EDA-&-Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import geopy\n",
    "\n",
    "# Visualizations\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#import plotly.express as px\n",
    "\n",
    "# Regression Models\n",
    "from sklearn.linear_model import LinearRegression, Ridge, LassoCV, RidgeCV\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Pipeline \n",
    "from sklearn.pipeline import make_pipeline\n",
    "from category_encoders import OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "# Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Baseline\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "# Tunning\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#           ### CONCAT FILES IN CSVs FOLDER ###\n",
    "\n",
    "# files = [file_path for file_path in os.listdir('Seasons Data/Spring') if file_path.endswith('.csv')]\n",
    "\n",
    "# df = pd.read_csv(os.path.join('Seasons Data/Spring', files[0]))\n",
    "\n",
    "# for csv_path in files[1:]:\n",
    "#     df_to_add = pd.read_csv(os.path.join('Seasons Data/Spring', csv_path))\n",
    "#     df = pd.concat([df,df_to_add])\n",
    "    \n",
    "    \n",
    "# #df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# df.to_csv('Spring_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2218,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = ['review_scores_accuracy','review_scores_location','review_scores_cleanliness','review_scores_checkin','review_scores_communication',\n",
    "        'neighbourhood_cleansed','picture_url','description','has_availability','host_since','host_response_time','host_is_superhost', 'host_listings_count',\n",
    "        'host_has_profile_pic', 'host_identity_verified', 'latitude','longitude', 'room_type', 'accommodates','bathrooms_text', 'bedrooms', 'beds', 'amenities',\n",
    "        'price','minimum_nights', 'maximum_nights', 'number_of_reviews', 'number_of_reviews_l30d',\n",
    "        'review_scores_rating', 'instant_bookable','reviews_per_month']\n",
    "\n",
    "def wrangle_data(PATH):\n",
    "    df = pd.read_csv(PATH, usecols=COLS, low_memory=False)\n",
    "    \n",
    "    df = df[df['has_availability'] == 't']\n",
    "    \n",
    "#     df['host_acceptance_rate'] = df['host_acceptance_rate'].astype(str).apply(lambda x: x.replace('%','') if x!='nan' else np.nan)\n",
    "#     df['host_acceptance_rate'] = df['host_acceptance_rate'].astype(float)/100\n",
    "    \n",
    "    df['host_since'] = pd.to_datetime(df['host_since']).dt.year\n",
    "    \n",
    "    # Bool Columns\n",
    "    df['host_is_superhost'] = [True if i == 't' else False if i == 'f' else np.nan for i in df['host_is_superhost']]\n",
    "    df['host_has_profile_pic'] = [True if i == 't' else False if i == 'f' else np.nan for i in df['host_has_profile_pic']]\n",
    "    df['host_identity_verified'] = [True if i == 't' else False if i == 'f' else np.nan for i in df['host_identity_verified']]\n",
    "    df['instant_bookable'] = [True if i == 't' else False if i == 'f' else np.nan for i in df['instant_bookable']]\n",
    "    \n",
    "    \n",
    "    df['description_len'] = df['description'].astype(str).apply(lambda x: len(x) if x!='nan' else np.nan)\n",
    "    \n",
    "    df['n_amenities'] = df['amenities'].apply(lambda x: len(x.strip('][').split(', ')) if len(x)>2 else np.nan)\n",
    "\n",
    "    df['n_bathrooms'] = [np.nan if i=='nan' else\n",
    "                         0.5 if i.split()[0]=='Half-bath' else\n",
    "                         1.0 if i.split()[0] in ['Private','Shared'] else\n",
    "                         float(i.split()[0]) for i in df['bathrooms_text'].astype(str)]\n",
    "    \n",
    "    df['price'] = df['price'].apply(lambda x: x.replace('$','').replace(',',''))\n",
    "    df['price'] = df['price'].astype(float)\n",
    "    \n",
    "    # Engineer Features\n",
    "    df['baths_are_shared'] = [np.nan if i=='nan' else True if i.count('shared')!=0 else False for i in df['bathrooms_text'].astype(str)]\n",
    "    \n",
    "    df['multi_listing_host'] = [np.nan if i==np.nan else True if i>1 else False for i in df['host_listings_count']]\n",
    "                                   \n",
    "    df['total_satisfaction'] = (df['review_scores_accuracy']+df['review_scores_cleanliness']+df['review_scores_checkin']+df['review_scores_communication']+df['review_scores_location'])/5\n",
    "             \n",
    "    df.drop(columns=['has_availability','description', 'amenities','bathrooms_text', 'host_listings_count',\n",
    "                     'review_scores_accuracy','review_scores_location','review_scores_cleanliness',\n",
    "                     'review_scores_checkin','review_scores_communication'], inplace=True)\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    # Rename Columns\n",
    "    df.columns = ['picture_url', 'host_since', 'response_time', 'superhost',\n",
    "       'host_profile_pic', 'id_verified', 'neighbourhood_cleansed', 'lat',\n",
    "       'lon', 'room_type', 'accommodates', 'n_bedrooms', 'n_beds', 'price',\n",
    "       'min_nights', 'max_nights', 'total_reviews',\n",
    "       'n_reviews_l30d', 'reviews_rating', 'instant_bookable',\n",
    "       'reviews_per_month', 'description_len', 'n_amenities', 'n_baths',\n",
    "       'shared_baths', 'multi_listing_host', 'total_satisfaction']\n",
    "    \n",
    "    # Reorder Columns\n",
    "    df = df[['neighbourhood_cleansed','lat', 'lon','host_since','room_type','response_time','picture_url','host_profile_pic','id_verified','superhost','instant_bookable',\n",
    "             'multi_listing_host','description_len','n_amenities','accommodates','n_bedrooms','n_beds','n_baths','shared_baths','min_nights', 'max_nights','n_reviews_l30d',\n",
    "             'reviews_per_month','total_reviews','total_satisfaction','reviews_rating','price']]\n",
    "    \n",
    "    #Filtering Outliers From\n",
    "    # price Column\n",
    "    df = df[(df['price'] >= df['price'].quantile(0.1)) & (df['price'] <= df['price'].quantile(0.90))]\n",
    "    # min_nights Column\n",
    "    df = df[(df['min_nights'] >= df['min_nights'].quantile(0.01)) & (df['min_nights'] <= df['min_nights'].quantile(0.99))]\n",
    "    # max_nights Column\n",
    "    df = df[(df['max_nights'] >= df['max_nights'].quantile(0.001)) & (df['max_nights'] <= df['max_nights'].quantile(0.99))]\n",
    "    # n_amenities Column\n",
    "    df = df[(df['n_amenities'] >= df['n_amenities'].quantile(0.005)) & (df['n_amenities'] <= df['n_amenities'].quantile(0.999))]\n",
    "    # n_baths Column\n",
    "    df = df[(df['n_baths'] >= df['n_baths'].quantile(0.0001)) & (df['n_baths'] <= df['n_baths'].quantile(0.9999))]\n",
    "    # n_beds Column\n",
    "    df = df[(df['n_beds'] >= df['n_beds'].quantile(0.01)) & (df['n_beds'] <= df['n_beds'].quantile(0.99))]\n",
    "    # description_len Column\n",
    "    df = df[(df['description_len'] >= df['description_len'].quantile(0.01)) & (df['description_len'] <= df['description_len'].quantile(0.99))]\n",
    "#     # reviews_per_month Column\n",
    "    df = df[df['reviews_per_month'] <= df['reviews_per_month'].quantile(0.99)]\n",
    "\n",
    "    \n",
    "    \n",
    "    # Null Values\n",
    "    df['response_time'][df['response_time'].isnull()] = 'within an hour'\n",
    "    df = df[df['host_profile_pic'].notnull() & df['superhost'].notnull() & df['id_verified'].notnull()]\n",
    "    \n",
    "    \n",
    "    #df['zip_state'] = [i[:3] for i in df['neighbourhood_cleansed']]\n",
    "    #df['zip_region'] = [i[-2:] for i in df['neighbourhood_cleansed']]\n",
    "    \n",
    "    # #Scale Columns\n",
    "    # df['log_reviews_per_month'] = [np.nan if i==np.nan else 0 if i==0 else np.log(i) for i in df['reviews_per_month']]\n",
    "    # df['log_total_reviews'] = [np.nan if i==np.nan else 0 if i==0 else np.log(i) for i in df['total_reviews']]\n",
    "    \n",
    "    df['host_experience_yrs'] = round(2021 - df['host_since'])\n",
    "    #df['reviews_per_month'] = round(df['total_reviews']/(df['host_experience_yrs']*12),2)\n",
    "    #Drop Highly Correlated Features & last min drops\n",
    "    df.drop(columns=['n_reviews_l30d', 'picture_url', 'reviews_rating','host_profile_pic','host_since','response_time','id_verified','multi_listing_host','neighbourhood_cleansed'], inplace=True)\n",
    "    df.dropna(inplace=True)\n",
    "    \n",
    "    df['ppl_per_bed'] = [a/n if n!=0 else a/1 for a,n in zip(df['accommodates'],df['n_beds'])]\n",
    "    \n",
    "    df['superhost'] = df['superhost'].astype(bool)\n",
    "    df['shared_baths'] = df['shared_baths'].astype(bool)\n",
    "    #df['id_verified'] = df['id_verified'].astype(bool)\n",
    "    \n",
    "    df = df[(df['total_reviews']>37) & (df['reviews_per_month']>0.33)]\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2219,
   "metadata": {},
   "outputs": [],
   "source": [
    "Winter_df = wrangle_data('Concat_CSVs/Winter_Data.csv')\n",
    "Spring_df = wrangle_data('Concat_CSVs/Spring_Data.csv')\n",
    "Summer_df = wrangle_data('Concat_CSVs/Summer_Data.csv')\n",
    "Fall_df = wrangle_data('Concat_CSVs/Fall_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spring mean price: $137\n",
      "Summer mean price: $152\n",
      "Fall mean price: $158\n",
      "Winter mean price: $162\n"
     ]
    }
   ],
   "source": [
    "print('Spring mean price:', '$'+str(round(Spring_df['price'].mean())))\n",
    "print('Summer mean price:', '$'+str(round(Summer_df['price'].mean())))\n",
    "print('Fall mean price:', '$'+str(round(Fall_df['price'].mean())))\n",
    "print('Winter mean price:', '$'+str(round(Winter_df['price'].mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spring Data shape: (35298, 20)\n",
      "Summer Data shape: (37676, 20)\n",
      "Fall Data shape: (40102, 20)\n",
      "Winter Data shape: (41591, 20)\n"
     ]
    }
   ],
   "source": [
    "print('Spring Data shape:', Spring_df.shape)\n",
    "print('Summer Data shape:', Summer_df.shape)\n",
    "print('Fall Data shape:', Fall_df.shape)\n",
    "print('Winter Data shape:', Winter_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20752, 20)"
      ]
     },
     "execution_count": 2222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring_df[Spring_df['total_satisfaction']==10.0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2223,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# geolocator = geopy.Nominatim(user_agent='myusername')\n",
    "\n",
    "# def get_zipcode(df, geolocator, lat_field, lon_field):\n",
    "#     location = geolocator.reverse((df[lat_field], df[lon_field]))\n",
    "#     return location.raw['address']['postcode']\n",
    "\n",
    "# zipcodes = Spring_df[['lat','lon']].head(2).apply(get_zipcode, axis=1, geolocator=geolocator, lat_field='lat', lon_field='lon')\n",
    "\n",
    "# zipcodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 35298 entries, 7 to 192888\n",
      "Data columns (total 20 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   lat                  35298 non-null  float64\n",
      " 1   lon                  35298 non-null  float64\n",
      " 2   room_type            35298 non-null  object \n",
      " 3   superhost            35298 non-null  bool   \n",
      " 4   instant_bookable     35298 non-null  bool   \n",
      " 5   description_len      35298 non-null  float64\n",
      " 6   n_amenities          35298 non-null  float64\n",
      " 7   accommodates         35298 non-null  int64  \n",
      " 8   n_bedrooms           35298 non-null  float64\n",
      " 9   n_beds               35298 non-null  float64\n",
      " 10  n_baths              35298 non-null  float64\n",
      " 11  shared_baths         35298 non-null  bool   \n",
      " 12  min_nights           35298 non-null  int64  \n",
      " 13  max_nights           35298 non-null  int64  \n",
      " 14  reviews_per_month    35298 non-null  float64\n",
      " 15  total_reviews        35298 non-null  int64  \n",
      " 16  total_satisfaction   35298 non-null  float64\n",
      " 17  price                35298 non-null  float64\n",
      " 18  host_experience_yrs  35298 non-null  float64\n",
      " 19  ppl_per_bed          35298 non-null  float64\n",
      "dtypes: bool(3), float64(12), int64(4), object(1)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "Spring_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2225,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>description_len</th>\n",
       "      <th>n_amenities</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>n_bedrooms</th>\n",
       "      <th>n_beds</th>\n",
       "      <th>n_baths</th>\n",
       "      <th>min_nights</th>\n",
       "      <th>max_nights</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>total_reviews</th>\n",
       "      <th>total_satisfaction</th>\n",
       "      <th>price</th>\n",
       "      <th>host_experience_yrs</th>\n",
       "      <th>ppl_per_bed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.00000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "      <td>35298.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.702307</td>\n",
       "      <td>-105.226282</td>\n",
       "      <td>899.760525</td>\n",
       "      <td>31.440903</td>\n",
       "      <td>3.964587</td>\n",
       "      <td>1.553799</td>\n",
       "      <td>2.077172</td>\n",
       "      <td>1.270908</td>\n",
       "      <td>8.004476</td>\n",
       "      <td>569.198906</td>\n",
       "      <td>2.511845</td>\n",
       "      <td>112.45776</td>\n",
       "      <td>9.832007</td>\n",
       "      <td>136.718058</td>\n",
       "      <td>6.158848</td>\n",
       "      <td>2.043149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.786429</td>\n",
       "      <td>24.875374</td>\n",
       "      <td>202.429163</td>\n",
       "      <td>8.821004</td>\n",
       "      <td>2.204029</td>\n",
       "      <td>0.822495</td>\n",
       "      <td>1.298886</td>\n",
       "      <td>0.540042</td>\n",
       "      <td>12.268970</td>\n",
       "      <td>520.036085</td>\n",
       "      <td>1.452868</td>\n",
       "      <td>80.67884</td>\n",
       "      <td>0.270378</td>\n",
       "      <td>72.935345</td>\n",
       "      <td>2.274392</td>\n",
       "      <td>0.735435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.982690</td>\n",
       "      <td>-159.711430</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.743477</td>\n",
       "      <td>-122.016023</td>\n",
       "      <td>934.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.957185</td>\n",
       "      <td>-115.174660</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>86.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.713100</td>\n",
       "      <td>-80.157742</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>141.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>47.733320</td>\n",
       "      <td>-70.997010</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>839.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                lat           lon  description_len   n_amenities  \\\n",
       "count  35298.000000  35298.000000     35298.000000  35298.000000   \n",
       "mean      35.702307   -105.226282       899.760525     31.440903   \n",
       "std        6.786429     24.875374       202.429163      8.821004   \n",
       "min       18.982690   -159.711430        85.000000      6.000000   \n",
       "25%       32.743477   -122.016023       934.000000     26.000000   \n",
       "50%       36.957185   -115.174660      1000.000000     31.000000   \n",
       "75%       40.713100    -80.157742      1000.000000     37.000000   \n",
       "max       47.733320    -70.997010      1000.000000     67.000000   \n",
       "\n",
       "       accommodates    n_bedrooms        n_beds       n_baths    min_nights  \\\n",
       "count  35298.000000  35298.000000  35298.000000  35298.000000  35298.000000   \n",
       "mean       3.964587      1.553799      2.077172      1.270908      8.004476   \n",
       "std        2.204029      0.822495      1.298886      0.540042     12.268970   \n",
       "min        1.000000      1.000000      0.000000      0.000000      1.000000   \n",
       "25%        2.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "50%        4.000000      1.000000      2.000000      1.000000      2.000000   \n",
       "75%        5.000000      2.000000      3.000000      1.500000      5.000000   \n",
       "max       16.000000      9.000000      7.000000     10.000000     91.000000   \n",
       "\n",
       "         max_nights  reviews_per_month  total_reviews  total_satisfaction  \\\n",
       "count  35298.000000       35298.000000    35298.00000        35298.000000   \n",
       "mean     569.198906           2.511845      112.45776            9.832007   \n",
       "std      520.036085           1.452868       80.67884            0.270378   \n",
       "min        2.000000           0.340000       38.00000            7.800000   \n",
       "25%       30.000000           1.400000       56.00000            9.800000   \n",
       "50%      365.000000           2.200000       86.00000           10.000000   \n",
       "75%     1125.000000           3.300000      141.00000           10.000000   \n",
       "max     1125.000000           7.500000      839.00000           10.000000   \n",
       "\n",
       "              price  host_experience_yrs   ppl_per_bed  \n",
       "count  35298.000000         35298.000000  35298.000000  \n",
       "mean     136.718058             6.158848      2.043149  \n",
       "std       72.935345             2.274392      0.735435  \n",
       "min       50.000000             0.000000      0.142857  \n",
       "25%       82.000000             5.000000      1.666667  \n",
       "50%      116.000000             6.000000      2.000000  \n",
       "75%      171.000000             8.000000      2.000000  \n",
       "max      395.000000            13.000000     12.000000  "
      ]
     },
     "execution_count": 2225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Select Target ( X,y = FM,TV )**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2226,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Spring = Spring_df.drop(columns=['price'])\n",
    "y_Spring = Spring_df['price']\n",
    "\n",
    "X_Summer = Summer_df.drop(columns=['price'])\n",
    "y_Summer = Summer_df['price']\n",
    "\n",
    "X_Fall = Fall_df.drop(columns=['price'])\n",
    "y_Fall = Fall_df['price']\n",
    "\n",
    "X_Winter = Winter_df.drop(columns=['price'])\n",
    "y_Winter = Winter_df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Features Correlation Spring Data')"
      ]
     },
     "execution_count": 2227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GI/h9Xwbztlm2l51bufHkkHDCfZ15+bdk1h0+az73/uQYogNd2ZdayLRF1m+TLobeYR48Va3P3gy+a0ifoeFE+Drz8q/JrK2lT3PxyqQY4qJ80VcYeGpewnnb2vemdEVnp2HtgWxmmtraadd2YHisP0YFcovLeWpeAlmFZbjobPjwzm4EeTig1Qo+X32chdtOW0X+PmGeTBvRFo2AX/dm8O0Wy+fYagWvXB9JZIALhfoqXvjlABmFZWg1gpevbU8Hfxe0GsFfiZl8s+U0bTwdmD0+ynx9kIcDn60/yY87mqZOAPQJV3XSCvg1IYNvGtDp1bE1Oj2/WNXJplqngBqd/m+zdd77+fgnjAmvifDiuTHt0QrBL7vT+GrjqXo6zJ4QTcdAVYdnFiaSXlBmPu/vpuO3R/vwyboTfLs5BYApfVozvnsQigJHs0p4eckBKqqMVpHfGv33fYPDGNstEACtRkOEnzPdXl5FYWklUweGcmPvVigKHM4o4pkfE62mW0vnX+YoIz1lrhLcgYeaW4ix48bzv8++bG4xLojRaGD1t/9h0jOzufutLzm4dR1n01LqpSvXl7J7xRICwjuYj+1btxSAqXO+YPJzb7Luh89QjE3XEGqE2vjf+9UuRr+7iWs7BxDu62SRZlLPYAr1lQybu5FvNp7imdHtVH0qjXy44ihv/XW43n0fjAsnt6SCEW9vZNS7m9h5Iq9eGmsxtFMg4X4udJ/2B09+vYN37+rRYLoVCWkMnbmi3vHU3FIe/nwbi7bWz0NrM6ijL6E+Tgx6fR0v/pTIG5NiGkz3+uQYXvgpkUGvr1PTR/oAsOlwDsPfXM+otzZwMruEh4ZGAFBeZeTdpYeZ/Vtyk+gRF+VHqK8z/Wat5rn5e5lzU8OGxjk3d+bZ+XvpN2s1ob7ODO7oaz4X4OHAgEgfUnNLzcc+XX2MEXPWMWLOOt78LZltR89azSCj6uBEv5mreO6HBObc3Pk8OnTi2fkJ9Ju5ilBfJwZH+ZnPBXo4MKCjr4UOj45sz4HUQoa9sZbHv9nNq5NiG132QZE+hPg4MXh2PC8sTOL1idENpnt9YgwvLExi8Ox4QnycGNhBLUcPDgln89Fc4mbHs/loLg8OUcvRw0MjSE4vYtTbG3nqh73MuCHK4n53DQjlWFZJvedcKY2RF1+sOcbw2esYPnsdaw+oHwNllQbm/nGQ135JavB+jc2QGH/C/Jzp9cIynv52F3Nv79ZgurlTuvL0N7vo9cIywvyciYvxB2DGpFje+f0AcbNW8daS/cyoVXY0QvDypFjiDzSd8VsjYNrwtjy5MImbv9jJ8I6+hHg5WqTJKirjtb8OszK5vlzzt5/hlT8PNZW4f4tGwDPD2/LEwiRu+lzVJ7Qhff48zMomfM8XYnCU2mf0n7WG537Yx+zztLWzb+rEsz/spf+sNWqfYWprP119jOGz4xk5J57V+7N4fJTav98xMJSjGcWMmBPP5A828/L4KGy1Fw5UeTloBDw3qh2P/biPSZ/uYESUH6Helu98bOcAisuquOGT7fyw/QyPxoUBMDTSBzuthps+38ltX+5ifNdAAtx0pOTpufXLXdz65S6mfLWLskoD6w7nNLrsF9Lp+ZHteOyHfUz83w5GRNfXaVznAIrKqhj33+3M336Gx4aYdOrog62Nhhs/28ltX9To1JSyX+1jQo2Al67rwEPfJTD2P1sYFetPmI+lDuO7BVGkr2LMB5uZtzWFJ4e3tTj/zKh2bDqaa/7b18WeW/q05qb/bWf8x1vRamBUjB/WwFr99+frTjDmnU2MeWcTb/91iO3HcyksrcTPzZ47+4dw/XubGDl3A1qN4LougVbR7WpAUazz01KRRpkWhBDCWQixRgixRwiRJIQYazr1JhAuhNgrhHi7ueTr1r0Hrm5uzfX4iyLj+GHc/QJx9w1Aa2NLZO9BHNu9pV66TYu/ode1N2Jja2c+lpuWQpuOnQFwcvPA3tGJzJNHmkp0Ylu5k3K2lDN5eioNCn/ty2RolGVHM6SjH0t2pQOwPCmLPhFeAOgrDew+VUB5ZX0j0oQeQXy29gSgNkb5VvpwbojRXYP4aZM6s7PreC6ujnb4NTCo2XU8l6zCsnrHz5w9R/KZAozN0IoOj/bjl53qbF5CSgEuDrb4uNpbpPFxtcdFZ0NCSgEAv+xMZbjpo23j4bMYjIr5en93BwD0FQZ2nchvMK+sokesP4u2q7N7e07l4+poi28dPXxd7XHW2bDnlOrFtGj7aUZ0CjCfnzUhmjeWHDjvrMW47kH8tst6M58jOgWwyDRLvudkPm7n0cFFZ8uekyYdtp1hZG0dJsbwxi/7LaL5t/N3YbPpA+F4VgnBXo54u1je90oZFu3HLzvTANibUoDrecqRs86GveZylMZw0yBzWLQfi03lcPHOVPPxCH8Xth5VPQNOZJ8j2NMBb2e1PfN30zG4oy8LtjW+Z1lj5EVD6CsM7Dye22T1YlSXIBZuOQXA7hN5qh512iZfNx0uDrbsNn20LNxyitFdggB1Bs9FZwuAq6MtmQV683X3DI3gr91pnC2q36ZZi44BrqTm60kvLKPKqLAqOZsBbb0s0mQUlnMs51yDg9JdKQWUVlQ1kbR/T8dAkz4FJn0OZjOgXcP6GFvIIHt4bACLt6t1I+FUPq4O529rE0xt7eLtZ8xtbUlZzft3tNOaf1cUcNapju1O9jYUlFZQZQWlowJdOZOnJ830zlceyGJgO2+LNAPb+fBnYiYAaw7m0DPUw3xOZ6tFKwQ6Ww2VBoVz5ZblqUeoB2n5ZWQWlje67OcjKtCVM/mWOg1qX0en9j78uc+kU3KNTooCDiad7M+jkzX5J4wJY4LdOJ1bSmq+niqDwrKkTAabJq6qGdzBh9/3qjqsOpBNrzBP87m4SB/S8vUcy7acYLDRqHmi1Qh0tlqyi6xTpqzVf9fmui6B/LEn3fx3tU41ujVdPyJpXqRRpmVRBtygKEpXYDDwrhBCAM8DxxVF6awoyjPNKmELpyT/LC6eNQ2+i6c3xfmWLs2Zp45SnJtDeOdeFsd9WodzbM9WjAYDBdkZZJ06SlFe083o+LnZk1lYM7DPLCzDr07j7+dmT4YpjcGoUFxWhYej7Xnv6WIayD0xoi1LHu/Dh7d1xsvZ7rzpG5sAD9Vls5r0vFICPB0vcEXLwc9dR3qBZX741/lo83fTkVErTUZBGX7u9Y1Ok3q1Iv5gtvWEvQD+7g6k59eSMb/MbCCqncZCj1pphsf6k1lYxsG0+i67oA7EB3X0Y2lCeoPnG4P6Ouj/XocCfS0dAsgo0JNcR4fktEJGd1ZnoTq38SDY05EAD8v7Xil+DZSRBstRLaNkZqHebLz0drEnxzTgzCkqNxuNDqYVMSJWNQB2au1GkIcD/qayN+OGjrz5x0GrGDOvNC8A7hoUxqqX4nh3SlfcLtB+WRN/DwfS82pkTM/T18v7AA8HMvIt0/ib0kz/MYGZk2NJeOdaZk3uxBuLVQ8ff3cHRncN4v/WHWsCLWrwcbEju7jmwyS7uByfRjYwNiW+znZkFV1d+vi7WfYZdcs9VNeNMss0tdqDZ6+LZPvrw7mhRzDvmDyXvll/kgh/Z3bNHsGqlwYz8+f9Vpnt9XWxJ6vWB2B2cTm+dd65r0tNvhgUhZJyA24Otqw+mENZpYHlT/Tlz0f78v220xSVWRowRnT0Y0UTezX5ulrqlFVUvxz51NWpzIC7gy1rDuagrzSw4sm+/PVYX+Ztra+TNfknjAl9Xe0tjHBZheX41S1TrjoyTf2fwahQUq4u83aw0zK1Xwj/W3fCIn12cTnfbDrFqqf7s/bZAZSUVbH1uHW8fazVf1ejs9UwsIMPy0yGzqzCcr6IP8HmGXFsf2UIxWVVbGwByzKbC8VK/1oq0ijTshDAbCFEIrAaCAKs45P3L0UxGlk3/1MG33J/vXOxA0fi7OnDdzMeYu38/xEU0RGNuLqriI1GEODuwJ6UfG74cCt7Uwp4fkz75hbrX8XDwyIwGBV+3ZXW3KJcMjpbLY+OaMc7fxw8b5phsf7sPJFntaVLV4rOVsujIxvW4eMVR3B1sGXli4OZOjiM/WcKMbaUaffzUP0x9uma47jqbPlrWj/u6B/CgbQiDEaI6+jL2eIK9jew7r0l8N2Gk/R9eSXDZ68lu7CMGRMaXhbY0rlzcAQzftpLl2l/8vJPe/nAtDTz9Zs789rPiS3aRVrScpn7x0F6TV/Jkp2p3DkwFICBHX1ITi2i+4srGDknntcmx5g9Z1oK0YGuGBSFkR9u4fqPt3Jb79YE1ZqgsNEIBrTzYnUzTU5cDlGBrhiNCiM/2MJ1/9nKbX0sdboauZrGhA8NDmPe1tPoKwwWx111NgyO9GXke5sYMncDDnZaru3k30xSXhp1+4UhUX7sPpVPoWn85Opgw7BoPwa8to7eM9fgaKdlXLegZpBU0hy0rFZdcivgA3RTFKVSCHEK+NseQAhxH3AfwMeffMbd995nVSFbMs4e3hTX8m4pzjuLi0eNq2pFmZ6zqaf4cfY0AM4V5vHL+zMY/+SrBIS1Z8htD5rTfv/K43gEBDeZ7FmF5fi71cyq+bvpLGYKq9MEuDmQVViOViNw0dlc0PU0v7SS0ooqcxC3ZYmZTOxh3Qb+7qFtuX2Qum424UQuQbU8YwI9Hcmo5TnT0pjSrw0392kNwL7ThQS6OwCqm7m/W81sTjWZhWUE1JoJDXDXkVVrFnRiz2CGRPlxy3+3Wl/4WtwxIJRbrgkBYF9KPoG1PAACPHQWSy0AMgv0lnqY0oT4ONHK24mVL8Wpx911LH9hENfOXW+e/RnbLYjfdjb+0qU7BoZyq0mHvSkFdXRw+Hsd3B3MOrT2dmLV9Djz8RUvDmbMW/HkFJXz1Lw95mu2vT6clLPnrlj2Kde04aY+ahDkxNOFJrnyTc8/TzmqNfvm7+ZgXs53trgcH1d1ts3H1Z7cEvW9l5RX8exPNcFwN748mDO5pVzbJYCh0b4M7jgYexsNzjpb3r+1M0/O33vZ+jRWXlTrU838Taf49uE+ly3XpTI1LoLbBqgfugkn8wn0rJEx0NPSKwZUL6Da3jOBng5kmtLc2LeNOejv7ztTef9O1SjTKcSDzx5QdfJytmNIbAAGo5FlVvQkA8gprrDwavB1sSenuPwCV7RssksqLLwCWqo+dwwI5eZr2gCmtvY85b4atW7oLNM0sHR3yc5UvnuoN+/9dZjJvVvzycqjAJzKOceZ3FIi/JzNyyUai+zicvxca2TzdbG38L5S06j5kl1cjlYInO21FOorGRHty9bjeRiMCvmllew7U0hkgAtppv7wmggvDmWWkHeuaY332UWWOvm51i9HOXV10mkp0Fdyf7QvW47nUVVLp46BNTpZm3/CmDC7qBx/t5p67OdmT1bdMlVUZtZNqxE429tQUFpJTLAbw6L8eHJ4W1x0NigKVFQZyS0pJy1fb9ZzdXI2nVq5m5egXSlN0X9Xc12XQH6vtXSpXztvzuTqyTtXAcCKxEy6hnjw6+6rb1KvUfiXTW5c3W4A/zzcgGyTQWYw0MZ0vBhwOd9FiqJ8rihKd0VRuv+bDTIAAWHtyc9MoyA7A0NVJQe3xRPRtWbQb+/oxKP/W8wD73/PA+9/T2B4pNkgU1leRkWZOoA6lbQbjVaLd1Cb8z2q0UlKLSTE25FgDwdstYIxnfxZk2w5q7Q2OZsbuqvLLUbG+LH1WG5Dt7JgXXKOeY1unwgvjmVf+Ufnhfhq9VEGTl/GwOnL+Gt3Kjf1Uz+Cuod7UVRa2WDsmJbCvE0pjH57I6Pf3sjKpEzG91CNcl3auFNcVmU2RFSTU1ROcVkVXdq4AzC+R7B5sDOwgw/3Dwnnni92UtZEcTKq+XbDSXMQ3uWJGUzspRqauoZ4UKyvqrf+OruonJKyKrqGqGvpJ/ZqzcrETA6lF9H5uWX0eXklfV5eSUZBGSPnxJvfg4vOht5tvVmRmNH4Oqw/aQ4Gu2JfOhN7q4OkrqEeFOkrG9ShuKySrqZ4ABN7t2LFvgwOpRfR6dml9J6+kt7TV5JRoGfE7HXkFJXj6mBrDph5yzUhbD+aaxHX4XKZtznFHMRv5f4sxpsGvZ3buFOsb7gclZRV0dlcjoJYZSpHq/dnMcFUDif0CDYfd9HZmGW/qXcrdhzPo6S8irf/OkzfV9bS/7V1PPpdAluOnr0igww0Xl4AFjE2RnUO4HB603n0fL32GHGzVhE3axXLEtKY3DcEgG5hnhSVVpJdp23KLiyjWF9JN1P7OblvCMsS1MFxZkEZfdurS2X7R/pyIkvdtavHc0vp/uxfdH/2L/7Ylcpz8/ZY3SADcDCjiFaeDgS46bDRCIZ19GXjRfQPLZWD6UW08qilT6QvG462PH2+3XCSkXPU4Lwr9mUyoZdaN7qEeFB8nrpRUlZFF1NbO6FXK1aa2s+QWgFQh8f6mwN1p+frucZU1rxd7An3cyblbONPbiSnF9PK04FAd/WdD4/yY8MRy6UTG46c5VrTsskhkT7sPFUAqMaB7iaddLYaooNcOVUrqPqIKN8mX7oEDeu0vo5O64+cNXtaDOlYo1NmUTk9aukUE+TKSSu89/PxTxgT7k8roo2XI0HuOmy0glEx/sQfsgwLEH8oh+tNy4iHRfmyw7SL3Z1f7WLke5sY+d4mvt96mi82nOTH7WfIKCwjtpUbOlv1E7ZXmCcncxpPh6bov0Htw3uFe1ocS88vo0uIu1m3vu28OZ7d+AH7rxYUK/20VIQifWybneotsYUQ3sAfgDOwC+gNjFIU5ZQQ4gcgFlh2obgyZVXWK2/PTXuKXTt3UFCQj6eXFw8+/CjjJ0yyyrPm77n8bQeP793O2vn/QzEaiRkwgj5jb2Xj4m/wD21H2659LdL++MbTDLr5PgLC2lOYk8nCuS8gNAIXD29G3vM0bt5XtnrsrUWXtsPOwA7evHhdJFqNYNHOVD5de4LHhkewP7WQtck52NloePumWDoGulBYWsmTP+zjjCkuwtrnB+Ks02Kr1VCsr+KuL3dyPPscge463r4pFhcHW/JLKnj+5ySLNe1/x9mMK1urO/eO7gyJCUBfYeCRL7ax19Thrn99FAOnLwNg1k2dmdgnBH/TzOK8+OO8tSSJLqGezHtiAG5OdpRXGMgu1NP3haWXJYerp+slX/PqxGgGRvqgrzDwzA/7SDJtb7r0mf6MfnsjoG5vWr0ldnxyjnmL6/jpg7Gz0VBgmvFISCngpYVq3IlNM+Jw1tlga6OhSF/JlE+2X9ROOVWVl2cweP3GWAZ19KOsooqn5iWQaNr6d8ULgxkxZx0Asa3dec+0hXH8gSymL6y/JfHW14Yz+s148k06TerdmkEdfXn460vbcvly+p03burEoI6mrWa/22PWYeWLgxk+u0aH9+9QtyledyCrwW2Vt70+nFFzVB26hXrywR3dUFA4nF7MtO/3mN2ILwY73cWtxX91QhQDOqjl6NmfEs3l6K9p/RjzziZALUfqlpoa1h/MYaZpa3V3R1s+vqMrgR4OpOXreeRbVcYubdx595ZOKMCRzBKe+2kfRXrL8tEr3JN7B4ddcEvsirKKi9a3mivJi4/u7EbHYDcUBVLzSnlufoL5w3Xb68Nx1tlip1Xrxc0fbeZo5sVtUV5Zfukz8G/e1pW4aH9KK6p4/Oud7DMFX107axhxs1YBqufLR1N74mCnZU1SBi/MV71jerX15vWbO2Oj1VBWaeC5eXtITMm3uP9HU3uwcl/GRW+JHdbuyvqbPmGePDk0HI0Q/JmYyTdbT3Nv/xAOZRSz8Vgukf4uvDU+ChedDRUGI7klFdzylVo2Pr21M228HHCw1VKkr+KNZYfZfjL/b57YMI01rOwbXqPPH6Ytlu/rH8LBan0CXJhbR5+bG3H79/TUgku+5vXJsea68fT3NW3t8hcGMXJOPGBqa6d0QWerZV1yFi+b+oXP7ulBuJ8zRkUhNU/Piz/uU+OIuOl4b0oXfF11CAH/XXmUJRfpnegXcGn93jXhnjw1vC1a0zbkX29O4f6BoRxML2LD0VzstBpeHRtJe39nivRVvLjkAGkFZTjYapl5XQdCfZwQwB+1tmTX2Wr489G+jP3vVs6VGy4swHm4kjJ1TYQnTw9vi1YIftuXwdebUnhgYCjJGUVsOKLq9No4VadCfRUv/lKj06zra3T6fV8G87ZefvD04svw9GqJY0J7e+3fJ6pF/7bePDu6HVqNYMmedL5Yf5KH48I5kF5E/CFVhzkToukQ4EKhvpJnFyaRWsdr8cHBYZRWGMxbYj8UF8bIaH+qjAqHMoqY+WsylYZLKyQlJRfX91mj/wbVSDOwgw+PzUuweN4TI9tybedAqowKyWmFPP9TEhWGhif2Tr4/pvG3YWtBFJVZZz25q07TIt+bNMr8w7CmUaYpuRKjTEviUo0yLZErNcq0FC7HKNPSuFyjTEvjn9LvXKxRpiVzOUaZlsjlGGVaGldqlGkp/EOq92UZZVoal2qUaan8E8rU5RhlWiKXapRpqVysUaYl8083yhTqrWOUcXNomUYZuXxJIpFIJBKJRCKRSCQSiaQZkIF+JRKJRCKRSCQSiUQikbQIWvL21dZAGmUkEolEIpFIJBKJRCKRtAj+CcsWLwW5fEkikUgkEolEIpFIJBKJpBmQRhmJRCKRSCQSiUQikUgkkmZAGmUkEolEIpFIJBKJRCKRSJoBGVNGIpFIJBKJRCKRSCQSSYtAxpSRSCQSiUQikUgkEolEIpFYHekp8w9j/p7TzS1Co3Br19bNLUKj8PLXu5pbhCvG3sG+uUVoFMr15c0twhVjqDI0twiNgpObU3OL0CjY21/9XahG88+Ym1H+AVNqQojmFqFRKC+vam4RGgUXN4fmFuGKKSy8+vs9gKrKq7/vO1d8rrlFaBScXP4Z/bfBcPWXqX86cktsiUQikUgkEolEIpFIJJJm4B8w13JJ/DOmyCQSiUQikUgkEolEIpFIrjKkp4xEIpFIJBKJRCKRSCSSFsG/zFFGespIJBKJRCKRSCQSiUQikTQH0lNGIpFIJBKJRCKRSCQSScvgX+YqI40yEolEIpFIJBKJRCKRSFoEcvclSZMhhChRFMW5ueWozYnEnayZ9wmK0UjsoFH0vu6mBtMd3rmR3z56lSmvfExAWHsMVZWs+PoDMk8eQQgNQ6Y8ROvITk0s/cUxY/oLbFgfj6enF7/89mdzi1OP1ybHEhftj77CwJPf7ibpTEG9NDGt3fngjm7obLWs3Z/JywsTAXj62khu6RdCXrG6Deac3w6wdn8WwV6OrJ85jBNZxQDsPpnH8z/stZ4ON3ZiSIyqwxPf7CLpdH0dYlu788Fd3dHZalmTlMnLC/aZz00dHM5dg8MxGBVWJ2Xw+uL95nNBng6snzWcd/5I5tNVR62mgzX1iAxyZe5tXXFxsMWoKIx6Yy3lVUar6fHGLV0YatLj0a92NKxHGw8+ursHDrZaVidl8tIPCQBEt3Jn7u3d0NlqqDIqPDdvDwkn8wDo296H12/ujI1WQ15JOePeim9UuWfe0JFBkb6UVRqY9uM+DqQW1UsTHezK2zd3QmerJf5gNq8sSQbAzdGWj2/vQpCnI2l5pTz87R6K9OpWvb3CPZlxQ0dstBrySyq46b/bAJg6MJQbe7dCUeBwRhHP/JhIRSPmS7923rx0XQc0QrBoZypfrD9pcd5WK3hrcgxRQW4UlFbw1I/7SMsvo2+EF0+PbIutjYbKKiNzlx1h+3E1D0bF+vPA4DA0GkH8wRzeXX6k0eStZkB7b14eF4lWI1iwPZXP1p6wOG+n1fDOLbFEB7uSf66Sx+btJS1fD8ADcWFM7hWMwajw6q8H2Xj4LHY2Gn56uBd2Nhq0GsHyxEw+XHEMgLk3xdAzzJPiMjWvnv0pkYPpxY2uE1inrbXRCN6Z0pWY1u7YaAQ/bzvNxysaP08aoneoB08MCUerEfy+L5N5289YnO8c7MYTQ8II93Vmxu8HWXf4rPnc+5OiiQp0JTG1kGmLDzSJvNX0DffkmZFt0WgEv+7J4P82p1ict9UKXhvXkchAFwpLK3lu0QEyCssAaOvrxPRrO+Bkr8WowG1f7KLCYGRktB9T+7VBAXKKy5n+SzIF+kqr6tGvrRcvXNsBrUat319uOFVPjzcnxRAV5EpBaSVP/biP9IIyYoJdeWVcRzWREPx3zXHWJGcD4KKz4dXxUbT1c0ZRFKYvPsC+M4VW06F/e2+mXx+JVgMLd6Ty+TrLNspOK5h7k1rXC0orefz7faTl63F3tOU/UzoT08qNX3al8eqvBwHQ2Wr4z5TOtPJyxGhUWJucwzvLmqY+zBxfq//44QL9xy21+o9f1P5jdCd/Hh/Zjgg/Z8a9v5mkWu+8Q4ALb9wYg7O9DUZFYex7mxu1n6jLGzfX6r+/vkD/PbVW//1jrf57Sq3++3u1/x7ZOZDnx0VjVBSqjAov/7iX7cfO1rtvY2GNvAjydGD18wM5kVMCQMKpAqb/vL/efRuTVyZEMzjKD32Fgae/T2B/av26GNPKjXdv64LOVsu6A1nMNI37XhzbkaExflRWKaScPce0+QkU6auw0Qjm3tKZ6FZuaDWCX3ac4b+rjllVD0nLRBplJGaMRgOrv/0Pk597CxdPb76b8QgRXfvgHdTGIl25vpTdK5YQEN7BfGzfuqUATJ3zBecK81n0zkvc/srHCE3LC1s0dtx4br7lNl564bnmFqUecdF+hPo6c82MlXQN9WDOLZ25toEP3Tdv6cwz3+9hz8l8vn+kL4Oj/Fh3IAuAL9Yca9BYkZJTwrA31lpbBeKi/Qnzc6bv9BV0DfXkzVu7MGbOuvo63NqFad/tYc/JPOY/dg1x0X6s3Z9F3/Y+jOgcyJBXV1NRZcTLxd7iulmTYll7IPOq1UOrEXx8d08e/XonyamFeDjZUWmw3oBuSIyqR68XltEtzJO5t3dj1Otr6qWbO6UrT3+zi90n8vjxyf7ExfizNimTGZNieef3A6xNymRIjD8zJsVyw9x4XB1seWtKV256byNpeaV418mnK2VQpA8hPk4Mnh1P5zbuvD4xmhs+2FIv3esTY3hhYRJ7Uwr4v/t6MLCDD+sP5fDgkHA2H83l0zU7eGBIOA8OieCtPw/horPhtYnR3PnZDtILyvBytgPAz82eO/uHMOyt9ZRXGvn4ji5c1yWQxTtTG0UfjYAZYyOZ+tUusgrL+PmRPqw9mM3x7HPmNBN7BFOkr2LEOxsZHevP0yPb8dSPieSXVvDgtwlkF5fT1s+ZL6d2Y+Cc9bg72vLM6HZM+M9W8s9V8uakaHqHe7LNZLBpLLlnjY/ijs92kFlYxpIn+rLmQDbHskrMaSb1CqawtJK4ORu4tnMAz13bnsfm7SXCz5lruwQwcu4mfN3s+e7+ngx9cz0VVUZu+98OSisM2GgECx7pzfqDZ9lr+th488/DLE+0bh23Vlt7Xbcg7G00DHltDQ62WuJnDeXXXamk5pZaVR+NgKeHRfD4giSyi8v5+o4ubDyWy6laz80sKuO1pUe4tWdwvevn70hFZ6NhXOcAq8pZF42A50e358F5CWQVlTP/3u6sP5zDibM1co/rEkhxWRVj/7ONEVG+PD40nOcXH0ArBK+Pj+LlJckcySrBzcGGKqMRrRA8M7ItE/67nQJ9JY8PDefGnsF8VscI2th6TL8+knu+3k1WURkLHurNukM5FvV7QvdgivSVjHx3E6NM9fvpnxI5mlXCpE+2YzAqeLvYseTRvsQfysFgVHjh2g5sOnKWJ3/Yh61WoLPVWlWHWTd05M7Pd5JZWMbix/qw9kA2x2q3UT1VHYa+tZExnfx5ZnQ7npi/j/JKIx+sOEo7fxfa+lvONX65/hTbj+dhqxV8e18PBrT3ZsNh6xkAoFb/8Yap/5gUzQ3vN9B/TIrhhQWm/uP+HgyM9GH9wRwOZ5bw4P/t5o3JMRbptRrB+1M689T3ezmYXoy7oy1VTdF/v2jqv6d0Y9QbDfTft3Xl6W9N/fcT/YmL9mft/lr9935T/z0xlhvejmfjwWyW710JQMdgN754oA/XTF9uFR2slRcAKbmljHl7k1Xkrsvgjr6E+Dox4NU1dAnx4I0bYxn77sZ66d64MZbnftxHwql8vn2wF4M6+hKfnM3Gwzm89cdBtV5fH8nDw9oy5/eDjOkSiJ2NhuFz4tXJvZcG89vuNFLz9E2iV0tGboktaXKEyttCiP1CiCQhxI2m44OEEPFCiEVCiENCiPlCCGEtOTKOH8bdLxB33wC0NrZE9h7Esd31G85Ni7+h17U3YmNrZz6Wm5ZCm46dAXBy88De0YnMk00zG3KpdOveA1c3t+YWo0FGxAayaNtpAPaczMfNwRZfV51FGl9XHS46W/aczAdg0bbTjOwU2OSyno+RnQP4eas607nnZB6uDrb4utXRwU2Hi4Mte0weFz9vTWFkZ1WHOwaG8fHyw+aZp1zTTLR670BOny3lcHr9WZarRY+BHf04mFpIsmmGJf9cBUYrdjyjugSxcMspAHafyMPN8fx67D6h6rFwyylGdwkC1CW9LjpbAFwdbcksUAcKE3q35q/daaTlqR9PZ2vlU2MwLNqPX3amAbA3pQBXB1t8XC0NPz6u9jjrbNibUgDALzvTGB7jZ76+2qCyeGeq+fjYbkGsSMwkvUCdac8tqTDfT6tRP3qq/59dVNZo+sS2cuN0bimpeXoqDQpL92UwpKOvRZohHX35dY+q84r9WfSJ8ALgYHox2ab3ezSrBHtbLbZaQbCnAylnS8k/p3oAbDmWy/Bov0aTGaBTa3dScs9xxiT3nwkZDI2ylHtotC+/7FLlXpaYSZ+2qtxDo3z5MyGDCoOR1Dw9Kbnn6NTaHYDSCgMANlqBjVY0uZuytdpaRQFHexu1DNlpqagyUmJlDw2AjgEupBboSS8so8qosPpgDgNM+VBNZlE5x3POYWxgpLsrpYBzpjxpSqKDXDmTV0pagSr3igPZDOrgY5FmUHtv/tiXAcDq5Bx6hnkA0Cfck6NZJRwxGQgL9VUYFRACBOBgpxownO1tyGnk9qkuMcGm+p2v1pNliZnERVrWk7hIH37dkw7Ayv1Z9A73BKCs0ojB1AnY22jNdcHZ3obuIR4sNtWtSoNi9iCzBrGt3Uk5W2qu63/tzWRIlGV7MjTKj192qzosT8oy13V9pYHdpwrqeXyWVRrNXn2VBoXktCL86/Q/1mBYzJX1H8ezSjhRyxhVTf/23hxKLzZ77xWUVlq3/+58hf23Ai4Opv7boab/PldeU44c7W2s+vFrrbxoaobH+LN4hzqmSDiVr44J6+jha9Ij4ZTaZyzekcqIGH8ANpoMrQB7TuXj7+4AqGMsR7vqcYeGSoPRqvVc0nKRnjItg/FAZ6AT4A3sFEJsMJ3rAkQB6cBm4BrAKmbhkvyzuHjWDIZcPL1JP37IIk3mqaMU5+YQ3rkXO/5aaD7u0zqcY3u2EtknjqLcbLJOHaUoL8fCm0by9/i760jPr7GOpxfo8XfXWXwc+rvryGggTTV3DQpjYq/WJKbk88riJApL1Q+C1t5OrHwxjuKySt76PZkdx3KtpIODhQ4Z+XoC3HVkF9boEFBHz4x8vbmDCvNzpleEF8+Pi6K80sArPyexLyUfR3stD49ox40fbOTB4e2sIntT6BHu54yCwo+P98PLxY5fd6byiRWXN/h7OJBea8YlPU9PgIeDpR4eDpZlKk+Pv4eqx/QfE1jw1ABm3dgJjYAxs1VvqzB/F2y1GpY8OwhnnQ1frD7Kwi2Wyw6uBD83HRkFtd5tQRn+bjpyimo+rvzddOZlDACZhXr8TANWbxd7c9qconKzJ0+ojxO2WsGPD/fGyd6Gbzac5JddaWQVlvNF/Ak2z4ijrNLAxsNn2diIs7l+rnVlLaNTK3eLNL6u9mSYjEUGo/oB5u5oS0FpzUf9iGg/ktOKqDQonM4tJdTHiSAPHZmF5QyN8sNW27h2ezUf6sjd2lJuf1edpdz6KjycbPFz05kH2QCZBWXm/NEI+O3Ja2jj7cj3m0+z73SNG/jTo9ry6LBwthzN5e2/jlBhhZloa7W1f+5JY0SnAPa+NRoHOy0zf060yD9r4eNiT3atupFdXE5UgIvVn3ul+LrYk1VL7qyicqKDXC3TuNqTWaimMSgKJWUG3B1sae3lgKLAf2/thIeTHSv2Z/HtltNUGRVm/3WYhQ/2RF9h4EyenjlLD1tVDz83HZl16ndsK7fzpqlbv2OD3Xh9QhSB7jqe+3k/BqNCsKcDeecqeGNCFB0CXDiQVsScPw+jr7SO8czf1d6izVXrel0d7M0f9gajQklZFR6OtuRfRBl30dkQ19GXbzc1Xj9xPvzcLOvuefuP2m1bQU3/cT5CfZ1QFIVvH+iJp5Mdfyak11vO2ZjU67/z9QS41+m/3ev03/m1+u+fEljw5ABmTTb133NqvKVHdwnipQkxeLvac+uH1vM2sVZeALTydODPaf0oKavi3aWH2Xkiv3GFr0Xd/iCzQI+/m86i3fV305FZR4/afUY1N/ZuzR8mA+3ShHSGx/iz6/XhONhpefWXA+Zx+7+df5mjjPSUaSH0A35UFMWgKEoWsB7oYTq3Q1GUVEVRjMBeIKR5RATFaGTd/E8ZfMv99c7FDhyJs6cP3814iLXz/0dQREc0Qhavpubb9SfoM30Fw95YQ1ZRGTMnqO6e2YVl9HhxOcNnr2XWoiQ+mdoDZ13LtMnaaATuTnaMmbOOVxcl8fn9vQCYdl1HPl99lNLypp/NvRzOp4dWI+gZ4c3DX+1g7Nz1jOocSL86M8MtiTsHRzDjp710mfYnL/+0lw/uUpsmG40gto0Ht36wkRvf28BT13UkzK9FhciyoHom0EYjiA52Y+oXO7njs+08MrwtoT5OuDrYMCzajwGvraP3zDU42mkZ1y2oeYWuQ4SvE0+PasfMJWrMjyJ9Fa/8msx7N3di/v09ScvXm2fiWjpGBa57bzPXvLqOTq3daGda8vD2X0cY9tZGbvhgK+6OttwXF9bMkjbM+draLqEeGBSFLs8tpdf0FTwwtC2tvR2bWdp/JlqNoEtrN176JZmpX+8mroMPPUM9sNEIJnYP4ubPdjL8vc0cySphar+Q5hb3giSmFnL9h1uY/Ml27h0Yao631DHQhQXbU5nw8Tb0lQbuGRjS3KJeFlqN4P1bO/HdphTOXMXLMmw0GrqHefLEvAQmfbSF4bH+9K3jkdaSuHNQBDMW7KXLM6b++84e5nNLE9K4Zvpy7vh4M8+Pi25GKS+PnMJyrnllLde+s4nXf03mgyldcLZvmePa2jwyvC1VRoUlu1Svm85tPDAYFXpMX8k1s1Zzb1w4rb1knwGoVhlr/LRQWn7pldT2uTXQQJ4JIe4D7gOY8vwcBt5wy2U9yNnDm+K8HPPfxXlncfHwNv9dUabnbOopfpw9DYBzhXn88v4Mxj/5KgFh7Rly24PmtN+/8jgeAfXXrUvqc+fAMG41DRj3puQTaJrhAAh0d7CwuoM62xxwnjS1l5DM33SK7x7qA0BFlZGKKnWZRtLpAk6dPUeYrzOJDQSMuywdBoVxa/9QAPadstQhwMPBYgYE1JmSummqZ98y8vUsTVBnEPaeyseoKHg529E11JNruwbx8oQYXB1tMSpQXmXk/9YdbxQdmkqPjAI9246cJc+0bGbt/kxiWnuw6VAOjcXUuAhuG6DqkXAyn0DPWuXF03JWrVpWizLl6UCmKc2NfduYg/7+vjOV902DuvR8PfklmZRWGCitMLD1SA5Rrdw5USvWyKUy5Zo23NSnFQCJpwsJcHcA1JmvAHfLmWhQZ3IDas2m+bs5kFVYUxd8XFVvGR9Xe3JL1LqRUVhGfmkF+goD+goDO47nERnoArhwJldP3jk1X1YkZtI1xINfd6ddtj61ySqqK6uOrDrLo7KLyglw15FVVI5WI3DR2Zi9LPxc7fl4SheeW5hk8VGz7mAO6w6qZWdyz+BGN8pkFZYR4F5H7rr5UFRmzh+tRuDiYEP+ucr617rXv7a4rIqtx/IY0MGHI5kl5mUmFQYji3amcc+g0EbTpSna2ht6tGLdgSyqjAq5xeXsPJ5LpzYenD5r3ZgyOcXlFq70vi725NRamtdSyS4ux6+W3H6u9vWWGmUXlePvZk92cTlaIXDWaSnQV5JdVM6elAJzAN9Nx3LpEOBCiWlpRqqpDVt1IJu7+rW2qh5ZhWUWy3LqzqDXTtNQ/a7mRM45SisMtPVzJquwjKyichJNS11X7s/ingGNVx/qkllUbmpza3TIKqyrQzn+7g5kFqo6OOtsLspL5vUJUaScLeUbK3rJTOlXp//wcICTf9N/WLRPDvXap7pkFOjZcTzPvGQ0Pjmb6GA3thxtPM/jqYNr9d+n6vTfHg4W3kzVMlm0Ux51+m9T0N/fd9X037XZduQsbXyc8HS2M49LrpSmyIsKg5GKUtWLcn9qkeo56utkEZT5Srm9fwg3921j0qPA4j2r9aC+Hv519Kjdr0zs1Yoh0X7c/J+t5mNjuwex/mC22meUVLDrRB6xrd05beU4ZJKWh3RlaBlsBG4UQmiFED7AAGDHxV6sKMrniqJ0VxSl++UaZAACwtqTn5lGQXYGhqpKDm6LJ6JrH/N5e0cnHv3fYh54/3seeP97AsMjzQaZyvIyKsrUTuBU0m40Wm29AMGShvlm/QmGvbGWYW+sZfneDCb2VgePXUM9KCqrrBfXIruojOKySrqGquvqJ/ZuzYpE9eO/dkyEUZ0DzbFXPJ3t0JhWNbT2diTU15nTZxtvje438ScY9toahr22hmV705nUp41JB0+K9ZUWrrageu4U6yvpGqquqZ/Upw3L96oxA5bvTeea9qrnSJivM7ZaDbklFYx7ez09X1xOzxeX88WaY3y09FCjGmSaSo/4A1lEBrviYFpD3LudD0cyGjdGztdrjxE3axVxs1axLCGNyX1DAOgW5klR6fn16Bam6jG5bwjLElRjRGZBGX1NevSP9DXv4LU8IY2ebb3RagQOdlq6hnpx9Ar1mLc5hTHvbGLMO5tYuT+L8T1UT5XObdwp1ldZuDuDuiyppKyKzm3cARjfI4hV+9UgrKv3ZzGhh2oYntAj2Hx8VVIW3UM9zeu3O7dx51hWCen5ZXQJcUdnq3aLfdt5czz78g1MdUlKLaKNlyNBHg7YagWjOwWw1rTDSjVrk7MZ11XVeUS0nzlgr4vOhs/u6sa7y4+QUGs5EICnkxrby9XBhpt7t2JRIwUmribxTCEh3k4Ee6pyX9slgDUHLOVecyCb8d1VuUfF+rPV9IGy5kA213YJwE6rIdjTgRBvJ/adLsDTyQ4Xk6eevY2Gfu28OG4y5vnUChg9LNqPI5mNt/NSU7S1aXl6+rVXY4k42GnpGubJsUbU4XwczCimlYcDAW46bDSCoZE+bLTSEtXG5EBaMa29HAl0V+UeEeVLfJ1lg+uPnOW6TmoA4qEdfdhp+rjbcjyPCD9ndDYatELQrY07J3LOkVNUTpiPEx6OaiyN3uEenLSyUWx/WhFtvGvq96hYf9YdtKwn6w7lMK6rGpNoeLQf200xQII8HNCaOuhAdx1hPo6k5es5W1JBZmEZISZPq97hXhaBgxubpDOFhHg7EmzSYUxnf/MuUNWsSc5mfDdVh5Exfmy7iDL25Ii2uDjY8PrvB60idzXzNqUw5u1NjHl7EyuTLrP/SMq64DM2HMqhfYALOlvVk6lnuBdHr2AioiG+XneMuFdWEffKFfTfey/cf4f61ni1xrR2x85G02gGGWiavPB0qhnXtvJS+5fGNmR8t/EUo95az6i31rMiMYMJpiDpXUI8KC6rrGd4zTbp0SVE7TMm9AxmZZIatH5gpA8PDong7s93UFZrCWJ6vp6+7dQJcAc7LV1DPCwC6f+bUaz0r6UilH9baOMWRPWW2KbgvXOBUaiOVa8rirJACDEImKYoyrWm9B8DuxRF+eZ89/xqx+krytDje7ezdv7/UIxGYgaMoM/YW9m4+Bv8Q9vRtmtfi7Q/vvE0g26+j4Cw9hTmZLJw7gsIjcDFw5uR9zyNm/flB5y8tav1ZrWem/YUu3buoKAgH08vLx58+FHGT5hklWeFPfLLJV8z+6ZODDJtuffkt7vN3iyrXooz754UW71Nq5265d5LP6nbMH90Z3eiWrmhKJCaW8qz8xPILipjdJdAnrmuI1UGI0YF3vkjmVVJF7e7iXIZM++zb+7M4GiTDt/sYp/pQ3LVy0MY9pq6c0CnNu58cGd3dHZa1u7P4qUf9wLqtqHv36HqUWkw8srPSWw+bOlF8vR1kZwrq7L6ltjW0mNCr1Y8OqoDiqKwZn+mxZbfF8JQdXlLt968rStx0f6UVlTx+Nc72WcKQrd21jDiZq1S9Qjx4KOpPXGw07ImKYMX5quza73aepu3vS6rNPDcvD0kpqjXPzyyPTf1C8FohPkbT/D5ReaHk5vTRaV7dUIUAzr4oK8w8OxPieYZsL+m9WPMO+oa+JhWbqYtsTWsP5jDzF/UpT3ujrZ8fEdXAj0cSMvX88i3e8zrtO8bHMbEnsEYFViw7TT/Z9q69omRbbm2cyBVRoXktEKe/ynpgvFM7C/RVXpAe29evLYDGo1g8a40Plt3gkeHRbA/tZB1B3Ows9Ewd3IMkYGuFOrVLXNT8/Q8EBfGfYNCSan1YXn3V7vJO1fBuzfF0t4UO+STNcdZeom7FlVW/n28lkEdfJg+LlLdyntHKp+sOc4TI9qSlFrImgPZ2NloePeWWPNWv4/P22v25nloSDgTewZjMBp5/beDrD90lvYBLrx9cyxaARoh+GtfJh+btgD9/oGeeDrbIYDk9CJeXnTAHBT4QujPXfqSCGu0tY72Wt6/vRvtAlwRAhZsSeF/F1kv2oR6/32iC9AnTN0SWyMEfyZl8u3WM9zbrw0HM4vZdCyPSH9n3hwfhYu9DRUGI7nnKrj1q90A/O+WTrTxcsDRVkthWRWzlx1h+8nLi89QdolBKvtFeDFtZFs0QvDb3nS+2pjCg4NCSU4vZv2Rs9hpNbx+Q0faBzhTpK/i+UX7STPNPo+Oqdn6etPRXD5crRrqJ3YL5OZeragyKmQUlDHzt2QK9ZcmV3n5paUf0M6b569tj0YIluxO47P4kzwyNJwDqUWsO6TW77cmRRMZqNaTaT8lkpqv57rOAdw7MNTcP/9v7XHWmLzfOgS48OoNHbHVakjN1/PSov0UXcL7vZj6XZuBHbx56fpIdVvvHan8b+0JHh8eQVJqIWuTVR3euSmWjkEuFJRW8uT8fea6vu6FgTjrtNhqNRTrq7jri52UlFexcfpgjmeVmNvSeZtP8/OOSzMeV11GHJ1XJ0QxINLUf/xYq/94pp95x56YVm6mbZhN/YdpO/jhMX7MmhCFp7MdxfoqktOKuONTda50XLcgHhwajoLqKfPmH4cafH5dzhVfnkHtzVvr9N+m/nftzGHEvWLqv9t48NHdPXGwNfXfJu/WXhF1+u/v1f770VEdmNSnDVUGI2WVBl5ZmHjRW2I7uVxc/10ba+TFyFh/nhzVjiqjEaMRPlh+pN6EwYUwGC69TL02KYZBkb7oKw1M+z6BRJMey54byKi31gNqUH/zltgHs5nxcxIAG2YMwc5GQ77JGzfhVD4vLkjE0U7Lu7d1oa2/MwLBwu2n+WzNxU04nv7P9Vbb/KUlkFFYYRUjRYCbXYt8b9Io8w/jSo0yLQVrGmWakssxyrQ0LscoI7EOl2uUaWlcrFGmpXOpRpmWyKV+tLVULsco09K4UqNMS+FSjTItlUs1yrRE/in1+3KMMi2NyzXKtDQuxyjTErkco0xL459ulEkvsI5RJtC9ZRpl5PIliUQikUgkEolEIpFIJJJm4Oqf5pNIJBKJRCKRSCQSiUTyj+Df5qcvjTISiUQikUgkEolEIpFIWgT/tggrcvmSRCKRSCQSiUQikUgkEkkzID1lJBKJRCKRSCQSiUQikbQQ/l2uMtJTRiKRSCQSiUQikUgkEomkGZCeMhKJRCKRSCQSiUQikUhaBP+2mDLSKCORSCQSiUQikUgkEomkRfAvs8kglH+bGeofTrtnl/8jMrSkqLS5RWgUTnw8vrlFuGLaPvFbc4vQKLi5Oza3CFdMgL9Lc4vQKOxPTGtuERoFG7urf17DaDA2twiNgkZz9a/GNhgMzS1Co6Av0Te3CI2C1kbb3CJcMZExwc0tQqNQVXX1t1P3xoU0twiNwtzFB5tbhEZBX1rR3CJcMSkfXSeaWwZrcjqv3CrftK097Vvke7v6R5QSiUQikUgkEolEIpFI/hH82/xGrv6pJYlEIpFIJBKJRCKRSCSSqxDpKSORSCQSiUQikUgkEomkRaD8y6LKSKOMRCKRSCQSiUQikUgkkpbBv8smI5cvSSQSiUQikUgkEolEIpE0B9JTRiKRSCQSiUQikUgkEkmL4F/mKCM9ZSQSiUQikUgkEolEIpFImgPpKWNFhBAhwJ+KokRf4X0GARWKomxpBLHq0b+dNy+NjUQr4OcdqXwef9LivK1W8PZNsUQFuVJQWskT8/eRlq/H3dGWj6Z0JibYjSW70nj1t4MW18wY15GeYZ4oisJ7y4+ycn+WNcQ389rkWOKi/dFXGHjy290knSmolyamtTsf3NENna2WtfszeXlhIgBPXxvJLf1CyCsuB2DObwdYuz+LYC9H1s8cxomsYgB2n8zj+R/2WlWPi2HG9BfYsD4eT08vfvntz+YWpx6vTIwhLsoXfYWBp+YlsD+1sF6amFZuvDelKzpbDWsPZDNzURIA08Z0YHisP0YFcovLeer7BLIKy7h/SAQ39AgGwEYjiPB3ofPzyygorbSKDte09eL50e3RagSLd6fx1YZTFudttYI5E6PpGKjWi2kLEkkvKDOf93fT8ftjffhk7Qm+2ZwCgIvOhlfGdSTCzxkUhZeXJLPvTP13Yy26t3bjof4haIRgWXI2C/akW5yPCXThwX4hhHk78saKo2w8ngdApyBXHuzXxpyulYcDb6w4ypaT+U0m+2s3dmJIjFq/n/hmF0mnC+qliW3tzgd3dUdnq2VNUiYvL9hnPjd1cDh3DQ7HYFRYnZTB64v3Y6MVvDulGzFt3LHRCH7eepr/LD9sVT2sUTeq6dTanV+f7s/D/7eLpXszrKbDq5NjiYvyU9va73azv4EyHNPanfdv76q2tQeymGFqa58a08GirX3zt2TWHsjCw8mOz+/tSac2HizclsL0BYlWk7+aVybVyYuG9KjOCztTXvxsyotr6+TFPDUvXHQ2fHhnN4I8HNBqBZ+vPs7Cbaetqoc1+r5qgjwciJ85jHf/Osinq45aVY9q3pzSjWGdg9CXV/HQ51tJPFW/nZk+qRM39QvFzcmOVvcsNB9/aFQHpgyKwGAwcra4nEc/38aZ3HNNIjfA7Fu7MjQ2AH2FgUe/3E5iSn3ZO7Xx4D/39EJnp2V1YgYvzt8DQFQrd965oztO9jacyT3H/Z9upaSsiol92vDwqA7m66OC3YmbtYL9DbSBjUnPEHceHxyGRgj+3J/F/B2plnoEufLY4DDCfJx45c9DxB/NBcDPxZ7ZYyMRQu2rFydk8FtiplVlvRC9Qz14Ykg4Wo3g932ZzNt+xuJ852A3nhgSRrivMzN+P8i6w2fN596fFE1UoCuJqYVMW3ygqUU/Lyf27WTNvE8wGo10GjSK3tffZHE+acMK1v34BS4eXgB0HTaWToNHN4eo9GvnzUvXdUAjBIt2pvLF+vrfGG9NjiEqyI2C0gqe+nEfafll9I3w4umRbbG10VBZZWTusiNsN41JxnTy5/7BYSgKZBeV88yCRKuNBWsza0IUgzuqfd+0+Xsb7L+jW7nx7q2d0dlqWZecxSxTuXlxbCRDov2prDKScvYcz/ywlyJ9lfm6QA8HVr84iA+WHebztSesrsvVwL9tS2xplLESQghB43kiDQJKgEY3ymgEzLyhI3d9sZPMwjIWP9qHNcnZHM+uGcRM6hlMob6SYXM3MqaTP8+MbscT8/dRXmnkwxVHaevvQjs/Z4v7PhgXTm5JBSPe3ogQ4O5g29iiWxAX7UeorzPXzFhJ11AP5tzSmWvfiq+X7s1bOvPM93vYczKf7x/py+AoP9YdUAegX6w51uCgMyWnhGFvrLWq/JfK2HHjufmW23jpheeaW5R6DO7oS6iPE/1fWUOXEA9m39SJ69/ZUC/d7Bs78ewPe0k4lc93D/ZmUEdf4pOz+XTNMd756xAAdw0M4/FR7Xjxp0Q+W3OMz9YcA2BotB/3DA63WiesETD9ug7c+397yCwqY8EDvVh3MIcTOTX1Yny3IIr0VYx+fzOjYvx4akRbpi1IMp9/dlQ7NpoGqdU8P6Y9m4/m8tRPidhoBQ62WqvIfz6dHh0YynO/HeRsSQUfT45m68l8TufrzWmyiyt4e81xJnUJsLh2X1oRD5h0c7HX8s2ULuxuQmNSXLQ/YX7O9J2+gq6hnrx5axfGzFlXL92bt3Zh2nd72HMyj/mPXUNctB9r92fRt70PIzoHMuTV1VRUGfFysQfgum7B2NlqiHtlNQ52WtbPGsaSnWdIzS21ih7Wqhug5u8LYzuy4VCOVWSvJi7Kj1BfJ/rNXKW2tTd35rq56+ulm3NzJ56dn8Cek/nMe6RPvbb2s9XHLNKXVRqY+8dBOgS60D7Q1ao6AAyOMuXFrFp58XYDeXFTrbx4qFZerD7GO3+a8mJQTV7cMTCUoxnFTP10O57OdqyfMYQlO89QabDO6NKafR/AzEmxrD3QdB/UwzoFEu7vSrenf6d7uBfv3tmTYbNW1Eu3fE8qX6w6zK53rrc4nngqn7iXl6GvMDB1SFtm3dyFuz/e1CSyD40NIMzPmZ7P/UW3cC/evr07I15bVS/d23d058lvdrL7eC4/PTWAITEBrEnK4IO7ejBzwV62HM7hlv6hPDI6kjd/SWLR1hQWbVUN+5HBbnz3WH+rG2Q0Ap4aEs6Ti/aTU1zBF7d2ZvOxXE7l1fQXWcXlzF5+hJu6B1tcm3uuggd+3EelQcHBVsO3d3Rl0/E8cs9VWFXmhtAIeHpYBI8vSCK7uJyv7+jCxmO5nKrVxmcWlfHa0iPc2jO43vXzd6Sis9EwrnNAvXPNhdFoYNW3/+HG59/CxdObb2c8QkS3PngHtbFIF9l7IMPueLSZpFTRCJgxNpKpX+0iq7CMnx/pw9qDlt8YE3sEU6SvYsQ7Gxkd68/TI9vx1I+J5JdW8OC3CWQXl9PWz5kvp3Zj4Jz1aDWCF6/rwJj3NqsTYqPacVvf1ny8+rhVdVH7b2cGvraWLiHuvD45hnHv1W9b3pgcw/M/7SPhVAHfPtCLQZG+xB/MZuPhs7z1xyEMRoXnr4/koWFtefP3msnsl2/oSHxytlV1kLRs/nXLl4QQTkKIv4QQ+4QQ+4UQNwohTgkhvE3nuwsh4k2/zxJCzBNCbBVCHBVC3FvrPs8IIXYKIRKFEK+YjoUIIQ4LIb4D9gOtAK0Q4gshxAEhxEohhIMpbWchxDbT9UuEEB6m448JIZJNx38yeds8ADwphNgrhOjfmO8jtpU7KWdLOZOnp9Kg8Ne+TIZG+VmkGdLRjyW71Bn15UlZ9IlQLe/6SgO7TxVQXmmsd98JPYL4zGTpVRTIt7IFe0RsIItMs5F7Tubj5mCLr6vOIo2vqw4XnS17TLP7i7adZmSnQKvKZS26de+Bq5tbc4vRIMNjA1i8Q52JSjiVj6uDLb6u9hZpfF3tcdbZkGCaAV284wwjYtVBT0lZzcyBo722wUWlY7sH89vuNCtpADHBbpzOLSU1X0+VQWFZUiZxkT4WaeIiffgtQa0XKw9k0yvM0+JcWr6e49kl5mPO9jZ0C/FgsUnuKoNCcS1drU17P2fSC8vILCqnyqgQfzSXvmEeFmmyiss5mVt6wdmJ/hFe7EwpoLyqfr23FiM7B/Cz6aNkz8k8tUy51anfbjpcHGzZc1KdSft5awojO6v1+46BYXy8/DAVJplzTV4BiqLgaGeDViPQ2WqpMBgp0VuvrbJm3bhrYBjL9mWYdbMWIzoFsGibqsOek/m4OTasg2Vbe4aRnS78UaOvMLDzeG6D/Yk1GB4bwOLtl5gX288wolMDeWFXY1xVFHDWqfNdTvY2FJRWUGW03nSfNfu+kZ0COHP2HEcyihtf8PMwulswP21Sxw67jufi5mSHn7uuXrpdx3PJquWZWM2mg1noKwwA7Dx2liBPR+sKXItRXYJYuPkUALuP5+LmaItfnXbKz9RO7T6uGuwXbj7F6K5BAIT7u7DlsGpUjT+QxXXd6hsJxvdqw5LtKVbUQiXS34W0gjIyCtX+Ys3hHPqZxn7VZBaVc/xsKUqdDqPKqJiNkLZaDRphdXHPS8cAF1IL9KQXllFlVFh9MIcBbRvQI+ccxgY6vl0pBZwzlaeWQsbxw7j7BeLuG4DWxpbI3oM4utsqjvRXTGwr01jK9I2xdF8GQzr6WqQZ0tGXX/eo46IV+2u+MQ6mF5Nt6s+OZpVgb6vFVisQgECY211nexuyi6zb7wEMi/Gv1X8XXKDPsCXhVAGg9t/DY/0B2HgoB4OpL0g4lU9ArXZteIw/Z3JLOZLZdG3t1YBipX8tlX+dUQYYCaQritLJtKxo+d+kjwXigD7ADCFEoBBiONAW6Al0BroJIQaY0rcFPlEUJQpIMf39X9PfBcAEU7rvgOcURYkFkoCZpuPPA11Mxx9QFOUU8CnwvqIonRVF2XhF2tfBz82ezMKamY/MwjL86jQyfm72ZJjSGIzqh6SH4/k9X1xMA9InRrRlyeN9+PC2zng52zWm2PXwd9eRXmvGP71Aj3+dgZy/u46MC6S5a1AYq6cP4b0pXXGrpV9rbydWvhjH4qf607POoERSn7p5kVGgx9/doU4aBzJqDagz6uTFs9dFsv214dzQPdjsGVCNzlbLoEhflu21XHrTmPi62pNZWNPJZxWVN9D56sg0LR0xGBVKyqtwd7TFwU7L1P4hfLLO0v00yENH/rkKXh8fxc8P9eKVcR1xsG26JtjbyY6c4pqZyrMlFXg7XXq9HNTWi3VHz/59wkbE393Bskzl6y0GNAABdctdfk25C/NzpleEF3+9MJhfpg2gUxvVGPXnnjRKK6rY9/YYdr05ik9XHrWqC7S16oa/m46RnQL4bqOlW7g1aCgvGtbh/HreNSiMVS/F8W6dtrYp8XfTkX4BGeE8eeFWJy9eH84NPYLNXjPfrD9JhL8zu2aPYNVLg5n5836rumBbq+9ztNfy0Ih2vPvXQZqSAA9H0mp5MaTnlRLgcXmGlSkDw1m1z3r9RF0CPBxIy6sle76eAA+HemnSz5PmUFoRo0wGmrE9WjVoUBrXqzW/WHk5HICPs535gxggp7gc70sYx/m62PHN7V1YfF8P5u9MaxYvGQAfF3uLD/bs4nJ8rDwetTbF+Wdx9ayZJHLx9KYkv36ffHjHJr5+4T6WfPgqRbnN44Hh56ojo9YSW/Ubo67R2N7czlZ/Y7jX6RdGRPuRnFZEpUGhyqjwyq/J/P7ENWx4cRDhfk4s2mm5tM4aqH1GLV0K9A0aXTMt+pUyiz6jmsm9W5m9YhzttDw4NJwPlh2xkuRXMYqVfloo/0ajTBIwTAjxlhCiv6Iof+eD/5uiKHpFUc4C61ANMcNNPwnAHqADqvEFIEVRlG21rj+pKMpe0++7gRAhhBvgrihKtc/3t0C1UScRmC+EuA24qGl0IcR9QohdQohdhfuWXswlVsVGIwhwd2BPSj43fLiVvSkFPD+mfXOLdUG+XX+CPtNXMOyNNWQVlTFzQgwA2YVl9HhxOcNnr2XWoiQ+mdrDPAsqsR5z/zhIr5dXsmRXKncOCLU4NyzGj50n8ppk/fDl8HBcGPO2nDbP1lZjo9EQGeDCgh1nmPTJdvQVBu6uo1tLx9PRllAvR3adbrqlS42BjUbg7mTHmDnreHVREp/f3wuALiGeGI0KnZ/9i54vLuf+YW1p7e3UzNJemIbqxswJ0cz+LfmqWH/93YaT9H15JcNnryW7sIwZprb2amTuHwfpNX0lS3amcudANS8GdvQhObWI7i+uYOSceF6bHNOi+4zz9X3Tro3kizXHKC1vWV4CF8vka0LoHObFf/5Kbm5RLprHvt7O1Li2rJk1HGedDRUGS6+xrmGe6MurOJTW8tvf7OIK7vwugZu+2s3Ijr4XnMiTND4RXfrwwAfzmDrnc0Kju/LXZ283t0iXTYSvE0+PasfMJWpsFhuN4Kberbjhoy0MmB3PkYwS7hsc1sxSXjyPDG9LlUFhyS7VO+jJUe35Mv4EpS3MI0vS9LTckYKVUBTliBCiKzAaeF0IsQbV+FFtoKpr0qw7zFUAAcxRFOWz2idMS43qRpSr7VNnABy4MGNQDTTXAS8JIf52xKooyufA5wDtnl1+ScPyrMJy/N1qRPJ305FVxw0wq7CcADcHsgrL0WoELjqbCy5Hyi+tpLSiyhzYd1liJhN7BF2KWBfFnQPDuLVfCAB7U/IJrDUjFejuQGYd1+bMgjKLWavaac7WmhGav+kU3z3UB4CKKiMVVeoMT9LpAk6dPUeYrzOJVl7PfbVxx4BQbu6rrmfeVycvAtwdLGYOQJ1hqO3pENBAfgEs2ZnKdw/25r2lNcFXr+8WzO+7rTsrkl1Ujr9bjWeMn6t9PffY7KIyc33RagTO9jYUlFYSE+zGsCg1xoyLzgZFgfIqIysPZJFVVE5SahEAKw9kcc+AEKvqUZuz5yrwcamZIfR2tuPsJc5eDozwYvOJPLMLrjW5c1AYt/ZXP3T3napTpjwsPRhAnZGqm6a63GXk61lqWmq291Q+RkXBy9mOG3q2Yt2BLKoMCrnF5ew8nkunNu6cPtt4gUGbom7Etnbnv3d1B8DT2Y7BUX4YjAorGim45h0DQ7n1mhAA9qYUnPc9W+rQsJ5129pvH+7TKDJeDHcMCOXma2rlxXlkrKbBvCg8T1481Jv3/jrM5N6t+WSlGp/lVM45zuSWEuHnzN6UgkbToyn6vi4hnozpGsT08dG4OthiVKC80sD/xTd+AMp7hrbj9sHhAOw5kUeQV42HSKCnIxn5lxbjaWCUP09dH821b6wyL1m0FlOHRDBloCr73pN5Ft4tgR4OFh5KoLZFgedJcyyjmEnvxAMQ7ufCsDpLzMb3asMv263vJQOQU1KBr0tNH+jjYs/Zkkv3dsk9V8HJ3FI6BbmaAwE3JTnFll6uvi725FyGHi0JFw9vivJqYocV553F2cPbIo2DS01crtjBo1j30xdNJl9tsorKCKjlKaKOmSzbp+yicgLca8ZSLjob86Sbn6s9H0/pwnMLkzhjimfUIdAFwPz3sqRM7h1onQmu2/uHcFOf1gAkni4gsFZ/4O/uYBFoHyCrsMzC4zLAXWfRZ0zsGcyQKF9u/rhm7r5ziDujOgfwwvUdcXWwRVEUyiuNfLvxlFV0uppornkmIcRI4ENAC3ypKMqbdc63RnWocDeleV5RlCv2ivjXecoIIQKBUkVRvgfeBroCp4BupiQT6lwyVgihE0J4oQbc3QmsAKYKIZxN9wwSQvhykZi8c/JrxYeZAqwXQmiAVoqirAOeA9wAZ6AYcLlUXS+GpNRCQrwdCfZwwFYrGNPJnzV1Ak2tTc7mhu7q4GBkjB9bj/19x7ouOcccY6NPhBfHsht/94Nv1p9g2BtrGfbGWpbvzWBib7Xh7BrqQVFZJdn1Gv4yissq6RqqLl2Y2Ls1KxLVD7Xaa/BHdQ7kcLr64ezpbGdeD93a25FQX+dG/WD7p/DthpOMfDOekW/GsyIxkwk9WwHQJcSDYn1lAwaNckrKqugSoubFhJ6tWJmo7hYT4lPjqTA81p9jWTVxWVx0NvSO8Gq0j83zsT+tiNZejgR56LDRCkbF+LOuTgDVdYdyGNtFrRfDo3zZfkKNZXLHl7sY8e4mRry7ie+3nuaL9Sf5cfsZcksqyCwsI8RbHZD3Dve0CHZnbQ5nlRDkpsPfxR4bjWBQWy+2XuLuSYPbebHuSNMMrL+JP8Gw19Yw7LU1LNubzqQ+6sd011BPtUzVGQxlF5ZRrK+ka6ja7kzq04blph2Ilu9N55r2qrt3mK8ztloNuSUVpOWVmo872GnpFurJsUZe090UdeOaWavpO3MVfWeuYmlCOi8t2NeodeTb9ScZPnsdw2evY8W+dCb2VnXoGupB0Xl0sGxrW7Fin6pD7Q+kUZ0DzG1tU/DthpOMnBPPyDnxrNiXyYRel5gXvf4+L9Lz9eYy5e1iT7ifMylnGzdwdFP0fTe8u4FeL62g10sr+HLtcf6z/LBVDDIAX64+woCXljHgpWUs3X2Gm/qps97dw70oKq1oMHbM+Yhp48H7U3tyy3vrOdsEcSa+XnOMwTNWMHjGCpbuSWWyyXjZLdyLIn1lgx9txfpKuoWrS6EnXxPCsgR1xtzbZAQRAp66viPfrKsJhi0EjO3ZqkniyQAcyiwm2N2BAFe1vxjS3odNpp1v/g4fZzvsbNTPC2d7LbGBrhYB5ZuSgxnFtPJwIMBNh41GMDTSh40XMYZtyQSEtSc/M42C7AwMVZUc3BZPRFdL43ZJfo2Ox3ZvxSuwdVOLCUBSahFtvBwJMn1jjO4UwNoGvjHGmZbtjYj2Y5upnLnobPjsrm68u/wICbWM2tmF5YT7OuPhpHpf9Y3wstiEoTH5buMpRs/dwOi5G1hp0X+7U1x2vj6jki4h7oDaf69KUvvigZE+PDA0gru/2ElZZY1XzKQPt9DvlTX0e2UNX68/wX9XHZUGmWZECKEF/guMAjoCNwshOtZJNh1YqChKF+Am4JPGePa/zlMGiAHeFkIYgUrgQVTvla+EEK8B8XXSJ6IuW/IGXlMUJR1IF0JEAlvVTZYoAW5D9YS5WO4APhVCOAIngLtQrW3fm5Y3CeAjRVEKhBB/AIuEEGOBRxszrozBqPDqb8l8dU93tBp1u7pjWSU8NjyC/amFrE3O4eedqbx9Uyyrnu1PYWklT/5Qs83s2ucH4qzTYqvVMDTKj7u+3Mnx7HO8vfQwb98Uy4vX25JfUsHzPyddQIorZ83+TIZE+7HlteHmbUGrWfVSnHn3pBd+2KtuC2qnZd2BLPPWn9PHRxPVyg1FgdTcUp6dnwBA77bePHNdR6oMRowKPD8/oUUsm3lu2lPs2rmDgoJ8hsUN4MGHH2X8hEnNLRYAaw9kERflx6aZQ9FXGnj6+wTzueXPD2Lkm/EAvLQwkfdu62LeNnCdqaN+YWxHwn2dMSoKqXl6XvyppryN7BTAhkPZ9ZYGNTYGo8LsPw/z2R1d0WoES3anczz7HA8PCedAWhHxh3L4ZXc6cyZGs/TJayjUV/LMgr8v47P/PMRbk2Kw1QrO5Ol5+Zem22LTqMDHG04xZ6y6NeWK5GxS8vTc0TOYI9nn2Hoqn3a+Tswa3Q5next6h7pze89g7v1R3d3Hz8UeH2d7EtOa7iO6mjVJmQyJ9mfrGyPU+v3NLvO5VS8PYdhrawB44YcEPrizOzo7LWv3Z7F2vzoY+nHzKd6/ozvrZg6l0mDk8f9Tr/+/+ON8cGd34mcNQwA/bUnhoBX1s2bdaCrW7M8iLtqfza8OU7eS/m6P+dzKFwczfLa6K9aLP+7j/Tu6obPVqG3tgZq2tmOwqa3NK+W5+TXvYNvrw3HW2WKn1TCyUyA3f7SZo1YKfGjOi1lD0VfUyYsXBjFyTjwALy1I5L0ptfLiQK288KuVFz+qefHhsiO8N6ULq14cjBAw+9dk8q0YT8NafV9zsXJvOsM6BbHn3evRVxh4+POt5nMb3hjFgJeWAfDKTV2Y0DcERzsb9n90A/Pij/HWL0m8enMXnHQ2fPNYP0DV6Zb36u8OZg1W7ctgaGwgO+dei768ise+2m4+t+7VEQyeoe4i9ex3u0xbYtuwJjGd1SZD3/jebbh7SAQAf+5O5YdaMaL6tvclLa+UFCt9fNbFoMD7a4/z7oRoNBr4a38Wp3JLubtvaw5llbD5eB4d/Jx5Y2wkLjob+oZ7MrVva27/NoE2Xo48MjAURVGNST/uSuVEIxsmL0WPd1cd44PJ0erW3kmZnDxbyr392nAws5hNx/KI9HfmzfFRuNjb0C/Ci3v6teHWr9R69L9bOtHGywFHWy2/PdSL2cuOsP0SJzMaG41Wy7A7HmHh3BdQjEZiBo7AJziEjYu+wT+0HW279WX3yl85umcrGq0WBycXxtz/TLPIajAqvPb7Qb6a2g2NRrB4VxrHss/x6DD1G2PdwRwW7Upj7uQYVkzrT6G+kqdMbemtfVvT2suBh4aE89AQ1Rvt7q92k11czn/XHOP7+3tSZVBIL9Dzws/7ra7L2uRsBkf5smFGnHlL7GqWPjuA0XPV3fumL0xSt8S20xKfnG3uv1+dGIOdjYbvH+oNqMF+X1po3W+jq51mWpLdEzimKMoJACHET8BYoPZaWAWodkdzAxoleJmoGzVdUoMQYhZQoijKO80ty8VyqcuXWiolRc3TgTc2Jz4e39wiXDFtn/ituUVoFNzcm24XDmsR4G8Vh7kmZ3+i9XbOakps7K7+eQ2joWl2O7I2Gs3V7/hrMPwzYgroS5rHK6Kx0dpo/z5RCycypv4OTlcjVU2425+1uDcupLlFaBTmLm7a4N/WQl96dS9jA0j56Lpm3NvM+hzJKrXKN207P8fzvjchxERgpKIo95j+ngL0UhTlkVppAoCVgAfgBAxVFGV3Q/e7FK7+UYxEIpFIJBKJRCKRSCQSyQWovUGO6ee+S7zFzcA3iqIEo8aonWcKQXJFXP3TfFZEUZRZzS2DRCKRSCQSiUQikUgk/xqstPaj9gY5DZAGtKr1d7DpWG3uBkaa7rVVCKFDDXNyRXvPS08ZiUQikUgkEolEIpFIJP9mdgJthRChQgg71EC+v9dJcxoYAmCKMasDcrhCpKeMRCKRSCQSiUQikUgkkhZBcwRJVRSlSgjxCOpOy1rga0VRDgghXgV2KYryO/A08IUQ4kmTmHcqjRCkVxplJBKJRCKRSCQSiUQikbQImmsvIkVRlgJL6xybUev3ZOCaxn6uXL4kkUgkEolEIpFIJBKJRNIMSE8ZiUQikUgkEolEIpFIJC0CpVkWMDUf0ijzD+NsRl5zi9Ao2DvYN7cIjULbJ35rbhGumKMfjG1uERqFiMd+bW4RrpjKSmNzi9Ao+Ad5NLcIjUJxcXlzi3DF6HT/jGGA0Xj1D96Ki/TNLUKjoNH8M5ywg9r4NLcIV4xW+8/Ii7S0ouYW4Yp5/tNtzS1Co3Cu8Fxzi9AohHYIbm4RJBIL/hmjMYlEIpFIJBKJRCKRSCRXP1f/XMsl8c8woUskEolEIpFIJBKJRCKRXGVITxmJRCKRSCQSiUQikUgkLYJ/maOMNMpIJBKJRCKRSCQSiUQiaRk015bYzYVcviSRSCQSiUQikUgkEolE0gxITxmJRCKRSCQSiUQikUgkLYJ/25bY0lNGIpFIJBKJRCKRSCQSiaQZaBJPGSHEFkVR+l7GdeOAI4qiJF/mc0OAvoqi/HCBNHcC3RVFeeRynlHnWX8qihJ9kenP+1whRImiKM5XIs+VMGdKN4Z1CkRfXsXDn28jMSW/XpqXJsZyU79Q3JzsaH3vz+bjfdr7MPu2bkS1cuee/27m951nmkzu127sxJAYf/QVBp74ZhdJpwvqpYlt7c4Hd3VHZ6tlTVImLy/YZz43dXA4dw0Ox2BUWJ2UweuL95vPBXk6sH7WcN75I5lPVx21qh6vTIwhLsoXfYWBp+YlsD+1sF6amFZuvDelKzpbDWsPZDNzURIA08Z0YHisP0YFcovLeer7BLIKy7h/SAQ39AgGwEYjiPB3ofPzyygorbSqLn/HjOkvsGF9PJ6eXvzy25/NKktDvDophrgoP/SVBp78bg/7zzScF+/f3hWdrZa1B7KY8XOSxfn7hkQwY0I0Mc8sJf9cBQ8MjeCGHq0A0GoFbf1d6PTsUqvlRf923rx0fQc0QvDzzlS+iD9pcd5WK5h7YwxRQW4UlFbw5A/7SMsvo29bL54e2RZbrYZKg5G3lx5h2/E8AL67rwe+rvaUVRoAmPrlbvLOVVhFfoC+4Z48M7ItGo3g1z0Z/N/mlHo6vDauI5GBLhSWVvLcogNkFJYB0NbXienXdsDJXotRgdu+2EWFwcjwKF/u7h+CVsCGo7l8tPq4VWQf0N6b6WMj0WoEC7en8tm6Exbn7bQa3r45luhgV/JLK3l83l7S8vUAPBAXxqSewRiMCq/9epCNR84C4KKzYc7kGNr6O6Mo8MLCJBJSCnhiRFuGRvliVCCvpIJnFySSXVTeqPpc09aL50e3R6sRLN6dxlcbTlmct9UK5kyMpmOgKwWllUxbkEh6QZn5vL+bjt8f68Mna0/wTa181AhY8GAvsovKefj7vY0qc0P0a+vFC9d2QKsRLNqZypcN6PHmpBiiglQ9nvpxH+kFZcQEu/LKuI5qIiH475rjrEnOxt/NnjmTYvB2tkNRYOHOVL7fctrqeoB1+ozebb346r5enMktBWDZ3nQ+XH6kSfSZc1tXhnYKRF9u4JEvzj8GufGaENyc7Ghz3yLz8T7tfXjj1q7qGOSTLfzRhGOQvhGePDeqHRohWLInna831W+n3hgfRWSAC4X6Sp79eT/pBWUEuutY8khvTp1V33VSaiGv/3kYgJHRftwzIARFUcgpruDFXw40Wb/ds407jwwMRauBv/Zn88OuNIvzsUGuPDIwhHBvJ15deoT1x3LN59Y81oeTprKTVVTOS38cahKZZ9zQkUGRPpRVGHjmx0QOpBXVSxMd7MrbN3fC3lZD/MEcXl2ifmK4OdrynyldCPZ0IDVPzyPf7aFIX8XQKF+eGtUOo4KpLU5m10m1TAa665hzYwwB7g4oisLUL3aZ2+/G4o2buzDUNLZ99OsdDY9t23jw0dQeONhqWZ2UyUs/Jqi6tnJn7pRu6Gw1VBkVnvt+Dwkn84jwd+GjqT2Iae3BnCX7+WTF4UaV+UK8PbUXI7oEo6+o4v6PN7H3ZG69NDNv7sotAyNwd7LDb8r3FufG9wnhxcldUFDYfyqPuz7c0CRyW6PvW/F0P86VV5nL1o3/294kulw1/LscZZrGKHM5BhkT44A/gcsyygAhwC3AeY0yEkuGdgok3M+F7tP+oHu4F+/e1YNhs1bWS7ciIY0vVx1h5zvXWRxPzS3l4c+38cjoyKYSGYC4aH/C/JzpO30FXUM9efPWLoyZs65eujdv7cK07/aw52Qe8x+7hrhoP9buz6Jvex9GdA5kyKurqagy4uVib3HdrEmxrD2QaXU9Bnf0JdTHif6vrKFLiAezb+rE9e/U73Bm39iJZ3/YS8KpfL57sDeDOvoSn5zNp2uO8c5f6uDnroFhPD6qHS/+lMhna47x2ZpjAAyN9uOeweHNbpABGDtuPDffchsvvfBcc4tSj7goP0J9nek3azVdQzyYc1Mnrnu7fl7Mubkzz87fy55T+cx7uA+DO/qyLjkbgAAPBwZE+pBqGpwCfLr6GJ+uNuVFjD/3xlkvLzQCZoyL5K4vd5FVWMaiR/qwNjmb49nnzGkm9QimSF/F8Lc3MrqTP9NGtePJHxLJP1fBg98kkF1cTls/Z766uxsDZq83Xzftx0T2NzDgtYYOz49uz4PzEsgqKmf+vd1ZfziHE2dr3um4LoEUl1Ux9j/bGBHly+NDw3l+8QG0QvD6+CheXpLMkawS3BxsqDIacXOw4YlhEdz6+U7ySyt5dWwkPUM92HGy/sfflco+64Yo7vh8B5mFZfzyeF/WJGdzLKvEnGZSr2AK9ZUMeXMDYzoH8OyY9jz+/V4i/JwZ0zmAUW9vwtfNnu/u68nQt9ZjVODlcZFsOJTDI98lYKsV6Gy1AHwZf5IPVqhG49v7teGRYRHMWHygUfWZfl0H7v2/PWQWlbHggV6sO5jDiZya8jS+WxBF+ipGv7+ZUTF+PDWiLdMW1Bgqnx3Vjo1H6w/Cb+vTmhM553C2t/6wRCNg+vWR3PP1brKKyljwUG/WHcqxqBcTugdTpK9k5LubGBXrz9Mj2/H0T4kczSph0ifbMRgVvF3sWPJoX+IP5VBlVJi79DAH04txtNOy6JHebD2Wa3FPa2CtPgNgx/Fc7vq0aT8ShsYGEObnQo9n/qR7uBfv3Nmd4a+sqpeuegyy4+1rLY6n5pbyyBfbeWRUh6YSGVDL1Itj2nP/d2o79cN9PYg/fNaibtzQNZAifSXXfbSVkdF+PDEsgmd/Vid/UvP03PjpDot7ajWC50a144b/bqOgtJInhkVwU89gPq1jWLeWPo8PDmPaLwfIKang05tj2Xwij5S8GoNDdnE5b648xo1dA+tdX1Fl5J75++odtyaDIn0I8XYkbvZ6Ordx57WJ0Yz/cEu9dK9NjOaFhUnsTSng63u7M7CDD+sP5fBAXBhbjp7l07UneCAujAeHhPPWn4fZcjSX1Qc2AdAhwIX/3N6FYW+pdeydWzrxyerjbDpyFkc7LcZGjkw6JEYd2/Z6cRndwjyZO6Ubo95YUy/d3Nu68vS3u9h9Io8fn+hPXLQ/a/dnMmNSLO/8foC1+zMZEuPPjImx3PB2PAXnKnjxhwRGdQlqVHn/jhFdgokIcCX20cX0aOvDB/f1YdAL9Sfklu46w2fLDrLvPxMsjof7uzJtfCxDp/9FwbkKfFx1TSK3Nfu+qV/vbhHj8ZbIv8wm0zTLl4QQJab/DxJCxAshFgkhDgkh5gshhOncm0KIZCFEohDiHSFEX+B64G0hxF4hRLgQ4l4hxE4hxD4hxGIhhKPp2m+EEB8JIbYIIU4IISaaHv0m0N90/ZMXELGVSa6jQoiZteR+Sgix3/TzxN8dr3U+TAiRIIToIYToKYTYavp7ixCi/d89t869njHpnCiEeOVi3veVMLprED9tUjv8XcdzcXW0w8+tfqO363guWYVl9Y6fOXuO5DMFjd4x/R0jOwfw81bV8rznZB6uDrb41pHb102Hi4Mte06qs/0/b01hZGd1MHHHwDA+Xn6YiiojoM4Y1tw7kNNnSzmcbv0P0OGxASzeoc7sJZzKV/VwtTQQ+bra46yzIeGU+gG5eMcZRsQGAFBSVmVO52ivbbBFG9s9mN92p9U/0Qx0694DVze35hajQYbH+rNouzrTvedUPq6O58+LPaa8WLT9NCM6BZjPz5oQzRtLDpy3YxnXPYjfdqVaRX6A2FZupOSWkpqnp9Kg8Ne+DIZ09LVIExflyxJTeViRlEWfCC8ADqYXk22qB0ezSrC31WKrFVaT9XxEB7lyJq+UtIIyqowKKw5kM6iDj0WaQe29+WNfBgCrk3PoGeYBQJ9wT45mlXDEZAQp1KszUkEeDpzOLSXfNBDafjKfIZGW92wMOrV2JyX3HGeq3//eDIZGWb7/oVG+LDHNQC9PzKRPWy/z8b/2ZlBhMJKapycl9xydWrvjrLOhR5gnC3eo5abSoFBsqvcl5bXqv5220XcuiAl243RuKan5eqoMCsuSMomr897iIn34LSEdgJUHsukV5mlxLi1fz/HsEotr/FztGdDem8VN1C7V1qPSoLAsMZO4yDr1ItKHX/eY9NifRe9wVY+ySiMGo/pi7W205jXvZ4srOJheDEBphYET2efqtRfWoCn6jKZkVNdgFmw+BajjDLerZAyitlN60vLLqDIoLN+fxaAO3hZpBnfw4fe9aju1KjmbnqEeF7ynMP3HwWR0dbbXklPcuJ5v56ODvzNphXoyisqpMiqsPXKWa8I9LdJkFpVz4mxpcxcZM0Oj/cxt6d6UAlwdbPCpM8Hm42KPs70Ne1MKAFiyK41hMX4ADIv2Y/FO9frFO9MYFq0eL60wmK93sNOa9Y3wc8ZGI9hk8mAsrTBQVmlsVJ1GdQ5i4ZZTAOw+kYeb4/nHtrtPqGPbhVtOMdpkbFEUcHGwBcDVwZbMAtWodra4nL2n8qkyNG3ujenRmh/i1UmpnUdzcHO0w9/doV66nUdzzLLW5q6h7fhs+UEKTJ65OUX12wBrYK2+TyKpTXME+u0CRAHpwGbgGiHEQeAGoIOiKIoQwl1RlAIhxO+oS4IWAQghChRF+cL0++vA3cB/TPcNAPoBHYDfgUXA88A0RVEsp1Lq0xOIBkqBnUKIv1CHJncBvVD7xu1CiPWohqyGjueb5GoP/ATcqSjKPiGEK9BfUZQqIcRQYDYw4XzPVRRlV7VQQojhQFtTOgH8LoQYoCiK1Xz1AjwcScurmYFOzyslwNOxwcFPS8Lf3YH0Wi6jGfl6Atx1ZNeSO8BdVy9NdWcQ5udMrwgvnh8XRXmlgVd+TmJfSj6O9loeHtGOGz/YyIPD2zWBHnVkLFBlrL0Ewd/dgYxaLpFqmppO+tnrIpnQsxXF+komf7TZ4v46Wy2DIn15eWGiFbX4Z1C/TJWdJy/qpwHVqJNZWMbB83iT6Gy1DOrox/QF1ssLPzcdmbXKSlZhGbGt3S3TuNqbl/oYjOoHvoejrdlgATAixo/ktCIqaw3gZk+KxqgorNyfxSdrLJfkNCa+LvZk1XrnWUXlRAe5WqZxtSezUE1jUBRKygy4O9jS2ssBRYH/3toJDyc7VuzP4tstpzmTpyfE25EANx3ZReUMbu+Njbbx5yj83HQWdTWzoIxObdzPm8ZgVCjRq+/fz01n/nCovtbPTUdZpYG8kgreujGGyEBX9qcW8tpvB9GbPhyeGtmWG7oHUVxWxW3/s5x5v1Jqv2dQ8yImuG5e6MisVZ5Kyqtwd7SlvMrI1P4h3PvNHu7q18bimudGt+e9FUdxagIvGTDVi1p9Q2ZhGbGt3M6bprpeuDvaUlBaSWywG69PiCLQXcdzP+83G2mqCXTXERnoQmIDyx0bG2v2Gd1CPVnx/CCyCst4fckBjmQWW1kbCPB0IC2vZvb5ahmD1C73ANmFDdQNF3syTflSu26Aaihe8EBPSsqr+HjNCRJOF1BlVHjjz8MseqgX+koDp3NLmf1X0ywz8XGyJ6e4ZklqTnEFHf0vflW9nY2Gz26OxWBU+GFXGptMS1+tib9r/fbW301nYcjyr1v3C8rwN3lbeLvYm9PmFJfjXcugMzzGj2dGt8fLxY67v1CH6aE+ThTpq/jfnV0J9nRg89Fc5v55CGMj2jn8PRxIr+WdlJ6vJ8Ddoc7Y1oGMfMs0/h7qOGT6TwkseHIAsyZ3QiNgzJy1jSfcZRDo5Uhqbu36fY4AL8cGDTANERGo1qnVr49GqxHMXriXVXutb8y3Vt+nAJ/f2RVFgZ93prJoV8uYMG0pyC2xrc8ORVFSFUUxAntRlxgVAmXAV0KI8ahGioaIFkJsFEIkAbeiGneq+VVRFKMp/ozfJcq0SlGUXEVR9MAvqMadfsASRVHOKYpSYjre/wLHAXyA34BbFUWp9tt0A34WQuwH3q8jc0PPrc1w008CsAfV4NS2rvBCiPuEELuEELvKjzZvg3u1YqMRuDvZMWbOOl5dlMTn9/cCYNp1Hfl89VFKyw1/c4eWw9w/DtLr5ZUs2ZXKnQNCLc4Ni/Fj54k86SppZXS2Wh4d0Y53/jh43jTDYv2viryI8HNi2qh2zPilZhnMtJ8Suf6DLdz6vx10C/FgbAPu6y0BrUbQpbUbL/2SzNSvdxPXwYeeoR4Ul1Ux+6/DvDUxiq/v6kp6YVmTz6xfLlqNICrIlR+2nub69zdTWmHg/sFh5vPvLT9K/9fj+X1POlOuad2MklrycFwY87acNhuPqhnY3pu8cxUkp1v/g7+xSEwt5PoPtzD5k+3cOzAUO5uaoZSjnZYPb+3MnL8Oc+4q6Tca6jP2nymk98srGfFmPP+3/gRf3tezmaX855JTXM6I9zZx46c7eGf5Ud6cGIWTvRYbjWByjyBu/HQHQ9/ZxNGsEu7uH9Lc4l4UN361m/t/TOS1ZUd4ZGAIgW7W9xprbGp3CSuTshj21gbu/3o3T41SJ+hsNIIeYR7M/v0g4z7YQmsvRyb2DG4maRvmzkERzFiwly7P/MnLP+3lgzt7NLdIV4SNVkN4gCsjZy7jzg/W8/ED1+DmaNfcYl2Q8/V9ALd/vpPJn2znwe/2cHOvVnQLcW96ASUthubwlKnte2kAbExeJD2BIcBE4BEgroFrvwHGmTxQ7gQGnee+l+pjX3c0frmj80LgNKpxpToOzmvAOkVRbjAFA46/hOcKYI6iKJ9d6KGKonwOfA7gOeWHS5b97qFtuX1QBAAJJ3IJ8nQ0nwv0dCQj73w2sublzkFh3NpfHUDuO5VPoEeNC2SAh+XMIEBGQVm9NNXW+Yx8PUtNbod7T+VjVBS8nO3oGurJtV2DeHlCDK6OthgVKK8y8n/rGi8o6B0DQrm5r2o935dSRw93h3ozCJkFqheQZZr6s4hLdqby3YO9eW9pzcza9d2C+X239ZbLXO3cMSCUW64JARrICw/defKifpoQHydaeTux8iW1GQtw17H8hUFcO3c9OaaZ0rHdgvhtp3XzIquwzGJG3M9NV2/GOauonAA3HVmF5Wg1AhedjdlLxs/Nno+ndOG5BUmcqR1LwKTDuQoDf+7NILaVG7+Zlno0NtnF5fjVWo7h52pfz4U/u6gcfzd7sovL0QqBs05Lgb6S7KJy9qQUUKBX9dl0LJcOAS7sOJnPhiO5bDiiru8e3zWwnrdDY5BVWGZRV/3dG3j/pjSZhWVoNQJnB/X9n+/aTNPPvtOqF8byxEzujwujLr/tSeere7rz4cpjjaZP9Xuuxs/Vvl4g4ewidXY6q0gtT872NhSUVhIT7MawKHWdvYvOBsXUlvq52jOogw/923ljb6PByd6GNydG8/yi/XUf32hkFaoyVuNv8phqKE21Hi46m3oG1BM55yitMNDWz5kDaUXYaAQf3NKJP/dmsPpAttXkb4o+o/aypnXJ2byh1eDhZEe+FQJ63z2kLVMGhQOQcDKXIE8nQF0S0pLHILWpLvfV+LrZk1W3nSoux99UZ2rXDVCXVgIczCjmTJ6eNl6O5oFsqskLYsWBbKbWmWm3FjnnyvFxqfnY9XGxI+cS8v6sKW1GUTl7U4to6+NMemH531x16Uy5pg039lYD5yeeKajXZmbWaW8z69Z9dx2ZpiUwZ4vL8TF5y/i42JNbUl/enSfyaeXliIeTLRmFZSSnF5n7xpVJmXRp4wFcWb8+dXAEt5mMowmn8gn0rKnfgR6W3rmger4FeFimyTSVmRv7tjEH/f19VyrvN4NR5r6RHbhriGrI2n38LMFeTjWyejqRkXvx9Tst9xy7juZQZVBIyS7hWEYh4QGu7Dl+ttHlro01+r4ft58xLxHPO1fJmoPZxAS5sftUgVV1uZqQW2I3A0IIZ8BNUZSlwJNAJ9OpYsClVlIXIEMIYYvqKfN31L3+fAwTQngKIRxQgwtvBjYC44QQjkIIJ9TlVRsvcBygwvT37UKIW0zH3IBqf7Q7L+K5tVkBTDW9H4QQQUIIXxqZr1YfZeD0ZQycvoy/dqdyUz+1M+ge7kWR6eOgJfJN/AmGvbaGYa+tYdnedCb1UQcrXUM9KdZXWrh3AmQXllGsr6RrqLrOc1KfNiw3re9evjeda9qr60PDfJ2x1WrILalg3Nvr6fnicnq+uJwv1hzjo6WHGtUgA/DthpOMfDOekW/GsyIxkwk91QFGlxAPVY96DX85JWVVdAlR16NP6NmKlYmqHiE+NZ3d8Fh/i4CiLjobekd4sSLR+gGLr1a+3XCSEXPWMWLOOpYnZjCxl+pp0DXEg2J91XnzoqspLyb2as3KxEwOpRfR+bll9Hl5JX1eXklGQRkj58SbDTIuOht6t/VmhSnfrEVSahEhXo4EezhgqxWM6RTA2oOWH4trk7O5oZu6/nxEjJ95hyUXnQ2f39mNd5cdYU+tZTRajcDD5HJvoxEMivThqBWXNRxIK6a1lyOB7jpsNIIRUb7EH7YcgK0/cpbrTLF8hnb0YacpYO+W43lE+Dmjs9GgFYJubdzNgfmqdXDR2TC5RxBLrGBUSjxTSBtvJ4I9Te+/cwBr6nysrzmQzQ3d1fc/MtafbaYdTNYcyGZM5wDstBqCPR1o4+3EvtMFnC2uIKOgjFBTXe/b1stcz9t41xjUh0b5caKRg8zuTyuitZcjQR46bLSCUTH+rDuUY5Fm3aEcxnZRPaeGR/my3RTn4I4vdzHi3U2MeHcT3289zRfrT/Lj9jN8sOoYQ9/eyIh3N/HMwiR2nMizqkGmWo823o4EmerFqFh/1tWpF+sO5TDO5AE2PNrPrEeQhwNajfq5HOiuI8zH0bzbymvjoziRc45v6+wO1tg0RZ9ROxZH5zbuaARWMcgAfLXmKINeXs6gl5ezdHcaN5oM4y19DFKbA+nFtPZ0JMhdrRsjo/1Yf8iynYo/fJbrO6vt1LCOvubA4h6OtpiKFEEeOtp4OZCarye7uJwwHydzW9Un3JOTZ60bOLqaw5klBLs74O9qj41GENfOmy0XuQTJ2b4m/pibzoboABdOWcmwNm9zCte+u4lr393EqqQsc1vauY37/7N33vFRFF8A/85dyqX3HkihQwKh9xZ6UUCK7YeIvRcUsQCiIChg7x27ooKi9F6lhkBC6D2990u7298fexy5JAhKLgk4Xz/5yO2+3X1vZ3dm9s2bNxSUVFRz4GcUlFJYWkGUaRrpmE5BrItPA2DdoXTGdlaPH9s5iLWm7ZXr1TZBrtjZaMgpKufguVxcHWzxdFKdVz2aeVv0uf4tX2w8QfRLa4l+aS0r9ycxoUcoAB3DPckvvnTftqMph8mEHqGsNE3pSc0toYepb9u7lS+n0uo+IvGTVUfoPnUZ3acu44/d57jNNAjcuZkP+cVlVzx1CeDP3efo3UZ9h7xc7Gka4MaZOrDJGm2fg60GRzs1X5SDrYYeTb04LnPOWKJY6a+BUh+RMjXhAvwuhNChRodMMW3/EfhUCPEYagTNDGAXkGH6/+UcLgcBgxDiALBIUZQ3LyG3G/gVCAa+vZDXRQixyLQP4DNFUfZfarspCgZFUYqEECOBtaYEx/OBr4QQ04HlV3LdCyiKskYI0Qr4y5QPuRD4H2C1Ibi1B5IZFBXIvoU3oC9Tl6O8wOY5w+g7fSUAs26JYlz3UBztbIh/ezTfbDrJa0vjaB/myTdP9MHNyY6hUUE8e1MkPZ5bYS11zayPS2VAhD9/vTIEfZmBJxddvJVrZwxg0Gw1W/1z3+/nrTs7obPTsiE+jQ3xqoPih+1neHNSJza+OJByg5HHv9xb43WszYZDaUS38WPbiwPRlxt46tv95n2rnu3H0Fc3AfDC4oO88b/26Gy1bExIM6/289yo1jTxdcaoKCRm63n+x4urHwxtF8CWI+k1hlDWF9OensLePbvJzc1hUHQfHnz4UW4aO76+1QJgQ7ypLF4aRElZBVO+uVgWq5/rzxDT6l7P/3iAN0xLYm86lMaGQ2mXPffQqEA2H7Z+WRiMCi//fpjP7u6oLuO4J4kTaUU8Nqgp8Yl5bDicwS97klhwcyRrpvYmT1/Ok9+rz8z/ejSmsbcDDw9swsMD1VHsuz7bh77MwGd3d8RWq0GjEfx1PMucdNYqNigKr604xgf/i0IjBL/HJnMqo4gH+4WRkFzA5mOZ/BaTwpwxrfn90W7k6yvMH/UFJRV8+9c5vr23Ewqw7XgW20yrHzwztDnNTTkSPtl8mnPZV94xvGLdjQovLU3gy3s7ozUtSX48rZDHhzQj/nwe6xPSWbw7kddvbcv6Z/uoK6yYloM+nlbIigOprJramwqjkVlLD5lzFbz8WwJv3NYOW63gfLaeaaa8RFOHtyDc1wmjUSE5t4QZtezcMBgV5v55lI8ndUCrESzdl8zJ9CIeHtCEQ0n5bDqSwZJ9ycwbF8GKJ3uSpy9naqXVJxoKBqPCK8uO8OnkDuryxfuSOJFexCMDm3AoMZ+NRzL4dW8Sr42PYNVTvdTlTU0rEnUIcefevmFUGIwYFZj9+2Fyi8vpEOLOqA6BHE0pYMkj3QB4a80Jthyz7giutdqM4e0Dmdg7FINBoaTcwMN11CauPZDMoHYB7F0wUl0C+LOLqz9tmj2UfjNWAfDizVGM6x6Co50NcW+N4pvNJ5m/NJ72YZ58/Xhv3JzsGNI+iGfHRNLzeev3QQxGhXkrjvLhxPZoNPDb/hROZhTxUP9wDiXns/loJktjknnlptb88Vh38vXlPGN6PzuEuPNwdDjlBgVFUZjzx1HyTZEzH286zRd3daTCYCQlr4QZS//tYqT/0B4F3t54igVjWqMRgpWH0jiTrWdyt0YcTS9kx6kcWvg5M2dkC5x1NnQP8+DO7o2Y/E0sIZ6OPDUgHKOirlrz/d4ki1WbrMXGwxn0a+XLxuf7UlJu5JkfLuZr+/OpXox8XV1BaeYvh5h/a1t0tho2H8lg02H14/qj9Sd57472TOjaiKQcPY98rb5LQ9v6M6ZTEBWmd+Ex03ajAvOWHeHbB7sghCDufB4/7jxXqzatO5jCwMgAds8bTnFZBY9/sce8b8OLg4g2rUw27dsY3rm7Cw62WtbHpbA+Tu3bPvXVXubcGoWNVkNJuYGnvt4HqPlP1swYiIuDLUZF4b6Bzeg1Y5VFhJw1WB2TyJAOwcS9NxZ9qYH7P9hq3vfXghvpPnUZAHP+14kJvcNxtLfh2McTWLT+mDl/zIB2gex9cwxGo8IL3+whu4aIptrGGm2fl7M9b9+mxiBoNYIVB1PZXsPqTJL/DkK5RubRS66MfzN9qSFi73DtzT+uCRu7huL3/Pccf2tUfatQKzR97Lf6VuGqcXKpvkrBtYijaeT3WqegjlZCsSY63bVfRwEYrTD9rK4pyLf+h2tdUJzf8KcbXQnBYbUemFzneHhcH23GuXO59a3CVVOYd31EQRTl1U20lrUJa9mw8v/8G+LnDKr7JTHrkNhzBVZp2KMauzTI+9Ygpi9JJBKJRCKRSCQSiUQikfzXuD6GyK4AIcQQ4LUqm08rijKmPvSRSCQSiUQikUgkEolEYsl/bTLPf8YpoyjKatTEuRKJRCKRSCQSiUQikUgk9c5/xikjkUgkEolEIpFIJBKJpGHzX1sSWzplJBKJRCKRSCQSiUQikTQM/ls+GZnoVyKRSCQSiUQikUgkEomkPpCRMhKJRCKRSCQSiUQikUgaBP+xQBnplLnecPV0rW8VaoVSfWl9q1AruLk71rcKV03Tx36rbxVqhRPvjK5vFa6aB3+Jq28VaoU98an1rUKtIISobxWuGo3m2rcBoLzcWN8qXDUazfURvOzg7FDfKtQKGWl59a3CVWM0XvvvBYCX17Xfl3J0tK1vFWoFh3Df+lahVshIL6xvFSQSC6RTRiKRSCQSiUQikUgkEkmDQC6JLZFIJBKJRCKRSCQSiURSD/zXVl+6PmJlJRKJRCKRSCQSiUQikUiuMWSkjEQikUgkEolEIpFIJJKGwX8rUEZGykgkEolEIpFIJBKJRCKR1AcyUkYikUgkEolEIpFIJBJJg+A/FigjnTISiUQikUgkEolEIpFIGgZy9aUGghBiFlCoKMpCK5z7ZWCLoijr/kZmNHBMUZSEKz3mH+pwJ9BJUZRHauN8V8uLN7Whf2tf9OUGnv4ulkOJ+dVkIoLdWHh7O3S2WjYmpPPSkkMAPHdjKwZG+FFmMHIus5ip38eSr6/A3dGWD+/qSNvG7vyyK5EXf423qg2zb27HgEh/9GUGnli0l7hzudVk2jZ2563JndDZalkfl8qMnw6Y993VvwmT+zfBYFRYF5fCHJO+rYJcmf+/Drg42GJUFIa9soHSCqNVbOjZzItnh7dAqxH8ui+Jz7ecsdhvqxXMGxdB60BXcovLefqngyTnlpj3+7vpWPZYdz7YcIpF288C4KKz4aXRrWnq5wyKwoylCRw4n2cV/S/w8vhIotv4oS838OTXMcTXcL3IRm68eUcHdLZaNhxKY+bPcRb77xvQlJljI4icuoKcojIeGNiUMZ0bAaDVCpr5u9DumRXkFpdb1ZbLMXP6c2zZvAlPTy+W/P5nvepyOSL8nbmtQyAaAVtO5bDicIbF/sEtvOkT7oFRUSgoNfDFrkSyTPd3fDt/2gW6IIBDaYV8H5NSZ3pb472YPaY1fVr4kF1Uxph3/6ozWy7Qu4U3029shVYDi3cn8snG0xb77bSC+be0JSJYtenxbw+QlKPH3dGWdydGEdnIjSV7k3j5t8N1qnfPpl5MG94cjRAsiUnii61nLfbbagWv3NSG1oGu5OnLmbo4juTcEgLddfz2aHfOZBYDcDAxjzl/HAHg88kd8XGxo6RcrVcf+DqG7CLrvte9m3vzwo0t0QjBz3sS+XST5f231Qrm3xxJmyA3covLePL7AyTllNCjmRdPDW2GrVZDucHIghXH2HkyG4AnhjRldIdAXB1s6TBzvVX1r8yssW3o39oPfZnafscnVq9vIxq58frtUab2O41Zv6rt9/OjWjEgwp/yCiNnM4vM7XevFt48e2Mrs51zf0tgx/Esq9lgjTYDoHszb2aNi8RGK8gpKmPcm9usZgPA7AltiY5Q+yFPfrWPuPO51e1o7M5bkzqqdsSnMmPxQQCeGtmK23qFkl1QCsC83w+xIT4NG41g4cQORDZ2x0Yj+HnnOd5bfcwq+vds6sW0ES3QCsGSfUl8vvWMxX5brWDu2It17dTFB83v9++P9bj4fp/PY/Yfat306MAm3BgViKvOhq5zNlpF76p0D/fkqcFN0QjB77EpfPXXuWp2vHRjK1r6u5CnL+f5pQmk5JVgoxE8P7w5rQJcMCrw+poTxJzLxd5Gw6tj2xDs7oBRUdh6PIv3Np6yuh3XQ5+wtssC4MF+YYyI9MNFZ0vfBVutpntVruZbaXhUAE8MbU5TP2dGvbGNONM9H9UxiPujw83Htwx0ZeTCrSQkVT+35PrmP5dTRgihVRRl5hU4V0YDrS/8uMJjrkn6tfYlzMeJfnM28vyPB3llfGSNcnMmRPLcjwfpN2ejKt/KB4BtRzMY/Opmhr22hdPphTw0sCkApRVGXl9xlLm/J1jdhugIf8L9nOkxfTVTv4nh1dvb1yj36u3tefrrGHpMX024nzPREX4A9Gjhw5CoQAa8vI5+s9by4ZrjAGg1gvfu7sK07/bTb9Zaxi7cQrnBOg4ZjYDpN7Tkwa/3c+M7Oxge6U+4j5OFzE0dg8jXVzD8ze18s+MsU4Y0s9j/zLDmbK3ScX52RAu2H8/ixrd3cNP7OzmVUWQV/S8Q3caPMF9nes1ax7TvYpl3S7sa5ebdGsUz38XSa9Y6wnyd6d/a17wvwMOBPq18SMwqNm/7aN0JhszbyJB5G3n19wR2Hs+sd4cMwKjRN/Hhx5/VtxqXRQiY2CmQNzef5oWVx+na2I1AV3sLmXM5el5ec4KZq06w93weE6L8AWjq5Ugzb0dmrDrO9FXHCfN0pIWvU02XqXWs9V78tj+ZB76Ksbr+NaERMGtMa+75fC/DFm5jZFQATavcz3FdgsnXlzPwta18ueUMU4c3B6C03Mhbq4/z2p9H60Xv50e24MFvYhn93l8Mq6ksOgSRX1LByLd38M2OczwxqKl5X2K2ngkf7mLCh7vMDpkLPPtLvHmftR0yGgEzR7fini/2MeKNbYxsF0CTKvd/fOdg8vUVDF6wlUXbzvL0MPX+5xSV8eCi/dz41g6eXRzP/JsvtpcbD2cw/r2dVtW9Kv1b+xLm40zf2Rt47qcDzJlQc/v9yoRInv3xAH1nbyDMx5l+rdT6duvRTAbP28TQ1zZzOqOIhwap705OURl3fbybIa9uZsq3sbw5seY2tTawVpvh6mDLK7e0ZfJHOxkwZwP3f7rbajYAREeodvScuYZnvoth3m1RNcq9elsUU7+NoefMNaodbfzM+z5df4JBr2xg0Csb2BCfBsANHYOwt9EwYPZ6hs7dyMQ+YQR7Oda6/hoBL9zQkoe+3s+od3cwrO2l69oRb23nm7/O8uTgi3Xt+Ww94z/YyfgPdpodMgCbj2Ry60e7al3fv7PjmaHNePzHg0z4eDeD2/gS5m15v0ZFBZBfUsFNH+7i+92JPGr6KB7TPgCAWz/dyyPfH+CJgU0QpmO+3Xme8R/v5vbP9tI22JUeTTytbse13ie0VllsPZbFpC/rtv2+2m+loykFPPDFXnabnPgX+H1fEsMXbGX4gq08+W0s57OLpUPGhGKl/xoqDcopI4R4QQhxTAixDWhh2tZECLFKCLFPCLFVCNHStH28ECJeCHFACLHFtE0rhFho2n5QCPGoafsZIcRrQogYYLwQYpEQYlylffOFEHFCiN1CiKZCiB7AjcACIUSsSYfKxwwQQuw3HfOFEMK+0rleEkLEmPa1vEK7fYQQvwoh9pj+epq2zzKdf5MQ4pQQ4rHavN8XGBzhx5I9iQDsP5uLi4MtPlU+1nxc7XHR2bD/bC4AS/YkMjhS/WDbejQTg1ExH+/v7gCAvszA3lM5lJZbx4lRmaFRAfz8lzoKEHM6G1cHW3zddBYyvm46XBxsiTmtVog//3WWoVGBAEzqG857q45SZoqAyTKNVPVt7cfhxDwSTCOPOUVlGK30PkcGu3Euq5jEHD0VBoWVcalEmyrzC0S38uH3/ckArDmUTtdwT4t9STl6TqYXmrc529vQMdSDX/clAVBhUCgoqbCOASYGt/Xnl13qSEjMmRxcHW3xrfI8+bra46yzIeZMDgC/7DrHkHYB5v2zxkbwytJDl6w6R3cK4ve9iVbR/5/SsVNnXN3c6luNyxLu6Uh6QRkZReUYjAq7z+XRPsjVQuZIehFlBvWun8wsxsPBFlDn9dpqNdhoBLYagVYD+VZ+ji5gjfcCYN+ZXPL09ePUa9vYnbOZxZzP1lNuUFgem8qASh9mAAPb+LFkn2rTqrg0ujfzAkBfbmDfmVyrRev9HRHBbpzL1pNkKotVcWn0b2lZFv1a+bAsVo2iWptgWRYNhbaN3DibVUzihft/IIUBlT7wAaLb+LLUVG+ujkuje1P1/h9OLiDd1D4cTyvE3laLrVb9VDhwLo+MgrI6tAQGRfrz6+7zAOw/k6u2fTXWt7bsP5MLwK+7zzO4ran9PpJxsf0+k0OAu9puHkrMJz1ftfNYSgE6Wy12NtbpMlqrzRjdOZiVsSkk5+gByCq0btkMaRvILztNdpzOwc3BFl/XKv0QVx0uOltiTpvs2HmOoe0C//a8igKO9jZoNQKdnZayCiOFVqi7aqpr+1epa/u39GFZrFovrT10Ze/3wcQ8Mq187yvTJtCV89l6knJLqDAqrE1Ip29zbwuZPs28WX4wFYANhzPoHOoBQJi3E3tM70lOcTmFJRW0CnShtMLIPlP/t8KocDS1EF8Xy2e0trke+oTWKAuA+OR8q7/PVbnab6WTaYWcSv97B9iNHQP5Iya59pWXXBM0GKeMEKIjcAsQBQwHOpt2fQI8qihKR+Bp4APT9pnAEEVR2qE6UADuA0KBKEVR2gLfVbpElqIoHRRF+bGGy+cpihIJvAe8pSjKDmAZMFVRlChFUU5W0lMHLAJuNh1jAzxY6VyZiqJ0AD406XslvA28qShKZ2AsUHnYvSUwBOgCvCiEsL3Cc14xfu46knP15t+peSX4V3Fo+LvpSKkkk5Jbgp+7pQzA+K6N2HQ4vbZVvCz+7g7mjhdASo7e3Lm8QIC7rprMBQdSuJ8zXZt6sfy5/ix5ug/tQtRGoYmfMwoKPzzeizXTo3loSHOr2eDrak9qXqn5d1p+aQ0dUx2peWpoqsGoUFiqThNzsNNyV+9QPqgSThvkoSOnqIw5N7Xh54e68tLo1jjYWve1r14WJeb7XFnG4nmqJDO4rT+peSUcvsRIgc5WS7/WfqzYLxuuf4KHgw3ZlSKLsvXlZqdLTfQJ9yQupQCAk1nFHEkv5K1RrXhzVCviUwpJyS+95LG1iTXei/rG39Xe4vlPzSvBz83SJj83e1JNMgajQmFJBR6OtV79/yP8XOxJy7sYGp+WX1KtLCrLVC4LgCAPB356sCtf3NWRDiHuFsfNHtOGxQ925b6+YdY1AvBz05FaKcQ/La8Evyptnp+rPSmV7Cio4f4PifQjISmfckP9jbz5u+kspiuk5uqr2+KmMz9LoLbfVdt4gAndGrEpoXr7PTwqgPjEPPOgRW1jrTYj3NcZN0dbfn6iFyue7cfYro2sov9FHS37GMm5evyr9EP83XWk/I3M5H7hrJs+gDcmdsDN9Lz9GZNEcWkFsa8NZ8/coXy09rhVokSr1bV5pfi5XFldC+r7vfihrnx5V6dq73dd4uNiT1qBZZvhU9UOF3vSTG2YQVHtcHOw5Xh6IX2ae6EVgkA3HS0DXKrdA2d7G3o382KPyUFoLa6HPqG1y6Iuqc1vpUsxsn0gy6RT5iKKlf4aKA3GKQP0BpYqilKsKEo+qlNEB/QAfhZCxAIfAxeGRrYDi4QQ9wJa07aBwMeKolQAKIpSOUbsp7+59g+V/t/9Mnq2AE4rinJhQu9XQJ9K+5eY/r8P1UF0JQwE3jPZuAxwFUI4m/YtVxSlVFGUTCAd8LvEOeqdhwc1xWBU+G1vUn2r8o+x0QjcnewYMW8jL/8Sxyf3dwXU6Utdmnrz8Oe7GTV/M8OiAulVZWS4IfBwdDjf7DiHvsxgsd1Go6FVgAs/7T7P+A92oS8zcHcf63/0/Ft0tloeHdKchZVCn6syqK0/e05lN4ipS9cr3UPcCfV0YOWRTAB8ne0IcLVnyrIjTFl2hFZ+zjTzqf3w+drmUu+FpO7JKChl8OvbuPnDXSxYeYxXx0XgZK823c/9Es/Y93dy5+d76RDizg2VIiAaKk39nHh6WHNmmvIFXOs8MrgZFQaFpVXa72b+zjx7Yyue++lgPWn29/xdm2GjEbRt7M4dH/zF7e/u4IlhLQiro2mX/4avNp+i+/TVDHplPWn5Jbw4Vp0e0T7MA4Oi0H7aCrpOX80DA5vR2Lth1b8ZBaUMXriVCR/sYsGqo7w2PtL8fl9LLItNJT2/lK/v7siUwU05mJhnER2tFYJXxrTipz1JJFVyhjY0roc+4eXK4nojKsQdfZmBY6bBMMl/jwab6NeEBshVFCWq6g5FUR4QQnQFRgD7TJE2f8ffxYwpl/j3v+GCS9jAld9fDdBNURSLGl4IUfl8lzynEOI+1CghPKMfxiVi6GUvOLFXCLd2bwyoIdeB7g6A6vX3d7voeb9Aal4JAZVGrgLcdaRVapDGdQlmQBs/bnu/7hJm3tkvnNt7q43JgTM5BHpU0s/DgZQqDWZKbkk1mQujhyk5enPkReyZHIyKgpezHSm5enYeyyTbFCa5IT6VyMYebDtimSC1NkjPL8W/0mi5n6u9OXz8oozqmU/LL0WrETjb25BbXE5ksBuD2vgxZUgzXHQ2KIqa02fNoTTS8kuJMyUjW3MojXv6hNa67pP6hHFbT/W8B85WLQvLUVpQR3MtnieTTKiPE428nVjzQrS63V3Hquf6MXL+ZjJM92JUxyB+39Mwpi5dS+ToK/CsNNLv6WBLTg0h8K39nBjZ2odXN5yiwtQD6hDsysksvXnKTFxKAU29HDmeUVzt+NrGGu/FD7vOW13vvyM1v9Ti+fd305GWZ2lTWl4p/u4OpOaZbNLZkFPPjsi0glKLKAw/V121srggU7UsAPN0scMpBZzP1hPi5UhCpelAxWUGVhxMJSLYlT8OWC+RdFpeiUV0gp+bziICCNQR3QBTuWg1ApdK99/PzZ73JrZn2k9xnM+2rNvqgjt6h3KLqf0+eC6XwEq2+Ls7VLclzzLyJMDdso1X229fbq2SD8ffXccn93Rmyjf7OZdZu+96XbQZKbl6corK0JcZ0JcZ2HUii9ZBbpy+zBSCf8KdfcO5vZdqR2wVOwLdHSwislQ7Sgi4hExmpYiC77ad4euH1DHCMZ0bsfFQGhVGhayCUvaczKJdiEetl0m1utbNMspBlam5roWL73dCcgHns4sJ8XIiIbnuc2NkFFhG+Pi52pNR1Y6CUrUtKShFK1Q7Luj/5jpzcDyfT2rPueyL9/n5Ec05l63nhzrog1zLfcILWLMs6oLa/lb6O27oIKNkqnId++BqpCFFymwBRgshHIQQLsANQDFwWggxHkCotDP9u4miKLsURZkJZACNgLXA/UIIG5PMlU5mv7nS/y94FQoAlxpkjwKhQogL2QsnApv/gZ01sQZ49MIPIUTUPzlYUZRPFEXppChKpytxyAB8s+2sObHUmrhUbuocDED7EHcKSirMH8AXyMgvpaCkgvamkNSbOgezxpSErm9LH+4f0IR7Pt1jXj2jLli06RSDZq9n0Oz1rIxNZnz3EAA6hHlSoC8nvUplmZ5XQoG+nA5h6mMxvnsIq0y5D1bFJtOzhRoBE+7rjK1WQ1ZhGZsOpdEq2BUHOy1ajaBbcx+OpVinkxGflE9jL0eCPHTYaAXDIv3ZWMX5s/FIBqPaq/PPB7fxZdcpNRhs0md7GfL6Noa8vo1v/zrHp5tP88Ou82QVlpGaV0KoaVStWxNPTtZih/QCX205bU7Cu+pgCuO6qo1Yh1APCvQVNXQkSiksqaCDae7wuK6NWXMwlSPJ+URNW0n3GWvoPmMNKbklDJ23yfw8uuhs6NbMm9UH627ln+uF09nF+LrY4+1kq0aANXZjf5Vw/8buOiZ1DuKdrWcpKL04wpZdVE4LHyc0ArQCWvg6kVxH05es8V7UN3Hn8wj1diTYwwFbrWBElD/rq0wbWZ+Qzk0dVZuGRvqx84T1Vr65Ug4l5RPi6UCQu1oWQyP92FSlLDYdyeDGKDXSZVBrX3abcmd4ONqiMWVpDPJwoLGXA4k5erQaYZ7+YKMR9G3hzYk0y/w/tU1cYj6hXpXuf7sANlSZdrshIZ0xHYMAdZrShRWWXHQ2fHJnR15feYwYU96AuubrrWcYPn8Lw+dvYc3BVMZ2UafltA91p6Ck/BL1bTntQ90BGNulEWvj1BwOfVv58MDAptz96R5Kyi++864ONnx5fxdeW3aYvadrf5pGXbQZqw+m0LmJl5qLxVZLVKgHJ1JrdxR60eZT5sS8q2JTGNfNZEeYB/kl5aTnV+mH5JdQUFJOhzCTHd0as/qg+iFWOf/MsKhAjpocGknZenq1UHMeOdhp6RDuWet2gFrXhng5mt/vYZH+l3i/1XppUBtfdpty9FV+v4M9HGjs5UhiTt1+QF8gIbmAxp4OBLrpsNEIBrX2ZcuxTAuZrcczGWHKqxTdysc8FcneRoPONJ2nS5gHFUaF0ybn1wN9w3C2t+GNNSfqxI5ruU94AWuVRV1Rm99Kf4cQMCJK5pOpilFRrPLXUGkwkTKKosQIIX4CDqBO09lj2nU78KEQYjpgC/xoklkghGgGCGC9aVs80Bw4KIQoBz5FzRNzOTyEEAdRo1JuNW37EfjUlFx3XCU9S4QQk1GnVNmY9Pzo31sOwGPA+yYdbFAdVA9c5TmvmI0J6fRv7cvmGf3RlxmY+v3FZaJXTO3NcNNyczN+jjMv87YpIcM89/ylcRHY2Wj49iF1ys/+s7m8sFhdqnLbzGicdTbY2mgY3NaPiR/sskqHe31cKgMi/PnrlSHqUpSL9pr3rZ0xgEGz1eVJn/t+P2/d2QmdnZYN8WlsiFc7pj9sP8Obkzqx8cWBlBuMPP6lenxecTkfrz3OyuejURSF9fGprDd1Zmsbg1Fh7p9H+XhSB7QawdJ9yZxML+LhAU04lJTPpiMZLNmXzLxxEax4sqe63OxPcZc979w/j/Da+EhstYLz2XpmWDncfkN8GtFt/Nj20iBKyiqY8s1+877Vz/VnyDx1ScznfzzAG6blTTcdSmPDocs3XEOjAtl8OL1BTUeZ9vQU9u7ZTW5uDoOi+/Dgw49y09jx9a1WNYwKfLcvmaf6hqHRwNZTOSTnlzI6wpcz2XpikwuYEBWAvY2Gh3qqHxZZxeW8s/UsexLzaOXnxOyhzVCA+JRCDiTXTYittd6L+RMi6RzmgbujLeum9uaDDSfNiXWtjcGo8NJvCXxxbye0GsEvuxM5kVbI44ObEpeYx4aEDH7encjCW9qyblpvcovLefK7i/Xyxuf64qzTYqvVMKiNH5M/3cMJK3asK+s9d/lRPryjPVqN4LeYZE5mFPFQdDgJSflsOprJ0phk5t7Uhj8f70Gevpxnfo4HoGOoBw9Fh1NhUFAUhTl/HCFfX4GDrYaP7miPjUag0Qh2ncw2J6G0ph0v/36Yz+7uqC41uyeJE2lFPDaoKfGJeWw4nMEve5JYcHMka6b2Jk9fzpOmdvF/PRrT2NuBhwc24eGBTQC467N9ZBeVMXVYc0a2D8DBVsvm5/vy8+5E3qs00msNNiSk07+NL1tmRpuXxL7Aimf6MHz+FgCmL45Tl8S207IpIZ2Npvb75XGRpva7G6Am+31hcRyTeocR6u3EY0Ob89hQNZfaxA92WiW5prXajBOphWxKSGPtC/0xKvDD9rMcteLUgPXxqQyI8GPH7MHmJbEvsPaFaAa9sgGA576PVZfEttOy8VCaeZWl6TdF0KaRG4oCiVnFPPOdeh++3HySN+/oyMaZAxECftpx9pI5166GC3XtRxfq2hhTXRvdhEPJpro2Jpl5YyNY/oRa1z5j6ut1DPXg4QFNqDCoHz2zlx0mX68mkH1ycDNGtPVHZ6tl3dO9+XVfEh9aMc+XQVGYv/o479zaFq1GsOxACqcyi7m/TyiHUwrYcjyL32NTeWlUS5Y82JX8knJeWKquEurpZMe7t7bFqChkFJTx4u/qtDhfF3vu7hXC6cwivr2nEwCL9ybxe6z1Boiuhz6hNcoC4NHocIa08UNnq+HPR7vze2wKn1ZZvr22udpvpSFt/Zk1tg2eznZ8cX8XDifmccdH6opwXZt4kZKr53xW/TgyJQ0DoTRgj1FdIIQ4A3Qy5Wy55gl9/M/rokBL9XUzCm9tvHxdLy/UwMnNtv7HXl1w4p3R9a3CVfPgL5fvcF0L7Im3jmOzriktbTjOwX+Lg0ODGZu5Kq6HsijR1+1qItbCUHHtlwWA0Vj3K5zVNl4+NQV8X3vodPWb5Lw20NfTan+1jcPfLBBwLZGRbt2IzLrgzNsjxeWlrl22Hc+xyjdtr2YeDfK+NaTpSxKJRCKRSCQSiUQikUgk/xmujyGyq0BRlFBrnt801enxKpu3K4rysDWvK5FIJBKJRCKRSCQSybXGf20yz3/eKWNtFEX5EviyvvWQSCQSiUQikUgkEomkoaP8x9ZfktOXJBKJRCKRSCQSiUQikUjqARkpI5FIJBKJRCKRSCQSiaRBYPxvBcrISBmJRCKRSCQSiUQikUgkkvpARspIJBKJRCKRSCQSiUQiaRD813LKSKeMRCKRSCQSiUQikUgkkgaBXH1Jck1TUV5R3yrUCoYKQ32rUCsE+LvUtwpXTXm5sb5VqBUe/CWuvlW4aj4cF1nfKtQKjwhR3yrUCoUl5fWtwlWjs9PWtwq1QkqOvr5VuGq2bUyobxVqBZ2Drr5VqBXcvN3qW4WrxtnZvr5VqBUyMorqW4WrJjj42n+eAI4dSa1vFWoFR2eH+lZBIrFAOmUkEolEIpFIJBKJRCKRNAj+a9OXZKJfiUQikUgkEolEIpFIJJJ6QEbKSCQSiUQikUgkEolEImkQyCWxJRKJRCKRSCQSiUQikUgkVkc6ZSQSiUQikUgkEolEIpE0CBQr/Xc5hBBDhRBHhRAnhBDPXkJmghAiQQhxSAjxfW3YK6cvSSQSiUQikUgkEolEImkQ1MeS2EIILfA+MAhIBPYIIZYpipJQSaYZ8BzQU1GUHCGEb21cWzplJAC8PD6S6DZ+6MsNPPl1DPHn86rJRDZy4807OqCz1bLhUBozf7ZcYvi+AU2ZOTaCyKkryCkq44GBTRnTuREAWq2gmb8L7Z5ZQW6xdZaRfeW29gyM9EdfZuDRz3cTdy63mkzbEA/eubszDrZa1sWl8sL3+wGIaOTO/Ds6orPVUGFUmPZNDPtPZwPQo4UPc26NwkarIbuwlNGvbbKK/lXp1NiNh3qHohGClQnp/BSTbLE/MtCFB3uFEu7tyCurj7P1pKpvuyBXHuwVYpZr5OHAK6uPs+N0Tp3o3bu5Ny/c2BKNEPy8J5FPN5222G+rFcy/OZI2QW7kFpfx5PcHSMopoUczL54a2gxbrYZyg5EFK46x02TT1/d1xtfVnpJydan0uz7bR3ZRWZ3YAxDh78xtHQLRCNhyKocVhzMs9g9u4U2fcA+MikJBqYEvdiWSZXrOx7fzp12gCwI4lFbI9zEpdab3P2Hm9OfYsnkTnp5eLPn9z/pW55K08Xfm1vYBaARsPZXDyiOZFvsHNfeid7gHRgUKSiv4cncS2aayGNvWj7aB6jL1fx5KZ8/5/DrX/wLtAl2Y1CUYjRBsOJ7Fsvg0i/3DW/sQ3cwLg1G146PtZ8ksUu34fmIU53LVJaAzi8pZuOFUnesPEBngzG0dgtT34mQ2y6u8F0NaeNOniaf6XpRU8Hml92JClD/tAl3V9yK1kO+q1G91SadGbjzQKwStRq1rF++3fEcjAlx4oFcI4V6OzF1zgm2nss377u7eiK4h7gghiDmfx4fbzta1+hYsmNyVIR2C0ZdWcP/724g9nVVN5sVbO3Bbn6a4O9vhN/Fb8/b/9WvKnImdSclWlx/+aOVhvtpwvM50v8C8/3VgYLtA9KUGHvl0JwfPVm+7XhjXlpt7huLmZEfIfb+Yt3dv4cMrt3egTSN37vlgB3/sOW91fWeOaU2/Vj6UlBmY+sNBDiVVr1cigl1ZcGs77G01bDqcwctL1f69m6Mt705sT7CnA4nZeh75OoZ8fQWjOgRyf3Q4QggKSyqY8Ws8R5ILALizdyg3d2uEEPDTzvN8ueWMVezqFubBEwOaoNUIlh1I5ZtdlvcyKtiNJwaE08TXmZnLDrPx6MW6+M3xEbQJdOVgYh5P/3rIKvpVpU8Lb6aPaoVWI1i8K5GPN1rWi3ZaDQtubUtEsCs5xeU8/k0sSTlqPfpAdDjjuwRjMCrM/u0wW4+ptkzqFaLea+CnXYks2noGgGkjWxDd2pfyCiPnsoqZ9lMcBSUVVrWvS4g7j/QNQ6uB5fHpfL83yWJ/2yBXHukbShNvJ15ecYzNJy6+++sf687prGIA0vJLeeGPI1bVtSqzJ7QlOkLtnz/51T7izudWk4ls7M5bkzqq3xjxqcxYfBCAp0a24rZeoWQXlAIw7/dDbIhPw0YjWDixA5GN3bHRCH7eeY73Vh+rVb2t8UxN7h3KhK7BKMDRlAKm/RRHWYWRiT0bc2fvUEK8neg8cx05VvpWklyWLsAJRVFOAQghfgRGAQmVZO4F3lcUJQdAUZT02riwnL5UBwghbrwQ/iSEGC2EaF1p38tCiIH1px1Et/EjzNeZXrPWMe27WObd0q5GuXm3RvHMd7H0mrWOMF9n+re+6BgM8HCgTysfEk2VPsBH604wZN5GhszbyKu/J7DzeKbVHDIDIv0J93Om63Mreeqrvcy/o2ONcvMnduCpRXvp+txKwv2ciY70B2Dm+LYsXHaI6FlreW1pPDPHtwXA1cGW1yZ2YOI72+kzYzX3fPCXVfSvikbAo33DeP6PI9zz/QH6N/eisYeDhUx6QRkL1p9kwzHLj9IDSfk88FMcD/wUx9TfEiipMLKvBiebtfSeOboV93yxjxFvbGNkuwCa+DpZyIzvHEy+voLBC7ayaNtZnh7WHICcojIeXLSfG9/awbOL45l/c6TFcU//cJDRb//F6Lf/qlOHjBAwsVMgb24+zQsrj9O1sRuBrvYWMudy9Ly85gQzV51g7/k8JkSpz1VTL0eaeTsyY9Vxpq86TpinIy2q3I+GwqjRN/Hhx5/Vtxp/ixBwe8dA3tpyhhmrTtAlxI2AqmWRW8KctSeZtfoE+87nM76dWhaRAc6EeDjw0uoTvLL2JINbeqOzqZ8mUAi4q1sjXl13kqd+P0zPMA+C3HQWMmey9Tz/51Gm/XGEXWdzub1jkHlfmcHIs38c5dk/jtabQ0YImNgxiDc2neb5FcfoGuJe7b04m6PnpdXHmbHyOHvO5zEhKgCApt6ONPN2YvrKY7yw8hhhXg60rKf3QiPg4T6hTF9+lHt/OEj/ZtXr2ozCUl7fcJKNxy3r2tb+zrTxd+GBn+K4/8eDNPd1Mjv96oMh7YNpGuBK20d/5ZGPd/DWvd1rlFux9zx9n/ujxn2/7jhN96nL6D51Wb04ZAa2DSDcz4XOU/9kype7WXhnpxrlVu9PYtCsNdW2J2YV88inu/j1r7pxjvVr5UOotyPRczfz/M/xzB4XUaPc7HERPLc4jui5mwn1dqRvSx9A/XDbcTyT6Hmb2XE8kwcHNAHgfHYxt7y/k2ELtvLe2hPMHa+2h839nbm5WyPGvLWdEQu3Ed3alxBvx1q3SyPgqUFNmfJzPLd+tpdBrX0I9bK8Tmp+CbNXHGNtQvXvke92J/Lyn3X34a8RMGtMG+7+bC9DF2xlZPsAmvo5W8iM7xpMnr6cAa9u4cstZ3hmRAsAmvo5MyIqgGELtnHXZ3t56aY2aAQ0M93rm97ewcg3ttO/lQ8hpnuw/VgWwxduY+Qb2zmdWcwDpnKzpn2P9w9n2m8JTPo6lugW3oR4Vu0TlvLqmhOsO5JR7fiyCiP3fHeAe747UOcOmegI9Ruj58w1PPNdDPNui6pR7tXbopj6bQw9Z65RvzHa+Jn3fbr+BINe2cCgVzawwTSAcUPHIOxtNAyYvZ6hczcysU8YwV619y5Y45nyc7Xnjt4hjH5rB8MXbkOrEYw0tYv7Tudyx8d7SMwurqbLfxVFsc7fZQgCKnugE03bKtMcaC6E2C6E2CmEGFob9kqnTB2gKMoyRVFeNf0cDbSutG+moijr6kUxE4Pb+vPLrnMAxJzJwdXRFt8qnWtfV3ucdTbEnFFHrH7ZdY4h7QLM+2eNjeCVpYcuOVNvdKcgft+baBX9AYa1D2LxjjMA7DuVjZujLb5VPnJ83XS4ONiyzzTKuXjHGYa3V98zBXDR2QLg6mhLqmkUemy3xizfl0SSqZLMNHnqrU0LP2eS80pIzS+lwqiw6XgWPcI9LGTSCko5nVX8txVM76Ze7DmbS2mF0coaq7Rt5MbZrGISs/WUGxSWH0hhQGvLqL7oNr4s3aeO8KyOS6N7Uy8ADicXkG66v8fTCrG31WKrFXWi998R7ulIekEZGUXlGIwKu8/l0T7I1ULmSHoRZQa1IE5mFuPhoD5LCmCr1WCjEdhqBFoN5Ft5NO3f0rFTZ1zd3Opbjb8lzNOB9IJSMiuVRVSQ5Ufw0cplkVWMh6MaEBroquNYRhFGBcoMCom5pUQEOFe7Rl3Q1NuR1PxS0gvLMBgVdpzOoVMjy3ufkFpotuN4RhGeTrb1oeolCfd0JK2wjIwi1YZd53JpH/w370VWMZ6OpvdCUSPmzO+FEOTV03vRwrdKXXsim+5hVevaMk5n6autBKEo6kipjUaY3/Mcff2Nbo7o3JjvN58AYM/xDNyc7PB3d6gmt+d4hrmNa2gM6xDMT9vPALD3ZBZujnb4VWnLL+xLyyuptv18ZhEJ53Mx1lHc+8AIP5aaIhZiz+bi6mCDj4tl/8nHxR5nextiz+YCsHRvEoMi1Q/OQRF+/LpHPf7XPUkMilC3x5zJJV+vvhP7z+bg767egyZ+zhw4l0tJuVF9705mM8Q0uFSbtA5wITFXT3JeCRVGhXWHM+jTzMtCJjW/lJMZRTXe671ncykqM9S6XpeiXWN3zmYVcf5C3yM2hYFtLPseA9v4mstq1cFUupvsGdjGl+WxKZQZjCRm6zmbVUS7xu409XXmwNmL93r3qWwGm8pt27FMDKYKIfZsLv41PKO1SUt/Z5Ly9KSY6qkNxzLp2cTTQiY1v5RTmcVXkDGjbhnSNpBfdpq+MU7n4OZgi69rlf65qw4XnS0xpojuX3aeY2i7wL89r6KAo70NWo1AZ6elrMJIYS3Wv9Z4pgBsNAKdrVbV21ZLer7a701IzjdH2UisixDiPiHE3kp/9/3DU9gAzYB+wK3Ap0II96vV67pxygghQoUQh4UQn5qS7qwRQlTvjaiy9woh9gghDgghfhVCOJq2LxJCfGjyep0SQvQTQnxhOu+iSscPFkL8JYSIEUL8LIRwNm0/I4R4ybQ9TgjR0rT9TiHEe0KIHsCNwAIhRKwQoonpmuNMch2FEJuFEPuEEKuFEAGm7Y+ZkgkdNIVR1Sr+7g4kV6oIUnJKqnXk/N0dSMmtWWZwW39S80o4XEPILoDOVku/1n6s2G+98HR/DweSsy/ql5ytJ6DKaGeAhwMpOZYy/iaZ6T/s58UJbdm/cCSzJrTjlV/VqVnh/i64O9mx9Jl+rJ05kAk9QqgLvJ3syCi4GA2SWViGt5PdPz5Pv2Ze1UZ3rYmfm47U3Isd5bS8kmodaj9Xe1JMnWmDUZ3W4OFo+cE5JNKPhKR8yg0Xuxdzx0fw2+PdeWhAuBUtqI6Hg415+gtAtr7c7HSpiT7hnsSlqGHmJ7OKOZJeyFujWvHmqFbEpxSSkl83jr3rEQ8HW4uP3pziir8ti97hHsSlFAJwPreEiABn7LQCZzstLX2dzE6CusbT0Y6sStFe2cVlf+t06d/Mi9hK9autVsMrI1owe3jzas6cusLD0dbivcgpvvx7cbDSe3E4rYi3R7fmrdGtiU8tqLf3wsvJjozCqnXtlT0Xh9MKOZCczw93duCHSe3Zdz6P8znVHQV1RaCnI4lZRebfyVlFBHj+s5Hj0V1D2LVwFN8+1Z8gr7qPXgrwdCApu5IN2cX/2Ia6xN9VR0qlNi81t6TaB7q/m47UvCoypo9Sbxd7MkyDERkFpXhXcegATOjaiM2mqYHHUgroHOaJu6MtOlsN/Vr5EOBe+w4BHxd788ciqFEYPs7/vA9SV/i5VS+Han2PSjIGo0KhXu17XOrYY6kFdAqvdK9b1nyvx3cJZksN0Sm1iY+TvUWfMKOgDJ9/0Ce0s9Hw8a1t+eDmSHpVceZYG393ncU3RnKu3uxkrCyT8jcyk/uFs276AN6Y2AE3U7v9Z0wSxaUVxL42nD1zh/LR2uO1Go1vjWcqLb+UzzadZsv0fvw1M5qCkgq2Hau7Pvq1hhHFKn+KonyiKEqnSn+fVLpsEtCo0u9g07bKJALLFEUpVxTlNHAM1UlzVVxvOWWaAbcqinKvEGIxMBb4tga5JYqifAoghJgD3A28a9rnAXRHdZ4sA3oC96Am+olCLYjpwEBFUYqEENOAKcDLpuMzFUXpIIR4CHjadCwAiqLsEEIsA/5UFOUX0/Ux/d/WpMMoRVEyhBA3A68AdwHPAmGKopTWhieuNtHZanl0SHNue3fHJWUGtfVnz6lsq01dqg3u7N+UmT/G8ue+JG7sHMxbkzszbuFmbDSCtiEejFuwCZ2dlhUvDGDvySxOpRXWt8qXxdPRljAvR/aeq5upS7VFUz8nnh7WnLs+22ve9vSPB0nPL8XJTss7E6MY1SGQ3+sxB8Wl6B7iTqinA69uUHNS+DrbEeBqz5Rlarjw0/3CaJZawPEMGZ5qbbqFuBHi6cCCDWpOo4S0QsI8HXh2QDiFpQZOZhVXi3xoiPQK9yDcy5GXVl2cSvLIr4fIKS7H19mOGUOacj5XT1pB3U3p+6d0D3UnzNOBeevVqVa+znYEutrz5O+HAZjaP4zmPgUcu8bei0BXexp56Lj9KzU32bwbWxIR4EK8yfl0rbFi73kWbztFWYWRuwa24NNHejP8pVX1rdZ/iqpBJ92aejKhayMmvKtOnT6ZXsTHG0/y1f1d0JcZOJyUX2dRQf81TqYX8cnGUyy6rzPFZQYSkgswVAk6fnBAEyoMxgbZH6nMzZ/vI7OojABXe94c14ZTmUUk510bA0RfbT7Fm8sPowDP3NiaF8dGMuWbGNqHeWBQFNpPW4Gbkx2/PdWHrUfSOZfZcNsRVwcbBkb40X/uZvL15bx7R/sG259tCNRT1bYHaCaECEN1xtwC3FZF5jfUCJkvhRDeqNOZrnou+fXmlDmtKEqs6d/7gNBLyEWYnDHugDOwutK+PxRFUYQQcUCaoihxAEKIQ6bzBaNOP9pucqjYAZUTjSypdP2b/oHuLYAIYK3pvFrgQrbBg8B3QojfUB8EC0xhV/cBuPd9EKfWgy97sUl9writZygAB87mEFgpqiTAQ1cttDk1V0+Ae3WZUB8nGnk7seaFaHW7u45Vz/Vj5PzNZJhGWUZ1DOL3PbU/demu6Kb8r08YAPtP5xBYaX5toKdlVAxASo5l9EygpwOpJpmbe4SYk/4u25PIm3d2BiA5R09OYSrFZQaKywz8dSyDNo3cre6UySwqw8fl4iiIt7Mdmf8wj0rfpl5sP5VtDrGtC9LySixGN/zcdNVCzNPySwlw05GWV4pWI3DR2ZgTmvm52fPexPZM+ymO85Uiny6M2BWVGfgzNoW2jdzqrBHL0VdYRFR4VonWuEBrPydGtvbh1Q2nqDDd8w7BrpzM0punj8WlFNDUy1E6Zf4lOVWilDwcbWosi1Z+Toxo7cP8DafNZQGw/HCGORntvd2CSauj6YhVyS4uw6vSKKenox3ZRdXtiAhwYUykPy+tPm5hx4X3Jb2wjITUQkI9HevcKZNTXG7xXng4Xuq9cOaG1r7MW3/SbEPHYDdOZhWb34uDyQU08XaqF6dMVlGZRQSAWtde2QBCj3BPjqQWUmKyY++5PFr5OdepU+a+IS2ZPFDNy7XvRCbBlaJbAr2cSPkH+QmyCy++D4s2HGPOxJrzudQ2dw9oxsR+ak6O/aezCPJ0AtTR40BPx39kQ10wsaea/BXg4Plci+gJf3fLqBiA1DzL6Bl/dx2p+apMZkEpPqZoGR8Xe7IqlUHLABfmTYjkrk/3WgxqLd6VyOJdap/q6eHNLaJTa4uMglKLaey+LvYWEWUNjbS8kmrlUK3vYZJJzStBqxE4O6h9j7879ufdify8W73XTw1rblG2N3UKIrqVDxM/3m1N0wDIKCq16BP6uNiR8Q/6hBf6jyn5pcQm5tPMx9mqTpk7+4Zze69QAGKrfGMEujtUe2ZTc0ss++eVZCqnDfhu2xm+fkjNlTWmcyM2HkqjwqiQVVDKnpNZtAvxqDWnjDWeqZ7NvEnMKjbnRVwdl0qHUHfplGlAKIpSIYR4BNU3oAW+UBTlkBDiZWCvoijLTPsGCyESAAMwVVGU6ln1/yHXzfQlE5VrGAOXdjotAh5RFCUSeAmoHI924RzGKuczms4ngLWKokSZ/lorinJ3Dcf/3fVrQgCHKp03UlGUC96VEajLc3VAjdixOG/lMKwrccgAfLXltDkJ76qDKYzr2hiADqEeFOgrLMJWQf0oLiypoEOoOtd+XNfGrDmYypHkfKKmraT7jDV0n7GGlNwShs7bZHbIuOhs6NbMm9UHa3/FmS82nCB61lqiZ61l5f4kJvQIBaBjuCf5xeWkV6k80/NKKNCX0zFcDd2c0COUlfvViLTU3BJ6tFAT7/Vu5cupNLVTvWp/El2aeaPVCBzstHQI8+J4ivVXbDmaVkiQmw5/F3tsNIJ+zbz46x+untS/uRcbj111HfGPiEvMJ9TLkWAPB2y1ghHtAthw2DIJ4IaEdMaYkpYOifQzr7DkorPhkzs78vrKY8SY5t4DaDXCPL3JRiPo18qH46l199FzOrsYXxd7vJ1s0WoEXRq7sb/KVL3G7jomdQ7ina1nKSi9OI8+u6icFj5OaARoBbTwdSJZTl/615zJ1uNXpSwOJFk+C43cdUzsFMS7W89ZlIUQ4GSnBSDYzZ5gdx2HUusn4u1kZjH+rvb4ONuh1Qh6hHmwL9Eyoi3U04F7uzdiwYZTFnmInOy02GjUCEsXey3NfZ1ItMJH2eU4nV2Mn4uduSy6NnZnf2KV98JDx52dg3h7yxmLssgqLqOF78X3oqWvk3lKY11zNF2ta/0u1LVNPdl5hXVtRmEpbQNdVTs0gshAF87VcU6AT1YfMSfm/WPPOW7r2xSAzs18yC8u+0e5YypPWx7RqRFHE3NrW90a+Xz9cfrNWEW/GatYsS+Jm00DRp2aeJFv+sBpSHyz/SwjX9/GyNe3sTYujTGd1PYsKsSdgpIK83SkC2QUlFJYWkFUiDsAYzoFsc6UrHTdoXTGdlaPH9s5iLWm7YHuOj6Y3IGnvj/A6Ywii/N5mZyIge46hkT6W+WD7nBKAY08HAhw02GjEQxs5cPWE3Xbn/gnHDyfR4i3E8Gepr5HVADrD1n2PdYfSjeX1dC2/uw02bP+UDojogKw02oI9nQgxNuJA6bVOz1N9zrAXcfgSD+Wme51nxbe3Nc/nPu/jKGk3Po5+46mFhLs7oC/q1pPRTf3ZsfJ7MsfCDjbX8zP56azISLAhTNWdnQu2nzKnJh3VWwK47qZvjHCPMgvKSc9v0r/PL+EgpJyOpjyeY3r1pjVB9V7XTn/zLCoQI4mq+1MUraeXi3UHC8Odlo6hHtyohb7htZ4ppJz9USFuKOzVT+/ezTz4kSa5fstuYhipf8ue11FWaEoSnNFUZooivKKadtMk0MGRWWKyQcQqShKraQWud4iZa4UFyDFNGXodqrPFfs7dgLvCyGaKopyQgjhBAQpinKl67AVmK5flaOAjxCiu6Iof5l0aw4cBhopirJRCLENNYzKGcj9Bzr/LRvi04hu48e2lwZRUlbBlG/2m/etfq4/Q+ZtBOD5Hw/whmlJ7E2H0thwKO1SpzQzNCqQzYfT0Vs54du6gykMbBvA7leHU1xWweNf7DHv2zBrENGz1gIw7dsY3rmrCw52WtbHpbA+LhWAp77aa172uqTcwFNf7QPgeEoBG+NT2fTyYIxG+G7rKY5cIndObWJU4L0tZ5g3Sl1aenVCOmez9UzqEsyx9CL+OpNDc18nZg1vjrO9Dd3C3LmjSzD3/qAuIejnYo+Psz0H60DXyhiMCi//fpjP7u6IViP4dU8SJ9KKeGxQU+IT89hwOINf9iSx4OZI1kztTZ6+nCe/PwDA/3o0prG3Aw8PbMLDA9VR07s+24e+zMBnd3fEVqtBoxH8dTyLxbutlzS6KkYFvtuXzFN9w9Bo1GWYk/NLGR3hy5lsPbHJBUyICsDeRsNDPdWOR1ZxOe9sPcuexDxa+Tkxe2gzFCA+pZADyQ1zasO0p6ewd89ucnNzGBTdhwcffpSbxo6vb7UsMCrwfUwyT/RVl4rfbiqLUaayOJBcwPh2/uhsNDzQQx3Jzi4u571t59AKwbRoNbJOX2Hks52J9TZ9yajAl7sSeX5gEzQawcbjWSTmljA+yp9TWcXsO5/P7aaVJZ7oFwpcXPo6yE3HPd0boSiqo2lZXBpJ9fDRalTg273JPN0v3Lw8eXJ+KWMi/TidrSc2KZ+bowKwt9XwcC81F1dWUTlvbz3DnvNqRMmcYc1RUCPIYuvpvTAq8P7WM8y9oQUaIVhzJIOzOXru6BzEsYwidp7JpbmvEzOHNsfFXku3UHfu6BLEfT/GsfVkNu2CXPn4lrYoCuw9l8uuSg7lumZ1TCJD2gcT9+5Y9GUG7n9/q3nfXwtupPvUZQDM+V8nJvQKx9HOhmMfTWDR+mPM/TmWB4e3ZninRhgMCtmFpdz//rY6t2HtgWQGtQtg74KR6MsMPPrZLvO+TbOH0m+GOp3qxZujGNc9BEc7G+LeGsU3m08yf2k87cM8+frx3rg52TGkfRDPjomk5/MrrKbvxsMZ9Gvly8bn+1JSbuQZUxsM8OdTvRj5unoPZ/5yiPm3tkVnq2HzkQw2mSL2Plp/kvfuaM+Ero1IytHzyNdq/+vRwc3wcLTj5bHqak4Go8KoN7cD8MGdHXB3tKXCqPDikkNWWYrZoMDra0/w1oQINELwZ1wqpzOLubdXCIdTC9h2IptW/s68elMbXOxt6NXUi3t6hXD752rf6cPb2hHi5YCjrZbfH+rK3JXH2PUPB5b+kb5GhZeWJvDlvZ3RCsHPexI5nlbI40OaEX8+j/UJ6Szencjrt7Zl/bN9yC0u54lvYwF1YYEVB1JZNbU3FUYjs5YeMrcN79/RHg8nO8oNRmYtSTDf6xfHtMbORsOi+9So6thzucy04tLfBgXe3niKBWNaoxGClYfSOJOtZ3K3RhxNL2THqRxa+DkzZ2QLnHU2dA/z4M7ujZj8TSwhno48NSAco6KuKPT93iTOZted83h9fCoDIvzYMXuweUnsC6x9IZpBr2wA4LnvY9Ulse20bDyUZl5lafpNEbRp5IaiqKurPfOd+o58ufkkb97RkY0zB6rLw+84e8nclv8GazxTB87lsepgKr8/2RODUSEhKZ+fdqoL/dzRK4T7+oXj7WLHn0/1YvORDJ7/Ob7W7JE0fIRyncxFFUKEouZqiTD9fhpwVhRlVg2yDwLPABnALsBFUZQ7Tcl8/1QU5Zcazld5XzTwGnAhtnO6oijLhBBngE6KomQKIToBCxVF6SeEuNO0/REhRE/gU9SImnHAjErnjQLeAdxQHWZvoUb1bDRtE8C3lVZyqkbwQ79dFwVaVtJww2T/Ce06NK5vFa6ac3W0nLa16dH+7zP5Xwt8OC7y8kLXAI/8en10NApLGm6erCtFZ4oeutapOl31WmTbxoT6VqFW0DlYdyWausLNu2GvRncl+PnVzwpztU1GxrUfTRAcfO0/TwDHjqTWtwq1gqNzjWvBXFOcWDis/pcotSJ/xKVZ5Zv2hki/BnnfrptIGUVRzqDmZLnwe+HfyH4IfFjD9jv/5nyV920AOtdwfGilf+9FXSoLRVEWoTpXUBRlO5WWxAYqnzcW6FODyr0uZYtEIpFIJBKJRCKRSCTXC8YGt8C7dbnecspIJBKJRCKRSCQSiUQikVwTXDeRMjUhhHgfdUnryrytKMqX9aGPRCKRSCQSiUQikUgkkktznWRYuWKua6eMoigP17cOEolEIpFIJBKJRCKRSCQ1cV07ZSQSiUQikUgkEolEIpFcO/zHAmWkU0YikUgkEolEIpFIJBJJw+B6WSH6SpGJfiUSiUQikUgkEolEIpFI6gEZKSORSCQSiUQikUgkEomkQWCsbwXqGOmUuc64XkK9nNyc6luFWiH+YFJ9q3DV+Ad51LcKtcKe+NT6VuGqeUSI+lahVnhvbER9q1ArnMsqrm8VrhrtdfJM7U3Krm8Vrpot3yytbxVqBduITvWtQq3g63vt90NycvT1rUKtkJuZV98qXDVZqdd+HQVgNFwfn8ptIgLrWwWJxALplJFIJBKJRCKRSCQSiUTSILheAg2uFJlTRiKRSCQSiUQikUgkEomkHpCRMhKJRCKRSCQSiUQikUgaBP+tOBnplJFIJBKJRCKRSCQSiUTSQJDTlyQSiUQikUgkEolEIpFIJFZHRspIJBKJRCKRSCQSiUQiaRBcH+t8XTnSKWMFhBChwJ+Kolxy3VeTTA9FUb6vK73+jpcntCW6jR/6MgNPfr2P+PPVlx+MbOzOm3d0QGerZcOhNGYuPmix//4BTZk5LpKIp5eTU1SGm6Mtr0/sQIi3E6UVBp76JoajyQW1qveLY1rTr5UvJeUGnv7hAIcS86vJRAS7suDWduhstWw6nM5LSxMAcHO05b072hPk6UhSdjEPfxVDvr4CgK5NPJk5pjU2Wg05hWXc8v5OAO7qG8bN3RqhKHA0JZ+pPxykrKJ2q43ZN7djQKQ/+jIDTyzaS9y53GoybRu789bkTuhstayPS2XGTwfM++7q34TJ/ZtgMCqsi0thzq/x2GgFr0/sSGSIOzYawc9/nePdVUdrVe8L9GjiydShzdBoBL/FpPDl9rMW+221gtmjW9Mq0IW84nKm/XKIlLwSAJr5OjF9ZEuc7LUYFfjfp3spMxgZ3MaXu3uHohWw5XgW76w7aRXdK9OzmRfPDm+BViP4dV8Sn285U82OeeMiaB3oSm5xOU//dJDk3BLzfn83Hcse684HG06xyHQPZo9pTZ8WPmQXlTHm3b+sbkNV2vg7c2v7ADQCtp7KYeWRTIv9g5p70TvcA6MCBaUVfLk7iezicgDGtvWjbaALAH8eSmfP+ervWkNg5vTn2LJ5E56eXiz5/c/6VueK2LdrO5++swCj0cigEaMZ/7+7LPanp6Xw1tyZFBUWYDQYmXT/o3Tq3ruetL3I3l3b+fjt+RiNRoaMHMOEGvR+45UZFJr0nvzAY3Tu3puYPX+x6KN3KK8ox9bGlrseepKojl3qyQo4HrubFV+9h2I00iF6OH1G3Waxf/+mVaz+7mNcPb0B6DpkNB2jR5CbkcoPr89EURQMhgq6DRlD50E31ocJNfL6kzcwpEcLikvKuW/2z8QeS64m8/ubk/H3csVGq2H7gdM8sfB3jMa6Dxmfc0s7BkQGoC+r4PEvL93uvT25Mzo7LevjUpj+o9rufXxfV5r4q3WTm4MtefpyBr68jvahHiy4oyMAAlj4RwIr91e/B9amW5gHTw5sikYjWHYghW92nrfYH9XIjScHNKGJrzMzfk9g49HMS5zJ+vRq7s0LN7REIwS/7Enk082nLfbbagWvTYikTZAbucVlTPnhAEk5JfRo6sVTQ5tha6OhvMLI/JXH2HVSXQJ6RDt/7u8fjqJAen4pU386SK6pXbEmc2/vwMC2AejLDDz62S4Ons2pJtMuxIN37+mKzk7LuoMpPP9dDABtGrmzcFInnOxtOJ9VxP0f/UVhSQU2WsFbk7vQNsQDG63gp+1neHv5YavbcoFXJ3ZkUFQQ+tIKHvrkLw6eqW7T9PHtuKVXGG5OdjS6Z7F5+0PDWjKxX1MMBiOZBaU8+slOzmcV1ZnuF3htUmcGRwVSXGbgoQ93cOBM9aXCZ0yI4pY+4bg72RE0+Ufz9rsGNuOeQS0wGBWKSip4/LOdHE2q++XSOzV246HeoWiEYGVCOj/FWNYrkYEuPNgrlHBvR15ZfZytpnehXZArD/YKMcs18nDgldXH2XG6ejlK/nvI6Uv1Ryhw2+WE6oLoNn6E+TrR68W1TPt+P/NujapRbt6t7Xjmu/30enEtYb5O9G/jZ94X6OFAn9a+JGYVm7c9OrQFhxLzGPTKBh5ftI+Xx7etVb37tfIh1MeJ/nM38dziOOaMq9kHNmdcJM8tjqP/3E2E+jjRt6UPAA8OaML241lEz93E9uNZPDigKQAuOhtmj4vg3s/2MuS1LTz8ldpI+7nZc2fvUG58YxtD529BqxHc0D6wVm2KjvAn3M+ZHtNXM/WbGF69vX2Ncq/e3p6nv46hx/TVhPs5Ex2hlkWPFj4MiQpkwMvr6DdrLR+uOQ7ADR2DsbPVEP3SOoa8soGJfcII9nKsVd0BNAKeHd6CR747wNj3dzE0wpdwb8vrjG4fSEFJBaPe3cl3O8/z+MAmAGiFYM5NbXhl+VHGfbibe7+KocJoxM3BhicGNeWBr/cz7sPdeDvZ0SXMo9Z1r2rH9Bta8uDX+7nxnR0Mj/Qn3MfJQuamjkHk6ysY/uZ2vtlxlilDmlnsf2ZYc7Yez7LY9tv+ZB4wPU91jRBwe8dA3tpyhhmrTtAlxI0AV3sLmXO5JcxZe5JZq0+w73w+49v5AxAZ4EyIhwMvrT7BK2tPMrilNzqbhtl8jBp9Ex9+/Fl9q3HFGAwGPnrzVWYteI/3v/6VLetXce6MpdNx8def0av/IN7+/EemzprHh2/OqydtL2IwGPjgjXm8vPB9PvpmCZvXreLcaUu9f/zqU3r3H8x7X/zEs7Ne5f035gLg5ubBi6+9zYdf/cKUF2bz+pwX6sMEAIxGA39+8TYTn32VR17/krjtG0hPPFNNLqJ7Px567VMeeu1TOkaPAMDZw4t7Z7/HQ699yn1zPmDr7z+Qn11/H9SVGdK9BU0aeRMxfiGPvLqEd54ZXaPc/174nq53vE3H29/Ex92ZsdGRdasoMCDCn3BfF7q/sIqnv4nhtds71Cj32v868NQ3++j+wirCfV2IjlDrp/s/2cXAl9cx8OV1LI9JYkVMEgBHkvMZMmc9A19ex61vb2PB/zqg1Yg6swvUtuTpwc14cnEct366h8GtfQmt0vam5Zcwe/lR1iSk1aluVdEImDmqFfd+uY+Rb25jRFQATXwt271xnYPJ11cwZOFWvtp2lqeGNgcgp7iMB7/az41v7eDZn+OZP0F9jrQawfM3tOSOT/Yw6u0dHE0t4H89GlvdloFtAwj3c6bLtOVMWbSHBXd0qlFuwaROPLloD12mLSfcz5kBkQEAvDW5M7N/PkCfGatYvi+RR4a3AmBU58bY22roM2MVA2atYVL/pjTydqrx3LXNoHaBNPF3peNTy3ji8128fmfNjuxVMYkMeHFVte0Hz+QQPWMlvZ5fwbLd55h1a839S2syKCqQJv4utH/ydx7/dCdv3N21RrmVMYlET19ZbfvP28/QY9qf9H5uOW//eYi5EztaW+VqaAQ82jeM5/84wj3fH6B/cy8aezhYyKQXlLFg/Uk2HLNsDw4k5fPAT3E88FMcU39LoKTCyL4aBsElKoqiWOWvodKgetVCiN+EEPuEEIeEEPeZtg0VQsQIIQ4IIdabtjkLIb4UQsQJIQ4KIcaatt9q2hYvhHit0nkLhRALTOddJ4ToIoTYJIQ4JYS40SRzp+n6a4UQZ4QQjwghpggh9gshdgohPE1yUabfB4UQS4UQHqbtHU06HgAernTtUCHEVpMNMUKIHqZdrwK9hRCxQognhRBak457TOe+33R8gBBii0kuXghR68OjQ9oF8Itp5CbmdA5ujrb4VvlY83W1x0VnS4zJm/vLzvMMbRdg3j9rXCSvLIlHqZQru7m/C9uPZgBwMq2QYC9HvF0sz3s1DIrwY8ketfMVezYXVwdbfKro7eNqj7POhtizuQAs2ZPE4Eg/8/G/7kkE4Nc9iebtozoGsfpgqjnqIauwzHw+rUags9Wa/5+eX0JtMjQqgJ//UqMqYk5n4+pgi6+bzkLG102Hi4MtMadVz/vPf51laJTqHJrUN5z3Vh01R+9kFZQCasXmaGdj1rvMYKRQX/sjVRFBrpzPLiYpt4QKo8LqQ+n0MznBLtCvhTd/HEgBYF1CBl3CVQdL9yaeHE8r5FhaIQB5+gqMCgR5OHAuq5gc08jartM5DGhlec7aJjLYjXNZxSTm6KkwKKyMSyW6yjWjW/nwu2nUdc2hdLqGe1rsS8rRczK90OKYfWdyybPCfb8SwjwdSC8oJbOoHINRYfe5PKKCXCxkjqYXUWZQ3+GTWcV4OKrBlIGuOo5lFGFUoMygkJhbSkSAc53bcCV07NQZVze3+lbjijl+OJ6AoEb4BwZja2tLnwFD2LVtUxUpQXGROqJZXFiIp5d1n/8r4djheAKDGhFQSe+/qugthKC4WNW7qKgQL29V7ybNW+Ll7QtASFgTSktLKS8roz5IPHEET/8gPP0CsbGxJbJHNEf27riiY21sbLGxtQPAUF7WoDp7I/u05vuVqgN496HzuDk74O/lUk2uoFhtI2y0GmxttdSHCUOiAlm809TuncrG1bHmds9ZZ0PMKbXdW7zzYrtXmRs6BbN0t9qf0ZcZMJiifnS2mnpZyaN1gCuJOXqS89Q2cW1COn2aeVnIpOSVciKjqF7ufWXaNjK1e9l6yg0KKw6kMKC1r4XMgNa+/GZyeq2OT6N7U9WWw8kFpJv6G8fTCrG31WKrFQhAIHC00wLgbG9Den6p1W0Z1j6IxdvPALDvZBZujrb4VXmm/Ex9qX0n1cGTxdvPMLxDEABN/F3YYeq/bjqUxg0dgwFTX8r+Yl+qvMJAQR216cM7BvPjtlMA7D2ZhZuTHX7uumpye09mkZZbvW+67XAa+jIDAHtOZBLkWfsDc5djRMdG/LDVZMOJTLVc3B2qye09kUlarr7a9sr32tHepl7emRZ+ziTnlZCaX0qFUWHT8Sx6hFsOFKYVlHI6q/hv9evd1Is9Z3MpreVo++sJRbHOX0OlQTllgLsURekIdAIeE0L4AZ8CYxVFaQeMN8nNAPIURYlUFKUtsEEIEQi8BkQDUUBnIcRok7wTsEFRlDZAATAHGASMAV6udP0I4CagM/AKUKwoSnvgL+AOk8zXwDTTdeOAF03bvwQeNelZmXRgkKIoHYCbgXdM258FtiqKEqUoypvA3SabOpuuf68QIgw1mma1oihRQDsg9kpv5pXi7+5Acs7Fyi8lR49/lUrS392BlEoVZEruRZnBbQNIydWTkGQ5nSEhKY/hpk5TVIgHwZ6OBHhUr3z/LX5uuio6leBfpdH1d9OZp8YApObpzQ2zt4s9GabOQUZ+qdlhFObjhJujLT883I1lU3pxUye1kU7LK+XTTafYPjOaXS8NoKCkgq21HGZcU1kEVGl0A9x1lyyvcD9nujb1Yvlz/VnydB/ahagNxZ8xSRSXVXBgwQj2vjqMj9Yct0r4sK+LPWmVOlxp+aX4uFR38KXmqTIGRaGwxIC7gy2NvRxQFHj/9nZ8f19nJplG085n6wn1diTATYdWCPq38MbPtXpHpFbtqKTjBTuqOyp1pJqeLYNRobC0AndHWxzstNzVO5QPNp6yqo7/FA8HW3IqdWhyiivwcLC9pHzvcA/iUlSn0vncEiICnLHTCpzttLT0dcLT8dLHSq6crMx0vH0vRh16+fiRlZFhIXPb5PvZtGYFd44dwqxnHuX+J6bVtZrVyMpIx9vX3/zb28ePrMx0C5nbJz/AhjXLmXjTYF6c+ggPPPFstfNs37SOps1bYWtnZ3Wda6IgOxM3r4sfnq6e3uRnZ1STS9i9lfefuYcf35hFXiU78zLTef+Ze3j94VvodeMt5ilO9U2gjyuJabnm30kZeQT6uNYou+zNuzi3YgaFxaUs2RhXRxpeJMDDgeTsi1G2artn2VcIcHcgpWrbWKU/0a2ZN5n5JZyu5AxvH+bJ5pcGsfHFwTzzbYzZSVNX+LjYmZ0VAOkF1dvEhoKfa9X+Ukm1ttbX1Z6U3IvtXkGJ2u5VZkiEHwlJ+ZQbFCqMCi/9lsCyJ3qy5fl+NPFz4hfTYJg1CfBwIKnSM5Vcw/NS9bmrLHMkKZ9hJgfNqM6NzA6MZXvPU1xawaG3RhH7xo28v/IouUV141AO8HAkqVI0enJ2MQEe/86xMrFvE9YeqPupfAGejiRVmjKVnF1MoOc/+y64Z1BzYt8azcu3deCZr/bUtoqXxdvJjoyCi2WeWViGt9M/b7/6NfNi4/GGEVkpaRg0NKfMY6ZIk51AI+A+YIuiKKcBFEW5MPFwIPD+hYMURclBdWRsUhQlQ1GUCuA7oI9JpAy4EMsXB2xWFKXc9O/QStffqChKgaIoGUAe8EelY0KFEG6Au6Iom03bvwL6CCHcTdu3mLZ/U+mctsCnQog44Geg9SVsHwzcIYSIBXYBXkAzYA8wWQgxC4hUFKV2k7JcJTpbLY8Obc7CP6rPqX1v9TFcHWxZ83x/7uofTvz5vHqZq36lXPCe2mgEEcFu3PXpHiZ9vItHBjcjzMcJVwcbBkX40Wf2Rrq9uB5HOy2jOwbVr9JVsNEI3J3sGDFvIy//Escn96uhoe1DPTEaFaKeWU6X51dx/6BmNK6jkNsrRasRtG/sxgtLErjri31Et/ShS5gHBSUVzF1+lNfGteGLyR1IzivB2IBd3Q9Hh/PNjnPmEalrkW4hboR4OrDalHMmIa2QuORCnh0Qzn3dG3Eyq5gG/Cpfd2xZv4oBw25g0a+rmTX/Xd6YMx2jseGPrm1at4pBw27kmyVreGnBeyycban32dMn+OKjt3l06vR61PLytOjYnSnvfs/D8z+jSduOLPnwVfM+N29fHp7/GY+/9Q2xW1ZTmFs9P0JD58YnvyDshlewt7WhX8cm9a3Ov2ZMl0bmKJkL7D+dTd8X1zL0lfU8Nqwl9g102uX1QlNfJ54a1pwXlx4C1D7JLd0aMeadHfSZu4ljKYXc1z+8nrW8PI99sYu7opuxftZgnHU2lBnUeqtDmBcGo0LEk7/T8ek/eGhoC0J8GlZf6nJM6BlKVLgX7y5PqG9V/hWfrT1G1BO/8eL3MUwdU/fTLWsDT0dbwrwc2XtOTl36OxQr/TVUGkyiXyFEP1RnS3dFUYqFEJtQo0Ja1sLpy5WLccVGoBRAURSjEKLyPagcU2ms9NvIv79XTwJpqFEuGuBS810EaqTN6mo7hOgDjAAWCSHeUBTl6yr770N1YOHW5wGcWg++rFKT+oZxe89QQJ36E1hpBCHAw4HUKmGDqbmWI1cB7qpMqI8Tjb2dWDs92rx99fP9GfHaJjLyS5nyzcX8GTvnDOZs5tUlFZvYM4RbujcC4OC5PJNOOaZrX4xcMOudV0JApegZfzcH0kwymQWl+Liq0TI+rvZkFarFnZJXQk5xGfoyA/oyA7tPZtMq0AVw4XyWnmzTqMjqg6l0CPXgt31JV2XTnf3Cub13GAAHzuRUK4uUKmGoKbkllyyvlBw9K0xTamLP5GBUFLyc7RjTpREbD6VRYVDIKihlz8ks2oW4c+4qy6Mq6QWl+FWKKPFztSejwDJUOT2/FH83e9ILStEKgbNOS66+nPT8UmLO5pJriubYdiKLlgEu7D6dw5ZjWWw5poYY39Qh0OqjnRd0rGxH1ZDr9Hw1MistvxStRuBsb0NucTmRwW4MauPHlCHNcNGp4bWlFUZ+2HW+6mXqlBx9uUVkjIejjUXkzAVa+TkxorUP8zecpqLSfV5+OIPlh9UIgnu7BZNWYP0Q9P8CXt6+ZKZfzCWRlZGGl4/l9KQ1y3/jpQXqOETLiHaUlZWRn5eLu4cn9YWXjy+Z6anm35kZaeYpSRdYs3wpsxd+AECriHaUl5Wa9c5MT2P281N46oXZBAQ1qlPdK+Pi6U1e1sXIl/zsTFw9Le+/o8vF6XAdo4ez5rtPqp3H1dMb30ZhnD0SR5tufa2n8N9w/9huTL5RzTOx73AiwX7ugDotKMjHjeSMSyfnLi2r4I+tCdzQpzUb9pywuq6T+zXh9j5quxd7OptAT0dArePVds+yD5KSaxnpEOBhGTmj1QiGdwhi8Jz1NV7veGoBRaUVtAxy40ANCV+tRUZBGb6VImN8Xaq3iQ2FtPyq/SUdaVWmaKfnlxLgfrHdc9HZmKNu/VzteW9ie6YtjuN8tlo2LU3J4S/8XhmXyr19w6yi/10DmjKxr+pUjD2dbTE9J7DK8wJqfynwEjInUgoYv3ATAE38XBjUTo36Hts9hPVxqVQYFDILStl1PJOoUE/OZlgnYe49A5tzR3/VpphT2QRVykcU6OlISk7xpQ6tkb5t/JlyYwQjX1lb64tUXIp7BjVnUrSac2//qSyCvJwAtS8R6OlIcnb1aUpXwq9/nblkThprkllUho/LxcgYb2c7Mv9htFTfpl5sP5Vd55F7koZNQxoycANyTA6ZlkA3QIcaiRIGcCGvC7AWy7wtHsBuoK8QwlsIoQVuBTZTiyiKkgfkVMrrMhE16iYXyBVC9DJtv72KXSmKohhN8lrT9gKg8gTv1cCDQghbk03NhRBOQogQIE1RlE+Bz4BqGfAURflEUZROiqJ0uhKHDMBXm08zeO5GBs/dyOoDyYzrpnaKO4R5kG/6QK5Men4pBSXldDAlWB3XrRGrD6RwJDmfds+soNv0NXSbvoaUXD1D5m4kI78UVwdbbLVqUr3beoay63gWhSUVV6Tfpfhm+1lGLNzGiIXbWBOfxk2d1UiVqBB3CvQV5ulIF8jIL6WwpIKoEHcAbuocxNp49QNoXXwaYzur84THdg42b18bl0anME/TnGENUSHunEgrJDmnhPah7uhs1demR3PvajlD/g2LNp1i0Oz1DJq9npWxyYzvrmZm7xDmSYG+nPQqjqb0vBIK9OV0CFNfh/HdQ1gVq+ZoWRWbTM8W6gdFuK8ztloNWYVlJGUXm7c72GnpGObJidTaD7o6lFRAYy9HAt112GgEQ9r4sqnKFK/NxzK5wZSPaGBrH/aY8hTtOJlNUz9ndDYatELQMcSdU6aOjocpPNpFZ8OEzkEsjbFu2G18Uj6NvRwJ8tBhoxUMi/Rn4xHLKQ0bj2QwypToeXAbX3aZch1M+mwvQ17fxpDXt/HtX+f4dPPpenfIAJzJ1uPnYo+3ky1ajaBLYzcOJFk+A43cdUzsFMS7W89RUHox0kcIcDLlBAh2syfYXceh1Kt/9iXQrGUbkhPPkZqcRHl5OVvWr6ZLz34WMj5+/hyI2Q3A+TOnKC8rxc3dusmuL0fzGvTu1svSGeHjF0Dsvl0AnDtzirKyMtzcPSgsyOfFZx5l8gOP06Zt3SebrExQk5ZkpyaRk55CRUU5cTs20LJjdwuZgpyLCbuP7N2BT5A6tTIvK4PyMrXN0RcWcO5IPN6B9edg+vjXnXSb9A7dJr3DH1sOcdswtbvQpU0j8otKSM2yfN+dHOzMeWa0Wg3DerTg6NnqU7eswZebTpqT866KTWZCN1O7F37pdq+wpIIOptxdE7qFsDr2YjvQp5UvJ1IKLD68G3s7mhP7Bns60tTfpc5Xmzmckk8jTwcC3NQ2cVBrX7aeyLr8gfVAXGI+IV6OBHk4YKsVDG8XwIYEyymJGxLSGW2a1jMkwo+dplVlXHQ2fDy5I6+vOsZ+Uw4/gPS8Upr4OuPhpLbhPZp6mdv12uaL9SfoP3M1/WeuZkVMIhNMA48dm3iRry83D8hdIM3Ul+rYRM2LM6FnKCv3q4NsF6azCwFTbmzNoo2qozIxq4jerVTns6Odlk5NvDieYr2VCD9bd4w+L6ykzwsrWbHvPLf0UqOMOjXxIr+4rMbcMZciMsSDN+/qwm1vbCazDvL6XOCztcfo/dxyej+3nD/3nufW3iYbmnqTX1xeY+6YSxHuf/GzaUj7YE5ZoR97OY6mFRLkpsPfxR4bjaBfMy/++oerJ/Vv7sXGYw2zHmhIGBXFKn8NlQYTKYM6vegBIcRh4CjqFKYM1AiQJUIIDab8LKg5Yd4XQsQDBuAlRVGWCCGeBTaiRp0sVxTldyvoOQn4SAjhCJwCJpu2Twa+EEIowJpK8h8Avwoh7jDZeKE1OggYTNO1FgFvo06lihFCCJPto4F+wFQhRDlQyMXcNrXG+vg0oiP82f7yIPRlBqZ8fTG6Zc3z/Rk8dyMAz/9wgDcndURnq2HjoTQ2HPr7lQKa+bvw1qSOKCgcTS7g6W9rd9WZjQnp9G/lw6YX+qEvM/DMjxeX6F7+dC9GLNwGwIxf401LYmvYfDiDTabR/g/Xn+S9SR2Y0LURSTl6HjGtinMyvZAtRzJYObU3RgV+2nmOY6YP0JUHUvjzqd5UGBUSkvL4Yce5WrVpfVwqAyL8+euVIery5Iv2mvetnTGAQbPVUcDnvt/PW3d2QmenZUN8Ghvi1RHrH7af4c1Jndj44kDKDUYe/1I9/stNJ3nrzk5smjUIAfy44yyHk2q/I2FQFF5bcYwP/heFRgh+j03mVEYRD/YLIyG5gM3HMvktJoU5Y1rz+6PdyNdX8Owv8QAUlFTw7V/n+PbeTijAtuNZbDOtXvTM0OY091cTy36y+TTn/uXIyhXbYVSY++dRPp6krtaxdF8yJ9OLeHhAEw4l5bPpSAZL9iUzb1wEK57sSZ6+nKk/XT4Xw/wJkXQO88Dd0ZZ1U3vzwYaTLNlXN/O6jQp8H5PME33VZRy3n8ohOb+UURG+nMnWcyC5gPHt/NHZaHigh/phmV1cznvbzqEVgmnR6uimvsLIZzsTG+z0pWlPT2Hvnt3k5uYwKLoPDz78KDeNHX/5A+sJrY0NDzwxjReffgij0cjA4aMICWvCt59/QLMWrenaqx93PzyF9+bP5vfF3yKE4PHnXkZtJupX7weffJbpTz2I0Whk8IhRhIQ15ZvPPqBZy9Z069WPex+ewtvzX+a3xd+pHzfPv4QQgj+W/ERy0jl+WPQxPyz6GIA5b3xUL5E/Wq2WEZMf5eu50zAaDXToPwzfRmGsX/wlQeHNadmpJztXLeHIvh1oNFocnF0Z86Ca0ycj6Syrv/3IfK6eIyfg17hhTM1YteMoQ3q05NDPUykuLef+OT+b9+386jG6TXoHJ50dv8y/Azs7GzRCsCXmFJ8u3VXnuq6LS2VApD87XxmKvszAE5XavXUzBzLw5XUAPPvdft6e3AmdrZYN8amsj78YqTW6SyOW7rF0fndp6s2jw1pQblAwGhWe/W4/2YV1k//jAgYFFq45wds3R6IRgj8PpnI6s5h7e4dyJKWArSeyaOXvwms3tcFFZ0Ovpl7c2yuU2z7fe/mT17auRoXZyw7z+V0d0WgEv+5N4kR6EY8Oakp8Yh4bD2fwy94k5k+IZPXTvcnTlzPlB3VZ8tt7NKaxlwMPDWjCQwPUyI67P99HekEp768/wbf3d6HCoJCcq+e5n+OtbsvaAykMbBvInvkj0ZdW8NjnF5/rjS8Pof9MNSj9ma/3mpbEtmH9wWTWHVQHuG7qFsLdphU5/9yXyPdb1aXBv1h/gnfu6cK2V4YhgB+2nSYhsW6moKyJTWZQuyBiXr8RfZmBhz/5y7xvyyvD6POCulrRS7e0Z2yPUBztbIh/ZwzfbDrBa0viePnW9jjpbFj0mDp+nJhVzG1v1OrY9eVt2J/E4KggYt8aTXFpBQ9/fDGp+tZ5I+j93HIAXr6tA+NMNiS8dxNfbzzBq78e5L7BLegXGUB5hZHcojIe+HB7neoPal/qvS1nmDdKXTp+dUI6Z7P1TOoSzLH0Iv46k0NzXydmDW+Os70N3cLcuaNLMPf+oH6j+LnY4+Nsz0Er9MGvNxpoN9NqiIa0WoDk6gl6cOl1UaB2uvpJ+ljblBTV7upM9YF/UP2OyNcW5eXXbo6XC3SLDLi80DXAe2NrXr7+WuNc1j8LHW+IaOvZuVNb7E269nK5VOXOh96tbxVqBbeImpcfvtYIbVL/q5xdLbn/IJKiIZOZWndTzqyFoeLa74MAGA0NP5/ZldClZ7P6VuGqWftIt+ujAb8EX+09b5Vv2kmdGjXI+9aQImUkEolEIpFIJBKJRCKR/If5rwWONKScMhKJRCKRSCQSiUQikUgk/xlkpIxEIpFIJBKJRCKRSCSSBsH1MVHuypGRMhKJRCKRSCQSiUQikUgk9YCMlJFIJBKJRCKRSCQSiUTSIPiPpZSRThmJRCKRSCQSiUQikUgkDQPjf8wrI6cvSSQSiUQikUgkEolEIpHUAzJSRiKRSCQSiUQikUgkEkmD4D8WKCOdMtcbdjq7+lahVrC3vz4ezYrya9+OgoLS+lahVhBC1LcKV01hSXl9q1ArnMsqrm8VaoXGXo71rcJVczq9qL5VqBXc7a+Dts/dr741qBVs7W3rW4VaobTUUN8qXDVFBSX1rUKtYDRc++uwODg51LcKtUJZaVl9q1ArxO47W98q1ALd6lsBSS1y7X8xSiQSiUQikUgkEolEIrku+K/llJFOGYlEIpFIJBKJRCKRSCQNAuN/yycjE/1KJBKJRCKRSCQSiUQikdQHMlJGIpFIJBKJRCKRSCQSSYPgPzZ7SUbKSCQSiUQikUgkEolEIpHUBzJSRiKRSCQSiUQikUgkEkmDwMh/K1RGOmX+o7w4pjX9WvlSUm7g6R8OcCgxv5pMRLArC25th85Wy6bD6by0NAEAN0db3rujPUGejiRlF/PwVzHk6ytwdbBh/i3tCPF2pLTcyDM/HuBYaqH5fBoBy6b0IjWvhHs+21ur9vRq7s0LN7REIwS/7Enk082nLfbbagWvTYikTZAbucVlTPnhAEk5JfRo6sVTQ5tha6OhvMLI/JXH2HUyG4Bhbf15oH84Go1g0+EMXl91rFZ1vhQvjYskuo0v+jIDU77ZT3xiXjWZyEZuvDGxAzpbDRsOpfPiL3EAPD2iJYPb+mNUIKuglCnf7ict7+KSmO0au/PbU715+Mu9rIhNqTWd+7TwZvqoVmg1gsW7Evl44ymL/XZaDQtubUtEsCs5xeU8/k0sSTl6AB6IDmd8l2AMRoXZvx1m67FMAFx0NsybEEkzf2cUBZ5bHMf+s7k8MaQZA9v4YlQgu7CMZ346SHq+dZft7t3Cm+k3tkKrgcW7E/lko+XzZacVzL9FtS+3uJzHvz1AUo4ed0db3p0YRWQjN5bsTeLl3w5bVc/L0S7QhUldgtEIwYbjWSyLT7PYP7y1D9HNvDAYoaC0go+2nyWzSF2G+/uJUZzLVcsss6ichRtOVTt/XbNv13Y+fWcBRqORQSNGM/5/d1nsT09L4a25MykqLMBoMDLp/kfp1L13PWl75cyc/hxbNm/C09OLJb//Wd/qXJKY3dv57L2FGA0GBo0Yw9jbJlvs//z9hcTtV+v6stIScnOy+f7PLcTt38Pn779ulks6d4anZs6jW6/+dar/BRJidrLk87cxGo10HziSQWMnVpOJ2b6elT9+iRAQFNqUSVNmAfDBy1M4ezSB8FZtuX/6/LpVvAZef6AfQzqHUVxazn2vryH2ZLrFfgd7G757fgThAe4YjAordp1ixpfbAHhsTAfuHBpBhcFIZp6eB95cw7n0gjrR2xrt3uBIf54e2RKjAgajwqxf4thzKttqNvRo4snUoc3QaAS/xaTw5XbLJXdttYLZo1vTKtCFvOJypv1yiBRT+9zM14npI1viZK/FqMD/Pt1LmcHI0Ag/7uoVggJkFJQyfUkCufpyq9kA8NL4KmVx/m/Kws5UFj+bymJklbL4Ri0LF50Nb9/ZkSAPB7RawSfrTrJ45zmr2lGZeRM7MqhdIPrSCh7+ZCcHz+ZUk3lhXFtu6RWGm5Mdje/92bz9oaEtmdivifpeFJTy6Kc7ScwqrjPdZ9/clugIf/RlBp5ctI+487nVZCIbu/PWnR3R2WrZEJ/KjJ8OAvDUyFbc1iuU7EK1jzTvt0NsiE/DViuY/78OtA1xx2hUmLn4IH+Z+l7WYO7tHRjYNgB9mYFHP9tV4/1vF+LBu/d0RWenZd3BFJ7/LgaANo3cWTipE072NpzPKuL+j/6isKSCcd1DeHhYS/PxbYLdiZ61mvhzudaz47b2DGwbQHGZgcc+312jHW1DPHj3ni442Jrs+H6/2Y4Fd3TESWfD+cwiHvh4J4UlFXg42fHFwz1oH+bJj9vP8Oy3MVbT/1pDTl+6zhFCbBJCdKqv4xsC/Vr5EOrjRP+5m3hucRxzxkXUKDdnXCTPLY6j/9xNhPo40belDwAPDmjC9uNZRM/dxPbjWTw4oCkADw9sSkJyPsMWbGXK97HMHNPG4nyT+4RxIq2w2nWuFo2AmaNace+X+xj55jZGRAXQxNfJQmZc52Dy9RUMWbiVr7ad5amhzQHIKS7jwa/2c+NbO3j253jmT4gEwN3RlqnDm3PnZ3u44c3t+LjY0a2JZ63rXpX+rX0J83Gi90vrmfbDAebe0q5Gubk3t+OZ72Pp/dJ6wnyc6NfaF4CP1p9g8LxNDH11E+vi03h8WHPzMRoBz41qzZYjGbWqs0bArDFtuPuzvQxdsJWR7QNo6udsITO+azB5+nIGvLqFL7ec4ZkRLQBo6ufMiKgAhi3Yxl2f7eWlm9qgEeoxM0a3YsuRDIbM38oNb2wzPzufbTrNyDe2c+Ob29lwOJ1HBjWtVXtqtq8193y+l2ELtzEyKoCmVZ+vLsHk68sZ+NpWvtxyhqnD1fteWm7krdXHee3Po1bV8UoQAu7q1ohX153kqd8P0zPMgyA3nYXMmWw9z/95lGl/HGHX2Vxu7xhk3ldmMPLsH0d59o+jDcIhYzAY+OjNV5m14D3e//pXtqxfxbkzJy1kFn/9Gb36D+Ltz39k6qx5fPjmvHrS9p8xavRNfPjxZ/Wtxt9iMBj4+O3XmPnqu7y76Fe2rl/F+TOWz8XdDz/NW5/9yFuf/cjwMbfQvXc0AJHtO5u3z37jY+x1Otp36lYfZmA0GPj5kzd4YMZCnn/nW/ZtW0fKeUuna3ryedb++i1PzvuA59/5lpvuety8b8Do2/jfE9PrWu0aGdI5lCaB7kTc/SWPvLOOdx6JrlHurV/3EXXfV3R75Fu6tw5kcKdQAGJPptPzse/p8tC3LN12nFfuqhsHprXavW1HM8zbn/p2P/Nvi7KaDRoBzw5vwSPfHWDs+7sYGuFLuLejhczo9oEUlFQw6t2dfLfzPI8PbAKAVgjm3NSGV5YfZdyHu7n3qxgqjEa0QjB1aDPu+2o/N3+0m+NphdzcJdhqNgD0b2Mqi1nrmfb935TFLaaymFWlLNadYPDcTQydZ1kWk/qGcTylgCHzNjHhre3MuKkNtlphVVsuMLBdIE38XOj09B88+cVuXp/cuUa51fuTGPji6mrbD57NJnrmKnq/sJJle87x0i3tra2ymegIP8J8nek5Yw3PfBvDvNujapR79bYopn4TQ88ZawjzdaZ/Gz/zvk/Xn2DQnA0MmrOBDaaBmNt7hwEw4OX13PL2dl4cF4mwUnEMbBtAuJ8zXaYtZ8qiPSy4o+ZPqAWTOvHkoj10mbaccD9nBkQGAPDW5M7M/vkAfWasYvm+RB4Z3gqAX/46S/+Zq+k/czUPfbKTs5lFVnXIqHa40OXZFTy1aC/zJ3as2Y47OjLly710eXYF4X4uDIj0B+DNyZ2Z88tB+s5YzYqYJB4xOZRKyw28ujSeF386YDXdJdcG/zmnjLUQQmjrW4crZVCEH0v2JAEQezYXVwdbfFztLWR8XO1x1tkQezYXgCV7khgc6Wc+/tc9iQD8uifRvL2pvwt/HVc97afSiwj2dMDb2Q4Afzcd/Vv78tPO87VuT9tGbpzLKiYxW0+5QWHFgRQGmDoIFxjQ2pffYlSbV8en0b2pFwCHkwtIL1BHEI6nFWJvq8VWKwj2dOBsZjE5piiBHSeyGBzhh7UZ3DaAX3er92j/mRxcHWzxrVI2vqay2X9G9dD/uvs8Q9qqjVdhSYVZztFeS+XIv8l9w1l5IIWsgtqNKmnX2J2zWUWcN93/5bEpDGxjef8HtvFl6V71/q86mEr3Zl7m7ctjUygzGEnM1nM2q4h2jd1x1tnQOdyTxbvV56zcoFBgsq2wtJKNdlqre9LbNnbnbGZxJftSGdDG8lkY2MaPJfuSVfvi0sz26csN7DuTS2mF0bpKXgFNvR1JzS8lvbAMg1Fhx+kcOjVys5BJSC2kzKDe0OMZRXg62daHqlfE8cPxBAQ1wj8wGFtbW/oMGMKubZuqSAmKi4oAKC4sxNPLp871/Dd07NQZVze3ywvWI8ePxBMQGGy+/72ih7Br+6ZLym/dsIreA4ZW275j8zo6dOmJvc7BitpemrPHD+MTEIy3fxA2trZ06DWQuN3bLGT+WvsHvYfdhKOzKwAu7h7mfS3adkLnYPnxXV+M7NaE79er0Xi7j6Ti5myPv4elA1lfWsGWg6Z6tcJI7Il0grxVJ/qWg4noTfXr7iMpBHm71Ine1mr3issMFtut2VREBLlyPruYpNwSKowKqw+l06+lZX3Tr4U3fxxQI1TXJWTQJVx9jro38eR4WiHHTAMPefoKjIrqSBeAg53avXS2tyGjltvvqgxuG8Cvu/5hWew6z5B2NZSF3cVusaKAs04NzneytyG3uIyKOlrvdniHIH7cpjpa957MwtXRDr8qAxIX9lWOLL7AtsPp6E3P0t4TWQR61t37PqRdIL+YIopiTufg5mCLr6ul7r6uOlwcbIk5rZbHLzvPMTQq8G/P2zzAhW1H1Ci6rIJS8vTltAvx+Ntj/i3D2gexePsZAPadzMLN0bba/fdzU23YdzILgMXbzzC8gzoo1MTfhR1H1cHETYfSuKFjdcfkTV1DWLrrbLXttcnQ9kH8tOMMAPtOXcaOU6odP+04w7AOqr5N/Jwr2ZHKSJMdxWUGdh3PpLTcgMQSo2Kdv4bKdeGUEUKECiEOCyE+FUIcEkKsEUL8XQ9vohAiVggRL4ToYjqHkxDiCyHEbiHEfiHEKNN2ByHEj6bzLwXM5xVCFAohXhdCHAC6CyGmmM4ZL4R4opJcte0mnY8IIRYJIY4JIb4TQgwUQmwXQhyvpFdfk66xJr2uupfk56YjxTQNASAltwT/KhWLv5vOHFYLkJqnN1c+3i72ZJimi2Tkl+LtojbYh5PyGdJW9Qi3a+xGkIcD/u7qMTPHtObVPw5jtMIXtJ9rVV1L8KvWaNmTkqvKGIzqB767o+UH55AIPxKS8ik3KJzLKibMx4kgDx1ajWBgGz8C3Ks34rWNv7uO5JzKZaPH392hioyD2ZaLMhd1e+aGVuyaPZgxnYJZuPyIeoybjqHtAvh6q+UIcG2gPk+V7n9uSY0NVeX7X6ivwMPUoNV0bCNPB7ILy3jt5kiWPdmTueMjzB1TgClDm7F1ej9u7BDI26uP17pNlfF3tbd4X1LzSvBzs+yk+rnZk2qSMRgVNSTVsWE5NDwd7cgqKjP/zi4u+1unS/9mXsQmXZzWaKvV8MqIFswe3ryaM6c+yMpMx9v3onPMy8ePrAzLKLDbJt/PpjUruHPsEGY98yj3PzGtrtW8bsnOzMDb19/828vHl+zM9Bpl01OTSU9JJrJ99RHqbRtX03vAEKvpeTlyszNw977oRHb38iEvy/I5Sk8+T0byed587kFen3YfCTE761rNKyLQy5nEzIvTjZIyCwn0dr6kvJuTPcO7hrMxtvpgyZ2DI1i9t/bbi5qwVrsHMLRtABunR/PVA914+rv9VrPB18WeJZKh/wABAABJREFUtErTaNPyS/Fxqe7MSM1TZQyKQmGJAXcHWxp7OaAo8P7t7fj+vs5M6tEYgAqjwtzlR1n8YBfWTOlJuI8Tv+1PtpoNoPYVknP/RVm4VSmLOYMZ0zmYhX+qZbFo82ma+juzd+4Q1r7Qnxd/jq+zqQkBHupU+wskZxcT8C8dK//r24R1B61bBpXxd9eRnH2xPJJz9fh7VOmve+hIqfT+JOdYvhuT+4WzbsYA3rijA26mfsmhxDwGtwtAqxE08nKkbWN3Aj2s4xgP8HCwvP85egKqXCvAw4HkS8gcScpnmMlBM6pzI4JqKLvRXRuzxMrT4QLcq+voX8UO/yp2pGQXE2B6f44k5zOsvWrHjZ1qtkPy3+a6cMqYaAa8ryhKGyAXGPs3so6KokQBDwFfmLa9AGxQFKUL0B9YIIRwAh4EihVFaQW8CFSOV3MCdimK0g7QA5OBrkA34F4hRHshRMeatpuObwq8DrQ0/d0G9AKeBp43yTwNPGzSt7fpOg2KCw3rR+tP4qqzZfnTvZjUO5RDSfkYjBDd2pfMgjLia8hb01Bo6uvEU8Oa8+LSQwDk6yt46bcE3ri1Hd/d34WkHD2GhuxercT8Pw7TdcYalu5N5M4+aojqi2MjmPt7wjUzP1OrEbQJcuX7v85x45vbKS4zcH//cPP+N1Ydp/ecTSyLSWZiz8b1qOn1Sa9wD8K9HPkj/uJH9iO/HuKF5Ud5d8sZJnUJws/Frh41vDK2rF/FgGE3sOjX1cya/y5vzJmO0Vj/UUv/NbZtXEP3vgPQai0DSrOzMjh76gTtO3evJ82uDKPBQEbKeR6b/S53TpnFjx/Mp7iobnKtWAutRvDVtGF8sGw/Z1Itc4bc0r8lHZr78eav++pJu39OTe0ewKqDKfSfs4F7PtnN0yNa1aOGl0arEbRv7MYLSxK464t9RLf0oUuYBzYawbhOQdz68R4Gv7GdY2mF3NUrtL7VvSzz/zhM1+lrWLonkTv7qmXRt7UPCYn5dHp+NUPnbWL2hEhz5My1wvgeobQP8+Td5fWbG+6f8NXmU3SfvppBc9aTllfCi+PUKfo/bj9LSo6eVc/35+UJbdl7MhtjA+3jPvbFLu6Kbsb6WYNx1tlQZrBswzuEe6IvreBIUvXcRw2Jxz/fzeTopqx7cRDODrbV7JBUx6goVvlrqFxbNeLfc1pRlFjTv/cBoX8j+wOAoihbhBCuQgh3YDBwoxDiaZOMDmgM9AHeMckfFEIcrHQeA/Cr6d+9gKWKohQBCCGWoDpRxCW2LzPpHGfafghYryiKIoSIq6T/duANIcR3wBJFURKrGiOEuA+4D8BrwCO4RFYPEZ/YM4RbujcC4OC5PJPnVg11DHDXkVolZDM1r4SASiMf/m4O5rDOzIJSfFzVaBkfV3uyTAnECksreObHi7dn64z+nM8qZmT7AAZG+NK/dX/sbTQ462x58/Yonvwutpqe/4a0/Kq66kjLt7QnPb+UAHcdafmlaDUCF50NucXq1CQ/V3vem9ieaYvjOF9pRGLj4Qw2HlZHTCeYEtFag0l9wri1RwgAB87mWIxWBLg7mCMwLpCaq7eI2lFlqofcLt2TyNcPduONFUdp29id9yer83g9ne3o38YPg1Fh9cHUq9Y/La/EQh9/d121EOALMql5JWg1AmcHG3KKyy95bKrp78A5tZFddTCV+6PDqcrvMcl8fk8n3l5z4qrtuBSp+aXmkQ4wPV95liHkaXml+Ls7kJqnPl/OOtW+hkR2cRleThcdKZ6OdmQXVdcxIsCFMZH+vLT6uEV4+QV70gvLSEgtJNTTkbSCsmrH1xVe3r5kpl9MVJyVkYaXj+V0gTXLf+OlBe8D0DKiHWVlZeTn5eLuYf38UNc7nt4+ZKZfrD+yMtLx9PatUXbrhtXc//iz1bZv37iWrr36Y2NTf1Fl7p4+5FaK8MnNysCtyjQ3dy8fQpq3Rmtjg5dfIL6BjchITiSkWf1/5N8/sh2Th6p54fYdSyO40pSjIG9nkjNrzuP2/uMDOZmcy3u/WUaP9I9qzLRbujD4mZ8ps2I4fV20e5XZdTKLxt6OeDjZkVNU+/VWekEpfpWm+fi52lebapSeX4q/mz3pBaVohcBZpyVXX056fikxZ3PNCXy3nciiZYCLeapuoikKYu2hdCb3qv1BiEl9wri1Z6WycP8XZVHDtJ+lexL5+qFuvLH8KBO6NeaDNWpU65mMIs5nFdPUz9k8Rb62uXtgM+7op+ab238qyyIqIdDTkZTsf5aot28bP566sQ0j566jzMrTke/sF87tJudb7JkcAj0dwJQuLdDdgdScKv31nBKLyJNAj4vvRmalZ/C7bWf4+mHVAW4wKswyJWcGWPZMX06m117Ox7sGNGViXzVnUuzpbMv77+FgEdkDkJKjt5gWVlnmREoB4xduAqCJnwuD2llOzbqpawhLdlknSuau6KZM7Kv2Pfefzq6mY2oVO1Kr2BHg6WiOtD6RWsCE1zcDEO7nzCDT9EuJ5ALXU6RM5dbPwN87nKp+XSuozpOxiqJEmf4aK4pyOXd4iaIoV9NrqayzsdJvIyb9FUV5FbgHddrUdiFES6qgKMoniqJ0UhSlU00OGYBvtp9lxMJtjFi4jTXxadzUWQ2hiwpxp0BfYZ6OdIGM/FIKSyqICnEH4KbOQaw1JQhbF5/G2M7qXMixnYPN2110NubEbbd0a8Tuk9kUllawYPlRery0gd6zN/Lo1/vZcTyz1hwyAHGJ+YR4ORLk4YCtVjC8XQAbEizD6DckpDPaFP44JMKPnaYVllx0Nnw8uSOv/5+98wyPqugC8Du76b1XSgo9hSRACL0TmlhAVAQFe68oSBEFC4K9d0GwIYqVHgiIdEgIJLTQ03vvu/f7cZckmwTFz2wCOO/z5Ely77l3z9lbzsyZM2fWHSeuQePAxdCBdbA245aotqza2yge1iws23aaUYvUYoTrEzKYEKkGz8L9nCk2NNjqk2W4NuF+6vzfCZFt2ZCgzlP3c6+rHzAy1Ku2OG6/5zbRd/5G+s7fyJq4NOZ8d7BZAjIACecLae9mSxsX9fsfG+ZNTKLx9x+TmMX1PdXvf1SoF7uSc2u3jw3zxkKroY2LNe3dbDl4roCc4irSCyrwN9jTt6NrrS3t6xVPHB7kyams0max42IcOl+In5sNbZwv2OdFTIP7KyYpixt6qA2FUSGetfZdTpzMKcPLwRJ3Owu0GkFff2f2N1jhxM/Fmrv7tGXJ5lMU1asNYGuhxcxQgdneUksnD1tSmugQtSQduwSRlnKOjLRUqqur2Raznsh+g41k3D29OHhgDwDnz5yiuqoSRyfTzJv/r9GxSxDpqefJTFe//+2b1xPZd1AjuZRzpykpLqJzUGijfX9sXsfAJurMtCTtOnYhO/08uZlp1FRXc2D7JkJ69TOSCek9gOTDavCipKiArLTzuHn+dc2GluKj3w4S9dBXRD30Fb/uPMnkYWqgKLKLF0WlVWTkN34/zr+tL442lsz4KNZoe/dAd959ZBgTn/+F7ELTJuW2hN/zc6vbHtzGEUszjUkCMgCJqcW0c7XBx8kKM40gOsiD2GPGq9lsPZ7DNYbaK8O7ubPXUANkx8k8OnjaYWWmQSsEPdo7cSq7lOyiSgLcbWunwkYFOnM6p/lX/Vm27TSjXlaL864/mMGE3v/wWvT++2uRll9Ov85qsNPN3pJATzvOmsCWC3y26QSD5q5l0Ny1/L4/hZv7qxk7PQNdKTIMCF0qIe2deX16JJPf2EaOiVd6BFgae6q2MO+6+HQmRqmBuAh/Z4rKq8lqNOhYQXF5NRH+6vWYGNWO9QfVKVb168+MDvPhWJqasW5trq2dEj6wqwc1eoUT6c2X/fd5THJtEd41B1KY1M8PgB6BrhSVN/7+MwtVG3oEqvX4JvXzY22cWofwQokEIeCJ8d1YuqVuEE4IuDayrcnqyXy+OZkh8zcwZP4G1h5I5aa+BjsC/saOANWOm/r6sa4pO64JYlms8cIEksYoiml+LleupkyZf8JNwBYhRH+gUFGUQiHEeuBhIcTDhmyVcEVR4oBtqNOKNgshgoHGLUuVP4ClQohFqAGe64Gphr+b2n5JCCECDdk0h4QQvVCnOR39m8P+ki1JWQzp6k7snMGUV+mMslt+n9Gfsa+qRQ7n/XDYsCS2hq1Hsok1ZI18EHOSd2+PYFLvtqTml/PQMnX5tg6edrw2uTsKcDyjhJnftkwlcZ1eYeEvR/jsjh5oNIIf9qWSnFXKwyM6cDilkC1Hslm1L5XFk0JYP2MAheXVPPGNqtutfdvRztWaB4YF8sAwNap/52f7ySutYs41XejsrY46vh9zkjMmbEBcYHNiJkODPNk+fzjl1TqeXFE3irlu1mBGLYoFYM7KBF6fEo6VuZYtSZlsMQQJnrm2G4EedugVhZS8cma3wDXQ6RWeX53EF3f3QisE3+9N4URmCY9Gd+Tw+UJikrJYuSeF124JJWbWQArKqnlsRTygFldeczCDdU8NoEav57nVibVFuBb8lMTrk7tjrhWczytnpmGJx6fGdCbAwxa9XiGtoIJ5qw6b3r6fkvj87p5oNYJVe1JIzizh0ZEdOJRSyOakbL7fk8KrN4eyaeYACsqqefyruu99yzODsLPSYq7VMCLIk+mf7CXZxIGkptAr8MXuFGYPD0SjEWw5kUtKQQU3hnlxKreM/eeLuLWHL5ZmGh4b7AfULX3t62jFXX3aohiKT/5yKJPUf9CoNQVaMzPue2wm82c8gF6vZ/iYa2nvH8iKz96nY+du9O4/mDsffIJ3Fy/k55UrEELw6DMLEKZaYqIZmTnjCfbt3UNBQT4jhg7k/gcf5oYJN7a2WkZotWbc/chMnn/6QXR6PcNHj6edfyBff/4BHTp3I7KfGqD5Y/N6BgyNbvS9Z2akkZOdSVD3plewaCm0WjMm3v0E7z//BHq9nqhhY/FuF8DvX39Kuw5dCInsT9fw3hyN38uLD09Bo9Fw7e0PYOug1lV6c/YDZKaeo6qijHl3Xc/kB2fRNbx3q9iybu9ponv5kfj5dMoqarj3jQ21+3a9eytRD32Fr5sds27pzdFzuex851YAPvz1IEvXH+alOwdia2XOV7PHAnA+u5gbn//F5Hqbyu+NDvNmQu+21OgUKqp1PPD5PpPZoFMUXllznPenhKERgp/j0ziVXcr9g/1JSitm6/EcfjqQzgvXd+Pnh6MoKq9hlsF3FVfUsGLnOVbc3RMF2H4il+0n1MD+x1tP8+m0CGr0CukFFcz/OclkNkC9a/HccMqrGlyLZwYz6uVYAOZ8l8DrU+tdi8R618Kz3rUwtLXeWnuc16eGs3H2EISAl35KMlmArCEbD6YxIsyH/a9eQ3mVjoc+qasJtfWF0QyauxaA524OY2IfP2wszDj81nUsjz3JK6sP8fzN4dhamfHFw/0BSMkt5dY3trWI7jGHMxgW4smOF0aqS2Ivq5tSuHHuUEa8sBmAZ76J583be2BloWXL4czaVZbmTggmqK0jigIpuWU8bbierg6WfPNIP/SKQkZBBQ9/vtdkNmw8mM7wUB/2Lh5HeWUNj3y2u3bflgXRDHlWXfHq6S/3GZbENiMmIY1NhkDfDVHtudOwyutv+1P4ul5txL6dPUjNK+NstunbVBsT0hke6s2eV8ZSXlXDI5/tqbPj+ZEMma++b59evp937lSX9t58KL2eHe24Y2hHAH5vYMf+JeOwtzLDwkzD6HBfbnxtK8fTLt+SDy3FZTqjzmQI5XIOGV0iQgg/4DdFUYIN/88A7BRFea4J2VggHhgEmAN3KIqyx1AY+E2gL2oG0WlFUcYZtn8BdAeOAL6oNV72CSFKFEWxq3fuJ4A7DP9+qijKmxfb3oTOSw3/r6q/TwjxDmqNGz2QCExTFOWioXr/x3+/8i8oYGl5dcQLS0tat+PaHFhaXf61Qy6FK6Ez/nf0aIEVwFqCBaM6t7YKzUI71yu/UN/pVggQmoJzBaYPmpua655c0doqNAtugY2nml6JuLm3zOpTpiQn68qufXSB0qIr/z1lZWP6xSJagqrK1pu23JxcDW3C7C9uuvKN+AuWxJ4ySZ/2qcEBl+X3dlX0fBVFOQME1/v/1b+QHXyR7eXAvRfZfvNFjrFr8P/rwOtNyDXa3oTO05rapyjKw01bIpFIJBKJRCKRSCQSydXF1ZA48k+4mmrKSCQSiUQikUgkEolEIpFcMVwVmTJNIYR4D+jXYPNbiqJ80Rr6SCQSiUQikUgkEolEIvlr/ms1Za7aoIyiKA+2tg4SiUQikUgkEolEIpFILp3/WlBGTl+SSCQSiUQikUgkEolEImkFrtpMGYlEIpFIJBKJRCKRSCRXFgr/rVQZmSkjkUgkEolEIpFIJBKJ5D+NEGKUEOKYECJZCDHrL+QmCCEUIUTP5vhcmSkjkUgkEolEIpFIJBKJ5LKgNWrKCCG0wHvACCAF2CuE+EVRlKQGcvbAo8Du5vpsGZS5yqiqqGptFZoFjebqSOLS6/StrcK/xsrq6nhNaDSitVX411hZaFtbhWZBK678awFwOqu0tVX41/h72La2Cs3C7tTc1lbh31Nzdfjvq8HvXS1YWFm0tgrNQnlpeWur8K+prqpubRWahZrqmtZWoVmoTNrV2io0Aze1tgImRWmd2UuRQLKiKKcAhBDfAtcCSQ3kFgKvAE811wdfHT1fiUQikUgkEolEIpFIJJKLIIS4Rwixr97PPfV2+wLn6/2fYthW//gIoK2iKL83p15XxxC4RCKRSCQSiUQikUgkkisevYlSZRRF+Rj4+P85VgihAV4HpjWnTiAzZSQSiUQikUgkEolEIpH8t0kF2tb7v41h2wXsgWAgVghxBogCfmmOYr8yU0YikUgkEolEIpFIJBLJZUFrFPoF9gIdhRD+qMGYm4HJF3YqilIIuF34XwgRC8xQFGXfv/1gmSkjkUgkEolEIpFIJBKJ5D+Loig1wEPAeuAIsFJRlEQhxAIhxHhTfrbMlJFIJBKJRCKRSCQSiURyWdBKqy+hKMoaYE2Dbc9eRHZwc32uDMpIJBKJRCKRSCQSiUQiuSwwVaHfyxUZlDER/3aOWXPOUbsUFkwKZWiQJ+VVOh7/cj+Hzxc2kglp58Qbt0VgZa5lc2Imz65MAOCJsV2Y3N+PvOJKABb9nMTmxEycbS34+O5Iurd3ZuWus8z9LqFZdR7Y2Y1513VFqxF8tzuFjzafMtpvodXw6uRQgts4kF9azSPL40nNLwfgvqEBTOrdBp1eYcFPR/jjWA4WZhq+fbA3FmYatBrBuoQM3lqfDMDim0OIDHChuKIGgKe/TeBIWnGz2nOBK/Fa1KdfR1dmjemMViP4YX8qn207Y7TfXCt4eWIw3XwcKCirZsZ3CaQVVNTu93K04pdH+vD+5lMs/fNs7XaNgO/u701WUSUProg3mf61dnRwZeaYTmiE4McDqXz+x1mj/eZawYs3BNHNx4HC8mqeWnmItIIKfJys+OnhPpzJKQMgIaWQF349CsBn03vgbm9BRbUegPu+PEBeabXJbblAiLcdkyN80QjYdjKP349kG+2P7uzGwEAX9IpCcUUNn+1OIbdM1W9SmBfdfRwQQGJGCV8dSGsxveuzb/effPTWYvR6PdHjrmfSlDuM9mdlpvP6i/MoKSlGr9Mz/b5H6NVnAAf27mTph29TXVONuZk5dzzwOGE9IlvFBoADe/7k03dfRa/TMWLs9UyYPN1o/2fvvcqhOPX1X1VZQUF+Hl//to1DcXv57L3XauVSz53hyWdfJqr/kBbV/1J4du4zbNsai4uLKz/+/Ftrq3NJnDy4l03L30ev1xM2eDR9xt9stD9h23o2f/MJ9s6uAPQYcS1hQ8a0hqpN8toDw4iODKSsspp7lqwhPjnTaL+1pRlfzbuOAG8ndHqFNbuSmffZViOZ6/p34pv519PvwWUcOJ7RInpf6X4PoG+gC0+N6ohGI/jpQDpf/NnYZyy8rhtdfewpLKtm5qpE0gtV39fRw5a547pga6lFr8CUT/ZRpdMzKtiTO/q3RwGyiyuZ+2MSBeXN6zNasi0F8OTojozu7o1Or/D1jnMs2278PTUXL90awfBQb8qrdDz86W4SzuY3kune3pl37uqNlYWWTQnpzP7qAABBbZ149fae2FqacT63lHs/3ElJRQ1mWsGb0yMJbe+MmVbw3Z9neOv3IybRv9aOyeEMD/WmrErHI5/tadKO0PbOvHNXJNbmBju+jqu1Y8ltPbC1MuN8Tin3fbSLkooanG0t+PzBvoT7u/Dtn2eYteKASW2ozyu39WREd1/Kq2p44KOdHDyT10hm7o3duXlAAE62FrS587va7Q+O7srUIYHodAo5RRU89MkuzueUtpju9Xnt6YlE9wuirKKKe+YvJ/5oSiOZn999AC93B8y0Wv6MO8ljL3+Hvl6hlEenDmXREzfQZshMcgtaxw7J5YMMykgYGuSJv4ct/edvJMLfmZdvCeOaxVsbyb18S3ee/iqOA6fzWf5QH4YEebIlUW3wfRKTzEebko3kK6p1LP71CF187Ons49CsOmsEPHdDELd/tIeMwgpWP9aXmMQskjNLamVu7N2GwrJqhr68jXFh3swc15lHlsfTwdOOceHejFq8HQ9HS768N5Lhi7ZSVaNnygd7KKvSYaYRfPdQFFuP5BB/rgCARb8dY12CaRuoV+K1qI9GwNxrunD3FwfIKKrgu/t6s+VINqey65zNDT18KSqvYcwbfzI6xJMnojsy47tDtfufHt2JP07kNjr3lD7tOJVdip2l6V9bGgGzx3XmnmVxZBZV8M29kcQezTG2I8KXoooaxr21g1HBnjw2ogNPf38YgJS8ciZ9sLvJc89adZgkEwX0/gohYGoPX5ZsOU1eeTXzR3YgLrWItKLKWpmz+eU8v/4EVTqFIR1cmBTmzQc7ztHBzYaObrbMXXscgDnDA+niYcvRrJZtROh0Ot5//WVefOND3Nw9eezuW4nqN4h2/oG1Mt8u+4QBQ0Yy9vpJnDt9kmeffoil36/F0dGZ+a+8haubB2dOJTPvyftZvnpji+pf346P3nqF55e8j6u7J0/dN4XIvoNo6xdQK3PngzNq//7tx285fUIN7IWE9+LNT78FoLiokPunXEt4z6iWNeASufa6G7hl8hTmPDOztVW5JPR6HRuWvcPNs17BwcWNpc8+RMcefXDzbW8k1zVqENG3P9xKWl6c6MgAAn1dCJ72MZFdfXj7kZEMfGR5I7k3v9/DtoPnMDfTsHbxzYzsFcCGvWpH3M7aggev78meIy0XdL3S/R6oPmPWmM7cvzyOzKJKvrq7J1uPZXPKEJwHuC7ch+KKGq59ZxfRQR48OjyQWT8kohWCF24IYt7qJI5nluBobUaNXo9WCJ4a1ZEJ7+2moLyaR4cHclNkGz7aerpZ9W7JttSEXr54O1kz4pVtKAq42lk0my31GR7qTYCnHZEzf6dHoCtLbutJ9MLG7/slt/fk8aV72X8yl2+fGMiwEG9iDqXz5vRezP8unh3Hspk8wJ+HxnRl0Y+HuLZXOyzNNQyctw5rCy1/vjSGH3efM1lgQLXDnshZa+gR4MriqT0Y9cKmxnbc1oMnvtjH/lO5fPv4QIaFeBFzKIM3pvfiufp2jO7CotWHqazWsWj1Ybr4OtK1jaNJdG+KEd19CPCyJ+LJn+nZwY3XpkcyfP66RnLr4lL5ZONx9r9mXMIj4WweQ+Yep7xKxx3DOvL8LeHc8c72llK/luj+3Qhs507wtc8TGeLH27NvZuBtrzaSmzLzc4pL1cDrN6/exYQREXy/fj8AbTydGBbVlXPpjYNSEpX/WKKMLPT7TxBC+AkhjgghPhFCJAohNgghrP/ikKlCiHghxGEhRKThHLZCiM+FEHuEEHFCiGsN262FEN8azr8asDZs1wohlhrOcUgI8Xhz2xXd3ZtVu84DcOB0Po425ng4WBrJeDhYYm9lzoHTaoR+1a7zjOru/ZfnLa/SsfdkLpWGrIDmpHs7J87mlnI+r5xqncJvcekMD/Iwkhke7MGP+9RVzNYmZNCnozqyOTzIg9/i0qnS6UnJK+dsbind2zkBUFalA8BMKzDTChRa9o1wJV6L+oS0ceRcbhkp+eXU6BTWHspgaFd3I5mhXd35OU5t8G9IzKJ3gIvRvtT8ck5mlRgd4+lgycDObvywP5WWILiNI+fyykk12LHuUCZDuhjbMbirO7/EpwOwMcnYjsuRABcbMkuqyC6tQqdX2H2ugPA2xh2Vo1mlVOnUe/5kbhkuNuaA6hjNtQIzjcBcI9AKQaEha6wlOX7kMD6+bfH2aYO5uTkDh0Wzc3uskYwQgrIytYFcWlqCq5t63QI7dcHVTX1HtPcPpLKykuqqqhbV/wInjh7G26cNXgY7+g+NZvefsReV/2PzOgYMG9Vo+46tm4iI7Iel1V+5odajR89eODi2XIP/35J28hjOnj44e3ijNTOna9Rgju/f0dpqXTLj+nTk601qYHjPkTQc7SzxcrE1kimvrGHbwXMAVNfoiU/OxNfNvnb//GkDeO27XVRUtdzzfaX7PYBgXwfO55WRWlBBjV5hfWIWgxv6jM5u/HpQ9RmbkrKJDHAGoE+gCycySzhuCIQUltegV9RAugCsLbQA2FmakV1cSXPS0m2pW/u2450NybWdrdwS07yDR4f7svLPMwDsP5mLo405no5WRjKejlbYW5uz/6Q6CLTyzzOMifAFINDLnh3H1EzS2MRMrunRBgBFUbCxNEOrEViZa6mu0VHczJlL9RkV7st3Owx2nPobO06pdny34wyjI1R9Az3t6tmRwTiDHWVVOnafyKGyWmcy3ZtiTI+2fPuHGlTcl5yDo40Fnk6N/de+5BwyC8obbf8jKZNyw721LzkHHxcb0yp8EcYNCuXr3/YAsOfQGRztrfFyaxz4vRCQMTPTYG6mRakXZVg8YwJz3vrJaJvkv40MyvxzOgLvKYoSBBQAE/5C1kZRlDDgAeBzw7Y5wGZFUSKBIcASIYQtcD9QpihKV2A+0MMgHwb4KooSrChKCPBF85oDXk7WpOXXvfzS88vxavCS9HKyJr3eCzK9wFhm+uAANs4ZymtTI3A0dORMiaejFen1prxkFFY0clReDnUyOr1CcXkNzrbmjY8tqDtWI+DXJ/qx5/lh/Hk8l4Pn6lKonxzdkd+f7Mec8V2w0Jrm0bkSr0V9PBwsySisazRmFlU20bi2IqOw7rqUVNbgZGOOtYWWOwb48f4W49RpgJljOvP6+hMtFjX3tLcks7DuHsksqmhkR32Z+nYA+Dpb8939vfn8jh5EtHcyOm7h9UGsvL839wzyN60RDXC2MSevrK7xmF9WjbP1xe+PgQEuJKSrGT0nc8s4klnKW9d1483runE4o5j0oubtHFwKudlZuHl41f7v5u5Jbk6Wkcyt0+9j84bfmXrDSOY/9RD3PTar0Xn+jN1Eh05dMbcwzSjt35GXk21kh6u7B3kN7LhAVkYaWelphIT3arRv+5b1DBgWbTI9/2uU5Ofg4FLXkbZ3caM4P6eR3LE92/n0mXv48a0FFOU2fd1aAx83O1Kyimr/T80pxqdewKUhjraWjInqwJa4MwCEdfCkjbs96/Y0fgebkivd7wF42FuSWWTs+9ztGweWLvhHnaJQUqHDydqcdq7WKAq8d2t3vr6nF7f3bQdAjV7hpd+PsfL+SDY80Y8Ad1t+imveDKaWbku1c7VhbJg3Pz3Wl8/v6omfm2k61d7O1qTm1WUppeWX4+1s3Ugm7SIyR1OLGG0I0Fzbqy2+hs7/L/vOU1ZZQ+Kb1xL/+njeW3uMglLTBfe9nRrr6NXADq8GdqTnleFteDaOphUxOly1Y3zPOjtaC28Xa1Jz67KK0vJKG12XS2XK4A5sOtg606h9PJxIyaibRpaaWYCPh1OTsr+89yDnYhZRUlbJj5vUaWXjBoeQllXAoeMtM9B4paI30c/ligzK/HNOK4oSb/h7P+D3F7LfACiKsg1wEEI4ASOBWUKIeCAWsALaAQOBFQb5BODC5OdTQIAQ4h0hxCigiAYIIe4RQuwTQuwrTdrwb2z7v/hy22n6ztvAyJc2k1VYwbMTQlpch+ZCr8A1r/9JvwVb6N7OkU5edgAs+f04I175g+vf3ImTjTn3DA34mzO1DlfytXhwaADLd5yrHQW5wKDObuSVVrXKlJ//h+ziSka+tp2bPtjNkrXHWTQxGFtLdaTzmVWHmfDeLqZ9to+I9k5c8zcjva1FHz8n/F2sWWuoOeNhZ4GPgyWP/3yEx38+QldPOzq5t27j7mLEblrHiNHjWf7jBp5f8i6vLpyLXl/nhs+eTubzD9/i4afmtqKWl872LRvoM2gYWq3WaHtebjZnTyUT3qtPK2n236RDeB8eeHM5d738Mf7BEfz20ZLWVun/QqsRLJs9nvdX7+dMRiFCwCv3DWXmR5tbW7V/zJXs90C9FuHtHJnzYxJ3fL6foV3cifR3xkwjmNjTl1s+2svI1//keGYJd/T3a211L4mLtaUszDRU1ui47s0dfLv7PItuujyv1SOf7+aOoR2JeW4kdlZmVOlUHxLh74pOrxD8+M/0mPErD4zqTHt32785W+vx6Gd7mD60A5vmj8DO2rzWjiudSf38CQ9w4e3fklpblb9l/IPv4T9iNpYWZgzu1RlrK3OeviOaBR/83tqqXfboFcUkP5crsqbMP6f+8LAOwzSji9Dwyiuo2agTFEU5Vn+HEKLpEyhKvhCiOxAN3AdMAu5oIPMx8DGA7/2rL+luu32QP7f28wMg/mwBPvUi1d7O1mQ0SBvMKCivjbyDGr2/IJNTL532q+1nWPag6TsJmYUVeDvVjeZ4OVoZZTYAZBSpMhmFFWg1AntrM/JLqxsf69T42OKKGnYm5zGwizvHM0pqU4ardHpW7U3lrsHNl+VwpV+L+mQVVeLlWDc66OlgSVaDjIqsogr1ehVVotUI7CzNKCirJqSNIyOC1Boz9lZmKApU1ujxdLBkcBd3BnRyw9JMg62lGYsmBjNr1WGT2ZFZXGk0WujpYNXIjgsyDe0AKDSkMx9JL+Z8XjntXW1ISismy3B9yqp0rEnIILiNQ206u6nJL6uunY4EauZMfhNp19087bimmwcvx5ykxlCQrkcbR07mllFZozboEtKKCXSz5Xh2WaPjTYmruwc5WXV1nXKyM2unJF1gw++rWfjq+wB0De5OdVUlRYUFODm7kJOVycLZT/DknIV4+7ZtUd3r4+LmbmRHbnYWLg3suMAfm9dz76NNZPts2Ujv/kMwM2v5rICrFTtnN4ry6opfF+flYO/sZiRjY1+Xot59yGi2fPtJi+nXFPeOD2f6mO4A7D+WQRsPB0hUR1993exJy2k6mP3e46M4mZrHu6vVYtL21hZ083Njw6uTAfB0sWXVghuY+OyPJin2ezX5PYCs4ko8HYx9X8OpRhf8Y1ZxJVohsLPSUlBeTVZRJQfOFtQW8N2enEsXb3tKKtUpZCmGLKKNiVlM79+uWfVu6bZURmEF6w+pdYA2HMpkcTMGZe4Y1oGpg9T6YvGn84yyQnycrUnPN76n0vPLjaa/1JdJTi/mxldjAQj0tGdEdx8AJvRpT8yhDGp0CjnFlew+kUOYnwtns5uvpswdQzswdZA68Bd3Oq+RjhkN7MhoYIe3i01tVllyRjGTXlPrMwV42jEitOUHgu4a0Ynbh3QA4MCpXHxdbQH1PevjYtvouvwdg4K8ePLaYMa+sIGqmpYLMt07aSDTb+gLwP7Es7Txcq7d5+vpRFpWwUWPrayq4dfYBK4ZHEJmbhHtfV3Z890z6rEeTuz8eiYDpi4hM/fKGHyUmAaZKWNabgIQQvQHChVFKQTWAw8LQxRGCBFukN0GTDZsCwZCDX+7ARpFUX4A5gIRzaHYsq2nGfnSFka+tIX1B9OYGKV2UCL8nSkyNBLqk1VUSXFFNRH+6ktoYlRb1hs6k/WndYwO8+ZYWqNknmYn4Xwhfm62tHGxxlwrGBfuTUyicRp5TGIWN/RU0zZHh3qx01A8NiYxi3Hh3lhoNbRxscbPzZaD5wpwsbXA3kqNU1qaaejfyZWThjne9dOQRwR7cjyj+V6cV/q1qM/h1CLaudrg62yFmVYwOsSLLUeNV/jZcjSba8PVBs7IIA92n1KLnN3+6T6iX9tO9GvbWbHzHJ9sPc03u8/z5sZkhi/5g+jXtvPUykPsOZVn0oAMQGJqEe1drPF1Uu0YFeJJbAM7Yo9mMz5MbeCM6ObBHkOtA2cbczSGGKuvszXtXK1JyS9HqxG105vMNIJBnd2MiimamtN5ZXjaW+Bma45WI+jdzom4FOP7o52zFdN6+fLWtjMUV9ZlLOWWVdHZwxaNAK2ALh62tauGtCSdugSRlnKOjLRUqqur2Raznqj+g4xk3D29id+vFlk+d+YUVVVVODo5U1JcxPynH2b6fY8SFBre1OlbjI5dgkhPPU9mumrH9s3riew7qJFcyrnTlBQX0TkotNG+PzavY2ATdWYk/z8+AZ3Jz0ilICsdXU01R3bF0jHCuINfkl9XhPzE/p24+jRvJ/mf8tEvcUTdt5So+5by65/HmTw8GIDIrj4UlVaSkde4szh/2gAcbS2Z8UFM7baisiraTnyHLlM/pMvUD9lzJM1kARm4uvweQGJqMe1cbfBxssJMI4gO8iD2mPHUt63Hc2qzI4d3c2evwWfsOJlHB087rMw0aIWgR3snTmWXkl1USYC7Lc4GvxEV6MzpnOYNhLd0W2rj4UyiOqg1aXoHunC6GYMZn8ckM+TZ9Qx5dj1rDqQwyRD06xHoSlF5daOAUWZhBcXl1fQIVPWZ1M+PtXFqQNPN0OYTAp4Y342lW9Qi0im5pQzoqgbQbSy09Ax05UR6895vn29OZsj8DQyZv4G1B1K5qa/BjoC/sSNAteOmvn6sa8qOa4JYFnuyWXW9FD7deJwBs9cwYPYaft+Xws0D1EHNnh3cKCqvarJ2zMUIbe/Mm3f25pbXYslp4SnUH63cRtTNi4i6eRG/bklg8jh19cbIED+KSsrJyDG+D2ytLWrrzGi1Gkb3D+LYmUwSk9NoP+wZuoydT5ex80nNKqDP5FdkQKYJFMU0P5crMlPGtFQIIeIAc+qyWxYCbwIJQggNcBoYB3wAfCGEOAIcQZ0aBeBr2H4hgPZMcysZcziTocFe/LlgBOVVOp74sm5pvA2zhzDypS0AzP7mIG/c3gMrcw1bEjPZbFj1YO4NwXRr44iiQEpeGTO/iqs9ftcLI7GzMsdCq2FUdx9ueftPTjRDQEOnV3j+xySW3tMLjRCs2pPCicwSHovuyKGUQmISs1i5O4XXJoey+ZmBFJRV8+jyeABOZJawJj6DdU8PQKfX89yPiegVcHewZMktoWgFaITg94MZbDFM33jj1u642FkggKS0IuatOv6vbWiKK/Fa1EenV3jpt2N8dHsEWo1g9f40TmaV8uCwQBJTi4g9ms2P+9N4eWIwax7vpy4lXW/lpcsFnWEu/we3haPVCH46kMbJ7FIeGBpAUmoRscdyWH0gjZduCOK3R/tSWF5du/JSDz9nHhgaQI1OQVEUXvj1KEXlNViba/jwtnDMNAKNRrD7ZF6LFS4GNZ18xb40ZgwOQCPgj1P5pBVVcn2IJ6fzyolPLeKmMG8szTU82F9dbSa3tJq3/jjD3vOFdPW044XRnVCAQ+nFxLfCdDKtmRn3Pz6LuU/ej16vZ+TYa2nv34Hln75Pxy7diOo/mLsffIK3Fi/gp5VfqQ3R2c8jhODXH78jLfUc3yz9iG+WfgTAC69/iJNzyxdo1mrNuPuRmTz/9IPo9HqGjx5PO/9Avv78Azp07kZkPzVA88fm9QwYGt0okzIzI42c7EyCuvdo6vSXDTNnPMG+vXsoKMhnxNCB3P/gw9ww4cbWVuuiaLRaRtz+EN8ufgZFryd0UDTubfzYtmop3v6d6NijL/s2/MSJAzvRaLVY2doz7t6nWlvtWtbtOUV070ASl91DWWUN9766pnbfrg+nEXXfUnzd7Jl1a1+Onstl5wfTAPjw5wMsXWva5aL/iivd74FaI+aVNcd5f0oYGiH4OT6NU9ml3D/Yn6S0YrYez+GnA+m8cH03fn44iqLymtrBheKKGlbsPMeKu3uiANtP5LLdEPj4eOtpPp0WQY1eIb2ggvk/N+90jZZuS30Yc4o3bu3OHQP9KK2s4ZmVphlg2XgwneGhPuxdPI7yyhoe+axuNcQtC6IZ8ux6AJ7+cp9hSWwzYhLS2JSgBvpuiGrPncPU7I7f9qfwtaE47ecxybx9VyTbXxyNAL7ZfpqklMbLtzebHQnpDA/1Zs8rYymvquGRz/bU2fH8SIbMV0sWPL18P+/cqS7tvflQej072nHH0I4A/F7PDoD9S8Zhb2WGhZmG0eG+3PjaVo6bOKC5IT6VEWE+xL1+LWVVNTz40c7afX+8NIYBs9V31vO3hDOxrx82FmYkvnM9y7ecZNGPCSyYHIGtlRnLHh0AQEpOGbe8HmtSnZti3fZEovsHkfjLfMoqqrn3uRW1+3Z9O4uomxdha23JqjfvxcLcDI1GsG3fCT5Z1fIrRUmuHISs+nx1canTly53rGys/l7oCqCirOWzCZobZ1e71lahWdBomp4ieCUR0bXpKS5XGvOGdWxtFZqFlkydNhX+HpdvPYR/wrdx51pbhX/N/bO/aW0VmgWXDp1aW4VmwcPrylk57GIUtUJBdlNQmGu6wEdLcbEyBVca1VWmW22qJalM2tXaKvxryuPevTpuqovw5K/HTNKnfe2azpfl9yYzZSQSiUQikUgkEolEIpFcFvzXEkdkUOZfIoR4D+jXYPNbiqI0+9LVEolEIpFIJBKJRCKRSK4eZFDmX6IoyoOtrYNEIpFIJBKJRCKRSCRXA/r/VqKMXH1JIpFIJBKJRCKRSCQSiaQ1kJkyEolEIpFIJBKJRCKRSC4LZKaMRCKRSCQSiUQikUgkEonE5MhMGYlEIpFIJBKJRCKRSCSXBXL1JckVTXVldWur0CxcLQ+iVqttbRX+NfqrJH+wulrf2ir8a9Lzy1tbhWZhX2pea6vQLDhZWrS2Cv+a3am5ra1Cs3BzeLvWVuFfM69zl9ZWQVKPq8H3lRWXtbYKzYJyFVwLC5sr318AaLRXxyQL+74jWlsFyd9w5bfa/xlXx5MlkUgkEolEIpFIJBKJRHKFITNlJBKJRCKRSCQSiUQikVwWXC2zJi4VmSkjkUgkEolEIpFIJBKJRNIKyEwZiUQikUgkEolEIpFIJJcF/7FEGRmUkUgkEolEIpFIJBKJRHJ5oP+PRWXk9CWJRCKRSCQSiUQikUgkklZAZspIJBKJRCKRSCQSiUQiuSz4jyXKyKBMcyGEiAVmKIqy7xLlwwAfRVHWGP5/DihRFOVVU+n4V7w4OZzhIV6UV+l4+LM9HDpX0EgmtL0zb9/ZC2tzLZsOZTDn6zgAgts6sfi2HliZa6jRK8xcfoC403m1x4X5ObNmzjDu+XAXv+1PMZkNCyeFMjRYteHxZfs5dL6xDSHtnHjz9h5YmWvZfDiDeSsTAHhyXFcm9/cjr7gSgJd/TmTz4UzMNIJXp0YQ0s4JM43g+13neHf9cZPZAPD8jSEMDfKgvErHE8vjOHy+sLEdbR15fWoEVhYaNidmMf/7QwDMGNeFkaFe6BXILa7kieVxZBZWYG9lxlvTeuDrbI1WK/h400lW7jpnEv37d3TlmXFd0GoEq/am8Om2M0b7zbWCRTeGEOTrQEFZNU98c5C0ggpC2jjw/HXdVCEheC/mJDFJWXg5WvLyjSG42VmgKLBybwordphG9/oM6OTGnPFd0AjB93tT+CT2dCM7Ft8UQpCvIwVlVTz+9UFS8yvo29GVJ0d1xFyroVqnZ8ma4+w6qT4Pj0V34LoIHxyszYl4NsbkNjSkZ1tH7uvfHq1GsDYpi5Vx6Ub7g73tua9/ewJcbXhpQzLbT9U9x3f2aUvv9k4IIThwvpAPtp9tafUBOBG/hzXL3kXR64kYOoaB10422h8Xu471X32Eg4sbAL2jr6PH0LEUZGfwzWvPoigKOl0NUdHX02vE+NYwAYCkA7v48bO30Ov19Bk+jhETpjaSOfBnDGu//QIhwNevA7c/8RwA7y94grPHkgjoGsq9cxe3rOJ/wcmDe9m0/H30ej1hg0fTZ/zNRvsTtq1n8zefYO/sCkCPEdcSNmRMa6h6yTw79xm2bY3FxcWVH3/+rbXVaZLnJzbwGSl/4TPMDT5jlcFnjG3gM1aoPgMgqqMrz00IwUwryC+p4sa3/ryibIjq6Mpn9/TmfG4ZAGvj03hrnen8d98OLswc3QmNEKw+kMbnDd6R5lrBizcE0dXbnsLyap7+/jBpBep33dHTjnnXdMHOUotegckf76WqRs9DwwK4prs3DlZm9Hlpq8l0r8/Cm7ozzNAefGzpvqbbg+2ceHN6T6zMtcQcymDedwdr990xJJDpQwLR6RU2HUrnhR8OA9DV14HFUyKwtzZHryiMfnEzlTX6FrHp5SkRDO/uQ3mljoc+2UXC2fxGMnMmhnJTPz8cbS1of8+q2u19Orvz4q0RBLV14q73d/Dr3vMtovMFTNG2vYCvszWx80fw2u9H+HDjCZPZ8OItYQwL8aa8qoZHPt97kT6GE29Pj8TKQkvMoXTmfBMPwMf3RhHoaQ+Ag405RWXVDFuwkbauNvyxcBQnM4oB2H8ql6dXHDCZDWCa9rmjtTmvTgmnvbsNldV6ZqyI41h6sUntkFyeyKBM6xEG9ATWtLIeDAvxIsDTjt7PrKVHgAuLb+vB6BcadxgXT43gyaX72H8qj28eH8DQEC82H8rg2RtDefWXRDYfymBYiBfP3hjK9YtjAdAIwbwbQ4lNzGx0vuZkaLAn/h529Ht2AxH+zrw8OYxxr8Q2kls0OYynVhzgwOl8VjzUlyFBnmwx6PZJTHIjp3RND18szTQMWxiDtbmW2OeG89O+FFIMjbzmZkiQB/7utgx4LoZwP2deurk745dsayT30s3defrreOLO5PPlA1EM7uZBbFIWH25K5tXfjgIwfXAAj47uxOxvE7h9kD8n0ou548PduNhZsPXZYazee55qXfOGoTUC5o7vyl2f7yezqILvHohiy9FsTmaV1spM6NmGovJqRr22ndGhXjw5qhNPfpvAicwSbnx/Nzq9gpu9Basf7kvs0Wxq9AqL1xzjSFoxNhZaVj0Uxc7kXKNzNjcaAc9e15Xpn+4js7CCVQ/1YXNSltFn3tirDUXlNYxc8gdjunsxY3QnHv86gfzSKu5fGkdWcSUdPe347M4eDDQ0prccyearHedY/9QAk+n+VzY9ONCPZ349Sk5JFe9MDGLXmQLO5ZfXymSXVPLa5pNMDPM2Orablx1BXvbc953auHjt+m6E+tiTkNayDQe9Xsdvn7/F7XOW4ODqzkez76dLj754tPEzkgvuM5hxdzxqtM3O2ZW7F76LmbkFlRXlvDfjDjr36FsbvGlJ9Dod33/8Og8+9wZOrh68+vRdBEf2x7utf61MVtp5Nv6wgsdffh8bOweKC+o6EcOum0xVZQU71v/S4rpfDL1ex4Zl73DzrFdwcHFj6bMP0bFHH9x82xvJdY0aRPTtD7eSlv+ca6+7gVsmT2HOMzNbW5UmGdLN4DOer+czXm3CZ9xUz2fcX89nxCTz6u8GnzGozmc4WJvx4qTuTH1/J2n55bjaWVxxNgDsOZnL9A93m0z3C2gEzB7bmXu/jCOzqJKv7+lF7LEcTmXX+YzrI3woKq/mmrd3MirYk8dGdODp7w+j1QheuqEbc35M4nhmCY7WZtTo1GDF1mM5fLs7hV8f6WNyGwCGBqvtwb5z1xPh78KiW8MZ+/KWRnKLbg1nxpcHOHA6j68e6cfQYE82H86kb2d3osN8GLZgE1U1elztLQHQagTv3hnJw5/vJSmlEGdbC6p1LROQGR7qTYCnPb2e+o2ega68Oq0nI5/f2EhufVwqn248zp4l44y2p+SW8dAnu3lodJcW0bc+pmrbXmD+jaFsTswwpQkMC/HC38OOqNmGPsaUCEa/tLmR3OIpPXjyS7WP8fWj/Rka7MXmwxnc89GuWpnnJoVSVFZd+//Z7BKGLWh8LU2BqdrnD43qSGJqIXd/sodATzteuCmUW97e0SI2Xe7IJbEltQgh/IQQR4QQnwghEoUQG4QQ1n9xyFQhRLwQ4rAQItJwjkghxE4hRJwQYocQorMQwgJYANxkkL/JcHw3IUSsEOKUEOIRw/G2QojfhRAHDee96SKf/X8zOtyXlTvOALD/VB6ONuZ4OFoZyXg4WmFvbc5+w8j5yh1nGBPuC4AC2FuZA2oUO6OgrpN31/AO/L4/lZyiiuZW24joUB9WGTI/DpzOx9HaHA+HBjY4WGFvZc6B02rnZtWuc4zq7vOX51UUsLE0Q6sRWFloqarRU1Je/ZfH/BtGhnrzw251FCbuTD4O1uZ4OFgayXg4WGJnZUbcGdWOH3afJ7q72okuqaiplbOx0BrZYWelxmBtLc0oKKuiRt/8L7uQNo6cyy0jJb+cap3C2oQMhnb1MJIZ2tWdnw6kAbDhcCZRgS4AVFTr0Rl0sjTToqD+nVNcxRFD57+sSseprNJG30lzE9rWkbO5ZaTkqXb8fjCdYd0a2BHkwer9qQCsP5RJnw7q6P+RtGKyDKNSJzJLsDTXYq4VABw8V0h2cZVJdb8YnT3sSCusIKOokhq9QmxyHn38nY1kMourOJ1bTsNbQ1HAQqvBTCMwN/zON+FzcDFSko/i4uWLi6cPZmbmhPQdytF9l9Z4MTMzx8xc7Vjqqqta1dmfPXEEd+82uHn5YmZuTkT/4Rzas91IZufGXxkw+gZs7BwAsHequ1adQ3tiZW3Tojr/HWknj+Hs6YOzhzdaM3O6Rg3m+P4rv2HZo2cvHBwdW1uNizIy1Jsf9vxDn7HnPNGhTfgMSy2G1y7X9WzDuoNppBmCtrklpntvmcqGliTY14HzeeWk5ldQo1NYdziTwV2MA75DurjzS7yanbgxKYtIw/u3T6ALJzJLOJ5ZAkBheU3tO/hQShE5JvzuGzIqzJvvd6oZPgdO56nX4iLtwQOGjOjvd55lVJjalrp9UADvrjtGlSEDJtfgCwd18+RISiFJhgyo/NKqRn7GVIyOaMN3f54BYN/JXBxtLPBsYNOFfReyxOpzPqeUpPMFrVJ01FRtW4BR3b05n1PKcRNnZYwK86m9p/afysPBxqLJe8rOyqy2j/H9zrOMDm9sw/iebVm9p2UzlS5gqvZ5Ry97dhzLBuBkZgltXWxwszdtG/dKQVFM83O5IjNl/p6OwC2KotwthFgJTABWXETWRlGUMCHEQOBzIBg4CgxQFKVGCDEceElRlAlCiGeBnoqiPAS105e6AEMAe+CYEOIDYBSQpijKWINcs7cOvZytScurC6Sk5ZXj7WxNVj3n5O1sTXq+sYyXsxqfmvtNHN89MZDnbuqORsBYQwTcy8maMRG+XL84lrem92putY1tcLKqbTwCpBWU4+VkRVa9YJCXk5WxDQaZC0wfHMDE3u1IOJvP8z8corCsmt8OpBLd3Zv4V8ZgbaFl/vcJFJSZrjPq5WhFWr2gVnpBOV5O1mQVVdazw5r0ggpjmXoO7ulrujKhd1uKy6uZZEg3X7r1NJ/fF8m+l6KxszLjgc/2meTF5OloRUa9+yajsILQto4XldHpFYoranCyMaegrJrQNo68MCEIHycrZn5/uDZIcwEfJyu6+tiT0ETKaLPbUe87ziysILSdk7GMgyXpDexwtjEnv979ER3iSVJqUbNnJP0/uNpakF2vcZ9TUkUXT9tLOvZIZgkH04r4ZloEAvjlcCbn800baG2K4rwcHF3rgmMOLm6kJB9pJJe05w/OHj2Eq1cbRt/2AI5u6jGFOVmsWDybvIxURt56b6tkyQAU5GXj5FZnh5OrO2ePJxnJZKWpjb83nrkfvV7H6JvuoFtEVIvq+U8oyc/BwcW99n97FzfSTh5tJHdsz3bOHz2Ei1cbhk+5DwdXj0Yykkunoe+7ZJ/h1MBnRBp8xtuqz/D3sMNcq2Hlo/2wtTTj89hTtYGTK8UGgB7+LqyfNZjMwgpeWJ3I8QzTdEA9HIx9X1ZhJSFtHIxl7C3JMNik0yuUVKq+r72rDQrwwdQwnG3MWXc4k6V/mn6KblN4OVkbX4v8crydrIzbgw2vV756vQA167qDK7OuC6KyWsfz3x/i4Nl8Aj3tUFD45tH+uNpb8NPeFN438VTwWn1drEnNq8tYSssrw9vFpskAzOWGqdq2NpZaHojuxM1vbef+EZ1MaoO3kzWpeXXZ5en5ZXg7NehjODXoY+SX4+1kPAYe1dGN7KIKTmeV1G5r52bLpmeHU1xew6KfDrP7RI7J7DBV+/xIahGjw3zYczKPsPZO+LpY4+1kRU5x3Xkl/w1kpszfc1pRlHjD3/sBv7+Q/QZAUZRtgIMQwglwBL4XQhwG3gCC/uL43xVFqVQUJQfIAjyBQ8AIIcQrQogBiqI06o0KIe4RQuwTQuwrP7bpn1nXDEwb0oFnv40nfMZvzPs2njcNAZgXbglj4fcJl3VU8gLLtp6iz9z1jHgxhsyiCuZPCAEg3N8ZnaIQPnMNveeu577hHWnndnmNUDdk8a9H6D13A6v3pjBtkDodYlA3d5JSiug5ez2jXo5l4aSQ2syZy4mElELGv7WDSe/v5u5B/liY1b2ibCy0vHVrGC//fozSSl0ranlpdPC0ZcboTjz7Y2Jrq/Kv8XGwpK2zFbcui2Pysji6+zoQ7G3f2mo1SecefXjina95cPGnBIb24McPFtXuc3Tz4MHFn/Lom8uJ37aekoK8vzhT66LX6chOP88jC99h2hPP8e37iykrvbLnmXcI78MDby7nrpc/xj84gt8+WtLaKkkw+Ix5G1i9L4VpA1WfYaYRhLR15PYPdjHlvZ08OqoT/h6XFsRtDZqy4fD5QqLmbSB6USxfbD3Fp/dEtrKWTaPVCMLbOfHMD4lM+3w/Q7t61GbRXGmYaQROthaMfXkLC1Yd4uN7ewOqjZEd3Hjwsz1cu3gro8N86N/F/W/OJvm3XKxtO2NcVz6JSabsCmhLXeD63u2MsmQyCyuIePp3hi/YxPyV8Xxwd+/Lsl1bn6ba5+9tOIGDtTnrnhnMtMEBJKYUorsSOk4tgF5RTPJzuXJ5372XB/VDlTrgr6YvNbzSCrAQ2KIoyvVCCD8g9h98lpmiKMeFEBHAGOAFIUSMoigLjD5EUT4GPgbwuGPlJd1tdwztwBRDwyXudD4+LnVm+bgYR6zBMFribCyTYZC5qW/72qK/v+xN4Y1palCmu58zH92nzoN2tbNgWKg3Or2etXFpl6Li3zJtUAC39vcDIP5sPj719XOyNsp0AMgoqDC2oZ5M/Yj0V9vP8OUDqt7X92rLlsRMavQKucWV7D2ZS/f2zpzLab6aMrcP9OeWfmrdhYNn8/GpNzrg7WRtNB1MtUMduTKSaWLEZ/XeFL58IIrXfz/GpKh2vL9BnVN8JruU87lldPC0I/5sQbPZAaqTrD8q4OVoZTSKUF8ms6gSrUZgb2XWKPvoVHYpZVU6OnrakZhahJlG8Obk7vwWn86mxKxm1fmidtT7jj0drRqNqmUWVeLtaEVmYZ0dF7JkPB0teXdqODO/O8T5POPr11rkllbhXq8uhJudBTmll5b11TfAhaMZJVQYUtL3nSukq6cdh1u4GJ29ixuFuXXXvyjPODsDwMa+LjOrx9AxbPjq40bncXBxw6OtP2ePHiIoapDpFL4ITi7uFOTU2VGQm42jq7EdTq7utO/UDa2ZGa6ePnj4tCU7LYX2Hbu2tLqXhJ2zG0V52bX/F+flYO9snIlkY1+XOdB9yGi2fPtJi+l3NXH7QH9u6VvPZzj/Hz6j4CI+4/4oXl9zjPSCCvJLsyiv0lFepWN3ci7dfB053Uy1vFrChvrTBbYkZfGiVoOzrQX5pc0/HSiryNj3eThaktlgpDuruBIvB0uyDL7PzlL1fVlFlew/W1DrB7efyKGrjz17TjcuRmsKpg0O4NYBanvw4JkG18LZeOQfIL2gopHMheuVnl/OGkMbL/5MPnpFwdXOgvSCcnYdzyHPkK25+XAGIe2c2X40G1Nw57COTB0cCEDc6Vx8XWwBNYvCx8WG9DzT1AVsDlqibRvu58LYCF/m3hCMg7U5egUqq3V8EXuqWWyYPiSQKQMCVBvO5OHrYgPkAuDtbEN6g+c7vaBBH8PZ2khGqxGMjfBlxMK6geeqGj1VNer9lHC2gDPZJQR62nOwiSLO/y8t0T4vqajhyRVxtft2LBjRrH0MyZWDzJRpXm4CEEL0BwoNWS2OQKph/7R6ssWo05T+EiGED1CmKMoKYAkQ0RyKfr45maHPbWTocxtZG5fKpL5+APQIcKGorNoorRAgq7CC4vJqegSo9T8m9fVjbZxqVkZBBX07qx2KAV09OJWpdtR6zVxDz6d/p+fTv/PrvhRmLj/QbAEZgKVbTzHixc2MeHEz6+LTmRjVDoAIf2eKKqqN0jtBbTQVV1QTYRiBmhjVjvUJqj715+iODvPhWFoRAKl55fTvrKbXW1toiQhwIbmZ05+XbTvNqJdjGfVyLOsPZjChd1sAwv2cKS6vbhTUyCqqpKSihnA/1Y4JvduyIUGdp+7nXjeSOTLUi2TDHPW0/HL6Ga6Rm70lgZ52nDXBS/9wahHt3WzwdbbGXCsYHerFliPGQZQtR7O5LkKdKzwy2JPdhjnEvs7WaDVq7RUfJysC3G1INQT+Ft4QxKnsUpb92TIr/hxKKcLP1YY2BjvGdvdmcwM7NidlcX0Pta5SdIhn7QpL9lZmfDytB6+tPc6BZg56/RuOZZXg62iFp70lZhrB4A4u7LrERn92SSWhPg5ohNo4CvGxNyoQ3FL4BnYhLyOV/Kx0amqqObRjM116GBfALM7Prf376L4duPuq74XC3Gyqq9RnqbykmHNHD+Pm07bllK9Hu45dyE4/T25mGjXV1RzYvomQXv2MZEJ6DyD5sNpQKykqICvtPG6ef18noLXwCehMfkYqBVnp6GqqObIrlo4RxtempN61ObF/J64+7VpazauCZdtOM2pRLKMWxbI+IYMJkf/QZ0T+vc/YkJBOr0BXtZ6auZZwP+dm9X0tYYN7vboMYe2d0AhMEpABSEwrpp2LDb5OVphpBaOCPdl61HgqReyxHMYbiqiP6OZRG3T5MzmXjh62WJlr0GoEPdo7c8qEhewbsjT2FCMWxjBiYQxr49O4sY/aCY3wd1GvxUXagxH+anvwxj7tWWeolbMuPq22rRFgmAKXW1JFbGImXds4YG2hRasRRHVy53h6kcls+izmBIPnrWPwvHWs2Z/KTf38AOgZ6EpRWfVlPXWpJdq217+2jd5z1tN7zno+3XySd9Yda7aADMAXW04ybMFGhi1Q+xgX7qkeARe/p0oqamr7GOo9VddfGNjVgxPpxUYDxq52FhiajLR3syXAw56zOSU0Jy3RPnewNqutO3hL3/bsTs41Cij/l1FM9HO5IjNlmpcKIUQcYA7cYdi2GFgmhJgL/F5PdgswSwgRD7z8F+cMAZYIIfRANXB/cyu9KSGd4aHe7Fk0hrKqGh79fG/tvs3PjWDoc2pl85krDvD2HZFYG5arizmkVmx/ctk+XrglDDOthopqHU8u29/cKv4tMYczGBbsyY6FI2uXDbzAxjlDGfGiWufmma/j1WUDLbRsScysXRpw7g3BBLV1RFHUSvtPf6V2hr7YepI3buvBlmeHIwR8t+MsR1JN15DYnJjJ0CBPtj83nPIqnVH0fN0zgxn1ciwAc75L4PWp4ViZa9mSlMkWQ/bIM9d2I9DTDr2ikJJXzuxv1GUq31p7nNenhrNx9hCEgJd+SjJJ41SnV3jxl6N8Mj1CXRZ0fyrJWaU8NDyQxJQithzN5od9qbxyYzDrnuxPQVk1MwyrZES0d+LuQf7U6PToFVj48xEKyqqJaO/EtRE+HEsv5seH1Joab25IZttx080d1ukVFvx8hE/v7IFWI/hhbyrJmaU8MqIDh1MK2Xwkm1V7U1lyUwgbnhpAYXk1j3+tftdT+rajnZs1Dw4P5MHh6kjdHZ/uJ6+0iqdGd2JcuDfW5lq2zh7E93tSeHfTSZPZUR+9Au/9cYaXrumMRgg2HM3mbH45t/Xy5Xh2KbvOFNDJw5ZnR3XC3lJLlJ8Tt0X6cs+3h/jjZB7dfR346OZQFAX2nStgdysEnLRaLWOnP8yXL81Er9cRMWQ0Hm39iVn5Bb4BnejSsx+71v3I0f070Gi0WNs5cP396qo52alnWb/iw9pz9Rs3Cc92AS1ug2qHGRPvfoL3n38CvV5P1LCxeLcL4PevP6Vdhy6ERPana3hvjsbv5cWHp6DRaLj29gewdVCzgN6c/QCZqeeoqihj3l3XM/nBWXQN790qtlxAo9Uy4vaH+HbxMyh6PaGDonFv48e2VUvx9u9Exx592bfhJ04c2IlGq8XK1p5x9z7VqjpfCjNnPMG+vXsoKMhnxNCB3P/gw9ww4cbWVquWWp8xfzjl1Q18xqzBjFoUC8CclQm8PqWez0iq5zM86vmMb9X3WHJmCbFJWWx4ZgiKovDNjrMmW6bVVDaMCfdh6gA/dDqFimodD36xzyT6g+ozXl5zjA+mhqPRwE9x6ZzMLuWBIQEkphWx9VgOqw+k8eIN3fj1kT4UlVfz9Cp1qejiihqW7zzP1/f0QlHgjxO5/HFCDWA+NqIDY0I8sTLXsuGJfvx4II0PY0+bzI6YQxkMC/Zi54vRaltqad13tnHeMEYsVFfmfObrON6c1hMrCy2bD2ey+bDaHvzmzzO8cXtPtswfTrVOz6OG77ywrJqPNp5g7eyhKIpCzOGM2jakqdl4MI0R3b3Zt2Qc5VU6Hv60bjWu2IWjGDxvHQDzbwpjYp/22FiYcejNa1m+9SSLVx8m3N+FLx8dgKOtBdHhvsy6PoR+s1tm4VRTtW1bkk2HMhgW4s3ul0ZTXqXj0S/q+hgxz46oXT1J7WP0UpdZb3B/XBfZjtV7jOssRXVy5+lrg6jRqVNSnl6xn4JLzP79fzBV+7yDlz1vTI1AAY6nF/HUiniT2SC5vBH/teWmrnYudfrS5Y6ZxdURL9RqtX8vdJlj7/hXM/auHHSXQbHdf4tfe6fWVqFZuKNPm9ZWoVlwsjTdMsEtRXrp5TG97t9yc/iVn3XT8bGfW1sFST1cXO1aW4V/TVZ6QWur0CxUVbTOqoXNiaXN1bGijq7myqlD81dYXAX++/x714rW1sGUTPsmwSQN96W3hF6W39vV0fOVSCQSiUQikUgkEolEcsWjv/LHUv8RMijzDxFCvAf0a7D5LUVRvmgNfSQSiUQikUgkEolEIpFcmcigzD9EUZQHW1sHiUQikUgkEolEIpFIrkb+ayVW5OpLEolEIpFIJBKJRCKRSCStgMyUkUgkEolEIpFIJBKJRHJZ8B9LlJFBGYlEIpFIJBKJRCKRSCSXB3L6kkQikUgkEolEIpFIJBKJxOTITJmrjIBOnq2tQrMgxGW5hPw/5uzpnNZW4V9TXFTe2io0CxrNlR+D3r4lqbVVaBa2LV/d2io0D05Xwfu2pqq1NWgW5nXu0toq/GtOvHlta6vQLES9ENPaKjQL+Xllra3Cv+ZqGWl29nBqbRX+NaVFV/79BODq4djaKjQLGeev/Pb51c5/bUnsK7+XIpFIJBKJRCKRSCQSiURyBSIzZSQSiUQikUgkEolEIpFcFlwtmX6XigzKSCQSiUQikUgkEolEIrks+G+FZOT0JYlEIpFIJBKJRCKRSCSSVkFmykgkEolEIpFIJBKJRCK5LND/x6YvyUwZiUQikUgkEolEIpFIJJJWQGbKSCQSiUQikUgkEolEIrks+I8lylz5QRkhxBmgp6IoJl1wXggRC8xQFGXfRfaXKIpi9w/Odx1wXFGUpEs5f0sR5e/M48M7oNEIfjmYzvJd5432h7V15PFhgQR62DHv5yS2HKv72t+YFEKwjwMHUwqZsepwS6teS5S/M48NC0SrEfxyMIPluxvY0MaRx4YFEOhhx7O/HDG24cZggnwcSEgpZMYPiS2tOgALJ4UyNNiL8iodjy/bz6HzBY1kQto58ebtPbAy17L5cAbzViYA8OS4rkzu70decSUAL/+cyObDmbXH+TpbEzt/BK/9foQPN54wmQ3PTwxhaJAH5VU6nlgex+GUwsY2tHXk9akRWJlr2JyYxfxVhwCYMbYLI0O90CuQW1zJEyviyCysIKqjK5/d05vzuWUArI1P4611x01mA8BzE4IY0s2T8iodM76Kb9KO4LaOvHZrGFbmWrYkZfKc4b6ZfW1XhgV7UV2j52xOKU99HU9ReQ39O7sxa3xXzLUaqnV6XvopiR0nck1qR32WTO9NdEQbyitruPe97cSfbvzZ82+JYPLADjjZWeA5dUXt9imDO/DC1F6k55UC8OHaIyzbbLr76FJ57fFriO7bmbKKau5Z+D3xx9Mayfz8xnS8XB0w02r48+BpHnv1Z/T61vX4r903mOhe/pRVVnPPaxuIP5lltN/a0oyvZo8lwNsJnV5hze5TzPtiOwCPXB/BtFHB1Oj05BSWc98bGziXVdwaZvDaA8OIjgxU7ViyhvjkTKP91pZmfDXvujo7diUz77OtRjLX9e/EN/Ovp9+DyzhwPKNF9DbFewogqqMrz00IwUwryC+p4sa3/mwRe/6KZ+c+w7atsbi4uPLjz7+1tjpG9O3gwtOjOqHRCFYfSOOL7WeN9ptrBS9cH0RXH3sKy6qZueowaQXqd93R046547pgZ6lFr8Ctn+zFTCP44o4etcd7OFiyJiGDJetM/64yhc9o42JNzOwhnMwqASDuTD5zVh4ymQ0v3NydYSHelFfV8OgX+zh0rqCRTGg7J96a3gsrCy0xh9KZ++1BAD66pzeBXvYAOFqbU1hezfAFm3C2teDT+6II83Phux1nmP1NfLPrPbCzG3Ov7YpWI1i5O4WPtpwy2m+h1bDkllCC2ziQX1bNo8vjSc0vB+C+oQHcGNkGnV5h4U9H+OO42i60tzLj5UkhdPSyQ1HgmZWHiDtbwFtTwvB3twXAwdqMovIaxr9hmud84U3dGRaitgkfW3rx6/Hm9J5YmWuJOZTBvO8O1u67Y0gg04cEotMrbDqUzgs/1LXPfV2s2frcSF79NclkbcL+HV15ZlwXtBrBqr0pfLrtjNF+c61g0Y0hBPk6UFBWzRPfHCStoIKQNg48f103VUgI3os5SUxSnY/UCPj+wSgyiyp54Ms4k+j+V7w8tQcjuvtQXlnDgx/vIuFsfiOZORNDubm/P462FrS7+/va7X06u/PSlB4EtXXirvf+5Je95xsdK/nvccUHZf4NQggzRVFqWunjrwN+A5Ja6fMboREwY2RHHvk2gaziSr6YFsEfJ3I5Y+gEA2QWVbDw92NM7t2m0fFf7T6PlbmW68K8W1JtIzQCnhzRgUe/O0RWcSWf3x7OH8nGNmQUVbBwzXFujWzChj0pWJlpWs2GocGe+HvY0e/ZDUT4O/Py5DDGvRLbSG7R5DCeWnGAA6fzWfFQX4YEebIlUe0IfRKTfFHnOv/GUDYnmrbDM6SbB/7utgx4PoZwP2deurk741/d1kjupZu68/TX8cSdyefL+6MY3M2D2KQsPoxJ5tXfjwIwfVAAj47uxOxv1aDTnpO5TP9wt0n1N7bDjkELNxPu58QLk0K47vXtjeRenBTCrG8PEnemgGX39WZwVw9ij2Txx7EcXvn1KDq9wqzxXXlgREcW/XKE/NIq7vhoD1lFlXTytmf5/b3p/eymFrEpOrwNHbwdCH34B3p1dOfNu/sweHbjDtqafef5aO0RDr4zodG+H3ac5snPdrWEupdEdJ/OBLZ1I/jGV4kMasvbT1/HwLvebyQ3Zc7XFJepwcpvXprChKEhfL8poaXVrSW6lx+BPk4E3/kFkV28ePuhoQx8/NtGcm/+sJ9tCSmYm2lY+/JERvb0Y8O+M8SfzKLfI19TXlnD3WNDefGOAUxdtKbl7YgMINDXheBpHxPZ1Ye3HxnJwEeWN7bj+z1sO3hOtWPxzYzsFcCGvWqnyc7aggev78meI42DaabCVO8pB2szXpzUnanv7yQtvxxXO4sWs+mvuPa6G7hl8hTmPDOztVUxQiPgmTGduW95HJlFlXx1dy+2HsvhVHZprcz1ET4UVVQz/u2dRAd78ujwDsxcdRitRvDiDd2Y+2MSxzNLcLQ2o0anp6oGbvpwT+3xX9/Ti5gj2Sa3xVQ+A+BsTiljFje+P5ubYcFeBHjY02fOOiICXHjl1gjGvLy5kdwrUyJ4cvl+DpzK4+tH+jM02IvNhzO49+M6//zcjaEUlVcDUFmt45WfE+ni60gXH4dm11sj4Lnrg7j94z1kFFbw46N9iUnKIjmzpFbmxt5tKCyvZtiibYwN8+bpsZ15dEU8HTztGBvmzegl2/FwtOTLeyIZ/spW9ArMu64r245m89CXcZhrBVbmWgAeXRFfe95nrulCcYVpuhJDg70I8LSj79z1RPi7sOjWcMa+vKWR3KJbw5nx5QEOnM7jq0f6MTTYk82HM+nb2Z3oMB+GLdhEVY0eV3tLo+OeM3GbUCNg7viu3PX5fjKLKvjugSi2HM3mZFbd8z2hZxuKyqsZ9dp2Rod68eSoTjz5bQInMku48f3d6PQKbvYWrH64L7FHs9EZBlOm9m3PyexS7Cxbvis7vLsPgZ729JzxKz0DXXltei9GPLehkdz6uFQ+3Xicva9eY7Q9JbeMBz/exUNjuraUylck/7Ulsa+omjJCCFshxO9CiINCiMNCiJsMux4WQhwQQhwSQnQxyEYKIXYKIeKEEDuEEJ0N26cJIX4RQmwGYgzn/FwIsccge61BzloI8a0Q4ogQYjVgfQn6vSGESBRCxAgh3A3b7hZC7DXo/IMQwkYI0RcYDywRQsQLIQINp7jRoMdxIcQAw/FBhm3xQogEIUTH5vxO69PN24GU/HLSCiuo0StsTMpiYEdXI5n0wkqSs0ubTCnbd7aAsqrWinGpdPO2J6WgzoZNR7Ib2ZBRVMnJ7NImC0jtO1tAaZWupdRtRHSoD6t2nQPgwOl8HK3N8XCwMpLxcLDC3sqcA6fVqPyqXecY1d3nb889qrs353NKOZ5u2tH0kaHe/LBHjfrHncnHwdocDwfjhoCHgyV2VmbEnVFt+GHPeaJD1UBYSb3GjY2lttXWxBsR4lXPjoK/sMOcuDMFgGrHyFAvAP6o13iIO5OPt5N6HRNTisgqUoMDx9OLsTLXYmHWMq/isb3a8fXWZAD2nsjG0dYCL6fGr7a9J7LJKChvEZ3+LeMGduPrtQcA2JN4Hkc7a7xc7RvJXQjImGk1mJtrWz0tdlxUIF/HqB2uPUczcLSzxMvZ1kimvLKGbQkpAFTX6IlPzsLXTU3I3JaQQnlljeH4dHzdGtvcEozr05GvN6kjr3uOpKl2uDRhx0H1vabakWmk7/xpA3jtu11UtKD/MNV76rqebVh3MI00wwh8bkmVqU25JHr07IWDo2Nrq9GIYF8HzueVk5pfQY1OYf3hTAZ3djOSGdzZnV/j0wHYlJRFZIAzAH0CXTiRWcJxQ8e7sLyGhslv7VytcbG14MDZApPbYiqf0ZJEh/mwcpeaqXTgVB4ONuZ4ODZogzhaYWdlxoFTeQCs3HWWUWGN2yDX9GzDasP3UValY09yLpXVpmlfdW/nxNncUs7nlVOtU/g9Pp3hQR5GMsODPFi9LxWAdQkZ9DG0DYcHefB7fDpVOj0peeWczS2lezsn7KzM6BXgwso9hnewTmky+DKmuxe/xpkmoDwqzJvvdxqux+k89Z5q4nrYW5tz4LR6Pb7fWXc9bh8UwLvrjlFVowfUrL66c/twLqeMY2lFJtEdIKSNI+dyy0jJV6/L2oQMhnY1vi5Du7rz0wH1+9twOJOoQBcAKqr1tc+DpZkWpV5j0NPBkkFd3Phhb6rJdP8rxkT48u320wDsO5mLg40Fno6Nn9d9J3NrMyjrcz6nlKTzBf+5Qrb/FEUxzc/lyhUVlAFGAWmKonRXFCUYWGfYnqMoSgTwATDDsO0oMEBRlHDgWeCleueJACYqijIImANsVhQlEhiCGiixBe4HyhRF6QrMB3rw19gC+xRFCQK2Go4B+FFRlF6KonQHjgB3KoqyA/gFeEpRlDBFUU4aZM0MejxW7/j7gLcURQkDegIpl/RN/R+421uQVe+FnVVciXuDqPrljru9ZW2HFww2XCYjlZeCl5NVbWMeIK2gHK8GDTMvJyvS/0Jm+uAANs0dxutTI3C0MQfUTsMD0Z147fcjJragsQ3pBeWNOv5eTtakF1Q0kKmz4elrurJ74Uiu79mmdjQaoIe/C+tnDebL+6Po5GXaTqiXo1VtijxARkF5I6fr6WhlFLxIL6jAqwnHPCmqLbFJWY22jwnz5nBKYW2DydT4uNiQkls3QpWWW4q3i80/Osd1vduz+9VrWfHkEHxdbf/+ABPj4+5ASmZB7f+p2YX4uDc9EvvLG3dwbs08Ssoq+XGL6dL/LwUfVztScuoCpKk5Jfi4XXwGrKOtJWN6B7AlvnGa87SRwazfd9okev4dPm52pGTVNepTc4rx+YsAkaOtJWOiOrAl7gwAYR08aeNuz7o9py56jCkw1XvK38MORxsLVj7aj9+fHsSEyLYmtuTKxsPBioyiuu84s6iyyUBGhsGv6/QKJRU1ONmY097VBkWB96eE8c29vZjWr12j848K9mJ9Ymaj7abAlD6jrasNa54eyHeP9KVXgIsJtFfxdrYmLa8uszg9vxzvBs+Ft5O1URskPb8cb2djmaiObuQUVXA6q4SWwNPRyuhZzSioaPK7vyCj0yuUlNfgbGN+0WPbuliTV1LFKzeF8Mvj/XjpxmCsLbRG5+wV4ExOcRVnc8owBV5O1sbvqfzyRsE674bvsvy6d1mApx29O7jy+zND+HHGQLq3VwOaNpZaHozuxGu/mTZZ39PRiox6QYmMwopGz3d9GZ1eDXw5GdquoW0c+eXRvvz8SB+e/+lIbZBm1rguvLr2eKsFNbydbUit95yk5ZX947aURNKQKy0ocwgYIYR4RQgxQFGUC5N1fzT83g/4Gf52BL4XQhwG3gCC6p1no6IoeYa/RwKzhBDxQCxgBbQDBgIrABRFSQD+Ls9dD3xn+HsF0N/wd7AQ4g8hxCHg1gZ6NKQpO3YCs4UQM4H2iqI0Gr4WQtwjhNgnhNiXtefXv1FTcjWzbOsp+sxdz4gXY8gsqmD+hBAAZozryicxyZRVtl4W0D9h8a9H6D1vA6v3pTBtoD8Ah88XEjVvA9GLYvli6yk+vSeylbW8NB4a2ZEanVI7QneBjl52zBrflWe+a70pNP+UNfvO0/WB7+k942c2H0zjk4cGtLZK/4jxj3+O/zUvYmluxuAegX9/wGWCViNYNnM07/8Sx5kM4xoVNw/pQkQnT974YX8raXfpaDWCZbPH8/7q/ZzJKEQIeOW+ocz8qPH0iCuBpt5TZhpBSFtHbv9gF1Pe28mjozrh79H6wcurEa1GEN7Oidk/JjL98/0M6eJBpL+zkUx0sCfrDrVMUKa5aOgzsooq6TN/E2MWb2Ph6kTevj0CO6vLu/rA9ZFta7NkrlS0GkGQrwNf7zzH+Df+pKxKx71DAoxkxoX58Ft8y027/KeYaQROthaMfXkLC1Yd4uN7ewMw45pufLzpxGXfJkxIKWT8WzuY9P5u7h7kj4WZhkGd3cgrqSIprXVqqElaDr2imOTncuXyfqs3QFGU40KICGAM8IIQIsaw60JqhI46mxYCWxRFuV4I4YcacLlAab2/BTBBUZRj9T9LCPGv1TX8XgpcpyjKQSHENGDwXxzTyA5FUb4WQuwGxgJrhBD3Kopi1IJVFOVj4GOAqEVb/++7Lbu4Co96mTEe9pZk18ucuRLILjYeZfOwtyT7MkkfvxjTBgVwa38/AOLP5uNTb8TJx8majALj1MeMggqjUan6Mjn1rtdX28/w5QN9AAj3c2FshC9zbwjGwdocvaLO8f4itnlGp28f6M8tfdsDcLCBDd5O1o2mwmQUGI/2eDdhJ8DqvSl8eX8Ur685ZjRdYEtSFi9qNTjbWpBf2nzX97YBftzcRx1tTThXgE89Hb2crBuloWYWVhiNrns7GY8KTYxsw7AgD25517gGi5eTFR/f1YsnlsdxzkQjbBe4J7oL04d3AmB/cg5t6mW3+Ljakp536Z+fV1J3fy3dfJwXpvZsPkX/AfdOiGL6eDUot/9ICm08nQA1xdvX3ZG07IunY1dW1fDrH0lcM7Abm/cmt4C2ddw7rjvTRwUDsP94Jm3qZZT4utmRltP0qPJ7jw7nZFoB7/5kXMxwSFg7Zt4cycinv6fKRNMCmuLe8eFMH9MdgP3HMmjj4QCJagfS182etJymG8vvPT6Kk6l5vLtarWdvb21BNz83Nrw6GQBPF1tWLbiBic/+aJJivy3xnkovqCC/NIvyKh3lVTp2J+fSzdeR01mljY6TQFZRBV71puh6Ohhnu6oylXgZtms1AjsrMwrKqsksquTA2QIKytS6JdtP5NDV2549hqm9nTztMNMIjphwym5L+IyqGn1tNuXh84WczSnF392WQ+cbFxH+f5g+OJBbDUHF+NN5+LjYAGoBeG9na9IbPBfpBcaZMd7OxpkzWo1gTIQvI1+IoaXILKwwela9nKya/O4vfN9ajcDO2oz8suqLHpth+Dl4Tv2e1yVkcO/QuqCMViOIDvHkujd3NKst0wYHcOsA9XocPNPgPeVsnL0HarZVQ5kL77L0/HLWGKZWxZ/JR68ouNpZEOHvwrgIX+ZNCMHBxtAmrNHzxZaTNCeZhcaZYF6OVo2e7wsymYbn297wfNfnVHYpZVU6OnraEdHeiSFd3RnY2Q1LMw22lma8cmMwM7837QIjdw7vyG2DOwAQdyoX33qZMT4uNv+oLSWRNMUVlSkjhPBBnVK0AliCOg3pYjgCF4amp/2F3HrUmjTC8Bnhhu3bgMmGbcFA6N+opwEmGv6eDFyo7mYPpAshzFEzZS5QbNj3lwghAoBTiqK8Dfx8CXr83xxJL6KtizXejlaYaQQjunnwR3LLrQrTHBxJL6atc50Nw7u6X/Y2LN16ihEvbmbEi5tZF5/OxCi1gRfh70xRRTVZRcYOOKuoguKKaiIMI4ITo9qxPkF1uvXrz4wO86mdK3z9a9voPWc9vees59PNJ3ln3bFmC8gALNt2mlGLYhm1KJb1CRm1Kfvhfs4Ul1c32cguqagh3E+1YUJkWzYkqDUD/NzrggYjQ71qC/XVn0oX1t4JjaBZAzIAX/5xhjGLtzFm8TY2GNnhRHHFxeyoJtzPqdaOjYfUzuSgru7cN7wDd36yl4p6HWYHazO+uDeSV345wr7Tjav1Nzcfrz9Kn6d+oc9Tv/Dr3nNMHqQ2Knp1dKeorOof1Y6p35kY27Mtx1IKmlvdS+KjH3YRdfvbRN3+Nr9uS2TyaNUVRAa1pai0goxc406YrbVFbZ0ZrVbD6L6dOXbW9IU/G+n920GiHvqKqIe+4tedJ5k8TC3yF9nFi6LSKjLyG3fc59/WF0cbS2Z8FGu0vXugO+8+MoyJz/9CdmHL1v/56Jc4ou5bStR9S/n1z+NMHq4GmiK7+lBUWklGXhN2TBuAo60lMz6o66gVlVXRduI7dJn6IV2mfsieI2kmC8hAy7ynNiSk0yvQFa1GLQoa7udMcoYc0b0YiWnFtHO1wcfJCjOtIDrYk63HjBfT3Hosh2sMxfeHd/Ngr+G9uSM5lw6etliZa9BqBD38nI0KBI8K8WTdIdMWtm8Jn+FiZ4HGME7Y1tUGf3dbzuU2Xwfwi9iTDF+wieELNrEuPo1JUWrgMiLARX0uGgQ3sgorKKmoIcIwjWpSVHvW18sWGdjVg+T0YqNAjalJOF9Iezdb2rhYY64VjA3zJibReMpwTGIW1/f0BWBUqBe7DG3DmMQsxoZ5Y6HV0MbFmvZuthw8V0BOcRXpBRW1qyz17ehqVDi4X0dXTmWVGgXVmoOlsacYsTCGEQtjWBufxo19DNfD/+LXo7i8mgh/9Xrc2Kc96ww1mNbFp9GvszsAAR52mGs15JZUcd2SrUTOXkfk7HV8EpPM22uONntABuBwahHt3WzwdVavy+hQL7YcMb4uW45mc12EWgNnZLAnuw21inydrdEabnwfJysC3G1IzS/njQ3JDH1lGyOW/MGT3yaw+1SeyQMyAJ9tOsGguWsZNHctv+9P4eb+auCsZ6ArRYbgnqR5+a/VlLmiMmWAENSaL3qgGrXuy6qLyC4Glgkh5gK//8U5FwJvAglCCA1wGhiHWp/mCyHEEdRaMH+XG14KRBo+Lwu4UIR4HrAbyDb8vhCI+Rb4RAjxCHXBnKaYBEwVQlQDGRjXxmlWdAq8uiGZt24KQSMEvyVkcDqnjLsH+HE0vZg/knPp6mXPKzcEYW9lRv8Ortzd34/Jn6mjnh/eGkZ7V2uszbX88kAUL649xu4W6HQ2tOG1jcm8OSlYteGQwYb+7TmSUcz25Dy6etmx6IYg7C1VG+7q355bP1Mv7weTu9Pe1Robcy0/P9Cbl9Yeb1EbYg5nMCzYkx0LR9YuiX2BjXOGMuJFNUnqma/j1SWxLbRsScysXfZ67g3BBLV1RFHU6u5Pf9XyywRuTsxkaJAn2+cPp7xax5Mr6nRYN2swoxbFAjBnZQKvTwmvXRZ0i2H+/DPXdiPQww69opCSV85sw1KbY8J9mDrAD51OoaJax4NfmHb1+M1JWQwJ8mDbs0Nrlze9wJqnB9auhDF35SF1eVMLLbFJWbV2LJgYgoWZhhUPRAF1y5jePsAfPzdbHhnViUdGqRksU9/f1SIFQdcfSCE6vA2H3plAeZWOe9/7o3bfziXj6fPULwC8MKUnk/oHYGNhxvEPJ7E05jgvfR/P/WO6MaZnW3Q6hbySSu59r/HKIi3Nuh3HiO7bhcTvn6Ksspp7X6hbdnLXskeIuv1tbK0sWLX4NiwszNAIwbYDp/hkdcus4nUx1u09TXQvPxI/n05ZRQ33vlG3csOud28l6qGv8HWzY9YtvTl6Lped76gx/Q9/PcjS9Yd56c6B2FqZ89XssQCczy7mxud/aXk79pwiuncgicvuoayyhntfrVsBateH04i6bym+bvbMurWvascH01Q7fj7A0rWtN3XPVO+p5MwSYpOy2PDMEBRF4ZsdZzlm4uLql8LMGU+wb+8eCgryGTF0IPc/+DA3TLixtdVCp1dYtOYYH0wNRyPg57h0TmaXcv+QAJLSith6LIfVcWm8eH03fnmkD0Xl6pLYAMUVNSzfeZ6v7u6FAmw/kcsfJ+oGYUYGefJQvfe2qTGVz+gd6MoTYzpTrdOjKDB75SEKG2QSNBebDmUwLMSLXS+Oql2CuXbfs8MZvkBdKXDWV3G8ZViCefPhDGIO1wW/rotsy+omlvjd+/Jo7KzNsdBqGBXuw81v/NFsCw/o9ArPr07ii7t7oRWC7/emcCKzhEejO3L4fCExSVms3JPCa7eEEjNrIAVl1TxmWEHpRGYJaw5msO6pAdTo9Ty3OrG2YPSCn5J4fXJ3zLWC83nlzKw33XhsmDe/mnjqUsyhDIYFe7HzxWi1TVjvemycN4wRC9Ug9zNfx/HmtJ5YWWjZfDiTzYbr8c2fZ3jj9p5smT+cap2eR03cbmqITq/w4i9H+WR6BBohWL0/leSsUh4aHkhiShFbjmbzw75UXrkxmHVP9qegrJoZhtU2I9o7cfcgf2p0evQKLPz5SKMMmtZi48E0RoT5sP/Vayiv0vHQJ3WZbVtfGM2guWsBeO7mMCb28cPGwozDb13H8tiTvLL6EOH+Lix/bCCOthaMCvNl1g0h9H2m5VdPvNxprdWXhBCjgLcALfCpoiiLGux/ArgLqEHt39+hKMrZf/25/7Xlpq52/s30pcuJZpg+dllw9nTO3wtd5mjNtH8vdAWg0VxRiYFNkpN+5d9PAPqUY38vdCXg5NnaGvx7ai7v6Z2XilvnLq2twr/mxJvXtrYKzUJUC05bMSX5V8F0hMryK2sK+sWwc7zy6zKVFl359xOAcyut9NfcZJy/8ttTecsnXx2dpYsw/uO9JunT/nJPr4t+b0IILXAcGIG6uM5e4BZFUZLqyQwBdiuKUiaEuB8YrCjKTU2e8B9w5fdSJBKJRCKRSCQSiUQikVwV6BXT/PwNkUCyoiinFEWpQp3ZYjRioijKFkVRLkRZdwFtmsPeK236UqtjKLrbcJ3oqYqitO76qhKJRCKRSCQSiUQikUiaRAhxD3BPvU0fGxbNAfAF6s+/TAF6/8Xp7gTWNodeMijzD1EU5a8ujEQikUgkEolEIpFIJJL/EwXTVOSov2rxv0EIMQXoCQz610ohgzISiUQikUgkEolEIpFILhNaqextKtC23v9tqFvNuRYhxHBgDjBIUZRmKd4la8pIJBKJRCKRSCQSiUQi+S+zF+gohPAXQlgANwNGS1sKIcKBj4DxiqJkNXGO/wuZKSORSCQSiUQikUgkEonksqA1VohWFKVGCPEQsB51SezPFUVJFEIsAPYpivILsASwA743rBZ8TlGU8f/2s2VQRiKRSCQSiUQikUgkEsl/GkVR1gBrGmx7tt7fw03xuTIoI5FIJBKJRCKRSCQSieSy4BKWr76qkEGZq4xWKorU7FRW1rS2Cs1CeUl5a6vwr9Foro7SU9Z21q2twr/GytqqtVVoFsyDe7a2Cs2CuaV5a6vwr9Hr9K2tgsRA1Asxra1Cs7Br7rDWVqFZ6PvS5tZW4V9zZPvR1lahWVDa+bW2Cv+a1piKYQpcXK78thRASbFda6sg+RuulmfmUrk6elsSiUQikUgkEolEIpFIJFcYMlNGIpFIJBKJRCKRSCQSyWXBfyxRRmbKSCQSiUQikUgkEolEIpG0BjJTRiKRSCQSiUQikUgkEsllgf4/liojM2UkEolEIpFIJBKJRCKRSFoBmSkjkUgkEolEIpFIJBKJ5LLgP5YoI4MyQojxQDdFURY14zk/BV5XFCXpL2SWAr8pirKqwXY/oK+iKF83lz7/L1EBzjwxvAMajeCX+HS+3HXeaH9YW0ceHx5IBw875v2UxOZjOa2kKfQNdOGpUR3RaAQ/HUjniz/PGu031woWXteNrj72FJZVM3NVIumFFQB09LBl7rgu2Fpq0Ssw5ZN9VOn0jAr25I7+7VGA7OJK5v6YREF5dYvatWhqD0aE+VJeWcMDH+8k4Ux+I5m5N3bn5v7+ONpa0PaulbXbHxjdhamDO6DT6ckpruThj3dxPre0JdUH4OUpEQzv7kN5pY6HPtlFwtnGNsyZGMpN/fxwtLWg/T11j0Sfzu68eGsEQW2duOv9Hfy693yjY03JghtDGBrkSXm1jse/PMDh84WNZELaOvLGbRFYmWvZnJjJs98fMtp/z7AOPDshmJCn1pBfWgVAn45uPDcxBDOtIL+0iolvbG8Re+DKvR4v3NydYSHelFfV8OgX+zh0rqCRTGg7J96a3gsrCy0xh9KZ++1BAD66pzeBXvYAOFqbU1hezfAFmwj3c2bJbT0AEMCrvyaxNi7NpHY8PzGEoUEelFfpeGJ5HIdTmr6nXp8agZW5hs2JWcxfpd5TM8Z2YWSoF3oFcosreWJFHJmFFYwM8WLGuC7oFdDpFZ5bdYi9p/JMZsOCSaHqc1Gl4/Ev9zf9XLRzMn4uViYA8MTYLkzu70decSUAi35OYnNiJs62Fnx8dyTd2zuzctdZ5n6XYDL9L2CKaxHV0ZXP7unN+dwyANbGp/HWuuMms6FvBxeeHtUJjUaw+kAaX2xv7PteuD6onu87TFqBwfd52jF3XBfsDL7v1k/2YqYRfHFHj9rjPRwsWZOQwZJ1J0xmwz/h2bnPsG1rLC4urvz482+trY4RfQNdmBHdEa1GsDounaUXa4d421NQXs0sQztkdLAnt/VtVyvX0dOOyR/v5XhmCR/fFo6bnQWVNerS9Q+siCe/rGXbIa89MJToXgGUVdZwz6triE/OMtpvbWnGV3PHE+DjhE6nsGbXSeZ9vg2Au8Z2597x4ej0CqXlVTz45gaOnsttEb1fvCWs1mc88vnepn1Geyfenh5Z6zPmfBMPwMf3RhHoqfoMBxtzisqqGbZgIwDd2jiyZGoP7KzMUBSIfmFT7fUxjR3hDA/xorxKx8Of77mIHc68fUcvrM21bDqUwZxv4gAIbuvE4qk9sDLXUKNXmLniAHGn8xgV5sOs64LRKwo1eoV538SzO9n0bfjefs48OjQAjRD8diiDFXtSjPZ3b+PAI0MCCXS35bnfjhJ7XNXJ08GSl67thkaAmUawKi6Nnw9mmFzfhjw/IZghBt/35IqL+4zXpoRjZa5lS2Im8384DMCTYzszMsQbvaKQW1zJkyviyCyqJNDTjldvDSO4jSNLfjvKx5tPtrRZly3/tSWx//NBGUVRfgF+aeZz3vUvDvcDJgOtGpTRCHhqZEce/jaBrKJKlk6L4I8TuZw2NDQBMosqWPjbMW7t3aYVNVV1nTWmM/cvV19wX93dk63HsjmVU6frdeE+FFfUcO07u4gO8uDR4YHM+iERrRC8cEMQ81YncTyzBEdrM2r0erRC8NSojkx4bzcF5dU8OjyQmyLb8NHW0y1m14juPgR6OdDjyV/oGejKa9MiGfHc+kZy6w6k8MnGY+x7dbzR9oQz+Qydt5byKh13DOvIc7eEc+e7Ldf5Bxge6k2Apz29nvqNnoGuvDqtJyOf39hIbn1cKp9uPM6eJeOMtqfklvHQJ7t5aHSXllK5lqFBnvh72NH/uU1E+Dnz8s3duWbJtkZyL98SxtNfxXPgTD7LH+zDkG4ebElSG63eztYM7OpOSr3nxsHanBdvDmXKuztJyy/H1c6ixWy6Uq/HsGAvAjzs6TNnHREBLrxyawRjXt7cSO6VKRE8uXw/B07l8fUj/Rka7MXmwxnc+/HuWpnnbgylyBBcPZpWRPQLMej0Ch6OVmx+djgbDqaj05umITCkmwf+7rYMeD6GcD9nXrq5O+NfbXxPvXRTd57+Op64M/l8eX8Ug7t5EJuUxYcxybz6+1EApg8K4NHRnZj9bQLbj2Wz4ZDaQO3i48AHd/RkyAuNv5/mQH0ubOk/fyMR/s68fEsY1yze2kju5Vu68/RXcRw4nc/yh/owJMiTLYmZAHwSk8xHm5KN5CuqdSz+9QhdfOzp7ONgEt3rY6prAbDnZC7TP9zd6FzNjUbAM2M6c1+t7+vF1mM5nMquC75fH+FDUUU149/eSXSwJ48O78DMVYfRagQv3tCNuT/W8306PVU1cNOHe2qP//qeXsQcyTa5LZfKtdfdwC2TpzDnmZmtrYoRGgEzR3fmAUNHa8VdajvkdIN2SFF5Dde+u4uR9dohaw9nsvaw+mx08LDltUmhHM8sqT1uzuokjqQXt7hNANG9/An0dSZ4+qdEdvHm7UdGMPCRrxrJvblqL9sOnsfcTMPaV25iZC9/Nuw9zXdbjvDp72pwfGxUIK/cO4Rr56xqdHxzMyzEC38PO6Jmr6VHgAuLp0Qw+qXG78TFU3rw5Jf72H8qj68frfMZ93y0q1bmuUmhFBkCYVqN4L27Innw0z0kpRTibGtBtc50AZlhIV4EeNrR+4IdU3sw+sWYJuyI4Mllqh3fPDag1o5nbwzl1V8S2Xw4g2EhXjw7MZTrl8Tyx5Es1sVvANQg0yf39aHf3HUmswPUZ+SJ4YE8/v1hsoor+XRKGNtP5nHGqF9RyUtrj3FLL+N+RW5JFfd9HU+1TsHaXMOX03qwPTmPXMNAV0swpJsHfh62DFyg+owXbwrl2tf+aCT34k2hzPzmIHFn8ll2f+9an/FRzEle+/0YANMH+fPo6M7M/i6BgtIq5q86THSoV4vZIrk8uapryggh/IQQR4UQS4UQx4UQXwkhhgsh/hRCnBBCRAohpgkh3jXILxVCvC2E2CGEOCWEmPgX5x4shIgVQqwyfMZXQghh2BcrhOhp+PtOw2fvEUJ8cuGzDAxs4rMWAQOEEPFCiMeFEEGGY+OFEAlCiI4m+rqM6ObjQEp+OWkFFdToFTYeyWJgJ1cjmfTCSpKzSzFR/+WSCfZ14HxeGakGXdcnZjG4i7uRzODObvx6MB2ATUnZRAY4A9An0IUTmSW1DaDC8hr0CgihjpxbW2gBsLM0I9swsttSjOnRhm+3nwJg38lcHG0t8HSyaiS372QumYaRz/psP5JJeZUOgL3JOfi62JhW4SYYHdGG7/48AxhssLHA0/EiNhQ2tuF8TilJ5wtapdjXyFAvVu0+B8CBM/k42Jjj4WBpJOPhYImdlRkHDBlMq3afI7q7d+3+5yYE8+LqROprf12vNqyNTyctvxxQGxstxZV6PaLDfFi5Sx11PnAqT70WDfT2cLRSr4UhQ2TlrrOMCvNpdK5rerZh9R41w6e8SlcbgLEy12Bqq0aGevOD4bPjzuTjYH3xeyrOcE/9sOc80aHqPVVSUVMrZ2Op5YLCZYbn/MJ2U9oR3d2bVYasyQOn83G8yHNhb2XOgdOG52LXeUbVey6aorxKx96TuVRWm66DUx9TXYuWRPV95aTmV1CjU1h/OJPBnd2MZAZ3dufX+Au+L+tvfV992rla42JrwYGzBSa35VLp0bMXDo6Ora1GI4J9HUjJb9AO6dy4HfJbgnotYpKy6eXv3Og8o4I92WAIXl4OjOvbka83JgKw52g6jrZWeLnYGsmUV9aw7aD6LFXX6IlPzsTXTc0yKS6r82+2VuYoLfSgjArz4fudqs/YfyoPBxuLi/qM/Qaf8f3Os4wOb+wzxvdsW+szBgd5kpRSSJIhQyK/tMqkbeDRYb6s3HGm1g7Hi/g+e2vzWjtW7jjDmHBfQJ3+YW9tDqgDQhkFarujtLL++8usRaaJdPWyJyW/grRC9RnZdDSb/oEuRjIZRZWczClr9J3W6BWqdepGc60GjTC9vg0ZGeLFD4bMnkv3GSlEh6jBFiOfYaGt/c5zS6pIOFdAje6/lRVyKSiKaX4uV/4LmTIdgBuBO4C9qFko/YHxwGzgpwby3ob9XVAzaP4qpB8OBAFpwJ9AP6A2FUEI4QPMAyKAYmAzcPBvPmsWMENRlHGGc7wDvKUoyldCCAtA+0+M/3/xsLMgs6guCJFVXElQC4xe/j942Fsa6ZpZVEmwr7GuHg6WZBSqMjpFoaRCh5O1Oe1crVEUeO/W7jjbWrD+cCbLdpyjRq/w0u/HWHl/JOVVOs7nlfPymmMtape3sw2p9UYQ0vLK8Ha2aTIA83dMHRTIxoOmnZbRFN4u1qTm1Y3apuWV4e1i02SH/3LDy8m6NnACkJ5fgZeTNVn17jUvJ2vSCxrLgBrUySis4EhqkdF5AzzsMNMKvn+sP7ZWZny25SQ/7G6ZaUBX6vXwdrYmLa/uWUjPL8fbyZqsenp7O1mTbnS9yvF2tjY6T1RHN3KKKjidVTcKHe7vwpvTetDGxZaHPt9jsiwZAC8nK+N7qqD8IvdURQOZukb409d0ZUJkW4rLq5n09p+120eFejNzfFfc7C25/cO6Ud7mt6Hhc3ExGxrbeYHpgwOY2LsdCecKWPDDIQpbeDqGqqPprkUPfxfWzxpMZmEFL6xO5HiGabIcPBysyCiq0y+zqJKQNk34PoNNOr1CSUUNTjbmtHe1QVHg/SlhONuas/5wJkv/PGd07KhgL9ZfRgGCyxl3+7o2BkBWE+2Q+jL12yH1p0WP6ObJEw2m7j03vit6RSHmSDaf/nHGdEY0gY+rHSnZdfdvak4xPq52ZOQ1PRXa0daSMVGBvLt6f+22e68J55EJPbEw1zDqqe9MrjOo/iDVyGeU/a3PSDP4lfpEdXQju57PCPS0R1Hg28cG4GpvyU97z/PeOtO1Db2crUnLa6zj39nhZfB9c7+N47vHB/LcpO5oBIytl2E6JtyXORNCcHOw5Na3TJ9F7W5vSVa9wc3skiq6edtf8vEe9hYsviGYNk5WvL/1dItmyYDqM+p/zxkF5Xg5Whn7DEcrMur5jIwGPuOpcV1qfcZN7+xoGcUlVwxXdaaMgdOKohxSFEUPJAIxijpJ7RDqVKGG/KQoit5QD8bzb869R1GUFMO545s4XySwVVGUPEVRqoHv/4/P2gnMFkLMBNorilJ+ETnJ/4FWIwhv58icH5O44/P9DO3iTqS/M2YawcSevtzy0V5Gvv4nxzNLuKO/X2ur+38xqZ8fYQGuvPP7RUscSZoZK3MtD0d34tVfjzTaZ6YRhLZz4rb3d3LrOzt4bHRn/D1smziLpLm5PrJuxPMCcafzGDR/I6NejOGR0V2wNLu83eLiX4/Qe94GVu9LYdpA/9rt6xLSGfLCZu76eA8zxnZtRQ3/mi+3nabvvA2MfGkzWYUVPDshpLVV+r9p6locPl9I1LwNRC+K5Yutp/j0nshW1rJpVN/nxOwfE5n++X6GdPEgskHmRnSwJ+sOyaBMSxHs60BFtY6T9aafzVmdyE0f7eHOpQcIb+fE2Mt4ioNWI1g2exzv/3SAMxl1tTY++jWOoGmfMPfTbcy6tU8ravjPub53OyOfodUIendw44FPdzP+lS2MCfdlQBePVtTwr5k2uAPPfhdP+FO/Me/beN6c1qt235q4VPrNXcft7/7JrOuCW1HLSyOruIppyw5w06f7GBXkibONeWur9I9Z8ttRop7dyE8N/LekaRRFMcnP5cp/IVOm/pwTfb3/9TRtf335v0uQqy+ru8j5LvX4Jj9LUZSvhRC7gbHAGiHEvYqiGE2MFULcA9wD4Hfdk3hEXvMP1WhMVkkVnvXS8jzsLVt8+s6lklVcaaSrp0NjXbOKKvFyVKP0WiGws9JSUF5NVlElB84W1I5UbU/OpYu3PSWG1M4UQ1R8Y2IW0/u3w9TcNbwTtw0JBNRpGr6udVOOfFxsSM8vu9ihTTIoyIsnxgcz7sWNVJmwEF197hzWkamDVRviTufi62ILqMXafFxsSM/7Zza0JLcP9GdyPz8ADp7Nx6depoW3s1Vt6u8FMgqMR9YuyPi529LWzZYNc4aq252sWPfMYMYt3kp6QTn5pVWUV+kor9KxOzmXbr6OnM4yTRHmK/V6TB8cyK2GRkv86Tx8XGwAtUCkt7NxJgaoWQzeRtfLePRQqxGMifBl5AuN5+MDnMgoprSyhi6+jhxsovjx/8vtA/25pW97oIl7ysn6IveUVQOZxplMq/em8OX9UbzeIINv98lc2rnZ4GxrUVtY+l/bMMifWw3PRfzZggbPxcVsaNrOnHrv5q+2n2HZgy3XSWuJa1E/RX1LUhYvajXNei3qk1VUgZdDnX6eDpZGo7aqTCVehu1ajcDOyoyCsmoyL/g+Q5bS9hM5dPW2Z49hylknTzvMNKLVaplcaWQXq22MC3g4GGcF1Jdp2A65QHSQR6PMpOxi9b4pq9Kx7nAGwT4O/J5g2gKn914TzvQxoQDsP5ZOG/e6bAZfN3vSckuaPO69x6I5mZpvlCVTn5WxR3jrkRHA2mbXGWD6kECmDAgAIP5MnmHK9gWfYfO3PsOngV/RagRjI3wZsXBT3TH55ew8kU2eYdrxpkPphLR34o+jxsWP/w13DOnAFIPvizuTj4/LxXW8mB0ZBt93U9/2tUV/f9mXwhv1gjIX2HU8h/butrjYWdTaZQqyiyvxsK97RtztLP6vfkVuaRWnc0rp3saxthCwqbhtgF+tz0g4V2D0PXs5WZPRIMs4o7DCKDPG62I+Y18qy+7r3ch/S4y5jOMnJuHyHhK88tkLDBJCOAshzIAJl3BMMVDrAYUQAcApRVHeBn4GQhseoCjKx4qi9FQUpWdzBGQAjqQV0dbZGm9HK8w0ghFdPdh2omUq5v9TElOLaedqg4+Tqmt0kAexDVaC2no8h2sMNQ2Gd3Nnr6HhueNkHh087bAy06AVgh7tnTiVXUp2USUB7ra1kfioQGejgn2m4tNNxxk4Zy0D56xlzf7z3NxfbWD0DHSlqKzqH01dCmnvzBt3RDL59a3kFLVcQO2zmBMMnreOwfPWsWZ/KjcZOnOqDdWX9VSZZdtOE/3yFqJf3sK6hHQm9lYDcRF+zhSX1zTZ4SmpqCHCTx1hnti7HRsSMjiaVkTYzLX0mbeBPvM2kF5QwaiXY8kuqmR9Qjq9Al3RagRW5lrC/JxJNtH0Brhyr8cXsScZvmATwxdsYl18GpOi1IZRRIALxeXVRunbAFmFFeq1CFDnqE+Kas/6+LopewO7epCcXmwUqGnnZoPWMDm9jYsNHbzsm32FsmXbTjNqUSyjFsWyPiGDCZFtAQj3c1btuMg9FW64pyZEtmWDoQ6Fn3tdRtXIUC+SDfVA/Nzqtge3ccTSTNOsQYBlW08z8qUtjHxpC+sPpjExSrUhwt+ZoovYUFxRTYQh82JiVFvWG2p61Z+DPzrMm2NpxtP7TElLXAv3ep2OsPZOaAQmCcgAJKbV831aQXSwJ1sb+r5jOVwTdsH3edT5vuRcOnjaYmWuQasR9PBzNioQPCrEk3WHWn51kyuVxNRi2roYt0O2Hm98LcYZahINq9cOAXVUbkQ3T9YfrgvKaIXAyVAPxEwjGNDRjeTspgMizclHv8YRdf8you5fxq87kpk8IgiAyC7eFJVWNjl1af60/jjaWjLjA+NiuoE+TrV/j+4dSHJq8wW8G/LFlpMMW7CRYQs2sjYulRv7qD6jx9/4jB4Gn3Fjn/asa+AzTjTwGVsSM+jq64i1hRatRtC3kzvHm/kd9vmWZIY+v5Ghz6t2TOrrV2tHUVnTdhSXV9faMamvH2vjUwHIKKigr6G20YCuHpzKVNsa/h52tceHtHPCwkxj0oAMwNGMYto6W+HtaImZRjC8izt/nry0VQLd7SywMGSx2luaEerrwLkWGFD68o8zjH5lK6Nf2cr6hHQmRKoFiMP9nCmuuBSf0aa2CL+Rzwjx4mSm6Z9lyZXFfyFTptVQFCVVCPESsAfIA44CjddPMyYB0AkhDgJLAUtgqhCiGsgAXjKdxnXoFHh1YzJv3xyCRgh+TcjgdE4Z9wzw40h6MX8k59LV257FNwRhb2XGgI6u3D3Aj1s+3dcS6jXQVeGVNcd5f0oYGiH4OT6NU9ml3D/Yn6S0YrYez+GnA+m8cH03fn44iqLyGmatUpeoK66oYcXOc6y4uycKsP1ELtsNwaePt57m02kR1OgV0gsqmP9zy07/2RCfxojuvhx4bTzlVToe/Hhn7b5tL45m4Bx1tOn5m8OZ0NcPGwszDr99Pctjk3nlx0MsuCUcWyszlj7SH1BXzpn8euNVUkzJxoNpjOjuzb4l49TlHD+tW5EkduEoBs9Tq/3PvymMiX3aY2NhxqE3r2X51pMsXn2YcH8Xvnx0AI62FkSH+zLr+hD6zV7TIrpvPpzJ0CBPtj8/goqqGp5YHle7b/0zQ4h+eQsAs789yOuGpX9jEzPZ/Dc1GJIzSohNymTjnCHoFfjmz7Mc+x97Zx0fxfEF8O/cxYm7AAkJFiK4u0tpaaFAjR9199JCKaUUWqDubtRbKlQoHtwlSHCXuPvF7ub3xx6XXBIKLbkE6Hw/n0Cy+3b3zc6OvXnzpp5moy/X/FiRoO0csfnFYRjKjDw6r7KeWTF9EINmajOZU77dyVu3ddK2Yd6bStzeykHltV2asKDaFt5dmvvy0PBWlBslJpNkyrc7bdoxXbnP/E09NwhDubal5lmWTOnHsLmrAXhm/h5eP7ul5v40y25eT49qQ4S/KyYpScw2MNW85ffwdkGM6dqECqOkpNzI/Z/brh6O25vGgOhANswcrG0l/VW85dyyqf0ZMttcLr7fzRsTta1YV1UpF9NGR9OmsQdSQmJ2MZO/rXwHm18YgquTPQ56HcPaBnPj2xs4YiODpa3yYkT7YCb0DsNozosHvrBdXhhNkrmLDvHBhPboBPy+M4VjGUXc1z+c/cn5rDmUyYKdybx4XRv+eLg7+QZtS2zQ2r6vN53h27s6W9q+dVUmXoZEBfDgt7tspvu/ZfKkx9m+bSu5uTkMHtCH+x54iNFjxja0Wlo/ZPFh3rtZ64f8Ye6H3Gvuh6w9nMlvO1OYdV0bfn+wG3mGCp42b5UL0CHUk7T8EpKqTLzY2wneu7ktdubApltO5LAgvn5jwy3ZepyhXcLZN+8uikvLuefVSi+XzR9MpNt9XxLi68qUm7pz8HQWm96fCMCHv8czb0kC943qQP/2oZQbTeQWlHDXK/XTfmttRhBbZg/HUGbkkS+2Wc7FTR9s2d568jfxvH17Z5zs9cTtTSUuoWqb0ZQFW63jLOUVl/Ph8sMseWag+TkprLCh8XLFnhQGxQSxdc4IissqeOTzynSsfG4wA56vko47uuBsr23tfTYdT3y5nRdubIedXkdJuZEnvtK8mEZ2bMzY7qFUGE2UlBu524ZxyM5ilPB63DFeHxONTif4KyGNE1nF3NEzlIOpBWw4lk3rQFdmj2qDm5MdPSO8uaNHUybMiyfUx4UH+4VrrhNC8P32JKsdVuuDlfvS6d8mgHXTB2IoNzKpSpuxeHJfhr+k9a+n/binckvsA+mWNmPKNZHmNgOSsot52hw7ys/NkYVP9sHVyQ6ThDv6hTNw9iorr8v/KpfyUiNbIP5rCa5vhBCuUspCs6fMAuBzKeUCWz2v65w1V0SGllXZTeRy5uSRlIZW4aLR6a4MhzpnV+fzC13iGAqvjJBS9o6X31rw2rgS0mGy4Xau9YlOf/nXUz6+rucXugzYPG1gQ6tQJ/SoZQvly40D62tfSnS54d40rKFVuGiulPFWy8i/30nvcuF0HS5TbihOv3NNA+xDVX/0fm29TQrNuid6XZLvTXnK2J4ZQohBgBOwjJq7PSkUCoVCoVAoFAqFQqHgyjFkXijKKHMehBAxwNfVDpdKKbteyPVSykl1r5VCoVAoFAqFQqFQKBRXHv8xm4wyypwPKWUC0K6h9VAoFAqFQqFQKBQKhUJxZaGMMgqFQqFQKBQKhUKhUCguCf5ry5cu/8h4CoVCoVAoFAqFQqFQKBSXIcpTRqFQKBQKhUKhUCgUCsUlwX/MUUZ5yigUCoVCoVAoFAqFQqFQNATKU0ahUCgUCoVCoVAoFArFJcF/LaaMMspcYSQn5ja0CnWCm4dzQ6tQJ+jt9A2twkUTEurX0CrUCRlpeQ2twkXj4evR0CrUCf7+jRpahTqhtNTY0CoozJhMl3/nLSe7uKFVqBN6zF7Z0CrUCRunDmhoFS6aFo8UNLQKdUJFeUVDq3DRlJWWNbQKdcKpk9kNrUKd4Onl0tAqKM7Df8wmo5YvKRQKhUKhUCgUCoVCoVA0BMpTRqFQKBQKhUKhUCgUCsUlwX9t+ZLylFEoFAqFQqFQKBQKhUKhaACUp4xCoVAoFAqFQqFQKBSKS4L/mKOMMsooFAqFQqFQKBQKhUKhuDRQy5cUCoVCoVAoFAqFQqFQKBQ2R3nKKAB4fmwMA6L8MZQZefzrnew9U3P74JgmHrw+oQNODjpW7kvnuZ8SAJg0sjVDYgMxScgqKOXxr3eSlleCm5Mdb93akRAvZ/R6wccrjjF/82mb6N+rhQ9Pj2yNXif4eVsin649aXXeXi+YOzaGqBB3covLefz73STnlhDT2J3nr22jCQnBe3HHiNufDoCbkx0zR0fRIsAVKSXTftnH7lreS10z++YODIoNwlBm5KFPt7DnVE4NmbahXrxzZ1ecHPSs2JPC1G/jAYhq4smrEzvRyNGOM1lF3PPhJgpLKri+eygPDG9tuT6qsScDZixl7+ncOte/R3NvJg9viU4IFsQn8/n6U1bn7fWCF0dHERnkRp6hnKd+2ktybgnBnk4seLAbJzO1bWETEvN4YeEhAIZFB3BnnzCklGQUlDH1133kFpfXue7VmTUulgHRgRjKjDz25Q4SzuTWkIlp6smbEzviZK9n5d5Unp2/B4AnRkZyU68wsgtKAZjz+z5W7k3DTid4dUIHYpp6YqcT/LT5NO8uPVynek+/rg39Iv0oKTPy5Pd72JeUX0MmurE7r9zYFkd7HasPZDBzwX4APFzseWdCexp7O5OYbeDBr+LJN1QwqkMw9wwIRwhBYUkFz/6yl4PJ2nart/YOY3y3JggBP24+wxfVyp+t6NbMi8cGNUenE/yxO4WvN5+xOt+uiQePDYwgwt+VZ3/fz6pDmfWi17noEeHNk8NaoNMJfotP4YsNNcvGrGvbEBnsRl5xOZN/3kdKXgkALfwbMW1kaxo56jFJuOWT7ZQZTQyLDuD2XqFIIKOglGm/7ifXYLuycSWkAf59PQXQIsCVZ69ujas5HTd9vI2yChMPDgzn6rZBuDvZ0X32GpvqX5UZY6Lo3yYAQ5mRSd/uYm9izXYquokHr93cDid7Pav2pzHjl30ATB0VycDoQMorTJzKLOLJ73aRb6igsbczcVP7cyy9EICdJ3N4Zn6CTfTvEeHNpKEt0OsEC3amMO9c31SQG7mGcqaYv6nh0QH8r0dTi1yLAFdu+ngbh9MK+fh/7fF1daC0wgTA/d/sIqce2owLYfq0p1m7ZjXe3j78+vvChlbnvFxM//Asdw+M4NnR0cQ+tZicovrZGnrW+LYMjNHa70fnbSehlv5ObFNP3rytE072euISUnn2x92Wc7f3j+C2/hEYTZIVCSm88Mtey7kQb2fWzBjCq3/u58PlR+ojOTWYc3MHBrUNxlBm5MFPNtfaV3xmTCzje4bh0ciB0Ht+bgAtNZ6/vto3VEsdZfmG7M3f0M/mMcZV1cYY32hjjG4tfPjs7q6cydL6jIt3JfPWkrrtS1WlZ3MfJl/VCr0Q/Lojic/WnbQ6b68XzB4TTZtgbZzx5Pw9ljYDINDDid8f6s77q47z5YZThPm68Mq4WMv5xl7OvLfyGN9sss046XLkP+YoozxlbIEQYuMFyJwUQvjWcryfEKKHbTSrnf5R/jTza0TvGXFM/m43s29oW6vc7Bva8tR3u+g9I45mfo3o18YfgA9XHGXI7NUMm7OaFXvTeGR4SwAm9m3GkZQChs5Zzbg3N/Ds6Cjs9aLO9dcJmHZNJPfMi+fqNzcwom0QEf6NrGTGdGpMvqGcYa+t58sNp3himKbjkbRCxr6/hdHvbubueTuYcW0b9DpNx6dHtmb94UxGvrGB0e9s4nhGUZ3rXp1BsUGEB7jSZfJfPD5vG6/8r1Otcq9M7MRj87bRZfJfhAe4MjAmCIA3b+vMrJ920+fZJfy1I5EHR0QC8POmU/SfvpT+05dy/8ebOZVZZBODjE7A1Ktacf83u7juvc0Miwkg3M86L67rEEy+oZyr397EN5vO8Ojg5pZzidkGxn+4lfEfbrUYZPQ6weThLblzXjxjP9jK4bRCbujSuM51r86A6ACa+bvSc/oynvo2njk3tatVbu5N7Xjym3h6Tl9GM39X+kcFWM59EneUwS+uZPCLK1m5Nw2AqzuG4GinY+CsOIbNXsWEPs1o7ONSZ3r3i/QjzNeFAbPXMPWnvcy6PrpWuVnXR/P0/AQGzF5DmK8LfVv7AXDvgHA2HslkwJw1bDySyX0DIwA4k13MDe9tZvgr63h3+VFmj40BoGWgK+O7NeG6Nzdw1avrGdDGn1DfukvPudAJmDSkBY/NT+DGT7YxpI0/YdXeY1p+CbP+OsSy/Wk21+d86ARMGdGKB7/dzZj3tjAs2p/wau/p2vbBFJRUMOqdzXy7+QyPDNLevV4IXhgdxYt/HeL6D7Zy15fxVJhM6IXgyWEtuPvLnYz/cCtH0goZb8OycSWk4Ww6/m09pdcJZo9uwwt/HmT0e1u444sdVBi1gf+aQ5nc/PE2m+penf5t/Gnm50rfWSt5+sfdvDAupla5F8fFMOWH3fSdtZJmfq70i9Ta73WHMhkyZzXDXlrDiYwi7h/cwnLNqcwiRry8lhEvr7WZQUYnYPLwVjz03W7GvL+FYVH+NKvlm8o3VDDqXetvavHeNG78eBs3fryNZ3/bT1JOCYfTCi3XPbNgv+X8pWKQARh17Wg++OjThlbjgrjY/iFAkKcTfVr7k5hdXF9qMyA6kPAAV3pMW8qTX8cz9+b2tcrNvbk9k76Kp8e0pYQHuDIgWmu/e7TyY2i7YAbOXEG/Gcv5YJm14WXG2FhW7ku1eTrOxaDYIMID3ej81EIe/2Irr06sva+4dFcSg59fVs/aWaPVUY3o/Xwck7//m29ovPkber7aGCPuqFZHzbUeYwBsPZbFsLnaOVsaZHQCnrm6Nfd/tZNR72xkeGxgjTZjdMcQ8g0VXPXmBr7edIrHhrSwOv/k8JasP5Jl+ftkZjFj39/M2Pc3M/6DzZSUGy2Twor/JsooYwOklBdjVOkH1KtRZkhsEL9s0WaYd57Mwd3ZHn93RysZf3dHXJ3s2HlSs8T/suUMQ9tqhoDCkgqLnIuD3vK7lODqpDljNXK0I7e4jApT3Zs9Yxp7cDqrmMQcA+VGyeI9qQyI9LeSGRDpx2/xyQAs25tGtwhvAErKTRjNOjna6ZFov7s62tEpzItfticBUG6UFFRJp60Y3j6E+RtOArDjWBYeLvYEeDhZyQR4OOHmbM+OY1rlPn/DSUZ0CAEgItCNjYcyAFi9L42rO9Yc3IzuGsqCLadqHK8LokPcOZNtICmnhAqjZMneNPq1trY99m/txx+7UgBYvj+dLs28/vaewvyPs732bbk66skwe5/YkqGxwfxs9uyKP5GDh7M9/u7WeeHv7oSbkz3xJ7Ry8fPm0wxrG/y395USXBzt0OsETg56yipMFNahV8Cg6AAWmL/bXadycXe2w8/Nujz7uTni6mjHrlO5ACzYnsTgGK0zOjg6gF+2adf/si2JweZOavzJXPINWhnYeSqHQE/tXUQEuLL7dK6lLG05ls3QmMA6S8+5aBPkTmKOgeS8EipMkuX70+nTwsdKJiWvlKMZRZfEbItWNopJytX0XbovnX5mQ9hZ+rXy5c/dWtlYsT+DLuFa2ege4c2RtELLgDPPUIFJghBa+XB2OFs27GxaNq6ENFSm49/VU+dKB0BCYj6ZhfXjBXCWwTGB/LL1bPud+zfttz07T+YC8MvWMwyJ1crouoMZljZw58kcgjyt6zhbEx3iTmJOtW+qVc1vauEeLS/i9mfQuZY2Y1h0AMv2Nbzx9ULo2Kkz7h4eDa3GBXGx/UOA566P4cXf9tVrPTysXRA/bdL6OfEnsjW9q/Wl/M19qfgT2QD8tOkUw9pp7ffEvuG8u+QQZWZPq6wqddKwdsGczizmUHJND9T6YniHxvxo7ituP5aFh4tDjb7i2XNpeSU1jtcnQ2KDqtRRF/gNbT3D0NhaxhiOemiA9rzqOKPCKFmckEr/SOt6SmsztHHG8n3pdA33tpwbEOlHUo6Bo+mF1EbXcG/OZBssXqUKDSmlTX4uVa44o4wQIkwIcVAIMU8IcVgI8a0QYpAQYoMQ4ogQoov5Z5MQYqcQYqMQopX52seEEJ+bf48RQuwVQtQ65SuEmCGE+FwIsVoIcVwI8XCVc4Xm/3VCiPfN+iwXQiwSQlxf5TYPCSHihRAJQojWQogw4F7gMSHELiFEbyHEWLMeu4UQa23xzgI9nEjONVj+Tsk1EOjpbC3j6UxKFTe8lFwDgVUagKeujmTLC0O4rnNjXl14EIB5a07QPNCV7bOHsvyZ/jz3016bNMoBHk6kVqnIUvNKalT4VWWMJs3A4uliD0BsYw/+eKQHvz/cned/O4DRJGns7Ux2URkvjonilwe7MfO6NhajgC0J8nImqcpsUnKOgSAv5xoyyeeQOZiUz3CzgWZU5yaEeNf8fK/t2pRfbbSMzN/dOi/S80oJqGYQ8HdzJDVf6+AYTZLC0sq8CPFy5sd7u/DZbR1o39QTgAqT5MWFh/j5/q6smNSLcL9GLDAb2GxJoKcTyTmV5SI512AxRFSVSfkbmdv6hbNi2kBen9ABD3MaF8YnUVxawa6XRrBt9jA+XH6kTpdiBbo7WZXV1NwSq7IKWpm3KjO5JQSaDU6+bo6WQXFGQSm+1fIPYFzXJqw5oBn/DqcU0LmZN54u9jjZ6+gX6VcvAzs/NwfSq3SU0wtKaxifLiX83RxJy6/UNy2/pr7+7o6k5pnLhpQUlhjxdLanqY8zUsJ7N7flu7s7M9G8ZKPCJJn91yHm39eFZY/3JNyvEb/ttF3ZuBLSoOn47+upUB8XJPDBhHb8cE9nbu3ZlIZEa7+rlmVDrYb8VKs2vmadADCuWxNWV5mpbeLjwqKn+vDjwz3oXGWAUZf4uVV+LwDp+aX412JEru2bqsrgNgEs2WttlJlxTSTf392ZO3uH2UT3/wIX2z8cEhtIaq6BA7UsobUlgZ7OVu13So6hRrsUVK2NT8mpTFt4gCtdm/vw19P9+XVSH9qGaoZAF0c9DwxtyWsL99dDKs5NkJczSVmV3tvJ2cUEedneQ/XfUL0vdcHfkGe1McasIVzXqTGv/nXQcrxjM2+WTunHV/d1o2Wgm83SULVdA0irrc1wtx5nnG0znB303N4rjA9WHT/n/YfHBLI4oeE8ry5VpLTNz6XKFWeUMdMceA1obf65CegFTAKmAgeB3lLK9sB0YLb5ureA5kKI64AvgHuklH/nb9kaGAp0AZ4TQthXOz8aCAPaABOA7tXOZ0opOwAfAJOklCeBD4E3pJTtpJTrzPoNlVK2Ba75Jy+hPnn5zwN0nbaMBdsSubVvMwD6tvFjf2I+naYuZdic1cwaF2PxnLmU2JOYxzVvbWTc+1u4q28zHOx06HWCNsFu/LglkTHvbsZQbuTOvmENrep5efjzLdw+oAVxM4bg6mRHmdmt/iwdwr0xlFZwMMn2sXH+KRkFpQx9fT3jP9zKq0uOMPf6KBo56rHTCcZ1DmH8h1sZ9Op6jqQVcsdl0Mn+cs1xuk9byuAX40jLL+G5MdqygvbNvDBKSfvJi+g6bSn3DmpB03pY7vNvqd6AdWvuzbiuTXjJbHw9ll7ER6uO8eU9XZh3dxcOJOVjupRbvcsQvU7QvqkHz/y6n9s/38GA1n50aeaFnU5wfacQbvxoG0Ne38DhtEJu7xXW0OrWypWQBjibDk+e/mUft36+gwGR/uf19rsceHBICyqM0uJll55fSvfnVjDi5bXMWrCPtyd2uCTbb9C8bUrKjRyrssT4mQX7GP/RVu6YF0/7pp5cFWt77z2FNU72eh4c2pLXFh48v/Alhp1O4NnIgavmrGLmzwl8fE9XACZd3YaPVxyhuNTYwBr+t3j5zwN0fXYZC7YncmsfbYyx90we3Z5dxtC5q/lizXE+vbtLA2tZO/f3D+frTacxlNX+zdjpBf1a+7Fs7+Xh6aewHZdmC3vxnJBSJgAIIfYBcVJKKYRIQDOSeABfCiFaoDnC2QNIKU1CiFuBPcBHUsoN53nOX1LKUqBUCJEOBACJVc73An6SUpqAVCHEqmrX/2r+fweaAac2NgDzhBDzq8hbIYS4G7gbwLPvfbhGDT2P2jCxTzNu7BkKwO5TOQRXsVoHeTpbzaqBNvtWdZYhyNPZaqbxLAu2JfLV/d14/a9DjOvWlPfN63BPZhRxJquY5gGuliUTdUVanvWsX6CHE+n5pbXKpOWXotcJ3JzsangnHM8oorjMSIsAV9LySkjLL2WPORjZsr1p3GluCOqa2wc2Z0JfbY38rhPZVt4twV7OVp4YoM3mBJ9D5mhKAWNfXQ1ARIAbg6stpRndNZRft9guiFh6vnVe+Hs4klZtKUJ6QSmB7o6km/PC1bEyL/LMy2MOpBRwJttAqI8LZ6MQJZrTuHRfOrf3CrWJ/rf2Dedm84Bw16kcgqt4KQV7OpOaa/3Np+aWWHkyVZXJrJLub9ef5Kv7NZvsdZ2bsGpfGhUmSVZBKduOZdE21IvTmf9+vf2EnqGM79YEgD1ncq3KaqCnU42ymlq9zHg6kZpfqbef2VvGz82RrMLKdLQOcmPOuBhu/2S7VfmZvyWR+Vu0qm/SiJY13pMtyCgos5pR96/i4XMpkl5QSkAVD74A95r6pueXEujhSHpBKXohcHXSk2soJz2/lPhTuZbgt+uPZtE6yI3CUq28nC0by/elc1sv23luXAlp0HT89/VUen4pO07lWr7/9UcyiQx2Y+uJmkE2bcX/eodxQ3ftHe05nUuwVXl3rrFcIS2vxGpmOqhanXB9l8YMjPLnxnc3W46VVZgsSzf2nsnjVGYRzfwakVDHwe4zCrTv5Sz+7o5WHnBVZap/U2cZGuXP0mpLlzIKtGVkxWVGluxNJTrYnb/2qJnoC6Gu+odhfi408XFh6dT+5uNOLJ7Sl6tfWUtGft3X1bf2C+fm3lo/bfdJ6/Y7yMvaEwM0j7HqMmfTlpJjYJHZY2/XyRxMUuLj6kCHZt6M7BDCs2NicHexxyShtMLEF6uO1Xl6qnPHwBaWvuLOE1mE+DSCI1rw+mBvF1Jy6i9mz/mY2KcZN/ao8g15/YtvqJZ+xIJtiXx1XzdeX3TIalnTqv3pvKjX4dXIwSaBpM+2a2cJqK3NyLceZ5xtM2IaezA4KoDHhrTAzckOKbX69XvzssDeLXw5kFJAVj0FwL6cuJSXGtmCK9VTpmpJMVX524RmiJoFrJJSRgNXA1V9GlsAhcDfB4ao+Rwj/9zIdfb6c14rpbwXmAY0AXYIIXxqkflYStlJStnpQgwyAF+uPcGwOVpw3qW7UxnTVRvQtQ/zosDcga5Ken4phSUVtA/TZgTHdG3CMvMa77Aqwa6GxAZy1LzWPjnHQE/z2nBfN0ciAlw5dREDz3OxNymfUF8XQrycsdcLhscGsuqAdbCsVQczuLaDlqVDogPYclxbQxzi5WwJ7Bvs6US4nwtJOQYyC8u0ToXZg6FbhA/H0m0T6PfzuKOWILyL4hMZ1zMMgI4RPuQbymvtYBcYyukYoX0K43qGsXinNrt5dqmJEPD4NW2Yt+qo5TohYFSXJjaLJwOwL7mApt4uhHg6YacXDIsOYM1B6x1vVh/K5Jp22lrhwW38LYMZLxd7zFlBiJcToT7OJOYYSC8oJdyvEV7m5T/dI7w5kWmbvJi35rglMO+SXSlc300b+HRo5kV+STnp+dZ5kZ5fQkFJOR3MM+XXd2vK0j1aR65q/Jnh7YIt68+Tsg30aqXFPHJ20NMh3JujqQUXpffXG04x8rX1jHxtPcsT0riuk7aErV2oJwUlFTUGzhkFpRSWVtAu1BOA6zqFsMI8S7NiXzpjOmvXj+kcwnLz8WBPJ96/rQNPfLebE9WCXvu4OlhkhsYE8ns9LC87kJJPE29ngjycsNMJBrfxZ93RrPNf2EDsSyqgqY8LwZ6avkOj/FldbTeoNYczudoci2FQGz+2mcvGxmPZNA9wxclOh14IOoZ6cjyjiIx867LRLcKLEzaoY6+kNMDF1VMbjmbRwr8RTvaaR2XHUC+O26htOBdfrTtpCcC7bE8qY7qcbb89KSg5V/tdTvswTwDGdGnCcrOrfN9IP+4d1Jw7PtlGSXnlTK63q4OlPm7i40Izv0aczqr7fNmXVEATb+tvas3hat/UoUxGmuNLDKzyTYEWj2hwmwCWVpll1gthWd5kpxP0buHL0YzaYzkoalJX/cODyQW0n7KEHtOX02P6clJySxg+d41NDDIA81YfZ/CsOAbPimPxrmTGdteMAh2aeWt6V+tLpZv7Uh2aaUvzxnYPZYk5jtSSXcmW/mu4vyv2eh1ZhWVc+8oaukxdQpepS/gk7ihvLzpYLwYZgM/ijtBv+hL6TV/Covgkxpv7ip3O0VdsSL5ce8ISgHepVR11gd9Ql/OPMaounW0X6olOYLOdvfYm5RPqU9lmDI8JZPXBDCuZ1QczuMYck2hwlD9bzbGKbv1sO8NeX8+w19fzzabTfLL2hMUgAzA8NpDFymCs4Mr1lDkfHkCS+fdbzx4UQngAbwN9gHeFENdLKS9mD7kNwEQhxJeAH1oQ3+/Oc00B4F5Fpwgp5RZgixBiOJpxpk5HHiv3pTEgKoD1MwZhKDPyxDc7LeeWPN2PYXNWA/DMj3t4fUJ7y5aaq/Zpho+nR7UhIsAVk5QkZhuY+r22peBbiw/z+oT2LJ/aHyFg9m/7bVJhGk2SF/84yCe3ddC2N92RxNH0Ih4cFMG+xHxWHczgl+1JvDQ2miVP9CK3uJxJP2jbFncI9eSuvs2oMJowSZj1+wHLDOiLfx7k5XEx2Ot1JOYYeObnvX+nRp2wfHcKg2KD2fbySAylFTz82RbLuVUzh9J/+lIAnvpqu3lLbDvi9iSzwtx4je4Wyh0DtV1CFu5I5Lt1JyzX92jlT1J2MadsuIuU0SSZs+gQH0xoj04Hv+1M4VhGEff3D2dfcj5rDmWyID6ZF0e34c+Hu5NvKOcp83vtEOrJAwPCKTdqgbhe+POQJbDsR6tP8PntHakwmkjJK+HZBbZfzx23N5WB0QFsnDXEsiX2WZY/M4DBL64E4OnvdmlbYjvoWbUvzbLL0rTR0UQ18UBKSMwq5qlvtXL1xZpjvPG/jqyaPkjbQnrjqTpdb7/qQAb9Iv1ZNbUvJeUmnvp+j+Xcwid6MfK19QBM/3kfL98Yi5O9jjUHM1htjhHzYdwx3v1fe8Z1bUJSjoEHv9L0fmhIC7xcHJg5RtvNyWiSjHpDcyZ8/9YOeLrYU2GSPPfrvnoJim2U8Oqyo7w1PgadECzck8qJzGLu6h3GwZQC1h3NIjLQjZdGR+HmZEev5j7c1SuMmz7bbnPdatdX8tKiw7x/Szt0QvD7rmSOZxRxX79m7E8uYM3hTH6LT+GF69rw+0PdyDdUMMVcNgpKKvhm02m+uasTElh/JMuyi8PHa07w6a0dqDBJUnJLeO5325WNKyENcHH1VEFJBV9vOsN3d3dGSlh3JIt15nQ8Org5I2ICcLLXs+zxnvwan8yHq0/8nSoXzcr96fSP8mft9AGWLbHPsuipPox4WQtFN21+grYltoOe1fvTWWWOHTPz+hgc7HR8c383oHLr664RPjw+ohXlRhNSwtT5CeTZYAcjo5S8tPgw792sfVN/mL+pe83f1NrDmfy2M4VZ17Xh9we7kWeo4OkqWxN3CPUkLb+EpCqz6vZ2gvduboudXodOwJYTOfUSh+xCmTzpcbZv20pubg6DB/ThvgceYvSYsQ2tVq1cbP+woYhLSGVgdCCbXhyqtd/zKuv95c8OZPCsOACe/m4nb97aCScHPSv3prFyrzY4/n7DSd6Y2IlVzw2i3GjikS8apt04F8t3JzM4Nojtr4zEUGrkoU8r+4qrZw6j3/QlADw3rh3Xdw/FxcGOhDdG8fWaY7z8m+37slWxfEPPDcJQXu0bmtKPYXNXA/DM/D28fkuVb2h/lTGGf5Uxxg/aGGNE+2Am9A7DaJSUlBt5wIZ5ZDRJZi88xIcTO6DXCRbEJ3MsvYgHBkSwLzmf1Qcz+DU+mTljovnr0Z7kGcp56gJ2rHO219E9wpuZvx+wme6XM/8xRxnEleYaZA6Wu9DsBYMQYp7575/PngPuAr4EioC/gFuklGHmIL+7pJRvCyGaAKuAHlLKGq2LEGIGUCilfNX8915gpJTypBCiUErpKoTQAe+jGWPOoE3qvCSlXC6EOAl0klJmCiE6Aa9KKfsJIVoCP6N59TwEPIbmvSOAOOBR+TeZ1uSB36+IDHXzcD6/0GVAenJ2Q6tw0YSE+p1f6DIgI+3Si6PzT3Fyqd+dUWyFf7Ut6y9XSlVcgUsGkw129qtvci+h5QcXg4/vlVG+N04d0NAqXDQtHvm9oVWoEyrKbW/otzVlpVfG8hQXt0s3Bt4/wfMSDYz8T0iYNVicX+rype1zcTZp2Hc/P/CSfG9XnKeMOVhudJW/bz3HuZZVLptmPn97FdkzaAGDz/WcGdX+rvpMV/P/JiHEJClloXnZ0VYgwXwurIr8djTDDVLKw0BslVuvO5cOCoVCoVAoFAqFQqFQXElcaY4j5+OKM8pcgiwUQngCDsAsKaVaOKhQKBQKhUKhUCgUCkUtKKOMwgohxG3AI9UOb5BSPnAh10sp+9W5UgqFQqFQKBQKhUKhUCgue5RR5jxIKb8AvmhoPRQKhUKhUCgUCoVCobjS+Y85ylyxW2IrFAqFQqFQKBQKhUKhUFzSKE8ZhUKhUCgUCoVCoVAoFJcEKqaMQqFQKBQKhUKhUCgUCkUD8B+zyajlSwqFQqFQKBQKhUKhUCgUDYHylLnCCAhyb2gV6oS8vNKGVqFOiIxp3NAqXDR6/ZVhuzWZTA2twkXj6urY0CrUCTk5hoZWoU4oKihpaBUuGgcnh4ZWoU4oLihuaBUumivFVfvA+oMNrUKd0OKRgoZW4aI58taohlahTlh3JLOhVbhoHv9uZ0OrUCcc3bG/oVWoE9zbRza0CorzYDJdGW3ihXJljLYUCoVCoVAoFAqFQqFQKC4zlKeMQqFQKBQKhUKhUCgUikuCK8R59IJRRhmFQqFQKBQKhUKhUCgUlwRXypLeC0UtX1IoFAqFQqFQKBQKhUKhaACUp4xCoVAoFAqFQqFQKBSKS4L/mKOM8pRRKBQKhUKhUCgUCoVC8d9GCDFMCHFICHFUCDGllvOOQogfzee3CCHC6uK5ylNGQfdwbyYNbYFOwG+7Uvhy42mr8/Z6wfPXRBIZ5EaeoYKnf91HSl4Jep3g2ZGtaB3ohl4n+GtPKvM2nibU25nZo6Ms14d4OfPRmhN8vzXRZmno3cqXaddEotfB/K2JfLzqhNV5B73g5RtiiW7sTm5xOY98s5ukHAOeLva8M6EdMU08+HV7EjN/OwCAk72Odya0o4mPCyaTZOX+DF5dfNhm+tdGlzBPHukfjk4IFu5N49tq769tiDsP9w8n3K8Rzy88yOojWQAEuDkye1QkQoCdTvDLzhR+35Nar7qfpUuoJw/2bYZeB3/tTee77UlW52ND3HmwbxgRvo2Yuegwa45mWc7FPdydE1naNrdp+aU882f9brPas7kPk69qhV4Ift2RxGfrTlqdt9cLZo+Jpk2w9k09OX8PybklBHs68fvDPTiZqem+50wes/7UvquHBkVwTbtg3J3s6PrCqnpND0C3Zl48OjACvU7wx+5Uvt5yxup8u8YePDownAh/V6b/cYBVhyq3IX1jbDRRwe7sScxj0i/76lXvXi19eebq1uiE4OdtiXyyxrp82+sFL42LISrEg9ziMh7/fjdJOSX0aO7DE8NaYG+no7zCxMuLD7PlWDYAV7UN5J7+4UgJ6fmlPPnjHnKLy22elufHxjAgyh9DmZHHv97J3jN5NWRimnjw+oQOODnoWLkvned+SgBg0sjWDIkNxCQhq6CUx7/eSVpeCW5Odrx1a0dCvJzR6wUfrzjG/M2na9z339KnlS/PXhuJXif4cUsiH608bnXeQa/j1Zu0+jWnqJyHv95Fknnb83sHhDOua2OMJsnM3w6w7lAmDnY6fnigKw52OvQ6wZI9qby19Kjlfk8Mb8HwtkEYTZLvNp7my/Wn6iwtVZk1vi0DYwIxlBl5dN52Ek7n1pCJberJm7d1wsleT1xCKs/+uNty7vb+EdzWPwKjSbIiIYUXftkLQGSIOy/f0gE3Z3tMUjL8xZWUVphskgaAF25oy8CYIAxlFTzyxbnT8dZtnXFy0BOXkMK0H7R0fHR3VyIC3QDwcLYnz1DOoJkr8GrkwKf3dqNdmDc/bjzJ1O932Uz/6rx2/wCGdg6nuLSCu19dxK6j6VbnnR3t+HbaNYQHe2I0ShZtPsazn68F4M6r2nLPNe0xmiRFhjIeeHMZB09n1fYYm3MxZf0sdw+M4NnR0cQ+tZicorL6Uv2CmD7tadauWY23tw+//r6wodU5J/vjN/Pzp29iMpnoMfhqhoyZUEMmfn0ci374HASEhLXgtidmkHj8MD989ColxUXodHqGjv0fHXsNqlfdr8R+yGv39GVo5zCtfL++jF3HMqzOOzva8e3TIwgP8sBokizacoJn520AoGd0MK/c3ZeYZr78b+5iFmw4WtsjbEKvFj5Muao1ep3gl+2JfLr2pNV5e71gzvUxRIVoefHED7tJzi0hprE7M65tA4BA8N7KY8TtTyfM14XXboi1XN/Yy4V3447y9ca6a7svdxoipowQQg+8BwwGEoFtQog/pJRV94K/A8iRUjYXQtwAvASMv9hn17tRRggRDLwtpby+vp99qSOEaAcESykXmf+eARRKKV+11TN1AiYPb8kD3+4iLb+Ur+7oxNrDmZwwV+QAo9oFUVBSwXXvb2FIG38eGhDO1AX7GRTph4Nexw0fb8PRTsdP93Zh6b50TmUbuPnT7Zb7L3qkB6sOZZxLhTpJw4zr2nDrx9tIzSvhl4e7s3JfOkfTiywy13dpTL6hnEEvreOqtoE8OaIlj367m9JyE28uPULLQDdaBLpa3ffTNSfZciwbe73gy7s706eVL2urDFJtiU7A4wMjeOznvWQUlPHJze3YcDSLk9kGi0xaQSmzlxzmhk6Nra7NKirj3u93U26UONvr+HJiB9Yfyyarnjt0OgGP9A9n0q/7yCgs48MbY9lwPJtTVdKQXlDK3GVHGd8huMb1ZRUm7vx2d43j9YFOwDNXt+buefGk5pfww71dWXUwg+MZld/U6I4h5BsquOrNDQyLCeCxIS14cr7WoT6TbWDs+5tr3HfNwUy+33yGvx7tWW9pOYtOwBODm/PIjwmkF5Ty+cT2rDuaxcmsyrKeml/CrEWHublL4xrXf7s1ESc7Hde2C6pPtdEJmD4qkts/205aXgk/PdidlQfSOVa1fHduTL6hgqGvrmNEbCBPDGvJ49/vIae4jPu+3El6QSktAlz59PaO9J2zBr1OMPXq1lz1+gZyi8uZNLwlt/Royrsrjtk0Lf2j/Gnm14jeM+JoH+bF7Bvacs0ra2vIzb6hLU99t4udJ3P46v5u9Gvjz+r96Xy44iivLtSMk7f1C+eR4S2Z+sMeJvZtxpGUAm7/cAverg6smT6QBdvOUG68+A6NTsCM0VFM/GgrqXklLHi0B3H70jmaVmiRGdu1MXnF5QyYs5aR7YKYPLIVD3+9i+YBroxsH8Swl9fj7+HIV/d0YdDcNZRVmLjlg60Ulxmx0wl+fLAbaw5ksut0LmM6hxDk6czgl9YiJfi4Olx0GmpjQHQg4QGu9Ji2lA7NvJl7c3uumlNzgDL35vZM+iqe+BPZfPtwTwZEB7Bybxo9WvkxtF0wA2euoKzChI+bIwB6neDdO7rw0Ofb2J+Yh1cjB8qNtjPIDIwOJNzfje7PLKFDuDcv3dyBEXNW1pB76ZYOPPH1DuKPZ/Pdw70YEB3Iyr2p3PPxFovMjLGx5Bs0w2RpuZGXft9H6xAPWge720z/6gzt3IyIEC+ib/uULq2DePvhwfR5+Nsacm/+vI21u89gb6dj8UvjGdK5Gcu2neDHVQf49C+t3biqWwQv3dOfUc/8XG/6n+ViyzpAkKcTfVr7k5hdXOO6S4FR147mxptu4ZmnJze0KufEZDQy/6PXePD5N/H08eeVJ+8kpksvgpo0s8ikJ59h2S9f8/jcD3BxdacgNwcAe0cn/vfIs/gHNyE3O4OXn7iDyHZdcXF1qxfdr8R+yNBOYUSEeBJ955d0aRXI2w8OoM9jP9aQe/PXeNbuSdTK9+zRDOkUyrLtpziTXsDdry/n0TEd6lVvLS8iueuLHaTll/Djfd1YdSCDY1XyYkynxuSXlDP89fUMjwnk8aEtmfTjHo6kFTLu/S0YTRJfNwd+fbAHqw9mcDKzmDHvbrbcf9XkvqzYn34uFRT1RxfgqJTyOIAQ4gdgFFDVKDMKmGH+/WfgXSGEkBdpRbro5UtC44LvI6VMvlwMMkKI+jZatQNG1OcDo4LdOZNtICm3hAqTZNm+NPq29LWS6dvSj4VmT4u4Axl0aeZlOedkr0cvBE72OsqNkqLSCqtrOzfzIimnhNS8UpulIbapJ6cyizmTbaDcKPlrVyoDowKsZAZFBfDrjmQAliSk0b2FDwCGciM7TubWmMUsKTdZZtTLjZL9SfkEejjZLA3ViQx0Iym3hJS8UipMkrhDGfRq7mMlk5pfyrHM4hqW5AqTtAzG7PU6dKLe1LaidaArSXkGUvK1NKw8nEnPCG8rmdT8Uo5nFnOpLRuNaezB6axiEnMMVBglixNS6R/pZyXTv7Uff+zSvqnl+9LpGu5d262s2JOYR2Zhw8x2tglyIzHXQHKeVtZXHMigT4tavqmMIky1tCvbT+VSVGasL3UtxDYx54W5fC/ancLANv5WMgPb+PNbvOaFtXRvGt3NZeVAcgHpBVrdcyStEEd7PfZ6gUCbsXJx0APg6mhHer7t6qizDIkN4hezd9LOkzm4O9vj7+5oJePv7oirkx07T2oDg1+2nGFoW80QVlhSWb+e1R20ddeuTlpz1cjRjtziMipMdVOq2jb15FRWkaV+XbgzhUFR1u9/ULQ/v5q94BbvSbXUr4Oi/Fm4M4Uyo4nEbAOnsopo29QTgGLzt2SnF9jpBdJcC9zcoynvLDtqWUueZaPyMqxdED9t0jxw4k9ka3lRrY7393DCzdme+BNaW/DTplMMa6cZkCf2DefdJYcoM7cdWebvrG+bAA4k5rE/UfOKyCkqo46yolaGtgtm/mZzOo5n4+5SezpcneyIP66lY/7mynRU5epOjVmwVfs+i8uMbD2aRWl5/Zb5kT1a8N1yzRNv68EUPBo5EejdyErGUFrB2t2anuUVJnYdTSPEVxsoFxRXfi+NnOwt31V9c7FlHeC562N48bd9l2xchY6dOuPu4dHQavwtJ48cwDeoMb6BIdjZ29Oh10D2bFlnJbNx2R/0GTEaF1fN+OjmqfVxA0Ka4h/cBABPbz/cPLwozM+tN92vxH7IyG7hfBeneexsPZSKRyNHAr1crGQMpRWs3aN5hpdXmNh1LJ0QH23S9HR6AXtPZmKyZaVaCzGNPTiTreVFuVGyaE8q/SOt28EBkX78Hq/lxbJ9aXQz93dLyk0Yzfo62ulrrZO6RfhwJruYlNwSG6fk8kJK2/ychxCgqht5ovlYrTJSygogD/DhIvlXRgfz2qmlwBagIzBfCDEScAQWSCmfE0LMBc5IKd8zXzMDKESzKC2UUkabXYTmAv3M174npfxICPEesFRK+YcQYgGai9DtQojbgQhgNjAfaAzogVlSypqmVu25J82ywwEDcJOU8qgQwg/4EGhqFn1USrnBrGcEEA6cBm6s5Z63AtcCjYAWwKuAAzABKAVGSCmzzZ4vHwIuwDHgdilljhBitfnd9Qc80dygtgAzAWchRC9gjvlxbczyTYE3pZRv15op/xJ/N0fS8isrgfSCUqKrzYr5uzmQZh6wGKWksNSIh7M9Kw5k0LelL0se7YGTvZ7Xlx8hv8TaKDO0TQBL96XVpco1CHR3JCW30vsiNa+Etk2tOwoBHo6kmmWMJklhSQVeLvbkXMByBTcnOwa08beZC31t+Lk6WAaTABkFpUQGXfjsjL+bAy9fF0WIpxPvrz1Z714yAH6NHMkoqHxuRkEZbap5I/0dDnY6ProxVlvCsD2J9WYjWX3g7+5oZUhMyysltnG1cuHuRGqeVnaMJklhaQWeLvaAtmRv/v1dKSox8k7cUeJP5dab7ufCz83RyvCQXlBK1D/4phqKAHcnUvIq66jUvBLaNvG0kvF3d7R0ZowmSUGJlhdVlyMNjQ5gf1K+xWD5/G/7+ePRnhSXGTmVVcTM3/djawI9nEiuUlel5BoI9HS2ypdAT2erjllKrsHKIPzU1ZGM6dqEAkM5497SXLrnrTnB5/d2Yfvsobg62XH/Z9vrbCAX4OFkpY9Wv3pap8vdyfr9GyrwamRPgIcTu6p8+6m5JQSY06IT8PtjPQn1deGbDafZfVozYjT1ceGqdkEMiQkgu7CMmb/tt7jg1yWBns4k51TJixwDQZ5OpFf51oI8nWrIBHo6AxAe4ErX5j5MuTaK0nIjz/+UwO5TOUQEuCKRfP9IL3zcHPhtWyLvL7Xd0tcgL2eSq3hSaOlwrpYOZ1Kqp9XL2eo+3Vr4kplfwon0QhqSYB9XEjMKLH8nZRYQ7ONKanZRrfIejRwZ0S2CdxfssBy75+r2PDymEw72OoY9WWvX0OZcbFkfEhtIaq6BA0n59af0FUhedgZevpWDZy8ff04esV5+m56sjb9en3IvJpORETfcQZsO3axkTh7eT0VFOb6B1cdmtuNK7IcE+7qSmFFZxyRlFhLs60pqTu11vEcjB0Z0Cefd33fVk4a1U70fkpZfQmwT63FG9byo2g+JaezBC6OjCPZ0YsrPey1GmrMMjw1kUQOFGbiUsdXyJSHE3cDdVQ59LKX82CYP+wdcjKdMC+B94DE0i1EXNE+PjkKIPsCPwLgq8uPMx6pyB5AnpewMdAbuEkI0A9YBvc0yIUAb8++9gbXAMCBZStlWShkNLDmPrnlSyhjgXeBN87G3gDfMzx4DfFpFvg0wSEpZwyBThWhgtFnvF4FiKWV7YBPwP7PMV8BkKWUskAA8V+V6OyllF+BR4DkpZRkwHfhRStmuipGpNTAU7f0+J4SwP09a643oYHeMUjLsrY1c8+4mbunWlBDPysGDnU7Qp6UPKw5cvu54ep3gjZvb8tX6U5ypsuzmUie9oIxbv9rJDZ/tYFgbf7xcLpnP5oIZ/9kO7vl+D7MWH+bBvmEEezie/6JLgIyCUoa8uo5x72/hlSWHeGlsDI0c9ee/UGEzmvs34onhLXlugdYZt9MJbujWhOve3kif2as5nFLI3f3DG1jLC+PlPw/QddoyFmxL5Na+mgt+3zZ+7E/Mp9PUpQybs5pZ42IsnjOXKiYJV7++gZ4zV9G2qQctzQZbBzsdpRVGrn1zIz9sOcPc8TENrGnt2OkEno0cuGrOKmb+nMDH93QFtDajS3NfHvhsK6NeXsPwdsH0au13nrs1PNd1aWLxkrlc0OsEX04dyfu/xXMytTJey0d/7iTq1k+Y9ulaptzcvQE1/Hc42et5cGhLXltYv3HU/qsYTUbSUxJ55IV3ufWJ5/nuvZcoLqw0DOZlZ/LVmzO55aGp6HSXx/4oV0I/RK8TfDl5OO//sYuTqZe3cTIhMY9Rb29k/AdbuKtvMxzsKr8je72gf2s/libYdgJbUYmU8mMpZacqP1UNMklAkyp/NzYfozYZ86oaD+Cig5ddTO1ySkq5GRhi/tkJxKMZEVpIKXcC/kKIYCFEWzRvl+ot/hDgf0KIXWieIj5oxp51QG8hRBu0NVxpQoggoDuwEc3AMVgI8ZIQoreUsmb0NGu+r/L/2RZ6ENoasF3AH4C7EOLsNP4fUsrzjcBXSSkLpJQZaG5Lf5qPJwBhQggPwFNKucZ8/EugT5XrfzX/vwMI+5vn/CWlLJVSZgLpQEB1ASHE3UKI7UKI7Rnb/lmwtfSCUgLcKw0p/m6OVh4amkwZAWa3W70QuDrqyTOUMzTan03HsjGaJDnF5ew+k2flzdGzuQ8HUwvJLrJt8MzU/FKCPCtn/QI9nEirtlwqLa/UMsOp1wlcnewuyEvmhTFRnMosZl49eskAZBSW4e9WaYTwc3P8V+6mWUVlnMgqpm1I/cUEOEtGUSl+bpXxIPzcHMj4Bx47mWbZlPxSdiXm08Lvwr1sLpb0/FICqxiBAjwcSateLvJLLLOaep3A1dGO3OJyyo2SPHNchv3JBZzJLibUx9r9viHIKCi1cp/3d3Mko4FcmP8JafklBFXxFAn0cLLy7gMtv4I8K/PCzcnO4iUT4O7IuxPaM3l+gsWw2jpYq6fO/r04IZX21bw/6oqJfZqx5Ol+LHm6H+n5JQRXqauCPJ0tHnxnSc01WNJikcmzTi/Agm2JjDAvQRnXrSmLzS7sJzOKOJNVTPOAuikvaXklVvpo9au1Pqn5Jdbv39mOnKLymtd61ry2oKSCTUez6WM2XKTmlVg6p8sS0mhdh95ct/YLZ/mzA1n+7EDS80oIruItEuTlXMN1PCW3pszZ/ErJMbBop/bOd53MwSQlPq4OpOQa2Hw4k+zCMgxlRlbuTSWmqRd1yW39IlgxfRArpg8iLddAsHel+7+WDutvKiXX2jMmyMvac0avE4zoEMLv220XjP/vuOfq9mz+YCKbP5hIanYhjf0q8zzE143krNq9d957dCjHknKsvGSqMn/1Aa7u0cImOtdGXZX1MD8Xmvi4sHRqfzbOHEyQpxOLp/TFz/3ymJi4lPDw9iMns3JiMCcrHQ9vayOpp48fMV16obezwzcgGP/gJmSkaGXBUFzEBy88ydW33EOzVtH1qvuV0g+5Z2Qsm9+5ic3v3ERqdhGNq/TlQnxdSc48R/l+eCDHknIb3EsGavZDAtxrjjOq50XVfshZjmcUUVxqpEWV9rlXS1/2J+c3iEf7pY6U0iY/52Eb0EII0UwI4QDcgGYnqMofwETz79cDKy82ngxcnFHmrC+pAOaYvTvaSSmbSyk/M5/7CU3Z8dT0kjl77UNVrm0mpVwmpUxCW9YzDM0zZh2ap02h2RByGOiAZgB5QQgx/Ty6ylp+1wHdqjw7REp5tmao3U/Wmqql0VTlbxMXtizsrLzxPPJVn1OrbFWLn1/nkRfw6Er2JxfQxNuZYE8n7HSCIVEBrD1sHcx27eFMRsYGAjAw0o9tJ3MBzdDRKUzrbDrZ64gOcbcKGjo0yt/mS5cAEs7kEebrQmMvZ+z1gqvaBRJXLVhW3P50RnfUBjDDYgLYfPT8Bs3HhrbAzdmOF/44YBO9/46DqQU09nQmyN0RO51gYCu/C16+4+fqYLHCuzrqiQ1253RO/Xv5HEotpLGnM4HmNAxo6cvGC0yDq6MW+wPAw8mO6CA3TtZjsMO9SfmE+rgQ4umEnV4wPCaQ1Qetg1WvPpjBNeZB8eAof7aa4054udhb4vg09nKmqY8Liedwza1PDqQU0MTLmSAPrawPivRj3QWUg4YmIdGcF+byPaJtECurle+V+9O5toPmVj40OoDN5u/MzcmOj27ryGtLDrOziut2el4pEf6ueDXSPMh6NPexCp5Yl3y59gTD5qxm2JzVLN2dypiu2gRM+zAvCgzlNWLZpOeXUlhSQXtz3TqmaxOW7UkBIMyvslM9JDbQEmw3OcdAz1baQMPXzZGIAFdO1dGSnz1n8gjzbURjb+39j2wfRNy+avXrvnRGd9Le//DYQDaZd4KL25fOyPZBOOh1NPZ2Jsy3EbtP5+LdyAE3syePo52OXi19OGZOy/K9aXQzxwTqGuHNiTrMl3mrjzN4VhyDZ8WxeFcyY7uHAtChmbeWF9UMRul5JRQYyunQTIsNMLZ7KEt2aXmxZFey5Z2H+7tir9eRVVjG6n1pRDZ2x9lBj14n6NbSj8MpdTvL+8XqYwyauYJBM1ewZFcy47qZ0xF+7nQUllTQwRxvYly3UJaajXgAfSL9OZpSYGWoqU8++nMn3e77km73fcmfG49y02Bt98YurYPILyqtdenSc7f2wqORI5M+sA5qHBHsafl9eNcIjibl2FT3qtRVWT+YXED7KUvoMX05PaYvJyW3hOFz15BRD3GvrjRCW7QmIyWRzLRkKsrLiV8fR2yXXlYybbv24cjeeAAK83NJTz6DT0AwFeXlfDLnabr2G0b7Hv3rXfcrpR/y0cI9dHvoO7o99B1/bjrGTQMjAejSKlAr37Xo9dz/umvl++M1Nc41BHuT8mlatR8SG8iqg9bt4KoDGYwyb1oxJCqALeYYXiFezujNmRHk6UQzPxfL7oQAI9TSpUsKc4yYB9HCtBwA5ksp9wkhZgohrjGLfQb4CCGOAo8DNbbN/jfUhX/zUmCWEOJbKWWhECIEKJdSpqMZYj4BfIG+57j2PiHESilluRCiJZAkpSwCNqMt7RmA5kHzs/nn7A5O2VLKb4QQucCd59FxPFrsmvFoy4sAlgEPAa+Y79lOSrnrX6S/VqSUeUKIHLMnzzq0eDPnq10KgHoN8mCUkleWHOadG9tq2+TuSuF4ZjH39G3GgeR81h7J4vddKcwcFcmC+7uSb6hgqnkJwPztSTx3dWt+vKcLAvhzd4plxyMnex1dmnnz4qJDtk+DSfL8b/v5/K5O6HWCn7cmcjStkEeGNCchMY+V+zP4aWsir94Qy4rJvcktLuexKrv6rHq6L65Oeuz1OgZHBXDbJ9soLK3g/kERHEsr5PdHewDw9YbT/GTDbb2t0iThjZXHeG1MNDod/LU3jZNZxdzRoykH0wrZcCyb1gGuvDgqEjcnO3pEeHN7j6b878udhPq48GDfZkgJQsD32xM5boOYDBeShrdWHeeV69qgE4LF+9I4mW3gtm5NOJReyMbjObQKcOWFka1wdbKjezMvbu3ehNu+3kWotwtPDAzHJLXYE99tT7Latcnmupsksxce4sOJHdDrBAvikzmWXsQDAyLYl5zP6oMZ/BqfzJwx0fz1aE/yDOU8Zd7xoGOYFw8MjKDCKDFJyaw/DpBv0GItPTakBVfFBuJkr2fFpN78siOJD1Yd/ztV6i5NEl5bfpQ3x0Vr26wnpHIis5i7eoVyILWA9UeziQx0Ze7oKNwc7ejV3Ic7e4Vy82faLPQHN7Ul1McZF3s9v9/fldmLD7PlhO0HPEaT9g4/u70jOp3gl+1JHE0v4qHBzdmbmMeqAxn8vD2Jl8fFsHRSb/IM5Tz+vVa+b+7RlKY+ztw/MIL7B0YAcMdnO0gvKOW9uKN8c08XKoyS5FwDT/+01+ZpWbkvjQFRAayfMQhDmZEnvtlpObfk6X4Mm7MagGd+3MPrE9rjZK9n1f40VpmNIE+PakNEgCsmKUnMNjDVnM63Fh/m9QntWT61P0LA7N/219n2uUaT5Plf9zPv7s7aluRbEzmSVsijQ1uQkJhH3L505m9J5LWbYln5dB9yi8t55OtdgBZcedGuVJY81RujycSMX/dhkuDn7sgrN8aiF6ATgr92p7LqgDbY+DDuOG/c3Jbb+4RRVFrB0/Ntky9xCakMjA5k04tDMZQZeWzedsu55c8OZPCsOACe/m4nb97aCScHPSv3prFyr9Z5/n7DSd6Y2IlVzw2i3GjikS+06/OKy/lo+REWTx2AlJK4vanEJdiuw70iIZWBMYFsfnGYZWtvy7npgxg0cwUAU77dyVvmrb1X7k0lbm+lTtd2acKCbTWXLm2bMxxXZ3sc9DqGtQ/mhjfWcTiloIZcXbJk63GGdgln37y7KC4t555XF1vObf5gIt3u+5IQX1em3NSdg6ez2PS+Nln54e/xzFuSwH2jOtC/fSjlRhO5BSXc9coim+p7Li62rF8OTJ70ONu3bSU3N4fBA/pw3wMPMXrM2IZWywq93o5xdz3Ge88/jjQa6TZoJEFNw1n43Sc0bd6a2C69iWzflQO7tvLCgzcjdDquvfUBXN092Lp6KUf376KoII/NK7XvaMLDz9A4vGW96H4l9kOWbDvJ0M5h7PtsIsWlFdzzxnLLuc3v3ES3h74jxMeVKTd04eDpbDa9fRMAHy7czbyl++jYIoAfn70KT1cnRnRtxrRbutHxvm9srrfRJHnxz4N8fGsHdEKwID6JY+lFPDgwgn1J+aw6mMEvO5KYe300ix/vRZ6hnEk/7AGgQ6gnd/ZpRoXJhEnCrD8OWDxonO319Gjuw/O/1f/k72VBAwU5N++CvKjaselVfi8B6ryyE//G28Yc6HehOZ4LQohHqDSMFAK3SCmPmc8lAJlSyv7VrzXv2vQCcDWa10wGcK3ZoHEHWgDfYHMclVxggpTyVyHEUDRjigkoB+6TUlb2RKx1PYlmHBqO5nVyoznQry/aPuSRaMaptVLKey9kG2pzoN9OUsoHqzyjk5Qys+q5aoF+jwO3VQn0O0lKud2sx3YpZZgQwhvNUGWPFug3sqouQoi9wEgp5clz6dbphVWXaJz+f0aeDXdrqk8C/0Fg20sVvf7yWEN9PrKybOMJUZ+4ul4Z7us5DTQjX9cUFVz+OyU4ONlmy+n6prig4b3RLhZbBTWsb/JOnWhoFeoE3xb1M/i2JUfeGtXQKtQJ645knl/oEufx73aeX+gy4OgO2wfFrw/C20c2tAoXzb4XhzTQ/qr1Q9gjC23SKJ58a+Ql+d7+laeM2SgQXeXvt9AC59YmG1Ptb8u1UkoTMNX8U/26z9Dcg5BSlqPtdHT23FI048WF8oqUcnK1+2eiec5Uf+6M891MSjkPmFfl77Dazpk9b6xDuGvH+1XTI8z8ezZa4OBzPbd+F7QqFAqFQqFQKBQKhUKhsBmX9vYMCoVCoVAoFAqFQqFQKP4zXCneoxfKFWOUEUIsAJpVOzy5qhfLv7jnUOClaodPSCmv+7f3VCgUCoVCoVAoFAqFQqGAK8goYwtDyb9YJqVQKBQKhUKhUCgUCoXiX6I8ZRQKhUKhUCgUCoVCoVAoGoD/mlHmythWRaFQKBQKhUKhUCgUCoXiMkN5yigUCoVCoVAoFAqFQqG4NPhvOcooTxmFQqFQKBQKhUKhUCgUioZAecpcYVwpy+8qyo0NrUKdUFFhamgVLpqkpPyGVqFO8PFxaWgVLpqMjKKGVqFOyM3Ma2gV6gST8fIv34YiQ0OrUCdI0+Xf+Hn5eza0CnWCbBrW0CrUCRXlFQ2twkWz7khmQ6tQJ/Ru4dvQKlw0dnb6hlahThhxfa+GVqFO2HMgo6FVUJwHFVNGoVAoFAqFQqFQKBQKhUJhc5SnjEKhUCgUCoVCoVAoFIpLgv+ap4wyyigUCoVCoVAoFAqFQqG4JPivGWXU8iWFQqFQKBQKhUKhUCgUigZAecooFAqFQqFQKBQKhUKhuDT4bznKKE8ZhUKhUCgUCoVCoVAoFIqGQHnKKBQKhUKhUCgUCoVCobgk+K/FlLnsjDJCCE/gJinl+38jEwb0kFJ+d557hQELpZTRdaljlftfA7SRUs61xf1tQfcIbyYNbYFewG87U5i38bTVeXu9YOaoSCKD3MgzVDDll32k5JVgpxM8O7IVrYPc0OsEf+1J5YsNp8/xFNvw3Og29Iv0p6TcyKTvdrMvMb+GTHRjd165qS1O9npWH0jn+V/3AzCibSCPDGtJ8wBXrn1jAwln8izXtA5y48XxMbg62mGSklGvb6CswmTz9HRr5sWjAyPQ6wR/7E7l6y1nrM63a+zBowPDifB3ZfofB1h1KNNy7o2x0UQFu7MnMY9Jv+yzua4A069rQ79IP0rKjDz5/R72JZ3j/d/YFkd7HasPZDBzgfb+PVzseWdCexp7O5OYbeDBr+LJN1QwKMqfx4e3xCTBaJLM+m0/20/kABDs6cSc8TEEeTojpeT2T7aTlGOos/R0D/fmiSHN0QnB77tS+HJTzbLw/DWRtA50I89QztQF+y1lYeqIlkQGuWGS8Nqyo8SfzsXRTsfcMVE09nTGJCXrjmTx7qrjdabvWfq08mXaqEj0OsH8LYl8VO0ZDnodr9wYS3Rjd3KKy3nk612W93bvgHDGdmlsftcHWHdY+6Ym9gplfLcmCODHLYnMW3cSgMkjWzGgjT/lFSZOZxUz+ccECkoq6jxNALNv7sCg2CAMZUYe+nQLe07l1JBpG+rFO3d2xclBz4o9KUz9Nh6AqCaevDqxE40c7TiTVcQ9H26isKQCO73gzdu6EBvqhZ1e8OOGk7z11wGb6F8bcyZ0ZHDbYAylFTzw8eZa0/TM9bHc0KsZHo0caHrXT5bj9w9rzYR+EVQYTWQWlPLQJ5tJzCquF72vyLy4pQOD2gZjKDXy4CfnzovxPcPwaORA6N0/W453b+XHizd3IKqJJ3e+v5E/t52pcW1dYYvy7eZkx5xxMbQIdEVKeHp+AjtP5fLWLe1o5tcIAHdnO/INFVzzxgabpOvFG9sxMCYIQ1kFD3++jYTTuTVkYkM9efu2Ljg56IlLSOGZ73cB8PE93YgIcNP0dLEnv7icgTOXA9CmsQevTOiIq5MdUsLQF1ZQasP2e9b4tgyMCcRQZuTRedtrT0dTT968rRNO9nriElJ59sfdlnO394/gtv4RGE2SFQkpvPDLXsu5EG9n1swYwqt/7ufD5Udsloaz7I/fzM+fvonJZKLH4KsZMmZCDZn49XEs+uFzEBAS1oLbnphB4vHD/PDRq5QUF6HT6Rk69n907DXI5vr+G6ZPe5q1a1bj7e3Dr78vbGh1atAjwpsnh7VApxP8Fp/CFxtOWZ231wtmXduGyGA38orLmfyz1icfHhPAxB5NLXItAly58aNtHE4r5N2b2+Ln6oBeJ9h5Oo85iw5hqqexb7sQd27v1gSdgLjDmSzYk2Z1/uoofwa29MUkJXklFby/7hQZRWX4NXLgqYERCAF2OsGi/eksq9LnrQ96t/Jl2jWR6HUwf2siH686YXXeQS94+Qat7s0tLueRb3aTlGPA08Wedya0I6aJB79uT2LmbzXbtQ9v7UATH2eues029evlyn/NKHM5Ll/yBO4/j0wYcFNdPlQI8Y8NWFLKPy4ng4xOwJRhLXn4u91c/8FWhkYH0MzXxUrm2nZB5JdUcO17W/h2yxkeHhgOwKA2ftjb6Rj/0TZu+WQ7ozsEE+ThVG+694v0I8yvEf1fXM3TPybwwtja7WwvjI3h6R8T6P/iasL8GtE30g+AQ6mF3PfFDrYez7aS1+sEb0xox7T5CQx9aS03vruZCqPtDTI6AU8Mbs7jP+3lxk+3M7iNH2E+1nmRml/CrEWHWb4/vcb1325NZObCgzbX8yz9Iv0I83VhwOw1TP1pL7Our/39z7o+mqfnJzBg9hrCfF3o21p7//cOCGfjkUwGzFnDxiOZ3DcwAoCNR7IY8ep6Rr62nsk/7GHOuBjLvV69qS2frDrBkJfWct2bG8kqLK2z9OgEPDWsBY/8sIdxH21lSJR/jbIwylwWRn+whe+2JvLQAK0sXNc+CIAbP9nOg9/t5tFBEQjzNd9sPsPYj7Zy86fbiW3sTo8I7zrT+azeM66L4o5PtzPslXWMbB9E8wBXK5mxXRuTZyhn4Ny1fLH2JE9d1QqA5gGuXNUuiOGvrOf2T7fz/OgodAJaBLoyvlsTRr+1kZGvb6B/pB+h5m9xw2Fz/ry+gROZxdxrzre6ZlBsEOEBrnSZ/BePz9vGK//rVKvcKxM78di8bXSZ/BfhAa4MjNHy4s3bOjPrp930eXYJf+1I5MERkQCM6twUR3sdfZ5dwsAZy5jYvzlNfBvZJA010tQ2mIgANzpN+pPHPt/Ka7d1rlVu6c4kBj23tMbxPaeyGTB9Cb2fWcwf207z/A3tba0ycIXmRWwQ4QFudH5yIY9/sZVXb609TUt3JjF4xrIaxxOzinnwky38sulULVfVHbYo3wDPXhvJ2oMZDH15HVe/vp6jaYUAPPLNLq55YwPXvLGBpQlpLNtrPYCqKwbGBNLM35VuUxcz6asdvHxLh1rlXr6lI098tZ1uUxfTzN+VAdGBANz90WYGzlzOwJnL+Ss+kb/iEwGt/X7vzi48+fUO+j63jOteWU25DdvvAdGBhAe40mPaUp78Op65N9deJufe3J5JX8XTY9pSwgNcGRAdAECPVn4MbRfMwJkr6DdjOR8ssza8zBgby8p9qTbTvyomo5H5H73G/dNfY9o737Jj3QpSzlgPQtOTz7Dsl695fO4HTHvnW66/4xEA7B2d+N8jzzLtnW+5/7nX+OWztykuLKgXvf8po64dzQcffdrQatSKTsCUEa148NvdjHlvC8Oi/Qmv3idvH0xBSQWj3tnMt5vP8MggrQ1enJDGDR9t44aPtjFtwX6Scko4bC7Xk3/ay/iPtnH9B1vxcrFncBv/ekvPXd2b8uKyIzz66356hXvT2NN6nHAiq5in/jjA478dYPPJHCZ0DgEgx1DO0wsPMun3A0z58yDXxQbi5WxfL3qf1X3GdW2487PtDH91PSPbBdHc37p9ur5LY/IN5Qx6aR1frD3JkyNaAlBabuLNpUd4aeGhWu89JDqA4jLbTGYpLi8uR6PMXCBCCLFLCPGK+WevECJBCDG+ikxvs8xjQogwIcQ6IUS8+afHhTxICHGrEOIPIcRKIE4I0UgI8bkQYqsQYqcQYpRZbrMQIqrKdauFEJ3M179rPuYnhPhFCLHN/NPTfDxBCOEpNLKEEP8zH/9KCDFYCBFlft4uIcQeIUSLunuV1kQFu3Mmx0BSbgkVJsmyfWn0a+VrJdO3lR8Ld2udgrj9GXRp5gWAlOBsr0cvBI72OsqNkqLS+qtkBscE8Ou2JAB2ncrF3dkeP3dHKxk/d0dcnezYdSoXgF+3JTEkRusMHUsr5Hh6UY379m7ly8HkAg4kax2K3OLyeplRaBPkRmKugeQ8LS9WHMigTwsfK5nU/FKOZRRhqsWSvP1ULkVlRtsramZQdAALtld9/3b4uVV7/26OuDpWvv8F25MYbH7/g6MD+MWcf79sS2KwuZNaXCUNzg56S8yv5gGu2OkE680zvcVlRkrK666zHRXszpnsyrKwfH86fVtal4U+LXz5a49WFlYeyKBzmFYWmvk2YttJLY05xeUUllQQGexGaYWJHea0V5gkh1IL8a/2ji6Wtk09OZVVxJlsA+VGyV+7UhgUZd3hGhTlb8mrJXtS6W7+rgZF+fPXrhTKjCYSsw2cyiqibVNPmvu7svtULiXlJowmydbj2ZZys/5wJkZzgdh1KpdAGxlih7cPYf6GkwDsOJaFh4s9AdWeFeDhhJuzPTuOZQEwf8NJRnTQOnQRgW5sPJQBwOp9aVzdsTGgzcK4ONqh1wmc7PWUVxgpMJTbJA3VGdEhhB/Wa4Oc7ceycHdxqJGms+fS8kpqHF9/IB2DuXxsP5pFsLdLDRlbcCXmxfAOjfnRnKbtx7Lw+Id5cSaziP1ncmuti+sSW5RvVyc7Ood7M3+rZsgoN8pavd1GtA3kz53JNknXsHbB/GQ2aO04no27iwP+1d6/v4cTrk527DBPnPy06RTD2wfXuNc1nZqwYKvmqdQvKoD9iXnsT9Q8X3OKymzafg9rF2RJR/yJbNyd7WtNh5uzPfEnKtMxrJ2Wjol9w3l3ySGLJ25WQWmVewdzOrOYQ8k1PVBtwckjB/ANaoxvYAh29vZ06DWQPVvWWclsXPYHfUaMxsXVHQA3T60NDAhpin9wEwA8vf1w8/CiMD+3XvT+p3Ts1Bl3D4+GVqNWokPcOZNdbOmHLN2XTj/zRNZZ+rXy5c/dKQCs2J9Bl3CvGvcZFh3A0n2VBtWzfUM7ncBOr6u3WKrNfRuRml9CWkEZFSbJ+uM5dG7qaSWzN7WQMqOm0eH0InwaOQBan6nCXHjt9AIhBPVJbFNPTmUWV6l7UxkYFWAlMygqgF93aHXkkoQ0S91rKDey42RurR56Lg56busTxvsrjtk+EZchUkqb/FyqXI5GmSnAMSllO2Az0A5oCwwCXhFCBJll1kkp20kp3wDSgcFSyg7AeODtf/C8DsD1Usq+wDPASillF6C/+XmNgB+BcQDm5wdJKbdXu89bwBtSys7AGOCsaX4D0BOIAo4Dvc3HuwMbgXuBt8zp7QQk/gPd/xH+7o6k5Vd2ONPyS2sZWDuQlq91FIxSUlhixNPZnrgDGRjKjSx9rAd/PdyDrzedJt9GyxhqI8DDiZQqS1dScktqDBADPZxIya1MX2quodaOd1Wa+TdCSsmX93bhzyd6cY/ZG8LW+Lk5kp5f2SFLLyjFz9WhXp79bwh0r/5ua3//qXnVZNw1GV83RzLMHdCMglJ8q3x3Q2ICWD65D5/d1YnJP+wBoJlfI/INFXxwawf+fLwnU65ubZn1rQv83BxJq9Ihrq0s+Ls5WpeF0go8nO05kl5In5Y+6IUg2MOJ1kFuBFS71tXRjt4tfNh2suYSiYshoMY3XlLrgPmsjNEkKTRU4GUeWNd27eHUAjqFe+PpYo+TvY5+rf0I8qxZbsZ2aczagxl1mp6zBHk5k5RduTQnOcdAkJdzDZnkc8gcTMpnuNkoMKpzE0LMBow/tp+huLSCfW+OYtfr1/De4kPkFpXZJA3VCfJysU5TdjFB/9KwckvfCFbssc2AuTpXZF54O5OUXWmUv5i8sCW2KN9NvJ3JLizjpfEx/PFYT2aPjcbZQW91z87hXmQWlHEq0zbL44I8rb+plJxigjyda8hUbeOTcww1ZLq18CUjv4QT6ZpHQESAG1LCD4/2Zvmzg3hgWCub6H+WQE9nkqv2Q3IMNerKIE+nGjKB5nSEB7jStbkPfz3dn18n9aFtqDbAdnHU88DQlry2cL9N9a9KXnYGXr6VBj8vH3/ysq3r9/TkM6QnneH1Kffy6lN3sT9+c437nDy8n4qKcnwDQ2yu85VG1T4GnKMf4u5Ial7NPnlVhkQFsCTB2svtvZvbEjepF8VlFayoxdvaFng3siezqNLQnl1Uho/Lub1dBrb0JT6xMpSATyN7Xr82ko/Hx/JbQio59WS0Bwh0dyQlt7LcpuaVEOBhnRcBHo6kmmWMJklhiVb3/h2PDm3B52tPYKjDSUXF5cvlaJSpSi/geymlUUqZBqwBavMBtwc+EUIkAD8Bbf7BM5ZLKc+uaRkCTBFC7AJWA05AU2A+cL1ZZhzwMzUZBLxrvvYPwF0I4QqsA/qYfz4AYoQQIUCOlLII2ARMFUJMBkKllDWCZggh7hZCbBdCbM/c3jBrYqOC3TGZJMPe3MjV72zilu5NCall4Ha5YafT0Sncm0e/3snYtzcyJDaQHtU8VhR1T1VD9rKENAa/tJZ7Pt/B48M1d1A7naBzuBez/zjAtW9upKmPC9d3adxA2lrzx65U0vNL+eqOjjw+pDl7EvOsZmf1QvDidZH8uC2JpNyas+6XGsfSi/h41XHm3d2Zz+/qzP7kAqqvALhvoBbb5Pf4+jEM/FMe/nwLtw9oQdyMIbg62VFmTkCHZj4YTZLox36n46Q/uX9YK0L96mfJTF0xtkcY7Zt58049xl+5GK7kvLgc0esEUSHufLfpNNe8sYHiMiP39LeefBjZLpiFuy7Nsl2V67o2tXjJgJa2rs19uf/TLVzz0ipGtA+hd+v6Warxb7DTCTwbOXDVnFXM/DmBj+/pCsCkq9vw8YojFJfWn/frhWA0GUlPSeSRF97l1iee57v3XrJappSXnclXb87kloemotNd7sONy5PoEHdKyo0cy7D2BH/g290Mfm0DDnodnZvV9K5paPpEeBPh68LvVYxJWUXlPP7bAR74aS/9mvvg4XTZhUW1IjLYjaY+LizfWz9GscuR/5qnzOX9RV84jwFpaB41OuCfjISq1mQCGCOlrLEw0Lz0KBbNE+feWu6jA7pJKUuqXbcWeADNuPMMcB2agWcdgJTyOyHEFuAqYJEQ4h4p5cqq95BSfgx8DNBx1qp//bWl55cS4F5pSAlwr/ReOEtGQRkB7o6kF5SiFwJXJz25hnLuifZn47FsKkySnOJydp/Jo02wm00HnRN6hXJDd81Fds/pPG0m1hwENsjT2isDNMt21VmrQE/nWl3Rq5KSa2DrsWxyzNb91fvTiW7swcYjWXWZlBpkFJTiX2X5lb+bIxmF9TNrfKFM6KkFfwXYcya32rut/f1X9Z4J9HQi1eyZlVmgzQBlmP+vLT7MtuM5NPFxwauRPSl5JexPzudMtmajXJaQSvtQL+rKkSyjoNTKu6W2spBeUGpdFhztyDPP3LxRxRX1s4ntOV1lFnjqVS05nW3g+2117/SWVuMbd6rxjZ+VSc0rQa8TuDrbkVNc/rfX/rQ1kZ/MyxueGN7SKm9HdwphQKQfEz7aWqdpuX1gcyb01dbH7zqRbfGoAAj2sp41B23GOfgcMkdTChj76mpAmz0f3FZbLjCmeyhxCalUGCWZBaVsOZJJuzBvTmXUXMpYF9wxqAX/69ccgJ3Hs6zT5O1CSvY/80ToGxXAE9dEMXL2CpsGH78i82JgCyb009K080QWId6NAG055L/Ji/rAFuU71fyz+7Q2I71kT6qVR6heJxgaE8C1b26s07Tc1j+CW3prz9l18uw3pbWrQV4uVrPSoLXFVT2ygr2crWT0OsFVHUIYPGtF5TU5BjYdySDb3HauSEghJtSTdQfrbhB0a79wbu7dDIDdJ3MIrqJjkJezlXeSlo6SGjJnZ9dTcgwsMi8R23UyB5OU+Lg60KGZNyM7hPDsmBjcXewxSSitMPHFKtstefDw9iMns/I95WSl4+FtvXTG08ePsJZR6O3s8A0Ixj+4CRkpiYS2iMRQXMQHLzzJ1bfcQ7NWNtlL44rnbB/jLLX2Q/JLCfSo2Sc/y9Bof5acIxZUmdHE6kOZ9Gvlx5bjdeu1WxvZReX4Nqr0HPFu5EBWcU1vl9hgN8a0DeTZRYctS5aqkmMo53SOgchAVzabl4rbmtT8UivPvEAPJ9LyrPMiLa+UQE9nUvNKtbrXSat7z0X7UE+iG7uz6um+2OkE3q4OfHNvF275sG77Upc1l679xCZcjqbrAsDN/Ps6YLwQQi+E8EPzNtlaTQbAA0iRUpqACYC1b+6FsxR4SJgXMwohqkZx+xF4CvCQUu6p5dplwENn/xBCtAOQUp4BfIEWUsrjwHpgErDWLBcOHJdSvg38DsT+S93Py/7kApp4OxPs6YSdTjAkKoA1h62jm685nMnItlpwvYFt/CyxM1LzSy0xNZzsdcSEuHPCRq7OZ/l6/SmuemU9V72ynmUJaYw2BwRrF+pJgaGCjPxqBqX8UgpLKmgX6gnA6M4hLE+ovbE6y9qDGbQKcsPJXodeJ+gS4cMRc7A0W3IgpYAmXs4EeWh5MSjSj3VHbWsI+qd8veEUI1/TgvAuT0jjuk5V3n9JRS0GvVIKSyvf/3WdQlhh7iys2JfOGHP+jekcwnLz8dAqQe2iQtxxsNORU1TOntNa3CBv83rjHi18LcEp64L9yQU09XYm2Pz+B7fxZ221srDuSCZXxWplYUCkn2UpkqOdDid7rWrt0syLCpO0lIV7+zbD1dGO15cdrTNdq7LnTB6hvo1o7O2MvV5wVbsg4vZZD0Di9qVb8mpYbCCbzd9V3L50rmoXhINeR2NvZ0J9G7HbvHOIt3npXJCnE0NiAvjD7BHTp5Uvd/cP554v4us0pg/A53FH6T99Kf2nL2VRfCLjeoYB0DHCh3xDea2D0QJDOR0jNE+2cT3DWLxTi61xdjmcEPD4NW2Yt0p7/4lZRfSO1GbOXRz0dIrw4UiK7WI2fLbiCH2nLabvtMX8tSORG3ppg7lOET7kF9dM098RE+rF67d14aY31pKZX9OIWZdckXkRd4R+zy6h37NLWLQjifHmNP2bvKgvbFG+MwvKSMktseyy1KOFj1Vd2rOFD8fTi2oY2S+WL1YdswTnXbwzibHdQwHoGO5NgaGc9GrPS88robCkgo7hWnD0sd1DWVLFe6dPpD9HUgqsDISr9qUSGeKBs4MevU7Qo6Ufh+s4Jsu81ccZPCuOwbPiWLwr2ZKODs3OnY4CQzkdmlVNhxYTZMmuZHq20gwf4f6u2Ot1ZBWWce0ra+gydQldpi7hk7ijvL3ooE0NMgChLVqTkZJIZloyFeXlxK+PI7ZLLyuZtl37cGSvtqtaYX4u6cln8AkIpqK8nE/mPE3XfsNo36O/TfW8ktmXVEBTHxdLn3xolD+rD9Xsk1/dVguiPqiNH9tOVBpXBDCkTQBLqxhlnO31+Jrbc70Q9Grhw8lM2xi+q3M0s4ggDyf8XR2w0wl6hXuxvdruZM28nbmnRyhzVxyzCoHg7WKPg15bo97IQU9kgCvJ9VhHJ5zJI8zXhcZeZ+veQOKqLfuK25/O6I7aJMOwmABL3Xsuvtt0hl4vrKb/nDXc8P4WTmYWKYPMfxxxKbvxnAshxHdoxonF5kPD0expL0gpfxRC2KMZUHyAecBC4BezzBLgASml6/m2xBZC3Ap0klI+aP7bGXgT6IFm0DohpRxpPhcAJAGzpJTPV79eCOELvAdEonkorZVS3muW+xrQSylvMgchXg/4SSmzhBBT0AxJ5UAq2nbg1lsEVeFiPGUAejb35okhLdALwe+7U/h8/Snu7duM/Sn5rD2chYNex6xrI2kV6EqeoYKpv+4jKbcEZ3s9M65pTTO/Rgjgj90pfL3p328Lmv0vtnadOSaKPpF+GMqMPPX9Hsu21n892YurXlkPQEwTD/OW2DrWHMjgOfN20UNiApgxJgpvVwcKDBXsT8pnorlyvLZjCPcNikCiecrM/fPCdzUKDHI7v9A56B6ubYmtE4KFCal8uekMd/UK5UBqAeuPZhMZ6Mrc0VG4OWpLALKKyrj5sx0AfHBTW0J9nHGx15NXUsHsxYfZcuLfzYSkXaCx4/nRUfRp7UtJuUl7/+a1wAuf6MXI18zvv7EHL98Yq73/gxnMMG9J7uliz7v/a0+wlzNJOQYe/GonecXl3DMgnOs6hVBhlJSUG5n750HLlti9Wvoy9ZrWCCFIOJPHMz8lUG489+fv4/PPYkT0iPDm8cHNzVuSp/DFhtPc0yeMAykFrD2ilYXnR7WmVYAb+SXlPLNgP0m5JQR5OPHOjbGYpCSjoIxZCw+Sml+Kv5sjfz3cnROZRRY9529P4ndzh/xCyLmALb/7tvbTtswVgp+2JfJB3DEeGdqCvWfyiNufjoOdjtdujKVNiLZt46Pf7LJ4HN03MIKxnRtTYTLxwh8HWHtQ6wB+f39XvBo5UG40MfuPg2w6O9Cb0sdiKAPYdTqX6RewBXtuZt55Zarz0oSODIgJwlBawcOfbWGX2Qi2auZQ+k/XdidqF3Z2G2Y74vYkM+UbbcBw9+CW3DFQ81BZuCORWT9pdvNGjna8fWcXWgV7IIDv15/g3cUXXr5NF7mTy8sTO5m3Ada2Yd5lDv655oXh9J2mNW8zbmjH9d3DtBm4XANfrz7GSwsS+HXyANo08SDNPBOfmFXEzW+s/cc66PT/fG7mUswLeZERXF/+nzlN5m2+z+bF6lnD6PfsEgCeG9+O67uHVubFmmO8vGAv7Zt589UjvfFo5EBpuZH03BJ6Tl30j3Xw8vc8r4wtyndksBuzx8ZgrxecyTYw+cc95Bu0AdFL42PYdTqX7/9Be16Q+88N5HNuas+AaG0r6Ue+2MZu85bkcdMHW7a3bhvqxdu3d9a2kt6bytTvdlquf+u2zuw4nsVXa6y3CB/TrSkPD28NaJ4ys35OuGCd/s2Sm9k3tqN/dACGMiOPzdvObnNw9+XPDmTwrDhzOjx589ZOODnoWbk3zbK1t71e8MbETkQ18aDcaOL5nxLYcMg6jssTV0dSVFJxwVtif/VY33+chrPs276Rnz9/G2k00m3QSIaNncjC7z6hafPWxHbpjZSSX794hwPxWxA6HUPHTqRT70FsXb2Ub955kaAmzSz3mvDwMzQOb/mvdendwvf8Qv+CyZMeZ/u2reTm5uDt48N9DzzE6DFjbfKs7i+uPL9QNXo192HSsBbohOD3Xcl8tu4U9/Vrxv7kAtYczsRBr+OF69rQKsiVfEMFU37ea/FQ7xjqycODIpho7heCFtfl7RvbYm+nQydg+8kcXl1yFOM/GAuGN/n3gZE7NHbntq5N0AnByiOZ/LI7lRvaB3E0s5jtZ/J4blgLmno5WzxMMovKmLviGLHBbtzapTESzdi0+EAGyy9yS+w9B/5ZDLy+rX155ppI9DrBz1sT+WDlcR4Z0pyExDxW7s/AwU7HqzfE0ibEjdzich77drel7l31dF9cnfTY63UUGCq47ZNtHK2yuUiIlzMf397hH2+JfeSVYfUb8bie8bvtR5sYKTK+GH9JvrfL0iijODcXa5S5VPg3RplLkYsxylwqXKhR5lLnnxplLkUuxChzOfBvjDKXIhdrlLkU+DdGmUuRizXKXApciFHmcuDfGGUuRa6EOCgXY5S5lLCVUaY++TdGmUuRizHKXEr8U6PMpYgyyvw7LlWjzH8lpoxCoVAoFAqFQqFQKBSKS5z/muOIMsoAQoihwEvVDp+QUl7XEPooFAqFQqFQKBQKhUKhuPJRRhlASrkULQaNQqFQKBQKhUKhUCgUigZCecooFAqFQqFQKBQKhUKhUDQE/y2bzGW5JbZCoVAoFAqFQqFQKBQKxWWP8pRRKBQKhUKhUCgUCoVCcUnwX1u+pDxlFAqFQqFQKBQKhUKhUCgaAOUpc4VRUFDa0CrUCUUFRQ2tQp1w180xDa3CRTPlw80NrUKd4OJi39AqXDSNG3s0tAp1QlZqdkOrUCc4N3JuaBUumvKy8oZWoU5wcHFoaBUumqL84oZWoU64UmY3y0rLGlqFi+bx73Y2tAp1gp2dvqFVuGg2PTOgoVWoEzpMX9bQKtQJpSWXf/m+0rlS2pILRRllFAqFQqFQKBQKhUKhUFwS/NeMMmr5kkKhUCgUCoVCoVAoFApFA6A8ZRQKhUKhUCgUCoVCoVBcEihPGYVCoVAoFAqFQqFQKBQKhc1RnjIKhUKhUCgUCoVCoVAoLg3+W44yyiijUCgUCoVCoVAoFAqF4tJALV9SKBQKhUKhUCgUCoVCoVDYHJt7ygghPIGbpJTv/41MGNBDSvndee4VBiyUUkbXkW5TpZSzq/y9UUrZ42/kxwIzgVQpZf9/+KxbgWVSymTz358Cr0sp9/8r5euQ3i19eWZUJHoBP21N5OPVJ6zO2+sFr9wQS1SIO7nF5Tz67W6Scgx4utjz9oR2xDT2YMH2JGb+fsDqmunXtqFLuDdSSl5fcoRle9Nsmo4Xb2zPoJhADGVGHvp8Kwmnc2vIxIZ68fbtnXG217MiIZVnvt8JQHQTT16e0BEnex0VJsnkb+LZeSKbYe2CmXJtNCYpqTBJnv1+F1uOZto0HbVxfPc24r5+H5PJRNt+w+l2zQ1W5xPWLmXV95/g5uUDQIfBo2jbf0S963kWW+RF80A33r69MzFNvZizYC/vLz1k0zT0bOHDlBGt0OsEv+xI4rO1J63O2+sFc66Ppk2wVi4m/biH5NwSy/lADyf+eLg77688zrwNpwBwc7Lj+Wvb0DzAFaTk2QX72X0mz6bpqEqXUE8e7NsMvQ7+2pvOd9uTrM7HhrjzYN8wInwbMXPRYdYczbKci3u4OyeyigFIyy/lmT8P1pve1Zk7oSOD24VgKK3g/o83sedkTg2ZaWPbckOvZng0cqDJnfMtx+8f3poJ/ZpjNJrILCjloY83cyarqN50nzU+lgHRWtl4bN4OEs7k1pCJaerJm7d2xMlez8q9qTz74x4AnhgZyU29wsguLAVgzm/7WLk3DXu94OVbOhAb6onJJJk+fw+bDtuunpp9U3sGxQZRXGbk4c+2sudUzfcfG+rFO3d20cr3nhSmfqeV76gmnrzyv440crLjTGYR9360mcKSCrwaOfD5Az1o38ybHzacZMo38TbT/yyzxlXJiy//Ji8mVsmL+dXyosCcF79reXGWEC9nVj83mNf+OsCHy4/YNh3j2zLQXN8+Om977fVtU0/evK0TTvZ64hJSefbH3ZZzt/eP4Lb+ERhNkhUJKbzwy97KdHg7s2bGEF79c79N03Elt98Ac27uwKC2wRjKjDz4yeZay8wzY2IZ3zMMj0YOhN7zcwNoCT2b+zD5qlboheDXHUl8tu6k1Xl7vWD2mMp278n5WrsX7OnE7w/34GSm1kbsOZPHrD+1PuFDgyK4pl0w7k52dH1hVb2ko0eEN08Oa4FOJ/gtPoUvzG1w1XTMurYNkcFu5BWXM/nnfaTklTA8JoCJPZpa5FoEuHLjR9s4nFbIuze3xc/VAb1OsPN0HnMWHcJ0iUzkT5/2NGvXrMbb24dff1/Y0OpY0auFD0+PbI1eJ/h5WyKf1tKXmjs2xjLGePz73STnlhDT2J3nr22jCQnBe3HHiNufDsALo6Po29qP7KIyRr21sd7S8vyYaPpHBWAoM/LENzvZm1iz/xbTxIPXbmmPk72eVfvSeM5cnz5xVSuGxARhkpKsglKe+GYnafmlXNsphPsGtUAIKCyp4Jn5eziQlF9vabqUUZ4ydY8ncP95ZMKAm2yuSU2mVv3j7wwyZu4A7vqnBhkztwLBVZ5156VgkNEJeO66Ntz12XZGvLaeke2CiPBvZCUztktj8gzlDH55HfPWneTJES0BKC038dbSI7z0V83B8X0DIsgqLGPoK+sY/tp6th3Ptmk6BsYEEh7gStepi3niq+28PKFjrXIv39KBJ77cTtepiwkPcGVAdCAA08fG8uof+xjw/HJe+m0v06+PBWDdgXT6zVjGgOeX8+gX23j91k42TUdtmExGln/5DmOfms2dL3/K/s2ryEw6VUMusltfbpv9EbfN/qhBDTK2yovcojKmfrfT5sYY0MrFtKtbc99XO7nm7Y2MiAkk3M+6XIzuGEK+oYIRb2zg642neHxoC6vzTw1vybojWVbHplzVig1HsrjmrY2Mfm8zxzPqzxigE/BI/3Am/7afiV/tYkArX0K9na1k0gtKmbvsKCsOZtS4vqzCxJ3f7ubOb3c3qEFmcNtgIgLd6fjEHzz62RZeu7VLrXJL4hMZ+NySGsf3nMxhwLOL6TV1EX9sPc2MG9vbWmULA6IDaObvSs9nl/HUN/HMubldrXJzb2rHk1/H0/PZZTTzd6V/VIDl3CdxRxn8wkoGv7DSYgS4uXczAAbOjOOGtzbw3PUxCGGbNAyKDSI8wI0uUxbxxLxzl+9X/teRx7/YTpcpiwgPcGNgjFa+37itMy/8vIe+zy5lUXwSDw5vDUBpuZG5C/byXBVjgS2x5MX0ZTz1bTxzbmpXq9zcm9rx5Dfx9Jx+jrx4cSWDX1xpZZABeG5sLCv3pdoyCQAMiNbq2x7TlvLk1/HMvbn273nuze2Z9FU8PaYtNde3Wjp6tPJjaLtgBs5cQb8Zy/lgmbXhZUY9pONKbr/BXGYC3ej81EIe/2Irr06sXY+lu5IY/PyyetauEp2AZ65uzf1f7WTUOxsZHnvudu+qNzfw9aZTPDakst07k21g7PubGfv+ZotBBmDNwUxu/HBLvaZjyohWPPjtbsa8t4Vh0f6E+7pYyVzbPpiCkgpGvbOZbzef4ZFBEQAsTkjjho+2ccNH25i2YD9JOSUcTisEYPJPexn/0Tau/2ArXi72DG7jX29pOh+jrh3NBx992tBq1EAnYNo1kdwzL56r39zAiLY1xxhjOjUm31DOsNfW8+WGUzwxTBtjHEkrZOz7Wxj97mbunreDGde2Qa/TGrYF8cncPW9Hvaalfxt/wvwb0WdmHFN+2M2L42NrlXtxfCyTv99Nn5lxhPk3op/5O/ko7hhD565m+EtriNuXxiPDWwFwJquYcW9tYMic1by99DBzb2hbb2lSXFrUh1FmLhAhhNglhHjF/LNXCJEghBhfRaa3WeYxIUSYEGKdECLe/HM+YwkAQogoIcRW8332CCFamI//JoTYIYTYJ4S423xsLuBslv3WfKzQ/HG0Fv4AAJWeSURBVH+QEGKt+dxeIURvIcR0oBfwmTkN59RRCDHZnL7dQoi5QojrgU7At+Z7OgshVgshOpnlbzTL7xVCvFTlPoVCiBfN99kshKjsDdYRsU08OZVZzJlsA+VGyV+7UxkUZf2YgW0CWLA9GYAlCWl0b655YxjKjew4mUtpuanGfcd0DuGjlccBkBJyisvrWnUrhrcLYf7GkwDsOJ6Nh4s9/h5OVjL+Hk64Oduzw2wgmr/xJCPah1h0dHO2B8Dd2Z7UXAMARaUVlutdHO1oCKNtyrFDeAYE4+kfhN7Onshu/Tiyo/5mBv4ptsqLzIJSdp3MocJo+0yIaezB6axiEnMMVBglixNSGRDpZyUzINKP33dq5WLZvnS6hntbnUvKMXAsvdByzNXRjo5hXvyyQ/NOqTBKCkoqqC9aB7qSlGcgJb+UCpNk5eFMekZ4W8mk5pdyPLP4ko6tNqJjY35Yr9Ut249l4dHIgQBPpxpy249lkVbFc+ks6w+kYSgzArDtaCYh3i41ZGzF0LbB/Lz5NADxJ3LwcLbH371a2XDXykb8CW0m/efNpxnWLrjGvarSMsiN9Qe1GcSsglLyDOW0DfWyQQpgWPsQfrSU7yw8XOwJqFa+AyzlWzNK/rjxJMM7NAYgIsCVjYc0o9/qfamM7KgdLy4zsuVIJqXlRpvoXZ2hsReYF07V8qLt3+cFwLC2QZzJLOJwSkHdK179We2C+GmTZqSPP5GNu/O569v4E1p9+9OmU5ZvamLfcN5dcoiyCq0dzzJ7/mj3DuZ0ZjGHkm07a3slt98Awzs05scNJwFzneXiUKPMnD2Xllezzqovamv3+ldr9/q39uOPXVq7t7xau3cu9iTmkVlYZhOdayM6xJ0z2cUk5ZZQYZIs3ZdOv9bW6ejXypc/d6cAsGJ/Bl3Ca9aXw6IDWLqv0thaZG437HQCO73ukmonO3bqjLuHR0OrUYOq31S5UbJ4TyoDIq2NWQMi/fgt3tyX2ptGN3O/pKTchNHsiuRop0dWeeM7TuaQZ+NxRXWGxATyy9ZEAHaezNHqWndHKxl/d0dcnezYafbe/WVrIkPNExKFVfp7Lg56S32040QOeQYtLTtP5BBUS3/mv4qU0iY/lyr1Eeh3ChAtpWwnhBgD3Au0BXyBbUKItWaZSVLKkQBCCBdgsJSyxGxY+R7NqHE+7gXeklJ+K4RwAPTm47dLKbOFEM7mZ/4ipZwihHhQStmulvvcBCyVUr4ohNADLlLKdUKIAWY9t59LRyHEcGAU0FVKWSyE8DY/+8Gz15rTiPn/YOAloCOQAywTQlwrpfwNaARsllI+I4R4GbgLeOFCXvqFEuDhSGqewfJ3al4JbZt41JBJMcsYTdpA0svF/pyGFjcn7bN6dGgLukR4cTrLwMzf9pNlw0Y50MuZ5OzKdCTnGAjydCa9SgcnyNOZlBxrmUAvzVNg2g87+fGxPswY1xadgKvmrLTIjWgfwjNjYvB1d+Tmt9bbLA3noiAnE3fvyg6Fm7cvKcdqeioc2rqeMwcT8ApszMBb7sXdp2FmcWyZF/WFv7sjqXmVg5O0/FJiGrtXk3Ei1Zwmo0lSWFqBp4s9pRUmbu8dxl3z4rmtV6hFPsTLiZyiMl4YHUWrQFf2Jxcw96+DGGoxatoCv0aOZBRUlsGMgjLaBLpe8PUOdjo+ujEWo0ny3fYk1h+zrffbuQjyciHJvIwKIDm7mCAvl1oNMOdjQt8Ilu9Orkv1/pZATyfrspFrINDLifT8KsvevJxqlo0qnbTb+oVzfbem7DmVw/M/J5BXXM6+xDyGtA3it22JBHs5E9vUk2AvZ3bVsqzrYgnydCY5u8r7N5fdqoNJrQ6olEnJLibIUyvfB5PzGd4+hMU7k7imU5N6NYpVJdDTieScannhWS0vPKvlRW4tedHVnBe/aHnh4qjn/qEtueGt9dw3uGU9pMPZKh0pOQaCPJ2q1bdONWQCzfkRHuBK1+Y+TLk2itJyI8//lMDuUzm4OOp5YGhLxr+5jvuG2DYdV3L7DRDk5UxSlSWSljqrAQ0wtVGj3csrJfYC2z3QluzNv78rRSVG3ok7Svyp3HrT3UpHN0fS8q3b7+iQ6umoTKtRSgpLjHg625NrqOzXDokK4LEf9lhd997NbYkOcWfD0SxWmJfSKM5NgEfl9wLaGCO2xhjD+psqKNG+qdzicmIbe/DCmCiCPZ2Y/NNei5GmIajeHqTmGgj0cCK9yrcW6OFEapW+SGq1NuPJka0Z06UJBYZyxr9Tc3J1fPemrFLf1X+W+g702wv4XkpplFKmAWuAzrXI2QOfCCESgJ+ANhd4/03AVCHEZCBUSnm29DwshNgNbAaaAC3OdQMz24DbhBAzgBgpZW3TXefScRDwhZSyGEBKeb6RS2dgtZQyQ0pZAXwL9DGfKwPOLg7dgbbMqwZCiLuFENuFENvzdi86z+Nsj51OEOTpTPypHK57axO7TuUy5apWDa3W33Jrv+ZM/3EX7Z9cyLM/7OLNWys/y0U7k+g5bQkT393AlGvrJJxRndO8fXfuffNrbp/zMc2iO/DXR680tEr/mr/Li8uBBwaE8/XG0xZvjLPY6XREBrnx49YzjH1/C4YyI3f0adZAWv5zxn+2g3u+38OsxYd5sG8YwR6O57/oEmZczzDahfvwzl8Nvor0gvlyzXG6T1vK4BfiSMsr4bnrYwD4YcMpUnIMLJnan5njYtl+LBvTpRLsoBqPfLaV2wY0Z8Vzg3F1tqfMWD9GybrGkhcvxpGWX8JzY7S8mDQykk/ijlJcWj8ePxeLnU7g2ciBq+asYubPCXx8T1cAJl3dho9XHLks0nG5t99XAhkFpQx5dR3j3t/CK0sO8dLYGBo56s9/4SVKdIg7JeVGjlVbYvzAt7sZ/NoGHPQ6OjezjTeiopI9iXlc89ZGxr2/hbv6NsPB7vLen+aVhQfpNn05v21P5NZq/b/uLXwY370pc36/fPoktkZ5ylwaPAakoXnU6IALmk6QUn4nhNgCXAUsEkLcA5jQDCXdzZ4rq4G/9Q2TUq4VQvQx32eeEOJ1KeVXdaHjP6RcVn49Rs6RX1LKj4GPAVo+teQffW1peaUEelTGlQj0cLKaYTgrE+ThTFpeKXqdwM3J7m+XI+UUl1NcVmEJ7Lt4TyrXdw75J2pdELf3b84t5kpt58kcgqvExwj2ciYl12Aln5JrIMjLWibVbPUe3yPUEjTwj+2JvFGLIWDz4UxC/Rrh7epAdj264rp5+ZKfXRnjoyA7E1cvXysZZ7fKWaDY/sNZ9cMn9aYf1H9e2Jr0/FICqxgdAtwdrWZDNJkSS3nR6wSujnbkFpcT09iDwVEBPD60BW5Omst8aYWJZfvSSMsvJSFRWwqwbF8ad/YJq7c0ZRSV4ufmYPnbz82BjKIL/44zzbIp+aXsSsynhZ8ryXml57mqbrhzUEv+119b8x9/PJsQn0rvimBvF1Jyis91aa30jQrk8WuiGfnicsuyDVtxa79wbu4VBsCus2XjmHYu2NOZ1BzrpiM1p6Rm2TDPvGVWWVry7fqTfPVAd0CbXZzxU4Ll3B9P9bVaOnex3D6gORP6hgOw80Q2wVW8W6qW3co0GKxkgrxdLHXA0dQCxr22BtC8NAbHBtWZnufj1r5V8uJUDsFV37Ons9UMJ0BqbrW88PybvLhfy4v2Yd5c1SGEaaOjcXe2xyS1eDlfrD5ed+noF26JI7T7pHU6grycSamWjpTckhoyZ5f4pOQYWGRehrnrZA4mKfFxdaBDM29Gdgjh2TExuLuY01Fh4otVx+okDVd6+33HwBZM6KvVWTtPZBHi0wiOaEGG/02dVR/UaPc8HEkruLB2D7AswdifXMCZ7GJCfRqx38ZL32ojvaCUAHfr9jujRjq0tKYXlKIXAlcnvZWXzNBof5acY3OKMqOJ1Ycy6dfKjy3H694b8UoiLU/7Xs5S3bOkqszZb8rNqfKbOsvxjCKKy4y0CHBlXz0Gwf1f7zBu7KF5PO85nWtVBwV6Olt5AYHmCVTVMyawlnYFYMH2JL68tyuvL9JiJLYOduflG9vxvw8210j7f5pL135iE+rD5FgAuJl/XweMF0LohRB+aB4hW6vJAHgAKVJKEzCBymVIf4sQIhw4LqV8G/gdiDXfK8dskGkNdKtySbkQwr6W+4QCaVLKT4BPgQ61PO5cOi5H87JxMd/r7ILb6mk8y1agrxDC17xU6kY0D6J6ISExjzBfFxp7OWOvF1zVNtAS3fwsK/enc10nbf35sJgANh3Nqu1WVqzan2FZa9y9uQ9H0+s+oOnnq44y4PnlDHh+OYt3JjGuRxgAHcO9yS8ut3J9BkjPK6HAUE5Hs17jeoSxeJcW3yM1t4QerbQlQr0j/TmepjlHNfOvXN4R09QTBztdvRpkAILCW5GTmkRuegrGinIObF5N8w7drWQKcyrz5OiOTfgEN61+G5tSH3lRn+xNyqepjwshXk7Y6QXDYwJZVS347aqDGYxqr5WLIVH+bDHHOpj46XaGvraeoa+t55tNp/lkzQm+33KGrMIyUvNKCDMHHOwW4c0xG5SLc3EotZDGns4EujtipxMMaOnLxgtcguTqqMdery259HCyIzrIjZPZ9Teo+HTFYfo8s5g+zyxm0Y4z3NBLMxB0ivAhv7jsHy1dign14o3bu3DT62vIzLe9UWne6uOWwLxLdqVwfTetbHZo5kW+odxquQxog54CQzkdzLOw13drylLzEquqMU+Gtwu2xPpwttfj7KA1QX0i/akwSY7UYTyTz1cepf9zy+j/3DIWxycx3lK+fcg3lNdYhpFmKd9a/LHxPcJYslMr375u2mBJCHj86ii+XF03g/wLYd6a45bAvDXyouQceVFSLS/2/H1eXPfaWro+s5Suzyzl05XHeGfJoTo1yID5m5oVx+BZcSzelczY7qHmdHhTYDh3fduhmVbfju0eypJdWjyNJbuS6Wmub8P9XbHX68gqLOPaV9bQZeoSukxdwidxR3l70cE6M8jAld9+fxZ3hH7Tl9Bv+hIWxScxvmcYYK6zaikzlwJ7k/IJ9XEhxLOy3Vtdrd1bfTCDa8zxiAZH+bPVHKfIy8UecwxWGns509THhcQGMjztSyqgqY8LwZ5O2OkEQ6P8WX3IetetNYczubqtZhAe1MaPbScqjSsCGNImgKVVjDLO9np8XbVJDb0Q9Grhw8nM+mu/L1f2JuUT6utCiHmMMTw2kFUHrMcYqw5mcG0Hc18qOsDSlwrxcrYE9g32dCLcz4WkahMAtuardScZ/tIahr+0hqV7UhjTRYuB1j7Mi4KS8lom60opLKmgfZjWZozp0phlCVqg9LAqQbOHxARyzBxAOtjLmY/v7MyjX8dzoh43f1BcetjcU0ZKmSWE2CCE2AssBvYA/2/vrsOjuLoHjn9PBBIsQYM7tGhxaXFrqRfqb426uxt1o+3b/uru3tLSlwrFrS3u0lKsaNAkQBJi5/fHzCYbQRKSTGY5n+fJQ3Z2djk3s7Mzc+fecxbh9H/drapbRWQnkOlOMfoQeB34TkQuAX4FDvdTei5wsYikA1uBp9zXXisiK4C/cKYwBbwNLBaR+ar6n6Dl/YC73PfZC1xSwP9VYIyq+quIdADmikga8DNOlacPgTdFJAXIvqJW1S0ici8wGedY8JOqjjnM9h6xzCzlsTHLee/KLtnl6v6J38vNQ5qzdGMik5Zv55s5Gxl1fnvG392bxOR0bvs8pzrGpHv7UikqnMjwMAa1iWPEu3NYvW0fo37+i1Hnt+f+0yPZvTeNe4Pu4paECYu3MKhdHWY/fTLJaRnc8v6cnBhHDmbAo+MBuOfT+fzfFU6Z1olLtjDR/bK846O5PHFBByLCw0hNz+SOj52s7qd2rs85PRuRkZlFanomV7/5Z/7/vISFhYcz+NIb+fq5+9CsLNr1PZGa9Rsz/dsPqd2kJS06H8+8335g1fw/CAsPJ7piZU655q5SjzOgpLZFrSpR/PbQICpHR5KlytWDWtDroV9zJU8rLplZylNj/+KtSzsRHiZ8P28zq7ft44aBzVi2KYkpK7czet5mnj67LT/fdgKJKenc9dWhP+NPjV3Js+e0IzJc2LArhYdGLyv22A8kU+HlyWsYdVZrwkT4ZVk863alMKJHA/7atpff1+zmmLhKPHHqMVSKiqBnk6pc1rMBIz5ZSKNqFbhjYFOy1Kmm8PncTazfVbonRwG/LdzM4OPqMf+F00lJy+SGt//Ifm7ak0Pp88AvADx6fkeGH9+YCuUiWPp/Z/HJlH94dvQSHrugIxWjIvjw5l4AbNyZzIUvlk4/+MSlWxnYLo7fnxiSXYY5YPyDAxj8hJML474vFjplmMuFM3lpfHZlnweHt6VNgxhUnbjv/tQZHVC9Snm+uPkEslTZmpDKTUH7XHEbv3gLg9rXYfazp5CSlsHN783Ofm7yo0PoP9KpHnP3J/N45YruRJULZ9KSLUxY7HQCDOvRkMsHODOIf5q3kc+nr81+/bxRp1I5KoJyEWEM7ViPc16Yyt8ldKd94tKtDGwbx++PF7AtHhjA4CfdbfF50LZYFrQthuXZFp8tKJE4D9mOJVsZ2LY2fzx5oltmfW5OOx4ayODHJ7rtWMBLl3VxtsfSeCYtdb5vv5i5jv9e2oXJIweRnpnFLR/MLfD/KUmhfPwGGL9oM4Pb12HuqFNJ2Z/JTe/mVCKa8thJ9HvYqRI38twOnN2zERXKRbDkv2fwydTVPPfD0gO9bbELHPfeDBz35rvHvQHNWLbZPe7N38zTw9vy063Oce/ur53jXufGVblhYDMyMpUsVR7/cQVJKc6x+bYhLTilfW2iIsOZcGdvvpu3iTcmF29HZa52qPLsz3/z+kUdCBNhzMLNrNm+j+v6NWH55j1M/XsHP8zfwhNntWbMTT1ISsng3m9z/s6dGsWyNSmVTUGd/dHlwnjp/PZERoQRJjB33W6+nVt6+cgO5Z47b2funNkkJOxm8IA+XHfDTQwbfo7XYZGZpTz540reGdGJMBG+n7eJf7bt48ZBzVi2MYnJK7fz3dxNPHtOW369oxcJyenc6ebx6dQolqv6NiEjM4sshcfHrMgeRTLqvHZ0a1KN2IqRTLqnD69OWM1ot4hCSZm0bBv9W8cx/eGBpKRncuenOd/5v9zTl6HPOucRD361OKck9opt2Tli7j29Fc1qVSJLYdOuZO77ymnnLSe1pGrFSJ44t3323+zUUdNKtC1+UZanGpUEOdoaHOoKO32prErYWfpDXkvCM9f2OPRKZdy9Hp3IFrdadfw//7tGjYqHXskHFs1de+iVfCC6YvShVyrj0tNCY6h0ZPl8g159R8toHqDCysws+3loDkdmhv/bUbdhjUOv5AMREf7NTxPwxwMDvA6hWHR62Luy7cVp756yN1qtsP595XTxOoaSVGH4+yVyUEz+7vIy+XcrqzlljDHGGGOMMcYYc5Q52gaO+LJTRkROxCkjHWytqp7lRTzGGGOMMcYYY4wpBtYpU/ap6jhgnNdxGGOMMcYYY4wxxhSVLztljDHGGGOMMcYYE4I0y+sISlVplMQ2xhhjjDHGGGOMMXnYSBljjDHGGGOMMcaUDUdZThkbKWOMMcYYY4wxxpiyQbNK5ucIiEg1ERkvIqvcf6sWsE4HEflDRJaJyGIROe9w3ttGyoSY8uXDvQ6hWFSsXNHrEIrFc9+t8DqEI7YvcZ/XIRSL6Ka1vA7hiP29cqvXIRSLrMzQmCectj/N6xCOWEZ6htchFIuwcP/fY6peK8brEIpFtWrRXodQLNav2+V1CEfsn3nLvQ6hWJx8di+vQzhinR7+zesQisX8x4Z4HUKxaHPPL16HYPzpXmCiqj4jIve6j+/Js04ycImqrhKRusA8ERmnqgkHe2P/n8UYY4wxxhhjjDEmNKiWzM+ROQP4yP39I+DM/GHr36q6yv19M7ANqHmoN7ZOGWOMMcYYY4wxxpgDi1PVLe7vW4G4g60sIt2AcsDqQ72xTV8yxhhjjDHGGGNM2VBCJbFF5Grg6qBFb6vq20HPTwBqF/DSB3KFp6oicsChNyJSB/gEuFT10I2xThljjDHGGGOMMcaENLcD5u2DPD/oQM+JSLyI1FHVLW6ny7YDrFcF+Al4QFX/PJy4bPqSMcYYY4wxxhhjyoYyWH0J+BG41P39UmBM3hVEpBzwPfCxqn57uG9snTLGGGOMMcYYY4wpG8pmot9ngMEisgoY5D5GRLqIyLvuOucCfYDLRGSh+9PhUG9s05eMMcYYY4wxxhhjDkBVdwIDC1g+F7jS/f1T4NPCvvchO2VEpDEwVlXbFvbNg96jH5Cmqr8X9T2Km1s3/P9U9WyvY/HaCc2rc88pxxAuwuh5m3hv+rpcz0eGC08Nb0vrulVISE7nrq8XszkhNfv52jFRjLmpJ69PXsNHM9cDcHHPhgzrUg9VWBW/l4e+X0ZaRskkbAoYOaw1/VrVIjU9kzs/X8SyjUn51mlbvwqjLjyOqMhwpqzYxqOjlwNw8nG1ueWkljSPq8SZ/53Jkg2JANSrFs2Ee/uyZvteABasS+DBb5aWWBt6tazBA6cdS5gI387ZyDtT1+Z6PjJcePbcdrSpF0NCchq3f7GITbtTOb55de44qQWREWGkZ2Tx3C9/M2v1LgBOOa421/RviipsS9rPXV8tJiE5vcTakNeoy7tzYsf6pKRlcM2rM1i4dme+dUZe0IkL+zYntmI54i7O/T02rGdj7j+3I4qydN0uRrw8rVTi7tm0GncMaU6YCGMWbuGjP/7N9XxkuPDo6a04tnZlElPSuf/75WxJTCUiTLj/5Ja0qlOZLIUXfvuH+f8mAHBdvyac0i6OylGR9B01vVTaAfD4ue0Z0LY2KWmZ3PbRPJZsSMi3TruGsbx0aWeiIsOZtHQrD329GIA7Tm3Fhb0as2vPfgCeHrOMSUvjiQgTnr+4E+0axhIRJnzz57+8Ou7vUmnPs5d2ZUiHuiSnZXL9G7+zaN2ufOs8dG4Hzu/TlNiK5ag34svs5ZcPasGVg48hM0vZl5rBLe/+yV+bEkslboCn/tOJQe3rkJKWyU3vzmLx+t351jmuUVVeubI7UeXCmbB4C/d/Nh+ANg1ief7SLlQsH8GGnfu45s0/2Juawdk9G3HD0GOzX9+mfiwDHhnHUvdzV5KevaQLg4+rR0paBte/9UeB2+LBc47j/N7Otqh/xVfZy28Y2oqL+zcjM1PZkZTKje/8yYYd+0o85oAnL+jAwHZ1SEnL4Ob357CkgL9X+0ax/N+IbkSVC2fiki088MVCAN6+pgfN4ioDUKVCJEnJ6Qx8bDwNqldg+uMnsXrrHgDmrdnJ3Z/OL7E29GpRnftOPZbwMOeY8e60dbmejwwXnjmnHW3qOcfv279YxOaEVNrVr8KjZ7Z2VhLhtYmrmbg8Z2p8mMA3N/QgPmk/13+8oMTiz6t746rcMqApYSKMXbKVT2dvzPX8cfWrcHP/ZjSrWZFHxq5kyt87AIirUp6nzmhNmEBEmPDtgs2MWbS11OIOePTsdgxoU4uUtExu/2QBSzfm/25p1yCGFy/uRFRkGJOWbWPkt0sAuPOUYxnSvjZZCjv37Of2TxcQn5hKjxbVee/q7mzYmQzALws38/KvpfNd+8I1fTmxa2OS92dw9Yu/sXD19lzPR5eP4LP7TqZpnRgys5SfZ63loQ9nAnBC27qMurov7ZrU4JJnfuH7mf+USsx5dahXhct7NCBMYOLfO/h+cXyu509rU4uBLWuQpUpiagavT1/P9n1p1KxYjrsHNkPcz9TPy7fx2187SjX2kti/nxjWhr7H1mTXvjTOeLnMXJYB8PCD9zFt6hSqVavO6DFjS/3/73NMDR46sxXhYcJXszby1qQ1uZ4vFx7G8xe2p239Kuzel87Nnyxk0+4UAK4d0JRzu9cnM0t57IcVTHc/Kwd6z+fOb0e3ptXYk5oBwN1fLmbF5j10b1aNt0Z0YsMu533HLYnn1fHe7DueK6FEv2VVaY2U6QfsBcrE3i8iEW7d8DLdIePGmVGS/0eYwAOnHcvVH85na1IqX17bnckrt7Nme86J8bDO9UhKyeCUl2ZyUrs4bhvSgru+XpL9/F1DWzJjVc6Fdq3K5bmwZ0PO/L/f2Z+RxfPntWNouzjGLNhCSenXqiaNa1ak/5NT6NAolifOactZ/83/cXvinHbc99USFq5P4INrutK3VU2mrtjOX1v3ct0H83jy3Hb5XrN+ZzKnjJpRYrEHhAk8fEYrLn9vLvGJqXxzY08mrdjG6m052+LsrvVJSsngxOenc3L72txxUktu/2Ixu5PTuO6jBWzbs58WcZV49/LO9H16KuFhwv2nHcspL84kITmdO4e25KLjG/LqhENWZisWJ3asT/M6VWh/03d0bVGTl67uSb/78h9of567gbd+WcGiV4bnWt6sdhXuHNaeQQ/+RMK+NGpWiSqVuMME7j6pBTd+voj4pP18dHlnpq3awdodydnrnNGhDkmpGQx7YxaDW9fipgFNuf/75ZzVsQ4AF7wzl6oVInn5/PZc+v48FJj+906+nruJ0dd1L5V2AAxoG0eTWpU44eHf6NSkKk9f2IFTn52Sb71nLuzAXZ/OZ/7a3Xx64/H0bxPH5GXOyes7E//hzfGrcq1/Wud6lI8IY+DjE4mODGfKI4P4Ye5GNu5MzvfexWlwh7o0q12ZjreNoUvzGrx4RXcGPvRLvvV+mb+Rt3/7i/n/PSPX8m9mruP9CU5bhnauz1MXd2b4M5NKNOaAQe3r0DSuEt3u+YnOzaoz6pIunPj4+Hzrjbq0C7d9OId5q3fy5e19GNiuDhOXbOGlEV0Z+dVCfv9rOxf2bsKNJ7fimdFL+PaP9Xz7h9Mh3qp+DB/f3LtUOmQGH1eXprUr0+kOZ1u8MKIbg0b+mm+9Xxds4p3xfzPvhdNzLV+8fhf9H/yblLRMLh/Ygkcv6Mjlr5T8dy3AwHa1aVKrEj3u/4XOTavx3EWdGPpU/s/Bcxd15o6P5zJvzS4+v6UXA9rWZtLSrVz9Vk7evkfObU9SUEf3+u17GfhY/u1a3MIEHjy9FVe+P4/4pFS+ur4Hk1duz3XMGN6lPkkp6Zz0wgyGuseMO75czKr4vZzz+iwys5Qalcvx/U3HM2XldjKznKHdFx/fiNXb91GpfOkNqA4TuH1QM277Zinb9uzn3Ys6MGP1LtYFfafEJ+3nqV/+4oKu9XO9dufeNK79fCHpmUp0ZBgfX9aZGf/sYue+tFKLv3/rWjSpWZHej06kY+OqPHX+cZz+fP6bCE+ddxx3f76QBet28/F1PejXuhZTlm/jzYn/8PxPKwEY0bcptwxtyf1fOp3js1fvZMSbs0qtLQAndmlMs3qxtL3yI7odU5v/u3EAfW77Kt96L42ez7TFG4mMCOOXp4YxpEsjfpu7ng3b9nD1i+O5dXinUo07WJjAVT0b8ti4v9m5L51nTz+WOf8msjHoxuLancnc/eMK0jKVE4+twcVd6/HilLXsTknnvrErychSoiLC+O9ZrZnzbyK7U0rnplZJ7d/fz9/MZ3/+yzPn5D/f9doZZw7jggsv4oH77in1/ztM4JFhbbj0rdlsTUzl+1uPZ+KybfwTvzd7nXO61ycxOZ0BT0/j1A51uOfUY7j5k4U0j6vEqR3rcNJzM6gVU56Pr+nGoGemAgd/z2fG/sWvi/N3Hs9Zu5ur3ptXOg03Zcbh5pQJF5F3RGSZiPwmItEi0kFE/hSRxSLyvYhUBRCRm0Vkubv8S3ekzbXAbe6cqt4F/QciUlNEvhOROe7PCe7yMSJyifv7NSLymfv7FBF52X3PpW4dcESkooi8LyKzRWSBiJzhLr9MRH4UkUnARBFpLCJL3efCRWSU+/8uFpFr3OX93P/nWxFZKSKfiYi4z3UVkd9FZJH7f1U+0PscoL0fi8iZQY8/E5EzCoizjohMC2pngX+/ompXP4Z/dyazcXcKGZnKL0u20r9VzVzr9D+2Jj8u3AzA+GXb6N60WvZzA1rVZNPuFP7ZtjfXayLChPKRYYSHCVGR4WxL2l+cYeczuF0co+dsAmDh+gSqREdSs0r5XOvUrFKeSlERLFyfAMDoOZsY0s4pL786fi9rtpXeHdqCtG/gbotdKaRnKj8v2sLA1rVyrTOwdS1+mO+0c9zSeHo2rw7Ais172OaOZFgVv5fykeFEhgsCCEKFcuEAVCofUeLbItgpXRvy+RSnh3/Oqu3EVChH7djofOvNWbWdrQkp+ZaPGNSSt35dQYJ7Yr09KTXfOiWhTd0qbNiVwqaEVDKylPHLt9G3ZY1c6/RpUYOf3IPppBXb6dq4KgBNalRkzroEAHYnp7M3NYNWdZ076ks3J7Fzb+ldJACc2L4u3/7pjPKZv3Y3MdGR1MrTuVWrShSVoyKZv9YZtfHtn/9y0nF1D/q+qlChfISzj5cLJy0ji72lcLJ6SucGfDHdudM0958dxFSIJK6Az9Tcf3YQX8Bnak9QjBXKRxTD9OLDN7RjPb6euQ6Aeat3OrHH5N4WcTFRVI6OZN5qp6P765nrOLlTPQCa1a7M7385d6qnLIvntM65L0wBhnVvxPez1pdgK3Kc3LkBX053RvM526JcobbF9OXxpKRlZq9Tt1qFkg04yEkd6vKN25E1b80uqlQoR60826JWTBSVoiKYt8YZ/fPNH+sZ2jH/fnF6lwZ8P3tDyQedR/DxOz1T+WXxVga0yn3MGNCqJj/Md47fvy2Np0cz5/idmp6V3QFTPiIcJWdHiKtSnr7H1uA795haWlrVrszG3alsTnS+dyes3E6vZtVyrbM1aT+rdySTlWe/zchS0jOdhZHhYYRJaUWdY0j7Onznfg4WrNtNlehIauU5D6nlnocsWOd81343ewMntnc68vem5tyDq1A+HErxu6kgp/ZoyucTVwAw+6+txFQsT+2quffRlP0ZTFvsjGZKz8hi4ept1KteCYB/t+1h6bodZOXdWKWoeY2KbE1KJX5PGhlZyow1u+naMDbXOku37iXN/ez8vW0f1SuWA5zPVIYbe0S44J7+l5qS2r/nrdtNYimOli6Mzl26UiUmxpP/+7iGsazfuY8N7jn42AVbGNQm9997UNtajJ7rfC/+sngrPVs45+CD2tRi7IItpGVmsXFXCut37uO4hrGH9Z7mIMpmTpkSc7idMi2A11S1DZAADAc+Bu5R1fbAEmCku+69QEd3+bWqug54E/ivqnZQ1QON2X/ZXaer+/6BZDlXAw+7nRF3ADcFvaaCqnYArgfed5c9AExS1W5Af2CUiFR0n+sEnK2qffP831cAie7/3RW4SkSauM91BG4FWgNNgRPcrMpfAbeo6nE4iX5SDvE+eb0HXAYgIjHA8Tils/LGeSEwzm3nccDCA7xfkdSqUp6tiTkX6fGJ+4mrnPckIoqtic7FcGaWsnd/BrEVIokuF87lvRrzxuTcw/u27dnPhzPWMf6O3ky6uw97UzP4Y3X+Ie3FKS4mii27c076tySkUjvPCXbtmCi2BN0d2ZqQku+CqCANqkUz9s5efHljD7o2rVp8QecRVyWKLYlB8SWmEpfv4rl8dhsys5Q9qc62CHZi2ziWb0oiPdM5oXj0h+X8eOsJTLu/H83iKvLtnNzDwUtS3eoV2Lgzp7Nr86591Kl++BdezetWoUWdGCY8cTKTnzqFwR3qlUSY+dSsXJ74PUH7RdJ+aubdLyqXJ97t4MpUZ7+IiY5k1ba99GlZnXAR6sZEcWydyvn2qdJUOzaKzUH7xuaEFGrHRuVbZ8tB1hnRrykTHhzIixd3Isb9vI2dv4nk/RksfPZk5jx1Em+OX1Uq0+LqVKvAplyfqWTqVsvfEXAwVw5uycKXzuSxCztx90dzijvEA6pTNZpNu3Lu+m/enUKdqtH51tl8gHVWbkpiqNtBc0bXBtQroBPjzO4NGf3nv/mWl4Q61aLzbIt9+dpzuC7q15wJizYXV2iHVCc297bYsjuZOnk6lOrERufeL3an5FunR4sabE9KZW3QjYmGNSoy4eFBfH9XP7q3yN2ZW5ziYnKOzeAcM/J2AgSvk/eY0b5+DD/ecjxjbu7Joz+syL6Iu/fUY3n+l7/JKuWT15qVy2ffXADYvjct3/fuwdSqXI4PL+3E6Gu68dnsjaU6Sgbyf9duSUjJdxOidmx0rvOQLXm+a+8+rRWzHh/CWV3qZ4+aAejcpBrj7u3Hx9f1oGXtyiXYihx1a1Ri4/acz/WmHXupW6PSAdePqViOk7s1ZfKi0u+gPJBqFSPZsS/nuLRrXxrV85wzBRvYsgbzg6acVa8YyYtntuLt89rzw5KtpTZKBkpu/zYFi8t7jZCYmu8aoXaVqNzn4CkZVK0Ymf+1Cc5rD/WedwxtwU93nMADpx9LufCcS/KOjWIZe8cJvH9lF1rEHXifC3lls/pSiTncTpm1qrrQ/X0e0AyIVdWp7rKPcLIMAywGPhORi4DCTL0ZBLwqIgtxyk1VEZFKqhoPPAxMBu5Q1eCr+y8AVHWau34sMAS4132fKUAU0NBdf3ye1wcMAS5xXzMLqI7TEQUwW1U3qmoWTodIY+AYYIuqznH//yR3mtHB3icX92/XQkRqAhcA3wVNVQqOcw4wQkQeAdqp6p687yUiV4vIXBGZu2v+T3mfLjHX92/KJ3/8m32nM6BKVAT9W9XipBdnMPC5aUSXC+fU42qXWlzFaXvifk54dBKnPj+DJ35YzksXdyzV4dyF1bxWRe4Y2pKR3y8DnBFL5/dowFn/9zt9nprC31v2cnX/ph5HefgiwsNoVqcKJ438hctemsqr155ATIVyXod1UD8u3Mq2pP18fEVnbh/SnMUbE/Pd1fWTj6auoeeD4xj85ETik1IZOdwZ8tyxSVUyVel4z890f3Ac1w5qQcMapTfS4Ui8O/5vOtz6AyM/n89dZ5W9IdwHcvP7s7h8QAsmPjKESlERpGXmPrno1LQaKfszWFmKOXKKw7knNKFj02r839jlXodSaGd1b5hrlEx8Yiqd7v6JQY9NYOTXC3njqu5Uiiqbx4zFGxM5/eXfOff1WVzVtwnlIsLoe0wNdu1NY/nmfKcaZd62PWlc9tF8znt3Lie1iaPqQS6+y6rn/reC7g/9xvdzN3JZH+ee3tINifR46DdOfGYKH0xdw7tXd/M4yvzCw4SP7hnK6z8uZN3W/Pn8/KBPs2o0q1GBMUtycs7s3JfO7T+s4IZvltKveXViyui+XJCC9m9Tdoz66W8GPzuds176g9gKkVw9wDk3X7YxiT5PTOHUF2by8Yz1vDnCu+l/pnQd7rdL8HyHTCD2IOuegtNBcxrwgIgc7hlvGNBDVQuan9AO2AnkHTec91JHAQGGq+pfwU+ISHfgQPNTBLhJVcfleU0/8rf9YH+zAt/nID4GLgLOB0YELc+OU1WniUgfnL/rhyLyoqp+HPwmqvo28DZAu4fGF+ryb1vSfmrH5PS8x8XkHiHgrOOMOolP2k94mFCpfAQJyem0qx/D4DZOjpnKUc40gLSMLHbu3c+m3Snsdu+aT1i+jeMaxDK2mJPuXdyrEef3bADA4n8TnTu07vSLOrG57zCA00NdJ+iOVO3YaOITC/q45UjLzCIt2bnwWboxiX93JtOkVsXsRMDFKT4plTpBPejO3zx3fNuS9lMnNmdbVI6KyB6dEFelPK9e3JF7vl6SnSDsWHfaTODxL0u2clXfAw3eKh5Xn3QsIwa2BGDe6h3Ur14x+7m61SqypRA5Rzbt3MfcVdvJyFTWb9vLP1sSaVanCvNXl2yyve17co8Yi6tSnu1594s9+4mr4tzZDRdnv0h076L9Nyhnz3uXduTfXSWbZyWvy/o25T+9GgOwcP1u6gaNXqgbG83WhDz7RkJqrhEOwevsCGr3ZzPW8fH1PQE4q2sDJi+LJyNL2blnP3NW7+S4RlX5d0fxt/XKwS25dIDTv71gzU7qVa8IONN46larwOZd+afGHI7v/ljHi1eUbH6fywc25+K+zQBYuHZXrtEtdavmHokBsGV3Sq5pPMHr/LNlD+c8PwWAZnGVGZxnitmw7o0YPatkR8lcObgll/ZvDsD8fNuiYr72HErfNrW544y2nPLEbyWeDH5E/2Zc1Ns58V24LrAtnGlidapWYEueKVZbEnKPZKpbNTrXOuFhwimd6jH48QnZy9IyskjLcEZoLF6fwLrte2kWV5lFBSR0PlLxiblHhNaOico3PTWwTkHHjIA12/eRnJZJi7hKdGoUS/9WNelzTA3KR4RRsXwEz57TlntKMMF9wPY9+6kV9L1bs1K5fN+7h2PnvjTW7tjHcfVjshMBl5RL+zThguMbAbAoz3dtndjofNNytyak5DoPqVPA9zHA93M28vF1PXjx579yTWuavHwbT4aHUbViOXaXwEiga05tz4gTnboe81bFU79mzl36ejUqsXnH3gJf99rNA1m9KYFXxyws9piOxK596dSomNM5V61iOXYWMKKzfd3KDD+uNg/9/Hf2lKVgu1PS+Xd3Cq1qV+JPd3pySSuJ/XvZJn92mJWG+LzXCDFR+a4RtialZl9fhIcJlaMj2L0vPf9rY3Nee6D3DHy3pWVm8e2cTVzZzzk337s/Z3+fsnI7j4a3pmrFSHbvK5tTzkpUGZ5qVBKK2m2aCOwOym9yMTBVRMKABqo6GbgHiAEqAXuAQ423/I2gqUmBet5urpihONOI7swzHeg8d51eONOGEoFxwE1BuV86HkZ7xgHXiUik+5qWQVOeCvIXUEdEurrrVxaRiCK8z4c4U6NQ1QJvEYpIIyBeVd/BmdJVrF2mSzcl0ah6BerFRhERLgxtV5spK3Nn15+ycjund3BO/ge3qcXstc4gnsvem8tJL87gpBdn8Okf//LOtLV8MWsDWxJTad8ghqhI5+PVvWk11m4/UH9Y0X0yYz2njJrBKaNm8NuSeIZ1dYb1d2gUy56UDLbnOXhtT9rP3tQMOjSKBWBY13qMD7ojUpBqFctlz01vUD2axjUq8m8JJTJdstHdFlWjiQwXTj6uDpOCqmEATFq+jTPd6Qsnto3jT3daWOWoCN4a0ZkXfv2bBW7OHIBtiftpVqsSVd2TkuObV8+VxLkkvP3rSnre9SM97/qR/83+lwv7ORdwXVvUJCk5rcDcMQcydva/9G7jzLevXrk8zevEsC6+5O/gLt+8h4bVoqkbE0VEmDC4dS2m5Tm5n75qB6e0d0aADWhVkzlujoDyEWHZn/1uTaqSkaW5EgSXhg+nrmHwk5MY/OQkfl24hbN7OIMFOzWpSlJqOtvydfalsic1nU5NnOl5Z/doyLjFzlSS4PwzQzvU5a/Nzkndpl0p9DrGmRsdXS6cTk2r8c/Wktk2747/m973/UTv+35i7NwNXOBeWHdpXoOk5PQC85UcSNOgof8ndqzPmhKKOeD9if/Q/+Fx9H94HD/P38i5JzQGoHOz6iSlpOc76YtPTGVPSjqdmzlz1c89oTG/LHDmsNdwL1hF4PbTW/Ph5JyKDCJwRrcGJZ5P5t3xf9P7/p/pff/P/DR3I+f3dg7JXZrXICklrVDbon2jqrx0RXcueGEKO0oh19UHk1cz8LHxDHxsPL8s2MQ5PZ2L6c5Nq7EnJZ1tebbFtsRU9qZm0NnNo3ZOz0b8ujBnilWfVrVYtWVPro6o6pVyjhmNalSkaa3KrD/AheyRWropiUY1co4ZQ9vXZvKK3MeMySu3c2Yn5/g9pG0cs9z8OPWqRhPuBlo3NoqmNSuwaXcK//3tHwY8O43Bo6Zzx5eLmbVmV6l0yACs3LqHBlWjqBNTnogwYdCxNZl5mFOfa1Yqlz0SoHL5CNrXq1IqneEfTVvLSc9M4aRnpjBu8VaGd3NuFHVsXNX5TCXlvcnlnId0dHOQDe/WgN8WO0UQGtfMOV0c0r52djLQ4ClcHRrFEiaUSIcMwFtjF9Pjps/pcdPn/O+P1Vw4sBUA3Y6pTdK+/Wzdnf9vOvKSnsRULM+db0/N95zX/tmxjzoxUdSqVI6IMKFX06rMzZMEvUm1aK45vhHPTFhNUlAHWLUKkZQLd/aRiuXCaRVXic2HuJFXnEpi/zYHtnhDIo1rVKR+NefvfWrHOkxclvvvPXHZNoZ1cc7Bh7avzR9ukZOJy7Zxasc6lAsPo34151ph0b8JB33P4P16cNs4/nbPRWpUzhkN3r5BDGEiR2eHzFHoSMbhXQq8KSIVgDU4Iz3CgU/dHCmCU3I6QUT+B3zrJt296QB5ZW4GXhORxW5c00TkFuAdYISqbhaRO4D3RWSA+5pUEVkARAKXu8seB14CFrudRGuBUw/RlndxpiXNdztztgNnHmhlVU0TkfOAV0QkGiefzKAivE+8iKwAfjhIbP2Au0QkHaeC1SWHaEuhZGYpT439izcv7UR4mPD9/M2s3raPGwY0Y9nmJKas3M7o+Zt5enhbfrr1BBJT0rk7qPJSQZZsTGL8sni+vq4HGVnKyi1JfDO3ZPOYTF6+jf6tajLlwX6kpGVy9xeLs5/76a5e2dWTHvp2qVsSO4ypK7YzZYXTATWkXRyPDG9DtUrleP/qrizflMSlb86mW7Nq3Da0JRlZWWRlwYPfLCmxBGmZWcrjP67gvcs7ExYmfDd3E/9s28dNg5uzdGMik1ds59u5m3ju3HaMu7M3iSlO+UOA/xzfkIbVo7l+YDOuH+jclb/ivXls27Of1yb+w6fXdCMjU9mckMJ9pXSCDTBu/kZO7FSfJa8OJ2V/Jte8nrPr/zHqdHre9SMAT1zUhXN7N6VC+Qj+futcPpz4N099vZDxCzcx8Li6zP3vWWRlKQ98Modde0v+4i1TlefGreL/LmhPeJjw46ItrNmRzDV9GrNiyx6mrdrJmIVbefSMYxl9XXeSUtN54HunX7VaxXK8ckF7slTZvieNkWNWZL/vTQOacmKbOKIiwxh7U0/GLNzCO3lK0Be3iUu3MrBtHL8/PiS7JHbA+AcGMPhJp+LMfZ8vdEpilwtn8rJ4Ji11OiwfHNaWNg1iUIWNO5O5+zOnPO4HU1fz30s6M/nhQYjAV7+vZ0Up3IX7bcEmhnSox8KXziR5fwY3vJVTZW3606fQ+z5nCudjF3bi7OMbU6FcBMtfHcbHk//hme8Wc/WQY+jXrg7pGVkk7Evj2jdmlnjMAeMXbWFQ+7rMee5UUvZncPN7OdVUJj92Iv0fdgZZ3v3xXLckdgQTF29mgnvRNqxHI64Y6HRyjp23kc/dJLsAxx9Ti027kllfwp2uwX5buInBHeqy4MUzSE7L4Ia3/sh+bvpTJ9P7/p8BePSCjtnbYtkrZ/HJ5NU8M3oxj13YiYpREXx0i3N/Z+OOZC54cUqpxD5hyVYGtqvDrKeGkpKWyS0f5OQWmvjw4OzqSfd8Op//u7wrUZHhTFy6lYlLckZ8ntmtId/Pzj0yqUfLmtx9RhsyMpUsVe7+dB4JJXRCnZmlPPnjSt4Z0YkwEb6f5xwzbhzUjGUbk5i8cjvfzd3Es+e05dc7ejkV+NxqPp0axXJV3yZkZGaRpfD4mBWlkhPqoO1ReHHial4c3pawMOGnJfGs3ZnMFSc0YuXWPcxcvYtja1fiqTNaUzkqghOaVeOK4xty8YfzaVS9Ajf2a+rcWRXhi7mbWFPKneGTlsUzoE0cM0YOIiU9kzs+zSkl/uu9/TjpmSkAPPD1Yl68qCNRkeFMXh7PZPfmy31ntKZZrUpkqbJxVwr3f+kc30/uWJeLezcmM1NJTc/khg/mlkp7fp2zjhO7NmbZe5eSvD+Da/6bU1Hsz1cupMdNn1OveiXuPb8bK//dxR//dyEAb45dxIfjltG5RRxfPXQKsZWiOLl7Ex68qAedr/u0VGIPyFJ4949/eejEFoSJMGnVDjYkpHJ+xzr8syOZuRsSuaRbfaIiw7jDndq9Y18az0xYTf3YKC7rVj97CP6PS+P5d3fpdcqU1P496rx2dGtSjdiKkUy6pw+vTljN6Hmlm9T7QO6583bmzplNQsJuBg/ow3U33MSw4eeUyv+dmaU8Ono5H17dlTARvp29kVXxe7n1xBYs2ZjIxGXb+HrWRl64sD2T7utDQnI6t3yyEHAKbPy8cCu/3t2bzKwsHhm9zJm2rgW/J8B//3Mc1SqVQ4Dlm5N46FunzP3Q9rW58PiGZGYpqelZ3PLpwlJpf5lUhvO/lARRnw4NEpEpwJ2qWjpHpxLgdmgtATq5o3yOWGGnL5VVe/eUboK+klLeR/OPD2TDP6WXfLMkte5YstO2SsOmDSWbMLu07Ev0ttJZcYks77+cFXllpBcm9VvZVT7au2TaxaV6LW+qjhS3aoVMvF1WrV/n/+/bHWvWeR1CsTj57F5eh3DEVvyz0+sQisX8x4Z4HUKxaHPPL16HcMRWvzDUgzpzpSd60DMlck2bMuHeMvl3s6xPHhGRQcAK4JXi6pAxxhhjjDHGGGOMf5T6bXwReQDIOxbtG1V9sjDvo6r9ii2oEuQmOv4kz+L9qtodaORBSMYYY4wxxhhjTNl0lE1fKvVOGbfzpVAdMH6mqkuADl7HYYwxxhhjjDHGmLLF/wkvjDHGGGOMMcYYExp8mve2qKxTxhhjjDHGGGOMMWXDUTZ9yRL9GmOMMcYYY4wxxnjARsoYY4wxxhhjjDGmbMg6uqYv2UgZY4wxxhhjjDHGGA/YSJkQs3dvmtchFIvMzEyvQygWKcn+nw/Z5Nj6XodQLLZv2+t1CEesQqVor0MoFm3a1vU6hGKxcN56r0M4YvuX/+l1CMWi8vGDvQ7hiG3dsMPrEIrF3j2VvA6hWMRWreB1CEesSsdWXodQLBav2O51CEdsf2ponJ+3uecXr0MoFsueHep1COZQLKeMMcYYY4wxxhhjjClpNlLGGGOMMcYYY4wxZcNRNlLGOmWMMcYYY4wxxhhTNqgl+jXGGGOMMcYYY4wxJcxGyhhjjDHGGGOMMaZsOMqmL9lIGWOMMcYYY4wxxhgPHLUjZUTkEWCvqj5fiv9nP+BOVT3Vi9cHG3lWa/q1qkVqeiZ3frGIZRuT8q3Ttn4VRl1wHFGR4UxZsY1Hv18OQEyFSF69pCP1qlVg065kbvhoPkkpGVzdvylndHZK3YaHhdE8rhKdHxpPYnI6l/dtwnk9GqAKf21J4q4vFpOWUbw9oI8Ob0v/NnGkpGVyx6cLWLoxMd867RrE8MJFHYmKDGfysnhGfrcUgPvPaM2gdnGkZyjrd+zjzs8WkJSSQUSY8NyFHWjbIIbwMGH07A28Nv6fYo07r0eGt6F/a6cdd362sMB2tG0Qwwv/6eC0Y3k8j3y3zG1HKwa2rU16Rhbrd+zjrs8XkpSSkf26ulWjmXB/P1765S/enrSmROI/oUV17j35GMLDhO/mbeK9aetyPR8ZLjx9dlta161CQnI6d361mM0JqdnP146J4sebe/L6pDV8ONMpOTzujl7s259BlkJmlnLeG7NKJPa8Rg5rQ//WtUhJd7ZFwftJDM//5zh3W2zj0dHOtji5Qx1uPaklzeMqccaLM1iywdmOZ3SuxzUDmma//ti6VTj1+eks35T/vYuizzE1ePCMVoSHCV/P2shbk3Nv53LhYYy6oD1t61dhd3I6t3yykE27UwC4dkBTzulWn8ws5fEfVjD9b6dE74jejTm3e30U+GvLHu75aglpGVlcfEJDLuvdmEY1KtL14QnsTk4vljYcTJeGMVzfuzFhIvyyfBtfzd+c6/l2dStzXa/GNK1RgSfHrWL66l0AHFevCtf1apS9XoOq0Tw5bhW/r91d4jEHe+rCjgxqX4fktExufm82i9fn///bN6rKK1d2IzoynAmLt3D/5wsAaNMgllGXdKZiVAQbduzj2rf+ZG9qBlUrluP9G46nY5NqfDlzHfd+Or9U2/TC3Wdz4gltSE5N4+qRn7Bw5cZ864x59Xpq16xCRHg4Mxes5tanvyIrK2e++C0XD+CZ24dRv/897EzYVypxP3pOOwa0qUVKWia3f7KApRsKPma8eHEnosqFMWnZNkZ+swSAO089liHta5OlsHPPfm7/ZAHxianEREfy/EUdaVSzAvvTs7jz0wX8tWVPqbQH4OmLOzP4uLqk7M/ghrf/LPDz9cDZ7Tm/VxNiKpaj4VXfZC/veUxNnrqoM20axHLlazP5cc6GUov7SI7fd5xyDEPa1SFLlZ179nPHpwuIT9pPs7hKPP+fDrStH8OosSt5e9LqEm3DCc2rc88pxxAuwuh5m3hv+rpcz0eGC08Nzzn23fV1/mPfmJt68vrkNXw0cz2Na1Rg1Lnts5+vXzWa1yat5tM//i2xNvRqUZ17TznWOX7P3ci7BR6/29GmntOGO75cxOaEVNrVr8IjZ7YGQBBem7Saicu30bhGBV44P7gNFXh14j988nvJtQGg9zE1ePD0VoSHwdezN/L25LW5ni8XLjx3vnMcTEhO55ZPF7FpdwqxFSJ55eIOtGsQw+i5m3jshxX53vvNyzrRoHo0p7wws0TbEFAS+8aZXepx3aAWiMDe1Awe+HoxK47wHKTPMTV46Ezn3OOrWRt5a1L+c4/nL3TPPfalc3Oec49zuzvnHo/9sILpf+046Hs+d347ujWtxp5U5/z27i8Xs2LzHro3q8ZbIzqxYZfzvuOWxPNqCZ+z5/Xwg/cxbeoUqlWrzugxY0v1/w45llPGHCkRKdOdXf1a1aRxzYr0f2oK9329hCfOblvgek+c3Y77vl5C/6em0LhmRfoeWxOA6wY2Y+aqnQx4agozV+3kuoHNAXh78hpOeX4Gpzw/g1E/rWTW6p0kJqcTF1Oey3o35vQXZ3DSc9MIDxNO61i3WNvUv3UtGteqSJ/HJnLvl4t48rz2Ba735HntueeLRfR5bCKNa1WkX+taAEz/azuDn5rCic9MYe22vdwwuAUAp3SsS7mIMIY8PYVTnpvGhSc0pn616GKNPW87mtSsRN/HJ3HfV4t44tx2Bbfj3Hbc++Ui+j4+iSY1K9GvVaAdOxjy9BROenYqa7fv43q3HQEPndWaKcu3lVj8YQIPnnYs1328gNP/73dOblebpjUr5lpnWOd6JKVkcPJ/Z/LJ7+u5/cTcMd49tCXTV+3M996Xvz+Ps1/7s9Q6ZPq1rkWTmhXp98Rk7v9yMU+eU/C2eOLcdtz35WL6PTHZWb+Vs5/8tWUP174/l9lup0DAmHmbOHnUdE4eNZ3bPl3Ihl3JxdYhEybwyFltuOLduZw0ajqndqxD87hKudY5p3t9ElPSGfjMND6Yto67TzkGgOZxlTilQx2GjprB5e/O5dFhbQgTiKtSnkt6N+LMl37n5OdnEB4mnNqhDgDz1iZwyVtz2LgruVjiP5z23dS3Cff/byVXfr6I/i2r07Bq7v1x2540Rk1czSS3Qylg0aYkrv1qCdd+tYS7flhOakYW8wq4CC9Jg9rXoWlcZbrd+zN3fDiX5y7uXOB6oy7pzO0fzKXbvT/TNK4yA9vVBuC/I7ryxLeL6fvQOH6ev4kbhx4LwP70TJ75fikjv1pUam0JOLFXa5o1rEnbMx7lxie+4P/uP7/A9S665326n/cMnc9+kppVKzF8cKfs5+rHxTKwRyv+3bKrwNeWhP5tnP279yMTuefzRTx1/nEFrvfU+cdx9+cL6f3IRGf/do8Zb074hyFPTeGkp6cwYWk8twxtCcCNJ7Vg2aZEhjw1hVs/ns8jB/jeKAmDjqtLs7jKdLnzf9z2/mxeGNG1wPXGLdjEoJHj8i3fuDOZG97+k2//WF/SoeZypMfvtyau5sRnpjD02alMXBbPLUOd77SEfWmM/HZpiXfGgPPd9MBpx3L9xws445XfGdr+wMe+U16aySd/rOe2IbmPfXcNbcmMoGPfuh3JnPP6n5zz+p+c98afpKZnMrGEj98PnNaKaz+az+kvz+Tk9nVolqcNw7vUJyk1naEvzuDjmeu5/UTnc78qfi/nvj6L4a/+ydUfzWPkGa0JDxPW7Uhm+Kt/MvzVPznnNacNE0qwDYF2PHJWa658by5Dn5/BqR3q0LxW7nac3a0+SSnpDHp2Oh9MW8ddJzvt2J+exUvjVvHs2L8KfO8hbeNITsso8LmSUFL7xoadyZz78kyGPD2F/xv3N88c4PvvcIUJPDKsDZe/M5cTn5vOaQc690hOZ8DTzrnHPafmnHuc2rEOJz03gxHv5Jx7HOo9nxn7F6e9OJPTXpzJis05Hd9z1u7OXl7aHTIAZ5w5jDfeerfU/9+QpFkl81NGhUynjIg0FpGVIvKZiKwQkW9FpIKIrBOR50RkiYjMFpHmh/l+U0TkZRFZKCJLRaSbu7yiiLzvvtcCETnDXX6ZiPwoIpOAiQd56yoi8pOI/CUib4pImPv6ISLyh4jMF5FvRKSSu/wkt13zgWFH9EdyDW4bx+g5mwBYuD6BKtGR1KxSPtc6NauUp1JUBAvXJwAwes4mhrSLy379d3OcO6HfzdmYvTzYaR3r8r+gO9jhYUJUZHj2v9uSUvO95kgMaVeb72Y7MS1Yt5sq0ZHUytOmWm6bFqxz7hx+N3sjJ7oXO9NXbifTvWs7f91uasc6F3oKVCgXiDuM9Mys7J75kjC4XW2+m73BbUfCQdoRyYJ1CW47NjCkff52LFi3mzqxUdmvG9KuNht2JvP31pK7a9uufgz/7kxm4+4UMjKVX5ZsZYDbSREwoFVNxixwPhu/LdtG96bVcj23aXcKq7ftLbEYD9eQtnGMdj/nC9YnUPkA+0nlqAgWZO8nGxnifqZWx+9lzbaD3/E/vXPu/eRIHdcwlvU797FhVwrpmcpPC7cwqE2tXOsMalOL7+c6+/+vi7fSs0X17OU/LdxCWmYWG3elsH7nPo5rGAtARL79dz8AyzcnZd/pKg3HxFVic2IqW5P2k5GlTFm1k+ObVs21Tvye/azdmXzQGyy9m1dnzvoE9hfzaL1DOaljPb76fR0A89bsJKZCJHExUbnWiYuJonJ0JPPWOBdnX/2+jqGd6gPQLK4Sv/+1HYApy7ZyamdneXJaJrNW7WB/emYptSTHqX3b8/nY2QDMXrKOmMrR1K5RJd96e/Y53/kREWFERoSjQRvouTuH88DLP+RaVtKGtK/Dd7MC37WHecyYtYETj3M6JPcGHQcqlAvP/r1F7crZ22h1/F4aVKtAjcq537eknNypHl/OcEYEzF29kyoVyuX7fAWei0/MfwzesGMfyzckkFXKdyeP9Pidd1sEwt+5N43F/yaQkVny7Sno2Nc/z7Gv/7E1+XGh830//gDHvn8OcOzr3rQaG3alsKWA7Vacbdiwy2lDeqby8+Kt9G+V+/gxoFVNxswPHL/j6dHMaUNqelb2uUf5iHCU/H/zHs2qs2FXMlsSSq4NAO0bxrJ+R3LQcXArA9vkPk8d1CaO0fOcdvy6JD77OJiSnsm8dQUfGyqUC2dEn8a8PqHkO/kCSmrfmLd2N4kpzsjWBWtznysWRd5zj7ELCjj3aFuL0e65xy95zj3GLsh/7nE471kWde7SlSoxMV6HYXwoZDplXMcAr6tqKyAJuN5dnqiq7YBXgZcK8X4VVLWD+z7vu8seACapajegPzBKRAJd8J2As1W170HesxtwE9AaaAYME5EawIPAIFXtBMwFbheRKOAd4DSgM1C7ELEfUFxMFFsSci6ktiSkUjvPiVvtmKhcB/+tiSnZJ3c1Kpdnu3tRtj1pf74TzqjIMPoeW5NfFm8FID5xP+9MWcPMhwcw69GB7EnNyB6aWFxqx0axJejicGtCSoFt2hp0MrA1IYXaBRyIzuvRMHs0yc8LNpOclsncJ4bw52ODeXviahJLcIpG7ZioXMOZtyakFHjRtvUQ2w/g3B4NsttRoVw41w1qxku//F1CkTtqVSnP1sT92Y/jk/YXcAIRxVb3s5WZpezdn0FshUiiy4Vzee/GvJ5nug04nWNvX9aJr67rztld6pVoGwLiYqPYHPR33pp4gP0kz7aIK8TJzakd6/JjMXbKOPt28OcntcDPT2CdzCxlb0oGVd3OgYJeG5+0n3enrGXag/344+EB7EnNYMbfxbv/Hq4aFcuxfU9a9uMde9OoUbFcod+nX4vqTF5V+m2oExvN5qBRRZt3p1A7z0if2lVzr7NlVzJ13E7ilZuTGNrR+fyf3qUB9apVKIWoD65urVg2bs2ZIrMpPoG6tWILXPfH127g34nPsDd5P6MnOFOyTu3Xjs3bEljy96bSCDeb810bvO+mZHfGZ68TG51rn9iS57hy92mtmPXEEM7qWp/nx64EYMWmJIZ2cEaCdmgUS71q0Ud8wXO46lR1phQHbN6VTJ0y8Bk5lOI4ft916rH8+dhgzuxSnxd+XlnyQeeR79iXuJ+4yoU49vVqzBsFHPsChrarzS9LtpZM8K64KrnP++KTUomLOXgb9qQ6bQCnU2fMzcfzw009eWzMiuxOmuw2tK/Nz4tLtg0AtauUz3Vc3pqYvx1xMeWzz6Mys9SZBuq240BuPbEF709bS0p66XXml8a+cV7Phkw+wtFL+c4fEvOfe9SukvvcY09KBlUrHvjc41DvecfQFvx0xwk8cPqxlAvPuZzt2CiWsXecwPtXdqFFntE6xmdUS+anjAq1TpkNqhqY5Pkp0Mv9/Yugf3sW4v2+AFDVaTgjXGKBIcC9IrIQmAJEAQ3d9cer6qHGX89W1TWqmum+fy+gB04nzUz3fS8FGgHHAmtVdZU6txA/LUTspSbv53tgmzjmrdud3XlRJTqCwW3j6PP4ZHqMnEiFcuGc2bl0LqwL68YhLcjIUr6f69yZ6NCoKplZStcHf+OERyZw1YBmNKxe9k9ybxzSgoxMzR4RcdvQY3h3yhqS00r/TvrhumFAUz75/V9SCojxkrfncO7rs7ju4/lc0L0BnRvHln6AxaxDo1hS0jL5uxTzTRRFlegIBrWNo/9TUzn+sUlUKBfOGZ2Kd/phaapWIZIm1Ssw99/SnbpUHG55bzYjBjRnwsjBVIqOJC2z7A7DLcjpN7xGk8H3U75cBP26HkN0VCR3X34ij73xk9ehFclz/1tB9wd/4/s5G7msbxMAXvttFVWiI/n1vn5c1q8pyzYmklmGTwJDxaixK+nx8Hh+mLuRy/o08TqcQrm+f1M++aPgYx9ARLjQ79ia/LY0vpQjK5wlGxM54/9+57w3ZnFV3yaUi8i5xIgMF/ofW5NxS8p2Gw6kVd3KNKxegfFLS3bqVUk42L7Rs0V1zuvZkKfHLPcouqIZ9dPfDH52Ome99AexFSK52s3Tt2xjEn2emMKpL8zk4xnreXNEp0O8kzFlR5nOfVIEec98tIDlhTk7Kuj9BBiuqrkmnIpId+BwshMe6D3Hq+oFed6zw+EEKSJXA1cDVB94I5XbnZRvnYtPaMT5PRsAsPjfRPfOq3N3s05szp2PgK2JqdQJ6pGuHROdPdx5x5791KzijJapWaU8O/fuz/Xa0/Lc/e/VsgYbdqawa59zh3vc4q10alyVH+Yd2Z3RS3o35oLjG7ltSqBO0B3n2rHRBbYp+O5B7djoXHcXzu7egIFt47jglT+yl53RpR5TV2wjI0vZuTeNuWt20b5hLP/uLL4cGpf0bsz5PRtmt6NunhjzDjOPT0zNdUc37/Y7u1t9BrapxQWv/pm9rEPjWIZ2qMN9p7emSnQkqsr+9Cw+ypOI8EhtS9pP7aA7UnFVymdPdclZxxlxEp+0n/AwoVL5CBKS02lXP4bBbeK4/cQWVI6KQBX2Z2TxxawNbNvjvMeufelMXLGNdvVimOdO3ypOF/dqxAXutlj0byJ1g/aT2jEH2E/ybIv4wxyafVqn4h0lA85no06uz09UgZ+fwGcmPEyoFB3B7uT0A772hBY12LgzOWf/XbKVTo1js4ewl6Yd+9KoWTlnZEyNSuXYsS/tIK/Ir2/z6sxcsyvfXdyScvmA5lzc1zlhXLB2F3WDRi7UrRrN1jzTv7buTsm1Tp1qFbLv+v6zdQ/nvjAVgKZxlRjcvk5Jh1+ga87tw4hhxwMwb9l66tfOmUJWLy6WzdsSDvja/WkZ/G/KYk7r1474nUk0qled2V/d57y2Vix/fH4PvS8eRfzO4u+svLRPEy44wTlmLFq/292/HXVio3ONQATnjnPwPlGngOMKwPdzNvLx9T148ae/2JuawR2fLsh+7vfHBvPvjpLLuXTFoBZc0s+Zlb1gzc5co6fqVqvAllLK91RYxX38Dvh+7iY+urY7L/5ccE6QkpLv2BdTnvg9hTv23TYk59iX5h77AHq3qMGKLXvYWcjvusKKT8p93hdXJYr4xIO3oXKU04Zga7bvI3l/Ji3iKrHMzZfWq2UNlm9OKvE2AGxN2p/ruFw7Jn874hP3u58zd1tERRw0UX3HRrG0rV+Fyff1JSJMqFapHJ9e242L3pxd7PGX1r5xbN0qPHdBBy55489827Cw8p0/xOQ/99ialPvco3J0BLv3HfjcAzjge2539620zCy+nbOJK/s5nU179+dM15qycjuPhremasVIdu8r+SIEpgSU4fwvJSHURso0FJHASJgLgRnu7+cF/ftHvlcd2HkAItILZwpUIjAOuElExH2uYyFj7CYiTdxcMue5Mf4JnBDId+PmrWkJrAQai0gz97UXFPSGqvq2qnZR1S4FdcgAfDJzfXYS3t+WxjOsqzNSpUOjWPakZGRPRwrYnrSfvakZdGgUC8CwrvUY796lmbA0nuFdnVwGw7vWz14OUDkqgu7NquVatnl3Kh0bxxIV6Xzcjm9Zo1hyhnw8fR1Dn53K0GenMm7xFoZ3c2Lq2Lgqe1LTC+gMcNrUsbFzATG8W31+c4cD921Vk+sGNueKt2eTGpSXYfPuFI5vWQOA6HLhdGpclX/iizffycfT13Hyc9M4+blp/LZ4K8O7NXDbEXuQdqTT0R0tMrxbA8YHtePaQc254p05udpxzsu/0+vRifR6dCLvT13Da+NXFXuHDMDSTUk0rF6BelWjiAgXhrarzeSV23OtM3nlds5wEz0PaVOLWWucwWWXvjuXE1+YwYkvzODTP/7lnalr+WLWBqIjw7JzNkRHhnF88+qsKqGcM5/MWJ+dhPe3JVsZ5n7OOzaKZU9qwfvJntQMOmbvJ/UP626mCJzSoXjzyQAs3pBIoxoVqV8tmshw4ZQOdZi4LPedvYnLtnGWOwXspPa1+fOfndnLT+lQh3LhYdSvFk2jGhVZ9G8CmxNS6NAoaP9tUZ1/4kunOk5ef8XvpV5MFLUrlyciTOjXojp/FLJ6Uv+W1Zn8d/5E0iXl/Un/0H/kb/Qf+Ru/zN/Eecc3BqBz0+okpaQX2Gm2JyWdzk2d+fbnHd+YXxc4HdiBqaIicPtpbfhoSunlNgj21tfT6HH+M/Q4/xn+N3kxF57aDYBu7RqTtDeFrTtyJ66uGF0uO89MeHgYQ3u14a918Sz7ZzONBt7HsaeM5NhTRrJpWwI9L3y2RDpkAD6atpaTnnaS845btJXh3QPftVXZk3IYx4zuDfht8RYAGgclQB3Svnb2caFKdASR4QLABcc3YtY/O3PldShu701YRd8Hf6Hvg7/w07yNnN/LuTjp0qw6Scn5P19lRXEev3Nti3a1WV3Mx+jDsXRTEo2qV6BebM6xb0qeY9+Ulds53Z3aNrhNLWavdY59l703l5NenMFJL7rHvmlrsztkwJn280spTPvJOX47x4+T29dm8srcx4/JK7Znj5Qc0iYu+/hdr2o04WHO575ObBRNalbIlW/s5FKaugSwZEMijWtUoH7VwHGwdr4EyROXb2OYWy30pHZx2cfBA/n8jw30emIK/Z+eyvmvz2Ldjn0l0iEDpbNv1K0azdtXduXWT+azdvuRH88Xb0ikcdC5x6kdCz73GOaeewxtX5s/VuWce5zaMefco7F77nGw96wZNDVwcNu47FyJNYJu2rRvEEOYiHXI+NlRNn0p1EbK/AXcICLvA8uBN3Dyt1QVkcXAfg7QsXEAqSKyAIgELneXPY6Tl2ax27GyFihMieo5OLltmgOTge9VNUtELgO+EJHAN82Dqvq3OwrmJxFJBqYDlQvxfxVo8vJt9G9VkykP9CMlLZO7v1yc/dxPd/bilOedvqyHvlvqlsQOY+qK7UxZ4ZxgvDFxNa9e2olzuzdg0+4Ubvwop/zqkHa1mf7XjlzDcBf+m8Avi7Yw9o7eZGQpyzcl8kUxl0OctGwb/VvHMf3hgU754qA7lb/c05ehzzp3lx/8anFO2cAV27Ln0T5+TnvKRYTx2Q1On96Cdbu5/6vFfDRtLS9c1JEJ9/dDEL6e9S8rNxdPpZwC27F8G/3b1GLawwOyS2IH/Hx3H05+bprTjq+XOCWxy4UzZXlOOx47ux3lIsL49Poe2e144OslJRZvXplZylNj/+KtSzsRHiZ8P28zq7ft44aBzVi2KYkpK7czet5mnj67LT/fdgKJKenc9dXB46teqTwvX+hUBggPE35evJWZBVRnKm6Tl2+jf+taTH2oPylpmdz1eU5lm5/v6s3Jo6YD8NA3S7JLYk9Zvj07j8+J7WvzyPA2VKtUjvev6caKjYlc4p7EdW9WnS0JKWwoxhFX4Pz9H/1+OR9c1ZVwEb6Zs5FV8Xu55cQWLN2QyMTl2/h69kZeuKA9E+/tQ0JyOrd+uhBwqmf8vGgrv97Vm4ysLB75fhlZ6owY+nXxVsbcdgKZWcryTUl89adzwXBJr0Zc3a8pNSqXY+wdvZi6cjv3f7O0WNsULEvh1WnrePqMYwkTYdzybazflcKl3erz97Z9/LFuNy1rVeSRk1tSqXwEPZrEckm3+lz1hfMdF1e5PDUrlWdxMVW7Kqzxi7cwqH0dZj97CilpGdz8Xs5J/eRHh9B/5G8A3P3JPF65ojtR5cKZtGQLE9yOgGE9GnL5AKdiy0/zNvL59Jwyr/NGnUrlqAjKRYQxtGM9znlhKn+X4HdVwK8zlnFirzYs+3EkyanpXPNIzizbP7+8lx7nP0PF6PJ8+9I1lIuMICxMmDZ3Fe98O+Mg71ryJi2LZ0CbOGY8Mii71GzAr/f146SnpwDwwFeLefFi95ixPJ7J7kXBfWe0pllcJbJU2bgrhfu/cL4fmteuzH8v7oQCf29J4i53/yoN4xdtZnCHusx7/jRS0jK58Z2c0ZJTnxhK3wd/AeCR8ztwds/GVCgXwdKXz+STKat59vsldGxSjU9u7UNMxXKc1KEe9w5rx/H3/VzicR/p8fve01vRrFYlshQ27Urmvq+c/b1m5fKMvasPlaIiyFK4ol9TBj41uUQ6yQLHvjcDx7757rFvQDOWbXaPffM38/Twtvx0q3Psu/swjs3RkWH0bFaNx8bkL81cEm148n8refuyToSJ8P38Tazeto8b3eP35JXb+W7eJp45uy2/3N6LxJR07nTPHzs1iuXKPk3IyMoiS+HxH1dkj76Ijgzn+ObVebSA8tIl1Y5Hf1jO+1d1ITxM+Hb2Rv6J38stQ5qzZGMik5Zv55vZG3n+/PZMuKc3Ccnp3PZZzvF98n19qRQVTmR4GIPbxDHinTn8c4ik/SWlpPaNW05qSdWKkTzhllzPzFJOHTWtyHFmZimPjl7Oh1d3JUycv/mq+L3cemILlmxMZOKybXw9ayMvXNieSfc55x63fLIQcM89Fm7l17t7k5mVxSOjnXMPtOD3BPjvf46jWqVyCE7BgYe+dfIlDm1fmwuPb0hmlpKansUtpfj9G3DPnbczd85sEhJ2M3hAH6674SaGDT+n1OMw/iOlWe2gJIlIY2CsqrbNs3wd0EVVC5XVUUSmAHeq6tziirE0NLntp5DYoJkZZTf3SWG4A6p8rXKV0klSWdL25hlK7kcRkeGHXskHmjSueuiVfGDhvNItHVwS9i6c7nUIxaLG8YO9DuGI7Uvy5sKvuFWKDY3kmjF5Ej/7UVYpTdMsaWlp/p/GsD+15KdulYbIcgdPiOwXy54d6nUIRywqAv9fZBxEdNfbS+QLLGXOi2Xy7xZq05eMMcYYY4wxxhhjfCFkpi+p6jqgbQHLGx/sdSLyGnBCnsUvq2q/osYiIu2AT/Is3q+q3Yv6nsYYY4wxxhhjTMgLkdk8hytkOmWKSlVvKIH3XAJ0KO73NcYYY4wxxhhjTOg46jtljDHGGGOMMcYYU0YcZSWxrVPGGGOMMcYYY4wxZcNRNn3JEv0aY4wxxhhjjDHGeMBGyhhjjDHGGGOMMaZsOMqmL9lIGWOMMcYYY4wxxhgPiB5l87XMkRORq1X1ba/jOFKh0I5QaANYO8qSUGgDhEY7QqENYO0oS0KhDRAa7QiFNoC1oywJhTZAaLQjFNpgSpeNlDFFcbXXARSTUGhHKLQBrB1lSSi0AUKjHaHQBrB2lCWh0AYIjXaEQhvA2lGWhEIbIDTaEQptMKXIOmWMMcYYY4wxxhhjPGCdMsYYY4wxxhhjjDEesE4ZUxShMkcyFNoRCm0Aa0dZEgptgNBoRyi0AawdZUkotAFCox2h0AawdpQlodAGCI12hEIbTCmyRL/GGGOMMcYYY4wxHrCRMsYYY4wxxhhjjDEesE4ZY4wxxhhjjDHGGA9Yp4wxxhhjjDHGGGOMB6xTxhwWEXn2cJb5gYgcLyIXisglgR+vYyoMETnhcJb5gYj0EpER7u81RaSJ1zEVloiEi0hdEWkY+PE6piMhImEiUsXrOApLRE4QkYru7xeJyIsi0sjruIpCRCqKSJj7e0sROV1EIr2OqzBE5BYRqSKO90RkvogM8TquwhKRc0Sksvv7gyIyWkQ6eR1XYYhIMxEp7/7eT0RuFpFYj8MqtFBpRzARqSoi7b2Ooyj8fuwLxc+Tn4XSMdyYorBEv+awiMh8Ve2UZ9liVfXVyYSIfAI0AxYCme5iVdWbPQuqkA6wLfItK+tEZCTQBThGVVuKSF3gG1X1TQeTiNwEjATigSx3sfpwv/gcuBZnn5gDVAFeVtVRngZWCCKyGDgOaA98CLwLnKuqfb2MqyhEZB7QG6gKzMTZJmmq+h9PAysEEVmkqseJyInANcBDwCc+/J5arKrtRaQX8AQwCnhYVbt7HNphE5GFON+1jYGfgTFAG1U92cOwCi2E2jEFOB2IAOYB24CZqnq7l3EVRigc+0Lo81QTuAqnHRGB5ap6uVcxFYXfj+Ei8j/ggBfVqnp6KYZjfCji0KuYo5mIXAdcDzR1vzADKuNcLPhNF6C1+rA3UkR6AscDNUUk+OStChDuTVRH5CygIzAfQFU3B+5I+8gtOJ1KO70O5Ai1VtUkEfkP8AtwL87Fgm86ZYAMVVUROQN4VVXfE5ErvA6qiERVk934X1fV59wLCD8R99+TcTpjlomIHOwFZVSg8/4U4G1V/UlEnvAyoCLIUtUMETkLeEVVXxGRBV4HVQSh0o4Y9/v2SuBjVR2Z5/zKD0Lh2Bcqn6cxwHRgAjnfV37k92P48+6/w4DawKfu4wtwOi+NOSjrlDGH8jnORdrTOBdqAXtUdZc3IR2RpThfllu8DqQIygGVcPbb4M6LJOBsTyI6MmnuAVjBmbLhdUBFsAFI9DqIYhDpTo85E+dkKD2wXXxkj4jcB1wM9Han//hqyk8QcTth/wMETkr91vE6T0R+A5oA97kdrlmHeE1ZtElE3gIGA8+60x38NvU7XUQuAC4FTnOX+XHfCJV2RIhIHeBc4AGvgymiUDj2hcrnqYKq3uN1EMUgcAy/COjjt2O4qk4FEJEXVLVL0FP/E5G5HoVlfMQ6ZcxBqWoizoH3AgARqQVEAZVEpJKq/utlfEVQA1guIrOB/YGFfhhW6H7hTxWRD1V1PTj5P4BKqprkbXRF8rV7sRMrIlcBlwPveBxTYa0BpojIT+T+PL3oXUhF8hawDlgETHPncfvtM3UecCFwuapudfMb+GmkT7BbgfuA790RJk2Byd6GVGhXAB2ANe6on+rACG9DKpJzgZOA51U1wb2YvsvjmAprBM70xCdVda04ubs+8TimogiVdjwKjANmqOocd/9e5XFMhyVolG4oHPtC5fM0VkROVtWfvQ7kCAWO4Vf4/BheUUSaquoaAPdz5cebjqaUWU4Zc1hE5DTgRaAuzvznRsAKVW3jaWCFJCIFzk0N9HD7QSjk/wgQkcHAEJypDuNUdbzHIRWKmxcnH1V9tLRjKW4iEqGqGV7HURhuZ1ILVZ0gIhWAcFXd43VcR5NDJcFV1fmlFUtxEZFwII7c+Rr8dkPC90TkFlV9+VDLyjoROUFVZx5qWVl0oGOeS1X1sVIL5giJyEDgd1VN8TqWIyEie3Au+tOAdHexqqpvEva737ETVLW/17EcKRE5CXgbp+NScK6XrlHVcZ4GZso865Qxh0VEFgEDcL40O4pIf+AiVfXTfE8ARCQO6Oo+nK2q27yMp7BEZKGqdnDzf3TCzf/hpwR7oUZEKqhqstdxFJW7TzwF1FXVoSLSGuipqu95HNphc0dbXQ1UU9VmItICeFNVB3ocWqGJSBfgfvInbizz+7iIBEb0RAGdgcU4J6btgbmq2tOr2IoiRBKangA8gnNxEIGzPVRVm3oZV2EdIMn9AlXt6FVMRREKyfpF5BxV/eZQy8oyEfkI6AnswsnJMg1n9NJuTwM7SonIRGCYO0Lf19xprse6D1eq6v6DrW8M2PQlc/jSVXWnOOVyw1R1soi85HVQhSUi5+IMh5yCc2L6iojcparfehpY4fg6/4d7V6egeAMXCn66u9MTeA8n109DETkO547I9d5GVmgfAh+Qk9/gb+ArnLb5xQ1AN2AWgKqucqdb+tFnOFNkluCzPCyBO50iMhrorKpL3MdtcToG/CYUEpq+B9yGk7zbd4lA3bwfFwJNROTHoKcq41xQ+0KIJeu/D8jbAVPQsjJLVS8FEKfy49nAazijwX13bSQipwN93IdTVHWsl/EU0V5giYiMB/YFFqqPqqOCc5MOuB1opKpXiUgLETnGp9vElCLfffEYzySISCWcOwmficg2gr40feQBoGtgdIw4pQQnAH7qlPF1/g9V9VuFpYN5CTgR+BFAVReJSJ+DvqJsqqGqX7tJ9nArUvjt4m2/qqaJW+BHRCI4SHnKMm67qv546NXKtGMCHTIAqrpURFp5GVARhUJC00RV/cXrII7A7zjJ+WsALwQt34MzEssvfJ+sX0SG4lRUqyci/xf0VBXAb9NdLwJ6A+2AHcCrOCNmfEVEnsEZ/f2Zu+gWdzrcfR6GVRSj3R+/+wCnAzwwKnQTTmeldcqYg7LpS+awuJVxUnFGM/wHiAE+89vdQxFZoqrtgh6HAYuCl/mRH/N/QHb+iV44F88zVNVX5ShFZJaqdg8eQi8ii1T1OK9jKwwRmQIMB8araicR6QE8q6oF5mAqi0TkOSABuAS4CbgeWK6qvqtu4uY6uACYSO4kmr45YRWRL3A67gNlQf+Dk5T8Au+iOnxBIxnaAMcAvktoGpTf51yckRijyd0G3+X3CQUi0iiQrN9v3NGgHYDHgIeDntoDTPbT1B8R2QGsBt7EiX2dtxEVjTjl1Duoapb7OBxY4KcplpCdu/KnQDv8SkTmqmoXv58XmtJnI2XMYVHV4FExH3kWyJH7VUTGAV+4j88DfJWxXkRicHIcBEZkTMU5QfLV3VwReRg4h5w7Ix+KyDeq+oSHYRXWBhE5HlB3StktwAqPYyqK23FG+zQTkZlATXxy5zbIvTgVf5YA1wA/q6rfqnkFjMCZjx5JUB4T/HUXcQRwHc4+Ac4oyze8C6fQAiMZ/nV/yrk/4J8RWC/keRxcplVx8sT5hogMA54FauHcIPLdlFdXeRF5m/w5o8r89lDVRcAiEflcVdMP+YIyTFVriEgbnHOpJ908ZH+p6sUeh1YUseRM5YvxMI4jcR7wkoh8B7yvqiu9DqiI0kQkGvc4ISLNCOoMN+ZAbKSMOahQyv8RICLDgRPch9NV9Xsv4yks94C1lJzOsYuB41R1mHdRFZ6I/IUTd6r7OBpYqKrHeBvZ4RORGsDLwCCcfeI34Ba/jSCD7Ok+x+C04y+/nXCHSmUWcPYNP+0HB+Lu0w1V9S+vYymqEEloml2e9WDLyjoR+Qc4TVX92PGdzS2c8CZ5cvyo6jzPgiqkUEgeLSJVcM4F++JMY6oB/BnINeMXbs6lZ4DJONuhD3Cvqn7laWBF4G6TC3A69RVnKtAX6qMqiuJUFX0QaI1zTngCcJmqTvEyLlP2WaeMMT4TqL50qGVlnVul5SxVTXAfxwKj/XC3MFSIyABVneTegc7HZ9NlQqIyC4CIfACMUtXlXsdSVG7iyVFAOVVtIiIdgMdU9XRvIyucEKmUU1Ab5qlqZ69iKgoRmamqJxx6zbLNj3/7vERkJQUkj/bTDQl32s8M92eaqm70OKQiE5E65K4qutXLeI6EiFTHudl4K87I4+bA/6nqK17GVRhuG3rgdJL9qao7PA7J+IBNXzJHhRAb8ZMiIr1UdQZk37FK8TimwyYir+Bsi0RgmZtpX4HBwGwvYztcQW0okI+qBfQFJgGnFfCcL6bLhEplljx6AAtFZC3OsOfA95SfcgSMxKmGNQVAVReKSBNPIyqEUEhoKiLH4uTEicnT8VoFp2S5LwTFPldEvgJ+wIe5lkSkmvvr/0TkeuB7crfDT99Xfk8eTeD7VEQqqGqy1/EUlogcq6org3JHBTqV6opIXb/ljHI78kfgdMJ8DHRT1W1uNaPlgG86ZXDOrQL5EiNx9nVjDso6ZcxRIcQq/lwLfOzmlhGcC8/LPI2ocOa6/84j94FqSumHUmRzD71K2aeqI91fH1PVtcHP+egCOlQqswQ7yesAikG6qiYGqmG5/DQ0dzPOfn46zndVwB6cEQJ+cAxwKk6+ieCO1z3AVV4EVETBsScDQ4Ie+6Lz2DUPJ97ATnFX0HMK+GbqDzBZREbh4+TR4pQofw+nIlZDN4nxNap6vbeRHbbbgavJnzsKfJgzCqfYwH9VdVrwQlVNFpErPIqp0ETkdZyOpUDuymtEZJCq3uBhWMYHbPqSMT7lzr1FVX1TDvtoIyKvqOpNXsdxKKEyvSGUuBcIvd2H090Em74hIu/hVI+6F+dk+2YgUlWv9TSwQhKRSL/lV8pLRHqq6h9ex2FChzv9OC/10/RjEZmFk9D+x6AqOUtVta23kRWOiEQFcvMdbJnficgfqtrz0Gt6y53a10rdC2xxqrwuU9VW3kZmyjobKWOMz4hIeZyLnMZAROBOtKo+5mFYheZWOngaJxla9lB6PyUKPAxlOv9BqExvABCnjPcrQCucKjnhwD6fTU0EnATFOCMZAiMAPhWRt/00px6nLPkDOHfRvwDGAY97GlHRNBYRv39PLRCRG3D29eA2XO5dSIWXZxpZQCIwV1XHlHY8RXWAHF6JwBJV3Vba8RSFqvb3OobioKob8ozmyzzQumXY70DeHFcFLfM7v5yT/AM0BAJl7xu4y4w5KOuUMcZ/xuCcwM3D32X2PsDJO/FfoD/OXOIwTyM6+oTK9AaAV4HzgW9wSv9eArT0NKKiuwLorqr7AETkWeAPfDSn3s3R8IAbu/qpekYeofA99QmwEjgReAz4D04CTb+JwikVH6h8NRxYCxwnIv1V9VavAiukK4CeONVyAPrhHM+biMhjqvqJV4EdLnf69EicSj8AU3GmwSZ6F1WhbRCR4wEVkUjgFny0X4hIbaAeEC0iHcmZFlcFqOBZYCWnTE/tEJH/4cRYGVghIrPdx93xSb5E4y2bvmSMz/hxeG1BAtNjRGSJqrYLXuZ1bMXFL1VaQmF6g4jMVdUuIrI4KIGjX6svLQG6BpWLjwLmBPYTPxCRrsD7OCeo4HQkX+6nsr8QGt9Tgf0gsG+4F6DTVbWH17EVhoj8CZygqpnu4whgOk5CzSWq2trL+A6XiIwDLlHVePdxHE5i0wtwqgCV+eO7iHwHLAU+chddDBynqgVW8iuLRKQG8DIwCKdD4zfgFr9UkBKRS3HyCXYB5pDTKZMEfOSXBNiHq6yfT4lI34M9r6pTSysW4082UsYY//ldRNqp6hKvAzlC+925tqtE5EZgE07CvVAih17FOyJyt6o+B1zoVjHKxUdVpACSRaQcTtWi53CS//ptREPAB8AsEfke5zN0Bk5CSj95D7heVacDiEgvnHb5qYIUhMb3VCAnToKItAW2ArU8jKeoquL87QOjMSoC1VQ1U0T8NGq0QaBDxrXNXbZLRPySv6iZqg4PevyoiCz0KpiicMsU/8frOIpKVT8CPhKR4ar6ndfxlIIyfT51uJ0ufsmNY0qfdcoY4xPu3XPF2W9HiMga/FsuF5yhwhVwEoA+jlMp4BJPIyp+L3sdwCEEhmqHQjWpi3HyyNyIUx2nAc70Bt9R1RdFZAo5JTVHqOoCb6MqtMxAhwyAqs4QEV+Uks6joO+pSz2NqPDeFpGqwEPAjzgdGw95G1KRPIfT6ToF57jXB3hKRCoCE7wMrJCmiMhYck/DmuK2I8GzqAonRUR6qeoMABE5AUjxOKbDErgZISKvUMCUGJ/djADoLCITVTUBwN3X71DVB70Nq/BEpBHQQlUniEg0EBE09fViD0MrTn7JjWNKmU1fMsYn3INVGM7F5vq8z6tqvmVlmYh0zjuVQUROVdWxXsVUWCLSEqesaSOCOrn9VIHClE1u9aU+OBcNvqm+JCKB4eWXANE4SX4VOA9IVdXbvYrtSLjV7vycGyckiEgdoJv7cI6qbvYynqIQJ7PscHISwc8EvlMfnZCLSAecqUsxOB1ku4DL/PA9JSKnqer/3Ok/+bgjUHyjoGm6ZX2qT0FE5CqcEt/VVLWZWwziTVUd6HFoxcqP28aUDuuUMcZngnMb+JmIzMeZV7/UfXw+cJuqdvc2ssMnIouAN3GSNGZXbfBh3oyWwJ24Fb0Cy/3QuRQ0gqxAPhxBFlx96TucC56zAF9UXzpAqdwAX5XMBRCRLjjTrnybG0dEqgOP4HQCKE4elsd9lDvjWFVdGdThl4uqzi/tmIzD7axEVZO8jqWwRKRTKHx2RGQxTg6y/e7jaJyKZG28jaxw3Olv3YBZQSXKQ+J8N5h1ypgDselLxvjPfBHpqqpzvA7kCJ0NfCsiFwK9ce6sD/E2pELLUNU3vA6iGHyD07n0Lv4rCXqq++8N7r+ByiUXUcarNRyEb6svHW6pXBG51Cd3pN/H/7lxvgSmkTOd7z/AVzgJTv3gdpw76C8U8JziTCkr80Rkhqr2EpE95P5uCkxBruJRaIUmIrE4x+zGQESgrLTPpv684FYw+hb4KnCDyIc+AyaKyAfu4xHkJGD2k/2qmhb4LLmJvP16DD+YMp0bx3jHRsoY4zMishJoAawD9uHfnDKBERo/AP8CZ6mqL+akB4jIIzhJGr8nqDy5qu7yKqai8Fs1mYKEyhBuCI3qS4fil20TCp+rgir2heIdaFN6ROR34E9gCZAVWO6TjtZsbqfMuTjTK6vgdM484W1UhSciQ4HANJ/xqjrOy3iKwk3Qn4DT2XcTcD2wXFUf8DKuwhCRcGDCwW5OiEhbH3cAmhJknTLG+IybWyYfv+SUKWC6SS2cKQH7wV/TTURkbQGLVVWblnowRyAUOpfcoc83qOpM9/HxwOuq2sHLuIpCRG7HSSb7vbvoTOBDVX3Jq5iKW0GdHWVJKOXGEZEXgdnA1+6is4Fuqnqnd1EVjbtfNyb3NMuPPQuoiNyLtzhyt+Nf7yIqHL91TB6KiLQD7gbOU9VyXsdzNHKr3F2BM2JagHHAu37KtQQgIhOBYaqaeMiVjQlinTLG+JA7hL6Fqn4gIjWBSqpaUAdBmXOgTqUAv3QuhZJQ6FwSkc44U00CiSd34+T+8FXOAPfEtAeQilN9CZxEv36rvnRQZf2iLhRy4wRNkxGc8tGBqYnhwF4/TZcBEJFPgGbAQnLaoj6bMoOI3ASMBOLJGWXiq9GuInIbsBcYi3878lvhdLKeDezAmdL3napu8zSwQhKRHjhTW1sB5XD2730+3L8r4nR4Z7qPw4HyqprsbWSFIyJjgI7AeJzR7IDvpvYZD1injDE+IyIjgS7AMaraUkTqAt+o6gmHeGmZ41aY6e0+9E2FmQARiQSuw6mSAzAFeEtV0z0L6ignIjEAfr5LVdZHkRSHUGmjj3LjHJCItFHVZV7HcSgisgJo7bc753mJyD84OaN8kWi5ICJyA/AkznSTwPbwW0f+Hzj5lr5RH1bxChCRucD5OLnhuuCM7mupqvd5GlghicifwCBV3es+rgT8pqrHextZ4YRKVS9T+izRrzH+cxZOL/x8AFXdLCKVD/6Ssieowsxod9GnIuKLCjNB3gAigdfdxxe7y670LKIiEJEKOMk0G6rq1W4pymPUX+XJY3DuPvdxH08FHvNp58xEERkOjPbrBaiIlMdJLNuY3FM0HnN/nelBWCXhFvyZVDPYJ0CZHbUUZClQG9jidSBHaAPOlF0/uwNorqo7vA6kqFS1p1upqKHXsRwpVf1HRMLdUSYfiMgCwFedMkBUoEMGQFX3uucmvqKqHwU+V6r6l9fxGP+wThlj/CdNVVVEFLKHfPqRbyvMBOmqqscFPZ7klsn2mw9wynoH7khtwrnr5ptOGZypS0txkjaC00H2ATDMs4iK7hqcTrIMEUnFh9VZgDE4F57zCJreEKCqN5Z6RCUjFCpp+KUNNYDlIjKb3FNmTvcupCJZA0wRkZ/I3Y4XvQup0P4BfDWtJC8ROQ14HmfKTxMR6YDTke+3z1OyiJQDFrrJcrcAYR7HVBT7gsuUu1OSfVX8AULqc2VKmXXKGOM/X4vIW0CsiFwFXA6843FMRSHkLr+ciX8uDgIyRaSZqq4GEJGm+K+kNEAzVT1PRC4AUNVkCdSl9I9mqjo86PGjbvJf31FV3418K0B9VT3J6yBKgS9HMuXhlzY84nUAxeRf96ec++NH+3A6ASaTu2PJT3kzHgG64Uw7RlUXikgTLwMqootxOmFuBG4DGuCMUvSbW4FvRGQzzrlgbZycP37zCPk/V76Z1me8Y50yxviMqj4vIoOBJKAl8LCqjvc4rKL4AJglIt/jHIDPAN7zNqRCuwuYLCJrcNrQCBjhbUhFkuYOtw2MvmpGAaMbyrgUEemlqjMAROQEfHaXLajiT4F8lrT4dxFpp6pLvA6khPmt89K3VHXqwZ4XkT9UtWdpxVNUqvrowZ4XkVdU9abSiqeIfnB//CxdVRPz3H/wSwclIjJRVQcC16vqPTjJ4Q/62SrLVHWOiBwLHOMu+sun+fkK+lxlHWhlYwKsU8YYf1qCU6ZV3d99R1VfFJEpOBVmFBjhtwozqjoxkH/FXfSXqvqtMwOcXCy/Ag1E5DPgBOAyTyMqvOuAj9zcMgLswn9teMH9NwonYeMinLa0B+YCZf6CM0gv4DK3std+cqZg+abCDBw1uXHSvA6gmER5HUAxKfNJ+w+VtFREvsszcrEsWiYiFwLh7nH8ZuB3j2MqjDpuifjTReRL8nQQ+6wTP6ArOd+1nUTEjyXv/f65Mh6x6kvG+IyIXAk8DEzCOQj3xZmv+r6ngRWBW32pD06njG+qL4nIAFWdJCIF5itR1dEFLS/LRKQ6TilmAf70awJHEakCoKpJXsdSVCIyGhgZGGUiIm2BR1T1bG8jO3wi0qig5X4reS8iv5KTGyd7aqKqvnDAF5URITby6pDKepn1wxUK7fBDdTU3iewDwBB30TjgCVVN9S6qwyciZ+Pk5usFzCF3p4yq6gBPAiuiECp5H/y5EpzP1eN++VwZ71injDE+IyJ/AccHymm6F9O/q+oxB39l2RJUfek7nAPXWYAvqi+JyKOqOlJEPijgaVXVy0s9qCMkIu3JPxrAN51LIhKLUwq0Mbnb4KsTOgARWaaqbQ61zJQ8EVmqqm29jqMo3HwfcICRV36Y6lMYodCZAaHRjrLeBhEJByaoan+vYzlSIvKQqj7udRxHKlRK3ge4N4hUVfd4HYvxB5u+ZIz/7ASCv+T3uMv8xrfVl1R1pPvrY6q6Nvg5PyYKFJH3cS7UlpEz91nJKVfuBz8Df+JM5/P7/O3FIvIu8Kn7+D/AYg/jOZr5NjdO4ILTHXnVKe/IKw9DKymhkt8nVNpRZqlqpohkiUiMqvq9PPkkEamoqvtE5CKc8vYv+21UIiFS8l5EuuJUg6zsPk4ELlfVeZ4GZso865Qxxn/+wUmQOwbnwvkMnIu428FXZTVDofrSdzgnQMG+BTp7EMuR6KGqrb0O4ghFqertXgdRTEbg5Mi5xX08DXjDu3COaqGQG+eY4E4lVV0qIq28DKiEXOx1AIdDRKLyTmUQkRpBU0Zf9iCs4uaHY/leYImIjMepJgX4cnTlG8Bx7nTwO4B3gY9xprb7SaiUvH8PJ/nydAAR6YVT2MJPxwzjAeuUMcZ/Vrs/AWPcf/1WRte31ZfcCgFtgJg8eWWq4M9kk3+ISGtVXe51IEfgE7dE/Fhyn9Dt8i6kolHVVBF5E/hZVf/yOp6j3FCvAygGITHyyv2ufRaohXPMCHSQBfJILfUwvMKYIyJXqeqfACIyHHgap5oiqvqhh7Edkjv152NV/c9BVruntOI5AqPx12jQA8lQVRWRM4BXVfU9EbnC66CK4BGvAygmmYEOGQBVnSEiGV4GZPzBcsoYYzzjJqIMVF+a4ZfqS+7Jz5nA6cCPQU/tAb5UVV9l2heRvjjt2IpPRwOIyA3Ak0ACOWVNVVWbehZUEYnI6cAooJyqNhGRDjhT5fx2x9CUASIShTPyqo+7aBrwht8ST4rIP8BpqrrC61iOhIi0w5neMAWoC1QHrlTVjV7GVRgiMgMYoKqhUrkrH59UkEJEpuJUTxyBs49vAxapajtPAysCN0F8C1Wd4CbMDfdbThYReQmnOuoXOOci5+GUK/8UQi/Buik+1iljjM+4yRvz7bh+y7QP2Z0yvXFygMz028FKRHqq6h9ex3Gk3Iud28mTj8VPc9JFZA3Qza9Vo4KJyDxgADAlUMFERJb48STblA0iEg009PPIKxGZqaplvlz04RCRM4FPcDry+6jqP95GVDgi8jHQCqczP3jqj1+mTx+SHypIAYhIbeBCYI6qTheRhkA/v5WSdke6Xg1UU9VmbjnpN1V1oMehFUpQgvWC+K4qlik9Nn3JGP+5M+j3KGA44LuhkSLyMHAOOdWXPhCRb1T1CW8jK5R4EfkfTilpxUlUfJuqrvE2rELbrqo/Hnq1Mu0fINnrIIpJuqomiuRKy2B3UEyRBI+8Avw88mquiHwF/EDuKYq+moIiIu/hlP5tjzNlaayIvKKqr3kbWaEEplGH4b+p04fLF9+5qroVeDHo8b84OWX85gagGzALQFVXiUgtb0MqvENV9BKRS1X1o9KKx/iHdcoY4zMFZHCf6SZG85v/AMcFhtCLyDPAQsBPnTKfA6/hlPMGOB9nyGp3zyIqmgUi8jnwP/x7sbMPWOjepQpug9+SNgIsE5ELgXD3buHNgK+mxJkyZSTOxc4UAFVd6McqcTg5u5KBIUHL/FYlDpwRiVe6pX/Xikh3gi6q/UBVHwUQkQqqGiqd4b4iIjNUtZeI7CF3B1KuXEs+sl9V0wI3I0QkAp90jBXSLYB1yph8rFPGGJ8RkWpBD8OALkCMR+Ecic04I30CeQ3KA5u8C6dIKqjqJ0GPPxWRuzyLpuiicToy/Hyx84P7EwpuAh7A2SZfAOOAxz2NyPhZSIy8UtURXsdQHFT1pTyPEwFfJWYVkZ44ifkrAQ3dyj/XqOr13kZWrMp0BSlV7eX+GyojlaaKyP1AtIgMBq7HuVEUasr058p4x3LKGOMzbmlWxfliTwfW4QxFn+FlXIdLRF7Bib8h0BUY7z4eDMxW1WEHeXmZIiLPAruBL8lJ6FYVZ6qALyv/mLJDRKrg3PH0VaJDU7a402UmAvfiTHe9GYhU1Ws9Dewwicjdqvpc0LEjF7+NhnNHvz0NtCaoWp+fkpKLyCzgbODHoLxXS1W1rbeRFR8RGaKqv3kdx6GIyCeqevGhlpV1IhKG0zk5BOf8dhzwrobYhaqIzFfVTl7HYcoeGyljjP/cA/yqqkki8hDQCX/l0pjr/jsP+D5o+ZTSD+WInev+e02e5efjXDz44iTbrc5yBU6Z7+CLhMs9C6qQRORUnNEkjXCObX4dwo2IdMWpzlLZfZwIXF7A1EVjDkfwyKvPcS52/DRNNFBtaS4+HOFTgA9wppT9F+iPUzUnzNOIikBVN+QZfZXpVSyFISJLKPhzlKvqoB86ZFxtgh+40346exRLkalqFvCO+xPKbKSMKZB1yhjjPw+q6tci0gunQsvzwBv4JI/J4SY480M5SlX1Y16GgnwCrAROBB7Dyffjt7KzLwHDgCUhcGftPeB6VZ0O4O7rH+AkBjXmsIlIOPCTm3zyAa/jKQpVDUxhWA7cDzQm5/xV8V9S02hVnSgi4la4e8StuPaw14EVwgYROR5QEYnEyZPhl2PGqV4HUBxE5D6c/SFaRJICi4E04G3PAiskEflaVc89UGdZoJMshMz0OgBTNlmnjDH+E7gbdQrwjqr+JCJ+uut5uMrsKBMRGaCqk0SkwKlWPkuQC9BcVc8RkTNU9SM36e90r4MqpA3A0hDokAHIDHTIAKjqDBHxXYU14z1VzRSRLBGJcXOX+NmnwF04iXKzPI7lSOx3p2qsEpEbcXKpVfI4psK6FngZqIeTH24cTvWcMs/tCPM9VX0aeFpEnlbV+7yO5wjc4v7r684yEbn9YM8HysWr6o2lE5HxG+uUMcZ/NonIWzg5WJ4VkfL4cOjzYSjLF9d9gUnAaQU857cEueDkJgJIEJG2wFbAb6Uo7wZ+FpGp5K6+5JuqJiISmGc+1d3HvyAnV9EUr+IyvrcXWCIi43GqlAH+y8UCbFfVH70OohjcAlTAye3zOM4Upks8jaiQVHUHzohK3xKRHsArQCuccvHhwD6/TXlV1ftEpCrQgtzTj6d5F9XhU9Ut7oi+Dw9VTrqMC5WEy8YjlujXGJ8RkQrASTjTNFaJSB2gnY/mPx+Wsp4Mzb3Tebaqfu11LEdKRK4EvgPaAR/i3LV9SFXf8jKuwhCR33AvPgm6ix4o3eoHbjnvA1FVHVBqwZiQISKXFrT8cKeSlhUiMhC4ACdpcXDHq686wUWkC85UskZApLs4O5eJH4hIU5yRMj1wOo7/AG5T1TWeBlYIIjIXJ//bNzhVLC8BWvpt1Il7/L4FqA8sxNkmf/jteCEiE4FhITCiz5gisU4ZY0yZJCILAlUdyioRmauqXbyOo6SJyKVl/QIu1Cp/HIwftocxxU1EPgWOBZaR0/GqfkpIDiAif1HANCw/TasRkT+B13BG84HTuXGTqvoitx3kHL9FZHGgQ8wP5x15ublYugJ/qmoHETkWeMpPlSwBRGQM0BGnIqdvR/SFQoel8YZNXzLGeEJEblHVlw+y7B4PwiqsCSJyJ/AVuU8iQq0U9i1AWe8E+NkvJUyLgR+2hykjQqEEs6urqh7jdRDFIBSmYVVQ1U+CHn8qInd5Fk3RJItIOWChiDwHbMGfU8FTVTVVRBCR8qq6UkT8uJ+Mxn9TvwvyOU6H5Vnu4/NxOi9902FpvGEjZYwxnihoepLf7lKJyNoCFqsPL3YOyg/bRUT2ABVxKk8EcuT4siT2ofhhe5iyQ0RmkFOC+TTcEsyq6qdqP4jIB8AoVV3udSxHIhSmYYnIs8Bu4Ety8l5VBUaBP25MiEgjIB4nn8xtQAzwmqqu9jSwQhKR73H26VtxKnLuBiJV9WQv4yoKEYkGGqrqX17HUlTBI6+Cli1S1eO8isn4g3XKGGNKlYhcAFwI9CJ3hZ/KQJaqDvQkMHNAZT2/z9HGtocpDBGZp6qdRWSJqrYLXuZ1bIUhIiuAZsBanM4MwWe5WCA0pmEd4IZEgC9uTBzGaF3fEZG+OJ1Lv6pqmtfxFIaInAY8D5RT1SYi0gF4TFVP9zaywnE7LBPInajfVx2Wxhs2fckYU9p+xxkmXAN4IWj5HmCxJxEVkYjcAHymqgnu46rABar6uqeBFT/xOoDDISKnA33ch1NUdayX8ZQgX2wPU2aEQglmcBLchwLfT8NS1SYHe15EBqvq+NKKp4guxcn9EeyyApaVaSLSDNioqoGOysY41b181SkDPAJ0w600qKoL3fwsfnOu++9V7r+B4/X5OJ00fmyTKQXWKWOMKVVuMsP1QE+vYykGV6nqa4EHqrpbRK4CfNUp45ZVH45zMpd9XFDVx9xfZ3oQVqGIyDM4yQ4/cxfdIiIn+K2SBoTG9jBlSt4SzANwLkh9xU+JcA/hdxFp7fdpWIfwLE7C1jInaLRuExEJzu1TBfDjKIbvgC4i0hx4GxiDk9fEb9OX0lU1USTXPYesA61chrUGrscZDa44I8LfUNVUT6MyZZ51yhhjPCEiw3BO3Grh3EkIDEX3Uw6QcBERdeeBikg4zvx0vxkDJALzCMpxEKCqN5Z6RIV3MtBBVbMAROQjYAHgu04ZQmN7mDJCVee4v+7FyT1hvNUDJ7msr6dhHUJZHs0XMqN1XVmqmiEiZwGvqOorIrLA66CKYJmIXIhzXtUCpxP5d49jKoqPgCTg/9zHFwIfkzOCxpgCWaeMMcYrzwGnqeoKrwM5Ar8CX4nIW+7ja9xlflNfVUNhakAsOXc6YzyM40iFyvYwZYCItMQpwdyI3COvBngW1NHtaNi3y2zCyuDRuiIShzPCEmCFqmZ4F1mRpbujfy7FSeQNEOlhPEV1E/AATkflF8A4nJF9ftNWVVsHPZ4sIqE8Ks4UE+uUMcZ4Jd7nHTLglO2+GrjOfTweeNe7cIrsdxFpp6pLvA7kCDwNLBCRyTh3afsA93obUpGFwvYwZcc3wJvAO0Cmx7Ec9UJoGpavicg5OIllp+AcM14RkbtU9VtPAyu8EcC1wJOqulZEmgCfHOI1ZY6qJgMPuIlyVVX3eB1TEc0XkR6q+ieAiHQH5nock/EBq75kjPGEiLwM1AZ+wKdlQYOJSDWcEQ6+G/7s3sVpjv+rmtQh567nbFXd6mU8RRUq28OUDX6stGT8TURGq+owr+M4GBFZBAxW1W3u45rAhFArXSwi36nqcK/jOBQR6Qq8j1OJE5wpvJer6jzvoio8t0rcMcC/7qKGwF9ABnYcNwdhnTLGGE+IyAcFLPZbWdApwOk4ow7nAduA31X1Ni/jKiwRaVTQcj/d0XXn009S1UT3cSzQT1V/8DKuogiF7WG853YUg5ObYRvwPbk7wP2Y1NSUAe4ok19VdY+IPAh0Ap5Q1fkeh3bYgkvEu4/DgEXBy0KBiCxQ1Y5ex3EoIrIYuEFVp7uPewGv+60T40DH7wA7jpsDsU4ZY4wposDJjohcCTRQ1ZEisthvJxGhQEQWqmqHPMt8cTJqTElwE8kquZOuZp/0qaqVZjVFEjjOuRfOTwCjgIdVtbvHoR02EXkOOA4nfwnAecBiVb3Hu6iKn4jMV9VOXsdxKAUdr/0SuzHFwXLKGGM8ISJRwBVAGyAqsNxPI2WACHfKzLk4CeqMd8IKWGbHOHPUUtUmACJyLs6ohiQReQhnVIMfE2iasiOQm+gU4G1V/UlEnvAyoCJQ4C2c0sXglJPu4V04R72pbtGEL3C2zXnAFBHpBOCnUVjGFIWNlDHGeEJEvgFW4pQLfAz4D071g1s8DawQ3CHcDwEzVfU6EWkKjPLD/O1QIyLvAwnAa+6iG4BqqnqZVzEZUxbkGdXwOE5yU1+NajBli4iMBTYBg3E6+VJw8nj5Jh9LQaMwQnGkq19GjLpJ+g9ErVqcCXXWKWOM8UTQ1J/ABUMkMF1V7U6VKTQRqYjTQTYI5y7beJxqFPs8DcwYjwV91z4NLFHVz/1yoWbKJhGpgFPae4mqrnJHjLZT1d88Du2QROQ64HqgKbA66KnKODdYLvIksBIiIkP8sF2MOdpZp4wxxhMiMltVu4nINJwTpK04d9p8k+dARFoCbwBxqtpWRNoDp6uq34ZxG2NCVCiMajBli4g8DkzDSWzvq45vEYkBqgJPA/cGPbXHT8mvRWQJQTmigp/Ch1V+ROQT4MagZP2NgPdVdaC3kRlTOqxTxhjjCTc57ndAO+BDoBLwkKq+5WVchSEiU4G7gLcCd51FZKmqtvU2sqOPiIwHzlHVBPdxVeBLVT3R08CM8ZifRzWYsklERgC9gZ7AHmA6ME1Vx3ga2FEk1Kr8iMg1wG3A7UA9nHOrO1T1f54GZkwpsU4ZY4wnRKSJqq491LKyTETmqGrX4KkABVUBMiXvAJUbbIqGMcaUEBGpjZPo/k6gqqpW9jgk42Nu3qvJwA6go6pu9TgkY0pNQdUqjDGmNHxXwLJvSz2KI7NDRJrhDiEWkbOBLd6GdNTKEpGGgQci0piCh3YbY4w5AiLyroj8jjN9NwI4G2dKkCllItJDROaIyF4RSRORTBFJ8jquwhKRi4H3gUtwRk//LCI2xdIcNaxcqDGmVInIsThlsGNEZFjQU1UIKo3tEzfglNE8VkQ2AWtxqkiZ0vcAMMOdUiY4Q+uv9jYkY4wJSdWBcJyKd7uAHaqa4WlER69XgfOBb4AuOJ0aLT2NqGiGA71UdRvwhYh8D3wEdPA0KmNKiU1fMsaUKhE5AzgTOB34MeipPTg5QH73Iq7CEJHb8yyKxhl5uA9AVV8s9aAMIlILpyNmAc422aaq07yNyhhjQpOItAJOxMkFEq6q9T0O6agjInNVtUtwOW8/T90VkQqqmuz+Xk5V07yOyZjSYCNljDGlyk0EOEZEeqrqH17HU0SBefPHAF2BMTijMy4GZnsV1NHMTRx9C1AfWAj0AP4ABngYljHGhBwRORVnNGIfIBaYhJPs15S+ZBEpBywUkedwplD7Lj2FiPQE3sMp+tDQnbp0DU51TmNCno2UMcZ4wj15eAKnPOuvQHvgNlX91NPACsEt532Kqu5xH1cGflLVPt5GdvRxy4N2Bf5U1Q7uNLmnVHXYIV5qjDGmEETkVZxOmOmqutnreI5mbhWmeKAczoilGOA1VV3taWCFJCKzcHIT/WjVLM3RyHc9qcaYkDFEVZOAU4F1QHOcEoh+EgcED61Nc5eZ0peqqqkAIlJeVVfijGQyxhhTjFT1RuBPoDWAiES7NyVM6TtTVVNVNUlVH1XV23HOq3xHVTfkWZTpSSDGeMA6ZYwxXol0/z0F+EZVE70Mpog+BmaLyCMi8ggwC6dqgCl9G0UkFvgBGC8iY4D1nkZkjDEhSESuwqmW+Ja7qD7Od68pfZcWsOyy0g6iGGwQkeMBFZFIEbkTWOF1UMaUFpu+ZIzxhIg8g5PwNwXohjMvfayqdvcwrEITkU44c+sBpqnqAi/jMSAifXGGcP9qSQKNMaZ4ichCnOP2rKCpJktUtZ2ngR1FROQC4EKgF7nz+VQBMlV1oCeBFZGI1ABeBgbh5Oj7DbhFVXd6GpgxpcQ6ZYwxnhGRakCiqmaKSAWgiqpu9TouY4wxxhRMRGapavdAlR8RiQDmB6r/mJLn5pJpAjwN3Bv01B5gcaiVKBeR+1T1aa/jMKakWKeMMcYz7lDVxgRVglPVjz0LyBhjjDEH5SbqTwAuAW7CqZCzXFUf8DKuo5WIxOEkugeYrarbvIynJIjIfFXt5HUcxpQU65QxxnhCRD4BmuGULw4kc1NVvdmzoIwxxhhzUCISBlwBDMGZajIOeFftoqLUicg5wPPAFJxt0Ru4S1W/9TKu4hYYleV1HMaUFOuUMcZ4QkRWAK3tJM4YY4wxpvBEZBEwODA6RkRqAhNU9ThvIyteNlLGhLqIQ69ijDElYilQG9jidSDGGGOMOTgR+VpVzxWRJUC+GyqWU8YTYXmmK+0kNKvritcBGFOSrFPGGOOVGsByEZkN7A8sVNXTvQvJGGOMMQdwi/vvqZ5GYYL9IiLjgC/cx+cBP3sYT5GIyAmqOvMgy77xICxjSo1NXzLGeMItW5yPqk4t7ViMMcYYc3hE5HbgS1Xd7HUsRzsReRaYhVMaG5zy2D1U9R7voiq8gqYn2ZQlczSxThljjDHGGGPMYRGRkcC5wC7gK+AbVY33Nqqj0wE6Mxb7ZSqZiPQEjgduBf4b9FQV4KxQy41jzIGE4pxDY0wZJiIz3H/3iEhS0M8eEUnyOj5jjDHGHJiqPqqqbYAbgDrAVBGZ4HFYRxURuc7N7XOMiCwO+lkLLPY6vkIoB1TCSalROegnCTjbw7iMKVU2UsYYY4wxxhhTKCJSGzgHOB+o7JfRGaFARGKAqsDTwL1BT+1R1V3eRFV0ItJIVde7v4cBlVTVbtSZo4Z1yhhjjDHGGGMOi4hcjzN9qSZOAtavVXW5t1EZPxORz4FrgUxgDs70pZdVdZSngRlTSmz6kjHGGGOMMeZwNQBuVdU2qvqIdciYYtDaHRlzJvAL0AS42NOIjClF1iljjDHGGGOMOSyqeh9QSURGAIhITRFp4nFYxt8iRSQSp1PmR1VNB2w6hzlqWKeMMcYYY4wx5rC41ZfuAe5zF0UCn3oXkQkBbwHrgIrANBFphJPs15ijguWUMcYYY4wxxhwWEVkIdATmq2pHd5lvyjAbfxCRCFXN8DoOY0qDjZQxxhhjjDHGHK40de7qKoCIVPQ4HuNzIhIjIi+KyFz35wWcUTPGHBWsU8YYY4wxxhhzSCIiwFgReQuIFZGrgAnAO95GZnzufWAPTlWvc3GmLn3gaUTGlCKbvmSMMcYYY4w5LCKyBLgdGAIIME5Vx3sblfEzEVmoqh0OtcyYUBXhdQDGGGOMMcYY35gPJKjqXV4HYkJGioj0UtUZACJyApDicUzGlBobKWOMMcYYY4w5LCKyEmgOrAf2BZZbol9TVCLSAfgIiHEX7QYuVdXFngVlTCmyThljjDHGGGPMYXHLFeejqutLOxYTGkSkPHA20AyIBRIBVdXHvIzLmNJi05eMMcYYY4wxh8U6X0wJGAMk4EyN2+RtKMaUPhspY4wxxhhjjDHGEyKyVFXbeh2HMV6xktjGGGOMMcYYY7zyu4i08zoIY7xiI2WMMcYYY4wxxpQqt7y64qTUaAGsAfbjlFpXSx5tjhbWKWOMMcYYY4wxplQdKGl0gOUvMkcL65QxxhhjjDHGGGOM8YDllDHGGGOMMcYYY4zxgHXKGGOMMcYYY4wxxnjAOmWMMcYYY4wxxhhjPGCdMsYYY4wxxhhjjDEesE4ZY4wxxhhjjDHGGA/8PxwOtJWPCvqUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "sns.heatmap(X_Spring.corr(), annot=True, cmap=\"Blues_r\")\n",
    "plt.title('Features Correlation Spring Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scaled Feature (reviews_per_month) Spring Data')"
      ]
     },
     "execution_count": 2228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "Spring_df.hist('reviews_per_month', log = True)  #(X.corr(), annot=True, cmap=\"Blues_r\")\n",
    "plt.title('Scaled Feature (reviews_per_month) Spring Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scaled Feature (total_reviews) Spring Data')"
      ]
     },
     "execution_count": 2229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "Spring_df.hist('reviews_per_month', log = True)  #(X.corr(), annot=True, cmap=\"Blues_r\")\n",
    "plt.title('Scaled Feature (total_reviews) Spring Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Split Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train1, X_test1, y_train1, y_test1 = train_test_split(X_Spring, y_Spring, test_size=0.20, random_state=42) # Spring Data\n",
    "# X_train2, X_test2, y_train2, y_test2 = train_test_split(X_Summer, y_Summer, test_size=0.20, random_state=42) # Summer Data\n",
    "# X_train3, X_test3, y_train3, y_test3 = train_test_split(X_Fall, y_Fall, test_size=0.20, random_state=42)     # Fall Data\n",
    "# X_train4, X_test4, y_train4, y_test4 = train_test_split(X_Winter, y_Winter, test_size=0.20, random_state=42) # Winter Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train1.shape, X_test1.shape, y_train1.shape, y_test1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SET BASELINE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spring Data Baseline (MAE):  56.83500987573483\n",
      "Summer Data Baseline (MAE):  64.1140395445497\n",
      "Fall Data Baseline (MAE):  65.48904128943289\n",
      "Winter Data Baseline (MAE):  68.61208085276675\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = [y_Spring.mean()] * len(y_Spring)\n",
    "y_pred2 = [y_Summer.mean()] * len(y_Summer)\n",
    "y_pred3 = [y_Fall.mean()] * len(y_Fall)\n",
    "y_pred4 = [y_Winter.mean()] * len(y_Winter)\n",
    "\n",
    "print('Spring Data Baseline (MAE): ',mean_absolute_error(y_Spring,y_pred1))\n",
    "print('Summer Data Baseline (MAE): ',mean_absolute_error(y_Summer,y_pred2))\n",
    "print('Fall Data Baseline (MAE): ',mean_absolute_error(y_Fall,y_pred3))\n",
    "print('Winter Data Baseline (MAE): ',mean_absolute_error(y_Winter,y_pred4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Models Pipeline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2233,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rfr1 = make_pipeline(\n",
    "    OneHotEncoder(),\n",
    "    SimpleImputer(strategy='median'),\n",
    "    StandardScaler(),\n",
    "    RandomForestRegressor(n_jobs=-1, random_state=42))\n",
    "\n",
    "model_rfr2 = make_pipeline(\n",
    "    OneHotEncoder(),\n",
    "    SimpleImputer(strategy='median'),\n",
    "    StandardScaler(),\n",
    "    RandomForestRegressor(n_jobs=-1, random_state=42))\n",
    "\n",
    "model_rfr3 = make_pipeline(\n",
    "    OneHotEncoder(),\n",
    "    SimpleImputer(strategy='median'),\n",
    "    StandardScaler(),\n",
    "    RandomForestRegressor(n_jobs=-1, random_state=42))\n",
    "\n",
    "model_rfr4 = make_pipeline(\n",
    "    OneHotEncoder(),\n",
    "    SimpleImputer(strategy='median'),\n",
    "    StandardScaler(),\n",
    "    RandomForestRegressor(n_jobs=-1, random_state=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Fitting Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2234,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_rfr1.fit(X_train1,y_train1);\n",
    "# model_rfr2.fit(X_train2,y_train2);\n",
    "# model_rfr3.fit(X_train3,y_train3);\n",
    "# model_rfr4.fit(X_train4,y_train4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Checking and Comparing Metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def check_metrics(model_list, model_names, X_train_list, X_test_list, y_train_list, y_test_list):\n",
    "#   # print each model results\n",
    "#     for model, name, X_train, X_test, y_train, y_test in zip(model_list, model_names, X_train_list, X_test_list, y_train_list, y_test_list):\n",
    "#       print(name)\n",
    "#       print('Training MAE:', mean_absolute_error(y_train, model.predict(X_train)))\n",
    "#       print('Test MAE:', mean_absolute_error(y_test, model.predict(X_test)))\n",
    "#       print('Test R^2:', model.score(X_test, y_test))\n",
    "#       print()\n",
    "#       print()\n",
    "\n",
    "# models = [model_rfr1,model_rfr2,model_rfr3,model_rfr4]\n",
    "# models_names = ['RandomForestRegressor Spring:','RandomForestRegressor Summer:','RandomForestRegressor Fall:','RandomForestRegressor Winter:']\n",
    "# X_train_data = [X_train1, X_train2, X_train3, X_train4] \n",
    "# y_train_data = [y_train1, y_train2, y_train3, y_train4] \n",
    "# X_test_data = [X_test1, X_test2, X_test3, X_test4]\n",
    "# y_test_data = [y_test1, y_test2, y_test3, y_test4]\n",
    "\n",
    "\n",
    "# check_metrics(models, models_names, X_train_data, X_test_data, y_train_data, y_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importance_df['abs'] = [abs(i) for i in importance_df[0]]\n",
    "# importance_df.sort_values(by='abs',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2237,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred_train = model_la.predict(X_train_processed)\n",
    "# diff = y_train - y_pred_train\n",
    "# plt.figure(figsize=(14,10))\n",
    "# plt.scatter(np.arange(len(diff)),diff);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2238,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def check_metrics(model):\n",
    "#   #print(model)\n",
    "#   print('Training MAE:', mean_absolute_error(y_train, model.predict(X_train)))\n",
    "#   print('Test MAE:', mean_absolute_error(y_test, model.predict(X_test)))\n",
    "#   print('Test R^2:', model.score(X_test, y_test))\n",
    "#   print()\n",
    "#   print()\n",
    "\n",
    "# models = [model_lr, model_rd, model_rfr, model_xgb]\n",
    "# models_name = ['LinearRegression:','Ridge:','RandomForestRegressor','XGBRegressor:']\n",
    "# for i in range(4):\n",
    "#   print(models_name[i])\n",
    "#   check_metrics(models[i])\n",
    "    \n",
    "# LinearRegression:\n",
    "# Training MAE: 75.11048377427583\n",
    "# Test MAE: 74.0995566954852\n",
    "# Test R^2: 0.43788657500640593\n",
    "\n",
    "\n",
    "# Ridge:\n",
    "# Training MAE: 75.1104311013773\n",
    "# Test MAE: 74.09950469620095\n",
    "# Test R^2: 0.4378865254712687\n",
    "\n",
    "\n",
    "# RandomForestRegressor\n",
    "# Training MAE: 19.63839671554341\n",
    "# Test MAE: 52.93756419855532\n",
    "# Test R^2: 0.6586319303160858\n",
    "\n",
    "\n",
    "# XGBRegressor:\n",
    "# Training MAE: 51.04689117492001\n",
    "# Test MAE: 55.166844811515766\n",
    "# Test R^2: 0.6447322467459573"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Lists to Plot Models\n",
    "# models = [model_lr, model_rd, model_rfr, model_xgb]\n",
    "# models_type = ['Linear', 'Ridge', 'RForest', 'XGB']\n",
    "# Training_MAEs = []\n",
    "# Test_MAEs = []\n",
    "# models_R2s = []\n",
    "\n",
    "# def listing_metrics(model):\n",
    "#   # Fill lists to plot and compare models\n",
    "#   Training_MAEs.append(mean_absolute_error(y_train, model.predict(X_train)))\n",
    "#   Test_MAEs.append(mean_absolute_error(y_test, model.predict(X_test)))\n",
    "#   models_R2s.append(model.score(X_test, y_test))\n",
    "\n",
    "# for m in models:\n",
    "#   listing_metrics(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot Models MAEs\n",
    "# w = 0.4\n",
    "# bar1 = np.arange(len(models_type))\n",
    "# bar2 = [i + w for i in bar1]\n",
    "\n",
    "\n",
    "# plt.figure(figsize=(9,5.5))\n",
    "# plt.bar(bar1, Training_MAEs, w, label='Training MAE')\n",
    "# plt.bar(bar2, Test_MAEs, w, label='Test MAE')\n",
    "\n",
    "# plt.xlabel('Models')\n",
    "# plt.ylabel('Mean Absolute Error')\n",
    "# plt.xticks(bar1+(w/2), models_type)\n",
    "# plt.legend()\n",
    "# plt.title('Models MAE (Train-Test Data)')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2241,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot Models R^2s\n",
    "# w = 0.4\n",
    "# bar1 = np.arange(len(models_type))\n",
    "\n",
    "# plt.figure(figsize=(5,5.5))\n",
    "# plt.bar(bar1, models_R2s, w, label='R^2')\n",
    "\n",
    "# plt.xlabel('Models')\n",
    "# plt.ylabel('R-Square Values')\n",
    "# plt.xticks(bar1, models_type)\n",
    "# plt.legend()\n",
    "# plt.title('Models R2 (Test-Data)')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Tunning Random Forest Regressor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "CPU times: total: 3min 2s\n",
      "Wall time: 2min 26s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=Pipeline(steps=[('onehotencoder',\n",
       "                                        OneHotEncoder(cols=['room_type'])),\n",
       "                                       ('simpleimputer',\n",
       "                                        SimpleImputer(strategy='median')),\n",
       "                                       ('standardscaler', StandardScaler()),\n",
       "                                       ('randomforestregressor',\n",
       "                                        RandomForestRegressor(n_jobs=-1,\n",
       "                                                              random_state=42))]),\n",
       "             param_grid={'randomforestregressor__bootstrap': [True],\n",
       "                         'randomforestregressor__max_depth': [None],\n",
       "                         'randomforestregressor__max_features': ['log2'],\n",
       "                         'randomforestregressor__max_samples': [None],\n",
       "                         'randomforestregressor__min_samples_leaf': [1],\n",
       "                         'randomforestregressor__min_samples_split': range(2, 3),\n",
       "                         'randomforestregressor__n_estimators': range(105, 106)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 2242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "param_grid = {\"randomforestregressor__n_estimators\": range(105,106),\n",
    "              \"randomforestregressor__min_samples_split\": range(2,3),\n",
    "              \"randomforestregressor__min_samples_leaf\": [1],\n",
    "              \"randomforestregressor__max_samples\":  [None],\n",
    "              \"randomforestregressor__max_features\":['log2'],\n",
    "              \"randomforestregressor__max_depth\": [None],#range(27,32),  \n",
    "              \"randomforestregressor__bootstrap\": [True]\n",
    "              \n",
    "             }\n",
    "\n",
    "\n",
    "model_rfr_search1 = GridSearchCV(model_rfr, param_grid= param_grid, cv=10, verbose=1)\n",
    "model_rfr_search2 = GridSearchCV(model_rfr, param_grid= param_grid, cv=10, verbose=1)\n",
    "model_rfr_search3 = GridSearchCV(model_rfr, param_grid= param_grid, cv=10, verbose=1)\n",
    "model_rfr_search4 = GridSearchCV(model_rfr, param_grid= param_grid, cv=10, verbose=1)\n",
    "\n",
    "# Fit it to the data\n",
    "model_rfr_search1.fit(X_Spring, y_Spring);\n",
    "model_rfr_search2.fit(X_Summer, y_Summer);\n",
    "model_rfr_search3.fit(X_Fall, y_Fall);\n",
    "model_rfr_search4.fit(X_Winter, y_Winter);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2250,
   "metadata": {},
   "outputs": [],
   "source": [
    "M_spring = model_rfr_search1.best_estimator_\n",
    "M_summer = model_rfr_search2.best_estimator_\n",
    "M_fall = model_rfr_search3.best_estimator_\n",
    "M_winter = model_rfr_search4.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModelSpring\n",
      "Training MAE: 12.916228130596501\n",
      "Test MAE: 12.787486666525385\n",
      "Test R^2: 0.9406753148082793\n",
      "\n",
      "\n",
      "ModelSummer\n",
      "Training MAE: 14.384056421501416\n",
      "Test MAE: 14.023164132615351\n",
      "Test R^2: 0.942170642279377\n",
      "\n",
      "\n",
      "ModelFall\n",
      "Training MAE: 14.664364934670312\n",
      "Test MAE: 14.584466765873016\n",
      "Test R^2: 0.9413607560308604\n",
      "\n",
      "\n",
      "ModelWinter\n",
      "Training MAE: 14.8680968531113\n",
      "Test MAE: 15.341615530914702\n",
      "Test R^2: 0.9400988753905036\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = [M_spring, M_summer, M_fall, M_winter]\n",
    "models_names = ['ModelSpring','ModelSummer','ModelFall','ModelWinter']\n",
    "\n",
    "check_metrics(models, models_names, X_train_data, X_test_data, y_train_data, y_test_data)\n",
    "\n",
    "# print('Test MAE:', mean_absolute_error(y_test1, model_rfr_search1.predict(X_test1)))\n",
    "# print('Test R^2:', model_rfr_search.score(X_test1,y_test1))\n",
    "\n",
    "# ModelSpring\n",
    "# Training MAE: 12.946564300373362\n",
    "# Test MAE: 12.814512000715828\n",
    "# Test R^2: 0.9393268866860071\n",
    "\n",
    "\n",
    "# ModelSummer\n",
    "# Training MAE: 14.354514616583154\n",
    "# Test MAE: 14.020025824446556\n",
    "# Test R^2: 0.941747919906727\n",
    "\n",
    "\n",
    "# ModelFall\n",
    "# Training MAE: 14.661181391426215\n",
    "# Test MAE: 14.618241980820105\n",
    "# Test R^2: 0.9405215682554102\n",
    "\n",
    "\n",
    "# ModelWinter\n",
    "# Training MAE: 14.928769417577358\n",
    "# Test MAE: 15.345949410493494\n",
    "# Test R^2: 0.9401690129643316"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1955,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importances = model_rfr_search.best_estimator_.named_steps.randomforestregressor.feature_importances_\n",
    "# features = model_rfr_search.best_estimator_.named_steps.onehotencoder.feature_names\n",
    "\n",
    "# imp_df = pd.DataFrame(importances, index=features)\n",
    "# imp_df.columns = ['Importances']\n",
    "\n",
    "# imp_df.sort_values(by='Importances', ascending=False).head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2253,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model,name in zip(models, models_names):\n",
    "    \n",
    "    joblib.dump(model,name, compress=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # save the models\n",
    "# import pickle\n",
    "\n",
    "# for model,name in zip(models, models_names):\n",
    "    \n",
    "#     pickle.dump(model, open(name, \"wb\"))\n",
    "\n",
    "# # # load the models\n",
    "# # model = pickle.load(open(\"model.pkl\", \"rb\"))\n",
    "\n",
    "# # # use model to predict\n",
    "# # y_pred = model.predict(X_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2254,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = joblib.load(\"ModelSpring\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2258,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_values_example = Spring_df.drop(columns=['price'])[:][58:59] # Right Price $73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 19)"
      ]
     },
     "execution_count": 2261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_values_example.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([61.5047619])"
      ]
     },
     "execution_count": 2260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(user_values_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2015,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 2015,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "229-198"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1956,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {\"ridge__alpha\": [1000,100,50,20,10,1,0.1,0.01],\n",
    "#               \"ridge__fit_intercept\": [True,False]\n",
    "#              }\n",
    "\n",
    "# model_rd_search = GridSearchCV(\n",
    "#     model_rd,\n",
    "#     param_grid = param_grid,\n",
    "#     cv=5,\n",
    "#     verbose=1# status updates\n",
    "# )\n",
    "\n",
    "# # Fit it to the data\n",
    "# model_rd_search.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1957,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_rd_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1958,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check_metrics(model_rd_search)\n",
    "\n",
    "# print('Test MAE:', mean_absolute_error(y_test, model_rd_search.predict(X_test)))\n",
    "# print('Test R^2:', model_rd_search.score(X_test,y_test))"
   ]
  }
 ],
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