{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84dcd475",
   "metadata": {},
   "source": [
    "# DDRA - Contactless (Reduced)\n",
    "\n",
    "## General Idea\n",
    "The idea is to play only with numeric features (floats, integers or booleans) that are CONTACTLESS.\n",
    "\n",
    "This considers a subset of the features. This is mostly a copy from 002_contactless_full_attributes that just selects the most relevant attributes.\n",
    "\n",
    "## Initial setup\n",
    "This first section just ensures that the connection to DWH works correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12368ce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔌 Testing connection using credentials at: /home/uri/.superhog-dwh/credentials.yml\n",
      "✅ Connection successful.\n"
     ]
    }
   ],
   "source": [
    "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n",
    "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n",
    "\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(\"../../utils\"))  # Adjust path if needed\n",
    "\n",
    "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n",
    "\n",
    "# --- Connect to DWH ---\n",
    "creds = read_credentials()\n",
    "dwh_pg_engine = create_postgres_engine(creds)\n",
    "\n",
    "# --- Test Query ---\n",
    "test_connection()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c86f94f1",
   "metadata": {},
   "source": [
    "## Data Extraction\n",
    "In this section we extract the data.\n",
    "\n",
    "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n",
    "- A **unique booking ID**\n",
    "- Key **numeric features** such as number of services, time between booking creation and check-in, number of nights, etc.\n",
    "- Several **categorical (boolean) features** related to service usage\n",
    "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n",
    "\n",
    "Filters applied being:\n",
    "1. Bookings from **\"New Dash\" users** with a valid deal ID\n",
    "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n",
    "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n",
    "4. Bookings that are **already completed**\n",
    "\n",
    "The result is converted into a pandas DataFrame for further processing and modeling.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e3ed391",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise all imports needed for the Notebook\n",
    "from sklearn.model_selection import (\n",
    "    train_test_split, \n",
    "    GridSearchCV\n",
    ")\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "from sklearn.metrics import (\n",
    "    roc_auc_score, \n",
    "    average_precision_score,\n",
    "    classification_report,\n",
    "    roc_curve, \n",
    "    auc,\n",
    "    precision_recall_curve,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    fbeta_score,\n",
    "    confusion_matrix\n",
    ")\n",
    "import matplotlib.pyplot as plt\n",
    "import shap\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db5e3098",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Bookings: 21,384\n"
     ]
    }
   ],
   "source": [
    "# Query to extract data\n",
    "data_extraction_query = \"\"\"\n",
    "WITH \n",
    "service_information AS (\n",
    "\tSELECT\n",
    "\t\tid_booking,\n",
    "\t\tcount(DISTINCT CASE WHEN service_business_type = 'SCREENING' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_screening_services,\n",
    "\t\tcount(DISTINCT CASE WHEN service_business_type = 'DEPOSIT_MANAGEMENT' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_deposit_management_services,\n",
    "\t\tcount(DISTINCT CASE WHEN service_business_type = 'PROTECTION' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_protection_services,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'WAIVER PRO' THEN id_booking ELSE NULL END)>0 AS has_waiver_pro,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name IN ('BASIC DAMAGE DEPOSIT','BASIC DAMAGE DEPOSIT OR BASIC WAIVER','BASIC DAMAGE DEPOSIT OR WAIVER PLUS','BASIC WAIVER','WAIVER PLUS') THEN id_booking ELSE NULL END)>0 AS has_guest_facing_waiver_or_deposit,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'GUEST AGREEMENT' THEN id_booking ELSE NULL END)>0 AS has_guest_agreement,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'BASIC PROTECTION' THEN id_booking ELSE NULL END)>0 AS has_basic_protection,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PLUS' THEN id_booking ELSE NULL END)>0 AS has_protection_plus,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PRO' THEN id_booking ELSE NULL END)>0 AS has_protection_pro,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'ID VERIFICATION' THEN id_booking ELSE NULL END)>0 AS has_id_verification,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'SCREENING PLUS' THEN id_booking ELSE NULL END)>0 AS has_screening_plus,\n",
    "\t\tcount(DISTINCT CASE WHEN service_name = 'SEX OFFENDER CHECK' THEN id_booking ELSE NULL END)>0 AS has_sex_offender_check\n",
    "\tFROM\n",
    "\t\tintermediate.int_core__booking_service_detail\n",
    "\tGROUP BY\n",
    "\t\t1\n",
    "),\n",
    "listing_information AS (\n",
    "SELECT \n",
    "\tica.id_accommodation,\n",
    "\t-- Defaults to 0 if null\n",
    "\tCOALESCE(ica.number_of_bedrooms, 0) AS listing_number_of_bedrooms,\n",
    "\t-- Defaults to 0 if null\n",
    "\tCOALESCE(ica.number_of_bathrooms, 0) AS listing_number_of_bathrooms\n",
    "\tFROM intermediate.int_core__accommodation ica \n",
    "),\n",
    "raw_bookings_checked_in_prior_to_TCR AS (\n",
    "\tSELECT\n",
    "\t\tb.id_booking,\n",
    "\t\t-- Using group by on check-in date to remove booking duplicates\n",
    "\t\tb2.booking_check_in_date_utc,\n",
    "\t\t-- Using min as a conservative approach to reduce outliers\n",
    "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n",
    "\tFROM\n",
    "\t\tintermediate.int_booking_summary b\n",
    "\t-- Note that by joining with BS we're only considering New Dash bookings\n",
    "\tLEFT JOIN intermediate.int_booking_summary b2\n",
    "    ON\n",
    "\t\tb2.id_accommodation = b.id_accommodation\n",
    "\t\t-- Exclusion based on actual booking creation!\n",
    "\t\tAND b2.booking_check_in_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n",
    "\t\tAND b2.booking_check_in_date_utc < b.booking_created_date_utc\n",
    "\t\t-- Note that since is based on TCR we can remove Cancelled\n",
    "\t\tAND b2.booking_status NOT IN ('CANCELLED')\n",
    "\tGROUP BY\n",
    "\t\tb.id_booking,\n",
    "\t\tb2.booking_check_in_date_utc\n",
    "),\n",
    "bookings_checked_in_prior_to_TCR AS (\n",
    "\tSELECT\n",
    "\t\tid_booking,\n",
    "\t\tLEAST(\n",
    "\t\t\tcount(booking_check_in_date_utc),\n",
    "\t\t\t30\n",
    "\t\t) AS listing_check_ins_prior_to_TCR_in_30_days,\n",
    "\t\t-- Capping\n",
    "\t\tLEAST(\n",
    "\t\t\tGREATEST(\n",
    "\t\t\t\tsum(min_booking_number_of_nights),\n",
    "\t\t\t\t0\n",
    "\t\t\t),\n",
    "\t\t\t30\n",
    "\t\t) AS listing_occupancy_prior_to_TCR_in_30_days\n",
    "\tFROM\n",
    "\t\traw_bookings_checked_in_prior_to_TCR\n",
    "\tGROUP BY\n",
    "\t\t1\n",
    "),\n",
    "raw_known_bookings_checking_in_prior_to_TCI AS (\n",
    "\tSELECT\n",
    "\t\tb.id_booking,\n",
    "\t\tb.booking_check_in_date_utc,\n",
    "\t\t-- Using group by on check-in date to remove booking duplicates\n",
    "\t\tb2.booking_check_in_date_utc AS other_bookings_check_in_date_utc,\n",
    "\t\t-- Using min as a conservative approach to reduce outliers\n",
    "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n",
    "\tFROM\n",
    "\t\tintermediate.int_booking_summary b\n",
    "\t-- Note that by joining with BS we're only considering New Dash bookings\n",
    "\tLEFT JOIN intermediate.int_booking_summary b2\n",
    "    ON\n",
    "\t\tb2.id_accommodation = b.id_accommodation\n",
    "\t\t-- Exclusion based on check-in\n",
    "\t\tAND b2.booking_check_in_date_utc >= b.booking_check_in_date_utc - INTERVAL '30 days'\n",
    "\t\tAND b2.booking_check_in_date_utc < b.booking_check_in_date_utc\n",
    "\t\t-- that are known!\n",
    "\t\tAND b2.booking_created_date_utc < b.booking_created_date_utc\n",
    "\t\t-- Note that since is based on TCI we cannot remove Cancelled\n",
    "\tGROUP BY\n",
    "\t\tb.id_booking,\n",
    "\t\tb.booking_check_in_date_utc,\n",
    "\t\tb2.booking_check_in_date_utc\n",
    "),\n",
    "known_bookings_checking_in_prior_to_TCI AS (\n",
    "\tSELECT\n",
    "\t\tid_booking,\n",
    "\t\tLEAST(\n",
    "\t\t\tcount(other_bookings_check_in_date_utc),\n",
    "\t\t\t30\n",
    "\t\t) AS listing_known_check_ins_prior_to_TCI_in_30_days,\n",
    "\t\t-- Capping\n",
    "\t\tLEAST(\n",
    "\t\t\tGREATEST(\n",
    "\t\t\t\tsum(min_booking_number_of_nights),\n",
    "\t\t\t\t0\n",
    "\t\t\t),\n",
    "\t\t\t30\n",
    "\t\t) AS listing_known_occupancy_prior_to_TCI_in_30_days,\n",
    "\t\tCOALESCE(\n",
    "\t\t\tbooking_check_in_date_utc - max(other_bookings_check_in_date_utc),\n",
    "\t\t\t30\n",
    "\t\t) AS lead_time_between_prior_known_check_in_to_TCI_30_days\n",
    "\tFROM\n",
    "\t\traw_known_bookings_checking_in_prior_to_TCI\n",
    "\tGROUP BY\n",
    "\t\tid_booking, \n",
    "\t\tbooking_check_in_date_utc\n",
    "),\n",
    "incidents_prior_to_TCP AS (\n",
    "\tSELECT\n",
    "\t\tb.id_booking,\n",
    "\t\t-- Using distinct count on check-in date to remove booking duplicates\n",
    "\t\tCOUNT(DISTINCT b2.booking_check_in_date_utc) AS listing_incidents_prior_to_TCP_in_30_days\n",
    "\tFROM\n",
    "\t\tintermediate.int_booking_summary b\n",
    "\tLEFT JOIN intermediate.int_booking_summary b2\n",
    "    ON\n",
    "\t\tb2.id_accommodation = b.id_accommodation\n",
    "\t\t-- Filter on Check Out date\n",
    "\t\tAND b2.booking_completed_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n",
    "\t\tAND b2.booking_completed_date_utc < b.booking_created_date_utc\n",
    "\t\tAND b2.has_resolution_incident = TRUE\n",
    "\tGROUP BY\n",
    "\t\tb.id_booking\n",
    ")\n",
    "SELECT\n",
    "\t-- UNIQUE BOOKING ID --\n",
    "\tbooking_summary.id_booking,\n",
    "\t\n",
    "\t-- CONTEXTUAL SERVICE INFORMATION --\n",
    "\t-- We're not including number_of_applied_services as it 1-correlates with upgraded services\n",
    "\tbooking_summary.number_of_applied_upgraded_services,\n",
    "    --Removed! booking_summary.number_of_applied_billable_services,\n",
    "\tservice_information.number_of_applied_screening_services,\n",
    "\tservice_information.number_of_applied_deposit_management_services,\n",
    "\tservice_information.number_of_applied_protection_services,\n",
    "\t--Removed! service_information.has_waiver_pro,\n",
    "\t--Removed! service_information.has_guest_facing_waiver_or_deposit,\n",
    "\t--Removed! service_information.has_guest_agreement,\n",
    "\t--Removed! service_information.has_basic_protection,\n",
    "\t--Removed! service_information.has_protection_plus,\n",
    "\t--Removed! service_information.has_protection_pro,\n",
    "\t--Removed! service_information.has_id_verification,\n",
    "\t--Removed! service_information.has_screening_plus,\n",
    "\t--Removed! service_information.has_sex_offender_check,\n",
    "\tNOT booking_summary.has_verification_request AS is_contactless_booking,\n",
    "\t\n",
    "\t-- CONTEXTUAL LISTING INFORMATION --\n",
    "\tlisting_information.listing_number_of_bedrooms,\n",
    "\tlisting_information.listing_number_of_bathrooms,\n",
    "\t\n",
    "\t-- CONTEXTUAL TIMELINE OF OUR BOOKING\n",
    "\t-- Defaults to 0 if booking_created_date_utc > booking_check_in_date_utc\n",
    "\tGREATEST(booking_summary.booking_check_in_date_utc - booking_summary.booking_created_date_utc, 0) AS booking_lead_time,\n",
    "\tbooking_summary.booking_check_out_date_utc - booking_summary.booking_check_in_date_utc AS booking_duration,\n",
    "\t\n",
    "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCR\n",
    "\t-- Removed! bookings_checked_in_prior_to_TCR.listing_check_ins_prior_to_TCR_in_30_days,\n",
    "\tbookings_checked_in_prior_to_TCR.listing_occupancy_prior_to_TCR_in_30_days,\n",
    "\t\n",
    "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCI (KNOWN)\n",
    "\t-- Removed! known_bookings_checking_in_prior_to_TCI.listing_known_check_ins_prior_to_TCI_in_30_days,\n",
    "\tknown_bookings_checking_in_prior_to_TCI.listing_known_occupancy_prior_to_TCI_in_30_days,\n",
    "\tknown_bookings_checking_in_prior_to_TCI.lead_time_between_prior_known_check_in_to_TCI_30_days,\n",
    "\t\n",
    "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: INCIDENTAL BOOKINGS\n",
    "\t-- Removed! incidents_prior_to_TCP.listing_incidents_prior_to_TCP_in_30_days,\n",
    "\t\n",
    "\t-- TARGET (BOOLEAN) --\n",
    "\tbooking_summary.has_resolution_incident\n",
    "\n",
    "FROM\n",
    "\tintermediate.int_booking_summary booking_summary\n",
    "LEFT JOIN service_information \n",
    "\tON\n",
    "\tbooking_summary.id_booking = service_information.id_booking\n",
    "LEFT JOIN listing_information \n",
    "\tON booking_summary.id_accommodation = listing_information.id_accommodation\n",
    "LEFT JOIN bookings_checked_in_prior_to_TCR\n",
    "\tON booking_summary.id_booking = bookings_checked_in_prior_to_TCR.id_booking\n",
    "LEFT JOIN known_bookings_checking_in_prior_to_TCI\n",
    "\tON booking_summary.id_booking = known_bookings_checking_in_prior_to_TCI.id_booking\n",
    "LEFT JOIN incidents_prior_to_TCP\n",
    "\tON booking_summary.id_booking = incidents_prior_to_TCP.id_booking\n",
    "WHERE\n",
    "\t-- 1. Bookings from New Dash users with Id Deal\n",
    "\tbooking_summary.is_user_in_new_dash = TRUE\n",
    "\tAND \n",
    "    booking_summary.is_missing_id_deal = FALSE\n",
    "\tAND\n",
    "\t-- 2. Protected Bookings with a Protection or a Deposit Management service\n",
    "    (\n",
    "\t\tbooking_summary.has_protection_service_business_type\n",
    "\t\t\tOR \n",
    "    booking_summary.has_deposit_management_service_business_type\n",
    "\t)\n",
    "\tAND\n",
    "\t-- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n",
    "\tbooking_summary.is_booking_flagged_as_risk IS NOT NULL\n",
    "\tAND\n",
    "\t-- 4. Booking is completed\n",
    "\tbooking_summary.is_booking_past_completion_date = TRUE\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# Retrieve Data from Query\n",
    "df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n",
    "print(f\"Total Bookings: {len(df_extraction):,}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "Preprocessing in this notebook is quite straight-forward: we just drop id booking and split the features and target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop ID column\n",
    "df = df_extraction.copy().drop(columns=['id_booking'])\n",
    "\n",
    "# Separate features and target\n",
    "target_col = 'has_resolution_incident'\n",
    "X = df.drop(columns=[target_col])\n",
    "y = df[target_col]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "In this section we focus on explore the different features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA - Dataset Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (21384, 12)\n",
      "has_resolution_incident\n",
      "False    98.8\n",
      "True      1.2\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Shape and types\n",
    "print(f\"Shape: {X.shape}\")\n",
    "\n",
    "# Target distribution\n",
    "print(round(100*df[target_col].value_counts(normalize=True),2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th>std</th>\n",
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       "      <td>2.664282</td>\n",
       "      <td>1.532038</td>\n",
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       "      <th>number_of_applied_protection_services</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>0.727132</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_number_of_bedrooms</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>2.049476</td>\n",
       "      <td>1.755499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_number_of_bathrooms</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>1.590816</td>\n",
       "      <td>1.312573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booking_lead_time</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>18.151422</td>\n",
       "      <td>24.349579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booking_duration</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>4.175084</td>\n",
       "      <td>4.851055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_occupancy_prior_to_tcr_in_30_days</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>8.780817</td>\n",
       "      <td>9.260855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_known_occupancy_prior_to_tci_in_30_days</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>9.470913</td>\n",
       "      <td>9.715511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time_between_prior_known_check_in_to_tci_30_days</th>\n",
       "      <td>21384.0</td>\n",
       "      <td>15.287318</td>\n",
       "      <td>11.424657</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      count       mean  \\\n",
       "number_of_applied_upgraded_services                 21384.0   2.664282   \n",
       "number_of_applied_screening_services                21384.0   2.007903   \n",
       "number_of_applied_deposit_management_services       21384.0   0.620651   \n",
       "number_of_applied_protection_services               21384.0   0.727132   \n",
       "listing_number_of_bedrooms                          21384.0   2.049476   \n",
       "listing_number_of_bathrooms                         21384.0   1.590816   \n",
       "booking_lead_time                                   21384.0  18.151422   \n",
       "booking_duration                                    21384.0   4.175084   \n",
       "listing_occupancy_prior_to_tcr_in_30_days           21384.0   8.780817   \n",
       "listing_known_occupancy_prior_to_tci_in_30_days     21384.0   9.470913   \n",
       "lead_time_between_prior_known_check_in_to_tci_3...  21384.0  15.287318   \n",
       "\n",
       "                                                          std  min   5%  25%  \\\n",
       "number_of_applied_upgraded_services                  1.532038  1.0  1.0  1.0   \n",
       "number_of_applied_screening_services                 0.985649  1.0  1.0  1.0   \n",
       "number_of_applied_deposit_management_services        0.485814  0.0  0.0  0.0   \n",
       "number_of_applied_protection_services                0.445444  0.0  0.0  0.0   \n",
       "listing_number_of_bedrooms                           1.755499  0.0  0.0  1.0   \n",
       "listing_number_of_bathrooms                          1.312573  0.0  0.0  1.0   \n",
       "booking_lead_time                                   24.349579  0.0  0.0  2.0   \n",
       "booking_duration                                     4.851055  0.0  1.0  2.0   \n",
       "listing_occupancy_prior_to_tcr_in_30_days            9.260855  0.0  0.0  0.0   \n",
       "listing_known_occupancy_prior_to_tci_in_30_days      9.715511  0.0  0.0  0.0   \n",
       "lead_time_between_prior_known_check_in_to_tci_3...  11.424657  1.0  2.0  5.0   \n",
       "\n",
       "                                                     50%   75%   95%    99%  \\\n",
       "number_of_applied_upgraded_services                  2.0   4.0   5.0    6.0   \n",
       "number_of_applied_screening_services                 2.0   3.0   4.0    4.0   \n",
       "number_of_applied_deposit_management_services        1.0   1.0   1.0    1.0   \n",
       "number_of_applied_protection_services                1.0   1.0   1.0    1.0   \n",
       "listing_number_of_bedrooms                           2.0   3.0   5.0    8.0   \n",
       "listing_number_of_bathrooms                          1.0   2.0   4.0    6.0   \n",
       "booking_lead_time                                    9.0  25.0  69.0  113.0   \n",
       "booking_duration                                     3.0   5.0  10.0   28.0   \n",
       "listing_occupancy_prior_to_tcr_in_30_days            6.0  16.0  27.0   30.0   \n",
       "listing_known_occupancy_prior_to_tci_in_30_days      6.0  17.0  30.0   30.0   \n",
       "lead_time_between_prior_known_check_in_to_tci_3...  11.0  30.0  30.0   30.0   \n",
       "\n",
       "                                                      max  \n",
       "number_of_applied_upgraded_services                   7.0  \n",
       "number_of_applied_screening_services                  4.0  \n",
       "number_of_applied_deposit_management_services         2.0  \n",
       "number_of_applied_protection_services                 1.0  \n",
       "listing_number_of_bedrooms                           15.0  \n",
       "listing_number_of_bathrooms                          17.0  \n",
       "booking_lead_time                                   220.0  \n",
       "booking_duration                                    116.0  \n",
       "listing_occupancy_prior_to_tcr_in_30_days            30.0  \n",
       "listing_known_occupancy_prior_to_tci_in_30_days      30.0  \n",
       "lead_time_between_prior_known_check_in_to_tci_3...   30.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>freq/count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_contactless_booking</th>\n",
       "      <td>21384</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>13185</td>\n",
       "      <td>0.616582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_resolution_incident</th>\n",
       "      <td>21384</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>21127</td>\n",
       "      <td>0.987982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique    top   freq freq/count\n",
       "is_contactless_booking   21384      2  False  13185   0.616582\n",
       "has_resolution_incident  21384      2  False  21127   0.987982"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary statistics for numerical features\n",
    "display(df.describe(include= ['number'], percentiles=[.05,.25,.5,.75,.95,.99]).T)\n",
    "# Summary statistics for boolean features\n",
    "summary = df.describe(include= ['bool']).T\n",
    "summary['freq/count'] = summary['freq']/summary['count']\n",
    "display(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dg4OxtLRUWWZpaUl0dDTx8fFERESQkpKSaczt27fVkkNmpHFJCCHEP1Ze/VOgDpU3nOBMj6/zOo1cqbBwOwGLp+V1Grnm8f0Q7jwJzus0cs3dzoq6P/+W12nkyv6RPQl/dD+v08g1MwdnosPD8jqNXDMyM8f/WkBep5Er1Yt7cO7Og7xOI9e83J04dTswr9PINR9Pl39t/j6eLgSFvszrNHKtUEELnoT+e687dgXN8zqFf5wRI0YwcOBAlWXa2tp5lM3fRxqXhBBCCCGEEEIIIdRAW1v7izYmWVlZERISorIsJCQEIyMjdHV10dTURFNTM9MYKyurL5aXzLkkhBBCCCGEEEKI/xYNRd68vjAfHx8OHz6ssuzgwYP4+PgAkD9/fsqWLasSk5qayuHDh5UxX4I0LgkhhBBCCCGEEELkgZiYGC5fvszly5cBePDgAZcvXyYoKAhIH2bXoUMHZXzPnj25f/8+Q4cO5fbt2yxYsIANGzYwYMAAZczAgQNZvHgxK1eu5NatW/Tq1YvY2Fg6d+78xfZDhsUJIYQQQgghhBDiv0Xx5XsRqcP58+epUaOG8v3b+Zo6duzIihUreP78ubKhCcDJyYndu3czYMAA5syZg52dHUuWLKFu3brKmNatW/PixQvGjBlDcHAwpUqVYt++fRkm+VYnaVwSQgghhBBCCCGEyAPVq1cnLS0ty/UrVqzItMylS5eyrdfX1xdfX9/PTS/HpHFJCCGEEEIIIYQQ/ykKhcwC9HeSoy2EEEIIIYQQQgghck0al4QQQgghhBBCCCFErsmwOCGEEEIIIYQQQvy3/Esm9P6vkJ5LQgghhBBCCCGEECLXpOeSEEIIIYQQQggh/lNkQu+/lxxtIYQQQgghhBBCCJFr0rgkhBBCCCGEEEIIIXJNhsUJIYQQQgghhBDiv0VDJvT+O0nPJSGEEEIIIYQQQgiRa9K4JP6VqlevTv/+/fM6jUzdvn0bb29vdHR0KFWqVF6no/ThMXN0dGT27NmfVaefn98/ah+/BHWcaytWrMDExEQt+Xwp6jgfhBBCCCGE+MdQaOTN6/+UDIsTQs3Gjh2Lvr4+AQEBGBgY5HU6WTp37hz6+vp5nYb4h/g3nw9GhUti16Qd+k6eaJtZcHPacMLP/ZVtGeMipXHq0Ac9eycSw0J5vHklocf2qMRY122GbeN25DcxI/bRPQKXzSIm8NYX2QfL6g2wrtMULWNT4p485OG6RcQ+vJtprEJDE5v6LbDwqUF+E3Pig5/yeOtKom5cynWdn8ukSGnMSnihqatPYngooScPk/AiOMt4jfzaWJSrgqGTGxraOiTHRBN66gixjx/kus7PkZaWxuoVyziwZxexMTEULlacH/oNxMbOLttyu7dtZcuGdUSEh+Pk4kKPPv1w9ywMwKvoaNasXMal8+d5ERqCkYkJ3pUq822nruir+W9Dh6rlqFe6MAba2tx8EszcvX/xLCIqy/iVvdtjZWKYYfmO89f5Zf9xLI0N+d23faZlf9p8gL9u31db7pt27GT1xk2Eh0fg6uzMwN69KOrpkWns9j172XvoMPcfPgLAw82Vnp07qcSnpaWx+PdV7Ni7j1cxsZQoWoShfX2xt7VVW85vbdi0mT9WryYsPBw3V1eGDBxI0aJFMo0NvH+fhYuXcPv2bZ4HBzOgXz/atWmtErN85e8cPebPo0dBaGvnp0Tx4vj+8AOODg5qzx3Sj9XO9Wv469AB4uNicfEoTLvuvbC0tsmyzJ2b1zmwfStB9wOJigin19AfKVXeWyVmxfzZnPI/orKsSKnS9Bs1Tu35b169iqMH9hIXG4t74SJ0/qEPVjZZf9a3r19j95ZNPAi8S2R4OP1/HEM5n4qfXW9u89+65g+OHdxHXGwsbp5F6NCrd7bbCbhxjT1bN/Po3j0iI8LpM2IUZb3f5Z+cnMyW1b9z9cI5QoOD0dPTp0jJUrTs0BlTc3PJ/Y3tWzazce0awsPDcXFxpXf/AXgWyfx3F+DY0SOsXLKY4OBgbO3s6NazFxXenDfJycksX7yIs6dPEfzsGXr6+pQp50XXnj2xsCig1rzf2rZlMxvWrlbm36f/wI/mv3zJIoKDg7Gzs+P7nj8o8wdYuWwJRw8f4kVoKPnyaeHu4UGX73tQuGjRL5K/+O/6/21WE+IDKSkppKamfnY9gYGBVK5cGQcHB8zV/MdQnQoUKICenl5ep/G3SEpKyusU8szr169zFPdvPh80tXWJeXiP+0tn5Cheu4A1RYZPI+rGRS4N7cSz3Rtw6zkMk5LllTEWPrVw6tCHoE3LuDSsC7GP7lFs5Ey0jEzUnr9ZucoUatGFJ7vXc/3ngcQ9eYBnXz/yGRpnGm/XtD0Fq9Tl4brFXPXzJfTPfbj3HIGevVOu6/wchs4eFPCuzsuLJ3m09XcSw15gV78lmjpZnE8aGtg1aImWoRHPDu3gwYalBP+5n+TYmNzX+Zk2r1vLrq1b+KH/IKbP/w0dHR3GDB/M69eJWZb56+gRlvz2C207dGT2b4txcnFhzLDBREZEABAe9pKwsDC69OjF/KUr6D90BBfPnmXu9Klqzb2VTym+9irOvL1/0W/FFhKSkpjYtiFamppZlum7fDNtZq9Uvoav3pm+T7fSG41eRMeorG8zeyW/HztHXOJrzgUGqS33Q/7HmLtwEV2/bc+KBfNwc3ZiwI+jCI+IzDT+4pWr1KlenfnTJrNo9kwsCxSg/4iRhL58qYz5Y8NGNm7bwdC+fVg6dza6Ojr0HzGKxBxeC3PqwKFDzJ47l25du7BqxXLc3FzpM2AA4eHhmcYnJCRga2OD7w+9svzf4OKlS7Rs3pxlixcxf84ckpOT6dO/P/Hx8WrN/a3927ZwZM8u2nfvxfCJ09DW1mbuhLEkZXOsXickYufoRNtuPbKtu2ipMkxdvFL56tZ/iLrTZ9fmjRzYtZ0uP/Rl3PTZaOvoMGXMyGz/7iUmJFDIyYmOPXurtd7c2LNlEwd376BjL1/GTJuFto4OM/xGfzx/Rye+6/FDputfJybyKPAeTVq1ZdzMefiOGEXw0yfM+Vm9DXv/5tz9Dx9i4fx5fNupC78uWYazqysjBg0k4s21+0M3rl1j4jg/6jVsxK9Ll1OpShX8fhzBg/v3lft1704A33bsxIKlyxj780SeBAUxZvgwteb91tHDh/ht/lw6dOrCb0uW4+LqyrBBA4iIyPzac+PaNX4aN5b6DRuzcOkKKlWpypgfh/PgfqAyxs6+EH0GDGLxylXMWfArllbWDBvUX/n37N9MoVDkyev/lTQuic9SvXp1+vbty9ChQzEzM8PKygo/Pz8AHj58iEKh4PLly8r4yMhIFAoF/v7+APj7+6NQKNi/fz+lS5dGV1eXmjVrEhoayt69eylcuDBGRka0a9eOuLg4lW0nJyfj6+uLsbExFhYWjB49mrS0NOX6xMREBg8ejK2tLfr6+lSoUEG5XXg3VGnHjh0UKVIEbW1tgoKy/6c5NTWV8ePHY2dnh7a2NqVKlWLfvn3K9QqFggsXLjB+/HgUCoXyWGRn2LBhuLu7o6enh7OzM6NHj1ZpDHk79GzhwoXY29ujp6dHq1atiIp6d1e6U6dONG3alHHjxlGgQAGMjIzo2bNntn/kPxwGFRkZSbdu3ZTla9asyZUrV1TKTJ48GUtLSwwNDenatSsJCQkf3b+3Mhte1rRpUzp16qSS04QJE2jbti36+vrY2tryyy+/qJS5ffs2lStXRkdHhyJFinDo0CEUCgXbtm0D3p1369evp1q1aujo6LB69WrCwsJo27Yttra26OnpUbx4cdauXatSd2xsLB06dMDAwABra2tmzMjYWPGx8wrSz61ChQqhp6fHN998Q1hYWI6P05UrV6hRowaGhoYYGRlRtmxZzp8/r1x//PhxqlSpgq6uLvb29vTt25fY2NgMx7BDhw4YGRnRvXt3KlasyLBhqv/kvHjxAi0tLf78809luQ/Phx49emBpaYmOjg7FihVj165dOc5jwYIFuLm5oaOjg6WlJS1atMjxMfhUEZdPE7R+MWHn/sxRvPVXTUkIfc6DVfOJf/qI5/s38/K0P7YN3/UisG3UmuDDOwn130P804fcWzyNlNeJWNZopPb8rWt/TejxA7w8eZj45495sPpXUl8nUqBi7UzjLSrU4Nm+TURdv0DiyxBC/9xH5PULWNdpmus6P4dp8XJE3b5K9J3rvI4MI+T4AVKTkzD2KJZpvLFHcTS1dXl6YBvxIU9JjokmPvgJieEvcl3n50hLS2PHlo20+vY7vCtVxsnFhQHDfiT8ZRinjx/Psty2TRuo26ARtes1oJCjIz/0H4S2tg4H96X3gHNwcuZHvwmUr1gJaxtbSpYuw3ddu3H29ElSUpLVln/T8sVZe/wip+485EFoOFN3HMXcUI+KHo5ZlomKSyAiNl75quDmwLPwKK4GPQMgNS1NZX1EbDwVPZz481YgCUnqy33t5q00qV+fRnW/wsnBgaH9+qCtrc2u/QcyjR83YhjNmzTC3cUFx0L2jBjQj9S0VM5fugykf5brt26jU7s2VK3og6uzE2OGDuZlWBh/njiptrwB1qxdR9MmTWjSqBHOTk6MGDoUHW1tdrx3nXxf0SJF6NfHl6/q1CG/llamMfNmz6Jxw4a4ODvj7ubG2FGjCA4O4dbt22rNHdKP1eHdO2jQvBWlyntj5+hE5z4DiIwI5/LZ01mWK1amLE3bfkvpCj7Z1p9PSwtjU1PlS9299dLS0ti3Yytft2pLWW8fCjk503PAECLDw7hwOuvPumQ5L1p+1wkvn0pqrTc3+R/YuY0mLdtQpoIP9o5OfN9/EBHhYVw8fSrLciXKetH8246U/aC31Vt6+voMGT+R8pWrYm1nh6uHJ9/2+IGHgfcIexH6f587wOb166nfuDH1GjbEwcmJfoOHoK2jzf7dmf/ubt20Aa/yFWjVrj0Ojo506tYdV3d3tm/ZBIC+gQFTZs2hWs1a2BdyoEjRYvgOGMjdgABCQ9Tf23bT+nU0aNyEeg0b4ejkRP/BQ9HW0WZfFvlveZN/6zf5d+7WHTd3D7Zt2ayMqVXnK8qW88LGxhZHJ2d69Un/n+5+YGCmdQqRFWlcEp9t5cqV6Ovrc+bMGaZOncr48eM5ePDgJ9Xh5+fH/PnzOXnyJI8fP6ZVq1bMnj2bNWvWsHv3bg4cOMC8efMybDdfvnycPXuWOXPmMHPmTJYsWaJc7+vry6lTp1i3bh1Xr16lZcuW1KtXj7t33w0LiYuLY8qUKSxZsoQbN25QsGDBbPOcM2cOM2bMYPr06Vy9epW6devSpEkTZZ3Pnz+naNGiDBo0iOfPnzN48OCP7ruhoSErVqzg5s2bzJkzh8WLFzNr1iyVmHv37rFhwwZ27tzJvn37uHTpEj/8oHrn5/Dhw9y6dQt/f3/Wrl3Lli1bGDcu53d7WrZsqWzUu3DhAmXKlKFWrVrKu7AbNmzAz8+PiRMncv78eaytrVmwYEGO68+padOmUbJkSS5dusTw4cPp16+f8nxKSUmhadOm6OnpcebMGRYtWsTIkSMzredt2Vu3blG3bl0SEhIoW7Ysu3fv5vr163Tv3p3vvvuOs2fPKssMGTKEY8eOsX37dg4cOIC/vz8XL15Uqfdj59WZM2fo2rUrvr6+XL58mRo1avDTTz/leP/bt2+PnZ0d586d48KFCwwfPhytN19EAgMDqVevHs2bN+fq1ausX7+e48eP4+vrq1LH9OnTlcdw9OjRtG/fnnXr1qk0vq5fvx4bGxuqVKmSIYfU1FTq16/PiRMn+OOPP7h58yaTJ09G801PiI/lcf78efr27cv48eMJCAhg3759VK1aNcfH4EszdCtG5LXzKssirpzB0D294UKhmQ8DZw8ir517F5CWRuS188oYdVFo5kO/kAvRt95ryE1LI+r2FQydMx8apMiXj9Qk1Ybj1KTXGLoUznWduaahgY6FFXFPH6ksjnv6CJ2CmQ+tMSjkSkLIMywr1cal/Q84Nu+EWakK8PZOXy7q/Bwhz58TER5OqTJllcv0DQxwL1yY2zdvZFomKSmJe3fuUPK9MhoaGpQqU5aALMoAxMbEoqenh6amemYlsDIxxNxAn4sPnyiXxSW+5vbTUArbWuWojnwaGtQs5sb+K1k3YLhaWeBqZcH+y+pr5EhKSiLg7l28SpdSLtPQ0MCrdCmu38rZ8NOExESSk1MwMkwf4vcsOJiw8Ai8ypRWxhjo61PE04Prt9Sb++2AAMp7lVPJvbyXF9euX1fbdmJi0hvsjYyM1FbnWy9DQ4iOjKBwiZLKZbr6+ji5uXP/TsBn13/nxnUGd/mOMX17sXrRAmJeRX92ne97ERJMVEQExUq9+6z19PVxcffk7u3cD1/+UvVmtZ0iJUt9sB0PAgPUO/w6PjYWhUKBnr56Gvj+zbknJSVx504AZcp6KZdpaGhQplw5bt7I/Hf35vUblClXTmVZufIVuHU9m2t9bAwKhQJ9g4zDjz/Hu/xVrz1lynllk/91ypbzUllWrnwFbmZxrUpKSmL3ju3oGxjg4uqqvuTF/wWZc0l8thIlSjB27FgA3NzcmD9/PocPH8bNzS3Hdfz0009UqpR+F6lr166MGDGCwMBAnJ2dAWjRogVHjx5V6X1hb2/PrFmzUCgUeHh4cO3aNWbNmsX3339PUFAQy5cvJygoCBub9C8jgwcPZt++fSxfvpyJEycC6RfQBQsWULJkSXJi+vTpDBs2jDZt2gAwZcoUjh49yuzZs/nll1+wsrIiX758GBgYYGWVs3/sR40apfzZ0dGRwYMHs27dOoYOHapcnpCQwO+//47tmzkj5s2bR8OGDZkxY4ZyO/nz52fZsmXo6elRtGhRxo8fz5AhQ5gwYQIaGtm3Ix8/fpyzZ88SGhqKtra2cl+3bdvGpk2b6N69O7Nnz6Zr16507doVSP/MDh069Em9l3KiUqVKDB8+HAB3d3dOnDjBrFmzqFOnDgcPHiQwMBB/f3/lfv/888/UqVMnQz39+/enWbNmKsveb+zr06cP+/fvZ8OGDZQvX56YmBiWLl3KH3/8Qa1atYD0Bky79+Zcycl5NWfOHOrVq6f8/Nzd3Tl58qRKD7fsBAUFMWTIEDw9PQFUfo8mTZpE+/btlT3A3NzcmDt3LtWqVePXX39FR0cHgJo1azJo0CBluVatWtG/f39lbyOANWvW0LZt20y77h46dIizZ89y69Yt3N3dAZS/iznJIygoCH19fRo1aoShoSEODg6ULl06w3bySn4TM5KiVLuPJ0VFkE/PAA2t/OQzMEKhmY+kyA9iIsPRsymk1lzSt6VJ0qtI1W1FR6Jrlfl8P1E3L2FV+2ui794g8UUwRp4lMC3tg+LNBJK5qTO3NHV0UWhokByv2rM0JT6O/CZmmZbRMjJGy6AQ0YE3ebJ/M/mNTLCsVAeFhiZhF0/mqs7P8XYogYmpat0mpqZZDjOIjooiNTUFU1PTDGWePM68B2xUVCTr//idug0bqyHrdGb66cMEI2NVh01FxsZjZqCbozoqejhhoKPNgatZNyjUK1WYRy/Cufk0JPfJfiAyOpqU1FTMPjiGZqamPHr8JItSqhYsWUYBczNlY1JYePoQDjOTjHWGqXF4R2RkJCkpKZiZqZ4zZmZmPHz0KItSnyY1NZWZs2dTskQJXF1c1FLn+6LfHA+jDx42YWRsQlTk5x2roqXKULqCDxYFLXkREsy2NauY9/M4hv08FY1shmt+isis8jcxIeozPusvVe+H3tZl/MG5qu7tvH79mg2/L6dClWroqmno+78696hIUlNSMP3gd9fU1IzHjzK/dkeEh2HyYbyZGeHhmfdKf52YyJJff6VG7dpqn8sy+/wzv/aEh4dhaqb6WZmamWbI/9SJE/w0bgyJCQmYmZszdeZsjP/hD6PJkf/jybXzgjQuic9WokQJlffW1taEhn5a99X367C0tFQOEXt/2fs9TAC8vb1Vvhj7+PgwY8YMUlJSuHbtGikpKcovxm8lJiaqzHWQP3/+DPlnJTo6mmfPnikbwd6qVKlShuFjn2L9+vXMnTuXwMBAYmJiSE5OznCXslChQsqGJUjf19TUVAICApSNLCVLllSZM8fHx4eYmBgeP36Mw0cmA71y5QoxMTEZ5oGIj48n8E2X2Fu3btGzZ0+V9T4+Phw9evTTdzobPj4+Gd6/Ha4VEBCAvb29SsNd+fLlyUy5D+4ypaSkMHHiRDZs2MDTp095/fo1iYmJymMWGBjI69evqVChgrKMmZkZHh7venrk5Ly6desW33zzTYZ9yGnj0sCBA+nWrRurVq2idu3atGzZEpc3XyyuXLnC1atXWb16tTI+LS2N1NRUHjx4QOHChTPd9wIFCvDVV1+xevVqqlSpwoMHDzh16hQLFy7MNIfLly9jZ2eXYT/f+lgederUwcHBAWdnZ+rVq0e9evX45ptvsp3TKTExkcRE1flt3jZ0ClWP1i/B6bvelBz3C6RBwotgXp48TIGKtfI6tRxRKBSkJMQR8tcBSEsj8WUI+fQNMSvhRdhF9Q5dyoz/oYP8MuvdkNcxEyd/8W3GxcYy/sfh2Ds40K5j51zXU6OoG/0avOsFOHr9nmyic6ZuSU/OBQYRHhOX6fr8+TSpUdSVNccvfPa21On3dRs4eOwYC6ZNRTt//rxOR+2mTp9B4P37LF74m1rqO/OnP6sXvett7DtijFrqzYxX5XfnqK2DI7YOjozq3Z2AG9dVekp9ihP+R1j2y1zl+8Fjxn92nn+nk/5HWfnrux74A0ardx6hzCQnJ7Ng6iRIS6NjL9+PF8jCvzn3v1tycjITxqZP09F3kPrnGfuSSpUpw6JlK4mKimT3zh1MGDua+QsXY2qq/ps64r9LGpfEZ9P6YO4AhUJBamqqsrfM+0NxsppY+f06FApFlnXmVExMDJqamly4cEE5lOet95/gpqurm6eTrp06dYr27dszbtw46tati7GxMevWrct0rp8vKSYmBmtr6wxzBwGYqOmuhYaGhsq5AF92ou0P7xZNmzaNOXPmMHv2bIoXL46+vj79+/f/pAk6c3pefQ4/Pz/atWvH7t272bt3L2PHjmXdunV88803xMTE0KNHD/r27ZuhXKFC73rUZHanrH379vTt25d58+axZs0aihcvTvHixTPNQVc3+x4PH8sjf/78XLx4EX9/fw4cOMCYMWPw8/Pj3LlzWZ5PkyZNyjCMc+zYsah/hiB4HRmOlrHqP0taxqYkx8WQmvSapOhI0lKS0fqgl4yWiRmvIzPvyZJbyTHRpKWkoGVoorotIxOSojK/A5wcE83dXyehyKdFPgNDkiLDsW/WgYSXIbmuM7dSEuJJS00ln65qw6Gmrh7JcbGZlkmOiyUtNRXeux68jggjn54BaGjkqs5PUb5iJdzfNMTCu+tQZEQ4Zu81sEdGRODskvmQACNjYzQ0NDNMABsZEZHhjnJcXBxjhw9BV0+PkeN/Il++3P/rdfruQwKWvOs99HbSbhN9XZXGIRN9XQJDPj7XW0EjA0o72TJhc+ZzHAFU8XRGWysfh67dyXXemTExMkJTQ4PwD45heEQE5h/cZf/Q6o2bWLV+A3OnTMTV+d1E9m/LhUdGYGH+7nMIj4jAXY29f0xMTNDU1MwweXd4eDjm5p//RWzq9Bn8deIEi35dgOVHhuvnVEmv8ji5vbthkJycPndWdGQkxu99eYyOisTe0TlD+c9RwNIKAyMjXgQ/z3XjUpny3ri4eyrfJ78ZGhwdGYmp2bvf2+jISAo55z5/kzc96dRdb+nyFXB572ZV8pvrTlRkhEqvmOjISAo5ff7xf9s4E/YilGETJn1Wz59/c+4fMjY2QUNTk4gPfncjIsIxzeJ319TMnMgP48PDMTNTvSGbnJzMT2NGExocwrQ5c7/IE3izy98si/zNzMyJCFe9zkaER2TIX1dXF1s7O2zt7ChStBgd2rZi765dtPuug3p34u+m8f87uXZekH5i4ospUCD98ZvPnz9XLnt/cu/PdebMGZX3p0+fxs3NDU1NTUqXLk1KSgqhoaG4urqqvHI6XO1DRkZG2NjYcOLECZXlJ06coEg2j//MzsmTJ3FwcGDkyJGUK1cONzc3HmXSrTUoKIhnz54p358+fRoNDQ2VXjVXrlxReaLM6dOnMTAwwN7e/qN5lClThuDgYPLly5fheFlYWABQuHDhTI95ThUoUEDlXEhJSeF6JuO9P6zz9OnTyh45Hh4ePH78mJCQd1+uzp07R06cOHGCr7/+mm+//ZaSJUvi7OzMnTvvviy5uLigpaWlso8REREqMTk5rz73OEH6ULoBAwZw4MABmjVrxvLly4H0z+nmzZsZtu3q6kr+j9y5//rrr0lISGDfvn2sWbOG9u0zf8w4pPckfPLkicq+vy8neeTLl4/atWszdepUrl69ysOHDzly5Eim9QGMGDGCqKgoldeIESM+dqhy5dXd65gUL6uyzKSEF6/upJ+PaSnJxNwPwKTYez3AFApMipVVxqhLWkoysUGBGBV+rwelQoGxZwle3c9+3pO05CSSIsNRaGhiVroiEVfOfHadnyw1lYSXwejZqvaO1LNxICH0WaZF4oOfkv+Dp+5pGZumPy0uNTVXdX4KPT09bGztlK9CDo6Ymplx5b351eJiY7lz6xaeRTJ/DLOWlhau7u5cvfSuN09qaipXLl3E470ycbGxjBk6iHxaWoyaMJH8+T+vN1786ySeRUQrX49eRhAWE0tpx3c9W/Xya+FpW5BbTz8+kexXJT2JjIvnzN2sh3LVLVWY03ceEhWn3iHQWlpaeLi5cf69/wtSU1M5f/kyxd5r/PvQHxs2snz1WmZNnEDhD3pX2lhZYW5mqpzgG9If1HDzdgDFCnuiLlpaWnh6eHDuvOrnf+78eYoXy/28bGlpaUydPgP/Y8f4df48bG3UN8eYjq4eBa1tlC9rO3uMTEy5fe1dz+v4uDge3L2Ds7t652aLCHtJ7KtXGJtm32iYHV09PaxsbJQv20IOGJuacuPKZWVMXFwsgXdu4+aZ9fnzMQUsrb5Ivbp6elha2yhfNvaFMDY15eZV1eMfeCcAF4/cbwfeNc6EPH/GkPETMfjMObv+zbl/SEtLC3d3Dy5deDfvYmpqKpcuXKBI0cx/d4sUK8qlC6o9Ny+eP0fhYu+u9W8blp4+ecyUWbMxMlb/k1lV81e99ly6cD6b/Itx8YLqPJMXzp+lyEeuVampqSQl5fwGrBAgPZfEF6Srq4u3tzeTJ0/GycmJ0NBQlfmFPldQUBADBw6kR48eXLx4kXnz5il7/Li7u9O+fXs6dOjAjBkzKF26NC9evODw4cOUKFGChg0b5mqbQ4YMYezYsbi4uFCqVCmWL1/O5cuXVYYHfQo3NzeCgoJYt24dXl5e7N69m61bt2aI09HRoWPHjkyfPp3o6Gj69u1Lq1atVBrKXr9+TdeuXRk1ahQPHz5k7Nix+Pr6fnS+JYDatWvj4+ND06ZNmTp1Ku7u7jx79ozdu3fzzTffUK5cOfr160enTp0oV64clSpVYvXq1dy4cUNl+GJ2atasycCBA9m9ezcuLi7MnDmTyMjIDHEnTpxg6tSpNG3alIMHD7Jx40Z2794NQJ06dXBxcaFjx45MnTqVV69eKc+pj/VAc3NzY9OmTZw8eRJTU1NmzpxJSEiIsmHQwMCArl27MmTIEMzNzSlYsCAjR45UOX45Oa/69u1LpUqVmD59Ol9//TX79+/P8ZC4+Ph4hgwZQosWLXBycuLJkyecO3eO5s2bA+lPFvT29sbX15du3bqhr6/PzZs3OXjwIPPnz8+2bn19fZo2bcro0aO5desWbdu2zTK2WrVqVK1alebNmzNz5kxcXV25ffs2CoWCevXqfTSPXbt2cf/+fapWrYqpqSl79uwhNTVVpTH0Q9ra2rkeBqehrasyl5BOQRv0HdxIjokmMSwEh7Y90Taz4M4v6ROrPz+wDeu6zXFs/wMhR3dhXKwsBXxqcmPyuy7sT3etx733SGLu3+bVvZvYNGiFprYOIf67c5Vjdp4f2o5Lp37EPrxHzMO7WNVqjEZ+HV6cPASAc6f+JEWG8XjbKgD0Hd3Jb2pG3OMH5Dcxx7ZxG1AoeL5/a47rVKeIa+exqtaAhBfBJLx4jmmxcmhoaRH1piHOqnoDkmNf8fLcXwBE3rqMSdHSFKxYi4gbF8lvZIp5KW8iblzMcZ3qpFAoaNKsJetX/46NnR2WVlb8sXwZZhbmeFeurIwbOXgAPpWr0Khp+lxuTVu0YtaUSbi6e+Lu6cn2zZtISIindt36wJuGpWGDSUxIYNCPo4iPiyX+Tc8rI2OTDL0fc2vb2Wu0rVSWp+FRBEe+omM1L8JexXEy4KEyZnK7Rpy884Ad599NQKsAvirpwaGrd0j9oFfpWzamRhQvZM3odZ8//C4zbZt/w4RpM/B0c6OopwfrtmwjISGRRnXT59EbN3U6BczN+aFr+lDCVes3sPj3VYwbPgxrS0vC3ty919XVRe9NT+TW3zRlxZp12NvaYm1lyeIVq7AwN6dqpcyfUJVb7dq2YdyEnyjs6UnRokVYu2498QkJNG6U/kTJsePGU6BAAXx/6AWk95C7/+BB+s/Jybx48YKAO3fQ09XD3j79+jVl+nT2HzjI9ClT0NPT4+WbJ40a6Bugo6PeYcIKhYJaDZuwZ/MGClrbYFHQku3rVmNiakap8t7KuJl+oyhdwZsa9dP3KyE+nhfB724UvQwJ4fGD++gbGGJWoAAJ8fHs2riOMt4+GJmY8iI4mC1/rKCAlTVFSpVRa/71mnzDtvVrsbSxoaClFZv++B0TM3PKer/7rCeOHE45n4p81aiJMv+Q5+8aqV+EBPPofiD6BoZYFCyY43rVkf9XjZuyc8M6rKxtsLC0ZMuaVZiamVPG+930AFNGj6Csd0Vqv5mr7cP8X4aE8Oh+IAaGhpgXKEhycjK/TJnIo8B79B/tR2pqCpFv5o4zMDAkXxZPKvx/yR2geevWTJ34M+6enngULsLWjRtIiE+gboP07wZTfpqAhYUFXXum/+5+06IVg/r0ZuO6tVTwqYj/4UPcuX2b/kPS54FNTk5m/OiR3LtzhwlTppKamkr4m99dQyOjDKMxPleL1m2YMvEn3D098SxchM0b17/JP/13dPJP47GwKEC3N/k3a9GKAX1+YMO6NXj7VOTom/wHvsk/Pj6e1b+vpGLlypibmxMVFcX2LZt5+fIl1WrUVGvu4r9PGpfEF7Vs2TK6du1K2bJl8fDwYOrUqXz11VdqqbtDhw7Ex8dTvnx5NDU16devH927d1euX758OT/99BODBg3i6dOnWFhY4O3tTaNGuX+UeN++fYmKimLQoEGEhoZSpEgRduzY8UmTl7+vSZMmDBgwAF9fXxITE2nYsCGjR4/Gz89PJc7V1ZVmzZrRoEEDwsPDadSoUYYntdWqVQs3NzeqVq1KYmIibdu2zVBPVhQKBXv27GHkyJF07tyZFy9eYGVlRdWqVbG0tASgdevWBAYGMnToUBISEmjevDm9evVi//79OdpGly5duHLlCh06dCBfvnwMGDCAGjVqZIgbNGgQ58+fZ9y4cRgZGTFz5kzq1q0LgKamJtu2baNbt254eXnh7OzMtGnTaNy4sXIy66yMGjWK+/fvU7duXfT09OjevTtNmzYlKipKGTNt2jRiYmJo3LgxhoaGDBo0SGU9fPy88vb2ZvHixYwdO5YxY8ZQu3ZtRo0axYQJEz56jDQ1NQkLC6NDhw6EhIRgYWFBs2bNlMPFSpQowbFjxxg5ciRVqlQhLS0NFxcXWrdu/dG6IX1oXIMGDahatarKMLrMbN68mcGDB9O2bVtiY2NxdXVl8uTJOcrDxMSELVu24OfnR0JCAm5ubqxdu5aiRTPvBfK5DF08Ke73rnHNuWP6cL0Q/z3cXfAz+U3N0bawVK5PfPGcm5OH4NSxLzYNWpIY9oK7v00h8sq7ed1enjqMlpEJhVp1I7+JGbEP73J94iC1DysDCD9/HC0DI+yatEPLyJS4Jw+4PXccya/Szz1tMwtIezcsWENLC/sm36JdwJKUxAQir10gcNlsUuJjc1ynOr26H4Cmjh4WZSuhqadPYlgoT/ZuIuXNhNxa+oYqQ+CSY1/xZO8mCnrXwLFZJ5LjYoi4cYHw947/x+pUt+Zt2pKQEM/8mdOJjYmhSPHijJs0TaWnUfCzZ0S/dz2oUqMmUVGRrF6xjIiIcJxdXBk3eZpyWFzg3TsE3LoJQPfv2qlsb8nqdVhaWasl9w2nLqOjlY9+DaphoJOfG4+DGbluN0kpKcoYa1NjjD4Y7lrayQ5LY8NsnxJXt6QnL6NjuHD/sVpy/VDt6tWIiIpiye9/EBYRjpuzC7N+nqCc5DskNBSN924cbNm1m6SkZH6c8LNKPV2/bU+3Dt8C8G2rlsQnJDB59lxiYmIoUawosyZOUPu8TF/Vrk1kRCQLlywmLCwcdzc35s6aifmbzz84JATFezcnXrx8ybcdOynf/7FmDX+sWUOZ0qVZuOAXADZvSW8g7tm7t8q2xowaSeNc3hTLTt2mzXidmMAfC38hLjYWV88i9B3lh9Z7x+plSDAx0e+e9PYo8B4z/d49pXXjyqUA+FSvSSff/mhoaPD00UNO+x8hLi4WE1MzCpcsxddt2qv9S3aj5i1JTEhg2fy5xMXG4F6kKEPH/aTSkzc0+Bmvot/93t6/d4eJP757OMzqpYsAqFKzNj0GDM5xverQoFkLEhMSWL5gXvp2Chdl0NjxH+T/XCX/B/fuMmXUcOX7tcsWA1CpZm2+7zeQiLAwLp1N7y09pr/qXEXDfppM4eI5m2f0v5x79Vq1iYyMZOXSJUSEh+Pi6sbE6TOU1+7QkBCVG5ZFixdnxFg/VixexPJFC7G1s8Nv4iSc3txcffniBaeOHwegZ+dOKtuaPnceJUurr1EVoEat2kRFRrJi6WJl/pOnz1Q+YCA9/3fXnqLFizNy7DiWLV7Esjf5j584GSfn9KHCmhoaPA56hN+oPURHRWFkZIxHYU9mz1+AoxqGOea1vJz+5P+RIu3DSVCEEP8ofn5+bNu2LdshhZ06dSIyMpJt27b9bXl9CY6OjvTv31/5FLKcOHHiBJUrV+bevXvKia/Ff8fxVpU+HvQPVXnDCc70+Dqv08iVCgu3E7B4Wl6nkWse3w/hzpOPDwv7p3K3s6Luz+qZyPnvtn9kT8If3c/rNHLNzMGZ6CyeAvVvYGRmjv81NQ9//ZtUL+7BuTsP8jqNXPNyd+LU7cC8TiPXfDxd/rX5+3i6EBT6Mq/TyLVCBS14Evrvve7YFTT/eFAeOd2tQZ5s13vJl+nx+08nPZeEEP8qW7duxcDAADc3N+7du0e/fv2oVKmSNCwJIYQQQggh3lHIFNN/JznaQrzHwMAgy9dff/31yfVNnDgxy/rq16//BfYg76j72GXl1atX9O7dG09PTzp16oSXlxfbt29XW/1fWtGiRbM8Trmdu0sIIYQQQggh8pL0XBLiPdkNPbO1tc1yXVZ69uxJq1atMl33sce9v+Xn5/fRuZNWrFjxiZmpnzqO3cOHDz8a06FDBzp0+Pc+FnXPnj3KR59/6O38VkIIIYQQQojPI3Mu/b2kcUmI97i6uqq1PjMzM+UEe/916j52/1UODg4fDxJCCCGEEEKIfxEZFieEEEIIIYQQQgghck16LgkhhBBCCCGEEOK/RSb0/lvJ0RZCCCGEEEIIIYQQuSY9l4QQQgghhBBCCPHfoiETev+dpOeSEEIIIYQQQgghhMg1aVwSQgghhBBCCCGEELkmw+KEEEIIIYQQQgjxn6KQCb3/VnK0hRBCCCGEEEIIIUSuSc8lIYQQQgghhBBC/LcoZELvv5P0XBJCCCGEEEIIIYQQuSY9l4QQQgghhBBCCPHfIj2X/lbSc0kIIYQQQgghhBBC5Jo0LgkhhBBCCCGEEEKIXJNhcUIIIYQQQgghhPhPUWhIX5q/kxxtIYQQQgghhBBCCJFrirS0tLS8TkIIIYQQQgghhBBCXc71a50n2/Wasz5PtpvXZFicEEKIf6wzPb7O6xRyrcLC7RxvVSmv08iVyhtOELhhaV6nkWsurbqy5s9zeZ1GrrWr6kV1v/l5nUau+Pv5EvEkKK/TyDVTu0KERUXndRq5Zm5shP+1gLxOI1eqF/fgTMCDvE4j1yp4OHE64H5ep5Fr3h7OnLh1L6/TyJVKhV15FPIir9PINQfLAjwJDcvrNHLNrqB5Xqcg/iFkWJwQQgghhBBCCCGEyDXpuSSEEEIIIYQQQoj/FIVC+tL8neRoCyGEEEIIIYQQQohck55LQgghhBBCCCGE+G9RKPI6g/8r0nNJCCGEEEIIIYQQQuSa9FwSQgghhBBCCCHEf4vMufS3kqMthBBCCCGEEEIIIXJNGpeEEEIIIYQQQgghRK7JsDghhBBCCCGEEEL8pyg0ZELvv5P0XBJCCCGEEEIIIYQQuSY9l4QQQgghhBBCCPHfopCeS38n6bkkhBBCCCGEEEIIIXJNGpeEEEIIIYQQQgghRK7JsDghhBBCCCGEEEL8tyikL83fSY62+FeqXr06/fv3z+s0MnX79m28vb3R0dGhVKlSeZ2O0ofHzNHRkdmzZ39WnX5+fv+offwSOnXqRNOmTfM6jS/u/+GzFEIIIYQQQnwZ0nNJCDUbO3Ys+vr6BAQEYGBgkNfpZOncuXPo6+vndRr/eHPmzCEtLS2v0/jiBg8eTJ8+ffI6jVyzrN4A6zpN0TI2Je7JQx6uW0Tsw7uZxio0NLGp3wILnxrkNzEnPvgpj7euJOrGpVzX+TmMCpfErkk79J080Taz4Oa04YSf+yvbMsZFSuPUoQ969k4khoXyePNKQo/tUYmxrtsM28btyG9iRuyjewQum0VM4C215w+w88xFNh8/S0RMLE5WBenVsDYedtaZxh68eI1ZW/eqLNPKp8n2sYMASE5J4fdDf3Huzn2CI6LQ18lPKWdHOn9VFXMjwy+Sf1paGv47NnPxr6MkxMVh7+pOw/adMbe0yrLMX3t2cPviOV4GPydf/vzYu7hRu3lrLKxslDHhoSEc3LiGoHt3SE5OwrVoCeq364iBkbFa8+9cozyNyhTFQEeb64+fM3OXP0/Do7KM11Ao6FS9PHVKeGBmoMfLV7Hsu3yLVX+eV8ZUKexMk3LFcLcuiLGeDt1+W8e94JdqzRtg07bt/LFhI+Hh4bi6uDCoT2+KenpmGnv/4UMWrVjJ7Tt3CQ4Jof8PvWjTvJlKTEpKCkt+X8W+Q4cJDw/HwtychnW/ovO37VGoeWLXzRs3sPqPPwgPC8PVzY2Bg4dQpGjRLOOPHDrEooW/Efz8OXb29vzg24eKlSop18fFxfHrL/P589gxoqKisLGxoWWr1nzTvLla834rLS2NnevX8NehA8THxeLiUZh23XthaW2TZZk7N69zYPtWgu4HEhURTq+hP1KqvLdKzIr5sznlf0RlWZFSpek3apza89+yZhX+B/YSFxuLW+EidOrVBysb2yzL3L5+jT1bN/Ew8C6R4eH0+3EMZb0rqsScO3mco/v28CDwLrGvXjFh9i84OLuoNfe3+W9dswr/A/uU+Xfs5fvR/Pdu3cTDwHtEhofT98fRGfI/f/IER/bt5mHgPWJfvWL87Plqzz8tLY1ta//gz4P7iYuNxdWzMB169sYym9wDblxn39bNPAy8R1REOL7DR1HG20clZtva1Zw9/ifhL1+QL18+HFxcafZtB1zcM78m5NaOLZvZuG4t4eHhOLu40LvfADyLFMky/s+jR1ixdAkhwcHY2trRrWcvyvuk556cnMyKxYs4e/o0z58/Q19fnzLlytG1Ry/MLSzUmvdb27ZsZsPa1YSHh+Pi4kqf/gOzzf/Y0SMsX7KI4OBg7Ozs+L7nD1TweXferFy2hKOHD/EiNJR8+bRw9/Cgy/c9KJzN9ezfQt3XfZE96bkkxBspKSmkpqZ+dj2BgYFUrlwZBwcHzM3N1ZDZl1GgQAH09PTyOo1cef369d+2LWNjY0xMTP627albTo+VgYHBP/p8zY5ZucoUatGFJ7vXc/3ngcQ9eYBnXz/yGWb+Bd6uaXsKVqnLw3WLuernS+if+3DvOQI9e6dc1/k5NLV1iXl4j/tLZ+QoXruANUWGTyPqxkUuDe3Es90bcOs5DJOS5ZUxFj61cOrQh6BNy7g0rAuxj+5RbORMtIxM1J7/sWu3WLz3KO1qVGJer444WxVg9MoNRMbEZllGTzs/fwz9QflaMaincl1iUjL3nofQtnpF5vXqwKi23/AkLJxxq7eoPfe3TuzbxZnDB2j4bRe6/TiO/Pm1+WP2FJKTsv79eXTnFl416tB1hB/fDRhGakoyf8yawuvEBABeJybwx+wpoFDQYdCPdBk2lpSUFNbOm0GaGv7WvNW2UhmaVyjJzF3+9FqykfjXSUz7rgn582lmXaZyGb72KsacPcfo+MtqFh06SdtKZWhWoYQyRkdLi2tBz1l06KTacv3QwaP+zPltId06fMvK337FzcWZ/sNGEB4RkWl8QkIittbW9O7WFXMzs0xjVq1bz5YdOxncx5e1y5fS+/tu/LF+Axu2blNr7ocOHmDu7Nl06daN5b+vwtXNjQF9+xAeHp5p/LWrVxg7ehSNm3zNilV/ULVaNYYPGUxg4D1lzNzZszh96hRjx41n7foNtGrThpnTp/HXn8fUmvtb+7dt4cieXbTv3ovhE6ehra3N3AljScrm78brhETsHJ1o261HtnUXLVWGqYtXKl/d+g9Rd/rs3rKRg7u206lXX8ZOm422tg7Txo7M9u9eYmIChZyc6NCjd5YxrxMTcC9SlNYdu6g95/ft2bKRg7t20KlXH8a8yX/62FEfzd/eyZnvevyQbYx7kaK0+oL57926iUO7dtKhZ29GTZ2Jto4OM8aNzvbcSUxIwN7JiW979MoyxsrGlvbdezJ+zi+MmDQNi4KWzPQbTXRU1o3ln8r/8GEW/jKfbzt1ZsGSpTi7uvLj4IFEZHHduXHtGhPHj6New0b8umQZFatUwW/kCB7cv6/cr7t379C+Y0cWLFnG2J9+5nFQEGNGDFNbzu87evgQv82fS4dOXfhtyXJcXF0ZNmgAERGZX3tuXLvGT+PGUr9hYxYuXUGlKlUZ8+NwHtwPVMbY2Reiz4BBLF65ijkLfsXSypphg/oTmcUxESIr0rgkPkv16tXp27cvQ4cOxczMDCsrK/z8/AB4+PAhCoWCy5cvK+MjIyNRKBT4+/sD4O/vj0KhYP/+/ZQuXRpdXV1q1qxJaGgoe/fupXDhwhgZGdGuXTvi4uJUtp2cnIyvry/GxsZYWFgwevRolR4miYmJDB48GFtbW/T19alQoYJyuwArVqzAxMSEHTt2UKRIEbS1tQkKCsp2f1NTUxk/fjx2dnZoa2tTqlQp9u3bp1yvUCi4cOEC48ePR6FQKI9FdoYNG4a7uzt6eno4OzszevRokpKSlOvfDldauHAh9vb26Onp0apVK6Le+0P7dujWuHHjKFCgAEZGRvTs2TPbf1A+HBYXGRlJt27dlOVr1qzJlStXVMpMnjwZS0tLDA0N6dq1KwkJCR/dv7f8/f0pX748+vr6mJiYUKlSJR49eqRcv3PnTry8vNDR0cHCwoJvvvlGJdcJEybQoUMHjIyM6N69OwDHjx+nSpUq6OrqYm9vT9++fYmNffeFNqfnwP79+ylcuDAGBgbUq1eP58+fZzi2b2V3zr91+/ZtKleujI6ODkWKFOHQoUMoFAq2bdv20eP0+vVrfH19sba2RkdHBwcHByZNmqRc/7HP6e35smTJEpycnNDR0WHRokXY2NhkaDz9+uuv6dKli0q59y1btoyiRYuira2NtbU1vr6+Oc7jypUr1KhRA0NDQ4yMjChbtiznz5/nS7Cu/TWhxw/w8uRh4p8/5sHqX0l9nUiBirUzjbeoUINn+zYRdf0CiS9DCP1zH5HXL2Bdp2mu6/wcEZdPE7R+MWHn/sxRvPVXTUkIfc6DVfOJf/qI5/s38/K0P7YNWytjbBu1JvjwTkL99xD/9CH3Fk8j5XUiljUaqT3/rSfPU69cCb4qU5xCBS3wbVwXbS0tDly8lmUZhUKBmaGB8mVq8K4Xpb6ONhM7taZqcU/sCpjjaW/DDw1rc+9ZCKGR0WrPPy0tjTOH91G14dd4liqLpV0hmnbpyavISG5fupBluW/7D6NUpaoUtLXDyt6Brzv3ICo8jOePHgLw+N5dIl++oGnn7lja2WNpZ0/Tzj149ugBD27fVFv+LbxLsurP85wIeMD9kDAmbT2EhaE+lT2dsyxTzN6a47cfcPruI4IjX3HsZiDnAh9T2NZSGXPwagC/HzvHhfuP1Zbrh9Zu2szXDerTqF49nBwdGNa/Hzra2uzatz/T+CKeHvTp0Z06NWugpaWVacy1GzepWrEilbwrYGNlRc1qVSlfriw3bweoNfd1a9bQpGlTGjVugpOzM0OHj0BbR4ddO3dkGr9h3ToqePvQ/rvvcHRyonvPXnh4erJ5w8Z3uV+9SoOGDSlTtizWNjY0/aYZrm5u3LyhvvPlrbS0NA7v3kGD5q0oVd4bO0cnOvcZQGREOJfPns6yXLEyZWna9ltKV/DJMgYgn5YWxqamype+mntyp6WlsX/HVpq0aktZbx8KOTnTY8AQIsPDuHg66wbRkmW9aPFtJ8r5VMoyplKN2jRt056iJUurNef3pee/jcat2lDG24dCTk50HzA4h/l3/Ej+tb5o/mlpaRzcuZ3GrVpTuoIP9o5OdOs3iMjwcC6eOZVluRJly9GsfYcMPa3e512tOkVLlqaglTW2hRxo0+V74uPiePLwgdry37xhHfUbNaZug4Y4ODrRb9AQtHV02L97V6bx2zZtxKt8BVq1bUchR0c6dfseV3d3dmzZDIC+gQFTZs6mWs1a2BcqROGixfDtP5C7AQGEhgSrLe+3Nq1fR4PGTajXsBGOTk70HzwUbR1t9mWR/5ZNG/AqX4HW7drj4OhI527dcXP3YNub/AFq1fmKsuW8sLGxxdHJmV590v+fvh8YmGmdQmRFGpfEZ1u5ciX6+vqcOXOGqVOnMn78eA4ePPhJdfj5+TF//nxOnjzJ48ePadWqFbNnz2bNmjXs3r2bAwcOMG/evAzbzZcvH2fPnmXOnDnMnDmTJUuWKNf7+vpy6tQp1q1bx9WrV2nZsiX16tXj7t13w1ri4uKYMmUKS5Ys4caNGxQsWDDbPOfMmcOMGTOYPn06V69epW7dujRp0kRZ5/PnzylatCiDBg3i+fPnDB48+KP7bmhoyIoVK7h58yZz5sxh8eLFzJo1SyXm3r17bNiwgZ07d7Jv3z4uXbrEDz+o3rU6fPgwt27dwt/fn7Vr17JlyxbGjct5F/SWLVsqG/UuXLhAmTJlqFWrlvIu7IYNG/Dz82PixImcP38ea2trFixYkKO6k5OTadq0KdWqVePq1aucOnWK7t27K7uq7t69m2+++YYGDRpw6dIlDh8+TPny5VXqmD59OiVLluTSpUuMHj2awMBA6tWrR/Pmzbl69Srr16/n+PHjKg0gOT0Hpk+fzqpVq/jzzz8JCgr66OeW3TmfkpJC06ZN0dPT48yZMyxatIiRI0fm6DgBzJ07lx07drBhwwYCAgJYvXo1jo6OyvUf+5wg/XzZvHkzW7Zs4fLly7Rs2ZKwsDCOHj2qjAkPD2ffvn20b98+0zx+/fVXevfuTffu3bl27Ro7duzA1dU1x3m0b98eOzs7zp07x4ULFxg+fHiWXwY/h0IzH/qFXIi+9V5DaFoaUbevYOjskXmZfPlI/aBHSmrSawxdCue6zr+ToVsxIq+pNtRFXDmDoXsxID1/A2cPIq+dexeQlkbktfPKGHVJSk7h3rNgSjk7KpdpaCgo5eLA7cfPsiwX//o1Haf/RodpvzJ+9RYehWQ/3Co2MRGFAgx0tNWVulLkyxfEREXhXPjdsdHR08PO2YXH93M+DDIxPv0GiO6b4cbJyUmgUKCZ7915n09LC4VCQdA99TR0WJsaYW6or9IAFJv4mptPQihil/WQvuuPn1PW2Q47cxMAXCzNKV7ImjN3H2VZRt2SkpIIuHMHrzJllMs0NDTwKlOGazdz35hSvGgRzl26RNDjJwDcDQzkyrXr+JT3+uyc30pKSiLg9m3Keb37O6WhoYGXV3muX8u8UfX6tWt4fZBDBW9vlfjiJUrw159/8iI0lLS0NC6cP8/joCDKV6igttzfehkaQnRkBIVLlFQu09XXx8nNnft3Pv/8vHPjOoO7fMeYvr1YvWgBMa/U2zD8IiSYqIgIlQYUPX19nN09uRfwZYb/qlPW+XtwL+B2Hmb2cW9zL1KilHLZ29wD1Zh7clISxw7sRVdPH3snp48XyIGkpCTu3rlD6XLllMs0NDQoXbYct27cyLTMzRvXKV22nMqycuUrcOvG9Sy3Exsbg0KhQN9AvUO5k5KSuHMngDJlVfMvU86Lm1nkc/P6dcqWU732lCtfgZvXM49PSkpi947t6BsY4PLe/33/WhoaefP6PyVzLonPVqJECcaOHQuAm5sb8+fP5/Dhw7i5ueW4jp9++olKb+Yd6Nq1KyNGjCAwMBBn5/Q7ry1atODo0aMMG/aui6m9vT2zZs1CoVDg4eHBtWvXmDVrFt9//z1BQUEsX76coKAgbGzS5w4YPHgw+/btY/ny5UycOBFIv4AuWLCAkiVLkhPTp09n2LBhtGnTBoApU6Zw9OhRZs+ezS+//IKVlRX58uXDwMAAK6us/7F/36hRo5Q/Ozo6MnjwYNatW8fQoUOVyxMSEvj999+xtU0fyz5v3jwaNmzIjBkzlNvJnz8/y5YtQ09Pj6JFizJ+/HiGDBnChAkT0PjIRe748eOcPXuW0NBQtLW1lfu6bds2Nm3aRPfu3Zk9ezZdu3ala9euQPpndujQoRz1XoqOjiYqKopGjRrh4pI+7r9w4cLK9T///DNt2rRRaQz78DOpWbMmgwYNUr7v1q0b7du3V05S7ubmxty5c6lWrRq//voroaGhOT4HfvvtN2Vevr6+jB8/Ptv9yeqcr1OnDgcPHiQwMBB/f3/lZ/Pzzz9Tp06djx4ngKCgINzc3KhcuTIKhQIHBwflupx8TpDe++n333+nQIECyrL169dnzZo11KpVC4BNmzZhYWFBjRo1Ms3jp59+YtCgQfTr10+5zMvLK8d5BAUFMWTIEDzfzJ3yKdeDT5HPwAiFpiZJryJVlidFR6JrZZdpmaibl7Cq/TXRd2+Q+CIYI88SmJb2QfHmiSK5qfPvlN/EjKQo1e7vSVER5NMzQEMr/5v885EU+UFMZDh6NoXUmkt0XBypqWmYGqgOsTUx0Ofxy8y76NtZmDGgaX0crQoQl5DI5hPnGLT4D37r0xUL44z/iL9OSmb5gWNUK14YvS/QuBQTFQmAvpGRynJ9QyNiczgUIy01lX3r/sDe1Z2CtvYA2Dm7kl9bm0Ob11Hrm1akkcahzetJS03l1Zttfi6zN8c9PEa1Z29EbJxyXWbWHL+AvnZ+fvdtT2pqKhoaGiw5fJpD1+6oJa+ciIyKIiU1FTNTU5XlpqamPHyc+95SHdq2ITYujtadu6ChoUFqaio9u3SmXu1an5uyUmRkJCkpKZh9MDTPzMyMR296rn0oLCwMUzPVocemZmaEhYcp3w8cPIQpEyfydaOGaGpqoqGhwfAfR1L6vQY4dYl+M9zF6INh30bGJkRFft5QmKKlylC6gg8WBS15ERLMtjWrmPfzOIb9PBUNzayHa36KqDf5G3+Qv7GJyb9iKM+7/FXPfyMTU+W6f6royLfnzge5G5uoJffL586ycMYUXicmYmxqxuBxP2GopnnqoqOiSE1JwdRU9XfX1MyMx0GZN65HhIdjaqa6ryamplkOgX2dmMiS336leq3aap/bNCoqMj3/D649pqZmPH6Uef7h4WEZ8jc1MyX8vWsPwKkTJ/hp3BgSExIwMzdn6szZGX6/hPgYaVwSn61EiRIq762trQkNDc11HZaWlsohYu8vO3v2rEoZb29vlUnafHx8mDFjBikpKVy7do2UlBTc3d1VyiQmJqrMK5M/f/4M+WclOjqaZ8+eKRvB3qpUqVKG4WOfYv369cydO5fAwEBiYmJITk7G6IMvOYUKFVI2LEH6vqamphIQEKBswChZsqTKHEo+Pj7ExMTw+PFjlQaKzFy5coWYmJgMc+7Ex8cT+KZL7K1bt+jZs6fKeh8fH5XeMFkxMzOjU6dO1K1blzp16lC7dm1atWqFtXX6hL+XL1/m+++/z7aOcuVU7xpduXKFq1evsnr1auWytLQ0UlNTefDgAffv38/ROaCnp6dsWIKcnb/ZnfMBAQHY29urNC5+2AsrO506daJOnTp4eHhQr149GjVqxFdffaXc5499TgAODg4qDUuQ3pPo+++/Z8GCBWhra7N69WratGmTacNjaGgoz549UzZEfSgneQwcOJBu3bqxatUqateuTcuWLVWO84cSExNJTExUWfa24UrdHq1fgtN3vSk57hdIg4QXwbw8eZgCFdX35VNkrXAhWwoXslV532PuUvacu0yH2lVUYpNTUpi0fjtpaWn4Nv5KLdu/evoEu/5Ypnzfrs/He5h+zO41Kwl99oQuQ0crl+kbGtGyR192r17OmSMHUCgUFC/vg3UhR2VD5qeqXdydQY2rK98PX535MIiPqVHUjdrF3flp8wEehIbjamWBb70qhL2KZf+Vf3aviY857H+M/YePMP7HETg5OnI38B6zfvlVObH3P9mmDeu5cf0aU2fMwMrKmsuXLjFj2lQsCljgVf7zei+d+dOf1Yve9Tb2HTHmc9PNklflqsqfbR0csXVwZFTv7gTcuK7SU+pTnPQ/wvIFc5XvB43J/ibQP81J/yOsWPCuB/7AMeqd3PxLOnXsKL//Ol/5vv8ovy+6vcLFS+A3ax4x0dEcO7CPX6dNZtTUmRkaQv+JkpOT+WnsGEiDvoM+/2/L36lUmTIsWraSqKhIdu/cwYSxo5m/cHGGhrh/G5nQ++8ljUvis3041EWhUCjvhAIq8yC9P5dQVnUoFIos68ypmJgYNDU1uXDhApof3CV7/wluurq6eXrROXXqFO3bt2fcuHHUrVsXY2Nj1q1bx4wZOZvYV11iYmKwtrZWmY/oLXVNZr18+XL69u3Lvn37WL9+PaNGjeLgwYN4e3ujq6v70fIf3v2JiYmhR48e9O3bN0NsoUKFuHr1ao7OgczOtY89He5zz8/slClThgcPHrB3714OHTpEq1atqF27Nps2bcrx55TZnbLGjRuTlpbG7t278fLy4q+//sow/PKtj30eOcnDz8+Pdu3asXv3bvbu3cvYsWNZt26dylxa75s0aVKGYZxjx46lfraZQHJMNGkpKWgZmqgs1zIyISkq8zuoyTHR3P11Eop8WuQzMCQpMhz7Zh1IeBmS6zr/Tq8jw9EyVv1nT8vYlOS4GFKTXpMUHUlaSjJaJh/EmJjxOjLzO625ZaSnh4aGgogPes5ExsRiZpCzO7b5NDVxsbbkebjqsU1vWNpBaGQ0k7q0UVuvJY9SZbB778lJyUnJAMRGR2P43p342FfRWNp/vKfXnjUruXv1Ep2GjMLog54pLkWL03fiTOJevUJDUwMdPX2mD+pN0Q8af3PqRMADbj0NUb7XenNtMzPQU+m9ZKqvl+2T3XrWqcia4xc5cj192N+D0DCsTAxpX6Xs39a4ZGJsjKaGRobJuyMiIjD/4C77p5i3aDEd2rSmTs30Xpmuzk48Dwnl97Xr1Na4ZGJigqamZoaeC+Hh4Zhl8WAEc3NzIj7oKRARHo75m3MmMSGB3xYsYNLUaVSqXDk9dzc37t65w5o//vjsxqWSXuVxcnt3syU5Of28j46MxPi9L4/RUZHYO2Y9X1duFLC0wsDIiBfBz3PduFS6vLfKE8OSktOHNkdFRmLy3u9dVGQkDs7qzV8dMuaf/v9wVGQEJu/1QomOjKDQF3gy3ecoVb4Czu7vhoQnv/lfPvrD3KMiKeT0+cdeW0cHS2sbLK1tcPHwZHiv7/nr0AEatmj12XUbGRujoamZYfLriPBwzMwy/901NTMj4oO/T5ERERl6LqY3LI0mNCSYqbPnfpEnMhsbm6Tn/8G1JyIiHDPzzBuBzMzMM+QfER6RYX91dXWxtbPD1s6OIkWL0aFtK/bu2kW77zqodyfEf9r/74BA8cW97Tnx/uTI70/u/bnOnDmj8v706dO4ubmhqalJ6dKlSUlJITQ0FFdXV5VXToerfcjIyAgbGxtOnDihsvzEiRMUyebxn9k5efIkDg4OjBw5knLlyuHm5qYyyfVbQUFBPHv2bv6S06dPo6GhgYfHuz/2V65cIT4+XiXGwMAAe3v7j+ZRpkwZgoODyZcvX4bjZfHmMaqFCxfO9Jh/itKlSzNixAhOnjxJsWLFWLNmDZDeE+jw4cOfVFeZMmW4efNmhnxdXV3Jnz//FzkHcsLDw4PHjx8TEvLuC+C5c+eyKZGRkZERrVu3ZvHixaxfv57NmzcTHh6eo88pKzo6OjRr1ozVq1ezdu1aPDw8KJPFUAtDQ0McHR2z/Exymoe7uzsDBgzgwIEDNGvWjOXLl2eZ34gRI4iKilJ5jRgx4qPHKi0lmdigQIwKv9ebTKHA2LMEr+5nP29IWnISSZHhKDQ0MStdkYgrZz67zr/Dq7vXMSleVmWZSQkvXt1Jnz8hLSWZmPsBmBR7r7efQoFJsbLKGHXRyqeJq40VV+6/u26lpqZx+f4jPO2zfpz5+1JSU3kY8gJTw3eNvm8blp6FRTCxc2uM9D7eAJ1T2jq6mBW0Ur4K2NhiYGzM/dvv5ttIjI/jyf1A7J2zHs6ZlpbGnjUruX3pPB0G/Yhpgazn7NMzNERHT58Ht24Q+yoaj5K5G+YU/zqJp+FRytfDF+GEvYqljNO74Zp62loUsbPk5pOsJ5LV1tIi9YNG9JTUtL/1ZouWlhYe7u6cu3RJuSw1NZVzly5RPJd/UyF9GLnigx6Zmm+Gx6mLlpYWHp6eXHjv2p6amsr58+coVrx4pmWKFS/O+Q/+Fpw9c0YZn5ycTHJyMhoaqp+BhqZGhs8qN3R09ShobaN8WdvZY2Riyu1r73pex8fF8eDuHZWGBHWICHtJ7KtXGJvmvtFQV08PSxsb5cvW3gFjU1NuXrmsjImPi+X+ndu4ehTOuqI8kjH/QlnkH4Crh2fWFeUBXV09ZWOPpbUNNm9zv6p67ty/E4DLF8g9LTU1y5vTn0pLSws3d3cuX3j3sIbU1FQuX7xA4aJFMy1TpGgxLl1Unefw4rlzFC76bp6+tw1LT588YfKs2RgZq//Jsm/zd3f34NIH+V+6cJ4iRTOfU7FIsWJcvKCa/4XzZylSLPs5GFNTU0nK5omp/xoKjbx5/Z+Snkvii9HV1cXb25vJkyfj5OREaGioyvxCnysoKIiBAwfSo0cPLl68yLx585Q9ftzd3Wnfvj0dOnRgxowZlC5dmhcvXnD48GFKlChBw4YNc7XNIUOGMHbsWFxcXChVqhTLly/n8uXLKkOzPoWbmxtBQUGsW7cOLy8vdu/ezdatWzPE6ejo0LFjR6ZPn050dDR9+/alVatWKo0kr1+/pmvXrowaNYqHDx8yduxYfH19PzrfEkDt2rXx8fGhadOmTJ06FXd3d549e6acaLtcuXL069ePTp06Ua5cOSpVqsTq1au5ceOGyvDFrDx48IBFixbRpEkTbGxsCAgI4O7du3TokH43ZOzYsdSqVQsXFxfatGlDcnIye/bsUZlj60PDhg3D29sbX19funXrhr6+Pjdv3uTgwYPMnz//i50DH1OnTh1cXFzo2LEjU6dO5dWrV8rzPidf3GbOnIm1tTWlS5dGQ0ODjRs3YmVlhYmJSY4+p+y0b9+eRo0acePGDb799ttsY/38/OjZsycFCxakfv36vHr1ihMnTtCnT5+P5lG0aFGGDBlCixYtcHJy4smTJ5w7d47mzZtnuT1tbe1cD4N7fmg7Lp36EfvwHjEP72JVqzEa+XV4cfIQAM6d+pMUGcbjbasA0Hd0J7+pGXGPH5DfxBzbxm1AoeD5/q05rlOdNLR1VeZy0ilog76DG8kx0SSGheDQtifaZhbc+eWn9NwObMO6bnMc2/9AyNFdGBcrSwGfmtyY/O5R3093rce990hi7t/m1b2b2DRohaa2DiH+u9We/zcVyzFzyx7cbK1wt7Vm+6nzJL5Ook6Z9C/N0zftxtzIgM5fVQNgzdETeNrbYG1mSmxCApuPnyU0Mpp6ZdMb85JTUpi4bjv3noXg921zUlJTCX8VA4Chri5a+dQzZ8tbCoWCCrXq8dfubZgXtMTEoiBHt2/C0MQEz9LvGvF+nzERz9LlKF8zvffLnjUruHbmFG16D0BbR0c5d5O2rh5a+fMDcOnEMQpY2aJnaMiT+3fZt+4PvGvXw8IqZw1vObHp9BW+q1qOJ+GRPI94RdeaFXj5Kpbjt+8rY2Z0+Jrjt++z9Wz65NGn7jzgu6rlCI16xcMX4bhaFaCVTyn2XHo3kbahrjaWxoaYG6bffbd/M/l3eExchjmecqtti+ZMmDKVwu7uFPH0YP3mrSQkJNCwbl0Axk2eQgELC37olj7XX1JSEg/e3IBJTk7ixcuX3Ll3L/2JoW+Gjlf28WbF6jVYFSyIk6MDd+7dY+2mzTSqV1ctOb/Vpl07fho3Ds/ChSlStCjr160lIT6eRo0aAzB+7FgKFCxAr97pD5lo1aYNP/TowZrVf1CxUmUOHTjA7Vu3GPbjj0D6E6dKlynD/Llz0dbWwcrKikuXLrJ3zx769uuv1twh/byv1bAJezZvoKC1DRYFLdm+bjUmpmaUKu+tjJvpN4rSFbypUT/9SZMJ8fG8CH530/BlSAiPH9xH38AQswIFSIiPZ9fGdZTx9sHIxJQXwcFs+WMFBaysKVJKfXNHKRQK6jb5hu0b1mJpY0MBSys2r/4dEzNzyrz3NLLJo4ZT1rsidRo1UeYf8vzdzboXIcE8uh+IvqEhFm8aiGNevSLsRSiRb3qaPX+aPjm8sakpJmoaIpSef1N2bFiHpY0tBSwt2bJ6VYb8p4waTpls8w/h0f1ADAwNMc8i/2A1569QKKjT+Gt2bVyXfuwLWrF1zSpMzMwo895TBKeN/pEy3j7UathYmXvoe7m/DA0m6M2xNy9QkMSEBHZtXE+p8hUwNjUjJjqKI3t3ExEehlelyp+d91vNW7Vh2qSfcfPwxLNwYbZs3EBCfDx1G6T/Xzj15wmYWxSga4/0qSCatmjJ4L6+bFq3lvI+FfE/fIg7AbfpNyR9btTk5GQmjB7F3Tt3mDBlCqkpqYSHpR97QyMjtT/MpEXrNkyZ+BPunp54Fi7C5o3rSYhPoG6D9N/RyT+Nx8KiAN169gKgWYtWDOjzAxvWrcHbpyJHDx/izu3bDByS/j92fHw8q39fScXKlTE3NycqKortWzbz8uVLqtWoqdbcxX+fNC6JL2rZsmV07dqVsmXL4uHhwdSpU5Xzx3yuDh06EB8fT/ny5dHU1KRfv37KCY0hfRjW20mJnz59ioWFBd7e3jRqlPtHcfft25eoqCgGDRpEaGgoRYoUYceOHbmerLhJkyYMGDAAX19fEhMTadiwIaNHj87waHtXV1eaNWtGgwYNCA8Pp1GjRhme1FarVi3c3NyoWrUqiYmJtG3bNkM9WVEoFOzZs4eRI0fSuXNnXrx4gZWVFVWrVsXSMv3R1K1btyYwMJChQ4eSkJBA8+bN6dWrF/v3Z/7I6Pfp6elx+/ZtVq5cSVhYGNbW1vTu3ZsePXoAUL16dTZu3MiECROYPHkyRkZGVK1aNds6S5QowbFjxxg5ciRVqlQhLS0NFxcXWrd+9zj2L3EOfIympibbtm2jW7dueHl54ezszLRp02jcuDE6OjofLW9oaMjUqVO5e/cumpqaeHl5sWfPHmUj4cc+p+zUrFkTMzMzAgICaNeuXbaxHTt2JCEhgVmzZjF48GAsLCxo0aIF8PHzRVNTk7CwMDp06EBISAgWFhY0a9bsk55e+CnCzx9Hy8AIuybt0DIyJe7JA27PHUfyq/TJmLXNLCDtXa8FDS0t7Jt8i3YBS1ISE4i8doHAZbNJiY/NcZ3qZOjiSXG/d/NZOHdMH+oZ4r+Huwt+Jr+pOdoW7z7fxBfPuTl5CE4d+2LToCWJYS+4+9sUIq+8m5fu5anDaBmZUKhVN/KbmBH78C7XJw76IsP6qhUvTHRsPKsOHyciJhZn64KM79AS0zfD4l5ERav0xoiJT2DOtv1ExMRiqKuDq40lM75vT6GC6b3ewqJjOH37HgC+C1aobGtylzaUcFLvpOQAleo1Iul1IjtXLSMhLo5Cbu58228o+bTyK2PCX4QSF/NK+f68f3rPvpXTf1ap6+tO3SlVKf36FRb8nMNbNhAfG4OJeQGqNGiCd52PDfb8NGtPXEQnfz4GN66BgY4214KeM/SPnbxOTlHG2JoZY/xe7685e/6ka80K9G9YDVN9PV6+imXnheusPPauZ00lDyeGN62tfD+2ZT0AVvifZYW/6hyIuVWnRnUioyJZvGIlYRERuLm4MGvyROWwuODQUJVG+RdhYXTo0Uv5fvWGjazesJHSJUvw68z0m0uD+viyaPkKps2ZS0RkJBbm5jRt1JCu32XfoP6patf5isiISBYvWkh4WBhu7u7MnDNXOSwuJCRY5bwvXqIk4yb8xKLffmXhggXY2dszedp0XFzePY1p/E8/8+uCX/AbM5ro6GisrKzo0bMX32TTMP856jZtxuvEBP5Y+AtxsbG4ehah7yg/ZeMowMuQYGKi3z3p7VHgPWb6vXsC6saVSwHwqV6TTr790dDQ4Omjh5z2P0JcXCwmpmYULlmKr9u0V/uX7IbNWpKYkMDyX+YSFxuDW5GiDPb7ifzv5R8a/IxX0e+u2w/u3WHSyHc3rtYsXQRA5Zq16d4/fY6cS2dPsXjOTGXMgmmTAGjapj3N2n2ntvwbvMl/hUr+Ez7I/7nK8X9w7y6T38t/7Xv5f99/0Jv8T7NEJf/Jyvy/aaee34P637QgMSGBlQvmERcbi1vhIgwcM0Hl3AkNfs6r93J/eO8uU0e/65G8bln6E54r1ahF134D0dDQ4PnTx5yYcpiY6Cj0DY1wcnNjxMSp2BbKfu7QT1G9Vi2iIiP5fdkSIsLDcXZ15efpM5STZIeGhKjMi1e0eHFGjBnLiiWLWb54ETZ2dvj9PAmnNzdXX754wakTxwHo1aWzyramzZlLydLqnZC/Rq3aREVGsmLpYiLCw3FxdWPy9JnKYXqZ5T9y7DiWLV7EskULsbWzY/zEyTi9GX6pqaHB46BH+I3aQ3RUFEZGxngU9mT2/AU4qmGYo/j/okj72OQiQog85efnx7Zt27IdUtipUyciIyPZtm3b35aXyLkTJ05QuXJl7t27l+2k1iKjMz2+zusUcq3Cwu0cb1Xp44H/QJU3nCBww9K8TiPXXFp1Zc2fnzYc9Z+kXVUvqr/X4Phv4u/nS8SToLxOI9dM7QoRFhX98cB/KHNjI/yv5f3w3dyoXtyDMwEP8jqNXKvg4cTpgPsfD/yH8vZw5sSte3mdRq5UKuzKo5AXeZ1GrjlYFuBJaNjHA/+h7ApmPl/VP8GlUT3yZLulf1qYJ9vNa9JzSQgh1Gzr1q0YGBjg5ubGvXv36NevH5UqVZKGJSGEEEIIIcR/0v/vbFNCZMLAwCDL119//fXJ9U2cODHL+urXV+/QiLym7mP3b/bq1St69+6Np6cnnTp1wsvLi+3btwP/X+eEEEIIIYQQeUWh0MiT1/8r6bkkxHuyG3pm+2ay0E/Rs2dPWrXK/NGpH3vc+1t+fn4fnTtpxYoVn5iZ+qn72P2bdejQQTlZ+YfUcU4IIYQQQgghxD+JNC4J8R5XV9ePB30CMzMz5QR7/3XqPnb/Vf9P54QQQgghhBDi/4M0LgkhhBBCCCGEEOK/5b2ndoov7/93QKAQQgghhBBCCCFEHvvll19wdHRER0eHChUqcPbs2Sxjq1evjkKhyPBq2LChMqZTp04Z1terV++L7oP0XBJCCCGEEEIIIcR/i+Lf0XNp/fr1DBw4kN9++40KFSowe/Zs6tatS0BAAAULFswQv2XLFl6/fq18HxYWRsmSJWnZsqVKXL169Vi+fLnyvba29pfbCaTnkhBCCCGEEEIIIUSemDlzJt9//z2dO3emSJEi/Pbbb+jp6bFs2bJM483MzLCyslK+Dh48iJ6eXobGJW1tbZU4U1PTL7of0rgkhBBCCCGEEEKI/xSFQiNPXomJiURHR6u8EhMTM83x9evXXLhwgdq1ayuXaWhoULt2bU6dOpWj/Vy6dClt2rRBX19fZbm/vz8FCxbEw8ODXr16ERYWlvuDmQPSuCSEEEIIIYQQQgihBpMmTcLY2FjlNWnSpExjX758SUpKCpaWlirLLS0tCQ4O/ui2zp49y/Xr1+nWrZvK8nr16vH7779z+PBhpkyZwrFjx6hfvz4pKSm537GPkDmXhBBCCCGEEEIIIdRgxIgRDBw4UGXZl5rvaOnSpRQvXpzy5curLG/Tpo3y5+LFi1OiRAlcXFzw9/enVq1aXyQX6bkkhBBCCCGEEEKI/xaFIk9e2traGBkZqbyyalyysLBAU1OTkJAQleUhISFYWVllu3uxsbGsW7eOrl27fvRQODs7Y2Fhwb1793J+/D6RNC4JIYQQQgghhBBC/M3y589P2bJlOXz4sHJZamoqhw8fxsfHJ9uyGzduJDExkW+//faj23ny5AlhYWFYW1t/ds5ZkWFxQgghhBBCCCGE+E9RKP4dfWkGDhxIx44dKVeuHOXLl2f27NnExsbSuXNnADp06ICtrW2GeZuWLl1K06ZNMTc3V1keExPDuHHjaN68OVZWVgQGBjJ06FBcXV2pW7fuF9sPaVwSQgghhBBCCCGEyAOtW7fmxYsXjBkzhuDgYEqVKsW+ffuUk3wHBQWhoaHaUBYQEMDx48c5cOBAhvo0NTW5evUqK1euJDIyEhsbG7766ismTJjwxeZ+AmlcEkIIIYQQQgghhMgzvr6++Pr6ZrrO398/wzIPDw/S0tIyjdfV1WX//v3qTC9HpHFJCCGEEEIIIYQQ/y0airzO4P/Kv2MQohBCCCGEEEIIIYT4R5KeS0IIIYQQQgghhPhv+ZdM6P1foUjLaqCeEEIIIYQQQgghxL/Q1YmD8mS7JX6ckSfbzWvSc0kIIcQ/VsDiaXmdQq55fD+EwA1L8zqNXHFp1ZXjrSrldRq5VnnDCfzW783rNHLNr3V9Osxfnddp5Mrvvu2JCg3J6zRyzbigJSHhkXmdRq5ZmplwKTAor9PIldIuhTh1OzCv08g1H08XjlwNyOs0cq1mCQ/+unE3r9PIlSpF3QgKfZnXaeRaoYIWBIdF5HUauWZlbprXKWRJoZA5l/5O0k9MCCGEEEIIIYQQQuSaNC4JIYQQQgghhBBCiFyTYXFCCCGEEEIIIYT4b5FhcX8r6bkkhBBCCCGEEEIIIXJNei4JIYQQQgghhBDiv0VD+tL8neRoCyGEEEIIIYQQQohck8YlIYQQQgghhBBCCJFrMixOCCGEEEIIIYQQ/ykKmdD7byU9l4QQQgghhBBCCCFErknPJSGEEEIIIYQQQvy3KKQvzd9JjrYQQgghhBBCCCGEyDXpuSSEEEIIIYQQQoj/FJlz6e8lPZeEEEIIIYQQQgghRK5J45IQQgghhBBCCCGEyDUZFieEEEIIIYQQQoj/FpnQ+28lR/tvUr16dfr375/XaWTq9u3beHt7o6OjQ6lSpfI6HaUPj5mjoyOzZ8/+rDr9/Pw+ax87depE06ZNPysHdfsnn1vi3+OfeG4LIYQQQggh/h2k55Jg7Nix6OvrExAQgIGBQV6nk6Vz586hr6+f12n842zZsgUtLS3le0dHR/r37y8NTv8Q1atXp1SpUp/dMPqlzZkzh7S0tLxOI9dMipTGrIQXmrr6JIaHEnryMAkvgrOM18ivjUW5Khg6uaGhrUNyTDShp44Q+/hBruv8HDvPXGTz8bNExMTiZFWQXg1r42FnnWnswYvXmLV1r8oyrXyabB87CIDklBR+P/QX5+7cJzgiCn2d/JRydqTzV1UxNzJUa95GhUti16Qd+k6eaJtZcHPacMLP/ZVtGeMipXHq0Ac9eycSw0J5vHklocf2qMRY122GbeN25DcxI/bRPQKXzSIm8JZac/9QjWJulHG2Q0dLi8cvI9h14QbhMXE5KlvZ05naJT04fech+y69y9NAJz91SnriYmlBfi1Nwl7F8ufNQG49CVFr7s3Kl6B6UVf0tLW4+/wFK/zPERL1Ktsypvq6tKpYmpIONuTPp0lIVAxLDp/iQWg4AN/X8qZKYReVMlcfPWP6zqNqzX3jli38sXYdYeHhuLm4MLh/P4oWKZJpbOCDByxaupTbAXd4HhzMgD6+tG3VKsu6V/7xB78sXESbli0Y2LevWvMG2LJpI+tWryY8PAwXVzf6DRxEkaJFs4w/evgwSxctJDj4ObZ29vTs3RufipVUYh4+fMBvv/zClUsXSUlJwdHJiQkTJ2NpZaX2/NPS0tj4x0qO7NtLbGwMHkWK0rV3X6xt7bItt3/ndnZu3khURDiFnFzo3Ks3rh6eyvWR4eH8sXQR1y5fJCEuHms7O75p3Y4KlauoPf+ta/7g2MF9xMXG4uZZhA69emNlY5tlmYAb19izdTOP7t0jMiKcPiNGUda7onJ9cnIyW1b/ztUL5wgNDkZPT58iJUvRskNnTM3N1Z7/rvVrOH74APGxsTh7Fqbd970oaG2TZZm7N69zcMdWgu4HEhURTo8hP1KqvHeGuOdPHrP1j5XcvXmd1NQUrO3s6T5oBGYFCqgt9+3rVvPXwf3ExcXi6lmYb7v/gGU2x/7Ojevs276ZR4HpufceNpLSFXyyjF/123yOHdhH687fU6fx12rJ+63tWzazce0awsPDcXFxpXf/AXhmcd0BOHb0CCuXLCY4OBhbOzu69exFBZ/08yY5OZnlixdx9vQpgp89Q09fnzLlvOjasycWFuo53h/aunkT61b/kZ6/qyv9Bg6icJFsrj1HDrNs0aJ3154feuNd8d15P+mn8ezbo/p3uHwFb6bNmv1F8v9baciE3n8n6bn0L5aSkkJqaupn1xMYGEjlypVxcHDAXM1/ONWpQIEC6Onp5XUa/zhmZmYYGqr3C6P473j9+nWO4oyNjTExMfmyyXwhhs4eFPCuzsuLJ3m09XcSw15gV78lmjpZXC80NLBr0BItQyOeHdrBgw1LCf5zP8mxMbmv8zMcu3aLxXuP0q5GJeb16oizVQFGr9xAZExslmX0tPPzx9AflK8Vg3oq1yUmJXPveQhtq1dkXq8OjGr7DU/Cwhm3eovac9fU1iXm4T3uL52Ro3jtAtYUGT6NqBsXuTS0E892b8Ct5zBMSpZXxlj41MKpQx+CNi3j0rAuxD66R7GRM9EyMlF7/m9V8nSmgpsDu87fYMmhU7xOSeG7al7k0/j4v0k2ZsaUdbEnODI6w7pvKpTEwlCftccv8Ou+49x6EkJLn9JYmRipLfeGZYpQp6QHK/zPMm7jfhKTkhnSpAZamlnnrqedn1HNvyIlNZXpO44yfPUu1h6/QGyC6vXiyqNn9Fm2WflacOCE2vIGOHj4MLPn/0K3Tp34fckS3Fxd6TtoMOEREZnGJyYkYGttQ+8ePTA3M8u27pu3brFlxw5cXVyyjcutw4cO8svcOXTq2pUlK1bi6ubK4AH9iAgPzzT+2tWrjB87moaNG7Nk5e9UqVqVkcOGcj8wUBnz9MkTfHt0x8HBgTm//MryVavp0LkL+fPn/yL7sGPTevbt2EY33378NGse2jo6TBo9Itu/GyeP+bNq8UJatPuWSfN+xcHZmUmjRxAV+e4z+2XGFJ4/fcKQMeOZumAR5StWZvbkn3gQeE+t+e/ZsomDu3fQsZcvY6bNQltHhxl+o7PNPzEhgUKOTnzX44dM179OTORR4D2atGrLuJnz8B0xiuCnT5jz8zi15g5wYPsWju7dRbvuvRg6aRra2trM/WksSdnln5iIrYMTbbr2yDLmRfBzZowejpWtLQPH/cyo6XOp37w1+fJrZVnmU+3bupnDu3fybc/e/Dh5BtraOsyaMOYjuSdg7+hM++97Zhnz1sXTJ7l/JwCTj/ye54b/4UMsnD+Pbzt14dcly3B2dWXEoIFEZHHduXHtGhPH+VGvYSN+XbqcSlWq4PfjCB7cvw+kn1P37gTwbcdOLFi6jLE/T+RJUBBjhg9Te+4AR95cezp26cbi5StxcXVj8ID+WV57rl+7yoSxY2jQuDGLV6xMv/YMV732AJT39mbLzt3K15hx479I/uK/7f+ucal69er07duXoUOHYmZmhpWVFX5+fgA8fPgQhULB5cuXlfGRkZEoFAr8/f0B8Pf3R6FQsH//fkqXLo2uri41a9YkNDSUvXv3UrhwYYyMjGjXrh1xcap3PJOTk/H19cXY2BgLCwtGjx6t0lMgMTGRwYMHY2tri76+PhUqVFBuF2DFihWYmJiwY8cOihQpgra2NkFBQdnub2pqKuPHj8fOzg5tbW1KlSrFvn37lOsVCgUXLlxg/PjxKBQK5bHIzrBhw3B3d0dPTw9nZ2dGjx5NUlKScv3boWcLFy7E3t4ePT09WrVqRVRUlDLm7RCccePGUaBAAYyMjOjZs2e2/xB8OCwuMjKSbt26KcvXrFmTK1euqJSZPHkylpaWGBoa0rVrVxISEj66f2+lpKQwcOBATExMMDc3Z+jQoRl6dqSmpjJp0iScnJzQ1dWlZMmSbNq0Sbn+7fmye/duSpQogY6ODt7e3ly/fl2lns2bN1O0aFG0tbVxdHRkxgzVL2oLFizAzc0NHR0dLC0tadGihXLd+8PiqlevzqNHjxgwYAAKhSJHj998e17t2rULDw8P9PT0aNGiBXFxcaxcuRJHR0dMTU3p27cvKSkpynKrVq2iXLlyGBoaYmVlRbt27QgNDc2w74cPH6ZcuXLo6elRsWJFAgIClDGBgYF8/fXXWFpaYmBggJeXF4cOHVLJ7/nz5zRs2BBdXV2cnJxYs2bNJ58Lb8/JZcuWUahQIQwMDPjhhx9ISUlh6tSpWFlZUbBgQX7++WeVbee03lWrVuHo6IixsTFt2rTh1av0HgOdOnXi2LFjzJkzR/l5PHz4MNvPIyIigvbt21OgQAF0dXVxc3Nj+fLlyvWPHz+mVatWmJiYYGZmxtdff61S59vfrZ9//hkbGxs8PDz48ccfqVChQoZtlSxZkvHjx6uUeys1NZWpU6fi6uqKtrY2hQoVUjk+H8vD39+f8uXLo6+vj4mJCZUqVeLRo0fZ7ntumRYvR9Ttq0Tfuc7ryDBCjh8gNTkJY49imcYbexRHU1uXpwe2ER/ylOSYaOKDn5AY/iLXdX6OrSfPU69cCb4qU5xCBS3wbVwXbS0tDly8lmUZhUKBmaGB8mVq8K5Xp76ONhM7taZqcU/sCpjjaW/DDw1rc+9ZCKGZNIB8jojLpwlav5iwc3/mKN76q6YkhD7nwar5xD99xPP9m3l52h/bhq2VMbaNWhN8eCeh/nuIf/qQe4unkfI6EcsajdSa+/u83R3482YgAc9CCYl6xdYzVzHU1cbT1jLbcvnzadLcuyQ7z18n4XVShvX25iacufuIp+FRRMTG8+fNQBKSkrAxU1/jUt2Snuw4f52LD57wOCyShYdOYaKvRxln+yzLNCpThPCYOJYcPs390DBevorl+uNgQqNjVOKSU1KIiktQvuISc9ZYnVNr1m+gaeNGNG7YAGcnR4YPHoSOjg47d+/ONL5I4cL07f0DX9WulW2DS1xcHKPHT2Dk0KEYfaGbLxvWrqVRk69p0Kgxjk7ODBo6HB1tHXbv2plp/KYN6ylfwZu2336Ho6MT3Xr0xN3Dgy2bNipjFi/8Fe+KFenl2wd3Dw9s7eyoXKUqpl/gC3ZaWhp7t23lmzbtKedTEQcnZ3oPGkZEWBjnT2XdiLh762Zq1qtP9a/qYVfIgW6+/civrY3/gf3KmDu3blK38de4enhiaW1Ns7bt0dfX58HdO2rN/8DObTRp2YYyFXywd3Ti+/6DiAgP4+LpU1mWK1HWi+bfdqSsT8VM1+vp6zNk/ETKV66KtZ0drh6efNvjBx4G3iPsRWimZXKb/5HdO6jfvBUlvbyxc3Cik+8AoiLCuXzudJblipUuy9dtv6VUNj1+tq/9g6Kly9Lsu87YO7lQwMqakl4VMDI2UVvuh3Ztp1GL1pQu7429oxNd+g4kMjycS2ezPvbFy5Tjm3bfUcY782P/VkTYS9YuWUi3/oPR1FT/IJvN69dTv3Fj6jVsiIOTE/0GD0FbR5v9u3dlGr910wa8ylegVbv2ODg60qlbd1zd3dm+Jf3/fX0DA6bMmkO1mrWwL+RAkaLF8B0wkLsBAYSGqL+384Z1b689jXB0cmLQ0GHoaOuwZ1fm+SuvPe2/xdHRia7de+Du4cHWzZtU4vJr5cfc3Fz5MjRS398p8f/j/65xCWDlypXo6+tz5swZpk6dyvjx4zl48OAn1eHn58f8+fM5efKk8kvW7NmzWbNmDbt37+bAgQPMmzcvw3bz5cvH2bNnmTNnDjNnzmTJkiXK9b6+vpw6dYp169Zx9epVWrZsSb169bh7964yJi4ujilTprBkyRJu3LhBwYIFs81zzpw5zJgxg+nTp3P16lXq1q1LkyZNlHU+f/6cokWLMmjQIJ4/f87gwYM/uu+GhoasWLGCmzdvMmfOHBYvXsysWbNUYu7du8eGDRvYuXMn+/bt49KlS/zwg+pdosOHD3Pr1i38/f1Zu3YtW7ZsYdy4nN8ZatmypbJR78KFC5QpU4ZatWoR/qblfsOGDfj5+TFx4kTOnz+PtbU1CxYsyHH9M2bMYMWKFSxbtozjx48THh7O1q1bVWImTZrE77//zm+//caNGzcYMGAA3377LceOHVOJGzJkCDNmzODcuXMUKFCAxo0bKxvkLly4QKtWrWjTpg3Xrl3Dz8+P0aNHs2LFCgDOnz9P3759GT9+PAEBAezbt4+qVatmmvOWLVuws7Nj/PjxPH/+nOfPn+doX+Pi4pg7dy7r1q1j3759+Pv7880337Bnzx727NnDqlWrWLhwoUrDWVJSEhMmTODKlSts27aNhw8f0qlTpwx1jxw5khkzZnD+/Hny5ctHly5dlOtiYmJo0KABhw8f5tKlS9SrV4/GjRurNJp26NCBZ8+e4e/vz+bNm1m0aJFKIxZ8/FyA9IasvXv3sm/fPtauXcvSpUtp2LAhT5484dixY0yZMoVRo0Zx5syZT65327Zt7Nq1i127dnHs2DEmT54MpP/++fj48P333ys/D3v7rL/wAYwePZqbN2+yd+9ebt26xa+//oqFhYXymNetWxdDQ0P++usvTpw4gYGBAfXq1VNpmD18+DABAQEcPHiQXbt20b59e86ePUvge3epbty4wdWrV2nXrl2meYwYMYLJkycr81mzZg2WlpY5yiM5OZmmTZtSrVo1rl69yqlTp+jevXuOGjs/mYYGOhZWxD1VbbiKe/oInYKZDy0wKORKQsgzLCvVxqX9Dzg274RZqQrwNr9c1JlbSckp3HsWTClnx/d2SUEpFwduP36WZbn416/pOP03Okz7lfGrt/Ao5GW224lNTEShAAMdbXWlniuGbsWIvHZeZVnElTMYuqc32ik082Hg7EHktXPvAtLSiLx2Xhmjbqb6uhjq6nD/vWOYmJTMk7Ao7CxMsi3boEwR7jwL5X5IWKbrH4dFUqyQNbr5tVAAxeytyaepwcPQzO8wf6oCRgaY6Oty4/G7LzDxr5O4H/ISVyuLLMuVdrLjQWgYvvUqM79Lcya0rk/1Ihl7+HjaWjK/S3OmtG9Mx2peGOiorwdNUlISt+/cwatsOeUyDQ0NvMqV5dqNG59V99RZs6jk40P5cuU+HpwLSUlJ3Am4TTmvdz3uNDQ0KOvlxY3rmTcK37h+jbJeXirLylfwVsanpqZy6uRJ7O0LMah/X5o0qEePrl3464P/J9QlNDiYyIhwipcqrVz2P/buOzqK6gvg+HfTewdSSS+E3kF67wKiIKKggoiK9CrSld5BepfeRKQXAemE3gMEQigpkGTTE5Jsfn8ENlmSQAgLUX73c86cw+6+mb07mR1m39x3n4mpKV6+fty8fi3XddJSU7l7+yaly1VQP6ejo0PpchW4eSNrHZ8S/pz45zDxcbGoVCqOHz5I6tNU/MuU1Vr8j8PDiImOxr9sOY34PX18CQrU7hDapIQEFAoFJqbaKx3xJCKcWGU0fqWz9omxqSnuXj7czXYT7nWpVCqunDtDMUdHZv0ykoFdv2Di0AFcOJ13h9XrehIeTowymhIv7HsPb1+CAm+80bZVKhVLZk6jSZuPcCru+oaR5pSamsrNm4FUqJj1XdTR0aFCpUpcu3ol13WuXblKhRfOJZWqVOX6lbzPUwkJ8SgUCkzNtNu5nXnuCaRiJc34X37uuZLj3FM527nnuQvnz9G6eTM+/7Q9UydP1EgK+C97fnP3XS//r/4vay6VKVOGkSNHAuDt7c2cOXM4cOAA3t7e+d7GL7/8Qo0amePku3btytChQwkKCsLDwwOAjz/+mIMHDzJ4cFZKpIuLC9OnT0ehUODr68vly5eZPn0633zzDSEhISxbtoyQkBAcHTN/vAwYMIDdu3ezbNkyxo0bB2SeVObOnUvZsvn7D3rKlCkMHjyYTz/9FICJEydy8OBBZsyYwW+//Ya9vT16enqYmZlhn8/x/D///LP6325ubgwYMIB169YxaNAg9fPJycmsXLkSJ6fMsdezZ8+mRYsWTJ06Vf0+BgYGLF26FBMTE0qWLMmYMWMYOHAgY8eORecVQxGOHj3K6dOniYiIwNDQUP1Zt27dyqZNm+jevTszZsyga9eudO3aFcj8m+3fvz/f2UszZsxg6NChfPTRRwDMnz+fPXuy7sylpKQwbtw49u/fT/XqmXeQPDw8OHr0KAsWLKBOnTrqtiNHjqRRo0ZAZiejs7Mzf/zxB+3bt2fatGk0aNCA4cOHA+Dj48O1a9eYPHkyX375JSEhIZiamtKyZUvMzc1xdXWlfPmsi8HsbGxs0NXVVWcT5Vdqairz5s3D89nwgY8//pjff/+d8PBwzMzM8Pf3p169ehw8eJAOHTIzDLJ3Enl4eDBr1iwqV65MfHy8Ru2uX3/9Vb0vhgwZQosWLUhOTsbIyIiyZctqHMtjx47ljz/+YNu2bfTs2ZMbN26wf/9+AgICqPTsP/bFixdrfFfzcyxA5gXL0qVLMTc3V3+ewMBAdu7ciY6ODr6+vurvR9WqVV9ru8uXL1cPTfziiy84cOAAv/76K5aWlhgYGGBiYpLvv0dISAjly5dXf143Nzf1a+vXr0elUrF48WL1f1zLli3DysqKQ4cO0bhxYwBMTU1ZvHixxp39smXLsmbNGvVxtnr1aqpWrYqXl1eOGOLi4pg5cyZz5syhS5cuAHh6elKzZs18xVGpUiViYmJo2bKl+pgqUaJEvj7/69I1Mkaho0NakmamaHpSIgZWud/t17ewRN+sOLFB13iwZzMGFlYUq9EIhY4ukeeOF2ibBRWbmIhKlYG1meZwOyszU+4/yb0DwtnOhr5tmuFmX4TE5BQ2Hwug/6JVzP+xK3aWOS9kn6amsWzvYeqULoFJIXcuGVjZkBqj+blSY6LRMzFDR98APTMLFLp6pCpfaKOMwsSx+FuJ6XmHW/wLQ8ISklNe2hlXysUBB2tLFu07nmebjcfP83H1cgxu25B0lYrUtHTWHz2f71pOr2JpYgRATGKSxvMxiclYmRjnuV4RCzPql/Jh94Xr/HXmKu7FbPm8diXSVCqO3sisO3YpJJQzQfd5HJdAUQszPqlejv6t6jFm016t1GdTxsSQnp6OjY21xvM21jbcu/fyrOyX2bv/AIE3b7J84cI3DTFPMUol6enpOTKKbGxsCMkjQzMqMhKbF9pb29gQFZnZMRkdHU1SYiKrf19Jt+496PF9T06dPMHPQwczc85cylWokNtmC0wZnfkds7TW3P+WVtYo8xgeFBsbg0qlynWdh/fvqx/3GTqcmRN+oVuHdujq6mJgaEi/4SNfWgvpdcU8i9HSSjMWCysr9Wva8PTpUzasXEbVWnUw1mJphthnwwgtXhiObm5lpX6tIOJiYkhJTmLP1s18+OnntO3UhWsXzrFwynj6jPwVn5Jv3kn/fAjki5lQmfte+Ubb3v3HJnR0dWnQ4sM32k5eYmKUqHL57lpb23A/j/NOdFRkjuF51jY2REXlflPhaUoKi+fNo17DhlqvFZvXucfaxpqQe8G5rhMVGYm19Yuf11p97gGoUrU6tevUxd7RkUcPHrJowTwG9evL3IWL0NXV1epnEO+3/9vOpewcHBxyZEK8zjaKFSumHiKW/bnTp09rrFOtWjWNnszq1aszdepU0tPTuXz5Munp6fj4+Gisk5KSolEHycDAIEf8eYmNjeXRo0fqTrDnatSokWP42OtYv349s2bNIigoiPj4eNLS0rB4IXWyePHi6o4lyPysKpWKwMBA9Y/ssmXLatRQql69OvHx8dy/fx9X15ffrbh48SLx8fE5akQlJSWpszOuX79Ojx6a47qrV6/OwYOvLkYaExNDaGioxlAiPT09KlWqpL6ovn37NomJiepOo+eePn2ao/PneecTZF58+vr6cv36dXWcrVtrFiqsUaMGM2bMID09nUaNGuHq6oqHhwdNmzaladOmtG3bVqv1p0xMTNSdAJB5/Lq5uWl0EhUrVkzje3L27FlGjRrFxYsXiY6OVtf/CgkJwT9bUcTsx6uDQ2aB4oiICIoXL058fDyjRo1ix44dhIaGkpaWRlJSkjpzKTAwED09PSpku6j28vLCOtuFbX6OBcjspMlem6pYsWLo6upqdGRm/4wF3W5BzifZfffdd7Rr145z587RuHFj2rRpwwfPii5evHiR27dv56ixlZycrBFT6dKlcwwZ6dSpE0uXLlUPx127di39+vXLNYbr16+TkpJCgwYNcn39VXE0btyYL7/8kiZNmtCoUSMaNmxI+/bt1X//3KSkpJCSkqLx3PNOPW1TKBSkJycSfmQvZGSQ8iQcPVNzbMpUJvJc3h0F/xYlijtRoriTxuNvZy1hZ8AFOjfULJiblp7O+PV/kpGRQc9Wjd91qP9KpV0daVUxq/Dp6iNnX3sbFsZGNK1Qgt8PBZD2ktqH9Up7Y2Sgz4qDp0l8+hQ/p2J88kE5lv59koiY+DzXy0t1Hze+qpuVLTN1+6HX3gZk1je9GxHFppOZ1wL3nkTjbGNJ/VLe6s6lU7eyOkkeRCq5H6lkaufWlHAqyjUtFyTXlvDwcKbNmsXsadPe2vnjbcl4dhzVrFWb9h07AuDt48OVy5f5c+uWN+5cOnrwAItmz1A/Hjz6lzfa3sts+H05CfEJDBs3EQsLSwJOHGfm+F8YNWk6xd3dC7TN44cOsmJe1oiAvsO1XwPpRWlpacydNB4yMujyXc832tbpI4dYsyAre/77oSPeMLrcZWRkHkdlKlWlQcvMa0sXdw+CAm9wZN+uAnUunTx8kN8X/KZ+3GvYSO0E+4LgoNvs37GNEVNm/mczP9LS0hg7MvM6q1f/gYUdTr41yPZbxtPTC08vLzp+0o4L589pZEn9Jyn+LwdqFZr/y86l7DNrQeYPDZVKpf6Rmf2OXPZaQnltQ6FQ5LnN/IqPj0dXV5ezZ8/m6CHO/gPf2Ni4UE+4J06coFOnTowePZomTZpgaWnJunXrctQIetvi4+NxcHDQqEn13LsqShwfn/nDYMeOHRodaaDdH8Xm5uacO3eOQ4cOsXfvXkaMGMGoUaMICAjQ2mfN7fh92TGdkJBAkyZNaNKkCatXr6ZIkSKEhITQpEmTHHWzXvyuAOrtDBgwgH379jFlyhS8vLwwNjbm448/zncRasj/sfC6n/FNtvsmhfabNWvGvXv32LlzJ/v27aNBgwb88MMPTJkyhfj4eCpWrMjq1atzrFck2wwwud0p69ixI4MHD+bcuXMkJSVx//59dRbai4yN8854APIVx7Jly+jVqxe7d+9m/fr1/Pzzz+zbt49q1XLOagOZQ0xfHBY7cuRIOjq9/K5fenISGSoVesaana26xiakJeZeEDstMSHzh1y2c/3T6Ej0TMxAR6dA2ywoCxMTdHQURL+QyaKMT8DGLH93PPV0dfF0KEZolObd7syOpW1EKGMZ//WnhZ61BPBUGYW+peYdVH1La9IS41GlPiU1VklGehr6L2SI6VvZ8FSpnaFkgQ/DeRipVD/WffZ/v5mRAfHJWR2cpkaGuRbpBnC0scDMyJBvG2fVDtHR0cG1iA1VvIozdtMerExMqOrtxm+7jvD4WS2jcGUcrnbWVPFyZfvZ1x/6df7uA4KyDd/Tf3a9YGliTExiVlaupYkR957knf2gTEjmYZTmkIdH0bFU8sw7O+xxbDyxSckUszTXSueSlaUlurq6RL1w3EZFR2FrW7AMweuBN4mKjqZzt27q59LT0zl/8SIbt/zB0QP7tXIX3tLKCl1d3RwFdKOiorDJI3YbW1uNIdUA0VFR2Dy7gfF8m64vdL64urlx+Q1uCD5XsWp1jRndnl/fxkRHY22TdRMlRhmNq0fuRdAtLCzR0dHJkRkUo4zG6lkGWljoI/b89SeT5y3CxdUt8zN4eHLj6mX2bv+Tbj/2KVD85atUxdPXV/047Xn8ymiNrJJYpZLi7h451n9dzzuWIh9HMHjs+DfOWipTqQpuXlk3kNPS0oDMeC2zZZXEKZU4uxU8fjNzC3R0dXF4YQi+g7Mzt2/kPtzxVcpVqYq7T859HxujzLHvXQrYeQhw69pV4mJiGNT9K/VzKpWKDSuWsH/7n0xcsLTA237O0tIKnVy+u9HRUVjn8d21trFFmdt310bz5mNaWhq/jBhORFg4k2fOeiszXOd17omOis4Rz3M2trZER7/4eaPV557cODo5YWllxcMHD/77nUvinfq/7FzKy/MfRaGhoerMk+zFvd9U9louACdPnsTb2xtdXV3Kly9Peno6ERER1KqlnalaLSwscHR05NixYxpDtI4dO0aVKlVesmbejh8/jqurK8OGDVM/l1uR3pCQEB49eqQe4nfy5En10KPnLl68SFJSkvqH7MmTJzEzM3tlTRqAChUqEBYWhp6ensawoexKlCjBqVOn6Ny5s/q5kyfzN+bc0tISBwcHTp06pa5vlJaWpq67A2gUVc++f3Nz8uRJihfPvGiPjo7m5s2b6mFCJUqU4NgxzeKZx44dw8fHR30RrKenR8OGDWnYsCEjR47EysqKv//+Wz1kLzsDAwONwttvw40bN4iMjGTChAnqv9eZM2desVZOx44d48svv6Rt27ZAZqdF9qLQvr6+pKWlcf78eSpWrAhkZoxln9EjP8dCQWhruwX5exQpUoQuXbrQpUsXatWqxcCBA5kyZQoVKlRg/fr1FC1aNEe24Ks4OztTp04dVq9eTVJSEo0aNcqzZpu3tzfGxsYcOHCAbtl+pD2X3zjKly9P+fLlGTp0KNWrV2fNmjV5di4NHTo0RyaVoaEhwStnvfyDqVQkPwnDxMmV+HtZMxGZOLqivHYu11WSwh5i4aU5TE/f0jpztrhnHYOvu82C0tfTxcvRnot37vGBv/ezj5TBhTv3aFU1f5kK6SoVweGPqeST9YPkecfSo8hoJnz9KRYvGSL1LsXduoJ1ec1CtFZlKhN3M7PWRUZ6GvF3ArEqVYmogCOZDRQKrEpVJHT3Zq3E8DQtPcewtLikZNyL2RKmzCzGb6inh7OtJWdu5z5M4k54JHN3H9F4rnWV0jyJTeDYjTtkZIC+Xs4bVgCqjAwKeo8oOTWN5BcynpQJSfg7FyPkWWeSkb4eHsXsOHDlVm6bAOBW2GMcrDW/u/ZW5kTG5d15am1qjJmRIcqEpDzbvA59fX38fHwIOHuWurUzr3tUKhVnzp7jk4/aFmiblStVZO2K5RrPjRk/Abfixenc6TOtDe/Q19fHx9ePs2cCqPXs/3+VSsW5MwG0/fiTXNcpWao0586cof2nHdXPBZw+TclSpdXb9Cvhz/0QzWuqByEhrzXMPS/GJiYaHSQZGRlYWdtw5eJ53Dwzh0cnJiZwO/AGjVq0ynUbevr6uHv5cOXieSp/kJkVr1KpuHLhPE2eTRX/9FkHrc4LB7mOjg6qNxhOmVv8ltbWXLt0Ud0ZlpSYSNDNQOo1bVHg94GsjqXw0EcM/mUCZloobGxkbIKRsWb8FlbWBF65iMuzzrCkxETu3r5JrSbNCvw+evr6uHl6E/7wocbz4Y8eYWP38jqtrxO7pZU11y9dUHfkJSUmcudWIHWbFjz26nXr5ajLNX3sCKrVqU/N+g0LvN3s9PX18fHx5fzZM9R4dn2vUqk4f/YsrT9ql+s6/qVKcv7sWT5qn3VD7tyZAEqUysqAfd6x9PDBfSbPnI2FpaVW4s01fl9fzp7N5dzTLq9zTynOngngkw6fqp87k+3ck5uIiAhiY2L+1bOI59t/NAvuv0ryxLIxNjamWrVqTJgwgevXr3P48GGN+kJvKiQkhH79+hEYGMjatWuZPXs2vXv3BjLr7HTq1InOnTuzZcsW7t69y+nTpxk/fjw78pg1JT8GDhzIxIkTWb9+PYGBgQwZMoQLFy6o3/d1eXt7ExISwrp16wgKCmLWrFk5ilwDGBkZ0aVLFy5evMiRI0fo1asX7du317hAevr0KV27duXatWvs3LmTkSNH0rNnz1fWWwJo2LAh1atXp02bNuzdu5fg4GCOHz/OsGHD1J0cvXv3ZunSpSxbtoybN28ycuRIrr5GkdDevXszYcIEtm7dyo0bN/j+++9RKpXq183NzRkwYAB9+/ZlxYoVBAUFce7cOWbPns2KFSs0tjVmzBgOHDjAlStX+PLLL7Gzs1PPzNW/f38OHDjA2LFjuXnzJitWrGDOnDnq4urbt29n1qxZXLhwgXv37rFy5UpUKpVGR112bm5u/PPPPzx8+JAnT15e5LegihcvjoGBAbNnz+bOnTts27aNsWPHvvZ2vL292bJlCxcuXODixYt89tlnGlk/fn5+NGzYkO7du3P69GnOnz9P9+7dNTL48nMsFIS2tuvm5sapU6cIDg7myZMnr8xqGjFiBH/++Se3b9/m6tWrbN++Xd0R2alTJ+zs7GjdujVHjhzh7t27HDp0iF69evHgwYNXxtKpUyfWrVvHxo0b6dSpU57tjIyMGDx4MIMGDWLlypUEBQVx8uRJlixZkq847t69y9ChQzlx4gT37t1j79693Lp166V1lwwNDbGwsNBY8psBGH35DJa+ZbDwLomBlQ3FajZGR1+fmGcdFvZ1m2NXOavTXnn9AjqGRhT9oAH6ltaYunhgW64a0dfO53ub2tT2g0rsPnuR/eevEBIRyW9/7SXlaSqNKmRe+E3ZtINle7OK+q45eIxzt+8SGqXk9qMwpmzaToQylqYVM4egpqWnM27dn9x6GMbAj1uSrlIRFRdPVFw8qWna7XjWMTTG1NUbU9fMjjGjoo6YunpjaJtZ/N21Yw98fsj6fzR071aMijri1ul7jB2LY9+4LUWq1+fhjvXqNg+3r8e+QSuK1mmGsZMrnt0GoGtoRPihgv9f+Conb96jtr8Xvo5FKWppRtuqZYhLSuHGw6wMnc51K1PFK/MmwdO0dCJi4jWW1LR0kp6mqoe7PYlNIDIugVaVSuFkY4m1qQnVfd3wtLfjxkPtzTq15+INWlcqRXk3J5xtrfi20QcoExI5dyerBs7g1g1oWDora2L3het4FrOjVcWSFLU0o7qPG/VKerP/cuZsXob6enz6QXk8i9liZ26Kv3Mx+rSoQ0RMHJdD8jdRRH581qE9f27fzvZdu7gbHMzEqVNJSkqiZfPmAIz85Vd+m79A3T41NZWbt25x89YtUlNTefz4CTdv3eL+s/OfqYkJnh4eGouxkRGWlhZ4erx5Nkt27Tt2ZPu2P9m1YwfBwXeZOmkiScnJNG+ZOavhr6NHsWBu1lCij9t34NTJE6xbs5p7wcEsXbyIwBvX+ShbZ1THTp/z9/79/PXnVh7cv8/mjRs5fuwobdrl/qP3TSgUCpq1acsf69Zw5uRxQu7eZe6USVjb2lKpelY5hbFDB7L7r63qxy3atuPv3Ts5vH8vD0PuseS3WaSkJFOnURMAHF1csHd0ZNHsmdwOvEFY6CO2b9nI5fPnqJzHDG0Fjb9xqzb8tWEd50+d5H7wXRbOmIK1jS0VqmV1YE8cPpT9O7Jm8EtOSuLenSDu3ckcSv4kPJx7d4LUM8GlpaXx28RxBN++xbf9BqJSpaOMjkIZHaXO2NFW/PVbfMjOzRu4GHCKh/eCWTFnOpbWNpSrnHUTZsbonzm0K2sWsOSkJO7fvcP9u3cAiIwI5/7dO0Q9zprttNGHbTl7/ChH9+8hIvQRh3Zt5/LZ09Rp0lxrsTds2Zodm9Zz4fQpHtwLZsmsaVjZ2FC+Sta+nzLyJ/7eqbnvQ+7eIeRZ7I8jwgm5e0e9783MLXByddNYdHX1sLSyxt7JWSuxA7Tr0IGd2/9i766d3AsOZtbUKSQnJdOkeWan5MRfxrJk/jx1+7Yftyfg1Ek2rltLyL17rFy6hJs3btD6o8yZm9PS0hgzfBg3A28wZMRIVCoVUZGRREVG5jkC5k20/7QjO7ZtY/fOzHPPtMmTSEpOplnLzPh/HTOahfOyhmB+3L4Dp0+eZP2zc8+yZ+eetu0y409MTGTenNlcvXKF0NBHnD0TwLDBA3FydqZy1dxvCAqRF8lcesHSpUvp2rUrFStWxNfXl0mTJqmL5L6pzp07k5SURJUqVdDV1aV3797qosCQOYzkl19+oX///jx8+BA7OzuqVatGy5YFn365V69exMTE0L9/fyIiIvD392fbtm2vVbw8uw8//JC+ffvSs2dPUlJSaNGiBcOHD2fUqFEa7by8vPjoo49o3rw5UVFRtGzZMsdMbQ0aNMDb25vatWuTkpJCx44dc2wnLwqFgp07dzJs2DC++uorHj9+jL29PbVr11bPaNWhQweCgoIYNGgQycnJtGvXju+++06jKPfLPJ9Br0uXLujo6PD111/Ttm1bjdkTxo4dS5EiRRg/fjx37tzBysqKChUq8NNPP2lsa8KECfTu3Ztbt25Rrlw5/vrrL3VNnAoVKrBhwwZGjBjB2LFjcXBwYMyYMeqZ16ysrNiyZQujRo0iOTkZb29v1q5dS8mSJcnNmDFj+Pbbb/H09CQlJUUrhVdfVKRIEZYvX85PP/3ErFmzqFChAlOmTOHDD1+vAOO0adP4+uuv+eCDD7Czs2Pw4MHExmoOQ1m5ciVdu3aldu3a2NvbM378eK5evYqRUWYh2/wcCwWhre0OGDCALl264O/vT1JSEnfv3n1pJpSBgQFDhw4lODgYY2NjatWqxbp164DM2lj//PMPgwcP5qOPPiIuLg4nJycaNGiQr0ymjz/+mJ49e6Krq6vu3MzL8OHD0dPTY8SIETx69AgHBwd1DbNXxZGUlMSNGzdYsWIFkZGRODg48MMPP/Dtt9/me7+9jrg7gegamWBXsQa6JqakREbwYNcm0p8V5NY3NdcYApeWEMeDXZsoWq0ebh99SVpiPNFXzxJ18XS+t6lNdUqXIDYhid8PHCU6PgEPh6KM6fwJ1s+GxT2OiUVHJ+vOW3xSMjO37iE6PgFzYyO8HIsx9ZtOFC+aOTtYZGw8J29kZlz1nLtc470mfP0pZdy1Vxjb3NOP0qPmqB97dOkFQPihndya+ysG1rYY2mV9X1Ieh3JtwkDcu/TCsfknpEQ+5tb8iSiz7fsnJw6gb2FF8fbdMLCyISH4FlfG9Sc1RntFel907MYdDPR0aVWpFEYGeoQ8jmbVYc16SjZmJpgY5n+2NFVGBqv/OUPDMr50rFURAz1douIT+ePUJW6FPn71BvJpx7lrGOrp8VW9qpgYGnArNIIpfx0kNT0r9qKWZpgbZ3XW3o2IYtauf/ikejlaVy7Nk9h4Vh85w4mbwZmxqzJwsbOipp8HJob6RCckceV+KJtPXnppjanX1ahBA6KVShYuWUpkVBQ+Xl7MnDIF22dDbcLDwzUyYB4/ecLnX3dVP161bh2r1q2jQrlyzJ/9iixHLWvQsBHKaCVLFy8kKjISL28fpkyfoR6aEh4ejiLbzbLSZcowYvRYFi+cz6L583B2ceHXiZPwyFbvsHbduvQfNJhVK1cwc9o0irsWZ8y48ZTJNiuXNn34cQdSkpNZNHsGifHx+JYsxZAx4zVq9oWHhhIXk/X/8gd16hIbq2Tj7ytQRmcOoRsyZhxWz2oh6unpMXj0r6xdtoTJo4eTnJRMMUdHvus3kPKVq+aI4U00/+hjUpKTWTZ3NokJ8fiUKEn/kWM04o8ICyUuNuu67e7tW0z8eYj68dqliwCoUb8h3/TuR3RkJOefzaw2oo9mnaXBv0ygROn81T3Nj8atP+JpcjJrFvxGYmICnn7+/DhsFPrZ4n8cHkZ8XNb+D7lzm+mjskYPbFqRedOnWp36dOnZB4ByVavzWffv2P3HJjYsXUQxRye6DxiCV4msephvqmnbdqSkJLNy/mwSExLwLuFPn+FjNGMPCyMu2zVdcNAtpozIuj7esCxzxuwP6jXg6x/7ai22V6nboCFKpZIVSxYTHRWFp5c346ZMVRfJjggP1yhBUrJ0aYaOHMXyRQtZtnABTs7OjBo3HvdnHdZPHj/mxNGjAPT46kuN95oyazZly2u3GH/9ho1QKpUsXbSIqKhIvLy9mTxtuvrcExEepnHNUKp0GYaPHsOShQtYtGA+zs4u/Doh69yjq6tD0O3b7N65k/j4OOzs7KhUpSpdu3fPUb9TiFdRZLyNX57i/9qoUaPYunXrS4cUfvnllyiVSrZu3frO4ioMhw4dol69ekRHR7+zWlDvuwcPHuDi4sL+/fvzLDgt3h+BiyYXdggF5vvNQII2LCnsMArEs31Xjrav8eqG/1I1Nxxj1PpdhR1GgY3q0IzOc3LWNPsvWNmzEzER/86C3/lhWbQY4VHKwg6jwIrZWHE+qOCz7RWm8p7FOXEj6NUN/6Wq+3ny96XAwg6jwOqX8eXI1byH0/6b1SrpTUjE28nWfxeKF7UjLPLt3UB52+xtrV/dqJBc/+3tTV7wMiV+0N7op/8SyVwSQvyr/f3338THx1O6dGlCQ0MZNGgQbm5u6lpYQgghhBBCCCEKl9Rc+o8zMzPLczly5MirN/CCcePG5bm9Zs0KXqTv30jb++7fqlmzZnl+znHjxhV2eK+UmprKTz/9RMmSJWnbti1FihTh0KFDOWZp+6/o0aNHnn+P58POhBBCCCGEEG9IoSic5f+UZC79x71s6JmTk9Nrb69Hjx60b98+19deNT35c6NGjXpl7aTly5e/ZmTap+19l5u6deu+lZpHr2Px4sUkJeU+u4+NTe7Trv6bNGnShCZNmhR2GFozZswYdbH2F73uDHBCCCGEEEII8W8gnUv/cV5eXlrdno2NzX+iw0EbtL3v/q201VEmtKNo0aIULVqw6YCFEEIIIYQQ4t9IOpeEEEIIIYQQQgjxXlEopArQuyR7WwghhBBCCCGEEEIUmGQuCSGEEEIIIYQQ4v3yf1xcuzBI5pIQQgghhBBCCCGEKDDJXBJCCCGEEEIIIcT7RWouvVOyt4UQQgghhBBCCCFEgUnnkhBCCCGEEEIIIYQoMBkWJ4QQQgghhBBCiPeKQkcKer9LkrkkhBBCCCGEEEIIIQpMMpeEEEIIIYQQQgjxflFI5tK7JJlLQgghhBBCCCGEEKLApHNJCCGEEEIIIYQQQhSYDIsTQgghhBBCCCHE+0UhuTTvkuxtIYQQQgghhBBCCFFgioyMjIzCDkIIIYQQQgghhBBCW24unV4o7+vzdd9Ced/CJsPihBBC/GvdfBBW2CEUmI+zPWv+CSjsMArks9qVGbV+V2GHUWCjOjTjaPsahR1GgdXccIz7B7YXdhgF4tKgJSERTwo7jAIrXtSOyJjYwg6jwGwtLf6z8dtaWhAdG1fYYRSYtYU5YZHRhR1GgdnbWvNEGVPYYRSInZXlf/68c+vhf/d6x9vJvrBDEP8SMixOCCGEEEIIIYQQQhSYZC4JIYQQQgghhBDi/aIjuTTvkuxtIYQQQgghhBBCCFFgkrkkhBBCCCGEEEKI94pCoSjsEP6vSOaSEEIIIYQQQgghhCgwyVwSQgghhBBCCCHE+0UhuTTvkuxtIYQQQgghhBBCCFFg0rkkhBBCCCGEEEIIIQpMhsUJIYQQQgghhBDi/SIFvd8pyVwSQgghhBBCCCGEEAUmmUtCCCGEEEIIIYR4ryh0JJfmXZK9LYQQQgghhBBCCCEKTDqXhBBCCCGEEEIIIUSBybA4IYQQQgghhBBCvF8UkkvzLsneFkIIIYQQQgghhBAFJp1L4j+hbt269OnTp7DDyNWNGzeoVq0aRkZGlCtXrrDDUXtxn7m5uTFjxow32uaoUaP+VZ/xXfjyyy9p06ZNYYfx1mnj+BBCCCGEEOLfQqFQFMry/0qGxQnxhkaOHImpqSmBgYGYmZkVdjh5CggIwNTUtLDD0Iq6detSrlw5rXaGBAcH4+7uzvnz5zU60GbOnElGRobW3uff6n04PjIyMli9fCl7d24nIT6eEqVK833vfjg6O790vR1b/2DLhnVER0Xh7unJtz/2xsevBABxsbGsWbGU82fO8DgiHAsrK6rVqMnnX3bFVIvf94yMDA5t28y5IwdJTkzExcuHFp2+wraYfZ7rHNm5jRvnAngSFoqegQEunt40bNcBO3tHdZuoiHD2bVxDyO2bpKWl4lWyDM0+64KZhaXWYn+uXilvKng4Y6Svz/0n0Ww/e5Wo+MR8rVvTz4OGZX05eTOY3eevq583MzKgUVk/PIvZYaCvS2RcAv9cC+L6g3CtxGxRoizOH36GqbsfhjZ2XJs8hKiAIy9dx9K/PO6df8TExZ2UyAjub15BxOGdGm0cmnyEU6vPMLCyIeHebYKWTic+6HoeW3wzfx4+yoZ9h4iKjcPT2ZGe7dvi51b8lesdPHOeX5eu4oMyJRnT42v18xkZGazYvoedx04Sn5RESQ93endsh3PRIm8n/i2b2bh2DVFRUXh6evFDn774+fvn2f7wwb9ZsXgRYWFhODk7063Hd1St/oH69ZVLl3DowH4eR0Sgp6ePt68vX33TnRIlS2o99s0bN7B61SqiIiPx8vam34CB+L/kff7ev5+FC+YTFhqKs4sL3/f8kQ9q1FC/npiYyLzf5vDP4cPExMTg6OjIJ+070LZdO63H/j7Ev2nDBlat+l0df/+BAylZslSe7Q/s38/C+fMIDQ3FxcWFH378kQ9q1FS/HhkZyW+zZ3P61Eni4uIoX74C/QYOpHjxV3+fXtcfmzexbvWqzOPey4ve/fpTwj/vfX/w7wMsXbiQsLBQnJxd6PH9D1T7IOu4H//LGHbv1DwPValajcnTZ2g9doDNGzeyZnXWsdO3/4CXHzsH9rNowQL1sfPdDz01jp0aVavkut73PX+k0xdfaD1+bZ530tLSWLZoIadPniDs0SNMTE2pUKkyXXv0wM7u7Zw3n1/v7NmR7XqnTz+cXnG9s33rH2xZr3m941uihPr13du3cejAAYJu3SQpMZF127ZjZmb+Vj6DeD9J5pL4v5Weno5KpXrj7QQFBVGzZk1cXV2xtbXVQmRvR5EiRTAxMSnsMF7q6dOnhR1CDpaWllhZWRV2GAWW3336Xzg+XmXzurVs/2ML3/fpz5Q58zEyMmLEkAE8fZqS5zpHDv7N4vm/0bFzF2bMX4S7pycjBg9AGR0NQFTkEyIjI/n62++Ys2Q5fQYN5dzp08yaMkmrsR/bvZ1TB/bS4vOv6fbTaAwMDFk1YyJpqXn//e7dvE7leo3oOnQUX/QdjCo9jVXTJ/I0JRmApynJrJoxERQKOvf/ia8HjyQ9PZ21s6eSoYVzX3Y1/Dyo6u3K9jNXWbz/BE/T0/miTmX08jEFsKONJRU9XQhTxuZ4rW3VstiZm7L26Fnm7T7K9QfhfFK9PPZWFlqJW9fQmPjg29xZMjVf7Q2LOOA/ZDIxV89xftCXPNqxAe8eg7Eqm/XDyK56A9w7/0jIpqWcH/w1CfduU2rYNPQtrLQSc3YHz5xn/uZtfNGiMfOH9sXDyZEhsxcSHRf30vXCIqNYsOUvSnt55Hht/b6D/HHoCL07fsycgb0xMjRgyOyFPE1N1Xr8hw7sZ8Gc2Xz+5dfMW7wUDy8vhvbvR/Sz79+Lrl6+zLjRo2jaoiXzliyjRq1ajPppKHfv3FG3cXZxoWfffixcsZLpc+dSzN6eIf37qr/T2rJ/315mzZjB1926sWzl75k/sHv9SFRUVK7tL1+6yMjhP9Pqw9Ys/30VtevUYcjAAQQF3Va3mTVjOidPnGDk6DGsXb+B9p9+yrQpkznyz2Gtxv4+xL9v715mzphOt27fsOL3VXh7+9Dnx7zjv3TxIiN+Hkar1q1ZsWo1tevUZdCAAQTdzow/IyODwQMH8OjRQyZNmcrKVauxd7Cn1w/fk5SUpNXY/96/j99mzaTL191YtGwFnl7eDOjbh+g8Yr9y+RJjR46geatWLFq+glq1azNsyCDuBAVptKtSrRpb/tqhXkaMHqPVuJ/bv28fs2fO4Ouu3Vi6YiVeXt70690rz/gvX7rEqOHDadnqQ5at/J1ateswdNBAjfi37dypsfz083AUCgV169fXevzaPu+kJCdz+2Ygn3f5krlLljLy13E8CAlhxJDBWo/9uc3r1vLXli380Lc/U397dr0z+OXXO/8c/JvF8zKvd2YuyHm9k/lZUqhYuQrtP/v8rcX+zukoCmf5PyWdS+K11K1bl169ejFo0CBsbGywt7dn1KhRQGbmh0Kh4MKFC+r2SqUShULBoUOHADh06BAKhYI9e/ZQvnx5jI2NqV+/PhEREezatYsSJUpgYWHBZ599RmKi5h3vtLQ0evbsiaWlJXZ2dgwfPlwjoyQlJYUBAwbg5OSEqakpVatWVb8vwPLly7GysmLbtm34+/tjaGhISEjISz+vSqVizJgxODs7Y2hoSLly5di9e7f6dYVCwdmzZxkzZgwKhUK9L15m8ODB+Pj4YGJigoeHB8OHDyc120X786FnCxYswMXFBRMTE9q3b09MTIy6zfOhWqNHj6ZIkSJYWFjQo0ePl3YkvDjsSalU0q1bN/X69evX5+LFixrrTJgwgWLFimFubk7Xrl1JTk5+5ed7nRjr1q1Lz5496dOnD3Z2djRp0gSAw4cPU6VKFQwNDXFwcGDIkCGkpaWpt3v48GFmzpypTj0NDg4G4MqVKzRr1gwzMzOKFSvGF198wZMnT9Tvp1KpmDRpEl5eXhgaGlK8eHF+/fVXANzd3QEoX7585gVN3boan+O5lJQUevXqRdGiRTEyMqJmzZoEBASoX39+jB84cIBKlSphYmLCBx98QGBgYL7228WLF6lXrx7m5uZYWFhQsWJFzpw5o3796NGj1KpVC2NjY1xcXOjVqxcJCQnq193c3Bg7diydO3fGwsKC7t2788EHHzB4sOZFzuPHj9HX1+eff/5Rr/fi8fHtt99SrFgxjIyMKFWqFNu3b893HHPnzsXb2xsjIyOKFSvGxx9/nK/PX1AZGRls27KR9p9/QbUaNXH39KTv4J+IehLJyaNH81xv66YNNGnekoZNm1PczY3v+/TH0NCIfbsz7wC7unvw06ixVPmgBg6OTpQtX4Evunbj9MnjpKenaS32Uwd2U7tFa/zKVaSYc3HafN2DOKWSG+fP5rne530GU65GbYo6OWPv4krrr74lJiqS0HvBANy/fQvlk8e0+ao7xZxdKObsQpuvvuXRvbvcvXFNK7E/V83HlX+uBRH4KILwmDj+OHUJc2ND/JyKvXQ9Az1d2lUry19nrpD8NGfnhYutFadu3eNhVAzRCUn8cy2I5NRUHG2007kUfeEkIesXERnwT77aOzRuQ3JEKHd/n0PSw3uE7tnMk5OHcGrRQd3GqWUHwg78RcShnSQ9DOb2osmkP02hWL2WWok5u81//0PzGtVoWr0Krg729OnYDkMDfXYfP53nOukqFeOXraZLiyY42NlovJaRkcGWv/+hU9OG1ChbCg9nRwZ36UhkTCzHLl7Rfvzr19OsVSuatmiBq7s7vQcMxNDIkD07tufa/o9NG6hcpSrtP+uEq5sbX3brjpePD39u2aRuU79RYypUqoyDoxNu7h70+LEXiQkJOX6Iv6l1a9bwYZs2tGz1Ie4eHgwaMhRDIyO2/7Ut1/Yb1q2jarXqdPriC9zc3ene4zt8/fzYvGGjus3lS5do3qIFFSpWxMHRkTZtP8LL25trV7X7fX0f4l+7ZjWt27Sh5YeZ8Q8eOhQjIyO2b8s9/vXr1lGtenU+/6Iz7u7ufPtdZvybNm4A4H5ICFcuX2bQ4CH4lyyJq5sbg4YMJSUlhb179mg19g3r1tLyw9Y0b9kSN3d3+g8ajJGhETu3537cb9qwnipVq9Gx0+e4ubnTtfu3+Pj68sfmTRrtDPQNsLW1VS/mFto5T75o/do1tGrdhhatWuHu4cHAIUOeHTt/5dp+w/p1VK1WLdux0wMf36x9D2Bra6exHPnnMBUqVsTJyUnr8Wv7vGNqZsbE6TOpU78BLsVd8S9Zip59+3ErMJCI8DCtx5+RkcGfmzfSIdv1Tr8hmdc7J152vbMx83qnUbPM650f+j673tmVlfHW+uNP+OSzTvi+JItLiJeRziXx2lasWIGpqSmnTp1i0qRJjBkzhn379r3WNkaNGsWcOXM4fvw49+/fp3379syYMYM1a9awY8cO9u7dy+zZs3O8r56eHqdPn2bmzJlMmzaNxYsXq1/v2bMnJ06cYN26dVy6dIlPPvmEpk2bcuvWLXWbxMREJk6cyOLFi7l69SpFixZ9aZwzZ85k6tSpTJkyhUuXLtGkSRM+/PBD9TZDQ0MpWbIk/fv3JzQ0lAEDBrzys5ubm7N8+XKuXbvGzJkzWbRoEdOnT9doc/v2bTZs2MBff/3F7t27OX/+PN9//71GmwMHDnD9+nUOHTrE2rVr2bJlC6NHj37l+z/3ySefqDv1zp49S4UKFWjQoIH6rt+GDRsYNWoU48aN48yZMzg4ODB37tx8bz+/Ma5YsQIDAwOOHTvG/PnzefjwIc2bN6dy5cpcvHiRefPmsWTJEn755Rcg829SvXp1vvnmG0JDQ9Xp7Uqlkvr161O+fHnOnDnD7t27CQ8Pp3379ur3Gjp0KBMmTGD48OFcu3aNNWvWUKxY5o/f06czf4zt37+f0NBQtmzZkutnGjRoEJs3b2bFihWcO3cOLy8vmjRpkuNu6bBhw5g6dSpnzpxBT0+Pr7/+OtftvahTp044OzsTEBDA2bNnGTJkCPr6+kBmllzTpk1p164dly5dYv369Rw9epSePXtqbGPKlCmULVuW8+fPM3z4cDp16sS6des0OmPXr1+Po6MjtWrVyhGDSqWiWbNmHDt2jFWrVnHt2jUmTJiArq5uvuI4c+YMvXr1YsyYMQQGBrJ7925q166dr89fUOGhoURHRVGuQkX1c6ZmZviUKMGNa1dzXSc1NZXbN29SNts6Ojo6lKtQkcA81gFIiE/AxMQEXV3tjCxXPnlMfEwMHiWyhnMYmZjg7OHJ/Tu3XrKmppSkzA5542fDG9PSUkGhQFdPX91GT18fhUJByO38dXbmh7WpMebGRtwJz+rITUlN40FkDM52Vi9dt3kFf24+iuBOeGSur9+PVFKquAPGBvoogFIuDujp6hAckfsd8rfN3LsUystnNJ6LvngKc5/Mv51CVw8zD1+Ul7M6nMnIQHn5jLqNtqSmpXEz5AEVfL3Vz+no6FDBz4drd+/lud6qnXuxMjejWY2qOV4LjYwiKjaOCn4+6ufMjI0p4Vaca3fy3maB4k9N5ebNQCpUrKwZf6VKXLuae0fWtStXqVCpksZzlapU5fqVvL/jO7f9iamZGZ5eXlqNPfDGDSpVzspY09HRoXLlKly5fDnXda5cvkzlKpU1nqtarZpG+9JlynDkn394HBFBRkYGZ8+c4X5ICFWq5vxbSfw3qFwla7s6OjpUrlKFy5cv5RH/JSpX1hx6Va1adS4/i/95Zp6BoaHGNvX1DbiY7aapNmK/GRhIxUqax33FypW5eiX3fX/1yhUqVtbc95WrVsvR/sL5c7Ru3ozPP23P1MkTNW5KajP+zH2vGX+lypXzPHauXr6scaxB5rFzNY/2UZGRHD92jJYffqi9wJ95F+cdgISEeBQKBaZvYUiZ+nqnoub1jm8+rneyr6Ojo0O5ihXzXEeIgpCaS+K1lSlThpEjRwLg7e3NnDlzOHDgAN7e3q9YM8svv/xCjWdjrbt27crQoUMJCgrCwyMzRf/jjz/m4MGDGtkWLi4uTJ8+HYVCga+vL5cvX2b69Ol88803hISEsGzZMkJCQnB0zKw3MmDAAHbv3s2yZcsYN24ckHlynTt3LmXLls1XnFOmTGHw4MF8+umnAEycOJGDBw8yY8YMfvvtN+zt7dHT08PMzAx7+7xro2T3888/q//t5ubGgAEDWLduHYMGDVI/n5yczMqVK9V3bGbPnk2LFi2YOnWq+n0MDAxYunQpJiYmlCxZkjFjxjBw4EDGjh2LziuGohw9epTTp08TERGB4bMLqSlTprB161Y2bdpE9+7dmTFjBl27dqVr165A5t9s//79r5W9lJ8Yvb29mTQpa4jRsGHDcHFxYc6cOSgUCvz8/Hj06BGDBw9mxIgRWFpaYmBggImJicY+nzNnDuXLl1f/rQGWLl2Ki4sLN2/exMHBgZkzZzJnzhy6dOkCgKenJzVrZtZbKFIkc1y8ra1tnn/LhIQE5s2bx/Lly2nWrBkAixYtYt++fSxZsoSBAweq2/7666/UqVMHgCFDhtCiRQuSk5MxMjJ66T4LCQlh4MCB+Pn5qffPc+PHj6dTp07qQu3e3t7MmjWLOnXqMG/ePPW269evT//+/dXrtW/fnj59+qizjQDWrFlDx44dcy06uH//fk6fPs3169fx8cn8kfn8u5mfOEJCQjA1NaVly5aYm5vj6upK+fLlX/q531R0dGZng5W1ZiaGlbW1+rUXxcbEoFKlY21tnWOdB/dzz2qMiVGyftVKmrRopYWoM8XHKAEwfeEus6m5BQn5/HGQoVKxe90qXLx8KOrkAoCzhxcGhobs37yOBm3bk0EG+zevJ0OlIu7Ze2qDmVHmOSQ+WTNzMiE5Rf1abkq5OOBgbcmifcfzbLPx+Hk+rl6OwW0bkq5SkZqWzvqj5/Ndy0nbDKxsSI3RPJ5SY6LRMzFDR98APTMLFLp6pCpfaKOMwsRRu3VbYuITUKlUWFto/nixNjfjfnhErutcvn2HXcdPs+Cnfrm+Hh2TOTTxxW1aWZgTFZtz2OKbiIlRokpPx9pG8ztrbW3D/Xu5f/+ioyKxerG9jQ1RUZqdkyePHePX0SNJSU7GxtaWidNmYKnF4c1KpZL09HRsXojFxsaGe88yB18UGRmJtY3m0HlrGxsis8Xeb8BAJo4bR+uWLdDV1UVHR4chPw2jfIUKWov9fY7f2sZGncmcW/w2tjnbR0Zmxu/m5oa9vT3zfpvD4KE/YWxszNo1q4mICCcy8klumyyQmGex5zjubawJyWPfR0VGYm394vfEmqjIrH1fpWp1atepi72jI48ePGTRgnkM6teXuQsXqW8MacPLjp2Qe7l3QEdGRubaPjIy9/+bd+3cgYmpKXXq1tNO0Nm8zfPOc09TUlg8bx71GjZ8K7Usnw8/zO16R5nH0MTn1ztWuV3vvGIUx3+eQnJp3iXpXBKvrUyZMhqPHRwciIjI/UI2P9soVqyYeohY9ueeZ5I8V61aNY0fwtWrV2fq1Kmkp6dz+fJl0tPT1T+En0tJSdGog2RgYJAj/rzExsby6NEjdSfYczVq1MgxfOx1rF+/nlmzZhEUFER8fDxpaWlYvPCjsnjx4hqpwNWrV0elUhEYGKju+ChbtqxGjZzq1asTHx/P/fv3cXV1fWkMFy9eJD4+PkeNqKSkJIKeDR24fv06PXr00Hi9evXqHDx4MN+fNT8xVsx2F+X5+1avXl3jb12jRg3i4+N58OBBnoU1L168yMGDB3Mtqh4UFIRSqSQlJYUGDRrkO/7ctpOamqpxTOjr61OlShWuX9cs1pv9OHNwcAAgIiLilYVB+/XrR7du3fj9999p2LAhn3zyCZ6enurPeOnSJVavXq1un5GRgUql4u7du5R4VpSx0gt32IoUKULjxo1ZvXo1tWrV4u7du5w4cYIFCxbkGsOFCxdwdnbO8X167lVxNGrUCFdXVzw8PGjatClNmzalbdu2L63plJKSQkqKZq0AQ8O8OyYO7d/Hb9Oz6uSMGDchz7bakpiQwJifhuDi6spnXb4q8HYunTzG9lVL1Y8/+/HVGY+vsmPNCiIePeDrQcPVz5maW/DJt73YsXoZp/7ei0KhoHSV6jgUd0PxBhdbpV0daVUxq3Dr6iN5D93Li4WxEU0rlOD3QwGkvaT+U73S3hgZ6LPi4GkSnz7Fz6kYn3xQjqV/nyQiJr5A8f+/SkxOZuKKtfTr9AmW/+LJJ7ShbIUKzF+6nJgYJbv++otfRg5n1oJFOTqS/202bVjP1SuXmTR1Kvb2Dlw4f56pkydhV8ROI0vn3+q/HL+enh4TJk3m17FjadygPrq6ulSuXIXqH3zAf2FOjwaNGqn/7enphaeXFx0/aceF8+c0sqT+C7b/9ReNmzR56TXAv1VaWhpjR2aW7ejVf+CrV8iHg/v38du0rOudkePf/vWOEAUlnUvitT0fovOcQqFApVKpM1GyD71JzaMAaPZtKBSKPLeZX/Hx8ejq6nL27Nkcd2iydzYYGxsX6vSQJ06coFOnTowePZomTZpgaWnJunXrmDo1f8VktSU+Ph4HBweNmlTPvevi1dq6qxMfH0+rVq2YOHFijtccHBy4k63g67vw4jEO5OuYHjVqFJ999hk7duxg165djBw5knXr1tG2bVvi4+P59ttv6dWrV471snda5bZPO3XqRK9evZg9ezZr1qyhdOnSlC5dOtcYjI2NXxrjq+IwMDDg3LlzHDp0iL179zJixAhGjRpFQEBAnsfX+PHjcwyZHDlyJJ9165Fr+yof1MAn2wwnz881yugobLJ1miqjo/HwzH04jIWlJTo6ujmKeCqjo3Pc1UxMTGTkkIEYm5gwbMwv6OkV/L9P33IVcPbwVD9OS82s3ZQQG4u5VdaP34S4WIq5vDrbZeeaFdy6dJ4vB/6MxQuZBZ4lS9Nr3DQS4+LQ0dXByMSUKf1/oGSRgs9gE/gwnIeRSvVj3WfnfjMjA+KTszoITY0Mcy3SDeBoY4GZkSHfNs6a7UhHRwfXIjZU8SrO2E17sDIxoaq3G7/tOsLj2MyOpHBlHK521lTxcmX72Xefyv9UGYW+peaxoW9pTVpiPKrUp6TGKslIT0Pf6oU2VjY8VWp3KJ+lmSk6OjpEx2oW746Oi8+ReQTw6HEkYZFR/Dwvq2Pz+f/XjXsOZPnIwVhbZt7oiI6Nw9Yy66aHMjYOT2ft1j6xtLRCR1c3RxHg6OgorF/IMHnO2sY2x5356KgobF447o2NjXFydsbJ2Rn/kqXo0rEDu7f/RccvOmsldisrK3R1dXMMh46K0jz/ZGdra0v0C5kO0VFR2D6LPSU5mflz5zJ+0mRqPMuo9fL25tbNm6xZtUqrnTPva/zRUVF5Tqxia2tLVOTL2/uVKMHva9YQHx9Pamoq1tbWfP1lF0qU0F79Gctnsec47qOicxzHz9nY2ubIwI2Ojs7zbwXg6OSEpZUVDx880Grn0kuPnTzit7W1zbW9bS7f8wvnzxNy7x5jfvlVazFn9zbPO2lpafwyYjgRYeFMnjlLa9e3VT+ooTGjW+rTvK933PMY/vv8eufFiQ1yu9553xTm777X9dtvvzF58mTCwsIoW7Yss2fPpkqV3GdSXL58OV99pXmj09DQUGOESUZGBiNHjmTRokUolUpq1KjBvHnzXmu00euSPDGhNc+HFYWGhqqfu6DFceqnTp3SeHzy5Em8vb3R1dWlfPnypKenExERgZeXl8aS3+FqL7KwsMDR0ZFjx45pPH/s2DH8C1jo7vjx47i6ujJs2DAqVaqEt7c393JJIw4JCeHRo0fqxydPnkRHRwdfX1/1cxcvXtSYweTkyZOYmZnh4uLyyjgqVKhAWFgYenp6OfaXnZ0dACVKlMh1n7+OgsRYokQJTpw4odFJeezYMczNzXF+NsWqgYEB6enpOT7T1atXcXNzy/GZTE1N8fb2xtjYmAMHDuT6vgYGBgA5tpudp6enuj7Uc6mpqQQEBBT4mMiNj48Pffv2Ze/evXz00UcsW7ZM/RmvXbuW4/N5eXmp489L69atSU5OZvfu3axZs4ZOnTrl2bZMmTI8ePCAmzdv5vp6fuLQ09OjYcOGTJo0iUuXLhEcHMzff/+d53sOHTqUmJgYjWXo0KF5tjcxMcHRyVm9FHd1w9rGhovnzqnbJCYkcPP6dfzymN5ZX18fLx8fLmUrmq1Sqbh4/hy+2dZJTEhgxKD+6Onr8/PYcRgYvNndVEMjY2yK2quXIo5OmFlacudGVmdJSlIiD+4E4eKR9wVARkYGO9es4Mb5M3Tu/xPWRfKuIWdibo6RiSl3r18lIS4W37IFH6byNC2dqPhE9fI4Np64pGTci2Vd5Brq6eFsa8mDJ8pct3EnPJK5u48wf+8x9fIwSsmle4+Yv/cYGRmgr5fzhgWAKiODwrpWjLt1BavSmtmWVmUqE3czs1ZHRnoa8XcCsSqVLXtQocCqVEV1G23R19PDp7gz5wKz6nKpVCrOB97C3z1n9mpx+6Is+nkAC37qp16ql/annI8nC37qRxFrKxxsbbCxMOd8tm0mJCVzPTgEf4+XZ8S+dvz6+vj4+HL+bFYNK5VKxfmzZ/HPYzp5/1IlOX9WM1Pu3JkASpTKewp0yBw2mtfNroLQ19fH18+Ps9kmc1CpVJw5E0CpPDrtS5UuzZls7QFOnzqlbp+WlkZaWho6L8wypKOrg0rLqTPvS/wBAVkZ7iqVioCAAEqXzj07vVTpMhqTbzyPP7ebLGZmZlhbWxMSEsKN69ep/WyIu7Zi9/H15exZzX1/7kwAJUvlvu9LlirF2TOasZ85fTrP9pCZKR0bE6P1WYyf7/szLxw7ZwPO5HnslCxdOkf8AadPUTKX9tv/2oavnx/eeWROv6m3dd553rH08MF9Jk6fgYWlpdZiznG945Z5vXPhheudwHxc71w898L1zrlzea4j3q3169fTr18/Ro4cyblz5yhbtixNmjR56eggCwsLdf3Z0NDQHL8pJ02axKxZs5g/fz6nTp3C1NSUJk2avFaJk9clnUtCa4yNjalWrRoTJkzg+vXrHD58WKO+0JsKCQmhX79+BAYGsnbtWmbPnk3v3r2BzB/jnTp1onPnzmzZsoW7d+9y+vRpxo8fz44dOwr8ngMHDmTixImsX7+ewMBAhgwZwoULF9Tv+7q8vb0JCQlh3bp1BAUFMWvWLP74448c7YyMjOjSpQsXL17kyJEj9OrVi/bt22t0lD19+pSuXbty7do1du7cyciRI+nZs+cr6y0BNGzYkOrVq9OmTRv27t1LcHAwx48fZ9iwYeqZyXr37s3SpUtZtmwZN2/eZOTIkVy9+nqZAgWJ8fvvv+f+/fv8+OOP3Lhxgz///JORI0fSr18/9Xpubm6cOnWK4OBgnjx5gkql4ocffiAqKoqOHTsSEBBAUFAQe/bs4auvviI9PR0jIyMGDx7MoEGDWLlyJUFBQZw8eZIlS5YAULRoUYyNjdWFwHMrhGlqasp3333HwIED2b17N9euXeObb74hMTFRXZvqTSQlJdGzZ08OHTrEvXv3OHbsGAEBAerhboMHD+b48eP07NmTCxcucOvWLf78888cBb1zY2pqSps2bRg+fDjXr1+nY8eOebatU6cOtWvXpl27duzbt4+7d++ya9cu9UyJr4pj+/btzJo1iwsXLnDv3j1WrlyJSqXS6Bx9kaGhIRYWFhrL66TEKxQKPvzoE9avXsmp48cIvhPEtAnjsLGzpdqzu+gAwwb0ZfvWrGLtbT5uz54dOziwZzf37wUzd8Y0kpOTaNgks6ZWYkICIwYPICU5mV4DBpGUmEB0VCTRUZEv7Yh8HQqFgqoNmnJkx1YCL5wl/MF9/li6AHMrK/zKZ3VkrJw6jtN/71U/3rlmOZdOHuOjbt9jaGREfIyS+BglqdlmZDx/7DAPgm4TFRHOpZNH2bhgNtUaNsXO3lErsT938uY9avt74etYlKKWZrStWoa4pBRuPAxXt+lctzJVvDIzsZ6mpRMRE6+xpKalk/Q0VT3c7UlsApFxCbSqVAonG0usTU2o7uuGp70dNx6+3lDsvOgYGmPq6o2pa2YnnlFRR0xdvTG0zSz079qxBz4/ZP0/Frp3K0ZFHXHr9D3GjsWxb9yWItXr83DHenWbh9vXY9+gFUXrNMPYyRXPbgPQNTQi/FDB/y/KS7v6tdl57BR7TwZwLzScmes2k5zylKbVM+90Tli+hsVbM9/XQF8fd0cHjcXMxBhjQ0PcHR3Q19NDoVDwUf3arN61n+OXrnDnYSgTV6zB1tKCGmW1W5AcoF2HDuzc/hd7d+3kXnAws6ZOITkpmSbNWwAw8ZexLJk/T92+7cftCTh1ko3r1hJy7x4rly7h5o0btP4oczbKpKQkliyYz7WrVwgPC+Nm4A2mjB/HkydPqF1Pu/VbPv3sM7b9uZWd27cTfPcukydOIDkpiZYtM+uxjRk5knm/zVG3b//pp5w8cYI1q1cRHBzM4oULuXH9Ou3afwJkFuQtX6ECc2bN4tzZszx6+JAd2/9i186d1KlTV6uxvw/xd/ysE9u2bmXH9u3cvXuXSRPGk5yURItWmfGPHjmCuXOy4u/w6aecPHGc1asy41+0cAHXr1/j40+yJv04sH8/Z8+e4eGDB/xz+BC9ev5A7Tp1qFqtmlZjb/9pR3Zs28bunTsIDr7LtMmTSEpOplnLzOP+1zGjWTgvawKVj9t34PTJk6xfs5p7wcEsW7yIwBvXadsu87hPTExk3pzZXL1yhdDQR5w9E8CwwQNxcnamclXtxg7QoeNn/PXnn+zckXnsTJk4keTkJFq0zJwRc+yokcz77besz9sh89hZuzoz/iWLMo+d7PseICE+noMHDtCqdWutx5ydts87aWlpjBk+jJuBNxgyYiQqlYqoyEiiIiO12qn9nEKhoHW7T1i/aiWnjmle71TPdr3zU/++/PVHtuudT/K43mnaTN0mOiqSO7dvEfrwIQDBd+5w5/Yt4rRcc0/kNG3aNL755hu++uor/P39mT9/PiYmJixdujTPdRQKBfb29url+SRFkHljbsaMGfz888+0bt2aMmXKsHLlSh49esTWrVvf2ueQYXFCq5YuXUrXrl2pWLEivr6+TJo0icaNG2tl2507dyYpKYkqVaqgq6tL79696d69u/r1ZcuW8csvv9C/f38ePnyInZ0d1apVo2XLgk//3KtXL2JiYujfvz8RERH4+/uzbdu2AqcTfvjhh/Tt25eePXuSkpJCixYtGD58OKNGjdJo5+XlxUcffUTz5s2JioqiZcuWOWZqa9CgAd7e3tSuXZuUlBQ6duyYYzt5USgU7Ny5k2HDhvHVV1/x+PFj7O3tqV27tvrE1KFDB4KCghg0aBDJycm0a9eO7777jj2vMSVvQWJ0cnJi586dDBw4kLJly2JjY0PXrl01OioHDBhAly5d8Pf3Jykpibt37+Lm5saxY8cYPHgwjRs3JiUlBVdXV5o2barulBo+fDh6enqMGDGCR48e4eDgoK4rpaenx6xZsxgzZgwjRoygVq1auQ4bnDBhAiqVii+++IK4uDgqVarEnj17tFLLQ1dXl8jISDp37kx4eDh2dnZ89NFH6uFiZcqU4fDhwwwbNoxatWqRkZGBp6cnHTp0eMWWM3Xq1InmzZtTu3btV9Z+2rx5MwMGDKBjx44kJCTg5eXFhAkT8hWHlZUVW7ZsYdSoUSQnJ+Pt7c3atWspWfLt3h1r92lHkpOTmDNtCgnx8fiXLs3o8ZM1Mo3CHj0iNlvHYa169YmJUbJ6+VKio6Pw8PRi9ITJ6jTxoFs3CbyeOY129y8+03i/xavXUczeQSux12jaktSnKfz1+1KSExMp7u3D570HoaeflZEW9TiCxPisIVBnDmVm4a2Yojl8oPWX3SlXI3N2vsiwUA5s2UBSQjxWtkWo1fxDqjVqhrYdu3EHAz1dWlUqhZGBHiGPo1l1WLOeko2ZCSaGL8+wy06VkcHqf87QsIwvHWtVxEBPl6j4RP44dYlboY+1Ere5px+lR2X9APXokjnUM/zQTm7N/RUDa1sM7bIu1lIeh3JtwkDcu/TCsfknpEQ+5tb8iSgvZmVQPDlxAH0LK4q374aBlQ0Jwbe4Mq4/qTGawxG0oV6l8sTEJ7B8+x6iY2PxdHZifM9v1MPiIqKVOTJJXqVDo3okpzxl+ppNxCcmUcrTnQk9u2PwwvB1bajboCFKpZIVSxYTHRWFp5c346ZMVX//IsLDNYY0lCxdmqEjR7F80UKWLVyAk7Mzo8aNx/1ZzUZdHR3uh9xj38+7iI2JwdzCAt8SJZg+Zy5u7h65xlBQDRs1RhmtZNHCBURFRuLt48O0mbPUw1TCw8M09n3pMmUZPfYXFs6fx4K5c3F2cWHC5Cl4Zhu2O+aXX5k39zdGjRhObGws9vb2fNvjO9q2a6fV2N+H+Bs1boxSGc2iBfOJfBb/9Fmz1Zk6YWFhGrXlypQty5hffmXBvLnMn/sbLi4uTJoyRWMWwSdPnjBz+nSioiKxs7OjWfMWfN2tm9Zjr9+wEUqlkqWLFhEVFYmXtzeTp01XD7OKeGHflypdhuGjx7Bk4QIWLZiPs7MLv06YhMezeoy6ujoE3b7N7p07iY+Pw87OjkpVqtK1e/dXZjUXRMNGjVAqo1m8cKH62Jk6Y2a2YyccRbabiKXLlGHU2LEsnD+fBfMyj53xkyar439u/759ZGRk0KhxE63HnJ22zztPHj/mxNGjAPT46kuN95oyazZly2u3oD1kXe/Mzna9M2bCy693aterT4xSyaplWdc7YyZO1hgWt3PbNtauXK5+PKRP5v+JfQYN0eiE+k/5DxT0fvr0KWfPntXI2NfR0aFhw4acOHEiz/Xi4+NxdXVFpVJRoUIFxo0bp77Wvnv3LmFhYTRs2FDd3tLSkqpVq3LixAn1ZFXapsh4Md9cCFGoRo0axdatW186pPDLL79EqVS+1Z7nN/VfiFH8+918EFbYIRSYj7M9a/4JeHXDf6HPaldm1PpdhR1GgY3q0Iyj7Wu8uuG/VM0Nx7h/YHthh1EgLg1aEhKhvdm13rXiRe2IjPnv3qW3tbT4z8Zva2mRo4bYf4m1hTlhkdrvRH5X7G2teaLM3wyl/zZ2Vpb/+fPOrYf/3esdb6eClSB5F+5sWl4o7+vUqmOuE9XklpX/6NEjnJycOH78ONWrV1c/P2jQIA4fPpyjTAlk1vG9desWZcqUISYmhilTpvDPP/9w9epVnJ2dOX78ODVq1FDfTH+uffv2KBQK1q9fn2Ob2vDv78oTQgghhBBCCCGEeB06ikJZxo8fj6WlpcYyfvx4rX2s6tWr07lzZ8qVK0edOnXYsmULRYoUyXMm6HdFOpfE/zUzM7M8lyNHjrz29saNG5fn9po1+4+mk+ZB2/vu/0XJkiXz3G+rV68u7PCEEEIIIYQQb+B1Jqqxs7NDV1eX8PBwjefDw8PzPTGVvr4+5cuX5/bt2wDq9d5kmwUhNZfE/7WXDT1zcnr9aZd79OhB+/btc33tVdO7Pzdq1KhX1iVavnz5a0amfa/ad7Vq1Xp3wfyH7Ny5M88Cj9kL8QkhhBBCCCEKTlFINZfyGgKXGwMDAypWrMiBAwdo06YNkDmb34EDB/I1aQ9kznZ9+fJlmjdvDoC7uzv29vYcOHCAcuXKARAbG8upU6f47rvvXvvz5Jd0Lon/a17ZCjlqg42NDTbZCuO9z7S97/5fuLpqdzpvIYQQQgghxH9Xv3796NKlC5UqVaJKlSrMmDGDhIQEvvrqKyBzYisnJyf10LoxY8ZQrVo1vLy8UCqVTJ48mXv37tHt2SQECoWCPn368Msvv+Dt7Y27uzvDhw/H0dFR3YH1NkjnkhBCCCGEEEIIIUQh6NChA48fP2bEiBGEhYVRrlw5du/erR7VEBISop79GiA6OppvvvmGsLAwrK2tqVixIsePH8ff31/dZtCgQSQkJNC9e3eUSiU1a9Zk9+7dGBkZvbXPIZ1LQgghhBBCCCGEeL8oFIUdQb717Nkzz2Fwhw4d0ng8ffp0pk+f/tLtKRQKxowZw5gxY7QV4itJQW8hhBBCCCGEEEIIUWCSuSSEEEIIIYQQQoj3SmEV9P5/JXtbCCGEEEIIIYQQQhSYdC4JIYQQQgghhBBCiAKTYXFCCCGEEEIIIYR4v/yHCnq/DyRzSQghhBBCCCGEEEIUmGQuCSGEEEIIIYQQ4v2iI5lL75JkLgkhhBBCCCGEEEKIApPMJSGEEEIIIYQQQrxXFArJpXmXZG8LIYQQQgghhBBCiAKTziUhhBBCCCGEEEIIUWAyLE4IIYQQQgghhBDvF4UU9H6XJHNJCCGEEEIIIYQQQhSYIiMjI6OwgxBCCCGEEEIIIYTQlnu7NhfK+7o2a1co71vYZFicEEKIf60mv84v7BAKbM+wHtQdNaewwyiQQ6N60nnO6sIOo8BW9uzE/QPbCzuMAnNp0JKj7WsUdhgFUnPDMfZeuFbYYRRY43L+nLkVXNhhFFglbzdioyILO4wCsbCxJS46urDDKDBza2ui7wcXdhgFZu3ihvLR/cIOo0CsHF24eu9hYYdRYCVdnbj5IKywwygwH2f7wg5B/EvIsDghhBBCCCGEEEIIUWCSuSSEEEIIIYQQQoj3ikIKer9TkrkkhBBCCCGEEEIIIQpMMpeEEEIIIYQQQgjxftGRXJp3Sfa2EEIIIYQQQgghhCgwyVwSQgghhBBCCCHEe0VqLr1bkrkkhBBCCCGEEEIIIQpMOpeEEEIIIYQQQgghRIHJsDghhBBCCCGEEEK8X2RY3DslmUtCCCGEEEIIIYQQosAkc0kIIYQQQgghhBDvF4Xk0rxLsreFEEIIIYQQQgghRIFJ55IQQgghhBBCCCGEKDAZFieEEEIIIYQQQoj3ikJHCnq/S5K5JIQQQgghhBBCCCEKTDqXhPg/VrduXfr06VPYYfyrfPnll7Rp06aww0ChULB169a3+h7BwcEoFAouXLhQqHEIIYQQQgihdQqdwln+T8mwOCH+j23ZsgV9ff3CDuOV3Nzc6NOnj1Y7woKDg3F3d+f8+fOUK1dOa9t934SGhmJtbV3YYeRb59qVaFq+BGaGhlx7EMasXUd4FB2TZ/sVP3TC3so8x/Pbzlzhtz1HKWZpzsqenXJd95fNezly447WYv+qXhVaViiJmZEhV+6HMm37IR5G5R27jkLBl3Wr0KiMLzZmJjyJS2D3hev8/s8ZdZtaJTz4sFIpfByKYmliRLf567gd9kRrMWf3UZUy1C3phYmhPrdCH7P8UADhMXEvXcfa1Jj2H5SnrKsjBnq6hMfEs/jACe5GRAHwTYNq1CrhqbHOpXuPmPLXQa3F/efho2zYd4io2Dg8nR3p2b4tfm7FX7newTPn+XXpKj4oU5IxPb5WP5+RkcGK7XvYeewk8UlJlPRwp3fHdjgXLaK1mJ+zKFEW5w8/w9TdD0MbO65NHkJUwJGXrmPpXx73zj9i4uJOSmQE9zevIOLwTo02Dk0+wqnVZxhY2ZBw7zZBS6cTH3Rd6/FD5v7auXEtxw/sJykhAXdfPzp0+5aiDo55rnP72lUO/LWVkLtBxEZH023AEMpWrqrRJiU5iT/X/M7lgNMkxMVhW7QodZq1oGajplqPf/PqlRzcs5uEhHh8Svjz9fe9sHdyeul6e7dvY8eWTcRER1Hc3YMu336Pp6+f+vXw0EesWbKIwGtXSU1NpWzFinT59gcstXg+3rBpM6tWryYyKgpvLy8G9utHyZL+ubYNunOHBYsWc+PGDULDwujbuzeffdpBo82yFSs5ePgQ9+6FYGhoQJnSpen5/fe4ubpqLWbN+Dfx+6pVWfH370+pkiXzjH/+woXq+Pv16cNnn376QvwrOHjoEMH37mFoaEiZ0qX58Ycf3kr8m/7cxqoNm4iKisLL04P+Pb+npJ9frm3vBAezcPlKbty6TVh4OH2++5ZP232k0SYhMZGFy1dw+OhxopVKfLw86fv9d/j7+Wo9doCNf/zJ6vUbMve9pyf9e/WkZIk84r8bzIJlywm8eYvQ8HD6/PAdHT9up9GmzaedCA0Pz7Fuu9YfMqhPL63Hn5GRwbqVy9m3aweJ8fH4lSxF9159cHRyful6u7ZtZevG9SijonDz8KTbDz/i7VdC/frwAX25eumixjqNW7SiR+++Wo9/9fKl7N25nYT4eEqUKs33vfvh6Pzy+Hds/YMtG9YRHRWFu6cn3/7YG59n8cfFxrJmxVLOnznD44hwLKysqFajJp9/2RVTMzOtxi/eX/+/3WpCCGxsbDA3z/nDWojn7O3tMTQ0LOww8qV99XK0rlya2buO0Hv5FpJTUxnXsQX6urp5rtNr2WY+nbFCvQxZ/RcAR65ndho9jo3XeP3TGStYeTiAxJSnBASFaC32jjUq0K5qWaZtP8R3izeS9DSVyV98iIFe3rF3rFmB1pVLMXPnYbr8tpqF+4/TsUYFPqpaRt3GSF+fyyGhLNx/XGux5qZFBX8alfVl+aHTjN64h5TUNAZ+WA993bwvM0wMDfi5XWPSVSqmbDvIkNXbWXv0LAnJTzXaXbz3iB+XblYvc/ce01rcB8+cZ/7mbXzRojHzh/bFw8mRIbMXEh338k6xsMgoFmz5i9JeHjleW7/vIH8cOkLvjh8zZ2BvjAwNGDJ7IU9TU7UW93O6hsbEB9/mzpKp+WpvWMQB/yGTibl6jvODvuTRjg149xiMVdkq6jZ21Rvg3vlHQjYt5fzgr0m4d5tSw6ahb2Gl9fgB9m/7g8O7dtCh27f0/3UihkaGzB03htSnT/NcJyUlGSdXN9p/3T3PNltWLuP6hfN07tmHYdNmU7d5KzYuXcTlM6e1Gv/2zRvY89effPXDj4yZOhNDIyMmjPiJpy+J/8Q/h1i9eCEfdezELzN/o7i7BxNGDCNGqQQgOTmZCcN/AoWCn8ZNZOTkaaSlpTFlzAhUKpVW4t67fz8zZs2iW9ev+X35Mry9vfixb1+ioqJybZ+cnIyToyM9v/8OW1vbXNucO3+eT9q1Y+mihcyZOZO0tDR+7NOHpKQkrcSsEf++fUyfOZNvunVj1YoV+Hh782OfPi+N39nJiZ4//PDK+JctXsxvs2aRlpZGz969tR7/voOHmDl/Id2+6MSK+b/h7eFBnyHDiIpW5hF7Ck4ODvzQ7WtsbWxybTNu6nROnz3HyCGDWLVoPlUqVuTHQUOIeKL9mwn7/j7IzHnz6drlC1YsnI+Xpwe9Bw0hKjo69/hTknFydOD77t3yjH/Z/N/YuXmDepk9ZSIADerW1nr8AH9sWMeOrVvo0asvE2b9hqGREWOHDn7p9/booYMsWzCP9p93ZsrcBbh5eDLmp8EoX/jcjZq1YMm6Teqlc7e8z1MFtXndWrb/sYXv+/Rnypz5GBkZMWLIAJ4+TclznSMH/2bx/N/o2LkLM+Yvwt3TkxGDB6jjj4p8QmRkJF9/+x1zliynz6ChnDt9mllTJmk9/ndKoSic5f+UdC4J8X8s+7C4uXPn4u3tjZGREcWKFePjjz/O1zZUKhWTJk3Cy8sLQ0NDihcvzq+//qp+/fLly9SvXx9jY2NsbW3p3r078fHx6tefD0ObMmUKDg4O2Nra8sMPP5D67IdY3bp1uXfvHn379kWhUKB4dsKOjIykY8eOODk5YWJiQunSpVm7dm2+Y3N3dwegfPnyKBQK6tatm+fnGz9+PO7u7hgbG1O2bFk2bdqkfj06OppOnTpRpEgRjI2N8fb2ZtmyZQA8ffqUnj174uDggJGREa6urowfPz5f+xUys4aaNWuGsbExHh4eGu+bn32rUqkYM2YMzs7OGBoaUq5cOXbv3p3n+6Wnp/P111/j5+dHSEhmx0n2YXHPh9Ft2bKFevXqYWJiQtmyZTlx4oTGdhYtWoSLiwsmJia0bduWadOmYWVlle/PXVBtqpRm7dFznLgZzN2IKCZtO4ituQkf+LrluU5MYjLRCUnqpaq3K4+iYrgU8ggAVUaGxuvRCUl84OvOP9eDSE5N01rsH1cry+//nOFY4F3uhEcy/o/92JmbUtMvZ+fFc6VcHDh64y4nb90jTBnH4WtBBATdp4RTMXWbfZcCWXk4gLN37mst1tw0KevHtjNXOHf3AfcjlSzYfwIrUxMqeLjkuU7LCv5ExSey+MBJ7kRE8iQugSv3w4iIjddol5aeTkxisnpJTMn74v91bf77H5rXqEbT6lVwdbCnT8d2GBros/t43h0Q6SoV45etpkuLJjjYaf5QysjIYMvf/9CpaUNqlC2Fh7Mjg7t0JDImlmMXr2gt7ueiL5wkZP0iIgP+yVd7h8ZtSI4I5e7vc0h6eI/QPZt5cvIQTi2yMlCcWnYg7MBfRBzaSdLDYG4vmkz60xSK1Wup9fgzMjI4tHM7TT76hDKVq+Lk6sYXP/QmJjqKSwGn8lyvZPmKtPy0E2WrVMuzzd3AG1StUw/vkqWwLVqUGg0b4+Tqxr3bt7Qa/+4/t9KmQ0cqVfuA4u4efNdvEMqoSM6eyLtDd9fWLdRr0pQ6jZrgXNyVr3/ohaGhIYf37QHg5rWrPI4I59u+/Snu5k5xN3d69B3I3du3uHbpglZiX7N2HW0+/JAPW7bEw92doYMGYWRoyLbt23NtX9Lfn94/9qRxo0YY5JHxPHvGdFq1aIGnhwc+3t6M/PlnwsLCuX7jhlZizm712rW0ad06K/7BgzEyMnpF/D/S5KXxz6BVy5bq+EcNH05YWJjW41+7eQutmzelZdMmuLu6MrhPL4wMDdm+e0+u7f39fPnx229oVK9urtnmySkpHDpylJ7fdKN8mdK4ODnxTZcvcHZyZMu23PfHG8W/cTOtWzSnVbOmeLi5MqRfH4yMDPlrV+7XF/5+fvTq8S2N69fLc99bW1lha2OjXo6eOIWzoyMVypbVevwZGRls/2MzH3/2OVU+qIGbhye9Bg0hKvIJp48dzXO9vzZvpFGz5jRo0gwXVze+7d0XQ0ND/t6zS6OdgZEh1jY26sXE1FTr8W/bspH2n39BtRo1cff0pO/gn4h6EsnJo3nHv3XTBpo0b0nDps0p7ubG9336Y2hoxL7dmZmrru4e/DRqLFU+qIGDoxNly1fgi67dOH3yOOnp2rveEe836VwSQnDmzBl69erFmDFjCAwMZPfu3dSunb+7RUOHDmXChAkMHz6ca9eusWbNGooVy/xxm5CQQJMmTbC2tiYgIICNGzeyf/9+evbsqbGNgwcPEhQUxMGDB1mxYgXLly9n+fLlQObQPWdnZ8aMGUNoaCihoaFA5l3IihUrsmPHDq5cuUL37t354osvOH36dL5ie95u//79hIaGsmXLllw/3/jx41m5ciXz58/n6tWr9O3bl88//5zDhw8DqLe9a9curl+/zrx587CzswNg1qxZbNu2jQ0bNhAYGMjq1atxc3PL1359vu127dpx8eJFOnXqxKeffsr169fzvW9nzpzJ1KlTmTJlCpcuXaJJkyZ8+OGH3LqV88dVSkoKn3zyCRcuXODIkSMUL573sKBhw4YxYMAALly4gI+PDx07diQtLfPC49ixY/To0YPevXtz4cIFGjVqpNHZ+LbYW5lja2bKueAH6ucSU55y42EEJZzs87UNPR0d6pfyZs/FvH9IeNnb4WVvx54L2vux4WBtga25qUYHUELKU649CMffOe/Yr9wPpaKHM862VgB4FrOldHEHTt26p7XY8qOIhRlWpsZcvR+mfi7paSp3wp/gZW+X53rl3Z25GxFJz6Y1mfN1O8Z2aEZdf88c7fycijHn63ZM7NSKLnUqY2ZkoJW4U9PSuBnygAq+3urndHR0qODnw7W7ee/DVTv3YmVuRrMaVXO8FhoZRVRsHBX8fNTPmRkbU8KtONfuvNu/S27MvUuhvHxG47noi6cw9ykFgEJXDzMPX5SXA7IaZGSgvHxG3UabIiPCiVVG41s66weksYkpbl7e3L0V+Ebbdvf14/KZAJRRkWRkZHDzymUiQh/hV6bcG0ad5XF4GMroKEqWq6B+zsTUFE9fP27dyH0YYVpqKndv36JUtnV0dHQoVa48t25cU7dRgEZHgr6BPgqFgsCrV9847tTUVG4EBlKlciWNGKpUrszlK9rrBI2PTwDAwsJCa9uErPirVq6sfu55/JcuX9ba+zy/WaPN+FNTUwm8eYvKFTT//pUrlOfytWsF2mZ6ejrpKhUGBprnRkMDQy5eefPjJbvU1FRu3LxJlYovxl+By1cLFn9u77F7335aNWuqvqGoTeFhoSijoihboaL6OVNTM7z9ShB4PffPkJqaStCtm5Qpn7WOjo4OZcpXzLHOkb8P0OXjNvT+5mtWLVlESnKyduMPDSU6Kopy2eM3M8OnRAluXMv9752amsrtmzc1PrOOjg7lKlQkMI91ABLiEzAxMUFXVyrpiPyRI0UIQUhICKamprRs2RJzc3NcXV0pX778K9eLi4tj5syZzJkzhy5dugDg6elJzZo1AVizZg3JycmsXLkS02d3bubMmUOrVq2YOHGiuqPH2tqaOXPmoKuri5+fHy1atODAgQN888032NjYoKuri7m5Ofb2WT+0nZycGDBggPrxjz/+yJ49e9iwYQNVqlR5ZWxFimTWP7G1tdXYbnYpKSmMGzeO/fv3U716dQA8PDw4evQoCxYsoE6dOoSEhFC+fHkqVcq8SM/eeRQSEoK3tzc1a9ZEoVDg+pp1Gz755BO6desGwNixY9m3bx+zZ89m7ty5+dq3U6ZMYfDgwXz6rK7ExIkTOXjwIDNmzOC3335Tv098fDwtWrQgJSWFgwcPYmlp+dK4BgwYQIsWLQAYPXo0JUuW5Pbt2/j5+TF79myaNWum/tv4+Phw/PhxtudxN/m5lJQUUlI007lfZziejakJAMoEzeELyoQkbMyM87WND3zdMTMyZO+lvH/UNi1XgnuPo7j2MGdtiIKyMcuMPSo+UeP56IRE9Wu5WXP0LKaGBqzs2QmVSoWOjg6LD5xk/+WbWostPyxNjACISdTc9zGJyViZ5L3vi1iYUb+UD7svXOevM1dxL2bL57UrkaZScfTGXQAuhYRyJug+j+MSKGphxifVy9G/VT3GbNpLRkbGG8UdE5+ASqXC2kJzaLC1uRn3wyNyXefy7TvsOn6aBT/1y/X16JjYzG28sE0rC3OiYmPfKF5tMLCyITVGc9hQakw0eiZm6OgboGdmgUJXj1TlC22UUZg4vroO1euKfTYMzPyFc465pZX6tYL6+KtvWLdwLsO/64aOri46CgWfdv8eL//ca/IUhDI6cz9ZvpCZaWllhVKZ+/CsuNhYVCpVjnUsrKx59CCzg9nLzw9DIyPWLVtC+85fkQGsX74ElUqlfs83ilupJD09HZsXhijZ2NgQfE87naAqlYppM2ZQtkwZvDxzdhq/iTzjt7YmODhYK++hUqmY+hbiV8bEkq5SYWNtpfG8tbU1wfcLlmFqamJCaf8SLF21BrfixbGxtmLvwUNcuX4dZ8e8a5cVhDIm5ln8mrW/bKytuReinQzZw0ePZV6XNG2sle29SBn1/Hur+RmsrK2JzuP7FRcbg0qlwso65zoP72cNka9VrwFFihXDxtaW4Dt3+H3JQh4+uM/gkWO0Fv/zGK2sNY//l8UfGxODSpWeo4amlbU1D+7nPsQ/JkbJ+lUradKilRaiLjyK/+Pi2oVBOpeEEDRq1AhXV1c8PDxo2rQpTZs2pW3btpiY5P3DFuD69eukpKTQoEGDPF8vW7asuvMDoEaNGqhUKgIDA9WdSyVLlkQ3W10cBwcHLr/i7mN6ejrjxo1jw4YNPHz4kKdPn5KSkqKO+VWx5cft27dJTEykUaNGGs8/ffpU3fn23Xff0a5dO86dO0fjxo1p06YNH3zwAZA55K9Ro0b4+vrStGlTWrZsSePG+b9Yet6hlf3x85ndXrVvjY2NefToETVq1NDYRo0aNbh4UbPYZMeOHXF2dubvv//G2PjVHTFlymTV9HFwcAAgIiICPz8/AgMDadu2rUb7KlWqvLJzafz48YwePVrjuZEjR4J+7h1/9Up607t5Vnbd8PU7c233OpqU9SMgKCRHJ89zBnq61CvpxZqjZ9/ofRqW9qF/q7rqx0NWF2zYQr2S3jQs7cMvm/dyNyIKL3s7ejatRWRcwkuzr95UdR83vqqbVaNn6vZDBdqOjgLuRkSx6WTm8XjvSTTONpbUL+Wt7lzKnoX1IFLJ/UglUzu3poRTUa490F4HX34kJiczccVa+nX6BEspblogAUcOs27RfPXjHkOGvbX3+mf3DoJv3aT7oJ+wsSvC7evX2Lh0IZbWNviVKdhQm2MH/2bJbzPVjweOHKutcDVYWFrRa8jPLJs7mz1//YlCoaB6nXq4eXq9lUyOt2HSlKkE3bnDogXzX934X2ji5MkEBQWxeOHCwg4lX0YOGcSvU6bR6tPP0NXRwdfbi0b16nIjl0zlf7ttO3dRvWoVitjlnfn6Og4f2M+CmdPUj4f9kv/yBK+rcYus4cOu7h7Y2NgwcvAAwh49xN7x5UX+83Jo/z5+m55VV2/EuAlvHOerJCYkMOanIbi4uvJZl6/e+vuJ94d0LgkhMDc359y5cxw6dIi9e/cyYsQIRo0aRUBAwEtr5eSnIyI/XqwhoFAoXlm0dPLkycycOZMZM2ZQunRpTE1N6dOnj7oYozZie54Sv2PHDpxemPnneVZNs2bNuHfvHjt37mTfvn00aNCAH374gSlTplChQgXu3r3Lrl272L9/P+3bt6dhw4Y5aicVtubNm7Nq1SpOnDhB/fr1X9k++9/r+Q+dNy0yO3ToUPr108wGMTQ05MMpy3Jtf/JWMIGLszoXnhfttjI11ugcsjI1Jig88pXvX9TCjPLuTozdvDfPNrX8PDDU13vjzKBjgXe5/jBn7DZmJhqxW5uavHRmtx6NPmDN0XP8fSXzx8PdiEjsrczpVKviW+1cOn/3AUHhWXE9j9/SxJiYxKz0f0sTI+49yb3AK4AyITnHbHiPomOp5Jl3hszj2Hhik5IpZmn+xp1Llmam6OjoEB2rWbw7Oi4+R+YRwKPHkYRFRvHzvKXq555nTzXuOZDlIwdjbZk5fCY6Ng5by6yhNMrYODydC/bDQpueKqPQt9S8261vaU1aYjyq1KekxirJSE9D3+qFNlY2PM0jE+d1lK5UBTfvrCGDac9q68XFxGCZ7S58XIwSJzf3Ar/P06cp/LV2Nd0GDKZUhcysUidXNx4G3+Xv7X8WuHOpQtVqePpmzb71PP4YpRJrm6wi0TFKJa7uuWe7mFtYoKOjoy7e/VysMlpjJrgyFSoyffFy4mJi0NHVxdTMjO8//5Si9g4Fij07KysrdHV1cxS/joqKwtY294LLr2PSlKkcOXaMhfPmUqxo0Tfe3ovyjD86Os9i3a9j4pQpHD12jIXz52s9fitLC3R1dHIU746Ojsb2DWYCdHZ0ZN60KSQlJZOQmICdrS3Dxv6KkxaOl+ysLC2fxa95bo+KjsbG5s1nMgwNCyfg3HkmjB75xtt6rkr1D9QzogGkpmZeJ8Yoo7HJdrwoo6Nx9/TKdRvmFpbo6OjkKN6tjI7GKo8i5YB6JrnQR48K3LlU5YMa+JTIHn/qs/eOyhG/Rx7xW1haoqOjS3Qu8Vu/EH9iYiIjhwzE2MSEYWN+QU/vP95d8B/pkH9fSJ6YEAIAPT09GjZsyKRJk7h06RLBwcH8/fffL13H29sbY2NjDhw4kOvrJUqU4OLFiyQkJKifO3bsGDo6Ovj65n96XAMDA9LT0zWeO3bsGK1bt+bzzz+nbNmyeHh4cPNm1o/+V8X2vDbBi9vNzt/fH0NDQ0JCQvDy8tJYXFyyChUXKVKELl26sGrVKmbMmMHCbHc6LSws6NChA4sWLWL9+vVs3rw5z9lsXnTy5Mkcj0s8u8B41b61sLDA0dGRY8c0Z9Y6duwY/v6aU01/9913TJgwgQ8//FBdS6qgfH19CQgI0Hjuxce5MTQ0xMLCQmN52bC4pKepPIqOVS/3nkQTGZ9AebesizcTA338nIpy/WFYntt5rnFZP5SJSS+tV9SkXAlO3gzW6EApiKSnqTyMilEvwY+jiIxLoIJ71hTCJob6+DsX49qDvGM31NdH9cLQsHRVxlvPbEhOTSMiJl69PIyKQZmQhL9zViFxI309PIrZvbRz7FbYYxysNWuZ2FuZExmXkMcaYG1qjJmRYY7hjwWhr6eHT3FnzgVm3dlXqVScD7yFv3vOIazF7Yuy6OcBLPipn3qpXtqfcj6eLPipH0WsrXCwtcHGwpzz2baZkJTM9eAQ/D3eznTsryPu1hWsSlfUeM6qTGXibmbW2clITyP+TiBWpbJq8aBQYFWqorrNmzAyNqaIvYN6sXd2wcLKmsDLl9RtkhITCb59C3fvgk+hnp6WTnp6Wo7vgo6ODhkZBe8INzYxwd7RSb04FXfFytqGqxfOq9skJiYQFHhDY3ry7PT09XH38ubqxax1VCoVVy5ewNvPP0d7c0tLTM3MuHrxArExSipUzbuIeX7p6+vj5+tLwJmsLEyVSkXAmTOULlXw2loZGRlMmjKVQ4cPM2/ObJy0PCTruefxn872f4tKpSIgIIAypUsXeLsZGRlMnDLlWfxz3kr8+vr6+Pp4E3BO8+8fcP4Cpf1z/v1fl7GxEXa2tsTGxXHqzFlqf1D91Su9Bn19ffx8fAg4d079nEqlIuDceUqXfPP4t+/ejbWVFTWqv/lx/pyxiQkOTk7qxcXVDSsbGy6dz/oMiQkJ3LpxHd8SuX8GfX19PL19uHRB83NfunAuz3UA7t4JAsjRgfM6TExMcHRyVi/FXd2wtrHh4jnN+G9ev45fHsN+9fX18fLx4dJ5ze/8xfPn8M22TmJCAiMG9UdPX5+fx47DwOC/MVuw+Pf4j3dFCiG0Yfv27dy5c4fatWtjbW3Nzp07UalUr+wAMjIyYvDgwQwaNAgDAwNq1KjB48ePuXr1Kl27dqVTp06MHDmSLl26MGrUKB4/fsyPP/7IF198oR4Slx9ubm78888/fPrppxgaGmJnZ4e3tzebNm3i+PHjWFtbM23aNMLDw9UdJ6+KrWjRohgbG7N7926cnZ0xMjLKUWvI3NycAQMG0LdvX1QqFTVr1iQmJoZjx45hYWFBly5dGDFiBBUrVqRkyZKkpKSwfft2dQfQtGnTcHBwoHz58ujo6LBx40bs7e3zPXPaxo0bqVSpEjVr1mT16tWcPn2aJUuWAORr3w4cOJCRI0fi6elJuXLlWLZsGRcuXGD16tU53uvHH38kPT2dli1bsmvXLnVtqtf1448/Urt2baZNm0arVq34+++/2bVr1zsZyrH19GU61qjIw6gYwpRxdKlTmci4RI4HBqvbTPisJcdv3mXbmawClgqgcVlf9l+6maOz5jlHawtKF3dg+Lo3H36Xm00nL/JF7Uo8iFISGh1H1/pVeRKXwNEbd9RtpnZuzdEbd/jjdOaQ0RM37/JF7UpExMQR/DgKL/sitK9ejp3ns4qLmhsbUszSHFvzzOGTLs+Kf0fFJ+Y5/K8g9ly8QetKpQhXxvE4LoF2VcugTEjkXLYi5YNbN+DsnfvqzK/dF64zvF0TWlUsyanb9/AsZke9kt4sPZg5S5ihvh5tK5cmICiEmMRkilqa0eGD8kTExHE5JFQrcberX5tJK9fh6+qCr2txthz8h+SUpzStnjnsb8LyNdhZWdKtTQsM9PVxd9TMAjB7VlMq+/Mf1a/N6l37cSpqh72tLcv/2oWtpQU1ymq/ILaOoTHG9lmdkkZFHTF19SYtPpaUyHBcO/bA0MaOm7/9AkDo3q04NGmHW6fvCT+4HctSFSlSvT5XJwxUb+Ph9vX4/DCM+Ds3iLt9Dcfm7dE1NCL80A6tx69QKKjbvCV7/thIUQcHbIsWY/v6NVha21CmclbB9NljR1CmcjXqNG0OQEpyEo/DsjpeIyPCeRB8FxMzM2zsimBsYoKXf0n+XLUCAwNDrIsU4fa1q5z+5xBtO2tviIdCoaBp6zZsXb8WeycnihSzZ9OqFVjZ2FKx+gfqduN+Gkyl6h/QuFVrAJq1+YgF06fg7u2Dp48vu//8g5TkZOo0zBo2fXjfHhxdimNhacmtG9f5feE8mrZui6Nz3jMwvo7POn7K6LG/UMLPj5Il/Vm7bj1Jycm0apk5rGfk6DEUKVKEnt9/B2RmS9y5mzlcNTUtjcePHxN48yYmxia4uGQegxOnTGHP3n1MmTgRExMTnkRmZo2amZphZKTdH6mdOnZk1Nix+JcoQUl/f9asfxb/s3qAI0aPpmiRIvT8/vtXxG+svlk0cfJkdu/dy9RJkzAxNc0WvylGRkZai71ju48YO2kKJXx98Pf1Zf2WP0hOTlbXGBo9YRJF7Oz4vtvX6tjv3susi5OWlsrjJ5HcvB2EsbERLs+yqk8GnCEjIwNXFxfuP3rInIWLcXVxoeVbqFvU8ZN2jJkwiRI+vviX8GXdpi0kJyfTsmlTAEaNm0CRInb88E23bPFn3rhJTUvj8ZMn3Lx9G2NjY3X8kNnZsX33Hlo0aYRetlIJ2qZQKGjZth2b1qzCwcmJYvYOrF2+DBtbO6rUyLr2GTmoP1Vr1KR568yh/q3afcLsyRPw8vbF28+Pv7ZsJiU5mfpNMj932KOH/PP331SsUhVzCwuC7waxbP5c/EuXwc1De3W7FAoFH370CetXr8TR2Zli9vasWrYUGztbqmW7dhs2oC/Va9aiZZuPAGjzcXumTxyPl48fPn5+/Ll5E8nJSTRs0gx41rE0eAApycn0/+lnkhITSEp8VpTf0kqjfIUQeZHOJSEEVlZWbNmyhVGjRpGcnIy3tzdr166lZMlXFz4dPnw4enp6jBgxgkePHuHg4ECPHj2AzLste/bsoXfv3lSuXBkTExPatWvHtGnTXrFVTWPGjOHbb7/F09OTlJQUMjIy+Pnnn7lz5w5NmjTBxMSE7t2706ZNG2JisobZvCw2PT09Zs2axZgxYxgxYgS1atXi0KFDOd577NixFClShPHjx3Pnzh2srKyoUKECP/30E5CZATV06FCCg4MxNjamVq1arFu3DsjsnJo0aRK3bt1CV1eXypUrs3PnTnR08pc0Onr0aNatW8f333+Pg4MDa9euVXee5Wff9urVi5iYGPr3709ERAT+/v5s27YNb2/vXN+vT58+qFQqmjdvzu7du9W1o15HjRo1mD9/PqNHj+bnn3+mSZMm9O3blzlz5rz2tl7XhhMXMNLXo3fzOpgZGXD1fhjD1u0gNVt2moO1JRYvDJks7+5MMUvzlw4la1LWjyex8RozumnT2mPnMDLQY0CrepgZGXI5JJRBq/7iaVpW7E42llhmK5A9c+c/dK1flT4t6mBtasKTuAT+OnuFFYez7ubX8HVnSJuG6scjP8m8CF5+6DTLD2XNrPimdpy7hqGeHl/Vq4qJoQG3QiOY8tdBUtOzskSKWpphbpz1A/NuRBSzdv3DJ9XL0bpyaZ7ExrP6yBlO3AwGQKXKwMXOipp+HpgY6hOdkMSV+6FsPnmJtDcchvlcvUrliYlPYPn2PUTHxuLp7MT4nt+oh8VFRCvR0Xm9jtEOjeqRnPKU6Ws2EZ+YRClPdyb07J7nFNxvwtzTj9Kjsr5bHl16ARB+aCe35v6KgbUthnZZHfkpj0O5NmEg7l164dj8E1IiH3Nr/kSUF7OOhScnDqBvYUXx9t0wsLIhIfgWV8b1JzUm7yGOb6Lhh215mpLM2oXzSEpMwMO3BN8PHY5+tpmvnoSHkRCXVRA9JCiIWWOGqx//sTJz+GyVOvX44vvMffBV7/5sW7OKFbOnkxgfj3WRIrT89DNqNmqi1fhbtmtPSnIyS2bPJDEhHh//kgwe86vGzF3hYaHEZSvoXr12XeJiYti0aiUx0dG4engweMyvGsPiQh8+YP2KZcTHx1GkaDFat+9Is2c/ErWhccOGKKOVLFi8iMjIKHy8vZk1fRq2zzIswsLDUWT7v+rxkyd83uVL9eNVa9awas0aKpQvz4K5mRNEhj7U4AABAABJREFUbN7yBwA9fvhB471G/DxM3emjtfgbNSJaqWT+okVERkbi4+3N7OnT1cPiwsLC0Ml2U+Px48d06txZ/fj31av5ffVqKpQvz8J58wDY9GzW2G+fdUg9N/Lnn9WdbtrQqF5dlDExLFq+ksjoaLw9PZg+/lf1sLiwiMea+z4yks49smJavXETqzduonyZMsybNhmA+IQE5i1ZRsSTJ1iYm1OvVg16fPXVWxnS1Kh+PZQxMSxcvpzIqGh8PD2ZMXE8ts+GxYVHRGhc5zyOjOSLb3pkxb9+I6vXb6RC2TLMm5F1zXL67DnCwiNo1ayZ1mN+Udv2n5KSnMz8GdNIiI+nRKnSDB83QeN7Gxb6iNhs15Q169YjNkbJ2pXLMofQeXgy/NeJ6sLaenr6XDp/lu1/bCYlOQm7IkWpXrM2H3/2udbjb/dpR5KTk5gzbQoJ8fH4ly7N6PGTNTKNwh5pxl+rXn1iYpSsXr6U6OgoPDy9GD1hsjqrKujWTfXMd92/+Ezj/RavXkcxLQ+xfFcUr/l/uHgziow3nW5FCCGEeIlvvvmGGzducOTIkddet8mv/81isAB7hvWg7qi336n2Nhwa1ZPOc3JmuP1XrOzZifsHClYo/d/ApUFLjrav8eqG/0I1Nxxj7wXtTEleGBqX8+fMreDCDqPAKnm7ERv16jpz/0YWNrbERb+dTsx3wdzamuj7wYUdRoFZu7ihfPR2bqC8bVaOLly997Cwwyiwkq5O3HzJMPh/Ox/n3Cdf+Td4dGx/obyvY42Gr270HpLMJSGEEFo1ZcoUGjVqhKmpKbt27WLFihXMnTu3sMMSQgghhBD/TxRSYvpdkr0thMhTSEgIZmZmeS4hISGFHeJ/0urVq/Pcp/kZivhvd/r0aRo1akTp0qWZP38+s2bNolu3boUdlhBCCCGEEOItkcwlIUSeHB0duXDhwktfF6/vww8/pGrVqrm+pv8W6rK8axs2bCjsEIQQQgghhBDvkHQuCSHypKenh5eXV2GH8d4xNzfH3Ny8sMMQQgghhBDi/fUOZisWWWRYnBBCCCGEEEIIIYQoMMlcEkIIIYQQQgghxHtFIQW93ynZ20IIIYQQQgghhBCiwCRzSQghhBBCCCGEEO8XHam59C5J5pIQQgghhBBCCCGEKDDpXBJCCCGEEEIIIYQQBSbD4oQQQgghhBBCCPF+kYLe75TsbSGEEEIIIYQQQghRYJK5JIQQQgghhBBCiPeKQiEFvd8lyVwSQgghhBBCCCGEEAUmnUtCCCGEEEIIIYQQosBkWJwQQgghhBBCCCHeL1LQ+52SvS2EEEIIIYQQQgghCkwyl4QQQgghhBBCCPFekYLe75YiIyMjo7CDEEIIIYQQQgghhNCW8HMnC+V9i1WoVijvW9gkc0kIIcS/VtS9O4UdQoHZuHoQ/SCksMMoEGvn4sREhBd2GAVmWbQYIRFPCjuMAite1I69F64VdhgF0ricP0fb1yjsMAqs5oZjnP/528IOo8DK/7KAyJjYwg6jQGwtLbge8qiwwyiwEsUdOXDxRmGHUWANyvqx/8L1wg6jQBqWK0FcjLKwwygwc0srwqOUhR1GgRWzsSrsEPKmI5lL75LUXBJCCCGEEEIIIYQQBSadS0IIIYQQQgghhBCiwGRYnBBCCCGEEEIIId4vCsmleZdkbwshhBBCCCGEEEKIApPMJSGEEEIIIYQQQrxXFAop6P0uSeaSEEIIIYQQQgghhCgw6VwSQgghhBBCCCGEEAUmw+KEEEIIIYQQQgjxfpGC3u+U7G0hhBBCCCGEEEIIUWCSuSSEEEIIIYQQQoj3i44U9H6XJHNJCCGEEEIIIYQQQhSYZC4JIYQQQgghhBDivaKQmkvvlOxtIYQQQgghhBBCCFFg0rkkhBBCCCGEEEIIUUh+++033NzcMDIyomrVqpw+fTrPtosWLaJWrVpYW1tjbW1Nw4YNc7T/8ssvUSgUGkvTpk3f6meQziUhhBBCCCGEEEK8XxSKwlle0/r16+nXrx8jR47k3LlzlC1bliZNmhAREZFr+0OHDtGxY0cOHjzIiRMncHFxoXHjxjx8+FCjXdOmTQkNDVUva9euLdBuzC/pXBLiLapbty59+vQBwM3NjRkzZhR4W8uXL8fKykorcf0XvOn+epuOHTtG6dKl0dfXp02bNi9te+jQIRQKBUqlUutx/Jv3kRBCCCGEEOLVpk2bxjfffMNXX32Fv78/8+fPx8TEhKVLl+bafvXq1Xz//feUK1cOPz8/Fi9ejEql4sCBAxrtDA0Nsbe3Vy/W1tZv9XNIQW8h3pGAgABMTU3z1dbNzY0+ffqoO6YAOnToQPPmzd9SdOJ19OvXj3LlyrFr1y7MzMwKOxwBbNr2F6s3biIqKhovDw/6/fAdJf18c237585d7Np/gDvB9wDw9faix1dfarTPyMhg0crf2bZrN3HxCZQp6c+gXj1xcXJ6O/Fv/ZNVGzYSFRWFl6cn/X/8gZJ+frm2vRMczMLlK7hx8xZh4eH0+f47Pm33kUab9PR0Fq/8nd37DxAVFYWdrS0tmjTmq887oSjAHbVX2bhlC6vWriMyKgpvT08G9OlNSX//XNsG3b3LwiVLuBF4k9CwMPr+2JOO7dvnue0Vq1bx24KFfPrJx/Tr1Uvrsf+5ZTMb164hKioKT08vfujTF788Ygc4fPBvVixeRFhYGE7OznTr8R1Vq3+gfn3l0iUcOrCfxxER6Onp4+3ry1ffdKdEyZJajx0yj9WdG9dy/MB+khIScPf1o0O3bynq4JjnOrevXeXAX1sJuRtEbHQ03QYMoWzlqhptUpKT+HPN71wOOE1CXBy2RYtSp1kLajbSTkq9RYmyOH/4Gabufhja2HFt8hCiAo68dB1L//K4d/4RExd3UiIjuL95BRGHd2q0cWjyEU6tPsPAyoaEe7cJWjqd+KDrWon5RXZV61K0ZiP0zSxJCnvAg+3rSHwYnGtbr679MHfPeU6KCbzMnd/nAKBjYIhj47ZYliiHnokpKdFPeHziIJEB/7yV+Ddv3MDqVauIiozEy9ubfgMG4v+S4/Tv/ftZuGA+YaGhOLu48H3PH/mgRg3164mJicz7bQ7/HD5MTEwMjo6OfNK+A23btXsr8WdkZLB2xTL27dpBQnw8fiVL0aNXXxydnV+63s4//+CPjetRRkXh5unJNz/0wsevRK7bHztsCOcCTjNk1Fiq1aip9fi3b1jDsQP7SEpIwMPPj47dvnvpd/fWtavs2/YH9+/eJiY6mu4DhlKuSjWNNt+3b53rum0/70KjDz/K9bWCxL5j49qs2H39+LRbj1fGvv+vP7h/N+hZ7EMoW1kz9uRn551LAafU5526zVpSS0vnnec2bNzI76tWExkZibe3NwMH9KdUHsd+UNAd5i9cwI0bgYSGhtKvbx8+69hRo825c+f5fdUqrt+4wZMnT5gyaRJ169bRaszZbdm0kXWrVxP1P/buOyyKqwvg8A8QKSpdpUtvih2sUZNoNPbE2GM3sXexxN67Yu+9966xRYyxY8UGNkSNoC5Fugh8f6ALq4CKq0S/8z7PPMnO3pk5c525u9y990y4AkcnZ3r17ZftvXv0yBGWLlpIaOhjrKxt6NytGxUqVlIpExx8jwVz53L54gWSk5Oxs7dnzPiJFDY3/2Tn8Tl8iu887yMxMZHExESVdTo6Oujo6LxV9sWLF5w/f57Bgwcr12lqalK9enVOnTr1XseLi4sjKSkJExMTlfV+fn4UKlQIY2NjvvvuO8aOHYupqWkOzuj9yMglIT6TggULoq+vn+Pt9fT0KFSokBoj+v/z4sULteznzp07fPfdd1hbW/+nR5MlJSXldgifxWG/Y8xauIgOv7ZkxbzZODvY0+ePoYRHRGZa/sLlK9SoVo05UyayyHc6hQsWpPfgITx59kxZZs2mzWzesYsBPXuwdJYverq69B48lEQ1XUMZHTrqx8wFC+nY+ldWLpiPs6MDvQcOJjwiItPyCQmJWFlY0K1jB0zf+BLx2uoNG9m2azf9e3Rn/fKldPutI2s2bmLT9h3qj//IEXznzKVj27asWrIEZycnevbrn2X8iQkJWFlY0q1Tpyzjf+36jRts27ULJ0dHtccN4HfkMAvnzObXtu2Zv2QZDk5ODO7Xl4gsYr8WEMD4USOpVacu85cup9I33zDyj8Hcu3tXWcbaxobuffqyaOUqZsybR2Fzcwb160NkFvv8WId3befY/r007diJfuMmoaOrw7zxo0nK5lpNTEzAqogdTdr/nmWZbauWc+PSRVp3782Q6bOpVrsem5ctJsA/6xwQH0JLR4+Y4NvcXTrtvcrrFLTAY9AUoq5d4OKAtvy7dxPOnQdiVMJbWcaswvfYt+5ByJZlXBzYntj7tyk2ZDraBkZqiTkjo2JlsfrxF0KP7iVw3jjiQx/i2LYnefIVyLT8vXULCJjoo1xuzBpJanIykVfPK8tY/dgYA+ei3N+yjBszR/L05F/Y1G2GgVtxtcd/+NBBZvn60r5jR5avWo2TszN9evYgPDw80/IBVy4zYthQ6tVvwIrVa6hStSqDfPpz585tZZlZvjM4feoUI0aNZv3GTTRp1ozpU6dw/O9jao8fYPvGDezZsY3OvfowefY8dHV1GTV4QLaf9f/4/cWyhfNp9msbps9fhJ2DI6MGD8j0/ty9bQvw6f4wPbRzG37799L8ty74jJ+Cjo4us8eNzPbefZGYgLWdHU07dMqyzIRFK1SWVl16oKGhQalyFbPc5oNj37Udv/17aNaxMz7jJpNXV5c540e9O/Yi9jRpn3Xs21Yt4/qlC7Tp3pth02fzbe16bFq2iCtqancADh46xAzfmfzWsQNrVq3ExdmJHj17ZXntJyQmYG1lRfduXbP8ozw+IR5nZ2cG+vioLc6sHDl8iLmzZtK2QweWrFiJk7MT/fv0IiLLe/cKo0cMo069eixZuYpvqlRhyMAB3L1zR1nm0cOHdO/0O0WKFGHm3PksX72W1u3akzdv3k9+Pl+rCRMmYGhoqLJMmDAh07LPnj0jOTmZwoULq6wvXLgwoaGh73W8gQMHYmlpSfXq1ZXratWqxapVqzhy5AiTJk3i2LFj/PjjjyQnJ+f8xN5BOpeE+EwyTmFKTU1l5MiR2NraoqOjg6WlJT1f/SJfrVo17t+/T58+fZTJ1+DtaXEjR46kZMmSrF69Gjs7OwwNDWnWrBnR0dHKMtHR0bRs2ZJ8+fJhYWHBjBkzVKbqvU/M48ePp3379hQoUABbW1sWLVqkfD+zKV+XLl1CQ0OD4OBglbj37NmDq6sr+vr6/PLLL8TFxbFy5Urs7OwwNjamZ8+ebzV20dHRNG/enHz58mFlZcXcuXNV3o+MjKRjx44ULFgQAwMDvvvuOy5fvvxWHS1ZsgR7e3t0dXXfec6JiYn07NmTQoUKoaurS+XKlTl37hwAwcHBaGhooFAoaN++PRoaGqxYseK96vLEiRMUL14cXV1dypcvz9WrV1Xe/+eff/jmm2/Q09PDxsaGnj17Ehsbq3z/yZMn1KtXDz09Pezt7Vm7du1bx9DQ0GD+/PnUr1+ffPnyMW7cOADmz5+Po6MjefPmxdXVldWrV6tsFxISQoMGDcifPz8GBgY0adKEsLCwt+px2bJl2Nrakj9/frp27UpycjKTJ0/G3NycQoUKKY8H2V/j6rZ+63bq//gjdWv+gH2RIgzo1QMdHR32HDiYaflRgwfSqH5dXBwdsbO1YXCfXqSkpuB/8ZIy9o3bd9C2RTOqVKyAk4M9wwf055lCwd8nTqo//i1baVD7R+rWqoW9XREG9u6Fro4Oe/48kGl5DzdXenT6nRrffYu2tnamZQKuXadKxYpUKl8OS3NzvqtaBe+yZbh+M1Dt8a/buImG9epSr05tHOztGNS/H7q6uuzeuzfz+N3d6dmtKz9U/z7bL65xcXEMGz2GIQMGYFAg8z/YP9bWjRv5sV49atWpQxF7e3r190FHV4cDe/dkWn77lk14eZejSYuWFLGzo23H33FycWHnti3KMt/V+IHSZb2wsLTCzt6Bzj16Ehcbq/JFXl1SU1Px27eHmj83prhXOayK2NGqWy+iIsK5cu5MltsVLVWGus1aUuKNEQ8Z3Qu8Sbmq3+JctBimhQpRqfoPWBWx4/7tW2qJPeLSaUI2Ln7vUTkWPzQk4clj7q2eQ/yj+zw+sJVnp/2wqtNUWcaqblNCj+zmid8+4h8Fc3vxFJJfJFL427pqiTmjQpWqo/D/h/ALJ0l4+pgHu9aSkvQC0zKZ/wGfHB/Hy5jnyqWAowcpSS9UOpfy2TqguHiKmHtBvIhUoPA/TnzoQ/JZ26s9/g3r1lG/YUPq1quPvYMDAwYNRkdXlz27d2VaftOGDZQrX4GWrVphZ2/P75274OrmxtZNm5VlAq5coXadOpQuUwYLS0sa/vQzTs7OXL92Xe3xp6amsnv7Fpq0bEW5ipWxc3Ck18DBhCuecebEP1lut3PrZn74sQ7f1/oRmyJ2dOnVFx0dXY4c2K9S7u7t2+zcsoke/QeoPfbX8f+1bze1fm5MCa9yWBexo0333kRFhHP53Okstytaqgz1m/1KSe8KWZYxNDJWWS6fO4tLUU/MCqtnBEpqaipH9+2m1s9NKPGq3Wnzqt25/I52p16zlm+NtMrobmAg5at+i0tRT0wLFaZy9ZpqbXcA1q5bT8OGDahfrx4ODg4MHjQIXV1ddu3enXncHh706tmTmj/8kOVnVqWKFenapTPffltNbXFmZdP69dSt34DadethZ+9AvwGD0NXRZe+ezOPfsmkj3uXK0/zXVtjZ2dOxU2dcXF3ZtiX93l28cD7lK1akS/ceuLi6YmVtTeVvqmD8jh+ARNYGDx5MVFSUypJxZJI6TZw4kQ0bNrB9+3aVv3WaNWtG/fr18fT0pGHDhuzZs4dz587h5+f3SeIA6VwSIlds3bqVGTNmsHDhQm7dusWOHTvw9PQEYNu2bVhbWzN69Ghl8rWs3Llzhx07drBnzx727NnDsWPHmDhxovL9vn37cuLECXbt2sWhQ4c4fvw4Fy5c+KBYp02bRtmyZbl48SJdu3alS5cuBAZ+2B+ocXFxzJo1iw0bNvDnn3/i5+fHTz/9xL59+9i3bx+rV69m4cKFbNmyRWW7KVOmUKJECS5evMigQYPo1asXhw4dUr7fuHFjnjx5wv79+zl//jylS5fm+++/V/n16fbt22zdupVt27Zx6dKld8Y6YMAAtm7dysqVK7lw4QJOTk7UrFmT8PBwbGxsePz4MQYGBvj6+vL48WOaNm36zn0C+Pj4MG3aNM6dO0fBggWpV6+ecmTRnTt3qFWrFo0aNeLKlSts3LiRf/75h+7duyu3b9u2LQ8ePODo0aNs2bKFefPmZZrkb+TIkfz0008EBATQvn17tm/fTq9evejXrx9Xr16lU6dOtGvXjqNHjwKQkpJCgwYNCA8P59ixYxw6dIi7d+++dV537txh//79/Pnnn6xfv56lS5dSp04dHj58yLFjx5g0aRJDhw7lzJm0L5bZXePqlJSUROCtW3iVKqlcp6mpiVepkly98X5TYRISE3n5MlnZgfFvaCiK8Ai8SpdSlsmfLx8ebq5cvXFT/fEHBeFVurRq/KVLE3A953+QeRb14NzFi4Q8eAjArTt3uBxwlQreXh8dc0ZJSUncDArCq0xZ5TpNTU28ypYh4Nq1j9r35BkzqFShAt5ly767cA4kJSURFBRI6TLpdaKpqUnpsmW5fu1qpttcv3qN0m/EU9a7HDeuZn6uSUlJ7Nu1k3z58+Po5KS+4F9RPAnjeWQErp4llOv09PNh5+TMvVsf15Fo7+pGgP85IsMVpKamEnQ1gCeP/8WteMmPjDpnCjgXIzLAX2VdxOUzFHApBoCGVh7yO7gSGXAuvUBqKpEB/soy6qKhpYW+pS3RGafbpaYSfecm+jYO77UP0zKViAjwJyUpfaRHbMhdDN1KoF3ACID89i7omBXm+W31ds4kJSURePMmZb3SR31pamri5eXN1YCATLe5GhCA1xvtR7ny5VXKexYvzvG//+bpkyekpqZy3t+fByEheJcr9+buPlpY6GMiwsMpXqqMcl2+fPlxcXMn8HrW9+OdoCCKl07fRlNTkxKlS6tsk5iQwPQJY/m9R69P9sf163vXrfib964Ld4PU9yPA88hIrl70p+J31d9d+D2ltzvpI+pex/6x7Y6DqytXPmG7k5SUxM2bNyn3xrXv7eXFlSyu/f+SpKQkggLfvnfLeHlx7Wrm8V+7GkAZL9V717tceWX5lJQUTp08iY2NLf1696R+7Vp06tCe48c+zYjDz05DM1cWHR0dDAwMVJbMpsQBmJmZoaWlpfLDLkBYWBjm75iWOHXqVCZOnMjBgwcpXjz7Ua4ODg6YmZlx+/btbMt9DMm5JEQuCAkJwdzcnOrVq6OtrY2trS3e3mkfFCYmJmhpaVGgQIF3NigpKSmsWLGCAq/+KG7VqhVHjhxh3LhxREdHs3LlStatW8f3338PwPLly7G0zHo+fGZq165N165dgbQhlzNmzODo0aO4umaezyYzSUlJytEzAL/88gurV68mLCyM/Pnz4+HhwbfffsvRo0dVOjUqVarEoEGDAHBxceHEiRPMmDGDGjVq8M8//3D27FmePHmibKynTp3Kjh072LJlC7//njbd48WLF6xatYqCBQu+M87Y2Fjmz5/PihUr+PHHH4G0R30eOnSIpUuX4uPjg7m5ORoaGhgaGr7z3yejESNGUKNGDQBWrlyJtbU127dvp0mTJkyYMIGWLVsqR5Q5Ozsza9Ysqlatyvz58wkJCWH//v2cPXsWr1dfEJYuXYq7+9s5Ilq0aEG7du2Ur5s3b07btm2V/4Z9+/bl9OnTTJ06lW+//ZYjR44QEBDAvXv3sLGxAWDVqlUULVqUc+fOKY+XkpLCsmXLKFCggPLfKzAwkH379qGpqYmrqyuTJk3i6NGjlCtXLttrXJ0inz8nOSUFkzcSFJoYG3P/VcfKu8xbsoyCpibKziRFeNr0CBOjt/epUPPUpsioqEzjNzY2JvjBgxzvt3XzZsTGxdG0XXs0NTVJSUmhc/t21Kr+/ceGrCIyKork5GRMTN6sKxPu3w/J8X4PHj5CYFAQKzKMlFS3qKhIUpKT3/rj0djYhAdZxB4RrsDozfImJoSHK1TWnT5xgnGjRpCYkICJqSmTpvti+Amm0D5/NWq0gKGhyvoChkbK93Lql3a/sWHRPIZ16YimlhaaGho0+70rTh6fJnfUu+Q1MiEpSnXaR1JUBHn086OpnZc8+Q3Q0MpDUuQbZSLD0be0VWssWvr50dDSIikmWmX9y5jn6Jq9+3NB38oOPXMrQravUln/cM8GbBr+SrGBk0hNTiY1NYUHO9YQG6y+URuQNuo37b5VvZZNTEy4fz84020UCgXGJqpTgoxNTFBkuPb79vdh0vjxNKhbBy0tLTQ1NRn0xxBKZeg8V9s5vPoRyeiNttPQ2JiIiMynB0VHRZGSkpLpNg8fpN/zSxfMxc2jKOUqqjfHUkZRkWmfJQaGRirrDQyNeB6pvs+Z08f+QldXL9uRTh/qddvyZuwFDA0/OvbG7X5n/aJ5DOnSQdnutPi9G85qaneyu/aD799XyzE+pahX8b/5uWViYkJIFvGHKxRvna+xiQnhirR7NyIigvi4ONauXkXH3zvTuWt3zpw+xdDBA5k5Zx4lP8H9K1TlzZuXMmXKcOTIEeWDgl4n5874Q/ObJk+ezLhx4zhw4ABl3+OHuIcPH6JQKLCwsFBX6G+RziUhckHjxo3x9fXFwcGBWrVqUbt2berVq0eePB92S9rZ2Sk7lgAsLCyUo1nu3r1LUlKSyh/0hoaGH9QpBKj0gmtoaGBubp7lYzGzoq+vr+xYgrQ5xHZ2dirJsAsXLvzWfitUqPDW69dTCy9fvkxMTMxb89/j4+O5k2H6SZEiRd6rYwnSRuckJSVRKUOCUm1tbby9vbnxnqNgspLxXExMTHB1dVXu8/Lly1y5ckVlqltqaiopKSncu3ePoKAg8uTJQ5ky6b+2urm5ZZrv6c0Plxs3big72l6rVKkSM2fOVL5vY2Oj7FgC8PDwwMjIiBs3big7l9681goXLqz84yHjutf/hh96jWeV+PBTW7VhE4eOHWPelMnofEW5BY74HePAkb8Y/cdg7O3suHXnNjPmzlcm9v4vCwsLY/qsWcyePv2zXAOfQonSpVmwbAVRUZHs372bsSOGMWvh4o9+Ssu548fYsHiB8nXnQUM+NtQs/f3nXoJvBfH7gD8wMSvI7RvX2bxsEYbGJiqjLcSHMy1bifjQh28l/y5Y/lvyWdtzZ/VcXkQqyG/njHW95iRFRxJ9R72jJj+FLZs2cu1qAJOnTcPc3IJLFy8ybcpkzAqa4eX9caOXjh05xHzf6crXQ8dmnrvkY509eYKAixeZvmCxevd73I/1i+YrX3cZPEyt+8/KqaOH8fqmKtof8fl29vgx1i9Oj73roKHqCC1Tx/7cy71bgXQe8AcmZoW4deMaG5ctlHbnE0pNSQGg8jdVaPIqUbmziwtXAwLYuWPbl9+5pJk7Cb0/VN++fWnTpg1ly5bF29sbX19fYmNjlT8Yt27dGisrK2XepkmTJjF8+HDWrVuHnZ2dMjdT/vz5yZ8/PzExMYwaNYpGjRphbm7OnTt3GDBggHJGxqcinUtC5AIbGxsCAwM5fPgwhw4domvXrkyZMoVjx45lmUMlM2+W1dDQIOXVh4S6ZHeM1x0LqampyvczSyKd2T4+NvaYmBgsLCwynTecsdPlfZ/Ql5tiYmLo1KlTpjmJbG1tCQoKeu99farz/dB/ww+9xidMmMCoUaNU1o0YMYKe7VpnG5eRgQFamppvJY8Oj4jA1CT7P+TXbt7C6o2bmDVpPE4O6TlNXm8XHhmBmWn6r33hERG4qDmxtJGhYabxR7xH/NmZvWgxrZs1pcZ33wLg5GDP47AnrFq/Qa2dS0aGhmhpaREe/mb9h2NqapLFVtm7ERhEeEQErTt2VK5LTk7m4uXLbN62nX+OHEZLS+uj4gYwNDRCU0vrrSSoERHhGGcRu7GJqXLEhLJ8eDgmb4zo0NPTw8raGitrazyKFqNN86b8uWc3zVtlfz2/i2dZb+ycXZSvX75qb6OjojA0To85OioSK7uc5+l58SKR3evX0rH/QIqVTuuwtipix6Pge/y1Z2eu/JH3IjIcbUPVfxdtQ2NexsWQkvSCpOeRpCa/RNvojTJGJryIzHwkS04lx8WQmpyMdn7VXGB58huQFBOV7baa2nkx9vTi8RHV3EYaebSxqNGQe+vm8zwobVpmQtgj9CxsKFTpB7V2LhkZGb26b1XrJTw8HJMsEhabmpoS8cYIvYjwcExfXfuJCQksmDePCZOnUKly2ogfJ2dnbgUFsW7Nmo/uXPKuUAkXt/SnOCa9mk4YGRGhEnNURAT2jplPQS1gaIimpuZbybujIiIwfnX/XLl0kdDH/9KyoWqersmjR+BezJNx03xzFH/xst7YOaf/uPf63n0eFaly7z6PisT6I+7djG7fuEbYv4/o0PvjkkwXz6LdeTP26Kioj4r9xYtEdq1fw+/9B73V7hzes0Mt7U52135OP7M+J8NX8b/5uZV272Yev4mp6VvnG5HhXn+9zyL2qv92RezsCMiQx1R8Wk2bNuXp06cMHz6c0NBQSpYsyZ9//qlM8h0SEqLyg+78+fN58eIFv/zyi8p+RowYwciRI9HS0uLKlSusXLmSyMhILC0t+eGHHxgzZswn/eFOci4JkUv09PSoV68es2bNws/Pj1OnThHwar533rx5PzqTv4ODA9ra2spk1ABRUVEf1FHxLq9HBGXMC/U+eY3e1+nTp996/XoqWOnSpQkNDSVPnjw4OTmpLGZmZjk63uuk1ydOnFCuS0pK4ty5c3hk82jy95HxXCIiIggKClI5l+vXr791Hk5OTuTNmxc3NzdevnzJ+fPpiV8DAwNVEqlnxd3dXeV8IC25+OvzcXd358GDBzzIMAXr+vXrREZGfvQ5Z3eNvymniQ+1tbVxdXbGP8N1l5KSgv+lSxTLZNrga2s2bWb52vXMGD8GdxcXlfcszc0xNTFWJviGtCmT128GUszd7Z0xfQhtbW1cXVw4d/GiSvznLl7E8yPqPyEhAQ1N1Y94rVfT49RJW1sbNxcXzmW4NlNSUvA/fwHPbB6LnB2vsmVYv3IFa5YtVS7ubm7UqlGDNcuWqqVj6XXsLi6uXDyfnscnJSWFi+fP41E08xw9HsWKcjHDuQJc8D+He7HszzU1JUUtT2/U1dOjoLmFcjG3tsHAyJjAgCvKMvFxcQTfvoW984eNUs0o+WUyyckv33qEs6amJqmp6r2G3lf0rasYeZZRWWdU3IvoVx0xqckvibkbiFGxDKM3NTQwKlZGWUZdUpOTifs3hAIOGdoYDQ0KOLgR9+Bu1hsCRsXKoKGVh/BLqomPNbS00MyTBzL8WJN2sBS1//Kura2Nq5sb5zN8P0hJScHf/xzFssiNV8zTE/8M5QHOnjmjLP/y5UtevnyJ5huxamppkvLmOeWAnr4+FlZWysWmiB3GJiZcuZieRzIuNpagmzdwzWIKlba2No4uLirbpKSkcOXiBeU2jZq1wHfhUmYsWKJcANp37krP/gNzHL+unj6FzC2Ui0WW924QDi45v3czOvnXYWwdHD+6s0pXT++9Y/8U7Y6GGtsdbW1t3NzcOPvGtX/O/xzFP0FeSHXT1tbGxdWN8/6q8V/wP0fRYpnHX7SYJxf8VfPVnTt7VlleW1sbN3cPHoSoTqt7+Cq9wZdOQ0MzV5ac6N69O/fv3ycxMZEzZ85QLkO+Oj8/P5WHCAUHB5OamvrWMnLkSCDtO/iBAwd48uQJL168IDg4mEWLFr31RDp1k5FLQuSCFStWkJycTLly5dDX12fNmjXo6elRpEgRIG0K0t9//02zZs3Q0dHJUWdJgQIFaNOmDT4+PpiYmFCoUCFGjBiBpqbmWx/cOeXk5ISNjQ0jR45k3LhxBAUFMW3a+z1W+n2cOHGCyZMn07BhQw4dOsTmzZvZ++oJVNWrV6dChQo0bNiQyZMn4+Liwr///svevXv56aef3mvu8Zvy5ctHly5dlHVma2vL5MmTiYuLo0OHDh91LqNHj8bU1JTChQszZMgQzMzMlPOqBw4cSPny5enevTsdO3YkX758XL9+nUOHDjFnzhxcXV2pVasWnTp1Yv78+eTJk4fevXujp6f3zuP6+PjQpEkTSpUqRfXq1dm9ezfbtm3j8OHDQFo9enp60rJlS3x9fXn58iVdu3alatWqOarD1951jb9JR0cn019SYjMp+6bmjX5izJRpuDk7U9TNlQ3bdpCQkEjdmmk5rkZNnkpBU1O6dkgbWrx64yYWr1rNqEEDsShcGMWrX/T09PTQ19NDQ0ODpj81ZMW6DdhYWWFhXpjFK1ZjZmpKlUrqe5SzMv5fGjFm0mTcXVzwcHNl49btJCQkUOfVsOVREydR0MyMrh3TrsGkpCTuvcqt8PJlEk+fPSPo9u20Jw1aWQFQuUJ5Vqxdh3mhQtjbFSHo9m3Wb9lK3VrqHwrdomkTRo2fgLubK0Xd3dmweTPx8fHUrV0bgBFjx1HIzIxunTulx//qaZJJSUk8ffqMoFu30uK3tiafvj6ODqpJkfV0dTE0NHhr/cdq1LQpk8ePw8XNDVd3D7Zv3kRCfAI1a9cBYNLYMZiZmdGhcxcAfvqlCf16dGPzhvWUq1ARvyOHCbp5k94+aX90xsfHs27VSipUroypqRlRUZHs2raNZ8+eUeXbb9UaO6SNFqxWuy4Htm+mkIUFpoUKs2fjOgyNTSjulf6ldPaY4RT3Kk/VWmn/JokJ8TzN8HhjxZMwHgbfQz9/fkzMCqKnr4+TR1F2rllJ3rw6GBcsyO3r1zj7tx8/tW73Vhw5oamjh565tfK1biFL8hVx5mXMcxIVYRRp3hkdEzOC5o4F4PHBHVjUbIRdy66EHd2DYbEyFKzwHdcmpo/KeLRnIy7dhhBz9ybRt69jWbsJWjq6hPll/uTCj/HkxGGKNGpL3L/BxD4MplDF79HMmxfF+bQnShZp1JYXzyN5fGiHynamZSoRdeMSyfGqrVtKYgLR9wKxrNWIlJdJr6bFuWBSsjyP9m9G3Zq1aMHYUaNwc3fHo2hRNm5YT0J8PHXr1gNg9IgRFCxUkC7d0vJ9NGnWjK6dOrFu7RoqVqrM4YMHuXnjBgP/+AOAfPnzU6p0aebMmoWOji7m5uZcvHiB/fv20bNXb7XHr6GhQb2ffmHzutVYWllRyMKCdSuWYWJqRrlK6bmShvn0pXylb6jT8CcAGjRqzMzJE3FyccHZ1Z3d27eQkJDA9zVrAWm5aDJL4m1WqDCF1ZinRENDg+9q12P/tk3Ke3f3hrR7t4RX+tPUZo4eRgnv8lSrldYmJSTE8zQ0/Qc9xZMwHgTfJV/+ApiYpacAiI+L48LpE/zcSj3365uxf1u7Hn9u30whC0tMCxVStjslMrQ7M8cMo4RXdrE/UYldT18fZ4+ibF+zEu28eTEpWIhb169y9m8/flZTuwPQskVzRo4ajYe7O0WLerBuwwbi4xOoVzdttNrwESMpVKgg3bt1A9I+p+7eu6f8/6dPnxIYFIT+q6f7QtqDax48TM/z+OjffwkMCsLQwEDtHTRNmjdnwpjRuLq5417Ug80bNhCfkEDtV/GPGzUSs4IF6dQ1Lf5fmjSlZ9fObFi3lgoVK3Hk8CECb97AZ1D6D3jNW/7KyGFDKFGyFKVKl+HM6dOcPPEPM+fOU2vs4usnnUtC5AIjIyMmTpxI3759SU5OxtPTk927dyvzB40ePZpOnTrh6OhIYmKiyrSzDzF9+nQ6d+5M3bp1MTAwYMCAATx48EDlMZUfQ1tbm/Xr19OlSxeKFy+Ol5cXY8eOpXHjxmrZf79+/fD392fUqFEYGBgwffp05TxhDQ0N9u3bx5AhQ2jXrh1Pnz7F3NycKlWqfFSv/MSJE0lJSaFVq1ZER0dTtmxZDhw48NG5UiZOnEivXr24desWJUuWZPfu3cpH2hYvXpxjx44xZMgQvvnmG1JTU3F0dFRJbr58+XI6duxI1apVKVy4MGPHjmXYsHfnbGjYsCEzZ85k6tSp9OrVC3t7e5YvX061atWAtHrcuXMnPXr0oEqVKmhqalKrVi1mz579Uef7rmtcnapXq0pEVBRLVq1BERGOs4MjM8aNUSbJDnvyBM0MHarb9uwlKeklf4wZp7KfDr+2pGPrXwH4tUlj4hMSmOg7i5iYGIoXK8qM8WM+SV6mGt9WIzIqksUrVqKIiMDZ0ZEZE8crp8WFPnmi0iH8VKGgdacuytdrN21m7abNlCpRnPnT0zp3+/XozqLlK5gycxYRkZGYmZrSsG4dOrT6Vf3xf/89EZGRLFq6DEV4OC5OTsycOhXTV3+ghYWFqdT/02fP+LV9emftmg0bWLNhA6VLlmTB7Flqjy871b6vTmRkJCuXLiEiPBxHJ2fGT52m/OPySViYSt0X9fRk8IiRrFi8iOWLFmJlbc3I8ROwf9XppaWpyYOQ+xwaup/nUVEUMDDA1d2dGXPmYWev3o6x16rX/4kXiQmsXzSf+LhYHFzd6Tp4mEqOlWdhocRGP1e+Drlzh1mj09uP7auWA+Bd9VtadU2bntuuVz92rVvDytkziIuJwbhgQeo2a0HlGurpoCzg6IbnyDnK1w5t0o4b5rePW/PGkdfYFB2z9LY88eljrk/0wb5NTyxrNyZR8ZRbCyYRefls+nmeOoK2gRG2TTqS18iE2OBbXB3fj6Qo9SbiB4i86k+efPmx+L4+efIbEP/4IXdWzuJlbFqSb20jk7c+u3XMCpPfzpnby30z3WfwxiVY/vATRRq3J49ePl5EhvPvoZ08O/u32uOvXuMHIiMiWbxoIeEKBc4uLkyfOUs5VSYsLFRlFJJn8RKMGjOWRQvms3DePKxtbJg4ZSqOGaagjR47jvnz5jJy+DCeP3+Oubk5nTp34adGjdQeP8BPTZuRkBDPPN9pxMbE4F7Mk+ETJqk8Lj708b88f54+VbFyte+Iioxi/coVRESEY+/oyIjxkzAy/vxTomo0+JnExATWLZxHXFwsjm7udP9jhMq9+zQslJjnGe/d2/iOSs95tHXVMgDKV/2O1t16KdefP3mc1NRUvCpX+TSxv2p31i2aR3xcLI6u7nQbPPwd7c5tZmZod17HXq7qt7TumhZ7u1792bVuNStetTsmBQtSr1lLvqlRS22x/1CjBhERkSxYtAiFQoGLiwuzZ/oqv5+EhoWpTD96+vQpLX9tpXy9es1aVq9ZS+nSpVm0IC0X1fUbN+jcpauyzIxX+UHr1qnDyBHD1RY7wPfVaxAZEcmyJYsIVyhwcnZh6gxf5fTssLAwlZHLnsWLM3zUGJYsWsDiBfOxtrFh3KTJOGSY5l+lWjX6DRjImlUrmTl9OrZFbBk9fgLFS5RUa+zi66eRmtO/WoUQX5zY2FisrKyYNm3aR4/EEeJzCL+f/RST/zKTIg5EPMz5E9Nyk7G1LVFPwt5d8D/KsFBhQp48y+0wcsy2kBkHL6n38fOfyw8lPfinSaV3F/yPqrzpBBeHdsrtMHKs1NiFKKKev7vgf5CpoQE3Qv7N7TByzN3WkiOX//uJ17PyfQk3Dl/6uIeX5JbqJd2JjorM7TByrIChEWHhkbkdRo4VNjHK7RCypLgTmCvHNXVUz/TWL42MXBLiK3bx4kVu3ryJt7c3UVFRjB49GoAGDRrkcmRCCCGEEEIIIb4WktBbiK/c1KlTKVGiBNWrVyc2Npbjx49jZmbG8ePHlY+rzGz5GoWEhGR7ziEhHz7KpHPnzlnur3Pnzp/gLIQQQgghhBDvpKGZO8v/KRm5JMRXrFSpUipPGMuobNmyan2y25fA0tIy23O2tLT84H2OHj2a/v37Z/qegYHBB+9PCCGEEEIIIb400rkkxP8pPT09nJyc3l3wK5InTx61n3OhQoUoVKiQWvcphBBCCCGEEF8S6VwSQgghhBBCCCHEVyXjE1/Fp/f/OyFQCCGEEEIIIYQQQnw0GbkkhBBCCCGEEEKIr4umjKX5nKS2hRBCCCGEEEIIIUSOycglIYQQQgghhBBCfFUk59LnJSOXhBBCCCGEEEIIIUSOSeeSEEIIIYQQQgghhMgxmRYnhBBCCCGEEEKIr4tMi/usZOSSEEIIIYQQQgghhMgxGbkkhBBCCCGEEEKIr4uGjKX5nKS2hRBCCCGEEEIIIUSOSeeSEEIIIYQQQgghhMgxmRYnhBBCCCGEEEKIr4qGpiT0/pxk5JIQQgghhBBCCCGEyDGN1NTU1NwOQgghhBBCCCGEEEJdIh8/ypXjGllY5cpxc5tMixNCCPGf9Txckdsh5JiBiSmKqOe5HUaOmBoaEBYemdth5FhhE6Mvtu4hrf79bwXndhg5UtbZjotDO+V2GDlWauxC/mlSKbfDyLHKm04QFR2d22HkiGGBAgQ9DM3tMHLMxdqcc0H3cjuMHPNysf+i251nkVG5HUaOmRkZ8uhpeG6HkWNWBU1yOwTxHyGdS0IIIYQQQgghhPi6aEjOpc9Jci4JIYQQQgghhBBCiByTziUhhBBCCCGEEEIIkWMyLU4IIYQQQgghhBBfFQ0NGUvzOUltCyGEEEIIIYQQQogck5FLQgghhBBCCCGE+LpIQu/PSkYuCSGEEEIIIYQQQogck84lIYQQQgghhBBCCJFjMi1OCCGEEEIIIYQQXxUNTZkW9znJyCUhhBBCCCGEEEIIkWMyckkIIYQQQgghhBBfFw0ZS/M5SW0LIYQQQgghhBBCiByTziUhhBBCCCGEEEIIkWMyLU4IIYQQQgghhBBfFw1J6P05ycglIYQQQgghhBBCCJFj0rkkhBpUq1aN3r17A2BnZ4evr2+O97VixQqMjIzUEteX4GPr61M6ceIEnp6eaGtr07Bhw2zL+vn5oaGhQWRkpNrj+JT7FkIIIYQQ4mukoaGZK8v/K5kWJ4SanTt3jnz58r1XWTs7O3r37q3smAJo2rQptWvX/kTRiQ/Rt29fSpYsyf79+8mfP/9nOWa1atUoWbLkf7bD7b9q05atrFm7FkV4OM5OTvj07UvRoh6Zlr1z9y4LFy/h5s2bPA4NpU+vXrRo1lSlzPKVqzh6zI/790PQ0clLcU9Punftil2RIp8k/q2bN7F2zRrCFQqcnJ3p298Hj6JFsyz/1+HDLFq4gNDHj7G2saFr9x5UrFRJ+X5cXBzz587h72PHiIqKwtLSksZNmvJTo0afJP5tWzazYe1awsMVODo506tvv2zjP3rkCEsXLSQ09DFW1jZ07taNChUrqZQJDr7HgrlzuXzxAsnJydjZ2zNm/EQKm5urNfYvve5TU1PZunYVRw/8SWxsDC7uHrTv2hNzK6tstzu4Zxd7t20hKiIcW3sH2nTqiqOrm/L9sMf/sm7pYgKvXyMpKYkSZcrQplM3DI2N1Rq/WblqFKpcA+38hsSHPuThng3EPQrOtKxTh74UsHd9a31UYAB3V88BQDOvDpY//IShe0ny6OcjMeIZT08dRXHub7XGbeBeAuv6Lchn74aOiRnXpwwi/NzxbLcx9CiFfese6NvYk6h4woOtK3lybJ9KGYuaP2NVrwV5jUyIvX+bO8tmEHPnhlpjf23zpk2sWb0ahUKBs7Mz/X18KFqsWJblDx8+zML583n8+DE2NjZ079GDSpUrK99XKBTMmT2bM6dPEx0dTanSpenv44Otre0niT81NZW1K5ZxcN8eYmNicC/mSddefbG0ts52u707trNt0wYiwsOxd3SkU49euLi5AxD9/DnrVi7jor8/T5+EYWBkRPlKlfm1bQfyqfl7QNq9u5qjB/cTFxuLi7sH7br2wNwy+3v30N7X924EtvYOtO7UFUeX9Psi7PG/rFu2hKBX927x0mVo06mrWu/dL73d2bp5M+vWprf7ffr1z77dP3KYxQsXKtv9Lt26q7T7lcp5Z7pd1+49aNmqlVpjB9ixdQsb168lPDwcR0cnevTpi7tH1vH7/XWE5UsWERoairW1Nb916Ub5ChWV769YuoSjRw7x9MkT8uTRxsXVlQ6/d8Y9mzoRIjP/v91qQnwiBQsWRF9fP8fb6+npUahQITVG9P/nxYsXatnPnTt3+O6777C2tv7iRpOpqw6+BAcPH8Z31iw6dmjP6hXLcXZ2okefPoSHh2daPiEhAStLS7p37YKpqWmmZS5cvEjjRo1YtngRc2bO5OXLl/To3Zv4+Hi1x3/40EFm+frSvmNHlq9anfZFt2ePLOMPuHKZEcOGUq9+A1asXkOVqlUZ5NOfO3duK8vM8p3B6VOnGDFqNOs3bqJJs2ZMnzqF438fU3v8Rw4fYu6smbTt0IElK1bi5OxE/z69iMgy/iuMHjGMOvXqsWTlKr6pUoUhAwdw984dZZlHDx/SvdPvFClShJlz57N89Vpat2tP3rx51Rr7l173AHu2buLA7p2069aD0dNmoqOry8Thf2TbBpz624+1Sxbxc/OWjJ05F1t7ByYOH0LUq9GRCQkJTBz2B2ho8Mf4SYyYMp2XL18ydfRwUlJS1Ba7UbGyWP34C6FH9xI4bxzxoQ9xbNuTPPkKZFr+3roFBEz0US43Zo0kNTmZyKvnlWWsfmyMgXNR7m9Zxo2ZI3l68i9s6jbDwK242uIG0NLRIyb4NneXTnuv8joFLfAYNIWoaxe4OKAt/+7dhHPngRiVSP+j1KzC99i37kHIlmVcHNie2Pu3KTZkOtoGRmqNHeDQwYP4zphBx99+Y9WaNTi7uNCzR9bX/pXLlxk2ZAj1GzRg9dq1VK1WDZ/+/blzO+3aT01Nxad/fx49esTUadNYs3YtFubmdO/a9ZO0mwBbN6xnz/ZtdO3dj6lzFqCrq8vwQf158SIxy22OH/2LJQvm0rx1G3wXLMbe0ZHhA/sTGREBQLjiGQqFgvadujBn6Qp6DxjMhbNnmTV1strj37N1Mwf37KR9156MmuqLjq4uk4YPyfbePX38GGuXLOan5r8y1ncOtvYOTHrj3p00fAgaGvDHuImMmDyN5JcvmTZmhFrv3S+53Tl86BCzZ/rSvkNHlq1chZOTM3179cz2M2vksGHUrVef5atW802Vqgwe4KPymbVr3z6V5Y+hw9DQ0KDad9+pLe7Xjh45zPw5s2jdrgMLl67A0cmZgX37EBGRefxXA64wdtQIfqxbj0XLVlLpmyoMHzyQe3fT47exsaFnn34sWbmGmfMWYG5hwYC+vZT3xRdNUyN3lv9T0rkkhJplnOaVmprKyJEjsbW1RUdHB0tLS3r27AmkjVC5f/8+ffr0QUNDA41XCefenBY3cuRISpYsyerVq7Gzs8PQ0JBmzZoRHR2tLBMdHU3Lli3Jly8fFhYWzJgxQ2Wq3vvEPH78eNq3b0+BAgWwtbVl0aJFyvczm5Z16dIlNDQ0CA4OVol7z549uLq6oq+vzy+//EJcXBwrV67Ezs4OY2NjevbsSXJyssrxo6Ojad68Ofny5cPKyoq5c+eqvB8ZGUnHjh0pWLAgBgYGfPfdd1y+fPmtOlqyZAn29vbo6uq+85wTExPp2bMnhQoVQldXl8qVK3Pu3DkAgoOD0dDQSPuC2b49GhoarFix4r3q8sSJExQvXhxdXV3Kly/P1atXle8pFAqaN2+OlZUV+vr6eHp6sn79euX7bdu25dixY8ycOVN5TbyuX4Dz589TtmxZ9PX1qVixIoGBge+sg5CQEBo0aED+/PkxMDCgSZMmhIWFqcQ8f/58HB0dyZs3L66urqxevVrlfQ0NDRYuXEjdunXR19fH3d2dU6dOcfv2bapVq0a+fPmoWLEidzJ80bp8+TLffvstBQoUwMDAgDJlyuDv7/9edfih1q3fQMP69alfty4O9vYMHjAAXR0ddu3Zk2n5oh4e9OrRnR9q1CCvtnamZWb7zqBenTo4Ojjg4uzMiKFDCQ0N48bNm2qPf8O6ddRv2JC69epj7+DAgEGD0dHVZc/uXZmW37RhA+XKV6Blq1bY2dvze+cuuLq5sXXTZmWZgCtXqF2nDqXLlMHC0pKGP/2Mk7Mz169dV3v8m9avp279BtSuWw87ewf6DRiEro4ue/fszrT8lk0b8S5Xnua/tsLOzp6OnTrj4urKti3p8S9eOJ/yFSvSpXsPXFxdsbK2pvI3VTA2MVFr7F963aempvLnzh00bNqcsuUrYmvvQJe+A4gMV3D+1Mkst9u/Yxvf1qxF1Ro1sbYtQvtuPdHR0eHYoQMABF2/xtMnYXTq0w9bO3ts7ezp3MeHe7dvcf3KJbXFX6hSdRT+/xB+4SQJTx/zYNdaUpJeYFqmYqblk+PjeBnzXLkUcPQgJemFSudSPlsHFBdPEXMviBeRChT+x4kPfUg+a3u1xQ0Qcek0IRsXv/eIKIsfGpLw5DH3Vs8h/tF9Hh/YyrPTfljVSR81aVW3KaFHdvPEbx/xj4K5vXgKyS8SKfxtXbXGDrBu7VoaNmxIvfr1cXBwYNDgwejq6rJ7V+bX/oYNGyhfoQKtWrfG3t6ezl264ObmxqZNm4C0z5qrAQEMHDQIj6JFKWJnx8DBg0lMTOTAgQNqjz81NZVd2zbT5NdWlK9UGXtHR/oM/IPwZwpO//NPltvt2LKJmrXrUr1WbWzt7Ojaux86Oroc+jNtBFkRewf+GDkG74qVsLC0okSp0rTq0JGzp0+SnPxSrfH/uWs7DZo0p0z5CtjaO9C5j0/avXv6Pe7d6j9gZVuEdl17qNy7t17du7/37oeNnT02dvZ06tNfrfful97ubFy/jnoNGlKnXj3sHRzwGTToVbuf+WfWpo0bKFe+fIZ2vzMurm5s2bxJWcbU1ExlOf73MUqXKYPVO0Zy5cTmDeupXa8+P9api529PX18BqCjq8P+LL7zbNu8Ce9y5WjW4leK2NnR/rdOOLu4smPrFmWZ73+oSRkvbyytrLB3cKBLj17ExsZyN8MPJ0K8D+lcEuIT2rp1KzNmzGDhwoXcunWLHTt24OnpCcC2bduwtrZm9OjRPH78mMePH2e5nzt37rBjxw727NnDnj17OHbsGBMnTlS+37dvX06cOMGuXbs4dOgQx48f58KFCx8U67Rp0yhbtiwXL16ka9eudOnSRaXz4n3ExcUxa9YsNmzYwJ9//omfnx8//fQT+/btY9++faxevZqFCxeyZcsWle2mTJlCiRIluHjxIoMGDaJXr14cOnRI+X7jxo158uQJ+/fv5/z585QuXZrvv/9e5RfW27dvs3XrVrZt28alS5feGeuAAQPYunUrK1eu5MKFCzg5OVGzZk3Cw8OxsbHh8ePHGBgY4Ovry+PHj2natOk79wng4+PDtGnTOHfuHAULFqRevXokJSUBab/KlSlThr1793L16lV+//13WrVqxdmzZwGYOXMmFSpU4LffflNeEzY2Nsp9DxkyhGnTpuHv70+ePHlo3769yrHfrIOUlBQaNGhAeHg4x44d49ChQ9y9e1flXLZv306vXr3o168fV69epVOnTrRr146jR4+q7HvMmDG0bt2aS5cu4ebmRosWLejUqRODBw/G39+f1NRUunfvrizfsmVLrK2tOXfuHOfPn2fQoEFoZ9GR8zGSkpK4GRiIt1dZ5TpNTU28vbwIyNCx97FiYmIBMDAwUNs+IS3+wJs3KeuVPnpBU1MTLy9vrgYEZLrN1YAAvLy9VNaVK19epbxn8eIc//tvnj55QmpqKuf9/XkQEoJ3uXJqjz8o8O34y3h5ce1q5vFfuxpAGS/V+L3LlVeWT0lJ4dTJk9jY2NKvd0/q165Fpw7tOX5MvSN/vvS6B3gaFkpkRDhFS5ZWrtPPlw9HVzdu3cx8KtXLpCTu3b5FsQzbaGpqUqxkKW7dvK4sowEq96x2Xm00NDQIvHZNLbFraGmhb2lLdMYpX6mpRN+5ib6Nw3vtw7RMJSIC/ElJSh8tERtyF0O3EmgXMAIgv70LOmaFeX5b/Z17H6KAczEiA1Q72CMun6GAS9o0NA2tPOR3cCUy4Fx6gdRUIgP8lWXUJSkpiZs3b+KV4ZrU1NTEy9ubgCtXMt0m4MoVvL1Vp/6Ur1CBgFfX/uvPOR0dHZV9aufNy+X3+Ez+UGGPHxMRHk7J0mWU6/Llz4+Luzs3r2d+jSYlJXE7KIgSGbbR1NSkZOkyBGaxDUBsTCz6+vpoaakvm8jTsFCiIiIoVrKUcp1+vnw4urz73i1aIn0bTU1NipYsxe3AtG2SXmZz72Zzjh8a+5fa7rxu9zO245qampT18sqy3b8WEKDyOQFp7f61LMqHKxScPHGCuvXrqyXmjJKSkggKCqRMWdX4y5T14vq1zL/zXL96ldJlVT+3vMqV41oW35GSkpLYs3MH+fLnx9HJWX3Bi/8LknNJiE8oJCQEc3Nzqlevjra2Nra2tsovZyYmJmhpaVGgQAHM35FDJCUlhRUrVlCgQNpUgVatWnHkyBHGjRtHdHQ0K1euZN26dXz//fcALF++HEtLyw+KtXbt2nTt2hWAgQMHMmPGDI4ePYqr69v5LbKSlJSkHAUD8Msvv7B69WrCwsLInz8/Hh4efPvttxw9elSlg6NSpUoMGjQIABcXF06cOMGMGTOoUaMG//zzD2fPnuXJkyfKL61Tp05lx44dbNmyhd9//x1Imwa2atUqChYs+M44Y2NjmT9/PitWrODHH38EYPHixRw6dIilS5fi4+ODubk5GhoaGBoavvPfJ6MRI0ZQo0YNAFauXIm1tTXbt2+nSZMmWFlZ0b9/f2XZHj16cODAATZt2oS3tzeGhobkzZsXfX39TI85btw4qlatCsCgQYOoU6cOCQkJylFKb9bBoUOHCAgI4N69e8pOqlWrVlG0aFHOnTuHl5cXU6dOpW3btsp/+759+3L69GmmTp3Kt99+qzx2u3btaNKkCZB2fVSoUIFhw4ZRs2ZNAHr16kW7du2U5UNCQvDx8cHNLS2XgrPzp/mCEhkZSXJyMiZvjGgxMTEh+P59tRwjJSWF6b6+lCheHKdX17a6ZBf//fvBmW6jUCgwNlGdzmdsYoIiXKF83be/D5PGj6dB3TpoaWmhqanJoD+GUKp06Td391GiXsX/5ogiExMTQrKo/3CF4q3zNTYxIVyRFn9ERATxcXGsXb2Kjr93pnPX7pw5fYqhgwcyc848SqrpHL70ugeIfDUNwvCNabuGRkZERmY+RSL6+XNSUlLe2sbAyJh/Hz4AwMnNDR1dXTYsX0qT1u1IBTauWEpKSorymB9LSz8/GlpaJMVEq6x/GfMcXbN3t7n6VnbomVsRsn2VyvqHezZg0/BXig2cRGpyMqmpKTzYsYbY4FtqiTun8hqZkBSlWndJURHk0c+PpnZe8uQ3QEMrD0lv/LslRYajb6nenEXZXvsZRstmpMjkvjXJcN/a2dlhbm7O3DlzGPzHH+jp6bFu7VqehIXx7NkztcYPKKcAGRmrxmRkbJzl9KDnUVGkpCRj/Eb+HiNjYx4+CMl0m6ioSDauWUXNOvXUEHW619ONDN66D42IymIqkvLeNVbdxtDIiMev713XV/fuimU0adU27d5duSzt3s1i2teHx/7ltjvZXftZfWZlde0rFJnHtH/fXvTz5aNqtW8zff9jREVFkpLJZ65xdp+54QqM37hPjI1NiMjwuQVw6sQ/jBk5nMSEBExMTZkyY+Zb/15fpP/j5Nq5QTqXhPiEGjdujK+vLw4ODtSqVYvatWtTr1498uT5sFvPzs5O2bEEYGFhwZMnTwC4e/cuSUlJKr8oGhoaflCnEEDx4un5KDQ0NDA3N1ce433p6+srO5YAChcujJ2dnUoy7MKFC7+13woVKrz1+vXUwsuXLxMTE/NWbpz4+HiVaVhFihR5r44lSBsJlpSURKUMyRi1tbXx9vbmxo2PS5ya8VxMTExwdXVV7jM5OZnx48ezadMmHj16xIsXL0hMTHzvHF0Z/40sLCwAePLkiTJZ6pt1cOPGDWxsbFRGP3l4eGBkZMSNGzfw8vLixo0byg661ypVqsTMmTOzPHbhwoUBlKPwXq9LSEjg+fPnGBgY0LdvXzp27Mjq1aupXr06jRs3Vrk23pSYmEhiomqejIy/gOemyVOncefuXRYvXJDboby3LZs2cu1qAJOnTcPc3IJLFy8ybcpkzAqa4eWt/hE06pT6KrdG5W+q0KR5cwCcXVy4GhDAzh3b1Na59Kl8yro/cfQvls5Nvzd9Roz52HAzZWBoRM9BQ1k+bzYHdu9EQ0ODClW/xc7RSTmFO7eZlq1EfOjDt5J/Fyz/Lfms7bmzei4vIhXkt3PGul5zkqIjib6j/mmtIk2ePHmYNGUKY8eMofp336GlpYWXtzcVK1YkVQ379zt8iLkz0vNbDR8/MZvS6hEXG8voPwZhU6QILdq0e/cG2Tjh9xfL5s5Svu4/fPTHhpcpA0Mjeg4cwvL5czj4+t6tUg07Ryc0NXP2R7a0Ox9mz+7d/FCz5n/mO8z7Klm6DIuXryQqMoq9u3cyevhQ5i5a8lbHlBDZkc4lIT4hGxsbAgMDOXz4MIcOHaJr165MmTKFY8eOfdAUoTfLamhoqDW54buO8foLSWpq+lfE10Pg37WPj409JiYGCwsL/Pz83novY26q931CX26aMmUKM2fOxNfXF09PT/Lly0fv3r3fO/l2xrp8/UUrY11+yjrI7NjZxTNy5EhatGjB3r172b9/PyNGjGDDhg389NNPme5/woQJjBo1SmXdiBEj6NuzR7ZxGRkZoaWl9VYS2vDwcExNP/4L0eSp0zh+4gSL5s+j8CdItJ9d/CZZJBs3NTV96xfHiPBwTF+NqElMSGDBvHlMmDxF+SQnJ2dnbgUFsW7NGrV2Lhm+iv/NRKhp8Wde/yampm+db0SG8329zyL2qjlyitjZEZAh19rH+hLrvnS58jhm+OHg5at2OCoyUmVEVVRkJEXsM+/MLWBggKampjKJ7mvPIyNUnshUvHQZZixZQXRUFJpaWuTLn5+uvzajkLnFR53Da8lxMaQmJ6OdXzV5d578BiTFRGW7raZ2Xow9vXh8RDU/kEYebSxqNOTeuvk8D0qb8pEQ9gg9CxsKVfohVzuXXkSGo22oek9oGxrzMi6GlKQXJD2PJDX5JdpGb5QxMuFFFqNBcir7djPra/9d94q7uztr160jJiaGpKQkjI2NademDe4emT+580N4V6yEi7u78vXr7yCREaoxREZE4ODolOk+DAwN0dTUIuKNkUGRERFvjQSJi4tjxCAf9PT1GTJ67Af/KPim0t7lcXRJfyray1dTOZ+/ce8+j4zE1iHzaaHKezciUmV9VGSkyr3rWboM0xcvV7l3u7VqTsEcPmnza2p3sm33TT7s2s/sO8alixcJuX+f0WPHqSXeNxkaGqGZyWduRDafWyYmpm+N5ouICH9rFK6enh5W1jZYWdvgUawYrZo1Zv+e3bRo1Ua9J/GZpXxBHZNfAxknJsQnpqenR7169Zg1axZ+fn6cOnVKmaMgb968byW3/lAODg5oa2srk1EDREVFERQU9FH7zej1aJiMeaHeJ6/R+zp9+vRbr91ffYksXbo0oaGh5MmTBycnJ5XFzMwsR8d7nbz6xIkTynVJSUmcO3cOj4/8EpzxXCIiIggKClKey4kTJ2jQoAG//vorJUqUwMHB4a1/J3VcE6+5u7vz4MEDHjx4oFx3/fp1IiMjlefp7u6uUg+v4/zYeoC0KY59+vTh4MGD/PzzzyxfvjzLsoMHDyYqKkplGTx48DuPoa2tjZurK+f80xP6pqSkcM7fH89sHqn9LqmpqUyeOg2/Y8eYP2c2Vh84zfR9aWtr4+rmxvkM929KSgr+/ucolmFkWEbFPD3xz1Ae4OyZM8ryL1++5OXLl2i+8bQSTS1NUlLVMYZANX4XVzfO+6vGf8H/HEWLZR5/0WKeXHgjufu5s2eV5bW1tXFz9+BBiOoQ/4evphmrM/Yvre719PUxt7RSLla2RTAyNuHapYvKMnFxsdwJvImzm3um+8ijrY29kzPXLqdvk5KSwtXLl3B2e/u+L2BoSL78+bl2+RLPoyIpXa78R58HQGpyMnH/hlDAIUOcGhoUcHAj7sHdbLc1KlYGDa08hF86o7JeQ0sLzTx54M26Tk3J9af3RN+6ipFnGZV1RsW9iH7VCZaa/JKYu4EYFUvPH4eGBkbFyijLqIu2tjZubm6ce5XvD15d++fO4ZlhlGpGnsWLq3zPADhz5ozKCNbX8ufPj7GxMSEhIdy4cYMqr6Zzfwx9fX0srayVi20RO4xNTLicIb9kXGwsQTdu4JbFI9m1tbVxcnHhykXVz4vLFy/gmmGbuNhYhg/oRx5tbYaOGU/evB8/AiXt3rVULla2RTA0Nuba5Uvpx42L5U7Qe9y7GZJbp6SkcO3yJZxc397mrXvXO2f37tfU7rxu9/3faPfPn/PPst0v6ump8hkHcO7sGYpmUn7P7l24urnh7OKilnjfpK2tjYuLKxfOp3+GpqSkcOG8Px5FM//O41Gs2Fufuf7nzlL0Hd+RUlJSefHi7R+ShciOdC4J8QmtWLGCpUuXcvXqVe7evcuaNWvQ09OjSJEiQNp0t7///ptHjx7lOCdBgQIFaNOmDT4+Phw9epRr167RoUMHNDU11TaM2MnJCRsbG0aOHMmtW7fYu3cv06a93+OX38eJEyeYPHkyQUFBzJ07l82bN9OrVy8AqlevToUKFWjYsCEHDx4kODiYkydPMmTIkBw/fSxfvnx06dIFHx8f/vzzT65fv85vv/1GXFwcHTp0+KhzGT16NEeOHOHq1au0bdsWMzMzGjZsCKTlHTp06BAnT57kxo0bdOrU6a0nt9nZ2XHmzBmCg4N59uzZR41Qq169Op6enrRs2ZILFy5w9uxZWrduTdWqVSlbNu0PGB8fH1asWMH8+fO5desW06dPZ9u2bSq5oT5UfHw83bt3x8/Pj/v373PixAnOnTun7GTLjI6ODgYGBirL+w4pb9G8GTt27WLP3n3cCw5m4uQpxCckUK9u2hOWRowazZx585Xlk5KSCAwKIjAoiKSXL3n69CmBQUE8ePBQWWbS1KnsP3CAMaNGoa+vzzOFgmcKBQkJWT/iOqeatWjBrp072LdnD8H37jFl0kQS4uOpWzctx8foESOYP3eOsnyTZs04feoU69auITg4mCWLFnHzxg0aNWkMpCW1LVW6NHNmzeLC+fP8++gRe/fsZv++fVStWk3t8Tdp3pw9u3ayf+9egoPvMW3yJOITEqj9qv7HjRrJwnnpT4D8pUlTzpw+xYZ1a7kfHMyyJYsJvHmDn39prCzTvOWv/HX4MLt37uDhgwds3byZkyf+oWGjRmqN/Uuvew0NDWo1aMiOjes5f+YUIcH3WDB9CkYmppSpkP7EtfF/DOTg7p3K1z82/JmjB/bz95FDPHoQwvJ5s0lMSKBq9R+UZY4dOsCtmzcIe/wv/xw9wqyJY6nV4CcsrW1QlycnDmNatjImpcqjU9Acm/ot0MybF8X5tCdOFWnUFosaDd/azrRMJaJuXCI5PlZlfUpiAtH3ArGs1Yj89i7kNTbFpFQFTEqWJ+r6xbf28zE0dfTIV8SZfEXS8snpFrIkXxFndEzTpg0Xad4Zl25DleUfH9yBbiFL7Fp2Rc/SFvMffqJghe94tHejssyjPRsx/74ehar+iJ5VERw79kdLR5cwv71qjR2gRcuW7Hz1oJB79+4xacIE4uPjqVsv7dofMXw4c+ekX/vNmjXj1MmTrF2Tdu0vWriQG9evK3PxARw+fJjz/v48eviQY35+9OjWjapVq1K+vHo6BjLS0NCg/s+N2bh2FWdOniD47h2mTxyPiZkp5V+NGgQY0r8Pe3ZsU75u+EsTDuzdy5EDf/LgfjDzfKeTkBBP9Zpp+RfjYmMZPrA/iQkJ9Ow/gPi4WCLCFUSEK9T2w8/r+GvV/0l57z4IvsfC6VPT7t3yGe7dIYM4uCd9hN6PDX/G71337uGD3M5w786eNE6t9+6X3u40bd6C3Tt3sm9vWrs/ddIkEhLiqfPqM2vMyBHMz/DU4iZN09r99WvTPrOWLk5r939p3ERlv7ExMRw9coR6DRqoLdbMNG7WnL27d3Fg/17uBwfjO3UyCfEJ1KqTFv+EMaNYvGCesvzPjZtw7sxpNq1fR8j9YFYsXULQzZs0bPQLkPadbcnC+Vy/epXQ0McE3bzJ5PFjefbsKVW//e6Tnov4+si0OCE+ISMjIyZOnEjfvn1JTk7G09OT3bt3K4edjx49mk6dOuHo6EhiYqLKtLMPMX36dDp37kzdunUxMDBgwIABPHjwQJno+WNpa2uzfv16unTpQvHixfHy8mLs2LE0btz43Ru/h379+uHv78+oUaMwMDBg+vTpykTRGhoa7Nu3jyFDhtCuXTuePn2Kubk5VapUUeb+yYmJEyeSkpJCq1atiI6OpmzZshw4cOCtRJ852W+vXr24desWJUuWZPfu3eTNmxeAoUOHcvfuXWrWrIm+vj6///47DRs2JCoqfQpI//79adOmDR4eHsTHx3Pv3r0cx6KhocHOnTvp0aMHVapUQVNTk1q1ajF79mxlmYYNGzJz5kymTp1Kr169sLe3Z/ny5VSrVi3Hx9XS0kKhUNC6dWvCwsIwMzPj559/fmvam7r8UL06kRGRLFyyGIUiHBdnZ2bNmI7pq2kOoWFhaGTINfH02TN+bdNW+XrNunWsWbeO0qVKKTtBtm7bDkDnbt1UjjV86BDq1amj1vir1/iByIhIFi9aSLhCgbOLC9NnzlIOcQ8LC1UZCeNZvASjxoxl0YL5LJw3D2sbGyZOmYpjhqkgo8eOY/68uYwcPoznz59jbm5Op85d+EnNnTMA31evQWREJMuWLCJcocDJ2YWpM3yVUwzC3qh/z+LFGT5qDEsWLWDxgvlY29gwbtJkHDLk5KpSrRr9BgxkzaqVzJw+HdsitoweP4HiJUqqNfYvve4B6jZqQmJCAktnzyQuNgYXj6IMHD1O2e4AhIU+Jvr5c+XrClWqER0VxZY1q4iKiKCIgwMDR49TmZ7y+NFDNq5cTkxMNAULFaZBk+b82PBntcYeedWfPPnyY/F9ffLkNyD+8UPurJzFy9i0JN/aRiZvfS7qmBUmv50zt5f7ZrrP4I1LsPzhJ4o0bk8evXy8iAzn30M7eXb2b7XGXsDRDc+R6Z0vDm16AhDmt49b88aR19gUHbP0z6jEp4+5PtEH+zY9sazdmETFU24tmETk5fTRQ89OHUHbwAjbJh3Ja2RCbPAtro7vR1JU5gmeP0aNH34gIiKCRQsWoFAocHFxYebs2crvJ2GhoSo5eoqXKMGYceNYMG8e8+bOxcbGhilTp+LolH7tK549w3fGDMIVCszMzKhdpw4dOnZUe+yvNWrWnISEeOZMn0psTAwenp6MmjBFZaRR6L//8jzDZ+w3335HVFQka1csIyIiHAdHJ0ZNnKKcFnfnVhCBN9KeXvZ7qxYqx1uydgOF1TQ9C6Buo8YkJiSwbM4s5b07YNRYlXv3Sei/RD9Pj7/8N1V5HhXF1rWrlffugFFjVe/dhw/ZlOHerd+kGT82UO+9+yW3O9Vr1CAyMoIlixYp2/1pvjMztPtvf2aNHDOGRQsWsHB+Wrs/YfIUlc8sgMOHDpGamkqNH2qqNd43fft9dSIjI1i+ZAkR4QocnZyZNG2GMun4k7AwlXu3mGdxhowYxbLFi1i6aAFW1jaMnjAJe4e0+LU0NQm5f58D+/fxPCoKAwNDXN3dmTl3PvZZTNH8kqSod8C2eAeN1Jz+NSuE+M+KjY3FysqKadOmffRIHCFy0/M38tt8SQxMTFFEPX93wf8gU0MDwsIjczuMHCtsYvTF1j2k1b//reDcDiNHyjrbcXFop9wOI8dKjV3IP00qvbvgf1TlTSeIio5+d8H/IMMCBQh6GJrbYeSYi7U554Jy/oNQbvNysf+i251nkdnnavsvMzMy5NFT9eZW+5ysCv53k35HPM+d9tDYoMC7C32FZOSSEF+BixcvcvPmTby9vYmKimL06LSnkDT4xENzhRBCCCGEEOK/SN25JkX2JOeSEF+JqVOnUqJECapXr05sbCzHjx/HzMyM48ePkz9//iyXr1FISEi25xwSEvLB++zcuXOW++vcufMnOAshhBBCCCGE+DLIyCUhvgKlSpXi/Pnzmb5XtmxZtT7Z7UtgaWmZ7Tlb5uDJX6NHj84yybWBgcEH708IIYQQQgjx6UgGoM9LOpeE+Mrp6enhlCHh5v+DPHnyqP2cCxUqRKFChdS6TyGEEEIIIYT4Gsi0OCGEEEIIIYQQQgiRYzJySQghhBBCCCGEEF8VmRX3ecnIJSGEEEIIIYQQQgiRYzJySQghhBBCCCGEEF+VFBm69FnJyCUhhBBCCCGEEEIIkWPSuSSEEEIIIYQQQgghckymxQkhhBBCCCGEEOKrkirT4j4rGbkkhBBCCCGEEEIIIXJMRi4JIYQQQgghhBDiqyIjlz4vGbkkhBBCCCGEEEIIIXJMRi4JIYQQQgghhBDiq5IiA5c+Kxm5JIQQQgghhBBCCCFyTDqXhBBCCCGEEEIIIUSOybQ4IYQQQgghhBBCfFUkoffnJSOXhBBCCCGEEEIIIUSOaaRKd54QQgghhBBCCCG+IiFPnuXKcW0LmeXKcXObTIsTQgjxn+UXEJjbIeRYNU/XLzb+ap6uXLwTktth5FgpR1sUUc9zO4wcMzU04Hm4IrfDyBEDE9Mvvu6joqNzO4wcMyxQgH+aVMrtMHKk8qYThCoicjuMHDM3NebI5Zu5HUaOfV/CjZ1nLud2GDnSoFwJwi6dye0wcqxwyXIEPQzN7TByzMXaPLdDEP8RMi1OCCGEEEIIIYQQQuSYjFwSQgghhBBCCCHEV0UyAH1eMnJJCCGEEEIIIYQQQuSYdC4JIYQQQgghhBDiq5KamjtLTsydOxc7Ozt0dXUpV64cZ8+ezbb85s2bcXNzQ1dXF09PT/bt2/fGuacyfPhwLCws0NPTo3r16ty6dStnwb0n6VwSQgghhBBCCCGEyAUbN26kb9++jBgxggsXLlCiRAlq1qzJkydPMi1/8uRJmjdvTocOHbh48SINGzakYcOGXL16VVlm8uTJzJo1iwULFnDmzBny5ctHzZo1SUhI+GTnIZ1LQgghhBBCCCGE+KqkpKbmyvKhpk+fzm+//Ua7du3w8PBgwYIF6Ovrs2zZskzLz5w5k1q1auHj44O7uztjxoyhdOnSzJkzB0gbteTr68vQoUNp0KABxYsXZ9WqVfz777/s2LHjY6o0W9K5JIQQQgghhBBCCKEGiYmJPH/+XGVJTEzMtOyLFy84f/481atXV67T1NSkevXqnDp1KtNtTp06pVIeoGbNmsry9+7dIzQ0VKWMoaEh5cqVy3Kf6iCdS0IIIYQQQgghhBBqMGHCBAwNDVWWCRMmZFr22bNnJCcnU7hwYZX1hQsXJjQ0NNNtQkNDsy3/+r8fsk91yPPJ9iyEEEIIIYQQQgiRC1Jzml37Iw0ePJi+ffuqrNPR0cmVWD4n6VwSQgghhBBCCCGEUAMdHZ337kwyMzNDS0uLsLAwlfVhYWGYm5tnuo25uXm25V//NywsDAsLC5UyJUuWfN/T+GAyLU4IIYQQQgghhBBflS8hoXfevHkpU6YMR44cSY87JYUjR45QoUKFTLepUKGCSnmAQ4cOKcvb29tjbm6uUub58+ecOXMmy32qg4xcEkIIIYQQQgghhMgFffv2pU2bNpQtWxZvb298fX2JjY2lXbt2ALRu3RorKytl3qZevXpRtWpVpk2bRp06ddiwYQP+/v4sWrQIAA0NDXr37s3YsWNxdnbG3t6eYcOGYWlpScOGDT/ZeUjnkhBCCCGEEEIIIUQuaNq0KU+fPmX48OGEhoZSsmRJ/vzzT2VC7pCQEDQ10yedVaxYkXXr1jF06FD++OMPnJ2d2bFjB8WKFVOWGTBgALGxsfz+++9ERkZSuXJl/vzzT3R1dT/ZeUjnkhBCCCGEEEIIIb4quZTPO0e6d+9O9+7dM33Pz8/vrXWNGzemcePGWe5PQ0OD0aNHM3r0aHWF+E6Sc0kIIYQQQgghhBBC5Jh0LgkhPqtq1arRu3fvT3oMOzs7fH19czWGjPz8/NDQ0CAyMvI/f5x31Z0QQgghhBBfgtTU1FxZ/l/JtDghxP+dbdu2oa2tndth5KoVK1bQu3fvtzqizp07R758+XInqI+UmprK7o3rOH74IPFxsTi6utPi9y4UtrDMcpug61c5uHM7IXfvEBURTpcBf1DSu7xKmRVzfDnl95fKOo+Speg1dJTEniH+zWtW8tef+4mNjcHVoygduvXEwso62+0O7N7J7q2biYoIx9bekXZduuHk6qZ8PzI8nDVLFxFw6QIJcfFYWFvzU9MWlKv8jdpi37p5E2vXrCFcocDJ2Zm+/X3wKFo0y/J/HT7MooULCH38GGsbG7p270HFSpWU78fFxTF/7hz+PnaMqKgoLC0tadykKT81aqS2mDPatGUra9auRREejrOTEz59+1K0qEemZe/cvcvCxUu4efMmj0ND6dOrFy2aNVUps3zlKo4e8+P+/RB0dPJS3NOT7l27YlekyCeJ/0uu/82bNrFm9WoUCgXOzs709/GhaIZ8F286fPgwC+fP5/Hjx9jY2NC9Rw8qVa6sfF+hUDBn9mzOnD5NdHQ0pUqXpr+PD7a2tmqP3cC9BNb1W5DP3g0dEzOuTxlE+Lnj2W5j6FEK+9Y90LexJ1HxhAdbV/Lk2D6VMhY1f8aqXgvyGpkQe/82d5bNIObODbXHD7B96xY2rF1DeHg4jk5O9OrbD3ePrK+do38dYdmiRYSGPsbK2obOXbtRvmJF5fsTxo7mz32q5+NdrjxTZvh+kvhTU1PZs2kdJ44cIj42Fgc3N5p37EKhbNr9W9evcWjXdh7cu01URAS/9x/8VrvftUmDTLf96dc21Kj/s9piP7htE2f9jhAfF4udsxs/te1IQXOLLLf5a/d2rvqf5cnjR2hr58XO2YUfm/6qcr5bly/i1rUAnkeEo6OrSxEnV2o3bUkhSyu1xP3atgOH2bB7H+GRUTgWsaFXu1Z4ODlmWvbYmXOs2bGbR6FPeJn8Emtzc5rW/ZGaVdLbndTUVJZt3sbuI37ExMbh6epM345tsbHI/DHyHys1NZW1K5ZxcN8eYmNicC/mSddefbG0zv4zd++O7WzbtIGI8HDsHR3p1KMXLm7uAEQ/f866lcu46O/P0ydhGBgZUb5SZX5t24F8+fN/kvMQXx8ZuSSE+L9jYmJCgQIFcjuM/6SCBQuir6+f22HkyIEd2/hr3x5a/t6FQeOnoKOjw6wxI0h68SLLbV4kJGJtZ0/zjp2y3XfRkqWZvHilcunY20diz2DXlo38uWsHHbv3YuyM2ejo6jJh2GBeZBP/yWN+rF68kF9a/MqE2fMp4uDAhGGDiYqMUJaZO20Sjx89xGf4aCbPW4R3xcr4ThzLvTu31RL34UMHmeXrS/uOHVm+ajVOzs706dmD8PDwTMsHXLnMiGFDqVe/AStWr6FK1aoM8unPnQzxzPKdwelTpxgxajTrN26iSbNmTJ86heN/H1NLzBkdPHwY31mz6NihPatXLMfZ2YkeffpkGX9CQgJWlpZ079oFU1PTTMtcuHiRxo0asWzxIubMnMnLly/p0bs38fHxao//S67/QwcP4jtjBh1/+41Va9bg7OJCzx5Zx37l8mWGDRlC/QYNWL12LVWrVcOnf3/u3E6LPTU1FZ/+/Xn06BFTp01jzdq1WJib071r109S91o6esQE3+bu0mnvVV6noAUeg6YQde0CFwe05d+9m3DuPBCjEt7KMmYVvse+dQ9Ctizj4sD2xN6/TbEh09E2MFJ7/H8dPsTcWTNp074ji5evxNHJmf59ehORRf1fDbjCmBHDqV2vHotXrOSbKlUYMmgAd+/cUSnnXb4823bvVS7DR326XCWHdm7Db/9emv/WBZ/xU9DR0WX2uJHZt/uJCVjb2dG0Q9bt/oRFK1SWVl16oKGhQalyFbPc5kP57d3JiUP7+bntb/QYMZ68OjosnTIu29jv3rxOxeo16T58HL8NHEpycjJLJo/lRWKCsoyVnQNNOnah/8QZdPAZQiqpLJk8lpSUFLXFfuTkaeauWkfbRg1ZMnE0TkVs6T9+ChFRzzMtb5A/P61+qs+8McNYPnkcP1b7honzF3P20hVlmXW79rJ1/yH6dWzLwnEj0NXVof/4KSRmUx8fY+uG9ezZvo2uvfsxdc4CdHV1GT6oPy9eJGa5zfGjf7FkwVyat26D74LF2Ds6MnxgfyIj0j5zwxXPUCgUtO/UhTlLV9B7wGAunD3LrKmTP8k5fC4pqam5svy/ks4lIcRn9/LlS7p3746hoSFmZmYMGzZMOYQ0IiKC1q1bY2xsjL6+Pj/++CO3bt1S2X7r1q0ULVoUHR0d7OzsmDYt+y/HS5YswcjIiCNHjgBvT4uzs7Nj/PjxtG/fngIFCmBra6t8lOdrJ0+epGTJkujq6lK2bFl27NiBhoYGly5dylEd/PPPP3zzzTfo6elhY2NDz549iY2NVb6/evVqypYtS4ECBTA3N6dFixY8efJEZR/79u3DxcUFPT09vv32W4KDg9/r2H5+frRr146oqCg0NDTQ0NBg5MiRyrrIOC1OQ0ODhQsXUrduXfT19XF3d+fUqVPcvn2batWqkS9fPipWrMidN76g79y5k9KlS6Orq4uDgwOjRo3i5cuXOaqr95GamsqRvbuo3agJJb3LY21nT7sefYiMCOfS2dNZblesdBkaNv+VUuUqZLv/PNraGBobKxd1/or3Jcf+Ov79O7bzU7OWlK1QkSL2DnTrN5AIhQL/Uyey3G7v9q18V+tHqv1QC2vbInTs3ou8Ojr4HTygLBN04zo16zXAydWNwhYW/Ny8Jfny5ePerSC1xL5h3TrqN2xI3Xr1sXdwYMCgwejo6rJn965My2/asIFy5SvQslUr7Ozt+b1zF1zd3Ni6abOyTMCVK9SuU4fSZcpgYWlJw59+xsnZmevXrqsl5ozWrd9Aw/r1qV+3Lg729gweMABdHR127dmTafmiHh706tGdH2rUIG8Wozdn+86gXp06ODo44OLszIihQwkNDePGzZtqj/9Lrv91a9fSsGFD6tWvj4ODA4MGD0ZXV5fduzKPfcOGDZSvUIFWrVtjb29P5y5dcHNzY9OmTUDak4CuBgQwcNAgPIoWpYidHQMHDyYxMZEDBw5kus+PEXHpNCEbF6M49/d7lbf4oSEJTx5zb/Uc4h/d5/GBrTw77YdVnfSRb1Z1mxJ6ZDdP/PYR/yiY24unkPwikcLf1lV7/Js2rKdu/QbUrlsXO3t7+g0YiK6OLvuyuPa3bNqId7nyNG/5K3Z29nT4vRMurq5s37pFpVxe7byYmpoqlwIGBmqPHdLazb/27abWz40p4VUO6yJ2tOnem6iIcC6fy7rdL1qqDPWb/UpJ76zbfUMjY5Xl8rmzuBT1xKywekbRpKam8s+BfXxf/2eKlvHCwrYITTt153lkBNcunMtyu44+Qyj7TTXMrW2wtLWjyW/diFQ84+G9u8oy5b+tjoObByYFC2Ft50CtRs2IDFcQ8fRJlvv9UJv2/knd76tR+9sq2Flb0a9jW3Tz6rD3aOYd0KWKulPFuyx21lZYmRemce2aONjacCUwSFkfm/cdoNXP9fnGqwyORWwZ0q0TiohI/jl3QW1xv5aamsqubZtp8msryleqjL2jI30G/kH4MwWn//kny+12bNlEzdp1qV6rNrZ2dnTt3Q8dHV0O/Zk2Wq+IvQN/jByDd8VKWFhaUaJUaVp16MjZ0ydJTv5039/E10U6l4QQn93KlSvJkycPZ8+eZebMmUyfPp0lS5YA0LZtW/z9/dm1axenTp0iNTWV2rVrk5SUBMD58+dp0qQJzZo1IyAggJEjRzJs2DBWrFiR6bEmT57MoEGDOHjwIN9//32WMU2bNo2yZcty8eJFunbtSpcuXQgMDATg+fPn1KtXD09PTy5cuMCYMWMYOHBgjs//zp071KpVi0aNGnHlyhU2btzIP//8o/KEiKSkJMaMGcPly5fZsWMHwcHBtG3bVvn+gwcP+Pnnn6lXrx6XLl2iY8eODBo06L2OX7FiRXx9fTEwMODx48c8fvyY/v37Z1l+zJgxtG7dmkuXLuHm5kaLFi3o1KkTgwcPxt/fn9TUVJXYjx8/TuvWrenVqxfXr19n4cKFrFixgnHjxn14Zb2nZ0/CeB4ZgXvxEsp1evnyYe/swt2gwI/ef9C1q/Rv34rhPbuwdtE8YqIz/4UzJ77k2AGehIYSGRGOZ8lSynX6+fLh5OpG0I3M/6B/mZTEvdtBeJYsrVynqamJZ8nSBN1M38bF3YNTfx8jJvo5KSkpnDx2lKQXSXhkqKucSkpKIvDmTcp6pY+80NTUxMvLm6sBAZluczUgAC9vL5V15cqXVynvWbw4x//+m6dPnpCamsp5f38ehITgXa7cR8f8Zvw3AwPx9iqrEr+3lxcBV6+q7TgxMWmd3gZq/iP7S67/pKQkbt68iVeGfWpqauLl7U3AlSuZbhNw5Qre3t4q68pXqEDAq9hff8bp6Oio7FM7b14u5/BHDHUq4FyMyAB/lXURl89QwCVtGqCGVh7yO7gSGZChcyE1lcgAf2UZdUlKSiIoMJAyZdOvBU1NTcp4eXHtaubXzrWrVynjpXrteJUr/1b5Sxcv0KD2j/zarAnTpkwiKipKrbG/pnjV7rtlbPf182HnpJ52/7XnkZFcvehPxe+qq22f4U+fEB0ViXPR4sp1evr62Dg4cf/2+3f8J8THAaCfxQ8eLxITOHf8KCYFC2FoavZxQb+S9PIlQXeDKeuZPn1SU1OTMp4eXLv17hGxqampnA+4xoPHjynhnjaF+/GTp4RHRqnsM7++Pu5ODlx9j31+qLDHj4kID6dk6TLKdfny58fF3Z2b169luk1SUhK3g4IokWEbTU1NSpYuQ2AW2wDExsSir6+PlpZk0hHvR64UIcRnZ2Njw4wZM9DQ0MDV1ZWAgABmzJhBtWrV2LVrFydOnKDiqzwIa9euxcbGhh07dtC4cWOmT5/O999/z7BhwwBwcXHh+vXrTJkyRaXzBWDgwIGsXr2aY8eOUTSbHB4AtWvXpmvXrsrtZsyYwdGjR3F1dWXdunVoaGiwePFidHV18fDw4NGjR/z22285Ov8JEybQsmVL5egpZ2dnZs2aRdWqVZk/fz66urq0b99eWd7BwYFZs2bh5eVFTEwM+fPnZ/78+Tg6OipHbb2ux0mTJr3z+Hnz5sXQ0BANDQ3Mzd/9S2a7du1o0qQJkFY3FSpUYNiwYdSsWROAXr160a5dO2X5UaNGMWjQINq0aaOMf8yYMQwYMIARI0a8XyV9oOevhnUbGBmprDcwNFKZZpUTRUuWplS5CpgVKszTsFB2rFvN7HGjGDhuMppaWh+1b/iyYweIjEibhmJobKyy3tDIWDnc/k3Pn0eRkpKS6TaPHjxQvu49eBgzJ46lY9NGaGlpkVdHh77DRmCuhvwbkZGRJCcnY2JiorLexMSE+/eDM91GoVBgbKI6nczYxARFuEL5um9/HyaNH0+DunXQ0tJCU1OTQX8MoVTp0m/u7pPFH3z/vlqOkZKSwnRfX0oUL46TY+b5SHLqS67/bGPPYgSpQqHItHy4Ii12Ozs7zM3NmTtnDoP/+AM9PT3WrV3Lk7Awnj17prbYcyqvkQlJUapTzpKiIsijnx9N7bzkyW+AhlYekiLfKBMZjr6lenNGRb2qf+M36tPYxJiQLK6dcIUCY+M3yhsbK+sfwLtcBapUrYa5pSX/PnzE4oXzGdC3D/MWLUZLTe1l+jm8avcNjVTWGxga8fwj2/2MTh/7C11dvWxHOn2o6KhIAPIbGqqsL2BoSPR7PlAkJSWFXWtWYOfsirm16vVx8vAB9m1cw4vERApaWPLbgKHkyaOeP1mjnkeTnJKCsaFqZ7mJoSEh/z7OcruYuDgade7Fi5cv0dLUpE+H1ngVT+s0VUSmdUAav1EfJoaGhH+CB7lEvPrMNXrjejYyNla+96bnUVGkpCRj/MZnrpGxMQ8fhGS6TVRUJBvXrKJmnXpqiDr3/D8n184N0rkkhPjsypcvj4aGhvJ1hQoVmDZtGtevXydPnjyUy/BrsKmpKa6urty4kZYQ9MaNGzRooJqsslKlSvj6+pKcnKz8Ajht2jRiY2Px9/fHwcHhnTEVL57+C9zrTpfX09ACAwMpXrw4urq6yjJv/gL9IS5fvsyVK1dYu3atcl1qaiopKSncu3cPd3d3zp8/z8iRI7l8+TIRERHKfAMhISF4eHhw48YNlXqCtHr8FDLWTeHChQHw9PRUWZeQkMDz588xMDDg8uXLnDhxQmWkUnJyMgkJCcTFxWWa0ykxMZHERNVcARl/wX/Tmb/9WLtonvJ198HDP/zE3pNX5SrK/7cqYodVETuGdvudwGtXVUYbva8vOXaAf44eYfFsX+XrgaPGfmyYWdq0egWxMbEMGT8JAwNDzp06ycwJYxk5eQa29vaf7LgfY8umjVy7GsDkadMwN7fg0sWLTJsyGbOCZnh5q3f00qc2eeo07ty9y+KFC3I7lPf2pdZ/njx5mDRlCmPHjKH6d9+hpaWFl7c3FStWRP40+jy+r1FD+f+Ojk44OjnRvHEjLl28oDJKKifOHvdj/aL5ytddBg/7qP29r1NHD+P1TVW08+bN8T4unDzOtuXpqQLa9Rv80XHtWLWUsEcP6DL07ZxWpSp+g3Ox4kRHRnBs/27WzJ1B16FjPuocPpa+ri5LJ48lPiGB8wHXmbtqPZaFClGqqPsnP7bf4UPMnZGe/mH4+Imf/JhxsbGM/mMQNkWK0KJNu3dvIMQr0rkkhPgqffPNN+zdu5dNmza913SxN58ep6GhodYEkhnFxMTQqVMnevbs+dZ7tra2xMbGUrNmTWrWrMnatWspWLAgISEh1KxZM9sEyZ9Kxrp53SmY2brX9RUTE8OoUaP4+ee3n0qTsYMuowkTJjBqlOoTzEaMGEG1Rs0zLV/Cyxt7Zxfl69f5nJ5HRmKY4de851GR2Ni9u3PxQxQsbE5+AwOehj7OUQfNlxw7QJlyFVSe6PZ6Ok9URITKqJKoyAiKOGQ+2sXAwBBNTU2i3hjZFBUZgZFJ2i+roY//5cDunUyZvxibInYAFHFw5Oa1AA7u2UnHHr1zFP9rRkZGaGlpvZWAOTw8HJMskl2bmpoSkWGUDEBEeDimr847MSGBBfPmMWHyFOVTwJycnbkVFMS6NWvU2rmRXfympiZZbPX+Jk+dxvETJ1g0fx6FCxX66P296Uuu/+zrPuvY33Wu7u7urF23jpiYGJKSkjA2NqZdmza4e2T+9L/P6UVkONqGqteVtqExL+NiSEl6QdLzSFKTX6Jt9EYZIxNeRGY+miKnDF/V/5vJuyPCIzAxybz+TUxN3xrVERERkeW1BmBpZYWhkRGPHj786M6l4mW9sXN2Vb5++ardfB71drtvbaeejvPbN64R9u8jOnzkQxw8SpXF1tFZ+fp17DFRURgYpY+EiY6KwvJVW52dHauWcuPSBboMGYVRJv9eevr66OnrU9DcAlsnF0Z0bsfV82cpVaFyJnv7MIYGBdDS1HwreXd4VBQmRoZZbJU2hczaPO3HNWe7Itx/9C9rduymVFF3TF9tFxEVhZmxkco+new+/imb3hUr4eKe3on1+jM3MkK1/YiMiMDB0SnTfRgYGqKpqUXEG5+5kRERb40AjIuLY8QgH/T09RkyeqzaRo3lFumc/7wk55IQ4rM7c+aMyuvTp0/j7OyMh4cHL1++VHlfoVAQGBiIx6sv1+7u7pw4oZok+MSJE7i4uKgMW/f29mb//v2MHz+eqVOnflS8r6ecZRxZc+5c1kkr36V06dJcv34dJyent5a8efNy8+ZNFAoFEydO5JtvvsHNze2tZN7u7u6cPXtWZd3p01knAX1T3rx5SU5OzvE5ZKd06dIEBgZmen6ampl/7AwePJioqCiVZfDgrH8d1dXTp5CFpXKxsLbBwMiYmwGXlWXi4+K4dysIBxfXLPeTExGKZ8RGR781pet9fcmxQ9oXf3NLK+VibVsEI2MTrl6+qCwTFxfL7cCbuLhn/kdxHm1t7J1cVLZJSUnh6qWLuLilbfMiIe1+08wwyhHSvuSr40ks2trauLq5cT7DvZySkoK//zmKZRiZl1ExT0/837j3z545oyz/8uVLXr58iabmGzFrqSfmN+N3c3XlnP95lfjP+fvjWSznOW5SU1OZPHUafseOMX/ObKwss34s+sf4kutfW1sbNzc3zmVog1NSUvA/dw7PDCM9M/IsXvytz40zZ86ojAJ9LX/+/BgbGxMSEsKNGzeoUrWq2mLPqehbVzHyLKOyzqi4F9FBafm9UpNfEnM3EKNi6TnA0NDAqFgZZRl10dbWxsXVlfPnVa+dC/7nKFos82unaLFinPdXrX//s2ezLA/w5MkTnkdFZdlh+CF09fQpZG6hXF63+4EB6Tm64uPiCL6tvnb/5F+HsXVw/OjOKl09PcwKmyuXwlbWFDA04tb19HxVCfFxPLh7myJOLlnuJzU1lR2rlnL1/Fl+HzQck4Lv0WmdmgqkkqymB4Jo58mDi4Md5wPS8wylpKRw4ep1ijpn3jGTeVgpJL2KyaJQQUyMDDkfkJ4vMDYunhu371LsA/aZFX19fSytrJWLbRE7jE1MuHwhPVl4XGwsQTdu4OaReQoIbW1tnFxcuHJR9fPi8sULuGbYJi42luED+pFHW5uhY8aTN2/WI8iFyIx0LgkhPruQkBD69u1LYGAg69evZ/bs2fTq1QtnZ2caNGjAb7/9xj///MPly5f59ddfsbKyUk6F69evH0eOHGHMmDEEBQWxcuVK5syZk2lC6ooVK7Jv3z5GjRql8gS0D9WiRQtSUlL4/fffuXHjBgcOHFB2WGm88Yfv+xg4cCAnT56ke/fuXLp0iVu3brFz505lUmxbW1vy5s3L7NmzuXv3Lrt27WLMmDEq++jcuTO3bt3Cx8eHwMBA1q1bl2VS88zY2dkRExPDkSNHePbsGXFxcR98HlkZPnw4q1atYtSoUVy7do0bN26wYcMGhg4dmuU2Ojo6GBgYqCzZTYt7k4aGBt/Xqc++rZu4fO4Mj+4Hs3z2DIyMTSjpXV5ZbvrIoRzdn/40oYT4eB7cu8uDV0+reRYWxoN7dwl/+lT5/pZVy7kbdJNnT8K4ceUy8yaNo6C5BR4l1ZPD5UuO/XX8Pzb8ie0b1uF/+iQh9+4xb+pkjE1NKVuhkrLcmME+/Ll7h/J1nZ8a8def+zh2+CCPQu6zdO4sEhMTqFojLZeXpY0N5paWLJ49k9uBNwl9/C97tm0m4OIFvCqo55HazVq0YNfOHezbs4fge/eYMmkiCfHx1K2blmNi9IgRzJ87R1m+SbNmnD51inVr1xAcHMySRYu4eeMGjZo0BtKSqpYqXZo5s2Zx4fx5/n30iL17drN/3z6qVq2mlpgzatG8GTt27WLP3n3cCw5m4uQpxCckUK9u2tO5RowazZx56VNxkpKSCAwKIjAoiKSXL3n69CmBQUE8ePBQWWbS1KnsP3CAMaNGoa+vzzOFgmcKBQkJWT/iOqe+5Ppv0bIlO3fsYM+ePdy7d49JEyYQHx9P3XppsY8YPpy5c9Jjb9asGadOnmTtmrTYFy1cyI3r15X57AAOHz7MeX9/Hj18yDE/P3p060bVqlUpX778W8f/WJo6euQr4ky+ImkjUnQLWZKviDM6pmmjM4o074xLt/Q2+/HBHegWssSuZVf0LG0x/+EnClb4jkd7NyrLPNqzEfPv61Go6o/oWRXBsWN/tHR0CfPbq/b4mzRrzt5du/hz316Cg+8xfcpk4hMS+LFuHQDGjR7Fovnp049/adKUs6dPs3HdWu4HB7N8yWICb97gp0a/AGmjNebPmc21q1d5/PhfzvufY8hAH6ysrfEqp/7619DQ4Lva9di/bRNX/M/wKCSYlXN8MTQ2oYRX+vFmjh6G35/p9ZeQEM+D4Ls8CE5r9xVPwngQfJfwZ09V9h8fF8eF0yeo+F0N1E1DQ4PKNWvz185tXLvgz+MHIWxcOAcDI2OKlk4f4bVo4mhOHPpT+XrHyqVcOHmc5l16oaurR3RkJNGRkSS9GpGteBLGX7u38/DeXSKePSP4ViCr50xHWzsvbiVKvRVHTjWpU4s9fx1j/7HjBD98xLQlK4lPTKR2tbRp5OPmLGThuk3K8mu27+bclav8G/aE4IeP2LB7PweOn+SHyhWV9dG4dk1Wbd/JP/4XuBPygHFzF2JqbERlL/Xm2nt9vPo/N2bj2lWcOXmC4Lt3mD5xPCZmppSvnD66a0j/PuzZsU35uuEvTTiwdy9HDvzJg/vBzPOdTkJCPNVr/gi86lga2J/EhAR69h9AfFwsEeEKIsIVn+zHSPH1+bLHuQkhvkitW7cmPj4eb29vtLS06NWrF7///jsAy5cvp1evXtStW5cXL15QpUoV9u3bp5yGVbp0aTZt2sTw4cMZM2YMFhYWjB49+q1k3q9VrlyZvXv3Urt2bbS0tOjRo8cHx2tgYMDu3bvp0qULJUuWxNPTk+HDh9OiRYssp3llp3jx4hw7dowhQ4bwzTffkJqaiqOjI02bpj3SuWDBgqxYsYI//viDWbNmUbp0aaZOnUr9+vWV+7C1tWXr1q306dOH2bNn4+3tzfjx41USgWenYsWKdO7cmaZNm6JQKBgxYgQjR4784HPJTM2aNdmzZw+jR49m0qRJyl/5O3bsqJb9Z3nchj/zIjGBNQvnEhcbi5ObBz2HjlTJ0/AsLJSY5+nD4e/fuc30kUOUrzevXApAhWrf0bZ7bzQ1NXl0P5jTfn8RFxeLkbEJ7iVK0qBZy7emUv6/xg5Q/5emJCYksHi2L3ExMbgWLcag0RPImyH+sMePic4wFaFi1Wo8fx7J5tUriYxIm0I3aPR4jF6NqsqTJw8DR41j/fKlTBk1jIT4BApbWtKlrw+lvNQzval6jR+IjIhk8aKFhCsUOLu4MH3mLOVUg7CwUJVRMJ7FSzBqzFgWLZjPwnnzsLaxYeKUqThmmIoweuw45s+by8jhw3j+/Dnm5uZ06tyFnxo1UkvMGf1QvTqREZEsXLIYhSIcF2dnZs2YjumraQ6hYWFoZBgt+PTZM35t01b5es26daxZt47SpUqxcN5cALZu2w5A527dVI41fOgQ6tWpo9b4v+T6r/HDD0RERLBowQIUCgUuLi7MnD1bOcolLDRUZaRm8RIlGDNuHAvmzWPe3LnY2NgwZepUHJ3SY1c8e4bvjBmEKxSYmZlRu04dOnyidrOAoxueI9M7vxzapE3TDvPbx61548hrbIqOWWHl+4lPH3N9og/2bXpiWbsxiYqn3FowicjL6aO3np06graBEbZNOpLXyITY4FtcHd+PpCj1Jah+7bvqNYiMjGTZ4sWEhytwcnZmyvQZymlxT964dop5FmfYqNEsXbSQxQsXYG1tw7iJk3F4laheS0uTO7dv8+e+fcTERGNmZkZZ73J0+P13lXZMnWo0+JnExATWLZxHXFwsjm7udP9jhEq7//SNdj/kzm18R6V3+m1dtQyA8lW/o3W3Xsr1508eJzU1VSXvnjpVq9OAF4mJbF2+kIS4OOyc3ejQ/w+V2BVPwojN8HTSU38dBGDh+JEq+2ryW1fKflONPNra3Au8yT8H9hEfG0N+QyPsXd3pOnws+Q2ynrL2ob6vWJ7I59Es27SN8MgonOxsmTrYRzktLkyhQCPDtROfmMj0pSt5qghHJ29ebK0sGNq9E99XTO8EbFG/DgmJiUxdtJyYuDg8XZ2ZOrg/Op/o2mnUrDkJCfHMmT6V2JgYPDw9GTVhispIo9B//+V5hqcdfvPtd0RFRbJ2xTIiIsJxcHRi1MQpymlxd24FEfjqCa+/t2qhcrwlazdQ2Nzik5zLp6buUcMiexqpkkJdCCE+2Nq1a2nXrh1RUVHo6enldjhfLb8A9T2S+XOr5un6xcZfzdOVi3cyf4LMl6CUoy2KN3JqfElMDQ14/kZuoS+FgYnpF1/3UdHRuR1GjhkWKMA/TSq9u+B/UOVNJwhVqL8j6nMxNzXmyOWbuR1Gjn1fwo2dZy6/u+B/UINyJQi7dObdBf+jCpcsR9DD0NwOI8dcrN/95OHcEhD88N2FPgFPO+tcOW5uk5FLQgjxHlatWoWDgwNWVlZcvnyZgQMH0qRJE+lYEkIIIYQQ4j9IxtF8XpJzSQgh3kNoaCi//vor7u7u9OnTh8aNG7NoUdqjeTt37kz+/PkzXTp37pwr8f74449ZxjR+/PhciUkIIYQQQgjxdZKRS0II8R4GDBjAgAEDMn1v9OjRmSYUh7R8TblhyZIlxMfHZ/qeicnHP6ZcCCGEEEKI/zLJufR5SeeSEEJ8pEKFClGo0Hs8UvczsrKyyu0QhBBCCCGEEP8nZFqcEEIIIYQQQgghhMgxGbkkhBBCCCGEEEKIr4rMivu8ZOSSEEIIIYQQQgghhMgxGbkkhBBCCCGEEEKIr0qqDF36rGTkkhBCCCGEEEIIIYTIMelcEkIIIYQQQgghhBA5JtPihBBCCCGEEEII8VVJkWlxn5WMXBJCCCGEEEIIIYQQOSYjl4QQQgghhBBCCPFVkYTen5eMXBJCCCGEEEIIIYQQOSadS0IIIYQQQgghhBAix2RanBBCCCGEEEIIIb4qKTIr7rOSkUtCCCGEEEIIIYQQIsdk5JIQQgghhBBCCCG+KqnI0KXPSSNVUqgLIYQQQgghhBDiK3I68G6uHLe8q0OuHDe3ycglIYQQ/1nngu7ldgg55uViz5nALzP+cq72nLp5J7fDyLEKbo5EPI/O7TByzNigANEREbkdRo4UMDbmRsi/uR1GjrnbWhL0MDS3w8gxF2tzQhVf5rVjbmrMP00q5XYYOVZ50wluPfpyrx1nK3MCHzzO7TByxNXGguioyNwOI8cKGBrx8Ikit8PIMetCprkdQpZkHM3nJTmXhBBCCCGEEEIIIUSOSeeSEEIIIYQQQgghhMgxmRYnhBBCCCGEEEKIr0qKTIv7rGTkkhBCCCGEEEIIIYTIMRm5JIQQQgghhBBCiK+KDFz6vGTkkhBCCCGEEEIIIYTIMelcEkIIIYQQQgghhBA5JtPihBBCCCGEEEII8VVJlXlxn5WMXBJCCCGEEEIIIYQQOSYjl4QQQgghhBBCCPFVSZGRS5+VjFwSQgghhBBCCCGEEDkmI5eEEEIIIYQQQgjxVZGcS5+XjFwSQgghhBBCCCGEEDkmnUtCCCGEEEIIIYQQIsdkWpwQQgghhBBCCCG+KikyK+6zkpFLQoj/nGrVqtG7d+9Pegw7Ozt8fX1zNYZ38fPzQ0NDg8jIyFyL4b9QD0IIIYQQQoj/Nhm5JIQQmdi2bRva2tq5HcZn4+fnx7fffktERARGRkbK9V9SPaSmprJ17WqOHtxPXGwsLu4etOvaA3NLqyy3uXk1gL3btnDvzi0iw8Pp/cdwylao+NH7zUns29atxu/VMZzdPWjb5d2x79u+heBXsff6YzhlyqvGfu7kPxz9cx/37twiNjqaMb5zKeLgqLa4M8a/fd0ajh36My1+Nw9ad+mWbfyB1wLYt30r92/fJjIinB6Dh6rE//LlS7atXcWV8+d4EhqKvn4+PEqUpHHrdhibmqot9i2bNrFmzWrCFQqcnJ3p5+ND0aLFsix/5PBhFi2Yz+PHj7GxsaFbjx5UrFRZ+b5CoWDu7NmcPXOa6OhoSpUqTV8fH2xtbdUWc0abtmxh9Zo1KMLDcXZywqdfP4oVLZpp2Tt377Jg0SJu3rzJ49BQ+vbuTYtmzVTKLF+5kqN+fgTfv4+Ojg7FPT3p0a0bdkWKfJL4U1NTWb9yOYf27yU2Jga3osXo3LMPltbW2W63b+d2tm/eSGR4OHaOjvzWrScubu6Z7n/MkEFcOHeWQSPHUD7Dv5W64l+7YhkH9+0hNiYG92KedO3V953x792xnW2bNhARHo69oyOdevRSxh/9/DnrVi7jor8/T5+EYWBkRPlKlfm1bQfy5c+vtti3b93ChrVrCA8Px9HJiV59++Hukfm1A3D0ryMsW7SI0NDHWFnb0LlrN8pXTL9nJ4wdzZ/79qls412uPFNm+KotZgAD9xJY129BPns3dEzMuD5lEOHnjme7jaFHKexb90Dfxp5ExRMebF3Jk2OqsVrU/Bmrei3Ia2RC7P3b3Fk2g5g7N9Qae0avr50DezNcO737YvWOa2fPju1s26h67bi6p1/7f+7Zhd+RI9y5FUR8XBwbdu0hf/4Cao993crl6dd90WJ0eZ/rfud2tiuveyd+7656386dMY3LF84TrniGrp4ebh7FaPvb71jbqrf92bR5M6vXrEWhUODs7IxP/2zazTt3WbBoITdvBvL48WP69ulNi+bNVcpcuHCR1WvWcOPmTZ49e8bUyZOpVq2qWmPOaMe2rWxavzbt3nV0okfvvrh5eGRZ/tjRv1i+ZBGhoaFYW1vzW+eulMvwXWflsiUcPXKYp0+ekCePNi6urrT/rRPuWdTJl0QSen9eMnJJCCEyYWJiQoEC6v0ylhtevHjxUdt/SfWwZ+tmDu7ZSfuuPRk11RcdXV0mDR+SbR0kJiRga29Pm87d1LrfD7V322YO7dlJ2y49GTHFFx0dXaaMeEfsiWmxt+6UdewvEhNw8ShK0zbt1RZrZvZt28Khvbto06U7w6fMQEdXl2kjh7277u3sadWpa6bvv0hM5P6d29Rv0pxR02fTffBQQh89ZOa4UWqL+9DBg8z0nUHHjr+xcvUanJ1d6N2jB+Hh4ZmWv3L5MsOHDqFegwasXLOWKlWrMaB/f+7cvg2kfYkd6NOff/99xOSp01i1Zi3mFub07NaV+Ph4tcX92sFDh5gxcya/dezImpUrcXF2pkfv3lnGn5CQgLWVFd27dcM0iw66Cxcv0rhRI5YvWcLcWbN4+fIl3Xv1+iTxA2zfuIE9O7bRuVcfJs+eh66uLqMGD8j22vnH7y+WLZxPs1/bMH3+IuwcHBk1eACRERFvld29bQug8UliB9i6YT17tm+ja+9+TJ2zAF1dXYYP6s+LF4lZbnP86F8sWTCX5q3b4LtgMfaOjgwf2F8Zf7jiGQqFgvadujBn6Qp6DxjMhbNnmTV1stri/uvwIebOmkmb9h1ZvHwljk7O9O/Tm4gsrp2rAVcYM2I4tevVY/GKlXxTpQpDBg3g7p07KuW8y5dn2+69ymX4qNFqi/k1LR09YoJvc3fptPcqr1PQAo9BU4i6doGLA9ry795NOHceiFEJb2UZswrfY9+6ByFblnFxYHti79+m2JDpaBsYqT3+17ZuWM/ubdvo1qcf0+a+unYGZn/t/H30L5bMT7t2Zi58+9oBSExIpIyXN01a/PrJYt+2cT17tm+lS6++TJkzHx1dPUYM8nnndb90wTyatWrLjAWLsXNwZMQgH5XYHZ1d6OkzkLnLVjJq4hQgleEDfUhOTlZb7AcPHWKG70x+69iBNatW4uLsRI+evbJuNxNft5tds2w34xPicXZ2ZqCPj9rizMrRI4dZMGcWrdu2Z8GS5Tg6OTGwXx8iIjKP/1pAAGNHjeDHOvVYuHQFlb6pwvA/BnHvbvq9a21jS48+/Vi8cjUz582nsLkFA/v1zrRNFSI70rkkhPhPevnyJd27d8fQ0BAzMzOGDRum/PUhIiKC1q1bY2xsjL6+Pj/++CO3bt1S2X7r1q0ULVoUHR0d7OzsmDYt+y+hS5YswcjIiCNHjgBvTwezs/sfe/cdHlXRBXD4l947hBRCeoXQCU3pvYvSROldpFeVLr1XEZQiRUGaoUpA4ROkQ5ASEloAISSQZFNJSPv+CNlkSSGEhQCe1+c+j7s7d/bszdzL7twzM07MmDGDXr16YWJiQpkyZVi1apVKHX///TcVK1ZEX1+fqlWrsmvXLjQ0NAgMDCzUZ963bx8eHh4YGBhQv359QkNDVV6fPHkyFStWVHlu0aJFODk5KR/36NGDdu3aMX36dOzs7PD09ARgw4YNVK1aFRMTE2xsbPj000+JiIgAIDQ0lPr16wNgYWGBhoYGPXr0yPM4vOjYr1u3DnNzc37//Xe8vb0xNjamWbNmhIWFFeoYFFVGRgYH/HfStmMXqtSoSRlnFwYMH40iKpJzJ//Od78KVavR4fMeVKtZW631vmzsv/vvpE2O9+j/7D3OFxR7lWp88lkPquYTO0Dt+o1o17krZStUUkusecnIyODg7l206dCZytVr4uDkTN9hI4mOiuT8yRP57le+SjU+/qw7VZ7LFMtiaGTE6Kkz8PugDralS+Pm6cVn/QcRevMGkY8i1BL7z5s30bZdO1q1aYOziwtjx49HX1+fPf7+eZbf8ssv1KhZk88+74azszP9Bw7E08uLbb9uBeDe3btcvnSJMWPH4VO2LI5OTowZN57k5GQO/v67WmLOadPPP9OubVvatGqFi7Mz48eORV9fH/89e/IsX9bHh6FffknTxo3RzScjcemiRbRu1QpXFxc83N2ZPGECDx8+JOjaNbXHn5GRwe6d2+jY9XOq1/oAJxdXho4dT1TkY04dP5bvfr9t/5UmzVvSsFlzHBydGDh0BHp6+hz+fb9KuVs3bvDbtq18OWqM2mPPit9/x690/OxzatT+AGdXV4aP/Yqox5GcPJZ//Lu2baVpi1Y0ataCMk5ODBo2Ej09fQIOZGbSODq78NXkafjVqo2tnT0VKlXm8959OH3yb9LSUtUS+9ZffqZVm7a0aNUKJ2dnRo4Zi76ePvvyaTvbtm7Br3oNunT9DCcnZ3r364+Hpyc7t29TKaero4uVlZVyMzE1VUu8OUUHnuTultVEnvlfocrbNmlHUkQYtzcs48n9O4T9vp3HJ49g37KTsox9q048PLybiCP7eHI/lBur55L2NJlS9VupPX7IbDu/bf+VTjnazohxmW3nREFt59fMttO4eWbb+WL4s7azPzsLq+0nHejwaVc8C8hkedXY/Xdknrc1an+As4srw5+dtydfdN62aEmjZs0p4+jEoGGZ5+2hA9mxN2vVmnLlK1DKxhZXdw+69uzN40cRRIQ/VFv8mzb/TLt2bWnTujUuLi6MHzcu87q5e3ee5cv6+DB0yBCaNmmCrq5unmVq16rFoIEDqF+/ntrizM+2Lb/QonUbmrXMPHeHjRqDnr4eB/bmfe7u2LaVan7V6fRpVxydnOjZpx/uHp7s2rFdWaZh4yZUqVoNOzt7nJxdGPjlEBISEnJ1HgvxItK5JIR4K61fvx5tbW1Onz7N4sWLWbBgAT/88AOQ2YFy9uxZ/P39OXHiBBkZGbRo0YKUlBQAzp07R8eOHencuTOXLl1i8uTJTJgwgXXr1uX5XnPmzGHcuHEcPHiQhg0b5hvT/PnzqVq1KhcuXGDQoEEMHDiQ4OBgAGJjY2ndujW+vr6cP3+eadOmMXbs2EJ/3nv37tG+fXtat25NYGAgffr0Ydy4cYXeP6fDhw8THBxMQEAAe579UEhJSWHatGlcvHiRXbt2ERoaquxAcnBwYPv2zC8ZwcGZad+LFy/Os+4XHXuAxMRE5s2bx4YNG/jf//7H3bt3GTVqVJE+S2E9Cn9ITHQ05Spmd6IYGhnh6uHF9WtFH9bwuurN6z1ydgAZGhnh4uHFjeDXNyRDXbLi96lQUflc5jHy5Kaa43+SkICGhgaGRq8+NCglJYXga9eo5ldd+ZympibV/Py4dOmfPPe5fOkfqlXzU3muRo2aXLp0CYCnz84DXT09lTp1dHS5WMhO5peJ/1pwMNWrVVN5L79q1fjnWTzqEB8fD4Dpa+gkCH8YRnRUFOUrVVE+Z2RkjIeXN8FXr+S5T0pKCjdDQihfOXsfTU1NKlSurLJPclISC2Z+S78vh2Jhaan22AHCwzLjr5gjFiNjYzy8vblWQPw3QkKo8Fz8FStXyfczAyTEJ2BoaIiW1qvPaJGSkkJIcDBVqqq2nSrVqnHlct5t58rly1TJ0dYAqlWvkat84IXztG3RnM86d2T+3NnExMS8cryvysS9HIpLZ1Wei754ChOPzOGvGlraGLt4orh0JrtARgaKS2eVZdRN2XaqqLYdz0K0nZz7aGpqUrFKlXz3eR2yYq+Qq937EHz1ap77ZMYerHKuZJ63VbiWzz5JT55w+MB+StnYUqKktVpiT0lJ4dq1a1TPcR1/HdfN1yUlJYWQkGAqV6mqfE5TU5PKVatx9crlPPe5evmyyrkOUNWvOlcv510+JSWFvf6/YWRsjKubm/qCLyYZGRnFsv1XyZxLQoi3koODAwsXLkRDQwNPT08uXbrEwoULqVevHv7+/hw/fpxaz+Z62LRpEw4ODuzatYsOHTqwYMECGjZsyIQJEwDw8PDg6tWrzJ07V9mhkmXs2LFs2LCBo0ePUvYFY8tbtGjBoEGDlPstXLiQP//8E09PTzZv3oyGhgarV69GX18fHx8f7t+/T9++fQv1eb/77jtcXV2VGVZZn3n27Nkvc9gAMDIy4ocfflC5w9arV/awKBcXF5YsWUK1atWIj4/H2NgYy2c/vqytrVXmXMrp+vXrLzz2kPnFZOXKlbi6Zs7tM3jwYKZOVf/QiJyyUrdNn4vd1NycmFdI635d9eaUVY/Zc+9hZm7+TqSkZ8dvofK8Oo8RZA7x3PrTWqp/WBcDQ8NXrk+hUJCWlqZs+1ksLC1zZQ1miYyMxNIqd/nIyEggM8PRxsaG75YvY+z4rzAwMODnzZuIiAgnMvLxK8dcmPgtLSzyjf9lpaenM3/RIiqUL4+bq/rn6lI8G4ZibqHadswsLPId4hEXE0N6enqe+/x7767y8Y8rl+PlU5bqtdQ7x1JOWTGaW6j+DcwLiD82Job09DQsnovf/Ln4c4qJUbBl4080bdlaDVFDzLO283ynm4WlBXfvhOa5T1RkJBbPfU4LCwuinrV9AL/qNalTtx42dnY8+Pc+q7//jjEjhrNi1Wq0tLTUEntR6JpbkhKj+vdIiYlG29AYTR1dtI1N0dDSJkXxXBlFFIZ2r2eutOio/NuOIp/hWVlt5/m2b25hwb938247r0O+7d7cIt9hlbHK8zb3573/XLvf99su1q1eSVJSEvYODkydM09tcz/me920tCT0zh21vMfrFBOjID2vc9fCknv5xB8VFYmFpWqbsbC0ICoqUuW5E8eP8+2UiSQnJWFpZcWcBYtyfS8R4kWkc0kI8VaqUaMGGhrZ82TUrFmT+fPnc/XqVbS1talePTvbwMrKCk9PT4KCMrMkgoKCaNu2rUp9tWvXZtGiRaSlpSm/5M6fP5+EhATOnj2Li4vLC2MqX7688v81NDSwsbFRDi0LDg6mfPny6OvrK8v4+fnlqiM/QUFBKp8p6zMXha+vb67U7XPnzjF58mQuXrxIdHQ06enpANy9exefQqbOBwUFvfDYAxgaGio7lgBsbW2Vxyk/ycnJJCerztWglyP743nHj/zBmuVLlI9HTXy9nVfq9PeRP1i7Ijv2ke9Q7AB/H/mT9d8tVT4ePkF9cyDlJzU1lRVzZkJGBt0HDn7t71dU2trazJozl+nTptGkYQO0tLSoVs2PmrVq8S7eyJw9dy43b97kh+eGABfV0cMBfLdogfLxN9/OVEu9zzv993EuXbjAgpWr1VrvkUMBLF+YPcR64oxZaq0/L4kJCUz9ahwOjo582r3na3+/V9GwcWPl/7u6uuHq5kaXDh8TeOF8rsyJ/5o/DwWwfEF225k08/W3HXU5cjiAFTnb/fTXG3vdho2oWKUqUVGR7Pp1C3OmTWH24qXo6ub/nUC8uoqVK7NqzXpiYhTs3e3PtEkTWPb96lydyu+adN7Bf3zfYdK5JIT4z/rwww/Zu3cvW7duLdQQtOfvnGloaCg7ad4ETU3NXKm2OYejZTEyMlJ5nJCQQNOmTWnatCmbNm2iZMmS3L17l6ZNm6p1UuoseR2nF6UIz5w5kylTVDspJk2aRMtPu+dZvrJfDVw9vJSPU1MyP0esQoGFZfaEm7EKBWUK0XGYn6w7xOqst9JzsaekZsYeo1BgnuM9YhQKHF8h9telkl91XJ/N5QWQ+qwNxiiiMc9xNzVWoaCM86vHn9WxFPkogrHTZqolawnA3NwcLS2tXJO4RkdF5Ttpq5WVFVGRBZf38vZmw+bNxMfHk5KSgoWFBb16dMfbW73zn+QXf1R0dL7xv4zZ8+Zx7PhxVq1cSSlr9QxJ8atZGw+v7OOQ8uy8VURHY5kj5pjoaJxd8x6OYWJmhqamZq6svpjoaOWPoH8CL/Aw7AFd26nOlzNn6iS8y/kyff6iosVfqzYeOVblyrr+KqKjVOJXREfjkk/8pmZmaGpqEf1c/Iro6FzZCImJiUwaNxoDQ0O+nvot2trq+dpu9qztPJ9lEh0VjaVl3m3H0soqVzZW9HN/t+fZ2dtjZm7O/X//LdbOpaeKKHTMVI+tjpkFqYnxpKc8JSVWQUZaKjrmz5Uxt+SpIu9MnJdVvVZtlRXdUp7m33ac8xmKlNV2nm/7ebUddco8b7NjT82v3Ste1O41UTzXhhTR0bmymYyMjTEyNsaudGk8vX349KPWnDh2jLoN8p+2oLDyvW5GRWFl9fZ3opiZmaOZ17kbHZUrqzaLpaUV0VGqbSavc93AwAD70qWxL10an7Ll6NalI/v37OHTz7up90OI95rMuSSEeCudOnVK5fHJkydxd3fHx8eH1NRUldcjIyMJDg5WZuB4e3tz/Phxlf2PHz+Oh4eHSmq+n58f+/fvZ8aMGcybN++V4s0axpYz++bMmTMF7KHK29ub06dPqzx38uRJlcclS5bk4cOHKh01hZks/Nq1a0RGRjJr1iw+/PBDvLy8cmUSZWU6FbQii7e39wuPfVGNHz+emJgYlW38+PH5ljcwNMTGzk652ZdxxMzCgisXA5VlEhMTuBlyDfc8licvrJKlbNRer4GhIaXs7JSbvUNm7FdzvMeTxARuhVzDzbPosb8uBoaGlLK1U252DmUy4//norLMk8REboYE4/qK8Wd1LIWHPWD01BkYq3HeHx0dHTy9vDhzJvu8S09P58yZM/j6ls9zn3K+5XOd16dPncLX1zdXWWNjYywsLLh79y7XgoKoU1e9y1Lr6Ojg5enJ6RzxZMVfPo94CisjI4PZ8+Zx5OhRvlu2DHs7O3WEC2S2HVt7e+Xm4OiEhaUl/1w4ryyTmJBAyLUgPH3yHqaso6ODq4eHyj7p6en8c+G8cp+PO3/Kou9/ZOHKH5QbQK8BgxgyqvBz4T3P0NAQO/vSyq3Ms/gvnn8u/qAgvAqI383Dg38unFOJ/2KO+LPqmThmJNo6OnwzbYZaszZ0dDKXGj93TrXtnD97hrLl8m47ZcuV49xZ1bZ/9vTpfMsDREREEBsTo5bOzlcRd/0y5r5VVJ4zL1+NuJDMOWcy0lKJvxWMebnseWzQ0MC8XBVlmVeVq+04ZbadwOfaTnAh2s7F88+1nfPn893ndcSedd5efP68Dbqa7yTimbF7qpwrmeftObwK+v7wbP6arBtIr0pHRwcvL6/c182zr3bdfFN0dHTw8PDkwjnVNnDh3Fl8yuY9P5hPuXKcP6c659i5s6fxKVfwfGLp6enKGwDvsoyM4tn+q6RzSQjxVrp79y4jRowgODiYn3/+maVLlzJ06FDc3d1p27Ytffv25dixY1y8eJHPPvsMe3t75VC4kSNHcvjwYaZNm0ZISAjr169n2bJleU4qXatWLfbt28eUKVNYtGhRkeP99NNPSU9Pp1+/fgQFBfH7778rO6xyDu/Lz4ABA7h+/TqjR48mODiYzZs355qAvF69ejx69Ig5c+Zw8+ZNli9fzv79+/OuMIcyZcqgq6vL0qVLuXXrFv7+/kybNk2ljKOjIxoaGuzZs4dHjx4pJ/HNqTDHvqj09PQwNTVV2QoaFvc8DQ0NmrX5iF1bfubcqRPcC73N9wvmYW5pRZUa2auRzfh6HAf3ZK8ElvTkCXdu3eTOsyV5H4U/5M6tmzx+1vlW2HpfhYaGBk3bfMRvW3/mfNZ7LMx8j8o53mPWN+MIKEzsOVZSi4+L486tmzx4NqdF2P1/uXPrZq67x68af5PW7di99RcunDrJvdDbrFo0DwtLKyrXyB7aOXvCeA7tzV6N5/n4H4eHc+fWTeVKcKmpqSyfPYPQG9fpP2I06elpKKKjUERHKe+cv6oun3bFf9cu9u7Zw+3bt5kzayZJT57QsnXm3DZTJk1kxbJlyvKdOnfm5Im/2bRxI6Ghoaxe9T1BQVf5pENHZZnDhw5x7txZ7v/7L/87eoQhg7+gTt26VK9RQy0x59S1Sxd2+fuzZ+9ebt++zcw5c3iSlETrli0BmDhlCstWrFCWT0lJITgkhOCQEFJSU3n06BHBISHcu3dPWWb23LnsP3CAb6dMwdDIiMeRkTyOjCQpKUnt8WtoaND6o0/4dfMGTv99nNDbt1g0ZyaWViWoXjt7rqQJo0ewd9dO5eO2H3cgYN8e/jh4gHt37rByyUKSkpJo2LQZkDkPlqOzs8oGUMK6FKVsbdUaf5v2Hdiy6SdO/X2c0Fs3WTBrBpYlrKjxQXb8X48azp5dO5SP233Skd/37uXw7we4dyeUFYsWkJT0hEZNmwPPOpbGjiI5KYkho8bwJDGB6KhIoqMi1bYke8fOXdjr78+BfXsJDb3NgrmZbad5q8y2M33qFFZ9l912PunYidMnT7Jl8ybuhIay9ofVBF8L4qOPP8mMOTGR75Yt5crly4SFPeDc2TN8PXY09qVLU626etu+pp4BRo7uGDm6A6BvbYeRozt6VqUAcOwyAI8vvlGWDzu4C31rO5y6DsLArgw2TT6iZM0G3N+7RVnm/p4t2DRsjXXd5hjYO+LaZxRaevqEH9mr1tizaGho0PbjDmzZ+BOnjqu2nZo52s5XI4eze2eOttMhn7bTrLmyTHRUJLduXCfs/n0AQm/d4taN68TFxqot9jbtP2Hrpg3P2v0tFs6egaVVCWrkOG+/GT1Cpd23/bgDB/ft4fCz8/a7xc/O22exP3zwgF83b+JGSDCPwsMJunKZ2dMmo6erRxU/9bWhrp92Yddvv7Fnz7Pr5uzZPHmSROtWmZmOEydNZtny5cryKtfNlJQ8r5uJiYnKMgD3HzwgOCSEhw/Vt8pdlk86dWbvHn9+37+PO6GhLJo/l6QnSTRtkRn/rG+n8sPK75Tl23/SkTOnTrL1l83cvRPK+jU/EHLtGu3afwzAkydP+OH7lVy9cpnwh2GEBF9j7szpPH78mLr1G6g9fvF+k2FxQoi3Urdu3Xjy5Al+fn5oaWkxdOhQ+vXrB8DatWsZOnQorVq14unTp9SpU4d9+/Yph2NVrlyZrVu3MnHiRKZNm4atrS1Tp07NNZl3lg8++IC9e/fSokULtLS0+PLLL186XlNTU3bv3s3AgQOpWLEivr6+TJw4kU8//VRlHqb8lClThu3btzN8+HCWLl2Kn58fM2bMUJmI29vbmxUrVjBjxgymTZvGxx9/zKhRo1j1gvlQSpYsybp16/jqq69YsmQJlStXZt68ebRp00ZZxt7enilTpjBu3Dh69uxJt27d8lxd70XHvji1+rgDyUlJrFm2hMSEeDx8yjJmyrcq809FPHxAXGz26kW3boQw46vsTIZNP2Yeyw8bNKL/8FGFrvdVtWyf+R5rl2e+h7tPWUZNLjj22zdCmPl1duybn8X+QYNG9BuWGfuF0ydYvTh7jpsVczPnuGnXuSvtP/1cbfG3aP9JZvwrlmYeI++yjJw09bn4w56L/zqzv8kejvrzmsz5cWo3aETfoSOIjozkwunM7L2Jw1TnWRr77Sy888kuehmNmzRBoYhm9fcriYyMxN3Dg4VLliozLR4+fIiGRvZ9uPIVKjD12+l8/90KVq5YjoODA3PmzVNZUefx48csXriQqKhISpQoQfMWLenVp88rx5qXJo0bE61QsHL1aiIjI/Fwd2fpwoUq8Wvm6Nx+9OgRXbtlD3HYsGkTGzZtonKlSqz6LvPHyLYdmT8G+z9bvCDLpG++Uf74UqePOnUmKekJKxbNJyE+Hu9yvkycOVul7TwMe0BsjrbzQb0GxChi+Hn9OqKjo3B2dWXSjNm5hte8CR937kJS0hOWLZhHQnw8Pr6+TJk5VyXT6OGDB8TmWDXtw/oNiIlRsGndGqKjo3BxdWPKrLnKoU03r4cQHJS5gla/zz9Veb8fNv1CKZtX7yBr0KgxCoWCNatXExUViZu7O3MXLFQOlYkIf4imZnbbKedbnglTpvLjqu9Z/f1KSpd2YPqsObg8m19PS0uTmzducGDfPuLj4yhRogRV/arTu18/tV4rAUxcvfCdnN3p69J9CADhR/ZxfcV0dC2s0CtRSvl68qMwrs4ajXP3Idi16EBy5COur5yN4mJ21uLjE4fRMTWnTMc+6JpbkhB6ncszRpIS8/oWVchqO0tztJ2pswpuO3XqNyBGoWDj2uy2M3X2XJVhcfv8/fn5p3XKx+OGZR6fYWPGqXRCvYr2nbqQlJTE8oXPYi/ny+RZc56L/X6e7X7zurXK2CfPnKMczqqjq8vVy//gv2MbCfFxmFtYUNa3ArOXLMs1ifmraNK4MdHRClauWpV53fTwYOniRdnXzfBwNDWzr/uPHj2i62fZ/15u2LiJDRs3UblyZVY968S5GhTEgIHZ18yFz25WtmrZksmTJqotdoD6DRsRo1Cw7sfVREdF4ermzqx5C5STlEeEh6v8u1XW15evJ01hzepVrFn1PfalSzN1xiycXZ6du5qa3Lt7h8nf7CM2JgZTUzM8vb1YtGwFTmoY2i7+WzQy/str5QkhxGu0adMmevbsSUxMDAYGBsUdzjvpTMjt4g6hyKp5OHMq+N2Mv7qnMyeu3SzuMIqsppcr0bFxxR1GkVmYmhD3DqwUmBcTCwuC7j4o7jCKzLuMHSH/qj/b4E3xKG3Dw8h3s+3YWFlwrGPt4g6jyD7Yepzr99/dtuNub0PwvbDiDqNIPB1siYtRFHcYRWZiZs6/EZEvLviWKm1dvENfC3Lg/JVied9mlV/fUNW3mWQuCSGEmvz000+4uLhgb2/PxYsXGTt2LB07dpSOJSGEEEIIIcR7TeZcEkIINXn48CGfffYZ3t7eDB8+nA4dOiiHrA0YMABjY+M8twEDBhRz5EIIIYQQQrxf0jMyimX7r5LMJSGEUJMxY8YwZsyYPF+bOnVqnhOKQ+Z8TUIIIYQQQgjxrpLOJSGEeAOsra2xtrYu7jCEEEIIIYQQQu2kc0kIIYQQQgghhBDvFVm77M2SOZeEEEIIIYQQQgghRJFJ5pIQQgghhBBCCCHeK+mSuPRGSeaSEEIIIYQQQgghhCgyyVwSQgghhBBCCCHEe0XmXHqzJHNJCCGEEEIIIYQQQhSZdC4JIYQQQgghhBBCiCKTYXFCCCGEEEIIIYR4r8iwuDdLMpeEEEIIIYQQQgghRJFJ5pIQQgghhBBCCCHeK+mSufRGSeaSEEIIIYQQQgghxFsuKiqKrl27Ympqirm5Ob179yY+Pr7A8l9++SWenp4YGBhQpkwZhgwZQkxMjEo5DQ2NXNsvv/zyUrFJ5pIQQgghhBBCCCHEW65r166EhYUREBBASkoKPXv2pF+/fmzevDnP8g8ePODBgwfMmzcPHx8f7ty5w4ABA3jw4AHbtm1TKbt27VqaNWumfGxubv5SsUnnkhBCCCGEEEIIId4r79uouKCgIA4cOMCZM2eoWrUqAEuXLqVFixbMmzcPOzu7XPuUK1eO7du3Kx+7uroyffp0PvvsM1JTU9HWzu4SMjc3x8bGpsjxybA4IYQQQgghhBBCCDVITk4mNjZWZUtOTn7lek+cOIG5ubmyYwmgUaNGaGpqcurUqULXExMTg6mpqUrHEsAXX3xBiRIl8PPzY82aNS+92p50LgkhhBBCCCGEEOK9kk5GsWwzZ87EzMxMZZs5c+Yrf56HDx9ibW2t8py2tjaWlpY8fPiwUHU8fvyYadOm0a9fP5Xnp06dytatWwkICODjjz9m0KBBLF269KXi08h42e4oIYQQQgghhBBCiLfYthMXiuV9W1f2yZWppKenh56eXp7lx40bx+zZswusMygoiB07drB+/XqCg4NVXrO2tmbKlCkMHDiwwDpiY2Np3LgxlpaW+Pv7o6Ojk2/ZiRMnsnbtWu7du1dgnTnJnEtCCCHeWieu3SzuEIqsppcrJ4NvFXcYRVLD04U//gl+ccG3VIPynjyMjC7uMIrMxsqC6HuhxR1GkVg4OHH44rXiDqPIGlbw4kzI7eIOo8iqeTi/s8e/YQUvrt8v3J33t5G7vQ3HOtYu7jCK7IOtxzk/tmdxh1EklWevZejaHcUdRpEt7tme4HthxR1GkXk62BZ3CPkqrjyagjqS8jJy5Eh69OhRYBkXFxdsbGyIiIhQeT41NZWoqKgXzpUUFxdHs2bNMDExYefOnQV2LAFUr16dadOmkZycXOjPIp1LQgghhBBCCCGEEMWgZMmSlCxZ8oXlatasiUKh4Ny5c1SpUgWAP/74g/T0dKpXr57vfrGxsTRt2hQ9PT38/f3R19d/4XsFBgZiYWHxUp1k0rkkhBBCCCGEEEII8Rbz9vamWbNm9O3bl5UrV5KSksLgwYPp3LmzcqW4+/fv07BhQ3766Sf8/PyIjY2lSZMmJCYmsnHjRuUE45DZqaWlpcXu3bsJDw+nRo0a6OvrExAQwIwZMxg1atRLxSedS0IIIYQQQgghhHivvI/TS2/atInBgwfTsGFDNDU1+fjjj1myZIny9ZSUFIKDg0lMTATg/PnzypXk3NzcVOq6ffs2Tk5O6OjosHz5coYPH05GRgZubm4sWLCAvn37vlRs0rkkhBBCCCGEEEII8ZaztLRk8+bN+b7u5OSk0qlWr169F3ayNWvWjGbNmr1ybNK5JIQQQgghhBBCiPdK+vuXuPRW0yzuAIQQQgghhBBCCCHEu0s6l4QQQgghhBBCCCFEkcmwOCGEEEIIIYQQQrxX3scJvd9mkrkkhBBCCCGEEEIIIYpMMpeEEEIIIYQQQgjxXpHMpTdLMpeEEEIIIYQQQgghRJFJ5pIQQgghhBBCCCHeK+mSufRGSeaSEEIIIYQQQgghhCgy6VwSQgghhBBCCCGEEEUmw+KEEEIIIYQQQgjxXpFRcW+WdC79B9SrV4+KFSuyaNEinJycGDZsGMOGDStSXevWrWPYsGEoFAq1xijeXj169EChULBr167iDuWt9D4cn8mTJ7Nr1y4CAwOLO5RXkpGRwc7NGzkacIDEhATcvXzoNvALbOzs890n+Mol9u3czp0bN1BER/Hl+G+oUqOW8vXU1FR2bPqJf86dIeLhQwwNjfCpUJEO3XpiYWWl5tg3cOTgs9i9feg+cHCBsV+7fIn9O7cRevMGiqgohnw1QSV2gLN/H+ePA3sJvXmDhLg4pi5ahqOLq9rizhn/ni2bOXb4IE8SEnDx8ubTvgOxtrXLd5/rVy8T4L+Tu7duEhMdRf/RX1HRr0aucmH/3mPnxvVcv3qZ9PQ0bEs70G/keCxLllRL7Du3b+OXTRuJiorC1c2NoSNG4u1TNt/yf/5xmDWrVvHwYRj2pR0YMOgLatTKPu4zv53KgX37VPbxq16DuQsXqSXe5237zZ+NW7cRFRWFm6sLIwcPoqyXV55lb4WGsmrdT1y7foOH4eEMG9ifzh+3VymTkJjIqnXrOXrsb6IVCjzcXBk+aCA+Xp6vJf6MjAz2bN3M8cMBz9qOF136vKjtXCHAfyf3bt8gJjqafqPG52o7gzq2zXPfjz7rTuM27fN8rajxb9+0gT8P7icxIQEPbx96DvqywHMXIGCvP3t3bCMmOpoyzi506z8IV4/sYxwe9oDNa34g5OoVUlJSKF+5Ct37D8LMwkKtsb/rx37TujX8vncPCfHxeJfzZdCwEdiXLl3gfnt27WTHll+IjorC2dWV/l8OxdPbW/n6gT3+HDl8mJvXQ3iSmMgv/nswNjZRW9ym3hUo3eZTjJy90LMswdW544g681eB+5j5VMK525cYOjiTHBnBve3riTiqep2xbdoe+9afomtuScKdG9xcs5D4m0FqizunEjUbUKpOc3RMzHgSdpd7v20i8d/b+ZYv+UFjStaoj665FakJ8URfOsODA9vISE0FwNjZg1J1mmNQ2hFdUwturl9CzNULryX2LM0reVPTwxkDXR1uR0Ty64kLPIpNyLd8s4reNK/krfJcuCKOGTsDALA0NmRSh2Z57rv2z1MEht5XW+wZGRlsXr+Wg/uetf2y5Rg4dAR2L2j7e3/byc6tWW3fjX6Dh+DhlfmZ4mJj2bx+LYHnzvIoIhxTM3Nq1P6Arj16YWRsrLbYxftNhsX9x5w5c4Z+/foVqqyTkxOLFi1Sea5Tp06EhIS8hsjE22rx4sWsW7futb7H5MmTqVix4ltfZ17UfXz69++Pq6srBgYGlCxZkrZt23Lt2jWVMnfv3qVly5YYGhpibW3N6NGjSX32Be2/bN+ObQTs9af7wMFMnLsQPX195k+ewNOnT/PdJzkpiTJOznzef1Cerz9NTubOzRu06diFKQuWMnj8Nzy8/y+Lp09Rc+y/ErDHnx4Dv2Ti3EXo6ekzb9I3BceenISDs0u+sWeV8fApS8fuvdQa7/MO/raDP/fv4dN+Axkzcy56enos+XYSKQXGn4y9ozOde/fPt8yjh2HMnzAOG3t7RkyZzjfzltD8405o6+qoJe4/DgWwfMliuvfqw+q163F1c2fU8GFER0XlWf7ypX+YNmkiLVq3ZvW69XxYpw5fjxvDrZs3Vcr51ajBjt17ldvEKVPVEu/zAv48wuKVq+jzeVfWr1yOu4sLw8Z9TVS0Is/ySUnJ2Nva8kWfXlhZWuZZZsb8hZw+d55J48awcfVK/KpU4csx44h4/Pj1fIbfdnBk/1669B3I6Blz0dPTZ+n0yQW2nafJSZR2cqJTAW1n5qp1KtvnA79EQ0ODStVr5btPUezZ/isH9/xGr0FDmDJvEXr6+sye+HWB5+7Jv46y6YfVfNTlM75dtIwyzi7Mnvg1Mc9u3CUlJTF74tdoaMBX02cxac580lJTmT9tEunp6WqL/V0/9tt/+ZndO3bwxfCRzF++En19fSaOHcXTp8n57vO/P//gh++W06VbdxZ/vxpnV1cmjh2FIjpaWSY5KZkq1fzo+Olnao03i5aeAfGhN7j14/xCldcraYvPuLnEXDnPhTE9eLB3K+4DxmJewU9ZpkTNhjh3+5K729ZwYWwvEu7coNzXC9AxNVd7/Bbl/SjdqjNhh3/j2pLJPAm7h1vvkWgb5d0BZ1GxBvbNOhB2yJ+r87/izrY1WFTww67ZJ8oymrp6JIbd496ujWqPNy8NfT2o4+3K1hMXWLjnT56mpjKgyQdoaxX80zgsOoZvftmr3BbvO6p8LTohUeW1b37Zy77zV0lKSeHqvw/VGv+OLT+zZ+d2Bg4dwdxl36Gnb8CkcaMLbPt//fkHP65cQefPe7Bw5WqcXFyZNG60su1HRT4mKjKSnv0HsvSHtQwdM47zZ06zdP4ctcb+pqVnZBTL9l8lnUv/MSVLlsTQ0LDI+xsYGGBtba3GiMTbKi0tjfT0dMzMzDA3Ny9yPRkZGe9sx0fWMSjIqx6f51WpUoW1a9cSFBTE77//TkZGBk2aNCEtLU0ZU8uWLXn69Cl///0369evZ926dUycOFFtMbyLMjIyOLh7F206dKZy9Zo4ODnTd9hIoqMiOX/yRL77la9SjY8/606Vmnn/6DE0MmL01Bn4fVAH29KlcfP04rP+gwi9eYPIRxFqi/13/1207tiZyjVqUsbZmX7DR6GIiuT8yb/z3a9ClWp88ll3qtasnW+Z2vUb0q5zV8pWqKSWWPOSkZHBH3v9af5xRypUq0FpR2d6DB5OTHQUgWdO5rtfuUpVaNvlMypWr5lvmd9+3kjZSlVo/3lPHJxdKWljS4Vq1TE1M1dL7Ft/+ZlWbdrSolUrnJydGTlmLPp6+uzbsyfP8tu2bsGveg26dP0MJydnevfrj4enJzu3b1Mpp6uji5WVlXIzMTVVS7zP+3n7Dtq2aEarZk1xdnRk7LAh6OvpsefA73mW9/Hy5Mv+fWlcvx46Ork76JKSkzny1zEG9+1DpfK+ONjb07f755S2t2OHf97H5FVkZGTwx77dNGvfgQrVqlPa0Ynug4cREx3FxQLaTtlKVWjT+TMq+uXfdszMLVS2i2dO41HWlxKlbNQa/wH/nbTt2IUqNWpSxtmFAcNHo4iK5FwB5+7+XTuo37QZdRs1wb6MIz0HfYmenh5HAzL/btevXuFRRDj9ho3EwckZBydn+g8fxe0b17n6T6DaYn/Xj/1v23+l02efU6P2Bzi7ujJi3FdEPY7kxLFj+e6369etNG3RisbNW1DGyYkvho9ET0+fgP3ZWUBtP+lAh0+74unjo7Z4c4oOPMndLauJPPO/QpW3bdKOpIgwbm9YxpP7dwj7fTuPTx7BvmUnZRn7Vp14eHg3EUf28eR+KDdWzyXtaTKl6rdSe/zWHzbh8en/EXX2GEkRD7i78yfSU55iVe3DPMsbOboRf+c60YEneRodSdz1K0QHnsKotLOyTGzwJcIO7iDmynm1x5uXuj5uHPwnmMt3w3gQHcvG/53FzEAf3zL5Z+0BpKVnEPckWbklJGd3xGZkoPJa3JNkyjvaEXj7Pk9T09QWe0ZGBv47ttGx67O27+LK8LHjiYp8zMnj+bf937b/SpMWLWnUrDllHJ0YNGwEenr6HDqQ2fYdnV0YP3kqfjVrYWtnT4VKlfmsVx9OnzxBWtq7+T1evHnSufQfkzMbKSMjg8mTJ1OmTBn09PSws7NjyJAhQOZQujt37jB8+HA0NDTQ0NAAMofF5fwhnZUdsmHDBpycnDAzM6Nz587ExcUpy8TFxdG1a1eMjIywtbVl4cKF1KtXr9BD86Kjo+nWrRsWFhYYGhrSvHlzrl+/rlLm+PHj1KtXD0NDQywsLGjatCnRz3ri09PTmTNnDm5ubujp6VGmTBmmT58OwJEjR9DQ0FAZ5hcYGIiGhgahoaEqn3nXrl24u7ujr69P06ZNuXfvnnKfmzdv0rZtW0qVKoWxsTHVqlXj0KFDuY79jBkz6NWrFyYmJpQpU4ZVq1aplPn333/p0qULlpaWGBkZUbVqVU6dOkVoaCiampqcPXtWpfyiRYtwdHR8YQdI1ufcu3cv5cuXR19fnxo1anD58mVlmazP6e/vj4+PD3p6ety9e5cePXrQrl07Zbnk5GSGDBmCtbU1+vr6fPDBB5w5cybXe+3fv58qVaqgp6fHsQK+6K1bt44pU6Zw8eJFZVvLygS6e/cubdu2xdjYGFNTUzp27Eh4eHiBn/VFdSoUCvr370+pUqXQ19enXLly7Hn2QzK/Y1CQ549PvXr1GDJkCGPGjMHS0hIbGxsmT578wpiz9OvXjzp16uDk5ETlypX59ttvuXfvnrI9Hjx4kKtXr7Jx40YqVqxI8+bNmTZtGsuXLy/wTnlOs2bNolSpUpiYmNC7d2+SkpJUXj9z5gyNGzemRIkSmJmZUbduXc6fz/7C16tXL1q1Uv3CmpKSgrW1NT/++CMA27Ztw9fXFwMDA6ysrGjUqBEJCfmnm7+qR+EPiYmOxqdCReVzhkZGuHp4cjNYvcMCniQkoKGhgaGRetLEs2LP2QFkaGSEi4cnN4KvFbDn2+FxRDiximi8fCsonzMwMsLZzYPbwcFFrjc9PZ3L589Sys6OJd9OYnTvz5k9fhSBp/P/4fsyUlJSCAkOpkrVasrnNDU1qVKtGlcuX8pznyuXL1OlWjWV56pVr5GrfOCF87Rt0ZzPOndk/tzZxMTEqCXm5+MPDrlOtcqVVeKvVrkSl65eLVKdaWlppKWno6urq/K8nq4eFy9feaV48xKZ1XbK52g7hkY4uXlwK6Tobed5sQoFly+cpVaDRmqrE7LP3XIVVc9dVw8vrl/L+7qTmpLC7RvXVc53TU1NylasxI1n16qU1BQ0QKUDUEdXBw0NDYKvqufv8K4f+/CwMKKjoqhYpYryOSNjYzy9vbmWzzFKSUnhRkiIyj6amppUrFIl333eBibu5VBcUv3+F33xFCYe5QDQ0NLG2MUTxaXs72JkZKC4dFZZRl00tLQwtHci7nqO45WRQdyNqxiVcctzn4Q7NzC0d8LwWWeSrmVJzLzKExP8j1pjKywrY0PMDPUJeZB9gygpJZU7j6Nwts47ozNLSVNjpnZqzoRPmvJ5napYGBnkW7a0lTmlrcw5cT1UXaED2W2/QmXVtu/h7UNwPtf+zLYfTMXKqm2/QuUqXCvg34vEhHgMDQ3R0pKZdEThSOfSf9j27dtZuHAh33//PdevX2fXrl34+voCsGPHDkqXLs3UqVMJCwsjLCws33pu3rzJrl272LNnD3v27OHo0aPMmjVL+fqIESM4fvw4/v7+BAQE8Ndff6n8UH2RHj16cPbsWfz9/Tlx4gQZGRm0aNGClJQUILMzqGHDhvj4+HDixAmOHTtG69atlZke48ePZ9asWUyYMIGrV6+yefNmSpUq9VLHKjExkenTp/PTTz9x/PhxFAoFnTt3Vr4eHx9PixYtOHz4MBcuXKBZs2a0bt06V8fE/PnzqVq1KhcuXGDQoEEMHDiQ4Gc/vuLj46lbty7379/H39+fixcvMmbMGNLT03FycqJRo0asXbtWpb61a9fSo0cPNDULdyqPHj2a+fPnc+bMGUqWLEnr1q2VxzHrc86ePZsffviBK1eu5JmlNmbMGLZv38769es5f/48bm5uNG3alKjnhpGMGzeOWbNmERQURPny5fONqVOnTowcOZKyZcsq21qnTp1IT0+nbdu2REVFcfToUQICArh16xadOnXKt67C1Nm8eXOOHz/Oxo0buXr1KrNmzUJLS+uljsGLrF+/HiMjI06dOsWcOXOYOnUqAQEBL11PQkICa9euxdnZGQcHBwBOnDiBr6+vShtu2rQpsbGxXLny4i/HW7duZfLkycyYMYOzZ89ia2vLihUrVMrExcXRvXt3jh07xsmTJ3F3d6dFixbKTuM+ffpw4MABlevCnj17SExMpFOnToSFhdGlSxd69epFUFAQR44coX379mS8xjThmGedyWbmqvORmJqbK19Th6dPn7L1p7VU/7AuBq+QBZpT/rFbqDX21yVWkRmj6XMZfCbm5srXiiIuJobkpCf8vms7ZStWZsg3U6joV4NV82YScuXyiyt4gRiFgrS0NCyeGx5mYWlBVFRknvtERUZiYfFceQsLoiKzy/tVr8lXEyayYOlS+g/8gosXLjBmxHDlv0nqooiJJS09HUsL81zxRBax3RgZGuLr482ajZt59DiStLQ09h86zOWgICLzGSr4KmKy2s5zmWimZq/Wdp538ugf6OsbFJhtUxRZw0meb/sFXXfiYmMzs4Kf+7uZ5djHzdMLPX19flm3huSkJJKSkti85gfS09NRqOnv8K4f+6yhq+bPnY/mFhb5HqPYmBjS09Mwf27eKnMLi3yHwr4NdM0tSYlRjS8lJhptQ2M0dXTRMTVHQ0ubFMVzZRRR6JoX3FnysrQNTdDQ0iI1Plbl+dS4GHRM8s7QjA48SdjBnXgM/IpKM1ZTbuwc4m5eI/zPvWqNrbBMDPWBzCyjnOKeJGNioJ/vfnceRbH52DlWHjzOr39fwMrEiCEt6qKnnXfHS013Jx4qYgmNUG/bio7Op+2b59+OM9t+et7nS3R++yjYsnEDTVu2VkPUxSejmP77r5JuyP+wu3fvYmNjQ6NGjdDR0aFMmTL4+WWO37a0tERLSwsTExNsbApOY05PT2fdunWYmGSOtf788885fPgw06dPJy4ujvXr17N582YaNmwIZHaI2NkVnHaa5fr16/j7+3P8+HFqPZswddOmTTg4OLBr1y46dOjAnDlzqFq1qsoP5LJlMydjjYuLY/HixSxbtozu3bsD4OrqygcffPASRyqzx3/ZsmVUr14dyOw88Pb25vTp0/j5+VGhQgUqVMi++zdt2jR27tyJv78/gwcPVj7fokULBg3KnB9l7NixLFy4kD///BNPT082b97Mo0ePOHPmDJbPfuy4uWXfBerTpw8DBgxgwYIF6Onpcf78eS5dusRvv/1W6M8xadIkGjdurPwMpUuXZufOnXTs2FH5OVesWKHyWXJKSEjgu+++Y926dTRv3hyA1atXExAQwI8//sjo0aOVZadOnap8r4IYGBhgbGyMtra2SlsLCAjg0qVL3L59W9mx8tNPP1G2bFnOnDlDteeyBwpT58GDBzl9+jRBQUF4eHgA4OLiorLvi45BYZQvX55JkyYB4O7uzrJlyzh8+HChjgfAihUrGDNmDAkJCXh6ehIQEKDMJHj48GGuztGsxw8fvnhM/6JFi+jduze9e/cG4Ntvv+XQoUMq2UsNGjRQ2WfVqlWYm5tz9OhRWrVqRa1atfD09GTDhg2MGTMGyDyvO3TogLGxMSEhIaSmptK+fXscHR0BlB3X+UlOTiY5WfWLnp6eXr7l/z7yJ+u/W6p8PHyCeudAyktqaior5syEjAy6Dxz84h3y8feRP1i3Ijv2ERNff+zqdPqvI2z+Pvt6O2j86xmSmZGRmZFZvmp1GrbKnCDYwdmFm8HX+CtgPx5l1XtHXl0a5jjPXV3dcHVzo0uHjwm8cF4lS+ptNWncGKbPW0Drzp+ipamJp7sbjevX49pzGcNFcfqvI/y86jvl44HjJ7xynYVx4s9DVPuwLjrPZWS9rONH/mDN8iXKx6Mmvp65tEzNzBky9mvWfreMg7t/Q0NDg5p16uHk6lbom0nPe9eP/Z+HAli+IHuOokkzZxVQWrxNjF08sWnQinu7NpBw7xZ6VtY4tPmUlDgFDw/vfu3vX8XFgU61sjMFvw/If8hqQYLuZ2fOP4iO5c7jaCZ1aEYlZ3tOXr+jUlZHS5PKLqU5ePHVs4+PHA5gxcLstj9x+utv+4kJCUz9ejwOjo506dbjtb+feH9I59J/WIcOHVi0aBEuLi40a9aMFi1a0Lp1a7Tz6YHPj5OTk7JjCcDW1paIiMxU01u3bpGSkqLstILMOWo8PQu36kxQUBDa2trKTh0AKysrPD09CQrKTB8PDAykQ4cO+e6fnJys7NgqKm1tbZXODC8vL8zNzQkKCsLPz4/4+HgmT57M3r17CQsLIzU1lSdPnuTKXMqZwaOhoYGNjY3yWAUGBlKpUiVlx9Lz2rVrxxdffMHOnTvp3Lkz69ato379+jg5ORX6c9SsmX3n0NLSUuU4Aujq6haYZXTz5k1SUlKoXTt7jhcdHR38/PxU6gGoWrVqoePKS1BQEA4ODsqOJQAfHx/lcS+ocyk/gYGBlC5dWtmxlJcXHYPCeH7/nOdEYXTt2pXGjRsTFhbGvHnz6NixI8ePH0dfP/87aoUVFBTEgAEDVJ6rWbMmf/75p/JxeHg433zzDUeOHCEiIoK0tDQSExNV2nOfPn1YtWoVY8aMITw8nP379/PHH38AUKFCBRo2bIivry9NmzalSZMmfPLJJ1gUsMrRzJkzmTJFtZNl0qRJNO38eZ7lK/lVxzXHdST1WQZejCIa8xznUKxCQRlnl1z7v6ysjqXIRxGMnTbzlbKWKvnVwNUjezWvlNT8Yo+mzGtY2e1Vla/qh5Nb9jmUNadarEKBWY67onEKBaWdin7sjU1M0dTSwjbHNQDAtnRpblwr2rCvnMzMzdHS0sp1pzc6KhpLy7xXArS0slLeNVaWj47GsoCVA+3s7TEzN+f+v/+qtXPJ3MwULU3NXJN3R0dHY/UKK4qVtrPjuwXzePIkiYTEBEpYWfH1tOnY29i+YsTP2o577vM2Nka17cTGKCjt5Jxr/6K4EXSF8Af36T1s9IsLv0Dl587d1JTMocixCgUWOdpMrEJBGZe8276JqSmamprEPPd3i1EoVFaC861chQWr1xIXE4OmlhZGxsZ88XkXSr7ghl9+3vVjX71WbZUV3VKeZsaviI5SOf8U0dE4u+U9PMvUzAxNTS2Vybuz9nk+g/Ft8lQRhY6Zanw6ZhakJsaTnvKUlFgFGWmp6DyXpaRjbslThXqzZlIT48hIS0PbWDVLSdvEjJS42Dz3sWvSnqjzfyvnmEp6+C9aunqUad+dh3/see1rxV++G8adR9nHIWvSbhMDPWKfZN9YMzHQ435U4YcwP3mawqOYeEqY5h4iX8HJHl1tbU7fKHhqhcLwq1lbuaIbZJ+7udq+IhoX14LavmauLCVFdHSubKbExEQmjx+DgYEBX02Z9tK/C982rzNrXuQmw+L+wxwcHAgODmbFihUYGBgwaNAg6tSpozJMqjCenxRUQ0NDrauZvIiBQf7jnQt6DVDeAcx54XnZzw8watQodu7cyYwZM/jrr78IDAzE19c31xw4BR2rF8Wqq6tLt27dWLt2LU+fPmXz5s306qXeFaAMDAyU82u9KiMjI7XUo04vOsZZZV71GLzqOWFmZoa7uzt16tRh27ZtXLt2jZ07dwJgY2OTa96prMcvyjIsrO7duxMYGMjixYv5+++/CQwMxMrKSqU9d+vWjVu3bnHixAk2btyIs7MzH36YOZmnlpYWAQEB7N+/Hx8fH5YuXYqnpye3b+e/TPH48eOJiYlR2caPH59veQNDQ0rZ2ik3O4cymFlYcPWfi8oyTxITuRkSjKund771FEZWx1J42ANGT52B8StOzmxgaEgpOzvlZp8V+8XAHLEncCskGDfPvJeUL076BoZY29opN9vSDpiaWxB8WfXY374RgnMhbyTkRVtHBydXd8Lvqy7fHP7gAZYlXn1hCR0dHTw8PTl3LnuekvT0dM6fPUPZcnln2pUtV45zZ8+oPHf29Ol8ywNEREQQGxODVQEdUEWho6ODp4c7Z85nL9ednp7OmQuB+KphImIDA31KWFkRGxfHqbPnqFPr1Yc16RsYYm1jq9yUbedS9twrTxITCb0RgotH0dtOTn//cYgyLq5q6TAxMDTExs5OudmXccTMwoIrOc7dxMQEboZcw90r7+uOto4Ozm7uXMkxMXd6ejpXLgbilse1ysTMDCNjY65cDCQ2RkFlvxpFiv1dP/aGhobY2ZdWbmWcnLCwtCQwxzQLiQkJBAcF4eVTNs86dHR0cPPw4OL5c8rn0tPTuXj+fL77vA3irl/G3LeKynPm5asRF5I5PDgjLZX4W8GYl8txU09DA/NyVZRl1CUjLY3E+6GYuOW4xmhoYOLmTcLdG3nuo6mjm+sHfsYb/J2QnJrK47gE5fZQEUdMYhIetiWVZfR0tHEsYcntlxjCpquthZWpEbGJSbleq+HuxOV7YSoTfhfV823fwTGz7V+8oNr2Q4Ku5jsJfWbb9+RijvMlPT2dfy6cwyvHPokJCUwaOwptbW2+mTYDXd38M8iFyIt0Lv3HGRgY0Lp1a5YsWcKRI0c4ceIEly5lTkyqq6v7ynNEuLi4oKOjozLhc0xMDCEhIYXa39vbm9TUVE6dOqV8LjIykuDgYHyeXQzLly/P4cOH89zf3d0dAwODfF8vWTLzH5acc8cEBgbmKpeamqoymXZwcDAKhQLvZ3fRjh8/To8ePfjoo4/w9fXFxsZGOQFzYZUvX57AwMBccxfl1KdPHw4dOsSKFSuUw45exsmT2RPhRkdHExISovwMheHq6oquri7Hjx9XPpeSksKZM2eUf4+iyKuteXt7c+/ePZWJ069evYpCoSjUe+VVZ/ny5fn3338L3f7eBhkZGWRkZCiHjNWsWZNLly6pZEIFBARgampaqOPi7e2tcj6BaruAzPY8ZMgQWrRoQdmyZdHT0+Pxc8uQW1lZ0a5dO9auXcu6devo2bOnyusaGhrUrl2bKVOmcOHCBXR1dZUdZHnR09PD1NRUZStoWNzzNDQ0aNK6Hbu3/sKFUye5F3qbVYvmYWFpReUa2T+KZ08Yz6G92Wn4SU+ecOfWTe7cylxK/nF4OHdu3VSuBJeamsry2TMIvXGd/iNGk56ehiI6CkV0lPLu4avS0NCgaZt2+G/9hfNZsS+cj7mlFZVrZK9iN/ubcQTs8c839kfPxQ4QHxfHnVs3eXAvM2X/4f1/uXPrZr5zLBQ1/gYt27Bv+1YunjnF/TuhrF+2EDMLSypWy/4hvGjKNxzZn73iWNKTJ9y7fYt7t28BmRMM37t9i6hHj5RlGrf5iHN/H+PYod+JCHvAkf17uHTuNHWbtlBL7B07d2Gvvz8H9u0lNPQ2C+bO4UlSEs1btQRg+tQprPouewjgJx07cfrkSbZs3sSd0FDW/rCa4GtBfPRx5pLaiYmJfLdsKVcuXyYs7AHnzp7h67GjsS9dmmrVi9YpUJAuH7fHf99+9h4M4Padu8xZvJSkpCRaNmsCwJRZc1jxwxpl+ZSUFEJu3CTkxk1SU1N49DiSkBs3uZejA+/kmbOcOH2GB2EPOXXuHF+MGoOjgwOtntWpThoaGjRo0Zr9O7byz9lT3L8byvplizCzsKRCjrazeOoEjhzInp8lKekJ90JvcS80R9sJvUXU40cq9T9JTOT8yePUalC4IclFib9Zm4/YteVnzp06wb3Q23y/YB7mllZUyXHuzvh6HAdznLvN27XnyO/7+d/hAO7fu8vaFUtJTkqibqPsY3z00EFuXAsiPOwBx/48zNLZ02nW9iPsSqtm8r1K7O/6sW/7cQe2bPyJU8ePE3rrJgtmzcCyhBU1c0x98NXI4ezeuUP5uF2Hjvy+dy+Hfz/AvTuhrFi0gKSkJzRq1lxZJjoqkls3rhP27LwIvXWLWzeuExebd2bOy9LUM8DI0R0jR3cA9K3tMHJ0R88qc4i7Y5cBeHzxjbJ82MFd6Fvb4dR1EAZ2ZbBp8hElazbg/t4tyjL392zBpmFrrOs2x8DeEdc+o9DS0yf8iPrnNYr46yAl/OpiWbk2+ta2OHzUDU0dPSLPZi7e4tixD3bNPlGWjwkKpGSN+lhU8EPXogQm7j7YNvmImKCLyqwlTV09DGwdMLDNbN96liUxsHXIlY2lLkev3qBJBS/KOdhia2HKZx9WJeZJEpfuPlCW+aLpB3zonZ2B2LZaOVxLlcDS2BAna0v6NKhBRkYG527dU6m7hIkRrjYlOBES+lpi19DQoE37T9i6aQOn/j5O6K1bLJw9A0urEtSond32vxk9gj27stt+2487cHDfHg4fPMC9O3f4bvFCkpKSaPis7ScmJDBx7CiSkpL4ctQYEhMTiI6KJDoqUu1zBor317ud5yZeybp160hLS6N69eoYGhqyceNGDAwMlHOkODk58b///Y/OnTujp6dHiRIlXvo9TExM6N69O6NHj8bS0hJra2smTZqEpqZmobJD3N3dadu2LX379uX777/HxMSEcePGYW9vT9u2mXNwjB8/Hl9fXwYNGsSAAQPQ1dXlzz//pEOHDpQoUYKxY8cyZswYdHV1qV27No8ePeLKlSv07t0bNzc3HBwcmDx5MtOnTyckJIT58+fnikNHR4cvv/ySJUuWoK2tzeDBg6lRo4ZyuJ+7uzs7duygdevWaGhoMGHChJfO3urSpQszZsygXbt2zJw5E1tbWy5cuICdnZ1yOJu3tzc1atRg7Nix9OrVq1CZODlNnToVKysrSpUqxddff02JEiVUVjp7ESMjIwYOHKj8e5YpU4Y5c+aQmJionMOnKJycnLh9+7Zy2JqJiQmNGjXC19eXrl27smjRIlJTUxk0aBB169Yt1JC7vOqsW7cuderU4eOPP2bBggW4ublx7dq1zB8IzZoVOX51uXXrFlu2bKFJkyaULFmSf//9l1mzZmFgYECLFpk/pps0aYKPjw+ff/45c+bM4eHDh3zzzTd88cUXheqMGTp0KD169KBq1arUrl2bTZs2ceXKFZW5p9zd3dmwYQNVq1YlNjaW0aNH59nW+vTpQ6tWrUhLS1POaQZw6tQpDh8+TJMmTbC2tubUqVM8evTopToyi6JF+09ITkpi7YqlJCbE4+FdlpGTpqqsfBXxMIy42Oy099s3rjP7m3HKxz+vWQ1A7QaN6Dt0BNGRkVx4tjrZxGGq8yyN/XYW3r6vNoQyO/YOJCclsW75EhIT4nH3KcuoydNyxR6f48fN7RvXmfX12OzYf8xcffKDBo3oO2wkABdOn+SHxQuUZVbMzZyroV3nrnz06WdqiR2gSdv2PE1KYvP3y0lMTMDVy4cvv56sMs/Ko/CHxOcYNnH31g0WTv5a+Xjb+syVBmvUbUD3wcMAqFi9Jp/2G8iBndvYumY1pezs6TdqHG7e6lkivEGjxigUCtasXk1UVCRu7u7MXbBQOSwuIvwhmprZ/1aV8y3PhClT+XHV96z+fiWlSzswfdYcXFwzhy9qaWly88YNDuzbR3x8HCVKlKCqX3V69+uXawU2dWhcvx6KmBhWr/uJyOho3F1dWDhzunJY3MOIR2jkmKPnUWQk3QYMUj7e9Os2Nv26jUrly/PdgrkAxCck8N2Pa4l4/BhTExPqf1ibAT17vrahEY3btic5OYnN36941na8GfzVpNxtJ0fbv3vzBoumZP/43v5TZgdajboN6PbFUOXz5/7+i4yMDKp9UOe1xA7Q6uPMc3fNssxz18OnLGOmfPvcuftA5bpT48O6xMbEsH3TBmKio3F0cWHMlG9VhsWF/fsvW9evJT4+jpLWpWjTsTPN277cDaUXedeP/cedu5CU9ISlC+aREB+Pj68vU2fNVcm2ePjgAbE5VmusU78BMQoFG9euITo6ChdXN6bOnqsyLG6fvz8//7RO+XjcsMyVlIeNGafSCVVUJq5e+E5epnzs0j2z/vAj+7i+Yjq6FlbolcieWzH5URhXZ43GufsQ7Fp0IDnyEddXzkZx8bSyzOMTh9ExNadMxz7omluSEHqdyzNGkhKj/kUhov85jbaRCbZN2qFjYsaTB3e5sWaBcpJvXXMrlaFuYX/sJgOwbdIeXTMLUhPiiLkayIPftyvLGJZ2wqN/9r/FpVt3ASDy7DHu/Pqj2j/D4Ush6Gpr0alWJQx0dbgVEcnKg8dJTcv+7m5lYoRRju9V5oYGdK9XDSM9XeKTnnIr/DEL9hzJlZ1Uw92JmIQnBN9XzTJXp/adupCUlMTyhc/afjlfJs+a81zbv6/S9j+s34CYGAWb161Vtv3JM+coF6m4eT2EkGerXPbv1lXl/VZv/JlSahgaXRzSZVTcG6WRIQMR33v16tWjYsWKLFq0CCcnJ4YNG8awYcPYtWuXcjWvtLQ0fH19+fbbb5XzE508eZL+/fsTHBxMcnIyGRkZrFu3jmHDhqFQKACYPHkyu3btUsn2WbRoEYsWLVJm7sTFxTFgwAB27dqFqakpY8aM4ZdffqFBgwbMnDnzhfFHR0czdOhQ/P39efr0KXXq1GHp0qW4u7sryxw9epSvvvqKc+fOYWBgQPXq1fnll18wNzcnPT2dmTNnsnr1ah48eICtrS0DBgxQDrk5fvw4AwcO5Pr161SrVo0hQ4bQoUMHbt++jZOTk/Izr1mzhtGjR3P//n0+/PBDfvzxR8qUKQNAaGgovXr14uTJk8oOrV9//VV53AGVY5+lYsWKtGvXTrlU/Z07dxg5ciQBAQGkpqbi4+PD8uXLVeasWrNmDb179+b06dOFnnfoyJEj1K9fn927dzNu3DiuX79OxYoVWb16tXJ+oOf/tll69OiBQqFg165dACQlJTFmzBh+/vln4uLiqFq1KgsXLlTGkvVe0dHRmD+3gk5+kpOT6dq1K4cPH0ahUChXwbt79y5ffvklhw8fRlNTk2bNmrF06dJCrfaXX51RUVGMGjUKf39/EhIScHNzY9asWbRs2TLfY1CQ549PzvMtS7t27TA3N2fdunUF1vXgwQP69OnDuXPniI6OplSpUtSpU4eJEyeqzFN2584dBg4cyJEjRzAyMqJ79+7MmjWr0D/+ZsyYwcKFmXesPv74Y0qVKsXvv/+uPI8vXLhAv379uHz5Mg4ODsyYMYNRo0blar8ZGRk4OztTtmxZ9u7NvjsaFBTE8OHDOX/+PLGxsTg6OvLll1+qTG5fWCeu3Xzpfd4WNb1cORl8q7jDKJIani788Y/6liN/0xqU9+Rh5Nu/0l5+bKwsiL4XWtxhFImFgxOH1TCJbXFpWMGLMyH5D+F921XzcH5nj3/DCl5cv//ihSneVu72NhzrWPvFBd9SH2w9zvmxPV9c8C1UefZahq7d8eKCb6nFPdsTfC//lbnfdp4Ob2/H04+HTxTL+/ZuqN4VMt8V0rkk3riEhATs7e2ZP3/+K2W7vClF6XB4naZNm8avv/7KP//88+LCzxSlw0eIF4mPj8fe3p61a9e+9BDNwpLOpeIhnUvFSzqXio90LhUf6VwqXtK5VHykc+n1+eFQ0VYHfFV9GtV6caH3kAyLE6/dhQsXuHbtGn5+fsTExDB1aubSvVnD2kThxMfHExoayrJly/j222+LOxzxH5aens7jx4+ZP38+5ubmtGnTprhDEkIIIYQQQhQjmdBbvBHz5s2jQoUKNGrUiISEBP766y9KlCjBX3/9hbGxcb6byDZ48GCqVKlCvXr1cq0SN2DAgHyP4fPLzheHsmXL5hvfpk2b3po681JQ+/zrr79eqq5NmzblW1fZsupZqeZNHJe7d+9SqlQpNm/ezJo1a975ZWqFEEIIIcT7J2thnDe9/VfJLwLx2lWqVIlz587l+VrVqlXzXJ3tbdKjRw969OhR3GGwbt26fOfsmTp1KqNGjcrzNVNTU6ytrYv1Qrdv3z5S8llZqzDzJ72pOvNSUPu0t7d/qbratGlD9erV83xNR0fnperKz5s4Lk5OTv/pfziFEEIIIYQQqqRzSRQrAwMD3NzcijuMd561tTXW1tbFHUa+slYgfNvrzIs626eJiQkmJiZqqy8vb+q4CCGEEEIIIUQW6VwSQgghhBBCCCHEeyVdMu3fKJlzSQghhBBCCCGEEEIUmWQuCSGEEEIIIYQQ4r0imUtvlmQuCSGEEEIIIYQQQogik84lIYQQQgghhBBCCFFkMixOCCGEEEIIIYQQ75UMGRb3RknmkhBCCCGEEEIIIYQoMslcEkIIIYQQQgghxHslXRKX3ijJXBJCCCGEEEIIIYQQRSaZS0IIIYQQQgghhHivyJxLb5ZkLgkhhBBCCCGEEEKIIpPOJSGEEEIIIYQQQghRZDIsTgghhBBCCCGEEO8VGRb3ZknmkhBCCCGEEEIIIYQoMo0M6c4TQgghhBBCCCHEe2TRnj+L5X2HtapfLO9b3GRYnBBCiLfWiWs3izuEIqvp5crxoBvFHUaR1PZ2468r14s7jCL7sKw7jxUxxR1GkZUwN0Px4F5xh1Ek5nYOHAoMKu4wiqxRRW/OXg8t7jCKrKq7E7+duljcYRRJ2+oVCL4XVtxhFJmngy3nx/Ys7jCKrPLstRzrWLu4wyiSD7Yep/28NcUdRpHtGNXrnW/7QoAMixNCCCGEEEIIIYQQr0Ayl4QQQgghhBBCCPFekQmA3izJXBJCCCGEEEIIIYQQRSaZS0IIIYQQQgghhHivpEvq0hslmUtCCCGEEEIIIYQQosgkc0kIIYQQQgghhBDvlQwkc+lNkswlIYQQQgghhBBCCFFk0rkkhBBCCCGEEEIIIYpMhsUJIYQQQgghhBDivZIhE3q/UZK5JIQQQgghhBBCCCGKTDKXhBBCCCGEEEII8V5Jl8SlN0oyl4QQQgghhBBCCCFEkUnnkhBCCCGEEEIIIYQoMhkWJ4QQQgghhBBCiPeKTOj9ZknmkhBCCCGEEEIIIYQoMulcKkC9evUYNmwYAE5OTixatKjIda1btw5zc3O1xFVYrxqzEDmFhoaioaFBYGBgcYfyRrzM+aOhocGuXbteazyvm1wvhBBCCCHE+yQ9I6NYtv8qGRZXSGfOnMHIyKhQZZ2cnBg2bJiyYwqgU6dOtGjR4jVFJ8Tr5+DgQFhYGCVKlHit76OhocHOnTtp165dsdb5Mud8WFgYFhYWRYxOVXBwMAMGDODq1avExMRgZ2fHp59+yqRJk9DR0VGW+/XXX5kwYQKhoaG4u7sze/bs//w1JiMjg52bN3I04ACJCQm4e/nQbeAX2NjZ57tP8JVL7Nu5nTs3bqCIjuLL8d9QpUYt5eupqans2PQT/5w7Q8TDhxgaGuFToSIduvXEwspKrbHv+nkj/wv4ncSEBNy8vOk24AtKFRj7ZQ7s3E7ozRvEREcxeNw3VK5RU6XMrp83cfrY/4h6/AhtbW0cXd1o/1k3XD281BZ7Vvy//bKJvwJ+JzExM/7P+g0qMP6QK5c58Nt27ty8SUx0FF+M/ZpK1WvmW37DymUcPXiATj370rh1W7XFvv3XX9m8aSNRkZG4ubszfOQofMqWzbf8H4cPsfr773kYFkZpBwcGfjGYWrVrK1+vXd0vz/0GDf6Srp9/rra4s/y68zc2bdlKZFQU7q6ujBwymLLeef99b90O5fu16wgOuU5YeDjDvhhIl08+VinTrnNXwsLDc+37cds2jBk2RO3xZ2RksPfXnzl+OIAnCQm4eHrRuc8ArG3t8t3n+tUrHNq9k3u3bxITHU2/UeOoUK2GSpmkpCf8tnkD/5w5RUJcHFbW1tRr3ooPGzdTe/zbN/3En78fICEhHg9vH3oNGoKNff5tH+DgHn/27thGTHQUZZxd6N5/EK6e2X+38LAHbP5xNcFXr5CSkkKFKlXo3v8LzNT0b01W7Ad3bOX0kcM8SUzAyd2Lj3r0oaSNbb77/LF7J5fPniYi7D46Oro4uXvQvNNnKn+v7WtXcf3KJWKjo9DT18fRzZMWnbpiXcD1oKjxb16/loP79pAQH4932XIMHDoCu9KlC9xv72872bn1F6KjonB2daPf4CF4eHkrX1++cD4Xz58jKvIx+gYGePmUo0fffpQu46i22EvUbECpOs3RMTHjSdhd7v22icR/b+dbvuQHjSlZoz665lakJsQTfekMDw5sIyM1FQBjZw9K1WmOQWlHdE0tuLl+CTFXL6gt3pxMvStQus2nGDl7oWdZgqtzxxF15q8C9zHzqYRzty8xdHAmOTKCe9vXE3F0n0oZ26btsW/9KbrmliTcucHNNQuJvxn0Wj4DQOfalWjs64mhni7XHkSwKuBvwhSx+ZZf2bcD1mYmuZ7ffyGI1YdPANC4vCcfervgYm2FoZ4uny3dSGLyU7XH/i63ffF+k8ylQipZsiSGhoZF3t/AwABra2s1RiTEm/P06VO0tLSwsbFBW7vofdJPn6r/H9jX5WXOeRsbG/T09NTyvjo6OnTr1o2DBw8SHBzMokWLWL16NZMmTVKW+fvvv+nSpQu9e/fmwoULtGvXjnbt2nH58mW1xPCu2rdjGwF7/ek+cDAT5y5ET1+f+ZMnFNjukpOSKOPkzOf9B+X5+tPkZO7cvEGbjl2YsmApg8d/w8P7/7J4+hS1xr5/5zYO7dlNtwFf8M2cBZmxT5lAygtid3B25rP+A/MtY2NnT9d+A5i6eDnjZ86lhHUpFkyeQGxMjFrjP7BzO4f37uazAV/w1az56Onps3DaxILjT07CwcmFrn0HvLD+8yf/5lZIMOaWluoMm0MBASxdvIhevfuwZv1PuLm5M2LoEKKjovIsf+mff5g8YQKtWrdh7U8b+LBOXcaPGc2tmzeVZfz37VPZvvpmAhoaGtRr0ECtsQME/PEni79bSe/un7N+1UrcXF0YOmYcUdHReZZPSk7C3s6WQf36YJXPsVy7cjn7tm9VbkvnzQagYb06ao8fIMB/J0f276FznwGMnj4HXX19ls2YUmDbeZqcRGlHZzr26p9vmR0/reFq4Hm6Dx7GhAVLqd+iNVvXrOKfs6fVGv+e7Vv5ffdv9PziS6bOX4yevj6zJn5V4HXnxP+OsOmHVbTv0pVvFy+njLMLsyZ+TYxCAUBSUhKzJnwFGhp8NWM2k+YuIDU1lXlTJ5Kenq622I/s/Y3jAftp36MvX06aga6eHj/OnV7gsb917Sq1GjVl8MTp9B37DWlpafww51ueJicpy9g7udCxz0BGzVpI79Ffk0EGP8z5Vq2xA+zY8jN7dm5n4NARzF32HXr6BkwaN5qnT5Pz3eevP//gx5Ur6Px5DxauXI2TiyuTxo1GkeOccXX3YMjosSxfs54ps+YCGUwcO5q0tDS1xG1R3o/SrToTdvg3ri2ZzJOwe7j1Hom2Ue6OCwCLijWwb9aBsEP+XJ3/FXe2rcGigh92zT5RltHU1SMx7B73dm1US4wF0dIzID70Brd+nF+o8nolbfEZN5eYK+e5MKYHD/ZuxX3AWMwrZHfEl6jZEOduX3J32xoujO1Fwp0blPt6ATqm5q/lM3zk50vLSj6sDPibcZt2k5ySwoRPmqKjpZXvPmM27qbXip+V2+StBwD4OyS7U1BPW4sLt++z/dQ/ryXuLO9q2y8OGRkZxbL9V0nnUiHlHDKSkZHB5MmTKVOmDHp6etjZ2TFkSObdvHr16nHnzh2GDx+OhoYGGhoaQO5hcZMnT6ZixYps2LABJycnzMzM6Ny5M3FxccoycXFxdO3aFSMjI2xtbVm4cKHKUL2X9cMPP2Bubs7hw4eVsQ4ZMoQxY8ZgaWmJjY0NkydPVtnn7t27tG3bFmNjY0xNTenYsSPhz+5oxsTEoKWlxdmzZwFIT0/H0tKSGjWy7x5u3LgRBwcHIHtY1Y4dO6hfvz6GhoZUqFCBEydOFPozbN++nbJly6Knp4eTkxPz56v+w5acnMzYsWNxcHBAT08PNzc3fvzxR+XrV65coVWrVpiammJiYsKHH37IzWc/CvI6tu3ataNHjx7Kx05OTkybNo0uXbpgZGSEvb09y5cvV9lnwYIF+Pr6YmRkhIODA4MGDSI+Pl75elZb+P333/H29sbY2JhmzZoRFhamUs+aNWuUn9XW1pbBgwcD0KtXL1q1aqVSNiUlBWtra5XPmp969eoxePBgBg8ejJmZGSVKlGDChAkqF8Ksz9mtWzdMTU3p169fnsPijh49ip+fnzLGcePGkfrsLlrO9xo2bBglSpSgadOmBcbm5OQEwEcffYSGhobyMcB3332Hq6srurq6eHp6smHDhhd+1hfVuXv3bqpVq4a+vj4lSpTgo48+UtmvKMPiXrWdu7i40LNnTypUqICjoyNt2rSha9eu/PVX9l3BxYsX06xZM0aPHo23tzfTpk2jcuXKLFu2rFDvERERQevWrTEwMMDZ2ZlNmzblKlNQO05ISMDU1JRt27ap7LNr1y6MjIyIi4vj6dOnDB48GFtbW/T19XF0dGTmzJmFiq8oMjIyOLh7F206dKZy9Zo4ODnTd9hIoqMiOX8y/2Nfvko1Pv6sO1Vq1srzdUMjI0ZPnYHfB3WwLV0aN08vPus/iNCbN4h8FKG22AN2/0brjp2o9Cz2PkNHooiK4vypgmKvSvuu3VQyrZ5Xo249ylaohLWNLfZlHOncqy9PEhP5NzT/O+RFif/Qnt9o9UknKvnVwMHJmV5DRqCIiuLC6fzj961clY8+/ZzKBcQPEB35mJ9/+J4+w0ahpaXehOstP2+mddt2tGzdGmcXF0aPG4eevj57du/Os/zWLb9QvUYNun7+OU7OzvQbMAAPTy+2/bpVWcbKqoTK9tf/jlK5ShXsX5DJUhQ//7qdti1b0Lp5M1ycHBk3Yhj6+nrs3n8gz/I+Xl4MGdCfJg3qo5sjEzInC3NzrCwtlduxE6cobWdH5QoV1B5/RkYGf+7bTbP2HalQrTr2jk50/2IoMdFRXDxzKt/9ylaqQuvOXanoVyPfMreCg6lRtz4eZX2xsi7FB42aYu/oxJ0b19Ua/4HfdtGuUxeq1qhFGWcXBo4YgyIqknMn/s53v/27dlC/aTPqNm5K6TKO9PpiCHp6ehwN+B2AkKtXeBQRTv/hIynj5EwZJ2cGDB/N7RvXufpPoNpiP/b7Phq2aU/ZKtWwLeNIp/6DiVVEc+X8mXz36zP6a6p+WA+b0g7YlXGiY98vUEQ+5t/bt5RlatRvhIuXD5YlrSnt5EKzjzujiIokWk3XzKz4/Xdso2PXz6lR+wOcXVwZPnY8UZGPOXn8WL77/bb9V5q0aEmjZs0p4+jEoGEj0NPT59CB7CyaZq1aU658BUrZ2OLq7kHXnr15/CiCiPCHaond+sMmPD79P6LOHiMp4gF3d/5EespTrKp9mGd5I0c34u9cJzrwJE+jI4m7foXowFMYlXZWlokNvkTYwR3EXDmvlhgLEh14krtbVhN55n+FKm/bpB1JEWHc3rCMJ/fvEPb7dh6fPIJ9y07KMvatOvHw8G4ijuzjyf1QbqyeS9rTZErVb1VAzUXXqnJZtp28yJmbd7nzOJol+/6HpbEBfm5l8t0n9kkSisQnyq2qqwNh0bFcuZfdLvacv8rO0/8QEqa+tv68d7nti/efdC4Vwfbt21m4cCHff/89169fZ9euXfj6+gKwY8cOSpcuzdSpUwkLC8vVYZDTzZs32bVrF3v27GHPnj0cPXqUWbNmKV8fMWIEx48fx9/fn4CAAP766y/Ony/aPxpz5sxh3LhxHDx4kIYNGyqfX79+PUZGRpw6dYo5c+YwdepUAgICgMzOorZt2xIVFcXRo0cJCAjg1q1bdOqU+Y+BmZkZFStW5MiRIwBcunQJDQ0NLly4oPwRevToUerWrasSy9dff82oUaMIDAzEw8ODLl26qHRI5OfcuXN07NiRzp07c+nSJSZPnsyECRNYt26dsky3bt34+eefWbJkCUFBQXz//fcYGxsDcP/+ferUqYOenh5//PEH586do1evXoV675zmzp1LhQoVuHDhAuPGjWPo0KHKYwagqanJkiVLuHLlCuvXr+ePP/5gzJgxKnUkJiYyb948NmzYwP/+9z/u3r3LqFGjlK9/9913fPHFF/Tr149Lly7h7++Pm5sbAH369OHAgQMqbWvPnj0kJiYq/zYvsn79erS1tTl9+jSLFy9mwYIF/PDDDypl5s2bp/ycEyZMyFXH/fv3adGiBdWqVePixYt89913/Pjjj3z77be53ktXV5fjx4+zcuXKAuM6cybzC+3atWsJCwtTPt65cydDhw5l5MiRXL58mf79+9OzZ0/+/PPPF37W/Orcu3cvH330ES1atODChQscPnwYP7+8h7MURVHb+fNu3LjBgQMHVM6jEydO0KhRI5VyTZs2LXQHVo8ePbh37x5//vkn27ZtY8WKFUREqH4RKqgdGxkZ0blzZ9auXauyz9q1a/nkk08wMTFhyZIl+Pv7s3XrVoKDg9m0aZNKx566PQp/SEx0ND4VKiqfMzQywtXDk5vB6k2rf5KQgIaGBoZGxmqpTxl7+YrK5wyNjHDx8ORm8DW1vAdAakoKRw/ux8DQCAdn5xfvUEiPw8OJUUTj/dyxd3F/9fjT09P5cfECmrZrj72a0/JTUlIIvnaNan7VlM9pampStVo1Ll+6lOc+Vy5domo11etE9Ro1uJJP+ajISP4+fpxWbdqoL/BnUlJSuBYSgl+VysrnNDU1qVa5MpeuXFXbexwIOETr5s2UN8vUKTIinFhFNJ6+5ZXPGRga4eTmwe3rwa9Ut4unJ/+cPYMiKpKMjAxCLl8iIuwBXjnOs1f1KPwhiugoylbM/hsYGhnh6unF9Wt5X3dSU1K4feM65Sqq/t3KVazE9WtXlWU0QGUotI6uDhoaGgRfuaKW2KMeRRAXo8C9bM5jb4iDixt3boQUup6kJ4kAGBrnfT18mpzEmb/+xLKkNWZW6htWHx4WRnRUFBUqV1E+Z2RsjIe3D8FX827/KSkp3AgJpmKOfTQ1NalQuQrX8tkn6ckTDh/YTykbW0qUfPURCBpaWhjaOxF3PcffMSODuBtXMSrjluc+CXduYGjvhOGzziRdy5KYeZUnJvj1Zseoi4l7ORSXzqo8F33xFCYe5QDQ0NLG2MUTxaUcnZoZGSgunVWWUadSZiZYGBty8c4D5XOJT1O4HvYIT7vC/Y21NTWp4+3KH5cLf66oy7va9sV/g8y5VAR3797FxsaGRo0aoaOjQ5kyZZQ/Si0tLdHS0sLExAQbG5sC60lPT2fdunWYmGSmwX7++eccPnyY6dOnExcXx/r169m8ebOyM2jt2rXY2eU/B0F+xo4dy4YNGzh69Chln5tHonz58srhNu7u7ixbtozDhw/TuHFjDh8+zKVLl7h9+7Yy++inn36ibNmynDlzhmrVqlGvXj2OHDnCqFGjOHLkCI0bN+batWscO3aMZs2aceTIkVwdK6NGjaJly5YATJkyhbJly3Ljxg28vAqeA2TBggU0bNhQ2dHh4eHB1atXmTt3Lj169CAkJIStW7cSEBCg/OHt4uKi3H/58uWYmZnxyy+/KL+weXh4vPTxrF27NuPGjVPuf/z4cRYuXEjjxo0BVLKfnJyc+PbbbxkwYAArVqxQPp+SksLKlStxdXUFYPDgwUydOlX5+rfffsvIkSMZOnSo8rlq1TJ/ANWqVUuZuZN1bNeuXUuHDh2UHWkv4uDgwMKFC9HQ0MDT05NLly6xcOFC+vbtqyzToEEDRo4cqXwcGhqqUseKFStwcHBg2bJlaGho4OXlxYMHDxg7diwTJ05EUzOz79rd3Z05c+YUKq6SJUsCYG5urnL+zJs3jx49ejBoUObQpREjRnDy5EnmzZtH/fr1i1Tn9OnT6dy5M1OmZA9vqqDGO/NFbedZatWqxfnz50lOTqZfv34q7ePhw4eUKlVKpXypUqV4+PDFd5ZCQkLYv38/p0+fVrapH3/8EW9vb5VyL2rHffr0oVatWoSFhWFra0tERAT79u3j0KFDQOZ10t3dnQ8++AANDQ0cHV/veP2YZ2ndZuaq85GYmpsrX1OHp0+fsvWntVT/sC4GrzBUOqdYRWZ8ps/Hbqae2APPnOb7+bN5mpyMmYUlo6Z8i4mp2SvXmyUmK34zc5XnM4+94pXqPrBzG5paWjRsqf7OGYVCQVpaGpbPDQ+ztLTk7p07ee4TGRmZZ/nIyLyH0e3ftxdDIyPq1iv4OlUUipgY0tLTsXxuDh5LCwvu3L2nlvc4euw48fHxtGzWRC31PS/22TCw59uOiZmZ8rwoqg49+/HzqhV8PbA3mlpaaGpo8Gm/L3D3yX8+rZeliM78u5s9t2CLmbk5CkXebSIuNpb09PRc+5iaW/Dg38y/m5uXF3r6+vyy9kc6dutJBrBl3Y+kp6cr3/NVxcUoADA2U70WmJiZEffs7/Ii6enp+G9ch5O7JzalVTM+/j70O/u2bORpcjIlbe3oO+abVxpW/7zoZ8fB3EL1fDQ3t8h3WGtsTAzp6em597Gw4P69uyrP7fttF+tWryQpKQl7Bwemzpmn0tlXVNqGJmhoaZEarzq3T2pcDPol8/7dEB14Em1DYzwGfoWGRmZnzKMTfxD+595XjudN0DW3JCVG9W+SEhONtqExmjq6aBuboqGlTcpz50yKIgpDu/wziYrK3MgAgJjEJyrPKxKTsHj22ov4uTtipK/LH5fVlwlZWO9q2y8u/+ERasVCMpeKoEOHDjx58gQXFxf69u3Lzp07i5SR4OTkpOxYApQ/0ABu3bpFSkqKSiaFmZkZnp6eL/Ue8+fPZ/Xq1Rw7dixXxxJkdi7llDOGoKAgHBwclB1LAD4+PpibmxMUlHlHrm7duhw7doy0tDSOHj1KvXr1lB1ODx484MaNG9SrVy/f97S1zZw08vmsibwEBQVRO8ekqZDZ0XP9+nXS0tIIDAxES0srV6ZUlsDAQD788MNXvkDWrFkz1+Os4wFw6NAhGjZsiL29PSYmJnz++edERkaSmJioLGNoaKjsWALV4x4REcGDBw9UMsye16dPH2XWSHh4OPv376dXr16F/gw1atRQuQtds2ZN5XHMUrVq1QLrCAoKombNmir11K5dm/j4eP7991/lc1WqVMlr95eS398+53F/WYGBgQUe41dV1HaeZcuWLZw/f57Nmzezd+9e5s2bp5a4goKC0NbWVvm7eHl55VrN8kXt2M/Pj7Jly7J+/Xogcwiso6MjdepkzsvSo0cPAgMD8fT0ZMiQIRw8eLDAuJKTk4mNjVXZkpPznzvg7yN/0r9Te+X2JuYDSE1NZcWcmZCRQfeBg4tcz4mjfzKw88fKLS319cbu7VueyQuX8tWseZSrVJnv5s5S/qgvipNH/+SLTz9RbmlpL//vX2GE3rzBob3+9Ppy2GvJmnkT9uzeTZOmTdU2J9ub5r9vPzWr+1FSTQs5nP7rKMO7dVZur6vtABw9sJfb14MZMOYrxs2cz0ef92TLmu+59s/FItd5/M8/6PVJW+X2us5dUzNzhoz7hvOnT9G7Qzv6dvyIhIQEnFzdinwunP/7L77p+7lyU8c1c9dPPxJ+/x6ffjEs12uVan3I0GlzGPDVZErY2LJx+cIC53J6kSOHA+jYqplySyvC9+6XUbdhIxat/IEZCxZjX9qBOdOmFDifzetk7OKJTYNW3Nu1gaAlU7j501LMvCtg07B1scTzrqnj7cKmIZ8rNy3NV//527CcO+dv/0t0wpMXF35F/+W2L949krlUBA4ODgQHB3Po0CECAgIYNGgQc+fO5ejRoy/VcfF8WQ0NDbVPdvjhhx+yd+9etm7dqsy2UWcMderUIS4ujvPnz/O///2PGTNmYGNjw6xZs6hQoQJ2dna4u7vn+55ZX5LU8bkNDAq+2/Ci1zU1NXNNwJaSkvJSMYSGhtKqVSsGDhzI9OnTsbS05NixY/Tu3ZunT58qJ4jO67hnvfeL4oTM4X/jxo3jxIkT/P333zg7O/Phh3mP1S+qwq6U9qbqUbfCHOdX8artPKtT18fHh7S0NPr168fIkSOVE6uHP7eaU3h4+AuzJQursO24T58+LF++nHHjxrF27Vp69uyp/KyVK1fm9u3b7N+/n0OHDtGxY0caNWqUa56mLDNnzlTJIgOYNGkSTTvnvbpWJb/quObobE99dq7GKKJVJn2OVSgo4+ySa/+XldWxFPkogrHTZr5S1lJFv+q4eOSOPfb52GPUE7uevj6lbO0oZWuHq6cX4wb25a9DB2n5Scci1VfRrzrOecUfo8h17F9l+N31q1eIi4lhTL+eyufS09PZuv5HDu35jdnfryly3ZCZzailpUXUc3d6o6KisLTMeyVAKyurPMtbWeWeHDvwwgXu3rnD1G+nv1Kc+TE3M0NLUzPX5N1R0dFYWr76imJhD8M5c/4Cs6ZMenHhQipf1Q8n9+yM4ZxtxyzHHfW4mBhKOxW97Tx9moz/zxvpN2oc5Spn3iixd3TifuhtDu3ZhVf5omWpVq5eI5/rjgKLHG0mRqHA0dk11/4AJqamaGpqKifvzhKriFZZCa585Sos/GEdcTExaGppYWRszKDPOmNdwEpuBfGpVJUyrtnfx7Jij4+JUcmajIuJwc7R6YX17frpR4ICzzPw6ymY53G+GBgaYmBoSEkbW8q4eTBpQE8unztNpZofFCl+v5q1VVa1yopfER2FZY6VOxWKaFxc8x5eZmpmhqamZq7sL0V0dK6MDiNjY4yMjbErXRpPbx8+/ag1J44do26DV7splZoYR0ZaGtrGpirPa5uYkRKX90pldk3aE3X+b+UcR0kP/0VLV48y7bvz8I89b31qxlNFFDpmqsdXx8yC1MR40lOekhKrICMtFR3z58qYW/I0nwzAl3H6xl1Cwh5l1/ts0m4zQwOVziFzQ31uR7z4/UqaGlHe0Y45v/3xyrEVxvvS9otL+lt+frxvJHOpiAwMDGjdujVLlizhyJEjnDhxgkvP5lzQ1dV95TtCLi4u6OjoKOeHgcwJtENCXm5sr5+fH/v372fGjBkvnfng7e3NvXv3uHcvO73+6tWrKBQKfHx8gMwv5+XLl2fZsmXo6Ojg5eVFnTp1uHDhAnv27Mk3i6govL29OX78uMpzx48fx8PDAy0tLXx9fUlPT+fo0aN57l++fHn++uuvfDuMSpYsqTKPUVpaWp6rb508eTLX46whRefOnSM9PZ358+dTo0YNPDw8ePDgQa46CmJiYoKTk5Ny4vW8WFlZ0a5dO9auXcu6devo2bNnvmXzcuqU6kSpJ0+exN3dHa0CVsl4nre3NydOnFDpkDt+/DgmJiaUfsFSqAXR0dHJdf7k97fPaodFqbN8+fIFHuO3SXp6OikpKcrOqZo1a+aKPSAgIFdWXV68vLxITU3l3LlzyueCg4NR5PihU9h2/Nlnn3Hnzh2WLFnC1atX6d69u8rrpqamdOrUidWrV7Nlyxa2b9+e64d5lvHjx91/2n4AAQAASURBVBMTE6OyjR8/Pt/PYWBoqOwwKWVrh51DGcwsLLiaIyvhSWIiN0OCcfX0zreewsjqWAoPe8DoqTMwNjV98U4FMDAoXOy3QoJVliZXl4xn7amo9POK39yCoBwTDT9JTOTW9VeLv2a9+kxesJRJ85coN3NLS5q2bc/wiVNfXMEL6Ojo4Onlxdkc/86mp6dz7sxZyj2bR/F5ZX19OXdWdbLjM6dPUTaP8nt2++Pp5YV7EYZfF4aOjg5eHh6cyTEXY3p6OmfOX8C3bOGujQXZc+AAFubm1K6Z/6TZL0vfwABrG1vlZlvaAVNzC4IvZc8d8yQxkdAbITi7v1ymdk5pqWmkpaXmyvLR0NQkI6PoN7MMDA2xsbNXbvZlHDG3sORKYPaS74mJCdwMvoa7V97XHW0dHZzd3LlyMXuf9PR0Ll8MxN0r99/NxMwMI2NjrlwMJDZGQeXqRft76BsYUKKUjXIrZV8aEzNzrl/Nni8s6Uki927dwNEt/zabkZHBrp9+5PK50/QbNxHLwszFkpEBZLxSxoWhoSF29qWVm4OjExaWlly8kN3+ExMSCAm6imc+3w10dHRw8/Dk4nPnzD8XzuFV0PeJZ6s/paa8+oq3GWlpJN4PxcQtx/tpaGDi5k3C3Rt57qOpo5vr5meGmm9Gv05x1y9j7quaxW5evhpxIZnfsTPSUom/FYx5uRwZ8xoamJeroizzKpJSUnmoiFNu9yIVRMcnUt4xe6oRA10d3G1LEvzgxRnmDcp5EJuYxLlb6hl+/CLvS9sX/w2SuVQE69atIy0tjerVq2NoaMjGjRsxMDBQzini5OTE//73Pzp37oyenh4lipBObmJiQvfu3Rk9ejSWlpZYW1szadIkNDU1XzolulatWuzbt4/mzZujra1d6NXmGjVqhK+vL127dmXRokWkpqYyaNAg6tatqzJkql69eixdupRPPslcEtXS0hJvb2+2bNmSayW1VzFy5EiqVavGtGnT6NSpEydOnGDZsmXKOWCcnJzo3r07vXr1YsmSJVSoUIE7d+4QERFBx44dGTx4MEuXLqVz586MHz8eMzMzTp48iZ+fH56enjRo0IARI0awd+9eXF1dWbBggcoP7izHjx9nzpw5tGvXjoCAAH799Vf27s0c9+7m5kZKSgpLly6ldevWhZrEOi+TJ09mwIABWFtb07x5c+Li4jh+/DhffvmlskyfPn1o1aoVaWlpuX7Uv8jdu3cZMWIE/fv35/z58yxdujTXynsvMmjQIBYtWsSXX37J4MGDCQ4OZtKkSYwYMUI531JRZHWs1a5dGz09PSwsLBg9ejQdO3akUqVKNGrUiN27d7Njxw7l/D5FqXPSpEk0bNgQV1dXOnfuTGpqKvv27WPs2LFFjl0dNm3ahI6ODr6+vujp6XH27FnGjx9Pp06dlNlQQ4cOpW7dusyfP5+WLVvyyy+/cPbsWVatWvXC+j09PWnWrBn9+/fnu+++U14TcmZyFbYdW1hY0L59e0aPHk2TJk1UOhUXLFiAra0tlSpVQlNTk19//RUbG5tcw++y6OnpvdLQIQ0NDZq0bsfurb9gY2tHiVKl2LF5AxaWVlSukd3pNnvCeKrUqEWjlpnDCZKePCE8LLvj7HF4OHdu3cTYxASrktakpqayfPYM7ty8wbAJk0lPT1Pe+TM2NkFbDfMQaGho0Lh1W/b8+gul7OwoaW3Dzs0bMLe0pHL17NjnTviKyjVq0jBH7BE5Y494yN1bNzF6FntyUhJ7ft1CRb/qmFlYEh8bwx/79xIdFUm12kXLHsgv/kat2rJ32xZK2dpTolQpdv28EXNLSyr5Zcc/b9JXVK5ekwYtcsT/MLtD/1FEOHdv38LI2BirktYYm5hibKLakaelpY2ZuQU29kXvwM6pU5dPmT51Cl7e3vj4lGXrL7+QlPSEls9W5Jw2eRIlSloz8IsvAOjYqTNfDOjPz5s2Uat2bQ4FHORaUBBjx3+lUm9CfDx/Hj7M4Bzz5r0OXTp8zNRZc/D28MTH25Nftu0gKSmJVs2aATB5xixKlizBF337AJnZuLefzSeVkprKo8ePCblxAwMDAxxyrGaXnp7OngO/07JpY7Rf4qbDy9LQ0KB+i9Yc2Pkr1rZ2WFlbs2fLZswsLKlQrbqy3OJpE6hQrQb1mmXOY5eU9IRHOdpOZEQE90JvYWRsgmWJkhgYGuLuU5adG9ejo6uLZUlrrl+9zOn/HaF9t5e7GfOi+Ju1bceuLT9jY29PyVI2bNu4HnNLK5UVKGd8NZaqNWvRpHVbAJq3a8/3C+fh7O6Bq4cnB37bSXJSEnUbZc9tdTTgd+wcymBqZsb1a0FsWPUdzdp+hF1ph1xxFDX2D5q24I/fdlCilC2WJa05uP0XTM0tKFs5e5L7VbOmUraKH7UbZ7apXet/5MLJY3QfNgZ9fQPl/Ez6hobo6OoSGRHOxVN/41GuAkYmpsRER/Lnnl3o6OjiVaGSWmLPir9N+0/YumkDdvalKWVjy6Z1P2JpVYIaOa5v34weQY3aH9CqXXsA2n7cgUVzZuLm6YmHpzf+O7aRlJREw2bNAXj44AF/HfmTSlWrYmZmzuPHj9j+y2b0dPWoUsDqhC8j4q+DOHbsQ+K/oST+e4uSHzRBU0ePyLOZK305duxDSqyCBwcyM31jggKx/rApTx7cIeHuLfRKWGPb5CNigi4qs5Y0dfXQs8ru6NOzLImBrQOpTxJyzWX0qjT1DDCwyb4G61vbYeToTmp8LMmR4Th2GYCeZQlClmcu8BJ2cBe2TT/Gqesgwv/cg1m5KpSs2YArs0Yr67i/ZwseX3xN/K1rxN24il2Ljmjp6RN+5PXMK7Xn/BU+qVGBsOgYwmPi6VK7MlHxTzh9I3v+ockdmnHqxh32X8iegkEDaFDOnT+v3MgzI8bc0ABzIwNszTP/7XIsYcGTpyk8josnPkk9HTTvctsX7z/pXCoCc3NzZs2axYgRI0hLS8PX15fdu3dj9Sw1cerUqfTv3x9XV1eSk5Nz3W0orAULFjBgwABatWqFqakpY8aM4d69e+jr6790XR988AF79+6lRYsWaGlpqXRS5EdDQ4PffvuNL7/8kjp16qCpqUmzZs1YunSpSrm6deuyaNEilbmV6tWrx8WLF3PNt/QqKleuzNatW5k4cSLTpk3D1taWqVOn0qNHD2WZ7777jq+++opBgwYRGRlJmTJl+OqrzC/9VlZW/PHHH4wePZq6deuipaVFxYoVlXP59OrVi4sXL9KtWze0tbUZPnx4npNFjxw5krNnzzJlyhRMTU1ZsGABTZs2BTInhF6wYAGzZ89m/Pjx1KlTh5kzZ9KtW7eX+qzdu3cnKSmJhQsXMmrUKEqUKKHsvMvSqFEjbG1tKVu27EtP9N6tWzeePHmCn58fWlpaDB06lH79+r1UHfb29uzbt4/Ro0dToUIFLC0t6d27N998881L1fO8+fPnM2LECFavXo29vT2hoaG0a9eOxYsXM2/ePIYOHYqzszNr164tdPvKq8569erx66+/Mm3aNGbNmoWpqalyvqDipK2tzezZswkJCSEjIwNHR0cGDx7M8OHDlWVq1arF5s2b+eabb/jqq69wd3dn165dlCtXuFVV1q5dS58+fahbty6lSpXi22+/VVkR8GXace/evdm8eXOuOb9MTEyYM2cO169fR0tLi2rVqrFv375X6nh8kRbtPyE5KYm1K5aSmBCPh3dZRk6aiq6urrJMxMMw4mJjlI9v37jO7G+yhwz/vGY1ALUbNKLv0BFER0Zy4XRmtuLEYarzLI39dhbeOVa5ehXNP8qMff2KpSQmJODu7cOIidPQyRV79rCJ0BvXmTMhO7vrlzWZKz7Wrt+Q3kMzO3nD7t/j+OzDxMfGYGRiirO7O+NnzFH7ymvNPvqY5OQkflqZHf+wCVNV4n/08KFq/DevM29idqfM1rWZ8deq35BeX2a399epUePGKBTR/LBqFVGRkbh7eDB/0WLlUIPw8HA0crRZ3/LlmTxtGqtWruT771ZQ2sGBmXPm4uKqOgTqUEAAGRkZNG7S9LXG37hBfRQxMaxat47IqGg8XF1ZNHsmVs+GxYVHRKicc48iI/m87wDl401bfmXTll+pXKE83y1aoHz+9LnzPAyPoHXz5q81foDGbT7iaXISm1et4EliAq6e3nwxfqJK23kc/pCEHEOG7t68weKp2des7T9lDpGsXrc+3QZlduj1HDoK/80bWLd0IYnx8ViWLEnrzl358Fknibq0+rgjyUlJ/Lh0ceZ1x6csY6dOV7nuhD937tasU4+4mBi2bfyJmOhoHF1cGDt1usqwuLD7/7Jl/Vri4+MoaV2Kth270PzZj0R1qdeyLU+Tk9m+9nuSEhNxcvei96ivVI59ZES4yrE/8Ufm/Hnfz5isUlfHvoOo+mE9tHV0uB18jWO/7+NJQjzGZuY4e3ozaOK3GKtxIQGA9p26kJSUxPKF80iIj8ennC+TZ81BVzf7RsXDB/eJjcm+5n9YvwExMQo2r1tLdHQULq5uTJ45B4tnQ4N0dHW5evkf/HdsIyE+DnMLC8r6VmD2kmWYW7z6cFOA6H9Oo21kgm2TduiYmPHkwV1urFmgnORb19xKZahb2B+7yQBsm7RH18yC1IQ4Yq4G8uD37coyhqWd8Oif/W9Z6dZdAIg8e4w7v/6olrizmLh64Tt5mfKxS/chAIQf2cf1FdPRtbBCr0T2oiPJj8K4Oms0zt2HYNeiA8mRj7i+cjaKi6eVZR6fOIyOqTllOvZB19yShNDrXJ4xkpQY9S3IkdPO05fQ09FmQJPaGOnpEnQ/gmnbfyclR5a7jbkJpgaqv7nKO9pR0tSYw/msEte0ohedamV3ok7vktkhvnT///jzSt6ZaUXxrrb94lDU3+GiaDQy5Ii/MxISErC3t2f+/Pn07t27uMP5T3JycmLYsGGFzv56neLj47G3t2ft2rW0b1/4L5z16tWjYsWKLFq06PUFJ/4zNmzYwPDhw3nw4IHKjyl1OXHtptrrfFNqerlyPEh9XybfpNrebvx15c2vgqMuH5Z157Ei5sUF31IlzM1QPHgzQy7UzdzOgUOBRV9sobg1qujN2euhxR1GkVV1d+K3U0WftLw4ta1egeB7YS8u+JbydLDl/Fj1Zca9aZVnr+VYx9ovLvgW+mDrcdrPe7V5+IrTjlG93vm2/7aasnV/sbzvpI6v/+bM20gyl95iFy5c4Nq1a/j5+RETE6Ncirxt27bFHJkoTunp6Tx+/Jj58+djbm5OmzbqX6JbiBdJTEwkLCyMWbNm0b9//9fSsSSEEEIIIURRSRrNmyUTer/l5s2bR4UKFWjUqBEJCQn89ddflChRgr/++gtjY+N8t3dR8+bN8/08M2bMKO7w3hp3796lVKlSbN68mTVr1qCtra3yWkHt4u7duwXU/Ppt2rQp39jKli371tT5Out/3e38TV0b5syZg5eXFzY2NgVOvC2EEEIIIYR4/0nm0lusUqVKKis65VS1alUCAwPfbECv2Q8//MCTJ0/yfM3S0jLP59+00NDQ4g4BJyenfMcP29nZFdgu7OzsOHLkyOsJrBDatGlD9erV83xNp4gTI7+OOl9n/a+7nb+pa8PkyZOZPHnya38fIYQQQgghiiIdSV16k6Rz6R1lYGCAm5tbcYehVvY5VqoRRaOtrf1WtwsTExNMTEze+jpfZ/2vu52/j9cGIYQQQgghxNtNhsUJIYQQQgghhBBCvOWioqLo2rUrpqammJub07t3b+Lj4wvcp169emhoaKhsAwYMUClz9+5dWrZsiaGhIdbW1owePZrU1NSXik0yl4QQQgghhBBCCPFeyW8qkXdZ165dCQsLIyAggJSUFHr27Em/fv3YvHlzgfv17dtXuUAYgKGhofL/09LSaNmyJTY2Nvz999+EhYXRrVs3dHR0XmpOWOlcEkIIIYQQQgghhHiLBQUFceDAAc6cOUPVqlUBWLp0KS1atGDevHnY2dnlu6+hoSE2NjZ5vnbw4EGuXr3KoUOHKFWqFBUrVmTatGmMHTuWyZMnF3pVaBkWJ4QQQgghhBBCiPdKekZGsWyvy4kTJzA3N1d2LAE0atQITU1NTp06VeC+mzZtokSJEpQrV47x48eTmJioUq+vry+lSpVSPte0aVNiY2O5cuVKoeOTzCUhhBBCCCGEEEIINUhOTiY5OVnlOT09PfT09F6p3ocPH2Jtba3ynLa2NpaWljx8+DDf/T799FMcHR2xs7Pjn3/+YezYsQQHB7Njxw5lvTk7lgDl44LqfZ5kLgkhhBBCCCGEEEKowcyZMzEzM1PZZs6cmW/5cePG5Zpw+/nt2rVrRY6nX79+NG3aFF9fX7p27cpPP/3Ezp07uXnzZpHrzItkLgkhhBBCCCGEEOK9UlzzeY8fP54RI0aoPFdQ1tLIkSPp0aNHgXW6uLhgY2NDRESEyvOpqalERUXlO59SXqpXrw7AjRs3cHV1xcbGhtOnT6uUCQ8PB3ipeqVzSQghhBBCCCGEEEINXnYIXMmSJSlZsuQLy9WsWROFQsG5c+eoUqUKAH/88Qfp6enKDqPCCAwMBMDW1lZZ7/Tp04mIiFAOuwsICMDU1BQfH59C1yvD4oQQQgghhBBCCPFeycjIKJbtdfH29qZZs2b07duX06dPc/z4cQYPHkznzp2VK8Xdv38fLy8vZSbSzZs3mTZtGufOnSM0NBR/f3+6detGnTp1KF++PABNmjTBx8eHzz//nIsXL/L777/zzTff8MUXX7xUJ5l0LgkhhBBCCCGEEEK85TZt2oSXlxcNGzakRYsWfPDBB6xatUr5ekpKCsHBwcrV4HR1dTl06BBNmjTBy8uLkSNH8vHHH7N7927lPlpaWuzZswctLS1q1qzJZ599Rrdu3Zg6depLxSbD4oQQQgghhBBCCPFeSS+uSZdeI0tLSzZv3pzv605OTirZUw4ODhw9evSF9To6OrJv375Xik0yl4QQQgghhBBCCCFEkUnnkhBCCCGEEEIIIYQoMhkWJ4QQQgghhBBCiPfK65xcW+SmkSFHXAghhBBCCCGEEO+RMRt+K5b3nfN522J53+ImmUtCCCHeWncjHhd3CEVWxroEd8IfFXcYReJYquQ7f+zf9fiv3Llf3GEUSVlHe+JiFMUdRpGZmJnzWBFT3GEUWQlzM8IDTxV3GEVSqmL1d77tDF27o7jDKLLFPdvTft6a4g6jSHaM6sWxjrWLO4wi+2DrcUIfvpvfFwCcbEoWdwj5kjSaN0vmXBJCCCGEEEIIIYQQRSadS0IIIYQQQgghhBCiyGRYnBBCCCGEEEIIId4r6TIu7o2SzCUhhBBCCCGEEEIIUWSSuSSEEEIIIYQQQoj3SgaSufQmSeaSEEIIIYQQQgghhCgy6VwSQgghhBBCCCGEEEUmw+KEEEIIIYQQQgjxXpEJvd8syVwSQgghhBBCCCGEEEUmmUtCCCGEEEIIIYR4r0ji0pslmUtCCCGEEEIIIYQQosgkc0kIIYQQQgghhBDvlQxJXXqjJHNJCCGEEEIIIYQQQhSZdC4JIYQQQgghhBBCiCKTYXFCCCGEEEIIIYR4r6TLsLg36qUyl+rVq8ewYcNeUyjqeY9169Zhbm6utniKYvLkyVSsWLFYY3gfODk5sWjRotf6Hm+iTb9NevToQbt27V7re7zKMX0T8RWHN9GWX4Vcs4QQQgghhBCv4p3OXHJycmLYsGEqP2Q7depEixYtii8oNQkNDcXZ2ZkLF/7P3l2HRZX9cRx/gyIlbSPSBoqda67duu6auHav3f5cu1173bVFXHvtLhQDu7AQEEUMUCQMECXm9wfL6EiK6DD6fT3PPI/cuffymcO5450zJ65+tx/6Ll68iKGhobpjiK9o4cKFmTrxnpaWFjt27MjUBquMnPNT6nLv3r05evQoT548IWfOnPzwww/MmjWLokWLKvcJDAykb9++HD9+nJw5c9K5c2dmzJhB9uwa/Zb+WXZt38a/GzcQFhaGvb0Dvw0eQlEnpxT3P3H8GG4rVxAcHIxlwYL06NOXSlV+ACA2NhbXFcu5cO4swU+eYGBoSNnyFejepw+5cuX+Ivl3b9/Gv5s2EhYWhp29Pb8NSj3/yePHWLNqJU+Dg7G0TMhfsUoVZf41K5Zz4dw5goKeYGhoSNny5eneuy8WuXJ9kfyaXP6anB0SJivdtHYNRw7sI+r1a4oWL0GvgYMpYFkw1eMO7N7Jzn83ExEWho2dPT1+G4Bj0WLK58cNH8Kt614qx9Rv0ow+g4Zkav4t//7LP+vWExoaiqOjIyOGD6NE8eLJ7uvvf4+ly5dx544PQUFBDB0ymA7t26vsc+XKVf5Ztw7vO3d4/vw5c2bPplatmpmaOdG2f/9lw/p1hIWG4uDoyJBhw3FKITvAMfejrFi2jOCgIApaWdH3t/78ULWq8vmqlSome1y//gNw+fXXTM+//dBRNu3ZT1jEC+ytrRjU9VecHOyT3ffE+Yus27mHx8HPiI2LpWC+fLRt2ogGNd7nVygUrP53O3vcPXgdGYVzEUeG9uiCVf58mZ4dNLvuJGpUphhVCtuin0OH+89C+ffsVUJeRqa4f8PSxWhUppjKtqcRr5i+4wgA5jkNmNC6YbLHuh4/z7WAx5mWvV3VMtRzLoKBbg7uPHnG8iNnCIp4meL+S3u2Jo+JUZLtB656s8L9LAD1ShahejE77PJYYKCbg45/riPq7btMy2xcrBQFm3fA0LYouua5uP3HaMIunkr1GBOnMth2GoCBlS1vQ5/xcJsbz07sV9knf4NWWDbrQA5TcyIf3MV/9Xxe+3tnWu6P7d6xja0f3DP0GzSEosVSv2dwW/3+nqF7n75UrFxF+fzpkyfYt2snfr4+vHr5kr9XumLv6PjF8n9NMqH31/XNzbmkr69Pnjx51B1DfIZ37xL+E8mdOzcGBgaffR6hOUxMTNTe8/BL+JS6XK5cOVxdXfH29ubQoUMoFArq169PXFwcAHFxcTRp0oR3795x5swZ3NzcWLNmDePHj/+SLyFL83A/yrLFf9KxSzeWrFyNnYMDY4YNJTw8PNn9b924wfRJE2nYpClLVrlStXp1Jv5vDPfv3QPgbXQ0d3196Ni5C3+vWs2EadN5FBjI+NGjvlB+d5b9tZiOXbry98pV2Dk48L/haeSfPCkh/8rV/FC9OhPHqub38/PFpXNn/l65mglTp/EwMJDxY75Ufs0tf03OnmjHlk3s27mdPgOHMHPRX+jq6TFlzKhU/w887XEc12VLaNOxE3P+XoaNnT2T/zeKiI9ed71GTVi1aavy0alHr0zNfvjIEeYvWEjPHt1Zt9aNwo4ODBg4iLCwsGT3j34bTUFLS/r/1g8LC4tk93kT/QZHR0dGjRiRqVk/dvTIEf5cuIBu3Xuw2m0tDg6ODB00kPAUst+4fp2J48bRtFlzXNf+Q/UaNRkzcgT3/P2V++zev1/l8b/fx6GlpUWt2rUzPb/7mXP8tXYDXX5uycqZk3GwLsTw6X8Q/iL5xgHjnDn59afm/D1lHK6zp9GoVnVmLlnBhWvXlfts2L2PbQeOMKxHF5ZNm4Ceni7Dp//B2y9wP6bJdSdRHefC1Chmz5azV5m/9zjvYmPpU78a2bOl/vEsKPwFv2/ap3ws3H9C+Vx4ZJTKc79v2sf+K7eJjonh9qPgTMv+U0VnmpRxYumRM4xev4e3MTGM+6UBOtmypXjMyHV76Pb3RuVj4paDAJzxva/cRzd7Nq7ef8y289dTOs1nyaarz+uAu9xbNTdd++vmzo/T6D94cesKV0d24cm+LTj2GYVpqfcNwbmq1MG20wACt67m6qhuRD64S4mx89AxNv0ir8HjmDvL/1qMS+eu/LViFXb2DowdPjTJ+3eiWzdvMGPKJBo2bsrfKxLuGSaNHUPAf/9vAUS/eUNx55J07933i2QW348MNy69ffuW4cOHY2lpiaGhIZUqVcLDw0P5fGhoKO3bt8fS0hIDAwOcnZ3ZuHGjyjkiIyPp1KkTOXPmJH/+/Mydm74LHRKG3jx48IAhQ4agpaWFlpYWkHRYXOJwj9WrV1OoUCFy5sxJv379iIuLY/bs2eTLl488efIwbdo0lfNHRETQo0cPcufOjbGxMbVr18bLS/UbvLQsW7YMKysrDAwMaNOmDS9evFB5fuXKlRQrVgw9PT2KFi3K33//rXzO1tYWgDJlyiTcWNSqxc2bN9HW1iYkJASAsLAwtLW1adeunfK4qVOnUq1aNeXPN2/epFGjRuTMmZO8efPy66+/8vz5c+Xz8fHxzJgxA1tbW/T19SlVqhRbt25VPu/h4YGWlhbu7u6UL18eAwMDfvjhB3x8fNJVBonln1pZJA6FmjZtGgUKFKBIkSJA0qFEgYGBtGjRgpw5c2JsbEybNm14+vRpkt+1cuVKbG1t0dPTS1fGD+3btw8TExPWr1+vkm3OnDnkz58fCwsLfvvtN2JiYpTHhIeH06lTJ8zMzDAwMKBRo0b4+fkBCa3luXPnVinT0qVLkz9/fuXPp0+fRldXl6ioKCChZ8zKlSv56aefMDAwwNHRkd27d6f7Ndy6dYumTZtibGyMkZER1atXx/+Dm1cg1deT1rUN4OnpSa1atTAwMMDMzIwGDRqk+GHs4zJNzcfD4mrVqsXAgQMZOXIk5ubm5MuXj4kTJ6arHGxsbAD46aef0NLSUv4MsGTJEuzt7cmRIwdFihThn3/++exz7tmzhwoVKqCnp0euXLn46aefVI5L77C4Xr16UaNGDWxsbChbtixTp07l4cOHBAQEAHD48GFu377NunXrKF26NI0aNWLKlCn89ddf6W5QnTlzJnnz5sXIyIju3bsTHR2t8vzFixepV68euXLlwsTEhJo1a3LlyhXl8926daNp06Yqx8TExJAnTx5WrVoFwNatW3F2dkZfXx8LCwvq1q1LZGTK38Z+jm2bN9OoWTMaNmmCta0tg4aPQFdPl0P79ia7/46tW6hQsRJtOrhgbWNDlx69cChcmF3bE65Tw5w5mTV/ITVr18GqkDVOxUvQf8hQ/Hx8ePY0827Olfm3bKJR02Y0aNwEaxtbBg0bga6eXor5d279NyF/+w4UsrGhS4+eOBQuzO7t297nn7fgv/yFKFa8BP0Hf8H8Glz+mpwdEv6P2btjG7906EjFH6piY2fPwJGjCQt9zgXP0yket2fbv9Rr1Jg6DRphZW1D70FD0NXV5dihAyr75dDTxczcXPkwyOTexOs3bKRlyxY0b9YMOzs7xowejZ6eHrv37El2/+JOTgwaOJAG9euTI0eOZPep+sMP9Ovbhx9/rJWpWT+2eeMGmrVoSZNmzbC1s2PE6NHo6umxN4XsWzZvolLlyrj8+is2trb06tOHwkWKsvXfLcp9LCxyqTxOnTxB2XLlsLS0zPT8W/YdpGmdWjT+sQY2BS0Z1qMLejl02Xf8RLL7lylejBoVy2NT0BLLfHlp3bgBdoWsuO7jCyTUxX/3H+LXVs2pXqEc9taFGPtbb0LDIzh98Uqy5/wcmlx3EtV0cuDwdR9uBgbxJPwl605ewkRfD+dCBVI9Li5ewas3b5WPyA969igUqDz36s1bSloX4Nr9x7yLjcu07E3LFmfrOS8u+gfy4Hk4i/afxDynPhUdCqV4zMs30UREvVE+yttbERT+klsP37837r1ymx0XruMb9CzTsn4o/No5AjevIPTiyXTtn79+S6KfBXH/n8W8efyAoEPbeH7OA8smbZX7WDZtS7D7Hp557OfN4wDurviDuHdvyftj01TOnHHbt2yi4Qf3DAMT7xn2p3zPUL5iJVr/d8/QuXvCPcOuHduU+9Rt0JCOXbpSplz5L5JZfD8y3LjUv39/zp49y6ZNm7h+/TqtW7emYcOGyg/V0dHRlCtXjn379nHz5k169erFr7/+yoULF5TnGDFiBCdOnGDXrl0cPnwYDw8PlQ8wqdm+fTsFCxZk8uTJBAUFERQUlOK+/v7+HDhwgIMHD7Jx40ZWrVpFkyZNePToESdOnGDWrFn8/vvvnD9/XnlM69atefbsGQcOHODy5cuULVuWOnXqpPiNyMfu3r3Lli1b2LNnDwcPHuTq1av069dP+fz69esZP34806ZNw9vbm+nTpzNu3Djc3NwAlOV09OhRgoKC2L59O8WLF8fCwoITJxL+4z916pTKzwAnTpygVq1aQEIDWe3atSlTpgyXLl3i4MGDPH36lDZt2ij3nzFjBmvXrmXp0qXcunWLIUOG0LFjR5VzAowdO5a5c+dy6dIlsmfPTrdu3dJVDukpCwB3d3d8fHw4cuQIe/cmfXOMj4+nRYsWhIWFceLECY4cOcK9e/do27atyn53795l27ZtbN++nWvXrqU7I8CGDRto374969evx8XFRbn9+PHj+Pv7c/z4cWUvkTVr1iif79KlC5cuXWL37t2cPXsWhUJB48aNiYmJQUtLixo1aigbZ8LDw/H29ubNmzfcuXMHSPibVahQQaVny6RJk2jTpg3Xr1+ncePGuLi4pKvuPX78mBo1aiR8SDh2jMuXL9OtWzdiY2PT/XrSuravXbtGnTp1cHJy4uzZs5w+fZpmzZope9akp0w/hZubG4aGhpw/f57Zs2czefJkjhw5kuZxFy9eBMDV1ZWgoCDlzzt27GDQoEEMGzaMmzdv0rt3b7p27crx48czfM59+/bx008/0bhxY65evYq7uzsVKyY/vOFTREZG4urqiq2tLVZWVgCcPXsWZ2dn8ubNq9yvQYMGvHz5klu3bqV5zi1btjBx4kSmT5/OpUuXyJ8/v0rDNsCrV6/o3Lkzp0+f5ty5czg6OtK4cWNevXoFQI8ePTh48KDK++7evXuJioqibdu2BAUF0b59e7p164a3tzceHh60atXqi3RNjomJwdfXh7LlKii3aWtrU7Z8eW7fupnsMbdv3qJsedUbqPIVK+F9M+Xyi4x8jZaWFoY5k3bp/xwxMTH4+fpS5oM82tralClXHu8U/p63b91McgNYvmIlvFN4vfBl82tq+Wty9kRPg4OICAujVNlyym2GhjlxLFoMH+/byR4TExODv58vJcu8P0ZbW5uSZcolOebUMXc6/9KSQT27sW7VCt5+1BD9OWJiYrhz5w6VKrx/r9TW1qZihQpcv3Ej037PlxATE4PPnTtUqKhad8pXqMDNFLLfunGD8hVU/1+oVLkyt1LYPyw0lDOenjRt3jzzgv8nJjYW33sBlHd+P4RMW1ubcs5O3PK7m+bxCoWCyzdu8TAoiFLFEoZsBz0LISzihco5cxoYUMzBjpvpOOcn5dfgupPIIqcBJgZ6+D5534gSHRPLg+dh2OYxT/XY3MY5mdy2EeN+acCvNcpjZqif4r4FLUwpaGHKWb+AzIpOXhMjzHIa4PXgiXJb1LsY/IJCKFIgfaNHsmtrU6OYPcdu+mZari/ByLEEETcuqWwL9zqPUeESAGhly05OuyJE3Lj4fgeFgogbl5T7ZKbEe4ay5ZLeM9xO4Z7BO5l7hnIVUr9n+JbEK9Tz+F5laIKOwMBAXF1dCQwMpECBhNb14cOHc/DgQVxdXZk+fTqWlpYMHz5cecyAAQM4dOgQW7ZsoWLFirx+/ZpVq1axbt066tSpAyR8iCxYMPU5AhKZm5uTLVs2jIyMyJcv9bHc8fHxrF69GiMjI5ycnPjxxx/x8fFh//79aGtrU6RIEWbNmsXx48epVKkSp0+f5sKFCzx79gxdXV0goafHzp072bp1K716pd0tPDo6mrVr1yq/bfrzzz9p0qQJc+fOJV++fEyYMIG5c+fSqlUrIKGn0u3bt1m2bBmdO3cmd+6EuRksLCxUXl9iQ8Uvv/yCh4cHXbt2ZeXKldy5cwd7e3vOnDnDyJEjAVi8eDFlypRh+vTpyuNXr16NlZUVvr6+WFtbM336dI4ePUqV/+bqsLOz4/Tp0yxbtoyaNd+PM582bZry59GjR9OkSROio6PT1TsorbIAMDQ0ZOXKlSl+m+Tu7s6NGze4f/++8kP22rVrKV68OBcvXqRChYQbvHfv3rF27Vpl+aXXX3/9xdixY9mzZ4/K6wYwMzNj8eLFZMuWjaJFi9KkSRPc3d3p2bMnfn5+7N69G09PT374IWHOjfXr12NlZcXOnTtp3bo1tWrVYtmyZQCcPHmSMmXKkC9fPjw8PChatCgeHh5JfmeXLl1o/99cANOnT2fRokVcuHCBhg2TH0f/4eswMTFh06ZN6OjoAFC4cOF0v570XNuzZ8+mfPnyKg0SxZOZ4yC1Mv0UJUuWZMKECQA4OjqyePFi3N3dqVevXqrHJdYBU1NTlWtozpw5dOnSRdnAOXToUM6dO8ecOXP48ccfM3TOadOm0a5dOyZNmqTcVqpUqU94lar+/vtvRo4cSWRkJEWKFOHIkSPKayM4OFilYQlQ/hwcnHbPiAULFtC9e3e6d+8OJPR2PHr0qErvpdofDcFYvnw5pqamnDhxgqZNm/LDDz8oe3wlvt+4urrSunVrcubMia+vL7GxsbRq1Qpra2sAnJ2dM1gaqXvxIoL4uDjMzFVvxs3MzHn4IDDZY8LDQjH9eH9zc8LCQpPd/93bt6xcsoQf69bN9HngXr54kZDfLGmeh4EPkj0mPCwMM3MzlW2mZmYpNkC/e/uWlUuXUKtO5ufX5PLX5OyJIv77m5uYJq0P4eHJ14dXL18QHx+PqVnSYx4/fP+6q/9Yh9x582JuYUHAvXv8s2o5jx89ZNSEyZmTPSKCuLg4zD8qT3NzcwIeJF/3s4rUsgemkD00NDTZ/UNDk/87Hdi/DwNDQ2rWSv3/pYx48fIVcfHxmJkYq+YxMSHwScpf1r6OiuLnPoN4FxtLNm1thnTvRIWSCR+gQyMSeqSbmZgkOWdYRESm5tfkupPIyCDh/vnVm7cq21+9eYuRfsr31g9Cwthw+jLPXrzCWF+PhmWKMbBxTWbuOMrbD75ITFTF0YbgiJcEPEvfl+PpYfpfY9aLqDcq2yOiolNt6PpQRUdrDPVycOymX6bl+hJymJoT80K17GJehJPdICfaOjnIntMYrWzZiYn4aJ+IMAwKpNyLK6MS7xlMP75nMEvjnuGj93szM7MUh/AK8Tky1Lh048YN4uLiknxoffv2rXIcc1xcHNOnT2fLli08fvyYd+/e8fbtW2XvDH9/f969e0elSpWUx5ubmyuHRGUmGxsbjIzef2OYN29esmXLhra2tsq2Z88Svj3w8vLi9evXScZkv3nzJsnwopQUKlRIpRtzlSpViI+Px8fHByMjI/z9/enevTs9e/ZU7hMbG4vJR/8pf6xmzZosX74cSOjxMn36dHx9ffHw8CAsLIyYmBiq/jc5pJeXl3LC34/5+/sTExNDVFRUkg/p7969o0yZMirbSpYsqfx34pCuZ8+eUahQ2m+cqZVF4gd0Z2fnFBuWALy9vbGyslI2LAE4OTlhamqKt7e3snHJ2tr6kxuWtm7dyrNnz/D09FSe50PFixcn2wdjyPPnz8+N/74Z8/b2Jnv27Cr12MLCgiJFiuDtnTCRX82aNRk0aBAhISHKnmWJjUvdu3dXaRBM9GF5GxoaYmxsrKyfqbl27RrVq1dXNiwlJ7XXk55r+9q1a7Ru3TrVHGmV6af4sCwS86anLFLi7e2dpIG4atWqLFy4MMPnvHbtmsq1/LlcXFyoV68eQUFBzJkzhzZt2uDp6ZmhoZ4f8/b2pk+fPirbqlSpotJz6+nTp/z+++94eHjw7Nkz4uLiiIqKIjDw/QfPHj16sHz5ckaOHMnTp085cOAAx44dAxIa1urUqYOzszMNGjSgfv36/PLLL0lubj709u1b3r5VvclObNxXp9jYWKZMGIdCoWDgsK8zD0dmio2NZeqE8aCAgcOGp31AFqPJ5f8lsp9wP8qyhfOUP4+dOiNTzpuc+k3eD+mwtrXD3NycCaOGE/zkMfkKZP4wLaFq75491G/QIEu8DyYy0NNj1eypvImO5vKN2/y1diMF8uShTPFiaR/8nStnZ0XbH97fWy87ciZD5/F+/H46iCfhL3nwPJwJrRtSxtaSc36qjQs62bQpa1eQw153Mhb6PzWK2dG73vuJ26dtT7v3eFrqlHDkyv1HhEe+SXtnIT6DTOj9dWWocen169dky5aNy5cvq3xIBZQNGX/88QcLFy5kwYIFODs7Y2hoyODBg9UyyfLHH7S1tLSS3RYfHw8kvL78+fMnmWcGyJTJhl+/fg3AihUrVBolgCTl+bHEZd79/Py4ffs21apV486dO3h4eBAeHq6cFynx9zRr1oxZs2YlOU/+/Pm5eTOhO+S+ffuSjOf/+Gbmw/JKnN8qsbwyQ2Z9o5uR85QpU4YrV66wevVqypcvr3x9iVKrK+nh7OyMubk5J06c4MSJE0ybNo18+fIxa9YsLl68SExMjLLX0+f+Tn39tL8xSqvup3Vtp+d3pFWmn+Jzy/9rSE+ZfAoTExNMTExwdHSkcuXKmJmZsWPHDtq3b0++fPlUhhcDyrnH0urFmV6dO3cmNDSUhQsXYm1tja6uLlWqVFF5/+7UqROjR4/m7NmznDlzBltbW6pXrw4kvI8dOXKEM2fOcPjwYf7880/Gjh3L+fPnlfPJfWzGjBkqPb8AJkyYQLd+/VPNamJiina2bEm+gQsPD8PMIvmhBWbmFsoeH8r9w8IwN1f9QiE2Npap48fxLPgpfyxc9EV6nhibmCTkD087TyIzc3PCw1TnOIsID0/yLX5Cw9I4nj0NZvaCL5Nfk8tfE7NXrPIDhT9Y0S0mJuGafBERjvkHX4hFhIdja++Q7DmMjE3Q1tZOMvlrRHh4kl5ZH0pcSS7oyZNMaVwyNTUlW7ZsSXrchYWFYZFC+WcVqWVP6bq1sLBI92u9dvUqgQ8eMHnqtCTPZQYTYyOyaWsnmbw77MULzE1T/pJTW1ubgvkSeso62ljz4PET1u3cQ5nixbD477jwFy/IZWaqck4HG+tMza+JdedmYBAPQt7nTZy020hfl5dv3vccNtLX5XHYiyTHp+TNuxhCXrwml3HSL5JL2ViSI3t2LtxNvidmel24G4hvUIjy58RJu00M9FUah0wN9Lifjh5SuY0NKWldgNm7jn1Wrq/hXUQYOiaqdUrHxIzYqNfEx7wj5mUEirhYdEw/2sfUnHcRmd8zKPGeIeLje4bwMMxSu2f46P0+PDw8Sa9dITJDhuZcKlOmDHFxcTx79gwHBweVR+KHG09PT1q0aEHHjh0pVaoUdnZ2+Pq+H1drb2+Pjo6OyjxH4eHhKvukJUeOHMnO8/K5ypYtS3BwMNmzZ0/y+nKlcxnnwMBAnjx5Pxb53LlzyiF4efPmpUCBAty7dy/J+RM/eCX24vn49Tk7O2NmZsbUqVMpXbo0OXPmpFatWpw4cQIPDw/lfEuJr+PWrVvY2Ngk+T2GhoY4OTmhq6tLYGBgkuc/7CH0uVIri/QqVqwYDx8+5OHDh8ptt2/fJiIiAqdUloxOD3t7e44fP86uXbsYMGDAJx1brFgxYmNjVepxaGgoPj4+ylxaWlpUr16dXbt2cevWLapVq0bJkiV5+/Yty5Yto3z58pn2waNkyZKcOnVKZYLuT5Gea7tkyZK4u7unep7PKdPMpKOjk+QaKlasGJ6enirbPD09012PkjtnesokoxQKBQqFQtmrp0qVKty4cUOl99aRI0cwNjZO12soVqyYSn2FhGvyQ56engwcOJDGjRtTvHhxdHV1VRYCgIQPSi1btsTV1ZU1a9bQtWtXlee1tLSoWrUqkyZN4urVq+TIkYMdO3akmGvMmDG8ePFC5TFmzJg0X4+Ojg6FCxfh6uX3cyLEx8dz9fJlnIonP9+BU4niXL18WWXblUsXKVbi/fDOxMaBx48eMmv+AozT6FWaUTo6OjgWLsy1D/LEx8dz7cpliqWwpLZT8RJcvaI6B8SVixcp9sHrTWxYevzoETO/cH5NLX9NzK5vYEB+S0vlw8raBlNzc65ffT9fZVRkJH53vCmSwrLUOjo62DsW5vq198fEx8dz/dqVFI8BuH8voed2Zn0g0dHRoWjRoly4+H6ukvj4eC5eukjJLzSMNrPo6OhQpGhRLn2U/fLFS5RIIXtxZ2cuX7qosu3ihfMUT2b/vXt2U6RoURw/6kWcWXSyZ6ewnQ2Xb7yfoyU+Pp4rN29T3DH5RsnkKBTxxPw3FCt/ntyYm5pw+cb7ebsio97gffceJT7hnOmhiXXnbWwsz19FKh/BEa94ERVN4fzve9vr6mTHOpd5uhpoEuXIng0LY0NeRiWdD62yow03HwapTPidEdExsQRHvFI+HoZGEP46ipLW7yce18+hg2P+3Pg8Sbtnee0ShXkZFc3lew/T3FfdXvndxNS5nMo205IVeOWb8AW9Ii6W1/d8MC3xwZxGWlqYliin3CczJd4zXE3mnsEphXuGYsVLcO3yR/cMl1TvGb5liffRX/vxvcpQ41LhwoVxcXGhU6dObN++nfv373PhwgVmzJjBvn37gIS5URK/ufb29qZ3794qK3vlzJmT7t27M2LECI4dO8bNmzfp0qWLylC1tNjY2HDy5EkeP36c5IPP56hbty5VqlShZcuWHD58mICAAM6cOcPYsWO5dOlS2icA9PT06Ny5M15eXpw6dYqBAwfSpk0b5Qf0SZMmMWPGDBYtWoSvry83btzA1dWVefMSurvnyZMHfX195STciaurJU4QvX79emVDUmJDhbu7u8rcNr/99hthYWG0b9+eixcv4u/vz6FDh+jatStxcXEYGRkxfPhwhgwZgpubG/7+/ly5coU///xTObF4ZkirLNKjbt26ODs74+LiwpUrV7hw4QKdOnWiZs2alC//+SsbFC5cmOPHj7Nt2zYGDx6c7uMcHR1p0aIFPXv25PTp03h5edGxY0csLS1p0aKFcr9atWqxceNGZYOgtra28u/4OfMRfax///68fPmSdu3acenSJfz8/Pjnn3/Svbpfeq7tMWPGcPHiRfr168f169e5c+cOS5YsSXINZrRMM5ONjQ3u7u4EBwcrv7UZMWIEa9asYcmSJfj5+TFv3jy2b9+uMkfcp55zwoQJbNy4kQkTJuDt7c2NGzeS7TGYlnv37jFjxgwuX75MYGAgZ86coXXr1ujr69O4cWMA6tevj5OTE7/++iteXl4cOnSI33//nd9++y1dwycGDRrE6tWrcXV1xdfXlwkTJiSZCNzR0ZF//vkHb29vzp8/j4uLS7K9s3r06IGbmxve3t507txZuf38+fPKCcMDAwPZvn07ISEhFCuW8tAJXV1djI2NVR7pHQ7yc9u27N+7h8MH9vMgIIBFc+cQ/SaaBo2bADBr6hRWLV2i3P+nX9pw8fw5/t20kcAHD1i7ehW+d+7QotUvQELjwORxY/H1ucPo8ROIj48nLDSUsNDQDDfcppq/Tbv/8h8gUJn/jTL/7GlTWLVsqXL/lr+05tL582z9ML/PHZq3+lmZf8q43/G948PoceOJj/vC+TW4/DU5OyTcEzT96We2bljHhbOePLh/j0WzZ2JukYuKVd+vHDth5DD273rfuNvs59Yc3b+P44cP8SjwAcsWLeBtdDS1GyTM6xf85DFb1v2Dv68vz4KDuXDWk0WzZ+DkXBIbO/tMy+/SoT07d+1i79593L9/nxmzZvHmTTTN/luNcvyEiSz+6y/l/jExMfj4+uLj60tMTAwhISH4+PqqfPEUFRWl3Afg8ZMn+Pj6pmtOuk/Rtn0H9uzaxf59ewm4f585s2YRHf2GJv9lnzJxAks+yN6mbTvOnT3LxvXreRAQwKoVy7nj7c0vrduonDfy9WuOu7vT7IN7iC+hTZOG7D12ggMnThHw6DFzV7rx5u1bGteqAcC0xctYtuH9Snbrduzh4vWbPHn6jIBHj9m05wCHTp2hfrWEntdaWlq0btyAtTt2cfrSFfwDHzLtr2VYmJlSrULZTM+vyXUn0Ynbd6lfqiglrPKT38yYjtXL8+JNNDcC338h+1uDalQvZqf8uUWFEtjnzYV5TgNs8pjTo3blhAnWP2qoyWVkiH2+XJz1Dfgi2fdeucUvlUtRwd6KQrnMGNioBmGv36j0kprYuiGNyqj+v68F1C7hyPFbd4lP5gO4qYE+NrnNyW+aMB+YdS4zbHKbk1Mv5akzPoW2rj6G1o4YWjsCoJenAIbWjuhaJPTIs27fh8K//a7cP+jwTvTyFMDGpR/6BQqRr/5P5K5Sm8f7Niv3ebx3M/nqNCNPzUboW1pj32M42XT1eOqxL1Myf6xVm3Yc2LeHIwcT7hn+nJdwz1C/0ft7htXLP7pnuHCerZsT/t/6x3UVfj53aPHTz8p9Xr58ib+fH4EPAgB4+DAQfz8/wkKTn09QiJRkaFgcJEzeOnXqVIYNG8bjx4/JlSsXlStXVi5P/fvvv3Pv3j0aNGiAgYEBvXr1omXLlipL0P/xxx/KoVtGRkYMGzZM5fm0TJ48md69e2Nvb8/bt28zrZVQS0uL/fv3M3bsWLp27UpISAj58uWjRo0aSSbSTYmDgwOtWrWicePGhIWF0bRpU5UJkHv06IGBgQF//PEHI0aMwNDQEGdnZ+WH8OzZs7No0SImT57M+PHjqV69unKYXs2aNdm5c6eycSmxoWLfvn3K+ZYAChQogKenJ6NGjaJ+/fq8ffsWa2trGjZsqGzEmzJlCrlz52bGjBncu3cPU1NTypYty//+97/PL8h0lkV6aGlpKXvB1KhRA21tbRo2bMiff/6ZaTmLFCnCsWPHqFWrFtmyZWPu3LnpOs7V1ZVBgwbRtGlT3r17R40aNdi/f7/KcK6aNWsSFxen0rOsVq1a7Nq1S2Xb57KwsODYsWOMGDGCmjVrki1bNkqXLq1SL9LzelK7tgsXLszhw4f53//+R8WKFdHX16dSpUrKCcg/lNEyzSxz585l6NChrFixAktLSwICAmjZsiULFy5kzpw5DBo0CFtbW1xdXdP9d0junLVq1eLff/9lypQpzJw5E2NjY2rUqPHJefX09Dh16hQLFiwgPDycvHnzUqNGDc6cOUOePAkrsGTLlo29e/fSt29fqlSpgqGhIZ07d2by5PRNstu2bVv8/f0ZOXIk0dHR/Pzzz/Tt25dDhw4p91m1ahW9evWibNmyWFlZMX369GQb3+rWrUv+/PkpXry4cgJ4AGNjY06ePMmCBQt4+fIl1tbWzJ07l0aNGn1ymaRHrTp1iYiIwG3VSsLDwrB3cGT6nLnKHhbPnj5VGZpZ3NmZMRMmsmbFclyXL8OyYEEmTp+BrV3CDfzzkBDOnk5Yxr1P1y4qv2vOoj8pVSZzPyjVqlOHFxERrF2dkN/OwYFpSfK//+KluLMzY8ZPYM3KFbiuWE6BggWZOO2j/P8tQ9+3m2qPsj8WLvoC+TW3/DU5e6Kf2rTjbXQ0SxfMI/L1a4qVcGbc9Jkq8xgGBz3h5Qf3V9Vq/cjLFxFsXOuaMITOzp5x02YpJ4nNnl2H61cvs3fHNt5GvyFX7jxUqVaDXzp0zNTs9evVIzw8gqXLlxMaGkrhwoX5c+EC5Rx/wU+fqnzpGBISgkvHX5U//7NuPf+sW0/ZsmVZ/l8j4G1vb/r0fb8i7fwFCwBo2qQJEyeMz7TsdevVIyIinJXLlxMWGopj4cLMXbBQOTzx6dOnaH2Q3blkSSZOmcLypUtZtuRvClpZMWP2H9jZqzbWHT1yBIVCQb36DTIta3Lq/FCZiJevWL1lO2ERL3CwKcScMSOUw+Kehoaipf2+7r95+5Z5q9wICQ1DN0cOClnm5/f+vanzQ2XlPh2aNyH67VvmLHfldVQUzkUcmTNmOLqpzKmZUZpcdxK53/AlR/ZstP2hDPo5dLj3LJSlhz2JjXs/9N/CyBDDD75oMTXQp3OtChjq5uB19DvuPX3OvL0eSXonVXa04UXkG3w+mKMpM+24cANdnez0qV8VQ90ceD9+xpRth4j5oGd3PlMjjD+anLykdQFyG+fEPYVV4hqULqoyN9W09gkNJn8eOMnxW5+/6qCRfVGcJy5W/mzXeSAATz324/f3NHKYWaCb6/1nvbchQdyeOQLbzgMp0Lg1b0ND8Fs6iwiv99MTPD/rjo6xKYXa9CCHqTmRAX7cnD6MmBeqQ9EyS63aydwz/PH+/62QZ6p1v3gJZ0aPm4DbqhWs+e+eYcK0GdjYvW+0POd5mrkz3y8ANWNSwkI6Hbt05deu3b/I6xDfJi3F99xvS3xxEydOZOfOnVy7dk3dUYQQmez169dYWlri6uqqXPkyswU+y7xeqV9boTy5ePA0JO0dsyDrvLk1vuw1Pf+tB4/VHSNDiltb8upFhLpjZJiRiSnPI9L/RWdWk8vUhKfXzqe9YxaUt3Qlja87g1y3qztGhi3s2opWc1arO0aGbB/ejdNt0v9FalZTbYsnAcGaeb8AYJPv0xZT+pp6Lt2klt+7ok87tfxedctwzyUhhBDfp/j4eJ4/f87cuXMxNTWlefPm6o4khBBCCCGEUKMMzbn0NZw6dYqcOXOm+FCn4sWLp5hr/fr1as32tWXlsggMDEy1Dn24rLqm6NOnT4qv5+Pl5bOi1P4ep06d+qRzrV+/PsVzFU9hUkN1nPNrnh++zjUZGBhI3rx52bBhA6tXryZ7dvmeQgghhBBCZC0KNT2+V1n2E0H58uWz7FCq/fv3pzgxZ3rnZPpWpFUWRkZGTJw48euG+k+BAgVSrUMfzhGjKSZPnpzixNPGxsZfOc2nS+3vYWn5aUtbN2/enEqVKiX73IfzXan7nF/z/PB13p9sbGy+65UwhBBCCCGEEKqybOOSvr4+Dg6Zu3RpZrG2tlZ3hCwjK5dF9uzZs2wdyqg8efIoJ3bWRJn59zAyMsLIyCjTzvelzvk1zw9Z+5oUQgghhBBCfJuybOOSEEIIIYQQQgghREZIT/uvK8vOuSSEEEIIIYQQQgghsj7puSSEEEIIIYQQQohvSrz0XPqqpOeSEEIIIYQQQgghhMgw6bkkhBBCCCGEEEKIb4rMufR1Sc8lIYQQQgghhBBCCJFh0rgkhBBCCCGEEEIIITJMhsUJIYQQQgghhBDimxIvo+K+Kum5JIQQQgghhBBCCCEyTHouCSGEEEIIIYQQ4psiE3p/XdJzSQghhBBCCCGEEEJkmDQuCSGEEEIIIYQQQogMk2FxQgghhBBCCCGE+KbIsLivS3ouCSGEEEIIIYQQQogMk55LQgghhBBCCCGE+KbES8+lr0pLIX3FhBBCCCGEEEII8Q3psHCtWn7vhkGd1PJ71U16LgkhhMiyHj0LVXeEDCuYx0Jj8xfMY0FwaLi6Y2RYPgsz/B4HqztGhjla5sP3kWbmL1wwH0/DItQdI8PympvyOCRM3TEyzDK3uUbXHU19z4SE902fh0HqjpFhRazya2z+Ilb5CQgOUXeMDLPJl5vTbaqqO0aGVdviqe4IKZJuNF+XzLkkhBBCCCGEEEIIITJMGpeEEEIIIYQQQgghRIbJsDghhBBCCCGEEEJ8UxTIuLivSXouCSGEEEIIIYQQQogMk55LQgghhBBCCCGE+KbEy4zeX5X0XBJCCCGEEEIIIYQQGSaNS0IIIYQQQgghhBAiw2RYnBBCCCGEEEIIIb4pChkW91VJzyUhhBBCCCGEEEIIkWHSc0kIIYQQQgghhBDflHjpuPRVSc8lIYQQQgghhBBCCJFh0nNJCCGEEEIIIYQQ3xSZc+nrkp5LQgghhBBCCCGEECLDpHFJCCGEEEIIIYQQQmSYDIsTQgghhBBCCCHEN0WGxX1d0nNJiO9IrVq1GDx4sLpjfDVdunShZcuWn32eiRMnUrp06c8+T3qtWbMGU1PTTDtfQEAAWlpaXLt2LcV9PDw80NLSIiIiItN+rxBCCCGEEOL7ID2XhBDiA1paWuzYsUOlUWr48OEMGDDgq2Vo27YtjRs3zrTzWVlZERQURK5cuTLtnOkREBCAra0tV69e/eKNczu3b2PLxvWEhYVhb+/AgMFDKerklOL+J44fw3XlcoKDgylYsCA9+/SjUpUflM+7rV7JcfejhDx7RvbsOhQuUoRuPXtTrHhxyZ+MHdu2smn9uoT8Dg4MGjqMYk4p/67jx9xZvXw5wcFBWBa0ok+/36j8w/v8M6ZO5uD+/SrHVKxUmT/mL/gi+RUKBevXrObQvr1Evn5NsRLO9Bs8FMuCBVM9bu/OHWzfvInwsDBs7e3pPWAQRYoVUz5/cO9uPNzd8ffz5U1UFJt27yVnTqMvkv3w/g+yDxpKgTSy79u5g+1bVLMXLpqQ/dXLl2xwW83VS5cIefYUY1NTKletRscu3THMmTNT82/f+i+b1q8nLCwUewdHBg0dhlMq9fS4uzurli97X3d++40qP1RV2Scg4D5L//oLr6tXiIuLw8bWlinTZ5I3X75Mzb5z21Y2f3jdDhmaar33OOauet32/Y3KH1y3a1at5Lj7EZXrtnuvPl/sutX0uqPp75sKhYINbq7vy794Cfqmp/x37WCHsvwd6NV/4Efl78q1y/+Vv0lC+bt06Zap5f8lsgP8NX8uXlcuExb6HD19fYo6laBLz14ULGSdadkBdu/YxtZNGwkLC8PO3p5+g4ZQtFjKdefk8WO4rV7J0+BgLC0L0r1PXypWrqJ8/vTJE+zbtRM/Xx9evXzJ3ytdsXd0zNTMAMbFSlGweQcMbYuia56L23+MJuziqVSPMXEqg22nARhY2fI29BkPt7nx7ITq/6/5G7TCslkHcpiaE/ngLv6r5/Pa3zvT86tDvPRc+qqk55IQ4qtTKBTExsaqO0a65cyZEwsLi6/2+/T19cmTJ0+mnS9btmzky5eP7Nm/ze8TjrsfZeniRXTq0o2lK12xd3Bg1LAhhIeHJbv/rRs3mDppAo2aNGPZqjVUrV6D8f8bzf17/sp9CloVYsCQYaxw+4eFfy8hb778jBo2mIjwcMn/kWNHj/DXooV07taDFa5u2Ds4MnzIYMLDks9/88Z1pkwYT+NmzVixxo3qNWowdvRI7vn7q+xXsXJltu/Zp3yMnzQ507Mn2rZpI3u2b+e3IcOY+9dS9PT0GD9qOO/evU3xmJPHj7FyyV+079SZhctWYGtvz/hRw1XK+G30W8pVqEibDh2/aPa9O7bTb/Aw5iz+L/vo1LOfOn6MlUsTsi9YmjR7WOhzQkND6da7L4tXrWHwyDFcuXCBRXNmZ2p29//qTpfu3Vm5xg0HRweGDxmUYt25cf06kyeMo0mzZqx0W5tQd0ap1p3Hjx7Rv3cvrK2tWfjXElz/WU+nrt3IkSNHpmY/7n6UJYsX0alrd5atWoO9gyOjhqZ83d68cT3hum3ajOWr3RKu2zGjVK5bKysrBg4Zxkq3dSz8eyn58udn5NBBX+S6Bc2uO5r+vgmwffNG9u7YRt9BQ/lj8RJ09fSZMHpEmuW/aunftPu1C/OXrsDGzp4Jo0eolH9YaChde/flz5WuDBo5misXL/Dn3Mwt/y+RHcDesTADR4zir9VuTJr5B6Bg/KgRxMXFZVp2j2PuLP9rMS6du/LXilXY2TswdvjQFP/Ot27eYMaUSTRs3JS/V6zmh+rVmTR2DAH37in3iX7zhuLOJeneu2+m5UxONl19Xgfc5d6quenaXzd3fpxG/8GLW1e4OrILT/ZtwbHPKExLVVTuk6tKHWw7DSBw62qujupG5IO7lBg7Dx1j0y/0KsS3TBqXhPjOxMfHM3LkSMzNzcmXLx8TJ05UPjdv3jycnZ0xNDTEysqKfv368fr1a+XzDx48oFmzZpiZmWFoaEjx4sXZ/1HvguQkDrk6cOAA5cqVQ1dXl9OnTxMfH8+MGTOwtbVFX1+fUqVKsXXrVuVx4eHhuLi4kDt3bvT19XF0dMTV1VX5/I0bN6hduzb6+vpYWFjQq1cvlbwfs7GxYcGCBSrbSpcurSwDGxsbAH766Se0tLSUP388LC4+Pp7JkydTsGBBdHV1KV26NAcPHlQ+nzgMbfv27fz4448YGBhQqlQpzp49m2ZZQdJhcYm//59//sHGxgYTExPatWvHq1evVDLNnj0bBwcHdHV1KVSoENOmTVPJ8+GwuP3791O4cGH09fX58ccfCQgISJLj9OnTVK9eHX19/YQPPQMHEhkZqVKe06dPp1u3bhgZGVGoUCGWL1+ufN7W1haAMmXKoKWlRa1atdL1+j/V1s2baNysOQ2bNMXG1pbBw0eiq6fLwX17k91/+9YtVKhYibYdXLC2saFrj144Fi7Czu3blPvUqVefcuUrUKCAJTa2dvQdkPDaP24AkfywZdNGmjZvQeOmCfmHjRyFnq4e+/cmn3/rls1UrFSZ9i4dsbGxpXuv3hQuUoQd27aq7JdDJwcWFhbKh5GxcaZnh4TG7l3b/qVtx1+pXLUatvb2DB39P8Keh3L29OkUj9v57xYaNG5KvUaNKWRjw29DhqGrq8eRA+/fE1v80prWHVwokkpvis/Nvnv7v7T5IPuQUQnZz6WWfWtC9roNE7L3G/xf9oMJ2a1t7fjfxClU/KEq+QtYUqpMWX7t3oML584QF5d5Xwxs2ZhYd5phY2vHsJGj0dPVY9/ePcnur6w7HX/FxsaWHr37ULhIEbZv/Ve5z4plS6j8ww/07T+AwkWKYFmwINWq18DM3DzTcgP8u2kjjZs1p9F/1+2QEQnX7YEU6v32f7dQsVIl2nXoiLWNDd169k64bj+o93XqN6BchYoUsLTE1s6OvgMG/Xfd3s3U7KD5dUfT3zcTyn8rbVz+K387e4aMGkNY6HPOeaZc/ru2/Uv9xk2o27ARhaxt6Dd4KLq6ehz9oPzHTJxMxSo/KMu/Y7ceXDh3NtPK/0tlB2jYtBklSpYib7782DsWxqVrd56HPOPZ0+BMyQ6wfcsmGjZtRoPGTbC2sWXgsBHo6ulxaH/ydWfn1n8pX7ESrdt3oJCNDZ2798ShcGF27Xhfd+o2aEjHLl0pU658puVMTvi1cwRuXkHoxZPp2j9//ZZEPwvi/j+LefP4AUGHtvH8nAeWTdoq97Fs2pZg9z0889jPm8cB3F3xB3Hv3pL3x6Zf6mWIb5g0LgnxnXFzc8PQ0JDz588ze/ZsJk+ezJEjRwDQ1tZm0aJF3Lp1Czc3N44dO8bIkSOVx/7222+8ffuWkydPcuPGDWbNmkXOT+hmPXr0aGbOnIm3tzclS5ZkxowZrF27lqVLl3Lr1i2GDBlCx44dOXHiBADjxo3j9u3bHDhwAG9vb5YsWaIc2hUZGUmDBg0wMzPj4sWL/Pvvvxw9epT+/ftnuGwuXrwIgKurK0FBQcqfP7Zw4ULmzp3LnDlzuH79Og0aNKB58+b4+fmp7Dd27FiGDx/OtWvXKFy4MO3bt89wjy1/f3927tzJ3r172bt3LydOnGDmzJnK58eMGcPMmTOVZbZhwwby5s2b7LkePnxIq1ataNasGdeuXaNHjx6MHj06ye9r2LAhP//8M9evX2fz5s2cPn06SfnOnTuX8uXLc/XqVfr160ffvn3x8fEB4MKFCwAcPXqUoKAgtm/fnqHXnpqYmBh8fX0o+8ENnba2NmXLV+D2rZvJHnP75k3Kla+gsq18xUrcvpn8/jExMezbvQvDnDmxd3DIvPB8I/l9fFTyaGtrU65CBW7dvJHsMbdu3qRcBdX8FSpVTrL/tatXaNG4ER3btWHuH7N48eJFpmZP9DQoiPCwMEqXK6fcZpgzJ0WKFePO7VvJHhMTE8NdX1+VY7S1tSldrlyKx3wJyuxlVbMXTkf2UmU/yl62HD6pZI98HYmBgQHZsmVOD8iEunOH8hXef4Oedt25kaTuVPyg7sTHx3P2zBmsrAoxbPBAmjduSO/u3Tj13/8pmSXxuk1S79O4bsuW/7jeV+JWKtft3l07/7tuM394jcbXHQ1+34T35V8qSfk74XP7doqZ7vr6qPzNtLW1KVW2HHdSOAYgKvJ1ppb/18oe/eYN7gcPkDdffnLlzpze3DExMfj5+iapO2XKlef2reTrsPetm0kajcpVqIR3CnUtKzFyLEHEjUsq28K9zmNUuAQAWtmyk9OuCBE3PrjfVSiIuHFJuY+mUyjU8/hefZtjJIQQKSpZsiQTJkwAwNHRkcWLF+Pu7k69evVUJvu2sbFh6tSp9OnTh7///huAwMBAfv75Z5ydnQGws7P7pN89efJk6tWrB8Dbt2+ZPn06R48epUqVKsrznT59mmXLllGzZk0CAwMpU6YM5cuXV2ZKtGHDBqKjo1m7di2GhoYALF68mGbNmjFr1qwUG1ZSkzt3bgBMTU3Jl8rcHHPmzGHUqFG0a9cOgFmzZnH8+HEWLFjAX3/9pdxv+PDhNGnSBIBJkyZRvHhx7t69S9GiRT85W3x8PGvWrMHIKGG+ll9//RV3d3emTZvGq1evWLhwIYsXL6Zz584A2NvbU61atWTPtWTJEuzt7Zk7N6FbdZEiRZSNhYlmzJiBi4uLsk44OjqyaNEiatasyZIlS9DT0wOgcePG9OvXD4BRo0Yxf/58jh8/TpEiRZTlaWFhkWp5QkJ9ePtWtTu9rq5umuXy4kUE8XFxSXolmJmZ8/DBg2SPCQsLxczcTHV/czPCwkJVtp319GTqpPG8jY7G3MKC2fMWYJKJE61/E/kjIohLLr+5GYEPApI9Jiw0FDOzj1+vGWGh7/NXrFSFGjVrka9AAZ48esyKZUsYOXQIfy9fQbZs2TL1NSQOwTL9KJOpmRkRKQzPevniBfHxcZiamSU55lFgYKbmS03iEKDksqc0PCgxu1ly2R8mn/3Fiwg2r1tLgybNMiH1f+dMoe6Ym5sTmFLdDw3FPEldM1fWnfDwcN5ERbH+n7X06NWHPv36c/7cWX4fM4qFi/+mdNmymZM9pes2texhydV7c8KTXLenmTLx/XX7x/yFmX7dgobXHQ1/34RUyt/ULMVhoQnlH5/s3+xxCuX/8kUEm9f9k6nl/6Wz79+1kzUrlhIdHY2llRWTZ89BR0cnU7K/fPGC+Li4JDnMzMx5GJh83QkPC0tS583MUn6tWUkOU3NiXqjmjHkRTnaDnGjr5CB7TmO0smUnJuKjfSLCMChQ6GtGFd8I6bkkxHemZMmSKj/nz5+fZ8+eAQk9TOrUqYOlpSVGRkb8+uuvhIaGEhUVBcDAgQOZOnUqVatWZcKECVy/fv2TfndiIxHA3bt3iYqKol69euTMmVP5WLt2Lf7/dUHv27cvmzZtonTp0owcOZIzZ84oj/f29qZUqVLKhiWAqlWrEh8fr+w58yW8fPmSJ0+eULWq6gSyVatWxdtbdfLDD8s6f/78AMqy/lQ2NjbKhqXE8yWey9vbm7dv31KnTp10ncvb25tKlSqpbEts4Evk5eXFmjVrVP42DRo0ID4+nvv37yv3+/A1amlpkS9fvgy9xhkzZmBiYqLymDFjxiefJzOVLluW5avdWLRkGRUqVWbKhHEpfujKijQ5f5169ahavQb29g5Ur1mTmX/M5Y73ba5dvfLZ5z5+9Ai/NG6ofMRm4lCdL83j6BFaN2mofHyNueuiIiOZ/L/RWFlb06Fz1y/++z6HIj4egGrVa9CmfXscCxemY6fOVKlajV07M7/n5JdQumw5Vri68eeS5VSsVJnJ43/PlOtW6k76fKn3TQ/3I7Rp2lD5iPta5T92DFbW1rTv1CXD5/na2WvWqcuCpSuZPm8hlgWtmD1lUqpzOQmRmniFQi2P75X0XBLiO/Pxtz9aWlrEx8cTEBBA06ZN6du3L9OmTcPc3JzTp0/TvXt33r17h4GBAT169KBBgwbs27ePw4cPM2PGDObOnZvuldQ+bAhKnBtp3759WFpaquyX2GOlUaNGPHjwgP3793PkyBHq1KnDb7/9xpw5czL02rW1tVF89IYfExOToXOlx4dlraWlBST0QPrccyWeL/Fc+vr6GUyYstevX9O7d28GDhyY5LlChd5/m5Vark8xZswYhg4dqrJNV1eXkBcpz6EFYGJiina2bEm+QQwPD8PcIvk5VszNLQgPU524MzwsHHNz1Unb9fX1sSxYEMuCBXEqXoJO7dtwYO9eOvzaKb0vK00an9/UlGzJ5U8mjzK/hUWSD2vh4eGYpzJpfgFLS0xMTXn86FGSoS2fqtIPVVVWdIt5l/AeEBEeppIhIjwc2xSGwxibmKCtnS3JBLAR4eGZPrfPhyr+UJXCH2aPSTm7nX3q2cPTkT0qKooJo0egb2DA2MlTM3VRgJTqTlhYKnXfwoKwJHXt/WtPPKf1f/O9JbK2seGGl1fmZU/pug0LS7Eem5snV+/DMEv2urXCsqAVTiVK8Gu71hzYu4cOv3b+rMzfVN3RwPfNilWqqqyKFptS+UekVf7aRHxUjyLCw5P0xImKimLimJHo6+vzv0lTPqv8v3Z2w5w5McyZkwIFC1KkmBMdfmrG2dOnqVk7fV+gpcbYxATtbNmS5EjuWkxkZm6epM6Hf+H3+szyLiIMHRPVnDomZsRGvSY+5h0xLyNQxMWiY/rRPqbmvIvQjC+jvkdhYWEMGDCAPXv2oK2tzc8//8zChQtTnKokcfXm5GzZsoXWrVsD7z+rfGjjxo3KkRrpIT2XhBAAXL58mfj4eObOnUvlypUpXLgwT548SbKflZUVffr0Yfv27QwbNowVK1Zk6Pc5OTmhq6tLYGAgDg4OKg8rKyvlfrlz56Zz586sW7eOBQsWKCeMLlasGF5eXioTTHt6eqKtrU2RIkWS/Z25c+cmKChI+fPLly9VeuFAQmNJaquSGBsbU6BAATw9PVW2e3p64vSFJu1Ni6OjI/r6+ri7u6dr/2LFiinnQ0p07tw5lZ/Lli3L7du3k/xtHBwc0r3qUuJ+6VnlRVdXF2NjY5VHeobF6ejoULhwEa5evqzcFh8fz9XLl3Aqnvx8AU4lSnDlsuocBJcvXcCpROrzC8THxxMT8y7NTJ/im8hfpAiXL7+fryE+Pp4rly5SvIRzsscUL1GCy5dU5zO7dOFCivtDQo+/ly9eZMqqjQYGBhSwLKh8FLKxwczcnGtX3veKioqMxMfbm6IpLCuvo6ODQ+HCeF1R/bt5XbmS4jGZIUl264TsXh9l901H9utXP8p+9QpFPjgmKjKS8SOHkV1Hh9+nTCdHjrSvx0+RUHeKqtSFtOuOM1cuqdb9ix/UHR0dHYoWc0oyvOVRYGCaQ3M/OXvhIirXYXx8PFfSum4/yn7p4gWKp3ndKnj37vO/BPnm6o6GvW9+XP5WieV/9ePyv53iAgAJ5V9E5W8WHx/P9auXKfrBMVGRkUwYNZzs2bNnSvl/zexJKBQJKwxn0v9dOjo6OBYunKTuXLtyGafiydf7YsVLcO2junPl0kWKpVDXspJXfjcxdS6nss20ZAVe+SbMF6WIi+X1PR9MS3wwp5SWFqYlyin30XSK/+rQ1358SS4uLty6dYsjR46wd+9eTp48Sa9evVLc38rKiqCgIJXHpEmTyJkzJ40aNVLZN3He2cRHy5YtPymbNC4JIQBwcHAgJiaGP//8k3v37vHPP/+wdOlSlX0GDx7MoUOHuH//PleuXOH48eMU++Cb0E9hZGTE8OHDGTJkCG5ubvj7+3PlyhX+/PNP3NzcABg/fjy7du3i7t273Lp1i7179yp/n4uLC3p6enTu3JmbN29y/PhxBgwYwK+//prifEu1a9fmn3/+4dSpU9y4cYPOnTsnmb/FxsYGd3d3goODk3xTlWjEiBHMmjWLzZs34+Pjw+jRo7l27RqDBg3KUFl8Lj09PUaNGsXIkSOVwwrPnTvHqlWrkt2/T58++Pn5MWLECHx8fNiwYQNr1qxR2WfUqFGcOXOG/v37c+3aNfz8/Ni1a9cnTZieJ08e9PX1OXjwIE+fPv1iEzL/0rYd+/bu5tCB/TwICGDB3D+IfhNNg8YJK53MnDqZlUuXKPdv9UsbLp4/x5ZNGwh8EIDb6pX43rlDy1Y/A/DmzRtWLlvK7Vs3eRochK/PHf6YMY3nz59T88fakv8jbdq1Z9/u3Rzcv4+AgPvM+2M2b6KjadQ0Yb6xaZMnsXzJ3+9fb5u2XDh3js0b1vMgIADXlSvwuePNTz//AiR8475k8Z/cunmToKAnXL50kbGjRmBZsCAVKlXO9PxaWlq0+Lk1m9et5bynJwH3/Jk3czrmuSyo8sG8Zf8bNoQ9O94PrWrZug2H9u3D/dBBHj4I4O8F84iOfkPdhu9v1MLDQrl314+gx48BCLh3j3t3/Xj18mWmZW/eqjWb16/l/BnV7JU/yD52+BD2fjAsrOUvKWRvkJA9KjKS8aOG8zY6moHDR/ImKpLwsFDCw0IzdUnwNu3bs3f3Lg7sS6g7c2fP4k10NI2bJtT9aZMmsuzv9/PY/dKmLefPnWXTf3Vn9X91p9UvrZX7tHfpyLGjR9mzayePHj5k27//csbzNC1//jnTcgO0bteefXt2c+jAvoTrds5sot9E07BJQvYZUyaxYun7et+q9X/X7caE63bNqv+u2//qfcJ1u4TbN28SHByE7507zJ4+lefPQ77IdavpdUfT3zcTyv8Xtqz/57/yv8f8WdMxt8hF5arvy//3EUNVyr/Fz605vH8v7ocP8vDBA5YsnE90dDR1GqqWf3R0NAOGjyTqC5T/l8oe/OQJ/25Yz11fH0KePsX71k1mTZmIbg5dylXMvPf+Vm3acWDfHo4cPEBgQAB/zptD9Js31G+U8H/W7GlTWL38/f1vy19ac+nCebZu3kjggwf847oKP587tPjp/XvKy5cv8ffzU841+PBhIP5+fipzCWYGbV19DK0dMbROmORfL08BDK0d0bVIuO+1bt+Hwr/9rtw/6PBO9PIUwMalH/oFCpGv/k/krlKbx/s2K/d5vHcz+eo0I0/NRuhbWmPfYzjZdPV46rEvU7OLzOHt7c3BgwdZuXIllSpVolq1avz5559s2rQp2U4BANmyZSNfvnwqjx07dtCmTZskvZ0S551NfCTOsZpeMixOCAFAqVKlmDdvHrNmzWLMmDHUqFGDGTNm0KnT+67gcXFx/Pbbbzx69AhjY2MaNmzI/PnzM/w7p0yZQu7cuZkxYwb37t3D1NSUsmXL8r///Q9I6PkyZswYAgIC0NfXp3r16mzatAlI+Cbt0KFDDBo0iAoVKmBgYMDPP//MvHnzUvx9Y8aM4f79+zRt2hQTExOmTJmSpOfS3LlzGTp0KCtWrMDS0pKAgIAk5xk4cCAvXrxg2LBhPHv2DCcnJ3bv3o2jY+av6JNe48aNI3v27IwfP54nT56QP39++vTpk+y+hQoVYtu2bQwZMoQ///yTihUrMn36dLp166bcp2TJkpw4cYKxY8dSvXp1FAoF9vb2tG3bNtlzJid79uwsWrSIyZMnM378eKpXr46Hh8fnvtQkfqxTlxcREaxZtYLwsDDsHRyZOWeecuLfZ0+foqX1/ruU4s7OjJ0widUrlrN6+TIsCxZk8vSZ2NrZA5BNW5uHgQ+Y+Pt+Xr54gbGxCUWKFWXB4r+xsf20Sey/h/y169YjIiKC1StWEBYWioOjI3/Mm68cbvLsaTDa2u+7WpdwLsm4SZNZtXwZK5YtpWBBK6bNnI2d/X/5s2njf/cuB/fv5/XrV+TKlYvyFSvRvVevdPea+1Q/t2tPdPQb/pw3h8jXr3FydmbyzD9UvvEPfvKElx80kNb4sTYvIiJY57qa8PAw7OwdmDzrD5WhEvt372bj2jXKn0cPThhmOnjkaJVGqMzIvviD7JNmpJ69+o+1efEigvVr3mefNPN9dn8/X3y8E1Zw6vVrB5Xft3L9JvLmy58p2evUrUdEeASrVy4nLDQUB8fCzJm/QFl3nj59ipb2+7rvXLIk4ydNYeXypaxYuoSCVlZMm/W+7gDUqFWLYSNHsW6tGwvnzaOQdSEmT59ByVKlMyVzoh/r1CUiIhzXlSsJDwvF3sGRWXPnq1y32h9kL+FcUnndrlq+FMuCVkyeMUvlug188IBDBz68boux8K8l2H7i4hnppcl1R9PfNwFatW1PdHQ0f83/r/xLODNx5uyPyv9xsuW/YY2rsvwnzpitnCze388X3zsJ8z/27uSi8vtWrNuYaeX/JbLr5MjB7ZvX2b19K5GvX2FqZkZx51LMWrQ4yeIJn6NW7Tq8iIhg7eqVhIeFYefgwLQ/5irrcMgz1Wu3eAlnRo+bgNuqFaxZsZwCBQsyYdoMbD64Ls95nmbuzOnKn2dMSlg4p2OXrvzatXumZTeyL4rzxMXKn+06J/yf8tRjP35/TyOHmQW6ud5/wfo2JIjbM0dg23kgBRq35m1oCH5LZxHh9b73+vOz7ugYm1KoTQ9ymJoTGeDHzenDiHmR/BesIn1SWqgmPb3yU3P27FlMTU1V5rGtW7cu2tranD9/np9++inNc1y+fJlr166pLEKU6LfffqNHjx7Y2dnRp08funbtmuxwuZRoKb50vy0hhBAigx49y9xv/b6mgnksNDZ/wTwWBIdq7o1lPgsz/B4HqztGhjla5sP3kWbmL1wwH0/DItQdI8PympvyOERz5xqxzG2u0XVHU98zIeF90+dhUNo7ZlFFrPJrbP4iVvkJCA5Rd4wMs8mXm9Ntqqa9YxZVbYtn2jupSeMZy9Tyeyu+TRh69qEJEyYwceLEzzrv9OnTcXNzS7J4UZ48eZg0aRJ9+/ZN8xz9+vXDw8OD27dvq2yfMmUKtWvXxsDAgMOHDzNhwgRmz56d7PyrKZGeS0IIIYQQQgghhBCZIKWFalIyevRoZs2aleo5P16VOiPevHnDhg0bGDduXJLnPtxWpkwZIiMj+eOPPz6pcUnmXBJCfLY+ffqoLFn/4SOloVnfs0aNGqVYXtOnT0/7BEIIIYQQQohUxSvU8/jUhWqGDRuGt7d3qg87Ozvy5cvHs2fPVI6NjY0lLCwsXQtXbN26laioKJVpT1JSqVIlHj16lGR4X2qk55IQ4rNNnjyZ4cOHJ/ucsbHxV06T9a1cuZI3b94k+5y5BixtK4QQQgghhMgcuXPnJnfu3GnuV6VKFSIiIrh8+TLlyiWsBHjs2DHi4+OpVKlSmsevWrWK5s2bp+t3Xbt2DTMzs0+aJ0oal4QQny1PnjzkyZNH3TE0hqWlpbojCCGEEEIIITRIsWLFaNiwIT179mTp0qXExMTQv39/2rVrR4ECBQB4/PgxderUYe3atVSsWFF57N27dzl58iT79+9Pct49e/bw9OlTKleujJ6eHkeOHGH69Okpdh5IiTQuCSGEEEIIIYQQ4pvyLa5dtn79evr370+dOnXQ1tbm559/ZtGiRcrnY2Ji8PHxISoqSuW41atXU7BgQerXr5/knDo6Ovz1118MGTIEhUKBg4MD8+bNo2fPnp+UTRqXhBBCCCGEEEIIIbI4c3NzNmzYkOLzNjY2yTaqTZ8+PcW5XRs2bEjDhg0/O5s0LgkhhBBCCCGEEOKb8i32XMrKZLU4IYQQQgghhBBCCJFh0rgkhBBCCCGEEEIIITJMhsUJIYQQQgghhBDimxIvw+K+Kum5JIQQQgghhBBCCCEyTHouCSGEEEIIIYQQ4psiHZe+Lum5JIQQQgghhBBCCCEyTHouCSGEEEIIIYQQ4psicy59XdJzSQghhBBCCCGEEEJkmDQuCSGEEEIIIYQQQogMk2FxQgghhBBCCCGE+KYoZFjcVyU9l4QQQgghhBBCCCFEhmkppDlPCCHEd+bt27fMmDGDMWPGoKurq+44n0STs4PkVydNzg6SX900Ob8mZwfJr06anB00P7/QLNK4JIQQ4rvz8uVLTExMePHiBcbGxuqO80k0OTtIfnXS5Owg+dVNk/NrcnaQ/OqkydlB8/MLzSLD4oQQQgghhBBCCCFEhknjkhBCCCGEEEIIIYTIMGlcEkIIIYQQQgghhBAZJo1LQgghvju6urpMmDBBIye31OTsIPnVSZOzg+RXN03Or8nZQfKrkyZnB83PLzSLTOgthBBCCCGEEEIIITJMei4JIYQQQgghhBBCiAyTxiUhhBBCCCGEEEIIkWHSuCSEEEIIIYQQQgghMkwal4QQQgghhBBCCCFEhknjkhBCCCGEEEIIIYTIMGlcEkIIIYQQQgghhBAZJo1LQgghvksvX75k586deHt7qztKuly5coUbN24of961axctW7bkf//7H+/evVNjsrQ9fPiQR48eKX++cOECgwcPZvny5WpMlXGaVne+JXFxcVy7do3w8HB1R0kXTb5uPxYdHa3uCN+VN2/eEBUVpfz5wYMHLFiwgMOHD6sx1eeJiIhQd4R00+Tyr127drJl/fLlS2rXrv31A4nvhjQuCSGE+C60adOGxYsXAwk3jeXLl6dNmzaULFmSbdu2qTld2nr37o2vry8A9+7do127dhgYGPDvv/8ycuRINadLXYcOHTh+/DgAwcHB1KtXjwsXLjB27FgmT56s5nRp0/S6Y2Zmhrm5eZKHhYUFlpaW1KxZE1dXV3XHTNbgwYNZtWoVkNCwVLNmTcqWLYuVlRUeHh7qDZcOmnzdAsTHxzNlyhQsLS3JmTMn9+7dA2DcuHHKv0tW9vTpU3799VcKFChA9uzZyZYtm8ojK2vRogVr164FEhplKlWqxNy5c2nRogVLlixRc7q0zZo1i82bNyt/btOmjfI9x8vLS43J0keTy9/DwyPZxuvo6GhOnTqlhkTieyGNS0IIIb4LJ0+epHr16gDs2LEDhUJBREQEixYtYurUqWpOlzZfX19Kly4NwL///kuNGjXYsGEDa9asyfINHDdv3qRixYoAbNmyhRIlSnDmzBnWr1/PmjVr1BsuHTS97owfPx5tbW2aNGnCpEmTmDRpEk2aNEFbW5vffvuNwoUL07dvX1asWKHuqEls3bqVUqVKAbBnzx7u37/PnTt3GDJkCGPHjlVzurRp8nULMHXqVNasWcPs2bPJkSOHcnuJEiVYuXKlGpOlT5cuXbhy5Qrjxo1j69atbN++XeWRlV25ckX5vrN161by5s3LgwcPWLt2LYsWLVJzurQtXboUKysrAI4cOcKRI0c4cOAAjRo1YsSIEWpOlzZNLP/r169z/fp1AG7fvq38+fr161y9epVVq1ZhaWmp5pTiW5Zd3QGEEEKIr+HFixeYm5sDcPDgQX7++WcMDAxo0qSJRtzoKhQK4uPjATh69ChNmzYFwMrKiufPn6szWppiYmLQ1dUFErI3b94cgKJFixIUFKTOaOmi6XXn9OnTTJ06lT59+qhsX7ZsGYcPH2bbtm2ULFmSRYsW0bNnTzWlTN7z58/Jly8fAPv376d169YULlyYbt26sXDhQjWnS5smX7cAa9euZfny5dSpU0el/pQqVYo7d+6oMVn6nD59mlOnTikb+DRJVFQURkZGABw+fJhWrVqhra1N5cqVefDggZrTpS04OFjZuLR3717atGlD/fr1sbGxoVKlSmpOlzZNLP/SpUujpaWFlpZWssPf9PX1+fPPP9WQTHwvpOeSEEKI74KVlRVnz54lMjKSgwcPUr9+fQDCw8PR09NTc7q0lS9fnqlTp/LPP/9w4sQJmjRpAsD9+/fJmzevmtOlrnjx4ixdupRTp05x5MgRGjZsCMCTJ0+wsLBQc7q0aXrdOXToEHXr1k2yvU6dOhw6dAiAxo0bK4c8ZSV58+bl9u3bxMXFcfDgQerVqwckfPDL6sOaQLOvW4DHjx/j4OCQZHt8fDwxMTFqSPRprKysUCgU6o6RIQ4ODuzcuZOHDx9y6NAh5fvOs2fPMDY2VnO6tJmZmfHw4UMgoVE+8T1IoVAQFxenzmjpoonlf//+ffz9/VEoFFy4cIH79+8rH48fP+bly5d069ZN3THFN0wal4QQQnwXBg8ejIuLCwULFiR//vzUqlULSBjy5OzsrN5w6bBgwQKuXLlC//79GTt2rPID39atW/nhhx/UnC51s2bNYtmyZdSqVYv27dsrhznt3r1bOVwuK9P0umNubs6ePXuSbN+zZ4+yR1ZkZKTyW/qspGvXrrRp04YSJUqgpaWl/IB6/vx5ihYtquZ0adPk6xbAyckp2Tlatm7dSpkyZdSQ6NMsWLCA0aNHExAQoO4on2z8+PEMHz5c2dOnSpUqQEIvGk0o+1atWtGhQwfq1atHaGgojRo1AuDq1avJNlhmNZpY/tbW1tjY2BAfH0/58uWxtrZWPvLnz68RDfJCs2kpNLU5XwghhPhEly5d4uHDh9SrV4+cOXMCsG/fPkxNTalataqa02VMdHQ02bJlQ0dHR91RUhUXF8fLly8xMzNTbgsICMDAwIA8efKoMVn6aHLdWbFiBX379qVx48bKxryLFy+yf/9+li5dSvfu3Zk7dy4XLlxQmYA3q9i6dSsPHz6kdevWFCxYEAA3NzdMTU1p0aKFmtNljKZct7t27aJz586MGTOGyZMnM2nSJHx8fFi7di179+5V9iTLqszMzIiKiiI2NhYDA4Mk5R0WFqamZOkTHBxMUFAQpUqVQls7oU/AhQsXMDY2zvKNqzExMSxcuJCHDx/SpUsXZYPM/PnzMTIyokePHmpOmDZNLn8/Pz+OHz/Os2fPlENzE40fP15NqcS3ThqXhBBCfFfevXvH/fv3sbe3J3t2zZp6MCIigq1bt+Lv78+IESMwNzfnypUr5M2bN8tP0hkbG4uHhwf+/v506NABIyMjnjx5grGxsbKxJqvT5Lrj6enJ4sWL8fHxAaBIkSIMGDBAI3rPfCtev36d5ENeVh1e86FTp04xefJkvLy8eP36NWXLlmX8+PHKYUJZmZubW6rPd+7c+Ssl+XSurq60a9cOfX19dUfJkMjISAwNDdUdI8M0ufwTv1DIlSsX+fLlQ0tLS/mclpYWV65cUWM68S2TxiUhhBDfhaioKAYMGKD8sOHr64udnR0DBgzA0tKS0aNHqzlh6q5fv06dOnUwNTUlICAAHx8f7Ozs+P333wkMDFQumZwVPXjwgIYNGxIYGMjbt2+VZT9o0CDevn3L0qVL1R0xVZpedzTdxYsXU/wGft68eWpKlT7379+nf//+eHh4EB0drdyuUCjQ0tLSiLlnhHrkzZuXN2/e0Lp1a7p3765xDcE5c+akTZs2dOvWjWrVqqk7zifT5PK3tramX79+jBo1St1RxHdG5lwSQgjxXRgzZgxeXl54eHioTMJct27dLDkU6GNDhw6la9eu+Pn5qeRv3LgxJ0+eVGOytA0aNIjy5csTHh6u8i3wTz/9hLu7uxqTpY+m1x1ImIDZ19eX06dPc/LkSZVHVjZ9+nQqVaqEq6srly5d4urVq8rHtWvX1B0vTR07diQ8PJzVq1fj7u7OsWPHOHbsGMePH+fYsWPqjvdJXr9+zcuXL1UemiAuLo5t27YxdepUpk6dyo4dOzSiUe/x48e4ubnx/PlzatWqRdGiRZk1axbBwcHqjpYu69atIywsjNq1a1O4cGFmzpzJkydP1B0r3TS5/MPDw2ndurW6Y4jvkUIIIYT4DhQqVEhx9uxZhUKhUOTMmVPh7++vUCgUCj8/P4WRkZE6o6WLsbGx4u7duwqFQjV/QECAQldXV53R0mRubq64c+eOQqFQzX7//n2Fvr6+OqOli6bXnbNnzypsbW0V2traCi0tLZWHtra2uuOlKk+ePApXV1d1x8gwQ0NDZd3XRPfu3VM0btxYYWBgoNDW1lY+NKHuKBQJ16ijo6PCwMBAUaZMGUWZMmUUBgYGiiJFiijfTzVBcHCwYs6cOQpnZ2eFjo6OolmzZoqdO3cq4uLi1B0tTc+ePVPMnTtX4ezsrMiePbuiSZMmim3btiliYmLUHS3dNK38u3XrpliyZIm6Y4jvkGZNGCCEEEJkUEhISLITR0dGRqrMR5BV6erqJttTwNfXl9y5c6shUfrFx8cn21Pg0aNHWXKFso9pet3p06cP5cuXZ9++feTPn18jMifS1tbO8hOmp6ZChQo8fPiQIkWKqDtKhnTs2BGFQsHq1avJmzevRtUdgIEDB2Jvb8+5c+eUKyOGhobSsWNHBg4cyL59+9ScMH3y5s1LtWrV8PX1xdfXlxs3btC5c2fMzMxwdXVVrmCZFeXOnZuhQ4cydOhQ/vzzT0aMGMH+/fvJlSsXffr0YfTo0RgYGKg7Zqo0rfwdHBwYN24c586dw9nZOclE9gMHDlRTMvGtkzmXhBBCfBdq1KhB69atGTBgAEZGRly/fh1bW1sGDBiAn58fBw8eVHfEVPXo0YPQ0FC2bNmCubk5169fJ1u2bLRs2ZIaNWqwYMECdUdMUdu2bTExMWH58uXKss+dOzctWrSgUKFCuLq6qjtiqjS97hgaGuLl5aURy39/bPbs2Tx58iRL1+/U+Pv706dPHzp27EiJEiWSfMgrWbKkmpKlT86cObl8+bLGNo4ZGhoqP2B/yMvLi6pVq/L69Ws1JUufp0+f8s8//+Dq6sq9e/do2bIl3bt3p27dukRGRjJ58mQ2bdrEgwcP1B01RU+fPsXNzY01a9bw4MEDfvrpJ7p3786jR4+YNWsWBQoU4PDhw+qOmSxNLX9bW9sUn9PS0uLevXtfMY34nkjjkhBCiO/C6dOnadSoER07dmTNmjX07t2b27dvc+bMGU6cOEG5cuXUHTFVL1684JdffuHSpUu8evWKAgUKEBwcTJUqVdi/f3+WXpXn0aNHNGjQAIVCgZ+fH+XLl8fPz49cuXJx8uTJZHsFZSWaXndq167NyJEjadiwobqjfLL4+HiaNGmCr68vTk5OSRpntm/frqZk6XPu3Dk6dOhAQECAcpuWlpbGTOj9448/MnbsWOrWravuKBlibm7O3r17k0zG7OnpSbNmzQgLC1NTsrQ1a9aMQ4cOUbhwYXr06EGnTp2Uva8SPXv2jHz58iWZ6D4r2L59O66urhw6dAgnJyd69OhBx44dMTU1Ve7j7+9PsWLFePfunfqCpkDTy18IdZBhcUIIIb4L1apV49q1a8ycORNnZ2cOHz5M2bJlOXv2bJJvtbMiExMTjhw5gqenp8qS4Jrwoa9gwYJ4eXmxefNmZfbu3bvj4uKiEcs8a3rdGTBgAMOGDSM4ODjZIRJZuffMwIEDOX78OD/++CMWFhYaNyyrW7dulClTho0bN2rksLKVK1fSp08fHj9+rJE9r5o2bUqvXr1YtWoVFStWBOD8+fP06dOH5s2bqzld6vLkycOJEyeoUqVKivvkzp2b+/fvf8VU6de1a1fatWuHp6cnFSpUSHafAgUKMHbs2K+cLH00vfwB3r17x/3797G3tyd7dvnYL7486bkkhBBCCPEN09ZOujiwpvSeMTIyYtOmTTRp0kTdUTJEk4ckgub3vIqIiKBz587s2bNH2TAWGxtL8+bNWbNmDSYmJmpO+O2KiorK8nMpfauioqIYMGAAbm5uQMLcjHZ2dgwYMABLS0tGjx6t5oTiWyVNmEIIIb4L+/fvJ1u2bDRo0EBl+6FDh4iPj6dRo0ZqSpY+AwcOxMHBIclEnIsXL+bu3btZek6aGTNmkDdvXrp166ayffXq1YSEhDBq1Cg1JUsfTa87Wfmb9bSYm5tjb2+v7hgZVrt2bY1uXNL0nlempqbs2rULPz8/7ty5A0CxYsU05u8RGRnJiRMnCAwMTDJ0LKtPyvxhw1J0dHSS/MbGxl870ifT1PIfM2YMXl5eeHh4qAyHrlu3LhMnTpTGJfHFSM8lIYQQ34WSJUsyc+ZMGjdurLL94MGDjBo1Ci8vLzUlSx9LS0t2796dZH6fK1eu0Lx5cx49eqSmZGmzsbFhw4YNSeY9OX/+PO3atcvyjR+aXnc0maurKwcPHsTV1VUje0EsX76cqVOn0q1bt2SHJGb1oVma3vNKk129epXGjRsTFRVFZGQk5ubmPH/+HAMDA/LkyZPlJ2WOjIxk1KhRbNmyhdDQ0CTPZ/Veb5pc/tbW1mzevJnKlStjZGSEl5cXdnZ23L17l7Jlyya78qwQmUF6LgkhhPgu+Pn54eTklGR70aJFuXv3rhoSfZrQ0NBkh3AYGxvz/PlzNSRKv+DgYPLnz59ke+7cuQkKClJDok+jiXVn9+7dNGrUCB0dHXbv3p3qvlm5gWPRokX4+/uTN29ebGxskjTOXLlyRU3J0qdPnz4ATJ48OclzmjCsTBN7Xg0dOpQpU6ZgaGjI0KFDU9133rx5XynVpxsyZAjNmjVj6dKlmJiYcO7cOXR0dOjYsSODBg1Sd7w0jRw5kuPHj7NkyRJ+/fVX/vrrLx4/fsyyZcuYOXOmuuOlSZPLPyQkJNmFMiIjIzWu96HQLNK4JIQQ4rtgYmLCvXv3sLGxUdl+9+7dLL3SWiIHBwcOHjxI//79VbYfOHAAOzs7NaVKHysrKzw9PZMsj+zp6UmBAgXUlCr9NLHutGzZkuDgYPLkyUPLli1T3C+rN3Ckll0TaPoqUs2aNWPIkCHcuHFDY3peXb16lZiYGOW/NdW1a9dYtmwZ2traZMuWjbdv32JnZ8fs2bPp3LkzrVq1UnfEVO3Zs4e1a9dSq1YtunbtSvXq1XFwcMDa2pr169fj4uKi7oip0uTyL1++PPv27WPAgAEAygallStXpjpBuRCfSxqXhBBCfBdatGjB4MGD2bFjh3IOl7t37zJs2LAs+QHpY0OHDqV///6EhIRQu3ZtANzd3Zk7d26Wnm8JoGfPngwePJiYmBiV7CNHjmTYsGFqTpc2Taw7HzZqaHIDx4QJE9Qd4bumiT2vjh8/nuy/NY2Ojo5yMv48efIQGBhIsWLFMDEx4eHDh2pOl7awsDDlFx/GxsaEhYUBCatv9u3bV53R0kWTy3/69Ok0atSI27dvExsby8KFC7l9+zZnzpzhxIkT6o4nvmFJlw8RQgghvkGzZ8/G0NCQokWLYmtri62tLcWKFcPCwoI5c+aoO16aunXrxty5c1m1ahU//vgjP/74I+vWrWPJkiX07NlT3fFSNWLECLp3706/fv2ws7NTrlozcOBAxowZo+54adL0upPafFznzp37ikky7vLly6xbt45169ZpXG+UEydO0KxZMxwcHHBwcKB58+acOnVK3bHSJT4+PsVHVmxY+li3bt149epVku2RkZFJFhjIasqUKcPFixcBqFmzJuPHj2f9+vUMHjyYEiVKqDld2uzs7JTz6RUtWpQtW7YACT2aTE1N1ZgsfTS5/KtVq8a1a9eIjY3F2dmZw4cPkydPHs6ePZtk3kYhMpNM6C2EEOK7oVAoOHLkCF5eXujr61OyZElq1Kih7lifLCQkBH19fXLmzKnuKJ/k9evXeHt7o6+vj6OjI7q6uuqOlG6aXHecnJw4ffo05ubmKts9PT1p0qQJERER6gmWDs+ePaNdu3Z4eHgoP5BGRETw448/smnTJnLnzq3egGlYt24dXbt2pVWrVlStWhVIKPcdO3awZs0aOnTooOaE37Zs2bIRFBSUZP6Z58+fky9fPmJjY9WULG2XLl3i1atX/Pjjjzx79oxOnTpx5swZHB0dWb16NaVKlVJ3xFTNnz+fbNmyMXDgQI4ePUqzZs1QKBTExMQwb968LD9vkaaXvxDqII1LQgghhBDfsG7dunH9+nWOHz+OkZERACdPnqRZs2ZMnDiRIUOGqDlhytq2bcu9e/dYu3YtxYoVA+D27dt07twZBwcHNm7cqOaEqStWrBi9evVKUsbz5s1jxYoVeHt7qylZyhYtWkSvXr3Q09Nj0aJFqe6bVZdjf/nyJQqFAjMzM/z8/FQaIePi4tizZw+jR4/myZMnakz5fXnw4AGXL1/GwcGBkiVLqjvON+dTVoAzNjb+gknE90wal4QQQnyzNP1DUtmyZXF3d8fMzIwyZcqkuspLVls1q1WrVqxZswZjY+M0Jz7dvn37V0qVfppedz4UHx/PL7/8QlhYGIcOHeLMmTM0b96cqVOnZvneAyYmJhw9epQKFSqobL9w4QL169fP0r2uAHR1dbl161aS1dbu3r1LiRIliI6OVlOylNna2nLp0iUsLCySTML/IS0trSy7HLu2tnaq75daWlpMmjSJsWPHfsVUQnw5adX5D2nCkFahmWRCbyGEEN+s+fPn4+Ligp6eHvPnz09xPy0trSzZQNCiRQvl0LEWLVpo1BLCJiYmyrwmJiZqTvPpNL3ufEhbW5tNmzbRpEkTateuzfXr15kxY0aSlQezovj4+CQrlEHCZLuaMFG5lZUV7u7uSRqXjh49ipWVlZpSpS5xnpyP/61Jjh8/jkKhoHbt2mzbtk1lSGiOHDmwtrbOkitVpvUlwoey2hcKQJoN8R/Kiu+bmlz+H05eHxAQwOjRo+nSpYtydbizZ8/i5ubGjBkz1BVRfAek55IQQgghxDfm+vXrSba9evWK9u3b06RJE5XVmrLyEJUWLVoQERHBxo0blY0Bjx8/xsXFBTMzM3bs2KHmhKlbsmQJgwcPplu3bvzwww9AwpxLa9asYeHChfTu3VvNCVM3efJkhg8fjoGBgcr2N2/e8McffzB+/Hg1JUufBw8eYGVlpVz1K6ubNGmS8t/R0dH8/fffODk5KRsIzp07x61bt+jXr1+WbCT4uKdbSEgIUVFRKvOlGRgYkCdPnizZ603Tyz9RnTp16NGjB+3bt1fZvmHDBpYvX46Hh4d6golvnjQuCSGE+C6cPn2aatWqqTtGhvXo0YOOHTtSq1YtdUf5ZFOnTsXFxSXVITZZmSbWncQhEh/e5n34c+K/s+py8okePnxI8+bNuXXrlrKnz8OHDylRogS7d++mYMGCak6Yth07djB37lzl/ErFihVjxIgRtGjRQs3J0pbShNihoaHkyZMnS9edD0VFRREYGMi7d+9UtmflhtUePXqQP39+pkyZorJ9woQJPHz4kNWrV6spWfps2LCBv//+m1WrVlGkSBEAfHx86NmzJ71798bFxUXNCVOnyeVvYGCAl5cXjo6OKtt9fX0pXbo0UVFRakomvnXSuCSEEOK7kCNHDiwtLWnfvj0uLi4UL15c3ZE+SYsWLTh06BC5c+emXbt2dOzYUWNWqylVqhQ3b96kUqVKdOzYkTZt2pArVy51x0o3Taw7Dx48SPe+1tbWXzDJ51MoFBw9epQ7d+4ACY0zdevWVXOq74O2tjZPnz5NsirfsWPHaNu2LSEhIWpKlj4hISF07dqVAwcOJPt8Vm4cMzEx4dKlS0kaCPz8/ChfvjwvXrxQU7L0sbe3Z+vWrZQpU0Zl++XLl/nll1+y/JBLTS7/IkWK0KJFC2bPnq2yfeTIkezatQsfHx81JRPfOplzSQghxHfhyZMnbNq0iY0bNzJz5kxKliyJi4sL7du314jeD7t27SI8PJx///2XDRs2MG/ePIoWLYqLiwsdOnTAxsZG3RFT5OXlxa1bt1i/fj1z5sxh8ODB1KtXDxcXF1q2bJlkyE1Wo4l1J6s3GH0KLS0t6tWrR7169dQdJcMuX76s7LlUvHjxJB+4sxozMzO0tLTQ0tKicOHCKvPQxMXF8fr1a/r06aPGhOkzePBgIiIiOH/+PLVq1WLHjh08ffqUqVOnMnfuXHXHS5W+vj6enp5JGjc8PT3R09NTU6r0CwoKIjY2Nsn2uLg4nj59qoZEn0aTy3/+/Pn8/PPPHDhwgEqVKgEJiyD4+fmxbds2NacT3zLpuSSEEOK7c//+fTZs2MDGjRu5c+cONWrU4NixY+qO9UkePXrExo0bWb16NX5+fsnexGdVnp6ebNiwgX///Zfo6OhPWkJZ3TS17vj7+7NgwQJlA4eTkxODBg3C3t5ezcnSduLECebMmaOSfcSIEVSvXl3NydL27Nkz2rVrh4eHh8q8Mz/++CObNm1K0iMoq3Bzc0OhUNCtWzcWLFigMil/jhw5sLGxUc5Dk5Xlz5+fXbt2UbFiRYyNjbl06RKFCxdm9+7dzJ49m9OnT6s7YopmzpzJpEmT6NmzJxUrVgTg/PnzrF69mnHjxjF69Gg1J0xds2bNePz4MStXrqRs2bJAQiNrr169sLS0ZPfu3WpOmDpNL/+HDx+yZMkSlR6fffr0ybILCYhvgzQuCSGE+C7FxcVx4MABxo0bx/Xr17P08IiPxcTEsG/fPtatW8e+ffswNzfn8ePH6o6VbteuXWPdunVs2rSJ0NBQ3rx5o+5In0TT6s6hQ4do3rw5pUuXpmrVqkBCA5+Xlxd79uzJ0j2C1q1bR9euXWnVqpUy++nTp9m5cydr1qyhQ4cOak6YurZt23Lv3j3Wrl1LsWLFALh9+zadO3fGwcGBjRs3qjlh6k6cOEHVqlXJnj31wQ4zZ86kT58+yga0rMLY2Jjr169jY2ODtbU1GzZsoGrVqty/f5/ixYtn+blntmzZwsKFC1Xm6xo0aBBt2rRRc7K0hYSE0LlzZw4ePKhc8TE2NpYGDRqwZs2aJPN4ZUWaXP5CqIM0LgkhhPiueHp6sn79erZu3Up0dDQtWrTAxcWFhg0bqjtamo4fP86GDRvYtm0b8fHxtGrVChcXF2rXrp3u5ZPVJbHHz4YNG/Dx8aFmzZp06NCBX375RaVXRFamqXWnTJkyNGjQgJkzZ6psHz16NIcPH85yS2p/qFixYvTq1YshQ4aobJ83bx4rVqxQfujLqkxMTDh69CgVKlRQ2X7hwgXq169PRESEeoJlMmNjY65du4adnZ26o6ioUKECU6dOpUGDBjRv3hxTU1NmzJjBokWL2Lp1K/7+/uqO+Nk2btxI8+bNMTQ0VHeUZPn5+Smv06JFi1K4cGE1J8pcWaX8r1+/TokSJdDW1k52tdAPZeWJ7IVmk8YlIYQQ34XRo0ezefNmnjx5opzvp0WLFll+vp9ElpaWhIWF0bBhQ1xcXGjWrBm6urrqjpUulStX5uLFiypzFVlaWqo7Vrppet3R09Pjxo0bya4cVLJkSaKjo9WULG26urrcunULBwcHle13796lRIkSWTo7gJGREadOnaJ06dIq269evUrNmjU1akhoaoyMjPDy8spyjUvr1q0jNjaWLl26cPnyZRo2bEhYWBg5cuRgzZo1tG3bVt0RP1tWbdhLL8mfObS1tQkODiZPnjzJrhaaKKuvECo0m0zoLYQQ4rtw6tQpRowYoXErlSWaOHEirVu3znLDTtKjTp06rF69GicnJ3VHyRBNrzu5c+fm2rVrSRqXrl27luWHplhZWeHu7p6kceno0aMaMXdI7dq1GTRoEBs3bqRAgQIAPH78mCFDhlCnTh01p/v2dezYUfnvcuXK8eDBA+7cuUOhQoU08lpOjqb3E5D8meP+/fvKOdyy+kp84tsljUtCCCG+eTExMRQpUoRGjRpp5AeKmJgY+vbtS5UqVTSucSkmJoZNmzapfMjTJJpedwB69uxJr169uHfvHj/88AOQMMRv1qxZDB06VM3pUjds2DAGDhzItWvXVLKvWbOGhQsXqjld2hYvXkzz5s2xsbFRNoY9fPiQEiVKsG7dOjWn+7bFxMRQtGhR9u7dq5zvysDAQDm5tBDfkg9XCP2WVgsVmkUal4QQQnzzdHR02LZtG+PGjVN3lAzR0dGhUKFCGtmVXUdHJ8sPXUqNptcdgHHjxmFkZMTcuXMZM2YMAAUKFGDixIkMHDhQzelS17dvX/Lly8fcuXPZsmULkDAP0+bNm2nRooWa06XNysqKK1eucPToUZVVm+rWravmZN8+TX/vESKjZsyYQd68eenWrZvK9tWrVxMSEsKoUaPUlEx862TOJSGEEN+Fzp07U7p06SQTA2uKVatWsX37dv755x/Mzc3VHeeTTJ8+HV9fX1auXJnmqlNZkabXnQ+9evUKSJgjJ6uLjY1l+vTpdOvWjYIFC6o7zieLiYlBX1+fa9euUaJECXXH+aKy6pxLmv7ekx5ZtezTS/JnPhsbGzZs2KDs7Zno/PnztGvXTobNiS/m23yXFUIIIT7i6OjI5MmT8fT0pFy5cklWdsnqPTgWL17M3bt3KVCgANbW1knyZ+UVvy5evIi7uzuHDx/G2dk5Sfbt27erKVn6aHrdSRQSEoKPjw+QsGpTVh/mlz17dmbPnk2nTp3UHSVDNLnH4aeqXr06+vr66o6RhKa/93wPsvpKp5ooODiY/PnzJ9meO3dugoKC1JBIfC+kcUkIIcR3YdWqVZiamnL58mUuX76s8pyWllaWbyBo2bKluiNkmKmpKT///LO6Y2SYptedyMhIBgwYwNq1a4mPjwcgW7ZsdOrUiT///DNLr3pXp04dTpw4gY2NjbqjZMjYsWP53//+p1E9Dj9lBTtjY2MA9u/f/6XifBZNf+9JD2tra3R0dNQdI8M0fRBNVix/KysrPD09sbW1Vdnu6empXFhAiC9BhsUJIYQQQnzDevfuzdGjR1m8eDFVq1YF4PTp0wwcOJB69eqxZMkSNSdM2dKlS5k0aRIuLi7J9hpr3ry5mpKlT5kyZbh79y4xMTEa0+MwcRnz9PgeemWpS2xsLLdu3SI4OBiAfPny4eTklOUaMj7X6dOnqVChArq6uuqOokKTy3/27NnMnj2bP/74g9q1awPg7u7OyJEjGTZsmHLuPSEymzQuCSGE+K68e/eO+/fvY29vr3FzcERERLB161b8/f0ZMWIE5ubmXLlyhbx582JpaanueKmKjY3Fw8MDf39/OnTogJGREU+ePMHY2JicOXOqO166aGrdyZUrF1u3bqVWrVoq248fP06bNm0ICQlRT7B00NbWTvE5LS2tLN+4MWnSpFSfnzBhwldKkn4nTpxQ/jsgIIDRo0fTpUsXqlSpAsDZs2dxc3NjxowZdO7cWV0xv1nx8fGMHz+ev/76ixcvXqg8Z2JiQv/+/Zk0aVKq14a63b59m8WLF3P27FmVxpkqVarQv39/nJyc1JwwZd9C+SsUCkaPHs2iRYt49+4dAHp6eowaNYrx48erOZ34lknjkhBCiO9CVFQUAwYMwM3NDQBfX1/s7OwYMGAAlpaWjB49Ws0JU3f9+nXq1q2LiYkJAQEB+Pj4YGdnx++//05gYCBr165Vd8QUPXjwgIYNGxIYGMjbt2+VZT9o0CDevn3L0qVL1R0xVZpedwwMDLh8+bJyOfZEt27domLFikRGRqopmcjq6tSpQ48ePWjfvr3K9g0bNrB8+XI8PDzUEyydbG1tU+2Fde/eva+YJn1GjhzJmjVrmDJlCg0aNCBv3rwAPH36lMOHDzNu3Di6dOnCrFmz1Jw0eQcOHKBly5aULVs2Sf4jR45w+fJldu3aRYMGDdScNHmaXv4fev36Nd7e3ujr6+Po6JjleoeJb480LgkhhPguDBo0CE9PTxYsWEDDhg25fv06dnZ27Nq1i4kTJ3L16lV1R0xV3bp1KVu2LLNnz1ZZnebMmTN06NCBgIAAdUdMUcuWLTEyMmLVqlVYWFgos3t4eNCzZ0/8/PzUHTFVml536tSpg4WFBWvXrkVPTw+AN2/e0LlzZ8LCwjh69KiaE3773r17x7Nnz5RzXiUqVKiQmhKlj4GBAV5eXjg6Oqps9/X1pXTp0kRFRakpWfosXLhQ5eeYmBiuXr3KwYMHGTFiRJZsGM6XLx9ubm4pNr4cOnSITp068fTp06+cLH1KlSpFixYtmDx5crLPT5w4ke3bt3P9+vWvnCx9NL38hVAnzenTLYQQQnyGnTt3snnzZipXrqzyTXbx4sXx9/dXY7L0uXjxIsuWLUuy3dLSUjnsIKs6deoUZ86cIUeOHCrbbWxsePz4sZpSpZ+m152FCxfSoEEDChYsSKlSpQDw8vJCT0+PQ4cOqTldUosWLUr3vll9MnVfX1+6d+/OmTNnVLYrFAqNGNZnZWXFihUrmD17tsr2lStXYmVlpaZU6Tdo0KBkt//1119cunTpK6dJn1evXqU66XL+/PmzdG9DX19fXFxcUny+ffv2WbrXj6aXPyQs4jBz5kzc3d2TbdTOij32xLdBGpeEEEJ8F0JCQsiTJ0+S7ZGRkRqxFLKurm6yqzj5+vqSO3duNSRKv/j4+GQ/RD969AgjIyM1JPo0ml53SpQogZ+fH+vXr+fOnTtAwgc8FxeXLLl8/Pz581V+DgkJISoqClNTUyBh7jEDAwPy5MmT5RuXunbtSvbs2dm7dy/58+fXiPryofnz5/Pzzz9z4MABKlWqBMCFCxfw8/Nj27Ztak6XcY0aNWLMmDG4urqqO0oStWrVYvjw4axfv55cuXKpPPf8+XNGjRqVZP60rMTGxoZ9+/ZRpEiRZJ/ft28f1tbWXzlV+ml6+QP06NGDEydO8Ouvv2rk+47QXNK4JIQQ4rtQvnx59u3bx4ABAwCUN1srV65UTlSblTVv3pzJkyezZcsWICF/YGAgo0aNyvJLbdevX58FCxawfPlyICH769evmTBhAo0bN1ZzurRpet2BhOFNPXv2VHeMdLl//77y3xs2bODvv/9m1apVyg+rPj4+9OzZk969e6srYrpdu3aNy5cvU7RoUXVHyZDGjRvj5+fHkiVL8Pb2BqBZs2b06dNHI3oupWTr1q2Ym5urO0ayli5dSuPGjcmfPz/Ozs4qc/7cuHEDJycn9u7dq+aUKZs8eTIdOnTAw8ODunXrquR3d3fn4MGDbNiwQc0pU6bp5Q8J817t27dPuTqoEF+LzLkkhBDiu3D69GkaNWpEx44dWbNmDb179+b27ducOXOGEydOUK5cOXVHTNWLFy/45ZdfuHTpkrLbfnBwMFWqVGH//v1JljjPSh49ekSDBg1QKBT4+flRvnx5/Pz8yJUrFydPnky2V1BWoul1BxIaZP78809lA0GxYsXo379/lm/0sLe3Z+vWrZQpU0Zl++XLl/nll19UGqKyogoVKjB//nyqVaum7ijfpTJlyqj02lAoFAQHBxMSEsLff/9Nr1691JguZfHx8Rw6dIhz584lWW2tfv36WXqlMoAzZ86waNGiZFeLGzRoUJZvlNf08re1tWX//v1JFnEQ4kuTxiUhhBDfDX9/f2bOnImXlxevX7+mbNmyjBo1CmdnZ3VHSzdPT0+V/HXr1lV3pHSJjY1l8+bNKtmz6rCs5Ghy3dm2bRvt2rWjfPnyyg91586d4+LFi2zatClL93wzMDDgxIkTVKhQQWX7hQsXqFWrVpacUPrD4auXLl3i999/Z/r06Tg7O6Ojo6Oyr7Gx8deOlyFRUVEEBgYqlzVPVLJkSTUlSp9Jkyap/KytrU3u3LmpVatWlm9YFSKj1q1bx65du3Bzc8PAwEDdccR3RBqXhBBCCA0VERGhnIdGiJTY29vj4uKSZPWmCRMmsG7duiw9KXmzZs14/PgxK1eupGzZskBCr6VevXphaWnJ7t271ZwwKW1t7SS9ZT6e80RTJvQOCQmha9euHDhwINnns3p+TaVQKAgICMDKyors2bPz7t07duzYwdu3b2ncuHGSuYA0gUKhID4+nmzZsqk7Spo0vfzLlCmDv78/CoUCGxubJI3aV65cUVMy8a2TOZeEEEJ8F65cuYKOjo6yp8muXbtwdXXFycmJiRMnJlnJLKuZNWsWNjY2tG3bFoA2bdqwbds28uXLx/79+5WrgGVFbm5u5MqViyZNmgAwcuRIli9fjpOTExs3bszSk7uC5tedoKAgOnXqlGR7x44d+eOPP9SQKP1Wr15N586dKV++vPIDUmxsLA0aNGDlypVqTpe848ePqztCphk8eDARERGcP3+eWrVqsWPHDp4+fcrUqVOZO3euuuMlK7mFD1KSFXuO+fj40KBBAx4+fIidnR2HDx+mdevW3LlzB4VCgYGBAWfOnMHR0VHdUZMVGxvLxIkTOXXqFLVq1WLSpEn88ccfTJw4kdjYWNq1a8eKFSuy7Pumppc/QMuWLdUdQXyvFEIIIcR3oHz58oqtW7cqFAqFwt/fX6Grq6to3769wsHBQTFo0CD1hksHGxsbhaenp0KhUCgOHz6sMDU1VRw6dEjRvXt3Rb169dScLnWFCxdWuLu7KxQKheLMmTMKfX19xbJlyxTNmjVT/PTTT2pOlzZNrzuNGjVSrF69Osn21atXK+rXr6+GRJ/Ox8dHsWvXLsWuXbsUPj4+6o6Tbg8ePFDEx8cn2R4fH6948OCBGhJ9mnz58inOnz+vUCgUCiMjI2XZ79q1S1G1alV1RkuRlpaWQltbO12PrKhFixaK5s2bK65fv64YPHiwolixYooWLVoo3r17p4iOjlY0a9ZM0bFjR3XHTNHvv/+uyJs3r2Lo0KEKJycnRZ8+fRRWVlaKdevWKdzc3BSWlpaKWbNmqTtmijS9/IVQJxkWJ4QQ4rtgYmLClStXsLe3Z9asWRw7doxDhw7h6elJu3btePjwobojpkpfXx9fX1+srKwYNGgQ0dHRLFu2DF9fXypVqkR4eLi6I6bIwMCAO3fuUKhQIUaNGkVQUBBr167l1q1b1KpVi5CQEHVHTJUm1p0Ph4s9efKE8ePH06ZNGypXrgwkzLn077//MmnSJPr06aOumOn27t077t+/j729Pdmza07H+2zZshEUFJRk0vrQ0FDy5MmT5YeVGRsbc/36dWxsbLC2tmbDhg1UrVqV+/fvU7x48Sw559WJEyeU/w4ICGD06NF06dJFOd/Y2bNncXNzY8aMGXTu3FldMVOUJ08eDh8+TOnSpYmMjMTIyIiTJ08qJ4U/c+YM7du358GDB2pOmjx7e3sWLlxI06ZNuXv3LkWKFGHDhg3KXrdbtmxhypQp3LhxQ81Jk6fp5S+EOmnO/85CCCHEZ1D8N98DwNGjR2natCkAVlZWPH/+XJ3R0sXMzIyHDx9iZWXFwYMHmTp1KpDwurL6B9ScOXMSGhpKoUKFOHz4MEOHDgVAT0+PN2/eqDld2jSx7iQ3LOLvv//m77//Vtn222+/ZenGpaioKAYMGICbmxsAvr6+2NnZMWDAACwtLRk9erSaE6ZOkcx8SwCvX79GT09PDYk+TZEiRfDx8cHGxoZSpUqxbNkybGxsWLp0Kfnz51d3vGTVrFlT+e/Jkyczb9482rdvr9zWvHlznJ2dWb58eZZsXHr9+jXm5uYAGBoaYmhoqFLWVlZWPH36VF3x0vTkyRPlMG0HBwdy5MihMmy7QoUKWbphRlPL39zcHF9fX3LlyoWZmVmy7zuJwsLCvmIy8T2RxiUhhBDfhfLlyzN16lTq1q3LiRMnWLJkCQD3798nb968ak6XtlatWtGhQwccHR0JDQ2lUaNGAFy9ehUHBwc1p0tdvXr16NGjB2XKlMHX15fGjRsDcOvWLWxsbNQbLh00se4kNoZpujFjxuDl5YWHhwcNGzZUbq9bty4TJ07Mso1LiQ2oWlpajBs3TmXFpri4OM6fP0/p0qXVlC79Bg0aRFBQEJAwAXzDhg1Zv349OXLkYM2aNeoNlw5nz55l6dKlSbaXL1+eHj16qCFR2goUKEBgYCCFChUCYPbs2So930JCQjAzM1NXvDSZmJgQERGBlZUVAGXLlsXIyEj5/Nu3b1Nt+FA3TS3/+fPnK8t5wYIF6g0jvlvSuCSEEOK7sGDBAlxcXNi5cydjx45VNshs3bqVH374Qc3p0jZ//nxsbGx4+PAhs2fPJmfOnEDCZM39+vVTc7rU/fXXX/z+++88fPiQbdu2YWFhASSs+vVhj4KsStPrTno5Ozuzf/9+5YfCrGDnzp1s3ryZypUrq3wgLV68eJZe5e7q1atAQs+lGzduqExenNiTY/jw4eqKl24dO3ZU/rtcuXI8ePBAOcQ1q6+YBQm9TFasWMHs2bNVtq9cuTJL1fMP1a1blzt37iiHYfXt21fl+cOHDytXTsyKnJycuHLlinIBBE9PT5Xnb9y4kaUnw9bU8v+wF156e+TNnDmTPn36yKqzItPInEtCCCG+a9HR0WTLlk25EtXGjRtp3rw5hoaGak6WMU2aNGHlypVZdshKavr168fkyZM14kMrfHt1x8jICC8vL+zs7NQdRcnAwICbN29iZ2enks/Ly4saNWrw4sULdUdMVdeuXVm4cGGWXJXsU2jqnFf79+/n559/xsHBgUqVKgFw4cIF/Pz82LZtm7IXpSa5f/8+enp6WfY93tfXFx0dHWxtbZN9fsOGDWTPnp02bdp85WSZI6uX/6cwNjbm2rVrWeo9X2g2bXUHEEIIIdRJT09P2TgA0Lt37yw5n0J6nTx5UiPmMUrOunXrPmkZcXX71upOVlS+fHn27dun/Dmx99LKlSuVEzRnZa6urhrdsBQVFUX37t0xMDCgePHiBAYGAjBgwABmzpyp5nRpa9y4MX5+fjRv3pywsDDCwsJo1qyZyvBcTWNra6vSsOHs7JylFhUoXLhwig1LAB06dFBpWJo5cyYRERFfIVnmyOrl/ymkj4nIbJrz1YMQQgjxFcjNlvpoetlrev6saPr06TRq1Ijbt28TGxvLwoULuX37NmfOnFFZFSwru3TpElu2bCEwMJB3796pPLd9+3Y1pUofTZ3z6kMFCxZk2rRpqe6jab0mPxQQEEBMTIy6Y2TY9OnTadOmjcYOzdL08hciM0nPJSGEEEIIkSVVq1aNa9euERsbi7OzM4cPHyZPnjycPXuWcuXKqTtemjZt2sQPP/yAt7c3O3bsICYmhlu3bnHs2DFMTEzUHS9NO3fuZPHixVSrVk2j5rz6VJrWa/JbIo3yQnw7pOeSEEIIIYTIsuzt7VmxYoW6Y2TI9OnTmT9/Pr/99htGRkYsXLgQW1tbevfurRFztoSEhKislJUoMjIyS6/49amkgUMIIT6f9FwSQgghhBBZVlxcHFu3bmXKlClMmTKFbdu2ERsbq+5Y6eLv70+TJk2AhFXiEhtlhgwZwvLly9WcLm2aPueVEEKIr0d6LgkhhBBCfGciIiKSzHGybNky8ubNq55AKbh16xbNmzcnODiYIkWKADBr1ixy587Nnj17KFGihJoTps7MzIxXr14BYGlpyc2bN3F2diYiIoKoqCg1p0vbtzDnlRAiedWrV0dfX1/dMcQ3RHouCSGEEB+wtrZWWQFM0/zvf//D3Nxc3TFUBAYGJjvsRKFQKFefAujYsaNGr6yVVevOrFmz2Lx5s/LnNm3aYGFhgaWlJV5eXsrtHTp0wNDQUB0RU9SjRw+KFy/Oo0ePuHLlCleuXOHhw4eULFmSXr16qTtemmrUqMGRI0cAaN26NYMGDaJnz560b9+eOnXqqDld2jR9zishvlfx8fH4+vpy+vRpTp48qfJItH//fo0Ynis0h5ZCBhkLIYQQWd7u3buT3a6lpYWenh4ODg6pLv+sTtmyZSMoKCjJ3C2hoaHkyZOHuLg4NSX7NO/evePZs2fEx8erbC9UqJCaEqWPra0t/2fvzsNqTv//gT9P0aadoiitUilKfKxZx9ZMtsFgZKwzloqUzBAKSaMijDUSI9vYd5IoW2mxL5UUI0wmJFSnfn/063wdp+Vk6H6/z3k9rst16T5nruv56ZPqvM59P+8///wTnTp1wunTpzF8+HDs2rVLdIPZqVOnWEeskqqqKpKSkmBrayu2fvPmTbRr1w7v3r1jlEw6L1++xPv372FoaIjS0lIEBwfj4sWLsLS0xLx586Cjo8M6IgGgoaGBtLQ0mJmZsY5Sazt27MDAgQM5NxiW1oABAxAREcHbIQcXP/+XL1/GqFGj8OjRI4k3dgQCAW9+5hL+oWNxhBBCZJaOjo7UpbMvX778ymn+m0GDBkEgEFT6i2JZWRkEAgG6dOmCAwcOcO4Fa0W+TxUUFEBFRYVBotp58OABxo8fj4sXL4qtV/zv4vov6rm5uTAyMgIAHDlyBMOHD0efPn1gYmKC//3vf4zTVa9FixZ49uyZxHDp+fPnsLCwYJRKeh/vIlRQUMCcOXMYppFObW5N4/NOw49xdddkTEwMYmJiKh1qb968GUD5jkOuKi0tRXp6eqX5nZ2dAZTvnuGK8PBwTJ48GSoqKggPD6/2uR4eHgC4+fn/5ZdfRH1pBgYGMlW+T7iNhkuEEEJk1ooVK0R/z8vLw+LFi9G3b19REe2lS5dw8uRJ+Pn5MUoovdOnT2Pu3LlYsmQJ2rdvDwC4evUq/Pz8MG/ePGhpaeHnn3+Gt7c3IiIiGKct5+XlBaB8AObn5wc1NTXRY0KhEFeuXEGbNm0YpZPeTz/9hHr16uHIkSO8/EVdR0cHOTk5MDIywokTJ7B48WIA5cMxLg7GPh5uLF26FB4eHli4cCE6dOgAoPxd+YCAACxbtoxVxFoRCoXYv38/7ty5AwCwsbHBwIEDUa8eN38N19bWrvFrnC+D1evXr1e6XrHj09jYGMrKyli7dm0dJ6uZv78/AgIC4OTkxMvvO3zcPRMWFobRo0dDRUUFYWFhVT5PIBCIhktc9ODBA+zdu5cXA3giW+hYHCGEELkwdOhQ9OjRA9OnTxdbX716Nc6cOYMDBw6wCSalVq1aYcOGDejUqZPYekJCAiZPnoxbt27hzJkzGD9+vFiPEUs9evQAAMTFxaFjx45QUlISPaakpAQTExN4e3vD0tKSVUSpNGjQANeuXUPLli1ZR/ks06dPx5EjR2BpaYmUlBRkZWVBXV0dO3fuRHBwMJKTk1lHFKOgoCD2QrriV9WKtY8/5uIL1I9VVkh+//59TheS16aou1u3bl8xyX/36dfSp+rXr48RI0Zg/fr1nNtFaWBggODgYIwZM4Z1lM/Spk0btGjRAv7+/pUOx7S0tBglk309e/bE7Nmz0a9fP9ZRiJzh5lsmhBBCyBd28uTJSnc69OvXjxdHVTIyMio9tqGpqYnMzEwAgKWlJf7555+6jlal2NhYAMC4ceOwcuVKTh47kYaNjQ2nPq+1FRYWBhMTE+Tk5CA4OBjq6uoAgKdPn2Lq1KmM00mq+LqRBRWF5ElJSaLjqv/++y9++uknTJ48WeKoJRd8zsBo6tSpCAgIQKNGjb5Cos+3f/9++Pr6wsfHR2zHZ0hICBYsWICSkhLMmTMH8+bNw/LlyxmnFVdUVCTxZgKf0O4Zdtzd3TFr1izk5ubCzs5O4qIJe3t7RsmIrKOdS4QQQuRC8+bN4eHhgVmzZomth4SEIDw8HI8ePWKUTDpdunSBhoYGoqKioKenBwB48eIF3Nzc8PbtW5w/fx5nzpzBtGnTcO/ePcZpZcvZs2cxb948BAYGVvqLOl+HZrKEq8MNvheSS0tTUxOpqamcK8Ru3749Fi1ahL59+4qtVxyHvnr1Kg4cOIBZs2YhIyODUcrK+fr6Ql1dnRfHtivD990zQ4cORfv27eHr6yu2HhwcjMTEROzZs4dRspopKEheCP9xPyPXd3wS/qKdS4QQQuSCv78/Jk6ciHPnzolKjK9cuYITJ05g48aNjNPVLCIiAgMHDkSzZs1E5cw5OTkwMzPDwYMHAZQXZM+bN49lzEq9ffsWQUFBVRbTVuy84qrevXsDgMTV8Xz5RX3r1q1o1KgRXFxcAACzZ8/Ghg0bYGNjg+joaDRv3pxxwv9u+/bt8Pb25txwie+F5NLi6nvVN27cqPTru3nz5rhx4waA8uNbT58+retoNXr//j02bNiAM2fOwN7eXmKoHRoayiiZdPi+e+b8+fNYuHChxHr//v0REhJS94Fq4eHDh6wjEDlFwyVCCCFy4aeffoK1tTXCw8Oxb98+AIC1tTXi4+M5f2MWAFhZWeH27ds4deoU7t+/L1r75ptvRO9SDho0iGHCqk2cOBFxcXEYM2YML4tp+X5MKzAwUFRYfOnSJaxZswZhYWE4cuQIZs6cKfr3wGdcGm7IWiE5n7Vs2RJBQUHYsGGDqPOtuLgYQUFBog61J0+eoHHjxixjVur69euiCw9u3rwp9hgfvocOHToUADB+/HjRGp92zxQUFIj1BFaoX79+rW5UZEEW3jAg/ETH4gghhBDyVWlra+Po0aPo3Lkz6yhySU1NDXfv3oWxsTF8fX3x9OlTREVF4datW+jevTtevHjBOuJ/pqGhgbS0NE4cy5KlQnJpcenz/7GLFy/C1dUVCgoKop0yN27cgFAoxJEjR9ChQwds27YNubm58PHxYZxWttR01JzrA5D27dvj22+/xfz588XWFy5ciMOHD+PatWuMkkknIyMDK1asELul0tPTE+bm5oyTEVlGO5cIIYTIjYyMDGzZsgWZmZlYsWIF9PX1cfz4cRgbG0scW+GimJiYKo+Wbd68mVGqmuno6EBXV5d1jP8kPz8fERERol/UbW1tMX78eF7ceKSuro68vDwYGxvj1KlT8PLyAgCoqKjITOcPl/B9p5ss6dSpEx4+fIg///xTtONz2LBhGDVqFDQ0NACAt7excR3Xh0c18fPzw5AhQ5CRkYGePXsCKP8ZHB0dzem+JaC8U8zV1RVt2rQRvamTkJAAW1tbHD58GN988w3jhERW0c4lQgghciEuLg79+/dH586dcf78edy5cwdmZmYICgpCUlIS9u7dyzpitfz9/REQEAAnJ6dKj5bt37+fUbKabd++HQcPHsTWrVuhpqbGOk6tJSUloW/fvlBVVRXdOJWYmIh3797h1KlTcHR0ZJyweqNHj8bdu3fh4OCA6OhoZGdno2HDhjh06BB+++03iSM3fMTVnTPS4mohubT4/vnniiFDhiAyMhKampoYMmRItc/l4nHWQ4cOoX///qhfvz4OHTpU7XNdXV3rKNXnO3r0KAIDA5GamgpVVVXY29tjwYIFn3WjYl1ycHBA3759ERQUJLY+Z84cnDp1CsnJyYySEVlHwyVCCCFyoWPHjhg2bBi8vLzEXghdvXoVQ4YMwePHj1lHrJaBgQGCg4N5+S67g4MDMjIyUFZWBhMTE4liV67/otu1a1dYWFhg48aNqFevfNN3SUkJJk6ciMzMTJw/f55xwurl5+dj3rx5yMnJwZQpU0S3Ny1YsABKSkqYO3cu44T/Hd+HG1y9bU1aU6ZMwaJFizg5HHvw4AFiY2Mr3fH56ZEn1saNG4fw8HBoaGhg3Lhx1T53y5YtdZRKegoKCsjNzYW+vn6lN5ZVkKUjoVykoqKCGzduwNLSUmz9/v37sLe3x/v37xklI7KOjsURQgiRCzdu3MCOHTsk1vX19fHPP/8wSFQ7RUVF6NSpE+sYn4WrRePSSkpKEhssAUC9evUwe/ZsODk5MUwmHW1tbaxevVpi3d/fn0Gamn28eyMqKgojRoyAsrJytf/Njz/+CE1NzTpK+OVx9b3e69evV7ouEAigoqICY2NjKCsriwrjuWbjxo2YMmUKGjVqhCZNmojt+BQIBJwbLn08MJJ2eJSQkAAnJ6ca/43UhY+Hd58O8vjGzMwMiYmJaNiwodh6fn4+HB0dOX3LqZ6eHlJTUyWGS6mpqdDX12eUisgDGi4RQgiRC9ra2nj69ClMTU3F1lNSUtC0aVNGqaQ3ceJE7NixA35+fqyj1NqCBQtYR/hPNDU1kZ2dLbpdqkJOTo6ot4XrLly4gPXr1yMzMxN79uxB06ZNsW3bNpiamqJLly6s44k5cuQI3r59C01NTYwbNw79+vWr8QURV4cbfNemTZtqbyarX78+RowYgfXr10NFRaUOk0ln8eLFWLJkCXx9fVlH+Wr69+/P611vdnZ2OHbsGIyMjFhHEZOVlVXp7qoPHz7gyZMnDBJJb9KkSZg8eTIyMzNFb0olJCRg2bJlos49Qr4GGi4RQgiRCz/88AN8fX2xZ88eCAQClJaWIiEhAd7e3nBzc2Mdr0bv37/Hhg0bcObMGdjb20scLQsNDWWUTDr5+fnYu3cvMjIy4OPjA11dXSQnJ6Nx48acH+6NGDECEyZMwPLly8V+Uffx8cHIkSMZp6vZX3/9hTFjxmD06NFITk7Ghw8fAACvXr1CYGAgjh07xjihuJYtW+LXX39Fjx49UFZWht27d1e5K4kP/3b5bP/+/fD19YWPj4+ob+zq1asICQnBggULUFJSgjlz5mDevHlYvnw547SS/v33XwwbNox1jK+Kq7vepJWVlYXi4mLWMUQ+7oo6efKk2KUNQqEQMTExMDExYZBMen5+ftDQ0EBISAh+/fVXAIChoSEWLlwIDw8PxumILKPOJUIIIXKhqKgI06ZNQ2RkJIRCIerVqwehUIhRo0YhMjISioqKrCNWq0ePHlU+JhAIcPbs2TpMUzvXr19H7969oaWlhaysLNy7dw9mZmaYN28esrOzERUVxTpitYqKiuDj44N169ahpKQEQPmOjSlTpiAoKIgTx1Gq4+DggJkzZ8LNzU2smyglJQX9+/dHbm4u64hiLl68CC8vL2RkZODly5fQ0NCodPeMQCDAy5cvGST88rjaGdW+fXssWrQIffv2FVs/efIk/Pz8cPXqVRw4cACzZs1CRkYGo5RVmzBhAtq1a4dffvmFdZSvhqtfO9LiWv6KriiBQCAxuKtfvz5MTEwQEhKCb7/9lkW8Wnvz5g0A8GaXLeE3Gi4RQgiRK9nZ2bh58yYKCgrg4OAg0UlAvrzevXvD0dERwcHBYi8kLl68iFGjRiErK4t1RKkUFhaKXkCbm5vz5uY7NTU13L59GyYmJmKf/8zMTNjY2HC63PXjgmBZxrUX2BVUVVWRkpIicSS04vbBd+/eISsrCzY2NigsLGSUsmpLly5FaGgoXFxcYGdnJ7HjUxZ2cXD1a0daXM1vamqKxMRETpbUE8JVdCyOEEKIXDE2NoaxsTHrGHIlMTER69evl1hv2rQp53bNVEdNTQ12dnasY9RakyZNkJ6eLnGUIz4+nnMv6D718OFD6OnpsY5RK7JUSN6yZUsEBQVhw4YNUFJSAgAUFxcjKChINHB68uQJGjduzDJmlTZs2AB1dXXExcUhLi5O7DGBQCATwyXydTx8+FCq53GlM8rR0RExMTHQ0dGBg4NDtV1pXL+hlfAXDZcIIYTIrNoUV3Kxs+jjF6lDhgyp9rn79u2ro1S1p6ysjNevX0us379/n7ODA1n53APl5a6enp7YvHkzBAIB/v77b1y6dAne3t6cL4hv3rw58vPzERERgTt37gAAbGxsMGHCBLEuFC6RpULyNWvWwNXVFc2aNYO9vT2A8ps3hUIhjhw5AgDIzMzE1KlTWcaskrQDAj6rbohAvj6udEYNHDhQNMQeOHAgfV0QJmi4RAghRGalpKSIfZycnIySkhJYWVkBKB9uKCoqom3btizi1UhLS0v0CyJXX0hLw9XVFQEBAdi9ezeA8hdD2dnZ8PX1xdChQxmnq9zHn3tNTU1e/6I+Z84clJaWolevXigsLISzszOUlZXh7e0Nd3d31vGqlZSUhL59+0JVVVVUKB0WFobAwECcOnUKjo6OjBNKkqVC8k6dOuHhw4f4888/cf/+fQDAsGHDMGrUKFGHy5gxY1hGlEllZWXIycmBvr5+jbfwUcMJAcRvZV24cCG7IESuUecSIYQQuRAaGopz585h69at0NHRAVB+k9C4cePQtWtXzJo1i3FC2fXq1St8//33SEpKwps3b2BoaIjc3Fx07NgRx44dQ4MGDVhHlAtFRUVIT09HQUEBbGxsoK6uzjpSjbp27QoLCwts3LgR9eqVvydaUlKCiRMnIjMzE+fPn2ecUJI8FpJziZeXFxYtWoQGDRrUuHuViztWAaC0tBQqKiq4deuWzPcC7tixAwMHDuTtzwEudkaZmZkhMTERDRs2FFvPz8+Ho6MjMjMzGSUjso6GS4QQQuRC06ZNcerUKdja2oqt37x5E3369MHff//NKJn8iI+Px/Xr11FQUABHR0f07t2bdSSp9OzZE/v27YO2trbY+uvXrzFo0CBO39THd1UVSt++fRtOTk6cLJH+mCwUkj948ACxsbF4/vw5SktLxR6bP38+o1RV69GjB/bv3w9tbe1qb9kEgNjY2DpKVXu2traIiIhAhw4dWEeRWnh4OCZPngwVFRWEh4dX+1xZ6bvi4nCpqu87z549g5GREYqKihglI7KOjsURQgiRC69fv8aLFy8k1l+8eCG6qpfLnj17Bm9vb8TExOD58+cSRyGEQiGjZNLr0qULunTpwjpGrZ07d67SX8bfv3+PCxcuMEhUs5p6oj7G5c4oTU1NZGdnSwyXcnJyeHG1Nh8LyT+2ceNGTJkyBY0aNUKTJk3EdmAJBAJODpc+HhhxeXhUk6CgIPj4+GDt2rVo1aoV6zhSCQsLw+jRo6GiooKwsLAqn0dl6l/HoUOHRH8/efKk2HF6oVCImJgYmJqasohG5AQNlwghhMiFwYMHY9y4cQgJCRF1t1y5cgU+Pj61eiHOyk8//YTs7Gz4+fnBwMCAdx1AiYmJVe5+4OrRlOvXr4v+fvv2bbGb7YRCIU6cOIGmTZuyiFYjPnd0fWzEiBGYMGECli9fjk6dOgEAEhIS4OPjg5EjRzJOVzM+FpJ/bPHixViyZAl8fX1ZR/kssbGxVe5eWrNmDaZNm1bHiaTn5uaGwsJCtG7dGkpKSlBVVRV7nItHKj8uUJeHMnWuGTRoEIDy4d3YsWPFHqtfvz5MTEwQEhLCIBmRF3QsjhBCiFwoLCyEt7c3Nm/eLLrZpV69epgwYQJ+//13zvc9aGho4MKFC2jTpg3rKLUWGBiIefPmwcrKCo0bN5bY/cDVY2UKCgqirJX9uqSqqopVq1Zh/PjxdR1NbhQVFcHHxwfr1q1DSUkJgPIXSVOmTEFQUJDodiSuqqyQPDExEe/eveNsIfnHNDU1kZqayqkjP7Who6ODM2fOSFzasHLlSvj5+VV6iyVXbN26tdrHPx0eEDa42BllamqKxMRENGrUiHUUImdouEQIIUSuvH37FhkZGQAAc3NzTv1CWB0bGxv8+eefcHBwYB2l1ho3boxly5bhp59+Yh2lVh49eoSysjKYmZnh6tWrYseblJSUoK+vD0VFRYYJpSMLnVGFhYVi/27V1NTEHn/8+DEMDQ2hoKDAIl6V+FhI/rEJEyagXbt2+OWXX1hH+SybNm3Cb7/9hvPnz4uOVoaEhCAgIABHjhxB165dGSeUXUOHDkX79u0ldr0FBwcjMTERe/bsYZRMenFxcVi+fLnYrkMfHx/6uiGkCjRcIoQQQnjg1KlTCAkJwfr162FiYsI6Tq0YGBjg/PnzMn/rEVdVVe76/PlzNG3aVLSTj8+4usOG74XkS5cuRWhoKFxcXGBnZ4f69euLPc6H3pzg4GCEh4cjPj4eu3btQmBgII4dO4bOnTuzjlatY8eOQVFREX379hVbP3XqFIRCIfr3788omXT09PRw9uxZ2NnZia3fuHEDvXv3xrNnzxglk8727dsxbtw4DBkyRPS1kpCQgP379yMyMhKjRo1inLBqAQEB1T7Oxa40IhtouEQIIURuJCUlYffu3cjOzpYoaOZyqTFQfryjsLAQJSUlUFNTk3iRx8X+jQrBwcH4+++/sWLFCtZRPsvSpUvRuHFjieNvmzdvxosXLzjbR1PRGdWmTRucPXsWurq6oscqOqPWr1+PrKwsRgm/HC7e2ASU79rbtm0b+vTpI7Z+8uRJuLm5cf4FdnXlvwKBgDdXmvv6+iIiIgJCoRDHjx/nxQ1s9vb2CAoKwoABA8TWT5w4AV9fX6SlpTFKJh1VVVWkpqbCyspKbP3u3btwcHDAu3fvGCWTjrW1NSZPnoyZM2eKrYeGhmLjxo2i3Uxc9OkO5+LiYjx8+BD16tWDubk5kpOTGSUjso4KvQkhhMiFnTt3ws3NDX379sWpU6fQp08f3L9/H8+ePcPgwYNZx6sRXwczAODt7Q0XFxeYm5vDxsZGYjDG9cHe+vXrsWPHDol1W1tb/PDDD5wdLrVp0wYCgQACgQA9e/aUeLyiM4p8PXwvJOdjKXN4eLjEWtOmTaGmpgZnZ2dcvXoVV69eBcDtnVcPHjyAjY2NxHrLli2Rnp7OIFHt2NnZYdeuXRK7ZHbu3Fnp/y6uyczMxHfffSex7urqit9++41BIumlpKRIrL1+/Ro//fQTL37fIfxFwyVCCCFyITAwEGFhYZg2bRo0NDSwcuVKmJqa4ueff4aBgQHreDXic3mrh4eH6Namhg0b8u6mu9zc3Eq/RvT09PD06VMGiaTz8OFDmeiM4rPly5dDIBDAzc2t0kJy8uWFhYVVuq6oqIiEhAQkJCQAKN95xeXhkpaWFjIzMyWOQaenp/OiK9DPzw9DhgxBRkaGaLgdExOD6OhoXvQtGRkZISYmBhYWFmLrZ86cgZGREaNUn09TUxP+/v747rvvMGbMGNZxiIyi4RIhhBC5kJGRARcXFwDlL6zfvn0LgUCAmTNnomfPnvD392ecsGYZGRnYsmULMjIysHLlSujr6+P48eMwNjaGra0t63hV2rp1K/766y/R559vjIyMkJCQIHFEKCEhAYaGhoxS1ax58+YAgNLSUsZJ5JeSkhJWrlyJpUuX8qaQ3MvLC4sWLUKDBg3g5eVV7XNDQ0PrKJX0+LjbqjIDBw7EjBkzsH//fpibmwMoHyzNmjULrq6ujNPV7LvvvsOBAwcQGBiIvXv3QlVVFfb29jhz5gy6devGOl6NZs2aBQ8PD6SmportOoyMjMTKlSsZp/s8r169wqtXr1jHIDKMhkuEEELkgo6ODt68eQOg/IjEzZs3YWdnh/z8fM6X6gLlt9b0798fnTt3xvnz57FkyRLo6+sjLS0NERER2Lt3L+uIVdLV1RW9OOKjSZMmYcaMGSguLhZ7B3727NmYNWsW43Q142tnVG1wfTecmpqaRLHxx2xsbDhTSJ6SkiIqea/seA1fVdTMcv1rpUJwcDD69euHli1bolmzZgDKh5Bdu3bF8uXLGaeTjouLC2/fVJgyZQqaNGmCkJAQ7N69G0B5D9OuXbswcOBAxumq9+nR0LKyMjx9+hTbtm3jfBE84Tcq9CaEECIXRo0aBScnJ9G78qtWrcLAgQNx+vRpODo6cr73p2PHjhg2bBi8vLzEyouvXr2KIUOG4PHjx6wjVmnLli04ceIEtmzZIrFjgw/KysowZ84chIeHi4rgVVRU4Ovry4tbd0xMTLBjxw7Ru+8Vrly5gh9++EEmdnpwtdBbWnzPz2VRUVH4/fff8eDBAwBAixYt4OPjw4ujQWVlZTh9+jTS0tJEO3+cnZ1Zx5KKmZkZEhMT0bBhQ7H1/Px8ODo68qYMno8+3WWroKAAPT099OzZE7/++is0NDQYJSOyjoZLhBBC5MLLly/x/v17GBoaorS0FMHBwbh48SIsLS0xb9486OjosI5YLXV1ddy4cQOmpqZiL0SzsrLQsmVLvH//nnXEKjk4OCAjIwNlZWUwMTGRKPTmy801BQUFuHPnDlRVVWFpaQllZWXWkaSioqKCO3fuSLzgyMzMhI2NDWe/doqLi0U3TrVq1ara5+bk5MDQ0JC3HVJcHS5VdKVVZs2aNZg2bVodJ6qd0NBQ+Pn5Yfr06aLr5OPj47FmzRosXrxY4iYwLomKisKIESMkvs8UFRWJLqjgMgUFBeTm5kJfX19s/dmzZzA2NsaHDx8YJZMODccIqT06FkcIIUTmlZSU4MiRI+jbty+A8l9658yZwzhV7Whra+Pp06cSA4KUlBQ0bdqUUSrpDBo0iHWELyI3NxcvX76Es7MzlJWVUVZWxosjNnztjKpfvz6MjY0hFAprfC4fC3b5YMiQIThz5gzatm0rtr5y5Ur4+flxfri0atUqrF27VmwQ4+rqCltbWyxcuJDTw6Vx48ahX79+EsOZN2/eYNy4cZwdLh06dEj095MnT0JLS0v0sVAoRExMjERJORdlZWVV+r3nw4cPePLkCYNEhHAfDZcIIYTIvHr16uGXX37BnTt3WEf5bBVX3u/ZswcCgQClpaVISEiAt7c3Z19kVFiwYAHrCP9JXl4ehg8fjtjYWAgEAjx48ABmZmaYMGECdHR0EBISwjpitfjcGTV37lz89ttv2LZtG3R1dVnHkTu///47+vfvj/Pnz6Nly5YAgJCQEAQEBODo0aOM09Xs6dOnEsdBAaBTp06cvukRQJXD68ePH4sNbLim4s0EgUAgcctp/fr1YWJiwunvmXwdjg0ZMkTq53K9BoDwFw2XCCGEyIX27dsjNTVVdIMW3wQGBmLatGkwMjKCUCiEjY0NhEIhRo0ahXnz5rGOV6P8/Hzs3bsXGRkZ8PHxga6uLpKTk9G4cWPO77yaOXMm6tevj+zsbFhbW4vWR4wYAS8vL06/UAIAHx8f5OXlYerUqRKdUb/++ivjdNVbvXo10tPTYWhoiObNm0tcwc6XI5U14eoOuIkTJ+Lly5fo3bs34uPjsWvXLgQGBuLYsWOiY2ZcZmFhgd27d+O3334TW9+1axcsLS0Zpaqeg4MDBAIBBAIBevXqhXr1/u/lmlAoxMOHD9GvXz+GCatXcTulqakpEhMT0ahRI8aJaoevwzEuDxyJ/KDhEiGEELkwdepUeHl5IScnB23btpV4kWpvb88omXSUlJSwceNG+Pn54ebNmygoKICDgwNnXyB97Pr16+jduze0tLSQlZWFSZMmQVdXF/v27UN2djaioqJYR6zWqVOncPLkSdGNTRUsLS3x6NEjRqmkJxAIsGzZMvj5+fGuM0pWjlTWhMsVqLNnz0ZeXh6cnJwgFApx8uRJdOjQgXUsqfj7+2PEiBE4f/68aBiWkJCAmJgY0Q1gXFPxNZ+amoq+fftCXV1d9JiSkhJMTEwwdOhQRumkJ+1FAXZ2djh27BhnjrbydTi2ZcsW1hEIoUJvQggh8kFBQUFiTSAQiI4eSNPrwgVFRUV4+PAhzM3Nxd7R5rLevXvD0dERwcHBYsXFFy9exKhRo5CVlcU6YrU0NDSQnJwMS0tLsfxJSUno27cv8vLyWEckHMTXQvJPrzGvsHz5cjg7O6N9+/aiNQ8Pj7qK9dmuXbuGsLAw0bFoa2trzJo1Cw4ODoyTVW/r1q0YMWIEVFRUqn1edHQ0XF1dJd4w4QuultlLi2vDsY+9ePEC9+7dAwBYWVlBT0+PcSIi62i4RAghRC7UtMOE68flCgsL4e7ujq1btwIA7t+/DzMzM7i7u6Np06acLijX0tJCcnIyzM3NxV5IPHr0CFZWVpy9razCgAED0LZtWyxatAgaGhq4fv06mjdvjh9++AGlpaXYu3cv64g1SkpKwu7du5GdnS06GleB6/0bfD5SaWZmhv3796N169aso0jt0+L3qggEAroxiwM0NTWRmprK2+EM34dLXMz/9u1buLu7IyoqSrQTS1FREW5ubli1ahXU1NQYJySyih9veRJCCCH/EdeHRzX59ddfkZaWhnPnzon1bfTu3RsLFy7k9HBJWVkZr1+/lli/f/8+L95JDQ4ORq9evZCUlISioiLMnj0bt27dwsuXL5GQkMA6Xo0qri3v27cvTp06hT59+uD+/ft49uwZBg8ezDpetfh+pJKPheTSHmfiC6FQiAMHDoh2Ltna2sLV1ZUTu8S+BNonQD7l5eWFuLg4HD58WHQcND4+Hh4eHpg1axbWrl3LOCGRVbRziRBCiFz4+AaYjwkEAqioqMDCwkLqd+xZaN68OXbt2oUOHTqIvVOanp4OR0fHSoc3XDFx4kTk5eVh9+7d0NXVxfXr16GoqIhBgwbB2dkZK1asYB2xRq9evcLq1auRlpaGgoICODo6Ytq0aTAwMGAdrUb29vb4+eefMW3aNNHXjqmpKX7++WcYGBjA39+fdcQq8f1IpYODA9LT01FcXMz7QvKKlwxcLR+vTHp6OlxcXPD48WNYWVkBAO7duwcjIyMcPXoU5ubmjBP+d1zcOVMblP/La9SoEfbu3Yvu3buLrcfGxmL48OF48eIFm2BE5tHOJUIIIXJh0KBBoo6lj33cu9SlSxccOHAAOjo6jFJW7cWLF9DX15dYf/v2Ledf7IWEhOD777+Hvr4+3r17h27duiE3NxcdO3bEkiVLWMerUXZ2NoyMjDB37txKHzM2NmaQSnoZGRlwcXEBUF4IXPE1M3PmTPTs2ZPTw6XExESsX79eYr1p06bIzc1lkKh2ZKGQPCoqCr///jsePHgAAGjRogV8fHwwZswYxslq5uHhATMzM1y6dEm0cywvLw8//vgjPDw8cPToUcYJCfnyCgsL0bhxY4l1fX19FBYWMkhE5AUNlwghhMiF06dPY+7cuViyZImokPbq1avw8/PDvHnzoKWlhZ9//hne3t6IiIhgnFaSk5MTjh49Cnd3dwD/t3tg06ZN6NixI8toNdLS0sLp06cRHx+P69evi3b+9O7dm3U0qZiamuLp06cSw728vDyYmppyvgxeR0cHb968AVA+lLl58ybs7OyQn5/P+RcafD9SuWDBAtYR/pPQ0FD4+flh+vTpYsdrfvnlF/zzzz+YOXMm44TVi4uLw+XLl8WOJDZs2BBBQUGi/z2EyJqOHTtiwYIFiIqKEhXCv3v3Dv7+/pz/fYHwGw2XCCGEyAVPT09s2LABnTp1Eq316tULKioqmDx5Mm7duoUVK1Zg/PjxDFNWLTAwEP3798ft27dRUlKClStX4vbt27h48SLi4uJYx5NKly5d0KVLF9Yxaq1iZ9unCgoKarzJiQucnZ1x+vRp2NnZYdiwYfD09MTZs2dx+vRp9OrVi3W8arm6uiIgIEB0bbxAIEB2djZ8fX15cR07wO9C8lWrVmHt2rVwc3MTrbm6usLW1hYLFy7k/HBJWVlZNFj9WEFBAZSUlBgkIp9av359pbtsyOdbuXIl+vbti2bNmokuE0hLS4OKigpOnjzJOB2RZdS5RAghRC6oqqoiMTFR4krwGzduoH379nj37h0ePXoEa2trzu7myMzMxNKlS8V6f3x9fWFnZ8c6WrWqutr8474rZ2dnzhXsenl5ASj/RX3SpEliN+wIhUJcuXIFioqKnC/1fvnyJd6/fw9DQ0OUlpYiODgYFy9ehKWlJebNm8fJY6AVXr16he+//x5JSUl48+YNDA0NRUcqjx07xvnr1z8tJL937x7MzMwwb948XhSSq6io4ObNm7CwsBBbf/DgAezs7Dh/06ObmxuSk5MREREh2rF65coVTJo0CW3btkVkZCTbgF9Aq1atcPz4cRgZGbGOIiEuLg7Lly8Xlanb2NjAx8cHXbt2ZZzsy9mxYwcGDhzIue9FhYWF+PPPP3H37l0AgLW1NUaPHg1VVVXGyYgso+ESIYQQudClSxdoaGggKipKdJzmxYsXcHNzw9u3b3H+/HmcOXMG06ZNw7179xinFVdcXIyff/4Zfn5+nC4dr4qpqSlevHiBwsJC0SDj33//hZqaGtTV1fH8+XOYmZkhNjaWUy+QevToAaD8BVLHjh3FdjooKSnBxMQE3t7esLS0ZBVRbvD1SCXfC8lbtWqFUaNG4bfffhNbX7x4MXbt2oUbN24wSiad/Px8jB07FocPH0b9+vUBlH8/HThwICIjI6GlpcU4Yc2uXbsmNpxxdHRknEg627dvx7hx4zBkyBDREcSEhATs378fkZGRGDVqFOOENYuJiUFMTAyeP3+O0tJSscc2b97MKBUh3EXDJUIIIXLh3r17GDhwIB4+fCgaYOTk5MDMzAwHDx5EixYtcODAAbx584aTRbVaWlpITU3l5XApOjoaGzZswKZNm0S3M6Wnp+Pnn3/G5MmT0blzZ/zwww9o0qQJ9u7dyzitpHHjxmHlypXQ1NRkHeWzKCoqVtkZpa+vz/nOKD7T0tJCcnIyzM3NxYZLjx49gpWVFed3/vz1118YMWIEevfuLTYgiImJwe7duzF48GDGCaWTnp6O27dvAygf0Hy6E4uLnj9/jh9++AHnzp2DtrY2gPJhWY8ePbBz507Od45ZW1tj8uTJEkcnQ0NDsXHjRtHAjKv8/f0REBAAJycnGBgYSByN3r9/P6NkNdu6dSsaNWokushh9uzZ2LBhA2xsbBAdHY3mzZszTkhkFQ2XCCGEyI3S0lKcOnUK9+/fBwBYWVnhm2++gYKCAuNkNRs7dizatGnD+Y6Typibm+Ovv/5CmzZtxNZTUlIwdOhQZGZm4uLFixg6dCiePn3KJqSUHj9+DABo1qwZ4yTSU1BQQG5ursRw6e+//4a5uTnevXvHKJl0YmJiEBYWJnoxam1tjRkzZvBi95K+vj5OnjwJBwcHseHS6dOnMX78eOTk5LCOWKNr165JfP5nzZoFBwcHxsmkExERgbCwMNFtd5aWlpgxYwYmTpzIOFn1RowYgczMTERFRcHa2hoAcPv2bYwdOxYWFhaIjo5mnLB6ysrKuHXrlsQgLz09Ha1ateL8YNXAwADBwcGcfLOpJlZWVli7di169uyJS5cuoVevXlixYgWOHDmCevXqYd++fawjEhlFhd6EEELkhoKCAvr164d+/fqxjlJrlpaWCAgIQEJCAtq2bSvR7+Dh4cEoWc2ePn2KkpISifWSkhLRdfKGhoaVFu9yQWlpKRYvXoyQkBAUFBQAADQ0NDBr1izMnTuXs8PJiq4rgUCATZs2QV1dXfSYUCjE+fPn0bJlS1bxpPLHH3/A09MT33//PTw9PQEAly9fxoABAxAWFoZp06YxTlg9WSgkb9u2LbZv3846xmeZP38+QkND4e7uLrol69KlS5g5cyays7MREBDAOGHVTpw4gTNnzogGS0D5rqs1a9agT58+DJNJx8jICDExMRLDpTNnznDq+HNVioqKxC4A4ZOcnBzR5/3AgQP4/vvvRbuEu3fvzjYckWm0c4kQQojc4PMOiOqOwwkEAmRmZtZhmtpxcXFBbm4uNm3aJNrtkJKSgkmTJqFJkyY4cuQIDh8+jN9++42THS6//vorIiIi4O/vL3Yd+8KFCzFp0iQsWbKEccLKVXzNPHr0CM2aNRMrTK/ojAoICMD//vc/VhFr1KxZM8yZMwfTp08XW1+zZg0CAwPx5MkTRsmkw/dCcqB8EHngwAHR901bW1u4urpyroC/Mnp6eggPD8fIkSPF1qOjo+Hu7o5//vmHUbKaaWho4MKFC5Xu+OzWrRtev37NJpiU1q5dixkzZmD8+PGiIU1CQgIiIyOxcuVK/Pzzz4wTVs/X1xfq6urw8/NjHaXWPt4x6eDgAC8vL4wZMwYZGRlo3bq16E0SQr40Gi4RQgiRCx/vgKh4B/vy5cvYu3cvL3ZA8Flubi7GjBmDmJgYUaluSUkJevXqhW3btqFx48aIjY1FcXExJ9+RNzQ0xLp16+Dq6iq2fvDgQUydOpXzA44ePXpg3759nL4Vrirq6upITU2t9LYyBwcH3rxI4msheXp6OlxcXPD48WNYWVkBKO+vMzIywtGjR0Udalylra2NxMREidL9+/fvo3379sjPz2cTTAoDBw5Efn4+oqOjYWhoCAB48uQJRo8eDR0dHU53/lTYv38/QkJCxN7Q8fHxwcCBAxknq5mnpyeioqJgb28Pe3t70c+uCqGhoYyS1Wz06NG4e/cuHBwcEB0djezsbDRs2BCHDh3Cb7/9hps3b7KOSGQUDZcIIYTIBb7vgJAFd+/eFeu7qnixynUqKiq4fv06WrRoIbZ+7949tGnThvOdRXw2atQoODg4wMfHR2x9+fLlSEpKws6dOxklkw8DBgxAWVkZ/vzzT+jq6gIoL4L/8ccfoaCggKNHjzJOWD13d3fUr19fYhDg7e2Nd+/eYc2aNYyS1SwnJweurq64deuW2CUUrVq1wqFDh3jV+8ZHFbeFVkYgEODs2bN1mKZ28vPzMW/ePOTk5GDKlCmiKoAFCxZASUkJc+fOZZyQyCoaLhFCCJELfN8BMXToULRv3x6+vr5i68HBwUhMTMSePXsYJZN9//vf//C///1P1GFUwd3dHYmJibh8+TKjZNIRCoWIjIys8kptrr1I+vjz/Pr1ayxfvhydO3cW23GYkJCAWbNmYd68eaxiSo3Px3EbNGiAy5cvw87OTmw9LS0NnTt35uT3TS8vL9HfS0pKEBkZCWNjY3To0AEAcOXKFWRnZ8PNzQ2rVq1iFVMqZWVlOHPmDO7evQug/GuHD183AGBmZobExEQ0bNhQbD0/Px+Ojo6cPspNCPk8NFwihBAiF/i+A0JPTw9nz56VeJF348YN9O7dG8+ePWOUTDqPHz/GoUOHkJ2djaKiIrHHuHy8AADi4uLg4uICY2NjsVLgnJwcHDt2DF27dmWcsHrTp09HZGQkXFxcKr1SOywsjFGyylXXL/YxrneNAfw/jqurq4sjR45IFBsnJCTgu+++w8uXLxklq1p1O04+xvXdJ1FRURgxYgSUlZXF1ouKirBz5064ubkxSiadqm6pfPbsGYyNjfHhwwdGyeTDhQsXsH79emRmZmLPnj1o2rQptm3bBlNTU3Tp0oV1PCKjaLhECCFELixevLjaHRCampqi53Lx5jVVVVWkpqZKHCWr6FXg8tGsmJgYuLq6wszMDHfv3kWrVq2QlZWFsrIyODo6cvoFXoW///4ba9asEdtBMHXqVFEXCpc1atQIUVFRGDBgAOsocofvx3Hd3NyQnJyMiIgItG/fHkD5zp9Jkyahbdu2iIyMZBtQhikqKuLp06cSw5m8vDzo6+tDKBQySla9Q4cOAQAGDRqErVu3QktLS/SYUChETEwMTp8+jXv37rGKWKUhQ4YgMjISmpqaGDJkSLXP3bdvXx2lqr2//voLY8aMwejRo7Ft2zbcvn0bZmZmWL16NY4dO4Zjx46xjkhkFA2XCCGEyAW+74Zo3749vv32W8yfP19sfeHChTh8+DCuXbvGKFnN2rdvj/79+8Pf3x8aGhpIS0uDvr4+Ro8ejX79+mHKlCmsI1apuLgY/fr1w7p16yRKgfnC0NAQ586dk+iMIl8f34/j5ufnY+zYsTh8+LCo0Li4uBgDBw5EZGSk2OCAfFkKCgp49uwZ9PT0xNbT0tLQo0cPTu4aA8pzA+U/Sz99mVm/fn2YmJggJCQE3377LYt41Ro3bhzCw8OhoaGBcePGVfvcLVu21FGq2nNwcMDMmTPh5uYm+plrZmaGlJQU9O/fH7m5uawjEhlFwyVCCCGEBw4fPowhQ4Zg1KhR6NmzJ4DyHUHR0dHYs2cPBg0axDZgNTQ0NJCamgpzc3Po6OggPj4etra2SEtLw8CBA5GVlcU6YrX09PRw8eJF3g6XQkJCkJmZidWrV0scieO6srIy7N27F7GxsZX2RXF59wDA/+O4FdLT03H79m0AgI2NjcSwjHw5Dg4OEAgESEtLg62tLerVqyd6TCgU4uHDh+jXrx92797NMGXNTE1NkZiYiEaNGrGO8lUlJCTAyclJ4vgiS2pqarh9+zZMTEzEhkuZmZmwsbHB+/fvWUckMqpezU8hhBBCCGvfffcdDhw4gMDAQOzduxeqqqqwt7fHmTNn0K1bN9bxqtWgQQNRz5KBgQEyMjJga2sLAPjnn39YRpPKjz/+iIiICAQFBbGO8lni4+MRGxuL48ePw9bWVuJKbS4PaGbMmIH169ejR48eaNy4MS+GYx8XktvY2GDJkiU4d+5cpcdx+SAiIgJhYWF48OABAMDS0hIzZszAxIkTGSeTTRVvFKSmpqJv375QV1cXPaakpAQTExMMHTqUUTrpPXz4UKrn2dnZ4dixY6Ib8fimf//+SE1NhZmZGesoIk2aNEF6ejpMTEzE1uPj4zmVk8geGi4RQgiRC+PHj6/28c2bN9dRks/n4uICFxcX1jFqrUOHDoiPj4e1tTUGDBiAWbNm4caNG9i3b5/oBicuKykpwebNm3HmzBm0bdsWDRo0EHuc64Xk2traGDx4MOsYn2Xbtm3Yt28fr/qiPi1I19HRwe3bt0U7f4Dy/082b97M+dvu5s+fj9DQULi7u4uV2c+cORPZ2dkICAhgnFD2LFiwAABgYmKCESNGQEVFpdrnR0dHw9XVVeL7El9kZWWhuLiYdYzPxsVDQJMmTYKnpyc2b94MgUCAv//+G5cuXYK3tzf8/PxYxyMyjI7FEUIIkQufvrguLi7GzZs3kZ+fj549e3J69wYA5OTkQCAQoFmzZgCAq1evYseOHbCxscHkyZMZp6teZmYmCgoKYG9vj7dv32LWrFmiY2ahoaFo3rw564jVqu72Ka7fOMV3pqamOH78OFq2bMk6ilzS09NDeHg4Ro4cKbYeHR0Nd3d3Xuw8lHWampqc2zlTGx8f2+IjLuYvKytDYGAgli5disLCQgCAsrIyvL29sWjRIsbpiCyj4RIhhBC5VVpaiilTpsDc3ByzZ89mHadaXbt2xeTJkzFmzBjk5uaiRYsWaNWqFR48eAB3d3eJom9CPvXixQvRDU1WVlYSRcFctHXrVpw4cQKbN2+Gqqoq6zhyR1tbG4mJiRJ9Y/fv30f79u2Rn5/PJhgR4eJwozYo/5clFAqRkJAAe3t7qKmpIT09HQUFBbCxsRE7YknI10DDJUIIIXLt3r176N69O54+fco6SrV0dHRw+fJlWFlZITw8HLt27UJCQgJOnTqFX375hZM33H2qqKio0lJmY2NjRolqLycnBwB41Q/y9u1buLu7IyoqSvS5V1RUhJubG1atWgU1NTXGCav27t07DB48GAkJCTAxMZHoi0pOTmaUTDp8LyR3d3dH/fr1JY5+ent74927d1izZg2jZKQC14YbtUX5vzwVFRXcuXNH6ltyCflSqHOJEEKIXMvIyEBJSQnrGDUqLi4W3UZz5swZuLq6AgBatmzJ+cHY/fv3MWHCBFy8eFFsvaysDAKBAEKhkFEy6ZSUlMDf3x/h4eGiq+PV1dXh7u6OBQsWSAw8uMbLywtxcXE4fPgwOnfuDKC82NXDwwOzZs3C2rVrGSes2tixY3Ht2jX8+OOPvCn0/hgfC8m9vLxEfxcIBNi0aRNOnTol6ke7cuUKsrOz4ebmxioiIZzBxX/TrVq1QmZmJg2XSJ2j4RIhhBC58PELJqB8sPH06VMcPXoUY8eOZZRKera2tli3bh1cXFxw+vRpUW/C33//jYYNGzJOV71x48ahXr16OHLkCAwMDDj5y3h13N3dsW/fPgQHB4uVGi9cuBB5eXmcHs4AwF9//YW9e/eie/fuorUBAwZAVVUVw4cP53T+o0eP4uTJk+jSpQvrKJ+Fj4XkKSkpYh+3bdsWQPkgHgAaNWqERo0a4datW3WejRCu4eIhoMWLF4v6lSq7hEJTU5NRMiLraLhECCFELnz6gklBQQF6enoICQmp8SY5Lli2bBkGDx6M33//HWPHjkXr1q0BAIcOHUL79u0Zp6teamoqrl27xttS5h07dmDnzp3o37+/aM3e3h5GRkYYOXIkp4czAFBYWIjGjRtLrOvr64vKXrnKyMiI1y+EtLS0OHVcRhqxsbGsIxCeKy4uRr9+/bBu3TqJvq5PrV+/vtLvT6xFR0dLFNlX8PHxwe+//w4AePPmTV3GkkrFMNvV1VXszRy+7BYm/EWdS4QQQshHEhIS4OTkJDqCxiVCoRCvX7+Gjo6OaC0rKwtqamrQ19cHwM387dq1Q1hYGG93n+jr6yMuLg7W1tZi63fu3IGzszNevHjBKJl0evXqhYYNGyIqKkp0rfm7d+8wduxYvHz5EmfOnGGcsGpHjx7FqlWrsG7dOpiYmLCOU2tUSE4+x8elzNra2tU+t1WrVjh+/DjneuD09PREt4Lykba2NqKjo8XeVACAmTNnYufOnZw+jh4XF1ft4926daujJETe0HCJEEII+Qjfr3XmYv6zZ89i3rx5CAwMhJ2dnURHEdd3pgQEBODu3bvYsmWLaGj34cMHTJgwAZaWlliwYAHjhNW7efMm+vbtiw8fPoh2vKWlpUFFRQUnT56Era0t44RV09HRQWFhIUpKSqCmpibxtfPy5UtGyaTD90Jywg7fS5lnzpwJZWVlBAUFsY7yWY4ePYrRo0fjyJEjojdGKo5Ix8TE8HYn7semTp2KgIAANGrUiHUUIiPoWBwhhBDyEb6/58LF/L179wZQvoPmY3zZop+SkoKYmBg0a9ZMbDhTVFSEXr16YciQIaLncvH2r1atWuHBgwf4888/cffuXQDAyJEjMXr0aM7vplmxYgXrCP8J3wvJCTt8L2UuKSnB5s2bcebMmUp7fz69gZBrXFxc8Mcff8DV1RWnT59GREQEDh48iNjYWLRo0YJ1vC9i+/bt8Pb2puES+WJouEQIIYSQr4rvHS7a2toYOnSo2BrXjqDURE1NDZMmTWIdo9b4ULZfHb4XkhN2+F7KfPPmTTg6OgIovzH0Y3wZso4aNQr5+fno3Lkz9PT0EBcXBwsLC9axvhguvhlF+I2GS4QQQgj5qvje77BlyxapnpeQkIAPHz5wqu8KAJYuXYrGjRtLFNdv3rwZL168gK+vL6NkNcvOzq72cWNj4zpK8nn4XkhO2OF7KTMf31T49FbZCnp6enB0dMQff/whWuP6zitCWKDhEiGEEELqRGFhIbKzs1FUVCS2bm9vzyjRl9W/f3/O9V0B5bcx7dixQ2Ld1tYWP/zwA6eHSyYmJtXucuD6C+yQkBDMnj2bt4XkhB0+Dmeq8vjxYwBAs2bNGCep3qe3ylawsLDA69evRY/zZecVIXWNhkuEEELIR/j+SyMX87948QLjxo3D8ePHK32c6wMCaXH1iEFubi4MDAwk1vX09Dh94xEg+WKvuLgYKSkpCA0NxZIlSxilkt6PP/6IwsJCmJub87KQnLDD9x2fpaWlWLx4MUJCQlBQUAAA0NDQwKxZszB37lwoKCgwTihJlgZ6hLBAwyVCCCHkI1wdEEiLi/lnzJiB/Px8XLlyBd27d8f+/fvx7Nkz0QsP8nUZGRkhISFBohg4ISEBhoaGjFJJp6JA/WNOTk4wNDTE77//LlamzkV8LyQnbOXn5yMiIgJ37twBUL7bcPz48dDS0mKcrGZz585FREQEgoKC0LlzZwBAfHw8Fi5ciPfv33N+OPzq1SsIhULo6uqKrb98+RL16tWj466EVIKGS4QQQshH3rx5wzrCf8LF/GfPnsXBgwfh5OQEBQUFNG/eHN988w00NTWxdOlSuLi4sI4o0yZNmoQZM2aguLgYPXv2BADExMRg9uzZmDVrFuN0n8fKygqJiYmsY9SI74XkhJ2kpCT07dsXqqqqaN++PQCIduydOnVKVJbNVVu3bsWmTZvg6uoqWrO3t0fTpk0xdepUzg+XfvjhB3z33XeYOnWq2Pru3btx6NAhHDt2jFGyL+fHH3+kIRn5ogRlXHyLkxBCCPkCHBwcpD4mlpyc/JXT1B7f81fQ1NTE9evXYWJigubNm2PHjh3o3LkzHj58CFtbWxQWFrKO+EVoaGggLS2Nc51LZWVlmDNnDsLDw0V9VyoqKvD19cX8+fMZp6ve69evxT4uKyvD06dPsXDhQty9exepqalsgkmJ74XkhJ2uXbvCwsICGzduRL165fsBSkpKMHHiRGRmZuL8+fOME1ZPRUUF169fR4sWLcTW7927hzZt2uDdu3eMkklHV1cXCQkJsLa2Flu/e/cuOnfujLy8PEbJanbixAmoq6uLbqlcs2YNNm7cCBsbG6xZswY6OjqMExJZRTuXCCGEyKxBgwaJ/v7+/Xv88ccfsLGxQceOHQEAly9fxq1btyTemeQKvuevYGVlhXv37sHExAStW7fG+vXrYWJignXr1lXaBcRXXOy7AspzLVu2DH5+frhz5w5UVVVhaWkpcavd48ePYWhoyKkuFG1tbYnPa1lZGYyMjLBz505GqaTH90Jywk5SUpLYYAkA6tWrh9mzZ8PJyYlhMum0bt0aq1evRnh4uNj66tWrKz3uyjUfPnxASUmJxHpxcTHnB2M+Pj5YtmwZAODGjRuYNWsWvLy8EBsbCy8vL6lvQCWktmi4RAghRGYtWLBA9PeJEyfCw8MDixYtknhOTk5OXUeTCt/zV/D09BQVRy9YsAD9+vXDn3/+CSUlJURGRrIN9wVxfTO4uro62rVrV+XjNjY2nLvt7tOCXQUFBejp6cHCwkLsRTdX8b2QnLCjqamJ7OxstGzZUmw9JycHGhoajFJJLzg4GC4uLjhz5ozoDZFLly4hJyeHF0fK2rdvjw0bNmDVqlVi6+vWrUPbtm0ZpZLOw4cPYWNjAwD466+/8O233yIwMBDJyckYMGAA43REltGxOEIIIXJBS0sLSUlJsLS0FFt/8OABnJyc8OrVK0bJpMP3/B8rLCzE3bt3YWxsjEaNGrGOQ/4/rh7rk0VHjx7F77//jnPnzrGOQjjKw8MD+/fvx/Lly9GpUycA5SX8Pj4+GDp0KC/K4v/++2+sWbMGd+/eBQBYW1tj6tSpnL9IACj/XPfu3Rvt2rVDr169AJR31SUmJuLUqVPo2rUr44RV09XVRXx8PGxsbNClSxe4ublh8uTJyMrKgo2NjcwcRSfcw/23fAghhJAvQFVVFQkJCRLDmYSEBKioqDBKJT2+5/+Ympoa58toZaXvShY8ePAAsbGxeP78OUpLS8Ue43pnVFX4UkhO2Fm+fDkEAgHc3NxEx7Pq16+PKVOmICgoiHE66RgaGvJ2h17nzp1x6dIlBAcHY/fu3VBVVYW9vT0iIiIkfg5zTZcuXeDl5YXOnTvj6tWr2LVrFwDg/v37aNasGeN0RJbRcIkQQohcmDFjBqZMmYLk5GTRzTtXrlzB5s2b4efnxzhdzficv6ysDHv37q1yQLBv3z5GyaomK31XfLdx40ZMmTIFjRo1QpMmTcQGfgKBgPPDpeoKybn+ApWwpaSkhJUrV2Lp0qXIyMgAAJibm0NNTY1xsqpdv34drVq1goKCAq5fv17tc+3t7eso1edr06YNduzYwTpGra1evRpTp07F3r17sXbtWjRt2hQAcPz4cfTr149xOiLL6FgcIYQQubF7926sXLkSd+7cAVC+Rd/T0xPDhw9nnEw6fM3v6emJ9evXo0ePHmjcuLHEjiCul4tOnDgRBgYGVfZdbd68mVGyL4uLx+KaN2+OqVOnwtfXl3WUz6KgoFBtIXnFsJIQWaCgoIDc3Fzo6+uLvvYre6kpEAh4UWafkZGBLVu2IDMzEytWrIC+vj6OHz8OY2Nj2Nraso5HCOfQcIkQQgghX5Wuri62b9/O2yJRWeq7qo6mpibnCr25mKk24uLixD7mWyE5qVtDhgxBZGQkNDU1MWTIkGqfy8Udn48ePYKxsTEEAgEePXpU7XObN29eR6k+T1xcHPr374/OnTvj/PnzuHPnDszMzBAUFISkpCTs3buXdUSpvH//HkVFRWJrmpqajNIQWUc/1QghhMiN/Px87N27F5mZmfD29oauri6Sk5PRuHFj0bZxLuNrfi0tLd4OBwDZ6ruqDhffbxw2bBhOnTqFX375hXWUz9KtWzfWEQiPaGlpiXa6aWlpMU5TexUDo+LiYvj7+8PPzw+mpqaMU32eOXPmYPHixfDy8hK7na9nz55YvXo1w2Q1e/v2LXx9fbF7927k5eVJPM6HXWOEn2i4RAghRC5cv34dvXv3hpaWFrKysjBx4kTo6upi3759yM7ORlRUFOuI1eJz/oULF8Lf3x+bN2+Gqqoq6zi1xue+q8q8fv0aZ8+ehZWVFaytrUXrt2/f5twtThYWFvDz88Ply5dhZ2eH+vXriz3u4eHBKJn0ZLGQnHwdFUeEy8rK4O/vDz09PV5+z6xfvz7++usvXn5/rHDjxo1K+5b09fXxzz//MEgkvdmzZyM2NhZr167FmDFjsGbNGjx58gTr16/nTRk84Sc6FkcIIUQu9O7dG46OjggODhbrlrl48SJGjRqFrKws1hGrxef87969w+DBg5GQkAATExOJAQEfblvja98VAAwfPhzOzs6YPn063r17h9atWyMrKwtlZWXYuXMnhg4dyjpilarb9SAQCJCZmVmHaWqvpkJyPnztk7pXWloKFRUV3Lp1i7fF72PHjkWbNm0wc+ZM1lE+S7NmzbB792506tRJ7Gfu/v374e3tLSpZ5yJjY2NERUWhe/fu0NTURHJyMiwsLLBt2zZER0fj2LFjrCMSGUU7lwghhMiFxMRErF+/XmK9adOmyM3NZZCodvicf+zYsbh27Rp+/PHHSgu9+WD48OG8GCRV5vz585g7dy4AYP/+/SgrK0N+fj62bt2KxYsXc3q49PDhQ9YR/pPFixdjyZIlvC0kJ2woKCjA0tISeXl5vB0uWVpaIiAgAAkJCWjbti0aNGgg9jjXdx3+8MMP8PX1xZ49eyAQCFBaWoqEhAR4e3vDzc2NdbxqvXz5UnQUXVNTEy9fvgQAdOnSBVOmTGEZjcg4Gi4RQgiRC8rKyhLXggPA/fv3oaenxyBR7fA5/9GjR3Hy5El06dKFdZTPxte+KwB49eoVdHV1AQAnTpzA0KFDoaamBhcXF/j4+DBO92Vwtfj733//xbBhw1jHIDwUFBQEHx8frF27Fq1atWIdp9YiIiKgra2Na9eu4dq1a2KPCQQCzg+XAgMDMW3aNBgZGUEoFMLGxgZCoRCjRo3CvHnzWMerlpmZGR4+fAhjY2O0bNkSu3fvRvv27XH48GFoa2uzjkdkGA2XCCGEyAVXV1cEBARg9+7dAMp/uc3Ozoavry+nd25U4HN+IyMjXt9Ow+e+K6D883/p0iXo6urixIkT2LlzJ4DywYesFJJzteWB74XkhB03NzcUFhaidevWUFJSkuheqtiNwlV833WopKSEjRs3ws/PDzdv3kRBQQEcHBx4sZNs3LhxSEtLQ7du3TBnzhx89913WL16NYqLixEaGso6HpFh1LlECCFELrx69Qrff/89kpKS8ObNGxgaGiI3NxcdO3bEsWPHJLbscw2f8x89ehSrVq3CunXrYGJiwjpOrfG57woA/vjjD3h6ekJdXR3GxsZISUmBgoICVq1ahX379iE2NpZ1xP/s4/9fuGTp0qUIDQ2Fi4sLbwvJCRtbt26t9vGxY8fWUZL/ruLlJh+PRMuCR48e4dq1a7CwsIC9vT3rOESG0XCJEEKIXImPj8f169dRUFAAR0dH9O7dm3WkWuFjfh0dHRQWFqKkpARqamoSL7C5/g68lpYWkpOTYW5uLjbEePToEaysrPD+/XvWEWt07do1ZGdno0+fPqJB5NGjR6Gjo4NOnToxTvffcXW4xPdCckL+i4iICISFheHBgwcAynuYZsyYgYkTJzJOVjOhUIjIyEjExMRUetPj2bNnGSWTTkxMTJXZN2/ezCgVkXV0LI4QQohc6dKlC6+7f/iYf8WKFawj/Cd87Lvy8vLCokWL0KBBA3h5eYnWL1y4IPFcWRgucRXfjwYRtoRCIfbv3y+6pdLGxgYDBw5EvXrcfwk3f/58hIaGwt3dHR07dgQAXLp0CTNnzkR2djYCAgIYJ6yep6cnIiMj4eLiglatWvFq15W/vz8CAgLg5OQEAwMDXmUn/EY7lwghhMis8PBwTJ48GSoqKggPD6/2uVw8nsL3/LUVFBSEX375hXOFoxMnTkReXh52794NXV1dXL9+HYqKihg0aBCcnZ05OTzr0aMH9u/fD21tbfTo0aPK5wkEAs6/Ay8NrhZ6S4vv+cmXd+vWLbi6uiI3NxdWVlYA/m+gffjwYc6XfOvp6SE8PBwjR44UW4+Ojoa7uzv++ecfRsmk06hRI0RFRWHAgAGso9SagYEBgoODMWbMGNZRiJyh4RIhhBCZZWpqiqSkJDRs2JCXx1P4nr+2uPoCm899V/KCq8fipMX3/OTL69ixI/T09LB161bo6OgAKC/h/+mnn/DixQtcvHiRccLqaWtrIzExUaIA+/79+2jfvj3y8/PZBJOSoaEhzp07hxYtWrCOUmsNGzbE1atXYW5uzjoKkTM0XCKEEEIIJ3D9BTYf+67kRXx8PNq1awdlZWXWUT4L17/2Sd1TVVVFUlISbG1txdZv3ryJdu3a4d27d4ySScfd3R3169eXuJ3M29sb7969w5o1axglk05ISAgyMzOxevVq3h0r8/X1hbq6Ovz8/FhHIXKG+wd2CSGEEEI4gI99V3z3cV/UxwQCAVRUVGBhYYGBAwfS/y9E5rRo0QLPnj2TGC49f/4cFhYWjFLVTkREBE6dOoUOHToAAK5cuYLs7Gy4ubmJ/dv+dADFypAhQ8Q+Pnv2LI4fPw5bW1uJiyj27dtXl9Fq9PHns7S0FBs2bMCZM2dgb28vkZ0rn28ie2i4RAghRGZV9cK0Mlz8ZYvv+flM3vquuColJQXJyckQCoVivTOKiopo2bIl/vjjD8yaNQvx8fGwsbFhnJaQL2fp0qXw8PDAwoULRcOZy5cvIyAgAMuWLRO7ZEBTU5NVzCrdvHkTjo6OAICMjAwA5T1GjRo1ws2bN0XP49KuIC0tLbGPBw8ezChJ7aWkpIh93KZNGwAQ+1wD3Pp8E9lDx+IIIYTIrOqKjD/G1VJjvuevLS4dDZK3viuuWrFiBS5cuIAtW7aIXkC/evUKEydORJcuXTBp0iSMGjUK7969w8mTJxmn/Xxc7Rsj7CgoKIj+XjEQqHjZ9vHHAoEAQqGw7gN+IY8fP4ahoaHY/15CCD/RcIkQQgghnMCl4RLhhqZNm+L06dMSu5Ju3bqFPn364MmTJ0hOTkafPn04f/tUdehrn3wqLi5O6ud269btKyb5urg6WH348CFKSkokCskfPHiA+vXrw8TEhE0wQjiMjsURQgiROzk5OQAAIyMjxkk+D9/zV6Vr165QVVVlHYNwyKtXr/D8+XOJ4dKLFy9Ex4K0tbVRVFTEIt4Xc/z4cTRt2pR1DMIh0g6Mpk6dCltbWzRq1OgrJ/o6uLrP4aeffsL48eMlhktXrlzBpk2bcO7cOTbBCOEw2rlECCFELpSUlMDf3x/h4eEoKCgAAKirq8Pd3R0LFiyQKLzkGj7nV1RUxNOnT6Gvry+2npeXB319fU4e6aC+K24YPXo0Ll26hJCQELRr1w4AkJiYCG9vb3Tq1Anbtm3Dzp07sXz5ciQlJTFOK0naQnJdXd06TkZkBVd3/kiLq7v2NDU1kZycLFGenp6eDicnJ+Tn57MJRgiH0c4lQgghcsHd3R379u1DcHAwOnbsCAC4dOkSFi5ciLy8PKxdu5ZxwurxOX9V72N9+PABSkpKdZxGOp+Wo1aFylG/rvXr12PmzJn44YcfUFJSAgCoV68exo4di7CwMABAy5YtsWnTJpYxq0SF5ORro30CX4dAIMCbN28k1l+9esXJN0QI4QLauUQIIUQuaGlpYefOnejfv7/Y+rFjxzBy5Ei8evWKUTLp8DF/xS1rM2fOxKJFi6Curi56TCgU4vz588jKypJ6kEPkV0FBgag43czMTOxricvkpZCcsMPVnT/S4mr+7777DqqqqoiOjoaioiKA8p9bI0aMwNu3b3H8+HHGCQnhHtq5RAghRC4oKytXWsBpamrK2d0zH+Nj/oqdJWVlZVi3bp3oF3QAUFJSgomJCdatW8cq3meR1b4rrlNXV4e9vT3rGLX2+++/4/Tp02JXxWtpaWHhwoXo06cPPD09MX/+fPTp04dhSkLY4eruz2XLlsHZ2RlWVlbo2rUrAODChQt4/fq1TNzOSsjXQHc+EkIIkQvTp0/HokWL8OHDB9Hahw8fsGTJEkyfPp1hMunwMf/Dhw/x8OFDdOvWDWlpaaKPHz58iHv37uHkyZP43//+xzpmjUpKSuDn5wctLS2YmJjAxMQEWlpamDdvHoqLi1nHk2lv376Fn58fOnXqBAsLC5iZmYn94bqKQvJPyVohOSGfi6uHaGxsbHD9+nUMHz4cz58/x5s3b+Dm5oa7d++iVatWrOMRwkm0c4kQQohcSElJQUxMDJo1a4bWrVsDANLS0lBUVIRevXphyJAhoufu27ePVcwq8Tl/bGws6wj/CZ/7rvhu4sSJiIuLw5gxY2BgYMDZXQ5VGThwIMaPH19pIfmgQYMAAFevXkWLFi0YpiTky9uyZQtGjBgBNTW1ap93+/ZtGBoa1lGq2jE0NERgYGC1z5k6dSoCAgJ4e1sfIV8SdS4RQgiRC+PGjZP6uVu2bPmKST4P3/J7eXlh0aJFaNCgQY03r3H9tjU+9l3JCm1tbRw9ehSdO3dmHeWzFBQUYObMmYiKiqq0kLxBgwZITU0FALRp04ZdUMJbU6ZMwaJFizg33GjcuDHevXuHYcOGYcKECejUqRPrSF8F32/rI+RLouESIYQQQr64Hj16YP/+/dDW1kaPHj2qfJ5AIOB8f4W+vj7i4uJgbW0ttn7nzh04OzvjxYsXjJLJPlNTUxw7dkzic883fC0kJ+yYmJhg/Pjx+Omnn2BsbMw6Tq2VlJTg8OHDiIyMxPHjx2FmZoZx48Zh7NixaNKkCet4XwxXC8kJYYGGS4QQQuTK8+fPce/ePQCAlZUV9PX1GSeqHb7n56OAgADcvXsXW7ZsgbKyMoDyvqsJEybA0tISCxYsYJxQdm3fvh0HDx7E1q1bazxeQ4gsWbFiBSIjI3Hz5k306NEDEyZMwODBg0Xfg/jk2bNn2L59O7Zu3Yq7d++iX79+mDBhAr777jsoKPC7ApiGS4T8HxouEUIIkQuvX7/GtGnTsHPnTgiFQgCAoqIiRowYgTVr1kBLS4txwurxPf/HKm7badmyJVq2bMk6To0GDx6MmJgYKCsrV9p39TGu9V3xnYODAzIyMlBWVgYTExPUr19f7PHk5GRGyaTz9u1bBAUFISYmBs+fP0dpaanY4xW7mQipSnJyMiIjIxEdHQ2hUIhRo0Zh/PjxcHR0ZB2tVq5cuYLNmzdj69atMDAwwL///gsdHR1s2bIF3bt3Zx3vs9FwiZD/Q4XehBBC5MKkSZOQkpKCI0eOiJUye3p64ueff8bOnTsZJ6wen/MPHz4czs7OmD59Ot69ewcnJydkZWWhrKwMO3fuxNChQ1lHrJa2trZERiMjI0Zp5EtF6TVf8b2QnLDn6OgIR0dHhISE4I8//oCvry/Wrl0LOzs7eHh4YNy4cZz9unr27Bm2bduGLVu2IDMzE4MGDcKRI0fQu3dvvH37FgEBARg7diwePXrEOioh5AugnUuEEELkQoMGDXDy5El06dJFbP3ChQvo168f3r59yyiZdPicv0mTJjh58iRat26NHTt2YMGCBUhLS8PWrVuxYcMGpKSksI5IyFfB90Jywl5xcTH279+PLVu24PTp0+jQoQMmTJiAx48fY82aNejZsyd27NjBOqaE7777DidPnkSLFi0wceJEuLm5QVdXV+w5z58/R5MmTSR29PEJ7Vwi5P/QziVCCCFyoWHDhpUeHdPS0oKOjg6DRLXD5/yvXr0Svag4ceIEhg4dCjU1Nbi4uMDHx4dxOulR3xWpLR0dHYkX1IRIIzk5GVu2bEF0dDQUFBTg5uaGsLAwsaPEgwcPRrt27RimrFrFRQgVO20ro6enh4cPH9Zhqi/vxx9/hKamJusYhHAC7VwihBAiFzZs2IA9e/Zg27ZtoptqcnNzMXbsWAwZMgQ///wz44TV43P+Fi1aYPHixXBxcYGpqSl27tyJnj17Ii0tDb169cI///zDOmK1ZKnvig90dXVx//59NGrUCDo6OtUe+Xn58mUdJqs9KiQnn0tRURHffPMNJkyYgEGDBkn0jQHlnV7Tp0/Hli1bGCSUffn5+bh69WqlfWlubm6MUhHCXTRcIoQQIhccHByQnp6ODx8+iK51zs7OhrKyMiwtLcWey8WSYD7n/+OPP+Dp6Ql1dXU0b94cycnJUFBQwKpVq7Bv3z7ExsayjlitESNGICUlBatWrZLou2rTpg2n+674aOvWrfjhhx+grKyMyMjIaodLY8eOrcNktcf3QnLCzqNHj9C8eXPWMT6bh4cHLCws4OHhIba+evVqpKenY8WKFWyCSenw4cMYPXo0CgoKoKmpKfZ9SCAQcH6wTQgLNFwihBAiF/z9/aV+Lhevlud7/qSkJOTk5OCbb76Buro6AODo0aPQ1tbmfB8Nn/uuCFs1/bvl4r9Vwg2JiYkoLS3F//73P7H1K1euQFFREU5OToySSadp06Y4dOgQ2rZtK7aenJwMV1dXPH78mFEy6bRo0QIDBgxAYGAg7TokREo0XCKEEEJInan4tYOrtxtVxtjYGEePHoWdnZ3Y+vXr1zFgwADOv0jiM0VFRTx9+lSi3yovLw/6+vqiY4qEyJr27dtj9uzZ+P7778XW9+3bh2XLluHKlSuMkklHRUUFN2/ehIWFhdh6eno6WrVqhffv3zNKJp0GDRrgxo0bVNRNSC0osA5ACCGEENkXFRUFOzs7qKqqQlVVFfb29ti2bRvrWFKZN28evLy8kJubK1rLzc2Fj48P/Pz8GCaTfVW9B/rhwwcoKSnVcRpC6s7t27fh6Ogose7g4IDbt28zSFQ7FhYWOHHihMT68ePHeTGw6du3L5KSkljHIIRX6LY4QgghckEoFCIsLAy7d+9GdnY2ioqKxB7nen8Cn/OHhobCz88P06dPFx2Bi4+Pxy+//IJ//vkHM2fOZJywemvXrkV6ejqMjY0l+q5evHiB9evXi55LHTpfRnh4OIDyHW6bNm0SHaUEyv8tnD9/XuzWLC6RpUJywo6ysjKePXsmMYh5+vQp6tXj/ks4Ly8vTJ8+HS9evEDPnj0BADExMQgJCeF83xIA0W2mt2/fhp2dnURfmqurK6NkhHAXHYsjhBAiF+bPn49NmzZh1qxZmDdvHubOnYusrCwcOHAA8+fPlygd5Ro+5zc1NYW/v7/E7Tpbt27FwoULOX8VNd/7rvjI1NQUQHmpcbNmzaCoqCh6TElJCSYmJggICJDoo+ECWSokJ+yMHDkST58+xcGDB0U3Uubn52PQoEHQ19fH7t27GSes2dq1a7FkyRL8/fffAAATExMsXLiQFzetKShUfcBHIBDQkVxCKkHDJUIIIXLB3Nwc4eHhcHFxgYaGBlJTU0Vrly9fxo4dO1hHrBaf81fVvfHgwQPY2dlxvnuDsNOjRw/s27cPOjo6rKMQUqeePHkCZ2dn5OXlwcHBAQCQmpqKxo0b4/Tp0zAyMmKcUHovXryAqqqq2A5EQojsoc4lQgghciE3N1dUyKyuro5Xr14BAL799lscPXqUZTSp8Dm/hYVFpe+y79q1C5aWlgwSEb6IjY0VGywJhUKkpqbi33//ZZhKeoqKinj+/LnEel5enthuLEI+1bRpU1y/fh3BwcGwsbFB27ZtsXLlSty4cYNXgyUA0NPT491gid70IKT2uH9glxBCCPkCmjVrhqdPn8LY2Bjm5uY4deoUHB0dkZiYCGVlZdbxasTn/P7+/hgxYgTOnz8v6lxKSEhATEwML4528Lnviu9mzJgBOzs7TJgwAUKhEM7Ozrh06RLU1NRw5MgRdO/enXXEalEhOfkvGjRogMmTJ7OO8VmePXsGb29vxMTE4Pnz5xL/Frh+rExbWxvt27dHt27d0L17d3Tq1AmqqqqsYxHCaTRcIoQQIhcGDx6MmJgY/O9//4O7uzt+/PFHREREIDs7m/OF0gC/8w8dOhRXrlxBWFgYDhw4AACwtrbG1atXRcc9uMzf37/avivy9ezZswc//vgjAODw4cPIysrC3bt3sW3bNsydOxcJCQmME1aOz4XkhDsePHiA2NhYPH/+HKWlpWKPcf17z08//YTs7Gz4+fnBwMCg2u4xLjpz5gzOnz+Pc+fOISwsDCUlJXBychINm7755hvWEQnhHOpcIoQQIpcuX76MixcvwtLSEt999x3rOLXG9/x8wue+K75TUVFBeno6mjVrhsmTJ0NNTQ0rVqzAw4cP0bp1a7xHMUI+AABDeElEQVR+/Zp1xErxuZCccMPGjRsxZcoUNGrUCE2aNBEbzggEAs7fTKmhoYELFy6gTZs2rKP8ZyUlJUhMTMT69evx559/orS0lPM7rwhhgXYuEUIIkUsdOnRAhw4dJNZdXFywadMmGBgYMEglPb7lFwqF2L9/P+7cuQMAsLGxwcCBA3lxpXZ1fVd+fn4so8m8xo0b4/bt2zAwMMCJEyewdu1aAEBhYSGnO4sqbkCkQnLyuRYvXowlS5bA19eXdZTPYmRkVOWxUL64f/8+zp07J/rz4cMHfPvtt5w/jksIK9z/jY4QQgipQ+fPn8e7d+9Yx/hsXMx/69YtuLq6Ijc3F1ZWVgCAZcuWQU9PD4cPH0arVq0YJ6wen/uu+G7cuHEYPny46FhN7969AQBXrlzhxbGy2NhYsY+FQiFu3LiB5s2b08CJVOvff//FsGHDWMf4bCtWrMCcOXOwfv16mJiYsI5Ta02bNsW7d+/QvXt3dO/eHb6+vrC3t+fd8T5C6hLdFkcIIYSQr2rixImwtbXF48ePkZycjOTkZOTk5MDe3p4XZbUVfVcA4O7uDj8/P1haWsLNzQ3jx49nnE62LVy4EJs2bcLkyZORkJAgGuYpKipizpw5jNPVbMaMGYiIiAAAUSG5o6MjjIyMcO7cObbhCKcNGzYMp06dYh3js40YMQLnzp2Dubk5NDQ0oKurK/aH6/T09FBYWIjc3Fzk5ubi2bNnnHvjhhCuoc4lQggh5CMaGhpIS0uDmZkZ6yifhYv5VVVVkZSUBFtbW7H1mzdvol27drz7hZ36roi0mjZtioMHD8LJyQkHDhzAtGnTEBsbi23btuHs2bOcLSQn7C1duhShoaFwcXGBnZ0d6tevL/a4h4cHo2TS2bp1a7WPjx07to6SfL78/HycP38ecXFxiIuLw+3bt9GmTRv06NEDS5YsYR2PEM6h4RIhhBDyES4OZ2qDi/lbt26NsLAw9OzZU2z97Nmz8PT0xI0bNxgl+7K42nfFN+Hh4Zg8eTJUVFREt65VhesvsPlaSE7YqyiFr4xAIEBmZmYdppFveXl5OHfuHA4ePIjo6Ggq9CakCtS5RAghhJCvaunSpfDw8MDChQtFJeSXL19GQEAAli1bJvYCW1NTk1XM/4yLfVd8FBYWhtGjR0NFRQVhYWFVPk8gEHB+uMTXQnLCXkUpvCx4//49ioqKxNa4/r1+3759oiLv27dvQ1dXF126dEFISAi6devGOh4hnETDJUIIIYR8Vd9++y0AYPjw4aIy1IqN0xXHysrKyiAQCOjdYCL2oprvL7D5XkhOyOd6+/YtfH19sXv3buTl5Uk8zvXv9b/88gucnZ0xefJkdOvWTXRjKCGkajRcIoQQIrMcHR0RExMDHR0dBAQEwNvbG2pqatX+N7/99htnykb5nr/CpzdmEVIdLy8vqZ4nEAgQEhLyldP8NwsXLkSrVq2Qk5ODYcOG8a6QnLD1+PFjHDp0CNnZ2RI7f0JDQxmlks7s2bMRGxuLtWvXYsyYMVizZg2ePHmC9evXIygoiHW8Gj1//px1BEJ4hzqXCCGEyCxVVVU8ePAAzZo1g6KiIp4+fQp9fX3WsaTG9/zyhot9V3zUo0cPqZ4nEAhw9uzZr5yGEDZiYmLg6uoKMzMz3L17F61atUJWVhbKysrg6OjI+a99Y2NjREVFoXv37tDU1ERycjIsLCywbds2REdH49ixY6wj1kgoFOLAgQO4c+cOAMDGxgYDBw6kI62EVIF2LhFCCJFZbdq0wbhx49ClSxeUlZVh+fLlUFdXr/S58+fPr+N0NeN7/o9duHAB69evR2ZmJvbs2YOmTZti27ZtMDU1RZcuXVjHIxzC951uslRITtj59ddf4e3tDX9/f2hoaOCvv/6Cvr4+Ro8ejX79+rGOV6OXL1+KBu2ampp4+fIlAKBLly6YMmUKy2hSSU9Px4ABA/DkyRNYWVkBKO8PNDIywtGjR2Fubs44ISHcQzuXCCGEyKx79+5hwYIFyMjIQHJyMmxsbFCvnuT7KgKBAMnJyQwSVo/v+Sv89ddfGDNmDEaPHo1t27bh9u3bMDMzw+rVq3Hs2DFevIMtDdq5RIDyW76SkpLQsGFDuvGLfDYNDQ2kpqbC3NwcOjo6iI+Ph62tLdLS0jBw4EBkZWWxjlgte3t7rFq1Ct26dUPv3r3Rpk0bLF++HOHh4QgODsbjx49ZR6zWgAEDUFZWhj///FN01DwvLw8//vgjFBQUcPToUcYJCeEeGi4RQgiRCwoKCsjNzeXtsTI+53dwcMDMmTPh5uYmNoBJSUlB//79kZubyzqihM/pu1q6dCmmTJkCbW3tuglJCJFZTZo0QWxsLKytrWFjY4OgoCC4uroiLS0NnTt3RkFBAeuI1QoLC4OioiI8PDxw5swZfPfddygrK0NxcTFCQ0Ph6enJOmK1GjRogMuXL0sUefPl808IC3QsjhBCiFwoLS1lHeE/4XP+e/fuwdnZWWJdS0sL+fn5dR9ICnfu3MHbt2+ho6MDf39//PLLLzUOl3799dc6Ske4TJYKyQk7HTp0QHx8PKytrTFgwADMmjULN27cwL59+9ChQwfW8Wo0c+ZM0d979+6NO3fuiHqX7O3tGSaTjrKyMt68eSOxXlBQACUlJQaJCOE+Gi4RQgiRGxkZGVixYoVYOaenpydvuhP4mr9JkyZIT0+HiYmJ2Hp8fDxnj5DJUt8VqVspKSlSPU8gEHzlJITPQkNDRbtj/P39UVBQgF27dsHS0pLzN8VVxsTEROJnAJd9++23mDx5MiIiItC+fXsAwJUrV/DLL7/A1dWVcTpCuImOxRFCCJELJ0+ehKurK9q0aYPOnTsDABISEpCWlobDhw/jm2++YZywenzOv3TpUmzfvh2bN2/GN998g2PHjuHRo0eYOXMm/Pz84O7uzjqiBFnpuyKEEFZiYmIQFhYmekPE2toaM2bMQO/evRknq1l+fj7Gjh2Lw4cPo379+gCAkpISuLq6IjIyElpaWowTEsI9NFwihBAiFxwcHNC3b18EBQWJrc+ZMwenTp3i/ICAz/nLysoQGBiIpUuXorCwEED5kQNvb28sWrSIcbqa8bnvihDCb0lJSWK7Vdu2bcs4kXT++OMPeHp64vvvv0fHjh0BAJcvX8bevXsRFhaGadOmMU4onQcPHuDu3bsAyodjFhYWjBMRwl00XCKEECIXVFRUcOPGDVhaWoqt379/H/b29nj//j2jZNLhe34AKCoqQnp6OgoKCmBjY1PlMTNCCJF3jx8/xsiRI5GQkCC6JCA/Px+dOnXCzp070axZM7YBa9CsWTPMmTMH06dPF1tfs2YNAgMD8eTJE0bJCCFfC3UuEUIIkQt6enpITU2VGM6kpqbyYkcK3/MDgJKSEmxsbFjH+Cx87bsihPDTxIkTUVxcjDt37sDKygpA+XHdcePGYeLEiThx4gTjhNXLz89Hv379JNb79OkDX19fBolqRygUIjIyEjExMXj+/LnEpRpnz55llIwQ7qLhEiGEELkwadIkTJ48GZmZmejUqROA8s6iZcuWSX27E0t8yz9kyBCpn7tv376vmOS/q6rvytbWlvN9V4QQfoqLi8PFixdFgyUAsLKywqpVq9C1a1eGyaTj6uqK/fv3w8fHR2z94MGD+Pbbbxmlkp6npyciIyPh4uKCVq1aUQE/IVKgY3GEEELkQllZGVasWIGQkBD8/fffAABDQ0P4+PjAw8OD87848i3/uHHjRH8vKyvD/v37oaWlBScnJwDAtWvXkJ+fjyFDhmDLli2sYkqFz31XhBB+atGiBbZv3y66qazC1atXMWrUKKSnpzNKJp3Fixdj+fLl6Ny5s1jnUkJCAmbNmgVNTU3Rcz08PFjFrFKjRo0QFRWFAQMGsI5CCG/QcIkQQojcefPmDQBAQ0ND4rGEhAQ4OTlBWVm5rmNJjW/5fX198fLlS6xbtw6KiooAyo8cTJ06FZqamvj9998ZJ6yeLPRdEUL45eDBgwgMDMSaNWtEQ/mkpCS4u7vD19cXgwYNYhuwBqamplI9TyAQIDMz8yunqT1DQ0OcO3cOLVq0YB2FEN6g4RIhhBDyEU1NTaSmpsLMzIx1lM/Cxfx6enqIj48XO94BlPeHdOrUCXl5eYySScfIyAihoaEYNmyY2Pru3bvh7e2N7OxsRskIIbJKR0cHhYWFKCkpQb165U0mFX9v0KCB2HNfvnzJIqJMCwkJQWZmJlavXs25ncGEcBV1LhFCCCEf4ft7LlzMX1JSgrt370oMl+7evStRkspFfOu7IoTw34oVK1hHkGvx8fGIjY3F8ePHYWtri/r164s9zvWuQEJYoOESIYQQQr6qcePGYcKECcjIyBD1h1y5cgVBQUFi3Uxc5efnBw0NDYSEhODXX38FUH5kYuHChZzsCiGE8N/YsWNZR/hPxo8fX+3jmzdvrqMkn0dbWxuDBw9mHYMQXqFjcYQQQshHNDQ0kJaWxqljZbXBxfylpaVYvnw5Vq5ciadPnwIADAwM4OnpiVmzZol6mPiAb31XhBB+qum4rbGxcR0l+TyfDmaKi4tx8+ZN5Ofno2fPnrTzhxAZRMMlQggh5CNcHM7UBtfzv379GgDEbgqqwPfhDBf7rggh/KSgoFBt149QKKzDNF9GaWkppkyZAnNzc8yePZt1nGpFR0dj5MiRlT7m4+PD+YsoCGFBgXUAQgghhEv4XtzJ9fyampqVDpYAoH///njy5EkdJ/py6P06QsiXkpKSguTkZNGfK1euYN26dWjRogX27NnDOt5nUVBQgJeXF8LCwlhHqdGUKVNw/PhxifWZM2di+/btDBIRwn3UuUQIIUTmlZWVIScnB/r6+lBRUanxuVzD9/zS4nN2Qgj5klq3bi2x5uTkBENDQ/z+++8YMmQIg1T/XUZGBkpKSljHqNGff/6JkSNH4siRI+jSpQsAwN3dHfv27UNsbCzjdIRwEw2XCCGEyLyysjJYWFjg1q1bsLS0rPa5FZ06XML3/IQQQr4MKysrJCYmso5Ro09v0iwrK8PTp09x9OhRXpSVu7i44I8//oCrqytOnz6NiIgIHDx4ELGxsWjRogXreIRwEg2XCCGEyDwFBQVYWloiLy+vxuEMF/E9PyGEkNqp6KerUDGcWbhwIS9+DqSkpIh9rKCgAD09PYSEhNR4kxxXjBo1Cvn5+ejcuTP09PQQFxcHCwsL1rEI4SwaLhFCCJELQUFB8PHxwdq1a9GqVSvWcWqN7/nlAdf7rggh/KGtrS3xPaWsrAxGRkbYuXMno1TS4+PRsU93W1XQ09ODo6Mj/vjjD9FaaGhoXcUihDfotjhCCCFyQUdHB4WFhSgpKYGSkhJUVVXFHn/58iWjZNLhe35pcPG2tdr0XXH9pj5CCH+cO3dObLhUsfPHwsIC9epxf3/Aw4cPUVJSIrHL6sGDB6hfvz5MTEzYBKtGjx49pHqeQCDA2bNnv3IaQviH+9+ZCCGEkC9gxYoVrCP8J3zPLw0uvt9FfVeEEBa6d+/OOsJ/8tNPP2H8+PES3zevXLmCTZs24dy5c2yCVeNzdls9fvwYhoaGUFCgS9gJoZ1LhBBCCCHVsLW1RUREBDp06MA6CiFETixduhSNGzeW6CfavHkzXrx4AV9fX0bJpKOpqYnk5GSJjqL09HQ4OTkhPz+fTbAvjIs7bglhhXYuEUIIkRsZGRnYsmULMjIysHLlSujr6+P48eMwNjaGra0t63g14lN+BwcHqTuIkpOTv3Ka/4b6rgghdW39+vXYsWOHxLqtrS1++OEHzg+XBAJBpbs5X716BaFQyCDR10H7NAj5P7R/jxBCiFyIi4uDnZ0drly5gn379qGgoAAAkJaWhgULFjBOVzO+5R80aBAGDhyIgQMHom/fvsjIyICysjK6d++O7t27Q0VFBRkZGejbty/rqDVyc3PD1atX0bp1a6iqqkJXV1fsDyGEfGm5ubkwMDCQWNfT08PTp08ZJKodZ2dnLF26VGyQJBQKsXTpUnTp0oVhMkLI10I7lwghhMiFOXPmYPHixfDy8oKGhoZovWfPnli9ejXDZNLhW/6PB14TJ06Eh4cHFi1aJPGcnJycuo5Wa/LQd0UI4RYjIyMkJCTA1NRUbD0hIQGGhoaMUklv2bJlcHZ2hpWVFbp27QoAuHDhAl6/fk1l2ITIKBouEUIIkQs3btyo9IiBvr4+/vnnHwaJaofP+ffs2YOkpCSJ9R9//BFOTk7YvHkzg1TSGzt2LOsIhBA5M2nSJMyYMQPFxcXo2bMnACAmJgazZ8/GrFmzGKermY2NDa5fv47Vq1cjLS0NqqqqcHNzw/Tp02nHJyEyioZLhBBC5IK2tjaePn0q8S5wSkoKmjZtyiiV9PicX1VVFQkJCRK3BiUkJEBFRYVRqtrhU98VIYT/fHx8kJeXh6lTp6KoqAgAoKKiAl9fX8yZM4dxOukYGhoiMDCQdYyvStpuQULkAXUuEUIIkQsVBai5ubkQCAQoLS1FQkICvL294ebmxjpejficf8aMGZgyZQo8PDywfft2bN++He7u7pg2bRpmzpzJOl6N+NZ3RQjhP4FAgGXLluHFixe4fPky0tLS8PLlS8yfP58XA40tW7Zgz549Eut79uzB1q1bGST6OqjQm5D/IyijfxGEEELkQFFREaZNm4bIyEgIhULUq1cPQqEQo0aNQmRkJBQVFVlHrBbf8+/evRsrV67EnTt3AADW1tbw9PTE8OHDGSerWceOHTFs2DBR31VaWhrMzMxw9epVDBkyBI8fP2YdkRAiYypuVfv0CNnLly9Rr149aGpqMkomnRYtWmD9+vXo0aOH2HpcXBwmT56Me/fuMUpWs+LiYqiqqiI1NbXGG0JzcnJgaGjI+Z/BhNQFGi4RQgiRK9nZ2bh58yYKCgrg4OAgcVSL6/ien4/U1dVx48YNmJqaig2XsrKy0LJlS7x//551REKIjOnfvz++++47TJ06VWx93bp1OHToEI4dO8YomXRUVFRw9+5dmJiYiK1nZWXB2toa7969YxNMSmZmZti/fz9at27NOgohvEGdS4QQQuSKsbExjIyMAPCzK4Gv+fPz87F3715kZmbC29sburq6SE5ORuPGjTnfGcXnvitCCD9duXIFoaGhEuvdu3fH3LlzGSSqHX19fVy/fl1iuJSWloaGDRuyCVULc+fOxW+//YZt27ZRATkhUqLOJUIIIXIjIiICrVq1goqKClRUVNCqVSts2rSJdSyp8TX/9evX0aJFCyxbtgy///478vPzAQD79u3Dr7/+yjacFPjcd0UI4acPHz6gpKREYr24uJjzu34AYOTIkfDw8EBsbCyEQiGEQiHOnj0LT09P/PDDD6zj1Wj16tU4f/48DA0NYWVlBUdHR7E/hBBJtHOJEEKIXJg/fz5CQ0Ph7u6Ojh07AgAuXbqEmTNnIjs7GwEBAYwTVo/P+b28vPDTTz8hODgYGhoaovUBAwZg1KhRDJNJJzAwENOmTYORkRGEQiFsbGxEfVfz5s1jHY8QIoPat2+PDRs2YNWqVWLr69atQ9u2bRmlkt6iRYuQlZWFXr16oV698pecpaWlcHNz48UNcoMGDWIdgRDeoc4lQgghckFPTw/h4eEYOXKk2Hp0dDTc3d3xzz//MEomHT7n19LSQnJyMszNzcU6ix49egQrKyvedBZR3xUhpK4kJCSgd+/eaNeuHXr16gUAiImJQWJiIk6dOoWuXbsyTiid+/fvIy0tDaqqqrCzs0Pz5s1ZRyKEfCW0c4kQQohcKC4uhpOTk8R627ZtKz16wDV8zq+srIzXr19LrN+/fx96enoMEn0evvZdEUL4p3Pnzrh06RKCg4Oxe/duqKqqwt7eHhEREbwabLdo0UKUl4/fN69duya65dTW1hYODg6MExHCXdS5RAghRC6MGTMGa9eulVjfsGEDRo8ezSBR7fA5v6urKwICAlBcXAyg/AVGdnY2fH19MXToUMbppMPXvitCCH+1adMGO3bswK1bt5CUlITNmzfzarAUFRUFOzs7qKqqioZj27ZtYx1LKs+fP0fPnj3Rrl07eHh4wMPDA23btkWvXr3w4sUL1vEI4STauUQIIURmeXl5if4uEAiwadMmnDp1Ch06dABQfhtPdnY2Z0uZ+Z6/QkhICL7//nvo6+vj3bt36NatG3Jzc9GxY0csWbKEdbwa8bnvihDCX0KhEAcOHBDbOePq6gpFRUXGyWoWGhoKPz8/TJ8+HZ07dwYAxMfH45dffsE///yDmTNnMk5YPXd3d7x58wa3bt2CtbU1AOD27dsYO3YsPDw8EB0dzTghIdxDnUuEEEJkVo8ePaR6nkAgwNmzZ79ymtrje/5PxcfH4/r16ygoKICjoyN69+7NOpJU+Nx3RQjhp/T0dLi4uODx48ewsrICANy7dw9GRkY4evQozM3NGSesnqmpKfz9/SXe/Ni6dSsWLlyIhw8fMkomHS0tLZw5cwbt2rUTW7969Sr69OkjuvWUEPJ/aLhECCGEEFINbW1tJCYmShxHuX//Ptq3b08vMgghX9yAAQNQVlaGP//8E7q6ugCAvLw8/Pjjj1BQUMDRo0cZJ6yeiooKbt68CQsLC7H1Bw8ewM7OjvMXOWhoaODChQto06aN2HpKSgq6detWaY8gIfKOhkuEEEII+eLCw8MxefJkqKioIDw8vNrnenh41FGqz+Pu7o769esjNDRUbN3b2xvv3r3DmjVrGCUjhMiqBg0a4PLly7CzsxNbT0tLQ+fOnVFQUMAomXRatWqFUaNG4bfffhNbX7x4MXbt2oUbN24wSiadgQMHIj8/H9HR0TA0NAQAPHnyBKNHj4aOjg7279/POCEh3EOdS4QQQuTC+/fvsWrVKsTGxuL58+coLS0Vezw5OZlRMunwLX9YWBhGjx4NFRUVhIWFVfk8gUDAyeGSrPRdEUL4SVlZGW/evJFYLygogJKSEoNEtePv748RI0bg/Pnzos6lhIQExMTEYPfu3YzT1Wz16tVwdXWFiYmJ6JbQnJwctGrVCtu3b2ecjhBuop1LhBBC5MLo0aNx6tQpfP/992jcuLHElcgLFixglEw6fM/PN7LWd0UI4Rc3NzckJycjIiIC7du3B1A+1J40aRLatm2LyMhItgGlcO3aNYSFhYkKya2trTFr1iw4ODgwTiadsrIynDlzBnfv3gVQnp8vXYGEsEDDJUIIIXJBS0sLx44dE72Dyjd8z08IIUR6+fn5GDt2LA4fPoz69esDAEpKSuDq6orIyEhoaWkxTvhlBAUF4ZdffoG2tjbrKISQ/4iOxRFCCJELTZs2hYaGBusYn41v+T8+VlaTT7uMCCFE3mlra+PgwYNIT08X2/nzaUE23wUGBmL48OGcGC7JUlcgISzQziVCCCFy4fjx4wgPD8e6devQvHlz1nFqjW/5ZelYGd/6rggh8kNTUxOpqakwMzNjHeWzaGhoIC0tjRP5TU1NkZSUhIYNG8LU1LTK5wkEAmRmZtZhMkL4gXYuEUIIkQtOTk54//49zMzMoKamJjpmUOHly5eMkkmHb/ljY2NZR/hiJkyYIOq7at++vUTfFSGEsEL7BL6chw8fVvp3Qoh0aLhECCFELowcORJPnjxBYGBgpYXYXMf3/BVycnIAQHT7Dh8cOXKE+q4IIUROFBcXo2XLljhy5Aisra1ZxyGEN2i4RAghRC5cvHgRly5dQuvWrVlH+Sx8zl9SUgJ/f3+Eh4ejoKAAAKCurg53d3csWLBAYhcW1/Ct74oQQsjnq1+/Pt6/f886BiG8o8A6ACGEEFIXWrZsiXfv3rGO8dn4nN/d3R0bNmxAcHAwUlJSkJKSguDgYERERPCiFDUkJAS+vr549OgR6yiEEELqwLRp07Bs2TKUlJSwjkIIb9DOJUIIIXIhKCgIs2bNwpIlS2BnZyexW0ZTU5NRMunwOf+OHTuwc+dO9O/fX7Rmb28PIyMjjBw5EmvXrmWYrmZ867sihMgPvh6RrtC1a1eoqqqyjiEhMTERMTExOHXqFOzs7NCgQQOxx/ft28coGSHcRcMlQgghcqFfv34AgF69eomtl5WVQSAQQCgUsoglNT7nV1ZWhomJicS6qakplJSU6j5QLclK3xUhRPZwudC7tLQU6enpld6y6ezsDAA4duwYi2g10tbWxtChQ1nHIIRXBGVc/o5ECCGEfCFxcXHVPt6tW7c6SvJ5+Jw/ICAAd+/exZYtW6CsrAwA+PDhAyZMmABLS0ssWLCAccLqqamp8bbvihAi2+Lj49GuXTvR91auuHz5MkaNGoVHjx5JDMC4/oYIIeTz0HCJEEIIIV/V4MGDERMTA2VlZdGAJi0tDUVFRRI7sbh41MDR0RF//PEHOnTowDoKIUROeHl5VbouEAigoqICCwsLDBw4ELq6unWcTDpt2rRBixYt4O/vDwMDA4kdn1paWoyS1c7z589x7949AICVlRX09fUZJyKEu2i4RAghRC6cP3++2scrtuhzFZ/zjxs3Turnbtmy5Ssm+TynTp2Cv78/L/uuCCH81KNHDyQnJ0MoFMLKygoAcP/+fSgqKqJly5a4d+8eBAIB4uPjYWNjwzitpAYNGiAtLQ0WFhaso3yW169fY9q0adi5c6dol5WioiJGjBiBNWvW8GY4RkhdouESIYQQuaCgIHlB6sfvpHJ9iz7f8/NZxef+03fe+dB3RQjhpxUrVuDChQvYsmWLaID96tUrTJw4EV26dMGkSZMwatQovHv3DidPnmScVlLPnj0xe/ZsUV8g34wYMQIpKSlYtWoVOnbsCAC4dOkSPD090aZNG+zcuZNxQkK4h4ZLhBBC5MKrV6/EPi4uLkZKSgr8/PywZMkSieNZXMP3/AB/jxfwue+KEMJPTZs2xenTpyV2Jd26dQt9+vTBkydPkJycjD59+uCff/5hlLJq+/fvx7x58+Dj41Ppjk97e3tGyaTToEEDnDx5El26dBFbv3DhAvr164e3b98ySkYId9FtcYQQQuRCZVvYv/nmGygpKcHLywvXrl1jkEp6fM7P9+MFNDwihNS1V69e4fnz5xLDpRcvXuD169cAym80KyoqYhGvRhU3rY0fP160JhAIeLPjs2HDhpX+bNLS0oKOjg6DRIRwHw2XCCGEyLXGjRuLdtPwER/yT5o0CSkpKThy5IjE8YKff/6Z88cL+Nx3RQjhp4EDB2L8+PEICQlBu3btAACJiYnw9vbGoEGDAABXr15FixYtGKas2sOHD1lH+E/mzZsHLy8vbNu2DU2aNAEA5ObmwsfHB35+fozTEcJNdCyOEEKIXLh+/brYx2VlZXj69CmCgoJQUlKC+Ph4Rsmkw+f8fD9eQH1XhJC6VlBQgJkzZyIqKgolJSUAgHr16mHs2LEICwtDgwYNkJqaCqD8ZjbyZTk4OCA9PR0fPnyAsbExACA7OxvKysqwtLQUe25ycjKLiIRwDu1cIoQQIhfatGkj2pL/sQ4dOmDz5s2MUkmPz/n5frzg33//Ffv4074rQgj50tTV1bFx40aEhYUhMzMTAGBmZgZ1dXXRc7g8VDI2Nkb37t3RrVs3dO/eHebm5qwj1UrF7jBCiPRo5xIhhBC58OjRI7GPFRQUoKenBxUVFUaJaofP+Tds2IA9e/ZIHC8YO3YshgwZgp9//plxws8TFxfH+b4rQghhYfv27Th//jzOnTuH9PR0NG3aFN26dRMNmz7d/cNX0dHRcHV1RYMGDVhHIYQ5Gi4RQgiRGzExMYiJicHz589RWloq9hjXd/8A/M0vq8cL7t69CycnJxQUFLCOQgiRMW/fvkVQUFCV3/MrdjPxwdOnTxEXF4cjR45g165dKC0tlZnjxJqamkhNTYWZmRnrKIQwR8fiCCGEyAV/f38EBATAyckJBgYGYp05fMDn/Hw/XlBd3xWXj6UQQvhr4sSJiIuLw5gxY3j3Pb9CYWEh4uPjce7cOcTGxiIlJQWtWrVC9+7dWUf7YmifBiH/h3YuEUIIkQsGBgYIDg7GmDFjWEf5LHzPz2cKCgrV9l21bNmSUTJCiKzS1tbG0aNH0blzZ9ZRPkunTp2QkpICa2trUfeSs7MzL3r2akNDQwNpaWm0c4kQ0M4lQgghcqKoqAidOnViHeOz8T0/n316pTaf+q4IIfyko6MDXV1d1jE+2927d9GgQQO0bNkSLVu2hLW1tcwNlggh4mjnEiGEELng6+sLdXV1+Pn5sY7yWficXygUIiwsDLt370Z2djaKiorEHn/58iWjZNLja98VIYSftm/fjoMHD2Lr1q1QU1NjHafWysrKcOPGDZw7dw5xcXE4f/48lJSU0K1bN/To0QOTJk1iHfGLoJ1LhPwfGi4RQgiRC56enoiKioK9vT3s7e1Rv359scdDQ0MZJZMOn/PPnz8fmzZtwqxZszBv3jzMnTsXWVlZOHDgAObPnw8PDw/WEatVU9/V/v37GSUjhMgqBwcHZGRkoKysDCYmJhLf8/l0+UFZWRmuXbuG1atX488//5SpQm8aLhHyf+hYHCGEELlw/fp1UfnyzZs3xR7jQ1Eqn/P/+eef2LhxI1xcXLBw4UKMHDkS5ubmsLe3x+XLlzk/XFq3bh0iIyOp74oQUmf4fhFCcnIyzp07h3PnziE+Ph5v3ryBnZ0d3N3d0a1bN9bxvpjmzZtLDP4IkVe0c4kQQgghX1WDBg1w584dGBsbw8DAAEePHoWjoyMyMzPh4OCAV69esY5YrYYNG+Lq1aswNzdnHYUQQnihXr16cHBwQLdu3URl3lpaWqxj1VpRUVGlx6GNjY0ZJSKEu2jnEiGEEEK+qmbNmuHp06cwNjaGubk5Tp06BUdHRyQmJkJZWZl1vBpNnDgRO3bs4GXfFSGEsPDy5UtoamqyjvHZHjx4gPHjx+PixYti62VlZRAIBDJzrI+QL4mGS4QQQgj5qgYPHoyYmBj873//g7u7O3788UdEREQgOzsbM2fOZB2vRu/fv8eGDRtw5swZ3vVdEUL4Q1dXF/fv30ejRo2go6NT7ZFnrl+EUDFYunbtGu7cuQMAsLGxgaOjI8tYUvvpp59Qr149HDlypNKuPUKIJDoWRwghhJA6dfnyZVy8eBGWlpb47rvvWMepUY8ePap8TCAQ4OzZs3WYhhAiq7Zu3YoffvgBysrKiIyMrHagMXbs2DpMVnvPnz/HiBEjEBcXB21tbQBAfn4+evTogZ07d0JPT49twBo0aNAA165dQ8uWLVlHIYQ3aLhECCGEEE5wcXHBpk2bYGBgwDoKIYSQ/2DEiBHIzMxEVFQUrK2tAQC3b9/G2LFjYWFhgejoaMYJq9euXTuEhYWhS5curKMQwhs0XCKEEEIIJ9CVzoQQUk5RURFPnz6Fvr6+2HpeXh709fU53/mjpaWFM2fOoF27dmLrV69eRZ8+fZCfn88mmJTOnj2LefPmITAwEHZ2dhLHofncJ0XI10KdS4QQQgghhBDCIVW9///hwwcoKSnVcZraKy0tlRjIAED9+vUlbl7jot69ewMAevXqJbZOhd6EVI2GS4QQQgghhBDCAeHh4QDK+9w2bdoEdXV10WNCoRDnz5/nRQ9Qz5494enpiejoaBgaGgIAnjx5gpkzZ0oMbLgoNjaWdQRCeIeOxRFCCCGEE+hYHCFE3pmamgIAHj16hGbNmkFRUVH0mJKSEkxMTBAQEID//e9/rCJKJScnB66urrh16xaMjIxEa61atcKhQ4fQrFkzxgkJIV8aDZcIIYQQwgk0XCKEkHI9evTAvn37oKOjwzrKZysrK8OZM2dw9+5dAIC1tbXouBkfXLhwAevXr0dmZib27NmDpk2bYtu2bTA1NaWib0IqQcfiCCGEEEIIIYRDPj2WJRQKcePGDTRv3pw3AyeBQIBvvvkG33zzDesotfbXX39hzJgxGD16NJKTk/HhwwcAwKtXrxAYGIhjx44xTkgI9yiwDkAIIYQQ2ePo6Ih///0XABAQEIDCwsIa/5vffvsNurq6XzsaIYRw3owZMxAREQGgfLDk7OwMR0dHGBkZ4dy5c2zDSSkmJga//fYbJk6ciPHjx4v94brFixdj3bp12Lhxo1gxeefOnZGcnMwwGSHcRcMlQgghhHxxd+7cwdu3bwEA/v7+KCgoqPG/+fXXX6Gtrf2VkxFCCPft2bMHrVu3BgAcPnwYWVlZuHv3LmbOnIm5c+cyTlczf39/9OnTBzExMfjnn3/w77//iv3hunv37sHZ2VliXUtLC/n5+XUfiBAeoGNxhBBCCPni2rRpg3HjxqFLly4oKyvD8uXLxW49+tj8+fPrOB0hhHBbXl4emjRpAgA4duwYhg0bhhYtWmD8+PFYuXIl43Q1W7duHSIjIzFmzBjWUT5LkyZNkJ6eDhMTE7H1+Ph46gUkpAo0XCKEEELIFxcZGYkFCxbgyJEjEAgEOH78OOrVk/y1QyAQ0HCJEEI+0bhxY9y+fRsGBgY4ceIE1q5dCwAoLCwUu0GOq4qKitCpUyfWMT7bpEmT4Onpic2bN0MgEODvv//GpUuX4O3tDT8/P9bxCOEkui2OEEIIIV+VgoICcnNzoa+vzzoKIYTwwsKFC7FixQoYGBigsLAQ9+/fh7KyMjZv3oyNGzfi0qVLrCNWy9fXF+rq6rwdxJSVlSEwMBBLly4VdQYqKyvD29sbixYtYpyOEG6i4RIhhBBCCCGEcMzevXuRk5ODYcOGoVmzZgCArVu3QltbGwMHDmScrnqenp6IioqCvb097O3txUqxASA0NJRRstopKipCeno6CgoKYGNjU+XxbkIIDZcIIYQQUgcyMjKwYsUK3LlzBwBgY2MDT09PmJubM05GCCHkS+vRo0e1j8fGxtZRkv8mPT0dGRkZcHZ2hqqqKsrKyiAQCFjHIoSTaLhECCGEkK/q5MmTcHV1RZs2bdC5c2cAQEJCAtLS0nD48GF88803jBMSQgh74eHhmDx5MlRUVBAeHl7tcz08POoolXzKy8vD8OHDERsbC4FAgAcPHsDMzAzjx4+Hjo4OQkJCWEckhHNouEQIIYSQr8rBwQF9+/ZFUFCQ2PqcOXNw6tQpJCcnM0pGCCHcYWpqiqSkJDRs2BCmpqZVPk8gECAzM7MOk9VebGxslbuX1qxZg2nTptVxotpxc3PD8+fPsWnTJlhbWyMtLQ1mZmY4efIkvLy8cOvWLdYRCeEcGi4RQggh5KtSUVHBjRs3YGlpKbZ+//592Nvb4/3794ySEUII+Rp0dHRw5swZtG3bVmx95cqV8PPzw+vXrxklk06TJk1w8uRJtG7dGhoaGqLhUmZmJuzt7VFQUMA6IiGcI3knMCGEEELIF6Snp4fU1FSJ4VJqairdIEcIIf+fl5eXVM8TCAScP5b1+++/o3///jh//jxatmwJAAgJCUFAQACOHj3KOF3N3r59CzU1NYn1ly9fQllZmUEiQriPhkuEEEII+aomTZqEyZMnIzMzE506dQJQ3rm0bNkyqV9MEUKIrEtJSZHqeXwolJ44cSJevnyJ3r17Iz4+Hrt27UJgYCCOHTsm6t7jsq5duyIqKgqLFi0CUP45Ly0tRXBwcI1l5YTIKzoWRwghhJCvqqysDCtWrEBISAj+/vtvAIChoSF8fHzg4eHBixdKhBBCas/X1xcREREQCoU4fvw4OnTowDqSVG7evIlevXrB0dERZ8+ehaurK27duoWXL18iISGBbjolpBI0XCKEEEJInXnz5g0AQENDQ+KxhIQEODk50ZEDQgjhoapuuFu+fDmcnZ3Rvn170Rofbrt79eoVVq9ejbS0NBQUFMDR0RHTpk2DgYEB62iEcBINlwghhBDCCZqamkhNTYWZmRnrKIQQQmqpuhvuPsaH2+4IIbVHnUuEEEII4QR6v4sQQvjr4cOHrCN8Mc7OzujevTu6d++OTp06QUVFhXUkQjhPgXUAQgghhBBCCCHyR1NTk5O7mPr06YPLly/D1dUV2tra6NKlC+bNm4fTp0+jsLCQdTxCOIl2LhFCCCGEEEIIqXNc3bE6b948AEBJSQkSExMRFxeHc+fOITg4GAoKCnj//j3jhIRwDw2XCCGEEEIIIYSQT2RmZuLGjRtIS0vD9evXoaGhAWdnZ9axCOEkGi4RQgghhBMEAgHrCIQQQghGjRqFuLg4fPjwAc7OzujWrRvmzJkDe3t7+llFSBVouEQIIYSQr6asrAw5OTn/r737j8qyvv84/rq4FUhAfgiYUiC3mgzFxXLL0EjIeTp5tqiTpbn8EW47Zx1zMjq6DdOcY552AsV56jg1zdORUxs7OzUpXZCmOc2B/NApGyVYk4NCKj8k4Ob+/vFlFJGG6M3nQp+Pczjnvq/r+uP5n/jm8/lcCg8P/8YDUe26PQIAcHPJzc1VaGioFi1apOTkZE2dOlVDhgwxnQXYGgd6AwAAj3G73RozZoxOnz79jc82NDTI6XT2QxUAwA7sugqorq5OmzdvVmtrq375y18qNDRUCQkJ+tWvfqXdu3ebzgNsyXLzZ0IAAOBB48eP15YtWzR58mTTKQAAGwkICFBJSYnt/7Dwn//8R2vWrNFrr72mjo4OuVwu00mA7bAtDgAAeNTatWv17LPP6qWXXtKECRNM5wAAbCI/P18RERGmM3qoq6vrekPce++9p+PHjysoKEg/+MEPdN9995nOA2yJlUsAAMCjgoOD1dzcrPb2dnl7e+uWW27pdr++vt5QGQDAE9LS0r72umVZ8vX11ZgxY/TQQw8pJCSkn8t6x+FwKDQ0VPfee6/uu+8+TZs2TXFxcaazAFtjuAQAADxq+/btV7w/f/78fioBAPSHpKQkFRUVyeVyady4cZKkiooKORwOxcTE6OTJk7IsS/v371dsbKzh2p6OHTum8ePHm84ABhSGSwAAAACA62bdunV6//339corr2jo0KGSpAsXLmjRokWaOnWqfvzjH+uJJ57QpUuX9M477xiu7Sk5OVl5eXkKCgrqdv3ixYtKSUlRQUGBmTDAxhguAQAAj6usrNQrr7yiyspKrV+/XuHh4crPz1dkZCR/HQaAG0xERIT27NnTY1XSsWPHNGPGDH366acqKirSjBkzdO7cOUOVl+fl5aWamhqFh4d3u15bW6uIiAi1tbUZKgPsy8t0AAAAuLHt3btXcXFxOnTokPLy8tTY2ChJKikp0cqVKw3XAQCutwsXLqi2trbH9bNnz+rixYuSpKCgILW2tvZ32hWVlpaqtLRUknT8+PGu76WlpSouLtaWLVtseQA5YAe8LQ4AAHjU8uXLtWbNGqWlpSkgIKDrenJysv7whz8YLAMAeMJDDz2kp556Si+++KK++93vSpI+/PBDpaenKyUlRZJ0+PBh3XHHHQYre7rzzjtlWZYsy1JycnKP+7fccos2bNhgoAywP7bFAQAAj/L391dZWZmio6MVEBCgkpISOZ1OnTp1SjExMWppaTGdCAC4jhobG7V06VK9+uqram9vlyQNGjRI8+fPV3Z2tvz8/HT06FFJ/z/QsYuqqiq53W45nU4dPnxYYWFhXfe8vb0VHh4uh8NhsBCwL1YuAQAAjwoKCtKZM2cUHR3d7XpxcTHbCwDgBuTv768//vGPys7O1kcffSRJcjqd8vf373rGTkOl/4mKipIkdXR0GC4BBh7OXAIAAB41e/ZsLVu2TDU1NbIsSx0dHTpw4IDS09M1b94803kAAA/x9/fXxIkTNXHixG6DpYFgx44dmjJlikaOHKmqqipJUnZ2tv76178aLgPsieESAADwqMzMTMXExOj2229XY2OjYmNjlZiYqISEBGVkZJjOAwBcZ01NTVqxYoUSEhI0ZswYOZ3Obj9299JLLyktLU0PPvigzp8/L5fLJUkKDg7WunXrzMYBNsWZSwAAoF9UV1ervLxcjY2Nio+P19ixY00nAQA8YM6cOdq7d6+efPJJjRgxQpZldbu/ZMkSQ2W9Exsbq8zMTKWkpHQ7K7C8vFzTpk3TuXPnTCcCtsOZSwAAoF9ERkbq9ttvl6Qe/9EAANw48vPz9be//U1TpkwxndInH3/8seLj43tc9/HxUVNTk4EiwP7YFgcAADxuy5YtmjBhgnx9feXr66sJEyZo8+bNprMAAB4QHByskJAQ0xl9Fh0d3fU2uy97++239a1vfav/g4ABgJVLAADAo5577jllZWVp8eLFuueeeyRJBw8e1NKlS1VdXa3Vq1cbLgQAXE+/+c1v9Nxzz2n79u0aMmSI6ZyrlpaWpqefflotLS1yu906fPiwdu7cqd/97nf8YQS4DM5cAgAAHhUWFqacnBzNmTOn2/WdO3dq8eLFnF0BADeY+Ph4VVZWyu12a9SoURo8eHC3+0VFRYbKeu+1117TqlWrVFlZKUkaOXKknn/+eaWmphouA+yJlUsAAMCj2traNGnSpB7X77rrLrW3txsoAgB4UkpKiumEazZ37lzNnTtXzc3NamxsVHh4uOkkwNZYuQQAADxq8eLFGjx4sLKysrpdT09P16VLl7Rx40ZDZQAAXF5tba1OnjwpSYqJiVFYWJjhIsC+WLkEAACuu7S0tK7PlmVp8+bN2r17tyZPnixJOnTokKqrqzVv3jxTiQAAfK2Ghgb97Gc/086dO9XR0SFJcjgcevzxx7Vx40YFBgYaLgTsh5VLAADguktKSurVc5ZlqaCgwMM1AABPCwkJUUVFhUJDQxUcHCzLsi77bH19fT+WXb3HH39cxcXF2rBhQ7cXUSxZskR33nmncnNzDRcC9sNwCQAAAABwTbZv367Zs2fLx8dH27Ztu+Jwaf78+f1YdvX8/Pz0zjvvaOrUqd2uv//++3rggQfU1NRkqAywL7bFAQAAAACuyZcHRgsWLDAXch0MGzbsa7e+BQYGKjg42EARYH+sXAIAAB7V0tKiDRs2qLCwULW1tV3nV/zPQHglNQCg9xwOh86cOdPjDWt1dXUKDw+Xy+UyVNY7mzZt0htvvKEdO3bo1ltvlSTV1NRo/vz5euSRR/TTn/7UcCFgP6xcAgAAHpWamqrdu3fr0Ucf1fe+970rbpUAAAx8l1u/8Pnnn8vb27ufa3onPj6+279P//73vxUZGanIyEhJUnV1tXx8fHT27FmGS8DXYLgEAAA86q233tKuXbs0ZcoU0ykAAA/KycmR9MVbQv39/bvuuVwu7du3TzExMabyriglJcV0AjCgsS0OAAB4VGxsrHJzczVx4kTTKQAAD4qOjpYkVVVV6bbbbpPD4ei65+3trVGjRmn16tW6++67TSVeVzt37tQPf/hD+fn5mU4BjGO4BAAAPCo/P185OTl6+eWXFRUVZToHAOBhSUlJysvLu+EPvx46dKiOHj0qp9NpOgUwjm1xAADAoyZNmqSWlhY5nU4NGTJEgwcP7na/vr7eUBkAwBMKCwu7fXe5XCorK1NUVNQNNXBinQbwBYZLAADAo+bMmaNPP/1UmZmZGj58OAd6A8AN7uc//7ni4uKUmpoql8ulxMREHTx4UEOGDNFbb72ladOmmU4EcJ0xXAIAAB71wQcf6ODBg/r2t79tOgUA0A/eeOMN/ehHP5Ikvfnmmzp16pROnDihHTt26Ne//rUOHDhguBDA9eZlOgAAANzYYmJidOnSJdMZAIB+UldXp1tvvVWStGvXLs2aNUt33HGHnnrqKZWVlRmuA+AJDJcAAIBHrV27Vr/4xS/03nvvqa6uThcvXuz2AwC4sQwfPlzHjx+Xy+XS22+/re9///uSpObm5m5vkANw42BbHAAA8KgHHnhAknT//fd3u+52u2VZllwul4ksAICHLFy4UI899phGjBghy7I0ffp0SdKhQ4cUExNjuO76iYqK6vGSCuBmxXAJAAB41FffGgQAuLGtWrVKEyZM0OnTpzVr1iz5+PhIkhwOh5YvX264rnfOnz+vP/3pT6qsrNSzzz6rkJAQFRUVafjw4YqIiJAklZeXG64E7MNy8/5EAAAAAAAkSaWlpZo+fboCAwN16tQpnTx5Uk6nUxkZGaqurtarr75qOhGwHVYuAQAAj9q3b98V7ycmJvZTCQDAU3JycvSTn/xEvr6+ysnJueKzzzzzTD9V9U1aWpoWLFigF154QQEBAV3XH3zwQT3xxBMGywD7YuUSAADwKC+vnu8PsSyr6zNnLgHAwBcdHa0jR45o2LBhio6OvuxzlmXpo48+6seyqxcYGKiioiKNHj1aAQEBKikpkdPpVFVVlcaNG6eWlhbTiYDtsHIJAAB41Geffdbte1tbm4qLi7VixQr99re/NVQFALiePv7446/9PBD5+Ph87dtMKyoqFBYWZqAIsD9WLgEAACP27t2rtLQ0/fOf/zSdAgC4Rmlpab16zrIsvfjiix6uuTaLFi1SXV2dXn/9dYWEhKi0tFQOh0MpKSlKTEzUunXrTCcCtsNwCQAAGHHixAlNmjRJjY2NplMAANcoKSmpV89ZlqWCggIP11ybCxcu6NFHH9WRI0fU0NCgkSNHqqamRvfcc4927dolPz8/04mA7TBcAgAAHlVaWtrtu9vt1pkzZ7R27Vq1t7dr//79hsoAALi8/fv3q7S0VI2NjfrOd76j6dOnm04CbIvhEgAA8CgvLy9ZlqWv/soxefJkbd26VTExMYbKAAAAcD0wXAIAAB5VVVXV7buXl5fCwsLk6+trqAgAgCv78MMPVVhYqNraWnV0dHS7l5WVZagKsC/eFgcAADwqKipK7777rt59992v/SV969athsoAAOgpMzNTGRkZGjdunIYPHy7LsrruffkzgC8wXAIAAB71/PPPa/Xq1Zo0aZJGjBjBL+YAAFtbv369tm7dqgULFphOAQYMhksAAMCjXn75ZW3btk1PPvmk6RQAAL6Rl5eXpkyZYjoDGFC8TAcAAIAbW2trqxISEkxnAADQK0uXLtXGjRtNZwADCgd6AwAAj1q2bJn8/f21YsUK0ykAAHyjjo4OzZw5UxUVFYqNjdXgwYO73c/LyzNUBtgX2+IAAIBHtbS0aNOmTfr73/+uiRMn9vglnbfuAADs5JlnnlFhYaGSkpI0bNgwzgoEeoGVSwAAwKOSkpIue8+yLBUUFPRjDQAAVxYQEKDc3FzNnDnTdAowYLByCQAAeFRhYaHpBAAAei0kJESjR482nQEMKBzoDQAAAABAp1WrVmnlypVqbm42nQIMGGyLAwAAAACgU3x8vCorK+V2uzVq1KgeZwUWFRUZKgPsi21xAAAAAAB0SklJMZ0ADDisXAIAAAAAAECfceYSAAAAAAAA+oxtcQAAAACAm1pISIgqKioUGhqq4OBgWZZ12Wfr6+v7sQwYGBguAQAAAABuatnZ2QoICOj6fKXhEoCeOHMJAAAAAAAAfcaZSwAAAAAAdHI4HKqtre1xva6uTg6Hw0ARYH8MlwAAAAAA6HS5zT2ff/65vL29+7kGGBg4cwkAAAAAcNPLycmRJFmWpc2bN8vf37/rnsvl0r59+xQTE2MqD7A1zlwCAAAAANz0oqOjJUlVVVW67bbbum2B8/b21qhRo7R69WrdfffdphIB22K4BAAAAABAp6SkJOXl5Sk4ONh0CjBgMFwCAAAAAOAqDR06VEePHpXT6TSdAhjHgd4AAAAAAFwl1mkAX2C4BAAAAAAAgD5juAQAAAAAAIA+Y7gEAAAAAACAPmO4BAAAAADAVbIsy3QCYBsMlwAAAAAAuEoc6A18geESAAAAAABXKT8/XxEREaYzAFuw3IxbAQAAAAA3sbS0tF4/m5WV5cESYGAaZDoAAAAAAACTiouLu30vKipSe3u7xo0bJ0mqqKiQw+HQXXfdZSIPsD2GSwAAAACAm1phYWHX56ysLAUEBGj79u0KDg6WJH322WdauHCh7r33XlOJgK2xLQ4AAAAAgE4RERHavXu3xo8f3+16eXm5ZsyYof/+97+GygD74kBvAAAAAAA6Xbx4UWfPnu1x/ezZs2poaDBQBNgfwyUAAAAAADo9/PDDWrhwofLy8vTJJ5/ok08+0Z///GelpqbqkUceMZ0H2BLb4gAAAAAA6NTc3Kz09HRt3bpVbW1tkqRBgwYpNTVVv//97+Xn52e4ELAfhksAAAAAAHxFU1OTKisrJUmjR49mqARcAcMlAAAAAAAA9Nkg0wEAAAAAANjJkSNH9Prrr6u6ulqtra3d7uXl5RmqAuyLA70BAAAAAOiUm5urhIQE/etf/9Jf/vIXtbW16dixYyooKFBgYKDpPMCWGC4BAAAAANApMzNT2dnZevPNN+Xt7a3169frxIkTeuyxxxQZGWk6D7AlhksAAAAAAHSqrKzUzJkzJUne3t5qamqSZVlaunSpNm3aZLgOsCeGSwAAAAAAdAoODlZDQ4MkKSIiQuXl5ZKk8+fPq7m52WQaYFsc6A0AAAAAQKfExETt2bNHcXFxmjVrlpYsWaKCggLt2bNH999/v+k8wJYst9vtNh0BAAAAAIAd1NfXq6WlRSNHjlRHR4deeOEFffDBBxo7dqwyMjIUHBxsOhGwHYZLAAAAAAAA6DPOXAIAAAAA4EsqKyuVkZGhOXPmqLa2VpKUn5+vY8eOGS4D7InhEgAAAAAAnfbu3au4uDgdOnRIeXl5amxslCSVlJRo5cqVhusAe2K4BAAAAABAp+XLl2vNmjXas2ePvL29u64nJyfrH//4h8EywL4YLgEAAAAA0KmsrEwPP/xwj+vh4eE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      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "cmap = sns.diverging_palette(220, 20, as_cmap=True)\n",
    "sns.heatmap(df.corr(), annot=True, cmap=cmap, fmt=\".2f\", linewidths=.5,)\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing for modelling\n",
    "Afterwards, we split the dataset between train and test and display their sizes and target distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 14968 rows\n",
      "Test set size: 6416 rows\n",
      "\n",
      "Training target distribution:\n",
      "has_resolution_incident\n",
      "False    0.98744\n",
      "True     0.01256\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Test target distribution:\n",
      "has_resolution_incident\n",
      "False    0.989246\n",
      "True     0.010754\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)\n",
    "\n",
    "print(f\"Training set size: {X_train.shape[0]} rows\")\n",
    "print(f\"Test set size: {X_test.shape[0]} rows\")\n",
    "\n",
    "print(\"\\nTraining target distribution:\")\n",
    "print(y_train.value_counts(normalize=True))\n",
    "\n",
    "print(\"\\nTest target distribution:\")\n",
    "print(y_test.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36c9276",
   "metadata": {},
   "source": [
    "## Classification Model with Random Forest\n",
    "\n",
    "We define a machine learning pipeline that includes:\n",
    "- **Scaling numeric features** with `StandardScaler`\n",
    "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n",
    "\n",
    "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.).  \n",
    "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n",
    "\n",
    "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "943ef7d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 72 candidates, totalling 288 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   4.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   4.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   4.0s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   6.3s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.6s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   2.3s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.4s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.6s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.6s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   6.3s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=  10.5s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   6.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.6s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.6s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.7s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.2s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   8.9s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.6s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   7.0s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   4.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   4.9s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.7s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.5s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.2s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   5.4s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.1s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   6.2s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.3s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.6s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   4.9s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.2s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.2s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.0s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   5.0s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.6s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.5s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.5s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.8s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.6s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   4.3s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   4.3s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   3.2s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   3.6s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   7.0s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   6.9s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.2s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   4.0s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.7s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   3.9s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.8s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.2s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.8s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   4.9s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   4.9s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=  11.0s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   3.0s\n",
      "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=  11.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.0s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.6s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.2s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   6.1s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.7s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   4.0s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.4s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   5.5s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   5.4s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.7s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.7s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.8s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.0s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   4.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.2s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   6.2s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.5s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   4.7s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   4.7s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   4.8s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.6s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   4.8s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.2s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   4.9s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   3.0s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   7.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   7.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.7s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   7.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.2s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.6s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.4s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.4s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.7s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.9s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.2s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.3s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.2s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   4.2s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.5s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.8s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   4.0s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   3.9s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   5.9s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.0s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   6.1s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   3.1s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   2.7s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time=   3.2s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.3s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   1.9s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.4s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.5s\n",
      "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.7s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.4s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.4s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.2s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   2.1s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.8s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   5.8s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.8s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   3.4s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   4.0s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time=   3.4s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   3.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time=   4.0s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time=   7.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   2.0s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   5.5s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.4s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time=   3.3s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time=   1.7s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time=   7.3s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   4.7s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   4.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   4.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   2.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.0s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   2.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time=   4.9s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time=   3.0s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   3.6s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   3.8s\n",
      "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time=   3.5s\n",
      "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 1, 'model__min_samples_split': 5, 'model__n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define pipeline (scaling numeric features only)\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n",
    "                                     random_state=123))\n",
    "])\n",
    "\n",
    "# Define parameter grid\n",
    "param_grid = {\n",
    "    'model__n_estimators': [100, 200, 300],\n",
    "    'model__max_depth': [None, 10, 20],\n",
    "    'model__min_samples_split': [2, 5],\n",
    "    'model__min_samples_leaf': [1, 2],\n",
    "    'model__max_features': ['sqrt', 'log2']\n",
    "}\n",
    "\n",
    "# GridSearchCV\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_grid=param_grid,\n",
    "    scoring='average_precision',  # For imbalanced classification\n",
    "    cv=4, # 4-fold cross-validation\n",
    "    n_jobs=-1, # Use all available cores\n",
    "    verbose=2, # Verbose output for progress tracking,\n",
    "    refit=True # Refit the best model on the entire training set - it's already true by default\n",
    ")\n",
    "\n",
    "# Fit the grid search on training data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Best model\n",
    "best_pipeline = grid_search.best_estimator_\n",
    "print(\"Best hyperparameters:\", grid_search.best_params_)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n",
    "y_pred = best_pipeline.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_model__max_depth</th>\n",
       "      <th>param_model__max_features</th>\n",
       "      <th>param_model__min_samples_leaf</th>\n",
       "      <th>param_model__min_samples_split</th>\n",
       "      <th>param_model__n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.800567</td>\n",
       "      <td>0.367533</td>\n",
       "      <td>0.309758</td>\n",
       "      <td>0.016611</td>\n",
       "      <td>None</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>300</td>\n",
       "      <td>{'model__max_depth': None, 'model__max_feature...</td>\n",
       "      <td>0.032795</td>\n",
       "      <td>0.020415</td>\n",
       "      <td>0.031554</td>\n",
       "      <td>0.052539</td>\n",
       "      <td>0.034326</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.748260</td>\n",
       "      <td>0.156803</td>\n",
       "      <td>0.519434</td>\n",
       "      <td>0.307019</td>\n",
       "      <td>None</td>\n",
       "      <td>log2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>300</td>\n",
       "      <td>{'model__max_depth': None, 'model__max_feature...</td>\n",
       "      <td>0.032795</td>\n",
       "      <td>0.020415</td>\n",
       "      <td>0.031554</td>\n",
       "      <td>0.052539</td>\n",
       "      <td>0.034326</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.784500</td>\n",
       "      <td>0.083096</td>\n",
       "      <td>0.176412</td>\n",
       "      <td>0.006317</td>\n",
       "      <td>10</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>300</td>\n",
       "      <td>{'model__max_depth': 10, 'model__max_features'...</td>\n",
       "      <td>0.032233</td>\n",
       "      <td>0.018502</td>\n",
       "      <td>0.027846</td>\n",
       "      <td>0.058432</td>\n",
       "      <td>0.034253</td>\n",
       "      <td>0.014815</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.521759</td>\n",
       "      <td>0.073640</td>\n",
       "      <td>0.206560</td>\n",
       "      <td>0.009525</td>\n",
       "      <td>10</td>\n",
       "      <td>log2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>300</td>\n",
       "      <td>{'model__max_depth': 10, 'model__max_features'...</td>\n",
       "      <td>0.032233</td>\n",
       "      <td>0.018502</td>\n",
       "      <td>0.027846</td>\n",
       "      <td>0.058432</td>\n",
       "      <td>0.034253</td>\n",
       "      <td>0.014815</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.828132</td>\n",
       "      <td>0.550318</td>\n",
       "      <td>0.146853</td>\n",
       "      <td>0.016658</td>\n",
       "      <td>None</td>\n",
       "      <td>log2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'model__max_depth': None, 'model__max_feature...</td>\n",
       "      <td>0.033227</td>\n",
       "      <td>0.020472</td>\n",
       "      <td>0.030666</td>\n",
       "      <td>0.051437</td>\n",
       "      <td>0.033951</td>\n",
       "      <td>0.011166</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.745528</td>\n",
       "      <td>0.295001</td>\n",
       "      <td>0.159567</td>\n",
       "      <td>0.023629</td>\n",
       "      <td>None</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'model__max_depth': None, 'model__max_feature...</td>\n",
       "      <td>0.029798</td>\n",
       "      <td>0.017825</td>\n",
       "      <td>0.030080</td>\n",
       "      <td>0.039780</td>\n",
       "      <td>0.029371</td>\n",
       "      <td>0.007784</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>3.490800</td>\n",
       "      <td>0.096002</td>\n",
       "      <td>0.163926</td>\n",
       "      <td>0.008971</td>\n",
       "      <td>20</td>\n",
       "      <td>log2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'model__max_depth': 20, 'model__max_features'...</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.017032</td>\n",
       "      <td>0.028030</td>\n",
       "      <td>0.040588</td>\n",
       "      <td>0.029225</td>\n",
       "      <td>0.008416</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3.569089</td>\n",
       "      <td>0.091260</td>\n",
       "      <td>0.151084</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>20</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'model__max_depth': 20, 'model__max_features'...</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.017032</td>\n",
       "      <td>0.028030</td>\n",
       "      <td>0.040588</td>\n",
       "      <td>0.029225</td>\n",
       "      <td>0.008416</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.332713</td>\n",
       "      <td>0.932992</td>\n",
       "      <td>0.105683</td>\n",
       "      <td>0.037904</td>\n",
       "      <td>None</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'model__max_depth': None, 'model__max_feature...</td>\n",
       "      <td>0.030112</td>\n",
       "      <td>0.017368</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.039004</td>\n",
       "      <td>0.028853</td>\n",
       "      <td>0.007690</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.138600</td>\n",
       "      <td>0.426632</td>\n",
       "      <td>0.101666</td>\n",
       "      <td>0.020386</td>\n",
       "      <td>None</td>\n",
       "      <td>log2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'model__max_depth': None, 'model__max_feature...</td>\n",
       "      <td>0.030112</td>\n",
       "      <td>0.017368</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.039004</td>\n",
       "      <td>0.028853</td>\n",
       "      <td>0.007690</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "5        5.800567      0.367533         0.309758        0.016611   \n",
       "17       5.748260      0.156803         0.519434        0.307019   \n",
       "29       4.784500      0.083096         0.176412        0.006317   \n",
       "41       4.521759      0.073640         0.206560        0.009525   \n",
       "16       3.828132      0.550318         0.146853        0.016658   \n",
       "..            ...           ...              ...             ...   \n",
       "1        3.745528      0.295001         0.159567        0.023629   \n",
       "61       3.490800      0.096002         0.163926        0.008971   \n",
       "49       3.569089      0.091260         0.151084        0.003098   \n",
       "0        2.332713      0.932992         0.105683        0.037904   \n",
       "12       2.138600      0.426632         0.101666        0.020386   \n",
       "\n",
       "   param_model__max_depth param_model__max_features  \\\n",
       "5                    None                      sqrt   \n",
       "17                   None                      log2   \n",
       "29                     10                      sqrt   \n",
       "41                     10                      log2   \n",
       "16                   None                      log2   \n",
       "..                    ...                       ...   \n",
       "1                    None                      sqrt   \n",
       "61                     20                      log2   \n",
       "49                     20                      sqrt   \n",
       "0                    None                      sqrt   \n",
       "12                   None                      log2   \n",
       "\n",
       "    param_model__min_samples_leaf  param_model__min_samples_split  \\\n",
       "5                               1                               5   \n",
       "17                              1                               5   \n",
       "29                              1                               5   \n",
       "41                              1                               5   \n",
       "16                              1                               5   \n",
       "..                            ...                             ...   \n",
       "1                               1                               2   \n",
       "61                              1                               2   \n",
       "49                              1                               2   \n",
       "0                               1                               2   \n",
       "12                              1                               2   \n",
       "\n",
       "    param_model__n_estimators  \\\n",
       "5                         300   \n",
       "17                        300   \n",
       "29                        300   \n",
       "41                        300   \n",
       "16                        200   \n",
       "..                        ...   \n",
       "1                         200   \n",
       "61                        200   \n",
       "49                        200   \n",
       "0                         100   \n",
       "12                        100   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "5   {'model__max_depth': None, 'model__max_feature...           0.032795   \n",
       "17  {'model__max_depth': None, 'model__max_feature...           0.032795   \n",
       "29  {'model__max_depth': 10, 'model__max_features'...           0.032233   \n",
       "41  {'model__max_depth': 10, 'model__max_features'...           0.032233   \n",
       "16  {'model__max_depth': None, 'model__max_feature...           0.033227   \n",
       "..                                                ...                ...   \n",
       "1   {'model__max_depth': None, 'model__max_feature...           0.029798   \n",
       "61  {'model__max_depth': 20, 'model__max_features'...           0.031250   \n",
       "49  {'model__max_depth': 20, 'model__max_features'...           0.031250   \n",
       "0   {'model__max_depth': None, 'model__max_feature...           0.030112   \n",
       "12  {'model__max_depth': None, 'model__max_feature...           0.030112   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "5            0.020415           0.031554           0.052539         0.034326   \n",
       "17           0.020415           0.031554           0.052539         0.034326   \n",
       "29           0.018502           0.027846           0.058432         0.034253   \n",
       "41           0.018502           0.027846           0.058432         0.034253   \n",
       "16           0.020472           0.030666           0.051437         0.033951   \n",
       "..                ...                ...                ...              ...   \n",
       "1            0.017825           0.030080           0.039780         0.029371   \n",
       "61           0.017032           0.028030           0.040588         0.029225   \n",
       "49           0.017032           0.028030           0.040588         0.029225   \n",
       "0            0.017368           0.028927           0.039004         0.028853   \n",
       "12           0.017368           0.028927           0.039004         0.028853   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "5         0.011568                1  \n",
       "17        0.011568                1  \n",
       "29        0.014815                3  \n",
       "41        0.014815                3  \n",
       "16        0.011166                5  \n",
       "..             ...              ...  \n",
       "1         0.007784               67  \n",
       "61        0.008416               69  \n",
       "49        0.008416               69  \n",
       "0         0.007690               71  \n",
       "12        0.007690               71  \n",
       "\n",
       "[72 rows x 17 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve cv results\n",
    "pd.DataFrame(grid_search.cv_results_).sort_values(by='mean_test_score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply a threshold selector to find a proper value for F2 optimisation, rather than defaulting to 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the best threshold for F2 score\n",
    "\n",
    "def find_best_threshold(y_true, y_proba, beta=2.0):\n",
    "    thresholds = np.linspace(0, 1, 200)\n",
    "    f2_scores = []\n",
    "\n",
    "    for t in thresholds:\n",
    "        preds = (y_proba >= t).astype(int)\n",
    "        score = fbeta_score(y_true, preds, beta=beta)\n",
    "        f2_scores.append(score)\n",
    "\n",
    "    best_index = np.argmax(f2_scores)\n",
    "    return thresholds[best_index], f2_scores[best_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best threshold: 5.0% — F2 score: 13.95%\n"
     ]
    }
   ],
   "source": [
    "# Predict probabilities\n",
    "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Find best threshold for F2\n",
    "best_thresh, best_f2 = find_best_threshold(y_test, y_pred_proba, beta=2.0)\n",
    "print(f\"Best threshold: {100*best_thresh:.1f}% — F2 score: {100*best_f2:.2f}%\")\n",
    "\n",
    "# Use that threshold for final classification\n",
    "y_pred_opt = (y_pred_proba >= best_thresh).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2fcc89",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n",
    "\n",
    "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30786f7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " No Incident       0.99      0.88      0.93      6347\n",
      "    Incident       0.04      0.43      0.07        69\n",
      "\n",
      "    accuracy                           0.87      6416\n",
      "   macro avg       0.52      0.66      0.50      6416\n",
      "weighted avg       0.98      0.87      0.92      6416\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print classification report\n",
    "print(classification_report(y_test, y_pred_opt, target_names=['No Incident', 'Incident']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the Classification Report\n",
    "\n",
    "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n",
    "\n",
    "It includes the following metrics for each class (0 and 1):\n",
    "* Precision: Out of all predicted positives, how many were actually positive?\n",
    "* Recall: Out of all actual positives, how many did we correctly identify?\n",
    "* F1-score: Harmonic mean of precision and recall (balances both)\n",
    "* Support: Number of true samples of that class in the test data\n",
    "\n",
    "Interpretation:\n",
    "* Class 0 = No incident\n",
    "* Class 1 = Has resolution incident (rare, but important!)\n",
    "\n",
    "A few explanatory cases:\n",
    "* A high recall for class 1 means we're catching most incidents.\n",
    "* A high precision for class 1 means when we predict an incident, we're often correct.\n",
    "* The F1-score gives a single balanced measure (good for imbalanced data).\n",
    "\n",
    "Special note for imbalanced data:\n",
    "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n",
    "We want to maximize recall to catch as many real incidents as possible — without letting precision drop too low (to avoid too many false alarms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b4da914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the ROC Curve\n",
    "\n",
    "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n",
    "\n",
    "A quick reminder of the definitions:\n",
    "* True Positive Rate (TPR) = Recall\n",
    "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n",
    "\n",
    "What we display in this plot is:\n",
    "* The x-axis is False Positive Rate\n",
    "* The y-axis is True Positive Rate\n",
    "\n",
    "The curve shows how TPR and FPR change as the threshold varies\n",
    "\n",
    "It's important to note that:\n",
    "* A model with no skill will produce a diagonal line (AUC = 0.5)\n",
    "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n",
    "\n",
    "The Area Under the Curve (ROC AUC) gives a single performance score:\n",
    "* Closer to 1 means better at ranking positive cases higher than negative ones\n",
    "\n",
    "**Important!**\n",
    "\n",
    "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). That’s why we also MUST check the Precision-Recall curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6790d41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PR Curve\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n",
    "pr_auc = average_precision_score(y_test, y_pred_proba)\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.legend(loc='lower left')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the Precision-Recall (PR) Curve\n",
    "\n",
    "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n",
    "\n",
    "A quick reminder of the definitions:\n",
    "* Precision = How many of the predicted positives are actually positive\n",
    "* Recall = How many of the actual positives the model correctly identifies\n",
    "\n",
    "What we display in this plot is:\n",
    "* The x-axis is Recall \n",
    "* The y-axis is Precision \n",
    "\n",
    "The curve shows the trade-off between them at different model thresholds\n",
    "\n",
    "In imbalanced datasets, accuracy can be misleading — the PR curve focuses only on the positive class, making it much more meaningful:\n",
    "* A higher curve means better performance\n",
    "* The area under the curve (PR AUC) summarizes this: closer to 1 is better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_opt).ravel()\n",
    "\n",
    "# Total predictions\n",
    "total = tp + tn + fp + fn\n",
    "\n",
    "# Compute all requested metrics\n",
    "recall = recall_score(y_test, y_pred_opt)\n",
    "precision = precision_score(y_test, y_pred_opt)\n",
    "f1 = fbeta_score(y_test, y_pred_opt, beta=1)\n",
    "f2 = fbeta_score(y_test, y_pred_opt, beta=2)\n",
    "f3 = fbeta_score(y_test, y_pred_opt, beta=3)\n",
    "fpr = fp / (fp + tn) if (fp + tn) != 0 else 0\n",
    "\n",
    "# Scores relative to total\n",
    "tp_score = tp / total\n",
    "tn_score = tn / total\n",
    "fp_score = fp / total\n",
    "fn_score = fn / total\n",
    "\n",
    "# Create DataFrame\n",
    "summary_df = pd.DataFrame([{\n",
    "    \"flagging_analysis_type\": \"RISK_VS_CLAIM\",\n",
    "    \"count_total\": total,\n",
    "    \"count_true_positive\": tp,\n",
    "    \"count_true_negative\": tn,\n",
    "    \"count_false_positive\": fp,\n",
    "    \"count_false_negative\": fn,\n",
    "    \"true_positive_score\": tp_score,\n",
    "    \"true_negative_score\": tn_score,\n",
    "    \"false_positive_score\": fp_score,\n",
    "    \"false_negative_score\": fn_score,\n",
    "    \"recall_score\": recall,\n",
    "    \"precision_score\": precision,\n",
    "    \"false_positive_rate_score\": fpr,\n",
    "    \"f1_score\": f1,\n",
    "    \"f2_score\": f2,\n",
    "    \"f3_score\": f3,\n",
    "    \"roc_auc_score\": roc_auc,\n",
    "    \"pr_auc_score\": pr_auc\n",
    "}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix_from_df(df, flagging_analysis_type, name_of_the_experiment=\"\"):\n",
    "\n",
    "    # Subset - just retrieve one row depending on the flagging_analysis_type\n",
    "    row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n",
    "\n",
    "    # Define custom x-axis labels and wording\n",
    "    if flagging_analysis_type == 'RISK_VS_CLAIM':\n",
    "        x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n",
    "        outcome_label = \"submitted claim\"\n",
    "    elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n",
    "        x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n",
    "        outcome_label = \"submitted payout\"\n",
    "    else:\n",
    "        x_labels = ['Actual Positive', 'Actual Negative']  \n",
    "        outcome_label = \"outcome\"\n",
    "\n",
    "    # Confusion matrix structure\n",
    "    cm = np.array([\n",
    "        [row['count_true_positive'], row['count_false_positive']],\n",
    "        [row['count_false_negative'], row['count_true_negative']]\n",
    "    ])\n",
    "\n",
    "    # Create annotations for the confusion matrix\n",
    "    labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n",
    "    counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n",
    "              [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n",
    "    percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n",
    "                   [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n",
    "    annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n",
    "\n",
    "    # Scores formatted as percentages\n",
    "    recall = row['recall_score'] * 100\n",
    "    precision = row['precision_score'] * 100\n",
    "    f1 = row['f1_score'] * 100\n",
    "    f2 = row['f2_score'] * 100\n",
    "    f3 = row['f3_score'] * 100\n",
    "    roc_auc = row['roc_auc_score'] * 100\n",
    "    pr_auc = row['pr_auc_score'] * 100\n",
    "\n",
    "    # Set up figure and axes manually for precise control\n",
    "    fig = plt.figure(figsize=(9, 8))\n",
    "    grid = fig.add_gridspec(nrows=3, height_ratios=[1, 15, 2])\n",
    "\n",
    "    \n",
    "    ax_main_title = fig.add_subplot(grid[0])\n",
    "    ax_main_title.axis('off')\n",
    "    ax_main_title.set_title(f\"{name_of_the_experiment} - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n",
    "\n",
    "    # Heatmap\n",
    "    ax_heatmap = fig.add_subplot(grid[1])\n",
    "    ax_heatmap.set_title(f\"Confusion Matrix – Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n",
    "\n",
    "    cmap = sns.light_palette(\"#A73A52\", as_cmap=True)\n",
    "\n",
    "    sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n",
    "                xticklabels=x_labels,\n",
    "                yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n",
    "                ax=ax_heatmap,\n",
    "                linewidths=1.0,\n",
    "                annot_kws={'fontsize': 10, 'linespacing': 1.2})\n",
    "    ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n",
    "    ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n",
    "    \n",
    "    # Make borders visible\n",
    "    for _, spine in ax_heatmap.spines.items():\n",
    "        spine.set_visible(True)\n",
    "\n",
    "    # Footer with metrics and date\n",
    "    ax_footer = fig.add_subplot(grid[2])\n",
    "    ax_footer.axis('off')\n",
    "    metrics_text = f\"Total Booking Count: {row['count_total']}  |  Recall: {recall:.2f}%  |  Precision: {precision:.2f}%  |  F1 Score: {f1:.2f}%  |  F2 Score: {f2:.2f}%  |  ROC AUC: {roc_auc:.2f}%  |  PR AUC: {pr_auc:.2f}%\"\n",
    "    date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n",
    "    ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n",
    "    ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LiIgwtm7dakyZMsX45ptvjOHDh8f4/E6dOmUYhmGcO3fOsFgsZnn0Vt179+7FaNGLsnnzZqvy6C17V69etWqdid4asnDhQrPc0dHROHz4sDnt0aNHRr58+czp3bt3NwzjcRfw6N+T8uXLm595ZGSkUa1atQTv80968OCBsX79emPixInGqFGjjOHDhxtt2rSxalWI6l4+f/58s7x69eoxlnXv3r0EdwF98jOM7eHr62vMmDEjxrxxtbyNHDnSLH+yG65hPO5CHtUlM7YYBg0aZPW+Fy9enKD3EiV6j4GePXua5Z06dTLLa9asaZZ37drVLB8yZEiM5V29ejVGN9HEePjwYZytjFGPsmXLGrt377aa73m0cD55rAwNDTWcnZ3Nac2bNzenRW9pk2TVxTddunRW07Zt22YYxuPW8OjL+/bbb8154jtOJLTbavQ6kydPjjF99OjRVvtrWFiYOe3WrVtWLe6jR482DCPmMadFixbmPA8ePDDy5MmToNiii76PSdY9WZ4mvhZOwzCM27dvG2vWrDG+//57Y+TIkcbw4cON+vXrm/NkyZLFqn5CWzjbtWtnTvv444+tpnXu3Nmc9tZbb5nlhQsXTvD7Av4LaOEEEij6YBSpU6dWzZo1zdcBAQGqWbOm5s6dG6NudI6OjurQoYP5OkeOHFbTn3WI97t376pTp06aNm2aIiMj46wX9Uu8JG3cuNF8nitXLjVr1syqroODw3MblffQoUPmL+aS9Pnnn+vzzz+PtW5YWJgOHz6snDlzqly5clq0aJGkxy0uEyZMUPbs2ZUjRw6VKVNGxYsXN28BkCtXLvn7+yssLEwHDhxQSEiIChUqpOzZsyt//vyqUqWKgoKCEhTv119/HWt5Qlsln6fVq1erXbt2ZotSXM6cOaOMGTNqx44dVrfwiWp9kiRXV1c1bdo01tEct2/fbvU6+ny+vr6qX79+rLdR+PPPP83nERERyp49e5wxbtq0SdLjwUCebEl1cHh8dy6LxaLmzZtr1apVcS4nLjNnzlS3bt2s9rUn3b9/X1euXFFgYKCKFSsmV1dX3b9/XytXrlSePHmUP39+Zc+eXYUKFVLlypWVPn36Z44jLm3btjV7GCREmTJlZLFYZBiGJkyYoG3btil37tzKkSOHihYtqtdee01p0qSJc/6+fftKetwL49dff1W1atWeKd42bdqYPQPmzJmj4cOHKyIiwjy+RdWJUq5cOX377beSHrdgL1q0SDlz5lSOHDlUokQJlStXTo6Ojs8UQ2ycnJz0+++/a8iQIZo0aVKsg7pt3LhRVatW1b59+5Q6deokrzOKs7OzmjRpYr4ODg5W2bJltXbtWkmKcwCb4OBglSlTxnwdFBSkc+fOSZIyZ85stjR6enoqICBAZ8+elfTs/wuSKvr3+dq1a/L394+z7qZNm9S1a9cYx5zmzZubz52dndW4cWP179/fNgE/o5EjR6p///5Wx58nRf8/+SxatGhhPn/yf2f0731UzyDpxX++gL2RcOKVkz59eh05ckSSdPjwYRmGkaCbUkcfMS+2k73oZXH9M0mTJo3c3NzM166urlbT40saY/Pxxx8n6J5q9+/fN59Hfx+ZM2d+pvU9q+jrSojLly8rZ86c6tatm/bu3auffvpJ9+/f17p166y6xubNm1erVq1SYGCg3Nzc9PPPP6tNmzY6deqUjh8/ruPHj5t1XVxcNGTIEPXo0eOp64/e7Sy6xCScSbmdzrlz5/T666+b3RTjE/XZXr9+3ao8bdq08b6Oktj5nuWzjeoe+uS6AgICrF7Hl0TFZefOnWrZsmWCvjtR2ypDhgyaMmWKunTpoitXrpi3QYri4eGhH374wewS9yzee+89pU+fXqtWrTK7vo4YMUJhYWGaPHlygpZRvHhxjRw5Un379tWtW7e0c+dOq27kqVKl0ty5c596c3t3d/dEJc5vvvmm3n//fd28eVNnzpzR+vXrdffuXfNz9Pf3V/369a3q9+rVS2PGjNH9+/e1efNmqx/dgoKCtHTp0ucy0qqnp6e+/PJLffHFF9q/f7+2bt2q9evXa/78+bp586akx/vb9OnTY/3OR0+QJOtjY3z8/f1jJM3R99cn9+0o6dKls3rt4uIS5zQnp39PyZ71f0FSvSzf5yf314MHDypnzpzPvJzofv31V/Xs2fOp9R48eJCo5Uf/HKN/vk9Os+fnC9gbCSdeOZUrVzYTzmvXrmnhwoUJuo7Tz8/PfB7bL+vRy2K7dk96/KtvdAlJdOMT/dqdfPnyadasWcqRI4ecnJzUuHFjqxaJKNHfR2hoaJLW/zTR1yVJrVq1Mq8/jU1Ugubk5KRp06ZpxIgR2rRpkw4dOqRDhw5pwYIFunbtmv755x999NFHmjp1qiSpUqVKCg0N1c6dO7V7924dPXpUmzZt0oYNG/TgwQN9+OGHqlevns2u73reFi9ebJVsjhgxQu+88468vb21f//+WE/cfXx8rF5funTJ6nX066yeNl/0zy2u+aLXcXNz06BBg2KtJ/17ndjTYkzMbYjmzp1rnrxZLBb99NNPqlu3rlKmTKlly5apdu3asc731ltvqWHDhvrrr7/0999/68iRI1q7dq127dqlW7du6Z133lGdOnXM+2QmVJMmTVSxYkV98sknqlOnjpYvXy5JmjJlitq2baty5colaDndunVT+/bttWXLFu3bt09HjhzRihUrdOTIEV25ckWtWrXSyZMnY503Z86cOnjwoK5evaqqVatqw4YNVq0rT5MiRQo1adJEP/74oyRp1qxZunv3rjm9WbNmMU6shw8frs8++0ybNm3SwYMHdfjwYS1atEjnzp3TyZMn1alTp+dybWwUi8Vi3i6kbdu2GjBggLJmzWruC1HHeElmK7okq/fxZL34hIWFKSIiwirpjL6/PrlvR3nymB9d9ATE3qJ/nwMDA+P9gS7q2n1bfJ8rV66sTz/91Hw9ZcqUBI+zEJfo/yc9PDw0f/58lStXTm5ubho3bpw6d+6cpOUnl88YsCe+CXjlvP/++/rhhx/MQWk6duyozJkzq0CBAlb1Hj58qKlTp6pevXoKCAhQ6dKl9fPPP0t6/Avv8uXLzW61ly5dMk8spedzk+gn/4nF1toVFhZmPn/ttdfMROTy5ctxDpZTtmxZ/fXXX5IeD7gze/Zsq5YcwzB0+vRpZcqUKcFxxCVHjhxmd1fp8clebK2Fly5d0p9//mmeyBw6dEgZM2ZU6tSprVpS8ubNa54IRbX43Lt3T6GhocqVK5eKFi1qdlEzDEO+vr66ceOGIiMjtWfPnqcmnE+2fthL9M9Vetx9MSppi9oHnxR1D9mo9zBr1izVqFFD0uNWnFmzZsU6X9T2ijJr1iwNHDhQ0r8/yMQm+j5+79495cmTx6qbeZStW7eaLfk5c+aUh4eH2a1t1qxZat++vRwcHGQYhmbOnBnruuITfVt5e3urcePGZoIR17a6evWqbt68qaCgIJUpU8bs8njt2jXzxPvOnTs6dOiQOWDOs3JwcNC3336rnDlzmseafv36mV0w43Pu3Dk5OjoqTZo0qlSpkipVqiRJ2rVrlzlwzqlTpxQWFhZr18eVK1eqdOnSOnv2rM6fP6/KlStr48aNypAhQ4Ljb9u2rZlwzps3Tw8fPrSaFl1oaKh8fX3l4+OjmjVrmvtBtWrV1KBBA0myaqE9ceKEVe+KtWvXPrW1Vno8ONG9e/fUtGlTeXl5WU1LmTKlHBwczIQzejIU/fnly5d17NgxZc2aVffv34+zG/2THj58qDlz5piXIJw4ccLq8oTE7idJFdvxOUWKFDHqOTk56dGjR2adJz35/61atWrKnz+/VR3DMPTbb7+ZP14ULlzY6pgzc+ZM85jz8OHDOL9/8SlRooRKlixpDjS1cOFCDRs2TL17945Rd8eOHTp37pzq1q0b7zKjHyOyZMmiqlWrSnrcyhg1iBMA2yLhxCsnT548GjRokDki6oULF1S0aFHVqVNHhQoVksVi0dGjR7Vy5UpdvHhRVapUkfS4dW7QoEHmP6+GDRuqbdu28vLy0k8//WSeRFssFnXr1i3JcT7Ztahz586qXr26nJycVK9ePfOaxn/++UfS41FRHRwclCJFCk2fPt3s9vSkrl276n//+5/5S3+zZs00Z84cFSxYUNeuXdO6detUsWJFffPNN5IeX6/q7OxsnnB++umn2rNnj5ydnVWxYsUYCUt0Dg4O6tGjh/mL9c8//6zjx4+ratWq8vT01IULF7R9+3Zt3bpVZcuW1RtvvCFJGjVqlKZPn67KlSsrc+bMSpMmja5evapp06aZy446ibx+/bpy586tPHnyqHjx4kqXLp3c3d21ceNG3bhxI0b95ODJa3tr166tmjVrau/evXGeIAUGBqp27dpasmSJJGnatGm6ceOGChQooCVLlpijsj6pZMmSypcvn/7++29J0qBBgxQaGqpMmTLp559/jrN7eO3atZUrVy4dOHBAkvT666+rQYMGyp07tyIjI3Xs2DGtX79eJ0+e1OTJk1WwYEE5OTmpZcuW5kiQ69evV6VKlcxRap8cofJZt9X169dVu3ZtlS5dWhs3bozzetDDhw+rVKlSKlasmAoUKKB06dLJyclJK1assKqX1H0mJCRETZo00U8//SRJWrdunTZt2vTUH6TWr1+v5s2bq2zZssqVK5fSpUuniIgIzZ8/36zj4uISa2IhSZkyZdKKFStUrlw5Xb9+XSdPnlSVKlW0fv36GN0e41KqVCmzpTT6CXvBggVVsGBBq7pz5sxR//79VbFiRWXLlk2BgYG6ffu21Y8cz+P7FxoaqoEDB6pbt24qW7asChYsKD8/P4WFhWnevHlmQiXJTHwkqVixYlbLKVOmjCpUqKCdO3fq6NGjCV5/27ZttWHDBnOU2uhJeLt27ZLwzhLvyf8TzZo1U+nSpeXg4KC3337b7NaaPn16s0U8qou3u7u7ec1y1OjEV65c0aNHj1SmTBk1atRIISEhun//vg4dOqR169bp4sWLWrt2rTJnzqx06dKpZs2a5mi0M2bMUHh4uAoWLKjly5dr3759iXpPEydOVJkyZcwuu3369NGMGTNUo0YN+fn56dKlS9qwYYO2b9+u/v37PzXhzJEjhzna9t69e9W0aVPlypVLy5cvf24jKAN4CvuMVQTY3+jRow1XV9enjjAZfaTMP/74w/Dx8YmzroODg/H1119brSexIyQahmEUKlQo1vXMnTvXMAzDmDVrVqzTAwMDre6f+OSIfQm9D2eUN954I9Z6w4cPf+r7SMh9OJ+MsUOHDvHWdXBwMBYsWGAYxuMRa5+27OLFixsPHz5M0H6RWM/zPpwPHjywGuU1+uPJETCjb+v47olXo0YNq9fRxXUfTldXV6NSpUrm68yZM1vNd+jQoXjvwxn1iD4q5tWrV43s2bPHWu/J+6kmZBuGhYXFGPkzrm0VtbwnR+aN7dGgQYOnrju2z/DJ7/Dff/9tNZJn9NFd4zo2xPW9jv7o0aNHnDFEWb9+veHm5maWFyhQwLh27VqC3pdhGMbQoUNjrDf66KlRhgwZ8tR4o8+XkJGvY5PQkaDffffdGPOWK1cu1rpP3isyrlFqU6VKFWPU1ahHp06drNYV32ip0e+L+eS0uEZFjW8k2nv37hmBgYGxxhU1Aq5hGEb37t1jrRN9FNU///wz3vtwxraNjh8/bgQEBMRa78l7gD6L3bt3x3m/3uiP6Nspru1+5MiRWP/fOTk5Gc2bN48zxoSOUhv9OPXkZxV9WnznAsB/3b8XNgCvmK5duyo0NFQDBgxQ2bJllTp1ajk5OSlFihTKlSuXOnbsqHXr1lmNcFq+fHn9888/6tmzp/LkyaMUKVLIxcVFmTJlUvPmzbVp06YEDU6QUPPnz9cbb7whPz+/WK/3fOutt/Tzzz+rQIECcnZ2lr+/v5o0aaItW7bEGJAiulq1amnfvn368MMPlT9/fnl4eMjZ2Vnp0qVT7dq1VatWLav6P/zwg1q1aqU0adJYXQ+VEA4ODpo2bZqWLl2qhg0bKkOGDHJxcZGrq6uCgoJUt25dffPNN1atIe+884769Omj8uXLK2PGjHJzc5OLi4syZsyoRo0a6Y8//jCv6/H19dXYsWPVtGlT5c6d27zHmpeXl4oWLapBgwbpt99+S1bX0jg7O+v3339X69at5e/vL1dXV+XNm1fff/99rCPNRgkODtaWLVv01ltvycfHR+7u7ipVqpSWLl1qdY/AJ1ubihYtqk2bNql27dry8PCQh4eHKleurPXr1ytbtmxxzpc9e3bt3btXw4YNU+nSpeXr6ytHR0d5enoqf/78ateunRYsWGA1ErKvr682btyod999V6lTp5arq6sKFCigyZMnJ2pESz8/P23cuFENGjSQl5eX3N3dVaxYMc2fP1+tW7eOdZ4cOXJoxIgRatCggbJnzy5vb285OjrK19dXZcqU0ejRozV79uxnjiU2efPmtWqBWb58uVX30tiULVtWX3zxhWrXrq2sWbPK09NTTk5OSp06tSpXrqwpU6ZoxIgRT113uXLlNHv2bPO6wz179qhWrVq6fft2gmJ/++23ra5ZdHFxiTGqtfS4dbtfv36qUqWKgoODlSJFCjk5OZmt7osWLVKXLl0StM74dOvWTfPmzVOnTp1UvHhxZcqUSe7u7nJxcVH69OlVr149/fLLL/r+++9jzLto0SK1a9fO3Ofy58+vH3/8UWPHjk3QulOmTKmNGzeqS5cuSp8+vVxcXJQjRw6NHj06wcuwBVdXVy1btkzVqlWL0c04ui+++EIffPCBMmTIEOeIwaVLl9a+ffvUt29fFSlSRF5eXnJ0dJSPj4+KFCmi999/X6tXr1b58uXNeTJnzqwtW7aocePGVsecxYsXx/n9S4gCBQpo7969mjlzpho2bKigoCC5u7ub/6fq1KmjKVOmqHv37k9dVkhIiNavX69q1aopRYoU8vDwUIUKFfTbb7+ZPZgA2JbFMF6Si5YAAEkSGRmpR48exRjQJSIiQqVLlzav3a1atapVd9MHDx7Iyckpxo8Jt27dUt68ec2ueO+++26sJ/MAAABxST4/+QMA4hUeHq5s2bKpWbNmKliwoHlfvylTppjJpvS4dT+6/fv3q169emrevLly584tX19fnThxQuPHjzeTTQcHhySP5ggAAF49tHACwH/E9evX47wlj/R4QKuBAweqb9++VuW7d+9WoUKF4pzPxcVF//vf/2KMTgoAAPA0tHACwH9EihQp9PHHH2vt2rU6fvy4rl27JmdnZ2XMmFFly5ZVhw4dYozYKT2+r1737t21bt06nTp1Sjdu3JCbm5syZ86sihUrqlOnTkm++ToAAHg10cIJAAAAALAJRqkFAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAAAAAAJsg4QQAAAAA2AQJJwAAAADAJkg4AQAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgEyScAAAAAACbIOEEAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAAAAAAJsg4QQAAAAA2AQJJwAAAADAJkg4AQAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgEyScAAAAAACbIOEEAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAAAAAAJsg4QQAAAAA2AQJJwAAAADAJkg4AQAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgEyScAAAAAACbIOEEAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAXjo//fSTChQooBQpUshiscjHx8dm61q3bp0sFossFotat25ts/X8F1WsWNHcdidOnLB3OIkSHBxsvodnNWXKFHPeAQMGPP/g/iOitlFwcPALW+fLvG8OGDDAjG3KlCnPfflJ2acl6cSJE+b8FStWfL7BAXglkXACkCTdvn1bo0aNUvny5eXv7y83NzdlzpxZderU0YwZM/TgwYMXEsfmzZvVokUL7d27V3fv3n0h63xZRD8RtVgsqlatWow6O3bssKpjsVh07969RK3v119/1YABAzRgwICX7qQ8MZ7cfhaLRU5OTgoICFCNGjW0fPlye4f40tq/f7+aNWumdOnSydnZWX5+fsqRI4caNmyosWPH2ju852bKlCnmPn/9+vVnnm5P27ZtU5s2bZQ1a1a5u7vLz89PhQoVUu/evXXgwAF7hwcAcXKydwAA7G///v2qW7eujh8/blV+4sQJnThxQkuXLlXevHlVsGBBm8eydOlSGYYhSerQoYOaN28uZ2dnm62vUKFC2rBhgyQpTZo0NltPYvz22286efKkgoKCzLIffvjhuS3/119/1dSpUyU9bhF61haoMWPG6MaNG5KkwMDA5xbX8xQREaHLly9r5cqVWrVqlRYsWKD69eub0+fNm5fohP2/Yt++fSpZsqRu3bplll27dk3Xrl3T4cOHtWfPHr3//vt2jPDZxbVvTpkyRX/88YckqXXr1jF6Tzxtur189NFHGjp0qFXZvXv3dO3aNe3evVuHDx/Wr7/++lzWFRgYaB4Tvb29n8syAbzaSDiBV9zVq1dVs2ZNnTp1SpKULl06ffjhh8qXL59u3rypP/74Q5MnT35h8Zw7d8583qRJE5UrV86m6/P29lbZsmVtuo7EioyM1MSJE/X5559LetwK/dNPP9k5qsdxpEyZUvny5bN3KHGqWbOmPvnkE125ckUDBgzQnj17ZBiGxowZY5VwFi1a1I5Rvhy+/PJLM9ls3Lix3n77bTk5OSk0NFQbN27UP//8Y+cIn93LvG8+q6+//toq2WzSpImaNGkiLy8vHTlyRDNnznyu63N1dX1pj4kAkikDwCvt448/NiQZkgxvb2/jzJkzMepcvHjRCAsLM1/fv3/f+Oqrr4wCBQoYKVKkMNzd3Y38+fMbQ4YMMe7fv281b1BQkLn88+fPGy1atDB8fHwMDw8Po3HjxuZyQ0NDzXpPPipUqGAYhmG+DgoKslpHhQoVzGmhoaFm+bx584wyZcoYXl5ehrOzs5EmTRqjTJkyRu/evY3IyEjDMAxj7dq15rytWrWyWu758+eNLl26GFmyZDFcXFwMb29vo0KFCsbPP/9sVS967BUqVDD++usvo2LFioa7u7uRJk0a49NPPzUiIiKe+ln079/fXI6np6chyciQIYM578SJE62mRT3u3r1rLqNHjx5GqVKljLRp0xouLi5GypQpjUKFChnDhw83Hj58+NRtLclYu3ZtjO29d+9eo0qVKkbKlCnNz+PJ7R4ZGWlUqlTJLFuyZIkZV8eOHc3yL7/88qnbIjGib7/on+Uvv/xilmfPnt1qnuj7Z3Tjx483ihQpYqRMmdJwcXEx0qVLZ1SuXNkYOnSoWWfy5MnmvP379zfL33nnHbO8Vq1aMb4TUbp06WLWmz9/vtW0L774wpz23XffGYbx+HNr2rSpERgYaDg5ORne3t5Grly5jNatWxt79uxJzCYzDMMwcubMaa4rPDw8xvTbt29bvY5rm7Vq1SrGPmQY1vtRaGioUa9ePcPDw8Pw9/c3OnXqZNy6dcus++R36ffffzcKFy5suLm5GYUKFTKXO27cOCNz5syGq6urUbp0aWP37t1WsTy5b0b/nsf2iP5ZxvaIflz59ddfjcqVKxs+Pj6Gi4uLkT17dmPAgAHGnTt3Ymy7OXPmGLlz5zZcXV2NPHnyGHPmzLHaTydPnhzvZxMWFmZ4eHiY9Xv27Blrvf3798f7+dy6dct47733jCJFihgBAQGGs7Oz4eXlZZQsWdL48ccfrZb15GcQJXrcP/74ozFgwAAjbdq0hqenp/HWW28Z165dM8LCwowWLVoYXl5ehq+vr9GhQwer4xOAVxMJJ/CKy5Ili3kSMWDAgKfWv3fvnlG+fPk4T8zKly9vdYId/eQn+rqiHs2bNzcM4/knnOvWrTMcHBziXGZU8hVXwnn8+HEjbdq0cc7fp08fs2702AMDAw13d/cY9X/44YenbtvoJ3StW7c2nJ2dDUnG0qVLDcMwjBIlShiSjPbt28eZcLq6usYZc5s2bZ66rWNLOL29vQ1/f/8Yn0ds2z00NNQ8QQ4KCjJu3bplbNy40bBYLIYko3jx4sajR4+eui0SI66Ec968eWZ5xYoVreaJ7eR82rRpcW6b9OnTm/ViSzij/4BTuXLleE+2t2zZYtZt1qyZ1bRChQoZkgxnZ2fjypUrxsOHD43s2bPHGVdC9q+4FCtWzFxO27ZtjW3btpnfj9gkNuH08/MzMmTIECP2GjVqmHWj75vp06c33NzcrOq6u7sbvXr1irGM4OBgq5htlXD27ds3zjrlypWzOvb9/PPP5n4f/ZE/f36r9cYn+r7o7e1t3Lhx46mfZ2yfz/nz5+N9fwMHDoz1M4gr4cyaNWusn2Px4sVjlH/66adPjRnAfxuDBgGvsFu3blldt5mQ7qvffPON1q9fL0nKmDGjfvrpJ82aNUuZMmWSJK1fv16jRo2Kdd67d+9qxowZGjdunFxcXCRJs2fP1o0bN8zrhmrWrGnW//bbb7VhwwaNGTPmmd/b4sWLFRkZKelxl8HffvtNs2fP1meffabcuXM/dQTHTp066cKFC5IeX9+4aNEijRw5Um5ubpKkoUOHauvWrTHmO3/+vAoXLqyFCxeqa9euZvmECROeKf40adKoTp06kqQff/xRf//9t7m+du3axTnfp59+qlmzZmnFihVat26d5s+frxIlSkh6fH3amTNn4t3WGzZsUKFChayWeePGDTk6Our777/XypUr411/cHCwhg8fLkk6efKkPv74Y7Vv316GYcjNzU1Tp06Vo6PjM22LxLh06ZI2btyoX3/9VYMGDTLLO3To8NR5Fy5cKElycnLS+PHj9dtvv2nmzJnq2bOnMmfOHOd8o0eP1pAhQyQ9/i4tWrTI3F9iU6JECYWEhEiSlixZovv370uSjh8/rl27dkmSatSoIX9/fx08eFCHDx+WJFWpUkUrVqzQkiVLNGbMGNWsWVOurq5PfV9xqVKlivl80qRJKlasmLy9vVW1alX98MMPevjwYaKXHd3Vq1eVJk0a/frrrxozZoxSpEghSVqxYoUWL14co/7Zs2dVpUoVLV26VJUqVZL0+Bjy9ddfq127dlqyZIly5swp6fH15itXroxz3VHXake/Dn3u3LnmPv/GG2/EOz0wMFDbtm0z96XAwEBNnDhRK1asUO3atSVJGzZsMI99ERER6t69u3k9+ltvvaWlS5eqe/fu2rt3b4K32Z49e8zn+fPnl5eXV4LnjS5FihT6/PPP9fPPP2vVqlVau3atZs+erWzZskmShg8f/kwDw504cULDhg3TnDlz5OnpKenx57h//379+OOP+t///mfWfdZjH4D/IHtnvADs58yZM1a/RB84cOCp80T/dX7x4sVm+eLFi83yAgUKmOXRf21fsGCBWV6jRg2zPHp3uLhaSQzj2Vo4P/roI7Ns7ty5xpUrV2J9P7G1cIaFhZktE66urlbz9uzZ06z/wQcfGIZh3SLg4uJiXLhwwTAMw4iIiDBSpEhhSDJ8fHyeum2jtyD06dPHWLp0qdnK1bhxY7N1JPq2kKxbODdu3GjUr1/fSJs2reHk5BSjtWHhwoUJ2tZPrmPVqlUxpsfVldkwDKNKlSox1j1ixIinboN79+4ZGzZsiPVx8eLFBG+/Jx8BAQHG1KlTY8wTW2vQW2+9ZUgyUqRIYaxZsybOVqXorWKFCxc295kSJUrE2jX1aTEvWrTIMAzDGDp0qFk2e/ZswzAM4+DBg2bZ22+/bRw7dixB3bQTIjw83KhatWqc265EiRLGgwcPzPqJbeGUZBw5csQs//TTT83ytm3bGoZh/V1yd3c3t/3cuXPN8kyZMpld4ocPH26Wf/PNN+ay49o349tnnzb9gw8+MKd98skn5n4Z/diXN29ewzAMY+vWrWZZunTprFpfy5QpY057Wgtnu3btzLpNmjSJt26UuD6fxYsXG1WrVjVSpUplODo6xvico7plJ6SFM3qLfO3atc3yvn37muV58uQxy69fv56g2AH8N9HCCbzCnhyBMPqAPXGJamWRZLacSVLx4sVjrRNdhQoVzOf+/v7mc1vcfqB58+Zmq0+jRo2UKlUqpUmTRg0aNNCaNWvinffIkSNmy0TWrFmtYn3a+8yZM6c52q2Dg4N8fX0lJe491qhRQxkzZtTDhw/1888/S5LefffdOOv/9ddfeu2117Rw4UJduHBBjx49ilEnMXG4ubmpatWqzzTPxIkTlTJlSvN1yZIl1a1bt6fOd/78eZUrVy7Wx7Jly541dNPly5e1b9++BNVt06aNLBaL7ty5oypVqsjb21sZM2ZUixYttH379ljn2blzpwzDkKenp5YuXWq2+jxNixYtzOfz5s2z+uvp6al69epJkrJly2b2QJg+fbqyZs0qDw8PlSpVSsOHDzdbRxPD09NTK1eu1Jo1a9SxY0flypXLavrWrVufy8Bhfn5+ZouuZP1denKEbEnKkSOH2aLn5+dnlhcpUsTsoZAqVSqz3Na3MYn+ff/yyy/N/bJu3bpm+cGDByVZv5+CBQvKyenfMRqjv++niX6MTsjxOS7z589X3bp1tXr1al25ckUREREx6jzL9ov+HqJ/NtEH4XqRnw2AlxsJJ/AK8/DwUJYsWczXf/75Z6KXlZCbjEclX5KsTsCikruEevJk6cqVKzHq5M2bVzt27FDXrl1VokQJeXt769KlS1qwYIGqV6+uTZs2PdM6ozztfUZ/j5L1+3xWDg4OatOmjfnazc3NKkF50vjx483uj3Xq1NGyZcu0YcMGtWzZ0qwT1c34WQQEBDzzPCdPntSdO3fM16dOnVJ4ePgzLyexWrVqpYcPH2rFihVKkSKFDMPQsGHDYu26+aRq1arpzz//1LvvvqtChQopRYoUOnPmjGbOnKkKFSrEmhxFdRO+efOm+vXrl+A4Q0JCzB9uFi1apKNHj2rbtm2SpAYNGsjd3V3S431h2bJlGjFihGrUqKFMmTLp7t272rJli3r37q0PPvggweuMjcViUeXKlTVu3Djt379foaGhViOV7ty506pulOjfxdi+h09bZ3yiJ1sODv+ersTVrfRZjyO28OjRo6cm/wk5VkYpUKCA+Xzv3r26efNmouKKfi/V1q1ba9WqVdqwYYPVD0nPcmxIjp8NAPsh4QRecU2aNDGfjxw5MtZf0S9duqSrV69KkrJnz26W//XXX+bz6NczRq/zPEWd5ISFhZmJ1YkTJ8xWhegMw1CePHk0evRobdmyRdevXzdbjiIjI+O9Z11ISIh5Unjs2DGFhYWZ017E+4yubdu25gldw4YN470v4NmzZ83nQ4YMUc2aNVW2bFldvHgx1vrRTxTjO9l8lhNkSbpz547atGkjwzDMROzcuXMJauEMDg6W8XhAuxiP1q1bP1McTk5Oql69unr37m2W9e3b96nzGYahUqVK6fvvv9fOnTt18+ZNjRgxwnxvK1asiDFPx44dzda7cePG6auvvkpwnM2bN5f0uBWoU6dOZnn0HxcMw5CHh4d69Oih5cuX6+TJk7p06ZJ5Ten8+fMTvL4nrVmzJsb1e8HBwWrUqJH5OnpiGT3ZiLrO+ebNm0/9werq1as6evSo+Tr6dyn6D1+29LR9Pr7p0b/vkydPjnUfvX37tlxdXa3ez+7du622X2zXfseldu3a8vDwkPT4WurBgwfHWu/AgQPxLif6sWHMmDGqWrWqSpcubVUOALbCfTiBV1yvXr00c+ZMnTp1StevX1eJEiXUq1cv8z6c69at0+TJk7Vu3Tr5+fmpWbNm5qAXnTt31s2bN2WxWPTRRx+Zy2zatKlNYg0JCdGOHTt09+5dNWvWTOXLl9e4ceNi7R42bNgwrVu3TrVr11amTJmUMmVKq0FF4muF8Pf3V/Xq1bVixQrdv39fjRs3Vvfu3XXs2DGNGzfOrGer9xldUFCQvvvuO124cEFvvvnmU+tGGTJkiFq1aqXly5fHOZhK9NbYGTNmyNHRUY6Ojkm+B1+fPn107NgxSY8HmVq4cKHWrFmjqVOnqlGjRuYgKy9Kly5dNGzYMN25c0d79uzRqlWrVK1atTjrd+3aVefPn1fVqlWVMWNGOTk5acOGDeb02PYdf39/LV26VCVLltS1a9f0ySefKEOGDPG2SEd566231KNHDz169EirV6+W9Ph+uFED5Uj/DqDTuHFj5c6dW2nSpFFoaKguX74cI6YBAwZo4MCBkh4nRk9L1AcMGKBjx46pSZMmKlOmjFKlSqWTJ0+aSbYkFStWzHweEhJiDmbTsmVLNWzYUNOnT09Qt8lmzZrps88+05kzZ/TNN9+Y5dHvjWpL0ff5H374QbVq1ZK7u7vZFTS+6c2aNdPo0aMlSd27d9fVq1eVP39+Xb9+XceOHdOqVasUFBSkSZMmqUiRIkqfPr3Onj2rc+fOqWXLlmrRooV+++23Z+pJ4ufnp/79++vDDz+U9Pi4dvr0aTVu3FheXl46fPiwZs6cKX9//3h/RAsKCjK7BPfr10/Vq1fX9OnTtX///gTHAgCJ9kKvGAXwUtq3b1+styyJ/ti1a5dhGI8HdSlXrlyc9eK7LUp0cQ0wEt9ANhMmTIixPg8PD6tbLUQN9DFo0KA4Y3RwcDA2btxoGEbct0U5duxYom6LEn2Qjfjef2yeHDQoPtFjiRo0aOvWrTFuw2CxWIxSpUrFOkhJ9MFOoj+eXMeTgzRFiW2AlbVr15oxlCpVyoiIiDCOHz9upEyZ0hxA5dq1a0/dFokR121RDMMwOnfubE6rUqWKWR7b5xP9PppPPtzd3Y1jx44ZhhH7bVHWrl1r3s7G2dnZWL16dYJir1mzptV6evToYTX99OnT8X4/O3ToEOt2eNqgNIZhPYhNbI/cuXNb3WNy5cqVMeo4OTkZISEhsX53o8q8vb2N1KlTx5i3atWq5iBAcX2X4vqexnUv1LgG/xkzZkyM9Uffv582Pb7bojwZ26xZs2KtE307JeTzMQzD6NOnT7zrrV+/vlk3tn06+qBLUQ83NzejSJEiMT6zhAwaFD3uuI7ZTxugCcCrgy61AJQ7d27t3btXI0eOVNmyZeXn5ycXFxdlzJhR1atX19SpU5U7d25Jkqurq1avXq2vvvpK+fPnl7u7u9zc3JQvXz4NGTJEq1atMm958ry1a9dOH3/8sQICAuTu7q5KlSppw4YNypo1a4y6tWrVUocOHZQ3b175+vrK0dFRfn5+qlatmlauXKkyZcrEu64sWbJo586dev/995U5c2Y5OzvLy8tL5cuX15w5c56py+SLUrx4cS1YsED58uWTm5ub8uTJo7lz58bZmlenTh19/fXXypo1a5KuNY1y+/ZttW3bVoZhyNnZWT/88IMcHByUOXNmffHFF5Ied62NfruYF6Vbt25md8k1a9aYtx2JTfPmzdWqVSvlyJFD3t7ecnR0VEBAgF5//XVt2LAh3u6fFStWNG8D8fDhQzVs2NDq1hZxebIl9MnXUS1dFSpUUGBgoJydneXu7q78+fNr8ODBibp1UJSxY8dq4MCBqlChgoKCguTm5iZ3d3flypVLvXv31p9//mleSyo9vsb1m2++UYYMGeTq6qrixYsn6Dvl4+OjDRs2qEaNGkqZMqX8/Pz03nvvaf78+c/cbTuxOnTooD59+ihTpkxW3WcTOv3zzz/XkiVLzNvVODs7K3369Cpbtqy++uors2VZetxyPWvWLOXKlUsuLi7KkSOHJk2aZHahfhZfffWV/vrrL7Vq1UqZM2eWm5ubvL29lTdvXnXv3t28HU9c3nzzTU2YMEHZsmWTm5ubihUrphUrVihv3rzPHAsAPCuLYXAlNwAAAADg+aOFEwAAAABgEwwaBEmPR+M7d+6cPD09X1jXJgAAAPw3GYahmzdvKl26dLF2Ucerg4QTkh5fV5UxY0Z7hwEAAID/kNOnTytDhgz2DgN2RMIJSZKnp6ekxweFuG7cDAAAACREeHi4MmbMaJ5j4tVFwglJ/97Y3cvLSxbGkQKAWHl6e5vPLx46asdIAODl5hUYIElcqgUGDQIAAAAA2AYJJwAAAADAJkg4AQAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgEyScAAAAAACbIOEEAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAAAAAAJsg4QQAAAAA2AQJJwAAAADAJkg4AQAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgEyScAAAAAACbIOEEAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAAAAAAJsg4QQAAAAA2AQJJwAAAADAJkg4AQAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgEyScAAAAAACbIOEEAAAAANgECScAAAAAwCZIOAEAAAAANkHCCQAAAACwCRJOAAAAAIBNkHACAAAAAGyChBMAAAAAYBMknAAAAAAAmyDhBAAAAADYBAknAAAAAMAmSDgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoQTAAAAAGATJJwAAAAAAJsg4UyG/vnnnzin/frrry8uEAAAAACIBwlnMlS9enWFhobGKP/ll1/UvHlzO0QEAAAAADGRcCZD7dq1U5UqVXThwgWzbM6cOWrZsqWmTJliv8AAAAAAIBoneweAZzdw4EBdvXpVVapU0fr167VixQq1a9dO06dPV8OGDe0dHgAAAABIIuFMtsaMGaPmzZurZMmSOnv2rGbNmqX69evbOywAAAAAMJFwJhOLFi2KUdagQQNt2LBBTZs2lcViMevUq1fvRYcHAAAAADFYDMMw7B0Ens7BIWGX21osFkVERDzz8sPDw+Xt7a0bN27Iwi4BALHy9PY2n188dNSOkQDAy809MMA8t/Ty8rJ3OLAjWjiTicjISHuHAAAAAADPhFFq/yOuX79u7xAAAAAAwAoJZzI0dOhQzZkzx3zdqFEj+fn5KX369NqzZ48dIwMAAACAf5FwJkPjx49XxowZJUmrV6/WmjVrtGLFCtWsWVMffvihnaMDAAAAgMe4hjMZunDhgplwLlmyRI0bN1a1atUUHBysEiVK2Dk6AAAAAHiMFs5kyNfXV6dPn5YkrVixQlWqVJEkGYaRqBFqAQAAAMAWaOFMhho0aKBmzZopW7ZsCgsLU82aNSVJu3btUkhIiJ2jAwAAAIDHSDiToVGjRik4OFinT5/WsGHD5OHhIUk6f/68OnXqZOfoAAAAAOAxi2EYhr2DgP2Fh4ebN+e1sEsAQKw8vb3N5xcPHbVjJADwcnMPDDDPLb28vOwdDuyIFs5kYtGiRapZs6acnZ21aNGieOvWq1fvBUUFAAAAAHGjhTOZcHBw0IULFxQQECAHh7jHerJYLIkaOIgWTgB4Olo4ASBhaOFEFFo4k4nIyMhYnwMAAADAy4rbovzHnD171t4hAAAAAIAkEs7/jAsXLqhLly7Kli2bvUMBAAAAAEkknMnKtWvX1LRpU6VKlUrp0qXTt99+q8jISPXr109ZsmTRtm3bNHnyZHuHCQAAAACSuIYzWfnoo4+0adMmtW7dWitXrlT37t21YsUKOTg46Pfff1fJkiXtHSIAAAAAmGjhTEaWL1+uyZMn6+uvv9bixYtlGIYKFiyoJUuWkGwCAAAAeOmQcCYj586dU65cuSRJwcHBcnNzU4sWLewcFQAAAADEjoQzGTEMQ05O//aCdnR0lLu7ux0jAgAAAIC4cQ1nMmIYhipXrmwmnXfv3lXdunXl4uJiVW/nzp32CA8AAAAArJBwJiP9+/e3el2/fn07RQIAAAAAT0fCmYw8mXACAAAAwMuMazgBAAAAADZBwgkAAAAAsAkSTgAAAACATZBwAgAAAABsgoTzP+L69ev2DgEAAAAArJBwJkNDhw7VnDlzzNeNGzeWv7+/0qdPrz179tgxMgAAAAD4FwlnMjR+/HhlzJhRkrR69WqtXr1ay5cvV82aNfXhhx/aOToAAAAAeIz7cCZDFy5cMBPOJUuWqHHjxqpWrZqCg4NVokQJO0cHAAAAAI/RwpkM+fr66vTp05KkFStWqEqVKpIkwzAUERFhz9AAAAAAwETCmQw1aNBAzZo1U9WqVRUWFqaaNWtKknbt2qWQkBA7RwfYx+IlS1SxUqWn1itavLjWrVtn+4AAvBKW/7ZatZo2tncYiVa+Xm1t2LI53jpffjNSn3wx6AVFBOC/hi61ydCoUaMUHBys06dPa9iwYfLw8JAknT9/Xp06dbJzdHjZFC1ePN7p77Zrpw7t27+QWNq/95527twpSXJxcVH69OnVuFEjNXrzzSQvu2qVKipTurT5esL33+uPP/7QTzNnWtVbsWyZvLy8krw+AP8dX34zUit+/y1G+U/jf1CGdOnsENG/lv+2WkNGfyNJslgsSuXnp6IFC+m9Vm3k6+OT5OUvmDpdnh6ekqTzFy+qybttNfGbb5UtS1azTtd3O8gwjCSvC8CriYQzGXJ2dlavXr1ilHfv3t0O0eBlt2LZMvP56jVrNH7CBP0yd65ZliJFCvN5VLdsJyfbHRreeP11dWjfXvfu39fSpUs1dNgweXp6qkb16klarpubm9zc3J5aL1WqVElaD4D/phKFi+ijD7pZlfl4edsnmCekTJFCM/43QUakoaMnQvXV6FG6cvWqRgxMequjv6/fU+t4pEyZ5PUAeHWRcCZj+/fv16lTp/TgwQOr8nr16tkpIryMoidYHh4ej38h//+y7Tt26L2OHTX6m2/0v/HjdfToUY0dM0ZLlizRzZs3NeLrr815R4wcqUOHD+v78eMlSZGRkZo6bZoWLFigsKtXlSljRr3zzjuqUrlyvPG4ubmZ6+/Qvr1Wrlyp9Rs2qEb16rpw4YKGff21tm3bJgcHB5UqWVIf9uolf39/SdLhw4c1YtQoHThwQBaLRRkzZtQnH32k3Llza/GSJRoxcqTW/f67Fi9Zoh9+/FHSvy28/fv1U906dVS0eHF9PWyYKlasqLbvvKOCBQuqa5cuZnzXrl1TjVq19L/vvlPhwoX14MEDjfvf/7Ry1SrdvHlTWbNmVZf331fRIkUkPe5ZMGz4cO3es0cPHz5UusBAde3aVWXLlEnS5wbgxXJ2do41+Zrz6wIt+221zl+4IE9PT5UpVlzvtW6rFO7usS7naOhxjfnxex08elQWSRnSpVOvTl2UM1s2SdLe/fv0/bSpOnj0iLy9vFS+ZCm1b9la7vH8YGaxWMzYUvn7q2Gdepr40wzdv39fzs7OmvbzbC1euULXb9xQUMaM6tCytUoUKSpJevjwocZO/FF/bP5Tt27dkq+Pj+rXqKUWjR53Ay5fr7a++OQzlStZSk3ebStJeqdbV0lSwbz59O2XX+nLb0bq1u3b+vLTvlq0Yrkmz/5Jv0yaKgeHf6/M+njw5/L29DKT9g1bNmvK7Fk6efqU/P38VKNSFb3duImcHB1lGIYmz/pJy9as1rXr1+Tl5aWKpcvog/bvPcMnBiC5IOFMho4fP6433nhDf//9tywWi9nNxWKxSBIDB+GZjR07Vh988IEypE8vT0/PBM0zecoULV+xQh9/9JEyZsqkXbt2qV///vL19VWRwoUTvG5XV1c9fPhQkZGR6tGrl1K4u+v78eMVERGhocOG6eNPPzWT3M/69VOOHDn0cZ8+cnBw0OHDh2Ntja1apYqOHTumTZs3a9zYsZJkdj2PrkaNGpo2fbq6vP+++f1ZtXq1UqdOrUKFCkmShg0fruOhofpy8GClTp1aa9etU9cPPtDsn35SpkyZNHTYMD189Eg/TJggN3d3hR4/HueJKIDkx2Kx6IN3OygwTVqdu3BBo8aP0/gpk9SjY+dY6w8a8bWyZcmiHh07y8HBQUePH5eTk6Mk6ez58/pwQD+90/xt9en6ga7fuKFvJozXNxP+p48/SHgvJVdXF0VGRioiIkILVyzTnF8XqFen95UtS1YtXbNKH38xSFPHjlPGdOk1b8ki/fnXVg3s/ZHSpA7QpSuXdeny5ViXO2HEKHXo2V2jBn2h4EyZ5OzkHKPOa2XLavT347Xr770qUqCgJCn85k39tXOHhvUfKEnas+8fffnNSHV9t4MK5Mmjs+cvaPh3YyRJbZo20x+b/tTcRb+q/4d9lDlTJoVdu6ZjoaEJfv8AkhcGDUqGPvjgA2XOnFmXLl1SihQptG/fPq1fv15FixZlMBQkSocOHVSyRAllyJBB3t5P70L24MEDTZ4yRf0++0ylSpVShvTpVbdOHdWsUUPz589P0DojIiK0bPlyHTl6VMWKFtVf27bp2LFjGjx4sHLlyqW8efNq4IAB2rlzp/bt3y9JunjxokoUK6bg4GBlypRJVapUUfbs2WMs283NTe7u7nJydFSqVKmUKlWqWLvbVq1SRZcvX9bu3bvNspUrV6p6tWqyWCy6cOGCFi9ZoqFDhqhQoULKkCGD3m7RQgULFNDiJUskSRcuXlSB/PkVEhKiDOnTq1y5cir8DAk3gJfD5m1/qXrjhuaj31dfSpIa139dhfMXUGCaNCpSoIDatXhbazdujHM5Fy9fUpECBRWUIaMypkuv18qWU0jmLJKkGfN+VpUKFdW4/uvKmC698uXKrQ/ad9DKtb/r/hO9leJy+txZLVyxXDlDsilFihSavWCBmjV4U5XLV1CmDBnUsXVbhWTOormLFkqSLl2+rAzp0il/7jxKGxCg/LnzqEqFirEuO6oLsZenp/x9/eQVyw+Qnh6eKlGkqFb/sc4sW/fnRnl7eatQvvySpCmzf1Lzho1Us3IVpUsbqGKFCqld8xZatHL5/2+jy/Lz9VXRAgWVJnWAcmfPobrVayTo/QNIfmjhTIY2b96s33//XalSpZKDg4McHBxUtmxZDRkyRF27dtWuXbvsHSKSmdy5cj1T/dOnT+vevXvqHK0rqvS461aOHDninXfuvHn6deFCPXz4UI6OjmrWtKnebNhQP8+dqzQBAUqbJo1ZN0uWLPL09FRoaKjy5M6tZk2batAXX2jZ8uUqXry4qlSurAwZMjxT7NH5+vqqZMmSWr5ihQoVKqSzZ89q799/65OPP5YkHT16VBEREWrwxKBGDx48MBPztxo31pChQ7Vl61aVKF5clV57Tdn+v+scgOSjUL78Vq2WUT9Sbd+9SzPmzdWpM2d0++4dRURE6MGDB7p3/57cXGP+kNW4/hsaNvZbrVr3u4oUKKjXypRT+sBASdKx0FAdOxGqNdGSNcMwFBkZqfMXLyg4Y6ZYY7t1+7aqN26oyEhDDx4+UL5cudWnywe6feeOrlwNU77cua3q58uVS0f/v8WwRuUq6tnvMzXv2F4lChdRqWLFVbxQ0n4Uq1qhooZ/N0Y9OnaWi7OzVv+xTpXKlTe72B4NDdXfBw5o+tw55jwRkZHmdqtYpqzmLlqoJu3fUYnCRVSySFGVLl5CTo6OSYoLwMuJhDMZioiIMLs9pkqVSufOnVOOHDkUFBSkQ4cO2Tk6JEfuT3QBtTg46MnxCB89emQ+v3v3riTpm1GjFJA6tVU9ZxeXeNdVs0YNtW3TRq6uruaPJgnVoX171aheXRv//FObNm/WhO+/15eDB+u1115L8DJixFO9uoaPGKHeH36oFStXKiQkxLy90J27d+Xo6Kjp06bJ8Yk4o7bZ66+/rpKlSmnjxo3aunWrJk+Zom4ffKC3mjRJdEwAXjw3N7cYI9Kev3hRHw0aqPo1a+ndFi3l5empvfv3aeiY0Xr48JHcXGMup22z5qpaoaI2b/9LW3bs0OSfZqr/h31UvlRp3bl3V/Vq1FTDOjHHWkjzxLE0uhTu7vpx1LdycHh8Laer6+MV375z56nvK0fWEM35YZK27NiuHXt2a8Cwr1SkQEEN+uiTp84bl9LFS8gY+602b/tLObNl1979+/R+u3fN6Xfv3VPbps1VvlTpGPO6OLsoTerUmvm/Cdq+Z7e2796lkePHadaCXzTmy6E2HbQOgH3wrU6G8ubNqz179ihz5swqUaKEhg0bJhcXF33//ffKkiWLvcPDf4Cvj4+OHTtmVXYo2vWSmTNnlouLiy5cuPBM12tKj6+lzJgxY4zy4OBgXbx0SRcuXjRbOY8fP66bN28qS+bMZr2goCAFBQWpebNm+uSzz7RoyZJYE05nZ2dFREY+NZ4KFSroiyFDtGnzZq1cuVK1atUyp+XInl0RERG6dvWqeU1nbNKmSaM3GzbUmw0baux33+nXhQtJOIH/gEPHjirSMNS5bTvzx7HfN2546nwZ06dXxvRvqHH9NzRw+FAt+221ypcqrexZQ3Ti9KlnvtWKg4NDrPOkTJFCqfz89ff+/SqYN59Z/veBA8qVLbtVvcrlyqtyufKqWLqMeg3op/CbN2N0mXX+/2N85FOOna4uLipfqrRW/7FOZ8+fV8b06ZUj67/3Ac+eJatOnT0T7/t0dXVVmeIlVKZ4Cb1Rq45adOqgYydPWC0HwH8DCWcy9Nlnn+n27duSpM8//1x16tRRuXLl5O/vrzlz5jxlbuDpihUtqukzZmjJ0qXKny+flq9YoWPHjpndZVOmTKkWzZtr5KhRMiIjVbBgQd26dUu79+yRR8qUqlOnzjOvs0Tx4sqaNav69u2rnj166FFEhIYOHarChQsrd+7cunfvnkaPGaPKlSopfbp0unjpkvbv369KcbRupgsM1Llz53To8GGlCQhQihQp5BJL66u7u7sqVqig8ePHK/TECavbswQFBalmjRrqP2CAunXrphzZs+va9evatm2bsoWEqGzZshoxcqRKlyqlTJky6ebNm9q+Y4cyBwc/8/sH8PLJEBioR48e6Zcli1WmeHH9feCAFq1YFmf9+/fva9zkSapYpowC06TV5StXdPDIEZX//3sEN2/wpt77sKdGjf+f6lSrJjc3N504dUrbd+9W9/c6JirGt95ooMmzZipdYKCyZc6iZb+t1tHQ4+rb8/Ht0+b8ukD+vr7KljWrHCwOWvvnRvn5+sZ6qxMfHx+5urhq684dSu2fSi4uLnHeEqVqhYr6aNBAnTh1SlUrWh+HW7/VVH0GDVSa1AGqWKaMLBaLjoWG6vipk3q3RUst/221IiIilTtHDrm5umrVurVydXFV2tQBidoGAF5uJJzJUPVoJ8QhISE6ePCgrl69Kl9fX3OkTSApSpUqpXbvvKMxY8bo/oMHqle3rmrXqqWj0Vo9O773nnx9fTV56lSd/fJLeXp6KmeOHGrTunWi1mmxWDTy66817Ouv9W6HDla3RZEkR0dH3bhxQ/0HDNDVq1fl4+Oj1ypWVIf27WNdXqVKlfT7unV6r2NH3bx507wtSmxq1KihD7p1U+FChZQ2bVqraf379dPESZP0zTff6NLly/Lx8VG+vHlVrmxZSY+7uA8dPlyXLl1SypQpVapkSfXgnrjAf0JI5ix6/512+mn+PH0/baoK5Mmj9i1b64tRI2Kt7+DgoPCb4fpi1Ehdu35N3l7eKl+qlNo2ayFJypo5s74d8pV+mD5N73/cRzIMpUsbqEplyyU6xjfr1tPtO3c0btKPunbjhoIzZtSQT/sqY7r0kh53x501/xedOX9ODg4Oypktm4b1Gxjr5QxOjo76oH0HTZk9S5N+mqn8ufPo2y+/inW9hfMXkKenp06dPaOqFSpYTSteuIiG9u2vKbNn6adf5snJyVGZMmRQnaqPz188Unpo5ry5+m7Sj4qMjFSWoGB91befvL28Er0dALy8LEbUPTXwSgsPD5e3t7du3LghC7sEAMTKM9oozhcPHbVjJADwcnMPDDDPLb34MeGVxm1RAAAAAAA2QZfaV9T9+/d1//5983V4eLgdowEAAADwX0QL5ytqyJAh8vb2Nh+xjRoKAAAAAEnBNZyvqNhaODNmzMg1nAAQD67hBICE4RpORKFLbTKxaNGiBNetVy/mDaWf5Orqat44GgAAAABsgYQzmXj99detXlssFkVvnI5+O5SIiIgXFRaQICdOnlSHDh00/5dflDKOe7q9zMaMHau7d++q94cf2jsUAP9xp86cUddP+uin8T8oRYoU9g7HJrbu2K4J06box1Hfxnp7FgD/LSScyURkZKT5fM2aNerTp4++/PJLlSpVSpK0efNmffbZZ/ryyy/tFSKSuXnz5mne/Pk6f/68JClL5sxq166dyvz/Dculx12xvxk9WqtWrdKDhw9VsmRJfdS7t/z9/eNd9nfffafGjRtbJZtHjhzR0GHDtP/AAfn6+Khx48Zq1bJlgmK9fv26mrVooUuXLmntb7/J09NTkjRg4EAtWbo0Rv0smTPr5zlzJEnLV6zQ2LFjdefuXdWtU8fqnpnnzp3T+126aNrUqfLw8DDL327RQvXfeEPNmjVThvTpExQjgOSpcbs2unDpUozy12vVVo/3Opmv/zl4QD9Mn6YDhw/JwcFBIZmzaMTAQWbvoUPHjmrClMk6ePSIHBwcVKFUaXV+512lcHePd/0Tpk1Rgzp1zWTz/oMHGjFurA4dO6pTp0+rVLHi+vLTvlbzXLl6VeMm/aiDR4/o7Pnzalinnrq+G/s9iqPbsWe3fpw5XcdPnpS7q6uqV6qsd99uJSdHR7PO7xs3aMbcOTp99px8vL3UoHZdNW3Q0Jx++NgxDf32G505f06F8uXXJ916yOv/j8mPIiL0Xq/u6tGxs3Jnz2HOU6JIUU2cOUOr/1in6q9VemqcAJI3flZKhrp166bRo0erevXq8vLykpeXl6pXr66RI0eqa9eu9g4PyVRAmjR6v3NnTZ86VdOmTFHRokXVs1cvHTt2zKwzctQord+wQV8NGaLvx4/XlcuX9WGfPvEu98KFC9qwcaPq1qljlt26dUvvd+miwMBATZ86VV27dtX3P/yg+QsWJCjWQYMHKyQkJEZ5r549tWLZMvOxdPFieXt5qXLlypIeJ6qDv/hCH3zwgcaOGaPlK1Zow4YN5vxfDRum999/3yrZlCQfHx+VLFFCv/zyS4LiA5B8fT/iGy2YOt18jPx8sCTptTJlzTr/HDygDwf0U7FChTRhxCh9P+IbNahTV5b/b627EhamHn0/VfrAdBo/fKSGD/hcoadOacjoUfGu++LlS9q8fZtqVq5ilkVGRsrVxVVv1qmnIgUKxjrfw4cP5e3trZaN31JIcOYEvc+jocfVe2B/lShcRBNHfasBvT/Sn39t1YSpk806W3Zs16ARw1W/Ri1NHfuderzXST8v+lW/LFls1hk2drQK5S+gH0d9q1u3b2v63DnmtDm/zle+XLmtks0oNSpX0S+LE365EIDki4QzGTp27Jh8fHxilHt7e+vEiRMvPB78N5QvV05ly5RRpkyZFBQUpM6dOilFihT6+59/JD1OEhcuWqTu3bqpWLFiypUrl/r366e9e/fq77//jnO5q9esUfZs2RQQEGCWrVixQg8fPVK/vn2VNWtWVa9WTW81aaKZP/301DjnzZunm7du6e3mzWNM8/DwUKpUqczHgQMHFH7zpurVrStJOnP2rDxSplS1qlWVJ3duFS1SRKH//51ZsXKlnJycVOm112Jdb7ly5bRq1aqnxgcgefPx9pa/r5/52LRtm9KnDVTBvPnMOmN//EEN69RTizcbK3OmIGXKkEGVypaTi7OzJGnTtr/k5Oik7u91VKYMGZQrW3b17PS+/tj0p86cOxfnun/fuEEhwZmV2j+VWebu5qaenTqrbvUa8vP1jXW+wDRp9MG7HVSjUuUEX7bw+4YNyhqcWa3faqYM6dKpYN586ti6rRYsW6o7d+5Iklat/V3lSpRU/Zq1lC5toEoVK64WbzbST/PnmZf1nDx9RnWrV1fG9OlVpXwFnTxzWpJ07sJ5LV29Su+2iL3nSpnixc0WWQD/bSScyVCxYsXUo0cPXbx40Sy7ePGiPvzwQxUvXtyOkeG/IiIiQitXrdLdu3eVP9/jk6wDBw7o0aNHKhFtHwsODlbatGm1N56Ec9fu3cqVK5dV2d6//1ahggXl/P8nZ5JUqmRJnTx5Mt57wh4/flw/TJyozwcMMFsS4rNw0SIVL15cgYGBkqRMGTPq3v37OnjokG7cuKH9+/crW0iIwsPDNX7ChHiv0cybJ48uXrqkc/GcLAL4b3n48KFWr1urWlWqmmMlXLt+XfsPH5Kvj7c69u6p+m83V5eP+2jv/n3/zvfooZycnayuT3R1cZEk/X1gn+Kyd98+5QjJZqN3Y+3Bw4dy+f+Yori6uOjBgwc6dOxoPHVcdfnKFbPbcUjmzNq+e5ceRURox949yhr0uIX163HfqWPrtnFeh5omdYD8fHy0d/8/z/utAXjJkHAmQ5MmTdL58+eVKVMmhYSEKCQkRJkyZdLZs2c1ceJEe4eHZOzo0aMqV6GCSpctqyFffaXhw4YpS5YskqSwsDA5Ozub10tG8fPzU1hYWJzLvHD+vFKnTm1VFnb1qvyeuO7Tz8/PXE9sHjx4oE8/+0wfdO2qtGnTPvW9XL58WZs2b9br0UZt9vLy0oB+/dR/wAC1atNGtWrVUqlSpfTN6NFq3KiRzp09q2YtWqjxW29pzW+/WS0vVarHLQ7nL1x46roB/Dds2LpFt27fsurieu7/jwGTZ/2kutVqaPiAz5U9a1Z1/+wTnT53VpJUOH8BXb12TbPm/6KHDx/q5q2bmjBtiiQp7Oq1ONd38fIlpfr/Y6GtFS9cWP8cPKA1f6xTRESELodd0ZTZs/4/xquP6xQqrPWbN2nHnt2KjIzU6bNnNfvX+Y/rXHtcp3eXrlr3559q2v4dOTs5qUWjxlq59ne5uboqZ7Zs6tm/r5q2b6cfZkyLEYO/n78uXLr8Qt4vAPth0KBkKCQkRHv37tXq1at18OBBSVKuXLlUpUoVq9FqgWcVFBSkn2bM0K1bt/Tb779rwMCB+n78eDPpTIx79+/H+IU8McZ+952CM2dWrZo1E1R/ydKl8vDwUMWKFa3KX3vtNb0Wrdvsjp07dfToUfX+8EO93qCBvhg8WP7+/mrVurUKFypkJsJubm6P38+9e0l+LwCSh6WrV6lEkaJKFe0Hskjj8SB+9arXVK0qVSVJ2bNm1Y49e7Rs9Wp1aNVamTMF6ZNuPfTdxB/0/bQpcnBwUMO69eTn4yOLQ9z/p+8/ePBcjpcJUbxQYXVs3VYj/vedvhg1Qs7OzmrZ5C3t3b/P7EFSt3oNnb1wXn0GDVTEo0dKkSKF3qxbX5NnzZTD/59vZM4UpDFDhprLvREerkk/zdCYIcP0zYTxypszlwZ//Kna9+ym3NlzqEzxEmZdVxcX3bvPMRX4ryPhTKYsFouqVaum8uXLy9XVlUQTz4Wzs7MyZswo6fGPGPv379esOXP06ccfy9/f//Ev9TdvWrVyXr16Nd5Ran18fHTz5k2rMn8/P119oiXz6v//oh7XsrZv366jx46pxO+/S5J5/VCVatXUtk0bdWj/74iMhmFo0eLFqlWzplW33Sc9ePBAQ4cO1ecDB+r06dOKiIhQkcKFJUlBmTLpn337VL5cOUnSjRs3JEm+sVw/DeC/58KlS9qxZ7cGffSJVbm/7+MfoYL//1gZJShjRl288m9rXdUKFVW1QkVdvXZNbm5uslgs+nnhr0qXJu4eGt6eXrp569ZzfBfxa/L6G2pc/3WFXb0qTw8Pnb90Ud9Pm6p0/9+LxGKxqGPrtmr/ditdvX5NPl7e2rF3jyQpXdrAWJc5duKPalTvdQWkSqXd//ytd1u0lLubm0oVLaZdf/9tlXCG37opH29v279RAHZFl9pkKDIyUoMGDVL69Onl4eGh0NBQSVLfvn3pUovnKjIyUg8fPJD0OAF1cnLSX9u2mdNPnDypCxcumNd5xiZHjhw6fvy4VVn+fPm0a/duPXr0yCzb+tdfCgoKkpeXV6zLGTZ0qH6aOVMzZ8zQzBkz9Nmnn0qSfpgwQY3efNOq7o6dO3X69GnVr18/3vc3cdIklSpVSjlz5lREZKTVPWwfPXqkyGivjx07JicnpyS19gJIPpatWS0fb2+VKmY9NkJgmjRK5eevU2fPWpWfOXtWaVMH6El+vr5K4e6u3zesl4uzs4oWLBTnOrNlyaoTp089nzeQQBaLRan8/eXq6qrf1v+hgFSplT1LVqs6jo6OSu2fSs7Ozvpt/R/KkzNnrInijj27dfLMaTWo/XhU8sjISD2KeHycf/QowuoWb/cfPNC5CxdirAvAfw8JZzI0ePBgTZkyRcOGDbPqepM3b179+OOPdowMydnY777Tzp07de7cOR09elRjv/tOO3buVI0aNSQ9HgG2fr16GvXNN9q+fbsOHDigzz//XPnz5VO+eBLOUiVL6u9//rFK5mrUqCFnJyd9PmiQjh07plWrV2vW7Nlq3qyZWWft2rVq2KiR+TpDhgwKyZrVfKRLl06SlDlzZrPba5SFixYpb968Cska94nM8ePHtXr1ar3XoYMkKTgoSBaLRb8uXKiNGzfqxMmTyp07t1l/1+7dKlSwoNm1FsB/V2RkpJb/tlo1KlW2uiel9DhBe+uNBvplySKt+3Ojzpw7px9nTNfJs2dUu2o1s94vSxbr0LGjOn32rOYvXaJvJoxX+5at5fnEbZeiK164sPYdPGh1vJSkE6dO6cjxYwq/dVO379zWkePHdOT4Mas6UWV3793V9fAbOnL8mE6c+jd5Xb95k1p07GA1z6z5v+jYiRMKPXVSU2fP0sxf5umD9h3k+P/v+Xr4DS1cvkwnz5zWkePHNPqHCVr750Z1aRfzHp/3HzzQqAn/04ed3zcHS8qbK7cWLF2io6HH9cfmP5Uv2gBy+w8dlLOzs/LkzBnn9gDw30CX2mRo2rRp+v7771W5cmW99957ZnmBAgXMazqBZ3X16lX1HzhQV65ckYeHh7KFhGjMt9+qZIl/uz/16N5dDg4O6v3RR3rw4IFKlSypPr17x7vc0qVKydHRUX/99ZdKlSol6XHyOnbMGA0dNkxvt2olHx8ftXvnHTV44w1zvlu3b+vkyZPP/D5u3bql33//Xb169oyzjmEY+mLIEHXv1k3u/38Tdjc3Nw3o109Dhw/XwwcP1LtXL6tbuaxavVrt3333meMBkPxs37NbFy9fVu0q1WKd3rj+63rw8IHGTPxBN2/eVNbMmTXy88FKH/hvN9ODRw5r8qyZunv3rjJlyKhend9X9dcqxbveEkWKytHRUTv27FbxwkXM8t6f9zdHhZWkd7o9vuf2+kVLY5RJ0qGjR7Xmj3VKGxCgn398fF/N23du69TZM1br27Jju6bPnaMHDx8qJDizvvy0r0oWKWpVZ8Xvv2nc5IkyDEN5cubUt18MifW+mlNm/aRSRYspW7QWyw/e7aDPRwxXl4/7qGqFiqpQuow5bc36P1S1QkW5ufIjHvBfZzGiLoRCsuHu7q6DBw8qKChInp6e2rNnj7JkyaL9+/erePHiupWI6z/Cw8Pl7e2tGzduyMIugefs57lztX79eo0dM8beoSTKn5s26ZvRozVr5kw5OfE73avMM1o3wouHjtoxEvxXzV+6RH/+tVUjBg6ydyg2cz38hlp07KDvR3xjXi+K/x73wADz3DKuy2XwauDMKRnKnTu3NmzYoKCgIKvyefPmqVChuK8NAeylwRtv6ObNm7p9+3aCb0r+Mrl796769+1LsgnA5urVqKlbt2/pzp07cd7DMrm7cPGSur/XiWQTeEVw9pQM9evXT61atdLZs2cVGRmp+fPn69ChQ5o2bZqWLFli7/CAGJycnPRO27b2DiPRqlSubO8QALwinBwd1bLxW/YOw6ZyZsumnNmy2TsMAC8IgwYlQ/Xr19fixYu1Zs0apUyZUv369dOBAwe0ePFiVa1a1d7hAQAAAIAkWjiTrXLlymn16tX2DgMAAAAA4kQLJwAAAADAJmjhTIZ8fX1lsVhilFssFrm5uSkkJEStW7dWmzZt7BAdAAAAADxGwpkM9evXT1988YVq1qyp4sWLS5L++usvrVixQp07d1ZoaKg6duyoR48e6V3uGwgAAADATkg4k6GNGzdq8ODBeu+996zKJ0yYoFWrVumXX35R/vz59e2335JwAgAAALAbruFMhlauXKkqVarEKK9cubJWrlwpSapVq5aOHz/+okMDAAAAABMJZzLk5+enxYsXxyhfvHix/Pz8JEm3b9+Wp6fniw4NAAAAAEx0qU2G+vbtq44dO2rt2rXmNZzbtm3TsmXLNH78eEnS6tWrVaFCBXuGCQAAAOAVZzEMw7B3EHh2f/75p8aOHatDhw5JknLkyKEuXbqodOnSiVpeeHi4vL29dePGDVnYJQAgVp7e3ubzi4eO2jESAHi5uQcGmOeWXl5e9g4HdkQLZzJVpkwZlSlTxt5hAAAAAECcSDiTofDw8FjLLRaLXF1d5eLi8oIjAgAAAICYSDiTIR8fH1ksljinZ8iQQa1bt1b//v3l4MC4UAAAAADsg4QzGZoyZYo+/fRTtW7d2hw06K+//tLUqVP12Wef6fLly/r666/l6uqqTz75xM7RAgAAAHhVkXAmQ1OnTtWIESPUuHFjs6xu3brKly+fJkyYoN9++02ZMmXSF198QcIJAAAAwG7ob5kMbdq0SYUKFYpRXqhQIW3evFmSVLZsWZ06depFhwYAAAAAJhLOZChjxoyaOHFijPKJEycqY8aMkqSwsDD5+vq+6NAAAAAAwESX2mTo66+/VqNGjbR8+XIVK1ZMkrR9+3YdPHhQ8+bNkyRt27ZNTZo0sWeYAAAAAF5xFsMwDHsHgWd34sQJTZgwQYcOHZIk5ciRQx06dFBwcHCilhceHm7enNfCLgEAsfL09jafXzx01I6RAMDLzT0wwDy39PLysnc4sCNaOJOp4OBgDRkyxN5hAAAAAECcSDiTsTt37ujUqVN68OCBVXn+/PntFBEAAAAA/IuEMxm6fPmy2rRpo+XLl8c6PSIi4gVHBAAAAAAxMUptMtStWzddv35dW7dulbu7u1asWKGpU6cqW7ZsWrRokb3DAwAAAABJtHAmS7///rsWLlyookWLysHBQUFBQapataq8vLw0ZMgQ1a5d294hAgAAAAAtnMnR7du3FRAQIEny9fXV5cuXJUn58uXTzp077RkaAAAAAJhIOJOhHDlymLdDKVCggCZMmKCzZ89q/PjxCgwMtHN0AAAAAPAYXWqToQ8++EDnz5+XJPXv3181atTQzJkz5eLioilTptg3OAAAAAD4fxbDMAx7B4GkuXPnjg4ePKhMmTIpVapUiVpGeHi4eXNeC7sEAMTK09vbfH7x0FE7RgIALzf3wADz3NLLy8ve4cCOaOH8D0iRIoUKFy5s7zAAAAAAwAoJZzLRo0ePBNcdOXKkDSMBAAAAgIQh4Uwmdu3alaB6FovFxpEAAAAAQMKQcCYTa9eutXcIAAAAAPBMuC1KMnL8+HExxhMAAACA5IKEMxnJli2bLl++bL5u0qSJLl68aMeIAAAAACBuJJzPUUREhG7fvm2z5T/Zurls2TKbrg8AAAAAkoKEMwnCwsI0ZswY1atXT2nSpJGLi4u8vLzk7u6uAgUK6P3339cff/xh7zABAAAAwC4YNCgRTp06pX79+mn27Nny8/NTyZIl1alTJ6VKlUqurq66fv26Tpw4oe3bt2vChAnKnDmz+vfvr+bNmydpvRaLJcYotIxKCwAAAOBlRcKZCLlz51ajRo20evVqlS1bNt6k7/Lly/r555/1+eef6/Tp0/roo48SvV7DMNS6dWu5urpKku7du6f33ntPKVOmtKo3f/78RK8DAAAAAJ4Xi8Gwp8/s5MmTCgoKeqZ5DMPQuXPnlD59+kSvt02bNgmqN3ny5Gdednh4uLy9vXXjxg1Z2CUAIFae3t7m84uHjtoxEgB4ubkHBpjnll5eXvYOB3ZEwglJJJwAkBAknACQMCSciEKX2ufk3LlzOnPmjO7duxdjWvny5e0QEQAAAADYFwlnEh0/flxvv/22tmzZIinmrUssFosiIiLsERoAAAAA2BUJZxK9++67OnPmjCZNmqTcuXPLxcXF3iEBAAAAwEuBhDOJ/vrrL02dOlUNGjSwdygAAAAA8FJxsHcAyV369Onl6Oho7zAAAAAA4KVDwplEX3zxhb766itdvXrV3qEAAAAAwEuFLrVJNGXKFJ05c0bBwcEqWLCgfHx8rKZbLBYtXLjQPsEBAAAAgB2RcCbRrVu3FBISYr6+efOmHaMBAAAAgJcHCWcSrV271t4hAAAAAMBLiWs4AQAAAAA2QcL5HOzatUuNGjVSYGCgXF1dFRgYqMaNG2vXrl32Dg0AAAAA7IYutUm0YcMGVa1aVWnTplXTpk2VJk0aXbx4UQsWLFDp0qW1evVqlS1b1t5hAgAAAMALZzEMw7B3EMlZmTJl5OnpqSVLlsjJ6d/8PSIiQrVr19atW7e0ceNGO0aYMOHh4fL29taNGzdkYZcAgFh5enubzy8eOmrHSADg5eYeGGCeW3p5edk7HNgRXWqTaNeuXeratatVsilJjo6O6tq1q3bu3GmnyAAAAADAvkg4kyhlypS6dOlSrNMuXryolClTvuCIAAAAAODlQMKZRHXr1lWfPn20Zs0aq/I1a9bo448/Vr169ewUGQAAAADYF4MGJdGIESO0b98+Va9eXV5eXgoICNClS5cUHh6uYsWK6euvv7Z3iAAAAABgFyScSeTr66vNmzdryZIl2rhxo65duyY/Pz+VLVtWtWvXloMDjcgAAAAAXk2MUgtJjFILAAnBKLUAkDCMUosotHAmwtWrV+Xj4yMHBwddvXr1qfX9/PxeQFQAAAAA8HIh4UyE1KlTa/PmzSpevLhSpUoli8USb/2IiIgXFBkAAAAAvDxIOBNh0qRJypo1q/n8aQknAAAAALyKuIYTkriGEwASgms4ASBhuIYTURhCNYmyZMmiPXv2xDrtn3/+UZYsWV5wRAAAAADwciDhTKITJ07o/v37sU67c+eOTp8+/YIjAgAAAICXA9dwJsK9e/d0584dRfVGDg8PjzFa7b179/Trr78qXbp09ggRAAAAAOyOhDMRhg4dqs8//1ySZLFYVL169TjrDhgw4AVFBQAAAAAvFxLORHj99dcVHBwswzDUtm1bffbZZ+aotVFcXFyUK1cuFSxY0D5BAgAAAICdkXAmQoECBVSgQAFJj1s469SpI39/fztHBQAAAAAvFwYNSqJKlSrp5MmTsU7buXOnzpw584IjAgAAAICXAwlnEnXs2FHTp0+PddpPP/2kzp07v+CIAAAAAODlQMKZRFu3blWlSpVinfbaa69p8+bNLzgiAAAAAHg5kHAm0a1bt+Ts7BzrNAcHB928efMFRwQAAAAALwcSziTKlSuXFixYEOu0hQsXKkeOHC84IgAAAAB4OTBKbRJ169ZNrVu3lqOjo9q2bat06dLp3Llzmjx5sn744QdNmjTJ3iECAAAAgF2QcCZRy5YtdfHiRQ0cOFATJkwwy93d3fXVV1+pVatWdowOAAAAAOzHYhiGYe8g/gvCw8O1efNmhYWFyd/fX6VKlZKXl5e9w0qw8PBweXt768aNG7KwSwBArDy9vc3nFw8dtWMkAPBycw8MMM8tk9M5MZ4/WjifEy8vL1WvXt3eYQAAAADAS4OEMxHmz5+vSpUqycfHR/Pnz39q/QYNGryAqAAAAADg5UKX2kRwcHDQli1bVLx4cTk4xD/Qr8ViUURExAuKLPHoUgsAT0eXWgBIGLrUIgotnIkQGhqqwMBA8zkAAAAAICYSzkQICgqK9TkAAAAA4F8knIlw6tSpZ6qfKVMmG0UCAAAAAC8vEs5ECA4OlsViSXD95HANJwAAAAA8byScibBgwQLz+a1bt/TRRx8pa9asatiwodKkSaMLFy7ol19+0fHjxzV06FA7RgoAAAAA9sMotUn07rvvKiIiQpMmTYoxrU2bNrJYLLFOe9kwSi0APB2j1AJAwjBKLaLEf08PPNXcuXPVtGnTWKc1bdrUqjUUAAAAAF4lJJxJ5OjoqF27dsU6befOnU+9TycAAAAA/FdxDWcSvf322+rXr5/u3r2r119/XQEBAbp06ZIWLFigr776Su+99569QwQAAAAAuyDhTKKvv/5aTk5OGjZsmD7//HOz3M3NTZ07d9ZXX31lx+gAAAAAwH4YNOg5uXbtmvbu3asLFy4oMDBQ+fLlk6+vr73DSjAGDQKAp2PQIABIGAYNQhRaOJ8TX19fVahQwd5hAAAAAMBLgxFtnoMrV67oo48+UuXKlZUjRw7t27dPkjR69Ght2bLFztEBAAAAgH2QcCbRzp07lS1bNs2ePVsZMmTQ0aNHdf/+fUnS2bNnNWrUKDtHCAAAAAD2QcKZRN27d1epUqV05MgRTZw4UdEviS1RogQtnAAAAABeWVzDmUTbtm3T/Pnz5ezsrIiICKtpqVOn1qVLl+wUGQAAAADYFy2cSZQyZUqFh4fHOu3UqVPy9/d/wREBAAAAwMuBhDOJqlevrsGDByssLMwss1gsunv3rkaPHq1atWrZMToAAAAAsB8SziQaOnSowsPDlS1bNjVu3FgWi0WfffaZcufOrbCwMA0ePNjeIQIAAACAXZBwJlH69Om1e/dudenSRefPn1fWrFkVFham5s2ba/v27QoICLB3iAAAAABgFxYj+rCqeCb37t1T79699fbbb6tYsWL2DidJwsPD5e3trRs3bsjCLgEAsfL09jafXzx01I6RAMDLzT0wwDy39PLysnc4sCNaOJPAzc1NkyZN0p07d+wdCgAAAAC8dEg4k6h06dLcaxMAAAAAYsF9OJPo888/V/PmzeXo6KhatWopTZo0slgsVnX8/PzsFB0AAAAA2A/XcCaRg8O/jcRPJppRIiIiXlQ4icY1nADwdFzDCQAJwzWciEILZxJNmjQpzkQTAAAAAF5lJJxJ1Lp1a3uHAAAAAAAvJQYNSqQffvhB+fPnl6enp3LkyKG+ffvqwYMH9g4LAAAAAF4aJJyJMHnyZHXo0EH3799X7dq15ePjoy+++EI9evSwd2gAAAAA8NJg0KBEKFy4sLJly6ZZs2aZgwZ9+eWXGjhwoG7fvi0np+TXU5lBgwDg6Rg0CAAShkGDEIWEMxG8vLw0b948VatWzSy7evWqUqVKpcOHDyskJMSO0SVO9ISTgwIAAACSgnNLRKFLbSLcunVLPj4+VmXe//+rd3h4uB0iAgAAAICXT/Lr+/mSOHTokFXX2ah7bR48eDBG3cKFC7+wuAAAAADgZUGX2kRwcHCI9d6bUZsyapphGLJYLGYy+jLjGk4AeLro13DOLtPQjpEAwMut1vLJdKmFJFo4E2Xt2rX2DgEAAAAAXnoknIlQoUIFe4cAAAAAAC89Bg0CAAAAANgECWcilChRQr/++qsiIyMTVP/06dPq1auXRo4caePIAAAAAODlQZfaRGjZsqU6deqk9u3bq379+ipTpozy58+v1KlTy9XVVdevX1doaKh27Nih5cuXa8uWLapXr546duxo79ABAAAA4IUh4UyEzp07q23btpo9e7amTZumadOm6dGjR1Z1DMNQYGCg3nzzTY0bN0758uWzU7QAAAAAYB8knInk7u6uNm3aqE2bNrp37552796t8+fP6969e/Lz81OOHDkUHBxs7zABAAAAwG5IOJ8DNzc3lSxZ0t5hAAAAAMBLhUGDAAAAAAA2QcIJAAAAALAJEk4AAAAAgE2QcAIAAAAAbIKEEwAAAABgE4xSm0SnTp2Kc5qDg4O8vb3l6en5AiMCAAAAgJcDCWcSBQcHy2KxPLVOt27d1KVLlxcUFQAAAADYHwlnEs2YMUMff/yxsmfPrvr16ysgIECXLl3SggULdOTIEX300UfavHmzunfvLkkknQAAAABeGSScSbRhwwbVqFFDEyZMsCp///331b59e+3YsUPTp0+Xl5eXxo0bR8IJAAAA4JXBoEFJNHv2bL355puxTmvUqJHmz58vSapTp45CQ0NfZGgAAAAAYFcknEnk4OCgPXv2xDpt9+7dcnB4vIkdHR3l7u7+IkMDAAAAALuiS20SNW/eXH379tWDBw9Ut25dpU6dWpcvX9bChQs1ePBgdejQQZK0Y8cO5c6d287RAgAAAMCLQ8KZRCNGjJCTk5MGDx6svn37muWurq7q3Lmzhg4dKkkqU6aMqlevbq8wAQAAAOCFsxiGYdg7iP+Ca9eu6e+//9b58+cVGBiovHnzys/Pz95hJVh4eLi8vb1148YNWdglACBWnt7e5vPZZRraMRIAeLnVWj7ZPLf08vKydziwI1o4nxNfX1+VL1/e3mEAAAAAwEuDhPM5uHbtmpYvX64zZ87o3r17VtMsFotVV1sAAAAAeFWQcCbRqlWr9Oabb+rWrVtyd3eXi4uL1XQSTgAAAACvKhLOJOrZs6eKFSumSZMmKSgoyN7hAAAAAMBLg4QziY4fP66RI0eSbAIAAADAExzsHUByV7hwYZ0+fdreYQAAAADAS4eEM4n+97//6dtvv9XKlSv16NEje4cDAAAAAC8NutQmUalSpfTw4UPVqlVLDg4Ocnd3t5pusVh048YNO0UHAAAAAPZDwplEPXv2lMVisXcYAAAAAPDSIeFMogEDBtg7BAAAAAB4KXENJwAAAADAJmjhTIR69eppxIgRypYtm+rVqxdvXYvFooULF76gyAAAAADg5UHCmQg3b95URESEJCk8PJxrOAEAAAAgFiScibB27Vrz+bp16+wXCAAAAAC8xLiGEwAAAABgE7RwJtHnn38e5zQHBwd5e3urYMGCKleu3AuMCgAAAADsj4QziUaNGqUHDx7o7t27kiQ3Nzfdu3dPkuTu7q6HDx8qIiJChQsX1rJly5Q6dWp7hgsAAAAALwxdapPo999/V/r06TV9+nSFh4frzp07Cg8P19SpU5UuXTr98ccfWrVqlc6cOaMPP/zQ3uECAAAAwAtDC2cSde7cWT179lTz5s3NMg8PD7399tu6ffu2unXrpq1bt+qzzz6Lt/stAAAAAPzX0MKZRLt27VJQUFCs04KDg/X3339LkvLmzasbN268yNAAAAAAwK5IOJMoKChIP/74Y6zTvv/+ezMZDQsLU6pUqV5kaAAAAABgV3SpTaIhQ4aocePGypEjh+rUqaPUqVPr8uXLWrJkiY4fP665c+dKkn777TeVL1/eztECAAAAwItDwplEb7zxhv766y8NGTJECxYs0Pnz5xUYGKhixYppzpw5KliwoCTpu+++s2+gAAAAAPCCkXA+B4UKFdLPP/9s7zAAAAAA4KXCNZwAAAAAAJughTOJKlWqFOc0BwcHeXt7q1ChQmrTpo3Sp0//AiMDAAAAAPuihTOJvL29dfToUW3YsEHh4eFyc3NTeHi4NmzYoMOHD+vatWsaMWKEcufOrZ07d9o7XAAAAAB4YUg4k6hRo0by8fHR0aNHtX37di1btkzbt2/XkSNH5O3trVatWun48eMKCQnRxx9/bO9wAQAAAOCFIeFMooEDB2rAgAHm/TajBAcHq3///ho0aJB8fX3Vq1cvbdmyxU5RAgAAAMCLR8KZRKdOnZLFYol1msVi0dmzZyVJ6dKl06NHj15kaAAAAABgVyScSVSsWDH169dPp0+ftio/efKk+vfvr+LFi0uSTpw4waBBAAAAAF4pjFKbROPHj1fVqlWVNWtW5cuXT6lTp9bly5e1d+9epUmTRnPnzpUkXbx4Ue3bt7dztAAAAADw4pBwJlHu3Ll17NgxTZo0Sdu3b9f58+dVoEABtWvXTm3atJGbm5skqXfv3naOFAAAAABeLBLO58DNzU2dOnWydxgAAAAA8FLhGk4AAAAAgE2QcD4H06dPV9myZRUQECAvL68YDwAAAAB4FZFwJtGMGTP07rvvKm/evLpy5YoaN26shg0bysXFRQEBAerVq5e9QwQAAAAAuyDhTKIRI0aob9+++u677yRJnTp10uTJkxUaGqrUqVPLw8PDzhECAAAAgH2QcCbRkSNHVKZMGTk6OsrR0VHh4eGSJE9PT/Xp00fffvvtc1/nrFmz4pz24YcfPvf1AQAAAEBikHAmkbe3t+7fvy9JSp8+vfbv329Oi4iIUFhY2HNfZ8eOHbV8+fIY5d27d9eMGTOe+/oAAAAAIDG4LUoSFS1aVHv37lX16tVVr149DRw4UJGRkXJ2dtZXX32lkiVLPvd1zpw5U02bNtWSJUtUtmxZSVKXLl00f/58rV279rmvDwAAAAASg4QziT7++GOdPHlSkvT555/r5MmT6tatmyIjI1WsWDFNmDDhua+zdu3aGjdunOrVq6fVq1dr4sSJWrhwodauXavs2bM/9/UBAAAAQGKQcCZRyZIlzVZMHx8fLVy4UPfv39f9+/dtekuUZs2a6fr16ypTpoxSp06tP/74QyEhITZbHwAAAAA8KxJOG3B1dZWrq+tzXWaPHj1iLU+dOrUKFy6scePGmWUjR458rusGAAAAgMQg4UyErl27JriuxWLR6NGjk7zOXbt2xVoeEhKi8PBwc7rFYknyugAAAADgeSDhTITFixcnuO7zSjgZDAgAAABAckPCmQihoaH2DsFKeHi4fv/9d+XMmVM5c+a0dzgAAAAAIIn7cCZLjRs31tixYyVJd+/eVdGiRdW4cWPly5dPv/zyi52jAwAAAIDHSDgTIX/+/Prnn3+syn766Sddv379hax//fr1KleunCRpwYIFMgxD169f17fffqvBgwe/kBgAAAAA4GlIOBPhn3/+0Z07d8zXERERevvtt3X8+PEXsv4bN27Iz89PkrRixQo1bNhQKVKkUO3atXXkyJEXEgMAAAAAPA0J53NiGMYLW1fGjBm1efNm3b59WytWrFC1atUkSdeuXZObm9sLiwMAAAAA4sOgQclQt27d1Lx5c/0fe/cdFcXVhgH82aXD0kF6URFsiL0LqNh7jy1iL0nU2BN7LzFqjCb23nvFrthFLGDXiIAKiEoH6dzvDz8nrBTRuCLJ8zuHc9g77Z3Z2bvz7r1zR6FQwMHBAZ6engDedrV1dXUt3OCIiIiIiIj+jwnnJ8rteZdf6hmYQ4YMQfXq1fHs2TM0atQIcvnbhuoSJUrwHk4iIiIiIvpqyMSX7Av6LyGXy6GrqyslegCQmJiYowx4m4TGxcV96RA/Wnx8PAwNDREXFwcZTwkiolzpGxpK/2+r06EQIyEi+ro1P7JWurY0MDAo7HCoELGF8xNMnjz5i29zxIgRmD59OvT09DBixIh8512wYMEXioqIiIiIiChvTDg/QWEknDdv3kR6err0PxERERER0deOCWcRcebMmVz/JyIiIiIi+lrxsSj/Mrt27SrsEIiIiIiIiAAw4SxyMjIycOfOHTx69EipfP/+/XBzc0P37t0LKTIiIiIiIiJlTDiLkDt37sDJyQlubm4oU6YM2rdvj8jISHh4eKBPnz5o1qwZgoKCCjtMIiIiIiIiALyHs0gZO3YsnJycsGTJEmzduhVbt27F/fv30bdvXxw9ehQ6OjqFHSIREREREZGECWcR4u/vj+PHj6NixYqoV68etm7dip9//hk9e/Ys7NCIiIiIiIhyYJfaIuT169ewtrYGABgaGkJPTw81a9Ys5KiIiIiIiIhyxxbOIkQmkyEhIQHa2toQQkAmkyE5ORnx8fFK8xkYGBRShERERERERH9jwlmECCHg7Oys9LpSpUpKr2UyGTIzMwsjPCIiIiIiIiVMOIuQM2fOFHYIREREREREBcaEswjx8PAo7BCIiIiIiIgKjIMGERERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCWcR9/z5czx//rywwyAiIiIiIsqBCWcRlJWVhWnTpsHQ0BAODg5wcHCAkZERpk+fjqysrMIOj4iIiIiICAAfi1IkjR8/HqtXr8acOXNQp04dAMCFCxcwZcoUpKSkYObMmYUcIRERERERERPOImn9+vVYtWoVWrduLZVVqFABNjY2GDJkCBNOIiIiIiL6KrBLbREUHR2N0qVL5ygvXbo0oqOjCyEiIiIiIiKinJhwFkFubm5YsmRJjvIlS5bAzc2tECIiIiIiIiLKiV1qi6B58+ahRYsWOHnyJGrVqgUAuHz5Mp49ewYfH59Cjo6IiIiIiOgttnAWQR4eHnj06BHatWuH2NhYxMbGon379nj48CHq1atX2OEREREREREBYAtnkWVtbc3BgYiIiIiI6KvGhLMIefr0aYHms7e3V3EkREREREREH8aEswhxdHSETCbLUS6EkMplMhkyMjK+dGhEREREREQ5MOEsQm7evJlruRAC27Ztw+LFi6FQKL5wVERERERERLljwlmE5PbIk5MnT2LcuHF49OgRxowZg5EjRxZCZERERERERDkx4Syibty4gbFjx+L8+fPo168ffHx8UKxYscIOi4iIiIiISMLHohQxQUFB6NKlC6pXrw5zc3Pcu3cPS5YsYbJJRERERERfHSacRciQIUNQtmxZxMXF4dq1a9iyZQtKlChR2GERERERERHlil1qi5Bly5ZBW1sbL1++RJ8+ffKc78aNG18wKiIiIiIiotwx4SxCJk+eXNgh0L/UwUOH8OuCBfA9fbqwQylUU6ZORUJCAn6dP7+wQyEi+up8c3E3zo+bi7DzVws7FCIqQphwFiFMOCk/U6ZOxaHDh3OU7929G3Z2doUQ0d8OHjqEqdOmoVbNmvh98WKpPCEhAfUbNsSyP/9E1SpVvlg84eHhaN22LTZv2gQXZ2epfNTIkRBCfLE4iKjwfHNxd77T76zejjtrdnyRWBr8PhXFKpfHpUkL8PTURancuXMLuHRuiYMdB3+RON4p36czbNyr45j3KKXyfa36Ii0h8YvGQkRFHxNOon+R2rVqYdLEiUplxsbGhRSNMjU1NVz198e1a9dQtWrVwg4nV3yOLdF/x75WfaX/7RvWQfl+XeDTdahUlpGcojS/TE0OkZmlsngyUlPhOqArnvlegcjMVNl2/omU6NjCDoGIiiAmnET/IhoaGjAzM8tRvmnzZhw8dAhhYWEwNDBAvXr1MPSHH6Crq5vreh49eoRfFy7E/fv3IZPJYGdnh5/HjUPZsmUBAAEBAViydCnuP3gAI0NDeHp64vvvvoOOjk6eseno6KCRlxd+X7oU69euzXO+F5GRWLRoEa74+UEul6NixYoYNWIErK2tAQAZGRlYuGgRDvv4QE0uR5s2bRAVFYXExESpK+yly5exes0aBAUFQU0uh6urK0aNHAlbW1sAQOu2bQEA3Xv0AABUrlwZK5YtU+pSu2fvXqxYuRI+hw5BLv97fLURo0bB0NAQk/+f2PuePYuVq1YhODgY5mZmaNGiBfr07g11dXUIIbBi5UocOHgQ0dHRMDQ0RMMGDTB6lHKrARF9edmTp/SkN4D4u6xYpXJosGQazo6cAdf+XWFY0h5nf5yO4s3rQ0Ohhws/zZWWrTSsN4ydHHH6h//3QpLJUKZHW5Rs3QjapkZIeBqBu+t24rnvlXzjeXriAmzqVkPJ1l54vPdYnvPZ1K2Gcn06w9DRFsmvYxB85AzubdgtJcP69jaoPm4wTEqXRGJ4JG4sWoP6v01W6grrNrgHbNxrQLeYKVKiYhF64hzurNkJkZmJ4s3ro3zfLgD+bgX2m7kEwT5nlLrUei2biVeB9xH45yYpNi0jA7TZvxJnhk7Fq8B7kGuoo8KAbrBvVBeaCj3EPXmKwD834eXNuwAAXQtzVBnRD+YVSkOuoY6kF68QsHQDIi5zLAqifxMmnET/AXK5HKNHjoS1tTXCwsIwZ948LP79d4wbOzbX+SdMmgQXFxf8NHYs5HI5Hj16BHX1t9XF8+fP8cOwYRg8aBAmTZyImNhYzPvlF8z75RdMnjQp3zgG9O+Ptu3b4+SpU/Bq2DDH9IyMDPwwdChcy5fHqhUroKamhtVr1uCHYcOwbcsWaGhoYP2GDTh69CgmT5yI4sWLY+u2bfA9e1apS25ycjK6d+uGUk5OeJOcjGXLl2PUmDHYsmkT5HI51q9bh17e3vhjyRKUKFECGhoaOWLxatgQv8yfj2vXrqF69eoAgLi4OFy+fBm/LVwIALh58yYmT5mC0SNHomKlSnj+/DlmzZol7eup06exZetWzJo5EyVLlMDrqCj89ddfBXjHiOhrUGFQDwQsXY+k8EikxScVaJmyPdvDoYk7rv2yAgnPI1CsYlnUmjQMvrHxeBVwL8/l0t8k4+6G3SjXuxOCj/giMyU1xzzmbmVQY+IPuLFoDV4F3ofCxgLVxgwCANxduxMyuRz15ozFm8hXODFgHNR1dVDp+165bstv5hKkvI6GYUkHVBs7COlJyXiwZT+enrwIw+J2sKxZCb7Dpr6dP/FNjnWEHD+PMt3bKiWc9g3rIPl1DF4Fvt3PKiP6wcDRDpcnL0Ty62jYuNeAx68TcOTbEUh8HoEqI/tBTUMdp76biIyUVBg62iLjTUqObRFR0cbHohD9i1y4eBH1PDykv7HjxgEAunXtiqpVq8La2hrVqlXD4EGDcOLkyTzXExkZiRrVqsHR0RH29vbw8vKC8//vdVy7bh2aNm2Kbl27wt7eHm4VKmD0yJE47OOD1NScF0jZmZubo+s33+CPP/9ERkZGjunHT5xAVlYWJk6YACcnJxQvXhyTJ03CixcvcP36dQDAjh074O3tjfr168PR0RFjRo+Gvr6+0noaNmiABvXrw87ODi7Ozpg8cSIeP36MJ8HBAABjIyMAgKGREczMzGBoaJgjFgMDA9SuVQtHj/3d0nDq9GkYGRlJXYJXrloF71690LJlS9ja2KBmjRoYNGgQ9uzdCwB48eIFTE1NUaN6dVhaWqJ8uXJo9//WVSL6+t1ZtQ2R/reQGBZZoHsX5RrqKPtte1ydtRQvrgYgKTwSwT5nEHL8HJzaNP7g8o/3HEVWWjpKf9Mq1+nlenfG/U17EXLEF0nhkYj0v4XbK7fBqe3bdVtUqwCFjQWuTP8dsY9D8frWA9xasTXHeu6t342oOw+R9OIVwi9ew4OtB2DfsDYAIDMtDRnJKRCZmUiJjkVKdCwy09JyrOPZ6UvQMTOGuVsZqcyhUV2EnrgAANC1MEPx5g1wceJ8vAq8j8SwSDzcegCvbj1AiRb1AQB6FuZ4desB4p48RVJ4JMIvXZeSVSL692AL579EbGwsjP5/EU3/XVWqVMFP2Vot33Vx9bt6FevWrUNIaCiSkpKQmZmJ1NRUpKSkQFtbO8d6unXtiukzZ8LnyBFUr14dXg0bSt1R//rrL/z1+DGOHj0qzS+EQFZWFsLDw1G8ePF8Y+z17bfYs3cvDhw8iEZeXkrT/vrrLzx//hzunp5K5WlpaXj+/DkSExMRFR2Ncv/v2gu8vTe0TOnSyMr6+96qp0+fYtmKFbh75w5i4+KkaS9evIBTyZL5xpdds6ZNMWPWLIwbOxaampo4evQoGjdqJHWxffTXXwi8dQtrsnURzsrKko6tl5cXtm7bhjZt26JWrVqoU7s26tWrJ7UWE9HXLfpB0EfNr7C1grqONjwXKff2kGuoI/ZR8AeXz0rPwO1V21D5x774K5dutUZODjCr4IKy33aQymRqcqhraUFNSxMG9jZ4Exml1F046l7OXhV2DWvDuWMLKGwsoK6jDbmaGtLfJH/EngKpsfF4cTUQDo3r4VXgfehZFYOZa2n4z1sOADAsYQ+5uhpabP1daTk1TQ2kxScAAB7tOoyqowbAsnpFRF67hWe+VxAXFPpRcRDR149XPUXQ3Llz4ejoiC5d3t5j0blzZ+zevRuWlpbw8fGBm5tbIUdIhUVHWzvHiLTh4eH4ccQIdGjfHkMGD4aBgQECAgMxfcYMpKen55pwDhwwAE2bNMGFixdx6fJlLF+xArNmzED9+vXxJjkZ7du1wzf/P/+ys7S0/GCM+vr68O7VCytXrUK9unWVpr158walS5fGjGnTciz3MYMf/ThyJKwsLTH+559hbm6OrKwsdOnaFRnp6QVeBwDUq1cPQghcuHgRZcuWxc2AAIz48UdpenJyMgb0748G9evnWFZTUxOWFhbYvXMnrvr7w8/PD3PmzcPGTZuwYvlyJp1ERUBGinL3TpGVBciU55GrqUn/q+u8rU/PjZ6F5FfRSvNlFbD+CTl2DqW7tkE5745IinipNE1dVxt3Vm3H87N+OZbLTCvY+k3LOaPWpOG4s3o7Iq4GID3xDRy86sDlm9YFWj670OPnUHl4X1xfsBoOjeoh9nEo4p48lWLNysjE8b5jcgy29G5ApicHT+GFXwCsaleBZXU3lOnZDgFL1uOvXUc+OhYi+nrxiqcIWrZsGTZv3gwAOHHiBE6cOIEjR45gx44dGD16NI4fP17IEdLX5P6DB8jKysKPw4dLLXP5dad9x8HBAQ4ODujerRt+njABBw4dQv369VHaxQXBwcH/6FErXTp3xvbt27F12zal8tKlS+PEyZMwNjbOc8RYUxMT3Lt/H5UrVwYAZGZm4sHDh3AuVQrA29b+0NBQTPj5Z1SqVAnA20GOsnt3z2bWB0aC1NLSQoP69XHk6FE8e/YMDg4OKF26tDTdxcUFoaGh+R4LbW1tuNerB/d69dCpUyd07NQJjx8/VloPERUNqbHxMCxhr1RmVKo4xP9vEYgPeYbM1LS3XUXzuV8zX0IgcNkm1J01Bo/3KbdyxjwMhoG9DRLDXuS6aPzTMOhamELL2BCpMXEAANMyTkrzmLm64E3kK9zb8PdjYXQtzZXmycrIgEz+4buunp/3R9Uxg2BVsxIcGtdD8BFfaVrso2DI1dWgbWyIV4H381zHm5dRCNp3HEH7jqPCoO4o2aoRE06ifxnew1kEvXjxQrrAPXToEDp37ozGjRtjzJgx8Pf3L+To6GtjZ2uLjIwMbN+xA8/DwnDYx0e6xzA3KSkpmPvLL7h2/ToiIiIQEBiIe/fuobijI4C3XWIDb93C3F9+wcNHj/D06VP4nj2Lub/8UuCYtLS0MGDAAGzfofyMu2ZNm8LI0BAjR4/GzZs3ERYWhmvXr+OX+fMRGRkJ4G2L/tp16+B79ixCQkMxf8ECxMfHQyZ72+xgYGAAQ0ND7Nm3D8+ePYO/vz8WLFqktB1jY2NoaWnh0uXL0gi3eWnatCkuXryIAwcPommTJkrT+vfti8M+PlixciWCgoIQHByMY8eP448//wTw9vmj+/bvx+OgIDwPC8ORI0egpaVVoJZgIvr6RF6/A5PSJeHY1AMKWyuU79sFhiX+/sEp400KHmw9gEpDveHYzBMKGwsYOxdHqY7N4NjMs8Dbibh8A1H3/kLJNo2Uyu+u3QnHZh4o17sTDIrbwcDBBvYN68C1f9e38f3/ftOaE36AYUkHmLm6wHXA22n4/zOGE55HQNfCDPYN60BhY4FSHZvD1r2G0naSIl5Cz6oYjEo5QtNQH3KN3NsnMlNSEXb+Klz7fwMDBxs8PXlBmpbwLAIhx86ixoQfYOtRA3pWxWBSxglleraDVa23PxhWGtYbltUrQs+qGIydi6NY5fKID31e4ONEREUDWziLIGNjYzx79gx2dnY4evQoZsyYAeDtfXSZX+mzu6jwODs748fhw7F+wwYsWboUlStVwndDhmDylCm5zq+mpoa4uDhMnjIF0dHRMDIyQn1PTwwcMAAAUKpUKaxYvhx//Pkn+g8YACEEbG1s0KhRo1zXl5eWLVpg8+bN0kA+wNvWwBXLl+P3JUsweuxYvHnzBubm5qherRr09PQAvE14o6KiMHnKFKipqaFd27aoVbOm1K1NLpdj1syZmP/rr+jStSsc7O0xatQoDBw0SNqOuro6Ro8ciZWrV2P5ihWoWLEiVixblmuc1apWhYGBAUJDQ9G0aVOlabVq1cKiBQuwcvVqrN+wAerq6nB0dETbNm0AAPoKBdZt2ICFixYhKysLTiVLYuGvv/J+a6Ii6sXVANxdtwtuQ3pCTVMTTw6fRsjRszDK1up5e+VWpMbGo2zP9tCzLob0xDeIefgE9zbs+ahtBf6xEY1WzEb2odheXA3AudGzUa53J5Tp0Q5ZGRlICA1D0MFTAN52+T0/bi6qjxuMxqvmIjE8EoFLN8D9l5+lLrfhF67h4fZDqDKiH+SaGgi/dB131+1E+T5/3ybxzPcKbD1qosHiqdA0UEiPRclN6PHz8Ph1Al7evIs3ka+VpvnNXIpy3h1R8fte0DE3QVpcAl7ffYTwi28HgZPJ5agysh90zU2R/iYZEVdu4ubivB+bRURFk0yI///kRUXG999/j0OHDqFUqVK4efMmQkJCoFAosG3bNsybNw83bnz886vi4+NhaGiIuLg4yHhKUBGSlZWFjp07o5GXFwZnSyqJVEE/24jG2+p0yGdOoq+DmasLvJbNwqHOQ5AYFlnY4dB/SPMja6VrSwMDg8IOhwoRWziLoIULF8LR0RHPnj3DvHnzpHvdIiIiMGTIkEKOjki1IiIicMXPD5UrVUJ6ejq279yJ8PDwHN1diYj+i2zcqyMjOQWJzyKgsLVC5eF9pMeSEBEVBiacRZCGhgZGjRqVo/zHbKNnfkhqaqrSMxPj4+M/S2xEqiaTy3Hw0CEs+u03AEDJEiXwx5IlH3wcCxHRf4GGrg7cBveEnoUZUuMSEHntFm7+vq6wwyKi/zAmnEXYvXv38PTpU6S990Dm1q0/PLT57NmzMXXqVFWFRqQylhYWWLNqVWGHQUT0VQo5ehYhR88WdhhERBImnEXQkydP0K5dO9y+fRsymQzvbsN9N0pnQQYO+umnnzBixAjpdXx8/D96zAUREREREdH7+FiUImjYsGEoXrw4Xr58CV1dXdy9exfnzp1D1apV4evrW6B1aGlpwcDAQOmPiIiIvk769tZoc2AV1HW1CzsUlSnZtjHqzf2psMMgos+MLZxF0OXLl3H69GmYmZlBLpdDLpejbt26mD17NoYOHYqbN28WdohESmJjY9GpSxesX7sW1tbWhR3OR3vy5Am+HzoUu3fuhI6OTmGHQ0T/UPk+nVG+bxelsvjQMPh0G5rnMsWb10eN8d8rlWWmpmFng67S628u7s512YClG/Bgy34AgL6dFdy++xbmrqUh11BH7ONQ3F61DS9v3Mk35gqDuuOvXUeQ8SZFKrOsXhHl+3WBYXE7ZKam4VXgfQT8vg5JL15J88g11FGud2c4NnGHtokRUqJicGftTgQfPp3ntiyquMK1/zcwLOmAjOQUhBzxxa0VWyAys3LMq7CxRJN18yEys7Cn6bd/r6NaBVQZ0R86pkYIO++Pq7P/QFZGBgBAQ08XjVbNhe/waXgT+XeswYdOo5x3R5i7lcGrwPv5Hg8iKjqYcBZBmZmZ0NfXBwCYmZkhPDwcLi4ucHBwwMOHDws5Oiqqdu3ahV179iAiIgIAUKJ4cfTr1w91ateW5nn+/DkW/fYbAgIDkZ6ejlo1a2L0qFEwNTXNd91r1q6Fh7u7UrL54sULzJ47F9euXYOuri5atmiB74YMgbp6/tXShQsXsHL1ajx+/BiampqoXKkSfp0/X5r+y/z5CLx1C0FBQSju6IgtmzcrLR8eHo7JU6bg/oMHKFO6NKZOmaIU1/Aff0SrVq3QsEEDqaxEiRIoX748Nm/Zgn59++YbHxEVDbFPnsJ32N9jGWQV4HaUtMQk+HT9Oyl9/8ly+1op1w9WNSuh+k9D8Mz3ilRWb97PSHwegdNDpyAzNQ0unVvAfd5PONT5O6REx+a6XV0LM1jXroIbC1ZLZXpWxVBvzlg83H4QV6YugoaeLioN7Y06s8bgeJ/R0ny1p4+EtokRrs7+A4nPI6BtagyZXJbnPho5OcB9/njc27AbV6b/Dh1zE1QdPRAyuRwBSzcozStTU0OtqT/iVeB9mJV3yTZBhlqTh+P+xr2IuBqAOjNGoWSbRvhr9xEAQIXBPRC077hSsgkAWRkZCD1xAaU6NmfCSfQvwi61RVD58uURGBgIAKhRowbmzZuHixcvYtq0aShRokQhR0dFVTELC3z/3XfYuH49Nqxbh6pVq2LkqFEICgoCACQnJ+O7H36ATCbDsj/+wOqVK5Geno4fR45EVlbOX73fSUlJwf4DB9Am22BWmZmZGPbjj0hPT8ea1asxZfJkHDx0CMtXrMg3xlOnT2PSlClo1bIltmzahNUrV+b6OJTWrVqhkZdXrutY+NtvMC9WDFs2bYKZmRkWLV4sTTt+4gRkcrlSsimts2VL7Nq9Gxn//4WeiIo2kZmJlOhY6S8tLqEAC0FpmdSYOKXJ2aelRMfCpl51vLxxB0nhbx9JommoDwN7a9zftBdxQaFIfB6BwGWboK6jDcMS9nlu1q5BbcQ+DkXy62ipzNilBGRqctxasRWJYZGIeRSMB1sPwLiUI2RqagAAyxoVUaxiOZwbOROR124h6cUrRN19hNe38/5x2r5hHcQGheLu2p1IDHuBVwH3EPjHRjh1aJqjO2+FAV2REBqGZ6cvKZVrGepD29gQf+09ivjgZwi/4A8DBxsAgGl5F5iWKYlHOw/nuv3wC9dgU7ca1DQ184yRiIoWJpxF0IQJE6QL/GnTpiE4OBj16tWDj48PFme7eCb6GO716qFunTqwt7eHg4MDvhsyBLq6urh95203r8DAQERERGDypElwcnKCk5MTpk6Zgvv378P/2rU813vh4kVoamrC1dVVKrvi54fg4GBMnzoVLs7OqFO7NgYNHIgdO3ciPT091/VkZGTg1wULMPSHH9CxQwc4ODigRIkSaNSokdJ8o0eNQudOnWBjY5PrekJCQtCyRQvY29ujZcuWCAkOBgAkJCTgz2XLMHbMmFyXq1GjBuLj43Hjxo28DyIRFRn6tlZos38lWu74AzUnD4OuhdkHl1HX0Uar3cvQes9y1J0zFgbF8x5sT8vYENa1K+PJoVNSWVpcAuJDw+DY1ANq2lqQqclRsk1jpETHIvphUJ7rMncrg+gHytNjHj6ByBIo0aIBZHI5NPR04djEA5HXbkH8v7XWpm41RD8IQunubdF63wo03/o7Kn73bb7JnFxDA1nvjX6fmZoGdS0tmLiUlMqKVS4Pu/q1ce3XlTnWkRobj+TX0bCs7gY1LU2Yu5VBbFAoZGpqqDpqAPznLYfI44fK6AePIVOTw7RcqTxjJKKihQlnEdSkSRO0b98eAODk5IQHDx7g9evXePnyJRrk0jJD9LEyMzNx7PhxJCcno8L/E8W09HTIZDJoZrtQ0dTUhFwuR0BAQJ7rCggIQJnSpZXKbt++DaeSJZW64taqWRNJSUkIevIk1/U8ePgQL1++hFwuR7cePdCkWTMMHTYMj4PyvkjLTalSpXD16lVkZWXhypUrcCr19qLmt8WL0aljR1haWOS6nIaGBpydnXEzn30loqIh6t5f8Ju5BL4jZuDa/BVQWBVDwz9m5DsgT3xoGK7OXorz4+bg8rTfIJPJ4bVsJnTMTXKdv3gzT6S/Scazs35K5WeGTYGxc3F0PLEJnU5vQ+lvWsF3xAykJyTluW09S3OkZGvdBICkiJfw/XEaKgzshk5ntqHD8Y3QLWaCixN/leZRWFvAvEJpGJaww4Wf5uHm4rWwq18LVUb1z3NbL64GwLS8C+y96kIml0PHzATlencCAGibGgMANA0UqDH+e/jNXIKMN8m5rufixF9RzrsTmm1ahJhHwXhy6DTK9myHlzfvIDMtHQ3/nInmWxejVIdmSstlpqYhPekNdC3N84yRiIoWJpz/EiYmJtJjUYg+1ePHj1HPwwO169bF7Dlz8Mu8eVI3bdfy5aGtrY3flyxBSkoKkpOTsei335CZmYnXUVF5rjMiIgJmZsotB1FRUTAxUb5Ie5d8RuWxrrCwMADAipUr0bdPHyxasAD6+voYOGgQ4uLicl0mN8OHDkVISAhatWmDZ8+eYfjQobhx4wYePXqEFs2bY9xPP6FN27aYNXt2jtZWczMzvHjxosDbIqKvU8SVm3h25jLigkLx4moAzo6aCQ2FLuwb1Mlzmai7jxBy9Cxi/wrBq4B7uPDzPKTGxsOpbeNc5y/RsiFCj59HVppyPVJlZH+kxMTj1JAJONF/LJ6fuwr3eT9B29Qoz22raWki8731aJsYodrYwQg+4ovj/cbi1JCJyErPQJ0Zf9+/KZPLICBwZepviL7/GBGXb+Dm7+tQvJlnnq2cL64GInDpRlQdPQCdzmxDi22/I+Ly254dQrxtlaw2bjBCT1zAq8B7ecb8+tYDnOg3Foc6DcH1BaugsC4Gx6aeuL1iK2pOHIqgAydwavAElOvdCYYlHZSWfdeiSkT/Dkw4iUji4OCALZs2Yd2aNejYoQOmTJ2KJ/9vcTQ2Nsbc2bNx7vx51PPwgGeDBkhITETp0qUhz+fHjtTUVGh9hguHd92v+vTujYYNGqBMmTKYPGkSZDIZTp469YGl/1asWDEsWrgQhw8exKKFC2FkZIQ58+bhp3HjsHrNGujq6mL3rl149uwZdu/Zo7SslpYWUlJS8lgzERVV6YlvkPAsAgpbywIvIzIzEfMoGAobqxzTzN3KwMDBBk8OnlQqt6jiCuvaVXBp0gK8vv0QMY+Ccf3XlchMTUPxZvXz3FZqbAI09fWUykp1aIr0pDcI/GMjYv8KxqvAe7g87TdYVqsgdUdNjopB8qtopCe9kZaLD3n+tuWyWN6DvT3cfhB7mnyLAx0GYm/z3gg7fxUAkBT29l5Ui8quKN21NTqf3YHOZ3eg2rjB0NTXQ+ezO1C8Re49raqOGYSbS9YBMhlMXErg2elLSI2Nx8ubd1GsUlmleTUNFEiJLfgPiUT0deMotUQk0dDQgJ3d23uSypQpg3v37mHr9u0Y/9Pb56LVrFkT+/fuRWxsLNTU1KCvr48mTZvC5r37KLMzMjJCfHy8UpmpqSnu3r2rVPauZTOvEW/ftZKWKF5cKtPU1ISNjc0/anVcu24dataogTJlymDGrFkYPGgQ1NXVUb9+ffhfu4Zvuvz96IT4+HjY2Np+8raI6OukrqMNhY0FQo7GFHgZmVwOo5IOCL+c877uEi0bIvrBY8Q+DlUqV9P+/49v741uK0RWviPHxvwVDANH5ftF1bS0gPfug5Tui5S9bU94fesh7OrXhrqONjKS3/5Ypm9njazMTCS/zLtnyjspr98eD/tG9ZD04hViHr295/3kwJ8gk//dZmFTrzrK9GiLkwN/xpv3uv4Cb49HWnwiwi9cg8b/E2e5ujoyU9MgV1dXWpfCxgLqWlqI/Sv4g/ERUdHAFk4iylNWVhbS3xs8AnibROrr68Pf3x/RMTFwd3fPcx0uLi4IDla+cHB1dcXjoCBER/99YeJ39Sr09PSUEsrsSpcuDU1NTYSE/n0Bl5GRgYiICFhZ5WxhKIjg4GAcPXYMgwcNAvD2sQjvRqHNyMjI8ZiEoKAguDg7f9K2iOjrUfG7b2FesSz0LM1hWt4FdWePgcjMwtOTF/JcplzvTrCs7gY9awsYOxdHzUnDoGtplqMVU11XB3b1ayHoYM6eF6/vPER6QhJqTPgBRk4O0jM59ayKIfzS9Ty3/cIvAGblnZUSs/BL12FSxgnleneCwtYKxs7FUePn75EU8RKx/08MQ0+cR1pcAqr//B0MHG1h7lYWbt99i+DDp5H5/7rdxr06mm9RHnCwdLc2MCxhD4Pidijn3RFlerTFjUVrpIQ2PjQMccHPpL/k19EQWQJxwc9y3IuqZWSAsr064PrCVQCA9IQkxAU/g3PnFjAt5wyLqq54feuBNL+5W1kkhr1A4v9bU4mo6GMLJxEBAJYsXYratWrB0tISb968wdFjx3D9xg38nm3k4wMHD6K4oyOMjY1x6/Zt/Prrr+jWtSscHRzyXG+tmjWxZOlSxMfHw8DAAABQs0YNFC9eHJMmT8bQH35AVFQU/ly2DJ07dZIGJbpz9y4mT5mCP5cuRbFixaBQKNChfXusWLkSlhYWsLSywsaNGwEAXg0bStt79uwZ3iQnIyoqCimpqXj46BGAty2jGhoa0nxCCMycNQsjhg+Hjo4OAMDNzQ379u2Dg709Dvv4oEnjv+/NCg8Px8tXr1CjevV/eqiJqJDpFDNF7ak/QtNAH6mx8Xh16z5ODvwJqbF/98aoMf576Fma4/QPkwEAmvp6qDZ2MLRNjJCWkIiYh09wcuB4xIc8V1q3g1ddQCbD0xM5k9e0uAT4jpyBCgO6of7iqZCrqyEu+BkujJubozU0u4grN5CVmQmLqhXw4moAAODljTu4PGURSndvi9Ld2iAzNQ2v7zyE74gZUjKZkZyCM8OnocqIvmi8eh7S4hLw9PQl3F6xVVq3pkJPemTJO1Y1K6Hstx0g11RH7ONQXBg3FxFXbn7cQf6/ysP74uG2g1JrKQD4zVyCGhN+gHOnFniwZb/SCLz2XnURdOBkbqsioiJKJt5/ajF9lQ4cOFDgeVtne95hQcXHx8PQ0BBxcXGQ8ZT4T5o2fTr8r13D69evoVAoUMrJCd9++y1q1qghzfP7kiU4dOgQ4uLjYW1lhfbt26N7t24fHLCqV+/eaN2qFTr8f3Rl4O1gQrPnzsX169eho6ODli1a4PvvvoO6+tvfwa5dv45BgwfjwL59sLa2BvC21XHJ0qXwOXIEqampKFeuHEb++CNKlvx7qP4Bgwbl+uiS7OsBgN179sDv6lXMmzNHKouOjsaEiRNx99491KpZE1MmT4a29ttRK9euW4cb7yXg9N+jb2go/b+tTodCjIRUrcGSaXh54w7urNlR2KEAAJzaN4VN3Wo4O2J6YYeiMgbF7dBg8RQc/uYHpftOqWhqfmStdG357gdn+m9iwllEyOXKvZ9lMhmyv3XZL/gz3+sGWBBMOEmVLly4gN9+/x3bt27NcS4XBenp6WjXoQNmTJ+Oim5uhR0OFSImnP8NGnq6aLZpEXy6DZXufSxsMjU5ynRvh0e7DiPjzdcR0+dmUbUCZHK51IpLRRsTTnqn6F35/UdlZWVJf8ePH0fFihVx5MgRxMbGIjY2Fj4+PqhcuTKOHj1a2KES5VC3bl20a9sWL1+9KuxQPsmLFy/Q29ubySbRf0R60hscaDfgq0k2AUBkZuHeht3/2mQTACKv3WKySfQvxHs4i6Dhw4dj2bJlqFu3rlTWpEkT6OrqYsCAAbh//34hRkeUu25duxZ2CJ/Mzs5OGr2XiIiIiAqOLZxFUFBQEIyMjHKUGxoaIiQk5IvHQ0RERERElBsmnEVQtWrVMGLECERG/j1keGRkJEaPHo3qHEGTiIiIiIi+Ekw4i6A1a9YgIiIC9vb2cHJygpOTE+zt7REWFobVq1cXdnhEREREREQAeA9nkeTk5IRbt27hxIkTePDg7cOSy5QpAy8vrw8+noKIiIiIiOhLYcJZRMlkMjRu3Bju7u7Q0tJioklERERERF8ddqktgrKysjB9+nTY2NhAoVAgODgYADBx4kR2qSUiIiIioq8GE84iaMaMGVi3bh3mzZsHTU1Nqbx8+fJYtWpVIUZGRERERET0NyacRdCGDRuwYsUKdO/eHWpqalK5m5ubdE8nERERERFRYWPCWQSFhYXByckpR3lWVhbS09MLISIiIiIiIqKcmHAWQWXLlsX58+dzlO/atQuVKlUqhIiIiIiIiIhy4ii1RdCkSZPQq1cvhIWFISsrC3v27MHDhw+xYcMGHDp0qLDDIyIiIiIiAsAWziKpTZs2OHjwIE6ePAk9PT1MmjQJ9+/fx8GDB9GoUaPCDo+IiIiIiAgAWziLrHr16uHEiROFHQYREREREVGe2MJJREREREREKsEWziLI2NgYMpksR7lMJoO2tjacnJzg7e2N3r17F0J0REREREREbzHhLIImTZqEmTNnolmzZqhevToA4OrVqzh69Ci+++47BAcHY/DgwcjIyED//v0LOVoiIiIiIvqvYsJZBF24cAEzZszAoEGDlMqXL1+O48ePY/fu3ahQoQIWL17MhJOIiIiIiAoN7+Esgo4dOwYvL68c5Q0bNsSxY8cAAM2bN8eTJ0++dGhEREREREQSJpxFkImJCQ4ePJij/ODBgzAxMQEAJCUlQV9f/0uHRkREREREJGGX2iJo4sSJGDx4MM6cOSPdw+nv7w8fHx8sW7YMAHDixAl4eHgUZphERERERPQfx4SzCOrfvz/Kli2LJUuWYM+ePQAAFxcXnD17FrVr1wYAjBw5sjBDJCIiIiIiYsJZVNWpUwd16tQp7DCIiIiIiIjyxISzCIqPj8+1XCaTQUtLC5qaml84IiIiIiIiopyYcBZBRkZGkMlkeU63tbWFt7c3Jk+eDLmc40IREREREVHhYMJZBK1btw7jx4+Ht7e3NGjQ1atXsX79ekyYMAGvXr3C/PnzoaWlhZ9//rmQoyUiIiIiov8qJpxF0Pr16/Hrr7+ic+fOUlmrVq3g6uqK5cuX49SpU7C3t8fMmTOZcBIRERERUaFhf8si6NKlS6hUqVKO8kqVKuHy5csAgLp16+Lp06dfOjQiIiIiIiIJE84iyM7ODqtXr85Rvnr1atjZ2QEAoqKiYGxs/KVDIyIiIiIikrBLbRE0f/58dOrUCUeOHEG1atUAANeuXcODBw+wa9cuAIC/vz+6dOlSmGESEREREdF/HBPOIqh169Z4+PAhli9fjocPHwIAmjVrhn379sHR0REAMHjw4EKMkIiIiIiIiAlnkeXo6IjZs2cXdhhERERERER5YsJZhL158wZPnz5FWlqaUnmFChUKKSIiIiIiIqK/MeEsgl69eoXevXvjyJEjuU7PzMz8whERERERERHlxFFqi6Dhw4cjNjYWfn5+0NHRwdGjR7F+/XqUKlUKBw4cKOzwiIiIiIiIALCFs0g6ffo09u/fj6pVq0Iul8PBwQGNGjWCgYEBZs+ejRYtWhR2iERERERERGzhLIqSkpJQrFgxAICxsTFevXoFAHB1dcWNGzcKMzQiIiIiIiIJE84iyMXFRXocipubG5YvX46wsDAsW7YMVlZWhRwdERERERHRW+xSWwQNGzYMERERAIDJkyejadOm2Lx5MzQ1NbFu3brCDY6IiIiIiOj/mHAWQT169JD+r1KlCkJDQ/HgwQPY29vDzMysECMjIiIiIiL6GxPOfwFdXV1Urly5sMMgIiIiIiJSwoSziBgxYkSB512wYIEKIyEiIiIiIioYJpxFxM2bNws0n0wmU3EkREREREREBcOEs4g4c+ZMYYdARERERET0UfhYlCLkyZMnEEIUdhhEREREREQFwoSzCClVqhRevXolve7SpQsiIyMLMSIiIiIiIqK8MeEsQt5v3fTx8UFSUlIhRUNERERERJQ/JpxERERERESkEkw4ixCZTJZjFFqOSktERERERF8rjlJbhAgh4O3tDS0tLQBASkoKBg0aBD09PaX59uzZUxjhERERERERKWHCWYT06tVL6XWPHj0KKRIiIiIiIqIPY8JZhKxdu7awQyAiIiIiIiow3sNJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFRCvbADoK+DEAIAEB8fDwMDg0KOhojo6xQfHy/93/zI2kKMhIjo6/auvnx3jUn/XUw4CQCQkJAAALCzsyvkSIiIiIjo3yIhIQGGhoaFHQYVIpngzw4EICsrC+Hh4dDX14dMJivscIgAvP111M7ODs+ePWPLOxFRHlhX0tdICIGEhARYW1tDLuddfP9lbOEkAIBcLoetrW1hh0GUKwMDA15EERF9AOtK+tqwZZMADhpEREREREREKsKEk4iIiIiIiFSCCScRfbW0tLQwefJkaGlpFXYoRERfLdaVRPQ146BBREREREREpBJs4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOoiLA19cXMpkMsbGx+c7n6OiIRYsWfZGYsitofJ/iS+/TlClTULFixUJZj0wmw759+/7xton+bb72OlCVPled9L6QkBDIZDIEBAR89nXnxdPTE8OHD//i61HldxQRfRgTTqIvaNmyZdDX10dGRoZUlpiYCA0NDXh6eirN++4LMigoCLVr10ZERAQMDQ0BAOvWrYORkdFnienVq1cYPHgw7O3toaWlBUtLSzRp0gQXL178LOv/p/z9/TFgwADpdW5JmaouyPKze/dueHp6wtDQEAqFAhUqVMC0adMQHR39yeuMiIhAs2bNPmOURF+Xr7EO/BQFTWwDAwPRunVrFCtWDNra2nB0dESXLl3w8uVL1Qf5AXZ2doiIiED58uUB5J2Ufa4ksaDS0tIwb948uLm5QVdXF2ZmZqhTpw7Wrl2L9PT0T1rn++cPEX1ZTDiJvqD69esjMTER165dk8rOnz8PS0tL+Pn5ISUlRSo/c+YM7O3tUbJkSWhqasLS0hIymeyzx9ShQwfcvHkT69evx6NHj3DgwAF4enoiKirqs2/rU5ibm0NXV7eww1Ayfvx4dOnSBdWqVcORI0dw584d/PrrrwgMDMTGjRs/eb2WlpZ8jh79q32NdaCqvHr1Cg0bNoSJiQmOHTuG+/fvY+3atbC2tkZSUlJhhwc1NTVYWlpCXV29sEORpKWloUmTJpgzZw4GDBiAS5cu4erVq/juu+/w+++/4+7du5+03qJ4/hD9qwgi+qKsrKzE7NmzpddjxowR3333nShTpow4c+aMVO7u7i569eolhBDizJkzAoCIiYmR/s/+N3nyZCGEEA4ODmLmzJmid+/eQqFQCDs7O7F8+fI8Y4mJiREAhK+vb57zBAcHCwDi5s2bOZZ7F++7mA4dOiRcXV2FlpaWqFGjhrh9+7a0zNq1a4WhoaE4ePCgcHZ2Fjo6OqJDhw4iKSlJrFu3Tjg4OAgjIyPxww8/iIyMDGk5BwcHsXDhQun/7Pvt4OAg1q5dm+N4rF27Voqzb9++wszMTOjr64v69euLgIAApf2bPXu2KFasmFAoFKJPnz5i7Nixws3NLc/j4efnJwCIRYsW5XlMhRBi8uTJSuu5evWq8PLyEqampsLAwEC4u7uL69evKy0LQOzdu1fpuG/fvl3UrVtXaGtri6pVq4qHDx+Kq1eviipVqgg9PT3RtGlT8fLlyzzjJfraFHYdeOvWLVG/fn2hra0tTExMRP/+/UVCQoI03cPDQwwbNkxpmTZt2kixeHh45Nh+bvbu3SvU1dVFenp6nsfiXb34/nLZ1/muLlm2bJmwtbUVOjo6olOnTiI2Nlaap1evXqJNmzZi5syZolixYsLQ0FBMnTpVpKeni1GjRgljY2NhY2Mj1qxZIy2TvW5/93/2v169eolevXrlKA8ODhZCCHH79m3RtGlToaenJ4oVKyZ69OghXr16Ja0/MTFR9OzZU+jp6QlLS0sxf/78XI9tdnPnzhVyuVzcuHEjx7S0tDSRmJgovQfZ17NhwwZRpUoVoVAohIWFhejatauIjIyUpmc/f7If94/9PiKiT8MWTqIvrH79+jhz5oz0+syZM/D09ISHh4dUnpycDD8/P9SvXz/H8rVr18aiRYtgYGCAiIgIREREYNSoUdL0X3/9FVWrVsXNmzcxZMgQDB48GA8fPsw1FoVCAYVCgX379iE1NfUf79vo0aPx66+/wt/fH+bm5mjVqpVSF6g3b95g8eLF2LZtG44ePQpfX1+0a9cOPj4+8PHxwcaNG7F8+XLs2rUr1/X7+/sDANauXYuIiAj4+/ujS5cuGDlyJMqVKycdjy5dugAAOnXqhJcvX+LIkSO4fv06KleujIYNG0rdXnfs2IEpU6Zg1qxZuHbtGqysrPDHH3/ku4+bN2+GQqHAkCFDcp2eVze/hIQE9OrVCxcuXMCVK1dQqlQpNG/eHAkJCflub/LkyZgwYQJu3LgBdXV1dOvWDWPGjMFvv/2G8+fP4/Hjx5g0aVK+6yD6mhRmHZiUlIQmTZrA2NgY/v7+2LlzJ06ePInvv/++wPHv2bMHtra2mDZtmrT93FhaWiIjIwN79+6FEKLA68/N48ePsWPHDhw8eBBHjx6V9i2706dPIzw8HOfOncOCBQswefJktGzZEsbGxvDz88OgQYMwcOBAPH/+PMf67ezssHv3bgDAw4cPERERgd9++w2//fYbatWqhf79+0v7amdnh9jYWDRo0ACVKlXCtWvXcPToUURGRqJz587SOkePHo2zZ89i//79OH78OHx9fXHjxo1893Pz5s3w8vJCpUqVckzT0NCAnp5ersulp6dj+vTpCAwMxL59+xASEgJvb+98t/VPv4+I6CMUdsZL9F+zcuVKoaenJ9LT00V8fLxQV1cXL1++FFu2bBHu7u5CCCFOnTolAIjQ0FAhRN6/zr7PwcFB9OjRQ3qdlZUlihUrJv78888849m1a5cwNjYW2traonbt2uKnn34SgYGB0vSPaeHctm2bNE9UVJTQ0dER27dvl2IGIB4/fizNM3DgQKGrq6vUutCkSRMxcOBApX1618IphHIr4DvvtyYKIcT58+eFgYGBSElJUSovWbKk1OJRq1YtMWTIEKXpNWrUyLeFs1mzZqJChQp5Ts8vpuwyMzOFvr6+OHjwoFSGXFo4V61aJU3funWrACBOnTollc2ePVu4uLh8MB6ir0Vh1oErVqwQxsbGUkuZEEIcPnxYyOVy8eLFCyHEh1s4320ne72Ul59//lmoq6sLExMT0bRpUzFv3jxpO3ntR24tnGpqauL58+dS2ZEjR4RcLhcRERFCiLctnA4ODiIzM1Oax8XFRdSrV096nZGRIfT09MTWrVuFEDnr9veP8Tu5HY/p06eLxo0bK5U9e/ZMABAPHz4UCQkJQlNTU+zYsUOa/u47Ib8WTh0dHTF06NA8p+cXU3b+/v4CgPTdktv58ynfR0T0adjCSfSFeXp6IikpCf7+/jh//jycnZ1hbm4ODw8P6R4mX19flChRAvb29h+9/goVKkj/y2QyWFpa5jtARYcOHRAeHo4DBw6gadOm8PX1ReXKlbFu3bqP3natWrWk/01MTODi4oL79+9LZbq6uihZsqT02sLCAo6OjlAoFEpln2NAjcDAQCQmJsLU1FRqyVUoFAgODkZQUBAA4P79+6hRo0ae+5Ab8YktFZGRkejfvz9KlSoFQ0NDGBgYIDExEU+fPs13uezvp4WFBQDA1dVVqexrGICEqKAKsw68f/8+3NzclFrK6tSpg6ysrDx7gvwTM2fOxIsXL7Bs2TKUK1cOy5YtQ+nSpXH79u2PWo+9vT1sbGyk17Vq1coRc7ly5SCX/31ZZ2FhoVRXqKmpwdTU9LPVr2fOnFGqW0uXLg0ACAoKQlBQENLS0pTq13ffCfn51Pr1+vXraNWqFezt7aGvrw8PDw8AyLd+/ZLfR0T/dV/PneJE/xFOTk6wtbXFmTNnEBMTI30xWltbw87ODpcuXcKZM2fQoEGDT1q/hoaG0muZTIasrKx8l9HW1kajRo3QqFEjTJw4Ef369cPkyZPh7e0tXcBkvxD41JECc4vtU+ItiMTERFhZWcHX1zfHtH8yuqWzszMuXLiA9PT0HLHnp1evXoiKisJvv/0GBwcHaGlpoVatWkhLS8t3uezbeDfgxftln+N4EX0pX2MdmJ1cLs+R+HxqnQcApqam6NSpEzp16oRZs2ahUqVKmD9/PtavX/9Zt/Wl69dWrVph7ty5OaZZWVnh8ePHn7ReZ2dnPHjw4KOWeddNukmTJti8eTPMzc3x9OlTNGnSJN/69UseL6L/OrZwEhWC+vXrw9fXF76+vkqPAnB3d8eRI0dw9erVXO9dekdTUxOZmZkqi69s2bLSKIrm5uYAoHSfUl7Pbbty5Yr0f0xMDB49eoQyZcp81tg0NDRy7Htux6Ny5cp48eIF1NXV4eTkpPRnZmYGAChTpgz8/Pzy3IfcdOvWDYmJiXne65nXc94uXryIoUOHonnz5ihXrhy0tLTw+vXrfLdF9G9VWHVgmTJlEBgYqDRK7MWLFyGXy6XWN3Nzc6X6LjMzE3fu3Pks29fU1ETJkiWV6teEhASleHKrX58+fYrw8HDp9ZUrV5Ri/hw0NTUBoMD16927d+Ho6JijftXT00PJkiWhoaGhVL+++07IT7du3XDy5EncvHkzx7T09PRcR/d98OABoqKiMGfOHNSrVw+lS5dmqyTRV4YJJ1EhqF+/Pi5cuICAgADp130A8PDwwPLly5GWlpbvxZajoyMSExNx6tQpvH79Gm/evPmkOKKiotCgQQNs2rQJt27dQnBwMHbu3Il58+ahTZs2AAAdHR3UrFkTc+bMwf3793H27FlMmDAh1/VNmzYNp06dwp07d+Dt7Q0zMzO0bdv2k2LLi6OjI06dOoUXL14gJiZGKgsODkZAQABev36N1NRUeHl5oVatWmjbti2OHz+OkJAQXLp0CePHj5ceyTBs2DCsWbMGa9euxaNHjzB58uQPDrtfo0YNjBkzBiNHjsSYMWNw+fJlhIaG4tSpU+jUqRPWr1+f63KlSpXCxo0bcf/+ffj5+aF79+7Q0dH5rMeGqKgorDqwe/fu0NbWRq9evXDnzh2cOXMGP/zwA3r27Cl1WW/QoAEOHz6Mw4cP48GDBxg8eHCOH5IcHR1x7tw5hIWF5fnD0aFDh9CjRw8cOnQIjx49wsOHDzF//nz4+PhI9WuNGjWgq6uLn3/+GUFBQdiyZUuutzO8izkwMBDnz5/H0KFD0blzZ1haWhZovwvCwcEBMpkMhw4dwqtXr5CYmCjtq5+fH0JCQvD69WtkZWXhu+++Q3R0NLp27Qp/f38EBQXh2LFj6N27NzIzM6FQKNC3b1+MHj0ap0+flr4Tsnf5zc3w4cNRp04dNGzYEEuXLkVgYCCePHmCHTt2oGbNmvjrr79yLGNvbw9NTU38/vvvePLkCQ4cOIDp06d/tuNCRP8cE06iQlC/fn0kJyfDyclJusgB3l5sJSQkwMXFBVZWVnkuX7t2bQwaNAhdunSBubk55s2b90lxKBQK1KhRAwsXLoS7uzvKly+PiRMnon///liyZIk035o1a5CRkYEqVapg+PDhmDFjRq7rmzNnDoYNG4YqVargxYsXOHjwoPSr+efy66+/4sSJE7Czs5NGMuzQoQOaNm2K+vXrw9zcHFu3boVMJoOPjw/c3d3Ru3dvODs745tvvkFoaKh0zLt06YKJEydizJgxqFKlCkJDQzF48OAPxjB37lxs2bIFfn5+aNKkCcqVK4cRI0agQoUK6NWrV67LrF69GjExMahcuTJ69uyJoUOHolixYp/vwBAVIYVVB+rq6uLYsWOIjo5GtWrV0LFjRzRs2FCpvuvTpw969eqFb7/9Fh4eHihRokSO5HfatGkICQlByZIlpV4g7ytbtix0dXUxcuRIVKxYETVr1sSOHTuwatUq9OzZE8Db+xo3bdoEHx8fuLq6YuvWrZgyZUqOdTk5OaF9+/Zo3rw5GjdujAoVKnxwRO2PZWNjg6lTp2LcuHGwsLCQRu4dNWoU1NTUULZsWam7qrW1NS5evIjMzEw0btwYrq6uGD58OIyMjKSk8pdffkG9evXQqlUreHl5oW7duqhSpUq+MWhpaeHEiRMYM2YMli9fjpo1a6JatWpYvHgxhg4divLly+dYxtzcHOvWrcPOnTtRtmxZzJkzB/Pnz/+sx4aI/hmZ+NQ7tImIiIiIiIjywRZOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnERERERERKQSTDiJiIiIiIhIJZhwEhERERERkUow4SQiIiIiIiKVYMJJREREREREKsGEk4iIiIiIiFSCCScRERERERGpBBNOIiIiIiIiUgkmnEREhWzKlCmQyWTSn6mpKerWrQsfH59Ci8nT0xMtW7b86OUWLVqUa9yOjo74/vvvP0doBZaZmYklS5agcuXK0NXVhaGhIRo2bPjJxzU2NhZTpkzBvXv3PnOkhe/27dvQ19fHq1evckzbu3cvZDIZGjZs+Enr9vX1xaxZs/5piPmaMmUKFAqF9PrixYswMzNDfHy8SrdLREQfxoSTiOgroKOjg8uXL+Py5ctYuXIlUlJS0KpVK1y6dKmwQ/soeSWce/fuxahRo75YHFlZWejQoQNGjBiBBg0a4ODBg9i0aROMjIzQokUL/Prrrx+9ztjYWEydOvVfmXBOmDAB3t7eMDc3zzFt8+bNAN4mjuHh4R+97i+RcL6vTp06KFeu3Ce9z0RE9Hkx4SQi+grI5XLUrFkTNWvWRPv27bF//34IIbB+/frCDu2zqFSpEhwdHb/Y9pYsWYL9+/djxYoVmD9/Pho2bIhWrVph9+7d+PbbbzF27FgEBAR8sXi+Zk+ePMHBgwfRp0+fHNPi4+Nx+PBheHl5ISsrC9u2bSuECD9N37598eeffyI9Pb2wQyEi+k9jwklE9BWysbGBubk5nj59qlR++fJlNGjQAHp6ejA0NES3bt3w8uVLpXnmzJkDJycnaGtrw9zcHF5eXggODpamR0dHo0+fPjAzM4OOjg5q166Nc+fO5RuPt7c3ypcvr1QWGxsLmUyGdevWAXjbbTY0NBRLly6Vugdnn/Z+l9o9e/agYsWK0NbWhrW1NUaMGIGUlBRpuq+vL2QyGU6cOIFu3bpBX18fDg4OmDdv3geP36JFi+Di4oJvv/02x7Rp06ZBJpPh999/l8pyi2/fvn2QyWQICQlBSEgIihcvDgDo1KmTtH8hISEAgNTUVEyYMAElSpSAlpYWbG1t4e3t/Un7e+zYMXTu3BkKhQL29vbYsmULAGDx4sWwt7eHiYkJ+vXrh9TUVKX1P3/+HD169JDeV3d3d1y/fv2Dx2rDhg0oUaIEKlWqlGPanj17kJKSgilTpqBKlSpSa2d2WVlZWLBgAcqUKQMtLS1YWlqiU6dOiIuLw5QpUzB16lQkJSVJx8zT0xNAwc6pd/HVrVsXJiYmMDY2hqenJ65evfrB/Wrbti1iY2MLtWs6EREx4SQi+iolJiYiOjpaSnKAt8mmp6cnDA0NsX37dqxYsQL+/v5o06aNNM+GDRswceJE9O3bF0ePHsWqVatQsWJF6V62zMxMNGvWDAcPHsTcuXOxc+dOKBQKNGrUqEDJSX727t0LS0tLdOzYUeoe3KJFi1znPXDgADp27IiyZcti3759GDNmDJYtW4YePXrkmHfQoEFwdnbG3r170apVK4wdOxZHjx7NM45nz54hODgYLVq0gFye82vOwcEBFSpU+GCSnZ2VlRX27NkDAJg1a5a0f1ZWVgCADh06YMGCBejTpw8OHz6MX375BUlJSZ+0v4MHD0b58uWxd+9e1KxZEz179sTYsWNx7NgxLFu2DNOmTcOGDRuUuovGxMSgbt26CAgIwO+//47du3dDT08PDRo0yPGDxPtOnjyJ2rVr5zpt8+bNcHR0RO3atdGtWzfcuHEDDx8+VJrnhx9+wJgxY9CyZUscPHgQS5cuhb6+PhITE9GvXz/07dtXqcv4H3/8UbCD/n8hISH49ttvsXPnTmzZsgX29vZwd3fHo0eP8l3OwMAA5cqVw4kTJz5qe0RE9JkJIiIqVJMnTxZ6enoiPT1dpKeni9DQUNGlSxdhbGwsHjx4IM3n7u4uateuLbKysqSyu3fvCplMJg4fPiyEEOK7774TlStXznNb+/fvFwDE0aNHpbK0tDRhb28v2rdvL5V5eHiIFi1aSK979eolypUrp7SumJgYAUCsXbtWKnNwcBDfffddju2+X16pUiVRq1YtpXmWL18uAIhbt24JIYQ4c+aMACBGjx4tzZOVlSUcHR1F375989zHy5cvCwBi0aJFec7Ttm1boa2tnW/ce/fuFQBEcHCwEEKI4OBgAUDs3LlTab7jx48LAGLLli15bu9j9nfMmDHSPLGxsUJNTU3Y2dmJtLQ0qbxDhw6iYsWK0utJkyYJQ0NDERkZKZWlpKQIe3t7peP3vqysLKGlpSV++eWXHNMiIiKEmpqaGDdunBBCiLCwMCGXy8XEiROleR4+fChkMpmYNWtWntt4d36/r6DnVHaZmZkiPT1duLi4iJ9++qlA26hatWqesRERkeqxhZOI6CuQlJQEDQ0NaGhowMHBAbt27cLGjRvh4uICAHjz5g0uXryITp06ITMzExkZGcjIyICzszPs7Ozg7+8PAKhcuTJu3ryJESNG4MKFCznuXzt//jwMDAzQpEkTqUxDQwPt27fHhQsXvsi+JiYmIiAgAB07dlQq79KlCwDkiKNx48bS/zKZDGXKlMHz589VH2gBnTp1Crq6uvjmm29ynf6x+9uoUSPpf0NDQxQrVgzu7u7Q0NCQyp2dnfHs2TPp9fHjx1G/fn2YmJhI54aamho8PDykcyM3MTExSE1NzXWwoO3btyMzMxPdunUDAFhbW8PDw0Pq4gsAp0+fhhACffv2zXMb/9T9+/fRrl07WFhYQE1NDRoaGnj48OEHWzgBwMzMDBERESqLjYiIPowJJxHRV0BHRwf+/v7w8/PDpk2bYGVlhW+//Va6WI6JiUFmZiZ+/PFHKTF99/f06VMp+fD29sbChQtx7Ngx1KtXD+bm5hg2bBiSk5Ol9RQrVizH9i0sLBAdHf1F9jU2NhZCCFhYWCiVGxoaQktLK0ccRkZGSq81NTWV7n18n42NDQDkuP81u6dPn8LW1vYjI89dVFQUrKysIJPJcp3+Ofb3Q8fg9evX2LdvX45zY+PGjUqJ6fverUNLSyvHtM2bN8PFxQV2dnaIjY1FbGwsWrdujaCgIPj5+Un7rq6unus59TkkJCSgcePGCA0NxYIFC3D+/Hn4+/vDzc0t33PgHS0tLencJyKiwqFe2AEQEdHbUWqrVq0KAKhevTpcXFxQo0YNTJs2DX/++SeMjIwgk8nw888/o23btjmWNzMzk9YzbNgwDBs2DGFhYdi2bRvGjRsHMzMzTJw4ESYmJrne0xcZGQkTE5M849PW1kZaWppSWUxMzCft67t9eT+OuLg4pKam5htHQdjZ2aF48eI4cuQI5s+fnyMRfPr0KW7duqU0oNA/2T9TU1NERERACJFr0qnq/QUAExMTNG3aFNOnT88xLbdkMvtywNukOLvHjx9LLaPGxsY5ltu8eTNq1KgBU1NTZGRk4OXLlx+ddBbkmF++fBnPnz/HoUOH4ObmJpXHxcUV6AeD2NhYmJqaflRcRET0ebGFk4joK1S1alV07doVa9euxYsXL6Cnp4datWrh/v37qFq1ao6/3B45YmNjg5EjR6JChQq4f/8+AKBu3bqIj4/H8ePHpfkyMjKwd+9e1K1bN894bG1t8fz5cyQmJkpl2dfxzodaHwFAoVCgYsWK2LVrl1L5jh07pBj/qeHDh+P+/fvYuHFjjmlTpkyBEAI//PCDVGZraysdo3fe3z9NTU0AyLF/Xl5eePPmjRT/+77E/np5eeHevXsoU6ZMjnPD1dU1z+W0tbVhb2+vNIoxAGzZsgUymQx79+7FmTNnlP6aNGkidbdt0KABZDIZ1q5dm+c2NDU1c4yoCxTsnHrXOvnu2APApUuXpNGBPyQkJETqlk5ERIWDLZxERF+piRMnYtu2bVi0aBHmzJmDX375BQ0aNECXLl3wzTffwNjYGM+fP8eJEyfQu3dveHp6YuDAgTA2NkbNmjVhbGyMixcvIjAwEEOGDAEAtGjRAtWrV0ePHj0wZ84cWFhY4Pfff0dERAR+/vnnPGNp3749Jk2ahD59+qB///64e/cuVq1alWO+MmXK4PTp0zhx4gSMjY1RvHjxXFuYpkyZgrZt26JHjx7o0aMHHj58iJ9//hkdOnTIN0EqqO+//x6nT59Gv379cPv2bTRr1gzJyclYt24ddu3ahfnz56NixYrS/B07dsTgwYMxdepU1K5dGz4+Prh8+bLSOi0tLWFkZIStW7eiePHi0NLSQoUKFeDl5YXmzZujT58+CAoKQo0aNRAdHY1du3Zh+/btX2R/R4wYgc2bN8PDwwPDhg2Dvb09Xr16BT8/P1hbW+PHH3/Mc9k6derkGKF4y5YtqFevXq6t6fHx8WjTpg1OnjyJJk2aYNCgQZgwYQKio6PRsGFDvHnzBocPH8aUKVNgY2ODMmXKICMjA7/99htq164NAwMDuLi4FOicqlmzJhQKBb777juMGzcOYWFhmDx5stRt+kOuXbuGkSNHFmheIiJSkUIdsoiIiPIcYVMIIbp37y4MDAxEbGysEEIIf39/0bx5c2FoaCh0dHREqVKlxKBBg8SzZ8+EEEKsW7dO1KlTR5iYmAhtbW1RtmxZsXjxYqV1vn79Wnh7ewsTExOhpaUlatWqJXx9fZXmeX+UWiGE2LBhg3BychI6OjqiUaNGIiAgIMeIonfu3BH16tUT+vr6StNyGwV2165dokKFCkJTU1NYWlqK4cOHi+TkZGn6u1Fb/f39lZZr06aN8PDwyP+gCiEyMjLE4sWLRcWKFYWOjo4wMDAQ9evXl0b0zS49PV2MGjVKWFhYCENDQzFw4ECxZcsWpVFqhXg7cm2ZMmWElpaW0rTk5GQxbtw4YW9vLzQ0NIStra3o06fPZ9nf3I5dbudMRESE6Nu3r7CyshKamprC1tZWdOzYUVy8eDHf47R7926hra0t4uPjhRBCXLt2TQAQq1atynX+tLQ0YW5uLnr27CmEeDty7Lx580SpUqWEhoaGsLS0FF26dBFxcXHSsR0yZIiwsLAQMplM6b0ryDl15MgRUa5cOaGtrS0qVKggfHx8cpyfuR2P69evC5lMJh4/fpzv/hMRkWrJhBCi8NJdIiIiKkzp6emwt7fH3Llzle5rLepGjx6N69ev4/Tp04UdChHRfxoTTiIiov+43377DRs2bMjRtbaoio+Ph4ODA/bv3w93d/fCDoeI6D+N93ASERH9xw0aNAjx8fF4/fq1NOJxUfb06VNMnz6dySYR0VeALZxERERERESkEnwsChEREREREakEE04iIiIiIiJSCSacREREREREpBJMOImIiIiIiEglmHASERERERGRSjDhJCIiIiIiIpVgwklEREREREQqwYSTiIiIiIiIVIIJJxEREREREakEE04iIiIiIiJSCSacREREREREpBJMOImIiIiIiEglmHASERERERGRSjDhJCIiIiIiIpVgwklEREREREQqwYSTiIiIiIiIVIIJJxEREREREakEE04iIiIiIiJSCSacREREREREpBJMOImIiIiIiEglmHASERERERGRSjDhJCIiIiIiIpVgwklEREREREQq8VUmnDKZDAEBAYWybW9vbwwfPjzXaZs3b0bt2rW/bED0j3h6esLX17eww/ggX19fGBkZSa89PT2xaNGiQovna+Po6IiQkJDCDiOH8+fPw9bW9rPNR295e3tj3bp1hR0GqZivry88PT0LOwz6CnytdTwVrqJyDUcfVuCEU6FQSH9qamrQ0tKSXjdr1izP5fJL4D7FunXroKamJm3bysoKQ4YMQWpq6mfbRl66d++OS5cuqXQbBw8ehLu7O/T19WFqaorq1atj2bJlKt3mO46Ojti3b99HL3fx4kXUqVMHCoUCxYoVw6RJk3LMk5ycDCcnJ6WkCgAmTpwIV1dXqKur53qeCCEwe/ZsODo6Qk9PD87OzvDz8/voGAvK0dEROjo6UCgUMDMzQ4sWLfD48WOVbe9zi4yMhImJCSpWrCiVpaamwtPTE8WKFYOBgQFKly6NFStWFGh9x48fh0wmy/HerFq1Cs7OztDX10fp0qWxZcsWaVpYWBjq1KkDIyMj9OrVC1lZWdK0OXPmYOLEif9oHwtCJpNBV1cXCoUCFhYW6NatG169evXZt1OvXj08f/78s833uXTs2BFWVlYwMDBA8eLFMWPGjDznPX/+vFL9rlAoIJfLMXToUGme7J8LhUKh9DnOzMxEz549YWRkhLp16yI8PFyadunSJXh6ekIIoZL9fMfT01PpO0mhUOCPP/4AAOzYsQO1a9eGrq6u0uciLw8fPkSrVq1gZmYmfV7mzp2r0vj/idjYWPTr10+Kt2rVqnjz5k2u82ZkZGD8+PGws7ODgYEB2rVrh5cvX0rTz5w5g/r168PQ0DBHXQ0A27dvh42NDWxsbLBr1y6pPD09HVWrVsX9+/c/+/5l9/73v0KhkH4EPnz4MNzd3WFsbIxixYqhY8eO+X7mMjIy8PPPP8PR0VG6lmjZsiUSEhJUug+fKr/vyk+p47du3YoyZcpAoVCgWrVq8Pf3l6aFhIRAJpMpHedWrVpJ07/GOv6bb75ROpcBICEhAT/++CPs7Oygo6ODkiVLYtq0acjIyFCaLyoqCkOHDoWDgwMUCgUcHR3h7e2NR48e5RvDtGnTIJPJcOTIEaXy939AfmfKlClo27atUtnatWtRtWpVaT88PDywc+fOAh2DgtTdH3sNdf/+fdSpUwe6urpwdnbGgQMHlKZnP+4KhQJubm7StPj4eLRo0QKGhoZo2bIlEhMTpWnbtm1Dz549C7Rf/8T713CtWrVCUFAQAOX6Q19fH05OTli4cGGB1lvY7/XHfsY3b96c49yQyWRYsGCBNE9+dYBKPuPiE3h4eIiFCxcWaN5evXqJYcOGfdT6AYibN2/mOm3t2rXCzc1Neh0WFiYqVKggZsyY8VHbyMunxPu5/PHHH8LY2Fhs2LBBxMbGiqysLHHt2jXRokWLL7J9BwcHsXfv3o9aJjAwUJibm4u9e/eK1NRUkZiYKAIDA3PMN2rUKNGgQQNhaGioVL5u3Trh4+Mj2rVrl+tx/+mnn0SdOnXEX3/9JbKyskRISIgIDw8vcHweHh7izJkzBZ4/+zFISEgQ3bp1E/Xq1Svw8p/qzJkzSsfmYz5j2XXs2FE0aNBA6TOSkZEhbt26JdLT04UQQty9e1cUK1ZMnDt3Lt91JSYmChcXF1G7dm2l9+bGjRtCQ0NDnD59WmRlZYmTJ08KLS0tcffuXSGEEEOGDBFjx44VycnJolatWmLXrl1CCCGCgoKEm5ubSElJ+ej9cnBwEMHBwQWeP3sdEhkZKdzd3UW3bt1yzJeVlSUyMjI+Op6v3a1bt6TjHBoaKsqUKSM2btxYoGVfvHgh1NXVxcWLF6Wy/OqGHTt2iLp164qUlBQxYsQI8f333wshhEhLSxOVK1cW9+/f/+j4e/XqJdauXVvg+fP7vJw4cUJs375dzJgxQ+lzkZeSJUuK8ePHi6SkJJGRkSHu3LkjduzYUeBYCiotLe0fryMzM1PUqVNHDBo0SERFRYnMzExx48aNPNc9a9Ys4ebmJp4/fy7evHkjevXqJRo1aiRN9/PzExs2bBCrVq3KUVdnZGQIY2Njcfv2bXHz5k1hYmIifXZmzZolJk2a9NHxnzlzRnh4eBR4/ve//7PbvHmzOHTokEhISBCJiYmid+/eolatWnmua/r06aJKlSriyZMnQoi39cTq1atFfHz8x+zCB32uOia/78qPreMvXLggDAwMxJUrV0RGRoZYtmyZMDMzE7GxsUIIIYKDgwUAERMTk+vyX1sd//r1a9GgQQPRs2dPaXpaWpqoVauW8PDwEPfv3xcZGRni2rVrwtXVVXTs2FGaLzY2Vjg7O4vWrVuL+/fvi8zMTBETEyP++OMPsWjRojy3n5WVJRwdHYWJiYno0KGD0rT3v8/fmTx5smjTpo30euzYscLW1lYcOHBAJCYmioyMDOHr6yu6du1a4OOQXW5198dcQ6WlpYmSJUuKiRMniuTkZHHw4EGhp6cn/vrrL2me/K7P582bJ7755huRnp4uOnbsKObPny+EECImJkaULVtWvHr16qP36Z9cw8XFxYnOnTuL2rVrCyFy1h8XL14UOjo64tSpU/mu82t4rz/1Ou6da9euCblcLp4+fSqE+HAdoIrP+GfpUnv8+HFUqlQJhoaGqFy5Mk6ePAkAWLx4MTZv3ow//vgDCoUC5cqVAwBs2rQJ5cuXh76+Puzt7TFx4sRP/gXc2toaTZo0wd27d6WyyMhIdO7cGebm5rC3t8f48eOVftHKK973ZWRkwNvbG15eXkhISMC6deuUfiF3dHTEvHnzULNmTejr68PDwwPPnj2Tpt+9e1eaVr9+fYwZMybP7kMJCQkYO3YsFi9ejJ49e8LQ0BAymQxVqlTBoUOHChT7+90wAwICIJPJlKb/9NNPaNKkCfT19VG5cmXcvn0bANCpUyc8ffoUXbt2hUKhwKBBg/I/8P83ffp09OvXD23btoWmpib09PRQoUIFpXmuX7+Oo0ePYuzYsTmW79WrF5o1awYDA4Mc06Kjo7FgwQKsWbMGTk5OkMlkcHBwgJWVVYFi+6cUCgW6deuG69evS2Xp6emYNGkSSpYsCVNTU7Ru3VqpRefFixfo0aMHrKysYGRkBHd3dyQnJwMAxowZAwcHB+jr66Ns2bIF/mWroPbv34/o6OgcvyKqqalJv4wDb3+hlMlkH2y5HT9+PLp164ZSpUoplQcHB8PR0RH169eHTCZDw4YNYWdnh3v37gEAnjx5gvr160NbWxvu7u7Sr4uDBw/GwoULoaWl9bl2uUCKFSuGTp064datWwDefm5nz56NmjVrQldXF/fu3cPLly/RvXt3WFlZwdraGsOHD1fqNXH9+nU0aNAAJiYmMDc3xw8//AAg56+bmzdvRqlSpaCvrw8bGxtMnz491/kSEhIwYMAAWFlZwcrKCoMGDUJSUhKAv1sXNm7cKPUK8Pb2Rnp6eoH32dXVVTrOMpkMcrkcf/31V4GWXb9+PUqVKlXg2weePHmCunXrQktLC40aNZLe719++QWtWrVC6dKlCxy3Knh5eaFz586wsbH54LyvX79GUFAQBg4cCF1dXaipqaFcuXLo1KmTNE98fDy+//57ODg4wMDAANWqVZPq/fy+e96dA3/++Sfs7e2l43vy5ElUr14dRkZGKFeuXI4WhfwcOXIET58+xe+//w4TExPI5XJUqlQJGhoauc6/d+9eDB06FDY2NtDR0cHUqVNx4sQJqStj9erV0bNnT5QsWTLXY6OlpYXy5cujYsWK0NDQQFRUFIKCgrBjxw78/PPPBY5bFbp164YWLVpAoVBAT08Pw4cPh5+fX47WrHeuXLmCNm3aoHjx4gDe1hN9+vSBvr6+NM/WrVvh5uYGAwMDODg4SN28hRD49ddfUbJkSZiYmKBp06Z48uSJtNyn1DEfkt935cfW8fv370ebNm1Qo0YNqKmpYeDAgVAoFNi7d2+BYvna6nhTU1O0b99e6bt68+bNePjwIfbv34/SpUtDTU0NVapUwd69e7F//36pm+aiRYsgl8uxe/dulC5dGnK5HEZGRhg8eDCGDRuW5zZPnTqFsLAwLF++HAcOHPjoHjRPnjzB/PnzsWXLFrRq1Qp6enpQU1ODh4eHUo+hj/F+3f2x11Dnzp1DVFQUJk6cCG1tbbRs2RIeHh7YuHFjgffJ09MT6urqaNiwoXRejBkzBqNHj4aZmdkn7denMjAwQM+ePaXv/vfVrl0b5cqVUzpvcvM1vNefeh33zurVq9G4cWPY2dkB+HAdoIrP+D9OOB8/fow2bdpg4sSJiIqKws8//4zWrVsjODgYQ4cORffu3TFkyBAkJiZKSaGpqSn27NmD+Ph4HDhwACtWrPjkD9izZ89w9OhR1KlTRyrr1q0bNDQ0EBwcjPPnz2Pfvn2YN2/eB+PNLikpCa1bt0ZycjJ8fHyUvoSy27RpE7Zu3YpXr15BT09PamZOT09H69at0axZM0RFRWHOnDlYs2ZNnvtx+fJlvHnzBp07d85znoLGnp+NGzdi3rx5iImJQdWqVaUL5507d8Le3h5bt25FYmKi1I13yJAhGDJkSJ7rO3v2LNLS0lCxYkWYm5ujadOmePjwoTQ9IyMD/fv3x9KlS6GpqVngOIG3FwRaWlrYunUrrK2t4ejoiLFjxyItLe2j1vOp4uLisHHjRjg7O0tl48ePx8WLF3HhwgVERETA2dkZ33zzDQAgKysLrVq1grq6Ou7du4fXr19j1qxZkMvffszc3Nzg7++P2NhYTJo0CT179izwe1ehQoV8PyNxcXEYMWJEvt2vW7ZsCW1tbZQtWxYWFhZo165dnvP6+fnh5MmTGDduXI5p736wOHHiBLKysnDs2DHExsaibt26AN4mOydPnkRycjLOnz8PV1dXbN68GdbW1qhfv36B9vdzevHiBXbs2IHKlStLZevWrcP69euRmJgIZ2dntG7dGpaWlggKCsLt27cRGBgodUMNCwtDgwYN0LFjR4SHhyM0NDTXz2lSUhK8vb2xevVqJCQk4O7du2jatGmuMQ0bNgyPHz/GnTt3cPv2bTx48AA//vij0jxHjhzBzZs3ce/ePZw6dQqbN2+Wpn3ofADefnZ1dXVhb2+PxMREeHt7F+h4rVmzBn379s1RPnDgQJiZmaFWrVrw8fGRyl1dXXH+/HkkJyfj1KlTcHV1xePHj7Fz50789NNPBdrm18LU1BQuLi7o3bs3duzYgdDQ0BzzeHt74/Hjx7h8+TJiY2OxYsUK6OjoAMj/uwd4+0NDYGAgHjx4gLNnz+LWrVvo1KkT5syZg+joaCxfvhw9e/aU6tALFy7k2l3rnbNnz8LJyQk9e/aEqakpypUrh/Xr1+c5f1ZWltKPu++6SeV1QZadubk55HI5AgMDERgYCDU1NZiZmRVakvEhZ8+eRZkyZaQLtPfVqVMHS5cuxaJFi3Dt2rUcienBgwfx/fffY+HChYiNjYW/v7/UfXDjxo1YsGAB9u3bh/DwcJQrVw6tWrVSWsfH1DFAwT7TH1LQOv798wB4m0S/fx6UL18elpaWaN26NR48eCCVf211fGRkJHbu3Kn0XX3s2DGpe2d2JUuWRI0aNXD8+HFpvo4dO+Z5nuRl9erVaNmyJTp06ABra+sCJ2XvnDx5ElZWVqhXr16+8xkZGeHChQsFWuf7dffHXkPdunUL5cqVU/rBqmLFijnOi+bNm8Pc3BwNGzbElStXpHJXV1ecPn0aqampOHPmDFxdXXHhwgUEBQUV+Pvnc4qNjcWGDRuUvvvfEULg3LlzuHPnjtJ5k5uv6b3+mOu4d5KTk7Flyxb069dPKvtQHaCSz/hHt4kK5e5LM2bMEE2bNlWa3qhRIzFz5kwhRMG6qA4bNkz069dPeo0PdKmVy+XC0NBQGBgYCACidu3aIi4uTgghxPPnzwUA8eLFC2mZzZs3i1KlShU43u7du4vq1auLH374QWRmZiptO3tzvIODg/jzzz+l15s2bRLly5cXQghx7tw5YWhoKDV/C/G2iTqv7kObNm0SFhYWeR2iAsX+freymzdviuxvsYeHhxg7dqz0+sKFC0KhUCjtz8d2qVVTUxM2Njbi9u3bIiUlRYwZM0a4uLhI+z1r1izRp08fIUTeXQ+EyP082bhxowAgunfvLhISEkRoaKhwdXUV06ZNK3B8n9IdQ1dXVzq3nJ2dxZ07d4QQb7tV6OnpiYCAAGn+5ORkqZvClStXhJ6ennjz5k2BtuXm5iY2bdokhPjnXWoHDBggHZf8up2968YxderUPLtEpKWlCVdXV3H27FkhRM73JisrSyxYsEBoa2sLNTU1oampKe2HEEJER0eLbt26CVdXVzFhwgQRFRUlypUrJ16/fi0mTZok6tWrJ3r27Cl9ZgviU7pbKRQKYWRkJOzs7IS3t7eIioqS1pX92F69elWYmJgofdaPHz8uSpQoIYQQYs6cOaJ+/fq5bif7+5aYmCh0dHTEsmXLcuxb9vkyMzOFpqamuHLlijT94sWLQktLS2RmZkrd2bJ3Re3Xr5/UVfVjZGZmCn9/fzFx4sQ8u8dld+7cOaGhoSFevnyZozwpKUmkpKSIzZs3C21tbXH16lVp+vjx44Wrq6vo2rWriI6OFo0aNRJnz54Vu3btEh4eHqJp06bi3r17BY77U7rUamtrC0NDQ+kvMTFRaZ78PhfZRUREiBEjRoiyZcsKuVwuypQpI44fPy6EeNtlDYAIDQ3NsdyHvnvOnDmTo5vikCFDxPDhw5XW061btwLXcX379hUAxO+//y5SU1OlOv3dZ/d9kyZNEq6uriI0NFQkJCSIHj16CJlMlqO7dV519ZkzZ0StWrVErVq1xJkzZ8TGjRtFnz59xLNnz0Tbtm2Fu7u70nfih3xKl9p33//v/lasWJFjvhs3bghDQ0PpfctNZmamWLlypWjQoIHQ09MThoaGYuzYsVL316ZNm4qpU6fmuqyXl5eYM2eO9DolJUXo6+tLXRk/to75GB+6pipIHX/q1Cmhp6cnLly4INLS0sSSJUuETCYTffv2FUK8vZ3Ez89PpKWliZiYGDFixAhha2sr1WtfUx2vUCgEAFG9enXx/PlzabqXl5fS9U52nTt3lq45nZycPuqcFUKIqKgooaWlJfbt2yeEEGLChAmibNmy0vSCdLOcMWOGqFGjxkdtNz+51d0few01bdq0HLdwzZs3TzRs2FB6ffr0aZGSkiISExPF/Pnzhb6+vlQfpqSkiMGDB4vy5cuLwYMHi/j4eFGpUiXx6NEjsXTpUuHu7i7atWsnwsLCCrxfn3oNZ2RkJKytrUWHDh1ESEiIEEK5/tDU1BQAxPjx40VWVlae6/sa3+uCfMaz27BhgzA3N1e61eJDdYAqPuP/uIXz+fPncHR0VCorUaJEvjfrHzt2DLVr14aZmRkMDQ2xbNkyvH79usDbdHV1RWxsLOLi4pCQkIDq1atLLQnPnz+HtrY2LCwsco2nIPGePHkSQUFB+Omnn6TWqbxYWlpK/+vp6UkDDoSHh8PKykrpVzN7e/s812NmZobXr1/n23r3Kcf6Q/Fmv6n7UygUCvTu3Rvly5eHlpYWpk2bhsePH+PRo0d4/Pgxli1bhl9++eWT1w0AU6dOhUKhgL29PYYNG4aDBw/+o5g/ZPPmzYiLi8ODBw+QkZEhdSV4/fo1kpKS4O7uDiMjIxgZGcHS0hKampp49uwZQkNDpa5quVm4cCHKlSsnDchx586djzrv83L+/HlcvHgx1y7L73vXjSMyMjLP92Xu3LmoXr063N3dc52+Zs0azJ8/H1euXEFaWhquXr2KcePG4fDhwwAAY2NjbN68Gbdu3cL06dMxevRojBs3Dv7+/rh48SJ8fX1RokQJzJ49+9N3ugDOnz+PmJgYPH36FGvXroWJiYk0LftnMSQkBLGxsTAxMZHe144dOyIyMhIAEBoamqNbcW709PRw8OBB7N+/H3Z2dqhbty7OnDmTY75Xr14hLS1N6bNcokQJpKamKp0PedUtH0Mul6Nq1arQ19fHqFGjPjj/6tWr0bp1a5ibmyuV16tXD7q6utDS0kK3bt3QqlUr7N69W5o+Y8YM3Lp1C1u2bMGhQ4dgb2+P8uXLY9iwYdi7dy/Gjh2LPn36fHT8H2P27NmIjY2V/vT09D5pPZaWlvj1119x9+5dvHr1Cs2aNUO7du0QHR2N0NBQaGlp5VqXf+i7BwD09fWVWixDQkKwbNky6bwzMjLC/v37lbrp50ehUMDW1hbff/89NDU1UadOHbRt21bpNozsfvrpJ3h5eaFevXpwdnZGxYoVoVAoYGpqWqDteXp64tKlS7h06RIqVKiAOXPm4JdffsGoUaPQrl07HDt2DIsXL1bp4EHvvv/f/fXv319p+u3bt9GsWTMsWbIEjRo1ynM9crkc/fr1w6lTpxAbG4stW7Zg2bJlWL16NYD8P/fvfxdraWnB2tpa6b3+mDrmcypIHd+gQQMsWrQI/fv3h6WlJfz9/eHl5SWdBwqFAtWrV4eGhgaMjIwwf/58pKenS4Mmfk11fEJCAi5fvoznz58rfW7MzMzy/ByFh4dLdZyZmRnCwsI+arubN2+GgYEBmjdvDgD49ttvce/ePam1T0NDI9dbINLT06XWw0/Zbn5yq7s/9hpKoVAgLi5OqSwuLk6ph1/9+vWhpaUFPT09jBw5EqVLl5Z6vGhpaeGPP/7A7du38ccff2DRokVo37490tPTsXTpUhw/fhytW7fGyJEjP9t+52bz5s2IiYlBWFgYdu3aBQcHB2nau/ojISEBEydOxOnTp/Psdv9uXV/be12Qz3h2q1evxrfffqvUcv2hOkAVn/F/nHDa2trmGMo6JCREegTA+wlbWloa2rdvj4EDByIsLAxxcXEYNGjQJ9/DqVAo0LdvX1y+fBlRUVGwtbVFSkqKUkWePZ4PxQsA33zzDb777jt4enp+8klibW2NFy9eKJ3IT58+zXP+d6Mo5ndf34diVygUSqMTRkREfFTMH0quc5N9hDIASveMXrhwAZGRkXB2doaZmRnatGmD+Ph4mJmZFWik2ffX/aW5uLhg/vz5GDx4MJKTk2FqagpdXV34+fkpXfAkJyejdu3acHBwQFhYGFJSUnKs68KFC5gyZQo2bNiAmJgYxMbGonz58p9l9M5Tp07hyZMnsLa2hpmZGX744QfcuXMHZmZmeZ4D6enped7Td/LkSezcuRNmZmYwMzPDtm3bsHz5clSvXh0AcPPmTTRr1gxubm6Qy+Vwc3ND48aNc4zeBrzt1vb8+XP06NEDgYGBqFatGuRyOWrVqoXAwMB/vO+fKvu5bmdnh2LFiim9p3FxcdKPMQ4ODgW+T6Jhw4bw8fHB69ev0alTJ7Rt21ZpdDfgbddETU1Npc9ySEgItLS0VHaPS37v9zvx8fHYuXOnUrebvORVV0RFRWHu3Ln45Zdf8Ndff8HOzg7GxsaF/n5/KhMTE0yZMgVJSUkIDg6Gg4MDUlNTle7Vf+dD3z1AzuNmZ2eHYcOGKZ17iYmJ+PPPPwsU38fWkdra2liwYAFCQ0MRHh6O5s2bIy0tDTVq1Pio9QDAqFGj8NNPP8HExASBgYGoUaMGtLW14ebmJo0N8KXdvn0bXl5emD17Nnr06FHg5dTV1dG8eXM0bNhQij2/z/3738VpaWkIDw/P873+UB2jCh/6zPfr1w/37t1DVFQUVq5ciXv37sHDwyPXed/dL5abr6GOr1mzJkaNGoWBAwdK36mNGjWCj48P4uPjleYNDg6Gn5+f9GNEkyZNsHv37nyTjvetXr0acXFxsLOzg6WlJerVqweZTCb9WOHg4IA3b97kGDU3KChI+qHCy8sLERERuHjx4qfutiSvuvtj64cKFSrg7t27SglUQEAAXF1d81wmr++CR48eYd++fRgzZgxu376NChUqQEtL66v5LtDU1MTUqVORnJwsjWiem6/tvc6uIN/rjx8/xrlz53L9Xi9oHfC5PuP/OOHs0qULfH19sX//fmRkZGDPnj04d+6cdF+bhYUFnjx5IlUCqampSElJgampKbS0tODn5/eP7ltITk7G2rVrYW1tDRMTE9jY2KB+/foYNWoUkpKS8PTpU8ycORO9evUqULzvTJ06Fd27d4enp2euFxcfUrNmTRgZGWH27NlIT0+Hv78/duzYkef8+vr6mDt3LoYOHYrNmzcjPj4eQggEBASgdevWBYq9cuXK2LNnD+Li4vDy5Uule4cKwsLCQmrNK6gBAwZg3bp1ePjwIdLT0zF16lSUKlUKzs7O6Ny5Mx4/foyAgAAEBARg1apV0NfXR0BAACpVqgTg7QcmJSUFmZmZyMzMREpKilTZFS9eHF5eXpg2bRrevHmD8PBw/P7772jTps1HxfhPtG3bFqampli6dCnkcjkGDRqEkSNHSudEVFQUtm/fDgCoVq0aXFxcMGTIEMTGxiIjIwMXLlxAamoq4uPjoaamBnNzc2RlZWHNmjW4c+fOZ4lxxIgRePTokXScp02bBhcXFwQEBKBYsWIICAjAiRMnkJycjIyMDBw+fBibN29GkyZNcl3fzp07cffuXWl9rVu3Rvfu3aXBTGrVqoVjx45J92TfvXsXx44dk97Td1JTU/Hjjz9KF88lSpTAuXPnkJqaipMnT+Y6MElhqFatGuzs7DBhwgQkJCRACIHQ0FApge7evTuuXr2KZcuWITU1FW/evMH58+dzrCcyMhJ79+5FQkIC1NXVYWBgkOt9QXK5HN26dcP48eMRHR0t3Y/ds2fPT/rR532hoaHYvXs3EhMTkZWVhUuXLmHx4sV5vt/vbN26FaampmjcuLFS+dOnT6X3LT09HTt27MD+/ftzDPkOvE1Cxo8fD2NjYzg4OODRo0cICwvDiRMnCu39zl6vCCGQkpKS52AtMTExmDBhAh48eIDMzEy8efMGCxYsgImJCUqXLg0LCwu0adMGgwYNQkREBLKysnDz5k1ERUV98LsnNwMHDsTatWtx5swZZGZmIjU1FZcvXy5wC2G7du2QkpKCZcuWITMzE35+fti/f7/0nfG+iIgIhIaGQgiBv/76C3379sWIESOk1v+srCykpKRIPW1SUlJy/QHN19cX4eHh6N69O4C3n+0TJ04gPj4eV69eLZT3+u7du/Dy8sKMGTPQu3fvD86/cOFCnDx5EomJiRBCSL/avxtwZeDAgfjtt99w9uxZZGVl4eXLl7h58yYAoEePHliyZAnu3buH1NRUTJgwATY2NtKPcu/7UB1TEPl9V35sHZ+eno6AgABkZWUhKioK33//PYoXLy71FPPz88P9+/eRmZmJxMREjB07FjKZDLVq1VJaz9dUxw8aNAjPnz+Xel706NEDJUuWRNu2bfHw4UNkZmbixo0baNeuHVq2bCndh/bjjz8iMzMTnTt3xqNHj5CVlYW4uDisXLkSv/32W47tXL9+HYGBgThx4oT0HRkQEIDly5dj+/btSEpKgq2tLTw8PDBy5EjExMQgIyMDPj4+OHDgALp27Qrg7b2kI0eORLdu3XD48GG8efMGmZmZuHDhwkf9WALkXXd/7DWUu7s7TExMMHPmTKSmpsLHxwe+vr749ttvAQB37tzB9evXpXNx8eLFuHv3bq7n2ZAhQ7B48WJoamqiRIkSuHr1KuLi4gr1u+B9MpkM48ePx6xZs3J9lNTX9F5/7Gf8ndWrV6NWrVo5Bu/7UB3wzmf9jBe48202799f5uPjI9zc3IS+vr5wc3MTR48elaY9fvxYVK5cWRgZGQlXV1chhBB//vmnsLKyEvr6+qJVq1bi+++/Vxo+GAW4h1NPT0/o6ekJY2Nj4eXlpTR/RESE6NChgzA1NRW2trZi7NixSn2X84v3/fsjZs6cKUqUKCFCQkJyvYcz+z2Pe/fuFQ4ODtLrwMBAUb16daGnpyc8PT3F8OHDRePGjfM9tvv37xd169YVenp6wsTERFSrVk0sX768QLFHR0eLli1bCn19fVGuXDnx559/5riHM797PA8cOCAcHR2FoaGhGDx4sBBCiIEDB4qBAwfmG/OcOXOElZWVMDIyEo0bNxaPHj3Kdb7c+rr36tVLAFD669WrlzQ9MjJStGnTRigUCmFtbS3GjBnzUY8T+CdDar+zZcsWYW5uLhITE0VqaqqYPn26cHJyEgqFQjg4OEj3qArx9jE9Xbp0EcWKFROGhobCw8NDvHnzRmRmZor+/fsLAwMDYW5uLkaMGCHc3d2l9+ND93CWLVtW6T7J/Lx/nvr7+4uqVasKfX19YWBgICpUqCCWLVumtIyenl6ew2vnds/QrFmzRPHixYWenp6wt7cXEydOzHEfxOTJk8Xs2bOl1xkZGaJ79+7CwMBA1KlT56Pu4/gnQ+bntq733+PIyEjh7e0tbGxspM/P4sWLpel+fn6iXr16wtDQUJibm4uhQ4cKIZTft/DwcOHh4SEMDQ2Fvr6+qFKlijh9+nSO+YR4O1x73759hYWFhbCwsBD9+/eXHseQ2yMJhg0bpvS5yO98CAkJEXXr1pXicHFxETNmzFC6fyy35atVq5broy3u3r0r3NzcpPvcqlWrJg4cOJBjvjNnzuS4x3zhwoXCzMxMlChRIs/7CnPzOR+Lsnbt2hx1TPZ6OrvExETh7e0tndumpqaiUaNGws/PT5onNjZWDBw4UFhbWwt9fX1RvXp18ezZMyFE/t89ed3rc+rUKVG7dm1hbGwsTE1NRcOGDaVz99y5c0JPTy/ffffz8xNVq1YVurq6wtnZWWzYsEGa9v7yV65cESVKlBA6OjrC3t5ezJw5U+lz++4+0/f/sktJSRGVKlUSQUFBUtnt27dF+fLlhbGxcZ73zeXmcz4WxdvbW8hkMuna4N1fbvfbCiHE8uXLRbVq1YSBgYEwMDAQZcqUyfEYjPXr14ty5coJhUIh7O3txfr164UQb+9jnzt3rihevLj0vZf90RGfUsd8qI7P77vyY+v4pKQkUbFiRek6o0+fPkr1zZYtW0SJEiWErq6uMDMzEy1atBC3b9/OEdPXVsfPmjVLlCtXTqrr4uLixNChQ4W1tbXQ0tISxYsXF5MmTcpxDfH69Wvx/fffCzs7O6Grqyvs7e1Fr169lN7TdwYPHiw8PT1zlKelpQkrKyuxevVqIcTbuqBnz57C2tpaGBoaiipVqoj9+/fnWG716tWiSpUqQldXV5ibmwt3d3fpERRC5P/d/E5edbcQH76Gen/9d+/eFbVr1xba2trCyclJundRiLf3b5YuXVro6uoKExMT4eHhIS5cuJBjm2vXrs1x3Thq1ChhbGwsXF1dpUeoFcTnuIbLHtf79UdmZqYoXbq0mDt3bo75v6b3+lOu4zIyMoSVlZVYs2ZNjvV9qA5453N+xmVCqPiJ3CQZOHAgsrKysHLlysIO5T/D09MTU6ZMyfNxNFQ0ODo6wtfXN8c9zPTv5O3tDU9Pz0IZ2ZC+HF9fX0yZMkV6RAX9d7GOp9zwGu7f47M8h5Nyd/78eTx79gxZWVnSYw2yP8+NiIiIiIjo3+zjHjxEH+XJkyf45ptvEBMTA1tbW8yZMydHH3tSLW9vb/5i+i8wfPjwfJ9HSP8ubdu25ef2P8DR0ZGt2ASAdTzljtdw/x7sUktEREREREQqwS61REREREREpBJMOImIiIiIiEglmHASERERERGRSjDhJCIiIiIiIpVgwklEREREREQqwYSTiIiIiIiIVIIJJxEREREREakEE04iIiIiIiJSCSacREREREREpBJMOImIiIiIiEglmHASERERoVKPOwAACiBJREFUERGRSjDhJCIiIiIiIpVgwklEREREREQqwYSTiIiIiIiIVIIJJxEREREREakEE04iIiIiIiJSCSacREREREREpBJMOImIiIiIiEglmHASERERERGRSjDhJCIiIiIiIpVgwklEREREREQqwYSTiIiIiIiIVIIJJxEREREREakEE04iIiIiIiJSCSacREREREREpBJMOImIiIiIiEglmHASERERERGRSjDhJCIiIiIiIpVgwklEREREREQqoV7YARARUeHJzMzE+fPncefOHcjlcqipqcHQ0BCenp6wtLQs7PAAAL6+vqhbty7U1T/+K2vHjh1wdnZGxYoVP39gAPbt2wdLS0vUrFlTJet/3/79+xEeHg6ZTAY1NTU0bNgQJUqUAAAkJSVh7969iImJgZqaGlq0aAEHB4cCrfPp06fQ0NCApqYmmjRpAhsbGwBAeno6Dhw4gLCwMMhkMjRs2BBly5YFAJw6dQr379+Huro65HI5GjRoACcnJwBv3zN/f3/o6+sDAIoVK4b27dur4pAQEdFXjgknEdF/2P79+5GWloa+fftCR0cHAPDkyRO8fv36iyScQggAgEwmy3Oes2fPombNmp+UcP7bNGnSBNra2gCAiIgIbNiwAWPGjIFMJsPJkydha2uLHj16ICwsDNu3b8ewYcOgpqaW7zpLly6NVq1aQS6X49GjR9i5cyeGDx8OALh06RLU1NQwdOhQxMTEYNWqVXB0dISuri7s7e3h7u4ODQ0NvHjxAuvWrcOIESOgqakJAHB1dUXTpk1VejyIiOjrx29vIqL/qKioKDx48AA//vijlGwCkFrM3rl06RLu3r2LrKws6OnpoWXLljAyMoKvry9ev36N9PR0REdHQ6FQoHPnztK68lvu5cuXSEtLQ1xcHHr27IkrV64gNDQUmZmZ0NLSQqtWrWBmZoZDhw4BANauXQuZTIaePXtCXV0dx44dQ2RkJDIyMmBra4vmzZtDTU0Nr1+/xv79+5GamgoTExOkp6fnuf+BgYG4dOkSAMDQ0BAtW7aEgYEBAgICcOvWLejp6eHly5dQU1NDp06dYGxsnO/x9PX1RUpKipRkXb16FeHh4Wjbtu0H1xkYGAh/f39kZmZCU1MTzZo1yzXhf5dsAkBqaqrStLt372Lo0KEAABsbG+jr6yM0NDTH+/k+FxcX6X9bW1skJCQgKysLcrkcd+/eRevWrQEAxsbGcHR0xIMHD1C5cmWUKlVKWs7CwgJCCLx580ZKOImIiAAmnERE/1kvXryAiYmJUrL5vtu3b+P169fo27cv5HI5AgMD4ePjg27dugEAnj9/jgEDBkBXVxe7du3CtWvXUK9evQ8u9+zZMwwcOBAKhQIAUKdOHTRu3BgAcOfOHRw9ehQ9evRAy5Ytcf36dfTu3VtKtg4ePAgHBwe0bt0aQggcPHgQV65cQZ06dbB3715UqVIFlStXRmRkJFauXAlXV9cc+/Xy5UucOHECAwYMgIGBAc6dO4eDBw+ie/fuAIDw8HAMHDgQxsbGOHnyJC5cuIBWrVr9o+Od1zqfPn2KO3fuwNvbG+rq6ggNDcWePXswZMiQXNdz8uRJ3Lt373/t3d9L03scx/HXvkNrVmpbtCm1sBzrB1RQXfTzpo1CgtqN1MUkIqG/pyhCjIi6yC6yjfBqFEpgyKCSWgtiTanpgpbLajO3785F+D2ZqXnOGZxzfD6utu8+n88++9699v58Pl8VCgW1t7fLZrPp69evMk3Tup+S1NjYqHw+v6Q5Pn78WD6fT4bx/YiHfD6vhoaGRcd88uSJ1q5dO6ttIpFQOp2Ww+HQkSNH1NLSsqS5AAD+HwicAABJUi6X0507d1QqlbRx40adPHlSyWRSmUxGXV1dkv5cAjujtbVVdXV1kr5Xx96/fy9Ji/bz+XyzwlEqldLQ0JCmpqZUqVRUKBTmnWcymdTbt281ODgoSSqVSrLZbJqamtL4+Li1X9Ptdsvr9f5yjDdv3qi1tVX19fWSpH379mlgYECmaVq/Zab6uGHDBg0NDS1y9xY335ivXr1SNptVd3e31bZQKGh6elo1NTVzxgkEAgoEAkqlUorFYjp37tzfnpskDQ8PK5FI6OzZs0vql0ql1N/fr3A4bC2N3rt3rw4fPiy73a7R0VH19PSos7NTjY2N/8hcAQD/HQROAFimPB6PcrmcCoWCHA6HnE6nLly4oKdPnyqZTFrtDh06pD179vxyjB/3VRqGYQW2xfr9uOwyn8+rr69PnZ2dcjqdymazun79+oJzb29vl8vlmnXt5yWmS/HzHtKFftd8fm5XKpV+a8xKpaJdu3bp6NGjS5rz5s2b1dfXp2w2q+bmZhmGoc+fP1tBfmJiYlbFcSHPnz9Xf3+/Ojo6Zv0R0NDQoHw+bx3+MzExoS1btlifp9NpRSIRnTlzRuvWrbOu/ziG1+tVU1OTMpkMgRMAliEeiwIAy5TL5ZLf71c0GlWxWLSuf/v2zXrt9/sVj8etimO5XNbY2NiiYy+lX7FYlN1u15o1a1SpVOZUE2tra2fNz+/369GjR1ZgKxQKyuVyWrFihTwej549eybp+7LZ0dHRX35nS0uLXr9+rcnJSUlSPB5XS0uLtZT0r3A6nRobG5NpmpqentbLly9/q5/f79fw8LC1VLVSqSiTycxpVy6XlcvlrPfv3r3Tly9frKrp9u3bFY/Hrc8mJyetU2pjsdi8VdoXL17o4cOHCofDcwLqj2N+/PhR6XRaW7dulSSNjIyot7dXp0+fnrPf9NOnT9brDx8+aHx8XG63+7fuBwDg/4UKJwAsY6dOndLAwIC6u7tlGIZWrlypVatW6eDBg5KknTt3qlAo6MaNG5Ik0zS1e/duNTU1LTjuUvq53W7t2LFDV65ckcPhsALNjP379+vmzZuqqalROBzW8ePHFYvFdPXqVdlsNhmGoWAwKKfTqVAopEgkosHBQblcrnkfC7J+/XoFg0HdunVL0vdK3l/Zo2maplW53LZtmxKJhC5fvqz6+np5PJ4FDy2asWnTJgWDQfX09Mg0TZXLZfl8PjU3N8/5rnv37qlYLMowDNXW1s46pCkQCKi3t1eXLl2S3W5XKBSyTqidqYL+yt27d7V69Wrdvn3butbR0aG6ujodOHBA0WhUFy9elM1mU1tbm7WEOhqNqlwuKxKJWP1CoZDcbrcePHigTCYjwzBkGIba2trmVKQBAMuDrfLzxhoAALAo0zTV1dWlY8eO/asPxDFNU9euXdP58+cXfPwMAADVQOAEAGCJRkZGdP/+fXm9Xp04cYIgBwDAPAicAAAAAICq4NAgAAAAAEBVEDgBAAAAAFVB4AQAAAAAVAWBEwAAAABQFQROAAAAAEBVEDgBAAAAAFVB4AQAAAAAVAWBEwAAAABQFQROAAAAAEBVEDgBAAAAAFVB4AQAAAAAVAWBEwAAAABQFQROAAAAAEBV/AE6MhYjJRVYBgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix for claim scenario\n",
    "plot_confusion_matrix_from_df(summary_df, 'RISK_VS_CLAIM', 'Contactless')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance\n",
    "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n",
    "\n",
    "Important! Be aware that SHAP analysis might take quite a bit of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d66ffe2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## BUILT-IN\n",
    "\n",
    "# Get feature importances from the model\n",
    "importances = best_pipeline.named_steps['model'].feature_importances_\n",
    "features = X.columns\n",
    "\n",
    "# Create a Series and sort\n",
    "feat_series = pd.Series(importances, index=features).sort_values(ascending=True)  # ascending=True for horizontal plot\n",
    "\n",
    "# Plot Feature Importances\n",
    "plt.figure(figsize=(8, 8))\n",
    "feat_series.plot(kind='barh', color='skyblue')\n",
    "plt.title('Feature Importances')\n",
    "plt.xlabel('Importance')\n",
    "plt.grid(axis='x')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the Feature Importance Plot\n",
    "The **feature importance plot** shows how much each feature contributes to the model’s overall decision-making.\n",
    "\n",
    "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n",
    "A higher score means the feature plays a bigger role in improving prediction accuracy.\n",
    "\n",
    "In the graph you will see that:\n",
    "* Features are ranked from most to least important.\n",
    "* The values are relative and model-specific — not directly interpretable as weights or probabilities.\n",
    "\n",
    "This helps us identify which features the model relies on most when making predictions.\n",
    "\n",
    "**Important!**\n",
    "Unlike SHAP values, native importance doesn't show how a feature affects predictions — only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2197cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PermutationExplainer explainer: 6417it [1:26:06,  1.24it/s]                          \n"
     ]
    }
   ],
   "source": [
    "## SHAP VALUES\n",
    "\n",
    "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n",
    "X_test_shap = X_test.copy()\n",
    "X_test_shap = X_test_shap.astype(float)\n",
    "\n",
    "# Function that returns the probability of the positive class\n",
    "def model_predict(data):\n",
    "    return best_pipeline.predict_proba(data)[:, 1]\n",
    "\n",
    "# Ensure input to SHAP is numeric\n",
    "X_test_shap = X_test.astype(float)\n",
    "\n",
    "# Create SHAP explainer\n",
    "explainer = shap.Explainer(model_predict, X_test_shap)\n",
    "\n",
    "# Compute SHAP values\n",
    "shap_values = explainer(X_test_shap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cae1a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_795/3711913411.py:2: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
      "  shap.summary_plot(shap_values.values, X_test_shap)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot summary\n",
    "shap.summary_plot(shap_values.values, X_test_shap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the SHAP Summary Plot\n",
    "\n",
    "Each point on a row represents a SHAP value for a single prediction (row = feature).\n",
    "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n",
    "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n",
    "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n",
    "\n",
    "Color shows the actual feature value for that point:\n",
    "* Red = high value\n",
    "* Blue = low value\n",
    "\n",
    "In other words:\n",
    "* The position tells you impact.\n",
    "* The color tells you feature value.\n",
    "* The density (thickness) of dots shows how often a value occurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x750 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the individual prediction for the highest predicted instance\n",
    "highest_pred_index = np.argmax(shap_values.values[:, 0]) \n",
    "\n",
    "# Use waterfall plot for a single instance\n",
    "shap.plots.waterfall(shap_values[highest_pred_index], max_display=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x750 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the individual prediction for the lowest predicted instance\n",
    "lowest_pred_index = np.argmin(shap_values.values[:, 0])  \n",
    "\n",
    "# Use waterfall plot for a single instance\n",
    "shap.plots.waterfall(shap_values[lowest_pred_index], max_display=20)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}