO4me/+2FevPoCzV10G+124J35Nn4UE0dQLtiMLnb40JEBvDDGrAtGSw4r7dS9k7UMhBCrAea+2yxKYpbaAeMlh7xj3pKNtTbAm0iJZaYqq4ahYcAtJ6wgiHlvoYNp6HTdmMtLTc7fgKnBLMeHC9S7AUXHYLKSI4gTHG3rRX4vG/4cFg6PzqtCoTiUPHVimP/rp59gexn/61mOodZ0kYmglDNouSFBFLPY9CjYGu/Mt7i81GGx5ZN3DPQNAei+2+xIKcN4MTUKkGZjJTJ13Wy89lgly2Qle4uO0doKcTeOEQIGCxYvXl3h8pLLhSWXr19e4Y9ev0HLD3hwpEC+15o1SbbWqrhZb3IzU2zj2O43lGFQKBR35Mx4he85W9nVa2eWA5ZqLaotDzeIaHshfhCjawblnMlo0eb4YJY4kXT9CD+KGSnYqwFmTRM4ho6+ppZjq2ysjQZgKzK6jq4JLszXeW++QRCn6q2WLljpBMyudPCCKA2C32GRX1tvcrux3U8ow6BQKO5IwTZ58uQwU4Wdv7YFXO/Am/Me9bbHYstjttllpdlmqpIjaxocKWcRQmAbguGCw9RQbt3i3ncVBVFCoxsQRMldZWNZls6Tx0pcXmxTayd0Y5AJxHFax7DUCXhzrsEb16s0O2mQeqtTQ39sYSzp+BFhLLc9tr4a7O1OJAeBijEoFIo7Ylk6HzsxyBdfznCttTtx7wSYbSeEUZcoEdi6hmmaPDFZwTF1jHKGnG0yWc5uqWMkN/x3tySJJJKpnHhfZLCbpO1H8zokScKfvTlHzjIwNI3PPhEyVSlsGVTeaaZYkkg6QcRyy0fCoQtYqxODQqG4I1GUMFN3Ibr7RkHFjEbWTmsFXpmuc3G5xUzNZahgY+japgqjUZQwU+1i6oKBrIVtaJv6/rezA++nzS7UO7wyXb/l+SCGvGUAGlnLQEr4xoVl4iS+bbxhu26srh9xcbHFy9dqLDQ9dE1g6uKOsYz9RBkGhUJxR4IkwfMjGt7mPZl3QpwIpBC03QjL1BgtOkyUMyy3fKI4uaUngRfGXK12mK27LLZ8vDDeNMDrhTHT1S7Xq12mq126fnSLkehfc6Pucn6hwfImmuQBMFdv4hg6mkwVURtuGhe526ByX357senRcEOEgKWWjybuHMvYT5RhUCgUd8TSNCxbx9ttc4c1mLpGRk8XwsGsjW3qdIKYG3UPP4qZqbt4YUySSPwwZq7uYusaOdtAJpLlto8XRiSJXDUia1NGc7ZBnCS8PF3j2kqH6Wp39X79awqOybFybssFcLYG1bZHK4yQSU8RVRd3FVROEsmNhosm0r4ZpqFR7QRESYIfxYcqYK0Mg0KhuCOGoXFmbIDjR3J3dZ+CgKxtYhk6E5UcH5kaZKKURQjJkZLDUN7B1AXXVjpcXelwbaXDTM0lAYYLNghBre1zZblDmCSrRmRtymiSSGrdEF2kTXr6bpowTtallZ4eH+BUefMwayEDcRxjCkm1G/LoRAnbMO4q4N1XgrV6YoAjBRs/jHGDGClRstsKheL+Y7yQ4ZMPjfHC1UvspmnosyfzjFcKPPPQCBlTJ07ANHTcICZJBJODvYKwBBabPpOVDJmMSa0bMFd3mRrMMZyz6PoRU4NZHMtYLSabKGVWU0YlEERxqr6qpbt8P0pHvFbG3BI6/+l3nOK3vv4el+upi8wEJioWwzmHJ45XeOTIAKalcW5qkFLGuquFWxdp34Vy1qTWTes5BvMOj00MUHDMQ2MUQBkGhUKxTSxL57NPTjFT9/jN52d39Nq/c26Mo4NFTg7nKeXSUjk/THhsYgDH1LENF6O3MPo9bSbL1HvZOhmmV7q0vBBNS6XHHStduvqy41LcFBeM44REpv0k+oZAE2lgeK0AIcDTp0c4e7TI//2XF7hWbVN0LPIZm7xtcGokz0PjRXRNu2ujAOvlt8sZEylMjgzclN8+TAh5SIIdt+PcuXPyxRdfPOhhKBQKIAhirtda/PY33uF3X6hyp+TVjxy1+B8/+wRdL+FoJUPGMvCj1H0yNZjWK6zVGhJAECXkHWNVWtyPEo6WMuhCMFN3e/IT6XNhLFflJ/pCdmv7K2xMBV0rdhf0ThzXVtr8x7fmaHUjdEPjQ2MFPnZqkFLG3vM00v0U2xNCvCSlPLfj1ynDoFAodkOSSL5+cZ5/9jsvc627+TUZ4Hd+7mOcnajcdrHu32/jgr3ZtdsVrNvuArxqTPyYZhhiaRp520Tom/fUvp/YrWE4fGcYhUJxX6BpgqdOjPCPfvBx/vkXXmNpQ4/Qoga/9BOP8+ixwdUc/8nbtLJcqxjraFsXjG23mGy7CrSrne2yGlnMHX4K70+UYVAoFLvGMXW+75GjPHmszB+9dpmvvjVN14OnHxrjex47zkPjpVukLbYrF367a++npjf3I8qVpFAo9oS1VcfbrQJW3FuUK0mhUBwomiawb9O3QHH/oArcFAqFQrEOZRgUCoVCsQ5lGBQKhUKxDmUYFAqFQrEOZRgUCoVCsY77Il1VCLEEXNuHtxoClvfhfQ4LH7T5gprzB4EP2nxh6zlPSSmHd3qz+8Iw7BdCiBd3k/N7v/JBmy+oOX8Q+KDNF/Z+zsqVpFAoFIp1KMOgUCgUinUow7Ce3zjoAewzH7T5gprzB4EP2nxhj+esYgwKhUKhWIc6MSgUCoViHcowKBQKhWIdH0jDIIT4MSHEW0KIRAhxbs3jnxZCvCSEeKP33+/a5LVfFEK8ub8jvnt2OmchRFYI8cdCiPO91/3Lgxv9ztnN31gI8ZHe4xeFEL8ihLivNKNvM+dBIcRXhBBtIcSvbnjNT/bm/LoQ4s+EEEP7P/Lds8s5W0KI3xBCXOj9+/6R/R/57tjNfNdcs+216wNpGIA3gR8Gvrbh8WXgs1LKR4GfBn577ZNCiB8G2vsywr1nN3P+V1LKDwFPAN8hhPi+fRnp3rCb+f4a8PeAB3o/37sP49xLtpqzB/wz4L9f+6AQwgD+D+CTUsrHgNeBv78P49xLdjTnHv8UWJRSPgicBf7qno5wb9nNfHe8dn0g+zFIKd8B2LghlFK+subXt4CMEMKWUvpCiDzw88DPAJ/fr7HuFbuYcxf4Su+aQAjxMjCxT8O9a3Y6X6ACFKWUz/Ve91vADwJ/uh/j3QtuM+cO8HUhxOkNLxG9n5wQYgUoAhf3Yah7xi7mDPCfAx/qXZdwH1VJ72a+u1m7Pqgnhu3wI8DLUkq/9/svAv8bsEXb8/cFG+cMgBCiBHwW+MuDGNQ9ZO18jwIza56b6T32vkVKGQI/B7wB3CDdPf8/Bzqoe0zv3zLALwohXhZC/J4QYvQgx7QP7Hjtet+eGIQQfwGMbfLUP5VS/sEdXvsw8EvAZ3q/fxg4JaX874QQx/d4qHvGXs55zeMG8DngV6SUl/dqrHvBvZjvYedu5rzJvUxSw/AEcBn4P4F/AvyLux3nXrKXcyZd8yaAb0opf14I8fPAvwL+zl0Oc8/Y47/xh9nF2vW+NQxSyu/ezeuEEBPA7wM/JaW81Hv4aeCcEOIq6Wc2IoT4qpTy2b0Y616xx3Pu8xvAe1LK//0uh7fn7PF8Z1nvKpvoPXao2O2ct+DDvXteAhBCfB74hT28/56wx3NeId05//ve778H/Bd7eP+7Zo/nu6u1S7mS1tA7Zv4x8AtSym/0H5dS/pqU8oiU8jjwncCFw2YUdstWc+499y+AAeAf7v/I7g23+RvPAU0hxMd72Ug/Bex0N3q/MQucFUL01Tc/DbxzgOO558i0ovcPgWd7D30KePvABnSP2fXaJaX8wP0AP0TqQ/aBBeBLvcf/J6ADvLrmZ2TDa48Dbx70HO71nEl3zJJ0oeg//l8e9Dzu5d8YOEea9XEJ+FV6ygD3y89Wc+49dxWokmamzABne4//bO9v/Drpgjl40PPYhzlPkWb1vE4aN5s86Hncy/mueX7ba5eSxFAoFArFOpQrSaFQKBTrUIZBoVAoFOtQhkGhUCgU61CGQaFQKBTrUIZBoVAoFOtQhkHxgUAIsefih0KIHxBC/ELv/39QCHF2F/f46lqVTIXiMKAMg0KxS6SUX5RS9uXIf5BUa0ihuO9RhkHxgUKk/LIQ4s1eH4If7z3+bG/3/oWeRv+/6/djEEJ8f++xl3p9Gv6o9/jfFUL8qhDiE8APAL8shHhVCHFq7UlACDHUkyRACJERQvyOEOIdIcTvA5k1Y/uMEOJba8Td8vv76SgUKe9brSSFYgt+mFQj6HFgCPi2EKKvbf8E8DCp0ug3SHtQvAj8OvCMlPKKEOJzG28opfymEOKLwB9JKb8At8oir+HngK6U8owQ4jHg5d71Q6RV2d8tpewIIf4xqVTy/7oHc1YodoQyDIoPGt8JfE5KGQMLQoi/Aj4KNIEXpJQzAEKIV0klBNrAZSnlld7rP0eqa79bngF+BUBK+boQ4vXe4x8ndUV9o2dULOBbd/E+CsWuUYZBobjJ2j4UMXf3/Yi46ap1tnG9AP5cSvmTd/GeCsWeoGIMig8afw38uBBC76mKPgO8cJvr3wVOrtGy//EtrmsBhTW/XwU+0vv/H13z+NeAvw0ghHgEeKz3+HOkrqvTvedyQogHtzMhhWKvUYZB8UHj90lVNV8Dvgz8Iynl/FYXSyld4L8G/kwI8RKpAWhscunvAP+DEOIVIcQp0uYvPyeEeIU0ltHn14C8EOId0vjBS733WQL+LvC5nnvpW/TaTyoU+41SV1Uo7oAQIi+lbPeylP4NaeOif33Q41Io7hXqxKBQ3Jm/1wtGv0XauOjXD3Y4CsW9RZ0YFAqFQrEOdWJQKBQKxTqUYVAoFArFOpRhUCgUCsU6lGFQKBQKxTqUYVAoFArFOv5/Vmex/Xa98XUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", \n",
    "             x=\"longitude\",\n",
    "             y=\"latitude\",\n",
    "             alpha=0.1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa1f6b9",
   "metadata": {},
   "source": [
    "the alpha was intentionally used so as to fish out the high dense zone, that is location with more district."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c229c8e0",
   "metadata": {},
   "source": [
    "it will be a good idea to include some other features in the visualisation,\n",
    "features like district population and price(median housing value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8827fc24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7b31977340>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mTJgw5LrnnoPVq1WG+7FIJJQw+cY3lCP+eGhoaODyyy9n06ZNw9p+X++ggoKC45vob8RQ51gIsfpYPUaOxozqarlkmP1Mcu6++33NdToxIgVwWxvk5ys7dDisEsOOxY036vj9cMYZGjNnHvm0mSY88AgkkoJwGNZvlDzwyH6BPXk03HW94Lx5Gp/9mKC2DP74R7j/fti9e/9+Zsxwcs9nvVxzjXtYgiSdtnj00WZWrDgB7+4R6OlJk0yaVFf76OxME4sps8XYsW4sS5LJHPwg0tlpMGeO76TNfzqzcKESDH19R98uk1HX6+WXH78gsTkx7GiuE+O0NHMdi2nTVJx/KKR8IecPo0hzTY3Gvfce+/IZHISeXhCYbNhg4tDhxRcln71Ley96aXytesVi8MMfKnOHywX/+30DpyuGdFloZQH8OU7GlcP1Z4HvGGa4ZNJky5ZBLEsyZ07eMM7Csamo8FJZ6aOpKc6sWbmEQupyKSpycumlIf785wgOh8DhECQSFuPGuZk1a2hhsmFDkvXrk9TVuZg/3zviy8Dk5sKnPw333ae04+JidQ3sw7KUhhuLwVVXwfTpJzZPTU3NsLUSUJqMDYzQQMP3xYgRJgMDkoYGi4oKwQUXaITD0NoKo0Yp4XKy8PnA5YSBiHrCSachLzB0GGxnJ8TjUFWlPj/wcBJLc5F2mJRXxrn4mhA7WgXPLodF4y22b1f+milT9MO0lVDIyVe/OgaP5+T9DDwenbvvriUeNwkEDr5UFiwIMmqUh/XrE2zcqFFd7eSKK1xDBiY0NmZ4+OEIOTkaGzem8HgEZ5xxciPMPoqUlsK998KKFbBsmbpW9iElTJoEZ521//qw+WAQAkb4s84JMSKEiWVJ7rsvTXu7JByGr3zFzbx56mrZvj3KmjUGU6eGcDo10mkldMJhQXHx8SuyLhfcdAM8+IhOQRFIC/7x60PvJxBQN41kCgaBQVPgQNDR7qarB/Z2wcILYLDXYvMraYRQWszq1Saf/vTh4bWh0PHbQbq7TZYsSeHzCRYt8uD1HrxPTROHCZJ9lJQ4WbnSSVcX9PcrU90Xv3h4VvfAgJktZKiTSFh0dZ1AbOxHFCnlUQsShsNw4YXKb9fSAqmU8p8VFqqijzZH5lT6e4dTmcDmYEaIMFGaSSikNIFMBtxuaGyM87vfNWEYknjcZOHCfB56KMP27RZOJ9x9t4uKiuFdVZs2Gbz6qkluruDKK51886uCwUEH4bCaaygKC+Gqq+F/n4YmA0Zd6WbzqyaZhMDrFKRjkqVvwLQJBjPKobBQHUtjo0Vzs6Su7v09Pkkp+f3v48RiFskkpNOSq68+Pp9HY6My0fh80NSkboaHhr7W1jrJydFoasrgdAqmTj25ocofVjweDz09PeTn5x+zwq3L9eGoav1RYV8/k/eby3IkbM3k+BkRwsThENxyi5Nly0xmzNAIBNSVomnivV4RmibIZCS7dlnU1AiamiRtbdawhEl3t8Ujjxjk5wt277Z45pkMt9/uGlY+waSpUNcOZ4XA7XLw80adeItJpC/JQA94goKrzwIp9x+HlCenx4USshYlJRqRiKSv7/if9ObPVwEEADNnDp1DEQzq3HtvHu3tBnl5Ojk5I8MiXVFRQUtLyynpuWGzv9PiyUYIWzM5EUaEMAGordPRwzpSgmGCQ4fKSi933llNImEyYUIQXRfMn6+zbJlJKASjRg3vphePq7+BgEDToKtr+Ddlpw5eFxiAMGDCLIv6JQky6PiCgpAvSbIfvLkeGhstTFMyerROZeXxSZOhBJCuC664wsNzzyXxeATnn3/8GsMZZ6gS6qmU+nskfD6NujrXkTc4DXE6naekC6DNqccWJsfPiBAmlgWPLYeNLerz2BK4dYESKKNG7U++amvLMHq0gaYJ+vslra0GeXnH9kOUlgqqqzUaGlS5iWuuOfJpbe+Ed9fB3OlQUgQuB9w0Ex5fBxkTbj5Tsqk4TjTupKBAIzcIbrfkjjucdHSA1ysYP147qNrwsXh7Bbz4BgR8cNPVUHlAbsOcOW6mTnWh6xzkg4nFJLGYpKjo2L+q4uJhH4qNzYcfO2vxhBgRwqStH9Y3QmWesk3vaIemHqg7ILdu1ao4Tz01QFeXYPduwcKFPjZsMLjzTsHo0Uc/TU6n4PbbnbS0SHw+DqqjdShvvAuvLFVO949foZaNKYR/zJZ5SSY1rrzMRTIpiUZVgb/CQp2qKp2amuO3bbV1wJ9fhZICVQL/wT/CN79wsJbi8QiamtJs3pygttZFVZWHn/wkTn+/xcc+5mbmzJGlUdjY2MLk+DmthYmUqgHR4y/C85shPwemTYG84sNNPkuWxCgqchCNQiZjIKWFw6Gxd69Fefmx6ym5XGJYDvH5M1WdpfkzD16+73i8Xo1PfcrPQw/F8XgkgYDgllv8J5yXkUxBIg5vvAaxKHgDYHz24AS4gQGT3/62GyEEb70V5aab8olELAwDeno+/BUSbGxOOrYD/rg5rYXJ7t3w9NOwskn1gu+OQlM3fPrjUJV/8LbhsEZ7e4aiIh3DUH6PQABeXKLz8lK46TpVRPL9UlUOt33s6NuMGuXim990EotZBINHNmmZpqStTTm1fb6hH6UqSiEdUzkt/iB4nSq/pqZm/zbRqEkmI6mudlJfn0LXJTff7KGz02Lu3OPTSpJJk4EBg+LikRGxZXOaYguT4+a0FiaRiHriX7sBYtmipfFC2LELYnFIm9DWA7WlcM01YR59tJ/OToMvfMHHhAleevsdvPSGRtgNq9cqYbJ5s8nKlZJbb9XR9eFdcU2t0NENY2shPMyCqC6XwOU6egDA008Psnp1gtxcFS21T6Ds3WuwfLlFYaHG3Lk6Vy0WmEkoLoJM+vCilqWlTqZP97J+fYJRozzU1bnxeDQmDaPPy4GYpuSXv2yhrS3NddcVMXu2XfDR5iOI7TM5IU5rYVJXBzt2pIntdYAf0CDdrPHUG6ANQPUoQEBlEXz+agf33ntwcbstW6FxN5gSLj5PLQsGBYWFctihuV098OuHVQRZSSF88Y6hw3rTGdU62OMiqxmpDo5HayC1fXuawkIHXV0G/f0mPp/GU08l+OpXE8TikpJijc9/3s9tt7mIRlUeyIIFqsDlgWia4IYb8rjySgu3+/BqwMPFNCUDAyaZjEUk8sH1nrCxOemMjOj1k8ppLUxSKRNNT4PhULG3GuAAIwYrGqA7DuefCd0Dh4fOGgY89yxMGK2qtm7fCmfMgKoqjaqqgx9bli6Ht5bBpPFw+cUqrDCZgsEo9A0oQZKfC71DzAOwswUeegVMC66YD207YenbcOEFcNGFR/5+l18e4M9/jjJnjpeSEgexmMV3vpumP+LB5Yau7jR/+lOaz3zGxbXXwu4G6I9A/wDkDKE0NLdoLHsXpk+DqZOP/3y7XBp33VVOR0eaiRMPL1FuY/ORwG5WckKctsKkuTnDjbdGeWdZKFskC/W0kSMhT5D0wLgCKMyBs6cefoPPZFTRxooKVQyyqWnoefoH4K8vQ3EhvLMCJk+AjAW/fFhpFblhGD8KdtfDOfOG1kr+vFyF7Xpd8PxymJGjhM6xOvJNm+bB73fR3i7p6MgWruzXycmxiMUgmdEpLFTbrt8Cjzyr5g8HlYbkOyCowDDgwUfB7VJmwLqa4fcoP5DSUjelpba/xOYjjm3mOm5OW2Hynf9J8c47fjCFEiYpwAOMEVCpEgRvvgbOPEKRR69XlQl/5RVYsxbGjVOO6/JyFY317HPgcsP5i1S5lJ4+pZG4nPDV70BXrxImJcWqkN+kcnjlr5CJw+LFao4dO2DbNjCTMJiBzg5JW5OJt1yS1nWKyo5+RW/caPLQQxYOB2iaxb33Orj0Uo2nngbdAePHmdx6q7qx72qAYAAK86BprzreA4WJpkFOCDq6lNZilzu3GdHYmslxc1oKkzeWwssvOUFmBYlECRMn4IXCJJTVQvgAF4mU0JsAjwP82QCmxYuVEPn5L1QP+URCLW9ogJWr1FP+rJnw6Vtgwxaoq1YCZttu9WDT3gd7WsGvw6VnKk1j6VJVCdayVJMuXQcLMPMkD/00SUeviRkHX46D55e4+OUPNc6ZrjSGQ9mxQxIMgtsrqG+w2LtX8sP/83L++Wk6OySzZzuZPl39i8eNgpUbIBpX2lLBIVXqNQ0+fTs0NEJF+ZHridnYnPbYDvgT4rQTJn198OSTUFSsU78rASJru3cBfkGRF6ZNhzIfxFNqlWXBU1thbRs4NLhtOtRlb7a1tfDFLyhBMmqUWlZVBZMnqRvuvj4U5WVqXTyuisTFk+B1Z8dV7J9Hyv2lGnRdlR13ueHXvzForwesNEiId6VpEBr3/MTF3ZfAV25QGfsHUlsrePV1yaZ6gWFotHQKpmuCq686XBJMHgf3fEIlLtZWgneI+nihEEydcuRz2zkAPjcETk1tPRubDw+2ZnLcnHbCJJFQoa/ekIMxU93sbErCKDf4BCTgzBqYnA/9CfBlTTldcSVIqsIwkIKXdsE9c/bvs6JCRSpt3ZohJ0ejrMzB7bcdPO/AgMQwIBwWXH0+rN8GiSRUlsK8caq6LsCll6oKuwB33gn19fCj16Tq9igBBLh08Bkw2qTJL/nxq4JL58KoMugfhHAA/F6YMUPjgouh62lBcZFAImhqhkceV1rTTTdA5QH1smoqT/y87mqH3y6BXD/8/SWqDMyR6MJEIik6/S4vm5GCHc113JxWv/ZMBlq7oScJLT3Q1uMCzaGeMkyg1+Ltd3TWroLZE+FVJ9TeAG5dbRLLQDQFlUPkgqxbl+bhh6MEgxrf+EYYr1dj164Ura0Zioqc3Hef0jr+7u+cfPZWjb+8Bk4HjKpUmsrii5VD25MDq6Jqvgml4AjAq4+byiTndqpAAaQKZQ47kF5BjwueehMclooSczrhzCnQsFcAOpcuBo8TFs6FBx9Wwy0Jz/0ZPn/3STq3ptKsenrgqT+pDoBTJsDMqYf3r3+BGBZwG8NMqrGx+TBhm7lOiNNKmPxpCSzfAPVR0EqgtgQ2vQM0WOoCGTTIkMFX52HWWGjrhIYWmDgGbpwMz22AEjdcOvbwffv9ArdbEAho6Lqgvj7Nb37TixCSPXt06uu9OJ0a11yjs2CBxl2fgMYm+PmvlK/ksktg/lnwi05oTwMCKqIwdRAGMxq4JKSzAsUnle1MF1gxCy1H4+01cO50qCyG3S3wvd/DRXNVOHE8CV+4UTnOfT7ozFY8Ly5S/e2dTtXzfjikUrB1q9q+8gBNZnwZfGwmPP4E7OhS2t9Tf4LNWyXVpZJUSjJ2rEZtLVym+bGLsNh8pLHNXMfNaSVMdjdDWSHUxGB3P5RXgmu9RXpAgkOANBhsFpSWm6TTOgIVJgtAN8TfgpgFO3wwe/bB+x4/3sWXvxzG5xO4XILOzgyDgwZ79qTo7vYyY4ZASvD7999GnQ7QdHXDd7lgbxo6M1Cb9Tk0JiHfAHo08FqQARICBg1wmLDNgJAXL05SSYEn64Tvi6i/Odljj8ahYS8U5cG1V8FLr6iAAYeAH/1Ivb/x4zDlKP6Qfbz6Grz2uvKpfOXL+7v9CQHtTVmfiU/S3SVparT4659h7nRBeRm8+abFlCmweLFGKChUwIONzUcRWzM5bk4rYXLWTHjudSjyw6IZoDkhdIXg2Z8lwCEQhsQyJRUei+pSnVnToTybDf72O6rUicOhIq4OFSYABQXKkNrebhIMOvD5NCIRi7FjLUpKdEpLtYMqDJeVwT13KpMQIXhtA7QD5dXKPSKBIhc442A4UUECiYTq9WtZkJLwbhz35ADzFjro7IP+KAzEoOyASDTT5D1Bk5MDN1yv3v/rt5R2EY3C8uXDEybRqEF/nwm5OodeHpEIRAcs7v+VRU83xOMSt9/CYTrIz1NzvfACvPKKZPp0wT33KCFqY/ORwk5aPCFOK2EybzqMrlbvPR7Y0gAOXWPbCwatLU4MXSCcacqK/Jx/psoZ2cfoUfDSS+oGf/bCI8+RTkt+9rMoug7/8i/FdHQYVFW5kNLiT3/qZPt2H2eckfPe9lWVsLcffvIa+FyQiMAmlzIjXZYDtWkYUwR70hrRnRYIE3QTEpryn2RMEkGLi74CNRWqL/zV58Jba6ChTflpxlTDhCF6MI2qUyYrS6ouiMdi/foEa9f0owlIJWD37jBnnLG/je+UifCjH1r0doNhSAxDoCdgoN/gzTc1yqp1dm6XSKmi3OJxW5jYfEQ5yQ54IYQOrAJapZSXCyFqgUeBfGA1cKuUMi2EcAP3A2cAPcDHpZQN2X38A/BplAf4i1LKF7PLFwM/zB71b6SU38suH3KOk/vN9nNaCROAgtz97+dMABD8938F+c53YqTTFgUFQa69VqMsG8o7MAD9/ar9bHm5UgjGHuIzWb3aZHAQFi7UcLkEixa50TRBKOQgFFKnsL09zbJlffT0pA8SJgD92U6MRSGIZ2BxAOaWg1tT833iPPjx/ZDQNUxdh1RGqdmmhIwgKCQ+twox3hdmPLEWWjpUmHFVyeFOcIAbb4TNm9UNfeLEg9dZFjz/F3hnucrav+Rii0efiJBb6KS2VpBOWzzzzADjx3vw+5XOP2k8jKqFTetVxjwaiICgz9LoboC4JamshpZmySWX7jeR2dh8pDg1Dvi/A7bCe1Ep/wn8QEr5qBDiFygh8fPs3z4p5WghxI3Z7T4uhJgI3AhMAsqAV4QQ++5UPwUuBFqAlUKI56SUW44yxynhtBMm+9ixAzZuhHnz4PLLHYweHaanRyUh7isx0tAAv/udMhPl5cFnPqNKqLe0WJSXO3A6BfG45IknTNJpqKsTVFUJFi3an2ghpWTr1m7q6nL5whdqyck53FFQnQ+FQWjqhYALJpcqQQJKGNxxKSx5CYwIpNw++htNZMwEoaEXO9BqHLQPHLxPjxtGVx39HLjdR9ZIXlsC//l/SoNbugr+8IZFa7+XmgoH8ytSOFOStjZVuHGfMNE0+M/v6jTsNtm0CfBJ3CENQwrwStr6wEjDgrME8+aqed59N8aKFTGqqlwsXhzC7baN0TYfAU6imUsIUQFcBvwH8GWhKqmeB9yc3eQPwLdRN/qrsu8BngR+kt3+KuBRKWUKqBdC7AL2JTDsklLuyc71KHCVEGLrUeY4JZyWwiSVggcfVDe/PXvga1+DCROUSWjpUnjiCZg2Ta1zu6GgQOV7vPJKmlWrIlgW1NU5uf32EF4vXHCBTiQiKSk5/AozTUlDQz9FRX5qanxDHA343fDZc6F7UOVp+A/JKXQ4YNpo2NkAA6aGpyqESzcYTFjoOOhapfF/j8DiM1X9rpPBps1gGRYSSfMMSXRAYCZ1wv0WrQGdXW/HSafVOdqnxQFUVAheeknnF79I87NHNFIJSTyhsurTGVXT6x+/KnC5oKEhxTPP9FNY6GD58hher8ZFF9nhwjYfAU7uM8//AV8H9oX75AP9Usp9pbVbgH1G93KgGUBKaQghBrLblwPLD9jngWOaD1k+9xhznBJOy8dEhwNy81QNrQPLrdfXw5//rHzcf/qTWt/UBK+8Chs2wNq1afx+jZoaJ/X1Gbq7TYQQXHCBzoQJGvffbxGLyUPm0rj00jEUFAwtSPbhcUJF3uGCBFRI78zpgKkiwPJzJQNdaaw9CdJ7EsQaEix73OS731PmqZPBxHGqFlfPmAzm4ji+OSnyx3hwuAWueAKPV6e0Opekcfgl4vcLvvQlF9ddI5g1W1BZJcCpMWG84H/+UyOTFiQSEI1aCAGBgE4goNPTkzk5B29jc6oRw3xBgRBi1QGvzxy0GyEuBzqllKs/wKP/m3Baaia6Dp+5S3VXrDggAzyTUSGu+zLQx4yB555TJU38QVi7RaO00CSVkni9glBo/410cBDa2tQ+TgWlpXDlYlizC3buMmEggYqttQBJJmXy4GMaxaWCu29//4UYzzkHeno0Xu/SWeMSlE4RjKrU8eLnK3N83P8otLTBa8sF48epjo2gyur39kNlmeCrdzt46DmYMBGKC+CWq1TZ/u3boboabrvNRUGBg8bGFLoumD//BMoQ29h80Byfz6RbSjnrKOsXAFcKIS5FlZoNoZzlOUIIR1ZzqABas9u3ApVAixDCAYRRjvh9y/dx4JihlvccZY5TwikXJsONYjjZ8/r9qjnWgdTWKR/Cxo0q9Le8XJm7Qrnw1+UQH/ASKhdU1ppcdrHnoFa4c+ZozJolT7gX+7EoLQKnDufNhHSvRatloISJpopyaRbdg2nuf9yJw6fxmZv21/gCFTK8tRHmTjh4+ZFwu+Gmm+DjlpMoOpm0IJWB3b2Q4xPE4mCmYdNO2LZdCRPLgl89pLpG3nAFzJoKX7pDOeP3BQD09ChB19cHPp/OZz9bSHt7hnBYJy/vtHx2sTkdOUk2GynlPwD/ACCEOBf4qpTyE0KIJ4DrUffC24Bns0Oey35ell3/mpRSCiGeAx4WQnwf5YAfA6xAib4x2ftqK8pJf3N2zOtHmOOU8EGYufZFMexjX4TBaKAPFWFwynmzAf7tDfBPg//3/+Daa5UwCYbgLytgexukAwJPvpfJZwQoKTn8xneqBAnAqBr4+FWqRPxN1+iEfGkgAmTzThyCdDJDQ32Sd9eadPYcPL43AlsaVSOuA4lEJHv3HjkfXdMgpGnkewRlQVhYrWTXBWeqhmC5PnjjVZUrIwTZcveqRMw+Dowku/VWVbr/9tvV9l6vRm2t2xYkNh8d9mkmw3mdON9AOeN3oR6sf5td/lsgP7v8y8A3AaSUm4HHgS3AC8DnpZRmVuu4F3gRdZ99PLvt0eY4JZzSX/hxRjGcUt5pgqBL/V08Wt0QfT646ROwoSdbXNgFnf1QW3asvZ0aZkxWL3Awe3IJt9/RxfbtEmkkwPSTSQsMM8k7bztIJg4OhK8rU69DWbrUYvlyuOce/SBHOqiQ6FdegcmTYfz4g9dVl0PAC119kB/c3yHyrptVQ7DS4qG/Q0nJ4W2BbWw+cpyCx2wp5RJgSfb9HvZHYx24TRL42BHG/wfqXnro8r8Afxli+ZBznCpOtWbyf6gohn1u4w88wmAfF41WN8PFYw42A5UWw7RJMGc6jK6E2y5TZUlONX19Js89F+fRR6Ps2HG4lW/ceBcLzyvC5XKpTMp4EpnO4PE5KfAa+8/oMTBNjYEBjZ/8RIVLH8jmzSpR84UXDh8XDEJbQpnOOgehvUMt93mhrGTojpFHwzAkS5dm+OMfU9TXH6OFpI3N35rhO+BtspwyzeTAKIasrfB4x38G+AxAVdUxEiqGwcwy9ToUpwPuvApWbYWgT1UTPtXE4xa//nWUWMzC7RasX5/hjjtgzJj9cb9bdsCGdYOYpgNwgQVaOkOBDtde5qWoFFZ2wattKiptDNCwVlnErlgEk0bD2u3w9ipBYREkE6pT5IEJmZMmqQZgkyapcGpd32+yeuddmDUechdA0AmPPw233wJrNqpqwdUVB30lkkmV8R4Oq/0cyptvGrz0UppAQLBunckXvuChqOi0DCa0OR2wBcVxcyrNXMcbxXAQUspfAb8CmDVr1ikrQjuYhgEBi2YrB/iRMM3Db5KxmIWuCzye47vy9u41iUQsqqrU6ZfSZMOGzEHCJBGHhiYXlibRXBJdpikoMHnyyRDeMgdfWA7PbID+BpBRZYYqtOC2afDAc7ChETb2ACb4WmH2KLjtkH4mOTnKd7RuE/z7/0HQD5++CQryIRaHghAU56rv3tcHjz4LvX1KoPzLl/ZrJzt3wkMPSRIJlYtz082CkkPMYE1NFrm5gtxcjcZGi95eSVHRcZ02G5sPBrsE/QlxyoTJCUQxfODEM/DTTaoh1sRcuHX84dtEo/DAg9DSAmeeCZdeom6iq1enefrpBA4H3Habn9ra4Z9Kl0tgmhLLUtFhqZTE5ztYIG3eCD6vE5czgWFouJ0mX/qSG0dI51N/MFj2lkZcCnUwlgQNuhyCn74JNSZs7Uctd4LPJ0g44LlX4Et1h0d7vboU8nNVX/iN22DRAjhjOqxZD03NkDHg3LNgRwe8uxoWTFPTpjOwYRv88PuSgZ4UqYxJ0xtOlmxw8PXPayzIBkwaBri9Gnv2GOTnWwSDgvJy+9dq8yHGbo513PwtQmy+ATwqhPgOsJZTHGFwNAYzMJCGfA/sGRx6m9VroLlFFWxc+jbMmK4ywl9+OUlhoUY8Lnn99RTFxY738leORWWlzuzZblatSiGEID9fY8GC/SVaIhHYuAFqql2EAtDQKJk3y0ko5OLjn02zsd1FqoCsOiLBISEmIQ6JjM7WHgFpC2KABvGgJCU0OjqhqRWqyg8WKGNq4Z1VKkJrXxXl6ir43J3Q2KyqKY8fCy1vQ20KXGXQMQh/fh0eeAK2bLIwBqCuykE8I4j0myxfv1+YrFoLq9c70b0aF1woOWOmRjBo2xFsPqTY/pAT4gMRJsOJYvhbUOSFs0thax9cO0TV3VgCdIcy88Tj6ga8rwpudbWD9evTpFLQ2OiiuRnuvvvokUyZjIllSdxuB9dc42POHDeZjKS0VMfjUXf3/n5JIgEF+YL8AsHrb7oxBNS3ww9+kCFRrSMTKAd8HapsfUYoSZCyIG6BT4cBVG3RFJADXWlo74ZfPwRTxqsw5H1mqssugIljVQ+T8lKljWzfCReeB2Wl+4//vPGQNKEtCj98E9YsA68DnF6IdOl09YFA4g1oLDigJpjPq3JPiot1ZkxXfhUbmw81tuJ83Izo4H8h4JJq9ToQy4KnXlUO7FAApk6H3i649hpVxwvgmmu8jB6tk0wK/vpXJ9GoypI/mjD5wx/WE4mk+PKX5yOEoKLi4NO/bJnF889bhMOCKVM03l0pSKOKUPYPQtgD/jxwpiVpSyhVPI0SLC5AF0pb8SizF07UNsWCSVdDxU6llWzcCpdfCD6vZHBQ9a0ffYAwdTpVAchDGVUIl0+B3yyH6jxorQI8cEG+RqTGIBUzOWs+fPazXnLC0NUleeghg3nzBJ+9U2luubmH79fG5kOHrZkcNyNamByJvV2wZhtUlcLeTigYC5/4+MHbeDyC2bNVoa2KClXva9Soo+938uQiEokj12NZu1aSmyvo7YWrr4ap00DzKY1hdz0MdguaIibesCA2KJQgcaL+i6aEbql6yTsFlAhwA17JxPOhfAwktkF9E1SWKW3hhRcM3njD5LrrHMyZs/9SKC1Rr6Eo8IPfBY29MH4M3PspCHoEarKDefJJg9//3uTNN+HJJ/VTmvRpY3PSsB3wJ4QtTIbA41KtbgdjqrVIwHv07WuHMJENxbx5FUddf845gscek1RVKQH16gZY3wLt9XDBPPj653X+79fwTieY1ZK+TgH7cmKaJcRUarpXk0yps9iYZ5CxLJr3SF5pcjB9igs3MH0sdHUrAZhKSbbtgNFjIO8QraGpSRKJqIRGh0MJgqAHPncWtEWgLKQ+H4nR4zX0PEmgTCMSFeTYBYNtPiqMYAe8EOIsYIyU8ndCiEIgIKWsP9Y4W5gAuyKwOQJTcqDGD4MC5s6BPbvhnDNgzmRo6YR3t6o8jjPGnZwsectSiYOxmKoRNmmSxre+JdF1wV/Wwjd/KGlabiE1eP4VnSsuFjzwCwevtcGv3oC3/gSxNhjokxDWIaDKw/g7TVb/MYWZSYEZY9CCVW43e6fnklvhpKUSakJw68ccNLXrbNwhaOqAT1wHbpcqOtnfL/n1r00SCcG11wrOPHO/VpHjVS9QRTL/uhQGBuHycyAvZ//329qkc93HdJIpePlt+Ngl7/+c2dh8IIxQJVoI8S1gFjAO+B3K9vEgKtXjqIx4YfL0JrjrJYgZUJgLX5oBnVHlbPdUwsxp0NEHv3wO0ino7IbH/gp/fzMsmP7+5l67Fh57TPlu9uyBm28GXVdX8Ypd0LHcwuhPYZom3Skn69d7uPB8OK8U/HNgcA20BMA1XuDog8wgjK22WN6YwYykwEyi0ueBTIa9m5JIlxPnBKV5LV0ueOklQSSi6pT1tithcu45MH+e0kYOTGQciu0N8PYacDngdS9cd9H+dS6nCh82TPXexuYjwcg2c10DzADWAEgp9wohgkcfohjRwqR7AL78S+gJA2lozoH/bzt8czwEdOiOw593Q7hXZYj/9WXYshSsDLzyPPzm+3Dd+3ja7utTzu6cHGhvP3jd2FIVjKUJC8OSuDCZOEGtS6XgzT/CZAHnjIJLL4P7H4EdMYNVf4nQ1YjyoWCpl+4AJBgW/b3QsAm294PDBYNdypzncMD8Wcrx/vbbcPFFgs9/XiMSOboZLxxQVQQypipSeSDXL4YX31I+nwvOPPHzZGPzgTNCNRMgna04LAGEEP7hDhzRwsS0INoL9JmIcRJp6gykBd2WEiZ+J0RSELBgfT1seQusbD5KfyP876+hfBJU5MKuFujoh/MmQGGO2qa7W3V2vOQSVfL9UGbNUvWy+vvhmmsOXnf9PFj1WY2Hf+tGNw0+cZ2Liy8Cy5LE48pJX1KiSr4XFUAqDS+/MEgiMoi0QBl9TdSvQiqp5dbJDxg0NQrmTNbZ0w4TpwAW9MaUlhH0wKSJ0JOCVe2CXRvhhgBHzFavKoPP3wyJJNQe4hIKhuCKi2B7Dzy3G1I9kGmG66+A3Jzj/3/Z2HxgjFzN5HEhxC9RlUruAu4Afj2cgSNamBTnwicu7+Dpliia16DbLEYPetgasygKeGiPaVxYA8WFKknvvfKUOmDB1kH495XQMwCdXVCswx83wgN3gNuhnvILCoauVQVKI7n7bujqUu8PxOWA//kazKmGVasEHk+Gp5822LjRpKxM45xz3GzaJLjySmUm6+iwSMUzSEuCcIA0QXOqH4XQwalzzZUuWtsy9CV1Mn4d4YGq0bC9C6wCSJZAxgklZ8MPd8HyZ2FXGyyz4NtXQaUXPEN8l7IDBE0qDc++Bq0xeCgmWTcgsZwmgb06ZWiMS8CC2bYwsfkQM4KTFqWU/yOEuBDV/2Ic8K9SypeHM3ZECxOA2TdtZLDRYleLj7FjmhDdk0lEXDRkdC6tdnNOlbL5z50NnauhbTMgQSsC/1jY0ALdUeWYL8iBvTHVYGpiEQQCcNZZR5//iSdUy+DCQrjnHvAeEDm2c6fFunUmo0cLDAPuuy/DvHmC+nqT886zWLxYp6kJ/uXbsHULCN0DwkCZtwTC68Dpd6M7NMbNcZEocpJK6xTnQqgcqstN3pJp6nMFVXU6TQ4HzQge3GiRI6B4ssBRKngtCJENsLAQ7qyGPJdKZ1m2E7bthdl1MCVbi7OhFd7dCEv7YXsYzIAEtyTpkwymYMJ82JOB5A6YMeb4qw/b2HwgjOBorqzwGJYAOZARL0xMQkyqbqaqeJD1MocxLpOSCVEuEC6mZHMnHBpcMxcyDliyE7r7oMoNHcUwEFU1tpxCsDcpuHSUct4bBry2UiUbXjAX8obI+rYsJUgqK1Uv+p6eg9sMJ5PZRlSawOUC3St46lWdaJuD5haNj10Py5eruUIBgTvgxEijJJtTh0I/IbckAzQmdAaaBEmh49WhPSrZ4c/QmiPJTIHOPgMjqquyLIPQGYRuS1JaBkEElgUxE5b1wmUl0NgNz6+GHD88ugzK8yAvAOVFkFeiyoJVh2B7TGJGwJGESAh+1QHf3wa+HvjyFPjX2z+I/7KNzXEwgh3wQohB3ovawYWK5opJKY8Z2D/ihcm5TOAFHHg8Jp+nnEGvg3ycjJN+XlkByzdBTSlcswj2JKDdDY0DoLugZ4uF7JfgFpj5Jnn5gjk1OnW5sHWPqlslDdVM6jNDtLvRNDj/fHj1VRg37vDs+dpajUAAduww2bFDsny3Tk+rE68pePEl2L1LmdEuuQRqqwXkuli+PoTh0cEQyLYM3UgIOXF0wewZFr1pDV0Hl9uiq1sjXQ7EJaZL7C+/EgfcYJnQOigJZQRVDqWNGNnLzLLUFedyqL9Wtr9KwA//dCvcGoenNw7wzM44TQmNQbeHgsBeYntcDOh1WD6N596Gf7nN1k5sPoSM0GtSSvle5Fa2meFVwLzhjB3xwqSKIJ9kGiYWfvbHr+5qgVdWQEURbG2AwArotyAaA78Gm7aD3A5YQt1NoxpabpqxTh2XDnt2wrJl0JGEddvhuvMhPw8aO9RNuSYrOM4/X7W5dToPv6m6PILQWDcvP5SmNCjJc+lEMsoFgpQkEhb19dDerlFZKSgu1DEm+aDKgl8OqHK/QkA0haGHeOdNi/MvdVAU0OiICkYVx1knPeiahdWa/e4e1FXRD/hANIOpw54YTMyD2dWwow1CHjhvEmxphatmqnL1B51XH8ya20b59AQ/WZnPdjJcnniW3LJefr72izg6Kpk5yhYkNh9SRqhmciBSSgk8k809+eaxth/xwgTAg86hRtJ0Rt3oXE7wOGFzB8SCcNMkQMI3VqOeXryAAfSCsV3juzuh7l/gnQbYHVXLN7dZ1M2xuHiBReVcB26Pxlevh7yQyjUZGID58/dHfO3ereY2XLCjGTKGIFCkU9EhiOQKIgMSIQQut0lvv8ZTr1uUV+lEDOBM4LVscocQql6XJaEvRcblISdhkTdWozGiUeHzs6sNokkHFAillaSl+j4xZSoTaUFBLoQycGUerN4KS3eoc/KFi+CiqUc+ryXksoI+dvl1ekU+r7ov4FzeoLwsxC0z4ROXnuR/pI3NyWKEPuQIIa494KOGSmBMDmesLUyOQF05VJdCYzv43BAuBt1U92YEjCmGNa3ZwoomIEFEHeQWQ3MX/HQJUAoEJLRBJC546i3BmAGL8SUp3i6GqZM9PPaYcq47nbBggSpx8pvfKDnwla9DUY46nsoS6KyHBfMlkQGJ12sxMGCheyW5JQ4KS6GzDSUTdUv9GKysXUrXIGWQiaZZ8qqbii6T3lINPc/J5LBJz+Q4PUlJX9qFrDORSYeylnaC7HHS0QM1OTAzH56uB49DlZmJpqDgKOdwFCVMd2mEMzr9bU6c+mya+mYxoURw0XmQY1cPtvkwMoJ9JsAVB7w3gAaUqeuY2MLkCHjcqp1vbwQCPnh1B/zqRSADE0fBFWfBthZBoksgkCycI/jEBKgrg7GVkEyhhMy+/BIhMBFs6zfY2SV44/kUFy2yKC/x0d8v3ovi8njgoouUPyU/B/7hk4JiQ6OpyWLcOMHSpYK2NklursSyBJUlgtwwVFZA2gHb1gpSZU6VKBPJ5plIQBNYpkasx2LjKigstSg+20n3qA4CeSatXflIn8DpNJGGJOPUoAJy0xL324Kt2+FTjfCjr8Cb26EsF6ryj30ezxaFfBqDdSkod0tKrUaIG3T0jmZXvWTWGEHhMPZjY/OBMkKFiZTyUyc61hYmB2BZ0Nym3leWgqZDvyer4yVV0yqfD9ZshRkz4KJLwa9D/XJBfCu8HoHkdFg0H6r8UB9DFfoalOpJv1DAKAfmRkFfn+CxxyzKKzJcttjFnnqYOVNpJIsW7T8mXYebb9Z54QVYt07icEgqK1WkVyoF550j8AcsPn+3zh+el/TG4K2NTjKFPnXQhlT/5QIPDo9Of1qieSTdKehPpSkc34MR0tFibjD8pPtcaD4JaAhDY9AtSfYJPAI274WV26E0CAvGHt6xcSh0BGVejb84k9TlOLl95wO8sGwPtz35NXqThaSrA/zuXic3zLIvRZsPCSNQMxFC/Jj9UVyHIaX84rH2Yf+CD+CZV2HlRvX+jElQMBv+2quuqxk6TC+FAQPaDJhVCV8fB9//nSojsrkH+iIqzyKZghf/P/jaD+HPT4HI05ASvMUwuNGATkt1QrRMWlsdpNKHt+3dRyYDb6wUmB4HX/mapLzcoq0NdtVLGposkgbcdZODrk5J41qTiX5B3sUalumlscFNV5tBLKXhCwm6aiCZsBBJSE8UiDoLo7sQ30AM0SKRQR08AivjBMNCOgC/oHAi9K0GvQze2aGijqWEC2cM77xeP0ljVIWTMR748nPzWN17EfXJsYiUhdhs8NUlGc6b6qDAdTL+izY2J4ERJkyAVe93B7YwyRKJwurNUF2mrFN/boXcWtWwMBeoqwVPCvb2wqJpMK5SjQu6IOBUZepbuyE0BtYYMKEMnvk+/F0A7nsWLA84E6aSRL2opx+fA4SkfDzUTlQ36EOjm97ZBC+8AwE3uN2C66/XuO/3FgMpweJrnCTSAt2lyqw4HeA2YKAbnGE4e4FGd68LlwXriyV7YyAyAukFEhJpSGJrw6R1DwyCKBDIMRI0qQ4wKnD0SXpcgsBY0DPQ1geV+cPTSvbh0ASzc12sfP4NXu2rYq+jAmFILK8GEQdC02hJYgsTmw8PI8wBL6X8w/vdhy1MsricqthhIgnr8mB3CM70QWcaRrlhag7MGqJY4ccuhkf+AtU1ELgQNlXDG5theiF8swpuujPChl4Xm7e4KM7VKB8Hext0ejoNcOoESzSeHxQ0PQt3nQtnHxAdtbEFHt8AGyIwNaQy7AsKBFdfq9NrQigE8S7oisGGDkFDns47b0EcQX4+zJ3dy/wL+nlqbSWb+p3IBEhTKrNbsYkvM8iss1cyqng3puFgxZY57GyfgAxbWIbA44zjKs4gIw4Gm4LoDo1MAGZMBF8AHnpX+fvPHQ8lw3Cmi7oZDMQknkCKhCHQLA2zCs6c7CLfrips82FBqB5zI5Fs/5JvABNRiQIASCnPO9ZYW5hk8bjh5svgsdeg0QUX1EKZH6p90G2A6whP4qOr4R8/A8/3wusDsLsB2gehJwIeBqmt3cT824L4Xsjn3KoCtJSbJUslmsfBxk5BNA8SHlgbgze2HSxMmnshFITJ4+HySTA/21d9dA2cMxc274QpZ8AP3oDXnoeBXQJdg0wuhEsMdtLJ758vZa9DIzUA7JJK1UqDPtqg7OJ2Aqk4iZSXcLCfs6cuoWdZLoNaPkauE+kSyIiJ5ZQkC9O09rp4ulPj+TXg7YXBJSAsmDQZXv53KDpGjmxFdYhwCTS1pHAXpNB1D/Omu7izVtX9srH5sGCN3DvjQ8BjwGXAPcBtQNdwBo7cUzYE42rh65+CdBcUZZ+UUzJFWrTRTx8hpqBlT1k8DW83qDDZ+dUqsS/oBJ8T4hkDnzXIqx09VOtORo/vJ/Ouj70DGXavdrBtO6SFJOYUZEwD5+gEmhXAPMRvMn+UKjAZrIbzpqocxAefhTebYe4ZcPktFp99xmLJ65pSEcJAs8AnBRU1ghc3VpMWkpRHwDYL6oF2QBeYeS4GewJsd04kEfExpnIr0wrXUD6mjR4MogRIGh7i/QGMLgdCFxCUWBlId6qOwVSCowV2bIONrXD+MYRJjhcWnQldjW56+t309ENOBHKznYwHEyqj3m1rKTZ/Q6RQucgjlHwp5W+FEH8npXwDeEMIsXI4A21hcggeDc7zw8sx5YOL0MzFoTV004mGjxDjAHh5JyxrUKkcTh2mF8PqKJw5pg3LtZeQv4c4ATZF89jhz8VzQwZtW5zcmiT6aol3eZig16CgbiNVoS5qJ+vMKzuffZ6/11fCW2vhsrPgjInq2H74W3jwaWg34fXX4PFzTd6ICxgrYa2AGhU1FtdMdshBbnf9lojm5Tcdd2E2atAmYIqA6RKcgp5ICe7qFnTDoL57NA6RxhtMIGMalqYRCEWIdYTRKwyMThdmWqgrJomyKeeC0aYqJI8vHca51eGasbBUg44eiLhgfh28uRrC+fC7NyDXD5+7ELy2/8Tmb4ZAipHngc+SfbSjTQhxGbCX/c3Bj4otTIbgvADUumDAAktvJMfZhyo9tT9ybl8tKolynI/ywCcL4DmWMTGZYlXPeBpd5cRyAENiCY3IGb0s9mY4oybIuz4No8ek4vwm+nZX0lwAaxy9rGvK455SjaVrVRb+sg37hcmW7ZAbhHgPdDVDX8zAUSIxHC6oQBWNPkOCS4K/jzw6Ccc1rO0JMN1Q4wCvUNtVCQynk76eXMLBAYykzrbByVR5GzAdglDeAEbGgassiWE4cPuTRDcHseIOyIDwqLBlRzl843ro6YT7HoXcMHziMsg9gpZy8RQoCqtimcvfVX/PnwsdA5BIqx4zsaQtTGz+tkhtxKom3xFChIGvAD8GQsCXhjPQFiZHoDZ7M8swh34c6PgJMPq99ReNU+YYj0OFCQOM98Hm3gKSbKWioIUWEUJ3uNH6JIOxMD3lTpa52ymf0IOrqBBTN2n0heieqFPt7EEX9bxiuHC0Brj0LI3lm+CiA0qsXXKpwT/9aYDujItEgZuO+X24BjwYSZfSShLiPR29xVfOrzN30LUzD9nrg5CAQk251JoEdALzIJHyobst8EuK3O2kpRvTqeFEomkWnpwUwkhgDGgk8GEZ4HKD2wM4YWYNfOlc+O/fqiTLzl54YyVcfBb88R3VD2VqLVw5Bxy6MmPNrVPfZ+E4iCWgpEBl1KcykB84vM7XPqJxME0ID6uJqI3NiSNHWjjXft6VUg6gvKuLjrXxgdjC5Bg4CVLIOYct97vg0vH7P0sJD66D/+/NuSQclZg+i76KXIRuQlTH7U6TaPCRGAODFW1MC28jLkI0Zqo4L+9lCh3dJMwgu2M+6uPjWZgHzvFg+KE+AvkeaB/bRcWN/bgjFq2DhYQLe8DtJdniwxIuZXrSs616B3T29I+GdgPyNFXaJWWp0ir5KFvZHom3Lo4nJ47TlSYs++iXuWBoGGkHVkbHZaUJ+QV7Hw7h2eTCFQJZBzVuqHDBnEJwCHC5IJpQeTEeN7y1BbY0qzDid7dDTSHMzMripKEqL9eEIRRQyzwuWDTp8PO/ZRe8tVrtc1ezMitecz7MGmJbG5uTgURgjcBEkyxvCyEaUE74p6SUfcMdaAuTk8T2LnhiEzR0utGcuaTdbnJ8XQRyYki3RrrRRWRNDrtXTSI6J59Zix6mwrWXjKbT5iijSxSQ5+wl1/0Ofqp4oT6IYSb5ZW8zZUEHhqyiz2eQ8XZR6uvECkSJ+PwkmkM4U0nShobUwREyMZMaMorykg/qkJ8tL+8WkLYQlkTPyaB3m1g7Lfq0IEEzTupcJ7mBXnIzA8R6Qpg+HdESwtHvpK7DjyvPSVMnGEVKJm3ugYEd4MrAVYtg5SaYWAfnzIK/rFGmKl1XGkk8vf9cvdoAf90Dl42CS0Yd+ZzG4irsOuSHP78JNZVQUwbvrLOFic2pZaQKEynlWCHEHOBG4J+EEFuAR6WUDx5rrC1MThI7uySNnQbJlI5MhQjl9rJw9pt4XElWrZpN1+piRInEmWvRsbmUzbVTKR7TRaHexSAhPFoCL0kiso9E/ivkWMVMybyI7khRFhSsTI5ns1XOZP8ajIRGqCPC9p6xdPaWQ4sDoZlIHBj9DoTHQrgMpMsBEQl9KKf5ALhcaTAkWsgkr6QXhyfN3p1VUJgi3yuRW8OUBQUd/Rp9e3Lo3FJAPKXjjzlxaXDnTbC2A7ri0B6BNV2wuRlWbYan/mP/+Zg/DjY3QVMXBDwwqXL/uoogFPig/BjmKpl1UQmhKiwnEsqMdt6ck/3fs7HZzwjXTJBSrgBWCCG+C3wf+ANgC5MPAgtJV85e0mEXxaaBkGkqRzficGYYVbSL4hnt/HnNlTT7qjGTbjIZF73RPDDBK2MUeNrppIQOs5hWq5qkabA2L8KLvfM4x70OLZWiyRWjvzeBx+GlJ1aI5dHIp5/4gBunw8SZMsl0gR6QhC7qJtYaJJkIKpPWLgvCGqQletBCulUmvF5qUlLTQfvuCsqnDWCsC5OXKylNOAmnTJavLyTZ4sbrg+YQzLkI7t8DXY0go2D0QXIQTA2WroVYDPx+dU7K8uFLV0J3BJ58CJa8Atdco9ZNK1avYxHww42XwJur4KbLYHxttoNj2Sn7V9rYACPXZyKECAHXoDSTUcDTwLAe32xhchKIksZV3s2tF6TZvruZgKebgom7iOhhgmIQV3mKmbNWkdnjoHJyM7rLok8P83brmRiVko3pKeByEDP8RB1OejoKqFndzNl9SzErJRvmBYjFvexKlRFOd5Lj7MNZaPDOlgVIpwszL4mzx8RpSXxGmmLpIup10iwt5FkCXkNVL9YsXEUJ0lE3zoCBb0wChMRXnCK3LsWutaXEE2nqPCnqHOWsFm70NMQT4AjBC7sh1QEZH7i8YO0FTOUUzxewfges2QEzx8OZ0yDoU2VmaqtVj/sTYdIY9bKx+SAZqcIEWA88A/w/KeWy4xloC5OTgBsdFzo15U2MD7yEcOaw1xchbEWw0DEsjTFnb8U/JUYi7kP3GoQI09Bei1GgEQsGCZt9mLqOTApiHUEcAybmoANffZQ91XUsbv0rT3mv5c1N51JV1khC87KzbSxoEks4kVUmofIMN4z1YDjy2SZh0GHR1yVhilThwH06RsaFpyJJ/hmdGOi0tFVRVONERPKpyjVJ9BWC0w0GtDRDKAdMoWqXmRFVhNi0IJbJ+vv71UU0dx78ZTlEEvDom3DZ2VCTD8U5cO21x1fLy8bmb4lk5PpMgLpsh8UhEUL8WEr5haHW2cLkJODGwXnUsVmYSPNNMiKNP2KR8boI6H04cbOxezqm04HuN/C6kkihEat10+4qIUdGiMkQCekiY7roGixiSf65tAVK0EszBIoivNN4Jn1L80h1B4i0hzFSLjJeN7hBC1vkj4nz6XEO/jnXy6ut0JeEQFAj1SdJCYlZDpQJ4tJPuLSPmAwS3RZkcDCEL1+jtEQQc0MXGpsjGZpbHKQNQW+XSsrUUuDZDJkCwABPWkViJYMwcwxMmgSvbYPXN0LGCcuehRI33DAdxlap9sc2Nh8NTp7PRAjhAd5E2QYcwJNSym8JIWqBR1GG6NXArVLKtBDCDdwPnAH0AB+XUjZk9/UPwKdR4TRflFK+mF2+GPghqg7Gb6SU38suH3KOox3v0QRJlgVHWjFixe/JpgAf5zimcW7oq4xN1OGMz2T3wCI6EwvIaPlML9hM2nKhuSRpzYVOhsKiLop8ncRMHxl0fCLJQCJI3PQR83qpn15Nb0kOns4km0OTyZk+gO4zGWzNJbHXr/JKMgLTqVPZm8/HEwX84nGNPz0M0WZw5oMnJPD7NTwuDT1fINMa6a0u4mt8JFd6Yb1GfK3Ghsf9JHa4cJSniJekqO+TePyqRIzbAIcfjDxwp8AXh9x+GO2H2TNg0dmQ8sKGHsi4Qc8F4YCujEpiLLA7Ktp8xLAQw3oNgxRwnpRyGjAdWCyEmAf8J/ADKeVoVIjMp7Pbfxroyy7/QXY7hBATUX6MScBi4GdCCF0IoQM/BS5BFWe8KbstR5njlGBrJicbbyXFFfcwXXZQHg0RtSJ0itepyHkNn/M1GlLl6JpFp78AS+SgY1FsdqCnLPoIUyD76Hb04qhIkXjXT0u0lm5HIXqNgZXRCE/qpXtJCfiypU2KLPLcgkmGxe2/MNi4VseZErg1gX41+CuhvxVKDIgMSGRFN7m9vfSsK8IXihHrFhh7XPRN08jk6rg8bvKmS/rGaxCHQBHoTkj0gNeEgThoQUi5lblrdC1sjkJ8AHQPSCeqhbGA/Fy48xqVIyIl9ParhEOHfdXZfIiRgDxJz9nZJ/1o9qMz+5LAecDN2eV/AL4N/BzVIvfb2eVPAj8RQojs8kellCmgXgixi/2O8V1Syj0AQohHgauEEFuPMscp4ZT9rI+i3p0P/DdKK4oCt0spd52q4/hb4MHJJFFBJNjPGywnThdRFlHi34DTs5OVYhZtlBK0ouRZvRR4evGl42xITaW5Ix+PHiexMYAccIIlSCaD0GugV5k4C5Jo4zNYuhPcEjo0+hokL2smPfkapk8iSyWOekleu8BVq0MQKgKwtUUiCyR7V1dCsSQYGsTwODDcLvBBRtfIjWrMFPBcWvWfL3HApggUV8HicfDCW6oDpScMkW3wynrYm4acfBhfB/0WCBPKi+DMcfDGJlUlYNsWaG+CWZPh1uv+1v8hG5ujIU6qAz6rPawGRqO0iN1Av5TSyG7SApRn35cDzQBSSkMIMYAyU5UDyw/Y7YFjmg9ZPjc75khzvK+vc6QVp/IZcZ96FxVCOIGlQoi/kpW+UsqtQojPAf8M3H4Kj+NvRiuNJGjFj0E7DYzlbPqMNAN6DuF0HyVmLx7ihEUEQzgIaQPseutcIm055Izvw52XRPOYpLo9CF8Gd0EazW2i12Sw2nXY6wAXyHxojmuQK6BAQp5F5kwLfXqCC7u9FAXc+L2wbhASPWF8MoHwWESNIAn84AXCFulcDadPOdE1F0wYDbF2yJdQkAeVZXDNRaoIZfugqgjX16ryPzJxia9WksgHegS7uwVb++G3r0NhECZIsAYgGPhgzn1HJ/QPwKhaWxOyOX6OQ5gUCCEO7FL4Kynlrw7al5QmMF0IkYMKtR3PhxwhhE9KGR9i1Q+PNOaU/cyOot5JVPEwUEXT956qY/hbEySAQZoAPvIIU0aUjaQoEN2ME7soiHdT31NHb38+LtcgO3dV0v1UOQQkZjpG/jltkCtw5aYQNRYyoNTv4kntRMf46e8vhF6HynRvABLAhUClidOZxu/tpjWconZNHlaymGhGQoOLGAKWSqwU4DfRR2cwR7vwFWsEffDndtAc0OiGudNhYBuEvSrHY1sERCn4YxDtglRSNWY0Ci129EroF+rZxS1VnbB8QdcgFOfDtCKYMvPUn/f+fvj5b1RI8wXnwkXnn/o5bU4vjkOYdEspZw1rn1L2CyFeB+YDOUIIR1ZzqABas5u1ApVAixDCgbpH9hywfB8Hjhlqec9R5jgiQogzgd8AAaBKCDENuFtK+bnsd/j9kcYOyzAohBgrhHhVCLEp+3mqEOKfhzFOF0KsQ5UVfFlK+S5wJ/AXIUQLcCvwveEcw0eRcuqYylnkkUcdE9BwUZTpJE/0U5fYQ15zP+En+pmxYy25v9nL5v89A3o06NNI97roX5VPaEwf/tkD6OE0FIDMFzhKM+SH+nA4THRPApBQJZQSmwDdaaHpJqYDvDkD3DXrd1xU+65SJQywuiRW0gKvRIQymP0Z6LZwGSYDKRg0YEyVqge2qg8uHAuj8mBlM+zuViXnL54Ei8+H0hIo8Kv6XDQINYeKrQSnVPElSejtgtHjIJxz6s97xlAVl0EJFBub40EiMNGH9ToWQojCrEaCEMKLetzbCrwOXJ/d7Dbg2ez757Kfya5/Lftg/hxwoxDCnY3SGgOsAFYCY4QQtUIIF8pJ/1x2zJHmOBo/AC5GCSOklOuBs4cxbtiaya+BrwG/zE6wQQjxMPCdow06VL0TQkxGlTO+VEr5rhDia6h0/TsPHSuE+AzwGYCqqqphHuaHC4FgMvOZyBw0dPamdrF327M4czuRYUHuQB/uQBQiaYomdaI3SxgEhzuDo8kilDvIWe6lxPf46bHy2eScQtzpQ3cZlHS1Y/a66E95SWoetJhJQuYhQzqedIKawj2IDEzUG7Dy8jgv+CfomgVBHRImFJqAhdztgEEBXQbREh29AOqKoCMDk+rgslyYlDtIWzpD23o/TqeLiUUCnwvIAcdnYGArPLFCkBIoNUWi9NAyIAaEIW7Csnq47fCamSedwgL45E3K1DX7A9CEbE4/TqLPpBT4Q9ZvogGPSyn/tK/mlRDiO8Ba4LfZ7X8LPJB1sPeihANSys1CiMeBLYABfD57f0UIcS/wIurR7T4p5ebsvr5xhDmOipSyWfn838MczrjhChOflHLFIRMYR9r4UA5Q7y4BpmU1FFCVKV84wphfAb8CmDVr1rFinz/UaNknmJ1PvoJr7V7MTCfRORlqO3czdfsu4q2ClOnkwvHPsMy9mESnFzIaZ05aypee/Qm9e3J5ePbHkWhsGDOZsdt3Mufld0n1OVmReyb1RWUE3l1DrLSMHWMvw5nK0LOnGJGBjsEMm+u2k2N4qC5M0PimT/3neoGYhIxU9VC8AocmcElJRzckTYGVAM2S3BfNUJHbwOWTWyn3hXAm5oD00hkFIwF7+iHg0oj0ScyIgBwJBYAuwAeONqgrhz4XRA64LKUEw1J5LCeb8WPVy8bmRDhZeSZSyg3AjCGW72GIMiVSyiTwsSPs6z+A/xhi+V+Avwx3jmPQnDV1yayv++9QmtQxGa4w6RZCjCLbHUoIcT3QdrQB2cb0mawg2afe/ScQFkKMlVLuYL/KNyKQrd34i8YS3Rxh54NJ0tstKsIC3BrOaS6+uOCHXDLrJXb6p0C/Riro4cHNtzCheRtT2chAbg41bfXUbNsFZoL8ria+sPR+MNw8dO6VdPfuopnZJEwvXj3JjNzdJMwcXugv4x+tWXRNAd5MQUsGkkI1upYCxjjgIgcJAYmYqWxWmqBjLexYIfEWQUNeOeMmdTO2vIX6Zjep9nkEnaDF4aIzoDofXlkuGEwAmiCdUZWFc/ww/wyYVA24YUsPTMvW1vrLTljfAV+eryK+hqK52aC/36KkRKew8NhSZ09MycexfhWebGNzvCjH7oi9eO5BOdnLUT6Wl4DPD2fgcIXJ51FawnghRCuqm/gtxxhzJPXuLuCPQggLlUhzxzCP4SPPlCuvR3vmScypFfS89SyxQJBkpYVepiE+W4aR76OmpY2KwVZWls/nGfM2LIdgQtNOzDUO/MW9XKY/h2N7B2OTzRQl+0lOdJNqMji7/y025VTRF96A5gnS111CNF6KS8R5OVNG1848kg0umOlETDaREQ1iTujSwaupUAnNAq9ANwwcg2lS+V4GPBoDa3KgUfKbORP48j2NTJ24lGljiulvr+WJVgj7Yc4kmFgDzXvBCEF9P1g+8KSgrxeWDcCcmZDMwLad6gdb4IG6XHAc4SFwxYokzzwTR9MEQsAddwSorT1yg/j1XfB3r0DAB/+7CMYdoye9jc3QnNzQ4I8SUspu4BMnMnZYwiSrLl0ghPADmpRycBhjjqTePY0Kjxtx5I8fz9nf/GcM0rxycQ9Wu4ajeJCmeCX+Uaq1b9iI4PKlaIlUMpAIE0/72Vw0He/sGL7xMVIuFyWjmjH2FjD6mRcwZ7gxzg5RmY7jTDZRmPtXngneRFVnOzt3jqF60jYw3GTqg1gRB/iBMiegwR6nEiK5FmKyAZUgmxyYphPp1lTMnQs4E+gUtO0s5hd/XcAF85dTUrIcI1ODPODxP+CHCdmijEENGtIqEz4mwcxATwT8e+H3jWqb6ZPgxquHPldSSv761yRlZQ5cLkFfn8lrryX59KePLEw6I5BMQDoN0jq+/017Lzz6mhJ21y2EMRXHN97m9GKk1uYSQvwXyheeQLkgpgJfet/9TIQQXz7CcgCklN8/3oO1gR5akZNysErDdPvDxGJFuOM9OEMGptNB2g8Rv4/BJ3MYaMtBBjV8ZyXIpNysCC7g1tbfsLt2DNuu2k6oMkEq5CK/wCDXlFSwgfxEjOfG38O0QA+yPE5Hby6ZmI6K2ZXIHg33FTFSAyFICbR5GajMgE+HQoHcDAxKKJbgFBAWUAlag0Hv0kJe2rGI9NgMxSFBdy90t8KEseD3qe+XSMOuTpg6CmJuMFJQ/w4MdkNbCMbUKhPUxm1wXQacTtUIK50tvwLqGtM0sLJCwbKOXSzyrEr4ymxw+iQvuhM80A1Xh13Mdh77memZpdne8254+FX451tUYy+bkccIL/R4kZTy60KIa1AJB9eiks/fdz+Tfe2LxgGzUeFpAFegwtJsToA+9mIgSYW9dBp5aH7oT+Ri9WiUNLWzt7qawlA7l1/9BC/85DI6PVVIHXCbxLxe1pw3ixxjgGfrPkZd/zbGlO4mPx5Dc0ODs4opmc1s6lpLKFNOOu2lJZAmPKaPyK5czJSGa/wAYWeS/vkaVqcOVRLhsjAMHWudgEcspEyRe0M/g6PzMeIeGFDVieOaF21NmDcbXXzyFtCT8M4WGIxCIgrz58Obu8HSVH/3USGYPx5erQevE3JyYWeDOg+jqpQgAXjyBWjaC//0uf1C4/LLvTz5ZAwhwOEQnH++96jn1euCj02BO5NtPPuEi8RSP28tSvPwDRoVmkbShHUR5fSfHobAAVe/le1orGsqMOAjHfFh8z4Z0c2x9v0qLgOekFIOiGE6H48qTKSU/wYghHgTmLnPvCWE+Dbw5xM92pGOhgOkQUZzYeBk0AogXBI3BoOjfYhNPVz01COERge5dNorfGHcj0nofjzhGDMCayjxtOMSBiV5HawpmY+w3OQZa7CkJGxFSOEkdzBC2F3Npa+/S9OVZ+Ga10HnuBipqJPctEb04Vx8S7zERoFZLNGuSCG7gNcFJAxkRpB+x4VWTbb9r4CkTqrVh1UvyO2Hq8sg/1Z4+BVYthWau6G0XWko88eqG3PHAFwyC6wYlJRA1An3PQuWqXJVAHbWw/ot4HSoUvc5WV/HjBluysoc9PdbFBVp5OYeW1WwkOyWKVLbA1hpwd4dksFsIdRHWmFHVOlnawfgszX7/TVXLYCHXoVIDG44V7Uathm5jFSfCfAnIcQ2lJnrs9lAquRwBg5X/Bajbin7SGeX2ZwAhVTjlU5MS8ejxQnKKF6RRNMMjOYUBT9fRuuaCKnOJDWJJu7d/GOS7U7icTdlrr3EZYCE5SEpPOTo/awVM0i4nHitOLmih3prLFKr5MqGXUxOxbnZtYOZwQEWVDdzblkrntdz8L/rpsgS5LVKHFGd9AMe5FaHyg8p1MGpE4vnksYDHvY/dkjIhAXluZCMQHkJnDkKKgyYXw4lcbhlLnRFlCApMOBnP4O1a6G4GJZuh5w86E/Bfz8GXX3wwLOQltAXg1UbDj5XxcU648Y5hyVIADQEdzhyGHdThIqFCS79OFRqGikTdsWg1gc1PuhMQeSA4PbyQvj6jfAvn1SBBDYjG5l1wh/rdbohpfwmyks6S0qZQWWKXTWcscON5rof1RN4n+P8alQVSpsTIIciqrRppIxNNA2mEX4nMq1jbIpi/HANzoYIGZcT3Bp/lFexPn0Gize+zMb+yfTk5lPiacfrSGJaDtozJaQtNw2+ahoC5Th0F4ktE7nTkcMYdxx5xaeZ513NeHMHvaaFI2ce3BDm/rREGJIXdzhI7RSQlMgOS/lKnAJqNLhIKAf8FpSjXkdltueBzws//yl8vQGaO6GmWrXsHcyBKRPgy5coDeDXP1VCJBKBNWtUCPDaHUprQcLPn4b1DbButzI/WR61v3NmQ13lkc7g0bnFkcO88UFi4ySjdAcBIZAaVHqgMaHSX3KcELRrdtkMwUgODRZCfPKA9weuuv9YY4cbzfUf2SKNC7OLPiWlXHs8B2mzH4HGaC4mpG8kvfZtIvEGCpyDFL7ezepZ0/B64lQVQlwE2dVfy2RtDVPXrePaoJvfhj+Fd/QgaZ+Del8VLVoVU5wbCDsitKVK2G3VsjCvkdqazwIeBFBGEWG9kVrdjd9VhRwt2DJLsGsPyEEQEXCWCNKDOrhMyANygA1CNfEcQOXABgAfMCh54SWQKQsMVUW4L6lRWSfY0wpFRXDtleq7TpwIy7LNP+vqYEwQHv2r+uxzwosrYFQNBJKQisP6nRBwwVvbYfHZSikaWwnjj6MIghCCMbrjkGVwSyUs61XdIufmwPY4NCdhlFflpQB09EJ7DxTkKG1lH4Zpm75GDmJYpVJOU2Yf8N4DnA+s4WQJEyFEFdDNASG9QogqKWXT8R2nzT404aGI21kwKsKG+14m1tZPskqwOPku0QkVeGJJUpt3I+pS1LXtIRCPkNLd/O+6b6D1wwbfZH4y8W7yAz1MN9cwoIUp11ro6giwsdjDBLmdKWIaUSLoOAgx7r250yb0pOGKC2GXB9p2owRGK+quW44SHClUJpCOulL6Ue1/dQt5nYCYgAGwtlv0+Sz6MoChk14GN22EyePhsstgwgRwu6GiAtZthZpCdQxeNzT2wpYmCLhhYABiFvxxG8QMeKsPZlRA9Ra4/aJs4uP7IOCAC7MdH9cOwqMd4NfgjX74dKnkxUb4/55V5rsxEn5ym2BSHTz5JmxrhvwQfOJ8KM59f8dh8+FnpGomh7bkzZbCenQ4Y4er6P+Z/QEuXqAW2I7q+mVzgmiigsKaf2XhP11FtP0d9r71Duuf2c00ayOZXFi/ESp3PsjgqAmETDejavcyxrOTvm4f54XaaW4o49UxF/OyuBw9nmFe7GW0He2kt3tZcsHbVIsaVvMODhzM5WxcuAFwO+H28yE3AN8Iw7J10LNL5YRIt6aESD/q6jCleqVRAscAvEqIkEZ53eZqyrKaBgosdvZKrvx8hlnTBV+53cnCmco199Rf4J1V0NsCZ86DcB50rIIZY2AwBi4J0QTsSUHKD/0J6O6F0hnQ3Qf/VQ6FJ8k0tSMOYQcUOGFvCt6IwQ+XQKIfPEHYGoUfPie56VzBtmaoKoK+QeWk//L1x9q7zUeZkWzmGoIY6n5/TIZr5ppy4GchxEzgc8d/XDaHIkQAt2cB7poFpNqW0Rz4Bet2dzPV24YZgVkdS2lo3E5VhaAmPUAyZRCLuBAuD26ZIezowyuSOK0Uy1hEeqCNmvpNmOf1E9cGEGho6IhDYi1622DZdrjmGvjTv8Jv3oBnX4T+Hsi0Q0YDPKB5s+1J41K1VuyRMFGDPagQDGlBQlN6axrlYwlKWrel6Wo2aKl38vrDftxujVXrYXQNZEzo7IKiYrh0LsTSUJSrQonXNUFGAAJkBvo1SHSaOMqSLOl3c5nfge/oEcLDotYDawYBCQkLfA5IJdS8bqmUrqQF7X2QE1AKW14Imjptk9fpz8gNDRZCPM9+xUEHJgCPD2fsCT3nSSnXCCHmnshYmyNTNn8+4bF1NK78F3xdzzGtpJtdDToVvTHWb46T3u1kbLmkKC/GHquEdZ6xOIRJGidSSAZdYWrytrBLO5cxsoctibeZ47wAlyOEk4Mzx4uLVT4FwMwy+P51sGk5rNgFmTTqchqEubOhbRAi/YICj0afx6IrispA8gBx1LOLO7vjjFShWQjSwsWmTXDj3Wl++p8uRtVo7NwDPjfcda0yg0UTsGQDvLYRPCFVwZ4Uqk9ctoy88Ai6Oxy8tEGyIQaXnwNzp6l1u+vhqT/D9Mlw4bnDP9ezsuHHTSkY64W2HvBWQlcnJOMQ0ODj8yHohe0tatv+KIytsAXJSGAEayb/c8B7A2iUUrYMZ+BwfSYHZsJrwExO46ZWfys6WUtn/nqsxTqu5NfJqX2J0R2NEIuz4rtNbFqaoXmvk/AUHyvKamieU02YNA4MBgmQdATwTg1T29rHuK1bWRlqpqtjBVcUX4OreuFBlQ/z89VrH/EMaGFIa4APdKkc81UeWDQOZk2Cxl2Clk6dh1uhLWUpTcSLEij7HmZcAqIS2uNofi+5ORb9gw5+fJ/Jd7+psatBlV2pzpYrCXjB7VE+lTFhpZnkJGEwDkYY9EGo2qFRbrk5YwqYHnj+dZXwWJALy1dDXz+8vhQWnXVwV0XLAtPcnxh5IJqAOeH9JVU3bRBcGZDsXAh9nTA3DuvXCgwTxowC3DCxCs47rECQzenISBUmUso3hBDF7HfE7xzu2OFqJsED3hsoH8ofhzuJzbFJMUAX6/FRyCBuejwd7DjrSvS+tXS1ROh54Gwiv9tCd2OEwECSvBmD5ISi9KUKceoZ0paTCm8TEzs3M/O1jdSkmtCAbfPHsG3lNma2b4aZt4HLN+T8a5tg9iRo2ALNHWpZcT587XPQEoeSPLjsfNjSAnINdKc1Xl0n6WqH9OYUOFxKO8lYhIUJ+Q6SGYnTKehuFbzWp/HLPLjlE5CXd/DcO1qhKKzMR5k0lBVCMgmepGqmddlYiPWqzHghlNiKxZUwOWsu9PbB1EkHC5K9HXD/0xCNw6J5cP6ZRz//U6tg8zuCiVJpJUkDKkohk4HGBviHezgp5jWbDz9yBEdzCSFuAP4bWIKK7v+xEOJrUsonjzV2uMJki5TyiUMm/RjwxBG2tzlOlE9DYGGgM46EtZcNcS9JbRLhmnqaveU0jbmUnu58qpO7ucLzJy7peZq1wXlE3QFq/E0UJDuY+PJWKkUzfYV5rPXOoGdvAb6aKIG2DYzd+QJMunbI+R0auFzwqZvhqZdV4cNplXDFb5SioTng6nHw3x9TCkm/AbPHCgY0He9kyZp1GeIxndnzHaScOjvXZRhoETQ3a7gdgk/dqtHZBY89Dnfduf/Gn07Dlk3Q2qtK1VcHYG9MZaLrKVXefmotvNYKDW2wYpuKUi7ZAFVxyPXCF+46/Pv86TXVZri8GF5dBlPGQVH+4dvtY3IlfOkSFWW2dBnszHa/djiUhpMZdvcem9OBkaqZAP8EzJZSdsJ7rUReAU6aMPkHDhccQy2zOUFcBCllPh2sxkc1q6K3sj4h6TF2MDknQRI3scEQJVongbwMVsbDOZEVjFu+lYhw4YxL6lOlJCwvvRTwRM7H6cnJIzAQoyO3kM308cWdLzOq2gPmHvBfD46S9+afWQ2rGuGFteDKg1ACtkeh3QN6OaDBIw1wawNoYZPtr5j07hVcPVXj3n92EEs5ef4t6O5M8NcXe4g1G4i0Fyn8WIaJZrp46imLzi6LX/7C4JZPQlK4icUF0Q7oTsGGHTC6GvashFInpC1YvkPVchBBaGyFwjwoKoFfLIFSA2bmwZySw/uhnEgvk4KsH2X2FFi/FVrbIZmGqeMgFDix/6vNR48RXuhR2ydIsvQwzEopx6oafAlwKVAuhPjRAatCHEenRZvhkcdY8lDtAVdJqMZCSi+m0HBi4tBT+PwxEkEPy8Q8WgrKmNGykqJdgzw1+Vr6rRDtZpSW4kq0KExp2kws38tgIEAnhWweXEZ14nUcmgnpjQcJk6AHPr8IRApWb4PNzdCZBsrURaKlIZEPa7eYPPntKG27XAgheeg1iO1x84//qFEVyLDqpW6mjtfYhIPYjhQut5OC3CCPPWnRY1ngsujZbbH+KwZnzIsza1EezZ1QVgtmGcSCcP7ZsGOrMjHVFENNkar7VVsLZlol45dNhrJxcEnO0I21Lj9PmblaO5SJ62hayaHUVsI9N8OOBggHYNp4u9HWyOL0LJUyTF4QQrwIPJL9/HGG6OI4FMfSTPYCq4ArgdUHLB9E9XK3OUUsDoAuNEZlytGi9azPHYX0CwiqMvIOmabLU8Rbl51NQ1s1ZkrDbPOT7szgiBk05NcwuW0z8dFerLQg5XSztyqXJtcYikUnDnfVewFY+/A44YxRsLMNpkyGHY1KY8hkfRyOHti0LUrXHh2nQzmxk3FYvtzgO9/RKC+3cGoCPWpwxnidolwnV12dw3NLNFZuMcAnVfp5vg67LFZv09mjG2Q8DlIZcGUPSHerVr6zi6A8m/meMkH3KOFSFIYzx0AgAAv9Q5+/0iL42l3KROU4zphFw4CVK2DjJpgxDWYeI5vKsI7c4Mvmo8lIFSZSyq8JIa4DFmQX/Srbg+qYHKtq8HpgvRDiISmlrYl8gIR0uC4EDUaYX67/EqNn/4g9rho0TMIM4BAmBi7iviAtFTV0biylMt6CKZzoGZPBUJCe8lziXi9x3Ut7rJiXBi/gzV1Ozqt0sCDwBtUE8VH23pwDcXhpo8o078yF6mIo2gPLdijn979cALtesxDCgcspSadVB0RNs9i61aS9PUNJiZ9AIEFegYNgfi4tbRoJr6mEYH+2AbxHQK4G3ZJUNIZWbWE1eoh16sohIh1QKXgpV1CfhDOSqmz91CqwkjBjkjJtHQtNO3YPlKHYsRNWrITKCnjnXRg/DsaOGXrb+j54dDN8ZibkDx3bYPMRxBqhwgRASvlHTiDA6lhmrsellDcAa4UQh7V4kFJOPd4JbY6PyhzIFeMwdl1PenwfXpkggxMDJ2AREAlyjD7arTIaC2qothrxxuPkRHvZU1JH3OOjJVqB7IUdsdHUjdrNWzJNy2A/t4WXU8t+h7xAmXMsS9mNZ0+COy9SuSAFYSjJh13jfSx5NcqeendWkJhYlpPWvRl27RaEcyRXX+MnowWZNFqjvVOSn0kjdCfSIZRmogOZFIHKJLXzmonFQ8TyfHTtKcLSNchJwAYXVrvOjps10m7IqYbJVcr0taEF5mRzcpMppSG5XCfvnO+LGNuXh3M0E1e+D86sgOChap7NRxaJwBq50VzXAv8JFJG9JQBSSnnMJtjHMgD8Xfbv5e/rCG1OGF2Dm6fD71YuYra1jN1j+yjUu3EIA5crQ5HVwajMLrZ6J9MwWE19bjVePUE42EfY2UtYi2BEBfFokPlj3yKYEyWddBEJmGw3t1Cr7xcmIR9cPQte2gA1BbBoolp2IKNHu3nqKXjyySRdXRotLQH27o3Rv1UHYdHdo/HggwnGTNT4xM1+OrvBn5JcNN7kxc2a8qoPpqFHUnjWAD3bi0hJJ4mYH1IWmA7AAUY/NIWRb+tkLtPpScKqfqiUMLVcHcuyNfCXN1RHxBsvg/GjTs45HzsGFsyDTVtg4Zkwqu7I24bccE7NyZnX5sPDSDVzAf8FXCGl3Hq8A49l5mrLvv2clPIbB64TQvwn8I3DR9mcbCpy4Etnw6b2+XQ0zCNe8jKunMfJSTYRTHaxKT4OZ30GM+FECkFk0MtgQ5iO3Ar8/n4CpTHS1W5ycgaIJv3oWEgpMKw2pJZGiP2P9bPq1Oto6AE3l93opsAHP/sxRKMC0zJJxE2klcTMGGxdZ/CwruHP92HGIDNgkdsBfW2WqpdSHSTeHcPjTxDt9pDsN7CiGXCkIe1Gnx7CHJTwdoaWwSThsRqrRrm4crbOovFKI3nsr0oj0Sx45HklUJxOqKk4fj/JQd9PhysvVy+bkckIFiYdJyJIYPihwRdyuOC4ZIhlNqeIgBvmVYPSOi+i3czn1cQSlic1XmmaTMdgKf7gIIOtecgBiavWQCYEgwMFWP0OCop7iOYFEA6J5rbwOyxGWXGE1Q268ptYFmzYCUV5KnFwKFbuhKfeUaafwjBccx3093tYsyZCRCZwOCW5uRqppMVgJMOV1wqcws19v80oQWKllJkrodPdXEh+TjeZaBwr4oFMDNxFEI/jKdSIp/xI3YC3Uwxs0xi8XOPZOo3SBkGRLlm1TeLxCGIJQSahes1bEsaNgluueX8CxWbkMhJDg7PmLYBVQojHgGdQhY0AkFI+dax9HMtn8llUQcc6IcSBPfCCwNvHe8A2JwdJhs3xLayOZVjaPp/treNJxf2qGGPawuG0EC4L3QGGJUik/GR6k+zJmctU3xtM8naxQBukJpMPzv1p3U3t8JtnoK4Mvnzr0HOv2Q35QWX++v/be+9wOc76YPt+pmxvZ0/vRzrq1eqS5YIrNti4YtOCMYTeQ16SkHwhjSRvQvICgQQI3fRmY4zBDctNxZZk9a7Te9tztu9Oeb4/ZmVJVjuSJUu25r6uuc7Z2SnPzuw+v/n17hEor4Mvf1ln2TIvn/lMDtOUqIrANKE85vSGD4dUtIAAsghVQ0qJyBhYapChrmqgF+IGeHxgFsFbwChGEAULqdkQETBqYT9V5MGCwrMFG/P5FKbiJV7pIRDW0T0KjfWO/2TPQdixB6QJ1VVQV3f8zzJZ+kZgfw9UxSDmheefh1gMLr30+KVaXF7rXJShwTcf8X8WuP6I1xJ4ZcIE+DHwe+BfgL88Yn1KSjk2yUG6nGVeHL6PRzPbyJQ1Uu4bZkYl7Nk5l3yvnynTDlJePUzaE6azYyqKaiMtGM3FCY5E2Za7iZtrf8r0FoE/cBsoh5tz1JTDtcuh5SST75RqeGIrZPJOz/ZYECzL5vbbfaxfH+SBBzJkMhAKeZnREmTOFLAEzG4RHNymYJkGUhHoqoJlSkw/Tn/5kX7QDEBFnTcNBhXsHE75+/6c408pWqTbNdKagLSGUm4ykTKY1uqlujmIUvr9KwLWrocD+yEahb/68zN30A+PwzcedLS2bA4K7Y5QyWSc7P3rrjuz47pc2Fxs5VSklPdOZjshxF9JKf/leO+dymcygdO54u2lA1Xh1IoNCSFCbnOs88O6Nb/CnDebHcnpyL3jWFUZZtbsZZwY5TXDpMbDlLUkGPRlySQjqJqFYoHqtZFtDTxlf45x4G0Lj1blfV649aqTn/vqBRDywWjKyUnp7Uzxs58NYBg2N98c5z3vqeSZZyQHDuiEQiqtjZLp06Fth6DsXR4e+q1CKimoKldYtVrlxW02e7aFwC4H1UBv1tCrbLIJHfoM8GhgqU6p+4xwwoptEwwPdipNwauyt8OiLmbx4O9VFBWmNMO0pdDdCbGo4wM5kqIFv94PvWm4pRWmnaTZ1eCYk3fSXANdvdA24JRmSSSgv//E+7m8dnH7mZyUt+IoF8cw2arBNwP/CdQBQ0AzsBu3OdZ5QWwcolg2hbxHobK7i0A2hZg9lXdM/Iwnq1ajRW2McR3L0vDEcghh4w9IUh0+xH4vaoVgUyfcMsvpdng6aCpcOtv5v1Cw+OZ/DVBWpuHxKDz11Bgf/nCIj3zEz1NPQVtbkUceyfCHP0h8Pj+VcR/veodKWxtcf71AUSAcFPR1h0mmIxAAo9fCGFGdPBSPCQkDihogQdiOimAKsJKQMEEvYI7pdPlh1gInetGjwOWrobIB6quOFSYHxuHFIYj74DcH4TNLT/x5a+LO/t1DTmX95Uuhq8tZd+kpike6vFa5KM1ck+WEF2ayLsp/AlYCj0spFwkhrgLedTZG5nL6NO4qMhTdS+SKVYwsWIgW8TC1p4e6bD/T1x+kYto6NqWX0JzuZlfLdLKROHrBg15uUlEZ5Fe/1YlH4J/74ZplcOWiY3MptjLCAZLMoYzZHP/R3TAkhmHj9SooipPAmM/b+HyweLHF00+nqanR0HXo6Mhx7bWChQt9fOlLsKcDBsdh3ZM2uaKC5hOYNhBQQJeQsmHEctQCDECWCvjkwDRwXpTyVqTJ+JBk2QKD/j6TQl7nJ89q7B+BgAfefw3EQ+ApCZWY18lYH8vD4qqTX+uKGHzoFsdnUl3m9KMfHIRAwDGhubw+udgc8KfBMfmGh5isMDGklKNCCEUIoUgpnxRCfOnsjM3ldLn8L7+C8Sc3Idam2HzDPaxU9lBbHEDXDK4fe5yKwTFWqpsZ8k2hQl3ExpyXRJ8XvSnP3vECE2MqZQFB54jg4bVQGYO5R4QDJyjwAiNE8bCWQZoIEeRYT3MopLFkSYQXXphACEFNjZemJh8A4+M2Qgi8XkdKhcMKYwmL8SKkIrBlLRhZyKQkhmk5PYMNgY6FqhnkNcsxaeW9YFtgq04UmLBw/pGA6aS4W5LRCYO//9thLMsm4BfcGqvg0iVetvbAPzwJZTF4QxNcNxXqQvDRS2C8ANNip77edRXOcoja2jO6bZNGSsngYIZQyEModBazMV0mjauZnJBXrJmMCyFCwNPAj4QQQzj99VzOA7HVV7L8q19n7KffZfrmbxOaAg1xg+qQTciTJF3wkLEW0Rn9K2rGpnP53GE2BvMMpgWpHp1InY0vJti4U0UFOrrhm38NsVKOq46ChiCFgRcV7SRPabfeWsO8eREMw2bq1AA+n/P4X1GhoCiQStl4vYLeUclAr5dvfNVJxLz2DkgPSPZuNamssBkdF9hCQaFIMW2DR3daAAcFZD3gM/HKIoWEBroHVD8ICcKAehuzJw+WQPNq5HImOx9LUt9cycEkXNUKZUF4shNmV0BDxBEodRdoJeA1azp59NEDhMNePvrRZUSjvvM9pIsK12dyUk5YKX6ywuQWnErgnwbeCUSBf3jl43I5UyJvupv89ZfTP9jFNYkJFrQuZVjpIDWxjwj1XBldxmotjKaCQQX/msjQ26axoVolXxRs2QG5YRtvXDDQJvjr/4b//LTT8TCEzo000UeGJkJ4TxLZoiiCGTOOrbZoWCp1U4JsXJ+jIm5TrAoSDqh4VAgF4IV2WNUsAIXRUYHtczLoC0KBcg9opebsaQnBLJGoiSeVwar0YqoxqNScZ6SihKEM2FmwJWZBQRGQnZD4miBrwbABFSV5aNjn5HacVfbvHyUU8pBMFhgZybrC5FXnom6OVQm8H2jhCPkgpXxv6e8/n2jfSQkTKeWRWsj3z2iULmeVCBrv0GrJ1ldTX5VBCA+12gqoWvHSNoduro7CzKAHZtvMXiDZuw+KKQWlygYEkSaF7b2CviGn/DpANX6qObPWgskUfPOHYFoe6qZ5UD1QEZF0dFikRiThOgXbVska0LpYY9MGA1QbbMOxYOmAZYNfc3wiWpBolYWuwEguCjWa02FBA/ICan2Q0MDMATa2V6diYRmqDsvqYUMvYMOCKmg8ZYWh88/117fyq1/tZvr0OE1NrmPmfHARaya/AZ7BaYhlnc6Op0paTHF8h8uki3+5nDvi6MTHn4fB34Digcb3g+/4SSLX6zrDskjL9QbecpWhFxUSCnjqJPkcbNkO130K3roa/v5jr6xwYv+g03a3qdTnfV+bzeOPFuhvszFMaItC01Kd6GwP5VUqWkhx/Ca2U16frA1exdFMbAGyiG3GSBk22Jpj3kKAjSNUvMLxuUSDzvapHDo5VEVnZgXYCrx7Fswphxd6He1kZcNhh/yFRktLjM98ZtX5HsZFy0Vu5gq8vHTWZDlVnkn4ZO+7XAAkN4MWA3MCsgdPKExiQuHDHi8ZDwSuhWcqBZ/4KgwOCAY7Bb4Q6EH44bNwyTS4+y1nMJQMbNth8cD9BXYfFIQCXrw+hbbdBcx+E19Uo5iFQgYye/NoV6osvFJl684CA52yVLIYKJpOxxyfB8wUc2dqiJBKuxWEnlIUl1PzAhQJiRw0e5yYYBRIaGz6ZYLrPxSiN6NwayssqoadQ/Cr3c5pNAUubTzTi+7y+kZczCXoHxJCvElKOamGWEfiVi96rRNZBoO/djpHBZ2mG1JK7Bc2IIcGUa+4ChFxFEhFCA49HcxqhStuUMiOwS9/A+EKSJfm4rbe0x9GvgD/58sW3/2vFNZoAUWBzgNe3vrOCJVRgy5FYiRzZNICj67gMyUjwxbbfSrRujRmQmVkTEFBUOYz8QUtxjRAKoxlNXw+kIYKlgKDptNgS8Xp3jWWgWqfo9EgIeYjNVxk/ZdSXHZFlNSE5Kk2m64kSE1B1QSBI4LTJlJOHa/gmVn1XF6HyIs3NPiTwOeEEAWcmPyzVoL+jBFC+HCiv7yl8/xSSvl5IYTAyVt5K45N7n+klF858ZFcTkpsiSNEFN2JcAIY6Md66IGXDJTaTbccs1tfCqIxmN8M67dAbx9IBSp0uGbl0dtKCdt3w9AIzJkBdcdpTCUEvLDOwMjbeHUVy5KMDBV565ssnn5a4emnTDQFqsIwNCrJW5K8IVhYDna1hi9ZIB6yuPn6AFOn+vnRA1l6dxVhdJxct4GvpZzFy0KsHSw6XanbRiAIBGOIUBip5Jyew5oPpAABD/4iT2LIYOaCOIO9NrObJTOXSK66RmNaqXtkRx98+wFHCfro3RBzdXEXzp6ZSwjRCPwAqMb5RX5TSvllIUQc+BmOo7sDuEtKmSjNj1/GaZeeBd4jpdxcOtY9wN+UDv1PUsrvl9YvAb4H+HFa7H5SSilPdI6Tfu5XYI06l+K3AFwtpVwIXALcIIRYCbwHaARmSSlnAz89h2O4ONAjhwUJQDCECEdAEYiq6uPu4lGdZHIh4INvhytXwcxZ8KWPwvJFR2/7/Ivwo1/DMxvgG/fByOixx/N6YGmLxFM0kVKiaqCpTp7JNdfoxGIZ+vqKTIwbzJ9n4Q2rdI5oVOrwnrdF+OzHQ1x1aYBYTKevTyLTGehNQH4E8knyo+Pkwh68sz2OSQ8bMjbq2DChZDe+TLbU97cInYOlUdk880yR3v4co0lncqj2wvTyw0maYxOQzTsmumT6Fd0Fl9cJshTNNZllEpjAZ6SUc3ASvz8qhJiDU+vwCSnldOAJDtc+vBGYXlo+APwPQEkwfB5YASwHPi+EOJRN/D84EViH9ruhtP5E5zgpQogyIcRyIcQVh5bJ7HfONBMppQQO/Tz10iKBDwPvkFLape2GztUYLlZEJIL2wY9BJg01x8+wm14OdRHoHIeADnMWwZ/Uwc3HKZCzv91J+ovHnPpUQ6NQUX7sdn/xZx7WP6vR3W2i6/DeewM0NSl87nN9dHTkEEIhnRZMnxamam4ZS+YKbrsanGeaIJcutXjuOZNYTBCN+lm3LovzFZVo+QJlEZg2RSE5YjLeY0HRQggThTxl0qRrnwUi59TxUkOgCpA2ewcEN6wUXH+NyqpVRz8/zZsGN18BAR80TqIV8GsNy7J59NFBxsYK3HhjLfG42xJyMpwtzaTUE6q/9H9KCLEbqMdJt3hDabPvA2twWnrcAvygNH+uF0LEhBC1pW0fO1RgVwjxGM4D+hogIqVcX1r/A+BWnAK9JzrHCRFC/CmOqasB2IIjANcBV5/qs55Tn4kQQgU2AdOAr0kpNwghWoG7hRC3AcPAJ6SU+8/lOC5GRCQCkeObOXdRYItW4IrFXhK9XkazTiLf0vrjt6idNc0xc6XTTr5g7QlKkLROUXnqj2Vs3GxQXSlYsEBnaKjI+vVFwmGFpiaF0VGTnq4iX/9v5Zj+7C0tKi0tztPeddeVs3Nnnl/+0kDTJF/7Wg2dmQz390kGszYe3aKsTNDb69jyCvm8Y96SBuBzikFKIKARDql84v0qM6ccO2aPDm84SW2u1zqdnVnWrBlCVQWhkM4tt9Sf7yG9JjgNYVIhhNh4xOtvSim/ebwNhRAtwCJgA1B9RPPBARwzGDiCpvuI3XpK6062vuc46znJOU7GJ4FlwHop5VVCiFnACXNLjuScChMppQVcIoSIAfcLIebh+FDyUsqlpYYs3wEuf/m+QogP4Kh5NDU1ncthXlQUkPyWLAHgEU+GP5viQTvFD2fpQoiEYCQB01ocLeVIuugnR55WmigrU7numsNxxX6/QjCoYJomuZyNYUhqarRjBMnL8XgUvve9Rr7znQYURTAyUuRvP9/HvDI/2Ri098LYoIWUKn5/gNpaSTptkMn4gJBTggWVRUuCfO/fPMQv0Gz3c01ZmYdwWCObNamvdyMMJoPTA37SHoARKeUpH0dKFUR+BXxKSpkURzy1lfwbJ6x5dTY4jXPkpZR5IQRCCK+Uco8QYuZkzvGqRHNJKceFEE/i2PJ6ONxo5X7guyfY55vANwGWLl16Ti/0xYQOlKMwiEkd+qTzfGe0wozjrM+RZyt7sTCJEaaaiqPeD4c1Pve5Sj772WHGxiRz5/r43OeOYyM7AYoiGBws8j//04tRNOjem2F2Uxm7N9pICWCTy9l0dWkEg16EAl6fTjrvRfPqXHldkPu+L/Ao8Gd/BuXlTvl4KSEen/QwXrOUlXn45CdnkMtZVFW5mfSTRcqzFxoshNBxBMmPjuhYOCiEqJVS9pfMWIfM/b04PuVDNJTW9XLYZHVo/ZrS+objbH+yc5yMntLD/wPAY0KIBNA5mc95LqO5KnEKRI4LIfw4rX//b2mQVwHtwJXAvnM1BpdjURC8gzCDWNSgIk6glewYh+eGnUZT19bAlNKTfWLc8ZlUljs+FC9eKqxq2gpZ9hbCKAGofJlZ/oorwvzgBwGefdbmjjtUotHTi/vYvz+LaUqWLQvR1GTw858LpFQpRS0CNmNjkljMi6baGAWTQMDHnW/3UxFXGc87pjm/H3buhp/80nGr3HkrLFp4+DyGAbkChIPHN/dNhlzOZmLCJhx2NLILgXBYJxx2W0JOFikFlnV2MlpL0VnfBnZLKf/ziLceBO4B/rX09zdHrP+YEOKnOM72iZIweAT45yOc7tcDfyWlHBNCJEvBTRuAdwP/dYpznBAp5W2lf/+upABEgT9M5rOeS82kFvh+yW+iAD+XUj4khHgWp1jkp3Ec9H96DsfgchwCKEw5iRq/Pwk/bIdoMImhpvlmj4ePNccREwpf/CX0e5wivu+5GhbXCp7pnc24AbsEPCHg/Y3Q8DKLyowZKk1NKr4zeDguL9cxDEkqZdLWZuP1qjhfXRPnq+WkwudMm2lTBNNa/QxldcoaPGRycO+7Yc4051gvboOA3+lHsmnLYWHSPwTfewAyWZgxBea3woFOmDsD5kw/8dgmkvDos5DOwPRGkzWPpykUJIoCb397kFmz3Kq/r0WkfdY0k9XAnwDbhRBbSus+hzPB/1wI8T6cJ/+7Su89jBMWfAAnNPhegJLQ+EfghdJ2/3BEt9uPcDg0+PelhZOc4xiEEJGS+e1IfX176W8IOGVnXeEEDVzYLF26VG7cuPHUG7qcFR7ohs2FAbT4PkCQNGwW+MrZvWkOv+tVkArYRZhSC1XNEAbmlmo9jhSh1gv3nmZ2uWlCW7sTrtzc5GgRh5BSsnbtBNu3Z2hr85BK6XzjG0mkNDlcPkggAn6ueofKnYs12jtM3nlvNeUxQcMRUVrbd8JPf+mYuW57C1RWQu8IfPE+SGdh0QzoHXAKFk9rdoTLx+85cdDB1+5z8m+8XskTT+S54hKD8jLBgXabbNrmn78QpaLiwtBQLhaEEJsm48c4ES1Llsi/eW7dpLZ9v9/7is51oSCEeEhKeZMQoh1H3T9Smkop5dQT7PoSbga8CwASSRobD4KgDln9AHEzhIIKBdiJwRNGgYNFDaEXUIQk0eNjhldHhGGGH3QFfIpTqfd0MAy478ew/yAYhkUhl+PmN8HKlX4iERUhBKtXx1i9OsaDD+b4+c8zSFko7X3IHGEhFZOgrtLVZbB0iZ+Fs459upw/F2prSg0bLfjX/4ahjCSfgT0HJO2dAr8uyE2AmYLyakegHA/ThL5BaKpzGoWlM/DoMx7WbpNQKAIKf3i+yMM/9jFv9uldE5fzh5Rgnz3N5DWBlPKm0t/jxDxODleYuJAlw28ZZD82QUJcXh4iMCFJ5JyJusxnsNssEG1cz9J6k2wuwOBwLbl0mJHBGFlL52AQpvhhsAgrg/CNrdCdhqlRePMUqD62Sv1LtHc4gqShzuapJ0fo6TVJjsP27Rk++tFKfL7DT/ZbthR48MEsh81bhySXSkCTRFIml705zHXXnTiR91COjGnCm6+Br/5EsmcHjAyDtCQ1ZRJfSGHDdliSc4TF8dA0xwS2fQ+oqqCQVXn+oAWpZClMWdK9z+LTf63zmx+qBAKTvCEu5xnhlIO4iBBCLD7Z+4ey8E+GK0wucrIkeZineAyFCEkmCPGEPp8ryyL0hVP4pZ8BrRMx4SGdrkHVsoSDKQKBLPsOzCRAEG9WpycDlTpcEYWN7eDVoCEE/Wn4zk749GLwneDbZpXkQSppks6YxMq8VFdDIlFgYMCgpcXx6A8NWXzvezksSyDEISc8IASRMi+xmMYHPxDh8ssn55jRNLh8meQb/2uTTClEIxD1wegQTGsFbwCm1p3cGf/WG6G1CXJ5gZJT2bo75+yglPqtmAa7d9v0DahMO6Wh4NXFsiQbNkgSCcmKFQoVFRfX0/hJOYvRXK8R/qP01wcsBbbifIMXABuBU5axvrjEr8sxdLGTIQZRUAgQArL00M48tZkZ3ih53yCWUiRqV6N6IGd5SKhRrBCUNY4Srt7GjIZ1XBdN8FfToF6Fgg1xnxMJVhmAdBE6kiceQ3MTVFbA0IjCxAQIYRMvc0J/j4yI6umxGB6TFPwaMnCEZJJgmzaLl3ponaby6ONpfvLTBBs3psmewuamKBDzQGMMKqIQ8Dq5nn0DIIuwZIlkfNzGNI/vW/R4YOUiuGoV3H2HCsJp8oUlnZAxBQJBQeACTPF4/nnJb34jef55+P73bV4L/tNXBQmYyuSW1wlSyquklFfhZOsvllIulVIuwUmynFTpV1czucjJkKSMLDEko3gxEcwkjUqBxUzHxqJWiYNXo7d8lF7Tgx60kAgqWobpGqgjTjeVdWvJcxWKONaWI+VJGkcDgQB84L2wdbvG7JllHNg3Tj4Pt90Wo7LycEjrjh2gaQqWZYNPh5xwkhOFigh72a/6aLzawE56wFBQgkVCM3r5kw/qfPEt1fi0wz/+iQn40U9geFgwbbrC9p2Sgi3IS0msUmHuLPjw22zWPpNj0yabKVNU7rnHj66f+JPMmwNXXxvkj48ZUCiAFGihMHfeoVF3jvvGnwnj4xKv1wlCGByUWJajrblw/C5OFwczpZSHoriQUu4QQkzK4+d+dS5yKmniAFtYSQ9jeFAxaaCMfrZxgC4eKagclFEqFYP5yi7GYisxijq6amGYGt5QAWvcS8yXJkuS1liAoAZDWSjzwmgeYj6YcoqGgaEQrF4Fq1cFkNKPlE7CIjgT/49/Cv/9PUG40k+6I4VplAJOfF7QvKSiPnY+L2DC4zTPKujYuiQ5UMnXvihZ/0SadV8KvyQM1m+Avl6oqoKRUYVPfEzy1NNgq4IFCwT3vgu6Oi3GxmxaWlTa2y0GBmwaG0+ef/Dt/xZ84ksx9m+QKD7BdW8S/O27z8adOvusWKGwZ4/N4CDcfLOCpl10pp3jc6hXzsXJNiHEt4Afll6/E9g2mR1dYXKR08BMprCIbvZQhcRPnAhhUrTzTNHgV8Yimr095MjjiTWiFk2KeFGFxVg+ToU6TFwf5umRCLtyPt5WB++bD492QFcKpsXg+pbT62rolHI4/Pr++2HNNtCjAjHhI15pMTJUxFY1x06lq6DqUJCQl04rX9WCBh0KBhRsNq0XfOjPDb71JQ9CQDAIhgnjExAKwjvfKbjjDsd/Ey757svLnfphHR0Wfr8gFjv+ZJvJ2GzblsPvF8yf7+e/PyPYMyoIe2FR1YXb0TEeF3zqUwq27QQQuBzBxauZ3ItTjPeTpddPU6pcfCpcYXKRo6CwlGtpZBYpJvATJEqYF9nLC8Ua0t4wWduPUGBCixBXhpGBUQxbR2Aw29hPXXCUiPkeRnMxftYNH26Fdx+n+vCZIKVk82aD/h6LcEigNKgUUz70coWMapPKgCV9YKkgLMiKkl1NLbX/FYACBZv775/gT98eYdVKLyuWQ7EIo6Nw+WWOTHp5tFVdncoHPxigr89iyhSNcPhYG7mUkvvuS9DZWcS2JUNDFtdfH6bhNdIXRQiBeoEKu/PKRaqZlOpyfR14WEq593T2dYWJCwoKtTRxpFl/CivJiS50YRE0sliKjlfPY3tU8kU/ttTQfQaWLBJUskxo3YRlmN6JE8TRniF//GOB/v48432CsawkGNW48uYgzbO97E6arN8BB5/THY0kK0rOGQm2dCJyPM4nJGeQHCzwb19M8KdfqmJbUYFmWDYfYmWORlI0nSZZR2pFjY3qSU1bhiHp7i7S3KyTTtscPFgAXiOSxOX4XMRmLiHEW4B/x/nlTBFCXIKTbX/KRt6uMHE5Lk0sY6lW5KC0KCheLKESEVkydohcIYRHLeJTDfLBCNpoN0VLMuHdw5wyHzC5CorpNGSzjgP4eOG3pilZs6bAlVeqRCIGO/eZFA2Daxd5ufYNOl9qV+kvwMH1QJkNOROCAtK245gfLTr5AqaEoQyWFDy6zSL/+yw3vtEpNvbYCPxmL2htTjv5uhhcMx8umWTqlsejsGSJn40bcwBcddVrvzxxKuX4sM60PtnrgtNMvH0d8Xmc5ltrAKSUW4QQk/o1uMLE5YR8SlvEH/Id+CgQM8exVAVpKuRkEITEqxawbShENYLZvVR6G1kc34dkxQkLSB6irw/+93+doKcrr4Q3vvHYbWwbbFvyzLM5HngghVEUgMa//63ktrWVvK1BsFYHTwSKWaBKBWlCTwYM2/GX9OPkfFgSfIK8FHRuyuO7MYSUTsLk+kGoTwCjkoYQ9I1C0RQsP0FNrq5u+J/vQkcnrFgCd90WZenSAB6PIFKToYjAwwUYCzwJ9u2Db38bbrsNVq489favSyQXs8/EkFJOiKOfJCZ1NVxh4nJCytQAv9MFP0t3o+d78SWH6fDX8LxvOaamY0mBpehs0eawOL4bn9KJpQbJ46VAHSZFIlTi4dgkwr17nQz0+npYv/74wsTjEUxt1fnXfx/BNgwc85HF5s0FvvWtPB/+sJ+3T4X0LNj0jILpVaBOcUqZJApgeZ3H62wSoShIr0AJCYqlhLShJLzYDboPBjokI9slm4pQG3OSLi9pEXheVmw3nYY/+yvYvg0iURgcgsS44C8+5SEQgHHyCBQ4hTAxTclDD6XZtavI4sU+rr8+8FL02vnE74do9IR91S4eLlIzF7BTCPEOQBVCTAc+AaydzI6uMHE5KRVqhI96xsBWMXqeZV2olrbKqUhVEpBZsiJIVg2y32qglX4SJGhTtzLBQQQCHyHmcxUaR1fPbW2FJ5+Enp6TPwFHy3Rss2RzEDZIFds22by5APhZPgX2LoYqDZ56CpKjCmh+UGxHWhXSYOVASDyqF+IBZi3w0ZeCR/fCSBbIgjIiKeZB6DAwCGtfgNGbIOqFx5+Ezi5oaZZEo4Itm8G0oasTqgowloBde6ClCWKxGvJ5m729Bj6foKnJqS32cvbuLbJuXYHGRo2nnsoxfbpOa+v5rzDc2Aif+9z5HsUFwMUrTD4O/DVQAH4MPAL842R2dIWJy8lR68FzE8iHEYGpZLQq8raPsEhhKxp520sBLx6jyLaOWTybauaexTtpVuvR8ZBklCwTRKg86rBNTfCpT0Em42gnJ6K8XEXVFCzDApnBKdogGRkxSKdt5jQoLJ/mlJSvbYG9e2C4W0Mf1CmOp0FaDA9rZLMQUKDlsgDzrvLzQq+ToC59UBwA34Tjrz+kHBSKTsTxj38BGzdKnnnKZHwCLrkEbEtjaExgScgnwSjCd7/r9Hfx+y0KhRSmaWPbcOmlPm6++URFueRLfomTJZ/n85LeXgtVhaYm9YLQYF7XXNxmrjmlRSsttwBvwSmrclJcYeJyajyXgudSNHstswZ/Rm1xgP5ANbatMpGNUskwifYyHnnmRgYytTy+cTE/+NMnaNAjKCh4OP5kGo+futvh8iUKKy8v57mnhsGyAUl1TYCZMyVjYxZNIYWVcyEVgnwePnIzzK8TKEqQ8XEf7e0GxaJE0wR1dRrBcpXHxwQP7HS29ybA7gPNJ9B0iSiANwhXrYCA5vhFerstikWb6iqFbVvBF5FEAoKcBeEy6OyAy1Y4JWEefTRPJmOjaQqhkKRYzLNypZfKyqMjwmbO9LBihY+dO4usXu2lslLDNOUxiYPptM23v51haMjCtgXz5+vcfbffzQs511y8DvgfAX8O7OA09TNXmLhMnuilDGxvILh+D97L2hjPB2nw91Ls0rh/7V0kDUcypCbifOKx5dz/pgFmMwMfJykZfAqCQcGDvyjj3/5T58EH05THBFdc7lTgLS9X6cvA13eDR3F87k+MwcwapxR+LKayaNGxYb1XBGFFEgYLkFahPwwN5QK1DjJFaJgCFU2CB/dASoA/AIahkEgI4nGLm25TeWEzJDPQ2gLNVU7iI4Bh2PT0SObNg1RK0NVlYVnHPuZqmuD228PcdJPkxz8u8C//kkdRYNkywbe+L1i/XkdTBfMXWExphGVLNaSUbNlSJF7hobFRY2rL0X1fXotIeQFGjV3EocHAsJTyt2eyoytMXE6LntFGsvuq6F7TRKZqiH3GLA4MtGLN9aLVGNhSwU6ojCSiKFIQEZWnPugpiMcF//pPYT77ZwGeeSaDacJllwUIBhXW9zjbKJrT334g5ywtJ0n1EEB9BOaHnKjhgRqoj0FDmWBzPwgPWAr8Zh3sSYJHUSFqo9oWn/9bwbXXKDxd6p10xSrYtxd+/WtnUqyv99LfX2B01CKVkixfrh2jlRzJ3r0WmzfbbNmisG+/yb99WcWydVAFFOGJxxRqG7xMmVIkFhVs3ykYz0gqKqGqEj5wL/j9kq4uiWFAU5PA4zm7s7Np2qxbN0EgoLBkySnq4kySkRH40Y+cv1dfDVdddVYOe/a4eIXJ50vlVJ7A8ZsAcETv+hPiChOXSVMsWuzb3cfadTrIODseb6QQBt+qAvkqHcurOjO1CjIkQO4EcfbqrsfjKrfccnSYkVeDjUlHAMhST6qMDZva4HebQFXgtuUw54jOj+VhWNUKaw8479fF4f1vgP2jsH0CWuJOEuP4CGge2JIRBHUVj1T5wzPwphvgpusPH2/pUqitdWqI1dZ62LgxzC9/WaCuTuXjHz+1Saq9HXp7bQwEVlF3AsE0CbqADCQSgqefU5jSZJAvCObNc9oft3c6jv/Bfotnn5UIIWlsVHjve1W83rMnUDo789x//xAej8K0aUGi0Vc+bTz0ELywCfJF2LXHYsoUhZaWC0hFuXh9JvcCs3CezQ6JVAm4wsTl7PHrX3eSnkiwcnEl+9oDtDaadI+ZKDUpqFDIT/gBCTGDUG2eCk4vvtSyJC++aDI2Jpk7V6O+/tQlvqUX0EEYzuuGMnh2BLo2gO6FtgR860n4/B0QPcLadtMimN8IOQOayiHohQN7IVKKYjYAawq07wJTg0IdZBLw0y0Q/SN8/DJoOuLj1dcfDiS47jov113nndRnnjlTpbFRsHmzxJLqET2/SoUsNRtVtdG8GgsWCnxh70vNwoSAbE6ybp1Nc7NAURQ6Omw6OxVmzDh7E3NNjZc5c0JEoyqh0NmpvbJnP3T2gUfLsGfnOP/2Hx6+8v8qLoxikxe3mWuZlHLmmezoChOXSdHXl2XbtgQtLUGEyDGr1cn43tMPD2c8TFRP4CvLYhsKSaIsqO2nThwd82sVi3SsWcNYWxuxpiamXHMNmvfwpPvoowZr1jghtc8+a/Kxj/moqjq5QClIWN7oZKCoCqDC4Jgz0baPQ9s4ZH2QKRwtTA6k4OdDTovhZTbcXAe6Di8Og88P/SYkQxZqrYktdLIGkIRiJ3zxtwq/aId/eQPcPedwBNiBXth6AAI+WD0PIsdxFUkpGR4uUCzaxGIeQiGNf/5nD+PjOTbtUEiZJratgSkc7cS2CAbh7ju93HELfOM70NHlKIDRCMyaDo8/IjAM0HWJlOKsl5EPBlX+9E9PEnJ3BrROh00vwvBAgViZYGioSC5nEw5fIIXCLl7NZK0QYo6Uctfp7ugKE5dJ8cILI3g8yks5E0ppjm+pFshQEDuSwxMoYJkKtb1tfDNehSqOfjo/+OijdD33HMHKSrqefRYjm2XOnXe+9P727Rb19Qo+n6Cz06avzz6hMDmYhtEC1HigKMHvddwMnXm4ohp2tEEoB2U6zKuDiiN8KAnD4r4uSZmuUe6F9SMQ02FrHvrzoFhwwDYwfTnGpM95St2F87cAjNl0dih89jmnastb58DjW+GrD4NQnCiwtXvgr9/u1Po6xP79KX73u0FGRgqIUj3KJUtivPGN1XzoUwH+3zcKcEAwMgKZrEAtGkTCkksWa9z8plLfl3th5x5n31nTIRoV3Habwv3321iWZPnyC8xcdAKuvhLauiA5EaanW/Kud/guLEFinu9BnDdWAluEEO0433YBSCmlGxrscnZob08Rix2bVNdl+xhsUqjRJ/AbeSxdoUXsZM9EgYbgW/FSho0kPbKO/u5/oGqJQVFdSCi4mOGdO+EIYTJ1qsKmTRbRqERKqKg4viAZyMG325zorRXlcFsF/GHMcaZfFoXr43DpdfBiu6M1LJkKHh36mGAnA3SSYagSElYAUjUoMsaWBJgqvHEe/HavTdHKkR5TMHMqtONMLgoQwAkb9Ul6EfzjdtiwD7randVRn1Mi7Im9sHAT3FlqdrpzZ5IvfGGERCKKUDUaWorEq3J8+5EJvvxMkZZpjVDt5+b58Kbl8MyzMDziZcFcyR03C2Ilv3cwCMuXHH09Fi1SmTVLwTAgEjm+IBlMw2DGEUIxHzRGD2tU54PWKfDxD8DgkE51VTl1NedvLMfl4tVMbjjTHV1h4jJpjhfC2Sa8WLaG5dMYK5ShqpJh0UCquB+TLHkC/LzzITLP/4Ara8eI5xJ4rV52lxcJiTccdaybbvIQDBqMjEje8haVhobjCxNTOpOiSkmgRGB5GGwkPeIgOxilNTiLq+YdjjzayxCb6CaIh5AVYv8EpEURzbePwVQNlUk/3phNbUjDVzXIanUX7Z0NHHxmBulEDPI4gkQCGaBDImsEXQX48RjcEnNKq3g00DUo5OGpvXDFXLCyks//fYb2jnKGJzwkUiqFDWHsxSbeO0xC3hy56gzTfGHQFGbNhqsvfemqT+re+P3iuGHCA2n47T5oTxx9/+J+eNM0mP3Kg+3OmLoaLjwhcoiL1Gcipew8031dYeIyKRoaguzaNY7ff/RXJoJJbrScVGWEUCCFOmHTN9FEQ6iAnypeYJSOX/+MpeNbQY5QnBEmkk0Qq+5nyp23HHUsn2Zx4+os+IPOjHyisQTg7c2SfWaGSCzB41g0igiVqBxgLxo6B9jDIlYAkKbAi/QQJ0imqPDoEFiqiW0XyVkWDa07yVl+LG+BLqlQpQ/iJ8/MVJKZM/bzYtsi9nfMdh7lFcCHYwBIQU463YPXJ6FZgbGkI+im1kIsBF/7IezdZrJmY4xsQaOQtbANCSEbbIXsj7xkhY8hE7YEJUvqJZURwWdLl2brNqeOWXk5rL709PJK+lLwzU1Oc67m6NHCJF2E72+Fu+fCoguwpfB55+LVTM4YV5i4TIrlyyvYvHn0mPXL1DT3DVYxHKwjpWSx0ChPDxGKz0dBJ4oHI+wlM6Dhi9gIv4oiPQSmLyUYr2CcMYpj2wg+9QD601spSp2R/iztsemUrbqSuXfdhS1tiqkkgXgFqseDRJKNDZAb3IGnPQfxCtaXpwmhU04AixyxI8rgd5NAIFClwuYxyIscBS2PT8sTiIzj8eQoU0YJmVk0w6AiN0qFGGFm9ABdzS1ccvNWfrLuHWzfudCZkT04Hn+fDSggoM+AujBcWuNoJ8MSHm+D/Wsg7Bek0CgawklgiSlOtECfBLskoBokRSQb1jsl4N99JXQfgF/92im8uHWbE0J85x3w9NNOu+GVK53hTExY7NjhOPWnTfPS2KhjS/jJDgjojlnr5YQ8oClw/x6YWuaY584Fzz+fYd++InfeGX0pCu2C5+KO5jpjXGHiMikaG4PMmBGlrS1JQ8PhMCWvAv9KJ1/eWcVItUK92ccXb09xQCkyh1pmUsbKd9zG7s06M3rWUtkzjJ0sp2reCtYl7uMF7XnUbJbWTDvTwx1YtkG6kCP/wjZ++dTveeLLf0fsDTOx68uIRup4413/h1QsyNZ9X2V275Moto1YE4bGeWxffQk14VauZDpxq47/nTB5JFvE0CyWxDRmIkkYNgV1DMMKEPEnCPpSCCEJWFk8mNi6YEgpZ0ZmD/1KDWPRMpKFKCtWb6C/up6RrkpHmDRzqEwY2GD7YUsEZAQCGSimYDRRsorpCkWPdCIE/IBPgCEdC5Z96DgSPAq2Iti7G7bvgz8+AH39TkfIpian2OSDD8Lu3Y4QaWyEQMDi618fI5ORaBo89liWd70rgq/Wx2gOWk6SY+jTnNNvH4LLms7N9+bgwSJ79uTJ5cKvHWECF3M5lTPGFSYuk0IIwV13tfDjHx/k4MEU8biXaNRxyAdkkfcm95O+ZIjFS8rwxXyYqKgoCAS3B25n4LLFGFsfIzS4Dm3GTNJrfsm6e+PYfptMsJJZZjuJuigRLUllpSTSYbPjySyjRUHP3DjeVdWQsBno+BFyfg2V3p1snz4bn5ljeuV+YtueZ3CiiV9kZ/OXGY0qYwBD07GkgsBiezrM9FyWdNJGbRA06e2s8j1NwMrSZk+hn1oKBQ0bFUW12GLMY561G9Vr46vLUhYYw5vIO8JA4KR0GaX/JajlIAuOsrJTQJ0f+hvB9oBxQAGP7fzahACPcGbxvLPvS7klKUdLMQz4xn2Q7QTLhMQYHDwI06Y5Gsnu3eDzOc74p5/OUCxKmpudWvnZrM0DD6RZcrcPzyTm7rgPtg6cO2Fyxx1RbrwxQix28kgtKWHD87BlGzQ3wzVvAM/5KqLsaiZnhCtMXCZNIKBxzz3T2bNnnGeeGaSzM4MQ4POpXHN1HVMXT+VAtB8byQqaUTk8m9XQAoFloO+HnEGCJJa/HIFASo3E7DB1T/QR8OTxj+UwLwsyRffRpS5BCwfwBgV6IM9gfgKPHML01lDI+YmPjKGYklxU8NzwMtojTeSkn0RRx28ViOpJtIINiklPwCIylqBhqIN31/yAJGGytpcV1gs8Pn4NO8cXYtkCX7BAl97C2M448xZ2ElKzVESGkUFxOJnwUHSXBVoRDB3q/Y5syQFtHsAPwVZIxEsrx4FB8VKVAAycSUsFisJZLOd12xDcfTmsWweGCeMJqKl2+r7Mnu30Gykrg2TSxu8/fJ39fsHIiEWuKNEmEa6lCiiew6dwj0eZlFDYuw/uf9Dpuvn0s44V8Pprz924TonrMzltXGHiclrousL8+XHmz49TKDhFDH2+w2XRmyk78c6tCyCbgpF+Rlc2oapr0RQDC0HvJXWUB8dQ2g2SgQBMkRQeylBcWk5MZgkMaqSbQqS8PirsHCIqCfrSVA4NoQUNckaMT/zxazxYdyNbaxfSHWumKAT2SJqMx4+ievCG01RM76RFtKF4TAypYaYFxY40V3geRK8vsFvOw5IKI+lqRporUPIaV0aeZVv6cnQjRpkC0pT4NEFQU4iocEkTdAowLBgxTGzdZo7fpK3oJZNX0cNgLlRgo+10NM7gCBGJ48y3gIzq9LFHEI5AVRiiMbjmahgcga5Bx9m/ox1UzfHPALS2eti5s0A0qqAogv5+k2nTPFQGBYWBU9/PrAENF0AjrNExJ2k0EnZK2fT1necBuZrJaeMKE5czxus9zSQzRYGFlwPQTB41P4Bf306QCYroWFUCu1xB5C2Gni8ysc+gbEUOOb+JQkTiD9nsyE/FKnZSLkfoUJppmzGFBbmtHBifSbgyzSXD2xCjRYqzPeTqglxSsYu86qWr0IIWKZDyxulTaknZUdJjCsr3tmIkJkhbIfxlLxC6qondzXOxQgJPyGKzvgg0gxkTm1m1pQxPuIzg3GV4vQGawinunrmDGm8Pw2aaF8ZNzGKK+/ou48BEJX5h4vFZ2KkyCiKGndecpipKSTtJ45i6gjZUGaiGQcjQmdekEw7DT3ohEIRdHTCwU+LdLvjNbidseFaj5LIWME0vLS023d1ZhIC6Oo1Fi0KkB20yEwI7LI7KJzGKNuPDoHsgUi5IFQUrGs7O9+GV0DrF6UnT2eW0a77h+lPvc85wzVxnhCtMXM4Llfh4o+9DbOSPLOQ5jJzE3xkglBjB2JdH6fax+N53MTeeZl1IUij3kc350PIag8M19ISb0IIW0pY85r2WQDhLdG6KaW1tdIhWZqk7SIhqBr31KMKmtqyPQsCDZhoM2RW0203En1mPJzWObIzQmZ5H2/pKOuIh+gN16IEC8XCCat8gI55K9OZW+t+vopsTTO3+Gp6mLKJ6gqelgZIp0FLYwUKrj87CFPSJOgwRYFFkC8K2eTG3iGsij7GhbiVd3mZ80TwMC/IvBpzoLgkMq1hRgdFaIDJXoxAUdA/BqGEQeX8P9cECPV+Zwu49XuY0w+CAZNtjkmgADMPPDTd4mT1b8POfS/7rvyS2bdI2ZrOjEVZeqhIulzzy/QJ7XhBouoYvqBBrEVz7VpWpZQrt7TkKBZupU/14JuNsOcvU1MBHPwg9vU6Pm5ZmSKclIyNQUQGh0KucYemauU4bV5i4nDfmEGEqN5PnzQRDKvpSgbS2wlV5pqkLQfEjkcxlmBGyZIcmeDD/AG2DUdpDswgU82SFh0FPLb7yPFZymLXTK0nkYswN7OTu4e8zPLeB8nCScSXCo+IGDFMlIpLskzNp6tlHhZZlKNHAwbFpCC1NRAyDqpAnAmqSkVw1yQkf+W1pCukMNGj4lBTab4f4qXkNPeolCJ9KRWSEVXOeprO3lUZfN/8ndD/PWZfz/NhKEh0xnjWupFChE5k3SrR5guSvyzDLFMyUD2pwHPtZMITKH7cKmhuhcRno4ymozFDMaQTnJ8kcrGBvF0RUWDIfAj5BLifZsEFhcBi+9DUby3aau2SzFp4dFnueHiE7plLMRvAFVbJpi0xekMnYdIclv9UybNgwghAwY0aQ97yn9phWw488MkZ3d553v7vmnAmbqipnARgZkXzjGzb5vJNb86EPKcTjr5JAubjLqZwxrjBxOa/4UPFRMpcJENolR70vEFRQRQUgW2xGk79HOXCQSwZ2si26mK3heQQyaSKFFNlggH61gqb2bm4fv4/Bp0epNYYJTteZUZengxa2GAupMkeY3rWHbFWM8T3DKGGTmDFIUZGMBKZh+XT8Mo0mi2AYjNCAEs0Rnegi3+FlrKGSA4svoXfvJYxpFXi8Jrmin9SmNxOZN46dFGjBHOlkkEWVL/DHlusY3xwluz8CB2wMn5fiAR9WjweSwF6gCqhSMEwdIjZ9vZJ5ywW15V42WwLda6KUmXgKsHi6oCxvI6TT9ri93XHS/8c3YCylInQwiypkTMRwntqGAqnRCHrAcFotZ0GagkLWZu1zFlZhgsWLdUIhjQMHsmSzNsHg0SbMgYECvb0FDEO+KlFWO3dKcjmnP0tnp2TXLslll72K2omrmZw250yYCCF8wNOAt3SeX0opP3/E+18B3iulDJ2rMbi8vhAozPHE0QPPQ28PAVlgvzWdGbk2MnoQP8MkwmG0lIWyK4mSzTLWX0NN4ziioFJT6GLKblgkt6KYBun5dWRTAvnrNiK1w8TvKifS8jxrzCjjRgzfRJ6iIjEUhSHvVIYaWqgSB3ixtZXR/mqKC3xoPSbSAsPjIzEcR9tnsiHfypMPXQdjCqqw0NQCuYEotqpAr06u6HHS5gUQwXHIj+K81kDUGWRrTZ7bk2NapZdpZgMHt1k0bopy060Gf3KbgWn6+epXbXbuFNg2qH7B6DgofrCEdH5xES9yzGJ8zE8k6KWqSkMIyZgpmDAkwaCgWLDo6/NSV5dCVQ2amnxHRYcd4h3vqMYwJIHAq1OMMRp18mvSaafpV/Ts9OSaPK7P5LQ5l5pJAbhaSpkWQujAs0KI30sp1wshlsLJwn5cXI5P1HsZjXVP0zuuUZ5OEAkkGQ2W47eyYCtYRZ1lnevYWTabuLYT+WQPj7Ecb6Mfz7o93PnM/1I1u5IDVy0mlBpAGxylsipP5OY8WXWIlsQfmZfdyze0D2JGPOSLQaLJCcYjcWRMMBaZCjmVbCaMVMHAgz2q4A0XyCZCdA8EsAtQ2B9A9Rl452ZIbY4hDc1xuBs4We86EC29tgAhoVHCHIgdaMesLqMQkgxkhnh3RZhVtzcy97NOKLBp6sTjCtXVgmhUUijAr/4gsTWBXQChy9JkKMCvoKkhpk0LUlmpEokI9u83KM+bCCEo5KC+PsJ73hMml7OYPTv0UmTekei6gq6/evd5wQLB2Bjs2SO58UbB3LmvslbiaianzTkTJlJKifPzAeenowNSCKEC/w68A7jtXJ3f5fVJUMwgWncDY4n7qB0Z4B29P+aJsmvJCj89eh1Bxtn3llnMevR5EsMzEO1bmd33BBFPEXMwS1+XTTTbz5KRR2g/IOk5aNFwowdvVz/71AVk5kxlYXovV1c+zt7MTMS4TXtoOqruVJa0PToaJjXlvWTzQcby5dimD7ugYWU1tHAOq1fHU5/DU5UnXjtGrt6HudeP1AWHyq/gw7HLe4Ec+JbkCF6ews6Daki8CzMkx8oYPljFP72YZPApi6VTLP7t/9O4dIVCLgemKRgfl/z8GUkqpjhajgkkFCjYICEQtLlqdZBlizT+8AebYNCpxtzRYZHLSQIBuOMOnTlzzleG4PFRFMHVVwuuvvo8DcDVTE6bcxq2IYRQhRBbgCHgMSnlBuBjwINSyv5zeW6X1ycCjfLI25h5yRdIzFiFr6LALcb9zLZ3kK/y4psWYKERYMXzWa7Uy6nxVVMhwJ8yIG0Ti0H3mMpjW71s7/IyOAC23yLoS9HUu45L9ceZV7mLZdomCge92Du9BHNJ7KRADguynUHmatu4tuFx3lT1EOXpMQLFDIppUrewA38gi15TRI2b+IpFmqo6CczNOmMXpVLHOo5ACTmL/w1pat/ZQ7A+RXRBkuDtgqBIwXZBhTJMoVKnmLZZu1Phxreb/PXfF+nvtwkEJL97xGJiioIV4qVHN+kBxVQoCws+8O4Q//0VPx//uMYVVyj09NiMjwt8PpXWVoXPfU7n1lsvDEHS1QNbd57vUZSQk1xOgRDiO0KIISHEjiPWxYUQjwkh9pf+lpXWCyHEV4QQB4QQ24QQi4/Y557S9vuFEPccsX6JEGJ7aZ+viFLkxInOcS45pw54KaUFXCKEiAH3CyGuAN4KvOFU+wohPgB8AKCp6RzVenB5TSLQiPiXctOMJeySWR4x0ySEyVuF4Fo1zMxAkBfKF+JvaKGRJno3Po7w9KI2Qvn0OC1vXsJoT5qn/vG34IHeXRrl10Uon6HR5B+iN11HZ08zieFKRmQ5wy9UIesligLBUJqZzfuRCDyqwZRFBxnPlbE6+jTzB/fia0nx/dg72fvkPIy0zq6ymdgNCtrBItaQBlU2skd3fnl+ICSJXpnAslTMooY0FfzVaTKZEKphkc6EGJdxQIAtSOVU7v+1wYZnCixZ4qFvTDitiy2govRXgj4Od10viEcEPp/T5+QLX9Dp6FA5cMDxQcycKU7Y/+QQiQTs3OlEWc2YcXbv48BAlljMg8/nTENr1jkNs+bO5Kx3izwtzm401/eArwI/OGLdXwJPSCn/VQjxl6XXfwHcCEwvLSuA/wFWCCHiwOeBpaXRbRJCPCilTJS2eT+wAXgYpx/J709yjnPGq3LLpJTjQogngauAacCBkgANCCEOSCmnHWefbwLfBFi6dKlrwXQ5BiEEc0WQuZ6X9cetCVM+YwZjbW1EGxrwxe6kkO/DlO1Y5jixlfVUBhTCU708+8VnSXX08ftL3sb8p59DG5MQNunvD7OvMAND8WCkvQifSaxhHE/OoGusidWzn0G1LCwsUkaU6twQ/d4aakyLG4uPk3xrGUbBw1iyAhIG4sYM+WcDTuvfogYVEkwQGhCUSB2ECkKxEEjsqEqhyku2P4g9LCCoQFpCSpJvEAwlJH/YXkTGPU6J4kCpmrEFTAAeaB8ENPjBz5xM+rnToaZGIZmEKVMgHD7mkh6FacK3vgXj405W+vvfD62tZ+/+PfxwN4sXV3DJJeUA3HYj5AvnWZAc4iyZuaSUTwshWl62+hYOP1B/H1iDM9HfAvyg5CJYL4SICSFqS9s+JqUcAxBCPAbcIIRYA0SklOtL638A3IojTE50jnPGuYzmqgSMkiDxA9cB/1dKWXPENunjCRIXl1fK3LvuYucvfsHo3r0IVUXaXnyR5cx995uIRCoAL9HLIqjhh1nzmT/lD3vnkF0/yHh/lvhyi6qOrahRk5w3CH5Q/SbTige4Q9xPWd8IB8sbaFG7uU6uobvYzIGJKdSERkjZIeJGglAoSVpGUTULT6AAUuK9I4c+WqTwTJD8uJ+cEkRoknRnmKpFQ1jYCI+kmPSQ+FUlFgp2vwLjKtQDBad2V2GZSqROpaenAOWWk1GvC0gLyAvQoKia/LFTwXwKp4CkgEhY5Y7bwMjCbbfC7bef/Brm845m0tgIXV3O/6dDfz/09sKiRU52+8u5666pBAKHp6BwyFnOO6fngK8QQmw84vU3Sw/CJ6P6CDP/AFBd+r8e6D5iu57SupOt7znO+pOd45xxLp8BaoHvlxzuCvBzKeVD5/B8Li4voQcCXHLPPaQHB8mNjqL5/USbmlCOnNU08OeiVLfWcPngH9lveWjdOkYxaVJ7WTcNtV3sHl8AXpvG+k7eUfgJBCTZcj8t7f0813gZKLAwu5O9+hzSxT6uSj7J/ukt2KjoooDHUyBv+VGxEMImNRZD9UuKCS/YoFgm6ReimF0egpemsXWFVFcMc5cO7QJs6fhYmnDiH98oqFoOgTJBMSSgC4RtI3UFKiE40AdehUIwirnZC8mSw9+WJFMW3/0+rFisUFd36uioUAiuuAKeeQZqa2HmzNO7B7/7HWzZ4pjIjmepDoVexfCw02XywmRESrn0jE8jpRRCnFPLy6txDji30VzbgEWn2OZCeA5xeR0Tqq4mVH3ih7KRnXvw5FtYseUR+our2f+m1VxycD37PNNYMXcj4+1xiqqH5dPWUTU4QD6u02010yB6MDWNEauCMT3OWEWcUSNOOh9ide4p6jLdDHprqLD7SVgVaBGL5HAITfiJLxpBqzYwUyrja8qhVyHfEyS/OwiX2RC2nVDhoA1FBYo4ISx1oMUVQjHQ4xAxBekJiUjY6GETb26MxtRaiqkAo0wjKaaiT81TnPBim4pTSLIo2LbbZPNmjZUrTy1QbrgB3vAGpxy8cprhOocqHNfVnXibdNpmcNAmEBDU1r46OSyT4txGcw0KIWqllP0lM9ZQaX0v0HjEdg2ldb0c7WduwDFb9Zb+f/n2JzvHOeM11K3GxeXcEGpdgPpCkkVD6wiM9FAM6PjmlWEoOisb1tLo7WCadZBhTznlyTFqskPsis1md2Ampk9lWvU+gkqKtAiwObCYXCrEwuRWavUBaqIDzC3fQmtqCxJJrixAT08jWTtA0i5DZIAyC0Kl3JCxUufFFKhBC7XMfKlnCgjKw9BUbRP0wvQqL3qFiapKhJTYUqWgRzCjPvyeCaZ86AAtHz7IlE8cQG2woEmBGOSy8NBDFtns5K6Pz3dqQeKY+Y+msRFWrz6xD+T55wv8zd+k+MjHMtz73gxf/kqGfP4CcI9KHN/TZJYz40HgUETWPcBvjlj/7lJU10pgomSqegS4XghRVorKuh54pPReUgixshTF9e6XHet45zhnXAiuLheX80bl3Lkku7sRgQVcIrYRG3yB6re14q0eJlZ4gTGlnMrZw7w4uJjZYhdWTNJZ0cw6fRWV1giVYoQpvjamhtr5TepW+jrrefSJ64hXjBL7qyGUgAK6TU8xgtkHRp8HMQAFVKoWDTC+OUqxxw9aKWsdiZiwkQGwE8Lpylhrg19D8UqmtOTZoxjkDS8hdD5xyQTF+C95bNs1tI82YpbF8fqTTGSnEA31kxvw42/OEqjLkuqKgiZAdRpsPfywxbx5CtOmgaadWVLgzp0FHnssy/CwRXOzxhvfGHypUdfJGBiw+Pa3c/z+EY2hQS+q4nRlrKnOc/fdp9Ho/lxxljQTIcRPcLSKCiFED05U1r8CPxdCvA/oBO4qbf4w8CbgAJAF7gWQUo4JIf4ReKG03T8ccsYDH8GJGPPjON5/X1p/onOcM1xh4nJRU7NgAR1PPon3ireSeyJPfXYfhd+3UVZfRnG6hu2XHPA00b63jsJYgfwNKqvZwOXGekZCZWQLAQq2Hz95PHaR+v5egvkswxOVhMeSGAGBphpESVH91H5GIrMwi4Jo+ShR/zjcLEk9YmMkvNgRBaXZQg3ZqHcUKD7vxSz3oMZtrC6FO1bYzL5pBGH42DpR5LpAmPdXjjDU8DzL53Tz7d43U1AkOx9aiDnggbngiRXAEhgTHkhJJxelICkUBDt2wKZNNq2t8K53Kfh8pydQdu4scN99SSorNZqaNMbGbL71rQk+/OEYdXUnn1r27TPYudsmnVLx6BLLhsS4xuNPFC4MYXKWFCQp5dtP8NY1x9lWAh89wXG+A3znOOs3AvOOs370eOc4l7jCxOWixhuJsOjee9n6gx9grbgDOTFIYf928l8dwnO9h5nvnsobQzPwr5qF+ff/yL5YkeTSEM3jPYS0CUy/SoYwY0NxPr7mK0wXHfw2chP7FzSj1phggYJNy7ZdHGhvIDA3Sd72U1YzRhEd0SIJv2uCQn8Aw6ehhEFmBTWRYeqWdtM10oyegesXVfKeyyM8EZYE9DQrYjoLRyJ8e80cyr1/gRILMNf2sqldIVRnMNYXoPvhBoLxLOk9UfIH/Y4zP2ehCsf8dMgJ39Ymeeopydy5gpqayYfmPvFElooKjXDYsYHF4yqGIXnuuRxvfeuJ446llBw8mGVwcALNY2JZUbAF0ZhxYYQFu/1MzogL4da5uJxXIg0NXPrnf87I3r1MdHejed5OxaxZhOvrD5dirwHzH76P/4dfZre9h1yFjq8/TZsR42Cnlz/Z/SAtkQTj4Tjed6WZW7Yb0VOkfnCQAX8Vzb9/ls3xd6JTQC/LsWjGBgpBnX25WeQjAbQqi1R3DGmDv7JARWSEQNJkxsxBWq0Bmq3Z/HK4jN6xGsq1ItlRL4/u13iDB8bT88kPwJ8sheurYF+nzcw3wI9+HeKpxyxyHSZIEyyBLqCmxuLyyw87QaJR+MpXJAsXwvTpcM89k3O2Dw1ZNDQcPYWEQgqDgyd3JmzfnuHFF8eJRUxGhsdASaLpgsqKKG9+0wUSk+MKk9PGFSYuLsCEp53c/H4a5i8jQOVxt9Eqq2j+9BdosAyySpL1I7s48MCP8G5ZT2pkHAaga0U94YFxAiLPso1bUISFIXy03dLAzdExJrZJ/Mu3EQmNg1+gR3ayZXwJmmZRPasX4dUwLJ10LkBjYIKEYZAFetMeEgZ0+HUGUzq+jBOc5Q1AmQLdI9BYDnYBtuQUVl8CN18BIyOCv/s7E0VRSCSccvXl5TaDgxZDQyZTp6pks06tr+Zm2LcPslknLPhUtLToDA9blJcfjsIaH7dYseLkZqqtW3MYhsINNwT445M5EgmLaMzDRz7k44YbvJO+Z+cMt9DjGeEKE5eLHoMsfaxDRaef9bQa10PXThACGueAfvQEp6o6Ycq5rvJyrnj/Zex/zxi+TbeT+sVz9FdUoORz1HQOoGIyEQyjeyVlYxlaZu7nrYF30Flj0BaqRCoWNhpFRcfWVHxhSVyfYChZy4QVxlJHUDEZp4ZqTxO3NsCPxyBhg6XBFAWKKRiU4PdCWQjq5sOyObClCBtSsDSm8La36fz+90UCAUlXl0Zjo8Yf/mAwPm6xdi1cc42fmTM1Ojth/nxIJIqMjEBLy8lrdr3xjQG++c0JDEMSCimMj1v4fIJVq3wn3W/7dsGGDQWWLpVcukrjzW8uZ+nSEKHQBRRc6jbHOm1cYeJy0aPixUecPKME7WpYcx8MHHDerJ0OV997QruPF8E8vRzmf4T94xFifYMcaJiGuaNAfbIPPWRiF3UKXo2UXs/BeJJ15TVE9R50xWSCMK2R/aiGyXCuioKqUhEcJu4fxC5m8ak+/KOV3F7uocsCJQ8NHqjyQU0MqodB1+Cq+Y5AAdhVhF8knUCw7QX4zGoPs2ZppNOSm26SfOtbNsMjEr9P0N9vsmlTnk9+0scVV+gMDuZ43/sOYpqSz3++mSuvPHEjkcZGnY9+NMbatTkGBixWrfKzcqWPsrKT54tcd10Yy4LFi2HxYj+zZwdPuv2rjquZnBGuMHG56FFQmcqNFMngzQPDv4Hqqc6bQ21QyID/FIWsfNcyWH8AtTqD1TXIi6uvQO8vY1p3OwWvl72t0xi0r+Gxml6slMpYshXht/FX5lCwCGh5asN9hMwk1cERQrkUShHGRCvx8jZ+1e5FDc7k6jKVQ+1GurLw/ssg8rJI3KQEaUMhAeM+MONO2fmKCmhpgR27bTZvF4wNpamuLtLS4mHjRpsVKyQdHRnGxw0A9u/PviRMhoaKjI0Z1NV5iUQOTxs1NRq3336Ka/MyrrvOy3XXXQDmLJeziitMXFwABR0fMfCaEIhBos95IxgHzyRCVdVy9uc/TLmyhtXlz7LzN8/TPqeKA60rEBNBouIefhdWsPuTpJ8uQ5gWWT2OrFQJNKbRQwUqa1Wq1EEKpo9cMUDIn6ahezfDFYtRW3aA4aFQmIpfVTBsJ5dRP47CVJF0rHR7x2FxAWQ9Tt+UEksWK3z4gwpD/ZKf/ayI12vT0iIIhyX19X6uvTZMoSC59VanAOOBA1m++90BQKLrCh//eD3l5RdG2XqXCwdXmLi4HImqOWatnWuc1/OuctZNgrJQnC0Dt1Mbvhlx6RDx7m5Ia2hN02nz+8jKbRhrNeo8PfjLi/QWmkkk4oxRg9QgbwewylTK7QRm3oNfyTIi0lhjKk2VFhXxFN09GUQ2THcOlsfAsMF/hFVpXyf84EEIjIK3DcoWgfoygbNsibMMDIRoaFC4/no/jY067e0FXnwxz+c/34rXq2Ca8ItfwG9/m6KmRmFsLMeuXVnyeYu//dspr1oLX5fXBq4wcXF5OZEKWHXnae+2pA5e6AWJDpF6PHPrX3pvWM2iF8bxm2EqascZyNZh2wr4VcL6OBN2GcmJOF6tSNSXJmf6UDWbbrWMqGecg3aQfSmbJk8BJeenStdIFOC+DvjINCdWAGDjLggHoaUOGqqhOs5x2+0eOFDkO98Z5957YzQ2OhtMmeLlve/1oKrOwYaGYONGSCYV1qzJUyhIGhpUEgmDAweyLFhweuYtl9c3F1D4hIvLa5uGCFQGIFU49j0vIIVKZU0/E+NRdKWAIXVsW8GjmgifRBESL3kUy6Y22ottQ8CTwVA9JGwvQ5bNb8ctHjVHSZR1k6nuYms2zbNdMJF3zhMLQ+ZQzS0J0SAMHqfEX2WlyqpVfiorj9YuDgkSgIoKJ++kp0fB6w1j2z76+6MEAhrh8KmfQw1Dkky+FhM2DnXHmszicghXM3FxOUsIAVe2wM92QMhzWFsAaCl62VAdpWzKLozeIsMDVZQpo/jLCxQ0D7q/yPT4HlrCbeSLAfx2gbxdZO/wdLIEidUkGc7WINFAGOxXxxi2NCaGfPzNtgBT4gpfuQ7esAQGR+FAD1REYN922LUZbr4BVi0/PJ5oVOXmm0+uWfz85zl+9rMifX02uu4jHvdg2xZ33ulnypST+5GKRcnXv55mYMDm7rv9LFz4WvOxvBaF4PnFFSYuLmeRBTWwaxh2D0NT7PD6uK3Smvezt3EZi5qeZ5m6AV23sGyFvpE62lMNNNUPIHMKw8VqxpLlVIcG2DE2Hyuj0D3egqqZaJpF3tAJKh6qylMY3jJyElIWbBuDy2rg3luczohdPfCN74FHh47uo4XJqRgdtfn614v0DkH/kKdUIVegapIndwcZsuHGFeA7gYxIpWz6+y1sGzo6LBYuPPNren5whcnp4pq5XFzOIqoCd86F1jh0JJxyWIe4crSZGSJDm5jOqFKJWrCRBYVgJMO0eBsNuTaimSGag+1oXoOcCOATBQxPkFzWS7HgQSgSTVGZ6o0Q08LcOE1lSYvC9GqoDRwxDhWaGuDyldDcCNdccXqfQ9edpSOhgK5C0AseP5bp575vFdiwGzbtPfH+5eUqt97qZ+lSD1dc8VoLAz5UnGsyi8shXM3ExeUs49XgXQvh9/thQ49j7qoKgo8gl/bOZaDsB1iFNBPhCEoiQ/2m9QhfgNy8MsoqYELoVHiGyef8ZDqCZPFCRMFQJAVDRfeadOsWXf3VLDR9zGmC2+LQGjl6HKoKb37jmX2GSEThLz4XYM17DcgrENFgHDAgkxFYEpRTBHOtWOFlxYozO//5xxUUp4srTFxczgG6Cm+ZBVc0w7YBeKYLhtIgJqoRsQihxEHm/s+j7HxBY3P9fKjy4ZnWQtSEpB3l4P5WNq1ZST4XgAoBVTb4dPTIBLWRLjpzZQTRWeoLkTfgkRTMCjvtSo7ENCWqyuGCladBwdDRysAcM2HEcGq4KDaR2TpPbLBo9sLK2coZHftIxsct+voswmGFxsYLYUo61B3L5XS4EO6ci8vrlpgfrpgClzbBcBYKJjyi5hi++3ds98XZu2gZmVg1xmCRzgcX0F59HeawgAkPIJx5rRdAQfotvHU2lZ4h8qTQlDIS0kujXkZPUTBuQsURYcA9PRb/+79ZFi3SufXWk9fLOh6PPSUxdxRBKhyKcPLNUOjpVink4M/Ww9rnbL7zVRXfGViybFuya1een/0sh5QCy5LcdFOA1atPf6xnH1czOV1cYeLi8iqgqVBbCp6q6FYZslWsJY1UFUcYIE7CaKGreBnmkA9Gcfq+lwN5wAMkQDRC3vBStD1IwEOS3eNpRgsBFN2Hv+QB7RuFBzfAwAAMTAhGR4+dGG1bMjFhEY2qKMqxmoVtw4ZNJkgV5yldAgr5PkHeA54w2BOSJ/8IXQMwo/n0rkcqZfKFL3Szdm0B8PCmN5URDms88kiOVau8xx3Tq4fb0ORMcIWJi8urzJzKlWxeXEfjSj97NpiIwQn2lV1PoRByQmIiEtUyIQSWpYNTKgsRswmpSSSCgJYl0TeVjp4YYRTmZODxDExvgB8+ChE/+MMqsbkB7rr72In5oYfGWLs2xaWXhnnLW8qPeu/J5+DZ50F9Sds4FKcjIC+gx6boF4g0TF0FDVWnfw3uu2+Yp55KUiwq5PM2Bw7kWLgwNKk+Kq8ObqXH0+WCuXUuLhcLjb6ZNP7dEjoLNcy/w0/6lisZl02gaighE0W1EH028bERVL3oKAZNEkWxqfAM0eTpo1GxMHeXs9yT43aPwjQFvvgH+NazsGYfJPIQDYDuUbAkHDxoMT5++Gm7p6eIaUq6ugo8+WSeF144ItNSgpTwvndrjhcfnEJgqgAdKIJQYem18McHFQJn0GU3mTQJBhXKyxWKRZtMBgYGTG65JXCetZJDuNFcp4urmbi4vMoECTMnuJyRzoc5KKdyafFhntp/A/k5EWReQUmb+LQsxrAHX0WBzDIPWmWeeHAYyx/GE1hF2ZCf+TLGKulHQeGPA+BVoS4Gi6bDc/uAVrh8DjzwqwJ799qEQvDxj/uJRAR33lnOtm1Z5s71861v5amoUFm2zFFFrroMrlgFfUMCf0yQS2qOINFwlgJEAgqf/ZDAe4ZRv3fcUU53d4GuriIf+1iMG26ooLxcpaLiQqj35Zq5zgRXmLi4vMoIBCt9d6HftZe/e9csFlw2wuqx+3jq6fcRnJfBEAH8FUW8ZXlSw2EgR028l6qyBPOrfVioVEc9hNQAqawgGoCJDAR9TjrIvKmg+uGDV0JLleTzD9nU1wt6eyVjYzaRiEpVlYdrr3UyDj/5Sf2Y3uuqCnVVMHuWYOs2iS0deaJIiSEEU5vhxsvO/BpMnx7gy19uRUqnEvGFxaFyKi6nw4V2F11cLgo8wsuqln/gznddz2+e/TR7516PCKuoGUHQm0aYNt50gUx7iLLsBJfU7OKqeo0btEYWE2WxN8T7rhJIoHME5tZBUz0Mpp1kyWvnwNQaUBTBzTfrDA9LFixQaWg49icfiSgEAseuV1X4t38UNNQIPKpA2gLTUKiqFXzlnwTBwDG7nBaaJi5AQXII18x1uriaiYvL+UIo3HBTJf/4UITeLhupeBlLelEtE49SYGSiCttSaQzlqA1cRVTP0Y9BAIUlBIhVwGdvAcN0ui3uHYTOMagMwcLDBYtZvlxn+fLjlA6eBNdcpfDD7wm+/N82be0wc7bgzz8hWLzgLF2DCxbXAX+6uMLExeU80tgo+PSf6fz1/82TC9mQUrDSGjlTc0KCK4vMmdfAuwMa85QgGWzK0fAdYVTQS7/imdXOcra57FLBZZdeCL6MVwvXZ3ImXKg6povLRcM9q1VueYePCCZ4DNBtJ2pKh/fdo/Mfszxc5lWJoVGP5yhB4nKucM1cp4urmbi4nGfiPvh/b1VZPkPl0bUwMOT4Pz53K6w4zWRAl7OFKyhOF1eYuLhcANQE4dOXwvuXOpWGwy/rh+LyauLW5joTXGHi4nIBEXqt9ZB63eJqJqeLK0xcXFxcjsJ1wJ8JrjBxcXFxOQZXmJwurjBxcXFxOQpXMzkTXGHi4uLicgyuA/50EVJe+JmeQohhoPMsHrICGDmLxzsfvNY/w2t9/OB+hguB442/WUpZeaYHFEL8oXTcyTAipbzhTM/1euI1IUzONkKIjVLKped7HK+E1/pneK2PH9zPcCHwWh//6wk3ldbFxcXF5RXjChMXFxcXl1fMxSpMvnm+B3AWeK1/htf6+MH9DBcCr/Xxv264KH0mLi4uLi5nl4tVM3FxcXFxOYu4wsTFxcXF5RXjChMXFxcXl1eMK0xcXFxcXF4xrjBxcXFxcXnF/P/lox6E9MsC5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "s=housing[\"population\"]/100, label=\"population\",\n",
    "c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    ")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb33310",
   "metadata": {},
   "source": [
    "This image tells you that the housing prices are very much related to the location (close to the ocean)\n",
    "and to the population density"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ce756f",
   "metadata": {},
   "source": [
    "the model we are trying to build will predict the median housing value of a district given the other features, therefore, it will be a good idea to fine the features that best correlate to the median housing value.\n",
    "This can easily be achieved by using the standard correlation coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f1c5484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687151\n",
       "total_rooms           0.135140\n",
       "housing_median_age    0.114146\n",
       "households            0.064590\n",
       "total_bedrooms        0.047781\n",
       "population           -0.026882\n",
       "longitude            -0.047466\n",
       "latitude             -0.142673\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be347305",
   "metadata": {},
   "source": [
    "from the output, it is clear that the most promising attribute to predict the median house value is the median income"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e131108c",
   "metadata": {},
   "source": [
    "Experimenting with attributes combination, also called feature engineering. Some features when use alone are not quite useful, but it combination with another feature can be promising."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57020f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# so let create some Extra features\n",
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a84c1331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687151\n",
       "rooms_per_household         0.146255\n",
       "total_rooms                 0.135140\n",
       "housing_median_age          0.114146\n",
       "households                  0.064590\n",
       "total_bedrooms              0.047781\n",
       "population_per_household   -0.021991\n",
       "population                 -0.026882\n",
       "longitude                  -0.047466\n",
       "latitude                   -0.142673\n",
       "bedrooms_per_room          -0.259952\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let find the new correlate matrix;\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)                     \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899cc44b",
   "metadata": {},
   "source": [
    "from this new output, we can see clearly that the room_per_household and bedroom_per_room correlate to the median_income more than the features they are derived from."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1740bc4c",
   "metadata": {},
   "source": [
    "let get the original training set, rewrite all the previous computation using transformation pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d5e080",
   "metadata": {},
   "source": [
    "we will get the training set and separate the predictor and the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "899985e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5fb1ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 9)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47803214",
   "metadata": {},
   "source": [
    "it is time for some data cleaning and feature scaling process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60935df",
   "metadata": {},
   "source": [
    "let's start by creating some custom transformer;\n",
    "The first will be called a CombinedAttributesAdder which will be use for featute engineering;\n",
    "The second will be called a DataFrameSelector which will be use for selecting the desired features for our housing data,\n",
    "so let's get it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d3ed007",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe selector;\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values;\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "15b4eb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combined attribute adder;\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]                    \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "76608c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b65d5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's write the full transformation pipeline                  \n",
    "housing_attribs = list(housing.columns)\n",
    "housing_attribs.remove(\"ocean_proximity\")\n",
    "num_attribs = housing_attribs\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(num_attribs)),\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(cat_attribs)),\n",
    "    ('label_binarizer', MyLabelBinarizer()),\n",
    "])\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    (\"num_pipeline\", num_pipeline),\n",
    "    (\"cat_pipeline\", cat_pipeline),\n",
    "])\n",
    "# num_attribs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f36dd9",
   "metadata": {},
   "source": [
    "lets transform our housing data in one shot using the full pipeline transfomer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4ffacbc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_transform = full_pipeline.fit_transform(housing)                \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8c61ad0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_transform.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8da5dc",
   "metadata": {},
   "source": [
    "It is time to start training our models,\n",
    "as our probelm is a regression, let's start with a linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ed3e2605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_transform, housing_labels)                   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780b68cc",
   "metadata": {},
   "source": [
    "Done! You now have a working Linear Regression model. Let’s try it out on a few instances from the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4999cea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t [ 86208. 304704. 153536. 185728. 244416.]\n",
      "Labels:\t\t [72100.0, 279600.0, 82700.0, 112500.0, 238300.0]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "print(\"Predictions:\\t\", lin_reg.predict(some_data_prepared))                      \n",
    "print(\"Labels:\\t\\t\", list(some_labels))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebff072f",
   "metadata": {},
   "source": [
    "and that's it, although the prediction not quite accurate, let measure the error of the prediction using the root mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc06e5b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68633.40810776998"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = lin_reg.predict(housing_transform)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)                       \n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "912a859d",
   "metadata": {},
   "source": [
    "thus result is not promising, let's try some more complex algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0eba2b8",
   "metadata": {},
   "source": [
    "let's train a decision tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6ccb8a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_transform, housing_labels)                   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0b16bd",
   "metadata": {},
   "source": [
    "we finish Training our model, let's make some prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de2abbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_transform)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)                         \n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68499537",
   "metadata": {},
   "source": [
    "what!, error of 0, this is a sign that our model badly overfit the model.\n",
    "This is one of the reason u shouldn't touch your test set until you are ready to launch the trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5db3c52",
   "metadata": {},
   "source": [
    "Let's do some more training using cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f688f2e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(tree_reg, housing_transform, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)                               \n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a729c091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [71994.79198472 69550.15019781 70006.34035177 71063.05487599\n",
      " 70189.11708835 75808.45861294 71261.52246942 73000.1929939\n",
      " 69138.75953868 69656.18005709]\n",
      "Mean: 71166.85681706542\n",
      "Standard deviation: 1921.5264353919003\n"
     ]
    }
   ],
   "source": [
    "# this create a function display the error\n",
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "display_scores(tree_rmse_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d35af96",
   "metadata": {},
   "source": [
    "Now the Decision Tree doesn’t look as good as it did earlier. In fact, it seems to perform worse than the\n",
    "Linear Regression model! Notice that cross-validation allows you to get not only an estimate of the\n",
    "performance of your model, but also a measure of how precise this estimate is (i.e., its standard\n",
    "deviation)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e136cc",
   "metadata": {},
   "source": [
    "it will make sense to use the cross validation on the linear regression model also, so as to be sure of it's prediction accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0606b10a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [71800.38078269 64114.99166359 67844.95431254 68635.19072082\n",
      " 66801.98038821 72531.04505346 73992.85834976 68824.54092094\n",
      " 66474.60750419 70143.79750458]\n",
      "Mean: 69116.4347200802\n",
      "Standard deviation: 2880.6588594759014\n"
     ]
    }
   ],
   "source": [
    "lin_scores = cross_val_score(lin_reg, housing_transform, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)                           \n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a9eeb3f",
   "metadata": {},
   "source": [
    "as we can see, the linear regression ain't lieing, and the other hand, the Decision Tree model is overfitting so badly that it performs worse than the Linear\n",
    "Regression model when we use the cross validation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873ba790",
   "metadata": {},
   "source": [
    "let's try one last model, random forest regressor, and just like the previous model, we \n",
    "will use both the normal accuracy estimation and the cross validation accuracy estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7e694f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg = RandomForestRegressor()                  \n",
    "forest_reg.fit(housing_transform,\n",
    "               housing_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "517639ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18715.356196603687"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normal accuracy estimation\n",
    "housing_predictions = forest_reg.predict(housing_transform)                               \n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "73950b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51643.21390051 48846.13078784 47337.38126192 51938.14735602\n",
      " 47550.74761302 51831.85044928 52095.61051587 49864.95713692\n",
      " 48446.10445327 53787.66378541]\n",
      "Mean: 50334.1807260075\n",
      "Standard deviation: 2104.4256669572346\n"
     ]
    }
   ],
   "source": [
    "# accuracy estimation using cross validation\n",
    "forest_scores = cross_val_score(forest_reg, housing_transform, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46bd13c",
   "metadata": {},
   "source": [
    "Wow, this is much better.\n",
    "Random Forests look very promising. \n",
    "However, note that the score on the training set is still much lower than on the validation sets, meaning that the model is still overfitting the\n",
    "training set. Possible solutions for overfitting are to simplify the model, constrain it (i.e., regularize it), or\n",
    "get a lot more training data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0bfcf6",
   "metadata": {},
   "source": [
    "let fine tune our model, let's use grid search to find the best hyperparameter for our random forest regressor model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d8182193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [\n",
    "{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "{'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "]\n",
    "forest_reg = RandomForestRegressor()\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error')                               \n",
    "grid_search.fit(housing_transform, housing_labels)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2c6d2f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=8, n_estimators=30)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_\n",
    "grid_search.best_estimator_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3933603a",
   "metadata": {},
   "source": [
    "and there we have it, the best model, with the best hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bab1855d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94501493",
   "metadata": {},
   "source": [
    "Now is the time to evaluate our model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6656d923",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's get our test set \n",
    "X_test = strat_test_set.drop(\"median_house_value\", axis=1) # features/predictors\n",
    "y_test = strat_test_set[\"median_house_value\"].copy()   # labels                             \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "59db9ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48266.57470928415"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's transform the the predictor and run the final test\n",
    "X_test_transform = full_pipeline.transform(X_test)                                        \n",
    "final_predictions = final_model.predict(X_test_transform)\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)  # => evaluates to around 48,209.6\n",
    "\n",
    "final_rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abcc8fc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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