BlacksOVR_LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries \n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12806, 61)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset\n",
    "\n",
    "dataset = pd.read_csv('Mapping_Police_Violence.csv')\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with 'Unknown race': 1247\n"
     ]
    }
   ],
   "source": [
    "unknown_race_count = dataset['race'].value_counts()['Unknown race']\n",
    "print(\"Number of rows with 'Unknown race':\", unknown_race_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated DataFrame shape after removing rows with 'Unknown race': (11559, 61)\n"
     ]
    }
   ],
   "source": [
    "# Filter rows where race is not 'Unknown race'\n",
    "dataset = dataset[dataset['race'] != 'Unknown race']\n",
    "\n",
    "# Print the updated DataFrame shape\n",
    "print(\"Updated DataFrame shape after removing rows with 'Unknown race':\", dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11559, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing columns that are not required\n",
    "\n",
    "\n",
    "columns_to_remove = ['victim_image', 'street_address','city','county','zip','ori','news_urls','call_for_service','tract','urban_rural_uspsai',\n",
    "                     'wapo_id','fe_id','officer_names','officer_known_past_shootings','urban_rural_nchs','hhincome_median_census_tract',\n",
    "                     'latitude','longitude','pop_total_census_tract','pop_white_census_tract','pop_black_census_tract','pop_native_american_census_tract',\n",
    "                     'pop_asian_census_tract','pop_pacific_islander_census_tract','pop_other_multiple_census_tract','pop_hispanic_census_tract',\n",
    "                     'congressional_district_113','congressperson_lastname','congressperson_firstname','congressperson_party','prosecutor_head',\n",
    "                     'prosecutor_party','prosecutor_term','prosecutor_in_court','prosecutor_special','independent_investigation',\n",
    "                     'prosecutor_url','disposition_official','officer_charged','geography']\n",
    "dataset.drop(columns=columns_to_remove,inplace=True)\n",
    "# columns_to_remove = ['victim_image', 'street_address','city','county','zip','ori','news_urls','call_for_service','tract','urban_rural_uspsai',\n",
    "#                      'wapo_id','fe_id','officer_names','officer_races','officer_known_past_shootings','urban_rural_nchs','hhincome_median_census_tract',\n",
    "#                      'latitude','longitude','pop_total_census_tract','pop_white_census_tract','pop_black_census_tract','pop_native_american_census_tract',\n",
    "#                      'pop_asian_census_tract','pop_pacific_islander_census_tract','pop_other_multiple_census_tract','pop_hispanic_census_tract',\n",
    "#                      'congressional_district_113','congressperson_lastname','congressperson_firstname','congressperson_party','prosecutor_head',\n",
    "#                      'prosecutor_race','prosecutor_gender','prosecutor_party','prosecutor_term','prosecutor_in_court','prosecutor_special','independent_investigation',\n",
    "#                      'prosecutor_url','disposition_official','officer_charged','off_duty_killing','geography']\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "removing dae, circumsances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming your dataset is stored in a DataFrame called 'data'\n",
    "# Drop the 'date' and 'circumstances' columns\n",
    "dataset.drop(['date', 'circumstances', 'wapo_body_camera', 'initial_reason', 'agency_responsible', 'name'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>state</th>\n",
       "      <th>cause_of_death</th>\n",
       "      <th>signs_of_mental_illness</th>\n",
       "      <th>allegedly_armed</th>\n",
       "      <th>wapo_armed</th>\n",
       "      <th>wapo_threat_level</th>\n",
       "      <th>wapo_flee</th>\n",
       "      <th>off_duty_killing</th>\n",
       "      <th>encounter_type</th>\n",
       "      <th>officer_races</th>\n",
       "      <th>prosecutor_race</th>\n",
       "      <th>prosecutor_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>SC</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Car</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Knife</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mental Health/Welfare Check</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Unarmed/Did Not Have Actual Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sudden Threatening Movement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Native American</td>\n",
       "      <td>ID</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Unarmed/Did Not Have Actual Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car, Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Non-Violent Offense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>KY</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Unclear</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undetermined</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None/Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>MN</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Pipe</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Knife</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>AR</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Drug or Alcohol Use</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Used Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>WI</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>CO</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>CA</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic Stop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>CA</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>saw blade</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mental Health/Welfare Check</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>MO</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Hammer</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Non-Violent Offense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>TX</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Non-Violent Offense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>54.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Car</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>Sword</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>CA</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandished Weapon</td>\n",
       "      <td>Foot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Person with a Weapon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>IL</td>\n",
       "      <td>Gunshot</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Used Weapon</td>\n",
       "      <td>Not Fleeing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part 1 Violent Crime</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age gender             race state cause_of_death signs_of_mental_illness  \\\n",
       "2   62.0   Male            White    SC        Gunshot                     Yes   \n",
       "3   25.0   Male            White    TN        Gunshot                     Yes   \n",
       "4   30.0   Male            Black    TN        Gunshot                      No   \n",
       "6   39.0   Male  Native American    ID        Gunshot                      No   \n",
       "7   42.0   Male            White    KY        Gunshot                      No   \n",
       "8   67.0   Male            White    MN        Gunshot                      No   \n",
       "9   31.0   Male         Hispanic    TX        Gunshot                     Yes   \n",
       "11  36.0   Male            White    AR        Gunshot     Drug or Alcohol Use   \n",
       "14   NaN   Male            Black    TX        Gunshot                      No   \n",
       "15  52.0   Male            White    WI        Gunshot                      No   \n",
       "16  32.0   Male            Black    CO        Gunshot                 Unknown   \n",
       "17   NaN   Male         Hispanic    TX        Gunshot                     NaN   \n",
       "18  42.0   Male            White    CA        Gunshot                      No   \n",
       "19  36.0   Male         Hispanic    CA        Gunshot                     Yes   \n",
       "20  37.0   Male            White    MO        Gunshot                     Yes   \n",
       "21  43.0   Male         Hispanic    TX        Gunshot                      No   \n",
       "22  54.0   Male            White    FL        Gunshot                      No   \n",
       "23  23.0   Male            Black    AZ        Gunshot                     Yes   \n",
       "24  50.0   Male         Hispanic    CA        Gunshot                      No   \n",
       "25  37.0   Male            Black    IL        Gunshot                      No   \n",
       "\n",
       "                       allegedly_armed wapo_armed  \\\n",
       "2                      Allegedly Armed        NaN   \n",
       "3                      Allegedly Armed      Knife   \n",
       "4   Unarmed/Did Not Have Actual Weapon        NaN   \n",
       "6   Unarmed/Did Not Have Actual Weapon        NaN   \n",
       "7                              Unclear        NaN   \n",
       "8                      Allegedly Armed       Pipe   \n",
       "9                      Allegedly Armed      Knife   \n",
       "11                     Allegedly Armed        NaN   \n",
       "14                     Allegedly Armed        NaN   \n",
       "15                     Allegedly Armed        NaN   \n",
       "16                     Allegedly Armed        NaN   \n",
       "17                     Allegedly Armed        NaN   \n",
       "18                     Allegedly Armed        NaN   \n",
       "19                     Allegedly Armed  saw blade   \n",
       "20                     Allegedly Armed     Hammer   \n",
       "21                     Allegedly Armed        NaN   \n",
       "22                     Allegedly Armed        NaN   \n",
       "23                     Allegedly Armed      Sword   \n",
       "24                     Allegedly Armed        NaN   \n",
       "25                     Allegedly Armed        NaN   \n",
       "\n",
       "              wapo_threat_level    wapo_flee off_duty_killing  \\\n",
       "2             Brandished Weapon          Car              NaN   \n",
       "3             Brandished Weapon  Not Fleeing              NaN   \n",
       "4   Sudden Threatening Movement          NaN              NaN   \n",
       "6                           NaN    Car, Foot              NaN   \n",
       "7                  Undetermined  Not Fleeing              NaN   \n",
       "8             Brandished Weapon  Not Fleeing              NaN   \n",
       "9                        Attack  Not Fleeing              NaN   \n",
       "11            Brandished Weapon  Not Fleeing              NaN   \n",
       "14                  Used Weapon  Not Fleeing              NaN   \n",
       "15            Brandished Weapon  Not Fleeing              NaN   \n",
       "16                       Attack  Not Fleeing              NaN   \n",
       "17                       Attack         Foot              NaN   \n",
       "18            Brandished Weapon         Foot              NaN   \n",
       "19            Brandished Weapon         Foot              NaN   \n",
       "20            Brandished Weapon  Not Fleeing              NaN   \n",
       "21            Brandished Weapon  Not Fleeing              NaN   \n",
       "22            Brandished Weapon          Car              NaN   \n",
       "23            Brandished Weapon  Not Fleeing              NaN   \n",
       "24            Brandished Weapon         Foot              NaN   \n",
       "25                  Used Weapon  Not Fleeing              NaN   \n",
       "\n",
       "                 encounter_type officer_races prosecutor_race  \\\n",
       "2                  Traffic Stop           NaN             NaN   \n",
       "3   Mental Health/Welfare Check           NaN             NaN   \n",
       "4                  Traffic Stop           NaN             NaN   \n",
       "6     Other Non-Violent Offense           NaN             NaN   \n",
       "7                  None/Unknown           NaN             NaN   \n",
       "8                  Traffic Stop           NaN             NaN   \n",
       "9          Part 1 Violent Crime           NaN             NaN   \n",
       "11         Person with a Weapon           NaN             NaN   \n",
       "14         Part 1 Violent Crime           NaN             NaN   \n",
       "15         Person with a Weapon           NaN             NaN   \n",
       "16                 Traffic Stop           NaN             NaN   \n",
       "17         Part 1 Violent Crime           NaN             NaN   \n",
       "18                 Traffic Stop           NaN             NaN   \n",
       "19  Mental Health/Welfare Check           NaN             NaN   \n",
       "20    Other Non-Violent Offense           NaN             NaN   \n",
       "21    Other Non-Violent Offense           NaN             NaN   \n",
       "22         Part 1 Violent Crime           NaN             NaN   \n",
       "23         Person with a Weapon           NaN             NaN   \n",
       "24         Person with a Weapon           NaN             NaN   \n",
       "25         Part 1 Violent Crime           NaN             NaN   \n",
       "\n",
       "   prosecutor_gender  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "11               NaN  \n",
       "14               NaN  \n",
       "15               NaN  \n",
       "16               NaN  \n",
       "17               NaN  \n",
       "18               NaN  \n",
       "19               NaN  \n",
       "20               NaN  \n",
       "21               NaN  \n",
       "22               NaN  \n",
       "23               NaN  \n",
       "24               NaN  \n",
       "25               NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: ['age', 'gender', 'race', 'cause_of_death', 'signs_of_mental_illness', 'allegedly_armed', 'wapo_armed', 'wapo_threat_level', 'wapo_flee', 'off_duty_killing', 'encounter_type', 'officer_races', 'prosecutor_race', 'prosecutor_gender']\n"
     ]
    }
   ],
   "source": [
    "# Check for NaN values in each column\n",
    "columns_with_nan = dataset.columns[dataset.isna().any()].tolist()\n",
    "\n",
    "# Print columns with NaN values\n",
    "print(\"Columns with NaN values:\", columns_with_nan)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Replacing NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['age'].fillna(dataset['age'].median(), inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['race'].fillna(dataset['race'].mode()[0], inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['cause_of_death'].fillna(dataset['cause_of_death'].mode()[0], inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:8: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['signs_of_mental_illness'].fillna(dataset['signs_of_mental_illness'].mode()[0], inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['wapo_flee'].fillna(dataset['wapo_flee'].mode()[0], inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:10: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['off_duty_killing'].fillna('Not known', inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['encounter_type'].fillna(dataset['encounter_type'].mode()[0], inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:12: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['prosecutor_race'].fillna(dataset['prosecutor_race'].mode()[0], inplace=True)\n",
      "C:\\Users\\waheed.anwar\\AppData\\Local\\Temp\\ipykernel_17432\\1652911823.py:13: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  dataset['prosecutor_gender'].fillna(dataset['prosecutor_gender'].mode()[0], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Import pandas library\n",
    "import pandas as pd\n",
    "\n",
    "# Replace NaN values with appropriate strategies\n",
    "dataset['age'].fillna(dataset['age'].median(), inplace=True)\n",
    "dataset['race'].fillna(dataset['race'].mode()[0], inplace=True)\n",
    "dataset['cause_of_death'].fillna(dataset['cause_of_death'].mode()[0], inplace=True)\n",
    "dataset['signs_of_mental_illness'].fillna(dataset['signs_of_mental_illness'].mode()[0], inplace=True)\n",
    "dataset['wapo_flee'].fillna(dataset['wapo_flee'].mode()[0], inplace=True)\n",
    "dataset['off_duty_killing'].fillna('Not known', inplace=True)\n",
    "dataset['encounter_type'].fillna(dataset['encounter_type'].mode()[0], inplace=True)\n",
    "dataset['prosecutor_race'].fillna(dataset['prosecutor_race'].mode()[0], inplace=True)\n",
    "dataset['prosecutor_gender'].fillna(dataset['prosecutor_gender'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Analyzing redundant classes of columns for cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'age':\n",
      "[ 62.  25.  30.  39.  42.  67.  31.  36.  35.  52.  32.  37.  43.  54.\n",
      "  23.  50.  28.  58.  69.  34.  40.  26.  41.  53.  85.  44.  51.  63.\n",
      "  33.  29.  46.  55.  27.  45.  19.  49.  65.  17.  47.  59.  18.  38.\n",
      "  75.  24.  48.  81.  73.  66.  61.  16.  56.  20.  10.  12.  21.  57.\n",
      "  60.  22.   5.  78.  79.  68.  64.  13.  15.  71.  82.  88.  14.  86.\n",
      "  76.  77.  83.  72.  70.  74.   1.   2.  84.  80.   8.  91.  11.  92.\n",
      "   6.   7.   9.  95.  93. 107.  89.]\n",
      "\n",
      "Unique values in column 'state':\n",
      "['SC' 'TN' 'ID' 'KY' 'MN' 'TX' 'AR' 'WI' 'CO' 'CA' 'MO' 'FL' 'AZ' 'IL'\n",
      " 'NC' 'NY' 'GA' 'MT' 'PA' 'OH' 'MS' 'NM' 'NV' 'WA' 'KS' 'MI' 'WY' 'LA'\n",
      " 'NE' 'AL' 'NJ' 'CT' 'MD' 'ME' 'HI' 'IN' 'DC' 'NH' 'VA' 'UT' 'OR' 'IA'\n",
      " 'MA' 'AK' 'OK' 'WV' 'DE' 'ND' 'RI' 'VT' 'SD']\n",
      "\n",
      "Unique values in column 'signs_of_mental_illness':\n",
      "['Yes' 'No' 'Drug or Alcohol Use' 'Unknown'\n",
      " 'History of Drug or Alcohol Use']\n",
      "\n",
      "Unique values in column 'wapo_flee':\n",
      "['Car' 'Not Fleeing' 'Car, Foot' 'Foot' 'Other' 'Foot, Car' 'Motorcycle'\n",
      " 'bicycle' 'Motorcycle, Foot' 'tractor' 'ATV']\n",
      "\n",
      "Unique values in column 'prosecutor_race':\n",
      "['White' 'Black' 'Hispanic' 'Pacific Islander' 'Asian' 'White, White']\n",
      "\n",
      "Unique values in column 'prosecutor_gender':\n",
      "['Male' 'Female' 'Female, Male']\n",
      "\n",
      "Unique values in column 'race':\n",
      "['White' 'Black' 'Native American' 'Hispanic' 'Asian'\n",
      " 'Native Hawaiian and Pacific Islander']\n",
      "\n",
      "Unique values in column 'off_duty_killing':\n",
      "['Not known' 'Off-Duty']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns_to_check = ['age', 'state', 'signs_of_mental_illness', 'wapo_flee', 'prosecutor_race', 'prosecutor_gender', 'race', 'off_duty_killing']\n",
    "\n",
    "for column in columns_to_check:\n",
    "    unique_values = dataset[column].unique()\n",
    "    print(f\"Unique values in column '{column}':\")\n",
    "    print(unique_values)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing signs_of_mental_illness column.<br>\n",
    "-> Combining 'Drug or Alcohol Use' and 'History of Drug or Alcohol Use' into single column<br>\n",
    "-> Replacing unknown with mode values<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'signs_of_mental_illness' after fixing:\n",
      "['Yes' 'No' 'Drug or Alcohol Use']\n"
     ]
    }
   ],
   "source": [
    "# Replace 'Drug or Alcohol Use' and 'History of Drug or Alcohol Use' with 'Drug or Alcohol Use'\n",
    "dataset['signs_of_mental_illness'].replace({'Drug or Alcohol Use': 'Drug or Alcohol Use',\n",
    "                                            'History of Drug or Alcohol Use': 'Drug or Alcohol Use'}, inplace=True)\n",
    "\n",
    "# Replace 'Unknown' with the mode value (most frequent value)\n",
    "mode_value = dataset['signs_of_mental_illness'].mode()[0]\n",
    "dataset['signs_of_mental_illness'].replace({'Unknown': mode_value}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'signs_of_mental_illness' after fixing:\")\n",
    "print(dataset['signs_of_mental_illness'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing 'wapo_flee' column.<br>\n",
    "-> 'Car, foot' to Car<br>\n",
    "-> 'Foot,Car' to Car<br>\n",
    "-> 'Motorcycle,Foot' to Motorcycle<br>\n",
    "-> and other to the mode<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'wapo_flee' after fixing:\n",
      "['Car' 'Not Fleeing' 'Foot' 'Motorcycle' 'bicycle' 'tractor' 'ATV']\n"
     ]
    }
   ],
   "source": [
    "# Replace specified combinations of values\n",
    "dataset['wapo_flee'].replace({'Car, Foot': 'Car', 'Foot, Car': 'Car', 'Motorcycle, Foot': 'Motorcycle'}, inplace=True)\n",
    "\n",
    "# Replace 'Other' with the mode value\n",
    "mode_value = dataset['wapo_flee'].mode()[0]\n",
    "dataset['wapo_flee'].replace({'Other': mode_value}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'wapo_flee' after fixing:\")\n",
    "print(dataset['wapo_flee'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing 'prosecutor_race' column: <br> -> by replacing 'White, White' with White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'prosecutor_race' after fixing:\n",
      "['White' 'Black' 'Hispanic' 'Pacific Islander' 'Asian']\n"
     ]
    }
   ],
   "source": [
    "# Replace 'White, White' with 'White'\n",
    "dataset['prosecutor_race'].replace({'White, White': 'White'}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'prosecutor_race' after fixing:\")\n",
    "print(dataset['prosecutor_race'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fixing race column: <br> \n",
    "-> changing 'Native Hawaiian and Pacific Islander' to Pacific Islander "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in column 'race' after fixing:\n",
      "['White' 'Black' 'Native American' 'Hispanic' 'Asian' 'Pacific Islander']\n"
     ]
    }
   ],
   "source": [
    "# Replace 'Native Hawaiian and Pacific Islander' with 'Pacific Islander'\n",
    "dataset['race'].replace({'Native Hawaiian and Pacific Islander': 'Pacific Islander'}, inplace=True)\n",
    "\n",
    "# Print unique values after fixing the column\n",
    "print(\"Unique values in column 'race' after fixing:\")\n",
    "print(dataset['race'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Encoding string based columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Apply label encoding and add 1 to the encoded values\n",
    "dataset['signs_of_mental_illness'] = label_encoder.fit_transform(dataset['signs_of_mental_illness']) + 1\n",
    "dataset['allegedly_armed'] = label_encoder.fit_transform(dataset['allegedly_armed']) + 1\n",
    "dataset['wapo_armed'] = label_encoder.fit_transform(dataset['wapo_armed']) + 1\n",
    "dataset['wapo_threat_level'] = label_encoder.fit_transform(dataset['wapo_threat_level']) + 1\n",
    "dataset['wapo_flee'] = label_encoder.fit_transform(dataset['wapo_flee']) + 1\n",
    "dataset['off_duty_killing'] = label_encoder.fit_transform(dataset['off_duty_killing']) + 1\n",
    "dataset['encounter_type'] = label_encoder.fit_transform(dataset['encounter_type']) + 1\n",
    "\n",
    "dataset['officer_races'] = label_encoder.fit_transform(dataset['officer_races']) + 1\n",
    "dataset['cause_of_death'] = label_encoder.fit_transform(dataset['cause_of_death']) + 1\n",
    "dataset['prosecutor_gender'] = label_encoder.fit_transform(dataset['prosecutor_gender']) + 1\n",
    "dataset['prosecutor_race'] = label_encoder.fit_transform(dataset['prosecutor_race']) + 1\n",
    "\n",
    "dataset['gender'] = label_encoder.fit_transform(dataset['gender']) + 1\n",
    "dataset['state'] = label_encoder.fit_transform(dataset['state']) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Preparing race column for one vs rest comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['race_binary'] = dataset['race'].apply(lambda x: 1 if x.lower() == 'black' else 0)\n",
    "dataset.drop(columns=['race'], inplace=True)\n",
    "dataset.rename(columns={'race_binary': 'race'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Analyzing correlations between 'race' and other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ens7EiRMZOXIkqampPPLII9xzzz0sXbo0oPGUt3gIIYQQQgghhBC/EoqiMH/+fK655po6wzzxxBMsXLiQffv2uZfdfPPNFBYWsmTJkoDFTXpQCCGEEEIIIYQQlxmr1UpxcbHXx2q1+mXbmzZtYsyYMV7Lxo0bx6ZNm/yy/boYArp1IRrIQmNyQ0fhoq36966GjoJfhIebGjoKfqE0gubcJhH6ho6CX+xOPdfQUbhooeHmho6CX+j1jaBgAH17WBo6Chdtw9aSho6CX6gOtaGjcNFi40MbOgp+odcpDR0Fv3A4Lv/O4ltWHWroKPjFwg+6NnQUfpZf8n3Ftqdu4bnnnvNaNn36dJ599tmL3nZ2djbNmzf3Wta8eXOKi4upqKjAYgnMuVMaKIQQQgghhBBCiMvMk08+ybRp07yWmc2X90MQaaAQQgghhBBCCCEuM2azOWANErGxseTk5Hgty8nJITw8PGC9J0AaKIQQQgghhBBCCJ8UY+MY7vRTpaSksGjRIq9ly5YtIyUlJaC/2zgGjwohhBBCCCGEEMKn0tJSUlNTSU1NBVyvEU1NTSUjIwNwDRe544473OHvv/9+0tLSePzxxzl06BAzZszgyy+/5E9/+lNA4ykNFEIIIYQQQgghRCO2fft2evXqRa9evQCYNm0avXr14plnngEgKyvL3VgB0KZNGxYuXMiyZcvo0aMHr776Kh988AHjxo0LaDxliIcQQgghhBBCCOGDztA4hniMGDECTav7rTazZs3yuc6uXZf2zYLSg0IIIYQQQgghhBANThoohBBCCCGEEEII0eBkiIcQQgghhBBCCOGDYpRn+peS7G0hhBBCCCGEEEI0OGmgEEIIIYQQQgghRIOTIR5CCCGEEEIIIYQPjeUtHpcL6UEhhBBCCCGEEEKIBicNFEIIIYQQQgghhGhwMsRDCCGEEEIIIYTwQTHKEI9LSXpQCCGEEEIIIYQQosFJA4UQQgghhBBCCCEanAzxEEIIIYQQQgghfJC3eFxa0oNCCCGEEEIIIYQQDU56UIhfvaZD+pL06N1E9O5KUItmbL/uAXK+W1H/OsP60/mV/yO0c3sqT2Vx7MV3OP3JfK8wCb+/laRpd2OOjaF4zyH2P/I8Rdv2BjIpAIzrZ2RAJwMWM6Rnq8xbayOvSKt3nUFdDIzoaSAsWCErX2X+ejunclX399cNM9K+pZ6IEAWrHU5kO1m42c7Zwvq3+3P07aAwqJOOUAvkFMDi7U7O5NcdvlNrhZHddUSGQn4JrNilcuyMK146BUb20NEuXqFJKFhtkJatsSJVpbTC71H3Tkd7hZSOnnQs2eHkzLl60tFKYUR3HZEhcK4EVqSqHMuqlo7uOtq1UIg8n470HI0VuwOfjj3rP2fnyg8pL8kjukVHhl37NLEJ3esMfzR1CZsXv0nJuUwiYxIYNOkxEjsPd39vs5ax8ftXSdu7gsryQsKbtqTH0NvpNvjmgKbjqmEWhvYMItiscOy0nc+XlJFboNa7zog+ZsYNsBARquNUjoP//VDOiSyH+/uYSB03jA6hXSsDBj3sT7PzxQ9llJT5v1xUGd/fREoXA0FmhRNZTr5abf3R8j24m5FRvYyEBSucyVOZt9ZKRq7vtN87OYhOCQY+XFjBvnRnIJIAXP711JYVn7Nx8YeUFuXRvHVHrrztaVom+S4XuZlHWTn/32Sd2E9h/hnG3/IkKVfcWStccUEOy758haN712K3VdK0WWuuufsF4tt083v8q7tyoJlB3YxYzArpZ5zMWVnJ2cL6y8bQ7kZG9zUTHqyQmacyd1UFJ3M86zx8fTDtW3pfYq7fY2POysqApGHi4CAGdzNhMSuknXEwe1nFj6ZhWE8TY/oFER6ikHnWyZcrKjiZ7X3Mt4nTM3loEIlxBlQVMnOdvPV1KXZHHRu9CI3hnNGnncKAjgqhQZBTCD/sVMmqJw0dW8Lwbjoizqdh1R6V41me74d2UejcWiEsGJwqZJ+DNXvVeveLPzSW6xCA31zdjHFDmxASrOfgsXLe/uwMZ3JtdYbv0j6Y68ZH0y7BQlSkkeffOsnm1BKvMJHheu66LpZeXUIJsejZf7SMd7/Iqne7QvwU0oNC/OrpQ4Ip3nOYfQ8/d0HhLYkt6ffdf8lfvYX1fa8m/T8f0+2/fyd67BB3mLgbJtDp5Sc5+ve3Wd9/CiV7DjFg4YeYYpoGKhkAjOxpYEg3A1+vtfHvryux2TV+N8mMQV/3Oj3a6rlqsJFl2+28MbeSM/mudUItnjCnz6p8ucrGv2ZX8v73lSgK3DvJjOLnHm+dExSu6K1jzV6V9xY5yS7QuG2knmCz7/Ato+G6wTp2HXeFP3xK46ZhOmIiXN8bDRDXFNbtVXl/kZMv1zqJDle4eXg9O8Qf6WitMLaXjrX7VN5f4iSnUOPWH0nHtYN0pB53hT98WuPGod7piG0K6/apfLDEyVfrnUSFKdw0NLDpOLJrEeu++Sf9xz3IzY/OI7pFMt/99x7KS3xfqWWl72Tpp4/SZcD13PzYfJK6jmHhRw+Rn3XEHWb9N/8k49B6rvjNv/jN/y2k57A7WDPvedL2rQxYOsYPDGJ03yA+W1zKC7OKsNnhkZvD6y0XfTuZuHF0CAvWV/D8R0WcznXyyM1hhAW7DnqTER65JRwNjVc/L+alT4rR6xT+cEM4geoIOqq3kWE9jHy12sobX1VgtcP9V1nqTUfPdgauGWJi6TYbr84p50y+yn1XWQi11I7l8B5GtMC1rbhd7vXUvi2LWDr7n4y4+kHue3Yesa2S+fTVeygt9l0u7NZKmsS0YswNjxIaEeMzTEVZER/+4xZ0BgO/mfY+D/1jIeNufgJLSIR/I1/DmL4mhvcyMWdFJa/OLsNq13hgSnC9edG7g4Epw4JYvNnKv74oI/OskwemhNQ6pjbstfGX90rcn2/XB6ZxYmx/MyN6mZm9rJyXPy/BZoeHrg+pPw3JRq4dYWHRpkr++WkJp3OdPHR9CKHBnjS0idPz4PWhHDzh4OXPSvjXZyWsSbUGpIw0hnNGp1YKo3sqrN+v8dEPKrmFGjcP19WZhvgouCZFR2qaxodLVY5kalw/2JMGcN3sL92p8sESlU9XqBSV179Nf2gs1yEA14+PZvLoKN7+7AzTXjhOpVXl+T8lYqxnuEKQWUf6qUre+fxMnWGefjCB2BgTz7+VwcN/O0Zuvp1/PJqI2dR4h0EoRuUX+2mMpIFC+NWSJUsYMmQIkZGRREVFMWnSJI4fP+7+fuPGjfTs2ZOgoCD69u3LN998g6IopKamusPs27ePCRMmEBoaSvPmzbn99tvJy8sLWJzPLl3LkelvkPPt8gsKn3DvzVSkn+bg4y9ReiiNkzM+J/vrpbT541R3mDaP3MWpD7/k9MfzKD14nL0PTMdZXkmrqdcFKBUuQ7sbWb7Dzv4TTrLOacxeaSM8WKFrm7pPhMN7GNhywMG2w05yCjS+XmPDbtfo19Hz9GvLQSdpWSoFJRqZeRpLtthpEqajaZh/K8aUjjp2HtPYnaaRVwwLt6rYndCrre/fGdBRx7EsjU0HXeFX71HJKoB+ya6qzWqHz1aqHMjQyC+BzHxYvM1JiyiF8GC/Rt3LwGQdu45r7E4/n45tKnYH9EzynY7+Hc6n49D5dOw9n472nnR8vkrlwKlq6dgR+HSkrp5Fl5Qb6DzgOprGtmPkDc9hMAVxYMvXvsOv/ZSEjkPoPepumjZvy8Ar/0hMy87sWfe5O0zWiVQ69ruGlu0GEN60JV0H3UR0i2RyMvYELB2j+1tYuKGC3UftZJ518tGCUiLDdPRKNtW5ztj+QaxLtbJxj5WsPCefLS7D5oDBPVxXqe1aGomO0DFzgesGLfOsk5nfl5IQp6djojEg6Rjew8gP223sS3eSla/yxfJKwkMUuiXV3RlyRE8jm/bb2XrQQU6BxlerrNgcGgM6ea/TIlrHiF5GZq+0BiTu1V3u9dTGH2bRZ9gN9Bp6Hc3i2zHpjucwmoLYtc53uYhP6sa4mx6n24CJGAy+j431iz4gvGkcU+5+kZZJ3WkS05J2XYfQtFlrv8a9phG9TCzdYmVvmoMzeSqfLq0gIkShe9u6j6mRvc1s2mdnywE72edU5qyoxObQSOninTabQ6Ok3POpDNDD1ZG9zSzZXMme4640fLyojIhQHT3a1V0OR/c1s3Gvjc37bGTnq8xeVoHNDildPXXCdSMtrN5pZdlWK1n5KrkFKjsP23EEoGNRYzhn9E9WSE3T2HM+DYu3azgc0KON7zT066BwPBu2HHbFce0+jexCVy+MKgcyNE7kQGEZ5BXD8l0aQSaFZgFst2ss1yEAV4+JYs73uWxOLeHEaSuvfnSappEGUnqF17nOjn2lfPpNLpt2lfj8vkVzE53aBvP2Z2c4eqKCzBwbb392BpNRx/ABkQFKifi1kQYK4VdlZWVMmzaN7du3s2LFCnQ6HVOmTEFVVYqLi5k8eTLdunVj586dPP/88zzxxBNe6xcWFjJq1Ch69erF9u3bWbJkCTk5Odx4440NlKLaIgf2JG/lJq9lZ5etp8nAngAoRiMRvbuQt2KjJ4CmkbdyI5EDewUsXk3DFMJDFI6e9lw9VdogI1clobnvoq7XQXyMjiOnPV1hNeBoZt3rmAzQr6OB/GKVwlL/PUrS6VxPGdKzvbeZnq3RMtr3hUHLaIX0LO/wx8/UHR7AbFLQtMBdLNeZjpwfSUeOd/i0rPrTEWQMbDqcDhu5p/fTqsMg9zJFp6NV+xSyT6b6XCf7RKpXeIDWyYPJqhY+LrEn6ftWUlqYg6ZpnD66mcKzJ2idPDgQySA6UkdkqI6D6Xb3sgqrRtoZB0nxvm/C9DpIiDNw8IRn52rAwXQbbeNdNz0GvWuZw+nJN7tDQ9OgXSv/j56MClcID9Fx5JR3+T6Zo5IYW3f5btnMex0NOHraSUKspzHAaIDbrwji6zVWSsoD24Xicq+nHA4bWSf2k9TFc5zrdDqSOqdw6ljqz97u4dSVtGjTlTlv/5F/PTyId6ZPYfuaL/0Q47pFhStEhOg4fMozXqHS5hoa0ybOd2ORXgetmnmvowGHMxwk1linb7KRF+8L5cnfhDB5sBljAAYVR0XoiAjVcfhkjTRkOWnTou7y3aq5nkMnvdNwKMNB0vl1QoMV2rQwUFKu8ugtobz4+3AeuSmUtvH+f+rdGM4ZOh3ENYETObXTEF9HnOKjlFrh07LqDq/TuRoJKm0aOYV+ibbP32gM1yEAsdFGmkYaST1Y5l5WXqFyOK2Cjm0t9axZv6reFza7J82a5jr/dWkX4BYX8ashc1AIv7ruOu8eAh999BExMTEcOHCA9evXoygK77//PkFBQXTu3JnMzEx+97vfucO/9dZb9OrVixdeeMFrG61ateLIkSN06NDhkqWlLubm0VhzvHt0WHPyMEaEoQsyY2wSgc5gwJqbXyNMPiHJSQGLV1XX85IK7xNlabnm/q6mkCAFvU6htMY6JeUazSK9L/wHdTEwMcWI2aiQW6Dy3gIrzvqH+P4kwWbQ6RTKKr3jUlYJ0eG+4x8aBKU1eg2XVWqEBvkOr9fB6J469p3QsAVgDDF40lHqKx11PMkNDXJ9X11ppUaIj274UC0dJwOXjoqyAjTVSXBYlNfy4LBoCnLTfa5TXpLnM3x5sae8DL/ur6yc81dmPjccnc4AisKom54nvm0//ycCiAhxHcfFZd4Ha0mZ6v6uptBgV7korjGXRHGZRmyUK0/Szjiw2jSuGxnM/NXloMB1I4PR6xQiQv3f9l9VhkvLa5Zvte7ybXGlo2adULN8XzPEzIksZ0DnnKhyuddT5SUFqKqT0HDv4zw0Ipq8bN/l4kIU5J5i+8r/kTJuKsMm3Udm+l4Wf/4PDHojPYdMudho+xR+/vivOWdKSbnm/q6mqmOquLz2Os2bem7etx+yc65EpahUIz5ax1VDgmjeRMcH3/t30H14iOuYKS6vUb7LVfd3NYVWlQsfdUJsU9dlcXSEK/1XDgpi/ppKTuc6GdDZyB9uCOUfs0p+dH6Ln6IxnDOCTVXnb+/lZZUQVcfDel9pKLO6llfXLs41FMRogNIK+N8alYoA3dg3lusQgCYRrmO5oNj7RwqLHTSJ+Pm9/E5nW8nNtzH12ua89WkmlVaNa8ZGEdPU6P7Nxkje4nFpNd4jSTSIo0eP8swzz7Blyxby8vJQVddJPCMjg8OHD9O9e3eCgjxnn/79+3utv3v3blatWkVoaGitbR8/ftxnA4XVasVq9e6WbNdUjErj7iDUq72e64d7uqN+uDCwXbN3HnVw5LST8GCF4T0N3H6FmbfmVwaku2sg6BS4fqgORXF12bxc6RS4frDr2F607fJLx+51n5J9cjeT7p5BWNN4Mo9vY83XfyMkvBmtkwf9+AZ+xIAuJn4zwVN//OfL4ovepi+l5Rr/nV/KbeNDGNUvCE2DrfttnMxy+GWMeu8OBm4c4Rn0/L6fb+yqdEnU076lnlfmlAdk+1JPXRhN02iR2IUx108DIC6hM7mZR9m2erbfGij6Jhu4ebTnyem73wYmzwE27vP0WMrKVykuq+AP14cQHVH5o5Oh1qdfJyO3jPU8pZ0xr/Si4lmXqnlLNux2DQMBOJ3rJDnBSEo3E9+tC8x8GoFwuZ8zTubChz+oWMyuIS9TUnTMWq5SHvjRaH4XyOuQEQMieOj2Fu6/n/33Sb9uv4rTCf+YkcEf74xnzr8743RqpB4sZdvekoDNvyR+faSBQvjV5MmTSUhI4P3336dFixaoqkrXrl2x2S6subu0tJTJkyfz0ksv1fouLi7O5zovvvgizz3nPcHlLUpTbtNH//QEXABrTh7m5t7bNjePxl5UglppxZZXgOpwYG4WVSNMFNZs/82lceCEk9dyPBdJVROChVkUr27aoedn7velrFLDqWq1JjcLC679hKzSBpU2jbwijZM5Np7/rYWubfSkHvPPlX+5FVRVIyRIwdXh1iUkiFpPTquUVtZ+2hISpNR6mlF1URARovDpcmdAn1pUpSPUVzoq605HSI10hAYplNW4J9Uprsm4IkIUPl0Z2HRYQpqg6PS1JsQsL8kjONx32QoOi643vMNWyaaFb3DlXf+hTZcRAES3SCYv8xC7Vn/klwaK1KM20s4Uuv826l3HdniIjqIyz7EaFuJ6M4cvpeWuclHzCWx4iHevigPpdp56p5BQi4JTdQ0deeXhJpw9cPFlYn+6g1dyPNsxnE9HaI2yGRqs40ye798rq3ClI6ye8t2+pZ6oCIUXfhfiFeauCUGkZam8Pf/iGkYaWz0VHNYEnU5fa0LM0qI8QusoFxciNDKGmBbtvJZFx7XlwPYffvY2a9qb5uBEtueGvuqYCgvx3o9hwa63WvhSdUyFB/vIi7K6b7hOnH87RnSkjryin58Xe47ZOZHlGRtfdTyFB+sorl6+g3WczvX9O6VV5SJEB3jXCVXlu+rfrHzvbWTnO2ka5t+HH43hnFFuqzp/ey8P8dFLooqvNISYa/dGsDuhoNT1OZOvcf+VCj2SFDYd9P9wtMv5OmRLagmH0z1zvlUNxWgSbqCgyPNjkeEG0k5dXL1+7GQlf/jbcYItOgx6heJSJ6/9JYmjJy7Ba0nEr0LjfsQsLqn8/HwOHz7M008/zejRo+nUqRMFBQXu75OTk9m7d69Xb4dt27Z5baN3797s37+fxMRE2rVr5/UJCfG+gK7y5JNPUlRU5PW5URe4t2UUbk4latRAr2XRowdRsDkVAM1up2jnfqJHpXgCKApRI1Mo3LzLb/Gw2iG/WHN/cgo0iss02rf0dLM1G6F1M53X69+qc6qQeValfUtPVaAA7eLrXqe6+mZJ/6lUFbLOQZtY7wvfNrEKp/N8XxicztNqhU+K8w5fdVHQNEzhsxXOgHUNrVKVjsSa6Wj+I+loXn+6qy40m4YpfLYq8OnQG0w0a9mF00c8861oqsqpo5uJTejpc53YxJ6cOuI9P8upIxuJOx9eVR2oTjuKzvvUo+h0aKp/niZZbXC2QHV/zuQ5KSxVvSauDDIpJLUwkJbp+wrRqcLJLAedqq2jAJ0SjRzPtNcKX1qhUWHV6JhgICxEYffRi88cqx3yijT3J/ucSnGZSoca5TuhuY4T2XWX79O5Kh1aedZRcDVKVL1OccVOOy//r4JXZns+AN+st/G/5Rf/lLix1VMGg4m4xC6kHfAc56qqkn5wM63a9fzZ223drletISL5OSeIjGpRxxo/na9jqqhMJbnanClBJkiM1ZOe5fvm3qnCqVyVDtXWUYAOrQycqGMdgPgYVybUHDb1c9JwtlB1f7LyVYpKVZITaqQhTk/6mbrL96kcJ8mtvdOQ3NpA2vl18otUCktUr2ErAM2a6DhX7N8n343hnKGqkFUAiTXilNhcIbOONGTmayQ2q52GusJXURQwBOju5XK+DqmwqmTl2tyfjDNWzhXa6dHJc+1sCdKRnGTh0HH/NCSUV6gUlzpp0cxEu0RLrdeRNiaKXvnFfhojaaAQftOkSROioqJ47733OHbsGCtXrmTatGnu72+99VZUVeXee+/l4MGDLF26lFdeeQUA5Xx/ygcffJBz585xyy23sG3bNo4fP87SpUu56667cDp9X/yYzWbCw8O9Pj9leIc+JJjwHh0J79ERgOA2LQnv0ZGgVq4eG8l/n0aPmZ4eHSffm01wm1Z0fPHPhCQnkXD/rcTdMIH0N2e5w6S/MZNWd99I/O3XENoxia5vP4shxMKpj+ddcLx+jnV77IzuY6Rzop7Ypgq3jDZRXK55jS2/b7KZwV09F2ZrdjsY0MlA32Q9zSIVrh1mxGRU2HbIdaHWNExhVC8D8dEKkaEKCc113DHOhN0JhzL822960yGV3u0UurdRiA6Hif11GPWQmuY60V+domNUT0/ebjmk0raFwsCOClHhrvept2gK2w67LiB1CtwwVEdcU4X5G5woiutJSEiQazKsQNl8WKV3W086ruznGj+7O/18OgbqGNXDE4GtR1Taxp1PRxgM63o+HUc96bh+iCsd32y6dOnoOWIq+zd/xcGt8zmXc5xVc5/FYaug84BrAfjh8yfY+P2rnvDDbifj0Hp2rvqIczlpbFnyH3JP7af70NsAMAWFEt+2Hxu+e5nTx7ZQlH+ag1vncWj7t7TtPjZg6VixtYKJgy30aG8kPkbPbyeHUliisuuw5ypx2q3hjOzjeQy2bGslQ3sGkdLNTGyUntsmhGAyKmzY42lgHdTdTFILAzGROgZ0MXHflDCWb60k51xgulGv2W1nbF8TXRL1xEXpuG1sEMVlGnvTPDdiv786iCHdPA0rq1PtDOxspF9HA82aKFw/wozJoLDloGudknLXjWr1D0BBicq5ksBMmHm511ODrpjKzjVfkbp+PmfPHOf7T57FZq2g1xBXuZj3/hMs+8pTLhwOG1kZB8nKOIjTaae4IIesjIPk53i6YKdcMZXTabtZ+/275OecZM+mBexY/SX9R9/m17jXtHqXjXH9zXRNMhAXpeP2cRaKyjT2HPccUw9dG8ywHp5jatVOK4O6GunfyUjzJjpuHB2E2aiw+YCr8S46QmFcfxOtmuloGq7QNcnA7eMsHD3tqLOXzMVYtdPK+IFmurU10CJaxx0TQigqVdl9zNOY+PANIQzv5RlqtGK7lcHdTQzoYqR5Ux03j7VgNuIezgGwfJuVEb3N9OpgJCZSx6TBQTRvqmfjXv/fXTaGc8bWwxo9kxS6JbriNKGvgtEAe86nYfIAhRHdPDdT245oJMW53v4RFQZDuyjENYEdx1zhjXoY3k2hRRSEB0NsE5jYTyHMAgdPBW4y38ZyHQLw7fJ8bp7YjAE9wkiIN/Po3S05V+hg0y7P0Md/PJrIpJGeh3pBZh1JrYJIauU6H8bGmEhqFURMU08dMKRPON2SQ4iNNjKwZxh/n5bI5l3F7DoQmCFX4tdHhngIv9HpdMyePZuHH36Yrl27kpyczL///W9GjBgBQHh4OAsWLOD3v/89PXv2pFu3bjzzzDPceuut7nkpWrRowYYNG3jiiSe44oorsFqtJCQkMH78eHQBqskj+nQlZcWn7r87v/IXAE59Mo89dz+JOS4GSyvP8JKKE6fZdtV9dH71SRL/cAeVp7PZe9/T5C1b7w6T9dViTDFN6TD9YcyxMRTvPsjWSfdgqzFxpr+tSnVgMipcP9yExQTp2Srvf2/1Gn8dFa6c777osvu4k1CLnXH9jISd72b9wfdWSs83sDucrvfBD+1uxGJ2PS1OO6Py1vxKdxh/OXBSI8SsMqKHjtAgyCmAL1Y53V1EI0JcM19XOZ0H8zaojOyhY1RPOFcCc9aqnC1yfR8WDMmtXMfNfRO9q7uPlzk5mRuYi5wDGRrBZpXh3aqlY7UnHeHBtdMxf6PKyO46RnZ3pePLdTXScf7p8b0TvNPxyYrApaNDryupKD3HliX/oaz4LDHxnbjqvvcJDnN1ZS8tOONuXASIa9ObK25/hc2L3mDTwteJjElk4m/fIirOM3fMuDteY9PC1/jhsz9TWV5EWJMWpFz5CF0H3RyQNAAs2VyJyaRw+4RQgoMUjp6y8+acYq9yEROpI7Rat/XtB22EBZdz9TAL4eeHg7w5p8RrQsHYpnquHRFMiEUhv1Bl0cYKlm0N3Nj0lTvtmAwKN440YzErpGc5+e+CCq90REfoCLF4bgJTjzkItSiM728iPEQh86zKfxdU1Nld+VK43OuprgOupKzkHCu/+Q+lRWeJbd2J26e9T2iEq1wU5XuXi5LCXN6d7plHYuOSj9i45CMSk/tx1/+5zj3xSd24+aH/sHzua6z5dgaRMS0Zf+uTdE+Z7N/I17B8uw2TQeGW0UFYzAppZ5zMmF/ufUxF6gixeM6/O484CLVUMjHF7BoOkqcy45ty95Adh9PVG2FkLxMmo0JBiauxYOnWwEwasGyrFZNR4dYrgrGYFY5nOnj767IaadATYvEs2HnYTlhwBZMGW9xDWt6eW+Y17GjVTisGA1w3wkKwRSEz18lbc0vJK/J/I0tjOGccPKURbIZhXV1lN6cQ5qxRKbP6TkNmPny7yZXmEd0UCkph7gZPGlTNNTFl90QFixkqbK7eDZ+uVMkLzNRCQOO5DgGYuySPILOOP9zRgpBgPQeOlvPXN05gd3h+My7GRHiYJ17tEy38889t3H//7ibX9e/yDQW8PjMTgCaRBu65KY7IcD0FRQ5WbCxk9vdnA5YO8eujaJo/pvMS4uf5/PPPueuuuygqKsJi+fmvPappoTHZb9tqKKv+7b/hIA0pPNz044EuA41hztUmEf5/RV5D2J16rqGjcNFCw80/HugyoNc3goIB9O3hv/NPQ9mwtXF0r1Ydl99EjjXFxtee6PtypNc1ju7jDsflf6uzZdWhho6CXyz8oGtDR+FnWd+jd0NHoU5Ddu9s6Cj4nfSgEJfUJ598QlJSEvHx8ezevZsnnniCG2+80a+NE0IIIYQQQgghLj/SQCEuqezsbJ555hmys7OJi4vjhhtu4B//+EdDR0sIIYQQQgghRAOTBgpxST3++OM8/vjjDR0NIYQQQgghhPhRSiMZ7nS5aByDR4UQQgghhBBCCHFZkwYKIYQQQgghhBBCNDgZ4iGEEEIIIYQQQvigNJI3Vl0uZG8LIYQQQgghhBCiwUkDhRBCCCGEEEIIIRqcDPEQQgghhBBCCCF80OnlLR6XkvSgEEIIIYQQQgghRIOTBgohhBBCCCGEEEI0OBniIYQQQgghhBBC+KDoZIjHpSQ9KIQQQgghhBBCCNHgpIFCCCGEEEIIIYQQDU6GeAghhBBCCCGEED7IWzwuLelBIYQQQgghhBBCiAYnDRRCCCGEEEIIIYRocDLEQwghhBBCCCGE8EGRIR6XlPSgEEIIIYQQQgghRIOTBgohhBBCCCGEEEI0OBniIRqlVf/e1dBRuGgjH+7V0FHwi03v723oKPhF1qmiho7CRSuMCm7oKPhFULCxoaNw0TStoWPgHxVltoaOgl/s3HP5d98tPlfe0FHwi+QuMQ0dhYtmMl7+xxOA3dE4KqqgoMv/eeygMZ0aOgq/aoru8j+GLieyt4UQQgghhBBCCNHgpIFCCCGEEEIIIYQQDU6GeAghhBBCCCGEED4ousYxbOtyIT0ohBBCCCGEEEII0eCkgUIIIYQQQgghhBANToZ4CCGEEEIIIYQQPuj0MsTjUpIeFEIIIYQQQgghhGhw0kAhhBBCCCGEEEKIBidDPIQQQgghhBBCCB/kLR6XlvSgEEIIIYQQQgghRIOTBgohhBBCCCGEEEI0OBniIYQQQgghhBBC+KDo5Jn+pSR7WwghhBBCCCGEEA1OGiiEEEIIIYQQQgjR4GSIhxBCCCGEEEII4YO8xePSkh4UQgghhBBCCCGEaHDSQCGEEEIIIYQQQogGJ0M8hBBCCCGEEEIIH3R6GeJxKUkPCnHJTJ06lWuuuaahoyGEEEIIIYQQ4hdIelAIUc24fkYGdDJgMUN6tsq8tTbyirR61xnUxcCIngbCghWy8lXmr7dzKld1f3/dMCPtW+qJCFGw2uFEtpOFm+2cLax/uz9F0yF9SXr0biJ6dyWoRTO2X/cAOd+tqH+dYf3p/Mr/Edq5PZWnsjj24juc/mS+V5iE399K0rS7McfGULznEPsfeZ6ibXv9Fu/6jO6pp28HHUEmyMjV+G6Tg/yS+tcZ0FHHkK56Qi2QfU7j+y1OMvM8+9mgh/F99XRvo0Ovh2OZGt9tdlBWGZg0XD08mGG9gggO0nHslJ1PF5eSe85ZZ/gOrY2MS7GQGGcgMkzPW18Wseuw7aK3e7HG9TMysLMBi1khPUvl67XWHy0Xg7saGNHTSFiwwpl8lfnrbF7lorp7JprplGBg5uJK9qUHLh1XDjQzqJvRlY4zTuasrORsoe84VRna3cjovmbCgxUy81TmrqrgZI5nnYevD6Z9S+9T6fo9NuasDMxBNWGAiYFdqvLCyVerfjwvhnQzMqr3+bzIc+VfRrU0PDTFQruWeq91Nuy189Vqa0DSADBxcBCDu5mwmBXSzjiYvaziR/NiWE8TY/oFER6ikHnWyZcrKjiZ7TpemobreP7ecJ/rffBdGbuO2P2ehiv6Guh//nxxIltl/jr7j+ZFShc9w3saCLMoZOVrfLvBxqlczzrXDjPSPl5H+PnzxclslUVb/Hu+qGnKqFBG9LEQHKTjaIaNjxcUk1NPfZKcYGTCkBAS44w0Cdfz5hcF7Dzkfaz06WRmVL9gElsYCQ3W8dcZeWRkOwIS/z7tFAZ0VAgNgpxC+GGnSta5usN3bAnDu+mICIFzJbBqj8rxLM/3Q7sodG6tEBYMThWyz8GavSpn6tmmP+zf9Dl71n5ERWkeTWM7Muiqp2jWqnud4dP2LmH7sn9TWpBJeFQC/cc/SuuOw93fr/7qSY7u/MZrnZbthzDht+8HKgn0bqcwINmVF7mF8MOuH8+LYV09ebF6j8rxbN9hx/VR6N1Wx/JdKtuOBq48ABzY9Dl713nyImXyU8TUkxfpe5ewY9m/KS105UW/8Y/SKtmTF2vn1s6L+PZDGH9X4PICXGVjYKdqZWNH/cdxx1aushF5Pj9W7q5RNrq6ykZ4tbKxek/gy4b49ZEeFOKyoWkaDkdgLnAARvY0MKSbga/X2vj315XY7Bq/m2TGoK97nR5t9Vw12Miy7XbemFvJmXzXOqEWT5jTZ1W+XGXjX7Mref/7ShQF7p1kRvFjbzF9SDDFew6z7+HnLii8JbEl/b77L/mrt7C+79Wk/+djuv3370SPHeIOE3fDBDq9/CRH//426/tPoWTPIQYs/BBTTFP/RbwOQ7vqGNhZx7ebHLy70IHNAXdeYaw3L7om6pjQT8+qVCczvrOTfU5j6lgDIUGeMBP66enYSsfs1Q4+XOIgLBhuHRmYdtoJgyyM6W/h00Wl/OOjAqx2jWm3RtSbBpNR4XSOg88Wl/p1uxdjZC8jQ7sbmbvGxptfV2BzaNw7Kaje3+vZTs9Vg038sN3O619VcCZP5d5JQV7losqw7pemnXxMXxPDe5mYs6KSV2eXYbVrPDAluN509O5gYMqwIBZvtvKvL8rIPOvkgSkhhFq8C++GvTb+8l6J+/Pt+sA0TozubWRYDyNfrbLy+pcV2Oxw/9WWetPQq72Ba4aaWLLVxiuzy8nMU7n/KkutNGzcZ+evH5a5P99tCFzjxNj+Zkb0MjN7WTkvf16CzQ4PXR9Sf14kG7l2hIVFmyr556clnM518tD1IYQGu9JRUKLy5Iwir8/3GyqotGkcSPd/48SIngYGdzMwb52N/8yzYrPD3RNNP3q+mDzIyPLtDt782kpWvsrdE81edVTmWZUvV9t5ZY6VDxdaURS4Z6LJr+eL6q4cEsLYAcHMWlDM397Lx2rTeOyOJhjrKZZmk8KpbAefLiyuN8yRDBtf/vAjrcoXqVMrhdE9Fdbv1/joB5XcQo2bh+sINvsOHx8F16ToSE3T+HCpypFMjesH64iJ8ITJL4GlO1U+WKLy6QqVovL6t+kPx/csYvPCl+g9+kGmPPQ1UXHJLP7od1SU5vsMn3NyFytnP0Zy3+uY8od5JHYezbLP/sC57CNe4Vp2GMptf1nr/oy65ZWApaFTK4XRPc7nxTKVnEKNm4bVnxdXD9SxO92Vd0fPaFw3WEe0j3bGDvEQ31ShpDywDRMAaXsWsWXRS/Qa/SBXP/g1TeOSWTKz/rxYNecxOvS9jmsemkdC59EsryMvbnlyrfsz8ubA5QW48mNML4V1+1zHem6hxs0j6s+PKSk6dqdpfHC+bNwwxLtsnCuBpTtU3l+s8slylaIyjVvq2WZjouiUX+ynMZIGil+hkpISbrvtNkJCQoiLi+P1119nxIgRPPLIIwBYrVYee+wx4uPjCQkJYcCAAaxevdq9/qxZs4iMjGTp0qV06tSJ0NBQxo8fT1aWp5nV6XQybdo0IiMjiYqK4vHHH0fTvE8sqqry4osv0qZNGywWCz169GDu3Lnu71evXo2iKCxevJg+ffpgNptZv359wPbL0O5Glu+ws/+Ek6xzGrNX2ggPVujapu4rzuE9DGw54GDbYSc5BRpfr7Fht2v06+i5utty0ElalkpBiUZmnsaSLXaahOloGua/SuXs0rUcmf4GOd8uv6DwCffeTEX6aQ4+/hKlh9I4OeNzsr9eSps/TnWHafPIXZz68EtOfzyP0oPH2fvAdJzllbSaep3f4l2XQZ31rN7t5NApjZwCjbnrXI0JnVrXXWUN7qJj+xGVncdUzhbBd5uc2B3Qp71rHbPR9f/F2xykZWucydeYt8FBQnMdLWP8X8GP6W/h+3XlpB6xcTrXyYfflhAZpqN3x7rP5PuO25i/utxnr4mL2e7FGNbdwPIdNle5yNf43wor4SH1l4thPYxsPuBg2yGHp1w4NPp3NHqFaxGlY3hPI3NW1p1efxnRy8TSLVb2pjk4k6fy6dIKIkIUuret+05sZG8zm/bZ2XLATvY5lTkrKrE5NFK6eKfD5tAoKfd8KgOUnGE9jfywzca+dCdZ+SqfL6skIkShW1LdaRjR08im/Xa2HnTlxVerrNgcGgM6e69jr5EGq//v6d1G9jazZHMle4678uLjRWVEhOro0c5Y5zqj+5rZuNfG5n02svNVZi9zNdCkdDUBoGlQXK55fXq0M7LzsC0gaRnSzcCKnQ4OnFDJPqcxZ5XrfNElse5yMbS7gS0HnWw/7CS3QGPeWjt2B7XOF+nVzxdbXeeLJn48X1Q3LiWYBWtL2XXIyqkcB+/NKyIyTE/vjkF1rrPnqI2vV5Sy42DdjVgbd1fy7eoy9qcFtmz3T1ZITdPYk66RVwyLt2s4HNCjje/91a+DwvFs2HJYI78E1u7TyC50PWmuciBD40QOFJZBXjEs36URZFJoFuFzk36xd93HdOx3A8l9r6VJ83YMueZZDKYgDm+f5zP8vg2f0LL9EHoMu5smzdrS94o/Et2iE/s3feEVTm8wERwW4/6YLYFLRP8OCrvTNPae0MgvhiU7XHnRvY686NteIc1XXrT3Dh9qgbG9dHy3RcUZ+PYJ9q3/mOR+N9ChjysvBl/tyosjO3znxf6NrrzoPuxuIpu1pc/YPxLVohMHN3vnhU5/6fICYEBHhdTjnrKxaNv5spHkOz/6Jyscz4LNh1z5t2avRnaBK5/caT3pXTaWVZWNyIAmRfwKSQPFr9C0adPYsGED3333HcuWLWPdunXs3LnT/f1DDz3Epk2bmD17Nnv27OGGG25g/PjxHD161B2mvLycV155hU8//ZS1a9eSkZHBY4895v7+1VdfZdasWXz00UesX7+ec+fOMX++9/CBF198kU8++YR3332X/fv386c//Ynf/OY3rFmzxivc//3f//HPf/6TgwcP0r173V3sLkbTMIXwEIWjpz3dWittkJGrktDcdzHR6yA+RseR055uyRpwNLPudUwG18VofrFKYeklONPWIXJgT/JWbvJadnbZepoM7AmAYjQS0bsLeSs2egJoGnkrNxI5sFdA49YkFMKCFY5nefaP1Q6nz2q0qqMhQa+DFlEKx7O88+J4lkqrGFdexEcpGPTe280rgsJSjdZ+bqCIjtQRGabnQLrn4rzCqpGWaadt/M/vMRCo7dalabhCeIiOI6c8+7XSBhk5Kgmxvm/E9DpoGaPzKksacOS0k4RYT7kwGuC2sWbmrbVRUhHYshAVrhARouPwKU8PrEqba7hVm7i609Gqmfc6GnA4w0FijXX6Jht58b5QnvxNCJMHm+t9+nyxaThyyruOOpmjkhhbdx3Vspn3Ohpw5JSTxBr51yfZyN/vCeGJWy1MSjEFJA0AURE6IkJ1HD5ZIy+ynLRp4ftH9Tpo1VzPoZPeeXEow0FSHeu0aq6nVXMDG/f6/wa5rvPFqVzV6xivznW+UDhWo1wcPe2s83xhrHa+KArA+SKmiZ7IMD37j9euT9q1qrux6JdCp4O4JnAix3vfpOdoxEf7rtPjo5Ra4dOy6g6v00GvtgqVNo2cQr9Euxanw0bemf3Et0txL1N0OuLbppCbkepznZyM3V7hwTV8o2b4rLStfPr3wXz56gTWf/MslWUF/o4+4NpPsU1c+766E7ka8VEXnhfp2bXDT+6vY8th1012oFXlRYsaedGinrzIzdjtFR5850V2+lY+/8dg5r42gQ3fPEtleWDyAjxlo2Z+pOdotKwnP2qGT/ORH9V/w102ApcU8Sslc1D8ypSUlPDxxx/zxRdfMHr0aABmzpxJixYtAMjIyGDmzJlkZGS4lz322GMsWbKEmTNn8sILLwBgt9t59913adu2LeBq1Pjb3/7m/p033niDJ598kmuvvRaAd999l6VLl7q/t1qtvPDCCyxfvpyUFFfFnpSUxPr16/nvf//L8OGesXt/+9vfGDt2bKB2CeC6IQZq3SiVlmvu72oKCVLQ6xRKa6xTUq7RLNL7gnNQFwMTU4yYjQq5BSrvLbDirH+4dUCZm0djzcnzWmbNycMYEYYuyIyxSQQ6gwFrbn6NMPmEJCcFNG5VXc9r7tfSCo0wH0MEAILNnM8LaqwD0RGe7TqctZ9ul1Zotbq7X6yIUFf+F5d5p6G4TCU89Oe3Cwdqu3UJr6NclFRo7u9qqioXNbvillZoNGviiePVg02czHay/0Tg5pyoEh7i+t2Sstplteq7mkIsrnQUl9dep3lTz8399kN2zpW4biDjo3VcNSSI5k10fPB9Rc1NXhR3HVUrPirhIXXkhcV3XpSUazSvlhc7jtgpKNEoKtNoEaVj8mATMU10zFzk/6EqVXEtLveuAOtLR2hVOspqrFOmEtvU92XMoG4msvKdpJ/x//FVlRe16v566qiQIFcdVVKrjqp9vkjpoufKgZ7zxfvf2wJyvqiqT4pKvTdeXOp0f/dLFmwCnU6pNYdQWSVE+Z6OhNAgaoe3upZX1y7ONRTEaHCdR/63RqUiQJ1BKssL0VQnltAor+WWsCgKz6b7XKeiNA9LaLR3+NAoKko95/VWHYbQpstYwpq2pDg/g20/vMGSWfdx1e//h07n3zGBVXlRXqNTTVklRIX5XsdnXlR650VKRwVNg+0BnnOiSp15ERpF0U/Ii6DQKMpLPHkR334ICV3GEtakJcXnMtix9A2WzrqPyff7Py/Aj2WjEkJq1GntWriGglSVjS9WB65s/JIoul9+ndiYSAPFr0xaWhp2u53+/fu7l0VERJCcnAzA3r17cTqddOjQwWs9q9VKVJSnwg4ODnY3TgDExcWRm5sLQFFREVlZWQwYMMD9vcFgoG/fvu5hHseOHaO8vLxWw4PNZqNXL+8n9H379q03TVarFavV+6zosDsxGOvu8t6rvZ7rh5vcf3+4MHDjrQF2HnVw5LST8GCF4T0N3H6FmbfmV+II/L3ZL16PJB1XpXhO0J8uD9w8I4EyoKuZOyZ6rsLe/F9RA8bm5+vdXs/1Izzl5oOFgZlLoUuinnbxel770r838VX6Jhu4ebTnqurdb8sD8jvgmruhSla+SnFZBX+4PoToiMofnTCxPn06GLhxpCcv3lsQmH0FsGm/p8xl5asUl2s8OMVCVLhCfvHF3Rj062TklrHB7r9nzKt7fhV/MRqgb0cTSzb75/jt1V7PtcM8PQpmLgrs1fiuo06OnlYJC1YY3sPAb8aamPGN9aLPFyndg5g62XN38trn8tizLidz4cMfVCxm6JmkMCVFx6zlaq0b8F+ytj0muv/fNLYDTeOSmfPyFWSlba3V++KXKLaJa3jBzGUN+DTHT2rlRWwyX71yBdlpW2v1vvilO5kDHyx1lY1ebRWuHaRj5rLLq2yIXz5poBBeSktL0ev17NixA73eu1U3NDTU/X+j0bv7p6IoteaY+LHfAVi4cCHx8fFe35nN3g0LISEh9W7rxRdf5LnnvCeHTLnyLwya9FSd6xw44eS1HM/Fa9XEZmEW76eNoednvfelrFLDqdZ++h4WXPupa6UNKm0aeUUaJ3NsPP9bC13b6Ek91jAtFNacPMzNvVv8zc2jsReVoFZaseUVoDocmJtF1QgThTXbu+fFxTqYoXLqrGcfG86/azrU4t07JdSikHXO9zFWbuV8XngvD7Xg7lVRWqFh0CsEmfDqReH6nYu7ANp9xMZzmZ5prA0GVxrCQxSKqt2PhYfoOHURM9lXPen093ar7D/h5OQcz41wXeUizKKQmV9/uajZ8yi02jbaxeuJilD4+z3BXmHuHGcmLUvlnW8v7sZyb5qDE9meHVR1TIWFeJfNsGDX2yB8pqPClY6aPUXCghWKy+o+Xk6cf6tEdKSOvKKfX773pTs4meNZ352G4Jpp0P1oGmrmha86qrqqN2PEROrIL764OmrPMTsnsjwTJVYdU+HBOorLPNsOC9ZxOtf3b5VWpSNEB1RbJ0RXqzcRQK8ORkxG2LLfPw0JB044vd56UpWGUB/l4ky+7/1aVumqo2r2sKi5DfA+X2Tk2HjuriC/nC92HbJy/LSnV5zx/DEVEarz6kURHqonIyuAk5D4SbkNVFXzmmQUXL1V6nozU2kltcObXcurszuhoNT1OZOvcf+VCj2SFDYd9P+T/KDgSBSdvtYkjBUl+QSHRftcxxIa7dVbAqCiNL/Wk/zqwpu2IiikCcX5GX5voKjKi5qTJYYE1d63VXzmRbXwraIVQoLgwUmeJ9c6ncKoHtC3g8I7C/3fcFFnXpTmY/kJeVFZWnfewfm8CHblRSAaKOotG3W0ddeVHzXD1ywbv5+o0DNJYWMAyob49ZL+Kr8ySUlJGI1Gtm3b5l5WVFTEkSOu2YZ79eqF0+kkNzeXdu3aeX1iY2Mv6DciIiKIi4tjy5Yt7mUOh4MdO3a4/+7cuTNms5mMjIxav9OqVauflKYnn3ySoqIir0//cY/Vu47VDvnFmvuTU6BRXKbRvtqr9sxGaN1M5/VKweqcqmvG9fYtPcVIAdrF171OdYF668KFKNycStSogV7LokcPomBzKgCa3U7Rzv1Ej6p24lQUokamULh5l1/jYnO4Zoau+uQWuibpaxvnuakyG6FljMKps75PgE7VdaJMivPOi6Q4nbvxIzNfw+HUSKq23ehwiAxVyKhjuxeq0qaRW6C6P2fOOikscdKpjaeXTpBJISneyPHMn9+QkFeoBmS7VXyXC+9j3GyE1s117pvYmpyq68017eM9B7gCtG+p52S2Ky9W7rTz6pwKXvvS8wH4doONOSsv/jGM1Q55RZr7k31OpahMJbmVp00+yASJsXrSs+pOx6lclQ7V1lGADq0MnKhjHYD4GFe6fd04+yMN7Vt511EJzXWcyK67jjqdq3rVa6406N0NKb7TcL7r/0WmoSodZwtV9ycrX6WoVCU5oUZexOlJP+P7GHaqcCrHSXJr77xIbm0gzcc6Kd3M7D1urzUE42LS4PN8Ee+dF62a6dzHuK80ZJ7VaFejXLSL11/Q+ULvh/NFpU0j95zT/ck866CwxEnnpGr1idlVnxw79ctvoFBVyCqAxObeDXCJzRWv10tXl5mvkdjMO3yb2LrDV1EUMAToillvMBHdoguZxze7l2mqypnjm2nWuqfPdZq37sGZauEBTh/bWGd4gNKibCrLCwkOi/FHtL2oKmT7yIuEZgqZdTTaZeZrJPjKu/Ph9510vU3iwx88n5JyjS2HNeasDUyviqq8yDp24XnRzEdeZP5IXpQVZVNZUYgl3P95AfWXjdP15Eeb5j7KRh3hqyiKf+qnX7qGflPHr+0tHtKD4lcmLCyMO++8kz//+c80bdqUZs2aMX36dHQ6HYqi0KFDB2677TbuuOMOXn31VXr16sXZs2dZsWIF3bt3Z+LEiT/+I8Af//hH/vnPf9K+fXs6duzIa6+9RmFhoVc8HnvsMf70pz+hqipDhgyhqKiIDRs2EB4ezp133nnBaTKbzbV6XRiMP71L97o9dkb3MXK2SONcscr4/kaKyzX2pXsu5O+bbGZfupMN+1wXxWt2O7h5lInTZ1UyclSGdjdgMipsO+T6vmmYQs92eg6fclJWCREhCqN6G7A74VCG/3pP6EOCCWnX2v13cJuWhPfoiO1cEZWnskj++zSC4puz+64nADj53mwSHriNji/+mVOzviZ65EDibpjAtqvuc28j/Y2Z9PjoJQp37KNo2x4SH74TQ4iFUx/7nsnanzYecDKiu578Yo2CEhjdW09Juau3RZW7rjBwIENlyyHXsg37Va4bqudMnsbpPJVBnfWYDLDjqOt7q931/yv7GaiwOrDaYdIAPRm5KqcvsoHCl+VbK5g0JJicc07yCp1MGRFCYYnKzkOeG/DHfhPBzkNWVm53PTIyG6FZtfkNoiP1tGqup6zCdUxe6Hb9ae0eB2P6mMgr0sgvVpnQ30RxmXe5uP+qIPamOdzlYu1uOzePMnPqrEpGrpNh3Y2YDApbD7luekoqtFpj8cE1Yem5ksA8hVm9y8a4/mZyC1Xyi1QmDTJTVKax57jnBveha4PZc9zO2t2ueK7aaeU3V1jIyHFyMtvJiN4mzEaFzQdc30dHKPRJNnLghIOySo0W0XquHRbE0dOOOnteXYy1qXau6GvibKHKuWKNKweaKCrT2JvmScMD1wSxJ83J+j2uOK5OtXPrGDOnclUycpwM72nCZFDYcsC1TlS4Qp9kAwdOOCmv1IiL1jFlqJljma43hQTCqp1Wxg80k1vgdOXFYAtFpSq7j3luih++IYTdx+ys2eXqBbFiu5U7JgSTkePgRJaTUX3MmI2weZ93L4mYSB3tWup55+vADE+qsn6vg1F9DOQVqZwr0biin+t8UX1Old9NMrE/3cnG/a5l6/Y4uHGkkdNnVU7lqgzpbsBkhO2HPeeLHu30HKl2vhjZ6/z54mRgetst3VTOVcNDycl3crbAybWjQykscbLzkGf/PT61CTsPWFm+1XVeNZsUr3lYYproaR1roLRC5VyR65gJsShEReiJDHPd1cdGuy43i0rVWnNeXIythzUmD1DIOudqpO6frGA0wJ50Vz0yeYBCSTms3uv6e9sRjd+MUlxvLDij0bm1QlwT19s/AIx6GNRZ4egZjdIK1/xGfdophFng4KnAPSHuNvRO1nz1JDHxXYlp1Y19Gz7BbqugQ58pAKz68glCwpvTf/w0ALoOvoMF793BnnUzaZ08nON7FpGXuZ+hU1y9Se3WMnaumEFi17EEh8VQnJ/B1sWvEN60NS07DKkzHhdj6xGNSf0Vss/BmXMa/Tp458Wk/q45WNacz4vtRzVuG6nQv4PCsazaeVFho9bcBk7N1TvmXADfXtt1yJ2snfsk0S27EtPSlRcOWwUdervyYs1XTxAc3px+41x50WXQHSx8/w72rptJq+ThpJ3Pi8HXePJi18oZJHYZiyUshpL8DLYuOZ8X7QOTFwBbDmlcNfB82Tin0b8qP9KqlY0KWL3H9ffWwxq3j1YYkKxw7IxG5wRXfiza5ikbg7soHMn0lI2+7c+XjQzpPSH8SxoofoVee+017r//fiZNmkR4eDiPP/44p06dIijI1bdr5syZ/P3vf+fRRx8lMzOT6OhoBg4cyKRJky74Nx599FGysrK488470el0/Pa3v2XKlCkUFXnG5j///PPExMTw4osvkpaWRmRkJL179+Yvf/mL39N8IValOjAZFa4fbsJigvRslfe/9x73GxWuEBLkaa3cfdxJqMXOuH5Gws4PB/nge6t7WIHDCW3i9AztbsRidnVVTjuj8tb8yloTOl6MiD5dSVnxqfvvzq+49uGpT+ax5+4nMcfFYGkV5/6+4sRptl11H51ffZLEP9xB5els9t73NHnLPK9xzfpqMaaYpnSY/jDm2BiKdx9k66R7sNWYODMQ1u1TMRkUrh5kIMgEGTkaHy+ze+VF0xp5se+ESkgQjO6lJ9SiJ+ucxsfLHF5dfRdvc6JpcMtIAwYdHD2jsWBzYOa8WLyxApNR4c6JYQQHKRzNsPP6F0VeaYhpoic02PNYLrGFkcfviHT/ffMVrmFVG3ZX8tF3JRe8XX9atcuOyQDXjzBhMSmkZ6m8931l7XJRbahT6jEnIUE2xvU3Eh5sIjNP5f3v/XvM/1TLt9swGRRuGR2ExayQdsbJjPnlXumIjtQRYvHkx84jDkItlUxMMbuGg+SpzPim3N0l3+F0PcUf2cuEyahQUOK6yV66NTCNRSt22jEZFW4aaXalIcvJf7+r8E5DhI7QIM8N4K6jDkIsChMGmAgPUcg8q/Lf7yrcvQucqqtXyPAeJkxGVyPR7mMOftgWuHkWlm21YjIq3HpFMBazwvFMB29/XVYjL/SEWDwLdh62ExZcwaTBFvfQnLfnltUaHpHS1URhicbBE4Gdy2Z1qgOTAa4bbiLIBCeyVT5caPMuFxHe5WL3cSchQXBFP8P584XGhwurny802sTpGNLN4D5fpGepzJhvrXPIwsVatL4Ms0lh6lXhBAfpOJph45VPC7BX233NmhgIDfE0HrVpYeTJ3zZ1/33rBNe8Fut2VfDBfNc5vldyEL+71vMaxQdvjARg/qpSvlnlv3lIDp5yDSsY1tV1PsgphDlrVMrOF8HwYO/hp5n58O0mleHddIzoplBQCnM3uF5NDaBqEB2u0D1RwWJ23SBnnYNPV6oBfYtE2+5XUllawI7l/6a8JI+ouE5MuOs99zCBssIsFMVTNzVP6MWom19m+w9vsm3p60REJzD2N/+haaxr/jBFpyc/+zBHdn6DrbKE4LAYWrYfTJ+xD6M3mHzG4WJV5cXQ83mRWwhfrvXMTeArL77brDKsq47h5/Pi6w2B3c8XIqn7lVSWufKi4nxejLvrPfcQj1IfeTHyppfZsexNtv/wOuFRCYypkRfnsg9ztFpexLcfTJ8xgcsLcOVHSBAM7+YpG7NXe8pGRIiChnd+fLNJZUQ3HSO6K5wrga/We5eNqDCF6wdXKxv58MmKhs8z0fgo2k+ZOEA0SmVlZcTHx/Pqq69y9913N3R0/OKxdwI3Kd6lMvLhwL7O81LZ9P7eho6CX2SdujwnvqwuMir4xwNdBmy2y392Wb2+cYywtFsvv0ltfQkJrXtS5ctFzpnLv44CSO4SmG7vl5LJ2Di6XdsdjeMWQa+//PPDbm8cefHUzZfneJDDN41r6CjUKXnO0h8PdJmRHhS/Qrt27eLQoUP079+foqIi9+tBr7766gaOmRBCCCGEEEKIXytpoPiVeuWVVzh8+DAmk4k+ffqwbt06oqPrnnFYCCGEEEIIIYQIJGmg+BXq1auX1xs1hBBCCCGEEELU1ljflvFL1TgGwQohhBBCCCGEEOKyJg0UQgghhBBCCCGEaHAyxEMIIYQQQgghhPBB0ckz/UtJ9rYQQgghhBBCCCEanDRQCCGEEEIIIYQQosHJEA8hhBBCCCGEEMIHnV7e4nEpSQ8KIYQQQgghhBBCNDhpoBBCCCGEEEIIIUSDkyEeQgghhBBCCCGED4pOhnhcStKDQgghhBBCCCGEEA1OGiiEEEIIIYQQQgjR4GSIhxBCCCGEEEII4YOik2f6l5LsbSGEEEIIIYQQQjQ4aaAQQgghhBBCCCFEg5MhHkIIIYQQQgghhA/yFo9LS3pQCCGEEEIIIYQQosFJA4UQQgghhBBCCCEanAzxEEIIIYQQQgghfJAhHpeWNFCIRik83NTQUbhom97f29BR8IuU33Vr6Cj4xboZuxs6ChctPNzY0FHwi8ICW0NH4aKFhjWOvHA41IaOgjivbXJ0Q0fBL8rKHA0dhYuWX3n5pwHAbJbbhF8Ks7mxdHrXN3QExGWgsRztQgghhBBCCCGEuIxJ06gQQgghhBBCCOGDopNn+peS7G0hhBBCCCGEEEI0OGmgEEIIIYQQQgghRIOTIR5CCCGEEEIIIYQP8haPS0t6UAghhBBCCCGEEKLBSQOFEEIIIYQQQgghGpwM8RBCCCGEEEIIIXyQt3hcWrK3hRBCCCGEEEII0eCkgUIIIYQQQgghhBANToZ4CCGEEEIIIYQQvijyFo9LSXpQCCGEEEIIIYQQosFJA4UQQgghhBBCCCEanAzxEEIIIYQQQgghfFB0MsTjUpIeFEIIIYQQQgghhGhw0kAhhBBCCCGEEEI0cm+//TaJiYkEBQUxYMAAtm7dWm/4N954g+TkZCwWC61ateJPf/oTlZWVAY2jDPEQQgghhBBCCCF8UHSN45n+nDlzmDZtGu+++y4DBgzgjTfeYNy4cRw+fJhmzZrVCv/FF1/wf//3f3z00UcMGjSII0eOMHXqVBRF4bXXXgtYPBvH3hZCCCGEEEIIIYRPr732Gr/73e+466676Ny5M++++y7BwcF89NFHPsNv3LiRwYMHc+utt5KYmMgVV1zBLbfc8qO9Li6WNFAIIYQQQgghhBCXGavVSnFxsdfHarXWCmez2dixYwdjxoxxL9PpdIwZM4ZNmzb53PagQYPYsWOHu0EiLS2NRYsWceWVVwYmMefJEA9x0aZOnUphYSHffPPNT1rv2Wef5ZtvviE1NTUg8fop+nZQGNRJR6gFcgpg8XYnZ/LrDt+ptcLI7joiQyG/BFbsUjl2RgNAp8DIHjraxSs0CQWrDdKyNVakqpRWBD4to3vq6dtBR5AJMnI1vtvkIL+k/nUGdNQxpKueUAtkn9P4fouTzDzN/b1BD+P76uneRodeD8cyNb7b7KDMz0PQmg7pS9KjdxPRuytBLZqx/boHyPluRf3rDOtP51f+j9DO7ak8lcWxF9/h9CfzvcIk/P5WkqbdjTk2huI9h9j/yPMUbdvr38j7MLaPgX4d9VhMcCJH5Zv1DvKLtXrXGdhZz/DuBkItkHVO47uNdk6fda1jMbu22T5eR2SoQlkl7D/h5IftDqz2wKShTzuFAR0VQoMgpxB+2KmSda7u8B1bwvBuOiJC4FwJrNqjcjzL8/3QLgqdWyuEBYNThexzsGavypl6tukvV/Q10L+TAYsZTmSrzF9nJ6+o/vxI6aJneE8DYRaFrHyNbzfYOJXrWefaYUbax+sID1Gw2uFktsqiLXbOFta/3Z+jMdRT/ZN1DD5f1+Sc01i41buuqalLgsKoXgYiQ+FcscYPO5wczfQOP6qnnj7tPXXegs0Ozv1InSfpcGkMxxTA6F56+iXrCTLByVyN7zb+eF07oJOOoV1ddW12gcb3mxycrpaH/ZJ1dE/S0yJKIcik8PxnViptgU3HuH5GBnY2YDErpGepfL3W+qN11OCuBkb0NBIWrHAmX2X+OhunclX399cPN9G+pZ6I83XUiWwnCzfZyA1AHVVlTG89fZNd576TORrfXkB+DOykY2g3g/s6ZIGP/OjR1pMff/s0sPnRGNLQr4OOQZ117mN88TaVM/l1p6Fza4WRPfSu8l0My3c53eUboGMrhb7tdcRFKQSbFd5daCenIHDx/yX5Jb/F48UXX+S5557zWjZ9+nSeffZZr2V5eXk4nU6aN2/utbx58+YcOnTI57ZvvfVW8vLyGDJkCJqm4XA4uP/++/nLX/7i1zTUJD0oxK9e5wSFK3rrWLNX5b1FTrILNG4bqSfY7Dt8y2i4brCOXcdd4Q+f0rhpmI6YCNf3RgPENYV1e1XeX+Tky7VOosMVbh6uD3hahnbVMbCzjm83OXh3oQObA+68woihnp/umqhjQj89q1KdzPjOTvY5jaljDYQEecJM6KenYysds1c7+HCJg7BguHWk/9s39SHBFO85zL6Hn/vxwIAlsSX9vvsv+au3sL7v1aT/52O6/ffvRI8d4g4Td8MEOr38JEf//jbr+0+hZM8hBiz8EFNMU7/Hv7rhPfQM6qLnm/V23v7Wht0Ov51Qf150T9IxaaCB5Tsd/Ge+jax8lbsnmNx5ER6sEB6ssGiLg9fn2vhqjZ0OrXRcP8wYkDR0aqUwuqfC+v0aH/2gkluocfNwXZ1lIz4KrknRkZqm8eFSlSOZGtcP9pQNcN3ULN2p8sESlU9XqBSV179NfxnR08DgbgbmrbPxn3lWbHa4e6Kp3vzo0VbP5EFGlm938ObXVld+TDR7lY3MsypfrrbzyhwrHy60oihwz0QTip+vZRpDPdU1Ucf4fnpW73by7gI72QUad4zxrmuqaxWjcP0wAzuPOnlngZ2DGRq3jDTQLNKzc4d01TGgk44Fmx28t8hV590x1oghgFc3jSUdjeGYAhjaTU9KZz3fbnTwzgI7drvG1HH117Xd2ui4sr+BlakO3q46740zeuWhUa9wNFNlzR5nQONfZWQvI0O7G5m7xsabX1dgc2jcOymo3nT0bKfnqsEmfthu5/WvKjiTp3LvpCBCLZ4wp8+qzFlp5aX/VfDegkoU4N7JQX6vo6oM634+PzY4eOc7OzaHxl0Xkh8DDKzY5eDtb+1kndO4a3yN/DAoHDmtsnp34POjMaShS4LCFX10rNnj5L+LHOQUwG9G1Ve+Fa4bomfXcZX/LnRw+LTKzcP1XudvkwEyzmos33VpyoS4ME8++SRFRUVenyeffNIv2169ejUvvPACM2bMYOfOncybN4+FCxfy/PPP+2X7dZEGCnHB5s6dS7du3bBYLERFRTFmzBj+/Oc/8/HHH/Ptt9+iKAqKorB69WoAnnjiCTp06EBwcDBJSUn89a9/xW53PeadNWsWzz33HLt373avN2vWLAAKCwu55557iImJITw8nFGjRrF79+6ApSulo46dxzR2p2nkFcPCrSp2J/Rq6/vsPaCjjmNZGpsOusKv3qOSVeBqGQew2uGzlSoHMjTySyAzHxZvc9IiSiE8OGDJAGBQZ9dF86FTGjkFGnPXuRoTOrWuu6gP7qJj+xGVncdUzhbBd5uc2B3Qp71rHbPR9f/F2xykZWucydeYt8FBQnMdLWP8e4Vzdulajkx/g5xvl19Q+IR7b6Yi/TQHH3+J0kNpnJzxOdlfL6XNH6e6w7R55C5Offglpz+eR+nB4+x9YDrO8kpaTb3Or3GvaXBXAyt3OThwUiX7nMac1XbCgxU6J9SdF0O6Gdh6yMmOI05yCzW+We+6Uemb7LoqyinQ+Gy5nYMZKudKNI6fUflhm4NOCToC0bjfP1khNU1jT7rrWF+8XcPhgB5tfP9Yvw4Kx7Nhy2HXsb92n0Z2oasXRpUDGRoncqCwDPKKYfkujSCTQrMIn5v0myHdDKzY6eDAifP5scpGeLBCl8S6rziHdjew5aCT7Yed5BZozFtrx+6Afh09jXNbDjpJz1IpKNHIzNNYstVOkzAdTcL8myGNoZ4a1FnHjqMqu87XNQs2ObE7oXc732ViYCcdxzI1NuxXySuClalOss5pDOjoCZ/SSc/aPZ46b956V53XsZ46T9Jx/jcbwTEFMLiL67x3MEMlp0Djq7UOwiw/ct7rqmf7YZWdR1XOFmp8u8HhOu918NQHGw84WbvH6dUbIZCGdTewfIeN/SecZOVr/G+FlfAQha5t6q6jhvUwsvmAg22HHOQUaHy9xobdodG/o6fRevMBB2nuOkpl8VYbTcJ0NPVzHVVlUBfXA4+DGSrZBRpfrXEdy/We+7rq2XY+P3LP54etZn7sv3T50RjSMLCTjp3HVFLTNPKK4PstrnqqVx311ICOOo6d0dh4QCWvGFbtVsk6p9E/2RN+T7rG2r0qaVmB630jfjqz2Ux4eLjXx2yu3RIVHR2NXq8nJyfHa3lOTg6xsbE+t/3Xv/6V22+/nXvuuYdu3boxZcoUXnjhBV588UVUNXDHsTRQiAuSlZXFLbfcwm9/+1sOHjzI6tWrufbaa5k+fTo33ngj48ePJysri6ysLAYNGgRAWFgYs2bN4sCBA7z55pu8//77vP766wDcdNNNPProo3Tp0sW93k033QTADTfcQG5uLosXL2bHjh307t2b0aNHc+6c//uA63SuJz7p2d6VbXq2Rsto3yfvltEK6TUq5+Nn6g4PYDYpaJoW0K58TUIhLFjheLW4We1w+qxGqzoaEvQ6aBGlcDzLU8lowPEslVYxruohPkrBoPfebl4RFJZqtPZzA8VPFTmwJ3krvcfNnV22niYDewKgGI1E9O5C3oqNngCaRt7KjUQO7BWweDUNc/V0OJbp2a9WO5w6q5HQ3He1q9dBfLT3OhpwLFMloVndVXWQSaHSBqqfrxd0OohrAidyapSNHI34Oo71+CilVvi0rLrD63Sum6FKm0ZOoV+i7VPTMIXwEIWjpz1PfSptcCpXJSG2nvyIUThWbR0NOHraWWceGg2uxov8YpWiUv9lSGOop/Q6iItSOH6mRl1zRqVljO/92SpGR1qW9wXQsUxPfeau885413mZ9dR5F6uxpKMxHFMATcKq9p13XXv6rEbrZvWf947VyMNjZ9QGO6c1DVcID9Fx5JQnTpU2yMhRSYj13UCh10HLGJ1XvaYBR04766zXTAbo19FIfpFKoR/rqCpNwlw9/X5yfkTXzo/jZ9Q61wmkxpAGnQ5aNFVqNSSkZdVdXlvFKKTVqA+OZ2l11mu/NopO94v9XCiTyUSfPn1YscIzdFpVVVasWEFKSorPdcrLy9HV+A293lUnaVrgGqpkDgpxQbKysnA4HFx77bUkJCQA0K1bNwAsFgtWq7VW69vTTz/t/n9iYiKPPfYYs2fP5vHHH8disRAaGorBYPBab/369WzdupXc3Fx3698rr7zCN998w9y5c7n33ntrxc1qtdaaDMZh12Mw/nif8WAz6HQKZZXehaysEqLDfVfioUFQWmPuhbJKjdCguk9co3vq2HdCw+b40Sj9bKEW1++XVninpbRCI8ziaw1X+vU6pdb44NIKiI7wbNfhrH2BWVqhuX+zoZibR2PNyfNaZs3JwxgRhi7IjLFJBDqDAWtufo0w+YQkJwUsXlXda33lRV37LDioKi9qrxMT6fsEFGyGUb1cvS78LdhUVTa8l5dVQlS473VCg6gd3upaXl27ONdQEKPBdaz9b41KRQAb78KCfZeNknrKRsj5/CipVTY0mtXIj5Queq4caMRsVMgtUHn/extOPz5YaAz1VFVd4+t4qt6FuLpQS+00lFZqhFp0579X3Mtqh/FLtGtpTOm43I8pgLC6znuV9dS15p9e1wZa+Pk6qsRHHVX1XU0hQYqrjiqvnY5mTbzTMaiLgUmDTO466r8LKv1aR1WpMz9+7rkv4tLnR2NIg6d8ey8vq9SIjqi7fNesD0ora5+/xeVt2rRp3HnnnfTt25f+/fvzxhtvUFZWxl133QXAHXfcQXx8PC+++CIAkydP5rXXXqNXr14MGDCAY8eO8de//pXJkye7GyoCQRooxAXp0aMHo0ePplu3bowbN44rrriC66+/niZNmtS5zpw5c/j3v//N8ePHKS0txeFwEB5ex53Nebt376a0tJSoqCiv5RUVFRw/ftznOr4mhxkx5a+MvO6ZC0xd4OgUuH6oDkVxdZ/1px5JOq5K8VQOny4PYOuHqFfPtjqmDPV0qZ21JMAzqeEaejN1vIncQpXlOy6vvD+ZCx/+oGIxQ88khSkpOmYtVymvPen0z9KrvZ5rq83LMXNRYPNj11EnR0+rhAUrDO9h4DdjTcz4xorjMhmmG8h6Svw6BeqY6pGk4+rBnkvXT5YFaHbgAOvdXs/1IzwPUT5Y6OcZp2vYedTBkdNOwoMVRvQ0cvsVZt6aX3nRdVSPtjquqZ4fP1x++dEY0iDEhbrppps4e/YszzzzDNnZ2fTs2ZMlS5a4J87MyMjw6jHx9NNPoygKTz/9NJmZmcTExDB58mT+8Y9/BDSe0kAhLoher2fZsmVs3LiRH374gf/85z889dRTbNmyxWf4TZs2cdttt/Hcc88xbtw4IiIimD17Nq+++mq9v1NaWkpcXJx7HovqIiMjfa7z5JNPMm3aNK9lr8y7sFa9ciuoqkZIkIKrQ55LSFDt1nN3HH20KIcEKbWeLFVdoEWEKHy63On3J0gHM1ROnfVc+Bn0rlbxUIt3K36oRSHrnO+0lFvBqdZ+Shdqwd2rorRCw6BXCDLh1YvC9TsNezNjzcnD3Dzaa5m5eTT2ohLUSiu2vAJUhwNzs6gaYaKwZnv3vLgYBzJUTs3z7JyqRuVQi+L1RCzUopCV73uflVdW5YX3sRhqUSit8YTMZITfTjBhtWt8uszu9+EdAOW2qrLhvTzERy+JKqWV1A5vrv3U1e6EglLX50y+xv1XKvRIUth00D8JOXDCSUZO9bLh+jfU4v20Mcyi1Dmjedn5/KjZw6LmNsBVLiptGnlFGhk5Np67K4iubfSkHvNPC8XlXE9VqaprfB1PNXupVCmtqJ2G0CBP/Vb1b/VlVX/XVeddrMaUjsvxmHKd9zx1bfXzXkmtfVdHXWu98Lo2UPafcHJyjueAqaqjwnzUUZl1nDPKKjVXHVWjh8WP1VEnc6w8f3cw3dro2XWRddTBDJVTuReQH5Z68qOec1/NHiWB0BjSUCs+1rrO37V7zFZxnb9rxN9Hr6lfq1/yWzx+qoceeoiHHnrI53c1778MBgPTp09n+vTplyBmHjKwSFwwRVEYPHgwzz33HLt27cJkMjF//nxMJhNOp/dJbuPGjSQkJPDUU0/Rt29f2rdvz8mTJ73C+Fqvd+/eZGdnYzAYaNeundcnOtr7RrSKr8lhLmR4B4CqQtY5aBPrXfG0iVW8Xg1V3ek8rVb4pDjv8FUXaE3DFD5b4QxI93Wbw/Uax6pPbqFGSblG2zhP3MxGaBmjcOqs77Q4VdcNYlKcpypQgKQ4nbvxIzNfw+HUSKq23ehwiAxVyKhju5dK4eZUokYN9FoWPXoQBZtTAdDsdop27id6VLWxdYpC1MgUCjfv8ls8bHbIL9bcn9wCjeJyjXbxnv1qNrrGeJ7M8X2B41QhM897HQVo10LHyWoTapmNcPcEE04nfLLUHrCn9KoKWQWQ2Nz7WE9srtT5OsXMfI3EZrXLUn2vXwRQFPz6tgJrjfzIKdAoLtNoH+9puDQboVUzHSez68mPsxrtqq2jAO3i9XXmYXX+7Pl4OddTVZwqZNVR15w+63t/njqreoUHaNvCU58VlEJJuXfdZDa65g6pq867WI0lHZfrMVXXeS+phXdd2zJGISO3/vNe2xbeedi2he6SndN811Eq7Vt6p6N1cx0ns31X8k7V9YaO9jXqqPYt9XXWa9XDVd2IXwybvXZ+FJd779sLyo88jXZxPvKjjnX8qTGkoSZVhTPnNJJqltd6yveps3WU7zrqNSECSRooxAXZsmULL7zwAtu3bycjI4N58+Zx9uxZOnXqRGJiInv27OHw4cPk5eVht9tp3749GRkZzJ49m+PHj/Pvf/+b+fPne20zMTGR9PR0UlNTycvLw2q1MmbMGFJSUrjmmmv44YcfOHHiBBs3buSpp55i+/btAUnbpkMqvdspdG+jEB0OE/vrMOohNc1ViV+domNUT09R2XJIpW0LhYEdFaLCYXg3HS2awrbDrkpcp8ANQ3XENVWYv8GJorieSoUEuSYuCqSNB5yM6K6nYyuF5pEK1w01UFLuekJQ5a4rDF6zx2/Yr9K3g45ebV2vi7sqRY/JADuOutax2l3/v7KfgTaxCi2iFK4dYiAjV+W0ny/m9CHBhPfoSHiPjgAEt2lJeI+OBLWKAyD579PoMfMld/iT780muE0rOr74Z0KSk0i4/1bibphA+puz3GHS35hJq7tvJP72awjtmETXt5/FEGLh1Mfz/Br3mjbsczCql4FOrXU0b6Jw4wgjxeUaB0568uKeK42kdPZcXK7f66Bfsp7e7XXERCpcM8SAyQg7jrguUKsaJ0wGmLvWjtnk6u0SaiEgr4zbelijZ5JCt0SFqDCY0FfBaHDN5A0weYDCiG6eH952RCMpzvX2j6gwGNpFIa4J7DjmCm/Uw/BuCi2iIDwYYpvAxH4KYRY4eCqwF3Hr9zoY1cdA5wQdsU0VbhplorhcY/8Jz8X/7yaZGNTFkx/r9jjo30lPnw56mkUqTBlmxGSE7Yddj4SbhimM7GUgPlohMlQhobmO268wYXfCoZP+bTlqDPXUxgMqfTro6NlWR3QETBroqmt2HnPF6dohesb09uz/zQdV2sUrDOqsIzocRvbQ0yJKYcshTxnadNDJ8O56klspNIt01U0l5XAoI3AX1Y0lHY3hmALYsN/JyB6uV2E3b+J6pWtJhfd577fjjQzsVO28t8/pOu+10xEToXDVIIPrvHfEU25DLRDXVCHq/JwczZsoxDVVsJgCk461exyM6WOiS6Ke2KYKt442U1ymsS/dE6f7rwpicFdP5+e1u+0M6Gygb7KBZk0UrhtuwmRQ2HrINUyhabjCqN5GWsboiAxVSIzVcec4M3YnHMwITHepjfudjOypp+P5c98Nw13HcvVz390TvPNj/T4nfZM9+XH1YFd+7KwnP2IDmB+NIQ2bD6r0bq+jR5KrfE8a4Jr3KfW4Kw3XDNIzukb5btdCIaWTzlW+u+to0VRh62FPmoNM0LwJxJyfxyI6XKF5k9o9J4W4WDLEQ1yQ8PBw1q5dyxtvvEFxcTEJCQm8+uqrTJgwgb59+7J69Wr69u1LaWkpq1at4qqrruJPf/oTDz30EFarlYkTJ/LXv/6VZ5991r3N6667jnnz5jFy5EgKCwuZOXMmU6dOZdGiRTz11FPcddddnD17ltjYWIYNG+YeH+VvB05qhJhVRvTQERoEOQXwxSqnuxt7RIjiNVPt6TyYt0FlZA8do3q6WtznrHW9bg4gLBiSW7kq/fsmehexj5c5ORnA1vR1+1RMBoWrBxkIMkFGjsbHy7yfsjcNV85343PZd0IlJAhG99ITatGTdU7j42UOr278i7c50TS4ZaQBgw6OntFYsNn/FzcRfbqSsuJT99+dX/kLAKc+mceeu5/EHBeD5XxjBUDFidNsu+o+Or/6JIl/uIPK09nsve9p8patd4fJ+moxppimdJj+MObYGIp3H2TrpHuw1Zg409/W7HZiMihcO9RIkAlO5KjMXOKdF1HhOkKCPMfDnjSVkCAHY/sYCQt2PeX7aLHN3SUzPlpH6/NvkHj8Zu9eQi/9z0qBn2dlP3hKI9gMw7q6jpmcQpizRqXs/FwR4cHeZSMzH77dpDK8m44R3RQKSmHuBk/ZUDXXBU33RAWLGSpsrqe4n650vdYskFanOjAZ4LrhJld+ZKt8uNDmnR8RCiHVJkHbfdxJSBBc0c9AWLDCmTyNDxda3fnhcGq0idMxpJsBi9nVNT49S2XGfGudw2B+rsZQT+07oRIcBKN6uuqa7HMany531JmGU2c15q51MLqXgTG99eQXa/xvlYPcQk+Y9efrvKtSPHXep8vtOAL40K+xpKMxHFMA6/Y6MRngmsGufXcyV2NWjd5lTcMUgqud9/amu+ra0b0NhFkg65zGrB/sXuW2f0c9o3t50nHvRNdd5Ny1dnYd83/GrNplx2SA60eYsJgU0rNU3vu+ssY5w7uOSj3mJCTIxrj+RsKDTWTmqbz/faWnjnK4evcM625011FpZ1T+M6+izq7+F2vtHld+TKnKjxyNmT7yI8RHfozpcz4/8l3rVB9eMKCjntG9q+XHJE9+7Dzq3/xoDGnYf1Ij2KwyorueUAtkF2h8vrJ6+QZN88T/dJ7GvPWuhplRPXWcK4HZa5zu8g2Q3FLhmkGe+F8/1PX/1XucrNnTuHtaNKYhHpcDRQvkO0KEaCB/+/zymjTQF5u9cVT2Kb/r1tBR8It1M3Y3dBQuWni48ccDXQYKCwI/CWmghYY1jrxwBPIOWvwkBn+Ok2pAVutlMrtsPSorL/9rEACzWZ5j/lKYzY2jfE//zeV57st98o6GjkKdmr34SUNHwe8ax9EuhBBCCCGEEEKIy5o0UAghhBBCCCGEEKLBSd8tIYQQQgghhBDCl0DPci+8yN4WQgghhBBCCCFEg5MGCiGEEEIIIYQQQjQ4GeIhhBBCCCGEEEL4oCjymtFLSXpQCCGEEEIIIYQQosFJA4UQQgghhBBCCCEanAzxEEIIIYQQQgghfFDkLR6XlOxtIYQQQgghhBBCNDhpoBBCCCGEEEIIIUSDkyEeQgghhBBCCCGED4pO3uJxKUkPCiGEEEIIIYQQQjQ4aaAQQgghhBBCCCFEg5MhHkIIIYQQQgghhC/yFo9LSva2EEIIIYQQQgghGpw0UAghhBBCCCGEEKLByRAPIYQQQgghhBDCB3mLx6UlPSiEEEIIIYQQQgjR4KSBQgghhBBCCCGEEA1OhniIRklpBE1vWaeKGjoKfrFuxu6GjoJfDH2gR0NH4aIteXlbQ0fBL0YOCm3oKFy0YJOjoaPgF81Dihs6Cn6xcn9UQ0fhogUHNY4uyDt3Xv7nPk3TGjoKfhEVE9LQUfALo0nf0FG4aBbL5Z+Gy5nSGG4sLiOyt4UQQgghhBBCCNHgpIFCCCGEEEIIIYQQDU6GeAghhBBCCCGEEL7IWzwuKelBIYQQQgghhBBCiAYnDRRCCCGEEEIIIYRocDLEQwghhBBCCCGE8EHRyTP9S0n2thBCCCGEEEIIIRqcNFAIIYQQQgghhBCiwckQDyGEEEIIIYQQwgdF3uJxSUkPCiGEEEIIIYQQQjQ4aaAQQgghhBBCCCFEg5MhHkIIIYQQQgghhC+KPNO/lGRvCyGEEEIIIYQQosFJA4UQQgghhBBCCCEanAzxEEIIIYQQQgghfJC3eFxa0oNCCCGEEEIIIYQQDU4aKIQQQgghhBBCCNHgZIiHEEIIIYQQQgjhi06e6V9Ksrd/BZ599lmaN2+Ooih88803P3n9qVOncs011/g9XjWdOHECRVFITU0N+G8JIYQQQgghhPhlkR4UjdzBgwd57rnnmD9/PgMHDqRJkyYNHSXA1ehRWFj4sxpMAqFve4WUjjpCLZBTAEt2ODlzru7wnVopjOiuIzIEzpXAilSVY1kaADoFRnbX0a6FQmQoWG2QnqOxYrdKaUXg03L18GCG9QoiOEjHsVN2Pl1cSu45Z53hO7Q2Mi7FQmKcgcgwPW99WcSuw7aL3u7FGtvHQL+OeiwmOJGj8s16B/nFWr3rDOysZ3h3A6EWyDqn8d1GO6fPutaxmF3bbB+vIzJUoawS9p9w8sN2B1a7f+PedEhfkh69m4jeXQlq0Yzt1z1Azncr6l9nWH86v/J/hHZuT+WpLI69+A6nP5nvFSbh97eSNO1uzLExFO85xP5Hnqdo217/Rt6HKweaGdTNiMWskH7GyZyVlZwtVOtdZ2h3I6P7mgkPVsjMU5m7qoKTOd7rJMbpmTzITEKsHlWFzLNOZswvx+7nw2rTss9Zu+gjSovyiG3VkavueIpWbbv7DJtz+ijLvv4PmSf2U5h3hom3/R9Dxt/pFUZVnSyf9xapGxZQUpRHeJNm9B56DaOu/j2KEriJtNYt/R8rF8yiuDCP+IRkrrvrSRLadfMZduOKuWxbu4CsU0cBaNWmM5Nu+aNX+N1blrNh+ZecSjtAeWkRf37pK1omdgxY/AGWfv81C+Z9QVHBOVq3acdd9/2JdsmdfYbdunE133z5CdlZmTgdDmJbtGTilFsYNmq8V5hli78h/dhhSkuK+ee/Z5KY1CGgaQDYv/Fzdq/9kIqSPJrGdWTw1U/TrJXvYwogbc8Stv3wJqUFmYRHJzBgwmO07jjcK0xBznG2LH6FrLRtaKqTJs3bMvY3/ya0SYuApWP3us/ZvvJDyovPEh3fkZHX/ZXYhLrTcWTXYjYtepPic5lExiQyZPJjtOniSUdZcR7rF7xCxqH1WCtKiG/blxHX/ZUmzRIDlgaAq4ZZGNoziGCzwrHTdj5fUkZuQf111Ig+ZsYNsBARquNUjoP//VDOiSyH+/uYSB03jA6hXSsDBj3sT7PzxQ9llJTVfx66GFcPC2ZoL086Pltc+qPpGNkniHEDq6ejjPQzNdIxJoT2LY0YDLDvuJ3//VBKcQDTMbaPgf6dDK7zd7bK/PX2Hz1/p3TWM6yHgTCLQtY5jW832Gqcv410aFl1/tbYf0Llh212Kv18/q4yuqeevh10BJkgI1fju00O8kvqX2dARx1DuuoJtUD2OY3vtzjJzPOk26CH8X31dG+jQ6+HY5ka3212UFYZmDQc2PQ5e9d9REVpHk1jO5Iy+Sli6qmn0vcuYceyf1NamEl4VAL9xj9Kq2RP+V4790mO7vzGa5349kMYf9f7gUmA+NWSHhSN3PHjxwG4+uqriY2NxWw2N3CMfnk6t1YY20vH2n0q7y9xklOocetIPcF17KqW0XDtIB2px13hD5/WuHGojpgI1/dGA8Q2hXX7VD5Y4uSr9U6iwhRuGqoPeFomDLIwpr+FTxeV8o+PCrDaNabdGoGhnp82GRVO5zj4bHGpX7d7MYb30DOoi55v1tt5+1sbdjv8doKx3t/rnqRj0kADy3c6+M98G1n5KndPMBES5Po+PFghPFhh0RYHr8+18dUaOx1a6bh+mNHv8deHBFO85zD7Hn7ugsJbElvS77v/kr96C+v7Xk36fz6m23//TvTYIe4wcTdMoNPLT3L072+zvv8USvYcYsDCDzHFNPV7/Ksb09fE8F4m5qyo5NXZZVjtGg9MCa43L3p3MDBlWBCLN1v51xdlZJ518sCUEEItnpv3xDg9D1wTzKGTDl75XxmvzC5j7W4b/r5k3rN5EQu/eInRUx7koee/Jq51Mh/963eUFuX7DG+zVdK0WSvG3ziNsIhon2HWfP8BW1bM5qo7n2baSwsZf9OjrF34IRt/+MzPsffYuXEJ8z95mXHX3c+f//klLRI68M4L91FSRzqO7d9G70ETeOiZj/jT858RGRXLO/+4j8JzOe4wNmsFScm9uOrWPwUs3tVtXLucTz/4D9ff8ltefPMjEtq048VnplFUWOAzfEhoONfceCfPv/JfXnrrY4aPmci7b7zA7h1b3GEqKyvp2Lk7t079/SVJA8Dx3YvY9P0/6TP6Qa59eB5Rccks+vAeKkp950X2iZ2s+N+jdOx3Pdc+PJ/EzmP44ZOHOJd9xB2mOD+D7969lchmSUy+7xOu/9O39Br9AHpj4M7Zh3cuYu38Fxk47kFu/fN8Ylp0ZP47d1Ne4jsdZ9J3sviTR+ky8Hpu+/M3tO02mgUfPkjeGVc6NE1jwYcPUpx/isn3zODWP88nrGk882bchd1aHrB0jB8YxOi+QXy2uJQXZhVhs8MjN4fXW0f17WTixtEhLFhfwfMfFXE618kjN4cRFuyqo0xGeOSWcDQ0Xv28mJc+KUavU/jDDeEEqglyfIqF0f2q0lGI1a7xp1vqP8/262TixjEhLFhXzt8+LORUrpNHbg73Ssefbo0ADV75vIh/flyEQQ9/uDFw6Rjew8Dgrgbmr7Px1jdWbA64+0rTj5y/9UxKMbJih4N/z7O6zt9Xmr3P3yEKCzfbee0rK1+uttOhpY7rh5sCkoahXXUM7Kzj200O3l3owOaAO6+o/xqka6KOCf30rEp1MuM7O9nnNKaONbjTADChn56OrXTMXu3gwyUOwoLh1pGBeVactmcRWxa9RK/RD3L1g1/TNC6ZJTN/V2c9lXNyF6vmPEaHvtdxzUPzSOg8muWf/cGrngJo2WEotzy51v0ZefMrAYn/L42iKL/YT2MkDRSAqqr861//ol27dpjNZlq3bs0//vEPAJ544gk6dOhAcHAwSUlJ/PWvf8Vu9zTX+hr+8MgjjzBixAj333PnzqVbt25YLBaioqIYM2YMZWVl7u8/+OADOnXqRFBQEB07dmTGjBkXHPe9e/cyatQo97bvvfdeSktdN5rPPvsskydPBkCn013QQex0Opk2bRqRkZFERUXx+OOPo2netwyqqvLiiy/Spk0bLBYLPXr0YO7cuV7buPvuu93fJycn8+abb7q/f/bZZ/n444/59ttv3YVr9erV7u/T0tIYOXIkwcHB9OjRg02bNl3w/vg5Bibr2HVcY3e6Rl4xLNymYndAzyTf+6t/Bx3HsjQ2HXKFX71XJasA+rV3FSerHT5fpXLglEZ+CWTmw+IdTlpEKYQHBzQpjOlv4ft15aQesXE618mH35YQGaajd8e6L3L3Hbcxf3W5z14TF7PdizG4q4GVuxwcOKmSfU5jzmo74cEKnRPqrrKGdDOw9ZCTHUec5BZqfLPedVHRN9l1RZFToPHZcjsHM1TOlWgcP6PywzYHnRJ0+PvtUWeXruXI9DfI+Xb5BYVPuPdmKtJPc/Dxlyg9lMbJGZ+T/fVS2vxxqjtMm0fu4tSHX3L643mUHjzO3gem4yyvpNXU6/wb+RpG9DKxdIuVvWkOzuSpfLq0gogQhe5t676oGtnbzKZ9drYcsJN9TmXOikpsDo2ULp7GoGuHmVmTamPZdhvZ51RyC1R2HXXg8HPviXWLP6bfiBvoO+xamse345q7nsVkDmL72nk+w7dK6saVt/yZHikT0Rt9X/yePLqLzr1H0bHnCJrExNOt/zjadx3M6bTA9WZZvfATBo2+joEjpxDbsi033vMMJpOFzavm+wx/x8MvMXTczbRM7Ejz+CRuuf85VE3lyF7PzX2/YZMZf/3v6dBtYMDiXd3Cb+YwatxkRoydSMvWbbjnwT9jMptZvex7n+G7dO9N/0HDiW+VSGxcS668+kZat2nLoQO73WGGjRrPdbf8lq49+12SNADsWTeLjv1vILnfdTRp3o6hU57DYAzi8LavfYbft+FTWnUYQo/hd9OkeVv6jfsj0S06s3/j5+4wW5e8Qavk4Qy88s9Ex3cmPKo1iZ1HYQmNClg6dq6eSddBN9Jl4HVExbZj9I3PYTAFsX+z73TsWvMJiR2H0nf0PTSNbcugiY/QrGVndq9zNcwVnj1B9olURt3wLLEJ3WnaPInRNzyLw17J4Z0LA5aO0f0tLNxQwe6jdjLPOvloQSmRYTp6Jdd98zq2fxDrUq1s3GMlK8/JZ4vLsDlgcA/XOa1dSyPRETpmLnA1sGaedTLz+1IS4vR0TPR/ozacP8+ur3CfZz/67gLSMcDCutRKNlSlY1EpNofGkB5BXun4aEGpOx0fLSglIc4QsHQM6eZ9/v5ylY3wYIUuiXXf3Q/t7jp/bz9//p6/zo7dAf2SXeeZnAKNz5bZvM7fS7fZA3L+BhjUWc/q3U4OndLIKdCYu87VmNCpdd3XIIO76Nh+RGXnMZWzRfDdJid2B/Q5f21oNrr+v3ibg7RsjTP5GvM2OEhorqNljP8TsW/9xyT3u4EOfa6lSfN2DL76WQymII7s8H3u27/xE1q2H0L3YXcT2awtfcb+kagWnTi4+QuvcDq9ieCwGPfHbInwe9yFkAYK+H/27js8imp94Ph3tiabCklIAoSEGnrvvUlXKSpWLFi5XPQqV+VnvxauV1DsHRVEARVs9C699w4hBNJ7z252Z39/LOyyyQZQdoni+3mefWBnz0zOuzsz58yZc84wZcoU/vvf//Lcc89x6NAhvvnmGyIjIwEICgriyy+/5NChQ7z99tt8+umnvPXWW5e97dTUVG677Tbuu+8+Dh8+zNq1axk9erTzon/OnDk8//zzvPrqqxw+fJjXXnuN5557jq+++uqS2y4uLmbw4MHUqFGD7du3891337Fy5UomTpwIwOTJk/niiy+c+UhNTb3kNqdPn86XX37JzJkz2bBhAzk5OSxc6F4Bnjp1KrNmzeKjjz7i4MGD/Otf/+LOO+9k3bp1gKMBo27dunz33XccOnSI559/nv/7v/9j/vz5znzdcsstDBkyxJmv7t27O7f/zDPPMHnyZPbs2UOTJk247bbbsFqt+IJGA9E14VSaeyPMqXQ7dcM9Fxh1wxVOpbunT0itOj2An17BbrdTVnUbwBULD9UQGqTl0CnXHyk120lILqdhnT/eQu+r7ValZpCjp8OJZFe3VnM5nMm0Exvp+ZSl1UCdcPd17MCJZJXYWlWf5vwMCmUWUH3X0/WyhHZtS9Zq94a4zBUbqNG1LQCKXk9I+xZkrdrkSmC3k7V6E6Fd2/ksX2HBCiEBGo6ecR1/ZRZITLNRP9pzZVOrgZha7uvYgaNJVuLOrRPor1A/Wkdhicq/bjHx6gOBTLrJRIPa3u2SY7VaSEk8SKMW3ZzLNBoNDVt0I+nEnj+83djG7ThxaAuZqacASD19hNPHdtGkda8rzbJHVms5ZxIOuTUkaDQamrTqSuLxvRdZ08ViLkO1WjEFVk9l0lpezqkTR2l1QUOCRqOhVduOHDty4JLr2+129u/ZQerZJJq1bOvDnF6czWohK/kgdRu7yixFo6FOo26kJ+3xuE766T3UadTdbVndJj2c6e2qypkjawkNj2PxZ+OZ9Z/uLHzvFhIPXl4D5x9hs1rIOHOQmCbucdRr0p3UxN0e10k7tYeY+G5uy2Kb9iQ1cY9zm4Bbrw9Fo0GrM5CcsNPLETiEh2oIDdRw+JTrxlGp2U5CipUGVZRPWg3ERus4nOgq0+zA4VMWGtZxXLTrtI5lVpurcCi32rHboVGM98s9ZxyJFctZqzNPVcVx6ILYHXGU06CuI496nVJlHI1jvN9Acb78Pp7samkuK4czGSr1qiiLz5ffx8+61nGU3zbqVVHmg+/K7xqBEGRSOJnq2rC5HM5m2ompoiFBq4HaYQonU93rICdTVWIiHDHUCVPQad23m5UPeUV26nm5gcJmtZCVcpDajVzHq6LRULthNzKqOE9lJO11Sw9Qt3HPSunTTm1jzqs9+P7NoWz88UXKSjz3gBPiSvzt56AoLCzk7bff5r333uPuux3jjBs2bEjPno6u1c8++6wzbVxcHJMnT2bu3Lk8+eSTl7X91NRUrFYro0ePJjY2FoBWrVxjgF944QWmT5/O6NGjAahfvz6HDh3i448/duanKt988w1lZWXMmjWLgIAAAN577z2uv/56Xn/9dSIjIwkNDQUgKirqsvI7Y8YMpkyZ4szPRx99xLJly5yfm81mXnvtNVauXEm3bo4TWYMGDdiwYQMff/wxffr0Qa/X89JLrq7t9evXZ/PmzcyfP59bbrmFwMBA/P39MZvNHvM1efJkhg8fDsBLL71EixYtOHHiBE2ben9stMkIGo1CUZl7CVdcBuFBnguMQD8qjRcsKrMT4F91wTWgrYYDp+1YfNPOAkBIoKMQrDiutKBYJTjwj7dF+mq7VQn0d/xbVOr+94pK7W5DBC5k8gOtRvG4TkSo5zyajNC/neOuTXUzRoZjTs9yW2ZOz0IfEoTGz4i+RgganQ5zRnaFNNkExDfwWb6CAxzfXcUx14UldudnFQX4K2g1CgUlldeJrOlogAgPcaw7rKuRhevNJGfa6NxMz8TRJqZ+XXzJ+S0uV0lhHqpqIzDE/S50UHAYmSmn/vB2+4x4AHNpEW89NRxFo8Wu2hh002O063H9lWbZo+KCXFTVRlDFOELCyLjMOH6e8xbBNSOIv0q9JSoqKHD8FiGh7kOSQkJrknw2qcr1SoqLeOTukVjLLWg0Wu575Alat+vs6+xWqawkF7tqq9SzwT8onLxMz79FaVEW/kGV05cWOo750uJsyi0l7Fn7KR0HP0rnYZM5e3Q9y2f/kxEPfkXtBt6Pt7TYEYepQr5MQWHkZCR4XKe4MAtTUHil9CUFjjhqRDYgqEZtNv4ynQFj/4Pe4M+utV9SlJdGcUGm12MACAk4Xz65nzMKi1XnZxUFms6doyqVaXaiwhxlTEKKFbPFzph+JhauLQEFxvQzodUozjLxasRRUKxW+fcCTZpzcVReJyrM0fhwMrncEUf/ABauKT4XR8C5OLx/1/780JKiksplcVAVPUhd5bf78sJLlN8D2uvYdsT7larz9QxP9Ykgf8/rmIyeYygqhfAQ13attso3qi5Wt/mjykryPJ+nAsPIv9h5KtD9+PYLDKOk0FU3qdO4J7EtriOoRl0KcpLYuWwGy758iOsf/haNxvfDmKuVPMXjqvrbN1AcPnwYs9nMgAEDPH4+b9483nnnHU6ePElRURFWq5Xg4ODL3n6bNm0YMGAArVq1YvDgwQwaNIibbrqJGjVqUFxczMmTJxk/fjwPPPCAcx2r1UpIyKXvch0+fJg2bdo4GycAevTogaqqHD161NkL5HLl5+eTmppKly5dnMt0Oh0dO3Z09vg4ceIEJSUlXHfddW7rWiwW2rVz3cl9//33mTlzJklJSZSWlmKxWGjbtu1l5aN1a9cEPtHR0QBkZGRU2UBhNpsxm81uy6zlWnQ+HLt7uTQK3NTDcVJbvN07F13ndWlpZNzwIOf7t7/N9+r2r5a2DTWM6uW6k/PlUh92MznHqId7hhjIyFNZudOHrUZ/MR3jddw6wFUD++gn34wbPz/abON+xzAQgLOZZprE6OjaQs8vG80XWbv67d+6hD2bfmXsI28QWbcxKacP8+ucqQTVqEWHXiOrO3uVrPjxM3ZvWsLEF2aiN1T/efH38PM38fo7X1JWVsKBPTuZ/fm71IqqTYvW7as7a15jtzvKhtgW/Wnd6x4Awms3I+30bg5vmeuTBgpf0Gr1jBj/Liu+fYaPpnRG0Wip16Qbcc16Y/fS7DJdWhi4c2ig8/278wu8st2KikrsfLywiDuGBNC/kx92O2w7aOF0qhW7F0Lp0sLIXcNccbwzzzfld1GJnY8WFHLn0EAGOOMwey2Oto20jL6g/P7iKpXf9w41kpFrZ8WOKy+/2zTQcEM318X17JVSJ6hKwzbDnf+vGdWEmlHxfDdtEGkJ2yr1vhDiSvztGyj8/atoDgU2b97MHXfcwUsvvcTgwYMJCQlh7ty5TJ8+3ZlGo9FUmqPhwjkqtFotK1asYNOmTSxfvpx3332XZ555hq1bt2IyOZqTP/30U7dGgfPr/Rmdn99i0aJF1KlTx+2z8xNwzp07l8mTJzN9+nS6detGUFAQb7zxBlu3bq20PU/0eldhd37eDFWt+uJ+6tSpbj02APqOfo7+Nz1/yb9VYgZVtRPop8AFFagAPyr1qjivqAy3SY8AAv0Uiiu0nGsUGNNDQ0iAwuzVNq/3nth7zMJLya5Hjeh0ju8qOEAh/4L5LoMDNJxJ++N/PL9I9cl2zzuUpHJmgatSc37XD/RXKLzgDkagv0Jqtuf9oKQMbOr5uxDu61S8k2PQw31DDZjL7cxeUV7twzvA0VvCGOl+58IYGU55fiFqmRlLVi6q1YqxVliFNGGY09x7XlyJ/QlWEtNcP7JO69inggLce0QEmRSSMz33PCkutWNT7QSb3O8IBZlcd/rO/5ua4/57pueq1Ajy3l0KU1AoGo220oSYhQXZBIV6ngDzciyZO40+I+6nTTdHZS0qpgl5WSms++UTnzRQBATXQKPRVpoQszA/m6DQi89RsPqXL1n100wmPPspdWLjvZ63yxUc7Pgt8vPcH4+Un5dDaI2qJ3rVaDRE1a4LQFyDJiSfTeSn72ZXWwOFn6kGikZbaaK5Ug+9C87zDwyntLByev9z6R3b1FGjViO3NDVqNSQt0TdDI/wDHHFUnBCzpDCbgCriCAgKd7ubej69KdiVPjKmJXc++RPm0kJstnJMgTX59s2biYxp6ZV87zluISElz/lerz1f7mnIL3adk4ICHE+08KSo5Nw5KsD9HBUc4N6r4tCpcp75MI9AfwWb6hhyMW1SDTIPXXmvuz3HLZz6zNU1XndhHEWu7QdfNA71XBzu50zHd+E6tx46Vc7/fZDrFsf0R2t6pafaodM2zmS4tnN+EslAU+XyOyXbc2HrKr/dlwf5KxR6KL/HDzVgttiZtcLilfL7cJLKmcwLY1CceS6qWAfJqSIGs+cYAv1x9qooKrWj0yr4GXDrReH4O969geVnCvV8nirKdp53KvIPDKe0yP34LivKrvK8BhBcMwY/Uw0KspOkgUJ41d++v0rjxo3x9/dn1arKjwDctGkTsbGxPPPMM3Ts2JHGjRtz+vRptzQRERGV5nbYs2eP23tFUejRowcvvfQSu3fvxmAwsHDhQiIjI6lduzYJCQk0atTI7VW/fv1L5r1Zs2bs3bvXbcLNjRs3otFoiI///RXRkJAQoqOj3RoSrFYrO3e6KkjNmzfHaDSSlJRUKc8xMTHOPHTv3p0JEybQrl07GjVq5HyayHkGgwGbzTtd66dMmUJ+fr7bq/eNT13WuqoKqTkQF+VeUakfqXA2y3NBdDbLTv3ICumj3NOfb5yoGaTw9RobpT64qVBmsZORqzpfKZk28gptNKvvmlDLz6DQoI6ek8l/vCEhK0/1yXbPs5RDdoHd+crItVNQYqdRHdfpyaiHmAil0mMqz7OpkJzlvo4CNKqt4fQFlSfjucqNzQazlpV7fULGPypvyx7C+rt3vQ8f0J3cLXsAsJeXk7/rIOH9L6gAKAph/bqRt8XzePE/wlwOWfl25ystRyW/WCX+gjHXfgaIi9JyKtXzl2dTHeONm1ywjgI0idGReG6d7AI7eUUqkTXci6CIUA25Bd6rqOl0BmrHteDkoS3OZaqqcvLgFuo1avuHt2uxlKIo7nnXaLSodu9WMs/T6fTENGjuNsGlqqocO7CFuMZtqlxv1U8zWfbDxzw85UPqNWzhk7xdLp1eT/1G8RzYu8O5TFVVDuzdSZOml3/xalftbjcBrjatzkB4nRYkn3DNGWNXVVJObCGyXluP60TGtiX5pPscM8nHNznTa3UGatVtWWmISH5Wos8eMarVGagV04Izx9zjOHNsM9Fxnue1iarfljPHtrgtSzq6iei4tpXSGv2DMAXWJDcjkYykAzRs5bmX6u9ltkBmrup8pWTZyCtS3SZ89DMoNKitI6GK8smmwulUK80uWEcBmsXpOZlced8qKrVTarbTNFZHUIDC3uNXXqCbK5bf5+JoFlexnNV5zNPF4mgapyfhbOXYXXHoCQpQ2HPsyuOoWH6nny+/L5hPyKh3zEuUlHGp8tu1jqP81pKU7l5+3z/MiFWFr5ZZvFZ+W6yOx8Wff2Xk2SkssdMw2lXPM+qhboTCmUzP9UKbCinZdhpEu9dBGkRrnI0fydl2rDY7DS7YbngwhAYqJFWx3T9KqzMQXrsFqSdcx6tdVUk5uYVaVZynatVrQ8pJ9+M7+cSmKtMDFOenUVaah39whDey/aemaJQ/7eta9LfvQeHn58dTTz3Fk08+icFgoEePHmRmZnLw4EEaN25MUlISc+fOpVOnTixatKjShJH9+/fnjTfeYNasWXTr1o2vv/6aAwcOOIc7bN26lVWrVjFo0CBq1arF1q1byczMpFmzZoBjjoVJkyYREhLCkCFDMJvN7Nixg9zcXB5//PGL5v2OO+7ghRde4O677+bFF18kMzOTf/7zn9x1112/e3jHeY8++ij//e9/ady4MU2bNuXNN98kLy/P+XlQUBCTJ0/mX//6F6qq0rNnT/Lz89m4cSPBwcHcfffdNG7cmFmzZrFs2TLq16/P7Nmz2b59u1ujS1xcHMuWLePo0aOEhYVd1pCWqhiNxkqPT9XpL//CectRlRu7akjNcbTwd47XoNfB3lOOAuPGrhoKS2H1Xkchs+2YyrgBWro2VTiebKdFrIbaNR1P/4Bzwzp6aoiqoTDvNxuK4upxUWpxNIr4ysptpYzoaSI9x0ZWno1RfQPIK1TZdcTVZX7ynSHsOmJm9Q7HRBpGPdSq6aoYhIdqiYnUUlxqJ+fcxeLlbNebNh6w0r+djqx8OzmFdgZ11FFQYufQadeXd/8wPQcTVTafu5u1Yb+Vm/voOZupcibTTs+WWgx62HnM5oxz/FADeh3MXlOO0QDn95riMrzS3fU8bYCJgEb1nO9N9esS3KYplpx8ys6kEv/K4/jViWTvvY6GtNOfzCV2wh00nfpvznz5A+H9uhJ981C23/CQcxunZnxBm5mvk7fzAPnb9xE36W50Af6c+crzjNzesna3hcGdjWTkqWTnq4zobiS/2M6+k65jbOJoE/tOlvPbXkdFes0uM3cO8icp3cbpNBt92xsw6hW2HHJVtFfttDCsq5HkTBtnM210aW4gsqaGmYu825rXa+jdfPfJFOrUb0lMg1ZsXDYLi7mUDr1HATD/o6cIrhHJkLGO863VaiEj2dGgarOWU5CbQcrpwxj8TIRHOuYRata2H2t+/pjQ8Ggi6zQm5fQhNiz9kg69R3s17xfqO3wccz54hnoNW1CvYSvWLZ6NxVxKl74jAfj6vf8jpGYtrr/9MQBW/vQ5i+e/z7hJr1OzVh0K8hx3xox+Jox+jt57xUX55Galkp+bAUBGSiIAwaHhBF9BD5OqDB85lg/fepUGjZvSqElzFv80H3NZGX0GOnqivD/9ZWqGhXPbuUeG/jh/Fg0aNyUyug7W8nJ2b9/M+jVLGT9hsnObRYUFZGWmkZvtiC/l3HwWoTXCCK3hmydgtO51D2vnP01E3ZZE1G3N/g1fUV5eSpOOjt9/zbynCAiuReehTwDQssdd/PLxOPb9NpN6TftyYu8iMpMP0mvMf1zb7DOeVd88TnT9jtRu2IUzx9Zz+vAarn9wlk9iAGjf916Wz3mKyHotiarXml3rvqLcUkrzLo44ln39JAEhkfS83hFHuz7j+P6du9i5eib1W/Th6K7FpJ85wICxrjiO7V6Cf2BNgmvUJiv1KGsXvEbDVgOJbdrTYx68YdW2Uob38Ccj10ZWnsqNvU3kFapuT6Z6/PZgdh+1sGano9xbsa2M+64PJDHVxqkUKwM7+2HQK2zc5yrTurc2kpZlo7BEpUEdHbdeF8DKbWWk5/imEF95Lo7z5ezIPpXjeOL2YHYds7DmXPm9Ymsp990QxOlUqzMOo15h4z7XRFk9WhtJPRdHw7p6Rxxby0jP8U0L/Yb9Vvq315FVoJJbYGdQJz0FJXYOJrr+3gPDDRxItLH5oGPZ+n1WbunrKL/PZqr0bKVDr4cdxxzlzPnGCb0O5q72bfkNsOmQjb6ttWQX2MkthAHttRSWOHpbnHfvIB2HklS2HnEs23hQZUwvLSlZds5mqXRvrsWgg53HHZ+byx3/H9ZJR6nZirkcRnTRkpShctbLDRQALXvezW/fTyG8bksi6rbiwMZZWC2lNGnvKPvWffcUpuBIOg12lH0tuo9j0afj2L/+C2Li+5CwbzFZyQfpMdLRQ7ncXMzu1R8Q1+I6/IMiKMxOYtvSaQTXrEfdxr47vsXf09++gQLgueeeQ6fT8fzzz5OSkkJ0dDQPP/ww48eP51//+hcTJ07EbDYzfPhwnnvuOV588UXnuoMHD+a5557jySefpKysjPvuu49x48axf7/jcXPBwcH89ttvzJgxg4KCAmJjY5k+fTpDhw4F4P7778dkMvHGG2/w73//m4CAAFq1asVjjz12yXybTCaWLVvGo48+SqdOnTCZTIwZM4Y333zzD38XTzzxBKmpqdx9991oNBruu+8+Ro0aRX6+a3zkyy+/TEREBFOnTiUhIYHQ0FDat2/P//3f/wHw0EMPsXv3bsaOHYuiKNx2221MmDCBJUuWOLfxwAMPsHbtWjp27EhRURFr1qwhLi7uD+f7ShxKsmMyqvRppSHQD9Jz4Zu1NudEmMEmxW0Yz9ksWLhJpV9rDf1aO1rc5693PFYKIMgE8XUdregPDnU/xGatsnE6w3djCpZsKsWgV7h7eBAmP4XjSeW89U2+252GiBpaAk2uVv642nqeHBfqfH/rIMe42I17y5j5c+Flb9eb1u21YdApjO6lx88AiekqXyx17/EQFqwhwM/1Xe5LUAnws3JdBz1BJsfdjJlLLM7ulXXCNc4ZwZ+81b1B6/VvzeQWee93CenQkm6rZjvfN5/mODbOzFrAvvFTMEZH4B8T7fy8NPEs2294iObTpxD3z3GUnU1j/0PPkrVigzNN6ndLMETUpMkLkzBGRVCw9zDbRtyPpcLEmd62cocFg07htgF++BsVElJsfLCwxO23CA/VEODv2qd2HbMS6F/G8G5Gx3CQLJUPfixx6667drcFvRZG9/HD5OcYMvL+ghKy8r17fLTuOoyiwlxW/vAOhflZRNdrxr3//oSgEMcFeF52qltviMLcTN591tXQsH7xTNYvnkn9pp148BnHxeIN455l+Q9v89OX/6GoIIfgGrXo3O8W+o+a4NW8X6h99yEUFeSweP77FORlUTeuKQ9P+cjZkJCbnep2J2XjivnYrOV88aZ7Q/eQmx5h6M2OfB7YsYZvPnzO+dlXb/+7Uhpv6t57IAX5eXz39Wfk5eYQ26AxT/9nunOIR1ZmulsMZnMZMz+YTnZ2BgaDkdp1Y/nHE8/TvfdAZ5odW9fz0YzXnO/f+d8LAIy57T5uvmO812MAaNhmGKXFOexY/i4lhZmE1W7GsPs+dXaFLspLcXusd1RcewbcNo3ty2awbelbhITHMWjce9SMauJMU7/ldfQc9SJ71nzCpp9fJTSiPtfd+Q5R9Tv4JAaA+PbDKC3KYfPidygpyCS8bjNGPvwZAeeGbBTkpsIFx0bt+u0ZMm4amxfPYNOvbxIaEcf1498nvLYrjuKCTH778b+OoSLBETTrdCNdBvvuuABYuqUMg0HhrqGBjvLpTDlvzytwL/dCNQReMOxsx2ELQaYSbuzt7xxG8fa8QrcJgaNqahnd10SAv0J2nsriTaWs2FZhhmxvxrG5FKNeYdwwVxwz5lYuv4MuONduP2whMKCYG/uYnHHMmFvgNlQlKkzL6H4BBPgrZOWpLNpY4tM41u21YtDBmF4GR/mdpjJziXuPh5rBCgF+rt9jX4KNAH8Y1FFHkMlxs2jmYrPH8vup29zH2P73mzKvlt8A6w+oGHQKN3bX4WeApHQ7X60ov2gMBxJVAvxgQDstgf5aUnPsfLXC6jap+pLtNux2uK2fDp0GjqfY+WWLb+a8aNB6GGXFuexc+Q6lhVmERTdj8L2fOId4FOW5l32Rse3oN/YNdq54mx3L3yI4LJaBd77rPE8pGi05aUc5vutHLGWFmIIiqNO4Bx0GTkKrq/pRuEL8EYq94gQKQlwDXv72rz/JUeKJa+PRTRFRQZdO9BfQa0LVXen/Kpa+sb26s+AV/boHXjrRn5zJ8Nc/RwFEBvhmksKrbfVB3/S2uJpMftdGV99du3zb6Ho1XCtV67CIgEsn+gvQG/6c87r9HsFB18Y95SfH/DVnFyj64OnqzkKVAif8t7qz4HV/zb1ECCGEEEIIIYQQ1xRpoPgTe+211wgMDPT4Oj9E5PeqanuBgYGsX7/eyxEIIYQQQgghhBCX59roL3SNevjhh7nllls8fnaxx6NeTMUnjFyo4mNDhRBCCCGEEOJv7Rp9WsaflTRQ/InVrFmTmjWrfj78H9GoUaNLJxJCCCGEEEIIIa4yGeIhhBBCCCGEEEKIaic9KIQQQgghhBBCCA8ufCSr8D35toUQQgghhBBCCFHtpIFCCCGEEEIIIYQQ1U6GeAghhBBCCCGEEJ7IUzyuKulBIYQQQgghhBBCiGonDRRCCCGEEEIIIYSodjLEQwghhBBCCCGE8EDRyD39q0m+bSGEEEIIIYQQQlQ7aaAQQgghhBBCCCFEtZMhHkIIIYQQQgghhCeKPMXjapIeFEIIIYQQQgghhKh20kAhhBBCCCGEEEKIaidDPIQQQgghhBBCCE/kKR5XlXzbQgghhBBCCCGEqHbSQCGEEEIIIYQQQohqJ0M8xDWpRoi2urNwxfLCTNWdBa8IDtZXdxa8Yukb26s7C1dsyL87VXcWvGL+lHXVnYUrZgryq+4seIVd/eufawF6dPvrz9C+Y3dhdWdBnFMnJqS6s+AV0qv9zyP5bHF1Z8FLgqo7A3+MPMXjqpJTjxBCCCGEEEIIIaqdNFAIIYQQQgghhBCi2skQDyGEEEIIIYQQwgNFxjtdVfJtCyGEEEIIIYQQotpJA4UQQgghhBBCCCGqnQzxEEIIIYQQQgghPFHknv7VJN+2EEIIIYQQQgghqp00UAghhBBCCCGEEKLayRAPIYQQQgghhBDCE41S3Tn4W5EeFEIIIYQQQgghhKh20kAhhBBCCCGEEEKIaidDPIQQQgghhBBCCA8UeYrHVSXfthBCCCGEEEIIIaqdNFAIIYQQQgghhBCi2skQDyGEEEIIIYQQwhN5isdVJT0ohBBCCCGEEEIIUe2kgUIIIYQQQgghhBDVToZ4CCGEEEIIIYQQnshTPK4qr33b99xzDyNHjvTW5q6KTz75hJiYGDQaDTNmzKju7Hjd7/lN+vbty2OPPeZ8HxcX5/adKIrCjz/+6NX8CSGEEEIIIYQQ53mtB8Xbb7+N3W731uZ8rqCggIkTJ/Lmm28yZswYQkJCqjtLVerbty9t27b1aSPKggUL0Ov1Ptv+n92+DXPYtfpzSgqzCK/dlN6jnyUqtnWV6Y/vWcqWJW9TmJNMaEQs3UdMJq55H+fnFnMxm36dTsL+VZSV5BFcsy5tet1Fqx63+jyWwZ30dG2uw9+ocCpV5YffzGTlX/zY7NFSR9+2eoJMCinZKgvXWziToXpMe/9wI81idXyxpIwDp2xez3+HRgpdmioE+kF6HizfpZKaU3X6pnWhTysNIQGQUwhr9qmcTHV93quFQvN6CkEmsKmQlgPr9qukXGSb3jKsq5HurfSO3yLFxrzVZWTmef5enfltrWdARyPBJoXkLJXv15RyOt19nbhoLdd3NxIbpUVVITnTxgcLSyj34s9Rs2dHGjwxnpD2LfGrXYsdYyaQ/vOqi6/TuzPNpz1NYPPGlJ1J5cTUDzk7a6FbmthHbqfB4+MxRkVQsO8IBx97mfzt+72X8SqM6h9I3w7+mPw0HE+y8NUvBaTnVP2FxcfqGdozgLhoPTWCtbz9TS67jpjd0nRoZqR/JxNxtfUEmjQ890EWSWlWn8YxoocfPVsb8DcqJKRY+WZ56SX3qT7tDFzXyY/gAIWzGTbmrSrldJoj9prBGl59KNjjep/+VMyuY+VejwHg+p5+9GxjxN+ocDLZyrfLS8jIvVQcRgZ1MRIcoHHEsbKExFTXbxgcoDC6rz/N4vT4GRTSc2ws2VzGbh/EsGPNHDYv+5yi/EwiY5oy+LbnqFPfc5mRmXycdT+/Q+rpg+RnJ3Pd2Cl0GXhPldveuOQT1iyYTucB4xh06zNez3tFQ7sa6NbSdZ76bk0ZmXkXLzN6ttbTv4PBeZ76YW0ZSRecpyaO8adxXfcq5sb9FuavNlfclFfc0NufXm39MBkVTpwtZ87S4kvuT307GBncxZ+QQA1n0h37YGKq6/iNCNVw84AAGsXo0GnhYEI53ywvprDYN3XdzvEaurfQEOgP6Tl2Fm9TSc6u+m81j1Xo31ZLaCDkFMCKXTaOJ7vSN6un0LGJhtphCiajwoe/lJOW65OsO3VqoqF7c0cMabl2lmxXSblYDPUU+rVxxJBdACt32ziR4krfNEahY2MN0edi+GhROek+juFaigOujeNb/P14rQdFSEgIoaGh3tqczyUlJVFeXs7w4cOJjo7GZDJVd5aqVc2aNQkKCqrubFSLY7sXs/7H/9J58D+49YkFhNeO5+eP76ekMNtj+tRTu1g2+wladLmJWycvpEHLgSyaOZHs1GPONBt+/C9JRzYw6M7/cefTi2jbexzrFrxMwoHVPo2lXzs9vVrr+X6dhbd/KMVitfPgCD902qrXadtIyw09DCzfUc5b35WSkqXy4Ag/Av0rp+3d2rejwprFKAxoq7DhoJ2Zy1Uy8uzc2keDyeg5fZ0wGNlNw54EO58vUzmWbOemHhoiLmhvzC6EZbtUPluqMnuVSn7JxbfpLQM7GujTzsC8VWVMn1uMudzOhFGmi/4W7ZvoGNXbjyVbzPzvm2KSM21MGBVAoL9r9ui4aC0TRpo4ctrKtG+LmTa3mN/2WvB2lVkbYKJg31EOTHrpstL7x9Wl088fk712Kxs63sipd7+i1cevEH5dT2ea6JuH0uyNKRx/5X02dB5F4b4jdFn0OYaIml7OvbthPQO4rouJL38p4D+fZGO22Jk8rgb6i+zORoPCmTQrsxcVXDTNsSQL85cX+iDXlQ3qbKRfeyPfrCjhf3MKMVtg0s0BF92nOsTrGdPXn0WbynhtViFnM21MujmAIJNjn8otVHnqg3y31y8bSimz2Dl4yjeNE4O6GOnXwcg3y0p4fXYhlnI7/7wl8OJxNNVzU39/ft1YxmtfFnA2w8Y/bwl0xgFwz/AAompq+XBBES/PLGD3sXIeuDGAmFoX2fAfcHD7YlbMn0qv6//B/c8tJLJuU76dMZ7iAs9lRrmllNDwuvQf/QSBIREX3XbKqX3sWjeXWnXjvZrnqgzoYKB3WwPzV5t5a14JlnI7D4+8+HmqXWMdo3oZWbbVzBvflpCSaeORkSa38xTApv0Wnv20yPn6aYNvLl6GdPVjQEc/vl5SxGtf5mMph8duDb5oDB2bGbhlQAC/bCjl5Zn5nM2w8ditQc79yaCHx24Lxo6d6XMKeH1WAVqNwj9vDsYXc/m3iFMY3FHD2r02Pv7VSlou3DVQS4Cf5/QxEQo39dKy+4TKR79aOXJG5da+WmqFutLodZCUYWfFTu/fSPAYQ6zCoA4a1u2z8fFiK+m5cGd/bZVlbd1whTE9tew+qfLxIitHz6rc2kfrVn4bdJCUaWfl7qsTA1w7ccC1cXz/aSjKn/d1DfrdDRTff/89rVq1wt/fn7CwMAYOHEhxcXGl4QSFhYXccccdBAQEEB0dzVtvveVxGMFrr73GfffdR1BQEPXq1eOTTz5xfm6xWJg4cSLR0dH4+fkRGxvL1KlTLyufSUlJ3HjjjQQGBhIcHMwtt9xCeno6AF9++SWtWrUCoEGDBiiKQmJi4kW39+KLL9K2bVtmzpxJvXr1CAwMZMKECdhsNv73v/8RFRVFrVq1ePXVV93Wy8vL4/777yciIoLg4GD69+/P3r17K2139uzZxMXFERISwq233kphoaPCe88997Bu3TrefvttFEVx5tVmszF+/Hjq16+Pv78/8fHxvP3225f13XhS8be5mMTERBRFYcGCBfTr1w+TyUSbNm3YvHmzW7oNGzbQq1cv/P39iYmJYdKkSRQXFzs//+CDD2jcuDF+fn5ERkZy0003OT+raj/zhT1rv6RFt5tp3mUMNaMa0e/ml9AZ/Di09QfP6X+bTWzTnrTvP56akQ3pOuxRIuo2Z9/6Oc40qYl7aNppJHUbdSG4Zl1adh9LeO140pP2+SSG83q31rFyp4WDiTZSs+18u8pMcIBCy/pVl0a92+jZcsjK9iNW0nPt/LDOQrnVTuem7j1qaodp6NNWz7zVFp/lv3O8wp4EO/tO2ckqgCU77Fit0Ka+5xNwpyYKJ9Ng61E72YXw2wE7aXmOXhjnHUqyk5gOecWQVQArd9vxMyjU8nGnqb7tDCzbamZ/gpWULJXZy0oJCVBo3bDqq+J+7Y1sPlDO1kPlpOWozFtVhsVqp1sL128xureRdXssrNhhIS1HJSNXZfdxK1Yv13syl/3GsRdmkP7TystKH/vgrZSeOsvhJ1+n6EgCpz+YQ9oPy6j/6D3ONPUfu5czn8/n7FcLKDp8kv0TXsBWUkbMPWO8m/kKBncz8ctvRew+YuZMupVPFuQTGqSlfdMqav/AvuMWflhVxM7DVVe6Nu0t46e1xRxM8N0xcaH+HYws2VLGvhNWkjNVvlxcTEighraNq+79NqCjkY37LGw+YCEtW+Xb5aVYyqFbSwMAdjsUFNvdXm0b69l5xILZN+0TDOjox5LNZew9UU5ypo0vfi0mNFBD2yZVxzGwkx8b95rZvN9CarbKN8tKKC+H7q0MzjQN6uhYs8tMYqqNrHyVJZvLKDHbqRfl3QaKrSu+oF2vW2jbYwwRtRsx7M6X0Bv82LPRc5lRu35rBt78FC06D0erM3hMA2ApK+bHz/7N8HGv4Ge6Or06+7TTs3ybmQPnzlNfLy8jJECh1UXOU33bG9h0sJyth6yk56jMX23GYrXTtYX772exQmGJ3fky++gwGdDZn0UbS9l73LE/zfyliNAgDe3iq/6ur+vsx/o9ZjbtM5OaZePrJcVYrNCjjeMqtFFdPeEhGr74xdFQ7NhPi4iN1tI0zvu9Tbs307DzuMqek3Yy8+HXLTbKbdCukedqetdmGk6k2Nl4UCUrH1bvUUnNsdM53pV+X4KddftUElKvTu/mrs007DqhsifBTlY+/Lr14jF0aeqIYdMhlawCWLPXQwyn7Py2/+rFANdOHHBtHN/i7+l3NVCkpqZy2223cd9993H48GHWrl3L6NGjPQ7tePzxx9m4cSM///wzK1asYP369ezatatSuunTp9OxY0d2797NhAkTeOSRRzh69CgA77zzDj///DPz58/n6NGjzJkzh7i4uEvmU1VVbrzxRnJycli3bh0rVqwgISGBsWPHAjB27FhWrnRUurdt20ZqaioxMTGX3O7JkydZsmQJS5cu5dtvv+Xzzz9n+PDhnD17lnXr1vH666/z7LPPsnXrVuc6N998MxkZGSxZsoSdO3fSvn17BgwYQE5Ojtt2f/zxR3799Vd+/fVX1q1bx3//+1/AMXSmW7duPPDAA6SmpjrzqqoqdevW5bvvvuPQoUM8//zz/N///R/z58+/ZBze8swzzzB58mT27NlDkyZNuO2227Barc6YhgwZwpgxY9i3bx/z5s1jw4YNTJw4EYAdO3YwadIk/vOf/3D06FGWLl1K7969gd+3n10pm9VCxtmDxDTp7lymaDTENO5G2uk9HtdJS9zjlh6gXnwPUi9IHx3XllMHVlOUl47dbufs8S3kZSZSL76H12M4r2awQnCAhmNnXN3wyiyQlK4SW0UFXauBuhEajp91Xd3agWNnbcRGuU4Peh3ccZ2RBb9ZKCz1TQGr0UB0DUhMd9/+qXQ7dcI9N1DUCVMqpU9IrTq9RgPtGiqUWeyk53kl2x6FBSuEBGg4esbVXbjMAolpNupHV/1bxNRyX8cOHE2yEndunUB/hfrROgpLVP51i4lXHwhk0k0mGtT27gXYHxHatS1Zq90bKTNXbKBG17YAKHo9Ie1bkLVqkyuB3U7W6k2Edm3ns3xF1NASGqTl4ElX7anUbCchuZxGMX+dYW3hIRpCAjUcOe2+T51KtVG/tufKplYD9aK0buvYgSOnrTSoYp16kVpiInVs2u+b2ub5OA4nVogjpeo8nY/jcIU4DieW06COa52EZCsdmuox+SkoQMdmevRahWNJ3ht2Y7NaSD19kPrN3MuMuGbdST65+4q2veSb/9CodR8aNO9+6cRecP48dSzJdf4vs8DpNBv1L1JmxNRyX8cOHEuyERflXqXsGK/j1QcDePoOEyO6Gy7aY+mPCg/VEBqo4fAFvX1KzXYSUqxu+0bFGGKjdRxOdO3jduDwKQsN6zjOCTqtY5nV5ipfyq127HZoFOPdQLQaiA5T3C5e7TjKspgIz2VZ3Qil0sXuyRQ7MRHVM5GfRgO1a1bOU0KqnbpVlMcxEQoJaRViSLVTt5pigGsnDrg2jm/x9/W7dqfU1FSsViujR48mNjYWwNkT4UKFhYV89dVXfPPNNwwYMACAL774gtq1a1dKO2zYMCZMmADAU089xVtvvcWaNWuIj48nKSmJxo0b07NnTxRFcf7NS1m1ahX79+/n1KlTzoaHWbNm0aJFC7Zv306nTp0ICwsDICIigqioqMvarqqqzJw5k6CgIJo3b06/fv04evQoixcvRqPREB8fz+uvv86aNWvo0qULGzZsYNu2bWRkZGA0Olrlp02bxo8//sj333/Pgw8+6Nzul19+6Rxicdddd7Fq1SpeffVVQkJCMBgMmEwmt3xqtVpeesnV9bp+/fps3ryZ+fPnc8stt1xWPFdq8uTJDB8+HICXXnqJFi1acOLECZo2bcrUqVO54447nL0yGjduzDvvvEOfPn348MMPSUpKIiAggBEjRhAUFERsbCzt2jkuUi53P/OG0uJc7KoNU1CY23JTUDi5Gac8rlNSmOUxfUlBlvN9nzHPsXrec3zxUh80Gh0oCv3Hvkydhp28H8Q5wee6plZsQCgstTs/qyjAT0GrUSgscV+nqNROrRquwujGHgZOp9k4mOi77okmA2g0CsVl7suLyyDM8xB5Av2onN7sWH6hRtGOoSB6HRSVwrfrVEp92NofHOD47iqOVS4ssTs/qyjA3/FbFJRUXieypqMyER7iWHdYVyML15tJzrTRuZmeiaNNTP26+JJzEfiSMTIcc3qW2zJzehb6kCA0fkb0NULQ6HSYM7IrpMkmIL6Bz/IVEuj4zvKL3L+bgiKb87O/guAAxzFcUOweR2Gx6vysokDnPlUh9hKVyJqei//urQykZtlISPHNsR4cWEUcFzk2Ak3n4vCwTlSYq6L96U/F3H9jAG8+GorNZsdihY8WFnn1uCgpcpQZAcHuZUBgcBjZaQl/eLsHty0iLekQ45/5/kqzeNmCzu03Fc//hSV252cVnT9PFZZU/i1q1XT9FjuPWsktUMkvtlM7XMMNPYzUqqFh5qKyipu8IiHn9hlPx0XIJfcn97gLiu1EhTniTkixYrbYGdPPxMK1JaDAmH4mtBrF6+cNkxG0GoWiUvflRaV2woOrOLb9HJ+7pS/D49DMq8FkrKr8thMeUnUMxWUeYqi6Y5vPXStxwLVxfP+paP469YVrwe9qoGjTpg0DBgygVatWDB48mEGDBnHTTTdRo0YNt3QJCQmUl5fTuXNn57KQkBDi4yuPqWzd2jWplKIoREVFkZGRATiGN1x33XXEx8czZMgQRowYwaBBgy6Zz8OHDxMTE+PWK6J58+aEhoZy+PBhOnX6YxeJcXFxbvM0REZGotVq0Vyw00ZGRjrzv3fvXoqKipyNIeeVlpZy8uTJKrcbHR3t3MbFvP/++8ycOZOkpCRKS0uxWCy0bdv2D8X2R1z420VHRwOQkZFB06ZN2bt3L/v27WPOHNewB7vdjqqqnDp1iuuuu47Y2FgaNGjAkCFDGDJkCKNGjXIOF7mc/ew8s9mM2ezeDbu83IBe7+NJBi5i7/rZpJ3ey4jxHxBUsw7JJ7ez7of/EBBci3rx3rk71r6xlpv6umL8zEcFQ4s4LY3qaHlzfumlE/9Jnc6Az5er+BuhbQOFUd00fLlSpcRLQyY7xuu4dYCrZvjRTyXe2XAF54cabtzvGAYCcDbTTJMYHV1b6Pll4zU+BvQydGvtxz3Xu1q03pxzlWYi87JOzfTcPsg1N9IHPxT5/G/qddCpmYHFm713Lunc3MDtg11xvP+97+K4oZdjksS35hZSVGKnbRM9D9wYwLQ5haRkVV/j3aXk56SyfO6r3P74THQ+LLc6xOsY29911fTxz747p28+4OrRkJqtUlBsZ+IYE2EhZrIvMWnzxXRpYeDOoYHO9+/Or3qOmCtRVGLn44VF3DEkgP6d/LDbYdtBC6dTrfyF5oMXfyPXwvEtxHm/q4FCq9WyYsUKNm3axPLly3n33Xd55pln3IY0/F4VnxyhKAqq6qhItG/fnlOnTrFkyRJWrlzJLbfcwsCBA/n++6t3h+FCnvJ6sfwXFRURHR3N2rVrK23rwglFL7aNqsydO5fJkyczffp0unXrRlBQEG+88cYV/Ra/14X5Vs5dOV0Y+0MPPcSkSZMqrVevXj0MBgO7du1i7dq1LF++nOeff54XX3yR7du3ExoaWuV+Vr9+/Urbmzp1qltvEoChtz/PsDtevGQM/gE1UDTaShNilhRmYQoO97iOKSj8oumtljI2L5rBsHvfpX6LvgCE144nK/kIu9fO9FoDxcFEG6fnuQqg85MeBfm794gI8ldIzva8PxWX2bGpdreJ5sBx5/X8NhrV0RIWovDK/e4Tyd492EhCqsqHP3nnYqbEAqpqrzQpWICHXhLnFZVROb3RsfxC5TbILXK8UrLtPDxMoU0Dhc2HvVOQ7k+wkpjmuvDSaR3fZ1CAe4+IIJNCcqbnO9PFpY7fomJvlyCT687x+X9Tc9x/z/RclRpB1du6b07PwhjpfswYI8Mpzy9ELTNjycpFtVox1gqrkCYMc5p7z4srsfuImZNnXcen/txvERKocetFERyoJSnVR5MseMG+E+Ukprom3zx/fAcHaCgodu1DQeeeaOFJkXOf0gAXPO3CpKl09xigXRM9Bj1sPei97kV7T1g4leIaYqE7V+uoFIdJqTqOknNxBLjHceGxER6qoV8HP176PJ/Uc40RyZk2GtXV0be9H98s906joSnQUWZUnBCzqCCbwCrKjEtJO32Q4sJsPnt5tHOZXbWRdHw729fMYcqH+9FornwY14EEK6fTXHM5Oc9TJk/nqSrKjNLzZYYGUN3WKSyuut5y/qkxESEasvP/eO+cPcctJKTkOd+fP76DAzTkVzguzqR7Htrj2p/cz7XBAe69Kg6dKueZD/MI9FewqY6hI9Mm1SDzkHd7F5WYwabaK/V+CPRXKpVlzhjKODdpoSu/jl4VXs3aZSsxV1V+V+4Zcp6j/PYQQzXehP8rx3EtHN9CnPe7a7SKotCjRw9eeukldu/ejcFgYOFC98fINWjQAL1ez/bt253L8vPzOXbsWMXNXVJwcDBjx47l008/Zd68efzwww9u8zd40qxZM86cOcOZM2ecyw4dOkReXh7Nmzf/3Xn4o9q3b09aWho6nY5GjRq5vcLDL78iYzAYsNncD/iNGzfSvXt3JkyYQLt27WjUqJFbr4zq1r59ew4dOlQp7kaNGmEwOCau0ul0DBw4kP/973/s27ePxMREVq92POXicvaz86ZMmUJ+fr7b67pbplxWPrU6A7XqtuDsMdfYebuqcub4FqJi23pcJyquLWeOuY+1P3NsE9Hn0quqFdVWjlKhO5ii0WC/RMPT72Euh+wCu/OVnmunoFilcV3X3zXqoV6kxll4VGRT4WymSuM6rsqvAjSuq+V0miOvq3eVM31eKW/Od70AftpoYZ4XHymlqpCaC3GR7pXGuEiF5CzPDQnJ2Xbiarmnrx9VdfrzFAV0XryeN5dDVr7d+UrLUckvVom/YKyynwHiorScSq36tziTodLkgnUUoEmMzvkoxewCO3lFKpE13DMfEaoht6B67xDnbdlDWP+ubsvCB3Qnd8seAOzl5eTvOkh4/26uBIpCWL9u5G25srH7Fyqz2MnIsTlfyZlW8gptNG/gmjDPz6jQoI6eE2f+vA0U5nLIzFOdr9Rslfwilfh67vtU/WitWwPAhWwqJKXZiI9136fiY3UkeFinRysj+06UV+o6fkVxWCrEkeWIo2lshThqe87ThXE0rRBH0zg9CcmOdQznPqp4d1u1e3eSc63OQHRsC04ddi8zEg9vpk7DPzaXSlyzrjz44i888PyPzld0bEtadrmeB57/0SuNE1D1eapJjGv7RgPERmk5dZEyw3Geci8zmsRoSUyr+hxUJ8KRvuIQtt8dgwUyc1XnKyXLRl6R6jZxpZ9BoUFtnXPf8BTD6VQrzS5YRwGaxek5mVz5nFBUaqfUbKdprI6gAIW9x707PtCmQmq2nQbRrh1VwVGWncn0/H2dzbTTIMp9x24QrXCmigtPX1NVSMnxkKcohbNVlMdnMu3U9xDD2WqKAf7acVwLx/efmqL5876uQb+rB8XWrVtZtWoVgwYNolatWmzdupXMzEyaNWvGvn2upxMEBQVx99138+9//5uaNWtSq1YtXnjhBTQajfNO++V48803iY6Opl27dmg0Gr777juioqIu+TjTgQMH0qpVK+644w5mzJiB1WplwoQJ9OnTh44dO/6ekK/IwIED6datGyNHjuR///sfTZo0ISUlhUWLFjFq1KjLzktcXBxbt24lMTGRwMBAatasSePGjZk1axbLli2jfv36zJ49m+3bt3vsYVAdnnrqKbp27crEiRO5//77CQgI4NChQ6xYsYL33nuPX3/9lYSEBHr37k2NGjVYvHgxqqoSHx9/0f3ME6PR6Jzj4zy9/vJPkm373sPKb56mVkxLImNbs2fdV1gtpTTv4ribtXzOUwSG1KL7iCcc6XvfxYL3xrFrzUzimvfl+O5FZJw5SP9b/gOAwS+QOg07sfHnN9DpjQTVqEPKyW0c2fETvW58+o98nZftt31WBnYwkJVvJ7tAZWhnAwXFdg6cchVGD9/gx/4EKxsPOCpvv+0t59b+Rs5kqiRl2OjdWo9Bp7DtiKOiVlhqp9DDnYO8Ijs5hd4tjLYdtXN9F4XUHEdPh87xCnqdYwZsgOu7KBSWwNr9jvfbj9m5s79C53iFkyl2mtdTiK7hePoHgF4L3ZsrHE+xU1TqGF/aoZFCkD8cPuPbgnTtbguDOxvJyFPJzlcZ0d1IfrGdfSddleaJo03sO1nOb3sd3/WaXWbuHORPUrqN02k2+rY3YNQrbDnkqjSv2mlhWFcjyZk2zmba6NLcQGRNDTMXebfSrA0wEdConvO9qX5dgts0xZKTT9mZVOJfeRy/OpHsvfcpAE5/MpfYCXfQdOq/OfPlD4T360r0zUPZfsNDzm2cmvEFbWa+Tt7OA+Rv30fcpLvRBfhz5qsFXs17Rcs2l3BDn0DSs21k5toYPSCQvEIbu464bnE9eU8Ndh0ys3Kb40670aA45/4Ax2Sb9aJ0FJWq5OQ7KmoB/gphIVpCz/VeiQp3FKn5RWqlOS+8YfVOM8O6GcnMdTyl4vqe/uQXqew57to/Hr0lgD3Hy1m327E/rNph5u5hJpLSrCSm2ujf0YhRD5sPuO8vEaEaGsVoef9739/2W7WjjKHd/cjIVcnKs3FDL3/yilT2HHPF8djYQPYcL2ftLkcj6MrtZdwzPIDTaTYSU6307+iHQY9zMs+0HJWMHBt3DDbxw5pSikodTyNpFqfjAy8PK+ly3b38PPMpouNaUqd+a7au/IpySyltejjKjJ8+f5KgGpH0H+0oM2xWC5kpJ53/L8xNJy3pMAY/EzVrxWL0C6RWnSZuf0NvNGEKCK203NvW7S5nUGcjmXkq2QV2hnUzkF9sZ/8F56l/jPZn3wkr6/c5fp+1uyzcMciPpAwbSWkqfdrpMegV57CzsBCFDvF6DiVaKSl1jFEf1duPE2etPhlqs2pbKcN7+JORayMrT+XG3ibyClV2H3Xt44/fHszuoxbW7HTs3yu2lXHf9YEkpto4lWJlYGc/DHqFjftcje7dWxtJy7JRWKLSoI6OW68LYOW2MtJzvB/DpsMqo3poSc6yk5xtp1szDQYd7D7h+FujemgpLLGzcrfj/ZbDKvcO1tK9uYZjZ1Va1tdQO0zhly2ust7fACEBOHtIhoU47vIXlfrm7v6Wwyoju2tJybGTnGWnazPHvE97TjryPLK7I4ZVexzvtx5RuWeQlm7NNBxLVmkZp6F2TfcY/M7HcO4Rl445ORwxVNWzUuJwuRaOb/H39LsaKIKDg/ntt9+YMWMGBQUFxMbGMn36dIYOHcq8efPc0r755ps8/PDDjBgxguDgYJ588knOnDmDn9/lzxoTFBTE//73P44fP45Wq6VTp07OCSkvRlEUfvrpJ/75z3/Su3dvNBoNQ4YM4d133/094V4xRVFYvHgxzzzzDPfeey+ZmZlERUXRu3dvIiMjL3s7kydP5u6776Z58+aUlpZy6tQpHnroIXbv3s3YsWNRFIXbbruNCRMmsGTJEh9GdPlat27NunXreOaZZ+jVqxd2u52GDRs6n6QSGhrKggULePHFFykrK6Nx48Z8++23tGjRgsOHD1e5n/lCk3bDKC3KYevSdykuyCSiTjNueOhTTEGOXi5FuSluDWvR9dsz6K5pbFk8g82L3iI0Io7h971HWLSrIjl43JtsXvQmy7/+N2Ul+QTVqE23YY/RsvutPonhvDW7yzHo4Ka+BvwNCqdSVT75tcztEZRhwQoBFzzPes8JGwF+FgZ31hNsMpCcpfLpr2XV0lX08Bk7JiP0bqkQ4KeQngfz1qkUn6szBpsUt6e5JGfDT5tV+rTS0LeVQm4RfL9RJTPf8blqd1QEWscp+Buh1AKpOTB7teNxYL60cocFg07htgF++BsVElJsfLCwxO23CA/VEODvOp/tOmYl0L+M4d2Mjm6YWSof/FjiNmRn7W4Lei2M7uOHyc8xZOT9BSVkeXncZ0iHlnRbNdv5vvm0/wPgzKwF7Bs/BWN0BP4x0c7PSxPPsv2Gh2g+fQpx/xxH2dk09j/0LFkrNjjTpH63BENETZq8MAljVAQFew+zbcT9WCpMnOltizcUYzQo3HNDMCY/DceTLEybnUv5BTdYa9XQERjgukCuX1vPlPtqOt/fPtQxr8X63aV8ttCxg7WL9+OB0a7HQf7jllAAFq4p4sc13p9rYfk2Mwa9wu2DTZiMCieTrbz7fbHbPhURqiXQ37Vg59FyAk2ljOjhT3CAYxjFu98XV5o4rXsrA3mFdrena/jK8q1mjHqFOwabMPkpnDhr5d35Re5x1NCc68J+Lo4j5QSZSrm+px/B54a1vDu/yBmHqsJ73xcxso8/E8YEYtQrZObZ+GpRCQcSvBtTi07DKCnMYd1P71BckElkTDNue/Qz5xCP/JxUlAvuahXmZfDZyyOd77csn8mW5TOp16Qz4/49u+Lmr6pVOy0Y9DD2gvPURz+6n6fCQjRuZcbu41YC/c0M62ok2KRwNkvlowvOUzYbxMdo6dvWgEHvaMzee8LKsu2+mSNn6ZYyDAaFu4YGYvJTOH6mnLfnFVQ4LjQEXjB8bsdhC0GmEm7s7U/wueEgb88rdJvYOKqmltF9TQT4K2TnqSzeVMqKbb65mjyYaCfAqNK/rZZAf0jLsTN7lc158RoSAHa7K/9nMu18v97GgLZaBrTTkF0Ac9fayMhzbTM+RmFUD1c1/5bejv+v2Wtj7V7vX0gePG3HZFTp2/pcDLl25qyuOoazWXYWbLDRr62W/m015BTC3HU2Z/kNEF9XYWR3Vww39XL8f+0+G+v2+eZi+FqJA66N41v8PSl2Xzy70YPi4mLq1KnD9OnTGT9+/NX4k+Jv7L3Ff/1uZqcSfTPR4tUWFlbNU1l7SWpq8aUT/ckN+bfvniJzNc2fsq66s3DFTEHXxnFhV//651qAHt08T8L8V7Jjd+GlE/0FlBT78BFLV0ntulU8euovRh5c8OeRk31tPCHj7UeDLp3oT6jsx3eqOwtV8htZeb6/vzqfPbV29+7dHDlyhM6dO5Ofn89//uPo/n7jjTf66k8KIYQQQgghhBDiL8qnbaPTpk2jTZs2DBw4kOLiYtavX/+7Joesypw5cwgMDPT4atGixR/aZosWLarc5oWPyvwrSkpKqjK2wMBAkpKSqjuLQgghhBBCCCH+5nzWg6Jdu3bs3LnTJ9u+4YYb6NKli8fPKj6y83ItXryY8nLPM7r/nvki/oxq167Nnj17Lvq5EEIIIYQQQogKrtGnZfxZ+ayBwpeCgoIICvLuGKbY2Fivbu/P5PxjToUQQgghhBBCiD8raQ4SQgghhBBCCCFEtZMGCiGEEEIIIYQQwhNF+fO+fqf333+fuLg4/Pz86NKlC9u2bbto+ry8PP7xj38QHR2N0WikSZMmLF68+I9+k5flLznEQwghhBBCCCGEEJdn3rx5PP7443z00Ud06dKFGTNmMHjwYI4ePUqtWrUqpbdYLFx33XXUqlWL77//njp16nD69GlCQ0N9mk9poBBCCCGEEEIIIa5hb775Jg888AD33nsvAB999BGLFi1i5syZPP3005XSz5w5k5ycHDZt2uR8EEVcXJzP8ylDPIQQQgghhBBCCE80mj/v6zJZLBZ27tzJwIEDLwhLw8CBA9m8ebPHdX7++We6devGP/7xDyIjI2nZsiWvvfYaNpvtir/Si5EeFEIIIYQQQgghxF+M2WzGbDa7LTMajRiNRrdlWVlZ2Gw2IiMj3ZZHRkZy5MgRj9tOSEhg9erV3HHHHSxevJgTJ04wYcIEysvLeeGFF7wbyAWkB4UQQgghhBBCCPEXM3XqVEJCQtxeU6dO9cq2VVWlVq1afPLJJ3To0IGxY8fyzDPP8NFHH3ll+1WRHhRCCCGEEEIIIYQnf+BpGVfLlClTePzxx92WVew9ARAeHo5WqyU9Pd1teXp6OlFRUR63HR0djV6vR6vVOpc1a9aMtLQ0LBYLBoPBCxFUJj0ohBBCCCGEEEKIvxij0UhwcLDby1MDhcFgoEOHDqxatcq5TFVVVq1aRbdu3Txuu0ePHpw4cQJVVZ3Ljh07RnR0tM8aJ0AaKIQQQgghhBBCiGva448/zqeffspXX33F4cOHeeSRRyguLnY+1WPcuHFMmTLFmf6RRx4hJyeHRx99lGPHjrFo0SJee+01/vGPf/g0nzLEQwghhBBCCCGE8ES5Nu7pjx07lszMTJ5//nnS0tJo27YtS5cudU6cmZSUhOaCJ4PExMSwbNky/vWvf9G6dWvq1KnDo48+ylNPPeXTfEoDhRBCCCGEEEIIcY2bOHEiEydO9PjZ2rVrKy3r1q0bW7Zs8XGu3F0bzUFCCCGEEEIIIYT4S5MeFEIIIYQQQgghhCcauad/Ncm3LYQQQgghhBBCiGonDRRCCCGEEEIIIYSodjLEQwghhBBCCCGE8ERRqjsHfyvSQCGuSXv35FR3Fq6Yn0lf3VnwirxcS3VnwSv6dQ+s7ixcsflT1lV3Frzilql9qjsLV6zP+jeqOwtesSPwuurOgldMe/tQdWfhig0e2ay6s+AVObnW6s7CFcvLM1d3FrxCtdmrOwteodr/+nFERJiqOwtCXDUyxEMIIYQQQgghhBDVTnpQCCGEEEIIIYQQnihyT/9qkm9bCCGEEEIIIYQQ1U4aKIQQQgghhBBCCFHtZIiHEEIIIYQQQgjhiTzF46qSHhRCCCGEEEIIIYSodtJAIYQQQgghhBBCiGonQzyEEEIIIYQQQghPNHJP/2qSb1sIIYQQQgghhBDVThoohBBCCCGEEEIIUe1kiIcQQgghhBBCCOGBXZ7icVVJDwohhBBCCCGEEEJUO2mgEEIIIYQQQgghRLWTIR5CCCGEEEIIIYQnitzTv5rk2xZCCCGEEEIIIUS1kwYKIYQQQgghhBBCVDsZ4iGEEEIIIYQQQngiQzyuKvm2hRBCCCGEEEIIUe2kgeKcxMREFEVhz549AKxduxZFUcjLy6vWfAG8+OKLtG3b1vn+nnvuYeTIkdWWn6vtyy+/JDQ0tLqzIYQQQgghhBDCh2SIhxDn3NDbn15t/TAZFU6cLWfO0mIyctWLrtO3g5HBXfwJCdRwJt3Kt8tLSEy1Oj+PCNVw84AAGsXo0GnhYEI53ywvprDY7rM4hnU10r2VHn+jwqkUG/NWl5GZd/E4erXWM6CjkWCTQnKWyvdrSjmd7lpn0k0mGtd1P11s2Gdh3uoyn8QAMKijjs7NdPgbITFNZeH6crLyL/69dWuhpU9bHUH+CqnZdn7aaOFMhmud0b31NK6jIThAwVwOp9NUFm8tJzPP+7/H5hVz+G3xTIrys4iKacoN454hpmFrj2nTzx5nxQ/vkpx4kLysFIbf8TQ9h9ztlkZVbaxc8B57Nv5CYX4WwTVq0b7XSPrf+AiKong9/xca1T+Qvh38MflpOJ5k4atfCkjPsVWZPj5Wz9CeAcRF66kRrOXtb3LZdcTslqZDMyP9O5mIq60n0KThuQ+ySEqzVrHFP65mz440eGI8Ie1b4le7FjvGTCD951UXX6d3Z5pPe5rA5o0pO5PKiakfcnbWQrc0sY/cToPHx2OMiqBg3xEOPvYy+dv3ez3/F5q/ciOzlqwjO7+QxjHRPHnnSFo2rOcx7c/rt/PSZ/Pdlhn0OjZ/NhWAcquND39YyoZ9R0jOyCbQ5E+X5o345y3DiKgR4rMY1iyZy4qfviI/L5u6cU24dfxT1G/cymPa9St+YMu6X0lJOgFAvQbNGXnHxCrTz/n4FX5b/j033zuZgSPu9FkM591xQwSDe4USYNJy+EQJH8xJIyXDUmX6Fo1NjBkcRsNYP8JC9bzy/hm27Cl0SxMapOWemyJp1zyAAH8tB4+X8PG3F9/uldi3YQ6713xOSWEW4bWb0nvUs0TGej5PAZzYs5QtS9+mMCeZkPBYuo+YTFzzPs7P33u8qcf1uo/4N+37j/d6/gE6NdHQvbmGQH9Iy7WzZLtKSnbV5/Tm9RT6tdESGgjZBbByt40TKa70TWMUOjbWEB2mYDIqfLSonPRcn2S9kiGdDXRrocPPqJCYauO7teZLlns9Wunp305PkEkhJUtlwW9mkjI8l/kPXu9Hs1gdny8q5cCpqs/hV2poVwPdWrrqId+tKbtkOduztZ7+HQzOesgPa8tIuqAeMnGMf6V6yMb9FuavNlfclFf4oi4FEBet5fruRmKjtKgqJGfa+GBhCeU++Dk6NlHo3sxxbKTnwpIdNlKyq07frJ5Cv9Yax7FRCKt2q85jQ6NAvzYaGtVRqBEIZgskpNlZtUelqNT7ef+zsfu4niXcSQ8K8btZLL6pKFWnIV39GNDRj6+XFPHal/lYyuGxW4PRaatep2MzA7cMCOCXDaW8PDOfsxk2Hrs1iCCT4yRm0MNjtwVjx870OQW8PqsArUbhnzcH46vT3MCOBvq0MzBvVRnT5xZjLrczYZTponG0b6JjVG8/lmwx879viknOtDFhVACB/u653Ljfwv99Uuh8/bTBd40Tfdvq6NFKx4L1Ft5dYMZSDuOHGy4aR5uGWq7vrmflDitv/2AmNVtl/HAjAX6uNMmZKvPXljNtnpnPF5lRFLh/uAFvlzv7tixm0TevM2DUP5j48g9E14tn5v8eoCjfc83AYimjZq0YhtzyOEEh4R7TrPv1M7aumssNdz/L468vYsjYJ/ht0edsWv61dzNfwbCeAVzXxcSXvxTwn0+yMVvsTB5XA/1FmreNBoUzaVZmLyq4aJpjSRbmLy+sMo03aANMFOw7yoFJL11Wev+4unT6+WOy125lQ8cbOfXuV7T6+BXCr+vpTBN981CavTGF46+8z4bOoyjcd4Quiz7HEFHTV2GwfOse3vz2Fx688TrmvPQYTWJqM3HaZ+QUFFW5ToC/H8vefs75+nX6/zk/K7NYOHI6mftvGMic/zzGtH+OIzEtk3/N+NJnMWzfuIzvv5zO8Fse4pk3vqVubBPeeXkCBfk5HtMfO7iDTj2H8PhLn/LUa7OoER7J2/95hNzs9Eppd29dTcKxfYTWjPBZ/i80ZkgY1w+oyftfp/LEa6cos9j5z2P10OuqPpn4GTUknC3jo2/Sqkzz7D9iiAp3NF48+nICGdnlvPJ4PYwG75cax3cvZsNP/6XT4H8w9vEFhNWO5+dP7qek0PN5KvXULpZ9/QTNO9/E2CcW0qDVQBZ/MZHs1GPONPe+uN7t1f/WV0FRaNhmkNfzD9AiVmFQBw3r9tn4eLGV9Fy4s78Wk9Fz+rrhCmN6atl9UuXjRVaOnlW5tY+WiAva5Aw6SMq0s3K37y7gPenfXk/vNnq+W2tmxnelmMvh4Rv8L1rutW2kY2RPA8u2W5g+r4SUbJWHbvCvVH4D9Gmjx+67eyNOAzoY6N3WwPzVZt6aV4Kl3M7DIy9eD2nXWMeoXkaWbTXzxrclpGTaeGSkqVIcm/ZbePbTIufrpw2+aZzwVV0qLlrLhJEmjpy2Mu3bYqbNLea3vRZ88bM0j1UY1F7Duv0qnyy2kZZr545+Fzs2YEwPDbtPOtIfPWNnbG+N89jQ6yC6Jqzfr/LpYhvzf7MRHqxwa5+LfClC/EF/qwaKpUuX0rNnT0JDQwkLC2PEiBGcPHnystffsGEDvXr1wt/fn5iYGCZNmkRxcbHz89TUVIYPH46/vz/169fnm2++IS4ujhkzZjjT5OXlcf/99xMREUFwcDD9+/dn7969bn/nv//9L5GRkQQFBTF+/HjKyqq+EJw1axZhYWGYze4n6ZEjR3LXXXddMqaTJ09y4403EhkZSWBgIJ06dWLlypVuaeLi4nj55ZcZN24cwcHBPPjgg85hF7/++ivx8fGYTCZuuukmSkpK+Oqrr4iLi6NGjRpMmjQJm81VyJvNZiZPnkydOnUICAigS5curF271u3vffnll9SrVw+TycSoUaPIzr5Ic6+XDOjsz6KNpew9Xk5ypo2ZvxQRGqShXbyhynWu6+zH+j1mNu0zk5pl4+slxVis0KON4+zfqK6e8BANX/ziKKiSM2188WsRsdFamsbpfRJH33YGlm01sz/BSkqWyuxlpYQEKLRuWPXVZL/2RjYfKGfroXLSclTmrSrDYrXTrYV7Hi1WO4UlrleZD9uperbSsWqXlUOJKmk5duatsRBsUmgRV3VB2Ku1jq2Hbew4aiMj186C38opt0Knpq7Ytx62cSpVJbfQTnKWnaXbyqkRpKFGkHcr/+uXfEWnvjfTsfdoIus0YuS9L2Iw+rHjtwUe08c0aMWw2/5Nm27D0eo973Onj++mefv+NG3blxoRdWjVeTCNW/bgbIJv79oP7mbil9+K2H3EzJl0K58syCc0SEv7pn5VrrPvuIUfVhWx83DVlcdNe8v4aW0xBxN82+CZuew3jr0wg/SfVl46MRD74K2UnjrL4Sdfp+hIAqc/mEPaD8uo/+g9zjT1H7uXM5/P5+xXCyg6fJL9E17AVlJGzD1jfBQFfL30N0b16cINvTvRoE4k/3fPaPwMen76bVuV6ygKhIcGO19hIUHOz4JM/nzw5IMM6tKGuOhatGoUy1N3jeJw4llSs31zu3jlL7PpOXA0PfqPpHZMQ+546FkMRj82rfrRY/rxj02l75CxxNRvSlTd+ox75AXsdjtH9rvHnJudztzP/sv4R19Dq706HUNvHFCTeYuy2Lq3iMRkM2/OTKZmqI5u7YKqXGfngSK+/jGTzbs9N8rVjjTQtKGJD+akcTyxjOR0Cx/MScWg19Cns/d7texZ9yUtut5M885jqBnViH43vYRO78fhbT94TL93/WzqNe1J+/7jqRnZkK5DHyWiTnP2bZjjTBMQHOH2OnVgNXUbdSEkLMbr+Qfo2kzDrhMqexLsZOXDr1ttlNugXSPP1dsuTTWcSLGz6ZBKVgGs2auSmmOnc7wr/b5Tdn7br5KQehWu5i/Qp42e5TssHDhlIzVb5ZuVZQQHKLRqUPU+3betns0Hy9l22Ep6rp3v1pixWO10aea+Tu1wDX3b6Znro94GF+rTTs/ybWYOnKuHfL28jJAAhVYXqYf0bW9g08Fyth6ykp6jMn+1I46uleohuNVDzD4qPnxVlxrd28i6PRZW7LCQlqOSkauy+7gVqw/awro11bDrhJ29CXayCmDRNtVxbDT0XN/p0lTDiVQ7mw870q/dp5KaC53OHRvmcvh6tcqhJDvZhZCcDUu226gdphBs8n7+xd/b36qBori4mMcff5wdO3awatUqNBoNo0aNQlUv3mULHBfyQ4YMYcyYMezbt4958+axYcMGJk6c6Ewzbtw4UlJSWLt2LT/88AOffPIJGRkZbtu5+eabycjIYMmSJezcuZP27dszYMAAcnIcd5Dmz5/Piy++yGuvvcaOHTuIjo7mgw8+qDJfN998MzabjZ9//tm5LCMjg0WLFnHfffddMq6ioiKGDRvGqlWr2L17N0OGDOH6668nKSnJLd20adNo06YNu3fv5rnnngOgpKSEd955h7lz57J06VLWrl3LqFGjWLx4MYsXL2b27Nl8/PHHfP/9987tTJw4kc2bNzN37lz27dvHzTffzJAhQzh+/DgAW7duZfz48UycOJE9e/bQr18/XnnllUvGcSXCQzWEBmo4fKrcuazUbCchxUqDOp4LI60GYqN1HE50lY524PApCw3rOAojndaxzGpzVXLKrXbsdmgU4/1KdFiwQkiAhqNnXN3kyyyQmGajfrTnC3utBmJqua9jB44mWYmrsE7HeD1THwpkyp0BXN/DeNE76FeiZpBCcIDC8bOuErvMAmcyVGKjPJ+ytBqoE6Fw4oJ17MDxszZiIz2vo9c5Gi+yC1Tyi7xXEbVaLaQkHqRRi27OZRqNhoYtupF0Ys8f3m5s43acOLSFzNRTAKSePsLpY7to0rrXlWa5ShE1tIQGaTl40rWfl5rtJCSX0yjGN41s1S20a1uyVm92W5a5YgM1urYFQNHrCWnfgqxVm1wJ7HayVm8itGs7n+Sp3GrlSGIynVs0di7TaDR0btGY/SdOV7leaZmF4Y+/yrB/vcLjM77g5Nmq79wDFJWWoigKQSZ/r+X9PGt5OUknD9OsdRfnMo1GQ9PWXUg4tu+ytmGxlGGzWQkIdF2sq6rKF+88y6Ab76Z2vUZez7cnkeF6aobq2XPY1XulpFTlaEIpTRv88e/ufO8LS7mrTmK3O8qN5o29ewVgs1rIOHuQmCbdncsUjYa6TbqRlrjH4zppiXuIadzdbVm9pj2qTF9SmMXpQ+to1tk3DXcaDdSuqVRqSEhItVM33PNFWEyEQkKae/qTqXbqRlRvdTgsWCE4QMOxM+7l3ul0lbiLlHt1a7mv4yz3olzlt14Hdw3y44d1ZgpLfNvocr4eciypQhxpNupHXbwecuE6duBYkq1S7B3jdbz6YABP32FiRHeDT+ohvqpLBfor1I/WUVii8q9bTLz6QCCTbjLRoLb3eyBoNI7eDqcq7Oun0qo+NuqGK5yqcCydTKk6PTh6Qtrtvr1h9aehaP68r2vQ32oOijFj3AvJmTNnEhERwaFDhwgMDLzoulOnTuWOO+7gscceA6Bx48a888479OnThw8//JDExERWrlzJ9u3b6dixIwCfffYZjRu7KpQbNmxg27ZtZGRkYDQ67rJPmzaNH3/8ke+//54HH3yQGTNmMH78eMaPd4zVfOWVV1i5cmWVvSj8/f25/fbb+eKLL7j55psB+Prrr6lXrx59+/a95HfSpk0b2rRp43z/8ssvs3DhQn7++We3xpf+/fvzxBNPON+vX7+e8vJyPvzwQxo2bAjATTfdxOzZs0lPTycwMJDmzZvTr18/1qxZw9ixY0lKSuKLL74gKSmJ2rVrAzB58mSWLl3KF198wWuvvcbbb7/NkCFDePLJJwFo0qQJmzZtYunSpVXGYDabK/UgsVnNaHVV9GOrICTAcXAXFLs3VBUWq87PKgo0KWg1CgUV5pIoKLYTFeY4mSekWDFb7IzpZ2Lh2hJQYEw/E1qNQkig908owefyWnF+i8ISu/OzigL8z8VRUnmdyJquQnPHkXJyCh0X8nXCNdzQ04/IGho++9X7Aw/PD5EpKq2Qp1I7QVXU/QP8QKtRKKyQnaJSO7VC3WPv1kLLsK56jHqFjFyVT3+1YLt0G+VlKynMQ1VtBIaEuS0PCg4jM+XUH95unxEPYC4t4q2nhqNotNhVG4Nueox2Pa6/0ixX6fx+ml/k/gUVFNl8sg//GRgjwzGnZ7ktM6dnoQ8JQuNnRF8jBI1Ohzkju0KabALiG/gkT3mFxdhUlbAQ93IqLCSQxNQMj+vERUfw/PibaRwTTVFpGbOXrOPeV97nu9eeILJmaKX0Zks578xbzOCubQn0r7p3zB9VVJiLqtoICnU/LoJDwkhLTrysbSyYPYOQGhFujRzLfvwCjVZL/+G3ezO7F1UjxFF1yitwv+2ZV2glNOSPV6vOppnJyLZw9+havDc7FbNZ5cbrwoioqafmFWzXk9LiXOyqDf8g99/DFBROXobn81RJYRYmD+lLCrM8pj+y/Uf0xgAatvbN8A6TETQaheIK1aPiMjvhIZ4vqgL9HJ9fqKjMsbw6Ocu9CmVxUYnq/Kyi8+V3YcWyssS93BvZ00hiqs2nc06cFxSgOPNQMU/nP6vIGUeJWmmdWhfUQ3YetZJboJJfbKd2uIYbehipVUPDzEXeHW7qq7pUeIhj3WFdjSxcbyY500bnZnomjjYx9eviS85v8Xu4jg33/BSXQXhw1cdGkYdjKdDPc3qtBga01XAg0Y7F+9NHib+5v1UDxfHjx3n++efZunUrWVlZzp4TSUlJNG/e/KLr7t27l3379jFnjqsro91uR1VVTp06xbFjx9DpdLRv3975eaNGjahRo4bbNoqKiggLcy/gS0tLnUNNDh8+zMMPP+z2ebdu3VizZk2VeXvggQfo1KkTycnJ1KlThy+//JJ77rnnsibOKyoq4sUXX2TRokWkpqZitVopLS2t1IPifKPLhUwmk7NxAiAyMpK4uDi3xp7IyEhnL5L9+/djs9lo0qSJ23bMZrPzOzl8+DCjRo2qFP/FGiimTp3KSy+5jy9v1//fdBjwlMf0XVoYuHOoK4/vzq96nPyVKCqx8/HCIu4YEkD/Tn7Y7bDtoIXTqVavjAPtGK/j1gGuK/aPfiq58o1WYdMBV++S1GyVguJS/nlTAOEhZZecwOtS2jXWMrq36278F4t92xS/+7iN42cdlb4+bXTceZ2BD340+6SLpTft37qEPZt+ZewjbxBZtzEppw/z65ypBNWoRYdeI73yN7q19uOe64Od79+cc5VmhhNe17pRHK0bxbm9v2nKG/ywZgsTxgxxS1tutfH0+19jB6bcPfrqZvQyLV0wk+0bl/HES5+hNzgan0+fPMTqRd/wzBvf+nSi2L5dgvnHnbWd7196N+kiqf84mw1e/eAsj95Tm3lvN8Vms7PncDE79heCz2Yu8p1D236gSYcR6PSXd7Pg76R9Ex239HV9L5/6oLEfoEWclsZ1tUyb55v6QYd4HWP7u1p3Pv7Zd7Mlbq5UD7EzcYyJsBAz2VdQD7ladanzp6iN+x3DQADOZpppEqOjaws9v2z0/fAbb9EocFMvDYriGDoihLf9rRoorr/+emJjY/n000+pXbs2qqrSsmXLy5r0saioiIceeohJkyZV+qxevXocO3bMw1qVtxEdHV1pzgXgih6j2a5dO9q0acOsWbMYNGgQBw8eZNGiRZe17uTJk1mxYgXTpk2jUaNG+Pv7c9NNN1X6TgICAiqtq9e7d/FWFMXjsvMNQUVFRWi1Wnbu3IlW696l7VI9WC5mypQpPP74427LHptR9cRxe45bSEjJc77Xax2lRnCAhvxi11VqUIDjyRyeFJXYsal2givcEQgOcO9VcehUOc98mEegv4JNdXSPnzapBpmHrvxqeH+ClcQ0V5y6c3EEBbi34geZFJIzPf+94tJzcVS4QxNkUir1KLlQYppje+GhGrLyryyWQ4k2t5m6z09CFeivuN2FCfJXqpyZvbgMbGrlHhYVtwGOrpplFjtZ+XaS0i28dK8fLetr2XPCOy0UpqBQNBptpQkxCwuyCQr1PAHm5Vgydxp9RtxPm27DAYiKaUJeVgrrfvnEaw0Uu4+YOXnWle/zx0ZIoMatF0VwoJak1PJK618LzOlZGCPdfydjZDjl+YWoZWYsWbmoVivGWmEV0oRhTvN8J/lKhQYFoNVoyM53P69l5xcRHlL1nAcX0uu0xMfW4Wy6+37paJyYTWp2Lh89/ZBPek8ABAbVQKPRUpjn/vcL8rMJucRxsfynr1i6cCaPvfAxdeNcDdzHD++iMD+HKQ8NdS5TVRvff/Umq3+dw2sfLfFK3rfuKeJogmu+Kr3ecSc0NFhLbr6rjAgN0nHqzJXdzT2ZVMak/yRg8teg0yoUFNmYPqU+x09798LPP6AGikZLaYUJMR29JDz/Ho7eEpeXPiVhB3kZpxhy11vey3QFJWZQVbvbRMgAAX5KlU8VKCpzfM4F0xJ6unPsawdPWZmW7ipzzpffgSb38jvQpCEl6+Lld5C/h/L73DYa19USFqLw2gPudbh7h/qRkKry/sIr268OJFg5neaai81ZDzF5qod4rlM44zBpANVtncKL1ENOn6uHRIRoyL6CesjVqkud/zc1xz2m9FyVGkHe7ZHoOjbc9/UAv8q9U8/z1JMowE+pdGycb5wICVCYvdL29+k9IU/xuKr+Ng0U2dnZHD16lE8//ZRevRxjtjds2HDZ67dv355Dhw7RqJHnMa7x8fFYrVZ2795Nhw4dADhx4gS5ublu20hLS0On0xEXF+dxO82aNWPr1q2MGzfOuWzLli2XzN/999/PjBkzSE5OZuDAgcTEXN6EVBs3buSee+5x9looKioiMTHxstb9vdq1a4fNZiMjI8P5G1R0Pv4LXSp+o9HoHDJznlZX9cWT2QKZFvcCIq9IpWmcnjMZjsLHz6DQoLaOdbs811psKpxOtdIsTs+eY46/pQDN4vSs3ll5nfMFQtNYHUEBCnuPX3kvAXM5mN3uGtjJL1aJj9GRnGk5FwfERWnZsM/z37OpjrkdmsTo2HfS6oyjSYyO9XurzmOdCEcrQsUhLn84jvLKQ2Ua19GSmu3Ik1HvGN+5+aDn39WmQnKmnUZ1tBxMVJ1xNKqjZdOBS5eeWi8OAdXpDNSOa8HJQ1to0XEg4Bgnf/LgFrpdd8cf3q7FUopSYayhRqNFtXvv7kWZxU5ZTsVu6zaaNzA4HwHqZ1RoUEfP6m2+67FTnfK27CFiaG+3ZeEDupO7ZQ8A9vJy8ncdJLx/N9fjShWFsH7dOP2Bb56ootfpaBpXh+2HTtCvQ0vAsU9tP3SCWwZ2v8TaDjZV5cTZVHq2dj0G8nzjxJn0LD5++mFCAys3RHuLTq+nXsNmHN6/jbZd+gOOGI7s20a/obdWud6yH79g8Q+f8+hzHxDXqIXbZ137jKBZ665uy955+RG69B5B9/43ei3vpWaV0goXVzl55bRtGsCpM467nv5+GuIb+LNknXd6HZWUOv5e7VoGGsX58fVPnofy/FFanYFadVtw5vhmGrRynKfsqsrZ41to3dPzeSoqri1nj2+mbR/XI5DPHNtEVFzbSmkPbf2eiLotCK/j+bGj3qCqkJJjp0GUwtGzrjKkQZTCtmOez4tnMu3Uj1LYesS1rEG0wtkqLp59xVP5XVCs0qSulpQsR16MeoiN1Lj1YLyQTYWzGSpNYrTO4RsKjkaJDfsc66zaVc6WQ+5l4FO3m/hxg4WDp678yrKqekiTGC3J5+MwQGyUlg37q47jzLk49idcWA/Rsn5f1XU5Zz3kCufVuFp1qewCO3lFKpE13MvxiFANhxO9e5WvqpCaA/UrHBv1oxS2H/W8r5/NOndsHL3gWIpWOJvlen++caJmkMKslTZK/w5zT4hq8bdpoKhRowZhYWF88sknREdHk5SUxNNPP33Z6z/11FN07dqViRMncv/99xMQEMChQ4dYsWIF7733Hk2bNmXgwIE8+OCDfPjhh+j1ep544gn8/f2dXU8HDhxIt27dGDlyJP/73/9o0qQJKSkpLFq0iFGjRtGxY0ceffRR7rnnHjp27EiPHj2YM2cOBw8epEGDi49tvv3225k8eTKffvops2bNuuy4GjduzIIFC7j++utRFIXnnnvusiYN/SOaNGnCHXfcwbhx45g+fTrt2rUjMzOTVatW0bp1a4YPH86kSZPo0aMH06ZN48Ybb2TZsmUXHd7hLau2lTK8hz8ZuTay8lRu7G0ir1Bl91HX2ffx24PZfdTCmnMNECu2lXHf9YEkpto4lWJlYGc/DHqFjftc3fS6tzaSlmWjsESlQR0dt14XwMptZaTn+OY7XrvbwuDORjLyVLLzVUZ0N5JfbHcWmAATR5vYd7Kc3/Y6Cv41u8zcOcifpHQbp9Ns9G1vwKhX2HKuC2J4iEKHeD2HEq0Ul9mpHa5ldG8/jp+1OitS3rZhv5X+HXRk5avkFNoZ1ElPQYmdg4mui+cHRhg4eMrGpoOOZev3Wbmln56zmSpnMlR6ttZh0MOOo47YawYptGmk5dgZG8VlEBKg0K+djnIbHDnt3fEdvYbezXefTKFO/ZbENGjFxmWzsJhL6dDb0RA4/6OnCK4RyZCxjp4/VquFjGTHHVqbtZyC3AxSTh/G4GciPDIWgGZt+7Hm548JDY8msk5jUk4fYsPSL+nQ27dd8pdtLuGGPoGkZ9vIzLUxekAgeYU2dh1xNcQ9eU8Ndh0ys/Jco4XRoLjNYRJRQ0u9KB1FpSo5+Y59JsBfISxES+i5O0dR4Y7iKL9IrTTnxZXQBpgIaFTP+d5Uvy7BbZpiycmn7Ewq8a88jl+dSPbe6xgSdvqTucROuIOmU//NmS9/ILxfV6JvHsr2Gx5ybuPUjC9oM/N18nYeIH/7PuIm3Y0uwJ8zX3l+Sos33DmkNy98Oo9m9evSskEM3yxbT6nZwg29OgHw/MffElEjhH/eMgyAT35cQauG9YiJDKewpJTZi9eRlpXLyD6O+RvKrTaeem8WR04nM+Nf92FTVbLyHMPdQgJN6HXerx4MvP4uvnz3OeIaNieucUtW/ToHi7nU2ZjwxTvPElqzFqPudPRUXLrwC36Z+wHjH5tKWERt8nMdPVSMfib8/E0EBoUSGBTq9je0Wh3BNcKIqhPn9fxf6KdVOYwdHkFyhoX0rHLuvDGCnDyr2xM6Xn08ls27C/h1jaPRws+oEF3L9ZSeyHA99WOMFBXbyMxxnKd6dAiioNBGRk45cXWMPHhrFFt2F7L7UDHe1rbPPaz89mlqxbQksl5r9q77CqullGadHeeUFd88RUBwLbqPcMw/1abXXSx8fxy7184krllfju1eRMaZg/S7+T9u27WUFXFi7zJ63uB5mKU3bTmsMrK7lpQcx5OZujbToNfBnpOOc8jI7loKS+ys2uN4v/WIyj2DtHRrpuFYskrLOA21ayr8ssVVBvgZICQAZ88Ex5h9O0WlVJrvwpvW7S3nuo4GMvMc5d7QLgYKiu3Oi3aAR270Y3+CzXmxv3ZPObcPNHImQ+V0uo0+bQwYdApbDzvWOf+0i4pyCx1/wydx7C5nUGcjmXkq2QV2hnUzkF9sZ/8F9ZB/jPZn3wmrswFi7S4LdwzyIynDRlKaSp92egx6xTkUIuyCekhJqWMOilG9/Tjho3qIL+pSAKt2WhjW1Uhypo2zmTa6NDcQWVPDzEXev9LffERlZDcNKdmO3qddmmrQa2FPguN3v7GbhsJSWH3BsXH3dVq6NlU4nmKnZayG2jXh162OzzUK3NxLQ1RNhblrbSgKzt5LpRZHo4gQ3vK3aaDQaDTMnTuXSZMm0bJlS+Lj43nnnXcuayJJgNatW7Nu3TqeeeYZevXqhd1up2HDhowdO9aZZtasWYwfP57evXsTFRXF1KlTOXjwIH5+jiNYURQWL17MM888w7333ktmZiZRUVH07t2byMhIAMaOHcvJkyd58sknKSsrY8yYMTzyyCMsW7bsovkLCQlhzJgxLFq0iJEjR1729/Lmm29y33330b17d8LDw3nqqacoKPDNnAwAX3zxBa+88gpPPPEEycnJhIeH07VrV0aMGAFA165d+fTTT3nhhRd4/vnnGThwIM8++ywvv/yyz/IEsHRLGQaDwl1DAzH5KRw/U87b8wrc5iWICNUQeEH3vR2HLQSZSrixtz/B54aDvD2v0G1ipaiaWkb3NRHgr5Cdp7J4UykrtvmuhrNyhwWDTuG2AX74GxUSUmx8sLDELY7wUA0B/q4W/F3HrAT6lzG8m9HRhTFL5YMfS5yVGqsN4uvp6NfOgEGvkFuosvdEOcu2+W685No9Vgw6GNPHgJ8BEtNUPl9kcYsjLEQh4IKurXtP2gjwg0GddASZFFKy7Hy+yOzs6mu12akfraFnKx3+RkevllOpKh8sNHu90tm66zCKCnNZ+cM7FOZnEV2vGff++xOCQhxdofOyU916QxTmZvLus66GhvWLZ7J+8UzqN+3Eg884GhxvGPcsy394m5++/A9FBTkE16hF53630H/UBO9mvoLFG4oxGhTuuSEYk5+G40kWps3OpfyCGz61augIDHBVwurX1jPlvprO97cPdcxrsX53KZ8tzAegXbwfD4x2PY3hH7eEArBwTRE/rql6iNbvFdKhJd1WzXa+bz7t/wA4M2sB+8ZPwRgdgX9MtPPz0sSzbL/hIZpPn0LcP8dRdjaN/Q89S9YKV4+71O+WYIioSZMXJmGMiqBg72G2jbgfS4WJM71pUJe25BYU89GCZWTnF9KkXm3enXy/89GhaTl5KBrX8VBYXMorX3xPdn4hwQH+NI2ry8znJtKgjqOsyczNZ93uQwDc9px7N/yPn36Yjs0a4m2degymKD+Xn+d+SEFeFnXrxzPp2Q8IPjdxZk5WqttcEr8tm4/VWs7H0ya7bWfELQ9x/dhHvJ6/3+OHpdn4GTT8867aBJg0HDpewvNvJ1FuveD8H6EnONBVzWoc68/Uf8c53z8wNgqAlZvymPFFCgA1Q/Tcf0sUocE6cvPLWb05n7m/ZvokhsbthlFalMO2pe9SXJBJRJ1mXP/gp84hG4W5KW6/R3T99gy6cxpblsxg86K3CI2IY9i97xEW7T6v1LHdi8Bup3G74T7J94UOnrZjMqr0ba0l0B/Scu3MWW1zntNDAsBud8VwNsvOgg02+rXV0r+thpxCmLvORma+a5vxdRVGdnf9bjf1cvx/7T4b6/b57ips9a5yDDqFW/oZ8TcqnEq18fEvpe7ld4iGAH9XHvacsBLorzCks4HgAMcwio9/Ka2yG//VsGqnBYMexl5QD/nox5IK5bfGrfzefdxKoL+ZYV2NBJsUzmapfHRBPcRmg/gYLX3bGjDoIa/Izt4TVpZt9009xBd1KXA0fOi1MLqPHyY/x5CR9xeUXPFcXp4cOm0nwKjSt42GQD9Iz4Vv1lx4bDiewHHe2SxYsFGlXxsN/dtCTiHM+011HhtBJoiPccT70HD3y8evVtg4nVF9+9xVobk2Jwb/s1Lsdm9M1yc8OXv2LDExMaxcuZIBAwb4/O8NGDCAFi1a8M477/j8b/3ZPfCa7y4UrhY/07XxGEd/v2sjjq7t//oTvf30q3e7iVeXW6b2qe4sXLE+69+o7ix4xY7A66o7C14x7e0//oSdP4vBI5tVdxa8Iif3rz+oPS/vrzPh4cWotmvjEkG9Bi51IiK8+6jh6vL8HX/Ne+MlG3+o7ixUydTDN49yrk5/zb3kT2r16tUUFRXRqlUrUlNTefLJJ4mLi6N3796XXvkK5ObmsnbtWtauXcsHH3zg078lhBBCCCGEEEL4gjRQeFF5eTn/93//R0JCAkFBQXTv3p05c+ZUerKFt7Vr147c3Fxef/114uPj3T5r0aIFp0+f9rjexx9/zB13/PFJ+4QQQgghhBDiWmaXp3hcVdJA4UWDBw9m8ODBV/3vXuypG4sXL6a83PMsyOfnvRBCCCGEEEIIIaqbNFBc42JjY6s7C0IIIYQQQgghxCVJA4UQQgghhBBCCOGJIk/xuJrk2xZCCCGEEEIIIUS1kwYKIYQQQgghhBBCVDsZ4iGEEEIIIYQQQnhglyEeV5V820IIIYQQQgghhKh20kAhhBBCCCGEEEKIaidDPIQQQgghhBBCCE8Upbpz8LciPSiEEEIIIYQQQghR7aSBQgghhBBCCCGEENVOhngIIYQQQgghhBAeyFM8ri75toUQQgghhBBCCFHtpIFCCCGEEEIIIYQQ1U6GeAghhBBCCCGEEJ7IUzyuKulBIYQQQgghhBBCiGonDRRCCCGEEEIIIYSodjLEQwghhBBCCCGE8ESe4nFVSQOFuCYFBhurOwtXzG6v7hx4R2CQvrqz4BUmg7W6s3DFTEF+1Z0Fr+iz/o3qzsIVW9fr39WdBa+odWBrdWfBK4LDgqs7C1fsWhkiHWDSVncWrljKWUt1Z8EraoSZqjsLXmG/BipUWu01coALcRmkOUgIIYQQQgghhBDVTnpQCCGEEEIIIYQQHtivlS5qfxHSg0IIIYQQQgghhBDVThoohBBCCCGEEEIIUe1kiIcQQgghhBBCCOGJPMXjqpJvWwghhBBCCCGEENVOGiiEEEIIIYQQQghR7WSIhxBCCCGEEEII4YEdeYrH1SQ9KIQQQgghhBBCCFHtpIFCCCGEEEIIIYQQ1U6GeAghhBBCCCGEEB7Y5SkeV5V820IIIYQQQgghhKh20kAhhBBCCCGEEEKIaidDPIQQQgghhBBCCE9kiMdVJd+2EEIIIYQQQgghqp00UAghhBBCCCGEEKLayRAPIYQQQgghhBDCA7uiVHcW/lakB4UQQgghhBBCCCGqnTRQiD+1tWvXoigKeXl51Z0VIYQQQgghhBA+JEM8hLjAkM4GurXQ4WdUSEy18d1aM1n59ouu06OVnv7t9ASZFFKyVBb8ZiYpQ/WY9sHr/WgWq+PzRaUcOGXzRQgM7WKgawsd/kaFU6k2vltz6Rh6ttLTv70rhh9+M5OU7oph4ih/GtXVuq2zcX853601ez3/HZsodG+mIdAf0nNhyQ4bKdlVp29WT6Ffaw2hgZBdCKt2q5xIccSrUaBfGw2N6ijUCASzBRLS7Kzao1JU6vWsu1m/7FtW//IlBXlZ1ImNZ8y9U4ht1Mpj2k2rvmf7b7+QeuY4ADH1mzPitkfd0u/dupKNK+dzJuEQJUX5/Pv176gb19S3QZwzoocfPVsb8DcqJKRY+WZ5KZl5nvfx8/q0M3BdJz+CAxTOZtiYt6qU02mOfb5msIZXHwr2uN6nPxWz61i5V/M/f+VGZi1ZR3Z+IY1jonnyzpG0bFjPY9qf12/npc/muy0z6HVs/mwqAOVWGx/+sJQN+46QnJFNoMmfLs0b8c9bhhFRI8Sr+T6vZs+ONHhiPCHtW+JXuxY7xkwg/edVF1+nd2eaT3uawOaNKTuTyompH3J21kK3NLGP3E6Dx8djjIqgYN8RDj72Mvnb9/skhvNWLPqORQvnkJ+bTb36jRn34BM0bNLCY9o1y35k/ZrFnD2dAED9Rk255a5H3NLn52Yz96v32b9nKyVFhcS3aMfdDz1BVG3Pv6833XRdMP06BRDgr+FYopmZP+aRlm2tMn3T+gZG9A6ifh0DNYK1vDkrix2HytzSjBkYTLfW/tQM1WKzwamzFuYtL+DkGYtPYti3YQ67Vn9OSWEW4bWb0nv0s0TFtq4y/fE9S9my5G0Kc5IJjYil+4jJxDXv4/zcYi5m06/TSdi/irKSPIJr1qVNr7to1eNWn+Qf4ODmOez7bSalRVnUjGpK9xueoVZM1TEk7F/KjhXvUJSbTHBYLJ2HPEG9pq4Y1n43heO7fnRbp27jngy971NfheB0fS9/erUx4m9UOJls5ZtlxWTkXvxc27e9keu6+BESoOFsho25K4pJTHXVL4IDFMb0M9EsTo+fQSE9x8bizaXsPurd8+yFruugo1NTLf4GSExX+XGDleyCi9dDujbX0qe1jkB/SM2x8/Omcs5mOtbxNzq22biOhtBAheIyOJhoY/kOK2YfhXFdBx2dm+kcMaSpLNxQfskYujXX0ruNjiB/hdQcOz9ttFSIQU+TuudjsHMwUWX59nLKfBRDh0YKXZspBPpBeh4s36mSklN1+qYx0KeVhtAAyCmE1XtVTqa6Pu/VUqF5PYVgE9hUSMuBtfsuvs1rhV2e4nFVybctfheLxTeVpD+D/u319G6j57u1ZmZ8V4q5HB6+wR+dtup12jbSMbKngWXbLUyfV0JKtspDN/gT6F95rFqfNnrsFy/brtiA8zGsMfPW/FIs5fDwjRePoV1jHSN7GVi6zcK0uSUkZ6k87CGGTQfKee7zYufr543eb5xoHqswqL2GdftVPllsIy3Xzh39tJiMntPXDYcxPTTsPulIf/SMnbG9NUScu07U6yC6Jqzfr/LpYhvzf7MRHqxwa5+LfCFesGvTUhbOeoPBYx7m3/+dT+3YJnz42kMU5ntuaTlxcDvtuw9l4vMz+dfLXxMaFsWHrz5EXk66M43FXEqD+HbccPu/fJr3igZ1NtKvvZFvVpTwvzmFmC0w6eaAi+5THeL1jOnrz6JNikXaqAABAABJREFUZbw2q5CzmTYm3RxAkMmxT+UWqjz1Qb7b65cNpZRZ7Bw85d2a2vKte3jz21948MbrmPPSYzSJqc3EaZ+RU1BU5ToB/n4se/s55+vX6f/n/KzMYuHI6WTuv2Egc/7zGNP+OY7EtEz+NeNLr+b7QtoAEwX7jnJg0kuXld4/ri6dfv6Y7LVb2dDxRk69+xWtPn6F8Ot6OtNE3zyUZm9M4fgr77Oh8ygK9x2hy6LPMUTU9FUYbFm/gjmfv82oW8fzyltfUS+uEa+/8Cj5eZ5rt4cP7KJb70E88+oHvPjGZ9QMr8XrL0wiJzsDALvdzluvPUlGWjL/euYNXpkxm/BaUUx97p+Ulfm2BfL6PkEM7h7IzB9zee79DMrK7Tx9Xzj6i9z2Meo1nE4t54ufcqtMk5pZzpc/5/H0jHRe/DCDzDwbU8aHExTg/erasd2LWf/jf+k8+B/c+sQCwmvH8/PH91NS6Pk8lXpqF8tmP0GLLjdx6+SFNGg5kEUzJ5KdesyZZsOP/yXpyAYG3fk/7nx6EW17j2PdgpdJOLDa6/kHOLlvMVsWvU77Af9g1MQfCIuOZ8nMBygt8hxD+undrJ47mfiOYxj1zwXENR/Aiq//SU7aMbd0dZv04o7/+8356n/bNJ/k/0KDu/jRv4OROcuK+e+sAszldiaNDbroubZjUwM39TexaEMpr36Rz9kMK5PGBjnPtQD3jggksqaWD34o5D+f57P7mIUHbwwkJtI35WCfNlq6t9Dy44Zy3v/JQnk53DdUf9E4WjfQMKKrjpW7rLy70EJqtsr4oQYC/ByfB5sUgk0Ki7daeet7C9+tK6dJjIabeut9FIOOHi11LFxv4b0fzVisMH6Y4RIxaBnRTc+qnVbeWWB2xDDM6B5DgMKiLeW8+Z2Z+WvLaVJXw019DD6JoVmMwsB2CusP2Pl8mUpGnp1b+2qqrE/VCYNR3TTsTbDz2TKVY8l2bu7pqk+Bo9Fi2U6VT5eozFqpkl9s57aLbFOIP0oaKLzo119/JTQ0FJvN0XK9Z88eFEXh6aefdqa5//77ufPOO8nOzua2226jTp06mEwmWrVqxbfffuu2vb59+zJx4kQmTpxISEgI4eHhPPfcc9gvuMrNzc1l3Lhx1KhRA5PJxNChQzl+/Phl5ff35OGxxx4jPDycwYMHO4ddLFu2jHbt2uHv70///v3JyMhgyZIlNGvWjODgYG6//XZKSkqc21JVlalTp1K/fn38/f1p06YN33//vdvfW7x4MU2aNMHf359+/fqRmJh4WbF4Q582epbvsHDglI3UbJVvVpYRHKDQqkHVNc6+bfVsPljOtsNW0nPtfLfGjMVqp0sz93Vqh2vo207P3NXev6i/UO+2epZvd8UwZ0UZIX80hubu65Rb7RSWuF6+uGvRramGXSfs7E2wk1UAi7aplNugXUPPkxN1aarhRKqdzYcd6dfuU0nNhU7xjlObuRy+Xq1yKMlOdiEkZ8OS7TZqhznuAPjK2kWz6D5gDF37jSKqbkNuuf95DAZ/tqxZ6DH9uEmv02vwrdSNa0pknQbc9vBLqHaVY/u3OtN06n09Q256hCatuvou4x7072BkyZYy9p2wkpyp8uXiYkICNbRtXHXFcEBHIxv3Wdh8wEJatsq3yx2NZd1aOipidjsUFNvdXm0b69l5xOL1/errpb8xqk8XbujdiQZ1Ivm/e0bjZ9Dz02/bqlxHUSA8NNj5CgsJcn4WZPLngycfZFCXNsRF16JVo1ieumsUhxPPkppd9YXnlchc9hvHXphB+k8rLyt97IO3UnrqLIeffJ2iIwmc/mAOaT8so/6j9zjT1H/sXs58Pp+zXy2g6PBJ9k94AVtJGTH3jPFJDABLfvqWfoNupM/A66lTrwH3Tngao9GPdSt/8Zh+whP/4bphNxHboAm168bxwMRnUFWVg3t3AJCWcoYTRw9w74SnaNi4ObXrxnLvI09RbjGz+bflPosDYEiPQH5cXcDOQ2WcSSvnw3k5hAZr6djcv8p19h4r47vlBew4WFZlmk17SzlwwkxGjo3kDCtf/5qHyU9DvSjvX4jtWfslLbrdTPMuY6gZ1Yh+N7+EzuDHoa0/eE7/22xim/akff/x1IxsSNdhjxJRtzn71s9xpklN3EPTTiOp26gLwTXr0rL7WMJrx5OetM/r+QfYv/4rmna6mfiOo6kR2YieI19EZ/Dj6I4FHtMf2DiLuo170qb3eGrUakjHQY8SXrsZBzd/45ZOqzNgCopwvoz+vukddaEBnfxYvKmMvcfLSc608cWvxYQGamjbpOoL2IGd/diw18ym/Y6L+jlLS7CUQ/fWrivGBnV0rNlZRmKqjax8lcWbyigx26kX6ZtO1D1a6li928qh0yppOXbmrS0n2KTQPLbqS46erXRsO2Jj5zEbGXl2ftxgxWKFjvGOFoH0XDtfryzncJJKTqGdkykqy7dbaRarQeODuQt7tnKPYf4aC8EmhRZxVbdQ9GrtiGHHuRgWri+n3Aqd4nWuGFZY3GJYtr3cZzF0aaqw56Sdfacc9aPF2+1YrdCmgec/1jle4WQqbDliJ7sA1u23k5YLHRu70h88bScxHfKKIauA/2fvvsOjKvYGjn/PtiSbXiAhIRBCIKGH3kkABQEVBbtYQb16vWK96mvFxvVaQLGLFBEFlKqAgGCQ3kMNNYRASCG9Z7Pl/WNhl002gLBLgPv7PM95IGdnTmay58zMmTMzhxU7LHjqFBoGuD794n+bdFC4UN++fSkpKWHHjh0ArF69mpCQEJKSkmxhVq9eTWJiIpWVlXTu3JnFixezZ88eHn30Ue677z42b3ZsNE+fPh2NRsPmzZv55JNP+Pjjj5k8ebLt8wcffJCtW7eyaNEiNmzYgMViYejQoVRXn7+V/3fSoNPpWLduHV999ZVt/5tvvslnn33G+vXrOX78OHfccQcTJ07kxx9/ZPHixSxfvpxJkybZwo8fP57vv/+er776ir179/LMM88watQoVq9eDcDx48cZMWIEN910E8nJyYwZM8ahc8edgv0U/LxVHDxuHxZZaYBj2WaiwpxfJmoVNG7oGMcCHDphommYvRLTauC+QZ7MXV1FSbn7hlAE+yn4uygPB4+biApzrIg7x2p5Z4w3L97jxY09ded8UngxVCrraIejWY5/o6NZFhqHOK9QG4coHM10DH/kZN3hATx0ChaLhUo3DQYyGqs5nrrPoSNBpVLRsl0P0g7tvKBjGKoqMRuN6H3c3yg+lxB/Ff4+KvYfsw9brzTA0UwTzcKdnwBqFTQJUzvEsQD7jxmJriNOk1A1kaEa1u927ZdSbTSyPy2Dbm1a2PapVCq6tWnB7sPH6oxXUWlg2LPvMvSZd3h24lSOnMg65+8prahAURR89XXfnF5OAT3iyV21wWHfqRVrCewRD4Ci1eLfqQ25K9fbA1gs5K5aT0CPjm5Jk7G6mqOH99Mmvpttn0qlok2Hrhzef2HTSqqqKjGZTPj4+p0+pvV80WrtN3AqlQqNVsvBfRd2rV2MhkFqAv3U7Dls73CuqLJw5LiBFk1d9zRUrYYB3bwpqzCTnunanjuT0UDOib1Etuxl26eoVES26EnWsWSncbLSkh3CAzSJ7U3mWeEbRcVzdM8qSguzsVgsnDi0kcJTaTSJ7e3S9J/JQ+7JvUTE9HTIQ0TznuSkO89DdvpOh/Bgnb5RM3xm6mZmvNObOR8NYe2CN6ksc0/n4xlnytqUNPv3XFll4ehJI9ER5y5rz45jAfanVTvESc0w0qWVDr2nggJ0aaVDq1Y4mO76pwxBvtaRDocz7NNSqqrh+CkLTUPrbodEhDjGsQCHM8w0bVj3bYqnTqHSAGYXN6vO5OFQxlltqWo4nmOmSR3pOZOHQycc21KHM0w0qSPf4L48qFTQKBCOZtdoT2VbaBzsvH0UEazUCp+aZSGijvAqlfXhUaXBQrZ7L48rg6Jcuds1SNagcCF/f3/i4+NJSkqiS5cuJCUl8cwzzzBu3DhKS0spKiri8OHDJCQkEBERwfPPP2+L+69//Ytly5YxZ84cunWzN+AiIyOZMGECiqIQGxvL7t27mTBhAo888giHDh1i0aJFrFu3jl69rI2GmTNnEhkZyYIFC7j99tvPmd4LTUOLFi3473//a/s5M9M6Ie2dd96hd29ro2P06NG8/PLLHDlyhOjoaABuu+02/vzzT1588UWqqqp47733+OOPP+jZ09o4iI6OZu3atXz99dckJCTw5Zdf0rx5cz766CMAW37ff//9v/9l/E1nhkOW1uhAKC03OwyVPJu3l4JapVBS4RinpNxCwwB7hXRLHw/SMk1uW3PijDPprNkJUlJuxs/7PHmoFcdCaKA9D9sOVlNQYqGozEJ4sIqbeutoEKhi6pK6nwT+XXoPUKms8zLPVlYJIX7O0+/jCaU1klBWacHH03l4tQoGxqvYk2bBUPdU8UtSVlyA2WzC1z/YYb+vfzA5J49e0DEWzZyAX1ADYi/zaImazpw3xWWOc6BLyuo+p3xOn1PF5Y5xisvNhAY5r3J6tdORmWsi9aRrr5HCkjJMZjPB/j4O+4P9fUjLzHEaJ6pRA14ffTstIhtRWlHJjKWreeidz/n5vecIDQqoFb7KUM2ns5cwuEc8Pl6eLk3/xfIIDaEqO9dhX1V2Llp/X1SeHmgD/VFpNFTl5NUIk4d3bLRb0lRSXIjZbMI/wHEKiX9AEJkZdXcWnW3W9M8JDAqhTYeuADRqHEVwgzBmf/8Fo//5Eh4eXixd9BP5uTkUFuSe52gXz9/H2nlbVOp4vhaVmmyfXYqOcZ786+4gdFqFwhIz4787RUn5udch+LsqygqwmE3ofR3LKb1vCAU5zsup8pJcp+HLi+1/64SRr7Fq9mtMHZeASqUBRWHAnW8T0byrS9MPUFleiMVswsvHMU1evsEUnnKeh4rSXLx8QhzD+wRTUWrPQ2TLPjRrcz2+QY0pzktny/KJ/D7tMW5+/CdUKvdMi/Dzsda3Ncva4jIz/nWVtfrT9XeZpVacsGD7iJtvFpTyyHAfJjwdiMlkwVBt4ct5JeddR+hi+Jzuoy2t0S4qrbA4nfoKoPcEtUpxGqdBgPObe70HDOhoHbHganW2Byss+NYx8tKeB8f9JefJw8BOGjbvd31jRK87055y3F9WCcHOl3/CxxOn4b1r9LvHhFungmg1UFoBPyaZqbh2Z3+LeiIdFC6WkJBAUlISzz33HGvWrGH8+PHMmTOHtWvXkp+fT3h4OC1atMBkMvHee+8xZ84cMjIyMBgMVFVVodc7ln49evRAOat3rGfPnnz00UeYTCZSUlLQaDR0797d9nlwcDCxsbGkpKScN60XmobOnTs7jd++vX0RqtDQUPR6va1z4sy+M6MxDh8+THl5Oddff73DMQwGAx07Wp/YpaSkOOTlTH7Pp6qqiqoqx6kTxupqNNq6J8V1aqnhjkT759/+5p75ym2i1LRorObD2eXnD/w3dW6p4Y7+9jx886v75lxv2GuvQDPzzBSXW/jnrV4E+ynnXTTqSqFS4La+KhTFOnXkSrViwWR2rF/Kk29MQau7vBM7u7bScs8g+/X/xdy612lwFa0GurbSsWSD6zq7LkX7mCjax0Q5/Hzbyx8w98+NPDHyBoew1UYTL33+Axbg5QdGXN6E/o9Z9Mt0Nq5ZwSvvfoHu9HWh0Wh4+uX/8O2kd3nsnutRqdS06dCVDp17unS9n97xXoy+NdD283+nua/zA2DfkSpe/jQbX72a/t28eeqeYF7/PKfWzeuVaOeaGWQd28mNo7/ANyiCjCNbWD33Lbz9GtIkttf5D3AFaN5hmO3/QWEtCWoUy+wPBpGZurnW6IuL1a21jntv8Lb9/NnPJS45rjPD+3mh91SY8FMxpRXW6XSP3uLDBzNLOHnq0m7w45uruLWvvTNk2u/uv1P10MKDN+jIKTTzx7ZLv7mPj1Ez4qw8TL1MeXhoiAc5BRZWbHXT0xI3OZYNk5eZ8fKwjqAY0UvF1BVmyt07g1n8j5EOChdLTExkypQp7Ny5E61WS1xcHImJiSQlJVFQUEBCgnWl6A8++IBPPvmEiRMn0q5dO7y9vXn66acv6yKUF5oGb29vp/G1WnuBriiKw89n9pnN1gZVaan1Rmfx4sVEREQ4hPPwuLSbsPHjxzNunOPicd2HvEzPof9XRwzYe9TIh9n2ilmjtnYC+egVis/qNffRqziZ67wCL6uwYDJb8K3xVMD3rGO0aKwm2F/hvUcc/4YPDfEkNdPM5/MvvlNhz1Ejx5zkwbdGHnz1KjLqaITY8qCvOw/OnHkbQ4MAFXnFrnmCUV4FZrMFb08F6+BIK2/P2k9jziittPb6n83bU6k1quJM54S/t8KMP0xuGz0B4O0XiEqlrrUgZklRHr4BwXXEslr16zRWLpzCE69+S0TTWPclsg67DleTlmlvKJ9ZEMzPW0Vxmf179j29WrwzpafPKT+9CjhrJXm9iuKy2t9jx5ZadFrYtNf1ZV+ArzdqlYq8IseOlryiUkLOWlfiXLQaNbFNIziR7fh9WjsnZpCZV8BXLz12xYyeAOtoCY9Qx6fEHqEhVBeVYK6swpBbgNloxKNhcI0wwVRluefm29cvAJVKXWtBzKLC/FqjKmpaPP8Hfpv7PS+99RlNmrVw+KxZTCve++QHystKMRqr8fMP5I3nH6ZZjOvecLNtXyWHj9sXrD1T1vr7qCkssXca+PuoOZZ56edxVbWF7DwT2XkmDh838PHzoSR29WZRkutuYr28A1FU6loLYpaX5KL3C3EaR+8bcs7wRkMlGxZPZOhDk2jWJhGAkPBYcjP2syNpiss7KDz1ASgqda0FMStK8tD7Os+Dl0+Iw2gJgIrSvFqjKs7mFxSJp3cgxXnpLuug2HnYwNEp9opIo7GeUzXLWj9vFcfrKmvLT9ffNUZY+HmrKDrdmRUSoKJ/Z0/enFxE5um2zIkcEzGRWhI7efDjskt7eLIv3czxefZzXn26zvDxchxd6uOlkJnnvIOtvBJM5jMjLBzj1BzFoNPCw0N0VFVbmLGi2iVTI/YdM3H8rDevnan3fPS183Ayz/kvtOfBcb+vV+0RqjotjB6io8pg4fsVBpdP7wAoN5xpTznu9/aEsjqanaWVXFD4ahMUlFq3k3kWHh+mEB+tsD7l6nhYdbHkLR6Xl/y1XezMOhQTJkywdUac6aBISkoiMTERgHXr1jF8+HBGjRpFhw4diI6O5uDBg7WOt2nTJoefN27cSIsWLVCr1bRq1Qqj0egQJi8vjwMHDtC6devzpvVC0+AKrVu3xsPDg/T0dGJiYhy2yMhIAFq1alVr/YuNGzee99gvv/wyRUVFDlvX6587Z5yqasgtsti2rHwzxWVmWp71Kk0PLTQNVZGW5bxSNZnhRI6ZlpH2OArWTokzN/Art1fzwU8VfDjLvgEsWGvgpz8u7YmxszwUlZlpEfn389CisWMeWkaqScuqu+MhooG16ChycsN5scxmyMyHZmGOja1mYQoncp3/nhO5llrhoxs5hj/TORHkq/DDSpPbhyJqNFoio1s7LHBpNps5uGcjUS061Blv5cIpLJv7Nf94+UuaNHf+2kV3q6qGU4Vm25aZZ6ao1ExsE3tftqcOmjVSc/Sk814ekxnSs0zENrXHUYDYphpSncTp3c6DXYer6+yEuhRajYa4qAi27Dts22c2m9my7zDtYppe0DFMZjOHT2QSEmDv0DjTOXE8O5cv//0oAT7OO3HrS+HGZIIHOE4PChnYi4KNyQBYqqsp2r6XkAFn3WwpCsH9e1K4cYdb0qTRamkWE8fenVts+8xmM3t3bSEmzvnrdwF+mzuDBbOn8O83JhLdolWd4fTePvj5B5J1Mp3Uwyl07t7PZWmvNNg7DLLzrItXFhSbaBNj71z38lBoHqnj0DHXFzCKoqDVuHaesVqjo2HjNpw4aF+rxGI2c/zQRsKaxjuNExYVz/GDjmubHD+4nkanw5vNRsymahSVY9NSUamwmF0/+kOt0RES3oaMI/Z2gsVs5uSRjTRsEu80TmiTDpw84tiuOHF4fZ3hAUqLsqgsL0Tv28AVyQasr7x2KGtzTRSVmomLsj/o8dRBs3ANqRnnLmtbnRVHAeKaam1xdFrreWOpMaTIbMYlCzMaqiGv2GLbcgosFJdbiImwnwMeWohsoHAsu+52SEauYxwFiAlXceysjgOP0zf2JhN8v6wao4tmd9TMQ/aZPIQ7tqUiG6rqfIW8PQ+ObamYcLXDK9s9tDBmqAdGM0xfZnBZHmoymyGzAKJCHb/kqFCFE3V0smTkWWgWWrv9lVFH+DMUxd4xJYSrSAeFiwUGBtK+fXtmzpxp64zo168f27dv5+DBg7ZOixYtWrBixQrWr19PSkoKjz32GNnZ2bWOl56ezrPPPsuBAwf46aefmDRpEmPHjrUdY/jw4TzyyCOsXbuWnTt3MmrUKCIiIhg+fPh503qhaXAFX19fnn/+eZ555hmmT5/OkSNH2L59O5MmTWL69OkA/OMf/+DQoUO88MILHDhwgB9//JFp06ad99geHh74+fk5bOea3lGX1Turub6LjjZRahoFq7j3ek+KyyzsTrU3Dh4f7kmfdvbGQFJyNT1aa+kap6FhoMJtiR7oNAqbUqxxSsqtHQdnb2B9zWJ+ietvyP5KrmZQFx1tmlnzMGqQJ0U18vDELZ70ae+Yh55trHkIDVS4vf/pPOyzxgn2UxjUVUvjBtab/DbN1Nx7vSeHM0x1PhG5WBv2m+kUo9C+mUKIHwzrpkKrhuRU699qeE8VA+Ltxdam/Waahyv0iFMI9rO+vzs8CLYcsKZLpcDtfVU0ClKYv86EolifCHh7Whd4cpfEYfezYdVcNq9eSNaJVH6e/DaGqgq6J94CwA+f/R+//jjRFv6Phd+xeM5n3P34WwQ1jKC4MJfiwlyqKu1Pt8pKiziRtp+sjCMA5JxM40TafooL3TvcfNW2Kob29KB9cw3hISoeGOpNUamZ5EP2BdbG3uFNQkf74oArt1bRp72OHm20hAWpuHuQFx5a2LDH8eatQYCKmEg163a5r9do1A39mL96E7+u3crRk9mMnz6PiioDN/e1zol//eufmDRniS38NwtWsGH3AU7k5JGSdoLXvvqJrNwCbkmwTj+rNpp48bPvSUk7wTv/uAeT2UxuYTG5hcVUG90zNEftrcevQxx+HayjAvTNGuPXIQ7PyEYAxL7zLB2m2tfqOfbNLPTNIokb/wLesdE0/cc9NLp9CEc/mWYLc3TiVCJH30HEfbfgExdN28/fROPtxfHpzt9+4ApDht9N0vKF/LVyMRnHjzL1y/epqqwkYeCNAHw14U1mT//cFv7Xud/zy8yveeSpVwkJDaewII/CgjwqK+zXxaa1K9m3exs5WRls27ia/7z+FF2696NdR/eu3/L7ulJuHeBHp1aeRIZqePyOIAqLTWzdZ3/c+H9jQhjU09555aFTaNpIS9NG1vK3QZCGpo20BPtbW/ceWoU7B/sRE6kjJEBNswgtj94WSKCfmo27XD9NMD7xQfZu/JmUzfPJzz7Cn7+8idFQQevu1ulKy2e+yPrfPrKH73cf6fvXsv3PKeRnp7Lp90nkHN9L+773AqDz9CGieVfWLfqAE4c3UZR3gpTN89i/dSHN21/vNA2Xql3fBziw5WcObltAQc4R1i4cR7WhgpadbwXgzzkvsvn3j23h2/a+n+MH17JrzVQKc1LZ9sdn5GbspU3PewCoripj05IPyE5PpqQgg4zDG1jx/T/xC2pC45Z9nKbBVVZuqWRoL0/ax2gJb6DmoRt9KCw1k3zQXj4+c5cviZ3s7Zs/NlfSp4MHPdrqCAtWcc9gPTodrN9lHW+flWciO9/EqBu8iWqkJiRAxXXdPGnVTEPyQTe8igtYt8fIgI4aWjVRERqocEeiluJyC/uO2dsLY4Zq6dnafle7dreRrrFqOrVQ0SBA4ZY+GnRa2HbQegd/pnNCp4Ff/qrGQ2dd78LHyz1rBK7dbWRAJw2tmqoIC1S4s7+O4nILe9PsPQqPDNPRs409D2t2GekWp6ZTCzUNAxRu7atFq4WtB422PIwZ6mHNw2r352HTfgsdmyu0i7K2j4Z0UdBqYNfp9tRN3RUS29t/8eYDFqIbQfdYhWBf6NtWoVEgbD1kDa9VQ2J7hfBg8NNDWCDc2E3B1wtS0q/t0RPi8pMpHm6QkJBAcnKyrYMiKCiI1q1bk52dTWysddj2q6++SmpqKoMHD0av1/Poo49yyy23UFRU5HCs+++/n4qKCrp164ZarWbs2LE8+uijts+nTp3K2LFjufHGGzEYDPTr148lS5bUmm7hzIWmwVXefvttGjRowPjx40lNTSUgIIBOnTrxf/9nnYrRpEkT5s6dyzPPPMOkSZPo1q0b7733Hg8//LBb0lPTqu3V6DQKd/T3wMtD4Wimia9/rXDo4Q7xV+HtZa9kkw8b8fFSuKGbDj9vhYxTZr7+tcItT4MvxMrt1ei0CneezkNqpomvF9XOg4+nPQ87Dhnx9lIY0v2sPCyy58FkhpaRGhI66NBpobDUws7DRpZvcf1N5b5jFrw9zCR2UOHjCdkF8OOfJtvCTf7eisOToBO5MG+dmf4dVAyIt76je/ZfZk6dPoV99RAbae2JeGyYY3E3fYWJYznu+Z469bqB0uJ8lsz5nOLCXBpHxfGPl7/CL8A6jLggLxPlrMdX61bMwWSsZurHzzoc54bbHmfI7U8AsGfrn/z45Wv29H/yQq0w7rB8cxU6rcI9g/XoPRSOZBiZ9EuZwznVIECNj5d9x7YD1fjoK7ixtxd+3gonckxM+qWs1lDXXu10FJZYSElz35ybQd3jKSgu46t5y8grKqFlk3AmPT/G9urQrPxCh++ipKyCd6b+Ql5RCX7eXsRFNWbKa08SHREKwKmCIlbv2AfA3a9NcPhdX7/0D7q0au7yPPh3bkvPlTNsP7f+0FpmHv9+HrtGv4xHowZ4ne6sAKhIO8GWmx+j9UcvE/Wv+6k8kcXux14ld8VaW5jMn5eiaxBEyzeewiOsAcU7U9h84xgMNRbOdKUefa+nuKiQuT9+Q1FBHk2jW/LvNyfiH2idapJ7KhvlrGG0K5fOw2is5tP/vOxwnFvvGsPIex4BoLAgl5lTJlJUmE9AYAh9+g/h1jtHuy0PZ/y6ugQPncKYEYHoPVUcTKviP1NzqT7rVA4N1uDrbb+BiW6s47VH7U/h77sxAIDV28r4+ucCzBYLjRpoeXqUN77eKkrLzRw5YeCtr3PIyHH9NdKy41AqSvPZ9PskyopP0SCiFTc/9q1tekRpwUmHNbAaNevEoPs+ZOOSiWxYPIGABlEMe/gzghu1tIUZfP/HbFj8Mct/eIHK8iJ8A8PpOfRp2va6y+XpB2jefiiVpQVs++NTyktyCW7UiiEPfWPLQ1lhpsM5Fdq0IwPu+oCtyz9hy7IJ+Ic05fpRkwgKs+ZBUanJyzrAwe0LMFSWoPdtQOMWvel8/VOoNa57Q4szyzZVotMpjLrBG72nwuETRj6dXeJYfweq8NHb87N1vwEfvcLNfb3wOz317tPZJbay1my2rm9xa6Kef97mi4dWIafQxLTfytiT6p4OitU7Teg0CiP6avHUQVq2mam/O454CPZT4e1prw92pZrx9jRyfWctvnrr1IEpSw22RScjQlS2t2H8+y7HB1Dv/1RFQalr6/LVO43oNDCyr86ahywzU5Y6jngI8lNOT0k9kwcT3l4wqIsGX711OsiUJVVO8/Di3Y5zKf7zY6XL85By3DrFI6GdNZ3ZhTAryUzZ6bUi/L0VLGdNqcnIgwUbzCS2U5HYXiG/BH5ea29PmS0Q7KtwW28FLw+oMEBmHny/0kxusUuTfkWycG2+LeNKpVhqjvsSV4zExETi4+OZOHFifSflqvPMZ+5f3M/drpUrMyjoypmbfym6tbnyF6g7n4UrXP8Utj580Pev+k7CJVvd94X6ToJLNNyz6fyBrgITpl79dUavfhHnD3QVqKy8+iu/QwcL6zsJLhEYXMdrK64y18Ktjp+fezvILpdX7ro654Pk7tlw/kD1JKSta9bGuZLIFA8hhBBCCCGEEELUO+mguIYNGTIEHx8fp9t7771X38kTQgghhBBCiCuaRVFdsdu1SNaguIIlJSVdUvzJkydTUeH8fUJBQed+rZsQQgghhBBCCHE5SQfFNSwi4tqYjyqEEEIIIYQQ4tonHRRCCCGEEEIIIYQz7ngXrKjTtTlxRQghhBBCCCGEEFcV6aAQQgghhBBCCCFEvZMpHkIIIYQQQgghhBMWeaZ/WclfWwghhBBCCCGEEPVOOiiEEEIIIYQQQghR72SKhxBCCCGEEEII4YRF3uJxWckICiGEEEIIIYQQQtQ76aAQQgghhBBCCCFEvZMpHkIIIYQQQgghhBMWRZ7pX07y1xZCCCGEEEIIIUS9kw4KIYQQQgghhBBC1DuZ4iGEEEIIIYQQQjhhQd7icTnJCAohhBBCCCGEEELUO+mgEEIIIYQQQgghRL2TKR5CCCGEEEIIIYQT8haPy0s6KMQ1Sa2++guSijJDfSfBJYxGc30nwSVCvYvrOwmXzGJW13cSXGKrz/X1nYRL1nDPpvpOgkvktO1e30lwiTYz9tV3Ei5ZaJClvpPgEhu3V9R3Ei6Zp5e2vpPgEtdK/a1Srv71A66BLAhxwa7+uzghhBBCCCGEEEJc9WQEhRBCCCGEEEII4YRFhrBcVjKCQgghhBBCCCGEEPVOOiiEEEIIIYQQQghR76SDQgghhBBCCCGEcMKCcsVuf9fnn39OVFQUnp6edO/enc2bN19QvFmzZqEoCrfccsvf/p1/l3RQCCGEEEIIIYQQ17DZs2fz7LPP8sYbb7B9+3Y6dOjA4MGDycnJOWe8tLQ0nn/+efr27XtZ0ikdFEIIIYQQQgghxDXs448/5pFHHuGhhx6idevWfPXVV+j1eqZMmVJnHJPJxL333su4ceOIjo6+LOmUDgohhBBCCCGEEMIJi6K6YreqqiqKi4sdtqqqqlp5MBgMbNu2jeuuu862T6VScd1117Fhw4Y68/7WW2/RsGFDRo8e7Za/rTPSQSGEEEIIIYQQQlxlxo8fj7+/v8M2fvz4WuFyc3MxmUyEhoY67A8NDSUrK8vpsdeuXct3333Ht99+65a010VzWX+bEEIIIYQQQgghLtnLL7/Ms88+67DPw8Pjko9bUlLCfffdx7fffktISMglH+/vkA4KIYQQQgghhBDCiYt5W8bl4uHhcUEdEiEhIajVarKzsx32Z2dnExYWViv8kSNHSEtL46abbrLtM5vNAGg0Gg4cOEDz5s0vMfXOyRQPIYQQQgghhBDiGqXT6ejcuTMrV6607TObzaxcuZKePXvWCh8XF8fu3btJTk62bTfffDP9+/cnOTmZyMhIt6VVRlAIIYQQQgghhBDXsGeffZYHHniALl260K1bNyZOnEhZWRkPPfQQAPfffz8RERGMHz8eT09P2rZt6xA/ICAAoNZ+V5MOCiGEEEIIIYQQwgmLcm1MOrjzzjs5deoUr7/+OllZWcTHx/P777/bFs5MT09Hpar/vEoHhRBCCCGEEEIIcY178sknefLJJ51+lpSUdM6406ZNc32CnKj/LhIhhBBCCCGEEEL8z5MRFEIIIYQQQgghhBNX8ls8rkUygkIIIYQQQgghhBD1Tjoo6llUVBQTJ06s72RcMEVRWLBgwWX9nVfb30gIIYQQQgghxN8nUzwuk2nTpvH0009TWFhY30mxefPNN1mwYAHJycn1nZQrxuCuWrq30uDlAUezzMz7y0BukeWccXq10ZAYr8FXr5CZZ2b+2mqO55htn4/sp6VFYzX+3gpV1ZCWZWLxxmpOFZ77uBdrWG9PerfT4eWhkHrSyKwVFZwqNJ8zTr94Hdd19cTPWyHjlIk5Kys4lmUCIMhPxduP+jmNN3lRGTsOVrs0/d1iVfRuq8bHC7LzLSzebCIjt+6/VZumCgM6agjwgfxiC8u3mTiU4Rh+QLyazi1UeOogPcfCrxuN5Je4NNm1LPttLr/O+5GignyaNIvhoceeISa2tdOwm9cnsWDO92RlZmAyGgkLb8ywW++m34AbHMKsWLqAo4cPUFpSzH8+nUpUdEv3ZuK0m/p40qeDB14eCkcyjPy0vJycgnOfUwkdPRjU3QM/bxUnckzM/qOctEyT7XM/b4URiV60itLiqVPIzjexdEOly88ngD+XzmLFwukUFebROKold41+kWYt2jkNu2bFXDau/o2T6YcBaBLdmlvufbLO8DO/foe/lv/C7Q89z3U3jnJ52s+2YvHPLJ4/k6KCPJo0a8H9jz5H85ZtnIb9c9kC1vy5hBPHUgFoFhPHHfc97hC+qCCPWdM/Z3fyJspLS4ht05EHHnuOsPAmbkl/UJ8uRD83Gv9ObfEMb8jWkU+QvWjlueP060brD1/Cp3ULKo9ncnj8l5z4fr5DmKaP30P0s6PxCGtA8a797H36bYq27HZLHs7oHKPQo5WCjydkF8LybWZO5tcdPi4SEtqpCPCG/BJYtdPMkUz7533bKrRuouCnB5MZsvIhade5j3mpNv4xk7VLp1BalEtYZBw3jnqFxs3bOw2bfeIQK+dP4mTaXgpzTzL0npfoNfiBWuGK87NZNucjDu76i2pDJcGhTRgx5j0imrn3lXRgrb97tNbg5aFwNNPM3L+qzlt/926rITFei69e4WSemflrDA7199nGDPOgVVMNU5dWsueoyWmYSzWkh46ebbXWPJw08fOfledtK/Rpr2VAZx1+eoWMXDNzkypJz7bn4cmRXrRo7NjcX7fbwJxVVW7JA1wbbalBXTR0O52HtCwz89dUnzcPPduoSYjX4OulkJlnYeE6A8dz7HFG9NPSIkKF3+k8HMsys2ST+/LQOUahe9xZ5dR2M5nnKqcaW8sp/9Pl1J+7apRTbazllO9Z5dTq3e4tp64U18pbPK4W8te+ClVXu74BL6B/vIY+7TTM/cvAp3MrMVRbeORGDzTquuN0aK7m5t5aVmytZuIvlZzMs8bx8bKHOXHKzJw/Dfx3ViXf/laJosCjN3qguGE62/XdPEjs6MGsFeV8MLMEQzU8eZv3OfPQKVbLiEQvlmyo5D8zSjiRY+LJ27zx0VsTWFBi5uUvihy239ZVUGmwsO+oa8/FtlEqbuiqJmmnia9+rSarwML912nw9nQePrKBwm39NGw/ZOLLX6tJSbdwd38NDQPsf9w+bVV0b6Xi141GvllixGCE+6/XonFj6bf+rz+YMXkSt939MOM/mULTZjGMf/1ZigoLnIb39vHjljse4O0Pv+b9z6aTcN0wvpr4Hju3bbKFqaysJK51e+558HH3JdyJQd096N/Zgx+XlfP+jBIM1Rb+dYfPOc+pznFabhvgxW/rKnlvWjEnckz86w4ffPX27+XBYd6EBan5cl4pb08pZsfBah4Z7k1kw3Mc+CJsWbeMX6Z9xLA7HuOVD36icdOWfPr2ExQXOW9RHdy7la59buDZcd/y4nvfExgSyidvPU5BXnatsDs2rSL14C4Cghq4NM3ObFyzgpnffcKtd43mnQnTaRIVw/tvjKWo0Hk+UvZsp2e/Qbzy7he8+cFkgkIa8v4bT5GflwOAxWJhwnv/Jicrg2de+YB3Js4gpGEY41/7F5WVFW7Jg9pbT/GuA+x5atwFhfeKakzXRV+Tl7SJtV2Gc3TSdNp9/Q4h1/exhWl0+xBaffAyh975nLXdbqVk1366L/4OXYMgt+QBoFWkwnUdFdbssfDdMjM5hRbuSlSh93AePiIYbu2pYmeqhcnLzBzMsHB7HxUN/O1h8ktg2TYz3y418/0fZorKLNx9jmNeqt2blrD0p/fpP/yfPDFuLmGRsUz78BFKi/Ochq82VBLUIJJBtz+Lj3+I0zAVZUV88+49qNQaHnjuG54a/xs33PUinnrnHdyu1L+jlr7ttfyy2sAncyswGC08eqPnOcup+Bg1N/fWsXxrNRN+ruBkrplHb/R0qL/P6Nfe/c/zBnbW0S9ex5xVVUyYXY6h2sI/btGfMw8dW2i4ta8HyzZV8cFP5Zw8ZeLxW/T4eDk2MNbvNvDqt6W2beFa93VOXAttqcR4Db3baZi3xsCkeVUYqmH0MN1583BTLy1/bDXyydwqMvPMjB7m4dB+yThlZk5SNR/OruK7xVUoCowZpnNLHlpFKgyMV1i718KU5afLqYRzl1O39FSRnGot1w5mWLitt2M5lVcCy7abmfy7mRkrzRSVn/uYQlysq7aD4rfffiMgIACTydqLnZycjKIovPTSS7YwY8aMYdSoUeTl5XH33XcTERGBXq+nXbt2/PTTTw7HS0xMtL12xd/fn5CQEF577TUsFnuvZkFBAffffz+BgYHo9XqGDBnCoUOHzpvWpKQkHnroIYqKilAUBUVRePPNN22fl5eX8/DDD+Pr60uTJk345ptvbJ+lpaWhKAqzZ88mISEBT09PZs6cCcDkyZNp1aoVnp6exMXF8cUXXzj83hdffJGWLVui1+uJjo7mtddes3VuTJs2jXHjxrFz505bmi7m1THHjx/njjvuICAggKCgIIYPH05aWhoAy5cvx9PTs9aokbFjxzJgwADbz2vXrqVv3754eXkRGRnJU089RVlZ2d9Oy6Xq217LH9uq2ZtmIjPfwqxVBvz0Cm2b1V0jJXTQsGmfkS0HTGQXWJi72kB1tYWucfbGzKYUE6mZZgpKLGTkWvh9UzWBviqCfF1fI/Xv5MHvGyvZdcTIyVwz05eU4e+jokOMts44A7t4sH63gY17DGTlmZm1ogJDNfRsqwPAYoHicovD1iFGy/YDBqpc3FfWq7WKbYfM7Dhs5lQR/LrBRLUJOsU4L6p6tFJxOMPCur1mcotgVbL1u+seZw/fs5Wav3aZ2H/cQnaBhXlrjfjqIa6J+4q/xQtmM2DwTSReP4zGTZox5p8voPPwIGnFb07Dt2nfiW69EoiIjCKsUWOGDr+DJs2as3/fTluYfgNuYOTdD9M2vqvb0u3MwC6eLN1Qyc7D1WScMjH1tzICfFTEt6z7nLquqyfrdlaxYbeBzDwzPy4rp7oaerXT2cJER2j4c3sVaZkmcovMLN1QSXmVhSZhru2g+OPXGfS5bgS9B9xCeGRz7n3sVXQenqxfucBp+NFPjyfxhjuJbBZHWONm3P/4G1gsFvbv3uwQriAvm1mT/8Pose+hVrv/5mXpwp/oP2g4CdfdRESTaB564iU8PDxZ/cevTsM/8dxbXD/0NppGtyS8cRSPPPkKZrOZvTu3ApB18jiHD+zhoSdepHmL1oQ3bspDj79ItaGKDX8td0seTi37i4NvTCR74R8XFL7po3dRcfQEKf9+n9L9qRz7YiZZc5fRbOyDtjDNnn6I49/N4cT0eZSmHGH3E29gKq8k8sGRbskDQPc4heQjFnYdtZBbDEu2WDAaoUO08zK9W6zCkUzYuN9CXjGs3m0hqwC6tLCH33vMQlo2FJZBbjGs2GHBU6fQMMA9eVj3+3S6JNxO534jaBgRw80PvolW58m2v+Y5Dd84uh033PUC7XsMQ6PVOQ3z1+LJ+Ac1YuQj79G4eXuCGjSmRbveBIe6Z0TO2fq11/DHNoO1/s6z8NPKKvy8z11/9+ugZeM+I1v2G+31t9FCtzjHsi08WEVCvJbZqwxuzUNCRy3LN1exJ9Vaf/+wvBJ/b4V2zesuXxI76Vi/t5pN+4xk55uZs6oKg9FCjzaOeTAYoaTcYtuq3JiVa6Et1aedhpXbjexLM5OVb2H2n9Y8tImqOw9922vYlGJi6wETOQUW5v1VTbWRWnk4enYeNlvzEOiGPHSLVUhOtZdTS7eeLqeaOf9dXVsqHMmCTQcs5JXAX3ssZBVaR2GcsS/dsZz640w55e/0kEJctKu2g6Jv376UlJSwY8cOAFavXk1ISIjD+1tXr15NYmIilZWVdO7cmcWLF7Nnzx4effRR7rvvPjZvdmxwTp8+HY1Gw+bNm/nkk0/4+OOPmTx5su3zBx98kK1bt7Jo0SI2bNiAxWJh6NCh5x3R0KtXLyZOnIifnx+ZmZlkZmby/PPP2z7/6KOP6NKlCzt27OCJJ57g8ccf58CBAw7HeOmllxg7diwpKSkMHjyYmTNn8vrrr/Puu++SkpLCe++9x2uvvcb06dNtcXx9fZk2bRr79u3jk08+4dtvv2XChAkA3HnnnTz33HO0adPGlqY777zzb30H1dXVDB48GF9fX9asWcO6devw8fHhhhtuwGAwMHDgQAICApg7d64tjslkYvbs2dx7770AHDlyhBtuuIGRI0eya9cuZs+ezdq1a+t8P6+7BPkq+HkrHDphH7ZZaYD0HDNNQ51fJmoVRDRQcfCEfQiiBTiUUXccncZaWeUVmyksde2QvmB/Ff4+Kg4cMzrkIS3TRLNw5w0ctQoiQ9XsPyuOBdifbiS6jjiRoWoiQzWs3+3aFo5aBY2CFY6cdPx7HjlppnED53/PyAYqUjMdh+MezrAQ2cBaoQb6gK9e4chJ+9+6qhoyTtnDuJqxupqjhw/Q7qyOBJVKRbv4Lhzcv+e88S0WC7uTt5J5Ip1WbePdksYLFXL6nEpJczynjp6s+/xQq6BJmJqUGudUSlo10RH2OKkZRjrHadF7KihAl1ZatGqFg+nG2ge9SMbqatKPpNCqfXfbPpVKRVz77qQe3HVBxzAYKjGZjHj72FtgZrOZqZ++yqDhDxDeJMZl6a2L9ZzaT5v4brZ9KpWKNh26cnj/hU1lqKqqxGQy4ePrd/qY1utXe9bNpkqlQqPVcvCsjrH6FNAjntxVGxz2nVqxlsAe8QAoWi3+ndqQu3K9PYDFQu6q9QT06OiWNKlU0CgQjmY7lt9Hsy00DnZepkQEK7XCp2ZZiKgjvEoFHZsrVBosZDsfdHVJjEYDJ9P20rxNz7N+p4rmbXpy/HDyRR93/44/iYhqw0+fPc34J3vz+Wsj2JI0xwUpPrcgPwU/bxUHj9vrgkoDpGebaVpHh6daBY0bqBzqfAtw8ISJpmH2+kargXuv92DeXwZKKtwzDB8g2E/B31vFwXTHNsixLBPNzpGHyIaOcSzAwXQTUWGOdWaXWA3vPurNS/fqubGXDq2b+lSvhbZUXXk4nmN2ODdq50HhcI3z6dAJU5150J6VhyIX5+FMOZXmpJyKCKm7nKoZPjWz7vAO5VShS5J9RbOgXLHbteiqXYPC39+f+Ph4kpKS6NKlC0lJSTzzzDOMGzeO0tJSioqKOHz4MAkJCURERDh0CPzrX/9i2bJlzJkzh27d7A2+yMhIJkyYgKIoxMbGsnv3biZMmMAjjzzCoUOHWLRoEevWraNXr14AzJw5k8jISBYsWMDtt99eZ1p1Oh3+/v4oikJYWFitz4cOHcoTTzwBWEc9TJgwgT///JPY2FhbmKeffpoRI0bYfn7jjTf46KOPbPuaNWvGvn37+Prrr3ngAeu80FdffdUWPioqiueff55Zs2bx73//Gy8vL3x8fNBoNE7TdCFmz56N2Wxm8uTJKKfHp02dOpWAgACSkpIYNGgQd911Fz/++COjR48GYOXKlRQWFjJypPXp1vjx47n33nt5+umnAWjRogWffvopCQkJfPnll3h61jG238XODD2v2QApLbc4DEs/m7englqlUFojTkm5hYYBjhVSrzYahvXU4qFVyCkw882vVZjOPYX/b/PztqazuNzxwCXlZttnNfl4WfNQUlYjTpmZsCDnxUOvdjoy80wcPenaObh6D1CrFMoqHfeXVeIwxPBsPl5QWiN8aaUFHy/V6c8V277aYVyS7FqKiwsxm034BzgOMfcPCCLjRHqd8crLSnn8gVswVhtQqdQ8/PhztO/Yrc7wl4Ofz+lzqub5UW7Bz9t5o8tHbz2nnMUJC7Y3tL9dWMaY4d58PDYAk8mCwQhfzS8973opf0dpSQFmswnfgGCH/X7+wWRlpF3QMebNmIh/YAOHTo5lC6aiUqsZMOwel6X1XErOcU5lZhy7oGPMmv45gUEhtOlg7Thr1DiK4AZhzP7+C0b/8yU8PLxYuugn8nNzKCzIdXkeLoZHaAhV2Y5pqcrORevvi8rTA22gPyqNhqqcvBph8vCOjXZLmvQ6UNVRTgXXMZPBxxOn4b1rlEEx4dapIFoNlFbAj0lmKtzwpLu8xHo++fg7Xhc+/sHkZh696OMWnDrO5j9n0WvwgyTc9CgZqXtY/MN7qDU6OvW55RJTXTe/OurvkgqL7bOaztTfJeU16oYKCw0D7WXb8N46jmWZ2JvmnjUnzvA9XUfXTE9JucX2WU3eZ+rvWnW+hYZB9rJ22wEjBcXWaUPhISpu7u1Bw0AVUxZX1jzkJbsW2lJn0lkrPRUWfOtoN3h7WtsvJTVmx5VW1M5DzzZqhvaw5+Hb3wwuz4PLyqkq6/6zxTSyTgU5U079tNo95ZT433bVdlAAJCQkkJSUxHPPPceaNWsYP348c+bMYe3ateTn5xMeHk6LFi0wmUy89957zJkzh4yMDAwGA1VVVej1eofj9ejRw3ajDdCzZ08++ugjTCYTKSkpaDQaune3N1KDg4OJjY0lJSXlkvLRvr19UaoznRg5OTkOYbp06WL7f1lZGUeOHGH06NE88sgjtv1GoxF/f/ud3OzZs/n00085cuQIpaWlGI1G/PxcNxd0586dHD58GF9fX4f9lZWVHDlyBIB7772XHj16cPLkScLDw5k5cybDhg0jICDAdoxdu3bZpq2A9Qmy2Wzm6NGjtGrV6rzpqKqqoqrKcT6lsdqERlv3pLiOLdTclmB/cvjdYvfNxwTYfsjIwRMm/PQKCfEa7hvkwWfzKzFeQpunaystd19vP4e/mFfqgpSem1YDXeJ0/L7R9Q2b/3WeXnre/3QalZXl7EnexozvJtEwLJw27TtdtjR0a63jnsH2c+rzX9x3Tt3c1xO9h8KEWSWUlluIb6nlkeHefDizhJO5Lm6tXaTf501hy7plPDduMlqdtTw5dmQfqxb/yCsf/ORQX1zJFv0ynY1rVvDKu1+gO50PjUbD0y//h28nvctj91yPSqWmTYeudOjcE4v7HhSLcziWDZOXmfHysD6ZHNFLxdQVZsrdWz25jMVsIbxZGwbd/gwA4U1bk51xiC2rZrm0g6JTCzW3Jdrr98luuNEGaBOlJiZCzcdzXL8mS+dYDXcOsN/5fb3IPeu+AGzYYx/lm5lnprjMwpMj9QT7V5F3nkUfz+daaEt1bKFmRD/7lJipS9x7t73jkIlDJ8z46hUSOmgYdb2OLxZUXVIeLqdjOfDdcms5FR+tcGtPFdP+uHrKKXF1uKo7KBITE5kyZQo7d+5Eq9USFxdHYmIiSUlJFBQUkJCQAMAHH3zAJ598wsSJE2nXrh3e3t48/fTTGAxXRpefVus4V1BRFMxmxwa6t7e37f+lpdabhm+//dahwwRArbb2mm/YsIF7772XcePGMXjwYPz9/Zk1axYfffSRy9JdWlpK586dHToXzmjQwLpoXNeuXWnevDmzZs3i8ccfZ/78+Q5rXZSWlvLYY4/x1FNP1TpGkyYXNm91/PjxjBvnuOhaz6H/R68bX6kzzr40Ex9n2xs1ZxY+8vVyfKLio1fqvFkqq7RgMltqLUblq1corvEUpNIAlQYLuUUWjmUbePthL9o2U5N8+OJrpF2Hq0nLtL+K4kwe/PQqisvsx/XVW9+i4ExphTUPvt4q4Kw43iqKy2o3XDq21KLTwqa9rr92yqvAZLbUWhDT25NaTyXOKK2o3bvv42l/EnPm37P3nfk5M989d2F+fgGoVOpaixcWFeYTEFj3wn0qlYqw8MYAREW3JONEGgt/nnFZOyh2HjZw9KR9ioXmdA3h513znFLqPqfKreeUX81zSm8fVRESoKJ/Z0/GfVdE5unrK+OUiZjGGhI7efLj8nKX5MfHNxCVSk1JoeMT9uKiPPwDnC/0d8byhdP5ff4Unn7jaxpH2d+WcihlOyVF+bz82BDbPrPZxC/TP2bVbzN576ulLkn72XzPcU7VHFVR0+L5P/Db3O956a3PaNKshcNnzWJa8d4nP1BeVorRWI2ffyBvPP8wzWLiXJ6Hi1GVnYtHqOP35BEaQnVRCebKKgy5BZiNRjwaBtcIE0xVlntGgZQbwFxHOVVWVzlVyQWFrzZBQal1O5ln4fFhCvHRCutTXFtW6X2t51NpkeN1UVqUV+cCmBfCJyCEhuHNHfY1aBTN3i2uXdNkb5qJY7Ptf7y66m9fL4WMvHPX3zWf6vucdYyYCDXB/grvjHF8mPXAYA9SM818ufDiO0b2pBo5lmVfa0ujtqajZvvBV6+QcaqOPJypv/UqwOwQp+aoyLOdeUNXA38VeUWXdld8LbSl9qWZHN56ciYPPk7Op5N5zq/Fskpr+6XmCIuax6iZh/RsA+Me8rzkPNR0znKqjtPWaTnlUXuUas1y6h9DFTpEK2xwcTl1pbFcJQ8krhVX7RoUYF+HYsKECbbOiDMdFElJSSQmJgKwbt06hg8fzqhRo+jQoQPR0dEcPHiw1vE2bdrk8PPGjRtp0aIFarWaVq1aYTQaHcLk5eVx4MABWrd2/urAs+l0OtuCnpcqNDSU8PBwUlNTiYmJcdiaNWsGwPr162natCmvvPIKXbp0oUWLFhw75jgU+FLT1KlTJw4dOkTDhg1rpePskRz33nsvM2fO5Ndff0WlUjFs2DCHY+zbt69W/JiYGHQ65wtx1fTyyy9TVFTksHUb/Pw541RVQ16xxbZlF1goLrPQorF9WKSHFpo0VHEs23mlajJbV2Ru0dh+GSlATETdcc52rtWgL0RVNZwqNNu2zDwzRaVmYpva+x09dRDVSO1w01kzD8ezTcQ2scdRgNgmGlKdxOnZzoPdR6prDX10BZMZMvMsRDdy/HtGN1Jxoo4G2vFTZofwAM3DFY6fsqavoNQ6TDS6kb1i8dBa54qeCeNqGq2WZjGx7Dm9GCFY1yzYs3MbLeMu/FV7FrPlsr+xp8pQ45zKtZ5TcTXOqWbhzs8PsH6P6VkmhzgKEBelJTXDGkd3+qOaT+rNFly6mrlGq6VJ81aknLXApdlsZv+uzUS3dP46RbBO4Vj8y7c89doXRMU4vsazR8KNvPbxz7z60WzbFhDUgEE3P8BTr33pusTXyEezmDj27tzikI+9u7YQE+f89acAv82dwYLZU/j3GxOJblH3aDS9tw9+/oFknUwn9XAKnbv3c2n6L1bhxmSCB/Rw2BcysBcFG5MBsFRXU7R9LyED7GspoCgE9+9J4cYdbkmT2QyZBRAV6niiRoUqnKjj5iUjz0KzGuGbhSlk1BH+DEUBtWvXjAVAo9ERHtWG1H0bbfvMZjOp+zYSGRN/0cdt2qITuVlpDvvystIICAm/6GM647z+dqyLPbTQJFRluxmvyWS2vhWiRYT9D6wALRqrOZZlrW9Wba/mo9kVfDzHvgEsXGdg9iW+orOqGnKLLLYtK99MUZmZlpFntUF00DRMzdFz5OF4jmMcBWgZqSYtq+42SEQDa/iaN/8Xm49roS3lNA8RjnmIbKiynRvO82Ahpsb5FBOhvqA8uPo6P1c5Vddr2zPyLEQ1dFJOneM172Atp9z5VjTxv+mqPqUCAwNp3749M2fOtHVG9OvXj+3bt3Pw4EFbp0WLFi1YsWIF69evJyUlhccee4zs7NqvjEtPT+fZZ5/lwIED/PTTT0yaNImxY8fajjF8+HAeeeQR1q5dy86dOxk1ahQREREMHz78vGmNioqitLSUlStXkpubS3n5pT0hHDduHOPHj+fTTz/l4MGD7N69m6lTp/Lxxx/b0puens6sWbM4cuQIn376KfPnO747PioqiqNHj5KcnExubm6taRLnc++99xISEsLw4cNZs2YNR48eJSkpiaeeeooTJ044hNu+fTvvvvsut912Gx4e9qGZL774IuvXr+fJJ58kOTmZQ4cOsXDhwr+1SKaHhwd+fn4O27mmd9Rlza5qBnbW0jpKTViQwt0DdRSXWxzed/7YTR70bmu/8Vq900j3Vhq6xKppGKAwop8WnVZhy37rjViQr8KAjhoiQhQCfBSahqq4f7COahPsT3f9eL4/t1dxQw8P2jXXEB6i4v4h3hSVmtl52H6j+9Tt3iR0tHf+rNxaRe/2Orq30RIapOKu673w0MLGPY6jJBoEqIhprGb9LveNPFq/z0znlirim6sI8Ycbe6jRaWD7YWsFP6KPmus62WvyjSlmYiIUerVWEeIH/TuoCQ9W2LTf3iDYkGIiob2a2EjF+h310VBSDvvT3TeNYNgtd7Jq2a+sXrmEjONpfPfFh1RVVpJwnbVz7vOP3uanafab2QVzvmfXjs1kZ2WQcTyN3+b9xJo/f6dv/0G2MKUlxaSlHiQj3TpH/OSJdNJSD1JY4Py1gK6ycmslQ3p50j5GS3iIigeHeVNYaib5oP2cevpOHxI72a+5P7ZU0qeDBz3a6ggLVnH3YD06LbaFVbPyzeTkm7h3sJ6oRmpCAlRc19WDVlEadh5y7fl13U33sfaPeWz4cxGZJ1L58Zt3MVRV0GuAtdye+umrzP/hU1v43+dPZdFPX/DAE28S3CCcooJcigpyqaywltk+vgFENIlx2NRqDX6BwYRFRLk07WcbMvxukpYv5K+Vi8k4fpSpX75vPacG3gjAVxPeZPb0z23hf537Pb/M/JpHnnqVkNBwCgvyKCzIs+UDYNPalezbvY2crAy2bVzNf15/ii7d+9GuY49av98V1N56/DrE4dfBOkJD36wxfh3i8IxsBEDsO8/SYer7tvDHvpmFvlkkceNfwDs2mqb/uIdGtw/h6CfTbGGOTpxK5Og7iLjvFnziomn7+ZtovL04Pt352yhcYdN+Cx2bK7SLUgj2gyFdFLQa2JVqbcjf1F0hsb29ob/5gIXoRtA9ViHYF/q2VWgUCFsPWcNr1ZDYXiE8GPz0EBYIN3ZT8PWClHT3dKT2vuEBtq7+me1rF5Bz8giLpo/DUFVB5763AvDL1y+yfM7HtvBGo4HMYylkHkvBZKymuCCHzGMp5GXbH3z0GvwAx4/sJOnXr8nLPsbODb+xJelnug90/1otf+0ycl1nHW1O19/3DPSguMyx/v7HzZ4O9fdfO6vp3lpDl1gNDQMVRibo0GkUNu+3lm0lFRay8h03gMJSC/klrv9eVu+oZlA3D9o2U9MoWMWoQZ4UlVnYfcTeGfzPEV70bW8fdZu03UDPtlq6ttIQGqji9gEe6LQKm/ZZ8xDsrzCom47GDa1vu2jbTM2oQZ4cPmF021S6a6EttXa3kQGdNbRuqiIsSOHOAdY8nL0WySM36ujVxt4eWbPLSLdWajq3tObh1n7WEadbD9jz0L9GHu4bdDoPx1yfh80HLMRHny6nfM8qp46eVU61s5dTWw5ay6luZ8qpNtZyatthezmV0M6xnBrW9XQ5dfzaHj0hLr+reooHWNehSE5OtnVQBAUF0bp1a7Kzs22LTL766qukpqYyePBg9Ho9jz76KLfccgtFRUUOx7r//vupqKigW7duqNVqxo4dy6OPPmr7fOrUqYwdO5Ybb7wRg8FAv379WLJkSa0pGs706tWLf/zjH9x5553k5eXxxhtvOLxq9O8aM2YMer2eDz74gBdeeAFvb2/atWtnW2zy5ptv5plnnuHJJ5+kqqqKYcOG8dprrzn8zpEjRzJv3jz69+9PYWEhU6dO5cEHH7zgNOj1ev766y9efPFFRowYQUlJCREREQwcONBhrYuYmBi6devG5s2bmThxosMx2rdvz+rVq3nllVfo27cvFouF5s2b/+03irjCn8lGdFqF2xJ0eOngaJaZb39znBcY7Kfg7Wkv0HceMeHjVc3grlp8Tw9hnPxbFaWnR58aTdCskZq+7bV4eVinHKSeNPPZ/EpbGFdasbkKnVbhnkF6vDwUjmQY+XxumUMeQgLUeHvZd2w/UI2vvoIbe3udHk5q4vNfymoNS+zZVkdhicXhjQ6utifNjN4TBsSr8fFSk5VvYcYfRtuQRH9vxeHVv8dPWfjlLyMDO2q4rpOavGILP/1pJKfQHmbtHjM6jcLNPTV46iA928KMP6oxunGZg179rqO4qJCff5hMYUE+TaNb8NJbH9mmeOSeykZR2c+jqqpKpnzxEXl5Oeh0HoQ3bso/n3udXv2us4XZumkNX018z/bzp/99A4CRdz/M7feOdltelm+qwkOrcO9gPXpPhcMnjEyaU+pwTjUIVDkMz92233pO3dTHEz9v6xSjSXNKbeeU2Qyf/VLKLQlePDHSBw+twqlCE9MXl7Mn1bXnV9fegyktKmDRrC8pLsylcbNYnnr1C/xOL5yZn5vpsJbEX8vmYDRW8/WHjqOwbrzjMW6683GXpu3v6NH3eoqLCpn74zcUFeTRNLol/35zIv6B1nzknspGUezPHFYunYfRWM2n/3nZ4Ti33jWGkfdY1y4qLMhl5pSJp6cfhdCn/xBuvdN955J/57b0XDnD9nPrD/8PgOPfz2PX6JfxaNQAr9OdFQAVaSfYcvNjtP7oZaL+dT+VJ7LY/dir5K5YawuT+fNSdA2CaPnGU3iENaB4ZwqbbxyDIcd9HXcpx61DpxPaWeuD7EKYlWSm7HQfv7+3ggV7GZSRBws2mElspyKxvUJ+Cfy81voqZbCOHAr2Vbitt4KXB1QYIDMPvl9pJrfYPXlo130oZcUFrJz3KaVFuTRq0ooHnv/GNsWjMD8TRWU/n0oKTvH56/aFutcuncLapVOIiuvKmJe/B6yvIr3nqU9Z8fMEkhZ+QWBIY4be+xLxvW5yTybO8ueOanQauC1Rh5dO4WimmW9+q6xdf59VTiUfNuHtaWBwNy1+eh0ZuWa+/c09dfOFWLnNgE4Ldw70xMtDIfWkia8WlDvmwV/lkIcdh4z4eFUxtIcHfnqFE7lmvlpQbitrTSaIjVSTGK9Dp7V2ruw8bGTZFvctGHAttKWSko3oNDAyQYenDtKyzHy32FDju3A8n3YeMeHtCYO6ak7nwcJ3i8/Og4VmjVT0aaex5eFoppkv5lfVOe3iUqQct6D3gH5t7eXU7NX2cspP79ieysiDhRvMJLRTkdhOoaAUflnnWE6F+Cm0jzqrnMqHGavcV05dSSwWmeJxOSkWiyyHBdapIfHx8bVuoMXV6fkvXTOHvT5VlF0Za6RcquAG+vMHugrc2uvqr4G/nueG8eL14K5hl+ftPu7krb02VhTLadv9/IGuAskz9tV3Ei5Zy6hrowG9cXs99RC4kLH6yljo91JpdddGnaG6BtYPCAi8sGnPV7r/u/PqPKcOH7n4Nxy5W0zzZvWdBJe7qqd4CCGEEEIIIYQQ4togHRQuMmTIEHx8fJxu77333vkPcAWYOXNmnXlo06bN+Q8ghBBCCCGEENcQC6ordrsWXfVrULhKUlLSJcWfPHkyFRXOhyUGBZ37NXBXiptvvrnWa0vPuJB1NoQQQgghhBBCiIslHRQuEhERUd9JuGS+vr74+vrWdzKEEEIIIYQQQvwPkg4KIYQQQgghhBDCCQtX/0KrV5Nrc+KKEEIIIYQQQgghrirSQSGEEEIIIYQQQoh6J1M8hBBCCCGEEEIIJ2SKx+UlIyiEEEIIIYQQQghR76SDQgghhBBCCCGEEPVOpngIIYQQQgghhBBOyBSPy0tGUAghhBBCCCGEEKLeSQeFEEIIIYQQQggh6p1M8RBCCCGEEEIIIZyQKR6Xl4ygEEIIIYQQQgghRL2TDgohhBBCCCGEEELUO5niIYQQQgghhBBCOGGxyBSPy0lGUAghhBBCCCGEEKLeSQeFEEIIIYQQQggh6p1M8RBCCCGEEEIIIZyQt3hcXjKCQgghhBBCCCGEEPVORlCIa1KXDl71nYRLtn2X9NZeSVbtDa7vJFyy3j2vjXPqw0/21XcSLplfsF99J8El2sy4+r8LgPj7Wtd3Ei7Z0YX76zsJLrFp6db6TsIlSxzZs76TIM6iugaqvszMsvpOgotcG3WfcC/poBBCCCGEEEIIIZyQKR6Xl0zxEEIIIYQQQgghRL2TDgohhBBCCCGEEELUO5niIYQQQgghhBBCOCFTPC4vGUEhhBBCCCGEEEKIeicdFEIIIYQQQgghhKh3MsVDCCGEEEIIIYRwwmKRKR6Xk4ygEEIIIYQQQgghRL2TDgohhBBCCCGEEELUO5niIYQQQgghhBBCOGGWt3hcVjKCQgghhBBCCCGEEPVOOiiEEEIIIYQQQghR72SKhxBCCCGEEEII4YRFpnhcVjKCQgghhBBCCCGEEPVOOiiEEEIIIYQQQghR72SKhxBCCCGEEEII4YTFIlM8LicZQSGEEEIIIYQQQoh6Jx0UQgghhBBCCCGEqHfSQSFcZsGCBcTExKBWq3n66aeZNm0aAQEB9Z0sIYQQQgghhLgoFpQrdrsWyRoUwmUee+wxHnroIZ566il8fX2ZO3dufSfpgm1aOZP1S7+jtCiX0CZxDL33VRpHt3caNifjEKvmf0pm2l4K805yw90v03PQA7XCFRdks2LOhxza/RfVhkqCGjbhltHvEdGsnVvzMqiLhm6tNHh5QFqWmflrqsktspwzTs82ahLiNfh6KWTmWVi4zsDxHHucEf20tIhQ4eetUFUNx7LMLNlUzanCcx/3YnSLVdG7rRofL8jOt7B4s4mM3Lp/T5umCgM6agjwgfxiC8u3mTiU4Rh+QLyazi1UeOogPcfCrxuN5Je4POkO9q6fyc6/vqOiJJegRnH0Hv4qDSOdn1MAqbt+Z8vyTygtyMAvpCndhzxPk7gEhzAF2UfYtPRDMlO3YDGbCAxtzvWjPsUnMNxt+dj650w2LPuO0qJThEbGMfju14ho5jwfpzIOsXrRp2Qe20tRXgbX3/ky3a97sM5jr1v6DX/O+4huA+9n0F2vuCkHVvfe3IDBfQPw1qtJOVzOFzOzOJljqDN8mxZ6Rg4OpnlTT4IDtLzz+XE2JjueNAG+ah68LZSOrb3x9lKz91A5X/907uNeqtuu96N/V2+8vVQcTKtiyoJCsvKMdYaPa6bjxn6+NIvQEein5uPvc9m6r9IhzMjr/OjZ3ougADUmExw9YWD28mKOHHd9PjrHKPRopeDjCdmFsHybmZP5dYePi4SEdioCvCG/BFbtNHMk0/5537YKrZso+OnBZIasfEjade5jXqqgPl2Ifm40/p3a4hnekK0jnyB70cpzx+nXjdYfvoRP6xZUHs/k8PgvOfH9fIcwTR+/h+hnR+MR1oDiXfvZ+/TbFG3Z7b6MALvWzmT7qu8oL8klJDyOfiNeJaxp3eXUoeTf2bj0E0ryMwho0JReNz5PVGt7OWWoKmP9bx+RunslleWF+AU1pkPf+2jX+y635gNg9L1R3DQoDF9vDbtTivnwi0OcyKyoM/yo2yJJ6BVC0wg9VQYzu/cX8+W0VI5n2ONMeq8DHdsFOMRbsPQkH35xyC15GBivpktLe121aIORvPPUVd3jVPQ5XWdm5Vv4bZNjnalRww1d1LRvpkKthsMZFhZtNFJWeY6D/o/n41pphwAM7eFBr3ZavDwUjp40MXtVJacKzeeM07e9loFdPPDTK2TkmvnlzwqOZTvGiWqk5qZeHjQNU2M2Q8YpE1/ML6fa5M7ciP8VMoJCuERpaSk5OTkMHjyY8PBwfH196ztJF2zPpiUsm/UfEof/k8fenEdYZCwzPhpDaXGe0/DVVZUENojkutufw8e/gdMwFWVFfPfu3ag0GkY9+y1PvruYwXe9iJe3vzuzQmK8ht7tNMxbY2DSvCoM1TB6mA6Nuu44HZqruamXlj+2GvlkbhWZeWZGD/PA29MeJuOUmTlJ1Xw4u4rvFlehKDBmmA7FxR23baNU3NBVTdJOE1/9Wk1WgYX7r9M4pOVskQ0UbuunYfshE1/+Wk1KuoW7+2toGGBPWJ+2Krq3UvHrRiPfLDFiMML912vRuLH0O7JzCRt++w+dB/6TEU/NI7hRLEu+G0NFqfNzKittOyt/eo64rrcx4qn5RLW+juXfP0l+1kFbmOK8dBZ9dQ8BDaO56bHvue2ZhXQc+ARqrYfb8rF3yxJWzBlP35v+yZjX5hPaOI6fJo6mrK5rw1BBQEhjBoyo+9o44+TRXWxfPYuGjWPdkXQHI28I5qaBQXz+QybPvXeUSoOFt55uglZT9wns6aEi9UQlX/2YVWeYV/8ZSViItfNi7Nup5ORV886zTfDQueeJxk0Jvgzu5cOUBQW89nkOldUWXno4BO05HjV4aFUcy6xm6sKCOsNknqpm2qJCXpqYzZtf5nCq0MTLo0Pw9XbtRdIqUuG6jgpr9lj4bpmZnEILdyWq0NdxCkcEw609VexMtTB5mZmDGRZu76OiwVnFaH4JLNtm5tulZr7/w0xRmYW7z3FMV1B76ynedYA9T427oPBeUY3puuhr8pI2sbbLcI5Omk67r98h5Po+tjCNbh9Cqw9e5tA7n7O2262U7NpP98XfoWsQ5K5scHDHEtYs+A/dBv+Tu56bR0h4LIu+HkN5ifPrO/PodpbNeI423W/jrufnE932OhZPeZK8THs5tXbBf0jfv5ZBo/7LqJcWE9/vflbPe5vUPavclg+Ae0dGctuNEXz4xSEefX4HFZUmPn6rHTpt3ddix7YBzFt8ksde2MEzr+1Co1aY8FZ7PD0cz/tFv5/k5vvW27Yvpqa6JQ9926ro0VrFwg1GvlpsraseGKQ9Z/3dNkrFkK5q/kw28cWiarLyLTx4vWOdOaSrmrhIFbOSjHz3uxFfPdzT333PJ6/2fFwr7RCA67roSOioY/bKSj6aVUZVtYUnbtWf87vo1FLDrf08Wbqxiv/+WEbGKRNP3OqNj5c9P1GN1Dxxi579x4x8+FMZH84q46+dBlz/yEr8r5IOiivUb7/9RkBAACaTtSsyOTkZRVF46aWXbGHGjBnDqFGjyMvL4+677yYiIgK9Xk+7du346aefHI6XmJjIk08+yZNPPom/vz8hISG89tprWCz24qSgoID777+fwMBA9Ho9Q4YM4dCh8z8lSEpKsnVIDBgwAEVRSEpKchp24cKFdOrUCU9PT6Kjoxk3bhxGo/3pX2FhIWPGjKFBgwb4+fkxYMAAdu7cecF/t4uxfvk0Ove7nY59R9IwIoYb7x+HVufJjjXOR4BERLdj8J3/pl33YWg0Wqdh1i6ZjF9QI24dPZ7G0e0JbNCYmLZ9CGrYxJ1ZoU87DSu3G9mXZiYr38LsPw346RXaRNVdG/Vtr2FTiomtB0zkFFiY91c11UboGmev+DelmDiaaaagxEJGroXfN1cT6Ksi0Ne1N2K9WqvYdsjMjsNmThXBrxtMVJugU4zzoqpHKxWHMyys22smtwhWJZvIzLfQPc4evmcrNX/tMrH/uIXsAgvz1lobNnFN3Ff87VozjbhutxPbdSSBoTH0vXUcGq0nB7Y4P6f2rJtBZMs+dEgYTWBoc7oOHktIeGv2rp9pC7P594lExibQY+gLhES0xi+4CVGtB+DlE+y2fGxaMZWOfe8gvvdIGoTHMHSU9dpIXuc8H+HN2nPd7S/Sptsw1Bpdncc1VJaxYPILDLv/HTz17u20Axg+MIjZi3PZtLOUtIwqPp6SQVCAhp4d6+5I3banlB8WnGLDDuePuMJDdcQ11/PFzCwOpVWSkW3gi5mZ6LQqErq5J0839PZhwapitu2r5HhWNV/OzifAT02X1l51xtl5sJKflxezdW/djxnX76xgz+EqcvJNZOQY+eG3QvSeKpqEOS/fLlb3OIXkIxZ2HbWQWwxLtlgwGqFDtPNypFuswpFM2LjfQl4xrN5tIasAurSwh997zEJaNhSWQW4xrNhhwVOn0DDApUl3cGrZXxx8YyLZC/+4oPBNH72LiqMnSPn3+5TuT+XYFzPJmruMZmMftIVp9vRDHP9uDiemz6M05Qi7n3gDU3klkQ+OdFMuIDlpGm163k7r7iMJCouh/+3j0Og82bfJ+fWd/NcMmsb1odOA0QSFNqfH0LE0aNyaXWvs5VRmWjJxXW+hcUx3/IIa07bXnYSEx5Kdvstt+QC4/eYIvp9zjLWb8jiSVsY7E/YTHORB3x4hdcZ57s3dLF2ZzdH0cg6nlfHexAOENfQkNsaxXKisMpNfWG3byivc84i4V2vrTfGZuuqXNda6qtU56qrebVRsPWhm++k6c9EGE9VG6NzCGsdDa/3/0i1GUrMsnMyzMG+dkaahKho3cE9H6tWej2ulHQKQ2FHHsk1V7E41cjLXzIxlFfh7K7RvXnfHTv9OHmzYU82mfdVk5ZuZvbISg9FCzzb2+mBEPw9WJxtYsdVAVr6ZnAIzOw4ZMV7DoycsFuWK3a5F0kFxherbty8lJSXs2LEDgNWrVxMSEuJw47969WoSExOprKykc+fOLF68mD179vDoo49y3333sXnzZodjTp8+HY1Gw+bNm/nkk0/4+OOPmTx5su3zBx98kK1bt7Jo0SI2bNiAxWJh6NChVFdXnzOtvXr14sCBAwDMnTuXzMxMevXqVSvcmjVruP/++xk7diz79u3j66+/Ztq0abz77ru2MLfffjs5OTksXbqUbdu20alTJwYOHEh+vnvG6xqNBjLT9hLdxp5elUpFdOueHD+cfNHHPZC8ivBmbZn9+Vj++1QvvnzjVraunuOCFNctyFfBz1vh0Al7DVFpgOM5ZpqGOb/U1SqIaKBw+Kw4FuDQCRNNQ53H0WqsnRd5xWaKSl3XX65WQaNghSMn7cMILcCRk2YaN3CelsgGKlIzHYcdHs6wEHm6wRLoA756hSMn7emsqoaMU/YwrmYyGsjN2EvjFvZzSlGpiIjpSXZ6stM42ceSiYhxvGYat+xtC28xmzm+P4mAkCiWTB7N92/1Yv5nd5C298JukC6GyWgg89hemrVyzEdUq15kHNlxScde+uNbxLRPILp17XLC1UJDtAQFaElOKbXtK68wcyC1grjoum/sz+fM6AtD9VnnqwWqjRZat9BffILr0DBITaCfmj2Hq2z7KqosHDluoEXTujuD/i61GgZ086aswkx65rnL/r9DpYJGgXA027HMOJptoXGw82sxIlipFT41y0JEHeFVKujYXKHSYCG77gEjl11Aj3hyV21w2HdqxVoCe8QDoGi1+HdqQ+7K9fYAFgu5q9YT0KOjW9JkMhrIObGXyJaO13dki55kHUt2GicrLdkhPECT2N5knhW+UVQ8R/esorQwG4vFwolDGyk8lUaT2N7uyAYA4aGehAR5sCXZ/qWXlZvYd7CYtnF+F3wcb29rR35xieN5f31iQ36b2YvvP+vCY/c3w8PD9U1nW12V6VhXnThHXaVWQXiwwpHMGnVmppnI03VmRLCCRu143NwiKCy10MQNdeDVno9rpR0CEOyn4O+t4sBx+0PASgOkZZlo1sj5Qyu1CiIbOsaxAAfSjUSdjuPjpdCskYaScjPP3KHn3Ud8eOo2PdHh5xiWIcTfJGtQXKH8/f2Jj48nKSmJLl26kJSUxDPPPMO4ceMoLS2lqKiIw4cPk5CQQEREBM8//7wt7r/+9S+WLVvGnDlz6Natm21/ZGQkEyZMQFEUYmNj2b17NxMmTOCRRx7h0KFDLFq0iHXr1tk6F2bOnElkZCQLFizg9ttvrzOtOp2Ohg0bAhAUFERYWJjTcOPGjeOll17igQes6zVER0fz9ttv8+9//5s33niDtWvXsnnzZnJycvDwsI7P/fDDD1mwYAG//PILjz76qNPjVlVVUVVV5bCv2qBDqzv/GN/ykgLMZhM+fo5PoX38Q8jNOnre+HUpyDnO1lU/0XPwg/S78TEyju5m6cx30ai1xPe59aKPey6+emtFV1rh2KAvqbDgW8d9mLcnqFUKJTWm6ZZWWGgY4FgZ92yjZmgPLR5ahZwCM9/+ZsB07mmMf4vew5qWmvNJyypxGNJ9Nh8vKK0RvrTSgo+X6vTnim1f7TAuSXYtleUFWMymWiMbvHxDKDzl/JyqKM3Fy7d2+IqSXOvnZXlUG8pJTvqWLoPH0m3o85w4sIblM/7FjY9OJzy6m7PDXpLyUms+vGteG37B5GVd/BDnvZsXk5W+j9Gv/HKpSbwggf7Waq6w2PHRTmGJkQD/i68CT2RVkZNn4IERDflsRiZVVWaGXx9MgyAtQZdw3Lr4+1gbf0WljvkoKjXZPrsUHeM8+dfdQei0CoUlZsZ/d4qSctdd4HodqOq4voPruIf08cRpeO8a125MuHUqiFYDpRXwY5KZCvctA/K3eYSGUJWd67CvKjsXrb8vKk8PtIH+qDQaqnLyaoTJwzs22i1pqiizXt/6GuWO3jeEghzn5VR5Sa7T8OXF9rwljHyNVbNfY+q4BFQqDSgKA+58m4jmXV2fidOCAq0ddAWFjh0LBYUG22fnoyjw1CMx7NpXxNH0ctv+FatzyMqpJDffQPMobx5/MJomEV68Mn6f6zLAWXVVjfq79Bz195k6s7RW/Q0h/vbjGk0WKg01w1gchuy7ytWej2ulHQLgd3qKXklZjTZhucX2WU3eXgpqlUJxee04oUHWeibE3xp3aA8P5q+pIuOUiW6ttDw5Qs/4H8rOu76FEBdCOiiuYAkJCSQlJfHcc8+xZs0axo8fz5w5c1i7di35+fmEh4fTokULTCYT7733HnPmzCEjIwODwUBVVRV6veNTvB49eqCctWhAz549+eijjzCZTKSkpKDRaOjevbvt8+DgYGJjY0lJSXFJfnbu3Mm6descRkyYTCYqKyspLy9n586dlJaWEhzs2ACqqKjgyJEjdR53/PjxjBvnOBd45MOvc9voN12S7othsVgIj2rDdbc9C0Cjpq3JyTjElqRZLuug6NhCzYh+9iF3U5e4t0W+45CJQyfM+OoVEjpoGHW9ji8WVF3TQ/quFBaLtcJv2mYA7fs+CEBIeCuyju0gZeMst3RQuENRfibLZ73LPc9OQeOmtTMSu/vxz1H2RUPHTUp3y+8xmeDdL04w9sFwZn8Sh8lkITmljK27S8AFq2r3jvdi9K2Btp//Oy33HKEv3b4jVbz8aTa+ejX9u3nz1D3BvP55DsVlV35j81g2TF5mxsvDOoJiRC8VU1eYKa86f1zhWjvXzCDr2E5uHP0FvkERZBzZwuq5b+Ht15Amsa4ZMXV9QkNe+GdL28//fuvSFxJ99h8tiG7izRMvOo4OW7TMviJr6rEy8goMfPpuB8LDPDmZdfGrM3aIVnFzT3vH4ow/6l7o9kp2reTjWtAlVsNdA+09Hl8tLD9H6It35jZi3W7rNBCAE6eqaBmpoUcbLb+uuzYL3mv1bRlXKumguIIlJiYyZcoUdu7ciVarJS4ujsTERJKSkigoKCAhwbpy9gcffMAnn3zCxIkTadeuHd7e3jz99NMYDFfQIySsC2mOGzeOESNG1PrM09OT0tJSGjVq5HT9inO9rvTll1/m2Wefddi3cPuFPTXR+waiUqlrLYhZWpSLj1/dc1fPxyegAQ3CYxz2hTRqzr6tyy/6mDXtSzORftaqymcWPfLxUig5q/fb10vhZJ7zqRhllWAy136yUfMYYB0aWGmwkFtkIT3bwLiHPGnbTE3yYdf0UJRXWdNScyEqb09qjfA4o7TC+pTVIe2eiu3pzZl/z9535ufMfPcs5+SpD0RRqWstiFlRkove1/k55eUTQkVJ7fBep8Nbj6khsKHjORXYsDlZadtcmHo7vY81HzUXxCwtzrvoayPr2F7KSvKY/La9DLCYTaQf2sKWP2fy8pe7UakubTTApuRSDqTaOzS1WuvTngA/NQVF9sZzgK+Go8cvbfn3I+mVPPVWKnovFRq1QnGpiY9ebsahY3W/OeBCbdtXyeHj2bafNWpr48jfR01hif269/dRcyzz0sv6qmoL2XkmsvNMHD5u4OPnQ0ns6s2iJNcsM19uAHMd13dZXdd3JRcUvtoEBaXW7WSehceHKcRHK6xPuTKWbKvKzsUj1PGa8QgNobqoBHNlFYbcAsxGIx4Ng2uECaYqyz0dU17e1uu75oKY5SW56Ou4vvW+IecMbzRUsmHxRIY+NIlmbRIBCAmPJTdjPzuSprisg2Lt5jz2Hdxq+1l3+hoPDNCSV2C/FgIDdBxOLa0Vv6ZnHouhV9cgnnx5J6fyzn0t7TtQDEDjRl6X1EGRkm7m+Kmz62/r9e3jVaOu8qq7rjpTZ9Z8Cu/jhW00QmmFBY1awVOHw+gD6++59M7HayUfNdNyNbZDdqcaScuyn+9nvgtfb8cREb56hYxTztttZRUWTGYLfnrHm3FfvWLrrD7zb2a+4989u8BMoK+sHCBcQ86kK9iZdSgmTJhg64w400GRlJREYmIiAOvWrWP48OGMGjWKDh06EB0dzcGDB2sdb9OmTQ4/b9y4kRYtWqBWq2nVqhVGo9EhTF5eHgcOHKB169YuyU+nTp04cOAAMTExtTaVSkWnTp3IyspCo9HU+jwkpO4bIg8PD/z8/By2C5neAaDR6GgU1YbUffb5wWazmaMpG4mMib/ovDaJ6VhrikhedhoBwa57HWRVNeQVW2xbdoGF4jILLSLsN3geWut8wmNZzitwk9k6DzLmrDgKEBOhrvVKKWfULpxyaDJDZp6F6Eb2YkkBohupOHHKeVqOnzI7hAdoHq5w/JS1Mi4otQ5NjG5kr2w9tNZ1N86EcTW1RkdIRBsyDtvPKYvZzMnDGwltEu80TmjTeDKOOM5Rzzi03hZerdHRsHHbWlNEinLT3PaKUbVGR6OmbTia4piPtJQNRDS/uHnxUa168Oibv/LI6wtsW6OmbWnb/SYeeX3BJXdOAFRUmck8VW3b0k9WkV9YTXycty2Ml6eK2Ggv9qdeekcCWNe0KC41Ed5QR0yUJ5uSL/2mvtJg7zDIzrMuXllQbKJNjL1s8/JQaB6p49Ax13dGK4pyzrec/F1mM2QWQFSo4zGjQhVO1NGBmpFnoVmN8M3CFDLqCH+Gori2bLpUhRuTCR7Qw2FfyMBeFGxMBsBSXU3R9r2EDOhpD6AoBPfvSeHGS1vvpS7WMqUNJw46Xt/HD20krGm80zhhUfEcP+hYTh0/uJ5Gp8ObzUbMpmoUlWOZrKhUWMyuu4msqDCRkVlp246ml5ObX0WXDvYRR3ovNa1b+rFnf/E5j/XMYzH06xnC2Fd2kZl9/g6HFtE+AA4dIRfDYLS+gebMllNooaTcQvMadVXjc9RVJrO1Q85ZnXmm0yAjz4LR5FgHhvhBgI9CugvqwGslH2en5Wpth1RVQ26RxbZl5ZspKjMTG2l/Fu2pg6gwNUcznXdQmMzWdctanhVHAVpGakg7HSev2EJhqZnQQMc8NwhQUVB85Y+4E1cH6aC4ggUGBtK+fXtmzpxp64zo168f27dv5+DBg7ZOixYtWrBixQrWr19PSkoKjz32GNnZ2bWOl56ezrPPPsuBAwf46aefmDRpEmPHjrUdY/jw4TzyyCOsXbuWnTt3MmrUKCIiIhg+fLhL8vP666/z/fffM27cOPbu3UtKSgqzZs3i1VdfBeC6666jZ8+e3HLLLSxfvpy0tDTWr1/PK6+8wtatW89z9IvXa9CDbF/9M8lr53Pq5BF++/5NDFUVdOxjfco779sXWfHzR7bwRqOBzPQUMtNTMJmqKS7IJjM9hbzsY7YwPQc9yInUnfz121fkZR9j14Zf2ZY0h24D73VbPgDW7jYyoLOG1k1VhAUp3DlAR3G5hb1p9srokRt19Gpjb72v2WWkWys1nVuqaRigcGs/LTotbD1gfdoc5KvQv6OGiBCFAB+FpqEq7huko9oE+4+5dn7H+n1mOrdUEd9cRYg/3NhDjU4D2w9bK70RfdRc18me9o0pZmIiFHq1VhHiB/07qAkPVti0315JbkgxkdBeTWykQsMAhRF9NJSUw/5091Wk7fs+yP7NP3Nw23wKso+wZv6bVFdX0LKL9Zz6c/aLbF5qP6fa9r6P4wfWsuuvKRTmpLJ1xSROZeylTS/7+dI+YTSpu5aSsmkORbnH2LP+B46l/EmbHve4LR/dr3+IHWvmsHP9fHIzj7Bk5ptUGyro0Nuaj4Xf/ZtV8+z5MBkNZKWnkJWegslooKQgm6z0FPJzrNeGh6cPDSNaOmxaDz167wAaRrR0mgZXWLgynzuHNaBbBx+aRnjw7MPh5BcaHd7Q8e6zTbmxv/0Gx9NDoVmkB80irR0CoSFamkV60CDI3nDr3dmXdi31hIZo6d7Bh7efacLGHSXs2Ffmlnz8vq6UWwf40amVJ5GhGh6/I4jCYhNb99k7Wv5vTAiDeto7Yzx0Ck0baWnayDodrEGQhqaNtAT7W68jD63CnYP9iInUERKgplmElkdvCyTQT83GXa4dIrxpv4WOzRXaRSkE+8GQLgpaDexKtTbSb+qukNje3ojffMBCdCPoHqsQ7At92yo0CoSth6zhtWpIbK8QHgx+eggLhBu7Kfh6QUq6+0ZPqL31+HWIw69DHAD6Zo3x6xCHZ2QjAGLfeZYOU9+3hT/2zSz0zSKJG/8C3rHRNP3HPTS6fQhHP5lmC3N04lQiR99BxH234BMXTdvP30Tj7cXx6fPclo/4xAfZu/FnUjbPJz/7CH/+8iZGQwWtu1uv7+UzX2T9b/brO77ffaTvX8v2P6eQn53Kpt8nkXN8L+37WsspnacPEc27sm7RB5w4vImivBOkbJ7H/q0Lad7+erflA+DnRRk8cGcTencLJrqpN68+G0defhVrNtpHoEx8pz0jhtk7dJ97PIZBiaGM+zCF8gojQQHWBXV1OmvTODzMkwfubEJscx/CGnrQu1swrz4Tx449hRxJc/01vn6ficT2auIiFUIDFEb2tdZVKWfVVQ8N0ji8HWLdXjNdWqro2Nz6+t2be1rrzG2HrHGqqq3/H9pVQ7MwhfBgax2YnmPmhJs66a/2fFwr7RCApB0GBnfzoG20hkbBKu4b7EVRmYVdR+yjCZ8coadfB/t04T+3V9GrrZZurbSEBqq4Y6AnHlqFjfvsa7ys3GYgIV5HfIyGEH+FYT09CA1SsWHvlTVy25Xq+00d/2tv8ZApHle4hIQEkpOTbR0UQUFBtG7dmuzsbGJjYwF49dVXSU1NZfDgwej1eh599FFuueUWioqKHI51//33U1FRQbdu3VCr1YwdO9Zh4cmpU6cyduxYbrzxRgwGA/369WPJkiVota551dzgwYP57bffeOutt3j//fdt01bGjBkDWJ/YLVmyhFdeeYWHHnqIU6dOERYWRr9+/QgNDXVJGpxp230oZSX5rFowidKiU4Q1acV9z36Lj7911EZR3kmHtTtKCnP46g37OhLrf5/C+t+nEBXblYdemgFYX0V615OT+OOXj1m98AsCGjTmhntepn3Pm9yWD4CkZCM6DYxM0OGpg7QsM98tNjisExHsr+B91qJSO4+Y8PaEQV01+OoVTuZa+G5xlW1opdFkoVkjFX3aafDysA5XPJpp5ov5VbUWkrpUe9LM6D1hQLwaHy81WfkWZvxhtP0ef2/F4dW4x09Z+OUvIwM7ariuk5q8Ygs//Wkkp9AeZu0eMzqNws09NXjqID3bwow/qjG6sV3QvMNQKsry2bp8EuUlpwgOb8XQh7+1TfEoLXQ8p8KiOjHw7g/Zsmwim3+fgH9IFIPu/4ygMPtNe7O219Pn1jdJ/vMb1i96l4AGzbh+1KeENevstny06TqU8pJ8Vi/8lLLiU4RGtuLusZNtUzyK8jNRFHsjs6Qwh8lv32L7eePyKWxcPoUmLbtx/wsz3JbO85n7ex6eOhX/ui8cb72KfYfKef2TdKqN9vMkrIEWPx97ldiiqRfjX4iy/fzIndbFf/9YX8jEqScBCPLXMuaOMAL8NBQUVbNqQxGzfjvltnz8uroED53CmBGB6D1VHEyr4j9Tc6k+a9p3aLAGX2974zm6sY7XHm1g+/m+GwMAWL2tjK9/LsBssdCogZanR3nj662itNzMkRMG3vo6h4wc184nTzluHTqd0E7B21MhuxBmJZkpOz1l2d9bwYL9O8nIgwUbzCS2U5HYXiG/BH5ea331H4DZAsG+Crf1VvDygAoDZObB9yvN5J77wfkl8e/clp4r7edz6w//D4Dj389j1+iX8WjUAK/TnRUAFWkn2HLzY7T+6GWi/nU/lSey2P3Yq+SuWGsLk/nzUnQNgmj5xlN4hDWgeGcKm28cg6HGwpmu1LLjUCpK89n0+yTKik/RIKIVNz92VjlV4FhONWrWiUH3fcjGJRPZsHgCAQ2iGPbwZwQ3spdTg+//mA2LP2b5Dy9QWV6Eb2A4PYc+Tdted7ktHwAz5x7H01PNv59siY+3ht37injujd0Yqu3nU0SYFwF+9vbMrUMjAPhsfLzDsd6duJ+lK7MxGi10iQ/kjpsb4+mpJie3kqT1uUyffQx3WHO6rhrey15XTV9R7VB/B/lZr50z9qSZ8faEgR2tdWZmvoXpK4wOdfPSLSYsFri7vwaNCg6dtPDrRvetFXG15+NaaYcA/LHVgE6jcPdAT7w8FFJPmvhifrnDdxESoMLby16Pbz9oxMerkmE9PazTQXLNfLGg3GHab9IOA1o1jEjwRO9pnTLy+bxycouujGl14uqnWM6+ysQ1KzExkfj4eCZOnFjfSbksZq2/+k/r7btcfPdfTzy9rqCx1pfA3981HXX1qWHQtdHTPnuGa1fQrw9+db264irTJr7R+QNdBeLvc81Uxvp0dOH++k6CS8z68q/6TsIlSxzZ8/yBxGWjugaqvoKCa6NNOOnpq7Pu27y/6PyB6km3uDpeMXMVkxEUQgghhBBCCCGEE7K6xuUla1CICzJkyBB8fHycbu+99159J08IIYQQQgghxFVORlD8j3D26s6/Y/LkyVRUOF/xPigo6JKOLYQQQgghhBBCSAeFuCARERH1nQQhhBBCCCGEuKyu1bdlXKlkiocQQgghhBBCCCHqnXRQCCGEEEIIIYQQot7JFA8hhBBCCCGEEMIJCzLF43KSERRCCCGEEEIIIYSod9JBIYQQQgghhBBCiHonUzyEEEIIIYQQQggn5C0el5eMoBBCCCGEEEIIIUS9kw4KIYQQQgghhBBC1DuZ4iGEEEIIIYQQQjghb/G4vGQEhRBCCCGEEEIIIeqddFAIIYQQQgghhBCi3skUDyGEEEIIIYQQwgmzpb5T8L9FRlAIIYQQQgghhBCi3kkHhRBCCCGEEEIIIeqdTPEQQgghhBBCCCGckLd4XF4ygkIIIYQQQgghhBD1TkZQiGvSus0l9Z2ES1acX17fSXCJ5rEh9Z0El9B7Xv2951t3XP3XBcDgW1rVdxIumXL1n04AhAZdGyuHHV24v76TcMmaDY+r7yS4RNt3NtZ3Ei7ZgZ0Z9Z0El/AL8qnvJLhEtcFY30m4ZPFdwuo7CUJcNtJBIYQQQgghhBBCOGGxXCNPFa4SMsVDCCGEEEIIIYQQ9U46KIQQQgghhBBCCFHvZIqHEEIIIYQQQgjhhOXaWG7pqiEjKIQQQgghhBBCCFHvpINCCCGEEEIIIYQQ9U6meAghhBBCCCGEEE6Ykbd4XE4ygkIIIYQQQgghhBD1TjoohBBCCCGEEEIIUe9kiocQQgghhBBCCOGExSJTPC4nGUEhhBBCCCGEEEKIeicdFEIIIYQQQgghhKh3MsVDCCGEEEIIIYRwwmKp7xT8b5ERFEIIIYQQQgghhKh30kEhhBBCCCGEEEKIeidTPIQQQgghhBBCCCcsyFs8LicZQSGEEEIIIYQQQoh6Jx0UQgghhBBCCCGEqHfSQXER1q1bR7t27dBqtdxyyy117vs7kpKSUBSFwsJCl6b174qKimLixIl1fp6YmMjTTz9dZ3hFUViwYAEAaWlpKIpCcnKyW9IqhBBCCCGEEO5ktly527VI1qC4CM8++yzx8fEsXboUHx+fOve524MPPkhhYaGtQ+BymDdvHlqt9oLCRkZGkpmZSUhIiJtT5TpDe3jQq50WLw+FoydNzF5VyalC8znj9G2vZWAXD/z0Chm5Zn75s4Jj2fY4T92mp0Vjx0tt7S4Ds1dVuiUPtw7wIbGzF3pPFYfSDUz/tZjsfFOd4WObahnSx5uoRloC/dR88mMB2/dXOYTp3MqDAV31RIVr8dGreO2LXNKzjG5Jf5eWCr1aqfDxguwCWLrVxMm8usO3aqLQv72KAB/IK4GVO8wcPmktsVUK9O+gIiZCIdAHqgyQmmVhZbKZ0gq3JN9m55qZbF31HeXFpwiJiKP/yNcIa9q+zvAHdyxlw5JPKM7PIKBBFH1uep5mbRJsn5cV57L21w9J37+WqooSIpp3IXHkawQ2jHJvRoAhPXT0bGu/Ln7+s5JTheeuFfu01zKgs852XcxNqiT9rOviyZFeta6LdbsNzFlVVfNQl2zX2pns+PM7yktyCQmPo9+trxJ6ju/icPLvbPz9E0ryM/APaUqvG58nqrX9u/js2Tin8Xrd+AKdBox2efrP2LV2JttXnZWPEa+e85w6lPw7G5da8xHQoHY+DFVlrP/tI1J3r6SyvBC/oMZ06Hsf7Xrf5bY8bPxjJmuXTqG0KJewyDhuHPUKjZs7z0P2iUOsnD+Jk2l7Kcw9ydB7XqLX4AdqhSvOz2bZnI84uOsvqg2VBIc2YcSY94ho1tZt+bjav4ugPl2Ifm40/p3a4hnekK0jnyB70cpzx+nXjdYfvoRP6xZUHs/k8PgvOfH9fIcwTR+/h+hnR+MR1oDiXfvZ+/TbFG3Z7ZY8nO2mPp706eCBl4fCkQwjPy0vJ6fg3HV3QkcPBnX3wM9bxYkcE7P/KCct015X+nkrjEj0olWUFk+dQna+iaUbKtlxsNpt+bjzhgAG9vTF21PF/rQqvv05l6zcuuvaVtGe3DzAn+jGOoL8Nfz3u2y27Cl3CPPPu0NI7ObrsC85pZx3v8l2Sx4Ahifo6dfRE72nisPHq5mxtJScc7RDWjbRMrinF1GNNAT4qvlsThE7Dhgu+biXYsQAHxK76G1tqWmLis7TltIxtI83UeHWttTEH/PZnuJYn3Vp7Un/rnqanW5Lvfr5Kbe1pQD2rJ/JztXfUVGSS3CjOHoPf5WGTeoup47s+p2tyz6hpMBa93Uf8jxNWiU4hCnIPsKmJR+SeXQLZpOJwNDmXH/fp/gGhrstH+J/j4yguAhHjhxhwIABNG7cmICAgDr3XYuCgoLw9fU9f0BArVYTFhaGRnN19INd10VHQkcds1dW8tGsMqqqLTxxqx6Nuu44nVpquLWfJ0s3VvHfH8vIOGXiiVu98fFyXExn3W4D//dNiW1buNY9nRND+3hzfXc9034t5q1v8qgyWHj+/kC05/gKPHQKx7OMzFhcfM4wB9MNzFle4oZU27VuqjCok4rVu818s8REVoGFe/ur0Xs4D984BEb2VrHjiDX8geMW7uynooG/9XOtBhoFwZrdZr5dYmLOXyZC/BTuSjjHl+oCB7Yv4a/54+kx+J/c88J8GoTHMf/L0ZSXOO9pOXl0O0u/f442PW7j3hcW0LzdQH797p/knjwIgMVi4dfv/klx3nFuGvMF97wwH9+gCOZ98RDVVeVOj+kqAzvr6BevY86qKibMLsdQbeEft5z7uujYQsOtfT1YtqmKD34q5+QpE4/foq91XazfbeDVb0tt28K1ru+cOLRjCWsX/oeug//Jnc/OIzg8lkXfjKnzu8g8up1lPzxH6263cedz84ludx1Lpj5JXuZBW5iH3lzjsA24611QFJp3GOTy9J9xcMcS1iz4D90G/5O7nptHSHgsi74+Tz5mPEeb7rdx1/PziW57HYunOOZj7YL/kL5/LYNG/ZdRLy0mvt/9rJ73Nql7VrklD7s3LWHpT+/Tf/g/eWLcXMIiY5n24SOUFjvPQ7WhkqAGkQy6/Vl8/J13dFeUFfHNu/egUmt44LlveGr8b9xw14t46v3ckge4Nr4Ltbee4l0H2PPUuAsK7xXVmK6LviYvaRNruwzn6KTptPv6HUKu72ML0+j2IbT64GUOvfM5a7vdSsmu/XRf/B26BkFuycMZg7p70L+zBz8uK+f9GSUYqi386w6fc5ZRneO03DbAi9/WVfLetGJO5Jj41x0++OrtZdSDw7wJC1Lz5bxS3p5SzI6D1Twy3JvIhu6pP4YP8GdIPz+++TmPlyeepKrKzKv/CEOrqXtxPg+dwrEMA9/NPUcvPrAjpZxHXk+3bRNnnHJ18m2G9PLium5ezFhSyrtTCqiqtvDsPf7n/D50WoUT2UZ+WFrq0uNerGF9vbm+hzfTFhUx7utcqgwWXngg6LxtqfSsar7/rajOMDqtwsFjBmYvr7u95SqHk5ew4df/0Pm6fzJy7DyCGsWy+LsxVJQ6P1ey0raz8sfniO16GyPHzieqzXUs+/5J8rPs5VRRXjoLv7yHgIbR3PTY99z27EI6DXwCjbaORpoQF0k6KJyoqqriqaeeomHDhnh6etKnTx+2bNlim7KQl5fHww8/jKIoTJs2zem+81myZAktW7bEy8uL/v37k5aW5vD5m2++SXx8vMO+iRMnEhUVZft8+vTpLFy4EEVRUBSFpKQkBgwYwJNPPukQ79SpU+h0OlauPPfTEWcmT55MQECALW7NKR7nUnOKx5lpLCtXrqRLly7o9Xp69erFgQMHHOK98847NGzYEF9fX8aMGcNLL71U62/hDokddSzbVMXuVCMnc83MWFaBv7dC++Z110j9O3mwYU81m/ZVk5VvZvbKSgxGCz3bOI4yMRgtlJTbt8raDwZcYnBPPb/+VcqO/VUczzbyzbwiAnzVdIrzrDPOrkMG5q4sZVtK3TeH63dWsjCpjL2pbkr4aT3jVGw/bGFnqoXcYli82Uy1CTo2d95A6x6n4nCmhQ0p1vBJu8xkFkDXWGvRVlUNP6wysy/dQl4JZOTB0i0mwoMV/PTuy8f2pKm07XUHbXqMJDgshoF3jEOj82TvxrlOw+9Y/T1RcX3pMnAMQWHN6TXsaRo2bs3ONT8AUHgqjay0ZAbc/iZhTdsTFBrNwNvfxFhdyYHti92XESCho5blm6vYc/q6+GF5Jf7eCu3OcV0kdtKxfm81m/YZyc43M2dVFQajhR61rgscrosqN5xeyaun0abH7bTuNpKgsBj63zYOjdaTlM3Ov4uda2bQJK4PnQaMJii0OT2GjKVBRGt2rZ1pC+Pt18BhO7pnFY1juuMfHOn6DJzJR9I02vS8ndbdT+fjdus5tW+T83wk/zWDpmfnY+hYGjRuza419nxkpiUT1/UWGsd0xy+oMW173UlIeCzZ6bvckod1v0+nS8LtdO43goYRMdz84JtodZ5s+2ue0/CNo9txw10v0L7HMDRandMwfy2ejH9QI0Y+8h6Nm7cnqEFjWrTrTXBoE7fkAa6N7+LUsr84+MZEshf+cUHhmz56FxVHT5Dy7/cp3Z/KsS9mkjV3Gc3GPmgL0+zphzj+3RxOTJ9HacoRdj/xBqbySiIfHOmWPJwxsIsnSzdUsvNwNRmnTEz9rYwAHxXxLese7XldV0/W7axiw24DmXlmflxWTnU19GpnP8+iIzT8ub2KtEwTuUVmlm6opLzKQpMw93RQDEvwY+7yQrbuKSc9s5rPfjxFoJ+aru3qrqyS91cwa2kBm3efu6O62mihsMRk28oqzj265FJc182L39aUk3zQwIkcE98tLCHAV0WnuLpvYvccMTA/qdzpqIlLOe7FGtzTm0WrS9l+ui319dxCa1uq1bnaUlUX0JaqYGFSKXuPuLctBbB7zTRadb+duK4jCQyNod8Ia923f4vzcmr32hlEtuxDfOJoAkOb03XwWEIiWrNnnb2c2vL7RJrEJdBj2AuERLTGP7gJUW0G4OUT7Pb81DeLRblit2uRdFA48e9//5u5c+cyffp0tm/fTkxMDIMHD8bX15fMzEz8/PyYOHEimZmZ3H777bX23Xnnnec8/vHjxxkxYgQ33XQTycnJtpvwv+P555/njjvu4IYbbiAzM5PMzEx69erFmDFj+PHHH6mqsheQP/zwAxEREQwYMOBv/Y7//ve/vPTSSyxfvpyBAwf+rbjn8sorr/DRRx+xdetWNBoNDz/8sO2zmTNn8u677/L++++zbds2mjRpwpdffumy312XYD8Ff28VB47bh9pVGiAty0SzRs4bI2oVRDZ0jGMBDqQbiaoRp0uslvGP+fDyKG9u6u1xzl74i9UgUE2Ar9qh4quospCaUU1M5IVNy6lPKpV1tMPRLMepA0ezLDQOcV4ANw5ROJrpGP7IybrDg/Uph8Xivk4ik9FAzvG9RLbsZdunqFQ0admLzLQdTuNkHU0mMranw76mcX3ITEu2HRNAfdZTCkWlQq3RkZG6zcU5sDtzXRxMtw9rrTTAsSwTzepopJ+5Ls6OYwEOppuICnOscrrEanj3UW9eulfPjb10Lr8uTEYDOSdqfxeNW/Yk6/TftqastGQiW/Ry2Nckrned4ctLcjm2bzWturnvJqyufES26EnWMefpykpLdggP0CS2N5lnhW8UFc/RPasoLczGYrFw4tBGCk+l0SS2t8vzYDQaOJm2l+Zt7Oe5SqWieZueHD/sPA8XYv+OP4mIasNPnz3N+Cd78/lrI9iSNMcFKXbuWvguLkZAj3hyV21w2HdqxVoCe8QDoGi1+HdqQ+7K9fYAFgu5q9YT0KOj29IV4q/C30dFSppj3X30pJHocOcFiloFTcLUpBxzrLtT0qqJjrDHSc0w0jlOi95TQQG6tNKiVSscTHf9kPyGwRoC/TTsPmgfXVleaeHwsSpioy79BrxNjCeT32rCJy9H8Mhtwfjo3dP8DwlQEeCrZt/R2u2Q5hEXX8C767jO2NtS9nZ0RZWF1BMGYiKdd5ReaUxGA6cy9hIRU6Pua9GT7DrKqZz0ZCJq1H2NW/YmO90a3mI2k56ShH9IFIsnj2b6uF7Mn3QHR/dcWCenEH/H1TH2/jIqKyvjyy+/ZNq0aQwZMgSAb7/9lhUrVjBlyhReeOEFFEXB39+fsLAwALy9vWvtO5cvv/yS5s2b89FHHwEQGxvL7t27ef/99y84nT4+Pnh5eVFVVeXwO0eMGMGTTz7JwoULueOOOwCYNm0aDz74IIpy4b1sL774IjNmzGD16tW0adPmguNdiHfffZeEBOuctpdeeolhw4ZRWVmJp6cnkyZNYvTo0Tz00EMAvP766yxfvpzS0rqH/bmCn7e1si4pc7zZLSm32D6rydtLQa1SKC6vHSc0yH7ztnV/NfklZopKLUSEqLi5jyehgSom/+baRRD8fazpLCp1fDJSXGqyfXYl03uASqVQVun49yyrhBA/5+eujyeU1pgtU1ZpwcfTeXi1CgbGq9iTZsHgpmmfFWUFWMwm9L6OTxT0vsHk56Q6jVNWkoveN6RW+PLiXAACQ6PxDQxn3a8fMfDOt9DqvNieNI3SwizKit03VNfX2/p3LHFyjp/5rKYz10VJublWnIZnXRfbDhgpKDZTVGYhPETFzb09aBioYspi101/OvNdeNX6LkIozDnqNE55Sa6T7y6E8pJcp+H3b1mA1sOb5u3dN72j7nMqhIK/m49iez4SRr7GqtmvMXVcAiqVBhSFAXe+TUTzri7PQ3lJIWazCR9/xzT5+AeTm+k8Dxei4NRxNv85i16DHyTh/9m76/gojv6B4589lxgRSCCBENwtBJdCKYUK1L2lT/XpU2+fyvPrU2+pl+pTd2+p497iUNwhSEiIu5/s/v44cpdLLqGFHAH6fb9e+4Lsze7N3M7O7M7OzJ5zIxl7tzDjs6fRG0z0Hz75GGNd36lwLI6GuVU01dn+50B1dh7G8FB0FjPGFuHoDAaqc/LrhMnH3iUpaPEKC/GUQyXl9cubhuruENvhujvANrFRvjLq3Z/KuX6SnZfuiMDt9tQZb/1QdsR5qY5GRKjne4vK/Oc4KCpzez87Wut3VLJqUwU5BU5aRRm5/KwW/N+NsfzfK4eafIK9mmuNkjrXUiXlKmHHcB0SrP029l11r6WKy1UiToJrKYCqBuo+a8gR6r46PSFsIdFUHq77KsvzcToq2LDoXQaOv4NBE+/l4M7fmfvpbZxz48e07pASnMSIvyVpoKgjNTUVp9PJsGG+pxZGo5GUlBS2b9/eJN+xfft2Bg0a5LduyJAhDYT+aywWC1dddRUffPABF198MevWrWPLli38/PPPf3ofL774IuXl5axdu5akpKa/sOjd2zdBT1xcHAA5OTm0bduWnTt3csstt/iFT0lJYeHChsfhVldX+/UYAXC7qtEbGn7qkNzFwKVjrd6/3/opeOP4l2/xTaiVma9SUl7JbRfaiQ6vIq/46K8OhvS2MOUc3zjrlz4vPKZ4nup0Clw4QoeieIaOnEz0eiNnX/ca8778P956MAVFp6dt5yEkdhuJRtNdYQ7oYuCSMb4urG//HLyZRFfUOy80br3ARlR4NfnHcF4cb9tWT6fzgLNPyjG4G3//lKwDGzn7ujcJjWxDRuoalkx/HHtYS9p2GXrkHZwANFWjdfsenHHRXQC0bted7IzdrFn4VVAaKILlVDgWx0NKdxOXj/cNeXjju+A9vDh3hAWbWeHlr0opq9Do29nIDZPsvPB5KYfyjq0OGd7fzk0X+xqlp74bvAkrl68v9/4/LdPJgUwHbzyUQPeOFrbsPrYG4UE9zVx9lm9esle+bHj+hRPVkN4Wrj033Pv3i5/JtVQgmurJ84k9xtB75BQAolt3I3v/erat/OqUb6DQTp7LkiN64403eP7558nKyqJPnz689tprpKQEPn7vvvsun3zyCVu2bAFgwIABPP300w2GbyrSQHGC0ul0aHXOBqfzz80cff3119O3b1/S09P58MMPGTNmDO3atfvT3z1ixAhmzJjBN99885eHnvwZtd8CUtOrQ1WPvrKfOnUqjz3mP9HXwPEPMOjMBxvcZvNeF/uzfBc2Br0nHqF2/x4RoTaFjNzAszaXV2q4VY0wm/+T5FBb/Sczte3P8uwvOkJHXvHRzz69fkc1qem+p1XGw2kID9H5tfyHhehJywzerONNpaIaVFXDblGg1k233QJllYFrhrIqTy+K2uwWpV6viprGiXC7wqfz3UHrPQFgtbdA0enrTZhXUZqPPTTwRH/2AE/oK0rzsYX5wrdK6MmV9/1EdWUpbrcTW0gkX750Ea0Smu5NBVv2ujiQ5buY9Z4XtkDnReA8XnNehNp0gOq3TWkj58WBw+dFTLiO/GM4L2qrORaV9Y5F/R4rNTy9Jf5c+EN711KUs48zr3q5SeLbkIbzVJ5fHqmtwXQcDu9yVLFixjQmXvsa7XuMBiC6dRfyMnawfvEHTX5TbAuNQKfTU1bsH6ey4vwGJ8D8M0IiomnZuoPfupi4JLaumXvU+2zMqXAsjkZ1dh7mVv7pM7eKxllcilpVjSOvENXlwtwyqk6YKKqzAvc+Ohob9zjYd8hXgNfMwR1m11FS7is3Qm0K6TmBy5GyisN1t10H+G9TU3dHR+g4bYCFx94vJvNwY0RGrpuO8QZG97fwxdxje6ixdmsFe17IqJUOT1kbEaKnqMQXp4gQPfsPNe14xJx8FyVlbmKjjcfcQLFxl4PHMgq8f9ekI8yuUFyr7SjMruPgMbytouaapqn3CzXXUr48WjMpad1rqXC7jgNBfONGU7I0UPdVluVhbazuqzOBZkWt8BZ7C3Q6Ay1adfQLE9GqA1n7gjfUVDStr7/+mrvvvpu33nqLQYMGMW3aNMaPH8/OnTtp2bJlvfCLFy/msssuY+jQoVgsFp599lnOOOMMtm7dSps2bYIWz5Ojr9Jx1KFDB0wmE8uWLfOuczqdrFmzhu7duzfJd3Tr1o3Vq1f7rVu5cqXf3zExMWRlZfk1UtRMNlnDZDLhdtevgHv16kVycjLvvvsuX3zxhd8cD39GSkoKs2bN4umnn+aFF174S9seqy5durBmzRq/dXX/ruvBBx+kuLjYb0k+/e5Gt6l2Ql6x5l2yClSKy1W6JPja7CwmSIzVsy8z8EWOW4WDOSqda22jAJ0TDH6vKqurTYynu2bdrop/VZVDI6fA7V0ycl0UlbrpnuQbI2kxKyS1MbLn4InfQKGqkFkA7WP9G3zaxyqk5wX+rdLztHrhk+L8w9c0TkSGKny2wE1lkOem0htMtEzowcFdvvHamqpycNcK4hIDj8WObd+Xg7v8y4C0ncuJS+xbL6zZGootJJLCnP3kpG2hQ6+mmx+mofOic4Kvi7HZBO1i9ezLOtJ54dvGc17o2Z/VcAOF97yoaLrHFHqDiZbxPTi42/9YpO9eSWyA3xYgNrEv6bv9x9of3LU8YPhtq74jJr4H0W0Cv3a0qdSkI71untq9kth29eMFnnTUzoPgSUfc4fCq6kJ1O1F0/pcBik7nfVLWlAwGE60Te7B3my+fq6rK3m0rSejY96j3265Tf/Ky9vuty8/aT0R0cF55dyoci6NRtHIDUWMG+62LHjuUwpUbANCcTorXbSV6TK3eoIpC1GlDKFoZeO6do1HtgNwi1btk5qkUl6l0bedfd7dvbWDvocA3k24V0rLcftsoQNdEI3szPNuYDn9U96mpqsFfGC3boKpqjaw8l3dJz3JSWOKiZ2dfi7vVrNCxnZmd+5v27UaR4XpCbDq/hpCjVeXQyClUvcuhXM8knN3a17oOMXmuQ1Izjv7mPq9IDcp+vWmofS2VU3Mt5esVZzErJMWb2HMw+JNbNgW9wURMmx5k7PEvpzL2rKRVA+VUy7Z9/cIDZOxeTqu2fX37TOhJUa7/EJHi3P3yitGTyEsvvcQNN9zAtddeS/fu3Xnrrbew2Wx88MEHAcN//vnn3HLLLfTt25euXbvy3nvvoarqUb144a+QBoo67HY7//znP/n3v//N7Nmz2bZtGzfccAMVFRVcd13TvN/+5ptvZvfu3fz73/9m586dfPHFF/Xe/DF69Ghyc3N57rnnSE1N5Y033mDWrFl+YRITE9m0aRM7d+4kLy/Pr4fF9ddfzzPPPIOmaZx33nl/OY5Dhw5l5syZPPbYY0ybNu1oknlUbrvtNt5//30+/vhjdu/ezZNPPsmmTZsanT/DbDYTFhbmtzQ2vKMhi9c7GJ9ipmeSgbgoHVeNt1JcrrEp1Vf53Xq+jZF9fD1AFq2rZmhPIyndjLRqoePisRbMRoWV2zzHIjpcYXyKiYSWOiLDFHomGbhqvJXd6a5j7iIayJwVFZw7KoR+XczEtzRw4/nhFJW6WbfD95TkviktOD3F10XWbFJoG2ugbazniiymhZ62sQYiw33Fg93qCdP68E1kbLQnfFPPbbFih0r/jgq92ytEh8FZKTqMetiw13OVOGmIjjF9fd+5aodKh9YKg7sqRIXBqF46WkfCmp2e31anwEUjdMRFKvywzI2ieHpk2C2eSTmDpf/oa9my4hu2rf6BgqxUFnz7KE5HJd0HnQ/AnM/uY+kvL3rD9xt1NQe2/84fCz+gIDuVFbNeI/vgFvqMuNIbZtf6WRzcvYrivIOkbp7P9//7Bx16nU67rsPrfX9TWrLeyRkpZnq21xMXpePKMywUl2tsrnVe/Ot8KyN6+86LxescDOlpZGA3A61a6LhojBmTUWHV4fMiKlzhjBQT8S09DUc92+u58gwLe4JwXvQdNYVtK79l+5ofKMhOZfF3j+JyVNItxXMs5n1xP8t/9R2LPiOuIm3HUtYv/oDC7L2smv0aOQe30nv4FX77dVSVsWfjHHoMvqhJ49tgOkZPYevKb9m+2pOORYfTUZOn5n7un46+Iz3pWLfoAwpqp2OEJx0mSwhtOgxk2c/Pk75nFcX56Wxf/T071v5Eh97jgpKGYWdew9ol37Ju6Y/kHErl548fw1FdyYARnjrqu7fvZ+43L3nDu1wOMg9sJ/PAdtwuJyWFOWQe2E5+9gFvmKHjr+Fg6kYW//I2+dkH2LjiV9Ys/pZBYy8PShrg1DgWeruNsD5dCevjaVyztY8nrE9XLAmeYZddnrybPh/65sU68M5X2Non0HXqv7F3SaLdzZcTd9EE9r3ykTfMvmkfknDdxbS5ajIhXZPo+cajGOxWDn4c+C0tTWXB2iomDLXQu6OR1tE6ppxlp6hMZcMu3zXRnZeEMLq/77pg/poqhvcxM7inidgoHZeNt2Eyel59DJBVoJJT4OaK8TYS4/RER+g4faCZbokGNu4Ozk3qjCUlXDAuguQeNtrGGbn1ihgKS9ysqfWGjof/GcuZw31DKiwmhcTWJhJbe27cW0YZSGxtIjpC7/38qnNa0KmdmZgWBnp2snD/da3IynOxYUdwhrbOX13J2cNt9Olsok1LPddPDqWoVGXdDl9Dy71XhjMm2dcYYzZCQis9Ca088Y6O8Pw/Mkz3l/bbVOasKGfS6BD6dTUT38rATRdEeK6ltvuupe6fEsnpgxq5lorw/D8q4LWUJ0xckK6lAHqNmMKO1d+yc+0PFGan8vsPnuuQLsmecmrhV/ezapavnOo1/CrSdy5l45IPKMzZy9q5r5GbvpWew3x1X59R15G6cRbbV31Dcd4Btiz7jAPbF9F9SPDK2xOFinLCLtXV1ZSUlPgtdYe+AzgcDv744w9OP/107zqdTsfpp5/OihUr6oUPpKKiAqfTSWRkcF8fLUM8AnjmmWdQVZWrrrqK0tJSkpOTmTNnDi1atGiS/bdt25bp06dz1113ecf9PP300349Hbp168abb77J008/zRNPPMEFF1zAvffeyzvvvOMNc8MNN7B48WKSk5MpKytj0aJFjB49GoDLLruMO++8k8suuwyLpeHXIjVm+PDhzJgxg4kTJ6LX67ntttuOKd1/xhVXXMHevXu59957qaqq4uKLL2bKlCn1epwEw/y1DkwGhcvGWrCaFfYecvPmDxW4aj1kiI7QYbf6KpJ1u1yEWKs4a4jZ0+09T+XNHyu8kwq63NClrYHT+pkwGRUKS1U27nEyZ3XTV6gAM5eWYzYpTDk3DJtFx+40By98Woiz1gOGli0MhNh9F27tWxt58B++gubyCZ55LX5fX8l7P3jGk/brYuGG831jNP91cQQAPywq48dFTTcGeNsBDbtZZXQfHSEWyC6ELxa5KT98TRBuV/x6FaXnwffLVE7ro2NMXygoha9/U8k9PAw21AZdEjzH66az/Iu7j+e5OZATnEGFXfpPpLKsgBUzX6WiJJfo+G5Mvvk97Ie7dJcUZoLiy0et2/fnzKtfYMXMaSz/9SUiYhI557o3iG7d2RumvCSX3358xjNUJCyGbgMnMWj8LfW+u6kt+MOByQiX1Dov3vrR/7yICtdht/oaEdfvdhFirWbiYDNhNoX0PJW3ap0Xbjd0SdAzuq8JkxGKyjQ27nExZ03Tnxed+nmOxerZr1FekktMm26cc+O73iEbpYWH/BpA49r354wrX2DlrGmsmPEyETGJTLz2daLiOvvtd9f6GaBpdOp3VpPHOZDOh9OxqlY6zr3Jl46yQOm46gVWzvSl46x/+Kdj/NUvsWLGS8z97N9UVRQT2qI1QybeSc+hlwYlDb0GTaS8pJAF379KWXEecW27cc2973iHeBQVZPr1IigtzOWNh8/3/r101gcsnfUBiV0Hcv2DnwCeV5FefvurzPv2ZRb/9CYtouOZeMUD9B16TlDSAKfGsQgf0JMhCz71/t39hf8AcPCT79l03YOY42KwHm6sAKjcn86ac2+i+4sPknjb1VSlZ7H5pofIm7fUGybz21mYYiLp/MjtmGNjKNm4ndVnX4+jzsSZTW3uqmrMRoUrxtuwWRT2pLt47ZsyvzIqpoWOkFpl1B87nITaKjlnuIUwu470HDevfVPmLaNUFV7/rozJo6zcckEIZqNCbpGbj2dUsGVvcLr5/7SwGItJ4aaLo7BZdezYV81Tb2fhdPnqqVbRBkLtvt5pSQlmHrvVd5ymTPYMsVm8upQ3vsxD1aBtaxOjBoZit+ooKHGxaWclX80s9Pt9mtKs5ZWYjArXnBWKzaKwO83Jy18U1zkeer83iSS2NnLf1RHevy89IwSAZRur+ODn0j+936Yy4/dyzEaFa88N911LfVLgfy0VqT88lNGjfWsj/7nON8TpiomHr6XWVfBuzbVUVws3nu9L578u8dxX/LCwlB+a8FoKoGPfiVSVF7B27mtUlOYS3bobE6+rVU4V+ZdTsYn9GXP5C6yZPY3Vs18mPDqR8Ve/TmSsr5xq33McI85/lPUL32HZT08REdOeM656lbj2A5o07uKvCTTU/ZFHHuHRRx/1W5eXl4fb7aZVq1Z+61u1asWOHTv+1Hfdf//9tG7d2q+RIxgUre5EB+KUsH//fjp06MCaNWvo379/c0fnmIwbN47Y2Fg+/fTTIwc+7LZpJUGM0fFRUhC8iTuPpw5djn58+YkkJurkb8/dsbO0uaPQJDp2DGnuKByzpugmfiJoFXlqXEJkF5z8B6T9pOAONTpefnly5ZEDneDys4qaOwpNIizy5C9rAZzBnHjqOOmbfOS3BJ4M7p50cpa1v647cfPQuB7uej0mzGYzZrN/b/JDhw7Rpk0bli9f7vdyhvvuu48lS5awatWqRr/nmWee4bnnnmPx4sV+LzwIhpP/ilv4cTqd5Ofn89BDDzF48OCTrnGioqKCt956i/Hjx6PX6/nyyy+ZP38+8+bNa+6oCSGEEEIIIf5mTuTH+YEaIwKJjo5Gr9eTne3/1qDs7GxiYxtvAHvhhRd45plnmD9/ftAbJ0DmoAiKm2++mZCQkIDLzTffHNTvXrZsGXFxcaxZs4a33nrL77Pff/+9wXiFhJwYreSKojBz5kxGjhzJgAED+OWXX5g+fXrQuxIJIYQQQgghxKnIZDIxYMAAvwkuaya8rN2joq7nnnuOJ554gtmzZ5OcnHw8oio9KILh8ccf59577w34WVhYWFC/e/To0fVeT1ojOTm53ptATjRWq5X58+c3dzSEEEIIIYQQ4pRx9913c80115CcnExKSgrTpk2jvLyca6+9FoCrr76aNm3aMHXqVACeffZZHn74Yb744gsSExPJysoCCPrDbWmgCIKWLVsGfJdsc7NarXTs2PHIAYUQQgghhBBCoGkn59wZdV1yySXk5uby8MMPk5WVRd++fZk9e7Z34sy0tDR0tSar/t///ofD4eDCCy/020+gSTibkjRQCCGEEEIIIYQQp7hbb72VW2+9NeBnixcv9vt7//79wY9QADIHhRBCCCGEEEIIIZqd9KAQQgghhBBCCCECUE/gt3iciqQHhRBCCCGEEEIIIZqdNFAIIYQQQgghhBCi2ckQDyGEEEIIIYQQIgBNhngcV9KDQgghhBBCCCGEEM1OGiiEEEIIIYQQQgjR7GSIhxBCCCGEEEIIEYCG0txR+FuRHhRCCCGEEEIIIYRodtJAIYQQQgghhBBCiGYnQzyEEEIIIYQQQogAVHmLx3ElPSiEEEIIIYQQQgjR7KSBQgghhBBCCCGEEM1OhngIIYQQQgghhBABaDLE47iSHhRCCCGEEEIIIYRodtKDQpySVJfa3FE4Zl16xDR3FJpEebmruaPQJNatK27uKIjDCgpP/jxlt+mbOwpNYuW6yuaOQpNYNWttc0fhmPV8cmVzR6FJnPPQ4OaOwjHb8Om25o5Ck3CdAtdSAIqiNHcUjllV1alxLODUqPtEcEkDhRBCCCGEEEIIEYAM8Ti+ZIiHEEIIIYQQQgghmp00UAghhBBCCCGEEKLZyRAPIYQQQgghhBAiAFU7+ecxOZlIDwohhBBCCCGEEEI0O2mgEEIIIYQQQgghRLOTBgohhBBCCCGEEEI0O5mDQgghhBBCCCGECEBeM3p8SQ8KIYQQQgghhBBCNDtpoBBCCCGEEEIIIUSzkyEeQgghhBBCCCFEADLE4/iSHhRCCCGEEEIIIYRodtJAIYQQQgghhBBCiGYnQzyEEEIIIYQQQogAVBnicVxJDwohhBBCCCGEEEI0O2mgEEIIIYQQQgghRLOTIR5CCCGEEEIIIUQAmqY0dxT+VqQHhRBCCCGEEEIIIZqdNFAIIYQQQgghhBCi2ckQj1NIYmIid955J3feeWdQv2f//v20b9+e9evX07dv36B+1/F01jALw3qZsJoV9h5y8dW8SnKL1Ea3GdnXxOkDLYTZFTJy3XyzoJIDWW6/MO3j9JwzwkJinAFVhYwcN69PL8Ppavo0DOioMKirQogFsotg7jqVzIKGw3eNh1G9dITboaAUFm1SSc30fT6ih0L3tgqhNnCrkFUASzarHGpkn01lbD89A7vosZjgQI7Gz8td5Jc0Po3yoG46RvQ0EGKFrEKNX1e4SM/zbTOwi47eSXpaRylYTApPfFZNlSN4aTh3pJURfS3YzAp70p18PrucnMLG89ToAWbGD7ISHqLjYLaLL+dWsD/Tl1liInRcNNZOxwQDBj1s3evki7nllJYHb4rpkz0dAzvrGNpd580Xs9aoHMpv+Hu6t1U4rY+eiBDIL4H5693sOeQL3zVBIbmTjrgoBZtZ4a0ZTrILmzza9Wxd8TmbfvuAyrI8ImO7MvTc/6NlQu8Gw+/dPJu1816lrDCDsKh2pJx5D227jvJ+vvjbB9m97ke/beI7DWfCP94NVhK8xg80Mri7AatZYV+myvTfqskrbvzYD+tpYHRfI6E2hUP5Kj/87uBgTuB8eP1ZZrq1M/DhrCq27HMHDHOsrrsikXPOiCXUbmDz9hJeeHM36ZmVDYa/8sIERg2Npl0bG9UOlc07SvjfR3s5mOHb5rWn+9CvV4Tfdj/OOsQLb+4OShoAzhluYXgfM1azQmqG51w90vk9qp+ZMwaZCbPrSM9x8/X8CvZn+n7nMLvC+aOtdEs0YjEpZBe4mbWiivW7nE0a98jhySTdcx3h/Xtiad2StRfcQvbPCxrfZmQK3V94gJDunag6mMmeqf8j/ZMf/MK0++flJN19HebYGEo27WDrnU9QvGZzk8a9rgEdFQZ3q1V//9F4Xds1wVN/RxyuvxdurFN/9/TU32G16u/Fm4Jbf58qZW1yZ4Wh3TzpyC6EWWvdHMpvOHy3tgqn9dZ50lEKC9ar3nToFDitj46ObRRahEC1A/ZmaSzYoFLWcHHRJE6la8LmpslbPI4r6UEh6nE4gnjHdoIal2JmdD8zX82r4PnPS3E44dYL7Rj0DW/Tv4uR80dbmbmiimc+LSU9x82tF9oJsfnGqbWP0/OvC0PYvt/F85+V8txnpSzZUB2Ugq5bgsLYvgpLt2p8MFclp0jj0lE6bObA4dtEweQhOjbs1Xh/jsquDI0Lh+mICfeFyS+FOetU3put8ukCleKKxvfZVEb00jOku56flrv43y9OnE6NKeONjR6PXu11TEwxsHCDizd+dpJV4NnGbvGFMeoVdmeoLNkUnBuW2s4cbGFssoXPZpXx9EfFOJxw56VhjaYhuZuJi8fa+WVpJU98UEx6jps7Lw0l9HCeMhnhzsvC0NB48fMSnv2kBL1O4baLwgjW6MiTPR092imcMUDHkk1u3p7pIrsQrhyjbzAPx0crXDBcz/pUlbdnuNiZrnLpKL3feWEyQFquxvz1wc9HNVI3zWTljGfpP/ZfnHfrdKLiujDrgxuoLAt81Zx9YD0Lv7qXLskXcN5t35PYfSzzPruNgqxdfuHiO4/giv/85l3GXPZC0NNyWj8jI3ob+W6Jg1emV+Jwadx4tqXRPNW3o55zh5mYu9bJy99WcihP5cazLYRY64cd2Tv4z16uuCCBC89uwwtv7ubGe9dTWeXmpcd7YTI2nIP79Yzg+xmHuOnf67nrv5sw6BVefrw3FrP/pdjPsw9x7lXLvcubH+4NWjrOGGTmtAFmvphTwbOfluJwatx2cUijx2JAVyMXjrHy67Iqnv6ohPQcN7ddHOI9vwGmnGUnNlLP/74v44kPSli/y8kNk+wktGxkx0dBb7dRsmknW25/7E+FtybGM/Dnt8lfvIqlyZPY99rH9Hr7SaLHDfeGibtoAt2ef5DdT77B0pTzKN20g0Ez3scUE9mkca+tW4LC6f0Uft/iqY9zijQuHd14/X3eEB0b92q8d7j+vmi4f/1dUApz/lB5d5bKJ/NViss1Lmtkn8fqVClru7dTOKO/jiWbVd6Z6SarUOOK0xpLB1wwTMf6VE/4nQc1LhnpOxZGA8RFwu+bVd6d6eab39xEhylcOqppz4W6TqVrQvH3Iw0UgKqqTJ06lfbt22O1WunTpw/fffcdAIsXL0ZRFBYsWEBycjI2m42hQ4eyc+dOv3388ssvDBw4EIvFQnR0NOedd573s8LCQq6++mpatGiBzWZjwoQJ7N7texry6KOP1uuJMG3aNBITE71/T5kyhcmTJ/PCCy8QFxdHVFQU//rXv3A6PU8jRo8ezYEDB7jrrrtQFAVF8V0oLF26lBEjRmC1WklISOD222+nvLzc+3liYiJPPPEEV199NWFhYdx4442N/l7t27cHoF+/fiiKwujRo/ntt98wGo1kZWX5hb3zzjsZMWIEAB999BERERH8+OOPdOrUCYvFwvjx4zl48KDfNj/99BP9+/fHYrGQlJTEY489hssVhO4GtZzW38zslVVsSnVxKE/l45nlhIfo6NPR2OA2Y5PNLN/sYOUWB1n5Kl/Nq8ThhCE9Td4wF5xmZfG6auatriYzXyWnUGXdTieuINS1KV0UNuzV2LRPI68EZq3VcLmgT/vAF8wDOyukZsGqnRr5pfDbFo2sIk+Le41taRr7s6GoHPJKYP56DYtJoWV4wF02mWE99Cze6GZ7mkp2oca3v7kItUK3tg0XWcN66lm7U2XdbpXcIo2flrlwumBAZ99FwPJtbn7b5G7wqWtTGptiZcaySjbudpKR6+aDX8qICNXRr4upwW3GpVj4fUM1yzdVk5nn5rNZ5ThcMKyPp/bvGG8kOlzHh7+Uk5HrJiPXzYe/ltEuTk/XxIbz6t85HYO76Vi3R2XDXo28Yvh1lRunG/p1DJyXBnXVseeQxvJtKnklsGijSmaBRkoXX/hN+zR+26yyN/P4PVLZ/PvHdB14EV2Sz6dFq44Mn/woBpOFnWu/Dxh+y7JPiO80nD4jr6NFyw4kn3EH0a27sXXFF37h9AYTttAY72K2BvnkxtOAMP8PB1v3u8nM1/hyQTVhdoWe7Ru+YB/Zx8jKbS7W7HCRXagxfYkDp0sjpat/fmkdpWNUXyNfLwxuQ/tF57bhk28OsHRVPqn7y3ny5R1ERZoZMTi6wW3ueXQzsxZksy+tgj37y3l62k5iW1ro0jHUL1xVtUpBkdO7VFQG7+ZsbLKFWSuq2LjHefg8LCciREffzg2fh6cPtLBsYzUrNjvIzFf5Yk4FTicM7eUrE5LaGFi0rpr9mW7yilVmraiiolqjbWzT3pTlzvmNXY9MI/un+X8qfLsbL6VyXzrb73uWsh17OfDm52RNn0P7O6Z4w7S/81oOvv8N6R9/T9n2VDbf8gjuiioSplzQpHGvbVBXhQ2pvvp75prD9XdS4Po7pYtCaias3KGRXwJLNmtkFUJyJ1/4rQf86+95NfV3RHDScKqUtUO66li3R2PjXs+xmLFa9aSjQ+BjMairjj2ZGiu2e8Iv3qSSWejpsQlQ7YTPFqpsS/Nca2Xkw6w1blpHeXq3BMupdE0o/n6kgQKYOnUqn3zyCW+99RZbt27lrrvu4sorr2TJkiXeMP/3f//Hiy++yNq1azEYDPzjH//wfjZjxgzOO+88Jk6cyPr161mwYAEpKSnez6dMmcLatWv5+eefWbFiBZqmMXHiRG/jwp+1aNEiUlNTWbRoER9//DEfffQRH330EQDff/898fHxPP7442RmZpKZ6emTlZqayplnnskFF1zApk2b+Prrr1m6dCm33nqr375feOEF+vTpw/r16/nvf//baDxWr14NwPz588nMzOT7779n5MiRJCUl8emnn3rDOZ1OPv/8c7/fqqKigqeeeopPPvmEZcuWUVRUxKWXXur9/Pfff+fqq6/mjjvuYNu2bbz99tt89NFHPPXUU3/pt/orosJ1hIfo2HnA1whS5YD9mW7atw78JE6vg4RWenbU2kYDdqS5SDq8TYhNoX1rA6UVKvdcFsLUf4Zx5yUhdGjT9K3mOh3EtYD92f6V+L5sjTbRgSujNlFKvfB7MxsOr9N5Kugqh0Z2UZNEO6AWoRBqU0g95GtEqHZCeq5G25aB46bXQesohT21ttGAPYdU2sYc/5mXoyN0RITo2L7Pd45XVmvsPeQiqU3DeapdnIHt+303VhqwfZ+DDm08NwsGvWedy+07bk6XhqZBx4Smf2p8sqdDp4PWkUq9i9u9mRrxDeTzhBiFvVn+4VMzNeJjmq+6dLsc5B3aSpuOQ7zrFJ2ONh2GkJO2IeA22Wkb/cKDZ/hG3fCZe1fz6ZPD+ObFCSz98VGqyoPbfzoyTCHMrmPXQd+5WuWAtGyVdg3cvOp1EB+jY3e670ZdA3alu2kX6zsuRgNcMc7M9785KK0M3g1N61YWoiPNrNng+63KK9xs21VCz65hf3o/drsnvSWl/tcC40a35NfPh/LJ68ncdHV7zObg5L3ow3Xf9v3+dd++Q756rC69DtrG6tlep+7bvt/pVybszXAxoKsRm0VBAZK7GTHqFXalBfdhw5FEDO5L3sIVfuty5y2lxeC+AChGI+H9e5C3YLkvgKaRt3A5EYP7BSVONfX3vgD1d3xUw/V33fB7szTaNBDer/4Owil+qpS1Op2nt8O+OvHal9VwOuKjFfbVSXfqoYbDA5hNCpqmBW2I6al0TXiiULUTdzkV/e3noKiurubpp59m/vz5DBniuZhLSkpi6dKlvP32297eBE899RSjRnnG7j7wwAOcddZZVFVVYbFYeOqpp7j00kt57DFfF8M+ffoAsHv3bn7++WeWLVvG0KFDAfj8889JSEjgxx9/5KKLLvrTcW3RogWvv/46er2erl27ctZZZ7FgwQJuuOEGIiMj0ev1hIaGEhsb691m6tSpXHHFFd55KTp16sSrr77KqFGj+N///ofF4un/PmbMGO65554/FY+YmBgAoqKi/L7ruuuu48MPP+Tf//434OlVUlVVxcUXX+wN43Q6ef311xk0aBAAH3/8Md26dWP16tWkpKTw2GOP8cADD3DNNdcAnmPxxBNPcN999/HII4/86d/qrwizewrfkgr/p+qlFar3s7pCrAp6nUJpeZ1tylViIz2nVXS4p5KdONTCD0uqSM9xM6i7kdsuCuGpj0qPOL/FX2EzgU6nUF7lv768CqIauF4OsVA/fLVnfW0d4zzd/owGKKuEL5eoVAbx4WSo1fObl9W5wSir0gixBj4eNjPodUr9bSo1YiKO/8VOuN3znSUB8kfNZ3WF2Dx5qqTOHAwl5Rqxhy869x5yUe3QuOA0Gz8srgAFLjjNhl6nEB7S9Ok82dNhMzd0XmhEhzdwbls8n9dWVlX/vDieqiqK0FQ31pAov/XW0CiKcvcF3KayLA9riP/TfGtIFJVled6/EzoPp32PcYRGxlOSn8aaudOY/dFNnPvPL9HpgtP9OOzwMIC6DQillZr3s7rslsPlbUX987tlC19+mTTMxIEsN1v3B7c7eGQLT0+BwiL/hoXCIof3syNRFLj9ho5s2lbMvrQK7/p5S3LIyqkir8BBh0Q7/5ySRNs2Vv5v6ramS8BhYSGH676653eFRtgRz+/628RG+fLMuz+Vc/0kOy/dEYHbreFwwVs/lDVpvXc0zK2iqc7O81tXnZ2HMTwUncWMsUU4OoOB6pz8OmHysXdJCkqcmqz+rgJ7nSFPHVt7hoLU1N9fLA5O/X2qlLW+dPjHq7wKosMaTkdZgHSHWBp+oDK2r44t+z3nRTCcSteE4u/pb99AsWfPHioqKhg3bpzfeofDQb9+vtby3r19E5HFxcUBkJOTQ9u2bdmwYQM33HBDwP1v374dg8HgvSEHz419ly5d2L59+1+Ka48ePdDrfRcAcXFxbN7c+KRNGzduZNOmTXz++efedZqmoaoq+/bto1u3bgAkJyf/pbgEMmXKFB566CFWrlzJ4MGD+eijj7j44oux2+3eMAaDgYEDB3r/7tq1KxEREWzfvp2UlBQ2btzIsmXL/HpMuN1uqqqqqKiowGar3x+uurqa6upqv3VuVzV6Q+BBcQO7GblsnG8/b35fdtRpbkzNKJtlGz3DQADSc9x0aWdkSC8TP/9e1cjWJ44DOfD+XBWrGfomKZw3RMdH81Uqqo+87Z/RJ0nHpGG+ouiTeU07idrxMKiHiSsnhHj/fu2bkqB8T1mFxts/lHHFmXbGDLSgabB6q4MDma4mmdfkVEmH+HM69DnL+//I2M5ExnXh6+fPIHPv6nq9L45W/056LhztK4vfmxGccq9Hop6ObfS89E3Tzzo3blRL/v2vzt6/73v82CdLvPvmTiS1tXPL/ev91v88xzcj3d4D5eQXOnj1qT60jrVwKOvYfruU7iYuH++r+974Ljh1H8C5IzyT6r78VSllFRp9Oxu5YZKdFz4v5VBe8zZS/J0cyIb35njq734dFM4fquPDeU1Xf4u/RqfAhSN0KIpn6MjJKNjXhEKANFBQVuapoGfMmEGbNm38PjObzaSmpgJgNPrGY9bM76CqnsLFag0wS9dfoNPp0OpclQca/lE7DjXxqIlDQ8rKyrjpppu4/fbb633Wtm1b7/9rNyIcrZYtW3LOOefw4Ycf0r59e2bNmsXixYv/0j7Kysp47LHHOP/88+t9VtPbo66pU6f69V4BSB53PylnPBAw/KY9TvZnlnr/rpkMLMymo6Tc9+Qt1OaZnTxgPCs13KpGqF0H1NrGrvM+Oa75NzPffx9Z+W4iQ5v2aXeFA1RV85sQEsAeoEW8RlkV9cOb6z8JcLqhsMyzHMrXuHmiQp8khRXbm+ZOcnuaysFcX/O7Qe85v0Ksit9T1hCLQmZB4PxeUQ1utaaHRa1trAplFcG/492w28HeQ0Xev42H0xBm11Fc7p8/DmYHfmRSVuHJU3V77YTZ/XsjbNvn5P/+V0SIVcGteoZcvHB7C3K3HftT41MlHTUqqhs6L5QGZ0/3nBd18lGAJ2THk8UWgaLT15sQs7I0H1to4DkPrCHRfr0lACrL8uv1qqgtLDIBi70FJflpTdZAsXW/mwNf+37smvI21OrfIyLUqpCRH/j8Lq86XN7W6WERUmsfHdvoiQpXePJ6/0bsa8ab2Zup8r+fjv4ALl2dz7Zda71/m4ye8rtFhJH8Ql/Z1SLCxJ69R77pv+umjgwdGMmtD24kN7/xR4/bdnoaCePjrMfcQLFxj4N9h3znreHwFWCYvW7dpzRc93nP7zp1n83XqyI6QsdpAyw89n4xmYcbIzJy3XSMNzC6v4Uv5lYE2vVxUZ2dh7mV/zlgbhWNs7gUtaoaR14hqsuFuWVUnTBRVGf5n09NpdH6u9Fy6sjh69bf/zxLoW+SwvImqr9rnCplrS8d/vGyW+r36qwRqNeH3aLUS0dN40S4XeHT+e6g9Z6Ak/ua8EQlD0+Or7/9HBTdu3fHbDaTlpZGx44d/ZaEhIQ/tY/evXuzYEHg11p169YNl8vFqlWrvOvy8/PZuXMn3bt3BzxDJrKysvwaKTZs2PCX02IymXC7/S8q+vfvz7Zt2+qlrWPHjphMf64raqDvAep9F8D111/P119/zTvvvEOHDh0YNmyY3+cul4u1a30Xejt37qSoqMjbk6N///7s3LkzYHx1usDZ9cEHH6S4uNhvGTDmrgbjX+2E3CLVu2TmqxSXqXRp52uvs5ggMU7vdzFXm1uFg9luurT1baMAXdoa2Ht4m/xilaJSlVaR/l2lW7bQUVDStC3nqgqZhZDYyv8CPrGVQkZe4FI1I18jsc6cDu1jGw5fQ1HA0IQlh8PlmW28Zskp0iit0Ehq7fsSsxHiYxTScgLHza16KsoOtbZRgA6tdaTlBr9WqXZAbqHqXQ7luSkqU/0mfLSYFJJaG9ib0XCeOpDpolutbRSgW6KR1Iz6DZZllRqV1Rpd2xkItSts3H3sfSxPlXTUUFU4VKCRFOufz5NiFb/Xz9Z2MFejfd3wcQrpuc33tEtvMBHdugcZqSu96zRV5VDqSlq27Rtwm1Zt+3CoVniA9D3LGwwPUFacRVVFEbbQmKaINuApb/NLNO+SXahRUq7SKd7//G7bSlfvFc013Cqk56p0qjV/jwJ0itdzIMtzXBauc/Li15W89I1vAfhpmYOvFx7bo73KSjcZmVXeZV9aBXkF1ST3aeENY7Pq6d45jC07Gu91dNdNHRk5JJo7/m8TmdlHvhPrlOTp0VS7IeRoVTvq1H15nrqva526r31rXz1Wl1uFtCy33zYK0DXR6C0TTIc/qntBr2q+noXNpWjlBqLGDPZbFz12KIUrNwCgOZ0Ur9tK9JhaDXSKQtRpQyha6d/bpak0Vn+nN/CKzox8jfatAtTfjbzSEzy/vz4Io7dOlbJWVSGzgHrxat9IOtLzGkhHrfA1jRORoQqfLXAHfUjEyXxNKARIAwWhoaHce++93HXXXXz88cekpqaybt06XnvtNT7++OM/tY9HHnmEL7/8kkceeYTt27ezefNmnn32WcAz58OkSZO44YYbWLp0KRs3buTKK6+kTZs2TJo0CfC8gSM3N5fnnnuO1NRU3njjDWbNmvWX05KYmMhvv/1GRkYGeXmelv7777+f5cuXc+utt7JhwwZ2797NTz/9VG+SzL+iZcuWWK1WZs+eTXZ2NsXFxd7Pxo8fT1hYGE8++STXXnttvW2NRiO33XYbq1at4o8//mDKlCkMHjzYO6noww8/zCeffMJjjz3G1q1b2b59O1999RUPPfRQg/Exm82EhYX5LQ0N72jIonXVnDnYTK8OBlpH67h6gp3iMpWNe3w3VLdfZGdUP1+jzoK11QzrbWJQDyOtInVcOs6K2Yh3OAfA/DXVjO5vpl9nIzEROs4eZqFVpJ7lm5u+dlq9U6NvkkKvRIWoUJiQrGA0eGbBBjhnkMLoXr7KZ80ujaQ4z0zPUaGe91vHtYA/9njCG/UwqpdC6ygIs0FsCzhroEKoFbYfDO5N/7Ktbk7ro6drgo5WLRQuHGmgtNLT26LGP840MribrwhbtsVNcmcd/TrqiAlXOHeoAZMB/tjlu+kJsUJcpELU4bGkrVooxEUqWI+ura5RC1ZXctYwK306GWkTo+cf54RQVKqyfqfv2N99eRinDfA9spi3uooRfS0M6WUmNkrPFRPsmIwKyzb5brCG9jaT1NpATISOQT1M3HReKPNXV5HdQO+Sv3s6Vm5X6d9JR58khegwOHuQZ+zshlTP90weqmdsX18+WrVDpWNrhSHddESFwajeOlpHKqze6YuXxQStWkDM4bHV0WEKrVrUf/rUlHqNuIada75l1x8/UpiTytKfHsPpqKTzAM8boxZ9cz+rZ7/kDd9z2NUc3LWUTb9/SFHOXv6Y/zp5GVvpMeRyAJzV5aya+TzZaRsoLcwgY88K5n3yL8Ii2xLfeXjAODSV3za5OH2AiR6JemIjFS4fa6akXGPLPt+5evO5Fob19N0E/7bRyaDuBpK7GGjZQuGCUSZMBoXVOzxldGmlRlaB/wJQVKZRUNr05dW3P2dwzSVtGZYSRVI7Ow/d3ZX8gmp+X+l7yj7tyd6cf1Zr79/3/LMjZ4xuxWMvbKei0kVkhJHICCMmkyf/tY61cM0lbenSIYTYlmaGpUTx0F1dWb+liNT95fXi0BQWrK1iwlALvTsaaR2tY8pZdorKVDbs8tV9d14Swuj+vjp1/poqhvcxM7inidgoHZeNt2Ey4q3XsgpUcgrcXDHeRmKcnugIHacPNNMt0dCkDZDgec1oWJ+uhPXpCoCtfTxhfbpiSfAMxe3y5N30+fBZb/gD73yFrX0CXaf+G3uXJNrdfDlxF01g3ysfecPsm/YhCdddTJurJhPSNYmebzyKwW7l4MeB35jTFFbt0OjX4XD9HVar/t5bq/7u7au/V+/01N+Daurvnp76e+1uX/09urd//X12yuH6Oy049fepUtau2KHSv6NC7/aedJyVosOohw2Hj8WkITrG1ElHh9YKg7t6jt2oXjpaR8Kaw+nQKXDRCB1xkQo/LHOjKJ742y2eiSaD5VS6JhR/P3/7IR4ATzzxBDExMUydOpW9e/cSERFB//79+c9//nPEIRTgaWD49ttveeKJJ3jmmWcICwtj5MiR3s8//PBD7rjjDs4++2wcDgcjR45k5syZ3iEb3bp148033+Tpp5/miSee4IILLuDee+/lnXfe+UvpePzxx7npppvo0KED1dXVaJpG7969WbJkCf/3f//HiBEj0DSNDh06cMkll/y1H6kWg8HAq6++yuOPP87DDz/MiBEjvEM5dDodU6ZM4emnn+bqq6+ut63NZuP+++/n8ssvJyMjgxEjRvD+++97Px8/fjy//vorjz/+OM8++yxGo5GuXbty/fXXH3V8/4x5q6sxGRUuP8OG1ayQmuHijenlfq8DjY7QY7f6Vqzb6STUVsnZw6yE2hQyct288V25X7flReuqMRjggtFWbFaFjBw3r39XRl5x099Mbj+oYTPDyJ4KdotCdhF8vUSl/PA9YZhN8eulk5EPP61QGdVLx+heCoVl8N0yldzD7U2q5rkY6J2oYDVDpcPzZOHThZ5XggXT75vdmAwweZgBiwkO5Gh8NMf/9ayRoQq2WpNQbd6nYre4GNvfQKgVMgs0Pprr9OvOmNJVz9h+vmLvxrM8LRPf/eZk/Z6mPSazV1ZhMilcNSEEm0Vh90Enr3xd4peGmAgdIbW6ra/d7iDUVsGkkVbCDg+jeOXrUkprDY2IjdRz/mgbdqtCfpHKzOWVzFsdvD6xJ3s6th7QsJlVRvfWE2KFrEKNzxe6vfki3A6a5ot7ep7G90vdnNZXz5i+OgpK4aslbu95AdAlXmHyUF8+unCE5/+LN7lZsik4DUUdek+kqqyQP+a/SkVpHlFx3Zhw7TveIR7lRZkoiu9qt1W7foy59HnWzn2FNXNeJjy6HeOufI3IWM9cCopOT37WTnat+xFHVSm20BjiOw1jwLjb0RuC0GJXy6L1TkwGuHC0CatJYV+myju/VvnlqagwBXutSXE37HFjtzgYn2IkzGYiI0/l3V+rGuw+HmyfTz+IxaLnvls7E2I3sHlbMfc8shmH05fH28RaiQjz9SQ6b6JnGOnrU/v67eupaTuYtSAbl0sjuW8LLj43HotFT05eFYuX5/Hx1weClo65q6oxGxWuGG/DZlHYk+7itW/K/M/vFjq/CYr/2OGp+84ZbiHM7hkK+do3Zd66T1Xh9e/KmDzKyi0XhGA2KuQWufl4RgVb9jZtv/bwAT0ZssD39rDuL/wHgIOffM+m6x7EHBeD9XBjBUDl/nTWnHsT3V98kMTbrqYqPYvNNz1E3ryl3jCZ387CFBNJ50duxxwbQ8nG7aw++3ocdSbObErbD3q644/q5au/v1rsq7/D7Qoa/vX3jytURvfSMbq3QkEpfLvUv/6OClW4cFit+jsfPlkQvPr7VClrtx3QsJtVRvfREWKB7EL4YlHtdPhfS6XnwffLVE7ro2NMX09P0K9/8x2LUBt0SfCUzTed5X/b9fE8Nwca6Bl6rE6la8ITgQzxOL4Ure7kB0Ico+uuu47c3Fx+/vlnv/UfffQRd955J0VFRUGPw79eCP53BFubhNDmjkKTKC9v3tfKNZWcrOBNKCf+mvi2f/5Vjicquy04b8k43jKzmqmFoImtmrX2yIFOcD2H9z5yoJPAOQ8NPnKgE9yGT5v+rSvNweU6OSdyrEtp7rFFTcBgOPnTAPCfS07Ouu+Dhc0dg4b9Y0xzx6DpSQ8K0WSKi4vZvHkzX3zxRb3GCSGEEEIIIYQQojF/+zkoRH1PP/00ISEhAZcJEyY0uN2kSZM444wzuPnmm+u9tlUIIYQQQgghTjaqduIupyLpQSHqufnmm7n44osDftbYK1WP9ErRKVOmMGXKlGOImRBCCCGEEEKIU5U0UIh6IiMjiYyMbO5oCCGEEEIIIYT4G5EGCiGEEEIIIYQQIgB5pcTxJXNQCCGEEEIIIYQQotlJA4UQQgghhBBCCCGanQzxEEIIIYQQQgghAlDV5o7B34v0oBBCCCGEEEIIIUSzkwYKIYQQQgghhBBCNDsZ4iGEEEIIIYQQQgQgb/E4vqQHhRBCCCGEEEIIIZqdNFAIIYQQQgghhBCi2ckQDyGEEEIIIYQQIgAZ4nF8SQ8KIYQQQgghhBBCNDtpoBBCCCGEEEIIIUSzkyEeQgghhBBCCCFEAKoM8TiupAeFEEIIIYQQQgghmp00UAghhBBCCCGEEKLZyRAPIYQQQgghhBAiAO2Efo2H0twRaHLSg0IIIYQQQgghhBDNTnpQiFNSbJuQ5o7CMTMZT40W0fwqV3NHoUmc2K3nf06bhPDmjkKTKCqqbu4oHLND6Y7mjkKTsFiNzR2FJjH6giHNHYVjtnNjRnNHoUls+HRbc0fhmPW9qntzR6FJZM7e2dxRaBKnQPXNpi0lzR2FJhLW3BEQJwFpoBBCCCGEEEIIIQI4FRq5TiYyxEMIIYQQQgghhBDNThoohBBCCCGEEEII0exkiIcQQgghhBBCCBGAqjZ3DP5epAeFEEIIIYQQQgghmp00UAghhBBCCCGEEKLZyRAPIYQQQgghhBAiAHmLx/ElPSiEEEIIIYQQQgjR7KSBQgghhBBCCCGEEM1OhngIIYQQQgghhBABqDLE47iSHhRCCCGEEEIIIYRodtJAIYQQQgghhBBCiGYnQzyEEEIIIYQQQogA5C0ex5f0oBBCCCGEEEIIIUSzkwYKIYQQQgghhBBCNDsZ4iGEEEIIIYQQQgSgndCv8VCaOwJNTnpQCCGEEEIIIYQQotlJA4UQQgghhBBCCCGanTRQNINly5bRq1cvjEYjkydPDrhu8eLFKIpCUVFRs8ZVCCGEEEIIIf6uVO3EXU5FMgdFM7j77rvp27cvs2bNIiQkJOA6m81GZmYm4eHhzRzbv4fkTgpDuuoIsUJ2Icz+w82hgobDd0tQGN1bR4QdCkphwQaVPZmeUkKnwGm9dXRsrRARAtUO2JetsWCjSlllcNOxdcXnbPrtAyrL8oiM7crQc/+Plgm9Gwy/d/Ns1s57lbLCDMKi2pFy5j207TrK+/nibx9k97of/baJ7zScCf94N1hJ8Bo/0Mjg7gasZoV9mSrTf6smr7jxknhYTwOj+xoJtSkcylf54XcHB3NU7+cXjjLRKV5PuF2h2gn7s9zMWOEgpyg4JfykkTZG9LNgMyvsSXfy2awycgrVRrc5bYCF8YOthIfoOJjt4su55ew75PJ+HhOh46LT7XSKN2IwwJZUJ1/OLaOkPDhpSOmiY2iPw+dGgcbM1SoZ+Q1/V/d2CmP66okIgYISmLfOze4MX/hubRWSO+toHaVgMyv87xcnWYVBiXo9Z6aYGNLDgMWssD/TzbeL/0Se6mVkTL/DeSpP5fvfqknLCXwMbzzHQrd2Bt6fUcmWfe5gJIFzRlgZ0ceM1ayQmuHiiznlR8xTo/ubGTfIQrhdR3qOm6/mlbM/0xe/MLvCBafZ6JZoxGJSyC5wM3NFJet3OoOSBoAJg00M6Wn0nN+H3Hy7qIrcI5yHw3sbGTPARJhNISNPZfriKtKyfWm/9QIrneL9L2uWbXbwzcLqoKRhbF89yZ11WEyQlqPx8woX+aWNbzOoq47hPfWEWCGrQOPXVW4y8nzpNujhzGQ9vdvr0OthT4bGzytdlFcFJQkAXHJmBGOHhGK36Nixv5p3v80jK8/VYPhuSRbOHRNOUryJyHADz72fzZotFX5h/nVZNKNTQv3WbdhewVPvZDd5/Ad0VBjcTSHEAtlFMPcPtdH6u2sCjOrlq78XblRJzfR9PqKnQve2CmE2cKuQVQCLNzW+z2MROTyZpHuuI7x/TyytW7L2glvI/nlB49uMTKH7Cw8Q0r0TVQcz2TP1f6R/8oNfmHb/vJyku6/DHBtDyaYdbL3zCYrXbA5OIg5bt+Rz1sx7n/KSXFrGd2Xsxf8lLjHwNUjeod0s/fVVstO2UlKQwWkXPkjymCl+YQ7uXsOaee+TdXAL5cW5TL7xDTr1PT2oaQBYv+Rz1sz3pCOmzZHTsWxGrXRc8CADAqVj/vtkH07HpBvfoFOf4KcDYOJgM0N7+crarxdWkVvUeJ0xoreRsclmb1n73aJKDmT7b5MYp+ecoWbaxepRVcjIdfPmDxU4g1P1ib8Z6UHRDFJTUxkzZgzx8fFEREQEXGcymYiNjUVRgjPxiaZpuFwNX4AE4nA4ghKX5ta9rcK4fjp+26Ly7mw32UUal5+mx2YOHD4+Gs4fqmNDqif8znSNi0foiDnclmQ0QGwk/L5F5b3Zbr5d6iYqVOGSEfqgpiN100xWzniW/mP/xXm3TicqrguzPriByrL8gOGzD6xn4Vf30iX5As677XsSu49l3me3UZC1yz+9nUdwxX9+8y5jLnshqOkAOK2fkRG9jXy3xMEr0ytxuDRuPNuCoZGfsG9HPecOMzF3rZOXv63kUJ7KjWdbCLH6wqTnqny9sJpnv6zknV+qUPDcVAbjNDtziJWxAy18NquMpz8qotqpcddl4Y2mYWA3ExefbueX3yt4/P0iDua4ufPSMEJtngiajHDX5eGgwQufF/PMx8UY9HDbxWFBmSKpR6LC+GQdize6eftXF1mFcNXpeuyWwOETYhQuHKFn/R6Vt351seOgyqWj9bSM8IUxGjw3c/P+OL5XMWP6GxnZx8i3i6uZ9m0l1U64+VzrEfKUgcnDTcxZ4+DFrys4lK9y07lWQqz1f+1RfYxBf0/6+EEWxgww8/mccp75pIRqp8btl4Q2mobkriYuHGNjxtJKnvqwmPQcF7dfEurNUwDXnh1Cq0g9b04v5fH3i1m/y8GNk0JIaBWcMmvsABMj+5r4ZmE1L39dgcOpcfNkW6Pp6NfJwHkjzMxZVc3zX1ZwKNfNPyfb6h2L5ZsdPPRumXf5aWlwGidG9NQxuLuOn1a4eGuGC4cLrjnD2GgaeibqmDBQz6INbt782UlWgcaUcQa/82nCQD1dE3R8tdjF+7NdhNrg8tOC9yxp0phwJowM451v83lw2iGqq1UeujkWo6HhEsVsUjiQ4eD96YHrlhrrt1dww8Np3mXap7lNHX26JSic3k/h9y0a789RySnSuHS0rsH6u00UnDdEx8a9Gu/NUdmVoXHRcF/9DZ5Gizl/qLw7S+WT+SrF5RqXNbLPY6W32yjZtJMttz/2p8JbE+MZ+PPb5C9exdLkSex77WN6vf0k0eOGe8PEXTSBbs8/yO4n32BpynmUbtrBoBnvY4qJDE4igB1rZ7J4+lSGnvUvrn7wB2LadOXb166jvDRwPnE6KomIjmfk5Huwh8U0EKaCmPgunH7JI0GLd107/pjJ4u+nMmTiv7jqgR9oGd+V715vJB3OSsKj4hk5qfF0tIzvwukXH790AJyebGJUPxNfL6jixa/KqXZq3HJe42Vt/84GzhtpYdbKap77opyMXDe3nGf3K2sT4/TcMtnGjgMuXviynBe+Kue3jQ5O0Yf5ohlIA0UQVFdXc/vtt9OyZUssFgvDhw9nzZo17N+/H0VRyM/P5x//+AeKovDRRx8FXBdoiMeyZcsYPXo0NpuNFi1aMH78eAoLPY8eVVVl6tSptG/fHqvVSp8+ffjuu++829bsb9asWQwYMACz2czSpUsbTcejjz5K3759ee+992jfvj0Wi+cqavbs2QwfPpyIiAiioqI4++yzSU1N9ds2PT2dyy67jMjISOx2O8nJyaxatcr7+U8//UT//v2xWCwkJSXx2GOPeRtMNE3j0UcfpW3btpjNZlq3bs3tt99+TMekMYO76FifqrFxn0ZeCcxYo+J0Qd+kwBdoKZ117MnUWLHDE37xZpXMQhjYyXM6VTvh80Uq2w5q5JdCRj7M+sNN6yjPE5lg2fz7x3QdeBFdks+nRauODJ/8KAaThZ1rvw8YfsuyT4jvNJw+I6+jRcsOJJ9xB9Gtu7F1xRd+4fQGE7bQGO9itga/V8/I3gbm/+Fg6343mfkaXy6oJsyu0LN9w7XqyD5GVm5zsWaHi+xCjelLHDhdGildjd4wK7e52JupUliqkZGnMmu1gxahOiJDm/72/vQUK78urWTDLgfpOW4++LmMiFAd/bqYGtxm3CArv2+oYtmmajLz3Hw2swyHS2N4H8+51zHeSHS4jg9+KSMj101GrpsPfimjXZyBronGBvd7tIZ20/HHbpUNqRq5xfDrSjdON/TrGLjqGNxNx55DGsu2quQVw8INKpkFGildfOE37dVYskllb+bxvZQZ1cfI3LUOtuxzk5mv8sX8KsLsCr2SGr75G93XyIqtTlZv9+SpbxdV43BpDOrmv03raB2j+xn5KkhP6muMHWhh5vIqNu52kpHr5sNfy4kI0dG3c8N56vQUC0s3VrN8s4PMfJXPZ1fgcMLQ3r67raQ2Bhb9UcX+TDd5xSozl1dRUa3RtlVwboxH9TMyd3U1W/a6OJSn8tncKsLtCr06NHIs+ptYvtXJqm0usgtUvlnoORaDe/jne4cLSis071IdpHb1od31LN7oZsdBjexCje9+9zQmdGvb8GXVsB461u5SWbdHJbcYfl7hxumCAYfrDrPR8/9Za1zszdI4lK/x/TIX7VrpiI8JzsOKs0aFMX1uEWu3VJCW6eT1L3JpEaZnYK+GK6sNOyr5alYhqzdXNBgGwOnSKCp1e5fyysaf2h6NQV0VNqRqbDpcf89co+FyQZ+G6u8uCqmZsHKHRn4JLNmskVXo6UVZY+sBjf3ZUFQOeSUwb72GxaT4NbQ2pdw5v7HrkWlk/zT/T4Vvd+OlVO5LZ/t9z1K2Yy8H3vycrOlzaH/HFG+Y9ndey8H3vyH94+8p257K5lsewV1RRcKUC4KTCGDtwg/pPexieg25gOi4jpxx2WMYTRa2LJ8eMHxcYm9Gn38/3ZLPQm8IXIYl9RjFiHPvonPfcUGLd11rF3xIr6G+dIy79HA6VjSQjnaedHQ9QjqGn3MXnY5jOgBG9zMxZ1U1mw+XtZ/OqSTcrtC7kbL2tP5mVmxxsmqbk6wCla8XVOFwaQypVdaeP9LMkg0O5q11kFWgklOosn63C9cp3HtC007c5VQkDRRBcN999zF9+nQ+/vhj1q1bR8eOHRk/fjyhoaFkZmYSFhbGtGnTyMzM5KKLLqq37pJLLqm3zw0bNjB27Fi6d+/OihUrWLp0Keeccw5ut6c0mDp1Kp988glvvfUWW7du5a677uLKK69kyZIlfvt54IEHeOaZZ9i+fTu9ezfc9b/Gnj17mD59Ot9//z0bNmwAoLy8nLvvvpu1a9eyYMECdDod5513HqrqufgoKytj1KhRZGRk8PPPP7Nx40buu+8+7+e///47V199NXfccQfbtm3j7bff5qOPPuKpp54CYPr06bz88su8/fbb7N69mx9//JFevXod9fFojE4HcZGwL8v/DN+XrREfHfgCJz5aYV+2f/i9mQ2HB7AYFTRNoypIF8tul4O8Q1tp03GId52i09GmwxBy0jYE3CY7baNfePAM36gbPnPvaj59chjfvDiBpT8+SlV5cPvjR4YphNl17Drou5itckBatkq72MANFHodxMfo2J3uqx01YFe6m3axgYs5kwEGdjWSX6xSVNa0JXx0hI6IEB3b9/sOeGW1xt4MFx3aBG5I0OugXZyBbft83eo1YPs+J0mHu60bDQoa4HL74ut0aWgadEpo2gYKvQ7iohS/hgQNT15PaOBmKT5GqdfwkHpIIyGmeauaKG+e8uWPKgccyFZJbCB/6HUQ39J/Gw3Yne72y4dGA1x1hoXpS6oprQjelUJ0uI7wEB3b9/vyR1W1xr5DLpLaBL7Y1OugbazebxsN2LHf6bfN3gwXyd1M2CwKCpDczYRRr7ArremHeESFKYTbdexKq3Mssty0b+T8Tmjpv40G7Epz1zt+yV0MPHWjnQeusHH2UBPGILSxtAiBUJtCaq28Xu2E9NyGzw29DlpHKaRm+so1DUjNVL3nR5soBYPef795xVBUptE2CA0ULaMMtAgzsHmXb/xIRZXGngPVdEk89u4CPTpaeO/xtrzyYBtuuDCKEFvTlgM6HcS1oF59vC9bIz4q8O/VJipA/Z2l0aaB8Dod9OugUOXQyD5OQ9GOJGJwX/IWrvBblztvKS0G9wVAMRoJ79+DvAXLfQE0jbyFy4kY3C8ocXK7HGSlbaVdl6HedYpOR7uuQzm0b31QvjMY3C4H2Qe30q6rfzradh3Kob0nTzrAV9buPOjrLV3l8AxtbR/XeFlbexsN2JnmIvHwNiFWhfZxBkorVO662MZTN4Rw+4U2kloHt5ew+HuROSiaWHl5Of/73//46KOPmDBhAgDvvvsu8+bN44MPPuDf//43iqIQHh5ObGwsAHa7vd66up577jmSk5N58803vet69OgBeHpsPP3008yfP58hQzw3nElJSSxdupS3336bUaN8cwo8/vjjjBv351twHQ4Hn3zyCTExvm5rF1zg3wL/wQcfEBMTw7Zt2+jZsydffPEFubm5rFmzhshIT3fCjh07esM/9thjPPDAA1xzzTXeuD7xxBPcd999PPLII6SlpREbG8vpp5+O0Wikbdu2pKSkNBjH6upqqqv9n1y6nHoMxiNfYNnMoNMplFX5X7CUV0F0A0/VQyzUGw9cVqVhD9DtGzwF/ti+OrYc0HD8tVE1f1pVRRGa6sYaEuW33hoaRVHuvoDbVJblYQ2J9g8fEkVlWZ7374TOw2nfYxyhkfGU5KexZu40Zn90E+f+80t0uuBURmGHu56XVvofk9JKzftZXXaLgl6n1LtBLKvUaNnC/6J4aA8DZw81YTYq5BSqvP1LFe4mfrAXbvd8Z0m5/45LylXCQwJfpIfYdOh1SsBtYqM8jQ+pGU6qHRoXjLHzw6JyUOCC0+zodQrhIU17A2Mzg16n1Js3paxSIzqs4XOjrM5xK6vCb5hNc6gZzlBWN39UqH5DHWqzWw/nqbr5sEKjZYTvGE4ebmZ/pjtoc07UCAtpJE/ZGzgetsNpqDM/Se08BfDOj2XcMCmEl+9sgdut4XBq/O/70iOOUz4aoYfjWvdcLa3QvJ/V5T0WFWq9bVpG+sqhP3a6KCzxdMlvHa3j3GFmWrbQ8cGMpp3Aoaarc728XqkR2kBeb/h8guhw335d7voN2WWVWsBhRccqItTz2xWV+efdojK397OjtX5HJas2VZBT4KRVlJHLz2rB/90Yy/+9cqjJJnWzmTz1d936uLwKosICbxOo/i6vAnud49axtWcoiNHgOUZfLFapPEFGuZpbRVOdnee3rjo7D2N4KDqLGWOLcHQGA9U5+XXC5GPvkhSUOFWWFaKpbmxh/tcgttAoCrL3BuU7g6EmHfZQ/3TYQ6MoyDp50gEQdvg6pG75X1qheT+rq6asLQlQPrc6XNZGh3u2nTjYzA+/V5OR6yalm5Fbz7cx9bPyoNQb4u9HGiiaWGpqKk6nk2HDhnnXGY1GUlJS2L59+1Hvd8OGDVx00UUBP9uzZw8VFRX1Gh4cDgf9+vm3licnJ/+l723Xrp1f4wTA7t27efjhh1m1ahV5eXnenhFpaWn07NmTDRs20K9fP2/jRF0bN25k2bJl3h4TAG63m6qqKioqKrjooouYNm0aSUlJnHnmmUycOJFzzjkHgyFwdp06dSqPPeY/dnP0+f9lzIUP/6W0BoNOgQuHeQrzmWtOvkK7Q5+zvP+PjO1MZFwXvn7+DDL3rq7X++Jo9e+k58LRvsak95r4ZqKudbtd7Ep3E2ZTGN3XyFVnmHn9h6pj6po4qIeZqyaGeP9+9eviJohpfWUVGm99X8qVE0IYO9CCpsHqrdUcyHSdst38jkb/zgYurpWn3v01OLPT9kjU0ylezwtfN97V/WikdDdxxZl279+vf3uE2RePwaSRVmwWhZe/LKGsUqNvJyM3Tg7h+c9LOZR7bA0vA7oYuGSMb5KFt38O3kzBK7b4enxk5quUlGvceoGNqPBq8o8wGWpj+iTpOHeI74b90/lBamkOsuH97dx0sa9Reuq7TT9hZY3l68u9/0/LdHIg08EbDyXQvaOFLbuDW8Y3hQPZ8N4cFavZ04Pi/KE6PpynUhHcUVxCHLXkLgYuHetraXvrp6avlwDvnF3LNnuGgQCk51bTOcHA4B5Gfll2ap4k6qn6uowTlDRQnCSs1oYfQZaVlQEwY8YM2rRp4/eZ2ezfi8But/NXBAp/zjnn0K5dO959911at26Nqqr07NnTO4lmY3Gtie9jjz3G+eefX+8zi8VCQkICO3fuZP78+cybN49bbrmF559/niVLlmA01u/G/uCDD3L33Xf7rXvxxz/39Kei2lPohFgUqDW9j91CvV4V3vhXUW+SwBCLQnmd626dAhcM0xFuV/h0oTtovScALLYIFJ2+3oSYlaX52EKjA25jDYn26y0BUFmWX69XRW1hkQlY7C0oyU9rsgaKrfvdHPja9+PVTN4UavXvERFqVcjID9zIU16l4Va1ek/DQ6z1e1VUOaDKoZFXrHEgu5onrrPRq72e9XuO/kZsw24H+97z9f816D3xCLPrKC6r/bYEz5s5AimrUHGr9Z9shNl1FNd6ar5tn5P/vFlIiFXBrXqGjrx4R2STP7WoqAa3qtXr/RBiVShr4P7C01vC/1zy9Kpo0qgd0dZ9Ll7I9v3uNccjxOb/ZCjEpuNQXuDjXl55OE/VeXIdWmsfneL1RIUrPH2Dfzl57QQLezNV3vjh6BO+cY+DfR/48orB4MtTJeV18lRO4DSUVRxOQ52eCbXzVHSEjtMGWHj0vWIyD/8W6TluOiYYGd3fzBdzju0id8teFweyfDerNccitM6xCLUpZOQ2cH7XHAubDlD9tiktbzjfH8jypCcmXEd+8dGf39vTVA7Wips3P1kVv14UIVaFzILA9UbD55Pv/Cir1DDoFSwm/HpReL7n2M/vtVsr2PNChi8dh/NURIieohLf7xMRomf/oabtLpCT76KkzE1stLHJGigqHJ76u259bLdQrz6uEaj+DhTe6YbCMs9yKF/jn2cp9E1SWL69+W9SqrPzMLfyr6fNraJxFpeiVlXjyCtEdbkwt4yqEyaK6iz/Or+pWENaoOj0VJT4X4NUlOZjD2v4muJEU5OOuhNilp8E6di818X+rDLv396y1h6orG283qvbWzXU5uvdWfNvZoF/mZRdqNIiVGYOEE1DclIT69ChAyaTiWXLlnnXOZ1O1qxZQ/fu3Y96v71792bBgsCvnerevTtms5m0tDQ6duzotyQkJBz1dwaSn5/Pzp07eeihhxg7dizdunXzTtRZO64bNmygoCDwO7n69+/Pzp0768W1Y8eO6HSeLGm1WjnnnHN49dVXWbx4MStWrGDz5sCvxzKbzYSFhfktf2Z4B4CqQmYBJMb6F8btWymk5wW+EEnP02jfqk74WP/wNY0TkaEKny1yB71rqN5gIrp1DzJSV3rXaarKodSVtGzbN+A2rdr24VCt8ADpe5Y3GB6grDiLqooibKGBZ6o+GtVOyC/RvEt2oUZJuUqneF/xZDZC21Y67w1HXW7V84aOTm18DVMKnhvIA1mNX9gr+Cryo06DQyOnUPUuh/LcFJWpdEv0TZhlMSkktTGQmhF4XL9bhQOZLrrVmuxSAbomGtmbXr9Ro6xSo7Jao2s7I6F2hQ27mjaTuVXIzNdIivP9NgqevH4wt4FzI1cjqc65lBSn+N3cHQ/VTsgr1rxLVoFKSblK53hf/jAboV0rHfsbyB9uFdJzVDonBMpTnny4YJ2T57+s5IWvfAvAj0sdfDn/2G7Cqh2QW6R6l8w8N8Vlqt9kqBYTtG9tYG9G4EYvtwppWe76eaqd0buNyeg5XlqdLjiq6inHjlWgY1Fc7v+7mk3QLlbPvkbO74MBjkXnBH2Dxw+gTYwnfN3uyn+Vw+V5s0PNklPkmYCzQ61zw2z0zMHS0LnhVj03uklxvnJNAZLidN7zIyNfw+X2P+eiwyAiRCGtgf3+FVXVGll5Lu+SnuWksMRFz86+O3arWaFjOzM79zftU9DIcD0hNp1fQ8ixUlXILITEOvVxYiuF9AZehZyRH7j+buzVyeB5aqw/QYbYF63cQNSYwX7roscOpXDlBgA0p5PidVuJHlPrIYKiEHXaEIpWBmceBb3BRGzbHhzY6ZsbQ1NVDuxcQev2wZn3Ihj0BhOtEnqQVicdaTtX0DrpxE5HQ2VtlwTfs2iLCRJj9ezLPFJZ69vGU9YavK+mzi/RKCpTaVVn+GxMhI7CkpOvp7A4MUkPiiZmt9v55z//yb///W8iIyNp27Ytzz33HBUVFVx33XVHvd8HH3yQXr16ccstt3DzzTdjMplYtGgRF110EdHR0dx7773cddddqKrK8OHDKS4uZtmyZYSFhXnnemgKLVq0ICoqinfeeYe4uDjS0tJ44IEH/MJcdtllPP3000yePJmpU6cSFxfH+vXrad26NUOGDOHhhx/m7LPPpm3btlx44YXodDo2btzIli1bePLJJ/noo49wu90MGjQIm83GZ599htVqpV27dk2WjtpW7lSZNFhHZoHCoXzPGweMBti4z3PBMmmwjtJKz7vSAVbvUrl6rJ7BXRV2Z2j0aKejdaTn7R9weFjHcB2xLRS+/s2Novie2FQ6PBdVwdBrxDUs+fZBYtr0JCahF1uWfYLTUUnnAecBsOib+7GHtSLlTE9vk57DruaXd65m0+8f0rbLKFI3zSQvYysjzvMMl3FWl7NuwZsk9hyHLTSGkvw0Vs96gbDItsR3Ht5gPJrCb5tcnD7ARF6xRn6JyoQUEyXlmt84/5vPtbB5r4tlWzw3Wr9tdHLpGDMHc1XSctyM7G3EZFBYvcPTIBAZptC3o4FdB92UVWpEhCiM6WfE6YbtaU3fvWX+6krOGmYlu8BNXpGbyaNsFJWqrN/pa0i45/Iw1u1ysGit52Z23qpK/nFuKAcyXew75OL0FAtmo8KyTb6b3WG9zWTmuSmtUOkQb+TScXbmr6oiu6Dp50BYvl3lvGF6MvI0MvI1hnTTYTLA+j2eTHzeMD2lFRrz13v+Xrld5drxeoZ217ErXaVnex2toxR+WemLm9UE4XbfvBBR4Z4eF2WVNNgzoyks2ehkXLKJ3CKVglKNCYM8eWrzXt+x/+ckC5v3ulm62ZNnFm9wcvnpZg7mqBzIdjOqjwmTQWHVds82NW+KqKuw1PMdTW3BmiomDrWQU+B528akEVaKylS/xqm7Lg1l/S4Hi9d5bjDnr65iytl29me62J/pYmyyBZMJlm/yfJ6V7ya7wM2VZ9r5bmGFZ4hHZxPd2ht449vgHJAl652ckWImt0glv0Rj4hATxeUam1N9x+Jf51vZtMfF75sOH4t1Dq44w0Jajpu0LJVR/YyYjIq3i3FUuMKALka27XdRUemZg+K8kRb2pHtmr29qy7e5Gd1bT36JRmEpjO2vp7TC09uixrVnGNiWprJqh2fdsq0qF4zQcyhPIz1PZWh3PSYD/LHb83m10/P/iQMNVFa7qHbC2YP0pOWopDdBA0UgM5aUcMG4CLJyXeQUOLlkQgsKS9ysqfWGjof/GcvqzeXMXuoZZmQxKcRG+xq9WkYZSGxtoqzCU9ZZTAoXjY9g5aYKikrctIo2cNU5kWTludiwo2m7na/aoXHuYIXMAjhUoJHSWcFo8LwtCOCcQQqllbB4k+fv1Ts1rhqrMKiLwp5DGt3bKcS18Lz9A8Coh2E9FHZleMokm9nzho9QK2xPC84x0Ntt2Du29f5tax9PWJ+uOAqKqTqYSZcn78bSphUbr70fgAPvfEW7W66g69R/c/Cj6USfNpi4iyaw5tybvPvYN+1D+nzwLEV/bKF4zSYSb78Gg93KwY8Dv9WrKSSPuZaZn9xPbLuexLXrzdpFH+OsrqTnEE9v2Rkf3UdoRCtGTr4HODy5d6bnDXBut4OyomyyD27HZLbRoqXnes9RVU5hbpr3O4rz08k+uB2rPZywyNbBScfYa5n1yf20atuTuMTe/LHwcDoGe9Ix8+P7CIloxchJvnTk10pHaVE2OQe3Y6yTjqI66cg5uB1LENMBsHi9g/EpZnKKVPKLVc4eaqa4XGNTrbL21vNtbEp18ttGT1m6aF01V55hJS3bzYEsN6P7e+bsWrnN93BlwR8OJg42k5HrJj3XzaDuJlpF6vhgxgkyUUsQyDDa40saKILgmWeeQVVVrrrqKkpLS0lOTmbOnDm0aNHiqPfZuXNn5s6dy3/+8x9SUlKwWq0MGjSIyy67DIAnnniCmJgYpk6dyt69e4mIiKB///785z//aapkAaDT6fjqq6+4/fbb6dmzJ126dOHVV19l9OjR3jAmk4m5c+dyzz33MHHiRFwuF927d+eNN94AYPz48fz66688/vjjPPvssxiNRrp27cr1118PQEREBM888wx33303brebXr168csvvxAVFRUoSsdsW5qGzawyqpeOEAtkF8IXi93eibTCbIrf08X0PPhhucppvXWc1tvzRO2b3z2vjQMItUGXw0//b5zgf4p9ssDNgZzglHIdek+kqqyQP+a/SkVpHlFx3Zhw7TveIR7lRZkoiq/Fu1W7foy59HnWzn2FNXNeJjy6HeOufI3I2M4AKDo9+Vk72bXuRxxVpdhCY4jvNIwB425v8FVaTWXReicmA1w42oTVpLAvU+WdX/3niYgKU/wmJt2wx43d4mB8ipEwm4mMPJV3f63ydp92uTxPK0f2NmI1e3og7D2k8tr3lUEZgjB7RSVmo8LVE0OwWRR2H3Qy7ativzTEtNATavUdkzXbHYTYy5k0yuYdDjLtqxJKak1yFRul5/zT7NitCnlFKjOWVTBvdXBuJLfu17CbVcb01RNihawCjU8X+M6NcDtomu8YHMzV+O53N2P76hnbT0d+CXy12E1OkW+fXRIUzhvmOy8uHun5/6KNbhZvDN7Tl4XrnJgMChefZsZqVtiX6ebtXyr9jkd0uA671ReHDXtchFgVzkwxEWb3DEF4+5fKepMjHi9zVlVhMilceaYdm0VhT7qLV78u9U9DC53f2xLW7nAQYlM4d4SVMLuO9Bw3r35d6m1YUVXP/BbnjbbxrwtDPZPHFrn56Ndytuxt+rd4gOfi1mSES8ZasJoV9h5y89aPFf7nd7jO7/xev9tFiLWaiYPNhNkU0vNU3vqxwpsOtxu6JOgZ3deEyeh588XGPS7mrAnOeOjft6iYDAqThhqwmCAtW+PjeU6/NESGKdgtvjRs2a9it8DYfnpCrHoyCzQ+nufym7Rx1ho3mgaXnWbAoIPdhzR+WRm88YE/LSzGYlK46eIobFYdO/ZV89TbWThdvjzeKtpAqN3XfSApwcxjt8Z5/54y2VM3L15dyhtf5qFq0La1iVEDQ7FbdRSUuNi0s5KvZhY2+WsItx/0DPEY1cvzW2cXwVeLVcoPH/Zwu4JWa8hZRj78uEJldC8do3srFJTCt0t99beqQVSowoXDFKxmz0OFzHz4ZIFKXknTxr1G+ICeDFnwqffv7i94rtsOfvI9m657EHNcDNYE3+9duT+dNefeRPcXHyTxtqupSs9i800PkTfP9+r4zG9nYYqJpPMjt2OOjaFk43ZWn309jjoTZzalrskTqSgrYNmvr1JekkvL+G5ceOt73qERpYWZKDpf2VRWnMMnUyd7/14z/wPWzP+AhE4pXHqX5/fIStvC19Ou9oZZNH0qAD0Gn8fEq58JTjoGTKSi1JOOitJcYtp048J/+dJRUuh/LVVWnMMnz/jSsXbBB6xd8AHxnVK49E5fOr55xZeOxTXpGHQeE4KUDoD5ax2YDAqX1Spr3/zBv6yNjtBhr3Udsm6XixBrFWcNMXuGg+SpvFmrrAVPw4dRD+ePsmCzeIaMvPF9BXnHMNePELUpWt1+nUKcAp748uScxKw2q/nUGIGVmXWcJyAIkuLCkz8dbRLCmzsKTaKk5OSfhKui7NR40mSxNu3rbZtLaPixv1azue3cmHHkQCeBvoPaHjnQCa7vVUc/pPdEkjl7Z3NHoUmcCnc6m7YEqYXsOHvtzgZesXOCe/rr4L6p61j855ITZPxZE5IeFEIIIYQQQgghRACnQiPXyeTUeEQrjkqPHj0ICQkJuHz++efNHT0hhBBCCCGEEH8j0oPib2zmzJk4nYHHGLdq1eo4x0YIIYQQQgghxN+ZNFD8jQXrrRhCCCGEEEIIcSpQZYzHcSVDPIQQQgghhBBCCNHspIFCCCGEEEIIIYQQzU6GeAghhBBCCCGEEAFoanPH4O9FelAIIYQQQgghhBCi2UkDhRBCCCGEEEIIIZqdDPEQQgghhBBCCCEC0OQtHseV9KAQQgghhBBCCCFEs5MGCiGEEEIIIYQQQjQ7GeIhhBBCCCGEEEIEoMpbPI4r6UEhhBBCCCGEEEKc4t544w0SExOxWCwMGjSI1atXNxr+22+/pWvXrlgsFnr16sXMmTODHkdpoBBCCCGEEEIIIU5hX3/9NXfffTePPPII69ato0+fPowfP56cnJyA4ZcvX85ll13Gddddx/r165k8eTKTJ09my5YtQY2nNFAIIYQQQgghhBABaJp2wi5/xUsvvcQNN9zAtddeS/fu3Xnrrbew2Wx88MEHAcO/8sornHnmmfz73/+mW7duPPHEE/Tv35/XX3+9KX7WBkkDhRBCCCGEEEIIcYpyOBz88ccfnH766d51Op2O008/nRUrVgTcZsWKFX7hAcaPH99g+KYik2QKIYQQQgghhBAnmerqaqqrq/3Wmc1mzGaz37q8vDzcbjetWrXyW9+qVSt27NgRcN9ZWVkBw2dlZTVBzBsmPSiEEEIIIYQQQogAVO3EXaZOnUp4eLjfMnXq1Ob+yY6J9KAQQgghhBBCCCFOMg8++CB3332337q6vScAoqOj0ev1ZGdn+63Pzs4mNjY24L5jY2P/UvimIg0U4pSk1ynNHYVj5nT9tYlvTlRm86lRzETF2Js7CsdMd4r0mVPdJ/+50SLK1txRaBIul7wc/kQRFhnS3FFoEqdCnsqcvbO5o9Ak4s7s0txRaBIjl73U3FE4ZtsMo5o7CuIEFWg4RyAmk4kBAwawYMECJk+eDICqqixYsIBbb7014DZDhgxhwYIF3Hnnnd518+bNY8iQIU0R9QadGncOQgghhBBCCCFEE9PUk//BCMDdd9/NNddcQ3JyMikpKUybNo3y8nKuvfZaAK6++mratGnjHSJyxx13MGrUKF588UXOOussvvrqK9auXcs777wT1HhKA4UQQgghhBBCCHEKu+SSS8jNzeXhhx8mKyuLvn37Mnv2bO9EmGlpaehqdbcdOnQoX3zxBQ899BD/+c9/6NSpEz/++CM9e/YMajylgUIIIYQQQgghhDjF3XrrrQ0O6Vi8eHG9dRdddBEXXXRRkGPlTxoohBBCCCGEEEKIALRTY4THSeMUmTJNCCGEEEIIIYQQJzNpoBBCCCGEEEIIIUSzkyEeQgghhBBCCCFEAOop8haPk4X0oBBCCCGEEEIIIUSzkwYKIYQQQgghhBBCNDsZ4iGEEEIIIYQQQgSgyWs8jivpQSGEEEIIIYQQQohmJw0UQgghhBBCCCGEaHYyxEMIIYQQQgghhAhAU5s7Bn8v0oNCCCGEEEIIIYQQzU4aKIQQQgghhBBCCNHsZIiHEEIIIYQQQggRgCpv8TiupAeFEEIIIYQQQgghmp00UAghhBBCCCGEEKLZyRAPIYQQQgghhBAiAE2GeBxX0kBxCklMTOTOO+/kzjvvbO6onHQGdFQY1FUhxALZRTB3nUpmQcPhu8bDqF46wu1QUAqLNqmkZvo+H9FDoXtbhVAbuFXIKoAlm1UONbLPptC/o8KgLp505BTB3PVHTsfInr50LN6kkpoVOOz4AQr9O+iYv15lze7gF9Sn99eT3EWP1QQHsjV+Wu4iv6Tx7x3cTceIXgZCrJBVoPHLChfpeb5tBnbR0aeDntZRChaTwuOfVlPlCF4axg0wkNLNgNUE+7NUfljqPGIahnTXM7KPgVCrQmaBxk/LHKTneraxmmHcACOd43VEhCiUV2ls3a8yd42TKmdw0jCws46h3XWe37RQY9YalUP5Daehe1uF0/roiQiB/BKYv97NnkO+8F0TFJI76YiLUrCZFd6a4SS7MDhxr2vCYBNDehqxmhX2HXLz7aIqcosaPx7DexsZM8BEmE0hI09l+uIq0rJ97xu79QIrneL9q9Jlmx18s7A6KGkYN8DAwK6e82J/tsqPS//EedFdz6jenvMis0Dj5+XOOnnKQKc2NXkKtu53M3eti+og5SmA8QONDOpmwGqGfVkq3//mIK+48XQM7WFgdF8DoTaFzHzP+XQwx3csLhhppFO8nnC7QrUT9me5mbHSecRjfLTG9tWT3FmHxQRpORo/r3CRX9r4NoO66hjeU+8to35d5SajVhll0MOZyXp6t9eh18OeDI2fV7oorwpKEgCYNMrGyH4WbBYdew46+XRWGTkF7gbDd25rZPwQK4lxBiJC9bz+TTHrd9YvSP/qfo/WqVJGrVvyOWvmvU95SS4t47sy9uL/EpfYO2DYvEO7Wfrrq2SnbaWkIIPTLnyQ5DFT/MIc3L2GNfPeJ+vgFsqLc5l84xt06nt60OIfOTyZpHuuI7x/TyytW7L2glvI/nlB49uMTKH7Cw8Q0r0TVQcz2TP1f6R/8oNfmHb/vJyku6/DHBtDyaYdbL3zCYrXbA5aOgC+mbeUT2cuIr+4lE4Jrfn31efRs0O7gGF/+W01j737ld86k9HA8g+e8/69cM0mpi9czo796RSXVfD5k/fQpV2boKahxpkpJob0MGAxK+zPdPPt4uojlrXDehkZ089IqE3hUJ7K979Vk5YT+D2bN55joVs7A+/PqGTLvqY/v8Xfkwzx+BMcjiDewZyAjia9TmcQr2SDrFuCwti+Cku3anwwVyWnSOPSUTps5sDh20TB5CE6NuzVeH+Oyq4MjQuH6YgJ94XJL4U561Tem63y6QKV4orG99lk6ehzOB3zVLKLNC4Z2Xg6Jg3WsXGfJ927D2lcMExHdFj9sJ3bQJtIhdKK49OCPLK3niHd9fy0zMX/fnbicGlcO96IQd/wNr3a65g4yMCC9S7e+MlJZoHGtWcasVt8YYwGhV3pKos3Br8SHdXHwLCeBn743cHrP1bjcMF1E02NpqF3kp6zhxhZ8IeLV7+vJjNf5bqJZm8awmwKYXaFGSudvPRtNd8sdtI5XseFo0xBSUOPdgpnDNCxZJObt2e6yC6EK8foG8xT8dEKFwzXsz5V5e0ZLnamq1w6Su93bpgMkJarMX/98b2QGTvAxMi+Jr5ZWM3LX1fgcGrcPNnW6PHo18nAeSPMzFlVzfNfVnAo180/J9sIsSp+4ZZvdvDQu2Xe5aelwWmcGNVHz9Aeen5c6uSNnxw4nfCPCY2fF72TdJw92MD8dS5e+8HhyVMTTP55yqYwc5WLl79z8O0SJ50TdFw40hiUNACc1tfA8F4Gpv/m4NXpVTicGjecbW40HX066Dl3mJF5a51M+66KQ/mebUKsvjDpuSrfLHLw3FdVvPtrFYoCN55tRlEa3u/RGtFTx+DuOn5a4eKtGS4cLrjmjMaPRc9EHRMG6lm0wc2bPzvJKtCYMs7gV0ZNGKina4KOrxa7eH+2i1AbXH5a8J4lTRhq5fQUK5/OLOOpDwqpdmrcfXl4o+kwGRXSs118NqusSfd7NE6VMmrH2pksnj6VoWf9i6sf/IGYNl359rXrKC/NDxje6agkIjqekZPvwR4W00CYCmLiu3D6JY8EM+peeruNkk072XL7Y38qvDUxnoE/v03+4lUsTZ7Evtc+ptfbTxI9brg3TNxFE+j2/IPsfvINlqacR+mmHQya8T6mmMhgJYO5K9fz8hc/ccN54/nsibvp3LY1tz33DgXFDbc+2q0WZr/2qHf55eX/+n1eWe2gb+f23HbJ2UGLdyBj+hsZ2cfIt4urmfZtJdVOuPlca6PnYd+OBiYPNzFnjYMXv67gUL7KTeda69V7AKP6GJGOBSIY/pYNFKNHj+bWW2/l1ltvJTw8nOjoaP773/96u+8kJibyxBNPcPXVVxMWFsaNN94IwPTp0+nRowdms5nExERefPFFv/2++eabdOrUCYvFQqtWrbjwwgu9n6mqytSpU2nfvj1Wq5U+ffrw3Xff+W2/detWzj77bMLCwggNDWXEiBGkpqZ641y3Z8TkyZOZMmWK9/MDBw5w1113oSgKSq0rsiPFu6H0NmT//v0oisLXX3/NqFGjsFgsfP755+Tn53PZZZfRpk0bbDYbvXr14ssvv/TbVlVVnnvuOTp27IjZbKZt27Y89dRT3s8PHjzIxRdfTEREBJGRkUyaNIn9+/c3Gp9jldJFYcNejU37NPJKYNZaDZcL+rQPfFU7sLNCahas2qmRXwq/bdHIKvL0wqixLU1jfzYUlUNeCcxfr2ExKbQMD7jLpklHZ4WNezU279fIL4HZf3jS0buBdCR3UtgbKB2d/MOHWGFcPx0/r1JxH6eKaGgPz0X89jSVrEKNb5d4LtS7t2u4yBreU8+anSrrdnsamX5a5rlpGNDZVxMv3+rmt01uv6euwTK8l4GF611sO6CSVaDxzSIHYTaFHokNXxmM6G1g9Q43a3e5ySnS+OF3J04XDOziuUHJLtT4bJ6D7WkqBaUaqYdU5qxx0q2dDl0QbsIGd9Oxbo/Khr0aecXw6yo3Tjf06xj4OAzqqmPPIY3l21TySmDRRpXMAo2ULr7wm/Zp/LZZZW/m8b2qGdXPyNzV1WzZ6+JQnspnc6sItyv06tDwzd/o/iaWb3WyapuL7AKVbxZW43BpDO7hf/PucEFpheZdqoPUpj2sp3+e+nqxkzCb0vh50cuTp/44nKd+XOo5L5K7ePJhdqHGZ/Odfnlq7hpX0PIUwIjeRub/4WTrfjeZBRpfLfScGz3bN3xujOpjYNU2F2t2usku1Ji+xIHTqTGwq+/4rdruZm+mSmGpRkaexuxVTlqE6ogMbfqEDO2uZ/FGNzsOamQXanz3u6eM6ta24WMxrIeOtbtU1u1RyS2Gn1e4cbpgQCfPNmaj5/+z1rjYm6VxKF/j+2Uu2rXSER8TnINxeoqVX3+vYMMuB+k5bt7/qZSIUB39uzbcmr4l1cEPiysC9po4lv0ejVOljFq78EN6D7uYXkMuIDquI2dc9hhGk4Uty6cHDB+X2JvR599Pt+Sz0BsCN1An9RjFiHPvonPfccGMulfunN/Y9cg0sn+a/6fCt7vxUir3pbP9vmcp27GXA29+Ttb0ObS/Y4o3TPs7r+Xg+9+Q/vH3lG1PZfMtj+CuqCJhygVBSgV8PmsJk0cP5tyRKSS1ieXBay/EYjby82+rG9xGUQeGUfEAANMmSURBVCA6Isy7RIWH+n1+1vBkbjhvPCk9Ogct3oGM6mNk7loHW/a5ycxX+WJ+FWF2hV5JjdR7fY2s2Opk9XYX2YUa3y7y1HuDuvlv0zpax+h+Rr4KUm/BE42qaifscir6WzZQAHz88ccYDAZWr17NK6+8wksvvcR7773n/fyFF16gT58+rF+/nv/+97/88ccfXHzxxVx66aVs3ryZRx99lP/+97989NFHAKxdu5bbb7+dxx9/nJ07dzJ79mxGjhzp3d/UqVP55JNPeOutt9i6dSt33XUXV155JUuWLAEgIyODkSNHYjabWbhwIX/88Qf/+Mc/cLlcfyo933//PfHx8Tz++ONkZmaSmekZb3CkeDeU3j/jgQce4I477mD79u2MHz+eqqoqBgwYwIwZM9iyZQs33ngjV111FatX+wr1Bx98kGeeeYb//ve/bNu2jS+++IJWrVoBnl4Y48ePJzQ0lN9//51ly5YREhLCmWeeGbReLDodxLWA/dn+J/i+bI020YEvCNtEKfXC781sOLxOB/06KFQ5NLKLmiTaAb8jtoUn3rXtz9FoE/Xn07Evq374c1J0rNrpabw5HlqEep7qph7yNSJUOyE9V6Nty8Bp0eugdbTCnlrbaEDqIbXBbYIpMtTzVHp3hu8JXJUTDuaotG0ZuNjV66BNtMLudN82GrAnw03bVg0X1RaTQpUDmrqO0umgdaRS7yJ9b6ZGfAN5PSFGYW+Wf/jUTI34mOataqLCFMLtOnal1ToeDjiQ5aZ9bOCbYr0OElr6b6MBu9LcJMb6pye5i4GnbrTzwBU2zh5qwhiEB941eWpPhv95cTBXo10D+aMmT9XexpOnVNo1kA8heHkKDqfD7p/PqxyQlqM2no4YHbvS/dOxO6PhbUwGGNjVQH6JSlFZ0yakRQiE2hRSa50bNWVUQgMNCXodtI5SSM2sU0ZlqiQcPj/aRCkY9P77zSuGojKNtkFooIiO0BERqmfbPl/9WlmtsTfDSYc2R5+Jg7Xfuk6VMsrtcpCVtpV2XYZ61yk6He26DuXQvvXNFq9gixjcl7yFK/zW5c5bSovBfQFQjEbC+/cgb8FyXwBNI2/hciIG9wtKnJwuFzv2pzOoVkOCTqcjpUdnNu3Z3+B2lVUOzr7zCc6643Hufvl9UtMbGCt7HEWFKYTZdew6WKfey1br1WE19DqIb+m/jQbsTnfTrlZdaTTAVWdYmL6k+rj1rBV/L3/bOSgSEhJ4+eWXURSFLl26sHnzZl5++WVuuOEGAMaMGcM999zjDX/FFVcwduxY7817586d2bZtG88//zxTpkwhLS0Nu93O2WefTWhoKO3ataNfP08BWl1dzdNPP838+fMZMmQIAElJSSxdupS3336bUaNG8cYbbxAeHs5XX32F0Wj0fsefFRkZiV6vJzQ0lNjYWO/6l156qdF416ib3j/jzjvv5Pzzz/dbd++993r/f9tttzFnzhy++eYbUlJSKC0t5ZVXXuH111/nmmuuAaBDhw4MH+7pzvf111+jqirvvfeetwfIhx9+SEREBIsXL+aMM874S/H7M2wm0OmUeuN7y6sgKsBQB4AQC/XDV3vW19YxzjMUxGiAskr4colKZZCerNako6JOQ3Z5FUSFBt4mYDqq/NMxpKuCpsHa4zDnRI3Qw90Iyyr9v7OsUgvYxRDAZgG9Tgm4TUz48b/wDLUdTkNF/fiE2gJv40uD//rSSo2YiMBpsJlhbH8Dq3f8uYbMv8Jmbujc0IgOD3wcPHmqTpqr6p8bx1uo3RPfuhdSpRWa97O67FYFvU6htEKtt03LSN+F2h87XRSWqBSXa7SO1nHuMDMtW+j4YEbTThpQM5Shyc6LRvLUmH6eXhfBUHNulNaNU4Xm/awuu0UJmI7SCo2WddIxtIeBs4YYMRsVcgpV3vmlGncTd5gKaaSMCrUG2sLzuwY6v8sqITrct1+XW6s3L05jx/hYhId4fruScv90lJSrhIUcfbkZrP3WdaqUUZVlhWiqG1tYlN96W2gUBdl7mylWwWduFU11dp7fuursPIzhoegsZowtwtEZDFTn5NcJk4+9S1JQ4lRUWo5bVYms0wMiMiyU/YdyAm7TLq4l/73hEjoltKasopLPZi7mH4+/yjfP3EeryIigxPPPaPA6pEJtuKytqfeOUNZOHm5mf6Zb5pwQQfO3baAYPHiw3zCIIUOG8OKLL+J2e0625ORkv/Dbt29n0qRJfuuGDRvGtGnTcLvdjBs3jnbt2pGUlMSZZ57JmWeeyXnnnYfNZmPPnj1UVFQwbpx/NzuHw+FtxNiwYQMjRozwNk40lSPFW6/XB0zvn1F3G7fbzdNPP80333xDRkYGDoeD6upqbDabNy7V1dWMHTs24P42btzInj17CA31rxiqqqq8Q10Cqa6uprra/87c5TRgMAZxwoc/4UAOvD9XxWqGvkkK5w3R8dF8tV4jwokqtoVnGMiH84I7HKJPBx2Th/mKok/mnnzzmfTtqOf8Eb5z98PZwZ+3xmyEayeYySnUmLe26RsoTmYDuhi4ZIzvjuPtnysbCX1sVmzx5dfMfJWSco1bL7ARFV5N/hEmImtM3w46zquVpz46TnlqypkmcopU5v/RNHmqXye93xwp788IbgG4breLXeluwmwKo/oauOoMM6//UIXrGK6j+yTpOHeIr1Hq0/kn5/k2qKeZq8/y1a+vfFncjLER4tTQu1MivTslev/u06k9F97/DN8vXME/L5xw3OLRv7OBi0f7rnvf/TU49V6PRD2d4vW88HVFUPZ/opK5No6vv20DxZHY7fa/FD40NJR169axePFi5s6dy8MPP8yjjz7KmjVrKCvzTCQ1Y8YM2rTxn7XXbPYUJlZrA49dDtPpdPVecdOUE1P+1fQG2ub555/nlVdeYdq0afTq1Qu73c6dd97pHZ5xpDSWlZUxYMAAPv/883qfxcQEngQKPMNnHnvMf1KmMRf8l7EXHXliqAqHZ1yZvc7TE3uA3gXeeFZRP7zZs742pxsKyzzLoXyNmycq9ElSWLG96Uu5mnTUnRjMbqkfrxoB01ErfEK0gt0C/zrb12qu0ymM6QPJnRX+N6NpGi62p6kczPHdfBn0nobDEKt/K36IVSGzIPB3VlSBW615yui/Td0nAcGw7YD/vBY1E1CF2OqnoaHZ5X1p8F8faq0/OanJCNdNMFHt0PhkniMoXfErqhs6N+o/Ba7hyVN1jkEjeTBYtux1cSCr3Pt3TZ4KtSmU1PotQ20KGbmB81R5pYZb1Qi16QDVb5vS8obz/oEsz51wTLiO/OKjvyvelqZy8HvfeaGvyVOBzov8v35e1H2qZjLCPyaYqHZqfDrP2WR5att+Ny9l+zJAzblRN1+HHJ4tPpDyKq1WOnzqHk/wdGGucmjkFWscyHbwxD+s9GyvZ8Oeoz8W29NUDubWPr99ZVRZvTKqgfO7OvD5HWLFez6VVWoY9AoWE369KDzfc+zl7cZdDh7L8L3WyWDwpCPMrlBca77LMLuOg1lH3whTXKYGZb91ncxlVG3WkBYoOj0VJf49BSpK87GHRTdTrIKvOjsPcyv/9JlbReMsLkWtqsaRV4jqcmFuGVUnTBTVWf49L5pKRKgdvU5Xb0LMgpJSoiIa6I5ah8Ggp0u7eA5mByeODdm6z8UL2b5yzltO1SknQ2w6DuUFLg+99V4jZW2neD1R4QpP3+B/D3DtBAt7M1Xe+CF4DwTE38ffdg6KVatW+f29cuVKOnXq5O1RUFe3bt1YtmyZ37ply5bRuXNn7zYGg4HTTz+d5557jk2bNrF//34WLlxI9+7dMZvNpKWl0bFjR78lISEBgN69e/P777832OgQExPjnVcCPL0VtmzZ4hfGZDJ5e4D8lXg3lWXLljFp0iSuvPJK+vTpQ1JSErt27fJ+3qlTJ6xWKwsWBH7tVP/+/dm9ezctW7as9zuFhzc8u+SDDz5IcXGx3zJq8gN/Ks6qCpmFkNjKvzBObKX4vfqttox8jcQ68xq0j204fA1FAUOQzjhVhawA6WjXUiGjgRvijHyNdoHSfTj8lgMa781ReX+ubymt0Fi1U+Pr35quV4XD6XnFac2SU6RRUqHRobXvxzIbIT5GIS0ncFrcKhzK0+gY59tGATq01jW4TVNyOCG/RPMu2YWeNHRs7TvHzEbPnAYNvarLrUJGnkbHNr5tFKBja73fay3NRrh+ohmXCh/PcRzTk+HGqCocKtBIivXPI0mxit+rW2s7mKvRvm74OIX0BhoBgqXaCXnFmnfJKlApLlfpnFDreJigXayefVmBf0C36pkzpPY2CtA5Qc/+rIbT0ybGE77ujfNfVTdP5dTkqTb+50VCjMKB7CPlKf/zomNrHQdy/PPUdRNMuN3wyRxnk+ap6kDnRrlGp3j/c6NtS13j6chV6RRfJx1tGt6mtmN9c4TDVb+MKq3Q6BDny+s1ZdTB3EbKqHyNpDplVFKcztv4kZGv4XJrJNXab3QYRIQopDWw37+iyqGRU6h6l0O5bopK3XRr7+vhYjEpJLUxkppx9A0JeUVqUPZb18lcRtWmN5iIbduDAzt98zFoqsqBnSto3T44cy2cCIpWbiBqzGC/ddFjh1K4cgMAmtNJ8bqtRI8Z4gugKESdNoSilcGZm8NoMNA1MZ7V23Z716mqypqtu+ndMfFP7cOtquxJzyQ6ooFxwkESqN4rKVfpXKesbddK12Ad5lYhPUC91yle7218X7DOyfNfVvLCV74F4MelDr6c34wtfeKU8rftQZGWlsbdd9/NTTfdxLp163jttdfqvd2itnvuuYeBAwfyxBNPcMkll7BixQpef/113nzzTQB+/fVX9u7dy8iRI2nRogUzZ85EVVW6dOlCaGgo9957L3fddReqqjJ8+HCKi4tZtmwZYWFhXHPNNdx666289tprXHrppTz44IOEh4ezcuVKUlJS6NKlC2PGjOHuu+9mxowZdOjQgZdeeomioiK/OCYmJvLbb79x6aWXYjabiY6OPmK8m1KnTp347rvvWL58OS1atOCll14iOzub7t27A2CxWLj//vu57777MJlMDBs2jNzcXLZu3cp1113HFVdcwfPPP8+kSZN4/PHHiY+P58CBA3z//ffcd999xMfHB/xes9ns7YlSw2D881fYq3dqnDNIIbPAcwGZ0kXBaPDM5A1wziCF0gpYvNnz95pdGleOUUjpopB6SKN7W4W4Fp63fwAY9TC0u8LuQxpllZ5xsgM6KoRaYfvB4N0sr96lcXaKQlaB56JtYGf/dJydolBaCUsOp2Ptbo0rTlNI6aywJ7N+Oiod1Jszw615epYUNPy2rSaxfKub0/rqySvRKCzVGDdAT2kFbDvgq1Svm2Bk6343K7d71i3d4ubCkQbS83Sk52oM66nHZIB1u3x5IcTqeXIbFea5QI1toVDt9ExC19Tzgyzd7GJMfwN5JSqFJRpnDDRSUqGxdb8vPjecZWLLfjcrtnrW/b7JxcWjjaTnqqTnqgzvZcBohLW7PBf1NY0TRgN8tdCJ2QQ1Ob+8qum7IK7crjJ5qJ5DBZ63Igzu5plTZUOq5zefPFRPaYXGgg2ev1ftUJlyhp4h3XTsylDpmaijdaTCLyt9abaYINzum2skOszzNLOssuFeS01hyXonZ6SYyS1SyS/RmDjERHG5xuZU3w3Tv863smmPi983eRqKF69zcMUZFtJy3KRlqYzqZ8RkVFi1zfN5VLjCgC5Gtu13UVHpmYPivJEW9qS7GuwNcCyWbXExpp+BvGKNglKNM5INlFRofufF9RONbP1/9u47vqbzjwP459yZmz0RSWRIRJCICGLG3rWqqqhRtDpQtNVFlaIDVbRVsyjFz2pLa68ktshAkCkSGbJ3csfz++PKTW5yQ0Li5Mb3/XrdFzn3OTffJ/c8zznnOc+IU+HibfXfPDBcgdf81cfUg0cM3doIIRED1x+Xi9LGCbEI2HGm7o8pAAgIk6NPezEeZTNk5KgwsKO6bJQfz/zOK1LcjFUi6Kb6+zkXqsDY3hIkPFIhPkWF7l4iSMQcrj6ef8XShIO3qxB3HyiRXwSYGXHo7SOCXAncia/9VrwLt5Xo6SVEeg5DZi7Qx0ddR0XEl30XU/qLcDtehct31NuCbqnwanchHqYxJKSp0KWVuo66Hql+v1iu/v/gDiIUFitQLAeGdhIiPlWFhFpooNDl5JVCDO1miJQMJdKylBjZ0whZuSoE3ykbivPRBDME3ynG6WvqAioVQ2seFmtzIRwaC5FfqP4+q/u5taGh1FG+vafg3+3z0cSxDWwdvXDtzDbIiwvRprN6nq8jv38CE/PG6DFCPVeYUlGCtCT10FelsgR5WSlIeRABidQQFo0cAQAlRfnIfBSv+R3Z6QlIeRABmZEZTC2b1noehEaGMHJtpvnZ0Nkepm1boiQjG0UPkuD+zVwY2DVG6JT5AID7G3bD8b3xaLn8Yzz4fT+se/nB9rVBuDrsHc1nxK7eirZbvkPW9ZvIvhoGp1mTIDKS4cG2A7Uef6nxg/yxaMOfaOXsgNYuzbDr2DkUFpfglR4dAQAL1+9CIwtTfPB4ydCNB4/B09UJ9o2tkVdQiO1HziA5LQMjenbSfGZ2Xj6S07PwKFM9rOp+kno+CyszkzptyDgXKkc/XwkeZalXaRrUSYKcfIbwmLLz3rvDDRAeo0Rg+OPzXogc4/pK8SBVhfspSvi3lUAi4nA5Qr1P6WpVFWXmqn9HQ8Ua6GoZ9dVL20AxceJEFBYWomPHjhAKhZg9e/YTl9f08fHB3r17sXDhQixZsgS2trZYvHixZqJJc3NzHDhwAIsWLUJRURHc3Nzw559/onXr1gCAJUuWwMbGBsuXL0dMTAzMzc3h4+ODzz//HABgZWWF06dP4+OPP4a/vz+EQiG8vb3RtWtXAMBbb72F0NBQTJw4ESKRCHPmzEGvXr20Yly8eDHeeecdNG/eHMXFxWCMPTXu2vTll18iJiYGAwYMgKGhId5++22MGDEC2dll41wXLFgAkUiEhQsX4uHDh7C1tcWMGTMAAIaGhjh//jzmz5+PUaNGITc3F3Z2dujTpw9MTeuuAo94oB4a0aMNByMDDilZwJ5zKuQ/vo4yNeS0htckpgN/XVTB31OAnp4cMvOAfUHqZeMA9cz31qYcvJw4yKTqm/ykDGDHaVWdroRRmo/uj/ORmgXsPV8254WufPx9SYUebQTwf5yP/UF1G2N1nQ9TQiICRnYVwUAC3E9h2Frhya6lCfe4q65aeKwKRgYK9G0vgokMSEpX71O+626nlkL08Smr9t4eqn7Ct++8HMGRtXtDeS5UAYkIeLW7BAYSIC5ZhS3/afd4sDTVzkNYjBJGMqC/rwgmhurhIFv+LdZ0V7azFmhW9Jj/hna/5m93FSGzllcruHWfwVCqQk8vIYxlQHImw87TSs1FupkRwFhZ/AlpDAcC1Y1Lvb0FyMgFdp9TasoGALjbcxjRpew7GN1d/f+zYUqcC6u7p5inrpdAIgZe72MAmZRDzEMl1h8q0Po+rMwEMCrXtfVGpALGsmIM9pPC1JBDQpoK6w8VaC7OlErA3UGInt4SSMTqhq7QKAWOXa2beRbOhSohEXEY1V2sPqZSVNh6VLtcWJkKYGRQdhyExajLRb/2YpgYqhtht/xXovOY+mSsdkPvd38W1/oxBQBnQhSQiDmM9pdAJgFik1XYeLi4Qj60y0ZotBLGMjkGdBCry0aaCpsOl5UNhRJwthWiu5cYMql6uETMQxXWHSyqsrv/8wi4qYJExGF4F3UdFZ/CsO2E/Inl+2acCkYGQJ92QhjLhEjKYNh2QqF10/vfVSUYA97oJYJIAEQ+ZPjnUt3NefHfhUJIxBwmDTGBoQGHyHg5ftyVrZUPGwshjA3Len44NRXjk4nmmp/H9jcGAASFFmHL37nV/tza0FDqqJa+g1GQl4Ggw2uQn/MIjew9MPqDTZohHrmZSeAEZd9BXnYqti8fofn56sktuHpyCxzcOmLsnB0AgOT4m9izeqImzZn9ywEArf1GYvDEb2s9D2bt26DzqR2an1utUF/fPth+AGFTP4PU1gYyB1vN+4VxCbg67B20WvkZnGZORFFCMsLf+RJpJwI1aZL+9x8kNpZo8dUsSJvYICc0AleGTkNJhYkza1N/v3bIzM3D+v1HkZ6dgxbN7LD247c1S4cmp2dCUG4Ou5z8QnyzeS/Ss3NgamSIlk722LxwFlzsyiasPx98C19v3K35+fOf1X+n6SP7451RA+ssL6eD5ZCIOIzpJYVMyiE2SYnf/inUKofWZgIYycqO65AoBYxlHAZ2lMDUSD0M8rd/CitNCkxIXeJYxYkNXgI9e/aEt7c3Vq9ezXcopI4s26P/Mwtz3NPT6IPcXP2cUK4iZW0vBcADQ8OG0Sadka7/3UhlhrU7ITJfFAr9LxcAYCDT/7KR9KBhTHrZzNmc7xCem51twyjftgPd+Q6hVvQIWsV3CM9t4RV/vkOoFT9+YMx3CM9k9k913HX4Ofw0u3rzo+gT/T8jE0IIIYQQQgghdUD18j3P59VLO0kmqdqyZctgbGys8zVo0ItbMokQQgghhBBCyMvjpexBcfbsWb5DqNdmzJiBMWPG6HzvaUuFEkIIIYQQQgghz+KlbKAgT2ZpaQlLS0u+wyCEEEIIIYQQXtEqHi8WDfEghBBCCCGEEEII76iBghBCCCGEEEIIIbyjIR6EEEIIIYQQQogONMTjxaIeFIQQQgghhBBCCOEdNVAQQgghhBBCCCGEdzTEgxBCCCGEEEII0YFGeLxY1IOCEEIIIYQQQgghvKMGCkIIIYQQQgghhPCOhngQQgghhBBCCCE60CoeLxb1oCCEEEIIIYQQQgjvqIGCEEIIIYQQQgghvKMhHoQQQgghhBBCiA6M0RCPF4l6UBBCCCGEEEIIIYR31EBBCCGEEEIIIYQQ3tEQD0IIIYQQQgghRAcVreLxQlEPCkIIIYQQQgghhPCOelCQBkmh0P+WTgMDaj+sT8QSId8hkMdUDWCyqoYy4ZaA4/gOoVYIGkA25CUKvkOoFVwDOKYaSPFGj6BVfIdQK853nct3CM9NuOYG3yEQ8sJQAwUhhBBCCCGEEKJDQ3mooC/oES0hhBBCCCGEEEJ4Rw0UhBBCCCGEEEII4R0N8SCEEEIIIYQQQnRgtIrHC0U9KAghhBBCCCGEEMI7aqAghBBCCCGEEEII72iIByGEEEIIIYQQogMN8XixqAcFIYQQQgghhBBCeEcNFIQQQgghhBBCCOEdDfEghBBCCCGEEEJ0UDEa4vEiUQ8KQgghhBBCCCGE8I4aKAghhBBCCCGEEMI7GuJBCCGEEEIIIYToQKt4vFjUg4IQQgghhBBCCCG8owYKQgghhBBCCCGE8I6GeBBCCCGEEEIIITowWsXjhaIeFIQQQgghhBBCCOEdNVAQQgghhBBCCCGEd9RAQV4IjuNw6NAhvsMghBBCCCGEkGpTqVi9fTVENAcFz5ycnPDhhx/iww8/5DuUl5pvCw5dPAQwlgEpmcB/15R4mF51eo9mHHp5CWBuDKTnAqduqBD1UF1JCDigV1sBXO04WBgDxSVATDLDqRAV8grrNh+3L+5EeMAWFOalwbJJS3R+5QvYOHhVmT42/Ciun1iDvKxEmFo5osPAeXBw99e8f37fZ4gMPqS1j51bNwycsrGusqDR10cIX3chZBLgfgrDXxcUSM95ckXs5yFAd08RjGVAcgbDPxcVSEgr26eDuwBtmwvR1IqDgYTD4h3FKCqpuzz08RbCt4UABhIgPpXh74sKpOc+eZ9OLQXo1kaoycPhy0oklsuDSAgM9BXCy1kAoRCISmT4+5IC+UV1k4cOLQTo0kpdNpIzGf67qsLD9Kq/h1bNOPRqK1SXjRzg5A2lpmwAQEsHDr5uAthacTCUclh/RI6UzLqJvbzBflJ08RRDJuUQ+1CJPaeL8ChL9cR9unuJ0cdXClNDDolpKuw7U4j7Kdr7ONkK8UoXKRybCKFSAYmPlPjlYAHkyrrJR7/2InT0EEEmAeKSVTgYKH9quejcSogebUUwkXFIymD4K6gECY/U+8ikQL/2YrSwF8DcmEN+EcOtOBWOX5WjSF43eQCA/r6P8yF9nI8AOdKyn5KP1kL4ez/OR7o6Hw9Sy/YZ1UMMNzsBTI04FMuB+8kq/HtZjkdZtX8B19FdgK6Py2lKBsORK9rltKLWjhx6txPB3BjIyGE4fl2JyETt9L29hWjvVlZf/HNJgYyn1Be1YVRvY/T0NYShgQCR8SX4/e9spGRUfQC7O0owuJsRnJqKYWEqxOpdGQiOKNZK49vKAL06GMK5qRjGhgJ8+fMjxCcr6iT+hnL+vnFuJ66e3Iz8nEewsWuJPmMWwNZJ9/k77WEkgo6sQUr8LeRkJKLXq5+hfe/JWmkeRF7F1ZObkfLgJvKzH2H42z/DrW3fOs3D3hOB2PHvGaRn58LNoSk+njgSbZo76kz7z/kr+Hrjbq1tErEIF7Z8r/n59NUw7D99AXfiEpCdV4Cd38yDu6NdnebBspsvXOZNhZlPGxg0bYRrr76HlL9PPXmfHh3RasWnMG7lhqIHSYha/isSth/USuP47ji4zJ0KaRMb5ITdwa0PlyD7anhdZgUAMKCDGJ0e17WxySocOF/y1Lq2S2sRenqLYGLIISldfZ55kFp27nu1hxhu9kKYPa5r45KVOHKpbupa8nJqsD0oSkrq8K6jHmro+a3L/LVy5NDfR4Bz4Sps+FeJ5EyG8b2EMJTqTm9vDbzaVYAb0er0dx8wvN5DABsz9ftiEWBrCQSEq7DxXyX2nlfC2pTDWH9hneUBAGLC/sXlf79Duz7vY/j7+2Fp646jW6ejME/3lVrK/Rs4s+cjtPB9FSM+OADHVn1w8o+ZyEi+p53fFt3xxmfnNa9eY1fUaT4AoIeXEJ1bCfFXkAK//i1HiYJhygAxRE/4E3o6CzC4kwinbijw819yJGUwTBkohpFBWRqxiMO9BBXOhtbR3WM53dsI4NdKgL8uKrD+iAIlCmBS/yfnoY2TAIM6CHEmRIlf/pYjOYNhcj+RVh4GdRCipYMAu88qsPmoAiaGwLheddPW3NqRQ//2ApwLU+K3fxVIyQQm9H5S2eDwajchbkSr8NsRBe4mqDDWX6gpGwAgEQHxjxhO3qj776BUX18J/NtJsOdUEVbuzkexnOG9kYZP/C58WogwsocB/rtUjO935SPxkRLvjTSCsYzTpHGyFeK9EYa4c1+BFX/mY8XufJwPLUFdXaL5txWhaxsRDgaUYN2hYpQogKmDJU/Mh5eLEEM7i3HqugJrDhQjKV2FqYOlmmPK1JCDqRGHI5fkWPW/Yuw9K0cLewFG+0vqKBdAT28RunqKcCCgBGsPFKNEDkwd8uR8tG0uxCtdxDh5TYGf9j/OxxCpVtlIfKTC3rNyrNhTjM1HisFxwLQhEnBc1Z/7LNo4CTCwgxBnQ5VY/48cyZkME/tql9PyHGw4jO4hQnCkEr/+I0dEPMMbvURoZF4WWLc2AnTyEOCfSwps+FddX0zsJ4aojq/ShnQ3Qj8/I/z+dza+/i0NxSUMH0+yhPgJVYpUwiE+WY7th7OrTCMRc7h3vwR7jufUQdRlGsr5+871f3H2wHJ0Hvw+3vz0IBrZt8S+dVORn6v7/C2XF8LMyh49hs+DkamN7jQlBWhk746+Y76qy9A1jl+6gR93/YXpIwfgjyVz0aJZU8z8fgMysqtuZTOSGeDo2kWa1z8/LtB6v7C4BN4tnDHz9aF1Hb6G0MgQOWF3cXPW19VKL3OyR4e/f0P62csI9B2O2LXb4PnbN7Du102Txva1QfD44TNEfvMzAjuORG7YHXQ6shkSG8u6ygYAoJe3CN08Rdh/vgRr9hehRM4wfaj0qXXtsK5inLgmx+p9RXiYrt7HWFaWJuGRCnvPlOD73UXYeLgIHAe8PVRa63UteXnpTQNFz5498cEHH+CDDz6AmZkZrK2tsWDBAs2sqk5OTliyZAkmTpwIU1NTvP322wCA/fv3o3Xr1pBKpXBycsLKlSu1PveXX36Bm5sbDAwM0LhxY4wePVrznkqlwvLly+Hs7AyZTIa2bdti3759WvvfunULQ4cOhampKUxMTNC9e3dER0drYq7YM2LEiBGYPHmy5v379+9jzpw54DgOXLmS/bS4q8rvk1y4cAHe3t4wMDCAr68vDh06BI7jEBISoklz8+ZNDBo0CMbGxmjcuDHefPNNpKWlaX0Ps2bNwieffAJLS0s0adIEixYt0vo9kZGR6NGjBwwMDNCqVSucOHGiUiwPHjzAmDFjYG5uDktLSwwfPhxxcXGa9ydPnowRI0Zg6dKlaNq0Kdzd3Z+av2fVuaUAwVEMoTEMaTnAkSsqyJVAu+a6a9pOLQWISmK4GKFOfzZMhaRM9dN5ACiWA3+cVuF2PEN6LpCYDvx3VYmmVhxMDessG7gZuA3uHV5Di/ajYNHYFV2HL4JIYoB71w/oTH/rwnbYu3WDV4+pMG/UHO37zYZVUw9EXNqllU4glMDQxEbzksrMdH5eberSWn2THhGvQnImw//OqW/EWzlWXWV1ayPE1bsqBEeqkJrF8FeQ+iK/fYuyM/GFW0qcD1NqPQmoszy0Ut/A3HnAkJLJsC9AnQePZlXnoWtrAa7dUyE4SoVH2cDfF5WQK4D2bup9pGL1//+7qkBMMsPDdIYDQQo4NhbA3qb2rwz8PAQIjlIhJIYhLRs4fFmpLhuuuvPQqaUAUQ8ZLtxWIS0HOBOqQlIGQ0f3svRhsQznw1WISXpxT1p6tpPg2OVihMco8DBNhR3HCmFmxMGredV3Yb18pLh4U47Lt+VIzlBhz6kilCgYOrcWa9KM6iHFuZASnLhWguQMFVIzVbgRqYCijtpeunmKcPqGArfvq5CcwbD3TAlMDTm0dqr6arO7lwhX7ihx7Z4SqVkMBwPkkCuADu7qvKdkMvxxogQR8Spk5DJEP1Th2FU5PBwFENTRxWY3TxFOBStwO06djz3VzMflCCWu3VUiNZPhwPnH+WhZ9h1ejlAiNkmFzFyGxDSGo1fksDARwMKkdjPSpZUA1yNVuPG4nP5zUV0ufKooF34eAkQlMgTdUiEtGzgdokRSBkOnlmXpO3sIcT6srL44EKiuL1o+ob6oDQM6G+Hvc3kIvlOMBykK/LY/C+YmQvh4VNHaAiAsshj7T+XheoVeE+VdCC3EX2fzcCu6bh+eNJTz97VTW+HZZQw8O78Ka1tX9Bv7NcQSA9y8uF9neltHL/QcNR8tfYdAKNLdmOjS2h/dXpkDN+9+dRd4OTv/O4cRPf0wrEdHuNg1wWdTRsNAKsbf569UuQ/HAdbmppqXlZmJ1vtDuvli+sgB6Ni6RV2Hr/Ho2Hnc+2o1Uv46Wa30jm+PRWFsAiI++Q55d2Jw/5edSN5/DM6zJ2vSOH84BQ8270XCtgPIi4hG+HtfQVlQBIfJr9ZRLtS6e4lx8roct+LUdc7u0+q6to1z1XWtf1sRLt9W4OpdJVIyGfafK4FczirVtTHl69rL6rrWspbr2vqEqVi9fTVEetNAAQDbtm2DSCTClStX8NNPP2HVqlXYtGmT5v0VK1agbdu2uHHjBhYsWIDr169jzJgxGDt2LMLDw7Fo0SIsWLAAv//+OwDg2rVrmDVrFhYvXoy7d+/i6NGj6NGjh+bzli9fju3bt2P9+vW4desW5syZgwkTJuDcuXMAgMTERPTo0QNSqRSnT5/G9evX8dZbb0GhqF43xgMHDsDe3h6LFy9GUlISkpKSAOCpcVeV3yfJycnBK6+8Ak9PTwQHB2PJkiWYP3++VpqsrCz07t0b7dq1w7Vr13D06FGkpKRgzJgxlb4HIyMjXL58Gd9//z0WL16saYRQqVQYNWoUJBIJLl++jPXr11f6PXK5HAMGDICJiQkCAgIQFBQEY2NjDBw4UKunxKlTp3D37l2cOHEChw8frtbftKYEAvXTkthk7QIem8xgb627orW35hBb4eYq+mHV6QH1EyfGWJ0NJ1AqSpD28BaaunbWbOMEAjRt3hmp8SE690mND9VKDwD2bt0qpU+OvYKdS7ti36pBCDq0CEUFddsf38JE/VQ3+mFZI0KxHEh4xNCske6/sVAANLXmEFVuHwYg+qGqyn3qkoUxYGLIIbrccVKaB4cqGhKEAqCpFYfopAp5SFLBwUZdVdtZcRAJtT83LRvIymNoVssNFAIB0NSSq9SQEJNU9bHuYMMhpkJZik5isLfh71RjZcrBzEiAuw/K6uWiEnWXVGdb3RdpQgHg0Eh7HwbgbrwCTo/3MZZxcLYVIbdAhTljDLF0ujFmjTaES9O6edJqacLB1JBDZGJZ60eRHHiQqkKzRrr/vkIBYGfNITKhbB8GICpRiWaNq/5ODCQcikqAurjusTRR99goH1NRiTofjk2ekA8bDlEV8hGZoIRjFfkQi9SNF+k5KmTn1V5GhALA1kq7fiqta6o6zh1sBIhJ0m4UjUosqws09cVD7foi8Qn1RW2wsRDC3ESIW9FlDQ2FxQwxCSVwdai7HjS1pSGdv1Me3IJjyy6abZxAgGYtu+BhzI26+aW1TK5Q4E5cAjqVa0gQCATo2LoFwqLiqtyvsKgEQz9cgiGzF2Puj5sRnZD8AqKtXeZ+3kg7fVFr26MTgbDw8wYAcGIxzHxaI+3UhbIEjCHt9AWY+7Wrs7iqqmvjU1VV1pvqulaAewna9VtkYtX7SMrVtVm1WNeSl5tezUHh4OCAH3/8ERzHwd3dHeHh4fjxxx8xffp0AEDv3r0xb948Tfrx48ejT58+mpv3Fi1a4Pbt2/jhhx8wefJkxMfHw8jICEOHDoWJiQkcHR3Rrp26siguLsayZctw8uRJdO6svolzcXFBYGAgfvvtN/j7++Pnn3+GmZkZdu/eDbFYrPkd1WVpaQmhUAgTExM0adJEs33VqlVPjLtUxfw+ya5du8BxHDZu3Kjp2ZCYmKj52wHAunXr0K5dOyxbtkyzbcuWLXBwcMC9e/c0efPy8sJXX6m7DLq5uWHdunU4deoU+vXrh5MnT+LOnTs4duwYmjZtCgBYtmwZBg0apPnMPXv2QKVSYdOmTZpeI1u3boW5uTnOnj2L/v37AwCMjIywadMmSCR1d6FkKAUEAvW46/LyiwBrU90XLMYGQF6F8f75RQzGBlXfePbxFuBmHENJ3QzBRVFBFphKCZmxldZ2mbEVsh/F6tynMC8NMmNrrW0GxlYoyC3rMWPn1g2OrfvBxMIeORnxuH5sNY79/g5emfEnBIK6uREzedyFPq9Q+zvJK2Ra3evLMzQAhAJO5z42Zi/+5tj4CXkwkenaQ30sqvOACvsA1mZln6tQVr5QftLf5lmVlQ3t7flFDNZmVZeNimUpr0i9nS+mRurvPzdfO67cAqZ5ryIjGQehgENOQeV9Gluqj3vrx8fVYD8pDgYUI/GREh09xPhglCGW/5H/1PktasrE8PExVaDjmKriyW5ZudDenlvIYGOuO++GUqCPjwhX7tRNZaXJR4WykfuEsmH0OB+5lcoGQ6MK+ejcWojBfmJIxRxSM1XYeLgEylr8KkrLaeVyAa2hTOUZyyqfM/KKGIxlgsfvc5ptldPUStg6mRmrf392nvYfKDtfBXPj+v/8qqGcvwvzMsFUShiZaJ+/jUyskJEcUze/tJZl5eZDqVLBskIPCEtTE8Q9TNW5j6NtIyyY/jrcHJoir6AQf/x7Fm8tXoO9336CxpbmLyDq2iFtbI3ilDStbcUpaRCbmUBgIIXYwgwCkQjFqekV0qTDyN2lzuIqrWtzK16HFDDNexUZGXA6r6VyCyrXtV1aizCkc1ldu+Gf4lqta8nLTa8aKPz8/LSGQXTu3BkrV66EUqluHfT19dVKHxERgeHDh2tt69q1K1avXg2lUol+/frB0dERLi4uGDhwIAYOHIiRI0fC0NAQUVFRKCgoQL9+2l3jSkpKNI0YISEh6N69u6ZxorY8LW6hUKgzv09y9+5deHl5wcCg7E6hY8eOWmlCQ0Nx5swZGBsbV9o/Ojpaq4GiPFtbW6Smpmpid3Bw0DROANA08JT/PVFRUTAx0T6RFRUVaYbHAICnp2e1GieKi4tRXKzd1VQhF0IkrmIQ6gsk4IDR3QXgOHXXU33TvO0Qzf8tm7SAZRN3/G9FfyTHXKnU++JZtW0uwIiuZVXR9uN1ODNfHWnrIsCwzmUNNjtO1tGVLHkqX3cRxvYpu6tb/1dBnfye0lNRULh6GAgAJDwqRgsHEfxai/FPUNXd36vD21WIUd3Lzi1bj9b9PENSMTBlkBSpmQwnrtXOMdzOTYhRPcrl49+6zceNSCUiE1QwMeTg31aECf0k+OVQcZ0Nu9Ennb0MMGVYWSvKyj9ewOy0ekzfz9/1nZebE7zcnDQ/t3Vzxuj53+LA6Yt4d/SgqnckOrVzE2rNHbT5yPOdg54mOFKBewlKmBpy8PcW4c3+Uqw7WNRg69rSKQXIi6FXDRRPY2RkVKP0JiYmCA4OxtmzZ3H8+HEsXLgQixYtwtWrV5GXlwcAOHLkCOzstGcMlkrVN74y2ZMfbQgEgkoHtFxeezdfNc3v0+Tl5eGVV17Bd999V+k9W1tbzf8rNshwHAeVqvon77y8PLRv3x47d+6s9J6NTdlkT9XN3/Lly/H119qTGfUcuQC9Xl341H0LitVLBxkZcEC5qe2MDCo/4dPEr+OJsJEBV+mpTOnFjZkRhx0nlXX29AUADAzNwQmElSbELMxLh8zEWuc+MmNrFOZpt/oX5aXDsIr0AGBq6QADQwvkpMfXWgNFRLwKD1LLblpEQvWdn7GM02r5N5ZxSMrQfZwVFAFKVWkvAu19Kj49qAsR8So8eFQWW/k85FXKg+54CopL86C93VgGzRPwvEIGkZCDgQRavSjUv6d2L6DLyob2diODyk/kS+UVoVJZ0vXEsi6FxygQl5yn+bn0uzAx0u4RYWLIIfGR7iup/EIGpYrBtMJTJhNDDjn56r9z6b8Vj8mUTBUsTJ7/6fPt+9pzpZROamZsWLlcVLWqSlm50N5uIuOQW6EnhkQMTB0kQXEJw/YTJbU2vON2nBLxKTryUSEGkyfkI/9xPir2sKj4GYC6XBSVMKRlM8SnlODrKQZo4yxESFTtXDWXltPK5QKVeniUyiusfM4wNiirG0r/Lb+t9Oeq6otnceNOMaITyup8sUh9fJsZC7R6UZgZCXC/jlbcqE0N5fwtM7YAJxBWmhAzPzcdRqZVn4/rE3MTIwgFgkoTYmbk5MLK3KSKvbSJREK4O9rjQYXeCPVdcUoapI21vydpY2vIs3OhKipGSVomVAoFpI2sKqSxQnFy7eX1dpwSq1LKDuTSurZifW9syOFhmu7rhfwiVu5aqoyJYeUeheXr2vspJVjylqxW61rycqv/ffjKuXz5stbPly5dgpubm6ZHQUUeHh4ICgrS2hYUFIQWLVpo9hGJROjbty++//57hIWFIS4uDqdPn0arVq0glUoRHx8PV1dXrZeDgwMAdU+CgICAKhsdbGxsNPNKAIBSqcTNmze10kgkEk0PkJrEXVOlQ2LK9zS4evWqVhofHx/cunULTk5OlfJc3cYCDw8PPHjwQCvfly5dqvR7IiMj0ahRo0q/x8ys5hMwfvbZZ8jOztZ6dR82/+k7AlCpgKQMwLmJdmXs3ITTWp6yvIQ0Vim9i612+tKLG0sTDn+cUqKwjh9+CkUSWDdtjaSosr81U6nwMPoSGjXz1rlPo2Zt8TBa+7tJjLpQZXoAyM9ORlFhFmRVzBr+LErkQEZu2Ss1iyGngKF507LqSSoG7G04xKfq/k6UKuBhGoOrbdk+HIDmTQVV7lObShSV85BbwNDctuw4Kc3Dg0dPyEM6g0uFPLjYCjSNH4npDAolg0u5z7U2BcyNOcRX8bnPSqUCHmYwuFQ81p9QNh48qqJsPHpxTx+L5UBaNtO8kjNUyM5Xwd2hrD3eQAI4NREiNkn3hZRSpZ4ToUW5fTgALRxEiHu8T3oOQ1aeCo0ttE+jNuYCZOY8f35L5OrfUfpKyVSXC9dyc1xIxeq5MuKrmPRVqQIS0xhc7cr24QC4NhVqNRpIxcC0wVIoVMC2YyW1+gSsWFc+8hnc7Crn437yE/LxSEc+7ISVln3V5RlPm1XGklRFOa3qOH/wSKWVHgCaNy2rCzLz1F2oXSrUF3ZPqC+eRVEJQ2qGUvNKTFUgK1eJVi5lvQ0NpBxc7CWIelD/VwZrSOfvxg6tEX+3bB4DplIh/u5FNHWpuzkKapNYJEJLJ3tcuR2p2aZSqXD1ViS8XJ2q9RlKlQpRCUmwNjetoyjrRtalEFj19tPaZt2nCzIvhQAAmFyO7OBbsO5d7qEOx8GqV2dkXaq9OUaqrGvttevaZo0EVdab6rpWBTd77frN1a7qfcp70uoghNSEXjVQxMfHY+7cubh79y7+/PNPrF27FrNnz64y/bx583Dq1CksWbIE9+7dw7Zt27Bu3Tp89NFHAIDDhw9jzZo1CAkJwf3797F9+3aoVCq4u7vDxMQEH330EebMmYNt27YhOjoawcHBWLt2LbZt2wYA+OCDD5CTk4OxY8fi2rVriIyMxI4dO3D37l0A6jkijhw5giNHjuDOnTt49913kZWVpRWjk5MTzp8/j8TERM1qGU+L+1mMGzcOKpUKb7/9NiIiInDs2DGsWKFeLrJ02Mz777+PjIwMvPHGG7h69Sqio6Nx7NgxTJkypVIjSlX69u2LFi1aYNKkSQgNDUVAQAC++OILrTTjx4+HtbU1hg8fjoCAAMTGxuLs2bOYNWsWEhISapw3qVQKU1NTrVdNhndcvKOCjysHL2cO1qbAkI4CiIVASIz6gmV4ZwF6e5cVlct3VGjelINfSw5WpoC/pwBNLYGrd9WVt4ADXusugK0lh4NBSnCc+omOkYF6Uq+60qbbJNy99j9EBh9CVmo0gv76GoqSQrTwGQkAOPe/+bh6bJUmfesuE5FwLxDhAVuRlRqD4JPrkJZ4Cx5+4wAA8uJ8XPnvB6TGhyA3MxEPoy7ixI73YWrZDPZu3XTGUFsu3FKil7cQLZsJ0NiCw2v+IuQWALfvl50gpw4Sw8+j7A8aeFMJX3cB2rkKYGPGYXhXESQiIPhe2bFrLANsLTlYPR6f3MSCg60lB1kdTHNy4bYSPb2EaOnAobE5h1e7q/MQEV+Whyn9RVoz+QfdUsG3hQDtmquXvRvWWQiJCLgeqd6nWK7+/+AOIjg34dDUisOobiLEp6qQUMsNFABwKUIFHzcB2rqoy8bQTgKIRUBItDqeEV2E6FOhbLg25dDZQ6AuG14CNLXkcOVuWZ4NJEBjC8Dm8TwW1qYcGlugyiUaa8PZGyUY0FGKNi4i2FoJ8OYAGbLzGcKiyx6LfjDKED3alvUOOxNcjC5txOjoIUZjCwHG9DGAVMzh0u2yBulT10vg7y2Bt6sI1mYchnSWorGlABdv1c0dTWC4Ar19RPBwFKCJBYfXe0mQU8BwK67sGJ8+RILOrcuuEgPCFOjYUggfNyEamXMY2V0MsRi4dk+d99LGCYkI2HdODqlEXU6MZaizJeMCwxXo3V6EVo4CNLHk8HpvHfkYKkGXivnwEKJ9i8f56CGGRAxcu6vOh6UJh17tRLCz5mBuzMGxsQBv9pdArgTu3K/dJ3oXbqvQvoUA3s0FsDYDhvqpy2lwlPo4H9VNiL4+ZbFfilDB1Y5Dl1YCWJsCvdoK0dSKw+U7ZeXiYoQS/l5CuDtwaGSuLte5BcCd+Lpt3Dt2MR/DexqjXUsp7BuL8M6r5sjKVSI4ouxJ7PzJlujbqWyiE6mEQ7MmIjRrom7AszFX/9+q3Hw/RjJ1mqY26jS21uo0ZrU8t0VDOX/79pmCsKC9uHnpINKTo3Fi9yLIiwvRxm8UAODfbZ/g/F9lq7kpFSVIfRCB1AcRUCpLkJuVgtQHEchMva9JU1KUr0kDANnpCUh9EIGcjId1kofxg/xx6OwlHA64itjEFCz/fR8Ki0vwSg/1cOKF63dh3Z6yCc83HjyGS+F3kZCajjtxCVjw604kp2VgRM9OmjTZefm4ez8RMYnqyTPvJ6Xi7v1EpGXV3fK1QiNDmLZtCdO2LQEAhs72MG3bEgYO6p7E7t/MRdutZT2N72/YDUNnB7Rc/jGM3F3gOGMcbF8bhNifftekiV29FQ5Tx8DuzREwbumCNj8vgshIhgfbdK+yVlsCwuTo016MVk5CNLHk8EYfdV17M7asTnznFSm6tilrjD8XqkAnDxF83dV17ageYkjEHK7eKatre1eoaycOeFzXxjfc3hNMpaq3r4ZIr4Z4TJw4EYWFhejYsSOEQiFmz579xOU1fXx8sHfvXixcuBBLliyBra0tFi9erJlo0tzcHAcOHMCiRYtQVFQENzc3/Pnnn2jdujUAYMmSJbCxscHy5csRExMDc3Nz+Pj44PPPPwcAWFlZ4fTp0/j444/h7+8PoVAIb29vdO3aFQDw1ltvITQ0FBMnToRIJMKcOXPQq1cvrRgXL16Md955B82bN0dxcTEYY0+N+1mYmprin3/+wbvvvgtvb294enpi4cKFGDdunGZeiqZNmyIoKAjz589H//79UVxcDEdHRwwcOBCCap6ZBQIBDh48iKlTp6Jjx45wcnLCmjVrMHDgQE0aQ0NDnD9/HvPnz8eoUaOQm5sLOzs79OnTB6amL77l/PZ9BiOpCj3bCmBsAKRkArvOKDWToJkZcVpDdRLSgANBKvRqK0Bvb/UT8z3n1cvNAYCJIeDuoP57vTNEu4htO6HE/Tp6ou/iNRhF+Zm4fnINCnPTYGXrgQFTNmiGeORlJYHjyr7Hxo7t0Ov1H3D9xE+4dvxHmFo5ou+EtbBsop5rhBMIkZF8F5HBh1BSlAtDExvYuXVF+76zqlzWrLacD1NCIgJGdhXBQALcT2HYekyu9WTX0oR73LVXLTxWBSMDBfq2F8FEpn7KufWYXKvrbqeWQvTxKftO3h6qzse+83IER9ZuJR9wUwWJiMPwLuo8xKcwbDtRIQ+m2nm4GaeCkQHQp50QxjIhkjIYtp1QaE3I999VJRgD3uglgkgARD5k+OdS3fQ/vnWfwVCqQk8vIYxlQHImw87T5csGwFhZ/AlpDAcC1Y1Lvb0FyMgFdp9TasoGALjbcxjRpew7GN1d/f+zYUqcC6ubE+3JayWQiDi80ccAMimHmIdK/HKwQOu7sDYXwEhWVj6C7ylgLCvCkM5S9XCQNBV+OVSg1VX27I0SiIXAKH8DGBqoh4z8fKAAadl1U8bPhSogEQGvdpfAQALEJauw5b+SJx5TYTFKGMmA/r4imBiqh1Fs+bdYM0zHzlqgWdFj/hvarUTf7ipCZh3Myn425HE+/MvysfmIdj6szDgYletmHBqthJEB0L/D43ykMWw+UpYPhZLB2VaAbp4iyKTqLv6xSSr8crC40oSWz+tmnAqGBkBvb3U5Tc5g2HFSUeU548Ejhn3nFejTToS+PkKk5zD8eUaB1KyyNIGP64thncvqix0n5VDU8bXnkYB8SMUcpgwzg6GBAJHxJVixPQPyclVKI0shTAzLyoZzUzE+n1rWXX38YPV5OyC4ABsPqgt7u5YGeHuUuSbN+69bAAAOns7FwTNlQ7CeV0M5f7dsPxgFuRkIOrwGBbmPYGPngdHvb9IM8cjJ1D5/52WnYvu3IzQ/Xzu1BddObYG9W0eM/XAHACA5/ib2/jRRk+bs/uUAgNadRmLQxG9rPQ/9/dohMzcP6/cfRXp2Dlo0s8Paj9/WLB2anJ4JQblWz5z8QnyzeS/Ss3NgamSIlk722LxwFlzsyiaNPx98C19v3K35+fOf1XmbPrI/3hlVdl1Zm8zat0HnUzs0P7daob7mf7D9AMKmfgaprQ1kDmXDngvjEnB12DtotfIzOM2ciKKEZIS/8yXSTgRq0iT97z9IbCzR4qtZkDaxQU5oBK4MnYaSChNn1rYzIQpIxBxG+0sgkwCxySpsPKw9J49VhXNGaLQSxjI5BnQQP65rVdh0uHxdCzjbCtHdS6ypa2MeqrDuYFGVwz8JqSmO6cmsHz179oS3tzdWr17NdygNxs6dOzFlyhRkZ2c/dT4NfbN4Z/0fP/s0BgZ61cGpSllZ+jfppS4Cof6v71065lzfpaXp/1WQoWHtTq7MF9ZAHt4YyPS/b3JcVN3e7Lwori1rbwghX2wb69XzvyqNNT3Cdwi14nzXuXyH8NzOrNGPJWefZsW7VSw7Vc+98Uk83yFU6c/vm/EdQq1rGDUoqZbt27fDxcUFdnZ2CA0Nxfz58zFmzJgG1zhBCCGEEEIIIbVBVVszR5NqaRiPaAmWLVsGY2Njna9Bg9TLNSUnJ2PChAnw8PDAnDlz8Nprr2HDhg08R04IIYQQQgghhOhRD4qzZ8/yHUK9NmPGDIwZM0bne6U9JD755BN88sknLzIsQgghhBBCCCGkWvSmgYI8maWlJSwtLfkOgxBCCCGEEEIaDD2ZsrHBoCEehBBCCCGEEEII4R01UBBCCCGEEEIIIYR3NMSDEEIIIYQQQgjRgdEqHi8U9aAghBBCCCGEEEII76iBghBCCCGEEEIIIbyjIR6EEEIIIYQQQogONMTjxaIeFIQQQgghhBBCCOEdNVAQQgghhBBCCCGEdzTEgxBCCCGEEEII0UHFVHyH8FKhHhSEEEIIIYQQQgjhHTVQEEIIIYQQQgghhHc0xIMQQgghhBBCCNGBVvF4sagHBSGEEEIIIYQQQnhHDRSEEEIIIYQQQgjhHQ3xIIQQQgghhBBCdKAhHi8W9aAghBBCCCGEEEII76iBghBCCCGEEEIIIbyjIR6kQbp85g7fITy3Ln09+A6hVkilDaMdVCYT8h3Cc0tMyOc7hFphY2PIdwjPTSjk+A6hVnANIxtIStL/suHt24TvEGpFUZGK7xCeW9jNHL5DqBW3Rf58h1ArhGtu8B3Cc+s1qx3fIdSOd+/yHcEzYYyGeLxIDePOgRBCCCGEEEIIIXqNGigIIYQQQgghhBDCOxriQQghhBBCCCGE6KBS6f/QM31CPSgIIYQQQgghhBDCO2qgIIQQQgghhBBCCO9oiAchhBBCCCGEEKIDU9EqHi8S9aAghBBCCCGEEEII76iBghBCCCGEEEIIIbyjIR6EEEIIIYQQQogOjNEqHi8S9aAghBBCCCGEEEII76iBghBCCCGEEEIIIbyjIR6EEEIIIYQQQogOtIrHi0U9KAghhBBCCCGEEAIAyMjIwPjx42Fqagpzc3NMnToVeXl5T0w/c+ZMuLu7QyaToVmzZpg1axays7Nr/LupgYIQQgghhBBCCCEAgPHjx+PWrVs4ceIEDh8+jPPnz+Ptt9+uMv3Dhw/x8OFDrFixAjdv3sTvv/+Oo0ePYurUqTX+3TTEgxBCCCGEEEII0eFlG+IRERGBo0eP4urVq/D19QUArF27FoMHD8aKFSvQtGnTSvu0adMG+/fv1/zcvHlzLF26FBMmTIBCoYBIVP1mB+pBQQghhBBCCCGE6Jni4mLk5ORovYqLi5/rMy9evAhzc3NN4wQA9O3bFwKBAJcvX67252RnZ8PU1LRGjRMANVAQQgghhBBCCCF6Z/ny5TAzM9N6LV++/Lk+Mzk5GY0aNdLaJhKJYGlpieTk5Gp9RlpaGpYsWfLEYSFVoSEehBBCCCGEEEKIDiqm4juEKn322WeYO3eu1japVKoz7aefforvvvvuiZ8XERHx3DHl5ORgyJAhaNWqFRYtWlTj/amBghBCCCGEEEII0TNSqbTKBomK5s2bh8mTJz8xjYuLC5o0aYLU1FSt7QqFAhkZGWjSpMkT98/NzcXAgQNhYmKCgwcPQiwWVyu28qiBgpDHJgxvhAHdLWBkKEREVAF+/uMhHqaWVJm+tZshXh1oDVdHGazMxViy7j4uheRqpTE3FWLKq03QrrUxjGRC3IrMx/pdSU/83OfR3pWDnwcHYwMgJQs4fl2FhxlVp2/pAPh7CmBuBGTkAqdDVYhOKnu/exsOrZpxMDUElCogOQM4G/bkz3xeHVoI0KWVAMYyIDmT4b+rKjxMr3pyolbNOPRqK4S5MZCeA5y8oUTUw7L0LR04+LoJYGvFwVDKYf0ROVIy6y7+Urcv7kR4wBYU5qXBsklLdH7lC9g4eFWZPjb8KK6fWIO8rESYWjmiw8B5cHD317x/ft9niAw+pLWPnVs3DJyysa6yoDHIT4LObcSQSTnEPlTif2eK8CjryRNGdfMSo3d7CUwNOSSmqbD/bBHiU8qeQHzwqgxu9tqnoKDwEuw9/XzjJnXxbcGhi4f6mErJBP67psTD9KrTezTj0MtLoD6mcoFTN1SaY0rAAb3aCuBqx8HCGCguAWKSGU6FqJBXWOuha2kI5bu9K4dOLcvlIViFpCflwV6dB7PHeTgTViEPrdV5MCmXh3PhdZuHUoP9pOjiWVYu9pwuwqOsJz9l6+4lRh9fqaZc7DtTiPsp2vs42QrxShcpHJsIoVIBiY+U+OVgAeTK2s/DzQs7EXpuMwpz02Bl2xJdh3+JRs2qrqeiw47i2rGfkJuZCDNrR3Qa9BGaefhrpclMicblf1cgKfYqVEolLBo3R78318DEovKkarWBjqn6dUwBwMCOEnRuLYKBlENckhL/O1uMtOwnnzO6eorRu50YJoYcHqapcOB8MeJTdef97VcM4OEowuYjhbgZWzeZGNBBjE4eIsikQGyyCgfOlzw1D11ai9DTWwQTQw5J6SocDJTjQbk8vNpDDDd7IcyMOBTLgbhkJY5ckj/1fFpTlt184TJvKsx82sCgaSNce/U9pPx96sn79OiIVis+hXErNxQ9SELU8l+RsP2gVhrHd8fBZe5USJvYICfsDm59uATZV8NrNXZSt2xsbGBjY/PUdJ07d0ZWVhauX7+O9u3bAwBOnz4NlUqFTp06VblfTk4OBgwYAKlUir///hsGBgbPFCfNQUFqTUlJ3dx0vwijB1rjlT5W+PmPh5i7LBpFxSosmeMEsYirch8DqQCxD4rw686HVab58n1HNLGRYMm6eMxaHIXUdDmWznOCVFL15z4rDwcOfdtxCLjJsPmYCqlZDGN7CmBYRaOqnRUwsrMAoTEMm46pcC+R4bVuAtiYlaXJyAWOXVdh438qbD+pQnY+wxtP+Mzn1dqRQ//2ApwLU+K3fxVIyQQm9BZW+fvsrTm82k2IG9Eq/HZEgbsJKoz1F2rlQSIC4h8xnLxRR1diOsSE/YvL/36Hdn3ex/D398PS1h1Ht05HYZ7uu+KU+zdwZs9HaOH7KkZ8cACOrfrg5B8zkZF8TyudfYvueOOz85pXr7Er6jwvfdpL0MNbgr2ni/HjngKUyBlmjDCESFj1Pu3cRBjZXYpjl4vxw58FePhIiXdHGMJYpn3cXwgvwZcb8zSvvwJrv3GilSOH/j4CnAtXYcO/SiRnMozv9aRjCni1qwA3otXp7z5geL1HWbkQiwBbSyAgXIWN/yqx97wS1qYcxvo/4Q9SCxpC+fZw4NDHm0PgLYYtxx/nwf/JeRjRWYCQGHWe7yUyjO6qnYf0XOBYsAqbjqqw45QK2QVP/sza0tdXAv92Euw5VYSVu/NRLGd4b+STy4VPCxFG9jDAf5eK8f2ufCQ+UuK9kUZa5cLJVoj3Rhjizn0FVvyZjxW783E+tAR1MX98VMi/uPjPt2jf9328OvsALG3dcWTztCrrqeS4YJzaNQ/uHUbj1dkH4dS6L45t/0CrnspOj8dfv46DeSMXvPLOdoye+xd8+rwHkbhuvhA6purXMQUAvX3E6NFWjP+dLcbq/xWiWA7MGCZ7Yj68XUUY0U2CY1dLsHJPAR6mq/DOMFmlcwYA+LcVg9Xxggq9vEXo5inC/vMlWLO/CCVyhulDpU/MQ9vmQgzrKsaJa3Ks3leEh+nqfYxlZWkSHqmw90wJvt9dhI2Hi8BxwNtDpeBq+ZJQaGSInLC7uDnr62qllznZo8PfvyH97GUE+g5H7Npt8PztG1j366ZJY/vaIHj88Bkiv/kZgR1HIjfsDjod2QyJjWXtBl9PMRWrt6+64OHhgYEDB2L69Om4cuUKgoKC8MEHH2Ds2LGaFTwSExPRsmVLXLlyBYC6caJ///7Iz8/H5s2bkZOTg+TkZCQnJ0OprNk1ODVQkGfWs2dPfPDBB/jwww9hbW2NAQMGYNWqVfD09ISRkREcHBzw3nvvIS8vT2u/oKAg9OzZE4aGhrCwsMCAAQOQmal+pK1SqbB8+XI4OztDJpOhbdu22LdvX53nZXhfK+w5nIpLIbmISyjGyi0JsDQXoXM70yr3uX4zDzsOpeLijVyd7zdtLIFHc0P8/MdDRMYVIjGlBD//8RASsQD+ncxrPQ+dWnIIiWYIi2VIywH+vcqgUABtXXSf+Tq6c4hOAi7dYUjPAc6FMyRnAr5uZelv3WeISwGy8oG0HODEDQYDCYdGtR8+AMDPQ4DgKBVCYhjSsoHDl5WQK4F2rrqrqk4tBYh6yHDhtgppOcCZUBWSMhg6upelD4tlOB+uQkzSi1si6mbgNrh3eA0t2o+CRWNXdB2+CCKJAe5dP6Az/a0L22Hv1g1ePabCvFFztO83G1ZNPRBxaZdWOoFQAkMTG81LKjPT+Xm1yb+dGMevFONmjAIP01T443gRzIw4eDavugNeTx8JLtyS4/JtBVIyVNh7uhglCga/1trd/EoUQG4B07yK66CNs3NLAYKjGEJj1OXiyBWV+phqrrtcdGopQFQSw8UIdfqzYSokZQIdHh9TxXLgj9Mq3I5nSM8FEtOB/64q0dRK3ROhrjSE8t3RnUNITFke/rv2OA/OuvPQoQWH6GTg8l313/r8TYbkLPUT81K347XzcLI0D3VcNHq2k+DY5WKEPy4XO44VwsyIg9cTykUvHyku3pTj8m05kjNU2HOqCCUKhs7lysWoHlKcCynBiWslSM5QITVThRuRCijqoH01POB3eHR6DS07vAqLxq7oMepriMQGuHN1v+70gTvg0KIbvHtOhUXj5ugwYDas7VrhZtBOTZqrR1ejWUt/+A35GNZ2rWBm1QxOrXtDZmxV+xkAHVP17ZgC1A0Ix6+V4GasEknpKuw6WQRTIw6eLk84Z3iLcfGWHFciFEjJZPjfGfU5o5OH9j5NrQXo2U6M3XXQ06687l5inLwux604JZIyGHafLoGpIYc2zlW3UPi3FeHybQWu3lUiJZNh/7kSyOUMHVqW5eFyhBIxSSpk5jIkpjEcvSyHhYkAlia120Lx6Nh53PtqNVL+Olmt9I5vj0VhbAIiPvkOeXdicP+XnUjefwzOsydr0jh/OAUPNu9FwrYDyIuIRvh7X0FZUASHya/Wauyk/ti5cydatmyJPn36YPDgwejWrRs2bNigeV8ul+Pu3bsoKCgAAAQHB+Py5csIDw+Hq6srbG1tNa8HDx7U6HdTAwV5Ltu2bYNEIkFQUBDWr18PgUCANWvW4NatW9i2bRtOnz6NTz75RJM+JCQEffr0QatWrXDx4kUEBgbilVde0bSsLV++HNu3b8f69etx69YtzJkzBxMmTMC5c+fqLA9NrMWwNBcjJCJfs62gUIW7MYVo2Vz2hD2frLT3RYm87MaYMUCuYGjtWrt3MgIBYGsBxKZo34THpjDYW+k+8dlZcZXSxyQz2FWRXiBQ39QVlbA6GSIhEABNLblKDQkxSQz21rpjcrDhEJOsnT46icHehr+qTakoQdrDW2jq2lmzjRMI0LR5Z6TGh+jcJzU+VCs9ANi7dauUPjn2CnYu7Yp9qwYh6NAiFBXU7VgVK1MOZkYC3Isvu5ItKgHuJyvh3ET3hZpQADg00t6HAbgXr4RTE+3vxdddhKVvG+HT8YYY2kUCcS0POhQI1L0dYiscI7HJVR9T9tYcYiscg9EPq04PAFIJB8YYiuqoE1lDKd+2FkCcjjzYVfG3tbPiKqWPSao6vVYesmolbJ1Ky8XdBwrNtqISdXdtZ9snl4vy+zAAd+MVcHq8j7GMg7OtCLkFKswZY4il040xa7QhXJrWfu8cpaIEjxJvwc61i2YbJxDA3q0zUu6H6NwnNT4Edm5dtLbZt+iKlMf1FFOpEB9xFmbWTjiyaSq2fd0FB9eOQezN6t0k1RQdU/XrmCrNh6mRAPceVDhnpKgq1f/l82HfSHsfBiAyQQnHcucZsQh4s78B9p8rRm5B3T1wsDThYGrEITJBOw/xqSo4Nq46D3Y2AtxLKBvOwQBEJla9j0QEdGgpQnqOCll5L+4Bii7mft5IO31Ra9ujE4Gw8PMGAHBiMcx8WiPt1IWyBIwh7fQFmPu1e4GRkhfJ0tISu3btQm5uLrKzs7FlyxYYGxtr3ndycgJjDD179gSgfnDNGNP5cnJyqtHvpjkoyHNxc3PD999/r/nZ3d1d838nJyd88803mDFjBn755RcAwPfffw9fX1/NzwDQunVrAOp1fJctW4aTJ0+ic2f1zZqLiwsCAwPx22+/wd9fe5xrqeLi4krr/SqVJRAKJdXKg4WZuhhk5ii0tmflKGBhVvOJXUolJBcjNb0Ek0c1xrodiSgqZhjRzwo2lmLN76wthhJAIOCQX6S9Pb8IsKqiE4ixAXSmN6rQJuPaVN1VXCwC8gqBXWdVKKyDGzFDaVV5YLA2033xqM6D9ok9r0i9nS9FBVlgKmWlJ4YyYytkP4rVuU9hXhpkxtZa2wyMrVCQm6b52c6tGxxb94OJhT1yMuJx/dhqHPv9Hbwy408IBHVzsWlipP67V7wYzC1gmvcqMpJxEAo45BaoKu3TyLIszut3FcjMUQ8raGotwLCuUjSyEGDLkaKKH/nMyo4p7fjziwBr06qPqTwdx6Cxge70QgHQx1uAm3EMJQqdSZ5bgyjftZWH4srl29VW3W2/NA9/nqubPJQyNVLfcOTmVy4Xpe9VVFoucnSUpcaPy4W1mXrfwX5SHAwoRuIjJTp6iPHBKEMs/yP/qXMR1ERRfqa6njKpWE9ZIytVdz1VkJsGwwr1mqGxNQof11OF+emQlxQg5MxGdBgwG50Gf4QHdwNwfMdMvPL2NjRt3rHW4gfomKpvxxQAmBiq68m8CjHlFag071WVj9zCyvloZF6W9xHdpIhLUtbZnBOlSuOsGE9eAas6DwbqPOQ9JQ+Aep6KIZ3FkIo5pGaqsOGfYih5XiBC2tgaxSlpWtuKU9IgNjOBwEAKsYUZBCIRilPTK6RJh5G7y4sMlTdMVX9X8WiIqIGCPJfSiVNKnTx5EsuXL8edO3eQk5MDhUKBoqIiFBQUwNDQECEhIXjttdd0flZUVBQKCgrQr18/re0lJSVo167qFtrly5fj66+1x9m5tnsXLXze05m+ZyczfPBm2WRdi9bcf2Ien5VSCSz9JR6zJ9lhz5pWUCoZQiLycDU8F7U/A0XduZ8CbDqmgkyqfpI0qosAW0+oUFC3PSxJBc3bDtH837JJC1g2ccf/VvRHcsyVSr0vnlV7dxFe7112pf7b33U36+PFm3LN/5PSVcjJZ/jgVUNYmRUj/SkTkdUXAg4Y3V0AjlMPHdFHDaF8308FNh9X58HbhcPIzgL8frL28uDrLsLYPmUtO+v/KqidD66gdBx6ULi6yz4AJDwqRgsHEfxai/FPUP3+Ukov4J1a94ZXj8kAAOumHkiJu4Hbl3bXegNFXaJjqnp8WogwpmfZ5BwbD9fNOaO1kxBu9kKs2FP7f6d2bkKM9i97oLX5SN2Ws+BIBe4lKGFqyMHfW4Q3+0ux7mBRnQ25IUQfUQMFeS5GRkaa/8fFxWHo0KF49913sXTpUlhaWiIwMBBTp05FSUkJDA0NIZNVPWSidK6KI0eOwM7OTuu9Jy2fo2v93zGzo6pMfzkkF3djozU/lw7FsDAVITO77BGouakIMQ+e72Qbdb8IMxdHw1AmgEjIISdPiVWfuyAyrnZP4gUlgErFYFThKZCRAZBfxa/KK0K10suVQGae+vUwneHdIRy8XThciKjdm8iC4qrywFW5OoI6DxxQbrovXU/AXyQDQ3NwAmGlieYK89IhM7HWuY/M2BqFedpPL4ry0mFYRXoAMLV0gIGhBXLS42utgeJmjAL3k8uGOomE6rJhYqj9lM7EkEPiI9035PmFDEoVg4mhAIBKa5/c/Kpv4u8nq6/ObMwESM+unSu1smNK+xgxMkClJ12ldPXAMTLgKh1TpY0TZkYcdpxU1lnvCaCBlO8n5aGK8qozD9LK5btiHmYM5tDWhcPFWspDeIwCccllcylpyoWRrnKh+9gtLRemFZ7AmhhyyHlcLkr/TcrQLicpmSpYmNTusDUDIwt1PZVbsZ5Kq7KeMjSxRkGFeq2gXHoDIwsIBCJYNHbVSmPeuDmSY6/XYvSPfzcdU7wfU7diFViRUhZfaT6MK5wzjA0FeJj25HyYyHTk4/FnuNkLYWXGYdl0I600UwYZICZJhZ8PPvs11e04JVallB0ApRNhmsg4rd6Dxo9XF9GZhyJ1HipO6lnx3Amoh4sUlTCkZTPcTynBkrdkaOMsREgUfy0UxSlpkDbWLvfSxtaQZ+dCVVSMkrRMqBQKSBtZVUhjheJk7WsXQmoDzUFBas3169ehUqmwcuVK+Pn5oUWLFnj4UHuFCy8vL5w6pXupo1atWkEqlSI+Ph6urq5aLwcHhyp/r1QqhampqdbrScM7CotVSEot0bziHxYjI0uOth5lJz6ZgQDuLjLcia6dhoSCQhVy8pRo2kgCVydZpeVIn5dKBSRlAk6NtU+OTo05JFSxRGdiOoNzhfTOTTgkPmFJT0D9REZYByMKVCrgYQaDSxPtmFyacEhI0x3Tg0cMzhXT23JIqOLm+UUQiiSwbtoaSVGXNNuYSoWH0ZfQqJm3zn0aNWuLh9GXtLYlRl2oMj0A5Gcno6gwCzLTpy8XVV3FciAtm2leyRkqZOer0MKh7AuXSgDHJkLEJuu+mFKqgAep2vtwAFo4CBGXXPX3YmejTl/xYu55qFRAUgYqHSPOTzimEtKqOKbKpS9tnLA04fDHKWWddv0GGk75rioPiVV8F4npDE6NdOShivSlOA4Q1eLVTVXlwt2h7BmPgQRwaiJEbNLTykXZPupyIULc433Scxiy8lRobKEdvI25AJk5tVunCUUS2Ni1RmJU2bhzplIhMeoSGjt669ynUTNvrfQAkBh5AY0f11NCkQQ2Dm2QVWEoW/ajuDpZYpSOKf6PKV35yMlXoYV9uXOGGHBsLKiy/leqgAQd5ww3e6Gm4fpUsBw//FmIFbvLXgBwKLAEf558vicSxXL136n0lZLJkJPP4FYhD80aCSot31o+D4mPVHCzL/s7cwBc7arep7wnrQ7yImRdCoFVbz+tbdZ9uiDzUggAgMnlyA6+Beve5R6GcBysenVG1qUbLzBS/vC9UseLXsWDb9RAQWqNq6sr5HI51q5di5iYGOzYsQPr16/XSvPZZ5/h6tWreO+99xAWFoY7d+7g119/RVpaGkxMTPDRRx9hzpw52LZtG6KjoxEcHIy1a9di27ZtdRr7XyfTMXZII3RqawJHOynmTbVHRpYCF2/kaNIsneeEob3KllMykArg4mAAFwf145gmNhK4OBjAxrJs3opu7U3h6W6EJtZi+Hmb4Ju5Trh0Iwc3bmuvbFIbLt9haNecg6cTBytTYJAvB7EICItRV16vdOLQ06vswuzKXQYXW6CTOwcrE6B7Gw62FsC1SHV6sRDo6cWhqRVgagg0sQCGduRgIgMi4uumQrwUoYKPmwBtXThYmwJDO6nHAIdEq0/wI7oI0ce7rNq6fEcF16YcOnsIYGUK+HsJ0NSSw5W7ZRcEBhKgsQVg83geC2tTDo0tKj9Fq01tuk3C3Wv/Q2TwIWSlRiPor6+hKClEC5+RAIBz/5uPq8dWadK37jIRCfcCER6wFVmpMQg+uQ5pibfg4TcOACAvzseV/35AanwIcjMT8TDqIk7seB+mls1g79ZNZwy15dwNOfp3lKKNsxC2VgJM6G+A7HyG8OiyLgPvj5Khu1fZcX82uASd24jRwUOExhYCvNZbComY03QxtjLj0L+jBPaN1Df5bZyFmNDfAFEJiiqfUD2ri3dU8HHl4OWsPqaGdBRALARCHpeL4Z0F6F3hmGrelINfS3U58vcUoKklcPXxMSXggNe6C2BryeFgkBIcpz6WjAzUE+rVlYZQvq/cZfB2eZwHk3J5iC2XB8+yPFy9p85Dx9I8tFbn4XpUWR78PbXzMKTD4zw8qNuLtrM3SjCgoxRtXESwtRLgzQEyZOczhJUrFx+MMkSPtmXl4kxwMbq0EaOjhxiNLQQY08cAUjGHS7fLhjudul4Cf28JvF1FsDbjMKSzFI0tBbh4q/ZbwTy7T8adK//D3WsHkZkSjYCDiyAvKYS77ygAwOnd83H5v5Vl6bu9iYS7gQg9twWZqTG4dnwtHiXcQpuu4zVp2vpPRXTof4i4vBfZafdxM+gP3I84g1adx9V6/AAdU/XtmAKAc6Fy9POVoLWT+pwxvp8BcvIZwmPK8vHucAN08yx3zgiRw6+VGB1aitDIgsPonlJIRBwuR6j3yS1QN36UfwFAZq4KGbm1/70EhMnRp70YrZyEaGLJ4Y0+EuQUMK35L955RYqubcoah86FKtDJQwRfdyEamXMY1UMMiZjD1TvqPFiacOjdTgQ7aw7mxhwcGwswcYAEciVwJ752e08IjQxh2rYlTNu2BAAYOtvDtG1LGDjYAgDcv5mLtlu/06S/v2E3DJ0d0HL5xzByd4HjjHGwfW0QYn/6XZMmdvVWOEwdA7s3R8C4pQva/LwIIiMZHmzTvToZIc+DhniQWtO2bVusWrUK3333HT777DP06NEDy5cvx8SJEzVpWrRogePHj+Pzzz9Hx44dIZPJ0KlTJ7zxxhsAgCVLlsDGxgbLly9HTEwMzM3N4ePjg88//7xOY993NA0GUgFmTmwKI0MhbkcWYMHqOMgVZSc+WxsJTE3Kioybkwzffuys+Xn66+qK/2RQJn7cmggAsDAXYdrrtjA3FSIzW4FTF7Kw+/CjOslDxAN1V1d/Tw5GBhxSsoDdZ1XIfzyc0syIAyvXzT0xHTh0UYWengL09OKQkQv8L1CFR9nq91UMsDLhMLorB5kUKCwBktKB7afUS3rWhVv3GQylKvT0EsJYBiRnMuw8rdR01zUzAhgru9hMSGM4EKhEL28hensLkJEL7D6n1OQBANztOYzoUva9je6u/v/ZMCXOhdVNTwsXr8Eoys/E9ZNrUJibBitbDwyYskHTFTovKwkcV3Y329ixHXq9/gOun/gJ147/CFMrR/SdsBaWTVoAADiBEBnJdxEZfAglRbkwNLGBnVtXtO87C0JR9SaDfVanrpdAIgZe72MAmZRDzEMl1h8q0Bova2UmgFG5rq03IhUwlhVjsJ8UpoYcEtJUWH+oQNNdVqkE3B2E6OktgUQMZOUxhEYpcOxq7Y/9vX2fwUiqQs+2AhgbACmZwK4z5Y8p9QocpRLSgANBKvRqK0BvbyAjF9hzvqxcmBgC7g7q7+6dIdqn0G0nlLifWjc3MQ2hfEc8YDCUAj3alOVhz7myPJgaan8XienAXxdV8PcUoKcnh8w8YF+Qdh6sTTl4OZXLQwaw43Td5aHUyWslkIg4vFGuXPxyULtcWJsLYCQrK+fB9xQwlhVhSGepuut+mgq/lCsXgPomVSwERvkbwNBA3b3/5wMFSKuDeVlcvQejKD8D146vRUHuI1g39cDgqRs1Q8vysh6C48rKdRMnH/QetwJXj67GlaM/wszaCQMmrtPUUwDg3KYfuo9ahBunNyDor6Uwt3FG/zfXwNa5fcVfXyvomKpfxxQAnA6WQyLiMKaXFDIph9gkJX77p1A7H2YCGMnKzr8hUQoYyzgM7CiBqZF6COFv/xRWORSvrp0JUUAi5jDaXwKZBIhNVmHj4WLt854p93j4oFpotBLGMjkGdBDD5PFwkE2HizVDVBVKwNlWiO5eYsik6mGGMQ9VWHewqMphrM/KrH0bdD61Q/NzqxXqa+gH2w8gbOpnkNraQPa4sQIACuMScHXYO2i18jM4zZyIooRkhL/zJdJOBGrSJP3vP0hsLNHiq1mQNrFBTmgErgydhpIKE2cSUhs4Vr7mJqSBGDLtJt8hPLcufT34DqFWKBT6OXlgRTIZz30wa0FiQv7TE+kBK+tnX/63vhAK9Wmq3KpxDSMbSErS/7Lh7GzCdwi1oqhI/88ZDeF4AgBRbY5r4ZFQqP/56DWrYSznOUR+l+8QnkmfsVf4DqFKp3brzwTE1aX/JZYQQgghhBBCCCF6jxooCCGEEEIIIYQQwjuag4IQQgghhBBCCNFB1UBXy6ivqAcFIYQQQgghhBBCeEcNFIQQQgghhBBCCOEdDfEghBBCCCGEEEJ0YCr9X11In1APCkIIIYQQQgghhPCOGigIIYQQQgghhBDCOxriQQghhBBCCCGE6MBoFY8XinpQEEIIIYQQQgghhHfUQEEIIYQQQgghhBDe0RAPQgghhBBCCCFEB8ZoFY8XiXpQEEIIIYQQQgghhHfUQEEIIYQQQgghhBDe0RAPQgghhBBCCCFEB1rF48WiHhSEEEIIIYQQQgjhHTVQEEIIIYQQQgghhHc0xIMQQgghhBBCCNGBqWgVjxeJelAQQgghhBBCCCGEd9RAQQghhBBCCCGEEP4xQkiNFRUVsa+++ooVFRXxHcozawh5YKxh5KMh5IExykd90hDywFjDyEdDyANjlI/6pCHkgbGGkY+GkAfGGk4+SMPAMcZo3RRCaignJwdmZmbIzs6Gqakp3+E8k4aQB6Bh5KMh5AGgfNQnDSEPQMPIR0PIA0D5qE8aQh6AhpGPhpAHoOHkgzQMNMSDEEIIIYQQQgghvKMGCkIIIYQQQgghhPCOGigIIYQQQgghhBDCO2qgIOQZSKVSfPXVV5BKpXyH8swaQh6AhpGPhpAHgPJRnzSEPAANIx8NIQ8A5aM+aQh5ABpGPhpCHoCGkw/SMNAkmYQQQgghhBBCCOEd9aAghBBCCCGEEEII76iBghBCCCGEEEIIIbyjBgpCCCGEEEIIIYTwjhooCCGEEEIIIYQQwjtqoCDkJaFUKnH+/HlkZWXxHQohhBBCCCGEVEKreBBSQ1FRUYiOjkaPHj0gk8nAGAPHcXyHVS0GBgaIiIiAs7Mz36E8t6ysLOzbtw/R0dH4+OOPYWlpieDgYDRu3Bh2dnZ8h1ctKpUKUVFRSE1NhUql0nqvR48ePEVVM4WFhWCMwdDQEABw//59HDx4EK1atUL//v15jq7hy8nJqXZaU1PTOoyEVNQQ6qiGpqioCAYGBnyHUSuUSiXCw8Ph6OgICwsLvsMhhJBaI+I7AEL0RXp6Ol5//XWcPn0aHMchMjISLi4umDp1KiwsLLBy5Uq+Q3yqNm3aICYmRu8bKMLCwtC3b1+YmZkhLi4O06dPh6WlJQ4cOID4+Hhs376d7xCf6tKlSxg3bhzu37+Piu3EHMdBqVTyFFnNDB8+HKNGjcKMGTOQlZWFTp06QSwWIy0tDatWrcK7777Ld4hVsrCwqHbjYkZGRh1H82zMzc2rnYf6ekytWbOm2mlnzZpVh5HUnoZQRwFAu3btdB5fHMfBwMAArq6umDx5Mnr16sVDdNWjUqmwdOlSrF+/HikpKbh37x5cXFywYMECODk5YerUqXyHWC0ffvghPD09MXXqVCiVSvj7++PChQswNDTE4cOH0bNnT75DrDaFQoGzZ88iOjoa48aNg4mJCR4+fAhTU1MYGxvzHd5TyeVytGzZEocPH4aHhwff4RDS4FADBSHVNGfOHIhEIsTHx2udkF5//XXMnTtXLxoovvnmG3z00UdYsmQJ2rdvDyMjI6339eUJ69y5czF58mR8//33MDEx0WwfPHgwxo0bx2Nk1Tdjxgz4+vriyJEjsLW11ZteOBUFBwfjxx9/BADs27cPjRs3xo0bN7B//34sXLiwXjdQrF69WvP/9PR0fPPNNxgwYAA6d+4MALh48SKOHTuGBQsW8BTh0505c0bz/7i4OHz66aeYPHmyVh62bduG5cuX8xXiU5UeP0/DcZzeNFA0hDoKAAYOHIhff/0Vnp6e6NixIwDg6tWrCAsLw+TJk3H79m307dsXBw4cwPDhw3mOVrdvvvkG27Ztw/fff4/p06drtrdp0warV6/WmwaKffv2YcKECQCAf/75B7Gxsbhz5w527NiBL774AkFBQTxHWD3379/HwIEDER8fj+LiYvTr1w8mJib47rvvUFxcjPXr1/Md4lOJxWIUFRXxHUatCAgIwG+//Ybo6Gjs27cPdnZ22LFjB5ydndGtWze+wyMvK0YIqZbGjRuzkJAQxhhjxsbGLDo6mjHGWHR0NDMyMuIztGrjOE7zEggEmlfpz/rC1NSURUVFMca0v4u4uDgmlUr5DK3aDA0NWWRkJN9hPDeZTMbu37/PGGPstddeY4sWLWKMMRYfH89kMhmfodXIqFGj2Nq1ayttX7t2LRs+fPiLD+gZ9O7dm+3atavS9p07dzJ/f/8XH9BLrCHUUYwxNm3aNLZ48eJK25csWcKmTZvGGGNs4cKFrH379i86tGpr3rw5O3nyJGNM+7uIiIhg5ubmfIZWI1KplD148IAxxtj06dPZ7NmzGWOMxcTEMBMTEx4jq5nhw4ezCRMmsOLiYq3v48yZM8zV1ZXn6Kpv6dKlbNKkSUwul/MdyjPbt28fk8lkbNq0aUwqlWq+i7Vr17JBgwbxHB15mVEPCkKqKT8/XzPOvryMjAxIpVIeIqq58k9b9ZlUKtU59v7evXuwsbHhIaKa69SpE6KiouDq6sp3KM/F1dUVhw4dwsiRI3Hs2DHMmTMHAJCamqo3PXIA4NixY/juu+8qbR84cCA+/fRTHiKquYsXL+p8+ujr64tp06bxENGzKykpQWxsLJo3bw6RSP8uVRpCHQUAe/fuxfXr1yttHzt2LNq3b4+NGzfijTfewKpVq3iIrnoSExN11rMqlQpyuZyHiJ5N48aNcfv2bdja2uLo0aP49ddfAQAFBQUQCoU8R1d9AQEBuHDhAiQSidZ2JycnJCYm8hRVzV29ehWnTp3C8ePH4enpWalH6oEDB3iKrPq++eYbrF+/HhMnTsTu3bs127t27YpvvvmGx8jIy07/zvqE8KR79+7Yvn07lixZAkDd3VilUuH777+v1+Nvy/P39+c7hFoxbNgwLF68GHv37gWg/i7i4+Mxf/58vPrqqzxHV7WwsDDN/2fOnIl58+YhOTkZnp6eEIvFWmm9vLxedHjPZOHChRg3bhzmzJmDPn36aIYWHD9+HO3ateM5uuqzsrLCX3/9hXnz5mlt/+uvv2BlZcVTVDXj4OCAjRs34vvvv9favmnTJjg4OPAUVc0UFBRg5syZ2LZtGwBo5guYOXMm7Ozs9KaxSF/rqIoMDAxw4cKFSjf4Fy5c0Ew2qVKp6vXEk61atUJAQAAcHR21tu/bt0+v6qgpU6ZgzJgxmiGBffv2BQBcvnwZLVu25Dm66lOpVDrnw0lISNAaDlXfmZub61VZ1uXu3bs6J+Q2MzOjFd8Iv/juwkGIvggPD2eNGjViAwcOZBKJhI0ePZp5eHiwxo0ba7ry6oPz58+z8ePHs86dO7OEhATGGGPbt29nAQEBPEdWfVlZWaxv377M3NycCYVC5uDgwMRiMevRowfLy8vjO7wqlQ6lKT/UpuKwG30bbsMYY0lJSSw4OJgplUrNtsuXL7OIiAgeo6qZrVu3MqFQyIYOHcqWLFnClixZwoYOHcpEIhHbunUr3+FVy5EjR5iBgQFr06YNmzp1Kps6dSrz9PRkBgYG7MiRI3yHVy2zZs1i7du3ZwEBAczIyEjT5fjQoUPM29ub5+iqT1/rqIqWLFnCZDIZmzVrFtuxYwfbsWMHmzVrFjM0NGTffPMNY4yxVatWsb59+/IcadUOHTrEzMzM2LfffssMDQ3ZDz/8wKZNm8YkEgk7fvw43+HVyP/+9z+2atUqzVAPxhj7/fff2aFDh3iMqmbGjBnDpk+fzhhTD7mJiYlhubm5rHfv3mzy5Mk8R/dycXZ2ZidOnGCMaQ9/2rZtG/Pw8OAzNPKSo2VGCamB7OxsrFu3DqGhocjLy4OPjw/ef/992Nra8h1atezfvx9vvvkmxo8fjx07duD27dtwcXHBunXr8O+//+Lff//lO8QaCQoK0vouSp8o1Vf379+vdtqKT/v0RU5ODk6fPg13d3e9m9388uXLWLNmDSIiIgAAHh4emDVrFjp16sRzZNX34MED/Prrr7hz5w4AdR5mzJihNz0oHB0dsWfPHvj5+cHExAShoaFwcXFBVFQUfHx8arSsan0QGBiIsLAwvamjdNm5cyfWrVuHu3fvAgDc3d0xc+ZMzWSfhYWFmlU96quAgAAsXrxY63yxcOFCvV0KWZ+XS01ISMCAAQPAGENkZCR8fX0RGRkJa2trnD9/Ho0aNeI7xGrT99VIli9fjj/++ANbtmxBv3798O+//+L+/fuYM2cOFixYgJkzZ/IdInlJUQMFIS+Rdu3aYc6cOZg4caLWxf+NGzcwaNAgJCcn8x1itWzfvh2vv/56pbk/SkpKsHv3bkycOJGnyKrv/Pnz6NKlS6Xx9QqFAhcuXNDZ7bI+GjNmDHr06IEPPvgAhYWFaNu2LeLi4sAYw+7du/W+Cyx5sQwNDXHz5k24uLho1VGhoaHo0aMHsrOz+Q6xxoqKiiCVSvV2pR5SPyiVSixbtkzvl0sF1Oe5PXv2aDUYjR8/HjKZjO/Qqq3iaiSl38fs2bP1ZjUSxhiWLVuG5cuXo6CgAIB6/pzS1d4I4YuA7wAI0RdhYWE6X+Hh4YiMjERxcTHfIT5VQxlvOGXKFJ03Krm5uZgyZQoPEdVcr169kJGRUWl7dna23sxpAqgbWrp37w4AOHjwIBhjyMrKwpo1a/Rukq3o6Gh8+eWXGDduHFJTUwEA//33H27dusVzZNUXEBCACRMmoEuXLpoJ53bs2IHAwECeI6ue0qV3S5Xe1G/atEkzv4k+UKlUWLJkCezs7GBsbIzY2FgAwIIFC7B582aeo6u5kpISJCQkID4+XuulT65du4YdO3Zgx44dOif+rO+WLl2K33//Hd9//73WBJNt2rTBpk2beIys5kQiEcaPH4/vv/8ev/zyC6ZNm6ZXjRMAMHv2bPj6+iIzM1Mr9pEjR+LUqVM8RlZ9HMfhiy++QEZGBm7evIlLly7h0aNH1DhB+Mff6BJC9Ev5pTl1LdUplUrZxIkTWWFhId+hVqmhjDfkOI6lpqZW2h4SEsIsLCx4iKjmqsrD3bt39WrJOAMDAxYfH88YY+zNN99k8+fPZ4wxdv/+fb1Zfpcxxs6ePctkMhnr27cvk0gkmrKxfPly9uqrr/IcXfU0hCXjAgICmLGxMZsxYwYzMDBgs2fPZv369WNGRkbs2rVrfIdXbV9//TVzcXFhf/zxB5PJZJrvYvfu3czPz4/n6Krv3r17rFu3blrnOn2bK+fBgwesW7dujOM4ZmFhwSwsLBjHcaxr165acznUdw1ludRly5axzZs3V9q+efNm9u233/IQ0bOxtLRkd+7cYYxpfx+xsbF6s8R2VlYWS09Pr7Q9PT2dZWdn8xARIWrUg4KQajp48CDc3NywYcMGhIaGIjQ0FBs2bIC7uzt27dqFzZs34/Tp0/jyyy/5DrVK06dPx+zZs3H58mVwHIeHDx9i586d+Oijj/Duu+/yHd5TtWvXDj4+PuA4Dn369IGPj4/m1bZtW3Tv3r3ej/EeNWoURo0aBY7jMHnyZM3Po0aNwvDhwzFgwAB06dKF7zCrzcHBARcvXkR+fj6OHj2qGdOdmZmpV2OkP/30U3zzzTc4ceKE1tPJ3r1749KlSzxGVn2lS8Zt3LhRa1WYrl27Ijg4mMfIqq9bt24ICQmBQqGAp6cnjh8/jkaNGuHixYto37493+FV2/bt27FhwwaMHz9eawnItm3bauYH0QeTJ0+GQCDA4cOHcf36dQQHByM4OBg3btzQm2Nq2rRpkMvliIiIQEZGBjIyMhAREQGVSqVXy+82lOVSf/vtN52rjrRu3VovhkWUagirkYwdO1ZredFSe/fuxdixY3mIiBA1WmaUkGpaunQpfvrpJwwYMECzzdPTE/b29liwYAGuXLkCIyMjzJs3DytWrOAx0qp9+umnUKlU6NOnDwoKCtCjRw/NeEN9mAxpxIgRAICQkBAMGDBAaxIqiUQCJyenej/ngZmZGQD12E8TExOtrqESiQR+fn6YPn06X+HV2Icffojx48fD2NgYzZo1Q8+ePQGoh354enryG1wNhIeHY9euXZW2N2rUCGlpaTxEVHMNZQhX8+bNsXHjRr7DeC4N5WYyJCQE169f16tlLCs6d+4cLly4AHd3d802d3d3rF27VjM8TR80lOVSk5OTdU4sbmNjg6SkJB4iejb9+/fH6tWrsWHDBgDq4RJ5eXn46quvMHjwYJ6jq57Lly9j1apVlbb37NkTX3zxBQ8REaJGDRSEVFN4eLjOlRUcHR0RHh4OAPD29q7XJ9jS8YYff/wxoqKikJeXh1atWunFbNMA8NVXXwEAnJyc8Prrr+vVE/pSW7duBaDOw0cffQQjIyOeI3o+7733Hjp27IgHDx6gX79+EAjUHfNcXFz0ag4Kc3NzJCUlwdnZWWv7jRs3YGdnx1NUNdOkSRNERUXByclJa3tgYCBcXFz4CaqG+vbtiwkTJmDUqFEwNTXlO5xn1lBuJlu1aqU3DXRVcXBw0NkopFQq0bRpUx4iejYLFy7EpEmTkJiYCJVKhQMHDuDu3bvYvn07Dh8+zHd41ebg4ICgoKBKdW1QUJBefR8rV67EgAED0KpVKxQVFWHcuHGa1Uj+/PNPvsOrluLiYigUikrb5XI5CgsLeYiIkMf4HmNCiL7w9vZmkyZNYsXFxZptJSUlbNKkSczb25sxxlhgYCBzcnLiK0RCeFNcXMzu3LnD5HI536E8k3nz5rFu3bqxpKQkZmJiwiIjI1lgYCBzcXFhixYt4ju8alm2bBlr1aoVu3TpEjMxMWEBAQHsjz/+YDY2NmzNmjV8h1cts2bNYk2aNGEymYyNHj2aHTp0iJWUlPAdVo0dOnSImZmZsW+//ZYZGhqyH374gU2bNo1JJBJ2/PhxvsOrtlOnTrHOnTuzM2fOsLS0NJadna310geHDh1iHTt2ZFevXtVsu3r1KvPz82MHDx7kL7BncP78eda3b19mY2PDZDIZ69q1Kzt27BjfYdXId999x6ysrNiWLVtYXFwci4uLY5s3b2ZWVlZs2bJlfIdXI3K5nO3YsYN9/PHH7N1332UbN25kBQUFfIdVbT179mQffPBBpe3vvfce69atGw8REaJGy4wSUk0XLlzAsGHDIBAI4OXlBUDdq0KpVOLw4cPw8/PDjh07kJycjI8//pjnaMuMGjWq2mkPHDhQh5HUHqVSiR9//BF79+5FfHw8SkpKtN7XtTpGfbRv374q86Av47sLCgowc+ZMbNu2DQA0S63NnDkTdnZ2+PTTT3mOsHpKSkrw/vvv4/fff4dSqYRIJIJSqcS4cePw+++/a80jUF+xBrJknEqlwsmTJ7Fr1y4cPHgQQqEQo0ePxvjx4+Hv7893eNUWEBCAxYsXay2luHDhQs08LfqgtEdUxSVSGWPgOE7nGPz6xsLCAgUFBVAoFJplnUv/X7EHm76cO/QZYwyffvop1qxZoznvGRgYYP78+Vi4cCHP0b1cgoKC0LdvX3To0AF9+vQBAJw6dQpXr17F8ePH9WoIFGlYqIGCkBrIzc3Fzp07ce/ePQDqcazjxo2r1xMilV92kzGGgwcPwszMDL6+vgCA69evIysrC6NGjdIMP6jvFi5ciE2bNmHevHn48ssv8cUXXyAuLg6HDh3CwoULMWvWLL5DfKo1a9bgiy++wOTJk7FhwwZMmTIF0dHRuHr1Kt5//30sXbqU7xCrZfbs2QgKCsLq1asxcOBAhIWFwcXFBX/99RcWLVqEGzdu8B1ijcTHx+PmzZvIy8tDu3bt4ObmxndINVZSUqKXQ7h0KSoqwj///IOlS5dqGoTJi3Pu3Lknvq8PDUa///57pQaWqkyaNKmOo3l2V69ehUqlQqdOnbS2X758GUKhUHNO1xd5eXmIiIiATCaDm5sbpFIp3yE91d9//13ttMOGDavDSGpPSEgIfvjhB4SEhEAmk8HLywufffaZXp77SMNBDRSE1NDt27d1PvHWh5PR/PnzkZGRgfXr12ueCCuVSrz33nswNTXFDz/8wHOE1dO8eXOsWbMGQ4YMgYmJCUJCQjTbLl26pHOyw/qmZcuW+Oqrr/DGG2/AxMQEoaGhcHFxwcKFC5GRkYF169bxHWK1ODo6Ys+ePfDz89PKR1RUFHx8fJCTk8N3iERPJScnY/fu3fjjjz8QHByMjh076s2KKoTUto4dO+KTTz7B6NGjtbYfOHAA3333HS5fvsxTZC+P0h5FpTiOQ8XbqNLGMGpMJeTZ0SSZhFRTTEwMRo4cifDwcM1JqfxTGX04GW3ZsgWBgYFa3dWFQiHmzp2LLl266E0DRXJysmaFCGNjY2RnZwMAhg4digULFvAZWrXFx8drlhOVyWTIzc0FALz55pvw8/PTmwaKR48eoVGjRpW25+fnV/upZX3AGMO+fftw5swZpKamQqVSab2vD8OfioqKsHbt2irzoA/DhnJycrB//37s2rULZ8+ehYuLC8aPH489e/agefPmfIf3RBYWFtU+5uvzUIKwsDC0adMGAoEAYWFhT0xbOtyxPvP398fUqVPx2muvaa2apG9u374NHx+fStvbtWuH27dv8xDRs7t27VqVwxvrc11bvk49efIk5s+fj2XLlqFz584AgIsXL+LLL7/EsmXL+ArxmRUVFVX6LvR5omKi36iBgpBqmj17NpydnXHq1Ck4Ozvj8uXLyMjIqNfLilakUChw584dreXWAODOnTuVbmbqM3t7eyQlJaFZs2Zo3rw5jh8/Dh8fH1y9elUvuokC6hUXMjIy4OjoiGbNmuHSpUto27YtYmNjKz2Rqc98fX1x5MgRzTK1pTdomzZt0ly06YMPP/wQv/32G3r16oXGjRvrVeNKqalTp+L48eMYPXo0OnbsqJd5aNy4MSwsLPD6669j+fLletVtffXq1XyHUCu8vb2RnJyMRo0awdvbW+dTYgB6MwdFu3btNEtpjxkzBlOnToWfnx/fYdWYVCpFSkpKpRV5kpKSNHNr6IPdu3dj4sSJGDBgAI4fP47+/fvj3r17SElJwciRI/kOr9o+/PBDrF+/Ht26ddNsGzBgAAwNDfH2228jIiKCx+iqp6CgAJ988gn27t2L9PT0Su/rQ/kmDRQPE3MSopesrKxYaGgoY4wxU1NTdufOHcaYepbz0lU86rs5c+YwKysrtnLlShYQEMACAgLYihUrmLW1NZszZw7f4VXb/Pnz2dKlSxljjO3evZuJRCLm6urKJBIJmz9/Ps/RVc/UqVM1q0OsW7eOyWQy1rdvX2Zubs7eeustnqOrvoCAAGZsbMxmzJjBDAwM2OzZs1m/fv2YkZERu3btGt/hVZuFhQU7cuQI32E8F1NTUxYYGMh3GM/l+PHjTKlU8h3GM5kzZw7Ly8tjjDF27tw5vV3RJi4ujqlUKs3/n/TSF3K5nO3fv58NGzaMicVi5uHhwX744QeWnJzMd2jVNnbsWObv78+ysrI02zIzM5m/vz977bXXeIysZjw9Pdm6desYY4wZGxuz6OhoplKp2PTp09nChQt5jq76DAwMWHh4eKXtoaGhzMDAgIeIau69995jHh4ebN++fUwmk7EtW7awJUuWMHt7e/bHH3/wHR55iVEDBSHVZG5uzmJiYhhjjLm4uLDTp08zxhiLiopiMpmMz9CqTalUsu+++441bdqUcRzHOI5jTZs2Zd999x1TKBR8h/fMLl68yFauXMn+/vtvvkOpNqVSqXUD8+eff7KZM2eyNWvWaC1lqw+ioqLYtGnTWIcOHZiHhwcbP348CwsL4zusGnFycmIRERF8h/FcPDw8NI2o+kwul7MTJ06w9evXs5ycHMYYY4mJiSw3N5fnyJ5MJBJpbngFAgFLSUnhOSKiS0pKCluyZAkzMDBgYrGYDR8+nJ06dYrvsJ4qISGBubi4MDMzM9azZ0/Ws2dPZm5uztzd3Vl8fDzf4VWboaEhi42NZYwxZmlpqTlX3L59mzVp0oTHyGqme/furF+/flqNXMnJyax///6sR48ePEZWfQ4ODuzMmTOMMaZZXpsxxrZv384GDRrEY2TkZUeTZBJSTd27d8e8efMwYsQIjBs3DpmZmfjyyy+xYcMGXL9+HTdv3uQ7xBopnbxQH8cYnj9/Hl26dKnUrVWhUODChQvo0aMHT5ERfbVt2zYcPXoUW7Zs0dtx6v/99x/WrFmD9evXw9HRke9wnsn9+/cxcOBAxMfHo7i4WLNs7ezZs1FcXIz169fzHWKV3NzcMGbMGPTv3x+9evXCwYMHYWFhoTNtfa6jGuJKBaWuXLmCrVu3Yvfu3TA1NcXkyZORmJiIXbt24b333qv3wzXz8/Oxc+dOhIaGalZceOONNyAWi/kOrdrs7e3x33//wdPTU7NixBtvvIGLFy9i4MCBmjml6ruoqCiMHDkS9+7dg4ODAwDgwYMHcHNzw6FDh+Dq6spzhE9nbGyM27dvo1mzZrC3t8eBAwfQsWNHxMbGwtPTE3l5eXyHSF5S1EBBSDUdO3YM+fn5GDVqFKKiojB06FDcu3cPVlZW2LNnD3r37s13iC8NoVCIpKSkSpMzpqeno1GjRnozbjIgIAC//fYboqOjsW/fPtjZ2WHHjh1wdnbWGtda36lUKkRFRemcmLE+34iVV1hYiJEjRyIoKAhOTk6VLvj1YYLJR48eYcyYMTh//jwMDQ0r5aE+T8xYasSIETAxMcHmzZthZWWlWRXm7NmzmD59OiIjI/kOsUqHDh3CjBkzkJqaWuW8DUD9n7uh4koFVanv+SiVmpqKHTt2YOvWrYiMjMQrr7yCadOmYcCAAZp5WgIDAzFw4EC6IXsBxo0bB19fX8ydOxdLlizB2rVrMXz4cJw4cQI+Pj71epLMihhjOHHiBO7cuQMA8PDwQN++ffVm/h8vLy+sXbsW/v7+6Nu3L7y9vbFixQqsWbMG33//PRISEvgOkbyk9GdWHUJ4NmDAAM3/XV1dcefOHWRkZNRo5na+paSk4KOPPsKpU6eQmppa6QJaHy42AVRaQaVUeno6jIyMeIio5vbv348333wT48ePx40bN1BcXAwAyM7OxrJly/Dvv//yHGH1XLp0CePGjcP9+/d1LremL8fUpEmTcP36dUyYMEFvJ8l84403kJiYiGXLlultHgICAnDhwgVIJBKt7U5OTkhMTOQpquoZMWIERowYgby8PJiamuLu3bs6V7ip7/RpwuTqsLe3R/PmzfHWW29h8uTJsLGxqZTGy8sLHTp04CG6mtPnpc4BYN26dSgqKgIAfPHFFxCLxbhw4QJeffVVfPnllzxHVzMcx6F///7o378/36E8kylTpiA0NBT+/v749NNP8corr2DdunWQy+VYtWoV3+GRlxg1UBDyHCwtLfkOoUYmT56M+Ph4LFiwALa2tnp3AzNq1CgA6ouCyZMna63YoVQqERYWplm6s7775ptvsH79ekycOBG7d+/WbO/atSu++eYbHiOrmRkzZmhW8tDHY6rUkSNHcOzYMb3quVLRhQsXcPHiRbRt25bvUJ6ZSqXS2aiVkJAAExMTHiKqOWNjY5w5cwbOzs5PXV3h22+/xYwZM2Bubv5ignsJnTp1Ct27d39iGlNTU5w5c+YFRfRsdC11DpStnKQPjcEKhQKHDx/WPPARCAT49NNPeY7q2Z06dUrzwKdiw96WLVt4iqp65HI5Dh8+rBk217dvX9y5cwfXr1+Hq6urXiwhTBouaqAg5CUSGBiIgIAAeHt78x3KMzEzMwOg7kFhYmKiNVeARCKBn58fpk+fzld4NXL37l2dwx/MzMyQlZX14gN6RpGRkdi3b59ejLd9EgcHB72cj6W8li1borCwkO8wnkv//v2xevVqbNiwAYD65isvLw9fffUVBg8ezHN01efv71+tdMuWLcOYMWPqVQPFmjVrqp121qxZdRhJ7fjqq69w4MCBSn/jnJwcjBgxAqdPn+YnsBqquNT5lStXkJ6erldLnYtEIsyYMUMvluB8mq+//hqLFy+Gr6+vXjbOi8VihIWFaW1zdHTU2/mLSMNCDRSEvEQcHByqHBetD7Zu3QoAsLGxwaJFi2BoaAgAiIuLw6FDh+Dh4QFra2s+Q6y2Jk2aICoqCk5OTlrbAwMDK61zX5916tQJUVFRet9AsXLlSnzyySdYv359pe9EX3z77beYN28eli5dCk9Pz0pzUOhDA8zKlSsxYMAAtGrVCkVFRRg3bhwiIyNhbW2NP//8k+/wal19rI9//PHHaqXjOE4vGijOnTtXaTgEABQVFSEgIICHiJ7NxYsXcfr0aVhbW0MgEEAgEKBbt25Yvnw5Zs2ahRs3bvAdYrV07NgRISEhen8jvH79evz+++948803+Q7lmU2YMAGbN2/Gt99+y3cohGihBgpCXiKrV6/Gp59+it9++01vb8IA4MaNG9i+fTtmzJiBrKws+Pn5QSwWIy0tDatWrcK7777Ld4hPNX36dMyePRtbtmwBx3F4+PAhLl68iI8++ggLFizgO7xqmzlzJubNm4fk5GSdN8X60k10woQJKCgoQPPmzfV2gsmBAwcCAPr06aO1vXTOFn3oAm5vb4/Q0FDs3r0bYWFhyMvLw9SpUzF+/Hi9XV1F38TGxvIdQq0ofTrMGMPt27eRnJyseU+pVOLo0aOws7PjK7waUyqVmmFO1tbWePjwIdzd3eHo6Ii7d+/yHF31vffee5g7dy4ePHiA9u3bV5o3Sl/OGSUlJXozpLQqCoUCW7ZswcmTJ3V+FzQPBeELreJByEvEwsICBQUFUCgUensTBqgvzs6dO4fWrVtj06ZNWLt2LW7cuIH9+/dj4cKFetF9lDGGZcuWYfny5SgoKAAASKVSfPTRR1iyZAnP0VWfrhn/S8dH68tNMaBeZvRJJk2a9IIieXbnzp174vvVHXZAXhwTExPNSiWkdgkEAk23e12XujKZDGvXrsVbb731okN7Jg1lqfOGcs6YP38+jI2N9eqBQkW9evWq8j2O4/Rm+BNpeKgHBSEvkdWrV/MdQq0oKCjQPEk6fvw4Ro0aBYFAAD8/P9y/f5/n6KqH4zh88cUX+PjjjxEVFYW8vDy0atUKxsbGfIdWIw3haatcLse5c+ewYMECODs78x3OM5HL5Vi8eDHWr18PNzc3vsOpkb///rvaafVlpQJ9Vrr8o5GREebOnfvEtPX5CWtsbCwYY3BxccGVK1e0Vu+QSCRo1KgRhEIhjxHWzJdffon8/HwAwOLFizF06FB0795ds9S5vmgI5wxAPURow4YNOHnyJLy8vCo98KnPZaNUfZ8Ylry8qAcFIUTveHl5Ydq0aRg5ciTatGmDo0ePonPnzrh+/TqGDBmi1ZWXkOowMzNDSEiI3jZQAOq5WS5cuKB3DRS6nqjqok9PV6urPvag6NWrFw4ePAhzc/MnPmEFGtYNzpAhQ7Bp0ybY2tryHUq16dtS5zVR378P6n1ASN2hBgpCXjLR0dHYunUroqOj8dNPP6FRo0b477//0KxZM7Ru3Zrv8Kpl3759GDduHJRKJfr06YPjx48DAJYvX47z58/jv//+4zlC3UqXSa2OAwcO1GEkz6chPvGeNGkSvL29MWfOHL5DeWZz5syBVCqlCc/0yODBg7F58+Z6exP2MqmPjUWl5HI5ZDIZQkJC0KZNG77DeSHq8/dBCKlbNMSDkJfIuXPnMGjQIHTt2hXnz5/H0qVL0ahRI4SGhmLz5s3Yt28f3yFWy+jRo9GtWzckJSWhbdu2mu19+vTByJEjeYzsyUqXSQXUY6IPHjwIMzMz+Pr6AgCuX7+OrKysGjVk8GHEiBHVSqdPT7zd3NywePFiBAUF6ZwsTB9WK3iZJjzz9PTEv//+CwcHB75DqZJSqcShQ4c0c+K0bt0aw4YN0xpW8O+///IVXrWcOXOmyifFP//8M95///0XHNHLSSwWo1mzZnpTn75MoqKiEB0djR49ekAmk2nm0iCEPDvqQUHIS6Rz58547bXXMHfuXK2nE1euXMGoUaOQkJDAd4gvjfnz5yMjIwPr16/X3LAolUq89957MDU1xQ8//MBzhC+XJw3t4DgOMTExLzCaZ/MydTmu709Xo6KiMGTIECQkJMDd3R0AcPfuXTg4OODIkSNo3rw5zxFWj4WFhabBq7yffvoJCxYsQE5ODk+R1b76fkxt3rwZBw4cwI4dO2Bpacl3OHWuvn8f6enpGDNmDM6cOQOO4xAZGQkXFxe89dZbsLCwwMqVK/kOkRC9RQ0UhLxEjI2NER4eDmdnZ62Tf1xcHFq2bImioiK+Q3xp2NjYIDAwUHPzUuru3bvo0qUL0tPTeYqMkPqvvt+8DB48GIwx7Ny5U3MzmZ6ejgkTJkAgEODIkSM8R1g9mzZtwueff47z58+jZcuWAICVK1di8eLFOHz4MLp3785zhLWnvh9T7dq1Q1RUFORyORwdHSv1kAoODuYpsrpR37+PiRMnIjU1FZs2bYKHh4cm1mPHjmHu3Lm4desW3yESordoiAchLxFzc3MkJSVVelp848YNvVoPviFQKBS4c+dOpQaKO3fuQKVS8RRV9axZs6baafVhaAQhte3cuXO4dOmS1pNuKysrfPvtt+jatSuPkdXMtGnTkJGRgb59+yIwMBB79uzBsmXL8O+//+pVPhqC6g6tIy/G8ePHcezYMdjb22ttd3Nz05vVxAipr6iBgpCXyNixYzF//nz873//A8dxUKlUCAoKwkcffYSJEyfyHd5LZcqUKZg6dSqio6PRsWNHAMDly5fx7bffYsqUKTxH92Q//vhjtdJxHKdXDRQJCQn4+++/ER8fj5KSEq339GX+hmvXrmHv3r0681CfJ15taKRSKXJzcyttz8vLg0Qi4SGiZ/fJJ58gPT0dvr6+UCqVOHbsGPz8/PgO66Xz1VdfVSvdn3/+iWHDhlXqYUFqV35+PgwNDSttz8jIgFQq5SEiQhoOaqAg5CWybNkyvP/++3BwcIBSqUSrVq2gUCgwfvx4fPnll3yH91JZsWIFmjRpgpUrVyIpKQkAYGtri48//hjz5s3jObonayjr2Jd36tQpDBs2DC4uLrhz5w7atGmDuLg4MMbg4+PDd3jVsnv3bkycOBEDBgzA8ePH0b9/f9y7dw8pKSn1evLYhmjo0KF4++23sXnzZq0GyBkzZtT7lW109ZCys7ODoaEhevTogStXruDKlSsA6n8PKblcjnfeeQcLFix46hLCn3/+eYOY2+Gdd95Bp06d6uXQiIb0fXTv3h3bt2/HkiVLAEDz0Of7779/6vK8hJAnozkoCHkJPXjwAOHh4cjLy0O7du3g5ubGd0gvtdKJ5kxNTSu9FxQUBF9fX3oiU8c6duyIQYMG4euvv9aMfW7UqBHGjx+PgQMH4t133+U7xKfy8vLCO++8g/fff1+TB2dnZ7zzzjuwtbXF119/zXeItaa+j0/PysrCpEmT8M8//0AsFgNQD+saNmwYfv/9d60Vfeqbp904ltKXyWPNzMwQEhJS7Xzpu/peNhrK93Hz5k306dMHPj4+OH36NIYNG4Zbt24hIyMDQUFBejMRLiH1ETVQEPISmTt3rs7tHMfBwMAArq6uGD58eL1+avGyMTU1RUhISL262Jw7dy6WLFkCIyOjKo+pUvoyNMLExAQhISFo3rw5LCwsEBgYiNatWyM0NBTDhw9HXFwc3yE+lZGREW7dugUnJydYWVnh7Nmz8PT0REREBHr37q3pqVOfbd++Ha+//nqlBrmSkhJNDxEA2LVrF4YPH17vu7FHRkYiIiICHMfBw8MDrq6ufIf00pk0aRK8vb0xZ84cvkN5Iep7A0VD+j6ys7Oxbt06hIaGIi8vDz4+Pnj//fdha2vLd2iE6DUa4kHIS+TGjRsIDg6GUqnUTM547949CIVCtGzZEr/88gvmzZuHwMBAtGrViudoCQDUxzbkGzduQC6Xa/5fFX1aC97IyEgzZ4OtrS2io6PRunVrAEBaWhqfoVWbhYWFZt4DOzs73Lx5E56ensjKykJBQQHP0VXPlClTMHDgQDRq1Ehre25uLqZMmaJpoBg3bhwf4dWYm5ubplFCn8pDTdXHhtRSbm5uWLx4MYKCgtC+fftKjVr1fZhKQ9OQvg8zMzN88cUXfIdBSINDDRSEvERKe0ds3bpVM5wgOzsb06ZNQ7du3TB9+nSMGzcOc+bMwbFjx3iOltRXZ86c0fn/J0lISEDTpk0hEAjqKqzn4ufnh8DAQHh4eGDw4MGYN28ewsPDceDAAb2ZELBHjx44ceIEPD098dprr2H27Nk4ffo0Tpw4gT59+vAdXrUwxnTeyCckJNTrYRG6bN68GT/++CMiIyMBqG/MPvzwQ0ybNo3nyGpffWxILbV582aYm5vj+vXruH79utZ7+jaRb0PQUL6PsLAwndtLe6Q2a9aMhmYS8oxoiAchLxE7OzucOHGiUu+IW7duoX///khMTERwcDD69++vN0+NG7r63l23uurzE1YAiImJQV5eHry8vJCfn4958+bhwoULcHNzw6pVq+Do6Mh3iE+VkZGBoqIiNG3aVDNZW2kevvzyS1hYWPAdYpXatWsHjuMQGhqK1q1bQyQqe36iVCoRGxuLgQMHYu/evTxGWX0LFy7EqlWrMHPmTHTu3BkAcPHiRaxbtw5z5szB4sWLeY6wdjWUeqohoO/ixRAIBJrG1NJbqfKNq2KxGK+//jp+++03GBgY8BIjIfqKelAQ8hLJzs5GampqpQaKR48eaSZqNDc3r7Q8ISHPq763hZe/mDcyMsL69et1pqvPS/iVnztGIBDg008/1Znu22+/xYwZM2Bubv6CInu6ESNGAABCQkIwYMAAGBsba96TSCRwcnLCq6++ylN0Nffrr79i48aNeOONNzTbhg0bBi8vL8ycObPBNVDog5KSEsTGxqJ58+ZaDWD6QKlUIigoCF5eXk8tt46OjpqJWes7XTf2+uLgwYOYP38+Pv74Y81KPVeuXMHKlSvx1VdfQaFQ4NNPP8WXX36JFStW8BwtIfpFv2poQshzGT58ON566y2sXLkSHTp0AABcvXoVH330keYG4cqVK2jRogWPUZLy9PHCrSGrz0v4VdeyZcswZsyYetVA8dVXXwEAnJyc8Prrr+v9E0e5XA5fX99K29u3bw+FQsFDRC+vgoICzJw5E9u2bQOgnnfJxcUFM2fOhJ2dXZUNefWJUChE//79ERER8dRye/PmzRcT1HPYvn07fvjhB83wpxYtWuDjjz/Gm2++yXNk1bd06VL89NNPGDBggGabp6cn7O3tsWDBAly5cgVGRkaYN28eNVAQUkP1czAwIaRO/Pbbb+jTpw/Gjh0LR0dHODo6YuzYsejTp4/miXHLli2xadMmniNtmP7++2/N5JLVVd97HrxsGsL3UZ/zMGnSJL1vnACAN998E7/++mul7Rs2bMD48eN5iKhu1eeG1M8++wyhoaE4e/as1rHVt29f7Nmzh8fIaqZNmzZ6sazr06xatQrvvvsuBg8ejL1792Lv3r0YOHAgZsyYgR9//JHv8KotPDxc59A/R0dHhIeHAwC8vb31YvUkQuobmoOCkJdQXl6e5kLHxcVFqzs1qTtCoRDJycmwsbGBUChEUlJSpdUKGqqGMi66IeSjPudBqVTixx9/xN69exEfH19puFlGRgZPkdXMzJkzsX37djg4OGgmWb18+TLi4+MxceJErS74+rIU75PU52PK0dERe/bsgZ+fn1acUVFR8PHx0QxvrO+OHj2Kzz77DEuWLNG5+kXpxNf1nbOzM77++mvNijyltm3bhkWLFiE2NpanyGqmXbt2aNu2LTZs2ACJRAJA3XNq+vTpCA0NxY0bNxAUFIQJEyboTZ4IqS9oiAchLyFjY2N4eXnxHcZLx8bGBpcuXcIrr7xS5WoFDdXLlFfy7L7++mts2rQJ8+bNw5dffokvvvgCcXFxOHToEBYuXMh3eNV28+ZN+Pj4AACio6MBANbW1rC2ttbqgl/fy8WZM2fQq1evp6b777//YGdn9wIiqrlHjx7pbAjOz8+v93//8gYPHgxAPZdJ+bhLzyVKpZKv0GokKSkJXbp0qbS9S5cuetXb4Oeff8awYcNgb2+vuZ4KDw+HUqnE4cOHAagnX37vvff4DJMQvUQNFIQQ8oLMmDEDw4cPB8dx4DgOTZo0qTKtvlxsVhd11iPVsXPnTmzcuBFDhgzBokWL8MYbb6B58+bw8vLCpUuX9GYJwuouv1vfDRw4EPb29pgyZQomTZoEBwcHnem6dev2giOrPl9fXxw5cgQzZ84EUNYotGnTJs0KK/qgoRxTrq6u2Lt3Lz7//HOt7Xv27IGbmxtPUdVcly5dEBsbi507d+LevXsAgNdeew3jxo2DiYkJAOjVnBqE1CfUQEEIIS/IokWLMHbsWERFRWHYsGHYunVrvZqosC7dvn0bTZs25TsMUs8lJyfD09MTgLqnV3Z2NgBg6P/bu/OwmvP+f+DPE0VptdM0dVJSFOE20bRhGLotkxuDqaxjDSUzxgiZRo2hLHPfYWIsY6SxNHPfNdSNimS7W4wJbUhkrCExWs7vD7/Od85kqdD7fOr5uC7X5bzPmet6ujud+3xen/f79fr73xEQECAyWoN09epVbN++HVu3bkVgYCD69u2LSZMmYfjw4cpt7epu+fLlGDRoEDIzM1FWVoY1a9YgMzMTx44dQ2Jiouh41ebi4iI6wmsRGBiI0aNHIykpCY6OjgCA5ORkHDx4UDJjhCvp6elh2rRpL3yNu7s7IiIi0K5duzpKRSR9LFAQEdWhTp06oVOnTliyZAlGjhwJHR0d0ZFqzMPDo9qv3bt3LwA8986r1EhphN/zODk5QVtbW3SMZ3rrrbdQWFiIt99+Gx06dEBcXBy6d++OU6dOoUmTJqLj1cjp06ef20uj8vdC3bVs2RK+vr7w9fVFamoqvvvuO8yYMQMzZszA2LFjMWnSJHTt2lV0zBd69913kZ6ejpCQENja2irfUykpKcpimFQcOXIEGzZsQF5eHn788UcYGxtj+/btkMvlar2L5c9GjBiBEydOICwsDNHR0QAAa2trnDx5Evb29mLDvQFJSUl49OiR6BhEksImmUREAt28eRMXLlwAAFhZWaFVq1aCE73chAkTqv3a77777g0meX28vb0xadIkODs7i47ySsrLyxEdHY1z584BADp37oyhQ4eiUaNGgpNVz4IFC6Cvr4+FCxdi165d+Oijj2BmZob8/Hz4+voiJCREdMRqiYyMhJeXFwYOHIi4uDgMGDAAWVlZ+P333/HBBx9I5vfir65du4aNGzciJCQEjRs3xuPHj9G7d2+sX78enTt3Fh2vXtuzZw88PT0xbtw4bN++HZmZmTA3N8c333yD2NhYxMbGio5Iz6DODWSJ1BULFEREApSUlGDWrFnYvn27st9Eo0aN4OXlhXXr1klyZ4WUDR8+HLGxsTA1NVWet1fXpn/Pk5OTA3d3dxQUFMDKygoAcOHCBZiYmCAmJgYdOnQQnLDmUlJSkJKSAktLSwwZMkR0nGqzs7PD1KlTMXPmTOUFilwux9SpU9GuXTsEBgaKjlhtpaWl+Omnn7B582bEx8ejZ8+emDRpEsaMGYObN29i0aJFSE1NRWZmpuioz/S8iUm3b99G69atJdPvx97eHr6+vvDy8lK56E1LS8OgQYNw/fp10RGrpb78PKqLBQqimtMQHYCIqCHy9fVFYmIifv75ZxQVFaGoqAg//fQTEhMTMW/ePNHxGpzo6GhcvXoV06dPx65du2BmZoZBgwZh9+7dKC0tFR2vWmbPng1zc3NcuXIFqampSE1NRX5+PuRyuWSaS/5V79694efnJ6niBPB0coe7uzsAQEtLSzkxwtfXFxs3bhScrvp8fHzQrl07TJ06FR07dkRaWhpSUlIwefJkNGvWDGZmZli5ciXOnz8vOupzPe8+3B9//CGZPhrA02Ljs3Z4GRgYoKioqO4D1VJ9+XkQ0ZvDHhRERALs2bMHu3fvhqurq3Jt8ODB0NbWxqhRoxAeHi4uXA3t3r37uWftU1NTBaWquVatWsHPzw9+fn7K8/aenp7Q1dXFRx99hBkzZqh1l/nExEQcP34czZs3V661aNECISEhymZ0UrB9+3asX78eFy9eREpKCkxNTbF69WrI5XIMGzZMdLxqMTIywoMHDwAAxsbGOHv2LGxtbVFUVISSkhLB6aovMzMT69atg4eHx3N7gLRs2VItJ0ysXbsWwNOpHREREdDV1VU+V15ejqSkJHTq1ElUvBpr27YtcnJyYGZmprJ+9OhRSdydr28/DyJ6c1igICISoKSkBG3atKmy3rp1a0ldwKxduxaff/45xo8fj59++gkTJkxAbm4uTp06hZkzZ4qOVyuFhYWIj49HfHw8GjVqhMGDB+PXX3+FjY0NVqxYAV9fX9ERn6lJkybKi+I/Ky4ulsydyfDwcCxevBhz587Fl19+qdzubWhoiNWrV0umQOHs7Iz4+HjY2tpi5MiRmDNnDg4dOoT4+Hj069dPdLxqW7JkCfr06YPGjVW/LpaVleHYsWNwdnZG48aN1XLCRFhYGICnd+zXr1+v0odFS0sLZmZmWL9+vah4NTZlyhTMmTMHmzdvhkwmw7Vr15CSkgJ/f39JTLipbz8PInqDFEREVOf69u2rGDlypOLRo0fKtZKSEsXIkSMV/fr1E5isZqysrBQ//PCDQqFQKHR1dRW5ubkKhUKhCAgIUMycOVNktBp58uSJYvfu3Qp3d3eFpqamokePHorw8HDFvXv3lK/Zu3evwtDQUGDKF/P09FR07txZcfz4cUVFRYWioqJCkZKSoujSpYvC29tbdLxqsba2Vuzbt0+hUKi+n3799VdFixYtBCarmdu3byuuXr2qUCgUivLyckVwcLBiyJAhCj8/P8WdO3cEp6s+DQ0Nxe+//15l/datWwoNDQ0BiWrO1dVVUv+bP09FRYUiKChI0axZM4VMJlPIZDJF06ZNFYsWLRIdrUbqw8/jyZMnigkTJijy8vJe+trly5cr7t69++ZDEdUjbJJJRCTA2bNnMXDgQPzxxx/KMX0ZGRlo2rQpDhw4IJmO+Do6Ojh37hxMTU3RunVrxMfHo2vXrsjOzoaDgwNu374tOmK1tGzZEhUVFRgzZgymTJmCbt26VXlNUVER7O3tcfHixboPWA1FRUXw9vbGv//9b+Uo1LKyMgwdOhRbtmyBgYGB4IQvp62tjfPnz8PU1FSluVx2djbs7OwkM67Py8sLbm5ucHZ2lmRz0koaGhr4/fffq0wXysrKQs+ePXH//n1ByRquJ0+eICcnB8XFxbCxsVE5KkF1x8DAAOnp6ZDL5aKjENU7POJBRCRAly5dkJ2djR07digbzI0ZMwbjxo2Dtra24HTV17ZtW9y5cwempqZ4++23cfz4cXTt2hUXL158bjM0dRQWFoaRI0eiadOmz32NoaGh2hYngKf5fvrpJ2RnZ+PcuXOQyWSwtraGhYWF6GjVJpfLkZ6eDlNTU5X1/fv3w9raWlCqmtPS0kJwcDAmTZoEY2NjuLi4wNXVFS4uLmrdx6SSh4cHgKf9AsaPH6/Sf6K8vBxnzpxBnz59RMWrkfLycmzZsgUHDx7EjRs3UFFRofL8oUOHBCWrmXv37qG8vBzNmzeHjY2Ncv3OnTto3Lgx9PX1BaarvokTJ77w+c2bN9dRklczfPhwREdHq+2RPyIpY4GCiEgQHR0dTJky5YWvcXd3R0REBNq1a1dHqWqmb9+++Pnnn2Fvb48JEybA19cXu3fvxunTp5UXOVLg6ekpOsJrY2lpqSxKyGQywWlqxs/PDzNnzsTjx4+hUChw8uRJ7Ny5E8HBwYiIiBAdr9oqs169ehVJSUlITEzEqlWrlGNGCwoKBCd8scrdNgqFAnp6eipFUy0tLTg4OLz0s0tdzJkzB1u2bIG7uzu6dOkiud+JSh9++CGGDBmCGTNmqKxHRUXh559/RmxsrKBkNXP37l2Vx6WlpTh79iyKiorQt29fQalqztLSEsuWLUNycjJ69OiBZs2aqTwv1clJROqARzyIiNSYus9Qr6ioQEVFhbKJXmRkJI4dOwZLS0tMnTpVrZsz1qSAsnfv3jeY5PXZtGkTwsLCkJ2dDeDpl+i5c+di8uTJgpNV344dO7B06VLk5uYCANq3b4/AwEBMmjRJcLKaKykpwdGjR3H48GEkJCQgNTUVNjY2SEtLEx2tWgIDA+Hv71/l4ktKWrZsiW3btmHw4MGio7yS5s2bIzk5ucpOovPnz8PR0VEyx+mepaKiAtOnT0eHDh3wySefiI5TLS862iGTyZCXl1eHaYjqF+6gICKiWisoKICJiYny8YcffogPP/wQCoUCV65cwdtvvy0w3YtJoSdDTSxevBihoaHw8fFB7969AQApKSnw9fVFfn4+li1bJjjhi5WVleGHH37AwIEDMW7cOJSUlKC4uBitW7cWHa3GFi5ciISEBKSlpcHa2houLi5YsGABnJ2dYWRkJDpetS1ZskR0hFempaUlqWNOz/PHH3+grKysynppaalkerM8j4aGBvz8/ODq6iqZAoU6H/cjkjruoCAiUmPqvoOiUaNGKCwsrHIRefv2bbRu3Vo5JpLevFatWmHt2rUYM2aMyvrOnTvh4+ODW7duCUpWfX9uuiplGhoaaNWqFXx9feHh4YGOHTuKjlQrcrn8hUcipHCXeNWqVcjLy8M333wj2eMdAODm5oYuXbpg3bp1KuszZ87EmTNncOTIEUHJXo/Y2Fh4e3vj5s2boqPUWOWllJTfX0TqhDsoiIio1hQKxTO/lBUXF7+w4SS9fqWlpejZs2eV9R49ejzzzqs66tWrF9LS0iRfoEhLS0NiYiISEhKwatUqaGlpKRtlurq6SqZgMXfuXJXHpaWlSEtLw/79+zF//nwxoWqo8ojNL7/8gs6dOysn3FSSyvGtoKAg9O/fHxkZGejXrx8A4ODBgzh16hTi4uIEp6s+Pz8/lccKhQKFhYWIiYmBt7e3oFS1s23bNnz99dfKI3UdO3bE/Pnz61VPIyIRWKAgIqIaq/ySKZPJEBAQAB0dHeVz5eXlOHHixDNHdaoTe3v7at/xSk1NfcNpXp2npyfCw8MRGhqqsr5x40aMGzdOUKqamTFjBubNm4eCgoJnNp6zs7MTlKxmunbtiq5duyob5WVkZCAsLAwzZ85ERUWFZHYWzZkz55nr//znP3H69Ok6TlM7hoaG+OCDD0THeGWOjo5ISUnB119/jaioKGhra8POzg6bNm2SxGSYSn/tv1K522jVqlUvnfChTkJDQxEQEIBZs2bB0dERwNNi2LRp03Dr1i1O9yB6BTziQUSkxtT1iIebmxsAIDExEb1791ZphqmlpQUzMzP4+/ur9RfnwMBA5d8fP36Mf/3rX7CxsVH2bzh+/Dh+++03zJgxA8HBwaJiVpuPjw+2bdsGExMTODg4AABOnDiB/Px8eHl5qdw5/msRQ11oaGhUWZPJZMqdOlK5sFcoFEhLS0NCQgISEhJw9OhR3L9/H3Z2dnBxcUFYWJjoiK8kLy8P3bp1w/3790VHIRJCLpcjMDAQXl5eKutbt27F0qVL2aOC6BWwQEFEpMaCg4Mxffp0GBoaio7yTBMmTMCaNWugr68vOsormTx5Mtq1a4cvvvhCZX3JkiW4cuUKNm/eLChZ9VUWjV5GJpPh0KFDbzhN7Vy+fPmFz0vl6IeRkRGKi4vRtWtX5dEOJycntf09rqkVK1bgX//6Fy5duiQ6Sr12//595Wfry4pBUvkMvnjxIsrKyqoUr7Ozs6GpqQkzMzMxwWqoadOmOHv2bJUGrNnZ2bC1tcXjx48FJSOSPhYoiIgE2b59O9avX4+LFy8iJSUFpqamWL16NeRyOYYNGyY6XoNiYGCA06dPP/NLc8+ePXHv3j1ByUiKYmJi4OTkJJmLxuf56zEohUKB69ev4+bNm/jXv/6Fjz/+WGC66pFyo88/NyHW0NB45r9DaruLXFxcMHHixCr9Jr7//ntEREQgISFBTLAa6tKlC8aOHYuFCxeqrAcFBWHXrl349ddfBSUjkj72oCAiEiA8PByLFy/G3Llz8eWXXyq/XBoaGmL16tWSKVA8fPgQISEhOHjwIG7cuIGKigqV59X5y/+faWtrIzk5uUqBIjk5mc0+61h2djYOHz78zPfT4sWLBaWqGXd3d9ERXovhw4erPK7sF+Dq6opOnTqJCVVDUm70eejQITRv3hwAcPjwYcFpXo+0tDRlz4Y/c3BwwKxZswQkqp3AwECMHj0aSUlJyn9PcnIyDh48iKioKMHpiKSNBQoiIgHWrVuHb7/9FsOHD0dISIhyvWfPnvD39xeYrGYmT56MxMREeHp6ol27dpIdszZ37lxMnz4dqamp6NWrF4Cn/Rs2b96MgIAAwemq7/Tp04iKikJ+fj6ePHmi8pwUphV8++23mD59Olq2bIm2bduqvJ9kMplkChT1xZIlS0RHeGVSbvS5Zs0a2NvbQ19fH5cvX8bo0aPRpEkT0bFeiUwmw4MHD6qs37t3TzK7QABgxIgROHHiBMLCwhAdHQ0AsLa2xsmTJ2Fvby82HJHE8YgHEZEA2traOH/+PExNTVUaYWZnZ8POzg6PHj0SHbFaDA0NERMT88w7YlITFRWFNWvW4Ny5cwCeftmcM2cORo0aJThZ9URGRsLLywsDBw5EXFwcBgwYgKysLPz+++/44IMP8N1334mO+FKmpqaYMWMGPv30U9FRGqyaNL6U8hEWKTT61NLSwuXLl9GuXTuV4x5SNmTIEGhra2Pnzp1o1KgRgKeTn0aPHo2HDx/il19+EZyQiETjDgoiIgHkcjnS09OrNP3bv38/rK2tBaWqOSMjI+UWZKkbNWqUZIoRz7J8+XLlKEs9PT2sWbMGcrkcU6dORbt27UTHq5a7d+9i5MiRomM0aIaGhtXeCSWlO95/tXv3brX/7OrUqRM+++wzuLm5QaFQICoq6rlFob9Ok1BXX331FZydnWFlZQUnJycAwJEjR3D//n21bd77LM8rGN2+fRutW7eW9O8GkWgsUBARCeDn54eZM2fi8ePHUCgUOHnyJHbu3Ing4GBERESIjldtX3zxBRYvXoytW7dCR0dHdJxXUlRUhN27dyMvLw/+/v5o3rw5UlNT0aZNGxgbG4uO91K5ubnK3gdaWlp4+PAhZDIZfH190bdvX5Wxqupq5MiRiIuLw7Rp00RHabD+3Ovg0qVLWLBgAcaPH68cv5uSkoKtW7dKYvQu8PJGn+osPDwc8+bNQ0xMDGQyGRYtWvTM4pFMJpNMgcLGxgZnzpzBN998g4yMDGhra8PLywuzZs1S+4LRnz1vA/off/yhMnabiGqORzyIiATZsWMHli5ditzcXABA+/btERgYiEmTJglOVn329vbIzc2FQqGAmZkZNDU1VZ5PTU0VlKxmzpw5g/79+8PAwACXLl3ChQsXYG5ujkWLFiE/Px/btm0THfGl3nrrLfzyyy+wtbWFnZ0dPvvsM4wZMwYpKSl4//331XYSydq1a5V/f/jwIUJDQ+Hu7g5bW9sq76fZs2fXdbwGrV+/fpg8eTLGjBmjsv7DDz9g48aNkpi48NfCnBQbfQJPc1+/fl3yRzykrvLzytfXF1988QV0dXWVz5WXlyMpKQmXLl1CWlqaqIhEkscCBRGRYCUlJSguLpbkF8+X3ZWXSpO9/v37o3v37lixYoVKT5Bjx45h7NixuHTpkuiILzV27Fj07NkTfn5++OKLL7Bu3ToMGzYM8fHx6N69u9o2yZTL5dV6nUwmk8xUmPpCR0cHGRkZVabbZGVloVu3bigpKRGUrGHw8PDAli1boK+vj61bt2LUqFHQ1tYWHeuVHTlyBBs2bEBeXh5+/PFHGBsbY/v27ZDL5Xj33XdFx3uhys+ry5cv46233lL20QCe7lwzMzPDsmXL8M4774iKSCR5LFAQEQnw6NEjKBQK5bGIy5cvY9++fbCxscGAAQMEp2t4DAwMkJqaig4dOqgUKC5fvgwrKys8fvxYdMSXunPnDh4/foz27dujoqICK1aswLFjx2BpaYlFixbByMhIdESSGCsrKwwbNgwrVqxQWf/kk0/w008/4cKFC4KS1Ux5eTmio6OVDXA7d+6MoUOHqlxcqqP62CRzz5498PT0xLhx47B9+3ZkZmbC3Nwc33zzDWJjYxEbGys6YrW4ublh7969/FwlegPYg4KISIBhw4bBw8MD06ZNQ1FREXr16gUtLS3cunULoaGhmD59uuiI1VbZuyE3Nxfz58+XXO8GAGjSpMkzu/lnZWWhVatWAhLV3Ny5c+Hm5gZnZ2d06NABCxYsEB2pxpYtWwZ/f/8q/UwePXqEr7/+mmNG61hYWBhGjBiBX375RXlH+OTJk8jOzsaePXsEp6uenJwcDB48GFevXoWVlRUAIDg4GCYmJoiJiUGHDh0EJ3y++tgkMygoCOvXr4eXlxciIyOV646OjggKChKYrGb+3KuFiF4v7qAgIhKgZcuWSExMROfOnREREYF169YhLS0Ne/bsweLFi5V3+tRdfejdAACTJ0/G7du3ERUVhebNm+PMmTNo1KgRhg8fDmdnZ6xevVp0xJeaPHkykpKSkJOTA2NjY7i4uMDV1RUuLi5VtuirK3bGVz8FBQUIDw9XGb87bdo0mJiYCE5WPYMHD4ZCocCOHTuUTRhv376Njz76CBoaGoiJiRGc8PmOHTsGPz8/5Obm4s6dO9DT03tuk8w7d+4ISFhzOjo6yMzMhJmZmcputby8PNjY2EhitxoATJw48YXPb968uY6SENU/3EFBRCRASUkJ9PT0AABxcXHw8PCAhoYGHBwccPnyZcHpqs/Pzw/jx49X9m6oNHjwYIwdO1ZgsppZtWoV/vGPf6B169Z49OgRXFxccP36dfTu3Rtffvml6HjVUjn95erVq0hKSkJiYiJWrVqlHDNaUFAgOOHLKRSKZ16AZWRkSKrDf33y1ltvvfR3YMaMGVi2bBlatmxZR6mqLzExEcePH1d5/7Ro0QIhISFwdHQUmOzl+vTpg+PHjwN42iTzwoULaNOmjeBUr6Zt27bIycmBmZmZyvrRo0dhbm4uJlQt3L17V+VxaWkpzp49i6KiIvTt21dQKqL6gQUKIiIBLCwsEB0djQ8++AAHDhyAr68vAODGjRvP3cKrjk6dOoUNGzZUWTc2Nsb169cFJKodAwMDxMfH4+jRozhz5gyKi4vRvXt39O/fX3S0GjMyMkKLFi1gZGQEQ0NDNG7cWO2PqRgZGUEmk0Emk6Fjx44qRYry8nIUFxdz9Kga+/777+Hv76+WBYomTZrgwYMHVdaLi4slNQ7y4sWL0NLSwqpVq1R6aUyaNElS/58xZcoUzJkzB5s3b4ZMJsO1a9eQkpICf39/BAQEiI5Xbfv27auyVlFRgenTp6v1sSEiKeARDyIiAXbv3o2xY8eivLwc/fr1Q1xcHICnZ6OTkpLwyy+/CE5YPa1bt8aBAwdgb2+vsl03Pj4eEydOxJUrV0RHbDAWLlyIhIQEpKWlwdraWnnEw9nZWe0buW3duhUKhQITJ07E6tWrYWBgoHyusjN+7969BSakF/nz77668fLyQmpqKjZt2oRevXoBAE6cOIEpU6agR48e2LJli9iA1XT69GkMHDgQ2trayn/HqVOn8OjRIxw4cAA9evQQnLB6FAoFli9fjuDgYOUUmCZNmsDf3x9ffPGF4HSv7sKFC3B1dUVhYaHoKESSxQIFEZEg169fR2FhIbp27QoNDQ0ATxvQ6evro1OnToLTVU996N0A/N9s+7+SyWRo2rQpLCws4OzsrNZd/zU0NNCqVSv4+vrCw8MDHTt2FB2pxhITE+Ho6IjGjV+8wTMkJATTpk2DoaFh3QSjF1LnAkVRURG8vb3x73//G5qamgCAsrIyDB06FFu2bFEphqkzJycnWFhY4Ntvv1X+fpSVlWHy5MnIy8tDUlKS4IQ18+TJE+Tk5KC4uBg2NjbQ1dUVHem1iI2Nhbe3N27evCk6CpFksUBBRES1du/ePfzjH//A6dOn8eDBA7Rv317ZuyE2NhbNmjUTHbFa5HI5bt68iZKSEuVug7t370JHRwe6urq4ceMGzM3NcfjwYbVtDpiRkYHExEQkJCTgyJEj0NLSUu6icHV1lWTB4nn09fWRnp6ulhfEDZE6Fygq5eTkqDT6tLCwEJyoZrS1tZGWllaleJ2ZmYmePXsqdyNIzf3793Ho0CFYWVnB2tpadJxq8/PzU3msUChQWFiImJgYeHt745tvvhGUjEj6WKAgIhLAzc3tmc0AKx06dKgO07w6qfdu2LlzJzZu3IiIiAjl+eGcnBxMnToVH3/8MRwdHfHhhx+ibdu22L17t+C01ZORkYGwsDDs2LEDFRUV9WoChhQuiBsS/jzevDZt2mD79u0YMGCAyvqBAwfg5eWF33//XVCymhk1ahScnZ0xa9YsPHr0CN26dcPFixehUCgQGRmJESNGiI5YLW5ubiqPK3ew9e3bFxMnTnzpLjAiej7+9hARCdCtWzeVx6WlpUhPT8fZs2fh7e0tJtQrePfdd/Huu++KjlFrixYtwp49e1Sam1lYWGDlypUYMWIE8vLysGLFCrX+8qxQKJCWloaEhAQkJCTg6NGjuH//Puzs7ODi4iI6HpEQI0aMQK9evfDpp5+qrK9YsQKnTp3Cjz/+KChZzYwePRqTJk3CypUr0adPHwBAcnIy5s+fjzFjxghOV31JSUn4/PPPATxtNFlRUYGioiJs3boVQUFBav0Z+2eHDx8WHYGo3mKBgohIgLCwsGeuL126FMXFxXWc5tWcOnUKhw8fxo0bN1BRUaHyXGhoqKBUNVNYWIiysrIq62VlZcppJO3bt3/mNAB10bx5cxQXF6Nr165wcXHBlClT4OTkxD4NVCMeHh7YsmUL9PX1sW3bNowePRpNmjR54X/z0Ucfqe0kiaSkJCxdurTK+qBBg7Bq1aq6D1RLK1euhEwmg5eXl/KzSlNTE9OnT0dISIjgdNV379495cjX/fv3Y8SIEdDR0YG7uzvmz58vOF31Xbx4EWVlZbC0tFRZz87OhqamZpUxqkRUfSxQEBGpkY8++gi9evXCypUrRUepluXLl2PRokWwsrJCmzZtVI6tvOgIi7pxc3PD1KlTERERAXt7ewBAWloapk+frpxp/+uvv0Iul4uM+ULff/89nJyc1PZCkaThP//5Dx4+fAh9fX1MmDAB77//Plq3bv3C/yY8PLyO0tXc88aJampq4v79+wIS1Y6WlhbWrFmD4OBg5ObmAgA6dOgAHR0dwclqxsTEBCkpKWjevDn279+PyMhIAE97/jRt2lRwuuobP348Jk6cWKVAceLECURERCAhIUFMMKJ6gAUKIiI1kpKSIqkvaWvWrMHmzZsxfvx40VFeyaZNm+Dp6YkePXqodPrv168fNm3aBADQ1dVV6zuu7u7uoiNQPdCpUyd89tlncHNzg0KhQFRU1HOLXl5eXnWcruZsbW2xa9cuLF68WGU9MjISNjY2glLVno6ODmxtbUXHqLW5c+di3Lhx0NXVhampKVxdXQE83ekipX9XWloaHB0dq6w7ODhg1qxZAhIR1R8sUBARCeDh4aHyuLID+OnTpxEQECAoVc1paGg880ua1LRt2xbx8fE4f/48srKyAABWVlawsrJSvuavTdFIHCcnJ2hra4uOUS+Fh4dj3rx5iImJgUwmw6JFi565G6ryuIG6CwgIgIeHB3Jzc5W7oQ4ePIidO3dKpv9EfTJjxgz06tULV65cwXvvvaccsW1ubo6goCDB6apPJpM988jfvXv36lVDYiIROMWDiEiACRMmqDz+cwfwv3ZpV2crVqzAtWvXsHr1atFRXosnT57g4sWL6NChA7uwC1JeXo7o6GjlSMjOnTtj6NChaNSokeBkDY+GhgauX7/+0iMe6i4mJgbLly9Heno6tLW1YWdnhyVLlrB5rGCVlyBSOg5YaciQIdDW1sbOnTuVn03l5eUYPXo0Hj58iF9++UVwQiLpYoGCiIhqraKiAu7u7sjKyoKNjY3yeESlvXv3CkpWMyUlJfDx8cHWrVsBAFlZWTA3N4ePjw+MjY2xYMECwQkbhpycHLi7u6OgoEC5e+XChQswMTFBTEyMypQVejP+3CRz69atGDVqFHer0Gu1bds2fP3118jOzgYAdOzYEfPnz4enp6fgZNWXmZkJZ2dnGBoawsnJCQBw5MgR3L9/H4cOHUKXLl0EJySSLg3RAYiISLpmz56Nw4cPo2PHjmjRogUMDAxU/kjFZ599hoyMDCQkJKj0AOnfvz927dolMFnDMnv2bJibm+PKlStITU1Famoq8vPzIZfLMXv2bNHxGoTKJpkAMHHiRLWeXFMTT548QUFBAfLz81X+UN0KDQ3F9OnTMXjwYERFRSEqKgrvv/8+pk2b9tzpVurIxsYGZ86cwahRo3Djxg08ePAAXl5eOH/+PIsTRK+IOyiIiAQoLy9HWFgYoqKikJ+fjydPnqg8f+fOHUHJakZPTw+RkZGSb9BoamqKXbt2wcHBAXp6esjIyIC5uTlycnLQvXt3SXX7l7JmzZrh+PHjVZrlZWRkwNHRUXIjeKXIzs4O3bt3h5ubGyZMmIC1a9dKuklmdnY2Jk6ciGPHjqmsKxQKyGQy9guoY3K5HIGBgVXeO1u3bsXSpUtx8eJFQcmISF3wgC0RkQCBgYGIiIjAvHnzsGjRInz++ee4dOkSoqOjq3SbV2fNmzevF9vub968+cxz9g8fPpTk+WipatKkyTPv2D9vVCS9fuvXr4efn1+9aZI5fvx4NG7cGP/5z3/Qrl07/j4LVlhYiD59+lRZ79OnDwoLCwUkqr0jR45gw4YNyMvLw48//ghjY2Ns374dcrkc7777ruh4RJLFIx5ERALs2LED3377LebNm4fGjRtjzJgxiIiIwOLFi3H8+HHR8apt6dKlWLJkCUpKSkRHeSU9e/ZETEyM8nHlRUxERAR69+4tKlaD8/e//x0ff/wxTpw4AYVCAYVCgePHj2PatGkYOnSo6HgNQp8+fXD8+HHcvHkTCoUCFy5cwN27d6v8kcour/T0dGzYsAGDBg1Ct27d0LVrV5U/VLcsLCwQFRVVZX3Xrl2wtLQUkKh29uzZg4EDB0JbWxupqan4448/ADyd4rF8+XLB6YikjTsoiIgEuH79unIbu66uLu7duwfg6QWalMaMrl27Frm5uWjTpg3MzMyqNMlMTU0VlKxmli9fjkGDBiEzMxNlZWVYs2YNMjMzcezYMSQmJoqO12CsXbsW3t7e6N27t/K9VFZWhqFDh2LNmjWC0zU8Fy9ehJaWFlatWqUyVWXSpEnPPfahbmxsbHDr1i3RMej/CwwMxOjRo5GUlKQcUZ2cnIyDBw8+s3ChroKCgrB+/Xp4eXkhMjJSue7o6CipcalE6ogFCiIiAd566y0UFhbi7bffRocOHRAXF4fu3bvj1KlTaNKkieh41TZ8+HDREV6Ld999F+np6QgJCYGtra3y55GSklKlHwK9OYaGhvjpp5+QnZ2Nc+fOQSaTwdraGhYWFqKjNUg3b95E9+7doa2tjV69egF42uTwyy+/xIEDB9CjRw/BCV/uq6++wieffILly5fD1ta2ShFVKoWW+mLEiBE4efIkQkNDER0dDQCwtrbGyZMnYW9vLzZcDVy4cAHOzs5V1g0MDFBUVFT3gYjqETbJJCISYMGCBdDX18fChQuxa9cufPTRRzAzM0N+fj58fX0REhIiOuJrtXPnTgwdOhTNmjUTHYUkovLrCXsGiOPk5AQLCwt8++23aNz46T2tsrIyTJ48GXl5eUhKShKc8OU0NP7vNPOf30tskln3SktLMXXqVAQEBEAul4uO80rMzc2xceNG9O/fX6Wx8rZt2xASEoLMzEzREYkkiwUKIiI1cPz4cRw7dgyWlpYYMmSI6Divnb6+PtLT02Fubi46ilJNJnPwLmvd2bRpE8LCwpCdnQ0AsLS0xNy5czF58mTByRoebW1tpKWloVOnTirrmZmZ6NmzpyR6z7zsiJaLi0sdJSHg6Q6D9PR0yRcogoOD8f3332Pz5s147733EBsbi8uXL8PX1xcBAQHw8fERHZFIsnjEg4hIgODgYLRp0wYTJ04EADg4OMDBwQGbN2/GV199hU8//VRwwtdLHWvhhoaGL707z7usdWvx4sUIDQ2Fj4+PsjlpSkoKfH19kZ+fj2XLlglO2LDo6+sjPz+/SoHiypUr0NPTE5SqZlxcXJTTFnJzc7F7926VaQtUt4YPH47o6Gj4+vqKjvJKFixYgIqKCvTr1w8lJSVwdnZGkyZN4O/vz+IE0SviDgoiIgHMzMzwww8/VBm3duLECXz44Yf1bhb8n7fAqouaNL/kXda60apVK6xduxZjxoxRWd+5cyd8fHzY7LCOzZ49G/v27cPKlSuVn1XJycmYP38+RowYgdWrV4sNWA179uyBp6cnxo0bh+3btyMzMxPm5ub45ptvEBsbi9jYWNERG5SgoCCsWrUK/fr1Q48ePaoc+5s9e7agZLXz5MkT5OTkoLi4GDY2NtDV1RUdiUjyWKAgIhKgadOmOHfuXJU7eHl5ebCxscHjx48FJXsz1LFAQerH0NAQp06dqjJuMCsrC7169WLzuTr25MkTzJ8/H+vXr0dZWRkAQFNTE9OnT0dISIgkGvra29vD19cXXl5eKp9DaWlpGDRoEK5fvy46YoPyol0rMpkMeXl5dZjm9bl//z4OHToEKysrWFtbi45DJGk84kFEJICJiQmSk5OrfFlLTk5G+/btBaVqWM6cOVPt19rZ2b3BJFTJ09MT4eHhCA0NVVnfuHEjxo0bJyhVw6WlpYU1a9YgODgYubm5AIAOHTpAR0dHcLLq47QF9fLn3YFSboQ7atQoODs7Y9asWXj06BH+9re/4eLFi1AoFIiMjMSIESNERySSLBYoiIgEmDJlCubOnYvS0lL07dsXAHDw4EF88sknmDdvnuB0DUO3bt0gk8le2h+DPSjq1qZNmxAXFwcHBwcAT4895efnw8vLC35+fsrX/bWIQW+Ojo6OZMfttm3bFjk5OTAzM1NZP3r0KHd0CVIfGuEmJSXh888/BwDs27cPFRUVKCoqwtatWxEUFMQCBdErYIGCiEiA+fPn4/bt25gxYwaePHkC4Omxj08//RSfffaZ4HQvtnbtWnz88cdo2rQp8vPzYWJi8tI7YKamptDU1KyjhNVT3/p81Adnz55F9+7dAUB5x75ly5Zo2bIlzp49q3ydFO+4khhTpkzBnDlzsHnzZshkMly7dg0pKSnw9/dHQECA6HgNTn1phHvv3j00b94cALB//36MGDECOjo6cHd3x/z58wWnI5I29qAgIhKouLgY586dg7a2NiwtLSVxprtx48a4du0aWrdujUaNGqGwsBCtW7cWHeu1yMzMRH5+vrJoBDy9GK6Po1+JGgKFQoHly5cjODhYORa1ctrCF198IThdw1NfGuF27NgRQUFBcHd3h1wuR2RkJPr27YuMjAz069dPMv8OInXEHRRERALp6urib3/7m+gYNdK+fXvs2bMHgwcPhkKhQEFBwXOber799tt1nK528vLy8MEHH+DXX39VOfZReaeeRzyIpEkmk+Hzzz/H/PnzOW1BDZSWlqJnz55V1nv06KFsxCoFc+fOxbhx46CrqwtTU1O4uroCeHr0Q6rHoYjUBXdQEBFRjWzcuBE+Pj4v/DKpUCgk1bthyJAhaNSoESIiIiCXy3HixAncuXMH8+bNw8qVK+Hk5CQ6YoNx+vRpREVFVdnJAgB79+4VlIqIXgcfHx9oampW6SHj7++PR48e4Z///KegZDV3+vRpXLlyBe+9956y4BUTEwNDQ0M4OjoKTkckXSxQEBFRjT148ACXL1+GnZ0d/vvf/6JFixbPfF3Xrl3rOFnttGzZEocOHYKdnR0MDAxw8uRJWFlZ4dChQ5g3bx7S0tJER2wQIiMj4eXlhYEDByIuLg4DBgxAVlYWfv/9d3zwwQf47rvvREckolfg4+ODbdu2wcTE5JmNcP/cq0gqjXClPI2ESB3xiAcREdWYnp4eunTpgu+++w6Ojo6S6J3xIuXl5dDT0wPwtFhx7do1WFlZwdTUFBcuXBCcruFYvnw5wsLCMHPmTOjp6WHNmjWQy+WYOnUq2rVrJzoeEb2i+tQId9u2bfj666+V00g6duyI+fPnw9PTU3AyImljgYKIiGrN29sbAPC///0P586dAwDY2Ngov4BKRZcuXZCRkQG5XI533nkHK1asgJaWFjZu3MhRhHUoNzcX7u7uAAAtLS08fPgQMpkMvr6+6Nu3LwIDAwUnJKJXcfjwYdERXovQ0FAEBARg1qxZyuMcR48exbRp03Dr1i34+voKTkgkXSxQEBFRrd24cQMffvghEhISYGhoCAAoKiqCm5sbIiMj0apVK7EBq2nRokV4+PAhAGDZsmX4+9//DicnJ7Ro0QK7du0SnK7hMDIywoMHDwAAxsbGOHv2LGxtbVFUVKScwEBEJNq6desQHh4OLy8v5drQoUPRuXNnLF26lAUKolegIToAERFJl4+PDx48eIDffvsNd+7cwZ07d3D27Fncv38fs2fPFh2v2gYOHAgPDw8AgIWFBc6fP49bt27hxo0b6Nu3r+B0DYezszPi4+MBACNHjsScOXMwZcoUjBkzBv369ROcjojoqcLCQvTp06fKep8+fVBYWCggEVH9wSaZRERUawYGBvjvf/9bZVTqyZMnMWDAABQVFYkJRpJ0584dPH78GO3bt0dFRQVWrFiBY8eOwdLSEosWLYKRkZHoiERE6NKlC8aOHYuFCxeqrAcFBWHXrl349ddfBSUjkj4e8SAiolqrqKhQ6bpeSVNTExUVFQISkZTNnTsXbm5ucHZ2RocOHbBgwQLRkYiIqggMDMTo0aORlJSk7EGRnJyMgwcPIioqSnA6ImnjDgoiIqq1YcOGoaioCDt37kT79u0BAFevXsW4ceNgZGSEffv2CU5IUjJ58mQkJSUhJycHxsbGcHFxgaurK1xcXGBpaSk6HhGRUmpqKkJDQ5UNoq2trTFv3jzY29sLTkYkbSxQEBFRrV25cgVDhw7Fb7/9BhMTE+Valy5d8PPPP+Ott94SnJCk6OrVq0hKSkJiYiISExORlZWFdu3aoaCgQHQ0ImrgSktLMXXqVAQEBEAul4uOQ1Tv8IgHERHVmomJCVJTU/Hf//4X58+fB/D0LlL//v0FJyMpMzIyQosWLWBkZARDQ0M0btxYMhNhiKh+09TUxJ49exAQECA6ClG9xB0URET0xtna2iI2Nla5y4LoWRYuXIiEhASkpaXB2tpaecTD2dmZDTKJSG14e3ujW7duHCdK9AZwBwUREb1xly5dQmlpqegYpOZCQkLQqlUrLFmyBB4eHujYsaPoSEREVVhaWmLZsmVITk5Gjx490KxZM5XnpTRmm0jdcAcFERG9cXp6esjIyIC5ubnoKKTGMjIykJiYiISEBBw5cgRaWlrKXRSurq4sWBCRWnhR7wmZTIa8vLw6TENUv7BAQUREbxwLFFQbGRkZCAsLw44dO1BRUYHy8nLRkYiIVFReSslkMsFJiOoHDdEBiIiIiICnX/QrR/cNHToUbm5u+P7772Fra8st00SkVjZt2oQuXbqgadOmaNq0Kbp06YKIiAjRsYgkjz0oiIiISC00b94cxcXF6Nq1K1xcXDBlyhQ4OTnB0NBQdDQiIqXFixcjNDQUPj4+6N27NwAgJSUFvr6+yM/Px7JlywQnJJIuHvEgIqI3jkc8qDpiYmLg5OQEfX190VGIiJ6rVatWWLt2LcaMGaOyvnPnTvj4+ODWrVuCkhFJH494EBHRa1VUVFRlbcOGDWjTpk3dhyFJcXd3Z3GCiNReaWkpevbsWWW9R48eKCsrE5CIqP5ggYKIiGrtq6++wq5du5SPR40ahRYtWsDY2BgZGRnK9bFjx1YZw0ZERCRFnp6eCA8Pr7K+ceNGjBs3TkAiovqDRzyIiKjW5HI5duzYgT59+iA+Ph6jRo3Crl27EBUVhfz8fMTFxYmOSERE9Fr5+Phg27ZtMDExgYODAwDgxIkTyM/Ph5eXFzQ1NZWvDQ0NFRWTSJJYoCAiolrT1tZGVlYWTExMMGfOHDx+/BgbNmxAVlYW3nnnHdy9e1d0RCIiotfKzc2tWq+TyWQ4dOjQG05DVL9wigcREdWakZERrly5AhMTE+zfvx9BQUEAno6LLC8vF5yOiIjo9Tt8+LDoCET1FgsURERUax4eHhg7diwsLS1x+/ZtDBo0CACQlpYGCwsLwemIiIiISEpYoCAioloLCwuDmZkZrly5ghUrVkBXVxcAUFhYiBkzZghOR0RERERSwh4URERERERERCQcd1AQEdEryc7OxuHDh3Hjxg1UVFSoPLd48WJBqYiIiIhIariDgoiIau3bb7/F9OnT0bJlS7Rt2xYymUz5nEwmQ2pqqsB0RERERCQlLFAQEVGtmZqaYsaMGfj0009FRyEiIiIiiWOBgoiIak1fXx/p6ekwNzcXHYWIiIiIJE5DdAAiIpKukSNHIi4uTnQMIiIiIqoH2CSTiIhqzcLCAgEBATh+/DhsbW2hqamp8vzs2bMFJSMiIiIiqeERDyIiqjW5XP7c52QyGfLy8uowDRERERFJGQsURERERERERCQce1AQERERERERkXDsQUFERLXm5+f3zHWZTIamTZvCwsICw4YNQ/Pmzes4GRERERFJDY94EBFRrbm5uSE1NRXl5eWwsrICAGRlZaFRo0bo1KkTLly4AJlMhqNHj8LGxkZwWiIiIiJSZzziQUREtTZs2DD0798f165dw//+9z/873//Q0FBAd577z2MGTMGV69ehbOzM3x9fUVHJSIiIiI1xx0URERUa8bGxoiPj6+yO+K3337DgAEDcPXqVaSmpmLAgAG4deuWoJREREREJAXcQUFERLV279493Lhxo8r6zZs3cf/+fQCAoaEhnjx5UtfRiIiIiEhiWKAgIqJaGzZsGCZOnIh9+/ahoKAABQUF2LdvHyZNmoThw4cDAE6ePImOHTuKDUpEREREao9HPIiIqNaKi4vh6+uLbdu2oaysDADQuHFjeHt7IywsDM2aNUN6ejoAoFu3buKCEhEREZHaY4GCiIheWXFxMfLy8gAA5ubm0NXVFZyIiIiIiKSGBQoiIiIiIiIiEq6x6ABERCQtHh4e2LJlC/T19eHh4fHC1+7du7eOUhERERGR1LFAQURENWJgYACZTKb8OxERERHR68AjHkREVGuPHj1CRUUFmjVrBgC4dOkSoqOjYW1tjYEDBwpOR0RERERSwjGjRERUa8OGDcP27dsBAEVFRXBwcMCqVaswfPhwhIeHC05HRERERFLCAgUREdVaamoqnJycAAC7d+9GmzZtcPnyZWzbtg1r164VnI6IiIiIpIQFCiIiqrWSkhLo6ekBAOLi4uDh4QENDQ04ODjg8uXLgtMRERERkZSwQEFERLVmYWGB6OhoXLlyBQcOHMCAAQMAADdu3IC+vr7gdEREREQkJSxQEBFRrS1evBj+/v4wMzPDO++8g969ewN4upvC3t5ecDoiIiIikhJO8SAioldy/fp1FBYWomvXrtDQeFr3PnnyJPT19dGpUyfB6YiIiIhIKligICIiIiIiIiLheMSDiIiIiIiIiIRjgYKIiIiIiIiIhGOBgoiIiIiIiIiEY4GCiIiIiIiIiIRjgYKIiIiIiIiIhGOBgoiIiIiIiIiEY4GCiIiIiIiIiIRjgYKIiIiIiIiIhPt/Mv980irp498AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = dataset.corr()\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Train-Test Split<br>\n",
    "<h3>The variables we choose are: <br> <h4><i>'age', 'state', 'signs_of_mental_illness', 'wapo_flee', 'prosecutor_race', 'gender','wapo_threat_level', 'off_duty_killing'.<i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9247, 8), (2312, 8), (9247,), (2312,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset[['age', 'state', 'signs_of_mental_illness', 'wapo_flee', 'prosecutor_race', 'gender','wapo_threat_level', 'off_duty_killing']]\n",
    "y = dataset['race']\n",
    "\n",
    "# Split the datasetset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Running logistic regression to train our model in predicting black race incidents\n",
    "<h4><i>OVR strategy<i><h4> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7205882352941176\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Create an instance of Logistic Regression Classifier\n",
    "clf = LogisticRegression(multi_class='ovr', solver='liblinear')\n",
    "\n",
    "# Train the classifier on the training data\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the race on the test data\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test.values, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.99      0.84      1673\n",
      "           1       0.36      0.01      0.03       639\n",
      "\n",
      "    accuracy                           0.72      2312\n",
      "   macro avg       0.54      0.50      0.43      2312\n",
      "weighted avg       0.62      0.72      0.61      2312\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix\n",
    "cm_df = pd.DataFrame(cm, index=[i for i in np.unique(y_test)], columns=[i for i in np.unique(y_test)])\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# Create a heatmap from the DataFrame\n",
    "sns.heatmap(cm_df, annot=True, fmt='g')\n",
    "\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "# Generate the classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(\"\\nClassification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "# Compute predicted probabilities with the `predict_proba` function\n",
    "y_pred_proba = clf.predict_proba(X_test)[::,1]\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "\n",
    "# Compute the AUC score\n",
    "auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, label=\"AUC=\"+str(auc))\n",
    "\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend(loc=4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Next Experiment:  SMOTE (Synthetic Minority Over-sampling Technique) </h1>\n",
    "\n",
    "<h5> While we achieved a high accuracy for the initial experiment, it is not a good indicator of performance for the aforementioned reasons. In this experiment, we will be using SMOTE to mitigate class imbalances and re-evaluate our logistic regression model with our revised balanced dataset. Moreover, we will be using the 'saga' solver, or the Stochastic Average Gradient descent with Augmented factorization and Intrinsic momentum. It's a variation of the sag solver that also supports the non-smooth penalty='l1' option which we have commonly referred to in class as the L1 Lasso Regularization technique. Given our dataset, this is the best choice as it handles sparse multinomial logistic regression effectively and is very fast for datasets of considerable size.</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5700692041522492\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Create an instance of SMOTE\n",
    "smote = SMOTE(random_state=65)\n",
    "\n",
    "# Fit SMOTE on the training data\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Create an instance of Logistic Regression Classifier with 'saga' solver and 'l1' penalty\n",
    "clf2 = LogisticRegression(multi_class='ovr', solver='saga', penalty='l1', max_iter=10000)\n",
    "\n",
    "# Train the classifier on the balanced training data\n",
    "clf2.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Predict the race on the test data\n",
    "y_pred2 = clf2.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy2 = accuracy_score(y_test.values, y_pred2)\n",
    "print(\"Accuracy:\", accuracy2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.56      0.65      1673\n",
      "           1       0.34      0.61      0.44       639\n",
      "\n",
      "    accuracy                           0.57      2312\n",
      "   macro avg       0.57      0.58      0.55      2312\n",
      "weighted avg       0.67      0.57      0.59      2312\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate the confusion matrix\n",
    "cm2 = confusion_matrix(y_test, y_pred2)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix\n",
    "cm_df2 = pd.DataFrame(cm2, index=[i for i in np.unique(y_test)], columns=[i for i in np.unique(y_test)])\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# Create a heatmap from the DataFrame\n",
    "sns.heatmap(cm_df2, annot=True, fmt='g')\n",
    "\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()\n",
    "\n",
    "# Generate the classification report\n",
    "report2 = classification_report(y_test, y_pred2)\n",
    "print(\"\\nClassification Report:\\n\", report2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "# Compute predicted probabilities with the `predict_proba` function\n",
    "y_pred_proba2 = clf2.predict_proba(X_test)[::,1]\n",
    "\n",
    "# Compute the ROC curve\n",
    "fpr2, tpr2, _ = roc_curve(y_test, y_pred_proba2)\n",
    "\n",
    "# Compute the AUC score\n",
    "auc2 = roc_auc_score(y_test, y_pred_proba2)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr2, tpr2, label=\"Experiment 2 AUC=\"+str(auc2))\n",
    "\n",
    "plt.title('ROC Curve for Experiment 2')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend(loc=4)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}