Coding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **PakEstimate 🏠**\n",
    "\n",
    "## _**Name: Ahmad Talha Ansari**_\n",
    "## _**Email: ahmadtalha963@gmail.com**_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **The First View 🫵**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>index</th>\n",
       "      <th>property_name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Location</th>\n",
       "      <th>Bath(s)</th>\n",
       "      <th>Area</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Bedroom(s)</th>\n",
       "      <th>Added</th>\n",
       "      <th>...</th>\n",
       "      <th>Business and Communication</th>\n",
       "      <th>Community Features</th>\n",
       "      <th>Healthcare Recreational</th>\n",
       "      <th>Nearby Locations and Other Facilities</th>\n",
       "      <th>Other Facilities\\r\\n</th>\n",
       "      <th>Plot Features</th>\n",
       "      <th>level_0</th>\n",
       "      <th>Initial Amount</th>\n",
       "      <th>Monthly Installment</th>\n",
       "      <th>Remaining Installments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35197</th>\n",
       "      <td>https://zameen.com/Property/lahore_mominpur_g...</td>\n",
       "      <td>Google Property Offer 5 Marla Half Triple Stor...</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR90 Lakh</td>\n",
       "      <td>Mominpur, Lahore, Punjab</td>\n",
       "      <td>5</td>\n",
       "      <td>5 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>4</td>\n",
       "      <td>3 years ago</td>\n",
       "      <td>...</td>\n",
       "      <td>['Broadband Internet Access', 'Satellite or Ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Nearby Schools', 'Nearby Hospitals', 'Nearby...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17134</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_p...</td>\n",
       "      <td>1 Kanal House For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR11.5 Crore</td>\n",
       "      <td>DHA Defence, Lahore, Punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>2 months ago</td>\n",
       "      <td>...</td>\n",
       "      <td>['Broadband Internet Access', 'Satellite or Ca...</td>\n",
       "      <td>['Community Lawn or Garden', 'First Aid or Med...</td>\n",
       "      <td>['Lawn or Garden', 'Other Healthcare and Recre...</td>\n",
       "      <td>['Nearby Schools', 'Nearby Hospitals', 'Nearby...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16510</th>\n",
       "      <td>https://zameen.com/Property/dha_phase_6_dha_p...</td>\n",
       "      <td>1 Kanal Full Basement Brand New House for Sale...</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR14 Crore</td>\n",
       "      <td>DHA Defence, Lahore, Punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>1 week ago</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33585</th>\n",
       "      <td>https://zameen.com/Property/lahore_abdali_roa...</td>\n",
       "      <td>Triple Storey House Is Available For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR1.2 Crore</td>\n",
       "      <td>Abdali Road, Lahore, Punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>3.5 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>4</td>\n",
       "      <td>4 years ago</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   index   \n",
       "35197   https://zameen.com/Property/lahore_mominpur_g...  \\\n",
       "17134   https://zameen.com/Property/dha_defence_dha_p...   \n",
       "16510   https://zameen.com/Property/dha_phase_6_dha_p...   \n",
       "33585   https://zameen.com/Property/lahore_abdali_roa...   \n",
       "\n",
       "                                           property_name   Type   \n",
       "35197  Google Property Offer 5 Marla Half Triple Stor...  House  \\\n",
       "17134                             1 Kanal House For Sale  House   \n",
       "16510  1 Kanal Full Basement Brand New House for Sale...  House   \n",
       "33585          Triple Storey House Is Available For Sale  House   \n",
       "\n",
       "               Price                     Location Bath(s)       Area   \n",
       "35197     PKR90 Lakh     Mominpur, Lahore, Punjab       5    5 Marla  \\\n",
       "17134  PKR11.5 Crore  DHA Defence, Lahore, Punjab       6    1 Kanal   \n",
       "16510    PKR14 Crore  DHA Defence, Lahore, Punjab       6    1 Kanal   \n",
       "33585   PKR1.2 Crore  Abdali Road, Lahore, Punjab       -  3.5 Marla   \n",
       "\n",
       "        Purpose Bedroom(s)         Added  ...   \n",
       "35197  For Sale          4   3 years ago  ...  \\\n",
       "17134  For Sale          6  2 months ago  ...   \n",
       "16510  For Sale          6    1 week ago  ...   \n",
       "33585  For Sale          4   4 years ago  ...   \n",
       "\n",
       "                              Business and Communication   \n",
       "35197  ['Broadband Internet Access', 'Satellite or Ca...  \\\n",
       "17134  ['Broadband Internet Access', 'Satellite or Ca...   \n",
       "16510                                                NaN   \n",
       "33585                                                NaN   \n",
       "\n",
       "                                      Community Features   \n",
       "35197                                                NaN  \\\n",
       "17134  ['Community Lawn or Garden', 'First Aid or Med...   \n",
       "16510                                                NaN   \n",
       "33585                                                NaN   \n",
       "\n",
       "                                 Healthcare Recreational   \n",
       "35197                                                NaN  \\\n",
       "17134  ['Lawn or Garden', 'Other Healthcare and Recre...   \n",
       "16510                                                NaN   \n",
       "33585                                                NaN   \n",
       "\n",
       "                   Nearby Locations and Other Facilities Other Facilities\\r\\n   \n",
       "35197  ['Nearby Schools', 'Nearby Hospitals', 'Nearby...                  NaN  \\\n",
       "17134  ['Nearby Schools', 'Nearby Hospitals', 'Nearby...                  NaN   \n",
       "16510                                                NaN                  NaN   \n",
       "33585                                                NaN                  NaN   \n",
       "\n",
       "      Plot Features level_0 Initial Amount Monthly Installment   \n",
       "35197           NaN     NaN            NaN                 NaN  \\\n",
       "17134           NaN     NaN            NaN                 NaN   \n",
       "16510           NaN     NaN            NaN                 NaN   \n",
       "33585           NaN     NaN            NaN                 NaN   \n",
       "\n",
       "       Remaining Installments  \n",
       "35197                     NaN  \n",
       "17134                     NaN  \n",
       "16510                     NaN  \n",
       "33585                     NaN  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/House_Buy.csv\")\n",
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    No. of rows: 50719\n",
      "    No. of cols: 23\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "# shape of data\n",
    "rows, cols = df.shape\n",
    "print(f\"\"\"\n",
    "    No. of rows: {rows}\n",
    "    No. of cols: {cols}\n",
    "    \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of all columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['index',\n",
       " 'property_name',\n",
       " 'Type',\n",
       " 'Price',\n",
       " 'Location',\n",
       " 'Bath(s)',\n",
       " 'Area',\n",
       " 'Purpose',\n",
       " 'Bedroom(s)',\n",
       " 'Added',\n",
       " 'description',\n",
       " 'Main Features',\n",
       " 'Rooms',\n",
       " 'Business and Communication',\n",
       " 'Community Features',\n",
       " 'Healthcare Recreational',\n",
       " 'Nearby Locations and Other Facilities',\n",
       " 'Other Facilities\\r\\n',\n",
       " 'Plot Features',\n",
       " 'level_0',\n",
       " 'Initial Amount',\n",
       " 'Monthly Installment',\n",
       " 'Remaining Installments']"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column names\n",
    "print(\"Name of all columns\")\n",
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50719 entries, 0 to 50718\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   index                                  50719 non-null  object \n",
      " 1   property_name                          50715 non-null  object \n",
      " 2   Type                                   50719 non-null  object \n",
      " 3   Price                                  50719 non-null  object \n",
      " 4   Location                               50719 non-null  object \n",
      " 5   Bath(s)                                50719 non-null  object \n",
      " 6   Area                                   50719 non-null  object \n",
      " 7   Purpose                                50719 non-null  object \n",
      " 8   Bedroom(s)                             50719 non-null  object \n",
      " 9   Added                                  50719 non-null  object \n",
      " 10  description                            50719 non-null  object \n",
      " 11  Main Features                          31754 non-null  object \n",
      " 12  Rooms                                  33965 non-null  object \n",
      " 13  Business and Communication             21545 non-null  object \n",
      " 14  Community Features                     19403 non-null  object \n",
      " 15  Healthcare Recreational                16332 non-null  object \n",
      " 16  Nearby Locations and Other Facilities  24342 non-null  object \n",
      " 17  Other Facilities\n",
      "                     12868 non-null  object \n",
      " 18  Plot Features                          1111 non-null   object \n",
      " 19  level_0                                275 non-null    float64\n",
      " 20  Initial Amount                         305 non-null    object \n",
      " 21  Monthly Installment                    305 non-null    object \n",
      " 22  Remaining Installments                 305 non-null    object \n",
      "dtypes: float64(1), object(22)\n",
      "memory usage: 8.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# a brief info of every feature\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                                     0.000000\n",
       "property_name                             0.007887\n",
       "Type                                      0.000000\n",
       "Price                                     0.000000\n",
       "Location                                  0.000000\n",
       "Bath(s)                                   0.000000\n",
       "Area                                      0.000000\n",
       "Purpose                                   0.000000\n",
       "Bedroom(s)                                0.000000\n",
       "Added                                     0.000000\n",
       "description                               0.000000\n",
       "Main Features                            37.392299\n",
       "Rooms                                    33.032986\n",
       "Business and Communication               57.520850\n",
       "Community Features                       61.744120\n",
       "Healthcare Recreational                  67.799050\n",
       "Nearby Locations and Other Facilities    52.006152\n",
       "Other Facilities\\r\\n                     74.628837\n",
       "Plot Features                            97.809499\n",
       "level_0                                  99.457797\n",
       "Initial Amount                           99.398647\n",
       "Monthly Installment                      99.398647\n",
       "Remaining Installments                   99.398647\n",
       "dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of null values\n",
    "(df.isnull().sum() / df.shape[0])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Remove all columns/features having more than 50% null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1017"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preserving original DataFrame.\n",
    "df_clean = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Let's start cleaning process. 👨‍🦯**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Removing all duplicates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Original DataFrame\n",
      "    No. of rows: 50719\n",
      "    No. of cols: 23\n",
      "    \n",
      "\n",
      "    DataFrame without any duplicates\n",
      "    No. of rows: 49702\n",
      "    No. of cols: 23\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "# remove all duplicates first\n",
    "df_clean.drop_duplicates(inplace=True, keep='first')\n",
    "\n",
    "\n",
    "print(f\"\"\"\n",
    "    Original DataFrame\n",
    "    No. of rows: {rows}\n",
    "    No. of cols: {cols}\n",
    "    \"\"\")\n",
    "\n",
    "rows, cols = df_clean.shape\n",
    "print(f\"\"\"\n",
    "    DataFrame without any duplicates\n",
    "    No. of rows: {rows}\n",
    "    No. of cols: {cols}\n",
    "    \"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Lowercase Conversion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['url',\n",
       " 'property_name',\n",
       " 'type',\n",
       " 'price',\n",
       " 'location',\n",
       " 'baths',\n",
       " 'area',\n",
       " 'purpose',\n",
       " 'bedrooms',\n",
       " 'added',\n",
       " 'description',\n",
       " 'main_features',\n",
       " 'rooms',\n",
       " 'business_and_communication',\n",
       " 'community_features',\n",
       " 'healthcare_recreational',\n",
       " 'nearby_locations_and_other_facilities',\n",
       " 'other_facilities\\r\\n',\n",
       " 'plot_features',\n",
       " 'level_0',\n",
       " 'initial_amount',\n",
       " 'monthly_installment',\n",
       " 'remaining_installments']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column names lowercase conversion\n",
    "columns = df_clean.columns\n",
    "column_mapper = {}\n",
    "for col in columns:\n",
    "    updated_col = col.lower().replace(\"(s)\", \"s\")\n",
    "    updated_col = \"_\".join(updated_col.split(\" \"))\n",
    "    column_mapper[col] = updated_col\n",
    "column_mapper[\"index\"] = \"url\"\n",
    "df_clean.rename(columns=column_mapper, inplace=True)\n",
    "# column names\n",
    "df_clean.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Removing columns having more than 50% null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['business_and_communication', 'community_features',\n",
      "       'healthcare_recreational', 'nearby_locations_and_other_facilities',\n",
      "       'other_facilities\\r\\n', 'plot_features', 'level_0', 'initial_amount',\n",
      "       'monthly_installment', 'remaining_installments'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "null_percentage = (df_clean.isnull().sum() / df_clean.shape[0])\n",
    "cols_tobe_removed = null_percentage[null_percentage > 0.5].index\n",
    "print(cols_tobe_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49702, 13)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove columns with more than 50% null\n",
    "df_clean.drop(columns=cols_tobe_removed, inplace=True)\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Location column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        DHA Defence, Islamabad, Islamabad Capital\n",
       "1        DHA Defence, Islamabad, Islamabad Capital\n",
       "2        DHA Defence, Islamabad, Islamabad Capital\n",
       "3        DHA Defence, Islamabad, Islamabad Capital\n",
       "4        DHA Defence, Islamabad, Islamabad Capital\n",
       "                           ...                    \n",
       "50714            Jinnah Colony, Rawalpindi, Punjab\n",
       "50715            Jinnah Colony, Rawalpindi, Punjab\n",
       "50716            Jinnah Colony, Rawalpindi, Punjab\n",
       "50717            Jinnah Colony, Rawalpindi, Punjab\n",
       "50718            Jinnah Colony, Rawalpindi, Punjab\n",
       "Name: location, Length: 49702, dtype: object"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how it looks?\n",
    "df_clean['location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "DHA Defence, Lahore, Punjab                    5332\n",
       "Bahria Town Rawalpindi, Rawalpindi, Punjab     3005\n",
       "DHA Defence, Islamabad, Islamabad Capital      1769\n",
       "Bahria Town, Lahore, Punjab                    1701\n",
       "G-13, Islamabad, Islamabad Capital              735\n",
       "                                               ... \n",
       "Women Housing Society, Lahore, Punjab             2\n",
       "Usmania Colony, Lahore, Punjab                    2\n",
       "Daultala, Rawalpindi, Punjab                      2\n",
       "Pind Begwal, Islamabad, Islamabad Capital         1\n",
       "Baghban Pura Housing Scheme, Lahore, Punjab       1\n",
       "Name: count, Length: 1010, dtype: int64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts...\n",
    "df_clean['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversion to lowercase\n",
    "df_clean['location'] = df_clean['location'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The location column can be splitted into town, city and province. Let's create three new features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['city'] = df_clean['location'].apply(lambda x: x.split(\",\")[1])\n",
    "df_clean['province'] = df_clean['location'].apply(lambda x: x.split(\",\")[2])\n",
    "df_clean['town'] = df_clean['location'].apply(lambda x: x.split(\",\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>town</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dha defence</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dha defence</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dha defence</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dha defence</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dha defence</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50714</th>\n",
       "      <td>jinnah colony</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50715</th>\n",
       "      <td>jinnah colony</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50716</th>\n",
       "      <td>jinnah colony</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50717</th>\n",
       "      <td>jinnah colony</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50718</th>\n",
       "      <td>jinnah colony</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49702 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                town         city            province\n",
       "0        dha defence    islamabad   islamabad capital\n",
       "1        dha defence    islamabad   islamabad capital\n",
       "2        dha defence    islamabad   islamabad capital\n",
       "3        dha defence    islamabad   islamabad capital\n",
       "4        dha defence    islamabad   islamabad capital\n",
       "...              ...          ...                 ...\n",
       "50714  jinnah colony   rawalpindi              punjab\n",
       "50715  jinnah colony   rawalpindi              punjab\n",
       "50716  jinnah colony   rawalpindi              punjab\n",
       "50717  jinnah colony   rawalpindi              punjab\n",
       "50718  jinnah colony   rawalpindi              punjab\n",
       "\n",
       "[49702 rows x 3 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's examine new features\n",
    "df_clean[['town', 'city', 'province']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Bath column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baths\n",
       "6        15951\n",
       "4         9078\n",
       "5         7942\n",
       "3         6500\n",
       "-         6041\n",
       "2         2775\n",
       "1          558\n",
       "7          502\n",
       "8          220\n",
       "10          70\n",
       "9           64\n",
       "5,555        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['baths'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 6041 instances contain \"-\".\n",
    "2. 1 instance contains \"5,555\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>HUGE VILLA WITH TRIPPLE UNIT CORNER SEPRATE AC...</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR13 Crore</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>-</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>8</td>\n",
       "      <td>1 week ago</td>\n",
       "      <td>HOUSE AVAILABLE FOR SALE IN DHA PHASE 2 THIS H...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 4', '...</td>\n",
       "      <td>['Bedrooms: 8', 'Bathrooms: 10', 'Servant Quar...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>1 kanal luxury house for sale in DHA phase 2 i...</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR11.9 Crore</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>-</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>2 weeks ago</td>\n",
       "      <td>1 Kanal elegant house for Sale DHA-2 Islamabad...</td>\n",
       "      <td>['Flooring']</td>\n",
       "      <td>['Bedrooms: 6', 'Bathrooms: 7', 'Servant Quart...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>1 Kanal Elegant house For Sale - Sector A - DH...</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR11.3 Crore</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>-</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>9</td>\n",
       "      <td>2 days ago</td>\n",
       "      <td>Your dream home awaits!Looking for the perfect...</td>\n",
       "      <td>['Built in year: 2022', 'Parking Spaces', 'Flo...</td>\n",
       "      <td>['Bedrooms: 9', 'Bathrooms: 12', 'Servant Quar...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>Beautiful House At Vip 70ft Street</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR11.98 Crore</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>-</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>1 week ago</td>\n",
       "      <td>Top Class Location70 Ft Wide StreetBrand New O...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 3', '...</td>\n",
       "      <td>['Bedrooms: 6', 'Bathrooms: 8', 'Servant Quart...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>Ultra Modern Designer Luxury House at Outstand...</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR11.9 Crore</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>-</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>3 weeks ago</td>\n",
       "      <td>1 Kanal brand New House Double Unit 5 Bed TV L...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 3', '...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50705</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_jinnah...</td>\n",
       "      <td>Single Storey House Is Available For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR45 Lakh</td>\n",
       "      <td>jinnah colony, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>3 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>2</td>\n",
       "      <td>6 years ago</td>\n",
       "      <td>House for sale. Ideal Location. D/D, TV Lounge...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Bedrooms: 2']</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>jinnah colony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50706</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_jinnah...</td>\n",
       "      <td>3 Unit House Is Available For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR1.3 Crore</td>\n",
       "      <td>jinnah colony, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>7 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>6 years ago</td>\n",
       "      <td>House for sale. Ideal Location. D/D, TV Lounge...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Bedrooms: 6']</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>jinnah colony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50707</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_jinnah...</td>\n",
       "      <td>Double Storey House for Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR1.6 Crore</td>\n",
       "      <td>jinnah colony, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>10 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>8</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>House is available for sale. Location: Jinnah ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Bedrooms: 8']</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>jinnah colony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50716</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_jinnah...</td>\n",
       "      <td>Residential Plot Is Available For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR37 Lakh</td>\n",
       "      <td>jinnah colony, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>7 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>6 years ago</td>\n",
       "      <td>Plot For Sale. Good location. Street No: 205 B...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>jinnah colony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50718</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_jinnah...</td>\n",
       "      <td>6 Marla 3 Storey House For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR90 Lakh</td>\n",
       "      <td>jinnah colony, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>6 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>8</td>\n",
       "      <td>13 years ago</td>\n",
       "      <td>Beautiful house of 6 marla with all basic faci...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>jinnah colony</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6041 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "34      https://zameen.com/Property/dha_defence_dha_d...  \\\n",
       "84      https://zameen.com/Property/dha_defence_dha_d...   \n",
       "87      https://zameen.com/Property/dha_defence_dha_d...   \n",
       "138     https://zameen.com/Property/dha_defence_dha_d...   \n",
       "182     https://zameen.com/Property/dha_defence_dha_d...   \n",
       "...                                                  ...   \n",
       "50705   https://zameen.com/Property/rawalpindi_jinnah...   \n",
       "50706   https://zameen.com/Property/rawalpindi_jinnah...   \n",
       "50707   https://zameen.com/Property/rawalpindi_jinnah...   \n",
       "50716   https://zameen.com/Property/rawalpindi_jinnah...   \n",
       "50718   https://zameen.com/Property/rawalpindi_jinnah...   \n",
       "\n",
       "                                           property_name   type   \n",
       "34     HUGE VILLA WITH TRIPPLE UNIT CORNER SEPRATE AC...  House  \\\n",
       "84     1 kanal luxury house for sale in DHA phase 2 i...  House   \n",
       "87     1 Kanal Elegant house For Sale - Sector A - DH...  House   \n",
       "138                   Beautiful House At Vip 70ft Street  House   \n",
       "182    Ultra Modern Designer Luxury House at Outstand...  House   \n",
       "...                                                  ...    ...   \n",
       "50705          Single Storey House Is Available For Sale  House   \n",
       "50706                 3 Unit House Is Available For Sale  House   \n",
       "50707                       Double Storey House for Sale  House   \n",
       "50716             Residential Plot Is Available For Sale  House   \n",
       "50718                    6 Marla 3 Storey House For Sale  House   \n",
       "\n",
       "                price                                   location baths   \n",
       "34        PKR13 Crore  dha defence, islamabad, islamabad capital     -  \\\n",
       "84      PKR11.9 Crore  dha defence, islamabad, islamabad capital     -   \n",
       "87      PKR11.3 Crore  dha defence, islamabad, islamabad capital     -   \n",
       "138    PKR11.98 Crore  dha defence, islamabad, islamabad capital     -   \n",
       "182     PKR11.9 Crore  dha defence, islamabad, islamabad capital     -   \n",
       "...               ...                                        ...   ...   \n",
       "50705      PKR45 Lakh          jinnah colony, rawalpindi, punjab     -   \n",
       "50706    PKR1.3 Crore          jinnah colony, rawalpindi, punjab     -   \n",
       "50707    PKR1.6 Crore          jinnah colony, rawalpindi, punjab     -   \n",
       "50716      PKR37 Lakh          jinnah colony, rawalpindi, punjab     -   \n",
       "50718      PKR90 Lakh          jinnah colony, rawalpindi, punjab     -   \n",
       "\n",
       "           area   purpose bedrooms         added   \n",
       "34      1 Kanal  For Sale        8    1 week ago  \\\n",
       "84      1 Kanal  For Sale        6   2 weeks ago   \n",
       "87      1 Kanal  For Sale        9    2 days ago   \n",
       "138     1 Kanal  For Sale        6    1 week ago   \n",
       "182     1 Kanal  For Sale        5   3 weeks ago   \n",
       "...         ...       ...      ...           ...   \n",
       "50705   3 Marla  For Sale        2   6 years ago   \n",
       "50706   7 Marla  For Sale        6   6 years ago   \n",
       "50707  10 Marla  For Sale        8   7 years ago   \n",
       "50716   7 Marla  For Sale        -   6 years ago   \n",
       "50718   6 Marla  For Sale        8  13 years ago   \n",
       "\n",
       "                                             description   \n",
       "34     HOUSE AVAILABLE FOR SALE IN DHA PHASE 2 THIS H...  \\\n",
       "84     1 Kanal elegant house for Sale DHA-2 Islamabad...   \n",
       "87     Your dream home awaits!Looking for the perfect...   \n",
       "138    Top Class Location70 Ft Wide StreetBrand New O...   \n",
       "182    1 Kanal brand New House Double Unit 5 Bed TV L...   \n",
       "...                                                  ...   \n",
       "50705  House for sale. Ideal Location. D/D, TV Lounge...   \n",
       "50706  House for sale. Ideal Location. D/D, TV Lounge...   \n",
       "50707  House is available for sale. Location: Jinnah ...   \n",
       "50716  Plot For Sale. Good location. Street No: 205 B...   \n",
       "50718  Beautiful house of 6 marla with all basic faci...   \n",
       "\n",
       "                                           main_features   \n",
       "34     ['Built in year: 2023', 'Parking Spaces: 4', '...  \\\n",
       "84                                          ['Flooring']   \n",
       "87     ['Built in year: 2022', 'Parking Spaces', 'Flo...   \n",
       "138    ['Built in year: 2023', 'Parking Spaces: 3', '...   \n",
       "182    ['Built in year: 2023', 'Parking Spaces: 3', '...   \n",
       "...                                                  ...   \n",
       "50705                                                NaN   \n",
       "50706                                                NaN   \n",
       "50707                                                NaN   \n",
       "50716                                                NaN   \n",
       "50718                                                NaN   \n",
       "\n",
       "                                                   rooms         city   \n",
       "34     ['Bedrooms: 8', 'Bathrooms: 10', 'Servant Quar...    islamabad  \\\n",
       "84     ['Bedrooms: 6', 'Bathrooms: 7', 'Servant Quart...    islamabad   \n",
       "87     ['Bedrooms: 9', 'Bathrooms: 12', 'Servant Quar...    islamabad   \n",
       "138    ['Bedrooms: 6', 'Bathrooms: 8', 'Servant Quart...    islamabad   \n",
       "182    ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...    islamabad   \n",
       "...                                                  ...          ...   \n",
       "50705                                    ['Bedrooms: 2']   rawalpindi   \n",
       "50706                                    ['Bedrooms: 6']   rawalpindi   \n",
       "50707                                    ['Bedrooms: 8']   rawalpindi   \n",
       "50716                                                NaN   rawalpindi   \n",
       "50718                                                NaN   rawalpindi   \n",
       "\n",
       "                 province           town  \n",
       "34      islamabad capital    dha defence  \n",
       "84      islamabad capital    dha defence  \n",
       "87      islamabad capital    dha defence  \n",
       "138     islamabad capital    dha defence  \n",
       "182     islamabad capital    dha defence  \n",
       "...                   ...            ...  \n",
       "50705              punjab  jinnah colony  \n",
       "50706              punjab  jinnah colony  \n",
       "50707              punjab  jinnah colony  \n",
       "50716              punjab  jinnah colony  \n",
       "50718              punjab  jinnah colony  \n",
       "\n",
       "[6041 rows x 16 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows where bath column contains '-''\n",
    "df_clean[df_clean['baths'] == '-']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33807</th>\n",
       "      <td>https://zameen.com/Property/lahore_circular_r...</td>\n",
       "      <td>House Is Available For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>PKR5.5 Arab</td>\n",
       "      <td>circular road, lahore, punjab</td>\n",
       "      <td>5,555</td>\n",
       "      <td>175.1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5,555</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>House available for sale. Best option for livi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Bedrooms: 5555', 'Bathrooms: 5555', 'Drawing...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>circular road</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "33807   https://zameen.com/Property/lahore_circular_r...  \\\n",
       "\n",
       "                     property_name   type        price   \n",
       "33807  House Is Available For Sale  House  PKR5.5 Arab  \\\n",
       "\n",
       "                            location  baths         area   purpose bedrooms   \n",
       "33807  circular road, lahore, punjab  5,555  175.1 Kanal  For Sale    5,555  \\\n",
       "\n",
       "             added                                        description   \n",
       "33807  7 years ago  House available for sale. Best option for livi...  \\\n",
       "\n",
       "      main_features                                              rooms   \n",
       "33807           NaN  ['Bedrooms: 5555', 'Bathrooms: 5555', 'Drawing...  \\\n",
       "\n",
       "          city province           town  \n",
       "33807   lahore   punjab  circular road  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row where bath column contains '-'\n",
    "df_clean[df_clean['baths'] == '5,555']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's remove the row displayed in above code output\n",
    "df_clean.drop(index=33807, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will work on this column later on in this notebook.**\n",
    "\n",
    "We know that 6041 rows contain '-', so we need to extract the information of baths from other columns like 'description' column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Bedrooms column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        5\n",
       "2        5\n",
       "3        6\n",
       "4        5\n",
       "        ..\n",
       "50714    4\n",
       "50715    4\n",
       "50716    -\n",
       "50717    8\n",
       "50718    8\n",
       "Name: bedrooms, Length: 49701, dtype: object"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['bedrooms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bedrooms\n",
       "5      13244\n",
       "3      10421\n",
       "4       9349\n",
       "6       6083\n",
       "-       3934\n",
       "2       3201\n",
       "7       1663\n",
       "8        652\n",
       "1        433\n",
       "9        312\n",
       "10       253\n",
       "11       126\n",
       "12        20\n",
       "13         3\n",
       "17         3\n",
       "25         1\n",
       "100        1\n",
       "16         1\n",
       "33         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['bedrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 3934 instances contain \"-\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will work on this column later on in this notebook.**\n",
    "\n",
    "Similar to bathroom case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Price column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          PKR7.1 Crore\n",
       "1         PKR6.25 Crore\n",
       "2        PKR13.95 Crore\n",
       "3          PKR9.9 Crore\n",
       "4        PKR13.95 Crore\n",
       "              ...      \n",
       "50714      PKR1.5 Crore\n",
       "50715     PKR1.63 Crore\n",
       "50716        PKR37 Lakh\n",
       "50717     PKR1.15 Crore\n",
       "50718        PKR90 Lakh\n",
       "Name: price, Length: 49701, dtype: object"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['price'] = df_clean['price'].str[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_clean['price_unit'] = df_clean['price'].apply(lambda x: x.split(\" \")[-1].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>price_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31039</th>\n",
       "      <td>https://zameen.com/Property/lahore_bahaduraba...</td>\n",
       "      <td>0</td>\n",
       "      <td>House</td>\n",
       "      <td>tact for price</td>\n",
       "      <td>bahadurabad, lahore, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>bahadurabad</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31040</th>\n",
       "      <td>https://zameen.com/Property/lahore_bahaduraba...</td>\n",
       "      <td>0</td>\n",
       "      <td>House</td>\n",
       "      <td>tact for price</td>\n",
       "      <td>bahadurabad, lahore, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>bahadurabad</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47775</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_chah_s...</td>\n",
       "      <td>Chah Sultan, Rawalpindi 9260472</td>\n",
       "      <td>House</td>\n",
       "      <td>tact for price</td>\n",
       "      <td>chah sultan, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>6 years ago</td>\n",
       "      <td>Chah Sultan, Rawalpindi 9260472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>chah sultan</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "31039   https://zameen.com/Property/lahore_bahaduraba...  \\\n",
       "31040   https://zameen.com/Property/lahore_bahaduraba...   \n",
       "47775   https://zameen.com/Property/rawalpindi_chah_s...   \n",
       "\n",
       "                         property_name   type           price   \n",
       "31039                                0  House  tact for price  \\\n",
       "31040                                0  House  tact for price   \n",
       "47775  Chah Sultan, Rawalpindi 9260472  House  tact for price   \n",
       "\n",
       "                              location baths area   purpose bedrooms   \n",
       "31039      bahadurabad, lahore, punjab     -    -  For Sale        -  \\\n",
       "31040      bahadurabad, lahore, punjab     -    -  For Sale        -   \n",
       "47775  chah sultan, rawalpindi, punjab     -    -  For Sale        -   \n",
       "\n",
       "             added                      description main_features rooms   \n",
       "31039  7 years ago                                0           NaN   NaN  \\\n",
       "31040  7 years ago                                0           NaN   NaN   \n",
       "47775  6 years ago  Chah Sultan, Rawalpindi 9260472           NaN   NaN   \n",
       "\n",
       "              city province         town price_unit  \n",
       "31039       lahore   punjab  bahadurabad      price  \n",
       "31040       lahore   punjab  bahadurabad      price  \n",
       "47775   rawalpindi   punjab  chah sultan      price  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows where price column contains 'tact'\n",
    "df_clean[df_clean['price'].str.contains('tact')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>price_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7627</th>\n",
       "      <td>https://zameen.com/Property/islamabad_eden_li...</td>\n",
       "      <td>5 Marla House Is Available For Sale</td>\n",
       "      <td>House</td>\n",
       "      <td>1</td>\n",
       "      <td>eden life islamabad, islamabad, islamabad capital</td>\n",
       "      <td>3</td>\n",
       "      <td>5 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>2</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>5 Marla House Is Available For Sale  Having be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>eden life islamabad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20728</th>\n",
       "      <td>https://zameen.com/Property/gulberg_2_gulberg...</td>\n",
       "      <td>Looking For A Facing Park House In Lahore</td>\n",
       "      <td>House</td>\n",
       "      <td>600</td>\n",
       "      <td>gulberg, lahore, punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>5 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>1</td>\n",
       "      <td>2 months ago</td>\n",
       "      <td>All kinds of properties are available for sale...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>gulberg</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20729</th>\n",
       "      <td>https://zameen.com/Property/gulberg_2_gulberg...</td>\n",
       "      <td>Facing Park House For sale In Gulberg 2 - Block G</td>\n",
       "      <td>House</td>\n",
       "      <td>600</td>\n",
       "      <td>gulberg, lahore, punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>5 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>1</td>\n",
       "      <td>2 months ago</td>\n",
       "      <td>This can be the right property for you to buy ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>gulberg</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31039</th>\n",
       "      <td>https://zameen.com/Property/lahore_bahaduraba...</td>\n",
       "      <td>0</td>\n",
       "      <td>House</td>\n",
       "      <td>tact for price</td>\n",
       "      <td>bahadurabad, lahore, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>bahadurabad</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31040</th>\n",
       "      <td>https://zameen.com/Property/lahore_bahaduraba...</td>\n",
       "      <td>0</td>\n",
       "      <td>House</td>\n",
       "      <td>tact for price</td>\n",
       "      <td>bahadurabad, lahore, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>7 years ago</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>bahadurabad</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47775</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_chah_s...</td>\n",
       "      <td>Chah Sultan, Rawalpindi 9260472</td>\n",
       "      <td>House</td>\n",
       "      <td>tact for price</td>\n",
       "      <td>chah sultan, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>6 years ago</td>\n",
       "      <td>Chah Sultan, Rawalpindi 9260472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>chah sultan</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "7627    https://zameen.com/Property/islamabad_eden_li...  \\\n",
       "20728   https://zameen.com/Property/gulberg_2_gulberg...   \n",
       "20729   https://zameen.com/Property/gulberg_2_gulberg...   \n",
       "31039   https://zameen.com/Property/lahore_bahaduraba...   \n",
       "31040   https://zameen.com/Property/lahore_bahaduraba...   \n",
       "47775   https://zameen.com/Property/rawalpindi_chah_s...   \n",
       "\n",
       "                                           property_name   type   \n",
       "7627                 5 Marla House Is Available For Sale  House  \\\n",
       "20728          Looking For A Facing Park House In Lahore  House   \n",
       "20729  Facing Park House For sale In Gulberg 2 - Block G  House   \n",
       "31039                                                  0  House   \n",
       "31040                                                  0  House   \n",
       "47775                    Chah Sultan, Rawalpindi 9260472  House   \n",
       "\n",
       "                price                                           location   \n",
       "7627                1  eden life islamabad, islamabad, islamabad capital  \\\n",
       "20728             600                            gulberg, lahore, punjab   \n",
       "20729             600                            gulberg, lahore, punjab   \n",
       "31039  tact for price                        bahadurabad, lahore, punjab   \n",
       "31040  tact for price                        bahadurabad, lahore, punjab   \n",
       "47775  tact for price                    chah sultan, rawalpindi, punjab   \n",
       "\n",
       "      baths     area   purpose bedrooms         added   \n",
       "7627      3  5 Marla  For Sale        2   5 years ago  \\\n",
       "20728     2  5 Marla  For Sale        1  2 months ago   \n",
       "20729     2  5 Marla  For Sale        1  2 months ago   \n",
       "31039     -        -  For Sale        -   7 years ago   \n",
       "31040     -        -  For Sale        -   7 years ago   \n",
       "47775     -        -  For Sale        -   6 years ago   \n",
       "\n",
       "                                             description main_features rooms   \n",
       "7627   5 Marla House Is Available For Sale  Having be...           NaN   NaN  \\\n",
       "20728  All kinds of properties are available for sale...           NaN   NaN   \n",
       "20729  This can be the right property for you to buy ...           NaN   NaN   \n",
       "31039                                                  0           NaN   NaN   \n",
       "31040                                                  0           NaN   NaN   \n",
       "47775                    Chah Sultan, Rawalpindi 9260472           NaN   NaN   \n",
       "\n",
       "              city            province                 town price_unit  \n",
       "7627     islamabad   islamabad capital  eden life islamabad          1  \n",
       "20728       lahore              punjab              gulberg        600  \n",
       "20729       lahore              punjab              gulberg        600  \n",
       "31039       lahore              punjab          bahadurabad      price  \n",
       "31040       lahore              punjab          bahadurabad      price  \n",
       "47775   rawalpindi              punjab          chah sultan      price  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows where price column contains un-realistic data\n",
    "df_clean.loc[[7627, 20728, 20729, 31039, 31040, 47775]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's remove them\n",
    "df_clean.drop(index=[7627, 20728, 20729, 31039, 31040, 47775], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Crore', 'Lakh', 'Thousand', 'Arab'], dtype=object)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique price unit\n",
    "df_clean['price_unit'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting value from price column\n",
    "df_clean['price'] = df_clean['price'].apply(lambda x: x.split(\" \")[0]).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>price_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14024</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_p...</td>\n",
       "      <td>1 Kanal Brand New Spanish Design Top Location ...</td>\n",
       "      <td>House</td>\n",
       "      <td>7.95</td>\n",
       "      <td>dha defence, lahore, punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>HARMAIN REAL ESTATE OFFERS' HIDDEN OUT OF MARK...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 4', '...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18810</th>\n",
       "      <td>https://zameen.com/Property/bahria_town_bahri...</td>\n",
       "      <td>Beautiful 10 Marla House For Sale In Sector C ...</td>\n",
       "      <td>House</td>\n",
       "      <td>4.00</td>\n",
       "      <td>bahria town, lahore, punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>10 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>2 weeks ago</td>\n",
       "      <td>Dear Valued CustomerAssalam O alaikumLuxurious...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 2', '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>bahria town</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18434</th>\n",
       "      <td>https://zameen.com/Property/bahria_town_secto...</td>\n",
       "      <td>10 Marla LDA Approved Brand New House For Sale...</td>\n",
       "      <td>House</td>\n",
       "      <td>3.95</td>\n",
       "      <td>bahria town, lahore, punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>10 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>1 month ago</td>\n",
       "      <td>Meta Properties offers best investment opportu...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces', 'Flo...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>bahria town</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34156</th>\n",
       "      <td>https://zameen.com/Property/lahore_captain_ja...</td>\n",
       "      <td>Brand New Double Storey House Is Available For...</td>\n",
       "      <td>House</td>\n",
       "      <td>52.00</td>\n",
       "      <td>captain jamal road, lahore, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>2 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>3</td>\n",
       "      <td>4 years ago</td>\n",
       "      <td>House Is Available For Sale. Ideal Location. R...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Bedrooms: 3']</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>captain jamal road</td>\n",
       "      <td>Lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37429</th>\n",
       "      <td>https://zameen.com/Property/township_township...</td>\n",
       "      <td>20 Marla Semi Commercial House Haider Road tow...</td>\n",
       "      <td>House</td>\n",
       "      <td>7.00</td>\n",
       "      <td>township, lahore, punjab</td>\n",
       "      <td>5</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>4</td>\n",
       "      <td>3 weeks ago</td>\n",
       "      <td>Township A1Haider RoadSemi commercial House1 K...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>township</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "14024   https://zameen.com/Property/dha_defence_dha_p...  \\\n",
       "18810   https://zameen.com/Property/bahria_town_bahri...   \n",
       "18434   https://zameen.com/Property/bahria_town_secto...   \n",
       "34156   https://zameen.com/Property/lahore_captain_ja...   \n",
       "37429   https://zameen.com/Property/township_township...   \n",
       "\n",
       "                                           property_name   type  price   \n",
       "14024  1 Kanal Brand New Spanish Design Top Location ...  House   7.95  \\\n",
       "18810  Beautiful 10 Marla House For Sale In Sector C ...  House   4.00   \n",
       "18434  10 Marla LDA Approved Brand New House For Sale...  House   3.95   \n",
       "34156  Brand New Double Storey House Is Available For...  House  52.00   \n",
       "37429  20 Marla Semi Commercial House Haider Road tow...  House   7.00   \n",
       "\n",
       "                                 location baths      area   purpose bedrooms   \n",
       "14024         dha defence, lahore, punjab     6   1 Kanal  For Sale        5  \\\n",
       "18810         bahria town, lahore, punjab     4  10 Marla  For Sale        5   \n",
       "18434         bahria town, lahore, punjab     6  10 Marla  For Sale        5   \n",
       "34156  captain jamal road, lahore, punjab     -   2 Marla  For Sale        3   \n",
       "37429            township, lahore, punjab     5   1 Kanal  For Sale        4   \n",
       "\n",
       "             added                                        description   \n",
       "14024    1 day ago  HARMAIN REAL ESTATE OFFERS' HIDDEN OUT OF MARK...  \\\n",
       "18810  2 weeks ago  Dear Valued CustomerAssalam O alaikumLuxurious...   \n",
       "18434  1 month ago  Meta Properties offers best investment opportu...   \n",
       "34156  4 years ago  House Is Available For Sale. Ideal Location. R...   \n",
       "37429  3 weeks ago  Township A1Haider RoadSemi commercial House1 K...   \n",
       "\n",
       "                                           main_features   \n",
       "14024  ['Built in year: 2023', 'Parking Spaces: 4', '...  \\\n",
       "18810  ['Built in year: 2023', 'Parking Spaces: 2', '...   \n",
       "18434  ['Built in year: 2023', 'Parking Spaces', 'Flo...   \n",
       "34156                                                NaN   \n",
       "37429                                                NaN   \n",
       "\n",
       "                                                   rooms     city province   \n",
       "14024  ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...   lahore   punjab  \\\n",
       "18810                                                NaN   lahore   punjab   \n",
       "18434  ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...   lahore   punjab   \n",
       "34156                                    ['Bedrooms: 3']   lahore   punjab   \n",
       "37429                                                NaN   lahore   punjab   \n",
       "\n",
       "                     town price_unit  \n",
       "14024         dha defence      Crore  \n",
       "18810         bahria town      Crore  \n",
       "18434         bahria town      Crore  \n",
       "34156  captain jamal road       Lakh  \n",
       "37429            township      Crore  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate the price and its unit\n",
    "df_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting price col to one range i.e thousand\n",
    "price_units = df_clean['price_unit'].unique().tolist()\n",
    "for price_unit in price_units:\n",
    "    if price_unit == 'Crore':\n",
    "        df_clean.loc[df_clean['price_unit'] == 'Crore', 'price'] = df_clean.loc[df_clean['price_unit'] == 'Crore', 'price']*10000000\n",
    "    elif price_unit == 'Thousand':\n",
    "        df_clean.loc[df_clean['price_unit'] == 'Thousand', 'price'] = df_clean.loc[df_clean['price_unit'] == 'Thousand', 'price']*1000\n",
    "    elif price_unit == 'Arab':\n",
    "        df_clean.loc[df_clean['price_unit'] == 'Arab', 'price'] = df_clean.loc[df_clean['price_unit'] == 'Arab', 'price']*1000000000\n",
    "    elif price_unit == 'Lakh':\n",
    "        df_clean.loc[df_clean['price_unit'] == 'Lakh', 'price'] = df_clean.loc[df_clean['price_unit'] == 'Lakh', 'price']*100000\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>price_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16620</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_p...</td>\n",
       "      <td>1 Kanal Brand New Modern Design House Availabl...</td>\n",
       "      <td>House</td>\n",
       "      <td>78000000.0</td>\n",
       "      <td>dha defence, lahore, punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>2 days ago</td>\n",
       "      <td>Leppon Estate &amp; Builders Offers:1 Kanal Brand ...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 4', '...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33101</th>\n",
       "      <td>https://zameen.com/Property/lahore_kasur_road...</td>\n",
       "      <td>4 Marla Dabal Storey House Available For Sale ...</td>\n",
       "      <td>House</td>\n",
       "      <td>5500000.0</td>\n",
       "      <td>lahore - kasur road, lahore, punjab</td>\n",
       "      <td>3</td>\n",
       "      <td>4 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>3</td>\n",
       "      <td>1 year ago</td>\n",
       "      <td>4 Marla Double Storey House Available For Sale...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>lahore - kasur road</td>\n",
       "      <td>Lakh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14872</th>\n",
       "      <td>https://zameen.com/Property/dha_phase_3_dha_p...</td>\n",
       "      <td>1 kanal house for sale in DHA phase 3 Xx Block...</td>\n",
       "      <td>House</td>\n",
       "      <td>82500000.0</td>\n",
       "      <td>dha defence, lahore, punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>4 days ago</td>\n",
       "      <td>Bhatti Associates Offers1 kanal basement house...</td>\n",
       "      <td>['Parking Spaces: 4', 'Double Glazed Windows',...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13861</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_p...</td>\n",
       "      <td>2 Kanal Brand New Spanish Design Fully Furnish...</td>\n",
       "      <td>House</td>\n",
       "      <td>270000000.0</td>\n",
       "      <td>dha defence, lahore, punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>1 day ago</td>\n",
       "      <td>Yasir Brothers Offers:2 Kanal Bungalow Details...</td>\n",
       "      <td>['Built in year: 2023', 'Parking Spaces: 5', '...</td>\n",
       "      <td>['Bedrooms: 6', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9616</th>\n",
       "      <td>https://zameen.com/Property/islamabad_e-11_br...</td>\n",
       "      <td>Brand New Double Storey 8 Marla House Availabl...</td>\n",
       "      <td>House</td>\n",
       "      <td>75000000.0</td>\n",
       "      <td>e-11, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>8 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>4 weeks ago</td>\n",
       "      <td>8 Marla brand new double story house for sale ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>e-11</td>\n",
       "      <td>Crore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "16620   https://zameen.com/Property/dha_defence_dha_p...  \\\n",
       "33101   https://zameen.com/Property/lahore_kasur_road...   \n",
       "14872   https://zameen.com/Property/dha_phase_3_dha_p...   \n",
       "13861   https://zameen.com/Property/dha_defence_dha_p...   \n",
       "9616    https://zameen.com/Property/islamabad_e-11_br...   \n",
       "\n",
       "                                           property_name   type        price   \n",
       "16620  1 Kanal Brand New Modern Design House Availabl...  House   78000000.0  \\\n",
       "33101  4 Marla Dabal Storey House Available For Sale ...  House    5500000.0   \n",
       "14872  1 kanal house for sale in DHA phase 3 Xx Block...  House   82500000.0   \n",
       "13861  2 Kanal Brand New Spanish Design Fully Furnish...  House  270000000.0   \n",
       "9616   Brand New Double Storey 8 Marla House Availabl...  House   75000000.0   \n",
       "\n",
       "                                  location baths     area   purpose bedrooms   \n",
       "16620          dha defence, lahore, punjab     6  1 Kanal  For Sale        5  \\\n",
       "33101  lahore - kasur road, lahore, punjab     3  4 Marla  For Sale        3   \n",
       "14872          dha defence, lahore, punjab     6  1 Kanal  For Sale        5   \n",
       "13861          dha defence, lahore, punjab     6  1 Kanal  For Sale        6   \n",
       "9616    e-11, islamabad, islamabad capital     6  8 Marla  For Sale        5   \n",
       "\n",
       "             added                                        description   \n",
       "16620   2 days ago  Leppon Estate & Builders Offers:1 Kanal Brand ...  \\\n",
       "33101   1 year ago  4 Marla Double Storey House Available For Sale...   \n",
       "14872   4 days ago  Bhatti Associates Offers1 kanal basement house...   \n",
       "13861    1 day ago  Yasir Brothers Offers:2 Kanal Bungalow Details...   \n",
       "9616   4 weeks ago  8 Marla brand new double story house for sale ...   \n",
       "\n",
       "                                           main_features   \n",
       "16620  ['Built in year: 2023', 'Parking Spaces: 4', '...  \\\n",
       "33101                                                NaN   \n",
       "14872  ['Parking Spaces: 4', 'Double Glazed Windows',...   \n",
       "13861  ['Built in year: 2023', 'Parking Spaces: 5', '...   \n",
       "9616                                                 NaN   \n",
       "\n",
       "                                                   rooms        city   \n",
       "16620  ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...      lahore  \\\n",
       "33101                                                NaN      lahore   \n",
       "14872  ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...      lahore   \n",
       "13861  ['Bedrooms: 6', 'Bathrooms: 6', 'Servant Quart...      lahore   \n",
       "9616                                                 NaN   islamabad   \n",
       "\n",
       "                 province                 town price_unit  \n",
       "16620              punjab          dha defence      Crore  \n",
       "33101              punjab  lahore - kasur road       Lakh  \n",
       "14872              punjab          dha defence      Crore  \n",
       "13861              punjab          dha defence      Crore  \n",
       "9616    islamabad capital                 e-11      Crore  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Added column**\n",
    "\n",
    "It is necessary to remove all those houses data which were added before 2.5 years because price of house also  \n",
    "depends on dollar rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "added_time_unit\n",
       "weeks      10830\n",
       "years      10457\n",
       "days       10068\n",
       "months      4421\n",
       "week        4024\n",
       "month       3364\n",
       "hours       2821\n",
       "year        1884\n",
       "day         1627\n",
       "minutes      133\n",
       "hour          62\n",
       "minute         4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no need of 'ago'\n",
    "df_clean['added'] = df_clean['added'].str.replace(\" ago\", \"\")\n",
    "# extract the time\n",
    "df_clean[\"added_time\"] = df_clean[\"added\"].apply(lambda x: int(x.split(\" \")[0]))\n",
    "# extract time unit (days, weeks, months and years)\n",
    "df_clean['added_time_unit'] = df_clean['added'].apply(lambda x: x.split(\" \")[-1])\n",
    "df_clean['added_time_unit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>added</th>\n",
       "      <th>added_time</th>\n",
       "      <th>added_time_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42872</th>\n",
       "      <td>1 week</td>\n",
       "      <td>1</td>\n",
       "      <td>week</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21572</th>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "      <td>days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23225</th>\n",
       "      <td>1 month</td>\n",
       "      <td>1</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         added  added_time added_time_unit\n",
       "42872   1 week           1            week\n",
       "21572   4 days           4            days\n",
       "23225  1 month           1           month"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[['added', 'added_time', 'added_time_unit']].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "added_time_unit\n",
       "weeks      14854\n",
       "years      12341\n",
       "days       11695\n",
       "months      7785\n",
       "hours       2883\n",
       "minutes      137\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['added_time_unit'].replace({\"week\" : \"weeks\", \"month\" : \"months\",\n",
    "                            \"year\" : \"years\", \"hour\" : \"hours\", \"day\" : \"days\",\n",
    "                            \"minute\" : \"minutes\"}, inplace=True)\n",
    "df_clean['added_time_unit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting added_time to one range (days)\n",
    "df_clean.loc[df_clean['added_time_unit'] == 'hours', 'added_time'] = 1\n",
    "df_clean.loc[df_clean['added_time_unit'] == 'minutes', 'added_time'] = 1\n",
    "for unique_unit in df_clean[\"added_time_unit\"].unique().tolist():\n",
    "    if unique_unit == \"years\":\n",
    "        df_clean.loc[df_clean['added_time_unit'] == 'years', 'added_time'] = df_clean.loc[df_clean['added_time_unit'] == 'years', 'added_time']*365\n",
    "    elif unique_unit == \"months\":\n",
    "        df_clean.loc[df_clean['added_time_unit'] == 'months', 'added_time'] = df_clean.loc[df_clean['added_time_unit'] == 'months', 'added_time']*30\n",
    "    elif unique_unit == \"weeks\":\n",
    "        df_clean.loc[df_clean['added_time_unit'] == 'weeks', 'added_time'] = df_clean.loc[df_clean['added_time_unit'] == 'weeks', 'added_time']*7\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>added</th>\n",
       "      <th>added_time</th>\n",
       "      <th>added_time_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29239</th>\n",
       "      <td>1 year</td>\n",
       "      <td>365</td>\n",
       "      <td>years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4675</th>\n",
       "      <td>2 months</td>\n",
       "      <td>60</td>\n",
       "      <td>months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39794</th>\n",
       "      <td>2 months</td>\n",
       "      <td>60</td>\n",
       "      <td>months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32016</th>\n",
       "      <td>2 years</td>\n",
       "      <td>730</td>\n",
       "      <td>years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40026</th>\n",
       "      <td>2 weeks</td>\n",
       "      <td>14</td>\n",
       "      <td>weeks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          added  added_time added_time_unit\n",
       "29239    1 year         365           years\n",
       "4675   2 months          60          months\n",
       "39794  2 months          60          months\n",
       "32016   2 years         730           years\n",
       "40026   2 weeks          14           weeks"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[[\"added\", \"added_time\", \"added_time_unit\"]].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43328, 19)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data of only two years\n",
    "df_clean[df_clean[\"added_time\"] <= 365*2].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Area column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        10 Marla\n",
       "1        12 Marla\n",
       "2         1 Kanal\n",
       "3         1 Kanal\n",
       "4         1 Kanal\n",
       "           ...   \n",
       "50714    10 Marla\n",
       "50715     7 Marla\n",
       "50716     7 Marla\n",
       "50717    10 Marla\n",
       "50718     6 Marla\n",
       "Name: area, Length: 49695, dtype: object"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how looks\n",
    "df_clean[\"area\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area\n",
       "5 Marla      10704\n",
       "10 Marla      7935\n",
       "1 Kanal       7460\n",
       "3 Marla       3546\n",
       "4 Marla       2218\n",
       "             ...  \n",
       "5.9 Kanal        1\n",
       "14 Kanal         1\n",
       "19 Kanal         1\n",
       "6.5 Kanal        1\n",
       "50 Kanal         1\n",
       "Name: count, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[\"area\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(232, 19)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total no. of rows where area is -.\n",
    "df_clean[df_clean[\"area\"] == \"-\"].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 232 rows where area isn't mentioned. So we need to extract this data from either property name or description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of rows where property_name column contains area (kanal only)\n",
    "df_clean[df_clean[\"area\"] == \"-\"][\"property_name\"].str.contains(\"Kanal\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of rows where property_name column contains area (manal only)\n",
    "df_clean[df_clean[\"area\"] == \"-\"][\"property_name\"].str.contains(\"Marla\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of rows where description column contains area (manal only)\n",
    "df_clean[df_clean[\"area\"] == \"-\"][\"description\"].str.contains(\"Marla\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of rows where description column contains area (kanal only)\n",
    "df_clean[df_clean[\"area\"] == \"-\"][\"description\"].str.contains(\"Kanal\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting description and property name into lower case\n",
    "df_clean[\"description\"] = df_clean[\"description\"].str.lower()\n",
    "df_clean[\"property_name\"] = df_clean[\"property_name\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>description</th>\n",
       "      <th>main_features</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>price_unit</th>\n",
       "      <th>added_time</th>\n",
       "      <th>added_time_unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>20 marla house in central dha defence phase 2 ...</td>\n",
       "      <td>House</td>\n",
       "      <td>130000000.0</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>5</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>15 hours</td>\n",
       "      <td>house for saledha 2 islamabadbeautiful housema...</td>\n",
       "      <td>['Built in year: 0000', 'Double Glazed Windows...</td>\n",
       "      <td>['Drawing Room', 'Dining Room', 'Study Room', ...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "      <td>1</td>\n",
       "      <td>hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>20 marla spacious house is available in dha de...</td>\n",
       "      <td>House</td>\n",
       "      <td>105000000.0</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>16 hours</td>\n",
       "      <td>house for sale dha 2 islamabad beautiful house...</td>\n",
       "      <td>['Built in year: 0000', 'Double Glazed Windows...</td>\n",
       "      <td>['Drawing Room', 'Dining Room', 'Study Room', ...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "      <td>1</td>\n",
       "      <td>hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>a 20 marla house is up for grabs in dha defence</td>\n",
       "      <td>House</td>\n",
       "      <td>87500000.0</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>17 hours</td>\n",
       "      <td>brand new beautiful designer house  dha 2 isla...</td>\n",
       "      <td>['Built in year: 0000', 'Double Glazed Windows...</td>\n",
       "      <td>['Drawing Room', 'Dining Room', 'Study Room', ...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "      <td>1</td>\n",
       "      <td>hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>21 marla house is available in dha defence pha...</td>\n",
       "      <td>House</td>\n",
       "      <td>99000000.0</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>1 week</td>\n",
       "      <td>beautiful designer house dha 2 islamabad beaut...</td>\n",
       "      <td>['Built in year: 0000', 'Double Glazed Windows...</td>\n",
       "      <td>['Drawing Room', 'Dining Room', 'Study Room', ...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "      <td>7</td>\n",
       "      <td>weeks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>https://zameen.com/Property/dha_defence_dha_d...</td>\n",
       "      <td>good 20 marla house for sale in dha defence ph...</td>\n",
       "      <td>House</td>\n",
       "      <td>99000000.0</td>\n",
       "      <td>dha defence, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>22 hours</td>\n",
       "      <td>house for sale dha 2 islamabad beautiful house...</td>\n",
       "      <td>['Built in year: 0000', 'Double Glazed Windows...</td>\n",
       "      <td>['Drawing Room', 'Dining Room', 'Study Room', ...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "      <td>1</td>\n",
       "      <td>hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44356</th>\n",
       "      <td>https://zameen.com/Property/adiala_road_snobe...</td>\n",
       "      <td>prime location snober city house sized 5 marla</td>\n",
       "      <td>House</td>\n",
       "      <td>15500000.0</td>\n",
       "      <td>adiala road, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>1 month</td>\n",
       "      <td>just know that a purchasing opportunity like t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>adiala road</td>\n",
       "      <td>Crore</td>\n",
       "      <td>30</td>\n",
       "      <td>months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44366</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_adiala...</td>\n",
       "      <td>prime location adiala road house sized 4 marla...</td>\n",
       "      <td>House</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>adiala road, rawalpindi, punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>4</td>\n",
       "      <td>1 month</td>\n",
       "      <td>this is the newest, most attractive neighbourh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>adiala road</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>30</td>\n",
       "      <td>months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45100</th>\n",
       "      <td>https://zameen.com/Property/gulshan_abad_guls...</td>\n",
       "      <td>get this amazing prime location 10 marla house...</td>\n",
       "      <td>House</td>\n",
       "      <td>26500000.0</td>\n",
       "      <td>gulshan abad, rawalpindi, punjab</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>2 weeks</td>\n",
       "      <td>house for sale in gulshanabad sector 3 adiala ...</td>\n",
       "      <td>['Double Glazed Windows', 'Central Air Conditi...</td>\n",
       "      <td>['Bedrooms: 6', 'Bathrooms: 7', 'Servant Quart...</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>gulshan abad</td>\n",
       "      <td>Crore</td>\n",
       "      <td>14</td>\n",
       "      <td>weeks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45422</th>\n",
       "      <td>https://zameen.com/Property/islamabad_highway...</td>\n",
       "      <td>prime location house of 3 marla is available f...</td>\n",
       "      <td>House</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>islamabad highway, rawalpindi, punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>2</td>\n",
       "      <td>1 month</td>\n",
       "      <td>waterelectricitygas availablenear to airport h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>islamabad highway</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>30</td>\n",
       "      <td>months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47281</th>\n",
       "      <td>https://zameen.com/Property/rawalpindi_punjab...</td>\n",
       "      <td>5 marla house for sale in punjab government se...</td>\n",
       "      <td>House</td>\n",
       "      <td>9500000.0</td>\n",
       "      <td>punjab government servant housing foundation (...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>-</td>\n",
       "      <td>1 month</td>\n",
       "      <td>punjab government servant housing foundation (...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rawalpindi</td>\n",
       "      <td>punjab</td>\n",
       "      <td>punjab government servant housing foundation (...</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>30</td>\n",
       "      <td>months</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "42      https://zameen.com/Property/dha_defence_dha_d...  \\\n",
       "45      https://zameen.com/Property/dha_defence_dha_d...   \n",
       "70      https://zameen.com/Property/dha_defence_dha_d...   \n",
       "133     https://zameen.com/Property/dha_defence_dha_d...   \n",
       "146     https://zameen.com/Property/dha_defence_dha_d...   \n",
       "...                                                  ...   \n",
       "44356   https://zameen.com/Property/adiala_road_snobe...   \n",
       "44366   https://zameen.com/Property/rawalpindi_adiala...   \n",
       "45100   https://zameen.com/Property/gulshan_abad_guls...   \n",
       "45422   https://zameen.com/Property/islamabad_highway...   \n",
       "47281   https://zameen.com/Property/rawalpindi_punjab...   \n",
       "\n",
       "                                           property_name   type        price   \n",
       "42     20 marla house in central dha defence phase 2 ...  House  130000000.0  \\\n",
       "45     20 marla spacious house is available in dha de...  House  105000000.0   \n",
       "70       a 20 marla house is up for grabs in dha defence  House   87500000.0   \n",
       "133    21 marla house is available in dha defence pha...  House   99000000.0   \n",
       "146    good 20 marla house for sale in dha defence ph...  House   99000000.0   \n",
       "...                                                  ...    ...          ...   \n",
       "44356     prime location snober city house sized 5 marla  House   15500000.0   \n",
       "44366  prime location adiala road house sized 4 marla...  House    9000000.0   \n",
       "45100  get this amazing prime location 10 marla house...  House   26500000.0   \n",
       "45422  prime location house of 3 marla is available f...  House    4500000.0   \n",
       "47281  5 marla house for sale in punjab government se...  House    9500000.0   \n",
       "\n",
       "                                                location baths area   purpose   \n",
       "42             dha defence, islamabad, islamabad capital     5    -  For Sale  \\\n",
       "45             dha defence, islamabad, islamabad capital     6    -  For Sale   \n",
       "70             dha defence, islamabad, islamabad capital     6    -  For Sale   \n",
       "133            dha defence, islamabad, islamabad capital     6    -  For Sale   \n",
       "146            dha defence, islamabad, islamabad capital     6    -  For Sale   \n",
       "...                                                  ...   ...  ...       ...   \n",
       "44356                    adiala road, rawalpindi, punjab     -    -  For Sale   \n",
       "44366                    adiala road, rawalpindi, punjab     4    -  For Sale   \n",
       "45100                   gulshan abad, rawalpindi, punjab     -    -  For Sale   \n",
       "45422              islamabad highway, rawalpindi, punjab     2    -  For Sale   \n",
       "47281  punjab government servant housing foundation (...     -    -  For Sale   \n",
       "\n",
       "      bedrooms     added                                        description   \n",
       "42           5  15 hours  house for saledha 2 islamabadbeautiful housema...  \\\n",
       "45           6  16 hours  house for sale dha 2 islamabad beautiful house...   \n",
       "70           6  17 hours  brand new beautiful designer house  dha 2 isla...   \n",
       "133          6    1 week  beautiful designer house dha 2 islamabad beaut...   \n",
       "146          6  22 hours  house for sale dha 2 islamabad beautiful house...   \n",
       "...        ...       ...                                                ...   \n",
       "44356        -   1 month  just know that a purchasing opportunity like t...   \n",
       "44366        4   1 month  this is the newest, most attractive neighbourh...   \n",
       "45100        6   2 weeks  house for sale in gulshanabad sector 3 adiala ...   \n",
       "45422        2   1 month  waterelectricitygas availablenear to airport h...   \n",
       "47281        -   1 month  punjab government servant housing foundation (...   \n",
       "\n",
       "                                           main_features   \n",
       "42     ['Built in year: 0000', 'Double Glazed Windows...  \\\n",
       "45     ['Built in year: 0000', 'Double Glazed Windows...   \n",
       "70     ['Built in year: 0000', 'Double Glazed Windows...   \n",
       "133    ['Built in year: 0000', 'Double Glazed Windows...   \n",
       "146    ['Built in year: 0000', 'Double Glazed Windows...   \n",
       "...                                                  ...   \n",
       "44356                                                NaN   \n",
       "44366                                                NaN   \n",
       "45100  ['Double Glazed Windows', 'Central Air Conditi...   \n",
       "45422                                                NaN   \n",
       "47281                                                NaN   \n",
       "\n",
       "                                                   rooms         city   \n",
       "42     ['Drawing Room', 'Dining Room', 'Study Room', ...    islamabad  \\\n",
       "45     ['Drawing Room', 'Dining Room', 'Study Room', ...    islamabad   \n",
       "70     ['Drawing Room', 'Dining Room', 'Study Room', ...    islamabad   \n",
       "133    ['Drawing Room', 'Dining Room', 'Study Room', ...    islamabad   \n",
       "146    ['Drawing Room', 'Dining Room', 'Study Room', ...    islamabad   \n",
       "...                                                  ...          ...   \n",
       "44356                                                NaN   rawalpindi   \n",
       "44366                                                NaN   rawalpindi   \n",
       "45100  ['Bedrooms: 6', 'Bathrooms: 7', 'Servant Quart...   rawalpindi   \n",
       "45422                                                NaN   rawalpindi   \n",
       "47281                                                NaN   rawalpindi   \n",
       "\n",
       "                 province                                               town   \n",
       "42      islamabad capital                                        dha defence  \\\n",
       "45      islamabad capital                                        dha defence   \n",
       "70      islamabad capital                                        dha defence   \n",
       "133     islamabad capital                                        dha defence   \n",
       "146     islamabad capital                                        dha defence   \n",
       "...                   ...                                                ...   \n",
       "44356              punjab                                        adiala road   \n",
       "44366              punjab                                        adiala road   \n",
       "45100              punjab                                       gulshan abad   \n",
       "45422              punjab                                  islamabad highway   \n",
       "47281              punjab  punjab government servant housing foundation (...   \n",
       "\n",
       "      price_unit  added_time added_time_unit  \n",
       "42         Crore           1           hours  \n",
       "45         Crore           1           hours  \n",
       "70         Crore           1           hours  \n",
       "133        Crore           7           weeks  \n",
       "146        Crore           1           hours  \n",
       "...          ...         ...             ...  \n",
       "44356      Crore          30          months  \n",
       "44366       Lakh          30          months  \n",
       "45100      Crore          14           weeks  \n",
       "45422       Lakh          30          months  \n",
       "47281       Lakh          30          months  \n",
       "\n",
       "[132 rows x 19 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows where area is - and property_name contains area details (only for marla)\n",
    "df_clean[(df_clean[\"area\"] == \"-\") & (df_clean[\"property_name\"].str.contains(\"marla\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's fill null values with `-`\n",
    "df_clean[\"property_name\"].fillna(\"-\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to find the index where the string 'marla' exists in property_name\n",
    "df_clean[\"property_name_area_loc\"] = df_clean[\"property_name\"].apply(lambda x: x.find(\"marla\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3\n",
       "1       -1\n",
       "2       -1\n",
       "3        3\n",
       "4       -1\n",
       "        ..\n",
       "50714   -1\n",
       "50715   -1\n",
       "50716   -1\n",
       "50717   -1\n",
       "50718    2\n",
       "Name: property_name_area_loc, Length: 49695, dtype: int64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[\"property_name_area_loc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting area details\n",
    "def extract_area_from_property_name(row):\n",
    "    if row.__contains__(\"marla\"):\n",
    "        marla_idx = row.find(\"marla\")\n",
    "        return row[:marla_idx].strip().split(\" \")[-1] + \" Marla\"\n",
    "    elif row.__contains__(\"kanal\"):\n",
    "        kanal_idx = row.find(\"kanal\")\n",
    "        return row[:kanal_idx].strip().split(\" \")[-1] + \" Kanal\"\n",
    "    else:\n",
    "        return \"-\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean[\"area_from_property_name\"] = df_clean[\"property_name\"].apply(extract_area_from_property_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>property_name</th>\n",
       "      <th>area_from_property_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20 marla house in central dha defence phase 2 ...</td>\n",
       "      <td>20 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>20 marla spacious house is available in dha de...</td>\n",
       "      <td>20 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>a 20 marla house is up for grabs in dha defence</td>\n",
       "      <td>20 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>21 marla house is available in dha defence pha...</td>\n",
       "      <td>21 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>good 20 marla house for sale in dha defence ph...</td>\n",
       "      <td>20 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44356</th>\n",
       "      <td>prime location snober city house sized 5 marla</td>\n",
       "      <td>5 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44366</th>\n",
       "      <td>prime location adiala road house sized 4 marla...</td>\n",
       "      <td>4 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45100</th>\n",
       "      <td>get this amazing prime location 10 marla house...</td>\n",
       "      <td>10 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45422</th>\n",
       "      <td>prime location house of 3 marla is available f...</td>\n",
       "      <td>3 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47281</th>\n",
       "      <td>5 marla house for sale in punjab government se...</td>\n",
       "      <td>5 Marla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           property_name   \n",
       "42     20 marla house in central dha defence phase 2 ...  \\\n",
       "45     20 marla spacious house is available in dha de...   \n",
       "70       a 20 marla house is up for grabs in dha defence   \n",
       "133    21 marla house is available in dha defence pha...   \n",
       "146    good 20 marla house for sale in dha defence ph...   \n",
       "...                                                  ...   \n",
       "44356     prime location snober city house sized 5 marla   \n",
       "44366  prime location adiala road house sized 4 marla...   \n",
       "45100  get this amazing prime location 10 marla house...   \n",
       "45422  prime location house of 3 marla is available f...   \n",
       "47281  5 marla house for sale in punjab government se...   \n",
       "\n",
       "      area_from_property_name  \n",
       "42                   20 Marla  \n",
       "45                   20 Marla  \n",
       "70                   20 Marla  \n",
       "133                  21 Marla  \n",
       "146                  20 Marla  \n",
       "...                       ...  \n",
       "44356                 5 Marla  \n",
       "44366                 4 Marla  \n",
       "45100                10 Marla  \n",
       "45422                 3 Marla  \n",
       "47281                 5 Marla  \n",
       "\n",
       "[132 rows x 2 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[(df_clean[\"area\"] == \"-\") & (df_clean[\"property_name\"].str.contains(\"marla\"))][[\"property_name\",\n",
    "                                                                                            \"area_from_property_name\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's fill the area col\n",
    "df_clean.loc[df_clean[\"area\"] == \"-\", \"area\"] = df_clean.loc[df_clean[\"area\"] == \"-\", \"area_from_property_name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of rows where area is missing and some info about area exists in description col.\n",
    "df_clean[(df_clean[\"area\"] == \"-\") & ((df_clean[\"description\"].str.contains(\"marla\")) |\n",
    "                                        (df_clean[\"description\"].str.contains(\"kanal\")))].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting area details from description col\n",
    "def extract_area_from_description(row):\n",
    "    if row.__contains__(\"marla\"):\n",
    "        marla_idx = row.find(\"marla\")\n",
    "        return row[:marla_idx].strip().split(\" \")[-1] + \" Marla\"\n",
    "    elif row.__contains__(\"kanal\"):\n",
    "        kanal_idx = row.find(\"kanal\")\n",
    "        return row[:kanal_idx].strip().split(\" \")[-1] + \" Kanal\"\n",
    "    else:\n",
    "        return \"-\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[\"area_from_description\"] = df_clean[\"description\"].apply(extract_area_from_description)\n",
    "df_clean.loc[df_clean[\"area\"] == \"-\", \"area\"] = df_clean.loc[df_clean[\"area\"] == \"-\", \"area_from_description\"]\n",
    "\n",
    "# no. of rows where there is no info about area.\n",
    "df_clean[df_clean[\"area\"] == \"-\"].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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>url</th>\n",
       "      <th>property_name</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>baths</th>\n",
       "      <th>area</th>\n",
       "      <th>purpose</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>added</th>\n",
       "      <th>...</th>\n",
       "      <th>rooms</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>price_unit</th>\n",
       "      <th>added_time</th>\n",
       "      <th>added_time_unit</th>\n",
       "      <th>property_name_area_loc</th>\n",
       "      <th>area_from_property_name</th>\n",
       "      <th>area_from_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32187</th>\n",
       "      <td>https://zameen.com/Property/lahore_sherwani_t...</td>\n",
       "      <td>6 marla owner built house for sale  in sherwan...</td>\n",
       "      <td>House</td>\n",
       "      <td>9500000.0</td>\n",
       "      <td>sherwani town housing scheme, lahore, punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>6 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>3</td>\n",
       "      <td>3 years</td>\n",
       "      <td>...</td>\n",
       "      <td>['Bedrooms: 2', 'Bathrooms: 2', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>sherwani town housing scheme</td>\n",
       "      <td>Lakh</td>\n",
       "      <td>1095</td>\n",
       "      <td>years</td>\n",
       "      <td>2</td>\n",
       "      <td>6 Marla</td>\n",
       "      <td>offers6- Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14220</th>\n",
       "      <td>https://zameen.com/Property/dha_9_town_dha_9_...</td>\n",
       "      <td>5marla brand new house for sale</td>\n",
       "      <td>House</td>\n",
       "      <td>25500000.0</td>\n",
       "      <td>dha defence, lahore, punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>5 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>3</td>\n",
       "      <td>2 days</td>\n",
       "      <td>...</td>\n",
       "      <td>['Bedrooms: 3', 'Bathrooms: 4', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>dha defence</td>\n",
       "      <td>Crore</td>\n",
       "      <td>2</td>\n",
       "      <td>days</td>\n",
       "      <td>1</td>\n",
       "      <td>5 Marla</td>\n",
       "      <td>5 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10857</th>\n",
       "      <td>https://zameen.com/Property/islamabad_f-10_f-...</td>\n",
       "      <td>f-10 beautiful new double storey house 600 yd ...</td>\n",
       "      <td>House</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>f-10, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>1.2 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>6</td>\n",
       "      <td>1 week</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>f-10</td>\n",
       "      <td>Crore</td>\n",
       "      <td>7</td>\n",
       "      <td>weeks</td>\n",
       "      <td>-1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40096</th>\n",
       "      <td>https://zameen.com/Property/lahore_formanites...</td>\n",
       "      <td>10 marla house is available for sale in forman...</td>\n",
       "      <td>House</td>\n",
       "      <td>35500000.0</td>\n",
       "      <td>formanites housing scheme, lahore, punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>10 Marla</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>1 week</td>\n",
       "      <td>...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>lahore</td>\n",
       "      <td>punjab</td>\n",
       "      <td>formanites housing scheme</td>\n",
       "      <td>Crore</td>\n",
       "      <td>7</td>\n",
       "      <td>weeks</td>\n",
       "      <td>3</td>\n",
       "      <td>10 Marla</td>\n",
       "      <td>10 Marla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9187</th>\n",
       "      <td>https://zameen.com/Property/islamabad_f-7_you...</td>\n",
       "      <td>your dream home awaits: good livable house is ...</td>\n",
       "      <td>House</td>\n",
       "      <td>268000000.0</td>\n",
       "      <td>f-7, islamabad, islamabad capital</td>\n",
       "      <td>6</td>\n",
       "      <td>1.2 Kanal</td>\n",
       "      <td>For Sale</td>\n",
       "      <td>5</td>\n",
       "      <td>4 weeks</td>\n",
       "      <td>...</td>\n",
       "      <td>['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...</td>\n",
       "      <td>islamabad</td>\n",
       "      <td>islamabad capital</td>\n",
       "      <td>f-7</td>\n",
       "      <td>Crore</td>\n",
       "      <td>28</td>\n",
       "      <td>weeks</td>\n",
       "      <td>-1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url   \n",
       "32187   https://zameen.com/Property/lahore_sherwani_t...  \\\n",
       "14220   https://zameen.com/Property/dha_9_town_dha_9_...   \n",
       "10857   https://zameen.com/Property/islamabad_f-10_f-...   \n",
       "40096   https://zameen.com/Property/lahore_formanites...   \n",
       "9187    https://zameen.com/Property/islamabad_f-7_you...   \n",
       "\n",
       "                                           property_name   type        price   \n",
       "32187  6 marla owner built house for sale  in sherwan...  House    9500000.0  \\\n",
       "14220                    5marla brand new house for sale  House   25500000.0   \n",
       "10857  f-10 beautiful new double storey house 600 yd ...  House  245000000.0   \n",
       "40096  10 marla house is available for sale in forman...  House   35500000.0   \n",
       "9187   your dream home awaits: good livable house is ...  House  268000000.0   \n",
       "\n",
       "                                           location baths       area   \n",
       "32187  sherwani town housing scheme, lahore, punjab     4    6 Marla  \\\n",
       "14220                   dha defence, lahore, punjab     4    5 Marla   \n",
       "10857            f-10, islamabad, islamabad capital     6  1.2 Kanal   \n",
       "40096     formanites housing scheme, lahore, punjab     6   10 Marla   \n",
       "9187              f-7, islamabad, islamabad capital     6  1.2 Kanal   \n",
       "\n",
       "        purpose bedrooms    added  ...   \n",
       "32187  For Sale        3  3 years  ...  \\\n",
       "14220  For Sale        3   2 days  ...   \n",
       "10857  For Sale        6   1 week  ...   \n",
       "40096  For Sale        5   1 week  ...   \n",
       "9187   For Sale        5  4 weeks  ...   \n",
       "\n",
       "                                                   rooms        city   \n",
       "32187  ['Bedrooms: 2', 'Bathrooms: 2', 'Servant Quart...      lahore  \\\n",
       "14220  ['Bedrooms: 3', 'Bathrooms: 4', 'Servant Quart...      lahore   \n",
       "10857                                                NaN   islamabad   \n",
       "40096  ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...      lahore   \n",
       "9187   ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...   islamabad   \n",
       "\n",
       "                 province                          town price_unit added_time   \n",
       "32187              punjab  sherwani town housing scheme       Lakh       1095  \\\n",
       "14220              punjab                   dha defence      Crore          2   \n",
       "10857   islamabad capital                          f-10      Crore          7   \n",
       "40096              punjab     formanites housing scheme      Crore          7   \n",
       "9187    islamabad capital                           f-7      Crore         28   \n",
       "\n",
       "      added_time_unit  property_name_area_loc area_from_property_name   \n",
       "32187           years                       2                 6 Marla  \\\n",
       "14220            days                       1                 5 Marla   \n",
       "10857           weeks                      -1                       -   \n",
       "40096           weeks                       3                10 Marla   \n",
       "9187            weeks                      -1                       -   \n",
       "\n",
       "       area_from_description  \n",
       "32187         offers6- Marla  \n",
       "14220                5 Marla  \n",
       "10857                      -  \n",
       "40096               10 Marla  \n",
       "9187                       -  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have extracted area from property_name and description col\n",
    "# if there still exists some rows where this data of area is not available\n",
    "# then we have no choice but to remove it\n",
    "df_clean = df_clean[df_clean[\"area\"] != \"-\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting area (in numbers) and its unit from area col\n",
    "df_clean.loc[:, \"area_unit\"] = df_clean[\"area\"].apply(lambda x: x.split(\" \")[-1])\n",
    "df_clean.loc[:, \"area(sqr feet)\"] = df_clean[\"area\"].apply(lambda x: x.split(\" \")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>area_unit</th>\n",
       "      <th>area(sqr feet)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48167</th>\n",
       "      <td>Marla</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8117</th>\n",
       "      <td>Marla</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26270</th>\n",
       "      <td>Marla</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      area_unit area(sqr feet)\n",
       "48167     Marla            3.5\n",
       "8117      Marla             20\n",
       "26270     Marla              3"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[['area_unit', 'area(sqr feet)']].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rows which contain incorrect data\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"back1\"] = \"1\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"boulevard1\"] = \"1\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"boulevard1\"] = \"1\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"house21\"] = \"21\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"327,282.1\"] = \"327282.1\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"back21\"] = \"21\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"house1\"] = \"1\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"2,750\"] = \"2750\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"477,218.6\"] = \"477218.6\"\n",
    "df_clean[df_clean[\"area(sqr feet)\"] == \"6,000\"] = \"6000\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting area (sqr feet) into float\n",
    "df_clean[\"area(sqr feet)\"] = df_clean[\"area(sqr feet)\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting area to a sqr feet\n",
    "for unique_unit in df_clean[\"area_unit\"].unique().tolist():\n",
    "    if unique_unit == \"Marla\":\n",
    "        df_clean.loc[df_clean['area_unit'] == 'Marla', 'area(sqr feet)'] = df_clean.loc[df_clean['area_unit'] == 'Marla', 'area(sqr feet)']*225\n",
    "    elif unique_unit == \"Kanal\":\n",
    "        df_clean.loc[df_clean['area_unit'] == 'Kanal', 'area(sqr feet)'] = df_clean.loc[df_clean['area_unit'] == 'Kanal', 'area(sqr feet)']*225*20\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_unit\n",
       "Marla       38873\n",
       "Kanal       10765\n",
       "1              11\n",
       "21              4\n",
       "327282.1        1\n",
       "2750            1\n",
       "477218.6        1\n",
       "6000            1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[\"area_unit\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contains_(row):\n",
    "    return len(row[row == \"-\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                            0\n",
       "property_name                  3\n",
       "type                           0\n",
       "price                          0\n",
       "location                       0\n",
       "baths                       6035\n",
       "area                           0\n",
       "purpose                        0\n",
       "bedrooms                    3930\n",
       "added                          0\n",
       "description                    0\n",
       "main_features                  0\n",
       "rooms                          0\n",
       "city                           0\n",
       "province                       0\n",
       "town                           0\n",
       "price_unit                     0\n",
       "added_time                     0\n",
       "added_time_unit                0\n",
       "property_name_area_loc         0\n",
       "area_from_property_name    18292\n",
       "area_from_description      20283\n",
       "area_unit                      0\n",
       "area(sqr feet)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of instances where columns contains \"-\"\n",
    "df_clean.apply(contains_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Room column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38426    ['Bedrooms: 5', 'Bathrooms: 5', 'Servant Quart...\n",
       "47345                                                  NaN\n",
       "18131                                                  NaN\n",
       "14965    ['Bedrooms: 5', 'Bathrooms: 6', 'Servant Quart...\n",
       "27445                      ['Bedrooms: 3', 'Bathrooms: 3']\n",
       "Name: rooms, dtype: object"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how looks\n",
    "df_clean[\"rooms\"].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rooms\n",
       "['Drawing Room', 'Dining Room', 'Study Room', 'Prayer Room', 'Powder Room ', 'Gym', 'Steam Room', 'Lounge or Sitting Room', 'Laundry Room']                                                                                              478\n",
       "['Bedrooms: 3', 'Bathrooms: 3']                                                                                                                                                                                                          398\n",
       "['Drawing Room']                                                                                                                                                                                                                         333\n",
       "['Drawing Room', 'Dining Room', 'Lounge or Sitting Room', 'Laundry Room']                                                                                                                                                                309\n",
       "['Bedrooms: 3']                                                                                                                                                                                                                          296\n",
       "                                                                                                                                                                                                                                        ... \n",
       "['Bedrooms: 6', 'Bathrooms: 8', 'Servant Quarters: 2', 'Drawing Room', 'Dining Room', 'Kitchens: 2', 'Study Room', 'Prayer Room', 'Powder Room ', 'Gym', 'Store Rooms: 1', 'Steam Room', 'Lounge or Sitting Room', 'Laundry Room']         1\n",
       "['Bedrooms', 'Bathrooms: 6', 'Servant Quarters', 'Drawing Room', 'Dining Room', 'Kitchens', 'Study Room', 'Prayer Room', 'Powder Room ', 'Gym', 'Store Rooms', 'Steam Room', 'Lounge or Sitting Room', 'Laundry Room', 'Other Rooms']      1\n",
       "['Bedrooms: 04', 'Bathrooms: 05', 'Servant Quarters: 01', 'Kitchens: 01', 'Store Rooms: 01', 'Other Rooms']                                                                                                                                1\n",
       "['Bedrooms: 6', 'Bathrooms: 7', 'Servant Quarters: 1', 'Kitchens: 3', 'Store Rooms: 2']                                                                                                                                                    1\n",
       "['Drawing Room', 'Dining Room', 'Kitchens: 2', 'Store Rooms: 2', 'Lounge or Sitting Room', 'Laundry Room']                                                                                                                                 1\n",
       "Name: count, Length: 9080, dtype: int64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value count\n",
    "df_clean[\"rooms\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16499, 33.22592987896974)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no. of null\n",
    "# total null values, percentage of null values\n",
    "df_clean[\"rooms\"].isnull().sum(), (df_clean[\"rooms\"].isnull().sum() / df_clean.shape[0])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have to extract information of bedrooms and bathrooms for those rows where this info isn't available.\n",
    "# but there are null values in dataset. without handling null values it isn't possible to extract this info.\n",
    "# so to handle null values I will fill all null values with `Not Available`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling all null values with 'Not Available'\n",
    "df_clean[\"rooms\"].fillna(\"Not Available\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rooms', 'Bedrooms', 'Bathrooms', 'Servant', 'Drawing', 'Dining', 'Kitchens', 'Study', 'Prayer', 'Powder', 'Gym', 'Store', 'Steam', 'Lounge', 'Laundry', 'Other']\n"
     ]
    }
   ],
   "source": [
    "# New features from room column\n",
    "new_features_list = df_clean.loc[1247, \"rooms\"].replace(\"[\", \"\").replace(\"]\", \"\").replace('\\'', '').replace(\":\", \"\").split(\",\")\n",
    "new_features = []\n",
    "for idx in new_features_list:\n",
    "    new_features.append(idx.strip().split(\" \")[0])\n",
    "print(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_features = ['Bedroom', 'Bathroom', 'Servant Quarter', 'Drawing Room', 'Dining Room', 'Kitchen', 'Study Room', \n",
    "                'Prayer Room', 'Powder Room', 'Gym', 'Store Room', 'Steam Room', 'Lounge or Sitting Room', 'Laundry Room', 'Other Room']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting new features\n",
    "def str2list(row):\n",
    "    row_list = row.replace(\"[\", \"\").replace(\"]\", \"\").replace('\\'', '').split(\",\")\n",
    "    # print(row_list)\n",
    "    # print(new_features)\n",
    "    new_list = []\n",
    "    for new_feature in new_features:\n",
    "        isDone = False\n",
    "        for room in row_list:\n",
    "            if (room.__contains__(new_feature)):\n",
    "                # return room.split(\" \")[-1].strip()\n",
    "                new_list.append(room.strip())\n",
    "                isDone = True\n",
    "        if isDone == False:\n",
    "            new_list.append(\"-\")\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_features = ['Bedroom', 'Bathroom', 'Servant Quarter', 'Drawing Room', 'Dining Room', 'Kitchen', 'Study Room', \n",
    "                'Prayer Room', 'Powder Room', 'Gym', 'Store Room', 'Steam Room', 'Lounge or Sitting Room', 'Laundry Room', 'Other Room']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [Bedrooms: 5, Bathrooms: 6, Servant Quarters: ...\n",
       "1        [Bedrooms: 8, Bathrooms: 2, Servant Quarters: ...\n",
       "2        [Bedrooms: 5, Bathrooms: 5, Servant Quarters: ...\n",
       "3        [Bedrooms: 5, Bathrooms: 5, Servant Quarters: ...\n",
       "4        [Bedrooms: 5, Bathrooms: 6, Servant Quarters: ...\n",
       "                               ...                        \n",
       "50714    [-, -, -, Drawing Room, Dining Room, Kitchens:...\n",
       "50715    [Bedrooms: 4, Bathrooms: 5, -, -, -, Kitchens:...\n",
       "50716        [-, -, -, -, -, -, -, -, -, -, -, -, -, -, -]\n",
       "50717        [-, -, -, -, -, -, -, -, -, -, -, -, -, -, -]\n",
       "50718        [-, -, -, -, -, -, -, -, -, -, -, -, -, -, -]\n",
       "Name: rooms, Length: 49657, dtype: object"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df_clean[\"rooms\"].apply(str2list)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenating all new features to the dataframe.\n",
    "for idx in df_clean.index.tolist():\n",
    "    df_clean.loc[idx, new_features] = temp[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only useful to extract bedrooms, bathrooms and kitchen\n",
    "def extract_room_num(row):\n",
    "    row_list = row.split(\" \")\n",
    "    if len(row_list) > 1:\n",
    "        try:\n",
    "            return float(row_list[-1])\n",
    "        except ValueError:\n",
    "            return 1\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['Bedroom'] = df_clean['Bedroom'].apply(extract_room_num)\n",
    "df_clean['Bathroom'] = df_clean['Bathroom'].apply(extract_room_num)\n",
    "df_clean['Kitchen'] = df_clean['Kitchen'].apply(extract_room_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only useful to extract store room\n",
    "def extract_room_num(row):\n",
    "    row_list = row.split(\" \")\n",
    "    if len(row_list) > 1:\n",
    "        try:\n",
    "            return int(row_list[-1])\n",
    "        except ValueError:\n",
    "            return 1\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['Store Room'] = df_clean['Store Room'].apply(extract_room_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only useful to extract servant quarter\n",
    "def extract_servant_quarter(row):\n",
    "    row_list = row.split(\" \")\n",
    "    if len(row_list) > 1:\n",
    "        try:\n",
    "            return int(row_list[-1])\n",
    "        except ValueError:\n",
    "            return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean[\"Servant Quarter\"] = df_clean[\"Servant Quarter\"].apply(extract_servant_quarter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "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>Bedroom</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Servant Quarter</th>\n",
       "      <th>Drawing Room</th>\n",
       "      <th>Dining Room</th>\n",
       "      <th>Kitchen</th>\n",
       "      <th>Study Room</th>\n",
       "      <th>Prayer Room</th>\n",
       "      <th>Powder Room</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Store Room</th>\n",
       "      <th>Steam Room</th>\n",
       "      <th>Lounge or Sitting Room</th>\n",
       "      <th>Laundry Room</th>\n",
       "      <th>Other Room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Drawing Room</td>\n",
       "      <td>Dining Room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Study Room</td>\n",
       "      <td>Prayer Room</td>\n",
       "      <td>Powder Room</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>Steam Room</td>\n",
       "      <td>Lounge or Sitting Room</td>\n",
       "      <td>Laundry Room</td>\n",
       "      <td>Other Rooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Drawing Room</td>\n",
       "      <td>Dining Room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Study Room</td>\n",
       "      <td>Prayer Room</td>\n",
       "      <td>Powder Room</td>\n",
       "      <td>Gym</td>\n",
       "      <td>1</td>\n",
       "      <td>Steam Room</td>\n",
       "      <td>Lounge or Sitting Room</td>\n",
       "      <td>Laundry Room</td>\n",
       "      <td>Other Rooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Drawing Room</td>\n",
       "      <td>Dining Room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Powder Room</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>Lounge or Sitting Room</td>\n",
       "      <td>Laundry Room</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Drawing Room</td>\n",
       "      <td>Dining Room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Study Room</td>\n",
       "      <td>Prayer Room</td>\n",
       "      <td>Powder Room</td>\n",
       "      <td>Gym</td>\n",
       "      <td>1</td>\n",
       "      <td>Steam Room</td>\n",
       "      <td>Lounge or Sitting Room</td>\n",
       "      <td>Laundry Room</td>\n",
       "      <td>Other Rooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Drawing Room</td>\n",
       "      <td>Dining Room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50714</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Drawing Room</td>\n",
       "      <td>Dining Room</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>Lounge or Sitting Room</td>\n",
       "      <td>Laundry Room</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50715</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50716</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50717</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50718</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49657 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bedroom  Bathroom  Servant Quarter  Drawing Room  Dining Room  Kitchen   \n",
       "0          5.0       6.0                1  Drawing Room  Dining Room      2.0  \\\n",
       "1          8.0       2.0                2  Drawing Room  Dining Room      2.0   \n",
       "2          5.0       5.0                1  Drawing Room  Dining Room      2.0   \n",
       "3          5.0       5.0                1  Drawing Room  Dining Room      2.0   \n",
       "4          5.0       6.0                1  Drawing Room  Dining Room      2.0   \n",
       "...        ...       ...              ...           ...          ...      ...   \n",
       "50714      1.0       1.0                0  Drawing Room  Dining Room      2.0   \n",
       "50715      4.0       5.0                0             -            -      2.0   \n",
       "50716      1.0       1.0                0             -            -      1.0   \n",
       "50717      1.0       1.0                0             -            -      1.0   \n",
       "50718      1.0       1.0                0             -            -      1.0   \n",
       "\n",
       "       Study Room  Prayer Room  Powder Room  Gym  Store Room  Steam Room   \n",
       "0      Study Room  Prayer Room  Powder Room    -           1  Steam Room  \\\n",
       "1      Study Room  Prayer Room  Powder Room  Gym           1  Steam Room   \n",
       "2               -            -  Powder Room    -           1           -   \n",
       "3      Study Room  Prayer Room  Powder Room  Gym           1  Steam Room   \n",
       "4               -            -            -    -           1           -   \n",
       "...           ...          ...          ...  ...         ...         ...   \n",
       "50714           -            -            -    -           2           -   \n",
       "50715           -            -            -    -           1           -   \n",
       "50716           -            -            -    -           1           -   \n",
       "50717           -            -            -    -           1           -   \n",
       "50718           -            -            -    -           1           -   \n",
       "\n",
       "       Lounge or Sitting Room  Laundry Room   Other Room  \n",
       "0      Lounge or Sitting Room  Laundry Room  Other Rooms  \n",
       "1      Lounge or Sitting Room  Laundry Room  Other Rooms  \n",
       "2      Lounge or Sitting Room  Laundry Room            -  \n",
       "3      Lounge or Sitting Room  Laundry Room  Other Rooms  \n",
       "4                           -             -            -  \n",
       "...                       ...           ...          ...  \n",
       "50714  Lounge or Sitting Room  Laundry Room            -  \n",
       "50715                       -             -            -  \n",
       "50716                       -             -            -  \n",
       "50717                       -             -            -  \n",
       "50718                       -             -            -  \n",
       "\n",
       "[49657 rows x 15 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[new_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only useful to extract other rooms\n",
    "def extract_other_rooms(row):\n",
    "    return 1 if len(row) > 0 else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean[\"Drawing Room\"] = df_clean[\"Drawing Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Dining Room\"] = df_clean[\"Dining Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Study Room\"] = df_clean[\"Study Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Prayer Room\"] = df_clean[\"Prayer Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Powder Room\"] = df_clean[\"Powder Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Steam Room\"] = df_clean[\"Steam Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Gym\"] = df_clean[\"Gym\"].apply(extract_other_rooms)\n",
    "df_clean[\"Lounge or Sitting Room\"] = df_clean[\"Lounge or Sitting Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Laundry Room\"] = df_clean[\"Laundry Room\"].apply(extract_other_rooms)\n",
    "df_clean[\"Other Room\"] = df_clean[\"Other Room\"].apply(extract_other_rooms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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>Bedroom</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Servant Quarter</th>\n",
       "      <th>Drawing Room</th>\n",
       "      <th>Dining Room</th>\n",
       "      <th>Kitchen</th>\n",
       "      <th>Study Room</th>\n",
       "      <th>Prayer Room</th>\n",
       "      <th>Powder Room</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Store Room</th>\n",
       "      <th>Steam Room</th>\n",
       "      <th>Lounge or Sitting Room</th>\n",
       "      <th>Laundry Room</th>\n",
       "      <th>Other Room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50714</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50715</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50716</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50717</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50718</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49657 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bedroom  Bathroom  Servant Quarter  Drawing Room  Dining Room  Kitchen   \n",
       "0          5.0       6.0                1             1            1      2.0  \\\n",
       "1          8.0       2.0                2             1            1      2.0   \n",
       "2          5.0       5.0                1             1            1      2.0   \n",
       "3          5.0       5.0                1             1            1      2.0   \n",
       "4          5.0       6.0                1             1            1      2.0   \n",
       "...        ...       ...              ...           ...          ...      ...   \n",
       "50714      1.0       1.0                0             1            1      2.0   \n",
       "50715      4.0       5.0                0             1            1      2.0   \n",
       "50716      1.0       1.0                0             1            1      1.0   \n",
       "50717      1.0       1.0                0             1            1      1.0   \n",
       "50718      1.0       1.0                0             1            1      1.0   \n",
       "\n",
       "       Study Room  Prayer Room  Powder Room  Gym  Store Room  Steam Room   \n",
       "0               1            1            1    1           1           1  \\\n",
       "1               1            1            1    1           1           1   \n",
       "2               1            1            1    1           1           1   \n",
       "3               1            1            1    1           1           1   \n",
       "4               1            1            1    1           1           1   \n",
       "...           ...          ...          ...  ...         ...         ...   \n",
       "50714           1            1            1    1           2           1   \n",
       "50715           1            1            1    1           1           1   \n",
       "50716           1            1            1    1           1           1   \n",
       "50717           1            1            1    1           1           1   \n",
       "50718           1            1            1    1           1           1   \n",
       "\n",
       "       Lounge or Sitting Room  Laundry Room  Other Room  \n",
       "0                           1             1           1  \n",
       "1                           1             1           1  \n",
       "2                           1             1           1  \n",
       "3                           1             1           1  \n",
       "4                           1             1           1  \n",
       "...                       ...           ...         ...  \n",
       "50714                       1             1           1  \n",
       "50715                       1             1           1  \n",
       "50716                       1             1           1  \n",
       "50717                       1             1           1  \n",
       "50718                       1             1           1  \n",
       "\n",
       "[49657 rows x 15 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[new_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Working on Main Features column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill all null values with 'Not Available'\n",
    "df_clean['main_features'].fillna(\"Not Available\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['main_features'] = df_clean['main_features'].str.replace(\"Elevators\", \"E1evators\")\n",
    "df_clean['main_features'] = df_clean['main_features'].str.replace(\"Service E1evators in Building\",\n",
    "                                                                        \"Service E2evators in Building\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_features = ['Built in year', 'Parking Spaces', 'Double Glazed Windows', 'Central Air Conditioning', \n",
    "                'Lobby in Building', 'Central Heating', 'Flooring', 'Electricity Backup', 'Waste Disposal', \n",
    "                'Floor:', 'Floors:', 'Floors in Building', 'E1evators', 'Service E2evators in Building', \n",
    "                'Other Main Features', 'Furnished']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "def str2list(row):\n",
    "    row_list = row.replace(\"[\", \"\").replace(\"]\", \"\").replace('\\'', '').split(\",\")\n",
    "    new_list = []\n",
    "    for new_feature in new_features:\n",
    "        isDone = False\n",
    "        for room in row_list:\n",
    "            if (room.__contains__(new_feature)):\n",
    "                # return room.split(\" \")[-1].strip()\n",
    "                if room.strip() not in new_list:\n",
    "                    new_list.append(room.strip())\n",
    "                isDone = True\n",
    "        if isDone == False:\n",
    "            new_list.append(\"-\")\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [Built in year: 2023, Parking Spaces: 2, Doubl...\n",
       "1        [Built in year: 2023, Parking Spaces: 2, Doubl...\n",
       "2        [Built in year: 2023, Parking Spaces: 3, Doubl...\n",
       "3        [-, Parking Spaces: 00, Double Glazed Windows,...\n",
       "4        [Built in year: 2023, Parking Spaces: 4, -, -,...\n",
       "                               ...                        \n",
       "50714    [Built in year: 2020, Parking Spaces, Double G...\n",
       "50715    [Built in year: 2020, Parking Spaces: 3, -, -,...\n",
       "50716     [-, -, -, -, -, -, -, -, -, -, -, -, -, -, -, -]\n",
       "50717     [-, -, -, -, -, -, -, -, -, -, -, -, -, -, -, -]\n",
       "50718     [-, -, -, -, -, -, -, -, -, -, -, -, -, -, -, -]\n",
       "Name: main_features, Length: 49657, dtype: object"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df_clean[\"main_features\"].apply(str2list)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean[new_features] = None\n",
    "for idx in df_clean.index.tolist():\n",
    "    df_clean.loc[idx, new_features] = temp[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.loc[df_clean['bedrooms'] == '-', \"bedrooms\"] = df_clean.loc[df_clean['bedrooms'] == '-', \"Bedroom\"]\n",
    "df_clean.loc[df_clean['baths'] == '-', \"baths\"] = df_clean.loc[df_clean['baths'] == '-', \"Bathroom\"]\n",
    "df_clean['baths'] = df_clean['baths'].astype('float')\n",
    "df_clean['bedrooms'] = df_clean['bedrooms'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49657 entries, 0 to 50718\n",
      "Data columns (total 55 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   url                            49657 non-null  object \n",
      " 1   property_name                  49657 non-null  object \n",
      " 2   type                           49657 non-null  object \n",
      " 3   price                          49657 non-null  object \n",
      " 4   location                       49657 non-null  object \n",
      " 5   baths                          49657 non-null  float64\n",
      " 6   area                           49657 non-null  object \n",
      " 7   purpose                        49657 non-null  object \n",
      " 8   bedrooms                       49657 non-null  float64\n",
      " 9   added                          49657 non-null  object \n",
      " 10  description                    49657 non-null  object \n",
      " 11  main_features                  49657 non-null  object \n",
      " 12  rooms                          49657 non-null  object \n",
      " 13  city                           49657 non-null  object \n",
      " 14  province                       49657 non-null  object \n",
      " 15  town                           49657 non-null  object \n",
      " 16  price_unit                     49657 non-null  object \n",
      " 17  added_time                     49657 non-null  object \n",
      " 18  added_time_unit                49657 non-null  object \n",
      " 19  property_name_area_loc         49657 non-null  object \n",
      " 20  area_from_property_name        49657 non-null  object \n",
      " 21  area_from_description          49657 non-null  object \n",
      " 22  area_unit                      49657 non-null  object \n",
      " 23  area(sqr feet)                 49657 non-null  float64\n",
      " 24  Bedroom                        49657 non-null  float64\n",
      " 25  Bathroom                       49657 non-null  float64\n",
      " 26  Servant Quarter                49657 non-null  int64  \n",
      " 27  Drawing Room                   49657 non-null  int64  \n",
      " 28  Dining Room                    49657 non-null  int64  \n",
      " 29  Kitchen                        49657 non-null  float64\n",
      " 30  Study Room                     49657 non-null  int64  \n",
      " 31  Prayer Room                    49657 non-null  int64  \n",
      " 32  Powder Room                    49657 non-null  int64  \n",
      " 33  Gym                            49657 non-null  int64  \n",
      " 34  Store Room                     49657 non-null  int64  \n",
      " 35  Steam Room                     49657 non-null  int64  \n",
      " 36  Lounge or Sitting Room         49657 non-null  int64  \n",
      " 37  Laundry Room                   49657 non-null  int64  \n",
      " 38  Other Room                     49657 non-null  int64  \n",
      " 39  Built in year                  49657 non-null  object \n",
      " 40  Parking Spaces                 49657 non-null  object \n",
      " 41  Double Glazed Windows          49657 non-null  object \n",
      " 42  Central Air Conditioning       49657 non-null  object \n",
      " 43  Lobby in Building              49657 non-null  object \n",
      " 44  Central Heating                49657 non-null  object \n",
      " 45  Flooring                       49657 non-null  object \n",
      " 46  Electricity Backup             49657 non-null  object \n",
      " 47  Waste Disposal                 49657 non-null  object \n",
      " 48  Floor:                         49657 non-null  object \n",
      " 49  Floors:                        49657 non-null  object \n",
      " 50  Floors in Building             49657 non-null  object \n",
      " 51  E1evators                      49657 non-null  object \n",
      " 52  Service E2evators in Building  49657 non-null  object \n",
      " 53  Other Main Features            49657 non-null  object \n",
      " 54  Furnished                      49657 non-null  object \n",
      "dtypes: float64(6), int64(12), object(37)\n",
      "memory usage: 22.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['baths'] = df_clean['baths'].astype(int)\n",
    "df_clean['bedrooms'] = df_clean['bedrooms'].astype(int)\n",
    "df_clean['added_time'] = df_clean['added_time'].astype(float)\n",
    "df_clean['price'] = df_clean['price'].astype(float)\n",
    "df_clean['area(sqr feet)'] = df_clean['area(sqr feet)'].astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49657, 55)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of data after cleaning\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only those houses which were added before 2.5 years\n",
    "df_clean = df_clean[df_clean['added_time'] < 365*2.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols which contain '-'\n",
    "def contains_(col):\n",
    "    return col == \"-\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                              0.000000\n",
       "property_name                    0.000069\n",
       "type                             0.000000\n",
       "price                            0.000000\n",
       "location                         0.000000\n",
       "baths                            0.000000\n",
       "area                             0.000000\n",
       "purpose                          0.000000\n",
       "bedrooms                         0.000000\n",
       "added                            0.000000\n",
       "description                      0.000000\n",
       "main_features                    0.000000\n",
       "rooms                            0.000000\n",
       "city                             0.000000\n",
       "province                         0.000000\n",
       "town                             0.000000\n",
       "price_unit                       0.000000\n",
       "added_time                       0.000000\n",
       "added_time_unit                  0.000000\n",
       "property_name_area_loc           0.000000\n",
       "area_from_property_name          0.325625\n",
       "area_from_description            0.381390\n",
       "area_unit                        0.000000\n",
       "area(sqr feet)                   0.000000\n",
       "Bedroom                          0.000000\n",
       "Bathroom                         0.000000\n",
       "Servant Quarter                  0.000000\n",
       "Drawing Room                     0.000000\n",
       "Dining Room                      0.000000\n",
       "Kitchen                          0.000000\n",
       "Study Room                       0.000000\n",
       "Prayer Room                      0.000000\n",
       "Powder Room                      0.000000\n",
       "Gym                              0.000000\n",
       "Store Room                       0.000000\n",
       "Steam Room                       0.000000\n",
       "Lounge or Sitting Room           0.000000\n",
       "Laundry Room                     0.000000\n",
       "Other Room                       0.000000\n",
       "Built in year                    0.532999\n",
       "Parking Spaces                   0.478736\n",
       "Double Glazed Windows            0.643859\n",
       "Central Air Conditioning         0.710296\n",
       "Lobby in Building                0.986532\n",
       "Central Heating                  0.723879\n",
       "Flooring                         0.389522\n",
       "Electricity Backup               0.487560\n",
       "Waste Disposal                   0.632909\n",
       "Floor:                           0.992146\n",
       "Floors:                          0.697591\n",
       "Floors in Building               0.986186\n",
       "E1evators                        0.987225\n",
       "Service E2evators in Building    0.986925\n",
       "Other Main Features              0.672942\n",
       "Furnished                        0.877452\n",
       "dtype: float64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.apply(contains_).sum() / df_clean.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retaining necessary cols\n",
    "necessary_cols = [\"price\", \"baths\", \"area(sqr feet)\", \"bedrooms\", \"city\", \"province\", \"town\"]\n",
    "df_clean = df_clean.loc[:, necessary_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_to_be_removed = df_clean[df_clean['baths'] == 0].index\n",
    "df_clean.drop(index=row_to_be_removed, inplace=True)\n",
    "\n",
    "row_to_be_removed = df_clean[df_clean['area(sqr feet)'] < 225].index\n",
    "df_clean.drop(index=row_to_be_removed, inplace=True)\n",
    "\n",
    "row_to_be_removed = df_clean[df_clean['bedrooms'] == 0].index\n",
    "df_clean.drop(index=row_to_be_removed, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "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>price</th>\n",
       "      <th>baths</th>\n",
       "      <th>area(sqr feet)</th>\n",
       "      <th>bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.319200e+04</td>\n",
       "      <td>43192.000000</td>\n",
       "      <td>4.319200e+04</td>\n",
       "      <td>43192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.555943e+07</td>\n",
       "      <td>4.547115</td>\n",
       "      <td>2.744098e+03</td>\n",
       "      <td>4.277737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.149833e+08</td>\n",
       "      <td>1.647983</td>\n",
       "      <td>8.579534e+03</td>\n",
       "      <td>1.642833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.230000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.125000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.900000e+07</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+03</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.900000e+07</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.195000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.500000e+09</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.001250e+06</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price         baths  area(sqr feet)      bedrooms\n",
       "count  4.319200e+04  43192.000000    4.319200e+04  43192.000000\n",
       "mean   5.555943e+07      4.547115    2.744098e+03      4.277737\n",
       "std    1.149833e+08      1.647983    8.579534e+03      1.642833\n",
       "min    1.230000e+03      1.000000    2.250000e+02      1.000000\n",
       "25%    1.500000e+07      4.000000    1.125000e+03      3.000000\n",
       "50%    2.900000e+07      5.000000    1.800000e+03      4.000000\n",
       "75%    5.900000e+07      6.000000    3.195000e+03      5.000000\n",
       "max    8.500000e+09     30.000000    1.001250e+06     15.000000"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Outlier Detection and Removal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier(col:str, k=1.5):\n",
    "    q1 = np.percentile(df_clean[col], 25)\n",
    "    q3 = np.percentile(df_clean[col], 75)\n",
    "    iqr = q3 - q1\n",
    "    lower = q1-(k*iqr)\n",
    "    upper = q3+(k*iqr)\n",
    "    print(f\"\"\"\n",
    "            {col},\n",
    "            Lower: {lower},\n",
    "            Upper: {upper}\n",
    "        \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            price,\n",
      "            Lower: -51000000.0,\n",
      "            Upper: 125000000.0\n",
      "        \n",
      "\n",
      "            baths,\n",
      "            Lower: 1.0,\n",
      "            Upper: 9.0\n",
      "        \n",
      "\n",
      "            area(sqr feet),\n",
      "            Lower: -1980.0,\n",
      "            Upper: 6300.0\n",
      "        \n",
      "\n",
      "            bedrooms,\n",
      "            Lower: 0.0,\n",
      "            Upper: 8.0\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "remove_outlier('price', 1.5)\n",
    "remove_outlier('baths', 1.5)\n",
    "remove_outlier('area(sqr feet)', 1.5)\n",
    "remove_outlier('bedrooms', 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43192, 7)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape before removing outliers\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df_clean[df_clean['price'] <= 125000000.0]\n",
    "df_clean = df_clean[df_clean['baths'] <= 9.0]\n",
    "df_clean = df_clean[df_clean['bedrooms'] <= 8.0]\n",
    "df_clean = df_clean[df_clean['area(sqr feet)'] <= 6300.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39330, 7)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape after removing outliers\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'baths', 'area(sqr feet)', 'bedrooms'], dtype='object')"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.columns[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import shapiro, anderson, kstest, normaltest\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Col: Price\n",
      "Shapiro-Wilk Test:\n",
      "Statistic: 0.8721656203269958\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "Kolmogorov-Smirnov Test:\n",
      "Statistic: 1.0\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "D'Agostino's K-squared Test:\n",
      "Statistic: 6615.783446533887\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\talha\\AppData\\Roaming\\Python\\Python311\\site-packages\\scipy\\stats\\_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Col: Baths\n",
      "Shapiro-Wilk Test:\n",
      "Statistic: 0.8917292356491089\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "Kolmogorov-Smirnov Test:\n",
      "Statistic: 0.9040233234293952\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "D'Agostino's K-squared Test:\n",
      "Statistic: 2541.8524334218823\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Col: Area(sqr feet)\n",
      "Shapiro-Wilk Test:\n",
      "Statistic: 0.8374937176704407\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "Kolmogorov-Smirnov Test:\n",
      "Statistic: 1.0\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "D'Agostino's K-squared Test:\n",
      "Statistic: 4603.172050606492\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Col: Bedrooms\n",
      "Shapiro-Wilk Test:\n",
      "Statistic: 0.9443007111549377\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "Kolmogorov-Smirnov Test:\n",
      "Statistic: 0.913608881527539\n",
      "P-value: 0.0\n",
      "Data does not look normally distributed (reject H0)\n",
      "\n",
      "D'Agostino's K-squared Test:\n",
      "Statistic: 334.08780297287416\n",
      "P-value: 2.842859188160318e-73\n",
      "Data does not look normally distributed (reject H0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in df_clean.columns[:4]:\n",
    "    print(f\"Col: {col.capitalize()}\")\n",
    "    test_name = [\"Shapiro-Wilk\", \"Kolmogorov-Smirnov\", \"D'Agostino's K-squared\"]\n",
    "    test_p = []\n",
    "    test_statistics = []\n",
    "\n",
    "    # Generate some example data\n",
    "    np.random.seed(42)\n",
    "\n",
    "    data = df_clean[col]\n",
    "\n",
    "\n",
    "    # Shapiro-Wilk Test\n",
    "    stat_shapiro, p_shapiro = shapiro(data)\n",
    "    test_p.append(p_shapiro)\n",
    "    test_statistics.append(stat_shapiro)\n",
    "    print(f'Shapiro-Wilk Test:\\nStatistic: {stat_shapiro}\\nP-value: {p_shapiro}')\n",
    "    if p_shapiro > 0.05:\n",
    "        print('Data looks normally distributed (fail to reject H0)')\n",
    "    else:\n",
    "        print('Data does not look normally distributed (reject H0)')\n",
    "\n",
    "    # Kolmogorov-Smirnov Test\n",
    "    stat_kstest, p_kstest = kstest(data, 'norm')\n",
    "    test_p.append(p_kstest)\n",
    "    test_statistics.append(stat_kstest)\n",
    "    print(f'\\nKolmogorov-Smirnov Test:\\nStatistic: {stat_kstest}\\nP-value: {p_kstest}')\n",
    "    if p_kstest > 0.05:\n",
    "        print('Data looks normally distributed (fail to reject H0)')\n",
    "    else:\n",
    "        print('Data does not look normally distributed (reject H0)')\n",
    "\n",
    "    # D'Agostino's K-squared Test\n",
    "    stat_k2, p_k2 = normaltest(data)\n",
    "    test_p.append(p_k2)\n",
    "    test_statistics.append(stat_k2)\n",
    "    print(f'\\nD\\'Agostino\\'s K-squared Test:\\nStatistic: {stat_k2}\\nP-value: {p_k2}')\n",
    "    if p_k2 > 0.05:\n",
    "        print('Data looks normally distributed (fail to reject H0)')\n",
    "    else:\n",
    "        print('Data does not look normally distributed (reject H0)')\n",
    "\n",
    "    # Plot a histogram for visual inspection\n",
    "    plt.hist(data, bins='auto', density=True, alpha=0.7, color='g')\n",
    "    plt.title('Histogram of the Data')\n",
    "    plt.xlabel('Value')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Applying One Hot Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded = pd.get_dummies(df_clean[['city', 'province', 'town']],\n",
    "                                    prefix=['city', 'province', 'town'])\n",
    "one_hot_encoded = one_hot_encoded.astype(int)\n",
    "df_clean = pd.concat([df_clean[['price', 'baths', 'bedrooms', 'area(sqr feet)']], one_hot_encoded], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Applying Log Transformation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['baths', 'bedrooms', 'area(sqr feet)', 'price']:\n",
    "    df_clean[col] = np.log(df_clean[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XYaRRk7ZCA5loEwiEvEHIP0/3uHDvnkO8bz83L6zG2GJdtkzNC8JvtPs8fhRolDAbcq/b29nrxk/+dQwNdZdE9JwLtEoEaAZPNX2Ge26eItmDwBc9Lvz57c+xeMZ4OPr1Ngv6tdV3vXMS/33tBIxJYUxIMdZO97jw4D8Ox3zhDSdKOtZpw8O3VvKWccrqZEkTRdf/2IIqjIu7+X/R48KPBWzesrA6pbaIRsw/1+w5xPs1cDD75+keFx74x2FMTaGvM8U5uwf3//0Q7/h79L+qJUmGds7uweOvfYI76i+B289EfFCrlGN742dYeeOkpOo53ePCoy8ejflKbNBQOG/34u8fnMJ9t0wVbctU/Cdc989ePs577frfr0yOqfuc3YMf/b8Peev5+dcvxwijBp29btzzt4949/vJbZelfU0kz7ZDB5vLh5W7WzgTj11Xbsbji2py+hzA9VwChHS0aSYImgnC5aNRqL24LXp/gyakoy2Xy+Dw0ZAB2PTi0ZgJdJhZk0vwv1+aBK1SgQefO8y5T63FhIbaMpyze/Bi6xlOP6y3mHDXLAucXhrLdx5ArcWEOVWjQMlluNZiTuv6PXjXchEIhEHFUM5qzEehLvcT63hsbj/2fXgG+z48w7n9zustkk207Z4AftvYjt82tnNuv3Xa2JQynEsx1vo8Abx2vAuvRemARnP3V/jLcPhowfodcdqfQKgthI6xewKSZXvnos8T4Jxkh+sfzP7Z5wlg//GuGM3XaIT6OlP0OH2C40+qrOM9Th/+8u4p/OXdU5zbF8+YkFQ9fZ4AXjp8Di8dPse5feWNk0TLSMV/wnWLXbuiSTSzu83tF9zP5vYTLW1CwuR7Fnu+5xIhm4S2fXrewTmBBoD9x7vwwy9NQrvVybtPc5sVa/ozlfNnIbfix7MnQ69WRPZr6M96TrKOEwiEIcFQzmo8kMhmP2Uy63i65ZKs49mtP5fk47lnLeu4xPXk0veSzjqe4Lnn4/ggDFyGWhZ7sfONzz6ezj7RhPcnWccJBMKQwKhVYvOtk1E7aSQcPjqSDM2gotD8ydkhmbn1dI8LfZ4A7G4/CrWhJVe5Xp5r1Crx/v9eBY9cx+onDeNCd0DarOOv/eAqqJTsunx+F3zB3GYdf+IblaguNbNsO9TRLZp1fFyxFk8snhZZwmrUKuGnGdy16yBv1vE7ri3FktpLWPXtbP4sK1nHh6p/GrVKfHP6aNw1cxLr3J94/ZOcZR0XGn+SZR3XKAT93RpIrh6jVol/fOcqDDOyy7tgd0GV4azj62aX4yuVY1h1v3z4C3bWcY1C0E/DbWzUKiPJnOITBe5oah/UvkFgk2gSM679HN4ANEoK/3fnjNC4UlL4oseFt9svYEdTO1w+WpLxFK7b4/fDqFFF/KFQq4RRq+RcgRFtr0GtgJKSwUszYBjA7QugWKeCj2bQ5wnA0P/lOIggNAoKarkMQQABhDSw+zx+GDVKaFUUGCaUtKxARQEIws8ALj8Nl5eGUauAXklhfJEGjffcAIePhtvrh7lAA2d/OYVaJYIAXD7uF2MP3TIJX5o6OlL3/+tv2wIVhS6HG2oVyTpOIBCGAGaNAjPKR7Ji7+osJmycXwU9JcuhddknXxNPmbVKOAMU1vL0k1krLLmRVF0aBZw0xTsmzCmOiSKNAvXlZs7lefXlZhQlMEkxqyhox5n5x6uAacUaBf68/Cqs3XeYdeyfl18FDcVuQ7NGgW9de0nO/GMo+6dZo8B3b5iUV+du0qugERp/PJm3U6nH6eP3d5M+uXrMGgWcSn6fTqQti1SUsP/ynLtZo8BNFWMSrtukVwn6qV4Vuk4UaZXYvmQ6tr3eFqO5XWsxYfuS6SgSeTFAGDwkmsQsfr9xxVr8efnVnAn+NsyvxOkPnNi6qAb/915H2lnsw3V/3u3En5ZfxekP8c8ZXOdVbzFjxcyJWLW7BZsXVuPnr3wSU06txYTldWUw6SgU67TwMQwefO4Iq671cypwx8738YuvXwaaCeLxuGRmN00ZjnW3TMW6fa04frYPu+6YgfuibNapKOz57jU4b/ewspw/dMskzJoyGm6au+5NC6qgk6d3/ZbuiYdAIBAyiBdg3WSAUIzbun2t8ObGrJxwusfFmmQDobZYu7cVp3tcObIM8AaDwv0kYf7NTI0JBsCKGyai1mKK+b3WYsKKGywQXoCWvm0BgPXwfvHYw+B6L59r/8h1/bkkH8/dSzPCNtGJjOLs1yNFW6bqv8nWHTp3fj8NnzsTDOLJuMkBEIoDffKNT8EMrZzEQxaby8eajAKhuOo1ew7B5vLx7vfE4mm8Y3P9vsNoqLsETze346F5FWnFZ0fX/cTiaVjHM77X7m1FZ69b8Lwa27qx7fU2bFlYzcoYDoTG/1NN7SjSa9Fpd7MmuuG6NrxwBE8snoa28w7WJBsAJo8yYu2+kFLAloXVeOSF2HIa6srw039/jIoxhVg50xJzXbhx6mjButfuTf/6TSbaBAJhQECSoV0kn9sim7Zlqi67J4DlOw+gprQY25dMx5OLp2H7kumoKS3G8p3vJxR32ieSkKmPJyETkNp55XpM5Lr+XJKP554tm6SuR4ry7D5a2H8FkqElU3ei+9s9Ad5ETY0nuiWLlyfkN4kkMePbT0HJBceagpKjqc0Kjz+9F2jRdYvVaevPPSB0Xs1tVgw3qgWTkLl8NPRqhej5DTdqOPepGVcU+Z2rrppxRdh/vAsXHD689/kF3HvzZDy3shbP3jkDjgTqJsnQCATCkIAklLlIPrfFYEmG5vLRMcs8ky2XJEPLbv25JB/PPVs2SV2PVMnQUvHfpJOhJbh/Po4PQvZJNIkZ1359YmOof3u6Yym6btE6BeyNJj7JGKsctx+0yKKOPrefN5lZ9O9cdYW3u/w0fvnKCfzylRORbf935wwwYnWTZGgEAmEoIEWCqsFCPrdFNm3LVF1SJUNLtYxUjs31mMh1/bkkH889WzZJXU8ufS/Z4xLdPx/HByH7GEX6OTJeOPYrEBtD/dvTHUvRdYvWKWBvNAaNcJ6GAq0SAUb4S3yBVgmHl/vLslpxcXE2V13h7dH7hTEmUneabUqWjhMIhAFBgUaBuriYuzB1FhMKJMqiOxDI57bIpm0GFSVYlyHFhE9SlJtOGam0Ya7HRK7rzyX5eO7ZsqlAZJwXJOmDUvheqjYl22aJ7p+P44OQfcwGFa4rN3Nuu67cHElixrVfgGYEx1CAZmLKkMJGsToL+yfiQudVazHhvN3LypcQpt5igk5FwekNiJ5fOJlZPC2neiPHnrd7WeW0nOpFrcUU+X80hgTqTtc/yUSbQCAMCMYW67BpQRXrghjODJlrWatsks9toabk2DSf3zY1R8bsVNEwQcG6NGJrwnjL9WCjYLmeBMrwitjGn2Illf4tUCsEbS5QZ/ZhPp/HZKbJx3MvUCsEx59U40ENYNP8KtTH1VMf9vcky1OK+J4yAd9TgxYsQw3upazJ9mOi++fj+CBkn0KdCpsXVrMmpdeVm7FlYXUkiRnXfnftOsg7pjfMr8LTze0xZUhh4127DmLD/ErecRvOks53XvUWMx6eV4GPz9mxvK6MNcmtt5ixbs5UeAIBjCnU4qF5FZx1PTC3AnftOgjLcANWzSpnlXP8jD3SNvfuOYT1c2LL2dHUjpUzLTjeacey2lg7XjvaidFG/rql8E9ZMDi00h3a7XYUFhbCZrPBaDTm2hwCgRCFmH92WZ3wAHD4aPS5/SgI67UCKMmhpFUuONvrxucXXNCrKTg8NAwaCk4vjQnDdBjJoXGZLT4+a4dMFsQFpx96tSLGtmF6BYJBGS4dKc211+bywenywy/r197sHxM6FQVlENDrlCk9eBw/Y8e53j5cMryINdY+O9+LEUUFmDxK+BxOdNlRJKPgkXGM1yDQG6RRXsJfRrJj/dPzDtz394/ws69dHtIP7T9Gr6Jw918/xGP/dRkmDjck3RbREP/kJ9/8sb3LATkAGqEYxXB/qBVyUAhl5i4rSW88AEDb+T5QMuCdzy5guFEDb4CBWiHH+T4vZpQVgw4CluEFCZd3/Kwdh09242rLCNY4erftHCrHmzFZ5Ppx/KwdhZQXgX5t7+gyFIwLNlrNW0ZXrxsemmGPYUqOEp5+PN3jQp8ngD6PHwUaJQo0Cs6H80T3SwXybDtwCOtNh8eB2SCsox29n8MbgD1qDOlVFBgmiGIeLe50bfT6/Sjo19EO11kooKN91u7B6Z5QNvKWU73Y0dSOK8YX4+F5FZABER1tX4DB259ZI9rft1SOxP2zJ8foaBdoQvdyq8MDrUoZ0dH2MYC7X0e7QKuAQUlFrnUOHw23zw+z4aKOtlGrhIaSw0czYADIADBBwOkNQKuiMFxJwSeXseo2qigEmSDGpnmdJOtVCATCgKCz1417OCStgNCbx5/cdhnnxX8wYnP5cA+HnAYQejv++KIaSW+6yRAMAo+8cIyzn2otJqy/ZapkdfV5A7iXQ/IECI2JzSm+4derKPyh+RSa2j7kLPexBVWiZWgVCvxgzyFB2/hIZazb3D6893kvrvvpG5xl2kQS26TLUPbPfPXH+//BluYB+r+C3VopSR00E8SDLxzl9fcH5iTn7wa1Avtaz+PuvcdY2+osJsyYNFK0DBmAu//xadI22Vw+/CiFfkx0sky+XBOA0BfgRK4HXPsV6lQYkynDROpOhI0vHmPLfJ3oxkPPHcHji2pC+7zQgsa22H1ePHwWvR4/5lSNwnCjBst3HgAQ8rvVN1rw6ItHcVlpMVo6enivaT++eTIA4OevnGDts3KWRfDYy0uLWckTwz6fLmSiTSAQBgQ2tx8tHb1YOcuCmnFF8AYYaJQUDnb0YEdTO2xu/6B9kI8nEZmQnE20AV6pjOY2K6RcQpUpCSOnn8b3bpiIW6pHY3iBOjLWztk9GD9MC6dfOItq2Dah8Spkm83tx8dn+7B9yXQMN6rh8NAo0Chwzu7BvXsOcY51nUoBnYpCQ10ZZ326FOPVE2Uo+2c++qMnwAj6hocng2+yMEFhf082esPhDeCumdy+N8Gk5U2IFG+T0FjksynVfjxn96DH6YPdE4BRq0CxToURRk1S500gDHQSlS+Ln2SHaW6zoqG2DMONFwNODpzsgV6tRGObFUtry3iVBJrarLhPLgMll2HDvEq4/TSsTh9oJogPOnpQO9EkeOz3brDEbI9fzp8OZKJNIBAGBA6PH1sX1eCp5vaYC2KtxYSti2rg9A4diZREZUJygVPkQVhsezJkSjYnEPBjbJEOv33j0xj923qLCRvnV6HPIx4n6vT68ZdlV6DEGFq+aneHlrB944qxmFU+THC8un1+7LpjBh554UjMJKbOYsKuO2bA6fWxjqEoGbYvmY5tr7ex/GP7kulQULJETz8lhrJ/5qM/JirNky4OkZdZiUyMo2FoP0YX6vDk69y+5/SK+57LGxD0PZeP26ZU+rHD6sR9cSs56iwmPLqgCqWDPFyCQIgmEf8Re+/mDTARiS6disITt0+L3Od9dOjl4E2TzXhwbmWMbxtUFM72umDzMXiquT3GH+stZtx62WjoVBRcPu6X5HQwiO1LpsMbYDDepMPYIq1kL0fJRJtAIAwITAY1fvUae0lQ+G+plkIOBBKVCckFepEkS2LbkyFTsjmFWg3u29uKprix1thmxdp9rQktHR+jV8Mrk2ENx0P4xvlVUAukRzEbNKyHdyD05n3DC0fwKEf9KkqOJ19v4/QPOWTYuCCz/jGU/TMf/TFRaZ606xHJyJts0rUCTfq+N1qvSsn3DEleu87ZPbx+ev/eVvz865eTL9uEIYMU10G1Qh6R6GqoK8MZmxvj+19YlRjUuGmyGevmVPL69psHOlj+2NjWjUdeOIqGOv4v4sU6JU5dcEOjpKBTSrv6i2QdJxAIAwI/zb8UsrnNCj8tzVLIgUCiMiG5YDDIezl8NOtBP0xTmxUOnrfi0QTkMqzliB9varNi3b5WBOT8X5idPlpw2a+To35fgIn5AhhNY1s3fBItFeZjKPtnPvqjUcQPjRL5oVbEB7VJ+mAufU9FyXlliGotJqjiFBN6nD5BP+1xsleeEAiDlUSug2aDCvWCUmAenLeHFDlqxhVhhFGD/3xmDfmfQo4H51YK+vY3rizlLLvxRDeuuYT/OvXykXNYsesgGp5+Hw88dwQ9LulWIZEv2gQCYUBgcwsvQbSLbB9MhOU07o1L3FMvYVxRqoSlbNbGfZXKhJSN1eHBxvmV+M+n1kjGY42SwjmbG9dONMPq9GBcCss3HZ6AYLyz2HJZIDRhEIoTFZow2N1+VI8xYuuiGrj9TGR5nFYpx+rdLZxLWMWWAqcar54oQ9k/89Efx4j44RiJ/LC7z4MH51XA6vBGVAYKNAo4vH6YC9SwOjyRL1KJILoUPYO+1+v24X/qL4Ecspg40nqLGcvry2Bz+wBcPBe7iC1i2wmEwYDN5cP5Pi/sHj8enleBB547EnMdjI933ji/Euv2HY7Zp85iwl0zy2EuUOGuvxzE00uvRKlJB7ePxvXlJZhbPQoKuQxOv/BLaIePjvF7vUqBAMNAJpNBIZfhmf+5Gm9/ejHbeZ3FhKW1ZVi9uyVSTuOJbqz/x2FskyiJJZloEwiEAUGRVvhyVSiyfbARoBnMrhyJpddOuCipY/fkxZdDOUJZPu+dPTlK6igg+RIqvU4JGWR4qfVMXDynGddONEMvsnyWjwKtQjDeuCCBsZZOzLKpQImti6ax3tzXWUzYumgaAkH2REGnErYp08nQhrp/5qM/aig51t4yFUEgIlUl6/9dKgxaJYJB4PH9bayx+sDcCuiTXKJuEPnSLrYdSN33jBoltCoFZleNxNLa2H4cXaSFMu5LuFFkTIttJxAGOp29btz7t0ORF1M6FYV1t0zBmpsnwxOgUaRVxciXnbQ68fDzR3DZuKLItbJQq8S4Yi20AGwMjT98ezo6bW6sj1NNqLWY8PC8SsFY6z63Hy0dPdi2vw06FRW5DsTHbD+3sg7+AI0XD5/F6t0trPIaT3TjrN1DJtoEAmHooFOHlkJyLSuss5igkzD2N9/5osfFGRsIhNpiy8Jqyb5YJcsXPS5W/FQYqW3TyCnOuhrbuhOO5+RCTclZN2cgtARahsTijdOJWdYoFLiXQxosvDyOSxpMJgs9iPBJGskymwttSPtnPvqjzeXD3VmQHNOr+MfqI88fEZSx40KjkAuOI41C/CVBqr6nVlJY97ePBCXqojGIjHmxmG8CYSBjc/liJtlASIf6/r2HUWsxYU71aHy1cmTkOnO6x4X7+6+T+493xZQVlrwsUKrw2rFzeLH1DKf/Pvz8EcFY6wKtMnJcQ10Z5328sa0bDz93BBvnV/KWE7LXjZFGTdrXSRKjTSAQBgQObwBLa8tYMXS1/Ut/ks1uO5Cxi8ha5XLJYjZtc4jEMicSz8mFFNJI6cQspyJbFgSwjMc/ltWWSSqrxsVQ9s989MdEpXbSRWqJPS/NCI4jbwIrBFL1PbvbL9yPcZnch/KYJxC6HT5Bqa7hBWqcsXtgc4WuNYlcK3qcPowwanj3E4u1js7LUjOuiL+ctm64EpDplOI6SV63EQiEAYHN5ceaPYewZWE11kSWJCtwvl9b+MnF03JtYtbIlKyVFGTTtkzVJUWcqJhtQttTOq8gsOeDU2ioLYv4R1h7+28fnML3b5wkanM6DGX/zEd/zJbkmNTn3ucOYPXuFjTUlaGhtiyyfLvlVC9W727BzmVXpW0T33a7J4BxxVo8sXgaFJQcff25Efw0g7t2HWS9MOEa89F69/Fj3ubyodvhg90TKtesV+U0nwaBkA5i1xhvgMFnXU48+uIxbF5YDbvbD52Kwp3XXYI6ixkBOgi9mgIgw/6Pz8Hp9YMJXpTx4kPNseolnHvi4ecPx9QvRJ/Hj3qLiTOJaH25GXIZJJGlJBNtAoEwICjUKbF5YTV2xC0Fqu1fciQm9TSYyJSslRRk07ZM1SUqWZRAnKhBpG6h7amcl0wO/OCmSzm1t9fPqQBkmf2mPZT9Mx/9MVuSY1Kfu0GjgMtH8y7pTCRGO1XfK9Yr8KflV2HdvsMsH/rT8qsQYGIf3JMZ8529blayvOvKzdi8sBqji7Si50Qg5Bti1xh1f5jHWye6sWbPIdx/yxRsu70GTzW141evnojsV2sxYeVMC8x6Ndx+WlSpwE8zeGxBFRw+OpJ7wqCi0Od249XjF/1LLRJmYtAosaS2DAzA8t8VN1gglwVRqCUx2gQCYYigV1KCcbOpxuMORMLSPXyxgVJJ96SCUaPAjZNLMGV0ISvj77FOm6S2heW9eGMkU0wAJkW5BSJ9JDRZT+VYvUqBNTyxshteSD5WNlmGsn/moz+aDSp8acpwXDrKyPLDj8/YJZMcS2eccyGJ74mUUcBThlbJH2++ft9hlg8Z1ArBMR/e3+bysSbZwMUJiFTx8gRCNgnLeb3FEaJSazHhnN2DTpsHQGisb1JSeKqpnfUFOew/c6pHY/qEYrS0X+DNN1JvMaH5Uyu27W9DrcWEmtLiyEu5lbMsMX7fcqpXsJwAzcSsngGAUYVavHT4DJbvfB81pUWYUz0aRTplWv5JYrQJBMKAIBVt4cFKWLonXr9WaumeVBhTrMMDcyvQ0tGD5TsPRLQpP+zowQNzKyS1zcPQ2Di/CvVx7VDf3w4eJrUx4fDTwrGXCcR2pRO/6fIHsGF+JWf/bphfBZeffazDGxDWH85wvOhQ9s989MdCnQrr50zl9MP1c6ZKNrHzBxg8MLeC89wfmFsBf5L67a4AjfVz+MtzBRLwvRT9N9l4c4dXeP+wz2UrXp5AyCZhWcN4XexaiwmrZpbjyrJhqBpTiB1Lr8TKWRY4/QHOZdrAxZhur5/BxheP8eYbWT+3Ajua2iO5R3Y0tUe272hqx9LaMtRbzJG/l9WWsa4ltRYTHplfibt2HYysnlm+8wCW7zyA830ebNvfBlf//Wx4gTpt/yRftAkEwoDA7vaLxM/lLi45F4w36bF5YTX6PIGIznKBRiGpTnUq2Fw+rOPIwBzKmC2dNiUA9Dr8UFJ+3H/LFAQhi4wJAOhxehBgAJQkX67N5ReME3162ZUZLaPH4cfP/30cP/va5XD66Ej/6lUU7v7rh7j7K5NZx9hdqceES4FtiPtnvvmjzeXDln8e44zZ3/Kv43h0QZUkftjr9sHnp/HIrZXwBpjIUk61Qo4uuxsqZXKrSnqdftz1zEFsWVgdIw943u7F7X94J6FY/1TzBSQbby6mDR/enq14eQIh24wu0mLbohqc7/Oi1+WHWimHTkXh4MkeNOx8PyKbVWsx4YZJ3Ddjs0HVr8ygDeUa+d61sLn9+N8vX4p7b5bhjM0DhVyG1i9sYJgg/vbda9Ht8CIYDOLJ26fBbFADMsDjD/n5pgWVcPlD980CrRIb51fhgtOHc30eqKjQPfiC04fH/qsK77RfiGhqA2DJfHkDTNr+SSbaBAJhQFBsUCYVPzfYOWl1RqQywoS/oI036XNm1zm7h/etdeOJbpyTSJsSAEYa1fDSQTz4HDsu+aF5lVCnKB1t1CoF40QTiTst0gmXUSgQ21psUGLLbZexJKPqLCZsue0y+DnGulisrFg8XboMG+L+mW/+eN7hwWqBmP3zDmn80KRTwccEWZq3YR9UJblu0qhVotvhw/KdBzi3J+p7QrHTfL5XqBPxobjjxEICwtuzFS9PIOSCQl0oqZ/N5cNLh8/ihUOdnOEUd93AvgeYDSrsumMGtvzrGG6/ejwrFCP85frePYeweWE1Nr14NPJ8oVNR2L5kOrb861jMM0dd/8qVsD52OAZco6Bw1zOhr9jXXGLCO+0X0NLRg62LaiL7xsd1qxVy0ZhxMcjScQKBMCDQUBTrIR64GD+nodK7GA4kOnvdrId6INQWa/e2orPXnSPLICplJKXUURAy1iQbCLXDQ88dRhCpiUeH40S5SDROVK8ULkMv8KVPKzLWtRxjXS9isz7NhwUxhrJ/RuvDRhP2x9M9rqzbpJDJWZPssE0bXjgChUyixz+ZsA8mK+Auhe/pBPIFPN3cDh2P7xlEfNYQd5xOxFZdv63hWFYuris3SxYvTyDkkkKdCtNK+SW13v7MylpmvmVhNR554Qimji7k9dmnmtuxZWE1nmqOje9uqCvDttfbWC/2m/qPaagri5Sx7fU2nLG50VAXWpL+n8+sEfmv8L61FhNaTvVGyqm1mHDe7oUmyVU58Qy4iTZN01i/fj3Kysqg1WoxceJEbNiwAcFgplVCCQRCLsmUZvJAxCai92rL8DJhIfRq4a88YtuTIVNxwU6RGE9nIjHaacR5pzLWpbA5HYayf0qtJS0FUmjBJ4LUPihJjHaKYzFZn7U6vIK2Wh1eABdjWeMn29eVm7FlYTVJhEYYNMQvvY5mR1M7HplXEeNfw41qNLdZBTWvm9usGMmhrS12TM24opi/Rxg1uOaSi7HdYfmv5jYrrr0kNua71mLCqlnluGJCEVy+9K7fA27p+JYtW/Cb3/wGO3fuREVFBQ4cOIBly5ahsLAQq1evzrV5BAIhQ+SjVm2uyOe2kMuAeosZjW3s5D/1lpA2pVRkqh3sOY7RTuW87DnWsc7nMZlp8vHc+7Jkk9Tn3uv04w9vtUXke8Lx7gYVhYefP4w7r7dkzKZkfValoLD4j+/wxpPvbLio+T26SIuffu0y9Dh9sHsCMGoVKNapMMKoSaBVCISBQXyYhE5FoaGuDDXjiuCjGVxw+lBTWhzxL5c3NDEX07zmSuYpdkz8dm+AgVIh51wmrlLIoVHKsWPplShQK0AHg1BRcnT2eNL+ODDgJtpvv/02br31Vtxyyy0AgAkTJmD37t147733cmwZgUDIJPmoVZsr8rktlJQcK2ZOBIMgW5typgVKSrqFVJlqB6NIfHUimtDpxHmncl5imr5CMeFSkM9jMtPk47kXZMkmqc/dXKDEujmVWMMR775xfhXooPgX7VRtStZntSoKl44s4Iwnr7WYoI1ackp0tAlDgWjJL52KwtZFNXiquT3iU9uXTI/xr+dX1QIQ17zm8mmxY7jirf0BJhK3Hb1MXK9WYPavGyM2Lt95AC+uqsOBjh7MrR4lWI8YA27p+LXXXovXXnsNn3zyCQDgo48+QlNTE2bPns25v9frhd1uj/mPQCDkB8n4pxSxe4OFsHYtF6lo10qJn2awfOcB1JQWY/uS6Xhy8TRsXzIdNaXFWL7zffhp6ZJiFYiMCT69XDHSia8Ok854TaV/9QKavk83t6f0Vp74Z2Lkoz9mqz+kzg2gllNYu49PtaAVannmfC/Z49QKOVbOtHAuNV85szzyoC+mo21zJS8fRJ5tCflIdJhEQ10Z654U1rYOc97uRV3/pDfej8LUW0ywuX2s7ULHcMVbn7N7IsdELxOvt5ihVcpjjqu3mKBSynH8jB1FaYZ2DLgv2mvWrIHdbsfkyZNBURRomsamTZuwePFizv0fe+wxPPzww1m2kkAgJEIy/unqj58LAqwvdktry+DKcAxqPuHu11l+YN/hmEQg9f06y24OneVswZXsTBaVEEnKZGjefh3tdftaYzSkw9mevSnqaDtFxloi8c4Ugnh0fhU67W7o1YqIvJLD68eYQi0o8OcVcfoCeGBuBR55/gjrvB6cWwEnR8xYnyeAlo5erJxlQc24IngDDDRKCgc7erCjqT2lOGHin4nh9AXwYH9/xfvjAzz9lWncNI0N8yuxft9h1hjaML8Kblqa/rjg9OLBuRV4mGesXnB6UZpE1vU+kfjqvgRivmmGxmPzq/AFj+/RPNeFIBMUvJ4EmVifHVOsA8MEsWqWJUZCLVSPJqKfnoiOdrJx2uTZlpCvjC7S4vFFNThj87BWh+xoasfWRTUAQveJe/ccwjP/MwNb/hnSzg7/HiasnX37H97B5oXVAICPz/Zhy8JqDDdqUG8x4/6vTsFZWyhEqtvhi8k6Hi5j1cxylBjV6LJ7ACCyfLy+3IwNt1bix3/7EPUWE5bVleGZdzvw8K2V2Lb/BB6cMzXt8A5ZcIBlEXv22Wdxzz334Kc//SkqKirw4Ycf4gc/+AF+8YtfYMmSJaz9vV4vvF5v5G+73Y5x48bBZrPBaDRm03QCgRBHMv757mdWLHv6/Ui8T3T83I6mdjy97EpcVcb9dnOwcfDkBRi1SrzffgHDjZpIW5y3e3Bl2TDY3X5MGz8sJ7YdO2PDF70eXpmOMUUaTBlVKEld77VbsfGFo9i6qAZu/0UNX60yFIe1bs7UlMaEFGOtq8sKl0yD+/dxSz7pGA9KSrjL+OBzK4w6FawOb2SiYNBQcHoDMBnU6HP7MG187LHvt1th8wR4271Iq8D0Ccm1BfHPxAj31wEOf5xeNoyzvzLN+59fwI/+78MYXfMCrRKBfl3zX37jckyfkP414vhZGyi5nHes0gyDySMT9/d3P7PiG79/h3f7//vOjAR8zw6XjOL2vflV0AVplJSwn/8+6rDCpNPAFwyyNMFVchkuuDyoHhdbd4fVibV7W2NfsJSbsWl+ZeQFQ0tHDxY8+TavvftWXIvLS4sFzyke8mxLyHf4xr3ZoMKOpVfC4Qmg1+1HgVqBscVauPwByCFHUBbKs+Dx0zhwsgdVYwrxnT9/AJ2KwspZEzG7chSnjOTG+VXw0jRUcjl8DAO7KwCNUo4ggI/P2DHjEhPkADp63ehx+SP3p+Oddvz45sn46HQvrpwQ8kMvzeBYpx3TSouTelHIxYD7on3PPfdgzZo1+OY3vwkAqKqqwsmTJ/HYY49xTrTVajXUanW2zSQQCAmQjH8atUr8dMEUVJeaY5LkTBpuwJQSzaCOAY3HbFDj/r2xX13C1FlMeHRBVQ6sCmFQKbDng1NoqC2L+cpzzu7Bng9O4b7ZUySry6hVYuooA6j+L+bht8aUTIapowwJxVLzlVs5ugBfv2IsnFFjrXy4Ae991p3QWPPKNdj8z2Oc7bDln8dwv0A7mAs0ePGj05h72Vg4fDTkMhoKuRxjCrV44aPT+OplY1nHmAxq7Ghu56zvbx+cwj1fmZx0OxD/TAxzgQZrBfxxUw78kUvjOToPoVTL2fVKBf7yzkl8a8Z41ljd9c5JLJ4xPqnyjFolqscYIy/PwuMo/PIsMd+j8MtXPsa9N0+OvGQwapXw0wx+9erH+N8vXcp53DC9Bs++34FvXFkKLy5eT5RyGf7v/Q58/crSmP3P2T2c1+HGE91Yu7cVP/v65Rhh1GRER5s82xIyjc3lQ7fDB7sn5D9mvSqy8oJvm83lQ6/LD6cvgAAdxN+/dy1sbh9kkMHlp6FXKVCsV8LhDYAJBmEp0UOnpOBngkBQBocvlChwRIEaF1w+3FI1CnIZ8K8f1EOGkHRffP4G4KJs4cNzK+BnggjQIe9VUnKolXJcMaEY214/gRU3lMPlo3H3Xz+KyZDuDtCoKS3Gcx914pF5FejscWPji8cweWQBNi+sxtj+1SmpMOAm2i6XC3J5bGg5RVFgGOni/ggEQv5hVlHQjjPzJsnRS5jNOt9x+mjOh3ogPVkrKXAHaPzgpktZGr51FhPWz6mAOwF5nkQxKeX47g2TBMZEagu2hqko/OS2y3EfR7k/ue1yaBMYa06RdnAKtQND46vVY3nPCxxLX+kgI1gfHczsPXIo+6dbxB/dOfBHnUKOPy+/mhXvXGcx4c/LrxYMXUiGAENj0dXjefs9kGT4RrGKwtZF0zjt3rpoGjQJjCMXTeP7N03i/Oq1YX4lXDzL5v0Mjduml/Keiz/uXHpdPt5+b2yzotflwwijJiZBVDxER5uQj/Al79uysBpBgLXtS1OG44E5U/GFzYPH95+I8Z96ixnL6ibggX8cxuaF1djyr+Os7StmTsTynQcik9/wSqz/3v4eakqLsLyuDHoVhYBayRlaolNR+NaMCTjd68bjr7exyl85y4Lv3WCBO+DH082fY+uimsjycSC0XL2htgzb9rfhs24nnm5ux647ZmDxH99JW55xwCVDmzt3LjZt2oQXX3wRn3/+Ofbu3Ytf/OIXWLBgQa5NIxAIGcQDCCbJ8eTGrJzgELnwc0lhZAulXM6a7AEX3zgr5dLddryQCY4JL1Kb3fkhPNYSESxSibSDSqAd5CIJoeQcCaEUMuH6FLLM3u6Hsn+K+VtO/FEm7BuQSfPmQyEyVhUJJC+LJgDhcZRIS2ooijXJDpexft9haChum5Qi56KMOxexB/DwdqKjTRhICCXve+OTLtz7N/a2S0cZ0djWzZpkA0BjWzeeamrHloXVnAk7G9u6se31NjTUlUV+a26z4qnmdjTUlYX+3dSOT7ucvDleGurKcMbGnmRHyt9/Av/51AqtQhmyp7/saMJyYN4Ag8b+++aWhdVpSyEOuC/ajz/+ONavX48VK1bg/PnzGD16NL7zne/ggQceyLVpBAIhgzhEkuQ4cvgVN9sYRJZ9GtLUfUwHT4AR7CePiPZlMkiROClT5abTDqmM9Wy2OxdD2T/FMrqnq8OaCtnqD6nrkcL3nCJl8K34SfZckun3cIKobocPfR4/CjRKmA0qMskm5B1CyfuGF6jR2MbeVjOuCAB4/aexzYofz57Muz38RZnvt8Y2K5bWlsGg4X5Jlkj90Uk5ueoLqwSE/9/UZsW9sydDkebHgQE30S4oKMCvfvUr/OpXv8q1KQQCIYvY3cJvFdN96ziQkCG0tIrrplJrMaX4HVcaxPpByn7K1JiQotx02iGV+vty7B9D2T/z0R+z1R9S1yNFeamWkexxyfZ7oY5MrAn5j13Ax7w8L2z5fo/G5RV+ScZVRvRv3gATkQOLD9lIpP5wgkOuY+qjJMaiZcGcXhojCtKbKg+4peMEAmFoIpbYajAnW2IhA5bVlnHqty6rLUMuZ9pi/SBlP2VqTEhRbjrtkEr9BTn2jyHtn3noj9nqD6nrkaK8VMtI+rg87HcCIV2EkveFv/Zy/c63LYzYSjuu46N/UyvkuHfPIayfU8HSuy/sVwcQKz/6Phnev95ixoPzKnC00xajrx0uN10G3BdtAoEwNDGoKM43mUAoWY1BlVws4EBGp6Sw+92TqCktRkNtWYyU0u53T+LBuRU5sy2b/ZSpuqQot0BF4cbJJZgyupCla32s04YCgTJSqd8gUl+m/WMo+6daIceLH33BmeX6qabP8P2bJmXdpgKNQrA/pMo6LvW4k8r3BM+dp4xk28ygVuDvcQoLBo0C5/sVFu6/ZaqorQRCviGUvO98nzdmm05FoaGuDIVaJVQKOeotZs6l5fUWE87aPYIrQKK/JMf/Vm8x47zdi26HD4v/+A72fu9a2D0B2D0BGDQUZAAoObDrjqthc/sj16AdTe0hvWyLCXq1AjIAf2q4CkpKjpFGNV5cXQsVRUFDyXFzxfCYBGl1FhOcXj9GFxak1Z4DTkc7Xex2OwoLC4nWIIGQhwj5p83lQ4/Lj3X7YuVUwvI5RVrlkFmW19HlACOT8baFjAmitMSQE9u+6LoAv0zNa5uC8WJMiTQa3+1dVshkGt66GMaDMh6taiFOdNmhlFG85foYGuUcOrzRpKOj3dXlgFOgf3VMECVx/Rs+hitbM98xySLkn11WJ5xB8NsMoCRNPdJ85WSXA0Gett84vwqyYBDjs+yPXT0uOOkgf3/IZShJQ7ImUk8a45yLz/t9ei2P3UHGgwki5Z3qsoORUbxlyBka43j892S/LjbXceM5xu9JqxP3c2Qp59s/U5BnW4KUdPa6sWbPoZjJ9nXlZvxkYTUYAGv2HMKBkz3YuqgmkuCs3jIMG+ZX815zNjx/GN+8egIrIVoo67gFy3e+z8o6vnp3C64oLcbKWRZ4/DT+2NiOxrZu3DTZjHVzKrFuXysOdvRi2+01eKqpPUbPPlzGs+92YM1Xp2DzS0fx6vGumHqX1U3AymdaMK20CJsWVGH2rxtDE/NyMx6aWwGDisKIIm1abZmViXZ7ezsaGxtx8uRJuFwulJSUoKamBtdccw00Gk2mq4+BXIwIhPxFyD9P97jwRXcfRg8zwOGj0ef2o0CrhEFFofOCA2PMBWlpHQ4kOnvd2PDCEdx2xTgMN6pZX1LWzanA6DRvDqlyyurEQ88fifnCFf7afqzThofmVmCcRA+gmarrk7N2/PaNNiytuyTyhbJAq0SAZvB0czu+e/1ETBopfP84bXVijYC28uYFVRjLY9vpHhf+/PbnEW3i6LG+652T+Na1E1hj/XSPC2v2HOKvL00tUEDEP61OfHHBwe+fwwy85zvQ+aLHhR/vOcT5pabOYsKWhdUYk+VrU7b88Iv+cd7Ice715WZsnl+JMUnUc9rqxIMCdj88t0J0HJ2yOvHE6yew/LqJCNDByFhUUDJsb/wMd91g4Tz3ZP2us9eNH//tI16f+8ltl2XtOkyebQlSE9bK7uvXytarFXB4ArB7/DCoFVAr5Hjk+SORyetb99yAh54/gqk8vvvg3Arc/OtGNNSVoWZcEdQKOYYXqCM62i4/DZePhlGjgFIuR7fTC6NGiZaOHmx48Ri+c/0lKDGoMcKogTfAYJRBAbNRBz8TxIP/OMx7Ddp4awUeeeEoXouaZEe2W0y4rLQY2/a3ob7cjPtmTwYTBFpP9+KGS4djlAT+m9Gl47t27cKvf/1rHDhwACNGjMDo0aOh1Wpx4cIFfPrpp9BoNFi8eDHuvfdejB8/PpOmEAiEAY7DG0BfACyN0/Bby1xKWmWbPo8fC68Yhx1xb4bDbRFK2pObibbDR+O1412cNzUAuPsr0mWfzlRdDIC/f3gGf//wDOf2O6+fKFpGn4i2slD25D5PAL9rbMfvGts5t986bSznMYL1pakFKobDTwv7p3/wZh23ewKC2artngDGZNmmbPmhw09zPuACQOOJ7qT7vU8Cu51+Gs8e+ALPHviCc/vSuGzDkbqT9Dub2y/ocza3P2cvPAmEdIlO3tfZ68bdf/0oJht5fbkZS66dgLc/uwCXj4bTR2P/8S7sF/Bdl4/Gtv1tMb8/vexKjCrUotflx+I/vovnV9XiK79qBABsXzId9+09DACoHF2I5TsPsMrdvmS64DXI7g3wXk/C2cjD+y691oPhRjXu23sY//p+vSQT7YwlQ6upqcHWrVuxdOlSnDx5EmfOnMEHH3yApqYmHD16FHa7Hf/4xz/AMAymT5+Ov/71r5kyhUAgDAJkAKcGY1hvcSjlnQkGhdsilwFB2cw+nam6+twi+rgi20P7ZDfreM6zfouMSQziILWctz1XnVnLOi7sC2Lb2ftLkXU8Nf9Ntu587HcCQWr4dLUbT8TqUYv6A8f2WosJw/QqPPLCEdj6tzs8F1+mxWcd50Is43h0eWLHewNMZH8+ze5kydgX7c2bN+MrX/kK73a1Wo0bbrgBN9xwAzZt2oTPP/88U6YQCIRBABMEtAoZGu+5AQ4fDXt/wiGDisLDzx8GM4gf5OMJArjtshHYvKCK1RYHPjuf0zmNUavEzxZMwdWWESzb3m07NyCyjuvUFL5XPwG3z5jAOodn3vkcOnUCCZm0SkwabsAT35qGAB2MlKGgZLjrLwdFs47fUjkca2ZPZdW/+Z9HOY81akO6vFsWVkfCCQo0Cpyze3DvnkMZz/odhLB/Dmb3NGqVkaRA8QnBdjS15yTjeoFWiXWzy/GVyjGs/nj58BeS2aRTUYLjLhFfiUaKttSrKcFz57PJqFWieowRWxfVwO1nIsdplXKs3t3CqjtZW0/3uNDnCcDu9qNQq4RBoxgy4U75THiJtL1/ibRZH/qSG/+7Vkmhz+OHzR2AUatAsU6FEcbkw187e92wuf2RcWDUKnOy8uGc3YMepw92j/D5ROtqc413s0GFHU3tkftx9D4+msHwAg2UlAz+AIN/fb8eOhUFT4CGzRVKZGZQKbDx1kq4fDT++p1rUKRToOmeG9Dno+H0BvDvH9ZDq6Dg9NN4+Qf1UFJyuP00XF4aRq0CKoUcu5Zfhf+0X4gkP4umSCd8zdAoKaycZUHNuCIYNUoYtQq8sKoOaqU0n29IMjRC3sEwDDo7OyN/jx49GvI0BeMJAwMh//zk7AWoFGrehEO+gBeTRkqTZCvfOdd1AW4Zf1tog16MkCjhWLKEkzDx2aanpEnCBABnrE78eN9h1pt2ILSs7SfzKzEqhThUocRiG+dXQR8UTyx2zuqEOwj+PpIBI3hsCycW461fxk4sdq7HhT4/gwefO8w65qF5lShQyjEigzHaQ9k/z/S6cdLqwuOvn2Atm181sxzjTTpJliAmQ5fVAWdQYAzLgigxpZ+g7QurA24avONOSwFjkqin0+pEh82Dx/dztOWscpQWajBaxKdTPfdzPS64Ba5dWkoW40Nne934/IKL19YJw3QY2d/vmU6aRp5tU6Oz1836WvulKcOxfs5UrI27t9RZTFjan6DL5aNRZzHh0QVVKE2i//IleV6H1Yn7OOzgOp+Wjh4sePJt6FRUTPKzMPUWE5bUluHS4QY89PwRLLp6PGuf6ORm00qLsLS2DGv2HMJPb7sMGqUc215vQ3OblVVH+O9n3j2J/54xIWbfi/WbsX7uVPzylY+x8IpxMZnD68vNuKOuDH9o/IwzzKO+3Ix7b56Mx/55LLbMcjM2zq+UJIlnVmYvFEXh/PnzrN+tVisoavBKfhBSo7OzE8uefBkrdn2AZU++HDPpJgxdtEr2QzwQioVbt68VWqU6R5ZlH59cuC188ty1hZdhP6gCF23zSrj0gAGw4oaJnDq2K26wQHhBGT9eOfshHYg6B7n4m24/2BPl6DKEFtmlUr8f7MlO+JiHnjssWJ8UDGX/pINBPBE3yQZCy+afeL0NdA6+Z3ghMoYkCrYJQiY47oJJ1sMAeGI/T1vub0vIp1M992R9NhAMCtoa6O/30z0u1uQqXO7ava043eNK4KwIUsO3JPrSUUbct7eV9XtTfxhMeKl0U5sV9+9txTm7J6H6OnvdguOgs9edxtkkzjm7hzXJDtvBdT66fjm8hroyzvCgxv52efOTLjw4t0IwhKihrizSjlsWVuOMzR0zcY6vI/z31NGFrH0v1t+NDc+HksNG90+txYS7brAACOVliNferrOY8MitFfjVKx+zyzzRjXV7W+Hpb690yMpEm++judfrhUo1NOR4CMmhLTRDVzwc2kJzrk0h5AkOHy2YcMghkFxqsJHPbdEnYptQErBksftoLN95ADWlxdi+ZDqeXDwN25dMR01pMZbvfB/2FOuS4hzSKaNPJLkWV2KzVI6Rknwek5mmzxPgT8bT1p3xtuciW34odT19PoHkam3dGfW9ZH0o0X7PtW8SuIleEh1Nzbgi3v5qbrOiZlxR5O+mNit6nL6E6rO5/YLjwCYS4ywVPU6foB3x56Ok5Ki1mETbpXpckeBYj2675jYrhhtDGcSj94+vI/x3zbgi1r7RNPaX19xmxZenjog8BzTsfB/DDCqs3t2Cy0uL8fyqWvz1O9fg6WVX4vLSYngCTIzkV3yZDh+dcP/ykdGs41u3bgUAyGQy/PGPf4TBcHGpDk3TeOuttzB58uRMmkAgEAYJJPHMRfK5LbKdDI0ri2m6dUmTkGloJUPLdf25JB/PPVs2SV1PLn0vU8nQ8nF8EAA7T7uLJdeK355o0qx8GQdi9sZvtzq8WMaTqT+a0z3iX+Sj287hoVlt6aMZzv3F+iRcXtiOFbsOxvwefk6YOsqIYToVlj71PoDQsnMh+tx+QJbe6p+MTrR/+ctfAgh90f7tb38bs0xcpVJhwoQJ+O1vf5tJEwgEwiAhU4mvBiL53BbZtC1TdUlRbjplpHJsrsdEruvPJfl47tmySep6cul7yR6X6P75OD4IgJGn3dUK4cW+8duNmsSmUvkyDsTsjd9u0Cix5Kn38aeGqwSPE2u3+H0MGgou38W/dSoKY4u1nPsnUrZBQ3HuG/49vE0f9XeBWJ9olWkH2WR06Xh7ezva29tx/fXX46OPPor83d7ejo8//hgvv/wyrr766kyaQCAQBgkGFcWKsQlTZzHBoBo6+R7yuS0KRGwrkNC2TLWDFOeQThkFGoXwsRwPSnqR+vQZHhPZ7Pd8Ix/9MVv9IfW559L3kj2XRP00FX8mZB6zQYXrytlfNFtO9fL2V63FhJZTvZG/6ywmFOsTC4Et1CoFx0GhyKRPKor1KkE74s/HqFFgWmkR3viki5UPJfq4llO9aDnVy7tPdNvVWkw4b/finN0T2b+hrgytp20xx4fLaznVG7NvPPX95cX3T/j3i3V6In/XW0yQIcj7Vbu+3+cT7V8+spp13Ofzob29HRMnToRCkZsLC8nMmP+cPn0aK3Z9AF3xcLh6zuPJxVdg7NixuTaLkAWE/LOrP9N286fdGG7URKQlztncqLWYoWW8KMlRpu1s09Vlh0dG4Qu7G3q1IiKp4/D6MaZQCw1Do6QkN9e3LqsTrv6M2dFZPsOZVaXI4hmpq8sKt0wjMCY8KCnhvjEL0d5lh0xGYR3POTAMjTKR9g1nPuYrQwf+rM9dvW64Agx/G1JylMRlsW493QO1UoENzx+JiRutt5iwfm4FfP4AKscWJ9UO8Qj6p9UOV5DC23x9ARolpsF5z+3qssPVP17i237jgirocuCP2fLDri4rXDINfz1J+mCX1Q5nkN/3dAmMo1NddjAyitcmOUNjHEd/dHX1wClT8dfN+FBSEutDXVYnPABLRkyD2PY9aXVi7V7ucknW8dzR2evGmj2H8BZH1vF1+w7H/C5V1vFMjoNE6ejPfh5vB9/5nLQ68QhPRvG6/uvchueP4D+fXeDMTJ5I1vGG2jKs2t0Sc7zZoMKOpVfC6Q3AZFDhgsPPUncIZx3/1Ssf47+iso7Xl5vxwJypWPSHd3DpyAKsnFmO4UYVvvG7dzBlpBEPzasAZEGcuuDGH+OykkuZdTwrE223242VK1di586dAIBPPvkEl1xyCVatWoUxY8ZgzZo1mTYhArkY5T9koj10EfLPk/2Tn7XxD7PlZmy6tRLBII3xOZpcZpvwROx+DhmaTfOroFOwJ2LZoq3LjkIZBY8s9PDZ5/ajIPzwGQRsQRoWifrpRJcdKr4Jzvwq+II0ylOo6/32C9jwwpGInm74HMJ6uuvnVODKMuGXOsfP2PFO2zncOHU0qx1eO9qJGZYRmDyK27aOLju0Am3oDtIojTuvz7ocOHXBhc5ed2Siq1bIcd7uwegiLcYN0+ESEUkyMYT88+NzdmgpGdbuO8Lhn1PhpoO4dMTg9M8zVicCQaAp6iWDWiHH+T4v6i4xQSFDSjJz6XCiy44igTHUm6JvxPNplx0aGQW7P4AgZJF6ZACMSgqeII2JSdRz/Kwdr7R2Yv60cSy79x08hS9VjcbkkcLlffD5BQA0zAU6uKLK0KkodPe5AFC4YgLbfz8974BR5oZHrmO3GeOCPajFxOGxPpSMXFNYR7vP40eBRokCCXW0ybNt6oT1ssP9YjbE6miHf9eqonS0NQoU69PT0Q6XW5gPOtoJnM8XPS70eQNAEAgCcHoDMGgU0CspeBgaajkFp4+G0+vHML0aPpqB3RV6+aRWyHHS6upXDQiiWKeEnwYK+nW0A0wQ3Q4fvva7/0R0uK8cPwxjirV4+LnDaOyX+vrO9ZfgS1NGIAjA5aVRoFVArZDDTzPQKCi4/DQcngAKNApolRSsDi9USgpaJQWlXIZuhxdalQIGFQU5gHMOL/QaBbQKCg4fDYc3dGyBkoJKSaEkhf6NJyufldesWYOPPvoIb7zxBm6++ebI7zfddBMeeuihrE60CQTCwEQup7CGQ5Ki8UQ37t/XiscWVOXIsuzjDQZZk2ygXyZkXys2L6zOkWWAWk7hBxz9BIQeQKXsJ62cwr1cYyLcDinWpVNTOPSFHTf87E3e7WL0eQJ46MVP8NCLn3Bu/+t3+b/yyVJoQ42Swu8bP+M95ie3XSZqc1oEgfv2HeH0z/v2HcEDc6Zmtv4cEgCwhsMfAenHfKJosuSHajmFHwvUk6wPOjwB/Py1T/Hz1z7l3H5N+XDRMop0KjzAITkGhL6sPTKvkvM4s0GFVbuPxHzFDHNduRmPL6qJ+U1MtmvzwuqYibRUk2qCtBTqQhPrxH5Pf0I8ukibk4l1PCOMmqReFIxJYPyes3uw8cWjEZ/QqSg8e+cMbIj6LZq6/i/dACJL58OJy7YvmR4jHejy0fjlKyfwy1dOoM5iwgNzK/DlX77Fa8uzd85gadxH17tpfhUW/OY/rG3bl0zH9PHFnGMiFbIy0d63bx/+7//+DzNmzIAsKntbRUUFPv2U+2JKIBAI0Qxl+aB48lkuxuGj0dLRi5WzLKgZVxRZQnywowc7mtol7adMSRhplRRqLSbeB3WtUnyibRCJuzSo+benMtbtHmHpGLvHj9ESPCTywQQhKOsioXx63pGP16Zs2SS1D+oF/CKR7QDgZxjBsehnuDMYF+pU2LywmqWtXF9uxpaF1awH73y+DhMIuSBeOqyhrgwObyBm4t1QVxbzbFA6TIvTF9xQKeQx992wXBcXTW1WBERuKkatUvB4l5//2tTt8A2siXZXVxeGD2e/hXQ6nTETbwKBQOCjL0+kMfKBfJEJ4cLh8UdirKJlt2otJmxdVAOnV1p5LyFSbQc5gJUzLQBiJ4+1FhNWzixPKIuoXBaKHWtsY38dq7eYIRe49aUm7yX8UN8nsj1dHF7h8sW2D2Ty0R+zdb2U+txlgOBLrkSeGB0iE1yh7TTNYHblSCy9dkJM+EWAZk/O87HfCYRcEi8NNr20GPJ+r9WpKM5ng3qLGf/7lUthc/ojX7eb26wRuS4++tx+1FtMnFr2tRYTZCIvd7n8M5xMzZRmArRosjLRnj59Ol588UWsWrUKACKT6z/+8Y+45pprsmECgUAY4Bh1whk5xaQzBhP5IhPChcmgxq9eYy/XCv+94VbuZZupkKl28DIMzAVqrJplwZrZk+Hw0DBoFHB6/TAXqODl+SIWjYqSY8XMiWAQZE3WV8y0QEXxT9dTOa9CrYhki8j2dElWMmYwkY/+mK3rpeTnLgPuqLsEt1SNwoiopHpnbW6MKtQikZm2WBZvvu3n7B7O8CQgtNT051+/PGapbT72O4GQC8Lx7DIAO5ZeGVnBVqhTwtW/qqWhroyVJA0AGtu6wSCIh+dVYN62ZjTUlaGhtkw0C7tRq8SyujIwiH0hXmcx4Z6vTAYD4ft0vH9GJ2xbcPmYxE9ehKzc+R599FHMnj0bR48eRSAQwK9//WscPXoUb7/9Nt58kzsGjkAgEKJRU3LBLx1qgYnLYCMsQ9PE80CYS3kvPy2ybJPjy1CqZKod9AoKnXYPntjfFpdkzYxVN1owOoG4Nh/NYPnOA5GHhvDXsZZTvVi+833sXXGtpOelFPEPZYb9I9f15xK9SH9lWlqNi2xdL6X2QYOSgk5F4aXWM7FZgC0mrJxVDkMCYRupjsX4Za/RNLVZ0eP0xUy08/k6TCBki85eNyvcIryCTaWQ4bXjVtRaTKgZVxTzJTua5jYrbG4/rii9uM/2JdMF/euMzY2Vz7Rw3mN7nD7QwaDg8ToVhe1Lpscct3p3C6aPL4bZMMC+aNfV1eHDDz/E5s2bUVVVhX//+9+YNm0a/vOf/6CqaugkMCIQCKljdfpilhWFCb+FvOD0oawkV9ZlF3fAi83zq9AXlek39HUliAKVAq6AD0B2sxyHsbn9qB5jjGTsDkvehDN2iy23TIYLTi9+Mr8StEzGktehgkCX04txKWR7loPG60fP4dEFVaxyn3m3A/99tfjb7j6PHya9Cl+eOgIKSh7po5ICNf7R8oXgslIf48Wm+VW88kQ+xov4/rU6vPhO/USsmmVhSb75/EFccHrTzjouhNXhFfHPzNafS2xuHx6cVwGrw8tqe3OBGnZ39v3xgtOLlTMnYtWscujVVJRNASDI4ILTizIJ+uOC04vN8yvhCwLewEV/VyvkUMlksLqS80EaNF45fAaPLqiCM8r39CoKf3r7c/z3teMSsmnVDRPx6IIquKLK0KkonOlx8Y5FuyeAccVaPLF4WozP+mkGd+06yFoW66FDfsonaeehY/00JsuzVoFiXWpZqwmJYXP50Ovyw+kLwOmjUaRVYniBOunY2/DXWrsnNB7Meu7kaVIdlw5idUplU3gsA8DPXj6Oy8YVRcItwjlZnn2vA3d/+VLsaGrH1rhEgpwEEbnnOjx+lBjU2DC/Eg/840jMJL6uX1XkxdZOPL6oBt4AA7lMBrVCDkomw9RRRpgMKnxyrg+bFlTxyqkFmSB2xH1hv44nH0M6ZG0t18SJE/GHP/whW9URCIRBhlGjwLd3vMf59nL17hbsE/hCONhQyhWgAWx88RinvJdSnrsvKSUGJbYumoa1HNJjWxdNQzAoXWKoEq0Cfsi45XXmV6EkxeXSfgb4xtXjWctIwzd4PyN+DsV6Jf60/Cqs23eYVcafll8FJsj/ZV8BBbQAHut/6IiWGVIHATfHrduoU6JQJ8PDzx9h1ffA3AoEkdlsZIVaJZY89f6Q9E+DWgk/E8Tj+9s4216vzv4SYpNeCZmM4pUAlMoPC7UK0JBh/T+46ylMMmQgyADfurYM9/H4XjBB36NkFOd1YeP8KtA8516sVwj6bCAuZEQuU4JmGLzUeoYlaXd12TDIZRf7vcPq5DynZHWYCYlxpteNkxdcrKzT4cR2iWb95vpae125GZtFykj1uHQQq1Mqm6LH8tNLr8Q3+7W143OyLKstAx1k4PLRWL27BX9efpVguXqNAmv2tqKloxdbF9XgV6+dQEtHLxrqyrD02gkAgJFGDYwaBehgEG9/asVPX76o6hHWOb/7rx9FdLRHFmqw+kYL7r9lKuxuPwr776PHztoxeYQRjy2ohMvP4EyvB+YCFYwaJUZJ3D9ZW8v16aefYt26dbj99ttx/vx5AMA///lPHDlyJFsmEAiEAYxOrcC0/mVFy3cewIpdB7F85wFs29+GaaVF0CWQjXawoJCzH6CBi/JeihxOtBVyijXJBkK2rZPYNlqkHegU65KLnIM8gXJVcor1wB4uY/2+w1AJlEEpFPjhvlbU//QNzP51I77++3cw+9eNqP/pG/jRvlZQCvZY16sUrEl2uL5Hnj8CvSqz/jGU/VOpkAu2vVKR/WXzlIhvUBL5odTXIkrE9xKxWyVSBp/vaZUKQZ/VKmPHsFpBYf1zR1jJmBpPdOOB545ArQjVc87uYU2yw+Xev7cV5+we0XMiJI7N5cMbn3RxSjs1nujGmj2HYHP5EionfmIKAG+JlJHqcekgVuc5u0cSm+LHcqFOyRl33dxmxVPN7QBkqLWY4PLR6HX5UWfhlrWsLzfj4MkeNLdZY2K5w1Jfy3cewPKdB/DoP4/hC5sb63n89KnmdjTUhVZWNZ7oxm/f+BRvnbBi04tH0dTWjY39/6fkcty/rxUyyKCi5Pigoweb/3kcvoB0oW1hsnL1f/PNN1FVVYV3330Xe/bsgcPhAAB89NFHePDBB7NhAoFAGOA4vAEsrS1DbdyFurb/LeZgzmocTz7KCYXJpm2ZqkuKctMpIxXZoFxLDQ1l/8x123MxUOW9cul7yfZjovsnEvtNkI5uhw/DC/ilod460Y1uh3ibdzt8rIlpImWkelw6iNXZ45TGpvixrKBkorKOy2rLUG8xg5LJOO8R9eVmPHJrBTa+eAwAUDOuSLBMvVrBmWk8vL1mXFHk78b+v5uj/j/cqMFwozpyLfAGmMg2OgM6lFl5xbxmzRps3LgRP/rRj1BQUBD5fdasWdi2bVs2TCAQCAMcm8uP1bu5E1+s3t2Cp5ddmWsTs0Y+y8pk07ZM1SVFuemUkZq8V27HxFD2z1y3PRfZsknqenLpe8kel+j+8bHdrHqJ3rak2D1+eEW+TCY0jkT24R1HKR6XDmJ1io2xRG2KL6fHKXxcj8uH1btbsPt/roY3EMTSp0Lhf/fePBmne9wYW6yFipKju88byU4u1ndisl/xx4f/1qsVeHLxNIws1ECroPDjmyeBYYJweAMYXqDGylkWeAS0tVMlKxPt1tZWPPPMM6zfhw8fju5u7jcsBAKBEI1Rq4wsI+JiKEmp5LOsTDZty1RdUpSbThmpHJvrMTGU/TPXbc9FtmySup5c+l6yxyW6/1CWvssFRo0SF0RWCSQ0jkT24R1HKR6XDmJ1io2xRG2KLycg8gU4QIe2a5UKOLzeyD2iZlwRVuw6iO1LpmN0kRaGqPrVIqE2Bo1w+Ej88eG/nd4AVuw6CCAUz/3QvAp88/fvRL7m11pMuG3aWMGyUyErS8eLiopw5swZ1u8tLS0YM0Y6rTICgTB4KeiXUuGizmJCwRCSUjGItEUuZWWy2U+ZagcpzqFAoxAuQ+DBp0CjQL3FzLmt3mLmPDad+qRgKPtnPvpjtmySuh5JfC/FMpL1oUT3L9arBPcr1mc2E/VQw2xQ4Xyfl7VEOcx15eaE5JvMBhWuK+e+DguVkepx6SBWZ7FeGpvix/LBjh7edq61mHCwowcNdWV45IUj+M9n1si+Lad6UWsxoeVULwwqKuY6Et7GV6bTG+C9P4bLDFNvMaOkQI3tS6ajSKfCylkW6FQUmtqseOi5I9iysDqyb3ObFQ/847DkMfRZmWh/85vfxL333ouzZ89CJpOBYRg0Nzfj7rvvxre//e1smEAgEAY4aobGxvlVrAeWsFSDOoFstIMFr0BbbFxQBW8O20INYNP8Kjy2oBLbl0zHk4unYcfSK/HYgko8uqAKagnrYkTGBJNiO/hFyk0k67g6wAiPV6HlcQyDu2ZO5Ix3vmuWBWDYx9I0g4fmVXDW99C8StAS6pdzMZT9M4ggHp5XyXnuD99amfGM71w4fV7B/nD6vJLU4xPpd1+S/S7FOFIjKFwGT38EAgw2zOfuxw3zqxCI89kCtULQ5wr6EwCOMGrw6AJuex5dUEUkviSmUKfCDZNKsGpWOWc8cKLyTYU6FTYvrGZNUMUkoFI9Lh3E6hxh1EhiU/xY3tHUjmW1ZZxje1ltGZ59rwNfnjoCy2rLUDWmEA/OrUC9xRw57minDU6PC8ogIj4b3sZ1/1s5sxyjjVosr2dvD9e5o6kdQKivV8y04Ju/fwfLdx7Awt+8jZaOHmxdVBOZbA83xj6RZCKGXhYMBjN+B/D5fLjrrrvw9NNPg6ZpKBQK0DSN22+/HU8//TQoKntve+12OwoLC2Gz2WA0GrNWLyFxTp8+jRW7PoCueDhcPefx5OIrMHas9Ms5CPmHkH8eP2NHT58LY00FLMmj09Y+FBfoMHnU0PDpDqsTDOMDJVex2oJmfJDLVTmTjGnvcsDHBKCUK1i6un4mAJVcIYl+LwC0nLRirF4ND4eOtiYYxBcuLy4v5X4zLsR77VbsbP4Ma2ZPZZW7+Z9HsaT2ElxVJlzukS9sGK7ywSPXsW1jXDjvV6FidCHnscfO2PHf29/FloXVGG5URzSQw5lj/7z8akyJG+vHzthx558OxGgAF2iVCPRrAP/+29NZxySLmH/2OT0YWaxnjcmzPU4U6DWD1j8/7bJDLaPgCwbhDTCRcw9rSXuDNCaWZPfcP+tyoLWjG9MmlLDG38HPu1BVapZE1/zdz6z409sXfSW63xP1lWiOn7HDpPBG/Ca6PA3jgjWgFh1Hx87Y0fzJWXylcgzr3F8+/AVqJ43k9IWDJy/gs7O9uNoyglX3u23ncMnIYkwbXxzZ/9PzDnzj9/+J8VODhsJ5uxf37jmE/7vzGkwcfrGNY3S0NQoU66XT0SbPtmyidbRdPhqFaepo93n8KNAoYTYkp6Od7HHpIFan0PZENLjP2b2we/zQqxXQKOTw0gwcngAKNAqoKDkuuHzQqULbAgwDyAGlTA63/+KzgE4pRyDIIEADQYSSChZqFChQUqARSrDo9PoxTK+Gv798nVoBtUKOXpcPOrUCAZoBzQByuQw9Lh8YJgiaCYKSyaBWUijWK9FysgcbXjwWif0OU2sxoaa0GNv2t2H3/8zAoj+8E7N934prcXlpMaQiY2vJ7HZ7xNlVKhX+8Ic/4IEHHkBrayscDgdqampQXl6eqeoJBMIgQyYHnLSMpW0c1mscln0FnZyhBQ2nTMWr86xF7r4eauWATKbk1NHeOL8KGpl073bHGjRwBoG1PO0wRp/aQ2yxXonVN03mbN/1cyogl4ufw3AVDadMx2vbcCX/F0WXL4DNC6uxI042pdZiwuaF1XD52IltXN4AHpxXgS3/Os465sF5FZzHSIlCAZiLdJxttmF+JWgB3fCBjkZOwU0zePA5tob5Q/MqoMvix4QwOjC4bHwJ7zVCI5GOtqlAiXtunspbTyDJekwKCPqNSSFenkYRwE0VY3htCga5fW+cToFhE0fwHqePOxe7x49uhw/Ldx7gLC8+wdQIo4Z8vc4ihTppJrapliNV/VLWybc9IQ3uv30Uk+07rFu9endLRLd6/Zyp+OUrH2PhFePw4kdfYPVNk7CWQ5d+4/wq/OzVY/jn4XOR32stJjwwpwI/ffk49h/vAgDoVBS2L5mOX714NKbu8HPf6t0tqCktivzb5aOxY+mV0Aco3Lf3MGcbNLdZ0VAbkgDjiveWOoY+Y4+mxcXFEb3sWbNmobe3F+PGjcNXv/pVfP3rXyeTbAKBkBxB4Jl3T6KmtDhmSXJNaTGeefckcrA6M2d4IKwT60Hu4mH9kAna5odMsro8gEg7pIaGovDIC9y6yBteOAJNAhMnj1wtbJucfxF9sU4lqE1azPGgVKxP/hgpUckpbHjhKMs/Ly8txsYXjgrqhg90AgBrkg0gEgeYCw0Av4iWtF+i/lCL1KNOsh6PSHmeBMpTiPiegsf3kq1bJxIvrh3EeQkIg4eENLj/doglqcWlW/3I80dw2xXj8FRzO5bVXcKrS79uXytuu2JczO/N/ffXqVErvRrqyrDt9TZW3c1RdUf/u85iQjAYhMsrnpm8zmLCeXvsS7c6iwkGifOZZGyibTAYYLWGGuaNN96A3587uRkCgTDwCQK4/erxaOnowfKdB7Bi10E0PP0+Wjp6cPvV44fSPJvoaGe4rlzraPtoRlBH1McRb53KMVLi8tO8/rno6vFwZUA2JV9wifR1/NLFbJAtP5S6nlz6XrLHKSm5YNImJTWEllkRBiwJaXC3cW+P160Oxz03t1mhoOSC/hQfHx3+Pbo8MU3t8L7NbVZcc0noC7tORUFBCb/QL9IqsXF+Fe7dcyjyW23/F3qnV9rVXxlbOn7TTTdh5syZmDJlCgBgwYIFUKm436jv378/U2YQCITBQhC8X+wAYP0tU3NhVU7IR93eMERHO/0y7G4/dCoKDXVlqBlXBG+AgUZJ4WBHD3Y0tXOWLVaf2Pa0GcL+2ZeH/kh0tJMvQ8zv4o+zOrxY1r8ElSuc6YLTK0kcPIGQSdLV4I7XrQ7rXPNdF8M+JocMTy6eFuNjLh8dU56Ypnb0djoYxOrdLdix9Eo0tXWj1mLinKTXl5sxwqhBp82FLQur4Q0wUCvkaDnVi9W7W/DMHVcL1pksGZto/+Uvf8HOnTvx6aef4s0330RFRQV0Ol2mqiMQCIOcICD4ZnMofdHOR93eMERHO/0yjFolti6qwVPN7TG61LUWE7YuquEsW8ymTI+JoeyfBXnoj0RHO/kyCnXJ+Z1Bo8SSp95HQ10ZGmrLWA/se753raitBEKuEdf9Fp4qxutWh+Oeua6LOhUl6GOrd7fElCemqR293R9g4PLRcPlo/P6tz7B1UQ2A2PtSvSUUR37qggt0MMiZX0Hq63XGJtparRbf/e53AQAHDhzAli1bUFRUlKnqCATCIEdsOY/Uy33ymbBObBPHxCbXmsXZtM0gUleqWsFSlJtOGTolxft1WAbg0QVVrGPCmr687Z5hHe2h7J/56I/ZsklqH8yl7xnUCkG/2xyluQsARo0C00qLYiYM0fUYM+xzBIIUhDW43+JYPl5rMcHm9qG+3My5vDxetzoc91xrMSFAMyw/bKgrE1n5NCWmvLCmNtdL3Oi6w/8Oa3e7fDRW727Bulum4J4vT4bd6wclk4GSAx929IBmgvjCxs7ikgmd86wEkLz++utkkk0gENJCbKKQ6YlEPiGFznOm8IhofHsktM3lF9YKdvlT0wqWMwHBcuWM+KTRS9NYP4dbY/eBuRXw0vztkFLMr4huN0SW4KWL2KRiME86XDSNB+dy9/WDcyvgEujrTOEV8UOvRH5Ii1yL6CTr8dLCPu2lxX3aTQcEfc9Nc/uvyxsQ9ru4l0VjinXYxKOPvWlBFcYUk1WchPynUKfCI7dWcupWL6stw8pnWvDg3Kmoj9Pg5tKtfnBuBf72wSksqy3D002fsXTpRWOuS4txrNMW+W1HUztWzrSg3hJbd21U3eF/H+u0xdhTU1qEMrMBPS4fnm5uh1wmgwwyTDDrce1EM46fsceUmSmd88F75yMQCIMKhVwu+JVCIR86iWfO9/nxp7c/xmMLqni1a8tKcmObtc+P9fta8MS3piFAByO2KSgZ7tx5ABsXVAIS2RagKfz0X4c52+Hh5w/jh1+enFK5nQ4ajR93cpb71/c7UH/pCIwROYcLTj9W7DqILQurce/syTEau7f/4R08uXga77EOka+/XNu73H784t/H8diCapbN9+89hP/98mSMTejsU4OSy1FvMbGywwJAvcUEahD7Z4/Dj7ue4e7rRSJ9nSmsjgDW7eX3w00LqjBRAj88l8C1aEIS9fgDFNwBF2d5vU4XFErxh+BeJ40Vu97j9b3fLL6C+zi3sN9xbR9v0mPLwmrYPYGINrFRoyCTbMKAwub2oaa0mDMEwuWj8WmXExturYgszY7W0f5Tw1XQqigUqhVwBmh85/qJUCvkuGvWJHRecGPD/Ep4/Az63H7IZMJJymxuPxbPGI8VMy3w00Ho1RTUCjka6iZgWV0ZvAEaJQY1VAo5ztg8+Ot3rwEQ+mr839dMgFGjwI6lV0KnomBQK+CnGTBBBdZ+dSpkstB+rx0/D9NkFX7+tcuyonNOJtoEAmFA0OvyYdXMiXjk1kp4Awzsbj+MWiXUCjm67G70un25NjFrFOqUaKidEPk7Ov61oXYC9CIxipnEqFViRlkRNJQcDpqO2Kah5JhRViRp/JNWRWHySGPk7+h2mDzSCK0yteWxBVoFdKqLE8PocnUqOQq04rdOrjhRWZS0mVAcqZi8CNd2Y/8DfphgzDZFxuOEe10+rJ41ERsXVMHloyP+qVNROG9zDWr/NGqVGF2owcQSPdx+BnIZDYVcjokleowu1OQkRluvplCkU0BNyRGI8kM1JUeRTgGdWpql40atEqVRk8rocVdarEv63HVqBd5q78INk3Ws8j48ZcN1k0clYBPbP6J9j89/012VEQxCQvFCAiF7GNRKbNvfFpMMcOooI7bdPg0HO3qgpuT4tMsJSiZDn9ePitGF8DEM1JQcQbUCfW4/5DIZ9CoKRgUFPwCnj4ZCIYfHz4Bhgmj9woYZE7mz9IcxapUYWagJvUwO0ihSKSCTyzDepIe7f5JfoFEgQAehUYQm4Wd6PNCoKIwwqqBVKuH0BdDnDkBJyUHJZaBkgIqSwekP3ZeutZihlMvR5fAiwATBZNhxyUSbQCAMCEx6BSBj66PWWUzYOL8KCA5e+aB4TEoKGoUOa/Zyt4U+h097ZhWFhnpLVmwrkMtw2/RSzro2LahCqt+UTEoKN1ePTescDEo5nr3zGjz83OGYr7z1FhOevfMaaATmOYUaJW9MXH25GYUckxezQYV7bp7Ka3OxLrOTPbNegaBMjft56pcNYv80qyhsXTSN89q0ddG0nPhjsYrCT267HPdx9MdPbrscKonS05lVFL5x9QTJ/F3F0Ki/dBRveaoElqIXKyk8e+c1ePC5w6wynr3zGhTwSP8U61WCflesZ3/tOml1co75TQuqMN6kj9nX5vKh2+GD3RN6CWXWZ+YLGoGQLGaDCjdNGY5vXlXKSlRWZzHhpikj0OfyY5xJi3N2L37+8nF8/0uXYt0/Yn3sxsklWD+nAuv3tcbd98y4a+ZEvPHxedRbzJxyYbUWE/w0g/v3HkVLRy/++N9XgJHJ0NnrxrbX22KTmpWb8d3rL8Hml47hnpunYNUzB/H47dzX4PVzKvDYP4/hv64YF/lCH7anYeeBSCjWdeVmbF5YjdFFWknbNuNruQKBAB555BGcPn0601URCFmFYRicPn0ap0+fBsNkNv6RACjkFNbFXUSBUOzcun2tUMhzlwAs23gA1g0FuNgW7BQf2cML4JEXjqCmtBjbl0zHk4unYcfSK3F5aTE2vHAEqUVNcyPUDmv3pt4OXoFy1+1rTfAcZKxJNgA0tlnx8HNHIPQKnaEZrLhhImfM3IobLGA4NLH7vAFBm/synIyMEvFPahD7ZzbHfKIwMpngeGBElnAmitTXooCcEiwvkMA48gOsSXa4jIeeOww+MaOAiN8F4vzuix4Xa5Idrmft3lZ80eOK/NbZ68bK3S248RdvYsGTb+PGn7+JVbtb0NnrFj0fAiEb3D97CmeisqY2K3728nEU6pX4z6dWPL7/BP7rinF4+PkjrH2njC7E2rhJNgA0tnXj8dfbcHPFSDx8awVnzPWPvzIZv3r1EzS3WdFQV4aRRVo0f9rNmmQDQOOJbvz2zc9QObYIG144gie+NQ2PvMC2p6nNig0vHMHCK8bhqeZ2NNSVxdgT/hsIaYav2XMINpe0q68yPtFWKBT46U9/ikBg8GYcJQxNOjs7sezJl7HsyZfR2dmZa3MGPX0+mjM+GwhdTPu4EkQNUhwiybIcOWwLh5/G7VePR0tHD5bvPIAVuw6i4en30dLRg0VXj4fDL51tmWqHPpFyExlrDj/NGa8MhG7yQu1g99FYvvNAzMRt+5LpqCktxvKd78POUX+fRziRU5+IFmq6DGX/zOaYTxQpxnAiSO2DUpSX6rnbPQFhv4vzIbuIz4X3t7l8uHfPIdaX8kw92BMIydLt8KGjxyU4nmUyGYYbNWhqs2K4Uc25r1iys7N2L+Y83oTLSouwfcl0bF8yHf/8fj2unWhCj9OH/ce7IuV4AwxGGDW85TWe6EadxYymNisCdFDQ9rC9NeOKYuyJ/hsI+WS3Q1p/zMrS8VmzZuHNN9/EhAkTslEdgZA1tIVm8Z0IkuAQmSiIbR9M2N1832RC9HmEt2eUIETkO6ZKVlWm2kGKcu0iiZX6BLbb3X64fDSnbBBf/bkeE0PaP7M45hPF7vbHxFt6Aww0SgoHO3qwo6ldsvEg9biTxvdSKyNZv0u0nm6Hj3M5OnDxwZ4sISfkErvHz61mEYXTG4C3X73C4eHe1yuibmHj8LFw9nJX1AvJcB4esfICTCgEpk/EF8P2xpfHVb7U98qsTLRnz56NNWvWoLW1FVdccQX0+ti4lXnz5mXDDAKBMIBJJUHUYEUokRaAnCRfChMEBN9oSxMZGiJT7SBFuXqRZFNCyahSqT/XY2Io+2c2x3yiFOqU2HZ7DXY0xcZb1ltM2HZ7jeh4SZRwwju+CX2y406KcZxqGckel+j+dpEH95y+GCUQEEqmecEp/CXXqFXC2R+CxHc9VyuEF0pzbW9qs+Le2ZNx3u6N2c+oVYoqcOhUoftogYgvGvqTosTXz2WP1PfKrOhtrFixAufOncMvfvELLF68GPPnz4/8t2DBgmyYQCAQBjhyGVixc2FqLSbIh1C61wIVxdJuDVNnMaFAlbt4WKfIjVFsezIUqChWrFeYeos55XaQon11SkpwvOoEMqIXaBTC9XM85KRyjJQMZf/M5phPFIOSwlNN7azl/I1tVjzV9DkMKWbkj8eoorB9yXTOZfPbl0yHMUkflML3Ui0jWR8yiuwfzlJuFHlwz+WLUQIBCCVDO9/nFbyGn7W5cb7Pi3qLCcFgkHPst5zq5fWJWosJLad6Obc5PDRaTvVG6m851Qu1Qo7zdg+vTWFd7zqLCQpKJuiL5+1eVv1c9lxXbobZIO3qkqxMtBmG4f2Ppgdv3BaBQJAOuQxYVlvGmahmWW3ZoH6Qj8fP0Ng4v4p1Ywlnu/UnkJk3UxjUIl82RbYngwzAXTO5kxfdNcuSsmKHGhBsX3UCZchlwPdnlePRBZUxCbIeXVCJ788qFxyvgUBAsH6unCdUQHhMUIHMjomh7J/ZHPOJkk6OgGR5kiNZUXObFU++8WnSZakRxCZB3xNfH6BGUMR/uctgAozgcUzcMtMxxTpsWsC/f1hL22xQ4bpy7heCmXiwJxCSpVCnwg2TSrBqVjnvNfzHfzuEaaVFWDmrHB5fAOvnVLDG/rFOGzbMr4xMgsPUl5uxrLYMO5raOes3aCg8+14H7r15MnbdcTWqxhQCCOLaiWasnGlh2VRfbsYPbizHx2fs2DC/EudtHmy4tRI3Ti6J2a/OYsIDcyuw54NTMfXXW8xYNbM8xp7rys3YsrBa8jCOwbuWi0AgDCoUcjmefa8DNaXFaKgtgzfAQK2Qo+VUL559rwNrvzol1yZmjS5HAI+/dgSPLaiGw0ejz+1HgVYJg4rC/XsPYfWNl6KsRLycTFFrMXEupeV7M50qdh+Nhp0H0FBXxhoTDU+/jz3fuxZjUii320fj5y8fw2MLqljt+/Dzh/G/X5kCsea94PKhWK/CP/efYcl7PTC3Aj0uH8bzHHve4cfv3+Sv/87rLZgQZ8Bppx/Pt5ziPOYPb7VhXs04jMrgmBjq/pmtMZ8o6eQISKoen8CE/kQ37D46KR/s9tJQKIKc49hD++GiZaK+d95LY/NLR3mvj2u+OpWzDKvbj8deOoqffe1yOKOO06so3P3XD3H/V6eiNO6Y8SY9tiysht0TQJ/Hj4J+PfsxUdrihToVNi+sxpo9h/BWVKx2ph7sCYRksbl8cPloaBQybLy1Ei4/jVMX3BhTrIFWScHq8OFPDVcBMuCj0z2oKR2GxX98B1sWVuPe2ZPh8NAwaCict3vx7e3v4Y9LpoMJAu3dTqgVcpQUqPHrVz/hjAOvs5hgUClYcpg6FYWf3FaNy8cUYsOtlTE62ifO98GkV6HPo8HXfvsfdDt8qC83Y+P8Stxzc0gvu0CjhFohh59hsGb2FLj8NJ5ediWMmpBPe2kGf//etXD5aBRqlTAbMiO3l5WJ9tatWxPed/Xq1Rm0hEAgDFR8NIPldWXY9npbTMxhrcWElTPL4eOQPBqs6NUUGtsuoP6nb3BuX/PV3C0dDwaDWFYbksyInniE34oHJYxYdXgCgsmLUk3A1ef249Xj3Xj1+Buc2++8Xjym0qhR4QEOiaHGNiseev4IHplXyXtsgUYpWP//foU9adWrKPzp3dP407vcUprfvHqCqM3pMJT9M5tjPlHSyRGQDFInwZPJZHjouWO8Ly0SSSzn9NKC18fVXu6v+YVaJe683sLSHq+1mHDn9RbemOwxxTrRlwmji7R4fFENuh2+yIQ8Uw/2BEIydPa6WVnxn7njanz3Lx+w9n1y8TQ8+tLH2L5kOrodPizfeYCzTB8dhMdPY8WugwBCqzp23TED3gATE84S/uL8zyNn0NxmjfE7l4/GymdaUGsxoaH/+uoNMNCpKPR5Avjq400xE/fGE91Yt+8wZlwyDD99+ZPI79uXTMfynQfwz+/X46qy7L/4zMpE+5e//CW6urrgcrlQVFQEAOjt7YVOp0NJycX3ijKZjEy0CQQCJ0wQWM7z9XL5ztDXy6GCtj/+l+9hVCtR/GUqqJUUnnn3JOeXzWfePYl1EmZgzlQCLrHEKonEVPoZRjBBlp/hn3ga+mNMueSyQm//2f2rVykEx4Reldnb/VD2z2yO+UTRiVwjhHIEJEMmfFDIbxLBqBWus4Bnu16t4M0eLwPw869fznmczeVDt8MHu8cPo1YJs557Al2oIxNrQn7BJz339mdW1FtMrNUq4eRhLad6ObcDoevLy0fO4tpLLk5qux2+yBfw+2+ZCpvLH/kCfvsfQr//5F+fsMoCQv63ZvYUvHzkLHY0teNv37sW9+89zLlv44lu/PjmSyMT7XAcdjbylPCRlVo3bdqEJ598Etu3b8ell14KAPj444/xP//zP/jOd76DxYsXZ8MMAoEwgHF6A3jue1dArdLB4QstDTJqlZg03ID5VcPQm4OEQ7kiwDD41a3l8MirYtrCoKKgYTzoEZjEZRq1XIaVMy345asnYrMdl5vxw5vKoZYwWFcuAxZcPgrL6i6BgpKjr78d/DSDp5o+SzkuWK+isHHupbh+8ihW+755/Az0CSRkcngCgtmYnQJf+jRKCo/Nr8QXdg/0agUcntByOYfXjzGFWmg4JklFOqXgmFDpMptwyekN4N+rrkBQzvbPb9YMw1nn4PVPlVyGdbeUQylXsc7dXzMcqhwEqKvlMqyeVQ45wApdWDlLOj+Uy4AbJ5dgyuhC1jg/1mlL2gcdXhG/SeA6r1NSeOKbVageZ2L5wqFTVt6XDA5PAP4AjbfuuQHOqOPCS8cdngBGGGOP6ex1496/HUJj28WJSn3/kvDRRdqYfc/ZPehx+mD3BGDUKlCsU2GEUZNcA6VBoi8EBiune1zo8wRgd/tRqFXCoFHAFwjAFwgpBzi9ARi1SijlMlidPhg04n2USJum2u5c9o6NCkkQ2x5vwwWnDy4/DZeXRoFWAb2SgtNPY9UsC9bdMgUqSo4elw9FWhV8DIMbJpXgvq9SMKgV8DI0FJDDSzN4+Qf1UFNyLJw2Bk81taOhrgxuPxPxlwIVhQCCcHpo/PU718CoVaBQScHNBOHy03B6AijWhdQK9CoFdjZcBbePxj+/XweFXA5vgMEFpw80E8QH/X5/uscVSbBYoKKgU1Fw+WjOa4VSLse/flAPvZJCgKFxwenHN64YC6+fQUtHT6QPgNALAJvbB51aAblMBoVcBpPEfpGVifb69evxt7/9LTLJBoBLL70Uv/zlL3HbbbclNdGeMGECTp48yfp9xYoVeOKJJySxl0Ag5B/j9Qo4ZWqsiVvWV2cxYeP8KoxXDJ3EisVywCXT4H6Otti0oArFyF1bqBgaZr0asytHYum1EyJf987bPTAb1FBJmKitkAK+f9OlWLuPux3UKdalZQKou3QU51jbtKAKWkb8Yb9Qq8DWRTV4qrmdtZR666Ia4a9u3gAYmQyP729j1z+/CuCYbBTqVDjp5h8TJRl+oA7751o+/9QPXv9UMzRomYp3vKQ6DtNBydAYZdRgdtUoLI36yn7e7sGoQg2UEtlUQAHr51Rg7b5WljbupgVVSddjFPEbvq/R0WgZGpVjTQL+y21TgPHjJ7ddzlo6Xmcx4Se3XQ5vIFb+yOby4d6/fcT6qtd4ohv37jmEbYtqIg/sHVYnZ7mPLqhCqSlW8jYTcC0Pvq7cjM0cLwQGIyetTta18cbJJVg/pwIbXmSHCiyrLcO3d7yHaaVFvH2USJum2u5c9obH73iTXnR7NGd63ThpdeLx19n3k6W1ZVi9uwUuH41aiwkPzKnAxpeOYv/xrpj9NsyvxE//dRxfm14KnYrCH976FHfWl6Gh7pKY+6+uX4XgydfbYmKtdyy9Ek/sPxH70q/cjBU3TIwsPw/7fXxfbF1Ug1GFarR09OLJNz7FbdPGYOuiGqzZcwibF1azrhX1FjOW1E7A7ndPYv2cChTrZXjw+SORc7poT1vMC7Jwvz/20jE8fGulZH6RlazjZ86c4cySStM0zp07l1RZ77//Ps6cORP575VXXgEAfO1rX5PEVgKBkJ945BRrQgWENBjX7WuFR5675dLZxiOncD9PW6zdm9u28Mop3LevFffvPRyR+1m+8wDu23sY9+9thVdC2/wCY2Lt3lb4U6zLI1cIluuRiz/saxQU7zLUp5vboVHw2+aVy/j7d18rvByfCU/3uFgPXtE2n+5xidqcDkPZP70i/ijlmE/Wpkz7IS2nsE7g3Okk69GK+I1WwG/CpHp91ClVgmNYp4x9WXXW7hFMBHfW7gEQ+pIdP8kOl3v/3lac698vU/AtD37rRDfW7DkEm0tYP3mgw3dtnDK6kLO/m9useKo59KWWr48SadNU2z2Ra3mi13qby4c3Pu5iTbLD+4fPM3zeG144gqmjC1n7rd93GAuvGIczNjce338Ck0cXYkSRjtV+Df15Ohrjfns8bpINhHxk2+ttoXCjujJBv+/zBNBQV4bGE90YU6zD083t2NI/yWbnQenGU83tmDK6EOv2tcIbCMac00V7ull1PdXcjktHGSX1i6xMtG+88UZ85zvfwcGDByO/ffDBB/je976Hm266KamySkpKMHLkyMh/L7zwAiZOnIjrr79earMJBEIe4fDRvDF6TW1WODiyWQ5W8rkt+kRs65PQtky1gxTlplNGKm3Y5wkIH5NiYrhEyecxmWmyOeYTpc9Hc8b4A9La5BCpJ9l+z6XvJXucWGb38PYep0+w3B5nZie63Q4fa7IX5q0T3eh2DO6JNt+1sWZckWA+gJpxRQC4+yiRNk213RO5lid6re92+DDcqE7oPMPHR/8d/ftwoxojjJrIPi4Of+Fq00TaWWifpjYrXD4mYleACaKpzYqRRo1ouU1tVgT7bUjGHin9IisT7R07dmDkyJGYPn061Go11Go1rrrqKowYMQJ//OMfUy7X5/PhL3/5CxoaGiCTDWKRTgKBALtbONNzn0c8E/RgIZ/bIpu2ZaouKcpNp4xUjs31mMh1/bkkH889WzZJXU8ufS/Z43QiuRrCmd3tIi+5xLani12kzQazbwL8/eoNCOcyid4e30eJtGmq7S42DpMZp3aPP6nz5Po7jMNDR7Z5AwynHVzHJlK/2D4e/8W6w37nEMnXEN6/zx3bBom2h1R+kZUY7ZKSErz00ks4ceIEjh07BgCYPHkyJk2alFa5+/btQ29vL5YuXcq7j9frhdfrjfxtt9vTqpNAIEhHMv7JJ60SJpFM0IOFfG6LbNqWqbqkKDedMlI5NhNtQfwzMfLx3LNlk9T15NL3kj1Oq0pM/cEoku1YbDsXSfmmSJsNZt8E+Ps1nEGbj+jt8X0kRZumOg6TGadGjRIXRFZMxLcDX7sYNBRcPnlkHy47uI5Npp3F9qm1XMxkLiZRGD6mQKuMmZQnao9UfpGVL9physvLMW/ePNxyyy1wuVzo6elJq7zt27dj9uzZGD16NO8+jz32GAoLCyP/jRs3Lq06CQSCdCTjnwX9kkdc1FlMKEggE/RgwSDSFlzyT9kim7ZlakxIcQ7p2JZK/QUahXB9KTzME/9MjHz0x2zZJHW/59L3kvUhtUKOlTMtMQ//wEXt+PADe7FeJVhusT75RIXJ+KbZoMJ15WbObdeVm2E2DO7M43z9GpZ94iIsCwVw91EibZpquycyDhMdp2aDCuf7vKwxGib6PMPHR/8d/ft5uxfn7B7U9++j4/CzllO9rLq4fouvX2ifeosZ5+wenLN7sHJmOV7/+Dxq++0R6786iwmyfhuSsUdKv8jKRPsHP/gBtm/fDiCUAO3666/HtGnTMG7cOLzxxhsplXny5Em8+uqruOOOOwT3u++++2Cz2SL/nTp1KqX6CISBCMMwOH36dOQ/JoeyT1wk459qAJvmV7EurJHMvhm2NZ/QMB7BttAwXp4jM48GQWHbEJSsLrVIO6iZ1JIMibeveLlqxidiG/9XBo2Swkah+jnkicYW67BpQRXq446p7z+GT/ZFCOKfiZHNMZ+4TbSITdLEaKsZr8g4T+5apGFo4bGfQBbzVK8LYR/iOy7eh8YU6zCmSIs5VaNCmZYXT8P2JdMxp2oUxhRpMKZ//xFGDR7lKffRBVUpSXwl45uFOhU2L6xmTfqu65chG+wSX3z9eqzTxvl7OPv0jqZ23j5KpE1TbfdExmGi47RQp8INk0qwalY5a3JZF3We4fN+YG4FjnbaWPttmF+FPR+cwqhCLVbOKsfxTjvO9LhYvrqjqR0rZ1pQH3XOO5rasWpWecxvQCjr+MqZ5djR1I4dTe1YVlvGOqf6cjNW3ViOK8uGQSGXweOn8dGpXiyrLcPfD57GxvmVqLfElhvuv2OdNmycXwW9Uo7rLGa8sKoO25dMxz9avuC0J3zcx2fskvqFLBgMZvwOMHbsWOzbtw/Tp0/Hvn37sGLFCrzxxhv485//jP3796O5uTnpMh966CH87ne/w6lTp6BQJP6m3m63o7CwEDabDUajUfwAQtY5ffo0Vuz6ALri4XD1nMeTi6/A2LFjc20Wi7CdAPLaxmVPvgxtoRluWzeeWvGVvLQzjJB/fnLejmI5BY8slLSmz+1HQVgnOAj0MDQmDR8aPt3VZQdk/G2BII2Skty0haBtAMBIZ1tHlx1agXZwB2mUplBXZ5cdSoFy/UEao0XKPdflgFwm4y2DCQYxosTAe/x5qxNesI9VAxjOIwd0yupEU1s3hhs1F+Wc+ryom2jCOAkkhIT8s63LjkKBNrMFaVhyNCYzTTbHfL7ZJPW16MQ5O5RyL6h+Pfbo8mjGBT+jRnm8mHUc6fhvV68bHpphH0fJUcIj9xPWM+7z+FGgUaKAR884Rkdbo0CxXjod7USebcN6zmE7zYahqaMd3U8sHW2NEkqqX0dbLd5HibRpqu0uNq4SHXdhG2J0tDUK6FUhHe2wDreKkqPX5YNRq4KfYeDwhDTt43W0ZTJAJQ/9W0kBKjkV8pewHWEdbW+oroiOdjAIly/0m0GjgFYphxwyOHwBuHyh39SUHJ4AA6c3AINGAY1CDh/NgAkCCAIyORBkALkckCGIhqc/wK01YzCttBgahRzFehVkACAD9EoKBzt6cN/ew3D1JzIMy00W60LLwkM62n7oVBQouQzUQNXR7u7uxsiRIwEAL730Er7+9a9j0qRJaGhowK9//euky2MYBk899RSWLFmS1CSbQBiKaAvN0BUPz7UZaaOhKPyAQ9ICuPhlYKjgUyjQccGFbXGSHaElixaUDkv+66VUeOUU1uxt5cxEXGcxYbOE/UTJKbT1uvnbIUUdTEYuPNYSOQdGIcfdew5xyv/Ul5vxk//iL+N0jwsPPB+SWakZVwRvgEGfN4CWU7041mnDw7dWsh6oztk9LN3gaJt//vXLJXuo50It0maPDWL/9MkpdGRgHKaDV07h3jTHcCJIfe5alQIP/uM4pgiMfTFkCgV+lILv2Vy+0HEcmaKvKzfj8Shd7GgSXS0ywqjJqA+KEf7KOlTh6qfwJNju8aNYr4K5f5JVVpJYmYm0aartLjauklmllIwNH5+14+ZfNeLHN0/C7IpRMdJ0K2dZcLTThsVXj8fTze0xPqZTUZxa2HUWE5bXXYIxxVo8+tIxTs3yZ949iamjC7Ftfxvqy82YWz0KP97TyrKt1mJCTWkxPuzowdpbpuDWmjGxGtr9x15ziYnzfhiW6gvfD7PhD1mZpY4YMQJHjx7FqFGj8K9//Qu/+c1vAAAulwsUlXyc0KuvvoqOjg40NDRIbSqBQMhTnCLSK85BLB8UjzfAsB5sAUT+3pDAw2imyJasEAB4mWBG2kEKuSa7NyCosWv3BjCKr35PAPuPd2H/8S7O7Xd/hZ1tNREJoUw+5A9lea9MjcN0yJbkmNTn7vAGsOjq8XiquT3mATr8QC6WaRhI3fcSkWMayhPVwUZnr5ulc31duRmbF1ZjdA5ejuUDNpcPB0+GYpi/NHUk1v3jcIxv14wrwrb9bVh89XiWj/FpYTe1WfHVqlHY0fQZ73WiprQ4IsHVeKIbP775UuhUVORLdPT+DbVl2La/DUHIWFJkjSe68f0by2EXkUDL9P0wmqxMtJctW4avf/3rGDVqFGQyWUQ7+91338XkyZOTLu/LX/4ysrDinUAg5BH5KKGTK9wCD9HNbVa4/bmb1GSznzLVDna3H1dNKMLPvnY5nL7Q0jqjVgm9isLdf/0wQYmh0LK7hrqyyJc5Tf9Sth1N7YIavCnJe+VaQmgI+2c++qO9fzkk3/iTqj+kPneby481ew5hy8JqrJk9GQ5PaJnrObsH9+45hCcXTxMtI1XfG+oyWEMJm8vHmmQDoRcqa/Yc4l29MNjpdviw4cWj2LqoBn46yPLtaHmvaMwGFb5SMSLm5Vg0I4wa3pdf4cmzUiHH9iXT4Q0wkEGGxxfV4N49h/DNq0pj/LhYp4RORbFku8IEmGDO74fRZGWi/dBDD6GyshKnTp3C1772NajVobQoFEVhzZo12TCBQCAMcPJRQidXxL/lZW335m6inc1+ylQ7mAuU+Mltl8csmQNCS+B+ctvloIPi5RrUF5fRxX+Z27qoBgYN/2qulOS9MiAhlAxD2T9Fx2EOvuYX6pSC40+svxJFah8s1CmxeWE1dsR9Gau1mLB5YXVCdqfqe0NdBmsoQVYvcGP3+OHy0Vi9uwVPLb2StT2cST9aIstsUGHXHTNwusfFW24i2tUaJYVv7zwQ+e2mycOx644ZeOSFI7HLwy3m0DVMp+Rc4aJTUZDJBKvL+P0wmqzJe91222344Q9/GJOIacmSJbj11luzZQKBQBjA6EUkW/SDWD4ongKt8E1CbHsmyabUkVHkPMW286GSU1i7jz++SyUXPwedWsG5jK65zYqnm9uhU/HblopUVyYkhJIhHyWusoXoOMziQ10YvZISHH96jsz1qSC1D0phd6q+N9RlsIYSZPUCN+GXTeHkZPGEpbGiJbK2LKzGIy8cESxXTLu6UKvEfz6L9dfJo4145IUjLD9ubOvG083tUFNylhRZWHVDSPorG/fDaDJ29X/22WfxzW9+M6F9T506hY6ODtTW1mbKHAKBMMDx0DR+Ob8qkkk2vJw3nEW3hx68MaDxGJQUbpoyHJNHGVlLI4+fscMg0UN0KsgY4X7yJiDPkygqhVywHVQiN3c+pIg39vhpdFhdeG5lLRSUHH397eCnGdy16yA8Aktqxxbr8JMFVfAj9MATbkOdioISwGiOJDgahRyP3FqJB587EvOlpr7cjIfnVUCTYlskilek320S9nu+oaLkmDW5JCZ5XXgcHu20QUVl7ZtGBJc/lDX7jbuvh9vPRPpDq5Rj9e4WuCRazq5WCvugWpncuUvley0dvVg5y8K5dJzP98JyTGv2HMJbcXG7QnI/0Qm1jFplJKEWIX9JZfVCLvtZirrjyzCoFXB6A7C5L5ZpNqjQdM8NCDBBBAG8uKoOLl8oc3iBkkIQwMKaMWCCwC1VI0HJZGAA/OimSTAXqPHs/1yNQi0Fg1oVcx/Qq0LPLK8eO88K6yjWKTG2SAvqkmGYVnolDnb04Nn3OnDDpBJs29/GGwbiZxj8o+WLyPnVW8xYOcuC1z8+j4/P2LFxfhXW7WvFSasLTyyeFrkPF2qVoJnshR9nbKL9m9/8Bg8//DCWLVuGuXPnYsqUKTHbbTYbmpub8Ze//AWvvPJKRGebQCAQuCimKLiCwP0cy3k3za9CcQqJFQcqasaL9bdMxf37WmOWVMVq16Yv55SSbQjABYq3n3SQLjbK6/Nh3S1TsZanHdze1PTExb5mJPK1w+cP4E/Lr8K6fYdZ7fCn5VfB4ebX0QYAP3jGOk+2aJvTB0omw+zKkVh67YSL8l52DyiZDDZnZpdCFsqF/bMwgVUAAxVfwIsH5lTwjkOPzwOAX8otEzC0H1sXTWOtzKizmLB10TS4valpzMejByN4LdIlqdft8gr7lth2AHD7AoJLx90Ck3UZgNmVo7Ak2of6+K8jJKHWwCS8euEtngzz8asXctnPUtTNVUadxYSltWWhF28+GqtnTsRt08fhfJ8HATqIx+OSHNaXm/HQvKno7vPhd299im9fMwHbGz/DBx292LqoBk+80YY7asugValYGb/D9wG1Qo4F08ayfDMsu/Xjv30Ig0YZWYoenck83pfnXzYaf1w6HTaXH0aNEmqFHF/0uHHoVC8emFsBNYC7v1yOIp2G8zoYlvnK9MuSjL1mffPNN7Flyxa88sorqKyshNFoRHl5OaqqqjB27FiYTCY0NDSgtLQUhw8fxrx58zJlCoFAGAR4AdzPs5x37b5WpDalGph45Grepc1r97bCI1fnyDLAK1cL95OEtunVaqwTaAe9OrW6CkXiQMW2A0CRTs2aZIdtW7/vMIp0/Lad7nGxJqzhY9fubeWMhZPJZbh/Xyvu33sYy3cewIpdB7F85wHct/cw1u5rhUwuErSWJkPZP3UqjeA41KmyL+lk0LAfLsM2rdvXCoNGGpu84A+zWLu3FV4k94LFIPKlUWw7ABRqVbxLx59qbuf1X5vLhx//7RDu29sa60N/b8WP9xyCzeVj7S+UUCt+f0L+EF69EB8qwLV6IZf9LEXdfGU09ftDQ10ZAOBr08fhnc+sOHHOwZpkA6GM3u99dgGP7z+BqaMLsb3xMzS2WSPZxqeOLsTIIi3/9WBfK9bcPBk7eTKTr9vXip997XJMHV0YWYrOl8m8uc2Kh58/iuc/OoOv/+4d3PzrRjzwjyMYO0yH+26Zgl+98jE+t7lh0gtfBzttHnT2ukXbMB0yGjg0b948zJs3D93d3WhqasLJkyfhdrthNptRU1ODmpoayOXZX1JFIBAGHtmSqxkIOEQktHIppZTNfspUOygpOWotJs7zqLWYoExgKXA6S2D7RKRJ+jgypuZaXmso+2c++mO2xoPUcn5S+J6XZgQzoXtp7sRM5/u8aGzjTpDVeKIb5/u8MRMwklBrYDO6SIvHF9Wg2+FDn8ePAo0SZgN7SXYu+1mKuoXKCGf8BkLXjEtKDLC5/bz+M9yoQVObFcv6JbaAi5JfDbVl8Ab4fS983eHLPh6WaQ2XV1NajGsuMfFmMm9s68bS2gkxf6/fdxj33Hwp9n54Bg31l4heB4NAxrPMZyVDh9lsxvz587NRFYFAGKQMZfmgePK5LbJpW6bqsjq8WNb/8BGf+XhZbRkuOL24pER4KXA6tqUk75XjMZHr+nNJPp57tmySuh6rw4s76i7BLVWjMMKoicRknrW5MapQm5jvuYTr5LO5V+RcbHHbSUKtgU+hTjzWOZf9LEXdYmWEM4Lb3X4Eg8IZwrnkvaJ/S/d6EC3ZtaOpHXUTuZMTxtcdprGtG/fgUgCAw0NDLhN+0dfn9mf8ZUnuUtMSCARCEgxl+aB48rktsmlbpuoyaJRY8tT7aKgri7ylVytCGU5X727Bnu9dm1HbUpL3yvGYyHX9uSQfzz1bNkldT4FWiT5vAC+1non5Ul5vMWFZXVlCS8cLUrRJTLlCF7edyIENDXLZz1LULVZGOCN4OFmn08e/aoRL3iv6N7HrgV5AbQO46P9AKBGoWNJGrmzmYclBg4aCQmTVdPhakcmXJVlZt03TNH72s5/hqquuwsiRIzFs2LCY/wgEAkGMoSwfFE8+t0U2bctUXQYVhWmloeVr0bGa2/a3YVppUULlpmNbKvJeqRwjJfk8JjNNPp57tmySuh69ksL2pnbWcvTGNit2NCUm75WqTXqVIiJZFE+txcSaJBA5sKFBLvtZirqFyghLdQEhv1HIZThn9/D6wXm7B/X9x9RbQmVGS36pFXJe36u1mHDW7hGVaW051RvZ52BHj6BPxst7AYBCLkOdxQSnNyB6LQj0h5Fk8mVJVibaDz/8MH7xi1/gG9/4Bmw2G370ox/hv/7rvyCXy/HQQw9lwwQCgTDA0SCIjfOrWBfNcHZbDbIn15BrNIxHuC0YaTIKp0I2+8kTCGBpbRnrRlzbn03VE0gxwzkjfA5IQBpEwQQEy1Aw/LaNLdZh0wL+Y8dyyHulcoyUDGX/zEd/zFZ/SH3ubr9wTKU7AVmyVM+9SKfEqlnlnNeTVbPKUaSLfRhPJqEWYeCSy36Wom6+Mur6Q6F2NLUDAP564BSMagUmluixcqaF5Qf15WZcdckwrJxVjqOdNiyvL0N9uRk7mtqxrLYMRzttONvrxsb5VZFJeJhw2NW9ew5haW0Za3vYN+/+64c42mnD+jkVqLOYImXH2xJve8RGixmfdTnw0LxKjCnU4uHnD/NeCzbMr8Jduw5m/GWJLBgMZvzuN3HiRGzduhW33HILCgoK8OGHH0Z+e+edd/DMM89k2oQIdrsdhYWFsNlsMBqNWauXkDinT5/Gil0fQFc8HK6e83hy8RUYO3Zsrs1iEbYTQN7bmO9tGUbIPz85a0cx5YVHrmPr9DIu9NBqTBo5NHz62Bk79AovZP1t0ef2o6C/LYKMC86AGlNG5aYtPjlvR4FcDp9MBleUbToVBXUQsDM0Jg2Xxrb3P7+AJTvei9HYDC/x3tHUjj81XIXpE5JfNfXB5xegUQBatQreABM5B7VCDrfXBw8NXDFeuNzjZ+z46/sdWFJbBmdUO+hVFHY2t+NrV5Ziskgfne5xoc8TiBxboFGITpgjx/Qn90nkmEQR8s+Pz9oxTMA/L9BqXDpI/fPYGTtcLi+GF7H98XyvCzpd9v3x8y4HXD4f9Bo1yw+dHi90KhUmiMQ6J0J7lwO7/vM5vs0xzv/09udYPGM8ypKo5732C/j67/7Du/3/fecaXFUm7HsHPrfCqKWgVihZ5+6l/bC7aEyfwP2V60yvG2980oXhBeoYea+Zk0owkkdKKaxPLJRQK9OQZ9vMk8t+Tqfui8f6UKgN3dMc3gAMagVkMkBDySGTy+D00XB4/CjUKdHr8kOrVCCIIFw+GholhZL+F01+JgiZTAZ3gIYcQBCAwxtAsU4FP8OAkgM0DZzucbPuyS4fDZ2Kwt+/dy1oJlR2gVYBg5JCn9cLyBSQywCZHNBQFPq8AXh8NIbpVfAEGHRccEFJyWE2qPCrVz/B/uNdkfOsLzfjkVsroJDJoADQ6/VCpQhpgzt9NGxuP+z914IAzeCuXQdRZtZjy8JqjMqgRFtWYrTPnj2LqqqQ9qfBYIDNZgMAzJkzB+vXr8+GCQQCYYBTQAFOmQ5rOfQZN86vQgE1eLMax2NSUXAF+dvClMMPKcUyOVyQYd3e1pjsovVh3UqZdAupCtQKuHw0b1ZSgzq1W9xwvRKMTI7mT7sjCZn6vAGct3tw7UQzjEH+ZDFhZDLgmvIS3BfXR+E3+zIRta2TVievjvZ4E7dGemevG/f9vTUnWq9GEf80DmL/NKsoOJU6Tu3YjfOroM+sshonfobBub4AzpzuixnD5+wejCrUYmyxNI9/AYbBDIFxHmDEfSWaIq2wXWLbAcCgVmL+E2/HvIDr8wYiD/t/F8ixMKpIi69WjoyZ1EwfXyw4qUkkoRZh4JPLfk617rB+9gcne7B1UQ1++u9PYrWxLWZsmF+Bh58/EjtptZiwfk4F2q1OGNQKDNMpsfGfx/DmJ90RXet4je0VN0zEYy8dw9ZF03De4cbynQc4bXL5aHzRe3F7XX9d3X00/vup/0Sum488fxivHg/dy8LXkx/+34eRyXpDXRnu/spkfN7thFohh6XEgPHmi/fGUbj470JdKMt8+KWDz0/j6WVXZeVlSVYm2mPHjsWZM2dQWlqKiRMn4t///jemTZuG999/H+oUdU4JBMLQIiCnWA/xwEU9xMcWVOXIsuzjAwS1ITfnsC08chmnBnRjv46mlP2k64+/4pIXqrOYWMmLEkWmoHD/nkOcS1jrLCZsXlidUDl8+p8A8MCcqbzHielob15YzfpKLaa3mkn5EmBo+6cX+eePTBD4Y9NnvDJZQuMv2XpSHedc6NQKYZ9O4OWZQaOI5FjgKsMgkq+ATJwJg4Hoe8LKWRZOP21s68a6fYdxeWlxzES7sc2Kh184gprSYmzb34Y6iwmXlxZj4vAC7nJOdANB4NffrMH6fa24rLRYUKYvOra6qc2KDS8cwQNzKyJ/h+8Zrx5/A8DF60lDXUhWzOWj0dLRgxsnD8eKXQcBAPtWXIvx4H4JHSYXvp2VifaCBQvw2muv4eqrr8aqVavwrW99C9u3b0dHRwd++MMfZsMEAoEwwMm1TnA+0eej0dLRi5WzLJEvNholhYMdPdjR1J5TzeJs9pPLT6OhrgwygPX1fFldmWjGUj5S0bGOhwmCt4zmNqtgmHcq9eda03co+2c+aoinM/6SrUfoWpRsPQ5vKO9CEGxpvaW1ZXB4xX3P6QvgwXkVONB+AcOjJMLO2dyYXjYMTl+KuRsIhAFE9D0hrE3NRVgXO/yVONqPzQYVdvQnJwxLXsaXE31cl8OHZXWXoPWLXiyv45fIXL27hWVDIOpiwXXPiNb8Dpdji5Lyy9cs/1mZaG/evDny72984xsoLS3Ff/7zH5SXl2Pu3LnZMIFAIAxw8lGrNlc4PP7I8q3om16txYSti2rg9A4NHW2H2w8ZZJhdNQpLo2S4ztk9kEEGR4p1SXEOTpEJgdD2lHS0c6zpO5T9Mx/P3SHyMiiRCWsiuLwBwWuRK8l6bC4/Vu9u4ZXWe3rZlaJl2F1+OH00Xmo9E/cCzoxRRVoY1IM3Az6BECb6niCkjQ0APprh9OP6fj9evbuFswydiuL1/+oxRbhywrCIH48fpsM/j5zF6t0tEQmuaPrirqPxf4fqU2D7kuloOdWLZ9/riOQ5yecs/znR0b7mmmtwzTXX5KJqAoEwQMlHrdpcYTKo8avXTvAu19xwa2UuzAKQ3X4aZlDjyTePYuroQowwagAAMpkMnTYPXj12DutuSW15rBTnoBdZ4iq0PSUd7Rxr+g5l/8zHcy8QiWWWSu6tWK/CE2+0oaa0+P+zd+bxUVV3///M3NnXhBmCIIuRiUUhaBSXmoQKPs+jVsCktH1E+xTBroo+rW1FwV3W7nVrf1ZA2yrt01Jwae3mRpLWlSggqERStiAhIZl9vTO/P2bu5M7MvXeW3MySfN+vFy8y95zzPd+zfO/9npl7zjfpUHO/aD/zxqG8bdCiV0ueu5BLX9pMWvz02b2Cr8lGESvp/ZEgigX/mSAUb5rPeJMWP/nHR4JbvqKIv7ItJGN5Uy2eeeOQoP3/+vV/45xJ1uRe7OdvaRS1awAZ20LMAvfVYCS+QL9q5mlodthh1KrQ5LCjdpwh+cYWtw/bFYgfyGk3lnYrSNEW2r/+9a/xi1/8At3d3fjXv/6FadOm4ac//Slqa2txzTXXFEsNgiAqFHOW/bjmURynN50wG5V8LTTM5ncAkZyYsoyTnDGFI9Eorrt4muC36YUcxMTBxaQWnWs5LFIUCT3E9qhJHYZWSP1crNSdAq+PF+Pb/rFsn8YsbTeWoO1aRik5/7SMPIcSym2DctheOd8fCaJY8J8JXKxrsXNHNCqlpM3cdJkD/zoYT+fLmTO1GudNqRK1fxXvQecNRiRtu9cVSPmc7is019lxpt2YcfBik8OGdYlzMLjD30pxIKgYRYmj/fOf/xy33XYbPvvZz2JwcBAsG/9GoqqqCj/96U+LoQJBEBWOFpCMjTqWjlV0ZnlVNVv6SOILBSXHyRcKylZXNCp+ENOWjm4UuM6WJSZ1DDHc2FSL5vRYpA4bbmyqhVRkzULqL3VMXy3YLPY5evdo93kCybivfJocNtyzcCb6PMWPo93vDQrGn+Uc4FNeeexQ6jC0LR3579HWKhTS8yjbcf2IO/QGDYMV8x3YtHQOHrv+fGy+4UKsmO+IhzcLjt65SBAc/GeCWDzqZocda1rqccoTkpSlVSmxv8eZIcdqUEvavzUREqzRYUMoHMM9C8Xvkyu37U5+Xttaj/uf3zukZ50dDyyaiYMnvbix6cykLQPx/dyrtu8RXGQDQweCOn3SbRwpivKL9sMPP4xf/vKXaGlpSdmvPWfOHHz3u98thgoEQVQ4fSEWT3V8jPWt9Rmxah995SMsbZyO8aVWskhke32ylK/pRqHC3Tt2Y33r7IxxWrV9N1ZdPVO2utIPTOLT0dWP4Zz3ZGCUgnNNl+MvgToVA6OGEdw/btQw0Kmkf+WcZjNiw+LZecXEnlSlx8NLGkoS67UvBDzV8dGYtE+9Ro3rn3gdGxfPxsqrZsATYGHSMeh1BXHdL1/Hr2+8uOg6GbVqfHnzW6J7nbdJhLjKh1iWQ9ckvk8SpM8fxra3DgnOo6c6DmLxhdMwPssvU0atSnLfuJH2aBNjBP4zwRsMY11LPUJsFO5A/MsohQLwh8M4rUonKccbiuC6i6fhO1fMgNsfxv2LZsIfZqFSSv8SvvqzDLZ989MwaVVw+cOIRqNY21IPX5hNPqNMGga+CIvHrj8/+fmtf/dhycVn4HMXTEWt3Yh3jwzi6ofbk3u7OVvm9nu3d/XD6Q+X9EBQMYqy0O7u7kZDQ0PGda1WC6/XWwwVCIKocNyBMM473ZL8zPffzjvdMqoPW0rHpGFwx385cPW5k+EJsXD543uRTBoGf3rvqKyvZ+eLTqXE5TOGllT8cbp8xnjosuwVywful6v0k1K5E4+zHUgmhtMXwuF+D2qq4otafhsO93ug0TA5PbD/9F4PljefCX84mhyj6eON2NzWjWWJE1lzIRaLv4qeC+5gBCE2Gu8LNh4/uBjOhScQwX+dLTzu/3X2+KyHc1UyJg2D714+HWfVmOAJsVAq4g7oWTUmfPfy6SWxR51KiUWzT8M1501ChI0l599UmwF9Lr9sdii3Dbr8Yfz7lC/5mT+P/n3Kl9N9XqNS4kmRX9kUANa0SO/R7hn0w+kPw+UPw6pXw6JXS752esIVwIA3BFcgAotehWqDJnlmBCE/5bYHt9hw7Xf6Q8l9zbFYDAaNClV6dUpfOH0h9HtDYKNRaDUMgmz83qRVK+FOzFeTRgUFFHjmKxfDGQjjNIsOJp0KgRALX4iFVa+GXqWEQqmAN8TCqIlvTQkn7isv/m8zjBoGxwd86PdHUu0/FMEEswYKKBCNcW/bKWDWMGAYYNAbhgJAj9OP2/+wG30Cv6w/texCHBv045HrzodKqcA4owZhNopQhMWfb21C10kvlAoFFACeWnYR3jp0CpvbuzMOXCuVj1iUhXZtbS3effddTJs2LeX6X/7yF5x99tnFUIEgiAqn1qRGTd1puENgf86alnoYY2PndUBdlMVVsycL9sXa1nrooqXrCxOiuGzGRFHdDDLqZtVL/3JlzXIglBghbwg2q0F0roW8ISCrYxfF8ubpGfGVORmxLPP1UL83I5Y214fTbMKxQgspIxdnmFTwmqvHpH3qoiwuqZtQVvZoQhTfvKwOqwTmn5x2KLcNTjSrcdeCWaLzSJnDPApGooL7QIH4a6YBiROY87Whw/1e0T2jU0fY5sYi5bgHt5gItZ/bDvLMG4fwtbnTMW2cAROr9OgZ9OOeZ/fiuoun4pnXD+H6S86ATq3EI690pczX5jo7Vsxz4Cu/ih9a9tCSBvzwbx8m89hNGvz2a5/Gvdv3wO0P46El54va59oXd2H3MRcv6gCLnmgQj758ICUCQJPDhgdbZuE7//cejgz40eSw4ZmvXoLrfvl6ymLboGEwZZwBj7cdFNwD/ssX9uN7V87A9U+8jrMnWrD00jOwr8eZ8ms3R6ne9CvKHu3bbrsNN998M373u98hFovhzTffxNq1a3HnnXfi9ttvL4YKBEFUOAElk7FoAeKO01079iCgHDuvAwZVqgwHGoj3xertexBUlSSgBADpcVq9Xd5x0mtUovvDnuzohl5TWD8ElIoscy3778uqLPNVJdEPRwd8Gc4+V3b19j04OuCTpYycjGX7DKpUknO+FPYYUDKS9wi5xkOnYiRtMNsWiXSUEn151449UObQl1lDm4mk9wz6JW2oZ9Cfcv2EK5CxyObyr9q+Bydcxd+bP5px+kJluQe3WIi1n9sPfc4kKx5++QBe/egkTrgCWLltN2ZMtGBzezdmTLLiuNOfscgGgLYDfXjk5QPxbSZNtRn2vHHxbNz7XPwU/4eWNEja50NLGpI6PdnRjWqjGg+nLbK5/Hfv2ItHrz8/+fmB59/HxsWzU/LdveAc3CsQQYBr84xJFjz4Qrxc24G+ZD9s6ejGct5bY6UM/1WUhfZXvvIVbNy4EXfddRd8Ph+uu+46/PznP8fPfvYzXHvttcVQgSCICscTYkX3ArV39cMjEJdxtOIORCT7wl3C13SLOU4j1Q9ytGE4MgppV6nnxFi2z1L3vRDFGg+565GjLwsNref0hyXrTj9kcsAbksw/4B3dC79i0+cJZd2DO5qRan9HVz8aplSho6sfNWYtBrzxvA1TqtCeSJtg0YnO17ZEHk4GnxqLNnnNHxY/0b+9qx/+cDTls1KhkMyv4p150t7VjxrL0LG2jQ4bGqZWZSzS09vML8fvh4YpVQCKdyCoGCP+NWskEsEzzzyDK664Atdffz18Ph88Hg9qampGumqCIEYRriwnaY+lPdrl3BfF1G2k6pJD7nBkFFK21HOi1PWXknJse7F0krseOeRlDa0nU92uLIv+bOlEfriyjP1ovscA2dsfTGyJCEaiybnHv5YNsTyewNCXZVltJC092xdj6fn9ofihaNzBjS5fbm3m68hdM+vUeOm2zxTtQFAxRnyhrVKp8I1vfAP79+8HABgMBhgM2UOjEARB8LHoy/ek7WJTzn1RTN1Gqi455A5HRiFlSz0nSl1/KSnHthdLJ7nrkUWeAljWGH9tlL/Y5vZ1iq20863bkiWmd7Z0Ij8sZRxtoxhka782ccChVqVMzj3+tWyI5THphrZ/ZLWRtHR+2Vzym3VqfPH/vZ78fHX9RMnynM78erhrNqMG02tMkuWLQVFeHb/ooovQ2dlZjKoIghilmDRMRvxFjiaHraQnbRcbs04l2RfmEjp4xRynkapLjv4djoxCypZ6Toxl+yx13wtRrPGQux455Jm0Kmx94xAaplYn42hvWjoHDVOrsfWNQzCJvDpu1asl67amLQqqjRrJ/NXGsXMSdjGwmzSYW2cXTCvlHtxiIdX+RocNnUcG0eiwodcdRLUxnrfzyCCaE2knXIGMONocXB5OBp9eVzA5z/VqpeSc16uHlpWNDht6XUHROpscNkTYaMpnFTP0LVhz4rNYfVybmxL18K+V03woykL7pptuwne+8x088sgj+Ne//oXdu3en/CMIgsiGLspiTUt9xk23HE7aLjZsJCLZF2ykdK8sBiIsbmiszXi4NjpsuKGxFoGIfOMUyjInQgXOicnVBqxtFZcrFctaDhmFlJVD5+GgzTIW2lFsn5Es9hgpgT0GWBYPtswS1OnBlnoEWHnGQ+5x10VjWe7z2QNzT6424J6FM/Hu4QHc+NTbuOnpXbjxqbfx7uEB3LNwpqgtTKrSS9pQ+qnWEyw6rBPJv661nkJ8yYzVoMGGxbMzFpul3oNbLMTaz72psa/HiVvm12HeWeMxwaLDhsWz8eFxF5Y11eKDHhcmWvVYMc+R8WxurrNjxfw6bG7vxub2bixrrE2Z0yu37cZ9i+L3klu3dkra561bO1N0WrltN26ZX4dmhz0j/4Mt9bj56V3Jz/csnIlPBuMHCDY77HiwpR5P7PwYa1rq0SzS5v09TtyzcCZWbtudvPbBcVdZzQdFLBbLftcaJkpl5npeoVAgFotBoVCAlemGnwsulwtWqxVOpxMWiyV7AaLoHD16FDc9/Q4M1TXwDfTisesvwOTJk0utVgacngDKXsdy70sOKft842A/zjRHEVAa4AmxcPvDMCdiR+uiPnR7lLioVvibz9HGGwf78at/HsQdV52T0RcbXtyHpY1nlqwv3jzYjxuefCslri6332pzezeeXHahbLq93d2PMBvFBKsewUg02Q9alRInnH6oVUrMOaOwuk4O+hFgo5lzjVFifB5hZI4O+OAOROAOhGHWqWHWqXJe9BZSdjj1ZSObfe450o8rZp2e0Wd/3XsMs6faRq19vnGwHz/62wf44RfOg5fXdqOGwXd//y6+e8WMorf9re5+BMNR2MwaxKBI6qRADP3uEHSawm2Dz4efuLDv6CnMqR2fMe5vd5/EzMnjcNZpuftaTl8IIa9T9D6vMVpzdqALtQUujjZXzppPHG2dCtXG4sfRHku+LRdHmhufUu/BLTZDcbTDMGgYKBRANBaDQa1ClUE4jnYkGkUM8V0TKqUS/jALX5CFRa+CQa2EAgo4gxEcPuXDBLMWZr06GUfbrFNDr1aCScTRZtkwrHpd3D4TY2DSMAhGWZzyxGObaxklTvlC0KiU2PXvAVwwbRyUTPw+ZNHH5YWiUfS7wzDpGPS6gli5bTceu/58WPRqGNQMAiwLHcPAH44gwgJKpQLeYARGrQoKADEFoFEqccLth1mnRoSNwekL4/RqPeommEs1PBkU5X2m7u7uYlRDEMQoxqJX4zMP/zNlAecJsdh1eACb27ux7ZuXllrFomHRq/Gnvb34095ewfQVl3+qyBoNYTGo4QuxKTEvU9Kz7PHKh3EmLe4WCP0BxL/xfvCaWQXJdfpCuPPZvZgx0ZIx1z487sIPv3Buzo6dWatCMByFmlFCo1LCnOVEZD6FLJBH+pdrMSx6NX780kEMBmMi9nlaSfQqBha9Gm/+exBzf/CqYHop9o7aRsg20tGrGZxWbcqIq9vosGHFvDro1Pm9Om41aNATMmH1tt3YmRYreePi2Rifx4KqUFuYVKXPKybzBIuOfr0uIlbD2FpYp5NP+7PlPeEK4Lb/excdXf34wzc+jW/+Zpdo3k1L5+DGp95O+XzL1k787uuXoPkHbVgx34HOwwPJ+0B6/nSev6URA74Q/GEGnUcG4QuxcAcisBm1UDFKrNmxV/Ae4A+zmP+j10TlvnTbZ0TTSkFRFtrTpk0rRjUEQYxiTBoGm5bOwSOvdKUs4hodNmxaOmdU7wFNh9vH2C7gRJd6P6xOpURznV0wDElznR26HA5lyZUwKx5qpKMr/mt3IfR7Q7j2oqnY0tGdMdeWNdai3xvKydHpGfRnxD2dW2fHhsWz83LkKwFzFvs0j2L7NGsYNDvsaOsSmPMOe0naPlK2kY4vxOLGp96Ox+BtrE15g+XGp97CHwv4AnRSlR4PL2kY079aEkQx4Ieo06iUkqf1dx4ZzPjsC7HY+dFJNNfZcf7U6pR7P7ffW0hek8OGv75/Ipm/0WHD5hsuxJFTPsyZVg2rQSN6D3D6QphbZ09ZhHOU095sjqKe0LFv3z4cPnwYoVBqrLtFixYVUw2CICoQf5gF2Ag2tNbDE2LhSryCZNIwONrviaePEdhoAOta6tHxcR9qLDoEI1Ho1AxOOP1odNjBRgMAjKVRLgY8sOgcnHCHYNQy8ARYmHUqeIIRTDBrABk3K7kCEUyp1uPR68+HilEmX0sLs1Hc/PSugsPrRKIxvHvolOBc+91bhzFtXPZfypy+EDa3HcTallnw8mQYNQw2tx3ELZfXZV04pLySqleh2lD8V1JzJcRK22eoiFvEik0gwuI7/1WHm+c7Mua8TqWQbT90PrgDERg0TMobQDo1k3zDwCNT6Cl3IIJ5Z9nw3xdMThn3s2pM6O51wR0srB53MIIQG43rzUbhDkbyXmhzr467/GFY9WqYZNxGwaeS7JTIDe71bFcgPp/txsr6omfolfEYorEYVEwEOqUWoWgM/sQr4VVGFYwaBi/+bzPcgTC0KiXWt9aj4+N+TLToYDWooWKUcPnDsJk0OD7oh92kwWdnnoavzj0TnhCLy84aD6NWhctnTAAUQNt3LgIS2z5c/jCurp8Ik4aBy+vDJ964PVcbNLCbNegZCOCx689P3peeaDuIexacg1PeEAa8IRi0KsFTw60GDdZ9rj5p29wz/8mObnzvihllN05FWWgfPHgQra2t2LNnT3JvNhDfpw2gqHu0CYKoTKoZQDPOnPGKYpPDhjUt9TDGxs59xAg1fAD+vOc42nh90eyw49LpdhhRujAnRkThUzB4+OUDGeO0tqUeBhnHyWpQ4Vc3XoS7duzNqOtXN16EcLSwX+00yhg+P2eq6FxDDm1weUP40qfPwJ0iMlxZfhU/3O8VLLuutR5TbSX6EkUCswJQjlH7ZJRAlUGL1TuE2x4rQdstehUeWtIg+FbGQ0saYNbL4/7VmFT43pXniI67ooC2H+r3YpWAvLWt9ZiW49yXQ0YuVJqdEtmp9DeRegb9uOfZvcm3siaY1Pjf/5iBI4N+PPJKFzq6+mE3afD0Vy7BHc8NzV27SYPffu0SvLTvE1x78TT88G8fZszrP3zj01BAgTu370l5o67RYcP6a6aDVRiwWuRe4D15Erf+/v3ktRsaa/Hd378HX4hNvi12bNAPAFAqALiBYJjFlDQ7krLtiWU4PkU5dfx///d/UVtbi97eXhgMBrz//vvYuXMn5syZg1dffbUYKhAEUeEElEyGIwsA7V39uGvHHgSUo/fV1HSCKhVW7diTssgGgLauPqzesQdBVenCewWUDFaJjNNqmcdJzzAZi2yurrt37IWeKawuJstcY3Jpg1IhKSPuSQhzwhXIcN65squ278EJVyD3xhSJsWyfqixtV5Wg7XoVgy0d3Rk6dXT148mObuhV8uikzNJ2ZZ5tPzrgy3CiOXmrt+/B0QFfUWTkQiXaKSGN0xfKWGQDwM4Dfbhj2244fSGRkuUBp/+MiZak/X/7P2eg4+O+5CIbADYuno0HXng/Ze5uXDwb9z73PmZMsgreO9q7+nH3s++j4+O+jG1rHV39UCgNkveChjPGp1zb0tGN5U1D8e63dHRDp2bwyCtd+PikFx+f9KK9qy/FjnoG/ZK23ZNYqJcTRVlo/+tf/8IDDzwAu90OpVIJpVKJpqYmrF+/HrfeemsxVCAIosLxhFjRPYftXf3whEbvL2bpuAMRyb5wy/RaaCEUc5xGqi455A5HBn/fnFDZAW/5OXtj2T7Lse3F0knueuS4txXr/liJdkpI0+cJCZ4vAsQX232e8h5TTv+GKVXJuekJsZhg0aXM1RqLNmPuctf4ZdNpO9Anui0i33sBVxf/s4pRoKOrP3nAYI1Fl2JHTn9Ysg6nPyyYVkqKstBmWRZmc/yodbvdjp6eHgDxQ9I+/PDDYqhAEESF48pyA3UHyu8GO1KUc18UU7eRqksOucORkW1veaF7z0eScp6TI005tr1YOsldT6ltLx8q0U4JaVxZ5ka538c4/YORoW1TLn845TMAeAKZX4Bx19LzpiOWntXuBNLTZQ34hvTn/vHtqBzvtdkoyvuFs2bNwnvvvYfa2lpcfPHF+P73vw+NRoPHH38cZ555ZjFUIAiiwskWFqoUIXRKRTn3RTF1G6m65JA7HBkWnfSjOVt6KSjnOTnSlGPbi6WT3PWU2vbyoRLtlJDGkmVulPt9jNNfy4vuYdGr4Uk7lNCky9zSYUmc26DNEhlELD2r3Qmkp8uKsLGM63w7Ksd7bTaK8ov2XXfdhWjiUJoHHngA3d3daG5uxp///Gc89NBDxVCBIIgKhwtpJUSpQ1oVG2OWvjCWsC+KOU4jVZdZp5KUa87BgR6OjGqjRrJstbG8TlUFxrZ9lmPbi6WT3PWU2vbyoRLtlJDGbtJgbp1dMK0cQ0elw+nPhdYC4jba6wokPwNAryuYMXc1KiWaEmG7GkXmdXOdHb0iZw/key8QChm26/AAGh02nHAF0OsKot8TTLEjq14tWYc1y0K8FBRloX3FFVfgc5/7HADA4XDggw8+QF9fH3p7ezF//vxiqEAQRIWjiwaxpqU+4ybLnTapiwZLpFnx6fcEcPeCmYJ9cc/Cmej3lu4QHk8gIDlOnoB8uvnC0nX5woXVNbnagLWt4nJzCRE0HBkTLDqsEym7rrW+LEMH6aLSY6GLjt6DoTzBLHM+WPy2F+t+6c5i7+487b3UtpcPlWinhDRWgwYbFs/OWGzPrbNj4+LZZRc6Kh1O/w+Pu7CssRaNDht+8vcPcOl0O1bMcyQX0Cu37c7wIQ71+3D3gpnY3+NMluXT7LDj3oXn4NLp9ow53+iwIRb1Sd4LOv99MuXassZabG7vTpZf1liLfT1OrJhXh+njTThvqhWXnmlLsaNJVXpJ2y7HU+EVMS7W1hjB5XLBarXC6XTCYrGUWh1CgKNHj+Kmp9+BoboGvoFePHb9BZg8eXKp1cqA0xNA2etY7n3JIWWf+4+70Of04IzxVnhCLNz+MMyJOL3/PumE3WrC2RPHhk3vP+7C/2x6AxsXz0aNRQtPgIVJx6DXFcTKbbvx6xsvLllf7D/uwso/vIeHljTAH44mx0mvVuLWrZ3Y+PlzZdNt/3EXnur4GDfPOytjTjz6ykdY2jh9WHVxcXg5ueYC4vDyY/la8pSREp9Xp0K1sbTxebPZZygQxDiLIWMsTrl80Oi0o9Y+9x93Yd2f3se61tkZbV+1fTdWXT2z6G3ff9wFuyqIgDJzPHRRH/oi8ozHSLVdTttzB8Iw6wqTkQvlYKfk28oLF0ebmzt2U2XG0WajMSiVMViUDIJAPI52mIUvGI+jrWUYOAMR+IIRWPTqpF9xmlULJZSIKeL7ogNhFhOtOlz/xBv47dcvhlbJwBNi4Q1GYNSqoAAABWBTMwggfjAa13fxe04Mp9go3IF4fp1KiSAbhScQgUmnghIAG4tBxSgRDLN46YNenHt6FaaMMwjG0u4Z9MPpDyfrsOrVZbnIBoq0R5sgCGK4WDTAuo4jaDvQmZHWXGfHhpazS6BVabDoVDj7NAtufOrtjLRmh72kewPNGgZWgwaX/fC1jLTmOjvMcr46rlbiK3MdgjF871s0E1qJEFrZKIdYvtzJq5VAlUaJ2/98UPDE3uY6O77fMrMEWhUHs4ZBDAo0/+DVjLQmh03WOZ8rKgbwKvQiMW1nQcUUFmM+nSq9GjfNq8uwwUaHDSvm1aGqgFc55YpNPRKLaiEqyU6J3LAaKmthnQ5f/0P9XnxL5Dm055gTz7x5GB1d/Vgx34FPnWYW9CsaHTY0TK3Gp04z462DAzh7khXX/fJ1NEytwp1XzYA7EEkJH8aVWTHPgb/tO4X3jgzie1d8Cj/7x0cpYUm5PDaTFv/zxBspp7o/dv35ooebTarSl+3COp2ivDpOEAQxXGJgcNNl0zNeZ2p02HDTZQ7EMHr3gApx8zzhvrh5nqNEGsWJAVnGST4UCgXufe79jHAf7V39uO+596FQFLbQliMObyXG+xwOUSgkxz2Kwr/0KHeUAO5bJLyV475Fs0riaOkYBqtFYszftWMvdAXGmE8nGovhsTQHG4iH6nns1Y8RzfOlSYpNTRDyke1Zdu7p1uQr5ZvbuwVfGee/1r2ssRYP/mk/fvDXD7C8qRYdXf3Y1+MSvwe88jEmWXT41n+chXV/2p+yyObyPPJKF9759ylsXDw7JU2rUpbl4Wb5Qr9oEwRREbhDLG7Z2omNi2fjjqtmwBNgYdapcMIVwC1bd+HXN15cahWLhisQwQqRvliR6IvTS6SbJ8TixqfexvKmWixvrEUwEoVWpUTnkUHc+NRb2PbNS2Wry50lbqe7wFjBcsThzSXeZ6V8I58Lrhzss1RzcqRxhlh87Vdv49Hrz8cqRgl3YptAmI1i2ZY38fiX52BSkXUqVhxtVyCS4TxztB3ogysQyWvcc4lNTb8eE0RuZH2WhVi8+e9TWHnlDABAIMzigUWzMOgPIRoFdBolImwMTl8Y50yy4o5tu7G8qRYNU6pg1Khw/tRq2E0a3Pf8PsE62rr6cOdnz8YnroDofaKjqx/LG2tRY9EmrzU77Oh1BzFnWvUwe6D00EKbIIiKwBMIY8Pi2djc0Z3xetKGxbPhDZZf/MSRopz7wu0Pwxdi8cjLXcLpFEc7ZxmVRDnPyZHGEwjj3kUzsfEvH2S0/d5FM0vS9oqNo02xqQlCNnKxz5/8/QB+8vcDyWuPXX8+bnp6V/J/DoOGwUNLGrClozvl+d7ssOGhJQ24dWsnfAJf4DkTe7ylCEaiyTjejQ4b7l54DixaVUW/vs9Br44TBFER2ExabElz4oH4t6FbOroxzqgVKTn6KOe+EIqVmZJOcbRzllFJlPOcHGnKse0VG0ebYlMThGwUYp9cDOv0GNfLm2oF73Ntifvc8qZawTpMOian2NxWvRqbls5Bw9RqKBXAaaPkjS+6YxEEURGE2fgJ1q9+9zPwh6PJU5y506zDrDyH+1QCYTaKcITFzu9dBm+ITfaFUcPgu79/t6R9YdIwuO3y6Wg9fwo8PN1MGgbbdx2RNaawWafC5TPG4+xJVjRMqUIwEoVOzWDX4QHs73EWHC/XrFPhxkunYWljbUb/PtXRnZNcLt5nu8DrcuUa73M4hNkoms+sxobW+oxx/9N7R0e1fYbZKKp0DNq+d1lG2ze8uK8kbedi2orNP7ns0KxT4bOzJmDxBVOSERC4LQPb3jmStw1WGzX47zmn4ytzpyPCxpJ9qWIUeGLnx3nFpuZOJnb5w7Dq1bCU8cnEBDFcuJPSXYG4zdiNmuQz8sqZp+GiM8YBSgX8IRauQAQmLQOdSom/frsZiMUXu0qFAt4Qi9997RJY9Wq8/J3P4PigH1UGNQxaFS47azxumR+DRaeCTsWARRQ6hkne9xbOngi9moE3xMITjMCUOJF8yjg9fvu1SxAIsfCF2eRzenN7NxqmVqHXFYBjvAlWvRpX109EhI3hw09cMOvEbVaoveX4CzgttAmCqAiibAQPLTkfq3dknp750JLz4Q+OnTjaAIvvf/48wZN5v//58xCMhCTKjiy6aBCLGqYIngQ+FL8395ODpZhcbcA9C2di1fY9Ka+yDTde7uRqA77cWCvYv7nK5eJ9rt6+J2WxU87xPoeDgYngytmTBcd9TUs9mNjotU+GYfG9K88RbXskVnx75OJo37VDeP7JZYeTqw1YedXZoqfr52uDEyw63DSvTlRervuz5YgaQBCVQs+gHyu37U6J+vCfZ9fggQXn4L4FM3HC7Uc4FsP92/cKnvodjsSgVSvxyMsHUtK5CB4KAPfsSC37HzNqcNeCc3Bn4hnHf7U8fQvNssZabH3jEJZcPA3f/f178IVYNDps2LR0DhiFAjazFof6ffjG0+8k0+JlPsA9C2dm2KxQe+fW2bFh8eyye7bSq+MEQVQEJp02Y5ENcKfo7oFJN3pfTU3HoNZI9oVBXbpvdQNK8XFavX0PAkr5xunogC8jfBG/rlxOBxeTO9xTx48O+HD/8+/jvKnV2LR0Dh67/nxsWjoH502txgPPv1+wbmWLxLjftWMPIOO4lxtapbQ9apXFt8eAUouNf9mPZY21eP6WRmz96iV4/pZGLGusxcYX98tmh3LYitzyxtqJ/8TYxukLZSw6AeBTEy14tasP7R/3IRID7n/ufdFTvzVqBR5OW2QDcZt5q/sU7hMoO2OSBat5X+SJvVrObaE5e5I15RVzLjIBFMBf9h7H/2v7OCWNK5Nus2Lt3XmgD3ds2w2nr3Q/NAhBv2gTBFERFOsU3UqgnPuimLq5AxHBV2O5unI5HVxM7nBPHXcHInj5g5N4+YOTgunfvWJ0HepUznNypCnHtntCLF7cewIv7j0hmH7L5WfJUo8ctiK3vLF24j8xtunzhDIWnQDQMKUq+bdRq5I89fuOq2aIPktrLDrBsg1TqlLeJEv/nF7H8sZaPPJyF5Y3Du3lbjvQhxsuPQMzTrPgB3/9KCWNX4Zvs2LtBeKL7T5PqKxeIaeFNkEQFcFYO8VZinLui2LqRqeOlw9jrb18yrHtFXvqONkeQeSFS2Q+ByNDZ0NwJ3qLIZXOlyN1XSxferpUObE0vs2KtZej3OybFtoEQVQEY+0UZynKuS+KqRudOl4+jLX28inHtlfsqeNkewSRFxaR+cw/6dukkz78UCpd7MTw9Ou5nCyerZxYGt9mxdrLUW72XZF7tI8dO4YvfelLsNls0Ov1qK+vx9tvv11qtQiCGEG4U3SFkPMU3UrArFNJ9kWhp23LQTHHaaTqkkNuOY/RSDCW7bMc216s+Sd32+XQmzvxX0zGaDvxnxjb2E0azK2zZ1zvPDKIXlcAve4gvMEImkVsotFhk0zvdQXRLCKfb2edRwbRKFEHl955ZDDleq8rIJjW7LChxqzD+tZZqOLZrFh7gfiBaHZT+bw2DlTgQntgYACNjY1Qq9V48cUXsW/fPvzoRz9CdXV1qVUjCGIEcQcCWNNSn+FAcSfJugOBEmlWfCZXG7C2VbwvCj1tWw500ZDkOOmi8h1UMlJ1BVhpuQE2u9xyHqORQJk45Vqsvcro6D11XBeVvjfposW/NxVr/sltg3LozZ34LyaD9mcTowmrQYMNi2dnLD4/PO5Co8OOmRPNUCkVuHfRTDQ7UvPETx2vQygcw4r5dRnpzQ47po834qvNZ2Ystj847kqx/c3t3VjWWJthd9wJ4vt7nFjWWIvN7d3J67fMq8OkKr1g2tLGWvz34//Cn/d+glgO7Z1bZ8fGxbPLan82AChisVgse7by4Y477kBHRwfa2toKKu9yuWC1WuF0OmGxWGTWjpCDo0eP4qan34Ghuga+gV48dv0FmDx5cqnVyoDTE0DZ61jufckhZZ8HT3qwuf0gvtJ8JvzheExtcyKO9hNtB7G86UycOd5UIs2Ly9EBH57+579x3SXT4Amxyb4waRg888ZhXP/paSVbyB3p9+JovxuTbeYM3Y72ezDZZsIUmcLrHOn3Yv2L+1Ni+Jp0DHpdQfzxnSO446qzC6rr414P1v/5fdy7cFZGG+5/fi/u/OxMTK/Jba4dHfDBHYjAHQjDrFPDrFNV7CJbyj67T3qw+3Afzj9jfEaf7fr3ScyeakftKLXPI/1ePL7zY9wocG/a1HYQX5s7XbY5nytHB3z4+JMBnFlTlTEeB3udmH5alSzz8ONeDwbdftRUGTLq6R30ocqsz9lW0vUfrt1wcbQ5GdZRHEebfFuCiyvNzXe7KR5X+viAD5FoFNEoknG03YEIDFoGejWDcDSKWAzQ8eJouxOxqTsPDeDBP+0HANw8bzrmnjUegVAUZr0KnYcGsKXj3/jhF8+FilHC7Q/DYlBDxygRYqNwByIwJuJoK5SAXsXAH2Hh8sXrNiTq9odYKBUKqFVKHBuIny7eeWQQm9u74UscJDm3zo6HlzSkLKLF2ltuVNy7a8899xyuuOIKfOELX8Brr72G008/HTfddBO++tWvCuYPBoMI8uLrulyuYqlKEEQW8rHPEBvFb944gt+8cUQw/UufPkNu9coWdyCCn7d14+dt3YLpixpOL7JGQ3hCLJZsfkc0/cX/bZatLm+IxZ/3nsCfZT5Z2W7SIMQCzT94NSMt31fTKnVRDeRnn0E2iv/9/fui6X/5lnzjXm54Qix+/cYR/Frk3nTdJWcUVyHE7xFLn3pXNF0uO7SbNLj/+fexU+AUYM45LgQ57GZSlX7ULqzJtyXSsRqEF5q+cBSX//g10XIv3fYZTJ9ggtMXwoqtnckTvTctnYM7t+9N5vvBXz/CD/76UUbaokc6MmRuWjoHNz4lvKX3pds+g+k1Jnzc68F//aQtpzJCp4mLtbfcqLiF9sGDB/Hzn/8ct912G1atWoW33noLt956KzQaDZYuXZqRf/369bj//vtLoOnoIBqNoqenJ/l50qRJUCorbscBUabkY58ufxh2kwYbF89O/npp1qlwwhXAym27s540O5oo51N1Xf4wDBoGy5tq0TClCsFIFDo1g12HB7C5vVveU8ezhPrJli4G92raHdt2pywgyvXVtJEiL/v0SY/7aLbPcrTHYtkh2UppIN+W4OB+2XUlfoW2G4cWoE5fCMEIi8euPz/F/n28kIPcvcBq0OCBa2binh3vo62rT/IU8WwnjIdY8XSuvvTTw7PJLLfTxHOl4hba0WgUc+bMwbp16wAADQ0N2Lt3L37xi18ILrTvvPNO3HbbbcnPLpcLU6ZMKZq+lU5PTw+WPfZX6K12+J192HLTFWX96jFRWeRjn1aDGk9/5RI88ML7KTFSmxw2PP2VSwBFRe2CGRblfKqu1aDGQ0sasKWjOyWmZqPDhoeWNGTVPR8sWQ5GypYuxaQqPR5e0lARr6aNFPnYp6WI415ulKM9FtMOyVaKD/m2BBDfHrFy2+6UuNJz6+zYsHg2FABuT0vj7P/WrZ3JxTZ3f+oZ9GPtC/tw76JzcKjfh/FmrWi92U4Yn1yth0HDpCzoObj60k8Pzyaz3E4Tz5WK+2ly4sSJOOecc1KunX322Th8+LBgfq1WC4vFkvKPyA+91Q5DdQ30VuFT/giiUPKxT6NGlbHIBoD2rn48+ML7MGoq7nvDginnE61NWhW2dHRnjFNHVz+e7OiGSSufbtVGjWQ/VBuH5+hbDRpMrzHhvKnVmF5jGnMLh3zss5jjXm6Uoz0WezzGuq0UG/JtCacvlLHIBuKvWd+xbTde/ehkRlpHVz+2dHRjeVMtgKGtUJysf3xwEof6fdjS0Y2/7Tsheor4CVcg4+A0jkaHDXuPOpN18OFvvUo/PVzq1PJyPE08VyruydfY2IgPP/ww5dpHH32EadOmlUgjgiCKgTsQyXAaOdq7+uEu8DXhSsQTjOCGxlrEgJQ+aXTYcENjLTzB0vWFOxBB5+FBrJjvEHllVT7dJlh0WNdaj1Xb96A97S2Hda31mGDRyVYXIY0nKG2fpZyTI403FMHdC2biwRfez5iH9yycCW+o+G33BKXtcDSPB0GMBfo8oYyFNMfOA31YeukZgmkdXf1Y3libsr3j415PUtbKbbvx9Fcuwff/sh/LGmuTZTj+Y0YNLqodh/OnVuPYoB8KhSJ5X2mYWoVljbW4dWsnfvnlOSlv0zQLbCd54JpZuPvZvWg70IfN7d14aEkDFEDKfbTSt6FU3EL729/+Ni699FKsW7cOX/ziF/Hmm2/i8ccfx+OPP15q1QiCGEHKcR9kqXD6wrh1ayeWN9VieWMtgpEotColOo8M4tatnXhy2YUl080TCEu+suoNyjtOU21G/OiL52HAG4IrEIFFp0K1UUOL7CLj8mfZL58lvZIZ9IZx8zO7sHHxbKy8akbK6ffX/fJ1PHb9+UXXyROISNqhZwx9MUkQo5H0Pc7pSO15turVKad482X1eUK4/onX8fuvfxqBSARrW+rRM+jHoD8Mg5pBlVGD+55L/VKx2WHDjpsa8eL7x5OvpbOxGDYtnZP0T6aOM2Bi4nBC7pX3dw4NYHlTLW5IfCkwZZweP/riefDwIg5U+jaUiltoX3jhhdi+fTvuvPNOPPDAA6itrcVPf/pTXH/99aVWjSCIEaQc90GWCoteDV+ITXGg+ZSyL2wmLX760gHBV1YB4MFrZsle5wSLjhbWJcaiz7JfPkt6JWPRq9HnCYmemFsKe7QZNfjJPz4StcM1I2CHBEEUj/Q9zulI7XmuTjuxO11WnycEfySKP+85gc7DA8n7xor5jpTPHG1d/bj/hffRMLU6uS87HImm3BNfuu0zADJfeef7MVykggkFhAQsVypujzYALFiwAHv27EEgEMD+/ftFQ3sRBDF6MGoYyX2QRg1TZI1KhylLX5hK2BdhNir6CnFHVz/CEqeREpWLllGK7q9rdNigZSrS3ciJcrTHMBvLYodj5/BIghiNpO9x5jO3zo5ed1A0LX2/s5CsCBvFp8+0pdxHGqZUSd5XGqZUAYj/wt15ZFCwzmyvvPd5QoJplcro/YqZIIhRRZ8niPuvmYlQJIoYFHD7w4lfuWPQqJTo9wQx1WYstZpFwRUI4SctsxBQKOAJsXAl+sKkYaADcDIQAlCavhA6ZZSPP0t6IRwd8MEdiMDlD8OqV8OkU8kSh/fYgA8unlyzToXTKzgu9kjS7w1hxWXT8eA1sxCMRJNzUqtS4oTTj35vCLXjS63lyOALh/CDlnqEFPH5z7XdoGGgBeAMF98es+0L95Vg3zhBEPKRLbQeAPzn2TX41ERL8pyGaoMap1fp4fOH4faH4U7cr6x6Nda1zMKeHifOmmBGiI1CqVCAVSuxaemcZEhVRqlI1mPQMPja3DPR5LAjwsZg1DIwalR49TufAaMEnt9zHAYNg8bpNty7cCaOuwL48IQHJi2DFfMdGWHGOEbbNkBaaBMEURFYDSowCiXW/CkzvNealnpYDGPndlajUcMHBVZt35PRF2tb6jFeU7pXx7O9zib3a7SH+r3C/dBaj2nD+OJlpOSOVmxGFRQKLVbvyOyzNS31iMXk/4KlXBjHqOEDRO1xHFN8eyy2HRIEUXyyhda7a8E5uGv7npTXs+fPGI97FpyDu3bsTd1nXWfHvQvPwQ//9iEWXzAFL7x3DN/6j0/hh38f2oKyaekcAPFF9iPXNWBLezd++o8DSRmNDhtWzHOAUSrQON2OBbMmAQrgjj/uTqlLKMwYx2i7N43ed7kIghhVaFWqDCceiJ9OedeOPdCqxs5CO6hSYpVIX6zesQfBLPEoRxKtWin5Gq1WLZ9uRwd8GYsbINEP2/fg6ICvILnHssg9VqDc0Yw6i32qR7F9lqM9FtMOCYIoHWKh9Y4N+HDX9j1oS7svnTPJitVpi2wAaDvQh/ueex+fv2BKIgTYmRn3dC4E1/KmWmxu786Q3dHVj0de6UJXrwf7jrvR4wpgdVpUEC4fP8wYR5PDBlMJw5OOBHSnJQiiIqDwXkOUc19wocfS9+uOROixkeoHVxa5rjE013KlnOfkSFOObS+mHRIEUX64ApGMhTAgvc+6vasfNRYtOrr6oWKUGfk2t3djWWMtPn2mLWPxzNHR1Y8JFh1qzFoYtYygDlw+bk83MHRv8o6ye9Po+tqAIIhRC4X3GqKc+6KYocdGqh/KuX/LlbHcZ+XY9nIOAUgQxMgjdl+SCvsFAJ5A/FVut0B5X4jFrVs78YsvXSApg6uDkyWGUavCY9efn3JveuYrF0uWqTRooU0QREVA4b2GKOe+KGbosZHqh3Lu33JlLPdZOba9nEMAEgQx8ojdl6TCfgGASRePkmAWKe8LsVmjh3B1cLLE8AYjuOnpXSnXRtu9iV4dJwiiIjDrVJJ7Ds2jbF+PFOXcF8XUbaTqsmSRaxlDcy1XynlOjjTl2PZy1IkgiOJh0anQLBD+q/PIoOS9odcVRKPDhggbFc3X6woIygbir4CfcAXQ6w7CG4ygWSLsIz8EGCAceqzSoYU2QRAVgSYSxZqW+owbP3cStCbL61CjCW0kItkX2kjp9jhNrjZgbau4bnKE3Rrpuk7PIpdCfGWizWKf2lFsn+Voj8W0Q4Igyo/Tqw1Y0zIrY0G8r8eJNS2zMu4NzXV23LdoFv7wzhEsa6zF5vaDeFAgX5PDhjm143DLfEeG7Pip43Vw1JhwzkQzwpEY7l44M7Muhx23zK/D5vbu5DUuLBl3mNtogb7SJAiiIjjmD+NP7x7F+tZ6eEIs3P4wzInY0U91HMSC8yajptRKFoluTwQnBwcE+2L34T6MrzJjfAljFk+zGbFh8Wy4A5FkyBGzTLGti1XXNJsRGxfPhosn10JxtEU56g+jz+kRnJMf9JzCeKsJozSMNro9EZxyDQq2/f2j/RhnMZXEHotphwRBlB/TbEZs/Fw93MEIXP4IDFoGejUDjUKBDa31cIfYoXuDhoEawO1XzEAgwuKblzkQjkaxrrUeXl4+k4aBL8JCpVRgbctM+MNRuAIRGDQMdCoGjAJQKRXws1Es+eUbMGgYbFw8GyuvmgFPgIVJx6DPHYTdpMHzK5oEw5KNJmihTRBERWDRqbG7x4VY4jP//909Liy5ZHTt65HColPhhu37sbypFg1TqhCMROEJsdh1eACb27vxx29eWmoVYdaqEAxHoWaU0KiUMGtH7nEzUguH06sNOH1EJI8+LDo11v/5MH74hXEAUu3zlx2Hsf5z55ZMt5HGolPhhi37ytIei2mHBEGUB05fCH2eEFyBMCx6NU6z6PCp0zQp6b3uIDzBCExaFXQqJbpOehEFEAiz0KkZ2E0aXPv46xlxrjn+/u25mGoziepwqM+D337tEgDxfd2hSAxvdPfj8Z0H4QuxeOm2z2B6jXj50QLdcYlRTzQaRU9PDwBg0qRJUCppx0QlYjdp8P3Pn4c70+IbNzls+P7nz0O1YewstMcZNdi89EI8/MqBlMOOGh02bF56IcYZS/utcM+gHyu37Ubbgb7ktbl1dmxYPBuTqvQl1IwYKcayfZarPZIdEsTYI5vd9wz6sfIPu9HWNZTe7LDjpnnTceNTbycX1s2J+9fyp97KWGw3Omx4+9AAjFqV4L3kSL8Xx50BPPxKV8rzoNlhxyPXNWDrG4dH3V5sMWjFQYw40WgUR48eTf6LRou7V6+npwfLHvsrlj321+SCm6g83MEIVu/YkxHXsb2rH3ft2AP3KIu9KEWYjeLRVw5k9EVHVz8efaUr64mgI4nTF8p4yAPAzgN9uGPbbjh9oRJpRowkY9k+y9EeyQ4JYuyRze5PuAIZi2wAaOvqwyOvdGF5Uy3vWvz+dffVZ6fkbXTYsKyxFg++sE/wXnLCFUBbV1/GIpurZ0t7N+5bNHNUviYuBP2iTYw43EJXb7XD7+zDlpuuwOTJk4uqg94qfDoiUTm4A5GMmzZHe1c/3IHR68in4wpE0CbSF21dfXAFIiV75bnPE8p4yHPsPNCHPk9ozDxgxxJj2T7L0R7JDgli7JHN7ge8oYxFNkdHVz+WN9amXGvr6sOdnz0bm5bOQTASTYl37QuxgveSAW8IEyw60edBW1c/AuHRezhmOrTQJoqC3mqHoXqsHFVFjAQuf1gy3R2QTh9NlHNfuLLUPZbGaSxRznNypCnHtpMdEsTYI5vdu7J84RkUiA7h9Idx41Nvi5ZJv5e4AhFBOVJlRjO00CaIBLSXu7yx6NUwaJiUA4d0aiZ54JBZN3r3gKZj0Uu3tZR9YclS91gap7HEWLbPcrRHskOCGHtks3uLTnrZp1Vl+r0mHZNxbzdqVIhEo1AoFAizMezrccKoVaFKr4ZVr8q6kB5L9x9aaBNEAu4VdwAleb2dkMaiU2HT0jl45JWujAOHNi2dk/UBMpqw6lRorrMLviLWXGeHtYR9YTdpMLfOjp0Cus2ts4+ZA1DGGmPZPsvRHu0mjaROZIcEMfrI9vytNoqnNzps6DwymHKtyWFDnzuYcm83aBg8tKQBWzq6U14Pb3TYcMv8OpxepccpTxCNDpvg6+Nj7f5DP9kRowL+gWvDOWxNb7XTfu4yRcUo8ZjA4RodXf147NWPoWLGzu0sBuCmy6aj0WFLud7osOGmyxzJ0EqlwGrQYMPi2Zhbl2pHc+vs2Lh4Nu0LHaWMZfssV3u8eZ5DUKeb5zlKpBFBECNJtufvBIsO61rr0ZR2X2h22LFiXh02t3cnrzU6bLhn4UwMeEMp9/blTbUZi2wgfq9/+OUDaO/qwyVn2rBC4P7TXGfH98eYHzB6v2ImxhT0a/ToJ36Ih8jhGolDPiZYdEXWqjS4AhHc+NTbWN5Ui+WNtSmHlNz41FvY9s1LSxr/eVKVHg8vaUCfJwR3IAyzTg27STOmHq5jjbFsn+Voj32eEJY/+ZagTsuffAvPr2gieySIUUi252+IjeK8qdVYxrsv7DnmxJv/7sfDSxpg1KrgDUbQeWQQ1/3ydfziSxek3NsbplSlvLXEhztQLcTGMH28CWuumQVfmIUvyMKqV2OCRTvm7ju00CZGDfRL9Ogm2yEe2dJHEy5/GL4QK/qwK4eDRqwGWliPJcayfZajPboC5acTQRDFQer56/SHRe8LAPDY9efjpqd3JT/3uoMp6dkOOgtGonAHwpheY8pD49ELLbQJgqgIsu3xHM17QNMpx8OX0jnhCmDAG4IrEIFFr0K1QTNqf9EkxrZ9lqM90mFoBEEIke3ecIbdiK1fvQRmnQonXAFo0g4GFjowLT2d7i9DjN5NUwRBjCqqjZqMfUUcTQ4bqo1j59dTs04l2RfmEi9qDvd7cdv/vYsrf9aGL/6/f+HKn7bhO//3Lg73e0uqFzFyjGX7tCQOQxOiuc5eki8ZuEORhKBDCQli7CJ1b2hy2PDnPcex5JevY8HD7djS0Y1J4/T47KwJyTydRwYz9l5zNDps6HUH6f7CgxbaBEFUBGKHeDQ5bFjXWj+mfi2dXG3AWpG+WNtaj8nVhhJpFv8l+87tezIOSmnv6seq7XtwwhUokWbESDKW7VPNKCUPQ1OX4CA4OpSQIAghxO4NTQ4bbmisTTkQrb2rH/c+uxd3XnV28svEze3dWNZYm3Gv504dn3fWeLq/8Bi973IRBDHqmGoz4kdfPG/olWSdCtXGsflK8jSbERsWz4Y7EEkeeGLWqUq6yAbih2IJhfQA4g/t0Xwo1lhnrNrnKW9I8jC0P37zUtSUoA/oUEKCIIRIvzdo1Qz+tOc4bt3aCV+ITcnb3tUPb4jFD79wbvLebtWrsL61Ht4QC3cgAqOWgVGjQpVBTfeXNGihTRBFIhqNoqenBwAwadIkKJX0QkkhTLDoRr3jniulXlQLMZYPxSLGpn26AhHJg8dKOefpUEKCIITg3xve7D4leUCaKxDBjImWMXdvlwPy9AmiSHAhyJY99tfkgpsgRhtj+VAsYmxCc54giEqG7mEjB/UcQRQRCkE2fI4O+OAORODyh2HVq2Eqg9elS0U59gV3KFa7wOvjlXYoVqn71+kLoc8TgisQhkWvht1Y/r9OlrrPSkG1UYPPzpqAxRdMQY1FC0+ATZ7Yu+2dIxU158sFOeb+WJyLBMGHsyN3IASrXhMPvRWMoEqvhkHDwB2MwOkLo8qgxvrWWXjwT/vhC7EwaBgsb6pFw5QqAIBCARzt9yIcjQ3FxTaoMcFcfnGxT7gCGPCF4PbHX2k3aBhUJ369F7uvOH0hDPrC8IYi8IZYVOnVqJGpbbTQHmPQ68tEJXOo34tVaQdtcQeATbMZS6hZ8SnXvphg0WFtaz1Wb9+TstjmdKuUV89K3b89g36s3LYbbQf6ktfm1tmxYfFsTKrSj3j9hVDqPisVEyw6rLzqbNG2V8qcLxfkmPtjdS4SBAdnR+8cGsBDSxrwg799lGEPNzTWJvdlN9fZsWnpHNyytRMbFs/Glo7u5OvkBg2DTUvn4LFXutDGk9GcOFyxXJ5Jh/u9WL19T4qO3CFtp1t1uP+FffjH/t5k2tw6O9a2zEKPM4CHXj6Q0j9ytY1WWWMMen2ZqFSODvgyHCcgflDH6u17cHTAVyLNik8590XPoB8bXtyPZY21eP6WRmz96iV4/pZGLGusxcYX96Nn0F8y3XKl1P3r9IUyFhoAsPNAH+7YthtOX2hE6y+EUvdZKRnLbZcbOeY+jQcx1uHb0fKmWmzp6Ba0hy0d3VjeVAsAaDvQh8de+RjbvnEpnkrLv7ypFo+kLbK5MivL5Jl0whXIWGQDQEdXPx5++QDau/owY6IlJW3ngT60d/Xh4bRFNhBvmxzPW/pFu8Ip5Bdqen2ZqETcgYjkadbuMXTIVjn3hdMfxot7T+DFvScE02+5/Kyy+fZbjFL3b58nlLHQ4Nh5oA99nlDZva5X6j4rJWO57XIjx9yn8SDGOnw7aphSJXrQWUdXP5Y31iY/t3X1wRtmMxarUjLayuSZNOANZejNwbVTKPpDjUUnuNUNkOd5SwvtCof7hRoAttx0BSZPnlxijQhiZHD5w5Lp7oB0+miinPuinHXLlVK3wZVFfjn2Yan7rJSM5bbLjRxzn8aDGOvw7SgYiUrmTU93C9hPNhnlYFPZojuItWGk20YL7VFAqX6h5v+aDtCeb2JksejVkulmnXT6aKKc+6KcdcuVUrfBkkV+OfZhqfuslIzltsuNHHOfxoMY6/DtSKuS9svT080C9pNNRjnYVLaT0cXaMNJto1URUTDcr+k3Pf0O7fkmRhyzToUmh00wrclhg3kMhZ8o576w6tWSulmzOMHlQKn7127SYG6d8Beoc+vssJvK67VxoPR9VkrGctvlRo65T+NBjHX4dtR5ZBCNIvbQ6LCh88hg8nOTwwYFYhn5pWQ0l8kzqdqoQbNEO3tdgZS2cvS6AqLl5Hje0kKbGBZ6qx2G6ppRv+87Go3i6NGjOHr0KKJR6ddMhit/pOqodCZXG7C2tT7DgeJOkh1LYVvKuS8mVekldSv3/dlA6fvXatBgw+LZGQuOuYlTUEu9F06IUvdZKRnLbZcbOeY+jQcx1uHb0eb2bixrrM1YKDc5bFjWWIvN7d3Jzw+21OOUJ4QV8xwp+Te3d2PFPAeaHal22Vxnx/fL5JnERTxJ15E7dbzJYceHx10paXPr7Ghy2LFifl1G/zTL9Lylr/UIIgfy3Qsv9Fp9LvL1Vjt8g71Y/7nzMHHiRHodP41pNiM2LJ4NdyACdyAMs04N8xiNjVrOfTHNZsT3P38unP5wUjerXl0Ri2yOUvfvpCo9Hl7SkIiBGq/fbirvONql7rNSMpbbLjdyzH0aD2Ksk25Ha66ZhWAkCk8wAisvjvaTyy6EWaeGScOg3xuAzayFRavCmmtmJWNmm3UqmDQMHrhm5lAcbb0aEyzlFUd7qs2IH37x3GQcbYOWgZEXR/uHXzhX8L5i0auxrqUe3lAEvlC8bRRHmxgV5LogLYf94Pn8as9fOPudfdhy0xU5yTdU18Dv7MP3fvcO1Bp10Q+4K4d+zgY5SkOUc19MqtJX1MJaiFL3r9VQ3gtrIUrdZ6VkLLddbuSY+zQexFgnXzuaMgpizE+w6DBB4HRxQLw/RvJZSwttoiRwC7rjx49j1R93Q18lvSDN9xdf/oLx+PHjQAyAYiRblFrv8ePHobfEF86FoLPYoNEW38EW+oKATrInCIIgCIIgiPyghXaRqYRfDIsBt6ALuE7BWHNGTgvSfH7x5S8YB458BGPNGVCrVfFFN5DcA61UKmUdg4x2ZcnPX5gjlpoWi0aT+gJDv/b39PSk6M+lFdKG9DjswFA/EwRBEARBEARRGLTQFmGkFsT5/mJYiB5Ci6f0X3djsWjGopODv3jjwy38uPwnTpxILg7T07LJAAC9xS6ali6Xfw2I/+LLXzgL/WrNX5gDQMB9Ct/73QlYxk/EwJGPwOjMgot1fp3p/c1PE+pL7pdsqXal51/1x90IuDMX5nx9+b/2cwt5RmdOSZs0aVJe+8KBzL3nQvDn00h9QUEQBEEQBEEQo4kxt9COxeKrNZfLJZnv2LFj+MrPnoXWXI2gewBr/vtinHbaacOu/5NPPkGUjYKNsIiyURw4cABut1sy/12/e0NUj08++QTeU70AkJTFlQGANf99MQAkZbiO/xuGmslgA17c/P/2wlRdA9fxf0OpMyAa8EGpM8BUXZOsCwC8p3rBRlgMHuvCzf9vbzJfNOCLy2Iz03KRwenB6AJgGCYzjRXIz7LwDfaD0QUy2mComQy1WoMDBw6k1MnPz+iM8b6PxqCIsoiyTEq/eU/1IuAewM3/by/UKnWyv9PT+HUK6SHZLoH80WgMvsFeMAwjrG9irgBAlI0m9eenHThwIGOu8Psh4OrPmG/cfOTmj1B+vlxurqhVajzxv9fg9NNPz2nem81mKBTZ393P1T4JgpAPsk+CKE/INgmifMnFPhUxzjrHCEePHsWUKVNKrQZBjCmcTicsFkvWfGSfBFF8yD4Jojwh2ySI8iUX+xxzC23uNdhcvyUsB1wuF6ZMmYIjR47kdMOtdKi9o49c7S1X+6z0PiP9SwvpnwrZZ2XqDFSm3pWoM1AaveW2zdFCpc6hSoD6Nndysbcx9+q4Uqms2FOULRbLmJr01N6xR772Wel9RvqXFtI/P8aCfVaizkBl6l2JOgPlqXcl+7bDoRzHYrRAfSsPdJIRQRAEQRAEQRAEQcgILbQJgiAIgiAIgiAIQkZooV0BaLVa3HvvvdBqtaVWpShQe4lsVHqfkf6lhfQfWcpdPyEqUWegMvWuRJ2BytV7NEJjMXJQ38rLmDsMjSAIgiAIgiAIgiBGEvpFmyAIgiAIgiAIgiBkhBbaBEEQBEEQBEEQBCEjtNAmCIIgCIIgCIIgCBmhhTZBEARBEARBEARByMiYW2jHYjG4XC7QGXAEUX6QfRJE+UL2SRDlCdkmQZQnY26h7Xa7YbVa4Xa7S60KQRBpkH0SRPlC9kkQ5QnZJkGUJ2NuoU0QBEEQBEEQBEEQIwkttAmCIAiCIAiCIAhCRmihTRAEQRAEQRAEQRAyQgttgiAIgiAIgiAIgpARWmgTBEEQBEEQBEEQhIyoSq0AUb4cHfDBHYjA5Q/DqlfDpFNhcrUhJY/TF0KfJwRXIAyLXg27UQOrQSMqw6hhEIvGYDVqEIhEMeANwRWIwKJXodqgwQSLTrJupy+EQV8Y3lAE3hCLyUY1WBWTkdesVSX1Gq9XAyol3IEI3IF4HjWjxIAvBKNGBYNaCSWjzJDhDUXg8oVhN2kRYqNw+iOw6lXQqpTwBsMw6zTwhtgMHdN1t2gYxAC4eHkNGganvEFo1SooACgUgEkb/9uZKFtlUMOoVcETiMDpD8NqUMOkVSEciSIQZqFQKBAD4A9FYDVoYNKqEAyx8CT6pkqvRo1ZC3cwItiXxwZ8cPGum3Wp9Qv1pVU/pJPYmBMEMXbJ5bkxWsn2PCwFxRoPueuRQ16hMvItl2v+sWwbxOiiZ9APpz885Ocm/PuTniCgiEGjZOANsXD7w8k0P8ti0Dvkg7v8QVQbdfCFWLgDYVh0aug1DPrcARh1ahjVDLzhuN9s1qlh0jAIRlkwUCIQicITiMCU8FuVSkCnYuANReALshiXWGN4ghFYEnliCiAWjfvbBjWDcJTFoC+CaqMGYTaKaBSIAckyZq0Kk2SwT1poE4Ic6vdi1fY96OjqT15rctiwtrUe02xGAHFDW7ltN9oO9CXzzK2zY8Pi2ZhUpReX0VKPfm8Idz+7NyNtfWs9ooBo3YZoAKuePYCOrn58+eLJWN7swOptuzPyrmmpx4//th+uQAQPtszOyNPosGFZYy2+9dt3sWXZRbh3R6aMexbOhD8UxV1pes6fMR53LzgHq7bvQbuAjt9/cT/+tPdE8npznR03XTYdNz71NnwhNpn37gUzcf0Tr+NTp5mxrLEWv33jML7SXIvlT70NAHhoSQO2dHSn1N3ssGPFfAd8IRZPtB/MSLtp3lA9F51Rhe9//jys3iHclzs/OI67n/8w5fp9i2bifza9gT5PiNeXs3DDljfR7w0J6sQfc4Igxi65PDdGK9meh6WgWOMhdz1yyCtURr7lcs0/lm2DGF2IzeX7r5mJk+4Axpt1uFMg/Z6FM3HT07vQ5wnhqlkTcPuVMwTl3LNwJo4P+vF4W6qPe/mM8bh7wUzctSPV9+b8+a1vHML1l0yDAgr8+B8fZfjHy5rOwG9eP4TrLp6GrW8cwt0LZiIWC2LtC/tw7cXTMv3tOjvWtMwatn0qYmMsur3L5YLVaoXT6YTFYim1OmXJ0QEfVqYtTDmaHDZsWDwbZq0KK7Z2pjgVHHPr7Fj3uXpRGetbZ+HPe46jTSjtc/X48+4ewTRuId78g1cBAG3fuwx3pBlpel4AonkaHTasvHIGNv7lA1EZK+Y7cO3jb6RcXzHfgc7DA6JlljXW4sbEYplfV8PUajzycpdgXi698/AAGqZWA4BoHc0OG66qn4hV2/cKtomrZ+f3Lsu42aX3D9eXUvo3OWy4/coZ+Nu+E6I6za2z4+ElDcP+9YbskyDKFyn7zOW5MVp/vXP6QpLPQznujflSrPGQux455BUqI99yueYvxljQs5MoBj2DfnzvD++J+va14414+OWurP7xpqVzsDltYcvR7LDhs/UTcWeajyvle3O+73uHB0T942aHDefy/Ox3Dw/ggWtm4Y+dxyT8bTvWL64fln3SHm0iA3cgIjjhAKC9qx/uQAR9npCgUwEAOw/0ScqosegEF9IAUGPWiqa1d/XDk/hFGAA8IVZST0+IlczT0dUPFaOUlGHUZr700TClSrJMjUUrWFfDlCrRvFw6979UHW1d/clX7KXq8ebQP7no357oJymddh7oS/4KThDE2COX58ZoJdvzsBT3xmKNh9z1yCGvUBn5lss1/1i2DWJ04fSHJX17o1aVk39cY9FK+rg1Aj6ulA/K+b5S/nFbmp/d3tWPYCSaxd/uG7Z90qvjRAYuf1gy3R0IQ81If0cjJSMYiRaUBgBuntysevrDyPa6hjuLDE8gczGaTUehMmLl+Hm59Gzys+Xh0nLpHyGE9Hf7w9nHJiBdH0EQo5dcnhujFVeWtpWi7cUaD7nrkUNeoTLyLZdr/rFsG8ToIptvL+b/cnDp2fIJ+ZvZfNBcfOj0PMXwbWmhTWRg0asl0806NTRZFtpSMrQq8bJSaQBg5snNqmeW9FzymHRMxrVsOgqVESvHz8ulZ5OfLQ+XVmj/COlv1qvhCUp/q2fWZe9vgiBGJ7k8N0YrlixtK0XbizUectcjh7xCZeRbLtf8Y9k2iNFFNt9ezP/l4NKz5RPycbP5xrn40Ol5zHo13CPs29Kr40QGZp0KTQ6bYFqTwwazTgW7SYO5dXbBPHPr7JIyel0BNIuU7XUH0SxRt0kzZJwmDSOpp0nDSOZpdNgQYaOSMrwCBth5ZFCyTK8rKFhX55FB0bxcOvc/97cQzQ4bTrgCom3i6jHm0D+56N+U6CcpnebW2WE30cnjBDFWyeW5MVrJ9jwsxb2xWOMhdz1yyCtURr7lcs0/lm2DGF1Y9WpJ394bZHPyj3tdQdF8zQ4begV8XCnfm/N9pfzj5jQ/u8lhg1alzOJv24dtnyVdaK9fvx4XXnghzGYzampq0NLSgg8//DBrud///veYMWMGdDod6uvr8ec//7kI2o4dJlcbsLa1PmNCcydkTq42wGrQYMPi2RnOxdw6OzYmDvYQk9HosOP+RTMF05qn27BGom5dNJA0iF/u7MKaFvG8G17ch1Xbdwvm4U4pvPnpXbhv0SxBGfcsnIlQOJZhgPt6nHiwRbjM2tZ6/PGdIynXm+vsWDGvDpvbuzPkr9y2O6nLBz0u3DI/nm9zezeWNdZm1BE/dbwOE636DL2aHan1fPf370r2z6sfHM+4ft+iWVi5bXfKtTUt9bj56V2iOnFjXuowNgRBlI5cnhujlWzPw1LcG4s1HnLXI4e8QmXkWy7X/GPZNojRxaQqvehcvuhMG2KxqKRPzfmXf3jniKgffffCmZhUpc9I29/jlPTn9/c4saypVtQ/XtZUi309zmTeta31GPD68cFxF5Y11maWqbNjTeusYdtnSU8dv/LKK3HttdfiwgsvRCQSwapVq7B3717s27cPRqPwcer//Oc/MXfuXKxfvx4LFizAM888g40bN2LXrl2YNWtW1jrpZMbc4WI+ugPxGHZmiTjaXB67STiONpduEoujrVOh2pgZRzu9bn4cbV+Ixem8ONr8vFzsZ3cgDHtaHG2LPv7q+4AvBEMijjbDKDNkeEMRuP1hjDNqEWajcPkjMOtV0KmU8AXDMCXiaKfrmK67lRdHm7tmTIujDQVg5sXR5vQ0pcWsTsbRjrBQgIujzSZjcnJxtH2Ja/w42ul6cnG0uetcrEGnRF9aeHG0xcZ8OJB9EkT5kot95vLcGK1kex6WgmKNh9z1yCGvUBn5lss1/0iOBT07iWLCxdFO+rlicbR5vr+fZeH0xv1IoTjaZp0aBg2Dfk8ABm0OcbSDEZi0KigU8djYei6OdojFOMNQHG2zTgUlgCgQD5StAIwScbS9iTJyxdEuq/BeJ0+eRE1NDV577TXMnTtXMM9///d/w+v14oUXXkheu+SSS3DeeefhF7/4RdY66GZEEOUL2SdBlC9knwRRnpBtEkR5UlYbQ5xOJwBg3Lhxonn+9a9/4bbbbku5dsUVV2DHjh2C+YPBIILBoT2nLpdr+IoSBCELZJ8EUb6QfRJEeUK2SRCVQdkchhaNRvGtb30LjY2Nkq+Af/LJJ5gwYULKtQkTJuCTTz4RzL9+/XpYrdbkvylTpsiqN0EQhUP2SRDlC9knQZQnZJsEURmUzUL75ptvxt69e/Hb3/5WVrl33nknnE5n8t+RI0eyFyIIoiiQfRJE+UL2SRDlCdkmQVQGZfHq+IoVK/DCCy9g586dmDx5smTe0047DSdOnEi5duLECZx22mmC+bVaLbRabV76cAeacAdQ2Y2lP9BELk64AkMHkOlVqDYMHUCWDndwh8sfTh62lX5wR7Y8UuliaSddAQTCLDwhdihNw0AbjQFaVTLNHQijxqSGSqVKyrHo1bBoGCgZJZyBMDyBCEw6FQxqBr7wkDx14jA0o0YFk0YJKJUZurCRCCIIQ6PUJXWxJHTRRaMIqRgEI1H4Qyx8IRYWvRpVBjXCYRZunu42DYMAkNIeo4ZBLMoipmTQ5w7AqFWjSq9GJBbL0MMXjmDAE8Y4kxp6tSol3ahhMOgLQK2K5/WGIhj0xtMsejUmVelF+5l/nWsXA8DJ73edChqFAv3+cPyaQQ2zVoVgJAqnf/j2UYh9EgRRHAqxz1yeG6MV7oAgru3cPbiUFGs85K5HDnmFysi3XK755ewjenYSxYZbP/hCEdhNWvhCLDzBCGxGDUJs3Ce06tWJg8Zi8CV9YxX06sRBZUEWdqMGCqUCvhALVyACk5aBXs0gEouCUSgRYqPwBOIHkhnUDJQAgtEYz9dWQaNSQhcDokpFZhoDhKOASqnEKW8QJq0aek3c1zZohw5lZgH0J9KNGgbecARuPwuzTgWtWgkdo8REGe6VJV1ox2Ix3HLLLdi+fTteffVV1NbWZi3z6U9/Gi+99BK+9a1vJa/9/e9/x6c//WlZdOoZ9GPltt1oO9CXvDa3zo4Ni2eX/IE5XA73e3Hn9j3o6OpPXmty2LCutR5TbamnvB/q92KVQN61rfWYlsibLY9UuhrAyrS0y2eMx/qFM+GLAat3CJRrqYcyFMEdO/aio6sf/zHDjrsWxMNRcXkNGgabls7BY690oS2t/A2Ntbh1ayd8ITYZDuDrv34Hj1x3Ph59+UBG/h+31MMPBnek6Xnv1Wdh/tmT0HPKh0de6UpJa66z4zv/dRau++Ub8IVYNDvG4cGW2YLtWdNSjxffO4r550zCLc/swmNfugD3Prc3I9+DLbPwo799gI2fPzelrXw5t27dBYtejbsXzMTNz+xCnyeEt75zEQ71R0XH4Cd//xA73j2ecv2+RTPxP5veQJ8nxJM/C1/71ds4MuAX7MvRYh8EQQyPXJ4bo5VybHuxdJK7HjnkFSoj33K55i/H+UEQucKtHz78xI2nv3IJ7ty+B52HB/HQkgb85B8fpfrBDjtumjcdNz71NnwhFkB8ri9vqoVGqUTYEMP92/em+NzzZ4zHqs+eg3ufTb3eXGfHvQvPwbo/78fLH5xMub7mmln4xOnHQy9n+uE3XzYdj7cdxHUXT8OXN7+F86dW4e4FM3H9E69jxmlmrGmZhVOeANyBaEr6N59+E32eUCKUrgPhaCxjfZQvJT11/KabbsIzzzyDZ599Fp/61KeS161WK/T6uNP+5S9/GaeffjrWr18PIB7e6zOf+Qw2bNiAq6++Gr/97W+xbt06WcJ7OX0hrNjambLI5phbZ8fDSxoq9pftE64Abvu/d1MmI0eTw4YfffG8lNBa6Qs6ft4Ni2cDQNY8UulrW+vxmR+8mnJ9xXwH/vuCyRkLW365FfMduPbxNwAAbd+7LCPvivkOdB4eECzf6LChYWo1Hnm5K/l5eWMtNnd0C+YXks9db+vqw5/2HBcs11xnx7lTqvDIy12iMrj2rG+tx53b9+DeRTNx33Pvi+Zbl8gnlr6mZRYu++FraEp8gXDjU29nrfv2K2dg0SMdGde58lJ50/tSLvugk1MJonyRss9cnhuj9ZftnkE/vveH90Tb/v3Pn1v0LyKLNR5y1yOHvEJl5Fsu1/zFGAt6dhIjBX/9sGnpnKTPnI+/DQDNDhtWXX021v1pf8piGpD23ZscNpyXJguI+9pXzToNq7bvzSjD+eGdhweSevD92yaHDauvPhudhwfR4wxkpHP6fnb2JDTX2YdlnyXdo/3zn/8cTqcTl112GSZOnJj897vf/S6Z5/Dhwzh+fOhXt0svvRTPPPMMHn/8cZx77rn4wx/+gB07duS0yM5GnyckuMgGgJ0H+pK/8lUiA96Q4AQGgPaufgx4h9rmDkQk88bjQGbPI5XOfcvFp2FKFTwhVrKcUTv0EoZQ3oYpVaLlO7r60TClKuVzjUUrml9MF0+IxQSLTrRc24G+ZD3Z2uMJsWjv6keEjUnm82aR4w9Hk3/XWLQ51a1iMs2fX14qb3pfVrp9EAQxPHJ5JoxWnP6wZNud/nCRNSreeMhdjxzyCpWRb7lc849l2yAqH/76ge8z5+NvA0BbVz9iUGQssrPJaheQBcR9bbGtr5wfzteD79+2J3SZYNEJpnP61pi1w7bPkr86no1XX30149oXvvAFfOELX5BdH1dA+mHozpJezriyTBR+uiuLU+AOhJFt6LLKEEgPRqJZy3kCQwt0obzBSFSyfHo6X146Yrq4/OGc68m1HwrpL7F0rk2FyhTqE7HxSslTwfZBEMTwyOW5MVopx7YXSye565FDXqEy8i2Xa/5ynB8EkSv89QHfP8zX3wbE/c5CZGUrx6Xx8/D1dwv48un+bzASHbZ9lsVhaOWCRaeWTDdnSS9nLDrpoeanW/TD74esMgTStSpl1nImHSNZh1Yl/ZJGejpfXjpiulj0aniC0l9ccPXk2g+F9JdYOtemQmUK9YnYeKXkqWD7IAhieMjx3KhUyrHtxdJJ7nqK4n+IyMi3XK75y3F+EESu8NcHfP8wX38bEPc7C5GVrRyXxs/D198s4Mun+79alXLY9lk24b3KAbtJg7l1dsG0uXV22E2VuT8bAKqNGjQ5bIJpTQ4bqo1DbTPrVJJ5zTpVTnmk0g2azMVc55FBmDSMZDkvzyiE8nYeGUSjSPlGhw2dRwZTPve6gqL5xXQxaRj0ugKi5Zrr7Ml6srWHS1cxCsl8xixy9Gpl8u9eVzCnuiNs5jeB/PJSedP7stLtgyCI4ZHLM2G0YtWrJdtuzbLQGgmKNR5y1yOHvEJl5Fsu1/xj2TaIyoe/fuh1BZN/5+NvA/E9zwrE0CxQpvPIoKSNpMsC4r72CVdAsAznh/P14Pu3TQldTrgCgumcvr3u4LDtkxbaPKwGDTYsnp2x2J5bZ8fGxbMr9iA0AJhg0WFda33GROYO2uLvc5hcbcBakbxrW+sxudqQUx6pdHXibz77e5zQAljTIl7udIsumXb/83sz8m5u78aKeQ40p40hd8jB5vZuAEieOr5y227cMr8OzY7M/DoAawV0+ce+HjQ67Fgxz5Fxk2mus+O7/3VWsp5V23dLtueF947inoUzcfNvduG+RbME8z3YUo/v/v5dPNginL62tR63bu1Ek8OGexbOxMptuwEAuqhPcgy2tB/MuH7folnJ8ty1NS31uPnpXaJ9ORrsgyCI4ZHLM2G0MqlKL9n2UkRkKNZ4yF2PHPIKlZFvuVzzj2XbICof/vph5bbduHvBTDQ5bNjc3o1ljbWZfrDDjhXz6pI+IpDwG5tq0e8O4d5FMzN87n09Tty3aFbG9eY6O+5bNBP7epwZ19e2zML08UZBP/zmy6ZjX48z6avy/WPOr/UFI5ho1WekJ9swvw6N023Dts+SnjpeCnI5mZGLo+0OhGHWqWE3jdI42joVqo3Z42hz/WCWiKMtlkcqXSyNH0c7mZZDHG0ub0Ycba0KBs1QHG2LXg1NIo62QaOCmRdHm69LPI52BBqlNkWXjDjaYRa+YDx+X5VBk4yjnZw/vDjafBnRKAsoGfR7AtBr1KjmxdHm6+ELx2NjVxnVMKhT22rSMHD6A2CYeF5vKAKnL55mTYujnd7P6df5cbT5ebk42sn+1amS++nltg86OZUgypdc7DOX58ZohYujzbWduweXkmKNh9z1yCGvUBn5lss1/0iOBT07iZEm7zja4URsax0vjnaIhd0wFEfbHYjAoGVgSIuj7Q1EYEqPo83ztTUqJbQxIMbF0eanpcXRNmrVMGiGfO30ONrGRGxtLo62SaeCTsY42rTQJgiibCD7JIjyheyTIMoTsk2CKE/o1XGCIAiCIAiCIAiCkBFaaBMEQRAEQRAEQRCEjNBCmyAIgiAIgiAIgiBkhGIKEARBEIQALMvi8OHDyc9Tp04Fw2SGJiQIgiAIgkiHFtoEQRAEIcDhw4dx46MvwjBuAnynTmDTzVehtra21GoRBEEQBFEB0EK7guFCRbgSR+qbEqEiuBAj3HULL8RIehmjhoEvHIKKUcNuTA3TxM9r0cePv49EWQBKhKJRKBA/Pt+fOMLfrFVBoQCgAGJRwKqKIaJUwRNiU+ob9AWhUaugUzPwBSPwJI7kt2hVUEaiUCKMoFIDd1q5QISF0x+BWaeCSqmEKxDCOIMG0RjiYQQScoxqBkAMnnA8BNU4oxpahsnQQxcN4mSYwXg1g6BKmdEvoSiLAW88DIeGiYcciMYATyCCM0wqBJVMio4mDQNVlIUfSgQiUbgDYVgS4cZiAFy8vPpEqAFDItRAKMrCrFIhEI0l9awyqGHUquANRjDoG/rsCUbgCURg1KqgQLy/jWoGOgD9YRZOX2obA4nwZKltDyCg1KVct+hUcT3T5pQWQF/iWrpOVn08RMnpYyR8DzH2MIybAJN9UqnVqBjEnktjgXJse7F0krseOeQVKiPfcrnmL8f5QVQuXChiVyDuo6f78IXI8gTDqDJoEIlGoVQoEAgP+bJGDYNglIVGyYXLjcCkZeK+PwBP4ppZx8CsUSEKwB9hoVEmfGJ/GOaED8og7hO7A3FbMKgZeMNsMiQvAMQQg07FgI1Fkz68JxH2S6mI661mlBj0hWDSxn1/T4TTK66TUgnoVAz8YRbeYARVBg2CkSg8wUgy7BiiMSiVingI3sR1rUoJrUzhFsSKAAABAABJREFUvWihXaEc6vdi1fY96OjqT167fMZ43LNwJlZv34N23vUmhw3rWusBAHemleECt9/+h3ehU6uwYfFsTKrSC8rn8m5q+xj/c2ktBrwhPPTygZQ8jQ4bljXW4l8HTuJ/Lq3FagEZD7bMwpc3vYlpNgNuaKzFrVs74QuxaK6z48fXzIIPGsG6+XkbHTbct3AmTrgC+NnLXSl5mx123LvoHHzpiTcAAE9/5RLc8ZxwW8arffAqDFi9bbegnt/+3Xvo94awaekcPPZKF9q6+nHf1Wdh/NmTBNu2pqUev3j1AH739rEhfersuOmy6bjxqbfhC7HJvHcvmInrn3gdM04zY21LPfxsDHfuiMs0aBg8tKQBWzq6Uz4/2dGdMrZcf2994xDuXjATPn8Qy558B74Qi63L52DiOJOonsf73Viy+R0AgEHDYPPSC/HoKwfQJpD3R3/dj38eHEjRiZ9nbWs9ptmMIAhi7CL23BgL94dybHuxdJK7HjnkFSoj33K55i/H+UFULj2DfqzcthttB/qS1+bW2ZM+fCGy3jkU9/F+8drH+NZ/fAoPvLA3Y41x94L4GoPvJzbX2XHzZdOx/Km3AQC/uP4CqKuV+NHfPsS3//NTuOvZvRnz/r5FM/E/m96AL8RK+rbPvHEIX5s7HQqEsOzJIR+an37dxdPwpU1v4vypVSnrhPkzxmPllWdj1fY92HV4EA8tacCP/v5R6nqhzo4Hr5mFB55/H//44GTK9Vvm1yEcjWHqMO2T4mhXIEcHfFiZtjAEgBXzHeg8PJBxHYhPmqtmnYZV2/dmpHEL8bk/eBVz6+xY97l6Qflc3jUts/Cvj/vxpz3HUwyDo9Fhw/rW+oxFPV/G7VfOwKJHOtDosKFhajUeebkLAND2vctwh0i59LzNDhuuqp8o2KbmxMIcADanLQz5eqxvrRetj9Pzb/tOpPSrlI5NDhvuXTQT//njnZK6c3mXNdbixqfeRpPDhhXzHbj28fiXA+ljKTW2nOx3Dw9gfWs9fvfOUTzycldWPde31qP5B69mlc/l/d07RyXzbFw8e9i/bI8G+yRGD93d3bhl6y6Y7JPg6evBw0vOH9OvjkvZp9hzCYjfHzYsnj1qf70rx7YXSye565FDXqEy8i2Xa/5ijAU9O8cOTl8IK7Z2piyyOebW2fHwkoacf9nmy+L8wOWNtYJ+cy5+KBCf0w+/3CUqh8uzrLEWnUcGs8rsPDyAq+snoscZSPGh+emcf833tfn6Sq6PHHacO7UqRTZ3/bOzJ6K5zj4s+6RTxysQdyAiOFkaplQJXgeAtgN9mGDRCaa1d/XDm/iWaOeBPlH5XF5/OIoai05wkQ0AHQl5UjJUjDKZt2FKVTLNI1EuPW9bV79om9q6+lFj0aLGopXUQ6o+Ts/0fs1WJsJmfneVrjuXt8aiTf5t1A69YJJep9TYcrK59nD1ZNPTkxjzbPL5cqXyuAIRwTSCIEY/2Z4b7lF8fyjHthdLJ7nrkUNeoTLyLZdr/nKcH0Tl0ucJCS6ygbgP3+cJFSSL8/HE/OZc/NCGKVUwalWScoAh/zcXmR0JXz/dh+anc2n8v/myJddHXX0ZsrnrNWbtsO2TXh2vQFz+sOD1YCQqWU4q3c2TKSafnzdbXbnIENIrW7n0eqX08ARY0TQhPcTS0+vIp218hHTl68j/O5928tP5+hY6BmJ5s+YJSNdHEMToJev9ZhTfH8qx7cXSSe565JBXqIx8y+WavxznB1G5uLLMl3zmE18W5+OJ+c65+qFc+Ww+uCfA5ixTLJ9QutS1bPUIXR+ufdJCuwKx6NWC17Uq6RcUpNLNPJli8vl53UHpb3hykSGkV7Zy6W2QapNJlz0MjzkHPT1pbc2nbXyEdOXryP87n3by0/ljU+gYiOXNNuZmnXR9BEGMXrLeb0bx/aEc214sneSuRw55hcrIt1yu+ctxfhCViyXLfMlnPvFlcX6gmO+cqx/Klc/mg5t0DHyh3GSK1S2ULnUtWz1C14drn/TqeAVi1qnQ5LBlXO88Mih4HYjv0e51BQTTmhw2GDVxg5hbZxeVz+XVq5XodQXQLJKnMSFPSkaEjSbzdh4ZTKaZJMql52122HBCpE3NDht6XUH0uoKSekjVx+nZeWQQjbw82cqoGEVW3bm8va5g8m8vbyGbXmf6ZyHZXHu4erLpadIM3QSl5PPlSuWx6Oh7O4IYq2R7bphH8f2hHNteLJ3krkcOeYXKyLdcrvnLcX4QlYvdpMHcOrtg2tw6O+ym3E8e58vifDwxv1lqjcH5oZ1HBuENRpL+rdS873UFc/JtGxO+froPzU/n0vh/8/WVqqfZYc+QzV3vdQeHbZ+00K5AJlcbsLa1PmMC7+9xCl5vctiwtmUWGh124bTWenz39+9ibp0dGxOHcojKaa3HpraDmFM7Divm12VMXO4kwKc6ukVlPNhSj5uf3pU8DGFzezeA+JcBOgBrW4TL8fM2Omy4Z+FMOMabMnSInzo+Cyu37cbKbbtx94KZom3RRX1YI1Ifp+fm9m6smOdAsyN+M3ppX49ombWt9di08+NUfersWDGvLqk7l/eehTOxctvuZLlJFn1S5ub2bixrrM34nP7lBtff3NifdPmS9Rzt90iO49F+d/La5vZu3DKvLtnG9Lz3P783Q6f0PBTiiyDGLtmeG6P1IDSgPNteLJ3krkcOeYXKyLdcrvnLcX4QlYvVoMGGxbMzFtucD59PiC++LM7H+8M7RwT9Zs7PbE6rt7nOjlsSPu7m9m6EwjHct2gmtr1zBPcsFPa/70v46GJ+Jefb7utx4pb5dZg+3pTiQ/PTubVB+jphX48z2Q6unoz1Qp0dD7bMwgc9rsw2XV6Hpum2YdsnnTpewXAxGd2BeKxnc1ocbe66VSCONpdm4uJoK9Wwm4TjaPPzCsXRDiTiaBu1Q7HtYrHUONp8GSlxtEMReALicbT55QKJ+HhGnQpqpRLuQAjVaXG0zXoVTBJxtPnydNEgesMManhxtPnponG0gxGcYRyKo53sf4E42madGlZeHG3umiERR1uviZcLRllYeHG03Ym4iKZEzGqnf+hztjjaLn9qG7k42qltH4qjndSTF0ebP6e4ONpCOpl18fjbci2yR5N9EpUPnTqeSi72KfZcGguUY9uLpZPc9cghr1AZ+ZbLNf9IjgU9O8ceXOxrbj6l+/CFyPIGw7DqNYjEomAUCvjD0RTfkR9H2+2PwKBlYOHF0Xb7IzCJxdFOyEmPo23Rq2HkxdHmfNtscbRjADSJONpGbdz390TiOnAyFEpAnyWOdiwaA5OIo+0JxuXrKI42AUD0Bj2pSi8aR0+4jHCMuJFyEKaUTcxII8Zny5I1Q+6cnvZ5JGJnZqorVod43el6CsslCILIpNQLy1JSjm0vlk5y1yOHvEJlFPIr/EjIJQgprIbCF9YjKStXhHzN0Qi9Ok4QBEEQBEEQBEEQMkILbYIgCIIgCIIgCIKQEVpoEwRBEARBEARBEISM0EKbIAiCIAiCIAiCIGSEFtoEQRAEQRAEQRAEISO00CYIgiAIgiAIgiAIGaGFNkEQBEEQBEEQBEHICMXRFuDogA/uQAQufxhWvRomnUr2+IfHBnxw8eow61Q4PYc6uKDyUYShYzTwhNgMPaX0T08zahj4I0FEYyooFQqolArYjPF4evy8Fn08WL0uGoPGOBRvT6iuWIRFrzeMGqMG4ILAB8Kwm7QIsVE4/WFYEkHrQ1EWfe5EWQ0DZZSFGmEElLqUthk1DHTRCPxKFbwhFm5/GGa9GnYNgyAAdyKvRa+GWcNAC6AvcW2KWQ1WyWTIC7AsBrxhjDepoVYyKTJMGgY6xBCNBhFS6uCLsNAqlYgC8IdZKGIszDpthkwmysIVAWIAvInA94wS0DFD9XM6qgGc4o+fhgGLGHyhKNz+MKxGNQyqoXJVBjWMagaeMAu3PwKzTgWNSokBbwhGrQpGDYNglMWgJwybSYsgG4WNyexLsXli1qmgAOCUuFZlUMOoVY24fRAEUZkU4/lZrpRj24ulk9z1yCGvUBn5livHcSeGD+dvuwIJv1CrgjcYifuwejXsxtxiT8slR0hWPmWlZPa6gxj0h2HUMDBqVTCoGQTDLNwhFt5gBEZt3JfVqxhEYjEEwlF4AhGY9QyMahU8IRbuwJBvr0CqX27UMFACcIci8IdYVBs0CLJRuHxhWBO+rTshw6pXQ8MoccoXglGjglalRDgaBRtFok4VdColFAACkShcgbg/rGWUCEai8IUiqDZoEGITadq4jEg0CkaphD/MwhdkYdbH/VsoAMQAhQLQqRj0uQMwaOWzY1pop3Go34tV2/ego6s/ea3JYcPa1npMsxlLWkfPoB8rt+2GlgHuWjALd4jI+FVHNzb981DK9Z+01sMHCNa7pqUea17YC38khmWNtXC6/RhnNYjmPXzciak2E3xsVLgdLfXQqQBWocDq7XvQeXgQDy1pwE9fOiAo70d/24M3/z0Y17OlHj4wonW3f3Acdz3/IQDgP2bYcdeCWVi9Qzjva/uPwxeK4nNzpmK1gLy7F8zEUx0H8b0rz8FKkfp0Ch1+9PcPcdO8Ohx3BfDIK11w+8N4aMn5gv2/pqUeu/79CVY9+wEAYEq1Hr++8SLRvPc+G2+7QcNg09I5eOyVLrR19cOgYfDQkgZs6ehGh8BnjkaHDcsaa/E/m9/E+VOrsLalHkE2iLue3YuvNU6BbnxVRtsvnzEe9yycibu270Fbmk73LZqJ/9n0Bvo8oaw6pc87ueyDIIjKpBjPz3KlHNteLJ3krkcOeYXKyLdcOY47MXw4f7vtQF/yWpPDhhsaa3Hr1k74Qizm1tmxYfFsTKrSj7gcMVm5lpWU+YfdaOsakjl/xnis+uw5uP+5vUkf0W7S4HdfvwQ9zgAeefkA2rr6YTdp8PRXLsnwb5vr7Ljpsum48am34QuxyTbft2gWTnlCCLEx/OjvH+Xm2256ExdMrcZN84TkzcT1T7wBX4hNyuDWG5x8ADBoGGy5YQ5iMeDhV7pSdXXYsazpDDzz+mFcd8lUPPPGYay8cgaW/PJ1zDjNLIsdK2KxWGxYEioMl8sFq9UKp9MJi8WSknZ0wIeV23anDAJHk8OGDYtnD/vbjWMDPtwuUcfGxbMFf9l2+kJYsbUTbQf60Pa9yzImNl/GutZ6zP3BqynXs5VZ31qP5h+8ikaHDRta67PmBSCaZ33rLFx8pg13P7sXHV39WDHfgc7DAznpm6ueueaV0pPLky19wB/G3mNO/GnPcXR09ePV734Gq3fszUnH51Y0YuNfPsja9vQ+yvaZT6PDhoap1Xjk5S40O2z4bP1E3Ll9r2j/ZBuPZY21uPGpt/PSQS77kLJPgig23d3duGXrLpjsk+Dp68HDS85HbW1tqdUqGaV+fpYr5dj2Yukkdz1yyCtURr7lymnc6dkpH3x/Ox2+vwXEF7oPL2kQ/FVZLjnZZGUrK9nOZzpTFtmAsK+3aekc9LoC+NOe42hPXN+0dA42py2QxdoHxG1ixfw6HDzpwarte0XrEpIhJm9ZYy06jwwmZQjJWzHfgUlWXdKHT6fZYcO5U6vx3uGBxP+DuKHxDNz41Nuy2DHt0ebhDkQEBwEA2rv64Q5Ehl2HK0sdLpE6+jyhpIF5QqykDG/iGx8+2cp4EmU6En9nyyuVp8aiQzASTaY3TKnKWd9c9cw1by55sqWrGCUmWHTJfP5wNGcdVYwyp7an91G2z3w6uvrRMKUKANDW1Y8aiw6AeP9kG48aizZvHeSyD4IgKpNiPD/LlXJse7F0krseOeQVKiPfcuU47sTw4fvb6fD9LQDYeaAPfZ7QiMrJJitbWUmZXZkyhXy9GosWNRZdcpHNXcvFL+Vo7+qHUctgQsJHFatLSIaYvBqLNkWGkLyGKVUpPnw6bQnZQ//3Jf1gOeyYXh3n4fKHJdPdAen0kazDxbueVYZAej5lcskr9RpEMBJNkRGMRGWtWy490+VJyeC3IR8dc5EPZPZRts/p8NO5v8X0zCbLE2AL0kEO+yAIojIpxvOzXCnHthdLJ7nrkUNewX5WnuXKcdyJ4ePKMm7pvlAufvtw5OQiq5C5JiZTyNfzBNiM65yvKEYucgrxbfOVl60Ofh7uf37bhmvHtNDmYdGrJdPNOun0kazDwrueVYZAej5lCpHPR6tSpsjQqqRfnCi07uHqmUses16NGABPcOgbrXzqzUU+kNlH2T6nw0/n/hbTM5ssk44pSAc57IMgiMqkGM/PcqUc214sneSuRw55BftZeZYrx3Enho8ly7il+0K5+O3DkZOLrELmmphMIV/PpGPgCykzrkmRi5xCfNt85WWrg5+H+5/ftuHaMb06zsOsU6HJYRNMa3LYYNYN/3sJS5Y6LCJ12E0azK2zAwBMGkZShlGTOfmzlTElyjQm/s6WVypPrysArUqZTO88MojGHPXNVc9c8+aSJ1t6hI2i1xVItkGvVuasY4SN5jRW6X2U7TOfRocNnUcGAcT3mvS6gwDE+6fzyKCkTr2uYN46yGUfBEFUJsV4fpYr5dj2Yukkdz1yyCtURr7lynHcieHD97fT4ftbQHx/tN0kvDdaLjnZZGUrm69MIR+x1xVEryuAZt71XldQdP6ntw+I24Q3yOKEK5BSVy6+rZi8XlcwRYaQvM4jgzjB8+HTaU7IHvrfnvSD5bBjWmjzmFxtwNrW+oyJw50gKcehFqdnqUMsxJfVoMGGxbMxt86O+5/fizUt4jKe6ujOuK4DJOu9//m9yVP++pw+yby9gz7oGKVonkaHHf5gKKnj5vZuLGuszZjknLzv/v7dVD0l2vbaB8eT17L1w5/eO4rfv3VYNM89C2diw4v7JGWoAWxpP4gLzhiHFfMcaHTYcOvWTskybR99krx289O7sKZllmheru2b27uxYp4DzYmbHtdnXLn0zxzcmG1u746fZN5aj6nj9Gh02HCwd1BQz/09TqxtrU+5YXI63bdoFlZu252TTultGa0HHREEkZ1iPD/LlXJse7F0krseOeQVKiPfcuU47sTw4fvbfJp4/hYQX+BuXDxb9BAyueRIycqlbDaZzWky9/U4cd+iWSnXV27bjYvOHIcV8+vQ7LAnr929YGbG/G+us2PFvLpk+7g237doFlg2iolWfXI9kItv2+wQl7dy2+4UGULrjc3t3Zg+3ohb5tVlrEPip47X4oMeV/z/4y7cs/AcrNy2WzY7plPHBeBiIroDYZh18VjCIxVHm6vDMow42ul6SumfnmbSMPBHQohGGTBKBRiBONr8vGJxtPl1xSIsen1h1BiG4mh7g2GMM2oRTsTR5uSFoiz6PYmyAnG0U+vmxdFOXOfH0U7qwIuj7Q6EMdk0FEebL08ojnZKfRJxtJUxFqZEHG1+mfQ42katCipmKI42X0cujjb/WjKOdiJGolE9VM6iV8PEi6Nt0sVjAw74QjBoVDBxcbS9YdiMmXG0s80TCy9mttg1LgbkSNkHnZxKlBN06ngq5fL8LFfKse3F0knueuSQV6iMfMuVw7jTs1N+OH876ePp4vGvXQkf1m7KL472cOUIycqnrJTMXncQTn8YBg0Do0YFg6awONqcL8zF0eauicbR9g/5tm6er6tNxNE2aOIxs5NxtIMRmLQq6NVDcbTdgYQ/nIij7Q9FUMWLo23SxmWkxNEOsTBrVVAohvohGUfbE4BeI58d03stAhTjBnl6tQGnF1DOashuVFL6C6cJx4jL9ZtjIaZkLTnE9PF5ZM4DecSa8i4xKY+8E/KWngM5Nlxs7IT0z6dNBEGMXUq9sCwl5dj2Yukkdz1yyCtURiG/whOjj1z87WLKkVvWSMqUg1J9pT11mHGz0ynpq+M7d+7EwoULMWnSJCgUCuzYsUMy/6uvvgqFQpHx75NPPpEsRxAEQRAEQRAEQRDFoqQLba/Xi3PPPRePPvpoXuU+/PBDHD9+PPmvpqZmhDQkCIIgCIIgCIIgiPwo6avjV111Fa666qq8y9XU1KCqqkp+hQiCIAiCIAiCIAhimFTkqePnnXceJk6ciP/8z/9ER0dHqdUhCIIgCIIgCIIgiCQVdRjaxIkT8Ytf/AJz5sxBMBjEE088gcsuuwxvvPEGzj//fMEywWAQwWAw+dnlchVLXYIgskD2SRDlC9knQZQnZJsEURlU1C/an/rUp/D1r38dF1xwAS699FJs3rwZl156KX7yk5+Illm/fj2sVmvy35Qp+ZyHTRDESEL2SRDlC9knQZQnZJsEURlU1EJbiIsuughdXV2i6XfeeSecTmfy35EjR4qoHUEQUpB9EkT5QvZJEOUJ2SZBVAYV9eq4EO+++y4mTpwomq7VaqHVaouoEUEQuUL2SRDlC9knQZQnZJsEURmUdKHt8XhSfo3u7u7Gu+++i3HjxmHq1Km48847cezYMfzqV78CAPz0pz9FbW0tZs6ciUAggCeeeAIvv/wy/va3v5WqCQRBEARBEARBEASRQkkX2m+//TbmzZuX/HzbbbcBAJYuXYonn3wSx48fx+HDh5PpoVAI3/nOd3Ds2DEYDAbMnj0b//jHP1JkEARBEARBEARBEEQpKelC+7LLLkMsFhNNf/LJJ1M+33777bj99ttHWCuCIAiCIAiCIAiCKJyKPwyNIAiCIAiCIAiCIMqJij8MrVI5OuCDOxCByx+GVa+GSafC5GqDYN4TrgAGvCG4AhFY9CoY1Qx00RACSg08ITZDBl+2Ra+GWcNAASASZaFUMilljBoGoSiLUATwBiMwalVQANCoAHVaXpNOBX8oAqd/KJ9SCaiUSpzyBGHSq2FQM/CFWLiDEVTpGehUqoz6vMEgtBoNQpEoXP4IjFoGejUDjVIBNspCjzCCSh3caeXUURZOFojGAE8gArNOBYuGQRRI5rXo1TBpGOgAuKMx+EMsVAwLg1qb1IOf55gngHEGNRQC/cJEWZz0RzDOqM1I00UDCCh1OV03axjEeDpaE/VrwaIvNNQWm4ZBMC2fUcMgGmXxiTsMm1kNnUqVMm+MGgYD3gD0OjV0TOZ4mbUquPxhwXlybMAHF0+WTaeK1582LzUA+hPXqgxqGLUqeIIROH3Z5y4x9mBZNmXLz9SpU8EwTAk1IopJPs+20UY5tr1YOsldjxzyCpWR/mw061Q4XaKc0xdCnycEVyDuX9iNGlgNmoLzEfIg97ik++LVBg0mWHTDrn8k2iRUbtAXhjcUgTfEYpxRDb067h9y7bHo1FCwUUQAeEMsPIk1gVIB6NRKRKOAL8wiEGIxzqhBkI3CHYjAalBBn+5/auLP/HAsFvf1AxGYErIUCkCtVCIYiSbrUACIIQatioFKoQBiMSiUCnjDLFz+CExaBnpNPM0bZuFO+M1GNQNvmIU3EEG1QYNwNJpMUymVGPSFYNKpYFIz8EVYsDFAgcQ6IhiBRaeCQc3AG47A7Wdh0augVzNQAjDr1bLYJy20S8Chfi9Wbd+Djq7+5LUmhw1rW+sxzWZMyXu434s70/JePWsCvnfl2VgtIuOB59/HSx+cTLn+4DWzoFQwuEOgzJqWWfjar97EkQE/AOCLc07HTZfVCea9obEWt27thC/EotFhw7LGWjzzxiH8zyVnwBWMYFN7Nzq6+mHQMHhoSQO2dHQL1FePNS+8j3/wdGx02LBingMOqx4+MIL9s6alHr949UNsf/c4AMCgYbD5hgvx6MsH0CaQ94/vHMEHn7hw14JZIu2ux+lGLbxQCPdlSz3GG5T4nkhaej9fPmM87l4wE3dv35PUx6BhsHnphXj0FWEdn+r4CL99uwcXnVGF73/+PKzeIazn3/cewpcuPRMrt+0W1CWGWEYb/+fiKfhK83SsEpC5trUeTNSHq372JgCg2TEOD7bMFq3/f7d24qNej+A8EJu7xNjk8OHDuPHRF2EYNwG+Uyew6earUFtbW2q1iCKQz7NttFGObS+WTnLXI4e8QmXkW65n0I+V23aj7UBf8trcOjs2LJ6NSVX6vPMR8iD3uAj54k0OG9a11mPqMObFSLQpneODfhw65cPDLx9AR1c/7CYNnv7KJfjes+8J+nsPvjDk2xo0DLbcMAexGPDwK13oPDyIh5Y04Mf/+ChF1h3PCfuOP/zLB/jT3hPJ6/NnjMfKK8/G3c/uRTsvP3898dXmMzHJqsfGF/enlG122HHTvOm48am34QuxyXqWN9VCAUVSp3SZX//NO3jkuvPxxM6Pce3F0zLWJc0OO+5eeA6++fSb6POE0Oyw495F5yDkDcEfYnHaMO2TXh0vMkcHfBk3cQBo7+rH6u17cHTAl7x2whXIMGwA+NwFUzIWRHwZZ0+yZlx/vfsU7hIpc9eOvXj0+vOT1746d7qo/C0d3VjeFHeaOxKfz5lkxXGnH5vbhybv8qbajMk8VN8ezEjTsaOrH4+80oWAAhkLQ365ZU1nJq8tb6rFw2mLbH7e/75wKu5dOEu0LXft2IOAQiHelzv2gFUoRNPS+/nsSVas3rEnRZ/lTbV4+BVxHW+edxYA4IdfyFxk8/MtbTxTIn0v/vlxf0bajc1nivbl6u17EFMOfUO/rjVzkc2v/9EvnZ9yjT8PhOYuMbYxjJsAk30SDOMmlFoVokjk82wbbZRj24ulk9z1yCGvUBnHspQ7llbO6QtlLHwAYOeBPtyxbTecvlBe+Qh5kHtcxHzx9q5+rNq+BydcgYLqH4k2CZV79aOTyUU2AGxcPBsPvPC+qL/H922XN9Xi45NePPxKFzq6+jN8+2yyPnfBlJTr50yy4oEX3k9ZZAOp64lHXunCPz/uyyjb1tWHR17pSvqeXD1b2rtx3OnP0IGTuXHxbDz88gHMmGQVXJe0dfXhweffx8bFs5Of73/uffS6A/j3Kd+w7ZMW2kXGHYhkDDJHe1c/3IFI8vOANySYt8ailZTRMKUqs4xZm7HY45dRMUNTIcLGROV3pMnnPk+w6FIMp2FKVd46dnT1wxNiJcvx9cxWhyfEZpWXS3qubRDSJxcdgfhrOoXq2dbVJ/j6kj8czblt2fohwqYeWpg+D9LnLkEQY4t8nm2jjXJse7F0krseOeQVKsOVpZwrrVyfJ5Sx8OHYeaAPfZ5QXvkIeZB7XMR8cSA+Lwa8qeM3EuNdqMw+Twg15tQ1Qz5rCM6/5/Kn+7TZZNVYUmO9S/nEnF/Z0dWPGosuoyw/D5+2rn7RV/g7EjrwZQvRlqZrW1c/jFoVjFpm2PZJr44XGZc/LJnuDgylp9/UOTwB4cUfRzASzelaSr08vbLpmC6rkPrE0rP2Dy89lzaJn2mfKS/fdDn6gZOfrd35jkkuZfIZc6F+SK+TP3cJghhb5PNsG22UY9uLpZPc9cghr1AZ+ZZzZdGFy59rPkIe5B4XMV98qL5I2mf5x7tQma5AOMNXy2cNkc3PzSYrPT3XtUEwEhWVna+vzcnJVnd6fdxnls22kpCGFtpFxqJXS6abdUPpFp3w8Jh00gcLaVWZLyoIXUupl6dXNh3TZRVSn1h61v7hpefTpkLzSKXL0Q+c/GztzndMcimTz5gL9UN6nfy5SxDE2CKfZ9tooxzbXiyd5K5HDnmFysi3nCWLLlz+XPMR8iD3uIj54kP1qdI+yz/ehcq06NQ4lfaLez5riGx+bjZZ6em5rg20KqWo7Hx9bU5OtrrT6+M+G9TDWyoX9Or4rl27sGfPnuTnZ599Fi0tLVi1ahVCIXoFRgqzToUmh00wrclhg5lnsNVGjWDeXldQUkbnkcHMMu4gmiXKRNihb3pUjEJUfmOafO7zCVcgRX7nkUE05qljo8MGk4aRbBtfz2x1mDRMVnm5pOfahs4jgxmyctERAIzD0LPZYUdv2h4hANCrlTm3LVs/qBhFyrX0eZA+dwmCGFvk82wbbZRj24ulk9z1yCGvUBmWLOXSF1R2kwZz6+yC+efW2WE3afLKR8iD3OMi5osD8XlRbUwdv5EY70Jl2k0a9LqDKX5oPmsIzr/nyqf7tNlk9bqCKdeE/GQOzq9sdNjQ6wpklOXn4dPssGXsk+fn73UFU2QL0Zyma7PDBm8wAm+QHbZ9FrTQ/vrXv46PPvoIAHDw4EFce+21MBgM+P3vf4/bb799WAqNdiZXG7C2tT5jonGnWvJDT0yw6LBOIO8f3zkiKWN/jzPj+iW147CmRbjMmpZ63Pz0ruS1X+78WDTvssZabG7vBjB0ot++HicmWvVY1lSbLLO5vRvLGmtFdfzguCvlevzU8TroAKwVqXttaz22tB9MXtvc3o1b5teh2WEXzPu7tw7j/uf3irZlbWs9dDFIpjOxWM79vL/HibWt9SlfOGxu78Yt88R1fPSVuB199/fvSurxVMdB8fFrnYVPT7dlpG1qOyg5TxTRoYNdVm3fLVn/zb/ZlXKNPw+E5i5BEGOLfJ5to41ybHuxdJK7HjnkFSrj9Czl0kN8WQ0abFg8O2MBNLfOjo2LZydDA+Waj5AHucdFzBfnTh1P3x88EuNdqEyrQYPLzhqPW+bXJReZK7ftxt0LZubk225u78b08cZkec63z0XWmpZ6/HHX0ZTr+3qcgvn564kV8+pw6XR7Rtlmhx0r5tUlfU+unmVNtZho1WcsojmZK7ftxi3z6/DBcVeK7ny5dy+ciZXbdic/37toFmrMOtSOMwzbPhWxWCzvl8+tVit27dqF6dOnY+PGjXj55Zfx17/+FR0dHbj22mtx5MiRYSk1krhcLlitVjidTlgslpLpwcV3dAfCMOvicRpziqOtUyXiNQ/F0U6XkSFbII42l2bKIY42X75oHG1vECbdUBxtTzACKy+ONr++9DjahkQcba1AHG1+ufQ42qZEHG0uRjU/bzKOdpiFSjkURzs9zzFvANV6tWC/pMfRTinLi5ed7bqQjmZ+HO1gPL6gnRdHmy8zGmVxwhNGtUkNfSKONj99wBeATqtOxjHkjxc/jnb6POFihXLX7bw42vy8XBxtdyJuoykRR9vlzz5386Vc7JMonO7ubtyydRdM9knw9PXg4SXnV2x4r9HUFjnIxT7zebaNNsqx7cXSSe565JBXqIz0Z6MlxzjayWepSTpec7Z8hUDPzkzkHpd0X7zamFscbTnHu1CZ/DjavhCL6vQ42joVLPoc42iHWYwz8OJo61XQq9L8z7Q42u7AkCwoAE0ecbTdiXWCIT2Otja+HhKKo23SqaDOEkfbG4znM/LiaJt1Khg0ZRBHOxaLIRqNv8L7j3/8AwsWLAAATJkyBX19wqfiEank88CYYNEJGLN4HMhSPNjPHG/KI3fh8TtPyyPv+FzyZInJOUk0Raxcfm3LRUcAmCaRcUqWNojdKE6vNuD0HOuvyTEfQRBjm1IvLEtJOba9WDrJXY8c8gqVkc+zEYg/Y3NxyHPNR8iD3OMi7IsPX24+FCpTrBzFcB95Cnp1fM6cOVizZg1+/etf47XXXsPVV18NIP7t/4QJFDeVIAiCIAiCIAiCGLsUtND+6U9/il27dmHFihVYvXo1HA4HAOAPf/gDLr30UlkVJAiCIAiCIAiCIIhKoqBXx2fPnp1y6jjHD37wAzCM9FHvBEEQBEEQBEEQBDGaGXZ8B4/Hk9yvzaFWU0xAgiAIgiAIgiAIYmxS0Kvj3d3duPrqq2E0GmG1WlFdXY3q6mpUVVWhurpabh0JgiAIgiAIgiAIomIo6BftL33pS4jFYti8eTMmTJgAhUIht14EQRAEQRAEQRAEUZEUtNB+77338M477+BTn/qU3PoQBEEQBEEQBEEQREVT0KvjF154IY4cOSK3LgRBEARBEARBEARR8RT0i/YTTzyBb3zjGzh27BhmzZqVcfjZ7NmzZVGOIAiCIKRgWRaHDx9Ofp46dSpFvyAIgiAIouQUtNA+efIkPv74Yyxbtix5TaFQIBaLQaFQgGVZ2RQkCIIgCDEOHz6MGx99EYZxE+A7dQKbbr4KtbW1pVaLIAiCIIgxTkEL7eXLl6OhoQFbt26lw9AIgiCIkmIYNwEm+6RSq0EQBEEQBJGkoIX2oUOH8Nxzz8HhcMitD0EQBEEQBEEQBEFUNAUdhjZ//ny89957cutCEARBEARBEARBEBVPQb9oL1y4EN/+9rexZ88e1NfXZxyGtmjRIlmUIwiCIAiCIAiCIIhKo6CF9je+8Q0AwAMPPJCRRoehEQRBEARBEARBEGOZghba0WhUbj0IgiAIgiAIgiAIYlRQ0B5tgiAIgiAIgiAIgiCEKXih/dprr2HhwoVwOBxwOBxYtGgR2tra5NSNIAiCIAiCIAiCICqOgl4d/81vfoNly5bhc5/7HG699VYAQEdHBy6//HI8+eSTuO6662RVstj0DPrh9Ifh8odh1ath0asxqUqPE64ABrwhuAIRWPQqVBs0mGDRZZR3+kLo84TgCoRh0athN2pgNWhS8hwd8MEdiMATCMNu0iLERuELsbDo1dCqlPAEI3D6wrAa1DBpVUOf9WoYNQx00RACSg08ITapZ/x6EAGlFp4QC3cgDLNODb1KCZVSgXCUhUrJCJQJoJ9Vw6Bm4AuxcAciqDKooFMN5bXo1TBpGOiiLDxQIhCJQokIDBqtQJ4YBmJRVDNKBKBIqc+kYaCNBhGECrE0XeJpLKAUK8cipGQQjMYQDLPJ+O2eYARGrQoKAAoFYFQz0ALoC7Fw+8MYZ1ZDp2TgTtNTjRj8UcCkVCAIJNO5folEWZz0xD+rGSX6PUGYdIk2Aill+G0PqpRwByJJWWYNAwBwJfThxjjIsjCoVfCHWTgT141qBt5wBIgpEAPgDUZQbdQk59CxAR9cfNk6FU6vNgAAjg344A5G4PZHYNQy0KsZmLQqBNloij4mnQqTqw0Z87xar0Y4FkvVXaeCSatKmc8mrQreYCSps9D8JghibMI929LvN2OBcmx7sXSSux455JXjeBDFhe+Pm7QqaBglBv0hmHTCvku6nz/OoIFWpRT16XPx94eruycYRpVBg1AkCk8wklJPev1C/hkA9LqDYGMxxGIJn1kT9w+j0RgCLAsdwyAKIBhhocCQ/8n51kolYFAxYGMxKBUKsNEYlAAUykxfPRxlEWIBF88XVSsVCEYjCEeGZJt0qvi6Ixwvb9apoVMpEYlFoWUYMADCURZ93jBsxvg6yROIl+N8coteDZOagSfMJuvTqRgoFQAUQJ8nCJN2yOfWMnFdfBEWTn8E5oQOp7xB6DQq6NUMqvRqWX3aghbaa9euxfe//318+9vfTl679dZb8eMf/xgPPvhgRS+0D/V7sWr7HnR09SevfXbWBKy86uyM600OG9a11mOqzZi81jPox8ptu9F2oC95bW6dHRsWz8akKn1KHZ2HB/HQkgb89KUDGXJvaKzFrVs74QuxGZ+3Lp+DieNMWC2gz5qWejy36wh+/NLHAACDhsGmpXPw4u4eLG+ejjsEyqxtqYdKyeLORJpBw+ChJQ3Y0tEtKP+1/T146cM+PNgyW1SeWaGEN6bA6h3C6QYA3xIo+5OWevhiwCqBcuta6tHnCeKRV7pw3cXTMvRrdNiwrLEWW984hLsXzMTW17vxevcgHv/yHKwU6atQNAyfQi1Y35qWeqx54T3sPuZKyl665S28dssF8CoMgm1b01KPf38ygBueejfZ/5uXXohHXzmAtrS8dy+YiQ0v7sfnLpiSHNvLZ4zH7VeejQf+9H6K7Ll1djzYMgv3P/8+Xv7gZIqc9a31iAK4a/uelDqunjUB37vybOExaK2HIerDVT97EwAwpVqPX994EVbv2JvMy82dx17pytCdPx/T5zdBEGMToecnd7+ZxntOjkbKse3F0knueuSQV47jQRQXIX+c8+WW/PINzJlWneK7HO73Jv1gQNwH4nweBYDbs/j7w9X9nUMDeGhJA77/1w8zfMI1LbPwwAv78I/9vcnrfP8MAJ5cdhEef60L1wr4zM0OO2653IFqowbHXQH8su2gqG99Y1MtqnQqVBk1OOEKQKNSoMqgTVmHGDQMttxwIR55OdXfbXTYsGKeA5Oseqz9a6YPm77euXvBTGz82wf49n99Clolg2gsjLue3Zuh01eazkQgEsWa1z4WrI9RKhAMR/Hlzf/E+VOrcPeCmfj+Xz/A0ktrEY5E8fXfvJOs856FM3HdL1/H2adZsGK+A1PHGTBRJp+2oFfHDx48iIULF2ZcX7RoEbq7u4etVKnoGfRn3JgBYPEFUwSvt3f1Y9X2PTjhCgCIf/uUbtQAsPNAH+7YthtOXwhHB3xJWcubajMmNCd3S0c3ljfVCn6ebDNlLJ64fHft2IPW86ckry1vqsUjr3RhefOZomVW79iDPk8omSal11079uDycyZhXetsSR1CisxFNr++gAKCaQFF5iKbS+v4uA8/+ftHOGeSVVC/jkQ/nT3Jirt27MFX5zrw6JfOl9RTrVSh4+M+0fSHljSkyF7eVIuAMnORzS8zvaYqpf8fTltkc3kffOF9LL5gSsrYnj3JigdeeD9D9s4DfVi9fQ/OmWTNkHPM6c9YZAPA5y6YIj4G2/cgoBz6Zv/R689PWWRzuj+S9oDhyvN15s9vgiDGJvxnGx/ufnN0wFcizUaecmx7sXSSux455JXjeBDFRcwf5/tyfN/lhCuQssgGxH0grtyrH52U9Pfl0F3MH995oA+rtu/BjImWlOt8/2x5Uy0efukjzBDxmdu6+vDwS13oT/yAJelbt3dDr1XhjYOn0HXSC61aJegzPvxypr/b0dWPR17pwj8/7hP0YdPXOw++8D4+d8Fk3P/cXnjCETyc9mMkJ/O404/H0xbZ/Pq6ej04OuDH8qbapNzPXzAFD798IHmdq/OB59/HxsWz0dbVh0dePoBXPzopm09b0EJ7ypQpeOmllzKu/+Mf/8CUKVMESlQGTn84YzABoMaiFbwOxAdowBsfjD5PKMPoOHYe6EOfJwR3IJKU1TClSlRuR1c/GqZUCX72hFhJfTyhofBqXB3+cFSyjFGryigjJV9Kh7aufvjy0JGPlNwaiw5tiX7I1m9cHRE2JqlHMBIVfP2fS/eHh07Y52QX0v9iebm5xY1ttvz8OcFh1KoybjRA9nnL11PFKDPy5jM/uflNEMTYhP9sS6e9qx/uQKTIGhWPcmx7sXSSux455JXjeBDFRcof5/svnO8y4A3l5QPtPNCHGrNWNG04/hBf90J8Qq59DVOqsvrMbV19MGpVyTJSfn0MCtRYdJhg0SEGhWB/tUv4jDUWnaS+/HbVWLTJOoX8WwCYkFgTiNU3waJDjUWblM33ufnX+WlcW2vMWtl82oJeHf/Od76DW2+9Fe+++y4uvfRSAPE92k8++SR+9rOfyaJYKXD5w4LXPQHpuOCuxE3bFRAuz+EOhBGMDC3c+H8LkZ7OfRbTM1kPLz3XMvw2ZtPL7Q8jJpkjPx1zLcfplWu/5aKn2x+WlJeuZzASLaj/xeD6Pd+2CcnI9bqQnkLjka8u7izznyCI0UvW++Iovj+UY9uLpZPc9cghrxzHgygu2fxxvv/iDoQRYjO9xUL8Mb7MQuHrXqgO+aw10v1QMfj+shw+o1Qap5PYWqHQ+qTamr4Okus+UdBC+5vf/CZOO+00/OhHP8L//d//AQDOPvts/O53v8M111wji2KlwKJXC1436RjpcjpV4n/h8hxmnRpadmhwtSrpFwrS07nPYnom6+Gl51qG38ZsepmzyMqlPjEZUuU4vXLtt1z0NOvVcAfFv91Ol6FVKQvqfzG4fs+3bUIycr0upKdQX+WriznL/CcIYvSS9b44iu8P5dj2Yukkdz1yyCvH8SCKSzZ/nO+/mHVqhNnMhVch/hhfZqHwdS9UB/71fP1QMcx6NTwJf1kOn1EqjdNJyo8fjr+czZfWqpSy3ScKDu/V2tqK9vZ29Pf3o7+/H+3t7RW9yAYAq16NJoct43qvKyh4HYhv5K9OnOpnN2kwt84umG9unR12kwZmnSopq/PIIBpF5DY6bOg8Mij42aRhJPUxaYYmC1eHXq2ULOMNshllpORL6dDssMGQh458pOT2ugJorrPn1G9cHSpGIamHVqVEb2KPvVC6Xj1kIpzsQvpfLG+vK5gytpzuYvn5c4LDG4yg2ZE577LNW76eETaakTef+cnNb4Igxib8Z1s6TQ4bzLqCvtevCMqx7cXSSe565JBXjuNBFBcpf5zvv3C+S7VRk5cPNLfOjl53UDRtOP4QX/dsPqSQT8i1r/PIIJod0j5zs8MObzCSLCOezwYFYuh1BXHCFYAiUT+feH3iPmOvKyCpL79dva5gss5mkXE8kVgTiNV3whVAryuYlM33ufnX+WlcW3vdQdl82oIX2gDwzjvv4De/+Q1+85vfoLOzUxaFSsmkKj3WttZnTJ5t7xwRvM6dOs7t8bUaNNiweHaGcc+ts2Pj4tmwGjSYXG1Iytrc3o1ljbUZE7spcSri5vZuwc9H+z2i+qxtrcf2XUeS1za3d2PFPAc2t3djTYt4Gbtp6EsGTi+xvC/tP45V23eLylvTWg9NLCZZny6WaaRNDht0ANaKlGt02PHt/6jDvh6nYL9xp0nu73FibWs9frmzCzf/ZpekHuFoBI0Ou2g6d3JjI28MdFGfZP9/3DuY0v+3zKvLWAhzpxxue+dIytju73Hi7gUzM9o2t86Ota312N/jzJBzulWPNa2zMm44f5SYt2tb66GLDh0Ic/PTu7CmZVZKXm7upMtNn4/8+U0QxNiE/2zjw91vRnNYpXJse7F0krseOeSV43gQxUXMH+f7cnzfZYJFh3Vpc0bMB+LKXXbWeEl/Xw7dxdYJc+vsWNdajw+Pu1Ku8/2zze3duOXyOnxw3CUogzt13GbSYsU8h7Rv3VQLfzCCi88cB0eNCcFwOMO33tzejRXz6zL6K34KeB0aHXbsE/Bh09c79yyciT++cxT3LZoFs0aFW+Y7BHWaaNXja3PPFK3PUWPC5Go9Nrd3J+X+4Z0juGV+XfI6v86V23aj2WHHivl1mHfWeNl8WkUsFsu2jTWD3t5eXHvttXj11VdRVVUFABgcHMS8efPw29/+FuPHj5dFuZHA5XLBarXC6XTCYrEI5uHiC3NxqK1CcbR1KlQbpeNoc+XtJvE42t5gGOOMWoTZKPwhNv56ufr/s3fm8W2VV97/SVe7ZMmOlDgJcYJBppDEgQBls50Shr7AFBhnUjqlC5BAZ/oOgVK6BMJaSAi0wzIQWtpCoJ0C3TJJKVvLEkqcUnghCdlIicHFCdmwsS1rX98/pCtfSfde7bac/L6fTz6xn+c855znuc9zdY4l3ZOsoy3Wlasz6TJ+t2XV0Rbt2BTqaFv0WgiazDramWNG6mj7wjF4s+poZ8rK19HOlMmtoy3218nU0c7sy6yjnd2XrqOdqvUHZNbRhgawSetoByOYYBupoy31U66OtrQ/Go+hz5usRagXtPjUF4LVmFtHO3vuYh1tsd0uraMdHKkVmF1HW9SRU0fbYkjvIbGOdlq3Qh1tS6p2YZ2kjnZ6LbPqaIvtEyR1tKX6xTra6XmaknUaxf0ot7+reT5JbdPT04Nrnt4Em2sqvH378NClJ6O5uXlc2hvtudQ6hZxP8bUt+35zJFCLcx8tnyptpxL6avF6VAu+dsojjcetqTraQ4EwrEb52CU7zp9gHamjLRfTFxLvl+u7LxSBw2xAOBaHLxTNsJNtXy4+AzLraPtCUViKqaOtAbQa9Tra0lg9koghHE3W0RZjUYNcHW2jDhZDbh3tSCIOU1YdbTFPktbR/tQXgs2kR51YRzuYnJdJJ0CQ1NG2GkdibqMgQJeqo+0JJH2wGlJ1tPU6mA01Ukf7mmuuwfDwMHbs2IETTjgBALBz505cfvnluPbaa/H0008XpOf111/Hj370I7zzzjvYv38/1q5di87OTtUxr732Gq6//nrs2LEDTU1NuPnmm3HFFVeUMg1FptabZWvgNaaetpcPhyX/QSv/Zq9UB7KU+pBWFPOnkUJk1WVGp4ZlJf7c0yxRcsxEW1m6jirTFwA4qsGiqOeoIveU0j6Xg+9WE0IK4XBNZAqhFuc+Wj5V2k4l9NXi9SCji3w8rhyDKsX5SjFQIfF+qRSiu1D74zmGO1ohmC8kJj/aVVjcPt1ZvbykpI+Ov/jii/jxj3+cTrIBYObMmXj44YfxwgsvFKzH5/PhxBNPxMMPP1yQfE9PD77whS9g/vz52LJlC6677jpcddVV+NOf/lT0HAghhBBCCCGEkGpQ0jva8Xgcen3u09j0ej3icfXHrUu54IILcMEFFxQs/8gjj6C5uRn33nsvgOSTzru6unD//ffjvPPOK1gPIYQQQgghhBBSLUp6R/ucc87Bt771Lezbty/d9vHHH+Pb3/42/umf/qlizmXzxhtv4Nxzz81oO++88/DGG28ojgmFQvB4PBn/CCG1Ac8nIbULzychtQnPJiHjg5IS7VWrVsHj8eDoo4/Gsccei2OPPRbNzc3weDx46KGHKu1jmgMHDqCxsTGjrbGxER6PB4FAQHbMypUr4XA40v+ampqq5h8hpDh4PgmpXXg+CalNeDYJGR+UlGg3NTVh06ZNeO6553Ddddfhuuuuw/PPP49NmzZh2rRplfaxLG688UYMDQ2l/+3Zsyf/IELIqMDzSUjtwvNJSG3Cs0nI+KDo72hHIhGYzWZs2bIFn//85/H5z3++Gn7JMnnyZBw8eDCj7eDBg7Db7TCb5Z+ebDQaYTQaR8M9QkiR8HwSUrvwfBJSm/BsEjI+KPodbb1ej+nTpyMWi1XDH1XOPPNMvPLKKxltL730Es4888xR94UQQgghhBBCCJGjpI+O33TTTVi2bBk+/fTTsox7vV5s2bIFW7ZsAZAs37Vlyxb09vYCSH405rLLLkvLf/Ob38SHH36I73//+9i1axd+/OMf47e//S2+/e1vl+UHIYQQQgghhBBSKUoq77Vq1Sp0d3dj6tSpmDFjBqzWzELfmzZtKkjP22+/jfnz56d/v/766wEAl19+OZ544gns378/nXQDQHNzM5577jl8+9vfxn//939j2rRpePTRR1naixBCCCGEEEJIzVBSot3Z2VkR42effTYSiYRi/xNPPCE7ZvPmzRWxTwghhBBCCCGEVJqSEu3bbrut0n4QQgghhBBCCCGHBSV9R5sQQgghhBBCCCHyFPyOdkNDAzQaTUGy5T4kjRBCCCGEEEIIGa8UnGg/8MAD6Z/7+/uxfPlynHfeeenSWm+88Qb+9Kc/4ZZbbqm4k4QQQgghhBBCyHih4ET78ssvT/+8cOFC3HHHHViyZEm67dprr8WqVavw8ssvs9wWIYQQQgghhJAjlpK+o/2nP/0J559/fk77+eefj5dffrlspwghhBBCCCGEkPFKSYm20+nEH/7wh5z2P/zhD3A6nWU7RQghhBBCCCGEjFdKKu/1gx/8AFdddRVee+01nH766QCAN998Ey+++CJ+/vOfV9RBQgghhBBCCCFkPFFSon3FFVfghBNOwIMPPoj//d//BQCccMIJ6OrqSifehBBCCCGEEELIkUhJiTYAnH766XjyyScr6QshhBBCCCGEEDLuKek72gDwwQcf4Oabb8ZXvvIVHDp0CADwwgsvYMeOHRVzjhBCCCGEEEIIGW+UlGj/5S9/QWtrK958802sWbMGXq8XAPDuu+/itttuq6iDhBBCCCGEEELIeKKkRPuGG27A8uXL8dJLL8FgMKTbzznnHPztb3+rmHOEEEIIIYQQQsh4o6REe9u2bViwYEFO+6RJk9DX11e2U4QQQgghhBBCyHilpES7vr4e+/fvz2nfvHkzjjrqqLKdIoQQQgghhBBCxislJdpf/vKXsXTpUhw4cAAajQbxeBwbN27Ed7/7XVx22WWV9pEQQgghhBBCCBk3lJRo33XXXTj++OPR1NQEr9eLmTNnoqOjA2eddRZuvvnmSvtICCGEEEIIIYSMG0qqo20wGPDzn/8ct956K7Zt2wafz4e5c+fC7XZX2j9CCCGEEEIIIWRcUVKiDQCPPfYY7r//fuzevRsA0NLSguuuuw5XXXVVxZwjhBBCCCGEEELGGyUl2rfeeivuu+8+XHPNNTjzzDMBAG+88Qa+/e1vo7e3F3fccUdFnRxt9g74MRyMwhOIwGHWw2bSYVqDpSDZOpMOGgBDYptFD5tRB184iiF/FHazDk6LAaFYPGOc1SDAFA/DBz10Om26r96ih9Wok/Un27bVIMAfCQPQQS9o0O8Noc6U9AkA/JEodBotgtE4hgMR2FO6jBoNopEIoloB3nAMnkAEk+v0EHS5do3RKIIpueFgsl0vaNO2bAYBRgB94RhcugiCWlNa58g8QwhpjfBGYhgORGE1CjDrBRh0WhiiUQBK42IYiAM6rRaBSAz+UAwTbTrotAKGU7J2c9IHE4D+SAxD/gga6/QKMjEEISAUj8EombtoLxqPIRKPwqwzZvSJaxaKxdM6xTGGeAhDcT30Wg36fWHYTDo4THrEYnF4IzEgASQA+EJRWAzJeUficcQTCdj0Omi0Gngka2436zG13pzebx8P+DP660w6HJXam0P+MPq8YXiCyTm6rAY4LIai9rOSDkIIKYRi7jeHG7U499HyqRbnftATxIAvDE8wGXs1WAxotJvG1CdSPpWOU8ZT3FPsnj7oCcITDMMoaBGMxWHQaBEH0jG03ayDoAGMupG43m7Ww6oX4IvE4A1GUWfSwajXQoAmKZPKbQw6LSKxOGJxwB+KYoLNgHA0Dk8qrjfpBGg1gKDVQADgicTg8UdgtyT1+yMxeIJR2Iw6aDWARgPotVr0+8Lp+Fiv1SAaT8CX8rcu5a9J6q9JD6NOi2giDp1Gi0F/GPWp6+eXzNMgaDHgD8Nq1MGqFyAA6AuEYTUm8yxvMFqVPVBSov2Tn/wEP//5z3HppZem2y6++GLMmTMH11xzzbhOtD/q92HZ2m3Y2N2fbmt3O7FiQStmOK2qshaDgMcuPxU/Xt+NDVnjr2hrxrVPb8aPFpwAU5MLN62TsdHZCisSuG7NVmzs7ofFIODBS+fi8Y09GbL/d94x+PLp02X9XN7Ziu//fgv0OgGL2prxf5/chFWXnoz/eaMH1577Gdz87HZZuxqNgBtT+r4wexK+d/5MLE35ka3/xXf34u4/d6fb29xOLGprxuWP/z+cPL0+OQ9dCD6NBTfJrWVnK+784w68vOuTDB1L5rvhdpjhhyB/DTpb0aAN4ro/7MbG7n50uCfgzs45WKqwDr5ACL/86z+Sc1GQQSIMncaAGxTsWQRBduyKzlbc/eJ7eGH7wRydO3oP4ffvHsSitmZ881fv4KFL5+KxDR/iy6fPyLmW4rzj8QRMDQJuWqO895T25l0LWqETtFi6Zis27B4pr3dh62R87/zjC97P+wYDOTrmtbhw98I5Gck+IYTIUczr5+FGLc59tHyqxbn39vvSMY3Up7sWtGL6Yb4XD2cqHaeMp7in2D3d2+/DPS+8h2//n+Nxzwu78K3Pfwb7h4NYtb47Q0dHiwtL5rux6In/BwCyeUdHiwv/efaxuPIXb8MfjiXb3C785/xjcc3Tm/HDL87BAy+/n5H7iPGtVgO46kw4NBTC9b/bgrsXzsETG3vQlSW7qK0ZT735Eb5y+gxctvotzJ1ej2vmt0CjSWDRExK7En/Ftna3E7deNAs//PMufPvzn8Gh4SAefDVrnm4XFrUfja899hZOmdGA5f8yG1ajDns/DeCnGz7IkK3kHijpYWiRSASnnnpqTvspp5yCaDRatlNjxd4Bf86LBQB0dffjprXbsHfAryq7uL0Zq7KSbHH84xt7sLi9GXOm5ybZaRvrtiGo0aT7Frc352x2APiKTJIt6rh53Tb81yUnYWPK5j0L5+Ch9bvxr6c04Y5ndyiM2Y6uD/rSfTdcMFPRx5vXbcMXTpyW0b5RMj9xHhqtRXWex0915OhYtb4bQQ2wTG19tKZ0310L5qj6OcluyTsXQWtQ9XM4ElXs++IpTbI6Tz1mUsb6r1rfjeOnOmSvpTjvCTYDblq3XXXvKV3zjd19WPr7zBcKAFhw8rSC9/OQP5zzYgMAr+/uww1rtmLIHwYhhChRzOvn4UYtzn20fKrFuR/0BHMSEtGnZWu34aAnOOo+kfKpdJwynuKeYvf0QU8QN63dhn89pQk/eGY7/vWUJrz9j09zkmwA2LC7D6te3Y3F7c2KeceG3X1Ytb4bi9ubR9q6k233LJyD1V09ObmPGN9+8IkPb33YD4Neg3sWzsHjWUm2KPv4xh7MTMXKi9ubsbG7Hw+t340PPvFl2pX4K12HO/64A188pQl3/nEHPvjElzuH7j483pXUvWF3H25etx2ABga9Jke2knugpET761//On7yk5/ktP/sZz/DV7/61bKdGiuGg7lJlUhXdz+Gg1FV2blN9YrjN3b3Y25TPbzhmKoNb+qvM2r68unwpXRs7O7HJLsx4385NnT3ZXz0pBgfs+cnlVHTIcpm6yjGdiGyhcio9SegUeybZDeq+ihd93x7I4Hcgy7VqbY3J9lN2NDdJ9OufM2z93OfN5zzYiPy+u4+9Hlr5wWHEFJ7FPP6ebhRi3MfLZ9qce4DvrCqTwM+vp6NRyodp4ynuKfYPT3gC2NDKgYV/2+0m1TygGScWkgek902yW7MSZyl/Y12EybZTbAadapxqTRWFu2I47PtbpDxpUsyX6WP00vHbejuQygah9Uo/+HuSu2Bgj86fv3116d/1mg0ePTRR/HnP/8ZZ5xxBgDgzTffRG9v77iuo+0JRFT7h4Mj/XKyoWhcdXwoGs9vQ9KvpK8YHd5gLON/Nd9K0V+qjkrMrRDZhKpE6XMFlNdUbv3z7Q01O/n6lXTnu+YZ+zlY+N4nhJBsinn9PNyoxbmPlk81Ofc8yX2+flKbVDpOGU9xT7F7WvxdmgMUkqPkQ06m0Pwin5xUVmpHyS81X9TmIu0bDkQQU0kUKrEHCk60N2/enPH7KaecAgD44IMPAAAulwsulws7duwo26mxwm7Wq/bXmUb65WSNOvUPCBh12vw2JP1K+orRYTMJGf+r+VaK/lJ1VGJupfpZqr1slNZUbv3z7Y18vqr1K+nOd80z9rOp8L1PCCHZFPP6ebhRi3MfLZ9qcu4m9dA2Xz+pTSodp4ynuKfYPS3+Ls0B/OH8OUo+5GQKzS/yyUllpXaU/FLzRW0u0r46sx7RuHJSXok9UPBHx9evX1/Qv1dffbVsp8aKOpMO7W6nbF+725l+ereS7OY9g2hTGN/mdmLznkHYDIKqDZthZCMq6cunw5rS0eZ24pAnlP5faUyH24VDku93FONj9vykMmo6RNlsHcXYLkS2EBm1fo3Ce+LtqTVV81G6/vn2hgaJvHtPqf+QJ4iOFpdMu/I1z97PLpsB82R0AMmHQrhstfkETkJIbVDM6+fhRi3OfbR8qsW5N1gNqj41WPl6Nh6pdJwynuKeYvd0g9WQiu1D6EjFooc8QcU4tCMVpxaSx2S3iTaUxhz0BHHIE4QvFE3HxGr6pXbE8dl2O2R8aZf4ovQcBum4DrcLRp0WvpD8pwUqtQdK+o724cq0BgtWLGjN2czi0zOlpSrkZFd39WDJfHdO0tOeepre6q4ebO3tw/JOZRumxEjCtbqrB4vamnNkn3qzV9XP7/5uS/oJfkvXbMU181vwv5v24pYLZ8mOWb5gNtqOdaX77n5hp6qPz727N6O9TTI/USYR96vq2LXfk6NjyfwWmACsUFuf+MiNYtnarao2Dnn8eecSi4dV++v0uUGE2LfmnT2y7W9/eChj/ZfMd2PXfg8WtTXn3GDEeX/qDWN552zVvad0zdvcLtyzcE7OC8baTXsL3s8OiwF3y+iY15LUXaulLgghtUExr5+HG7U499HyqRbn3mg34S4Fn+5a0MoSX+OUSscp4ynuKXZPN9pNWLFgNv53017cdvFs/O87e3HK0ROwZL47Jw7taHFhyTktWN3Vo5h3JJ/0nZRJt7mTbUvXbMWi9mZ0uDPXUYxvj51ow2nHOBGOJJKyMvrFmHnnvqF0PtHmduKa1PgMuxJ/petw60Wz8Pt39uCWi2bh2InW3Hm6XVjUntTd0eLC8gWzASQQjuS+0VXJPaBJJBL5vsZ6WOHxeOBwODA0NAS73S4rI9aDHA5G0nWo89XRFmXtkjra0vHpOtomHZzWkTraooxNpo62WM/Olqqjne1Ptm1buo62AL2gxae+EKxGffojJBl1tCW6sutoDwcjaLSN1NHOkFWooy3aqjMIMADoz6qjnTnPzDrallQdbWO6jnYUQa1RZlxuHW2XTQd9qkZ2hiySdbQ9gQgm2vQKMjEEICAsqaMt7c+uo529ZmIdbekYQzyMoViqjrkvDJtRB4dZuY62SS8gGo8jFk+gTq+DRkjW0RZ1OhTqaEv3W3YdbbHPZcuso13IflbSMVoUcj5JbdPT04Nrnt4Em2sqvH378NClJ6O5uTn/wBq0N9pzqXUq/fp5uFGLcx8tn2px7hk1h006NFgP3zraR9JrZ6XjlLGOe4qh2D2dr452nVkHnRYwCAJ8hdbRTskYpXW0w1FMsBgQjsXhCabi21QdbZ20jnZgRL9SHe1PfWGYDAIsegE6rQYxsY52OIY6ow6CNrOOdp1JD1NWHW2HxQANRupo15l1MMrU0e4PhGGR1NGuxh44fD/LVQbFvDgoyU4tybIVE4uQlrc9UkvvmIm2IrSVVysu29YkVenSa1gWsz7FyFYOKxpTPzWX6MBRan0NFsV+h0X+xlDMflbSQQgpjVgsht7e3vTv06dPhyDk/67aeGWsk6uxpBbnPlo+1eLcG+2mwzaxPpKpdJwynuKeYvd0LZ2BQvOiUmPngnRn/T5Fko80VunvU0y0CSGEkCrR29uLKx9+AZYJjfB/ehCPXX3BEf2uOCGEEHKkwESbEEIIqSKWCY2wuUr7nBMhhBBCxid8GBohhBBCCCGEEFJBmGgTQgghhBBCCCEVhIk2IYQQQgghhBBSQZhoE0IIIYQQQgghFYSJNiGEEEIIIYQQUkGYaBNCCCGEEEIIIRWkJhLthx9+GEcffTRMJhNOP/10vPXWW4qyTzzxBDQaTcY/k6k2irETQgghhBBCCCFjnmj/5je/wfXXX4/bbrsNmzZtwoknnojzzjsPhw4dUhxjt9uxf//+9L+PPvpoFD0mhBBCCCGEEEKUGfNE+7777sM3vvENLFq0CDNnzsQjjzwCi8WC1atXK47RaDSYPHly+l9jY+MoekwIIYQQQgghhCgzpol2OBzGO++8g3PPPTfdptVqce655+KNN95QHOf1ejFjxgw0NTXhX/7lX7Bjx47RcJcQQgghhBBCCMmLbiyN9/X1IRaL5bwj3djYiF27dsmO+cxnPoPVq1djzpw5GBoawn/913/hrLPOwo4dOzBt2rQc+VAohFAolP7d4/FUdhKEkJLh+SSkduH5JKQ24dkkZHww5h8dL5YzzzwTl112GU466SR87nOfw//+7/9i4sSJ+OlPfyorv3LlSjgcjvS/pqamUfaYEKIEzychtQvPJyG1Cc8mIeODMU20XS4XBEHAwYMHM9oPHjyIyZMnF6RDr9dj7ty56O7ulu2/8cYbMTQ0lP63Z8+esv0mhFQGnk9CaheeT0JqE55NQsYHY/rRcYPBgFNOOQWvvPIKOjs7AQDxeByvvPIKlixZUpCOWCyGbdu24Z//+Z9l+41GI4xGY6VcJoRUEJ5PQmoXnk9CahOeTULGB2OaaAPA9ddfj8svvxynnnoqTjvtNDzwwAPw+XxYtGgRAOCyyy7DUUcdhZUrVwIA7rjjDpxxxhlwu90YHBzEj370I3z00Ue46qqrxnIahBBCCCGEEEIIgBpItP/t3/4Nn3zyCW699VYcOHAAJ510El588cX0A9J6e3uh1Y58wn1gYADf+MY3cODAATQ0NOCUU07BX//6V8ycOXOspkAIIYQQQgghhKQZ80QbAJYsWaL4UfHXXnst4/f7778f999//yh4RQghhBBCCCGEFM+4e+o4IYQQQgghhBBSyzDRJoQQQgghhBBCKggTbUIIIYQQQgghpIIw0SaEEEIIIYQQQioIE21CCCGEEEIIIaSCMNEmhBBCCCGEEEIqCBNtQgghhBBCCCGkgjDRJoQQQgghhBBCKggTbUIIIYQQQgghpIIw0SaEEEIIIYQQQioIE21CCCGEEEIIIaSC6MbaAUIIKZZYLIbe3t7079OnT4cgCGPoESGEEEIIISMw0SaEjDt6e3tx5cMvwDKhEf5PD+Kxqy9Ac3PzWLtFCCGEEEIIACbahJBximVCI2yuqWPtBiGEEEIIITnwO9qEEEIIIYQQQkgF4TvahBAyTpF+V53fUyeEEEIIqR34jjYhhIxTxO+qX/nwCxkPhyOEEEIIIWML39EmhJBxjGVC41i7QAghhBBCsuA72oQQQgghhBBCSAVhok0IIYQQQgghhFQQJtqEEEIIIYQQQkgFYaJNCCGEEEIIIYRUECbahBBCCCGEEEJIBWGiTQghhBBCCCGEVBAm2oQQQgghhBBCSAVhok0IIYQQQgghhFQQJtqEEEIIIYQQQkgFYaJNCCGEEEIIIYRUECbahBBCCCGEEEJIBWGiTQghhBBCCCGEVBAm2oQQQgghhBBCSAVhok0IIYQQQgghhFQQJtqEEEIIIYQQQkgFYaJNCCGEEEIIIYRUEN1YO0AIIYSQXGKxGHp7ewEA06dPhyAIY+wRIYQQQgqF72gTQgghNUhvby+ufPgFXPnwC+mEmxBCCCHjA76jTQghhNQolgmNY+0CIYQQQkqA72gTQgghhBBCCCEVpCbe0X744Yfxox/9CAcOHMCJJ56Ihx56CKeddpqi/O9+9zvccsst+Mc//oGWlhbcc889+Od//ueK+bN3wI/hYBSeQAQOsx42kw7TGiyysgc9QQz4wvAEo7CbdWiwGBCNxeHJGp+IxtEXiMBu1sNlNWA4FM2wYTUIGAyEYDLoYNbrcuz7wlEM+ZO/6wUtguEQ6kwmeMOxDB3eYAhaQQe9oEW/NwSbSY86gwABCejicQS1Qs4YUzyE/pgOFr0AfyQGjz+C5jq9gmwMPd4I7CY9nAYBIQDDKRm7WQ+bQYAuHsNwDGjQaxGEJkOHzSDACGAgFoNJEOCTjLUaBIRiMdQLmXqltj/yRWEz6qDRAAZBi2g8jngCiCcAbzCKOpMONoOAaDyGfl8UDRYDIvE4hoNRWI06aABAA2gAaDRAPA44dUBIK6Tt1Vv0sBp18Iei8ASjcFoNCMXi8PhH/DTFQwhqjTnrE4gEkYAeBkGLfl8INqMeTpMOoXgiYz7iOvRJ5l9nEACtJufaByJRfOqNwGHRw2bUwRuKYjgQgctmRDiWnFudSQdrqk/cJ3azHlPrzYr7Wa5dB2Agq63OqEOfNwxPcGT/OiyGip03QsjhQzGvn4cbtTj30fKp0nbkYqtGu6koHR8P+DNisTqTDkcV4NOQP3xYvuYdrvMqFLk9GoxG0T88Er/azXo4LIaqrtW+wQCGApG0H2KsVoi/0xosGPKH4fOFENNqc2JQTTwGf1wDjQbQa5LBbiIBhKIx+MMxNFiS8aw3GIXNpINeq4UnGEa9Odk+HIzCbtLBIGgRisYBAN5QUlbMEaLRCBzm3Pxj0B/EZJMeIa2AcDyBQDhp027Wwa4XEIjHEI4Bw4EorEYBJp0AnaBBMBpLtSVj+wQAJIAEEjDrBOi1GgyHY0nfzEk/fJEYhvwj1y2WivmdNgMSCcAficEfStq26QXEgHS+MeJvCHqdDk4dENYKiALwh2MYFmPyAu8X+RjzRPs3v/kNrr/+ejzyyCM4/fTT8cADD+C8887D3//+d0yaNClH/q9//SsuvfRSrFy5EhdeeCGeeuopdHZ2YtOmTZg9e3bZ/nzU78Oytduwsbs/3dbudmLFglbMcFozZHv7fbgxS7ajxYX/PPtYXPmLt+EPx9Ljl3e2YuXzOzFnqh1fO+sY3LQu18byzlbEEnEsXbM1p++WC2fhP5/chD5vGF87vQlXdRyLG2T8XN7Zimuf3oQ6sx6L2prxf5/chIcunQuLNgFHnQU3KYz5YH8fnn5nP65oa8aJLi18GkFR9sMDn2DLxx588+zjFOfRIPjhS1hk+1d0tqJBK+A6Gf33d7bCnwCWKeitN4Zw8U/ewmOXn4rHNnyIL58+A49v7JGVjcZCuPWZ7eiS9LW5nVjU1oyn3vwIXzl9BqxCAqYJdem5WgwCHrx0Lh7f2IPNvYN48NK5uP/l93Ou8Z3/Mht3/HEHXt31SY7d5c9uRyCawKK2ZvQN+WA6yqm4Th8eGMDVv9kOi0HA6is+i4df3Y0NWXJ3ds7Gd3/3LvYMBNDuduLK9mOQQAIPvLI7R+cVbc249unN8Idj+H/fOQ0f9ccV93P3gQFc+T/v5vj0rac34/1D3oy225/Zhg3dnwIA5rW4cPfCObIvDISQI5diXj8PN2px7qPlU6XtyMVW7W4n7lrQiukF6ivVp32DASxdsxUbdvel2w6H17zDdV6ForQflne2YsVz72Lrx550fDoUiOCmddurslaF7ks5uX86fiJuvWgWXtq+D+fOmqoYo7+262O4Gx1wmHSIxBJYtb4bm3oHseorc3H/S+9nxJjnHD8RS88/ATf/YXtal8Ug4LHLT8WP13fnxKNLzj4Wk+stsvnH/Z2t8CO511at786Jm2+98AT864//ms6N2txOLJnvRjASx9VPbYI/HMuI0b9+xtGwGASskomLpbGuOG+dJoQDQ0E8JLHd1GDG/1x5umwMfmfnbLzzwSGcdmwjwrE4bntmR1XulWP+0fH77rsP3/jGN7Bo0SLMnDkTjzzyCCwWC1avXi0r/9///d84//zz8b3vfQ8nnHAC7rzzTpx88slYtWpV2b7sHfDnbGwA6Orux01rt2HvgD/ddtATzHkhAIANu/uwan03Frc3Z4y/ed02/NclJ+HyttwkWyoTjsZl++58dgfuWTgHAHBVh7qOBy+di43d/Xh8Yw/uWTgHq9Z3Y4I9N+mVjpkz3YWu1JigVl227bjJuHp+bpItlVHTcdO6bQhqINsX1OQm2VK9RoMFi9ubsWp9N46f6shJsqWyE+3mjCQbQHpdZqbGTnPWZfi5uL05rVP6s5QNu/tw87ptmDnVIWv3totmp+3MmpabZGevu2j3oaybiSh3y7rtePirJ6d/3z8UwOou+Xk/vrEnvfeCWovqfj5ucoOsTw9/7eSctrsWzEm3vb67Dzes2YohfxiEEAIU9/p5uFGLcx8tnyptRym26urux7K123DQE8yr4+M8Pn2s4NOQP5yTjALj/zXvcJ1XoajtUTFmFn//6wd9uGnttqqs1b7BgOq+3DcYUPX3hKkOLFu7DefNnqoaV14w+yg83tWDSDyBh9Z3oysVz67u6smJMWdOdeCOZzMTTDHGlotHpzQox/ZBDbDxg76cJBtIxs13PPteRm60sbsfq9Z3Y/9QIN0ujdH3DwUU42JprCvOe4LNkpFkA8DDXz1Z0d9b1m3H6e5G7PMEcpJsUUbtflEoY5poh8NhvPPOOzj33HPTbVqtFueeey7eeOMN2TFvvPFGhjwAnHfeeYryoVAIHo8n458Sw8FozkKLdHX3YzgYTf8+4Asrym7s7sfcpvqc8b5wDN5wTNVGIvnhZtm+SXYjACAQyU3GpXKBSDztxyS7ERu7+/Pa9ab+wlSobCEyhdjLppBxc5vq02usJit+7CUb6dhse1Kd+fRnX+PsuRWz7vls6YSRo9poN+X8ASF7bkBhaynXHo0l8sq+vrsPfd7yX5yLOZ+EkNGlWq+fhxu1OPfR8qnSdtRiq67ufgz48r/uePL45FHwqc8bzkmwRCr1mlcpijmb42le1SDfHhVjZgCYZDflJHYi5a7VUCCi6sdQIKLqr1Lcmq3HG45hQ3c/rEZdRjwrFzfKxZ5q8ahfxbY3HEOj3aTYv2F3X07cvLG7H412U0a7GMcWGusCynmHTtDmXSvpOsnJKN0vCmVME+2+vj7EYjE0NmY+VbWxsREHDhyQHXPgwIGi5FeuXAmHw5H+19TUpOiPJ7XJlRgOjvTnW3i5JG849Z0MVRsq/d5gMtkpRkcpYwqRLWceav2F6BXXVimRLsQHcWy2PanOfPqV+otdy0JsSXUW6lep10iuXbYtqK6/EIo5n4SQ0aVar5+HG7U499HyqdJ28sVWhQS9pfrkyeNrLe3hos7mOJpXNSgmFsobi5WxVoXuSyW5YmM7Mf6XjlXSma9NRM22RxKfK6FkL7tdri2frkJj1+x+6TrJypR5Psb8o+PV5sYbb8TQ0FD63549exRl7Wa9qq4600i/3aT+9XajLndp61IPPVC1odJvMwlJ20XoKGVMIbLlzEOtvxC94trKrXGhPohjs+1JdebTr9Rf7FoWYkuqs1C/Sr1Gcu2ybSZ1/YVQzPkkhIwu1Xr9PNyoxbmPlk+VtpMvtsrXX45P9jy+1tIeLupsjqN5VYNiYqG8sVgZa1XovlSSKza2E+N/6VglnfnaRNRs2yXxuRJK9rLb5dry6So0ds3ul66TrEyZ52NME22XywVBEHDw4MGM9oMHD2Ly5MmyYyZPnlyUvNFohN1uz/inRJ1Jh3a3U7av3e1EneQG32A1KMq2uZ3YvGcwZ7zVIMBmEFRtaJBQ7DvkCQEAzHqtqg6zXpv245AnhDa3M69dm0FIjylEthCZQuxlU8i4zXsG02vcpiKrdEilY7PtSXXm0599jbPnVsy657MVjY385e6gJ4iOAvZeKdeg3e2ETtDklZ3X4oLLVv4TOIs5n4SQ0aVar5+HG7U499HyqdJ21GKrdrcTDdb8rzv2PD4pJesumwHzWlyyfZV6zasUxZzN8TSvapBvj4oxMwAc8gTRUaW1cpj1qn44Ukmhkr+b9wwWHF93uJ3whaIZ8axc3CjqzG5TikctKrZtBgGHPEHFsR0trpy4uc3txEFPMKNdjGMLjXWBkXln247G4nnXyheKlnS/KJQxTbQNBgNOOeUUvPLKK+m2eDyOV155BWeeeabsmDPPPDNDHgBeeuklRflimNZgwYoFrTkLLj55TlqqotFuwl0ysh0tLiyZ34LVXT0547/7uy34xcYPsbxT2YZBl5tEt7uduPWiWVi6ZisA4NEN6jqufXpz+sl9S9dsxZL5bnzq8auO2drbh/bUGFNcXXbj+wfw8Pr3VWXy6TAlINtnArBCZVwo7Mfqrh4sme/Grv0eLGprzjlYouwnnkCOHnFddu4bwqK2ZuztH8645qu7erCorRntbmf652z9HS0urFjQivf2Dcna/cEft6ft7Njbn3fdRbvXnNOCDrcrR+7OzlZc/eSm9O9THGYsapef96K25vTeM8X9qvt594EB2farf7Upp23Z2q3ptnktLtyzcM4RVRaEEKJOMa+fhxu1OPfR8qnSdpRiK/Gp44WU+Doqj09KJXscFgPuXjgnJykd7695h+u8CiXfHr326c3p39vcLqzonF2VtZpab1b1Q3yiuZK/7+0bwooFrfjTjn2qceUL2z/GovZm6LUaXHNOy0g8296cE2Pu3DeEWy6clRFPijG2XDy6f0A5tjcBOOtYF5bMd8vGzbddODMjN0o+dbwFUxzmdLs0Rp/iMGOJQlwsjXXFeX/q9eOa+S0Ztq9+cpOiv3d2tuLN7oOYajfj9otnFX2/KBRNIpGQfwt1lPjNb36Dyy+/HD/96U9x2mmn4YEHHsBvf/tb7Nq1C42Njbjssstw1FFHYeXKlQCS5b0+97nP4e6778YXvvAF/PrXv8Zdd91VcHkvj8cDh8OBoaEhxb8AirXrhoMR1JmStdQKqqNt0qHBOlJHWzo+EY2jP5D83WUbqaMtythSdbSNBh0sqTra0vFiHW27OVmjWVpHW6pDWkf7U18IVqN8HW3pGFM8hL6oDlZDqo52IIJmm15BNllHu86kh0tSR1sqI1dHOz0XmTraYp/VICAYi6FBkNcr1tG2GnXQKtTRtpl0qBPraPujqDcbEFWoow0NkMiqoz2cqploS9XRHg5GMUGsox3IXDOxjrbUx0AkiDj0MKbqaFuNergkdbSz16Evq02soy299oFIFAO+ZO1ymylZK9sbjGCC1YhIqu6hzaRL19gW/XRk1dHO3s9y7WIdbWmbWEdbbHPZqld7s5DzCQA9PT245ulNsLmmwtu3Dw9dejKam5sV5Q9XxHUAUDNrMNrXppr2KqG7HB3FXN9YLIbe3t7079OnT4cgqH8krlgq/fp5uFGLcx8tnyptRy62KrWOtuiTvcg62qPxmlcpCjmb43FelURujwajUXzqHYnhsutoV2OtxDraom4xVivEX7k62tIYFPEYgvHkpxL12jx1tI3JXEFaR9sbjKIuVUc7HEvG175QUtZiyK2jLbU9FAii0Sipoy2pZW3XCwjGYwjFkg97sxiSdbT1kjrallRsr1ZHu86sgzVVR1sak8dSMb/TYkACI3W068w61EnqaGfkXP4QdDodXNl1tIu8X+RjzD/L9W//9m/45JNPcOutt+LAgQM46aST8OKLL6YfeNbb2wutduSN97POOgtPPfUUbr75ZixbtgwtLS1Yt25dRWpoixTz4tBoN8ne/I+SkZU+qkLuwDahmFptNoX2ER3HTFSSyR0zsQjLEwsQnpJPRxH2irUtcnSJNgpD6VqNtDcXsP6lrkMxKO1npXa5L2EcSS/GhIxHent7ceXDL8AyoRH+Tw/isasvGJM/vIx1YjmW1OLcR8unSttRiq2K4agGi2wslg+H5fBMQA/XeRWK4h6VCcSquVZT680F1eNW8rdWr2NxOUzlObpI+aYy62MXypgn2gCwZMkSLFmyRLbvtddey2m75JJLcMkll1TZK0IIIWT8YJnQCJtr6li7QQghhBAcAU8dJ4QQQgghhBBCRhMm2oQQQgghhBBCSAWpiY+Ojybis988Hs8Ye0LIkUNdXR00Gk1euULP5/DwMLyf7Ec8GoN/4BDee+89DA8PV8TX8cTevXvh/WQ/ANTMGog+jda1qaa9SuguR0cx1zfbzvDwcMGvc5U+n4SQysCzSUjtUsj5HPOnjo82e/fuRVNTU35BQkjFyPcUcRGeT0JGH55PQmoTnk1CapdCzucRl2jH43Hs27ev4L8S1gIejwdNTU3Ys2dPQTfc8Q7ne/hR6Hkr9HyO9zWj/2ML/c+E53N8+gyMT7/Ho8/A2Phd6bN5uDBe99B4gGtbOIWctyPuo+NarRbTpk0bazdKwm63H1GbnvM98ij2fI73NaP/Ywv9L44j4XyOR5+B8en3ePQZqE2/x3NsWw61eC0OF7i2lYEPQyOEEEIIIYQQQioIE21CCCGEEEIIIaSCMNEeBxiNRtx2220wGo1j7cqowPmSfIz3NaP/Ywv9ry617p8c49FnYHz6PR59Bsav34cjvBbVg2tbWY64h6ERQgghhBBCCCHVhO9oE0IIIYQQQgghFYSJNiGEEEIIIYQQUkGYaBNCCCGEEEIIIRWEiTYhhBBCCCGEEFJBjrhEO5FIwOPxgM+AI6T24PkkpHbh+SSkNuHZJKQ2OeIS7eHhYTgcDgwPD4+1K4SQLHg+CaldeD4JqU14NgmpTY64RJsQQgghhBBCCKkmTLQJIYQQQgghhJAKwkSbEEIIIYQQQgipIEy0CSGEEEIIIYSQCsJEmxBCCCGEEEIIqSC6sXaAEDkOeoIY8IXhCUZhN+vQYDGg0W7KkRvyh9HnDcMTjMBu1sNlNcBhMVTNnpS9A34MB6PwBCJwmPWwmXSY1mAp2GYhvldifkp+7hsMYCgQSbfbzXpYDcKo+ERIJanlPVnOfaKUseXelwghhBy5VPv1tBT90hjdYdahzqhDLJ6ANxzDcCCCeoseFr0AbyT5u9NmRCQWRzAcRYPVCF84huFgBHaTHkadFpFEHGZBgCcYhtWoRzgeh16jRTAaT8qZ9QXlAYXARJvUHL39Pty4dhs2dven29rdTty1oBXTndZ0277BAJau2YoNu/vSbfNaXLh74RxMrTdX3J6Uj/p9WCYzZsWCVsxQGCOlEN8rMT81P3/wxx14ddcnGe23XzwbX/7ZG+jzhqvmEyGVpJb3ZDn3iVLGlntfIoQQcuRS7dfTUvTLxegdLS7859nH4spfvA1/OAYg+Vq3uL0ZGmjwwCvb8fcDw3jyqjNk4/tbL5qFlS+8h++dfzx+/Fo3rmw/Fjc/u72oPKBQ+NFxUlMc9ARzDgUAdHX3Y9nabTjoCQJI/kUs+7ACwOu7+3DDmq0Y8ocrak/K3gF/TjArjrlp7TbsHfCr2izE90rML5+fM6c6ctpvf2Y77lk4p2o+EVJJanlPlnOfKGVsufclQgghRy7Vfj0tRb9SjL5hdx9Wre/G4vbmdFtXdz8e7+rB/qEANnb3456Fc3DHsztkXxPv+OMOLDylCbes245vzDtWUU4pDygGJtqkphjwhXM2u0hXdz8GfMmD2OcN5xxWkdd396Xfka2UPSnDwajqmOFgVNVmIb5XYn75/JzbVC/bPslurJpPhFSSWt6T5dwnShlb7n2JEELIkUu1X09L0a8Wo2+UiWM3dPenP+49yW5UfU2cZDeiq7sf0Vii6DygGPjRcVJTePIEg2K/JxhRlRvO01+svYy2QHm2C/E9oSpR2Pzy+RmKxmXbvcFY1XwipJJU6j5QDcq5T5Qyttz7EiGHG9FoFO+//35G23HHHQedjqEvIdlU+/W0FP35YnS5OFZsk4tlpYj9+V478/mQD95tSE1hN6lvSbHfbtKrytXl6S/WXkabuTzblfC9EJl8fhp18h9osZmEqvlESCWp1H2gGpRznyhlbLn3JUION95//338x8PPwjZxGgDA+8le/PTqCzFz5swx9oyQ2qPar6el6M8Xo8vFsWKbXCwrRezP99qZz4d88KPjpKZosBrQ7nbK9rW7nWiwJp9M6LIZMK/FJSs3r8UFl62wJyQWak9KnUmnOqYuz6EsxPdKzC+fn5v3DMq2H/KEquYTIZWklvdkOfeJUsaWe18i5HDENnEaHFOb4ZjanE64CSG5VPv1tBT9ajF6m0wc2+F2pr9TfcgTUn1NFPt1gqboPKAYmGiTmqLRbsJdC1pzNr349D/xuxcOiwF3L5yTc2jntbhwz8I5BZciKNSelGkNFqxQGLNiQWveUjqF+F6J+eXz8719Qzntt188G0vXbK2aT4RUklrek+XcJ0oZW+59iRBCyJFLtV9PS9GvFKN3tLiwZH4LVnf1pNva3U4sam/GFIcZbW4nlq7ZilsunCX7mnjrRbOw5p09uLOzFT9//QNFOaU8oBg0iUQi31cvDys8Hg8cDgeGhoZgt9vH2h2iQEZda5MODVb1OtrDwQjqTHq4bBWoo61iT4pYr1a0XVdiHW013ysxPyU/xTraYrtDUke72j4pwfNJSqGae7JcyrlPlDK23PuSGjyfZDyxc+dOfOe3W+CYmnwy8dC+Htz7pZMOy4+O82ySSlHt19NS9GfE6GYd7NI62sFIMn5N1dH2BiOYYB2po11vNcKfkqsz6WHSaRGJx2HWCRgOhmGRq6Nt0heUBxQCP0tGapJGu6mgDS6+0zpa9qSUG7wW4nsl5qfk59R6s2zdwtHwiZBKUst7spz7RClj+c41IYSQUqn262kp+kuJ0QujvBrZhcCPjhNCCCGEEEIIIRWE72gfYWR//KLBovzRiGJklRA/IuIJRmA36+GyFvaXrI8H/AhF4wiEY/CHY7Cb9ZhsN8qO/XjAD08wCk8g+fGROpMOR2W9qyPKeIMRuGxGhGPxtF6X1YDhUBTDEh02kw6haAzxRAQmwQhvOJbRN63Bkv6IpieQnJvNIMBu1sMXjmEoEEnLO8x6xBMJDIeiQAJIAPCFkh9Rd2StqVRng1UPi0EHbzAKTzAKh1kHq1EHbyiK4cDIPIYC6tdHqlPqv9z1jcfiGJTI2k1Jm6VcQ0JGi1LvM/lQOjujpaOUsZXwmRBCCFFCfM0dCoRhMeogaDUwCFqEo3F4Q1HUm/Uw6gV4UrFwXeqriRoAA/4g6i0m+CRxdZ1Jh0Q8gXA8gUA4hmAkhglWA0KxeHK8KRljawF4UuPEuNsQT2AwlrTrTL3uxxJAMBqDPxSD3ayDBoBYCEyrAXQaLQYCYdiMOhiE5EfGfaFo+qGhWo0Gk+rkc45iYaJ9BNHb78ONa7dlFGYXv+w/3WktWVaJfYMBLF2zNaNA/bwWF+5eOEf2I8tS2x8PBrBqfXeG/Y7UAxOkYz/q92GZjJ8rFrRiRspPUWZz7yAevHQuHnhld1reZTPg1/9+Jm57ZnuOjjsungWD1ogbFPQ/9+5e/PDP3RntyztbceezO/DKrk8y/F4y/1j4wjE81tWTOaeUrulOa8ZcXDYDnrzqDCxdszXH9uL2ZmigwQOv5PqcfX3U1uf+l/6OdVv2Z7TffvEsfP2xN9HnDcNiEPDY5afix+u7sUEyvpBrSMhoUep9Jh+F3FuqqaOUsZXwmRBCCFEi+zU3O1Z02Qx46htn4Obfv5sbV//LLNjNppz84twTJuGGC47Hbc/sSMfq9738fs54aYwqti3vbMXjXR/gvNlHIRqLI55Abv7gdmFR+9H41d8+wldOn4Gn3vwIXz/jaAwFIjJxeVL2nhd34Y5/mV12rMuPjh8hHPQEczY2AHR192PZ2m3px+EXK6vEkD+cE/wCwOu7+3DDmq0Y8odlx+0d8KOruy/nkADAht19WCoZ+/GAPyeoFP28ae02fDzgz5BZ3N6MxzdmHqh7Fs7JSbJFHbf9cSc2ftCnqP+iE6fltN+8bhtOmOrI8fuDT3xYnXWYAWBDak33Zs3lnoVzcMezO2RtP97Vg/1DgbzXJ1tntv+L24/Jab/9mR24Z+EcAMDi9masykqygfzXkJDRotT7TD7ynZ29A/6q6ihlbCV8JoQQQpSQe83NjhXvWTgHP/ijfPx6aDiEm9blvk4dP8WO257ZoRiri+OlMarYdvO6bbh6/nHYPxTAB5/45POH7j483tWDmVMdeHxj8v/9QwGFuDwpe/wUe0bOUSpMtI8QBnzhnM0k0tXdjwFfuCRZJfq84ZzgV+T13X3pv0ZlMxyMotFuUrS/QTLWE4yq+ulJfeRalJnbVJ8jP8luVLWl9FH5ru5+eMMx2fa5TfU57Y12E7pUfB3OmouqX939qn6J1ydbZ7acTsg9/l3d/ZhkNwKQXy8RtWtIyGhR6n0mH/nOznAwWlUdpYythM+EEEKIEnKvudmxolr8ajXqZPukOtRiT2mMKm3zhmPpB6apxc6i7rlN9apxuSi7oQKxLj86foTgyRNkSfuLkVWWiaj2Dyv0ewIRhKJx2b7ssZ5AfhvS4nVyer3B3GRZipovwwr25cbknVOWrnL8Eq9P3vVR6BdtF3odCBkrSr3P5NVbwL2lmjpKGVsJnwkhhBAl5F5zs2NFtfhVqU+qI1/sKaejkNxBqrsY2XJfO5loHyHYTeqXWtpfjKyyjF61v06h327WwxtST+TFsXZzcTaMutx3cG0mQVWH3Ji0fgX7cmPU9MjpKscv8frkXR+FftF2Xp/zXGNCqk2p95m8eou8t1RaRyljK+EzIYQQooTca252rKgWvyr1SXXkiz3ldBSSO0h157MhlSn3tZMfHT9CaLAa0O52yva1u51osBpKklXCZTNgXotLtm9eiwsum7yOOpMOhzxBtCnY75CMtZt0qn7aTboMmc17BnP0HvKEFHV0tLhwSOH76O1uJ2yG3MPe7nZi857BnPaDniA6VHyty5qLql9up+L35KXXJ1tntlw0lvsXvXa3E4c8IQDy6yWidg0JGS1Kvc/kI9/ZqSvgj43l6ChlbCV8JoQQQpSQe83NjhXV4ldfKCrbt3nPoGqsLiKNUaVtNoOAg54gDqrlD6n4vC31v1pcLsp2VCDWHdNEe+XKlfjsZz+Luro6TJo0CZ2dnfj73/+ed9zvfvc7HH/88TCZTGhtbcXzzz8/Ct6ObxrtJty1oDVng4tPqpZ+57cYWSUcFgPuXjgn50DOSz05XOmR+dMaLGhzu7BkvjvnsHS0uPBDydijGixYoeDnigWtOKrBkiGzuqsHi9qaM/QuXbMVt188W1bHDy6eibOOdSnqf/bdvbLtu/YN5fh97EQbFrU3584ptabTsuaydM1W3HLhLFnbi9qbMcVhztGVfX2ydWb7+XjXhzntt188G0vXbAUArO7qwZL5bnQUeQ0JGS1Kvc/kI9/ZKaRcVjk6ShlbCZ8JIYQQJeRec9OxojvZtnTNVtx6kXz8OqnOhOWdua9Tu/Z7cPvFs9GmEKuL46Uxqti2YkErHl7/PqY4zDh2olU+f3C7sKi9GTv3DWFRW/L/KQ4zFrU35/giyu7a78nIOUpFk0hIv8U6upx//vn48pe/jM9+9rOIRqNYtmwZtm/fjp07d8JqlS9F8te//hXz5s3DypUrceGFF+Kpp57CPffcg02bNmH27Nl5bXo8HjgcDgwNDcFut1d6SjVPRu1kkw4N1gLraOeRVUKstTccTNbBc9mKrKMdGamDN9luUq2jLdqwq9TR9oUimGA1IhJL1ugWfRLraIs66mTqaEv7pHW0xfbsOtpie71CHe06kw71CnW0h4MROCx6WCV1tO1mHWypOtre4Mg8PIGkLqXrk+2n6L/c9RXraIuyDkkd7WKvYSkc6eeTlEap95l8KJ2d0dJRythK+KwEzycZT+zcuRPf+e0WOKY2AwCG9vXg3i+dhJkzZ46xZ5WHZ5OMJiN1tCOwGAToBA30qTravlAUDmkd7dRrkSVVB3vAH4TDYoJfElfbpXW0I6k62hblOtrSuFtaR3uCxQANJHW0wzHUmXTQIreO9mCqBrhRUkfbZkrW3NZogMY6+ZyjWMY00c7mk08+waRJk/CXv/wF8+bNk5X5t3/7N/h8Pjz77LPptjPOOAMnnXQSHnnkkbw2eDMipHbh+SSkduH5JOMJJtqEkLGmpr60NTSU/MjthAkTFGXeeOMNXH/99Rlt5513HtatWycrHwqFEAqNfJ7f4/GU7yghpCLwfBJSu/B8ElKb8GwSMj6omYehxeNxXHfddWhra1P9CPiBAwfQ2NiY0dbY2IgDBw7Iyq9cuRIOhyP9r6mpqaJ+E0JKh+eTkNqF55OQ2oRnk5DxQc28o3311Vdj+/bt6OrqqqjeG2+8MeMdcI/HwxtSgewbDGAoEIEnEIHDrIfdrMfUenPB44f8YQz6I/CFo/CFY6g36zGpzpj+zoP4HQ9PMAK7WQ+XdeR7leL3DEXbVoMAfyQMnaCHzaiDLxTFUCACp1kPrU6bIVtn0kEnaNPfP64362BJfbd5yD+izxSPYY8/+Z0OXZYOm0mHQCSKAV/yu9ENqe9Siz7bNREEtQZ4w7GMMdLvbovtTpMOISDdZjcnv1cSjceg0wro8wZhMepRb9QhHE+kddZb9LBm+Z3tl1UvYDAQhtWYkg1Gc9Yz2x8lPx0mHWISP6Wy2ddV6boVSynns5L2yeGB+AwG6T0g+zkNpaB0dkZLRyljK+GzCF8/CalNeDZJrSCNyepMOpgEbfK71uHkd6TtZh0Mghb93hDsFj0sOgFxAP5IDJ5AFDajALNegAbAJ95QMkbWC/Cm+q2pfr1Wg3A8Br1WgDccA+IR2EymdMwsxtZCPIEwgFAsjuFgFDajDnWGpE1pzG4xCOj3hWDUCagz6uCLpPSYkjmCUavBpArEETWRaC9ZsgTPPvssXn/9dUybNk1VdvLkyTh48GBG28GDBzF58mRZeaPRCKPRWDFfjxQ+6vdh2dpt2Njdn24Tn+43wyn/oDop+wcD+OhTPx56dXeGDvHJ4Qkkn0y4YXdfum9eiwv3LpwDfywua3t5Zyu+//stMOgEXNHWjJ/9pRs//OJJuHHN1rSsxSDgsctPxY/Xd2ND1vgr2ppx7dOb4Q/H0vo+PNCHhmMnYalEhyh/Z+dsfOe3W7BnIIB/On4ibr1oFm5etx33XXwMfBoLblJYn5+//gF+9eae5HzdE3Bn5xzctE5+Pk/+7SN8+fTpOOQJwKgT0nIWg4AHL52Lxzf2qPrV7nbi1otmofdTP36+4cMM2Zv/+Th8ftZUxeu4vbcPV/9mOwCgqcGM/7nyNNy0brvqNd83GJC9bncvnFPUH2FEij2flbZPxj/l3quqqbccHaWMrfRa8PWTkNqEZ5PUAtKYzGIQ8PBXToZJr8Wq9d2Zsb/bhSs7mhGKxtHoMOMHz2zPiNHb3E5cM78FkVgc4VgIy1/7IKf/2nNaMNluwo1rt0GDBO7snIMbFHIFIRHHVx99E33eMFw2A37972fgtmd25MjecuEsfPXRv+H4yXWyOcLH/T4cVUYcAYzxR8cTiQSWLFmCtWvX4tVXX0Vzc3PeMWeeeSZeeeWVjLaXXnoJZ555ZrXcPOLYNxjICdYAoKu7Hzet3YZ9gwHV8UP+MF57/5OcJBsANuzuw2vvf4Klv89MlgDg9d19CMok2aLtm9dtw39dchK6uvvx+MYe/NclJ+UksIvbm7EqK8kWxz++sQeL25sz9J3ubsTNWcml2H/Luu14+KsnAwBOmOrAsrXbsGF3H4JaS45d6fpc1XFMuu2uBblJttT+V8+YgTc+6Ierzpwht7i9OSfJlvOrq7sf7/zjUzyWlWQDwHkySbbUzznTR8ozPPzVk3OSbKns3gE/hvzhnCQXSF63G9ZsxZA/jGoy1vZJ7fHxgF91j3884C9J7948evcWoLccHaWMrYTPhBBCSCFkx2SL25uxfyiQk2QDwIbuPjy2oQcTbIacJBsANnb346H1u7F3IIDefr9s/6pXd2PjB33o6u7PG1tDq8U9C+cAAO5ZOCcnyRZl73x2B+5ZOEcxRwjFEyXHESJjmmhfffXV+NWvfoWnnnoKdXV1OHDgAA4cOIBAYCSRu+yyy3DjjTemf//Wt76FF198Effeey927dqF22+/HW+//TaWLFkyFlM4LBkKRHI2pEhXdz+GAhHV8X3eMCbVGRV1TKozYkN3n2yfNxxTte0LxwAkD51PRnZuU73i+I3d/ZjbVJ+hL589naDN0ZtvTCAST/+eT9YbjmGS3YRQNJ4hpzYPqV8AMMluyrkpFWpbRCdoVWWHg1H0ecM5Sa7I67v70OetbqI71vZJ7eEJRlX3rScYLUnvcB69wwXoLUdHKWMr4TMhhBBSCNkx2dymejTaTYqvQxu6+5CARjZeBZIx+iS7EZMUyghv6O5Pl7AtLLZOfuJjkl05H+lK2RTtZ+cIoWi85DhCZEwT7Z/85CcYGhrC2WefjSlTpqT//eY3v0nL9Pb2Yv/+/enfzzrrLDz11FP42c9+hhNPPBG///3vsW7duoJqaJPC8ORJpIeD6v2eYAShaFyxX60vr21Jv5ysmm65/kLtSceV62O2bCgaz5HLNw+pDSXZYvwcLuCae/Jc93z7olzG2j6pPcq9V1VTbzk6ShlbrbUghBBCssmOyULReFGxqxz5dIh9hcS33mDyzSTxfyWk/dm2hwORsl87x/Q72oWU8H7ttddy2i655BJccsklVfCIAIDdrFftrzOp99tNenzqU3530ahT/vtOXtuSfjlZNd1y/YXak44r18ds2eFQNEcu3zykNpRki/GzroBrbhDy+JRnX5SLPY/+atsntUe596pq6i1HRyljq7UWhBBCSDbZMVm+uBXIH2sWGsMXEt9G48mk2WYSVGWl/dn28/lbCDVT3ovUDg6zHu1up2xfu9sJR56N57IZcGg4hDYFHYeGQ5jX4pLtsxkEVdtWQ/JAtKV+zpbdvGdQ0W6b24nNewYz9OWzF43F03pFuXxjzPqRY5VP1mYQcMgThFGnzZBTm4fULwA45Amiw527noXYFonG4qqydSYdXDaD4nWb1+KCy1bdJ3+PtX1Se9hNOtV9azeV9rfkujx66wrQW46OUsZWwmdCCCGkELJjss17BnHQE1SMXTvcLmiQQIdKjH7IE8Kh4ZBsf4fbiUOeIIBCY+uknkOekKqsKCeXIxh12pLjCBEm2iSHqfVmrFjQmrMxxafX5nu6s8NiwNnHTcQ157TkHLiOFhfmHzcRdy+ck5M0zWtxwSRoVW1/93db0O52YlFbM777uy1Y3pkpu7qrB0vmu9GRpVscs7qrJ0Pfm90HcWfnbFl7d3a24uonNwEA3ts3hBULWpM+xv05dqU6H9vwYbpt2dqtqrJP/u0jnHmsC33DgQy51V09WNTWnNevdrcTpzZPwJUdubJ/2rFPdS239o58t+bqJzdhucI6rFjQimkNFjgsBsXrds/COVUvsTXW9kntcVSDRXWPl1ria1oevYWUyypHRyljK+EzIYQQUgjZMdnqrh5McZixZL47N/ZPPXX8U28Yt108O+fNIfGp49MazJg+wZwTw7e5nVhyTgva3C60u515Y2vEE1i6ZiuAZIWj2y+eJSt760WzsHTNVsUcwajVlF0qVJMo5PPbhxEejwcOhwNDQ0Ow2+1j7U5NI9bRHg5GUGfSw1FGHW1/OAaHQh1tUb/LlltHW+yziXW0tcm6sL5Qsk7sBEkdbVHWrlJH2xMY0Zesox3DBIs+XUdb1FEnrVdt0qHBqlxHWzpGWp86PS9JHW3pfCKpeoD9viBMBj0aJHW0h1M1om1Zfmf7ZTUIGAqEYZHU0c5ez2x/lPysl9TRzpbNvq5K161cCjmf1bRPxidiHW3pPaCSdbTVzkM1dZQythI+K8HXTzKe2LlzJ77z2y1wTE0+SXhoXw/u/dJJmDlz5hh7Vnl4NslYIY3JbNI62pEY/CFJHW1fCHVmPaySOtrDgSgsRgGWVB3tPl8INpMedZI62pZUHW2DVoNQPAaDWEc7EYHNaMqIw20GAUIigXBipI621aiD3SAgBsAnkbUq1NEW9RxWdbRJbTK13lxWbWKHRT0BUuuXDwyLq2XXqPDkQikTi9KYf06Aku/KNJVZo69Jsi6NMq+vSv6UGnwXsgbVZKztk9rjqAYLjqqC3kokqOXoKGUs37kmhBAyWhQakzVPtOWVOboAmUoyvcz4uxD40XFCCCGEEEIIIaSCMNEmhBBCCCGEEEIqCBNtQgghhBBCCCGkgjDRJoQQQgghhBBCKggTbUIIIYQQQgghpILwqeMyiOVRPIEIHOZkOalafJKrWH5L9NNeQPmtYua2d8APbygKJIAEAF8oijqTDgadFuFYHL5gFDajHjpBg35vCA6LHhZDssSUNxTBjDojgtAkH70fiMBh0cMgaJPlqAw6GPUC/KEovKnH/9uNOiSiUegRQUhrwnA4lvbTmirHFdAK8Kce+W8zCphoFBCCJkPWZhCgBzCQkqu3CjALOniz9EXjIYSiOjgN2hwdVoOAcDyGAV8ETqsR4VSZgDqTDjqtFvFEGCadMa3TnrJrAuCLJ+CTlDVw6AVEgRz9/b4gTPrkOMQTiCMOTapsgVTOH4lh0B9BvUUPq16ALxJLlyzQAIAGsOqTtj+NxjDkT66NWS/Apo0jBEFmLYMIak0Z/tcZBGgBDEnX0qSDEUBfas/UW/WwGnRjej7EUk6i/boKlXIajxz0BNNl7OxmHRoshoKetj8ajOZ9tFq2KqG3HB2ljB0vr1+EEFLLiGWrPKlyqy5rcRVPyh1fK/qHAmFYjTqY9QIC0RgS8cycwKZPluuKxhPwR2IIRWJosBgQisURCEUxwWpMlfKKJMt7GQToAAyndEEDaAAkEoA3NBJnD/rDsJmSdoPRGGJxwB+OYkJK93AgCptJB40G0GoAg6CFBkA8kSwdJsbgJr2AQCQGbzAKmzGZwwz6k3MyCFoMpH7Wa5M/21J5jlHQYirLe1Wej/p9WLZ2GzZ296fbxMLlM0bhMfCFUoqfxYz5qN+HH/xxB75y+gw8vrEnZ8wVbc249unN8IdjaHM78Y2OY1BvNWDpmq3Y3DuIdVefAV9Cg5vWZdprSxWFv/bRt3Dy9PoMPR0tLtz3L7PhhyDvZ2crtvf24erfbAcAdLgn4M7OOTk22t1O3H7xLHzt0TcBAE9edQZueCZXZnlnK7re/xjnzjpKVsfyzlZEoyHc/IftGX1fO70JV3UcixtkfFze2YooIrjkkTfhD8dw3CQbfnbZqbL67+ycjcseewsznBas6GyFFoKszivamnHDmq24e+EcPLGxB10y6/n0mx/hlgtnwecP4YrH34E/HMOab5wGjcOSY/ufjp+IWy6chVvWbsMGmXX7+mNvos8bzpjTqlf+jvXv9+PBS+fK7ofROh/j5XyOBr39PtwosxZ3LWgdlZIVaozmdaqWrUroLUdHte/xhBBC5Nk3GMDSNVuxYXdfum1eiwt3L5xTUNnbcsfXov52txO3XDgLP/zTe3h11ycAAJfNgN/8xxn4xBPCQ+u7sbl3EA9eOhf3vvQ+/n5gGE9edQaWycbos3FwMIQn3uiRzTPE2Pbrq9/CKTMasGS+G1c/tQl3L5yD+156X1b212/14oYLjsddzyf9sxgE2ZhVlP/aY29h7vT6nJ9Fm9ec40Y0nig7ntIkEolEWRrGGR6PBw6HA0NDQ7DbM4sO7x3wY+marRkXRKTd7cTdC+fUxDsD+wYD+N7v31X084dfPDHnoBUzN1F27vQGbO4dkB3T5nZi7vQGrHq1GwDQ4XbhgtbJWLZ2O5ac48Ylp0zLCfjkxmbr2fC9s3OSTamfKxe0ouNHrxUku6itGQCwOuuQZetT07FiQSs+l7In8tp3P4eb1m1X1fmbd/Zi1avdeOn6ebj9mR2Kst8//3hcvGoj2t1OLDnHjS//7E3Z9Vrc1qw4D3ENt/QOZNhWWp8l57gVr6u4blf+4m3ZOamNq8T5UDufHw/48X2VPXzPwjlHzDvbBz1BXP/bLYprce+XThqzd7ZH8z5aLVuV0FuOjlLGjsa6q51PQmqNnTt34ju/3QLH1GQsMLSvB/d+6STMnDlzjD2rPDyblWPIH8aSpzdnJJki81pceOjSuarvHJc7vtr+laO/3e3ESZKY/bHLT8UhTxDPbtuPjd39GfHlY5efqhp/LznHja7u/oLyjI7Um06FxMGif2qxrlIekmnThX9unYyO4yaW9drJ72hLGA5GZS8IAHR192M4GB1lj+QZCkRU/RwKRHLai5mbKDu3qV5xjNgvsqG7Lx3Yz22qhz8cK2hsth6vyriu7n54w7GCZSfZjZhkN+bVp9bvl9gTCUTieXWKc4rGEqqyOkGb/tlqlP+AycbUXPKtZ7ZtpbmpXVdx3ZTmNJbnw5NnD3tq5HyOBgO+sOpaDPjCo+zRCKN5H62WrUroLUdHKWPHy+sXIYTUMn3esGySCQCv7+5Lf+KvWuOr7V85+ruyYvZknG1Kv/ZI48R88bfVqCs4z9hQRBwsjilUt9LPG7r7MMluKvu1kx8dl+CRSVClDAfV+0eLUvwsZowoG4rGVcdk94u/h6LxvPakY6U/5/UzkOunEt5gbpKspq/Q/kJ8FOdUzHzU/M03F9FeIbbzXVc5W1K9SlT7fIyX8zka5Pujwlj+0WE0r1O1bFVCbzk6qn2PJ4QQIo8nz70y37203PH5GGv90ljQG4wpxvP54tbssYXYKkQ2+/9CdKv9XO568h1tCXazXrW/zqTeP1qU4mcxY0RZo059e2T3i78bddq89qRjpT/n9dOc66cSNpMAm0koWF+h/YX4KM6pmPmo+ZpvHqK9Qmznu65ytqR6laj2+Rgv53M0sJvU/0aar7+ajOZ1qpatSugtR0e17/GEEELksee5V+a7l5Y7Ph9jrV8aC9pMgmI8ny9uzR5biK1CZLP/L0S32s/lricTbQl1Jh3a3U7Zvna3E3VjGLxKcZj1qn46ZAKuYuYmym7eM4g2hTFtqX6RDrcLhzxBAMDmPYOwGARFe9Kx2XpsKuPa3c7kE7oLlD3kCeGQJ5RXn1q/xZB7sM16bV6d4px0gkZVNhqLp3/2heTfhWxLzSXftci2rTQ3UVbJp0OekOKclHwYjfNhz7OHxzK5HG0arAbVtWiwVu7Jo8UymvfRatmqhN5ydJQydry8fhFCSC3jshkwr8Ul2zevxQWXTf31tdzx1favHP1ibiCSjLOD6dhQGifmi799oWjBeUZHEXGwOKZQ3Uo/i3lNua+dTLQlTGuwYMWC1pyNIT4UqxYehAYAU+vNqn7KPXGwmLmJsu/tG8KituacjSo+MGt1Vw+A5Ma8sqMZpzZPQLvbidVdPQhFI1jemWuvTTI2W09HiwsmACtkxol+bt0z8n2LZWu3ytoQn2i4dM1WLF2zFbdcOEtR35+2f6yoY8WCVuwf8OfM/9ENH2K5ylqG4pH0nK7+1SZF/Xd2tuLqJzelxx1lN8vKLWprxtI1W7GorRkdCuv53r4hrFjQik88/rTtQ4N+WduibLYu6bplz+nuF3ZidVcPFrU1j9n5OCrPHj5SHoQGAI12E+5SWIu7FrSOaYmv0byPVstWJfSWo6OUsePl9YsQQmoZh8WAuxfOyUk257W4cM/COXkfNFbu+Gr7V6r+drcTt140Czv3DaXblq7ZitOOmYBr5regLRX/i3mDWvx9+8WzEY4ksFMhz5DmCh0tLiw5pyUdByvJ7trvwe0Xz077pxSzSnUr/dzR4sI1/+RGm9tV9msnnzoug1iHdDgYQZ0pWae3FoMUsY626KejiDrahcxNro62zaSDSadFKBqHLxSD1ShAL2jxqS+Uqo+nUEc7VefPKGgxFIjAbBBg0gvwh6PwBpXraIt+2rLqaA8HorAYBUyS1NFOz0lSR3s4EIVDUkdbqi8aDyEY1cElqaMt7RfraE+wGhFJ1dG2mZK19qR1tDN8RGYd7TqzDvWSOtpS2X5fEEZ90t9EPIGEpI62VM4fiWFIrNVdQB1tjz+5NpasOtqZazlSR1u6bmIdben+EOtoi9fQZtRV7XwUcj7FOtqifTvraCfraJt0aLDWXh3t0biPVstWJfSWo6OUsdVcdz7ZmIwn+NRxUg5iHWnxXuqylVZHu9TxtaJ/KBBJx5TZdbRtJh3q1Opoh6OYYEnV0U75qVRHO54Y0alP1dG2mnSwKNXRTtXF1moAjUId7Tpzqv53qo621aiDMVVH22LUwZhdRztVM9zIOtrVpRaTajmm1puLrpdXzNyKkT1moq0oP0aDxrwSBdTGm1i83UKHNFWhrm3h7irbnlqW3upzVIMFR421EzVCo91UM4l1NqN5H62WrUroLUdHKWPHy+sXIYTUMg5LeYlrueMPd/21QnOV9fOj44QQQgghhBBCSAVhok0IIYQQQgghhFQQJtqEEEIIIYQQQkgFYaJNCCGEEEIIIYRUECbahBBCCCGEEEJIBWGiTQghhBBCCCGEVBCW95JBrB3nSdUNdllr8xH3Yr1UTyACh1kPW6peakZtXbMODZaR2rpKY6QM+cMI+8IIapM1sKWy8WgU+4eTvzvNeoQSibS+eoseVqMO/lAUg4Eo6lP163yRGIYDEbhsRoRjcXiDEVhNelhTfUP+kbGBcBT1WiCYqict2rYakvX7PIFkjb1kbe8EXIaROtoesda0QYAuHkNYK8AfikKvi8EoGNP6RBlTPIb+KODUASGtkKPDhAS88TB0WiN8kVhGPfHpVgEhrS7HRyEeRQgCgtE4hlO67IZkjUGp/rpUHcEB6foaBBgA9KfaGqx6mHUj69Bg1cNi0MEbisIbTNYSDMfjGA5EUWfSwZqq/f2pd2StHUIEkVS97Oxrnr0XGkw6RICMtjpTslb3kHiNrcla6fn2UDVR29+kdijkXlPrtiqhtxwdpYwdzXUnhJBapdBYvlC5UmOPbP02ow6+UBRDgeJzjHLyk2LW41NfGBoNEIzG4QlEYTcLqNPrEAXgD8fgCyVrUusEwCQk41SxTrZJp4UWQCgeRyIVNw8HorAaBZj1AgxaDXyxGOIxZNTQDoajmGAzwp/SZTfrUacXAACheAKBcAz+cAx2sw46LSBotAjFkv5ZjQJsRl26XrbNpINB0OJT30hd7Gg8Dp1Wi2A0nqqpLcCkE6DVAOF4HACg0yTrattMOtiMOhzFOtqVZ99gAEvXbMWG3X3ptnktLty9cE7RNauryUf9Pixbuw0bu/vTbe1uJ1YsaMXD63fjt29/nNF+/4JW+AHFMTNSNZ33DQagj3jh01hwk4zs8s5W/Pz193D0BAu+dtYxuGldUsZiEPDgpXPx+MaenDGL25uhgQYPvLI9p++KtmbcsGYr7l44B0+9+RFu/UKzou0r2ppx7dOb4Q/H0OZ24vaLj4c/ocGydfJ+bti1Hyc1OWA2WHCDwlycOj/8GovsuizvbIVZY8QPXngPC09pSs/tO/90LFxzmxTXZ/mzO/DKrk8AABaDgNVXfBYPv7obG2Rkl/7+XWz92JPRdtsftuHDPj+evOqMtN8umwFPfeMM3LhmK97pHcSDl87F/S+/n3stO1sRjYdw8x+2Y0HrJJx6bKOsnysWtCIc8uOCB98CALhsBvz638/Ebc+MXCOLQcBjl5+KH6/vxoY811i6h6pJb78PN8rM564FrZg+CvZJYajdnyq9T6plqxJ6y9FRytjRXHdCCKlVCo3lC5UrNfaQ058dzxaaY5STnxSzHiue24lvnXsc7vzjDmxIxZ9P//sZCMTiuP2ZHehKrYEYN8rF17dfPAsHh0L42esfZMS+bW4nrj2nBQ1WA374p/fwldNn4PGNPfj7gWE8edUZOa9fHS0u3HbRTNz1/Ht4NRVXi+3X/VMLvr76LfjDMdl1bXM7saitGf/xq3fw8FfmAtBgVVYs3uZ2Ysl8NwSNBoFIDP/zxkf4yhnT8fXVb+GUGQ1Y3jm77NdOTSKRSJSlYZzh8XjgcDgwNDQEu92e0TfkD2PJ05szNqLIvBYXHrp0bk28s713wI+la7ZmbEaRdrcTt108C5+/7/WM9g3fOzvnMEjH3L1wDuqMOix5ejPu6pytKrtyQSsAZMgsOceNzb0DsmM63E5c0DoFy9Zuz+lrczuxuK0Zqzf2YO70BvzbKdMUbbe5nZg7vQGrXu0uaE5yfsrJ5Ot//5AXqyXJZT67J0l8VFuXZGI9G2f/118y2u5a0IrdWTYfu/xUPLGxBxu6+/Ou9T+3TsGNa7cXtD4dP3otrX91VgKdbSffXO5eOKfsd87UzudBTxDX/3aLov17v3QS39muAfLdnyqxT6ptqxJ6y9FRytjRWHe180lIrbFz505857db4JjaDAAY2teDe790EmbOnDnGnlUens0RCo3lC5UrNfZQ058dz+bLMcrJT4pdjyvOOjodbwLJ+NBqFPDQq90ZayAXN4pIY1G5vgtap2DfUDAdU6rpyo6r03paXDixqT6jPXtdxRzjoCeI57ftT/+RQEqb24kvtE6BBsDHQ0G82zuAE1M6OtwurFzYWtZrJ7+jLaHPG5bdiADw+u4+9HnDo+yRPMPBqOxmBICu7n5EY7l/O/GGY6pjhoPR9PzzyXrDsRyZuU31imM2dPcrJkAbu/sxyW7Exu5+zG2qV7UtyhQ6Jzk/5WTy9Yv+FWpX6qPaunR19yMQiee0+WRsTrIb0ze9fGs9KbXWhcxNqj9bNttOvrkMB6OyfZViwBdWtT/gq43zeaST7/5UyX1SLVuV0FuOjlLGjua6E0JIrVJoLF+oXKmxh5r+7Hg2X45RTn5S7HpI400gGR9ajbqcNZCLG0WksahcX6PdlBFTqunKjqvTenb35bRnr6uYYzTaTbJJtijTaDdhUsqnDRIdG7r7yn7t5EfHJXiCEdX+4Tz9o4UnkMdPmf68Y4IR6AVtwfqzU/lQNC4rW0i/NxhLy+SzLdVTip/ZFKIj++8WxfiYb13krpWcTXGNCtEp9hezT6T6lezknUuVz4cnz80uXz8ZHQq519S6rUroLUdHKWNHc90JIaRWKTSWL1yutNgjn/7smEr1NaGM/KTY9ciOB+XiQ7V2EbWYMbuvVF1y7XK6i81RpL+X+9rJRFuC3aRX7a/L0z9a2M15/JTpzzvGpIchlWiXot+oU/9whFq/zSSkZfLZluopxc9sCtERjWcewGJ8zLcucj7K2RTXqBCdYn8x6yPVr2Qn71yqfD7sJvXbVb5+MjoUcq+pdVuV0FuOjlLGjua6E0JIrVJoLF+4XGmxRz792TGV6mtCGflJseuRHQ/KxYdq7SJqMWN2X6m65NrldPvDxeUo0t/Lfe3kR8cluGwGzGtxyfbNa3HBZRv772cDQJ1Jh3a3U7av3e2ETtDktNsMguqYOpMuPf98sjaDkCOzec8g2hTGdLidOOgJyva1uZ045Amhze3E5j2DqrZFmULnJOennEy+ftG/Qu1KfVRbl3a3E2a9NqfNmrIptXHIE0JH6vd8a31oOFSQnzbDyI0t256cnXxzqatyottgNajOp8FaG+fzSCff/amS+6RatiqhtxwdpYwdzXUnhJBapdBYvlC5UmMPNf3Z8Wy+HKOc/KTY9ZDGm0AyPvSFojlrIBc3inSkYnvlvmBGTKmmKzuuTutpceW0Z6+rmGMc9AQz5pQ95qAnmPapQ6Kjw+0q+7WTibYEh8WAuxfOydmQ81pcuGfhnJp4EBoATGuwYMWC1pxNKT5d9tHXP8hpNwGqY6Y1WNLzN8X9WN6pLPuDP27HLzZ+mCGzuqsHi9qaZccsam/GFIc5J0lrTz0RcOmarVjU1oz39g0hpmJ7UVszVnf1AEgejGA8ghUqfr62az8GfepzMcX9quuiB7DmnT1Y1Nac9n/dpj2qOnftG0q3re7qwTXntKDD7ZKVvfbpzTlt3/3dFixdsxW3XDgrbWPpmq245aJZ6HC70mstt57LF7TiaGdyrd/sPqjqZzDkT7ctXbMVt188O0N2dVcPlsx3oyN1HtSusbiHqkmj3YS7FK7VXQta+SC0GiHf/amS+6RatiqhtxwdpYwdzXUnhJBapdBYvlC5UmMPJf3Z8WwhOUY5+Umx67F20950vAkk40OnzYjbL56VsQZycaM4v9suno1pDeac2LfN7cSSc1pwytETsHPfUDqWzY55RTpaXLj94lnYKYmrxfbr/qklvYai3ew8Qcwxjp1oxRKZWDz51PEWuCfaMLXejF37PFjUntTR0eLC8gWzy37t5FPHZRBrzYk14Vy22q6jLfpZJ1dH26RDgzW3jnb2GCnZdbSlsvFoFAe8qXWR1NEeltQHlKuj7Q1GMMFqRCRVR9tiTNaN9kUktatl6miLtm2pOtrJWnzJvy5l19GWyqbraIej0AsjdbSlMnJ1tDP6C6yjLR2TUUc71e6Q1NFOr6Wkjra0TayjPRyMwGHRw6IbWQeHJVnD2huKwheMot5iQCQex3AwCptRB1uqjvaAb2StHUIUEa0x5zpK62iL7RMkdbTFNrukjvZwMFUv3KjLu4dKpZDzqba/Se1QyL2m1m1VQm85OkoZW81155ONyXiCTx0/sik0li9UrtTYI1u/zZSso+0JFJ9jlJOfFLMe0jraw4Eo6oqso61BsjZ1uo52MAqLQYCliDradSY97AZJHe1IDP7QSB1trUaLcCwOTzAKq0GhjrY/DItBB5NMHW1Lqo62IK2jrdViwMc62lXHYanNxDobpeCp0W5SPPyFBFwOiwFQmf/0iYX5Nx7IN5VqT3WyTNukcpUW6HQxwffUEl2pBmr7m9QOo/kOarVsVUJvOTpKGct3rgkhpPBYvlC5UmOPSuYU5eiq9HqMB5pLGVThwH9MPzr++uuv46KLLsLUqVOh0Wiwbt06VfnXXnsNGo0m59+BAwdGx2FCCCGEEEIIISQPY5po+3w+nHjiiXj44YeLGvf3v/8d+/fvT/+bNKns9wAJIYQQQgghhJCKMKYfHb/gggtwwQUXFD1u0qRJqK+vr7xDhBBCCCGEEEJImYzL72ifdNJJCIVCmD17Nm6//Xa0tbUpyoZCIYRCI4+Y93g8o+EiIaQAeD4JqV14PgmpTXg2CRkfjKvyXlOmTMEjjzyCNWvWYM2aNWhqasLZZ5+NTZs2KY5ZuXIlHA5H+l9TU9MoekwIUYPnk5DaheeTkNqEZ5OQ8UHNlPfSaDRYu3YtOjs7ixr3uc99DtOnT8f//M//yPbL/dWvqamJJRAIqQF4PgmpXXg+yXjmcC7vxbNJyPhgXH50XMppp52Grq4uxX6j0Qij0TiKHhFCCoXnk5DaheeTkNqEZ5OQ8cG4+ui4HFu2bMGUKVPG2g1CCCGEEEIIIQTAGL+j7fV60d3dnf69p6cHW7ZswYQJEzB9+nTceOON+Pjjj/HLX/4SAPDAAw+gubkZs2bNQjAYxKOPPopXX30Vf/7zn8dqCoQQQgghhBBCSAZjmmi//fbbmD9/fvr366+/HgBw+eWX44knnsD+/fvR29ub7g+Hw/jOd76Djz/+GBaLBXPmzMHLL7+coYMQQgghhBBCCBlLxjTRPvvss6H2LLYnnngi4/fvf//7+P73v19lrwghhBBCCCGEkNIZ9w9DO9IY8ofR5w3DrokgqDXAG47BE4jAYdbDZtJhWoMFewf8GA5G0+11Jh20Gg0CkQj0WiFzjEGAEI9hMAp4g1HYzTqY9QJM8RhCWbJWg4BgNIahQBRWow5aDaDRADqtFgO+MCypNm2qzYYQglpTjj0jYvBBi1AkDk8gCqtRgFkvwKDVwBCPAYgipDViOMu2EI9hOAbEE0lf60w62A0CEgA8KVl7yoYJQF+qbXKdHlqZuYTiMfQPR+Cq08Mg0x+LJ+c6wWrM6TPFgzlzE330RIEEAF8oCptJB4deQFzGx2g8Bp1WgC8cxXAwhgk2XY4ftpSfQ/4oGiwG6HVaDAejGA5GYDfpYTYI6BsOos6sh0UnwBuJwROIwpZaU5tRh1AklrGW4j456AliwBeGJ3XdnRYDQrF4zt7RABiStNlMOvgjUQz6kuMaLAY02k2jdgay97c4nyORWl6L0fStWrYqobccHaWMreU9QQghtUx2XCSNb8T42xNMxnEuqwEOi2FU/KqGbalOm1EHg6CFNxSG3WSAPxLDcHAk1jfqtEgkgGA0Bq1Gk4xxg1E0WA0A4rK5BQD4ojEYtFoEo3H4QhE4rUaE43EgASQSgDeUtKEBoNUCeq0Wn/rCsFt0MAtCZuxqEBCOxxCOAcNi7mAQIEADXyQKDTTp2LvOpINe0OJTbwg2kx4mnRYJAKFYPJ0/aDXJfCKBZN6iF7QIx+Iw6wQYBC0m15vLu2gpmGiPI/YNBrB0zVZcd/YxEBwW3LR2GzZ296f7291OrFjQinteeA/Pbz+Ybu9oceG7/6cFDrMRN8iMWd7ZilWv7MT69/vx4KVzMez1Y27zJFn9V7Q149qnN8MfjqHN7cSitmY89eZH+MrpM/D11W9h7vR6LGprxt+6+/C1M4+W97GzFcuf3YGXd32Sbm9zO7Fkvhtuhxl+CFim4OcPX9yBVyTjOlpc+M+zj8WVv3gb/nAsQ/aZTXvQO+DH986fqTjvX/7174r9Kzpb4bRosTSr719PmoJrz/2M7NyWd7biwVdy11/Jxyf/9hEWnNKEO5/div++dK6in/e/tAM3XzgbN/9ha07/rRfNglYL3Lh2GzZI+s49YRJu/sJM3LxuG7pk9okl7sf5//0WAGDOUXY8eOnJuGndiH2LQcBjl5+KH6/vztDb7nbilgtn4T+ffAd93jDa3U7ctaAV051WVJuP+n2ye2PFglbMGAX7tUQtr8Vo+lYtW5XQW46OUsbW8p4ghJBaprffhxtl7p8rF7RCELRYumYrNuzuS/fNa3Hh7oVzMLVCCZkSYuxfSdtyOs85fiJuuXAmlkliSYtBwONXnIpEAvjZhg/xtTNm4PGuHrzTO4hVX5mLF7buw9fOapaNXW+/eDa0Gg1u/sN2bO4dxIOXzsWPX/sAXzl9Bh7f2JMhL+YTv36rF5efdTTqEvqc2LvD7cLV84/FYkks3eF2Yck5bvjDMTza9aGszm8+uQkPXTo3J5btaHHhyvZm/PKNf+Arp8/Ar9/qxdLzj8fdL7yH6//PZ7Cn34emCrxu1kwd7dHC4/HA4XCMu1qDQ/4wljy9GRt292HD987O2dQi7amNdeUv3s5of+FbHVj+3E7FMSsXtOI37+zF5t4B3L2gVVF/m9uJudMbsOrV7ozfN/cOpNvbUvqyb1hSeydJdEh1q9lWGzc3q12cEwBVfSvz2LvjX2bjnHv/ktH+zJI23PPirqLWX83HG9duw/LO2bhp3faS/Vxyjhtf/tmbGe1LznFjc++Aqs6OH70GAHjtu5/LsZ9vvHSe7W4n7v3SSWW/s612PvcO+LF0zVZFf+5eOOeIeeeultdiNH2rlq1K6C1HRyljR2Pdx+vrJzkyOZzraGfDs1keBz1BXP/bLbL3z5ULZuP5bQewobsvp29eiwsPXTq3au9sS2P/StlW0rnkHDfe7R3ISEaXnOPGVIcJz23bj7nTG7CldwBd3f1Yco4bW3oHcFeeWF+MTcV4UswX1PKLd/cM4oLZk7Fs7XZFGWks3eF24oLWKYryi9uasTorsR8Z68KJ0+vTvr3bO4gr2o7G4xt7sOQcN6ZPsJb9h5RxX97rSKHPG04fCm84JrthAKCrux+T7Lm1FROA6hhvOIa5TfXYmPpZSXZjdz/mNtXn/C5t39jdD18eH6U6pLryzU1pXHa7OKd8+vL1h6LxnHadoC16/dV87OruRyASL8tPqzH3wynidVHTKSJnP9946Ty7uvsx4AvLylaK4WBU1Z/hYLSq9muJWl6L0fStWrYqobccHaWMreU9QQghtcyAL6x4/5xkN8km2QDw+u4+9HmrF/tIY/9K2VbSObepPiPJFtsa7aZ0DCt+OlL8OV+sL8am0jwhX36xYXef4ps2crH0hu5+VflJdqOizQ3dfRm+bejuwyS7Me37UCAiO64Y+NHxcYInOHKxPXkuvDcYy2kbzjNmOBBJJ5X59Gcnn+Lv0vZidZQ7Tq59OBBBvo9r5LMnt2751lJu/QFlHwvxo5RrrrRW2baV9Ocbn23TU+VAPu+1CpZ/Qxwv1PJajKZv1bJVCb3l6ChlbC3vCUIIqWXU4pe8sVQV762ePLpLsa2kU26e0ja5nwuNTeXyBDUf1OTy+ankQ6E2RXlvMAZBqz62EJhojxPsJv3Iz2a9iiRgMwk5bXV5xtSZ9RgORQvSb9RpZX+Xthero9xxcu355lyIPTkd+fTKrT+g7mM+P0q55kprlW1bSX++8dk27abq3k7yXitT/ut9uFDLazGavlXLViX0lqOjlLG1vCcIIaSWUYtf8sZSVby32vPoLsW2kk65eUrb5H4uNDaVyxPUfFCTy+enkg+F2hTlbSYBOm35H/zmR8fHCS6bAfNaXAAAm0FAu9spK9fuduKQJ5TTrkn1KY2xGQRs3jOIttTPSrJtbic27xnM+V3a3uZ2wprHR6kOqa58c1Mal90uzimfvnz9coc3GosXvf5qPra7nTDrtWX56Qvl/jV2857BvDpF5OyL11VpvHSe7W5n6umT1aPOpFOdT12VE/1aopbXYjR9q5atSugtR0cpY2t5TxBCSC3TYDUo3j8PeYLoSMXf2cxrccFlq17sI439K2VbSefmPYPokIkDD3qC6RhW7Bd/zhfri7GpNE9QiivTNlpcOOQJqspI6XA7cVBF/pAnpGizw+3K8K3D7cIhTyjtu6OAN+zywUR7nOCwGHD3wjmY1+LCoUE/lne25mxu8emya97Zk9He0eJCJBpVHXP3CzuxuqsHi9qa8faHhxRlF7U1Y3VXD4CRJ/rt3DeUbhfbfvnXf2DFAmV7u/Z7MtqTTx1vgQnAChU/39s3lDO3JfNb0j5JZZ/62z9w9ws7FeeyPDVvtXUxaHL/QPFE14eqc5NbfyUfn/zbR7j1olm49unNuLNztqLOZWu34pYLZ8n233bRLLjqjOhwZ944d+33qPppivvTbdc+vTlnHVZ39WDJfHfOC4z4pPOla7amf79rQWvVS3xNa7CozudIeRAaUNtrMZq+VctWJfSWo6OUsbW8J8jhTzQaxc6dOzP+RaN8LgAZHzTaTbhL4f7Z7nbhnlT8LWVeS7K9miW+pLF/pWwr6dy5bwh3dM7OiPlWd/Xg2IlWXDO/JRnrtzejw+1K5gvtzfjlxn8oxtC3XzwbTpsR7W5nOr8Q84XsxFfMHXbt9+A/5h2DU46ekKOzw+3CNedkxtLJp463YIrDrKhz6ZqtsrFsR4sLV3aM+LRrvwe3XjQTa97Zg9svno2jHOaKPFGeTx0fZ8jV0R4ORlBnStY8ltbRFtvtMnW002OkdbRTtecsWXW0RVlbGXW0pfay62hbUjWfjTJ1tKW2s+to21J1tIFkjWqprFhHezgYwSSbHoLMXELxGD71RuC0jdTRlvbH4jEMBaOYYDHm9EnraGf7mFFH26iDwzBSR1sqm66jHUnW0W6w6mCUuT7JOtoxNFj0GXW060x6WAwC+r1BWE16WFN1tIdTa2rJqqOdvU8y6kWadHBaR+poS/eOWEdbOj5dR9ukQ4O1cnW0Czmf2fu77giuE1zLazGavlXLViX0lqOjlLHVXPfx/vpJqsfOnTvxHw8/C9vEaQAA7yd78dOrLxzTJ3zzqeOkWLLjIml8I8bf4r3VZRv9OtqVtC3VaU3V0faFwqiTq6MtJOtQZ9TRDkXRYMmsoy2NXYHcOtoTrEZECqijXWfRwZKqoy3Vma6jHYzCYhBgkdbR1miQSGTV0faFYDXqYVaoox1LZcDSOtomnQAj62gfuTgs+Q+XclClvGmmlOETAGCiXKOtOPEyOKpUGyU7olxbb6pCu5yPlaDU+riNdlPBCbLSnMaCWkkka4FaXovR9K1atiqhtxwdpYyt5T1BDm9sE6elk1pCxiNqcVEh8Xe1qIZteZ3l140ul+YKJgjHTFTOQ0YLfnScEEIIIYQQQgipIEy0CSGEEEIIIYSQCsJEmxBCCCGEEEIIqSBMtAkhhBBCCCGEkApSkUQ7Fothy5YtGBgYqIQ6QgghhBBCCCFk3FJSon3dddfhscceA5BMsj/3uc/h5JNPRlNTE1577bVK+kcIIYQQQgghhIwrSkq0f//73+PEE08EAPzxj39ET08Pdu3ahW9/+9u46aabKuogIYQQQgghhBAynigp0e7r68PkyZMBAM8//zwuueQSHHfccVi8eDG2bdtWUQcJIYQQQgghhJDxREmJdmNjI3bu3IlYLIYXX3wRn//85wEAfr8fgiBU1EFCCCGEEEIIIWQ8oStl0KJFi/ClL30JU6ZMgUajwbnnngsAePPNN3H88cdX1EFCCCGEEEIIIWQ8UVKiffvtt2P27NnYs2cPLrnkEhiNRgCAIAi44YYbKuogIYQQQgghhBAynigp0QaAL37xizltl19+eVnOEEIIIYQQQggh452SE+1XXnkFr7zyCg4dOoR4PJ7Rt3r16rIdI4QQQgghhBBCxiMlJdo/+MEPcMcdd+DUU09Nf0+bEEIIIYQQQgghJSbajzzyCJ544gl8/etfr7Q/hBBCCCGEEELIuKak8l7hcBhnnXVWpX0hhBBCCCGEEELGPSW9o33VVVfhqaeewi233FJpf8YdQ/4w+rxheIIR2M16uKwGAMhpc1gMGeP2DvgxHIzCG4zAZTMiHIvDH47BbtbDrBcwHIxgKBCFw6yD1aiDNxTFkD8Ch1kPm0mHaQ2WtA5PINluNQgwxUMIao3whmPwBJL2rQYBiCdgRhxBrZDuc5j1sBkERBMxBCOANxSF1aiDBoBOBxizZJP6Y/BCi2A0Dl8oAqfViFAsjuFAFDaTDloNoNUAeq0WVi0QhCZHhzcYhCAk5+mLxEbmZRBgjMcALRBErp/GeAwRrYBQPAF/JAZ/KAa7WQerXoA3S482kcBAOCkzwaaDUStgWLImNoMAfTwGP7QIR8OwGow5fsbiMfT7IpiQmqPHH0FdaqwJQAhI65Suf0BrhE9iy2EQENdqcq6VNxiCyWBIr5/dIsCi08EbjmE4kLRl0iX/FjYhtYfk9pu4tz4e8MOTsiFed4dZj+FQNMO2uH8OeoIY8IXhCUZhN+swwWJAAshoa7AY0Gg3jd6BKoDsfS/O50ikltdiNH2rlq1K6C1HRylja3lPEFII0WgU77//fkbbcccdB52u5McKEZIRIznMetSZdDiqgHujWtyl1lcucrqD0Xg6RnNYdKgz6hEIx9IyNqMOvlAUQ4HMnMQTiCAaTyAYjcMbjMJqFGAz6hCIxuAPReG0JuNWbzAZy2sAQANY9QIAJOPSYGrd9AJiAGLxBOIAApJ43CBoMegPw2E2IByLwxuKos6kg0kvwB+KwiuJ232RGAb9IzHxp74Q9DoBJr0AvVYDLYBoPIZgDNALWgQjMfjDMdSZdNALWnzqDcFmSsbkCQDRrNzArBcQjMYAAGZdUr/JoINBp0UkHkciDmg0gEHQIhpPYKLNWLFrJ6Xgu9b111+f/jkej+NnP/sZXn75ZcyZMwd6vT5D9r777quchzXMvsEAlq7Zig27+wAAFoOA1Vd8Fg+/2o0N3X1puXktLty9cA6m1psBAB/1+7Bs7TZs7h3Eg5fOxQOv7MbG7v60fLvbiSvamnHt05vhD8dyfv/F5XMRiyewbO22jHH/PLsR3z//BNyU1d7uduKuzlb4IeSM6XC7cPX8Y7H4F2/DH05uyM6TpuC6cz+DG2T0LO9sxSOv7cYftx7Ag5fOxf0vZ/re5nbiqvZj0FinA4xG3LROXse1T2+C3azPmef9na3wJ4BlMuPu6mzF/qEgHnx1d84cFrUfjSVPjehZ3tmKHzyzHYP+KH522alYqjCXbR99ghNnTJSd64rOVoRjIdz8h+0ZfY98ZQ6OnzpBcW7be/uw5Dfbs/bEbmyQkX3g5b9j3Zb9aGow45dXnibrxy0XzsJNa7fh++cfjzue3YmX3zuUsbd+tHAOgrF4zrX9t1OPwv89u0XWzxULWrGvfxiXrn5nxM/LP4uH1+f6edeCVkx3WlELiGdHbj4zasTH0aKW12I0fauWrUroLUdHKWNreU8QUijvv/8+/uPhZ2GbOA0A4P1kL3569YWYOXPmGHtGxiul3huz43xgJKbXAPi+Qp8Y75dKtl2XzYBf//uZuO2ZZDxqMQh48NK5eHzjTsX8AQD+Z/FnUW81oG84jFUycehVHcdgar0Zy9ZuQ1dWLH9lezNsBh0eSo2zGAQ88tVToJtgwqfeMGLxBB5a351h/5zjJ2Lp+Sfglme2q+Y1cnH7nZ2zcdljb2G604Il892YZDfCoBEQjIRxx7M7c3KNRW3NWLZ2O35+2SnYPxTM8aXD7cLtF8/CxwMB/PKNf2DpBcfj0p//DSdMseOWC2figZf/joWnNOHXb/Vi6fnHY9nabbjpCzPLvnbZaBKJRKIQwfnz5xesdP369SU7VG08Hg8cDgeGhoZgt9tL1jPkD2PJ05szDtiSc9zY3DuQcaFF5rW48NClczEcimLpmq3Y2N2vKt/mdmLu9AaserU75/cN3zs7JyEDgMcuPxWrN/bI6lu5YDae37Y/45Ap2XpmSRvueXGXrJ52txO3XTwLf9iyT9H3uxbMRpvblXNTk+pY3jkbZ//XX3JsK81NnMNz2/Zn3AxEOtxOnCjRIyaIoVgctz+zQ9GPlQtaFe21u51Yco4bX/7Zmxntaj6KOjt+9BoA9T3R7nbi++cfj4tXbcy75ovamvH4xh6cJJmjyAvf6sDy53bmjH3p+nl5516on/d+6aRReWdb7XzuHfCnz46cj3cvnHPEvHNXy2sxmr5Vy1Yl9Jajo5Sxo7HulXr9JIcfO3fuxHd+uwWOqc0AgKF9Pbj3SyeVlBxXSlclfap1eDaV+XjAj++r3BvvWThH9p1tuThfZF6LCxe0TsGN/7tNtu+hS+eW/O6onN3s+L6Q/EGc34ef+PC8YuzswgWtk7Fs7XaZPicuaJ2S7ltyjhvtbid6PvEBAJ7dtj/HfjF5jVzcLsbEbW4nLmydgpOm1+Ou595TzF1uv3gW3u75VNYX0cbV57jR1d2Pd3sHcEVbM678xdvoSCX+qzf2YO70BrzbO4gr2o7GE3/9B1aVce3kKPg72uvXry/435FAnzecc/jmNtXLXmgAeH13H/q8YQwHo2kZNfmN3f2Y21Qv+7s3HJMdN8luVNQ3yW6S3ahytnSCVlFPV3c/orGEqu+NdhP8Cj6KOgKRuKxtpbmJc5C7UQDAhiw9Xd398IVjiMYSqn6o2evq7ofVmPuhj3xjvKlPBgDq17irux86IXkE8635JLsRXVlzFEkAsmMLmXuhfg74wrJ9o4n07GTT1d2P4WB0lD0aO2p5LUbTt2rZqoTecnSUMraW9wQhhIwVnjz3Ro/CvVEuzhd5fXcfJtUZFfv6vKXHTHJ2s+P7QvKHuU31sBp1aFSNnfsU30TZ0N2f0Sfqm2Q3YZLdJGu/mLxGLm4XY+KN3f2YZDchAY1q7hKNJRR9EW1YjTrMbarHhlQsLbaLa5rs68MkuxEbyrx2cpT0MLTFixdjeHg4p93n82Hx4sVlOzUe8AQjOW2haFxGcoThYASewMi4fPLZ/eLvUh1SvMGYbHuxtoYV9Ev71fSFonFFH+VsSHWpjSt2vYYDkaL8kENuTUudm5psoX7I6VMaW0k/lV6IRpO885E5k4crtbwWo+lbtWxVQm85OkoZW8t7ghBCxopS741ycb4UtbipnPutnN3sWLSQeDgUjcMbjBUdOyv1SfUpjSk1rxHJjkvzxcaePPkIgIw1kK5jdlwt/l7p18qSEu1f/OIXCAQCOe2BQAC//OUvy3ZqPGA36XPajDr15awz6WE3j4zLJ5/dL/4u1SHFZhIK1qXWX6egX9qvps+o0yr6KGdDqkttXLHrVWfWF+WHHHJrWurc1GQL9UNOn9LYSvppN439Q2jyzkfmTB6u1PJajKZv1bJVCb3l6ChlbC3vCUIIGStKvTfKxflS1OKmcu63cnazY9FC4mGjTgubSSg6dlbqk+pTGlNqXiOSHZfmi43tefIRABlrIF3H7Lha/L3Sr5VFJdoejwdDQ0NIJBIYHh6Gx+NJ/xsYGMDzzz+PSZMmVdTBWsVlM2BeiyujbfOeQbS5nbLy81pccNkMqDPp0J6SUZNvczuxec+g7O82g5DWIeWQJyTbnuwLosPtku3LthWNxRX1tLud0AkaVd8PeoKwKPgo6jDrtbK2leY2Mgf5vo4sPe1uJ6wGATpBo+qHmr12txO+UO472vnG2AwjB1ltndrdTkRjyb+k5Vtz8dpK5yiiSclkU8jcC/WzwVr5JzEWi/TsZNPudqKuBv4YMFrU8lqMpm/VslUJveXoKGVsLe8JQggZK+x57o1KbyTIxfki81pcODQcUuxz2UqPmeTsZsf3heQPm/cMwheK5omdXTjkCSr0OXFQ0ifVd8gTlLW/ec+g4lpnx/tycbsYE7e5nTjkCUKDBDoUrkFbKh9R8kW04QtFsXnPIDpSsbTYfsgTSvuUXIcQOsq8dnIUlWjX19djwoQJ0Gg0OO6449DQ0JD+53K5sHjxYlx99dUVdbBWcVgMuHvhnIzDsLqrB9ec05KzKea1uHDPwjlwWAyY1mDBigWtaHc7sbqrB4vamnM2iPjwq9VdPbK/f3hoKK1Dypp39si2t7udaHO7sLxzdk5fh9uFa85pSetOzuNDLO+U17NiQSsee/0DRd/b3E5McZjhC4ZUdVz79GbZeZoArFAY1+Z2Yck5LTk2k08vzNSzYkErvvu7Lbj6V5tU/dj0j0+wXGHNVixoRSIRz7G34+NPFdd5xYJWbO0d+W5Nek9k/ZFDlF3d9SEA4OonN+FOmevT7nbi1otmYe2mvbhrQSv+vt+T0T+vxQWnWS/rz6Ovf6Dq595+b6af8+X9vGtBa02U+JKeHSnifI6UB6EBtb0Wo+lbtWxVQm85OkoZW8t7ghBCxoqj8twblUp8ycX5wEhMf/ZxExX7ynmYlpzdpWu24vaLR2JEMQaXm5MYV6/u6oFeq8FpxzixRCEOvbKjGaccPSFHT5vbiUXtzXBPtKXHre7qQTiSwGnHTIB7kg3XzM+Nx3fuG8ItF87Km9fIxe3LO1tx9ZOb0OZ2Ysn8Fpx2jBN1Bh2++3+Ok801FrU14+pfbcJZxzplfUk+dXw2wpEEdu3z4NaLZmHpmq3oaHHhlotm4ffv7MGitmbs2u/BrRfNxP9u2osflnnt5Cj4qeMA8Je//AWJRALnnHMO1qxZgwkTJqT7DAYDZsyYgalTp1bUwUpT6SczinXuhoMR1Jn06b+EZLcp1dH2hZJ1miOxOALhGOpMepgNI3W07WYdbKk62p5AUl9dVh1t0Y4tq4622G41CEjEE7BI6miLfXUGAbFEDP4I4FOoo52pf6SOtj80UmN6OBiFzahcR1uqwxsMQivoYUnV0UvPS6aOttRPuTradWYdbKk62lI9SnW0pX7I1dGW9ufU0Q5I1gEjdbSz11+soy22S+toZ6xDKASTXqGOdkpOqY623N4Sa0RKr7u0jnZ6LeXqaJt0mGDNqqNt0qHBOrp1tAs5n9n7vu4IrhNcy2sxmr5Vy1Yl9Jajo5Sx1Vx3PtmYKMGnjo8tPJv5yY6R7EXW0ZaLu9T6ykVOd0YdbbMOdaZkHe10bGlK1tEW41UxJym4jnYoGctrNEkfLHoBGozU0bab9bCLdbQTCcQT8nW07WYDItl1tMNReIOZdbSHJHH1p74QdDoBZr0AnVYDAXnqaPtCsBqTMX92He06sw6WVB3tBABLqo620aCDUadFOBYHEiN1tCPxOCbZTGNbRxsAPve5zwEAenp60NTUBK22pK94p3n99dfxox/9CO+88w7279+PtWvXorOzU3XMa6+9huuvvx47duxAU1MTbr75ZlxxxRVl+VEODov8ocp3sfIHPvnruMnrqH6t1Illaxideq6V+JPP0UVPtpi5Fb8OSvsNSP7V9iiFMXI02k2ySXQtvHutRq0kkrVALa/FaPpWLVuV0FuOjlLG1vKeIISQsUIpRsqHWtyl1lcucrodKC1Gq5aPlaRJpZ65HMdMtFVVf6Uo6UtbM2bMAAD4/X709vYiHM58FPqcOXMK0uPz+XDiiSdi8eLF+Nd//de88j09PfjCF76Ab37zm3jyySfxyiuv4KqrrsKUKVNw3nnnFT8RQgghhBBCCCGkwpSUaH/yySdYtGgRXnjhBdn+WEy5zJSUCy64ABdccEHBdh955BE0Nzfj3nvvBQCccMIJ6Orqwv33389EmxBCCCGEEEJITVDSZ7+vu+46DA4O4s0334TZbMaLL76IX/ziF2hpacEzzzxTaR/TvPHGGzj33HMz2s477zy88cYbimNCoVDG09E9Ho+iLCFkdOH5JKR24fkkpDbh2SRkfFBSov3qq6/ivvvuw6mnngqtVosZM2bga1/7Gn74wx9i5cqVlfYxzYEDB9DY2JjR1tjYCI/HI1vXGwBWrlwJh8OR/tfU1FQ1/wghxcHzSUjtwvNJSG3Cs0nI+KCkRNvn86XrZTc0NOCTTz4BALS2tmLTpk2V864C3HjjjRgaGkr/27Nnz1i7RAhJwfNJSO3C80lIbcKzScj4oKTvaH/mM5/B3//+dxx99NE48cQT8dOf/hRHH300HnnkEUyZMqXSPqaZPHkyDh48mNF28OBB2O12mM3yT+k2Go0wGo1V84kQUjo8n4TULjyfhNQmPJuEjA9KSrS/9a1vYf/+/QCA2267Deeffz5+9atfwWAw4Be/+EVFHZRy5pln4vnnn89oe+mll3DmmWdWzSYhhBBCCCGEEFIMJSXaX/va19I/n3zyyfjoo4+wa9cuTJ8+HS6Xq2A9Xq8X3d3d6d97enqwZcsWTJgwAdOnT8eNN96Ijz/+GL/85S8BAN/85jexatUqfP/738fixYvx6quv4re//S2ee+65UqZBCCGEEEIIIYRUnJK+ow0Ajz32GGbPng2TyYSGhgZcdtllWLduXVE63n77bcydOxdz584FAFx//fWYO3cubr31VgDA/v370dvbm5Zvbm7Gc889h5deegknnngi7r33Xjz66KMs7UUIIYQQQgghpGYo6R3tW2+9Fffddx+uueaa9Me233jjDXz7299Gb28v7rjjjoL0nH322UgkEor9TzzxhOyYzZs3l+I2IYQQQgghhBBSdUpKtH/yk5/g5z//OS699NJ028UXX4w5c+bgmmuuKTjRJoQQQgghhBBCDjdK+uh4JBLBqaeemtN+yimnIBqNlu0UIYQQQgghhBAyXikp0f7617+On/zkJzntP/vZz/DVr361bKcIIYQQQgghhJDxSsEfHb/++uvTP2s0Gjz66KP485//jDPOOAMA8Oabb6K3txeXXXZZ5b0khBBCCCGEEELGCQUn2tkPIDvllFMAAB988AEAwOVyweVyYceOHRV0jxBCCCGEEEIIGV8UnGivX7++mn4QQgghhJAKE41G8f7776d/P+6446DTlfQsXEIIIUXAOy0hhBBCyGHK+++/j/94+FnYJk6D95O9+OnVF2LmzJlj7RYhhBz2MNEmhBBCCDmMsU2cBsfU5rF2gxBCjihKeuo4IYQQQgghhBBC5GGiTQghhBBCCCGEVBAm2oQQQgghhBBCSAVhok0IIYQQQgghhFQQJtqEEEIIIYQQQkgFYaJNCCGEEEIIIYRUECbahBBCCCGEEEJIBWGiTQghhBBCCCGEVBAm2oQQQgghhBBCSAXRjbUDtcjeAT+Gg1F4AhE4zHrYTDpMa7DgoCeIAV8YnmAUdrMOEywGRGJxeCSydSYdNACGxDaLHjajDr5wFEP+5DinxYBQLJ5hw2oQYIqHEYQeGp023ddg1cNi0MnIBhHUmuANxzLaQ/EwYjEd9IIG/d4QbCY9HAYBcQDheAx6rZAzxhsMQavTwaIb6Wus00On0+WsgzEaRTClIxCOwGUzwZcaYzfrUWcQYATQF47BpYvI+ugPB2HQGRGOxeEJRGE1CjDrBRi0GhjiMUCrRRCajHE2gwBjPIaQVkA4nkAoEoNGowEAeENRWI3JdYcGsOlHfPAEImiw6WERBAyHYxgORFCX8kMAEIrHoNNqocm2Z9IhEo1jOBhGvcWYnmN6/QEEgZwx0xos2DcYwFAgkrEnEE/AE47BF4rAaTUiFIvDE4igzqSHSadFOB6HTScgodXI7j2Rjwf8OfvtqFT/kD+MPm8YnmDyWrisBjgsBsX9nN1eZ9LBZtTJ6qgllOZzJFLLazGavlXLViX0lqMj+15iN+sxtd6sOkbpPkAIIYcDxdzjpLJWow56rQb9vjBsqXjHF45i0DcS28XjCdTXyD1T7v5vNQg5cweguh5D/jAOeILwhaKotxgQisbhD0cxIZWLiPr1ghb93hDsZj0segGeUBT+UAx2sw5mvRZaaOCNxKDRAHqtFuFYDEZBQDAax3AqB7AaBETjMYRjSMf3Jp0ArQZIIA5Ai3Aq/7Eadal2AAlAowHMegH93hCM+mReEI7FkUACFr0O/kgMw8Eo7KaReF+vTb5fHIzGMRyMwG7Sw6jTQgcgodXAF45hOJicn0HQYsAfhtWog0HQYjAQhsWgg0ajgU6rgbNK152JdhYf9fuwbO02bOzuT7e1u51YsaAVd7/wHl7YfhAAYDEIeOzyU/Hj9d3YkJKVaxPHX9HWjGuf3oz7Fs6E6SgnblonY6OzFQYA312zFRu7++GyGfDkVWdgaep3ka+f3oQrO47FTTJ+Lu9sxf0v78RgMIZFbc24/rfv4vFFp+H3/68XXz59Bm5QGLNv0I87XvsAG7v7cdrR9fjhF0/KsSvK/u39A/ivVz7Ek1edgRsV9Pn9IfgcFkUf73h2B17d9Um6vc3txJL5brgdZvgTwDKF9YnFw1j+3G585fQZeHxjT4ZMm9uJRW3NePrNj3DLhbOw9PfvYt9QEE9/4wwslfHj9otnwaQVEIolcOszuf23XjQLZqM+Z47nHj8Rt1w4Czet24YumX1yxx934BXJ3ERb//7Lt3HbxbNw/8u7c2zddvEsBGIJ3Kaw92Y4rYp7864FrdAJWixdsxUbdvel+y5snYzvnX+84n7+0/aPcdcLuwEo7915LS7cvXBO3uB+tFA7nzOc1jH0bPSp5bUYTd+qZasSesvRUcrYfYOBnPtArZ1hQggplWLucXKyYpx42eq3cPL0etxy4Sxc/dQm9HnD6fh05Qvv4dp/Om5M75lK9//lna244vG3sGcgAItBwOorPouHX+3Ghm759dg3GMDS37+Ld3oH8eClc3HvS+9jc+rn+156PyeGvqr9GAyHonisayS+Fu08+voH+OoZR8Ok1+KXb/wD1537Gdz8h+0Zco9f8VmsenV3RhwpxveT7CasfG4nXs6K/Re1NeOpNz/CV06fgaff/AjfP/8EfPXRv+Ezk+uwZL4bTpsRt/8xM2focLvwjXnH4KgGE279ww7ZdbpHkrNJbX3tsbcwd3o9FrU146uPjvy88vn38IN/mV3x686PjkvYO+DP2dgA0NXdj5vWbsMXT2lKty1ub8aqrKRErk0c//jGHixub8asablJdtrGum0Ia5Duu2fhHNzx7I4c2Ss7jlHUcfO6bbjhgpnYmLL58FdPxm3PbMdXz5ihOkar1aT7/uuSk1Rl246brOibKDOp3qKqY+ZUR0b7xu5+rFrfjaAmN8mWro9ea8DMqY6cJFvU8fjGHpww1YGb123Dg5fOxT0L5+D2P8r7efszOxBOJHDrM9tl++/44w70e0M5fcdPdWBZVpKd9nHtNpyQNTfR1sNfPVnW767ufrzd8yl+oODHTWu3qe7Njd19WPr7zBcTAFhw8jTV/XzB7KPSbUp79/XdfbhhzVYM+cMYa/Kdz70D/jHybPSp5bUYTd+qZasSesvRsW8woDp232AgZ8yQP5wTVAK1dYYJIaRUirnHKcmKceLi9mZ0dffjzmd34J6FcwCMxKdXdhwzpvdMtfv/zeu24eGvngwgGbc99OrujCQbGFmPg55gMjbs7sfi9uZ0/Cn9WcrG7n7sHwpgdVdmn2jn+KkO7B8KYNX6bnzxlKacHGDEn1y9q9Z3460P+3G8TOz/+MaedFx/wlRH+pqI4975x6c5OcOG7j78fMOHePPDTxXXSZqzSW0tbm+W/fkzU+xVue5MtCUMB6M5F0ykq7sfk+zG9O9zm+pzZOXaRDZ292NuUz284ZiqDX84lv59kt0oKxuIxFV1eFM6Nnb3QydosTHVpjbGahz5cIMvj6w3HFP0TSqj1j+3qT6nvRA/veFYQevc1d2PQCSe189QVH0tpesiomZfaW5dkmshxyS7KefmJB2rtjeTY/tk2vNfIxG1Ob2+uw993rEP0vOdz+FgdJQ9GjtqeS1G07dq2aqE3nJ0DAUiqmOHApGc9j5vOCeoFKmVM0wIIaVSzD1OTXajJE7Lju27uvsRjSXG9J6Z7/6vE5KpW764bcAXTseGUlm1cY12U86bSKL83KZ6NNpN2JhaM7kcKHusSHKMSTH2l9qQXhO1cRt296HRbpK1l31ds20p/VyN686PjkvwyAQvUrzBkcQkFI3n9Mu1ZffnszEs6Zfak1KMDvHnYuZWiP5YQlUkrw6ltSrEdiHrLMqW66fcNSjUfjbDKrby6SxlrNL+kdOZ135QfZ1Gg7x7owZ8HC1qeS1G07dq2aqE3nJ0lDLWk8enI+l8EEIOP4q5x+WTlcY82bGSGBuN1T2z0Bg/X9zmkfwxVyqrNk4tt1Fbs0L8KcSu+H++fKuQPqUYWGkt0rlDha87E20JdrNetd9mEtI/G3W5HwaQa8vuz2ejTtIvtSelGB3iz8XMrRD90bj6gcqnQ2mtCrE9HFJ/N0nUXQk/5a5BIddZjjoVW/l0ljJWaf/I6cxr36S+TqNB3r1RAz6OFrW8FqPpW7VsVUJvOTpKGWvP49ORdD4IIYcfxdzj8slKY57sWEmMjcbqnllojJ8vbrObRlI8qazaOLXcRm3NCvGnELvi//nyrUL6lGJgpbVI5w4Vvu786LiEOpMO7W6nbF+724lDnlD69817BtGWJSvXJtLmdmLznkHYDIKqDYthZGMc8oRkZc16raoOW0pHm9uJaCyeblMb4wuN/OXHmkfWZhAUfZPKqPVv3jOY095WwDibQShondvdTpj12rx+GnXqa+mTSepF/cXMrT11LZT8PuQJokNFp9rePOQJoqPFJdOe/xqJqK3pvBYXXLaxfwJnvvNZZzpy/m5Yy2sxmr5Vy1Yl9Jajw2HWq451yARiLpsB82TuA0DtnGFCCCmVYu5xarJtkjgtO7ZvdzuhEzRjes/Md/+PxpJvIOWL2xqshnRsKJVVG3dQJhYV5TfvGcRBTxBtqTXL9nHznkHFODY5JqgY+0ttSK+J2riOFhcOeYKy9rKva7YtpZ+rcd2ZaEuY1mDBigWtOZtHfNLrmnf2pNtWd/VgyXx3RoIj1yaOX9TWjNVdPdixtx/LO5VtGBJI9y1dsxW3XDgrR/axDR+q+nn3CzvTT9e7+slNuP3i2Xjybx+p2o3H4+m+7/5ui6rsxvcPKvomyhwa9Kvq2LlvKKM9+VTCFpgArFAZF4mHsXPfEBa1NefcKMQ5v7dvCCsWtOLapzdj6ZqtuO0ieT9vv3g2jFoNfnDxbNn+2y6aBafNmNO3K6Vfycf3suYm2rr6yU2yfre7nTi1eQJuU/BjxYJW1b3Z5nbhnoVzcl5U1m7aq+rni9s/Trcp7d15LUndtVDqIt/5rJWyVqNBLa/FaPpWLVuV0FuOjqn1ZtWxck9FdVgMuFvmPlBLZ5gQQkqlmHuckqwYJ67u6klXl1m6ZiuA1NOqF7TisQ0fjuk9U+3+v7yzFVc/uQlAMm675pwWxbit0W7CPQvnoKPFhdVdPen4U/qzlDa3E1McZixqb86wLdrZtd+DKQ4zlsx34/fv7MnJAVZ39WCJjD9ifH/aMU7s2u/J6VvU1pyO69/bN5S+JuK4U5sn5MTVHW4XvtFxDE47ZkJBOZvU1uquHtmf/77fU5XrrkkkEnm+xXp44fF44HA4MDQ0BLvdLisj1j0dDibrHNfJ1dE26TDBOlJHW5QV67sNZY1P19E26eC0jtTRFmVsMnW0h4PJOtzWVB3tTNmROtrS9mQdbQF6QYtPfSFYjfJ1tKVjvMEQtIIOFv1I3yTbSB1t6TyU6minZRTqaEvtZdTRDkZhMSTr5Rll6mhn6JXW0Y7GoEH+OtrDwQjqrZI62il9FoMAPYBAPAa9pI62dK6RVF2+eoshY462rDra2ftErH0o3RPSOtoTrGINceU62tk6RcQ62lLd2XW0xT6XLbOOdrbO7Ha7pI52to7RopzzeSRSy2sxmr5Vy1Yl9JajI/te4iiijnY1znAh55PUHjt37sR3frsFjqnNGNrX8//ZO/PwqMrrj3/vvbPPZCPDFiQQnChkYRMVTYJAbZVVKNpWbWVzq6JtbV3Y901buwD+tBbUtqK2pSDg0lbRCrgLVlYhgoCyJiSZzD5z7/z+mMwwM3ebmUwyITmf5+F5yF3fe+e+977nPed8D37zg4EoKSlpsXMAaNZ50nWsdLaprdOR+mYy77jobU06DbRcUx1tvQaWyNj8wtiuLdbRjn7/h+toR187AMX7kUwd7fNOLywGLcxaDo3eAJyJ1tGOGh9H6mg3je8NGg5cAnW0wQCmpjrauqY62n5BQDAYhFGrgbupjnaWQQMWQJABdHF1tMNjag4Aoupoh+tr17l8MOk10DfV0TbqNOAYBhzV0W5d5AZBXbMNkgp3PSS2LUjpzMnUfJXb9sLyPp0tLXDexOjcyudLvg0tR0GuUXIwLPWcJEuPPJPscXJM0i8JuedZbnlb+MAo0VYMybZAW74Xrdm2ljpXOo7bnGPIvUuUkHsPEARBtAeSecdJbVuUqcFhksi9/6WuXel+JHO/ErcbWo7C/JazEYpa7MjyUOg4QRAEQRAEQRAEQaQRMrQJgiAIgiAIgiAIIo2QoU0QBEEQBEEQBEEQaYQMbYIgCIIgCIIgCIJII2RoEwRBEARBEARBEEQaIUObIAiCIAiCIAiCINIIGdoEQRAEQRAEQRAEkUbI0CYIgiAIgiAIgiCINNImDO01a9agd+/eMBgMuPrqq/Hxxx/Lbvv888+DYZiYfwaDoRVbSxAEQRAEQRAEQRDyZNzQfuWVV/DQQw9h/vz52LVrFwYMGIAbbrgBZ8+eld0nOzsbp06divw7duxYK7aYIAiCIAiCIAiCIOTJuKH95JNP4q677sLUqVNRUlKCp59+GiaTCevWrZPdh2EYdOvWLfKva9eurdhigiAIgiAIgiAIgpBHk8mT+3w+fPbZZ5g5c2ZkGcuyuP766/HBBx/I7udwONCrVy8IgoDBgwdj2bJlKC0tldzW6/XC6/VG/rbb7em7AIIgmgX1T4Jou1D/JIi2CfVNgrg4yKhHu6amBjzPizzSXbt2xenTpyX3ufzyy7Fu3Tq8+uqr+Otf/wpBEHDttdfim2++kdx++fLlyMnJifzr2bNn2q+DIIjUoP5JEG0X6p8E0TahvkkQFwcZDx1PlmuuuQZ33HEHBg4ciOuuuw7//Oc/0blzZzzzzDOS28+cORMNDQ2RfydOnGjlFhMEIQf1T4Jou1D/JIi2CfVNgrg4yGjouNVqBcdxOHPmTMzyM2fOoFu3bgkdQ6vVYtCgQaiurpZcr9frodfrm91WgiDSD/VPgmi7UP8kiLYJ9U2CuDjIqEdbp9PhiiuuwNtvvx1ZJggC3n77bVxzzTUJHYPneezZswfdu3dvqWYSBEEQBEEQBEEQRMJk1KMNAA899BAmT56MIUOG4KqrrsLvfvc7OJ1OTJ06FQBwxx13oEePHli+fDkAYNGiRRg6dChsNhvq6+vxxBNP4NixY7jzzjszeRkEQRAEQRAEQRAEAaANGNo//OEPce7cOcybNw+nT5/GwIED8eabb0YE0o4fPw6WveB4r6urw1133YXTp08jLy8PV1xxBd5//32UlJRk6hIIgiAIgiAIgiAIIkLGDW0AmDFjBmbMmCG57t133435+7e//S1++9vftkKrCIIgCIIgCIIgCCJ5LjrVcYIgCIIgCIIgCIJoy7QJjzZBEARBEO2DQCCAQ4cORf6+7LLLoNHQcIMgCILoWNCXjyAIgiCItHHo0CHcs2YrLJ0vgePcN3jm/rGko0IQBEF0OMjQJgiCIAgirVg6X4KcgqJMN4MgCIIgMgblaBMEQRAEQRAEQRBEGiFDmyAIgiAIgiAIgiDSCBnaBEEQBEEQBEEQBJFGyNAmCIIgCIIgCIIgiDRChjZBEARBEARBEARBpBEytAmCIAiCIAiCIAgijZChTRAEQRAEQRAEQRBphAxtgiAIgiAIgiAIgkgjZGgTBEEQBEEQBEEQRBohQ5sgCIIgCIIgCIIg0ggZ2gRBEARBEARBEASRRsjQJgiCIAiCIAiCIIg0osl0AwiCIAiCINoDgUAAhw4divx92WWXQaOhoRZBEERHhN7+BEEQBEEQaeDQoUO4Z81WWDpfAse5b/DM/WNRUlKS6WYRBEEQGYAMbYIgCIIgiDRh6XwJcgqKMt0MgiAIIsNQjjZBEARBEARBEARBpBEytAmCIAiCIAiCIAgijZChTRAEQRAEQRAEQRBphAxtgiAIgiAIgiAIgkgjZGgTBEEQBEEQBEEQRBohQ5sgCIIgCIIgCIIg0ggZ2gRBEARBEARBEASRRsjQJgiCIAiCIAiCIIg0QoY2QRAEQRAEQRAEQaQRMrQJgiAIgiAIgiAIIo2QoU0QBEEQBEEQBEEQaYQMbYIgCIIgCIIgCIJII2RoEwRBEARBEARBEEQaIUObIAiCIAiCIAiCINIIGdoEQRAEQRAEQRAEkUbI0CYIgiAIgiAIgiCINKLJdAMAYM2aNXjiiSdw+vRpDBgwAKtWrcJVV10lu/3f//53zJ07F19//TWKi4uxcuVKjB49Om3t+abOhUZPAHa3HzlGLSwGDS7JM0lue8buQZ3TB7sngGyjBnkmHQK8AHvc/sGAgBq3H9lGLaxmHRq9gZhzmHUcHB4vOI0GZp0Gjd4A7O4AcowamPUaOLwB2F1+5Ji00HEsdPBBYPVw+PiYY0DwwsVroOVY1Dq8sBi0yNZxYAFoBB4elhPtYxC8OM9rYNJycPp5NLj8KMrSwiu5LY+vHQFYDBpYdYAXHBqbtsk2amHRcdAIPOw80EnLwgMm5hgWHQc9gAaBhz7q+OF9vQKPHJaDF4gcN/rcx5wBWPQaMAyg41gEBAFCEBCCgMMTQJZBA4uOAy/wqHEGQr+HEPo9zHoNGABgAJOWg0/gEQgA+RrAy164jlyTFma9Bi5vAA1uP6wWPXxNv6lFrwHLAJ24IDysRnR/3H4PgtBG7n+WQYt8nfh6wvehJur6s3QcwDKiZ8/lD6DOEfrtLU3PQmNUuxqbrjv8nDS4QvtmG7UoyDXKPs9Sy1kADXHL9ByL2rhnvGu2IW39LVmS6Z/tnbZ8L1qzbS11rnQctznHSGXftvxMEASRORpcPtQ4fLB7LoxFc0y6TDcro8jdk5a8Vyfr3Whw+yPv6PBYLZ5v61wxtkSWQYMeeSY0uHxwOr3gWTZ2fG3QQBsQcNLth1mvgZ5jwDIMeCGIIAAvz8PAcRAAuP08XF4e2UYNdBoWfFCAng3ZAAgCYAAGQDAIOLyhMb+5yUYIBPzIMRpE4996lwfdDCHbwScE4fbxcPlC58jWcvAIQbiazptlCJ1XwzLwBHg0upvG6Ezo9AgCQQRh1HDQsgwafTwam8ag0bZK+Nx+IYA6J498iw7BICLnyTZqYNFy4AE449pb5/JCp9EgXwP4WA4BAC4fj8bwmLzpfjeXjBvar7zyCh566CE8/fTTuPrqq/G73/0ON9xwA7788kt06dJFtP3777+PW2+9FcuXL8fYsWOxfv16TJgwAbt27UJZWVmz23Os1olZG/dgZ3VtZFmlLR9LJ5ajV745ZtvjtU7MjNu2qtiK+4ZfiukvfAqXj4/sv2RCOZa/vh93VRSC694JszeJz7FkQjlO1buw8N39onVTKorw4Eu74fLx+P0tpRjQqzNmS7RzyYRyvLP/G2w/UoepFUX46Yu7sOrWQbAag9DqTLL7VJ+qwUufncKUiiJ8U2NH134Fsts2uuzY8Nk53Dv8Mtnr6MS54AyaJNcvnVCOHIbDzyWO/9sJ5XAFgVkyx+1m9uJ7qz7G2slDsHb7Efzo6l54budRyW0FwYt5m/diR9S6Cls+plYU4aWPjmHO2BLUOV0w5Joj12rScfjDrYPw3M6j2H28Hn+4dRB+9/Zh0W+8+KYyLNqyD9sOnhOdd8nWvXAHgphaUYRauxOGHvmy9+nI6Trc/8pemHQc1k25Emu2Hcb2uO0WTyjDr/7+P5yoc6PSlo/plX0QRFDUrvjn5JNfXoVjtYLs81x9ug7T//K/C9dls+L+EZdiWtyzu2B8KX689iPUOHyRZcsmlqMwrj+0Bsn0z/ZOW74Xrdm2ljpXOo7bnGOksm9bfiYIgsgcJ+vdeHTDF9h+uCaybFixFSsm9Zc08joCUvfku/26YO7YEszetLdF7lWi72ip7b7TtzPmjSvFf/aexPWl8mP0jZ8dx+v7zuKVe65Brd0NPgj8+YOv8YvvXo5Tdg9Wv1MtOaZd9sYBTLqiJ9Z/dAy3yYytZwy/FN1yTXhMbvzedF+lzjFvbD/c8vQHkfFllc2KGSNtcPl43L9+F1w+PjJGX//RMfxkaG+YdBxWS4yLo8e64evWMD6cbvBgVdS5e+YZ8ZfpV0uOwRdPKMNnX53FVZd2hY8XMH/zvhb5dmY8dPzJJ5/EXXfdhalTp6KkpARPP/00TCYT1q1bJ7n973//e9x44414+OGH0a9fPyxevBiDBw/G6tWrm92Wb+pcogcbAHZU12L2xj34ps4VWXbG7hEZ2QCw/XANVr9TjWmVRTH7z9m0B7++ZSD6FoiN7OhtuuYYJdc9t/No5JiDe3dWPMaYAZdgZ9M+Kyf1x+p3qmHQi43e6H36F1oj5/lOSYHitsXd8nD/CLGRHb2Nh5U/3+xNe+BhILnOw4iN7OjjBlkTplUWYfU71ehbkCN6EURv2yXHFGNkA4jcl34FOZi7aS+65Zlj2jmtsihyzOj/R7P9cA3mbNqDkoIcyfPOH1cWOU/pJWIjO/6+h8+7Ku5lEt5u7qa9WHP74MjfpxrcWLdD+rqjnxMPa1J8ni/rlhd7XdU1WCXx7C7YvA8rJ/WPWTZr4x6csXvQmiTTP9s7bfletGbbWupc6Thuc46Ryr5t+ZkgCCJzNLh8IoMSAN47XIPHNnyBBpcvQy3LHHL35PLu2Zi5cU+L3KuT9W7Fd/TJejeAkCdbart+BTmYtXEPbihTHqPfNcyGlZP64+Mjtag+58Tqd6px8xU98enX50UGMBAa087dtAc3X9ETz+08ihKFsXX3PPmxvYcBdn5VI3uORVsPxIwvt1fXYPW2wzjV4I4sD4+dSwpycKrBLTsujh7rhq+7k8UUY2QDwJrbB8u2d+6mvbja1hUn7W6RkR39uzT325lRQ9vn8+Gzzz7D9ddfH1nGsiyuv/56fPDBB5L7fPDBBzHbA8ANN9wgu73X64Xdbo/5J0ejJyC60WF2VNei0ROI/F3n9Mluu7O6FoN65or2d/p4OHy84jm8AUH1mGrHcDTNFu2srkWXbD12Ni1LdJ9Etk1km0TOF08i+w3qmRu5H0rbumTOEd5Xqp3Rx1Q7fvxvHH9tydx3tXNpuAtdtWu2QTSBEH9tQOLPidz+0dt2ydaLltU5m/9xbqn+2d5py/eiNdvWUudKx3Gbc4xU9m2Je5FM/yQIovVIpm/WOHwiwzHMe4drItFqHQm5e6I0FmvuvWpw+xXf0Q1uPwDALvMuD7ctkbFdl2w9umQb0DXbELEFwv+XYnuUvaB0D1wK53b4eOVzHK4RjS+3V9eia7YhZnm4DYmOdaOvO/7cGo5VvVdmvaZFxywZNbRramrA8zy6du0as7xr1644ffq05D6nT59Oavvly5cjJycn8q9nz56y7bE3PeRyNHourLer3Hgpg7mxKSdD8RwK68PHTOYYDg+f9D6JbNuc61Ban8hxw/dBblIikTbI3cvoY6odX259svcykXNFHzPRdqX6G0kdP/wcRaPWBxKhpfpne6ct34vWbFtLnSsdx23OMVLZtyXuRTL9kyCI1iOpb6dK3+9I384wcvdEdSzWjHuV6DtabrtkxnYODw9vQIjsE/5bifA4T2k7pXPbo8bnckitj26n0jK1Y0mNaROxRaTGtzHbNLN/ZDx0vKWZOXMmGhoaIv9OnDghu222Uat4rCzDhfXZBuX0dr1GfGuzmkQPFM+hsD58zGSOYTFwSe+TyLbNuQ6l9YkcN3wfpO5xom2Qu5fRx1Q7vtz6ZO9lIueKPmai7Ur1N5I6fvg5ikatDyRCS/XP9k5bvhet2baWOlc6jtucY6Syb0vci2T6J0EQrUdS306Vvt+Rvp1h5O6J6lisGfcq0Xe03HbJjO0sBg56DRvZJ/y3EuFxntJ2SufOjhqfyyG1PrqdSsvUjiU1pk3EFpEa38Zs08z+kVFD22q1guM4nDlzJmb5mTNn0K1bN8l9unXrltT2er0e2dnZMf/kyDJoUGnLl1xXactHVpRhkWfWyW5bYcvH7hP1ov3NOg4WHad4DrkHK/qYasew6LjIPmftXlQ0LUt0n0S2TWSbRM4XTyL77T5RH7kfFQrbmmTOEd5Xqp3Rx1Q7fvxvHH9tydx3tXMF+Aszd2fsHlQl8Oyl8hvIPbtn7V7Rsjxz8xU4W6p/tnfa8r1ozba11LnScdzmHCOVfVviXiTTPwniYicQCGD//v2Rf4FA201HSqZvWi06DCu2Sq4bVmyF1dLxlMfl7kl4bChFc+9VjlGr+I7OaTIKs2Xe5XLj1vjjWHQcztq9OGv34IzdE7EFzjb9X4qqKHtBaTxqUjh36LwK5yi2isaXVbZ8nLF7YpaH25DoWDf6uuPPHeAF1Xvl9AZadMySUUNbp9PhiiuuwNtvvx1ZJggC3n77bVxzzTWS+1xzzTUx2wPAf/7zH9ntk+GSPBOWTiwX3fCw8lx0iZSu2QYsk9i2qtiKGSOKsW7HUdH+v/r75zh48jyWTJA/x5kGt+S6qRVFkWPu+vqc4jFe+983EeW+Rzd8gRkjbPB4XYr7fHG8JnKet/efVNz28Ok6rHnnkOI2BkH5fIYgJNcZACxV2I8RXFi34yhmjLDh4Ck7plYUiTpWeNuzDS5RJw3flwMnG7B4QjlO1zlj2rlux1FMrShCpS0/8v/441cVW7F0YjkOnGyQPO/CLXsj59n3Ta3qfQ+f94GRxaiyWUXbLZ5Qjvtf3BX5u3uOEVMrpa87+jkxCC7F5/nw6brY67JZ8cBI8bO7YHwZHt3wRcyyZRPLW73EVzL9s73Tlu9Fa7atpc6VjuM25xip7NuWnwmCuBg4dOgQ7lmzFb/82+e4Z81WHDp0KNNNSgs5Jh1WTOovMiyHFVuxclL/DlniS+6efHnKjmUTy1vkXhXkGhXf0WFF8x4y7/IDJxuwdGI5/rVPeYz+7HvVeHTDF7iqTz5sXSyYMcKGf3x2Alf07oQZI2ySY9olE8ux4bMTmFpRhP0nG2TH1qfq5Mf2BgDXXmqVPcf8sf1ixpch1fFidM8xRpaHx877Tzage44RM2TGxdFj3fB1n3e48MCI4phz3//iLtn2Lp5Qjo+qz6Ag24gF40tb7NvJBIPBYLOO0ExeeeUVTJ48Gc888wyuuuoq/O53v8Pf/vY3HDx4EF27dsUdd9yBHj16YPny5QBC5b2uu+46rFixAmPGjMHLL7+MZcuWJVzey263IycnBw0NDbIzgOE6pI0eP7IMoVpqCdXRNmiQZ75QRzt6/2BAQK079LfVcqGOdngbS1MdbVajgSWqjna2UROpnRyut6yPq6MdfYzoOtrnnV6Y9dJ1tKP3MQhe1PIXauTZ3X70tlyoox27rXQd7ehtpOpoR+6FRB3t6H3j62jHn/uYM1Rrj5Wpo20xaJAVrqPtCiDXqAOfRB3txqaaiZamOtp2jx+dzHr4FepoR7cxuo52+P5bdeLrCd+Hmrhl4Tra0c+Oyx9AvdOPbEOoTqLDG4Ajql2NTdcd/ZxkGbTIiaujHf88Sy0P19GOXhZTR7vpGW8pIzvd/bO905bvRWu2raXOlY7jNucYqezbkvc9kf4JAPv378cv//Y5cgqK0HDyKH7zg4EoKSlJSxvaOm3x2lujTdHnANCs86TrWMkepy3+domSSN8M14YOvxusFqqjLXdPWvJehetoh48dHqvFE66jHd4uW6aOdvS7XhsQcMrth0mngV7DgGMYBBTqaGcZNdBrWASCAgwSdbSFIOD0hsa+Zp24jnb0+LfB7UFXfVQd7aha1vF1tC2G0Hmj62ibmsbXSnW0s4yxtkr43HJ1tLOMGmRF1dGObm+9ywuNRgNrfB3tuPvdXDIea/nDH/4Q586dw7x583D69GkMHDgQb775ZkTw7Pjx42DZC473a6+9FuvXr8ecOXMwa9YsFBcXY9OmTWmpoR0mmUFJ1yZVv3h6SGwbLVUh3WHTUef0wjH6dLYkvE/nJM7QOYGNu6sdI4nzJXvuML1SPEfzSO7+p3ofkkHueZZbXiCxrHMre6+VaCuGZFugLd+L1mxbS50rHcdtzjFS2bctPxMEQWSOHBMZ1vHI3ZOWvFcFucaE6nH3yDNJ2hJqbesqu6Zl6ZkWG6YZJDmg7tnM+tiJknFDGwBmzJiBGTNmSK579913RctuueUW3HLLLS3cKoIgCIIgCIIgCIJInjZhaBMEQRAEQRDExUYgEBDlc1922WXQaGiITRAdHXoLEARBEARBEEQKhEXULJ0vAQA4zn2DZ+4fG8nvjjfEyQgniI5Dh+vpYe03u92e4ZYQRMchKysLDMOobkf9kyBan3T3T4fDAfvp4xB4Ho5z3+KLL1g4HI60tLWtc+TIkTZ37a3RpuhzAGjWedJ1rGSPk+p9OnLkCAReiJxH4AV88cUXkX2PHDmC5f/YCVNuF7jqz+LZR36Cvn37JnQN9O0kiLZLIv0z46rjrc0333yDnj17qm9IEETaUFMpDkP9kyBaH+qfBNE2ob5JEG2XRPpnhzO0BUHAyZMnE54lbAvY7Xb07NkTJ06cSOiFe7FD19v+SLS/Jdo/L/Z7Ru3PLNT+WKh/XpxtBi7Odl+MbQYy0+509832wsX6DF0M0L1NnET6W4cLHWdZFpdcckmmm5ES2dnZHeqhp+vteCTbPy/2e0btzyzU/uToCP3zYmwzcHG2+2JsM9A2230xj22bQ1v8LdoLdG/TA6u+CUEQBEEQBEEQBEEQiUKGNkEQBEEQBEEQBEGkETK0LwL0ej3mz58PvV6f6aa0CnS9hBoX+z2j9mcWan/L0tbbJ8XF2Gbg4mz3xdhm4OJtd3uEfouWg+5teulwYmgEQRAEQRAEQRAE0ZKQR5sgCIIgCIIgCIIg0ggZ2gRBEARBEARBEASRRsjQJgiCIAiCIAiCIIg0QoY2QRAEQRAEQRAEQaSRDmdoB4NB2O12kAYcQbQ9qH8SRNuF+idBtE2obxJE26TDGdqNjY3IyclBY2NjpptCEEQc1D8Jou1C/ZMg2ibUNwmibdLhDG2CIAiCIAiCIAiCaEnI0CYIgiAIgiAIgiCINEKGNkEQBEEQBEEQBEGkETK0CYIgCIIgCIIgCCKNkKFNEARBEARBEARBEGlEk+kGtEUaXD7UOHywe/zINmphNeuQY9JlullEG+OM3YM6pw92TwDZRg3yTDp0zTZIbtvg8uG80wcA8AQENLr9yDJqYdFxyDZqI8/XN3UuNHoCsLv9yDVpYdZr4PULzXoWo4+ZY9TCYtDgkjyTaHmWQQMGQEPctll6TaQ/5BhDbXJ4AhnrH9/WuWCPa3ePPFOrnZ9oe7TUO1uu77TWMVLZNx1tbg6ZPj/RPshk30v2G5Po+yeZMQNBEO0DMrTjOFnvxqP/+ALbq2siy4YVW7FiUn8U5Boz2DKiLXGu1gkfgCBC9SuDQcDn53Gu1onO+eaYbU/Xu/FNvQtmvRYNLj8sBg0avQHct34X+nbLwpIJ5fD5eHh4ATuqa9Al24CAEEQniw7bD51Dl2wDvAEBdS4/Pj56Htf2yUe92weLQf6D3uDyodHpA88wmLVpD3ZW10bWVdrysXRiOU6ed8EVEOANCHD5eAAG/PpfB/HWwXORbauKrVgyoQzeAA+vX4BfL8DpDcDHC/AFQv9ONnjg8fHo2gr941itE7M2Sl9Pr7j73hGgScFQ//r6vAtmPQePX4CW43GgsRG9O5nQrRnPZDqeteO1Tuxs6tPegACnj8eZEx5UXJqPQpVjpLLvsVon5mzcg+1Rba6yWbFkYlmr9A/qnx2Xk/VuNLj9EeM026hNecyUjuf4eK0TO6pr0DW6/xx3o8JmVex739Y64RUu1KIOAvAGBHxb60QPif2kxoxVxVasjBszHq91YqZE31g2sVz1XUAQxMULE+xg1e3tdjtycnLQ0NCA7OzsmHUNLh9mrN8d88IMU1VsxepbB3W4QWymaMsGxDm7By4fL23ATiiHgWNQ5wnA7gkgx6SBnuMwZ9Me7Ijbdu7YUtz+pw/Rt1sWnphYjhMNblSfc6JrtgFWiw5uH49V71THnGNk3854+HuXw+XnccbuRZ5Ji8JOppjZ9pP1bjy64QusmFCGR+I+7NHnXzmxHAfPOuANCDBoOZxpcKNbjhH3r9/VZHhf2HZgYR7W7TiKP9w6CM/tPCq67iUTypFn0jb7N1Lqn9/WufDIhi/kr2dS/w7l2Q7/ztsPt81Jwdboww0uH2qdXpxt9DZFWvDIMmjg8PrRJcuA/BTP+U2dC48qPGsrJvVX9YydqnPhRL0bq94+LDIYHvhOMXrmGtBd5hin6lw4XufGqm2HY94bVbZ8zBhZjMI8o2jfk01t3i7R5qpiK1Z+vxwFzewf1D8JKU7UOvFtg1vUB3vkGNEzSSPy2zoXHlN4jld8v1z1OTpZ58I3sn3PhktyjZJ94WydCy4+iNkS3/YlE8ph4hh0idqvweXDL//2OfoW5GBQz9zIt3TX8TocPGXHb24ZgByTDmfsHjz0t89l+8ZvfjCw2Z5tpb5JEETmII92FGcbvZJGNgBsP1yDs43eNmPstWfamgERbzDoWUb0IQaAHdW1mL1pD+aMKcGNv98OAJgx0obPj9fFDJbD2y7Zug9P3NwfU5//FF4hCC0HDO2TD29AgI5j8fi/vow5h0nH4bare2HJ6wdillfZrFg0oRSdmp7N8L2z+3jJD3v4/HYfj+kvfBpZVmHLx4wRNtxzXR/89j+HY7adVtkHqATWf3QMgwrzMK2iKGZQsXjrPiwYV9qi/cPuCShfjyeAHi129rZFg8sn6iMA8N7hGjy24QusyvCk4Kl6N949dA5dsvQx0RjDL+uM7mnsw41uP4QgsGZbddyAOh/zx5ei0e1P6T40egL48nQj1k4egi7Z+ojxcMbuwaMbvkCjJ6B6DC8viAb6AELfGAZYfFOp7L4eXsDqOCM7tG8tAAaLJoj3bfQGJI0TIPT9avSqt7k5UP/smJyud4ee9W3VIuN0wfhSnK53JxVZ4vAG8NnxeswYaRMZr+t2HIUjgedYre8tkul7XkFsZAOh53fOpj1YPrE8ZvmZRg9uG9oL63Ycxept1ZHlVbZ8TK0swplGD3JMOtQ5fYp9o87poxBygminkKEdRb3br7i+QWU90XwaXD7Me3UvBvTMxZRre8d8ZOe/uhe/bpohjt9HzXMWvU0yecZho/+zY3WYVlmEQT1z0S3bIBoAh9lRXQuGAbY8UAGHh0euSRv5AJt0XOQY4evqnW/C2slDwADINxux86tQqGi3bIPowzytskjkTQZCg4d5r+7DkgllcPv4iPHVqPK8Nnpi14eP++iNfSOGdrjNXbL0qLRZcWNpN3zxTT0eeGl3xOtdYcvH1IoiOP08WpJkr6c9U+PwiYzsMO8drkGNw5cxQ7vB5cPx8y689sVJkTe2j9UMk45LW9sEAAs375MYUNdi4eb9WDqxLKXjur1+vHTXUCzask/kEXvprqFo9PhUj+EJCIqGrycgyO/rV9i3ugYev3hfu4rxn8jkQHOg/tk2I7Fauk1eXsBv/v0lplUU4bFRfWMmpZ7895d4dFS/pI7n8ASw+rZBksbr6tsGwZHIJFdAUDTWvTJ9z6EyOe3wxX7jgkHguR1HZSfE5owNXbtd5dlXW08QxMULGdpRmHWc4nqTynqi+dQ6ffjRVYV4bmfsRzZszNU6Yw2IRLzf0duYdJxk+POwYisW3VSGepcXBp0GLMNAr2ExZ+NefHa8LrLP6m3V+Ns91yheQ6MngAa3H96AgGyDBjNG2vDyx8exYlJ/0XVVFVuxaHwptCyD4/VubN1zCjura/HyXUNFhnlhJ1PMvtFsP1yDb+vcMZNFWUatYjuzDNrIhECWQYNahxcAoOVYvHTXUOQYNdCwLBZu3Sf6Lf5w6yA82GRsh+/j3DEliudrLolcT0dBbWCWSaOm3u1X9MYumViWtoG+y8crGqQuX2qTP9YsAxZt2YcBhXmYEhe9sfLNg5g3Vv1ZVzNslQwGhycAq0WHlZP6S3rUpfa16JU/52aV9c2lo/fPthaJFWlTAvnDzcET4PHQdy+XnJSaN64EnkByfTDPpMP/vVuNgYV5mBrX99Z/eByzxqgb7k5PIOabHSb87XLKeMUbPX7JCfGwgR7/Xg0Ciu+fcF6m2rPf3vsGQXRkyNCOwqzToMKWLzmjWWHLh1lHt6ulCQhBSa9t+O8F4y6EfCUSPgsgZhs5r/B7h2swe9MeDCrMw+pt1aiw5eOxUX2xvboGM0baYvbJMig/B76AEBOSXVVsxT/uvRZ/2v4VdkvMsn9wpBZVNmtMPrZOw4gGCk/dPljxvPVuP/SaCxX7TDoOlbZ8Se97pS0fJh0Hu+fCzL5ey6FLlg7uJs+0XsNhwea9sr/FtMqiSNt2VteipcUeErmejkJ2Gx64uX08DiiEXbtTNH6lsKsYpKl6cT0BXjEkNBHjoTmGb5aRw4t3DsWirftE4bgv3jkUQYg9cgyg+P1iVFvcPDpy/0wlEqs12rR06z5MqeiNR0ZdHtM3lr62H8smlqelTTqOxdKt+2UnpWYnYBhHEwgK+Mk1vXGy3h2zvEeOAVf2ykNAkI8ECZNn0uHJtw7JfrsWj5eOdMk2ahUN9Oy4ySQ173p4vUHDKvYNg4Yq7RJEe4UsxyhyTVo8OLIYLCDK95sxshi5Jpp1TIZUQtYEISgburWzuhZ8lBpoIuGzAGK2GdQzV9YrvLO6Fo/e2BeDC/Ow63gdahp9kvtYVAaU7x+J8+QdrsHcV/di8fhS/OSaIiyR8BBf0ycf/gCPd391Hdz+kLK3WcdhUGEedh+vh8vHxxjR8Zh0HC7JM8IXEEKh6AwDDYAF40uxYPM+kRDbgvFl0AAYt2pnZHk4r/W/h87h8TcPYe3kIaLZ+ujZfrNOE7lXiebONYdErqejYDFoFJ9Bi8pkUEvi9gew/q6hWLhFbCSuv2soXN70eduzjRzW3zUUi0XetNC5hKD6oFyKYBBY/+ExWa/awzdernqMLB2H6/t1Qd/u2ZJCSVkKhqdZq8FjEkKGO6prsXjrPlGuKACwLDC1oggAYvYLRwOxLTyW78j9M9lIrNbgrMODnyt4ms86PGlpk58X8KOre8leu59Pvg8ataxo4jYIwKhNbLLGLwiSk9rhb5Vfxlg3aznZiX4GEPU7tSiR8Hp/UMDcsaVYvFXcN+aNK4U/xfcUQRBtn/b87UuaHJMOBTkGjCrvHpmZ1WtYnLV7UJBjyHiu1cVEqmF0Lp+ysRYdCtrgVs6TbHD7wcS5ceRys8J4fDy65xjw/UE94GsaIMTv4w7wuLOqDxgwopC8u6v64LPjdVg7eYjo4x4IBrFkqzifdGd1LZa+fgBP3DJQVNIkOkx794l6SY+VScdh3ZQr8cSbB2P2/eSX14FnWcwYacOjTblzFgMHpzcAPcfgP/tPxhwnnNe6ZGIpeudbRIOI6LB7qdl+NU9/c9EJPHiWk70endCyOeJtCac3gOmVfTC6vHukfI1By+F0gxvdc4yyoZGtQb5ZLyrxBISMxEVb9mGZhJGYKmatBrPi+gwQepYXb9mPZSnmaANQ9GgnAgNgzpgSzN20J+4YViyeUKboYXb4eEVDIT5XFAilfLz88fEYsUK9hsXuE/V4+ePjSXsWk6Uj989kIrFaCy3LYomCp3lOmp4HISgvkrn+o2N45Ma+SR1Px7Lgg8BrTWlUYcJinboEZoycXuXQcZdc6LhXRQQxbr9EPdU6lsVT71Vj/vhSBPggGt1+ZBm10HAMnn3vK9w33JbQvSEI4uKDDO0oGlw+zNq0V9JLOqzYmnEl34uFcEj31zVObJ5RAQ3HotEd8mo3uP0wK4gh5RiV729OVOiWSSWU36TjoOViP8p6DasYaho2LrUsAw3LosKWL/IkOz0B9OxkxKwxfREEE7k2hgnCoOHw9Htf4XdvXVDtDn/cPX75fNK+3bNFRjYQG6YdLq8VvRwA5ozphzXbxAqrZ/08PvnqLL5TUgCHjwfH8NCwLHrkGPH2/pNY8NohUTu2V9fA6RVw34u7sHbykJh1cmH3crP96aYmAOw7fgZX27rC4ePBRl3PR9VnUFpoRecWbUHbodHjh0nLRFTq7U3PYO98E87Z3XBkMEfb6eMVxQKdaQwddyr0qe3VNc0S6EtE5EgJAcDcTVKTAKEIl2UT5CcBnF4/1tw2GKcaYsNnC3IMWHPbYDglogLqnD789LreyDMbY56JIqsZdZd1Qp3ThyKrarNTpiP3TyGoEomVgSqqPhVPsy8FT7Mcd1zTG2u3HxFNKE2vSmxSKho+CKyOK2sJXPjmLZIJ+44mz6TDb/4jHzq+5CbpY3gDvGLKhssX2++8vIBplUVgII6CnFpZBG/TPXZ6/fjpcBvmbNorOu7iCWVpjfIhCKJtQYZ2FG1Zyfdiosbhw9c1Tvx5+lWSH5YlE+Rzw6wWHaqKrZK/Q1WxFVbLhf04lsH1fbugb4FEaOZJOziWER3vyzN2vHz3UMzfLP6Qvnz3UPx7/2k8/uYhVNrysWxCOZbdVAZXgMdrD1TC6QsZ5dk6Dm4+iGWvHYj5uC6fWIbX42bhgQsfdyWxsEE9c7Fux1FZD9bdw/rg1d3fomuWHnPGlEQG0WYdhzqXD7M27hUd0+XjseC1QyKD+uW7hkoa2WGc3gBeumsock1aLP9+ORZv3Q+Xj1cMu99RXdvi5YMCgQCuuLSLKKQ2PFhxqEQ4tCesJh0CRh3mxBlyVTYrlkwog6alE3IVcHgDioJC6UwxaI7gmBLBoIrIUQJ2k0tpEuBwDVwKkwD5Zj0YeNAtJzdmMq97jh4urx+dzHrRPrkmDVhGL1sDWAi2rEe5I/dPp8ozn4kIk2AQ2PDZCUk18H98dgI/v/6ytJyHY4C124/IltKal8CkVDSegLzy987q2oT0Eby8cui4V2aSId+sx0yFlI34aBynNwAGjCgK8ozdAwZMxHOebdBh2RsHJH+LJ948iJlJKrMTBHHx0KYM7ffeew9PPPEEPvvsM5w6dQobN27EhAkTIusdDgcee+wxbNq0CbW1tSgqKsKDDz6Ie++9Ny3nb8tKvhcTdo8fa24fLDKygQv1KB+/eQA4lkGd0we7J4BsowZ5Jh0MGhb3j7CJPAQVtnzcPyI2vMrAsXhsdF/M3xyb8xzOCdQywDmHFwvGl+Bcow9ZBg4mrQanGjy4/epemF7ZJ/Lh3VFdiwWb92HR+DIMK+6CM3YPvvi2HiUFOSEvvF4DXgiCF4IIAli4eW/MwMJq0aHskhzMlDB4gQtiYU/dPjjmgx8OhQ8IQVGom0nHYc6YfnjlnqHw+nn8edpVmPfqXpFhtWB8KUw6TqSwLCfGZNIr57nptSxuWb0zcvx/3HsNztq90Kvkxzm9LTuQzzXpZfNW527a2+Ie9bZEEBAZ2UBocDvn1b1YruAtbWmyjRo88+Mr8E2dK2Z5jxwjnvnxFcg2pu+z01JK22oGfCKTSmrltpTWcwDyLQbMlpyoLAMrYehrWQ7zt+xTrHPfknTk/mnRaxRLUqk9py0CA/z8+sslvbNzx5YCTHq87ILSpNThGghJnsal8h1RWx/aJrXQcadKea/4aJw8ow5r3qlGSUFOpA42wzA42eDBWwfOYM7o0OS6O8DjF9+9XFK3Yt64UriTVGYnCOLioU0Z2k6nEwMGDMC0adPw/e9/X7T+oYcewrZt2/DXv/4VvXv3xr///W/cd999KCgowPjx45t9/ras5HsxkW3QwssLih+sBrcfS17bL+l5efjv/8NNg3qI8gynPf8JtsyojHjDA8GgyDMdPv78zXuxYFwpfvm3z7Hq1sE4XuvEwJ55IkM1Ogd6R3Ut3H4eZxu9OGv3YJjNCjcvYNW22DC2N39WJVJVzjNpVYuLaFAAALUPSURBVOusn3N4cd+Lu0Tndfl49MwzYunrByLnic6HnrVxL2aMtGH38TrJGtoLNu+LUQAPwzLKKsRyy9moxPbt1TUIvn4AAwvzMKhnruL1tbQAV7I1TtszTj+vWCe2pWuaK2HRcKjjmEipujAVtnw8MLIYFk361KcZlWecTdGzrzYZlYiCtppxpbSeYRnMljFa58gYrU4/j9sUQoVb+pnoyP3TqOUUxfPmjWvZ0odS6FgWc1+VnuxevHUfFsuETyeL6qRUklElat+RRL4zuSqh43LXbk+yFnwgKCj2uUCTyJmWZTFH5rdYtCV9vwVBEG2PNmVojxo1CqNGjZJd//7772Py5MkYPnw4AODuu+/GM888g48//jgthrbVosN3+3XB5RIqsV+esseELRPyWC06HDzdqLiN3e2X9XYvGF8aUx4rmugPncuvLBjk4wU8ddtgfNvgRmG+CYtlhMiAC6Wqjp134b4Xd6HClo8Km1XSkHf5pFWVX7zzasVr7mzR45W7hyLbqAXHMmhwefH6g5V4+r9fgWWZmGvRciw0HBNRHVcK295eXYN7h18qWh+EtArxwVN2PDCiWLQ8JDZTDIMuNid9R3UtplYUYc+3DaiyWWME4MJUFVsVVZTTQbKDoPaM0xtQ9KBlUgzNJwRjStWFUcuPTBUlpe1UfXZGLadowCeifqzXsIrHUKoi4PDxiqJMkkarmijVDcmJUiVLR+6f7gCvmA+dCY+lNyAoaiWoCYMmSjoM42gMGlb+O2OzJlQKyxuQn+jfqXDt8eW74ol3tiQqBOdRaM+O6lp40vRbEATR9mhThrYa1157LTZv3oxp06ahoKAA7777Lg4dOoTf/va3svt4vV54vd7I33a7XXbbHJMO88aWYEfcC75HjgE/vOKSDp+fnWi5rhyTTv2DJbN+R3UtHh0lPyDMMmjxTZ0LjZ4APD5eMTzM4+Nh0nIQgsrhbTura/GL6y+LlKxaN+VK7Dpeh0ZPQPLjmG/RY6FEiKbHz0sqkJp0HOaO6QenN4Bapw9OH48zDW5cWdQJT/zrSzx6Q1/UOL0x4kcObwAGLRcRP1LLS9NxLJZPLMfi1/ZHQshZAC9/JFYh3nuyAUVWM8b2L4hZfsbugcfPw8+LzRNvQADDAPeNuBQCxGH9qaqmJtM/kx0EtWfyTDr89j+HZMW6Ft3U+irHYVx+ZSNRKTc5WRhAUWn74RvUy3BJwQuC7GTUAyOKwSdQy7fe5VOcBKh3yecsu7x+RVEmp1diXwaK3rVUCmlT/0yMYBCKquPzxra+R9vpDeCyLhas+fFgBPhgRNdDwzG4/6+70jYZp+OUDWMdl1xduYAgYMZIG4CgRJlVW0J1tNWuTW69WaV0p1liMlmxzzWhNsmUyiRUMn2TIIjMcVEZ2qtWrcLdd9+NSy65BBqNBizL4tlnn8WwYcNk91m+fDkWLlyY0PEbXD58W++RDHks6mxBtlHbYY3tZMt15Ri1ih+sgILiqVyub1WxFTqOwaMbvsDO6lps++V1WPNutexs8twxJRCCwLPbj2DKtcrqpw1uf4wXvcKWjyqbtESvWyZEc2Tfzpg7thRLXtsfuU8mHYfnplyJ1dsOY3tU/naVLR99OlvwoysLsfOrWgwszIE3IMiWNMlRGcQ2egN4fe8pbHmgEidqXeCDQeg4FtOrirD6nWrRIGBoUT4qLs1Hg8ffVIYn9Cp4+B//w5rbBouOr9ewKCvIwfQXPsW0yiKRUTP9hU+w4afXKrZRimT6ZyqDoPaKlxcUxbrkxH5aA5+fV6yjLaWYnSoWLYs5Y0owJ66EVqUtH0snlkOTok9bw7JgmCDGlHcXTUYxTGi9Gma9Bj9e+7Fkf3nwpd3YeJ98f7FmGWRLpC3eug9LJULHGaTf2KP+mRjBIBQ9qMnmKaeDXLMGz94xBO9/VYMuTSUAw5O8z94xBIE0iePxQQHzx5dI1k+fP74UfJI1okN9KygpMHZhvTKpetlZKNeCjz9zon1ObZIplUmoZPomQRCZ46IztD/88ENs3rwZvXr1wnvvvYf7778fBQUFuP766yX3mTlzJh566KHI33a7HT179pTcts7lw6p3Dsu+NJdOKOuQhna4XFe8Evh7h2vw2IYvJMuemXUclkwox5xNe0QfrKUTy/HjP30ke778LC22PzwcDh8fmYW36Dg4PB58/k19xLD2CwJ+MrS3ZAmcKwrz4BMECEIQt13dC5xCsqZJx6Fzll5U+zpb7mOtEC72f+8extIJZREDNs+sxe5jdfjseH3MIaI9jz5eAMswiiVNlk4oUxzEBoNBbD9cgwWv7sOAwlCY+Zs/r5I1jKc+/wn+ed+1GLdqZ+Q4FbZ8rJjUH9mm2I9+hS0fu0/Uo6R7Nlw+XjaEPZVZ+WT6JwdgyYQyzNm0V/RMLZlQjvY7jBdjd6vkRaqsb0nyLfLKvemuoy2Awco3D2BqRVGkdnPYe77yjQMpq/l6AgKmPh/qO2GRIwA42eDBktcOJDSpZNRyGFwonfJRqRJ+7lLJd44XPgTUjb1UKkxR/0wMNXX7VNXvm4OB5fBtg1s01RQEcMbuQY8cg9RuSaNhWDz+r4OSffA3/z6YdMqCJyDg/vW7RSU4AeD+9bvwl+nKKVpAqO8pfS/l+p7Dx+PHaz/Cykn9Y2rBn7V78aM/foC/xp070T5nUpmESkTzIZ5k+iZBEJnjojG03W43Zs2ahY0bN2LMmDEAgP79++Pzzz/Hr3/9a1lDW6/XQ68Xl0KRQklxcqeE4mRHIZWyZzUOH5a/vg/LJ5bD4ePR6PYjq8lgrnV60CvfhBN1btHxfjikB3QsJ1kiZsmEcpQXaPHyJycihqRBy8p6gb1+HjqOw/qPjuH2q3tJhreZdBzWTh6CJ948KApTu2lAAb7TtzPePngutpFMKC/0j//9KmYQfX2/Lnj0xr6YHVcPO174LMz26hp4AgK8AQE+BfG4ndW1cPsFTGnKOY2/1ikVReCaBMy2V9dgamXIe293+xUN4/icynA97PnjL4QdVzYd/8GXdmNVUw1vOVKZlU+mfzb6edz1wqdYc/tgzGqqzZ5l1CLAC/jJ2o/wbFzd7/aMWU2sS2V9S+L08Thn9+I/Dw2TDllNcx3tSVf0xLo4r1JzBcAc3oBi30mkRJk7wGPu2FIs3ir2jqkpDTd6/IrloqQmtdTalEpZNeqfiZHuPOV04BeC4FgWb+w5FfttK7bi598phj9NbnZPQLkPJlKOKxqXN4AVk/pLHm/FpP5w+RLoe35e8XvplnkvOLwB1Dh8shox8X0o0T5X7/QqvgvqnV70yjerXlc0yfRNgiAyx0VjaPv9fvj9frBxYUMcx0FIIGcnEdJRVqI9Ec7JrnX6InnL0SWpwkgN+nwBH+aOLcOsjfF1fvOxckK5rLd7xohi1RI14Q+njmMVvcCLx5fBJ4RUQV/86BgmV/QW5RfPHVuCp96pliiTVItFW/dj8U2l8Ab2Ytfx+sigV8sCf/zvV6J9+nbPxoItYvG0eMG1aByeAPQaFh6foDywdvvx4Eu7ZcNQf33LgMgxNVzI6E4lXG1HdS18fiFShsxq0eF3bx2Cy8dj94l6WXGnSlt+xOvQUtjdAZyoc2P86p2S6zPpxW1tTCpiXaYExLpaCr/fj2fvGIKdX9Wgq0TIaoPbk76TqeTGKtWuV0I2miXB9QAQFIDH/3UgRok63GdXvHFA0dOXbdTi/24fjJP1sZORPXIM+L/bB0vmQ2fa2OvI/dOs4/Cdvp3RryBH9P4+cLIhI2HzQQb43VuHxN+2pknzdOk4MGDS2gc7mXV48i15xfBExBQb3QHF7+ULU6+S3C/LoFH8Dsd/49TKB4bXG3Qa3P6nDyU95bc9+6GqkCpBEBcvbcrQdjgcqK6+YIgcPXoUn3/+OTp16oTCwkJcd911ePjhh2E0GtGrVy/897//xZ///Gc8+eSTaTl/lkp9V7X17QmpnGw5z6yUwZat12PZGwcwpaIIj0SFkzU4ffABonDPbIMG2QZNUiVq3H6VCAR/AAE+GBkEfHjkPO4e1ge/uP4yBIQgzHoOWo7FzH/ukTzG9sM1sHsCmDW6X0jQbNNerN5WjX/9vEoyP1ZJGXxndS2mVYjzxE16DofPNuKSTkZFYbdsk1bRwxatYJzfFF2QarhaWHk9fIxplUV4+Ia+ONPgwaTBPTBvU2yJtHA6wCV5JsnjpYu27MXNBD8bWYwx5d0jxqxBy+F0gxu2zpaMtquT2YAT9W7JSJPeVjN6Smg6pEq8xyqacO36VNBxyorhiQg8BQFsO3gO2+IjYpp4WMHQtmg5eIxa/Gn7EZE38pffvQwWiYmUdLS5OXTk/hkUgpg3thQ7vxILqd4y+BIEM5Ck7Qso6DgcroEvTUrX6e6DSqVBd1bXJqQ/YdJzit9LuWdRx7H4571DYNYbY1LXfnjFJRhbJu5DDJTLC4YT1rQci8u7ZUl6yits+dC2cN8kCCJztCnL8dNPP8WIESMif4fzTyZPnoznn38eL7/8MmbOnInbb78d58+fR69evbB06VLce++9aTl/nkmnqJ6Z18bysxNVAU/2eHwwiMffOIABPXMx5dreIpGxaM/ssGKrZNkzl8Dj4Rv7ot4VgNPDw6LnEAwCBXlGLNq6DwN65qJzlh7BYKgWricgYN+RWvS/JFd1dnzGSFvIs64SYeD28TDquEjZrMGFeTBpOQQEAU4vD5ePh1HLKc5gf1Pnxv5T9pga1o2egOQ+eSYtTDpOMn8SgKikSNjzeGlnC3QsK3vdDIBlE8tln81wDjXQlH+mC3k7z6uEq9U5vaJjAbFGu8vHY/fxOkwa1ANTnv8E1/e1Ylk4HcDjR5ZBiyyDpsWNbKBte3Fbm3qXD3lmHd7YdkoUMTJvXKmionVL4xOCypEmaSzvlaq6sBp1KorhdS4flOUVQ2GjSu8WpbYJCOI3//5S1hu5dILYG1nn9OHOyj6Sky/dc4yoc/qAzgnegBToyP3Twwuoc/okJ5eKrBZ0srS+4rqajoPa+kRJdx90yHxfI/0mgXx3k5bDyL6dUSIRYbD/ZIPss8hCgEFnlE1dY+MF5Bjl8oJhS7vW4VXsm+edXvTJ8AQpQRAtQ5sytIcPH46ggmJLt27d8Nxzz7XY+btmG7B0Ypkox7bKlo+lE8tiRHEyTbIq4Mkc7/kpVyrWBNU05QMPK7Zi5aT+ksa9luEwd1P8fbRi8YQyTLu2CFoNi8ffPBhjAFYVW1F+Sa7ibDYfDOK7/brihpJu0GmV69VkG7SocXhjPMUmHRf5e2d1LZ6fcqWiJ1nDMCJPtcWgkdynymaV9PiHiTZgw2WCGABdsvTwCcp1Np0+HpMreiOIIAwaBvPHlYnE4iAIGNirE9x+HoMK86DTcIrhai9ME4fPVUYZ7eF2Tq0ownlnyHB762AN3jr4Lipt+Vg+sRw9k8wraw4MoFhyKYXqRRctuSYdHv/XQVHEyBm7B0/+58sWr5mshFtF60IuPzIV1NIVUk1nMOmUFcP/mYAYWpbMeyL8blEK5Xb7lb2Rbr/Yq2fSc2jw+PH6nlOx71VbPqZWFrW4R5lhVPpnO+6gWo5VrB2fzsmlRFET2ErX85Bo+HSiqPabBCILWQaYNboE8zfvFVUjWDC+DHLaqKyEPgwQ+gbP2bQHy+OEHE1aDi/FCaOG3xMvfXQM88eFJsSyjNpQZRCZvmlpx6XvCKKj06YM7bZAYb4Zv/7BQNQ5fbB7Asg2aJBn1rUpIzsVFfAwJ+vdaHD7YXf7kWPUItuohVnHxRwvx6TFr//9peSggWUYzB7dD2/+rArZRi26Nxn10d71TmadyMgGQkJdczftxaKbSjHv1b3iGsCHayIGnRzHakNhzVXFViy+qQzX9+2Ctw6eFW1XZbNCyzHINenw5H++jHwItRwLDcdgUGEedh+vBx8MKnqSw7mV0Zi0HP752QlMqyjCY3FGzobPTkjmYlfZ8tE5S4+nbh8cKVXi9vMYvWoHBhfmYsmEclgtOtQ4pK/f6Q3lnC25qR8G97LKzrg/8eZ+TK28FMOKO8Oo5RTD1eKVVyts+Xj4hr6oc/oi7QwbFn+755qYc80bV4oGtw890XqGdo3TC6NGwIomj3r0JMN5hws1Ti96dxCvQCAo4BffvVyyhNa8caUIJFlSJ53IRXRE1qdR60LLKodLaxMoBSSFXsMqKoZHT5rJYdFyeF7h3bJCQX09FW+kSafBix8ei8kJD3vxXvzwGOaNa9na6rUOn2L/rHX40Fu6YuJFj1oaUzonlxIlHNkk1zeUVO+TQTV8OskJFoOWU/wmS5W2iycIYMXr+yW1Xla8cQBzxkhXI3CoqP074t5ttU4vHrmxHxZv3Scy6OeNK0Wt04vCfDMsWg5rd4ivaXtTaL3Su4AgiIsbMrQl6JptaFOGdTypqIADwLFap6g2a9hA+7rGGVmm4RjZj832wzX4tt6N6S98ikpbPpZNLIeOY/FwlKG+dvIQxRq/HoXcsXijNp7wAHf74RrMfXUvHrnhcngDvCgCYWplb9S7/DAbNLI533+4dRA4Vv5ad1TXYlplH9Gg2hPg8bPrL8eirWIjZ+7YUjS6faJz/eqGvuDYC+0Plwly+fjIbPnKSf1l1U4N2lBI+pW9xUZ2uK3hGfdTdi8c3gB6ZOuxULYmaCn0LBMpadY734zPT9Th1mc/FBlKFU353C/dNTTGI/7K3degNbGatQAjr0iPNNWFvRjQMCzmbN4rW0IrEx60MNkqHie19cmQSIh3Knh5ZZX/RPJEnX5eUh8BaIpSUTC+UvFGegI8blWIREpW/TlZ8swcWEYv2z+FYPsVQ2uLQqo6lsGMETYA4md4xohi6BRKXiZDEEHFPqgUpSiF0xtQju5KIBTdxwuKUXk+mf4bX4kjnnjhV1OCImdOhYkYtXcBQRAXN2RoX4TYVeoVS6mAn6x3i4xs4IKBtub2wRG12DqnH1aLTlTH8ozdg0c3fBExhndU12LWxj2YMdIWY/irGctKH7Ndx+sSykUGQsb23VV98PCNfTGzqZxMtlELPy/g/hd3YcH4Uhh08rPjAHDPsEsV2+rx89h/yh4zY69lWcx9VdrIWbw1ZOT8495rcLbRiyKrGa/tOYVbn/0Qr9wzVNaQ3lFdK1vzt8pmRVhRJpEZ9x8880HoGh8ejhWvy6keH8TCsSWR39egZdGnswWDCnNFg6UHRhRDyzK49dkPI8vlcvNbEi7JsL72jCegnGrgSZPQUSpYtByqiq2Sk4FVxVZJIa9UMek1uOevn2HlpP6i6JJHN3yRUM1dKRo9yqrFf5ZIu4jHrpJr2qiQa5qSN1JFgX3e2NQU2BNFy2o6bP/MtOK7FAyAghwjxpZ3j3mGz9o96JFrSFuqjUmrUYzwevTG5GrZO1UmJRIpDyik2Bek1PyjiRd+1XIspl1biMu6WODw8WAZHhqWxWVdLJh2bWFE5KzBpTxma1Ax8AmCuHghQ/siJDuFsk0Nbr/iwHxWlOolxwDr7xqKT78+DyBkOLv9PM7aPVh/11CcqffE7PvoqH6YMdKGIYV5yDFpoVcZSOeYtLJh0i9/fBz/uPcazNkUG1oenol+8KXdkWU984zonW9CozeAAC8giFDY6ldnG/H8tCvxr32n0T3XqBjS99go5VxWvYbFuh1H8YdbB4HBBSNGyVPlCQi4Y93HcPl4bH94OD4/XgeXj4fDozKA8AZEg+sKWz7uG2FDjcMLk45Tn3GPWm/38Xjr4Dm8JaN6/MvvXY5xq0KTKyYdh+emDMHY/gWiQVnnbD3c/IW2K+XmtyTJhvW1ZxqT9Ly0Jo1+HncP6wMAMcZ2VbEV9wzrg8Y0em+MWharbxuMVdsOi/rN6tsGw6hNLXRcqnwXExUDm0jud5ZBo1iiS8n40rMMHhxpkxRPurSzBXoJb6QQVFZ/bmnh647cPw0aFtf364K+3bNFkyoHT9lhSCDVIN3YfTx+9tJurPnxYAT4YKSueWG+CXe+8Cl+f+sg9EjDebyCcoSXV0jud882KpfYSqi0Xop9waJSqcMSF2kiBH0Y3f8S2SiOgBASHM0yalXKhlGONkG0V8jQvgixWnQYVmzFexIeIzlPYzIGWtdcA87ZvbIKqt07GWLUtd3eAA6ebMDAnrn49b9D+dAVtnzsjqo97eMFdMkywKhlUef04S/TroJRx8ETENDgCnmiNRyDw6cbsWTr/ogX1qzXwOkNRDxJ4XOadBzWTbkSJxs8WLXtsEhUrbfVjOv7dUWd06/4gQvwQUXP0cHTdqy6dRC6ZRvw4HeKMXtMiWromsMbiLSzxu6K1Ay3GJQnIIx6jaSoyvQXPsFTtw8OlflSm3GPWu9QUWeNXu/y8Zj6/KeYO7YEBblG1Lv8kfb+8JkP8JfpV2PTfdciyxCaJGltIxsIPcPKHsKO4xVoywO3Bpcfv3jlc6yc1B+P3Hh5UzilBmftHvz8lc/x1O2D03YuDcNgTZyRDVzQk1g+IbUQej3H4vmpV6L6rCNmeUGOAc9PvRL6BMrxGDgWeg2LrRLv0QdGFMOgcAwdgG7ZRqzeVi0SlFwysQxSvU+1v6eowJ4oHbl/ahgGj43qh4Vx4ltVtnzMH18WERBtTRo9fnxT78arn5+M/B4OH49dx+vwTb07bb+HwAOLX9snGcmwaOs+zE0yksKo4fDclCuxatth0b18bsqVCeWWp9oXnD5v5Hsdn261dGI5nD4vEKVLoufEqRKAOIrDqGGxdvIQrH6nWhTKvnbyEBgzMBFDEETrQIb2RUiOSYcVk/rjsQ1fxBjbSp5GNQMt26iNGO8sGMXyPLNH94tR1zbrNehbkBMJ1dp9vB5rbhsMg5aV/LBMrSjC9Bc+xeDCXExp8lK7fHzTDHgJ3j9yHn0LcgCEaoEaJfIVp1UW4fPjddj8v5OSomoA8OgNfeH28YoKpg53AHdV9QELJiZcPayQDgQxe1NsmPhrD1Qq3ktLlMrqKUcAa/67F8snlkMAFGfLz9o9snU/WZbBc9uPYMXE8oRn3JMNZ3T5eMz85x6snTxEFOLu9AYwpHcnxeO1NDkmrXKtcZVnvD2RpVMejGap5Pi2JDkmLVZM6o91caGbFbZ8rJjUP62/k8PHK6pzp+pF9QsCgkFIqgTPGFkMv6Aemh8EFJWolyjk0TuEIOa9uldSUHLOpr1YNL5UVKlLtb8nqf6cLB25fwYQxMLNUr9XLRZu3odFEuXYWppsY+v8Hqp1tJOMpOCDQayOmzwHQvcSDINF49XvZZaKDoRcRAof5DB30xdYPrF/qIRlUxSARcdh1sYv8Njo2EmDRKM4ggzwlMy7gGUYLLqp9Z8PgiBaBzK0L1IKco1Ydesg1Dh8kXrGSp7GHKNW0UDLMWqx6tZBqHX6VMvzBISQUnfY2D1t98SUwHL5ePzvm3p8+vV52UFmWJmbAYMX77wadS4/dh2vg8Pjx//dPjgUspyljxzvu/264sbSbpjy3MeocfhwTZ+QQJfSIHvWaEZVVXzxhDKwAEaVd8OUit4xIdMcA8zcKM7F9vFCwiqreg0bKYlltejw4p1DJetaL55Qjluefl/yWgDAatZjZ3UtFm7ZqzjjvnDL3sgyNTVYOS2cLIMWL901NCbXNdUySenErKJG255zQOPxqw1GMzhwa83fSc1Lm6oXV8Ox8vcXTEKGk0tFidqlEELvVRCM3H64RlIHw6BhFd/xLR2+3JH7p1epHFt1DbwS5dhaGpPK77EsTb9Huutoe3mVZz8BIUI9p1yNQC4ixaTlIIBB1RPvSu4XX3/b7lbWswlHDaTSnwmCaB9kfvRMpEyOKfEQ3oJcI5ZOLMfsjdIGWrj2ttPHo1amxFQYV5Mhft9wG24efAlufvp9LIrzzgwqzMXv3josuf/O6lpMa1Ip3V5dgykVvTH9hU9RYcvHD6+4BLzgxiWdTFj55sGYD2VVsRUbfnotXH4edpcfvErSodMbAMMoq4r7AgJWvnkQJQU5EaV5hmHwbYMH39S7JQetDS5/wiqru0/URwa/NQ5fjEKp08sjx6iFQcPijb0ncXm3LNRInK/KZgUD4IWpVyEgCDjd4MbPr78Ms8doQoI2TGiQu3DLXrx18IJXnmGV1WDlohkbPf6IR7vSlo+X774GDJP5gQApt17Ao1Jn2ZOBgX2Y1vydLAblfM5URagU7291YvdXTdRJSYk6lQkEHoLsJNySieUIJJkrmywduX8qCdsBQGMLh+1L4VLxtqqV4UsUi16lDyYZSZFMypMciVQjKJLYTwNg2YRyvP9VDbpEaSOcaXCjwmYFEzfmyDVr8eKdQyXz01+8cyjAhLZPpVwfQRDtAzK0LwKia1RnG7WwmlPLke2Vb8bjNw9Ag9sf8YLnGLURIztcn3u2TI3JMJomV6hew4BlgOemXgm3T8C6KVdGPq5GrfKjFT2DG/7/ziYhsa9rXQgIQUyrKMLtV/eK+WjP3rgHAwvzMKhnbqTdcpj1GpxzeBW3cXgC+PHQXli346goBLfSJl309dPjddh/skEyn3r9R8cwe3RJpGyWScvheyVdMfrbhoioEcMw2PuNHQMLc/DOl2cwuLATVm/7KkZwLUylLR+TK3rjJ+s+wopJ/UUeigpbPqZXFqEo3wxvIHYQYNJo8NJHxyTb+dJHx7BAoq5uvLL7jupaLNi8N23ej+bQqDIYUVvfnkjHYLSlUDu3M41t03PKuY+J5FJL4VBRDE/EU64m6qQU3mpWMU6k1utZDgu27JOsMrB4yz7J/p5OOnL/NEuUW4tZn4FUjpaK9ojHrOOwbvKVWPXOYVEfXDf5yqSvPZVnPx6jToPH/hJbjSCsE6FUjYBhGZxu8OA1iZSRPp0t6JETW/bVpJGvhLF4675IFIeagFsiAm8EQVycUO9u45ysd+PRqBrVQCgXe8Wk/mAA1Lv9sLv9yDFqkR1lNMtRkGuU3SZcn1vHsvLleWxW7KiugUnHId+ix+yNe2I8P+H8L7VQ4+ja1NH/DwgCBvXKwydHz0eWMQgZnKPKuuL4eTd65plwxu5RDY1mAHS26BXbkWvS4XdvHZIMEf3pcJvkPmEV8hd2fi0aWDwwohgcg4hH2KTjsPq2QXhjzylRre+uOXqUdMuJCMtt3PUNFo0vg9PPw+UNwKLX4HTTwOBHVxUqhgE++J1iDCjMC9X4bRpgO/087rimd4zqMcMw6JFjwJW98kQeJilld6DJG9UGFIPVnqm2EN7eWpj0nKIRJ1VnubVQ+x3SWerIxwvyuY9gMHdscqWFwlgMnGJ+qyWB+2vWcnh+ypWoPichqDblSpgVRJ0YACP7dkZJQY7o991/skGyNJPDx+Ptg+fwtkyVgV/d0LJ9uCP3zyy9Btf37YK+BRKq4yftyGrh/HgppLRNklmfKEEAa96RESQEg6UTkxMkTOXZj4djgTVN1QjiDeY1tw2G3PybTwjiDxLiiturayEAWBwXuZdojnaeWaeY1pFnbn1xUYIgWof2++VrB4Q9zPEG73uHa/Dohi8wqqwbZm28kJMbDgPvlW+OP1RChOtzu3w+LLmpDHNe3SsqzzNvbAmO1Dix4d5rMG+TWPwl/NGZO6ZE0QgOe03jPagcy6DO6RMZppW2fEypKMIv//Y/uHw8KpqE05TCw8CEZorl6nJX2azwC/Ihou8fqZWccHD5eLz80XHcP/LSmLzuM3YPGOaCxx8I5aKv3SE2kMMf7kdv7AujLjQg75Ktx/w4QZ2wgJSGYWSF0nZU12LO2BJ8frwuZpsN9w5F91wj/rT9iMjInzuuFA6PD28/dB0aPX5oORZv7Dsdo+weTVtQDDbrOMXfMhNeo0xh1obKsX11zhmzvCDHgOemDFE04loaLaecK6xN0csshRCEYog3n2JJK7NO0+z8Vi/PQwgGJas3PDCyGF5e3vBlWODRG/th0dZ9MX06XDIpHJIajVofbek+3JH7Jy8EMXNMX3x05HzM8h45Bkwc1EM1zaklMGiUS1UZNOn5PZxKgoTVNUlP0qby7Mdj0HBYvW2/rIbFUplqBGr6NO64yelE+1zXbAOWTSzHLInUvWUTyyNpawRBtD/I0G7DhD3MUnx2rA4P33B5JEQ5POO7aMs+LJ5QLuu1PmP3oM7pg90TQLZRgzyTLvKSD9fnzjLoUONwY9bovgiCgcPtR7ZJi93H6nDTmp1w+XisnTxE9uO6s7oWYKBoBD/40m5U2vIxb1wpbnv2w8h6Hcdi1duHRcfeUV2LIC6IqO2sroWeY1VDoxs8ftw34lIICErWp1YKY1234yi2zKjE/M1742bFrbhtaCE+OnoeZU3q6ABwssGDJa8dwKb7ro38LkVWM9btOIoZI22Snkctx+L1Pafg54PYfbxOVjzuV9+7XLadQEiUZWpFEe4bYUNACMIfEGDNMmCWRFjb9upaLNqyL2ZS5sApu6whD0jXZm9teCGIGSNtAIKiiYMZI20ZGcxmCo5lEAxC2ogbUQxOTumuFQjwXixVyHP0BDwALGk5V0uVtHL5eew+Xi/bb5WEzMJwDIs/bj8S834KH+OP732FuWPkyx6ZNBxmJhCSGk0ilSVako7cPwUAZ2VKYva2mjNiSJkZKApn6tP0ezSqlQ5NcoLHrBKOvSKBSS6Xn8dnKfRftbz1eF2FZPpcYb4Zv/nBwAvjL4MGeWYdGdkE0c4hQ7sNY5f5QJl0obDGJ948KPJ+Tq0ogt3jRwGMIqM6S8vhZKMHBi0HXggiwAdxtMYBv5/HJfnmSH3uYBDINevx0ZHz6JptQCezDku37o85lzcg4Pq+VswfVwaHj4fdHcoft+hColzxRrCPF9DZoodOw+K8w4c/T7sK7x46hxVvHMCPriqMGHkeBXXOndW1mF7ZJ/L3PX/5DH+8YwjmbNojmvleOrEc9S4vwLKY/sKnmFZZJFmfeuN918ref5ePBx8UMLWiCLNGl6DBHaotzYLBLc98IPtRrndfEBP72z1DFUNQ3b4AnvnvEay6dZCsobuzuhaPjVI2nCwGLX7/9uHIJIbLx+ONn1UlLIZj1il7PyxtwBvlDvBw+XiMKu8eEyJ/xu6By8fDHch8eHtrERCCKZeOamlMWgNONXrxeny6RLEVtq5Z6J6VvoFllooYWqohuw6PX7ksYALGgyfA47are0keY2pFETwKz6vLz0v2RaCp70oYCmatch9u6SiHjtw/g0EolsRcNL71+6OfZfDUO4cxf3wpAnwwUqpKw4WWzxhRnJbzZKkYm8lO0jp8ys9+IiX7HJ6AYv+Vm2DPVikLFr8+WXXzrtkGMqwJooNBhnYbJlvmAzWtskg2rBEIhW0frXHEhHZbLTq8cs9QHD7jiIhyuf08zjS40TlLj3N2DzpnG7BiUn8EAgLONnoj4dtS3uvuFg3mjC0TzTxX2vKxZEI5vDyPR27sh8US4V9hL3ZNk7r5Q9+9HFcU5uF0g0fVQ6XhLhich846cLzWJTmQuOuFT/HbHw1ErcMHl4+XNWK1Kh9KTZOhHl1beu3kIYoz39Gz2PlmPX7/tnT+GhAyiFw+Hn6VGNcGl1+2nZW2fJi0LAYV5sWEficTSioIQSyZUIYPvqoVeSGvvdTaJrxRDBj85cOvRQrxJxs8eOvAGTxyQ98Mt7D1SIfHtaXwB4N4fscRTKkowiNNQkThkjfP7zyKX92gHJ2RDBYdhxemDgEfDIkkhc9VacvHdcWpTxDlm/X47VvK/VYNBozqe1oOVRVrifVOP4+7qvpgTHl3UR8uyDW2uOp3R+6fngCPL083Yu3kIZJlnpQmVVoKN8/jp8NtmLNpr+gbvXhCGdwKqQvJYNZx+E7fzugnkVN94GRD0ikDcg6GRNcDQJ5Jh9++dSjpiQ+TlpPXpym2isp7nXcqq5ufl1E3Jwii40CGdhtATlXc0jRgjJ/dja5ZHc/OphDr+I/rr28eoBjapstl0eAK1dBmWcSEb0vVeLRmm2TDu+Zs2oO5Y0uw8s0Dkgq48V7s4+dduO/FXagqtmJWobJ4Uac4tXUNx+C7T74n2q7KFvoongsGFT1etQ6v8ofSGVIt332iPmLoRv8/nvAs9pYHKuDw8AgIgmLOl6+pJmiPPOVZ7lyTLlSSC2JV8qUTy/HA+l342fWX4ZV7hkYGeWrhw9GehjyzDofPOiS9kMVds1DcJT2hvs2CgaKHMCGVnHaC28cremw8GRSv8wsCHrz+csmSN3PHlsIvpK/0WEAIIs9swPzNYmNiwfgyBFKcIPLyyv02kVq+QUDxGEotS0VYzOnxq2oytCgduH/6/LximSeXr/U1LgycfPrB3E3pqyRR5/RiztgSzNu0V1S5Y9GEMtS5vChMQjdGzQOeiIfcr/LdlXsHuQM87r2uD0aXd0eXLP2FySq7B706GUVRGQYthwfXfSwZMffgS7vx93uvUW0rQRDtGzK0M0SDy4dapw9BAAtejRXACquKe/w8plQUKQ7YpHB6A6LtL+lkwrLX98eEcnfJMkDLMTjv8MHl5/FtgwceHw+zXoN7hl2KeU2eYrvbj60PVEZm52scPlW1TSEYCr/8wRWXwBkVWl7cxYJfHanBoJ65ke3DquPbD9fAoFEWUopWKK8qtiLPpBUZvSP7dsbD3+sLLx/6SP793mvwvxP1eCDK2xsu/5Nl1GLyc5/Ifig3/DQUWh5WGo//v9ws9qT/+wAA8A+VD23YM2XUcorGu0nHoSDHgCUTyuD2CzHl2Xz+AH79g4H49OuQEE84WuHSzmbFe2mK8zT8/i1xbnx4Zn910/UC6Ss3lywMoOghnDdW3kPY3sg36/DUu9WS+b/rPzqm6C1tabQMizlb90oO8Bdv3SdS7m0ODMtg2ZZ9kvdh+ev7MS/FklZ2lbxTtfVA6D2c6nqjimfNKBEGnogmQ0vSkftnvkWPBTLP4eNvHsD8Fi6tJoXTx+N4rQubZ1RAw7FobPoG+3kB97+4K22VJLJNodSy6KoX4Wtf+toB1XKh8VjSkMaUavnDRncAAT4YygWIJhiEnw+KStQZdRwGFUo7Pips+ZL9lCCIjgUZ2hkgXLJrQM9cSQGs9w7X4LENX+Dn1xfjwZd2i4zA7jlGWS/tq7u/RWeLPuJRDYevBQTlfMGJT72PwYW5mFJRhIf+9jn+NPlKLNgsPTt/+58+jBloSrUFwQAev3mgaEa90paPx28eiNP1LswYacM1ffLBC8FIDW4vL0hOLlQ0qY4HmmaiK2z5WDi+FD945gP86KrCyP0xaTnkmnVY8vp+0f5/uHVQJLQ6XHpk2cQyDI77UIav59k7hsDpDWD9XVfj/a9q8VhTma37h9ug4Rg8dmM/eAM8zjm80HGxs9hrJw8BEApnVfKoh2uC1jpUQtCcXtz89IeoKrZi0fhSOBAa2ArBIMwsi1N2tyha4R/3DsWC8aVYsHmfyAu+YHxZTOevcfgk1YKBkLFd4/Ahx6RTLDenVlquuQhBZQ9hG4hubzX8gqDYn9PpNU4WT0Dem7SjuhYeiQiZVHH7ld9r8SrBiZIOr5paGTOl9YIQxPxxJZJ9d/64UggSD7tLZfJTTeipuXTk/ulSeQ4zkcrh8fnx5+lXSYaO/3n6VWhwedNyHj8v4EdK76IEoj+icQV4zB1bisVbxc/+vHGlcCUQhp9qLe48sw4+XsBWici/GSNsojJcWpbBQtlvbCm0cVFlmZqkJggic5Ch3cpEl+yacm1v2RDw9w7XYNbofpL5xa8/UIl1k6/EqncOx6wbVdYVf5l+NWZtEhu3SyeUK3obwmreQQBrbh8sCoEDLnikVk7qH/pIWHR4/Ob+6JZjhN3lh6XJqH/kH//Dxp9eqxhavnxiOXYf/0r0Yb7uss6SkwthI/bP067C2slDsPtEPU7Wu1Hj8MUcY8ZIm6J6d/g6gVDpEbefj1FmDQvNxQ8aqmxWvHTXUPh4HhadFgu37sM2iXq1FU1lU57beTSUqx4IKbSvfqdadK1rJw8BywB/u3sosoxaTH1e2bMenoxY/Np+lBTkYPW26kh5kDUSQjz3/nUX/nbPNZgx0oZHm3JlLQYOTm8Aeo6JieRUy3tr9PgVy809tuELrLp1UIsOGlwqHkK19e2JYLDteg9btcyUyn1I1bOfpVKqKisBr1pzxMkEAMtel069Wfb6fsyRuK5Ml/fq0P2zhZ7D5mC1GGS/wXM37ZVUrk8FQUjvtdtdAfz0xc+wclL/mO/WWbsXtz37If7v9itUj8EwUIwQk8uqYqAsaielzbBCpp+ueOMg5ke9hzM5SU0QROYgQ7uViS7ZJZX7HA3HMhhWbMV7cYaNEAT+9skxPHpj35iQMKOWxeNvHpAMXzvZ4Fb0NkyL8qZqOFbRMzJ7TAmydRxevnso5st4vdVCy51+HtMqinD71b1iPLzx4cxASFAnjFHH4eanP4DVosOEgT1EnnuOla83HX2dYRo8AXQ2e7F8YjkcPh4MgKWv7Re1fXt1DRZt2Y8F40vBMsDdVZfCG+e5C5dW0nEM7qzsg9f2nMTosu54SubDzYLBoptK8YM/foh3fnmdyLMefU9P1rsx/YVPI14CTdM92VFdixN1bsnBfI3Dh8nrPsb6O6+Gw8eDZXho2FBExHmnB1lGfWRbOeG9MFkGrWK5ufeivN4thdmggdWiw8pJ/SUFh8wqHsT2RFv2HmYZtMpK4GksFdecPGglAkIQ98uUBby/qYSeGk6Z1J9wdI6SOJnbz2PbwXOSk3kA8MvvifdNhxe+OXTk/hkEFMUJM9Ed1b7Biah3J0K6+6BJz6HG4YuIj0qtTwSlCDG5Nrn8ynW04yMTHD4ebx08h7dk+2lI+DHTk9QEQWSO9vvla6PYPf7IILRnJyOeun1wrFpw1MePYxmsmNQfj234IsbY1mqAB66/TBQSVmWzYk6TCFm897TyUqvi4Dfa6G90+xW3bfT4YdZxIiMbuOD1/vn1lyneh2/r3DEf0nBot0nLKXqAjVoOVosOL945VFIA6WffUT5v/ORGtkGDJa99hb4F2RjUMxeds/Sy5cW2V9fg61onLrWawTBBjCnvHuN9PmP3gGFC9WQDwSDe/6oW1/frqni8sBjakRqnYrhcuNa4VF3tBpl8UZOOw/zxpZi1cY9IHGlqZRF02gvd32rRKeaEWi06HKlxSp4nTEt7zIwcKzu58/LdQ6Fl2rHaUhxq9aHV8oNbEqOGxXNTrsSqbYdF4kjPTbkSRg2rsHdyNCcPWnE/P49pMmUBp73wSUIiR3aXXzE65/mpVyq0W9kIiq/nC6Qnt7U5dOT+6fYpl5Ny+1q/P9oT+I6ng3T3QTW9koTynoPA+qjyotF9b/1Hx2QV8KX6ldJ6Na2G8D3O9CQ1QRCZgwztViA6L8eklw5Njs8hHlZsRX5T/s6qWwehxuGLCGBpOUZSTXR7dQ0Wb92HgYV5MZ6QndW1mDHCpnheLctGZuNzTFo8dftgrN1+RBQ+/dTtg5Ft1MKpMls+e0xyj1b4WCsmlit6gJdMLMPKSf1lQ9t/Olw5SqBXJ1NE2O2fu74BywC3DS3EcztC9+Wp2wcr7u8NCHD6eNy/frfIcwMA96/fhRemXRUJpTvvUFb6Da83ajnc/qcPI+FyLi8Pk/5CuFxN1HHi62rrZQwXuTJw26trIQCYGxdefP8IG4SgtAcPSMzr3ZIIgOzkzoLN6RXZautkp6BK3WqwwOpth0UG3/bqWoBhsHhC+oShUlHnTgSXj1csC+hOwBuYZQx59gf1zI15TwzqmQuTjlPsL2r1fLMk1rsCPOaNK8WiLanntjYHARDpegAdo3/mmnT4zX/ky0ll4tpzTVqsvm0Q1u2IT4PKx+rbBiFHpf51oqS7D/p5ATOavjniPOnihHK+WRaYck0RdFpGVPbvql6dwMrM9SWrq5CdYA3xRFKzCIJon5Ch3cLE5+WsmzJENVf6ixP1WDmpf2SGM8cUK5hx4JRd0cidGhceDYQGjnLnZQDMH1+K3dvrsHpbNV57oBJr40rEACFDHgywYkIZvmnwiM4RPXvOALI5jhW2fOw+US9avrO6Fk4fr+gBdngD6JKtl73+D47UynpmK2z5eGPf6Uhu85IJ5eCDPGasD3mdplQUoXOWXuKoF9BrWDh9AayY1B/r4u5nhS0/ohYfXq4mguMOCKiw5cNi0KBvt6yIl/+p2wfjh3/cJbtfnevCh3n3iXrJa1YtAxcVP1fj8GGaTI74tOc/wZYZlbBadJKpDEAo18xqadkZ+dYU2WrrGFXyfzOpduv1C/J9+HANvP70/U46DauYS61L0XtuSVFMKZoshfSal+8eCgMn3za9ynVJTa7ZXX64fYKkJsPpeg9M+vRFEkjhCQiSzyPQ/vunX6UcXLKCYOnArOXw3I6j0hNeYLB8YnqMfx3HKnqgdQrPuRQMC1iz9HhgpA2PNT3HWQYNHF4/rFk6CPGK4BJYNBwK8oyyZf9MnHR0RbL9zqwSRRKuIZ7pSWqCIDIHGdotwMl6Nxrc/lCItV6DKdf2xoFT9lB4kFGr+EGeO6YEd1UWxRjW8UqVaqHdUrnfDMMoGinnGr2R9UFAcaDs8POimdx4EbHw30EEYz5CVTYrJlf0xoMv7ZY8fqNKWQ63jwevkB+5bsdRbJ5RiQWb98acN5ybFT5vWJRt6YQyXFGYFzFIZ4y0KQ4adp+ox7j+3fH7tw8nJJiiVHO70paPnp2MofC25z/Bikn9I22T81KHCfAX7sGBkw1YMqEMczbtlQ1PkyI6/LjB7VP04DW4/bi0i0UylWFYsTVmYqilyLTYU1vCpZL/mwmV4zB2t3Iftqv08WQICALmj5dR5x5fEqlSkCwsEyoTWFKQI3rH7j/ZICumFI9SBMYKBTEqHy9g7rgSLN6yT6Imdkkk5SSafIsec18Vl1UDQs9FS3tVVUsqtWMxNLXvltr6lsDpV560VtIISIY6l3LFjDqXD+Kpf3lMHIdvGzxYva06buyQjxkji9Ejx6B6jAAgMrKBcN+TryHOIDQGAIKifjdjpE1UCr7e5YkRUw1TacvH0onlqHd5UJhvzvgkNUEQmYMM7TRzrNYpqmUaXRbLH1CejXV4AzEGi5RS5Zs/r1IMA9dI5MJ5VD6q9VEeUrUyMHZ3AF2y9DEzufFhyi4fj8c2fIGVk/pj5qh+cHoDyDJqYdZx+P7/vS97DrXQLbNeozh4dvl4+AM8HrmxL2ZzLByeAOwefyQvMvq8O6pr4fKHBrQLt4QGxOEa2UzT+jDRhvqIyzsrTpZ4owbBSjW3p1QU4dvz7shvGM7nnD2mJBQRoOCZ33W8DsCFsNBe+WasjksxUDMyokP6TDrl+x4WqSvINYpSGayW1ilRkmmxp7ZEozsQ6V+PRbyXGpxtEp5KRJm3pTDpOcWJQCnBw1ThGBZLXtsvo859QFKdOxGCCOKxUf2wcMu+mHdsuL8JQXUDvlElvaZR4T0bDAL/9+5hPHxjX8yMq4H81DuHce9wm2gfPy8oCnK1tFdVNew2gSiAi5VUy0m1JHZ3QLEfqk2IJYpRp8GDaz9WrJiRDH4hiD9sE09kh1OepJS/41FLbZOrIc6xDIAgRpV3j9QED+uvXFh/gTyTAX5BOoqEFwTkmUKTAjkmXUYnqQmCyBxt6sv33nvv4YknnsBnn32GU6dOYePGjZgwYULMNgcOHMCjjz6K//73vwgEAigpKcGGDRtQWFiYmUZHcbLeLTKygdiyWGqKmdGDUDmlSrOWUwwDlwodz1XJJYr2oJpVBsImPYfzTg8WTyjD3E0hz3F8mLJJx0mGV4fywwZj2vOfiIztKls+GCiX5WAAnLV7I0Z+/EAi16SFRsNi9b8P4ZYre8Ko5WTVS4GQ8Fud249BhXm4b7gNWo6FyxvA1Ioi/OK7l0UmIKIN9bONXpUBjD9yDdETDo+N6gunl0eOURuZQFkUNWgIe5SHX9YZWo7B4pvKMO/VvTEf5qpiKxbdVIpahxdjyrsjy6DBJXkmAOIUg6PnHIr3Uh8V0seyjOK20QOM+PO0FkYti+/07Yx+El7GAycbYNK2bGhsW8JikO5f4fQFiyFzoeNmLYd1EmJoFbZ8rJtyper7JRlSUedOBJNWg5VvHMC0iqKY8NUzdg9+/59DeHSUtJhSNIkKJUkTxE+HF4u0KCpt+Zg7thRBiI1mpzegqK3R0gJ5TJqiAC5G1N5Nxgy8mywqejDpekcwAAbJVMwIf7OTIVnlbylSjX5y+Xn4A0EMKsxFEExkgqsg14DaRp/o3AKAua+Ko1aAC17tMJmcpCYIInO0KUPb6XRiwIABmDZtGr7//e+L1n/11VeorKzE9OnTsXDhQmRnZ2Pfvn0wGNRDiVqDBrdfcRb10VF9EQwqG5JmvSYSKu4N8JIeTaefV/Rc3D+CEx33kjwjXpw6GIXWbDh8POxNHxCLjsPxmkZ8cKw+sr1Bq5ynZNJy0LAMHn/zIKZWFEXEu6JREuICGMwdW4KZ/9xz4bjFVjx8w+XQaIClTWHQ8aFYSyaUIxDk8eiGL/DinUPx+JsHcOvVvUQDiXAe1ul6t7pYiVGLRm8Anx+vw8Ceufj02Hl8fPQ8dlbH1tSOvo48o1ZxAJNr0mLOmBLY3X7kmLQwajnMkVD+fvzm/mAlhiF2TwDTnv8EJh2HuWNLMGtMP9jdAWQbNcgz6dA124Aiq0W0X3yKQUAQML1SPqTvfFRIn4ZlML2yCCwgqVAeP5OfCfQMg/ljSzF7055YQ6LYiqU3lUGb+Sa2GmadBlv/962oxJ+fF/DcjiP4xXcvVz9IC6FhGfzpva8kywz+6b0jWDAufTWFk1UJThQvz+PB6y+XNXS9vPpxExVKksKk1ShO2kqFvuaZdfjtfw7Jamssuil9InRScCywYGwJZm/aK9E/S+FNMYz/YsAvCHh0VD98+vX5mOU9cgwYP6AA/gxcu0mvUZyQX5qmOtpglEtpJWtpp6NPpxr95PYG0CXHEIlwCxOOZHF6Yw10l4rnPN6ZkKlJaoIgMkebMrRHjRqFUaNGya6fPXs2Ro8ejccffzyy7NJLL22NpiWEmgfD4eHx8de1eHBksaRB8+B3LoOGYTBj/W5sr66RVcB2eJRLieg1LF7/WVVMfW2j4EGP/Gw8JhHWvmRCOSoYYHXTMrefx30yNWTvG2GDlmVw3hXAG3vP4I29ZwAAaycPiWmjkhDX9uoazB7bD2snD4kJzap1+NDZaMbcLfskQ0GXvLYfi8eWoMbhw+1/+hDrplyJJ948KDkYnb95LxaNLwPLQkU0ikWXLAOmVhSBYxj07ZaNUaXdsXhrKDcyHM5933AbOJaBEAyiR65RdB+BCwOYGSNt+NEfPwKa/r/7eJ3shMO0yt4xy6PDwl0+HjP/uQcVtnwMasojH1ZsxYpJ/VGQa4zZTyrFoKrYirsq+2B6ZZFkePFfp18d2TbfrEONwysZMmfRa5BvbhuDg9mb9ogNicM1mP3qXjw+of2qGsfjE3j8/PrL8P5XteiabYio4Z9pcONn118Gn5C5HG2Hn8ePJCbAwgNvRxrzx9XUudXWy6FhWMzZKp3juXhrYgrazSm35fbzisJibol76Asoi9D5WliMzMJxeGSjXP/c1677pyAA551evLbnlOib2dtqRqcMvD89Cp7hHdW1qulkiaLnWLwkU0rrpY+OJZ2+kY4+bVIRizTJiEXmW/SSFV12VNdi0RbxBFdjs6JWCILoCLQpQ1sJQRDw2muv4ZFHHsENN9yA3bt3o6ioCDNnzhSFl0fj9Xrh9Xojf9vt9hZro5oHw2Lg8JcPjmFc/wLcP9KGR+IUNbtn6zF/876IJ1lOECvPpMNv35IvJbJofBm+8+R/I8urbFYsm1iG2TIfkDmb9mD5xPKIp73O6ccv/vZ5TP5nlkEDIRhEndOPU3YvDLpQObBw7e940S8pQbZo7C6/KKQ75C0qwVsHz+EtmVDQh28IeepqHD6ca/TKDix3VtfCE+BR7/DFhLhHn2vxhHL4gzymPv8xfjy0FyptVhj4ILw8jzljS+HneXj8PPRaDgE+iAaXH58er8Oo0m6KA5hplX0if6tNOPz8u8WRv+MF26KvZVpFEUw6Dv175uLrGidON7iRY9LB2jSAk0ox+OxYHe4bzuCP7xwV5ZuvunUQDHHP16q3q6WjGIqtWN2UZ55ukumfdiVxn8M1sPt5dE97C9smepbDN/Vu2YH9JXETMa1KEIqVFeammDcthV7LYkx5N3x/8CUxJbTC5fv0KYbspkPh3iPwskJJSyaWw6MwGZKKuJaqCF0KObnUPxNDy7FYLVOWEshMea/WEmjzCQIeG90PtQ6vqJTWhEEF8CXpzddxKpUEElAxP+/0Yu7YUizeKl3q7rzLi14wi/ZLNrdbbcynpjbeHFpzbEsQROpcNIb22bNn4XA4sGLFCixZsgQrV67Em2++ie9///t45513cN1110nut3z5cixcuLBV2phj1Kp4MDT4573X4kyjB0fPOdGlyRPl9oc8URzDYHDvXPQtyMagnrnQcizW33U13v+qNmLQAqEwNaUcpnhF2u3VNXCofEAcPj4yI23SS+d/VjYJeE1+7jO4fDyqbPnYdH8F3th7Cn/54FhENXtnAqrZWRIfqB3VtQkNFtffeTW0GjYkj66Ay8uje54Rq946hEdu7ItZTSG2WUYtAryAVW99iQevvwyP39wfz+04it+9dTiyb6hmpw1dsw0Yu2pHTAjY8Ms6K5432lOgNuEgCMCm+66FTsPi9b2nRYJtYQJCEGtuG4xTDW64/Tzq3X7UuwP4+Oh5DC7MlUwxmFZZJDv4Y8FgXlQIb43DJzmwAUKD5BqHr0VC3pLpny1hSFys+IVgmxvYh4lXQo9mZ3WtWrdNClYAHr6hL2Zvko7UYRUqFCiRDoV7PcthzTuHMX98KQJ8MPLu0XAM/u+dw7h/RLHsvma9RlEHQkpcS01kTk0fRArqn4nhVskrlopAaGmSrQmdKiYNBy8fxKpt1aI+uGB8GUxJltirc/kwvSoUih79TaqyWTG9KjEVc6Neg9v/9CFWTuofI1J21u7Fbc9+iL/eebXkfsl6qLNVxnxqhnhzaM2xLUEQqXPRGNpC06zoTTfdhF/84hcAgIEDB+L999/H008/LWtoz5w5Ew899FDkb7vdjp49e7ZIGwtyjVg6sRyzN0qXeuiVb8bxWid4IYitEp6oB0YUY0xZd8yKy3GrbAoJDxthamVUpELYVYV53P7IObf98jo8+R+xx3xH0yB5WmURVm+rxvbqWizcsg9jyrtHFDV/dFUh7h9uQ75FJzsrrRQ2aVYZDFr0HEb94QOsnTxEFD4t2tagwXmnD6MH9MDKuBDzsPfYGxDw3I6jIk9MeNux5d0j13uhjcrdJnqSQW3CwaznUFKQg6/OOmQ93wDQI9eARk9A0oPZxyqemQfUvenRZdLsGSqdlUz/bAlD4mLFrTBxlqmBfRg10a20inKxjGKkjlwZHzWyDFpFQzcRhXsWwF3DLpUsPbZgfBmU3gwsE0rHWf1OtSj8fu3kIZLCYkYdp6j/kUptdeqfiaGaV6xSyaMl0LLK9a21bHoE2hgol9JSKmMnhanJK37/yEvxyKjLY6L+fP4gTAkouOs5Fn27ZUkKoVbGCYFGI+UAiFkf1++9fh7zxpVi0RaJ8oLjSuFtwfdwa45tCYJInYvG0LZardBoNCgpiQ077NevH3bs2CG7n16vh16vb7F2nbF7UOf0we65IFb165sHoK6pjnaWQYscozZiFPJCEH/cfkRSKOiP27/CzNH9Yj5YJh2HgYV5MGk5PD/1Kph0HLScsrqI1CxqIqJgYXy8ssd8WpSqefjvdTuP4kdXFeLz43UYXdYNDBvE4gllsmGTTr+04Zal1yhHBeg1mDHShs5ZerCMfAmsKpsVZh0HXgjisb9+JgqDP9OUp/yX6Vcrhp9PqyhCYb45ZrCrpoy++0R95G+lOtoVtnyYm8pqKdXZrLDlK4Ym3idR6gdQ96ZHD/7UQtxaqnRWMv2zJQyJixW1gXuqImDpIEvFU6a2PhlSLeOjRpaOUzR0sxJQTnf4eZxt8EqW/jlZ50aXHPnnXsexeEohGkVK2EzLMpgxwhbZLrrNM0YUQ5uCoCH1z8RQzStO4zOfKPUun6LAZb3Ll5bzNKeMnRQGjoVWw4g85OEoM0MCoeNeXsCUiiJRdE24rKZXptRdsroK9W4/7vrzp5Ke81uf/RB/umOI6DjpoqXHtgRBpIeLxtDW6XS48sor8eWXX8YsP3ToEHr16pWRNh2vdYqEMypt+Vg2sRz9umdL7uMJ8LhNQSgoEPUBiFa9jt723V9dl7TQRzIfkEZ3AP17ZOMPtw6C2y9EFMqNWhYPvrRbZMB5m/IZH2sqecNxDDQMi6felQ6bfOqdw/jx0N6S7XD6ecUPZEhxvQ6rt1Xj4Rsuw/3DL4UQFIu23T/CBiEYBANg1a2DREZqOE/Z5VP2roVLhr35s6rQZIpBI6uyWlVsxX3DbZj+wieRZUp1uR8YWYxcU8iAlauzGe15lxvMfHCkVnLCQc2bnhM1uaJk6A8rtsJqybwYmk7FkNC1AWX01qKlRMDSgVFFiCidBlc6Qryl8AeD8oYuI23oxmN3+3Hvi5/FeMVdvpBI1LodR/H81Ctl9/XxCsJm1TWi9CAglCvbPccQisKJEqU6a/egIMeQdK5ssnTk/mnScqqVOlqbLKMGdk9AUuCSAYOsNL0jmlfGTowANDstptEdiAiZStX2fmHaVZL7eXheMbfbE1dtINugRY3DJ1tCtKUmqAmCuHhoU4a2w+FAdfUFg/Lo0aP4/PPP0alTJxQWFuLhhx/GD3/4QwwbNgwjRozAm2++iS1btuDdd99t9baesXtk1SlnbdyD3/xgILpmi8uOMWBUhYLCImNyJbLcPmVj1OUXG48GISgrzLN0YjkMAP48/SoE+CByLRz+cOtgybzHP9w6GGfqXTHHDht0ziYv2pl6L4qsJkwcdAkWbN4nauMDI4rx5enGmGOE297g8it+IJ+fcmXkeFcX5eOOdR9LbjvthU+w4afXwqznsOKNA/ID5vHKA2a9hkWuUYdLu1wop3W81impsto9xwCzToPBhbmRe+zy8Xjpo2NYPrEcdk8Ax867IoPf7tmGmLzn+DqbRh2HXcdD171usvygfN2Oo9gyoxILt+yLMZTPNnplPf7xxrOcoT+s2IqVk/q3iZIkesGDghyjpCHRI9cAveABJARu2iMWLScfzVFshSWD3kNPQPn95Amkz9ueahkfNVw+ZWGvREKBswxauHy8bPqGUttSEbKymvWodfpQ1NkcEaWyGDiY9Rz4YBBWc8t6vzpy/3QHeEyt7A0gKOE97g13Gp/5RDFqOPxpxxHZGs/L01Teqzll7KRIR767xaBR7HsWmfDzRncAd//lM9nc7j/+JNZDbTFoFCdY0pUHTxDExUubegt8+umnGDFiROTvcP7J5MmT8fzzz2PixIl4+umnsXz5cjz44IO4/PLLsWHDBlRWVqa1HfH1iK1mce3DOqdPMVyqzumTNLTVhIL8goDdx+vwh1sHQcMwkh+KRi+vPFs79aoYRfAqmxW1vAAT58XyieVw+PiIh9mi4xAUXKjl9Xj2v19he3Uttj88XLJ8lVTeY3SotNMbwO7jdZg0uAecfh4ev4AxcYOuM3YPvAEeQ3rl4cU7r4ZOw8LpDeDTY3V48KXd2PDTa5UHp1Ef9QAfVNzW4QnAF2CVS97wAqps+ZLbVNjycbbRiyG98mKWh2a9S/DBVxf2YZiQt2bOpi+wbGJ/OKPusVnH4Zd//xxzx10w6r9t8GDxa/vxm1sGxDxb8XU2u2UbcFXvTvDLhLoBIaMgiGCMkZ5l0MJq0eG6yzonbDzHG/rhY7QFIxsAPKwB39TYUWmzxjzDl3Wx4HiNHYVW6SiS9ojDz+OuqpC6vaikW1WftJbQSpYGl4o3aaq0NykVzCqROuYEQrylSEfOrVnH4Tt9O6NfQY4oz/vAyQbFtqUSfu/08Zj7qjhXFgjdi8dvHoAck2qzU6Yj988GVwAz1oee+Slxz/yM9el95hMlEQHUdNCcMnZSpKvvpfJeyDHqFD3UOXGTCl6fcilUbwZy8wmCaFu0KUN7+PDhCAaVVWKnTZuGadOmtVgbTta78eg/vohVu2wyTKLFt9TFo0JGZ7yhriYEVOfyR17Yv/re5ZLbWPTKs7VZBg3+d6Iem2dUIiAIOFXvBoLAzFe/ks2fmzumBNMqi0IlxxLMe4wuRxU2uHdW12Leq/vws+8U4/71uzCtsihmwuFkgwdLXjuAP0+7Ch4/j5+9vBs1jgu5YmofbWNUuR41cR2TnoPLyyuKGjV6AphaWQSAifnNw+GO3XP0IkPTxHE40+jF63tOxXoviq2YO7YMR2tc+OTY+Ril+ApbPv6174zoN1NT8w4b3g0un6IHs7NFLzLSQ/sjKeNZ6hhtBYePx+3P7ZJd/8bPqlqxNZnF7g7goaYSfI/ceHlMjfSH/vY5/u/2KzLWNpOOU3w/pVMUiwOwYHyprOBYqmdSC6tNJM+cAzB3bClmb9ojErdcMqFcsW06TlnISqrEUYPbr/jebnD7VQUkm0NH7p8mfes984nSWjWeWQBLJpRhjkQJzSUTyhVF/6RIR767V6G03tKJ5fDKlNZLNoXK4Qtg+gufSk4qTn/hE/zj3mtU20oQRPumTRnamabB5cOj//ifyLu5/XANHt3wBVbfOihihKiFQzV6A5j2fChXd1ixFSuaDHW1AVqAD000hHKepXPaztjdiuFKRh2H7YdrMH/zXgwqzMPu43WYM6ZEteTO1Oc/hUnH4bkp8mHKQOgDvXbykIiHalBhbkz95+2HazBrdD/FgYdRx+Hmpz8Qr1AJcX9gfexATmkwatBw0HCMZJ57RZOSe5ZRgzvWfYy7h/XBz64vRkAIRtRz3/nyLCovtaK3Na6JCOJ3bx2SfE4Wbt0Xuedhpfj4+xN/LxMhx6QLCbqlENrdlo3nZEh3LuDFTJZRI1mCr8KWjxWT+qct/zIVWlMUq9HH48drP5IM9fzRHz/AX6dLl/FRQ69RruWrpn8AAE4/j8Vb90tGBs19dS/mju0nu2+t0yepAxGe3AyFiMfuk+n+kenzZxKjtu0JwSWroJ0qTp6HgWUxe0w/BMFEIhkYBME2rU8GnUrf0yXQ9/Qshyf/86VkWc/f/edL/OK70k6MZFOonD5ecZyTCbV5giDaFmRoR3HG7lUMMz5j90ZetGqe17Bn3qTj0L9nLr6uceJ0gztU9krGM1lhy8eu43WRv90+QfKDM+/VffjL9KskZ5AXTyiLlEILGeuhwVwiJXdmjLRhUM/chHKuTDoNSrpnY9WtgyIGd/RHRU2Z26CRHnicc/ng8/NYOrEcrqjwQ5OOgyfgj/nQvfPlWUXxHY4BtBwnmxPPAFjWdJ7fvXU4po52mBtKuomWufzyQkVhpfLV26rBMgxee6ASG3Z/K1sfO5nBTqqh3Sfr3Whw+2F3+5Fj1CI7SgX/YiLduYAXM3qOVXyuM1lHuzVFsRzegGKopyPFUmLegKCYc6um6A8AfBCK3xOFbBBY9BpZ/YkHX9qNf/70WtE+me4fmT5/JvHzguIz729hITopDBpWcYxiSLK+tRwahsWTbx3CtMo+0ERVRPHzQTz53y/x4HcuS+p46eh7bj+PTZ+fwqbPT0muv1emUgeQ3Hc2V+WZjw81Jwii40GGdhQNKjPy0etdKurYHMNIqoZvvv8aLLqpFHPjjOToMOwwbn8Ac8aWiBQw7xthw5Kt+zGwMC+iSB0ehC3Zuh8zR1/wlJw478Lu43UY17+74rVlGTQRNe/tDw9XzbmyewO470X5MEGGheLAg2XEhniVLR96LYcfr/04JtS70RvA7hP1ePnj4/jbPddE8hCf+e8R9O+RKxLfOWP3gGMBvyDA5w8qhlM2egOK15pnFn9cG93qSuVAaDAdEIL44kS9pJGdipp3st7pY7VOzJJQxg/Xdb+YSHcu4MWMR0GFfkd1LTwJDEZbChcfkBXFKsg1wMWnr462Wk17tfVyNLqbn3PrUBE0U5oEMOk4DC7MlfSUVdryJWtW5xi1iv2jpQf9Hbl/CsGgrCaJx89DEFp/qOUXBNkaz/PGlabN+K93+TB2QA+sfPOgZPRFsmXEGj3Kfe/PMorh0dhVvtF2lb6Z6He2S5ZeMaWrSxaV3yKIjg4Z2lEkkvMbpkGlfMSvbxkgqRqeY9Jj4ZZ9GFiYh59ff1nEeI/3Clfa8pFn0mHlmwdEBnVBrhEz/7kHbx08J9nOn11fHPm/XsNiZ3UtDp1pxPX9uqBv92xRrvLBU3YcOtMYaWeD26MYvt3g9oBjtco5hCwLDctIDrY1bKh8TfygpNbhhUHDYZDMALPClg8AEbVvi16DLIMGxqac0DAMGHj9AupcfoR86/K4vDyWTSzHrI3ia102sVxS0E5qkBtNdFip0xvImJr3yXq3yMgGQobY7I178PjNAy4qz3Yiz2XPdqpqHI+qEaeyviXRMhzWvHsYdw27NKa0X2G+CWveOYyfXifvTUoWtcgZJkXnuapqcQJ5omrbyCkfA0CNw6tYZqjG4UVh3ERZQa4RSyeWY7bEu2zpxPIW7+sduX/qOA5/+fBrlBTkiDRJ3jpwBrNHl7R6m7Qci1+/+SWmVhSJ0ip+/59D+NWN0uHTyaLXcnjwJfnoi78nmaeclYa+Z9Jzitosat/wRGlOShdBEB0DMrSjSKYWZrbKx0CvYTGop9hgdPl4bDt4DtsOnovUWI43xiNecRaRbaP52z1DFa8jWqxs94l6mHQcTtndmDumBLNkhHl+svajyDKO0+LBl3ZF6miHB8pGLYsH1u/CypsHAMGgYg6hPyggx6SDlLRdaHkQJxs8MYOS43VuXNlb2RPOAJEPZx+rHktf24++Taq+Yb5tcOPtg2fw4HeKJUWDoskyalCYb8ZvfjAQdU5fpFZ2nlknaWQD6jmoYRV2IBQuyQAYVd4dk6/tfWHCodGr2K50kGlxpHSj0Whxut4lmVZwqs6FXEvH8R6oGnEZLCujYRl8f1BP2dJ+mjSGjgeh/B5SE9eUIx3eWYtOpQSbwjFMOg1u/9OHsmWG5HLPe+Wb8fjNA9Dg9kfCXnNaKVWkI/dPjgHuquqD1e9Ui7RAwmlMrU4QuOPa3li97bAoBHvGyGJIfpxTQG1yXC5NTA6jyjgskXx3k5bD2slDJH+PtZOHpLWueVuv1kEQRGYhQzsKLcsolmrQRg0Q88w62YFY2Ngq6S4uZxKtBOryict09epkwhv7TuPBl3ZjrUztZE5loKphmchA87ENX+APtw6CRc+J6mIDF0p2LRhfGslzNOs4ZBu1GP7r/4qOHS6NUe/2Y71ELendJ+qx/qNjWDiuFDPjPCvRx1g+sVzywzxpUA9wDCMZghcerITb+a+fV+FHV/fCCxJCZ1MrivCjP36IP0+7UvGjndf0MeyabZA1rOPRKuSgRof/DysO1dH81d//J1vLelWUwF66aW/iRCYth8XvSivnhyMQOgomrXLpqHQOJJOFAdA5WycZzdI5W68SY5Iceg2n+B6aMyY1T2KqqsXRMADuG34phKDE92S4TfE+5Jl16NstSzL3XC6lJUxBrjEjE2hmlf6ZrrrNbRIGyt+tDBjatU4vXD4eo8q7x4Rgn7F74PLxqHV6UdTZ0uzzcIzy5HjSkwxCEPPHl2Dh5n2iCYL540sBQX2GgGMZPPVOtaSGBQsGyyamV8OivQiOEgSRfsjQjsLuTbxUQ9dsA5ZOLMecjXtjS4HZrLhvhA3TX/gEq24dJDpHvBJovFd8ywMVkb8tBunB8vbDNfLGY7EV+WYdBhXmRYz49R8dw6++11fS6AVCxvZjo/pFxNCUBplLmgaZ73x5FvcM64Ovzjkj6xmGQUGOAdcVW+Hy84rnc8nU+WUAcCzQp7MZZr0GDg+PLIMGZj0Hjo0dr9Q5/Xhswxd4/Ob+mDmmBHaXHxaDBkIwiAanH6tuHYRckw5LJpZhzsY9oo/20ollCRvX0WQbtegiBDG2vDumV/aBSceBF4L44EhtJPw/HDrm9AYkjWwAeO9wjWp5r+bQ3sSJnAmWnesIMMEg5o4twexNeyUjVJgUPbnpwBXgcbbBi4GFuREV4pAAnwGn6tzokpM+z6aOZRQ9iakKr/kDwH0vfYo1Px4cE/6u4Rjc9cKn+L3Euz2eRh+v+D3ZICFoFqZrtiHplJZM05H7JxME8rP0+OqcI3Y5AGuWHkwGuqNZr8Ud6z6RLbGp9PwlC8tAcpIhle7HA6hzenH/SFuo3GhTNIfTG0Cd04v8BCIjXH5eXoiwugZOmfFHS9Pg8qHG4YPd4xeVfiUIon1ChnYUiZRqOHjKHgovNmpg1nKYOKgAUypCIcFdsvQ4cs6Bj7+uxapbB6GTWScyiE1a5ZDEs/ZQSHGVzQqLTiNpUP/xvSN4fupVAIMYI66q2IrFN5Xh2/OuyDUMKczDwJ65OGN3K167289HxNC2zKjAb989JJnb9fgbBzBzVD8MuCQXLMuiyGrGzq9qIzWjq2z5uGZCGU7WexTP1yiRQ1ppyweD0Oyw9Gx2WYyhnWXksGJSf6zdcVQk9vWrG/qi3unDoTMOdLZosXxSfzg8gYRCw9XIMeng9vEo6myBualWd9dsPUaVdsOIyzsj16iLhI7tjlKRl74PLedVbm/iRGrK+a4UFaYvSlgGc2Xy7+e+uhdLJ2ROddzhCcAnCFj62oGYZy+kGlyU1vzxOpdHVnitR64B9W4PLkkhL9jpDeDQWQe+++R7suvVaPT4Fb8nan0/2ZSWTKOm8J7IPbtYCQQFLH/9IPoW5KBL1O/zbYMHy14/gJmj0pMPnQxmFUE9c5re/3VuH/LMejBRk+5AaJKhk0WHendyYmh1Th8cXgGnGtzomm2ANyDA5QsZ7t1zjGAZH9BZ+Rhq3wKXt/UN7ZP1bjy64YuYMVt06VeCINonZGhHoSROA4TUpG9+envk70pbPuaOLcXtf/oQNQ4fTDoOa24bDL4ptOm03YNHR/XF6G/qsfi1AyHBrmAQC8aXYsFmaZGb2579EJW2fEyu6A1PIIDJFb1FoezXXpqPfIsOo8u6YUp03q/dixqHFzWuC/m/OSYtfv3vL/HYqL6K16Zl2cg5/HwQr+89g9f3npHc9p7rbJjy3CeRv6tsVmy871qcbvDik2PncbrBg2yVOr7xasCVtnwsGF8GDy9gydb94hrV1bVYuHkf5kTVnuUFSJY4Ct3XgxhYmIfV26ph0nGYO6YfBvfKg5ZjoNWwzS5t0i3XCKOOQ43DB54PggGDS/KMotnpbBWvcUt6le0eHxZPKBMp3IfKwJWj0eMDLiJxIrUa9JnMS25tXD4Fj83hmozWb8016fCb/xwS9cvt1bUQkN7SY2aDHgu37kOJhIGzYMu+lEPH1cSSEhFTUuv7auuB5FJaMk1H7p8BAXjr4DlZgdKHb2h9QxtCEEsnlOP9r2rQpclgNWg5nGlwo8JmTSgEOxGyDTosfX2/ZB/8z+sHkhaCMxs0+ElTaTspT/w/71P3xOcYlb3ErV12q8HlExnZQCiq7bENX7RoChlBEJml/X75UkCq5FSYSls+PjgiNugWb92HlZP6R3LpggjitT2n4jw5Vmy6vwJHa5wIIhQWNWOkLeItzjZqYNRwqHN58dc7r0a9ywePTwDHcnipKf/wsVF9ceK8G3oNi8u6WDArLhQ6+lzLJ5Zh7eQhkQ/rzupaGLXKAl5slN3pUyrwCsATF3a1vboGi7bux6DCvEgpMbefVz4fA7z2YCWc3gAsei30GhYn69zonKNXDPkKIuQZ215di/NOn2Ko4tSKopgSazM37o2sT8dMciJ5WVaLDsOKrTGKpNFtSLa8VzIYtFrcsfZjrLl9MGZxbCT8NcALuGPtR6GoiIsIHccq5turCd+1J9S8h6nWj04HPoXSYzura+FLY+kxPy9ICkaGefTG1M6VDkEms0pESbo8im0FLccqvvO17bh/qpWLUlvfIrAMTjd4JMYj+ejT2YLuOemZwFHrg4/ckFwfNGs5XNE0SR5Plc0KcwJ9L5PfXSlqHL6MpZARBJFZ2u+XLwUYBphaURQpIxWmqtiKKRVFWLfjqGifHdW16JIdyhmaVlmEtTuOigZW26trsHDLPuz5tgG1rlBY1JFzTpy1e1Hn8uF0gwcfHq2F3cPD5Q1gR3Ut+GAQx8+7MGdsCT4/XocT592478VdmP7Cp3AoebOqayK5gfe9uAsNrlB4Yq3DJ3ltYQGvUw0XQr2zVTwPUp6LndW1GNQzFzura7Fk6wEIQlDxfEIwiDF/2IH3v6rF8tf34ztP/hc/ee7jhMoWDSjMw/o7r1b1KnkDgmSJNeDCTHJDkjU+kyXHpMOKSf0xrNgas7w1yn9YLToUWc0Yv3onRv9+O374xw8x+vfbMX71ThRZza0+2GguPl7AfSMulXym7hthU50gak+oeQ/V1rckrTkJ0KAi+Ke2Xg4dy2D++BJUxj1rlbZ8zB9fklDud50rVKJL6hjzxpWiroXfPa1NvUv5G5NsPeWLibbYH3khiD9sOywxHqnFH7YdjkTeNZd09/d6tw9zx5WgKn4cZsvH3HElaEggFD2T310p7CppIhebMClBEIlDHu0oNCyLrf/7Fo/e2BeaJg9gtlELg4bFLc98IArHDNdpZMHgqdsHo2cnk2w+3s7qWkyrKIJBy+Gev3yGlZP6o0u2PiL2BQAP/+N/eGHaVTHHsFpCdRq7R3lek1GTDofrmfQcpj7/iWyty2jhNp1G2TOhkwm79jZ5qrZX12Dm6L5Y/1G1pBrwyx8dw6ymkM4/vncEf7h1EISme2TWaxTrX5r1GqzeFhI+euNnVYr3Qa7EWpjmziSfsXsu5E8aNcgzSedPZqr8R3iw0V5qfApBpCwu1d5oy97D+LSQZNcnQ5ZBq/i+SDU1wx8MYtnrBzCwMA9T4561Za8fwLyx6uGwOk1qJbouVow6DR5cK19PuT33T51Kf8xEtI3LLy9Ot1NBkDRZ0l1q0KTT4od//AArJ/WPEUML95tX7k6sLndbKruVyRQygiAyCxnaUTS6vfj59ZeLymBVNYUZhxWlAcSEJIcNuaduH6x4fG9AgFnLYdWtg7A6rvREhS0fq24dBKM29oNc4/Bh+gufYsZIWySUMRE16XDoeLjU11m7V1YYpcqWH1PX+XSDR7E27Wm7tNCZPsoA9/h5STXgKpsVM0baIh6hcImzuWNLIoNXpfqX0Y4kpVB/pRJr0aQ6k3y81omZcWJUYUXgwnxx3nOmyn+0pcFGc3F6A4riUu1ZbCmeWodXsY+ed3rRJw2le1KBgXK/TGeloyydcr3crBTDs10+XjEc9lffUzdSWJbB5TIluips+aplGi82GECxnnL7utpY6l1ezBtbisVbpbVX6l1eAK3bH50qgl/pEgQza5XrxScS6h2N1aJDSfdsyX6TbNh3Wym71dZC2QmCaD3I0I6ik9mAxySUfLcfrkEwGMS0yqLIIEIqJFmvYRW9KwYtB61KfcdffLdYsm0HT9mxaEIp5r26LyE16fBHausDFZhaUYR/7voGc2UGAvPHl0HHMZHBsY8Piup7R3smVt8mnlAIG7ZhTHoNOls4zB7dDwEhCJePh4ZlcOScA91zDNBrObz90HUi4+/oOYfi/Vl0U2lkGccyeGBkcWR99DVNaapnLVViLZpUZpLP2D0iIxsIpRHM2rgHv/nBwDYlYNRWBhvNpS2GZ2YKi0GLyc/JR6hk1HvYlIIDSE8CpNPiYgD59wXDYEWK6utqpagSEZvTsIzifWhvhnZr/u5tjVyTHkte2y8ZAbHijQMpi/I1BzVB0iyV9Yni5YVQHe1gUFQpZMYIG7xJpvS0t0gsoH1eE0EQidFxRqYJ4FCpA/rT4baIoS0Vkrz3ZIOid8XrF9CoUt9xzth+opnPYU1lu7rnGrH61kE43ejBXVV9MKa8u0hNtCDXCFfgwiBQCALrPzqGOaNL8G2dG/PGlSIghOrCmvQanLV78KM/foC/3nk1fvW9y/HYKAY6jsWIyzpjUM/cmPD2QT1zMeLyzjjT5NEOTypc0ycfHMPA7ecxY6QNB042wKTlsHDLXswfVwaHjwcLHma9Btf0ycfCLXsxc3QpLu0inuH38YLi/Qnn4Q4rtqKzRQ+zToOx/QsixoZJx6Ewz4Rlb4RU3nefqJf1rkXPJCdT37JORYStzulrU4Z2e6EthmdmCrOOw/DLrJJ9dPhl1owKbZm1F0Qc4ycBXvroGBaMK1U/SII0qqivN6aovp6rEjWUiGpxvlmH5a8fkLwPr3x8HL++ZUBKbWurmLQcXpNIvfLzAp7bcQS/+G4GlLdbiZYS5WsOZhVBv2Q9zXIE+GAkLW1K3HM+9flP8M8UJv3aUyRWmPZ4TQRBqEOGdhR2t1/RI63XsHjzZ1WwewKSk/PBoIJ3BQwW3lSKGoeykIfdHRC9jC0GDZzeAHYfr0O2UQunx4/uuUb8afsR0Qzy3HGlcHgunOOdL8/irqo+OOvw4ifPfax47T945kMAwPZfXYeHb+wrCqGvtOVjyYRyaBng9QcrodNwWLh5b8ykQngbCEG8f6QOr3z2TeReOnw8dh2vw/tH6mRDttXEi+xuf8wscI4JGF3WLeZ+5Vt0+M0tA1Dj8MHp9ePmwZdg3qt7ZWeSk61v2SYVZjsADW6fYnhmIiI57QW724NHbuwn20ftbg8yVbqt1unBnLElmLtJ/G5YPKEctU4PekqkV6RCS/XFLll6xXDYLll61WPkmHRYeFMZHtvwRcx9aK9eLIfHg59JpF5V2vKxZGI5HJ7MPZMtTUuJ8jWHGqcX88aVYNGWfaJxwrxxJah1etPSD10+5ZSe5pYaDALtJhqivUSXEQSROGRoR5Fj0oryroGQt+wPtw6CxaBB326hnN+vzjpE+5f3yMHv3joseezt1TXw+HmY9Sr1WfVczMv4ZL0bv/r7/2IGfO89PFwydHl7dS0WbdmHZRPLIxMCOUYNjFpONRQyJoSaYTBbJjR6zqY9WD6xHEYth3mv7hV5k3ZU12Lupr1YdFOp4r2UyzNXEzeyGLSimpNyH6/oZXIzyanUt1RTZVdbT6RGtlHX5sIzM0WOUTrNJbqPZgq9Vot7/vwZ1vx4MAJ8EPYmz6aGY3DPnz/F71XSOZKhpfpijikkQtncUM+O5MXKMig8kxsz+0y2NGq6KWrrWwKjVoNpz3+CNbcPxsy4CINpz3+CZ+8YkpbztETN6mQnvwmCINoqZBFEkaXXSJaC2lldCwbAykn9I8ukxC28KvVhXT4e+WadYvirKSqcS84IdKqEuIeN6mAwiGAwJMqTSE3XTfddiyyDVjWE3uHjIcTlY0WzvboGLj+veC9/84OBkvtaVMSNLDoupUGqnDGeSn3LPLNO8V7mmdvfILot4A0oh2c+nGS91ouZRPpoprDoOCy8qRQLNu8T5eouvKkUljSGtWcbtYp9sTkGTrqM5I7ixWrLz2RLo1dJa9FnIK3FouPw+M39sfLNg6J++PjN/dPWD9Mt9JXK5DdBEERbpeMkNSaAxy8oDhQ8/gsDeak6jXqZsleRfYxaGFkGD44slqwR+eDIYhijBHLkjMBEynuNaqqbfOPvt+OxDV+g1qFc07XW6cXAwjxc2sWCxgSOrxYO5vKqDLpkQjoDwaBs+P1T736FQDA9tT/DpFLfsmu2Acsmlkvey2UTyyk/u4VQrbHegVTHkynx19q4A7xiH/YE0mdwef284nvN28wSRjkmHS7tYom8G2mAL09bfiZbmvMqNcTPZ6CGuOK39J30fUvTXbM6kclvgiCIiwXyaEehluMZn2cV7/HIM+lU8/ocbj+sWTqMKu8eIxxy1u6BNUsPAReEuWqdPqybcmUkbDps3CZS3iuaHdW1mDmaU6zp+sK0qy7sn8DxNSqKuWoK0HKDLo9fQQztcE3MZEc6SLW+ZWG+Gb/5wcALdbQNGuSZpetoE+lBTSW3I6mOJ/sOaE14AYp9WCXwJynqXD7c/ZfPZN9rf/zJEBSl73SEAm35mWxpDFoOD74kX0P87/cmVvs5nTiVhAKra1TTyZIhnSkSqUx+EwRBtFU6zsg0AUx65dthksivjg8LXDmpvyjsqarYisebZnZrnT7Me3WfbIjZ8onlmPHS7pj9w3nN4TreiZT3iseg4RRruho0F/YxqRzfpONg0KqHoishN+hS9VqmWWisOWFvXbMNZFi3IiaVeq2mNKnoXgyk8g5oLdTqmaez3rlJp0GNwyf5Xgut7zjPRKZpy89kS2PRazBYpoZ4pS0fFpWxRUugVic7XXW0w6QrRSLVyW+CIIi2CIWOR6HXsKIQxDCVtnwYVELDgZA45ujy7lg7eQieun0w1k4egtHl3SPr3Qp5bDura/FNnVtkSOysrsVzO49iWmXIN+MVeCyZUCYZLrlkQjm8gvgDyjLAjBE2ydC2GSOKEe2grnMqh5nXO71wegOYIhMqN6WiCF5eEIWShVEyYKUmM5JZnyzpDnsjWg53gMf0yiJU2WJ/qyqbFdMri+BOY0hyW+e8y4vFMu+AxRPKcd7lzVDLALOKUaG2PhlYlhG9g8JU2PLbX63qNkydyjNZl8FnsqVx+3nF76G7mSkMqWBRifBRW58pwpPfUqSS800QBJFJ2uabNkP4AgKmVBQhCIjEQ8Jh3ko0uHx4RELEAwh9IFbdOkg1t7leJs9tZ3UtplWEDO0AH8Rpuxuzx/RDEAwa3X5kGbVgEMQ3513QcNKDS5YBxpR3jwltO2P3gGUAbdSA1KDVKIaZ/3X61Whw+/HgS7tlQ+XW33k1VqSg2mvRaRRFZSy69D+yHUkZ+GKm0R3AfS/uaqrX2jvmmbvvxV34c1T6Q3tHp9HgjrUfY83tgzGrSVE4y6hFgBdwx9qP8Mc0KQqnAgMo9mEmjbavhmUwtem9GP/OnlpRRIZ2K6LhOMVn8pmfXJHpJrYYjZ6A4vfwhQy8mwwaVrGOdiKOg0wQnvxuruI/QRBEW4AM7SicXuWP5V+nX624fyIiHtkqeaZKgmpZBi3efug6sAzw3PtH0a8g50KNam8Au0/U4+DJBvQtyBHt+/R/q3H/8GIcOeeMWc4A6J5jQL3bi0uaapwyDBTDzBkmFN6lVDszy6BNyYDNNWnxwMhiAOKB8wMji5FratmwsfZUs7O9YdZxis+cWrpCe8KgYdEr34Txq3eK1iUafdNScGrGbxot7XyzDstfP4BBhXmid/YrHx/Hr28ZkLZzEcoYNBwKZZ7J+PSk9ka2QaP4bspEyUcNy2DGSBuAoKiO9oyRNlWdlUxCk98EQbQX2pSh/d577+GJJ57AZ599hlOnTmHjxo2YMGGC5Lb33nsvnnnmGfz2t7/Fz3/+87ScP8eoU/xYqtWDTETEo0uWXjHPdPeJetn9880hBdzT9W7cNexSrNp2WFQCa9nEcizasi9mv0pbPu4bUYyVb+zHz7/XFwE+GPE2FOabcOcLn+J3PxoY2V7LsYoDZS3HIt+cWG5zsnlbOSYdenUyYWz/gpiB89lGL3p3MrXIh5Zqdl4cGLScoqfU0IFytANBAXPHlmLx1n0xObHh9I5AMHOlzrpk6bHyzYOSxu/LHx/Hb9Jo/OaYdFh4Uxke2/BFzLuQvF+tTzg9CRB/N+LTk9ob3bINit/1bhnQ8sg2auETghgtI7yaidreydBRyuIRBNG+aVOGttPpxIABAzBt2jR8//vfl91u48aN+PDDD1FQUJDW8ze3HmQiIh4FeSYsmVCGOZv2igTTlkwowxNvHsSMkbaIp9qg5bDreB2+PGWPnL9brhE+XsDYuDDws3YPdAAW3lSGX90QiMwEZxk0YPkg6j08vvvke6J2VdjyYY4KyU7ES9SS4V3dc40YXdYtZjZ7SK+8FvnoUs3Oiwcty+Bn3ynGmPLu6JptiPSP0w1u2LpYYtIf2jvZBh0WbdmHqRVFovSO3791CPPGlWa0fXcPuxSr3o6dCKyyWfHgd4rTfi7yfrUNtCwDjmUk05M4Fu26f+aYdKpCqJmg3ulFUWczzHpN5B1h1nOod3phNVP/IAiCaGnalKE9atQojBo1SnGbb7/9Fg888AD+9a9/YcyYMWk9f3ONx0QN9V75Zqz4fjnsngvGcLZBgx55JjxyY9//b+/e45uo0v+Bf9JL0vSStLRCW21LuSO0tIAid1jZBUUERBGWRRBRV0FEviJ4QVFEQOWyoOKiCCiI7i7CeltcARGoCHITEASK3SJyk/7apOktbXp+f2BjQ9MmbSdzST7v14uXdmaSPJ3Ok5lnzplz8OTGIy4XqFXzM1d9vqXYjhc+PYZ2CSY0rXan/BdLKZ799BhevasTrosJd/l8S7Hd6y7Z3rYS+fICV6672d509+cFuzqEBAehSYQB/zlyqkZXyOdu74iQYHU+c+gLzUxhmDGoHZ7eeNTlGczereIwd3hHRUfDv2yzY/yqvW6fpR+3ai8+mdxL8pxi65fygoODEBupx+lLrst1AGIjDQj28/ysGgh1fI/mLj2xlHLZZsfYd77DhF6pzhv3xfYrefjOrhyf5CEREblSVaHtSWVlJcaOHYvp06ejQwfvWmzKyspQVvb7yc5qtda5fWOKx/oU6tfGhOPaq15vKbbj6U1Ha3SN3ZWdh2c2HXW2rl622bHl+CVsOX7VFc1v3BWH9e2SnRhtxPwR6bCUlMNaUg6zMRQmYygSrupKrfUL3ECes7NqvnZraTlMxlDERcj/t6xPfpZVVOK5j2vmx87sPDz38VG8NCzNZ3GqUXJsBF4d2Ul1c7m7yyldteey/Tmn3FFDnjVUffKzyO7A+FXf4fUxnRHy22BoJmMorokyYPw7e7H6Xv8drNCbgVDl/pszD7Wde57U99qWiJShqUJ7wYIFCAkJwZQpU7x+zbx58/D888/X63MaUzw2plD3tnW1ocVhfbpkn8krwpMbj7gUNVUt68mxER5/l8Y6m1+MwtIKZ5EfGRZSo5VeCoE6Z6dankuvT34W2SuQFG3Azun9YLM7YP3tQj5SH4zXvzqJIru0c6xrgRrncjcbQ7F0dCZWZeXUGENi6ehM1T8bKiW15FlD1Sc/bWXlWD46DWZjKGzVZteIMYZi+eg0FJX5b2Gnxp5RgZ6HWs89TxpybUtE8tNMob1//3787W9/w4EDB1zuynry5JNPYtq0ac6frVYrkpKSfBGiU0MLdW8L6MYUh97EdtFaWqPIBq60rD+18QgWjszw6cV9bl4RnnJT5M8dnoYUiYv8xj6XrwVX39WPNITg2X8fVcVz6fXJT32oA3/t1wYz3RwbLw5LQ4Xw33l6tSTCEIJVWTk1vj+ysvOgA7BwZIYiccnNH8Z/qE9+Jhr1KIK+1vz0/e1Z5aixZ1Qg56E/5J4nSlzbElH9aabQ3rlzJy5duoTk5GTnMofDgf/7v//DkiVL8L///c/t6wwGAwwGg0xRNo63BbSvi8P8IjsOnilwOyjbO7tykF9k91mhfTa/uEaRDVwp8p/eeATzR6RL2rLt73N2urur37t1HMb1aI5vTufVmNdd7taX+uSnIchQ4yIeqHq04gjmDQ+sruNqZSutcDsyPHDlb2UrrUAzk8xBKUCNrZz1VZ/8LNUBTwdofqqxZ1Qg56E/5J4nWrq2JQpkmim0x44diwEDBrgsGzhwIMaOHYt7771Xoaik5W0B7evi0FZWUWeXM1uZ77roFnq4OCgslf6z/XXUYkuxHa9+8SOmD2yLJ29t73xmstxRiXd2/YQJvVLdTmWn1mf3bHZH3ReOV900CAQXraW/P6NtDEFMuDqf0a7OF8eXr57FbMz7qrGV05cCOT/V2DNK68cfc4+I/IGqCm2bzYbs7N8v/HNycnDo0CE0adIEycnJiI2Nddk+NDQU8fHxaNu2rdyh+kR9CmhfFoexEXos3nLSbZczAHhxaEev36u+J0triTInSK0P6uZOfpEdj9zcGs9cNcBer1axmDOsI87ll+A1N69T63PpSh0baqX0OAq1MYWFIlwf7DLacfUeMVIfX756FrOx76vGVk5fCuT8NIfr8eKwjnhq45Ea89q/OKyjIucWuY8/KW92MfeIyF+oqtDet28f+vfv7/y56vmTcePGYfXq1QpFJa/6FNC+Kg7LHaLWloms7DyUO4RX79OQk6WnAVp4gvReUJCu1mftZ206irluRulW83PpPDZ+p/Q4CnWJi9TjnfE3YNm2UzV6xLwz/gZJjy9fPYspxfuqsZXTlwI5P6um3MxIjsG91Wb1OPhzAeb8NuWm3MW2UR+MXq1iXQr/Kr1axcKoD5bss6S82cXcIyJ/Immh/d577+HNN99ETk4Odu/ejZSUFCxZsgSpqakYOnSox9f369cPQnhXxAGo9blsrVO6ddXT6M3FXozu3NCTZVRYSJ0XB1Fhqro3pGpFHrpyFpe7duVU+3PpkR4uHCMlvHBUu/wie51/W1+Oo+CN17dlu+0RE6TT4bXRmZJ9jq+exZTiff19/IerBXJ+NmTKTV8rLC3H+J6pEIBLLvZsFYvxPVN/62HQ+NG3pb7ZxdwjIn8iWdWyfPlyPPvss5g6dSrmzp0Lh+PKRXx0dDSWLFniVaFN6iBFt6uGniyviwnH3OFpeNpNF7y5w9N8MsWXv/KmK+fWaX0181x6cGUlXhyWhmc2uT82gisrFYxOXlYPYxV4Wu9Ll2127Mx2n/s7JR6IyFfPYkr1vv46/oM7gZyfanwm2FJSgSnrD2JCr1RMuKqVfcr6g5LNay71zS7mHhH5E8kK7WXLluGtt97CsGHDMH/+fOfyrl274vHHH5fqY0gGUnS7aszJMiU2AvNHpKOwtAKFJeWIMoYiykfzaPszb7pytmwaKVM0jRcRYcDn+3Ixb3gabHaH89iI1Afjix/O4a6uKUqHKBuTh54dntb7kpxFh6+exZTyfZXuoSSXQM5PU1gokmKMeH1MZ4QEB7kMPDlp3QFFus2bwkJQbHe4HfCyar0UpM535h4R+RPJrsZycnKQmVmzS6DBYEBRUZFUH0MykKLbVWNOlnLOo+3PTB664StZjDWEOVyPP3ZIxJO19HYIpAuqmAh9nX/bmAjl9oWcAxH56llMPuNZf4Gcn3GRerx3Xzc8vanmeeu9+7ohJlz+Qluu7wip8525R0T+JEiqN0pNTcWhQ4dqLN+8eTPat28v1ceQTKq6XW2d1hebHu6BrdP6YtnoTCR4ObBJ1cnSnbpOlp7m0T6bX1y/XySARRpC8OKwNPRq5Tpa/5WRcNMQadBWoX02vxjPf/IDMpJjsHJcV7wxpjNWjuuKjOQYvPDJDwF1bDQzheGl4e7/ti8NT1P0+ezI327wuNOrVSwiJbzBU3VT8OrvmsY+i+mr9/Vnv3jIz1/8OD+L7I4aRTbw+xziRQpMbRYWEoTZt3d0+x0x+/aOCAuR5vKvoef62jD3iMifSHbFM23aNEyaNAmlpaUQQmDv3r1Yv3495s2bh7fffluqjyEZNabbVUNbxZWYR9tfXbbZMX7VXrw+pjOe+q07Y5QxFBWOSoxduQer771RUxcthaUV2Pbjr9j2469u1z8+MLCOjeTYCCwcmfH7PNphIYiJUH4e7aKyijoHYSoqk/bv5KtnMfmMZ/1YvcjPa2WOSS6WkvI6z1uWkvJGTTXXEJdtdoxasRsLRqRjxi3tYCt1IDIsGJesZRi1Yjc+fKC7JMeyLwYeY+4Rkb+QrNCeOHEijEYjnnnmGRQXF+PPf/4zEhMT8be//Q2jRo2S6mNIQxpysgzkuVilZi0tx8/5Jbj9tSy367W2L3ls1NTMFKZ4YX01S0l5nYMwvT+xm+Sf6atnMfmMp/cCOT/V+LtbS8tx2WbHfWv2uV0vZUy+KIyZe0TkDyTtOzpmzBiMGTMGxcXFsNlsaNq0qZRvTxpU35NlIM/FKjU5n5WVA48NbTCFhdY5CBP/Tv4pkPNTjb+73N//LIyJiGqS7BntnJwcnDp1CgAQHh7uLLJPnTrlt/Ndk/SiPDzfyXm0vSf1s3NK47GhDf523JF3TB7yU2uDL9aH2Rha5+9u9lCI+wLzkIhIeZIV2uPHj8c333xTY/mePXswfvx4qT6G/FzVPNruBnDhPNr142+DyvDY0AZ/O+7IO9d6yM9r/Tg/E6ONdf7ucj+fDTAPiYjUQCeEEFK8kclkwoEDB9CqVSuX5dnZ2ejatSsKCgqk+JhGs1qtMJvNsFgsMJlMSodDtTibX3xlHu3fnvfiPNoNZym2a2ZQGW/yk8eGNmjpuCPveJOfv+QXw1otP01hIX5dZFd3rqAElpJy5+9uNoYqUmRXxzwMDLy2JVInyfpy6XQ6FBYW1lhusVjgcMg/tQVpGwsn6fjbs3M8NrTB34478s61MeF+O7q4J4nRRsUL66sxD4mIlCNZod2nTx/MmzcP69evR3BwMADA4XBg3rx56NWrl1QfQ6QaVa0X1pJymI2hMKmg9cKdqhYNa2k5TMZQxEVo+8JLK/udtM/fckcOzE+SAnOPiPyBZIX2ggUL0KdPH7Rt2xa9e/cGAOzcuRNWqxXbtm2T6mOIVCE3rwhPbTziMndq1fN4KbERCkbm6lxBCWZsOIydV81vOn9EuiYvfrWy30n7/C135MD8JCkw94jIX0g2GNr111+Pw4cPY+TIkbh06RIKCwtxzz334Mcff0THjh2l+hgixZ0rKKlxMQkAu7Lz8PTGIzhXUKJQZK4sxfYaFysAsOPUZczccBiWYrtCkTWMVvY7aZ+/5Y4cmJ8kBeYeEfkTSefbSExMxEsvvSTlWxKpjqWkvMbFZJVd2XmwlJSr4q77ZZu9xsVKlR2nLuOyza6prnha2e+kff6WO3JgfpIUmHtE5E8aVWgfPnwYHTt2RFBQEA4fPlzntunp6Y35KAowan4+y1pSXuf6wtK618vF6iEOtcTpLa3sd9I+a2k5wvXBmNArFZlJ0SirqERYaDAOnMnHO7tyeKy5wfwkKTD3iMifNKrQzsjIwIULF9C0aVNkZGRAp9PB3WxhOp2OI4+T19T+fJbJGFrn+qiwutfLxeQhDrXE6S2t7HfSPrMxFEtHZ2JVVg5e25btXN6zVSyWjs70eCwGIuYnSYG5R0T+pFGFdk5ODq655hrn/xM1lqfns5aNzvRJy3Z9WtDNxlD0ahWLXW66SfZqFQuzSi4E4iL16NM6DjvcdMPr0zoOcZHq6CHgLa3sd5Kf1D1gIgwhWJWVU6MrdFZ2HnQAFo7MaFzAfoj5SVKQMvfU3DOOiAJDowrtlJQUAEB5eTmef/55zJo1C6mpqZIERoFJieez6tuCnhhtxNzhaXh64xGXi8qq0XXV0OoOXJk/df6IdMzccNil2O7TOg4LRqRr7oJDK/ud5OWLHjC20oo6nze2lVagmalBb+23mJ8kBalyT+0944goMEgyGFpoaCg2bNiAWbNmSfF2FMDkfq64oS3oKbERePnOTrCUlKOwtBxRYaEwq3C+2MRoI5aNzsRlm90ZZ1ykdu/qa2W/kzx81QPG38Y3kAvzkxpLitxTqmccEdHVJBt1fNiwYdi0aRMee+wxqd6SApDczxU3pgU9MdqoiQtIc7h2C2t3tLLfyfd81QPG38Y3kBPzkxpDitzjyOVEpBaSFdqtW7fGCy+8gKysLHTp0gUREREu66dMmSLVR5Efk/u5YrZcEWmXr/LX38Y3INIKKXKP53UiUgvJCu2VK1ciOjoa+/fvx/79+13W6XQ6FtrkFbmfK2bLFZF2+Sp//W18AyKtkCL3eF4nIrWQrNCuPup41RRfOp1OqrenACLnc8VsuSLSLl/mr7+Nb0CkFY3NPZ7XiUgtgqR8s5UrV6Jjx44ICwtDWFgYOnbsiLffftvr1+/YsQNDhgxBYmIidDodNm3a5FxXXl6OGTNmIC0tDREREUhMTMQ999yDc+fOSfkrkMoIAPDh/Zqqu+d9Wse5LGfLFVHjWIrtOH3JhoNn8nH6VxssxXbJP8PX+WsO16Nl00hkJMegZdNIfh+Q5siRh77QmNzjeZ2I1EKyFu1nn30WixYtwiOPPILu3bsDAHbv3o3HHnsMZ86cwQsvvODxPYqKitCpUydMmDABd9xxh8u64uJiHDhwALNmzUKnTp2Qn5+PRx99FLfffjv27dsn1a9BKiD3tBxsuSKSlpw5zPwlci+Qp7ji9wIRqYFOVPXzbqRrrrkGS5cuxejRo12Wr1+/Ho888gguX3Y/AmStgel02LhxI4YNG1brNt999x1uvPFG5ObmIjk52av3tVqtMJvNsFgsMJk4EaraWIrtmLz+oNsRQ/u0juO0HH6O+al9zGH/xfzUDuZhYGFuEqmTZC3a5eXl6Nq1a43lXbp0QUVFhVQf48JisUCn0yE6OrrWbcrKylBWVub82Wq1+iQWkgan5QgszE//wxz2H8xP7WIe+jfmJpE2SPaM9tixY7F8+fIay1esWIExY8ZI9TFOpaWlmDFjBkaPHl3n3bt58+bBbDY7/yUlJUkeC0mH03IEFuan/2EO+w/mp3YxD/0bc5NIGyTrOv7II4/g3XffRVJSEm666SYAwJ49e3DmzBncc889CA39fTqFRYsWeQ6sjq7j5eXlGDFiBM6ePYvt27fXWWi7u+uXlJTE7jUysRTbcdlmh7W0HCZjKOIi6n5G6vQlG25e9HWt67dO64uWTSN9EWq91fd3o5qYn/5HiRy+aC1FfpEd1tIKmIwhiAnXo5kpTNLPCETMT+2SOw+lPh/y/Fo35iaRNkjWdfzo0aPo3LkzAOD06dMAgLi4OMTFxeHo0aPO7Ro75Vd5eTlGjhyJ3NxcbNu2zeMXisFggMFgaNRnUsM0ZCAWrUzLca6gBDP+dRg7swNvkBkpMT/9j9w5fCavCE9uPIKs7Dznsl6tYvHS8DQkx0Y06r0D/WKf+aldcuah1IOu8fzqGXOTSBska9GWmrsW7aoi+9SpU/jqq69wzTXX1Pt9OWCEPBozEMu5ghLM3HDY5QKhalqOBB+dZOtzQW0ptmPy+wddLgKq9G4dh9c4yEyDMT/9w/mCEmw/+SuaRhlQVlGJsNBgXLSWon+baxAvYQ5ftJZi2j8OuRTZVXq1isXCkRkNbtkO5BGba8P81BY58lDqQdd4fm0Y5iaROknWoi0Fm82G7Oxs5885OTk4dOgQmjRpgoSEBNx55504cOAAPv30UzgcDly4cAEA0KRJE+j1/OJVk8YMxCL3tBz1vaC+VFjm9iIAAHaeuoxLhWW8EKCAJgB8fvh8jRapvm3qf3O0LvlFdrdFNgDsys5DfpG9QYW2pdhe4zsBuPLdNXPDYY7YTJogRx5KPegaz69E5E9UVWjv27cP/fv3d/48bdo0AMC4ceMwe/ZsfPzxxwCAjIwMl9d99dVX6Nevn1xhkhcaOxCLOVyeLpoNuaAuKClHuD4YE3qlIjMp2tlScOBMPt7ZlQNLCQeZocDlzKls3xep1tK6Z7TwtL42l2127M/Nx+Q/tHKb4xyxmdROrjyUetC1Ag/nz/qcXwP90Q8iUp6qCu1+/fqhrp7sKu3lTm6YwkLrXB/lYb1cGnI3PtIQjKWjM7EqKwevbfu9B0bPVrFYOjoTEYZgn8ZMpGZyTitkCqv7FOZpfW1sZeV15nhRGW+mkbrJlYdSn+sj9HWfP8M9rK/CRz+ISA0km96LtMFSbMfpSzYcPJOP07/aYCm2++RzqgZicUdNg5o15G58pD4Eq7JyanRZzcrOw+qsHETqVXX/yu/IdQxTw8g5rVBMhB69WsW6XderVSxiIhr2PRNt1Nea46uycmA2quP7S42Yn+ogVx5Kfa6P0IegZy053bNVLCK8OL966qnGY5KI5MKKIIDIeYfXHK7H/BHptQ5qppbuWw25G19aUVnnc6GlFZWSxEY1sZVC/eTszdLMFIaXhqfhqY1HsMvNqOMNHQjN7qg9x7Oy82B3MMfdYX6qh1x5KPW5Pjo8FI/8oTUAuORgz1axeOQPrREd7jluOXvVEBHVhYV2gFBicJ/GDGom17y4DZkCxVZW93OfRR7WU8NwgCptkHt6r+TYCCwcmfH790VYCGIiGvd9wRyvP+anusiZh4nRRswfkQ5LSbnzXG82hjZolhBzuB4pTcJxW3oiJvRMRVlFJQwhQbhUWIbmTcK9Oobk7FVDRFQXFtoBQqk7vA0Z1MyX8+K6i6++d+O18vy5v2ErhTaYw/WYM6wjnnbTyjxnWEef/I2amcIkvRHHHK8/5qe6yNmrTOpzdkK0Ebd2jHe5Sd81JcbrmJm/RKQWLLQDhFbu8F60lmL2Jz8gMznGeTe7arTf5z/5AS/dkS55y3Z9W97lbrGjK7RyDMtJjaPqXrSW4vlPfkBGcgzurdYidfDnArzgoxyWej8wx+uP+am+fJRjqsyL1tIaRTZw5TGqpzYeadRc9sCVKcqgq99rmL9EpBYstAOEmu7w1nUxUlBsx5+7pbgd7ffenqkoKG7YvLie1Kfl3Ryux4vDOrp9LvRFH7XYkbqOYTVQ6/Ow+UV2bPvxV2z78dda10uZw77YD1oZY0JNAj0/1ZqPvp4q0xdz2Td2XzJ/iUgtWGgHCLXc4fV0AhUCtY72CwCzBl8vS5x1sRTb8cKnx9y22M359BhevasTT+Q+EBepR+/WcW67p/YOsFYKNT8P66u5rd3x5X6QozXQnwRyfqo5H31N6nyXal8yf4lIDVhoBwg13OH15gQqgDpH+1XDTOqXbXZsOX4JW45fqnU9T+a+8diAK6PRVj+GereOcy4PFGp+HtZXc1u74+v94OvWQH8zqX8rVApRY7ToSf1bKRiV71222XHRUoovp/VBhUPAWnKlt1ZIsA6T1h7w63OC1PkuZU4zf4lIaSy0A4jSd3i9OYEW2+u+++1pvRz4LKIyLEV2xEYYcEvHeIzv0fz30WitpYiNMMBS5L8Xs1dT8zFYNbf1Ljc3zBozt7U7at4PgeayzY4Jq7/DhF6pLqNFH/y5ABNWf4dPJvfy2/y0V5RjxT1d8fSmmgOCrbinK0rs/jtvs9T5zpwmIn/CQjvAKHmH15sTaKSh7kPS03o5BPqziErRBencDroDXLmgmzc8TYGolKHmY9BXc1u7o+b9EGispeUotjtcxtaozp8LpEiDHjNrGRDsmU1H/Pq7Sep8Z04TkT9RvmqhgOHNCVSnQ513x0ODg3wVntfU8rx7oLHZHSgsKcf2x/uipLzS2T3TGBqEKesPwmZ3KB2ibNR+DCbHRuDlOzvBUlIOa0k5zMZQmIyhkg8Kpfb9EEgCuUCy2R04caEQK8d1RVOTAbZSB6LCQnDRWooZGw4r+t30S34xrKUVzjyMCgvBtTHhkn6GlHPZM6eJyJ+w0CbZeHMCPZdfiBeHpeGZTTXvjs8dnobislIAkTJGXZManncPRBUV5Vg6urPb7plLR3eGrbRUwejkpfZjUK4RmNW+HwJJIBdIxWXlWDfxJrzw6Q81vpvWTbwJRWXKdB3PzSvCU27mt547PA0pDZjfui5SzWXPnCYif6ITQqhhfCnZWK1WmM1mWCwWmEwmpcMJOOcKSmo9gSZEG/FzXhGe++QHXJ9oRmZStMtzfsfPWTB7SAckSXyB0FBV05RxRFPp1JWfP+cVue2eCfzedVwtx4Zc1HgMWortmLz+oNvxGPq0jvPJCMxq3A/+yNP509P3u79yV9BW8VVh68kv+cV4YsPhWmNaMCJd8pZtKTGn64fXtkTqxBZtkpWnAdlsdkedc/A+PtA3XfDqmtu7NhzRVF42u6PO+VoDqet4FTUeg5dtduzPzcfkP7Ry3iwLCw3GgTP5eGdXjk9GYFbjfghESg+4qZRiuwMHzxTUeswXK/DdZC2tqPP70lpagWtljqk+mNNE5A9YaJPs6jqBWkvkH3FUrm6u1DhKHBtUf7ayciwdnYlVWTkuA2P1bBWLpaMzUVTGv5M/C8QCqaiswsMxL/9sGfy+JCJSnvIjSxFVYzLKO6COp7m9LcX+Oy2L1sh9bFDDRBv1WJWVU6M1LSs7D6uycmA2Sl+EWYrtOH3JhoNn8nH6VxvzlmQVG1H3MR8r4ZR23tL69yVzmoj8AVu0SVWiwkLqHHU8KkzaQ9abub0DrXVGreQ+Nqhh7I7KWrusZmXnwe6olPTz2COFlFbuEHUe8+UO+YfCMXn4vjSp+PuSOU1E/oIt2qQq18WEY+7wNPRqFeuyvGpAmeskHrzFUlL3XXKLh+53JB+5jw1qGJuHbrJSdqNljxRSgyJ73cd0sYf1vnCth+9LtQ6ExpwmIn+i3luaFLBSYiMwf0Q6CksrnAPqRIWF+KSQCtfXnQLh+mDJP5MaTs5jgxpGzvmU2SOF1ECtc4inxEZgwYh0WKt9X5p8MI+2lJjTRORPWGiTKslVOAUF6dCzVazbbn89W8UiOEgnSxzkPRbV6ibnfMpWDwM6ccAnkoOa5xC/NiZc1aOLX405TUT+hF3HKaCFBOlwb89U9Lyqe13PVrG4t2cqC22iejKH6zF/RDr6tI5zWV41n7KUrVFqbUmkwCLnMe/vmNNE5E/Yok0BLTZCj3mfH0dmcgwm9ExFWUUlDCFBOPhzAT7cewav3tVJ6RCJNEeu+ZTV3JJIgSVQ5xCXGnOaiPyJTggh/3CYCrJarTCbzbBYLDCZTEqHQypwrqAEMzccdjmxV7VEJHCEU1kxP6m+mL/yYX6SHJjT9cfcJFInFtpEuDLSKVsilMf8pIZg/sqD+UlyYU7XD3OTSJ1U9Yz2jh07MGTIECQmJkKn02HTpk0u64UQePbZZ5GQkACj0YgBAwbg1KlTygQbACzFdpy+ZMPBM/k4/avNr6fVMIfr0bJpJDKSY9CyaSRP6KQJgZSjdWH+khowH6XDnCYif6CqZ7SLiorQqVMnTJgwAXfccUeN9S+//DKWLl2KNWvWIDU1FbNmzcLAgQNx7NgxhIWFKRCx/zpXUFJjLss+reMwf0Q6Etl1i0hxzFEi9WA+EhHR1VTVon3LLbfgxRdfxPDhw2usE0JgyZIleOaZZzB06FCkp6fj3Xffxblz52q0fFPjWIrtNS4YgCtzWM7ccJh36YkUxhwlUg/mIxERuaOqFu265OTk4MKFCxgwYIBzmdlsRrdu3bB7926MGjXK7evKyspQVlbm/Nlqtfo8Vq27bLPXuGCosuPUZVy22dmNiyTB/GwY5ijJgfnpHeYjyY25SaQNqmrRrsuFCxcAAM2aNXNZ3qxZM+c6d+bNmwez2ez8l5SU5NM4/YG1tLzO9YUe1hN5i/nZMMxRkgPz0zvMR5Ibc5NIGzRTaDfUk08+CYvF4vz3888/Kx2S6pnCQutcH+VhPZG3mJ8NwxwlOTA/vcN8JLkxN4m0QTNdx+Pj4wEAFy9eREJCgnP5xYsXkZGRUevrDAYDDAaDr8PzK3GRevRpHecyh2WVPq3jEBfJLnAkDeZnwzBHSQ7MT+8wH0luzE0ibdBMi3Zqairi4+OxdetW5zKr1Yo9e/age/fuCkbmf8zheswfkY4+reNclvdpHYcFI9L5rBmRwpijROrBfCQiIndU1aJts9mQnZ3t/DknJweHDh1CkyZNkJycjKlTp+LFF19E69atndN7JSYmYtiwYcoF7acSo41YNjoTl212FJaWIyosFHGRel4wEKkEc5RIPZiPRER0NVUV2vv27UP//v2dP0+bNg0AMG7cOKxevRpPPPEEioqK8MADD6CgoAC9evXC5s2bOYe2j5jDeZFApGbMUSL1YD4SEVF1OiGEUDoIOVmtVpjNZlgsFphMJqXDIaJqmJ9E6sX8JFIn5iaROmnmGW0iIiIiIiIiLWChTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhFtpEREREREREEmKhTURERERERCQhzRXaDocDs2bNQmpqKoxGI1q2bIk5c+ZACKF0aEREREREREQIUTqA+lqwYAGWL1+ONWvWoEOHDti3bx/uvfdemM1mTJkyRenwiIiIiIiIKMBprtD+5ptvMHToUAwePBgA0Lx5c6xfvx579+5VODIiIiIiIiIiDRbaPXr0wIoVK3Dy5Em0adMG33//PXbt2oVFixa53b6srAxlZWXOn61Wq1yhEpEHzE8i9WJ+EqkTc5NIGzT3jPbMmTMxatQotGvXDqGhocjMzMTUqVMxZswYt9vPmzcPZrPZ+S8pKUnmiImoNsxPIvVifhKpE3OT/IkQAlarFVar1e/G3NIJjf1GH3zwAaZPn45XXnkFHTp0wKFDhzB16lQsWrQI48aNq7G9u7t+SUlJsFgsMJlMcoZORFdhfhKpF/OTSJ2Ym+RPrFYr/rL8KwDA2of6+9UxrLmu49OnT3e2agNAWloacnNzMW/ePLeFtsFggMFgkDtMIvIC85NIvZifROrE3CR/ExoWoXQIPqG5ruPFxcUICnINOzg4GJWVlQpFRERERERERPQ7zbVoDxkyBHPnzkVycjI6dOiAgwcPYtGiRZgwYYLSoREREREREZEPCCFQWFiIqKgo6HS6WpepheZatJctW4Y777wTDz/8MNq3b4/HH38cDz74IObMmaN0aEREREREROQDhYWFGLX4MxQWFroMola1TG0016IdFRWFJUuWYMmSJUqHQkRERERERBKoKp4jIyNhs9kQFRUFAM4WawAINhidBfYDK3dixX29ERIWrmTYtdJcizYRERERERFpkxACFosFFovFOaWXEALnzp3DqMWfOf9bWFjo0ooNABVlJXhwzV7cv3InEKx3ad1W22RaLLSJiIiIiIhIFoWFhbhz/r8wcuEn1QroYkxevctZPFdvub66xTo0LAKhYeGoKCvB5NW7UFZkw30rtquu+7jmuo4TERERERGRNrgbsCwkLBy634rqKsF6o7N4DtYb8eCavaisKIMuWA+r1ersPl5dsN545f0MnruPyz1wGlu0iYiIiIiIyCcKCwtx96JP8csvvzi7jANwFtWOCofL9lXFc/WW6wl//wrnzp1rdBxyDpzGFm0iIiIiIiJqtKpWYwDOFmir1QrodM4WanuxzVlMV/3XM52zpbsx5Bw4jYU2qZKl2I7LNjuspeUwGUMRF6GHOVyvdFikAjw2iNSL+UlEFNgKCwvxl+VfAQDWPtQfAHDv619AF2KAITIClRXBNVqwveVNkV29ezjwW5H/m6r/rxo8DYBPu5Gz0CbVOVdQghkbDmPnqcvOZX1ax2H+iHQkRjfuLhZpG48NIvVifhIREXCly3d1IWHhDS6u66uqe/gHjw0GANw5/1/QR8Y4W9LDTLEoLCzEw+v2A7hyM8BkMvkkFj6jTapiKbbXuFADgB2nLmPmhsOwFNsVioyUxmODSL2Yn0REpKSqVuqqUcqrfg4JC3c+6129RfzKsog63rHx2KJNqnLZZq9xoVZlx6nLuGyzsxtigOKxQaRezE8iIlJS1fzaVaOUnzt3DlPe/Qa6EINiMbFFm1TFWlpe5/pCD+vJf/HYIFIv5icREVV/9lkJV8+v7a7Irj5YW9XPVqsVQgjJ42GhTapiCgutc32Uh/Xkv3hsEKkX85OIKDAJIWCxWFBQUICzZ8/i3te/gKPC4dMC1hu1DZx29ZRi1acekzpWFtqkKnGRevRpHed2XZ/WcYiLZNfDQMVjg0i9mJ9ERIGhemFtsVjwyy+/4M75/8KIef/E+Nc2O1uRK8qKcd+K7bLNWV0fNYpwnc4nsfIZbVIVc7ge80ekY+aGw9hx1ci1C0ak8xm/AMZjg0i9mJ9ERIGhsLAQd87/FyorKxFmir3yTHSIAcFutg3WG1VZaFep3o08xCD9/NostEl1EqONWDY6E5dtdhSWliMqLBRxkZyLlXhsEKkZ85OIyH9VFaVCCOd0XaFhv82LbXNfTFd10/Zm/mslVJQVO+ML8kFVzEKbVMkczoszco/HBpF6MT+JiPxLVYFttVrxwMqdWHFf73q9Xq1FdhVfxsdCm4iIiIiIiJyFdWRkJGw2G4QQGPvmdpSXFiNIwamytIiDoREREREREQWoqgHOqgY3u3vRpzhx4gTuXvQpzp0755w26+qpsahubNEmIiIiIiLSkKqiNyoqCjqdrs5tACAyMtL5/1FRUbDZbM7l586dw72vfwF9ZAwqK8rgcFQ6n12evHoXopomA1D/M9dqE3CFdtX8aEpOpk4UaOo6CVTH/CSSH/OTSJ18kZvMX/9RWFiIyat34bXxvRAVFVXrNlPX7wcAvDikLR5ftxu6ED1eubsLnvnkRI3lwQ4HKiscsJdcKcIdv/23tMiKyvKyGsuDQg0uy6toYfnVyyorHfXKD2/yUyeUmkVcIWfPnkVSUpLSYRAFFIvFApPJ5HE75ieR/JifROrE3CRSL2/yM+AK7crKSpw7d87ru4RqYLVakZSUhJ9//tmrL1yt4+/rf7zNN2/zU+v7jPEri/G7Yn5qM2ZAm3FrMWZAmbilzk1/odVjSAu4b73nTb4FXNfxoKAgXHfddUqH0SAmkymgDnr+voGnvvmp9X3G+JXF+OsnEPJTizED2oxbizED6oxby9e2jaHGv4W/4L6VBkcdJyIiIiIiIpIQC20iIiIiIiIiCbHQ1gCDwYDnnnsOBkNgTBLP35c80fo+Y/zKYvy+pfb43NFizIA249ZizIB24/ZH/Fv4DvettAJuMDQiIiIiIiIiX2KLNhEREREREZGEWGgTERERERERSYiFNhEREREREZGEWGgTERERERERSYiFtsr98ssv+Mtf/oLY2FgYjUakpaVh3759SoflE82bN4dOp6vxb9KkSUqH5hMOhwOzZs1CamoqjEYjWrZsiTlz5oDjE17x+uuvo3nz5ggLC0O3bt2wd+/eOrf/5z//iXbt2iEsLAxpaWn4/PPPZYrUvfrEv3r16hrHfVhYmIzRutqxYweGDBmCxMRE6HQ6bNq0yeNrtm/fjs6dO8NgMKBVq1ZYvXq1z+OsTX3j3759u9vvngsXLsgTcDXz5s3DDTfcgKioKDRt2hTDhg3DiRMnPL5O7uNfi/mptZzUah5qMf+0kneBxtOxJITAs88+i4SEBBiNRgwYMACnTp1SJliN8bRvx48fXyMnBw0apEywGsZCW8Xy8/PRs2dPhIaG4j//+Q+OHTuGhQsXIiYmRunQfOK7777D+fPnnf++/PJLAMBdd92lcGS+sWDBAixfvhyvvfYajh8/jgULFuDll1/GsmXLlA5NcR9++CGmTZuG5557DgcOHECnTp0wcOBAXLp0ye3233zzDUaPHo377rsPBw8exLBhwzBs2DAcPXpU5sivqG/8AGAymVyO/9zcXBkjdlVUVIROnTrh9ddf92r7nJwcDB48GP3798ehQ4cwdepUTJw4EV988YWPI3WvvvFXOXHihMvfoGnTpj6KsHZff/01Jk2ahG+//RZffvklysvL8ac//QlFRUW1vkbu41+L+anFnNRqHmox/7SQd4HI07H08ssvY+nSpXjzzTexZ88eREREYODAgSgtLZU5Uu3xJk8HDRrkkpPr16+XMUI/IUi1ZsyYIXr16qV0GIp59NFHRcuWLUVlZaXSofjE4MGDxYQJE1yW3XHHHWLMmDEKRaQeN954o5g0aZLzZ4fDIRITE8W8efPcbj9y5EgxePBgl2XdunUTDz74oE/jrE1941+1apUwm80yRVc/AMTGjRvr3OaJJ54QHTp0cFl29913i4EDB/owMu94E/9XX30lAIj8/HxZYqqPS5cuCQDi66+/rnUbuY9/Lean1nNSq3mo1fxTY94FuquPpcrKShEfHy9eeeUV57KCggJhMBjE+vXrFYhQu9zl6bhx48TQoUMVicefsEVbxT7++GN07doVd911F5o2bYrMzEy89dZbSoclC7vdjrVr12LChAnQ6XRKh+MTPXr0wNatW3Hy5EkAwPfff49du3bhlltuUTgyZdntduzfvx8DBgxwLgsKCsKAAQOwe/dut6/ZvXu3y/YAMHDgwFq396WGxA8ANpsNKSkpSEpKwtChQ/HDDz/IEa4k1LT/GyMjIwMJCQn44x//iKysLKXDAQBYLBYAQJMmTWrdRs79r8X8DJScVHo/N5aa8k9teUc15eTk4MKFCy5/A7PZjG7duvFvIJHt27ejadOmaNu2LR566CHk5eUpHZLmsNBWsZ9++gnLly9H69at8cUXX+Chhx7ClClTsGbNGqVD87lNmzahoKAA48ePVzoUn5k5cyZGjRqFdu3aITQ0FJmZmZg6dSrGjBmjdGiKunz5MhwOB5o1a+ayvFmzZrU+s3fhwoV6be9LDYm/bdu2eOedd/Dvf/8ba9euRWVlJXr06IGzZ8/KEXKj1bb/rVYrSkpKFIrKewkJCXjzzTexYcMGbNiwAUlJSejXrx8OHDigaFyVlZWYOnUqevbsiY4dO9a6nZzHvxbzM1ByUqt5qLb8U2PeUU1V+5l/A98YNGgQ3n33XWzduhULFizA119/jVtuuQUOh0Pp0DQlROkAqHaVlZXo2rUrXnrpJQBAZmYmjh49ijfffBPjxo1TODrfWrlyJW655RYkJiYqHYrP/OMf/8C6devw/vvvo0OHDs5n6hITE/3+70uuunfvju7duzt/7tGjB9q3b4+///3vmDNnjoKRBYa2bduibdu2zp979OiB06dPY/HixXjvvfcUi2vSpEk4evQodu3apVgMgYo5KR+15R/zjggYNWqU8//T0tKQnp6Oli1bYvv27bj55psVjExb2KKtYgkJCbj++utdlrVv3x5nzpxRKCJ55ObmYsuWLZg4caLSofjU9OnTna3aaWlpGDt2LB577DHMmzdP6dAUFRcXh+DgYFy8eNFl+cWLFxEfH+/2NfHx8fXa3pcaEv/Vqno4ZGdn+yJEydW2/00mE4xGo0JRNc6NN96o6P6fPHkyPv30U3z11Ve47rrr6txWzuNfi/kZKDnpT3moVP6pNe+opqr9zL+BPFq0aIG4uDhVfweqEQttFevZs2eN6SVOnjyJlJQUhSKSx6pVq9C0aVMMHjxY6VB8qri4GEFBrikYHByMyspKhSJSB71ejy5dumDr1q3OZZWVldi6datLC1N13bt3d9keAL788stat/elhsR/NYfDgSNHjiAhIcFXYUpKTftfKocOHVJk/wshMHnyZGzcuBHbtm1Damqqx9fIuf+1mJ+BkpNK72cpyZ1/as87qik1NRXx8fEufwOr1Yo9e/bwb+ADZ8+eRV5enqq/A1VJ6dHYqHZ79+4VISEhYu7cueLUqVNi3bp1Ijw8XKxdu1bp0HzG4XCI5ORkMWPGDKVD8blx48aJa6+9Vnz66aciJydHfPTRRyIuLk488cQTSoemuA8++EAYDAaxevVqcezYMfHAAw+I6OhoceHCBSGEEGPHjhUzZ850bp+VlSVCQkLEq6++Ko4fPy6ee+45ERoaKo4cOaKJ+J9//nnxxRdfiNOnT4v9+/eLUaNGibCwMPHDDz8oEn9hYaE4ePCgOHjwoAAgFi1aJA4ePChyc3OFEELMnDlTjB071rn9Tz/9JMLDw8X06dPF8ePHxeuvvy6Cg4PF5s2bNRH/4sWLxaZNm8SpU6fEkSNHxKOPPiqCgoLEli1bZI/9oYceEmazWWzfvl2cP3/e+a+4uNi5jdLHvxbzU4s5qdU81GL+aSHvApGnY2n+/PkiOjpa/Pvf/xaHDx8WQ4cOFampqaKkpEThyNWvrn1bWFgoHn/8cbF7926Rk5MjtmzZIjp37ixat24tSktLlQ5dU1hoq9wnn3wiOnbsKAwGg2jXrp1YsWKF0iH51BdffCEAiBMnTigdis9ZrVbx6KOPiuTkZBEWFiZatGghnn76aVFWVqZ0aKqwbNkykZycLPR6vbjxxhvFt99+61zXt29fMW7cOJft//GPf4g2bdoIvV4vOnToID777DOZI3ZVn/inTp3q3LZZs2bi1ltvFQcOHFAg6iuqptu5+l9VzOPGjRN9+/at8ZqMjAyh1+tFixYtxKpVq2SPu3os9Yl/wYIFomXLliIsLEw0adJE9OvXT2zbtk2R2N3FDcBlf6rh+NdifmotJ7Wah1rMP63kXaDxdCxVVlaKWbNmiWbNmgmDwSBuvvnmgLh+lEJd+7a4uFj86U9/Etdcc40IDQ0VKSkp4v7773femCTv6YQQQvJmciIiIiIiIqIAxWe0iYiIiIiIiCTEQpuIiIiIiIhIQiy0iYiIiIiIiCTEQpuIiIiIiIhIQiy0iYiIiIiIiCTEQpuIiIiIiIhIQiy0iYiIiIiIiCTEQptk16dPH7z//vuyf25xcTFGjBgBk8kEnU6HgoICn33W5s2bkZGRgcrKSp99BhEA9OvXD1OnTpX0PVevXo3o6GhJ35OI6scXue3L9yUi7/zvf/+DTqfDoUOHlA6FfIyFNsnq448/xsWLFzFq1CjZP3vNmjXYuXMnvvnmG5w/fx5ms1mS923evDmWLFnismzQoEEIDQ3FunXrJPkMIiKi+ti+fbvPbyoTUf0lJSXh/Pnz6Nixo9KhkI+x0CaPysvLJXuvpUuX4t5770VQkO8OvdriPX36NNq3b4+OHTsiPj4eOp3OZzEAwPjx47F06VKffgaREux2u9IhEBERaY7dbkdwcDDi4+MREhKidDjkYyy0A8zmzZvRq1cvREdHIzY2FrfddhtOnz7tXF/VneXDDz9E3759ERYW5myVffvtt9G+fXuEhYWhXbt2eOONN1zee8aMGWjTpg3Cw8PRokULzJo1y6Xo/fXXX7Ft2zYMGTLEuUwIgdmzZyM5ORkGgwGJiYmYMmWKc/2lS5cwZMgQGI1GpKamYt26dTVakHU6HZYvX47bb78dERERmDt3bo3fu1+/fli4cCF27NgBnU6Hfv36AQDKysrw+OOP49prr0VERAS6deuG7du3u7x2165d6N27N4xGI5KSkjBlyhQUFRU53zc3NxePPfYYdDqdS/E+ZMgQ7Nu3z2X/EvlCRUUFJk+eDLPZjLi4OMyaNQtCCADeHeOrV69GcnIywsPDMXz4cOTl5bmsnz17NjIyMvD2228jNTUVYWFhAIAzZ85g6NChiIyMhMlkwsiRI3Hx4kWX1y5fvhwtW7aEXq9H27Zt8d5777ms1+l0+Pvf/47bbrsN4eHhaN++PXbv3o3s7Gz069cPERER6NGjh0seff/99+jfvz+ioqJgMpnQpUsX7Nu3T6rdSaQadeX2e++9h65duyIqKgrx8fH485//jEuXLgG4ci7v378/ACAmJgY6nQ7jx493vm9lZSWeeOIJNGnSBPHx8Zg9e7ZznafzMhH9rl+/fpg8eXKtedq8eXPMmTMH99xzD0wmEx544AG3Xcd/+OEH3HbbbTCZTIiKikLv3r1dznuersFJpQQFlH/9619iw4YN4tSpU+LgwYNiyJAhIi0tTTgcDiGEEDk5OQKAaN68udiwYYP46aefxLlz58TatWtFQkKCc9mGDRtEkyZNxOrVq53vPWfOHJGVlSVycnLExx9/LJo1ayYWLFjgXP/RRx+JiIgI52cJIcQ///lPYTKZxOeffy5yc3PFnj17xIoVK5zrb7nlFtGpUyexe/dusW/fPtGjRw9hNBrF4sWLndsAEE2bNhXvvPOOOH36tMjNza3xe+fl5Yn7779fdO/eXZw/f17k5eUJIYSYOHGi6NGjh9ixY4fIzs4Wr7zyijAYDOLkyZNCCCGys7NFRESEWLx4sTh58qTIysoSmZmZYvz48c73ve6668QLL7wgzp8/L86fP+/yuc2aNROrVq1q4F+LyLO+ffuKyMhI8eijj4off/xRrF27VoSHhzvzyNMx/u2334qgoCCxYMECceLECfG3v/1NREdHC7PZ7PyM5557TkRERIhBgwaJAwcOiO+//144HA6RkZEhevXqJfbt2ye+/fZb0aVLF9G3b1/n6z766CMRGhoqXn/9dXHixAmxcOFCERwcLLZt2+bcBoC49tprxYcffihOnDghhg0bJpo3by7+8Ic/iM2bN4tjx46Jm266SQwaNMj5mg4dOoi//OUv4vjx4+LkyZPiH//4hzh06JBvdzSRzDzl9sqVK8Xnn38uTp8+LXbv3i26d+8ubrnlFiGEEBUVFWLDhg0CgDhx4oQ4f/68KCgocL6vyWQSs2fPFidPnhRr1qwROp1O/Pe//xVCeD4vE9HvPOVpSkqKMJlM4tVXXxXZ2dkiOzvbea198OBBIYQQZ8+eFU2aNBF33HGH+O6778SJEyfEO++8I3788UchhPDqGpzUiYV2gPv1118FAHHkyBEhxO+F9pIlS1y2a9mypXj//fddls2ZM0d079691vd+5ZVXRJcuXZw/L168WLRo0cJlm4ULF4o2bdoIu91e4/UnTpwQAMTevXudy44fPy4A1Ci0p06d6vF3ffTRR12KgNzcXBEcHCx++eUXl+1uvvlm8eSTTwohhLjvvvvEAw884LJ+586dIigoSJSUlAghrnyJVo+nuszMTDF79myPsRE1VN++fUX79u1FZWWlc9mMGTNE+/btvTrGR48eLW699VaX9XfffXeNQjs0NFRcunTJuey///2vCA4OFmfOnHEu++GHH1xytkePHuL+++93ee+77rrL5fMAiGeeecb58+7duwUAsXLlSuey9evXi7CwMOfPUVFRvMAgv1dXbrvz3XffCQCisLBQCCHEV199JQCI/Pz8Gu/bq1cvl2U33HCDmDFjhhCi7vMyEbnylKcpKSli2LBhLq+5utB+8sknRWpqaq0515BrcFIHdh0PMKdOncLo0aPRokULmEwmNG/eHMCVLqDVde3a1fn/RUVFOH36NO677z5ERkY6/7344osu3Vo+/PBD9OzZE/Hx8YiMjMQzzzzj8r4lJSXOLqdV7rrrLpSUlKBFixa4//77sXHjRlRUVAAAjh8/jpCQEHTp0sW5fbt27dyOhlw9Xm8dOXIEDocDbdq0cfm9vv76a+fv9f3332P16tUu6wcOHIjKykrk5OR4/Ayj0Yji4uJ6x0ZUHzfddJPLYwvdu3fHqVOnvDrGjx8/jm7durm8X/fu3Wt8RkpKCq655hrnz8ePH0dSUhKSkpKcy66//npER0fj+PHjzm169uzp8j49e/Z0rq+Snp7u/P9mzZoBANLS0lyWlZaWwmq1AgCmTZuGiRMnYsCAAZg/fz4fzyC/VVtuOxwO7N+/H0OGDEFycjKioqLQt29fADXP5+5UzzkASEhIcHY7r+u8TEQ11ZWngOdr1EOHDqF3794IDQ2tsc7ba3BSJz6FH2CGDBmClJQUvPXWW0hMTERlZSU6duxYY3CjiIgI5//bbDYAwFtvvVXjgjw4OBgAsHv3bowZMwbPP/88Bg4cCLPZjA8++AALFy50bhsXF4f8/HyX1yclJeHEiRPYsmULvvzySzz88MN45ZVX8PXXX9fr96oer7dsNhuCg4Oxf/9+5+9RJTIy0rnNgw8+6Pb5tOTkZI+f8f/+3/9zKU6I5OTNMe6thuSYt6pfXFRdrLhbVjVd3uzZs/HnP/8Zn332Gf7zn//gueeewwcffIDhw4f7LEYiNSktLcXAgQMxcOBArFu3Dtdccw3OnDmDgQMHejVY4dUX9DqdzplfdZ2X3RUCRFQ3T+dPo9FY6zpvrsFJvVhoB5C8vDycOHECb731Fnr37g3gykBfnjRr1gyJiYn46aefMGbMGLfbfPPNN0hJScHTTz/tXJabm+uyTWZmJi5cuID8/HzExMQ4lxuNRgwZMgRDhgzBpEmT0K5dOxw5cgTt2rVDRUUF9u/fjxtuuAEAcOLECcmmKsnMzITD4cClS5ec++NqnTt3xrFjx9CqVata30ev1zvvWlZXWlqK06dPIzMzU5J4iWqzZ88el5+//fZbtG7d2qtjvH379m5f70n79u3x888/4+eff3a2ah87dgwFBQW4/vrrndtkZWVh3LhxztdlZWU51zdGmzZt0KZNGzz22GMYPXo0Vq1axUKb/E5tuf3jjz8iLy8P8+fPd+bf1QMC6vV6AHB7fvKktvNy586dG/ibEPmv2vLU20I4PT0da9asQXl5eY2bWd5cg5N6sdAOIDExMYiNjcWKFSuQkJCAM2fOYObMmV699vnnn8eUKVNgNpsxaNAglJWVYd++fcjPz8e0adPQunVrnDlzBh988AFuuOEGfPbZZ9i4caPLe2RmZiIuLg5ZWVm47bbbAFwZ7djhcKBbt24IDw/H2rVrYTQakZKSgtjYWAwaNAgPPvggli9fjpCQEEydOrXOO3/10aZNG4wZMwb33HMPFi5ciMzMTPz666/YunUr0tPTMXjwYMyYMQM33XQTJk+ejIkTJyIiIgLHjh3Dl19+iddeew3AlREld+zYgVGjRsFgMCAuLg7AlS9ag8HgthsukZTOnDmDadOm4cEHH8SBAwewbNkyLFy40KtjfMqUKejZsydeffVVDB06FF988QU2b97s8TMHDBiAtLQ0jBkzBkuWLEFFRQUefvhh9O3b19lNbvr06Rg5ciQyMzMxYMAAfPLJJ/joo4+wZcuWBv+uJSUlmD59Ou68806kpqbi7Nmz+O677zBixIgGvyeRWtWW28nJydDr9Vi2bBn++te/4ujRo5gzZ47La1NSUqDT6fDpp5/i1ltvhdFo9KonS13nZSKqqbY89dbkyZOxbNkyjBo1Ck8++STMZjO+/fZb3HjjjWjbtq3Ha3BSMaUfEid5ffnll6J9+/bCYDCI9PR0sX37dgFAbNy4UQhRc4CG6tatWycyMjKEXq8XMTExok+fPuKjjz5yrp8+fbqIjY0VkZGR4u677xaLFy92GVBJCCGeeOIJMWrUKOfPGzduFN26dRMmk0lERESIm266SWzZssW5/vz582Lw4MHCYDCI5ORk8e6779YYfKx6/HW5ejA0IYSw2+3i2WefFc2bNxehoaEiISFBDB8+XBw+fNi5zd69e8Uf//hHERkZKSIiIkR6erqYO3euc/3u3btFenq6MBgMonpKPfDAA+LBBx/0GBdRY/Tt21c8/PDD4q9//aswmUwiJiZGPPXUU86BWbw5xleuXCmuu+46YTQaxZAhQ8Srr75aYzC0Tp061fjs3Nxccfvtt4uIiAgRFRUl7rrrLnHhwgWXbd544w3RokULERoaKtq0aSPeffddl/VX56+776DqgzqVlZWJUaNGiaSkJKHX60ViYqKYPHmyc3BCIn/hKbfff/990bx5c2EwGET37t3Fxx9/XCN3XnjhBREfHy90Op0YN26c830fffRRl88aOnSoc72n8zIR/c5TnrobMNfdee77778Xf/rTn0R4eLiIiooSvXv3FqdPn3au93QNTuqkE+K3id6IZHDhwgV06NABBw4caPDd8ebNm2Pq1KmYOnWqtMFJ6PLly2jbti327duH1NRUpcMhIiIiIon169cPGRkZWLJkidKhkApx1HGSVXx8PFauXOnVqKha9r///Q9vvPEGi2wiIiIiogDEZ7RJdsOGDVM6BJ/r2rVrg6YcIyIiIiIi7WPXcSIiIiIiIiIJses4ERERERERkYRYaBMRERERERFJiIU2ERERERERkYRYaBMRERERERFJiIU2ERERERERkYRYaBMRERERERFJiIU2ERERERERkYRYaBMRERERERFJiIU2ERERERERkYT+P4TyquBQSzexAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pairwise plot\n",
    "sns.pairplot(df_clean, vars=['area(sqr feet)', 'bedrooms', 'baths', 'price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.histplot(df_clean['area(sqr feet)'], kde=True)\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(df_clean['bedrooms'], kde=True)\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.histplot(df_clean['baths'], kde=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p06cnzV2u29jcDhf3VnoPa0EMTmU2cVipVDh16hR9fX20tbUZcWi4rTBi0HBbUm8SCcOw0S0818l6rTaQhGHIoUOHyOVyPPLII3R2dnL27Nk1uda5WI0LphDiJhubsbExcrkcAwMDnDhxAtd1G8Iwm822nI1NKx4La1EMTmWqOMzn83R3dzfEYf0xUz0OjTg0tBpGDBpuO6aOlJvviXktRgbz+Tz9/f3E43H27t3bECmtkNKeidVet5SS9vZ22tvb2bZtG1EUkc/nGRkZ4dKlSxw9epREItG4wAdBsCI2NktBKwmQVhCDU9FaN2oImyOHSqmGOKzbVBlxaGgljBg03FZM9Q5cyAl4LTWQaK25cOECJ06cYPv27Wzfvv2m97JW1trqWJZFNpslm80CN2xszp07x/j4OF/+8pdJpVKNyGF7e/sdb2OzFLSiGKyfV+rMlFaOoogoiqhUKkYcGloCc0Yz3BbM5B24ENZKtC0IAg4dOsTo6CiPPvpoQ6Q006qdsWs1Fd9M3cZmfHwc3/fZvn17oxnl5MmTVCqVhsdhNpslk8lMmoixGtT3aSsJjFYUg3Otebq5ys3icGpDSvNc5VbbF4bbCyMGDS3PYtPCU1kLkcF8Ps/+/ftJJpPs27dvxiaRpVyruQjNjuu69Pb20tvbC0y2sTl8+PAkG5uOjg7S6XTLivWVpBXFoFJqwdmGmcRhGIaN+6ebq9xq+8bQ2hgxaGhp6tHAxaSFp7KakUGtNefPn+fkyZPs2LGDrVu3zhmBWKq1ruR7boXI4FzMx8amvb29IQ5TqdSyX9hNZHBlqGcdFstM4jAMQ4IgmCQOm+cqmx8XhuXGiEFDSzLVO3ApanBWq4HE930OHTrE2NgYjz322Ly8A1tVVLXaxX8uprOxKRQKDXF49uzZSXOXbycbm1ulFcXgQiODc2HEoWGtYMSgoeWoewfWhdtSpVRWI02cy+Xo7+8nnU6zd+/eeXsHtqoYvN0RQpBOp0mn02zevBmlFOPj44yMjExrY9PR0bEk86RNZHD5qQu15RRic4lDmH46ihGHhlvFiEFDy9A8Um4p0sJTkVI26g6XG601586d49SpU+zcuZMtW7YsuBapVcVgq657MUgpaWtro62tbZKNTS6X4/Llyxw7doxYLDZJHC6FmXgr0IpiEFZWcM8kDoMgwPf9xv1GHBpuFSMGDS3BUjWJzMZKRQZ93+fgwYMUCgVe8YpX0N7evuBttKoYbKWL/3Iw1cYmDENGR0fJ5XKcP3+ew4cPL8rGxkQGl5/6Pl5NoTWdOKz/QK5HDqeKw3q3ssEwG0YMGtY8zSPlltOfayUaSOpp4ba2Nvbu3btoM+NWFYNwZ0UG58K2bbq6uujq6gJqPxRGR0cZGRm5ycamo6ODtra2VbexWSpaTQw2l6WsFer1hHWaxWE9clivPUyn05O6lQ2GZowYNKxZFjNS7lZYzsig1pozZ85w5swZdu3axebNm2/pvbSqGDQXodlxXZeenh66urPkw4vky1cpFsYo5EOuHb2G7/sNG5tsNtuwsTGRweVnLUQG52I6cXjlyhVyuRx79uxpRBWnNqS00udgWB6MGDSsSVYiLTyV5YoMVqtVDh48SLFY5PHHH6etre2Wt7mUYnClLwStKGJXklBVOFv6PCPhKZSOIK7xkhl2bd9HMtrS6FS+dOkSSina29tJpVJAawmsVlorrM3I4Fw0T0dxHGfayOF0DSmt9B4NS4MRg4Y1x1J6By6E5bCWGRkZob+/n46OjltKC0/FRAZvXwb9IwwFx0nZ67CFh9aaYnSdi5Xn2ZNaz4YNG9iwYUPt9mKRkZERhoeHAfjyl788qRklkUisyX3eypHMVlozTB6h1xw5rH8G9RGezdNRjDi88zBi0LBmaPYOXOxIuVthqY2cT58+zdmzZ7n77rvZtGnTkvuTtaIYBBMZnIuh4DiOiGMLD5jwMrR6yIfnGQ8vE7PaGrenUilSqRRdXV288MILPPjgg+RyOQYHBzl16hS2bd/kcbgWaEUxuNQegyvF1HnKdZqjhmDE4Z2OEYOGNYFSijAMVzQtPJWligxWq1UOHDhAuVzmiSeeIJPJLMHqJtPKaeJWxldVLlVOE+mIDd42EnZqSbevtUbpACEmN4nUPyPFzNZHzTY2W7duJYoixsbGyOVyXL16lePHj+N5HtlsdtVtbFpRDC63x+ByMZMYnMp04rD+r1qt4vs+ML3PYSt9jobpMWLQsKo017CsdhpmKRpIhoeH6e/vp7Ozk4cffnhetiCLoVUjg6180ThZPMDzo59mLMoBmoSV5uH0Ph7JvGrJXkMIQbu9jSvVbxCXWYSoXcSrahxbxEha3dM+b7pjwbKshuiD6W1sksnkJBubpSpjmItWFIO3W2RwLprPxZZl3SQOmyOH9WYU27ZX5Ye84dYxYtCwakxtElntepxbaSDRWnPq1CnOnTvH7t272bhx47J3PreiGITWSRM3r3PQv8qzuX+grIqkrQ6kkIyHo3wt/1kyVpYdyfuW7HV7vPvIhxfIh+dxRIKIALSmz3uYpNU74/PmOt6m2tgEQdBoRjl9+jSlUmmSjU17e/uy2di0ohhs5cjgUvwonU0cViqVxmPq4rAeOTTisDUwYtCwKqyUd+BCWGyauFKp0N/fj+/7PPnkk6TT6WVY3WSWstmlfkJfCdbC57wYjhdfphiN0+X0Nd5Dh9PFkH+NY6WXllQMxq0OdiWfYcg/Rj68iC3idLo7yDo7Z9x/i/n8HMehp6eHnp4eoFbekMvlGBkZ4dixY5NsbDo6OshkMksmhlpRDN5pkcG5mK84rEcMjThc2xgxaFhRVto7cCEsJto2ODjIwYMH6erq4tFHH122tPB0tEqEbSqtuO5CND5t5NoWNmPh6JK/XsxqZ2P8STby5Lyfc6vfI8/z6Ovro6+vD6015XKZXC7HcG6IEwP7qYoi7YlONmZ20dXRTTqdXvRrtuIx0MqRwZVY90ziUCnVEIdSyptqDtfSNeBOxohBw4qxGt6BC2Eh0TalFKdOneL8+fPcc889bNy4cZlXN5m1tN8WQquuu92u1d0prZDixiiwQAdknZ7VXNqyIISo2dJ4EReSX8P3LxCEPleDCwxVTpE+sB1PpWlvb28YYC/ExsZEBleOlRKDU5lNHFarVSqVihGHawgjBg0rQt2uYK1FA5uZb2SwnhYOgoCnnnqqYfi7kiyHJ+JK0YpRod3JRzhafJmR4DpJO41AUozGiFtJ9iQfW+3lLZuB87Hi81yrnqbD7sNxPMJYQC52lWRPhXvlPnK5UYaGhjh9+vSCbGxWu1kMIFR5lK5gyyxSzN04YyKDt8bUz7suDqMoIoqiGRtSVvs4uVMwYtCwrNS/7PVu4bUqBGF+AmtwcJADBw7Q29vLPffcs2pzYlu1gWStfvZz0e508S2db+P50U8yHFxDacg6vbwi8xo2x3es9vKWhVI0xoB/lrTVgSNrnoe2cGizu8mFV9HtVba2bWXr1q0opcjn8zfZ2DSLQ8/zGttezekjoRpjuPpJiuFhFD6O6KTDezVp+7FZ12Qig0tLXeTV19YsDsMwpFAoUCqV2LBhw6TIoRGHy4MRg4ZlYy14By6E2bqJlVKcPHmSCxcucO+997J+/foVXt1kWlUM1mm1UWQAG2Pb+Z6en2YovEqkI7qd9dhybZxCl2N/hrqK0iGWTEy63RIOkQ4IdbVxm5SSYuw6L8Y/wLB9EboFHXIT8fIbuHixwJEjR0gkEg2PQ9d1V+Xz1zpkoPL3FMMD2KILW7QTqCGuVz6EiDmknYdmea6JDC4nU8VhoVBgZGSE3t5egiBo3D9dQ4rh1lkbZzLDbUW9LuTixYtkMpk1OxJrKjP5DJbLZfbv349SatXSwlNZrBhUuko1PEo1PAU6wrV3YIsdwMqYD7fCcTAbUkp63A2rvYwVIWG1E7cylKIxXBlr3F6KxohZKVJWZ+O2q+XTfHzw96ioCvVP+Jo6wZh3le9/6D8SJ9PwODxz5gzFYhGAU6dO0dHRQVtb24o0X5Wjs5SiE7hyI5aoiVzLSlKJzpH3nydlPzjjMWoigyuL1roh/Op/16dUTRWH9bSyEYeLx4hBw5LSPFLuzJkz7Nixg2QyudrLmhfTpYkHBgY4dOgQfX197N69e9XSwlNZjBjU2me8+hmqwSGEiCMQ+NWz2OIMnnw9FisnclsxMriWWY79aQuHu+KPcqDwOUaCK8Rkkqoqo4nYHdtHzLrxvX4+/3dUVQVXuFiy9h2JVEhJjfPV0Q/xpu6foru7m+7ummn28PAwBw8eJAgCjh8/TrVaJZPJNFLKbW1ty3JRD9QwWgfTRDsz+HoQTRVBbNrnmsjgylK3HaszU1q5Lg5h+ukorfjeVwMjBg1LRrN3YP1L20qpzGaBpZTi+PHjXL58mXvvvZd169at8uomsxgx6EfnqAZHcazNSFG74GkdUAlOItmCw0PLsNLJGAG4/CgdMRRcoRwViMkEXe4GLLG4U/2m2B5s4XCucoBimKPN6WZL7H42xfZMetywfwGgIQRr/20TRD4D1VM3bbceybnnnnsAGjY2uVyOK1euEIZhw+Mwm83eko1NM7bMIJAo7SPFjWi40iVc2YuYJUJuIoMrSxRFs/74nkkcBkEw6+i8VtwXK4ERg4ZbZibvwFbreK2vt1Qq0d/fj9aap556ak1GNhcjBoPoGqAbQrC2HQchPMLoEqyAGKzTSj8SWoF6ZLAcFdg/9gUGg0tEOkIKSaezjofSryVlty94u0II1sd2sc7bSUSIhT2tIJKziM3p7psayYzH48Tjcbp6A6rhZSrVAqVClfxQxIULNaFZt7Hp6OggmUwuSpjFrR3ErM2UozO4cgMSj0CPoPBpc55sjP+bDhMZXFmUUgvKxEwnDusBinrkUAgxSRzWu5UNRgwabpHZvANbTQzW1/3888+zfv167r777jWTFp7KYuYoC5wZRFiEECtTM2hYHuqf69HC17jqn6XD7sWRHqEOuO5f4lDhOZ5oe8uiL3xCCGxmtl/ZGNvDseLzhMrHlrVjKVA+AsH2+KPTrnfqWorBVyj4nyTSYyAFbsZiY3YP97rfT7GgagbYw8OcPn160tzluo3NfN6bFC49se9jsPoRKtG5iZRxhk7nTWScx2d9rokMrixRFN1SHWm9nrBOszj0fb8hHuvisLlb+U7EiEHDopnLO1BK2RCJa526iTTAnj17Vr1beC4WExl07E3IIEaohrFlrfg/UnlAYsutS7/IabjdTrS+qlKIxvGkR9Ja/jGEsxHICnn/PKmbrGC6GAqukA+HaHe6l+W1X93xI1yrniIfXieISghACEmPs5XHM2+96fFTxWCoBij4nwYknrUbqDU7VYIDuHI7mczryGQybNmyBaUUY2Nj5HI5BgYGOHHiBK7rNlLKU21spuJZfWyI/yRVdYlIl3FlH45sn/M9tmqda6uKwaVe93zEoZTypoaUVvzMF4MRg4YFM1/vQMuyWiIyWCwW6e/vb/xdn9W6llmUGJQbSDhPUQ6+RiU8gUAghEfceQyHbcu00ulp9TSx0ooTxUOcKh+mFBWxhcNGbysPpF9BzErMvYElRmuNEiERIZ6YXNZgC4dIh4TaX7bXj9spfqjvd/jG2Ce4WD2IFBbbYo/yWPszM663+UJfjU4T6TFcubNxmxQeUqQphy+Tcl9343YpaW9vp729nW3bthFFEfl8npGRy1y+9hxnL13GcxMkY/fT0b6Njo4OHGdyVFMIi5i1ZUHvsRVFVV3wtNq6Ye6awVtlvuJwas3h7SoOjRg0LIiFjJRrhTTx1atXOXz4MBs2bGDHjh187nOfW/NrhsWJQSEESe9xXHszQXQF0DhWH5K+Rk3NcnO7nEhPl4+yf/yrONIjY7Xj6yony4fwdZW97W9ojKxbSVydQMv0hBXMjQhgKRojLpOkJkbqLZQoirhYOUA+GqDX3UFfbHqTbceOsTf7PcD3zLnNm6NstfPJTceHkI37ZtyWGELGP0u855O4XdcABxW1EfovcunaExw6tIVUKtVIKbe3ty8q/diKNYP1c0SrrRsWXjN4qzSLw+ZGQt/3J01HuV3FoRGDhnlTjwbOd6TcWhaDURRx7Ngxrl27xv33309vb++kE8Ba51ZMpx2rD8fqa/y9Gu+3lSODkQ45VTqKJRza7SwADi6WsLlavUAuGKLTXdnostYaC5u7Eg9xYPyLjARX8WQSX5VRROxJPEVMLjxiOeRf5P8OvofhYJhIa2whWe9t5i097yBpZW5pvc04cjNSxInUKNZEylbrCKXzJOwnZtxOpEbJV95HJXiJUF9D4KF1iGUHeC6k04fYveu1jOfj5HI5Tp48SaVSmWRjk8lk5iU6WjFNXP9ut6IYnGots5I0z1SGO0McGjFomJNm70CY/ySRtSoGC4UC/f39SCnZu3dvY4Zq/T21glBZqgkkWmvOnDlDLpdr1FwttlNzPrTqibKZqqpSjgrE5eTZu56IMapHKEbjdDK9GLzuD3Bg/CU0mj2J+9kQ37Rk6xJCsDm2u2YFUz5MIRqlze5iS3wPm2N3L3h7URTxT4N/zIA/REzaeMIGSowGB/nC4K/xdOfPkrZ3zNpJPBNThZUjN5Own6IYfIkoGgJcNGVcuY2E89SM26lG/QTReRASoV0s2Y5GoVQedC8Royh5gt7eN9Lb2wvMbmPT0dFBOp2eVoSsdKSqGT86QTX4BpEewpabiDlPYMu5zc9bWQyu5v6eynTisP6vWq1OsrJ57rnnsG2bN77xjau23sVgxKBhVuo1FPWTykLmQq5FMXjlyhUOHz7Mpk2b2LVr100nybW45ulYCjHo+z4HDhygWCzS19dHLpfj7Nmzkzo1s9kssdj0Jry3QisI7plwpUdMJihFReJNxsu+ruIIe9JtzXxy8ON8Lf8cvq4Cmudyz3J/6mG+u+8HbnlN9f0phGBDbAfrvbuIdIAlnEUL8DOVrzPsjxCXDq50ScgxknIEdECgxzg+/qd0e69gc+J7sMTMDRszrbd5XUII0u4zONZmKuEhtC7hWjuI2480IoXTEURXQNhoHSJE7UItkCAkWhQmOugrk55Tt7FZv349WmtKpVJDHF64cAGt9SQbm1Qq1ejen1p7uBJUgucpVD+A0uMI4eGHL1MNXyAd+wlca9esz20+b7caqxkZnIvm66BlWZPE4Uc/+lGSyaQRg4bbg+Zi2vmmhacipVyxWrS5iKKIo0ePMjAwwIMPPjhjk0irzPy91XWOjo6yf/9+MpkMTz75ZKMeSilFPp9vRE2OHz9OPB5vCMP29vZbuiC22kVpuvXawmZ7fDcvjT/HWDhKwkoRqCr5MMem2DY6nZuPrYPj+/lq/ksoHZGSta7jsi6xf/xF1nnr2dvx6iVdqxAC+xbtgkbDa0QoEsSw8WtCEAjwiJRG4zDif5OUvY1ub++s29JaE+kQKSykkNOmXIWwiNsPEbcfmvcapUgDAZbI4qthBBPb1Qq0BGHhWDNH0IQQJJNJkskkGzduRGtNoVBoiMOzZ/tJbfgYXiygUn0LlvXwiqaLlS5Q9P8BTYhj14Sf1ppQnaTkfwIn9s5ZvREXe+5eC6ylyOBcNIvDYrHYEk2IUzFi0HATC2kSmY21EmUrFArs378f27bZt2/frJGutbLmuViMzyDUPtsLFy5w4sQJduzYwdatW9FaT0pz1CMi27dvJwzDxoXx9OnTlMtl0ul0w8JjucaGrXV2Ju8l0D6ny0fJBYM40mFrfCcPZ56atnmkf/ybBDqgramJI0mafJjjQOHlJRGDS02nvRFbSHx8krKCRYSvYygdIoUkafUQ6CFyQX9DDAbKJxdcxZMJ2iZsbK5Wr3CkcJhBfxBXuuxI7CSrOpdEoMTs+yiHzxPpAlKkiPQQaAEoNBVi1kO41n3z3p4QgnQ6XUsVd/w3vPCfandoSG5+iarfzvPP/6dJHofLETmvE0ZnJ1LDmyet0RK9hNE5lB7CEtMLD60VxfAUVuoMxXA9CeuuRvS0FVjLkcHZKBaLa2J+/UIxYtAwieaRcrf6i3It+AxeunSJo0ePsmXLFnbs2DHnyaWVxOBCI4NhGHLo0CFyuRyPPvoo2Wyt+WG27di2PWmmbKVSIZfLMTIy0qi3am9vJ5vNLqjesBWir7NhCYv704+xI3EP49EYnoyRsdpnfO/FqMB091hISlHhltezHNGqLbFH6Ha6ueIPEKoqWmpCHaHQZK0OHBkjjGyUrgJwcPxZDox/lfFoDFtYbPS2sD2xlxfHXqakiqRkimJU4Gv5F1gXrmOdmLvmbS4caytp960U/E+iKYPSaF3FsTaTdJ4h5b0WucAUNsBo9RMU6kIQQAAaHHeU7nv+ACv3e1y+fJljx44Ri8UmRc5ddykN3K0bLz4JDUJM3H8zgcpzpfy/yQeHkd0jnC8eIGHvYH38B3FldgnXt3y0UmSwmVKpZMSgoXWZaaTcrbCas4nDMOTIkSMMDg7y0EMPNcTMXLRKmnih+7ZQKPDyyy/jeR579+6d1ZR3NmKxGOvWrWPdunVorSkWiw1xePbsWaSUjajhdPWGrZiumo24lZyxRrCZHreP85WzKBUiZe20q5UiIqLb6Zvj2auDZVk80/0OPjX03xkNTxNqjYWizc6yOf4AWkeEukja3sexwld5LvcpFIqUlSLQPseKRzlWvEjSXsc6d0Pjsy9HJS5XL5GSi+9GbibuPI5r7SZQZwGwxSYs2T5r+nQuBkt/MON9Spxi1113AbXzzOjoKLlcjnPnzlEoFJbExqaObW3DFn2E6jK23DpxfoqI9HU861GkmF7YDVQ+ynjQj6W7EL6HI9spBIcZ4P+wMfETa/57WL8etWpkcC2OMJ0LIwYNS5YWnspqRQbHx8fZv38/ruvOmRaeyu0YGaw3zcw3OrqQNaRSKVKpFJs2bWpMhhgZGeHq1avT1hvWL4ytILiXkr3tr+Jo8RDj0TiOdhEIfF0lLhPs63jNLW9/uerY2t0+vm/9b3CtfIKLlQ+i9BVcmcJXQ4S6QNLaTJf7OC8Mvo9Qh3R7tZSlRwxbuJwuDxO3JqeEYzKOrwNKsjjra0dRRCn8BH50HEv0kXbfhmXFp32sJTNY8sEle98Rs0Vrbxy7tm3XfvRkvknHuq8QqQJBdSuFkSc4eXKISqVCOp1uiMO2trYFRbukiJP0/hnj1b8lVCcAC7TCtjaSdL9j2s/cV0MUwiM4sgtFDEQFS8RxZS/F6ARVdY2YtW4Be2PlqZ8fWjEyaNLEhpZkod6BC2GlJ5Borbl06RLHjh1j69at3HXXXQsWPq0SGZzPOpVSHDt2jCtXrszaNLNUNE+GAGasN4RaA0tPT09L/vJfDL3eOr6394f51PAnGPQHAMU6dz2vyb6R7YnpTZwrUZmSKk2Mupv/xWUsHGageoayKpCyO+hzt5O4BU9AgL74Lrpjv8iI/yK5oJ9IV+m299HlPo4UGfLRKHFr8o8uT3oINGU1WfQpFEILnFlmHfvRBYYKPwv6IoIIjaTk/yXt8d8h5jyBr8s4Ioa1CFub+WCRIqIyw703zpFKKUbLv08YfRK0j0Tiud/AXf8i23e8CxV0Nb4DR48eJQiChsdhNpud0camGc9+GEv2UA1eRukRLLkOz34Ua4Z0b6RLKO3jygwRN34kSBEjUCNEenYRvhZoDky0EvVsiRGDhpah2TtwtpFyt8JKRtnCMOTw4cMMDw/z8MMP09XVtajt3C6RwXK5zP79+9Fas3fvXhKJlR+RNlO94djYGCdPnuTYsWONesNmC4/blR3Ju9mRvJucP0JESNcMxtSRjjhZPMa5ymkqqoIjHNZ7G7kndT8xOX2Uux4ZHPDPcWj8WUpqHClsVCXkgnWEh9JvoH2iyzmKfE6VXybQFbYlHiJptc1r/Zbw6Pb20e3tu+m+uEgwqkamvI+QuJBoBOWoREzGUSiGgkHSIk2WyWKmqopcqhzlun+GTv0XJLmEFgkEaSCEaIjj47/JmPh+yqqAK+Jsit3H9sSjSy4Ku+LvYKD8H6a9zxU37Fwq0TcJo8+g8RCyDw1oHSDVGcYr76Mz+e/p7esh3jVKKhgl8H2ioqCQs7h06RJKqWltbKZiyw3Y3vxqLF3RiS0zBCqP1mnqmwv1KLbI4Mm13+nayv6IRgwaWgalFGEYLnlaeCorJazGxsbYv38/sViMffv2LboeDm6PyODg4CAHDhygr6+P3bt3r5lUS73e8NixYzz66KMopRZUb3i70OHOXsB/pnSKw8UDJGSCDjtLVVU5VT5BpEMezTw57XdVa01EyIni16jqCl3OpsYxMhRc5GTpGzyWeQtHi8/x5dzfUVZjxITPi3lFn7udx9p/kKyzB7nIbtPdqYd5fvQzjAd5UlaagIBcMMqGWDcbYo9ysXqZ0XAUgaDd6WCbvR2nyfrGV2X6xz/NkH+OhKwQk1eJqAlJWwgELtdUJ2d8iS0PknbuxldFjhS/QFUVuS/9ullWt3A6Ym+lGHyFQvipyXeoLBuS/6vxZzX8OugyQtwwDxfCQRFHRy+idMTF8icY9L+B1jXTfpG06Wx/hD17vp1SsdJkY3MWIcSkTuVEIrHgc7Mlk2SdpxmofgxfF8ESVKIraKp0e89gL1Gt5nISRVFjwkerYWoGDWueZu/AeiRhOSMxyy0GtdZcvHiR48ePs23bNu66664laXpp1cig1ppTp05x7tw59uzZw4YNt96tuZzMVW8Yi8UaXcq36m/YKoQq4HzlDDERI2PXIna2ZSORXK1eIR+O0u5MP2O4ao9RCEfION2N74EQgrTdSS64xoXKUT4//D58XabTLuEKH4ViJDjEi6N/zK7kM2xLfNui7EceSL2OQjjG8VI/Q+EQEkmP18urO76LXnc7Q8EguSCHK13WeesZuDjAuBhvPH/AP82wf4EOZwMxfRqBRmMT6QhJhMDmapAGFGlLEpMpYjKFFeW5VD3M1vhDpOyl7ZLdmP59qtVfZ9D/bZQe5drZp9m2/k2Tf2xqBdP2iQMo8sExBv1v4IkszkSqP1RFhv2XaHN2kE0/QDqdZvPmzSilGB8fJ5fLMTg4yKlTp7Bte1EG8J3e65DC41Ll0wgxiCvX0eHuI+u+8tZ2yjREQUAY/h4yfBaBJrKexHZ+HesWvq+t2klcTxPXy2FaCSMG7xCmNokstxCE5RVWQRBw+PBhcrkcjzzyCJ2dnUuy3cX69600U8VgtVrlwIEDlMtlnnzyyTV9MpruuJuu3nB0dJSRkRFOnz5NqVSaVGt1u/obVnWViioTn9IoEZMx8lGOiioDN4vB+rEwXaxYTIiVg4XPUtVV2i3wZECoXUDi64B8OMZ1/5tk3Xtod2afajEdlrR5Ovs9PJB+muv+BVwZZ4N3N7asCYIet5cet3fSepuPg1xwBSEsLGET6A0oHCx8IIYiItQuVa3xhMJqSnPGZYaR8BLFKDetGFQ65Fr1KNf9k0Q6oNPdyjrvPjw5v8iN52XY6P0uAFcrX7vpmPOcVxCEH0OTR9A+8d4CpC4j7dcwFp5G66ghBAFsmUSrQfLBSbLuA43bpZS0tbXRlvHYtnEQHeUolTXDo5IrV8qNH0jNkcOZbGyEkGS9pykGm9D5q2zf9IpFjQ2ciygI0JU34DKMRiMAK/oIKvosER/HchZXrtOqncTVapUoitb0+XcmjBi8A1hK78CFsFxiMJ/P09/fTzwevyWblOlYDTscv/IlKsEnQGtc93XEYt8653OaxWAul2P//v10dHTw8MMP35KVxUox1z62bZuurq5G7We1WmVkZIRcLsfhw4cn+RveTvWGnvCIyTiVqEysafZxRZVxhUdczlz7GVdtaLuD8XCYjgm7Gq014+EwPe4WzpcPAuDKKloLoHaxFQgCHaF0wFh4/iYxeKF0gJfHP4KvSvS4u9ib+QEcZ/p1tDm9tDm9097XzFQxaAsXrSfOFTJFSe0hzX4cyoCHRYAlPCKdxWmayRvoKpZwcKappVQ64mjhU1yq7EejkcLiun+cgfJL9NoZLlb/GkWAJ9bzQOLdJBKzCxel1E3HWMx6nLL1KqLo8wg1jhYSqSO03EI69sOMV/dPu62aQJ/m3KjHkP5fI9VR0Iq0p0n3tbF543cR8HTDxub8+fMcPnyYZDI5ycZmavRcK4kl0ssiBAHC4JdxGZ74S8LE+5KM4/u/iOW8d1HbbdXIYLFYa84xaWLDmmI5vAMXwlJbyzRPz9i+fTvbt29v6aYXgLGxd1KsPovStQkgovop4qW/oy3zF8hZRF09gnnu3DlOnDjBrl272LJlS0sIosWs0fO8Of0N61HDjo4O4vHpLUjWOrZ02BrbTn/hJfLhKAmZxNdV8mGe7fG7aHPap32e1hoLh12JV3Co8CUG/QvYwiFUPmk7y47EY4z4lxuPbc5sanRjdJ1gcjTmM4Pv5kz5OSBCCBj2T3Gw8EXi1i4KYYV2J8ND6ce5P/WqBUVyporBLncLFyuHKUfjxK0043wHgQ5JcwJXKKTw6LY3MKC2UVVFXJEg1D756Dq97nba7ZutUkaC81yuHiBuddQigWoQqb+OVhcZ9DWuEPjapqLP8fXid3I3/x/rEt8y65qnm2XeEf8VisGDVIIvgy5hO/eScv8ZjrWOtJ1nsPp1Ql3BFjXBGukqGk3avuum15DhFxHqEFpsAemB1qCvIsOPY7u7Jv1A8n2/IQ7r0fOp04Hq5/3lwo6+Ri0ebTUdUhKIsNWhRW+3VSODhUIBIcSqNOzdKkYM3qYsl3fgQlhKYRUEAYcOHWJ0dJTHHnuMjo7p66ZulZVsICmX/oZC9fOgBbZoB0BRohS+iFt5N8nUv5nxuUopqtUq586d4xWveMWy7Y/l4lb28Xz8Dev1hvWoSSvVG25P7CQi4lz5DPkohyNcdiZ3c0/y3lmfJ4Sgz7uLuExz1T9DORojZWVZ591Fyu7g4cybOFl6maKq0G4p0CHhRGovbSUAQdre0tjehdKBhhAEB5BUlGY4cFFcImO3caE8wOXqP1GIxtjX8R3zfo9TxWC3s4Vt8Uc4X+mnGORAC1z5OuLOz9AXy+JYG+kWCQ6Of5YB/xTjegRL2HQ7W7g/9YZpRwDmw8tEOpgQggWk/iQwhphIpksUMRFQ0RBhcbw4vRgsFGqeg9NFBgGktEl730na+86b7mt37iHr3M9IcAAxMS1EE9Hh3EuHO+Xz1AoRvQRkoD41RQigD6FOI9QJtHyq8XDXdensOEt34rcR6y+jsaiG93Dm6r/i6NFr+L6P67q4rsvo6CiZTGYZBFY44z2CxQcCWjky2KpZCiMGb0OUUvi+v+pDypfKZ3B0dJT+/n5SqRT79u1b4nFPk1nJyGC5/GmUDieNh7JIofQI5eoXZhSD4+PjHD58uGEbs5z7YzlYjmjuTPWGZ8+e5dChQy1VbyiF5O7kHrbGt1OKaj6DiTmmnDSL6zanhzbnZvuQXm8br83+IF8Z/TvKOsATVVzAlQ5SlIi0wlelxuNfHv/ohMefgxASpTX5KI4G4rJCxl6PQJALxvhG/us8lHn1vG1qpopBISQ7E0/S425jNLwKCDqcdbTZk1POj2SeIR9eoxjl8WScrLNxlg5o2Xgti0NAAbRCUw+MChDgEBLp2jaGSgfoStTq+A6O/Dhl1d/YmnO34Lr/L2jjZ+b1HgEs4bI1+d20+bvIByfQKNqcXWTd+7FuGpOngICbR8yJ2rjlKcIrCj6HVfkFJFHjPcXtl7hn0ztROz5PNQw5eeolqpURDh7sRyloa2tr/EBKp9O3/F2MxCakPjpRL1jbVr12MBJ9LPZb1qqRwWKxuKgO8LWAEYO3EfW0cL1beDWFINyov1vsdAStNefPn+fkyZPs2LGDrVu3Lvv7WckGEk2hcQKdjETPYAx7+fJljhw5Ql9fH0NDQy0nBJeCy5VLhDpko7tp2ujBWq031DqiFJ2nHF0GBHFrAwlryyxj0wS+8ol0REzGp41+TXr0PN7DfelXc0/iKV4c+3uuVl+g3UoSs9LEZBcawZXqC6TtjcStLIEqocWN1HGkBYGycERdlESATcpOMBYWuVo5w47kw9O+bqgKBDqPLdI4MjPtOUEIQbvTR/ss4/lqj1lHuzP3BI2ssxlHxqmoMVKi5oF4Uzxa60mCpaSPAw9wJPezlFX/hMSZeJaAUf6CwcIeulOvnvP161jCpct7lC7v0dkfKGy0vBcZPovWndD4vHNACi23TXq4rPxHBBGqUasHtXq9UaLw10DuprP3OQSKtsw9qPC15Ec7G6PzlsLGRnq/ga78IIKwaQ21tDHuv1/Qtppp5chgK9YLghGDtw0r5R24EOq/7BbzxfZ9n4MHDzI+Pr6saeGprGQDiePspBydROsQIepj2iI0CsfZOemxURRx9OhRBgYGeOihh3Ach6GhoRVZ53KwmH18qniCTw59jJHwOgpNymrjlW2v5cmO2e0yptYblkqlhjhcqXpDpUMGq19gNHi5UR8qhUu78yg93msm2blorTlVOsHBQj+FcBwpLLqcLh5re4Iud/oZ2wvZn5bl0uEkccQ9pOz1k7YxFp1jPLxI3MrS6+1mODiFJpxYn5qwfAGERExcPiIdIYXEvSnSBUr7DFa/yGjwMpEqYok4GedBlN6EnObxS0m7vZEtscc5V36BivbxhELBTdEq3fSDrEs8A0Ah+jpMCEHR9EiN4HzlV+hOPQdAKTzLYOXjVKPLuFY3Xd63knJmT+fPhrJfg1CnQJ8CnUDgAxJlvR7EZKso0WjcEJNu1VpRDj5PZF1DKw8hPcLoKFJeoW/d2+ldn6ESXKJcKVEaCxkc9G+ysZnv98B27sXX/wNZ/TUsrgMaRSfa+2Uc91WL3g+tGhksFAokk8lVv/YuBiMGW5yV9g5cCIsVg7lcjv7+fjKZzIqnQVcyMphI/Axl/3kClUPqWk2bIsARKRKxn2g8rlQqsX//foQQ7N27l3g8Tj6fX1LRupLHzGJea8gf5EMDf0tR5XGJYyMYC4f5zMjHSVhJHshMH5Ga7rWTySTJZHJSvWEul7up3rBarS5ZrWExPMOo/xKu7MSesBkJ1DijwTdJ2ltI2TdG0l2uXuLr+RdAQNbpJCLiqn+V53Jf4o1d30rcmr44fSH7NdLVxg+Qyc8XqIl05JPpH+JE8YtEegylI6TWxK0q42EcTwo0IUoL8mGZDV4nG2I3W9IMVr/IYPXz2CKDa3UT6QJD/hcJ3R2kw9cAUIrGKETDOCJGm907ZwR0vggh2JF4FVlnC9erWUr+3yKFQulS84MIVe3cZNPRVPhf+25Nllm1RK6mCsCo/1XOFX4fX43W9l2oGfG/xObEz9A1D0eAaZHriNyfQkZfA3UKLdJo62G0fAjm+fkGaCJ8LLkdpcaxpI0lNxCq44xW/pyACkoXasG7jhjbevaSdt5GPj/KSP4CV6/lOH48wPO8SeJwJtcG130S3M82/l4KUdGqkcFSqdSS00fAiMGWpnmkHKyMd+BCaBaD80Frzblz5zh16hQ7d+5cle7YlYwM2vZ2OtLvplD4farqJKCJy+2kku/A9R4H4Pr16xw4cID169eze/fuxj5d6nXWt7VS+3uha//q6JcpqjFSoh0paxcJV3mM6xxfzz83bzE4leZ6w23btk2qN8zn8wRBQKFQmNShuZiLVDE6gybCljcuFI5M44eDFMNzk8Tg6dIpAh3Q59bSpTY2vW4vA9VrXKpcZGfy7kW912Yy9hbGq8+jdVcjTR2oElLYJKxavaHjOLyl69/wqaHfp6KLCKlpFyECQVlZVKs5pHTodFK8qeu7KBZK/OP4L1NSOWIyxbem/i2j8uXaCDSrlrKv1clJStZJQvUwx4vPcbFykIoqYgmHrL2BPanXkLZrvqHVMMdYeIaMvR3PXnh2QAhBp7uVTvdfMlB2uFb+ACEKRQU0hFoQILHp4JXdn7jxPGpxUNGUWL5Ra1g7p10q/QWBHsWTvQgh0Vrh60Eul95Lu/s0tkyRrz7LQPk9hGoEW3bQG/852rzXzr5o2YOS3z7ne9N0IxmYiNXeSNEqNIgOhHAnBQiUlpTUS7jWg7hyB0IIIjVGwf8igS5Rda9id43Q3R1js3iEYllSFT/LqIwYLQFjDjr3R3R13LPsJvCtHBlsxU5iMGKwZWn2DlyrY3vqwmI+YrCeFi4UCjz++OO0tc2vEH2pWWnTadd7jKz3AVRYAUKkXRMLSilOnjzJhQsXuO+++1i37uYaqVYwx56OxQjOXDAC6IYQBBBSYkU2I+HSpcub6w0ty6JardLR0UEul+PIkSONesN6Wnm+9YZ62gQl3Ig33WAsHMWTk6PhUkgQgnJTg8ek7S+wLrfL20M+PEc+OocjkmgdEeHT7d5P2rqROralYk/6fhJ6AxDhODEqUYXTpeNUVcT2xD7uTT3FwbF/4OOFj6Gw0UBZj/F/8r/EXfEYm+OT0/i2SKHEZYbkUUZLA8Rkig57PZH2ue6fISoEPJr8Np4bfQe58NKE1BF02pt5quMP8OzFGfr2xn+MdudbGA0+hybAjjYTSGhj3zQXcAcIpzXxdsQ2StFxKtFVLJFBC42vCoTaRyNRepCc/xxBeIjr1f9VT6xTjXKcLfwbeoLvZ33q3y7qPTSjYr+FqPzsRL1eMxm02ILWasJGqHZc+GoYENjyhp+iJTOUo1OUqh/HlVuxZDtKlxn23wfW+cmbtQNE99s5dfy3KJcypNPpSR6HSxnJa9XIYKvOJQYjBluO1fYOXAh1kTqX1+DIyAj9/f20t7ezd+/eVbUBWWpvxHm/rn3DNLdarbJ//3583+epp56a9uTSKjOUZ2Kha69302qlEE0/fBQRSbl8bv+WZc1Yb3ju3Ll51xsmrM3kxX4iXW10kUa6Amji1sZJj806WYb8IbR9Q+BFOgI0SWtpLjSebOOu5FsYqh5lLLyALVza3R10OndPql8UQoKuiWQhat/LmBVja2Idnkxzb/otAOwvfIwIG4nCEqC0xschHwwRxYpY4sbxHekiKIcc13GES9JqB0CKGO1OH7ngKp8e+mEqlCeeUZNT18PzfHLwh3ig/f9jo3cvjvSIdAWtQywxvzotz95Ar/0jcz7u7th7OVb5IeoRt3rsTSB5qPODjAcHauvSmrLKE+oAKSSaCPA5X3wRhw83umzFRLWlRjNY/Tu6vZ+b0bh7vljOXiLei6j+R4S+ANho63Gk/YtY4R8TqbOgYwjhEalrteZp3T5pG1orApVHCxd34ji0RIqq+NR0ltgAdN39O2xOfLkxU/nc+X/Eu3YEx+3Asd5ER8eWW+7Yj6Kopeyg6hgxaFgR1oJ34EKxLGvGC7/WmjNnznDmzBl27drF5s2bV/39SCkJgmDVXr8ujLPZLI8++uiM00RWY1LKUrGYz/ixzBMcLfZT0HkSKoVAUtZFJBYPZB5Z1Do+NfSPfDP/AkVVwBMe96Qe4Lu6vnfGfT5dveH4+PhN/obN4rB+QUvZO0lbuxkLjyInRJXWIRlnz6QUMcBdiV1crFxgMBikzW4j0hGjYY4ep5eNsc3Trm0hkcFPXf8bDhS/DAgeSL6SN/XMLI7S1kZcmaGkBklOpI8j7ROoAn0T3bGfH/wjImEhtEZOLEEKQaShoGKcL32FLYlXYYsUkS5Siq5RDDOMyEFSYQZPphvTVmzh4oeXiShRk171Y1xPGK9UOTT6AYbij9DnQiE8hCYkYW2j23s9ySn7crFkUrvZwxc5Ufk+IgYRQDm/jX1b3w9A0tqNJ7soRhcJtYUtYrUpJ1TQxCmrQ9hSTQjIupFNXVgqhqp/zjrnHbe8Tst5CJwPNf6uz5UR1o9S9j+AZR9FyhEE64g7byQK+1G6gpwQ50qXUFRx5eTmlNlyDhFlPM+jp6edRPpdBOFXgSJaK5T6ABcvfR/9/U/S1tbW+B4s1MamlSODppvYsKysFe/AhTJTpK15lu5qpoWnslqzibXWnD17ltOnT3P33XezadOmWT/jOy0yuDWxnddl38KXcp+mpMYBcESMB1KP8XTHHDVY0/CRgb/n62NfRqGQSIra55vjz5MLRvjJTT83r200Zsm2tU2qN6x3KR86dGjSRIiu9JsYCbo4NHacQMHW5BY2J/cixeSUcJ+3jqfan+bg+H7y4ShSWGyNb+eR9GN4cvYO3FD5HCl+nrPll6lEJTrdDexJvor1sT0A/Kcz/xwhwkYvwv7iZ3j5zBf4d9v/atrteVY7G+N7uVT+CvnwLAACi6yzix73fgBywXlqUieiueVCoBlTCRIqQuuQirpKpGEkSHIxsAndgOHwCmVVotfbRMpqY8wfxlfHkVIw3aRlDVhimHzwf0EJknYfApuxoJ/x8AyDwUYKkabH3cJj6WduaTRjKpXikdQ/ArXz1XMHnkNur9fsuqyL/zAnC/8ZS9RKPAQKsBHyQQjPNfbCdEQUZn1tP/i/VP2/R6kLCNpx3TfhOj+OlPN7P7a1h1TsVzk7/A/09XWSij8EIklY+TMq4ctIYoAkVGNYoh3JwgVMufoegvBZtC43Pikhi6zf9Jds2NxHefwNjIyMcOHiCTzvGqlUG+nU/XR0dM/ZcduqNYPFYnHNXMsWihGDaxytNdVqtdHZ2EpCEKY3cR4eHubAgQNrcpbuakTcgiDg4MGDjI2NzVsY14+BxXo4Trettc6+jlfxQOphDhX2ExJyd+IeeryZPelmohJWeHn86ygUcXEjVVfVFc5VTnChdI7Nia0L3u50/ob1kXlHjhzhgL7MsdggoQWObdM/foGj4xE/tPG1pOzJs3W3xLeyMbaJfJjHFhZpKzPr51Q/Dp4ffT+nSvsRQmILh3Plo1yvXuDpju/jo9f/CjHhEahuWOchRMh7zv88P7flD6fddpd7Lwmrh7HwAkoHxK1OMvYWrAkRuyvxBq7l30vNsa+54aJ2Me+wX8X21I/iR6McKu4nrwbJaAstMpTlZSpqnIHqBU6Hp1EEtIkQdxYdYAuFZATYjjtR/5YPigwFL3LNP8H5SjdSHOTro5/lB9f/W7q8TRNzjxffYFc/h00aoRd7E6dKh/GjF3BFlZoX4B60uIs8MdKcphZja45w1WpHs973zvhaVf9DVKp/iNYVwEOTp1L9c6LoEsnEb6K0T6AuABJXbmqk76cihEelvAUptiMnjO2zsR+nGHyFSvgympCU+yYCVSIffJZQ5bBEG4oK00vxGpLa6/nBZyYJwWZC9Z/p6/0huntPUfE/ShAOEAaKSiXLocNPE/jbb7Kxad63rRoZLJVKbNiwYe4HrkHWzlXYcBP1tPDZs2fJ5/M89NBDLXPhrtMsBrXWnD59mrNnz84r+rUarHRkcGxsjJdffplkMrkgG52lFIPN21sJbiWqmXbSPNXx9C29/vHSUXxdxWbyRdTGJaDKwcLLixKDU/E8j76+Pvr6+rhSHuYfz5zEDVzaAgu/EhAJn5dKJ+jwPb5901M31RtawiLrZGfY+mS01oyJy5wrHyZmJUhatTpKpTTD4QCHCs8yrkawmOyrV6uF04yp4SnbUwz4F7lWPU9IQKezjo3efTjTRCbvb3+G5/N/iS8sQs1EdZwAIXB0yOu63wlAqG1yQZG03UGeceJkSblJcsEFRoKjOCLAIyREEJuI2aoJSakm1m2h8cQ6IgaRE002kQq5WD2JxCJjV0hZgkBrrgdVPjLwX3hl9s0M+WfQqgLiPKEawhIxery97Er8MJY193duurnEAOvjb6B/XJGyOvFkAq01hWgEV64jLu+mrI6jUA1xJRCkrHtITPERraNUSLX6PrSuIuhFyHoT3ihh9HnGK49RiL5BoAYAgSM30OF9NzF7em/DqbOJpUiSdt9E2n3TjcfoKlooisE3qKozCOFh8wABB6bd5nrnv6GUQuncjIIRIsaqv4xkCE2AY2/EsSM87xrt7S+gw32M5iwGBgY4ceLETTY2rRoZrPsMtiJGDK5R6pNE6r+QZpqLudapi8FqtUp/fz+VSoUnnniCTCaz2kublpWKDGqtuXz5MkePHmX79u1s3759QZ9vsxg0LIyMnW4U9DdT/9uV8zOd/utzv8c11Y8lNFqDLdbx89t+f9rHnixdoapDNqa6a2KYifrf0jAvj56m+7LA8zyy2SzZbBYnoxlVlympPAnZRpe7pWG3MhNFe4BA+6TFDQsWKQUJmWQkuDZDwrKGQPPH53+Ft/b+OBu9uzhYeIETpZcIlY8QklMc4IK3jSfb3oQn40Q6INQ+rogjhOT7O97N3+ffSaBBCYHU4BDxbR2/Pf0Lao0QkrTdR0x24Ecv4oqICAuQFAOHpBOgkEQTn5ZEI9hCIDU2Fp6oFepf988RKE3K0viRgxACV1hAQEkNcKL4JZKWSzV6HosKAkGI4ELpDEPVb7K347/OGYWa6fy7IbaHXHiVy5WjjIdDgCZmpdgZ38vW+M9yYfyXGQu+RESAhU2b8zRbMtMfI7XXOYPSgwhSDSFY+3wyBGqAUvW9ILPYsg/Q+Oocw5W/Iuv9GMOVv2c8PIQQNu3uXrq9n7pJDE6HFB5dsR+gzX0dgRpAiiSe3Mb14p+QD/6sZlUDSCR9zh+SStXnIyeB0enfB4Io6kfIdizrrhuvJbeg1BniiaN0tH8727ZtI4qiRnnFxYsXOXLkCFJKpJRYltVSs8VLpZIRg4alYap3oJQS27ZXpcN1KZBSks/nOXToEJ2dnTzyyCNrKi08lZWYTRxFEUeOHGFwcJCHH364kVZcCK0sBle73nFbYgcZq53RaHii+N8i0oqAKi5xnmqffaIJwH8+/bO41jBJS4OAUEkq0TV+49Q/59t6/gWbYzvodm+ksCOtJubL1hAIXMclEU+SsmM8/cjTjQvikQvf5Kp1EBlTJGNpbE9yNX6K3al9ZJ3pU1BaayT2hGyanJqMiHCEA1qCqHUlTx4dBloLBoMhPnD1T/jBvn/NydLLeDJGdmLGcagDrlTPcLp0AE+EXKkeINRVElYnW+KP0Zvezb/MfIiLY/2crH6Bzd6T7Mg8OWmNcZki6/ZypXIWhWys4HrxMK6lmHRISMFY5JAgQkhFqFwisZWMs4UIaLM2ohlBa4+KGscRIRpFLow1Onjj0keKCE+0EakD2FSIcAFRS3Frn3J0nPOVf2B78rtm/bxnigxawub+1LewwdvNaDiAxKLL3UzGrk2K2ZL5z7NudypCJic8C8Mp4j2iqjWR8InJG/6rrtxG2T/ESf/tVFSV+k+a8fACef+rROqnCEWesWAET3bgWTN7NTqyF0femAXdm/o5epm5ftZzvpeyP7m0oB4BVYAQMRCTyx+EqLUEKX0jEm1ZFp2dnXR21n7sBEHA17/+dYQQjdrbVCo1abb4Wr2GmG5iw5JQ9w5srk+Zrz3LWqQeETx79ix79uxhw4YNaz66udxCpVgssn//fizLYu/evcRisbmfNA2tLAbXAm/t/l4+MPDXVHWJYGIX2ri8IftmUvbcJ3Nb5rCkJlCCUNlUlA0IpFA8P/JZLiXO8VjmlWSdXgaDQWKWxhGSQlQhbdcij5FWFKMyj3fsatQbZjs7KI6fQle7cfwMpWKJUr7EiDzFsD3Kfck30JXtntbfsDPawaB1hNFomA66kFLiqypVVWZ74n5c+jhZ+dpE2rUe7aldvDUSTzgUoxLP5/+JkCrtsovh8Qtc1i+iRW028bD/PFnZTm98D7bwyIeXOTR+DdKaPm8PmzIPsokHp91nQgh2JR6iEOY5J04R6TKV6hg2PQQaLKEbtYxSTBg+C0Gg+nhzz39kNLyGQNLlbkarq1yrfIRh/5tofQ0pYlzzM4xGHlBGILFlLaaYtrOMBYMI5ER/skJrVRuHpwsMVJ+bUQyeuvan5PX7sNXDCPH90z5GCkmXu4Uud8ucx81cWHIDUu4hil5EqzhCumil0Ayj8JCiZ9LnLoSgyjXKqoyD1/DhDFXAaHCecP3fcCJYB+MhtojR5T7E5sQz2GJx551mUomfoej/JZJ84zalwUdi4+FZzxDqz04qZdE6qnU8i54Zt1uvjd+8eTMdHR34vt+wsTl+/DjVapVMJtNIKWcymTVTX2jEoOGWaB4pN123cD1N3EpUKhX6+/sJgoDt27ezcePGuZ+0BljOyOC1a9c4dOgQGzduZNeuXbdUE9PKYnAt/CC4J30f73B/mc/nPs1QMEjayvB0+2vnVSv4x2d/BVsqIiVAS/wmIWgBI+EF1qktfCX3LFIkKUQFtNbEbZ/BaoV86GELSUX5bIx1sy+7p7HtUpSnEI3Q5nbjxDzaMhk0UKp2kytdZzB/hQvnLjb8DevREq01LikezTzDC/mPMhReQyKRQrLO3cqDmbeQsrP81aVBrgVnG5YtEaC0hS3aQIAfhYxHORIyzkjhIpd4sWFaLCf+jakc5eJB1sV3k7I6qKgc58sv0uPejRSzX5Q7nG6ebH8jwWWJm7RYl9zAuuxWPjbwaWxRQBJNpIXBExGBtniq42fodDfR6W5q2lI3rt7MieLbkMLicrWN60E7llBYRITawpEQEwED1X/AEwGWuPFdqVu+zPTtuXTtHyk6/y8pLyKFRvEcid3Pc+jq60mnuqiEh3CtnWyI/bslH5cZ936RUvkXUfoKWtUbXzLEnFdSii5MEVeKSlRGICYZstvSYSwU+PErxNlEzMoS6AJXK59HqkN0u/fj2U9hWzeb2S+EbPIfGS7+M5QeqkUDkdjEyCT/K67VRaHyDZQ+j6S7JsLVAFJuxLWfnHW7zTWDruvS29tLb28talkulxvi8MqVK4RhSFtbW+P7kE6nV6XesO5Dmk4vn+/pcmLE4CozH+9Ay7JaKjI4ODjIgQMH6O7uxrKslqn3gOVpIFFKceLECS5dusR9991HX9/NHbA1URfO2Bk43TpvPK/1WAvrznpdfE/fDy74eSGVRhJWIVCT/PAANFK4nCydps/bSJ9bu+A6YpirVoHRYJx8OEbG1vTFNYXoKl0019BOrmcU1KIl6XSGPev3kJDtDX/DegG+ZVkE8TLkyrTZO/HkdVzhsi3+IHtSr8KeaLb45xt/gyCs8J8u/DRagWe1NbwBI1U7x3TY3UT4XFDP3ahba3p7IZKIAsPBNcbCEdrtLMVohECXG3V805ELLvFC7gNUVQnH6maP8130pWrfhbd0/B7/mHsnnvTxJuKWvrZpsx9ke3J64XC6/EEiDaF26XMLaC0Zi+JESGLSJy3LeDKgqmriKWEpQu0jhYMiQOkyEk2P98RN2y7Yv07WitAaIg1SaGwR4rufpBLUzg9++E3OjH+INu97afdei2c/hBS3Xi9m27tIJf8GP/goSp1DiCyO8x3EkVTLf4ivzuLIPjSaUNWipVOJNATaQmqBK9uQwiajc2ixn2IQUA4+jSPidHpvpj32C5OM3BeC4/TQ1/4VSv5X8IPPY1m7SMduRFAT3s9Q8T+IUlcAiW09QNz74UZ380zM1k0cj8eJx+OsX7++IcDq4vDChZpYbm9vb1g6zWVjs5SYBhLDomgeKTebZUyrpImbR6jV08Ivv/xyS0U1l7qBpFKpsH//fsIw5KmnnrrpRKF1SBT2E4Uvo/U4Uq7Hch6bVHQ9Ha0sBtdCZPBW+K7ET/GR8v+LLRShvnFs1xKQgja5nlE/T6Qjsk62NkoO6HI6uFLtJ+P5rE+kkEgGw9N8cugar+94LSmr5scXl1XGwzJZZ1OjbKEQDdNhrydptSPEzf6GLx/7Jhf0QYLRKqLi4HmdBPGAq1GO7bEKqabolWPH2BF7hGOlw1QjH0fYRCgCHRCTMd6Q/R5Olw9yrvw8WsuaNNUCOeEjWBe+SStNOSqSC6+x3tvRsJqZji8M/Skvjj9PqGvROBk/xcXyN/nR4I+IOym6k9v4seTH+Nzg7zPgH8IWCd7U/kt0J7fNuM2yGgGhQUscqdiZuI6nfcYjm6SMiKRDRXlUcKjqGLZWuCJCEzVq2yINV4p/RNa6m47YAwAcvPaTdHq1GsYACaJWA1ckwhH1BLvGFhohAor+/6Lk/y+0kCSc72N96tcXdVw1I2U7Me/Hbrq9M/bj5KsfJVCXAYFn7SLtwpB/BK0iRCNNHKKxEUEGERdYOodW3yQiQGoLVyYJqTBQ/QccayMpb3IKXOuI0fwAoX4ziCpoF1t8lo723pvWBJBwX0nCvbnW1rUfx7EeJlKXEMJGio3z+v7Pt5u42Qh+48aNte9KodCwdDpz5sykKPp0NjZLiUkTGxbEQkfKtUKauFwu09/f3xA99S9EK6y9maVMEw8PD9Pf309XVxf33nvvtL90Q/+LhMGzE8XWcaLwMCo6C953Ydl3z7jtVhaD0LrrBtjaezeVUx5Jp4IrFYGKCKlFYSIkD7Q/xYHCQeIygSdv1GaNhFcJKJEQadJ2rZA/oVNE+ignS+9lg7uplpLVARYOQ0GEJVwUipSVZUviodp4uCnYtk05ngeluLf7IaIoolQqUSgVuDh8ls+e/79sie2aNA3iu3p+kr+9+gdc8S9R0VUEkoSV4Fu7vp+0k+UB+2meH/2vBLp2iZBCTaTDm6xetJ5Id4/TZm3CnkYM+mqcFwf/kIvB1+lxBBVlUwyT+AhyOuCjg7/JD6y/0WTx+u5fnPfnsCn+Oi5XPotATbSMgGcpLOETCBcH8KwqKSJC7RKpBJEoNHpnbBRJUcUSmiPj/4r1wXdSjA4TWvux0IRaNB47Hmoc68Z5weJGs4QQEOra9krB/+biWMSmzG/M+30shLh9PzHrHgJ1hZq1zHqq6jzF8CepqHFQtVUpJI5IUq12IoTA0yepEGDjTNSi23hkKKkc+eo/NcSgUtcJg49Rqfw5UowiEfgAwkfxNEOje+hq/+iC1iyEg23NLOqnolRtpvJi6gCFEKTTadLpNJs3b540JageRXddd5I4XGzd9lS01kYMGubPYkbKrfU08fXr1zl48CC9vb3cc889k77EK9Gdu5QsRQNJ85i93bt3s3Hj9L+GlRomCr+JkFmknLANkV1E0Wmi4AWktXPai/9SrnU1WI5f5UpHtQaBFYo6/vsdf827Tv0EjlXEkWGtm1g77Ek9yWBwlU6ni1IUTarvKqlRatYjN6xrHFEiJQqUQo9UshYNjnQZyUWSdg+fufpP5GSVSAv+afCjWEKzXmT58e2TuzjLehwLGyEEtm2TyWTIZDLYPqyXm+gudzMyMsK5c+cQQtDR0cF97feTjJXJhaO40uW++BPcnaxFx6SQxESAK2sOf0oLAu1QVTaeDJFC46tRwMIVXayLP3TTPgpVic9f/16KOsIWNhJN3A6Iy5ChIE1VCy5Xr826n8fDC/gqR9rejjtlBnVf/BW4+XX4+gpC1+J9zd8HjUAIcIUiLiRBWMK1yihN0/dKorVCiDKD1X/CkW0EvkDbtfrI+lk3BFxuZMulmGpMVI8LQyn6MPAbFIvXOVn5PhQ5AAQ2bdYbSXu7iFu7STqPIGaJps6EEDaudWMsYczazt1tf8G10h8zHh5FYpNxHqMQbuO0/Tl8PYqmeGOlwkVOpJYtYeGrkdr+0nn8yu+i1AlgFI3AEbX9UNVMxFOP3LQerRUaH4G3JN+/+vViKer+pk4JiqKIfD5PLpfj8uXLHDt2jHg83hCG7e3ti64BLZfLaK2NGDTMTbN34EImidSja0tlMLxU1GvhLl68yL333sv69etvekyricFbXa/v+xw8eJBCoTDnNBGtrqP1GFJONqCVogulroMugJjZj3G1RuctBUslYnPBBS5V+hkLr+LKJOu8e1nv3YcUy39q+/c7/qLx3+WoxNXqRSqqRNxKkLE7+UruOa76V8lYtc+wGNa6XD1xw7jZFuMIHSKavAEtEccSMb5w9WMMCT2pJixCcFUP8eenfwLL7aCiK2yNPUSMPhSTfzBqrdFEtCeybOzcyMaNGxuRkoO5z3E2+CyRCkmrBJarOFP5EtWRcV7d9RMA3CW+hZN8FqiliqWICLVNVWniIkE2dh8FVaLD3kiXc/N3/+jI/6RKQKBtAm0jhQKticmQhFXFD20iIAhKOE6iseZIVyhFAxwt/AWjwWk0EY5IsjH+GnYm/p9JIuEN2b/gS/lfYDw4Saihql0SUmNpQRGwta4JLu2gajGuRkNMA1H/vxie1YOX6qEYfZ2MDNFKECGICTVl2N6kp1KXiRMW0QyPXOa8fgbZmMMCEp+q+gf8skQKj5h1D92JdxK3FzdbuxnP2sqW9B9Muq1YHuXc2BXoKNTS3YDExRa1HyO1fR0Rs2s1rVH4FZQ6RRCdRU8019RNihzAp3b8DeYeobvjJbT2KQafo+Q/i2IcW2wg6b6JmP3oLV2n6ue05egQtiyr4eUJTBohee7cOQqFQsPGpi4O52tjUyzWBLcRg4YZafYOrPtVLeTLUj/5raURPeVymf3796OUYu/evTMWzbaaGLyVaFs+n+fll18mk8mwd+/euRtnhAfCpnaavSEQNFUQTu3+OdbaiizVuof9cxwu/BO+LuKJNIVwiGPBZylHOXYkXrOi+yduJdiemJzWf7rjaQ4XDnO1ehWA3cmHOFX+GuMqR5voRAqJrypYQtPlTq7FEkgGRYTAmohkTfjKCR+HkLyIEKqEAI6WPoeyQEaa4dzX2GjfTda5j5JVIm230+Pe8CaUUpJMJxgPThILY6StdQRBgO/7lCt5zpZfRF3sZVP6Hu7P/hClYoGr4qsoIYhJn7ISSJ2iL/44BRWStLrYk3oCaxrxfT78GGiw6jG0iZyqRuOKAAUkBQ0hOBqc4GL5c+T8k4xFp4l0QEy04cgkvi5ypvQJNBZxaze58DKOiNHj7uT1XX+MEILx6hVCkSfnP0sp2I8KTlPVGqE0mhI2Ti2CpfUkQVj/L9e60dQwWLawYpq4VDhC4QnBgLJwJ3waNTdseSItsMSNtLEGLuufntRUJABH1GsVI5QOKEcHGSq9m97kb+BaW+c+yBaI0C6x0Sd48IGHqIQnGC79OhU1htS1vvBAl7GFQ7v73QCo6GRtpTqgKUNee6+T5tOVAMhX/zdF/1MI4SGJU1VH8Ctn6Ij9S+LO7N3CsxFFUcNWbbmZOkKy2cbm5MmTVCoV0ul0Qxy2tbXNeA0uFApYlrVkaeeVxojBZUYpRRiGC0oLT6V+8EVRtCbE4MDAAIcOHaKvr4/du3fPuiYpJUEQrODqbo3FiFetNRcvXuT48ePcddddbNu2bV6fsZSbkHITKjqHtLYihIPWRbQawXZeh5hDDC5ls8tKp5yXIhV/sfISgS7Rbt0Ya1iJxrhSPcw6735S9sLNvJdyjVkny9MdT1OOygDErTj94518dfTT5MJBQJOUNl1eBz3ODd81pQMUlYm03A0hKFB4VNDSrZUW6qa0pQRXhvQ4OSIGuB59GaI0D6b/oDGark5Z5amEOTyRRgiJ63q4rkeKNLngHKmkJMgHHD16lHjwFA+3vblhYSMSimv+OcbDURIyzfrYVjIzTEbRE3N407JMVTtEWiK0RgtBhIVEszNRs4sZDU5xePx/MhZeIlRlIkqApqKvY0VqQnQlODD+SeLWSYSw0TriQuUldsRfyY7EK0l764H1dLj3oLWmEl3kWuUjlKLLOCJNX+zbOZj/1whRrtmccEPwSCFx5I0ftJnUo5wa/xrdnoUrIFRw7cq3s37T57FEHqUVQtQaa0TTgL9I1z6XUF9GihsKympqWqnZEMVRuowfnaUUPL8sYrCegfJkO577ODHxmwyW/4hSdBmNJi476Ir/IHHvzbUniCSgQGQQjDX2j+DGsSYARC9BdJly8BUs0Y6UbUR6HEEbSuco+P9EzH4FaMG4/0Fy1U9R1cPEZS8d3reT8r59XutejR+7U21sKpVKQxwePXqUIAgaHofZbHaSjU19+sitprff9a538eEPf7iRwt67dy+/+7u/y913z1xDDvDBD36Q//Af/gPnzp1j586d/O7v/i5vectb5v26RgwuE83egfX07mIP7vrBtdp1g0opjh8/zuXLl7n33ntZt25uj6pWiwwuVGCFYcjhw4cZHh7m0UcfbaQf5oMQDo77rQT+J1DReWonYg/Lfhjb3TuvbbTSvl1KAl1mPBwgJtomfa88maYc5ihEQ7csBpeKeFON4IPpvWyJ7eJk6SChDuhzN2HrE+TClwnCEWo1bAFp527gEs0BGVdERMJFTgjB5iRlQlbotIpYhBP7Q4PO87XCL/CWxN8wGFwg0iEZu4ukzGBJj1BXaf65EaoKtnDoad/IlvU1QVUulxkZGWFkZIQLFy4A0NHRQW92Jx0dHSTsxIzv28bBFxEIQbtVYCxKoCe6iQNlsd0JeU1Hzez5cvlLjIdXCJVEaQ1CT0wmvuFxWFQRZcaJMYAQCkWZQCU4VVT0ervI2Deiq0II4vZmtqXeMWlNe9J/wpHxfw0U6lIbKRLYIk6oitgyiVKaQA/TldjO9tR/IO0+yKVLl9ByhHs7f4t89SVG/S9SrL4P1yo3IoRK19autaBmOjQxo3nSNOjma4BEExKq2esmF8vUUXSe8zgbrL8lVEdRuoxj3YtsGr1o2U8QhZ/DtXbhR99orFoD9Z/zFpru9i9SDl5A6XGE6KIcHERRqo0YRKJ1lUiNMFb9AFcqH0MRIbEpq5OMhe9mvbpEe+zHb/zYrZ9vJ77Ha2kucSwWY926daxbt67xfaiLw0uXLjE+Ps673/1u9u3bx7Zt25bEVuaLX/wib3/723nFK15BGIb8yq/8Cm984xs5cuTIjNt//vnn+YEf+AHe9a538W3f9m28//3v561vfSsvvfQS991337xe14jBZWBqk8ithrzXwhSSUqnE/v37Aaa1SJmJ1V73QllIHV6hUGD//v04jrPoaSLSWo8b+1FUdAaty0iZRchNiDnMe2Hl5igvNUvxi98SDpZwCLU/6XZFiBBy2s7W6ThTfJnPj/wJZVXCQrDZu59v6/vlW17fbLQ7Xbyi7bWNv5XeSTrYzlh4AqVD0s522ux7ccSnCbBrF8tG93j9Wc37UNMmywg0vrJw5ET8SWi0HufTQ+9BCw+NZn/ug2Td9SBqdYAqPM7u9lcSqgqFaIA2ZyMbvHsmni9IJBIkEolJ9Ya5XK7RmVmfp5zKpDjpvJ/rwQEUVWKik63in3Ne/wUhFrZQtNsllKpJpC3uAJti34Zn7URrxWh4gkhHOFYSP6zZpuiJfxG1iKKPg0vAuLpI1KjEG8WOrnC+tJP7M987577viN3HvtjnGah8hUJwiKzzStLuds4UfodCeJAgrI1Js0Sc7tgzpN0HJ/b7jXrtNu8R2rxHOFB5Fh2dRoobI/0EomYlpOsNJnqSoK/H1/REfafAwpY311suBdPNUxZS4sh7p328lPdjO28jDD6CZB2Cq2hEbd40YKOxJsbTCZFAA9XwBJoQKRK1EgCdx1dDFINvcL36SQASMgtobMaR+jrl6p8iohdwrSeISw+pDoKw0fJJcL6FKFo75VDNNH8fNmzYgNaaK1eu8PTTT/PlL3+Z97znPURRxNve9jZe//rX87rXvY5du3Yt+Hz3yU9+ctLf733ve+np6eGb3/wmr3rVq6Z9zrvf/W7e/OY380u/9EsA/NZv/Raf+cxneM973sOf/umfzut1jRhcYubrHbhQVtOipT45Y/369ezevXtBv9paTbDMN5J59epVDh06xObNm9m5c+ctThPxsOx7FvG81uwmhltPwVrCoce9m7Pl53FFAkfGUTpiLLpG2uqmfYYZvs28nP8nns29j0BPGIUIOFY5wOULP85Pbf7LW1rfQpDCpt29n3b3/km3v0a+jc9E/6fW+ao1oZa4VkiTqQlQq0WzRUSoJVZTE4nStVqvsfAUWxLfwhev/yFZbyO21ECERiJchxPFr5G20vS4O3gs/X0Ng+qb1tnUmbl161aiKGJ0dJSRkRG+XP11QjXUeGxBXuGw/js2y7vIqZN4VkRchLhWhCNCnPxe4vFnsGR7zUZExFA6xJkQjhFyQvDVqvBu/G9NHMqJzt2aVbvkWOF/M1r5H/i6lpIXSHaLv2d9zw2h9fy1R5F2ba8pQCu4K/3TAOxI/Tq54CsUg6MI4dHuPkHaufF5TCespLiAJayJVosbRXZ1Gai0mJh6UrN60RPRQqUhoowtLCyRwRJxIjWMJadPty+WhdaYCyFw3O/Gsh/HiQ4Cmnzxg2j9TRzxBF3tNxqmPGs3kEBxFkk3QlhoXYsfajIUgi/g6zIxUStRsBnH4zpMNOJE6ioV9T/RJEhauxBCI9QHQR9HqR9dk2JwKkIINmzYwK/92q8B8OEPf5jf+Z3f4eGHH+ZDH/oQP//zP09XVxeve93r+JEf+RHe8IY3LOp18vk8wKxZp69+9av8wi/8wqTb3vSmN/HRj3503q9jxOASsVDvwIWyGvYyURRx/Phxrly5MuPkjLloxcig1nrGzm2lFMeOHePKlSs88MADjdqS1aBVxeBSrXtz/DHKKsegfxqlBkFDyu7m7uTrseeotwT4cu5vCbCwiJBCoHQtuTeuqnx56G9Yx/xS9cvFk1uf4cKpk5wRLxMJuxZjikI0DkLSCBPW5IYAXasdrKOppS0TVs34OuNtxBZ6Ig1r3YhZCRiPxulC4KthThaOoYlIWb10urvwptQc1rEsi87OTka8l4jGailuSzu1EpkoQouIC+EYr1R/yWjsf+OLw2yy30x7ejcvnRzG7bkRgVzn7WXIP0qgah2ZHgFl3EZXqxSQoEqZGLKRwBQTNW2aig7wdYmYrJ1rFIJj+rvR19/Fhp7X8Pz1R3FtPSHJahc+LQVfHXyMp7pfREqXTu91dHqvm/a9Tk251m5zwKpOqj2skxBQUBIlBFJHWLWYdaPRRFAzy7ZlO/nShxjW/xEoAwJLbqLH+3O8+Nw/aGZjujXPh3otM0Bn+zPTPkYIF9feSzU6jRDjKFWz65GyF+gl0pWJcX81ayKHHAJNiI1GY1H7geMLn5hIYMsM6CpEh7HZP7Gd1qJardLV1cWv/uqv8qu/+qtUKhW++tWv8rnPfY6hoaG5NzANSine+c53sm/fvlnTvdeuXbvpWtTb28u1a/MvQTBicAlYjHfgQlnp2rtisUh/fz9CCPbu3UsiMXNt0Gy0Ys0gMK0YnNpBvdh9slS0qhhcKlwZ597UM4yGlylFIzgiRoezBbepDmomSuURAkBoNVHoX++YrEVxjpSenZcYXE67p7FwiO5slm3W9+BIr/F6J8Zf4FIwkZ4UEGpJWdmkZQWFmLgIayyh8ZXNOu/RG+9P1Ppgp9ayaQGXq4dRahDBOBU9QqR8EAKLNnq8R9idfDOd7l3YcnKX/IXKCxOxL7dREiORhGigzED+DNUL+4B9DHV0oLJZorA0aRub4q/lWnU/1/0X0VoihcYjoG5ybYsIS9qUVWxCfDVX4dVq8xSSEDVxW63G8pj6FUau/wtsyYQYqbdEgERhC8VXrj/DK3v+kfHqx7le/FvO+xUCHWd9/EnuTf8s0nKn/Zx7xIfJ85aJV9SN/V5fUzu/zWX115TUGJ5MY+kAR5TwGMfRRaTMktFbqOiPYk+IWBtIc4bQ/xYCP4GQd+HGfh5nEd25ixWD8yVu38eYvxMp4wihgCTQQajO4ln7iKtrFNUgSVJIEaKQKCIckUCIcMLuJyLSRWwyNfcEHSE5j5S3NjN5NZhqOB2LxXjta1/La1/72lmeNTtvf/vbOXToEF/5yleWYomzYsTgLaKUwvf9Ze+AWsnI4NWrVzl8+DAbNmzg7rvvvqUTSqtNIJlpskd93vJ0xtqrRauKwaX8jkhhkXU2k3U2z/3gJioTURjBzfuvntibCa0VA/5hrlYOUlGjpO0+1nsPkXW3L3D1sxPpAKVDrKZ51UIItqYeZJ3agSTOtdJ5RoIzhGEZ7UpsoSfSkYJIC0q6j0H/Ct3uxkZXKGJqU0NNHkU6pKhGscUokarWtqMhEoOcLX2Br+UPkXY2Y4XfIGsXUQpC9TDrMt03RcYaSMF9ux8gbmUn1RuGYciBAwfo7OxsTEZ5rO0XOFv+HMfzH8aTB+mUJcraoaA9LDRxAiTNtXgTDQfU0uMWAZPn9Gqk0OTVfyMu65bQN1aqqKVxJdcYKP46Z4pf4uVSH1WdQgNHq1/nYKGft/b+54YlWDN93Ru5PtiBJ3ONCGb9mKqqLja3b+Dy6I3Xi3CIdBu2KE+klxNU9GdwZITSYCPJSkFR64kxhwWEOkhU+imw7scSaYR9DzjfhZiI3M3GTant4o8QUweRorbWUNuU7fdhx2fvUp2JhHM/nr2DSnQaR3QhsAjUeWzZTlvsVaSdrVws/QElNYpHiBAaixgxawdCDzW6zRvz2CfOZZGy18T5daEs9Vzin/u5n+MTn/gEX/rSl9i4ceOsj+3r62NgYGDSbQMDAwvK5hkxuEjqaeF6t/Byt8KvhBiMooijR48yMDDAAw88QE9Pz9xPmoNWjQzW62201pw6dYpz58415i2vFVpVDMLqj6PLxjdgE1EV9kR08MZ9QmvWO3eDP/1zz5e/ytnylwFwRIzr/lFGgnNsib+KUEsCXSFlddA9EaVc7HkhaXfgyRSlKE/KvmFKXYryZOxuHs28BZmtXTSPHj1KQV3ifOwvURSxRIYO+UaqXOK6fx5FSKQ1tmQa+Uuj7k3rInoiviYm+nnLkYtE48hLrLPPIB3REGJK7+daEUKZRhBgSweJJCICFAmxjqRT6+purjd89tln2bZtG6VSifPnz3P48GFSqRTZ7F083v6bDIaf5Wr0J3gTvoq+dhDSIqkrjOv4hNzVjf/PyDL2FLFWj4E220FPh0AxVv4YL5fuwdcWCREgBQRacC3w+fLwf2O7+iGEEJSCU1wp/VcCXaDTfS0PdH+Ri4N/xZj4IyxRG2HXoX+du7u/h1AVSdkbKFVzRKqKJT20jpD4gEXC7oHoFFoASNqkoKo1AbULsxCCmJY4MgDVD6ID5R9ChM9B7LcR1g4AwiAPONjO5EzFpMhg8XtJ6FONRqSa72GIFX4/hfLz2PG5o+lTsUSS3viPk6t+nFJ0DE2VuL2LDu/NxKxtYG1ju7WN8erHCcMvIvRFbLkVQTuKKoqr2GRw6JjwrhkEUlT8e9ZMN/FCWKpRdFpr/tW/+ld85CMfaXxP5uKpp57ic5/7HO985zsbt33mM5/hqaeemvfrGjG4CJbCO3ChLHftXaFQoL+/Hykle/fuJb6Ik8N0tKoY1Frj+z79/f2Uy2WefPJJ0unpa6dWi1YVg2vFLHtX/EmOlr9GKKyaJciExPFExLe0v5PLxcs3PacSjXG58k0cESNhdaJUCDiM+Fe4VP17YrKLUFcpR2NIYbPO28pmr4d13k4S9jYsMf/IQUwm2Ry7l1Plb5ALKjgyRiUq4kqPLfH7kVM6ziMcHPsNdDvrGuPWtugsF8qHCXWIjiKUsGtdxhMyqbnT1RY14RHpejdyzQIm1DYxGbDNC7DqkfOJ50gBCQeK0TgWKZQqYYkIW4DWCV7R/pMzvr+Ojo7Gj6u62e/IyAgnjp/C99eTbX/PxBSIDFXvm+TDA5SrJ7kUniIfuROJ6YiELNNuFVDIhkit1aXVBF2X/BsK/Ch2Y0ZL7V1b1MbsoQMuh11UG0Kw3pijCbTmdOUoUn8bvclv5eu5F2vTPDQM+4cZqrybrtjr2OT+O1LOdyCbvAptmWRH8icohb/DeHSdQJVBCyqWS6cUpO27KEXP33g8UGEiiS9qMU5H1sSsAoToQOCh1WWo/g1leT9jlT8iqfIkbQsqgkB5aOc3cBNvntRAElMnJxlt15FC4Kj/B82HajfoMiJ6CaGvoUUb2noExMyNC67VQ2/iXxCq0VpFoMhOGqHpWJvJJt6O0j9KqfIegvBFlD4HSCy5haSMIzg3cUCl0c53UQk2YVml6V9wDVP3GbxV3v72t/P+97+fj33sY6TT6UbdX1tbW+O6/CM/8iNs2LCBd73rXQC84x3v4NWvfjV/8Ad/wDPPPMMHPvABXnzxRf7sz/5s3q9rxOACWErvwIWynOnWK1eucPjw4SXpjJ1Kq4nB+udZNxlta2vjqaeemnuayCrQqmIQVj8yCPCtve/EG/wzDhWfJRI1YZSyUnx313+a8cdQMRqkqsdJi/WcLn2T0XCASCsCLZDCYbO3joIaJtQBMa6jo6Ocyvl8OkpS0bXTrYXg3sQ+3tj79jnXuCV+H56V4GrlJGU1Tp93Fxu8XXS6k9NGWmtCfEBPuhhbwibrbiQh07yt99/ynjPfA5ZESjUhCKmNiLPStFleTTDpSn2rE12wICg0oqfNn1y9uaNTaAJCIqCiPdCSTllkoPTvKPubaXdfT9b93sZor6k1eM1mv83+hrlcjnNXDhC0vYSTLOG63fS4WdZZg8TsLjrdpxksvsAwn8ZGNaykLTSRlriinXt77uP5AYG0Jrp7tUaIWrdvqCVCxPGV1XgvN+aIgJzobG5vizEsvk6IhUNEm1UmJStIoSmE/0g5epZC9WPEvJ8nROPKNjL2Ttrc3TzR8Uecq3yYUf8YAptO9x5S4hyRuoAmBroEqAn5egN7oilIT8rn2widoBJ8iZz6ML2WRcJ2aqWgAjzLJ4r+HaXCSUIeRrjDVIoFErNcp2x1vuYjqAax/D9GqJO17nV1FV/nyCmF4FX0tP/JzNuQ7TPeV9uPKZKxXyZSJ4jUJYTI4MgHEPo8Wp0ALLDuBbkZpc60ZGSwUCjQ2XnrHeH//b//dwBe85rXTLr9r/7qr/ixH/sxAC5cuDBpH+3du5f3v//9/Nqv/Rq/8iu/ws6dO/noRz86b49BMGJw3jSPlINb9w5cKMuRJo6iiCNHjnD9+nUefPDBJUkLT6XVxGCd/v5+du3axZYtW9ZMJGsqSy0G19rs65Xgdd0/yeu6Z45eTUUKB4HNmco3yEW1DsFa8wJo7XO+cpC4lSEmQrrs6wg0R8M2LCmwRf2z0hwsf4XC5ev8sw2/MevrCSFZ5+1gnbdjzs8nLlIIJJEOG+PhtNZUVZkN3l1IIfnXd30YgN8+/lYcS/Bk57fjigQbvd1cq36Ny5UXQIwjtJoo4VLYIsLWTIoi3rROKYiUoKQdHHzucodIWVVGgjgX/PP4xfch+Bs8EaB1QLRdIsQnp9/WFD+3s6XjDJclVLZQKVQYr5SR8Qwpu49s4mF2dbyB/tEzjKvT2LJWGVjVFg4JXtnzfwF4vOerPH/lCRzXqjXRaEHoR+StDLYO6JRFpND4Wk6MnKtlLgNt0WGVEVISKgtHRHgiIm1VJux+xMR+SXGtepHA/10suQmBRdrewvbkDxK3u9mZ/HFI6oZ/aKiuUfK/hAoDQv0clgBfKxwhqOj6d9GGekOM8BCi7mOqKegR4ioi4djQ5FpYWxOMRn+O7zyF3VZhJOwkO0sJXn16sgw+iFDH0GITxfDT1LqABW0SBqLPcz63h6z4Ck7yMH74ElJkiLvfMW9LHCEEtnU3ttVco7gLrF2THrdWJm0tlGKxyJYtW255O/M5pz/77LM33fa2t72Nt73tbYt+XSMG50Gzd2DdAHqlWeo0cbNh8r59+5ZtnmIricEwDDl06BAA9913H+vXL48Z7FLRqpHB4/YRvlD8FPqMZl18PW/ueIZsbG1MC5mLNns9MdnGeHQdENjCIUQTIyQmqziigNJ5nFqMjFPFNqSoeW2qRnODxhaKM/6JxnaVUuTDlxkPjmDLJJ3eq/GmXGRnE4Jaa9ropurmue5fJmVlkFgUojwJK82m2I5Jj//Vuz960za63K10WNs5VvwwY9ElhAhxCYm05FpokXKiGQWh1pqKtnGJ6LHHSVlVhoMY16MOQmQjKR3oGDFhkXHH+Er+9QQj7dzX/mOsj38blrg5GltVI5Siy2S89TjxFHSAUppCOUexOsDZy/0cOeySSv1rNmezBMkyaaudjT2TGyxs4dDb9l+5VPpVUCEl7YEUCK0JcUAU6bGKXAtThFoihSLQFo5QbHaHUMKqWQkKSEh/wjb6RkPK9XCcsvLwxAgp55VE+OTDU5wr/g3r3V6Gy+8lYgC0xsIl4/4g2cQvkPa+m2L5CwxVf40RNU63BBtFKKCiIxxZq90U9AEW6ABNmVArso6L0IKIG6bkChhUET6glAdhJ1iaUGvsGY6fwHorQueRaj9aZCkFzzY+5QkjIzwhKKuIcV5JvJxA6IAIge//FZ73b0h63znjsblQFuqPuFZYqjTxamHE4Cwst3fgQljKNPGlS5c4evQoW7ZsYceOHcsqblvFZ3B8fJz9+/cTi8WwLGvN1QdORyuKwb+69GccsQ+iIhARXA2ucKxwlJ/Y8DNsTtz6r+rlRgobT64jQuCgETogLkISVgUxMXjLFSU8EaERVIghhSLUzd+xWqevJeDw8Ke5u+OVnBj/T4wGB7lcjbjoJxmPPo4jYFOsl+/s+TdknO4512YLh4fST3O6dJBr/gUC7dPrbeKu+H20O3OL7ZjVzl3pb6HdvZcXRt/HtWo/miLlyGE82oDSF7CmnP7q4jCYiCRKqWmzyghgVKUnBpFFjXI1paGqHQLpERchkOPQ2B9zbOzdCKFReGzyvp97JmoNax2nCtGUQpVSkEykkF6Ju9bdj6O6G/WGI2fGuOYPcf3SINlstjE/VgjBtsSjDFWewldfxiYi1HZD2A6obja5Y6SskCtBklBLOq0SG91hOpzxxuQXVcurN3wO659lVbvYIkKLkHJwAWlJbEJ09CmulwcIdYgLE/Y3ASP+X1MI/oH16f9BKvEGYu5rsG0bHfm4wXvxg88S6gqRAIdRIA9qrCap5WakqCKm6XCqaPA1xAQUtERIC1tmuepvYqN96aY2mqqKEPIa0v8/oCsgMuim7d6YTyywhSAmFBobLdK1z0XnqVZ/D89+DNtamua6KIpw3flNEFpLFItFIwZvR1bCO3AhLEWaOAxDjhw5wtDQEA899BDd3XNfYG6VVrCWqddMbt26lR07dvD5z3++JUTWUonBarXK8ePHcRyHzs5O2traluUHwqGxfo6WjqCBmIghLUmkIopRkY8MfJB3bPtFoBYZOFs+RS4cJm23c1d8J7ZcO6eqHm8nVZUgJMSRFkk5igQq2kZrgRISpxavmTRft4bCE2HN807bfC7/N1zX/wdbX2LAT3C43E5FOdhC4SvBkeIIw5d+lZ9e/18Y1pfIh1dIWV1s9O6b9BnVU8gJK8X96afYpR5CaUVMJhZ83hoJr4BMkYssItIIrYhJn0OlOPcnypM6rzXghxohYxPj78RElaGudQCjJ/UtSFETVKG2qUY+UtqNSFutgafItep7qeZGuVD8Co41BAiKUZlHOv5tYzuVaJCY1UnM6sayZ643bJ6n3NHRwf1tv8SJwCEKnkMTEWHVDJRFO9LawKuzP82R4e9FS4hLNVGvJ4AIj4CKdikrh5SoUo+eqRu92CgEBfUyLl3E9DmkrBBJgQONCGl9d1R1jhNjP0dV1WxkwMKz7mFX+r+QiE2IYaUg+jw6+BJQQFq7wXkrMf3blPzPEXclUteTydSaf0StbAGdbjTUjBHngF9lp23jTljLlFVEGYkTHSZJGXQBoW+8LwALiICqUsRlXRwmGvdq0YbQeUr+R/5/9v47TrK8rvfHn5/P54TK3dV5ck67s7uzgdnMLiDpEq6oiBJURAQxZ9HrTxS+1+tVLoqK6EUv6BUEEQEBWZJLWljY3cm7Mzt5ekLnruqKJ3w+n98fp6q6e9LOzE5qL+/HY2Z6qqtOnVR1Xuf1fr9eLwrpn7uo8+xctVCZwculJr5Wdf18w15HdaUi5Z5NSSkJw3N4XVxAtZkv3/cvOUf3UqodR3c9zqNprdm7dy8jIyPzwPFCaW1fDjBYKpXYvn07+XweYwx79uwhjmOKxWKHWclkLh5QnK0em/kOxmjUnK8dJRXSSk5FJ4njmBpV/m3sXzgZDBPZCEc49LtDvGLgB+j3ro9UgqWpG+hy+piKR5EGHGWIrUqUqQiU8AiRuFTpdmpM6gISiyci8iroZNkaGxLbNI34BK5UHAm7aWqXrAo7ACq2kolY8NHx3wBp0DZC4TBVe4p0uhcsmDjmufzhvGPkX4Dx9rlK24jEcMUgrEAI2VJZd/N0s5sl9ihp38VYmDLQ5a4DO0VKNGhYl4b1SLUArz2Ni2qfrgkcUaRlhLGy1XIFsHhCcyr4FDg+TZvBEZqM47C/8hcMpF6EReKJPAP+3ajTMqhPz1O21lKpVJiammJk7CRPVj+Mzg4jVQ+ulLgCMs4gazKvJeesJyIk1g08J9MRbliSxJasbOBYQ906NKxLWkYJ2LWJeXVsFdaAEQZhTiClRYoEoLUtqeceo0bsEovSHJYxItQ72V3+ETbn/40KTzITPY0viwykfgdHzqbq5Pw/YTx6gJypkZIS1VpPTwiENUQMJUKZ1g1DZE8R4/C07qEgAooyoImHJcSYCll3LZga2BAP0Q4rBGDGGOLOFpx+RFvvbEtnnEeN8HFGG++mqQ8BkFEbGMz+Nr5zfl/D9nV3odX3wOB/orra3oEXU5fKsFlrOX78OHv37u0wX1dzm0737bteql6vs337doAzrHSEEAsGDD6b9RweHmbv3r2sXbuWxYsXdwB7rVZjamqKyclJDh48iOu6HWDY09Nzycpq0+IvJPO/6IWYnUT7wsRnONI8REF1UVRpAtPkZHCcf5/4FK8f+qnr5iLxkt6f4dPj76FpSujWpdMRDq7sJiPzQIDFZZFXYLxexZeagmomKlZIouOw+LJJYCSxjqloH1fqeUyaKywZFVAzFVyy5EU/0/ppejIFXBEAoBU86r6delzj5fXfZknqLjx56RelgtuPbCSMkz7NmNtay7BYQT4u8LYVibq0Go/T1CUmgu/wZO3TnAqL5GWTjAyYMZlORrK1tHKFLR5hEl82rykJIKjEikCkAUtKRB3G0QrJSPNLpEwN5bhMBR8Baxji/awfvO2s2yKEoFAoUCgU8IcOUKmMkzI92NgjakbEpk7JPcET1S8i3CeQLnjp38CN/wwtBEpaZKs1DBDrgBvcJvviPFklycnEk9AjpGEzCWSylkgoPJuc1zKBgfP2ozGGWMyFVwKBg0Vj9CSPln+CwNYwNkIIyYHaP7K58Cv0eDcD4Lgu/bmvUmm8n0j/Hb5MtMgCkGIdkRzEijKINKEuJ8KV1qiCL3TnxmV2R7kgujFyHWFcQooRAmuoWkvNJp/bGPAQGFtDdiySAkDhqPnZ6s1oD0erP09MvWMEPqN30Ky8mZWF/4unzm0Sf6WTU65EWWup1+vfA4P/Gep6awufXpfSJo7jmD179jA5Ocltt912WWTvF1vXIxgcGxtj165dLFq0iI0bN57xxdNmM6/3utTz0xjTUZHfdttt9PT0zLNLyuVy5HI5li9fjtaacrnM1NRUxxw4n8/T09NDb28vhULhgr+4N+Vu5Knantb8lNtZF200/d4iyqbEseYRsjJLWiXg3JcpCk6ekeAUx4NjLE+vPGO5Xxn7vxxpfgtX5Hl57y9TzFx5BnEwtZY3Lno3T1T/ncngi0gxRZ+7mbKeoRxP44gq0Me4uZUVTkyFryAEaJtc9gUgpIO1MQ3r00UNiUWfBpSN0aScGIOlrhsE4XYK6WQ+LbIJK+MKQ05F5JTHrpn/zb7qR1mZfh5O0E9JPM2AewfLC3de8LYNeMsZ8FfwdO27aBu02Lx27kcCbbbkX9x5fs7pJ+f00+evY132RxiPHmMmfJTB+NOEIqRpPbRNgI/EkpN1PGWIznEfY6VDbAXZNhBsPR5bQUE2iaXfyiIBISwn+VkOnaqRFjksgiXyZ1k38Nozljsd7MaiSXvd4LX3b56jzeM07BH8aQ9ChUrXEakfJuV+iAE3neRWxzGL5f9my+BWZip34GrJlMlTMoksSFuLJ2IiqxAYFAmQx1qMSOZLEyPv1vu2jnMbCCa8XgxIqtajaUdw0CgUUvTTNGPsnnk3W/Kvo6kfw9gKvtpILvv9uOrnsdB6NfSbMuXwc1Ts1xGqhqtW4ohFTIf/jLGNBJCLGGwyyuDJAbBJcg1qHUHqVyjX3o4REbTWTghJaASuFEhbwRIlANM0CK0mrP0eldo7mDH3s77vfUw030tMHUWqY3VkrSG0FSYbf8mi3B+d8/xbyGri74HBBV5tNvBai0TOVxcrxJiZmWH79u2k02nuvfdefN9/5hddgZoLBq91WWvZv38/R48e5cYbbzynWnihtIkvZT3b+crAvHGBc53zSqkOIwjJfOHU1BRTU1Ps2rULY8y8lnI6fe7Ejbu77+NbU9/gRHiCpm0iY4nF4EufF/e+lJqeQduYlJr/hepKn5quUdNVIPm87ql/kX2VrzAZH0zaiKKJJw2fnXwj6ekH+MElv3lR++VSynFSbO1+FQ19N/ur/0BNHyclDChLYLsw3ML6zGbWZDawa2aaE8F3W1Nls98x1goiFI7j0K3qnIq60CZhpIwBg0pm0VqIyPWzOCJoAUGZ2L6ItjGxwBE+oTnBE+UPgVCgY/bJz2JmJJ6QbO36BVblvu/82yU8bso9j4wu8rnpvyIWGqzEioTNXOyt5p6es6tHlVIMqTsZSt3Jxyq7adpD5EUDKQRCGPKiQUa1Ej5a8FBh0C2BSJspU61Zw9n0EEve1ohJtVg202mvaiQZlcGTEVYHOM5/5/jUuwiMi1F3sqrw5zjK52w66BhLgCTnpli2dANxFNNoNJhujjFT+TGypTvp6x6kUqlgli4GBF2FJ1gRbOBp20uESpheYZEiJkeAzyYyagdW2IQHFS0QaAXG2g70O1sZowlJIdAtixsN9gSutXh2kvHGn+HJXoTwCPVhmvE2etO/hKdWzh4D2UVP6kc5XlpBtjvNQHozxkgCvY+63knFxGQ6TGKajHMb2FMgChi1mWb0SXAfwEQg7dcA0PSBgkD6uHYEaQOMraOtINViUC0WT36NUxMbqatkX831vBRCgrU04qfOe/5dT8TBxdT3wOACrtO9A69XIAgX3ia21jI8PMy+fftYtWoVa9asuabb1H7vaw2ugiBgx44dBEHwjGkiC0mlezHrOTk5yY4dOxgYGOCGG264pFaM7/ssWrSIRYsWYa2lWq0yNTXF+Pg4+/fvx/f9DjAsFotntJR/dvkv8cF9f8sp5zhGWgb9Ib6v58VsyG2iHldJyTT1VspGuxq6hi9TnZnBr0y/jxPNr6MJcWVyca3ZNIGOyasGDfNV9pTu4oau++ddjK5UpdUQm/JvYzraRVNP4MoCPe5mPFnsnP85ZwgZqBb7Z5C23Ro3NEyKBltYndpPw9aZiTOY1n2fJ6KOPAHaDiKJNAUsSiQClXb0Wt2cJNIOCIW0mjI5YjN7Yf3y9F9yc7iDrT2/et5t8mSKTcW72FS8i29M/Qv7at/GER73dv8Qa3O3XtB+qZlxHJGi3Hr/PmcGXwQYA1YoXCGwxAgUqgOOkjZrUnPaqkiEck8DgslzLDJRcesmK/yQmlGMmFTCsuonmJq+j0Hvh1t2O5rI1nFFIoIIbRMD5JxFCASu6+K6LumcT01Ps2HZWsISTE1NcfDgQYaHh1vn9lfZpH6bYXbStA4CyIgu0moj0jF4saGBRKPQRiOERrXkI6alzMW0BDWd42tbx1LgnJaN7WLJyIBIH8fYJpGpEVkPV5xC2n9mMP/rZ+x/E2dwRD9CuCgFS3PvZzr4ENXoYWpM0qUcUnIIyThW9GCcl4FcgRRpICbl5Yl1H9CFEgJtphFyKXArkRlBxY+QVRZtBXHrUHnCguNQMnXOBN8tz0TOP6++ENvExpjvtYkXarVFIm2QcrVNpC+2LqRNHEURe/bsYXp6mttvv73D5lzLavsyXkt7menpabZv306xWOS2227rpCCcqxYKM3ihoNVay5EjRzhw4AAbN25k2bJnDrm/0PfP5/Pk83lWrFiB1ppSqcTU1BSHDx8+o6Wcz+fxnRT32gcopAssXz5/bijj5Nic28Kj5W8wFU2SEilCGxDZiJtzt9Ln9XOq+TQngkcAO2fuyaKEJbIODeujpOW71b+gKQ6xOvMgRXfVZdne85Urswz4d53z92syL+ZY45tYU2oxfUl2rxAKR6yjyY10O3dzY3qS4ei7hKZObOtoYwmMi69isKbFECaAqB2fR+snATjCIRQCazQVMglIImqdKxBYhz21r7Kz+lVcmew3YwWRlVi9iTet+O9nAOj7en6Q+3p+8FntHwdNt2pg8NBIjBUUVB+YGlbUqWiFoJUIYiOEcNFWttTYs2B41tZlbiUgarHfJLCSEZOeB6UEluHmPyGROFikPkxMcg0IxGIycimeXDRviYGt4ck0fYVBvO40o6OjrFq1CsdxmJqa4tixY1Srr6dr2Zcodn8DIZoIMY2225BmPbHWuEoSIUAoYhQxiYdgRmRQlFBKMmXcFpS3nXV1WgAXZr8zpdAIDLGNGY2alHWuk8t8Iv4st6gCA5m3zNuG00GVUh59mTfTx5tbTxjHmkNoJFatBVEEIONsoRHvwNgIcIEAYw1CeDiiF0sNHX6NjOugrZh3RLS1uMKSpkoVF2MDkgZ2+ygK0tLDao04B/u3ENvE9Xoda+33wOBCqrmRctdzW/j0eiYwWC6X2b59O9lslnvuueeatYXPVtdqBm8uCFq/fj3Lly+/oGO9UAQkF7Jf20bapVKJ5zznOXR3d1+x9VFK0dvb25lNbbeUJycnOXHiBMYYenp6aDab51SzP7f4AhzhsKu6nYauk5Jpbstt5f7iCwDY3/g6lhBBwl4kJTDW4LYi1iDGWsN0dJg91Sluyb+OvDPUeY8kTxjkHLuaY8eOnQFOL2d1uUvZ2v1z7Jj5EDU9hhUGV+S4Mf9DINays7KT0WgcgEH/AW7O38zHR/4HsdAJ66LBlZpGc5JUpgtPxMRWYCwo0c6utQSRAekmbVjj4BJ3znkhwLEGYwVppWlfoJWwKGkIeJJvlD7L6vQNLPZXUYtKeHh43qVd4LKyn8CeBAy+jFDC0DQqaRmTjAg4soDCp999PsfDkxgboE1AVuwlwkUgEvW1TfwGk0vWHEFLsmVINClhGY69Fm/YztWApjF4UiXpHCgCCxqFERKHEn1qmowZRsdbsXItgQ1pmirr0nfhtVTZ1locx5k3MjFR+zwjja9ibUwc5VrPqRPZPXSpt9M0f0FO1ola6lyLRdtb0HISRZm0gCEcStYSGoMUhn7VpBvB0ThLaFWrhS6QLUV3xeSZ1hk8oVGtG4KacdlR+TBLGn+OEApBD4uzf4QxsgMGh0deR3f68SR9pbXjjIaunn1nHLeMcxuBe5hq+EjrhmsCyOKrVVjbwFBBCsXZDMjbsN0HrFxF3RzstMQFkMaS1k9RD99NNv0b814bNpt4qdSCVBPXajWA74HBhVLXu0jkfHUutspay9GjR9m/fz9r1qxh1apV1902XYkovWeqKIrYvXs35XL5okHQQhKQnG89a7Ua27Ztw/M87r777qt+g3B6S7lt8VEqlTh27Bjj4+PzWsqO4+BIh+f2vIC7uu+nEs+QdXKk5OnAUaCEQzQnIk3SypW1rWxakabgLKMcH2Uk2EXeGWIqPMl3yp/iRPg01lqG/NU8Nv0EWkiUAHsAtBXc7t3Lq1b8ZPJOl/GzNOjfzB2FX2Yk2okAlvp30O0lc6tL/CWMhwkY7Pf6yagMK1PrOdh8CgPUjQ9Go5wV+OEU2vXwZTyHU7IomyKm3nk/2/l7dhuaZpwet0Bi5yxnn2ENnjTsKf8Vo82VhGYY0ChhkbisSH8fazIvodefHx12vvq+nnfwyfG34co2FEqAp7YSL+gBp4VyhaRpQrSNcITLZDzOKbuEvNjFoFtEWEFgmtRNRI+TsGxtPiqZwDRIGyGAsNU+nwslPKHAJpyitpYIr8OqZUVIr2riixKK/YQmsY3BWsq1vyXyHsF1sxhjzjgXKvFDSBEhVT+u0yIatI+1E4xVPknt6B/T3d3dOr+7kf5JYnMSaxSN4M+wdi+OnKEPQEmEWI20+xkiARYndYrItrT3NtnGKdOHIxIrG0iYRJ+YunHROCg0lglO1N6Mdn8VKes0w0foTj82TzyMAOnAzPQGCsX5gFAIh6L/arLuHTTjpwjix7B2vPU7j5zzSiaa3yLTmX+ct9jkxsRoBtM/yUzzd2laC0KSEh4+PkrWiKPPY72fJ4rKNIP/iiNmEFhqgWDN2sUo9W8XfJ5dD1Wr1XBd97oiYS62/p8Bg9ejd+DF1NkA1VzAc8cdd1AsFq/R2p2/rjbT1hbPZDIZ7rnnnot2s18ozOD5wODY2Bg7d+5k6dKlrF+//hnvtK/052GuxUetViOdTlMoFDqzWI1Gg0Kh0AGHhUKBXu/M1Iy16fs4VP8S2tYBRZLKapHCYi14UhMZh9XprQghUMKnqkepxJN8buIvKOtxFB5CCPbWHiXrCZpaoq1CAkpangi/we3Hzi+yuNiKTciB+iNMRIcwNml9VvXDrLC3scjfSEZlWJGen8Dyo4t/k78e/k3Go9EWoyMxSBZlXsSrhn6RPz/wYpT0iKgy5PZilcZ3fRpaJyxVS5jRnj+z1pKiG4lBMx8bGBKgllIQmGM4IsZBE1iXJpb99c9zuPEQvsxDdBhHucQ64vuXPXrObe5JL+aV/e/hS1PvpKqnaOg6WRWTlWtpmBhJGUsVKdZjSWPRaB0RkbjXReIWhuMEtinhU9dNCvIwjrQtvW6yjdLGKEIim6h2o9NXpO0XaBNBThtApkXMIpVkEkdWYnBxhebpsJcxnSiT946+hkVegVTfEIF9HXB7Z7HaTs9rkQohUI7CGIdcAdZt2dKZpz1w4EDLommQYrFIV9cHCfkwYfxNwOKq55D1Xs905V6EtKzx6iw3NRooXAzHYgdjHYwVKDH/M9+O+jNW4ghDO0hu8dI/Qas8jcoEzjncoISC4ZG/Z9nQjwEQNd6Nij8JRCixgaz3HrqyL0ebaYwto2QvUuTJ+a9gbHozaWlRdlYpLYWlaSRDfU9Ra/xPclKSptCJy0v2m4ekRmwPEgQ/0hLKzN4s9OZO0Iy+nwxfmF1R2zorrsIM8KVUrVa7bH6s16r+04PBtnfggQMH6OvrI5fLLcgDdjoYLJVK7Nixg1wud0mA52rW1UwhaUftPRvxzEJmBq21HDhwgCNHjrB582YWLVp0jlfPr6u9vVJK+vr66OtLAF+j0ehEig0PDwNJakRvby/FYrHjA7k4tYHF/p2cbD6CIyxt+YQBFIZAu/S4m/GdRCCgbUhadrO7+jDleIKsKqJEMo9UttMoYXGEJbaJVQkm+f/fNN7BT6jfvmzbOxYeYCw8QMEZ7JgH1+JJjjW20eUMkVHdZ33dW5b9EcP1fTw681k84fHc7h+m2x8A4OfXPpRsR3yKLxx+P7l0ju50D5P1XZTlXjKmQdVmCK1DOx1DUMe2nN/OOOIWlEjmER1haBqXyDqkZYjEEOhTFFQR6XmJ4ld5fHH0Ppra0O/fy4b0Wylm1sxbZF96FT+y5O8AaOqjnKr9OY34CMpt4ApFaHtpmEEiW8LaGE/1oOIS4Zy1E7S/PwTKezUP9vw6h0a/TJPvkuIuhnk7QkqmtKZLhTR0ep6NS7uSqbu26x/kZYgShsAqLBJjLNvCpZRNmrYnYBOXI2GdRfknOGW+TmV0BqkUDRzqYUjOL3QU4skuNFgMnlzRuflZuXLlPIum4eFhnnyyQi63he7iA3jFMUJ/P1H9/2DNBqx9kl4hcaVoZVyDK6BhDZ7QBNbpMIOQJJsoDE5rbEIAOaGRUgMujnO2hu5sebwL+DF09Xn4dmK2lczjmOB5xHwUx9uIYj7ZUNKvQfAxPGES5beFhpHMaI8+QIgejBBgNXOhRtI0VzSbH8EVbXa7Pf2asLqeGCZslvC9SZz4YaQ5iBUptNyKdh6As+RYX8uqVqsLOooO/pODwblt4RMnTpDNZhdE5uzZqi3CmDsHt3btWlauXHndg9urIcjQWvPUU08xOjrKrbfe2gEZl1ILVUASRRE7d+6kVqs9o2L6WtbZQGw6nSadTnfMryuVCpOTk5w6dYp9+/aRTqc7rOED+Z/hSW8NB2vfILI1UrKP2AhcUSbvdZFVAxgbU9MTuDLDoH8TT9c/AdABgkHUTNajJT6Zs3IIYRFSsMc+zHjqMzx6POGamtrl1vRbuWPgJYyPj/OR6jto2AZLvXW8bunbz7vNE9ERHJmalyKRUT1MR8coxyPnBIMAyzIbWJJah8GcNZavoIbIx0sJGSUkppi9hbjaS8U+Sres0rQeMQqXGCUNsXVwhU0SRlqze4qk5SqERQoD1hLh4IoYJQzaCvq8IgpDjMBBExnBjE2hcTgaPEKkH8atJYIUh1vYOvCBeeuZUitYkX8nY+VHODK5kw2bnouhjxl9Am0ieuMxJuPj5OJRJuMasUksqhGCJhGelNyYeQUAqwdfACRzpGH1D9hf+ih1cYhBb5q8DKlYb5a16yQgJfOGtgWFPXRHvQswabLMmDQKjRK0vBGTyLpRnWGDOsWoyJNFIUWZtZ5iihBjRhAY6iaLI2OkyNGX/pF52366RVMYhkxPT3Oi+jWmyl/HaIPn5HH9dTS8AGkPUnRkB9H2E3JMePSrKsfjbhrGwRXJPKG2kj5VRQgNJD6HsoX/pMgDI+c9N0Mgqv4BfqsVTEuklDDuGhm8GbyvAxDrccLwg2BLrMzfiXJ+m0PTzyXLJCEuK/t20Q45TbmvphL+I4oy2uZI+N4QKWKMvBljvtlpK8+WaB0hi9HvxQsbCDuVxN6ZaRzzCYQ9Qey+MWEJbQNhJ7EiB6L7vNt5JWuh28rAf2IweLp3oOM411TR+mxLKYW1lieeeIJKpXLFxQCXs640uKrX62zbtg0p5RlpIpdSC8VaZu56VioVtm3bRjab5e67777klJDroea2lFetWkUcxx3WcP/+/TSbTbq6lnBHz8/Q09NDPp9HCMFE+DQH6l9MBBpYMrLIqsyDFN0VeCIFp7FNyb/zORNrDQgQtk4l8wn6VNBhmPIqYG/453zt0Keo2xqOSIYWj4VP8s6DP8arCj/L5v6zmzsbG58jeUUk73mOCk2TnZWvcaixm8A06XMXcWPubpanN85bTm/jZkRhgoBRIhOwJL+eZanXMNia8fv7o2+hIqaS9nEcIN1Wu7G1vcYKGrEk4+iWjlVgrcCRBmsFWT2DUg4xKmkzG2jYNBEOaWZY7DVaMXvJHtXmcb46dhdLMq8jq9bQ7z8fR6aQIk1a3IGupMi5Scu14K5o7XvLVHyEAbWRJyofo2IaREBkYzypuC33YgZSG+ftH2NjJqMjFLN3kFEvSp4PiLhE03yTXrWEbreHQ/UP4yZJxJ1WcYjspOwC1IyPhRbjPBsSJzGAyynTTY9ryFMmti4zSlIQcYdlNaZMw/oI22Sy8Qny3tZzHlfP8+jtzzGROcGgWYJje2g0GglDXl9HOd5Cf/SDNLqHGXa2U7czDMgTDLlPs9SZZtLkEoYQw6BTISvriQk0yfysAIyVIKHRsKQz51gRC17aRdlPzvkwzPlUWFBM89j4Bm7M/w5h+KdIkvziIPoUVv4Vq4ufRZ3lZkaqLlKp36DZ/J8oKi1ja4VmObn0O6jVfoozpw5nKyXKCFvFiNVJm1kAtooyO9BmP8IcQ+mHEXYGhI+Wt6PdV4C4+qCsVquRzWave2LmfPWfDgzO9Q6cGyl3LUQMl7MqlUrn5+u9LXx6XUkwODo6yq5du1iyZAkbNmy4LCq0hcYMnjx5kj179lxRX8ldM3v54sQ3GYvGyag0zyls4UW9912SBcTFrp/jOPT393eyoxuNRsf4+tixYwghOsbXNxXfQOBMAoa8WozbUoOuzdzBkWAXdT1DWuZxHSehRAQdHz5rTaLMtYLlqQZ5FRIa1RFaSAw5FdBQp4hMDm0l1hocLGkJ/1Z5Lzf0fmieSrldPe4yDjceI2OLyDY7aWo4wifnnJ3FNsbw1amPc6j5JK5wcXA51nya8egED/CDrEjPRoApXBar2+nrLhKbEE9mOu8D8GMr/pr3Hf0hrBBofCrRBCnZ1Ur4EIS6iZA9YGdaPngW0bKdkcIgHHdWBIKhkfCMSDSDbjCraAZCowhwQQgOVP8piZ0T/x3TUj+fCibYID5wxvYKIeh1V9HbtYobul7KvurnORnuwhMZbsi+nKK34ozXJEpiSWznTwoqVSAym+nLvIwh/wb+Y+pvWOln8ETSkoxxmDEp8iLCEwZtId1K3DD2dCdD0WJEHQZVHReXcaPoEhGhlYTAlFY40pIREYKIpv4c2ye+yIbsF0mnz27x1TQTRKZMWi5CCqdj06RtgWowykx9N3vibxOFMZ71GFZ9HA6KrE+nuCVzH54cRGvBaPxztBk1oKVcBqvTCCWQ2U1g9s5p/85W0ARSFmzcYiHPBGcCWO5IguAPW2DaJzGJiZD2KI3Gr5DL/d1Zt9H3Xoqr7qQZfgxjJ3DVZnznFQilSHnvJA7fgGS+16IgSZvxZAqLmjdviMiBGUXFn0eaJ4EUVhSBBkp/CUGV2P2p+a+5CtUGgwu5/lOBQWMMcRyfVS2slOqYSy+kstZy+PBhDhw4AMDmzZsXFBCEKwOujDHs37+fY8eOXdRs3IXUQhGQQGKGe+LECW655RYGBgauyHs8Mr2Nj576JE0TIJGUojKfbj7EsfoJ3rLyzNivC6lnw7ym02mWLFnCkiVLMMZ0WsonT55k374KmUyGnp4ebE+d7m4PpRSr07ezqfk0exvfoqInAYErHBomJrYJwEEmQCA2kJIhdo7iFlrGx2hSKkLrZPYsJQ1CJFFkvTLg4yM/wg3ZF7E883zyziqs2Y81hxmQAV+dfJgnqZFiiHV9d2GtYUnqRvLq7GDwZHiA4eZ+crKLjEouNHm6GQtP8mT1kXlgsL0/HeG30jbOLGUNVsgWy9JPE5iJUoRGsSHTRVVP0rCCtBAoJB5xR4BitQYpOxDJJBkhSGottXDL/dCQAEEEzRZAasMUJSxKwCK/j1NdD/HV8f+DIyVL/ZewIvcDZ6zvhtxL2MBLznsuSKEY8NZzqPEIKduFEgkjXtXjpFSBbifx1HzD8qdp6BH2V/8PWe0SM0zD7KVkoCin8QUsccscipJUEUlbiJFMD3aJGjllcIRlLBLkHE1kBUYIqpHFVSbJWW7l/yosGRmyv/79yNqdLJKfpWZjVgoXpKAeh8jiV5G4GBsixSyTr22I6/gc8w6jhKLPGUTrmCiKacQ19lXqmFN1urufxGZGEPL5pMXjKDEDCBy5jIy8gZp4CG0nweaoM4gbjjDbMBCUGgY/3Q1UiEnhcBajaAHagCeTJm+MRXVEHB7YJkY/QdicwUsVzn6MVA+Z9FvPeNxL3UEjugnP7mzJgZKzSFsQ7i+BGAdTmz/8aTVYizR7AR/b8YfMYPGQehfCOYYVZ7txuHL1PTB4ndRc78B2vurp7MNCbBOHYdiZAdu6dSvf/va3FwxImVuXGww2m0127NhBFEXcfffdl31WYyEISIIgYGRkBK01d9999xX7ItJa87mxL9M0ATmZRbSY17qus6v2FIfrw6zKXJyJ9eVkLqWUdHV10dXVxerVq4miqNNS3rdvH0EQdOw9but5Jeuzd3K4uZ1aXGcisgyXx4jEk8l2GYu0Lg/0vp6x5h+ee/1JLpm+1AgBKRHS49RRGAwhhxv/TkMfZpnfS488wf85sQ/r+RjfAVyajPPkzCd41aI/pdddcc6UlMnoJJENKar+eY9nZJaJaITQNPFOs91pxFWerH+H4cYhJILl6fVsyj0HX6Z4y8pP8MGjP0VDTCVbYS1Z0WBd7tXU9BNIoTDWwaeCbVmaWBJxTV3mcGi0lKuJGXTbIHluRa2Qs+H6BIPp5OYkUS63267JBd/peYSJKNnusfCveLr2jzzY809sn/ifVNhGD7/DlkUXlnSyNH0bZT3CVHS003JPqTxrMveRUrNzsw09Rmzr5NxVCNFLgS0A1PQ4NvoHQmtZ642zPxxobQeAJSsC1rsjVEjATpzAXnRrmxrCIUdMYGVnOw0ChSbUI6zy/o1+LenzkilFsKSUi6i9EBUbGuoHyYqlSOGhbUBgxsk5m6jqJ0jJJN7RcVwcxyVlfSajMZzBfdTiKvF0GhOlqabvwUkFLE69gNWFl2JMyKlTJfqGnsbYKkL44C2ibkcRpJDCw09boI7ARctfxdp3IYSdxYOtE71m40TFLOfaFFqwTRImL2CqeSde/CA9ub+6oGPWrq78x6g3PkwQvhdook2RfU//FHfd+Tp0/CUc889gKyDyYDXCHseKAtBo/Tu3csAowk5gubpgsFqtfm9m8FrX6d6B50oSWWht4qmpKXbs2EF3dzf33HMPrusuuG1o1+VMIGlHqvX19XH77bc/Y5rIpdT1zgyWSiW2bdvWsqrouaJ3pKPhBNNxCU+4HSAIkBIp6rbOY+XdFw0G4cqpl13XZWBggIGBAay181rKR44cQUpJsbiRiewJmqrEbX13YdjK4foxTgZjuNJhNBgnMC45FSAx89rEFmgaNzF5ERaspaCaidrWOigcGgYaZoSTzX18q2QxXrrFKsJEc4put4cIn7858maWZHpQ+KxM38P9vW+bvy0iBQi00Sg52/KNiUiLLM6cr29rLYGt8+8Tn+JkeAzZ4lqOBYcZbh7gxX2vxZM+P7HizPbseDjOrkqOk42v0aCJISCrmlgEMzpF3TqkraGpS/ie37J+DlG4BIhWK9m22sjJd2+/19tq/80CQYDIQLMl7lAigYnJ+xzly5PPR8kkk3jE/DTfGLF0ecvp8V7LktwPnfOY+zLLzflXMhEepqYncIRHj7uSvDOfKVfCQwhJEoE3y8QZLMr/Kbbk3oYULuWTN6MpEOGTkiE9skZaGaa1pm4kWUJ0q5Uezdm6uduZLFew2lMYrbGe01K+t44XUI1jlHBImU8Sm2RPGJuj4L+VxakX4lZ209CN05apcYXGcav0ZlfjFvMJY9hsUA1GOFT5Ksf3eBS7BpicfDWLB1eRyU6h1GKwDuXaa4nsCbRN5v4ELhnnxaSzryEKV0PzJ/Ba109tEiDYDkRkjq8nttHa5hbYt5og+jInSi/EdW/DkcvIug/iO5vPedzalUm/FtJJh2Fqagrs08n7q/sR5jjKbEOYsUTsI/qJnVeh4o8jbC0RjnSqTtI27n7G97zctdCj6GCBg8GL8Q5cKEDKWsuhQ4c4dOjQGakZ1zrW7VLrcjCD7Xb5wYMH2bBhA8uWLbtiw7rXMzM4PDzM3r17WbduHVEU0Ww2L8tyz7UvlUxiws7cGwkQUpdwDK7WkLUQgkwmQyaTYenSpRhjmJmZ4eTkKY6VjhMHMdaLidOGMTlJ0euiYZqkVYpaeBM1+QRZFWBb9h4CQUX7jARpfCfZI67QuFIT2WRs35U+sQ0RNiYgouRZrE2sSrQWDKS7EUkAGBknT2BiHBFxtP4pmvFHUcJQCRQNsYilqReQV11Mx2MUnQGUVDRNndAE3JC984zZxCNmDyf1MEW3D1ckoyShaXCseYj99e3cmDu7uKXf6+d5Pf+FwDzIaPgkO2c+TWC248sGOaUxOoMVaSpWMB4eYpHsRkqJL5pgszRNSEZpREseEOG0jAvPtHdJskdM+wABEJlpCiqdeORZ8E1Aj4qYNj4nwzFORH/Grtp7UXhsyP0Sy7MvP2MbHOEz5G884/G5lXNWkZaD1M1xMnIZUjjEpkZkZ+hzt3ZatY4skBaGmBCAik1RizyWqzIn4xyrpWbM+PTIGLejyIa5Bt+JVCKBx4OOR9wCgm3AWI80wzhJfjJA65bDEzMQvpfR8N+5y0/ap5Wm5CnxQ2g0pXiaHidLSmVwRHIT6LoOrpsnk1UEpsyyReuYmTRMTk6ybdsRPM+jqziJ2/sIgTtElgYpFeKKAfLez5NOvTjZbu85HC7HFD05R+XbuvaQgD4lwNrGnAk/iG3ClsYIrD2BiFfTMPuwzb+iIUybhMaKPnq6vn7eYzQvfUT4xO6Poe19SHMCK1IYuQlEAWFPoeJ/BesA3UAdYUcw6hasWHXe97gS1fYZXMi1IMFg2zswjuMLjpRTShEEwVVaw0urIAjYuXMnjUaDrVu30tXVNe/3V9Ov73LWs13vtmVKtVo963653CWEuO5Ad9s6Z2xsrJM7ffDgwSsOWgf9Pvq9Xk4GI3jGRUqFNYa6aeBJj7uLtz/zQs5SNV3ji+NfpKbrrMmu5obMDVc8j1RKSXd3N34+xaLxwwgDMpQcqh6j3qyjqxHatczoGZbmnsfeUoXu3BFSMmoxgh7r/VcjOMbhYA+OACvajU+BwkXbxPXNkxLbmimzVhBFlrSXtFalSObL2ia9xggKbkQQxkyJblIqxhGTjIQfw1oHaVYxFZuW0tVlRWoDt+SfO2/brLWMxseQQnSAIIAn01g7zcnmkXOCQUjO+ZTKsiL9HFaknwNAJT7ETPQ05bjCVNxgvPkNjIUJBI7RKCxGBIzHeQao4MskIk1iSHmSwCSK5XZIYFoEOMpiSFjHuvGoW4+MSOMIi7UCbQNyTsSUzhEjMWIuIxrzZPWPOTTzjs65EgVNnr9sxwUdfyV8lqZfxnDjM9TNMNZalPAoelvon5MrnbCc816JEYoTposJPYUnXQZkwLSTIitiirZJ2aZItSICLQJPGOJ2q1m0/5m9Ro0gWm3mZN+olp0KgKSBEDuQQEpY8llDr/0n/q2yiGVOL8u9+9CySWSreHNapZGt4socxfwQOR+OHDnC/fffz8zMDMfrH2Ta/jumaajRjXJihIzoYTdrWmAQYFX/PkZLm0gLWnZDyeoLAU0DaWct2IMdsJgAwcTrMAHABikFebsPZw4NKgQYJpgs30Fv12PnPEbGmPnfA0JixVq0XDvvedp5IVBD6UfBHgN8jLqV2P3Ra2JMXavVOgK3hVoLDgxeaqSc4zid/MDrsSYnJ9m5cyfFYpFbb731rO3PhcJunl7PhhlsZy7ncjnuvvvuqyKeud6YwUajwfbt24FESd7O9b1a7exXD72MDxz/CDVT78QBO8LhvuKdLPKf+QswMjHTURkhBD1uF/vsXr4YPkbtlEmsTuR3WZfp4+dW/jQZdeXvrtMqxSJ/gP31wyzKDdLjFGk0A4QFFQtoWoanjhE7a/Fr9/KCZc+lp6dn3rn3udEPs6f6aVCS0ChSMia0IcJKis4gFkNaOsTGABbXbV1UMZ3EiM5oloCG9WiKNFmVKFqlieh2mjjCUpVVuvhlYn+GlEyz1N8wZ4h/tpIW6JmViDcuHmjnndXkndUsAbSNqemXsr30Po4Fj6ORiUmL9ck6K0BlmNFHCEyIpJ9e16MR70CoRIyRFRGu0MRWoq3EEZq8bGJMGxQlroc9UhNZh9CKJB5jTmt1pHmKVekiwnE6zJSTSvHd8dvJOw/SpMri1I8ykH7wnNuUc1awLvtGKvoQ2jZJyT6yalnHlgUgtoq0DInNnGuLNSAFfe7tDBTexUMTf4qMHbIypluMgq7S5ZwiJROur2EcXLkGwVMdwjA56snywtbtgGjNEHa4RWtw5Wz7NbCCOLYcI8/t6Wm65AiKXeStAAthXKTk/jiRmSG2dQb9e1EiRWDqrVlDh0K3gxWP4dsUjugn1jFxFBPH04wGX2bq0Gb6utZ3bJrGIk2/q0gJOq3+poWpWLOp+G9MzLwWax5DW5F8t9tZTtQCxHtRknYyZMu6KImLVNSoV06Szlps/B9Y/RSIDFLdBc79F55LLDy0+8No9XyEHQWRx4plV11F3K5qtcrq1auvyXtfrlpQYNAYQxiGF8wGzq3rFUhZazl48CCHDx9m48aNLF269Nwtu+t0G56pLgUMWms5fvw4e/fuZfXq1axevfqqtRevJ2uZyclJtm/fztDQEJs2bZr3RXk598f5lnVDfh2/tvItPDT5dUaao2SdLHd338Zzum9+xuUeaZxg+8xTlOMKAsgon6+E36FuDF2ui0LSsJqnqmN85NQ/86alP37Ztul8dWN+AzNxhVPBGE3TpGmaZFWatcXVdLkFqlGNcOIYfc1iKzHiSXK5XMc4+FszX0GTI0fAtMjSJ6p4rTSFLieNr3pZ5q3hW9PfRLhuB9DIjn3HrKDCQMs0OwEEBVGn3692zLAzImBSv4dKeBOOyPMvJ/8/lqXXImVLrGDhmD3Iq5w/ZCw+QVM3SKnEUqeuqzhCsTQ1PxnkQqtUH+XrM+8kNGMokWdr16/y3L7fY6yxG6whlnVK0WEEktgMEJspHFlAIOiTKU7OfIVsKocShtgmKR9CQGQTQ+uMSAyi26WkpW5dpBCc/glcky5ixdw2rCFNxIjuZjh+nMVqmmPxdzhciWnagLxV1ITl/sG985bjyAxFee5Zti73JurxNnwZE1mFxKJkksiytetdjMQniAnIiy60EEzSDQJORU08c4xbc/eyIv1m0uk0xyc2MCpi+j2vNXE6q86YnTWcBVKeFB31dZt9O0WWNCEFFVLAEgvRvidDiml64z9l0qbI0IerVwD3d66RAHV9GGMrKFFACoHnuHiOi7EeoRmhMFihNlnj+PHjGGMoFj9OVCxSTb2ANJImljX9+xhsvWc+/fuU6q9IBFPWQKvVnWyPj8t4p63c2cZWq1gKiOxv4gdDYE4kVjE2wuinEOYQxrw4YQatAapACsR5CADZh+XSAwYuVzUaje+1ia92zfUOvJi6HoFUs9lk586dNJvNC0qMuJ5AysWUlJIoOiMx9JyltWbPnj1MTExw22230dvbewXX7sy6HgQkc5NmNm3axNKlS894ztU0x16cHuSNS889xH+2Ggsn+VZpG5GJ6HW7MFh2lB+najRpadFEGCS+dIiMYefMYSId4aorb5jd5eR5sOdujgcjlMMKS1OjTEYlKnGNalzDky5rnBWszC9j44aNhGHYEaL807H3YbICYS0N69OIXGaiFGkZ4EqLJMcrBn8BT3q8ecU9/O3JPyPC4UxubrYdqZF4GJQJEyCIJbaCmvEYiYtE1kFzAhOH3Na9CEE10XFGkppIsXZoJU8Ef063t5xGPE05LgASRyjWZTazJn3TRe+jnaVP8nT9fSjRyn5mim+Vf4Ge2ot4Xv9vdJ63OJW0WCNTZTL8DuVoN8ZqFvkvIlu8gRP1vwcEGoWUSWxabFUrZ9cQmjqOSCctZUOiaGXuBF5S7dC39uM5mgzrPpoGVrkT1KwlMA3yKoMkT2AFgXH56PEXY6zlnu4/YXFq0zMast/Z+5d8a+KnqMZP44gYg6AZp7ix61fpSq9hptFA4RAR4DGr5o5tgBGLGMi+nrSTgPGlffs4ObEBQoP0Zg1r8mhKLfFM+7HTtzcxsk6mCfMqJG0NkRTMFfxC8qJ+ETAcl6g1/5Kp5gfpEf+K4zTRZgaHLsDB2AAlZtfX2gApXAZ6V9G1aDPWWqrVKlNTU0xMTFAu/w2u61IsFhnRIxSLRXzfx3fX4Ts/SBj/S6Iqt7rlOCnx1T2gzz0XmIw6NMEcB7m2o6S3toLV30Kyiq5MCS/8HMKOYPEx6m5i58VcbxF0c+t7auKrXFLKSzYVvt58BicmJti5cyd9fX3cdtttF6SKvR4B7YXUxYDYWq3WUcrObYlezbrWbeI4jtm9ezelUum8M5LXA2g9Xx2uH6emGyxLDXUei22QtMtaaRUGg7EhSkhCo6mbOl3q7Ns7Ex9lKtxNYMpk1BB93s1sP7Sfu9fdfUnrl1Ip1mZWQgas3cxENM1YOAFAn9vDTG26cx54nsfQ0BBDQ0M8cuyT2KB14bYAkgCPRuxhWzN7vkqMhpXzPH508XP426M/geMl7Eiy7XOahBYckXyui06AEgkQNFYyprsJrUNahNSNJevGHZZFCIvjGpRpMBF3kXcskmEKLgzIIsb0IUVMTk4RRxrlX9zX/b7aX+NI05obS2LrBIKp6IuU6j9Odybhiqy11M0MAIP+8xj0nwck52dTTzLW+AKWI2AtRkhcDL6MsRZSapClzvPY1fwMrjSUrKToRFSsd0bHT4hZEORiqFufwLoscyewwqFLaDQZYhxiKzkc9HEi6mFzapghr0w5fAOlUKDkctbl/g7fPTejdHffB2g0GszwHUy1m/37yix7bjKnucTfSJe7mMnwKNCNi09oa0S2warUVnLOfJPpxX37sMDk1C/Rb78AUtKFoUqKmCYG0ZnNM7Yl0midIUnScZJy4kpJ3GYN5+4ca4mFbTF1EDID6qVs3igIawrkaoqqm0l9Amk9lEijbUDEJGm5lrxzS+d4tY2vV6xY0clTnp6e7rDj2WyWnp4eisVfJpv9ARrRu0GUSIulxBTQdpS6HcThJLLVHm6fr0okM4YpZwXY6jxLJSHyWDNK1vk22e4DCONiRTfC1nHiTyHsGJH75mvWBn6mqtVq123854XWggKDz6auF59BYwwHDhzg6NGjbNq0iSVLllwwy7mQ1cQXst4jIyPs3r2bpUuXsn79+suSJnIpdS1BVhsMe57H3Xffje+f3UAYLi8zaFv5rZezZuIq/hwz3dA0USIxaY6NxVUicWyzhsBohvwMOZljLBhjR2Ubo+EIWZnjhtyN9Lh1jtT/ncjW+c7YMGVXJbbHCr508K/RFt6x9oOXvK5CCPq9Hvq92Qt5RZTOuk+2dj2fw2MfSKbZ5qQ6WJvAu+yJQb479t3WRbPILrubntT3Uw4/hevYlgK11TK0grSMyDkhpTjdag1bQNG0HoFx8UTMVGDo8pPPkLEAybEXgC8Nnp2goAS+NGANxjYwdpheUcaNv8aj5f9DXXtIkee5/R8jpc7PYuwsfRKnJZwRnTZgAgilEHyr8m5emvmflKJRDjWeoByPAlBwBliTvp1uNwGKKdXLff3/lx1TP4oQw2gjiIXAteDJFCnZg1HHuCX/PHZUvo4QkkZcxxdNQtItc+v2/rWt9nHCVDZNYlHjCk3NaBo2xFd5rLVMxDmGo162pI6y2Cu32vGJP2AQHWN76cWtbUo4y9As5a7+j6HkbKsvnU6T5gFmohmEmBWpSOlwf/cb+Xrp7yhFp2hSQuGzxL+Ru7pec8592tvzp53Wdw7YABwpvZWm+RaJe6NA4yJoJPOQWNw551/yWnuGL2X7GVI0cPBZpEJ8GaBNBhAIs4de06BHSpQ4icUSYnmsGXNP33vPmpgD8/OU16xZM8/Dsx0LWSj8auc5XTlDZHaiTZmo/rsoGaLm9MINgNgCpIDS/G1obWfGPYQkwrYEI1Z0YW0GqbcjnMNYcX3O5dXr9e+ZTl/tutQL4PXAqrXNksMwvKC28Om1UNXEz8S0GWPYt28fJ06c4KabbmJwcPCcz70ada2YwbGxMXbu3HnBYPhaM5jPVN1ugaONE53/B6ZBWnlkZYWadrCxQQlLmMzmc0/xJk6GJ/nU2CeYjidxhIOxmoONPdyYqdDrFehyVzLjnsSVpnMVVCL5844Db+TXV76Xg43dxDZiRWoDvd7lP5du7rqLT0/8PXUToW2HpgMBrpC85vY3dS6aj+19nD3+U3T5efzUAxyPPkWfW8ARSRxd2hmiZhzSIqDLHqGmXQpKtDjTtlLZ4iofIRI2bc4kVkeK0q0kGRliEARWkRKGfreBKxJQORnnmNRZpJB87NTruLfn7ewc+UW8lMtMM+BHV3933jbOxMexneXPrWS6LTRl6rrM7urDVPU0eZWMckyEx2joCrcWXkK2xfDGUUi3/zq0eRQYJQzrxJGlK7eejLMSVy0h49zKf8n9PtVqlc+WfwhH1ijQREgP026wG4NVCokgshJXdBzwAMi3fQ8RjEYFjNUMejOJrU8L9kUmUSjLFjvtEpKRBusc4kD5DhwgI5fTn/2fKHVT53pz+mexx1vCK/rezolgD1U9TdFdwoC75qJvYFd2v7/zs7FVmvHjnJz5WbIyIiVAuEDkYmwzUZ63bwLmEAizP6XJyxBPWCIrsDaLg0+sy3iqbYid/O0JwQNZB6fxKkxTACmEehci81/Oua5zPTwhmZNrn+ez84ZFisXV9PQ8QhD/DMYmymGLwFFvoZj/BUz0GWz4EaytI0Qm+Q6zp4AcgibanmkmLRhHmlNoef2BQWsttVrte23ihVLXGgyOj4+zc+dOBgYGLtks+Vpvw6XW+ZjBZrPJ9u3br3iSxsXU1Z7NtNZy4MABjhw5clHReldzZvBSanV6KYfqw5xsjlF0uwhNTGg1S5wuKiJg2mqMFeQUrMn28pL+l/DxkY9RiicZcIeQoj0Af4Sp+Dj93p187MRX8T2VkGqdy2DykxKWvzr6m3hOotf9tsiwMXMXD/a86rKzzL889Me8d+Q3qZkmViSwLCt9fnbo9/B9v9NS7g36ODUySiryORodAbGRSjOF61VA9OHTRWwbVFjGiswLcVCE5r34sklGBIkCF8Uz9Q7yXsLiBdYlIwKG3ErLA04wGod0qwaDzgwGSYjkYOXXIKVxlUtvxuOhkfuphP08d+C3GEhtZVPmlYyVP4mcB7fo/G+p/1zGwiNU40l63VkvVE+kmYyOMRocZnVmC18e/0Wmoqdp20vnhMsy818R0+tZsfi5p28GuVyOdCVPaC0zIo2JE5AtsczYNIvENEZIhJCkRANPRITGISUUsa134GtsFb2yntjftEDQZFQnrwok7Bq4aNLSYETC6BYFTMcxhkNMVJP52NhCRRdA/K8z1lVKh2XpWy7yzDl3SZEj4z7A2t7dAOyf3IIiopcHmTJfQlIjJUUnJWTujKFj2/s/cYaeO1foSNERo1iSfak6n572EW0y3fwlvPCXO63YwG6gr/tT51zfdDpNOp1m8eLF8+YNJycnOXjwII7z0/T0/FYnN7zd5RDO88EcwOptWNMa3RJdSO8HCGr/iqdO906NkrUW1/7acK76HhhcQOU4zjWZGZyboXvDDTewZMmSS17WQgaDZwNXExMT7Nixg4GBAW644cr7zF1oXU2Q1fZQrNVqF80WX+9gsNcrcl/xdnZU9jIVlgDJgDdIqGuslSvp7ikyE5eJdMDNXbcSmCYj4QmyMtcBggA5lcdYzXRcQjgutMyJZ0ugrU1YRlulW65AIGmYCntqX6PHG2JL4V4g+dJ+aOb9aBvwYNeb6M9eGnOYzWZ5+5q/oFarcUzvZYlaRiF7ZjZ0l1OgK9VF7MfkghxhXMFDYdEYrWiGTWInJjABKIemU2d9/i8Zqb8LRxyjR1YY1d1UdEjakS2wM2vkkag2k3mxyDoYa+l1axgSYAhQVJKMDKiaVCJUERpfaHzXwVpN3XoEuCivwqOlP6DLWcGAexcpqwmMRSh3znsKYuNxe8/r2F15GCnc+SyVECjhUtclvjL+K4yHT3cAiAXKVlPnE6yyf3DOffvKRf/Mp0+9hoYpJykvgLGKe7p+l6W5e/jg0fUstymmTczi/BQjOseQqqCkR9ySmWRUyFToz7M86ZY+7W9PgSUlwo4nXVcLCHY580UcCkCEhEt+h22ll+DFAVn+iVBkWd+77WJPm4uqdb3bgeRmcar5dxytfZAldrQtSactTFdWMKwziR2NbfN/6VkF+6zpC20bm9NvLrSxpB1J05rOc5V4ipnp9RSKTz/jup5v3vD48eM89dRTZLPZDjDs7noLytmLNUcAH6FuRsillOsHGOz+GtgZIA9ESHscI5YnhtPXaX0PDF6DejZt4naG8dWaRWs0GuzYsYM4ji9Lhu5CVROf3t6ea6dzLqXstayrtZ8rlQpPPPFEx0PxmVSOp9f1DgYBlqQGWeT3U46rJBOCmi8e+XemzRRCW1zpsTK9gQ3ZGwhNhLWJoCS0Cpc0Qkhim0fgoe00HUrkjEpaq5GBo81RVmWWkJFdVOJx9ta+w5bCvXzo+NuZjg/jiOTYfnj8F/EmeviZFe+75O3LZrNs4tzG2ymVYkN2HU/MbEcIH4MlJMKTioyTQuJgtCat84yMnUATMyxihop/wfuq/w0Hg5GSrNJ0mQlSrTzkpDWd/BMaCbLVisPgCENgkviPiciwyNU0jEeEIiPCWZbIJiAhLSO0SdSgQTyDJ79NHH+NJb6kFmkySiGEpakVw/EgLyz+HQBplcdw5g22JkJGLpPRUy316BwrHWuJBUxkPw7ccc799spFH6VarVJiJ92sJpebFSH9xIoEnByp/hEzwT8yxAwBEm1iUgQomWWxO8l4tJLAOqRllBgf2yQNI1kb27GvkVjSAiLFPNhkLIzoAhXjQ8rQbf6BUEKfSgQ9euZGImtxM59Euesv7IS5hNJ2iqbey6LUgwjZnTwYfh5JmWnjE9laR1fRNAqrZMsF0jDLB7YlS5wBBAEQyY1Fu5FsWuDdSBgv/TT93X8DwPTkBlwlsRbq5m8Y7L3/rOt8rnnD6enpOfOGBXp6tlAsFikUCghgvHI7uWwD3z+AYILE8Hs5kffjIM49P30tS2tNo9G4Lrpaz6YWHBi81GqzThdsavksa2xsjF27djE4OMimTZsuC+u1EFJUzlZzwVUYhuzcuZN6vc6dd95JoXD6fMi1r6sBsk6ePMmePXtYtWoVa9asuSTPwIUABgGkkBTd2eN8i7yDCT3Gkq7FZFWeHrcXIQSHG0+BKFMzZcLYwxE+WTlAVQfk1XIKTgoZT2A99wyGrO3aFlgfsIwHk/T7vSgc6qbCQ2P/mxl9CE+aZM4PcKQhMpO8/9ivsSn/AFhYnFrBitQGPHn5LjwbsuvxpMfe6tM0dZmamQYUka0jcOn1BliVWUVVT9HPGvpqQ3xj7BHCTJoAgTSWwEA5zrAxNUzKTaYJjRWEkaKYuoPYfAMpDLFVWCsSaxogLT2gSYCLK0ySBNGaO0xAYZIr7IuYpvVY4tUoqJADVQ/l5zFIprRFmSYr/Arl+hG+WHo9ogxEEf25n2I6OkneSczHK/EEaVkg5YFu0jJTnj23pRBoC4F78rz7zFhDzW1izApC5+yOAitzv8n24F9wZRPXGrKuQOBwNKzSpzQ3p46xtzrA5vwpXGGYtBE5Ui1VctuvMGH/kjGD+cuvWZ+K8XHRLHZLNBGsc2QHeFkLIYLp2quoxhE5mfyuqS0r+/ZdzCly3rJEgAYx53LtvQRtY1L2GMtSP0dkJoEQn/vA/CVR8BEcpwrUWkfgnDCQyJj2yGvnc9TmoA2QEl9jdPJ3yDgfx3MEKSFxhKTA2wimDV739tZw47nrQucN63XNWPW/ku0RKHsKK3IYueG6BYJAJ8zie2riBVJtMBbH8UUzMBdTxhiefvpphoeHufHGG1m8ePFlW/ZCbxOXSiW2b99OoVC4JCbsatWVZAbnimW2bNnyrCKMFgoYPL2UUHTbHpanV3UeO9E8zLfLXyLnZAlNRGhDIio04jopOcCWwsu4Jb+aZemn+NsTH8LtzE4lDJk27e5Zcumr6SZ9xqIJ6XGGeLL+NXxpiK2gbZIbG0NgHWbiCY43DyGAY8F+TgZHuKfrxWdZ80srKSRrMqtZlV7Jgz33s7++hyONvVTi43hSkHe6CGydRf46NuXu5VG5k/3BwYTNmkODuiLmlO4jjBwcYXhx36sQQjAeHKNUupl8YTtgqRmfglPH6FkvlraRD/MEKAkoSlS6FhNHFLyQo80cxs+01LdJW9GoFEdjj56Ug7ZgjSBWaU7WP0SvnEabNTTELeTcDazN3EHW5jtmNHPLWpO0ks25DXorcY1HSo9xqjlGZCNSymdVajlbu7fgyfnfGRuzX2B/40fQZoTAGlLCsMrzkrQbp8q6lMvJUKGloOhmQIfEJBZAgU1m7GLaTOm8XUPduC0blylCXJYq2wKCiQq8ZKBiBSkbM+DOJhSnBZyc2kDD/A6Lum4i5dyAfBZgxhF9OHKIUB9DymznxjG24yhRxFVLSTlzW6i/zPjEFqz5IgMD3UyUP0BXysUTc3nPOWWT75LImtMf7vyUdT6BQJCXHkoITEvJnnUdKjMPkip8g2ZjmGb800ADz3kjmcxPnHObzjZvOD09TalU4siRo5w44XbU+D09cB5ThWtebTD4vTbxVa5LTV0QQlxxMFWv19mxYwfGGO65557LThsv1DaxEIIgCPjud7/L2rVrWbly5VVLE7mUulL7OQgCtm/fThRF3HPPPc/asf5ygsFrfTwO1HcTmoBBbwndTh/T0RT1uI6WTW7J3cw9xWTmL+ss4h1rn887nv5JhGq1v4zm1ExEfz5DWiWpEcJC1Yzjiiy35J/L4cZ/tNig2a5AjEtsHSSGkWAEV7is8Fcz3DzIMf8AD4lfozsLT48kA+0lnWG6bvn5dZ+7pG2UQpJ1smwpbGVLYSvWGsrxOE1TIyVzdDn9jARjPFHZTcEboBIcbSmKE+ZTCNuZlfSM4vDhI6TTaZqpKtNI8vaFpOXjTGlQwpCRIafiGnkSexrd7pG21KjGtpYtwBiBJ5I9FMo0bcbVEe01SABTRgQgk0i5lIjpknVSMqakn8axx2iE/8aeUNDn3EtGFZmJSwhrWoxTouqVWAZrP3jWfWSs4dulxzlaP06f14MvPeq6wZO1p0krn9u75qfepFLd3JT6fOf/5eanmQk+g6WKK9fQk/5JRqd+GF8mYiWkg4grWJWlaVN4ooEVkppNvB3VHKugNhD3SRjUVOt3AkEAVK1A6Yh0K2pQJ7uWmlUESGL5LqpVQaJnFigMjng1Pd3vuqjzRgiHgvcippr/SGAOoEQeY+vJ4+7LUOJMRiqKV1GrvY6ly29kceo3OHBqPT1pB0cIFLS2JTkfRMuuJ5zDsM+tUmTpcg056aKEQLdAoyFp++dUlVLlxcAx2lDdxP+DcvlP8N3vkHqG77m584ajo6MsX74cz/M6rOEZ84bd3ZckwLxSVavV8H3/uiU3LrSunz16FepKgsHR0VF27drFokWL2Lhx4xURQyxEZjCOYw4fPkwURWzdupWenp5nftE1rivBuJVKJbZt20ZPT88lq8lPr4XKDJ5tvSt6GrfF+vjSZ8hfBD6MhyfJO2feVL1j/d/xi7t+gXX5U3S7DdamYSqKGA0KONLiS4eiu5TnFF7MqsxGQIHVwGx8lrazwLAUjwMwHR8np/J87uSvsTLXJCPDzvOG3DKZbIq/2P9yHhh6GzlVpNdbSVb1oZ6hTXb2/SA7fnztOtI8Tl03WOIPMR0Jqok5GwbQVgExAs3dhZchugS1eo2xcBprBNXxAtnsyylkMsRUOBl/my7HUo73klcRdet3DGuMBYPFAYwVBNZBG83BmTQ2lWyvYhYIAriiNWdnLUpolNTU8SjFPpoEVBuSlI2R+BGKIk8kfOr6CGnylJkhq5bQF91LTpx9TngymuZkc5Q+r4eUSuigrJMhshH760fYnN+IL88dT9aVeiVdqVfOe2yxeScj4rfxW21y3820Zt4Ujvur1MO/pUqZwFp6BDgtPJSTESVjqVnInsaohTY5k9JOAm7b38oNK2lYiSSkS0iCOeAqRqLtvzBVCmiYxxGcwFqJx0P09y4/5zYBpJ0t9KbS1OJvEelhfLWWjHsnaXV2NfPps/FrF82KQKy12PojWPNWQONKQdW0w/Lmb6cCUq1tcJEYOxcst8yxLfjiBKGd+3qBI2Ka0f2keBxrjiGih4AIq+5DOGePstRa4zhOy6KmeN55wzY4LBQK18yTFhIwmMlkrvkN9bOt/+fA4OVWFM9t+914440XbAtyKbXQwGC1WmXbtm0opXBdd0EAQbi8zKC1luHhYfbt28e6detYsWLFZfvSWKhg8GxVdAc4GRyb56FmWgP/Baf7jOcPN3awtecorgiI22DNq5BXIY9Orea/rfpFFueWdy4Sy7ybOBU9gRK2w2y07Is7ZtEZGeJJTWwjulOKtIxoGrczXxZqRd36eCmH/bXPE9syebGLgkyMOspVjSbNfd2fvuRzPfFca7WWs9/HofqXqWmNRhIaSdYRbMqsAS/hcWyuSr9ZzOjEBF1dXcRxzOTkJHEUYTLrUKlF3NvzHr5WfRkpZYisTNgvY1AyuXg3jE9kJdLxUMSzittOl1kksWOtS71F4AmNg6FuXFIyRhICER4WRfK6yAbkpE+TxdSsQJteZmJLWX+dXvmis25/UweENjoD8PnSp64bNE1wXjB4tlo+9BLkyCYOi9fgCZ0AazPIfUOfoRTtoBLtomy+hRIxw3GVASFwFThEpGyDEktwGaFpBRnRBjyiwzPPmmJD0ybTdnkhCNrPmTNjWLEay6dBSAQOnrTEvITj04KezB+Rcu9EyrOPjqScDaScDRe0zecTSgohENl7gV2dx8LSTnzxanRrTlAACsFI0zKKZYsrWqrwubrk1vI6/z+tzw4oUYPgr1HRRxDUk+dFH8aoB7D+OxGnraMx5gwi5XzzhidOnMAYQ3d3d0ewcrWBWbVaXfDiEfh/EAxeTjBVr9fZvn07wGVp+z1TLaQEkrZAYsWKFQwNDfHoo49e61W64LpcIEtrzVNPPcXY2Bi33377ZQfDC7VNfLb1Xp+5mSONvUxGI+Sdbow1VHWZotvHqvTGM5axfebDicecTRG3wF1sLVkV8l+HellaWDnv+T+0+Dd539G3EtvpJNNCJK1UYyWhTYCfL2Mio4gR5FSjA3wgYc+qNuFIfBFjqbBI7EE5inLVsI+1RMLBINhX/nncGc2PFd530cd8WWoxKeVTjit0OXlWZ15AbGNONEe5s2sLdxc3c6SxncloGIAhfz3dzmL+Y+xTSN/S151ErDWjJqP1Y/i1brYf287j/es4pQtYLJ7UFJ0aPW6dyEqyKqKgYrJOihk7Top2m3iW/WnLD9qHTbRyhH2hESQt1qywrcgxSdmkiWwCkvOyyUScRWJZpvbjKhjL/gaPTvpk5BCDqR9mIP1yAApOjrRKUdX1eYxwVdcoqDxZeWn5tEuHVrCUb5/xeJdzM4syr+JYbRhjj+HJHNNWIAxYFHn/+cTB44zFCSBfLRRCCFIiAXn6NPzT/m97VlLO+Z0QiX0ySFJSgjUErTPMWkOp/qtzkRWS+xnsfi9CXPx15WJdM/q6bwb2sWvyTgbDaUYkVGUPSFpWNGUCq8lIZ95nV7VuEWKrOR0ItsU5MvoHQGMokoDoKkp/GR2tB//H573mQgSe55o3nPU3dObMG/acN8XpclTbVuZ7zOBVrmezwy9nJF07Om3x4sVs3LjxqtDUCyGBxBjD3r17OXnyJLfccgsDAwPUarXrfr3n1uVgBhuNBtu2bUMIccUylv8zMYP93mIeKL6CJ2a+znQ8gUCwLLWG5xSeR0adORNVN8MIIXCEO+9LTFCjYU+c8XyAt614P4eq2/nS9N+hdcxUWAWhUBKksAQ2YQF9EWGsnHdpi1AYK/FEBEIh2IVUEonlKdYRtjz9hLVokczUfWjmbbyl6wN8q/QfNHSNVal1bMxuOe8IybL0Ym7ObWJbZTeVqIaSktjGLEkNcnvXzeScbjbnn09g6oDFl1msNRSa36FuZwijOhKJJmZl10a2LHs+Lj4P7f8gRkscIiwSjSLAxRchShhqRtCnVjOt83SxlwAfY0XL56/NCM4RFVgIcUiJmNgKBHGSqWuhbNIE1sFppe5qBDe5R5FKYki8DqEKukzdTnG0/geMBJ8jar6IGn/EEgkHopcT2U2kpE9N19FWsym3Ducc0WmXWkIIBlIPUvRu5VjtryiFX8NSQ9JNf+r7WZZ9I7uH/5hy+p8ZM+CFZQZchScE3cIwHht8IVAimRlUWCLOfi2oGU1OCrASaQ31FhDUxqDmoEZPJH8EX2ei9HyULCJEFsd9KbnUTyDEM48gGWMuaYatZn2eVkWUmG90/kRTc3sKHKtxW+8vAGMtNSs6UXlzAaEgaS0LAgy9c67debCTiPgL2NPA4NmYwfPV3HnD5cuXY4yhXC53WMOrMW/4nyGKDhYgGHw2dTmYQa01+/bt4+TJk2zevJmhoaFnftFlquu9TdxoNNi+fTvW2nlMadvj8fQYpeu1ni3ImpycZPv27QwNDbFp06YrdqNwucHghRyb39vzP8iJHL9+w89d9vdZmlrDYn8V5XgSJRR5VTzncyU+WIud++tWW1mJc7NHq3Nb+OncewH47affkqhbjSWtEo5GCoMjNDM6TcFp4pAkgLTIxORtEPTbBEAeqmaJrAPWotpGbVi0garxedfB3yKyic7063yL1eq/obykzRnXG7x14xfOWMfn997LotQAB+pHaeqApalFbM6vp2uOPY8/J0NXCElfYw1L0r3U1AQxMb3uYhb5q/FkchPiddq8SaPPlRqsxVMabSSOMEw2yjSJGTGbWOQ+SYCLngsIbSIw0EDTODhi9rso1YIDIYrQOjjoDivWbSZQjkuYPDq73lIijKaCRyN+jLR8FFc4RMblRufj9HkxFofjPJ+l+bexPnPloshc2cWa/G8Bv3XG73L61cyYj2Ol5ThdHI9mf5cihRePknHAFZCVhpKRSAygOkmFkAhRrAVfJskwFosvIJxzjvsi+RNri8GnS1UJbRltLTrczVT419SaJXJugj57+s9uYXOpfrrWxmd85gQCJYrsjbtZyRhZFaKAkB8nV3gduvkf2PhdLRb59OaxmyzhtGVaFMLOzJtQtNY+a+s3KWVn3hASU/9SqXRanvLlnTf8Xpt4AdaznRms1Wps374dKeVVaQufXtezmrgdtzc0NHSGgKb9YbvYu75rVZe6n621HDlyhAMHDlwVM+2ryQz+5I5fIUK1pqCmeMOOX6cbhz+/5Q8vaXnnWu/Ek/CZ7XaG/K2cCj4DNoCOhjHEWsXK9AMXtA693mImohPENrFtycqQphG4UhBbw6kgxyKviisiXKFpWI+mdZMLZuubc8L2tjSY7UmrpIy1VOJ0q3XtUIv/g0255TjSbUksgIzP+478IN3eMrqcpXxf91vxvRxSSm7Mb+DG/IXNh0Ey1TfgrCCf33zW3//ehg/yC3veRt34YAyRlqRlayJQGKRIMW1mWoxfzLBZQ1YO4ThVrBnFFx4GjbUz5GQFhCAyFl8mnoazRs2ytT6zPFHadYhR6JZ73ZxOKEYqlI4pyIiS9ZAWUjJESpcZrTgcdxPYvZwq/QJPVQZ4btdb6fI2oFT3Be+bZ1vWWtKT/4u4/3eJbDWJH7SQUYvZ0vPJzvMOT2xsWfH8Nk31BTy+TcSsvU8TyNH2eUwqSThJfpYkZ7KxEOFSVJaajTGtM2xXw3BrqkJvJpW0Z10IZjZTCaIzQOGlgkFPdNEw01gxqyw2GKyAtFhMb89Dnee6JJuWzbyeUuXfUfbxDtS3QGx9sv5PQvyPLZDptPanQaLPyBlufydczmuE67r09/d3LLza84bT09Ps2rVr3rxhsVgkm81eNGFRq9W+BwavRV2rNnF7Bm7ZsmWsX7/+mqiXrkdmcG6u7rni9hYiGLxYJjOOY3bv3k2pVGLr1q10dXVd4bW8emDwTTt+JWHH5t37C8po/vDJP+XtN/zSRS3v2XyGjTEcbp4gJ+5HsgfDUQSN5HcoCu5z2Jx9+QUt61dX/h5/ePC3qOgppuMsaS9CC5+wBWhCa4lCD5cmSipqsUvTSrLKYybSdCsH5ywJHACRdYmtwhMaJRSbMstwZXKDoa0gMBIlIOcECPs01Xgv/zr+ZZanns+S6KWMOztYyYvp6+u7oG25kHN1mSc5FoWExmNGZ0g7MZaQtIxxpUdgQ5AOSgiE8YiCCK2nsdIB8hyJY0KTo9eN8GWDjDSEcYlep0oMeCRzmLNmOLNWLO0Zurlr2P7ZEZaqVSRaaZXM4bV+3+dUOR6lWO1OsNQ9xDeObyNb8HFb1ijNhuZFKx67oH10MXWquYtjjceIbI2YNEVxE8/p+8p5X7Oqb2/nZ2tfw2Tp7Ug+1dl2XwhiILKJtUsyWzhbbbGJtuBLSTQHCAoEW1KKLuUhgBiLRJCRDsoXHBtdz/LBWcXwpYLB5dk3sb/67pZ4qw0HLQqHFfkzWdN2def/kWa9TlO/Dew0nvMr5DIPYM0pbPwQknGsTVhqSYAli/XeMO98aF/bruS19ULmDdus4YXOG/5niKKDBQgGn01dCphqiwBGR0c7M3DXqq43MBiGITt27KDRaJw3V3cuGFwI1b6oXigYrNVqbNu2Dc/zuOeee/C8i1M7Xmq1QevlrnJY5jOT/8ah+kEs4ChNqJPBd0E7uSK5GO+NjgMJGP7i9CfZXdlFZGMW+UO8qO+VLEmtOOt7XMp6j4fTfGr0YU40RglsTEbeyPL0Ovq8Eq50WZt5Aasyd17UMt++5n9wsPo035x+LxXbQCNxWnpag6RuUww6vfzw4r/DWsvXpv+Fx8pfQDopCmYfq8RxdtsNxMIBo5FSYIzFtDgSX2ZpmhmkbHnRWUFskgu5LyMUGkcke7OmY3bVH2EHj9IXlxhRH8CetATG5Rbnt1k39LyL3mdz69fX/QWHjhziQ80/IJISTEzeWY5kFEEdT0iagBQuvpsn7ReI7DixVgRxK6fYSmaiFAN+kn88pQdoGo9eJ8QVZVyh8UVMw7qods7tObyO25WolhWxVYlquXWTk8AgyWp3jJVemZ21LEu7Y7Ky9d6xh5f2+erYc+j37iKlFrEo/QbSzrJntZ/2VP+NfdV/JzINhJBETsSE3M3yaBFF98KWLYSiWPgtdP1hoE7CCUrK0QQBMaFIZirjlnocMWt83VbyzuYoC3Y0NPdmfJLpzLbPn8VYiycU3f7sTfb09AZWtD525TJgwdjPUSyuecb17k//EJGpcLzxD0S2Dgh80c263O+Sdc/PVKcyGVJ8cP5+kIvQ6XcjgvcgzV7AYsQarPfTCGd+HGH7+nC1CIMLmTfMZDId1rBYLJ513vB7zOACrIsFU9Vqle3bt+M4Dvfccw/p9KUp2S5XtS/+VzNf+Vw1PT3N9u3b6e7u5p577jnvUG7HKmSBgMG54PWZ9vPY2Bg7d+5k2bJlrFu37qofl8u9TytRhT8bfg+leLrzWMoxuDKkEnm0Glut7NPZ+Kr3H/8TjoUnsDZ5dDoucahxmB9f9GbW5uYHzF8KMxibmE+NPszB2jD9XpF+6VHRdZ6qxtxbfJAX9s22hrXR1HSdtEp1vAvPV2ty6/lGeQwdSxyhOzNeiiTeraTLnfVem9nKt0tPMhUrnmKCTZlJsrUqNfJoodDWgmgtB0vT1JEt5WkbD0XWIaM01iaReKbVXAxFBoPDMmccbR0qJk3deIRIHor+ki8N/y3LUjdzV9dr6PPmg+wLvXFZvXI1Ly29ke+Uv8lifynWagzLgDJCT5EWHmmZJrBTRHoST0g8ZdHSQ+oQKRSujHCEJow8DJaSzJFlFYuy62jqb9ItDiJMEm8XaI01FiUM+rRwurY0xSAxthWYJtoMYoIgDQJHGD4/ZXmwu4HXMn42FmLlMG2gaX1K0bcpxg0OBx/CwWXaJLxkjh9k88A7nnG/tKscn+Lp2hewGApuMuZRC2cI5AR7Kp/ivp4Ln5VVqgfr/igm+hBQBaDL9QCPsdDBipkEbFqN3zo/IhLxSGBaecmtfTQAuKIzYDC/BDhCgv4u5crrk9nV034vxX8BLiwib3H2jSzOvpEoKgMurvsszfHVJsj8DdpMAjHQf4alDCTEixBnzhderTrfvOHBgwdpNBqdeUPf9+nr68P3/csGBr/2ta/xx3/8xzz++OOcOnWKf/3Xf+X7v//7z/n8hx9+mOc978wbxFOnTl2SlmHBgcFnc6IopWg2mxf03BMnTvDkk0+yfPnya3KRP1u175iuJRi01nL06FH2799/wb55Qojret7x9JrLDJ6r5rbHb7rppqsqJGrXlWgTf3biM5TiaRxcHJmcb9W4jpKQUTF13ToHsZ0L9nemv85weAIJeCppBRmjaZiAz058gl/M/Q4N3eCb0w8zHBwhCiKGxFJu0DdcMAtwtHmKE41R+r0imdZ7dDk5YhOzp3qIB3ufQ87J8FhpO98t7WE6qpJRHrcUNnBfz3OeERRqaxKV8BmncpKa0a4lqeVsyG5gZ2U3gbmT6cihaUMc8S0ysq/THn1h4fV8sfIIY2EZSQJe2mbGFokQccu7L7EhqcQxRmToF5MYHJrWoWp8QlRL9AG18ATTYorPj38VQzKfZ2zMC3r/f8TnaFefrZalVrOz8jileJpup4iil4bJ0MDnrsIDLPOHONx8nFI0jKWGtROkZJqTQZmKjnAtSOEgHIem0SgUquZzoLILUhIrb6PgDTKYGgQRcbTxGMud76CQHUDYroYOcUSqbbAybz3FrNEh93Z5eCJuzaIJjsdFZkzyutgKIuuBEKREDU9KlrsxIS4181l2T34GV7+WFT0/hCcXI8+jSD7V3EFo6uTV7OdZoHBsmonoaUJTx5MXDo6c1C+j5Tp0+E9gx0EMobwfY2n+BfOeNzr1N8Ti3QS0LXxCEC4Ki8EyDmhrcYWY11q2rb+0NUyXf5XWR/YMxz8BTE/fSrG4jfHpH8cX3yFGIAFhDF09ezm9XPfyjroI2Xve319vY0Snzxs2m02mpqaYnp7mve99Lx/5yEfYsmULXV1dnbbzs8EntVqNW265hZ/8yZ/kB37gBy74dfv27aNQmBWXXWr3csGBwWdTFzIzGMdxxxvu2WbHXu5qf1DaLu1Xu+I4ZteuXZRKJe64447OHdSF1EICg8/U1o6iiJ07d1Kr1c7bHr/SdSXaxAcbBwHbAYJzS0mD0bNzYAApLLtr29DWkBKz7XEpFUprRqMxymGJvz35l0xFY8mlzVj2mJ18++DnkvQGI3F0N++88Y/PuV7VuE5gIvpPNyNWPnXdpK4b7K48xefHvoW1kHNTVOIGX5p4jGpc4xVDLzzvdqdEiirRPPWntWCtwBXzz4OX9b+KXq+HPZXd1E2T1e4i7uj+YTZmb2I82M/uyqc4qh9iQ8bFYYrRqIDVYGQSd6ZEEo2mOtcNi20poNPKEFpF07jELcawXYN+GgTEVrWyPixCeHx56n8g1whU7XEezP7GebcTYElqGTfn7mBn9TFOBicQwiJRrE6t56b8baRkiqHUarSNiW1AXY8xEe6mqXexqzZMwxqqsUtG1RHCZ8BbhPGnic0YoXaIbJ2ZeoXSZA3Pk5RS05TjdWxyn8ZTiZDEGhizG3hJ798yEX2dg5XfJcLpZBfDrNAiJSJSMm7tKZg2GcomjUeMEpYYgUtM1glJC0jJiJpRVK1DRTukREQo/57Hpv8VY/OknX4azW+RQ3Db4vlsmZ3z97wHhWgxmRf+HRboaer6EK7YQCbz9+e9gR/s+Wngp5O3sxZrDlELPo6N/hlJjZvSlrqJ6VJea7bSdqxbYgyNQOO6o/OEO/NWH1CyztT0HRjRoNSai7UkWeGUbqCr+8kL3rYrUc9WSXylK5VKsXjxYhYvXsy73/1uXvva1/LQQw/x6U9/mi9/+ct87nOf4/u+7/s6f842P3++eulLX8pLX/rSi16vgYEBuru7L/p1p9f/U2DwmdrElUqFHTt24Lou99577xXxhns21abQr8XcYKVSYdu2baTTae69996LnotbSGDwfMxgpVLhiSeeIJ/Pc/fdd1/TPMorKSAxLVQkgbRI0SRh1Odml0pi/vqW/8XfHk+sWuZ+kYdRGUdqpLX89ZE/oiyq+CKNK10mo3G6M7PnghSaSJb4hT1v48eXvoUlqUUMev3z7rJ7vW5SyqeiG3TNMSOu6ToFJ0tapfhuaQ8gWJJOjJ67nCzTUZVdlUPcWZxgwD+7GCMyMXcWfpovlN5LZFSHsdNIlDAsT9007/mu9Li/+ELuL75wHkt/ormDr0+9h8hWSULKLL1eSMFpUDceKR3iSkFKGeraQSmDbXFlomWL0x7Yj3HmAcFsXEb5iqgDBAW6k/WReNydCr/Cv576QtKStiBsnk3p17Is+3zS3nxW5q7ic1maXs5w8wjaxgx4i1id3jDPx08JByUcfLmKoruKddlXcGP9KR6b+Q+momP4corFborIVqnrEq4s4qtlNPQwgQrQqRmsVhgsygj2hutxyIIBI2OsspT0MYZSL2A6/AoTwVcTxbGdhWMSk2QpM/tYWacR2CRJRYCDYcCptvZ4EpkWWUFWNHGdiMhKatqjT80gKYEdJuN5CKHYO3kDA9bSlbob1C8z5N3IUyJD05RIq+Q8Mhhi0WDI2YQvn1koYEzMifrfU4m+CLaCFQ6OXM/SzNvIuiuf8fVCCIRaQz7zm8BvEkZHseY43ym9kdtTgpx0cFtbG1pNWYcMdr2BqfqH4RnAqhV1mlbgQOc8iYEKAju9ke7iXkztl3HMwyQekTm0835U+qbzLvdy1PXGDJ6vlFJs3bqVrVu3snv3bt70pjexdetWvvSlL/H+97+fN73pTaxfv57Pf/7zLF9+/qjBZ1tbtmwhCAI2b97MO97xDu69995LWs6CA4PPtk18NmsZa21nYHTFihWsXbv2urxDuVbt1nbLfOXKlaxdu/aSjsFCSk9pg+7T93NbUb569WpWr159zT0TL1bociHV5/QxHo4S2CYIgSJJXFBIYkNnrH2l08/v35ioC2/K3srT9f2EJsSTHiaeJu3als+coMkInoVIayZqEcVssoykxSWoao/IOCDgCye+Qj4LBaXpcnMM+iu4KXcvS1ODbMitYHv5aWIT4avEjDgymtsLN1DXNUpRjYIzv4XX7WQ51phgNBw/Aww+Vd3LzpknmI7HSakMi9ybGY+2E1oHC3giptcM8KL+3z/n/pr7PbFt5sOEtoov8giRPB7HUzjSkEzXKfZU9rPOWw46pOrnwAFfRCiaxHhEth1zljy/XY6rWvssaUPrll9g+92zoo6vEsGGNpqcinFEneHozzhaei+SHobrdQRlBPfxYyv/F0tTK1maWnnhJwewMrOJlZlkBjS2TUabO/l2+SNYUcSRSwAHR5QwRIS2RtbvQoQJ2+oID2kksYmJiRBWMnJ8GqenwobCu8ipv+dk48NEtpFsmzWMxHlGKXCHGsaVFmuTtvocUpWUjHCFoWEcXBETWoUVEjC4NkDi0+vVkuSZltK5YRR506TPUy0Fy7chfg2FuMAi+SaOxw8xbcZwRZFANkhR5Ibcyy5oH400P8FM+HHARchehA2I9TaO1f6Y9YX/hZIXl4jhuSuAFdzfUguPjW7A95NP4q7A8NyuN7R2xRbgiXns4FyWsBJbUMkNXtsDUghwLMQWEBJRuZO0aLbocYFDDaNfR7Px66j0Gy5qvS+2rndm8FxVrVbp7u7mgQce4IEHHuCd73wnpVKJhx9+mMWLF1+x9120aBHvf//7ueOOOwiCgA984AM8+OCDPProo9x2220XvbwFBwafTZ2tTRzHMU8++SQTExPceuutF2zlcK3qaiqK5yqpn23LfCExgzB/fefmT19PowOXGwweqB2iHDaRKAyJKXHUYsl6nF5+Ze2vkVNnMiNbi/fznfI3OBaepBJNUPQSP7lOdBkCIUCpBp6bmNC2taKBdYisgxIaKSwzYg+uUTSMphSnOFB/km9MP8Lq9G0s9oYQ+bUcapykrpvknSx3FG7gnuItNHQTVypCGwGzjH7ThLhSkpXzWf7dld08PPUQkWniqyzlaIrpyGFT9qdYk/JomjJibAOevTAGvGkqzMTHUXgdIAiQUUXqZjJRjgrF2twGhLXgWG7veimlxn6qZid56RDrgDHjs9gL8GVMUzvzTbVJLu5SiJbgIhHrGBviO20gaCioCCUsEUkc3HityqKMYVnaEJDB2O/y+ZH7WOK/iHXZnyflXfi4x9xyRIqss4KYIp5MI0TCkntqKSbWRIyDFThCEllLSvhI1yG2EUZAr1yMqnaxZ88etNZ0d9/BUM99TGU+g1VNMmoxvUJgbMxM83/TI5L5y7wMaGgvOb8ECNuWVYhkPzPbAgWHvIqxFsKWOllgyEhNIFyYMwJQaRqeQuOIv2aFl3gJxhxlvPYi1vsvpt9f94z7xJiYcvhFQKBka+5QpLB4WHOYqfCb9Keef0n7u119g/vA1hHN3+e53kzn8UxqMc3gCYScBYBzT5+GgPNJID0srkg6ALPOkSCRePGfEIevwURvBr2TJC+viPDehfQujYk6vRYSMzi3arXaGaNC3d3d5xV/XI7asGEDGzbMKrzvueceDh48yHve8x7+4R/+4aKXtyDB4KW2x04HUpVKhe3bt+P7/hWLDLvcdbUYtnbucjtO7dkqqRdClN7cap9jQRCwfft24ji+Jkbj56sLEbpcTG2r7CAUIRvTNzESnqRqZlqNN5cX973srECwXW9d+us8NP2vfLf0uVkg2F4/EviXgJgmsybREBrV8VdzhCHrRCiRIrY+gU0zE1tCM0Ood3FYHUMT0eN30+sO8PVT/8RU84v887Emf37rx9iYW8G3p5/CEy45J0WgI0aDMqsyg6xIz7ZqtInZVvkukQ3o92fneipxmYP1p7i18HrW+YPsH9s/76J4vprlqk5/vsURHqvUC9gdPAJEPCfz49w59BJOBTtpxFP4Nk1Tl5kwRWIbMB6G9Ll1kDBtM2AFjTAim3YR9sz38FuMFwgcYXGkbaWeCIZrJdZkc0RW0rAuaaFBWDIyItKf5KmZT1KKB0Atpse5mY2Zn7moLNec6iWnuinFY6Rk0r6XwkfTgycybMy/lFo8zJPVz1O3dYjBCktOFLiv/+foWbIGay21Wo2pqSlOzWxjqnkIFQ/QSE+SSadJpVII7zWUo4+SV2V6VY2KSVG3Lta4WAlFmjgt7a0nLLGxLXJLIokJW7ZIyd6TWEzHz7Bd+8jRTlgRNtmnaRGypvgpXDPCZOMpRqv/gKFCgVeyvP8PztgfmhrWToOYry4VIo2xmlCPXvC+PW+JDNZ9CSL6CNgjQA7FFCUxSEGPMhdTWQPTOCiReEHWYd5sbNsuPSdVi3eev2OS/xtM8x5cESESUhrsFHHwMxh+H+m96llv0kJlBuv1+nVzXdi6dSvf+MY3Lum1CxIMXmq1waC1luPHj7N3795n1fq8FnU1mMG2XcrlzF1eiMxguVxm//799PT0cOONN14T0c756nKDwfFwnLRM4SqPZemVncdPNEdo6POr8B3H4WX9r+ax8ueS9tTp8VOtVQxiS963p0GnBMi4QiOsoqohthGxVijpkVaShq1gjKKhGzxVepS1hRSLiw4CQyHl8v8dfANHZ0Lu7/8RDtZPMhHOoIRkebqflw8+iJojiJnRFWaiKTJy/t18VuaZiE8yEoww6A9e1L7zZY6iu4rxcC/KGqRIxD2RbaCEy+a+F/Kg99Z5rxnybsIp+AzXMwi+iE+MwKNkB5gKFBJBjAYk2ukjbcoooYmsaMGZpBU691PlitlWKlgW+11Yq2lal5RIeN4e1SAjImqRZIQiwgagD1OOj3Kk+WkyqoebCr9On3cTzjMoZx3psDF7L4/NfJbJ8AS+TBPZAItlQ/peVmfuB2BF+rnsr32epilTGxdsyL6SHi/xvRNCkMvlkj9hlQO1x3GiPprNgOlSiSgMcdIRfuqldPuvx/e/ww1uLxNmgp3VTzMVp8iJgD5npmXorBNAiCS51Wj/mV9zT9GDTae1x5JZRNECgu1Wa8N+i0b9uywWTTwXhPgo1fJHibVAyS+T705uKhRZhOjGmlMgZhlXQxNQeOrizqvzlnoQSxahvwpmBE9tJuOuZjo+wvSkoLe4HJ9/JkmrTvaAZzWhUEQW5JzZzIxILJHOVRqLK8IEFs75unEQhM13wWUAgwuRGWzfyFwrEeHptX37dhYtWnRJr72+rm5XuBzHIYoiduzYwfT0NLfddhu9veeXu19vdSUZNmMM+/fv59ixY2zevPmST6qz1UICg20vxyeffJL169dfkH3OtajLDQZzKkclnM9caJu44GXUBTLDlqS1OZd6oG0sDN2pHJGJcGXCvLhCE5sk0MxYSQPZipgDZA1LjLWKwMSkpaTL7SLb5eNK0+LDEtWFpywrCh7rsn2MRtsIVMigt4wfHngZBX++RYYvfZRwzrBj0TZGoPAucKbrcP0gX5r8MjPxDN1uN3cVXkwlPkXTzLREEBYlXFZnnk+fd6bhrxCCfm8Dfe56QvOjrK5/lW/XvsFEOE7TWDQKly4aWhFbgbAb6RKPoJSLtRohEtuZAIm1AiEs+rS+squS+ULTshFxRUxaRIRWMfL/Z++94yO76rv/9znntqmSRlpJ23vzrtt6XdY2zRTTCYReEgIkIZ0neUJCIO355XlSSSAhIc8DoaUAIQmBAAnGNmAbg8H29ubtq93VqsxIM5p6yzm/P+7MSNrV7mqbvSJ8Xi+X0cy999xz2+d+y+dDZ3Ocksg0reKEoKrH+cHJXyclDbYMaUQ2VW145aqHZ5yHlclbsITNwerjFMNhUqqL5cmbWJuYFADP2ou4pfNdADx59Emc7My6bClrIY7sxLhVcsm4HCMMfAr1g8jqQo4cOYXRC+nq6uJgxzaGdJKk7GZfo58F+hQ5NUpNj5OSBoFsH2ExpXrOtIRsplw2eeMhBMg4LojVLFuIjKCqGyRVgkWiztR+MSHAtgwT/vMYy9sIqTFGYOm78KUh0kMI2YkxDYQZRclV5Jwt5zyfLhpCgHUbxrotDv8JSbddwjT+iTH5LUJxnISOSzQ0sd1dAUXORARC4jfPCQ9DoBWRSmFz9ktfy0FGIc7SOYy7kYOzlrkUzNXI4JXSGSyXyxw8eLD9+ciRI2zbto1cLseSJUt43/vex8mTJ/nMZz4DwIc+9CGWL1/Ohg0bqNfrfPzjH+fBBx/kvvvO9jufDeYkGbzUNHGtViOKIoIg4M4777yodMi1gqsVGWw0Gmzfvp1Go8GWLVuuuL3OXCGDURSxZ88ewjBk3bp1LFu27Jke0jlxJclgtVplftDHSX2K8bBIVmUJTUg+yNNhd7AmdeF6KYCbU89nW+2B5oPUNGMyIpbmMPC/136UB3Y8wH3253CtCFeG+FoRGdFMbU0+tOMIYwM/slAigytdCrWtdCQnLfGaM4HBUNUe/5n/ZNxaYTQnG8N8eOBJup0+ViVuYEvni0lbWZIqyVJvFXsqT+AJD1clCHXIWDhMl93LiuTyC+7nA/n7+erIfxKa+Fo8Vj/B3sp+XjXvddjyOHn/KI5IsSr1XJYnz19TJYTAVTkWZV7NazOvph5VGGwcoabLgCEfFDhWO4JvAmphioQJCE3cRmI1BYeroSRlawITu/+2IodxPWm8HdOKvmIohXG3shCayEgUUft8sioRyzJFHBW2j0YttPnqwedw77IHGAweRuHQa9/ejpYvTdzA0sQNs9JAPV+Nqys7mO/excn6A0wER5HCJqJOV2opK3tfQ2JFP+VymUKhwOnwaJzu1SF1KTkaLeaEXETNTLDIXcer+9+PHx7nZOlVuLIeR0sNSGEIjKJLRu1zSLasP5qfVbvmVZAUHpGpYdtnjznvGywlsQiJiAWybesh/NCirK4HPYIQFkrdwKLkL6LkVTIuaEb1lMzS4/w0e0/kWNW7kqTbjym9gGqb5sIICssY0txIT9c/ETb+FhN9hzCaT2T+EyUmay5baeOZdCBp/+rKIIqiORcZhJlrBi8Fjz/++DQR6V/91V8F4Cd/8if51Kc+xeDgIMePH29/7/s+v/Zrv8bJkydJJpPccMMN3H///TMKUc8Gc5IMXiyMMQwMDLBvXyysuWnTpjl50sHVIVWFQoHt27eTy+XYtGnTVUmHzgUyWKvV2Lp1K0IIPM+75v0mrxQZzOfzbNu2jQ43Q053MpQYoWCP4Sibbq+bF/Y8j85ZCtC+csFb2XXwMUJRbncsYmLB3F/s/DMArpt3Hc7g2+he3UWP00vKyvA3x/4PRT3M1Oo73ZRakVLjyTQhEbYtznosmcinHgks2ZSEMRJbapSIMMBYeIonyqfZWfkO1cY4kYwjIloHKGFTDPMIAR12D8/LvQj3ApHBWlDl66P3EegQVzgoJYkiTSMK+Orwt3nd/LfR0CuoYxhqpOmyJ+i0Z/+w8FSK5cmN0/52XXqYU/Xj3H/iq8hUGldGtLpLpDAIoSjWNEnXphRBRvkYDKVaQC6pzoiJCXzspnxQ3FDROpcq9RE2pBW2jKgENjRT0Uk7wElq/nX45e0uZ0fYbEi/ibWZN7THOZvIzoUannrd2/BUD2PBXgJdJKkW0u3cgNeUemlZiLknXOpBHUvaaK0Jo5Ag1DSUpF5rUCwW8bw+BoIcPVaepAxjYhsJDBpHTo5hrZzgAClCFMpE7cYd02x+WniOS0xISdnEIuG6SZ2SIiSlAgiPkDeCwCgsMUwjeISN3d9EkLiqmQZjDNrvIWXfgK1s5nftZWhsIyERBoGFweEXyHXFbipSriQKHwLLohbejWsebabKBZHRBGINyB6M/u5ZFYXx9aa4Ek/Ta8FZ62IRhiGNRuOKRAaf+9znnvde/qlPfWra5/e+9728970X1hWdLX7oyWAQBOzatYvx8XFuvvlmnnjiiTn7BgJXNjJojOHo0aMcPHiQtWvXsnjx4qt2k7rWpWVahKi/v5/169fz6KOPXjUNvyuFyyWDU91k1q1bx7x589isN3No9DCHxg5TLVbpOJWhXJjgePdxcrkcqVTqgufIb636KwZHD/Hx8T9Bm5AV7g28bcmvAFAP63yl9s8Mpg4ghyPAotdejiZAIHCER0SINrqppKaRQoGRFMMitoZW9KaTIvPdMm5TkLihLQYbaQbDXlQzxRcHe2ICVAmKhNKOGwiMJm2HSJ7E0r1syr2aGzJbSFsXvqk/WnqUhvZxhIVqen8pJVGhpBAG3D/6CMuTSxEI9pQPM+yP8bzuW8nMYt3nQrfdS7fdy/Xr7+ev970SnbCaqc9YGDtRtlnafyPH60Oc9CM2eQcQ0pBJuPja4EmfyEBDKyIpSFKngttk1ZP0uiPI4aVHqUUWtIVrJENBhjzpNuERCOomYGvxzzhe+SvqJp5XVwR4dPGc/i+dc18uRAaFEHTYK+mwz++lu8Rby87w+0Ro7OYLbCOqI42kw1/Mjh07qHU8jO41HPdzccSrudnQaI4T0UcXy8RhHBsadQtXRUQoGjoWWY8lfmYmKGO+oSodqsZqUuyYbJeNzYS2yWuXqDlfCRFiyTrHxjeTMJuAx+N9ZTF9uW+cdz8vFq37wVRi1de165y/F9btiPA7aL0PoTposAVjxpFyFcr7NYToQvo+Qf12HBnrPrb8hyIMwn7HBcbTwEQ7MaaIkP0IuQ4hzn7+zsWawXI5thm8VmoGLwdzkgzOlrAUi0W2bdtGKpXirrvuagsEX8uk5EK4UmQwCAJ27txJqVTitttuo6PjyloPnYlrNTI4lRCvX7+eRYtiP9JrdbxTcTlkMIoidu/eTT6f59ZbbyWbzeL7PpZlsbZ/DWv71wBx+rhQKJDP5zl8+DC2bZPL5eju7qarq+ucotvze1by2z3/96y/f37wk5zWezBCIHGJCBkK9qFNorkvuil2HP+/T4NO1cOm7F0cqR3h2yP7WW+7pOUES7wSjgzbdXIp5bPEK+LXFUXd0UxPx3Pj+3Ui6aERuPjM94oklY8xEFk1jlQ/QaHxCJ3OUhyZZbF3PTM1HQDUo9oZaeoY2kgiI0grly47tofKWilO1Ic5Wj3F9dnZpdovhF9Y9+Vpn6Mo4pvf+Qrzl4Sk1GGq0WHyOscae5BYGlxysqxIJGwCBKXII2s3yAexLEuIFZPvuGgu3qszTqlRnUIjkU16Awb0KGmVYiRKtUliQ9j4coL/GnoWnaofJZMs9X6C3uSk/dqVkkLa0vF6TtYPUQjy1HW8ToFgsbece5f8JHKNzWPDX6VgQJmmhWIz7y2NJmHVaegCR8UGBGN0OBNUGj5aSRCSmvHoUssZrx7ilAtnUlMfSd0o5DS6aAgNNIyNBiwicqpGp6rTiEKUFATicUIj8ExEwhpgZHw9Sn+NXO7C5QmzQeu+NdsomxBZlPuLEN6HiX4AGKR6Gcq+F9FsghGOg+QBfP9NCE4Tx1YTCOcXkO5PnGcsxwkbH8Xoo4AG4SDlRiz33Qgx/ZkTRdEzKuJ/KahUKgDXfBZpNpiTZPBCmBrxWLlyJcuXL2/ffJ5Onb6rgSsRYSuVSmzbto1kMsmdd9550W4il4JrUVomDMN21PhMQnw13T2uJC5lnPV6na1btwKwZcsWPM8757FJJpMkk0kWLVpEFEUUi0Xy+TxHjhxh9+7dZLPZNjnMZDLnfcgXGqOc8PdijERpD2XH1lpBVMUYHykUAT7SqGadUoREcXvn3dzZ+WzuMnfyvO7n8rHDv83argqODGlo1d5mqA2ujFjolCnW42PpErDQHcPzfKqhw0k/S9oKSEmfSuTEdXOApMGhYD9h/RDaCKT4Cgkry6rwbj62+2fQRHxwQ9xAcXP6Fh4ofIvQRFhTEmQNo5FYLEksbv9NCokrbYb9wkUdo4uFDDOsSDwLoxqsz9T4QfFhxsO/o1MNYZuI5VmNQnKgnuJomGC+XcQRDRo4WCIiMhINNEQdXytcFVGPWmQiJozNdp025imHUZ1EonFEhC0ilNAkZCNuRDGDEBl2VX4Hr/ZhjkzsYy2CMQwrxKOXvc9pu5vXzf8ATxb/g5P1AygUy5I3sCnzMpSK72nZxArGq98HGesz0mzskUQoIqJGgsiqoFQZhEfStYGQjL0FQY0u55WMHR9ALvgwDb+BO+VWaTUbK6y2OEtrtkTTGSXCEoaM9BFG02ObtjOzIRZ6Hgs1OUsSyBdTKjkIAgwuSr2UVOoPL2leoihqC+fPFkL2YDlvxpg3xp9n6CwWTg7lfL39+UIxPGNCosbHMPoQQixBCAdjKujocUL/C9juu84a91yQd5uKarVKIpGYcxHNmfBDRwanRrxm8s+d62TwcknViRMn2Lt379PuonGtRdoqlQpbt27FcZwZCfG1Nt5zYSanlPNhbGyMbdu20dPTw3XXXXdRNzGlFLlcjlwurt1qGbfn83mOHz+OlLJNDHO53FlzerIxgCZAMv3vlnAx+CxN3MTJxiGqegIwKBwWuksoRkPsq2xlfWoTyxPL+T8b/oGvDD632bA8tWM5blJxRFwblpFlrkuewhKacuRRVZJ5Xp1IC8rabS2ENoaKcfBRKBNTHt9ANRihZH2ZealFCCL++tiPY7TmZX3vZW1yDXsqe6mGdaSQzciaRZfVjXdG53VoIhLqws1qoQkYbBxl2B9AG808ZwHz3eW4F2g6aL0MCCGwZRpHpnle92sI9Ssp1L9NLXwUIQWdzvNRbOUH1f/iZNiD03Y/btBp+SgMTnoRpyZGWNxRIGk30FoipUY2IuLH/+R8+8JGI3EIkBiU0Nj4U47J5EtKnznA4g5FSbv0phUneCGHBgJuW7Tzsu5BKdXJs3LndsZYnfgpTtX+lYYJsUwcv9TGoERMeBw7g9bjmChC6xApQcgIE0UIJQn0MI3yOlbZD1ENHibyfw2vWbeasgyFqCkEfkYlnWnOrC0CIuPTacXRw6j9C7AFRDJ+mXOEAeM3v6ujw39jYmIAJ/EeIl1AyR4cdX1b2Pt8MMZccu3dTCTwUmH0AbQ5ihALEU3fciFSQA4T/QBjXjctOjgXawbL5TLJZPKaVJu4WMxJMniuiR8fH2fbtm1kMplzRrzOZUk3V3CpZLbVJTsyMvKMOK1cS+SqpaO4ePFiVq9ePeMN6GJJ1jOFi7kJtV4E1qxZw5IlSy77BjbVuF1rTalUIp/PMzAwwJ49e8hkMm1ymM1mmef0IVBNQjhJjiITgJBcn76FWzO380TlETpkN932PJRS1KIKx+sHmO8uocuO5UZ0s7D/zHSmEBAhCA2sSQ7hSE0hTDIaZjHEPrZSQM3YKAwWmghJYFq+K3F6L2jU8FwLIQxJUSdl+RitiYTkgfwfY0nB+pTHqJ9hPEyRtLJcl7oeIbKM+GN023GauhiWm3qH55dpikzI7vJjnKgfxBIWAsGQf5y9E7s5UC0wHk6QsTK8uPs5rM+sm9XxsaTVTM9Opmg3dm/hdPg9Tvl56iaOjDrCxRjDLV3v4XDl7ymnNcfDTlwdkhYNqr4bi9PYLefbyS7xlra4bKbjY8mglipxjKWigKUEp8I0vlFIYdAGIjfJl09tYan3HCyxB1ummZ/8n2SdW2a1f7OBbdtsyPwOuyf+gNA04lSxAEt04EkbQx1beRgaYGxCU8Voi5HTPk5ynKopEEVLMMawYN4rgFe0150yhnzhDyjzRVTbIDC+prQWhEgiA56Iu7un3rVbMuGONFRCyFhMSjGZeH7q0Q+oVP8QIRQNranTha02k679OUJK8n7E2vlPnbXPl0WqjAEzAHo/mADkEpDrQFwCVTCVmOCKM1+EXAwTYKpwBhmcaxG2crl8RZpHrgXMSTJ4JqbWfa1atYply5ad80E3kyXdXIKUkiC4OF2nSqXCtm3bUEo9Y04rlzLuKw1jDAcPHuTo0aNcf/319Pf3n/O3Uso5kSZujXN8fJzfOPLHVKIqNha/u+qXWNa5DIhvsvv27WNwcPCqaWtKKens7KSzs5OVK1fi+347arhz506MMXR1dZH15jPOUSIJllZEJkBTIyF7uT61ie9O3Eev00+HNTnGhEoxEY0zHubbZLASJUnKAEVEaOIHnyU02giqUYKf7v8FDtV+nSCyKEbp+MErImJtQ0MUxR210kSxBAsCjMHzT5JJeQhbEAYRRZMhbfsIDL5RCCkIjaQUeNSNw+3uNjo6IiSGfRNfo8AibHMrDb0RYyBledycWcdCt/e88zfqn+Jk4xCdVk9b53Bv+SgPF77fJBESGGF/+QgvmfcsXtU36ZM7NTI4G7ygapFAsAABAABJREFU7zOMjY2x3f91AjPBOvd/EDlH2D3xJ2gTKxIaYagrm0gI3GTEUC1Nlvj6bSU7Y9/fJJGRWCJqcpmzGXrK0pwOUwRIPBG/iFdwqBmbRW4VwX8RaU3NN4ThO5jQXlyZaGxun/cgSl5eHdn85F302F/mSP1zVMMT5JybWZJ6Maeqn2C4/gUCGigCjAiQQpG0V+EtnEfNj4jGlxOGYdxt39HRfrlJp9MIIVje9UucqroUGz8gZABJJ72JlzJR/zfqYUTFOOdoP4khgDPvigZBvUkelalRZTEj4QArzC4moic4LTOxTJCrGR67nt6undOWv2QyaAyE30SEX8NQQhgBwsKoTWC/dQZSd34IuRhEBhgDJq9nY8YQcgGI6fehuagzWKlU2ufCXMecJ4O+77Nz504mJia49dZb6ezsPO/vfxjSxBcz/tOnT7Nr1y4WLlzI2rVrn7GL7ZmODPq+z44dO6hWq9xxxx0X7P6aK5FBgH888UUerjxOS1rXJ+T9B/+cZd4ifnfNe9i2bRtBELBly5bz2iZdyRua4zj09/fT39+PMYaJiQny+Tw3FO7kO06R0C3hmxKgSMp5vLb3HViWhRIqljyZAtO0BpNTHqv3Jv+eb9deS9oKYpkVYkmZidBhZ6WXBd4uLBSWTAMentTopiZgS4evJbLsGqhhyNklsFPNb0DZkhxVjlcrzPfSCCnQBsbDBDXt8sL0DjwVsa8kOCnWxyRJQI39lP19rOZent13N93OhaPwxTDflDuZfOB+f+wQIeAgsZWN1hF1o/n66Hd4Xu5ZZJtNKpeCrq4unsvH43mLIr6V/98YEyGxmpqQEQZNYCw8fDxpGA0zuNTpUnXSqo6vfdKyyoROorWDEnXaHRpNnKiMsLYjtsKzmlG50EgaxqJLTGAJjTAGrSVK2RwMOnBEiCMihAj5zsizmOfdSda+gXneK3Dkpb3I2LbNGnt6OrnfeztKZMg37iPUh5GE2LIbJTw0Y3Sn7iGXewnDJ77Dxo0b22URx44da5dE5HI5+rp+md7EaUJdwJa9eGoRpH6ZxNhPMRTupmEkybYnzCQEcd3gmUUAGtqagBqL0XCQ+dWD5JNpipFNzdgERuKKiJpQyLHr6ZlCCC+ZVJkTiPBrgECI1bFHpKkgou9j5CqwnnNRqxOyD6WeQxR+FUw9ttAzRRAWyn5pO3U8ddxzLTJ4LVnRXS7mJBlsPbTGxsbYvn072Wx2Wrfw+fDfhQxqrXnqqac4ceIEGzduPG8U7OnAM0kGS6USW7duJZPJsGXLllmdJ3MpMvhw5QfNRoipNm+Co/UTfO3Rr7G0Y2lbP7KufSTgyLNLKC42wjRbCCHIZrNks1mWL1/OutPreWTgWzTSNURZ0ltZSHFighPdJ8il+sib0wTax26OcSIax5MpcvZkdC2Xy3Hi2PNxg+/R7cSuCXk/wVONRQgMj5YOc3taYUwJjYc2Ele6GO0TGU1ZuzjCImO5RNrQpYcAuym0OzUZKlmSiKNaIAiMpK4deswhlI4QCk6wHm1UuwvXYEAYjoivURj6HkpqbOGyzLuV2zKvn/H8i+3rJj8fqZ6iZiIUoJp6eFIqHG1o6IhvFh5uRweNMQSqzLaJz1II9iGFTb97I+uSL203UpwP4+E2IlMnbiVuCn4L1dwXjY/LAlmioS167SpJ2cCREVLZBLqCKwJqxkUbCIxgqjbzotQ8BKNt2RWAoOmI4qkQYzTlMKTLcngq6CQh/dg2z8SRXiEhHzzKWPA4hyf+iB4lUQIiA7XoLjb1feyC+3cuSCnpS7yOvsTriKIavjlKPToEgKdW4qk1CBHXaCcSCbq7u1m4cCFa6/bLzcmTJ9m3bx/JZLJJDhPYnTGpWd71SZYDExMTlKNbcMQkGWwRw5pWdFtN6td07Zm87wjqzCfUx/HSLidDh9Eo1WxwMlSNwwQutjC0XjfC4BRS/izr1x2hWAQpF+F5f4ptX3/hCdH74oigWD2ZshYpMA5EWy+aDAIo540ImSMKvwWmhJSrkfaLkOrOszc/R2sGfxg6iWGOkkFjDIcPH+bQoUOsXr36ouzCLMua0zWDs+kmrtfrbN++vR0NuhZqGp4pMnjq1Cl279590Q0zcyUy+E31fWgRwam7ZmJa8x/p7/Dxm17F0eoAXzn+AEeqAwgEq1JLeEX/i1h0gVq2qwGlFEvMCm5bf1vb27NQKDAyMkL+4Ci1Dk0pfQjHc3Acm4RKsTZ1Exmrc9p6Rv1BItPPidDGGGiYWExaCE0+aHC4ej3LEz8gJSoUdQoRhdhSkJSLcawcPfZintf1Tqo6z7+dfldTHmVSgFkA2sTVca0HtSNClrh5DJ3sjToRUYQQEcK0iFRssWeJWKMuZLBJkgwfO34/T2T/FaFUXBcW1vmVVV8BIGf34UiHSlQipbI0orA9BjEl0tZyhGhEk8nFSlRgbOGXKVXH27owY8Fuhho7eU7Xb1yQEMoZNN9a2wLBPO/lXJf5Tb5feBflaATfKOoakjLEVRaOCcjgo41AIwkNTPW51QgSImBCe6gpTsotQpSSDiXjYRC4IiRE4sgwtjA0cbw7GQ2TcVtVirFUjBGP8sTwDXR5r6DTeQFd7nMv+UVGqQQJ1pOw1k/7uzHmLCkcKSUdHR10dHSwYsUKgiBgbGyMQqHA/v378X2/nVLO5XKk02n88gephb+OKzSIOCJY04oM9yAtiQ7vi+fDmHabjhB97e3WjKQQxREor6mraQw0sBiKkhRPr+GO7keoVF+MEv5kcFYfpVp9I8nkP2LbN51/EkwQB3blmXNoIUz9zPLcWUEIG2W/DGm9GKgD5262mIuRwStlRXctYE6SweHhYQYGBi5JH++HITJ4PpKSz+fZvn07PT09bN68+Zq5uJ5uMqi1Zv/+/Zw6dYqbbrqJefPmXdTycyUyOGyPnfM7AwREnG4M8/+O/RMFv4inXAyGbcV9nKwP856V76LH6TrnOq4GzuwATqfTpNNplixZQhiG5MdGOVI4wPDwKUI/IpXoR+Q8yrnyNNHrqUdH0+rqjB+o4w2b/dFqIu3S5+4lLTUV7VGPkkiZo9tawObsq1DS4gejTzSZyUz6d820p4kdTbSIWwVinTVBLJpd5bSfAFwEoITB6AjXiq3ftBHszh/glq71KGFA6DhVrTz+9vhrWJK4kRX2XSySyzihj1HxT5CREUoIQmOmCfT6RmMJwR2dm9t/21f9IsYdxZY9qGbqLTRVCsEuDtW+yZr0vdP26P7hf2EiGufu7CuYl+ojq67HEikCMzFJao1Bo5FCstR7A7XoOLYM6VYbEMYmTiX61MOTNMQYxgQoEVdf2gJOhzYeBkcE7C5q1nXU8bFoGKsdAZ16/EIjkU2i2Ipwtxzi/GicjCvb5FEbQUXbNLCRUjMRfIVa8B8M15Ik5CoctZruxE/iqGWzPSXPiZkEnM+Ebdv09vbS29uLMYZarUahUKBQKHD06FGUUnR1rSCX+ybSqyPUQTzuoL9zslSlVv8HQv9jQBkhOkjIO2mYQ9hmHCUMNW0RInGn+GkLAZaJqBuL27r/hUb9V5D4TaGb1jUSy+jU67+Obcfi1oXiK1AciOddp0hZ/4SXWQtyGQgHTBlEM9plQgxlUM+7rHmMz+Hzk6a52EBSrVZ/FBl8JtHb28tdd911SbZpPwxkcKbxt6Klhw8fZt26dSxatOiaKmp9Oh1IGo0G27ZtIwzDC9bJnQvPdI3jbOFFSSbsGjMV7gsMC9xevjX6XQp+kR6nE9mM2EQqYqQxxkOj3+U1C176tI/7XETbsiz65vXTNy8uazif6HXO6iUfDhBp3Y5ExV61CkmSjOUwHK5iOFzFmtQakrLAUGOQWztu4sbsJizh8L79P0eEZkMKQGDMZGQwHmPs8huvXxO3V+i2oLUxsRpil9VgLHJJiRo5q4pqEr5GpKjjsbprPUqa9jolGiU0AsOo/33GGo+hUUCaSGdR1Lg+vYLtEzWqUdA0hYuP8vr0cpYkF7XnbDTYA0a2iSCAJZKEeoIhfxdriMnglwc/ydbKQ83OX8PO6iOkVDe/tuyDrE3/LHvKH4rrKk2LkEn6nWeRdlZQDLaj8bHpQsjWA9vBs5YidZaUXE052o3BJ6FW4oanqJPHoOhKZfl2scStyQpJadNAEQYRVZEkpapUtU9CTU2dT6+smydUOyJoDNS1ooGFRJMUAXE8EjBlqtF2ytEORhv/jqtW0Zv4GbLO3UhxaZ7AsyGDUyGEmKbNqbWmWCxSKBQ4ceIEExMTpNMd5HJDBEFAR0cHSikS3lvBe+uU7UbUggepBQ/QpY8Q1k6BiM+B1uy3hM/jY2UR6n3NYobpkWQwaH0KgPHx69o1tgCWKhPoVxJN/Bmp9EswajMiegxMHrAwlBFiBWaGtO6VxlxtIPlRZPAZhJTykv1z57q0zEykqtVEUy6XnxY3kUvB00WuWjp6uVyOjRs3XvKb5lxJEzuWRcscypgWIRTt2rdfW/3T/N3xz6KEahNBACUUQsDx2smz1vnEwW182v/YtLSziQR/ufEjV2TMF/OSMpPodaFQ4MiRIyz21zLaO4iUAUYLBCp+LAqHHruPyGhC6vQ6vVjSwegeJBa9zmos4fDbT/0iWmgwUJ5okM4k4s5iY5rJWNn8r+bN/f/I54Z+csa0rQZsqcmaMj12dVLyRhiSKsQyNcZMMiYWIhYgUUKD0SRlAAZqxqEW+TiyhtGn6LMlthrmuZ1xlO67E324Yhk3d6znbQvfeMYszUysp7o47yk+wbbqt7FkLFtiAIWhFuX566Mf4BeW/QEJaxEHyx+jFg1iiwyLkz/G4uSPA+DJ+SiRIDRl7ClyIKGpYMsMq7P/E0tORrquB758+iX4xlAzDmvSGRoill+xwzSLEl1Iaykl//skLI1FgCMifKOwxWSKHAwtfhBTWEGAwgCeCJuWhTRr7SCME/2AphYd4FT1w5ya+BpKfKN9XAxrWdv9r7M6/1r3gEt9sZZS0tXVRVdXFytXriQIgnbUcO/evfhBg0xO0NGZpa9rBZl0Z1MwWpF0XkjCfj6dpk7B28pA8f00jEKYqL0voZEkZY2UWscE5x9jofjiaUSwPUYBof51EK/AyJchwieR5ikMNlq9CpxXwyU27lwMflQz+MxiTpLBy4FlWdRqtWd6GJeMM9PExWKRrVu3ks1mufPOO69ZO5+r7UBijGFgYID9+/dfdB3pTJgraWIPFx35SOWc8SjQmMiQUkkSVgLNDHNv4u9h8mF34OABPhN+rN200CITxoJf2vXz/NXGv7ki476UuZ0qer1q1SpurN/I8tMreLD+H4yTx0JgmxQL7GUMRSFDfoUFXhfz7Dh9d7oxzjwnS58bk5mG8RHETg3H1QrWhMexLGuKip4GND+16CsEQdCmVlPT1GaK+0TOrrXr3Fo/EMSSNhYhEYaMbOAQxVGe5u9qxiEwNo4UCJ0nbSfJhy5jUYqqhqVuied3ncSIE2ie5AeFUTZ1/Ey7FjBnracSnCAyAaopShyaKghJr7MRgK/kP40UhshAy3M4lpHWjIbxC0HO2cRtuY/OOPeu6iVn38lI4z6MDlEiSWQqaBr0OS+ZRgRbeGX/f3Jg4ovsr3wKY2p0OAuw5RjVRhVLdmDLiIXeq6k0vk7dlOiX44zpFCEWFgFCxBNYDzUJa5LathpRmjFbVPN46DYRnCTC9fAYSXmMBlbzZQkUh9ib38jyji8T6mEcazmO7Jtxv690U5Vt2/T19dHX10c5OMnx8oOM145xolHn8DEHt7aO3tTG9nnuOA5KJJnn3cV1+l1sL30CH6vN/5MqzaaO30NKiVLrMdETtEsboN22I+Ui0EfPOS5LGEy4Faf+yyiqk/sffgxfWBj37Vdk/88FrXVcLznH0sSVSqUtwj/X8d+ODP6wpImnkp8zLfeuRVzNyOBUQe1bbrnlilyccyEyaIzh+uoKjmeOg9D4YRwTjDAkLMnS7CqSdoLNHTfw1MRRSmGFdJP8TYQVbGmzqWOyy1AIwUcaH0JZcfSqRYrafbVq5vNLa41Bo+TTezvxPI9Ny25lE7e2Ra+P5o9wpHQE6mMYZdMIJUeDEWzbptvNcHf3epz2OMWUxyY85S8BH5ZzFMuS5H2f16iYHNm2TVKkmDDVto+vMTT9eg3zwvXUne1TOoLjeWuJMqdkHUc2xZkNgMFqB0EClA6IsOmyHca1y3iUoqwF1yeH6RBVSiTJl4ZYkO3haO1L7K9+LXZboc6d2T9mYPwHBNYYgWkdL0GnfR2rEvcA4JtaM9I7PfKiISZd50FD18gHpzDiVrJ2SDXaSmTKWCJNj/tK+r1XnXPZ1ZlXszrz6vbnyJT53lP/yML1i0l6i/DUekaEwAm+hDZZlJ7AN1GzBlShsahbHYSmhCXiaGYsW67aszy5L2LK3+J9SilD3dgoNFKYdvRQIDg09hq0sDBCozDYopO+5O/R4U12zV5smni28HWRgfp/0ZAFurOLEUiquREi/wRiYgkDA5Vpwu25XI4FHW8mo9Zyov4l6tEYaWshixNvIGnHrsme+2Gq1ecjaLT3vyVPk/T+gnr1Necdk934LRQVDBYIBUYjCHGCv6VuvZKqHuJ09WMEehBLdjMv8ZN0OLdekfm4WD/lawXVavVHaeJnEpdDen4YyGAYhuzcuZPR0dErRn6uNq4WGazVamzduhUhxBUV1H46axwvBVEUsXPnTrJBkrsTd/A9/3GE7YOJ5VA67G5+ecVPAbCl6xaOVI/zg/Ed5INxAFzp8Ozu27i5Y+P0FctW7d1UNAmGEHxi3yd44eJ7WZxaSEPXeWTsKxyqbsU3dbrs+WzK3MP69Kbzjv1qvLS0RK9v6ryZm7gZ3/c5NTrE7tEjnC7ksY1gTUcWy/Kpi3rzPDHTUr4A2mgOi6U4ocVPqvdM++553R/gv0Z/i4DY4SSeacNCZw0397yNb4/taNLn1hwajInPeVvQJiMQp+ZakS5LREgFiggtJBlRJzSSTgK6VI2iTnCodIrVmX6K2qUYJdAa5jslLAz3l/6AhuVhRR0kpEeP1ckC72bWpV7ejh4q46Dxienf5ANXAqE59/HI+yfZXX6UiWiMuFO6QZfaQJ8zj4y9iqx9PfIi3CkkKRoTq0lZd+JZ8bXanfglRk2dIHoEx0gMIbGwtYsxEQLJqK/pcRRKGFwREhiFj8IVYbvT+0zUtY+ULhLTbpAVwmAZjdaGUFjYIsBtipHDMEPVXyTUv0l38i0cKf4v/GAcLS69S/lcKIWHqesRMmpZ2wIuYy2ixBGy86usX3FPW7i9UCiwe/duwjCkq6uLXO5nWZbLnWWDZtk9JJMPUCy9C6UOIwVIuYyk90FsezUT2sFVjRnHYwwoM4JBxkQQQEiMUQgC8pXfJR/8AFvUcDAQCU5PPEbV+znmp95x2fPRejbMxcjghTRr5wrmJBm8HMx1aZl6vY7Wmlqt9oy5iVwKrgYZzOfzbNu2jf7+ftavX39F3yqv5chgiwArpUin07y850ZekX0BXxq6n2pUY316Fff2P7v9eyklb13842zJbWZ3aR9CSG7IrmfplCaE2eK7/qPsP/EYvraY53gkrBoShUQx7B/i/sJJtAnZkLmN39rzNqStCOoRf3z9309bz9VOwTuOw7IFi1m2YHFb9LpQKDA4OMj+/ftJJpM4HRaBiKPszRhe/HA18LKeN0Bh+jp7vfW8vu/TPFr8cwrBESzhsjHzWlalXgSAxEVTb/YZx92vLZLiSLep5Tdp3TY5A2aaNp8QhpxVJURQilwa2KzN9FDBoRo5+NpmtTdIXdscC3rxjSIyFRZTRBhDMYSxiSc4VfoTMlaOZdYnuLPzJTxU/BcUhqjdtRv/KyFmFq/2dYPd5Ucp63Fydj+YUwR6H6WwiKCXmj5AOdxPv/dKbDk7AeyZ0q5KJuhL/w5+NEAmOEQx+B7l8Em0KSJkgg7nueSrnyQhfAZ8TQJDSI1QJpFSxwLVtNLGk9Hes1uqmNxv6SKIcEXI5NIxBT1e+ROGa3+EIzSWhEU3fIOtI3/GutznsEUnlrx8kWE/miA+36bfs5TwqOv4xDtTuL0lwZTP5zl06FC7mar1j23bWHYP4+MfJAxD1q5di9aaRvBvlMt/gG9SuJyDDLIIQRHDmaRXEEWakfD7uKJGwySbLzsaR9QYr/8dPd5rsdWlC6BD/HIb10peu9mtmfCjBpJrAGKaOOfsMZcjg4ODg+zatQuAW2655ZKbaJ4JXEkyaIzhyJEjHDp0iPXr17No0cWTmgvhWq0ZLBQKbN26tU2Av//972OMoT85j59Z/qbzLrsytZSVqaXn30AzcDQlyUm7btAYur1YKS5pNdCiTD306HG7m9+nqeg8nzz6lyQTHrYbu2nIhOT3Dv0UNb/BWxb+PAv0uqd1bqeKXi9btqytC/eW/C/xaT6MEQbZJIHGGJa5K9jS/WwOFA6ctS7PznJPz+/NuJ31qTezu/IpQDPVidYS/QhTJkI0U8ztkRE3k0xfT2QkUdPyrU7sH1whhTGxH3O3VcIyEYNBL4GRhEax1PGx0ARGkSNPkQ4G9AIqdZet/DI91gQdMo+KYFxuAJoaisblF/r/z4z7kw9OUo7G6LL7EfhEZh8KSSh6qWmXHrmISniAcf9x5nn3zOpYnK8Gz1GLcdRiOrznorVPoEdQsgtLJilXbGrmb1jiNH2NDRwM0/imQa+KpVAElSmEMO4xtpop5VgrcHLaIy1wZTglmRrXGVYjQVpF2EITmdhxpm4sajR4svBa+lUjTisLly7nJ+hM/vys9vvsfc0250NPI4SRqeHN0KxxpgTT1GaqY8eOsXv37nZKuVqt4javvZr/cWqNTwEBlnKYCFTcsNN8IdEGAlbQlf4CuvZCJP4ZvdwReaOw8QmM0456gyQwLo6okK//O/2pn7ikeWjvd7OTeC6SwR9FBuco5iIZbGnmnTx5kg0bNrBjx45rkqicD1cq7dpKkReLxavaOX2tSctMrRFdt24dixcvBi79pehc+PCGv+JX9v4SSk7vmTVAEAbYTVkRI2I5j4bx278KTcDW0SGWdeRociuEiXBlLLQrHZsvj3wCg4WTS3J6aC8v6n4daevyogoXi6m6cH9oPsp3Tn2Thya+jhVY3Dr6AjzPY39jP41Go/1QnQ2uy76e0/4oo8GDCOoIbBy5ku+MaVYki3RZrXTmlHquadqGplmHOOXzGZEaR0ZYTfeJjKywxp5AmpCSThDgkNRjjKgeClGS8TBJzqowzwyTVJKs5RHiMF+faHahPouX9n3gnBH1yARxtEwotM5jTAXBPKRoEBEhhcKSHZSjvfSYZyNmkS6ebUOGlA6uXNj+vK73F9g6vBVffp+EjBDCkBQ1RsMMfXYntog4FWTRpkZC2iihcWQ3kv0IJCES2dy2EpPzf+b8WlJgCY1v4rrCknGpGIcMNWwpKGhFLLkSMR79Hb3Bl+lN/S6hzmOpNdhqzQXnACBrrSShepmIjpGQfQghqUcj2CJLl7PugstPbaaCWE6rlVLO5/MYY6j7I/Qt+CxSRSgZSzU5To5AjxCSpSv9z0jZ2V5nqO7Fif4DYXymvgQGYh6IIYyZfp605i4yl9+QORc7iSEmgz+yo5ujmGtksFarsW3bNrTW7bTwjh07iKLomu0cnglXglyVy2W2bduG67rceeedOM6FrbYuFVeaZF0OtNbs2bOH4eFhNm/eTFdXV/u7qzHOv7zuI/zi7p9HyWYjiRE0ohoZZ5IYGSPazREA1ajCRFhiSSbbJoLG6DYRxIA2kpBYrtlX42yb+A5PlL6LADIqyY/1vZ3lietw5NNX+iCE4O6F93A3cWQriqK2m8T4+DhhGFKtVmNdw1xumuj1mSiHReomQ855AwkVp44eGX0c3wxxqDKPm7MnYv1B07R9pS2T3RyLxBjZisPSUpADg2g6ysRkUdPnlFjmFtpUptvUGYsSBNKmENqUQ4+srNMRnSabkAQIfONR1h6RgYQMaJiH+ZfBF+KIBCCZ79zJ7T2/2d6ftMphS5d6VMGZ0mTi6wYdVg8ChUBijJ7WiHMmauEIx+r3MdLYDQjquRS+uRmbrnMsMTNu7v34tM/LjeZk4wlG6v+OME/QqXwKUZLA2EjZC3IVeb2cnPkvQBCi2jOqhG76+05GB1uqkvEnQWQEdWMjMSSlpmhcbKL42BlwiRDmCKPldzUjwRIlFtGR/G08+1nn3RdHZliSeAmD9UeoRKfAaBKqj373TpLq4l2BXNdl/vz5zJ8/nz179iClJJXZhjYl/GoCIaooS6GUhZQZjCkSRNtw5XPb69CJ36NRT2BFX0OaKgabUG0m47yfkYlXY4sqgWn5o4CFT2gcct6LL3q8Z2Iuuo+0Uvc/igw+w7jUh+BcqhkcHR1l+/bt9PX1sX79+vbFci3Xs50LrbTrmdZOs8XQ0BA7d+5k8eLFrF69+qq/RV4rkcFGo8HWrVvRWrNlyxYSieniuVeLtH5kw6SEzK/t+U0yboOGmWw+CIxCmziKEumIcjQBRIgpx8VqSoNgINS0pIHBBGhsIq2ACFsY6qbM509/hLpfoseNNRCrAbx/zeev+L6dD0openp66OnpQQhBEARks9kZRa+7urqmvZAFpkFkAuwpNXin/WE0Ah+LbaWFrEjmSVs17GY60xYdGDOOEPHcymYXJ8R0MK5DDDCi1V9r6LPKJGU4LYoohKHLqjLsp2kYm8BYdNsFMtJBUifEoaJdfCPpkDUMIhbrloo6IQrDQPAgJ0/dxwpnC/3JzXQ7L2CBu4qjtd3YIsJFEJohHNlJl92HMZpAj9Hl3HHOJpJaVOCJ4ocYCwewhIM2Gj83zu7yb7DE24wje0hYG5o+wBd3XxBCssi7lfnuTdTDY6APkg32MRKcIkQQmSqd9moWJ36VpJrHUP5dQI2st5KB2sPUqRKJWPxbm/jMjmg5nRgiFBqBS50K9rRGFFdGdMh68ziFxFLQhsgcY6zyP+lOfxLHuu6840+qflYkX0NdFzBEeLL7oppxzgVjDIlEgr6+ZZQqDrblYoxLGEb4fgOooSzN4OAYXR1FMplM+35qvN8g4DcwUQ1wEErhAAnn5fjBv+KICtpIpDBoJLZ6NgnrAqUns8BcdB+BH9UMzmnMhcigMYZDhw5x5MgRrrvuOhYuXDjt+7mwD2eidaFf7EVvjOHgwYMcPXqU66+/nv7+/qs1xGm4Fgh3sVjkySefpLu7mw0bNsw4b0/HOG/NbmFr5Ws4QuM3deokmoa2cKWhrEcRBDExjTSo+MEy9dFumoLQsYeyarqEaGyp22m7lKiSS0oi4shL0pH82ZE30R8+h1cufz1Z6+nvmrdtm0WLFrXdJMbHx9ui17t37yabzbbJYTKVJaHSVHSJjmbdV1ugRwh8HPZVW1EfQ9aqcXvnRqJoDN88AUZj8ONoWxy/AgQJmSM0dZSwCfUwHaraDCZOzrAxAikMWatOfJAMlojpjI+DaUa5Mm0i6EMzAmuLCIjIygZJGfC90hfJ1h8nFJ+II346ohGMsTL9MtLWECnlgilS1afx1AI67UlrvDNxonY/4+EAGdWHkjZCV4jUCbTOM+ZHpK1eKuF2OpwXknXuvqRjpIRNyl4FrCLlvpgFukItGkIKh5Ra0K7Jm5f9cXT4ryAM870tDNQeQ+syiAglIgSCUEMkBLYwhCauPrSYKuUewxNhU7aldRRU8+wOMExQaXwKx/qTaeN8amQtSgkwgnIQcWP/UwghSaieS9rvc6GVcrXkZqRcgNZHkbIP13UwRqHNOCZaSGViBQPHdqC1ntaIkkgkEGrypbMaDTNheijrNXhmAEc0iMjQ6b6CBYlfuSJjnovuIxBLy/woMjhHoVRcTHyt1ij4vs/27dup1WrccccdM55o10rU6mLQmuuLIYO+77Njxw6q1Spbtmx5WpXen+kGkpMnT7Jnzx5WrVrFsmXLzhk1eToKrt+46FXs3LeTkIHYwcBEaATlIMVbFr6b7dUH2VfZiUWC4xPH2jWDkZm0zoKWxRmEppmuE7rdPGGFNRzPEBmBMAY/TsihjeaE9R3+ZuA77TL/LtXLjdnncXvny676vk+FlHKa6HW9Xm/XaA0MDCCEQPWkmUgOE7g+CTtNS6R6qs0dgEFSDFM48nqQEPiLaehv48gqtnDQWGgTEVGnoYtYIklGLcKo+Ujup6UG05ItFPFKUWiSssEYKaqRS8pqNLcn0EagmvRFSpsQ0Xb7SImQlAzYMVEm4a6gjkJrCLHQRmDbDsP+wyxkOSE2rsgxz72OjL0BR5473ZsP9iOFQsk4gmoxgpQhgXHxDbhqFYEeZiJ4mKS1Aes865otbJnClivOPn72CzHmNCZ6Eo8aKTtB1bi46mZss5e0OUlSSkZDQUoGeCLCEwEVI+lCU8VCNd0/rGaSf/J4hsSP0/jABNFk89G203fQ6YyRsVvONuBJwcH8elZ1773s/T0TrWeblBZp79eZqP0u2gy3zxUpeshkfove3I3TOu2HhoZ46qmn8DyvfZ53dKY5UP08xfAwSXUTRtzGhC4SUKXbur19LzdmHPQgiBSIxRd9X7pWn8fng+/7+L7/IweSZxqX+hBsnbxhGF7VmrNLwfj4ONu2baOjo4MtW7acsyZwLkYGp5LB2aBUKrF161Yymcx55+Jq4Zki3FprnnrqKU6ePMnNN99MT8/5owZP1zj/97oP8I3hh7g//wCakKXeSn5xTawvtig5n8PVP6KhG9yQ28hw+QhewgVEm6mIpqtqK8IyqfAXPxwznkYbiSKi0SSCwkQIVJy+a5IpIQzj0Wm+OfY5vjX2L9yRfQVfHvxHAD64cXYWY1cKnuexYMECFixY0Ba9zhfyBIWQwegIxUSJLGnKohzT4ObLhRFxDCmjOtvr8nEomxWsdq8joVIEukQ5OM5EdBhtKrgyTWAqODJNaCxcEbQdTNoQ0NAWC60xGsZmLEzRJSfwlE+A1Za0af+YydhiUsaNQFl7IfUmhfeNjQCyska/GqNo0uyqjxKYMaR4il65j5XpAp3OApa5N8/4kiexp9CmEEWZILIxFrReFSzRQ0MfwtcDV4QMngtCJFDOu0AfxpiT9OkKQ/7j1KNBIu2SAMYjj+EoTSXQuDToVFVM5OI6NRpC4xuFNIa6UaSakjammSJmisuPEB0MNZ5gLNhP1iniSjPtF606xKdOr2FN/1NXdD+nEivHvoMO9Uka/pfQehApF+A6P4bVbCg5s9M+DMN29PvQoUNMmIOE83eTtPrQroXlKDzVTRT5nK4/Rr+zGcKvYaJvgSkRp5bXgf1WhJw36zHPxZrBSqUC8CMyOFfROuGuJTJljOH48eM89dRTF4wEwdwkg639mQ1xOXXqFLt372bFihWsWLHiGZEbeCYaSFpR4UajwR133DHrWpSna5wv7H02L+x99ll/z9idbO64g0fHH6YYFkl4PWgTYaJxapbEMRpH2tRN/EBUQhMa1SQzrZ7O1j60Cv0hrqGLO26lEM3/j2sPDQJTn+BJ8c8szcS3sb849kYC3/De1U9vnSFMil53dnaykpU0GnVGCiNsKhT5dPQJQtVod18KQAqbu3JxWrSu61T0BEnl4TU17GyZpcvdSFKvpBrlWZO8DkcmeWj0CwybbtYnhiZ9eYkbebQRrO/4c3rcu9kx/D84Emyjoj2UNEgRkZQ+gbGwmiLLU68qKTSVUNFt+wjqREhGowShdOi3S5SiFANBDoxAEhEYOBoNM1H4EJszRQ7UNAZBOVIkgo+xceFNAPS6NzIS7MPXZRyZiBuLZIDEI21NpszjUOfVJwNCSFCrEKwiASy276Ea7iWIxjhY/iwDwSDGGKSQlI1NNcrRWXgR89e8BK/yy0xEsbWdbxQZWmniqHkcWjW1LuO6l8HyZzHASjlZA9qCFnHhhOVM7nOl8Smq9U9gKAA2trqVbOJPUeriFBPOjLJZcgGW93OzWtayrHbNLMDxiYAD5ceI6oJ8eRSIm1WUJ8AeR4f3I4IvNSOCC4E6JnwcY+pI938ixOxe4udizWC5XAb4Uc3gXIUQ4poiU2EYsmvXLsbGxs7qFD0XrnV3jJkghLhgFKsloXPq1Cluuukm5s2b/Zvllcb5xnq8OsjeicOM+mPknE7WZ5azLLlwxt/OFhMTEzz55JNkMhnuuOOOWWtIXknSejnrelHPj5Gze3iy+H0moiKdVo7bOu/i+kxcTzYRjnOycZR/Pf0panoMS0hCYzX9dONOY5hKbA1GxKpmkTBYIqaLsX6cICiX6cpabXu32MnDIG3Nh46+ASUsLGOzIXsPWzrfgC1nLxFzJeC6HovmL2bR/MX8ifkgTww+zpfH/51QRywfW47OGg7lD+G6DgknwVJvFYE5QUNX8dTkw6WhKyRlN0sSd2MJm1OND1E283FFwFKv0K639I1kyL+OTW5MMG/o/QtuaM2kifjiieeRVDXKxsMSoHWIkJLISJTQVCKXpAqwxKRMSMrzGYtcUqbOgbAPTKzNFxiJry1WmiPc2FEmJUPAUNOQsgSu/VYOFiTFqIMu6+fpta5nNNxNNRonKTQJFZBRK0irRRhj8PUpbNmNq5Y9TUdnEkp4ZOybqcgRhvQXsWR/O3UOGcphiQnvKClrCenOL3Os+lXG6t9AqmWMiVEy+n4k47RC4IIERt3NoK9JyB4smUDqmcSv4/i4xGBMhUrj01QbH2l2JQsgxI++zVjlx+lKff2iiNKVTLlmvX68IImXSKBEF0EQUK/XKTeGYSzNaPXfSSYCLDuL46i4U1laoA+A3g9q44U3wtysGaxWqySTyTk37nNhzpLBHwZLuomJiWlSKbPVNFNKzbmaQTg/wWo0Gmzbto0wDNmyZcszrt10LmL0VPkYD458j1rUIKE8RhpjHKmc4LnzbuW6zMpL2tbQ0BA7duxg2bJlrFq16qLO7We6tnEqNnfczeaOmZsAMlYn66ybeGffb/IHx/43StbJqpCoKbMyWg3pTcUCyhJN1E69gWyKLcdptmaKNeMgmzItLRhtiLCJjKBhBJExfG/8q1T9v6fXdkiqBTi1N7Bi/guu/mRMgRCCzQtuZfOC2Mc1CAKGCkMcLBxgbHgMy7fozsxnvLPKmDNA1unBkR51XcbXdVanb8USNk+M/hd1HCyhOegv4KA/jy5VI4gkI7qLLlU9x/YVr1n8UPtzxR/heONrHJj4KjXGMSYkoUKU0PhGQtthIiIjAypaEhiFEhqIsNB0yJBViRopGeIbSUMbtDTkgEN+jpNRmtBIpP8ZFlmKNV4/mjQPjH6PG8w9ZPs6aEQHAYOSXXS496LEMxdhyfuHCEyVpOyZJgJtE+Hbo9T0GEnVTY9zMyP+41SiE0Sqh5p4FUT7SMkSPfbtZN3Xcbj+OIbvY8m4CSMCLANGTBXgieOEEeDXPkkt+Ayxq7hgakFoZE5QD79ASr0RHR6nVv8d6uEBhLBJ2s/CdX4XccaL4xUlg9YKOqxVFILdeDKHslxUskqWLCvnvYSO4J9oNBKMjxeJohDHcfE8l4RbR9iFtrPdhTAXawbL5fJ5pabmGuYsGbwcXAvyMq1U6KUQgGuFzF4szkUGx8bG2LZtG7lcjo0bN14T6YKZxhrqkMfHdhGYiMXJSS2woUaex8d2szK5GFfNvg51atf4pXZKX0t6iLNBf7ofiwQGw0RkCJq6ep70SPoF0k7U1iQ0RmNEnAw9Uxx4ev1bHP3SsWsq2sSp5m5V5JbUMSzgsfJyLDmCMB/hgYG/JjSSTe7b2dz7EtQVkPO4GNi2zaK+RSzqWzTNZizM++SDAqdSJ7E9RdbrYk32dpZ4cXSlbkpnromxyCY+TU3cTTILpJx5rBJvxmIttUrAKX4fS2jCNhEEkIQGXBlS0wqJwREBOavSjGfBoaibMWosFiVCacgKOBl2cizMxoSdKmu8Eb453kCIYaSEGzo6gScZqBp2lYu8adHHSdprsWXflZja8yKIqkistl/zVChhNSPM088009R3lMTpzpS1gJWpNzBQ+y+q0WkAktYm+hIvpdNe3VzqiWnrbmiJpaKmomTzBYfYE7rsa7T9fTSl5ouNmBQmj1Xbqfr/hac2MlL+Sao6aEcZi9GX6PQfpTP99WmE8EoSKykUa9Jv4Gj1qxSCfYS6iCs7WOA9m17nVoz8Jo5zkmxHH2EY0mg0aDSKhH6dY08N4Hi72s0o57NOnas1gz8sKWJjzH9PMvhMkimtNXv37uX06dOXnAqdy2Rw6rinumqsWbOGJUuWXDNvWTORwbGgxFhQosue7pjRZWfJN8bIB0UWqJmP5zeHnuRLpx9GIXnH4pexNruEHTt2MDExcc6u8dlgrpFBgD9Y+ju8//DvE6gGslk/WIsSpN3nMhIcwI1OIWRA2UDGkkghm3VcTdeOKY0pLYhmiwrGtLXibkyewBKGx8orcGSEMeBjoU3cyPKDxt9TGRtmc/aVZK1npiThTJuxTdFmThdOMDI2RHmgzonaBOXO7eRyOW7sfimPlr5IxXgoo2k97yMhUWgysndW23ww//84XPsm4GMQzFetLteZr70Gik5ZxlUREbItfBMZwWiYRClDtyqSAo4HKcDgyIjlToGkCrgh29GO7AIERnIySpH2evlq/k9I0qDTqrGGn2NB71suc0bPxuHKg+wuf5mJaBSJpM9Zy60dP0Panjzm85z1eKqTuh4jIbtjySYTEVIhFazGm+K922Wvo8NaRTUaBARJNT/Wh2x/v5qhxg8IdA1bJpiw3o2IPoorzJS0vqAWwHUdrycKpjSQTPXMa5LCqq7hVN9LRQfY0NQ6FITGMK5HSPp/hmv9JloPEYXfoTu3DSWHMeb5CHH5zQ2OzLIm/SYa0RihqeOpHErEWSxj34P2P4XRp1Cqi2TSJ+mVQd6BlX4JhUJxmh94ixh2dnZOI39a6zllrwqTZPBaeWZdDrTWc5cMzsU0cbVaZdu2bQCXlQqdi9IyMH3cURSxZ88eRkZGuOWWW9q2StcKZiJZSiikkERm+txHJkIKiTVDTqRer/Mzu/6MUlBENB8E79//UbqjDt7t3suWLVsuq6t9LpJBgNeMvZznPOc5HC0fJ6UTDJhTHKkfJ6iWKAuJRJISeQwhWhuEjCMlslmkH0YCS569301VP7JM4IqI0ZLAtuOiLR+LaIoLBcDu8gMI4Dldb0cKxW/supvrs2mEgHI95B7vo0/jrMT3poXzlrJwXizkW61W2/I1R44cIbHQxpcRDWMhtUGbuMM6I2u8Y8mnL7j+7439M4dq9wEamnWaY6FFh5JYwhC2pzQWY/a1Rad6K4ivUtG6TQQhtnULjaSgPXooIgG/2f3t4JOWPvtLaZJJ0ebuoZGcDLowRiGJuN49jYdPHZuj4QcZGP4gnSoEbPqsj9HZsemy5vNo9SG+V/wEvvGxhU1oAo7Vt1MK/xcvnfdn2ComNY5McWPmTTxZ+gyVaISWA0zC9NJXP7tpSgqLtLV4xm32OBuZ597ESGMrtWbLd5V78fz/JK0EIjKs6HwjJMCYEsrqpNFI4Moqpt2EotsEvaA3YfHZZuNRaytxD7MP1MIHscJ78Bsfxpg8ua4aUjxGo/YAjvc+pLx4R5OZ4KouzipkUs9C2HVMeD+YUcBBWM9C2G+gI9FFR0eO5cuXt/3AC4UC+/fHVo+dnZ1tcngtqntcCD9MkUEp5dwlg5cDpdTTniYeHh5m586d9Pf3s379+ssK48/VyGCr1rFWq7F161aklG2LvWsNMxHuLjvLwkQfB8rHcJWDJRSR0Yw0xliWnE+P03nWej6w/++YCMcR06JYhrwq8p3sYUTFZbw4QYeVZmVqKbkZ1nE+zEUyOPVFbll6CQDzmMfN2RsI9YvZOvEYe8rbGQ0gIo8UBomFJuR0xdDhGgYa46yUHTiqZd5m2jpqAK4VXx9HWYAypt2FDGfapgUMNY4w7B/hgeFfZHN3qt3Z7PQonoh+kW8cCbg791bWJ9/2tEscJZNJkslkW/T6hvEb+MLY+yjLIiEW0kQ4QrP81Jswyy/s7rOv8nXirlcL2ayNq9NBWdfJqjqOCNFN2esIiVIruKvnZ/n26C4q/kFUK21vWsn7OLIbRdCwwRMRZWNhiZjMeO708UxoD60VUhg2ukMkZEAlcqhqi/l2FVdEVIxNSMip4O0MFpbTk/xVMvY6PHXxqeQ95f/ANw1SItu+54bapxgNc6D6Na7LvLr924XeZjLWAk7WH6ehJ8haC4iG+/BnaP84H6SwWZt6Pd32dYwFsd5gl72aLm6H8CsgchgTABWMHkZad3FC51gmvoxqCoBDTEdP+X043kZMqM+K27Ztrk2I3/ho08lmCY3GGAkvhdb7Cfy/x/Xey0jpTQR6a3MBm7T7S2QTP3vR83kmhJAI+16M9Swww0BqRkmZqX7gxhhqtVrbS/no0aNorUmlUm2Nw4vxBH+m0KoZ/GHAf9s0sWVZTxuZMsZw4MABjh07xoYNG1iwYMFlr3MudhNDPO5isXjFSPHVxEwkSwjBXbmbqIRVBmvD7b/3uTnu7r6l/XCdisO1Y62lp6zIINE8Vnwco8oooQhNyM7Sfl4w727mOTmO1QbxpMuSRP9552gukkGYWQ5HCIGtbG7rvJvbOuNGlN0Tj7G19BClKE9DKzZk17Cj8hid7mIGG5JeNY5lC0CiCEFYSKMZCjNowKNGILLt2q+pLhKC2LljsHGc/zzxC6STbfc8tIkToo4y5ITDvsrnear6ObS2WJp8Mbd3/fLTMEvT0RK9/tnc/wVoi14PDg5S9Is88sgj7UhLd3f3jJGWkFgOQzJ5TkkkA0GGhTh4oo4iIsCiw7qZ27o/DEBC9QIHMYam3qMk1o0MsUSEhaGkBYusCZ7yuyhFiWY96FShbVBE9NlF4sYRTSOSVLWgSzVwZcR45KKJ6z7LxiOKhimU/w9JawNhYydSjMTyldpic9/jF5yzYngaC+sMqRWHelijEBw86/dZawHZ9Cvbn4+YIwRTOqxnCylset2b6XVvntx/cx2RiDDhd8GcBDyk9WyU82YWJg/w2Pgoy+2DZFWN0Fgc9vvwxVruJI/A0EA05z9eX9R0e4z9rocQYn7T3xoQDoJOdPQEp8ZvBOpTRhdQbvw5friHnsyHL3rfZoIQSRDLZvlbcdZLzpNPPollWZw8eZK9e/eSSqXa53FHR8c1WU9YqVR+aDQGYQ43kMyFNHGj0ZimG3elbGvmYmTQGIPv+22LvUWLFj3TQzovzpWK73Y6efX853O0epKJsErKSrAsuZCkmjm6qY1hplPVkhojNIsT89uE7kR1kL8f+DKlsEY5rCCFZIHXxxsXvpjlqZmla+YiGbyYa3dD5nY2ZG6nGFT4+vCTdNgpnhj7Hg0UjtKMmSz4gtAIIi24PpOkGJykYSwG/U42ZMZ4spaNnU5MS+y6FU2ERqQwQpPwJALd/EYghcImoq4tXBm1lxkJJfur97F74uuUdZLQWKwQN/Oyhf/jaY8atkSvPc9j//79rF+/nkKhwIkTJ9i7dy+ZTKb9QM1m48iYwMUQTFuPRiOEQ9ms52X9fzrjtlYmXs/x+neIjEEa054nkCxw72B97vcZrz0IlfexyCpxOkozGHawp5znho5smxQmZEBk4jhvWVsxJRWCTlWhou1YmtzAhHHRCCwijKmggwepiiQBGTDgipA9+ZuxzUtY2PFqPHXLjC9NtnAJzBn7qzUIsMSFy3SMMVfshVUIF8t5G8Z6CcaMIEQniH6EECxNbKah38K+8jeoBUUEkg57Plsyr8fUfhtJhCssGma660xaQCOqkZQeoJpNKM17jrCoBseYTgQn4Uf/dUX263IhpUQpRV9fH/Pnz2+nlPP5PHv37iUIgmkp5WulTq9arf7QkMEfpYmvIlodsl1dXWzatOmKFscqpfB9/4qt72ojDEN27txJo9Fg+fLl1zwRhPOTLE+5rMucbXc1E6SIoydTOx5iKzaDwm3f1IQQNIxm38RREpZHh50l0iFHKgP8wb6/RcsxLMJY5NZk+P9W/zrzM73XhIfypeBiCaynHBLKoRLWed3Cn+e+gX+h5ORRIk4BJ6MkL130Ao7Xj+BZOSrBCXbXNxJxEMIGxnZxCPGN3ZaniYwAoWhoq13PCXHjgC3A17SJYISkGLh4lqEUulR0AiU0QsBBs5W/PvmTLPVW0eMs55aO15C8SKHgy4ExcXq4JXq9YsUKfN9v1xru3Lmz7T/b1bGWvHwCjQ9t190QUFyffvk5t/GD0n9Q1R5JUUNPOY8VNjen3w9AZ+IeOhOPsRqoR0VG6vtZnfoWT5X/BasZ2BGYWHAcgUtIi2ZLY/Cb8UpfKMImYRQC3KhIUWRjTUQiumTAfMvHFhrBf+BXH6au7sJXL6eqj9NhbaDbvRGARd4N7K8+gq/rONJD64iaqWALh1XJC0sMaa2vOPEQsgfB2c5Ca1LPY0ViC/ngGJZw6LKWIqVkPFhNEG4nYUKS0qJu4qpCT2h8o0EsQggFpgAiXm98eRVoUDjvWCZqnyOTeOMV3b9LwVSdwTNTyq262UKhwOHDh7Esa5qX8jNVa/jDVDMIczgyeDm4mmliYwxHjx7l4MGDV61Ddi5FBsvlMlu3bsXzPLq6uuZELQhM6ve1HrSXgrGxMZY3+jjknGqSjamieII16ekF6Cdqw2gMObsDR9ogHQr1MVy7gGop1wqAcd7/1O/y4TV/8kMfGWzBlTarUvN5YvwgIhA8d+GrqEU+xbDCxsxSNnWuohbVSKsMh2r7mIjyBKbOYX8DSilOlAdYnqijRESIpBEpfGwakUVF2xgjmtIscexQYNBYCBHGpBGwlcaPRJsIagTCRGRUgBKafLiTY5XH+VbhWzSMT6+qY0nDa+f9Bb2p5Vd4FqfjzDl1HIf+/n76+/un+c8uHXkxY9kBtDsCIgAhEFgs8Z7DdZnnzLjuSjTGYLCHyGQQYh42RQSaunZoIDkafJc1zvRlPdXB4tRtwG10ez/Hd0fuQam4FlGIENMkhEDcAS4E0hjCpodyvE9xCCyULqFW2IR4MmKh3UAA1abHtTGSQ41HMDyCEHDcKBy5mNWpl7PKW85YcIzR4BSNsIgQ4AiXDemX0eOuveC8XsnI4GxgSY++M8bVmf5fDI3/G0ZEQIgt4tYSHwNCkXH+HqH+jTD4PEYfx7YDoIwQHcSP+HM/K2qN/yDlvhQps+f8zdOBczmQCCFIpVKkUikWL16M1ppisUihUGBgYIA9e/aQTqendSk/XcerXC7/0EQG4b8pGbxakbUgCNi1axfFYpFbb72Vzs7OK74NmDvdxENDQ+zcuZPFixezevVqtm/fPifGDdO9lC+lXmVgYIB9+/bxy2tew0eKX+FA5Wg7+mSMYJG3gE432Sabxhgmwgq2tLDl5GUp1RiW1M0uT9nuqPXskN8/8lf8bOJ1bTL4tkffTSTiG78yir+/828vetyXQ36vNtakF6ENHK4OMhZUcKXFDdnlbMjETSgJleDWjju5IbOJUX+Q3RM/4IR/EBCsTubYVT6BwZBUAUrqputJ7H5cqYRkM7IpXaObac1Jkj3WCHFth4kpRNAYQ1oFWDIiNIJqIDnSWMGq5BA5u9p0Yob7xn4BPeawPv1yViVfSJez9IrOy9SXgfFghNHgJHVdJSM7mecuIqmy0/xnbwhuYNfIQzxVe5igoeke3URfx3IGagN0d3eTSCSmnQPFYDB2isEGFAGtzn+DoUQxGJxxXBPhIQr+k1TDEyTshSjhIaMeRK1KkBikog1JGSBMRCF0yaqQkpHIJk00ptUdHiv0CQFZGdcnVprHLtJwOAJJ1JQMckmJCbJyO+O17SiZYIkIWOeCT4Q00OF+nt7s+lnN7bVik+ZZ76IRfhzQzY5jAIWl7sVNJjHmzUi5AL/xdcLgAJa1Bct5KZ75HPXwn8+53pBxKv7nyXg//bTsx7kwWwcSKSVdXV10dXWxcuVKfN9vp5T37NlDGIZ0dXW1yWEymbxq97NKpUJv7+zknK51zOkGkmutZrBUKrFt2zaSySR33nnnVQ1dX+uRwalNM1PFlOdS40vr/LrYqJvWmn379jE4OMimTZvo7u7mT/kFxusT/Ouph0lIm9f0P4sRM8b9ww8zUBvElhaBDvFkK3EWb3uiNoGjdNPDd/JGGfeCQiEcbBPJtzz+8+C67V9p4C2P/zz/uPlvLncqrgouJZqphGRDdgkrU/Op6QaejFPHZ8KVHgu95Sz0pkfjPnz0DzlUPYmvLTwZIEQcBUyKFPv0am4MDuPaBiUUkTFYRGgjmqSu6T5rrHYNqMRgCU1kBOP+GCPBerqtMXrsalMWBCIjCZtr2F76N/ZMfAktJBLBPHslL+374EXPw0wQQnC6cYyD1a0EpoGFw7A5xpB/nHXpW8la3e3f2rbNzQuez808f5ro9ejoKAcPHsR1Xbq7u+nu7o5Tz/ZClLCJTIDF5HxH+AgUXfbZZR/FYA8D1S8SmjKWSGPwaOghLCRCeMxz1uGbIZRIsyT1ExyZ+DOq4hAJEVIzVruJxRJJpBmDZvOEahN0QWSgZBS2qBFphRACV/p0yipgCJA4QYNhnWaxPUGCkABBIXwDhbxNf+LjdCRuOu+z5OmODJ4LHen30Ki+lZL/ExgzCqKDrPP/8JLxOS6ExLLvIQi38NSh77FwyfMAyKX/P06Nf4GZDPEgi2MtxA+/j9avRcoLW6FeLVwq6XYch76+Pvr6+qady/l8nkOHDmHbdpsYdnV1XdHncrVa/aFJE4dhOHfJIFx68fyVJlOtgu3ly5ezcuXKqx5ZuZbJoO/77Nixg2q1ypYtW6aF0edKRBOmRwZnC9/32bp164yWep1ehneueGn782Lm88r5L+RA+QiFoESnlWFZcin3jXyXQlAko1LUdSMWFjuPs4QQgj+s/hXSSkz7u9GgcXj9Y7/MivRyliT6ecG8LazJLJv1/lwtTCXal3KteMrGUxffrPEry97HwYm9fHnkX2mYCn1OFyGaol9EodhRX0FlfBt3dGRBaCwhEChcGdHjKUoROCKggduOV8WRRPBEJ5XI5brUBKJZx6iNIGjeYo1ugHCpYmG0QBJxyj/EJ0+8iqxaxKrUPdyY/fGL3qcWIgKO1/YCgh47bjYyxpAPTjFQ3891qS0zzvWZotdRFLX14A4cOEC9Xo8JYW4FI3IPDV3BwiEiJMKnQ/Wz3NsybZ3ahAzVHyIydVJqedwlLrdQ9B8kNKcxdpK6LmHJLAsTb6PDvZubEnH3eKk0QmD+gx6rQDHS1PQIfuNrcSMIFjUdoiU0DFSNIUTjCYNRIb4xJISPwMREMvI5EC1kpTOERciEsfFpEQ7N0do76DNvI6EWoWQnCWszlpyud3o1agYvFW6yh3nJr533NzO5j+S8bRTqdwMT7b8J5uE5z0KbCkYX0aaM5Jkjg1fCm3imc7mVUj527Bi7d+9uN1Xlcjk6Ojoua5uVSuWKNYU+07Bte26TwUvFlbKji6KIvXv3MjQ0xM0330xPz9lFwVcD1yqpKpVKbN26lUwmw5YtW87qrpxLnsoXGxkslUo8+eSTdHZ2csstt8yqYajb6aI7N3kD1jqOAn6nsJWxoIS0LRqRIGGBNi0x2lbSDHqcRbHMhzW9k1lriFrywFJQCio82djLzuK3sKygmQCV3JDazE+vePes9u9K4pl8uK7KrOdXMx8A4jmtRWWG/EG2lh5n28STSO8WdjcENmWKYYqsmGBFepSkDKgHmqSsU41cLGGIiKWBFQbTlBWy2mKHoqlrGHujSGlT1TYSjUWELSOkMBgDlWiAnfm/5kD5b9vacY0Q3rzkG7PaJ2MMvqqidYmcNWlpKIQgrTopBnkapoY3i+5ZpRQ9PT3te1mreF/nX0TZHqeaHCSSVaRQ5KwlPL/7l86K6Pi6QEOP4Mqe9rFWIknWfj7jta1Q62NB5210OLeSUNPrZrPZecA7AOgDQlNFm/eza/TZVEgybmyyOqBO7JoSyzLFGpOuCFEtf19AiiSBDrFFSM0oGiisdgGAxhKaUuMfaKglSOEwIb9Czns3nnXdtLm9VsjgbDATGfQ8jz7nq4xXfwuDQsnJKLEx40iZQ82gDfh04mqk45VSbeIHsbpH60Vn9+7dRFFEZ2cn3d3d5HK5s8ojLoQflgaSUqk0t9PEl4MrEVmrVqvThJMTicSFF7pCuBYjgy2v5RUrVrBixYpzRiHmGhmczXgHBwfZtWvXefd9NpBS8uMLX8Dz5t3K4coJXOny8Mj32V7+drNzVbeTPbXA4o/X/Dyjo6OcKaMMMKnvJsg5WcbDHdgymlIJF/Hdsa089MR7sIVFp93Bu5e8lSXulXErmAuQQpKysqywsqxIruXeea/i8eKjjAfjHB//Lg1ZpagzbJ2I3/4FsFIeojNZYjzKIoWgEdkklE9DF3Dl/LjarVXw1pxtAfjGajqfaCwZO3nEjSkC11RxXYuoWRMKYKuIz5+8l25nI1lrIRuybyF1XtFlgWy2vUwtKWiZzIkZzpHZYKoe3M16MwP5fRwv7cYfl8hSjsPZIXK5gO7ubjKZDEIIpLARQqHPkLARAgS9qPJz6E+8ZFbbt0QSRJJNvTvYNfR9fN7JeCRRysIVCmM0DSQIDWgiIxEiBDQBivlWGQE0jGrOQ2u9zWNAiBAZbDmfQB9nrP4phImoht8EGnj9Fjq6iTD8uzlhl3YuX2Il+3CtZ1MLvkqkGwhSaFNEYEg4L0WIZ0743xhzRf2UzwXXdac1VbVSyiMjIxw8eBDHcaallC8kFfXDojP4+te/nlqtNrfJ4DOVJm41RixcuJC1a9c+7TUl1xIZnFojdyGvZaUUQRCc8/trCbGw7vnJqzGGp556ioGBAW688cYrUkz88OjjDFRPc3fPzSxJLuT6jlV8YaCbr418FSV8jAFLdPGXa36LTCZDPp/nTKNe3aZ88X+L/nEsK4rFlImJQb6RbBpaGXwTMNQY4X8d+AvWJJfS6+a4KXMDmzqvv+z9OR+utahLWqV5bu5F8Ye+1/OxPb/HgHWMhrZjNw0VcKv1S9y68Pl87tQvkBKHOBrkCEKXDjuHYYzhRpJF3kQzLjupZ9gi53F62TTlbQQVf4isl2l6/sZSKpEGHweDYNjfQzncxlDjK00LtJXc0vG7pKdEAI0xJEwGpQylME+n1ZQcMppyNM5CdyWunP3LamgCRvwB8v4gkamRtZL02ItJWwtYOu86ls6LI2ct0et8Ps/AwABCiPhBmusikVpKSe+Ma/6EjTERteg0junFRJcWhdrYdxuD9U8wVP0ImBOE9BGJGoqA0ORRNAia7Sdxq0tIyVjtis+p8tetms64CtEghMKSC6gH38RQaf9SiACsHzBYeR1d6m2E0e8BGiGeR0f2ry5pP64mzkeqUu6bkLIz3kczgSUXkXDuxbWe9zSPcjpa99ins1FnppTy+Pg4hUKBI0eOsGvXLrLZbJsctnQ6W2hJ3vwwRAZvuukmRkZG5jYZvFRcqs6g1poDBw5w/PhxNm7cyPz5z0wU5VpJEzcaDbZt2zZjjdxMuFbGPVu05GVmQhAE7Nixg0qlwh133HHZb4iP5bfxwUOfo96cn8+feohFiQ4+dN17ed3ie3nd4ntnXE6IWN4Eceal3Hr0GbQoYjFJBCsN2SSCzV80myWSVshQsI98aPHVoa/T53Uim/Vv5XqJz2y+sPftbJAP8uxNPsWBE8dwhMO69Cpuzt6Idw7h7mcKz7XfAsDqdaun/X1XaTvH/Sw3JGGxyfOUv4SKdpAmQkmYiBzSykeJiKjpwmER0sBpU5AWOq00mljwWggwRhPiNAWXQ9LKb5cFGBNSDvfzUP5d3NP9D/zziVeQsm20hgI5Xpf8CE9VnmAkOIFEYdB02r0sTlxYQqWFyIQ8VXmcU/XDwCjaDHK6XuOk6mKRdxP93t14Kk4ht0SvFyxYgNaaUqlEoVDg5ImTlGpp7H6Pamo/jmNj2TYJ1Y+r76Y4g4f3hVAPx/lu4VcQHMEVsW1eXU9gyXkx4aQPzSiO2EjKXkcQfZ1qdASfrqamoabS9l0x7VpPKdJYIr52ozCcQgRV85dxNDehdxPwm+3xGPN1CsU1SPltnKTBlV0o8fRlh86F85FBIRySzqtI2C/BmCpCpBFn3TeefrQCG89ko45Sqt00BfGzraVt2NLp7Orq4siRI6xevZr169dTLpcvu2bwoYce4k//9E954oknGBwc5Itf/CI/9mM/dt5lvvWtb/Grv/qr7N69m8WLF/OBD3yAt7/97Zc8hj/6oz8C/ptKy1yKzmC9Xmf79u0EQXBWY8TTjWshMtgS1e7u7mbDhg2zequbi2RwpvGWy2WefPJJUqnUjLWRF4tKUOWPD36W0Ex2rRoEA7Uiv773z/nQDb9xzmWFELyHn+JDfJrJmsKW+IRhVXI5xXDXtGVqTH9oSSBlB2gt8I1FOQhYmOyY4sYByUSad+/5FSSKZe4C3rvyf17Svo40Rvn30f/kmDvEvCBHJAwn8kOcbJzmx3pfhiWv/VvShC5hhOJk/Q7mu4+TVRWKUYoIh7qGWpBgPCzSbYElDdpEKBOhhBX7+GJQ6JicnxEYjY0KJRJDQgZIaB7LyR8er+b5z+i1dHsWslk64C4q8MDpN/OyBV+iEJzG13WSKk3Ono8jZ0+y88EpTvvHSCkI9ChCpsB0U4mKjAb7Ac3i5MtQYrpeqJTyLNHr0cJNnC5uo1QdxIQuMrGBSHho3bjoOf/e2G9i8RQhNjWdQskJbBEQ6WGQvQjqCDIYOkk5d5BRP0Pd7CBTfC+lwCVt+TSMjlPxiGYkVmKLeVgiFgiv+Hv5Znkd+TCNAPrtEnclD5C1Q9QZPCUygqHQYzB4E8Pj86lpRc7u4JaOH2dJ6p6L3r8rhdmkW4VwEOKZEWqeCa177LXQtd2C67rMnz+f+fPnY4yhXC5TKBT47Gc/y3333ce8efPwPI/HHnuMG264gY6OSxOZr1Qq3HjjjbzjHe/gNa95zQV/f+TIEV72spfx7ne/m3/8x3/kgQce4F3vehfz58/n3ntnDhhcCGEYEkXRf08y2CJTs01T5fN5tm/fTk9Pz6ybA64mLnb8VxLGGAYGBti/f/9Fi2rPNTI4UxnC8PAwO3bsYMmSJaxevfqKzP//Pfr5NhGU09K9cLSapxJUSdkzR11b0ct/2PxXvPnRdyHdBFIapO/Tm1rMsF/AGBdbheesGrNEy2UjPq8TdpOQGE3DRAhhY3AwJlY5O1w7yq/s/UWS0qPHmcdPLHgH89zZpf6eKG5lJBijM0rR4+QQUlAN6xyoHOVg9TDr0msuYuauLs51ffU787GwqWBzKnoRjizQaU5RMSHGQNbKcFvmtZQYpBqN8/ipb7Gsy5AwNQJhE2qJlAYlDON+mT4Vp4mNmR43VGLq5zjRaQz0uSk8FcZp/6ZAsyM0wpZ8duDlvHPZty95n4vBaFPfbwQQKJEBAcLYRDpBTQ9TCU+Stc/vwOM4Dgv6l7Kgf2n7YZrP5xkcHKRWq/HYY4/N2nu2FJzCmH3ECoMeRhgmdIqkrGGLkNBUEMLG1xWq0Xc4Wn+CEItu1cV12fezMXEvx0dfRFIMEqAIhYct12GLCYRQhGaE8UaZzxcWc9rvbIthH693c8rP8tO57zElkI4xcDpKcszv4UC9lwgLS2jG6hOc8v+BF+oSazI/dsnH4HLwdNTeXWm0OomvpZKRqRBCkMlkyGQyfO5zn6NYLPL1r3+d973vfXz0ox/lN3/zN7n99tu59957edGLXsTmzZtnzRFe8pKX8JKXzK5+FuBv//ZvWb58OR/8YCxJtX79eh555BH+4i/+4pLJoGVZ8T+XtPQ1gks9eZRSs3KXMMZw5MgRDh06xNq1a1m8ePE1ccK2LvanmwxGUcSePXsYGRnhlltuaXdpzRZzjQxOHa8xhsOHD3P48OErXiJwrDYUb+8suhbXlh2qHueGjnUzLjuVsP7TnR+f9t3hygl+MLaLmq7zxPhXsGQExCnhcEqTgWi3GYAf+HiOhSD2b417ZePaQgHYIsCxWk0RVU42DvCh4+9rpzI9OvjA6nNr5x2vnyIh3aYdWoyk5ZH3xzndGLqmyOC5sDy5ikXuco7U9xMGIbb0qJv5GDRbOp/HPd0vBiDvD7Cn8m1uXfB8TlS+TadVQxkIkLH0jxQk7F5CU0cJ4u5jE7WPx/Q6t7j+c6BWY3EiARi0mWQooYmbIrqcmW/pI429DNWfpM/bxDz33ILLUigwGk1tevTIGJS0AUNkZva6PRemPkyVUhQKBebPnz/Ne7YlFDyT6HUpPIokIjKqOSGxEV5Zp/BEDUQC25QphBbDYexdLIWhFFUZKfwNd3f5LOm576xx1cPdlIMHCKIjfGPMMOh34ooQW0ZoA4GxGGh08x/l63htbk97uQaK8dDjuJ9DYOiy4nuENiGlSPBY8X5WpF6MJT0q4XHGg60EeoKB8Q8hpAHdxd0L77+oOZwt5iIZvFaEvWeLjo4OXvGKV/Cud72rnS38xje+wX333ceHPvQhbrrpJh588MGrsu3vfve7vOAF020U7733Xt7znvdc8jprtRqf/exn5zYZvFS0WHsYhucUoQyCgJ07d1IqlbjtttsuOQx8NdC6cK6ENtNsUavVpnVPe97F13fNJdFpmCRaYRiya9cuxsfHuf3228lmr6x1U4/TwZFq4YyHP7R6QBcmzk08p5LBalDl0yc+zUDtFB12mjcvfBNvWBQTk9d03sPvH/sAPmUc6sQSo/HWIhSCADAoOWkRFucwVXMkYMwkETQGbHwcRZu4aAMVJviN/e/m1o5ncUNmE2tS04mHLSwio6ftp9YGBFiXUEv2TOHVfW/kvtGvcKS+H1/7pFWGjelNPKfzhe3fdDuLudN+I+PhaT55+knIGNLKJykDNFDXNo1QgHMHnnkMR4YgJMbEgsuhUThi+vWiaEYMzZkvDk3BICH4zviXWJ/cRJe9iGo4zIP5n0dTBOBQ7ZNIsjyn+6/J2mefV51WL0ruRxsXYwookSbUPgKBJz0EAba89BIZYwxKqWnesxcSvc7ZqziAjRI+EU5bGknSQCNwRI1AS0bDNI4IcZUBYzDGpxQl2VP6JxYkX3HWWDxrA561AYDh4GcAsGWzfk3EUjUTocegP/3eHxhJzbjUtE1a1qFZdiGFJClDxsM6Y8F+JDWO1f6JSvU7JB1Dpxd384dmlO8Nb+aO3scveR7PhTPJoK+rHK09TDkcxFNdLPPuJjlFgPxawNP5HLtSqFTi2tJ0Ok0mk+Gd73wn73znO4miiOHh4au23dOnT9PXN11VoK+vj1KpRK1WuyRVk7GxMf7oj/7ovycZbJ145yImxWKRbdu2kU6nr7qbyKVgKhm83Hq12WB0dJTt27czf/581q1bd8kX7lyMDNZqNfbu3YtlWVftXPi5JW/gifE/QtMqWG/1Ogr63DTdzrlfRFpkcFdxJx8+/H8JTGx3d7I+zu/u+xNeMG8Lb178FrLZLB+8/i/by52uD/P7+z7MRFQhMgI/svCsiFO1CXqsjjOK2WKy6cpJ1ZSQBinVihcatBFNUhkv+t3Ct/nO2CNISZO4ROwrjPD+tb/OifppNEGcjtSGQjBGSiVYkbw0/968P8ru8h4wmrXpdfS5/Rde6DKRstK8uv+NVMIypahEt9WDM4MbihIW3fYifnnDx/i3ob9kvPoQQrloDCPFkJv6X8pocJL57hZGwu/imICEDKnhUtcCSzGt6SEiTg2rtp5hCy2rQ8PeiX/D9z9Gzgo42tBniZZrSnwr/7O8rO8LlIKdBHocV/WRsdaTs/tY7K3jeG0CPzpJLTqKEmmyVieCKim1kqRacMnzdmY2Y7ai18G8VXjOLiQ1tLGxCFEioK5TuDKioQUhiqQIoCUiI8CVEaNRlTCsY1nnfoGNjJwSizVnfTcVVrPmM75Kp8v4aETcpmJCBupfoFz+BlnPwxERkRFowBYaSwU8Onwrd/b+4JLnciZMJYNjwXG+O/6XlMMhWu1jByvfYHPHO1ng3XxFt3s5mGuRQYjJoBDirKZJpdQz1lh6qejq6uIzn/nM3CaDl5oiFULM2IRhjOHEiRPs27fvsjXjriZasidXO8o2NU2+fv16Fi0623bqYjDXyGArLb5w4cLLIsEXQk8yx1sXP59/HHiQOJELIMgqmz/c8CvnXbZFBj965O/wtUAKjZKgjSFC8sDId3lRz730JKYLovd7vXz0pv/d/rz19A7++fi/sSDh0dCNeL0QEwkjpySSY7jNMbYeoLqZwjNAGBlQVvMxqXFkhC00t/Z2sbPynwT6JCIT8VT1OClrFSmV4K6uzSzwLv4m+mD+Ab6V/x7lME5dJkcfYUvXTbys9+UXva5LQcpKk7IuHClzpEePvYiKexPz7EVIKVmcgkpYxJEeK703UKnspGYmcEREigaelGgNRqpmHR90OxkCrVFKI9FNiRpQQhMaxUSjzo3pfXiyzrjvgpg5ii0o88TYLyPxY0ojFCeLO0h6DgiItGZZ5g+ohIewREBCZclYK+h2bkJeRgfqhUpbziV6bQrv4mT6L0klj6NkA43EN0tYk/k58o2/RIrimVtqdwzHr8vnH3POjsgHksgYVHN8UTM43mklEOJGjNkOgCdCOlSFjGpQjFJ0Cg1Co7WmphMs9zoQhPh6DNtJY4sGgRHQFCYPAYsQT16+8cGZmEoGt5U+w0Q4SELmsKSN1hEVPcrW0mfodTZgyWsjyDFXI4OpVOpp5wf9/f0MDQ1N+9vQ0BDZbPaStY4TiQR33HHH3CaDl4MzXUiiKGL37t2Mjo62PWWvZVxtN48wDNm5cyfFYvGKpcnnigOJMYbjx49Tr9dZvHgx11133YUXuky8duG9vKT3WXx64EsUghJ3dt3APb1bLricEIJBc4qqDhECpGi5Lwhodk9+dvCz/NKKXzrnOoaHhxndOcTbl7wJ27b5H4O/xbxEB5YEo8NYsw5JoMFqvsC3bt2x5Ant/Pa20WE25Prb8RVHaDwVghHUw4BxeZguN0IIQzmICMxOalGC4UYncOtFzdneiT3cP/odQhMxz8kiEIyHZR4qPE6/08ctnRe3vquNDek7yAenGAlO4MokkYmJ2OrkLSzOrKLP/Szfn/gd8v52LFFHCYMSCYxYSE0fBWL9waFGnsVeElfqtldvYBR+BEtTWTx5isAoypxL6sngipBqdIxu52aksBms3k/odjJu4kaWhAg4Vf1tJoI6z573/0ip67DVhR1MLoSLrXOeKnp9k/4Mx/M7OFn+Po3xJHJ8IaPZLFb/LSTsB+J90jZJ4UPrnDWKZU7XBQv6X9D9Oob8f6cUJVDN/u0ISbdV5t7Uz9CZvQWA0sR70GaY5d6vEaovsLU0wFhkEVf8SnrsBHfn3oIQdcDEYvHQJoItaCOxheZ7J/+ZOxa+/iJm8PxokcFicJJCcARHprBkTIelVCToohrlGWxsZXHi9iu23ctBFEVzLjJYLpefETK4ZcsWvva16ZaE3/jGN9iy5cLPivPhv203MUyXZ6lUKmzduhXbti+5Hu7pxtWUlymXy2zduhXP865oanQuRAa11uzZs4fh4WFSqdRFN8lcDlJ2kp9f8aaLWkYIQUGOTZE3nn5zMsBEUJpx2amR3+uvv57u7m6iKOILiz6DMYbXP/F2LAyO8Eh4SSJho00DKQR1A06rw3VKWm1DJtvMMMeVhK6MwAgqoU+Pq/FkSKQFxSCBrTQeGm2qPFZ6gEfGHuHl3T/BmuxK+twLWztuK22nrgPmu5OWft1OllONMbaVdl5zZHC+t4Ln5l7P/vIPGAlP4okky5IbWZ/cDMQduHd3x5pfhyuH+N74ffS6fSjmM1r/DBl7PwpNl9NNRUOjUcZxFBjDEb9Gr/sC0vK+5swrbEL8GW7xEoMUmtAIGrpEvvIElWZHs0VIVtVxRYjAMM9VnKp9hG7npfQ4P05N78GYgJS1EXkJkaXLaXqTUrJs3k0sm3cTMCl6PVp4OZFVoj/7OIM6SUknQMQvLPMsuLHr1y+47s25F+GbOg/mv8Ro4CEx9Do+a/PPZuHCVe3fZTMfav//bWYTS5OPcqTyEA0d0mUvYXX6xSStPhrRKJbM4oetZj+NmEIIpTD4Rl0UEawHT1KtfxCooNS9dCR/7qzftMhgaGoYNJIzJICa+pOBqc56u1cbc7Hp5UpZ0ZXLZQ4ePNj+fOTIEbZt20Yul2PJkiW8733v4+TJk3zmM58B4N3vfjcf+chHeO9738s73vEOHnzwQf75n/+Zr371q5c1DqXU3CaDl8PKW2Tq9OnT7Nq1i0WLFrFmzZo5c1JeLTLYcldZvHgxa9asuaJvPtc6GazX62zbtg2tNXfeeSfbt2+/JIebpxNCCJaES1DONs4Uj2lVH27IbjxrOa01u3btIp/PtyO/U91hhBB8YYrItO/77Dq2i1ONUzwivolREJlGXLvWlDxBgA4NrdI5iUGIuJ4wJSWuDAi1pBw4OM0OzMAIxoMkgbaohSe4f/xv+XYpXi7SITaG5/f8ODclX3HWS0klqs4ol2MJwURUmeGbZx597hL63CUX/F3G7kLJpYQmjaUkizNv53jpo6ScAerGjaNedprTjQyVqIucCjBmMtMhgB7bp+R7WGJ6FZxsfgpNnVJ4DGFZaCNRRCRlgCcCIiTaKKQxCOMwVP88e0t/Ntlg1ITHMjb3fG7W94krqYAwXfT6BsaKJ+mc+AwFdhFGEcloPn3+W4jK/eiOCxOOO7tfyZ3dr5z2t4dPP3weEWdBv3cX/d5dZ33nqh763Xs5WP4WgSdxRBTX8/7/7L13fGRXfff/Pue2aRp1aVer7b3YXtvb5IJNM2BqAqGlEAIkeRKSJ5Ce/J4UCCEhPJQQCHlCCCUJEAghVBNjY2zc7dX25m1arbpG0oym3nLO7487MyvtSruSVlvk8Hm99mVryp1z7z33ns/9ls+HckuWgJKa+fI7Mv4OIvoxYpXHPv8IY+l/IhF7ANM6l7WpEKtacxkRWUtBjWBxLrhR0uNYMkqTPbU6wbXAQq0ZnI/I4DPPPMMLX3jOAea9730vAG9729v43Oc+R19fH2fOnKm+v3LlSr7zne/wnve8h49//OO0t7fzmc98Zs6yMnBuzixoMng5kFJy5swZxsbGuOGGGy7o0LneMd/ESmvNc889R1dXFzfccAOLFs1/Ef71TAbHxsbo7OycJKJ9PY+3AiEEUR1leayVk/mBst9tJVYXkrDXLn7tpO+USiU6OzvRWtPR0TGjSLht29yy9hZu4RbuVfcyPj5OT38P/6I/gpQaCAgwcGJRPAVCirJeW1jTJuU5CRthiLDbU0DOd3CVSTHoZmksiW0EKOWTkD6BMAi0wQOpb/D94e+wJtrBHQ2vZkmkBSkkrU4zB7On8FWAKctNVUrhK8Ui+9KRRYCz+f08Nvot+twBLGGwNrGBO+reQtS8PGeBy0WjVceSSCunCt3U6SS2tIhF3oynAtaIVuIyg5VwcUQjTw6coN9/HGEOkw7iNJtFhAhN2UoKhAET6YyHxNQCQYDWHrpMDysdtGFkMVzkNJANDpD1PUwZCjZXoIECXTw2/BpaIy8lad9Ao92BcRGf2yslhyWlpLF+KY31fwyEDy8VB4kDBw6glKpaizU2Ns44+6OUmvN4F0deQbSljUNj7yIhQ+mfsAlIkFMWyrtpRtvJFr9ORD82QUw+PAsRkSVTeDMN1vcAKLoPYca+h9QtCP0brI3dw4HsV8n6gxjCIdBhV/jyyO0kzeunyWEh1wxeLu6+++6LBhw+97nPTfmdzs7Oy/7tCiq//z+SDBaLRXK5HKZp0tHRsSD9BeczMui6Lvv27aNQKFxRd5XrlVz19PRw6NAh1q5dy/Lly6s3/0t5E18PqDSQ/OHqP+BvTn6IE/k+Ah1GHxrsGL+36rcnfT6TybB7927q6+vZsmXLnJ7IpZTU1tZSU1PDX8rPUCqVSKVSPJc6ygF/DyftA2jpIJD4SmAKFTZClImhIJSS0RpKykIKTatVhy0DtIaYVLgqTEIXA4MhN4mrTUb9ZziYfwohQGhJvdlGXBoMlMZIWBEEgqxfos6Ksav+0jU0XflO/mPgs6R9D0sIAg2Do530Fs7w1sX/36yPy3xCCsmtyS1EhE13qY+iXyJuxFiXXMHq6GShd2G1cDSXhPoUuaCBev0IliwgcJHCwAuCMMonNUoJEAHSMIkIn0AXyp2xoTRQxU+58h+JRmsPSxqTWohCn+vwbCpSjLhPMFR4kOfEH5S74sFEssX5Z5LJc1GoS2q7KhfwEfLy6hNt22bRokUsWrRokuh1f38/x44dIxaLzUj0+nLIqxCCensrt7c8zcM9d2BYpZAM+oo7256d8XZc9++JVYlgpbccQGHThR+MMJD9OXJBDyoavnMm81WWx/8CK/kLnMg/SC4YImY0sjx6Oxtir5zT/lwpLMTIYKVm8PmEBU0G53KRDg8Ps2/fPkzTZNmyZQv2hM6XZl8mk6Gzs5Oamho6OjquqLvK9aYzqJTi6NGj9Pb2cvPNN1c7GCu4mDfx9YIKGbQsiz9a/8d4nsfpwmlaoi3UTkgfQVgCsG/fvmk75bP+OGOlMerseuLm1NeFUopHxh5kd+YR8moMRybYktjJPYteRVtbG3equ0in0/zpqd/EjCYoaJOY4VPUHjFl4MiAIJCYomKYF8IwqBLEc8I6mmE3SaAl9VYWywhjWMUgpC+u24ulc9hmHFeDjctYqYdfXvoxVsRWXPLYPT7yLcZ8n0bDwSj7jRUClzOlFAdzPyDGtRXAjhoO2+puYGOwBk/5xI0o1hR2fYYwWKTbubHpXrRWFIIzDBf+iby/j3qhGcOtpvMNCQiDXGBQElbYEVvMYTsOLiZFbZEQxep5kWXKN/VVcG7+BME4LkN42qqK4EgRcNj9OaKpdmqdO2mLvQOt9ZRRIBUcxy/8Mb46iEZhikXYznsQ9oX6gLPFRNHrFStW4Pt+NWp4KdHr6cY7W7xgyY/nPn6dLx/qC8ch0Qznfo3x4CwSgRHSd4o6T2/uj1iefJCV0bvxg+dA9yFk3XWnkLFQI4PX0pJ2PlGZDwuaDM4GWmtOnDjBqVOn2LhxYyhXcJ0v9BfDfHTm9vb2cvDgwasmo1MhV9fCRu98uK7L3r17KZVKdHR0XKAXBQsrMliBZVmstdZO+sxE95SpSgBKqsQPBx/gwPghSqpERDpsiK/nroa7seXkAvTvj3yLZzP/jdYKKUwKwQhPZ+4j7Y3ypsVvQ0pJfX09f1v/RSCMwg8M9bM/8xTP+vejY3lsUUJhIQVEpE8usJlELDAQaPKBjacNHFnAkmUi6EsQAlNotA4YowEdhI0qWposS7Tzrz0f4Oa6XWxK7GBjYvqOyV5vCEdQJYIAUcMmGxTpLh5j/TUmgxXEjegkO7TzMfF6EkISM1ewrOb9AGwAdo99kcPZf8OSYXSp5Fv0lppodUZDaRq7DssfwLIc8srGER522aJQCjGV9F5VX7Jy3hT9eDgIFAaKqPQxy6Te1d2kSl8j4z6OsF6MNH2Gi8tImNuImEvQagQ3/1Z8naGiEejpXvziHxIVDtK6Z16Pp2mak0Sv8/k8qVTqAtHrhoaGeSODlwMl20CnCIVyKmMJSXpJG2TVUQQCQ5jhXCCM17q6SLb0SWyp8P1n0eQBG0OuIRp5D1IuuVa7NAkLMTKYz+cXbCDpfFTm9/8IMlhJg+bz+aqDRCaTmSQts9BwOWlipRRHjhyhr6+PrVu30tw8M1/Zy0Xlgr/WF//4+Di7d+8mmUxy8803TxsNXUiRwekQBAEHDhxgdHR0WveU+wfv45n0HqIiQkLGKagCT409y7CbZml0Fa7yWew002rXs3f8YbTW2LJcUyfA0zmOF3YzVHo5zc7k2ttIJMLypStYzgruVW/g6KmjfDX4KG5QwrYhZpRwlUmpWMKJm8iyoK+POcF3N2woCcWuDWyhMFSBtA67iKOGS61ZAq0wjQBpwonCk5wsPMn9qc+wLXEvy2KLabTWEp3gviCRnE/1gyA8lsZlaOldb7il7uf5ysCT5YhbRaReY3oBjvSIShfsdoQwkfnj5A3JsCzQ7sTRhE0+Qmv0RR7gPMxqc4SNLvesQiXxbGDh6VMQ/wJKQFfORyBxZDsr7Ca0ziAwEdU0qELj45c+gT3PZHAihBDE43Hi8fgFoteVLs8DBw7Q1NREY2PjNZETSUQ+iFd4DSaqfFyo2j8GbEPpzknS12GdrgQUgf8IngyQohlBC1AkUAcpFP+WWPQvEdeB608QBFc0I3UlkM1mnzeRwQoW1hmYA8bGxtizZw/JZJKOjo6qY8eVlGa5Gpjr+IvFInv37sX3/WkjYlcKlSeQa0kG+/v72b9/PytXrmT16tUXvbEvlMjgdGMslUrs3r0bCPWpHMe54DMjborD2WPEjRi1ZhKtNI7hkPYGeDZzmFwAURmhq9DDcK6bnJEh0IK8SuMIm6gRxRARfJ3neOHYBWRwIqSUbFy9kT/h//HDH/6QrVu3cnhsH2ezPYwWSnRHHiFm+CA0QlPWaNP46lwzQ7jECQIRAw1CByQMF60FlqHK/s4heRTAYrML1Ad5si+gS6zBFKDQ1LCSNbGVPJ05SinwcYzwVphVLo4UrI/vJBi7jBNzFTGTSHvSqCUdjKB0EPoPIxhy4+QCg+3JFpKWwhQ2fsJEcIJaagiwEGVSprRX/o1yBHKC+4YkhiJXje0asnJfqryiUXo8/D/pliNXEk1AMTiD5x/DFCG10eXolyg3sfjqDFdTGnmi6LXv+zz88MM0NTWRTqc5ffo0hmFUo4YNDQ1XxQHKsdbgBx+kVPozLFEMrwkMPHEbdfH/y1jmhfjan5REDmVlQJJH0IIQlYaoKJJFBOoYgTqKaVx5DdVL4VoHB+aCXC53XVnUXi5++MMfLmwyeNFCZK3p7u7m6NGjrFmzhhUrVkz6vGEYuK57NYZ5RTCXZozR0VH27NkzqWP2amIiGbzSGPMyHMgco6c4QMyIsj6xEno9urq6uPHGG2fUPb4QIoPTpbBm2igy4o5QCko02Of0FAvKo6QVoKgxIzRaTfz34P2kVYpm41xXcEm7eL5H3IwCgugsi/4dx+G2lXcBYXRgbOw1fHjgPRQth0azhDA0McMlG1jEtI8hwkSZicbVYQesJTWmVJQCiHDOzk8Ay61BVtgpnhpvRhgJ4tIn4zlkghj9apSDhQyOiDPkF7CNAkKAIwU312xmdXwnxzg2q/25nvHa5jfyrwP/RKA9lC6TNQFRuYJXtv5p6MGtXZ4Y+0+E+h6Ck4BLGGWK4slbcJSP1p1glEmhDttEDCmQKDxdkcCeDFmtIizH/YRGa4XGIAB8LcokPTRzEwTVNLQUFz7AXC1Urv329naWL1+OUoqxsTFGRkY4ffo0hw4doqampuqjXFNTc8WihvHIayHyWoreblQwQMR4CYkyEa01tzHsPY6v/WryXqOJyRhS1iMu6O6OAgNoPXpFxjpbLMSawXw+z5Il10eafa6oPETef//9/MZv/MbCJoMwdZrM930OHjzIyMgIt95665TCwf+TIoMTifG6detYtmzZNanZq/zmlSaDQ6URvt73fQZKw1jCwlc+j/c8y+piG2/c9RpqamYmG3K9dj+fj/PHWIl+rl69mpUrV170XCfNULakqPIkjPC4lJSHp30i0iIiw4VkNBhFICgFBlHDR5WFBRUKX+WJyga2JG7CVz4pbwhbONTbF153FZw/Jk+V6DL28a5lv8fK+GYKhQKHBh/lsHyQAU8yXkygIsNYUqEROMLH1WHMSENVS68ikaJ1QJs1FuocGjU4UjHqRRjz4ygEo26UQmCTNIu0REphjZtwWBJZx/ban5n1ObgYikGeQ7n99BW7MYTJytg61kbnz95wJpHBLbU38Q7jN7gv9Q0G3QEsYbE+tonXNb0JKFt0YmJJB49XEBOKQB8BIkg2kwseJeYkaLLDlK3SaXLudymqPEI4FP0xLFQYsVIGhvSZWFNYkTqqQmiUVigkA4HN8nLEkHJssNLVbJl3zcsxmgsq11Xl2EopqxHBNWvWVLvoR0ZG6O7uRghRfb+hoWHKSPxc4akcfcX7yPqnsWUNi4wuEoRi2PWxT6Dyv0rG7yTQAQJB3GihOfYhPPfv0TqFEOfq2zRpBAmkXDpv47scLEQHklwud1WzalcClfvGRz7yEW677baFTwbPR8U9w3EcbrvttmkvSMMwFnTNoJRyRuOv2OylUim2bdtGfX39Jb9zpSCEuCoE66nRvfSXhmm1m0gVMwxm0gTCp6t+mKxZpIaZkcGFkiauPAxNbBSZafSzJdLKitgKDo4fQmiJg0MhKOIpnyWRVuJGgh8O/zBcEDVkPQdDaGx57kGkqAxe3/IW9mf38UzmKdJeGkNIlkaW8YL6F1Jn1WMKAymmJj//2vM+xoN9GCJsOAhGHDpqf5VtK17KNl6K7/uMjo7ydOoRduuHyOCRK55CRtfhqZB8WOdJbkcImyAOZGLYpiIIIO3HQEC65JDzHdqiYzQ7OQIlKCiL/uGj+C3jfOLMPkBQaxaglOO39DfKqdXZI+dn+dbwv9NT7CqfI8XR/H7OxG/mxQ33zgshnGn0ek1iLe9OTO/GIYVBq72S4/lniJit2MYdaK0Y9fqxhSZunOu2FySx5DKUPkDEWExMlBjxRjBFgKcFAaJcRTiRBk4eZ/iuYESb1KsISVmk0poS0sEapPPn0443UHk81YUpF2HK+b+vnU8Gz4fjOBNErxXj4+OkUinOnj3L4cOHSSQS1ZRybW3tnM91we9hd/r9ZPz+qoLo6fwDbEi8lfbYazEMm5aaz9IQ9HL42L+xqOVWmmtDIWPBqymWPoNSvQiRROs8mhyWeQ+GvLT4+dXAQnUgeb7UDB48eJDf/M3ffH6Rwb6+Pg4cOMDy5ctZs2bNRSeYaZoLPjJYKpUu+pl8Ps+ePXuQUs5YXPhK40qTQU/5nMifISajnBrvI5UbI2JFcOwYA+4o3xl4jF9c9qqqSPGlxnq9z5FKKnsmjSLT4RWtr0BpxanCadJ+BolJs93AYjtMg+SDQjX9pJCMlqI4RoAUKmzyUFEEkgdHfoCnPWqMBL4O2Dt+iIPjZ1gd20DciLM+sZx18RWYZWKltearfX9DXu0tE8Fw0bVkkSfSn6DdWUNTpA3TNGlubube5p/mFfqnyOVynB7q4l9zf0bRbCTjO9SZoCiFtWZa42KhEGRJYgnwdJisDAJBIXCwhUejnQcNrjZ5LnWUtY1rGHRj1Jp5Wu00Uvgoy+Afu9+AoIG2yA7ubvh5EubMIwK7x5/kbPE0dWYTTtm+bdzPcDi3lzXxDayMrrnEFmaG+Yr0t0c2kAvGGHRPo8pzP27WUSc3onR60u9Zxo24aqSc2rVIGjZp5WDLsGwgYBxJuup4ItFoIc9pGU7Aab+WWtlAs8wi8MnoOnJyIzeI6AWfVconVfwrit73QBdAmFhyJ03x980rKaxETmZybCvam7W1taxatWreRK8BjmU/w5jXS8yoxRAWWivyKs2x3FdosncRMcOHPtNoIzt2O+aic7qOlvkqQOK630LrERBRbPOVOPabZn08rhQWas3g84UMZjIZlixZsvDJoBCCIAg4cuQIvb293HTTTbS0tFzye8+HNPHFSNXw8DB79+5l8eLFbNgwfympy8WVJoMCEFowlBthpJClIVpLxHHCmii/RG8hxZnCIKvioQL/ydwAB9Kn0cCGmnbW15yrA9FakwpGGPfHqbnGjhTToRIZfOqppxBCTNsocjHUmEne3P4WzmS7GCoM0eQ0MeLl2Jc9SnexjyV6Ocf0QaprohCUVEU4RHB7zTb2ZvfgKpfWcgPJuF+iGNgUVYYGL/ROfnR0D+N+np11N1QX2GHvGUypy53D5TICDYYI+G7qk/zCkg9csL+JRIItic18kK/i+z7v3v9aPCQ3iDvI1BwB6aGFwagfZQn99KlVmLKS1pYoLaizixhCUwhMDKHZ2ryMAS9GjVlgWWSEAAgw8ZRJX7GWrB/hQPYgj46+nxvjabToxSTGyxJ/RW3t9BGWU/nnMIVVJYKV4z1Q6qWrcGISGVRKcbKwh9OFI3iUWGQvZ31sO7FLzL35rGu1ZIRNiTtZ7K8lH6QxhUW91YYbnKW3+D2KwQC2bEBpl4IaIm6/jEVOB0qPkff3YbqPM+qdxUdjiqUEQYa44eOWKwcVCl1u85HVlLBEYJPVNhk/dDkxZZJmc2t1nuT8fobc3eSDfmz1CI5+FpAgbNAefvAQg9nfoC35L/N2LC5HVuZSotfRaLQaNayrq5uWDHlqnGH3CJZ0MMqd4EJIYjJJTqUZKP2I5eY5f+Pzo2xCSGzrVVjmPWg9ghA1k1LG1wMWas3gQpeWqVxbhUKBhx9+eOGTwUKhwO7du9Fac9ttt804j7/Q08TTkVmtNadOneLEiRNs2rTpuityvdLRNqEFsbTFqJchFo1XiWBeFYjKCBEZZaiUZlV8MV87+xgPDO4lGxQRGqKmQ0fDen5h2Qt5OPVjvl96kHGV4xtH7mN9YjVvaHvtpEaL6wHZbBaAeDzOli1b5nxT1VrTai2igVB6pSnSTJNVz6CXIhM/iZV+gBqzSKAlA26CnkIdqix+97blb+LvznwMx4hUtzXqFhBCYEuBEAHNdj3jfo7ncmdYH1/Bw7En+JeT/8X2Oh+tJ0deKp7KhWDgkuM2TZNP3/yd6u9ms1kODD/JqXwn/ellrGp9hNNFg5jhY4kALTWGUARaTkhgaorKRmlBo5VDoPB16IBytlDHuB/Flj5xMcqNNUcwPI/97kqyyuHx7F9j9gdsT27jjYvfecH4VFkPboojXhYJKX9OKX489nUO5TrxdfidE7mjnMgf4OVNP0fCvHrzTgqDRmsJjda5e4cj1tHqFBlxd1NQfUhMkuZaWpw7cYxwzsStrTRE3kDOf45xrxufIifT3USNEWyC6pHQ+HiY+CpKk/NiRr1n8RhH6RICA0MkiBhLaY28AoC0d4Kj2X+jqFIYwGL5LIoARAwDC4SD1gUCdYic9xRxa8cF+xQ2C/YBiy/wuJ4Ol2NFNxEXE70+cuTIJUSvXRTBFCUWobiM0sULxjzVPUAIGyHm32J0PrDQaga11s+LyGBljq1YsYLPfvazC58M7tu3j2QyyYYNG2Y1oRZ6mniqCJvv++zfv590Os2OHTuuy9b3KxkZLBQKdHZ2slIupiuZYtAdJu1r0BpLWqyILacUSAwh6Rw9yfcHd2MIybJoqLOY9nI8nDqEZpQ948/iKhdLmwQ6oDN9kJSb4nfX/BbmFC4Q1wKVRhGAzZs3XxYRrDwYGYZRFQavl0k8eZaDxc/RZPt4SiOlT3skTY1R4kh2CR9oC23b4kYNWbcfgEBrSirALOv4WeWO0IQRY8wb5Avd/0ZXZAAhQGmBIdQEEeNzMGbRnayUYsx7lqLRzaYl6+iwfxfP8xhMDWKrf+Mx/Qx1RpZhv4aE6ZL2ovg6Ux7hudSlI3x8LRECcr5NLojgGB6m0GxJjJCUJX5Y3ICrz+n1+Zg8nunkYO6XaTRNesd7caSDgeIFi9/JM+k+PO1jlbUL80EOU1q0O+ciir2lYxzJ7cWWERqNMMXvaY++Ui/7so9yW91kJ46sP86IO0iLveSqiLiH1mo3kbTWUQpGkMLGkU0X/K4hYiStm0haoe/u8tgbeKjvDuJWnogI77dFbZLzYty9+MfhwuofZaD4bXLBCaSIUGvdQrPzEiLGYrRWdBW+T0mNUmOswKAv1DDUErSLFGbYOy4iCJ2m5O+bRAZd12Uwfy951UeAQGiwRJyG6IdJRm7FENPPsSslOD0b0eva2lpqzEWMeF1YMlY93p7OYwqLOnuyv/FCrL9biGPOZrMzbkS83vGtb32LfD6/8Mngtm3b5nQjfD6kiSeOv9I4E4lEuO2222b89Hu1caXI4OjoKJ2dnbS0tLBr0yYaM+38Z99DSKFIGDEa7Ub8snZde7SZ7/Q+Q9H3WBE/V1JQbyfI5Ad4Nr0bhU/SiBMEiqgZwVYuPcVBnhnbza6Gc4vNhw59hmfGD4Rp5thK3n/D/573fTsfE910Nm3aVCWEc9mO1hqlFFprDMOoir8qpQiCgEPprxFoD4lNxAjnlNIBTbbP/1nz8zREwojVDYkbGEz1M+aNEZcJNIpcUCBpxmh1wmPsaR9DGuzPHQ2pn4YxP0KjlZ9EBWVZIKbZuDDCMxVy/hnOjv8+DieQBIyULPrzN7Ki5q9ZsmgJS/hddmrNwdMH2a2/y6jrMKgKZPKSxugABS2IyhJSRMkrmzrTIwA8HdremUJhkSVulDiab8bTVliOICoiHgGtVo7B3Ag5eyklYwmOVSBp5Xl85AukXUk+KFJj1aB1OPfXxrawKnrO4aSndAJXudRb55o0LGFhS4uuwtEqGcz643yl//Mcz3fhqQDHsFiu2rhV30FJlXDklZViMUSUmDm7bMPdi0MrtoP9n2Swr58Ni36NxYvDMg0hBAlrAwlrw5TfLaoUOb+HiGwMG7p0AnRFpTAUYRYYgAcYmHJy09RA/kWMqzQgURqK2kLpgJHce4i722mJ/DSN9kumXEPmKzJ4MVxK9LpYLBJtvQVZ30tWpzClXZYG0rQ5t1Br3nDBmBcasVqINYP5fH7BRwYrWLcuvA8teDI41whfhUxdD9Zoc8FEMjgwMMD+/ftZunQp69atu673Zz5s9M5Hd3c3R44cYf369SxduhQhBFtqVzLm5TmQOY2rfEbcPHEzws76DSyK1FPQpdBq6zxIISjpIgkzAurc+460yfoFeoq91dfe+NRvhTZQ5Xvvc4XTvPHJ3+Lfd35sXvdvIoIgqEZ/d+3aVS1En23dWIUEVs6FlHLSvJFSglCk/GMIJFKcK/uvuBv0Fw7QYob1f1vjt5DxM+zL7mXETyFFgCUcVkXXEJERPOUz4KZoc1ombEdwPFtPJOkRN/yQXOlQVHrQrSXjDV1yP5RSnB3/fWIcwdMxfBFH6iIxnqZr/E9YV/eJ8HMEtLU3s1z8MjVmHa7rMpAa4ujoXrrUUzyU+wHrGh1G3DhxWUALsESAROFrSYssYqAZ9uvKaWxVrZmsNUqh17K5lJxvsyHej6VdDmaXoRAsjmRIyG7GSxGichHLzZvZIe6cIsJ86ev2M92f4nSpDwOJKSR5v8QBfYJeOUT3cDftTjsbExuJG9dfPdPmRb9OqXf3LO9PYS1pdXaLJEXdSkz0oDSgFVoohM6jxSIS1suq3yy5R8ipMSB0UClqJ7TfK2+t6J+kN/95LNFArX0rAN/v6UAZJqZfYlv8+1edWE0UvYaQdIyMrMVIO4yZD+E5KRyZpNncybroL0waX+XhbiGRwUrz20Ibcy6XW/A1g+djwZPBucI0zeqCuNCeSuCctMyxY8fo6uqa0nP2esR8RgaVUhw+fJj+/v4L9CQNYXBn0xbWJNoYKI1hIGiLNtHshKnzFbFWnho5jqt87PKi7OuAQCkSwsZXAQ4WlXhVpY4rYYZPg2958neQMoxnqfJKJdBICW968j18ZedH52UfJ6JYLLJ7924Mw6CjowPbtqvp3dmQwcoNuPKd6W7EAokhLPxyXdLEJVyjsWS0eg0B3Jm8ixvjW+n3epEY9JXGOVscpLvQjxSSRXYzu+pu4j8HvlHdjpQGJwstREWRWqtEoAVaL6OEpkiKjJclaU39BP67R99DvUzxMw1HKGAjZdh5qkQ8bGDQz5DzznDGHWJ35kfkgjQSSXtkDbfV3cvSxUtYungJWr+CN2Tew/8+8mo2Ny8h7cepMfPEDZeEWSLjRenRMdZURXonH+uY4TKe8ylJh8VOmgYrz+Ojq9HApkQfWsNz2RbaYqMIeYYeuvjX3DdIZLMsj9SzLvF6ljjr2SufIBtkSExIE3vKZXl0PQBHswc4WxrAEgYxaYc6fVIRKEWacdzA5VDuEGk/zZ31d2LL6y87MNuH74hspMZcyoh3BEvEEEKS5y7Q3yYqskhyoCWIFhpjf42csM8594tlGWuNi1ElgpVfl+RRushI6QGeHvxVcmYSXSbRJdPhkdLry41HP5zHIzA7xGIxYrEY7bwFpd5UFb1OpVI8nn96kuh1pV5+IRGryr1jIa3BhUIBpdTzJk1cwf9YMliZfAuteLUCpRSlUomBgQE6OjoWTMh6vshgqVRiz549+L7PbbfdRjR6oQSFFJIl0SaWRJsueO+Oxk08OXKUU7lB4mYEBOT8IkuiTWyoXczTo50UKGFoia990l6WpBlnV912AHx8TMLO10qbbWiXplEiJAtBEPC1vu/y0FAnGd+lzorw0pbtvK7tZReM51JIp9Ps3r2bpqamSfWB5wrNZ0YGJ0YEz48Gng8hJCtjd/Nc9j6UVkhRlrHBQwqD1ckXYEu7Si6VUiRlkqQTkpm1EcFIIs14kCdiOLTajVjSJCJtStoNiUE5TjOuooyXYjjSptGKoYIMlrCJTEFoHjrxIP/lfw0QJM0ipghIBwqtznV9B9rGEgWO5Z7gqexBtA6IygSB9jme3082SPO65ndhSgshBLW1tXxu58NAOLeGRwbpHPoGIraXbrNENmgkp4ZZag9wpLgsjA5qDSIkya6ZQAWCBivLYLEGF5PlkWGi0mX32DLa4ykihkIIsHSBFc4oUsBY4PHQ2GfRGgq+QukSgxjUWDejtGKRs4QtidsBOF04ga81NTKsV/R0gNIaA4HS4OkSi+xF9Lv99JX6WB5dPqM5cTUxWzIohGB57OUUsyOMB10IDDQBrriHpL0cWxYwRSs19quRcrJci2Esn+DIUVEvnLBtTISIcGTsq7hWcgJNDKGQ+MuvH53RS4leVzA4OEhTU9O8il5fKUzMTCwU5HI5gAWz5s4UC54MzjUlWpl8C7FuMJPJcPDgQbTWdHR0LCiT7/kgg+l0ms7OTurq6ti2bducyHydHefXV7+S7/Q9w4HMGRSKWxtXc++iW2myExT8LIczxyniUvJ9aq0a3tj2OmrtMLJY1mCuEsEKJkrsfvL0v/DQ0GECHTp4Zf0sX+z+IaNemrcvfyMzRUU/cypbxdmQwYmk7VJEsIKtybcy7B5lxD1V7fqVwmBH3buoMRdXP1c5B5Vaw0odYr1MUi+ToeC4DkW8f2f5r/GBUx8DICinhQ2hCbSg0WrAUyV87bEuugnbuJAM/pf3VSh3V6aDKJ42cERAUUsKfoGoGcUQJQJsThT7CbRHvdlc/raDJW0G3bN0FY+yOrblgu07jsOSxUvJjb+MdeplNLc0c3roDCd7jyPaPkPcLZFTkXK6GAItMcui1xJIBxEqotWjXoxxv4AtdblhBlY4oxhC0+02UlA2Rb+PhNWCKxwM06MODziMEazkJQ1vJVnuJK4z65FC4GmFI2RVpCUo/27MiGGKMOORDbLTnlNXFThb3E0xGCVmNLEkcjPWFa41rGAuZTk15nK21Pwyw+5e8sEQjqyjyb6R+IT5NxVqo7/MUOFvK+qT4e+X3xOAKTfg6SEwnQuIYAUKyQ/Obucl7U+Hf/sPod2vofUoUq5COL+EkCtntT/zhfNFr1OpFPv376e3t5ejR4/Om+j1lcRCjAzmcjkMw7gudHvnEwuHRcwzhBALUl6mp6eHQ4cO0d7ezpkzZxYUEYTLJ4O9vb0cPHhwRlZrl8KiSD3vWPnSKZ9Of3XFO3my+yn2Dx5g4/IN3Fp7C7FJYsPhAqO1PkcIdbjga60ZKA7xaOoIGkhaJr4OKAUerpJ8s+8phvIuv7fx5y46Pq01x48fp6uri61bt9Lc3HzBZ2ZKBivRwNkQQYCIkeQVLR/iTOEJhkpHsGWclbEXUGu1T/l5KeUkD+qJ/yoPXm32Il6fvocfNz7DSJAmCAwsQxM1JBl/BCEkLVYbr2195QXbP3HiRJUIAgwHCc64daxxUgilcXWAEZqikRd3kgkyOOd5s5rCQgNpP3XJ/ZdSUl9fT319PTdzE8XiK2n3v8wj7g9JqygKwZgXxfV6sMwa0n6UhBE6aYDA0wZ1aAwRlhM0yjEcGTDgJikom4jw0OYissqm1c5QCgyG/Ro8ZQCjfH3wn+moewV1Vj0rImtIyghjZRFwiaBEgNKQlBFqzfrqPLDF1CniUbebzsw/UgjOUolln8wv45bad1FntV3yeFwu5lqjHTEaaY++aNbfi9tvQrlfLp8NXU0bm6IeX49iiBjqEu4ynhESZVX6GL77eTQeAIHai/R/gBH9GNLcOeuxzSeklCQSCYQQbN++/aKi1w0NDVNmUq4FgiCYsbD39YKKFd31SK4vBwuLScwzFpK8jFKKI0eO0NfXx80330w8Hqerq2vBNcDMVWdQa82xY8fo7u6elhhdzpimem1VZCXaU9zWeNsF7zcYSUaDDELo6gIcEkFBVNo8ObqHUhASHFf5uCp86JCEGnePjnbyzmd7+cytv1fd5mNnn+IjPV9CaYEQYCrNr7mvY+fOndPWp1yKDFaKyivHfDZEsAJDWKyM3cnK2J2z+t75xLCSou7r6yPiR/jdtt+oaqr1u4M8M76boiqxJLKYXbXbp4wKPhY8dsFrPxhfi6cNVjojxKSHxqYo72RV4s/YV/xnUl4/sbIFoa99xv1xiipPPijMan8AIpEIdy7/Re7kF0M5m7ExuoZP8ZT4Pj0cYsitYUO8H7MQMOrHSMoCWUIyIYAaw0MQpsUNoTBVnjwtNJrjaK3p9+oItMASPkJrTuSPcbbYT6MdEJMjrIuXyPqaIS/OoJcAIYlIg9WsI9ABw94wSTNJW+RCYqeUYl/mXygEXdiyDUM4BLpIITjB/sy/cWfj78z6eMwWV/N+lfX76A8aCcQbaZI/wFZZcsrEIwmiAdtooTXyBrpLf3zR7ZhBCa3O4rv/giZAkEAIidYKxTii+JfIxH8BobzXaOmjuOogtlxLvfP7V+2BfWIn8XyJXl9pLMQyrWw2+7xLEcPzgAxezo1locjLFItF9uzZQxAEdHR0EIvFyiKqC68tfy6RQc/z2Lt3L4VCgV27dl21C3Gi7+/5+PStf85bnvwdAgKkqHgDAxq+sOOv+e+BH1VTyZ6eEH0uFzEFWnC2MMJDA7u5u/UWPnzon3gkfRhf2ehyUksKzd86/8V/1Nw1p3HOtFHkaqBim3fq1Cm6u7u56aabqKmpqRLEVquZVza8rPrZ6XyMf37dz7P76BOTXitpi/8eX09NrkBC+rx7+Qer8iebEtt5ZPRb5IIMgVakvGECXISWHB7/Gin3m6yKbKEtchuNztZZ71Ml0nIzt1IoFPjQ8Z/jTL6WZZFBeoqNJGMFGiMmnhLYhqagJEkq0iigDBsCiEuXYS9BoCW28BAi3C/QJM1e6owsUoQEs6Qt4qqPDfE4lQj1eOkoQ97PUmfWcXPy5im7iVPeKfJBF6ZsxihrPxoigimbyPonGXW7qbeXzuoYzBZXkwyOecdxVY5aaxNFNgEQ0ZqSt59aZyvLY69CCoda/48YMj9MRcj5HASCgJe2P40qfQpNCUEcISr1uhK0RaC78L1n8VWJ0cKvYOAiAS94nH7vSyRjHyVpv/SK7+/0gtNzF72+VmO+nvF87CSG5wEZvBwsBDI4OjrKnj17aGxsZPPmzVXit1AbYGZLBrPZLLt37yYej7Nr1y4sy7r0l+YJlxrrl3Z+mMHBQf707KfQWvO+pb9etUK8s2knX+y+n7Tnh0ZcQqDRKGViGT65IBTL/UrPQ9zdeguPZw7iKRspVOjTq0Nh3aKyePOTv8OXd3542nEIIS4Y52waRa4GgiDg4MGDZDIZtm/fPonQV6KGFeI6UQRbCFEdf3XRUBrkhfszrqK8SL5+kg7epvgOcn6GPeOPMuQNoFE4wqbNThEzxvEC6Cr2k/J2szJ4Lctjr75guzNFNBrlT2/4j+r+Dg0N8YPR76HFXsayeZI1PoPU0KBLJI08g34tUpyrVnO1AVRKDcBVJkmjQNIoYAmPQb+WkjKJkEc5SVxdWUQ18YiFCL7AjbW/RkIUCAIPw5h8rbgqi8LHYnLEVWITkMXV09cZAvQWT3OycJhA+yxxVrAyuhFjlgLsV5MMltQ4UkweXziP6kHUIMuE+Pb2V/DfZ99HwYxNqh0UKJxMDhYB+JzfRa60W04Za7rynySqn8LCQ034pIFHOv/bxOTusoJFgK9OAgGmXIWYJp0/F8yUWE0nep1KpThx4gS2bVejhvX19Vc0srnQ1i8I5X5isdg1v6fON/7Hk8HrtWZQa82ZM2c4duwY69atY9myZRfqwMFl1d9dC8yGDA4ODrJv3z6WLVvG2rVrr/rFd7HIYAUtLS38fcufXfB61Ijws0tfwj+dvo+CMhAqJIOmDFBKlGNDoMrb95QZStOIc78datwZeCh6iv0sdlrwlIclrUmRs/PHOZdGkSuJUqnE3r17AdixY8cFguiVuXx+E0qF0FYe2Cq1Rf93/d/x20d/HSYtfJoa6rl79eS6MikkO+vuIRcohsYepN5soMk8iiOzBNSEUdvAIm4EnCl8hxZ7F1FzcgnCk8MPEDVj3FjXMeN9NgyDRYsW8XOL3g6EC8jegUd52P8C3SrJ4kiWuC4y6GYwZJK8sokInywCrSlHhwVRU+FIn6KycZVFyT+LY7egtCzPF02TkSFhlNBodqf/DoSBIRwarBvYkPh56u1QVLbeXoYpE7hqjIhxTmzdU2OYMkmdOb3H8mOj3+fZzOMUVClsvhBPsSq6hlc0vxl7Fs0n8+mjfClEjSaUewCtVTWap7VCa5+YMVlh4J72x3n07PfIGB9ACQND+WwOvsxYaQwAYbwEwT8DRSCG0h5KFxEiQBFHqRymCIlgRRsRQjtCgxIZ93Mk2E6u9Gl81QVoDLGYuPNLONYd87K/cxHJnkr0emxsjFQqVRW9rq2trcrXxOPxeb2fLMTI4E/SxNcpLmdiXq81g5UoSiqVYtu2bdTX11/wmUqk5Hoc/8VQ0Ue8GLTWnDx5kpMnT15T/cTLbXZ5eevdrIkt5w8P/R0ajdZQ8g2UMMseG4J7WraFpGeK7wsR2mdpDZ8/8+9sqFlFISiQNGvYVLOeVbEVkyNmzL1R5Eqh4oxTV1fHpk2bZhQFmKrWsEJuK//+evXHue/U93mI+6mhlj9f//6LblMhEMIhYljYMo3CAcLjE6CIiGbyqoeUt5d28yUAfJ9PErNPkSi56BI8kY5Sa7yIN7X/xqyPQywWo2PlS+ngpQRBQO9QL4eCDxIzLQp6lHSQJGkUsQMfV5vY+Ag0gQqt8fyyl3KtWU9QJoJCQI0sUmOUylHCMM0ZPkQUGHJ3kxnrotXcSlfp/jIxcVAqgcYLmyd0FpAsidyFM41Q9ZnCczyTeRw0tFgtSCHIB3meyx+jbfwxtte+cMbH4WqKIjda60i5B8n4XUSNRjRQDEaIGa3UWWsu+Pzt7a8AXlH9u6urq3r9CHMThvkSfP8+lB4Lm8UAMBljK4YYvuhYBgpPMFr8Fo5MEzFawgS07ma8+GGkbMAyNqG1j1bHAYWQa2YdNZyPkiHDMKrED0JNvYp8zenTp6vvV8ojLjdTs9DKnIDnhS/xVFjwZPBycD2mifP5PHv27EFKSUdHx0Xb16/H8V8KhmFU6x2nwkR/5Z07d5JMJq/i6CajUud2OVhTs5Kdddv5wdDec0kmHVYj1VsxXrv4dvbs2YMpAlxtlaMY5aiCphr96S50k7RiNNn1DLpDDA4N0lszjC0cjpu9NOXP8k+Hv8y4OYgtFQjwAol0Y/zD9g9d3oGYI1KpVDWyu2rVqjkR0+mihkopXrHq5bxch3WGnuddmE6egBa7FYnEUyWorD1ao7QiJiudlefG989n3kdT7Bi29PGURACNZpYh70G+0G2xLL6OVruV5dGVxIyZeyhX9mXpoqUs5VPVNN3X+n8NX+ZostKk/TilIE5SNuHrDErnMMppSilCKZkK4rJY1tGT1dEbwkFrD0mEnNfDGdVNICRh4jOPZYwTER6GTiDkDSyOvpw1sekJ3cnCEYqqxCL7nNVbzIiRVzmeyx2elgxqrXHVGAiwRV01gn21HlAco45VsXvpKz3FuH8WEDTaG2mL7MSRl76vTCSuWivG9W1oThHhLIocrjZJBUvwjUUI4YA+PIXDdoijo1nWJgfodeuJGSXa40lM0Uaguym692HYJYLSp9Eq1AsUshXDfjvSfMGM9/dKRNmi0Sjt7e20t7ejlCKdTpNKpejq6uLQoUOTRK9rampmfW4XmvsI/KRm8HmJ641MDQ8Ps3fvXhYvXsyGDRsueZFcKZ/fK4mLjTmfz9PZ2YllWdeFv/JUtXhzwe+sfzP1dpLv9D1OUfkYQrKupo2/WPtLPPnkk1iWhSUVgVYE2kBoXdUrtKSP6fuARY2RoMZMkDDi7M8c51j/Q7Q6LQxZQzxy/FPE4i5ReW4pMswAZWT5lad/j7+/9a8YdFPEzAhJc3rl/GKQJReMUWM2Ysu5y090d3dz7NgxNm3aVPWhnQ9cLGo48VqufK7y2Q3xTXRmnqa71EWNjBAz0pS0QgqLWquOkh7GkgkarNDrtaj2kDR98oFV7hyFAS9CVsXo8Z7FNBN0FU/TVezijroXVJ1pZotKmu5tqz8PUC7uT9GVOs3I6CgDRg/jTQF1kS5s6ZEvjmE5zVQ6kaTQ55GPc3/5QQ7TUPhI0BJLKGpkgWYjhy0CBKMI0jSbN50vlzkJvvamfF1i4OnSlO9l/bP0lR4l74f2jQlzCYucO666+kHcbGWN+WpcNQ6ALWfuGjEx7eqqM+TUHkx5G76ooa/0CK7KYEiNpYaxjXZKfpSIKBD68+hqsnjMj2HIekxxBluaZH2X/kKWpfFahLYJgsMExSfQehREIyDQqoeg9BEQTUhj04zHeyWJ1USZJeAC0WshxCT5mpmIXi/EmsGfkMHrFJebJr4eagYrXZYnTpxg06ZNLFkyMyP4643MzgTTkcFUKsWePXtmTISvBuaTbL9j5b28Y+W91b/HxsZ4/PHHaWlpYdOmTXxV7uBnnnwPnpL42sAATOlRExhEEw4R6VRt2TJ+njGvgCKg1WlEqRL9sQBTVpagCfsgNL5V4P8892FGvQwSwcpoK/c0d7A0upxasxEhBK4q8PDIFzmWewKlfSwZ4YbEi9lZ/3oMMfPbREUCqK+vj1tuuWXKEof5wsWihuc3oRjS4LXNP8OPxx6iO6+x5WFi0iNiOCiVwpAOSyP3EDPD6FfcKKGQ1VozV5tkVBxH+GhZotluJtAB/aV+juefY2vy5vBzKkfa7ybQLnGjmYSx6JL3qHwwwEDpMQrBEFGjmdbG29jesrPqgXp2qIPjYw+jo08yZptIUcLVFkoL8oFNg+lRoWuWjFHROAykF7aK6DDVHBEuLUYWWwSUdFiqYJNlvPQFHj77MRI1VugPTdjtvm98nN9ac4BWuw0DSUmVcMr1gYEOcLVLu3Ohy0kxSNFV+BbFIEVENgOaUe8oRTVKIJddk9KF2ZDACiZGBj3Vh9ZFDCPstq4xl5Py9hPoAPQIUtSS1Vuo5zC2yFYjhCNeki+d/m1uqt8PIux7sqRB1vNwAx8pPCxRIlBnCRCgswhiGKIB9AjK+/6syODVPLbni16Pj4+TSqXo6enh8OHDMxK9Xog1gz9JEz8PcT2QqUpaNJPJzDotej2Mf7Y4n2BNbJTZuHEj7e1TixlfC8ykgWQuqAhnr127luXLl1dv4F/d+VG+M/AADw49jkYTkQ65IEdJ5WmLtGKXbcjSXg5XeSStKBJBY2MDVmnqeeAriY/JYGmEiDSIyAzDfh//0b+ftshSLLGIldEbGXDvZ9A9jMQIIz6qyLOZb6NR3N7wlhntl+/7HDhwgFwux44dO6peqVcLlxK8jhHjnrp78evuIeufIa2eIeOfxhH1tEZ20uxsq27L0wZxzkXESsoiUALLDCip8LZpCIOYEeNsqZut3MyId5IT+QcpBCOhd7OI0GJvYlXshRd0tVYw4h7i4PgnKanR6mtnCz9gc82v02BvIpFIsCGxiQ1swvPeQSo1zL+l/opYTTc+BpkgStwoYQsPX9sEOvTbkDgI3KpLjklAQrrYIqCoTSpNDh6afaMjJGvNsqBRWYZIwM3JBB87fgO/tuoZDmf30lU8jSVMJJKiKtFoN3JT8sKmmlHvCIVgiBrjXGmAKeKMB6fxHeOa17HOFBPJlcCAcpe/EIKEsYxAe6T9QwSUCHSeOnsnbdGPgE6T856kJ7+avzrQS3s8yansjaxLPE2NOULWr8FTGkV/WLepRhE6ixYRQKJ1Gk0eU8TQ+sysxnutomxSSmpra6mtrWXVqlUzFr1eiJHBbDZbral8PuF5QQbnumhfqn7tSqNSXB+JROjo6Jh1WnShpokrBFYpxcGDBxkeHp62UeZaYr6P70Th7Jtvvpmmpgs9k1/R/ELiRpynx/aQ9rIsi7QRNW2k0LjKxZY2rnJxlUutsQgTk/8ee4iaaTK6rgpvtEkZwzbHMEWeQMOIJ+nJZwjI8gyHWJnsKXex+kghiBtxAu2yf/wBbq19DZFpmgsqqGhhmqbJjh07rqoE0FSYTvBaKYWlLerMVdSxCuGck66ZiLyqJ6n7sPDKki8KQ4aWeYY4122sw/ghJTXOifwDlNQ4SbMdKQxKapzeUidxs5nFZQ3Dj598Jx4FDEzeGv8rTtlfpKTGiMpF1ftYQQ1wLPdFdlh/gZzgjmFZFosWLea9iz6O1roaiek+dQg3todS4x58cxwtBApVCRCGHUhSYwg1yS6xgmjMqtq1TXxPCrgxkcCWDvc2vJknMp8gU3oSSxYRrGJn02/SZF9YAlAMhkIyOsk2USIwUMb4LM/ktcPEyKBjrMYQDXiqF0u2IYQgabZhC4+ouYsa+3Ycubi8zy045lqQBWqtFKOlAqZs4tHhn2Zbw31EjWFihsCWy3Cse/C8f8EUBgKb0OzaQus8Ch9DhJHIlNtDV/EwRZWjwVrE8shmosbk6NT1FGWbTvR6YGBgkuh1qVRacC5a+XyeZcum77xfqFhYZ2GecS2lZQYGBti/f/9lyaYs5MhgsViks7MT4JKNMtcKE909Ljea4fs++/btI5vNXlQ4W0rJ3U27uLtpF77yMaVJ2svw45HH6SsOhGkpFLVmkjqztpwCDXCVgSkvnMsB4eJQdPPY5ji+1vjaYsyNolHYOkBbRYBqR3OgNeN+loQRxdcuGX/oomQwk8lUtTA3btx43SxIFcxUuqbyWSklb2v4BF9MvZ0mO0dMehhCkw5ipIMEdzaEAsKe8iiqIpsTNzDmdVEIRqk1l1ZTy46swVVZBkuH6Uw9xsHSjwmEgcbAR/GVwv9mqcrRZJ+zVRRC4Mh68kEf4/5paq3VU+6TEIJkMkkymaRQKGDb60lY76A7/TQp9TRaa2qN5YwkvwQidL1xlQEybErS5USmQJZF0yvM8fxjB4VgiKfS70SpQRqsiqLPPg6nX88ZYzO3Nnxm0ndsWYti8kO21jqU2Q4iCyoyWJkzpmyk1r6HtHsfpeB4yNkwSVjbqXNejRQX3r8anCgvWrKKb5w+zNlchpFSO08Pv5UlsQFeuXQ1GxMvJOf9iKKOExFZJIVylztIocKzY76Y4/lOnsl8n2KQC4+d1jxnPctdDW8kaTZNGu/1eGynEr0eHR2tahtW1oNKI8rVFL2eC35SM/g8xLWQltFa89xzz9HV1XXZsikLlQy6rsvjjz9+gZD29YaJkaXLGWOhUGD37t1YlsWuXbtmHAE2y4K+tVaSl7W8hL5CP3m/QFQ4HM6e4UjuJFlVRAQmI34BO25gyskRckFI8kwHhFBoJSn4BoGWOEJTQkE57VnxX6j8K6gCUSOCr6Ez/TSudmmxW1kWXYlRjlgNDg5y4MABVq1aNSnlfT1jJtI1kUiEF5c+wI9KX6LknAAkbfatRKxmeos9oewPgqXOMtbE1jLiHgKoEsEKDGHj6hwHiz/GFxaSAFGm3YGQBPh4vo9hTSy2D4maZmYPqlprTNMsR2JejdavIpPJkEqlCIZdxhq/jpKajI5QpwpEpY9XlqqJSAehKr95IQSwL/1+fJXCkedsuCvIBQfZn34PN9R+tPpanbWelLeXnN9D1GhBoykEAziyFplfPOc5Muo+x7B3CInJImcbcbP10l+6DJz/EBizbsEy2ikFJ9DaxZKLcIzViIvU1P7Uig3U2Q4P9Z1mpFhgZbKFly7ZRUfL0rDzXUTR2OTZRJTnkIQWiQEWJdYCK+gc/0cC7dFohRFJpQOGvV72jz/M7fU/Xf2t6ykyeDGYpklzczPNzc3VYxyLxa6J6PVc8JOawesYl5MmvppkynVd9u7dS7FYpKOj47In1ELUGRwdHSWfz7Nhw4brnjzMBxkcHR2ls7OT1tbWy4qaGUgW260oM3z6b422siy2iFP5HlasaOMbA/9Of86jIWpjy3BOeEpS9Dxq7CQF3ydmSITUqDCsgSUNCoFk3DcpBBZRwzvns0xIR/qLig8c/xASRYvZRI0TZ2VsDQ/3PkreKILW3Fl3Fy9a8aKLjP76xXRRw1wux8jICPcs/kWWLFlSlawZ9oYY8AbwlEej3UibswRb2sSMJqQw8VQBq9yFrbUO08eZXpQwkARlUfFwznvKwNeSMXWKVr25eh8LRaGbqTFXzng/XHmK07nvUQz6iRpLaYq/iFW1q1jFr1EqvYMzw4c5UfhXuqOHaLWGSBg+tqgnZmwk8J7BmOYy9AJNMdiLJYNpO47HvCco+mN8ue9dDHkWPgYmijqjwEZ7A/FYkojRRJvzAob8s7O+BpTy2Zv5Z7pLz+DqIiCI5L7Lxvi9rI6/clbbmt3vXkiuLNmCJVum+caFMITkJUtW8+K2VbgqwJaTayYd4yYM0UxJjxKI7Zgih9J5SjpL0v5pBrwz5IMM9ea5RiQpDGJGkp7ScUqqgFOebwtRs08pVRW8Pl/0+sSJExQKharodUNDA4lE4pqvGT8hg89DXE0ymE6n2bNnD8lkko6Ojnl52jEMY8HUDCqlOHr0KGfPnsVxHFasWHGth3RJTEwTzwU9PT0cOnSIdevWsXz5hV2XM8VUjiISWJdYwbrECgCsQZuvxv4FgcbXoeacRLPJXEt9TR17xw+TCywSZpGI4ZP3JVqcSxieyDaxOjFM1PCqacSs75ByY2gR1h72uKM0a83R7IP4hgnYIOAh83Ee3vsjPn3TP8x5H68XSCmrae/FixezYsWKSU0o9bKBeqdhUnQRIGkuoclax6B7EFPFMIRFSaWJynrG1XNoQbku79xCpoFhv4ZFZpqCGkBioXAxRYzV8Z/BmKHocMnuJBP5L2TJRWAy5j1JX+ErRIwW4uZ6mp2XsaZtB2vFVpRSYdRw5CSpsX5OpW2Kq58iHpXnjUygNBzM5dkQM6dy/zv3aQ2f730HQ14CVfbW8TCwtcdZtZsNehsxuQJLhKm12S7mXcUHOFV8HEtGqJWLAE1WjXAo9y3qrTU02Otntb2ZYj4FsoUQOMaF93xD1lEX/SXGCv+Ep7vxNAgsouYdJJxXMFx4Lvz++dtDoCcZ3y2cyOBEnE9gZyJ6XfFQng/R67kgn8//JE38fMPVkpapkIJVq1bNWXx3KiyUNLHruuzZswfXddmyZQtHjx691kOaEeZq+TeTRpHZbGsm1nL33LCLe9jFB/b8LceC52iQTXz05j+tvn//8MM8PPwYESNPg1nEV5q0ByY+LhaFwOJQZhE1ZglL+BQCGyEColZAUE4jKgTD/gi+rtyAy6QBjWVI3n3wVzGFRdKoYWfdLu5svIukWTvnfb8WGBgYqHZ6L126dNJ7F5OukVKyKvpi4mYLg6VDBNql1bmBxc5WRvMme0o/RFcT8RUIsoFDWi9lY82t5II+4sZi2iIvpN7acMHYcn4/z+W+RtrrwjYSLHXuZnF0B8X494ASjmgj0GMEOo/GoxC4+CrDuLeP9tgv0RK5FykldXV11NXdAoSNPyMj32BP7rXEYmaoW6gFSgsOjGd498p9/GDoHrTOT5dJpuBqRvzQ19cW4f2oyczQYOYRQjPiHmLUO82+8a+S2DjCs2lBxGhndfzXqbG3X5BaPx9ni0+jUcSMitKCIEEjab+fntKTV4wMXq0avIh5K83xVRT9zrCLWC7HMTYhhEGzvYyIjJMN0tSYYYOd1oq8SrMicgO2ONc5thAjg5cSnb6Y6PXBgwdJJpNVYphMJq/4+arIPdXUzF6q6HrH84IMznUCXGkypZTiyJEj9PX1XTYpmAoLIU08Pj7O7t27SSaT3HLLLeRyuQUTzZxLZND3ffbu3Usul6Ojo2POT5Ba62qDQyVCMZN5/sdbf3PK11/YcBu2sDmWPUFRFVgRKzDuaobdNMNemkBLAi0Y88LFxRI+lgxAgy7XlwEE2kBDuekAIMCRAZS9dAMdMOqP8dXeB/hSzyNIAVHDYF2khZ9pfz3LYnOPkF5JVCSOTpw4wQ033EBzc/MFn7mUdA0YtMqtLIptRUgwjTCy97K2X2L/qfvxsAh0hT5LNAJT+7xj+Wcu+K3zkXKP8MToByiodPiCB/2lPSwt3YCWGUxdC2g8NUjYGOIACkvW4+ss/YWv0mjfhSEnz8dIJBJqxfE0SinGxsYYGRkhlUqxJZ9nb2Yvda0vJ218neloxrBqKGtjhvciW3g0mHkU4CkLpXJEjQwrrWEc4SG0pqSHOZr5dYRso95+KYtjrydmtE25fVdlMcTkCJCUYamDr/KXPHZzxdUUyDZkPXH7wlKLGrOezfHb2Zv9ISmvFwMTX3skzUZuSNwxaXxKqWvexT9bzCaaeSnRa2BS1HAmotdzwU/SxM9DXEkyWJHaUErR0dFxRTTXDMPA86Z2B7ge0N/fz/79+1m5ciWrV6+uRlAWChmE2cnL5PN5du/ejeM4dHR0zPnGPFEGBcoexZe5KJnS5M6GHayNr2TITWEKg7bIIpJmDf/Zcx/fHPoBvhJhelkEFAOBFFbYAVr2UQ6FVMSkAJFRjg5qKPcta0ZKESypseU4MUPgaZOjpVN84MTfEBGa/7Pqz2mKTf9gpJTPSOn7+HqMevtFOOb8OZhMBa01R48eZWBggFtvvZXa2ktHM88nhsCkqKEKNG7gVuf8m6N/ypcLf06AUbWOM7RHR+wNMxrjwcw/UVBpIiKOlCZKKVydo7/USS0+IcUsovHgPNpmilo8PUY+OEmNvOGi+1TRgVuzZk01RZcavoch8zhm4z7M8xihEJK4bJnUoRyVHlIo3HJjUiAki8whbOGDhpx2SKty920wjFn8AoH7GWx8IjLscI6bv0ui5m0A1JvLGfX7JhGHQHkIBDXmldMlvV7SrpsTt1NnNXO6cJC8GqfJWsLq2FZqzcnX0PUy3tngcnQG50P0ei7I5/M/IYPPN1TI4Hw/AY6OjlalNq5kt6xhGBSLxSuy7cvB3pHT3H/yGeRIiTdtvnuSo8pCiGZOxEwt6UZHR9m9e/dlOagE2udg5kGOZh/FVXnanI3clHw5tdb8dE1KIWmLtNIWmby9N7Tfi5Kah1I/JlA+WU+BsCipgGJgEjHKMiyA0EaZEJbFieU5EgDg+mDKsF4xIgWuNqvvaQQFLfjzk/+HT2z5+ynHmCp8jxPjf1aVJjnNh6m3bmNt7SeuyEIXBAH79+8nn8+zY8eOqhjubFAZ18Wihi0Nq/hNPs8TQ9/gkPsjljmbubft12a0fTfIMuqfwsBAljvMpZTYKk5RZ6gRgkCksXQ9lVyuxscQUaSIEOgiAomYYQ1iBRNTdFuCT5EaSXE4+0eo6FEEEsNdxxL+F2sjbTzn/jb5wC57KEPZVRuJJiZ8osIr61gKMjokgqZQJIWLLV2SUuNqTV4Liloy4H4Uc+QfaIz8DsuiHfS7hxkLBojoOFoHuKpAg7WEpdGZe/fOFlfbOm86CCFoj6ynPXLxdPhCTBPPF4Gdieh1fX19lRzO5TqvjPcn0jLXMeZ6wZqmWY3CzMdFNNFNY926dSxbdmWtl643YjVWyvKezi9wMjeE0iBNwSOn+viT2Ou5uT7sjJRSVlOg18ON9lKojPdiOHv2LIcPH2b9+vVzFiPVWnH/4Kc4kXsqjLEIwajXy6nCM7ym9Q+pt6ZOoc0X3tj2ShY7zTw68jT7x44jRYDCIOM6uGZAxPCRKAzho7WF1mUCeJ48XS6IYEuNKYr42FCulCvvJYrQ2u2bff/JnU13UWfWI4TgXU+9hdYag1c2HAyJ5oS5Meo9Rlf2/axMnquBnA+USiX27NmDlJLt27fPW4ptOukarTUdLT/FLv06ADzPO9cQdJEFUTG1WHT4isAvrcSJDuPq0MVEUwJsHNmC1opi0I+rJd/t+2sCGaU9soPb6t6CYcycHBqGQUtzCy3NYUo7n89XdeLOjB2hfamgS/q42mTUj9FkZXGEhyVkWYYnPKVFZaC0wCzL6zhCEQVKWqM05LQVeikDgS4wUPw4SfulbEv+Isfz/82IfwYpLFZGt7A+8QYcOXPHposhH6QYKh2jqNPEZANN9roFF2m7XnUGL4Yr5UAyU9HrhoYG6urqZjyGfD6P1vonNYPPN1QmwHxMyCAIOHjwIKlU6qq5aVxv3cS/v+dfeS43hAFEDJMAxVApx58c+Cpf7XgPEdOaN+2+q4WLpYkr6cWenh5uueWWy7IoOls4xMn80xjCwpRO2ThCkw/S7E5/kxc3/eqctz1T3Nm4gzsbdwDwvv0f50ipi0BIir5J3reJywgl1UV9RBJoAxD4SmIZFbJyDraE6bx9NPDj0YcYDVJEZJInR/8bK1bP5vixcrox/NDEdW2o+N15JYO5XI7Ozk5qa2vZvHnzFVv05yJ4ff5YIkaSGmMxo37XJILi6QJSGETzd9FiLsWMHyHrHyUfHEJrH19ncFUfpcDjlNdIVuVB5RnKfo9D+Ye5p/HdrIzdPKf9isVixGIxli5dShAEjI5uYe/oZzhmP4OrDfKBTZspiFsNDHijeBhEtDepY1koF2WEEcISAg8DHwMDRVlbGalzZP1D1Np3cFvDH+CpHAIDKQxy/inGvTQxcyWGmHt92Ih3mqPZ75IPRpDCROPTU+zEl8sRYuE4TSw08gpXZx24mOj1kSNH8DyPurq6KjmMxWLTkupcLgfwkzTx8w3V+pPLjK7l83k6OzsxDOOqumlcT93ER86e5Fi2HwlEzTDCYmAgEGS8Il/veZK3Lr9j0qK4EMjgdBqWlUaRfD7Prl27LrtR5Ex+P0orLGlXF0whBIYw6CrsvYw9mD1c1+UVxdt4mdrF1q1bsW2bM4UBDmdPczBzmqdGj5GkCyNiI5TADzxMwwIEUcPF01aoZXgRWIHDifFBMsETlHBQWlBrFMrvltPQobEuAEq4DLk9NFqLJlm0zQWVMo729nbWrFlzVaMpMxG8rnyuUisqpWRTzVt4Jv1ximocGchyjFWw2LqJuLsehyaWxEOfYE+NMuI+Qs7r4kj2e/R59eT0xCigpqTzPDjyj7zV+dAFtmazhWEYNDU18eKmP+BFWlejhgPDh8gE+0g4OQbqc7Rb/USEjykCAi2RerLQjqfDbutJp0OE8il5/yj19t1YMk7a2093/ssUVT9ojW00siT6ehrtXbMeu9I+J/MPUVQZ6q0V5etdMeZ3U4iMI8Qtl3VsriYWIhm8VDfxlcD5otcTo9yXEr3O5/OYpjkvzSmf/OQn+Zu/+Rv6+/u56aab+MQnPsGOHTum/OznPvc53v72t096zXGceS0T+x9NBoUQl21JNzw8zN69ey+rVmyuuB7IoNaa06dP88BzT6FMgXmBA4PAVZrTuSFg7nIt1wpTRQYnNors2rVrXhpFZLno/3xaotGXTX5mg0rErKamhi1btlQJ+/LYIpbHFvHyll0cyJziv/oeoTvfTcQ+ixYGQisCBFELckUHgcAxFAEGCoWEsiqaxMJHqFoyhRzakSglMFCMBw4NZm6CT254BACUFnzs1B9RdDUuDhqLlzfdw6sWv25W+9ff38/BgwdZv3497e1XrvlgJpguajjRSxnC+1SLtZMdyT/geP7rZIJuLBFjSaSDdbE3ceDs4UnbtWQ9rZHXsKf0JUa8CHnC+VnRCtSUo84qw4HsA2yvfe0FYyupEXyVx5H1mHLmDzpCCOLxeCgkzDJ8/yWMjo7Sm3oFXeZnaa7ZR8JwyegInkwi9BhCCqrh4ArK/yuJlHuvw8W3EPRxKvePeCqNI1sQQlAKhunKfR5bNlBjrpvxWAHG/X5y/hAJo2WCJaAkbjSRNk9TEmlg5iLT1xIL5QF7Iq41gZ00X6cRvU4mk9x///3cfffdRCIR4vH4ZT9AfuUrX+G9730vn/70p9m5cycf+9jHeNnLXsbRo0dpaZl6viWTyUmybPP9EPu8IIOXc1DmakmntebkyZOcPHmSTZs2TWqSuFq41p25QRBw4MABRkdHefnW2/n6oeP4SsGEKrFAa6QQrIqHE7xyrqYb9wP9u/n8mfspBC4tTh2/t+aNLE9eWdupi+H8BpKRkRE6Oztpa2tj/fr1c76RTawjA1gZu4U949/B00UsIlXbKaUVa2I752VfLoWRkRH27t17yYjZluRKtiTDGtDB4iDfGvwaZ4tdSCSr4+t42H+KbOBgqyKGDPUJK0fQJGCJ00JeCZqNRvpLqVCURsK+XDvLnNHzyGCIfreWrGfhCxsRqopwX+p7/Gj0W9xRdysrE5vZHL9j2sWw8tBy6tQpbrrppnmXeZoPXCxqGAQB9cZmttdsnvS5Sk3rlOdKB2XiN0WtoQDQjPvDk1731Dg9xf8m4x0hwMUSCRrt7bQ6dyAvYrs2HSZGYVZnt/D000+zclUbQf4hxvUJsjVHsWLHsaTCFApXGwR6wh1EtiGFQ8IK09mj7pO4aoSYPOdeFJGLyaszpEqPTUkGgyCg3+0EDBbZN14wR/T5NQmUY9MLpK65goVWM1iZ49cTgZ1K9Lq7u5uHH36Yj3zkI0SjUaLRKF/5yle45557aGhomNPvfOQjH+Fd73pXNdr36U9/mu985zt89rOf5Q/+4A+m/I4Q4rLsay+F5wUZvBzMJbrm+z779+8nk8mwc+dOksn5KWKeLa5lZLBQKNDZ2YmUko6ODhzHYdOZxexJn6UQ+FjCIEDhKUWjHeMNS0JCczF5mT8+8M88mTpOUHbQSJUGeOeev+V/r34Nr1pydQjR+ZjYQNLd3c2RI0fYsGHDBYLEs8FUQtLNzkpuqrmXPePfoaiyZaEOQaPdzi21r5mv3ZkWvb29HD58mA0bNszqwaYl0sI7lk3uis10u+yP78YywAsK2MKs9hPXBjUsja3i8Hg3BgJDKFxtopWmIGwOFNrYHO1FTiCEKT/O4+kl+NhUgkgmHg1OAQTsyz/GI6kfELc/g0IiUGxhB29c/xvAOfebwcFBtm3bds2u19ngUlHDiYLXlVKD87Eq/iKOZP4r9KTWEx5ayl29hjBotM5FR7XWdBe+xai7D8dowhK1eCpDX/F+JDatkdvmZb9WLF/PCtbjeR6jo6MMjn2ZZO1nqJEeAZISBgoDKRpBJGmwX0rCvBGAkhoud0afIz1CCAzsMG18HvZlvsbuzDdwKQGaGinZGNvF6vjLSZjrSZgtxI1G8sEwNcbi6vHMBcOYXi0xObfF/lrgWkfZZovKGnA9kcHzEY1GWbduHd/97ncpFov8/d//PZ/61Kf44Ac/yM/+7M+yY8cOXv7yl/Pyl7+cbdu2zWhfXNfl2Wef5Q//8A+rr0kpeclLXsLjjz8+7fey2SzLly9HKcUtt9zCX/7lX7J58+Z52U/4CRmcNaHKZrN0dnYSiUTo6OjAtmcn1zCfuFbdxCMjI+zZs+cCv92/uOGt/O7eL3I8N4CrAySCRZEa/nzzz0yqu5iKDJ7M9JSJYEgQKjflQEk+dfLbvGrJTk5nB/hKz4/I+Hk21Szj9W23EzGvjLBoBUIIgiDg8OHD9Pb2XnajyMSI4PlC0jvqXk97dBMn8k/jqjyLnLWsjd+GI+dfo3LieE6cOFF1S5nrk+5E/OquX+HLT3yZH0QexjYkvgbX12wLbsRZZOCpAoYQZIM8WgVIoQm0xAsMetwGerx6lpqjOIbH6UItroyR1zGkrGQPA+rtQjmnrimVFFG7BikVshyD3K+e4cDht/Ina7/A/v37KRQKc5aOuR4wneB1Pp+vFrW7rjupCSVptdEe62A8/ziFsr1cBUIIEkYjG+MvrL6WD3rI+M8RNRZVU8OG0UwhCEi5T9PsbEPOUp5mIs6PtFmWRUtLCy0tv4nWv0E2m6U4vIdi4QBZbxTHjlAbvQmn9ha0E343IlvRKLRWVecSrTUBLlE5+SHmZP5HPJn5dxQKE0WzmSYiPYbc/6QUPEG9fRMr4r/CitgdHMt9n1G/C1PY+NolKusIcosxprCQu16x0NLElbVroRDYSCTCqlWrWLx4MZ2dnfT29vL973+f++67j49//OO8733v493vfvcltzM8PEwQBLS2Ts54tba2cuTIkSm/s379ej772c9y4403kk6n+fCHP8xtt93GwYMH563cZeHM9IvgctPEM60ZrIgoL1++nLVr117zkPy16CY+c+YMR48enVJGpdaJ8v92/DLH0r3sHjtFe6yRO5ovtNWaigx+4uS3UFpgiHMLRihJoSkp+IvD/8YTI0fwdFh59szocb7Z+yQfvfFdtF1EwPhyIYTg5MmT8yIePnERn8pRRAjBksgmlkQ2Xe6wZ4RKB3wmk2HHjh3zqp315l1v5s28edJrSisOZ/dzOHuQhJkh5eZJ2ivI+l24WHhYeFpiCUW334D0NEUlMCVlH+UQUelX05wgEKaNJRWBFuV4qsaUCh+L//fMP7DD3EnNRsmXhv+GiIzzmuZfIWEv3G7ACtkbHx9n7969tLS0VCU0zo8adtT/b+JiEZ35+8hpDw0YWDSay3hx07uwJ8jLeDpNoIsYcrLItykSeDqLr/PYFyGDgc7iBmcRmDjGcsR5riEXS7ue6/i8E7izqhOXSqU40H0ArXVoOda4EivSREGdxZZNCAQllcKStTQ5d0zaZmf66yitMISkycwQky5eWSczq8YxvAOcyX+e1fH34tQkebbvdXimhdAa1K3YpRuu+T1+NliIaeL5ENS/mshms9VO4ra2Nt7+9rfz9re/Hd/3r6gBREdHBx0dHdW/b7vtNjZu3Mg//MM/8P73v39efuN5QQYvBzOJDE70mr3xxhsvYPTXClczTayU4vDhw1WXhotFkNbVtrGudnpdvKnIYC6YrisqlBp5NHUYhcLCCKN1WjHmZ3n/kS/x8a3/i8dTBzmVHyBuONzeuAXHUBwZP8DiSDur4mvnsMdho8j4+DixWGzeGkWAKYng1UapVGLv3r0IIdixY8dViXBLIdlccxNr4xtJe6OcyvdyPNdFdzHCs6lHWRxrJOdKbKNEzLRRAgwClJaIMpHQhFIkEFJB39MYhq4SwfD1MApmSE1/3aM8aO2nOJyrjuMjZ36NddFbePOS37ri+3ylkEql2LdvHytWrGDFihWTanHPl67ZXPMGNte8gZLOM+SdpMZupNm+0BbQEkkMESHQeUxx7sHA11lMUYMppn4Q0lqT9R4j7d6Pr0cQGNjGEurt1xEx10z63FTzXmtN2nuIkeJ3cVU/EWMljZHXsGjRtirJzWQyYYfy2RTj3jasxU/jRYcxTZO41c6S6BuImysnbTerRsL9QhGVJXxtVP2hfR1gy0ay/mGe7vk/pM2HEE54fWsEeb0bueYZpLx/difmGmKhpYkrncTX+l44G0wnOG2a5qTs18XQ1NSEYRgMDAxMen1gYGDGNYGWZXHzzTdz/PjxGX1+JvgJGbwEoXJdl71791IsFtm1a9d1pS9UiQxe6ULnijhvEAR0dHRcdqptqvT2XU03cCL7w6rxGZRJlBaYRkCAwMbAkGEaRAqJUh5d+UH+v4Of40x+iAAN2ueHw1+lpMMOVCE0TbbDb636LdqiM6/zS6VS7NmzB9u2Wb58+WURwYmNItfDzbpS6lBXV3dFNfamgy1tmp1Wmp1WttdtpahKnF30aj7f/fcYxgiBdhguRTGExhIBlvCIkSOvE0gBJWUQo6xZJwChL2iSCO3xNFJIiio3eQBacyz/LPszj3FD8jY+cTLsphVoXiE/yuoVq6/GYZgz+vr6OHToEBs3bqStbfJD18UEryM6Tru9BZha8DpmLCFprmXE20uEFkwRxVVpfF1gkfPCaVPEheAQI6X/RCBx5DI0PqWgi1TpS7TKd6O0RVf+G4yVenGTJkGwY5Lg9VDx3xkofh6tfSQ2GdVP1t/DkthvUe+8CCHEee4SNzGcupuh0aOMpUcpek3QYFFs7KexsbF6rUZknKIqhGUnYV/+ueOEgRQOvsqERLD8VrX0UoAyJE8Ov5wXxR+57HN2NbDQyOBCS2vD9GRwNrBtm1tvvZUHHniA173udUB4LB544IEZpZnhnHPSvffee1ljmYjnBRm8HCJ0MWmZdDpdFabt6OiYMfO/WrgaAs6VY1BfXz9JauRyMFVk8GeXv5ivdD9C1vcJys19WgukgIQRIa/cC9OqhB23x3N9NFo1xEybQBwh64fuGBKF1oLBksufHf0Avbkof1nzdm688caLjq/SKLJx40YGBgYu6UAyHSZJx1wnT8DDw8Ps37+fZcuWsWrVqms+JiEEUSPC2sQG/mLjx3lw4L/5wciXiRoeGT+KY6ykxWpjIPgxhi4RaAcXG62LCCHwvRKWjCLLvsnV7RI+SBgXWRu/dfYDPBh10KISEYJv6t+Hk7Cj9vXsqnv9rFw6rgZOnz7NyZMnZ9QRPVPB64l6hkujr0YKk7T3HJ5OY4oEbc5LaHam1/DLec+gcXGMVeH2MHHkSorqBGdzX2NP9iHSfil0JarTfH/4IHc1/ilxcxm+GmWo+O8ILbBkqDigtcbTwwwWv0itfSfyvHSzbdu0LW6nbXE7Sqlq1PDMmTMcPnyYZDJJY2MjK+Id7Am+TUER6hqiqu4mSXMRvs4wmD2KaZ/bfuVy0GVnnWDC6XeDPjLuQ5TUSQzRQNK+g5g5vdfz1USlRGAhkatroTF4ucjlcvMSEHrve9/L2972NrZt28aOHTv42Mc+Ri6Xq3YX/8Iv/AJLlizhgx/8IADve9/72LVrF2vWrGFsbIy/+Zu/oauri3e+852XPZYKri92cw0wnbRMT08Phw4dYvXq1axcufKaL5pTYT4dVKZCb28vBw8eZM2aNZNSUZeL6bqJ/3n7e3nP3r+nr5hBabANwU21K7i5fi2f67qfQCtk+RFeaUWgA6QQRKVNzAwJQkEFQFh7CBVSoPGUSYNT4o+zX+BbfJhAB3jKx5bWuW0qxZEjR+jr66umwgcHB+dUlzlVx/C1Rnd3N8899xwbN25k8eLFl/7CNcCLWu/hRa33ABDogKFSaLFmiFfymTNf4WS+C4M8/X6cpkieaNShFIBV9kPW5QcJAXjq3DyYComoU1ZA1BWn5fANAU9l/pMD2Qf52SUfIiotNB6GqLvq5/GxkYfYPfI4OZ3FChzaRlbxiltfTW1t7ay3NTPBa4cl1k/RbI2gdIGI2YglL2695akhJJOF9oWQBL5if+G/GQs0NdJCIij6LgNuhqdH/i93t3ycvH+EQGexRMOE7wpManDVIKXgDFFz+kitlJK6ujrq6upYvXo1pVKpKiBc7FpB3aJlpGNnyAQR6s0cFpqIrMMWgkDnsWlGibHzXRXPKc2U/1v0n6M3/2E81Y/AQuOT9R6hKfLz1DmvmOEZmBt87dFX6iYXZInJOIudZVhyMkGeqEm5UPA/NTII8KY3vYmhoSH+5E/+hP7+frZu3cp9991XLUE7c+bMJKI8OjrKu971Lvr7+6mvr+fWW2/lscceY9Om+asvf96QwemcIi6F89PEEwnBzTfffF3qkVUwXw4q56Nis3b27Fm2bt1Kc3PzvG5/usaXRifJF3b8PhCmpisq777v882+xxkuZVHKCyOCKCSSqGFjllPHQmRRWkySJYHwfq7KIsiGhNc99h5e195BISjSaNdzc+0WlttL2Lt3L6VSaVKjyEy8ic/HpRpFrjYqNa99fX3ccsst1NXVXdPxzBSGMFgUOXf9/ebKX+SJ0d105XvoGznLkdxZ0IqIeZZFkThChHRO6zASJFwJEQ1ceP5iaoTQFfc8IliB1gRqiPv7XssiZzmnSwaurmd5rINb6l5G0pw9GZst/rPvX3k2/SgBCh2Eln9jDUMs9Vu5nZde1rYvJV1j6XqgHgJBoIOL+ifbRjtucGpSuYrWPhk1xrhSJKSFKcNr3kSC0PR7/WS90wjhAApX9ZU9lUGKGFLEEEjkLG3mHMehra2NtrY2lFKk0zdyJrWPU/4PKZlnqXVcbFsgZQ2Loz+FlatjQP/h+TKD5x4qyhG3VPHL+KofWy6rdjF7aoBU6SskrF2Ysh6tNUPeAAOlPmzpsDy6ioi8PDeqcX+MR8d+wKDbV459S5qtRdxe/xKS5jnL04n1yAsFCzUyOF/r4bvf/e5p08IPPfTQpL8/+tGP8tGPfnRefnc6PG/I4FxhGAalUngTKhaL7NmzZ146Ry8HWuuy88TFL5TKDXo+yaDneezdu5dCoUBHR8e8dphWMBOx7Il2P6Zp8pEbf5n3H/o3ThcGUVoTNyK8qHkrA6URjmTPkjSiSBEPPX0rdmYTIND4WmDJANNQjPs5HGlzttBHT66P5qEkRyInGU/m+O6pH/G2pW9ifXLNBaLTF0MlVTNRMuFaE8GKJmZFWmUmc1opRXexn57iIADtkVbaI63X/MYdN2O8uLncMbocvnria3w782MK/nLOFgJM0UuLlHhYLK+5jZuat/D4yL9T0OkLtmWbBufO6oXnyBQBGyJ9DHo1fG9UIHAQjNOb+xoncp/DEGEJQqBtfqb+C3OK1F0MQ24/e9JPotA4QSSsgTQERQo8nHqA7TV3zWvTz6UEr8//3MS5kLB2UPD3U1KnsWQzWvt4aghNDYEewZjooCPAFBJXK4pqmITRgtZ5AoqhfiCCQKcJ9Dg11k5sOXcxfykl9fX11NffxU3cRaFQIDUywPDQWUZTRbJmNJSJqoWyAVD1rlHxRrb9OgI9SiE4hiHqq0QQwBRNeKqHgn+IqLWTB1Lf43DuAG65pKXWrOOexleyIjq3GlStNc9kHqW/dJYmqxVTWvjKY8Dt4Zn0I7yw4dUXiPhf62t0NliokcGVK1de+oMLEP/jyWAlTVzxK21sbGTz5s3XZJIWgiKPpJ6kM30QV7msii/jzoadLItNf0OcT3mZbDbL7t27icfjV7RGci7OKYsiDXzylneT84uk3Swtdh2mafLUyBG6Tw7TVxojZtjELEVRGyhNlRAqJJYMGCo4RMywlqzJDtNSlm+wZ2Q/+yJjmEaAUFBQKf76xEe4pXYbd8nt1YeFi+H8RpHrQTKh8nBjWRbbt2+fUROMr3x+OPwM+zLPUQhcBBAxHG6pXc9djbdeV4vNq5e8ivbBJZw1zqLiBjUNDil3iEa7iRa7magR4SX1b+Prw3+HJc/NNw3kC4rIRZ5zGo0sEsWRQhtKS5ptAxEMUxfNIUUYTRRCscgY41jhHjbpV2NFb0AZ27GN1su2EOwcexKXEkYQnjPLspBCEAQ+WZXhVOkY68vNIPON2QheSymxxQoaI28l7d6Pp/oASdzaRtRq50jh/1HSHlHOEdeiUsQMi6S5gdHS5zGFRmsr1A8Mt4xAE5XtZeUAjyH3OdJeL1JYNNkrqTOXzvr6ikajtC9ZQfuSFQRBQDqdJpVK4Z1+PdaKryPkOV9krQXSc7lzyTdRXPgwcT72ZJ5hX7aTqIxSYyVRKMa8Ue4b/hY/3/ZO4nPwf84EYwyUzlJr1mOW08KmtKgzGxhwexnzU9RbYeS8ItNyPV2fl8JCjAzm8/krEiC5HvC8IYNzTRNLKclmszzzzDOsX7+epUtnf5OZD/g64N/OfoOj2ePY0sZAsid9iFP5bn5x6Rtpj05d4zVf8jIDAwNVDcWLWZHNBy7HRi9uRoib51IvOxo2IIXgu/1P05UfxNSrSZinyfmU3Sg0lgzIuSaGkAihqByu8fEMIyMjpCNpbCMg0ALKrQimUDw2sodH1QECNPJpQavTwF9s+C1qztOoux4bRTKZDJ2dnTQ3N8/KM/u53Bk6M0epMWIsjoYLzag7zjPpw7RHW1mbWHaJLVwdjI+P09nZSWNjI6/f+HqklPQWz3I0d5BGq7kajdKmIu/GSXkGjU4eH0GqFEHTws74SRQGXFAxBlHpMuzX4GqDqHDxfZ8GJ18lggBrrHFWWuOgNYeL3yaV/zE+/0zM2sLq+CtYGr1tznPBcz1UAAYCy7Ym9NhffUwneD0xamiyliZrDUqMYkgLQzSEupn2tzld6iEISkjAJUAKg7WxbdhGgqJ/EFPY2LIJT+fCjmJhIXQWX/fhqSL7x7/JoHsMRfhbpwtPsjp2Oysv4/gahkFDQwMNDQ2sXfse8vlf4Yn+N4MzBCrAOfNRSqUSI5ER6urqiJobyXpPIHUCIYyQFOshTFlPxNjE/uyXkUhiRlmsG4N6q4ERL8WJ/DFurLll1mP0lEugA8zzGmhMYYU1z/qcrt1C0xiEhRkZnKgz+HzD84YMzgVBENDb20s+n2fHjh3U19df+ktXCMeyJzmeO0WdVYsjw6foGp1gwB3m0ZGnedOSqS3JLjdNXHGgOHXqFDfccMMV9T6sYL5T29vq17Otfv0kaYU3P/Fu4rZPoCWDBQchzHJ3Mbxy8e2kUsPkcjnMeguzFJRThuF3BZJxzyDQRnXhDzT0Fkf4zX1/wT9v+6vqbweqRLp0GKU0SXt9uQbq2mJwcJADBw6watUqli9fPqtF4mSuh0ApaqPnbnj1dg3pbJZT+Z7rggxWNPaWL18+qbmr2W5hxBtiyB3EEhZKKzSa/7XqN/n06U8y6CZROmwYsYVHKp+kIZZBM3lB0ho8bVbdTCo414wiSEqXFVYWheCoW8dQEMUSAkvkSLtH2RukSHujPJV5mhFvCEdGeVnjW1hfs+2S+zc2NoY+bmMtslD4VSIYBAEeHklZx0rnQg/eq4Gp0skVYqi1RqgGlAIt/NCqsv79xMb/L12Fo7jKJ4bJppo72Jj4dQCEiAIKKSwcUVf9HV9lkUTpKe1lwD1MjbEIU4bXVj4Y5WT+MRqtldRa0+uZzgaxWIwXrfomUPYylv0cPXqUY8eO4boudU1bcVqeo2SdQQoTTYAhEjRG3ooha8kHeazzSJsUEiGgoApzGlPSrCNuJsgGGerludrZbJAhZiSoPa9mcKFF2RZqZLCm5uINVQsV/2PJYD6fp7OzE6UU8Xj8mhJBgN5iP4FWVSIIZdkN6XAy1zXt9y4nTTzRY3nXrl1XbZJfTmTwUtut4Mu7/o5X/fj3MIzwd5TSBMCd1gaOD52gJojR1tbG8dypqgxJBV4ASstq7aEsk41Aa3KqxDd6f8Dr2l7CYP5hTmU/TV/hJEIIxn2Ho/2r+LOd/zjv+zYTaK3p6uri5MmTbNmyhZaWlllvw1N+dX8nQkiBp2bm1HMlUfFQnkpjz5I26+ObabRbGPVSGBg02E00WE18ePMnSKfTPJz9Nivja9lSt4PT+R7uG3yEvPtVpOmhtYkSBgIY8hOstgdwhE9JmyTMCR2mQJNRxEQxFDiMKgdLBJhoJD4Ww3TlCzw9/lUqEcfxIM+/9v8dW8Zv5I1t7512/4aGhti/fz83rtlKYGV5Ov0o40G6PBM1Ng53Nb38susFSypHPhhFIIgbDVhy9tqhM5GuEUTYmvhjbojn6UsdJ9Xns2XZOZmaGvsuCv5uApVGitAzWukxwKTGfgFHC0cwhFMlggBRWceof4YRr2veyOBEGIZBXV1d1Xc9n8+HHcr9EYr6Mez4CFGnldr43dSY25FCsshp40T+GHGdqD6cuMpFIGmy5tZwYEuHTfGbeTr9MENuPxEZpaQKIAQ3xW/CmdCcshDJ4EIccy6Xu2a9BFcazxsyOJvox9DQEPv27WPx4sU0Nzdz+PDhKziymcGRDqAvEJD2dUDMmP5GPdc0cT6fZ/fu3di2fdU9lq8UGTwf377jQ5P+zmazPNT5MDqmUQ2Cfm+IuIgTIDFEUI4ahQ0oFSI4cVpJwq7kJ0f28KLGdo5lPsSIO4yrLdCQNItsXXKMn3vyN/iXnZ+44vs3EZUu+KGhIW699dY5NzQsibZwMHsKT/lYMrw9uIGH1pol0fntKp8NtNacPHmSM2fOsHXr1mn9oS1ps9hZwmLnwjrb2tpaXl37s9W/V8SW8Pq2l3E0u5avn/wEkQTl2sIAiCBRLLNTnHKbGPF96kyBJcPu5Mq0KGgTH4mDDyIINbDRDAQNTNWYciC3j7vy3bTGQgF0Xw0TqCEM2UJ/b4GjR4+yZcsWWltbaVevZpV9gIL3OK6GEXUD62veyfrkxXUyL3Uch9wTDJaeo6TzAERkDW2RTdRbl+dxerEmFIMYFBuQjOL7frWmtta6l7y1h5z3MIHuAUAQIWG9hKT1MlT+nxHndXpXmya4+D1EaY9x/yiF4CwSixprA1FjZg0pE73D4/E48XicZcuW4fsvYHR0lFQqxYkzKY54D9PQ0MCyulV0c5qUN0zciONrn6IqsiK6as4NJABrY1uwpMNz+YOM+2O02G2siW1mZXRyZHghEqsrJYl2paC1JpfL/SQy+HxAZUE5efIkmzZtYsmSJYyNjV01S7eLYWPNGh4c+jEj3hj1Vh0CKKoSgQ64uW76QvG5pFwr7hptbW2sX7/+qt9ErhYZnIjh4WH27NnD2qWreemal3C22EchKFJv1fLxE0OM+n1I4aO1KBNAq+xiceGCHjOi9OW/y6g7TC6wKQuakAts4obLDbUDvO7J3+Y1bR2sii1la3IjdVbyiu2b53ns27cP13XZuXMnkcjc5Sw21aziWO4Mp/K9xKSNAkqBx5rEEtbHr00XXcUKcWRkhO3bt89rzU6zXU9zw63c0fA5/qjzlylEfCwZns+uUiOqVOAXVryO/dkjDOWHqHM8pNCMBDaBJYiKYLK+odAMlyKT3C7Ox1cGPsQLG15CvdiLVnv41oiHj4WvJWKl5mhW8ctN/0h/5meooYcaK5yJ7ZwlLqJoPXfP3PFgiN7SQUwRoc5oI5y3I/QU9hORNUSN+emKPj9q2NPTw5kzZ9iwYUM1cgghsWtx/pCC9VIKwVOEzSc7iRnbkFLSZK9hNN+N0kG1KaekchiAQ4Fxdw+WUY8j2yZ5IQe6QHf+S2S8AyjCiLZZepBFkXtpcm6/5Pinq8EzTZPm5maam5ur5CCVSpEa9mkvrKY32UXOyeOYDrckd3B7/V2Tu6lnCSEEK6PrWBFZW5XTmmpcC5EMKqWuOyOHS2G+RKevRyysM3EZ8H2fffv2MT4+zs6dO0kmw8X5avr7XgxNdgOvXvRSvj3wA4bdFBqwpMlNtZvZVX/rtN+bTZq4kkasCA+3t19eJGCukFJO6/pyJdDV1cWxY8eqDwAAq+PnvFnfv/YP+LPjf8NAqRcpFJaUuIEmQKJ0mCbWuqLyBT+36NWkg49N0Kc7V0smBDQ6OWzDIONl+UHPj/nimW+T9lw0EDMN3rn0p7irefu87FuhUKCzs5NoNMr27dsv++YaN6O8ZtEL2J95judy3Ugka+Lt3JhcR9S8+vWQFaLreR7bt2+/LKJ7Kfzlzf9v2ve21MJXz/40IyUPyzRIaYczXpx2M0dCeGS1ha09XC3JcvHjVFB5UoV/AKOHh7MryOsoAUYYb9RQQvOjgVey2hnj3NwKr/Gc+3US3muI2pcmNFMh7fWhdECsqpMoSJhNjHrdjPvD80YGJ6Krq4sTJ05UI7rnRw211kTErUTMWy+QrVkauZlh7ySj3hlM4aB0QKDHaTBjSH2anN8FviRirCJp76rqEqbcJxjz9hAxFmOIKFprSmqQ/uL3SJiriRgXr42uRAYvBiEEiUSCRCLB8uXLudG/kVQqRe9ID+nBDKJbcKLhJI2NjTQ2Nk6Sy5othBAYTE8qFyIZDILgqmak5gM/6SZeALjYk3LFizUajV6QEq3Y0V1pf9+Z4Ja6G1gZW8aR7HFc5bE0upgVsaUX1RucKZkNgoCDBw+SSqXYvn37NRUeNgwD13Wv+O9UIkoDAwNs27Zt2rpQx3H44Ob/j1KpxInSadqdxezJHOVTp79EoAWBBrRACs2O2s0srWnDyy4tRxAVlXSgKCeYR70ogRY02fU87h6m4BvVJgXXhY+e/Dq9xQHesvRVl7V/Y2Nj7Nmzh8WLF7Nu3bp5m781ZpzbGrZyW8PWedneXFEsFuns7MRxHLZt23bNowgvbvkYPxj8NeJylIQoMa6hs9hEveFT0gYZbeNpSUS7TNWhXEHcK1Aj+nh0vJmCds4RQcIUdER6LLXSMGUa1CBXum/OZNDXJQwxlcSQJNDze01WmtPOnj07qXRhNtI1loxzU+Kn6XcPkvJOIVFExCiN5iIcoyyrol0KwXEsvx5HmgTqOKni9xHaqxIoIQSObCEfnGLcP3ZJMjiX7lzTNGltbaW1tRWtNdlsllQqRV9fH0ePHiUej1eJYTKZnFfythDJ4ELrJg6CgHw+/5PI4EJFf39/VTJl7dq1F1zglQXmepmY9XYtHQ3TRwLPx0zSxJVFFaCjo+OKRldmgquRJnZdlz179uB5Hh0dHUSjly6QdxyHTc56AF7QvJ21sWV87LkvMOyNUBdJ8sbFL+fW+jBF1xp5OfvT/0HccCmqcHGNSg9PmxzNLUYAT40coBCEN2iJRkjQZZ/U/+h7pEoGf5x6loeGn8aUJq9f9GJWxpcw5veSMJuIyKlvPH19fRw+fJi1a9eydOnSeThi1xcq0jFNTU2zksa5kmiwl/HG9m/z4ODH6S8+SK1pUNJN7C1JsqUci6P9WMIHQxBTRfL6wjknUEQi40jhE4g4SlfUMEM6aIuAmHQR09joKR1wOPtjosEDbKt78az3IW40kHK70FpVBZSV9hEaIsb81UJprTl8+HD14fNi0ZRLSdeYRGk3t7HM3kFJHSfjP1olggBS2EjtUHLvRxoRhIiidRF0lkCdwZDL0BgoXUJpDz1BkuVi47+cOSeEoKamhpqaGlasWIHneYyMjJBKpdi/fz9aaxoaGqrk8HIjZAuRDC60buJcLgfwk5rBhYaKBVd3dzc33nhj1fPvfFxpf98rjUuliSti2k1NTWzatOm62McrTQYr4tmJRIJbbrllzhGlRbEW/lfdz3DmzBnWtq+lIdlQfZhIWOu4f3A9tzeepMYMRakzfpSnx1cw4sVBBwy6GXRZoqQS3BUShNJ4WpJOp/mj05+kzx0nlMhUjLkP0hYrhXVogOHW8ktrP1ElhVprTp06RVdXFzfeeON1bZc4VwwPD7Nv3z5Wrlw5r57Y84VtDb/IN/trOBsUabZaaDJzWLKfcX+MOrNARAascQbpchtJqziVCKFJgIlHUVsE+rymiPJ/HeGBhkE/wWIzgzxv16WAs55Df/ELOEaEG2pmFyGss9oY9c4y5vUQMZJoNKVgnDprCUlz6nvk/8/efwbGkZ1X/vDvVlXniAYakQjMOYchZzTKozBB2bJlry3Lkl9ny5bTSpYcpF1rd/2XLNuyLa8l2bK8ylbOo1GcqCEBZoKZBECk7gYa6Fzh3vdDo5sACZLIBOk5H6Qh0KGqUX3r3Od5zjmzhZSSY8eOkcvlZt3av5YYAlOrho6N4zg42IgJ6xYhNBBZlOxD0+5HCD9BYxVJ8wRSZrFVDw5gyQwSE9MZwJJpXFr0puewkNedy+WaUjXMZDIkk0n6+vo4deoUoVBoStVwtu99J5LB5VKAmSny+bLg6rnK4DLH5C+PaZocOXKEYrHI/v37b/rHq3yBbNu+4+YX4OZt4spCs27dOtra2pbNTXUxyWAikeDIkSO0tbVNWwmeKSpD7rW1teRyOc6cOUOpVCIWi1UHyO3RWj5VqCPmzqMQJEpBgh4TXTi8quk+vj701I1eHYC/6f8P+ksZADQh2BTopcFXrD5KANIzxt+efwt/svbzSCk5efIko6Oj7Nmz567cofb19XH69Gk2bdpEU9P0Ruu3G2Ejwr7oAZ5JP0l/6Qqg0ISftsDbKJqHSDsHEUIS1YvsD76OvTWv5t8H3s24cwVbaeSlh4QdQigTgRcmbGMEVE2tz5XqiRtZDCXRJqLRABJ2mCGnAaksvjf4SU5cPMaOxn1Y3gHOZp/AUgXqPWvZHXk9Ydf15M6t+Wn37SZpXmLcHkQgaPZuoc7dcYP28exg2zZHjhzBtm327NkzrzW1sjZPUShrDeSlH0dl0FT5+lfKQqphDBFCiLLtR41rLVl7gJzThyQ5YVujE9TXoFSBtPkUtZ6Xot3gnOdbGbwZhBCEw2HC4TCrVq3CNM2yCCWVoq+vDyHElKrhTJKD7kQyeCdWBt1u94z+Hnci7hoyWMHY2BhdXV1EIpEZRaoJIaqRdHciJmcrV1CxGRkYGGDXrl03tOG4XVgMMjhZHLN58+brPOhm+1qVaoTX660qIHO5HIlEgv7+frq7u/mV0Guoq6vjV3s/jNvwAw75gskrWnfQVxrEr+sU7KntPiXLDUGXkJzJ9wNgCIGBTf0EEVSTZs2EUgQMh/Pjz5Dshn41TOOGJkz3rVtddxIq82W9vb3s3LmTWCx2uw/pplgX3Eidu57ewmUsLGqMGlp9Hbi1V0/7+GEzh0sT6EgcIeguNbDBM8SZohcHD2qiFmwrDY8mKSgXP8130GiM02iMYyudS1YtF0oN2MqemK3L8LlvpTj9/L+jtiFTvbGmrD4u5g/yqsY/o87dcd2xePUQK3xbkWoTwLyj8yowTZOuri4Mw2D37t0LPuOpaRpeVwNhtpA1j+KoDEppSIq4tSY0xrAsC00TuESQVu/99BW/TUFm8eitBPQOAsZKQGHKAUpyAJ8+1URdKgtTpijJBIiluSe43W6amppoampCSsn4+DipVIqenp4pVcO6ujqCweC0G9w7kQzeaZXBbDZLIBBYNkWVhcZdRQavXLnCyZMnWb169ZRkglthuSiK54JridW1s3LL0SBzoRNIKhWz4eHheYtjJhPBydFyk5WDK1euxDRNkskkiUSCd8tHMORVywk7oOgtDbDPt5V/vPJVLKkjJRMSAYEmJPdF1/PU2Nnq+3r18aqEYMrxULYs+eeefwRjEyNaCdl3CK/uIqordB3avG38YuMbb/ss6FwxueK50NYxi4mYu5aYe2YbLQ0fWauEW3fQNAcHN4eLbQg7QdAlq0KSknLhUTY6ClMZHCm00lVN2dZAgWULDF3h2DorVieoqc/iTCwBbt2No2wKcpwfJT/C65v/142PaYFIIJRV7Z2dnYRCIbZs2bKoVbWQayduLU7R6UUpE7fehEtEsErfQaoESsXLM5FSEhAhgq5d+FwbptwPFAqpilNeO+/0kTa7MGWKLFmcoCJvN+E32q89jEWDpmlEo1Gi0SirV6+mVCpVq4Y9PT3VGL26ujpqamqqVao7kQzeaZXBChm8W3HXkMFUKkV3dzc7d+6c9RzVnUwGJx/7+Pg4nZ2dRCKRec3KLTYWsjJYqUbYtj1jociNcCMiOB3cbjfNzc00NzcjpWR0dJREIsGpU6ewLItYLMaGeAd/t/YPeM+lvydtWSjKX7iXxXfyqyt/ll/u/DPGbAsJmLJsOzEdIUQxkWuRJ6x5kE6JkhziiqOhlKCnkOTp9CF+pfXnuK/2wJzP/3bAsqxqW3Hfvn3zst9Yrrh8+TIre+/lROu3KaGBdEFZ0gBaE2OmgaEpBA4eIShh4NEcQODTimSlb+K6KCflaEIiBIwngtQ2jyGEQjoCpUnsqiJYMWSe52L2ECuDMxekzQWVGd1KDvZiV06E0PAabXiNqVU9TdyHZT2NlD0oBZrmwq2tK89oTqyRQmgoSqAEurh6YzflKCOlJ3FUEa/WQFF6UHoPKfNpdC2IR5t7d8WRaZTKomm1aGJ265PH45myzoyNjZFKpbh48SInTpwgEolQW1tLsVi84ypWd1plsGIrc6d9zjPF8mQLc0BtbS3333//nGZUDMNYUt+7hUSlylZRTa9atYpVq1Yt6wt2ochgNpvl0KFDhMPhebWllFJTMlZvRQSvhaZp1fme9evXk81mSSaTXLlyhfHxcX49/Ah1dXXE4/EpbZ6X1e/jCwNPIJUibekUHAOfYV8dEGNiblAJegtRWrw+3JqLlBzElDq6kNUZM1Nq/FvvZ/gfh/8NTzBaNj1WCjs/wjdefGP/vNuJyR6JO3bsWLabl7lCKcXZs2fp7+/nFbtfwWjqNP3OxYnfCpwJ25Ot3j3U+ps5nHkcU2aQuDBVAU0UCOgSC0VJaggUugaOEqRTQVIXYoQ3l1M7NG1CkzxRgUaAEooT44/S6FuLT18c0/PKWE5ra+uCrDtKOZgyhULi0mrQZ5H1behr0UQDUg2AshFaLYa0SBV/gC2H0IiipIkpk/j0lWhODAcHIQR5uw9LjRPQOyqvhiHrsFWGgt2LZ1IF2JRJSk4/ChuXiOHRW6adPZQqS670RUr2syiKaCKK1/US/K6XVZXcs4GmadTU1FBTU8OaNWsoFovVqmEqlaquW7W1tdTU1Cz779OdVhnM5XLPVQbvBAgh5jysfCdXBjVNI5vNcvz4cbZv3z6nPNqlxnzylCuoCEXa29tZs2bNvIUileOZLRG8FpMtJSrt5EQiQTKZ5NKlS7hcLuLxOHV1dfxc84OkrXF+lDqOcHk4km5md6wPl5BVealUgu5MHL8ewK25yFhJHKVNEMGJ9yxTRoYKCi1QhzUpZxl/Pa95+o/58v7/c92x3k6Mj4/T1dVFfX39bUnBWWxIKTlx4gRjY2NVa5Vfj/wZxxPP8sX0v2Bj4cbH26K/T2N8LQAvr38NUkmKTo5/7f0AGbuXqCtPVHewhSCrdMbGDM4ebAVLx3Ap0okQ0frxqlpd1zQkslyFdrwMpa/w+NC3WRPde9OZs7kglUpx5MgR1qxZQ1tb262fcAsUnSFGzWcpOcMoJG4tSsS9k6Ax8zg3TQujcZX4+jSIee8na53AkqMIDCLaDgLaZoS6OitessZQUkNWSbUEIdAwsFW2+no5+wxj5k9xVBYmUmb8xipq3M9DE1fHNJRSZEr/TtF6HE3UIkQMR6XJlT6NwMDvful8PioAvF4vLS0ttLS00N3djW3b6LrO+fPnKRQKRKPR6ibV7/cvuwLBnVYZzGazd8wIy1xw15DB+eBOJYOWZdHT04Npmtx77713zIU6n8rgYghF1EQlbjEIidvtri7YjuNc106+r3YzD8Xvo8c7zP849xl+klxJq2+MsKtIwXFxORfFUoIat6AkLSQmUgn0a3zoBCBFkKsG2FfbzRZuXv3kb9EeaqAoS8TcUV5T/zL2125f8POdCRKJRLWK3d7efttvUkopTFVCoOHW5u8oUFHUVlJTJre+t8T3suUm6TOa0PAbITr8e3kmbaGLWtxaDk1Y4IDlKuESMDLqwxcsMdwboaZhlJr6sgeag0O5BW3QFthC3puh1okxNjLGxYsX0QyNurpaGuKNxGKx6s14ND/EZ4feR15mqXM18Ya699x03nhwcJATJ04smOrblllSpR9jylE8WiMCDVMmSZWewBB+vPrc38NvrMSnt2KrHAIDQ7ta3al4GbpllIxj4UzkJjsTQ76OMnGJ6MQxjjFmHQQEPmNV+fnKJGefwyXqCbuvZkabTjdF66doIo6ulcVQmvBjyz4K1mP4XM9HiIVzr1BK4ff7WbVqFWvXrqVQKFQrhhcuXMDtdleJYU1NzW0nYZVN+J20CXyuMniHYD43lEoKyZ2ESqqKruv4fL47hgjC3MlgRWiQSCQWTSiymNB1nbq6Ourq6qoJBYlEgsHBQUqZDO8NvZFSscT/Nr+M4Ypg2ZJv3vcBSk6J9575J3oLo3iEC00UkQj0SdOFybyD0ssLa+VMKoQwaJRw617SzhiWo0iaDv9z7HOIc5+n0RPiza2v4L4lIoa9vb2cOXOGzZs309h48xSIpcCoNcKlwnnS1igaGvWeJlb6VuHV5zZ7WiqV6OrqwuVyzSs15UDNAS7kL5IwB9GFgQIkitXhjTzy8lb+/SePMTzowza9nO3sYN+LenB5TYTQ8OsR4u412MrE74qwvmE3eovBpWwPg7lD9JVy9PTXop9aQzy4mtPhrzOoHcGn2QR0Sc4Z5R/638wrYr/J1tiLrju23t5ezp49y/bt2xfM57Lg9FFyUvj19moL1as3kXcukbMvzosMAghh4BLXR+1VfA2j+moK6gIlpx/hhEiPD+AN2hhqFS7VhG3bFOUgjszgrbaSy4bXughQlBcJsw2pbLLWUbLWd3CcPhAmLizcWh2go4kIUo0i1Ri6iM/rnCbjWmLl8/lYsWIFK1aswHEc0uk0qVSKM2fOYJrmdVXDpUZl/b/dpHQ2uJuj6OAuIoNQJoRKXTd+f0vcadYylRZpa2srtbW1nDx58nYf0qwwFzJYEYo4jjPvFJXJkVdLRQSvxeR28qpVq0ilUhw9ehRd13lH6SHcyk08HieVSlFTU8NvdPws/9b7ZXrySXTyWFKnLDGYMKfWPdOHlykbty4BBY5BzjawZdmsFwV9hSz/37n/pDtxiqdLPylXHKWHj+z4mwU9X6UU586d48qVK+zateuG0YBLiXF7jKPjneSdHEEjjFSSC7kz5OwMO8K7MbTZ+Ynlcjm6urqIRqNs2rRpXlWPmDvGL7T8PE+PPsOlwkV0YbAmsJZ7a/bj033sfMPzOZZ8nNHiKDub7kXX4afpz5Gxh9GFm7yTRhduNgRehFvzcWzs30hbxzHcIQJuL3agH73RQo3HGBRH8WsWptSRlGdRw0aRb458ZAoZVEpx4cIFenp62LVr14JGWjqqABUD6UnQ8GLJsWmfU3JGGbPOUHAGMUSAkKuDkLFqTvN4hhak3vd8EtmDnLtyEK/fS2v9HsKuLbiITBheW+UNJBKhCQRi4v81pCqvJ1nrGGnrpxjKixB+FDamTKCUwqM3olQWIQIIsbAeoTersum6XiV+a9euJZ/PMzIyQjKZ5Ny5c3i93urvo9HokhC0yv32TqoMPtcm/i+AO6VNXEmfOH/+fLVFmk6n74hjn4zZWstkMpmqSnrr1q1zXqwmC0UqprK3u0UJ5cSNSmTiypUrkVJWF+sTJ05g2za1tbX8cu0jFBpMzhcu8cXBr2AqgaIsLEjkxogGrq9meY0yRVRASQpsqaELhZg0bxhxpzgmf0DQBSiFz5XnL7t/gZBex4vqf4adkefN6/wqudjj4+O3jCZbSvQX+8g6WRrcTdXrwKv7SJhDpKwkDZ6ZV6MqQoqWlpZ5zbBORswd48GGV077O5fLxa6mqVW7AzX/jb7CUUasPrxakBbfZurdaxgoHmLc6iagt2Fo5WtEanGy9jme0j+Ft0oEyzdmR+loQFAv8b2Ln+T5LT+Lbjh0X/w8WbOTldvjKG8JRz4P/QZxibOFIYKgygISMcnyxlF53NMoeYtOkoHiDyk6KdxakJIcIWtfwnSnqfPumdMx2EUvF476iNe9gnUr12FoV+crpZRIrZGs9GOrMXRZnktUjoOp0kRd9yBVkZxzBkOEcGltWOoMSl5GqQC2Gp2QC2XxuV4xZb5wITDTlqsQgkAgQCAQoLW1Fdu2GR0drbpxWJZFTU1NlRzOx53hZrgTyeBzbeL/ArgTyKDjOBw7dox0Os2+ffuqoe93wrFfC03TqsTsVjfN4eFhjh49uuyEIguFSstt06ZN1baprutVz8INGzaQyWRIJBJcuXKFTCZDbSTCO+t+j7PGRbqts2wObOQlO5/Pq595J7bSr7emAQyhk1MaIKbk3go1TtRjl1vKStLkGiPmyqMcxYlCgP/v3JdQfJ1mTw07Ii3cG7uHVYG1Mz4/y7I4fPgwUkr27du3rFJ+xqw0Xs035TowJlqyeSd/y+f35E9jyRL+TA2nTp1dMCHFXBE0YmwIvfC6n2edARROlQhCeTZR1wIUZYqQppBM/S44SmAIyeXscX78kziBpu/i8p8jVBfCEmMkCqfIWV00BX5nQQihz2jFazSTty/j0eKAhilTuLQIgYn5vMlIW92UnBQh42pcoSnHSFunCLlW49FnV3mubDibm5unXWc0TcPnqieqdjBmdmKRBakjVQGPaMEtVlIojWHZGdx6rGwsb7wI2/4RUl5BMYoigN/1CgLuB+f8Od0Ic52/M4yr/qgVY/1UKsXw8DBnz57F7/dX01Ci0eiCkbelHM9ZKOTz+WUx2rJYeI4MUv5CXJvisZxQseDQdZ0DBw5MGUhfaAPnpcDkeKkbVfmUUly6dIlz586xZcuWeQ2pL4VQZLZQSnH69GkGBwdv2nKbHF21evVqisVi1exaH1Hs8mwut5ONFHY2jRGIYE/6Wpcc8BgKWTabARRSUVUiNwSs6mxhWC9S58qRsVx0ZdvJSw8KhxW+EXzGAGcKJznR+32UivDi2lezr2Y3UdeN2135fJ6uri4CgcC8KrqLBZ/uZ9QamfKz8jWicN2kRdyd6eTHI18ibaeQykYrudix+oW0tb14kY94bjAorxdSSbRJLVSpTCJ6EEvl0IXEUVf/PoaQWErjgfY3MCZOofwX0KglPVZEEyZeVxDLexi/8QQx38vnfYy68FLnuZ8xcZiCcwWlJD5jBRH3Njz61LlEqWzydj9uLTqFTLi1CBlnlJJM3ZIMOqqIQEMTbtLpNF1dXXR0dLBy5cobPkcIQdi9E5deS9G+jKNKeI0mPKIdoXw4soBm+7GcDC7NDfgR2oMgLqGRIep9Ax5jzbw+pxthIcQYk43129vbsW2bkZERUqkUJ0+exHGcKTF58/EEvdOUxPBcZfCOwlxnBpdzdW1kZISuri4aGxvZuHHjdV94XddnXGVbLrgVGazYciSTySlV0LlgPv6BiwXbtjl27BiFQoF77rlnVq0Yr9c7ZTB8ZGSERCLBiRMn+EPtjdT6a/nviX9A08CR8JWX/DPvO/MPnM1fxNAshNRRCKRSGJpWJYUANUYeR2kMWREK0oNA0eLNEnaVUArSppec42GkMETG/izfSHwaIUAqhWUq/mbb3+LVy8PoY2NjHD58mIaGBtavX78sPvdr0ehpZrDUz7g9RlAPoZCMWilCRpg61/TD/cPFPr6d/CQFJ4OOC2UKbE+Jo84PaR5vZUt4/xKfxa1R59lMf+HHFJx+fHozmtAoOiOA4pUNv8yn+j9ExCiioZATtkVCKIrSx0i3Qo/04Ak6mGoYQ5koJSk6LsyioDvxTbyleFUYVREjKKUoOj2UnH50LUDA2IB2C/WsW6sh7n0RlswAciJr+HqCUyZxRnnOcBKUkuU5Pm5MMkpOgjHrGEXnCqAh83EunLBZs3rLjKq6Qmj4jQ78Rsd1v3PhIqI2MWo+jUMaTQVxVA5LSYL6AXQ6cJyyr+FCb0gXQ5lrGAb19fXU19dXBW+pVIqBgQFOnz5NIBCoEsNwODyr97/TPAahTAafmxm8y7EcyaBSit7eXk6fPs2GDRtobW2d9nEVMuU4zrI3Ga2gcszTiUhKpRKHDx9eMKHIUiuGb4VisUhXVxdut5u9e/fOK/R8cjtZKVVtJ/9Z/i1ks1kikQiXLl3iHS2/zFdGv8/jiYPYskRJGkilYTpgS0H53qnQhUQCaTuIQiCwCbrKFfOC4yJj+0gXh2kNhdCFRCHxag6GplAG/MWZ32KFfxUr2YR+wc/GVZtob1+6KK/ZIu6uZ31gI5cKF0iaQwghiBhR1gc23VBNfHD8MQpOBrf0Ix1F0OdF03TGrCRdYz9almQw7GqhI/Agl3PfJmtfoPy39lPvvpdm7z5eXf8Ovjz8IQJ6EW2iIliSfnYNvRlPwEO40cOIOYIkiEsLoFA4Wh7NVSIWrMWVqSWRSHDmzBn8fj+1dWG0mh9iGceRqoAQOl69lWb/W/BNQ6KuhUu7ubhCCI2QsZpE6WlcWhhdeFBKUXAGces1+PTpW3mmHGG4+D1MmcKtxchmx+lPdNK8bicrmsrPOZv5JgVnhBW++4l5Zn/thj3bEEKRtbtx1Ag6HvxiN0F9e3VjWj4HUV2TZkyKlI1QvQg5UP6n1oQSrSCMRbdpmSx46+jowLKsatXw2LFjKKWmVA1vNQ7iOM4dVxl8TkDyXwDLjQxOztrds2fPTZWXNyNWyxWTh7InI5PJcOjQIaLR6LzbipVq4HIigpVqWWUWcCEX75u1k8+fP0+bp4a3x3+empoanrBP8pORozhK0uS0MaAOIoRg3PbS5BlDTOiSvZpTTjhRZTKogHpfuJp64tVtDAFSgUSgadCTu8Q5+qBB48f5b9F+uYl7Iy9iU2jvslv8hRC0+1fR4Gki44yjoRFx1WCIGy+LY+YIjlQoCT6vDzHxNzSEwbgzMu1zTDlO1rpM0Unh0oIEXe349FvbipTso2TyH6Fkd6FpUYKeNxL0/iLiJsd3I6zw30uNey0p8zRS2YRdbcTc5Vm89eH9/En4Mxwb+QE9pWOsMvaT7C4Ri8XYuHEj3WPfRAEuDEBDAAIPDjmk7qe9vb3aVkylUvRnvkQh9xjSDuE2oni9OjnXRa7kP8aq0J9eJ564MvZtvt77T5wdj6MLxYbIMK9a8X5qw1tueD5R9wZMmWLcvohSNiBwa1Hinn1TZiMnI2udw5RJ/PoqxsbGGOjPsGLFDlyBcU6NfZZT2S9gqwJSwanMJ4m5N/BAw9/O6nPWhIuIZw9B1yZslUMXXowJclsRrlU2qJPvORWLmxuuCcpGd55Ak8fLMTMAUkNqW3D0+6qCuKWCy+WioaGBhoaG6kY0lUpx5coVTp06RSgUmlI1vHb9vdM8BqE89vIcGbxDMNcb/nKKo6v4lEkpZ5S1Wznn5URmb4XKbngyGRweHubIkSOsXLmS1atXz0soUllwobzIHh0/z7Oj3QyZo9S4QuyOrmdvZGlTL4aHhzl+/DirV6+mra1t0cnpte3kVCpFMpnk1KlTRKXil2tfWE1Cef/ZMRLyHKMqQNgossqXIGkFKUmjnIwnwFFlEuDWBaLcaMaYlJKiCYXlCEwMpIKQXkAXOY6NjvPM6ACSzyBQhHQPf7d1eaWheHXfjHwFLcsiny6CS+F2u6tEEMBRDn79+opW0UnSX/gBRSeJJlxIZZG2umnw3kvYdb0wovo866ckxt8COIBEOinS+fdTso9QG/ybOV0/ASNOwLgxCd0aexHt47vp7Oycooo2lQeHegyyoMrCGoGOo6JIrr6eYRjE62OMey/ikY0IJ0apWCKfK2JaBnn/KeyRx2iO3U8oFEIIwYXRT/F/jj7FyfQ9VeHTM4lVnEh/iHdu+b0bEkJdeGjwPp+wsw5TjqEJF369GddNxCwlOYguAoyOjjI4OEhraxuhUIjhXC/dha9gIdEoj044SBKlU/wo8W5eEP8fKOXgqJGJ945NUTxPe3yaH52p/n2V9WbyBr5CDCfbXU1XNRSqB00eR1EHFdNslUOTx5Ba621tu07eiFZSlypVwyNHjiCEqFYNY7EYbrf7jqwMPjcz+F8Ay6UyODY2RmdnJ7FYjC1btszoyyKEWDbHPxtUyOBku5ytW7fOS601nVDkmZGTfG3oSUrSJqB5uGQN0lsYImPneGl8bhYUsz2my5cvc+HCBbZs2XJb4gJ1XZ8y+zM+Pk4ikeDy5cucOHGCB6MvpK7uDfxr5nOcz/WwygeN7gKDpp+84yJgWHg0m6K82tKuJKBUxCgKsDBQCIJaCQEkCgZFypXEym0qI01+5fDb+fiO2VVcbjcqIq4O/1bSvivk5Bg+gmhoFFUOXRhsDt1z3fOSpSMUnRTBSarXvD1AsnSIgNFyw+zdsdz/AcoVL6ozcJKC+U1M+y14XAtvEj4yMlLdkHV0dFR/HnS1MO7EcWtrEYyCUigRoeRkCLimJgBJVcJRBTThxeV243a7CRHCsW2yZoF8OsnBgwcxDIO6ujoetf6Tk+ndRFx5XHp5c1i0DQ4mV/K1vnfyy5u+dsPj1YRBwFhBgBUzOj9dBBlNnyA97Ka9vZ1AIIBSiivmKWwlMYSONkHydMDEYrjUhen0kjef5njmFM+kE2QV+PUa9kbv54V1D8zuQ558/JMqgZM7GdNVDV2yr1wRnJSeggiAHEaTV5ByqjDOUTlKziUcmUETATx6R7VCudhwu900NjbS2NhYXW9SqRS9vb3VqqHb7a6u/8uha3MrVJTWodDSfIa3A8+RQZYHGbxy5QonT55kzZo1dHR0zOoLcqcqiitCilQqtShCEVOa5XaolLT7GqqPHS6N8PTISfZFNxJ2Ld5OT0pJd3c3yWSSPXv2EA6Hb/2kRYYQgkgkQiQSqYbdJxIJEokEzx/djdd7H3FvnLoVdZxSPXw38TimPELAMClIN6at0N3laiCUSaAAHAcUZcsgQ3eQSqNICE0oJFfTUAQKJQTvPvVOXla/lxFzEISPuHsl6wNbqfNc7yl3u5HJZOjq6iIej3NgwwHCY16eHP0mBZlFAR7hZXNwP/uiU4mBJbMUnAE8eu2U77NXj5Oz+yk6SQJGy3XvJ2UG0zk28a/J60C5QVu0Hl9wMlipXG/YsOG6iMd67z2kzCNknVE8ehNKSEpOEr/RSp1795TH6sKPW2+kYJ3DpUWrP1daAa8nTMea+/CuW8Xo6CjJZJLjwy0IVJUIAngNm4zt5fTYwiV0KKVI9euM5TK0tK/E7/OhlE3RGcTCLOcQX1PtK4/SlhjNfZWDmSv8eHwUe8KEZ1wO8Y3Elxgo9fOmljfP+/iuJYaT17PyJtdGIJHKQdPK14Emyv8PCimvtoktmSBd+h6mHKCcnyxxafVE3S/Grc89vnMumLzerFq1ilKpRCqVoq+vj1wux+OPP15tJ8disXnNUC82nksguYMw1x3G7Yyjk1Jy5swZ+vr62LFjB/H47BdAXdfvqJlBKP+tTpw4UbXLWQyhyFAxzaiVpeYa+5MaV5jB0gi9xQSbF4kMWpbF0aNHsSyLffv2zev8FhNer5fW1taqAW1FnXzs2DGklLyh7gXE42/ggt7N0+M/pb+Yp6QGYGJWUIeJJJSrmcggGC+WJShqQorCxGMUCr9mokSaU9kuBksWz6ROsj5cP3FzUyAUBQv+bus/33R+bylQqZZVDMGFEOyNvpStwf2czB7ExmKdbwdRz3Tf28p6dL3DwU1XKuGaeMR032mFuEE1ca7o6+vjzJkzN6xch10drAn+An3575Bz+hEIatxbaQ88hM+ITT10oVHreRlX7B6K9mUMrQapithqnKj7AD69PAJSIQBO/41m5AQOYkFmyyo2TsPDsH7bq7BcZ8g7lxEI3FotAX0FWXsUiTOFEEogpNlYzijPZDPYSDzo6EJD4VBSkiOZg7wg/1Ka/deT+rliunayEq0I+zioAlKW1xKpCmVLIJpQ6krVvzVj/RRTDuLRViKEjlISU15m3HqaWu3Vt2xxLyY8Hg/Nzc1YlsX4+DgrVqwglUpx6dIlTp48STgcpra2lrq6OgKBwLKqGj6nJv4vgEoc3VKXrE3T5MiRI5RKJQ4cODDnXcdyqGzOBuPj45imSSAQYPfu3YsmFHFrLnShYTGV6FvSRhManlnGjc0U+Xyew4cP4/P55pVPu9S41kqi0k6+ePEi+ZzN/dEXoWoMPpr8F3RfuTfsN2xQoOsgHImsUj7tBoxHETKKoGDEMngmdYQNkXoUOmpC0SwEhNzwJ6d+jZfW3E9ODrHSv5PdNS9b0s9jaGioWi1raZl6s/caQXZFX3jT57u0AH69mTHrNC4RrNqkFJxhPHoM7zX+eRVowovP9WIK1ve5WnstpxODht/9CvJ2Dl248OhzN/GueHleunSJnTt33lSoVuvZTI1rIwU5iMDAb9x43CHs2g0BSar4HUpyCE14qfe8iDrvg9etr5uiA5wbr8eWAkMrk2bT0dGEZG04wWM/fpT6mkbi8ficvO2UUpw8eZLR0VH27tmL3+/HkpswZRKBjkdvwG/s53vDv47NxD0AgcRBKIi4VtBnKwrSwkBDrxBTBS50SsqmM/MMzf7Xzeq4ZgNN08C9Gk3bhmYfn0hqUSihsNUGimYjUvYipcS0RyjZfbi0hirpE0LDpTViOYNYMolbb7jFOy4+Ku4XNTU11NTUVLsUqVSKVCrF5cuXMQyDWCxGXV0dNTU1t3UdtW2bYrH4HBm82zF5B7ZUQ62V1lMwGGT//v3zutDvJDI4NDTE0aNHcblctLe3zztabrJQ5NobTYO3hpW+Jo5lLuLTPLg1F7ayGTLTdPgb6PAt/KKYTqc5fPgwTU1NrFu3blntbGeDa9vJhUKhqk5+Y+Z1qHFFTU0NK1e2c0W/QFf2aS7nLzJiF7GkTsSryBbL84RyUnXQEBJDSGxlMGYXWRNqhInbr0ubiM5TgGPR6s1yMv8NFIKLxUN8P/VR1gceYHP4Xlb6rvfcXEj09PRw7tw5tm3bNqdqfQW1nu2YcpSMfRlNGEhl49bCxD17bjgvCBANvAtz/BiOHKZCAkFg67/CZ/q/QtLsQSBo8qzhBXUP0eCdnSm7UoqzZ88yMDDAnj17ZjQLpWkaAe3WbUYhBBH3PsKuPeXoNuG7YfzaK1v2cHRkgEvZumodGWBLzRXaotsI1tl4sga9A12cOjdK0NdAXWwV8Xi8KkK5EaSUHDt2jFwux549e6rVeZcWxqVdHdmIedrZGv5Vjmc+hq2sqjl3xN3BlsAv0p17dOoLVwu9S/jdFi6k8XyE1oaQfeV31lYg7RZOHD5GNBrF7XZjO1kcaSOUQtPkpLxnnfJ1tDw6SNNVfL1eLy0tLbS0tCClJJ1Ok0qlOH/+PIVCgWg0Wq0o+/3+JV1bs9kswHMzg3cK5tMmhqXzPqoQoo6OjgXJMb1WmbscUQm5v3DhAlu3buXChQtzMgivvNZkoYgQ4oaf4YMN9zBu57hSTJYD5pWgyRvjVY33YWgLe/kPDAxw6tQp1q5de0NfyDsVPp+P1tZWpJSMjo7S1taGZVkcO3YCgN11L+GBWIxCIMuPx75Pb7ELFyVs5Zmwopn4WwEKQbu7jYtmDpeukEqgi6sZyghBkzeLKYxys1SBpQw0ofhh6mm+kzyEQODSPLyg5gFe0/zQgp2nUopz585x5coVdu/ePa85VgCvXssK/8vI2j0UnVFcIkDQ1YZXv/lspKGvoDHydXKlL1KyD6NrMXLqAF9P/Ii8cxk3fiRwsdhFamCAn2/+bSKemUWwVayr0uk0e/furRpFLzSE0HCJmx/T5vo/4k+2/jXfvvIsJ9NNGEKyJdbPzvge/P69pM3z1MV7aa4vYTtFSoV+xsaucPlgC7rupq6ujng8TiwWm7KhdhyHI0eOYJome/bsuaXv3cbI61jlfYhT+c9gyjFafffT5N+JLVOsFd34RofJSYkuyxscgYaJg0u42Rs6sCCf1y0hXCh9LUovx0FalkXX4U5cLg5xnrwAAPbOSURBVBfbt29HCIFLxnHLBkpOH5ryTXyhJJYaxBA1aMRu+hZLhVvdazVNIxaLEYvFWLt2LYVCoVo1vHDhAm63u0oMa2pqFv2+nc+XVfTPVQbvclSqSrZtL2p2qlKK8+fPc/HixXkrZydjuVcGHcfh+PHjjI6Ocs899xAOh7l8+fKcCOxsjaQbvDF+tf1hTmYuM2plCBt+Noc7CBoLdwOsEN2enh62bdtGXd307b87GZW5q6GhIfbs2VMlSUopxsbGqurkXC7Htpq9vCT+INSX+Ovej5Bx7InmcdmiZltwFyP2EIbQMCcugat/RYFXFZFCR0cihY6tdAxN0pePoIQ+0YoGjSwn85+k58KnqXGtps2/m73RB/DPwCZmOlRI0ujoKHv37l2wYXGXFqLGvXnWz9O0CCHfW6jUIn7Y/x/knTQhrbHarrRlgIwzxKHxJ3lx/Nak2HEcjh49SrFYZO/evfOKFFsobK7/I1yBg+wsnKHWfbXyqJSDS10ga9mEfXvx6G68rnH8wQSrO7bg5FeRSCQ4e/YshUKBmpqaakvx9OnTAOzevXvGogSPx8MOz1QxiKHVEvK8jBdExvju6FlKSISiPNKAzu7wPhr884vKlLIfKcstXKE14NU3IbSb34dM06SzsxOv18u2bduqVTZNc1PjO8Bo6VEs53LZD1IW0Aji03chHYElrVkZXptmlkzpj5DOswhMJFHc7jcQ9f/unM9bSjkrsYjP55tilVWpGp49e5ZSqXRd1XChkcvl8Hq9d5wdzmzwHBlkaexZbNvm6NGjZDIZ9u/fv6Dl5uVMBkulEp2dnQBTcpXnUs2ca6KI3/Cyp2b97A58hnAcZ0qV5W7cOTqOU223XVtJEkIQjUaJRqPVHXxFnTx6ZpQ3+V9fTUmJRCIIIRi1hvlR8vMkrR4yNrg1JtqD5Vxgr1bWbAoFSpTbxjlbTKiVBRqSsKtAo2ecknRxueBjoHiRY9levj78FbyahSYULxe/zL1rXzKjc6x8P03TZN++fcuCJF2LhNWLjuvq3BpgaC6QkCj13/L5lmVx+PBhAPbs2bOslJs6LuS1LUw1imAEQ1uHNtFSN0QEqQoUnFM01OwgFouxfv168vk8yWSS4eFhzpw5g6ZptLS0MD4+Tk1NzbxGCtx6Gy9u+D0afE/zePoJRswMfi3APTX3cSB2/5xfVymbkvVDCqUfUbKPgxpHCJ2MtpKA75cIuO+b9nmlUolDhw4RDAbZsmXL9e1Wo41a8QgF+yyWTGJoUbzaGlxa45wMr8fyP4suLqBT/p4aDOOY/8yoylITeNeczn0+XThd16vED8pVu0rV8Ny5c3i93urvo9HoghC4bDa77AQtC427igzO5w+1mIQqn8/T2dmJx+PhwIEDC159XK5t4vHx8apv4ubNm6d8KWd7zMsxUaQiAFJKLVsCMV+YpklXVxeaprFv375bEgifz0dbWxttbW3VRIpEIlElIZW23ktj/401wW6eTR/hWOanSKWhIREoSlLDYzhMuNegC8WYFUKhIZAIIYm7szhScKUQxa+VsIQHt7CJunLE9Cwe4XCGD3K59x/YEnqATeG3od9AMFQxene5XMta8OMVAdJcv0YpVDUT+kaobMq8Xi9tG5o4VXgGd9HLGv82jHmIUBYKMXcTfcUzZO00Ab28acjLkbKY6BqjbE34cCggsdAnbmF+v5/6+nr6+vqoq6ujubm5bF3T/SOkq5eIfzUNNTupq6ub0/dUCJ0tkfvYEpmeoM0FttNN0fwJpn0cTWWRIozCQjhnyeY/wpg9jkm5Oh02Woi62jFLNocOHSIcDrN58+YbEji3Xo9bv17kM9l5YiaG17niV9C4hFQaiIqK2UGnhG19CdP8w+r9zJEppBoCDAytFSFuXKFfyAQSv9+P3++ntbVsvj06OkoqlaK7uxvLsqipqamSw9nkwE9GhQzezVieq95twGLZyySTSY4cOUJzczPr1y9O6sVyrAwODg5y7NgxVq9eXbXkmIyZksGZCEVuB3K5HF1dXdVFeaa7z7SVIWWm0YQg7o4taLt6oTHXc6zAMIwpkVXpdJpkMsn58+fJ5/PU1NTwkvgr+PLYo3SEomiaQhNQ1HzUUMTEBao8M1gtHAJezcYlJCO2n6I9itsTQiCpceeo1XOAoIQLHUneMXlq5Ds8mT5BUQbRhM764HZeFHsNLt1d3ahFIpGb3lyXA9YGtzM0ep68ncarhVFAQY7gEl7WB2/sO1g5x1A0xFD9CX448CkKTgldCKKuKC+tez2rA9uW7kSmQdSIs9K3hZ7iKVLWlQn/RjcxowX3Nd93W47h0VegcZXUFQqFapTlpk2bkNJk1PUBvPFDONKkqAQ9pSZOPvk6wv4O6urqqKurIxwOI1WSXOFfsewuwIvX/RJ8np9DW+CZ4mthO2cwnWEEWZRWV1X/2o4gYaYYN7+C17WzbOBeOkWE1QydcBOrqWXTpk1zXgevtpRvbXhdsL6LJiRSeaqjHELoSKWjkcehE6X2UbKfwHI6kWocgYYm6vG6Xoyhr572GBZrPl/X9erfdt26ddWq4fDwMGfPnsXn802pGs70+17xGFwO957FwnNkcAIVe5mFQiV54uzZs2zatOk6a4qFxHIig5OFItu2baOhYXrF7kzI4GTjVbi5UGQpkUqlOHr0KK2trTOOzpNKcjxzju7sRfJ2EYQiZATYEd7A6sDyE5uMjo5y5MiRKbFk84EQomojsXbtWvL5fLWd/Du5XyNAgLq6OmJ1MSxfkXPZgxwc+3csdBSCkJElbUXLQXiqzAttpRMSLhQaAa2EgYOOxFIGBg5iwgvxihXGUnkQBQwccmMXOZf7Bhvd95DvCbGmfh/r1i5/5fe+yPMYKvZyodBFRg4B4BZ+toVfwIbQ9LFtmUyGzs5OGhsbGYmf59DIMxhCp8YVwVY2SXOEbw1/hl9saSbivn2zrkII2vwbqXU3M2YnUSjCegzL6SRrdaGcEprwYstRhNAIurdXrXpyuRyHDh2ivr6e9evXI4Tg/Pj7GLGeQBM6huZDYiF9PcQ3f5V6+69JJpP09PTgdqdoX/33GK4EiPIsarZwkJL1JNHghxd1c6BUATAnPoCrxCgrIS01/HqJiKu8NuRLY5wc+Amt8eezad3cieB0uJnhNdVIPcVVmyOq/y2IYstTmPbjaCKKoa0FHBzZR9F6FL8WRxPXm+0vRTaxEIJAIEAgEKh2KioxeSdPnsRxnClVw5t5wd7tUXTwHBmsYiEJleM4nDhxglQqxd69e4lGowvyujfCcmkTTycUuRFulZoyeT5wpoPOS4G+vj5Onz7Nxo0br0tquBmuFIc5Nn4Gn+5lha/cvklZaQ6NnaTGFSbmnp9qdSExODjIiRMnWLdu3aKpov1+P+3t7bS3t2NZVrWdfPTwUYQQxONreFPdP/LD0icZNE+iGzBuKSSCgnRjSR23sCmo8nWvCYlbODjoZa/CifdJ2OGyEhmJV4OwnkcIB0uanMk/STqqOKi+jX7GIWIUcQsPb+34Nwxtbu2kxYSu67ym+b9xOXeA8/nuiSrnVpq80280R0dHOXz4MB0dHXR0dPDx3s+hlCTiLitKdQxiwmDUSnM8+xT3xR5ZytOZFgEjQsC4+l2Qxv3oIkje7kZSwKM3E3TvwG+sAa6OoqxYsaK6MSvZCUbNZybUzFEAdLxYaBTpxVPTzbbmFyOlJDH6e0g1jGX7UVJH0zR0vYhpP07J+hY+z8Ip1a+Frq2kSrKUBKGBsslLhUBhaOXPwSyZXOkZxh/2Em/0Leqm5VrD65B6G5nct9ExcZQHITSUstGFg62acBvrKNpfBDQ0raJUNtC1Nhx5Dtu5hNu4vup8O7KJr/VRzWazpFIpBgcHOXPmDH6/v0oMI5HIlHtONptdkHnwf/iHf+Cv//qvGRwcZPv27fz93/89+/btu+HjP//5z/Oe97yHS5cusXbtWv73//7fPPjgg/M+julwV5HB5TAzWCwW6ezsRAgx72SNmULXdUql0qK/z81QLBbp6uqqnvet5nJuRmDnKhRZTEy2HNm5cyex2OwsGnoLgzhKEnFdXVDq3DX05AcZLCWXBRmcnKM8X3+92cDlclWzTKWUVXXyuXPnqC3soC12H+6YgdEMX0t9l6HSAMOlIA3ecUbdtRg4WMrAqWqWryIv3ZRTOxR+rYQ24XEolWLIdjBx0+IaZY13GI9mgRI8NvgS1ob+BENfT9gVI+pa+jzpm6E9sJr2wPTttwoqKTLr1q1jxYpydm/WzuK+RqWqawYKyDrjN30908xyNPclCs4obd57aA9en8O8GNCEh4hnPyH3LpQy0YSv2k5Np9N0dXVdl6VcsM8hMdGYuvbqeLFUgbx9llpeXF5btMNoyoVXDyJVpU3qQVNj9PZ9DmWvqSqUF3pD6jI24TF2UDCvIOQASgQqdu0I3Lj0NZRKJXou9xCJRnDXuGGJ10K/dxMF8/XY1hfQRalaHHRUAK/xp9i2jWWNAi6kKOv8hSYmqrYCKE77uktRGbwZhBCEQiFCoRAdHR1YllWtGh4/fhylFLqu09XVxSOPPLIgUXSf/exnecc73sFHPvIR7rnnHj70oQ/x8pe/nNOnT0+b/PPkk0/ypje9ife///08/PDDfOpTn+I1r3kNnZ2dbNkyfSdgPriryCCU/8hz8a8zDGPeM4Ojo6N0dXVRX1/Ppk2bluxiv91t4rGxMTo7O6mtrZ1W3TYdbkQGlyMRrFQ8s9nsnC1HStLENc0MkhBgSqv670y+wLs+/RW6L44gJTQ3+Hn3z7yC9c0LY0N0I0gpJyK7hm9rjrKmadV28rp168jlclWz68S5NC8JvJh4PE4ikOZg4SfEXWdJmwVKuMk7Xmq0AkXlxo2NmHQZ6oAhHByloRQUSiUcPUyNnmWjbwANRVEaKCnos6L8ZPxL2CqAIVysCbTwcMPPE9QjeDR/tT25XNHf38+pU6fYsmXLlDGNqKuGK8UrUx5rSwuBIGLceHNzMvMNnh75BDYmCjiV+z7R0QZe0/AB3O6lUc9rwg3iKpFNpVIcOXJkWk9Pr9GOwIXCBK5WeCXmROrIimteXUy8h4amaxi6gVQZQpEQo8lBzpx/BtvSCAc3UFfXNGcRynXnpEUJ+X4RRIhC6RsIMuh4CBp1ZGQcy6rlSs9lamI1RGMBCtIk7FrabGGA2vD7yBVfQK74cRRZNG0lUe8f4DbKNi+GbMN0nganvjzKowRQQikdTUzvp3k7KoM3g8vlmjLfnMlkePrpp/nkJz/Ju9/9btra2giFQjz55JPcc889czr2D37wg/zqr/4qb3nLWwD4yEc+wje+8Q0+/vGP89//+3+/7vF/+7d/yyte8Qr+6I/+CID3ve99PProo3z4wx/mIx/5yPxOeBrcdWRwrpgvoert7aW7u5t169bR1ta2pCTmdraJK0KRNWvW0NHRMePz1jRtCvlerkKRUqnE4cOH0TSNvXv3zlkJHnfHuJS/Uk03gHIsnkAQdZWJV65U5PXv/zcKmbKLHsCli0V+7UNf5O9+6xG2tS9Oy7biPVcoFNi3b9+cFXeLgcrMT6WdnEwmSSaTFHszbNK2IWu3E41E+Vbmo4zZNgYh6t1ZLGWgFHg0G9vxoChXwFAKhEDixlQGHZ4UhpDkZPnmfsmq5WyhodymE0VKjsX5/HE+2vNH+PQga/xb2OR/PlF3HT49itdYXlZCly9f5vz589NWr7eF9zNU+goj5gh+3YejJDknT527lm3h5037ellzkKdG/hUbG60sDUDikJZDfCf1Xl7e8F5GrEFsWcKvR4i66qvX92JheHiYY8eOsWnTJpqarvf48xrNhPTNpO2DWGTQ8SMp4qgCPq2ZmPvl1cca+i4s+7soZSMmcrCVygEGHm+c+tZnqFN5LNOhmB9gYGgzp065CQaDxOPxqghl7mKOGiL+txDyvomScwaUSVRfSXHkWU4PPEGoNog7UqAgS8Q9mwgb1xLZpUHA+1IC3pde93NN09D0XajCRRx1CahBSgupRjG0zTh2Iyh7WtHKchn9uRZCCMLhMC972ct42cteRn9/P3/+53/Os88+yyOPlEcpXv7yl/Pggw/y8pe/fEYdFNM0OXToEO985zurP9M0jZe+9KU89dRT0z7nqaee4h3veMeUn7385S/ny1/+8txP7iZ4jgxOYK5kUEpJd3c3AwMD7Nq1q+p9tJS4HZXByQba27dvn7bMfTNMJrDLVSiSyWQ4fPgwNTU18670dvibuVzop68wTMjlRypF1s7T4W+m2VteTP7yc98oE0Ghqt0gpcAxNd772e/whT9+20Kc1hRUbFUMw2Dv3r3LynvuWrhcLpqammhqaqrGVSUSCZI9Se4rvr48DF5fwvGfJOUcQSJZ763nB2P9FKVFURr4dRNHQaVs6NcsnAkPG0cK+krlxIyAbmIrDb+rhFu3kUqQtzNcyv2QvuJj1fH5oFbDy+LvJu69edt2sTGT5JSdkRdScgocGnucnJNHExrt3jZeEv8Z/Pr0vqfPjn1ygghq6BPtWQ0NS1n0ly7wTOrr5MlO5ErrNLjb2Rjcj0tbHJulStVz69atN11zVkfew9n0O8nKc1gqjRA6fr2F1eE/n2IdFPL/LqPZIyg5jFIVybqG0NZgSxvDCOPSV+DWLdzuS8RqB/FrbySVGq+KUDRNqypYa2tr52RNpGlefFp5ti6dTjN43MX6Va8kGC9fm2FXM2FjBZpY+GpaKvP3mNbTGPpa4pG/mPXzda2RgPe1mPZBbNkDegBD7EbXdoDyTGtds9wqgzdDc3Mz0WiUhx9+mA9+8IM8++yzfPOb3+RDH/oQb37zmzl//vyUMYXpkEwmcRznOkFlQ0MD3d3d0z5ncHBw2scPDg7O63xuhLuODM61TTwXQlXxYLNtmwMHDixarNOtsNRksGJCPDY2NmcD7Yrf1bXRcstlt1iZuarMI82XnAYNP/fHdnEmd5m+whC6prEh0sHaYDvuCf+74xcTwNSxIDFhupxImLN6P1s66OLm1dVsNktXVxfRaHTZ26pci8lxVevXryeXy5FIJBgeSDA21k4wuLk661Uce5qT/ifJ60mUyhDSdYKaiz5sMo6HkF4AJTGVB1O5MIRNOXBM4dZspBQ4aIT1PJruVBNQFIpxOcJXh/6EX2j5BF7j9qgNlVKcOnWqKli72RjD/tgr2Rl5IYPFS3j1AA3etpu+dtZOAlSJ4NU3haKEUTlAk2ctmtAxZZErxXO4lYWtfsJoqRxVWOPZzKrgr+E35tfi7O3t5ezZs+zYseOWm26PEWdL3UcZK/6UnHMKj2im1v/AdY9zGauIBf+dXOmjmNZhhPDgcb2IknQQooQ+kWEshAuX3o7l9CJcgzQ3r6G5ubm6KalYJh07Vs4JrlQNZ5uhOzIywuHDh1m7dvHEWxUU8p0M596KRgkhFLbTxeXEF4n5/4xQ4A2zei1Db8bQX4VSJUBDiKubymuta3K5XPW/bdu+qeH1ckHFCkvXdfbv38/+/ft573vfy9DQ0KwLIcsVdx0ZnCsMw5iVCKOiYotGo+zevfu2GtXeSpm7kKgIZHRdn5eBduWYl9t8IEBPTw/nzp1j06ZNCxYZCBB2BdkT3cyuyEaA69tpN9vDzGB/o5TiYmGA05nLjNlZwkaAdcE2VvtbrvtsR0ZGOHLkyKzscZYzKu3kyjB4MplkcHCQixcvomleXuJ7E/WRekI1Xgatk2TsYb488Dn6zChxVxa/ZiGlmBCj6EglEaI8TSYnWvZu3akmpQgE5SwUiYnFT9P/wvPrfm/KMSlVRKpxNBFBiMWplEkpp6TDzESw5tF9tAc2zuj1o64W+s3TOMpGF5Oyf8uNdOL6ymq1yq158ShJb/4DKPJUXMMLhR8yWjrB3tp/wGtMbzV1K1y8eJFLly6xa9euWbkzRLz7iHBjtSaAYbQSMf6y+m+pClj5/1s1Wa5ACDfgoLh6n5i8KVm3bl01gSeZTHLu3Bl8Xo1YbSPxeOMtRSiVOcj169cvqhVZBYnc29BFEakESmmAQhcmI/n3zZoMVjDddT6Z7OVyOY4fP05zczNer/emhtfLCblcblo18Y2s065FXV0duq4zNDQ05edDQ0M3vMc0NjbO6vHzxXNkcAKzqa4NDAxw/PhxVq1axapVq277jXSyq/xioiIUqaurm3clSQhBsVgkn8/Peve8WJBScubMGYaGhmZ905kNbjRTtWllHc+MJitjbUC5KghQF791+/ZsrpfHR47iSBMlDjJqXeZyQeO4ezePNPxOtS0zMDDAyZMnWb9+fVVpejfB5XIRCoU4e/Yszc3NNDQ0kEqlOHPmDKVSiVgsRjy+iz9ufxl/0/MbnC6arHSnCOgWze5RLhQbyEuBV9gonQmFZ7laWAnOq0BDw0EybJ6t/kwpi0LpP3l69NsczdsUlcCnBdgbfYR7YwtnVWLbNkeOHMFxHPbs2bMoueq7wm/mXO4JTEooVRabyIlPxK/HcBnXvuezOGTxEESf+J3jWBRkkgu5j7Mp8s7r3+QmmNz+3rNnz4LGeN4IAi+G1khJnkGnpvpzR44hhB/9BqIIuJrA09qSRZoDFAu9ZLOKvkuNHD3STk0sXm0pTybulU7Exo0bp52DXGhkcl9BUCxfzZOqvlKBJiyG0++kPvr+BX3PQqFQvX9s2LABIcRNDa9vFZO3lMjlcvO69txuN7t37+axxx7jNa95DVC+3zz22GP89m//9rTPOXDgAI899hi/93u/V/3Zo48+yoEDB+Z8HDfDc2RwAjMhg0opzp49S09Pz5zm5BYLS9EmrhDg2QpFrkVFKBKJRBgYGOCpp56qDmPX19cTDAZvCzGsZNMWi8XbJqL48zc8xGtP/yulnMbkSQfNJfmT17/8xk+krEg+Nn4eoUpI8VlcIo8hyi+Ssr/HJ/uP8N+aPkJvb391zrOu7vaZDC8mKv56bW1t1c1aJZGgok4eGBigu7ubFwV/jXg8jsercVo+Rp0nzbgcIGllyEk3PmXi1iS2rMyTVXMYAJATJVu3uNqazRf/g28mv8npYqVKosg4WX4w8hlG7GEern/LvM/RNE06Oztxu93s2rVr0ToTAXeEF9e+gx+l/p5iZTYQjXrPGqLuXRRkBr9+VXluOX0AVSIIoOsuLFsxNtE2nimUUnR3d5NIJOas4p8LhBB4XbuxSn2Y9nl0rWaiypvF57oHQ795ZUY5J8D8KpqS+H0R/L489XV9FKxGhkYi9Pf3093dTTAYrFaMzp8/z9atW2dcaZovCvazICjPSU5Zbst035bnFvb9CgUOHjw4hQjC9IbXlbGh5VQ1XAjT6Xe84x28+c1vZs+ePezbt48PfehD5HK5qrr4l37pl2hpaeH97y+T8Le//e284AUv4AMf+AAPPfQQn/nMZzh48CD/9//+33mfz3S468jgXInEreLoLMvi6NGj5HI59u/fvyAGlAuFxawMVnbmly9fZseOHfPynpssFAkEAuzZs6fa0kskEly+fBmXy1UlhrOJC5oPCoUChw8fxuPx3FYRRcjv47P//Rd55398hfO940gJDXEv73rDA7dUEmfsPON2Dpsf4hI5yrbL5daPAKRK8KWL/4eGgRewd+/eJamw3A4MDQ1x/PjxaaueQgiCwSDBYJCOjg5M06yqky9fTqIba2mr9bCxJsCwdonvjXydnO3Cr9sYmiwT9ErFlnLFUE0kJu+O/gIAUiZJFx/lbMkz8Z7l26tSCqkUXWOPcXn8i6CBhyjPj78ES44RdW+kyfPSGV3vlQpLKBSasZXTfNAevIdfCt7DpexTZJwhVvj2UuNu4Wyukwv5I5iyiCHcFGUWTRjlHNtpIGZxu5FScvLkSdLpNHv37l3yzZnbWENIvIaieQhbDaCJMH73/XiNnTd9nlI2WM+U/6F3TPy0BuQIPvcZOtrvY+XKlZimSSqVore3l7GxMQzDIJEozwzHYrFFX4P8rhdSMr+IEOqaCRSFUgK3PrN4QqUUBWeYvDOMQCNorMCjTxUvVYhgPB6vJsRMh2sNryuVwsp943ZWDfP5/Lzv+T/7sz9LIpHgz/7szxgcHGTHjh18+9vfrm4AKmKkCu69914+9alP8e53v5t3vetdrF27li9/+cuL4jEIINRc1BbLGLZtz6lKVskuvO++64PIs9ksnZ2d+P1+tm/fvuwUl5lMhmeeeYaXvvR66f98ULEcGR8fZ9euXfMiEDMRijiOw8jISDWmTEpJXV0d9fX1c1bp3QpjY2McPny4GmW1HFoSc8G4leMrQz+h4PxfDFGqzrlVoCFx7BC/2PxvS2KEfjtQmfXcunXrrDctUkpGR0er816lUglV43DQe4gRlcCrOzR5I7jFSUyu+kJqCNb6n8eL438IgGUf5YfD7+WZnLdKBAEUspyzjCCgFah3jRPWSwzkdep8CrfmIABbCUbMML+y8uvTHmdlLYrH41MqLLcDUjn0ly7QXzxHSRaIGnFwnmCg+G10PLj08nVmOUUcSqwM/Axrw79169edmIPM5/Ps2rWr6uk3lPgpDfGbz/8tNJRSKEwERtXs+qaPl6NQ+hiIIIhJ66VSIM+B5xcQ+lqAaoVw69atGIZR3RTn83mi0Wi1nbxYmbg9ib1oIotCVJXUmlA4yk17/Mitz1U59BefJFk6iq2KgMKjRWjy3UetexMwcyJ4K0yuGlb+u4JKxXAxq4Z79uzhAx/4AA8//PCivP5ywF1XGZwrbtRqHR4erubQrlu3PPNLF6NNvFBCEZi5kbSu68TjceLxOEopxsfHGR4erqr0yrNe5d8vBKEZGhrixIkTrF69esm9IRcaYVeAVm89Z3Lymqk2qt1N3RCz/tzGrHGOpE9T761lQ2jVwh3wAmLyXNlcZz01TatGUSmlqurkcCLK+Pg4oVCIOldZnXyGL9JfOo5b+Nkd/W80eTdVX0eIMDpTSYNiwkJp4t+GsgnpJYYLkqaAQudqdcYlFPWeMT5+8eHrCGE6nebw4cO0trYui1llTeis8K5lhXctSkmE0LDtneSt04w5F7HtcvqEEIKIvpKV/jff8jUdx+Hw4cPYts3u3btxu908ObQPbSLD9+JQ+XPs0P6GpviLFvP0gAmbK2Yh/hFewA2qOJUMUiybZotyhbOvr48zZ86wffv2qjK6kttdKBSqxPD8+fN4PJ4qMawoWhcC9YFPM5z7OQR5hCgHfjvKS23gQzN6fto6z3DxIG49SkBrqlYJ+wuP49frwQzQ2fk4K5ocOjpqEWoMJuIBZ4sbVQ0nZynD4rSTK+vB3dpNqeCuI4PzaRNPJlRKKS5cuMCFCxfYsmXLkgz1zhWaplW/EAvxBajEPMXj8Xn7602uCM5GMSyEIBKJEIlEWLt2Lfl8nuHhYQYHBzl9+jShUKhKDGc7Z6iU4tKlS1y8eJEtW7Ysm9nP+WJ3dANnc7UIBifIhZggguV/RVwds3q9v7nwr5zMdGOrctUqbIT51bY3sTm8dmEPfB6otBNHR0cXbK5scju50tKbPMpgGOvYGL+Puro6Yq6pxs661s6W4Hqezl/AUaBNfPaoq9KTmCuPJhQBtwed0tW/FeXWs0ARc0+Nh0smkxw9epQ1a9bQ1nZzO5jbgUoqi2EE2R37e3rznyVldQJQ69pFq/9nMW5h0G1ZFocPHwaoOjQ8MbQXfVIlFsqf1GXn92ni8EKfxrwhhA+lbwH7h6B85QqhKoHsBX0diKaqRc7OnTupqam57jV8Ph+tra20trZWuyXJZJJTp05hmia1tbXTilBmC69/DW3+g2Ry3yRvfg+3azs1wVsT9grS5llA4JnIUBZC4DcaGLPOk8ydI3H6POvbz1AbA2E+WSaCrueBMf/q7o1mDRernVwROt7NuOvaxI7jzClWLpvN8tRTT/HAAw9g2zbHjx8nnU6za9eu2xbNNVNYlsVjjz3GS1/60nm3Uvv7+zlx4gRr166lvb19XtWHySqxhbSOqdych4eHSaVSeDyeKjG81ZyhlLLqybZjx45l/7edLS7mjvBY6j1oONXhcIFC4uZ1jR8m5p6ZZcU/Xvp/PJvuRAE6GgqFROIVfj6w6V0EXLd/YayIfkqlEjt37lyS9vfkdnIikajenCu+ch6PBymTfHfwzziUH0epqa5APlGixTNKUC9hKw23cK6v5E5QwkeafwKUzWdPnDjBpk2byAV7SZl9rA+8iKj3eiJxp6IiiPF4PGzbtg1d18uVMXm9N+BV1LO/4bsAFMyTjJvfQ2HiN/YS8jz/tlVOlSqA9R1wuiln8+og2sH9ID29OS5cuMDOnTtnXcFWSpHNZqtzrmNjY1URSl1dHZFIZEnP+WzmC+SdIQLG1EJJqnAGs8/HpvphYjV+hNZCeWOaAExwvxH0NYt2XNdWDSsUZ65VQ6UU8XicI0eOsGHDhsU67NuOu64yOFdUKoP5fL6ayHDvvfcuil3DQqNSOnccZ85ksKKUfuLyCU7XFPjM8EVio0EebtnNC+rXz/q1JieKLLSHoNvtprm5mebm5ilzhseOHUNKWSWG184ZWpbFkSNHsG2bffv23ZWzcysD29kx8jt0lj6B5hoHJfDqzbyk7h0zJoIAR8dOoFB4tKufkSNtSirPfw5+h19qfe1iHP6MUTF8NwyDPXv2LNkc7+R28vr166s35ytXrnDq1Klqxfreur+ixf99nh5/nBFzFIciQQq4dBtT6qBPJomTVcpTUakiBdZn+VzxD5DF8kb3yfHPENVb2R75JdJWmpARZGNwPcFlFo83E1RGUoLB4BRBzLD6wE2fJykLLoazH2as9DkKTpGc1LHUV3HrK+gI/h71vp1oYmlnvIXwoVyvBmMvqDTgB20Fly71cfnyZXbv3j2nTagQglAoRCgUmiJCSSaTdHV1VVXzlSSUxf5OBI0VjNsXqyMCAIVSlmRihA01bcRqBEJrveqTJRrAOQfO6UUlg9dWDedrXWOaJqZpPtcmvtMwnzYxlPMAm5qa2LBhwx0jJqgMz851btC2bY4dO8YT6Qt823OGUt5EIOgpKo6fvsj5zP38yurnz+i1ljpR5No5w7GxseqsTWXOsL6+nkAgwMmTJwkEAuzYseO2moQvFiqjDZkeg9dv+9s5RyMmiyNYykS7RoSiawa2tBkoDi/E4c4Z+Xyezs5OwuHwkqhpb4Rrb86lUqlatbl06RIuV5zn1f0q8Xicz4z/Ji5howkwlYalNDKWB7c7P/FqFUJYrhMWpcH58+fp7e1l1aZmvlT4c5SaqCEKMB2d44UMRzP/Sr07RL2rl6HcGM3eVlr8L6XO+wC6WD4Z0zdCPp/n0KFDxGIxNm3aNGX99om1ZNS3b/JsQbb0JGOlz1B0HNIygq1Ax6To9HAu+8+YvIlW30twnDGy9hMYhAh6Z7aWzQdCCBAtQEs1urOvr4/du3cvGKlwu91T4hnHxsZIJpNcvHiR02e/Q00UwqEXLJoIJebeSNo+x5h9EY8WwbSKDCYuU+PZwMqmNoQzPDVOCUB4Jgjy0mC6dnKFGM7UuiaXywEsKweRxcDdd0ecA5RS9Pf3A7Bq1SpWrlx5m49odqhcwHOxl6nYVOi6zhOeK5RsE7/mmwiklxRkkS8NPMWrWnZS5735Ijb5i3Y7EkWEEESjUaLRKGvXrq2KAHp7e8lms7jdbsLhMMVicdEUercLk9vf8zXnjeghNGFgq6mzWlKWNxtR18yrGh88988MmUPUu+P8RvOb5z13MzY2RldXF01NTctO0OXxeGhpaaGlpQXHcart5FOnTrHJ+nUuNn6NgvciYDBY8lGDoiiLeDVZnRgEsBGo9L1cGSkbLX9z7P8AlbxqgVKQsgKUpE5YT7M90INbjGBKRbI0wrjZTVp+BltE8WlR1gdfzJrgwjoNLASy2SyHDh2isbFx2r/lqvhbGR78+xsVTWnUfotx89tIVSCn6nBQ+HQN8CHlOEoNMWKeQrN/QN78PpIcoKHn6mgIvIuw98WLfo4VcVN/fz979uxZNEKhaRo1NTV4/c/SVPcu3OQBhaX+moGBbQwM/kZ1lGGhRCgePcJK/4MkS8dJ5k+T6B+nwf88tne8HE2dAQdQNlSSa5QEVQCxuDF7N8JcrWsqZHCpfC5vF/7Lk8HKAPrwcLnasVSmnwuNuSiK0+k0nZ2dNDQ0oJojjB77Hi7hrlaENDS8mpeiLPL94VO8se3q4G+yNMah0bOkzAy1njB7omuJ6P4qEbycH+Lfer5HfzGJV3fz4rodvKb53gU951shEAgwPj5OPp9n7dq1uFwuEokEFy9enNWc4XJHJYnCNM0FaX+7XC7W+FfSnTuNKU1cGEgkNjYu4eY1DTeb4yrjB0NP8qnBT5cFFALS+WHefvZPeF3963hl09xUoBURxapVq24ZDH+7oet6tWVXmfXqSHSQSCTIZDKEw2Hi8TixUJgvj/48QaOcD1u0DQKjr6M5t4Nde3fh8/kYSw1PpNKUWZGlNErShSEkKzyjuBkl73hQaGQcyCgPtsqhiRKKYYbM05zLfoGYq5aA0Uib/xFCsxQTLTQqpP5WyuiI9irG1Fen+Y2PVfG30jf+DqQCS4Ex6TUEGpqwUPZTZDg+8TMPILHVIIPZP8VjfAaP0b4IZ1eGUorTp08zPDzMnj17Fp1MmKXLkH8HPiGRqqxQ9wqHtrrDRKNfJpF8W1WEUnFmmLcIRY8RYw/nTypWNjyfdasnSL1aB9pKkOdB1AIaqCRoTWBsXrBzng9uZng9ecxpfHwcn893R98jZoK7jgzOplJQLBY5fPgwUkruvfdeHn/88SXL+F1ozJYMVoQi69ato62tjeNjV4AbbcKvDuECnMn08dFL3yZlZiq/5vtDh3nzihcT98HJ0ct86Pz3yUunmqRxIfcdusbO85cbf3FuJzhLVFqmPT09U4LtK1WbVCpFIpHg6NGjQDk7cro5w+WOYrFIV1dX1TB7oY79tzt+kf9x5h8YsoYwKY8NeISH1zc9SIPv1sklnxr8NJomyyKWiWtA0yRfHP7inMhgf39/ucK2adOyVvZPh8nt5FWrVlXbyYlEggsXLrDK/e4yMYzF6B3sxbIsdu3dVZ1X9mpBss4IlVZyWW9cthSvMzLYSiDQkQpyysBWGi4cNKEwhCSgjaLkIKOmIm0KBgtfYXXwV2kPvPG2VFYrCTErV668JanfWP9ehhIPc17+NgIThaBGPMLG+vcC4NHXkOMngFM2uxYClELhoFOLW50DITEmWZpI5cIhy0jhYzSF3rso56iUqlbql8o0u1R6F34hsRQwYW8kkbgE+IzH2LjxA1NskyopPIFAoEoMZytCyefzHDx4kMaGa6q7wg/u14D9dHlGEAnGLjD2gza38ZXFxM2qhp/4xCcolUpYllX1vLwbcefc9RYYFfuU2tpaNm/ejK7rSxLrtljQNG1Gx66U4syZM/T29rJz585qJNnmcDMRI8SIncYQerVNXJJF3MLDi+rLwfaOcvhM348YMTM0eWrRBDjSYdzu4bupv6bJq9NbSLAlotGbayBtR5BSYiqHzvQ5nhnt5p6axVVkOY4zJb3g2taMruvU19dTX19fnTMcHh7m3LlzHD9+fIqf4XL+8mcyGbq6uqoRTwu5cw24/Lx/8x/ROXqCI5luQoafV8ZfMCMV8ed6v1pONlCCydsLNWG18q+XP8Nb2n9uRsdRsQG6dOnSFFJ/J+PadvLIyAhDQ0McPXq0qlxMJpPU1dXhdrt5Ud2v8PnBv5ioDipcwsYlHCypozAQwgY0bOVgKR0dRXme38Ir8viFiQIcJSbSaPJcyH6UkL6entJpLuZ/gsSh0bOFvdE349EXbzaqUt1dt27djHOxG+L7aOCn0/4u6v05MqXv4pMDjEsvtgOaMBH4UFobghNwje9jOSVFYU5E5y00lFJVu6M9e/YsWXqKkD0T+67J64CGVA7GhE/jtbZJlQSoZDJZtfWZqQilQgQbGhqmH9nQasD9SlAvAmSZIN4hqIw5/dM//ROf+tSneOyxx5b1vWAh8F+SDF65coWTJ09eZ59yq0i65YyZRNJVrDiy2ex1kXqapvGWjhfz9+e/Tl4WyhFaKHR0HmzcS72vPCd2KTfMQHGUmCs8sQlXGFqRjsBFpCpiqSZyjiBgmKwLD3By3EXW9uOWUFQO3xo8uKhk0DTN6qJ2zz333FINPnnOsJJdm0gkqukAlXZePB5fVnOGqVSKo0eP0t7ezsqVKxftuHbVbGZXzezaOpdLvZUCzfUQ0F8anNHrVNpsQ0ND856DXK7QdZ1QKMTZs2epq6ujo6OjGlN28uTJ6vW3xn0v58wngTK9jroKpMwQSWsFUeMsUpYAozxXWKnEInELC4RCKgMNqt/Zkp3n28m/wlJ5KuF65+weLuWf4HVNH8ZvxKY93vmgEhW4efNmGhtvnu87U7j0GM3hD6Bn/g7HPEZeSQRxvMY6wu6NGOZTOIxMeY5U5XXS0BY+m1tKyYkTJ8hkMuzZs2dJHQsUfgQpQDKZEJbThqffKLpcrqoIZbIAr+fySc6fSeAPNFBT23GdCOWWRHAyxJ3n2qCU4mMf+xjve9/7+MY3vsHznve8231Ii467jgze7KKUUnL69Gn6+/unVMUqMAzjjq0M3qqqWRGKuFwu9u/fPy1JekXzVuLeEJ/veZq+4ggxV5AHm3byiuat1cdIJEpJtMrdXgi8+iCGVqLgeBG4kEoj77gJGSb1njRZu7wjFIClFo9sZ7NZDh8+TDgcrlZ7Z4tAIEAgEKCjo6PazhseHubChQt4PB7q6+urc4a3ixheuXKF7u7uZdsy3RPaydn8qWl/pxRsD96aXDqOQ+fxLvryA3Rs7iBtZPApP8YMIsHuJORyOTo7O6mtrWXjxo3Vzcnq1aspFovVdrJ/ZCtbPWsYqH8S6Ta5N7KfOu9Onk0/zZiTJmYkcSMpWAY5ZaApDYREQ8FE1Fj5ei3/d0Z5sVRu4t86ZfLgYKs03x7+Y9b61uDWa2n1PYzfNf9rrHLNbtu2bV755tPBa6ymveZvabFz5J1eTGx04SZktJLMD5Iu/j9slUHDh0KiyCPwEfPN3GD5WkiZxVYJBF4MrREhxJQYvT179iy5LZnheStO8S8wAHsi9Uaf+Lvb4tbpQUIIopEAsUAn2ooubHuMQuEEiXQjzz67EsMoexqGQiHOnz9PU1MTa9euXTYb5IWCUopPfvKT/Omf/ilf+9rX/ksQQbgLyeCNYJomR44coVQqceDAgWlVjXd6m/hGlcHR0VG6urpoaGhg48aNN20n7o51sDvWccPfr/DWEXUFGTUzxD1RNEATORwpcWtuwoYfl9AxpYOjBH6jBIA1sSgdqNl0w9euQClFX7Gfy4VeCk6Rek8dq/wdhG7ioVaplLW2trJ69eoFWaCubedV5gyPHCnndk7OTV6oiKiboWJRUWnxx2ILX71ZCLyo4V4+Pfg5hGaDKsfjCcobByV1Hm6+uQDFsix+fORxTorzuJo8JPJH0fMaK3zN3B/bh19f/pYpM8H4+DidnZ20tLSwZs2a665Zr9fLihUrWLFiRbWd3JxYRXIwScFxyNfmeGH8xfhr9jJofYqCfZF2zeRcsYQFSKWw0HHhTDTrK+VaRUG5YcJS/GoVSUMph7Q1QL+4hAD6C99iQ/h3aPLNPfqtkhm92NesYQQIG1O7Do3BP8F2BshaP5lQEwt0wtT5fwufa/ZCBqUkOfNxCtZTOCqNJty4tdX4Xa/gxPErlEqlaozeUsPneyMZ6zu4nadwTVxKUkFBhfD4Pjaj19DsJ9Ds76OIortaCBo5wsE+2tpaSGY3MjAwwKlTpxBCkMvl6OvrW7B40OUApRSf/vSn+cM//EO+/OUv88IXvvB2H9KS4a5LIIEy8Zt8WplMhs7OTkKhENu2bbvhkP3Bgwepr69flnFPt0KlutDePlUdV2mJr1+/ft7nVVFYPZU6xWf7f0zBMXFrBhHXJRq8fdS526hxBxksjtJbGMavm1wpROjOlHfOrZ4Y/7zr7bd8n6PjJ+gaP4qjHAzhoiSLxN11vLD2eURdkese39fXx+nTp9m4cSPNzc3zOseZQClFOp2uplAUi8VFnzOcHLu2c+fOZe951Zsf4H+e+2skJpXOpcDFu9f8Ma3+G1eaisUiB7sOcdx7Di2m0+RtQBc6pjQZLCXYHt7MgZpdS3Yei4WRkRGOHDnCqlWrrvvO3gpKKTKZTPX6y2azhKM+ovESkZoAtmFxJvckQ6XjoIaI6Fm06gynQhMwYMUmMpOvkhZFmbyDYLUvjJQOJTWCV6vjQN3H0ZSbR5N/yYjZiUuUMKWXBu9+Hmh49w2P8+LFi1y+fJldu3YRiVz/3V0qFOyz5MzH0AgQdr/2ltF4N3wd6yBjxf9EE350EUOqEpbTR2o4TGH0ZezcuXfJDNBvhGLpaazS34HKoxkvIhC49ZoLgMpjlD6CUjZokyI6VRqhiow7v8DBzos0NDTQ3Nxc3RyPjY0RCASqIrylTkJZSHzhC1/gN37jN/j85z/Pgw8+eLsPZ0lx15PBwcFBjh07xsqVK29ZMTp8+DCRSOSO8xkEOHLkSFWtCFeFIn19fWzfvv26lvhsUIn0qVRNhRCczvbxROokQ6U0jV4dv/E0giJ+vQaEIFEaIG0WOTK2gpITYkd0Fb+78jX4XTffQY5Z43xz+LtoQiPiilB0bKSUpOwEm4MbuDd21d6mkprS39/P9u3bp835XGxMVuclEgnGx8erc14Vs+v5YnJyys6dO++oQeYT6VMcGjvOtvBGdtRsueljs9ksXV1dyBrB2VAvcU8tLu3qxm3MGkcgeG3jK/Do5c/gUu5ZujNfougM4BIxVgYfYlN48f3j5oPK7NxCbV4mt5NHRkaqtkm1tVGGtM/RU/wWkiwGDgiBS9SRVS2MWJcoVwV1QCGVhSZAx6DdW75uHWlikWFT6O08k/4mujqOJhSO0tCFg6M0NG03r27+4JRjqnw3BwYG2LVr110x76mUZKTwz9hOPy69vLGWUtI/cAnNSLCq+Q/wezbe5qOcB+QQRulfUKIWJhuWKxvHusjBkzsI12y/rjVsWVY1CSWZTAJMyU++3eR4pvjKV77C2972Nj71qU/x6le/+nYfzpLjrm0TV8w+L126xLZt22bkH3int4krx17xncvlcuzfv39ehOTaRJFK2smGUCsbQlfNQweLWzg8/kUy9jCgaPCs4KXxh3nHmt2zer+EmSLvFFDSz9f7j5AolRMaoi432ZhgX3QXhlae7Tx27Bi5XI69e/feNkPQa9V5pVKpSgwvXLiA1+ud4mc42x1zoVCgq6sLn893RyanbI5uZHP01jfIit1Ia2srRrOH04nL180HGsKgJE0sZePBw5H0V7mU+wgucrjQEOoC58aPMWKe5Xl1v7ZYpzQv9PX1cebMmQWdnbu2nVyp2Jw40Y2UW6ir70DUnEO4i4TcbdR6tuHRG/jSwO/gqCwgy1Y1AqQS1E9qcQoMUIpUqQepTqEDBTmxGVEuvKJEyTnKmDlAxF2u+E62VVkKf72lgsJCyjSaKFcVpZT09/cjhIfaWA1Cy93mI5wnRKis+FWZKWTQMkdJJDLEattZufr6GUGXy0VjYyONjY1VEUolhefEiRNEIpEqMQwGg8uyaviNb3yDt73tbXziE5/4L0kE4S4lg47jcPjwYTKZDPv375/xrvROJoMVNXElqsvj8XDgwIF57coqRHAmiSKN3g084PljRq0elJLUuNswxOznZoQQpK0SPxjqpeg46BPjTMlSiUeHL/NgwxDtrloOHz6MYRjs27dvWe08PR7PdTfm4eHh6pzh5NzkW80Zjo+P09XVRX19PevXr79rTU+Hh4c5fvx41W4kY2cJ6H7G7QyRSWknY3aGZm8Dft2HbdtcyP4HHpGjpGIIIVBK4SZNqvQ1xq3XE3YtvFp0rphskbNz585Fq2Jfa5s0Pj5e3pxcriOXy6GiUdx1IbzxAK9u+Dt+NPJBRqyzoCSacqjRTYL61e+TrTJowkPCTOAWNiXHNcWMtCRdeDSL7sw3uaf2rVU17fj4OFt3tTMiHmMwM4rPaKTOsx+3dvtaxfOFwI2u1WI6lxEyypUrV9A1jYamGhxlok3yMrwjIfxIfSea/T2U1ECEscw06ZEzaO57WLli7y2J3GR3hjVr1kypWl+4cAG3210lhrFYbElmrW+FRx99lLe85S189KMf5Q1veMPtPpzbhruODCqlePbZZ9E0jQMHDsxqkPdOt5bJ5XILlq08GyJYPQZhUOe+tWrtZmh0xzmXyVF0HDyT3lcTDpZUfOrSj7lnNM55f4ZBX5GvnbvA3prVvKhuCwFjebVPJ9+YK9mhw8PDnDlzhlKpRG1tbZUcXnudJhIJjh07Vp0pW4676YVAb28vZ8+eZcuWLdTXl+eUQkaQTaG1dI4doyhLeDQ3ObuAT/eyNbQBTWhcKh7EYARL+aufjRACS4VwkeFs9nvsrpmZl+FiozKyMTg4uKQWOUIIIpEIkUiENWvWUCgUqjfmc+fO4fP5WB9/K3V1dUSjUS7kPsHF3OcoOkk03EjKcYT17udRkE1k7Yrh1FVoojx56NGCOI7D0aNHKRaLrNnupbvwV+SdK1iyXH00RIDNkXfR6Lsz1ZlCCPyueyhaF+lPdKEbMerrI9iyB6+xBbfecbsPcd6QxvNAOWjOYWyrh+HhcXT3fupafn5Oa9C1VevR0VGSySSnT5+mVCoRi8Wq5HCp/Bgn44c//CG/8Au/wD/+4z/ycz+3PNaL24W7cmZwdHQUr9c7azJ0/vx5crkc27ZtW6QjWzwcOnSIZDLJxo0bF0wocrsyht988O+4UhzDrV19Xw0NU0rCykWrN06CLG7NQCFxyBD1CDp8tawLrmR/zS7q3FdViz/z1AdImDkqMobVwTo+tvs3l/ScJuNGc4YV25rR0VFOnz69oH5syw3XKqOj0eiU30slOZ+/zNncRbJ2jrg7xvrgGpq95XGP89knOZl+DxIParKPmbJxiXHq/b/G3po3LeEZTY+K8CedTrNr1655ZzMvFGzbZmRkhEQiQTKZREpZjs6r+ynj+k8oyREMEaTR+3zWBH8FSxX5Uv/PENDzFBwPCIFQCp9eYtwJ8vr6L3HixAkcx2H7js0czbybMesElsxVyWPZ2EZja+QvaAm85Hae/pxRLBY5duozBCLdxOpcaJobj7GRoPsB9Du46nkt8tlBjh97grp4BytX71rwe0BlDazMGabT6aoIpZKEstidkMcff5zXv/71/M3f/A1vfetb79oN90xxV5JB27bn1O69dOlSVa15p6BizNvT00M0GmXfvn23ftJNXutaocjt+IL8/pF/5XjmCj7dgAljEhTkHYtaVwDDcNHgDqMLjay8Qok0jpTUuIMYmkadO8ZbVryRek8dr3j8f1KoXgtlMgjQ6Anx2f2/v+TnNh0qc4bDw8OkUikAGhsbaW1tvaOVeTeClJJTp04xMjIyI2W0Uuq6z8C2bb4++HO4RaLaJgZwqVEs4efF9Z+YUZv4fO4ox7PPkLYShIwYmwJ72BDaM/eTm4TJlbJdu3YtW+HP5DmvRCJBLpcjFA3RGG8kHo9XCewzqY9xOf9pfJo5QfAURemhw/dLcGEDuq6zfft2MvIUR9PvJW/3T7yDmGjjT5g9iwgPNH2z+v5fu/gSskoHodCUYp3/ADsb37ekn8FMUCwWOXToEOFwmI2b1qBIowkPurY4djmlwn8gS3+PIA94Ee5fx+N/66K812TkcjkOHjxIc3PztJZHi4FrRShKqSlJKAtt1fP000/z2te+lr/6q7/iN3/zN++6NXYueI4MTkJvby+Dg4Ps3bt3EY5q4VERiuTzeRobG8lkMuzaNTfbjWvDuW8XEQT4+uBB/uH8dwHwaAaO7WBiI4RGRyBOSVrUeyJYKse4vAxoWFIScwdp8kRJmCN0lJr5tnkWpa7OpAhsMqa/7JUN/MeeX2dFoH76g1hiVGat0uk07e3tjI+Pk0wmEUJU/QyXy4zNfDCZIO3cuXNe/mRd6S/Sm/sXDHIopSOEg42XGs9rub/uN275/GOZp/jJyFcmIhfdDKcT9MgQWceLAryaxf5gLb+9fnrrlJvBsqxqEs6OHTuW1VzrrVAoFKoVw5GREfx+f9U2JOu+wE/T/0rJGcWr17In+MtcOVnC6/Wybds2dF0nVTpE5+g7sWXZ1+/qOlLebCp09sX+jjrvDj57/gFcHmvC6bC8WTMdnSZXI/c3/7/b9RFch0KhwKFDh6ipqWHTpk2LvjYWM7+Nbn9vSm6IBGx9L77wJxftfW8HEbwWkzcnyWSSbDa7oCKUQ4cO8cgjj/AXf/EXvP3tb3+OCE7grpsZnA/upASSilDE6/Wyf/9+hoaGSKfTc3qtyfOBQojbLlR4sH4XJ8b7+FHiJDm7bFptaDr3xtbi0d2cGO8FwFJ5FBIdFyARCDSh4dU9fKt4Dq5RoyoMQp4C40UvCsHHL/2IP9v8M0t9etehQhyklOzbt69aQZJSVv0MKzM2N5szXO4wTZOuri50XWfPnj3zJkg7o68jpDdyOvNFLAbRRQ0rA69ga+ShWz7XliaHxr6PrSzq3E2kRoc4ZdWWW6AAKIqOix+MZSl2v4c/3DDzSlWpVKp+NysE6U6Cz+ejra2NtrY2bNu+zmx9fd2vV+PJjh49Wk38qawbIddaBNee8+RmMVhyjO9efgiX20YAtqwkoUjcukNvcXgpTnVGyOfzHDp0qJoBvtjkoVQooE0Qwcl3Ix3QnWcpFa7g8bUs+PsuByIINxahJJNJLl68iMvlqq6Ds90gHzlyhFe/+tW8613veo4IXoPnyOAk3Clq4pGREbq6umhubq6qTG+WQHIzzEUostjQNI3fbX057Uk3J10J6mrreGH9FrZF2vlB4gTHx3vJO2b1WG3loKERNsoDyCfTwyCmb8mVK4UZIEzEmPnAsi0dFGqK791CoGId4/f72bp165SFTdM0YrEYsVismps8PDxMX18fp06dIhKJTMlNXs7I5/N0dXURCoXYsmXLgm041oTuZU3o3lk/b8jsI2unCehltfJJq0jBiaILiSYmmqAKTEenKzdzy5DKJi0ajbJp06bbvrGaLwzDoKGhgYaGhinZtefOnaNQKODxeAiHwxSLxWo72a2FafI+QE/+P7lKAstVPwkYuKlz3cuIbeL1qAkieDUFRSqJx+WQGj2KFqjn+NgzDJlDhPUQm8I7WeFfvGzza5HL5Th06NDMMngXCMr+M3TAmTTWwsS/NQSW9R7wfXxB37NCBFtaWhYswWmhMFmEIqVkdHR0yga5pqaGeDx+SxHK8ePHeeSRR/j93/99/uiP/mhZneNywF1JBuf6R74TyGBvby/d3d1s2LCB1tarPn9zOfblSAQBxsbGOHz4MNvrV/LG9a+YckO9r3Y9J8d7eTZ9Hls56LpCEzY1rghBw4slLUx188s67NXIFOHt6x++5bGkSuP855Wn6UpfxlKStcEGXt28hw2hFQt2ng0NDaxfv/6mn/9kP8NVq1ZRLBarApRz587h9/urxHC5zRlWLHIaGxuX7IZ6KxgYCKEhVfk7k5U+FAJNXN1QCQFCKArOzCqwlaSj5XSeC4lKxUbXdfr7+2lubiYYDJJMJjl79uyUa3Bj+PcYKjxOUQ2VZz4pV7k0dFr8L7tlVVigGDbP8OjI1xguZdAROCi6xrt5Rf2L2R59waKfbzab5dChQ7ehUpa/wc8nVPOqsKDvtpyJ4LXQNI3a2lpqa2tRSpHP50kkEgwNDXH69OnqNRgMBonFYtUuy6lTp3jkkUf4jd/4Dd797ncv63O8XbgryeBcsZzbxEopuru76e/vZ9euXdTW1k75/WTT6Zm81uQZweVEBIeGhjhx4gRr1qyhtbX1uuNyawa/vupl3Dt2me5sP/3FywyZl0A4DJdSE4+/xRiso7gvtvaGvz6UPso3E98na2cZM22SeT9+PYAuNJ4ducjFXII/WPsQa0NzT4+oeOutXr2atra2WX/+Xq+X1tZWWltbq6284eFhurq60DStelO+3XOGqVRqSuzacrnO4u4V1LqaGChdwi3mn6taMc3u6Oigo6Nj2ZznQmNsbIzOzk7a29tZuXIlQgja29untJMrs5I1de+hEPsEWU5iY+EVAVb4H2JDpDzPGdAdbMqk++rkerkym7dcdDHIcCnDCm8UXZQ3hAPFMb6f/BFrgjsIGFfVu44cpmj+BKnGMPRVeF0HEDfoDswEmUyGQ4cO0drayqpVq5b07ymM30Haj6FNVFKrP2eivupeOEP1CuG9E4jgtRBCEAgECAQCdHR0YFlWVSH/8Y9/nI997GMcOHCA/fv38y//8i+8+c1v5i//8i/vqHNcStyVAhLHcebkF5jNZnnqqad44IEHFuGo5o5KHFmhUGD37t3T2lOMjIxw7NgxXvCCm++Yl5NQZDIqprwXL15k69ats0pn6Clc4VT2LCVpssLbzPGRC3xu6Kc3fPyvr3g1D6+YXiT08Z5P8/jos9e4qQmCdOARUaRU9BRGeWF8Pb+95pVTnvs7nR8iaWX4xZYHeHDF/hu+f8Vbb/PmzTNKxpkNJs8ZDg8PY5pmdfi/rq5uSecMBwYGOHnyJJs2baKp6cZ5xLcLV4oX+E7y/zFupTiZNhksRRFCoU9qE1tKJ+oq8B+7P3jD16l4Qta2e3lU/QtpWyKVwKs57Arv5eGm312qU1pUjIyMcPjwYdasWXNT+6pKdndFnZzP56utvHg8Xm3lHR86yNHcO/G5rQn/QlGeH1QaAcfgSGk/htCIuK6ud7ZyGCiN87PNr2Jz5AAARetJ0rm/RqoRypRJw21soibwl+ha7fUHeAtUCG9HR8dtiyYtpF+MofrRKOfDaAgUYIlafNEnFuQ97mQieCs4jsNPfvITPve5z/Hoo48yMDDA/v37eeihh3jooYfYvn37XXW+C4G7sjI43zbxdFYWtwuV4WWfz8f+/ftv2F6ZSZv42mi55TLPVLEaSaVS7N27d9amvG2+FtomDVTvimzhC8NPIdX1FTGfUDckgv2FIZ6YIIJioiVTJoWKHD14iKJpAp9mcDY7WH3e73R+iEvWEJU2zj9d+Sof6fsyX93/v6a8/uQs5V27dl3nrbcQuHbOMJvNkkgk6Onp4eTJk0Qikaqf4WJ53imluHz5MhcuXGDHjh3XVbGXC1q8q3hD429xMvss0eJRvuUMkre9SEl1VMuj2zwvdOONSX9/P93d3axcvZJPFP+MjONFoBATApQn0l046oO8uvkdS3NSi4QK4V2/fj0tLTcXLwghqKmpoaamhrVr11ZbeYlEgjNnzlT95FrqVjHq/P8YKn4ES9PQhcJxIKb72Nf8Lxy5+I9cvwxfVSYDSDnOWP6DSDWGLloQQkOpEqZ9hLHcXxH0vBKhxTD0TYgZVIDT6TRdXV3VSvbtgi/6fYpjP4OUx8sEGZDaOnyRry7I69/NRBDK98OVK1fygx/8oCoY+c53vsM3vvEN3v/+9xONRnnooYf40Ic+dFvMrpcj7koyOFdU2mlSymWhAEylUhw+fJiWlpZbzpTdigwu1/nAStXTtm327ds3L6uRyfjqPe/nrZ3/k2GzbG+BkOwLr+Y9G992w+d8bfi7yAkiWG7RXK0OKhwslccl/FhKEjTKx/mjwSNcsoaZepMSKKHxqqffyVf3vx8o71QrMV1LlaUshCAUChEKha6bM5w841VfX084HF6Qa+LatI1wOHzrJ91GhI0Y+6MvZ3/05by0r4+/SP4vxq2y2tyvm7wyuomfWzV9W65Syd6xYwdfyfwvstKDS9jo2kRlUToUpYvDmS7u5LTTwcFBTpw4wZYtW+ZUyfb7/bS3t9Pe3l71kyu3k48gRJy2+F+hRwQ10RjNvha8evnm3OKt5XRugKDuRZtoE6fMLBHDS/uEiKRkP4sjU+iiATHxGIRCySIF6wcYZBHCha6vxuv5RTTtxt6Tlcrn2rVrp8xj3y54I59flNe924kglDdpDz30EC972cv48Ic/jKZpvPWtb+Wtb30rpVKJH//4xzzxxBMLdr+5G/AcGZwEwyh/HLZt33YyWBGKbNy4kRUrbi1WqKiJp6tqKqWqbfPlRARzuRyHDx8mEAiwc+fOBf/MP7brT2f1eFNaU/4tJlozVP/XJm3lUSjuqV0NwAcvfZYVgTQKDUdp6JQrr/3FKGriczZNkyeOPMVZ0YuvLUgmd5B9rm3Uuhcnn/ZGuHbOsNLG6+zsXJA5Qyklx48frxLe5ZK2MVOsWLGCj6748C0fp5Ti3LlzXLlyhd27dxMOhxkaGUYpF7p+dQMhNDCUpCCnLrN5J8e53Gl6i5cBQZu3g7WB9VUStJzQ19fHmTNn2L59O3V18896drlcNDY20tjYWI1oLGcnJ7jQfYl0bLw60vD8+AMkrS/QW0zjEjqWcvDrLp5fe4Cgq2z0rFSB8vezcr0qpEyAshHCjaavQqBjO6cpmd/E534VmnMMoYZQRJDGZtCaq7OtM6l83smoEMEVK1Ys+SzkUmFwcJAHH3yQ+++/n4985CPXdcA8Hg8PPPDAshsHu924K8ngXC/wyvzc7RSRSCnp7u5mYGCA3bt3E4vNzN1e1/WqMKRy/pOFIkqpZUUER0dHOXLkCM3Nzaxdu3ZZHNc90Z10jR+v/nvKESlBf6GEV4d761bzyoayuXdLIE1BulFKIITCUjqaULT60/Tmo3z5/A+Rw+M85TtB0bBgHBhXPDnayc83P8LG0JolPccKDMOYclOu2DV0d3djWdYUP8OZ+AFWKryO47Bv3747zgNxpqh8PysjDZUKr14ZK5BlEljBVHMQKDoFfjzyffqLfXi1srl1f/EKh8dPUO9uw6d7afM10+ZrqlbDbhcmt/pnug7NBpqmVdvJFeukZDJZVYYGAgHur3sRg8ELpEkTNiJsDu9gdXBH9TVc+gaE8KHUOEJEUBRRqoBCYWgxBN6yd6poQtld6KoXTSVhwrxFc54klXshR446bNy4cVnOti4UJhPB1atX3+7DWRQMDw/z8MMPs3v3bj72sY/d9qLOnYS7kgzOFUKI22ovUzEfLpVKHDhwYFaVlcpF7zgOmqZdJxRZTkSwv7+fU6dOsX79+hlVPZcK+2p28sWBbzFkJSZ5fJXbxit9a9kQ387GYAtbIuVZop8kfkxJlYmSoZU/Z4VCIjCVgUGJwsUBuuODFHSTuKsGXTNwpCRppfji0Hf5k0AHhmbgKEmiNI6jJHFPGPcC+xneDJPtGtavX082m2V4eLg6ZxiNRqvEcLprslgs0tXVhdfrXZQK73KB4zgcP36cXC7H3r17p7SYNgZ2krSOYCkdl3QQGkgpcNCIua6K2S4VLjBQ7KPe04ghDCxp01vIMFQ6Q61rjIDh5+j4GXZFNnGgZsdtS4C4cOECvb297Nq1i0hkaTJ3K8rQSju5YjQc6GsjpHVQV1dHyIjj+JzqNWboq/G5Xkre/DpS5hGAVHk0EcBrbKt+fgIdt7qCkEWUtqlsSK8UpeI58iNfYMvmP6S+4e4mggcPHqS1tfWuJYKpVIpXvepVbNy4kX//93+vdvqew8zw3Kd1DXRdn5MSeb7I5XJ0dnbi9/vZv3//rC/kSinccZyqRc5yE4oopTh//jy9vb3LVljw3vV/yD9e/ndOZc9gKwef7ucldc/jtY2vuO6xX018Fak0NK7xplMKR2nEvQXa42s4WLhIWAuiTxA8XdOIGGGSpVHO5i4RMmr43vBx+gsjSBR17jB1Lg+dY2fZGuzgTR0vXrLznzxnuHr16uqc4fDwMGfPniUQCFSJYTgcJpfL0dXVRSwWY+PGjcvmWlto2LZdTYnZu3fvddXSh5t+l+7cWxi2tPIGwSl7FAY0k1fHr86pDpUG0IWBIcrXwmBpjLSVx6+7CRouVviaGLezHB7vZqV/BU3emavqFwLXznzeKjd6seByuWhqaqKpqWmKQv7s2bMcO3Zsijo54v9dXPpK8ua3cGQCl+7Crbfi0q+2e5UcwECitBWIiWSibD5PIiFobgAjXLiVIdUdi/8KRHB0dJRXv/rVdHR08OlPf/qOin9cLrgryeB8dtO3w2twNkKRG6FS+auIRJabUORaAcXtusncCm7dze+turHIZDJ0UW413Qi1IkptvBbZo65r+WlCQ6IYNbM8Nnye4dIYDe4IuWyOLyV/TEkaSCV4PHWFj/b8mN9sfxGvb198o91rMXnOsDL8Pzw8TGdnZ/V6i8fjbNiw4a4lgqZp0tnZidvtvmnl8w/X/Cv/2f+/OZs/hVSSiBHkdQ3vpMl/9Qbs0tzISZuHpDmOIcrBY9oESQnpAdJWhivFoRuSQVua5OxB3FoYnxFdkPNUSk1R9S+Xmc/JCvn169eTy+VIJBIMDg5y+vRpgsEgdXXbiMdfQjgSxrJ/TMn8Mo5zDiECSDWOLjzoohkxkXSSyWZJJpM0NDTi9Qxh36VU8L8CERwbG+O1r30tDQ0NfO5zn7trR1QWG3clGZwPlrpN3NPTw+nTp2csFLkZdF3HsixcLteyIoKmaVaNaO+mebK3tf7/+F8X/hZb6ujIqnmuVBouzeaP1/wBPneAkBFk3Mrg1a+a4GbsLEHd//9n77zj7KjL/f+emVO295Zk03vbbElCIPQeUnYjIKKoKKIiioheFfUiIFJEuShXUC8q5SegJJtQkhACSSghQLItvWc3m21n++7pZ2a+vz82M9lN3WTLObt73vflfenZU76TM2e+n3me5/M8BISFWm8LY6NSkSWZt5yf4FatHRHGY01u/LrC/1Zs4NqkXGJjY9GEiioCWCV7v9aVdS7+NxymcXFxtLa28sEHH5CcnExaWhopKSmD5s7c4/FQXFx80vzd03Hj8J+d8e+ZEaM46N6LS3USbYkBAQERIEJWiLV0OK8lSeJ0v1xd1znoXsMh1zp8WjOyZCXFNp2s+K8RZTn/SLth/nE6nSelwEONExsNn2yESiEldQnR0fuBBqzKNKyWuaBtBm0bbU5obGwmPT2daHsTQkpByKfvmzhQGQpCsL29nRtvvJG4uDgKCwtD+rwNdQatGJQkifPpp91fYtAoRDfSMYmJPXOW6rqO3W5n586dpKenk5aWFhLzap1OJyUlJeas1sFUTzYhZjzxFgvNAQ1NyObgE0XWSbVFkxjVsTlfnjyXVY6N1Pnqsck2/HoAq2ThkqQ8VF0gSxKyJFPX3oRXV1A9foa7dLyKBQWB4ldpzIjkR/v+yn2TruKgew8+3UOcJZFkOZNyt5NZieOZENM/G5rhMJ05cyZpaWkIIWhvb6e+vp6Kigp27txJQkKC2c9woPbxcjqdFBcXk5aWdt4R+xPJjBjF1JiZ7HXuos3biiT5COgaIyIyiFU6+mu2qy5sso3hEWknvf6wex272l5DoGGVY9BFgGrvZjx6E5ck/zcW+dxvtDRNY9u2bfh8PmbPnj2gbtZOl07et9eKzzeepKQ5pjvZaovC3XYQv3snIzLSsNmOIohFs14DUvCvlb3JUBCCLpeLm2++GavVysqVKwfsdSZUGJQTSKAjGnU+h1ZUVERqauoZO+z3lM5Gkdzc3B6lYwzHsKZp+P1+8y65sbGRqKgoc0PurT5y50JjYyPbtm1j1KhRg7aNAcAf9j/JQW85upCQZUFWzDS+O+buLs/Z0rKdT5tLaAg0k2iJY15CNnMTZlHUepg3qrcyNiqVoqbd7K04QkBPoMkbjTgWH1IknfFR9cSM8nFN+ijssh3Vq/NGYzmqAP1YExybrPD7afcwMqp3p5oYnFjzebobGI/HY/YzbG5uNusM09LSiI2NHRDngdF8uC/OXSEE9X4H9f5afJrKLmclDn8LiqQghI4iKWTHT2V+Yk6XyK+u66xv+BlOtYooy/HvWNU9+PV2Zid+n8zIC89pLZ1rIXNycgZNRFcIYbqT6+vraW1txWazIYkGZk5RSUrwgJSAULIQSnCmjPQVnUfpDVYh6PF4uPnmm/H5fLzzzjvnPKggzMmExeAJlJaWEh8f32djiIxoQ3R0NLNmzeqR4+lME0WMPnIOh4OGhgasVqu5ISckJPR5fZcRPRrs7RoMA4WRRjzXyGdrwM3LRz7C4W1FC3goO3yIw64UFEk3jSmqsCBLOlOsNdx6yVSiLbH89fDnaOa0UgMJBVg+9zG+veUJqv0tREt2/n3Rgz0+TmNKTFNTEzk5Od2u+eycxmtoaMBisXTpZxiKdYYNDQ1s27at35oPezUfB1wVVHkdWGULY6JGMCZyxEklAD6tjXWOexFI2JWuzbydag1TYm5kWtzN3f5cv99PSUkJFouF7OzsQRW1P5EDBw5w5MgR4uPjaW1txWKxmGMagz2/uzcxhKBxEzMY8Xq93HrrrbS0tPDuu+/2m9t9sDNoxWAgEDDbqpwL27dvJzIykgkTer//W0NDA6WlpYwcOZJJkyb1KNpgtI4xWsmc6b10XaepqQmHw0F9fT1CCHNDTk5O7tULoeFGrKmpYdasWT1Of4cyLS0tlJaW9rhX4lFPo+kmXl5yGJ9mxaocd7QLHQLCwtjoBn4wP4tDzkbebTiEADrLBQG0e/zIcjQax79TRdKZEBHLs7P/67zWZ6QRvV4vOTk5512XY/QzNM5DVVW7zE0OhaiUMU95+vTpZGRkBHs5XVB1P+sc9+HXW4m0HG8AresBPHojWXHfYHzMtd16L5/PR3FxMZGRkWRlZYWkKO8NjGj20aNHycvLIzY2tktfzYaGBnw+H0lJSeY10W63n/2NQ5ChIAT9fj+33XYb1dXVvPfee33S/3KoEhaDJ7Br1y5kWWbKlCm9up6Kigr27dvHtGnTetzhviej5Ywh8saG7PP5SElJ6ZXCf1VVzT5sOTk5IeNG7Avq6urYsWMHkyZN6pXokdFn8LrCF9GFhEXuWrfq1yyMjG7iBxdNZlXNfqr97mMzcI+jI0ADiwxu1YJPt9Lx65ZQJI1nhhUQP6ISVXcTY51MvCX7rCLAMP9IkkR2dnavCTajztA4D10uV5d2IcGo/zly5AgHDhxg1qxZIdn2CGB768sccK3CIkVjk2LRCODVGoiypHNl6qPY5LNHbD0eD0VFRWYdb0dfUh1VeFEkG7I0OErJjUkx1dXV5OXlnTKabaSTjbKGtrY2YmNjzZuUgVLWMBSEYCAQ4Pbbb+fgwYOsX7++VybihDlOWAyewL59+wgEAkyfPr1X1mGk1+rq6sjJyekVo0hvtY4RQpgNhh0Oh7khG3WG5xIB8nq9lJaWYrFYmDVrVkhEefoCIYQ5mWHmzJmkpvZuH7hLlz1GozcGq6Sakyx0XUITCpOjasmfm8HeVg87Xc2AMCODVkljWEQD8XYNGYEuJBzeGA62p6DpMhNjarkyZS8jIuwIIWjVYvCLSQyP+gKjo0aTbks96VwynLQxMTHMmDGjT1Npp6ozNM7Dvt6QOzdZzsnJCem0k6p7KWr+C3W+UlThRZZkIpVUsuO/RXrEzLO+3uVymXXRxg1vk38nNd5P8Wj1WOQoUm05DIuchyINHCPJiQgh2Lt3Lw6Hg7y8vG6b6U6suzbKGlJSUkI2nTwUhKCqqtx5551s376djRs3kpZ2srkqTM8YtGJQVdXzcgUfPHgQl8tFVlZWj9dgRFUCgQC5ubk9inZ0NorA8dF5vYnb7TYbDLe2thIXF2fWGZ7pYtrW1kZpaSnJycmDuvGwscHU1dWRnZ3dJ6Lh1mW/ZqcvCU3IyFLHT1MXEnFWD7+ZdSFqch0NfgeFVRXoyMeig4IJMQ3EWr34j/UnVCQdi6xT5YqnzhXLrZmfE23xk2Ybxy5XJJU+Cb+uYlcySLZNIid+Bhck5ODX/OjoqB6V4uJi0tPTe81J211OrDM06l1TU1NJTEzs1fNLCMGePXuor68nNzc3ZPtfnkiT/yDN/gPYlFiG2XOxyGe/cTNEw4gRI5gwYQKSJNHg285B5wo0/NjkeDTdiyrcDIu4kLHRiwdEVOxEOvdLnD179nlfdzunk+vr6/H7/eaYxpSUlH5JJxvb8+m+B+M7HT16dJ/VuQcbTdO466672LJlCxs3bhzUNejBJCwGT6C8vJzm5mZycnJ69PmGUSQmJoasrKxeNYr0hRA8EZ/PZ14EOzuTT3SE1tfXs337dsaNG8fo0aMH5ObRHYy6OY/HQ05OTp+mMb+07L85KqJp80ciS4I0exs3pIzn3su+gipUHL4q3qr+hLUNpejIRCk+psTVo2oCFas5ENcqawgh4fPBxckHaQ/YibONo7jdTqQisEouZCKItFxKc6CBtkATjYFWdAGRuoXLoi+kYOLSoH6nRr2rcS5qmmb2M0xOTu5RBNrordfe3t7jm7VQx3BHjxkzxhQNQmjsaPs77WolsZbjpQ4+vRVd+JgRd2cX1/JAQAjBrl27aG5uJi8vr9e+UyOLYtykGOlk4yYlJiamV38nAd1DhaeEGt8uNOEnzTaBUZF5xHTqJTkUhKCu6/zgBz/go48+YsOGDf1i6BqqhMXgCRw9epSamhrmzJlz3p9dX19PWVkZo0aN6pGxAHpWH9hbnM6ZLISgurqaGTNmkJ4+sDaNc8Hn81FaWoosy71aN3c2isp3kRGVwojTpEQa3G385sDf8YujjIyuJ802jMNuB8YvWpZ0LLIAVWV2fAVJjGZ3II6jPgtJVh1d+ABIsF7F5pbPUEWHIUUcE5MWWeH2EV/kgqR5/XK8Z0MIQVtbmykMe1JnqKoq27Ztw+/3k5ubO6B6650rjY2NlJWVneSO9mmtlLX+L4oUgU0+3ppDCEGbWs6U2K+QYp8RjCWfF7qus3PnTlPc92UDYp/PZ85ObmxsxGq1dnEn9yR6rYkAJa1vUOPbhU2OREbBqzuJs6QzO/5moi1JQ0YI/vjHP2bt2rVs3LiRMWPGBHtJg5rBUSl8Cs5XNPWk6bRRT7Z//36mT5/O8OHDz+t9Or9fsIUgdIzoMyZP6LpOQ0MDBw4cwOVyYbFYaGhoQJKkXncmhwLGzOiEhIRuTaDoTfLGTDvj31Oi4vhj1o+o91dSWPt7JEknVrHTrvkQgCILNCHR7IsBIWOx66j+YwO5BOgigE1O4pD7IKoQKEjIQkZWZASCgK6x2vFuyIhBSZKIj48nPj6eCRMmdClr2LdvHzExMWZZw5kiNUb5hqIozJ49e1APtHc4HGzfvp2pU6eedD3qGNFmRxM+4LgY1PF3zE+WB06kVNd1tm/fjtvt7pfG2Xa7nREjRjBixAg0TTPTybt37yYQCJCcnGyKw3NdS73/EHX+vcRbhmGVO1LR0SKJxkAFR73bGC7yhoQQvP/++1m9ejUbNmwIC8F+YPBeBc+T8xWDuq6za9cuHA4Hc+bMISEhoUfr6E2jSG+iaRpHjx5FkiTmz5+Pz+czN2OjpmawjCRrbm42WwGNHz8+ZL6DE0mxZpIZMYVyzzZSIqLJkBKpcB1FlgQ17ljujf0hnqjncKq7ibdIVPvi8elurLKFGMtEDnqOACAhoSgySBId/6fRGGg97ee+W/shaxs/wCIpfGPELUyJ798Gt1FRUYwePZrRo0cTCATMiGFFRcVp6wy9Xq/Z53PmzJmDtr4VjrfJOV3k3iJHkGLLotKzHosUgUWKRieAS60mzjqGOMvoIKz63NF1nbKyMnw+H3l5ef0e5VUUhZSUFFJSUsx0cn19PUePHmX37t1m7XVKSkq30sntqgMhdFMIAkiSjE2Ootq5j5o9DHoh+Otf/5rly5ezYcOGPmnz1pnHHnuMwsJC9uzZQ2RkJBdddBFPPPEEkydPPuPrXn/9df77v/+b8vJyJk6cyBNPPMENN9zQp2vtSwZtmljTNFRVPfsTT6CxsZGdO3dy6aWXdvs1A80ocr54PB5KSkqIiIg4qQ7yTM7ktLS0Ade7y9hIJ0+e3OOZ0f2BR3PyUdN/qPBuR9NVrLKdydHzmJeYj0Wy4tMaqHD+gzpfEcVt0TSrscRZR2CTU9nbth0PKjbJ0kUc+fQAMUoE/zP9ia6f5fHwg30PouFFOdb0WhUy6bY0npj2i3497lNhRGqMtjW6rpOSkkJcXBwVFRWkpKQwderUkPld9QVG0/eztclRdQ+HXG/R5N+NJrxIkkKMZQTjowuItoR+ob6maZSVlaGqakhOUDFqr410ss1mO6sZ6qB7M7va15Fs7VqDXe8ux1dvY3b8LYNWCAoh+O1vf8vf//531q9f32tdPc7E9ddfz5e+9CXmzJmDqqr84he/YMeOHezateu0xslPPvmESy+9lMcee4xFixbxyiuv8MQTT1BcXMyMGQOntKIzg1YM6rpOIBA459e1trZSVFTElVde2a3nO51OioqKiIuLY+bMmT02ihiNpA0RGCobVktLC2VlZaSnpzNp0qSzRlRO5Uw2WoWEwszk0yGEoLy8nMOHD5OVlTXgelm1qY241BbiralEnTClAkDV3bQFGtnvauCQu4IGRwMB4WKv9TBCCCyyAkjoQkdDZ3bcdL4z5ttd3uNHOx6mTWtEANqxq4f1WEr6ioSL+dro7k/B6GuMOsPKykpqamoASEpKOq/2SQMF4/w908jAzgghcKqVePRGrFI0cdYxA6KtjKZplJSUIIQgJycn5NP9mqaZZqiGhgZUVe3iTjYimm2qg8+a/4VAI0bpaF3V7m2irqWSaVHXM3tM9xqLDzSEEDz55JP87//+L+vXr++Vjh7nQ319PWlpaXzwwQenDQrdcsstuFwu3n77bfOxefPmkZ2dzV/+8pf+WmqvEtq/niBwLmliwygyevRos1XD+dK5PlCSpJBKX9XW1rJr1y4mTJjQ7ZnNnVN4xt2xw+HgwIEDp3UmBxtd1802I7NnzyYu7mQxFerEWZKJs5w+EmSRo0iyR5FNGvpeL5Pto8ialcXfKp+nrH0PAf14RDrDmsTXMr960ns0BZqPiT/ZbHqt6xp2SefD5q2mGPy8ZQNFLZ/i1t0kWJKYETOXnPg52JT+ExqSJKGqKg6Hg0mTJpGamkp9fT11dXXs3buXmJgYUxj2tiO0vzlx2kZ3z19Jkoi1jiKWvpvH3tuoqkpJSQmSJA0IIQgde4sRFTSartfX13PkyBF27dplppNTU1OZHH05+9wf0BQ4gqqqtLe4GR2dR87o7gUpBhpCCP70pz/xpz/9iXXr1gVNCEJHQAg443STzZs3c99993V57LrrrmPlypV9ubQ+JfR/Qf2MIQaFEKfdGDobRWbMmNHjvkehYhQ5ESEEhw8fpry8vEcNlu12O5mZmWRmZnZxJm/durXfZyafDlVV2b59Ox6Ph7lz5w7qNiOGKSYxMdGcQPG9sd9lW2sZHzVtwq+rzIybzuWJl51yk5UkYc4+iZR9DI9swyYbc5Rb2NX+Ebuce9nSWopfV9EEVFBPWfteltctY27ChSxNu7FfzEbGpJjOBgrjJqVzg+Hy8vKQORfPh85NlmfPnj1g+iWeD4FAgOLiYqxWK7NmzRqQpjVJkoiLiyMuLo7x48fj9XrNc/HQoUPY7XZSUy4iENVMVXUlORkzmT56DpI0cM7J7iKE4LnnnuOJJ55g7dq15OXlBW0tuq5z7733Mn/+/DOme2tra0+qw01PT6e2travl9hnhMXgCRgXFk3TTrkRGkaR+vr6XjGKhKoQNI6zubmZOXPmEBsbe/YXdYPOzuTOaZNt27YBmJtxf3b79/l8lJSUYLFYmDNnTsjVHfUmxjzlzo2HDbLiZ5EVP+us7yGEhCTpWCSV0dEtdHiPO7BKgjX1f6UlEIWqKx3PNf8u8OkePmn5EBmZhWlLqPbVAzDMnkpEL0cMjbq5rKysU97I2Gw2hg8fzvDhw7uci9u3bzfrDI1+hqEceTKmHBm/1cF8I+P3+ykuLsZut5OVlTUgheCpiIiIMG+YjXOxqqqK5kqZGHkselskNTW1XdLJgwEhBH//+9/5zW9+w+rVq7nggguCup67776bHTt28PHHHwd1HcEgdK9wPeR8RZVx0T+VGPT7/ZSUlKBpGhdeeGGP6o1ONIqEkhD0+/2UlZWh6zpz587tM/NH57TJ1KlTzZnJe/bsMdsz9LUz2el0UlJS0iVKNlgxhM65pPtPxcSo0RzyHmZ4hPOYEDx+3kqSjIZGpOLErycTEIFjj0sIcex5QrCpeQs1XkFLoB2ARFsc5a2f0qy5scoSz2b/+bzXZ9R9lpeXd3sE5IkpvNbWVurr6zl48CDbt28nKSnJ/Hso1RkaLVVcLhezZ88OqbX1Nj6fj+LiYqKioga1E1xRFOx2O83NzUyYMIGkpCQaGhrMdHJ8fLxZZxgdHR0y+8a5IoTgpZde4pe//CVvvfUW8+fPD+p6vv/97/P222/z4YcfntU0mJGRQV1dXZfH6urqyMjI6Msl9imD1kAihMDv95/Xa9euXcsll1xCVFSU+Vh7ezvFxcXEx8czc+bMHt2RGkYRY3ZyKBlFXC4XJSUlxMbG9vk82tNxKmeysRn3pjO5qanJbA4+bty4kPkO+oKjR4+yd+/eXmsQ/l87HyHOthu7rJliUJZkLJJCQAQQAup9yQSECsf+XY0rjYSNFr+VcZETGR6RxoHmXTRKtciGVjz2HzsSv5vxONFK1MkLOA1CCPbt20dtbS25ubm9EtF2uVxm25rW1tY+nTxxLhhO2qHQONtoCRQbG9vv/T77m7a2NoqKihg7duxJ/fW8Xq9pQGlqaupIJx87FwdSaYMQgldffZV7772XN954g6uuuiqoa/nBD37AihUr2LhxIxMnTjzra2655RbcbjdvvfWW+dhFF11EVlbWgDWQhMXgKXjvvfe44IILzI3E4XCwbdu2XjWKGP/sofTjbWpqYtu2badMIQYTt9tttgnpLWey0TpmypQpjBgxopdXHDoIITh06BBHjhzptru0u/y/ow9Q7d8PSFiwGJqPgAigC4k6XwK60I9Nx+tIFsuSjEe14VZtXJo0D6ts5eOmd1Fk0AWmcORYanmEnExCVDRRlmjy4uYyO2H2addjlDa0tLSQm5vb5WautzDqDB0OB42NjUHbjAOBAKWlpQD9OhUnGHg8HoqKiszofahcl/qC1tZWiouLTykET0TTNBobG01xqOt6F3dyKJ8Ty5Yt43vf+x6vv/46CxYsCOpavve97/HKK6/wxhtvdOktGB8fb5ZcfO1rX2PEiBE89thjQEdrmcsuu4zHH3+chQsX8tprr/Hoo4+GW8uEKj6f77xet3HjRmbNmkVCQgLl5eUcOHBgUBtFAKqrq9m9e3fIi6POzuSmpqZzdiYbppiKigqysrLO2INtoGO4oxsaGsjNze11U8Hu9k9YVf9nBAKFjgiyjo5AEKsMY7/LTUB0xA3FMUlokS20+S0oJHBhYh47Gj+hVXZ2vGGX704QqQSwSjqSBBIysqQwJTqH2zO/iSacyJIdWeqIEhuzo71eL7m5uf3S19Ko7TLGNAohzKkTfVlnaNTN2Wy2AWug6C4ej4etW7eSnJw86HtDGkLQmPN+LpxqVKORTg61dl5vvPEG3/rWt3j11VdZsmRJsJdz2nPqn//8J7fffjsAl19+OWPGjOGFF14w//7666/zq1/9ymw6/bvf/S7cdDpU8fv9nM/hffTRR0yePJm6ujpzI42Pj+/RWkJVCBrtKCorK5k1a9YZ7fShRiAQoLGxscvMZCNimJiYeNK/sVFo39jYSE5OTq+ZYkIRTdPM8Vx9Oad1bf1f2dH+ETrHWtIgkxkxiRtTf87mtvf5sPFDGtQ2QCAhIUsKVlKwkcaYqBF81rgGoVg6DCbHvi8FnYmRdWTamvELhaOBNNq0FDThI8XazlUJw0iwqMiSnVjbXGKUK9letg8IXpSsc51hfX09bre7Sz/D3hKnQ2mCisvloqioyOxtGirXzL6gJ0LwVHg8HtOd3NTURGRkpHmjEsx08qpVq7j99tt56aWXuPHGG4OyhjCnJiwGT8HHH3+MEAJFUXq8kRpGkVAdLbdz507a2trIyckJqbvHc6VzlKa+vsOh2tmZLIRg27Zt+Hw+cnJyBnWhvTERR5KkfhFHdd5DbHOuRxMqk6LmMS46u8vfnaqTzS2baA+0Mz56Apm2Mfy7Zh0N/lYc7p34ZdUUgzZJ5YaE7WTampEQIIEmFHZ5x1MTSGKi/QBxlmhGR2WhCx+q1kyrYwQ2343MmDETD20oWIhS4oL6OztVnWHn0obzWVvndOnUqVMHtRA0mvkPHz48pEpW+oLeFoInoqpql2bXhlPe+E9/3TytW7eOL3/5yzz//PPceuut/fKZYbpPWAyeQHt7O5s3byY2Npa5c+cOWqOIz+ejrKwMSZKYNWvWoCo+F0KYzmSHw4Hf70eSJCIiIvo0ShYKeDweiouLiYmJCZoBqDtUe+v5oKGYKm89e5yfIcsdscO50YeZG3MYv25BO/ZbsUsaEhKHfenYZA/ImcyMnYWqBqhvOII1wkd8wu0c8lbSojYgSxJpttFkxV5KvPX8emP2Jn6/3xSG51tn6HQ6KS4uJi0tjcmTJ4fMdaQvaG9vp6ioiJEjRw56Y1dfC8ETMSLYRtTQ5XKRkJDQxZ3cF2zcuJEvfvGLPPvss3z1q18d1N/pQCUsBjvhcDgoKyvDbrebjWnPl1A2ihjtVBISEoaEM8/oSwaYzuTeTt+FAobjPS0tjSlTpoT8BVeIjtYy/9n3ClvUnUgS3Jb6GXGKB69uRZI66gxlIFL206pF4gjEkhYxmWHKMBz19URHR+GLrOeQbziaFEWskogudNq1JpKtw7ky5Vbscu8bSc6XzkX/RgT7bHWGxjmcmZnJ+PHjQ/577QmGOBozZky/zd/tuEbrSFL/3jj1txA8FR6Pp4s7OSoqyjwf4+Pje2Vv+Oijj7jpppv4n//5H+64445Bff4OZAa1GAwEAmZU7kwYpoKDBw8yc+ZMamtriY+PP++LUajWBwI0NDSwffv2IdFOpbGx0XSBjx07FkmSTGeyw+Ggra3NdCanpaX1ifu0vzDa5IwZM4YxY8YMyO/1rtLvc1vaJ0TKKrqIRpP8cKxddaTsp0WLxKknMt46j8bGJuLj4oiOjWSH8yCNejoZtuMGA11oNAaqmZewiPEnpK1DBSNKY5Q2eL3eLv0MjV5zpaWl3XKXDnRaWlooKSnpxyiZH9SNEPgYcII8DqxXISlT+/yzDSE4fvz4HvX87E1UVe3iTgbMVHJycvJ5pZM//fRTli5dyqOPPsr3vve9AXldGioMeTGo6zo7duygsbHRNIps376dyMhIJkyYcM6f2TkiKEkSzV4X9+/5O7XeFqySws0j5nPLyCvO95B6RGVlJfv27WPatGk9dkaHOlVVVezZs6fLGLITOdGZHB0dbdYZhtLM5LNRW1vLzp07z3isAwWH8ze0+95GkVLwCT9OtR2EF6usU6vlki4HcLdaiY8dQVS0Hb92hG1uG7o0mgRr14av9f5KZsRezKy4y4NzMOeIy+UyhWFbWxuRkZF4PB7Gjh076G/cmpubKSkpYeLEiYwcObLPP08IAf6XILAeJDsQAaIVpDiI+C6SMr3PPjsUheCJnMoQlZiYaKaTu3PjvHXrVpYsWcJDDz3EPffcM6jP38HAkBaDxhgyIUQXU8Hu3buRJIkpU6ac0+cZ9YFGRPDzpr08sOcVVF3umL4gCWQEwyNjeGn2f/Xo2M4FoxFvTU0N2dnZPR6hF8p07qt3Lu7oQCBg1tF0x5kcKlRUVHDw4EGysrJISUkJ9nJ6jF89RE37Pah6PRI2QEOgE2Wdh+b8PuX1L5M0rA7FGkBCwa5kctifSbWvhhTb8akBQug0BKqYk3A9k6JP35twZ9su3qv/hCOeRuItkcxLmsFVKZehyMGttTx69Ch79uwhJiYGl8uF3W43z8eEhISQPR/Ph8bGRsrKypg0adJZJz/0FkKrAO/jQBTICcceFKAfAstMsN/XJ//GA0EIngq3221eH5ubm4mKijIj2PHx8Sf9W5WWlrJw4UJ+8Ytf8JOf/GRQna+DlUEtBlVVNce9nYhRh5OYmHhSof2+ffsIBAJMn969u8PTGUUWb34QdwBkST82jkugCxlJEtw55vJ+iRCqqsr27dvxeDxkZ2cP6FTo2eg8TzknJ+e8++qdzZkcCqYMIQT79++nurqanJycHrc+CiX82mFaPa/iCXyOJEURY7+Wlrp5lB+uISsri5gEDz6tClmKIMoyhRrfETY1r0RCPlYzqNGi1ROjxHNV8m1EW079b1PUUszfK1bTrgawywoBveN3elnyZL4x+kv9fNTHMXp+zpw5k7S0tFPWGRobcXJyckicj+eLMSJxypQp/RrVFoFN4PsryBO69rfUmwEVoh5DknrXTGGkwQeaEDyRU6WT//3vfzN37lzy8/Oprq7mhhtu4N577+WXv/xlWAgOEAbtbOIzUVdXx7Zt2xg3btwp0y+KouD1erv1XqczijR5nHi1jtYYxvtLkoSMjobM69Wb+lwMer1eSktLsVqtzJkzJ6Q70veUQCDAtm3bCAQCzJkzp0eO4RPn1J44M9kosA5Wl39d19m5cyetra3MnTt30Al8mzKW1JhfAB2/rwMHDlBVVUVeXh5xcXEA2JXjEaTh9gnkxF3FLucnNAVqkSWZREs6ufFXnVYI6rrOW7Uf0a4GyLAfb/XS6vfyafN+rko9wqio/t+wKysr2b9/P9nZ2WZDdEVRzLpW43ysr69n//79J81NHkiGKIfDwfbt25k+fXr/z3SVIgAZUIHOv2EfSJEnPNZzBosQBLBYLKSnp5Oeno4QgoaGBmRZ5ne/+x333nsviYmJzJ49m6985SthITiAGFJi0EghHjp0iJkzZ572AqQoymkjiie+3+n6B7aobQjRMYSrM5IkgS4R0M/+/j2hra2N0tJSUlJSmDJlyqB2DHu9XkpKSrDb7cyePbtXJz9IkkRiYiKJiYlMmjSJ9vZ2HA4H5eXl7Ny5s9+dyaqqUlZWZoregbT5nytGk/CmpibmzJlz2rYXkiQxMTqXkRGTaQ7UIUsKKbYRKNLpz4OmQBM13jZiLdYuv9s4q406n5s9zv39LgYPHz5MeXk5ubm5py3l6Hw+Tpw40exnWF1dzZ49e4iLizOj2KHcN9SoczWin/2OMgXk4aBXgjwKJAsIF4g2sFyJJPVeq63BJARPRJIkUlNT+fOf/8y+ffv44he/yJgxYwgEAkyaNIkpU6awePFiFi9e3ONWbWH6lkEtBjtf5I0Gy01NTVxwwQVmhOFUWCwWVFU943ufzTE8LnY4iqwT0BQkNPPvHcYSwbCI7tWy6UKnyltLq9pGrBJNZuRwlLO0QHA4HOzYscN05Q3mu7O2tjZKSkpITU3tc9ErSRJxcXHExcUxYcIE05nceSPuS2eyUeNqs9l6XfSGGp0nqHQ30huhRDNMGdet97fKViQktBOKZHQEKbYWnOoq3nO8Q6JtPOOjryfBOuY8jqJ7dI5+zp49u9uTcSRJIiYmhpiYGMaOHWsaourr6zl06BARERGmMDxVXVewqKmpYffu3WRlZZGaGpw+kJIUjbB/FXwvgF5Oh2vdBpa5YLu+1z7HEIITJkzoF2NMsDh06BCLFi3ipptu4qmnnkKWZVpaWli7di1vvfUWCxcu5I477uDJJ58M9lLDnIZBXTOoaRqqquLz+SguLgbo1tzSmpoaKioqmDdv3in/fqJR5HQX2e+W/IkDziZ0ISFLekeMUEhYFI1Xc39GUuSZa9pcqps19e9T7qkkoAewSAojIoaxIO1qEq0np7+EEFRUVHDo0CFmzJgRnDvufqShoYFt27aZbTeCudn1tTPZ5XKZNa7Tpk0b1JFeVVUpLS1F13VycnL6LBX/9IHn2dpSSao9EqusIIROpHKI8dFVRCpWZElBCIFNjuKipJ+Tau99h6kQgj179lBfX09eXl6vRfNOVdcVCnWGVVVV7N27l1mzZoXEXHAh2kHb3hEVlIeDPKXX+g0OFSFYUVHB9ddfz8KFC/nf//3fU16bVFU15yWHCU0GvRhsamqiuLiYpKQkpk+f3q2LoMPhYP/+/cyfP7/L48ZoOSOF3J2JIt8t+ROH3Q1oeodxJEKR+NWkL3FB8tmdyqsd71HatoMUayIRSgQ+3U+9v5GJ0WO5KWMJkiTxSX0ZRa27yY2bTGKDnfr6erKzswf9j+7o0aPs3bs3JNvknM6ZnJaWdl5O0NbWVkpKShgxYsSgH83l9/spLi7GZrMxa9asPhUt1d4anjn4L6q8TgQCu+RnfvIOIhQLMZZEoOM379NbSbJN5MqUx3v1394wPLW0tJCXl0dkZGSvvfeJn9O5n6HP5yM5OdkUh/01fcioh8zJySExMbFfPjNYDBUhWFVVxXXXXcdVV13FX//610F9kzrYGdRisK6uji1btjB+/Hiz6XB3aGxsZMeOHVx22WXmYz2dKHKovZoES9xZo4EGbWo7L1S+hiLJxFqOv8ajeXBpHi5JuJgn9/8bpyqh02FMiZI1Hpz4TWakTDyntQ0khBAcPHiQysrKc2odEyx66kw23JYTJkwYdPVGJ2LM3o2Pj++3yTjtgTY2NX1OlbeOKKUaWdpEpJyAJB3/bFX3ItC5Pu0Zoiy9k9bUdZ1t27bh8Xi6la3oLYQQZp2hw+Ggvb2d+Ph4Uxj2VZ2hkbHIyckZ1K2tYOgIwdraWq6//nouvPBC/vGPf4TrAQc4g1oMulwumpqazjld2traSlFREVdeeSVw3CiiaVq/TRRx+Bp4ueo/xFliscvH79w1oeHwN7KlvpZ2VUE+1rtQR0IXErEWjWXzfosQAo/uQkIiUgndQvJzwXDRtrS09Kh1TLDQdd10gjocjrM6k42U2vTp00lPTw/SqvsHp9NJUVER6enpQZu9e8T9IZ81P41djjuFGBQsSP9fIpWepzY1TaO0tBRVVcnNzQ2qy9/r9ZpR7KamJiIjI8/YP+586GyMGewZi6EiBB0OBzfccAPZ2dm89NJLg7p+eagwqL/ByMjI86qb6+wmDtZouXhrHDFKDE7Vhb1TGsepumj2enBrMrKkY5GORSolgapLuDSZV4+sIC1Ko9Z3BEmSyLCPJjvuYpKsA7eGMBAIUFZWhqqqzJ07d0C6aGVZJikpiaSkpDM6k1NSUqiurqaiooLs7OyQj372FGMD7Tw2MBik2bOwytH4hRMbscd6g+qowkuqfUavCMFAIEBJSQmyLJOXlxf0TTQiIoLMzEwyMzO71Bkaa0xJSTnv/ppG94bKyspzMsYMVFpaWiguLu63KSrBorGxkSVLljBt2jRefPHFoJ/DYXqHQR0ZFELg9/vP+XUej4cPPviAa665xowKBmPG8JaWEjY0bkKRZKLkSDy6F78ewOHysau9HUXSkCXg2LKEDpIkyE7yMjo6gRglHgG41FYSbelcl3IrwiPx20MvAfDLcV8bEBdoj8dDSUkJkZGRZGVlDcp0ROeZya2trUiSxMiRIxk5cuSg6yPYGcMEFCob6CHXOkpb/44mjOuGRISSwMVJvyTR1j2n8ukw6iHtdrt5Htf5GtnaspPD7ioilQiy4iaSHTcFqxzcDbZzFLtznaFxs3K2OkPDIV1dXU1eXt6Ai+KfK/09Ti9YNDc3s3jxYkaOHMnrr7/eb/WmYfqesBg8BX6/n/Xr13PZZZdhsVi6ZRTpC3Shs619FyWt22lT24lWopgZO42tR/bytnMXEgJFPv71abpEcoSLqXEwPXa6merShU6Dv5qNtX7cQkUVHWLKImnEKZG8MPvX/X5s3aW1tZXS0lLS0tKYPHnyoC5QNtqpOJ1Ohg8fTktLi+lMNgwoMTExg8ZAUlNTw65du4LTdPgMNPr3UeH+AI/WSIJ1LGOjruxxraDX66WoqIjY2FhmzJiBLMvU+Rp5tWoNDf5mopUIAkJDExo58VNZkn45shQa57oQAqfTaQrDs9UZGuMv6+rqetUhHaoYQrA/x+kFg9bWVpYsWUJKSgorV64ckNmZMKcnLAZP8ZpAIMDmzZvRNM3chBMTE4MmRDSh4dG82LCyb88+mpubecq6BpdqxSJpZs2gJhTGxTYwNymTZFvXGrPCI/to1iU0XUGRO1Lgxn+fHDGcuybeQKXnCPGWeGbEZqEEOTIBx80TQ6FfYiAQoLS01JyTbdSRGc5kh8NBY2Njj53JocKRI0c4cOBAyLQY6UvcbjdFRUUkJyczdepU8zt7o3YDW1p2kBmRjnzsMZfaYRD7auZiRkf133i2c8Hr9ZrCsKmpqcuc2ri4OPbu3UtDQwN5eXmDOqoNQ0cItre3s3TpUqKionjrrbf6zPkeJniExeAJzzfqAwFzDJnD4UAIYbpAk5OT+10Y+v1+ysrK0HWd7Oxsitr28OTBf+PVlGN9DAURssbSEWPwS3Wk2UZ0Oa6XKvbg0+1YJNXceHQh0IVESoSTaEssfl1HkSTS7XHcPvIWRkf1LC3WEyorK9m3b1/IRY36AiMNHhUVxcyZM0+bBh8IM5PPhuEGP3r06KCbqXwqDGNMRkYGkyZN6tJ8/n8Ov4xfD5Bk7doA/6jHwYK0+VyYlB2EFZ8bRp2hw+GgoaEB/dh858mTJ5ORkTEgzsnzZagIQZfLxY033ogsy6xatWrQR3qHKoNaDEJHM+DucCajSOf5tA6HA1VVSUlJIT09vV8auLpcLkpKSswUk/F5Pp+Pv1e+wQF3FaMjM/juqC/QJGp5r/E/CCGIsyQBgla1iTermvALC1ZJ7/LeMVY3IJNkiyJKthEQOq2qj3RrDHMTrmZ7axUSEnOSxnF56kwsct8eqxCC/fv3U11dzaxZswZ9P7L29vYuE1S6G+k7nTPZqOkKxaLuzg2Wc3NzB30dmdEfcuTIkSfNQBdC8Gz5a7So7aTajp/juhBUeR0sybic2fET8alFCOHHZpmFIofub0HXdXbs2EFraytJSUk0NTXh9/u7uOUHU33ZUBGCHo+Hm2++Gb/fz5o1awZEjXmY82PQi0G/38/ZDrG7E0Wg4yLe1taGw+Ggrq4On89nCsO+2ISbmpooKysjMzOzWw2HhRDsdZVQ2vYRLq0NkIhR4nm9shKvbusSGURoxNn9IGBE1PFUnTcQYF8bePzRCBQQYFUk5qVk8qupN+FUW5GRiLP17uZkjAxsa2sjJydn0N+BGt9tT120QgjTmexwOHC73f0+M/lsGGKhvb2d3NzcQZ9mam5uprS01CxxOBUfNGzlvYZPSbUlEqHYEEJQ72/GLtv4+vAMNN9TaKIehECSoomL/DYxEV8PudIAXdfN0YFGz8TOdYYOhwOn00l8fLx5Tg7k9PFQEYJer5dbb72VlpYW3n333UEfxR/qDGkxeD4TRU58vdPpNIWhx+MhKSmJ9PR0UlNTe9w/rKqqij179jBlyhRGjBhx9hd0wq05qfNVIiGRbh/J90ueokH1oCNh4djxyoI4qw8ZGBZ5XAxWO1UOtNqItSok2jruBJ1qgAilnmExLtpVAUik2Cx8cfgXyEu8qEfHCcfT4EIIsrOzB1UU4VTU1dWxY8eO8/puz0bnpsJtbW1msX9fzUw+G6qqmm2BcnJyBv13a9S6nk0seDUfK2rfZ5+rAk1oCAGxliiuTxlHsv4LhHAhSdGAhMANSCTHPEGk7Zp+O5azoes6ZWVl+Hw+cnNzT/vdnqrO0BCGcXFxISdwT8dQEYJ+v5/bbruNmpoa1q1bN+jbW4UZwmLQaBlj1Af2hmPY5XKZwtDpdJKYmGgKw3OJzhhtGY4ePdqrUzZu+fzn+ISEJjrqHS2SRqLNQ4QSRaLtuEjY3qDR7LOSERmJTe5Yt11pRrbU4dMtWOSOfzNVKETJKtelX4lf14myRHFhwmxS7SnntC63201JSQnR0dFnrJkbLBjmiZkzZ5Ka2jsTLU6Hz+czawyD4Uz2+/2UlJRgsViYNWtWSKavexND5He31lXVVQ66j1Lra8AmWZgYMxqr+grtnv9DkrrOtNb1VuzWuaTG/Z/5WLv7H7i8zwJ+rMpM4qJe6Lcm1pqmUVZWRiAQOKfm2YFAoMvcZFmWTQNKKNe+DhUhGAgEuP322zl06BDvv/8+KSnndj0PMzAZ9GIwEAiYgs+gc32gJEl9YgbxeDzU1dV1ic6kp6eTlpZGRETEaV+naZqZTuuLVOl/Kt7njbqNAOSnX05ERDtr6j5DBiIVK36hsb/ZgidgZ0TU8bRAQuRe2lSFCFlFOfbvpQmBLoFVlrBIViQJIuQIFqct5Nltb9Mq+fg2F7H08i+cdj1GXVVGRkbQJk/0F4bIr6qqCop5orMzuaGhAbvdbkYM+8KZ7PV6KS4uNkX+YG4LBMcnxvRU5Dc5/xu37w1kOaHL47pwosjpDEtYA4Cj6WI8ohbjAi4BFiRS41Zhs04678/vDpqmUVJSYrrfz1fk67pOc3OzGTUMBAJd5iYHczpLZ5qamigtLWXy5Mm9HskPJVRV5Vvf+hY7d+5kw4YN5zW0IczAZMiJwWBMFPF6vWY9V0tLC3FxcWZ0pnPazufzUVpaiizLzJo1q1/Sabqu82bdSj5pLsOtBbBIMv5AHHtbJFLsdtMwEhuxG7duIVLRkY51udYQSJKRMk5BFzqlta145QQExzf+CMXH2osfOemzHQ4HO3bsYPz48aetqxos6LrOrl27aG5uJjc3N+j1kCc6kyVJMjfg3nDLu1wuiouLT2qnMlgxor29MTGmzfM8bZ5nkIg1e4V2lLS0EWG7hJTY/6XN+STNvr/S8es7jgAipCgykrb1aA1nQlVVSkpKkCSJ7OzsXov2di67qa+vx+l0kpCQYJ6XwaozHCpCUNM07rrrLrZs2cLGjRsZNmxYsJcUph8ZUmIwWKPlOuP3+01h2NTURExMjJmy27t3L4mJiUybNq3foygezUOtr5oEawKqpvDTba9yxOXCKoMQMCb+MG7dRrTl+L+lLgMIFEkmyZrMJ9VNCCUJIY7JRanjtVZFI97u4eKUySRY4rgi5QKUho4o2VCYu6uqKtu2bcPv95OTkxMSho7OGM5kYxPuqTPZiPZmZmYyfvz4QS0EhRAcPnyYioqKXpu9q+l11LXegq43I0kRgIwQbiTJSnLsM0RYL6S2KQuvcNNpABEAOiADo5IP9HgdpyIQCFBcXIzVamXWrFl9mtL1eDxmJLu5uZno6Ogu/Qz747waKkJQ13V+8IMf8NFHH7Fhw4ZBPUUlzKkZEmJQ07QuNYLBEoKnWlt9fT1Hjx6ltbUVq9VKZmYm6enpQZ80Uett4t+Vn1DSXImMxMgYBxXeJmRJYJVAIBDHxGCUEkm0EkNRgx+vau+yO0UoARIivMiSwK50pHysQsHrcxER4UfTZX4w4k5y0mcG5Tj7Gp/PR0lJibl5hnrNXE+dyY2NjZSVlTFhwgRGjRrVT6sODkYbpJqaGnJzc3u17YZf3UGz62FU7SACgSKlEB/1Q6LsCwGoaZyKjwAn3jIaF/PRfSAGTzVOr78w6gyN5uuKonSpM+yLm+ehJAR//OMf8+6777JhwwbGjBnTp5/34Ycf8uSTT1JUVERNTQ0rVqygoKDgtM/fuHEjV1xxxUmP19TUDPoetP1JaO9MvURno0ioCEEAq9WKpmk4nU6mTZuGoig4HA62bNmCzWYjLS2N9PT0oLjtMiKS+OHEReb/VlWVJw4+zEF3Gx5dQiCwSToWWSJS7kjfqLrlhLSVTtwxIagLiYCqovm9xMd7ibUff+JLjc/w56OxPDztuyTY0olSujbhHagY/SHj4+OZPn36gKiZkySJuLg44uLimDBhgulMrq6uZs+ePWdsD2KYJ6ZNmzboU0xCCHbv3k1jYyOzZ8/u9bS/zTKDtLh/HxODPqzKRCTpeNmIImeAXtnl9yaO/cdC718rfD4fxcXFZmP0/j6XrVYrGRkZZGRkdKkz3L17txnJNvoZ9kad4VASgvfffz+rV69m48aNfS4EoeO6OGvWLL75zW/yhS+cvp78RPbu3Utc3PG9IVzP2LsM6sigEILf//73XHXVVUycODGkXGrG/M6amhqys7NJSEgw/6ZpmnkXXF9fj6IoXcbiBVPMft70IZubNyMhkWLPZEvbdlQ9gFWysr3Jik/rMJIA2JQAyZEehOh4IAJBbEQbNkWYIQyBTqwlgKrLCCxYJYmxkZncMuLeAS0KjVTp8OHDmThxYsjcgPQEoz1I57SdcV62tLSwf//+fnFIB5vOPRPz8vLOaAjrK/yBCuraru6o2z32mCEMYywXkRz/Uq99lmEEio2NDbmbGiOSbRhQjDpD44blfPpZGkJwypQpDB8emiMBewNd13nggQd47bXX2LhxI5Mm9a3p6FRIktTtyGBzc3OXfTJM7zKoxWBrayu33XYb69atY/z48eTn57N06VKmTp0a1Auaqqps374dj8dDTk7OGS9Yuq6bhf4Oh8Ms9E9PTw/qvGSDzc1bWd/wAY2BRnY5/HikeIQASQK7EiCpkxiU/D6GJ3i6vD5G8eIVVhAgIxBCoSkQSUCPJFpJIsOeyoK0+cxOnByMwzsvGhoa2LZt26A2xnR2JtfX1yOEID09nZEjRw7omclnQ9M0tm3bhtfrNRssBwu3921aXD8mcKxvqIJEhCWPlPjXeu0zPB4PRUVFZi1zqH+vHo/HFIad6wzT0tKIjY096/qHihAUQvDII4/wj3/8gw0bNjBt2rSgrONcxODo0aPx+XzMmDGDBx98kPnz5/ffQocAg1oMGrS0tPDWW29RWFjI2rVryczMNIVhVlZWvwoqr9dLSUkJNpuNrKysc0ppdC70r6urQ9d1UxgGuz+XV/NikSws/ORBvJqdjrNKJyPGhSKJDieJpjMs1mm+RkLHJmvoQsYi6wgBdZ5EXHpH9YJVttLSWEdsUhx+reMxuxJA8mj8v/l/CsJRnp3q6mp27949JGYqd45ujx071pw40dvO5FDBaJ6taRo5OTkh0/YkEGhApwa7tXfrbj0eD1u3bh2wjnDjhsXoZ2ixWM5YZziUhODvfvc7/vznP7N+/XqysrKCtpbuiMG9e/eyceNGZs+ejc/n4/nnn+fll1/ms88+Izc3t/8WO8gZEmKwM+3t7axevZrly5ezZs0aUlJSyM/Pp6CggNmzZ/fpxtXW1tZlDm1PPksIQWtrq9nL0KibMcbiBVMYLtj4E1QlEh2ZWKuHOAvIigW318mIeK+Z1rJLAXRJRkYgSwK3aqHGmwjHUl/O5mZscckExPFjiZS8JNh9yBIk2RL50vAvMjMueBczg86u0t5sFB6qGK1yWlpayMvLM6Pbve1MDhUCgQAlJSUoijIgjEA9xeVyUVRURHp6OpMmTRpwQvBEjDpD47w05ssbdYZtbW2UlZUNCSH4xz/+kd///vesW7eOvLy8oK6nO2LwVFx22WWMGjWKl19+uW8WNgQZcmKwM263m3feeYfly5ezatUq4uLiWLJkCfn5+cybN69XBVXnnnqjRo3q1YurUTdjCEOv19tlA+7vCIYQgiNHjnDw4EFmzJjBEWsd7zduxuFrxq1VEGlVQYCCiiJ3mFEskqDJG0ezakc6VlAooeFUI5AQyBIkWp3YrcdPV13vqJNKtCQxOmo0V6dczqSY8f16rMbx7tmzB4fD0euu0lDESJX6fL4ztso5lTPZaCiclpY2YMbSGeaJyMjIITEhx+l0UlRUxPDhw7s1D32g0fm8NOoMATIyMpgwYcKgnZsthODZZ5/l0Ucf5Z133uGCCy4I9pLOWwz+13/9Fx9//DGbN2/um4UNQYa0GOyM1+tl3bp1LF++nDfffBO73c7ixYtZunQp8+fPP+9IgBCCiooKDh06xIwZM/rcASWEwOVymcLQ5XKRnJxsFlT39QYshGDv3r3U1dWRnZ19Ut+1uro6Hqp7iAiLCnTUDHp0GxZJ0OaPoTFgBwQKEl5NIiAUFEkg4Sc9yme+j64LNKGgCRlxrA22DS+qsCIhMy12JPdN+HGfHiscnxjjdDrJzc0dtBuJgREhMxqjn8uNhjGusb6+3pzKYxhQQvXfzePxUFxcTFxcXMiZJ/qC9vZ2ioqKGDlyJOPGjRt0QvBEGhsbKS0tJS0tDZ/PR0tLCzExMWY6uTt1hgMBIQTPP/88DzzwAKtXrw6ZervzFYPXXHMNsbGxFBYW9s3ChiBhMXgK/H4/GzZsYNmyZbzxxhsALFy4kKVLl3LppZd2W1Dpus6ePXtoaGggOzu7iy2+vzA2YIfDQXt7O4mJieYG3NvF75qmsX37dlwu11mF0Qv7V/Jh28dkWOOIj66nOeDFp0nUeOMBiWg5ipaAxxSDSVYntk5RQVWXCOgWJAQB1Y8iW2lRozAabUjoxFu8fGfErYxPGEuaLbXXL+qBQIDS0lKEEGRnZw+YSNf5YtS79kaE7EzO5GD32DQYalNU2traKC4uZvTo0YwdO7ZfPlMIFU0/BEJDUcYea7LdPxg9MTunhk+sM7RaraYwDAXD3vkghOCll17ipz/9KW+99RaXX355UNfjdDo5cKCjD2ZOTg5PPfUUV1xxBUlJSYwaNYr777+fqqoqXnqpwxH/9NNPM3bsWKZPn47X6+X555/nmWee4d133+Wqq64K5qEMKsJi8CyoqsqHH37IsmXLWLlyJV6vl4ULF1JQUMAVV1xx2rYSgUCAbdu2EQgEyM7ODkr7iRPxeDymMGxtbe3VyIzf7zcjRtnZ2ecUMVI1P1vb1lLjreCwW7CjvZGACKDqXlzH0sapdidWy/FT1acp6EJCkQSK8FPnTwBAQmCTNaIsAWQJbLKO26cRZRVIEvg0iaPNbbx7Zc9qTYx2G0bftcGeOnS73RQVFZGUlNTrbvz+npncHdrb2ykuLmbYsGGDpjXQmWhpaaGkpIRx48b1mwNeVXfi8f0dXatAoCPLaUTYv4zNelmff7YhBKdOnXranphGJwfDnaxpmpllSU5ODhkD0ZkQQvDKK6/wox/9iDfeeCMkxNPpmkh//etf54UXXuD222+nvLycjRs3AvC73/2Ov/3tb1RVVREVFUVWVhYPPPDAKd8jzPkTFoPngKZpbNq0ieXLl7NixQpaW1tZsGABBQUFXH311WYT3p07d/L973/ftL+HYrG5z+czhWFzczOxsbGmMDzXBrpGc2UjldZTYbTXeYT1DUVsrHyHgD0BVShESl6SovzH169ZEKKjltAdsODW7cjoyJJOnNWPhEAVEpFygBS7C6vU0XTcpyu0BKKob/fz+5xfMzpyxDlv9E6nk+LiYlJSUnpsBBoIGMan/hBGnXtsNjQ0mM7ktLS0Pps0cSKtra1dImSDXQg2NzdTWlrKhAkT+m0Mma47cLp+ji4cyFI6IKOLeiTJTnTkL7FY+s4U1h0heCJCCNra2kxh6HK5SExMNM/NULjZPxWvv/46d999N6+//joLFiwI9nLChDBhMXie6LrO559/zrJly1ixYgV1dXVcd911zJgxg6effppFixbx3HPPhaQQPBG/32+m7BobG82UXXp6OtHR0WfcDI2NJDMzs0+Kzb+06TtIETH4NAupEU5kueP9A5qMKhQkdFwBKz5hRUYQoahEKgE0ISGJAJlRLmRJx6ta8OpWfMKGQCKgS2TY44mQrWREpDErbiZz4+edVWw0NTVRVlbGqFGjhkRNlXG8Y8eO7ZfpBJ0JhjPZaC8yFMbpwXFhNGnSJDIzM/vtc32+FXi8/4csj0WSOn5zHSNDD2OzXUNU5L198rnnIwRPhdvtNoVh5zrDUCpzWLlyJXfeeSevvvoqS5YsCfZywoQ4YTHYC+i6TklJCQ899BCrVq1i2rRpjB07lvz8fG644YagjJM7X05M2UVERJjC8MRi6traWnbu3MnkyZP7ZSPx+/08Xv44Nf56NB1U0ZEqFrpOixqNhE6UEiDCoqLpMtGKl7QIJx7ViioU2rXIDuey0NCRCAg7AoiQ/ccMLRKRcgQ5cbNYmnbbSWLDGLfWX8cbbAwHfCiM5OocmekrZ3J9fT3btm1j6tSpg7q9iIHRHD0Y7VQ83r/h861EUbq6/zW9GkUZS2z073v9M3tLCJ6I3+836wwbGxtDos7w7bff5hvf+AYvvfQSN954Y79/fpiBR1gM9gJCCB5++GGeeuopXnvtNTIzM3n99ddZsWIF+/bt48orryQ/P59FixYFfZzcuaBpGg0NDdTV1ZnF1EYqubm5mfLy8qCOH3u3fgNrHetp9zdSH4hDR8YmBzrG2wmJWIuXVLsLj2bFrdnwaDYUScMiabg1OzZZxi57QdaPTUkRxCo+Yi1eYhUrCe5x7KeGSDmKL0f/iAMHDgyJcWsAVVVV7N27t18c8OdDbzuTjRubGTNmkJ6e3gcrDi0MoT9t2rSgNEfv78hgXwnBE9E0rUudoa7rXfoZ9kemaO3atXz1q1/l+eef50tf+lKff16YwUFYDPYQr9fLHXfcwccff8yqVauYMWOG+Tej/5yRSt6xYweXXnopBQUFLFq0iNTU3ne49hXGRa6uro7a2lpz/FhmZiYJCQlBrZtzqW7+evhZPmupxScsxFr92GQVBZ1hEW34NYV2PQKvbsWCjgD8uoVIxYJVdqIfa02TZHWSYOkYl3fEHY+iyB1zlkWH61FBcEP6LVyaPLhrb8rLyzl8+PCAaZ59ojP5XFN2R48eZd++fWRlZZGSktJPqw4eRoR75syZQRP6HTWD96OL2hNqBiOJjvwVFsuMs75HdzEioH0tBE/kVNHspKQkM2rYF3WGGzZs4JZbbuHZZ5/lq1/96oDZX8IEn7AY7CFvvvkmjz76KCtXrjzjHbYQgoMHD7J8+XIKCwspLi7moosuIj8/nyVLljBs2LCQ/+EaM5Xdbjdjx46lpaXFnEtrRGX6q8j/dJQ0FuPWXbjw8+ftLzIh3U6s1YszYKdVi8IqdaSQfbqFaIuGInf0O7TKGiPszQBUu6ORFAuSJCFUFUUBj25DIKMJsALXpd7EhUlzSbAOngbTQgj2799PTU0NOTk5QWmF1FMCgYAZlTGcyca5GR8ff9JvzBC+2dnZJCYmBmnV/UdNTQ27d+8OiQi3qu4+5iYuB3RkOQO7/VZs1kt67TMaGhooKytj2rRp/SoET4VRZ2h0c4iJiTH7v/ZGneFHH33ETTfdxNNPP803v/nNkN9PwoQWYTHYC6iqek7hf2NChyEMP/vsM+bOnWtOPxk5cmTI/ZB9Ph8lJSVYLJYuzYaFEOaYJ4fDgaZpZlQmOTk5qC1XAnqAr2/9PomREnGKjzYtsiM6KGn4dRsRkg+rpcNlHGPxkm5txy8Uan1xKLIAISGj4tQiUCQdj2rFo9vQkVAQTIieyIK0S5iT0HtRjGCh6zq7d++mqamJ3Nzcc3aUhyJnciYnJiZSXl5OZWUlOTk5JzVHH4wYqf9Zs2aRnJwc7OUAIISGrpcjUFHkMUhS7/U+DVZEsDt0rjNsaGjAZrOZwvB8Mi2bN29m6dKlPP7449x1110ht3+ECX3CYjDICCGorq6msLCQwsJCPv74Y3JycsjPzyc/Pz8kWls4nU5KSkpISEg44xQGIy1iTD/x+/0hM5f2mvW3ESnJ3DvzOxS3lHLIcwivBrFWFzoSMYqPDFsbfqHg8MchSQJJ0/BiR5IEAV2mLRCJdGxuMkCiNQVJkrFIMq2qkyhvCRkJUQhAcibx89y/BO14zwWjWbjH4yEnJydk22T0hM7OZOPclCSJ8ePHk5mZOSBc/z2hsrKS/fv3k5OTMyQioEZEcPr06UGpiTwXTldnaNxQn+3c3Lp1K0uWLOGhhx7innvuCfp+EWZgEhaDIYQQgrq6OlauXElhYSEbN25k+vTppjAMxsB4o7XIyJEjGT9+fLc/XwiB0+k0haHH4+kyFi/YDVvrfLWsdbzDrvYy3HiRhU5mRDMWSafKl4gkCRRdxSkisUoqrYFI/LoFi9RRcyiQiJAicOsqkiQxLaYCgczxCSgCSej8bOK/g3qcZyMQCFBWVoau6+Tk5AT9e+lrhBDs3LmTxsZG0wjl8XhISkrqt5GN/Y0xDjMnJ4eEhIRgL6fPGUhC8ESEELS2tprC8Gx1hqWlpSxcuJBf/OIX/OQnPwkLwTDnTVgMhihCCJqamkxh+N577zFx4kTy8/NZunRpv4zHqqmpYdeuXUyZMqXHrUWcTqcZlXE6nebm21ttQXrC9rYtvOVYiVd1kGRt44g3DosCkq7iFR2RwVZ/BJpQUCQdgYQQIElWVCGYGXMEXVI6IoJ0/Jz0Y6JQCAmbHIlFTWaEnMuVw68hNTo0TBlG6t9mszFr1qxBP0VF1/Uuc6SNjfXEkY0DYWZydzl8+DDl5eXk5uYOiVS4kRoOlku6t3G5XKYwbG1t5c9//jOTJk3ixhtvJCIigoULF3Lffffxi1/8IiwEw/SIsBgcABh3i2+++SaFhYW8++67jBw5kvz8fAoKCsjKyupV04YQwtxE+sJh6Xa7zc23ra2NhIQEc/MNVorS2ESGj0tiv+dTNuhFWBSQRUfNoE+z4NFtKOgggYIgIGyAYHpsJTpylxSyeixK6PfJ7N85kqbKRBCQktHCuHHwjawvMjp6GooUnPSkx+OhqKiI+Pj4M6b+BwuaplFWVobf7yc3N/e0NyA9dSaHCkIIDh06RGVlJXl5ecTGDh6j0+kw+kQOxIhgd/D5fPzf//0fq1at4tNPP8VutzNt2jQee+wxLrnkkkFf6hCmbwmLwQFIe3s7q1atYvny5axZs4a0tDSWLFnC0qVLycvL69HGrus6e/bsoaGhgZycnD7fRLxeLw6Hg7q6OlpbW4mLizObXPdXVKa6uprdu3ef5Dj86fbbUZGwWcCp2WkLRKILGausk2zNoM7fgITEtNhKdCTkY1FBDRB0RNlKN4+n6UgiMfFups6qICrGS2yMC0kRSJKEpkGUksbijB+RGTmxX47XmLubnp7O5MmTB4zAOV9UVaW0tBQhxDnNzT5XZ3KoIITgwIEDVFdXk5eXR0xMTLCX1OcMdiHYmX379rFo0SIuuugiYmNjefvtt1FVlYULF5Kfn8911103JL7zML1LWAwOcFwuF++88w7Lly9n1apVxMfHs2TJEgoKCrjgggvOKfWnqirbtm3D5/MFxUjg8/mor6+nrq7OjMqkp6ef17zk7iCEoLy83IyAnslh+b7jDXY5d+PWYvEKGRmJen89raqTqTEV6MidxKCEQKapOZrtGyYjyxpZcw4Rn+QkKtKDrICqgQ8bmLFEiWg5nm+N+i0xloReP1aDlpYWSkpKhszcXb/fT0lJCVartUep8M7O5Pr6emRZ7veZyd1BCMG+ffuoq6sjLy9vULjCz4YhBIdCw/BDhw5x/fXXc/PNN/OHP/wBWZbRNI3PPvuMN954gzfeeIMxY8bwzjvvBHupYQYYYTE4iPB4PKxbt47CwkLefPNNIiIiWLx4MUuXLuWiiy46YxrB6/VSWlqK1WolKysr6EYCIypTV1dHY2MjUVFRpjDsjXSdEIK9e/dSV1dHbm7uOUVAfbofGZlqbx1PH36BdPs2BMdrBvVjYrCyIoX9n44heVgLM3LK0QN+ElP9ALi0zkIQdDpMKRIyNjmKBEsKFyYsICtufo+OszP19fVs376diRMnMnLkyF5731DF5/NRVFREdHQ0M2fO7DXBpus6zc3NZjpZ07Rzcn/2FUaT+4aGBvLy8oiKigrKOvqToSQEKyoquP7661m0aBHPPPPMac9np9MZjgyGOWfCYnCQ4vf7Wb9+PcuXL2flypVIksSiRYtYunQpl1xySZeaqc8//5y1a9eydOlSpk2bFjJRDgNVVbuMxbPb7aYwPJ+5z5qmdTES9CQd7VRdvN+wmT3O/0MgI5COCTuZutp4dn88geSMFqbnHCYqwoPFJgho4Od4PzVDCBriUMGKp66BEcNBljse03VBVTU8Mv/N81qnYQYaCpsmHK+JTEhI6NNz2minZEQMg+VMFkKwa9cumpubycvLG/DGl+4wlIRgVVUV1113HVdffTV/+ctfQu4aHWbgExaDQwBVVfnggw9YtmwZK1euxOfzsWjRIvLz83G73dx111184xvf4PHHHw/5tKGRrjOEocViMeu4EhISzrr+QCDQpX6sNzfrX5cuJTLGhiTAq8qAhaIPp+JvtzL3st3ExbdhsYJXs6BxPF1piEcDf10rIzO7HoeqS3h0Gy6PTG7qlWTHXUFmxIRufV9HjhzhwIEDIdVsuC9xuVwUFRWRlpbW7zWRwXAm67rOzp07aWtrIy8vb1D2iTyRoSQEa2true6665g/fz5///vfB73rP0xwCIvBIYamaWzatIlly5bx//7f/8PpdHLttddy2223cfXVVw+o1JKu613quCRJMjfexMTEk+6evV4vJSUlREREkJWV1ecX1U1Nb7Ku4kOKP0siNaWFUeOPEJ+g4tckAhwXoSeKwVSl1YwIAng1BY/e0eJGRkeWIFoKMMwez6ToeSSJPKItU09KDRkjEI8ePTpkpmy0tbVRXFzMiBEjmDChe2K5rziVM9k4P6Ojo3tlbbqumyMic3Nzsdt7b4JHqDKUhKDD4WDBggXk5uby4osvhh3DYfqMsBgcgggh+M1vfsNTTz3Fww8/TEVFBStWrKC+vp5rr72WgoKCAedIM+q4jKiMEKLLWDy3201xcTHJyclMnTq1X9MsnoCXf+16kwZtL5EJOwjoGr6T0sTGeiTSra2djgtatSgkSUdGIEuC4ZZWkixO2tQI6rU42lQLPt2OJEFW9IUsSv8uiqKwe/dus35sKBgJDHPMmDFjGDt2bLCX0wWjBtbhcNDY2NgrzmRd19m2bRter/eM7XIGE0NJCDY0NLBw4UImT57Mq6++GvQ67jCDm7AYHGIEAgG+/e1v895777F69WpmzpwJdGwsxcXFLFu2jMLCQo4ePcrVV19NQUEBCxYsOK/avGAhhOgyeiwQCKDrOmlpaUybNi2od9e60Hm//s9sb/sYDx3r0M0OhRJ2KZIES635fJ+m4NbtSAgssk6c4maMrZHGQBSVgRTcqhW3sGORVOIVD1bJD1gQEiAES+z/zYTM7GAcar/S2NhIWVnZgDDH9IYz2eibGAgEyM3NHRJCweFwsH379iEhBJubm1m0aBGjRo3i9ddfHxJCP0xwCYvBIcadd97J1q1bWbVqFcOHDz/lc4xJDYYwPHDgAFdeeSX5+fksXLiQxMTEASMMa2tr2blzJ4mJibjdbnw+n+n8TE1NDaowVFWVTS0rqPUfQdV1Kn37UUWAZKXRTBN7NQWvbkeSdBRJMMraQJzkZq9/OO2qlTY9BgsqGbY2VAEaNrRjwrLd00RSZAwSEjo6Uf5h3DX1z0E73r7CEAlTp0497TkdqpyPM1nTNEpLS80RgkMhdTiUhGBraytLliwhNTWVFStWDInU/9kQQgyYPWegEhaDQ4zDhw+TkpLS7VYqRrsKQxju3LmTyy67jIKCAhYtWkRKSkrI/kgrKyvZv38/M2bMIC0tzZyXbDS5drvdJCcnk56eHhLzkmu8FZS0beCT/YWmgcRIE8sIFFlnnK0OITQO+kfgVG14hY0ki5MYyYsHOxoKEjqS5kexWLBIGnGKhyjJh4aMT7cwL+n7TIy5DkUa+NEkwyU9c+ZM0tLSgr2cHtHZmexwOPB6vSc5k1VVpaSkBEmSyM7OHlJCcDB8x2ejvb2dgoICYmJiePPNN4eEK/xs6LpuRsubm5tJTEwM8ooGJ2ExGKbbGJMNli9fTmFhISUlJcyfP5/8/HyWLFlCRkZGSAjDzsaJ7OxsEhISTvk8w/lZV1eH0+kkMTHRFIbBvhv/1aYlZmsZl2bDrytYZZ0MSyuxcjv7fJm0qhH4dSsj7M1oOvglGxKCRk8z6VHxKJJOurUVKypuYT82Mg90IRNjGcOoyIuJswxjZOQsoi0D7wJriP3B6pI+0ZkcFxeHz+cjIiKC3NzcIeEqHUpC0OVyceONNyLLMqtWrRoSdb5no7MQ/MMf/sD69ev505/+xPjx44O8ssFHWAyGOS+EEFRUVJjC8PPPP+eCCy5gyZIl5Ofnk5mZGRRhqOs6u3fvpqmpiZycnG6bYDwejykM29raiI+PN3sZhkKrjq0t6ylqXUVAVDLWWkt1II4qXwJuYWe4rQUhwI8NCR1ZC6BYFWJlN0mKC9cxIQgCXcj4hAVVWNCFBUlSsEh2xljTiLFqpEfMYVLsl4J9uGfFmJ2dk5NzWrE/mGhvb6ekpARd11FVtU+cyaHGUBKCHo+Hm2++Gb/fz5o1a4bELOlz4be//S1//OMfeeSRR7jmmmtCziA2GAiLwTA9RghBVVUVhYWFFBYWsmnTJnJycigoKCA/P58xY8b0y2ZlFNX3dJye0RKkrq6OlpYWYmNjTWEY7NY7ft3DEfcm9rc9w363TLWaSIziJVr2EMCOACzCj6wopCitRMoBXCICECDAK2yoQiYgLICMTRIkW5rQhIyGjC5kJASzYm9lRsKXQy6V3Dnqm5ubS1xcXLCX1Of4/X6KioqIiopi5syZZhP23nQmhxpDSQh6vV5uvfVWWltbWbt27ZBoAXUubNq0iZtuuomXX36Zq6++Gui4Odq3bx9jxowZlFmBYBAWg2F6FSEEdXV1rFixgsLCQj744AOmT59uCsOJEyf2yWZlzKBVFIVZs2b1Wv2f3+83U3VNTU1ER0d3GYsXTPZVv8snvqc46E8j3uJFRhDAggg4sdsiSLa0Eyn7cQs7INCEjE9YCegKOgoSEhnWRjSh4BcKOhLNXgfjoyJBAh0ZHQmL0NF8c/jCuN8HVWgYIwQdDseQaZfj9XopLi4mNjaW6dOnn+Q0PpUz2agxDKWZyefCUBKCfr+f2267jZqaGt57771wPdwpWLFiBQ8//DAlJSVm/fq//vUvysvLyc/P5w9/+AMjRowI9jIHPENeDD744IM89NBDXR6bPHkye/bsOeXzX3jhBb7xjW90ecxut+P1evtsjQMVIQSNjY288cYbLF++nPfff59JkyaRn59PQUEBU6dO7RVxYfQQjIuLY8aMGX22AZ7YKy4yMtKMyMTGxvarUDJc0tOmTWPYsGG0+Rv5oPofHNA/RxUyCipRlgCpShsuEYFAQhMSXt2KX1iRkIiS3MRY/KhCRhUWQJAoO5EkgR8rdklluKWNikAsoMAxl7JEJF8YsarfjhU60v+7du2ipaVlyIxb83q9bN26lcTERKZNm3bW8ytUZyafC3V1dezYsWNICMFAIMDXv/51Dh8+zPr168MRLk7tGjbGaN5www2UlpZyxRVXcOmllzJ+/HiuvfZa1qxZwzXXXBOkFQ8ewmLwwQdZtmwZ7733nvmYxWIhJSXllM9/4YUX+OEPf8jevXvNxyRJGvTtDnqKEILW1lbefPNNli9fzrvvvsvo0aNNYThz5szzEnFtbW2UlJSQkZHBpEmT+k2QdU7VNTQ0YLPZ+i1VZxgnsrKyTnmeljasprD2WRIj7SRY3ETIKgEs6MfSxD7dikAmTW5FUnR8uqVDLHqbyYix4BMKMjDR1sBefzIca4gt6xoWRWDMULagMSHiy0xI/A6S1HdCw5iy4XK5yM3NDYkazr7G4/GwdetWs0n6uZ5P3XEmhxpDSQiqqsq3vvUtdu7cyYYNGwb98XaHzkKwqqqKuLg4dF0nPj6e9evX88ILL3DppZeyaNEiMjIy0HWdiy++mF//+tdcd911QV79wCcsBh98kJUrV1JaWtqt57/wwgvce++9tLS09Om6BjttbW2sWrWK5cuX884775Cens6SJUtYunQpubm53RKGRqPhcePGMXr06KClME9M1SmK0mUsXm+tSwjB4cOHqaio6LZx4p+776fNup0ESwRR1kg8uoZT0wgIiJHcRFm8phhMkNqxKuARVlIUF02aBnQUsncIwY73tKExyurCjkqbsKKTQpx9PmtqNxPFCO6Y/I9eOV7o2lw5JycnJEVMb9MXs5WdTqcZMWxvbychIcEUhqEQZR1KQlDTNO666y62bt3Kxo0bycjICPaSQopXXnmFhx56CEmSuPDCC7n77ruZPXu2KRZ1Xcfr9fLNb36T3bt38/nnnwe9+8NgICwGH3yQJ598kvj4eCIiIrjwwgt57LHHGDVq1Cmf/8ILL/Ctb32LESNGoOs6ubm5PProo0yfPr2fVz54cLlcrFmzhuXLl7N69WoSEhJYsmQJBQUFzJ0795QtNHbu3Eltba2ZJg0VdF2nqanJjMhIkkRqairp6emnnJfcXYx6ubq6OvLy8s6rXlEIQUD3ctj9OVtaV9CiNpBqqcEnrKhCJkG0Y7OCR9gYaWmhSo3BiAraJA0ACcE0WzOakKjSotCERLkv0RSKHR8EzW6de6esPa9jNQgEAuZN2lBprux0OikqKmLYsGF9Vl/r9XrNG5e+mpl8LhhCMCsri9TU1H797P5G0zR+8IMf8PHHH7Nx40YyMzODvaSQYufOnVxzzTU8+OCD1NTUUFRUxNGjR/nDH/7AFVdcgaZpvPbaa7zwwgvU1NSwadOmsOGmlxjyYnDNmjU4nU4mT55MTU0NDz30EFVVVezYseOU9v7NmzebKbrW1lZ+//vf8+GHH7Jz587wD7sX8Hg8vPvuuxQWFvLWW28RGRnJ4sWLKSgo4KKLLkKWZX7961/z0ksvsXnz5pCeOKHrepexeJqmmcIwKSmp233idF1n586dtLa29mq9XEuglnLn++x1/hOvsNLgrWdSTCReYSVDacehRXGiGEyUfUywtrEvEIsqZA4fE4LHk8cGAs2dx7Wjb8WttjAsYiqxlu7XRPn9foqLi7HZbMyaNWtI9NRrb2+nqKiIzMxMxo8f3y+izO/3d3EmR0REmBHD/nAmDyUhqOs69913H+vWrWPDhg2MGTMm2EsKCTqnh7dt28bzzz/Pn/70JwA++ugj/vznP7Nr1y7+53/+h6uuuoqNGzeyYcMG7rvvvrAQ7EWGvBg8kZaWFkaPHs1TTz3FHXfccdbnBwIBpk6dyq233spvfvObfljh0MHv9/Pee+9RWFjIG2+8AcDEiRPZvXs3r7zyCpdddlmQV9h9jJpJo5dhIBAgJSWF9PR0UlJSTit2jDSp3+8nNze3z9KkZc1/5oj3M9TAYSRFQpHAL3xARysdQwwOt7hIlH0cDsSwu1UjNjoF4wLSWTaouoRLi0BIER3tblAYH5XLdak/PGt01HDQxsTE9KkhKJRoa2ujuLiYUaNGMW7cuKCsQdM0GhoaqK+v7+JMNsodevt7GGpC8Oc//zlvvvkmGzZs6JemyR9++CFPPvkkRUVF1NTUsGLFCgoKCs74mo0bN3Lfffexc+dORo4cya9+9Stuv/32Pluj0VR606ZNrF+/nubmZqqrq3nttdfM52zevJlnnnmGXbt28cgjj7Bo0aIuzajD9A5hMXgK5syZw9VXX81jjz3WrefffPPNWCwWXn311T5e2dDF6XSyaNEiysrKSEtLo6GhgUWLFpGfn88VV1wxoGpGhBC0t7ebwtDr9Zpj8VJSUsy2OIFAgJKSEmRZ7tfRY/86cDEREQpxip823Q5I2CQVkEhVPKQpHg4HYtjeYicx5uS+i5oO7XoUqlCwSVYkFDRUBIJRllQc2hECuoxF1siNWcAFKd81X2s4w7vroB0MtLS0UFJSwtixY0MmWmQ4k410cm87k4eaEHzggQd47bXX2LhxI5MmTeqXz12zZg2bNm0iLy+PL3zhC2cVg4cPH2bGjBl897vf5Vvf+hbvv/8+9957L6tWrepTg8b69etZtGgRU6dOpbW1lYqKCt5//30uvfRS8zlbtmzh4YcfxuPxsGrVqgF1vR8ohMXgCTidTkaNGsWDDz7IPffcc9bna5rG9OnTueGGG3jqqaf6YYVDj9bWVgoKCnC73bz99tskJSXx8ccfs2zZMlauXEl7ezs33HAD+fn5XH311SFREN9dhBC4XC7q6upwOBy4XC6SkpJISkqiqqqK6OhoZs6cGbQ0aUVVKVu5D10LEGGRUNCZbmuiUo2hpAXiok9O/fo0BaceiYQgQj5e26jodTiJQT4WS7ThZ7ithWhFJc2eQ7pyO3tL3aSnp/eacSLUaW5upqSkhIkTJzJy5MhgL+eU9LYzeSgJQSEEjzzyCP/4xz/YsGED06ZNC8o6JEk6qxj82c9+xqpVq9ixY4f52Je+9CVaWlp45513enU9RmrY7Xbzl7/8BUVR+OEPf0hRURF/+MMfWLt2La+//jpXXnml+Zpt27YxcuTIcC/GPmLIi8Gf/OQnLF68mNGjR1NdXc2vf/1rSktL2bVrF6mpqXzta19jxIgRZpTw4YcfZt68eUyYMIGWlhaefPJJVq5cSVFRUdB+6IOZqqoqFixYwMiRI/nPf/5zUqNhXdf59NNPTWFYX1/PddddR0FBAdddd92Aa0zsdruprKyksrISIYQ5LzktLS3od8NVrZ9R7PopsZKPVIuXBs3OYX9Xt7QAPJoNj25HQjfFoKq7CAAyAh1IUpyMsjchIZARWCQdCVC0UUxMuo9E+yWDXgwabvhJkyYNmHpj4+blfJ3JRn/MoSIEf/e73/Hss8+yfv16Zs6cGbS1dEcMXnrppeTm5vL000+bj/3zn//k3nvvpbW1tdfXdPjwYebMmcPYsWO5//77+cIXvgDA3r17eeyxx3jzzTd57bXXuPbaa3v9s8OczOC3552Fo0ePcuutt9LY2EhqaioXX3wxn376qXmhOnLkSJfahObmZu68805qa2tJTEwkLy+PTz75JCwE+4jy8nLmz5/Pn/70p1NOFZFlmYsuuoiLLrqI3//+9xQVFbFs2TIefvhhvvOd73D11VdTUFDAggULBsToMlVVqampYfTo0WRmZuJwOKitrWXv3r3Ex8ebNVzBiH6OiL+AEfEfACCEj1bfKkrLnyMh2mhI3fH/FXQArNLxFHKk5EIV0QgkQGOkvenYcwWWY/WIALpSQYXz96w49Cu8EQloQiZa9tDaXs99WcX9dKR9T0NDA9u2bWPKlCkhbYI6EUmSiImJISYmhrFjx5rOZIfDwb59+87oTB5qQvCPf/wjzzzzDO+9915QhWB3qa2tPalfbnp6Om1tbXg8nl6/5kRHR7No0SJeeuklGhoazMcnT57Mr371KyIiIrj++utZvXo1119/fa9+dpiTGfKRwTCDE13X2bZtG8uXL6ewsJCDBw9y1VVXkZ+fz8KFC0lISAi5yFNTUxNlZWWnrB3z+Xzmptvc3ExsbGyXTTeYvLD3v9HsW1FkQEC7V2CLGIFbc2GRLMjI2KQGXHo0OoJExckYexMgsEkqEiCQTANKmXs4hz3p+IQVBMiSToq1nVjXPu6auQkJy4BuM2OMW5s+ffqg6jF3Jmeyx+Nh165dQ0YIPvvsszz66KOsXbuWuXPnBntJ3YoMTpo0iW984xvcf//95mOrV69m4cKFuN3uHovBU5k+HA4Hv/jFL/h//+//8dZbb3WZJLJ//36effZZvvOd7zBlypQefXaYsxMWg2EGPUIIdu/ezbJlyygsLGTXrl1cfvnl5Ofns2jRIlJSUoIuDB0OBzt27OhWpMjv93cZixcdHU1aWhrp6elB6RN3Ko56d7DW8b84tTYEAovuISBZAUGq0sYIewsSOjZJO9aWpmPN+5wJ7PSNw69bsMkBJEAVMmPs9WRGtGCVNGQEipzArPgfkxF5GZI0cFyFQ6W5cmdncl1dHbquk5KSwqhRo/rEmRwqCCF4/vnneeCBB1izZg0XXXRRsJcEBD9NrGkaiqLgcDg4cuQITU1NXHLJJURGRuL1evn+97/Pyy+/zIoVK7jhhhvM1wUCgV6bMx/mzITFYJghhRCCAwcOmMKwtLSUiy++mPz8fJYsWUJ6enq/i6mqqir27t3LjBkzzlkgBAKBLmPxjGhMWloacXFxQRWGuq5z2PM5bq2FEREzePXoDwhgQSHAjKhqpGORwc5icItzNIc86djlAMbSR9vqGRnRgoTALgewoJKIl0YRQ4MWg0Ah0z6ReUm/x2IJ3VF1NTU17N69m5kzZw766JhBbW0tO3bsYNy4cWZ02xCGaWlpZ2yrNNAQQvDiiy/y85//nLfeeiukWl9110CyevVqtm/fbj725S9/maamph4ZSIyI4L59+1iwYAFRUVHs3LmTCy64gFtvvZW7774bXde55557ePHFF3nxxRe5+eabz/vzwpwfYTEYZsgihKC8vJzly5ezYsUKPv/8c+bNm8eSJUvIz89nxIgRfS6mysvLOXz4MNnZ2T12yRnRGKMdiNVqNSOG/dFA+Gy0t7fzSuNtuHQb4+21xCleM9LXgcTm9nEc8aYQoQQAEELjwrhyLJJGhBxARmWE5KLIP5JmzahB7JiMkqiofHH4a1gtoWcaMgT/rFmzSE7ufvPtgUxtba2ZGjZmaBv9No3IttFWyRCGA3XcoBCCV155hfvuu4833nijiws2WDidTg4cOAB0TPB56qmnuOKKK0hKSmLUqFHcf//9VFVV8dJLLwHHW8vcfffdfPOb32T9+vXcc889vdJaprGxkQsuuIDFixdzzz33oCgKDzzwAAcPHiQ/P5/77rsPl8vFT37yE1588UWqq6tJSkrq8b9BmO4TFoNhwtBxMT969CiFhYUUFhbyySefkJubS0FBAfn5+b0++1gIwf79+6mpqSE3N/eU0256gqZpXcbiGQ2E09PTSUhICEqazhDf5eXlTJg0AVf0P6n3vYuMGxmQJTtbnans8wzHKqkoksAmfMyNP4KMToQSIEHyUK/FcsCXjoSOjECSQBMdvuRp0aO5NOVR6nzFeLQG9jZuQSGe60f/V78fr0FlZSX79+8nOzt7yGxwNTU17Nq1i1mzZplC8EQMZ7JxjjqdzpCbmdxdXn/9de6++26WLVsWMmaHjRs3csUVV5z0+Ne//nVeeOEFbr/9dsrLy9m4cWOX1/zoRz9i165dZGZm8t///d+90nR6/fr1fOc73+H99983R702NTXxy1/+ks8//5zVq1eTnp5Oa2srTU1NjB07tsefGebcCIvBAUhVVRU/+9nPWLNmDW63mwkTJvDPf/6T2bNnn/Y1/d1ZfiAjhKC2tpYVK1ZQWFjIBx98wMyZM01hOGHChB4JQ13X2b17N83NzeTm5hIVdXLj5t7EaCBcV1dHfX09QoguY/H6Qxga6fnq6uqTxK+qt9Pi34Rfd7Bs9x+pj55BqxqJIulY8HNJwiEzMjhMcbHFM5JWLQprJxcyQEAoJCgBRkZOZlfbLrB0jTIl643MT7iJzMQf9vnxGlRUVHDo0CFycnJISEjot88NJkY6vHNEsDt4PB4zYtjS0hL0mcndZeXKldx555289tprLF68ONjLCUk2bdrELbfcwpo1a5g5c6ZZC+h2u0lJSeGZZ57p1sSvMH1HWAwOMJqbm8nJyeGKK67grrvuIjU1lf379zN+/PjTjjgKVmf5wYAQgsbGRt544w2WLVvG+vXrmTx5Mvn5+eTn5zN16tRz2qQ0TWP79u14PB5yc3P7vXegEIKWlhazybUxL9mYLNEX9VtCCPbs2UNDQwO5ublndT+/eOAKqvR0mtRYdCExN/YgMYqfSMXPMMXJZ+7ROPWIU4hBmWSLm5aABMrx2sFZ8kEi7ODW7fiEBQlBpKwzL20zstR3ruTDhw9TXl5Obm7ukJmher5C8ERO50wOhVrYzrz99tt84xvf4OWXXzb75A11TuUarqysJDs7m9tuu40//vGPQMd1wePxcOWVV/LjH/84XCcYZMJicIDx85//nE2bNvHRRx91+zX92Vl+MGMIqTfffJPly5ezbt06xowZw5IlS1i6dOlZ5+gGAgFKS0sByM7ODrpLzpgsYQhDv9/fpbC/N9q36LrOzp07aWtrIzc395xSf3/ffglKpIU2bzujE+KIkD0kKh6qAokcDSRiQTNNJqLDhUKK1Ut9IALjD+OkSlIiAjRp0QSEYravkYVKrMWPRbIgEUtmzH0Mj1rY4+PtWIvg0KFDVFZWkpeX1+slAKFKbwnBE1FVlcbGRtMkpSiKeQMTTGfy2rVrue222/jHP/7BLbfcEpQ1hBqqqmKxWPD7/ezcuROr1UpycjLDhg1j5cqV3HrrrXznO9/hZz/7GXa7nffee49vf/vbvPPOO8ybNy/Yyx/ShMXgAGPatGlcd911HD16lA8++IARI0bwve99jzvvvPO0r+nvzvJDhba2Nt5++22WL1/OO++8w7Bhw0xhmJOT02WTOnLkCK+++irXXnstWVlZIeegFELgdDpNYejxeMzC/tTU1PMSrr0ZBXWrLXze+ARefSsp1FHiH41XtyIdM58IJKJkH2m2KCr9AfN1F0UeoFWPxKnbUdCRJLAQIMniQUagCZmAUNCQUKR0xsXeS0rEtefdrqZzOjwvL4+YmJizv2gQYAjBvjbIdJ6ZbDiTU1NTSU1N7Vdn8oYNG7jlllt47rnnuO2220ImUhlMjPYxbrebq6++moaGBtxuNwDPPfccixcvprCwkK997WtkZGSgKArNzc08/vjjfPOb3wzy6sOExeAAIyKiI/113333cfPNN7NlyxZ++MMf8pe//IWvf/3rp3xNXzcTDdPh3FuzZg2FhYWsWrWKpKQkFi9ezNKlS4mJiaGgoIDc3FxeffXVkBOCp8LpdHYp7Ddm0aalpXXL8amqKmVlZWiaRk5OTq9GQf2qm4NtD1Dq3E6bbgckEhQrFyZ9h72u5ez31JrPvSjqAHVqHJqQUCSBEDppFheKpOMXCgFhOdbaRiAAmzwcWyAGj3IAgcCntZMg/47sjKVnXZcQgn379lFXV0deXl7Qm4H3F/0lBE8kWM7kjz76iJtuuomnn36ab37zm2Eh2AlN07jooosYNmwYjzzyCPX19RQWFvLXv/6VF198kVtvvZWjR4/y8ccfI0kSY8aM4YILLgj2ssMQFoMDDpvNxuzZs/nkk0/Mx+655x62bNnC5s2bT/masBjsXzweD2vXrqWwsJCVK1eiaRpz5szhpz/9KRdffPGAm57hdrtNYdjW1mY6PtPS0sybk84EAgFKSkqQZZns7Ox+Pd79zpWsq/87yB2C+7gYlI8ZUgKkWNxoQsInrMeE4HFkHTyShTSlDQk4NlQFgYRPTCA77e1Tfm7nusi8vLw+NwWFCsESgidyJmfy6c7T82Hz5s0sXbqUJ554gu9+97thIUjXGsFt27bx5S9/mVdffbXLCL5f/epXPPnkk3z22WdkZ2cHaaVhzsTA2pXCMGzYsJPmIE+dOpXly5ef9jUZGRnU1dV1eayuro64uLiwEOwDIiMjKSgoICUlhbfeeouCggIsFgtf+9rXUBSFxYsXU1BQwCWXXBL0usHuEBUVxZgxYxgzZsxJs2jj4uLMljWRkZH4/X6KioqIiIgISjp8QvQSjjbWs5u3AYlAACJkP04RgRCYm7dAOtaj8Pi9sOp3olkTSFPaMFZtiEUJgV3az+e1v0JSSrBIsUyKe5Jo20iEEOzatYvm5mZmz549ZH5ToSIEoevM5HHjxnVxJu/bt88c35iamnreqfutW7dy44038sgjj4SF4DGM1LCqqhw9ehSr1cquXbswYkzG3++55x7eeOMNtm7dSnZ29ilNJmGCS1gMDjDmz5/P3r17uzy2b98+Ro8efdrXXHjhhaxevbrLY+vWrePCCy/skzWGgVWrVnHLLbfwhz/8ge985ztAR8Tsgw8+YNmyZXzrW98iEAiwePFi8vPzufzyy/vdWXw+REREMGrUKEaNGoXf7zeF4YEDB4iKisLv9xMXF8esWbOCcrGXJJkrRt/J5eJbPFfxBbb4x3NB5CH8kpWAUPDoCjoSMjqdhSCAzRaLW29FxhCMx6OGfk3QQixOfT3l7hQcqgWa7me0LYqpzm+itmrMnj271yJQoU51dTV79uwJCSF4KiIjI7ucp4YwPHTo0Hk5k0tLS8nPz+dXv/oVP/jBD8JCkI5orHGzd8kll5Cbm8uTTz7JZZddxlNPPcXjjz9ORkYGQgiSkpKIjIzE6XQChIVgCBJOEw8wtmzZwkUXXcRDDz3EF7/4RT7//HPuvPNO/va3v/GVr3wFoF87y4c5GaNI+oUXXuCmm2465XNUVeXjjz9m2bJlrFy5EqfTyQ033EBBQQFXXXXVgIsutba2UlJSgqIo+P1+IiMjzYhhTExM0DfPbY6raRdN+HULVkkjVvHh1RU0OjYlY45JpOYk0iqdlD5u0KNp12wUucbh1CKOiUnQkYlVExgTV41HcyJJMCPmC8xNGbwF8aEuBM/E+TiTd+zYwYIFC/jxj3/M/fffH/RzORQwIn4A//rXv/j3v//NSy+9REJCAk8//TSvv/46c+fO5Ze//CUpKSls2rSJL3zhCzz//PPhXowhSlgMDkDefvtt7r//fvbv38/YsWO57777uriJ+7OzfJiTKS8v59ChQ90eSaVpGp9++qk5Fq+xsZHrrruOgoICrr322pA3IjidToqKihg2bBgTJ040x+LV1dXR0NCA3W4nPT09ZHrEBQIBtrV+A03bgYaEjoSEDUX3IXCRaOkqBlVNUE88h70p7PNmYJcCyMf+qAgPiVYND3YzzigjSJK93D7mzSAcXd8ykIXgiZzKmWyxWDh8+DBLly4lNjaW3bt3s2DBAu666y4efPDBoJ+7wcTr9VJZWcnEiRPNxx555BGzKf9TTz1lPv7YY4/xxhtvmLO49+7dy7e//W1++9vfBmPpYbpBWAyGCRNC6LrO1q1bTWFYVVXFNddcQ0FBAddffz1xcXHBXmIXjIjgqFGjGDt27EmbpaZpNDY2msLQYrGYKbqEhISgb66a7qbZ/wle7SieNi818vOkyu2mcUSiI0XcSDzFrtHUB2KJlI+3rhlma6VVjwJzSnLHf5MQzIxMY2p0LJpeg1UZTUrkHdgto/r/IHuJwSQET8RwJq9fv56f//zn1NfXM3v2bCoqKrjxxht5+umng36uBhNd17nrrrvYvXs3zzzzDLNmzcLr9fKVr3yFdevWkZeXx/vvv98lsrpz504+//xzfD4fI0eOZOHC3unjGaZvCIvBMGFCFF3X2bZtG8uWLaOwsJBDhw5x9dVXk5+fz8KFC4mPjw/qBtXU1ERpaSnjx48/Y82qga7rZoquvr4eSZJMYRjM5sGd2Vl3BwH9I+wWic6rqdViKXWNojYQ30UMxlp8aHR9rgCGW5rJiazE2unr0aUo0mL+h3j7ZX19GL2OIQSHwnxlXddZs2YNjz/+OFVVVTQ2NnLZZZexdOlS8vPzyczMDPYSg8ILL7zAK6+8QkREBL/4xS+YN28ePp+Pn/3sZ7z11lvcfvvt3HvvvUOmyfpgIywGw4QZABiOVUMY7t69myuuuIL8/HwWLVpEcnJyvwrD+vp6tm/fzuTJkxkxYsQ5v17X9S5j8Yx5ycZYvGALw89rZxCjBJCOOY6btCiOBpIoc4061qJGA/zEWDvEX+fVymhcHbubCElFEIkkSQghkPFSGcjkYGAsLr0VGZkM+3gWpt1PpBK6G+hQEoLQMU/6+uuvZ/HixfzpT3+isrKSlStXsnLlSj766CNyc3N56KGHWLBgQbCX2i8IIcxry8qVK3n55Zfx+Xw88MADzJ07l0AgwA9/+EO2bNnC0qVLufvuu4mPjw87hgcYYTEYJswAQwjB/v37TWFYVlbGJZdcQn5+PosXLyY9Pb1PhWFtbS07d+5k+vTpZGRk9Pj9jBSdIQwDgYApDPtzqsSZcLlclDTfwA5/PEcDSeii4983w9aGH4vZqAZguLWJvMhKNCFhkY+7i4/4ItnuH05XawpESFF8K/N5FKVvmiT3hKqqKvbu3TtkhGBVVRXXXnst1157Lc8999xJYqahoYG33nqLGTNmMGfOnCCtsn/pbBYpLCzk9ddf5+2332bevHk8+OCDzJ8/H13X+eEPf8jnn3/OokWLuPvuu4fE+TKYCIvBMH1OVVUVP/vZz1izZg1ut5sJEybwz3/+k9mzZ5/y+Rs3buSKK6446fGamppeER+DCSEEhw8fNmsMt2zZwrx588jPzyc/P5/hw4f3qjA0xMHMmTNJTU3ttfc1MOYlG0X9Xq/XnJecmpoalIbdfr+f4uJi7HY74yYnsN31PA3+GBIiZlPe/kca9aguPQtHWxvJjjyKwIosHV/vuvbR+LEA0rGII8fmnkBe3CIuTrq9n4/szAw1IVhbW8t1113HxRdfzPPPPx8SNyGhxC233ML+/ftZvHgxdXV1vPPOO0yePJmf/exnplnu3nvvZcWKFfztb38Ld6oYYITFYJg+pbm5mZycHK644gruuusuUlNT2b9/P+PHj2f8+PGnfI0hBvfu3dvFMJGWlhZOO5wBIQRHjx6lsLCQwsJCNm3axOzZs8nPz6egoIBRo0b1SBhWVFRw6NAhZs2a1S/iwJiXbAhDl8vVZV5yX40b64zRRDsqKoqZM2ee8vz7Z/liWnW72aYmQXJySezhjv8ldfSOVFXBWs8EgC4VhoYYTLYM57bMP5mP19ZMJmCNwC1krJIghgBpKTv76ChPZqgJQYfDwYIFC8jNzeXFF18ccFOC+pr169fzta99jXXr1jF16lSgI2X8xz/+EUmSePDBB7n00ksBWLFiBUuXnn18Y5jQIiwGw/QpP//5z9m0aRMfffRRt19jiMHm5mYSEhL6bnGDGCEEtbW1rFixguXLl/Phhx+SlZVFQUEB+fn5jB8/vtvCUAjBoUOHqKysJCcnh/j4+D5e/akxxo3V1dXhdDpJTEw0DSh90bDb5/NRVFREbGws06dPP+ONSHvbUTY4/4c06yTmpX6HI2334fevOTbzWEZC523ndODUYjDNOopbR3S05qivn8YREYlXHH+egmCUxcfw5O29fpwnMtSEYENDAwsXLmTKlCm88sorA2IqUH/z8ccfs2TJEt5//31ycnLMx5ctW8Ztt93GBRdcwD333MONN94YxFWG6QnhMEuYPuXNN99k9uzZ3HzzzaSlpZGTk8P//d//deu12dnZDBs2jGuuuYZNmzb18UoHF5IkMWzYML73ve/x3nvvUV1dzXe/+102bdrEnDlzuPDCC3n88cfZvXs3Z7ofFEKwb98+jh49yuzZs4MmBAGio6MZO3Ys8+bNY/78+aSkpFBTU8NHH33Eli1bOHLkCF6vt1c+y+v1smXLFuLj45kxY8ZZI9KxcZksGf4H5qV2TJvJjPk90RF3oktpCMmKkEYQI0fAMXkIx4UgwMzY4ym1Rux4hYQNgV0S2BDoQKVqp7Z2aq8c3+kYakKwubnZvDn617/+FRaC0OV6YPz3qKgokpKSzOlXxuNLlixh2rRptLS0UF5e3u9rDdN7hCODYfoUYzzXfffdx80338yWLVv44Q9/yF/+8he+/vWvn/I1e/fuZePGjcyePRufz8fzzz/Pyy+/zGeffUZubm5/Ln/QIYSgubmZN998k+XLl7Nu3TrGjRvHkiVLWLp0aZcImKqq3HPPPVxxxRUsXLiQqKioIK/+1Hi9Xurr66mrq6OlpYXY2FizyfX5rNnj8bB161aSk5OZOnVqr9VcOrwH+E/tL9HQujyebhvDl4b/HoDqyslU2mIRgKXTxwoBfiTGW7xkHIsOtrTfjytQCMfezyZPIyF6+XkLmqEmBFtbW03DVWFh4YAYB9nXdHYAu1wu3G63WRv8ve99j1dffZVly5Zx+eWXoygKFRUV/PjHP+b2229n0aJFwVx6mB4SFoNh+hSbzcbs2bP55JNPzMfuuecetmzZwubNm7v9PpdddhmjRo3i5Zdf7otlDllaW1t5++23KSws5J133mHYsGFmu5rf/va37Nu3j1WrVp22vjPUMObQ1tXV0dTURHR0tCkMY2Jizvp6l8tFcXExqampTJ48uddd2e1+Bxua/0aDvxxFsjEt5grmJNxs/r26ejJHjrWZOZUYHGfxMix5O42tt+HVPj3p/WViGZZUcs7rOnr0KPv27SMnJ4fExMRzP7ABRnt7OwUFBcTGxvLmm28OmZnSZ6JzC5nf/e53rF69miNHjjBp0iR++ctfcskll/DVr36VN998ky9+8YskJyfz9ttvM27cON58c/BN2xlqhMVgmD5l9OjRXHPNNTz//PPmY8899xyPPPIIVVVV3X6f//qv/+Ljjz8+JwEZ5txwOp2sXr2a//znP6xevZqEhATy8/O5+eabmTNnzoBzVwYCAXMsXmNjozkvOS0tjdjY2JOE3olj9YLR0Hvz4VuIi9lPi65gQ2AsISA6GtKMw0li0nYc7adPFydG/paoyFsAqGn4JUJfhhASdmUVKSkni/qhJgRdLhc33ngjiqLw9ttvh/y4x/7m0Ucf5emnn+bxxx/nkksuMW/EV65cSUZGBn/4wx/YsmUL9fX1TJo0ieeeey7YSw7TC4QtU2H6lPnz55t1Jgb79u3r1sSKzpSWljJs2LDeXFqYE4iJiWHBggX85S9/YdasWXz/+9/nnXfe4cYbbyQ6OpolS5ZQUFDAhRdeOCCEodVqZdiwYQwbNgxVVc2xeFu3bsVms5nCMD4+3hSCmZmZ52Su6W0uHPtvHA3TcUuR+IWEJDqG4kkIhikB0lL24HT/7Yzv4fT9lajIW6hqmIJF0lEsHff7frGQaodgeNrx3+NQE4Iej4cvfvGL6LrOqlWrwkKwE7quU1VVxfLly/nrX//K0qVL+fjjj2lvb+fOO+8023r9+Mc/RtM6ShMGwnUgTPcIi8EwfcqPfvQjLrroIh599FG++MUv8vnnn/O3v/2Nv/3t+IZ2//33U1VVxUsvvQTA008/zdixY5k+fTper5fnn3+e9evX8+677wbrMIYETU1NLFiwgLi4ON577z2io6P5yle+gtfr5f3332f58uV8+ctfxmKxsHjxYgoKCrj44osHRNG9xWIhPT2d9PR0c16yw+GgpKQESZLQNI2MjIygCkGDtJSdSPXTaJdtOIWCDY1EOcChtumMTgFZOpuJR6G6YQp2WUMCNNEhJyMkjYAiUem4iJFpnww5Iej1ern11lvxeDysXbt2SI9N61wbaPx3WZbRdR2v10tBQQHvv/8+BQUFPPHEE9xxxx20t7fz6quv8qUvfSnkZqSH6TnhNHGYPuftt9/m/vvvZ//+/YwdO5b77ruPO++80/z77bffTnl5ORs3bgQ66lX+9re/UVVVRVRUFFlZWTzwwAOnbEQdpncIBALMnTuXMWPG8Oqrr562hioQCLBx40aWLVvGypUr0TSNxYsXk5+fz+WXX94vvf96k+bmZoqLi4mJicHj8QCYEcOkpKSQ7GsZCATOmCZOin6WZuc92CUN9ZgQhA73skUCr7AgPOuoPvoOUyYfwWZtRlYmYrHlIyvnFrEfKPh8Pm677Tbq6upYt27dkBC/p8OYKFJcXMwrr7zC4cOHufXWW7nppptwuVxccMEFXHbZZbzyyiv85je/4fvf/z7QYey74447eOCBB7j22muDfBRhepuwGAwTJgwAGzZs4JJLLul2w11VVfn44495/fXXWblyJW63mxtuuIH8/HyuvvrqkC/Kb25uprS0lAkTJjBy5EjTaW00udY0rcu85FBKibW0/xxXYNlJj1ukEaQnfoCjcRIyAv2E0XeKJPAIhabKrzFu9CokyQ9IIAkkKRF71O9QLNn9cxD9RCAQ4Gtf+xrl5eWsX7+e5OTkYC8paBhC8OOPPzYj+36/n3feeYd///vf3HzzzTz++OM8+eSTXHrppaxYsQLoENM333wzPp+Pd955J+jR8zC9T1gMhgkTpsdomsbmzZvNsXhNTU1cf/31FBQUcM0114RcbVZTUxOlpaVMmjSJzMzMk/5uzEs2mlwHAgFSUlJIT08nOTk5JCZUuDz/os3zO3TcgIUo60ISY4+1qGmcjF3S0cTxTduIDPqETJw8Akl2A4lIkoQQOtCIrEwnIvqfg2azV1WVO+64g927d7N+/XrS0tKCvaSgYaSDP/vsMy699FIefvhhfvrTn6KqKjfffDOXXnop9957L5WVlTz88MN89tlnzJo1i4yMDMrKyqiqqqKsrGzARf/DdI+wGAwTJkyvous6W7ZsMYVhdXU11157Lfn5+SxYsCDotVoNDQ1s27aNKVOmMHz48LM+XwhBe3u7KQy9Xi/Jycmkp6eTkpISkjWTVY1TiJA0hOjoQigBigSakBCaQow9lg4heDzaKYQHiQARMa8hKycL5IGGpml897vfpbi4mA0bNoTnmgOHDx8mKyuL22+/nWeeecZ8/KqrrsLr9VJZWcnll1/O5MmTGT58OC+99BKpqamMHTuWhx56KOSj/WHOn7AYDBMmTJ+h6zplZWUsW7aMwsJCysvLufrqq1myZAkLFy4kPj6+X6NQDoeD7du3M3369PMWB8a85Lq6OlwuF0lJSaSnp/fbvOTu0NDwLl7uwS7pyFLHJV4VEpoukZbwLD73z4Gk04jBV5GVkUFaee+gaRr33HMPH///9u4zKqpz/fv4d8CCHQui2NDYUBCsCBqVRIMUM8NKEY8riiWmqBFJTkQTiIkxSsQTjHrUJCoaY6WpSDSCoqLEgiWWA3bwqIAcARGClNnPi/yd9aBYIzPAXJ+1eME9ew/X7VL8zb33vq+EBOLj42nVqpWhS6oUDh48yMSJE+nTpw+BgYF06tSJWbNm8e9//5vPPvuMhg0b8s0339CiRQvCw8PLXTUX1ZOEQSGEXiiKwtmzZwkLCyMyMpLk5GRcXFx0m1w3adKkQoNhRkYGZ86cwc7O7oVdLiwoKNAFw7y8PMzNzXXBsDKsovw3wxYT0xJAobjUhXaWy1G02fx5900UpQAw/7/LxAqQhYlpN8zqhVbpy8RarRY/Pz9iY2PZu3fvM29jVd3t37+fwMBAWrduTf369dm+fTubNm1i4MCBABw/fpw+ffqwYcMGRo4caeBqhb5UvkflhDAwa2trVCrVQ1+TJ09+5Dlbtmyha9eumJmZYWdnR0xMjB4rrhpUKhW2trbMnj2bkydP8scffzBo0CBWrlxJhw4dGDFiBD/99BMZGRmP7Zf8PG7evMnZs2fp0aPHC71vrG7dulhbW+Po6MjAgQNp3rw56enpJCQkcOTIEVJTU3VPKRuCUvQrKaeXUbfGYdpZLgdAZdKYmrU/RIUp8D8U7f+ALFSqRtQy863yQdDf35+dO3cSGxsrQbAcgwYNYu7cuaSmphIaGsqXX37JwIEDKS0tpaSkhPr169OtWzejfuLaGEkYFOIBR48e5ebNm7qv3bt3A/DWW2+Ve/yhQ4cYNWoUEyZM4MSJE2g0GjQaDWfOnNFn2VWKSqWiS5cuzJo1i6NHj5KcnMzw4cPZsGEDnTt3xs3NjWXLlnH9+vW/HQyvX7/Of/7zH+zt7XV9ViuCmZkZbdu2pW/fvrz88su0bNmSrKwsDh48yO+//86VK1fIz8+vsJ//oGvXrnHx4kV69uyJubl5mddq1n6D2vUWUaOmB6Y1elKj1j8wq/cjpjV66q2+F02r1RIQEEBkZCSxsbF06NBBbz976dKlWFtbY2ZmhqOjI0eOHHnksaGhoQ990NT3KvKAAQP47rvv6N+/PzExMRw/fhxTU1Nq1KjBkiVLAOjXr59eaxKGJZeJhXgCX19foqOjuXDhQrmrJiNHjiQ/P5/o6GjdWP/+/XFwcGD58uX6LLXKUxSFa9euERERQUREBIcOHaJv376o1WrUajVt27Z9ppWra9euceHCBRwcHGjSpEkFVv5o9/slZ2Zm8r///Y969erp9jKsX79+hazEPS4IVkeKojBnzhxCQ0PZu3cvNjaP3ofxRdu0aRNjxoxh+fLlODo6EhISwpYtW0hJSSl3FTo0NJRp06aV6cykUqmwtLTUW833JSUlMWPGDOrUqcP8+fPZtGkTISEhnDp1ivbt2+u9HmE4EgaFeIyioiKsrKzw8/Nj1qxZ5R7Ttm1b/Pz88PX11Y198cUXREVFcerUKT1VWv0oisLNmzeJjIwkPDycAwcOYG9vrwuGT+oWkpqayuXLlytVICopKdEFw6ysLMzMzHTBsGHDhi8kGBpjEAwKCmLZsmXs2bMHOzs7vf58R0dH+vbtq1tR02q1tGnThqlTp+Lv7//Q8aGhofj6+pKTk6PXOh/l1KlTzJw5k6SkJPLy8khKStJrmBaVg1wmFuIxoqKiyMnJwcfH55HHpKenP/Sp3tLSkvT09AqurnpTqVRYWVkxefJk4uLiuHHjBpMmTSIhIYE+ffrg7OzM/PnzSU5OfuhS8pw5c9i3bx+9evWqVIGoRo0atGzZEnt7e4YMGULHjh0pLCwkKSmJhIQEUlJSyMnJee5L42lpaUYXBENCQliyZAm//fab3oNgUVERSUlJDB06VDdmYmLC0KFDSUxMfOR5d+/epV27drRp0wa1Ws3Zs2f1UW657O3tCQoKwtHRkZMnT0oQNFKG3zlViEps5cqVuLm5PdV+dKLiqFQqLCwsmDRpEu+++y7Z2dls3bqViIgIvv32W1566SVef/111Go1a9eu5ZdffiEiIoJGjZ7Ux9dwTE1Ndf2StVqtrl/yyZMnUalUNG/eHEtLS8zNzZ+qLV5aWhqXLl0yqiC4dOlSgoOD2bVrFz176v9+x6ysLEpLS8v9MJicnFzuOV26dGHVqlX06NGD3NxcgoODcXZ25uzZswbbysXOzo6wsLBKszWS0D8Jg0I8QmpqKrGxsURERDz2uBYtWpCRkVFmLCMjQza5rSAqlYomTZowbtw4xo0bR25uLtu3byc8PJyQkBBq1qzJ6NGjMTMz03VdqOxMTEywsLDAwsICrVara4t3+vRpFEUp0xavvPncD4K9evWq1AH4RVEUhR9//JG5c+fy66+/VqmHHZycnHByctJ97+zsjI2NDStWrGDOnDkGq0uCoHGr/L8lhTCQ1atX07x5czw8PB57nJOTE3FxcWXGdu/eXeYXvqg4jRo1YvTo0bRv354mTZrw5Zdf6trh2dnZMXPmTA4fPoxWqzV0qU/FxMSEpk2bYmNjw6BBg7C3t6dGjRokJyezb98+Tp8+reudDMYZBNesWUNgYCDbt2/H2dnZYLU0a9YMU1PTv/VhsGbNmvTs2ZOLFy9WRIlCPBVZGRSiHFqtltWrVzN27NiH+tCOGTOGVq1aMW/ePACmTZvG4MGDWbhwIR4eHmzcuJFjx47xww8/GKJ0o6PVapkyZQoxMTEcOHBAt6VIQUEBu3btIjw8HC8vLxo0aKC7lOzk5ISpqekT3tnwVCoVjRs3pnHjxnTu3Jk7d+6QmZnJ+fPnuXfvHnXr1qWgoICePXsaTRBcv349M2bMYOvWrQwaNMig9dSqVYvevXsTFxeHRqMB/vr7GBcXx5QpU57qPUpLSzl9+jTu7u4VWKkQjydPEwtRjt9++w1XV1dSUlLo3LlzmdeGDBmCtbU1oaGhurEtW7bw+eefc/XqVTp16sS3334rv9z15IMPPmD37t3ExcU9cpPhwsJCYmNjCQ8PZ9u2bdSqVQtPT0+8vLwYMGBApewv/DiKonDhwgXS0tIwMzPj3r17ZdriVbX5PA1FUdiyZQtTpkwhLCyM4cOHG7ok4K+tZcaOHcuKFSvo168fISEhbN68meTkZCwtLR/68PjVV1/Rv39/OnbsSE5ODgsWLCAqKoqkpCS6detm4NkIYyVhUAhRpcXExGBvb//U/WeLi4vZu3cvYWFhbN26Fa1Wi4eHB15eXgwePLhK3Dt1f9uc+5eG8/PzycjIIDMzk7t379KkSRPdljVVYT5PIyoqikmTJrFx40Y8PT0NXU4ZS5YsYcGCBaSnp+Pg4MD333+Po6Mj8PCHx+nTpxMREUF6ejqNGzemd+/efP311wZ5AEaI+yQMCiGMVklJCQcOHCAsLIyoqCgKCgrw8PBArVbz6quvVor+wg96MAg+6H6/5MzMTO7cuYO5ubkuGFbG+TyN6Ohoxo0bx7p16/Dy8jJ0OUJUOxIGhRCCv+7dOnToEOHh4URGRpKTk8Pw4cPRaDQMGzaMunXrGrrEJwbBBxUWFuqCYU5ODg0bNtRtWVOnTh09VPz37dq1i3feeYeVK1cycuRIQ5cjRLUkYVAIIR6g1Wo5cuSILhimp6fz2muvoVarGT58OA0aNNB7Tc8aBB9UVFSkC4a3b9+mfv36ZdriVUZ79uzB29ub5cuXM3r06App3SeEkDAohBCPpdVqOXnyJGFhYURERJCamsrQoUNRq9W4u7vTqFGjCg8pfzcIPqi4uLhMv+Q6deroVgwrql/ys9q/fz9vvfUWixYtYty4cZWiJiGqKwmDQlQT1tbWpKamPjT+4YcfsnTp0ofGQ0NDGTduXJmx2rVrU1hYWGE1VnWKonDmzBnCwsKIjIwkJSUFFxcXNBoNHh4eNGnS5IWHlqtXr3LlypUK20ewpKSErKwsXb/kWrVq6YLhi+qX/KwSExPx8vIiKCiI999/X4KgEBVMwqAQ1cStW7d0GxEDnDlzhmHDhrF3716GDBny0PGhoaFMmzaNlJQU3ZhKpXqotZYon6IopKSkEB4eTkREBKdPn+bll19Go9EwYsQILCws/naIuR8Ee/fuTcOGDV9Q5Y9WWlqqa4t369YtTE1Ny7TF00coO3r0KGq1mq+++oqpU6dKEBRCDyQMClFN+fr6Eh0dzYULF8r9DzU0NBRfX19ycnL0X1w1oygKly9f1gXDpKQknJyc0Gg0vP7667Rs2fKZQ42+g+CDtFott2/fJiMjg1u3bun6Q1taWtK4ceMKafN34sQJPD09+eyzz/j4448lCAqhJxIGhaiGioqKsLKyws/Pj1mzZpV7TGhoKBMnTqRVq1ZotVp69erFN998Q/fu3fVcbfWiKAppaWlEREQQERFBYmIi/fr1Q61Wo1aradOmzRNDjqGD4IO0Wi05OTm6vQyfpl/ys7rfhePjjz9m5syZEgSF0CMJg0JUQ5s3b+Yf//gHaWlpWFlZlXtMYmIiFy5coEePHuTm5hIcHMz+/fs5e/YsrVu31nPF1ZOiKNy4cYPIyEjCw8NJSEjAwcFBFww7dOjwUOjZuHEjzZs3p0+fPpUiCD5IURRyc3N1wbC4uFgXDO/36n1W586dw93dnQ8//JAvvvhCgqAQeiZhUIhqyNXVlVq1arF9+/anPqe4uBgbGxtGjRrFnDlzKrA646QoCpmZmURFRREeHk58fDzdunVDrVaj0Wjo3LkzgYGB/Pjjj+zZs6dKtCZTFEXXLzkzM5PCwkKaNWtG8+bNsbCweKivd3nOnz+Pm5sbPj4+fPPNNxIEhTAACYNCVDOpqal06NCBiIgI1Gr1M5371ltvUaNGDTZs2FBB1Qn4K0Tdvn2brVu3EhERQWxsLO3atePGjRssXryYN998s0LuyatIiqKUaYuXn59P06ZNdcGwvLZ4ly5dws3NjZEjR7JgwYIqN2chqgsJg8LoJCYmMnDgQIYPH86OHTsMXc4LN3v2bFasWMG1a9eeamXmvtLSUrp37467uzv/+te/KrBC8aDZs2cTHBzMwIED2b9/P61bt0atVuPl5UWPHj2qZEjKz8/XrRjm5eVRs2ZNDh8+jLe3N23btuXq1au4ubkxYsQIvv/++yo5RyGqCwmDwuhMnDiR+vXrs3LlSlJSUh55T52iKJSWlj5ToDI0rVZL+/btGTVqFPPnzy/z2pgxY2jVqhXz5s0D4KuvvqJ///507NiRnJwcFixYQFRUFElJSVXiEmV1MX/+fBYsWEBcXBwODg7k5eURExNDeHg4v/76K82aNdNdSu7Tp0+VDE1//vknv//+O/7+/pw7dw4bGxsKCgpwcnLi559/rpJzEqI6kX+BwqjcvXuXTZs28cEHH+Dh4UFoaKjutfj4eFQqFb/++iu9e/emdu3aJCQkoNVqmTdvHu3bt6dOnTrY29sTFhamO6+0tJQJEyboXu/SpQuLFi0ywOwgNjaWtLQ0xo8f/9BraWlp3Lx5U/d9dnY27777LjY2Nri7u3Pnzh0OHTokQVCP5s+fT3BwsC4IAjRo0ICRI0eyefNmMjIyWLhwIbdu3UKj0dCtWzc+/fRTDh48WGZPycquTp06uLi4cPjwYfbu3Uu9evUoKipi8+bN9O/fn6CgIC5evGjoMoUwWrIyKIzKqlWrWLZsGUePHiU6OhpfX1/dPnzx8fG4uLjQo0cPgoOD6dChA40bN2bZsmWsW7eOkJAQOnXqxP79+3n//ffZtWsXgwcPpri4mK+//poRI0bQtGlTDh06xKRJk1i9ejVvv/22oacsKqni4mK8vb0JDAzE3t7+iccXFhaye/duwsPD2bZtG7Vr12bEiBF4eXkxYMCAKrGCnZmZiZubG7169WLt2rVkZ2ezbds2wsPDiY2NpWvXrkybNq3cDzNCiIojYVAYlQEDBvD2228zbdo0SkpKaNmyJVu2bGHIkCG6MBgVFaV78OLevXs0adKE2NhYnJycdO8zceJECgoKWL9+fbk/Z8qUKaSnp5dZQRTiRSkqKmLv3r2EhYWxdetWADw8PPDy8mLQoEHlPqxhaFlZWXh4eGBjY8P69esfCq+5ublER0djZmbGG2+8YaAqhTBOEgaF0UhJScHW1pbr16/TvHlz4K/Qlpuby88//6wLg//9739p1aoVAGfPnsXW1pZ69eqVea+ioiJ69uzJ4cOHAVi6dCmrVq0iLS2NP//8k6KiIhwcHDhy5Ih+JymMTklJCfv37ycsLIyoqCgKCwvx8PBAo9Hg4uKCmZmZoUskOzsbT09P2rVrx+bNmytlWBXCmFX+6wpCvCArV66kpKSkzAMjiqJQu3ZtlixZohv7/4Pf3bt3AdixY4cuIN5Xu3Zt4K9Ngj/55BMWLlyIk5MTDRo0YMGCBbqgKERFqlGjBq+88gqvvPIKixcv5uDBg4SHhzN9+nRyc3Nxc3NDo9EwdOhQ6tatq/f6cnNzUavVWFlZsWnTJgmCQlRCsjIojEJJSQmtW7fm008/5bXXXivzmkaj4ZNPPqFr1664uLiQnZ2Nubk5AHl5eVhYWPDjjz/yzjvvlPveU6dO5dy5c8TFxenGhg4dSlZWFidPnqyoKQnxWFqtliNHjhAWFkZkZCQZGRm4urqiVqsZPnw49evXr/Aa8vLyUKvVNGzYkG3btlWKVUohxMPkaWJhFKKjo8nOzmbChAnY2tqW+XrjjTdYuXJluec1aNCATz75hOnTp7NmzRouXbrE8ePHWbx4MWvWrAGgU6dOHDt2jF27dnH+/HkCAgI4evSoPqdXpZWWlhIQEKB7Gvull15izpw5POlzanx8PL169aJ27dp07NixzJPhAkxMTOjfvz/BwcFcuHCBffv20blzZ+bOnYu1tTXe3t5s2LCB3NzcJ/5ZP4/8/HzefPNN6tSpQ1RUlARBISozRQgj4Onpqbi7u5f72uHDhxVAWbRokQIo2dnZZV7XarVKSEiI0qVLF6VmzZqKhYWF4urqquzbt09RFEUpLCxUfHx8lEaNGinm5ubKBx98oPj7+yv29vYVPKvqYe7cuUrTpk2V6Oho5cqVK8qWLVuU+vXrK4sWLXrkOZcvX1bq1q2r+Pn5KefOnVMWL16smJqaKjt37tRj5VWTVqtV/vjjDyUgIECxtbVVatWqpQwfPlxZtmyZcu3aNeXu3btKfn7+3/rKyspShgwZogwcOFDJy8sz9JSFEE8gl4mFEAbl6emJpaVlmdXZN954gzp16rBu3bpyz5kxYwY7duzgzJkzujFvb29ycnLYuXNnhddcXSiKQnJysu5S8pkzZxg0aBAajQZPT08sLCyeuVdwYWEh3t7e5OXlsXPnTho1alRB1QshXhS5TCyEMChnZ2fi4uI4f/48AKdOnSIhIQE3N7dHnpOYmMjQoUPLjLm6upKYmFihtVY3KpUKGxsbAgICSEpK4ty5cwwbNoyff/6ZTp064e7uzvLly7lx48ZTXUq+d+8e77zzDrdv3yYmJkaCoBBVhIRBIYRB+fv74+3tTdeuXalZsyY9e/bE19eX0aNHP/Kc9PR0LC0ty4xZWlpy584d/vzzz4ouuVpSqVR07NiRGTNm8Pvvv3Px4kXUajURERHY2Njw2muvsXjxYtLS0soNhsXFxfj4+HD9+nV27dpF48aNDTALIcTzkDAohDCozZs388svv7B+/XqOHz/OmjVrCA4O1j2gI/RPpVLRrl07/Pz8OHDgAFevXsXb25udO3diZ2eHi4sL3333HZcvX0ZRFEpKSpg4cSKXLl1i9+7dNG3a1NBTEEI8A7lnUAhhUG3atMHf35/Jkyfrxr7++mvWrVtHcnJyuecMGjSIXr16ERISohtbvXo1vr6+5ObmVnTJRktRFDIyMoiKiiIiIoL4+HhsbGxQqVQUFBSwf/9+WrRoYegyhRDPSFYGhRAGVVBQgIlJ2V9FpqamaLXaR57j5ORUZl9HgN27d5dpGShePJVKRYsWLXS9uW/evMnEiRO5desWMTExEgSFqKJkZVAIYVA+Pj7ExsayYsUKunfvzokTJ5g0aRLjx48nKCgIgJkzZ3L9+nXWrl0LwJUrV7C1tWXy5MmMHz+ePXv28NFHH7Fjxw5cXV0NOR0hhKhyJAwKIQwqLy+PgIAAIiMjyczMxMrKilGjRhEYGKhrXebj48PVq1eJj4/XnRcfH8/06dM5d+4crVu3JiAgAB8fH8NMQgghqjAJg0IIIYQQRkzuGRRCCCGEMGISBoUQQgghjJiEQSGEEEIIIyZhUAghRKW1dOlSrK2tMTMzw9HRkSNHjjz2+C1bttC1a1fMzMyws7MjJiZGT5UKUXVJGBRCCFEpbdq0CT8/P7744guOHz+Ovb09rq6uZGZmlnv8oUOHGDVqFBMmTODEiRNoNBo0Gg1nzpzRc+VCVC3yNLEQQjyn0tJSZs+ezbp160hPT8fKygofHx8+//xzVCpVuefEx8fj4uLy0PjNmzdl0+YHODo60rdvX5YsWQKAVqulTZs2TJ06FX9//4eOHzlyJPn5+URHR+vG+vfvj4ODA8uXL9db3UJUNTUMXYAQQlRVQUFBLFu2jDVr1tC9e3eOHTvGuHHjaNSoER999NFjz01JSaFhw4a675s3b17R5VYpRUVFJCUlMXPmTN2YiYkJQ4cOJTExsdxzEhMT8fPzKzPm6upKVFRURZYqRJUnYVAIIZ7ToUOHUKvVeHh4AGBtbc2GDRueeF8b/BX+zM3NK7jCqisrK4vS0lIsLS3LjFtaWj6yZ3V6enq5x6enp1dYnUJUB3LPoBBCPCdnZ2fi4uI4f/48AKdOnSIhIQE3N7cnnuvg4EDLli0ZNmwYBw8erOhShRDikWRlUAghnpO/vz937tyha9eumJqaUlpayty5cxk9evQjz2nZsiXLly+nT58+3Lt3j59++okhQ4Zw+PBhevXqpcfqK7dmzZphampKRkZGmfGMjIxH3lvZokWLZzpeCPEXWRkUQojntHnzZn755RfWr1/P8ePHWbNmDcHBwaxZs+aR53Tp0oX33nuP3r174+zszKpVq3B2dua7777TY+WVX61atejduzdxcXG6Ma1WS1xcHE5OTuWe4+TkVOZ4gN27dz/yeCHEX2RlUAghntM///lP/P398fb2BsDOzo7U1FTmzZvH2LFjn/p9+vXrR0JCQkWVWWX5+fkxduxY+vTpQ79+/QgJCSE/P59x48YBMGbMGFq1asW8efMAmDZtGoMHD2bhwoV4eHiwceNGjh07xg8//GDIaQhR6UkYFEKI51RQUICJSdkLLKampmi12md6n5MnT9KyZcsXWVq1MHLkSG7dukVgYCDp6ek4ODiwc+dO3UMiaWlpZf78nZ2dWb9+PZ9//jmzZs2iU6dOREVFYWtra6gpCFElyD6DQgjxnHx8fIiNjWXFihV0796dEydOMGnSJMaPH09QUBAAM2fO5Pr166xduxaAkJAQ2rdvT/fu3SksLOSnn35i8eLF/Pbbb7z66quGnI4QwkjJyqAQQjynxYsXExAQwIcffkhmZiZWVla89957BAYG6o65efMmaWlpuu+Lior4+OOPuX79OnXr1qVHjx7ExsaWuxG1EELog6wMCiGEEEIYMXmaWAghhBDCiEkYFEIIIYQwYhIGhRBCCCGMmIRBIYQQQggjJmFQCCGEEMKISRgUQgghhDBiEgaFEEIIIYyYhEEhhBBCCCMmYVAIIYQQwohJGBRCCCGEMGISBoUQQgghjJiEQSGEEEIIIyZhUAghhBDCiEkYFEIIIYQwYhIGhRBCCCGMmIRBIYQQQggjJmFQCCGEEMKISRgUQgghhDBiEgaFEEIIIYyYhEEhhBBCCCMmYVAIIYQQwohJGBRCCCGEMGISBoUQQgghjJiEQSGEEEIIIyZhUAghhBDCiEkYFEIIIYQwYhIGhRBCCCGMmIRBIYQQQggjJmFQCCGEEMKI/T97OwFus1wunAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(df_clean['area(sqr feet)'], df_clean['bedrooms'], df_clean['baths'], c=df_clean['price'], cmap='viridis')\n",
    "ax.set_xlabel('Area')\n",
    "ax.set_ylabel('Bedrooms')\n",
    "ax.set_zlabel('Bathrooms')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation analysis\n",
    "\n",
    "corr = df_clean[['price', 'baths', 'bedrooms', 'area(sqr feet)']].corr('spearman')\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_clean.drop(['price'], axis=1).values\n",
    "y = df_clean[['price']].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_metrics(X_test, y_test, model):\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    explained_var = explained_variance_score(y_test, y_pred)\n",
    "    \n",
    "    # Calculate adjusted R-squared\n",
    "    n = X_test.shape[0]  # number of samples in the test set\n",
    "    p = X_test.shape[1]  # number of features\n",
    "    adj_r2 = 1 - ((1 - r2) * (n - 1) / (n - p - 1))\n",
    "\n",
    "    # Print the results\n",
    "    print(\"Mean Squared Error:\", mse)\n",
    "    print(\"Root Mean Squared Error:\", rmse)\n",
    "    print(\"Mean Absolute Error:\", mae)\n",
    "    print(\"R-squared:\", r2)\n",
    "    print(\"Adjusted R-squared:\", adj_r2)\n",
    "    print(\"Explained Variance Score:\", explained_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 8.047134553145608e+18\n",
      "Root Mean Squared Error: 2836747178.2211413\n",
      "Mean Absolute Error: 77305236.9562288\n",
      "R-squared: -1.0657741760884873e+19\n",
      "Adjusted R-squared: -1.213948427941485e+19\n",
      "Explained Variance Score: -1.065512809276242e+19\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression().fit(X_train, y_train)\n",
    "print_metrics(X_test, y_test, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.09078168474914194\n",
      "Root Mean Squared Error: 0.301299991286329\n",
      "Mean Absolute Error: 0.15814810817215247\n",
      "R-squared: 0.8797674195349187\n",
      "Adjusted R-squared: 0.8630515213095055\n",
      "Explained Variance Score: 0.8798523522298427\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor().fit(X_train, y_train)\n",
    "print_metrics(X_test, y_test, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\talha\\AppData\\Local\\Temp\\ipykernel_36484\\2285815048.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf = RandomForestRegressor().fit(X_train, y_train)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.07891904117994143\n",
      "Root Mean Squared Error: 0.2809253302568877\n",
      "Mean Absolute Error: 0.15401837681728386\n",
      "R-squared: 0.8954784767972258\n",
      "Adjusted R-squared: 0.8809468819710617\n",
      "Explained Variance Score: 0.8956675036621128\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor().fit(X_train, y_train)\n",
    "print_metrics(X_test, y_test, rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Explainable AI**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9+/HFF18gOTkZPXv2RHFxca3bzJs3D4WFhZqvtLQ0fZdPZBBsu2Su2HZNQ3JOCQDAwdZK5EqMz2x6N23YsAHjxo2DnZ1dna+7/5Ja27Zt0blzZ4SGhuK7776r9eyRTCaDTCbTa71ExsC2S+aKbdc03C4oAwC42NuIXInxmUUAOnr0KBITE7F9+3adt3Vzc0NERARu3LhhgMqIiIjMx+vfnYejzBofjGgNAMgqUs/15enU+GZ7fxizuAS2fv16tG/fHtHR0TpvK5fLkZSUBH9/fwNURkREZB6uZBRi59l0bI5LwZ83cwEAedUjPvu71H11pTESNQDJ5XKcP38e58+fBwAkJyfj/PnzWp2Wi4qKsGPHDkyePLnGffTr1w8rV67ULP/nP//BkSNHcOvWLZw4cQKjRo2ClZUVxo4da9BjISIiMmXnU/++42vpr4kAgOJyBQAg2MNBlJrEJOolsPj4ePTt21ezPHv2bADA+PHjsWnTJgDAtm3bIAhCrQEmKSkJOTk5muXbt29j7NixyM3Nhbe3N3r06IGTJ0/C29vbcAdCRERk4q5l/X0zUHxKPvp/cgSllerZ3pv5OIlVlmhEDUB9+vSBIAh1vubll1/Gyy+/XOvzt27d0lretm2bPkojIiJqVFLzSrSWb2TLNY8j/Rr3oIc1MYs+QERERPRoMgrUHZ6tpBK42tvAz0V9F54EjX/U55qYxV1gRERE9Ghyqjs8D27th5XPPwYA+OF8OuxtrGBna3lxwPKOmIiIyAIVl1cBAMK9HDXrRrQLFKsc0TXoEphCocDBgwfx5ZdfakZYvnPnDuRy+UO2JCIiIjFUVKkAAJF+LiJXYhp0PgOUkpKCQYMGITU1FRUVFXjiiSfg7OyMJUuWoKKiAmvWrDFEnURERNRAefJK3LvlqE2wq6i1mAqdzwDNnDkTHTp0QH5+Puzt7TXrR40ahUOHDum1OCIiInp0F9MLNI8DXS1v0MOa6HwG6OjRozhx4gRsbbWHzW7SpAnS09P1VhgRERE9uiW/XMX648kAAFsrqWbWd0uncwBSqVRQKpUPrL99+zacnS3vNjoiIiJTpFKp0GvpYdzOL9Osc5RZ3qzvtdE5Bg4YMADLly/XLEskEsjlcixYsABDhgzRZ21ERETUQBduF2rCj6ejLawkEjzZNkDkqkyHzmeAli1bhoEDB6Jly5YoLy/H888/j+vXr8PLywvffvutIWokIiIiHWUXVQBQD3R4Zv4T4hZjgnQOQEFBQbhw4QK2b9+OCxcuQC6XY9KkSRg3bpxWp2giIiIST06JOgBZSSUiV2KaGjQQorW1NcaNG4dx48bpux4iIiLSg1y5euRnaysGoJro3Ado8eLF2LBhwwPrN2zYgCVLluilKCIiIno0BaXqAGRjxbu+aqLzp/Lll18iMjLygfWtWrXiIIhEREQmoqBUPfWFzJoBqCY6fyqZmZnw9/d/YL23tzcyMjL0UhQRERE9mqLqub/sbHjre010DkDBwcE4fvz4A+uPHz+OgADeXkdERGQKissVAAAHWwagmujcCXrKlCmYNWsWqqqq8PjjjwMADh06hDfeeAOvv/663gskIiIi3ckr7gWgBt3v1Ojp/KnMmTMHubm5eOWVV1BZqe5gZWdnh7lz52LevHl6L5CIiIh0V1qpnrXB2Y4BqCY6fyoSiQRLlizB/PnzkZCQAHt7ezRv3hwymcwQ9REREVEDlFcxANWlwZ+Kk5MTOnbsqM9aiIiISE/uBSBXOxuRKzFNOgegkpISfPjhhzh06BCys7OhUqm0nr9586beiiMiIqKGqVSofz+7OdiKXIlp0jkATZ48GUeOHMG//vUv+Pv7QyLhCJNERESmpkopAAA8HHkGqCY6B6BffvkF+/btQ/fu3Q1RDxEREemBsvoKjYcjzwDVROdxgNzd3eHh4WGIWoiIiEhPqk8AwcuJNynVROcAtHDhQrz77rsoLS01RD1ERET0iO7vn+vtwgBUE50vgS1btgxJSUnw9fVFkyZNYGOjfW3x7NmzeiuOiIiIdJdXPREqAPg524tYienSOQCNHDnSAGUQERGRvmQXVWgeuzlwHKCa6PypLFiwwBB1EBERkZ5kF6sDkASAVMrZ4GvSoE+loKAA69atw7x585CXlwdAfekrPT1dr8URERGR7nKqA5CUQ9XUSuczQBcvXkT//v3h6uqKW7duYcqUKfDw8MCuXbuQmpqKzZs3G6JOIiIiqqfcEnUfIGsrBqDa6HwGaPbs2ZgwYQKuX78OOzs7zfohQ4YgNjZWr8URERGR7vKqA5ANA1CtdA5Ap0+fxtSpUx9YHxgYiMzMTL0URURERA1XUFYFALC1thK5EtOlcwCSyWQoKip6YP21a9fg7e2tl6KIiIio4Qqrb4OXWbMDdG10/mSGDx+ODz74AFVV6nQpkUiQmpqKuXPn4umnn9Z7gURERKSblFz1YMVeTpwGozY6B6Bly5ZBLpfDx8cHZWVl6N27N5o1awZnZ2csWrTIEDUSERGRDtILygAALQNcRa7EdOl8F5irqysOHDiAY8eO4eLFi5DL5XjsscfQv39/Q9RHREREOlCpVCguVwAAuoZ7ilyN6Wrw8JA9evRAjx499FkLERERPaKrWcWongcVfVqwb25t6hWAPv/883rv8NVXX21wMURERPRoDifeBaC+Bd7NgX2AalOvAPTpp59qLd+9exelpaVwc3MDoB4Z2sHBAT4+PgxAREREIimtVODMrXwAgIcjw09d6tUJOjk5WfO1aNEitGvXDgkJCcjLy0NeXh4SEhLw2GOPYeHChYaul4iIiGrw66VMtF7wKw5dzQYANPF0FLki06bzXWDz58/HihUr0KJFC826Fi1a4NNPP8U777yj1+KIiIjo4f68mYtpW85AJfy9rkMTD/EKMgM6d4LOyMiAQqF4YL1SqURWVpZeiiIiIqL6e+/HyxAE9cCHTz8WiFx5JV7r11zsskyazmeA+vXrh6lTp+Ls2bOadWfOnMG///1v3gpPRERkZCqVCtcy5QCAST3C8P+eaosvX+wAa44CXSedP50NGzbAz88PHTp0gEwmg0wmQ6dOneDr64t169YZokYiIjKASoUK8vIHz+iTefn5r0woBfW1r8k9wkWuxnzofAnM29sbP//8M65du4arV68CACIjIxEREaH34oiISDeFpZX4I/EuTiTl4E5BGQrLqiCvUKCkQonyKiUqFSpUKQXNL8xeEV7Y/FJnkaumusjLFcgvq0Swu8MDz128XYDPDl0HoJ72woNTX9RbgwdCjIiIYOghIhKJSqXCX+mF+P1qNs6mFiDpbgnuFpejSik8fOP7FFbPGk6mSaFQoduHh1BUrsD7w1thfLcmmuf+vJmL0WtPapZ7R3DQQ13oHICUSiU2bdqEQ4cOITs7GyqVSuv533//XW/FERFZKoVChZu5JUjIKMLNu3Kk5JYho7AMOfIK3C2uQHG5ArVFHQkAF3treDjK4GxnDVd7G7ja28DD0RZeTjL4usjg62KHADd7BLnbG/OwSEdzd11EUfVlyvd/vIyOTdw183vN3XkRACCVAIFu9nh7SEvR6jRHOgegmTNnYtOmTRg6dChat24NiURiiLqIHolKpULs9RzsOpsOmbUUw9sFoHtTT0il7BRIpiWjoAwL9l5GWl4p8koqUVyhQEWVEvU9kWNrJYW3swzNfJzwWKgb+kX6oqW/M9t6I5BeUIpdZ9M1yyoBGLHqOL6f1hUFpVW4VT3j++Kn2mB0xxCxyjRbOgegbdu24bvvvsOQIUMMUQ9Rg13NKMKWP1Nx9PpdpOWVav0C2XHmNqQSwNfFDu1D3TGsbQD6RfrwLgkSlbxcgSc+jYW8ovaOyBKJ+tZmJ1truDrYwtvZFqGejujW1BN9IrzhyqkOGq35ey5BgHpKi69e7IBJm06jSilg1OoTsK4OuJ6Otgw/DaRzALK1tUWzZs0MUQuRTnLk5djyZyoOXM7C9Ww5KhSqB17jYmcNAUBxuQIqAcgoLMdPFzPw08UMSAB4OcsQHeSKbk294GpvDSeZDRxkVnCwtYajrRWc7a3hZGsNZztr/kVNevfMmhOa8BMT4gZ/VzsEu9sj3NsJEb7OaOHnDAfbBnfVJDNWXqnAkeo5vUa2C0SfFj74blo3jFkbhyqlgEqlClIJ8OW/2otcqfnS+Tvr9ddfx2effYaVK1fy8hcZlUqlwu9Xs/F1XArOpxWguIbbd+2spWjh54wBrfwwtmOI5o6IwtJK7DqXjoNXsnA5owgFpVUQANwtrsDBhGwcTMiuVw1SCWAllcDGSgpbKylsraWwt7HCU+0DMbMfbwqg+pu38yKuZhYDAKb3bYY5A1s8ZAuyFCt/v46vjt6EUlCfAXxnaBQAoH2oO37/Tx+MWnUCeSUVWPZsO472/Ah0DkDHjh3DH3/8gV9++QWtWrWCjY2N1vO7du3SW3FElQoVdpxJw3en03D5ThEUKu2OEVYSCUI8HdA7whsvdA5BM1/nGvfj6mCLid3DMLF7GAD1hIE/XriD/ZcycfF2IQrLqiAIAlQCau1YCqivwauUAqqUSpRCqVl/6XbhIx8rWY4959Lx7ek0AECnMA+GH9KoVKiw7Ldrmp9Dg1r5aV3mDHZ3QPw7/aFSqXhW+hHpHIDc3NwwatQoQ9RCBEB9tmbD8WT8eCEDybklEP6RSDwcbdGtqSdGdwxucMdmB1trjO4YUue1c5VKhZJKpWYMjlz5va8K5JdWoaC0EkXlChSVV2FQaz+dayDLlHRXjte/uwAA8HK0xdZJHIOH/vb9mdua8LN8dDuMjAms8XUMP49O5wC0ceNGQ9RBFi4ttxRfxibhYEIWMosqHng+yN0eA1v54eVe4fB1sTNKTVKpFM52Ujjb2cDfjbcK06Mrr1TgqdXHoRQEWEsl2D2jOzviEwpLK5FRWI5Ifxd8f0Z9ZjDA1a7W8EP60aDedQqFAocPH0ZSUhKef/55ODs7486dO3BxcYGTk5O+a6RGqri8Cm/uvIhjN3IfGIxNKgGa+zhjVEwgXugSCic7dgQl8/fc2pMoLFP3XVs97rEaR/Yly9Nr6WEUllWhf5QPLqUXAQAej/QRuarGT+ffKikpKRg0aBBSU1NRUVGBJ554As7OzliyZAkqKiqwZs0aQ9RJjdDgz47idn6ZZtnWWoq2ga4Y2ykYo2ICeYqXGpX39l7Gxeq+YpN6hGFAK142JbV7fwDefzPGpB5hYpVjMRo0EGKHDh1w4cIFeHp6ataPGjUKU6ZM0Wtx1Hh9dTRJE356R3hjSs8w9GjOYdypcfrlUgY2nbgFAIgOdsX8JzliL9UuwtcJYd68mmJoOgego0eP4sSJE7C11R58q0mTJkhPT69lK6K/lVYq8NH+RABAC19nfP1SJ5ErIjKctNxS/N/WcwAAN3sbbJ/SReSKyFT9ML0booPdxS7DYuh8jUGlUkGpVD6w/vbt23B2rvkWZKL7Td9yFlVKARIA617sIHY5RAajUKgwYvVxKFQCrKQS7H6lG+w4sCHdp7zy7/HMXOxs6ngl6ZvOAWjAgAFYvny5ZlkikUAul2PBggWcHoMe6kJaPv6oHt30uY7BCPZkJ1BqvJ5fdxJ5JZUAgE+ei+ZlDXpA4X0Duro7cloTY9L5T5Fly5Zh4MCBaNmyJcrLy/H888/j+vXr8PLywrfffmuIGqkRmfbNWQCAk8wKi0a0FrkaIsP5aP9VnLqVDwAY1zkEI9rxlmZ6UH51QAbUU/eQ8ej8aQcFBeHChQvYtm0bLl68CLlcjkmTJmHcuHGwt+dYKVS7NYeTkFFYDgD48Km2HP+EGq3fr2Zj9eEkAECUvzMWjWojckVkqorK/x4ChHe+GleD4qa1tTVeeOEFfddCjVhxeRWWHVB3fI7yd8aT0QEiV0RkGBkFZZj6v3gAgLOdNXb+u5vIFZEpuzenIWfWNL4GBaDExESsWLECCQkJAICoqCjMmDEDkZGRei2OGo/pW6s7PkuAdePZ8ZkaJ4VChRGrjqNKKUAqAXZM7crZ3KlORdUDY3JucePT+Xzbzp070bp1a5w5cwbR0dGIjo7G2bNn0aZNG+zcuVOnfcXGxmLYsGEICAiARCLBnj17tJ6XSCQ1fi1durTO/a5atQpNmjSBnZ0dOnfujFOnTul6mKRHZ1LyEXstBwDwfKcQBLqx4zM1ThO/Po3sYvVULh8+3RaR/i4iV0SmrrhCfQlMygRkdDoHoDfeeAPz5s1DXFwcPvnkE3zyySc4ceIE3nrrLbzxxhs67aukpATR0dFYtWpVjc9nZGRofW3YsAESiQRPP/10rfvcvn07Zs+ejQULFuDs2bOIjo7GwIEDkZ2dXes2ZFivfHMGgPpywMIRrUSuhsgwPjt0DUevq4P+UzGBeK5DsMgVkTm4dwnMSsoAZGw6n5vNyMjAiy+++MD6F1544aFnZv5p8ODBGDx4cK3P+/lpDxX/ww8/oG/fvggPD691m08++QRTpkzBxIkTAQBr1qzBvn37sGHDBrz55ps1blNRUYGKir8n4CwqKtLlMKgOnx+6jqzqv4g/fiaanfz0jG3XNCz++Qq+jE0GADT1dsIno9uJW5AZYNtVK6lgABKLzr+N+vTpg6NHjz6w/tixY+jZs6deiqpJVlYW9u3bh0mTJtX6msrKSpw5cwb9+/fXrJNKpejfvz/i4uJq3W7x4sVwdXXVfAUH8y83fSgsrcTnh64DAFoHuGBga859pG9su+L7v2/PasKPt7MMe6az03N9sO2qyasDkDUDkNHpfAZo+PDhmDt3Ls6cOYMuXdRDup88eRI7duzA+++/j71792q9Vl++/vprODs746mnnqr1NTk5OVAqlfD19dVa7+vri6tXr9a63bx58zB79mzNclFRkcV+M+rTK1vOQqFSdwb9iiM+GwTbrnhUKhVGrz2J09Vj/TT1dsK+/+vOkZ7riW1XraxSPbOCjRXPjhubzt+pr7zyCgBg9erVWL16dY3PAeoOzDVNmdFQGzZswLhx42BnZ6e3fd4jk8kgk8n0vl9Ldio5F8eTcgEAL3QJhb8bx4gyBLZdcZRXKjD482NIzikBAHQJ98DWyZ15iVcHbLtqpdVTYdhY8QyQsekcgFQqlSHqqNPRo0eRmJiI7du31/k6Ly8vWFlZISsrS2t9VlbWA/2JyLCmV0/+6GJnjfeGceZrajzy5JUY8OkR5FSP4DuyXSCWj2knblFktkqrzwDZcmBYo3ukT7y8vFxfddRp/fr1aN++PaKjo+t8na2tLdq3b49Dhw5p1qlUKhw6dAhdu3Y1dJlU7dMDibhb3fH5k+fY8Zkaj+S7cvT86HdN+HmlT1OGH3ok5VXqkwq21lYiV2J5dP7NpFQqsXDhQgQGBsLJyQk3b94EAMyfPx/r16/XaV9yuRznz5/H+fPnAQDJyck4f/48UlNTNa8pKirCjh07MHny5Br30a9fP6xcuVKzPHv2bHz11Vf4+uuvkZCQgH//+98oKSnR3BVGhlVQWomVf6inAIgOckX/ljzzRo1D/K08DFgei5JKJSQA/juiFd4YxMFf6dGUVanPAMl4BsjodP7EFy1ahE2bNuGjjz6Cre3fM9e2bt0a69at02lf8fHxiImJQUxMDAB1eImJicG7776rec22bdsgCALGjh1b4z6SkpKQk5OjWR49ejQ+/vhjvPvuu2jXrh3Onz+P/fv3P9Axmgxj2jdnoGTHZ2pkfrmUgee+jNOM8Pzlv9rjha5NxC6LGoEKhToA2dkwABmbzn2ANm/ejLVr16Jfv36YNm2aZn10dHSdd1rVpE+fPhAEoc7XvPzyy3j55Zdrff7WrVsPrJsxYwZmzJihUy306E7cyMHJm3kAgIndw+Djov8O60TGtuFYMj746QoAwNZKih3TuiA62F3kqqixqKi+BGbHS2BGp3MASk9PR7NmzR5Yr1KpUFVVVcMWZAlUKhVmfKvu+Oxmb4O3h/DSAJm/Rfuu4Kuj6jF+nGXW+PnVngj25FQupD+VSnUAsrdlADI2nc+5tWzZssaBEL///nvNpSyyPMsOXEdedcfQ5WPaseMzmb0ZW89qwo+PswxH5/Zl+CG9q1IwAIlF5zNA7777LsaPH4/09HSoVCrs2rULiYmJ2Lx5M3766SdD1EgmLk9eiTWH1R2fHwtxQ58WPiJXRNRwKpUKz315EvEp6gEOm/s4Yd+rPXmbMhnEvTNADhxA0+h0/o4eMWIEfvzxRxw8eBCOjo549913kZCQgB9//BFPPPGEIWokEzf1m3goBQFWEmDti+3FLoeowcorFej3yRFN+One1BO/zmL4IcNRqNT9YB15BsjoGhQ5e/bsiQMHDui7FjJDR6/f1UwFMLlnGLyc2PGZzFOevBJPfHoEudWXcp+KCeSkpmRwCqU6ADnJeAbI2PiJU4OpVCq8Wt3x2d3BBnM5JgqZqaS7cgxbcUwzKu/0vs0wZ2ALkasiS6CsvhPakQHI6Or1ibu7u0Miqd88JXl5eY9UEJmPJfuvIr9Ufeff52Nj2PGZzNKp5FyMW/cnqpSCeoDDUa0xrnOo2GWRhVBVXwJzsmMAMrZ6feLLly/XPM7NzcV///tfDBw4UDO9RFxcHH799VfMnz/fIEWS6cmRl2Nd9R0yHZu4o2dzb5ErItLd0et3MX7DKagEwEoiwdoX26NfFAdNJeNRVZ8BcmYAMrp6feLjx4/XPH766afxwQcfaA00+Oqrr2LlypU4ePAgXnvtNf1XaSIyCsowbt2f6BzmgVGPBaJDqLtFnvXIkZdj7No/oaz+pfHlCxzxmcxP/K08TKgOP7ZWUnz/765oG+QmdllkYapPAMHFzkbcQiyQzpHz119/xZIlSx5YP2jQILz55pt6KcpU7ThzGzdzSnAzpwTfnk6DVAL4u9qjfag7nmzrj8db+MC6Ed8tcighCx//moiEzGLNuml9msLDybaOrYhMz6X0QoxZexJKAbCxkuCnV3sgwtdZ7LLIgrnaMwAZm84ByNPTEz/88ANef/11rfU//PADPD099VaYKWrp74zoIFdcz5ajtFIJlQCkF5QhvaAMey/cgQSAl7MMbQNdMbC1H55s62/2YzvIyxX45EAivj9zG0XlCs16a6kET7YNwOtPNBexOiLdXcsqxqjVx6FQCbCWSrDz390YfkgUldWDIAKACwOQ0en82/n999/H5MmTcfjwYXTu3BkA8Oeff2L//v346quv9F6gKenf0k8zu3mOvBy7z6bjYEI2rmYWobBMAQHA3eIKHLqajUNXs/HG9xfhZm+DKH8XPB7ljadjgs3mbMm51Hz8v58TcCYlX3OKFgC8nWUY3zUU03o1bdRnu6hxSsktwbAVxzSTmm6d0pmXvUg0BaWVmsfu9ubxu6Ex0TkATZgwAVFRUfj888+xa9cuAEBUVBSOHTumCUSWwMvJDlN6NcWUXk0BqM+U/HjxDn67nIm/0guRI1c37IKyKsTdzEXczVws2ncVjrZWaObrhD4R3nimQzCC3U1naH2FQoXVR5KwOS4FOfIKzXqpBGgf6o63hkQhJoSTQJJ5yigow+DPjqJCoYJUAnz9Uid0CmvcZ63JtBWV/T1/pgs7QRtdgz7xzp07Y8uWLfquxaw52VljbKcQjO0UAkAdJg5czcK+ixk4m1qAzMIyqASgpFKJC2mFuJBWiM8O3YDMWoowL0d0a+qJZ9oHoWWAq9FrT7orx39/uoKj13M0o5IC6m/IZzsE4/UBEWZ/KY8sW468HAM+jUVppRISAGteaM87F0l0BfcFIJ5RNz7+VjMQa2spBrf2x+DW/gDUgwaeSs7H7vPpOJWci7S8MihUAioUKlzNLMbVzGJsOH4L1lIJgtzt0THMAyOiA9CtqadB7jRTqVT49nQa1hxOQlp+mdZzUf7O+M+AFrwdmBqFwtJK9F8Wi+IKdR+2T0e3w4BWfiJXRQRNv8r6jbJH+sYAZCRSqRRdmnqiS9O/T7lfySjEzjPpOHEjB8k5JShXqKBQCbiVW4pbuaXYEX8bUgng62KHmBA3PNkmAANa+j7SXwrZReVY9HMC9l/KRMV9HfDsbawwpI0f5g2J5HQW1GjIyxXot+yI5i/txU+1wciYQJGrIlIrLle3y3qOM0x6xgAkopb+rmj55N+XvNLyS7HzzG0cuXYX17OKIa9Q32mWUViOjL8y8fNfmQAAT0dbtAlyxYCWfhgeHVCvEUR/u5yJZb9dQ2JWsdb6UE8H/F/fZnimQ7B+D45IZOWVCjzx6RHkVM/t9e6TLTWXqIlMQWL1kCI2Vrz8JQYGIBMS7O6AWf0jMKt/BAD1HQK7zqbjUEIWLmcUoaB62onckkocTryLw4l38dbuv+BiZ41Ifxc83sIHz3QI1JzBKS6vwtJfE7H7bLrm9D+gvoW9bwtvvPNkS4R6Ohr/QIkMTKFQYcCnscgoLAcA/GdABF7qESZyVUTaTt7MBQAEuNmLXIllYgAyYW4OtnipR5jmB3d5pQL7/srAL5cycfF2Ie4WV0CA+jryqeQ8nErOw4f7r8LB1go+zjKk5JVCuO8Wdl9nGSZ2D8PkHmHscEeNlkqlwqDPjyK1um/bK32aYsbjHK+KTE/SXTkAoF2wm7iFWKh6BaCnnnqq3ju8d2s86Z+drTWebh+Mp9urL1epVCr8fjUbP17MwJmUfGQUlEEpAKWVStzKLQWgvoW9U5gH3hoSxfFOqNFTqVQYtuI4bmSrf7GM79YEbwyKFLkqogdVKlQoLFOfmX+CN5yIol4ByNXV+Ldm08NJpVKtwRkB9fxGu8+l41J6IbqEe2Bmf97CTpbjuS9P4nJGEQDg2Q5BeH94K5ErIqrZ4cRszeP+LRmAxFCv34wbN240dB2kJx2aeKBDEw+xyyATVKlQwbYRX/r81/o/EZ+SDwB4sq0/lj4TLXJFRLU7cCULgHoW+Mb8fWnKeGqAyAKk5Zai59I/4OVkiw6h7nimfRAej/QxyBhTYnh5czyOXs8BADwe6Y2Vzz8mckVEdYur7gAd7s0bUcTSoAD0/fff47vvvkNqaioqKyu1njt79qxeCiMi/dl6KhUAkCOvxP7LWdh/OQtSCRDkZo/uzbwwtnOI2fYRm7XtPH6r/mu6a7gnNkzoJHJFRHXLk1fidnUn/TEdOTSDWHT+8+/zzz/HxIkT4evri3PnzqFTp07w9PTEzZs3MXjwYEPUSESP6PUnIrB8dDv0aeENNwf1rNMqAUjNL8O3p9MwfOVxNH/7ZwxeHouPf01ERkHZQ/ZoGt7adRF7zqcDAKKDXbFlMsMPmS55uQJ9P/4DPT/6HQBgJZFgdIcgkauyXDqfAVq9ejXWrl2LsWPHYtOmTXjjjTcQHh6Od999F3l5eYaokYgekbW1FCNjAjWjIBeWVmLb6TTsv5yJqxnFKKtSokopICGzGAmZxVj5xw04yqzQKsAVQ9v44+nHguo14KYx/XffFWw9lQYAiPRzxu5/d2s0l/SocdpwPBnJOaWa5TZBrmyzItL5J1pqaiq6desGALC3t0dxsXoky3/961/o0qULVq5cqd8KiUjvXB1sMbV3U0zt3RQAkJJbgm9OpuBw4l0k55RAoRJQUqHUjC+1YO9leDjaon2IG55pH4wnWorbf+jTA4lYdzQZABDm5YifZvTgLxIyeX/dLtBanto7XJxCCEADApCfnx/y8vIQGhqKkJAQnDx5EtHR0UhOToZw/6h7RGQ2Qj0d8fbQlnh7qHr5VHIutp9OQ9zNPGQUlEEAkFdSiQMJ2TiQkA2JBAh0s0e3pp4Y1zkE0cHuRqt1zeEkfHboBgAgyN0ev87qxYE9ySzczCkBoG63/+7TVDNZNolD5wD0+OOPY+/evYiJicHEiRPx2muv4fvvv0d8fLxOAyYSkenqFOaJTmHqiXsVChV+vpyBPefScS61APmlVRAE4HZ+Gb6Lv43v4m/DxkqCcC8nPB7pg+e7hCDY3cEgdW2Ou4UP918FAPg4y/Dba714CzGZjeyiCgBA3xY+GNc5VORqSOcAtHbtWqhU6lnEp0+fDk9PT5w4cQLDhw/H1KlT9V4gEYnL2lqK4dGBGB6t7j9UXF6F706n4ZdLmbiSUYTSSnX/ocSsYiRmFeOLI0lwsLVCqwAXDG7tj2c7BMHZzuaR6/g+Pg3v/nAZAODuYINDr/fmIJ9kVuTVczK2DzXeGVOqnc4/PaRSqda19jFjxmDMmDF6LYqITJeznQ0m9QzHpJ7q/gtp+aXYejIVh65mITmnBFVKAaWVSpy+lY/Tt/LxwU9X4O5gg8dC3DEqJhCDWvnpfMnqpwt3MOf7iwAAFztr/PGfPnoJVUTGknRXjnudRHo09xS1FlKrVwC6ePEiWrduDalUiosXL9b52rZt2+qlMCIyD8HuDpg7OBJzB6vn3DqTko9tp1MRl5SL9IIyCAKQX1qFQ1ezcehqNiQA/N3s0b2pJ8Z0CnnoX8OHErLwf9+egwDA0dYKB2f3hpuDreEPjEiPjt9QD9RpJZHAy8lO5GoIqGcAateuHTIzM+Hj44N27dpBIpHU2OFZIpFAqVTqvUgiMh/tQ901oUalUuHXK1nYdeY2zqQWIK+kEgKAOwVl2HHmNnacuQ1rqQTh3o7o28IbL3RugmDPv/sPnbiRgymb4yEAsLOR4rfZveDjwl8eZF4+2n8Va44kAQBc7HnZ1lTU638iOTkZ3t7emsdERPUhlUoxuLW/5m4XebkCO86k4Ze/MnD5ThFKKpVQqARcy5LjWpYcX8Ymw97GClH+zugS7okvj9yESgBsraX45dWeCHQzTOdqIkP59zdn8MulTM2yvysDvKmoVwAKDf27t3pKSgq6desGa2vtTRUKBU6cOKH1WiKi+znZWWNi9zBM7B4GAEgvKMXWP9Pwe0IWbtyVo0opoKxKibOpBTibWgAAsLGSYM/0bgjzdhKxciLdrTmcpAk/bvY2kEoleGNQpMhV0T06n4vr27cvMjIy4OPjo7W+sLAQffv25SUwIqq3QDcHzBnYAnMGtgAAXLxdgG//TMXxGzm4XVAGa6kUO6Z1QUt/V5ErJdKNQqHC8oPXAABNvZ1w4LWeHKzTxOgcgARBgEQieWB9bm4uHB05qy0RNVzbIDeznZSV6H7v7r2EcoV6yJi1L7Zn+DFB9Q5A9wY5lEgkmDBhAmQymeY5pVKJixcvaqbIICIisiRpuaVYfzwZr/RpirySSnxbPU9drwgvNOXlW5NU7wDk6qo+BS0IApydnWFvb695ztbWFl26dMGUKVP0XyEREZGJm7DpNJLuyvG/kymwlkrUdy5aS7H6+fZil0a1qHcA2rhxo+bW9xUrVsDJiYmWiIhIpVLhZo4cAKBUCVCq1L8rP3muHZzseNu7qdLpoqQgCNiyZQsyMjIMVQ8REZFZOZiQjXtD43UK80D/KB98P60rhrTlZKemTKdoKpVK0bx5c+Tm5qJ58+aGqomIiMhs7IhX9/dxd7DBd1O7ilwN1ZfO3dI//PBDzJkzB5cuXTJEPURERGYlPiUfACc5NTc6X5x88cUXUVpaiujoaNja2mp1hgaAvLw8vRVHRERkyorLq5BfWgUAeLp9kMjVkC50DkDLly83QBlERETm5/sztwEAEgkwsKWvyNWQLnQOQOPHjzdEHURERGZnf/VUF/6u9hzs0Mw80v155eXlqKys1Frn4uLySAURERGZiysZRQCATk08RK6EdKVzXC0pKcGMGTPg4+MDR0dHuLu7a30RERFZgjx5JYrLFQCAZ9oHilwN6UrnAPTGG2/g999/xxdffAGZTIZ169bh/fffR0BAADZv3myIGomIiEzOx79dBQBIJUC3pp4iV0O60vkS2I8//ojNmzejT58+mDhxInr27IlmzZohNDQUW7Zswbhx4wxRJxERkcm4llWsme+rS7gn+/+YIZ3/x/Ly8hAeHg5A3d/n3m3vPXr0QGxsrH6rIyIiMkH//uYsBAAyaynW/quD2OVQA+gcgMLDw5GcnAwAiIyMxHfffQdAfWbIzc1Nr8URERGZmrT8UiTdVc/9NXdQJOf7MlM6B6CJEyfiwoULAIA333wTq1atgp2dHV577TXMmTNH7wUSERGZkv+3LwEAYGslxYRuoSJXQw2lc2x97bXXNI/79++Pq1ev4syZM2jWrBnatm2r1+KIiIhMSXF5FQ4mZAEAnmjpw74/ZqzeAUilUmHp0qXYu3cvKisr0a9fPyxYsAChoaEIDWUCJiKixu/pL06gSilAAuCtoVFil0OPoN7RddGiRXjrrbfg5OSEwMBAfPbZZ5g+fbohayMiIjIZ355KxbUsdd+f156IQKCbg8gV0aOodwDavHkzVq9ejV9//RV79uzBjz/+iC1btkClUhmyPiIiIpNw4Ip62gtvZxle7ddc5GroUdU7AKWmpmLIkCGa5f79+0MikeDOnTsGKYyIiMiUJGWXAACaeTuJXAnpQ70DkEKhgJ2dndY6GxsbVFVV6b0oIiIiU5NVXA4AaBvEOS8bg3p3ghYEARMmTIBMJtOsKy8vx7Rp0+Do6KhZt2vXLv1WSEREJLJKhQrlVeouHz2ae4tcDelDvQPQ+PHjH1j3wgsv6LUYIiIiU3QiKUfzuHMY5/1qDOodgDZu3GjIOoiIiEzWsRvqAGRvYwVba4790xjwf5GIiKgOyw9ew/bT6olPfV1kD3k1mQtRA1BsbCyGDRuGgIAASCQS7Nmz54HXJCQkYPjw4XB1dYWjoyM6duyI1NTUWve5adMmSCQSra9/dt4mIiKqj5TcEiw/eB3F5QoAQKsAdoBuLEQNQCUlJYiOjsaqVatqfD4pKQk9evRAZGQkDh8+jIsXL2L+/PkPDTQuLi7IyMjQfKWkpBiifCIiauRW/n4DAGAllWBAS18sHNFG5IpIX0Sdwnbw4MEYPHhwrc+//fbbGDJkCD766CPNuqZNmz50vxKJBH5+fnqpkYiILNe9eb+6hntg7YsdRK6G9Mlk+wCpVCrs27cPERERGDhwIHx8fNC5c+caL5P9k1wuR2hoKIKDgzFixAhcvny5ztdXVFSgqKhI64vIHLDtkrky5bb73JoT6Lr4EGZtO4/8UvVYd1N6hotcFembyQag7OxsyOVyfPjhhxg0aBB+++03jBo1Ck899RSOHDlS63YtWrTAhg0b8MMPP+Cbb76BSqVCt27dcPv27Vq3Wbx4MVxdXTVfwcHBhjgkIr1j2yVzZapt90hiNk7dykdGYTn2nE8HANhZS9G7hY/IlZG+SQRBEMQuAlBfttq9ezdGjhwJALhz5w4CAwMxduxYbN26VfO64cOHw9HREd9++2299ltVVYWoqCiMHTsWCxcurPE1FRUVqKio0CwXFRUhODgYhYWFcHFhhzdqmKKiIri6uhq0HbHtkiFYctt9ccOfiL2mvuXd2c4aXk4yvP5EBJ6MDhCtJqo/XdquqH2A6uLl5QVra2u0bNlSa31UVBSOHTtW7/3Y2NggJiYGN27cqPU1MplMa4RrInPBtkvmylTb7unkfADAyHaBWD6mnbjFkEGZ7CUwW1tbdOzYEYmJiVrrr127htDQ0HrvR6lU4q+//oK/v7++SyQiokbkTEo+yqqUAIBpvdnnp7ET9QyQXC7XOjOTnJyM8+fPw8PDAyEhIZgzZw5Gjx6NXr16oW/fvti/fz9+/PFHHD58WLPNiy++iMDAQCxevBgA8MEHH6BLly5o1qwZCgoKsHTpUqSkpGDy5MnGPjwiIjIjW06qh0xxklkh0p+XkBs7UQNQfHw8+vbtq1mePXs2APW8Y5s2bcKoUaOwZs0aLF68GK+++ipatGiBnTt3okePHpptUlNTIZX+fSIrPz8fU6ZMQWZmJtzd3dG+fXucOHHigUtpRERE97twuxAA0MzHWeRKyBhMphO0KTFGB0Bq/MRoR2y7pA+W2nYj5/+C8ioVJvcMwztD+UezOdKlHZlsHyAiIiJjKSytRHmVCgAwqBUH0rUEDEBERGTxfr2iHvFZAuCxEDdRayHjYAAiIiKLF3vtLgDAzcFGq18pNV78XyYiIot38XYBAKC5j5O4hZDRMAAREZFFy5NXIjWvDADwRCtfkashY2EAIiIii/bJAfWAu1ZSCSZ2DRO5GjIWk50Kg4iIyJAUChU+/PUqdpxRT5bdsYk7rK15XsBSMAAREZFFevuHS9h+Ok2zPPuJCBGrIWNjACIiIov004U7AAB3BxuM6xyKTmGeIldExsQAREREFue3y5koqVRPfLplSme09HcVuSIyNl7sJCIii7Pi9+sAAG9nGcOPhWIAIiIii3PzbgkATnthyRiAiIjI4pRWX/7q0MRd5EpILAxARERkUdILSiFUP+7UxEPUWkg8DEBERGRRTifnAwAkEsDfzV7kakgsDEBERGRR7s375WBrJW4hJCoGICIisijXs+UAAE9HW5ErITExABERkUVJyysFAAS5O4hcCYmJAYiIiCxKrrwSANDMx0nkSkhMDEBERGRRSioVAIC2gRwA0ZIxABERkcXIKCiDqvoe+I5hvAXekjEAERGRxfjhvHoCVCupBKGejiJXQ2JiACIiIotx5NpdAIC/q53IlZDYGICIiMhiJGQWAQCig9j/x9IxABERkUWoVKhQUFoFABjYmpOgWjoGICIisgiHrmZpHg+I8hWxEjIFDEBERGQRvo+/DQBwtbeBna21yNWQ2BiAiIio0VOpVIi9ru4A3TvCW+RqyBQwABERUaP3zZ+pqFKqBwCaNzhS5GrIFDAAERFRo3bwSib+388JAIAwLwf4u9mLXBGZAl4EJSKiRiujoAxTNp9B9eDPmDc4StR6yHQwABERUaP1ZexNCACsJBJ8N60r2oe6i10SmQgGICIiahQUChU2nkhG3xY+WPRzAs6nFaBCoQIAtAlyZfghLQxARETUKHyw7wo2x6Vg0c9XH3hubKdgESoiU8ZO0ERE1Cjcyi2p9bmnY4KMWAmZAwYgIiJqFOTlCgCAzFqK5aPb4ZU+TQEA3Zt6wtqav+5IGy+BERFRo1BaqQQAtAl0xciYQADAjMebwYGjPlMNGImJiKhRKK1UnwFysvs78DD8UG0YgIiIqFEor1Lf8eVsx9BDD8cAREREjcK9W95d7WxEroTMAQMQERE1ClXK6gDkYCtyJWQOGICIiKhRuBeAPBx4BogejgGIiIgaBaVKPeOXh6NM5ErIHDAAERFRo1Cdf+DlzEtg9HAMQEREZPYU1R2gAcDTiQGIHo4BiIiIzF5uaaXmsY8zL4HRwzEAERGR2btbXKF57MG7wKgeGICIiMjsZd8XgKRS/mqjh2MrISIis5cnVwcgK4nIhZDZYAAiIiKzl1/dB8iKZ3+onthSiIjI7OWVVAEArHkKiOqJAYiIiMxeYZn6DJCtNX+tUf2wpRARkdkrLFOfAZIxAFE9saUQEZHZK65QAADsbKxEroTMBQMQERGZPXm5OgA52DIAUf0wABERkdkrrVQCABxtrUWuhMwFAxAREZm90kr1GSAnOwYgqh8GICIiMnvlVerJUJ0ZgKieGICIiMisZRSUae4Cc7GzEbkaMheMykREZLYKSyvRa+kfqFIKAICYYDdxCyKzwQBERERma9vpNFQpBUgAzB0UiWc6BItdEpkJXgIjIiKzdeBKFgAgyN0e0/o0FbkaMicMQEREZLYSMosAAF3CPUWuhMwNAxAREZml7KJylFSox/95pn2QyNWQuRE1AMXGxmLYsGEICAiARCLBnj17HnhNQkIChg8fDldXVzg6OqJjx45ITU2tc787duxAZGQk7Ozs0KZNG/z8888GOgIiIjI2ebkCaw4nYczakwAAK4kEnXkGiHQkagAqKSlBdHQ0Vq1aVePzSUlJ6NGjByIjI3H48GFcvHgR8+fPh52dXa37PHHiBMaOHYtJkybh3LlzGDlyJEaOHIlLly4Z6jCIiMhIsovKEf3Bb/hw/1XczCkBAIR7O4pcFZkjiSAIgthFAIBEIsHu3bsxcuRIzboxY8bAxsYG//vf/+q9n9GjR6OkpAQ//fSTZl2XLl3Qrl07rFmzpl77KCoqgqurKwoLC+Hi4lLv9ya6nxjtiG2X9MGU2+4HP17GhuO3AAChng7oFOaBuYNawMup9j+MyXLo0nZNtg+QSqXCvn37EBERgYEDB8LHxwedO3eu8TLZ/eLi4tC/f3+tdQMHDkRcXFyt21RUVKCoqEjri8gcsO2SuWpo291/WX3XV3SQK47M6Yulz0Qz/FCDmGwAys7Ohlwux4cffohBgwbht99+w6hRo/DUU0/hyJEjtW6XmZkJX19frXW+vr7IzMysdZvFixfD1dVV8xUczHEkyDyw7ZK5akjbLS6vwp2CMgDA6I4hhi6RGjmTDUAqlXpelxEjRuC1115Du3bt8Oabb+LJJ5+s96Ws+po3bx4KCws1X2lpaXrdP5GhsO2SuWpI291wPBkAIJUAz/GuL3pEJjsStJeXF6ytrdGyZUut9VFRUTh27Fit2/n5+SErK0trXVZWFvz8/GrdRiaTQSaTPVrBRCJg2yVz1ZC2u/8v9Zn8MC8nWFub7N/vZCZMtgXZ2tqiY8eOSExM1Fp/7do1hIaG1rpd165dcejQIa11Bw4cQNeuXQ1SJxERGUfSXfVdX30jvUWuhBoDUc8AyeVy3LhxQ7OcnJyM8+fPw8PDAyEhIZgzZw5Gjx6NXr16oW/fvti/fz9+/PFHHD58WLPNiy++iMDAQCxevBgAMHPmTPTu3RvLli3D0KFDsW3bNsTHx2Pt2rXGPjwiItKTKxmFqFSqu0aM68T+P/ToRD0DFB8fj5iYGMTExAAAZs+ejZiYGLz77rsAgFGjRmHNmjX46KOP0KZNG6xbtw47d+5Ejx49NPtITU1FRkaGZrlbt27YunUr1q5di+joaHz//ffYs2cPWrdubdyDIyIivdl2St1HyM5GijBvJ5GrocZA1DNAffr0wcOGIXrppZfw0ksv1fr8/WeD7nn22Wfx7LPPPmp5RERkIo5dzwEANPdh+CH9MNk+QERERPekV9/+3jvCR+RKqLFgACIiIpNXoVD3/3ks1F3kSqixYAAiIiKTll5QqnncNohTvJB+MAAREZFJu3RbPU2GBOC0F6Q3DEBERGTSEjLVAUjGwQ9Jj9iaiIjIpCXnqAdAdLIz2ckLyAwxABERkUm7na/uA+TpyGlfSH8YgIiIyKRlF1UAAPzd2P+H9IcBiIiITFpBWRUAIMTDQeRKqDFhACIiIpNWWqkEAERwFGjSIwagRig7OxsSiQQSiQTZ2dkmuf/Lly9r9nH58mWDbBcbG6t5bWxsbIPqJHEYug3f09B2qK/96NpGjfW5mJLbdzJxc/FQpCx5Ev6yKrHLMejPFWP8/+qrzdfGGMegr/dgACIiIpOVdLdE87i5L88Akf4wABERkcm6fd8o0HY2ViJWQo0NAxAREZmsJ6J8xS6BGikGICIiIrI4DEBERERkcRiAiIiIyOIwABEREZHFYQAiIiIii8MARERERBaHAYiIiIgsDgMQERERWRwGICIiIrI4DEBERERkcRiAiIiIyOIwABEREZHFYQAiIiIii8MARERERBbHWuwCTJEgCACAoqIikStpmOLiYq3HdnZ2Jrd/uVyu9bi+n7Uu25WUlGg9Nvb/5733u9eejMHc2+49hm7D9zS0HeprP7q2UWN9LqbUdo11zPVlyJ8rxjhWfbX52hjjGOp6D13arkQwZgs3E7dv30ZwcLDYZVAjkZaWhqCgIKO8F9su6RPbLpmr+rRdBqAaqFQq3LlzB87OzpBIJFrPFRUVITg4GGlpaXBxcRGpQuOxtOMF9HfMgiCguLgYAQEBkEqNc7WZbbd2lnz8uh67qbVdU2HJbagupvS56NJ2eQmsBlKp9KHJ0cXFRfT/aGOytOMF9HPMrq6ueqqmfth2H86Sj1+XYzfFtmsqLLkN1cVUPpf6tl12giYiIiKLwwBEREREFocBSEcymQwLFiyATCYTuxSjsLTjBRrvMTfW46ovSz5+Sz52feLnWDNz/VzYCZqIiIgsDs8AERERkcVhACIiIiKLwwBEREREFocBiIiIiCwOA5AOVq1ahSZNmsDOzg6dO3fGqVOnxC7JYGJjYzFs2DAEBARAIpFgz549YpdkUIsXL0bHjh3h7OwMHx8fjBw5EomJiWKXpTeW1Hb/ydLa8v0ae7s2liZNmkAikTzwNX36dLFLE41SqcT8+fMRFhYGe3t7NG3aFAsXLjTq/HGPigGonrZv347Zs2djwYIFOHv2LKKjozFw4EBkZ2eLXZpBlJSUIDo6GqtWrRK7FKM4cuQIpk+fjpMnT+LAgQOoqqrCgAEDtCY+NFeW1nb/ydLa8v0ac7s2ptOnTyMjI0PzdeDAAQDAs88+K3Jl4lmyZAm++OILrFy5EgkJCViyZAk++ugjrFixQuzS6k+geunUqZMwffp0zbJSqRQCAgKExYsXi1iVcQAQdu/eLXYZRpWdnS0AEI4cOSJ2KY/MktvuP1liW75fY2rXYpo5c6bQtGlTQaVSiV2KaIYOHSq89NJLWuueeuopYdy4cSJVpDueAaqHyspKnDlzBv3799esk0ql6N+/P+Li4kSsjAylsLAQAODh4SFyJY+GbZfu11jatZgqKyvxzTff4KWXXjLZSVuNoVu3bjh06BCuXbsGALhw4QKOHTuGwYMHi1xZ/XEy1HrIycmBUqmEr6+v1npfX19cvXpVpKrIUFQqFWbNmoXu3bujdevWYpfzSNh26Z7G1K7FtGfPHhQUFGDChAlilyKqN998E0VFRYiMjISVlRWUSiUWLVqEcePGiV1avTEAEf3D9OnTcenSJRw7dkzsUoj0hu1aP9avX4/BgwcjICBA7FJE9d1332HLli3YunUrWrVqhfPnz2PWrFkICAjA+PHjxS6vXhiA6sHLywtWVlbIysrSWp+VlQU/Pz+RqiJDmDFjBn766SfExsYiKChI7HIeGdsuAY2vXYslJSUFBw8exK5du8QuRXRz5szBm2++iTFjxgAA2rRpg5SUFCxevNhsAhD7ANWDra0t2rdvj0OHDmnWqVQqHDp0CF27dhWxMtIXQRAwY8YM7N69G7///jvCwsLELkkv2HYtW2Nt12LZuHEjfHx8MHToULFLEV1paSmkUu0IYWVlBZVKJVJFuuMZoHqaPXs2xo8fjw4dOqBTp05Yvnw5SkpKMHHiRLFLMwi5XI4bN25olpOTk3H+/Hl4eHggJCRExMoMY/r06di6dSt++OEHODs7IzMzEwDg6uoKe3t7kat7NJbWdv/J0try/RpzuzY2lUqFjRs3Yvz48bC25q/OYcOGYdGiRQgJCUGrVq1w7tw5fPLJJ3jppZfELq3+xL4NzZysWLFCCAkJEWxtbYVOnToJJ0+eFLskg/njjz8EAA98jR8/XuzSDKKmYwUgbNy4UezS9MKS2u4/WVpbvl9jb9fG9OuvvwoAhMTERLFLMQlFRUXCzJkzhZCQEMHOzk4IDw8X3n77baGiokLs0upNIghmNGwjERERkR6wDxARERFZHAYgIiIisjgMQERERGRxGICIiIjI4jAAERERkcVhACIiIiKLwwBEREREFocBiIiIiCwOA5AF6tOnD2bNmmU2+6XGxRDtZNOmTXBzc9PrPokMqVevXti6davR37e0tBRPP/00XFxcIJFIUFBQYLD32r9/P9q1a2ey84MxAJHODh8+bPBvHCKixmrv3r3IysrSzKRuTF9//TWOHj2KEydOICMjA66urnrZb5MmTbB8+XKtdYMGDYKNjQ22bNmil/fQNwYgImr0KisrxS6BzFxVVZXe9vX5559j4sSJD8ymrk+11ZuUlISoqCi0bt0afn5+kEgkBqsBACZMmIDPP//coO/RUAxAFkqhUGDGjBlwdXWFl5cX5s+fj3vTwv3vf/9Dhw4d4OzsDD8/Pzz//PPIzs4GANy6dQt9+/YFALi7u0MikWDChAma/apUKrzxxhvw8PCAn58f3nvvPc1zgiDgvffeQ0hICGQyGQICAvDqq68a7ZjJdNTV/ioqKvCf//wHgYGBcHR0ROfOnXH48GGt7Tdt2oSQkBA4ODhg1KhRyM3N1Xr+vffeQ7t27bBu3TqEhYXBzs4OAJCamooRI0bAyckJLi4ueO6555CVlaW17RdffIGmTZvC1tYWLVq0wP/+9z+t5yUSCb788ks8+eSTcHBwQFRUFOLi4nDjxg306dMHjo6O6NatG5KSkjTbXLhwAX379oWzszNcXFzQvn17xMfH6+vjJB3t378fPXr0gJubGzw9PfHkk09q/X/dunULEokE27dvR+/evWFnZ6c5i7Fu3TpERUXBzs4OkZGRWL16tda+586di4iICDg4OCA8PBzz58/XCiN3797F77//jmHDhmnWPexnY3Z2NoYNGwZ7e3uEhYVhy5YtD5xxkUgk+OKLLzB8+HA4Ojpi0aJFDxx3nz59sGzZMsTGxkIikaBPnz4A6vc9d+zYMfTs2RP29vYIDg7Gq6++ipKSEs1+U1JS8Nprr0EikWiFqmHDhiE+Pl7r8zUZYs7ESuLo3bu34OTkJMycOVO4evWq8M033wgODg7C2rVrBUEQhPXr1ws///yzkJSUJMTFxQldu3YVBg8eLAiCICgUCmHnzp2aWZEzMjKEgoICzX5dXFyE9957T7h27Zrw9ddfCxKJRPjtt98EQRCEHTt2CC4uLsLPP/8spKSkCH/++afmPclyPKz9TZ48WejWrZsQGxsr3LhxQ1i6dKkgk8mEa9euCYIgCCdPnhSkUqmwZMkSITExUfjss88ENzc3wdXVVfMeCxYsEBwdHYVBgwYJZ8+eFS5cuCAolUqhXbt2Qo8ePYT4+Hjh5MmTQvv27YXevXtrttu1a5dgY2MjrFq1SkhMTBSWLVsmWFlZCb///rvmNQCEwMBAYfv27UJiYqIwcuRIoUmTJsLjjz8u7N+/X7hy5YrQpUsXYdCgQZptWrVqJbzwwgtCQkKCcO3aNeG7774Tzp8/b9gPmmr1/fffCzt37hSuX78unDt3Thg2bJjQpk0bQalUCoIgCMnJyQIAoUmTJsLOnTuFmzdvCnfu3BG++eYbwd/fX7Nu586dgoeHh7Bp0ybNvhcuXCgcP35cSE5OFvbu3Sv4+voKS5Ys0Ty/a9cuwdHRUfNegvDwn42DBw8WoqOjhbi4OCE+Pl7o1q2bYG9vL3z66aea1wAQfHx8hA0bNghJSUlCSkrKA8edm5srTJkyRejatauQkZEh5ObmCoLw8O+5GzduCI6OjsKnn34qXLt2TTh+/LgQExMjTJgwQbPfoKAg4YMPPhAyMjKEjIwMrff19fUVNm7c2MD/LcNhALJAvXv3FqKiogSVSqVZN3fuXCEqKqrG158+fVoAIBQXFwuCIAh//PGHAEDIz89/YL89evTQWtexY0dh7ty5giAIwrJly4SIiAihsrJSj0dD5qau9peSkiJYWVkJ6enpWtv069dPmDdvniAIgjB27FhhyJAhWs+PHj36gQBkY2MjZGdna9b99ttvgpWVlZCamqpZd/nyZQGAcOrUKUEQBKFbt27ClClTtPb97LPPar0fAOGdd97RLMfFxQkAhPXr12vWffvtt4KdnZ1m2dnZWeuXJJmWu3fvCgCEv/76SxCEvwPQ8uXLtV7XtGlTYevWrVrrFi5cKHTt2rXWfS9dulRo3769ZvnTTz8VwsPDtV5T18/GxMRErTYqCIKQkJAgAHggAM2aNeuhxzpz5kyt0F+f77lJkyYJL7/8stbzR48eFaRSqVBWViYIgiCEhoZq1XO/mJgY4b333ntobcbGS2AWqkuXLlqnKbt27Yrr169DqVTizJkzGDZsGEJCQuDs7IzevXsDUF8+eJi2bdtqLfv7+2sunz377LMoKytDeHg4pkyZgt27d0OhUOjxqMhc1Nb+/vrrLyiVSkRERMDJyUnzdeTIEc0p9ISEBHTu3Flrf127dn3gPUJDQ+Ht7a1ZTkhIQHBwMIKDgzXrWrZsCTc3NyQkJGhe0717d639dO/eXfP8Pfe3c19fXwBAmzZttNaVl5ejqKgIADB79mxMnjwZ/fv3x4cffmialwMsyPXr1zF27FiEh4fDxcUFTZo0AfDgz7gOHTpoHpeUlCApKQmTJk3Sapv//e9/tf4/t2/fju7du8PPzw9OTk545513tPZbVlamuSR7T10/GxMSEmBtbY327dtrXh8ZGVnjXY/311tf9fmeu3DhAjZt2qT1/MCBA6FSqZCcnPzQ97C3t0dpaanOtRmatdgFkGkpLy/HwIEDMXDgQGzZsgXe3t5ITU3FwIED69WR1MbGRmtZIpFoboEMDg5GYmIiDh48iAMHDuCVV17B0qVLceTIkQe2I8skl8thZWWFM2fOwMrKSus5Jycnnfbl6Oioz9K03N9e7wW5mtbda/vvvfcenn/+eezbtw+//PILFixYgG3btmHUqFEGq5FqN2zYMISGhuKrr75CQEAAVCoVWrdu/cDPuPvbkFwuBwB89dVXDwTwe201Li4O48aNw/vvv4+BAwfC1dUV27Ztw7JlyzSv9fLyQn5+vtb2df1s1EVD2nx9vufkcjmmTp1aY5/NkJCQh75HXl6e1h8jpoIByEL9+eefWssnT55E8+bNcfXqVeTm5uLDDz/U/KX8z86atra2AAClUqnz+9rb22PYsGEYNmwYpk+fjsjISPz111947LHHGngkZI5qa38xMTFQKpXIzs5Gz549a9w2Kiqqxu0fJioqCmlpaUhLS9O07StXrqCgoAAtW7bUvOb48eMYP368Zrvjx49rnn8UERERiIiIwGuvvYaxY8di48aNDEAiyM3NRWJiIr766itNGzt27NhDt/P19UVAQABu3ryJcePG1fiaEydOIDQ0FG+//bZmXUpKitZrYmJikJmZifz8fLi7u2vW1/azMTIyEgqFAmfOnEHHjh0BAImJiXobhqQ+33OPPfYYrly5gmbNmtW6H1tb2xp/J5SXlyMpKQkxMTF6qVefGIAsVGpqKmbPno2pU6fi7NmzWLFiBZYtW4aQkBDY2tpixYoVmDZtGi5duoSFCxdqbRsaGgqJRIKffvoJQ4YMgb29fb3+Ot+0aROUSiU6d+4MBwcHfPPNN7C3t0doaKihDpNMVG3tLyIiAuPGjcOLL76IZcuWISYmBnfv3sWhQ4fQtm1bDB06FK+++iq6d++Ojz/+GCNGjMCvv/6K/fv3P/Q9+/fvjzZt2mDcuHFYvnw5FAoFXnnlFfTu3Vtz6WDOnDl47rnnEBMTg/79++PHH3/Erl27cPDgwQYfa1lZGebMmYNnnnkGYWFhuH37Nk6fPo2nn366wfukhnN3d4enpyfWrl0Lf39/pKam4s0336zXtu+//z5effVVuLq6YtCgQaioqEB8fDzy8/Mxe/ZsNG/eHKmpqdi2bRs6duyIffv2Yffu3Vr7iImJgZeXF44fP44nn3wSQN0/Gz09PTFo0CBMnToVX3zxBaytrTFr1izY29vr5fOoz/fc3Llz0aVLF8yYMQOTJ0+Go6Mjrly5ggMHDmDlypUA1OMAxcbGYsyYMZDJZPDy8gKg/uNEJpPVeJladGJ3QiLj6927t/DKK68I06ZNE1xcXAR3d3fhrbfe0nRK3bp1q9CkSRNBJpMJXbt2Ffbu3SsAEM6dO6fZxwcffCD4+fkJEolEGD9+vGa/M2fO1HqvESNGaJ7fvXu30LlzZ8HFxUVwdHQUunTpIhw8eNAIR0ym5GHtr7KyUnj33XeFJk2aCDY2NoK/v78watQo4eLFi5p9rF+/XggKChLs7e2FYcOGCR9//PEDnaCjo6MfeO+UlBRh+PDhgqOjo+Ds7Cw8++yzQmZmptZrVq9eLYSHhws2NjZCRESEsHnzZq3nAQi7d+/WLN/rMHv/98f9NwpUVFQIY8aMEYKDgwVbW1shICBAmDFjhqbzKBnfgQMHhKioKEEmkwlt27YVDh8+rPX/WtP/6T1btmwR2rVrJ9ja2gru7u5Cr169hF27dmmenzNnjuDp6Sk4OTkJo0ePFj799FOttikIgvDGG28IY8aM0Sw/7GdjRkaGMHToUEEmkwkhISHC5s2bH+h0/M92WZt/doIWhPp9z506dUp44oknBCcnJ8HR0VFo27atsGjRIs3zcXFxQtu2bQWZTCbcHy1efvllYerUqQ+tSwwSQagefIOIiIgMLjMzE61atcLZs2cbfAa8SZMmmDVrlklPP5STk4MWLVogPj4eYWFhYpfzAN4FRkREZER+fn5Yv359ve6sNWe3bt3C6tWrTTL8AOwDREREZHQjR44UuwSD69ChQ4NuzTcWXgIjIiIii8NLYERERGRxGICIiIjI4jAAERERkcVhACIiIiKLwwBEREREFocBiIiIiCwOAxARERFZHAYgIiIisjj/H5CHnugwTuQdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "disp1 = PartialDependenceDisplay.from_estimator(rf, df_clean[['baths', 'bedrooms', 'area(sqr feet)']],\n",
    "                                                [0, 1, 2])\n",
    "disp2 = PartialDependenceDisplay.from_estimator(rf, df_clean[['baths', 'bedrooms', 'area(sqr feet)']], \n",
    "                                                [0, 1, 2], ax=disp1.axes_)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"decision_tree.pkl\", \"wb\") as file:\n",
    "    pickle.dump(dt, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"random_forest.pkl\", \"wb\") as file:\n",
    "    pickle.dump(rf, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Area plays more role in the prediction of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save cleaned data frame.\n",
    "df_clean.to_csv(\"House_Buy_cleaned.csv\", index=False)\n",
    "df_clean.to_excel(\"House_Buy_cleaned.xlsx\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mypython",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}