리뷰전처리.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ytHz6gH_1LZC",
    "outputId": "1a99654e-0f86-4feb-9035-d6cba985eaca"
   },
   "source": [
    "!pip install git+https://github.com/ssut/py-hanspell.git\n",
    "!pip install git+https://github.com/haven-jeon/PyKoSpacing.git\n",
    "!pip install konlpy\n",
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "yUsyjoOh0QzB"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import sys\n",
    "from konlpy.tag import Okt\n",
    "from collections import Counter\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "OETcbTdt_dr4"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('야놀자호텔리뷰.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 659
    },
    "id": "JgxaDqXS_rLM",
    "outputId": "0b384f5b-0ff5-4a17-84a5-869d9e643c46"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>4</td>\n",
       "      <td>발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요~!</td>\n",
       "      <td>10시간 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>굳굳굳굳굳</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>3</td>\n",
       "      <td>생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다. 오픈초기에 사소한 부분에...</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>5</td>\n",
       "      <td>객실이 깨끗하고 너무 예뻣어요!</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150504</th>\n",
       "      <td>150504</td>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>코로나로 인해 바베큐를 못해 아쉬웠지만... \\n호캉스 잘 즐기다 왔습니다 또 오고...</td>\n",
       "      <td>2020. 08. 22</td>\n",
       "      <td>2020. 08. 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150505</th>\n",
       "      <td>150505</td>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>4</td>\n",
       "      <td>호텔 좋고 수영장도 좋다\\n재방문의사 있음</td>\n",
       "      <td>2020. 08. 20</td>\n",
       "      <td>2020. 08. 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150506</th>\n",
       "      <td>150506</td>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>4</td>\n",
       "      <td>오성급 호텔 스멜 풀풀 남\\n수영장 넓고 좋으나 6시까지하는건 아쉬움\\n풀바 음식은...</td>\n",
       "      <td>2020. 08. 20</td>\n",
       "      <td>2020. 08. 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150507</th>\n",
       "      <td>150507</td>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>뷰도좋고  직원들도  친절하고 잘쉬다가 갑니다</td>\n",
       "      <td>2020. 08. 14</td>\n",
       "      <td>2020. 08. 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150508</th>\n",
       "      <td>150508</td>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>방이 생각보다 작지만 호텔이라 이해하고 좋았어요</td>\n",
       "      <td>2020. 08. 12</td>\n",
       "      <td>2020. 08. 12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150509 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                hotel  star  \\\n",
       "0                0                  AC 호텔 바이 메리어트 서울 강남     4   \n",
       "1                1                  AC 호텔 바이 메리어트 서울 강남     5   \n",
       "2                2                  AC 호텔 바이 메리어트 서울 강남     3   \n",
       "3                3                  AC 호텔 바이 메리어트 서울 강남     5   \n",
       "4                4  몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "...            ...                                  ...   ...   \n",
       "150504      150504                           그랜드 하얏트 서울     5   \n",
       "150505      150505                           그랜드 하얏트 서울     4   \n",
       "150506      150506                           그랜드 하얏트 서울     4   \n",
       "150507      150507                           그랜드 하얏트 서울     5   \n",
       "150508      150508                           그랜드 하얏트 서울     5   \n",
       "\n",
       "                                                   review          date  \\\n",
       "0                   발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요~!        10시간 전   \n",
       "1                                                   굳굳굳굳굳          6일 전   \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...          6일 전   \n",
       "3       오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다. 오픈초기에 사소한 부분에...  2022. 03. 27   \n",
       "4                                       객실이 깨끗하고 너무 예뻣어요!          3일 전   \n",
       "...                                                   ...           ...   \n",
       "150504  코로나로 인해 바베큐를 못해 아쉬웠지만... \\n호캉스 잘 즐기다 왔습니다 또 오고...  2020. 08. 22   \n",
       "150505                            호텔 좋고 수영장도 좋다\\n재방문의사 있음  2020. 08. 20   \n",
       "150506  오성급 호텔 스멜 풀풀 남\\n수영장 넓고 좋으나 6시까지하는건 아쉬움\\n풀바 음식은...  2020. 08. 20   \n",
       "150507                          뷰도좋고  직원들도  친절하고 잘쉬다가 갑니다  2020. 08. 14   \n",
       "150508                         방이 생각보다 작지만 호텔이라 이해하고 좋았어요  2020. 08. 12   \n",
       "\n",
       "           real_date  \n",
       "0       2022. 04. 03  \n",
       "1       2022. 04. 01  \n",
       "2       2022. 04. 01  \n",
       "3       2022. 03. 27  \n",
       "4       2022. 04. 01  \n",
       "...              ...  \n",
       "150504  2020. 08. 22  \n",
       "150505  2020. 08. 20  \n",
       "150506  2020. 08. 20  \n",
       "150507  2020. 08. 14  \n",
       "150508  2020. 08. 12  \n",
       "\n",
       "[150509 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "rxnOHE7AN5Op"
   },
   "outputs": [],
   "source": [
    "ddup = df.drop_duplicates(subset=['hotel','star','review','date'], keep='first', inplace=False, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "S0g3S84GO270"
   },
   "outputs": [],
   "source": [
    "ddup = ddup.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "id": "WktDkzDHxI2I",
    "outputId": "46d12e61-eaf5-4029-c1f1-d05bbb6f5a4d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>4</td>\n",
       "      <td>발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요~!</td>\n",
       "      <td>10시간 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>굳굳굳굳굳</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>3</td>\n",
       "      <td>생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다. 오픈초기에 사소한 부분에...</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>5</td>\n",
       "      <td>객실이 깨끗하고 너무 예뻣어요!</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124796</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>4</td>\n",
       "      <td>친절하시고 조식도 맛있었어요~!\\n하지만 방음...이...많이 안되더라구요;;</td>\n",
       "      <td>1시간 전</td>\n",
       "      <td>2022. 04. 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124797</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>역시 5성급 하얏트답습니다. 조식 포함시 좀 비싸긴 한데 조식빼고 생각하면 매우 괜...</td>\n",
       "      <td>1시간 전</td>\n",
       "      <td>2022. 04. 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124798</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>완전 좋았습니디</td>\n",
       "      <td>6시간 전</td>\n",
       "      <td>2022. 04. 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124799</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>깔끔하고 좋았어용</td>\n",
       "      <td>1일 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124800</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>너무좋았습니다</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124801 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      hotel  star  \\\n",
       "0                       AC 호텔 바이 메리어트 서울 강남     4   \n",
       "1                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "2                       AC 호텔 바이 메리어트 서울 강남     3   \n",
       "3                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "4       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "...                                     ...   ...   \n",
       "124796                           그랜드 하얏트 서울     4   \n",
       "124797                           그랜드 하얏트 서울     5   \n",
       "124798                           그랜드 하얏트 서울     5   \n",
       "124799                           그랜드 하얏트 서울     5   \n",
       "124800                           그랜드 하얏트 서울     5   \n",
       "\n",
       "                                                   review          date  \\\n",
       "0                   발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요~!        10시간 전   \n",
       "1                                                   굳굳굳굳굳          6일 전   \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...          6일 전   \n",
       "3       오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다. 오픈초기에 사소한 부분에...  2022. 03. 27   \n",
       "4                                       객실이 깨끗하고 너무 예뻣어요!          3일 전   \n",
       "...                                                   ...           ...   \n",
       "124796        친절하시고 조식도 맛있었어요~!\\n하지만 방음...이...많이 안되더라구요;;         1시간 전   \n",
       "124797  역시 5성급 하얏트답습니다. 조식 포함시 좀 비싸긴 한데 조식빼고 생각하면 매우 괜...         1시간 전   \n",
       "124798                                           완전 좋았습니디         6시간 전   \n",
       "124799                                          깔끔하고 좋았어용          1일 전   \n",
       "124800                                            너무좋았습니다          3일 전   \n",
       "\n",
       "           real_date  \n",
       "0       2022. 04. 03  \n",
       "1       2022. 04. 01  \n",
       "2       2022. 04. 01  \n",
       "3       2022. 03. 27  \n",
       "4       2022. 04. 01  \n",
       "...              ...  \n",
       "124796  2022. 04. 04  \n",
       "124797  2022. 04. 04  \n",
       "124798  2022. 04. 04  \n",
       "124799  2022. 04. 03  \n",
       "124800  2022. 04. 01  \n",
       "\n",
       "[124801 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0jD-ShivvBC3",
    "outputId": "17860ecb-00fc-43dd-c162-c8ade8633428"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124801 entries, 0 to 124800\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   hotel      124801 non-null  object\n",
      " 1   star       124801 non-null  int64 \n",
      " 2   review     124801 non-null  object\n",
      " 3   date       124801 non-null  object\n",
      " 4   real_date  124801 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "ddup.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "oASZeOEKOwEX"
   },
   "outputs": [],
   "source": [
    "rawdf = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pfPnAikhlYOZ"
   },
   "outputs": [],
   "source": [
    "rawdf = rawdf.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MkyIxKwVlPYJ"
   },
   "outputs": [],
   "source": [
    "dupp = rawdf.loc[rawdf.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HCKgNyBCnmqL",
    "outputId": "fdbd597d-cbcc-4c65-838a-0e4d8375cc11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              25708\n",
       "unique                88\n",
       "top       노보텔 앰배서더 서울 용산\n",
       "freq                4480\n",
       "Name: hotel, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dupp.hotel.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "2KQ7fnu3SiVw"
   },
   "outputs": [],
   "source": [
    "df = ddup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "vG1Y80aU_2HW"
   },
   "outputs": [],
   "source": [
    "# 각 전처리 후, 18글자 이하의 리뷰 갯수 비율 확인하는 함수 \n",
    "def del_percent():\n",
    "    under_18 = df.loc[df['length'] <= 18,'review'].count()\n",
    "    all_count = df['review'].count()\n",
    "    print('18글자 이하 리뷰갯수:', under_18)\n",
    "    print(under_18 / all_count * 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "1F78Cf9E_5Ap"
   },
   "outputs": [],
   "source": [
    "# 리뷰의 length 측정해서 컬럼으로 넣어줌\n",
    "df['length'] = df['review'].apply(lambda x: len(str(x)))\n",
    "df['review']=df['review'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XnIUTO4d_63I",
    "outputId": "c88811bd-22dd-479d-afdf-f5930005fb27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18글자 이하 리뷰갯수: 73659\n",
      "59.02116168940954\n"
     ]
    }
   ],
   "source": [
    "#1.'\\n' -> ''\n",
    "df['review'] = df['review'].apply(lambda x: x.replace('\\n',''))\n",
    "df['length'] = df['review'].apply(lambda x: len(x))\n",
    "del_percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-9AjIujM_-ZI",
    "outputId": "0fda4329-a6d2-4be5-f564-f10e1d3b6365"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18글자 이하 리뷰갯수: 75138\n",
      "60.206248347369005\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "# 2. ~, !, ., >\n",
    "def cleanText(readData):\n",
    " \n",
    "    \n",
    "    text = re.sub('[-=+,#/\\?:;^$.@*\\\"※~&%ㆍ!』\\\\‘|\\(\\)\\[\\]\\<\\>`\\'…》❤]', '', readData)\n",
    "    return text\n",
    "\n",
    "df['review'] = df['review'].apply(cleanText)\n",
    "df['length'] = df['review'].apply(lambda x: len(x))\n",
    "del_percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rBgtC_KeAHdf",
    "outputId": "a91b77fc-b439-40a4-cd30-b81c1ad26452"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18글자 이하 리뷰갯수: 75471\n",
      "60.473073132426826\n"
     ]
    }
   ],
   "source": [
    "def cleanText2(readData):\n",
    " \n",
    "    text = re.sub('[ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㅃㅉㄸㄲㅆㅛㅕㅑㅐㅔㅗㅓㅏㅣㅜㅠㅡ]', '', readData)\n",
    "    return text\n",
    "df['review'] = df['review'].apply(cleanText2)\n",
    "df['length'] = df['review'].apply(lambda x: len(x))\n",
    "del_percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0nvgvi4MALrC",
    "outputId": "90e565e1-d342-45b1-c929-d90db5aedf46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18글자 이하 리뷰갯수: 75498\n",
      "60.49470757445854\n"
     ]
    }
   ],
   "source": [
    "def cleanText3(readData):\n",
    "    \n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "\n",
    "    text = emoji_pattern.sub(r'', readData)\n",
    "    return text\n",
    "\n",
    "df['review'] = df['review'].apply(cleanText3)\n",
    "df['length'] = df['review'].apply(lambda x: len(x))\n",
    "del_percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6NGiQYazAOuw",
    "outputId": "f66bd740-2333-4ce2-f8b1-52d6dd6f5922"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(249, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['length'] > 500].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E5sOUmhVAXy3",
    "outputId": "4d0b8afa-c2de-4a2f-91b8-130b2e221ea6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124552, 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['length'] <= 500]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "10KUvC_b0r5W"
   },
   "outputs": [],
   "source": [
    "from pykospacing import Spacing\n",
    "spacing = Spacing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "MU9gRl_f2EOn"
   },
   "outputs": [],
   "source": [
    "def space_check(review):\n",
    "    result = spacing(review)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I7lVISrS2tS0",
    "outputId": "57c9e704-26cf-4d67-beb7-977b0f5521dd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-7574688e9d9b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['review']=df['review'].astype('str')\n"
     ]
    }
   ],
   "source": [
    "df['review']=df['review'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 461
    },
    "id": "bkcoG-xx1qpY",
    "outputId": "44c1550e-a351-492d-c245-bf6c7162baf4"
   },
   "outputs": [],
   "source": [
    "df['review_space'] = df['review'].apply(space_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "mxf0QzGRAg9A"
   },
   "outputs": [],
   "source": [
    "df['review']=df['review'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "tGdOZXEFClaw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "넘조아효 \n",
      "굿굿굿굿굿\n",
      "나쁘지읺음\n",
      "좋아옄\n",
      "역대급 최고\n",
      "좋아욬\n",
      "따듯하게 호캉스 하고 돌아갑니다\n",
      "짱조름요 \n",
      "굳굳굳굳굳♡♡\n"
     ]
    }
   ],
   "source": [
    "df['review_spell_check'] = df['review'].apply(spell_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "rhdVae_h2OW3",
    "outputId": "705e0091-7753-4fe3-fba1-44ada5659377"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'아주좋앗습다'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spell_check(\"아주좋앗습다\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cOXQQhj5FJhN"
   },
   "outputs": [],
   "source": [
    "df=df.drop(df.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "s_bOLMss4C_e"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('야놀자스페이스.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3lk_jlBD4ewe"
   },
   "outputs": [],
   "source": [
    "df=df.drop(df.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "      <th>review_space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>4</td>\n",
       "      <td>발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요</td>\n",
       "      <td>10시간 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "      <td>35</td>\n",
       "      <td>발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>굳굳굳굳굳</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>5</td>\n",
       "      <td>굳굳 굳굳 굳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>3</td>\n",
       "      <td>생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...</td>\n",
       "      <td>6일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>84</td>\n",
       "      <td>생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AC 호텔 바이 메리어트 서울 강남</td>\n",
       "      <td>5</td>\n",
       "      <td>오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈초기에 사소한 부분에서...</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "      <td>2022. 03. 27</td>\n",
       "      <td>99</td>\n",
       "      <td>오픈하고 얼마 안 되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈 초기에 사소한 부...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>몬드리안 서울 이태원(Mondrian Seoul Itaewon)</td>\n",
       "      <td>5</td>\n",
       "      <td>객실이 깨끗하고 너무 예뻣어요</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>16</td>\n",
       "      <td>객실이 깨끗하고 너무 예 뻣어요</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124547</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>4</td>\n",
       "      <td>친절하시고 조식도 맛있었어요하지만 방음이많이 안되더라구요</td>\n",
       "      <td>1시간 전</td>\n",
       "      <td>2022. 04. 04</td>\n",
       "      <td>31</td>\n",
       "      <td>친절하시고 조식도 맛있었어요 하지만 방음이 많이 안 되더라구요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124548</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>역시 5성급 하얏트답습니다 조식 포함시 좀 비싸긴 한데 조식빼고 생각하면 매우 괜찮...</td>\n",
       "      <td>1시간 전</td>\n",
       "      <td>2022. 04. 04</td>\n",
       "      <td>89</td>\n",
       "      <td>역시 5 성급 하얏트 답습니다 조식 포함 시 좀 비싸긴 한데 조식 빼고 생각하면 매...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124549</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>완전 좋았습니디</td>\n",
       "      <td>6시간 전</td>\n",
       "      <td>2022. 04. 04</td>\n",
       "      <td>8</td>\n",
       "      <td>완전 좋았습니디</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124550</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>깔끔하고 좋았어용</td>\n",
       "      <td>1일 전</td>\n",
       "      <td>2022. 04. 03</td>\n",
       "      <td>9</td>\n",
       "      <td>깔끔하고 좋았어용</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124551</th>\n",
       "      <td>그랜드 하얏트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>너무좋았습니다</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>7</td>\n",
       "      <td>너무 좋았습니다</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124552 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      hotel  star  \\\n",
       "0                       AC 호텔 바이 메리어트 서울 강남     4   \n",
       "1                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "2                       AC 호텔 바이 메리어트 서울 강남     3   \n",
       "3                       AC 호텔 바이 메리어트 서울 강남     5   \n",
       "4       몬드리안 서울 이태원(Mondrian Seoul Itaewon)     5   \n",
       "...                                     ...   ...   \n",
       "124547                           그랜드 하얏트 서울     4   \n",
       "124548                           그랜드 하얏트 서울     5   \n",
       "124549                           그랜드 하얏트 서울     5   \n",
       "124550                           그랜드 하얏트 서울     5   \n",
       "124551                           그랜드 하얏트 서울     5   \n",
       "\n",
       "                                                   review          date  \\\n",
       "0                     발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요        10시간 전   \n",
       "1                                                   굳굳굳굳굳          6일 전   \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...          6일 전   \n",
       "3       오픈하고 얼마안되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈초기에 사소한 부분에서...  2022. 03. 27   \n",
       "4                                        객실이 깨끗하고 너무 예뻣어요          3일 전   \n",
       "...                                                   ...           ...   \n",
       "124547                    친절하시고 조식도 맛있었어요하지만 방음이많이 안되더라구요         1시간 전   \n",
       "124548  역시 5성급 하얏트답습니다 조식 포함시 좀 비싸긴 한데 조식빼고 생각하면 매우 괜찮...         1시간 전   \n",
       "124549                                           완전 좋았습니디         6시간 전   \n",
       "124550                                          깔끔하고 좋았어용          1일 전   \n",
       "124551                                            너무좋았습니다          3일 전   \n",
       "\n",
       "           real_date  length  \\\n",
       "0       2022. 04. 03      35   \n",
       "1       2022. 04. 01       5   \n",
       "2       2022. 04. 01      84   \n",
       "3       2022. 03. 27      99   \n",
       "4       2022. 04. 01      16   \n",
       "...              ...     ...   \n",
       "124547  2022. 04. 04      31   \n",
       "124548  2022. 04. 04      89   \n",
       "124549  2022. 04. 04       8   \n",
       "124550  2022. 04. 03       9   \n",
       "124551  2022. 04. 01       7   \n",
       "\n",
       "                                             review_space  \n",
       "0                     발렛파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요  \n",
       "1                                                 굳굳 굳굳 굳  \n",
       "2       생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...  \n",
       "3       오픈하고 얼마 안 되서 갔었는데 깔끔하고 좋은 호텔이였습니다 오픈 초기에 사소한 부...  \n",
       "4                                       객실이 깨끗하고 너무 예 뻣어요  \n",
       "...                                                   ...  \n",
       "124547                 친절하시고 조식도 맛있었어요 하지만 방음이 많이 안 되더라구요  \n",
       "124548  역시 5 성급 하얏트 답습니다 조식 포함 시 좀 비싸긴 한데 조식 빼고 생각하면 매...  \n",
       "124549                                           완전 좋았습니디  \n",
       "124550                                          깔끔하고 좋았어용  \n",
       "124551                                           너무 좋았습니다  \n",
       "\n",
       "[124552 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lEIZbJleAz0C"
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j0GsXqmb9Q2P",
    "outputId": "af0567a5-6d10-4ede-a3ab-13711abd28d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "결측치가 존재하지 않습니다\n"
     ]
    }
   ],
   "source": [
    "# 결측치 존재 여부를 확인해주는 함수\n",
    "def check_missing_col(dataframe):\n",
    "    missing_col = []\n",
    "    counted_missing_col = 0\n",
    "    for i, col in enumerate(dataframe.columns):\n",
    "        missing_values = sum(dataframe[col].isna())\n",
    "        is_missing = True if missing_values >= 1 else False\n",
    "        if is_missing:\n",
    "            counted_missing_col += 1\n",
    "            print(f'결측치가 있는 컬럼은: {col}입니다')\n",
    "            print(f'해당 컬럼에 총 {missing_values}개의 결측치가 존재합니다.')\n",
    "            missing_col.append([col, dataframe[col].dtype])\n",
    "    if counted_missing_col == 0:\n",
    "        print('결측치가 존재하지 않습니다')\n",
    "    return missing_col\n",
    "\n",
    "missing_col = check_missing_col(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hanspell import spell_checker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spell_check(review):\n",
    "    try:\n",
    "      result = spell_checker.check(review)\n",
    "      return result.checked\n",
    "    except:\n",
    "      print(review)\n",
    "      return review\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-7574688e9d9b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['review']=df['review'].astype('str')\n"
     ]
    }
   ],
   "source": [
    "df['review']=df['review'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "업청깨끗해여\n",
      "편하곶늫음\n",
      "츄홓\n",
      "역시 갓라머뭄 짱짱\n",
      "조아요오옹\n",
      "괜차나욬\n",
      "너무좋았습니\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-be5ec2530679>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['review_spell_check'] = df['review'].apply(spell_check)\n"
     ]
    }
   ],
   "source": [
    "df['review_spell_check'] = df['review'].apply(spell_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"야놀자한스펠.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('야놀자한스펠.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df.star>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_Q-ltLK79oVp",
    "outputId": "51fd7779-8037-4346-ccfd-dc5dfedc8e0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    91681\n",
       "4    21419\n",
       "3     6314\n",
       "1     1604\n",
       "2     1554\n",
       "Name: star, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_count = df['star'].value_counts() # 유니크값의 개수를 확인합니다.\n",
    "val_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad = df.loc[df.star<=3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-04ed5c8ffe1c>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  bad['label'] = 0\n"
     ]
    }
   ],
   "source": [
    "bad['label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "good = df.loc[df.star==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-8290465e9f27>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  good['label']= 1\n"
     ]
    }
   ],
   "source": [
    "good['label']= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "postive = good.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "      <th>review_space</th>\n",
       "      <th>review_spell_check</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46692</th>\n",
       "      <td>호텔 더 디자이너스 홍대</td>\n",
       "      <td>5</td>\n",
       "      <td>친절하고 따뜻하고 넘 좋았습니다</td>\n",
       "      <td>2020. 04. 19</td>\n",
       "      <td>2020. 04. 19</td>\n",
       "      <td>17</td>\n",
       "      <td>친절하고 따뜻하고 넘 좋았습니다</td>\n",
       "      <td>친절하고 따뜻하고 너무 좋았습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108126</th>\n",
       "      <td>호텔 로프트 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>편하게 쉬다가 갑니다</td>\n",
       "      <td>2020. 06. 14</td>\n",
       "      <td>2020. 06. 14</td>\n",
       "      <td>11</td>\n",
       "      <td>편하게 쉬다가 갑니다</td>\n",
       "      <td>편하게 쉬다가 갑니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99198</th>\n",
       "      <td>신라스테이 마포</td>\n",
       "      <td>5</td>\n",
       "      <td>깨끗하고 편하긴한데 생각보다 청결도는 떨어짐</td>\n",
       "      <td>2020. 11. 28</td>\n",
       "      <td>2020. 11. 28</td>\n",
       "      <td>24</td>\n",
       "      <td>깨끗하고 편하긴 한데 생각보다 청결도는 떨어짐</td>\n",
       "      <td>깨끗하고 편하긴 한데 생각보다 청결 도는 떨어짐</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88352</th>\n",
       "      <td>글래드 마포</td>\n",
       "      <td>5</td>\n",
       "      <td>위치도 너무 좋고 깔끔한 인테리어에 잘 지냈습니다</td>\n",
       "      <td>2022. 02. 19</td>\n",
       "      <td>2022. 02. 19</td>\n",
       "      <td>27</td>\n",
       "      <td>위치도 너무 좋고 깔끔한 인테리어에 잘 지냈습니다</td>\n",
       "      <td>위치도 너무 좋고 깔끔한 인테리어에 잘 지냈습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68100</th>\n",
       "      <td>호텔 선샤인 서울</td>\n",
       "      <td>5</td>\n",
       "      <td>깨끗하고 괜찮았습니다</td>\n",
       "      <td>2018. 10. 30</td>\n",
       "      <td>2018. 10. 30</td>\n",
       "      <td>11</td>\n",
       "      <td>깨끗하고 괜찮았습니다</td>\n",
       "      <td>깨끗하고 괜찮았습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88843</th>\n",
       "      <td>글래드 마포</td>\n",
       "      <td>5</td>\n",
       "      <td>항상 가지만 침대에서나오는 구스털이 날려요 먼지가 많고 환기가 잘 안되서 목이 자주...</td>\n",
       "      <td>2021. 11. 15</td>\n",
       "      <td>2021. 11. 15</td>\n",
       "      <td>53</td>\n",
       "      <td>항상 가지만 침대에서 나오는 구스털이 날려 요 먼지가 많고 환기가 잘 안 되서 목이...</td>\n",
       "      <td>항상 가지만 침대에서 나오는 구스 털이 날려요 먼지가 많고 환기가 잘 안돼서 목이 ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6959</th>\n",
       "      <td>L7 강남 바이 롯데</td>\n",
       "      <td>5</td>\n",
       "      <td>헬스장도 좋고 룸컨디션도 깔끔하고 선정릉 뷰도 좋습니다</td>\n",
       "      <td>2021. 07. 31</td>\n",
       "      <td>2021. 07. 31</td>\n",
       "      <td>30</td>\n",
       "      <td>헬스장도 좋고 룸 컨디션도 깔끔하고 선정릉 뷰도 좋습니다</td>\n",
       "      <td>헬스장도 좋고 룸 컨디션도 깔끔하고 선정릉 뷰도 좋습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25829</th>\n",
       "      <td>L7 명동 바이 롯데</td>\n",
       "      <td>5</td>\n",
       "      <td>깨끗했어요</td>\n",
       "      <td>2022. 02. 19</td>\n",
       "      <td>2022. 02. 19</td>\n",
       "      <td>5</td>\n",
       "      <td>깨끗했어요</td>\n",
       "      <td>깨끗했어요</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45982</th>\n",
       "      <td>메리골드 호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>깨끗한 청소와 편리성좋습니다이지역 출장은 이곳으로 옵니다</td>\n",
       "      <td>2018. 07. 27</td>\n",
       "      <td>2018. 07. 27</td>\n",
       "      <td>31</td>\n",
       "      <td>깨끗한 청소와 편리성 좋습니다이 지역 출장은 이곳으로 옵니다</td>\n",
       "      <td>깨끗한 청소와 편리성 좋습니다 이 지역 출장은 이곳으로 옵니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120667</th>\n",
       "      <td>명동 멀린호텔</td>\n",
       "      <td>5</td>\n",
       "      <td>좋아요오오오</td>\n",
       "      <td>2020. 05. 25</td>\n",
       "      <td>2020. 05. 25</td>\n",
       "      <td>6</td>\n",
       "      <td>좋아요오오오</td>\n",
       "      <td>좋아요 오 오오</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                hotel  star  \\\n",
       "46692   호텔 더 디자이너스 홍대     5   \n",
       "108126      호텔 로프트 서울     5   \n",
       "99198        신라스테이 마포     5   \n",
       "88352          글래드 마포     5   \n",
       "68100       호텔 선샤인 서울     5   \n",
       "...               ...   ...   \n",
       "88843          글래드 마포     5   \n",
       "6959      L7 강남 바이 롯데     5   \n",
       "25829     L7 명동 바이 롯데     5   \n",
       "45982         메리골드 호텔     5   \n",
       "120667        명동 멀린호텔     5   \n",
       "\n",
       "                                                   review          date  \\\n",
       "46692                                   친절하고 따뜻하고 넘 좋았습니다  2020. 04. 19   \n",
       "108126                                        편하게 쉬다가 갑니다  2020. 06. 14   \n",
       "99198                            깨끗하고 편하긴한데 생각보다 청결도는 떨어짐  2020. 11. 28   \n",
       "88352                         위치도 너무 좋고 깔끔한 인테리어에 잘 지냈습니다  2022. 02. 19   \n",
       "68100                                         깨끗하고 괜찮았습니다  2018. 10. 30   \n",
       "...                                                   ...           ...   \n",
       "88843   항상 가지만 침대에서나오는 구스털이 날려요 먼지가 많고 환기가 잘 안되서 목이 자주...  2021. 11. 15   \n",
       "6959                       헬스장도 좋고 룸컨디션도 깔끔하고 선정릉 뷰도 좋습니다  2021. 07. 31   \n",
       "25829                                               깨끗했어요  2022. 02. 19   \n",
       "45982                     깨끗한 청소와 편리성좋습니다이지역 출장은 이곳으로 옵니다  2018. 07. 27   \n",
       "120667                                             좋아요오오오  2020. 05. 25   \n",
       "\n",
       "           real_date  length  \\\n",
       "46692   2020. 04. 19      17   \n",
       "108126  2020. 06. 14      11   \n",
       "99198   2020. 11. 28      24   \n",
       "88352   2022. 02. 19      27   \n",
       "68100   2018. 10. 30      11   \n",
       "...              ...     ...   \n",
       "88843   2021. 11. 15      53   \n",
       "6959    2021. 07. 31      30   \n",
       "25829   2022. 02. 19       5   \n",
       "45982   2018. 07. 27      31   \n",
       "120667  2020. 05. 25       6   \n",
       "\n",
       "                                             review_space  \\\n",
       "46692                                   친절하고 따뜻하고 넘 좋았습니다   \n",
       "108126                                        편하게 쉬다가 갑니다   \n",
       "99198                           깨끗하고 편하긴 한데 생각보다 청결도는 떨어짐   \n",
       "88352                         위치도 너무 좋고 깔끔한 인테리어에 잘 지냈습니다   \n",
       "68100                                         깨끗하고 괜찮았습니다   \n",
       "...                                                   ...   \n",
       "88843   항상 가지만 침대에서 나오는 구스털이 날려 요 먼지가 많고 환기가 잘 안 되서 목이...   \n",
       "6959                      헬스장도 좋고 룸 컨디션도 깔끔하고 선정릉 뷰도 좋습니다   \n",
       "25829                                               깨끗했어요   \n",
       "45982                   깨끗한 청소와 편리성 좋습니다이 지역 출장은 이곳으로 옵니다   \n",
       "120667                                             좋아요오오오   \n",
       "\n",
       "                                       review_spell_check  label  \n",
       "46692                                  친절하고 따뜻하고 너무 좋았습니다      1  \n",
       "108126                                        편하게 쉬다가 갑니다      1  \n",
       "99198                          깨끗하고 편하긴 한데 생각보다 청결 도는 떨어짐      1  \n",
       "88352                         위치도 너무 좋고 깔끔한 인테리어에 잘 지냈습니다      1  \n",
       "68100                                         깨끗하고 괜찮았습니다      1  \n",
       "...                                                   ...    ...  \n",
       "88843   항상 가지만 침대에서 나오는 구스 털이 날려요 먼지가 많고 환기가 잘 안돼서 목이 ...      1  \n",
       "6959                      헬스장도 좋고 룸 컨디션도 깔끔하고 선정릉 뷰도 좋습니다      1  \n",
       "25829                                               깨끗했어요      1  \n",
       "45982                  깨끗한 청소와 편리성 좋습니다 이 지역 출장은 이곳으로 옵니다      1  \n",
       "120667                                           좋아요 오 오오      1  \n",
       "\n",
       "[10000 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([postive, bad])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "      <th>review_space</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102896</th>\n",
       "      <td>신라스테이 마포</td>\n",
       "      <td>5</td>\n",
       "      <td>좋았습니다</td>\n",
       "      <td>2018. 05. 22</td>\n",
       "      <td>2018. 05. 22</td>\n",
       "      <td>5</td>\n",
       "      <td>좋았습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17701</th>\n",
       "      <td>신라스테이 서초</td>\n",
       "      <td>5</td>\n",
       "      <td>직원분들도 정말 친절하시고 객실도 깔끔하고 뷰도 좋고 전체적으로 만족스럽고 좋았습니다</td>\n",
       "      <td>2020. 12. 23</td>\n",
       "      <td>2020. 12. 23</td>\n",
       "      <td>47</td>\n",
       "      <td>직원 분들도 정말 친절하시고 객실도 깔끔하고 뷰도 좋고 전체적으로 만족스럽고 좋았습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91027</th>\n",
       "      <td>글래드 마포</td>\n",
       "      <td>5</td>\n",
       "      <td>편히쉬다왔습니다</td>\n",
       "      <td>2021. 06. 22</td>\n",
       "      <td>2021. 06. 22</td>\n",
       "      <td>8</td>\n",
       "      <td>편히 쉬다 왔습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5705</th>\n",
       "      <td>노보텔 앰배서더 서울 용산</td>\n",
       "      <td>5</td>\n",
       "      <td>너무 편하고 좋았습니다</td>\n",
       "      <td>2020. 03. 24</td>\n",
       "      <td>2020. 03. 24</td>\n",
       "      <td>12</td>\n",
       "      <td>너무 편하고 좋았습니다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30082</th>\n",
       "      <td>롯데시티호텔 명동</td>\n",
       "      <td>5</td>\n",
       "      <td>좋아요역시</td>\n",
       "      <td>2022. 02. 02</td>\n",
       "      <td>2022. 02. 02</td>\n",
       "      <td>5</td>\n",
       "      <td>좋아요 역시</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124518</th>\n",
       "      <td>호텔 더 디자이너스 리즈 강남</td>\n",
       "      <td>2</td>\n",
       "      <td>서비스 제품은 부실했고 무엇보다 방음이 하나도 안되서 실망했어요</td>\n",
       "      <td>5일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>35</td>\n",
       "      <td>서비스 제품은 부실했고 무엇보다 방음이 하나도 안 되서 실망했어요</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124525</th>\n",
       "      <td>신라스테이 서초</td>\n",
       "      <td>3</td>\n",
       "      <td>좋아좋아좋아</td>\n",
       "      <td>3일 전</td>\n",
       "      <td>2022. 04. 01</td>\n",
       "      <td>6</td>\n",
       "      <td>좋아 좋아 좋아</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124530</th>\n",
       "      <td>신라스테이 서초</td>\n",
       "      <td>3</td>\n",
       "      <td>라운지를 이용했는데 정말 먹을게 하나도 없고 해피아워관련되서 이렇게 안좋은데는 처음...</td>\n",
       "      <td>2022. 03. 28</td>\n",
       "      <td>2022. 03. 28</td>\n",
       "      <td>76</td>\n",
       "      <td>라운지를 이용했는데 정말 먹을 게 하나도 없고 해피 아워 관련되서 이렇게 안 좋은 ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124534</th>\n",
       "      <td>안테룸 서울</td>\n",
       "      <td>1</td>\n",
       "      <td>밤에 너무 추워요 자다가 감기걸릴뻔했어요</td>\n",
       "      <td>2일 전</td>\n",
       "      <td>2022. 04. 02</td>\n",
       "      <td>22</td>\n",
       "      <td>밤에 너무 추워요 자 다가 감기 걸릴 뻔 했어요</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124537</th>\n",
       "      <td>호텔 포코</td>\n",
       "      <td>2</td>\n",
       "      <td>더블침대가 싱글 2개로 붙여 놓은 거라 굉장히 불편했습니다</td>\n",
       "      <td>2022. 03. 28</td>\n",
       "      <td>2022. 03. 28</td>\n",
       "      <td>32</td>\n",
       "      <td>더블침대가 싱글 2개로 붙여 놓은 거라 굉장히 불편했습니다</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19472 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   hotel  star  \\\n",
       "102896          신라스테이 마포     5   \n",
       "17701           신라스테이 서초     5   \n",
       "91027             글래드 마포     5   \n",
       "5705      노보텔 앰배서더 서울 용산     5   \n",
       "30082          롯데시티호텔 명동     5   \n",
       "...                  ...   ...   \n",
       "124518  호텔 더 디자이너스 리즈 강남     2   \n",
       "124525          신라스테이 서초     3   \n",
       "124530          신라스테이 서초     3   \n",
       "124534            안테룸 서울     1   \n",
       "124537             호텔 포코     2   \n",
       "\n",
       "                                                   review          date  \\\n",
       "102896                                              좋았습니다  2018. 05. 22   \n",
       "17701     직원분들도 정말 친절하시고 객실도 깔끔하고 뷰도 좋고 전체적으로 만족스럽고 좋았습니다  2020. 12. 23   \n",
       "91027                                            편히쉬다왔습니다  2021. 06. 22   \n",
       "5705                                         너무 편하고 좋았습니다  2020. 03. 24   \n",
       "30082                                               좋아요역시  2022. 02. 02   \n",
       "...                                                   ...           ...   \n",
       "124518                서비스 제품은 부실했고 무엇보다 방음이 하나도 안되서 실망했어요          5일 전   \n",
       "124525                                             좋아좋아좋아          3일 전   \n",
       "124530  라운지를 이용했는데 정말 먹을게 하나도 없고 해피아워관련되서 이렇게 안좋은데는 처음...  2022. 03. 28   \n",
       "124534                             밤에 너무 추워요 자다가 감기걸릴뻔했어요          2일 전   \n",
       "124537                   더블침대가 싱글 2개로 붙여 놓은 거라 굉장히 불편했습니다  2022. 03. 28   \n",
       "\n",
       "           real_date  length  \\\n",
       "102896  2018. 05. 22       5   \n",
       "17701   2020. 12. 23      47   \n",
       "91027   2021. 06. 22       8   \n",
       "5705    2020. 03. 24      12   \n",
       "30082   2022. 02. 02       5   \n",
       "...              ...     ...   \n",
       "124518  2022. 04. 01      35   \n",
       "124525  2022. 04. 01       6   \n",
       "124530  2022. 03. 28      76   \n",
       "124534  2022. 04. 02      22   \n",
       "124537  2022. 03. 28      32   \n",
       "\n",
       "                                             review_space  label  \n",
       "102896                                              좋았습니다      1  \n",
       "17701    직원 분들도 정말 친절하시고 객실도 깔끔하고 뷰도 좋고 전체적으로 만족스럽고 좋았습니다      1  \n",
       "91027                                          편히 쉬다 왔습니다      1  \n",
       "5705                                         너무 편하고 좋았습니다      1  \n",
       "30082                                              좋아요 역시      1  \n",
       "...                                                   ...    ...  \n",
       "124518               서비스 제품은 부실했고 무엇보다 방음이 하나도 안 되서 실망했어요      0  \n",
       "124525                                           좋아 좋아 좋아      0  \n",
       "124530  라운지를 이용했는데 정말 먹을 게 하나도 없고 해피 아워 관련되서 이렇게 안 좋은 ...      0  \n",
       "124534                         밤에 너무 추워요 자 다가 감기 걸릴 뻔 했어요      0  \n",
       "124537                   더블침대가 싱글 2개로 붙여 놓은 거라 굉장히 불편했습니다      0  \n",
       "\n",
       "[19472 rows x 8 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tmp1Qc-3yYXS"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # 그래프 그리기 위한 패키지\n",
    "%matplotlib inline\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "id": "7SnKNPm1ycss",
    "outputId": "e24e0aa8-d5e9-496c-dedd-4d13454a4d41"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAIECAYAAAAEgdi5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdfXRU1b3/8c9MBpBhkpDJEGKQIuGhvVBoAqECtUmAsa1FehForbVUQYXKvbBAbld9+Fl1KdxYC6Hhwd4ijfVqSxUl1dbWNo1JJLm0QRIQbA00FIuAIZkxZhggJDm/P1iZ7khIAiYTJrxfa2WtmXP2OXvv+Yrrk5M959gsy7IEAAAAQJJk7+kBAAAAAJcTAjIAAABgICADAAAABgIyAAAAYCAgAwAAAAYCMgAAAGAgIAPAJcrMzJTNZpPNZuvpofSoO+64I/Q5/OMf/+jp4QDAJ0ZABtCuluBzMSGwsLAwdExmZmb3DU7Shx9+qEceeUSPPPKI8vLyurUvAMCVwdHTAwCAT+LDDz/Uo48+Kkm6/fbbNXv27B4eEQAg0nEFGQDwiTzzzDOyLEuWZenaa6/t6eEAwCdGQAYAAAAMBGQAAADAQEAG0OP+8Ic/aP78+UpOTpbT6VR0dLQ+85nP6Lvf/a7eeuutNo/5xz/+IZvNpuHDh4e2/fznP2/1pcKO7qxQU1OjVatW6Ytf/KISExPVt29fDRo0SF/84hf1wx/+UIFAoDume55rr71WNpsttDzh9OnTysnJ0fXXX6/BgwfLbre3+WXH5uZmvfDCC7rllls0fPjwVp/dPffco7fffrvN/vbu3Rv6bObOndupMebk5ISOWb9+fat9F3MXi0AgoHXr1umGG25QUlKS+vXrJ7fbrUmTJukHP/iBTpw40eZxR44cCfXxn//5n222aW5ultvtDrV7/PHH22xXV1cnh8Mhm82mW2+9tePJA7jyWADQDkmhn8564403QsdkZGRcsF19fb01a9asVn18/Mdms1nLli2zmpqaWh176NChdo8zfw4dOnRe37m5uVZ0dHS7xw0ePNgqLS294PgzMjIu+rNpy7BhwyxJ1rBhw6yqqipr7Nix543l45/jwYMHrZSUlHbHb7fbrYceeqjNPsePH29Jsvr162f5/f4Oxzhp0iRLkuVwOKzq6upW+26//fZ2P+sWr732mpWQkNDumKOjo61f//rXbR4/YsQIS5I1ZsyYNvfv3r271bmmT5/eZrtXXnkl1OZ//ud/Opw7gCsPd7EA0COampp04403aseOHZKkgQMHauHChZowYYIaGxu1Y8cOPfvss2poaFBOTo5OnTqln/70p6HjExIStH37dlVXV2vx4sWSpGnTpmnZsmXn9ZWQkNDq/Y9//GMtX75ckuR0OjVv3jxNnTpV8fHxqqmp0e9//3u98sor+uCDD+T1elVWVqYxY8Z010cRcubMGc2ZM0f79+/X9ddfr7lz5yopKUknTpzQBx98EGr397//XZMnT1ZNTY0k6frrr9dNN92kYcOGqampSW+99ZaeeeYZ+f1+PfbYY7Lb7XrkkUda9TV//nx973vf05kzZ/Tiiy/q7rvvvuC4KisrVVZWJkn6yle+okGDBl303F566SXdcsstampqUp8+ffS1r31NmZmZGjx4sD766CO98cYbeuGFF1RfX6+bb75Zf/zjHzV9+vRW55g2bZr+/ve/65133tEHH3ygwYMHt9r/xhtvtHpfWlqqM2fOqF+/fhdsN23atIueC4ArQE8ndACXN3XTFeSsrKxQm09/+tPW+++/f16b3bt3W263O9TuN7/5zXltzCvJt99+e4djKysrsxwOhyXJSklJsQ4fPtxmu1dffdXq06ePJcm67rrr2mzT1VeQW37Wrl17wbZNTU3WhAkTQld/X3jhhTbbHT9+PHSF2W63W/v27Wu1//3337fsdrslyUpPT293fA899FBobFu3bj1vf0dXkN977z0rJibGkmR96lOfsvbu3dtmP3/+85+t2NhYS5J1zTXXWA0NDa32P/fcc+2O46abbrIkWVOmTAm1KywsPK9damqqJckaMmRIu/MGcOUiIANolxncLuWnrYB85swZa/DgwaE/2V8oMFmWZb344ouhc33hC184b//FBuSWJR3R0dHWkSNH2m1rBsOSkpLz9ndHQL755pvbbfvSSy+F2ubk5LTb9t1337WioqIsSdbdd9993n6v1xtaxnKhXxQsy7KSk5MtSVZMTIwVDAbP299RQF66dKklyYqKirLKy8vbHfOWLVtC53r++edb7Xv//fdD+xYvXtxqX2NjYyiEb9q0yRo5cqQlyXr44YdbtfP5fKFfDG677bZ2xwLgysWX9ACEXWlpaWjJwI033qhx48ZdsO28efM0cuRISVJJSYmqq6svuV+/36/f/va3kqRbb71VQ4YMabf9t7/97dDrP/zhD5fc78VYunRpu/v/93//V5IUExPT7rIISRo9erQ+//nPS2p7/PPnz5ckWZal559/vs1zlJaWqqqqStK5WvTv37/9CXyMee4ZM2YoJSWl3fa33HKLHA5Hm2NOSkrSqFGjJJ2/nGL37t366KOPJJ1bNtGydOLj7YqLi9Xc3BxqBwBtYQ0ygE7bvn17p9rt27dPDz300AX3/+Uvfwm9/tKXvtTh+W644QYdPHhQkvTnP/9Zs2bN6tQ4Pq6kpCQUjqKiojp8NPXZs2dDr//6179eUp8XIyoqSlOmTGm3zZtvvilJuvrqq/X73/++U+eUpMOHD+vUqVOtAu6cOXN0zz33KBgM6vnnn9f9999/3vHPPfdc6LX5C0Nn7d+/Xz6fT5IUHR3dqceBu1wuffjhh21+5tOmTdOBAwdUWVmpo0ePKikpSdK5x5tLUmJioj7zmc9o2rRp2rx5s3bu3Nlq3mZg7u7HoAOIXARkAJ3W2cc4Dxw4sN39x44dC70ePXp0h+cz25jHXizzFmRPPfWUnnrqqU4f6/f7L7nfzoqPj9dVV111wf2BQEC1tbWSpHfffVc333zzRZ3f7/e3Csgul0uzZ8/WL37xC+3fv1/l5eVKTU0N7T979qxeeOEFSdLQoUMvKVCan/lLL72kl1566aLG+3GZmZmhL2sWFBSEQntL8G25Ktwy1oaGBpWUlMjr9Ur6V5AeOnSoRowYcVFzAXDlYIkFgLCrr68PvR4wYECH7V0uV5vHXqy6urpLPrahoeGSj+2sjpYvfJLxS23PoWWZhdT6arEk/e53vwsF8ttuu002m+2i++zqz9wM6S2huLGxMXRlvSUgX3311frMZz7Tqp3P59PevXtbtQOAthCQAYRddHR06PXJkyc7bG8+sMM89mKZQftnP/uZrHNfVO7UT8uVx55kjj89Pf2ixm9ZVuhBJKYbbrhBiYmJkqRf/vKXoSUoUuvAbAbpSx3zD37wg4sab1sPHbn66qv16U9/WtK/gu+uXbtC/42YwbclTLe0KyoqkmVZ57UDgI8jIAMIu6uvvjr0+sCBAx22N9u0rDm9FOaX8o4cOXLJ5+kpsbGxocDZVeOPiorSN7/5TUnnlq/86U9/kiR99NFHevXVVyVJqampl3wf6O74zFvC7aFDh3T48OFQAB46dGjoC51mu7KyMgUCAdYfA+g0AjKAsGu5s4Ik/fGPf+ywvdnGPFaS7PZ//W+s5erghaSnp4eWCYTrrhRdLT09XZJUVVUV+uLiJ9XWMott27bp9OnT5+2/WKmpqYqJiZEk/elPf2p1hfpSfXyZxcfXH3+8XcuDZ1r+CnDttde2eTUdAFoQkAGE3dSpU0N/1v/tb3+rd95554JtX3755dAV5Ouvv/68p+KZf8LvaLlGQkKCvvKVr0iSduzYEZEh+fbbbw+9/sEPftAl55wwYULoCvH27dt16tSpUFCOiorSrbfeesnnjoqK0m233Sbp3J00nn766U88XjMgv/766yotLZV0fkBOSEjQ2LFjJUkvvvii9u3b12Y7APg4AjKAsOvbt69WrFgh6dzVva9//ett3p1i7969ocdIS9J99913Xhu3263Y2FhJUkVFRYdXkR9//HH16dNHkvTNb36zw1ulHT58WP/1X//1ie6/3JXmzZunSZMmSTq3Znj58uXtfoHw1KlTeuaZZ7R169Z2z9tyN4j6+npt2LBBRUVFkiSv1xv6ZeZSPfDAA6E7myxbtkzPPvtsu+2rq6v12GOPhb5Q93GDBw/Wv/3bv0k6d6W75RejtoJvy7Znn3029N8GyysAdITbvAHoEStXrtSrr76qHTt26J133tHYsWO1cOFCTZgwQY2NjSopKdHPf/5znTlzRpJ09913a+bMmW2ea/r06dq+fbv+/ve/65ZbbtGcOXNa3WouIyMjdIeICRMm6KmnntLdd98tv9+vG2+8UV/4whd04403avjw4erTp498Pp/+9re/aceOHdq1a5ckafny5d38iXSO3W7XSy+9pClTpuj999/Xj3/8Y73wwgv6+te/rs997nOKjY1VIBDQe++9p127dulPf/qTTp48qccee6zd895222168MEHZVmW/t//+3+hpRCfZHlFi2uuuUZbt27V1772NZ05c0a333671q5dq6997WsaNWqU+vfvr7q6OlVWVmrnzp0qKSlRU1NTu1d6p02bpr/+9a9qbGyUJA0fPlzDhg1rs92GDRtC7Vq2AUC7uu0ZfQB6BRmPje6sN954o91HTbeor6+3brrppnYfVW2z2aylS5daTU1NFzxPeXm51b9//wueo63HH7/yyiuhx1139BMfH2+dOHHivHN09aOmhw0b1uljjh49as2YMaNT44+KirI2b97c4TkzMzNbHedyuayTJ092eFxHj5pu8X//93+hx1Z39ONyudp9BPkLL7zQqv3ChQvbbFdTU2PZbLZQuxEjRnQ4HwDgCjKAHuNyufTqq6/q9ddf17PPPquSkhJ98MEHioqK0pAhQ5SZmalFixZp4sSJ7Z4nJSVFb731ltauXas333xT//znPxUMBts9ZtasWTp06JCeffZZvfbaayovL1dNTY2ampoUGxurkSNHKi0tTV/60pf0pS99SX379u3KqX9iV199tfLz81VUVKRf/vKX2rFjh95//33V19drwIABuuaaazRu3DhlZmbq3//931vdOeRC5s+f3+p2djfffLOcTmeXjXny5Ml699139atf/UqvvPKKysrKdOLECZ0+fVoxMTFKTk5WamqqvF6vZs6c2e49sjMzM2Wz2Tq8bVt8fLzGjx+vPXv2hI4DgI7YLKuDBXsAAADAFYQv6QEAAAAGAjIAAABgICADAAAABgIyAAAAYCAgA0AEarmLAwCg6xGQAQAAAAMBGQAAADAQkAHgMvPKK69oxowZuvrqq9WvXz8lJSUpIyNDmzZt0j/+8Q/ZbDYVFRVJkmw2W+jHfAjGG2+8oUWLFmnMmDGKiYlR//799dnPflaPPvqoTp8+fV6fjzzyiGw2mwoLC/WLX/xC1113nVwul6699towzRoALh88SQ8ALiM//elPtXjxYiUmJmrWrFnyeDyqrq7W3r17lZubq29961t6+OGH9cwzz+jw4cN6+OGHQ8eaYfaJJ57Q3/72N02dOlUzZ87U6dOnVVJSokceeUSFhYXKz89XVFTUef2vWbNGf/zjHzVr1ixNmzZNdXV14Zg2AFxWeJIeAFxGJk6cqH379umf//ynEhISWu2rqamRx+ORdO5LekVFRbrQ/8Krqqo0fPjw877I99BDD+nxxx/X1q1bdcstt4S2P/LII3r00UfldDq1Y8cOpaamdvHMACBysMQCAC4zDodDffr0OW97SzjujOTk5DbvcrFixQpJ0uuvv97mcYsWLSIcA7jiEZAB4DJy2223KRgMasyYMVqxYoXy8vJ04sSJiz7PyZMntXr1ak2aNEmxsbGy2+2y2WyKj4+XJL3//vttHvf5z3/+E40fAHoD1iADwGXk3nvvlcfj0aZNm5STk6N169bJZrMpIyNDTz75pNLS0jo8x9mzZzV9+nT95S9/0Wc/+1ndcsstGjRoUOiq9KOPPqozZ860eWxiYmKXzgcAIhEBGQAuM9/5znf0ne98Rx9++KFKS0u1fft2/exnP9OXv/xl/e1vf9OgQYPaPf7Xv/61/vKXv+iOO+5Qbm5uq33Hjh3To48+esFjefgIALDEAgAuWwMHDtRXv/pVbd68WXfccYd8Pp+Ki4slKXQHiqampvOOO3jwoCRpzpw55+1ruT0cAODCCMgAcBl544032rwzRXV1tSTJ6XRKUmgt8XvvvXde25bbvRUWFrbaXlVVpe9///tdOFoA6J1YYgEAl5Gbb75ZLpdLkydP1rXXXivLsvTmm2+qrKxMEydOlNfrlSTNmDFDL774oubMmaOvfvWr6t+/v4YNG6b58+dr1qxZGjlypNauXau3335bqampeu+99/Sb3/xGM2fObDNUAwD+hYAMAJeRrKwsvf7669q9e7dee+01XXXVVRo2bJieeOIJ3XPPPaEv2t111106fPiwtm7dqh/+8IdqbGxURkaG5s+frwEDBqigoED33XefCgsL9eabbyo5OVkPPfSQ7r33Xv3qV7/q4VkCwOWNB4UAAAAABtYgAwAAAAYCMgAAAGAgIAMAAAAGAjIAAABgICADAAAABgIyAAAAYIjo+yAfPXq0p4fQrTwej2pqanp6GLgI1CzyULPIQ80iDzWLPFdKzZKSktrczhVkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADI6eHgAAAAA6tmZNdNj6cjqjFAyGr7+VK+vD1ldncAUZAAAAMBCQAQAAAEOnllj85je/UUFBgWw2m4YOHaolS5boww8/1Lp161RfX6/k5GQtXbpUDodDZ8+e1YYNG1RVVaXo6GgtX75cCQkJkqTt27eroKBAdrtdCxYsUEpKiiSpoqJCubm5am5u1owZMzR79uzumzEAAADQjg6vIPt8Pv3ud79TVlaW1qxZo+bmZpWWluq5557TzJkztX79eg0YMEAFBQWSpIKCAg0YMEDr16/XzJkz9fzzz0uSjhw5otLSUq1du1YPPvigtmzZoubmZjU3N2vLli164IEHlJ2drZKSEh05cqR7Zw0AAABcQKeWWDQ3N6uhoUFNTU1qaGjQwIEDtX//fk2ePFmSlJmZqbKyMknSrl27lJmZKUmaPHmy9u3bJ8uyVFZWpqlTp6pPnz5KSEhQYmKiDh48qIMHDyoxMVGDBw+Ww+HQ1KlTQ+cCAAAAwq3DJRZut1uzZs3SPffco759++pzn/uckpOT5XQ6FRUVFWrj8/kknbviHB8fL0mKioqS0+lUfX29fD6fRo0a1eq8Lce0tG95feDAgTbHkp+fr/z8fElSVlaWPB7Ppcw5Yjgcjl4/x96GmkUeahZ5qFnkoWZdw+mMCltfdrtdTqczbP15PP3C1ldndBiQA4GAysrKtHHjRjmdTq1du1YVFRXhGNt5vF6vvF5v6H1NTU2PjCNcPB5Pr59jb0PNIg81izzULPJQs64RztuuOZ1OBYPBsPVXU9Mzt3lLSkpqc3uHSyzefvttJSQkKCYmRg6HQ9ddd53effddBYNBNTU1STp31djtdks6d2W4trZWktTU1KRgMKjo6OhW281jPr69trY2dC4AAAAg3DoMyB6PRwcOHNCZM2dkWZbefvttXXPNNRo7dqx27twpSSosLFRaWpokaeLEiSosLJQk7dy5U2PHjpXNZlNaWppKS0t19uxZVVdX69ixYxo5cqRGjBihY8eOqbq6Wo2NjSotLQ2dCwAAAAi3DpdYjBo1SpMnT9b3v/99RUVF6dprr5XX69WECRO0bt06bd26VcOHD9f06dMlSdOnT9eGDRu0dOlSuVwuLV++XJI0dOhQTZkyRffee6/sdrvuvPNO2e3n8vnChQu1atUqNTc3a9q0aRo6dGg3ThkAAAC4MJtlWVZPD+JSHT16tKeH0K1YsxV5qFnkoWaRh5pFHmrWNcL7qOnwrkHuqUdNX/IaZAAAAOBKQkAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMjo4aHD16VNnZ2aH31dXV+sY3vqGMjAxlZ2frxIkTGjRokFasWCGXyyXLspSbm6vy8nL169dPS5YsUXJysiSpsLBQL7/8siRpzpw5yszMlCRVVVVp48aNamhoUGpqqhYsWCCbzdYN0wUAAADa1+EV5KSkJD355JN68skn9cQTT6hv3776/Oc/r7y8PI0bN045OTkaN26c8vLyJEnl5eU6fvy4cnJytGjRIj399NOSpEAgoG3btmn16tVavXq1tm3bpkAgIEnavHmzFi9erJycHB0/flwVFRXdOGUAAADgwi5qicXbb7+txMREDRo0SGVlZcrIyJAkZWRkqKysTJK0a9cupaeny2azafTo0Tp58qT8fr8qKio0fvx4uVwuuVwujR8/XhUVFfL7/Tp16pRGjx4tm82m9PT00LkAAACAcOtwiYWppKREX/jCFyRJdXV1iouLkyQNHDhQdXV1kiSfzyePxxM6Jj4+Xj6fTz6fT/Hx8aHtbre7ze0t7duSn5+v/Px8SVJWVlarfnojh8PR6+fY21CzyEPNIg81izzUrGs4nVFh68tut8vpdIatP4+nX9j66oxOB+TGxka99dZb+ta3vnXePpvNFpY1w16vV16vN/S+pqam2/vsSR6Pp9fPsbehZpGHmkUeahZ5qFnXCAajw9aX0+lUMBgMW381NfVh68uUlJTU5vZOL7EoLy/X8OHDNXDgQElSbGys/H6/JMnv9ysmJkbSuSvD5j+C2tpaud1uud1u1dbWhrb7fL42t7e0BwAAAHpCpwOyubxCktLS0lRUVCRJKioq0qRJk0Lbi4uLZVmWKisr5XQ6FRcXp5SUFO3Zs0eBQECBQEB79uxRSkqK4uLi1L9/f1VWVsqyLBUXFystLa2LpwkAAAB0TqeWWJw+fVp79+7VokWLQttmz56t7OxsFRQUhG7zJkmpqanavXu3li1bpr59+2rJkiWSJJfLpblz5+r++++XJM2bN08ul0uSdNddd2nTpk1qaGhQSkqKUlNTu3SSAAAAQGfZLMuyenoQl+ro0aM9PYRuxZqtyEPNIg81izzULPJQs66xZk3vXYO8cmWErkEGAAAArgQEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwODoTKOTJ0/qJz/5if75z3/KZrPpnnvuUVJSkrKzs3XixAkNGjRIK1askMvlkmVZys3NVXl5ufr166clS5YoOTlZklRYWKiXX35ZkjRnzhxlZmZKkqqqqrRx40Y1NDQoNTVVCxYskM1m654ZAwAAAO3o1BXk3NxcpaSkaN26dXryySc1ZMgQ5eXlady4ccrJydG4ceOUl5cnSSovL9fx48eVk5OjRYsW6emnn5YkBQIBbdu2TatXr9bq1au1bds2BQIBSdLmzZu1ePFi5eTk6Pjx46qoqOim6QIAAADt6zAgB4NB/fWvf9X06dMlSQ6HQwMGDFBZWZkyMjIkSRkZGSorK5Mk7dq1S+np6bLZbBo9erROnjwpv9+viooKjR8/Xi6XSy6XS+PHj1dFRYX8fr9OnTql0aNHy2azKT09PXQuAAAAINw6XGJRXV2tmJgYbdq0SYcPH1ZycrLuuOMO1dXVKS4uTpI0cOBA1dXVSZJ8Pp88Hk/o+Pj4ePl8Pvl8PsXHx4e2u93uNre3tG9Lfn6+8vPzJUlZWVmt+umNHA5Hr59jb0PNIg81izzULPJQs67hdEaFrS+73S6n0xm2/jyefmHrqzM6DMhNTU06dOiQFi5cqFGjRik3Nze0nKKFzWYLy5phr9crr9cbel9TU9PtffYkj8fT6+fY21CzyEPNIg81izzUrGsEg9Fh68vpdCoYDIatv5qa+rD1ZUpKSmpze4dLLOLj4xUfH69Ro0ZJkiZPnqxDhw4pNjZWfr9fkuT3+xUTEyPp3JVh8x9BbW2t3G633G63amtrQ9t9Pl+b21vaAwAAAD2hw4A8cOBAxcfH6+jRo5Kkt99+W9dcc43S0tJUVFQkSSoqKtKkSZMkSWlpaSouLpZlWaqsrJTT6VRcXJxSUlK0Z88eBQIBBQIB7dmzRykpKYqLi1P//v1VWVkpy7JUXFystLS0bpwyAAAAcGGdus3bwoULlZOTo8bGRiUkJGjJkiWyLEvZ2dkqKCgI3eZNklJTU7V7924tW7ZMffv21ZIlSyRJLpdLc+fO1f333y9JmjdvnlwulyTprrvu0qZNm9TQ0KCUlBSlpqZ2x1wBAACADtksy7J6ehCXquWqdm/Fmq3IQ80iDzWLPNQs8lCzrrFmTe9dg7xyZYStQQYAAFMk1TIAAB0SSURBVACuJARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADA4OhMo//4j//QVVddJbvdrqioKGVlZSkQCCg7O1snTpzQoEGDtGLFCrlcLlmWpdzcXJWXl6tfv35asmSJkpOTJUmFhYV6+eWXJUlz5sxRZmamJKmqqkobN25UQ0ODUlNTtWDBAtlstu6ZMQAAANCOTgVkSXr44YcVExMTep+Xl6dx48Zp9uzZysvLU15enr797W+rvLxcx48fV05Ojg4cOKCnn35aq1evViAQ0LZt25SVlSVJuu+++5SWliaXy6XNmzdr8eLFGjVqlP77v/9bFRUVSk1N7frZAgAAAB245CUWZWVlysjIkCRlZGSorKxMkrRr1y6lp6fLZrNp9OjROnnypPx+vyoqKjR+/Hi5XC65XC6NHz9eFRUV8vv9OnXqlEaPHi2bzab09PTQuQAAAIBw6/QV5FWrVkmSbrjhBnm9XtXV1SkuLk6SNHDgQNXV1UmSfD6fPB5P6Lj4+Hj5fD75fD7Fx8eHtrvd7ja3t7RvS35+vvLz8yVJWVlZrfrpjRwOR6+fY29DzSIPNYs81CzyULOu4XRGha0vu90up9MZtv48nn5h66szOhWQH3vsMbndbtXV1enxxx9XUlJSq/02my0sa4a9Xq+8Xm/ofU1NTbf32ZM8Hk+vn2NvQ80iDzWLPNQs8lCzrhEMRoetL6fTqWAwGLb+amrqw9aX6eOZtkWnlli43W5JUmxsrCZNmqSDBw8qNjZWfr9fkuT3+0Prk91ud6t/BLW1tXK73XK73aqtrQ1t9/l8bW5vaQ8AAAD0hA4D8unTp3Xq1KnQ67179+pTn/qU0tLSVFRUJEkqKirSpEmTJElpaWkqLi6WZVmqrKyU0+lUXFycUlJStGfPHgUCAQUCAe3Zs0cpKSmKi4tT//79VVlZKcuyVFxcrLS0tG6cMgAAAHBhHS6xqKur049+9CNJUlNTk66//nqlpKRoxIgRys7OVkFBQeg2b5KUmpqq3bt3a9myZerbt6+WLFkiSXK5XJo7d67uv/9+SdK8efPkcrkkSXfddZc2bdqkhoYGpaSkcAcLAAAA9BibZVlWTw/iUh09erSnh9CtWLMVeahZ5KFmkYeaRR5q1jXWrOm9a5BXrozANcgAAADAlYKADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGBydbdjc3Kz77rtPbrdb9913n6qrq7Vu3TrV19crOTlZS5culcPh0NmzZ7VhwwZVVVUpOjpay5cvV0JCgiRp+/btKigokN1u14IFC5SSkiJJqqioUG5urpqbmzVjxgzNnj27e2YLAAAAdKDTV5Bfe+01DRkyJPT+ueee08yZM7V+/XoNGDBABQUFkqSCggINGDBA69ev18yZM/X8889Lko4cOaLS0lKtXbtWDz74oLZs2aLm5mY1Nzdry5YteuCBB5Sdna2SkhIdOXKki6cJAAAAdE6nAnJtba12796tGTNmSJIsy9L+/fs1efJkSVJmZqbKysokSbt27VJmZqYkafLkydq3b58sy1JZWZmmTp2qPn36KCEhQYmJiTp48KAOHjyoxMREDR48WA6HQ1OnTg2dCwAAAAi3Ti2xeOaZZ/Ttb39bp06dkiTV19fL6XQqKipKkuR2u+Xz+SRJPp9P8fHxkqSoqCg5nU7V19fL5/Np1KhRoXOax7S0b3l94MCBNseRn5+v/Px8SVJWVpY8Hs9FTTbSOByOXj/H3oaaRR5qFnmoWeShZl3D6YwKW192u11OpzNs/Xk8/cLWV2d0GJDfeustxcbGKjk5Wfv37w/HmC7I6/XK6/WG3tfU1PTgaLqfx+Pp9XPsbahZ5KFmkYeaRR5q1jWCweiw9eV0OhUMBsPWX01Nfdj6MiUlJbW5vcOA/O6772rXrl0qLy9XQ0ODTp06pWeeeUbBYFBNTU2KioqSz+eT2+2WdO7KcG1treLj49XU1KRgMKjo6OjQ9hbmMeb22tra0HYAAAAg3Dpcg/ytb31LP/nJT7Rx40YtX75cn/3sZ7Vs2TKNHTtWO3fulCQVFhYqLS1NkjRx4kQVFhZKknbu3KmxY8fKZrMpLS1NpaWlOnv2rKqrq3Xs2DGNHDlSI0aM0LFjx1RdXa3GxkaVlpaGzgUAAACEW6dv8/Zxt912m9atW6etW7dq+PDhmj59uiRp+vTp2rBhg5YuXSqXy6Xly5dLkoYOHaopU6bo3nvvld1u15133im7/Vw+X7hwoVatWqXm5mZNmzZNQ4cO7YKpAQAAABfPZlmW1dODuFRHjx7t6SF0K9ZsRR5qFnmoWeShZpGHmnWNNWt67xrklSsvrzXIPEkPAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAg6OjBg0NDXr44YfV2NiopqYmTZ48Wd/4xjdUXV2tdevWqb6+XsnJyVq6dKkcDofOnj2rDRs2qKqqStHR0Vq+fLkSEhIkSdu3b1dBQYHsdrsWLFiglJQUSVJFRYVyc3PV3NysGTNmaPbs2d07awAAAOACOryC3KdPHz388MN68skn9cMf/lAVFRWqrKzUc889p5kzZ2r9+vUaMGCACgoKJEkFBQUaMGCA1q9fr5kzZ+r555+XJB05ckSlpaVau3atHnzwQW3ZskXNzc1qbm7Wli1b9MADDyg7O1slJSU6cuRI984aAAAAuIAOA7LNZtNVV10lSWpqalJTU5NsNpv279+vyZMnS5IyMzNVVlYmSdq1a5cyMzMlSZMnT9a+fftkWZbKyso0depU9enTRwkJCUpMTNTBgwd18OBBJSYmavDgwXI4HJo6dWroXAAAAEC4dbjEQpKam5v1/e9/X8ePH9eXv/xlDR48WE6nU1FRUZIkt9stn88nSfL5fIqPj5ckRUVFyel0qr6+Xj6fT6NGjQqd0zympX3L6wMHDrQ5jvz8fOXn50uSsrKy5PF4Lna+EcXhcPT6OfY21CzyULPIQ80iDzXrGk5nVNj6stvtcjqdYevP4+kXtr46o1MB2W6368knn9TJkyf1ox/9SEePHu3ucbXJ6/XK6/WG3tfU1PTIOMLF4/H0+jn2NtQs8lCzyEPNIg816xrBYHTY+nI6nQoGg2Hrr6amPmx9mZKSktrcflF3sRgwYIDGjh2ryspKBYNBNTU1STp31djtdks6d2W4trZW0rklGcFgUNHR0a22m8d8fHttbW3oXAAAAEC4dRiQP/roI508eVLSuTta7N27V0OGDNHYsWO1c+dOSVJhYaHS0tIkSRMnTlRhYaEkaefOnRo7dqxsNpvS0tJUWlqqs2fPqrq6WseOHdPIkSM1YsQIHTt2TNXV1WpsbFRpaWnoXAAAAEC4dbjEwu/3a+PGjWpubpZlWZoyZYomTpyoa665RuvWrdPWrVs1fPhwTZ8+XZI0ffp0bdiwQUuXLpXL5dLy5cslSUOHDtWUKVN07733ym63684775Tdfi6fL1y4UKtWrVJzc7OmTZumoUOHduOUAQAAgAuzWZZl9fQgLlVPrYUOF9ZsRR5qFnmoWeShZpGHmnWNNWt67xrklSsjeA0yAAAA0NsRkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMDg6alBTU6ONGzfqww8/lM1mk9fr1Ve/+lUFAgFlZ2frxIkTGjRokFasWCGXyyXLspSbm6vy8nL169dPS5YsUXJysiSpsLBQL7/8siRpzpw5yszMlCRVVVVp48aNamhoUGpqqhYsWCCbzdZ9swYAAAAuoMMryFFRUZo/f76ys7O1atUqvf766zpy5Ijy8vI0btw45eTkaNy4ccrLy5MklZeX6/jx48rJydGiRYv09NNPS5ICgYC2bdum1atXa/Xq1dq2bZsCgYAkafPmzVq8eLFycnJ0/PhxVVRUdOOUAQAAgAvrMCDHxcWFrgD3799fQ4YMkc/nU1lZmTIyMiRJGRkZKisrkyTt2rVL6enpstlsGj16tE6ePCm/36+KigqNHz9eLpdLLpdL48ePV0VFhfx+v06dOqXRo0fLZrMpPT09dC4AAAAg3C5qDXJ1dbUOHTqkkSNHqq6uTnFxcZKkgQMHqq6uTpLk8/nk8XhCx8THx8vn88nn8yk+Pj603e12t7m9pT0AAADQEzpcg9zi9OnTWrNmje644w45nc5W+2w2W1jWDOfn5ys/P1+SlJWV1SqI90YOh6PXz7G3oWaRh5pFHmoWeahZ13A6o8LWl91uPy/vdSePp1/Y+uqMTgXkxsZGrVmzRl/84hd13XXXSZJiY2Pl9/sVFxcnv9+vmJgYSeeuDNfU1ISOra2tldvtltvt1jvvvBPa7vP5NGbMGLndbtXW1p7Xvi1er1derzf03uynN/J4PL1+jr0NNYs81CzyULOusWZNdNj6cjqjFAyeCVt/K1fWh62vcAoGw1kzp4LBYNj6q6npmZolJSW1ub3DJRaWZeknP/mJhgwZoptuuim0PS0tTUVFRZKkoqIiTZo0KbS9uLhYlmWpsrJSTqdTcXFxSklJ0Z49exQIBBQIBLRnzx6lpKQoLi5O/fv3V2VlpSzLUnFxsdLS0rpizgAAAMBF6/AK8rvvvqvi4mJ96lOf0ve+9z1J0q233qrZs2crOztbBQUFodu8SVJqaqp2796tZcuWqW/fvlqyZIkkyeVyae7cubr//vslSfPmzZPL5ZIk3XXXXdq0aZMaGhqUkpKi1NTUbpksAAAA0BGbZVlWTw/iUh09erSnh9Ct+DNi5KFmkYeaRR5q1jXCu8QivH+u761LLKhZ17vkJRYAAADAlYSADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGBwdNdi0aZN2796t2NhYrVmzRpIUCASUnZ2tEydOaNCgQVqxYoVcLpcsy1Jubq7Ky8vVr18/LVmyRMnJyZKkwsJCvfzyy5KkOXPmKDMzU5JUVVWljRs3qqGhQampqVqwYIFsNls3TRcAAABoX4dXkDMzM/XAAw+02paXl6dx48YpJydH48aNU15eniSpvLxcx48fV05OjhYtWqSnn35a0rlAvW3bNq1evVqrV6/Wtm3bFAgEJEmbN2/W4sWLlZOTo+PHj6uioqKr5wgAAAB0WocBecyYMXK5XK22lZWVKSMjQ5KUkZGhsrIySdKuXbuUnp4um82m0aNH6+TJk/L7/aqoqND48ePlcrnkcrk0fvx4VVRUyO/369SpUxo9erRsNpvS09ND5wIAAAB6wiWtQa6rq1NcXJwkaeDAgaqrq5Mk+Xw+eTyeULv4+Hj5fD75fD7Fx8eHtrvd7ja3t7QHAAAAekqHa5A7YrPZwrZmOD8/X/n5+ZKkrKysVmG8N3I4HL1+jr0NNYs81CzyULOu4XRGha0vu90up9MZtv48nn5h6yucqFn4XFJAjo2Nld/vV1xcnPx+v2JiYiSduzJcU1MTaldbWyu32y2326133nkntN3n82nMmDFyu92qra09r/2FeL1eeb3e0Huzr97I4/H0+jn2NtQs8lCzyEPNukYwGB22vpxOp4LBYNj6q6mpD1tf4UTNul5SUlKb2y9piUVaWpqKiookSUVFRZo0aVJoe3FxsSzLUmVlpZxOp+Li4pSSkqI9e/YoEAgoEAhoz549SklJUVxcnPr376/KykpZlqXi4mKlpaVd4hQBAACAT67DK8jr1q3TO++8o/r6en33u9/VN77xDc2ePVvZ2dkqKCgI3eZNklJTU7V7924tW7ZMffv21ZIlSyRJLpdLc+fO1f333y9JmjdvXuiLf3fddZc2bdqkhoYGpaSkKDU1tbvmCgAAAHTIZlmW1dODuFRHjx7t6SF0K/6MGHmoWeShZpGHmnWNNWt675/rV67snUssqFnX69IlFgAAAEBvRUAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADI6eHgAAIPKtWRMdtr6czigFg+Hpb+XK+rD0A+DywhVkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMjp4eANDd1qyJDltfTmeUgsHw9LdyZX1Y+gEA4EpDQAZw2eGXGgBAT2KJBQAAAGAgIAMAAAAGAjIAAABguGzWIFdUVCg3N1fNzc2aMWOGZs+e3dNDAgAAwBXosriC3NzcrC1btuiBBx5Qdna2SkpKdOTIkZ4eFgAAAK5Al0VAPnjwoBITEzV48GA5HA5NnTpVZWVlPT0sAAAAXIEui4Ds8/kUHx8feh8fHy+fz9eDIwIAAMCVymZZltXTg9i5c6cqKir03e9+V5JUXFysAwcO6M4772zVLj8/X/n5+ZKkrKyssI8TAAAAvd9lcQXZ7XartrY29L62tlZut/u8dl6vV1lZWVdMOL7vvvt6egi4SNQs8lCzyEPNIg81izxXes0ui4A8YsQIHTt2TNXV1WpsbFRpaanS0tJ6elgAAAC4Al0Wt3mLiorSwoULtWrVKjU3N2vatGkaOnRoTw8LAAAAV6DLIiBL0oQJEzRhwoSeHsZlxev19vQQcJGoWeShZpGHmkUeahZ5rvSaXRZf0gMAAAAuF5fFGmQAAADgckFABgAAAAyXzRpk/EsgENBTTz2lvXv3Kjo6Wv+/vbsJhe2P4zj+mScdorg0nhMSISUyC4TYKCkSYqMJkWKjZEGzUCKSh6SMrTJZWUkpqVGyEOOhKA01DU0KeRjDHHdxS2MxzL/++Z3rfl67+a3eu/M9v3Pmd5qamlBUVCQ6i/xYXV3FxsYGLi4uUFhYiK6uLtFJ9IWXlxeYzWbYbDbc398jOjoaTU1NyM3NFZ1Gn5iamsLBwQGen58RHh6O6upqlJeXi86iADidTvT29sJgMKC7u1t0Dn3CZDLh9PQUavWfPdRfv35hcnJScNX344CsQGazGVqtFvPz87Db7RgeHkZSUhJP9lCoiIgI1NbWYm9vDx6PR3QOBcDr9SIyMhImkwlRUVHY3d3FxMQExsbGoNfrReeRHzU1Nejs7IROp4PD4YDJZEJycjJSUlJEp9EXFhYWkJqaKjqDAmQ0Gv/5m0++YqEwbrcb29vbaGhogCRJyMjIQH5+PjY3N0WnkR8GgwEFBQUICwsTnUIBkiQJ9fX10Ov1UKvVyMvLg16vx9nZmeg0+kRiYiJ0Oh0AQKVSQaVS4fLyUnAVfcVqtSIkJATZ2dmiU4gCxh1khXE6ndBoNIiLi3tfS0pKwtHRkcAqop/t5uYGTqeTT2n+AmazGRsbG/B4PEhOTubxoAr3+PgIi8WCwcFBrK+vi86hAC0uLmJxcRFxcXFobGxEVlaW6KRvxwFZYdxuN4KDgz+shYSEwO12Cyoi+tleX18xPT2NkpISxMfHi86hL7S2tsJoNOLk5ASHh4fQankZU7KlpSWUlZUhMjJSdAoFqLm5GQkJCdBqtbBarRgZGcHo6ChiYmJEp30rvmKhMJIk4enp6cPa09MTJEkSVET0c8myjJmZGWi1WhiNRtE5FCC1Wo2MjAxcX19jbW1NdA75YbfbYbPZUFVVJTqF/oO0tDQEBwdDp9OhtLQU6enp2N3dFZ317XjrrTCxsbHwer1wOp2IjY0FAJyfn/PRL9H/7O3tDXNzc7i9vUV/fz93Iv9Csizj6upKdAb5cXh4CJfLhc7OTgB/npDKsoy+vj6MjIwIrqNAqVQq/IvflOMVQWEkSYLBYMDS0hI6Ojpgt9uxs7ODoaEh0Wnkh9frhdfrhSzLkGUZHo8HGo0GGo1GdBp9Yn5+Hg6HAwMDAwgKChKdQ1+4vb3FwcEB8vLyEBQUhP39fVitVvT09IhOIz8qKipQWFj4/ntlZQUulwttbW0Cq+gzDw8POD09RWZmJjQaDba2tnB8fIyWlhbRad+On5pWoPv7e8zOzsJmsyE0NBTNzc08B1nBLBYLlpeXP6zV1dWhvr5eUBF9xeVyoaurCzqd7v2sTwBob29HcXGxwDLy5+7uDuPj4zg/P8fb2xuioqJQWVmJiooK0WkUIIvFgsvLS56DrGB3d3cYHh6Gw+GAWq1GfHw8GhoakJOTIzrt23FAJiIiIiLywT/pERERERH54IBMREREROSDAzIRERERkQ8OyEREREREPjggExERERH54IBMREREROSDAzIRERERkQ8OyEREREREPjggExERERH5+A0pPJD5lrABJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히스토그램 을 사용해서 데이터의 분포를 살펴봅니다.\n",
    "feature = df['star']\n",
    "\n",
    "plt.figure(figsize=(10,7.5)) # 그래프 이미지 크기 설정\n",
    "plt.suptitle(\"Hotel review\", fontsize=30) # 부제목과 폰트 크기 설정\n",
    "\n",
    "plt.title('star', fontsize=20) # 제목과 폰트 크기 설정\n",
    "temp = feature.value_counts() # feature 변수의 변수별 개수 계산\n",
    "plt.bar(temp.keys(), temp.values, width=0.5, color='b', alpha=0.5) # 막대그래프 생성\n",
    "plt.xticks(temp.keys(), fontsize=12) # x축 값, 폰트 크기 설정\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # 레이아웃 설정\n",
    "plt.show() # 그래프 나타내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KAWMAZXzytkM",
    "outputId": "8f2426f4-629b-4212-bbf9-29db8398c637"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c28eb884-e71e-4d90-9b62-1e4a6527c233\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Unnamed: 0</th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110102</th>\n",
       "      <td>127713</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>조식은 기대하지말고 주변에 뭐 편의시설은 부족하니 사서 들고가길 추천</td>\n",
       "      <td>2018. 08. 13</td>\n",
       "      <td>2018. 08. 13</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110103</th>\n",
       "      <td>127714</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>나쁘진 않은데 그렇다고 좋지도 않아요 주변 편의시설이 부족한게 아쉽고 조식은 별로였어요</td>\n",
       "      <td>2018. 08. 13</td>\n",
       "      <td>2018. 08. 13</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110106</th>\n",
       "      <td>127717</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>2</td>\n",
       "      <td>바닥도 더럽고 커피포트는 정말 충격이였어요수압도 약하고청소</td>\n",
       "      <td>2018. 08. 06</td>\n",
       "      <td>2018. 08. 06</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110109</th>\n",
       "      <td>127721</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>2</td>\n",
       "      <td>에어컨 상태가 절 돌아가긴 하지만 너무 케케한 남새가 나네요 뷰는 괜찮았습니다</td>\n",
       "      <td>2018. 08. 04</td>\n",
       "      <td>2018. 08. 04</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110121</th>\n",
       "      <td>127737</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>1</td>\n",
       "      <td>생각보다 별루예요</td>\n",
       "      <td>2018. 07. 08</td>\n",
       "      <td>2018. 07. 08</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110135</th>\n",
       "      <td>127754</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>편의점안에 날짜 잘보고 사야될듯해요</td>\n",
       "      <td>2018. 06. 08</td>\n",
       "      <td>2018. 06. 08</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110145</th>\n",
       "      <td>127765</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>깨끗하고 좋은편이나 외국인용 버스로 관광호텔인진 몰랐네요</td>\n",
       "      <td>2018. 05. 21</td>\n",
       "      <td>2018. 05. 21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110164</th>\n",
       "      <td>127792</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>2</td>\n",
       "      <td>다 좋습니다 시설 위치 등등 방 세번바뀠는데 결국 남들 자던  사용한 시트에서 잠 ...</td>\n",
       "      <td>2018. 04. 22</td>\n",
       "      <td>2018. 04. 22</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110166</th>\n",
       "      <td>127794</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>1</td>\n",
       "      <td>너무 낡고더럽고누군가 먹다 넣어둔 배스킨라빈스 아이스크림 보는 순간 정말뛰쳐나가고싶...</td>\n",
       "      <td>2018. 04. 19</td>\n",
       "      <td>2018. 04. 19</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110170</th>\n",
       "      <td>127798</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>교통은좋지만 상대적으로 길가라 시끄럽고 무엇보다도 호텔이좀 춥네요   나름깔끔햇어요</td>\n",
       "      <td>2018. 04. 02</td>\n",
       "      <td>2018. 04. 02</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110183</th>\n",
       "      <td>127812</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>남직원은 불친절하고 천장에 모기 눌러붙어 죽은거 몇마리 있어요 수압도 약해요</td>\n",
       "      <td>2018. 03. 03</td>\n",
       "      <td>2018. 03. 03</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110194</th>\n",
       "      <td>127825</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>완전 따뜻해요</td>\n",
       "      <td>2018. 02. 18</td>\n",
       "      <td>2018. 02. 18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110201</th>\n",
       "      <td>127834</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>저번에 왔을 때 만족이었는데 이번엔 청결에서아쉬웠어요가운에 머리카락들 붙어있구</td>\n",
       "      <td>2018. 02. 05</td>\n",
       "      <td>2018. 02. 05</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110203</th>\n",
       "      <td>127837</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>두번째 방문이었습니다처음에 방문했을때 너무 좋았던데다가 이번에 가격이 너무 좋게 나...</td>\n",
       "      <td>2018. 02. 03</td>\n",
       "      <td>2018. 02. 03</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110221</th>\n",
       "      <td>127856</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>위치도 좋고 침구류는 편했지만 커피포트에 물때인지 곰팡이인지 있어서 상당히 불쾌했는...</td>\n",
       "      <td>2017. 11. 08</td>\n",
       "      <td>2017. 11. 08</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110222</th>\n",
       "      <td>127859</td>\n",
       "      <td>은평 씨에스에비뉴 호텔</td>\n",
       "      <td>3</td>\n",
       "      <td>녹번역에서 가까운곳이지만 녹번역 자체가 번화가가 아니라 주변 먹을데가 많지 않음털 ...</td>\n",
       "      <td>2017. 11. 06</td>\n",
       "      <td>2017. 11. 06</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110235</th>\n",
       "      <td>127875</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>야놀자에서 예약해서 그런가 꼭대기층에 난방이 안된 방으로추워 죽는 줄</td>\n",
       "      <td>2022. 01. 24</td>\n",
       "      <td>2022. 01. 24</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110238</th>\n",
       "      <td>127878</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>크리스마스라서 사람들이 몰릴 걸 예상해 가격을 비싸게 올린 건 이해가 가나그래도 1...</td>\n",
       "      <td>2021. 12. 26</td>\n",
       "      <td>2021. 12. 26</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110239</th>\n",
       "      <td>127879</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>2</td>\n",
       "      <td>일회용품에 클렌징폼 없어요 종이컵 포트 없고 의자 하나뿐이라 뭐먹을때 한명만 앉을수...</td>\n",
       "      <td>2021. 12. 26</td>\n",
       "      <td>2021. 12. 26</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110245</th>\n",
       "      <td>127885</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>교통은 정말 좋은데 시설이나머 편의성은별로애여 정말 텅텅비고 그 흔한 넷플릭스두 없어영</td>\n",
       "      <td>2021. 11. 22</td>\n",
       "      <td>2021. 11. 22</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110247</th>\n",
       "      <td>127887</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>입실후 차키 맡기고 잠깐 식사하고와서차량이 옮겨저있음 주차장 자리가 좁으니 이해함 ...</td>\n",
       "      <td>2021. 11. 21</td>\n",
       "      <td>2021. 11. 21</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110250</th>\n",
       "      <td>127890</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>2일 잡았는데 첫날 배정받아 들어간 방이 주인 있는 방이었어서 당황하고 방이 없는지...</td>\n",
       "      <td>2021. 11. 15</td>\n",
       "      <td>2021. 11. 15</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110260</th>\n",
       "      <td>127900</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>2</td>\n",
       "      <td>별로예요</td>\n",
       "      <td>2021. 03. 27</td>\n",
       "      <td>2021. 03. 27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110261</th>\n",
       "      <td>127901</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>넓고 깨끗합니다 약간 쌀쌀했는데 라디에이터로 온도 조절을 하는게 조금 아쉬웠습니다</td>\n",
       "      <td>2021. 03. 26</td>\n",
       "      <td>2021. 03. 26</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110265</th>\n",
       "      <td>127905</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>교통편리 및 주변 편의시설 좋음</td>\n",
       "      <td>2020. 11. 07</td>\n",
       "      <td>2020. 11. 07</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110269</th>\n",
       "      <td>127909</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>가운때문에 전화드렸었는데 다시 전화하니 계속 부재중이시고 결국 못받아았네여고거빼고는...</td>\n",
       "      <td>2020. 09. 08</td>\n",
       "      <td>2020. 09. 08</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110273</th>\n",
       "      <td>127913</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>베니키아 체인인줄 알았으나 그냥 모텔입니다 이럴 줄 알았으면 옆에 머큐어 갔을듯화장...</td>\n",
       "      <td>2020. 07. 10</td>\n",
       "      <td>2020. 07. 10</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110277</th>\n",
       "      <td>127917</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>2020. 06. 17</td>\n",
       "      <td>2020. 06. 17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110279</th>\n",
       "      <td>127919</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>오래된 호텔 잠깐 머물기 괜찮은 곳 통풍이 잘 안되어 전반적으로 방이 습한</td>\n",
       "      <td>2020. 06. 08</td>\n",
       "      <td>2020. 06. 08</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110280</th>\n",
       "      <td>127920</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>위치는 정말 좋았는데 호텔이 그닥 청결하지 않아요 서랍같은데 열어보니까 전에 썼던 ...</td>\n",
       "      <td>2020. 05. 13</td>\n",
       "      <td>2020. 05. 13</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110281</th>\n",
       "      <td>127921</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>퇴실시간 지키기  빡셈 추가요금냄</td>\n",
       "      <td>2020. 04. 24</td>\n",
       "      <td>2020. 04. 24</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110290</th>\n",
       "      <td>127931</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>2</td>\n",
       "      <td>그닥역이랑 가까워서 고르긴 했으나침대딱딱하고 베게 푹 꺼지고무엇보다 침대에 누웠더니...</td>\n",
       "      <td>2019. 11. 28</td>\n",
       "      <td>2019. 11. 28</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110293</th>\n",
       "      <td>127934</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>최악입니다</td>\n",
       "      <td>2019. 10. 15</td>\n",
       "      <td>2019. 10. 15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110294</th>\n",
       "      <td>127935</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>객실 엄청 지저분하고 식당은 4명이 앉으면 다닐수 없고 욕실도 엄청 지저분좋은점은 ...</td>\n",
       "      <td>2019. 10. 04</td>\n",
       "      <td>2019. 10. 04</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110297</th>\n",
       "      <td>127938</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>사진과다른 객실</td>\n",
       "      <td>2019. 09. 25</td>\n",
       "      <td>2019. 09. 25</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110303</th>\n",
       "      <td>127944</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>되게 오래된 느낌이 듬무난무난 함</td>\n",
       "      <td>2019. 09. 18</td>\n",
       "      <td>2019. 09. 18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110342</th>\n",
       "      <td>127983</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>지하철역가깝고 찾기쉬워요근처 편의점 치킨집 빵집 음식점  등 인접성 좋아요낡긴 했지...</td>\n",
       "      <td>2018. 12. 08</td>\n",
       "      <td>2018. 12. 08</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110343</th>\n",
       "      <td>127984</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>역삼역 나와서 골목 들어가면 바로 있어요 곳곳에 모텔 느낌이 나는 요소가 있지만 방...</td>\n",
       "      <td>2018. 12. 02</td>\n",
       "      <td>2018. 12. 02</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110352</th>\n",
       "      <td>127994</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>괜찮습니다 방충망에 구멍이 났어요 모텔과 호텔 중간사이</td>\n",
       "      <td>2018. 09. 09</td>\n",
       "      <td>2018. 09. 09</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110358</th>\n",
       "      <td>128000</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>깔끔합니다</td>\n",
       "      <td>2018. 07. 26</td>\n",
       "      <td>2018. 07. 26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110363</th>\n",
       "      <td>128005</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>사진이랑은 너무다르네요 시설이 많이 노후되었습니다</td>\n",
       "      <td>2018. 07. 01</td>\n",
       "      <td>2018. 07. 01</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110365</th>\n",
       "      <td>128007</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>조어냐오어</td>\n",
       "      <td>2018. 05. 22</td>\n",
       "      <td>2018. 05. 22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110369</th>\n",
       "      <td>128011</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>위치가 좋습니다 하지만 객실 청소상태가 좋지 않았습니다 서랍 안까지는 청소 안하시는...</td>\n",
       "      <td>2018. 05. 08</td>\n",
       "      <td>2018. 05. 08</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110370</th>\n",
       "      <td>128012</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>직원분들은 친절했어요객실 내 준비되어 있는 세면도구 세트를 여행용 세트정도로  작지...</td>\n",
       "      <td>2018. 05. 03</td>\n",
       "      <td>2018. 05. 03</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110372</th>\n",
       "      <td>128014</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>화장실은 군데군데 곰팡이가 있었고 별로였고 추웠지만 나름 괜찮네요</td>\n",
       "      <td>2018. 04. 16</td>\n",
       "      <td>2018. 04. 16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110379</th>\n",
       "      <td>128021</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>나쁘지 않아요</td>\n",
       "      <td>2018. 02. 22</td>\n",
       "      <td>2018. 02. 22</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110386</th>\n",
       "      <td>128028</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>3</td>\n",
       "      <td>너무 추웠어요 난방 올려달라해도 안올려주셨음</td>\n",
       "      <td>2018. 01. 06</td>\n",
       "      <td>2018. 01. 06</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110387</th>\n",
       "      <td>128029</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>바닥에 밟히는게 너무 많고 냄새도 나고 객실 청소가 잘 안되고있는거 같네요</td>\n",
       "      <td>2018. 01. 06</td>\n",
       "      <td>2018. 01. 06</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110393</th>\n",
       "      <td>128035</td>\n",
       "      <td>베니키아 강남 노블레스</td>\n",
       "      <td>1</td>\n",
       "      <td>진짜 제가 갔던 곳 중에서 최악이었어요 방안엔 먼지 투성이에다가 욕실엔 스파가 있는...</td>\n",
       "      <td>2017. 12. 26</td>\n",
       "      <td>2017. 12. 26</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110418</th>\n",
       "      <td>139098</td>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>3</td>\n",
       "      <td>괜찮아요 또 가고싶네요</td>\n",
       "      <td>47분 전</td>\n",
       "      <td>47분 전</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c28eb884-e71e-4d90-9b62-1e4a6527c233')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-c28eb884-e71e-4d90-9b62-1e4a6527c233 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-c28eb884-e71e-4d90-9b62-1e4a6527c233');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        Unnamed: 0         hotel  star  \\\n",
       "110102      127713  은평 씨에스에비뉴 호텔     3   \n",
       "110103      127714  은평 씨에스에비뉴 호텔     3   \n",
       "110106      127717  은평 씨에스에비뉴 호텔     2   \n",
       "110109      127721  은평 씨에스에비뉴 호텔     2   \n",
       "110121      127737  은평 씨에스에비뉴 호텔     1   \n",
       "110135      127754  은평 씨에스에비뉴 호텔     3   \n",
       "110145      127765  은평 씨에스에비뉴 호텔     3   \n",
       "110164      127792  은평 씨에스에비뉴 호텔     2   \n",
       "110166      127794  은평 씨에스에비뉴 호텔     1   \n",
       "110170      127798  은평 씨에스에비뉴 호텔     3   \n",
       "110183      127812  은평 씨에스에비뉴 호텔     3   \n",
       "110194      127825  은평 씨에스에비뉴 호텔     3   \n",
       "110201      127834  은평 씨에스에비뉴 호텔     3   \n",
       "110203      127837  은평 씨에스에비뉴 호텔     3   \n",
       "110221      127856  은평 씨에스에비뉴 호텔     3   \n",
       "110222      127859  은평 씨에스에비뉴 호텔     3   \n",
       "110235      127875  베니키아 강남 노블레스     1   \n",
       "110238      127878  베니키아 강남 노블레스     1   \n",
       "110239      127879  베니키아 강남 노블레스     2   \n",
       "110245      127885  베니키아 강남 노블레스     3   \n",
       "110247      127887  베니키아 강남 노블레스     1   \n",
       "110250      127890  베니키아 강남 노블레스     3   \n",
       "110260      127900  베니키아 강남 노블레스     2   \n",
       "110261      127901  베니키아 강남 노블레스     3   \n",
       "110265      127905  베니키아 강남 노블레스     3   \n",
       "110269      127909  베니키아 강남 노블레스     3   \n",
       "110273      127913  베니키아 강남 노블레스     1   \n",
       "110277      127917  베니키아 강남 노블레스     1   \n",
       "110279      127919  베니키아 강남 노블레스     3   \n",
       "110280      127920  베니키아 강남 노블레스     3   \n",
       "110281      127921  베니키아 강남 노블레스     3   \n",
       "110290      127931  베니키아 강남 노블레스     2   \n",
       "110293      127934  베니키아 강남 노블레스     1   \n",
       "110294      127935  베니키아 강남 노블레스     1   \n",
       "110297      127938  베니키아 강남 노블레스     3   \n",
       "110303      127944  베니키아 강남 노블레스     3   \n",
       "110342      127983  베니키아 강남 노블레스     3   \n",
       "110343      127984  베니키아 강남 노블레스     3   \n",
       "110352      127994  베니키아 강남 노블레스     3   \n",
       "110358      128000  베니키아 강남 노블레스     3   \n",
       "110363      128005  베니키아 강남 노블레스     1   \n",
       "110365      128007  베니키아 강남 노블레스     3   \n",
       "110369      128011  베니키아 강남 노블레스     3   \n",
       "110370      128012  베니키아 강남 노블레스     3   \n",
       "110372      128014  베니키아 강남 노블레스     3   \n",
       "110379      128021  베니키아 강남 노블레스     3   \n",
       "110386      128028  베니키아 강남 노블레스     3   \n",
       "110387      128029  베니키아 강남 노블레스     1   \n",
       "110393      128035  베니키아 강남 노블레스     1   \n",
       "110418      139098      신라스테이 역삼     3   \n",
       "\n",
       "                                                   review          date  \\\n",
       "110102             조식은 기대하지말고 주변에 뭐 편의시설은 부족하니 사서 들고가길 추천  2018. 08. 13   \n",
       "110103   나쁘진 않은데 그렇다고 좋지도 않아요 주변 편의시설이 부족한게 아쉽고 조식은 별로였어요  2018. 08. 13   \n",
       "110106                   바닥도 더럽고 커피포트는 정말 충격이였어요수압도 약하고청소  2018. 08. 06   \n",
       "110109        에어컨 상태가 절 돌아가긴 하지만 너무 케케한 남새가 나네요 뷰는 괜찮았습니다  2018. 08. 04   \n",
       "110121                                          생각보다 별루예요  2018. 07. 08   \n",
       "110135                                편의점안에 날짜 잘보고 사야될듯해요  2018. 06. 08   \n",
       "110145                    깨끗하고 좋은편이나 외국인용 버스로 관광호텔인진 몰랐네요  2018. 05. 21   \n",
       "110164  다 좋습니다 시설 위치 등등 방 세번바뀠는데 결국 남들 자던  사용한 시트에서 잠 ...  2018. 04. 22   \n",
       "110166  너무 낡고더럽고누군가 먹다 넣어둔 배스킨라빈스 아이스크림 보는 순간 정말뛰쳐나가고싶...  2018. 04. 19   \n",
       "110170     교통은좋지만 상대적으로 길가라 시끄럽고 무엇보다도 호텔이좀 춥네요   나름깔끔햇어요  2018. 04. 02   \n",
       "110183         남직원은 불친절하고 천장에 모기 눌러붙어 죽은거 몇마리 있어요 수압도 약해요  2018. 03. 03   \n",
       "110194                                            완전 따뜻해요  2018. 02. 18   \n",
       "110201        저번에 왔을 때 만족이었는데 이번엔 청결에서아쉬웠어요가운에 머리카락들 붙어있구  2018. 02. 05   \n",
       "110203  두번째 방문이었습니다처음에 방문했을때 너무 좋았던데다가 이번에 가격이 너무 좋게 나...  2018. 02. 03   \n",
       "110221  위치도 좋고 침구류는 편했지만 커피포트에 물때인지 곰팡이인지 있어서 상당히 불쾌했는...  2017. 11. 08   \n",
       "110222  녹번역에서 가까운곳이지만 녹번역 자체가 번화가가 아니라 주변 먹을데가 많지 않음털 ...  2017. 11. 06   \n",
       "110235             야놀자에서 예약해서 그런가 꼭대기층에 난방이 안된 방으로추워 죽는 줄  2022. 01. 24   \n",
       "110238  크리스마스라서 사람들이 몰릴 걸 예상해 가격을 비싸게 올린 건 이해가 가나그래도 1...  2021. 12. 26   \n",
       "110239  일회용품에 클렌징폼 없어요 종이컵 포트 없고 의자 하나뿐이라 뭐먹을때 한명만 앉을수...  2021. 12. 26   \n",
       "110245   교통은 정말 좋은데 시설이나머 편의성은별로애여 정말 텅텅비고 그 흔한 넷플릭스두 없어영  2021. 11. 22   \n",
       "110247  입실후 차키 맡기고 잠깐 식사하고와서차량이 옮겨저있음 주차장 자리가 좁으니 이해함 ...  2021. 11. 21   \n",
       "110250  2일 잡았는데 첫날 배정받아 들어간 방이 주인 있는 방이었어서 당황하고 방이 없는지...  2021. 11. 15   \n",
       "110260                                               별로예요  2021. 03. 27   \n",
       "110261      넓고 깨끗합니다 약간 쌀쌀했는데 라디에이터로 온도 조절을 하는게 조금 아쉬웠습니다  2021. 03. 26   \n",
       "110265                                  교통편리 및 주변 편의시설 좋음  2020. 11. 07   \n",
       "110269  가운때문에 전화드렸었는데 다시 전화하니 계속 부재중이시고 결국 못받아았네여고거빼고는...  2020. 09. 08   \n",
       "110273  베니키아 체인인줄 알았으나 그냥 모텔입니다 이럴 줄 알았으면 옆에 머큐어 갔을듯화장...  2020. 07. 10   \n",
       "110277                                                     2020. 06. 17   \n",
       "110279          오래된 호텔 잠깐 머물기 괜찮은 곳 통풍이 잘 안되어 전반적으로 방이 습한  2020. 06. 08   \n",
       "110280  위치는 정말 좋았는데 호텔이 그닥 청결하지 않아요 서랍같은데 열어보니까 전에 썼던 ...  2020. 05. 13   \n",
       "110281                                 퇴실시간 지키기  빡셈 추가요금냄  2020. 04. 24   \n",
       "110290  그닥역이랑 가까워서 고르긴 했으나침대딱딱하고 베게 푹 꺼지고무엇보다 침대에 누웠더니...  2019. 11. 28   \n",
       "110293                                              최악입니다  2019. 10. 15   \n",
       "110294  객실 엄청 지저분하고 식당은 4명이 앉으면 다닐수 없고 욕실도 엄청 지저분좋은점은 ...  2019. 10. 04   \n",
       "110297                                           사진과다른 객실  2019. 09. 25   \n",
       "110303                                 되게 오래된 느낌이 듬무난무난 함  2019. 09. 18   \n",
       "110342  지하철역가깝고 찾기쉬워요근처 편의점 치킨집 빵집 음식점  등 인접성 좋아요낡긴 했지...  2018. 12. 08   \n",
       "110343  역삼역 나와서 골목 들어가면 바로 있어요 곳곳에 모텔 느낌이 나는 요소가 있지만 방...  2018. 12. 02   \n",
       "110352                     괜찮습니다 방충망에 구멍이 났어요 모텔과 호텔 중간사이  2018. 09. 09   \n",
       "110358                                              깔끔합니다  2018. 07. 26   \n",
       "110363                        사진이랑은 너무다르네요 시설이 많이 노후되었습니다  2018. 07. 01   \n",
       "110365                                              조어냐오어  2018. 05. 22   \n",
       "110369  위치가 좋습니다 하지만 객실 청소상태가 좋지 않았습니다 서랍 안까지는 청소 안하시는...  2018. 05. 08   \n",
       "110370  직원분들은 친절했어요객실 내 준비되어 있는 세면도구 세트를 여행용 세트정도로  작지...  2018. 05. 03   \n",
       "110372               화장실은 군데군데 곰팡이가 있었고 별로였고 추웠지만 나름 괜찮네요  2018. 04. 16   \n",
       "110379                                            나쁘지 않아요  2018. 02. 22   \n",
       "110386                           너무 추웠어요 난방 올려달라해도 안올려주셨음  2018. 01. 06   \n",
       "110387          바닥에 밟히는게 너무 많고 냄새도 나고 객실 청소가 잘 안되고있는거 같네요  2018. 01. 06   \n",
       "110393  진짜 제가 갔던 곳 중에서 최악이었어요 방안엔 먼지 투성이에다가 욕실엔 스파가 있는...  2017. 12. 26   \n",
       "110418                                       괜찮아요 또 가고싶네요         47분 전   \n",
       "\n",
       "           real_date  length  \n",
       "110102  2018. 08. 13      38  \n",
       "110103  2018. 08. 13      48  \n",
       "110106  2018. 08. 06      32  \n",
       "110109  2018. 08. 04      43  \n",
       "110121  2018. 07. 08       9  \n",
       "110135  2018. 06. 08      19  \n",
       "110145  2018. 05. 21      31  \n",
       "110164  2018. 04. 22      89  \n",
       "110166  2018. 04. 19     144  \n",
       "110170  2018. 04. 02      46  \n",
       "110183  2018. 03. 03      42  \n",
       "110194  2018. 02. 18       7  \n",
       "110201  2018. 02. 05      43  \n",
       "110203  2018. 02. 03     421  \n",
       "110221  2017. 11. 08      95  \n",
       "110222  2017. 11. 06      75  \n",
       "110235  2022. 01. 24      38  \n",
       "110238  2021. 12. 26     258  \n",
       "110239  2021. 12. 26     131  \n",
       "110245  2021. 11. 22      48  \n",
       "110247  2021. 11. 21     949  \n",
       "110250  2021. 11. 15     142  \n",
       "110260  2021. 03. 27       4  \n",
       "110261  2021. 03. 26      45  \n",
       "110265  2020. 11. 07      17  \n",
       "110269  2020. 09. 08      52  \n",
       "110273  2020. 07. 10      84  \n",
       "110277  2020. 06. 17       0  \n",
       "110279  2020. 06. 08      41  \n",
       "110280  2020. 05. 13     134  \n",
       "110281  2020. 04. 24      18  \n",
       "110290  2019. 11. 28      56  \n",
       "110293  2019. 10. 15       5  \n",
       "110294  2019. 10. 04      58  \n",
       "110297  2019. 09. 25       8  \n",
       "110303  2019. 09. 18      18  \n",
       "110342  2018. 12. 08      76  \n",
       "110343  2018. 12. 02     142  \n",
       "110352  2018. 09. 09      30  \n",
       "110358  2018. 07. 26       5  \n",
       "110363  2018. 07. 01      27  \n",
       "110365  2018. 05. 22       5  \n",
       "110369  2018. 05. 08     114  \n",
       "110370  2018. 05. 03      90  \n",
       "110372  2018. 04. 16      36  \n",
       "110379  2018. 02. 22       7  \n",
       "110386  2018. 01. 06      24  \n",
       "110387  2018. 01. 06      41  \n",
       "110393  2017. 12. 26     326  \n",
       "110418         47분 전      12  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad=df.loc[df.star<4]\n",
    "bad.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B_cjmsBS9N6D",
    "outputId": "b2199a59-33bb-42ed-fa17-b47b46bcc625"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9477.000000\n",
       "mean       80.800781\n",
       "std       121.085304\n",
       "min         0.000000\n",
       "25%        13.000000\n",
       "50%        37.000000\n",
       "75%        96.000000\n",
       "max      1677.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "XPLcT8ODkUsW",
    "outputId": "4dd72a8b-606b-4a62-bade-9db7268eb362"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-577fb385-459e-4cc0-aee2-10c495f407cd\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Unnamed: 0</th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>review</th>\n",
       "      <th>date</th>\n",
       "      <th>real_date</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12096</th>\n",
       "      <td>13763</td>\n",
       "      <td>신라스테이 역삼</td>\n",
       "      <td>0</td>\n",
       "      <td>바른후기 운영 정책에 의해 블라인드 조치되었습니다</td>\n",
       "      <td>2018. 01. 01</td>\n",
       "      <td>2018. 01. 01</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36525</th>\n",
       "      <td>41160</td>\n",
       "      <td>홍대 나인브릭 호텔</td>\n",
       "      <td>0</td>\n",
       "      <td>바른후기 운영 정책에 의해 블라인드 조치되었습니다</td>\n",
       "      <td>2021. 09. 26</td>\n",
       "      <td>2021. 09. 26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65823</th>\n",
       "      <td>75024</td>\n",
       "      <td>라마다 서울 동대문</td>\n",
       "      <td>0</td>\n",
       "      <td>바른후기 운영 정책에 의해 블라인드 조치되었습니다</td>\n",
       "      <td>2021. 04. 14</td>\n",
       "      <td>2021. 04. 14</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69130</th>\n",
       "      <td>78801</td>\n",
       "      <td>호텔 피제이 명동</td>\n",
       "      <td>0</td>\n",
       "      <td>바른후기 운영 정책에 의해 블라인드 조치되었습니다</td>\n",
       "      <td>2017. 11. 03</td>\n",
       "      <td>2017. 11. 03</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79966</th>\n",
       "      <td>90829</td>\n",
       "      <td>아리랑힐 호텔 동대문</td>\n",
       "      <td>0</td>\n",
       "      <td>바른후기 운영 정책에 의해 블라인드 조치되었습니다</td>\n",
       "      <td>2018. 01. 09</td>\n",
       "      <td>2018. 01. 09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-577fb385-459e-4cc0-aee2-10c495f407cd')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-577fb385-459e-4cc0-aee2-10c495f407cd button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-577fb385-459e-4cc0-aee2-10c495f407cd');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       Unnamed: 0        hotel  star                       review  \\\n",
       "12096       13763     신라스테이 역삼     0  바른후기 운영 정책에 의해 블라인드 조치되었습니다   \n",
       "36525       41160   홍대 나인브릭 호텔     0  바른후기 운영 정책에 의해 블라인드 조치되었습니다   \n",
       "65823       75024   라마다 서울 동대문     0  바른후기 운영 정책에 의해 블라인드 조치되었습니다   \n",
       "69130       78801    호텔 피제이 명동     0  바른후기 운영 정책에 의해 블라인드 조치되었습니다   \n",
       "79966       90829  아리랑힐 호텔 동대문     0  바른후기 운영 정책에 의해 블라인드 조치되었습니다   \n",
       "\n",
       "               date     real_date  length  \n",
       "12096  2018. 01. 01  2018. 01. 01      27  \n",
       "36525  2021. 09. 26  2021. 09. 26      27  \n",
       "65823  2021. 04. 14  2021. 04. 14      27  \n",
       "69130  2017. 11. 03  2017. 11. 03      27  \n",
       "79966  2018. 01. 09  2018. 01. 09      27  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.star==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hydWdFdK9Z1L",
    "outputId": "b51f5a6b-345a-4921-fd8c-6864b298ec1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2668.000000\n",
       "mean       28.050225\n",
       "std        42.675207\n",
       "min         0.000000\n",
       "25%         7.000000\n",
       "50%        13.000000\n",
       "75%        30.000000\n",
       "max       463.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "ytW16KsA-o_L"
   },
   "outputs": [],
   "source": [
    "df_train = df.review_spell_check.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qI5MjBIw--4Z",
    "outputId": "75982c96-e392-4a81-d759-6fbbaa7c1959"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                       발레파킹 비용이 있긴 했지만 방도 나름 괜찮고 조식도 맛있었어요\n",
       "1                                                     굳굳굳굳굳\n",
       "2         생각보다 방이 좁아서 놀랐어요 아무래도 안마의자나 고급 블루투스 스피커처럼 주변기기...\n",
       "3         오픈하고 얼마 안 돼서 갔었는데 깔끔하고 좋은 호텔이었습니다 오픈 초기에 사소한 부...\n",
       "4                                          객실이 깨끗하고 너무 예뻤어요\n",
       "                                ...                        \n",
       "122573                    친절하시고 조식도 맛있었어요 하지만 방음이 많이 안되더라고요\n",
       "122574    역시 5성급 하얏트 답습니다 조식 포함 시 좀 비싸긴 한데 조식 빼고 생각하면 매우...\n",
       "122575                                             완전 좋았습니디\n",
       "122576                                            깔끔하고 좋았어요\n",
       "122577                                             너무 좋았습니다\n",
       "Name: review_spell_check, Length: 122572, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "9BZ2XQx7-R5K"
   },
   "outputs": [],
   "source": [
    "def df2str(df):\n",
    "    #전체 리뷰들을 하나의 문자열로 만듦\n",
    "    s = [s for s in df]\n",
    "    document = \"\"\n",
    "    for i in range(len(s)):\n",
    "      #print(s[i])\n",
    "      document += s[i]\n",
    "    return document\n",
    "\n",
    "def get_noun(text):\n",
    "    # okt tagger를 이용해 명사 추출\n",
    "    okt = Okt()\n",
    "    noun = okt.nouns(text)\n",
    "    for i,v in enumerate(noun):\n",
    "        if len(v)<2:\n",
    "            noun.pop(i)\n",
    "    count = Counter(noun)\n",
    "    noun_list = count.most_common()\n",
    "\n",
    "    return noun_list\n",
    "\n",
    "def visualize(noun_list, title):\n",
    "    # 워드클라우드 이미지 생성\n",
    "    wc= WordCloud(\n",
    "        font_path = 'malgun',    # 한글폰트 경로 설정\n",
    "        background_color='white',                     # 배경 색깔 정하기\n",
    "        colormap = 'Dark2',                           # 폰트 색깔 정하기\n",
    "        width = 800,\n",
    "        height = 800).generate_from_frequencies(dict(noun_list)) \n",
    "    \n",
    "    plt.figure(figsize=(10,10)) #이미지 사이즈 지정\n",
    "    plt.suptitle(\"Word Cloud\", fontsize=40)\n",
    "    plt.title(title, fontsize=20)\n",
    "    plt.imshow(wc, interpolation='lanczos') #이미지의 부드럽기 정도\n",
    "    plt.axis('off') #x y 축 숫자 제거\n",
    "    plt.show()  # 워드클라우드 이미지 확인\n",
    "    \n",
    "    return wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HnE3-iHZ-XUB",
    "outputId": "92159e07-cfc7-44b3-e921-110bdba29fd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('직원', 7900),\n",
       " ('호텔', 7833),\n",
       " ('이용', 7816),\n",
       " ('위치', 7089),\n",
       " ('객실', 6242),\n",
       " ('시설', 6173),\n",
       " ('정말', 5488),\n",
       " ('침대', 4587),\n",
       " ('최고', 4508),\n",
       " ('조금', 3931),\n",
       " ('방문', 3906),\n",
       " ('조식', 3694),\n",
       " ('가격', 3546),\n",
       " ('아주', 3531),\n",
       " ('항상', 3384),\n",
       " ('성비', 3352),\n",
       " ('서비스', 3255),\n",
       " ('체크', 2924),\n",
       " ('생각', 2851),\n",
       " ('주차', 2849)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#document = df2str(df_train) # 전체 리뷰들을 하나의 문자열로 만듦\n",
    "noun_list = get_noun(document) # 명사 추출, 개수 셈\n",
    "noun_list[:20] # 상위 20개 단어 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_noun = pd.DataFrame(noun_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_noun.to_csv(\"야놀자명사.csv\", encoding='utf-8', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "top = df_noun[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>직원</td>\n",
       "      <td>7900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>호텔</td>\n",
       "      <td>7833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>이용</td>\n",
       "      <td>7816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>위치</td>\n",
       "      <td>7089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>객실</td>\n",
       "      <td>6242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>모든</td>\n",
       "      <td>893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>방</td>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>안</td>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>바닥</td>\n",
       "      <td>859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>코로나</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1\n",
       "0    직원  7900\n",
       "1    호텔  7833\n",
       "2    이용  7816\n",
       "3    위치  7089\n",
       "4    객실  6242\n",
       "..  ...   ...\n",
       "95   모든   893\n",
       "96    방   892\n",
       "97    안   883\n",
       "98   바닥   859\n",
       "99  코로나   847\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = pd.read_csv(\"타겟.csv\", encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    t\n",
       "0   2\n",
       "1   1\n",
       "2   2\n",
       "3   3\n",
       "4   1\n",
       ".. ..\n",
       "95  5\n",
       "96  1\n",
       "97  1\n",
       "98  1\n",
       "99  0\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dDVL41FeCg0t",
    "outputId": "16f2ff1a-46b0-48b1-bf86-c435952d7cca"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'sudo'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는\n",
      "배치 파일이 아닙니다.\n",
      "'sudo'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는\n",
      "배치 파일이 아닙니다.\n",
      "'rm'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는\n",
      "배치 파일이 아닙니다.\n"
     ]
    }
   ],
   "source": [
    "!sudo apt-get install -y fonts-nanum\n",
    "!sudo fc-cache -fv\n",
    "!rm ~/.cache/matplotlib -rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HlEPpIQHCrNJ",
    "outputId": "90d2f0b8-5890-4e5e-906f-57946e50c53a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'apt-get'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는\n",
      "배치 파일이 아닙니다.\n",
      "'apt-get'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는\n",
      "배치 파일이 아닙니다.\n"
     ]
    }
   ],
   "source": [
    "!apt-get update -qq\n",
    "!apt-get install fonts-nanum* -qq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "S2aO1ReID225"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font', family='malgun') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "eJp-hWABCGjV",
    "outputId": "e1a3bbbd-87b9-480a-f52b-88c72728a540"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', family='malgun')\n",
    "top_10 = dict(noun_list[:10]) # 추출한 명사 중 상위 10개\n",
    "plt.figure(figsize=(10,7.5))\n",
    "plt.suptitle(\"Bar Plot\", fontsize=40)\n",
    "plt.title('total reviews', fontsize=20)\n",
    "plt.bar(top_10.keys(), top_10.values(), width=0.5, color='b', alpha=0.5)\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "kUKscogmEvwS",
    "outputId": "9c520ac3-bf94-47f0-dcf2-55ef8397225a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAJ1CAYAAAAVNMDoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd5wdVd3/32fKrds3m95DQnooCSX03qsgohFQEMSGIqKP3ccuKiqKgqJgAUS6SC+BCIQSSggtIb1t2vbdW6ac3x9nZvbeu3d375a05zefV27u3innnDkzc87nfKuQUhIiRIgQIUKECLGnQ9vdDQgRIkSIECFChCgFIWkJESJEiBAhQuwVCElLiBAhQoQIEWKvQEhaQoQIESJEiBB7BULSEiJEiBAhQoTYKxCSlhAhQoQIESLEXoGQtIQIESJEiBAh9goYu7sBIUKE2PkQQlwC/CVn0zFSyoW7uA0TgJnAOKAckEAjsANYCqyQJQaOEkIsBI7yfq6VUo4f7Pb+X4EQ4mjgmZxNn5BS3rpbGhMixAARkpYQuxRCiF8AV+dsukpK+Zs+nH898MWCzeOklOv6UMZ6YHTOpglSyjWlnh+idAghpgBXAueR3+fF0CSEeAS4DXhSSuns7PaFCBFi70KoHgqxq/Fcwe8j+3h+seNLLsNb7edOnutCwjL4EELUCiFuAd5FkczeCAtAFXAh8CjwthDirJ3WwBAhQuyVCCUtIXY1FqHUAsL7fUSpJwohyoE5RXYdCfy9xGIKCU4hiQoxQAghDgDuB8YU2d0MvAdsBdqBocBIYAr5i6h9gfuFEB+VUt6xUxscIkSIvQYhaQmxSyGlbBBCLANmeZuGCiGmSinfK+H0wwC9yPa+SGsKSdKzfTg3RC8QQswDngQqcjZL4E7gZuC/Ukq7yHnDgDOATwEH5eyK7rzWhggRYm9DqB4KsTvQXxVR7nELUZMhwL5CiKH9KKNYW0L0E0KIauBu8gnLBuBQKeVHpZQLixEWACnlFinln6SUBwMXAGt3fotDhAixtyEkLSF2BwqlG/0hLQ8Cb+f87lXNJIQYDkzO2VQvpVxeYt0hesctwNic36uBw6WUL/WlECnlXcABwFOD2LYQIUL8H0BIWkLsDvRZ0iKEiAFzC8r4b1/KoCuxCaUsgwTPrfacnE0O8BEpZb8kJlLKBuBk4OkBNy5EiBD/ZxDatITY5ZBSbhFCvI8ytgQYI4QY34sXz8F02je0AW+gjHo/7W0rhbQUHlOSPYsQYgxwCDAMFV+kAdgELJJSNpVSRqkQQkwC9gNGoNQsW4HbpJRWN8cLVN9MRxm1NgDrvba1DWbbesGXC37/Vkr58kAK9FRJJbuy9wVCCBOYD0wE6gAb1ddvSylf3xl17gwIIWbT+by0otRxz0spd+zOdoUIsbMQkpYQuwvP0UlaQBGKNT0cn0s4XpBSOkKIRTnbZgshKqWUzSWW4behWwghPgx8neIeSwC2F+TsW1LKxT2VlVPmQooERRNCnAF8A0VACnE30FSkrE8C30YFaytEmxDiDq9tW0ppW38hhJgInJazSQIlx97ZlRBCjAS+B3yYfNub3GM2ATcBP5dSdpRQ5neB7+RsKjnuT3+D5AkhzgR+AkwrsjsrhLgP+IaUcmUp5YUIsbcgVA+F2F3oq4ooV7WzCEBKuR4lVQD1LB/e3clCiCpUNFYfO8i3ick9tkII8STwT7onLKBI//HAi0KIXwkh+vw+CYVfoWx0ihGWYudEhBAPoGxIihEWgDKUJ84b3mp8Z+J4Ol3YAZ6VUq7ayXX2GUKI84EVwGV0Q1g8+MTmvV3Qd32C97zcCDxAccICEEEZM78uhDhulzUuRIhdgJC0hNhdKNkYVwhhAIfmbFrUzd89EZ/DyX/eFxULGe/FgnkWKBzsG4HHUK67zwKZgv1XAX/tof7ucK13ro+3UTFO/gW8hLINyW2fQJGpMwvKaQOeAO5AGbC2e9uHAw+jVCA7C4VkcY9zIxdCXIy6d4mCXa+jJFkPAIVSiTHAc0KIuew5+BUqwnAuMqg+vxP1jDZ628uB+8iXaIYIsXdDShl+ws9u+aC8S2TOZ3g3xx2Uc0wGiOfsuzJn3ws91PWzgrq+2M1xfyk4rgW4AjALjqsEfgG4Bcdf0cs1L8w5NoWypZDAI8DUIsePAoyc358uqC+LUmHFC85LoFRHlndcQ8F5Rw/ifVxeUPbpu+DZye3HNb0cOxXoKGjjE8DkIsceBbxfcOwKINlD+d8tOH78TrqOkwrqcVEkprLguIj3XrR1c+8v2dn3J/yEn531CSUtIXYnSpW25KqGXpVSpnJ+53oQzRVCFK6ki5UBRexZhBCHA5fkbEoBp0gpb5IFhrBSymYp5ZeBLxQU8wsvXkkpiKGC5d0OnCaLBNiTUm6UXmwTIUQFyo4h2A0skFL+qKBPkFJ2SCn/F7jY21Rqm/qDkQW/39+JdfUHvwXiOb/vA06WUq4oPFBK+SwqiGHuNeyDIoa7DZ7q8XcFm6+VUn5RFthxSSmzUsrfA6ejSP7OvPchQuxShKQlxO5EqXYtudsXFexbRqc43ER5+eTBIzIH5mxqRnkfFeKqgt/fk1I+302bAJBS/hZ4KGdTEri0p3MKsAn4tJTSLeHYj6MkPD5ukyqmSU/tux34Rx/a0ycIISKoa85FT8bQuxRCiBnkq/q2oLIcd5uMUUq5HdXXuffkCs/tfnfhFGBSzu+FUsqf93SCVFm8ezwmRIi9DSFpCbE70Stp8Ww4cm0m8s6RUkrghZ7KQNnDmDm//1tIEoQQSeDsnE2NKNF7Kfhmwe8FJZ4HcJOUsrXEYz9W8PuHJZ73v31oT19RbBW/x5AWuvbZLwslE8UgpXwFZRztoxYVN2Z3ofA6flDieT8D0oPclhAhdhtC0hJit0FK+QGwMWfTTCFETcFhMwB/m0s+QfHRmzFuKUHl5pEfAuA+KWWhsW1RSCnfBN7J2TTLM+gtBf8u5SBPonFAzqbXvP4rpX3LKS5Z2lnoYuC8GzG/4Hdfki/e3ktZuxK5hujbgWdKOUlK2YLKmh0ixP8JhKQlxO5GLuEolKpAPgl5SxYP5pZr13KIFzisuzKguHfLgQW/+xR6HsiN06KhAn71Bgel3ioFM8hPHvhKief19/hS0VhkW2WRbbsLufd1s1Ru8qWiMPZO4TOyS+DZSI3P2bSkRHWij51170OE2OUISUuI3Y3ejHF7smfx8QqdIvA4OeH+PQKTG/+kHVhSpIxCl+AuRpq9oND4tBQX46ZCA98eMKzgd1+DhpUklekrpJRZlGdOLqp2Rl19hRAiiopX46NP99QjOLkGzjvTbbwn7JH3PkSI3YGQtITY3ejNrqVLULlCeBNn7moyt4y55MfmeEEWzzRcVfC7pVhdPaDQTqIUj42+hNmvKvg90PYNJjYV/N5T4oIU3oO+9hnk99vu8sKpKvi9J937ECF2KULSEmK3Qkr5DrAtZ9P+QogyCPLw5LrTdidpge6TJ/YpdH9u00o8zofo/ZBBxZ7Uvj1CjVIC+mNrs6vvaynYk+59iBC7FCFpCbEnIJeMGHQaPOYSjpVSys09lJFLWg7LCalfapLEpoLffbXLKAwLX8zWYyBoKvg90PYNJgrJ5FFFj9r1KLwH/bG1ye23wb6nUNoY3FTwe0+69yFC7FKEpCXEnoDuVES9qoZy8DydcTUqgTkeccn1+EgD3WUe3lbwe59e6ivElF7KGygKkx5OKnpU9+jr9fQFT5K/+j9aCDFhJ9ZXEjzvr1wVXJ/6QAgxmvygdN3d00J1Y18S0VaVcMzWgt970r0PEWKXIiQtIfYEdGeMmysl6VGt48XeyPXEORKYTf6k8FIPbsyFxrklJS/MQW5QO5fBdzF+m/x8R/P6eH5fjy8ZUiVHfCRnk6BrpODdhdz7OtIjIqWiMFBhMQNu6GpjUlVK4Z6ReK+EQkrZQH4G9AP7mJxzp937ECF2NULSEmJPwFLyReAHeSv13BVlb5IWyFcRHUHf7FleIX/FfI4XG6VXCCFmoVySfSzrQ8C4kuAZG7+Ws+kAIURJK2ghxBRKc8EeCH5R8PvzA000KIQwhBBjB1IGXeP6XNCHcz9a8PvFbo4rlMBMLbH8I8mX5PSE3LqHAMeUcpKX+mF3BsULEWJQEZKWELsdXsyJ3HD5UeCanN/1JQZSyyU2xUhLt9mHpZTtqEy/PmqAz5ZQJ8D3C37/vcTz+orCcPzfKPG8bw92QwohpXya/AiyOnBnf0mHF2TwUeDYATatMEDcl3xD717qPwA4K2fTDroP0vZmwe9SScJXSzwOut77wijM3eFaVI6rECH+TyAkLSH2FBQSik/m/F2KlKXwuKHAaTm/LbpfKfv4TcHv7wshehStCyGuJH9yawdu6aWe/uLv5LuvXiyEOL+nE4QQH6VrCPidhU8AG3J+TwIW9daHhfCuaQn5OYP6BSnlMvKjx44C/tiTekUIUYvq69xj/liYlDIH7wH1Ob/PF0JM66ldQoj/AU7o6ZgCPAKsyvl9tBDimu4O9uo4mnzyHyLEXo+QtITYU1CousldHZZEWqSUG4G13ZTxqpSyMAha4fnPAX/L2ZQEnhBCXFoYZVcIUSGE+Bkqg3AuvuLZIAw6PLudr+U2A/iHEOLrQog8NYMQIiGE+BZwq7epaWe0qaB9DcD5QK5qbCzwkhDi70KII4UQRY1UhRDDvH5+CbiL/AiwA8XnyM+/8xHgISHExCLtOBIl9cslHavoIc+TJym8NWdTBHhECNHFLkoIMVIIcQvwI29TSTFUvDoKJX8/E0JcL4TI8yYSQpgemf43SmrZVEodIULsDRAq31yIELsX3mTWSH4EUx/7SynfKLGcv1E8YeFPpZRfK7K98PwKFEmaXbBrB8rzqAkYjsoFUyh2v1NKeWEv5S+k0yV4rZRyfG9tKjhfAPeRL90BRRRe9No5xGuf35ebgetRyfN8HONlAR50eJKV+1BSjUI0o/I0bUNJpYaiYvHsS/FF1AXFMln3tR+FEJ8E/kR+zBKJshNahUqoOZOuhrEtwAlSyu68zvzyq4F36Rq9dilKEiOACaj8Uf51/gIV/LAv1/Fruho5Z1C2O/WoAHgH0Zmvqw34CvD7nOM/IaW8tad6QoTYYyGlDD/hZ4/4AI+jJpLcTxOg9aGMK4qUIYFT+lBGFfB0N+V09/ltKe0EFuacs6af/RRF2Y+U0q4tKCPcSwq2H72T7+UQlPTB7WM/+p9lPd2z/vQjSsLS0Yc2bEAR5lKv+XAUKSul7BtRRKZP1+Gd8/sS62hDqaCOLth+ye58z8NP+BnIJ1QPhdiTUMxQ9gXZt+RwxVRJDvmGvj1CqqSMx6G8R5b2cKgDPAUcJqX8XB/b2W9IKTNSyjOBy4B13RzWAfwF2E+WKKUaTEgpt0spLwGmo2yFCkP9F0MjyuD0RGC2lPKRXo7va5vuBCajbI568u7aDHwP2FdK+Xofyv8vylW+p3a/DnxYSvkZKWWfxdxS4UrgbJRkpxgs4B7gACnlE32tI0SIPRmheihEiF7gecAcghL9lwMNqEl4kZRyZ0RJ7UvbhNe26ShVSwNKQvCcHGS364HCS8swE2XnUo5a9TcC21HkcEV/JvJ+tsUEDgMmohIh2qggbm8Drw+0HUKIESi1z0hUsLkNKFf4nkhwf+rZDyVJG44iYhuA56WU2weznhAh9hSEpCVEiBAhQoQIsVcgVA+FCBEiRIgQIfYKhKQlRIgQIUKECLFXICQtIUKECBEiRIi9AiFpCREiRIgQIULsFQhJS4gQIUKECBFir0BIWkKECBEiRIgQewVC0hIiRIgQIUKE2CsQkpYQIUKECBEixF6BkLSECBEiRIgQIfYKhKQlRIgQIUKECLFXICQtIUKECBEiRIi9AiFpCREiRIgQIULsFQhJS4gQIUKECBFir0BIWkKECBEiRIgQewVC0hIiRIgQIUKE2CsQkpYQIUKECBEixF6BkLSECBEiRIgQIfYKhKQlRIj/jyGEWCiEkLu7HaVib2tviBAhBhchaQkRYidBCDFeCCGFELcOYplSCLFwsMoLESJEiL0Jxu5uQIgQIUL0ARcBid3diBAhQuwehKQlRIgQew2klOt2dxtChAix+xCqh0KE2AkQQnwXWO39vNhT6/ifS3KO04QQnxZCvCKEaBNCtHt/XymE0HKOuyTHluOogvK+W3DcPUKIVUKIlBCiRQjxvBBiwSBd1xrvUyGE+KX3t1XQhqlCiFuFEOuFEBkhxBYhxO1CiH0LynrMa/+cbur6iLf/upxt3dq0CCFOEkI8LITY7tW7UghxnRCiquC4F739yYLtz3n13VKwfbq3/a8528qFEN8SQizz+rjVq++fQogDe+/JECFC9AehpCVEiJ2DhUAVcBXwJnB/zr43cv7+G/BRYD3wJ0AC5wA3AocDH8s553vAd4C1wK0Fdfn4PfAO8BywGagFTgX+JoTYV0r5rYFclIcI8DRQAzwOtOARNCHEycC9gAn8G/gAGA2cC5wmhDhGSvmaV86twIkolc+Xi9Rzkfd9W28NEkJ8G9U/DcBDwFZgNnANcKoQ4lApZYt3+FPAIcARwKPe+QngYG//cQXFH5tzHkII4Z03H3gRdd9sYAxwNLAIWNJbm0OECNEPSCnDT/gJPzvhA4xHkZBbu9l/obf/NaAsZ3sSeNXb99GCcySwsIc6JxXZFkFNuBYwqmDfQjUMlHxNa7w2PAkkC/ZVA43AdmB6wb4ZQBvwWs62GNAE1ANGwfHDUURgSW/tBY7x2vQCUFWw7xJv3/U52471tl2Xs+0kb9vj3veknH33edvGeL9neb/vK9I/GlC9u5+98BN+/q9+QvVQiBC7D5/0vr8mpWzzN0op24Gvej8v60uBUsqVRbZlgd+hJKuFUoT+4steO3NxEUq69B0p5TsFbXgb+COwvxBiurctDdwFDEORhlwsAHRKkLIAX/C+PyWlbCqo91aUlOpjOZtfANLk98VxKJL0nZzfeCq6o4EVUsr1BfWmChsipXSllI0ltDlEiBD9QKgeChFi9+EAwCVfvePjWcAB9u9LgUKIsSjCcxwwFogXHDKqz63sijSwtMj2Q73vObk2LjmY4n1PQ6mwQKmIPgVcDPwn59iLUZKh20toz6HesecLIc4vsj8C1AkhaqWUO6SUaSHEC8Ax/jaU9OUVKeWLQogtqP67GXWPqoB/5pT3DooIXSiEGAc8APwXeNUjiCFChNhJCElLiBC7D5VAQ7GJTkppCyG2A0NLLUwIMRF4GaWmWYRSdTSjyM94FBGIDrzZbJVSFjOGrfW+P9XL+WX+H1LKF4QQy4EzhRDVUspGIcQBwEzgfinl9hLaU4say77Ty3FlwA7v76dQROUYIcRTKHL4I2/f08AJnu3KcTnH+212hBDHAt8GzgN+6u1qFULcBvxPruQsRIgQg4eQtIQIsfvQDNQIIUwppZW7QwhhAENQRq6l4mrUBP4JTy2SW96FKNIyGOguIm2z9z1HSllMEtMd/gr8ALgA+AOd7SxFNeTXq0kpa/pQ59Pe9/EoaZdGJzF5GmVvNAdFWiTwTO7JngroS8CXhBD7AEcBVwCfQ0lmPt6HtoQIEaJEhDYtIULsPDjet97N/tdR7+CRRfYd6Z33WsF2t4fy9vG+7ymy76jumzloWOx9H9HH8/6Kuq6LhRAmijBsJ19d1Fu91UKIGX2o8xUUITwOJXFJoTyBoJO8nAocBiztSeIjpfxASnkLqo/bgLP60I4QIUL0ASFpCRFi56ERtUof283+P3vfP/ZcboHA/fYn3s9bCs7ZgXKtLYY13vfRuRuFECfRR4PefuIvKG+g7wghDirc6cWkObpwu2fg+jTKDfkqoA64vVD61AOu977/KIQYWaTepBDikII6HZRb+D7A+cB/pZQZb99qVF9ehYq++3RBeRO6IUjVKPVbFwPdECFCDA5C9VCIEDsJUso2IcRLwBFCiH8Ay1HSlwellEullLcLIc4CPgy8LYS4H0VyzgYmAHdJKf9RUOxTwEeEEP9GxQKxgeeklM+hYrt8AviXEOIeYCPKNuRklJfOBTv5encIIc5DuQgv9mxF3kZJUcaiDGZrUa7OhbgNpar5Uc7vUut9SgjxNeDHwAohxMOouDFlwDiUBOS/qH7IxVPA6Si7oaeK7Ls05+9czAHuE0IsAZYBm1BE6yxUfJqfEiJEiJ2D3e1zHX7Cz//lD2ol/2+UhMRFkZJLcvZrwGdQcVk6vM8S4LMoO43C8oaiPGq2oAiQBL6bs38+SjLQCLSiJuuzUdKXvGO94xfS9zgta3o5ZjzwW2AFytOoBXgPFUjv7G7OSaBsUyTwVg9ld9teVDC+u1AkIgtsQ3n5/BKYW+R4P96KBOYV7PNj6FhAecG+0Shy9TwqxkwG2AA8Apyyu5+58BN+/i9/hJRhlvcQIUKECBEixJ6P0KYlRIgQIUKECLFXICQtIUKECBEiRIi9AiFpCREiRIgQIULsFQhJS4gQIUKECBFir0BvLs+73EpXSomKnt3z/t6O61fdrovQNJz6VuyV2xFRA+lKkCAMDXP/UQhNgJRICOov1qae2lnqcf8/YE+57p7asbPvUW750rW77NM0HaHpe0xf7Q50d+299p1uIIQW9l0vY1F/+8c/r6OjgzVr1mCaJo7jMHHiRCKRSJ/LGay29XbuQJ4H/1zHtfhg26toQsNyMoyunk5FbAiudNFE/+UBA7neUspxpYMmdBo6NlHf/AGGHkETOpOGzO12Hsv9u7v9OwFFC96j4rQEneK6YMuuTTa0nTvBZxyIa6TuWUrD5/6FMbwa2WGpwa88xoiVX4OYCSjC4rrKBUvXtWCb47gIIdA0kXeT/f2d57pICbr+/8+AWtgfwB5B2Pz6XcdGSheBADqJqaabu4SwCCEQutnNcW6fJt+9kRj35MnYG2Hpb9/tiX0z2G3KLctxnLzxSNcHRoZt28Y0TdasWcOXvvQl6urqaG5u5qabbmLkyJGkUil0XceyLJLJJP/6178oKyvjlFNOob29HdM0MU0zaJtt22iahmEYee3uaxtzz3Glg+s63nwiMLTiz0mpkLgIdDqyLXzj30cQ0eNsa2vju6feyWGTLsB2skSMYqGIcsooMhbmtr1P7emmrO7KsZwMUSPB8yv/yW8WXk1tMkbUSPKXj29B5ATb9sv174mmdb5Druviui6GYezyd2ePIi1kHIgZpO5/m+ZrHkIfVYFMWaAJ0AS1916MMbISpISd0UlekaIyjjFmCProSmTKBinRyiIgBBKwbRfXlUSjBiBIp20MQ3iDgCIwrivRNHVzNc3f5noPFznbZB7B+b+KPfn6XDuDbsZ4+d6vsfKl24lXDEW6Dtl0C0MnHMxxV9yFdGyEMbDBrhjyJhQrzdbVLyGlq6R7mo5jpageNYtk1cg86V5J5VoZ0I09XkrTHyIG+X1nZ1NsXfViJznRdBwrzZBxBxArGxJIUQuRV4Ztd9m/K6EWQHre5DBY8MnFLbfcwuuvv44QglmzZnHllVcG+/oDv42RSIRRo0ZRXV1NIpHAMNTUEo/Hg/0Ab731FkOHqhygyWQyKMd1XXRdR9fVpBnE5BgAebOcDBEjxhPv/pG7Xv8eFdE6ymI1fP/0hSglQn/7V52nCY0RFftg6lEixiZipsoD2ltbS7mewbr/xcoRnlVIMlLFqKohVMWHETES3R5/9dVXc+6553L00UeTSqVIJBI88sgjLFy4kOuuu67P92mg93XPIi2+Nirr4DZ2IMoiyA6PtOgCXJl32E6D5SDbssh2S5EmKXGFUBMJYJrqxdq2tY2mphSTp9QFp775+iaGDS9j+IgKbNvFMNQAlE7bxOMmjuOi6xqrVzWQTltMmz4sOO7/Mva01Wwu/MfJzrSTaW9ANyK4rkM21YyVbvWO2YkPnXRB6KRat/H4b8/CdbJIKTEjCVp3NHD0Jb9i+nFXYbdtx4gkFakJBtzcvxVERA1AwlQJnfsqpdmVGGicKOk6CN2go3kzD19/IkYkgWtnMaJJ2hsbOO2ahxg76zRcJ4uudV395g6g/kS7uzGYkpbCVXg6naatrQ0hBOl0uuix/YHrusGElslkAgJ45513sm3bNgzDwDRNduzYQWtrK3/+859JpVKYpsnHP/5x4vE4K1euZOHChQwfPpzjjz+eaDQaLPr60rbOY9U1Z500belGdGGgaR4pov+UxS9XAmm7DUfapKxWHOnk7e8Ou38sVO2zXYuU1UrMSKrnQ4LQBI7rYFs2Ukqi0Sjt7e2kUiozRTqdJpFIkEqlaG9vD7ZFo1F0XSeb7ZKwvrNWKdE0DdMcmOR6z3hLC6EJMHWEqSNNR9mRFFkl7TQIFEnyJDxIr03en0tf20gsYfLOsi3s2N6BoWts2tTCYUdMYPWqBgxTY/iICnRdsG5tIy/8dw2uIxlSl+SwIyeQTEbYvKmF9vYs06YPw3E6yc2eNqmUilIGWn+762T517emcfQnb2P45COCiWeg5Q8UQtPRDBOhm2hCQ9NNhLYrXhER1J+oGoFrWyBdjGgZjp0hWjcJALNsSM7RvaPj3YVERuyLUTVC9bHWXZ7F3QelDnZA6KRbt/Hw9SegaQbStdEjCTqaNnHERTczesZJingY0YIC1Jem6SSrRqJH4ri2hRFJgJTovpi+h2fSf6bWrl1LOp3eKZKOnuAP5pZlMXToUGpra/MktIMB/1p81Yv/92CWr2lKfZ/bb9u3b2fz5s3EYjGy2SxjxoxBSsnq1auD42KxGB988AE///nPqa6u5pVXXmHVqlV8/vOf7+c7n3+sJjQM3UTXTPRBeZ9zpH1oaqwQWp9IkEQiEHRkm/nOf47F1KM0dmzm00fcxH6jT8J2s5h6tPeCIDj2zy9+kTc3Pg4IZo48hssP+12P5QghVLuFjhCa0iRIia7p6FE97zhfKlZToxKpx+PxQCqWSHRKaUqxY/o/JmnxICW4UhnBur5twZ4RuVcC9ZtbSZRFaGnJIKVk48YWtm5RK/JE0kT3BoO2tiz/fW41Rx4zkdGjq3jy8eW88N81nHDSFAxDw8ixhfG/ZcZWEqXOhcKeC7+NukBEOnWb0nVxrFSXiUJKF00zsDLttDdtJNPRCICVaUU340riUAApJboR2al2JTmVKXsq6XoqGlc9i7sKUuJkUziOyhMoXRsZidP46C/Z8coDpO00uqYjXRctXgGOhZvtAKEF/S7MGEM/fgOaGWX1tccw8gu/ofqEzyOtDCKa2O02HEWN+fAeJenQ0bw5IC1GJEF70wZsKxX0T9cC/S+JnU0BAtfJKnWP1YH0V7893EbHcTAMgz/84Q8sX76cZDKJbdu7rH9c1yUWi7F161YuueQSzj//fCzLIhotbdLqT33d2VMMBnw1F8DnPve5ks657777GDVqFN/61rdYsWIFP/zhD3nrrbeYNWsWjuME5ZXYAnKJhUTieu/0zrrmvsOX1rg0dmzGNGLsaN9I1k7n7S+tJHVsW6aBhvZNCCFoyzT0qRzfpsg0TDZu3MDChc8Gqr1oNMprr71GS0sLbW1tlJWV8e677yKE4KGHHqKxsZFZs2ax33778fe//51YLNbFHknTNLLZLEOHDuXYY48dECnfM0kLdD5zYiBivMGD3wYNOOm0qYAiL5m0zeFHTgiOy2YcXFdNvju2t5NIRhg9ugqAI46ayN3/XApAMhnh0YffR9MERx07Ccd10YVARPfcW9ITfJsB/9uIJrs9VjdjmJEEyWqVrDiaqO61fNd10HaFbYbI+9q1D5/AIyBq1YOUaGaM7NaVdGxeSUYINOkijAjp1SvREhAZvg9uNo3QDaRjo8XKAvJnVIPm34dcYpwzmBR63Ozc6xOKkHR7DwW6EUXTDVxHRzOj6GYMIUqbsETQd1rQj6LLDe0elZWV1NbWkkgk+kxa/HdetUP06VxfDO+6bjBRDNYzXqyc3iShg1lnNpslEokERra+4a/runR0dFBdXU19fT0bNmxgwYIFAEyaNImZM2eyePFiZs2a1WeiUfSac/7fM9A5wZl6DFOLYhqxfnkd+SUZWgRTV4Sh09i4VBs4AtKyZs0afve73zF06FBSqRTV1dVs3LiRRx99FF3XcRyHeDyOYRj88Y9/ZMuWLVx00UXst99+PPDAA1iWRSaTySPGpmnS0tLCnDlzOPbYY3EcZ/eSlkFbvfnnFq7Q8Q2/fVnw4Oh5+3SO953J2rz+ykbee28rJ526L8uW1nPD9f/l5NP2ZfKUOoToFLsOHVpGJm3zzttbmLJvHQ//+12GDlPGWlu3tnH4keM56thJZDI20YgOQtD+55dxtrUhIkanDc+eCAkYGrI9izFtKIlzZ+FmLbSISXvjRpY9+SuMaNKbFEXnSULDtbM4dpa3nvglVcOnYmfaEcFKSk3WUrroRpRMRwNjZ5/OqGnH4zoWWhEPkYE+d3kTWzddPli2BSXDddCiZdjbVzH08n8y5oBz83avumYS5XPPpe4j1xWtC0BmQTo+KZF57ejN42ZnoeexQiKlg3Q1tSp2XaU6GozVcQlFtLe309LSEkywfblfviFrrhdMqWOi67pEo1Gam5vJZDIl19kfOI4TtNMnWjtjEeCXbVkW9957LyeffDJVVVWB0W9DQwO//vWvufrqq8lkMjQ1NTF+/PhgBV5bW8uaNWuA/ts9+ddlORn8kGRSujiujSb0os9iKfdLBBRoIP3WeU2udHCli+s6A7Kdc3FxpYNA4BaRWhdH53UbhhqDJ0+ewre//W1isRiO42BZFoZh5Hma2bYdkJf29nb22WcfXNelsrKSY445hnHjxgWeY7nqz+pqtUDtm+QsH4NCWvJWb64Ep9QOy4EmkJajJA12/vnBo2F5ot6sg9RF/9Qn/XCbFsITqUZ0Vr67jQ3rmzjnvFkkEiaHHzmBoUPLWPLKBo+0iIBbJZIRjj52Em+8tpE1qxqYuE8tc/YbCcCUfevYUq9USpquKYKiC9pufglr2Wa08ijScvesxUEuXImImzhb2kh8aBaJc2chbRsiJtlUE6tfu4doshrXzqqVbwClyx22z2E0179Hw/o3gv3+BCV0A+k6GNEE7Y0bqBi6jyItrtOFtAxUaiCEpmwlzKiSEuU9E50rBde26JfOTuk20fS+ugYKkK7ygmnbAUC2ZRu6pqGX1SLtDE5HEwBO2w60WLnqP0AYUfXQauQsALpKWhw7S+u2VcpIdyfqItUE6WBGk5TVjA0Mg4tfdeeaOPhrMN6BHsrwB9AvfvGLZLPZbm1aCrcF5FBKrr/+epqbm2lvb+e4447jwx/+MI2NjUQikbxJt6dyHcehoqICoN/ePN2117/GdDod2J90dHTkST8GU23oX3N7ezu33XYbc+fODUiLYRjccccdWJZFZWUlGzduxLIsysrKAtLiG4AOBJonoWtNb0fXdIQQWG6G5tQWapKjcIPwBp1tFkLFX+nubRCAK10MLYLtdm902jsKjOdFt2ZXfSox/20vBf4zTCDJHjp0KCeffHKf60+n07S3tzNv3jymTJnSfY0eidmtNi25hEUZzfaPRYkypcMVCbOoebeoUEZ1A1Wh9N1FS8VTcTM206cPZfp05bK37K16tm5p5djjJzNnf0VG3JwBKp22GDa8nCF1ZWxY38T2be089OA7aELFczn40LEA6F7AOgB9WBluYyVaWRRp74Gkxe8vKRFRAxE10IYoFYSmm0gk1SNn8JEfrxrUaqXrYpixoquigUoNzFg5ALoRyfHMEUhk4G1gRBMDa39/JgVPReS7WpsVdZ0+Q+2N4Nm+CDOOMCKd8qxeVlmBx03TRu7/4VzlcZMnERtESIluRkm3bmf0zJM46fMP4TpWV4PanY1uZqHc+1JbW9uvorPZbB6BMU2TZDKZ59Lbp6YOEoEIDN89ItDU1ERra2tgjNvQ0MDWrVsZOXJkYDcy2PZOuq5TV1cXqL1isRh33nkn7733Hl/72teAThsYx3ECQ85cu5i+ILfvdM0ka6eob1mJoUUQCNJWGxua3qMmOUq9J549GJB3Xo/X5H0nI1W7xS5scJHTfikRmobrOliWis3ix/WJRCLBtdq2HZBPn2z7+zVNo7VVLcbb29u7kHYhxJ7hPeQbkIqoQfqx92n58dPow8qRaatvqhwpwdBxd7Sj1cQhY3dOkq6k4aI7lNqkP+JCCZgaNbd+BC0Z6SRYpVwfIF2Jpmts39HB1s0t1AxJsn5tI9u3d1C/uYXNm1qZOq3Oe/DVeb6aaEhdknjcRNMFluUwZEiS5xau4o3XN3Hiyfti2Q5RTzQnHSWpko6rJFZ7yjvhkUhpu0oqJCU4EtmeVcbDHgQCKV1PMkGnIWQBcr1yHDtDJFbO20//lqb69zjso7/FSreiGVFP2mAUlVTkDrCuY/PUTR/GSrd4ZffBkE1KNN2kbcdaIvEqXMcClMtxU/17PPrrU3HdvqkL/DKtdAu1Y/Zj/oU3dKve6qUgEBqNd1xDxslSc+xniIycytjvvYpmeiTezCcA3UkxCiGERiReiR5J5KvxpOuH1iN/9VDMUTT3uLzS1TYp0YwoppVRHj09t6ikdvcL3RSdJ3lyij+r3Z1n2zaRSISVK1fS1tYWuHNu2rSJdDpNJBIJjF77Uu5geC/lnu+Tlvr6erZu3YphGBiGwdatW1m1ahUjR47sooIZLOLiui6ZTIbt27djWRYPPPAA69ev58orr2TUqFGAitcSi8VoaGgItnV0dFBZWdn/a5YOujBo7NjM6h2ve7YeGu3pJt7f8gKzRx2njvGmQP/6hRD8/MkP05LehqFHvfGr6zMvEDieWsfbtJcip+HCl7goSZdPwEEREMuy8D2JfI8h6CoV9H/HYrGixHPP8R7ybrqzvZ3sS+vQx1Yh27NdSUtPEnYhlNohoiNiKoR+YMwjJdayes+zplMyURL8Faup9091JVT0W0yddWsa+eD9bUTjJq7jUl4e5eXF62lry3iMVO80vfHmjnHjq7sUOWe/kWzb1uYV71+PQLakcBtSCMtV6rA9hckruSiiLIIwlAcLulCfgjYKoaGbpa+kfbXO9rWv0lT/vipDN9GNTve53h90yfZ1r6k4K3qkm8Gmh2uTEt2Me+TI0/VrOnamna2rXvSeoZIvCYnyesq0N6DpnavHvkJ6hrdtr9xD2k6T/eBFouMOYNgnblL77YxSB6H6UVrKJkLoZq/tlUgcx0I4WaTjBO+JZkSUhEkWiUrd9wtA003Pfby3lfOuH/nzVuZ9XNn7i5L333+f1tZWKioqiEQirFq1im3btjFmzJi84/rTpsEgDn4Zy5cvp729ncrKysC25IUXXuCwww7Lu/bB9CxyHIeysjJ+/vOfY9sq9seVV17J9OnTyWQyRKNRampqqKqq4r333mPs2LFks1nWr1/PrFmz8tpfynV29plq/+sbHqMj20wiUontWkSMOG9ufIJTZnyOsmh10ZD7m5rfVx49erRX25C9X9KSD18l6xPd7du389hjj/Hmm2+yfft2TNNk3LhxHH744Rx55JEAXTyBApW6FzCwSx05kq3+YBBdVbyGRA20mgRaVRwZMdSkVir8RZsjkbaT9/IIIRCVsb5P4p3+lIq09KOjlNuthpOyOODAURxw4Khuj73nrqU4HjHy33vbVq52mhBkMjaJZIRt29ro6PBdWxUhAqi++fzOKMADiYDkx5YpdfCRKBsISdd6hYCsjVadoOlr/yH78nq08mi3hsK50o9U61Z0I6ruJZ6hopVGN+NEE1V55+lmHDPwOsovu5QHPZqoRtBpP9InCFQYf9fJVz9pBtGy2n7Mp9IjK4JIvKKvJ+cUo9yYpZ2l5tSvULH/GSz/xBQ63nmK4Zf/lfjk+crYVtMRmpGnOi3F6UaoHkMKj6jpOpn2HThWRnmDSTkwc8NAPdRCunW7t7H71uxq9HcA9UOYNzc3s3jx4sD7R9d12tvbWbRoER/96Ee7BEcrydBzgIN6oQpV13VSqRSLFy8mHo/jOOoZj8VivPvuu7z66qvMmzcvEPkPJmnSdZ2Ojg4+//nPU1lZybPPPssdd9xBS0sLZ5xxBplMhoqKCmbNmsX999/PgQceyOLFi1m3bh2f+cxnSu6HQEWco+LJ2B38d+Wd6JrpjQeSmFnGuoa3WLz6Ho6fehmuaweLCh9pu52U1YrtWoFha5EaEUILouAOCmSnlFT0IZdx57FigEHz1Mm+qnDTpk386le/YvXq1QwfPpwhQ4YgpWTlypW88cYbrFu3jgULFgwawS0Vg0havIZbDrIto9QGHdl+BIWToGmIgmBrUkrosPq3GPOkBDLSf4tlJTYT2K6Lk3U91ZJ6RIQAy3JIJCJMmVpHTa1n4+FJmXKj3UaF6vKx46pJpxVp8UP/Sykxpw7tdxt3BbTKONhOj2o/6doI3aRl2wc8+ONDAbCzHSAEuhHDyrQy45jPcehHfo1jZ4MXVbo2ru/t0o/7bGfasDKtaLpvm9IXSDTNDCZq8FduDla6Hx4dnsTCyrSpax8IpEQ6Fm42hYiWk5g1heTME1n/k2MZ89UniU85HIBs/Qraltyn3J41A6dDXVeJlagYOtkOxs05k/K6idjZlDLOG2DbNd3AyrRRNWIawB4Z5K5UBITcIyNPPPEEGzdupKKignQ6ja7rxONxnnvuOebPn8/48eODSWCwJSg9tS9X5WUYBi+++CKbNm0KSEs2myUajaJpGvfeey/Tpk3LM4QdrLb69jMTJ05k+PDhzJgxgxdeeIGbb76ZTCbDeeedB8BZZ51FfX09X/jCF4jH41x00UUMHz6829V6T9fsB1R7ZvmtbGh8m3ikQqmsPUP1qFnGw2//lv3HnEJtclReOAWAY6dcQnu22SM7bhHlkEQTOlk7zYur/9XvvvEhfCmuENiOGmuyjopN5NLzOKahkXVSGHpEEawCw/u+QhnJquf7L3/5C9u2bePiiy/moIMOora2FsuyWL9+PQ8//DAPPfQQo0eP5uijjw6I8K7A4JEWf3UQN9GGlaPVJZEdkdJtWnwOoAlI27jtWYRWIGkZkuxZvdRT26REmFq/aahA1WtoGkasKxEzTfXQz5o9AlA5hQqTIapVj4brykBl5LoyLxqutJzSJSw5killD6TlmRiIiJ5XXvC3lHkGtYFaoCyqpD5WASkRApmx0JJRRViCl6zn5mmaieNYHPWJW6mom0SmfQdlteNY+KePe4HAyJcEDdCEPlZep1QbuqnUV6UWJUFoGla6FcfOdJIoT7URS9aq57AvTZNK0qIbEaJJFUVyQEaVmobQleGbtW01Qz9+AwiNDT87gbHffYXo6Jmkli9i801fxazRAYmeAC3qrQR7qTvXBmfGsZ+nbsJB/W5rz/W4PXhS7ZmGAV0mRU8i8c477/D4449TXl5OKpVi9OjRtLa2Bvr/O+64g6985StFCcvOIC6FZedKgx577LG8vD6HHnooS5eqmFGbN2/mb3/7G1deeWXRcnLH4L7CcRwaGhpobW1l+PDhpNNp5s+fTzQa5Ze//CUVFRWceOKJVFZW8sUvfpG1a9dSXl7O8OHDu4ydxervQtJcC1OPsql5OQ+//VuiZpKsk6Y2OZpjp1zCvW/8BFOL0NCxkX+8/D984Zi/dinnvP2/WfL1LV5zj/pjALdSemOwlC7JqBorEpHS7XkMT1IU0WPq/RLq/e8rhKcW0nWdt95ayvLly7n44os59thjg2M0TWPy5MlcddVVtLS0sHDhQg499FCi0SiWZfW5zv5g8EiLJ8WInTWD4WdM7zu58MiKiJu03/IyTV9/GH1kBaRsVZYmGHLPxegjK9Tkq2t9s2vJrQcG/JB177bYNcuzD/+l8JmslPlZnqWvwuprW8ziL3b21fVE5o4J9mm9lJ19cxPG+Bq0yljXa5Teo9IXtZUQuHaWYZPmk6waGWyOJqtxHd9dcHAmKqEZnHHton6cp2NnOjCiCZ7/x2dY/uJfSVQMQ7oOVrqZYZPmc9IX/oNjpQPD4H60Lmhj/yCQjo10LBXV1rawdqxj6IJf077sMbbcchljv7OYyiM/SeWRnwzOUuHxvbpL8NZRiz2VAwkgm2pGN2L9kFp1aX5ADHUj2sOEvefZB3QnuWhoaOC2227z3mOlgrniiitYvHgx99xzD0OGDOGdd97h9ttv56KLLsrztthZxKWwTF9qcvfdd7Np0yaqqqpobW1lypQpXH755dx8880sWrSI6upqXnzxRaqrq/nIRz4SXFOhxKWvbQFljHnqqacG8TkikQiWZXHggQdywgknsGzZMk488cTAA2Xy5MkAeVFwSyUsrnQCj6HbXvoKLeltlEdrSWVbOHTCeZw8/TOs3v46i9fcS0WsjlfX/Zt/LvkeFxz4HWwni66pbNMqKm33Ts9SOhh6hJb09kFRjSiCaRPR47y16SkcN0t7pglDj/RavhAC28mSjFazrW0tph714tL047kSAsdRLvBLly5l5MiRHH300UHMId+7zLdFOvPMM/nNb37Djh07GDlyZJ+NzvuLnRN+tY/EIGDTplrhOvUtCEPHl4wJIZCOi72pWREZTXhSmP43cTA7N9+YT+uyrXC14A8I3R3XW11++2XWQUR0sq+ux6lvJX76dGTWVh5WQNsNz1N147kITSDiJm1/eBHrrc0qr5MmAgmL25wmdto0rDc2ETttGtGDxyppS05o/v4mqxRCkO1oJFk1knTbDmJltco7xzd+yzOC67sYLa/P+hFNEshRYRap3y/fD5XfB13z4EC1SYtXKuLhONg7CAxuh11yE2u/dTSNj/2K6pO+iGulEUbEk6D1PSYRSHTPK8mIJPru7dRb6T0+7ztR0tLHoostOPyJNJVKceONN1JfX09FRQVNTU1ccMEFjBo1ihNPPJG33nqLtWvXUllZyZNPPkllZSVnnXVWEBOlu3Gh35dWpE8ty8I0TR577DH++9//UlZWFkSnPfdcFajw/PPPZ+3atWzevJmKigoefvhhhBBccMEFQD5xyK3L74+e2uOPb+Xl5Xz2s5/N2+6fe9ZZZwUuzn77fTVDIWEp1l+FhMW37fjzi1/k3c2LqIjX0ZrZwb7D5nPajM8DcOHcH7C24S22ta+jPFbHf97+NTEjwVlzvoLj2gg0IkbXxJrFEDUGFgbBh0DDdi0SkUoeeft3PLj05wihl7Qo9+0EXemQMCtJRitJWW39fp78+9vU1EQymQzcnnOTifr3bMSIEaRSqbwkibvCvmWPSS0sXam8UrI21rJ6RMzo9PTRlRTGWrJB/Xb2LDFyTw9I4cBXyrZCFD4IwbHedmd9M/ZyZeQocwLzaUPL0JIRRFxNPPEPzaLiByfjbmkl8fEDSX5iHk59K5XfP5nEubOUaq867p08MGPAXGieF5DvUSSEFngMOVYax1L5NqTbndHbzsae9TwFkCB0AzfVwphfrPRISYphn/gGRs1opJQkph5F2dzj6HjvWUBJc4JQ9uQP+qXX6xuS9xLvRcq871LQ8/Mkdt6dKPGxklIWnSh9wtHR0cFvfvMbli9fTlVVFY2NjRx55JGcfvrpWJZFeXk5n/zkJykrKyOTyVBeXs69997Lgw8+GBi55ur/+yvJyG1vd4Tl1Vdf5e6778Y0TTRNI5VKceqppzJ16lSy2SzV1dVceumlQWbm8vJyHn74YW699VYsy0LX9cDjJ3ec6q29uft9+5ncfEf+d3V1NclkMgjpLoQIIq8WllVMYu3/bbsWmlBk8K8vfZUXVv+L8lgNGbuD8mgtHz/op0SMOFknTVViGJ+c/2tMLYLlpCmL1nDf0p9xzxs/QtcMNKHlBY3LvU7/b9+jqPSos73DJx7l0RqGlI2lJjGSmuToXj+1ydHUJEZSVzaWiBHrEjCvP+0AJSHzM3UX3mt/e3t7O6Zp5t0v0zR3urRl0EhL7sPV1w/QOQGva8J6qx4RVS7PXuFIxyXzwlr1W89/gfpVX78usuum3IG7u4Go2INfbFt3f/tldSnXI3X2+iayr29Ux0YU8QNwNjbT+Nl7afnZM0jXRa8rQ0QM3O3tSMtBq00g27MBqZEdWdz6VhV3xXa7tKffCK65czLUTUWOIvGKwLtGBY/r30AwkGfBX6G5rp3/wguVhNAvfyD19H81LTpJhGtjVI1gyHk/QIvEg34dfumfGH75rd7hnUk4B1x3L7e+cBJyO7K4HZb3nUWmLGTaQqa8bd63THeGuC9ytTsHvbl/57xrufdaShmodrZv384vfvEL3nnnHaqqqmhoaODAAw/kkksuAdSA7TgOY8eO5dOf/jS6rpPNZikrK+Puu+/m9ttvB5RHja//L1y0+O95b20ttpDx22qaJq+99hp/+ctfkFIFBmtpaWHevHmcffbZuK4b5AOaOHEil19+edDW8vJynnrqKa677jo2b94cqLQsywrqLeV5yr2uSCQSqJpy+/qGG25gxYoVATnKvbbca8ytq7B+y8lgaCYZO8UfFl3Bk+/9kbJINY7r4Lo2Fx98HWNrZip3Zz2G7WSZMvRgLp1/A45r4XgSjvvfvI4/LLqS9mwzhhbx9tlF69xZ8ImL49o40saVFq60cHK+nYJt6m8bx/XI5cB8/QIJ2eTJk9m4cSMrVqwIVHr+x5e6PPPMM9TV1QXxdIQQNDU1BfdyZ2FQ1UP9ual56gcdUvctw23NoFXGvDglyoBUK4+RfXkdmRfWEJ0/XoX8NztFiP2tv2+NLeGQEnSQnUZjNhKJEbjk+fpNB9fTm3a1LfHKsB20RATrg+2kH3+f5IIDSP3nXeKnTQsO1YaWUf65w1SkYduFiEb68ffRJ9SSuv11IoeNx9nUQurBt0lePBdcSdsfXkT/z7uUf/EI9BFmkF6g/8hZIXl/60aU9sb1NG56m/YmRbaSVaNoa1iH7otmd/a9zG2h581iZdoQXkI/377DtlK4jhVs7y8J6Nez6Vt/B0TED0BoIXTl6QRgDhmfcy1d1yH9Ji69HF7YHyLRfVr6LoqgosEdd6LEq5tr6W7scF03MGQ1DIO33nqL2267jR07dlBRUUFjYyMHHXQQV1xxBaZpBvYj/gQ8Y8YMPv/5z3PjjTeSTqeprKzkkUceYdOmTSxYsCAwNPWlDP5kkUteirUruJxuJEGGYbBo0SL+/ve/4zgOsVgssGO59NJL88rw2zp79myuvPJKbrrpJlKpFFVVVSxfvpwf/ehHnHLKKRx33HFdMk739jwFUokCCYvfT67r8uSTT7L//vszefLkoP3FEk8WPmdSSmzXQhcGph5lfePb/O3lr/Helhcoi1YjkaSsFi4++OfMHXcGtpMNxlJDj2A7WeaNOwPL+Q23vPAFACpiQ3hh9T9Z37iMD+3/dQ4Yc0rRa9oZc4zExdCiNHZs4rNH/ZnDJn6YlvT2wLC2JwihkXXSVMSG8OtnPs7rGx4tyRamaDtkZ7qHAw88kHvuuYc///nPXHrppUyaNCm49lQqxX//+1+efPJJzjzzTCoqKlSyRdNk/vz5VFVVeW3bOWP4bkkpXLiqka4yQHW2tJJ64G1ERPeirtJpZqALZGuW9j+/QnT++DzvlmDVNmAn9b5fR+HgIb1oicVetLyXV7ronmFmboAjV7pomo5GvsdB58V2qtKySzbQ/O3HSF40l8QFc+j415u0fP8JEhfujz6uGndHB8Y+Q4JTs29sIvPMSmpuPo/GLz6A9eYmqv90Pg2f+Cex4ycjquJU/uBkjIm1gRRH6AMVxuW23Ztk45Wsef1eHvzJfC/6rEoB4NhZxs05Qx26k0lLZ98qb5Z02w5SzfVKlSVdkC6abpJtb6B1+2oqh03BdR2EJ4beJfDsUqSnPnPTrWiRBBKJtLOBTVIQW0b4gXby2yciiQFKe4ojeHc1gduUovmbj3YSXNsFU0cri+I2pbx26LhtWYwpQ6i45mik7SKMXa+h7ol8Oo7K0u6rVFpbW/n3v//N008/jaZpxONxmpubOeGEE1iwYEEwAedKEgzDCIjLl7/8ZW666Sbq6+uprq5m2bJl/OhHP+K0007jmGOOCewDfHVMMfJS2NZCEiClxDAMMpkMd999N8888wxCqDgsbW1tjB49ms9+9rMkEok89+vcts6ZM4errrqKm2++me3btwcu3HfccQevvPIKRx55JHPnzqW8vLxPkpbCaKn+9WmaxogRIygvVyk0YjG1WClmQ1Ns/DSESdZO8/TyP/Pvt66nPdtEebQGx7VIWW1cOPf7HLvvJ3BcK/Cu8dtk6BFs12L+xPMx9Sh/fP5zZOwOKmND2dSynN89eykHjj2NY/f9JJPr5nmBFnfemKSuSUmgfTuZZKQqmB96g6krQunHoxHBONDnliCEFgQE/NjHPsaNN97Ij3/8Y2bPnk1NTQ2O47BmzRreeecdZs2axWmnnQaovtF1nWuvvTa4ptyYP4OJ3UJaoGClYLsQ0Wn7/YvYaxvRauLIjOPlMVKqIZl10KripJ9aQcddb5D48H5B6oC8B9rzd9/V15C100SMGI+/exOLVt7OD89YlMfw8wmLgyZ0NjUvx3LSjKuZHZSpCY0d7Rto7NjMPnXzyNgdRI0Ev3jqAqoSw7n00F+TtVNEIwmyL6+j/JqjiB2zDwCJ8+dgvbsFZ1MLxj5DiB23j1L1CIGI6DhrGkh8dH9VT2WM2GnTiMwZSfmXj8LZ3IJszeBsbsWYWKvuid6/cOK+l0xZ7Tgu/Ok6okmV08WIKJXQYR+7kfkX3uB5kvjqF025G3tSj8KAT4OJXBLoOja6EaF1+yqatyz38vA4+AapbU0badiwlMphU5QdjtazR8MgtlIRFsdGrxwOgFmm+rE/te609kpFkqQjsVfuUKQ666CVRXC2tmO/v5XIwWODMVSmLETSzDk3F7v+vfUlKv4CStd1dF2npaWFl19+mccff5wtW7YENiqO43DRRRdx/PHHK5VYQVwTdVkyIAMTJ07k2muv5dZbb+WNN96goqKCTCbDP/7xD1588UVOOukk5syZE4RF90mIlLLbXEC544lPAt566y3uv/9+li9fTiKRwDAMWltbGTNmDFdddVUw4RS6X0Mncdl333255ppruOWWW3j//fcpLy8nEomwfv16br75ZtatW8fFF19c0rPkqwi+853vsHr1aqLRaBfD3qamJq6//vogP41frm87NGHCBL73ve91OU9KyeI19/LYO79n9Y43MPUoyUg1lpPCdrMsOOjHnDD1UziuHeQR6kJ6NBPbtZg37kySkSr+9MLn2dG+kfJYDY7rsHjNPSzd+CTXnnAPE4ccgCNtdLGTp0tvbgBwXAtdM3q1m9GEFlynxPWmvoFJLFVYDpeDDz4YTdN44IEHePnll2lpacEwDIYOHcqxxx7LBRdcQHl5ed474KuPBiMVRXfYpaSl0EZDiE4PmPTTK+i483Xlbpv11EJSQkbtl44E20XEDFp/uhBz1gjMacOC8/NexF2gf/ThkxC/uhXbXmJr62qaU9uojNdhuxaG1pm2HsCy00TNJHe99r+sb3ybX5z7Ou2ZJiQuZdEaHnv3Dzz13i3csmAzjqskES3pbQGj9iPXll05H4Dm7zyG9KLrimQEBHTc9xbmzOGBbZCUkvjZMwFwd3QowrLfKNy2DImP7u+pnNzO4Hae6q0//dg5oOrEK4YB/qChHuTCSLg+TMpyjt25QbgKbQnqlz9HNtVEvHwYjhfgTgiBdCw2vvskEw48Dy1n5bOz24fQkHYGvayGpid+w44lD5G20+iaHvSlFi/HTbfl5w3KLwSQDP34b9DjlSqLdV+CPZZgByI8YqtVxah75LK8/Q2X/QvZnCZ28lTKv3B4/rm202f3/gEh51oKJ/3cEOSrVq3itddeY8mSJWzYsCFIfNjS0sI+++zDRRddxIQJE/LUOoXPgV+2YRg4jkNtbS1XX301Dz30EA899BDZbJbKyko2bNjAjTfeyMSJEznggAM48MADGT16dH6zu5G0+BNFW1sbf/3rX1myZAlSykBy0dLSwtSpU/nMZz5DdXV1t4TFr8MnLiNGjODaa6/lrrvu4umnnw5IxMSJEznjjDNK7m6/T0866SRaWlooKysLjHL96/DVQbnzgt9O27aDjNf5hE1Jot/b8l/e3/oidWVjsV2LjmwTiUgll83/LQeNPytv3O1WWqOZ2E6W6SOO5Ksn3s9ti7/MW5uepixag6FFOGnapxVhce2SpR4Dgwzs6/xYUYUpBoqhM4quGBRtg5QEXkPz5s1jxowZQX4tXdcZNmwY48aNA+hC2nMTXu6sMXKXS1ryHh5bEQ5nQxMt335cuTj7L5MEkTCJzB5B9uX1OWoRDbc5RdPVD1Lz14+i1yUD+5bcjtopE4ro/HKl49XjYugRNF3n4WU38P6WF5BScuer3+KKI/4QvBhCaGhCUw+Y1664WU5tUg1SyWhVUE1dcixlXpAhH4YWDVKtO64NhrL1ASj7/OEgfFsXV5G2mEHj5+4nefFc5ZVlOWDqZBavpf1PL6OPrKDjr0tUQLqMDVEDt6GDqutO9/rfW0HT93cgn0B2ZlINtrsu3c+IojN8fA8i8v6isFwVUK2NVUvuxoyW47qWd8ECx84SSVSxftmjNG56m+qRM4LombCTVVhCKLWPESG75QM6Nn1ARniW85qBtDKkV28kNnYoWrwc6Vh06lKDQtRv2w/61Fd/9T4c6pEXd3ML6SdX0PqTZ4gePYnaOz5Gw+X/Iv3QO5R/5WjMOSPRh5Z5+auK2bXsJOTZV3c+S83Nzaxfv54PPviApUuXUl9fT2trK7FYLIhrEo/HOeusszj99NMDrwrfGDGwT8tJtihEZ9JDf2IWQnDGGWcwZcoU7r77bpYvX04kEqG6upoNGzawatUqnnjiCUaNGsV+++3HxIkTmTBhQiCFKHzWfLVUWVkZiUQC27aprKwMXFDnz5/PJz7xCWKxWBfCktsHuX/7JCsSibBgwQJmzJjB/fffT319PQsWLKCmpqZLqP9iyJUAzZ+vFlZ+nxaqi3qDX1ZgN+XV+YlDrqehbSNvbnwCIXQmDTmAiw/+OeNqZ5dEWAIpk6cqGlY+gauPvZNH3rmRh5Zdz4wRR3H2nGtV/aJQuppD+HBxpfrIQbHJkgXffTlncOFn3k4kEkEeKB++B1x30ZMLvwcTu4S05D5weYTF0HFbMzR+7j6cTc2IZFSpfOImbkMH8VOnUvmT09h+1l+wP9iukihmHUQ8gv3+dhqvuJuaW85Hq04EEhdAqUPYCQa6Pp/ypAj+QNieaeKu177H86vu4kvH/IO1jW9x6+IvI5FcdPDPukQ3DAxSNZN1Dct4YOkvaMvsACAZqWbpxicCqYrPvB1pY2iKxSYi3grEW6mmH3oHZ3MrwtSQOYa6siOrJgcpg2Ojh4wjesi4/MtyXISu0fQ/D+NsakEfVq7cyvWcsd7vQ1d2zo3e5C4KDHXzHlihd93ey2o/EN07Fq7reF5Fg0dYcsvXjQgrX7mTli0rMOPlyujWiygppYsQGtlUC28/fQOHL/iD58nTdcU66C+n66BFy7C3r2Lo5f9kzAHn5u12Ui0sv6iSMd94jsiIfUu79r6Gz+9hLAyu33t2UvctI/Xwu9jvbkG6UP6t40leqFSRdY9dTuvPF9J01QPooysxJtWSvPJQIrNHKruWoFm7Lk5LLmm54YYbSKVSRCIRIpEIFRUVpFIp2tvbmTt3Lqeffjrjx48HyItZkfsM9GSL4Q/svgrma1/7GgsXLuSxxx5j8+bNAUGyLItVq1bxxhtvMGfOHL7yla/kSUQKy/afuUsuuYTW1lZeeOEFhgwZwnnnncdJJ50UtLeYhCX3/MLrkFIZCPtGsuvWrWPatGmBYXKxd9FfKPrl+9+tra2Ul5fz9a9/nUmTJvHFL34xIIKgjDrLy8t54YUXuPvuu7nqqqsYM2YMmUwGwzAwTbPgvdUCycdFB1/Hb5/7BLNHHc8ZM79ExIiXRFj86/dhaKZnY2hyxqwvMnPkUZRFaz2JVrFFigj+jxpJTD1KzEjmqI9KGQv2lDALBW0tWKXmPg/+PfUNzoMS+khQBjqW73TSUvjACCECyYjblqHx8rux3tiMqPAIS0RHpiyM8dWUXXUEIqJT8b0TafzEXSrLsK6pAGrJCNYbG2m45J9U/+FD6CMqgsBqoqDeQYPwDG01ZXeyevvrLN30FK+tf4Sq+HC+dfIjjKudzYyRR1MRq+OOV7/FV+6bx2ETP8ysUceyT91BxM2ygJGbWoS2zA6eX3knabsdgcDQI7Smt1NXpoiF/8KURat5dd1DfPrOCcwdezqXzb8BCwujDdKPL6fql2ci2zJKcgLgSso+e1jR+5GXKVsTygU1GUEri+QQv67XDkralZ9Mkc46gzpcXDtL15V/9x0r8bIBa54e33WC7MC+WmMgD3uXVabrohsROprree+5m9AM34NL4LqWJ3JVSR/NWDlr33iAcfudxZiZp+DYWXQjsnOesQACpIvQdJw2RWit1m3okQRaNKkMcC0CMimtjCdtAXQjz15EmLH+Sap6OLRLOQLcpjRlVx1B9IiJ6MPLST+9gub/eYShz15J5fdPJnnJXFIPvEN2yYZO4ipKfUYGiJzm+kTCtm3Gjh3Lpz71KW666SY0TaOlpYVkMsmcOXM46aSTmDp1KtDpnZNLAHLVGb/61a/YsWMHHR0dHH/88ZxyyilBvJRAHeFJMjRN47jjjmPevHm88MILPPfcc2zcuBEhBKZpMmHCBK688srAhbo7KUlu/QsWLKCqqoqjjz6aMWPGBNKdUnIeFVWdeOqisrIypk+f3mtOIv8c/+Pv8wleTU1N4BprmmbQF+Xl5WzdupXbb7+diRMnMnLkyMDg2b9Xhe3TNZWJva58HF874X7i3iLO8QhLoQSpcFKVnmTEexr8DHK4SBw7y4RaRbYtJ4MmdE9Fr87VhBY8Sq502d62jogeY1tbExm7I2hznx7I3YqCtqrJE6fAiys3KKBPYvqDQulMf7DTSEt3D45PLJzt7TR+5l6yL69TdixpO89TpeI7J2KMq8ZNWUQPGkvFd0+g6Sv/QSuPqn7N2oiyKNayenYsuJ2qX55JZM7IIMS/0PJfcB8DWxELXBx0DN7fspjfL7qM8bX7cdK0T3P81MuIGWWksq1oQuPwSR9hQu3+PLPiVl5aez/Pr7qLr5/0IKOqpgaJu9qzTUweegjfOfXxvFoefedGHnzrlwBBrADbyTAkOYaDJ5zDkKSX8h4NYQK2S/rR95FeXiAhUCvYZITkxw4I2HNwHwLJiPft9ZXblkVa3sPa3Xvn5rp0eX2sd05A0rNA96Oq9hVSSo+wGGx6fyHNW5Yz7cjLVTbWfuqVi68MVQTNNx/9KS1bPyCSqMZ1LKRrk6gahZPtIJtqJjfK7pIHv8OQsQcQrxiWl2QteMa9Phg8KGIpDLVy1MvrEJl22t54iPTKl0CDxkd/SXyf+URGzyQ2/oAerrcfg0QJkhb/euNnzwxspoLWx02cDU2ImGq/MWkI5VcfmX+MLnDdQe62og3OqTNnQrVtm7lz57Jq1SoWL17Msccey3777ce++yrpVa53Tu75hf25efNm6uvraW9vp6mpSVVZRArnkwjfZuPkk0/myCOPZMmSJbz11lu89957XHjhhVRXV3eriikmGamurubjH/840DWSbSkr4aISCK/u3KSF3ZXlui5tbW2Bx5Lvuuz3m2VZwUTnq4j8cPE33XQTEydO5Atf+AKGYRQ1bu5KrDRc6RKPVOB4weU6vWfyyZQPx1WxZgw9gt6NrYgvpYFOr5xC2E4WTehEjSQfnfsDNKHTYbUwrkapUHaN/ctgodg4odFTjsrBlnj3FTuld4uJIqUrwXEREQPrva00XfUA1vJtaBWKsKAJMDTcljSV3z6B2PGTVXyWuIm0HBLnz0G2ZGj+3yfQKqIIoSmik4jgrGui4eI7qfjm8STOU544MuuAqXVVS/WzozyhJxrqbs6feB4Hjz8HvUDkHo+UB3+PqtqXBfN+zIJ5P847xpeeRPQ4tpMha6do6NiElJLa5CiaUltImGr14BtiWU6WfYbO45w5Xw3K0aUOUaj49gk4m1uUxMNV0hNsF1HuvXS+LU531x1Vj0H5VUeo+DgAZsFL3bm86Pzbnwg8SYvrWOjEaNz0Nk/87mwiyRpcO9tjf/uB5jqaNnLohTcwfr+zyKZbiCZrWPv6/axachfTjrwc6djQj8Egl7AGq1M7g25EWfnynax86XYiiSocO4MRiZNqaWLWCVcD8N+/XUG8cjh2th3djNO6bSUv3nkVx15+p3r+PPVRF+IyWPDUb3gTQOPjv6Lp/u/jpFsRRpT4PvvQ+tJdtCy+AxybyKgZDP/kH4mOnaPsYbR8qUD/6u9ml/AkdDGDjn++QfO3HkMfVoZMWWBoiKiB25RCZhy2zP9tQKRxpXq2sg61912CMaZKRb7W9J4rHCgKJC3+NfiT8TnnnMOJJ54YxJjwJ9hi4eT9c3MRjUaJx+OBCqUQxSQZPiFKJBIcccQRHHHEETQ2NlJdXR0Qpe5UG8UkELnSFR+5cU/6C99+phj8useOHcsvfvGLQIIyZIgKtdDc3ExlZSVSSjKZDFJK6uvr2bFjB4sXL+b111/n0EMP5eMf/3ie5KjYOF14vZr3DnbnIRSogaWr4l55x6WtNho6NrO+8W02N69gR/sGWtPbSVmtXjRcZaQbNROUR2upSY5iRMU+jKmeSU1yFElP3W8IOHP2l/Pa6Iey6O8iITCqFaWrckUQI7Y/Noi54yNomsot1NLSkufGP1AoVZsKJVBVVbXnSFoKX+hAuuIHgtN0Ug+9S8v3Hsdt6EArVyohNKHURc1pyq86nOQnD1KSAt+DxdSRtkPy0oNAF7T875OIuKFUSb7bc8am+dqHyL6ynvJrjkKvK1Or9hzyktum3DaX0nkqA6aSsmxrXcvzq++iIjqEjN2B5WYwtag3OWieSNHGlTaOaxE1koEQ8sSpl+c8ZJLXNzzHRX+tIWOnUUIQg6xjM7ZGiaWzdoqYWUZHtommjnoA2jONJKPVSCEREsyZwzFnDu++7TlivS6SL6mMIaUr0YeXd3tcsBQuHLyEUMkrc+ox45WMnnUqZjSJ69g99K+Kk6IbETLtDUFSRd9lOpqsIVk9Or/+PqDLdQrhqXai7Fj3Okse+BaaYSq3Z93ESrUwdMLBjN//HDTDZOXLt7N11UuYsXIcO00kXsWGd55g8b+u4ZDzf+4N5MUN/Aa6GvEuGulaiGgSa9kTbL7xS9SeeAFVJ3yB6Nj90KLKTdZu3U7q/UXsuPdbrPvBEYz7/hIiwyYHxGWnwZPY6WOqiJ8+DW1I0ouvJJEShE9eOrJqQNaEEh45Urk+e5GYO+/trtPzF94zfzDNTQ7no5R76zhO8Ck20BebTHPzj/nqJ5+wFEpWSimvmF2N1hdvsR7Q2/Mcj8eZMGFC8NsnOddddx3vvvsuI0eOpL6+ngsuuCBop2maXH755Rx++OHYth3YSvS0wOz6u3gOt+B99+xfNKGxescbvLb+Yd7e9CzrGpcFEprOcguv0bPTQRncCgSjKvdl+oij2H/0SUwdfhiudMnY7ejCwNAiedLXPvWtF6pDJTuErN2B1OP0FlxOExoZJ4WhRzwv1gIpeonQNIFtW+gRJf364Q9/yJAhQ0in0wN+hvz3q7W1lenTpwdu7MXIfSkYMGnpjqiAMvBEKiNQtyVN6y+fo+PvS8DQEQlTrdQMDXQNtzlN2ecPo/zqo/KCTgUrRcMjLpfMQ6tO0Pz1R5SKKG52qpaSUTrueJ3s4rWUfe4w4mfNVMQGT/KiC0WQepjEu4MQyhhWJ8qW1tXc/doPqIjXkbXTniGXir2SslpUoKL4MHwVivSYfjJSxbFTLgnEjmfOvobjpl5GTWIUjmup60R4RrfqmIp4HQCThx5MMlIFQMQLQCSkNwlYTmdCQ3J+ayo+ix+cLzDKLRCfBsTFcYP+KZzwEcoWSVpOTqZnJXURMfUYCc1AIimrHs38j/y6D09RPvwkfY6dwc50eBfVtzKKERbXVoa3bQ3ree6vn8LKtKEbUS+Bo4HQdOac/LUgnszcs3/Ik78/F9ex0DQDx8oQiVey4vlbiUTLOeDM7yBdR4UTLJC4DA6kN9nr7Lj/+1TMP4ERn7mz4DpdjPIhlM89h7L9TmfV1eNo+M/PGP7JPyLtLMK7lsGG9N5r6bhE549XAR/7VRA5gSKLv4NBaPIiIn3fNkaU4BoanNONpCJXDVTKhFm4rzeJVk+SEl9l4qtGumtDd+UVwj+3tbUVy7IGddXcHXyPEiklVVVVaJrGFVdcQTwe55e//CVTp07l4osvZvPmzWzZsoWXXnqJe+65h7a2Nk4++eRAWlSKzUNvUphcwrKjfSP3v/kzlqx/iJbUduJmOTGzDNe1yTppFQbfsZGBB5BEoBa6ujDQNRNTj6FrEba0rmb1jjd4ZvmtzB51HOfM+RqjqvYt+jwVa2cxBOOrdCmPqlhMhc4bPcEPnudH0NU8R4K+QMrOtsbjcUaPHk1VVVWei/pAoOs6FRUVgQRuIOg3aSkmdg5uluWqSdJbgaefWE7rrxZhLd2MVhUDV3Z6+0iJbM9Q/pWjKf/cYUUJSx5xsRziZ81AG1FO87X/wV69A60qrkhJ1kariuNsbqXpmodI3b2UxIIDVbTXeKeu0k8qKIzSDYKklJh6FAnMHHk0f7+kuehxD799A0+890eu/9DSHstzXJuh5ePJ2B28tOY+ooYX1Ex4Uh0pSdttAGhCZ1zNbMZUTQ9+qwvwvgriXYho8dvqu5gWm9CllMH9KiqS1gRuh43MFqQfF7nh26WKBuy62FaqVwmLEUkgXQfHVqsL3Yii6Qb+CyeE1m9JQXEJS4RUyxYW3rKA9sb1GJEkjpXGiCRIt21n2tGfZtT049V9kJLaMXPY79Rv8NK/riGarMZxM7iOhRkv5+1nbkBKhwPP+l812Do2mr4zIkBKEBp202aiBykPIqe9AS1aplzHBSAEbqYDPVlNZNg+2DvWe6cOXkK3QuQ+M25bBq0sSvutr9D46XsQVXGV00r3V69Seb5HdUREZ+hzn0EfX6MM60292/HVN5B0HJXAzrFSBdclcF0bM6ruo3ofSh+su4wvRd4J/1oHG4Xktpj0pdQFVTH4K9kbbriBZcuWUV5ejmVZ/SrLlwT05NLr95+u66TTaX76058ybNgwxo8fj6ZpJJNJampqqK6uprq6munTp3PEEUfw2GOPcdddd1FfX88ll1xSlHz0pZ2FhOWd+kX8+YUvsqV1FeXRGiridaStNho7NlMRq2NU1VTKo7WURWsw9SiGHkEgsN0stpOlLdNIW2YH29vX09C+CVOPUh4bgiY0Xln7IO/Vv8CCg37MIRPODersT7sd1yZixHlt/cO0ZxvpyDaja5He32Gh4bhZEpEqtrSuwtRjZJ0UfVYS5RD2GTNn8Nvf/rZP5/enrv4+2/0mLV1YpSOV6sDUAw+U7Bsbab/5JdJPLAdXBpFukVKpdNIWGDqVPziFxEf3D9yg/Qsr9gALU0dmHaIHjaX2zgW0fPdxUo+8h5aMgK8uMjRELEb25fVkX1qHOWck0aP3IXbqVIzx1cGkXmwS7xGeOYfrup5/voPlpNGEQcZupzI+lFS2NRDptWUaAslJMlINEGQj9Y9Zue1VfrPwYqriw8g6aTRfLykEumbiug5xs4x1jas4adrFTBl2iMq7UcS+I891eV0jImoqO6Ed7cTPnEHy4rmBIXTufcz9LrrNV5SmbZUPSuvcLjSBlsgX8wtNw4wmu+9Gr7/XvHYfdePnkqwZ03vfl4hiE5FPWDqaNvHMLQto3PwuZrQMx0qjm1GsdAtDxh3Ifqd8Pbhuoem4rs2+h3+Spvr3eO+5m4mV12FnOwKPoncW3kimo4lDLrgeTTeCenLbUfh336AmfSkdyg46j83/+SnVh19CZOS0YK8P3YjSsvhO2pctYsSn/6T2632LidEX5BFaX40bM9FHVlBzxwJk2vLUQcLLHRbFWraF5m8+gqiKKy9ACgdkmfeXuo8O0YR6dyLxrqtPHSWRjFcMU8vFEgfrYs974e+dQVZ2dRsiEZNEIkE8Hg+kOcUkqT1Ja9RYVTz5ZiDFdN1AxZOrTkilUiSTSRobGwMD5fb2dhKJBJqmcdpppzF+/Hh+/vOfk8lkuPTSS7u1aekJuQRTRZM1eX/Li9yw8GIsJ01lfChtmUZiRpIDx5zK3HFnMrJyClXxYSQild0az0rp0pFtoTm9jS0tK3l9w6O8tv4R2tI7KIvWkHXT3Pz8ZxBCcPD4cwKX6760XxM6rrRJRqp48r0/8p+3f4MmvD7o7bq9a/Yl+clIFWm7PScoXWlSu9x+FEKQTqfzSG5vBL6U8qVUhuOJRGJAi7sBS1qCyg0BaMiMTWbxWjrueYvMkytw27NonkGob3AroiayOY0+ppLKn5xG9LDxXRIgFmXcUiKFUnlIy0EfXk71Hz5E5M8v0/rrRbjNabSKGDiuIi/JCGiC7JubsJZupu2mFzGnDcWcMZzYqVNV4kVvou8LHNfCNKKsqH+Jrz5wONXxWrJOGt84UyL52K2+Ia3O8IqJ/PSslwN9J3SK9KaPOJK7L+s+K6avdvrx42flWLP3sDQFss+vwVpWj0hGVE6njc0Ykz2xnNMPEbE3GbgZW0m0NOHLE9Un2X3I/S4iW29ib9z8Dov+9inmnvNDph15RWAc21/kvlS5dfqxWJo2v8uzt36C1m2rFGGxM4E9SzRZy/wLbyASrwgkJqosHSldDvrQT0m1bGHtG/cTKx+aQ1wq+OClfygj4o/8mmT1aC/OS76EqP8rZqmMabNpqs7/MQ1LH2H1NdMpO+hM4pMOQa8YCq6D1biR9rceo2PZS1Sf8ikqj7pUuYkbA0uF4Ktlel35Sv940CpiRA8eW7Q8fUINzV9/uEcJXPCXUFIUPRJn03tP4zoWmY4mz9W8U9IiXQfdjNHRvFn1Fe4e40w6mOir9MF/H9pa29m+fTuZTKZbSYsQAoHmuQF3HR98dUOxkPJ+m3RdJ5lM5kW4hU5PocMPP5y6OqXqjkZ9+z9l9DljxgwuuugiHnjgAbZs2cKoUaO6eED1Bv+6/HgrHdlm/vbyV8nYHcTNClrTO5g77gzOnfNVRlVN7dJXtmupsTtnHBEINM0gGa0iGa1iZOVk9h9zMmfNuoZ/L7ue5z74h8oVpJnc/uo3GV8zh2EVE0uUuHT2kUqc6+K4DoloFeViSFGplqAzMKnXcEAiPTdsy0ljSxtXOkE6AOl0gFZOrvq6O2hCI5u1iEQi/O1vf+OZZ56hurqabDYbuDx3Z5Dtq/S6U0EahkFbWxtTp07l61//+u6xacmbHBwX6/VNZBatJLNoDdbSzcisjVYe7QzLL2WQCNFt7CB62AQqrzsdY3Rlj4Qlty6gMwaLp08HSH7yICIHj6X1F8+SeWalUk0lo2A5KhtyWedkaL25CXvFduIfyo/w1xf4rHx09XS+e+ojnhFU52AphKbYfWwoL6y+m/+uvANN071juk4EUrpFF4gCLTDETVutZD2blt6gVcXR6pKIuKkIXtZGS+Z7EvULGU/Skvvga+Sp3oAu987/9gmElJIX/vE5how9gH0Pv1TpYfuZa6g7NaWfmVk3Iqx/62EW/+saMu0NGNEyHDvtuU8LpGtz8Pk/p3rkdFw7i2ZEKEaADl/wexw7w4a3HyVWVoeT7cC1s0QTVWxe/iyP//ZMDjzr+4ydrRKIOXYWTTfo9AbIX50Wbit+cYCmIZ0sCBjz9YV0vHI3LS/ezo7//ASntQWhgVE1jPi+RzDmG9+kbL/T/U7oci9K7lPv27ZtIkaky3vou8AGRNRxAINMe4qOpRupv+A2ZHsWzVBZh6XrBob2ssNSEtbcirrUrN4h17aIxCt5++nf8uZj11E8EZwi0ELTiZXVetGWuydW/e2T7tAdCehu30Dgt9t2szhefqwudaMRMWKq33E5/8JzOOvsszGMzsi8Xct1sZwMph7Lf049246M3Y4mdCJ6DJfOMQyUGioej7NmzRpuv/32INGiD18NcP755wN08a7yI/4edthhHHDAAVRWVnZ5vvrWRw4IjZfXPsDahreoig+jJb2Nk6ZfyUfn/kC12bW9a1MLE+F5C3XX535cFz8Zbm3ZaC455BcMr5jEP5d8l7JoNTvaN/DflXfyof2/HtyXYhKpzmvynhEE8Uh5IM2SQT1F2uKk8tRFymFBB6cDEJgIIkIjaySIGErSrXu2Mb6nY49957UIFNmMxWJEo9EgppGUMpCSFPZROp0mEokUNdr11UG2bQdh/geCARniCiECFUv735fQ8Y/X0KriiKihDG0tB2zf2FZHtmcholP+hSMou+pwRTys4jlIuhOXFrPBwHYxZwyn5s8XkL53GW03LSb7Tr2atKOeztxRCQCJGko6s/+oLuH/S4XmsdayaDX7jzm5x2O3tqxi0Yq/4zU+f8IPrq34wySECOxXInq827gBhZBZu9ONXEpkxlYxXPoL7yF127NqwvENcZWPXicp9G1shArKJgMbHQ1Ep4HtM3+8kExHI6d88VE0zfC8XDr7IOgPoewhijepeBwgZY/iBlmjlz52He8s/J0amCJxJWHRlNGtne3gkA//krGzT8sjLPnPmoZ0lf3NUZ+4lUV//RTr3nxQERc7g22lMWMVdLTU89xtlzJp3keYdcLVlNUqaYPr2CiJidF3iYti6IHURpgxKo+6jMqjLuv2FNXnWt/rKqgWIBJL4Dgutm0FuUhisVgQml7TNFwpMTxCXDVvMsnPHgcRDZF1sWyb9o52tQpzJXpFjMTkIUHcFro89p0um8F77rpEk9XExBB6sleRSFzbylFnFL+oXKI1UHdgv5xoNJo3kPvu0r4B7EDryc3+7EoXQ4sUxnPMg59k9bF3buLJlX/gujPeGFD9P1t4BuNr5vDh2T/o9phkMhl4XhXa6miaFkh5fFdvf6HhP59+NOJSDXG7g++Z+eaGx4kYcVJWC5OGzOW8/b4JECSxLbk8j2DkxnVxXQeJ5OTpn2H51sW8vv4xEmYFyzYv5MzZ12DmJsl1MiB05aSQu5jzHshEpJKfn7OkGBUPtonAyzKet93/LqTyrpQIRxHH7IZ7MCtnY5RPRrp24JlZDFK6mKbav2DBAhYsWEAmkyGZTPLnP/+Z999/n5/+9Ke0tLQQiUQCaUlraytf+MIX+OY3v8n06dNJpVJBUMX88ju92foiRSvEgNVDAAhB5Y9Pxa1vJbtkA2h0egbFdGTKRrZmiOw/ivKvH0f04LGKwTpul5xBQghlKGs5XRdM0fyB37JUsCCEgFQWhEA/dyblh4/B/s/7tP/tNZzVDbiOi1YRw21JU/E/xxI9fIKa1GNGXh6RXpFr4iGU/7/jWkgr7TFg36bDICsckpFKWtw2Wp22oJ+CokqcVHwxYVumgaiZ9Du/53ZqAuF9AhVOPwaATrWc97s963lqeWQVoeqq8j1U/MneVSoWPb9fW7Z+wCLPa+ekz/9bBWlzLDRd2e44VhpN07Ey7ehmDNcz0M0pOq9decTFdZRLuhEBdLasWszrD/0vW1e+SCSu4kQ4dsYjTgIr3cZB5/2UfQ7+WJ4til9+UK6UKhqvl07gqEv+wkt3f4UVL9zm2VhIT3JjIgyNFS/exqb3nmbyoRcxef5FxMuHFm13SQOy9C7cv9/SVfFqAITW2ScSkJ40M8empvBaSoEQAtubdJ984lGOP+FkdI8sm6bJli1bWL9+PXPnziWdThOLxVj61ls8+8wzVA0bgnW8RiQWoT3Vwb777svR0+YVjR3h92uX+ktqZdezej3PV2F5Im4/uu1A4Y8duQa00ajqLz9z82DUgwRbKnuJp9//C29ufAJdM4Lgk7pmkLHaGVE5hY/N+yEALg4NTdtAQtpK5WUpVrZ2FlEzzoaG97nzte9w+WE3koxU47hZNM0ga3eQiJWzbccWytgGQFuqmbhZpqTG3hgcj8dpa2srKhmXUpLNZvMS6eX2lU/ocolwn9+TXHiHb21biy4M2rNNTBtxOBEj5kmTov0qP/ccTdOxPePwOaNOYPHqe4lHytnWthbHzWLqEXXDJAjv3SmM6ZRbv6n3EojTy9/W/OplOG0fIIxypJMiMuRwzNpDaFr2LfSYCnkhcSmf/m2MmoMB2LHwPCoP+D7l07+JdDM9khbovHe5XnT+t/8c+4ksffjSNB+RSKTHZ35A95fBUA9pimRocZOq605nx4X/wNnSikhGkCkL2ZLGGF9D4hNzSX7sAETMk8AYWpBkLY+weEkRu1tK5B6fl3wr9++hlUQ/cRDxD80m9cDbWE+vJLt4HWUf3p/yK+crg1/PTbdPVswy/09NaGh6FIpIQIStmO6QNpsDyvZXG10HihhHdl3dd7bFl7TMHHks5THlDtffyLB9ht8O/8Fty6j4GomIuhZA6BpadSw4XkoHIXRatq2iZesH6GaMbKqJbatfYdWrdzFi36M47GO/V+oyx/bC9KvosppnvOtH042VDyUIsiRzm5X7vKi4GJpuoGs6LdtW8t5zN7Py5TuwrXRnpFvpdro3Izj0wt+wz0EX4nhu0OoyixsZSk/aIb2w+odecD3lQ8bz5qM/BSkxIgkcK4N0HaLJGtJt23njkR/xwUt/Z/z+5zJ2zhlUj5iObkb79aIGA4KUCN1QxEU6OX0iPBKj1F1dlm1aYcK3ntCpt16xfAW2KxgxfDhNTU3U1dWxZMkSampUMs9sNkssFmP5++8zbfp0jjz8CLZv3kIkEsWxHWKxKKRtz8upc4FDVO9m0MpVD3USxkx7A3Y2pSROhYQ9kEYZSj0kuzHG9Tb5E6Pvduuv7vsDX9ISiUTIZBTBNgyD+vp6Vq9eTXNzc9EVZ1/Kt22bSZMmUVZWhm1nMTSTpZue5OW1DzBv3Jm0ZxrRhIGuGaStNs/jsHM1H4lEQUDEjHR6Hfrle4EiO5xGFq78J5cfcQO6IdCkqSZYL9dZNBoLiJg/IenkR8jNXfjl3ldd14N9ft93dHSQyWSorq7mpptuIpVK8eUvfzlQHwyKWk12/iG6ivQGDblS8rwn2X926x9DL5uEUbZPMDYG+wukUt1COgih4VhN2OktaHGT7LZnsFPrqaiei5PZAloUKS1S614mNvbjmB5p0ZNlaF6g0hKofX77PVLqq4o6OjrYsGEDjY2NmKYZ3M9USnn1+akXLMsK1HvF+6z/hAUGyxDX0JRh7Jgqqn51Jg2fuAu3MYU+upLEZQeT+Nj+6CPVajc3aFwXwuKpazIvr6Pj76+hVcfVNgTogopvHIeIdXbW3XffHUTucxxH5epwHeLROB86+xzMihjJjx9I5sJZyOdWIqYOJe1kiXn6S9u2WbRoEcccc0yePq8U+CJsgK1/+xxO8xZEJIF0skRHzaDm1GsB2GftVvYfdZU6yc4i9PzcGKojXKU50vIHcymVm7Urnc7Vk3TyVgs7Fb4Uy58z27KKtJRHwfJ03rqGVu3FjdEEtpXFiMRZ/vxfePmeH1A+pFZJToyIyqNjpWnc9Da1o2crDx1P0tKwcRlv/OeHRJM1WJk2YmW1bHjn8U6C1gOZEEDT5nf54KV/sOb1+2hv3EgkUamC29kZEALDjGGlW4kmqzn0whsYPf3Eot4+xWypOr+9TMauzczjrqJ65Axeue8btGxZQSRRhZTK1VvTTXQzRqplK8ue+hXvPvsHxu13Jode8Cv0viR/FAAumkfifFIn9J5e257JfknwDbqtLGvXriObyeA4Di0tLdTX11NeroIQ+ivmaDRKXV0dkViUoeNGYRS4qRdrUW/t8e2cMh2NTD/6swzb5zCyqSbP9ilH/eC66GaUjuZ6Xn/of5WKz+nesN2yLKLRKI899hj/+te/qKurG3AALSklyWQyyJnz8ssv8+yzzw6oTNd1iUajNDQ08OMf/5iZM2fi2A6YKgDlvHFncvWxdxQ911cPSSk9Gx9vrCrobn8c0oVBdbyKTkmpVK+9K0FTbXFzyylSRi58aRaohIm33347559/PrW1atH19ttv88ADD/CDH/yAVCoVTHqFZfVrfPM4a135OLa0riRmlPH+lhe9vESRwLOor+Xn2ebl2CAu2/QMUSOB7WQZVrVvUDbSBnS2P30y5TOvpWL2T5FOGuHZmvRp4hYmSEnNoXcHm1rf/SHZ+keJjTyd2MjTg+3191ehx0d0dofdjnQt+gSRo75KJNi2bRubNm1i27ZtfPnLX+7UcHiIRqPEYjFeeOEF6urqKCsry4s3VIiBzl39l7RQMNB79imRuWOo/OHJOGsbSXzsALQaNaEFkWm7icECgK1cpu33ttJ2w3/Rx1YjO5TaR0R0yr9yNCJmBh165JFHBqJHwzBob28nHo9z9z13k5UOpjfpPvPYUyzfvIbKjUk2rtvASaeczIEHHEA2m+WNN97g6KOPxkX2nY97K++6D/8UN9uBMGNYW1bQ/MxNalUOWIZGk9tKDZAbHEsIgZtNoUXibL/nW1Qc+lGio2ciHVutpnPFkcITRwrRZ3e6gaCwDrctq1R3AdkCTF15bOGJECPq7/1P+yazT7oGITSsTDstW1ewbul/WLH4b6x/62EO+fAvmXzoRcoGRTexsx001b9HvFzZirQ3ridePozaMfuptmj5fedHe9225lWWPfVrtq95mY7mesxoObGyWhw7iyvtwH4l095A7dgDOPQjv6Zm1MxeCUteXQX7hTBwHYtR046nZvRsljz4HVYvuRshdC8KsKVURrqBGR2ClW1n5L7HKJVXjndSj9A03EwHRtUo6m+5DO36j5E1QHNB9uHWCw0m/a4eo2JYiRFyJZpnKzZz5kyamtuJRCK8//77zJ49mxEjRjB9+vS8M3xjTKALYenORqH485tPTDXdwMl2MGr6CYycekyPrc6mmnntwe8ghNbFkTqvBq/eZDLJkCFDqKmpIZPJDDjqp2+oCGrFWVZWNqDyfEmypmmdEmWvexzXpj3TCEBrpoGYT1CQ6MLIW1EHRtWu5XnDdLXhS1mtWE6ajNUBsU6V9EDbD5BOp3nwwQc58cQTA9KyevXqYH+hi/SA60VlYZ8z6gSWrPsPVfFhLN/6Ig++9UvOmfNVdGF2TYDYm4Gq9D2nlAmAirAbYeHyv/HquodIRqtpSm1hxoijMPWo8h7yytQTFYhuJB19Vk05Kfycael1txMbrYybncw29Ggd6c0PI7QYerRTJe1LYPsKv86nn36af/zjH2SzWUaNGkVlZSXRaDRQ/9i2TSqVoqGhgQcffJAlS5Zw2WWX5SXZLMRA567+6xlEp649j7g4LvEzZwSH+RFUg+zBXYoRuT/UVyKCPrpKZW5OWYr5mXqwX0qXbMamrq4OIQTbt29n06ZNzJypErZFzAiZdJpkIoGL5OTTTuFkF9Dg2eeepTxZFtRdVVWFEAIdgaQ0Y1WBQDoWQjfpePcZ2t/8D2gmevkQrM3vU37IRzpXx1Jg+EawvkpFqPN9EtOx9BH0ZJUiLVYKRCIvb4yUMjAeG6g+sF/w+70ppexk/FWbI9GqYgERDfpGSnQzim56thCxchKVwxk++QhmHPs5Xn3g2yz62xW0NW5g/1O/juvaDJ1wEOd++/Wi1Ss9cnE1nmaYbF/zKtlUC/HyYdhWygs0JpQ6yM5gZ9vZ55AFzDv3x5jRssCLyS/b/+6uP4sRF003cR2LePlQDv/Y7xkz81SWPnYdDRuXYkbL0A2VHyvVupU5J3+VifMuUEkf9e4jrhZcNMKI4HQ0UnHYAkbueywpz+U3N7haV12Qfz6K+GQ7AglNT4OX8Or08a/7/kOENGVl5bS3t9HU1EQqlaK2tpbXX3+d999/n9NOU55SkUiEe+65hylTptDe3o5pmnR0dDB58mSOPPLIbhP/9YTORZFOpqMBUMREN+N51+8Hl0u1bMkbk4r2Rw6y2WywyMlkMgN+l3KNSqXs3vWzVEgpiUQitLe3d1VfCRGs6GNGootNRMZOBX/78Tqihp81udOLxP/e0PQeQmjUt3xAXXlxd/X+QtO0YJwGFaPl+eef56yzzgI6o+iq9nS9B30d73z1y8Hjz+bJ9//E5uYVlEVreHDpL9jRvpFz5lxLbXJ03jmudALjWnXfvMUJKiWLpunoQgdPJdaWaeDht2/g8XdvJmLEydopahMjOWLSR/1W4D9w0u4At3vJX3coer2aUvFZjUuwW5cTG3WOqk1XgoHstmfQ4iMwKqYpqY4eo9vxoQc4joOhGyxZsoQ//elP7Lfffhx33HGMGjWKmpqaLh5AHR0dbNu2jVWrVvHAAw/w+9//nu9+97vU1tb2KHHpLwbuPVRoWKRrajUupcq2XEQV5J/bFV4H2y6yQ6kipGdgi+0Gj4Km60RzDH1aW1t55513AtKyzz77BHp3XEk6m1G69+XLqd9cz1FHHhXoohsbG3nzzTfZd9pUYoGtSDcvRyBgkIGdQGTkNKRtYVSPJL3qFbJ2htjk+bh2Bs2MISIJGh6+jrY3/0P1yVcT3+dQFV7diCB02PbPa0HTsRs20vrqfZTPPadL/xb2eff9t5PgBZNzdnQESRkV8XJUvhk/uaKnSVLtdjttYbxipOuQrB7NUZf8mYq6Sbz+0P8SjVcx/ZjPKJsQf42cYzQphI5m5EuXfLWQ61jUjp7D0Zf9nadv+jC21aEGYk25Gmc7miirGcOcU7/OpHkXAHTa0RQS7l76s5C4AJ49jouULuPmnMHwyUfw7sLfs+LF20i37UC6NmNmnsKcU76qcjD1Zlsi1aQifXsVI4Kb7SAyeibxeeehNW3CiMQ7Rf6uV2aOCq/znmlIK4tZMyro+56kLMrmt7NNxxx9DG+88hxNLa1EoxHmz59Pe3s7AJMnT2bkyJHBNRx//PFMnTqVeDzObbfdxllnnUUikSAW84h7EGq/b8+set9lYAhsmHG0gtgzmvQyBpsx/yK6L4zOuCFnn302J5100i4Jb99f+GqWykrltmrkSeg8j75i6hnv29Aj7GjfyDX3Hcjm5hWcMPVTXHLIL8jaKSJGPFB1LNv8DLXJUbxd/yyzRh2bI2kZnH5Jp9MB8br77rspKytj3rx5qo2GEdgD+fmXfNInhCiZ7AbvpNBwXItEpJIF837CrxcuIGN3kIxWseiD21m68Qn2H30Sc8eeyfCKSZTFaombZWg9GI5m7A7aMg1sbV3L6xseZcnaf7O9fQPJaBWOa2E5GT556PUMq5jYGVzOddR9EDrgq9Yszxattyi3Rv5YJ3xbNQd0nebXPkN0+MmYVXNUbjI9gZQ26Y0PEB1xmnee3WUhUip8ovv4448zffp0vvzlL+clDs3NryWEIJFIMG7cOMaNG8ekSZP48Y9/zPPPP8+ZZ56J4zh7FmmBbohLgRFtHltWJ/VSKGqi1ARoyktC6BqO62IAW7ds5dHHHg2CFG3evJnt27cDKgJjRUUFf/3rXznxxBMZPnw4sViMNWvWsGjRIj784Q8H7RZCkM1mWb58OZMm7wNmia5wEs+jxMWoHI4xaziZTe9iN26k7iM/RzNjOKkWMGNIJ0vth75P+dxzkZbnDaObZDe/R+Njv8Zu2szoax5FS1TS8OAP2b5xGeUHnE1k9Aygn9l5BxFSSvCiBrvb25TbuJfXSDou+rBylV7B0393rpA7vVuCK/CMbwH2P+0bZNp3sOTBbzNy6jFUjZjW7cTaVT3jqc10E8dKUzfuQA654HoW/fVyDDOGbaVBSibMPZ8DTvsmyZoxHmnQ8kLtBy6Fpa7+CwiOb2wn0HAdi2iiiv1O/R8mzbuAdxb+nm1rXuHg81V8ESkK7LeKDcRCYEYSuI4JQkO4NvFhU9j216sQP7kKKwKa9Imhp+q21RiYJ3NxQYtBdguM/dbPqDn1K0g7g4gkSr7OIdUJhg0fRtraRDwWo62tjbq6OpYtW0YikeCAAw4gk8kQjUaJRCJMnDgRgCFDhjB58uQuxpc9L1Z8dOPeHgz6XQd76bqeQX8vE0EOEfZtUJLJ7qM275EQXX8UM670+9iVKpr2ydM+Q2PHJvap84iCHg08aV5cfQ872jZw9bF3cttLX2Fb2zrqysZSSlyPvuLhhx/mySef5Bvf+EZgFwWdRry+t1UuSpW05L6TumbiuDbThh/G5478M39+8Ytsb1tPeawGy8nw3Mo7eHr5bVQnRjC8YhIVsToqYkMw9RiGrswPLCeL7WZoTTfQkt7G1tbVbG9bh66ZRI0EZdFq2jONJKJVfOqw6zhkwodwXFsRlrwLcBBeHjlhVKh2lpDBucuiSjMAg5Y3v4zV9AZ1Jy71ik+jmeW0r7wFp30lySlfUudppbp15/dpLoFfvXo1H/nIR9B1nVQqRTyuiG4xzyApJalUirFjxzJu3DhWr14NDE6W8UIMihtKb7rqwZAO5D60PlPXdZ14PE4ymQxE0rZt09raGqxQLMvimWeeYd26dZx33nmUl5cH0Raz2Sx1dXWdgY/SFppp0qPInXybimz9choe+gnStak9+7voFXVqn9dWp3U7WqJKle+FjU+9t5DWl+8iOfskyg44Oyi+9pzvkvrgRVoW30F01HQq5i/Y+Zl6S4DQBDJj4+7oUITUIzJYLvpQT3fvdrqw5t7nwpdP0w1cT1w65+SvUjFkIuV1k/yKitdf8PzkDmS6GcOxs4ybcyYdZ27ilfu+Tt34ecw64WpGzzgJoGT7lZL6okgbfHWRlBLXyVJeN5GDz7+ObKpZuVt73lHd1+1JpFybtob1uE5WqQQjCdoaVzH7Iz9g6vFfIt20UenKdRNr+1rWfutIxnzrXuITD8ZJNSuVZPCsCHBt9Iph6pfZi1ul3xIp0b0yZs3en1mz98/bP2LECN544w1AqYXuvvtuNm3aFKyIm5ubufHGG/My+V588cVdYpnscvhSUq/v/citxaS/hfcpt92Fz3Z3+wdaRp5Nm6Z1OcfxjCtVGpHOlAhC6GTtFKYew3VVaPfjp16ady5SGfi3Z5u4940fc9K0TzNxyAHsP/pE7n39R1xxxB9UALZBuF+2bVNWVsb111+P4zhcccUVTJ06NXCXTyQSLFmyhGuuuSZQ0/mT5Lhx4/if//mf0qPj5o4Lnjv4zJHH8D8n/pt73/wxr61/hI5sEzGznGSkCstJs2r7EhzpYjsZpHQCKZNAJUHVNQNdqISJFfGhWE6a9mwzUSPBnNEn8qH9v8nY6ukq3IInqVcLCG/MMspwMltxrWac9jXKELcXgq2XTaLQy8i1mml962u0vf8Hao74F0b5vki7A80sx+lYR8sbnyc55csYibFIN5tDjGQv0pbu98ViMZqbm1WbPJf0Ys+E3+e+ZLWjoyPQdOyMRfcu8p0dHPir40QywbRpKv/K8uXLWbFiBZlMhng8zvDhw5k6tTNM87Zt2ygrK+Oyy1RALj9Gg19eKpVSwaA0gdab91Debm+iyaZITD+WivkLOg/TdIiqybxs3oeCHDCat9JNTDuGxDRlWOivEFUQM4f4PocS3+fQnDr7ljti0OFZ48uMjbPVk7QIofIXRXS0EZ7Pfjcvhv9A5w/CaoKLVwxj+rGf807vO5nwj/MJw9QjL6d8yASGTjw0CMmPEHkxWIqp3PqDYm0VwrOjcWwQBIRFFBg/FpOwAETiVRx41vdUgDjvujIdjYzY70yIJogOm9wp+q8eDQ7EJx6MUT0So3pkt231JV+l9q0/cGfS7d6KTcXaKCsrY/v27UHwtGw2y3nnnVdKd+2+5zdogPrKJQJ7KwzdDHKZJaNVXfdHq72/JI4Xzr0j20LEiKMLA01TxObnT36YfYceyvFTLyNrpzh7zlf5/aLLefCtX3LmrKtJW0odWMpd68mOx3Vdxo4dS3t7O5MmTQqOB0VqqqurOeaYY2hpacEwlGrEsqw+T3xSFdyFuNSVj+WKw3/P8q0vqSSHW55nQ+M7Xsh/A2lniRpxZW/me8x7l+JKB4lL2m5DWpIRlZM5ZMKHOHDMqcwedTw4LradwTA8Ui482Ze/ANMMWpf+mtZlvy5J2yY0GHF+AyJS7RFMB4SB3fo+6Q13U33oX4iPPg/pZBBGAjeznR3PnYBRPpXymV7wP9G58BZGEtFNtF+8eyZxc0hvp+fXnDlzeOyxx5g5cyaTJ08u6R48+uijrF27llNPPVVd/k54z/Yq0gLqeXJsRTLuv+8+TNNk5Eg1YEspWbFiBcuWLeOcc87BdV3q6uqoqanhvvvu45xzzgkGXP/mVFdXKz1qKU+U7/rrRTiVUhIdO4fo2Dlk1i+l5fm/47Rtx023okXL0JLVVB37aSLDpwR2LFK6KrGkdJVNgh4BoeKbALh+PAqhKRXBAKJDDg7UWyxtFzK2SnSZsZC2i7upBa3SW8G73fdfT6tP3y6jL5Nqd2oWKWUgXfFdqXPrLfx7oOiunFzvoCCabY+ibfViR+IVzDrh6qLHuFYGzYzS8f5zND/7Z9Ir/otrwZpvzMIcOonY+AOpmL+A+JTDPJdff5bW+/wM+UeZphn0oW98N3v2bPbdd99gf+FENTCPgZ34jOcUvbtVrv2HJ711LJ5f9U8aOjZ62YCNYM2gawYpq5Wfn7OEiBEPxpW4qdQxQgja0g387MlzqU6M5LLDblBSPc9u6PLDfsevnlnAfW/+lDNnXZ1Tc+82X37k3tz7b5ombW1tXHLJJaxbt45f/vKXfPvb3w68zbLZLGPGjAmMugvhE8xS7c78c9T47iCkxLaVFGXK0IOZMvRgWlLbaOjYxPrGt9nUvJx2LcuOxpW0dmz1SJ5EQyOqx6mIDaEmMYrhFfswrnYWdeXjqUqoQG6udNB0HYMoucHj1CJE9UHt0c8qQ1zNxPc+6hki8DYCgnkmUnMQdaesRDMr8FMVSOnQ8MK5uB0bGHLiWwgtoiLwaia4FuhRnNZ2XKtJ9Uun/AcATdMx9RimFsXUusYaO/XUU1m6dCk/+tGPOOigg5g6dSojRoygoqIiePfT6TRNTU2sX7+eN998k6VLl3LwwQczb948RRwHI7BiAfY60gKKvQlNsGnTJs4880zGjRsX7Fu3bh0PPPBAnkh169at1NfXA+QFpItGo3zsYx/LsXHo4wpcuiB0spvfo+Hhn1Nx6EeJjJqOMGNIK421ZQXb7/02tWd8neiY2QFxwXMpFXTVO4pIPL+KAaozBgxfmlERo/rm83EbO3AbU8i2DM6GZswDlKGnf009tbMY2RAFxGIgxEUIzcv5Y/ZIWBw7kx/wbCfAL7lPT5RU0XXzSvGC4hnRJE1P/Z7NN32GyLBhxPY5lMScU8HK4qZbyKx9nXVP/oHKYy5l2CdvJtceqr/PkE9Ics+PxWLEYrFuJ5NSImF293uwDD8HE9313WC/l733DRie+uGQCR+iJjnKywJvew+bEg/omklbupGIEffcdDvtW/zgclGzjNNnfon9xyiCL3HRhO4l+jP5zBF/4pV1D5K22klGq0oaF6WUZDIZstks2Ww2b7thGDQ1NTF37lyWLVvG3//+d6688kpAvce+IW5ra2ueGlEIEeQmKlnaEtwXF01THj+FFLoiXkdFvI7xtXMAWPHcLYye8xnitaO7lFcMfu4iTQrefva3jJtzBmW143CLqIHNium9F9jjdfjfjiIsfih+715WzPk5Qk9glCmbMj8Crx/wtGL/rxMdeqzaF0hcPEmqnaI51YQmdGJmp32XrwoaMWIE11xzDbfffjuLFi3iueeeQ9d1b6ztlKBLKYOwB6eddhof+tCH8iIcDzb2StKiYqDonHfeeTzzzDM8+uijQbyWsrIyPvShD+WJuaqrq2lububmm2/OCxudTqcZMWIE5513Ho50+6geQrk9azp2Uz3CiJCcnZ+HyKwdS9trD2DtWEd0zGyEESG16mV23P1N9PI6pJ3uasfhuohIDLtlKxXzF1B5+MVIK4Mw+58Bub/Ie3EMDXPa0OLHeVGM+7IaKjyuP3ZPxYhL79Ft3QFlk97ZMApIqw+rYQPb7ryGqqM/wrBP3owWK+9yTNvr/2bjL84kOnoG1Sd9KXhu+tO3uegiHevD6rewnF6O6Ff7dibyr93ziBtgbqdC5Nm1eESjUDqm1B0mrnQ5ZMK5HDLh3F7LtZx0TvtyJlI9wkHjz/LupSIsvjrFlS7xSDlH7vMxLCfjnQluj9FvlDHt0KFDqa6uDvJT+RBCBIvFc889l+9+97u8+eabzJkzJ/AYAmVDkRflnL6TQyGEMs7WNDqa6+lo3hxkBhcIhGZgxMqIJKowIglcx+KJWy7jhM/+ncm1HyObbiUSK6e1cT2pli1q8eOpUDTNJJqsIZ4cgmFGsTLtPHvr5zn9mvGU1Y5TYSy8AKFBW7rJnN3zNehFxi4dKR0leXFt3HQ9aCZm5Syka+GktyCE0aWu8hnfBT99g1B960vV5k88n8l1B2FoKgKyHznYf79d12XMmDFcc801fPDBB7zzzjusX7+epqamILZRPB5nyJAhTJgwgenTpzNqlFrEDjSHVE/YK0mLn7BwxIgRfPSjH81j9rk5Lnz3uVgsxrXXXks2m+0yAAcJnIRWcpyWoB1mDOm6JKYdTXrVK2z+48VE6iapF8SIYG1bjVk7juSc04IXKTbuAEZ+/p7eV/lSBnlkMEq1BB9cdDEkdD2jLqnahyvB1PPSMeyONuZ+d7ddDWbKvmT9ske9wcwT6+7aJpcEiUTTI2TbdjBq/7NIpNuwdnRQd8FP0WLlOO2N6hlyHS/Hk6Bs/zNIzDyGtjcfVqTFtRH0g6AFfZYf0M//7o0EqZWy9P75hoCyc1IWGppu7hVqmvxJs9Mjrjv1ZH+uKa+cImHec1e2mtCwnWxgq9JpxJ1FaBGEdMg4WcpjtRhaNMj265erJMpg2+0YelypNMixD/N+O3ZHMP2ZmlFUfeBLUaSUTJw4kd/+9rfBPl8Nn5ssEVTemhkzZrBo0SLmzJmDlJ35h4p5mvSHcLuuja5F2LHuNda++W9iZbVBpGo7084Hi//BKV96jLrx85ASqoaOIhpTbuW+Gqxh7WtsePtxoslqHCujAh1aad597ibOuHYRQ8YdiHRtKuqGYgReeSK4F7i29x71/XmQePOZFusce8EjzOB0rGHrQ5PVUOzHySusRiqu4qah+rBfUjblS+CkwEh486dLVXwYVfFh+acVzI2+y/K+++4bqIV7gh93p5CwDKaKfq8kLf5L5j/kucFu/G2Fibc0TQusm7uUV2onFiHMQlMvec1pXyFbv5zMxrfVCteIUHbA2URHKfGgFF49uoGId10ld1vlblQPdalb736S2q0qrBKhBjOdNx7+ERvfXUSsrAzHyjDYrp2DASkdzFg5LVsaOe6zUaYdchHEoPnZW6g993voyeou52S3fED6g8VUHf9ZtaFfXmcC11aDpp1NoZv+6r/rcb5aQn0JzwjRM9T2JvienoZdqh7yDSsdS0Vh7sGrwp/IdT2SJ/GwMu04VgozVuGt3rsOxL26XnetzatT2WBkO5rQdCNI8llMxWfoEQyfGAbPbqe7sNCUijGVbWBHywfqfCeFFLpHXiSmR2Z8gindDEHCBaFjGAmyXlLAlmwbbZntOcf7/dRVAudv96FpGpWVlXm5iaZMmcIdd6gUBNFoNM/OcKCQUqp74zqMmXUqY2admre/afO7bFj2KGa03Dvewcq0BqEYfKP+cfudxbj9zso717bSvLPwxjzpuJVuC85VJEUq4/WSXY57v54uCzItgnSg6tA/YFYdgLSaQTPyn2fpIIxytj91CKLAxdon4H5QPb/tfkLN3HvqL/z9DN49L1TIi+cihPCkVJ3PigS0Ac4VAyctEhC+yfWuRW620MJtULDKVkfmnC26HNdX5N0Y1yYyfAqR4VPyj3EsL9iZGsGDVWhJyM8GuqtR6ipnoOqHXQfVvkTlcMprhxNN1uDYHmnxROW7/DkWIlAJAIHbuHRdjGgCJBhCA8Ok7iM/YNvfv0nbW4+RnH0yRuUIhGHidDSR3bCM5uf+olRDp35FldVHCZ1AqV6jSeW1EYlX9HxCEbiug2tncK0Mtp3Gyaaxs+1YmXbsTBuZjkayqRbGzjqVeMWw/8feW8fJcVzr39+qhqGdRa2YyTLIMsnMkBgTBx2HyWEncZJ7gzfkMCc3zLmxHby2EwccU8zMsiTbkiWLabW8Aw1V7x/V3QM7SwLH+b33fCzPbE93dVV1ddWpc57znDpL137s+3jzUoV3Gq+ooIzlpHn4z5/mkb9+jXPefy1zDntxctwUX2WNmaCoiF17sOtZrr78MDrnHs15H7zZ9E21haQK32COV5Qkr+sOUp2n4O26DdlxPADzppzMCw//DAC2XctJ4+2+D7v5wCiZngCr1i3pd9+PyM0H3+fYyYcxbZpxfVt1kSj11qBY4nm4paWFH//4x8mu3bIsFixYwCWXXAJAf38/hUIBqJ3H91SS+kTkkyp24XtFUtlWVt/2Q/KdC2idZiJMbTdb88x05EZToSFAkrabYFW8Qi+Wk65pp6hyB6F8wMXrugu/74kovDlgT8d1ZtYrEVamwfwv0QpSnadh5xePeH3UIWgd1B2KrXbWiKR69RvRaqVzPFJtIaoZIw3KnqhMWGkZdsMEBLYPJG7ASA1pdFjrGmKtiimt8vuIf0fftWMZevoJV7faCuGgQ78qckMYq0odIFRM0GT4fLde/DtK4BWjJI4pwsAwLktpDUvG11BGfX4Ndu+NFjFdIUvTYVhz36A8lPwGmF1gdFn7eR+mafGJ9Nz0Xfr++WOCnm3oEGQ2Q2rmwXS+6iu0nHoJVpS8caIRWWHo42ZaeOSvnyfbMs0odI0yKycuCcMGrLUy2bZVkCwUYeARBmWUXyLwiwRegaBcwC8PYDsZWqcuMUpL5DatLnd/iArKWLbLU3f+jE1PXI/lpPAKvRFpV3xf40JM5zsJyoMcccGnaZt+kEn74KSjtlbmmOraJvOiirk+ol03IlHkhOVgWU7t75HyEbc/7tNG/SGEMKGuVoryjptQfi+ZmS9HlbsYXP15Up2nMLDqM3ScdAMAh4hBDgyeoOf+NyDQKK+bzJzXkZ75cgZWfJiWI3+EdJrxex+hf8VHsTKzCQvP0nHyDQyt+zG5Re9lYNv1vGrBi2Daiwn9QRynacJzUvWuXSnF9OnTk4jPiy++OLG01ONZ9kRqrOtVCqplpwi8ItvW3MGsg8/GK/ZTGtyVuIgTl2hVWUFQxh/sQqsQ28lQ6N1Kobc/yhTPsPdCKw/IMfTMDxhcfSXW3ukspKa+EMvKQJRPqSJmfITFTVi5eSivOyKwq1j5tA6Rdn4Ua+L4N6KN8IHjkXorYTn0CZUm60wMXF0vE1ZaajR+pcEPJ/BQxMh/SmEyOqdsCEcwscbHVRWevaqMmk4QDU7YxzLM72w5iZLS6Lw9kQldN5riJcZxznMk/2p3kpAy+hch4aXxVxf7dyDEyOHvAkzk0QgvrZS2MS+jEJiQxND3hg1By0mBNqZoy03j9++IdnuCVFOH6RMV1VPIxM+ugzKZJaeQWXLKqO1rxA0zukRh7SrEslPsWHsXYehFVsRRLqt3D8URS4l7KKp/FLpvuWmcjAnZDLyhqntXCtxfNtvYDL5746M8dccfmXPYyWRbZxhlIlJctFZIJ8P2p2+ne8szHHRazCGkqtrbWHQYIGyHv3z1FHauuxc32xYtboYAMZVtI/AKeMU+0Bo7lWOgazunvuXHLDnJWB0M1cHo7TD07CmCwWdQpW0w8+VgpbHSJgxXpqeiVBkpbfq2XY9oPZrWBW8DYPDpbzKw8lMEfU8QlnchIuuK37cSOzefliO+j9/ziCnHaUGrAGll6PcGacKM72r8SyzjfYerAZ7x2IyZlPcncDPwithuhsf+/iWEEExecCz3/PZ9uNkWiJTK+I6BXzJWtb98ljX3/A8Lj3l1pNxaCGmz5JTXkMpGrlkpqR6/cQ6gtmOuoO2YK/ZJ3WMQbo0ICyHBzh+IkA5WekrjiwFhZ4ZfP0FpiEsZ5XxZd34x8Mk6Lp+498/cs20dd778PyiHAWl7z5TUvbO0SAGpfQeLEU0R2CtVRfJT/Xt0r+o0AboKGGq+a7QXoEsBuuBD0UcVfZPHqOChh3zUYBk96KH7S6hBj+xrDsdZOAkd6tHnjPr6jMMtkvRXqMCbiII3URHoMETkUsM5UwQmgzaYPoFReVX2m2jANvmoRpucRgM57stJTWtlwK6lPqYsPJFDz/oApaHuBK9QLbFZM53vTCIKauKahcAv9eOXBozCoEIsJ026aZIhi4vboUKK/Tux3Sx9O59m+9O3c+Cp76I8tButNff94UOo0KNSeJUdUVqgwmhnGC9w0e9aG1BulNW6vr+q+63Sf7WfMSbDzbZG/DK1L+Gwb6MCVipfErtE5D7xSwP4pYHoGegaDpD99XrEbU7nJ2E5cNi5H0s4ferltl+8kcHuDcMAlozi2RWRmf3ICz9HORpDoV/Czbaxfc0dPPKXy5lz2Is46LT3UC70YLtZgvIgnXOXJ2WY6KSx2lFJdhgrHeHQs6ggstCFxeQcx23DzS9KzpNOK7mF7yG38D0EA6tRwSAWIISFlTPKg5U3JGIqGMTOzSPoX4krDNpFaYVkfJuwkebG6uAHMORywIQTao523+oxH/olbDfDpieuZ+Wt3+Ok1/6QOctexJxlL0quWXP3L6ss9JGFs9hHrnU6y1/yhRHvJetxJFVt3FdS++5Gb1JYRAXQc/dLkO4kg0cSFjWDR4cIu5mgrxhZgPZOhm3QRzk3UCEacOowdV4YUIqtVHshe2ZpUUZhUQUP74FNxrwbKw9x1EASaaJBRQ9SaWMtCTUohQ7N3zowx3TJR6QdvHs3IHIu+Koyi4WKvk/dYMou+uhykPyjFKBLvlFUSgEEYaUOSpn7Km3+ju6JipScQJE6cS7Owknm3NFkgmOx2iIlLAmZ/Qv4FPHjjJUCc9BQ7OcMvkG2Ng6pfS5FhyZfzEhSa83zAJmQLE1UcRn1XG12t6FfJt8+m2kHnDpmebuefZDSYFcE9otYJKXhh2mfuZRc64zkXKVCNq/8B3bkWkCbkOY5h5kJs3nKIgp925m2+OTkmgf+98MmKWy1z7N6ipAWAqtuYoysG3bjyBOo7dNAedgyEyleoqZ448/3IKiUryGhAzCveGX1TjBaWieRQgm4UxsQb/ypVIDtZikX+ygP9USFV79z+1GRjooOAw8VQqHP8DaVh7qx3awJ6/XLuNkWvGI/oR8kboCYodgMmAZFR8qY1orpDcaQtBzu++NnaJ68iBkHnjHs9yTNxLgWO/Pe+P2rQHmo8i76Hnobwh4O7teqjK7Cssj0ZPofvQy/bwVe9wNIuyn5TXm7zWd5F9JuQtrNDK35FsHgGjKzLgaqFaY9VyzqlYoYK7EvNiX12BohBLabYfPKG7jt529g2Qv/k3lHvjyxlpr5JRqf9bcUkjDKFVfs34nlppP1TEjb4M1Grw11L/EeSP0uOep/K0t23lnosEhY3kEjHJXWCqE8MrOPxs5HDPF7YXERQuCrEEdarO7exuG//TwtqQx+GCZoDq3BtSwCpXjrwSfypeNfgq/CxPIihUi+743smZkkSpindgzS/ZqrjCIw0gsnGvwxbIBE/zPbWYRrI3MuOqiEIGulKfz20cr5sbYXfTfHozKqyxOVewohwBHgRuA2x0KXfKMcwT6fM4WIXF6OhffIFoZ+8QCyLYP2wj3C0IwlWmmEY6F2DBiafQ34CplPU759HX1+iB7yRlUY9odopRGuheotkj5tIZkLD0GXy2CHw/rcsA2nIiVFJYm/6jEaOixHpvJR8CXCRlipkSdCYc4TQpoki4BX7MN2MtREgURYBctJ07t9NcW+7Ug7ZRZkFWKnmhjoWsfTd/+CM972O8pD3aRy7Qx1b6Rn8wrsVI7QL+JmW1n/4B847NyPM+2AUyj0bSX0iua+pQFTXqN2NPg+2sTeaJdb3QeOHe28pUXCCxK5dcDge2K3jnH1WATaNwknI0uOlE6EGTAuMctJIS0XaaewnTSWk8Z2M1hOFieVxU7lsVNZsrlJ2Haa9pmHmjrYLjFJ436VZIqIKA4i3iM71ZRw+6iIvE1aNogKZ068QAlpN5wjqkG4YeAlYzX0i7iZFooDO5ES/PIgYMaY5Ri2WstyGy469RIri8JKoUMPVd6BDoog00w6/W567nl51MwKRkbYTQys+AjlSSehwyLezltpPvzbZGa8lJ77X4fy+yOkhMbKGGI1Ozcv6S/ldaP9gWSx3FfSaHzuCzB/7XhXhH6JFTd+i1W3fo/Dz/8EB59+KSoManiahiss0QJr2bhRhF6meQR+qggLUz8kanGLezfP1ypzEZ9OZjodJ98wwXLU6HPhOMSKxsG0XAs/Pv21ONJCVW2O/TCkLZXlU/dfFxEb1lqd9ChqwkRkz5SWOK1CSxpn6TTCDT1GEQj3AcuowFhdQjUMoyJa04w6CKrN9fFQ0nXH4r8jS5Au+Kihcu25+1JCBY5FsLaLwf++C2t2K7rg7SdsiQCtkW2ZhOyNUCMyNsGTO41VzJLofTFyJiJKI7Iu4aZehOuQufAQhEjVYssqLQBImB+9rruQ6WnYTfOJoybiF3A8HCSjZqtNvB86odu3nIxZSEeQRce+bsTf7vujidpx0ibqJj9pHsvO+XDNOSr0yXeahcFJ5ZP7DlOUEhE135VWqImg+6LF1KqyVG3Y/TgFf4B57ct4xWdXo7RqMJGJ6D8zAafSbVUbgvHffvSqmWejdIwp2G++00Y3jz6qNkZR/4dBmVSulRu//5IkJF5ro5ymmlKEQbmuqKoQ0bo8VwChV0RI0IGx3NippoitNbp+HCZzAcbqaKUYeua/cduPxcofwODqy8kv/SI6SkKqCRGWYSzOLf4g7uTTUMWt2C1LyS16HzI1OaqbQ3V/l7Zei9N+FF7X3aSmnIkOS+QPvpzy9r+hvJ64puPo2H+txGMq8Ir886evZqDrWU6/5CqmLT6lLrVHNC8Me+dMG/3iAN2bHmXHM/dQ7N+O5WQiK4tF4A3ROu0gmicvNPix/ThuG1lKTSoYL9rkxO7VOlAw0bsrBAinxlK9J2KijUwdWlNZXr/k2BHP/f4Tt7GgpRMAW1r4at+FtMMehzxHN0/ZyPYswbPdSEuglWigtNQpD9Ul6CpNrFqpEBhLRPXvCvCDihsquU5H1mpd+S25ScX0nXRYbH1RGiFAD5bRA+WkrH0u8aDLOFgzm7Gm5tFFf/8CYr3QuMJis11olAarJf2vmXeURmQcY/zIGwWi3HUnhS0/QNodaFUChOEfCIbIzn8bbod5Kbr+eSK5Be+g5YgfoMMSRIRYQ+t+hL/7foSdNaGGiY0SkBY6LOG0LCO36NIK9XW91DyCWp92vSSuFb9ksCPR/ZQKcVJN9O9cm1hNtArAstEqRIU+fnmIdFMHzz56LUPdm2lqn43WOuJDiRdPNYLSUl03Qy4mxZ5xQCgdYgmb799xCSu33c8PL17NzNYlKBhGdV4vf131XYpef6T8qEqtonfGD0soHeJIFyGsitVIG4CrJW2GSj0cO++lLJ52QpJk7l8nsZo0fLcvpU3oFVh0xvtonrwAr9BHKtfG+of+SN/2tQbLUF1Sg4UlOgDAQNd6bNemNNRFUC5guY3CWEcXk6csRTDwNMHAGpqXfQ1pN1HecQPezn8i3faadqE8hJVC2i0Utv2E1hkvMdZJZeY65fcl74Q76SSCwXV4u+5EBwOgAxA2weAawtJ2rMyMBjXafzIRV9EwrEXkpnNSOQ4/9xM0T16QUBuMyIRdtW7I6JzFJ76ZtpmH0LXhIWJrLICwHPxiH5nmqYayfyxIwT6S2nZKsKJQ+xHOH7YKj4Bx25M+VlpTDn1EBJyXQuCFAXk3zZ1b1/JUzw5OmLYQX4X4YUgx9ElZNr4K94niskdKi5AGWCpzLsK1CNd1ozuyaC9iAaxWEOIdW+K6MZ8hGi3AtqJwYxldF30XUhjQZvXflgRLIqzKd2lLhC2NPz/6FGnLLJJZB7IuIusgci4i5yBzKUQ+hWxKIZrTiKyDNd3sjMVYoOK96W+l0V6I9s2//eEeSsQSpo+qRWOAwP8C0UqDLcELQJl62bmF5Ba8B4QbJRSzUaXtdN9+PunpFyTXWumpCDt6PkImicCG1vw3Ohyi46TrCQsbE+S+DgtY2dn03HsR5e03kFt0KYY2cnxDvZFOV+ODd9J1vxkrhZ3KVZnR4/Fv3Cvppg78coFn7v8tx77iazz69y8xsGsdxf4dCV/E6BIvipLtA+vw/EI0ierkd6UNQFcKK/lNIAl1QMbJMzk/N4HE5tMdtGVbkFF943DcWqXd7N7iHdsV9/4nXYNFXLsWwx2PslzKxrFSDJaHTEBh1fBTGjIubO+HtuZZLJ52AiHqX8RsqSODZJSgNAyQltmxxjtxs1P3WHjMa2iZUslu27N1FYF3LcJqXPNaU7jJZq4Cj13PPkAq28bA7g0M9m6mdcpiVBiMWE5DiZ6VTE+j5bBvJ6b+lmVfJyzvRD/7C3NaPAZj3cXvQzot5pCVSnLTtB//v5GirLGys8gf9Im6xvwM6bQkIN7nUoZFqKoR+JOidaH6/TTXG2ty5zwDcjaYIdPuRou0rHLRxclOJ887msnzjh61nn5p0IC1nwMD4d66z+qvH97HI+xmG/SxFIJMnTU6dh3dte0ZJqWbOKTDhLQ70iLrmHNb3AzhhMkXh8uYb82IGln0Nfem5bhHzURkXRMhIypKSqyoaEGilMRKSMZNISxJSQdoiXFbyEghsS2EHX9Ks+BFUSc4FtKxEK4FroWyBaEt0K4Ftp3snGT0b0Ke8v0x+OJNcqjQhTiKaT9bWvaFJH7ZsSMaxhIdvRBqyIPIEi7TU7FSU2vOK276HXbLYtzOU1Hl3chUBzoYNDu/mnqBlZmBlZ2JnT8AOz+cXjo15Sy8rrvji8Zd1+oz68d8wwkj9onr4Su5EALLTlEu9HD7L9/CwmNfQ65tJkvP+gCW7dK7/SmevvPn466UZaX48g0Xsq5rBVk3S6hiDm+NY6WxpI0XFI0Cg8aRafpLgxw5+3Q+c97NBKGHLR2C0MMLislzjbNsj9RNAnj3KT+nFAyZpHtxWyNTdaA8bl9zJTsGnuGFB76DOR3LCJUXWVwMQNeSNkVvgKXTTwPAlo3CHfc/EBeMMS1exJx0U9JGGUcLRc+52L+dlimLKPbvJNM8mcAbMrrDKBbZZIxEOJ2+nWvYvfFhg3vatY7ebatonbJ4FKtag6pXAX2lEzO5xtcLpNNmsCeADgrJcQDptlLa+meElUV53YapVYcIK0P+4M9ErqQQHZYQwjI4l/RktPLQyo+sM+OP+NibHXzD41KMyuw8WqShCn2EsIaxF9eLV+ytgK6jfo35hoaLQKsAy80ipKRc6Kkw4laB08fTB/8qyoeKclfdx2PXc6RPpRW2lPSWC3z38X/yxeNfwsaBbr780D+Ykm1mwC/RmW7i5s1P0uJWyBj3VMZUWurNQkGEBrajsMrUyfNJnTy/4bUBVMHCauVZhigpn4WydY92XEEUf2gjR75egQpDAhViSRnlF0qADBWJLTljyZ7MqRHoN33WYqY+chnYjci6nn+ilWGEFJZjJvG9rbIQEISIOHrJCY1pVSu0DhBWmv7HP0x2zmuQblvlOitN9c3jHCvh0Dr83ofxuu4iLGwy7JMIdDCIlZ1Facs1aH+wco3VOIXD6FWufUFrQ5Hj+oTYbgYVegmuIAb7BV6BLatvYuXN32HJyW9j3hEvq5kIQ7/E2FK72h4w5Xia052k7GxkXTEKQdfgRgbLPcxpP5SMkydUPraVolDuZcGkIwESy0psRq80pQH8Nza3R99PXPCqEWu4qWcVf1nxLbyghG2lOHXRyLgfMKGz1l762Ccs8W2ExEmZiJDS4G5KAzsNXgFNGHi4mRbz/BwnOl4B4soRgLjQwIQepYxYc8//IG2XGQedybMPX83Gx/7C3MMuHOZiGrXqVW6BmLW5BqclJC1H/hCAlqN+Eh2LSNWaFtF23O9RXi+VsNgIAxHRtgthRe8PWNF7kj/4cqTTipWZTgKSbpB/aOS6RjQPI+3gLVmzgx9WRjlApGwKv3mEgW/fgTW12QRNACJtE+4covUbF5A6do4JbHCtYYtxfab3xuNNMGnOUaQi0G3F4jI2c7ISgs65y3EzLVFRlfcruWcwPNggui3CngiX0r6TRLmLgkQKv3mEge/ehTUpZ6JxAZF2CLcP0PLZF5B+wQHJ82jUl74KSVmSz9z/F/JumtcecAw3b3qS/33mEWY1tTHol8g6KTYP9HDE5Flx8/dYJmRpkULgWnUhan5oOEBE1eugQVoWti0hjGiqIzdRqBW2tLnwN1/jsa5NPPP6zzO/fQq+7+FIq2GUZ+xmMlCUilsJBOt6d7Guv4uu8iCFwEdpRcp2aHEzzMi1cnD7NNKReSrUCktYjdSW8cl49JrqQahJXkzZlEIufP5mF34uRUeANq0l6BLSztH30DsQdhOZOa+n7+H3IKwMwmlBFbuSyVJrlYRpNh/+3/g9D+F13WF286oEaITMEBbWk537ZuyWQwAiqvJx1q2+nlUv6GjZodNNk3Ai2nsnZRaA1bf+gC1P3sKxF32T9hlLk9DWWHERcmQiu7oOAyBQZd5x4g8bnvLd297MP1b/gg+e8TsOmHJcw3PqKdhHvB3Dd61+WKr0B+CHZdJ2joLXy8/veR/9pS7actO4YfUPWTz5GJbPeRFD5R5sK2WyCEfh0JZ0nnuFhcrusjzUDcCDf/ov7vnte+sYcY0Smsq1owI/IcCLQ54bhsbSSGExJH2Duzey9r6rmHbAqRz14svZ9tRtbFl9E73bn6J16gEJCeBEpBGoXAjLKBeQ4E8qbgCJ03r4aD3DMEUclRCWxe4kXYXraNTmYcdjmodRmjeqMhEpO2HXEP6K7ai+EnrIsFeLjEO4tQ896MUF1bR5pO+1YzqKRJSSF3/0nuS4YcgdeWzWuoozXPjx+6uurfA7Je2yx2clqq/zcyLRfVXcx9ObDd5Sa0TOBE6o3lLNufV9WQp80rbD79Y8yE9X3sVvX/hWAM6YtYTtb/lKze0+cvc13LRptbnnXmzcx2VpqY7Pvuj6n3LGrAP45kmvNMcjlw2Yoe/pEFdY3L5tLW+95de8+aDj+cjhL6SsQ1LCIua8bG5uosVvxso4YBH5d+WoeoFGIyPI4J/XP86vn7qXVd3b2DTQw0B5CALT4VgWtpNmWraF+S2TOGvWgbz9kJOZlGmKFJc9ZF4co58rL2wYa24ILcb2zdbcQBjTcpQNlwS7MNJ1dddMVB2TsnKNAMIA4bps+dZLKT1zH7M+eivu9EXosgd2Pe5hoqLNpGNJdOghLBesDP1PfJzixquYdNZDyPR0UlPOAukg3XYGV32GGBwrhI3ftxLl7cLKzMDOzTe7RR2YqAghTcp2YQMKrTzKu25DOi04rYcxUXNR9YQaeEW6Nz9eRbpmOsyEPOcY6FpP77bV9G5bjVIh7TMOYeGxr+WQsy5DRJwPcZht7T3GgsDGbjrzXWmV4DGEkHhBibSTQ+sQS4AXmkmm5A/h2mkTGYTAmsDOPhlSVe+IE+3A4/JcK01/qYtf3ftBVm67jbMPehfLZ7+I79z2Bq64/yNk3RYOnnZKdE1o3Eoj9G1tW/ePxH1/2Lkf48CT32Zo+ZWCqsixWLRWqMAj37kAIEqzMLIMa4s2tOuPXf8lAm+IA09+O5nmKRxw0lt56NqP8+TtP+LYV35j3EpLDfZgpPdPh8YiEn9Wi/KppnhPJLGc1JcZ5asRIrqfACHRcRly5JxoNVaS3z/G4PfvxpqSr7WS7Bqi9cvn4S6flVhJhkk8JbkWstlgEHVkDRcZB51PG9jAHki9BdUA9a2KIjPKujDea2Mus+63/YFwSz8iHTG9WxJdDrCm5Gn/2SuT8/41Et036WMXHWEhRdZF51Pg1OL04jaGWhEqRdp2uH7DSt52yxV84PAzOW/eUnwVYgtJOQywpGTQL9OWyjLkl587npZYExz0y6zYtpbpuZaa49WiQgW2RXdxkDWbnmLdtEU1xw0GQOAHAZ7vJ3gHw/3S+N7VA6UYeHzk7mv41ep7KIY+c/MdXDDvUGbkWmh2M0hhaIO7ioOs6tnKQzs3ct/29Vy77jG+f+rFLJ8yl1AprKpki/tCw631w1q1xxFj+marSqL2sWjGju2ov2bPRUeLqLflcYprtkYWDMCOwb37oq8UwnJRXg+9D74Vv+dBOk67A7vJjJX0jBcn5wqnmQTTIl3KO24g6HsiipZQaFVGupMorPsxWpXILbzU+O7NndBhGSs31ygt48ARDGtdhE3wS/2sf/iPhl+jivabqp1S55yjePKOn9A+cxntMw4h02x2qyZ3TdUOLn5vtKpyF41HoTLRQ9W8GRVsSDTZRJ+2dExCtD3kP6lYWMqAxpJuFLlkJueHNv6VPz76edbtephTF72eVx35WbJuM5cc/9/8/N7L+NrNr+TCQ/+DM5dcQs5tScoNQi/ygTs1Yb/j74M9k1g5zLVOJ9c6fYLXxk9bR0Ootp7Vc0jgl7CdNGvu+TVP3vFTDj/v40xddCIqDDj4tHez9cmbefruXzFr6XnMOPAMQr+cgD9Hu3+CPRjx/ZN1n1Vi7YmFt1GdRsCdjGQl2T6Ad99GQ/NQZyVRvcW4oNGroTTaV+ggRMdko4EyFv69YPWuthjU54ebiOJSf20ikbLnr9hOuL4bkXXQgTJUFEUfNbutceHPqUT9F+ooQCQiejWmVNPfdX0cY1gsIbEsyRVP3ce7bv0Nr168nM8cc4F5t4VECJHQ9Kej8W1LuU/e8AmtdJaUWKksTdGuRWnDehlTYllCJt9tISHdRDZCGQ/zJIrxseMlk4FWOMLicw/8je+vuI0D2qby3kNP4/x5S+lM53EaZKvsLRdY2b2NbzxyE//YuJJLbvk1f73gPcxoakNF7q4JKSyjnFqzK+/fifZL2C1TEWOAwKolMS+XBggHdiOb2rEyzYzGNZJovsU+VGkQUU/nPOK9THZdq3Va7S4hDv1LN2M1Udm17aP1xNxXMvjUVxha+12ctqOYdPo9WJnpBhMiLFBltPKMW0dXAnJ1OETT4sui70WU348OC9i5eZS2/hnldZM/+DMEg89gZaYPj3wYxwI+zD0kDS9MpnkKx7z8q+Nvp4rZnEWS7j4ZA9GntN0ko7IJHxwfOLO6rHhc+KqElFDw+8Zdx1jBqR9b1WZ0p2rBG/J6Wb3tDm5/5ioe2vhX0k4TFx/1OV506GVIYeGHZY6eeyHtuZlccf+HueKBj3Lv+v/llEWv48jZ59GenYFdZbUYPq73444z3hupMApZl5QGdqJCn3TzlAhjUjdBxzvo6G9pOdguyaYksX8l/aWwnTSbVl7PPb97P7OXnc+ycz6SlGe7WY6/6Ntc/53zuO8PH+QF77mOpvZZxgo3DrCp6i3uf7qE0UQAoUa2Z2vwDQ3PA0TKRrZmkC1ptG3SToi0jS5kEuv8uO8rRJ3RUezz4VLv+hjPuSP9HYtsTqFa0oiMgwiUsQ65NiK/51CBiYCdG103TEbt3zrXoTYbp61DvXzm/r/yP6vv4T3LTuMrJ7w0KqpihSOCdCR7tH20hkxIadFaE6ogCVtyLdv4+qsa5kYvnxQSQn/MEKdqit/q+1Q/lNg9tap7G795+kFm5Fr50nEv4fx5S5PzvTAwSlRUliUkraksJ0xbwKEdM3jdjb/gho2r+OXqe/j48nMJVIhr7WPfeuiD7bLlG+cx+MCDLPjuA6TnHZUcH0u0V0SksvT+80ds/tJ/MP3SjzHp5Z9HeyXECLTR8TVdv/8IXX/4Ic4kC1UORzbOxNZeATKTY/53tmJlmhMLWHJa6JtI4X2++zW4AJmeRvOyb5GZaQZ7DZeKlU6sCVr7xKnVhbDQQQFhZxl6+lv4fSuQqUloVSLVeSoISe8Db0amp1DceCXCyjD57KdQXi/SbR1X7WqcFVWKQcywObrmivEHS2sY/qV6jMUAv9apSzj8vI9HWB0XVS4wHqlWKixpU/D6GSr34UiXroGNSZ1jqRBQ1YqxooAXFBO8iy1rx+ljW25i1+AGNnU/wcptt7Gtfy1aK46cfR4XLP0AiycfYzYuWuFYKULls7DzKD78gmu5YfWPuH7VD/j5PZdx7eNfZVHn0SyZcgItmckcMPk4OvNzEnfTfpfoFlJaaGGArDf98BV0b36Ml39mJU0dc1AqbGD9IZltvWIfQz0VAHU1HsmkadCse+j33P2b99IyeSHHX/xdLDtlyrVsVOjTMvUATnzdj7j5R6/k1p+9llPe9Ety7bOjOo7QD+UQ0jZ9H/0bhaufwJrcZNwtz6XyojUiZaN2F+j47WtN8IUfgjvKEqIU2g8SqgfAuEe8vbOSPJeypwpCcr2vTD/Z0lhatDZ/+3tOP1GDnwoDk419xLoZJULaqb1qhxCGpsQSgl+uvoeP3nMNSmu+d+rFvPmgE6K1t0JAV6247GuZuE9BSAKt8FVIb7mAK+3k5Y1dM1OyzQz6pQgvMYJEYzZOoFQMfGwpcaRd03CogHa2DPWyvm8XF8xfxnlzD8ELA2xp1QCE66UYeOTdNBctOoo/rX2Ip3t31jZnIg9wnO+ZkLYBoMUT4HhpsKP+EtIyRoHYMjBaFWOlL9uG3SGw22egygWTD6pedOWaoG8HVn5SRVEYbgrbp+OtAjYzbcrOMREm5R03MfDEx+g4/d76CgCGpyXmmUCrJMqh6cCPAiaxmxA2WpnwRh0WkKlJBANP4+26zZQ0Hq6JZDcQhy3WK9uiKoHeOIobb0irjv4nGMHSMnJkj9IBlnDYObCe3UObybqtPL3zXs7l0iRSKCmi7llqNPn0JADSThP1EiofSzqs63qIn9z1ESblWmlKd3Ds3Jdy8qLXsnT6aUhhESgfS9iJ28iSDqEy3DAvPvRDHDvvZdyz7g/cs/5/WbXtdm5f+3umt8zj8gtuH1db94tEgNo407euPt5AYpbbpWd9kLlHvJSOWYeb45HVyHC92Oze8hh3/+ZS8pPmceqb/4dc6/TktziaRYUBMw48g9PeeiW3/vQ13PuHD3L6Jb81c8UIQN/kmJS1FBDPqdKCce/kU4hs5JIcz9wpRER3QdW/fW8lKYZ+DaHmXos261nadvZuY5u0t/of4+u7kapWhZ00aTTGyUGVAOkbb2JGvZZYSRfMbmrjzJkH8l9Hn8vi1ikmOjdyCe2tkjcemZDS4itF1kmxcvdWZv/yoyPCZpXWZGyHTLpp5KyOkdmoM214B3JVIMWRGm6sVQYEFLt3VESo1Ui01sk85IWG+nw/9WPdfY1fUEc7MuUVkBGmYSQRQqK9IjhpdOBF9R47J5KIyM4mvfxzTHrZ5aNXTIUI28Xf9SzrPjAPu3UawnH3m0ZcU88q64AQIiG9UuUuihsfQPt9YKWNxSWONlEek858JLrOKCw6LCKsDF03H43f+yjpma9EeV0mR5E2rxbCRqsy2blvMjcfBwg1sarEpv+9zLcy7uurur0ha2/Dsit9CbB+96N0DW2kyW1nbdeD7BrcSGfT7MT9Uj956AhT8tDGv9LZNBsvygxsS5dlM84yvvronbpg6QfIua1knGYOmnYyHbkZyb1D5dfwrcT3iaODQuUzJT+PC5f9J+cefCnrdz/K0zvvZWHnUUzKzSRUAZa0/zU0/sTzw9iKUuwibJmyKCGbM3mxrEgZMSzBHTOXccobf0G+cz4tUxYnZHXV85ixuATMOuRsznzX1TipfIX6v8o9O0KFk+SvqHHFne0zEQgIlCHgHIuE818gGcuZICnX+CReZ/4VockjSWxhkZZNz7bVbFl5A046j1I+Mmas1maMWnYKJ5Nn5sFn46Ryo0INRhUNNsZVevqsJZw+y5Bixl4Q2Hur1HhlQqPPWL+NT6vJSQ3T1gQVjcyOF56RFBulcC2L99/xe7K2ixAG6Pvpoy9gfsskAhVix/lgog6Ym+/ggLYpPLprE79f+xAXL66kdfdVSBhRKhseGQMazDou24b6uOrp+8m6GZZ21FJS748OFlH8t9VkdrJWFAo71l3iyUum89HkNZF6iVG1dzOYTP+Ut65ClcGdcZDJSbEHoZcTlQS4FisHMQ21ncVqEgk3S71xaViLqiwh2XlvpeWI7++D2hleDQC/PIBW2fFbSvaFaIWQdpJQr7bRjZ9pbNVQKuT+Z68l4+SZ1ryQp3fdx33PXsP5h7yPQPk4DZKkhTokZWf54Z1vo+ApbMtkyGjL5rjijX2gpcHYRO/6Cw58e3KtUqFJByBtLOmMuujblkuofDQm+uiAKccl4dixwlLXqgl02gRlLGWg6qTGbTJkZVqFCMuAiBthi2YtPReIrS+1Ckv8PVZcpi2OoqsCL0ocOUb7BcbtG+3U9/ciWtMPkogFPY2ciKVlP4mA5JkGWvG6G37GQLlowJ5jbMJGszQYUkaLAa/E0o4ZfOOkV9QszM8bid2dloOTzuOkmxJXUZxV3knl6d6ygvt/+kle/62te6e0VN02qEpdMBGFRTO+TcJYMiGlxZaSou9xcPs0/nT+uxjwSqQsuwLERVIIPFpSGa555lFe+qdvJ8jhelHR4Pif1XeDXwYnBUN9vP2Qk5nfMolQ66RycdKlha2TeeOBx/Gp+67j4/f+ifX9XVw4/zBmNbWRd9M1AytQii1DPTyycyPfefyf3Ll1LUdOns0blhyXlAmjuIf2om+NVQCGVlxP0L0JVRqAYTTptSKkRHlFrOZOyhsfQ7gk/BD7agMaT7iltXejhiC9IEp6FQbjjGzacxnWz3H+mmCAoE8z9Mz3ScxvybkasECVyMx+jcGvYADE2u+nuPkPuB0nEBY2gpWhEtapEcJGBYOkp56L03Y46JD6jjRZecu42Va2rL6Z33988fh4U/ajqKCM6Qc16nOPFYcnd9zNyu23M7vtYC468tN857Y3cPvaKzhpwcW0ZCY3DDeWQuKHHqcvfhMZpzlJAZBx8zVAOiEEAosgNJEflrCR0kJWbWnHWjiruWGUCgl1iKxL4FgRMco2Zy+lUaFRlu5qZUXH1rpGRUg7UUTqOTYqRQYIIROXUL3CUq+4CCHG7z7WJIleqbIi73NJcG9R+yLXEL5CdmQrBJHPI1Zv17JIWTZ2VTBILLGCEr/b9X9Xi9ZmIXYtu2Fwx7ilegrbx6K1SRGhVUjL5IW0TF444rntMw/liZu+WYWx2/tnZtdBD8ZrYXGkeUZ7ff+JnW5sKTFoNm07wzTQNGaScq3Ro1gsISmFAb9+4VuYnmvFCw3A9+D2acBwDc6OfOb/ccQL8MKA76+4nU/c+yf++/F/smzSTKZmW8g7KaQQlEKf3aUh1vTuZOXuraRsh5OmL+S7p1xcw9VSXX7DplaaXHtsDNGBh8zAtu++l3DIWA/GM8EIYSgV3Mlg5yvupVHdQxMwxwnLQauQwurbsFogPefwyo33kdQP4EYTN2DoxAF30ilMOvNX6LBgMpdWm8ijybM6E6+IuCVajvop4eAaVNCHiDEvlVqAdBDKoyE/RTLpq6hOoEMfP/T2uv2jTYjju1okfuc4iWKjsuLQw+tX/QAvKHLM3JewZOoJnLTg1Vzz2Je44ckf84rDPxGBQOuVFgsvLPLqoz5Ha3bqsLKTiDQV1OzMQl2VsHRizSLumRhrE4ReA7I7HSmzuqJA6L1PIxEXXS+Wk8FJ5RJgtMk7FAMvRpaRFJEYt9Lot+EuOp1gEcaT5RmoRJ7ECaD2taUjnuuEMCDaQFUUF2mIRK2OHLIt+y/mF6k1RNtC8j9nvHG/3Edps7meiEVeRxsvHUSA42GXiSiUOzQh0OMsexhcIkrYGAYmBYPWitt/+SYOOv1Spiw4HiEExf4dWE4lsGFvn1h9HWqtjSNbWAD6vRJdxYilfC/qsEdqT3xDQ3RVFRIY4U3Mb6NXy0yKinPnHEJ7Oldbvq4oRvUdYiH45NHn84LZB3HlU/fzWNdmHu/azE3F1YS+T5RYhOZUljn5dl664HBeMPsgXr/k2ASsa1XtbBov+qLmY6LAMSEttA/NJ5yFzLaiQ98AY0frEiHQYYBw0vg7n2HwoQcrCdXGuPeIg6VaYQg8hO1SXHM3hSfuJLv0JFJzDjfWl3FENiW70bBsrBaiyiqitXkpZGrYJJ64pSIXSDUgV2uFlZ1Jdu7rx3H/GEfgoLUi1XkydJ48zuvCCKNhlB9pOVh2Ktp9VC0oY2EKxhCzGTWLrpnoan+PQ+y11jUbsfhY/M4EKkRpQcpOYznpYREtfljGsVLcvvZKHtz4F5ZMPYHTFr8RgPMPeR8rt93GX5/4NgdNPZGDp52anF9TVwT9pS5as1Mp+YORAmFyGPmqjGulueKBj3LH2qtozU7FD4qjmpWj0TEmwE+jsYRDKRjk0+fexOT8XJQOIuuNwLLTCMs2EVhOCstJV4HK92LKbXDpUPdGiv27uObyI8Znto6ULwS84rOrSOciy18VfXtyasP3YIQJPm6flAirAcA2Il9r/er5tHzu7DGUlb2wVQnAC5HtGfo+exNDP7gHMaUJCp7BhfkKOSlnWL7LwfMK25IAcatECAgjfGUQWa27SwUO/c3l/OGct3HqjMX0lgukrDo3ZzSlWWLPgbhCCFTRN4Ry1dfGG7FQoQseoiVT2dGO8ejqo4ZMbiVpxl80d62973fMOezFiIUnABB4BUr9u1ChTxh45vzqhk5QxhvqXS2xZebIybPJRNwt1gT7s6a8Pb4Skl1h9ew8OqdtlUTrXVdpkPZ0jiG/XBM9BMbF46kgmgjjJ6oJteKYKfM4dup8NgzsZvNgLz2lIUqhj9Ia17LJO2kmZ/MsaplM2nYoBh5Dfrku/5BRjlLDckzomo9kdznOBU1YDqoEk9/wPdyqLLHjld6bf0DfbQ8iYpPeHiykw1606Hv/Xf+DKkB++cuNchWUYTw7iWj3L8YgqmqksIiIEC3+XjGvS2NFUZ6xfIzSUGFlqPDJSLTyQAUjXhPzngiZQtTt6L1iH8XBLrTWY4QLTkSM5aPJMYrbgFcaxkNUCsz4bPSK2MKYTkOtaHLSoBWFoExpYIDAL0Z3MFmbHSvFpu6VXPPol0nbOV667CM0pdooB0Xy6Q5eu/wLfP2Wi/jFvR/kQ2f8ganN8/HD8jDLRqzIuHam1oUUdWnZH2Kw3I1jpfGCOLP03oslbUr+ICq2oMWvm1aUBrsS14mdylEc6CcMyjUVG2nxNyNIVVnSqkZH9TCJ2nHsRd/ELw2Ysa3ixSUigyR+5atWEq2jBTvEClPochi5lxpZaoXhJKl/H7wQHarKFUIYS0s6gy54qCHPcLHUFCUM6RcgmyeeQ2tPRG0fMGyosUVHaXAt7AUdNec9XwCqjYC4MYj20a5N5J00C1o6cS0bX4UJf1iTk0qgAo1kIuDS5BylwRKoXUPoUmDcaMl7b36jHKB2DiFbMsn5Y0X1jCdqKN85h6aOOcnfMw48g3M/cCOZfOfwej4HrnCtNSnLIdSKtx9iNplKq72iG5mQ0hKokJyb4cme7Rz+288Dw1/X6o7PZvJ4aoR49OjCdKQwZGynJkxTaY0t5TDeiHqZk+9gTr5j1HNM+SOXEw/ueOeQvegw0ucdaHZAsUImBKTG5+PUmAEaDnbDFFDlIaOAjAbulBaqNIiVbUGVBysDfE+kfqIMA4Nl2fAIvf/8Man5M8gf92pzbtT/Yw4eHSJwKO+8lbC42SgvysOAb0KE00xq8mkIKzNMYVF+H6q8C7tpIcofQDp5hp75IcJuIjvntWgkwk6Nqe7WTCDSBemOeo2ouy5uY+fco5G2i5tuNoy0E3lxtMFjSSrWN0G0o7McHtu9mUApjuicTSn0E8VFa83UbAuOVUnsFovSmqHAo7s0RM5xeXTXZkIVsHz6Yrp7NtMU8XgoHeKIFP3FLn5534fY3r+WVx75KQ6dcQaB8knZGQLls2TqCbz6qM/z07sv5Yd3XMKlp/6SjtwsgtCroaTXiRJgWH9jiZWZ1x3zZV511GcRxKkk6vpXSIKwTC7VxjdveTUPbPgT/3XOPzhw6okMlnsiEPBIY16Ti8DXcfJAy04x46CzkNJCqRDHzVAa2EWmOXJhNUhI56sAV1oIBJblRmHIAmkba1qiaCV6R2UMzT70/FEf9XhEjBKyMkxhCRXCtYZdY0XTcMvsJbz2Zz2mvzXEEVxaqSTUeZg1QQpUwUNmXQa+fhulm9fQ+be3msUyZY1/CjGDGGFL1O4hgjVdFep5KSBQyCYX5+ApUaWHW5f+1TIccG4iS7/72D/pLhe4+tx3mDXMdpP3cjwegerP8Zwbrxnhtn50yTc5mKpc3sIyrLjBpl7sRZOS88e6hxAi4RLq3/UMXRsfxnFz0Rxm1qr+HRtY9+AfUGFAaWAnTjqP7WbZ+PhfTLj9QWfipJpGDZLZlxKPf0tIfBWitCa1l/xoE1JaFBpfBQz5ZbYX+s2Dr8N7CMxAyDkpAq0Ss1y9xOHI8aAxioP5LUZrr+jawpVP309rKoMfhrXmV4DIxRSHQFf2RSa02RKyJn48uTcaW0gKgc/c5g7ectAJCbW/1hqRcUxOpBHqPe7OjifsJJvxyEqL0KJy/l4g7EbbGez+3/8i7NNMfvUHsfOT0IGPsEegoW5ULhAWNxH0rzZstcJG+33IVAeFp7+OlZ6C03Yk6ACEYwA6Vgq/9xGKz/6K1uW/QIdFcPLoYDBZhKjCrIxWj4lMII36QVoOGs3yl35hzOsnKgHmZfr2LVfRUy7w/nPemhyL5Wur7mHLYC/paOcBBhDuhQGnzTyA86bNBeDbN19Fr2fKiPmAQxXguFl6izv44R1vY+W2Wzl54Wu48ND/MG42EVNlG56U0xa/gcFyN7958JN8/eZX8fpjvsriyRHwejy7Oa3JOHkyTn7UdseKqSVtAhXSlGrDsVK0ZiaPyzJjQIVGkUs3dXDWu66u/FZVUxO+WZeQDnAja2SoQsqFbsIgiwo8VJCjPNSX4EUqnt7KGFJBOXHLlUKftOUki5nlpCEoE6gI1xG7TrIZCn98mODebcjWjCFKq961SmFYYzuy5N93UqU/44y6f3wc7/6NyHzaEK5VG0OlRGiJnNKE885jQIMOQoRt4T22leL/rqDlsy+seU7VbVKDZdSuIeIGiwm6tWMlNtjSR7But6Ge9xXCMpYemU/hHDy10k4mOB/uZ6l+3wOlcKTF+v4u/rZhJV8/8eXmHIy7Yn8QGiZ9EWqwIVjThRooI5vT0TjBYIFSFqq7SLB6J5y+MDl/XH0Z5bXyCn30bX86ykruE+PgznjHFWil6NrwEEJKtAqjNCIZVOCZiLUUPBdWFqidT/ZVaPS4lJb4Zkd0zmbN6y4f9tCFEJGirhJWWrTGV4qmiAckLiN2H9nSisoZLkGktNy/81m+fOMvoaUT/BINUfYi+V8DGQk0qMGyoTjIAbMP4i0HnUCgFRZVCk4jxsZREoXVFq/RIVhR7hmZyVeqOopYUXp0mcmbdXyC46rhROaVkG6anuu/Sd9dfyV3+NG0nPq26IbjR8fHANiYFK5B7Rs/H0AI1+QQMn9En3airOiIp2NfToD1WALCSmcq/AkBZQXCMCjbLtsGezj+j1/huvPfzSGT51DyiqQtm5JfpindRLo8QMYz7pxSaZAmJ4UXBrhOitvX3M+TPdtpihT6UClaUhnu2fI0Ow86gfOmmeeS8Qcplc3i01/oJ59KY1kum3tW8c1/vpYtvU+yfM6LeNNx3zSWLK1quCQsaROqgAuWXoZjpbjygY/zz6d/wcLO5ZE1cxQ3XLXfPALFjtwvJgrJtdPEgOZAGTCzwdGkGS01gWA4IZUKPQQGG+dEHERBGGDX7c5i6+jjuzYyOd9JR7qJyQtPwHGy0SSdpjSwCytjQNq+1lgR8DoWaaWwhPEqOFQICEMND+3axJK2qeTcVEV5il6X7AlLCBZNRzhVCHsp0EUfa0YLg9+5k4Gv3Ub+fSdFC5JILBPuUTOx57Ub15GKsE3xtTNb6f/iTZSuup/cO48xc1BgcrYFq3cy8PXbaPnsC4dZ6uL6CVsixmkNTq5t8M55922MXFjxu2osLfaiTuSkXA0Id1zv634CUjeSeBwZygzJJ+/9M8smzUzoMSxpJWsP1OIbG952AgtsBRtnPvyndkE5cg8Ru8Ojp6UU/qrtRJWovX6UusgomGLSnCOYNOeIhucN9W4dNb9W4qYnGPV++0IabTQnsvlsJGMqLdUPLWXZzM63j3reSGJFVpFyGJB1XIqBx4BfGhaeBuBE5uJTpi/iv1/yfvJOGl+FBshIbVhkvZGrglapPYfkuLHwSCkoBwHTouSPdn0CRatxW8Y1eG0XuwV6/vFN7LbphobfsiO/pkD7ZQPOddK1xwMfmclTXHM3TjtVC/yot6upV83E7huFZfDhP7Hrd/+J3WzT+aqvIVO5xMoyUnTDsPKDQcBB+f0IO0fQv5qBJz5G+/F/AmHh9zyI3Ra/RFUuNUB5Xajy7qgpFeuKSXpIwlg7Ee17pMlkRPCjXW2lGw/wuFas+BlZLs8O9RHEHEJ2CqRERk0OpUMoze7edtJg2biWgwb+/OL3NSz7pX//ESm3AkYPhA2RBaE125wcb81OZWHnUcxuO5h3nPQjUnY2CWmub68UFkorzj7oXcztOIw57UuHJVdsJDWRMOOwlMgql021S0MKK9rMjL6I1tfbslP4SuFIyf88eR+WlLxm8XKj+FUpLr4KSVk251z3PV6/5Fi+ePxLeMFlN9LYPgqpSAFq1PTu0hBfe+RG3rfsdKZkm9ldGuCoKz/Nba/4CCfPWIQXBiZUU2AiaqbmcKcNZxHWoUJYEvuATuTkyu86xsKECntuO8xtPIcCOIunUL51fXQhyTsksg7WzJYROjH6VKBj5Xyc60Gjd638z2cqmAetjVJVDkidHoXWKjUxioR9sQ8Zo4wQjRcEkSXFIm07fOvRm7lz2zP88yWXJedJzFoUk54OBuUaVvdEtImOHS+xXAVvohCuTdhdwF+xDZFzjeJJtHkSQBAi8mm8R7YQbOjBntOWJFOsLmtYFySucasKWGuIIANviFSunTX3XsHNP3qd4WVJR/xg8UZFayw3U2UBfX5YyCYqYyot9QtAGJtnMe9KoELStgkVLAYe/V6JYuBHERA6GkA2WdulNZUlG2FUfn/221BaMTOyLlSHINtSorRmYetk3tM6ef+0vEpU5HObkDY9ivhdGwh6YNeV30UVqc0DpMHKgsxAOGBgIRXbtfGsOG0Q9EPQF2nio7iLaszl9QqLk2ZoxT/Y/pM3Eg4FTH3T18gecFISSTQ+BSFyraTMC2BFCobbvhyn9TCEbajt3SkvwG07KrrEqam33/Mw4eBaw2ERKyhWCq/rbqymBUh3EnZuLpUon7GlZlwqbXzvdt3OPZoIgg09dL/ht8j2DGrXEG0/eQXOksmJyX40qcbnGNyHMsBtXYUHoYJ9EvFED/hhQLpqoX2yZzvlMEjycxVDn1lNbewY6mNSqqK02EIw6BUp+B5r+3Yyr3kSeTdFU6qdd574I3zl4VipxL89kqImMGHLS6YcD0CQMNiObmmp/hy/RBNy3C/jvKr6fnG9vdDHkSl+tvIOHMvmNYuX40e5wpLzo+vb07mETbtcGsJxUiitUJhQ2E2FfoYCz/BJJc9IRFFMkrnNHewqDvLFO//IqxcvZ0q2Ga01+Uw+sQ4nmx+lk4WlYVsiawqWRA9WwMNCSHQ5QKRs+j57I+V/rsU9cqbJfBxfozQiZRNu7cNdPisqR8QBb1G0yRih0dWPLPE0j8PtW/Wu+Cu24a/eYfB9YQTCjVzm7lEz4yaNq+znShQaC5FEpiit+NR91/GDFbdx9bnvYG7zJEqBT9p2zLlCMrvJKI4t7ugpPsZjaan5LdQgjWsoWLkDkU8lrqG4HB0aF1G4oRfv/k3Yc9rGBQmo2VBYThJer0I/Sby69clbyDa3smPtXcw78uUEXhG7ro0V3Olz4yLa1zIhS4sQArvuwdlSsqJrCzduWs0DO5/l6Z6d7Cj2M+gbf3Dasml208zKt7O0YwbHTJnLuXOXMr9l0oj30JHpN44ekghDRiesvSP8iaTge0aDxuwm9xYYlEg0yXVc8FH84zYgc5XBKISJEpCZZgbu+z2FlXfT8dLXkJ63HFXqNxwqVQM37N9JbqnxXwtruGWgkSso1vRRvlFYHvsb2378BvyuXtrPfydt53zQgHLjzNtj7SIERosCBp/6JmFxY0SXHxKWtuPtuBGw0OEQwm5mYNWnQXk0HfRJsJsRVoqwsNlkXHZb8XbeQmrqC0zR0sXvfQSxKU165kshN9f4a6uUlvFMFPEuFmmhlUaHBgMQnWQ+ygH+yu1Yk5sIt/ZXojP24J0d8Eooren3SjV9n9QLnYTz5RzTz3GG8jfc+Ese3LkhiRLSWpOxXfqLgxw2aVZSRksqwxVPPc2sX36U7p7tfPfsS3j3oacy5BfJORmjsOjhCsuwfolcRXF+oOckMeEeSg32KNrAdGbySWRHvdUnfnTlsJLA1XZSYNlITNoO27L58N3X8Jsn7yHrZiiFZiynLJtiaYgjpy/kwYs+BmhaWjprlJRy6CdWYC0wSoUUhFv7GLryEYQljGVDaygHiLSDDkJka4by7euQkyIlNGpTHDEUrO1CZF1av3YBqqeIaHKrCOMw4c1SoL0A4VZRBCDGJnOrsszgDActj9bnKKN8l25ag9o1ZNxARR/h2qiBEqnls7AXRxEo9vMDhCsQKMCyHDYN9XD1mkdwpMUNm1axdaiXa857JydMW0AQWeWg0s1fe+RGZuRaKYV+VURppdxQK9699FRaUplKoMZI9aj+Lfpeum6VUXJ15ZxkfjdYCkTapviHR8m8bGmivI6mJA2ba2KraqS83P2b95Ju6uCQMy/j7t9cSsvUJbTPOGTMcsfo5OedjMvSEn/WN7wcBnz+gb/xyyfvYVPfLlozeaZmm5mWbSFl2YniUQw8Ng10c9e2tfxgxW0cNXkOHznqhbxk/uG1ANi6e8TRQ/E5D+3cyLtv+w1TMnnCyIc+Vp/q6H+2lOwoDnDGzCVcfuyLatIE7C0wKCkjylHSesa7Rj3X2/okvTfdTf7YV9F02OgRDIZHZThXQM1LEN2f0DcKiUzRe9P32PW7/yToK9B61uuY8vrvmXOq2gxj9J+unOG0LcduXoiQaYTTirSbEId+BVXehQ4LqHIXyDgSyUnqOvTUV8kd+FFkdg6DT34hUVqU30fT4stIz3hJVTscdFjBQAhrZOuXEBVwo//EdryHN5N7/VGJ8iJk1eCQAplPIXKuSQkfT/5jrQE19zafccLNR7s2cfKMRcOKsCKAt9aaZ/q6mNnUliyGxdDnnUtP4eNHncOmwZ5kZ1gKfDrSTQz6JZqcND2lAifPWMx157+LtX27WNBsFousnUnq1cglVF/vZKMRu4Wepxur+vrHrto+r4QVPat67EF8thXNMQAFv5wwcKtIkfFVyHnzD+c3L3wLGwa6ydrGVWesq5WyY2UUTDfVoPY0yZgRTSlSx842i5PS4FjI9iyqu4Ae8rCm5QnWdxNu6KlcS2UelTk3se7JtlF2+bHCMsLYr/k7dmWnLNSOAXo/dB0i59LymRcad0WD5KnVfZ64NLqGKF63CpFxwFeVjlYa9+T5yJybWIyeDyIwhihpw7O7dvOlh66nJZVhTe9OXrbgcI7snI2OrehUxkyoFFc/YxQcpfUweIEVrW1vXHIcLanMiM+hXmJLXLh9gNKNT5voK2WYrYfhkEKFyDh4D2+lfPMa0mctTuaz+nOHtbvq3Q79MjvW3cNTd/yU/KT5HHXhZwGwnBS3/PgiDjnj/Sw45tXYbnbP1rfn4ZwxsdxDsesheuE/dOcf+cETt7O4dTKXH/9Sjp82n4Utk+nMNCUhxmZXWmTLYC+re7Zx3foVXLf+cd5+y5W40ua8uUtrcjs0Mk/HINnu0hD3bXuGjkye3cUBxk9/rWhyMwz6ZaZljV84UGoYlf/e7BwqA0mi/FIyWCsoOYkuF7Ca2lFeAeFCONAFmNBokcqSsH9GZnZhOSO6cWrcQoEH0blB33Z2X/1Jem/+CcqH9nPezuQ3/iCaZM0EVrM7H6vNkeUjNfnE5FB51214fY+hgiEsdxJO+3LcjuNq6xeW6X/sMuyWZVjpqVhTp1Le9hd6H3gzrct/DgjC4lZzbjCAcJqjx1l5pmMqkxFYOtjQQ+97r8W7byNN7zreRDjoqmlKa7RvGD7xx8ewWn9vFbmBrlv/OJMyOW7atJpLDj4p2cHFO7EWN8OVT91H508/xO6e7XzvnLfxrqWnJPU4sG0q03ItCZaqkfgqJO+kaXYzHNE5u6Y+MHwDUS9jjeeYaXeirpzRRGvdELs+1jUxnir+Hkd9PNO3i82D3Qz6Hqu6t3FQ+zTKMbakqs6h1rSmzOJfTVIZt7wcBqRtm7ybZmFLJ2l7OOpFChE9v9j11KDfooVHNqdJnTS/5qfC7x4le9Fhyd/uYdPxH9ocN7K2zaUA776NDF3xEOG2AWRbBmFJVH9kuXMsY9k4fi6pk+aj/BCRijAXSqOLfuXdl9LwexQ8o0h4IXJyk7HiFCJ25xHGQM17Fbk0Sn9/kmDNLmR7Fl2O2Fq9EDkpR+a8A82Fo7jHxi9V885I1qP491HmJ4UmjbGknjRtIdve8hUGvBLXb1zJx+65llOu+QZ/u+DddKSbCELDDG0JiacC/vHi97GkbTgb9LCaal2zqa7/zVQxOh4ocC0Kv3mEcPsAsiVd4xqK1wMdm3uEgFAx+JP7SJ26wLgVq5XJEea92C28ZdWNPH33r7BTOZac9FamHXCqebeVYtnZH2ba4lN58vYfsfHxv7Dk5Lcx+9Dzkmv/nWXCKnOsYNyy+Ul+u+ZBlk2ayU9Oey1HTJ6dnGOwLyoZm62pLK2pLAd3TOflC4/km4/ezKfvu45vP3YLp844gJzjjmmCA8jaJlHZyTMW8dUTXkZvuZCA8xpJbA1qT2W5bcsa3nzzr5KJbW8tm40W1GQ35TQmgNIRwFhYDiiQrsGDyExzhf12hHY0unfyu+2iA4++e66k+89foLR+LbJJMvniL9Bx3oeNH7VOYRmtvIZ1D8qE5S0MrPgYVm4udtNCpN2ECgcZXP05rOxsmg76FDosIJ0WvF23Id12cgvejlY+Qjo0L/s6fY+8B608hJWlwrsR7Y77SuhCROXvWFgdY+wOogkv3NKHaM1Q+vuT+Cu2Men6S5BZNzJ5g5k1qiaOcVpYKhgLAwJd07uD3699kC8cdyGru7fzjUdv4uNHnUMpqGAN+r0SJ0xbyB/PfTtre3dyUPu0pBxfhXzyvuv4zdMPMOiXk8nQEoJAK+Y1T+Lqc9+BI62kiv1ekbyTHnGs7YmibVspXLsCyNsXFmDbcklZqT0qs7pdcdTHj5+4A6VhQcsk/vOuq/nLBe8mZdmUQ5+U5SRWks5ME9957J9cv2ElW4Z6+f3Zb+OQjul4KozigTRu9N7VKyzxc41FEbN5N454ShaTQKG9AJl18R7eQterfsH0oz+FNaMF4VqEOwYMANNcRHRzAJovfyFq2wDhrkGcQ6fT/5kb8FfuoOOqVxPuGERmHVTBx5oWAbBjC4+UiKyDyDjD+la0ZeOOTO4Xu0hHs8QlvzkWqqfA0C8fQGRi4Ki5tx70SJ+1CHt+RwI0rn9mExUhBHhR5GApimCp1ngtkVh0YlK9sTRrjUnCm3NSvGLhkZw4bSEX/vUHvOr6n/KXC96TWO+EMBvW7pKJzhvyyyOuIY60xrTAJ2Mi4uAJNvdS+MPjRokMVQ2WpaYNAmNZyafw7t1A4YqHyb1pOdoLEivbWG6ithlLOeycj9I242AAwsDDsl2EJQkDj8nzj2Hy/GPo3f4UTrqp5tpxS6I87htiyXrZEy/HhJWWMNoJPdm9na6Bbi477AyOmDybQb9M1naIWXItKuZ5rXUC2rWE5J2HnMwVT93H412b6S4NknPax6W0gMmImrVdFrR0jnlutWwd6gPfSyakMSxwY0rNLtwvjVmgkBaqPISVazPcEQJUxHSqSgMmomckIr5EtCFhS1w8JnSt/+4r6Pnb1yg9+xiqCOmFBzD51d8kt+wcdJQMUUREeQkYTAWJL3T0wRK97HaK0rqrcTuOJ7fovbWnzH87Pfe8gnBoPXZ+MVr5pKa+gNTUF0Rus8i9JV1ajviRuZ8OEJZ5kXQpQDRB/5duofDLBxFZB+fQaUy65o3GMuKOsDOIJvTgmd0gBCLjmoUjBjfu4Wpcb2GJrYAfvPOPzG7q4JKDTuLZgd1c+NcfcOK0BZwyYzE9pQJp2yHQIW3pLFOzzUyNIn8CpbCF4K0Hn8j6/i7sCItiTNMi8jToBK81FJQZiPJOOdJu6JrdGxko7aa70EcYZbbem1chxgIMlrrZXSgnIc/jCSmv7+dQadK2w7XrHuU7j9/CL898IydNX8ixf/gKF1//U35+5usTC25cuq9CpmWbWT5lLlMHummKcESx2pe2Xdb27eS69Y+zbagvsdQoNC+dfzhuFUDXjqyKIzGkai8wgFVLVkLobYk9szMaa+aYc9gMY61Q2pCLNaXAD1G+wpregj2zNSnTOXQauuDhHjFz+P1iYjKMNSHc2Mvgz+4HLwBbIjtyiLSN2jFI7k3LDS4iXvxjK9oICksifgiuzdCvHiJY01XFPWOaI9I2udcdWVPmHktkXdBKI1vNxk7mGzNsi+bIhRZjfsYyCFOxdhYDn2m5Fv58/rs47g9f4eP3/Imvnfiy5FxjZTf96khr1AzOYykPyXGlwYLBH9xDuKkX2Z5Bl6P53JHoniK5Ny8neHoX5bueRbZkTG6iQCFyLoPfuwv3hLk4izsTUHSje8fWfK012ZapZFumGnI5wKoiULVs12Ql15rWqQfUXpsAcUeTyFIdv89hAbTdmApkT0Rrkx9OTjxNwh47Jy0pIUrhDUT+QZAizpRa7ymM/IVS0lMu4IdBMiGPVwzEouLDHvBKpC2nYdg0GL90MfRpdtMM+mWzsMU/7sW8X+Oa0XpEy0q9xINKuhl0UOFliT/HWyUTsRK5hZw0oVek8MxjyJRF63lvovOln8VunZZECcXXaK1AGQVKWk4Nk6IQIlKG6vuysutMTXkBg099FbnxKqzsXINh0YGxqmRmYOXmmhMjy4lWfvI9een8PoTbigoGqlI+xOZuYczP8b8GnVLzIlsS7QX4D24yO9TeAs7BU80OrT7nxwSk+h4xAPRdt17FHVuf4foXXYpjWSxqncwnlp/L6274Bb886w2cPnNJVF2RECr2lYs0u2nsyKLywcPPGvPeSms+VHWeuw9dmDFV/9tP/CEFr5fO/BwArHFGbDWSODXA64/5Ci869EPMbjuk5vhoUlFYTD9LC3626i7ed/vveP+yM7hokYlGu/5Fl3LR9T/h6N9/iY8fdQ7nzzuUVGQ96S4N8a6lp/KBw8+sKduJlJOc7bJi91bedetVlEIfgUhcBKfNOMAALTEbpkJgJuihJG2AERHhOoRrI2IDSoTrsKY3o70Aa7qxsgCkT11QubYplWBGRKNIeyFG1BpFuoLxSp22kM5/XIIaLBtLiCWRzanh+X/qhsdoC62xDtj4T+5k6H8erITnatNW1VskffYS3KNnR6y8tRjACYsU4AXIljT9X7gF2ZkzSpOs6wOBUQrTNuH6bmRb1lhmYjr8ESSuU8Z2KAU+U7LNfOfki3jNDT/jlYuO5Ogpc/FViK9CwnEuvo3eu2H4ywiLUrptHcU/PF7rFpIC7YVYU/PkLzsZ75EtlO/ZWOMqEo5E7S7Q/6l/0P4/Fxvm4/p7NHClKhWCVsOSdMbfq6OLELIGuD+OhgMK4bSaR5duMnXex56l6tQu+83SEmuoR02ew8zmSfxh7UMcPWUuL1lw+ChXmcqkLEm/V+ILD/6dNX07uWDuMqZGIYbjSaAUb2ZijTpru0Z5Gu0aEd87bureG8PrfY6Dj/0VVRpESJsRqfqFRAdlrFw7QdezOB1QWH0bwnIJh7qH0fwPwy1IC+0VyB50OnZEHCQiZant1EtIt88CaZM5xEzetQqL0bBj073Wip3rH2DyvKNrqtgYmhspnEEJp+VQ8gd9itLm/8Xrugutykg7j916GLlF70NIt3YQysrLFLcpDnl22o5AyDjFvV25ldKVf5XbJ9+TgR3l6wjW7ibY2m8y39oWzmERqVKoR+TaGUsqC6lgyC/zxpt+xZ/XP84VL3gTx0ydFyklgosXL2fzYA8v+sv3+cBhZ/LZY18UEb2Zfk5ZtVaSQb9Mk5PilKu/zu1bnkZGxHBVd6bJSeFYFhcvWs6J0xcmeK69leqxNH/S4XXHJzZpVF8ro2tnty9ldoPjo5UphEhA9s/27+ayO37PX599gg8f+QIuP/bFgLHOHtQ+jTte9iHeddtvuPyBv3H0lHlJ9GGTk04iubqKg0zKGOtdqBRYRqm5YN6h/P7sS2rA93HZEDF42ynOuOabDPpl8m4qGQNg3EG4NqVbnyF4ciciAqTKrEvwbDd6oMzgd+8y3CxeYMa7woRHO5Lca4+kdPMaQ42fcw3GqhxiTW7Cu3cDwfpuClevQHUXDEhXYHbgTSmyr1yGVhqrM4fVWYulGfY8AjXqoj4MgxHJwBdvQXUXkfmUYemVxnIgMg7Z10XcSxHuZSyQ6GgiIiuLcC28R7dUKSKNTsbUIe0gMmYTMhKGq5GkbcM6fe7cQziiczZfeuh6rj73HUDEiLuHy8AwZSJUCMci3D1E/+U31va/xigg/UUylxyDbM2QPm0h2YuWUfjNIyZbdjkwrMP5FOW7nqXvk/+g9YvnmufgWMPXASprg8GmDA8mqT5HCNFQqRlVhAAVIqyM2ZAOPoraEUBaDh9fMTZHBWgdIKw0o9puNQhpo4JBnNbDcVoOMVQYcvyqyB4oLRaBUhw5eQ7vPvRUPnv/X3jHrVfxp/WPc+qMxRzSMZ1p2RaanBRSCDwV0lMa4tmB3Ty8cyN/2/AE925fz+x8Ox864qyovHBEk+wwESKZ6Pv9Eukq8269SCEpBR5t6RzFwNt7IEu9RCG6m798Pt5mw71SxUhPgreqVN1o1ylwcrDzim+gy98w/s2qc4WAMDSEtWH0Xltp8HbCgu9cQfPxr6HYszmKoDLAX2vB0SAEA7ueId3UiZNprhnAfnmIYv8OnHSevh1P8/SdP2fS7MPp3/kMTroJaafI5DtHnhSirMx200Kalny4cXc00JqHfbdSaB2SnnqOORYlNazcZ5TuRlcUl9CYY8t3rANfobWPPa8d9/AZ5mRb7pUp08yZJh3F3OYOfn7G63nFwiNrcBBeGPAfR7ygZpeutE76MLYA1ivOXhhw8eKj+fCRL2TLYG8SRVQIPGbkWnnv7b/j2YHdUXmj5KuaSHuqJrFA+eZZSnevXE6Ny3QmpATF5+SdFBnb5Senv5Y3HHiccZ9FZuwBz0RV/fT017FxoJv5LZMY8Erk3TTbC32UI/N4k9N4HMV9WA6DxDUnRJwW0fC5FEOPH5z6Gha1drKjMMBrbvjZcCtF2kbkUwZXkrZBg3PgFDqvv4Swa8hgGNK2KTXK10MUKVR9LaFCZF10yafpvScaTMnuArI1U8GlhAqRdZK2aBWDyKvGtBAJJ4xszZi8RunR04/EY1N7IcK1GPzxvZRufaZiHcDgYVR/iexLl5I+ab5xVzgT2KWPIEkdlEbm02ZTMcwqn5wdA1AmpLBU1zFUJjHfWw4+Ifldac2uoT78MIzvMvH6N+iH/k/eYNxr1VYWW6ILPs7BU2h6y9HJo81/+DS8BzcRbug1zMiBMoDn1gyFqx7BmtFC/j0nJM9oJMWlWhpZhEY6ZywRSLQqId2pDKz8L/qu2wL9wEgUT9oY1IUNqtzg9/pzHQgGoO24t9N65A/RqoSQw8kaR5I9cg/F5G8fOfKFTMnk+d6KW/nV6rv57dMP0OymSVk2VgQmVFrjq5BS4NNTHiLvZjh/7lI+vvwcDu+cTRhF8YznhfBUiOukeGDnBub96uPjd6dg3FeZVDZhQtw3oVymBu3nvY+gb7uxlsRRQ4BdlWZeKU0YKrSGtKspF4pomYoAuBq7ihgt8BWZrEOx6JPJOJSKHlguaqgHe4phpdy44u/sWH9vlHsiQIceUtoEKsAb6uaMt/8e0CZk2nLY9ewDPHXHT8hPmk95qJsNj/0J3xukbdpBDOzeQNu0gzj0hR9C6RCrEa9osiMPQQVU3EbCuJhE7WJVbc5Meis5bhnXEQIhbTRjYXkq1ydiS3SoKN/6jJn4fI1z6DSsGS3Jy67Vni349RPEV08w/vA4g3j8exwy+cHDz0oUZ1m1Q69PUhHjwYYCj/nNk1g2aSbLJg3HMsxrnsSOYr+pyx61YPQ22VWum73ByuxtmTqyZimt6cg0cdUL3wKYfg5jLJGAvGssirZ0k4iP+NjLFhyRRFg1ule1xcRTIa4KUdpQ1PsqJCdTSCHxleKsWQcyo6mVUuCbBSYuI1KAU8fOIXXsnAn1EYBWmtQJ80idMG/i18Z9KASkRrC4RS4i1Vsi3N7f8JRh7gzXonzXswx+6w6DGwmqQKOhQrZnaXr38dHFw8vYE6mpgxdUKSwjmFq0sZYOi3gcQ+JzYqzKaw84Jvkta7u8bekpzMxHrvlxtqUhviRyCw187TaKf12VKCxCVrHfKk3+Q6cYrFCg0AKs9iwtnz+Hnrf83syr0fnaD5HNaQa/cRsybZN76zEjKi57i2sbta1ohHDQwQDpGS/DPnw3ereDSClQVfcVwmAj7Rx+/xP4PQ/TdMCrop37SPVTYKVRpZ2kppwZFTO2K7laJqi06EhbNCGCoVK86aDjOWfOwdy17Rnu3bGeVd3b2DrUx6BfIlQqYcKd29zO4Z2zOXbKPE6YtgBLysTCMpFdWdqysaVFwS+Pi2YcIpCjtEhZTg2KfG/EuD/MyzT5Nd9qeE4Js/kCw/XkYB7lfY8NsXRZjmzVuUUidmwJGeCO+/s46egW7n6on2OPbK4l1VUBB5x8CQecfEnD+97563dE30SUq0Ix/YBTmX7AqQRegcf+/mXmL7+IbMtUFh33hiSLMFRwD8Ml1uStEXMWjfVS1fw+DsxDo2uhMlmU79uE98gWY64vBWRfdVjciAmVXS8VPItZ8GIuBysZO1Vt1JpQKQb9Mi2pDK5lk7LNa2XCbStAs9it6QiZWGEG/TK2kLiWTU+5QEcU3ZaJQdJ71ZLaNlV/jnX8uSizup/DSMHUEb7EkRblMOCmTau5Z9s6thf6KYY+GcthfsskTptxAMdNm8/3Tr0YYHj22GhtU1qTjdykban4jTPj163idJEIdpcGmdHUSldpsBaRpzERLV5orA7VPwqSJIkjtjPjGBBvqIc/TyGSxW34hRhcy1jzY2TNyb3+SNJnLTLH6ijhk8/IahJu7afvY38zf8cKvgZcie4ukn/38dgLJw1jjd4nlhatjaWqOvtxIxEYK2oQjlthqceexJ+hVlhCknfT/Oj01wIkjO2j9q+OkvDWKywR+d/QFQ8x+IO7E9danK5BpG1UT5Hca44gfebiJPElmPkrdewcmj9xJr0f+7sBI/uR5ScIEVmX/i/dggaa3nqMcftZw/Pe7b3yMlKbldkke900LX4/6SXzGJb9tU6G1v0Yb+ddEZ3F+EXrMLK+7zdMi4l0iG9gSYkXBkzNtfCyhUfwsoVH4KuQQIUR3b9OgG+uZSVKRqhVwpOSDORq03+dxGDEE6ctYOMbvziuKKNGEisvpsy9I0iqHkDV0UPGtaOx0za3/mkVmzf10dqWYcOz3Zz5wsUsO2o+j9/xIAsmH0K2Mwe2w5//+CgbN/TS1pYhl3O58KJlrLjzQU5afhIr736YwxcdSSbnEgYh0kkZn2Dom2gjUWVijczI9dmkhZRsWvE3ujY9SqF3KzMOPIO5h7+Evh1reOqOnyKdFLnWmcw+9HzSTR3mIq0YI19ewz6p/pzo7+MpO/oDgOI1K0zIpAL38Om4x80xE9UeYlliqQXiiprx1ggQZ4kKhXi/V2J30YRTtqQqOZXAKDGuZZNzUnzxvuv40RO344UhU7PN9HoFclG+oQ29Ozgvwog9n1ls91aq+zlWCGMs0d+efYIP3flHPBUyo6klCnOWhFqxumcb319xKwe0TuVrJ7yMw6voFipjzPydsmy2DvWycvfWhNAvTttRCDyOnToPN2IoTkWKYsqyG+/9XSsB2064na69R09yXBsBW6KVwjloCs5BUwxuxGoA5gxNVIoa8uh577WEG3sNUDhaMIUj0QNlUifMJfe2KCN4lfIDe6e0VDYsAtVVQMfJBBspIyJykTWlkDGYeQ9cRPF948gwrXXyHo4nr1BjhSVEuDaFPz5O/2duNApYpLTqKB2DHjARYflPRABxq7LtFI6FDkKyFx9OuK2fgW/dkeBbECTEc/2fvxk9UCZ/2cnGEhNHFZmG7AOry2jYEw3CQnndwLwxd097Mk9p5UU4yP0QPVQ7UMwCGR9zLYtQhQRaYwuJI2XDEDIduYl0ZGKPyZpqOr5BvWtN0FayEOyN7AszW83CVhc9ZDuml85+8WHJsXvu3oiVMuc1tbViZ5qSpHieTnHR64+js7Nie8l3tBNql2xrK1YmZ6IFIoye1hEy3GpsrZDJcfOsyoVeyoVe2mcs5ZDTL8VJ5ykN7KKpfRZHvvizdG14iK6Nj1Ac2Ek612H0HzeLzGBMP88DqZ94vUe3UvzzKpP2fcgj9+ajK6bvMfIJjSXVytWo+Jzo936/lLiO3rDkOJyoz3626i7ectAJNbs8gAUtnXz0qLOTfDhfePDvfObgC7jqqQc4f+5SWlKZhHxutHDMf3epV2LjUNS7tq7lDTf9kosXLefSZacxv2VSDSNuwfd4snc7H737Gt5406/4x4vfy9RcSw0IOp5E826aa9c9ynF//DJeGCJEBXjbM7Cbn5/zdl6+8AiUCpLpp54ldV+1c2+uHW0jkFh9g9DQw6eG83xopRJFpve91+I9sAnZmjZhuZFbiFAj8imaP3lWtLDWht7urcQgXNVbpOndx+McMQPdXx7GAiuiBI2yPUvxulUUr1lhFnVvYm7kkd7fmK9ntHWg0eZERwECwrUo/PYR+j75j0SJ1TradNsmz5TsyND6tfMNi3BVP8b1Erbp3/wHTkEPegz+5D4TJu8FBvcYKGRTioFv3UG4rZ+Wz77QWN0iC0+9q2Dfu4wit51MMbT1C/gbNiBzWXRY9QyEABUg7Dx+z0MIJ0Pfo5dhUr+IyjlGy4r+ttBhATs3j6YlHyHON7fPLS21hQ2fwC1pJZFQWkOo/YgDwjRcCkPH71SF1I7XR1nvy9M10NaJirl2vJk7x6pX9ScMfwnCyE9cLPps39rP0rOnmPNUiKp6+BJNYagMnVk8L8B1bVQQYEkQWplzHZmA9Mw9DOjVLw3w8F8u55iXf8VMvMLCiZLvGTyL5Mnbf0yxfzvSctj42J/NPW0XHQaEgYeTzpPKtbPqn9/niPM+TqZlKv7uDfjbgQhgOppWvtdK4DjcOUm5ER/C0I/uMZEaHqROXUD6hQeYiXkvFZYR71v3PR6Fg36Zj959LUN+ma1DvSxpm8pxU+fjq5B/bFjFWw46IfFv37L5SfrKJXrKBdpSOQqBx0nTF3LYpCd49eKjWbl7K2888LhkUlV6ZDbO/xclJpb73orbWD55Dt855SIgwsWFYRKanLYdjuiczdXnvoNDrvosf3jmYS499DTKYUjGrkRuAXzzxFfwleNfagD7VXOHFIJS4DMp02S4cywnSQb77yQ1755tgd3AJRSqhMOl99JrKd20xigspcAoMkqDI1F9JVo+d7ax1lRRyu8zHIXWBjhcCki/YDHuUbPGvCTc2k/hyoehU0JkiZiIjPT+Vv89ksJSo7jEWZilYPAHdzPwtdsSMHaijERRV0hBy5fPq3GvNVKehGWeTfMnzwKlGfzZ/YbfxQsjxSVEtqQp/PZRgmd7aP3c2diLO83zjBSk+vWxUXv2XIy3pP+xj1NeAzIPDeGHVUDcvge/VdXBDF82JGgP3E6XpiUfMfiXKCP8eGVcCRNjCZSH1gopLKS08YJitLMROFaM3DegPHtEzMLEO7lmAO3VHmhf+QJHKL1OCQuVwnVtVq/cSWdnjqYm00f1YdpaG5AuGNcSQKHg89srHqGrawinbiGOtXpjrVKUh7qjH6JjCe+J2XEtO/s/x9+IqPqTX/NtgnM2YbdHE8soO/6aF8cPE1DfuFxLlkAXTGK2xoVXVS1iySz+dTWlG9cY9k6laXpvlGIgSbi8n4FqukKEmLVdvn/qxewuDvKZ+//Kd065CK01hcBjUsYoj8XAJ+9abOjvpqdcoLdc4PYtT7O90I8XBhQCjwGvxKBfZttQH3ObOyiFAZkGOaf+X5Z4uBQCL7FKlQIfS0oUBsSuMGPeloKck8K1bMoJI/HwDdFoltlcVaoRXVODEernhQnL6ZhjOz5HACl73y389bcZw6qQ7PJDRc+l11D862oDCi2bDY3BXziongK5NxxF7nVHJriXRuXuXWUxbg0pUN0FAFR/KcrRU3WeFOiih2zJmPQGcgzsyz6SRm4loALs9wL6PnUDhd88mpDeJRYWSwBm/mv96vmkT1/UUPGLy03uJY07qPnTL4CUzeAP70E2pyFUhpzQN1FF/oOb2X3xFTRddjK51xqyvzg8PSYgNDCLqC2AiDa5ey7m2vaTriGYuw2RzYCqB9nGnpcAlJ9QWiBEwoSOisgKkdFxD5mKyGHFxGEa40qYGHfwlQ98jCGvlyD0KHh9TM7PZaDczZT8fC4+6rOEKsCSNtv61rK9fy22dAh1QM5tZdHkYyh4fVzz2Jd5zfIvJOeOV/bly76/FoHqQa9Co7CsX9fN+md288pXH4bvhTju8IRYQgg6OoxrKJMxyp7jWpxw8jxuuP4pwlDXYF+rBydCYjuVgSKEoH3GoTXtVIGHtF02PH4dO56+A98roIIytpvDzbYwf/lFtE07iDAoY9kGFNV87Kuq2qWiZJBjAGwjzgImaO0YUWGBZLLSUQ6WYHMf/V+4GZEyOVry7zkBd9n0hjua51K2DPUmLLZCmCyx1S4KpTVvOshEY6zr7+I/j3xhcu3VzzxC3k1jCUnaNskmY0vB/lrsno8Sg+TfdOBxvOGmX/GDFbfx5oOOjyy0tWOqr1zko/dcS8H3OH+eGe+OHN5nSQh6XffF0UMpy2S/dmXtbi/UVZy+gsQtsKfsWvvUYjHO8uPFVvWX6H3ftZRueSZSWALjTgoVImWj+kyuo+b/ivEXdRaBvbawDK8r0TsvMk5j66iOgOhOZJ3fz8O/Yf/FFg3XInhmN30f+zvle541fRjhgCoKC+iyT8vlZ5N58cE1bup6RWiYJSxWXD56OqIlzeDXb0OkbKNsBqF5jjkHNeTT94l/UL7lGfKXnYSzdJq5b8ytI+vcWeyDbtOQarqQ1JJ9UVhd0VqzJxxR47K0CCEIVcAbjvkaQgiueewrbO17ircc/53kPKUrANCrHvw4U/Lzyac7sKXL3ev/wEfOupaUnaF7aGtcck35/y9IoiQojbQkjz+2jZUrtnPBiw8CKlar+k2DUoo//O5x5s1rZ/fuIY48aiZNTS6zZreSSTsopUgmy8hFZtxDNlqF+OUB85MK0UKy6LjXJbmGYkVk86ob2bXuPg456zLcTAvScgmDEj1bV7H6th9x5Is+QyrbalxK0kqSPgo3PebAqsaSlG5Zi3fXekRz2iDiR3u2AqN5lwOaP3J6LSgvvkxWylYFj973/wnVNQShxl0+i6b3nmT6024Q2bOfpHpC8FRARro81rWZR7s2s2mwh1lNbUgh2TCwm4/cfQ1z8x28Y+nJFAOPruIQz/TtYstgL3985mE2DnSzrt8kzgxUmFjJdNUuaX8ucvujzD0tOw4hf/H8w/jcsT188r4/89NVd3HYpJm0p3I40qIU+uwsDnDv9nUA/OT017KkbWqCh6nfzUohRhyDcbir0opiuZDwuYiI5C8Jh/UVpGyKf1mF/+hWkyncV6NP4pESL3MuuXccl4S17q8+H0lhSZKJPro1wbAkCotrowsezqIOWr/1YvN3qBqCePdKhl1e9Z6PxKUU/74fXXY1GJM6ixW+SvAqhT8+zsCX/0nYNYRsyyQ4ILQ2GyXfRJS1fOk8si8/tEZhadSPjRQXLIEOQvLvOh5rSp7+T//DuO+yDrocmntIgWhOUbplDd6Dm8i8dCm5Nx6FPbfd3EtpdGiilPb1WFPFgciyXU8jUeUyVz7oEGGlEVYab/c99N7/OlJTz6F52TfRYTEiYNUgJMJK71E9R1VaKh2rEqvIbWt+TU9hK684/L+479lrmduxjCn5eYbdM7quPTud1x79xaScwXI35aBAPt1BT3Eb37v9rbz+6K+QT7ePMw/Cv4+EocKyJFs297F5Uy8vfcVSUhEwTkYauYzj+DF9fPZ5S9i5fRDLlizQHUyZmse/awNKReG2VYM99Mvc+/sPEIae6XMVUi70cOev34FO4uM1KvTJtc3iiAs+BUChdyvppk6yLdOSukqric55R/PUnT/DL/UbpUUbEGI1uHhcE1hEpFW68WkGvnQL1qxW9JA3Ol4lLitQVUpL0gRz7ygxneou0POea8yi4VqIFpfWL59nzLZVE23cT6M/JJXcV1ui1jQ9ThHCZB/P2C5bh3q5bcsa3n/Y6Xz/8dv44vEXAtDsZnj5wiNwpQHcZmyXzUObeWjnBh7Y+SwvW3A4mwd72DTYE5W570HPIz27Rn5848Ku3xbHF1QdE9H/RGPMQLJwRu/2eCamivvXfH/Poadx7pylXLPuUe7atpYVu7dSDn1yTor5zZP4xPLzuGDuUjoyTYS6orCM1saRJGU5LJsyL4ke0mh6+rsohVFupiCEFBT/tJKhn92PNaMZXfBHH9tSGsr6yU3k3n4so2s4eyYNsRfR2BauRfn2dfR+7G+orf2GQyTBsEQKS8nHmpKn7cevwJqar+A2aOx2Gg2wWv/930WGYUJi15hrEazvZuCrt1L6+5OQspFNbq3CEil9oilF69cvIH3GojEVlpHuK0QEzvVDsi9bij27ld7/+AvB+m6Tp8lXkeUnIuYLFEM/u5/SX1eTPvsAshcdhnPIVMOcrvS+U5Kjy4VMIYSDliMpkRppmTVD+b0Mrf1vBlddTjA0QHq6i5A2QuZrr9hDxXhUpcWYVw0i/+md9/HAhj+TcXK84vD/Ip82obGPb7mRB4IhDpp6CvM6DgOg6A/QU9iGY6VpSrWxvf8Z/LDMkNdLU6qNd5/8U0Jl/NDjofv+dxGtNVbkX5w+o5kZM00EiAoV0pLJehD4YWWjoTRNTSmaFtYmDiuXAqQUBIGq2nRoLCfFCa/53oTqBLBg+UWsuOmb3H/1R3EzLejQR1gOhd6tzDrkHJraZ5u05Q2yTdcvRg0l+km2ZrBmt5mcLGNN7KJipYkqG32SXCezLuU71tP/uZsInu2Ocr5oWr/5YpN5dgS/8WgiIhdc/LknLPkasJH0lgp85O5rePG8Zbxs4RF86r7r+O7j/+TNB55As5vmqMlzgIhWXgi++vANvHfZ6TzZs4P5zZM4dup8cvYdiVVgbzaWI+3qqj9NBISq8IpYAmREghgrIxO4nw6jsiIAoilPRApI3b0nMIkHKmR+yyQ+ePiZfLAur1C1xFnnG5r3xzgWs3DPyrfx6MWfSMpsdtN86qRXMr/ZpAqIz5MdWew5bcgpTSbUfpT3Ic45Iydl95NZXQx/tpF1BWDwh/cw8K07zOKac81vVpWFpewjW9K0/vBl2PPajWXBMdgRRRiNR4tQ+diWO6wP4/vGx4LQw7YaJVd6/krNmIu5UBwLNeRR+PVDDP3iAcLtAybHk9LoKuuaSDmoviL2vHZav34B7hEzx62wNLx//Ewdo7i4y2fR8dvX0vfxv1O68WlkU8o8Nz9M3EGy3WB+hn71IMU/rcQ5bAb5S0/AXT6rJiP3XvRQMieL6NmOtgYE/aspbfsrhfU/wdv1NHZTnvbjf0J2/lsxjCbDleyxymwk43APKRCSxZOPYXbbwaSdCt3u5PxczlxyCQPlbvKp9uR4xm3mNw9+kin5eQTKw7XTTGsxTK7zOqJcFux9ivPnm1QvmkIIwoh+WiZAKXNeS2sGy648MKV05AIy1zuORVu7wam0tmbq1n09fDMchY2NVifLSXPYOR+lf+czFPq3oZXCsl1apiwmlWtHa2VyWVQNrpGk4QsZ18kL0ENldMEbh9Ii0IKEWAkRWVosiR7yKN3wNIWrHqZ870ZjHQkMTXrrt19C6oS5hohpAhEOWpuFNdzUi5zchO4rGfzNBDUFISWe55Hp7OAv25/grJkH8rKFR+CFAZ855gK+/siNPNq1KSEviynnv/rwDQz5Hp9Yfi5re3fyjluv5Dsnv8rQBmhFFYpiQjLiIqYjZSK2oAhMhIm0hr35uhygS4GZEJU2/a20UXKEaXOslAhLGnBp5Hevh3lobQCEsfsPIYxpO65f1XlxfeufoS1MuhClh6dDFRgXjpSiRmFp1CcNrRFVFmQV+kjLSSgbADK2y6ePOR8wRrgk3LocooY8RMFHF0cf21oK8EJ0wdlnINJG/QV12IuNvfRffiOlG8xCBzpxLcQYFl30kR052n/ycpyl09C+QjkaqU20iBRW8pBsyzWbGWnSeKgqq3uoAuIoxhrF5t8kCCsZB0on+JHCn1cx9NP78J/Yjsg6xkLlV0JmYkVA9RRIn7WYli+di9XZNGGFJSlvFMXFmpqn/WevZPCH9zD4vbvQg2XjmgwVOtRGcZYCOSmH7i2hegrIafm44H3RQxCtK37/U8hsxnCDGTIZtN9PWHgWr+dBvF134Pc8SDBQxMpCbtFF5A/+DHb+ALQKQFrjtrqOJaMqLUqFWNJme/8z3Lv+anKpVvqKO0jZTTiWS8HrRwhBPtXBYLmHkxe+hvbcdN507DfY2P0ED236G23pSXTkZnLzUz/HC4osnGQyt8b1/n9FYYmluj1WnaZr2xYaOD/GuGBcRUCU/KoiL3vloWjgwpcdkpxbWZTq7znyY4yv01qhlaJ58gKaJy+oOUepABmDoqtdMyMMsJoXLX5ZRfKjMY1Hi9xI2n5StgAdR1MJY4qXGYdgbRfdb/sD6Ch9vS2xj5hC80fPwD1sesTXYtWUN6pIYXaT+RQ977y6ku5+ghOsVhqRdVBb+shdeiKv/fw54Js8Q9W0/v1eEf20KTwduR2OmjwnyVy8sHUynzv2xWRtl0wUxZKS9rjnmlFN9qEySodjDev/sGuIcFMv4eY+87lrELW7gOouoHYX0ANlw97qhWbRjSMUXNvs4qP8NrI9g2zPmn8dWazpLdizWrBmtmDNahtGwqaDSBlyrKpx3NgikiguUjIeM1i1EjSaWyMe55VjEiviSnKqulBrTTH0TTqSiL1YgOmHCPAY08uP+GykREu11+zMjdqVtC9SLuPFsvDHxxn4+m2E2/prFlshhXEJpW30oIc1vZm2n7wCZ8nkKDGfNEk5I0tbeElUKwABAABJREFU1+Am+ktdTG9ZxDNdD3HwtFMIlI8tnRq+nOpAihVbb2Hp9NON4v1vMqfH7jRhScoPbab/U/8geHKnmXNaM0Y58MJkgAnXMsoq0HTpiTR/8JQazN1ISuW46lFvDXSsJFN90zuOwz1uDgNf/iflO9ebJJJp27zjlkR1F3CXz6L9V69CZt1huKQ97RuUh3Sa8YP72PbHY7HbQZvsK1UngpCABqtpNvnZLyAz66KEoj+JIGLk9WSiMqZ7CCDrtjC9ZTEAc9oP5bHNN/BM15O8aOkH6RrchG25NKc7STu55Nq004TWIUW/Hz8s4VpZ+ko7uHPdb1k643RCFSAt43/7t1HN94kYy4qIdqEj7a4jA5dJZRTNE3vaSxqzUxaWRKkA7fkVrV7aJjIoNNwAOJXY/+T66uyxkSKS7FBi5P++eoQi0iQsiWxPJRYWXQpInTjPKCyRKRcm6BeNihbNqap8JhOrno6y3+qij8waZcP3yzhOCkScnM+4ebpKg5X7AqfNPCCpswaOnWqy9m4c6MaWFrtLQ4RjJHkccbetI+uIxjyTyBUQdg/hP7Ed7+4NeI9vRXUVUD1GSdFFP7KUyOQTK7KMRAqliPLaECpUIYQhQBcIN/ZUlKPIbC5aMsiOLLI1jT23Hffo2bjHzMaa2RLt+qO2e2HFYlPXlj2a2EYwM1eXFwYeVhziHAZIy6Z/1zpu+8UbOfMdfyDTPCWxKAghEur/qsf3nMuozzoGikoLf9UOBr55O+Wb14BjGcLFqsU2ZmlVfSWcg6fS9v2XYs9pA18T2CEOFv9Y/UMOnHICs9uX8uzuR9nYs5Lm9CRuX3MFB087BUvY9BS2cde633P+Ie8D4M+Pf535k47EtdL8feX3WDr9dEJC9oz7918kUZ9aU5qQsdu4OY0uVBLsCssodKqniHPQFJo/cQapk+bXKI17iqeqP7/mPYjeD+2HuMum0/7riyn89lGGfnIvwTPdJqFmwcc9ahbtP3ulUVji8Ox9oiAYi4qwczitTVgtM9B+kTgwA+WhwxKq1IOVnUzz0i+RmW3SauiwCMJCSHd4u/anpcVUTtGcnsTRc1+cHLeERT7dwfxJRzB/0hE113hBEdfOsH73I+wa3MCBU09isNSNa2eQ0uK8gy8FQEbWAZNQ8N9okO8DETW7rxH8nbL2c28lHixS2JAe3StYM0GqCjgv+V1p4yu3JOX7NuAsnVbZWe/to9TRTs1XqMFBRMrsKkTWZeBrtxGs3U3rV89D2PYwwNmYL4Q256tdQ5HJnD20tLiEm/tQfUWAxK2gtU6SJLqWzXlzlwKVxIm+CpHCpLUQGFr/lGVz4fzD0FrzwtkHkXfN4j6aH7z6u1YReNC1ILI8Bet2U777Wcq3rcN/eDOqt1SZYF0rWdhoy8TEJ9E/Kp9RW+M/RJUigxAVi4mksrP2FWrXEGprP/6K7ZT+/hQ4EnteO6mT5uGeMI/UMbMTLJGOXFDCsYY/v3gc7KWo0MeyXR75y+U0T17E4uNfTxiUkZZNUB5ky6o7CKNQ9b0CFO1jabQI1lhWXItw9xCFXz3I0K8fRnUXzDNVqpK0L3o/cCSqu0j6hQcYltYoZFc4FioIwHbZ2vc0c9sPA8Cx0uRSrdjSSbCLQghK/iC7BjYkddoxsI6dg8/SlGon49aCLP8dREfzgQ4V9sxW2q94NT1v/19K/1yLbDFgV2yZBBTk3nAU+Q+eYiKI/BBs2TCZ494uyg3dRUEIljS5jF54AIXfPkLx948jO3O0//QVRlENahWWPVIQqqzsSAfl92NzKFMv7ENUJezUWqG9HoKhZ/B234O38xZ67389A6s+S/7gT5OZZYghY0vLXm9MqmQc0UMSpUNC5eOHZbJuC/2lLgqeySY6WO4hbedQOsS1M4nloGtwI9NbDuDkha+ha3AjaaeJUIW4tkEYx3Tbqr8UETb9/0txec4kArXG5mLhWAz+4G68BzebEEgvCkuWAl3yafn8uVgd2WTBElJSvmM9xT89gcg4ZF93JM7iTlTBR2RdCr96iPyHTzM7t31QV2FJKAVYc1rJvOQQin9cQbihB1IWsj1D8Y+Po0s+bf99oQGmjVthARwL1TdEy5fPwzlkKnqgPHFMi8ZYI8oB1gwDtKbKFRLXJWM7vPaAY2pYbWNK/nhyixP8vX6JyfPyikVHJr/LBi957W4biLL14lroUkDp5jUU/7wSf8V2wg3dRrHIOOZfrHBEVOSGdbNOMRhV36vqIx39T9ccMBYaJ8K7iKiSShOs68Z/fBviVw9hL+kkddxcsq9chr2gA6zIkicqeIE9WQQa42MMbqs00MX6h/+XA054U801Qtqkm5pGJU58rqWhu09pCEy+G6QJ/S/+/jGG/uchgjW7kPl0LfZCkLBD60BBf4ncJcfQ/IkzzSIdVPAX8TycsnOY6ENQKLyggBeW8EPDiK1USFOqjWe6HuSHd74DtGZz72o+eMbvkMLiigc+Gt1aRFFjz3+ptmjEPFBtP3o5Pe+9htL1T5lw494S7hHTaXrfSaRPM7jMPcWvjLdOcbmxaK2TMGbth1iTcuTfcyLZly41rtoqJXRi9amzDEXrgPmbBI9mKhEATk29ZKoDN9WB2340LHof3u77GFz9BbrvfBXp6T+l5YjvRpgWH8S+I1gc0z0Uo8ilZSWN1Gi80OwyHStVgxq3Iv/Vkikn8LeV3+Xn97wfPyxjS5eiP8Ds9kN40dIPEAY+tuOy++IrCZ7ciWhKQTAGr8f/yfhF64gy28ea2crkW96RLM7Bhh6aP36Gib4pG/ClbErR+4nrUbsGsTqyUA4QGQfv0S0MXfkQLZ88i3DHIIPfuoOWz52NaImUz47sMEvMXlQ68RHL9iz595xI7nVHUfj9owx+727jj5+ap3TdKvraMrR+6byI1n84T0Wj/hCRBcc9ZjbOwkn7psZ1yemg8t74KsStzjwcSb0p2AsDHGklkTCNFJUahSXe5bkWaneB0vVPMvTbRwlW7TALUspBdjaZqJ5QJWGwNWxT8aTE+JSChiIqG5t4MjPWgMriCSAciehsAqXwH9+O/9g2Cr991IRqvvxQ3OWzKu2y5ITDNUdSckK/hO1mWXPv/xAGJQ489V0AlQg5rQiD8vPKwlKPF0MI8365Nqq/RPGvqylc8TD+yu0Ix0K258APa9xBQJK0T7Smab78bMMfonQFC5YA7s27u7H7CeZ1LAPMLjrjNNOSmZyUKaXFkNfPws7lvPHYrwPw07sv5Zf3fpCMk4/StoBGIfYkHO9fIDXvVoQhESmLtu+/lN73/xnvnmfJXXYyudcfZfBAgcEo7S+FpVpGwnvhRJAKP8SabjZNus5FNSa+r9IDcUeYtsXBDlGZVMMCjEm1xhKqdfS7DkDauB3H0H7inyht+yu997+OXf84mJblvyA753WN27KHMi5G3Fhi4NXCSUcxrdmkQHdkJVRXa0Phr7RiQedRXHrqLxuWqVDYMXg02o2hDG3x/+ks+0bMwNDJzhowxFKujR7yCHcOVny30UBVvUV0ya9cD5T+9iS5NyzHmt6CNb2F4JT5FP+yitzrDaBUe+EEXpKxRFQ+ojqLjEPTJcfiHjmT3sv+TLi5Dzk1T+GKh7EXddL0lqOTXUb9i95QJOheo3CrIQ+ZMtEdSgX4qkzKqoSoxu0KlY/SIY6VJln5I6WwPqFcYtIVokZhqa9T9fE4yij+HBV4GblTdKAYuuohs9t+apeJ5Mk4xoccGgxQdZdCvVty30h9PZN76gpaSyud5I2R+Wi+8EMKVz1C6c8rSZ2xiKZ3Ho9zcJSbay9DR0O/iLQcbDfLrvX38+jfv8hRL/osTiqHCk1+rujCfb7Y7K0k7ajCi4VdQxSvfYLi1SvwV2xHpGzD1aF1khk4wWtFGwi1u4B79GxaPn82zoFTGrozlAqxpcvq7XfSkunk6Z33cty8l9Oc7uS6Fd/k8S03J1GfSofk3Gae3f0YP7vbYFo27H6cyy+4DYCv32zcAQL572dpqba4RFw1LV8+F7VzEHtue2LhqGbu3Z8KS30dqyV2aZGyE6DunpMBRu+uayObU8ZwEPP0ZByD/bNHcPuLiNJAQBw+qJUPaNLTzqPzhavpuftCum9/PcFhq8gf8gXiuXO/WlqqJba4KK1oznTSnOk0HC5VIYcVy4whPQtUBGaKd3hRGbblognr+uHfYZj/m0q8W4oWjPQLDqD09yeR+bTJKiqNGdCe0YJ72AxzSYQ7UN0Fk6QrepntJZMpXbeqUvY+xSRVKT9VFgHtBbhHzKT9J69g95t+h+oaQrZlGfzGbbiHTzccCcFw7M2It4jcOSJl4VseoQpIOzlSOHjK4Bu01jiWi0Bik44u1cOIEBtiD0axklSfV/3ZsKp11hXhWCAFpdueYfC7d+E9sAlhW8j2bMWiEucpFcMBnM+piEpPJdgBXQmFFkIg2zIQaop/Xkn5zvVkX3UYuUuOwerIVRbZcfjC63+3XZMSY8Ojf+LOK9/FlIUnsPikt5q5ybJRoT+sjOeLJAqLFHgrt1P68ypK160i2NqHkBLZnoFAG4wD1GJXXIke9MCxaHrXCeTffyIi69YuuNrcI06j8viWm3hi662886Qf8/WbX8XDG/9OS6aTJVNO4Oi5L+b2NVcAhocln57Ex8/+CwKJ0ipx9QMcM/clpj5IGmfV+9dIY9dq1bH6v22TQFJmXeTcdmPBcmTSfyYbeK3y91xIo/E/kjt13OKY67NvXE72tUdU1mpI1uskJUt9NGADd2wSJRSWsNJT6Dj5ZrpuOR5V2h6N63CfgDTHrbTUKCQRZb8l7REHg5QWrmycrCxqbs3fopq+/P9k30nkEgEY/NG9+E9sR3bmQArUUNlMen6lz/s+9nd0ySf/kdOxJhsSLZmxK0yZrmV2d/tbqvUX10aXAuzFnbR+/QJ63vJ7QKOLAf2fv5mO377WAOaqlOfxlC2wcISFY6XYOfAsv3vo0w2tg3ev+z1rdz3I64/5CqHyExcoVJSO+J3QmHcELYcpJlpXwqxHquNI7iDhmBwyA1+5lcJvHwWlIuBl1W7bNKqhq+RfKaJ+MiTqi8ilIVsz4IUMfu9uSjc8TfPHTid9polWbBi+WYfFMROiSVtRGurmrivfRVAaYPemR+mYtYwTLv4uluWgVMRn9HyfYpQCaRGu7WLg23eYsPJow5Ewskai45xIgTLRLYdOo/kjp5M6cZ75PaiLbhEVKouewjYe2vQ3XrT0AwC8/PCPc/+z1zKnfSlt2al05GYmqQ0C5eOSYXPPam566qc0pToo+YNIaVH2h5KADOMe+tfJMBBr1feEN6ve/Vi35iQWydD0bZJaQ6mEGiLmp3ku37PRLI17UofkOlsmFpZG0mhOGnX+stIGgGtnaT/5BqTTHN+wprw9lQlZWqoVF8Rwk9R4Oy+at2v+Tr4/DybZ/xckUf60TsKDMxceTPoFixH5FLIlTbipl/7Lb6L9FxeZrKtRenpCbdDzgMi5qP4y0rYMhmLQMwRHz7GItI0uB6SOnUPT+06i/3M3YXU24d2/iaFfPUTTW482u6LxhPvF2J6wxD0bryHntLJy223cuubXLJt5Fq6VpRwMMad9KXM7lvHkjru579lref0xX0HpEIuK0qJ0SNkfIuM28+iWm/j+HZfw/YvWYkuLclDEsVIJ2BEqQMXG1RpuoSE0bgLvwc30feof+I9vRbZmjWsgxjFUe2YmOIHt7SZh3O+roMZKVWN9iZWX9gzhpl563nk1uTctJ/+BU8xzrwcZmgLq7h21Q4X4pX68Qh8AS8+4jFzbTEK/hBWnpnieTzHCNtEimRcfQsvOQfo/fzMisooKGbc/WmyEQPWXkPkUTe88nqZLT0A2pSqWKnu4RSDGseRTHbzxmK8bV5FWzGo7iDntS3l8y82EQ5vxgmICCYg/pzYv5PxD3o+UNlorXCtDf6mLqx/7ImcueSs0SsuyL5TEcZQxkkXOuMIqbOUjYcyEEKgwMJY4IZCWhcRBUOH4Gehaj5NqIp3vTPK0AQlY+nk/uBqKHt6/8SJtyzE5XxpamqVjDBvpyeYOWiHEvgnFnpClpVEF64/9nzwPJdo5WFNMWGLht4/gPbIVYUvCLX30f+4ms1u3Jc0fPcPsLqJIBHtuG6Wb15B7jdlJef9ci7u0kr9ov+9aqy0ukR8399ajKd20Bv+hzch8iqFf3E/mnAOwZrRMiL5aacVguYcg8AiUR0duBoPlHlyrZPiFVBmAjNNMa2ZKXAtzrTIgSSksMm4zu4c2c81jX6bkDXDF/R/hjcd+nZRtLI2hChBCjpqyYpjCEmN6bMnQLx9g4Cu3GoByW7aWNGwCvuyGZuQ6C8hEJWbMjWW8dampR7zhLZsIDgQM/uAe/Ie30PK18w2uoA5TYKw3xuJiJkkzIabznZz93r+htWLlzd/mzivfyclv+CnTDjg1Yb/9dxCjuCiaLjmW4OkuCr9/zET7lYJkZ6yHPAg16dMW0nTZSbiHTgdGxwRVf8YstkqHSGGU7JSdQWtFOSgihAlzrpaMmx8W3tySmUzGiY/VRQ9Vzw/10WbjlXGWUf/++EGIjAgAbVuy4vFttLdnmTGzhULBw7atBHvlOBZCGPdhdSqT2KIS+mVkymbNvb+mY+ZhzDnsRYSBh+1mqtxz415O/71kFMtwI6noBdXW79qcVrDnuKD/R3v5/6RGYqNLxG6aPudA0i84wGjR+RRq2wDW9GZ63nct/optuEfOTBhVMy9fRv9nbzRWlu4iqr9M+pwlZvJM28NMq3sn48B3RGHR+fecQPebfwdpm3BDD0NXPkzzf542vvpEL4hrZznnQBNR8sTWf3Lj6p9w7sHvaXR3vKBQcyRmMN4xsJ4VW27m9rVXcsCU43nLcd/mD498jm/cfDGnLX4jCzuPSrgu6pWGhj52IWoUr77P3sjQT+9H5l2jtHkm5814zbU1fQcV7hUhEVGeoOikuKnj6L+qL7HvOzSU/4kLrO6+DYupn8CkMFEMgGzP4j20me7XXkXrN16Me/Ssmtw6SR8yHEcU+iWkZXPImZchLZe7rnwX537gJrKt09FRuoznsyT9Fj2bls+dTbCpF//ejYj2DHqwjB4o4xw4mdzbjiX7skPNdbF1ZQIZhgFD2w/YkTWlKd3OlOb5OFaaWW0H15wTU/mDwXjZ0qEcDCXko6Laji5ERCIo0HE+qlFYshvKBMqoaa8UuJESUSr52Lbk2XXdBL5ixswWstlGeZIEA7vW0b1lBSDItk6jc+5ydq6/jy0rb+Dw8/8LKR3sVDbpS/MqCVRPAf/JXYiUZaL2nt9DbHSJaB10OcRe2GrgBEE4PnZnrSF2nys/8sjEhVaLiKwxE7e8/J/S8v8zEZakdOtavPs3mURqcf6K5jTBU7sMvgCS0DqrM0fzp8+ieO1KZGua3Jui5HX7BXs0cpnV/lcdKlInzyd97oEU/7QS2ZqhePUKcm9ejjWpqbLzGetuOqTgD5K2ctz37DWknRwPbfwrh886h7I/iBAyybUlqiZtgAc3/oUHnv0zQgqaU5N43dFfZtHkowG47PQreWDDdTyy+e/cv+FabOly5pK3Mqd9abKrrdShscKivZDe//gLxatXGMBqYIC2MQCwuk8at612YUomBydivfVCdMk3SeCI/PgR5iH5bFwwMQmdjvIaCcvsMhP23CBMLEX17qex6lyxuviIfIpw5xDdl/zBZNE9c9Ewxs9GgEDbzaC1JvBLHHTau9m+9i4e/NN/cfIbfha55p6/oJb6hVcHJl9Q2zdfxO5XX0Xw1E7shZPIvuYIshcfZkjFlALFMGWl/nssjXa3WmusyKS/YNKRLJhkOIPOOfjdUeRcKrpG1lD5g2FMf/GhH0qgAzHfC6GCclhJCQHRYhgk0YEjdwQTLiN2A0kp6Osr8Y+/Pcn0GS0cc9xsABzXIt+coqtriD9fvZJ8cwqtNJ6vOemkacyZP5VH/v5lUpk8ufbZrLzlO5z7gZsIvSJeaQAAFXqR4ouhZ/BCRMqifPcGul7yS+yZLYbDaj9E6j1XokONzLkEG7vpuOrNZF95IGBPPLGsNTqM4P8sLf8nY0tkPSn+dTVN7zzemN0LnpkEQk3ukmOQeTM5xZl6tVJYHTma3mIW5ERZiV9KOcoCtw+lZmArDRZkL1pm0sbbknDbAMX/fYKmtx9rQrjr0O61hZkPPyiRy7SwevudbO9fy7tO/im3r72SI2efR8ZtrjONR4tw9HnU7PM5dPoZuHYFbL6u62H++9Y38M2Xr+CYuRdyzNwLASj5Q1UpLqpAuQzf+cZhl73v/xPF61Yh27MJ0HZc/CWRuTtxMYkI+2AQmKi+ErocYLVnsWa3IdsyWHPasOd3YHXmkJ1NyPasYSF2LQOCjkOo/RDVVyTcOYTePUSwqZdg7W7U7iHCXUOo7QMGa9L8/7H33/F2VXX+P/5cu516+73pvZKE0BJ67yBFEEVU7Do4MtYZxzKOdRxxZHRmFHWwl1GaqCggoEgvIQQSEiAJ6fUmt9972i5r/f5Ye+9zzr3nthSFz/f3fojn5pxd1l5777Xe6/V+vV/vRDmUIxUqXHmONEBVkZVBO2clXzsork/PR35L439foR2XGkJalc5R9H2Ehi294B/58/feRO/e9TRMWojv6TCRUiosAPfqsSHPg6WFz8zJ9TR+/RKK92/QGVZt2pkuZ5XVPsZI56l93nKihc4WDYYkXERW2U6pfO30QKy4nX7HcpKvX6JRERWmkApNrjZbw/dhuPf0AI+ho4aKX/zkWfa1D/D8qt0UCx7nXbiAQt4jkbDYvrWbZ5/Zyaw5TSgFu3b1M2dOhplzJmHadSx7/RexnAxdO1frY5pWRW22QTN32CUiYWFOrMNozSJGKab5qjepECkT02tG+lvxUcjOIiJllh3F8BkRRqULYaBkEat+CagAv3ctwsqiZbcrtykhzBR2w5Fx+G089rd3WlTIgFfoAW7wvT7Ye38Qi6qxilxVrfoGZ+2K4UEJESKptThQw690x3FJSpVl2iuPn3Io3LUOc0pDXK1WGAJV8Mi870StrllR3bdSZ4NEWFkkOuRYVk2HyMIhS09YUuGcNBNn2TSd+msbFP+0kczfnRgTj4frxIjM6SQzdOR28LOn/5mrl32eY6ddyI7udfz4yY/z7pO/UYWIRMdKWGnWtz/BM9t+TzbRTF9xP0U/R8ZpYGfPy2ztWstPnvpHpAoARdLKkkk0knf7ueTID1GfbIvRliGIScjR6P3k3dphaUlrZ2GUCV/vWn0P4qwSqVA5V6dx1iVJXrQQ57hpWEdMwF40AXNCtubxxmMy7+Jv2I//0j7ctXtxH92Mv71HtzVh6TCiG8TP1EhIURVCUKGboQJJz8fuovn7b8Q5aeawCqCVfWSEGUNtM5dR3zaHlx65mZOu/k9U4IGdRBgGVjL7N+Hk1XJa4+sefD2h8JmzfDrO8rIQn6gIBQ0+9oFcU/ReRIkWAKawYie41vbRpynCuj36C0BLLBijkPZHI3eOdozKa/V9iWUZrHthL07C5Iv/fiHbtnbz+9+u47wLF2DHacuK6z96KotDXaAX1rTT3dEFQBB49O/fRMOkxfhuPjpJGT1SsvawIiWq4Gn0svgadFoq74MSKFXAUG30rvoIPYmN0AfYEAOVZuggRj6/AmGD3wuT3vAgIGm/6zzMDOUiiwqtkxVAom02E163Wf/j1eq0xINqIKu+F6Gyp65lQnXngZ4QDyYUMZ74aaUptLroOByWGH4vHyKETGsfI1ASQ4gqtr0AfCUJpIyWDUR3XAhd+XYIO3+Y9mMKHV+NBrbws/5TZ+Nv6tR9M9ihieoSVfISDAGpQQTGcHVT9+lzYjRAJKyDQ94l+n4HQ+95vKIO/47q7STOX6CdlrSDv34f3qpdOMumxdLwQ0xojQobWL/7CX707Me54qhPcOy0Cyn5ea48+pPcsvLzfPvhd/PW479Cs5UCRJw9IZVkYv1cjp52PvXJVuoSrfFE7JhJ/vm8X9Od36OhahXg+gX6i50Eyidp1YVdW6NQYDgJ9/3HX8jf9jxGc2ZcDkul0yzCwoeyt4iwTexjppK6fDGJs+dhTmuo4gPExTAreSo1/tQbD/ojAt3SDs4xU3GOmUoaLYbmPb+Lwp1rKT25Fbkvh9GQLNdQYfRrGhIisQyU69P98bto+cVbtZrzoHToWmhLdPR5J7+D+jZdoDJaHUrfo39/LzKIUvgPn/M9XMgm+jsqojqEKB2aCAtgEiowEzlswxxPhcUzD6itY/xuRBOUhS0raC7lyUsMHevHewzbHEJGB9i1q5cTT9IhoZmzmmibkCU3UCKZtJAh76o3FJgE6OstVgw3AiuRCQm5esxzUg3sevFPrLQ+x441dzN1yYXhphXtN0SITJp6jnstOS0SLRlQGW41TRQudsNRGFMdVDoDTgBSIowEQWEn0u3EbliKUr5GWK0MVt1mjMQEZGkfRgoSE08Jiydqb0VYGfzedSECAwfykI7qtBxo3Gnw/vG+ocJeJC0u+0vIngJGIHV8vfIUirCS6QE8ABGKUSqNf18AITAyzphg1jJjXaK6S/E1GEJgCQNXBlTW5tPvoiJlObjSJwgHHwEE4fcZ20FGgxIgEPgqYMArIcfixCl0hkHBw6hPxNeklMKcXI85uX7EfUe65/FkolQZpnYPAczumIi0g8g4iGToJFXOoZVtCdGU5Flz6P/Ph7XuzN5+3NBpiVCLWtcWmSUs3n3if3LExFPxA5eElUaqgGuWf5F1ex6Ow0NeUIz/DqRLY2oijamJ7O3bxJ/X/5C004AvPZQK8KRLwkyjkNhmkrRdz1kL3lHdd4O5GKHDUvjtWnLffVJnCLkH4LCg3xdV0FyV5DnzSF1zDMnz5lcJUalSKCgYVXc+SFNKxSUDMA3M1gzmeQtInrcAb91eCne8QP7ONcieon4Ww5BRPOHojhhy3GrHRWcWBXv66fnk3bT8/K06HXpQ31T2l5Q6fdUUCRac/I64rVG153TDZE6+5vMkMk3hvoevBpEQWicEwDSreUlKaR5G1L7BDojvS8yoKrZZRhwRQyfsyjDj39wORXeOcozq/tLfFfIe9Q1l4TvbNigWfYy434XOHArNsgx8Nz5izFmRvhYhzPe1M3Xx+Sx//ZcwTBuv0BedvNwOX2o5iJyrq6e/VpyWcJ6oQi2lBCOJLHXQeOSXSJ20GFkoYqSSSK8Pw66n74VPk9v4P7Se+zTSzyOMhH4e/TyGnaWw82UE0HrO4+VThcfvff5jFLb9LPxS1mzWSDYmGf/yxahx5KIrsMqecGTd772NYE8/zT9/C2ZrhqYfvAnCrJbq1DYFpqEr8ubdmqjAiBYoMEW5qN1YTHsNenBs76frPbfqFY4sDwhDrjKKO+dcrPltNP3vVcici2lbFH2XfrfE5HQdvgwQCAwhKPge2UwDNzz5W66aexzzWyZTdIsIBAnTYmt/J8/17scxLKQqe8BJy2ZJ8xRSll12dES4YqnVNQKNXjjhyj56kWTYP/E1SKIDCDsUU5LlNNKRiHxxpsvBvKPhfVXdBfwd3RjZhK6dU/FbVX8LUU7jntmMPa8Vb/0+jba81K43tIwhqF54BMyQ6zB38gkgtNqnZTp4QQnbTHDrs1/AC4pce8INALRkpjGpfl547aZOYUaQtLIcO/3iuGCoTms2cYMCWaeJl9sf56v3v40jp5wdCnWFcf/KgTasG+K/0knfV/6sY/lhu8fssETEY1MgewrYiyeS/dBpJF93RNmZjup6GaJMmB18HKXiGPNw5y6fM6yLE26LJbSTDGG4UKCCAHvJJOwlk0i9aSkD33uK4h9e0gqjTqi/UmOyrrSy46I5LkZ9Evep7fTd+BANnz1P85fs6uc0Di0YVoUYWMU7HIbmUvUTWHb5F8rnP0wqpxGSEumERGTRyuseGCiRTNpY1lCHxrLKDmf0fbHoo6QilR70PIV/B3v7tBCdIcY3br4WTAgIJObEOkRqaAaKlLpIaXlzES8AKw9R/kf5T8O0iDgWdlKPQXUtsxDR8SrGyhD21ftlE9iLJmBMyEKY3PCqt2iO7SsiO3KxGnDZBEpqb85IhLXmQqE4YZa5fIaVLu9hVyAoAqSf0w4NoIIcwm4A6cJBLBDGh7QcQC764JWA98Je/K1d2lGBUQvXdX/rcQp3rMGYqNVZR/Ng41WGVBitGSY88sFxtTcyoy0zNuQqfHBVoBApC2tWMx56gbC5sI8fv7iCbyy4AglEvn/Ug7v2J/GXToBkYxwuBNje089ftvfSksziBj6+krQkMty3YzXXTajnorYF+IAT7iMJqlMNq0zrJvhecfDXMbRs2A6GqKx7Y8Qhj1qTV9X30YruQMdFRcxRSV60ENGQ1KS2yaHuwzArxthhsgzs5dPwXtyLSFqUHt+K7C9pQvEwTnw0hvtuCeFEZSXKF7Buz8Ps6d3Im5d9AUNYXHLkh7nkyA/rCcSwY06KGxTY07sRhQy1WHSoL5A+CTvD3r7NNKYy2GZC/6aGahVE1vfVPxPsH9DZIG4wKp+qymGxDY1SugGZdx1P3T+dqdVyFeBJXXl5DAtvjYgOr+8R97tSZVGtWqEOJDLwsOykDu2YBvbiSTT9zxUUzplH39f+gmzvR2QT+lpHQZTi8xsC5foYjUnyP3+W5JlzSJw+p6wYHAqDKRVgORl6dq9jz8ZHWHDquxGEiIZlYxi64qzvFQncAk6qvspBP5SOixAQBBLTNtm9qxelYOq0Bv2daeB5AY5jcd8961mydBJHLJqA70ts24ydmxfXtTN/fiu2Y+KWfJyExZrndzMwUOKc8+bHxwBiJ67rfbfjrdypC5t6/w8VopUKkbIJdvfR8ut3kDx3vlZWjrh2gJOwKBTc8i5S6f6scN48r4wM+17ZEfGKA7qIJtC9+0VefvT7tG96kmRGyxYYpl0mn6rw3VOKxGmzmfj0hw/fdR8GixY7+V+vofu6X2POaIScW7kFCJNi/x3423djZNIoL4+RmIDX/SzCcMhv/RnK7wdhIgwHWdpHet4/IEw9/gjDKfeXjNDzg4Pgxoa0hIOC99I+ej7xe13rBCKMsnoHqRAJXX22/nPn4xw7tZo0V5fAaEjFnqgq+Rrqr5z0DKHl4zMOImFiNKf0YJ4Y3YONfpX7B1CuX4bCx2KhB68rpI4xrFSO38TohcoVIZOEvhLWQFj9tFgEK/Q4/RIkkyTzkqC3AAkIXBfHtJBKcUZ2BkvmNOMGAaYwyPklZtW1sHtvO537uqANgmIJTAthm5hjwWFrEPYqTaMtOoznFvoIvAKp+onDIi2Vn3E/HICp8HlR/SWSFy4keeHC6t9qVFCOLVBggj2vBdmRx5rXQvra4xDZUINhuIk6ilQaNgiDQHoEMsCxktyz7lvYZoJFk07nlme/wNtPuAEvKKGUwg5T+LygRMJKs2rHvXz74Q/yhUt+j+vn4wlfh/8ks1uP4Quv+xMJKxO2fVCtkPC9yN++mtKDrwx1WIbrs8EOS8HHqE9Q/+WLSF2ySHdNoYAyFRgGuFH/CVSVJ6cwTDvOpAHI9+4hmWnGtJPDTuJCCAp97VhOJl6NRt8rGWAYZrkmmWWQ79uDbddhO1lSVxypi19+6m5Kj2zBaEqFocWRHYbYMYpWgl5A/389inPCDP2+ymCIMFjf/k08+rPrWXxW9cIlQoksO4kVquRGZQAOteMSh3dsk43rO5BKhU6LwjTL12WaBq1hNkw1siJ4/JEtzJjZiO2Y8TBpGGUuXC3nXCQsRNrWNcRC5dz/Jyx0WkTaHrKgiS6xuTnFxg0dLDhiAqWST1dXnsbGFK4bxBL+leGjbJ1NX49+t4++6JPUtWrxzFPe+i3yvXuZe8JbqG+dDUDgu/ilHMAQR/41pc8SJl6IjBOX0xi68NRfdD/2JkobwahDl5UKSbfCgo4H3qkXuBIMRxNxJzcei2E1hM9lxWI3Fh08OORvbLBJeHNkb4HSI5sxmtPIfQO1V8GBQqRtgs4eMu8/EY6dGpJBw9/9QIsgRe22jTL0VmEi5JM03ngZ3HjZuC5KuQF7F35NOyshWWvM+0bbjjLJ1zQxaF/bQFkV30VZLeHkFpiC+kwGBNgJm2IQkLQs/mv1gzy0az0nTJxNdymPbWg1Vc9QnDpDV9d2HAMMk/3bVtK7dz2Wk0apABn4KBVgWuVJJ/BLOjRlOiH8H646TQuvlGPSvFOpa52NV+zHTtax+o9fY9vzv+ONX1yL9F1M+zDK9kcvi6knYVXwtO6H0GGMESeP8Lk0JzfgnDqLxq9fir14YlU4MtxQ8ygiUl+M7uqXyDRsTMPmT+t/yF82/JSPnPVzWrMzuOH+K7h91b/xpuM+O6jJ+nlNWhmmN81m+YxLR73MylolZYfDRPYWyf1whX42KrJrxoawmKiCizmtkaZvXYG9dLJGXAyFmapd96uWRUqxvpvntn+Zx/kf/C3TllwQ3/soxBQELirwsJN1/P4/zmDeiW/luMs+TynfjeVkUEpi2Un69m9i74ZHmH/KOwHBHZ9bwilv+RbzTnwbfm4Aa3ojzT96M73/ci/529fobLXQcRm5D0N+iycR2QTuyh0U7lxL6i3HIAyT7j3r2LvhUUwrgZXI0L7pSepaprLh8R8jDAuvNMDURefSMHEBMvDY9fKfcAs9TFt8IU66IZZlP5RIS0UEgWTSohRyvzwvwDQFrhtg2yaGIXjgjxuYOq2eo4+dQkNjKkYEkykbz6v2TKRS2CEZ3rJrj8PKl4iokOb/Q05LXBx0UNaiEc5Hi5ZM5Nv/9ThOwmLr5q7YCSwVNRpqWQZPP7mdYkGntax+fnecSdQ4eU681mmdcdyQ00+afzrpxinh+QZVZbdeY30c8dmGnR/19/XH3IDXtF0jLYFW91bSBeUjzDQRIoOwkYWd2I3H4HWvCncfP2dl1GaPbbNwlZvUnrtz/HQavnCBJh7ZxlAuiqGZ/vYRuu5AVA9GH0RUe6PDjFPRKnTg+09TvH89Rkum/JAOuwRF3wAv0GROJ1JsHcfKKdz+wOLA0WwYflbwiiuHFSPqTxTrOndhC4OWZAZfBmBadBUGuGbecq5ZcDz7C/0kQha7LwMSpoWnFNIrYSVs1j7wTVb94ZfUtYJXACcFhmVTynmasC0hkbGQgY8XkbjR39tJ6NsHl/zT91h0xnUEvosNCMMsr1j/Wu+h0pwbkSkrVY666g2dQ3vZVFp//Q5E0o7rD1WaECJcdYarTyPqf0EpKLB214Os2PY7AunxyfN/Q2t2OkopPnXBb/jVys/xPw+9g+Omv46lU86lIdUWh5Is08H1C3z30b/D9QsxkhLVGjING9cvML1pCW845lNxZV0g1pIp/G4t3rp2jOZUXAxvzCGhgoc5s4nmH78Za0YTeBJp+Jimw3N3f4Vtq39Hqm4CfimPk27AMG1KOZ3aadpJCn37OOL093PEGe/XxQQNS/83hMeiUQjLSOqHBv2MOSnNF0ukm6q2b9/0JH/54XuYf8q7dD8bFpVjCBKEY+oFSdoh/9NnMBpSo4bF4hWthrIQlkn+/57DvmQOdn09ezc8yuP/90GyLbPwin1YiSyJTBMrfv1JrESGgc5tnPXeX9AwcQFP3vIRtjz7a0DROnMZZ77nZ6Tq2oYgLofSAqmoD0nxmfA5z1Q87w2NSZpbMliWGXNZurvzbNywn82bOlm2fBq+L0kkIJWyeejPm9iyuYtly6ex+MiJ+IHEitpsiPg/YRwk5+xvaWoQxSC+DjHkmgxDk50bG1NccvkifvGTZ5k2vZFr36mdD8syCHxJti7Bk49t5eUX96GUIp/34mwjNz+A49goBNJ30anOAsPSwfipi84FNOJiWqMnaryqbSxznILM5E/C5NE3rTQ3GNCZzIeBTzXulGeV8xBJG3vJpLFtH62Oxtv40EHx1+2ldP8GSFio3kJIyB1mH03S0ARgx4wlwf9mJsAN9Moq57mIMPxS8F0SoVPw45eeZE79Rv7x2PNpS2mo/dLZS7l57WOs3LcNNxS/cgyT7lKefz3+EmbVt6DCUNOsY68kkWnGTmQxrAT7Nj9Frnsns5ddhVccwEnVs/W535DINDN5wVl4xT6EaYMMMKwEpVwXrTO0+mXsqISETP33X6uzwtNVqNnWDAlF21U8V0Z9CPH7ZbVUKsjJypcEu/rAkwS7e+PaPQiDoj/Aju4XOXXO1Rw19bz4+EIIknaWd5/8DbZ2rmH1rgfiWkSROuhpc6/htLnX4AXVoUQdTpIxahLpvcRS6KGGisy75G9bXSX9PezwV+nAWQaq6GNOrqP5R1djzWjSxfQcC8Ls3USmmWzzTJxkHUZTgvZXHqPQv59Zx16JV+rHdnQowrC0Qxx4RZT0QcnyLY9RH4NC/352vXg/hmlj2kkCr0TH9ufYue5+SvkuUIps80wmzjsF00pQ1zK18qZW/CnBDr+SksYvXQhuQP6XqzAax+64qEAi0jbei3vw/7QN+w1LWXTKdSw68wPV3VZR1C6yXS8+wManfsGFH7qLlunHcucXj+bFB7/Nstd/ESk9TCNx6DgtVW2HJx7fhudLBvpLZDIO/f0lZs9pxvMCjj9pOpMn1yPD9FOA39yxllNOnYVlGry0rp1FISJQKvocc+wULrh4IfmQh2CaWohOgCZz+/q/YdP/XwsmRM205lqmlIozs449birHHqefQT9Ms7dsg/6BEkuPmsz3fvTGmBgd7QuQTJfDnVGWWS0zLeewOrivNpO5Ph1qlGXwQakAISyUP8C+e+ZQf+y3Sc18G0KYCHH41FTGfWRhiji7QeZcnZI85EEKkQp7aPZQbBEkVYPgWbVZcwoSFvWfPofE2XM118SOyssPlzKjdPE1x4w5EeO2AxFMi3YJL0EpRWvoiDQnM/FmjWHtCqXgu2e9hZaQF/C7zau5ZeMzLGqazEmTZofhi+oo4PfXPcaWvg7+6bjzOa5tBrOWvZHZy94YH/u5u/+Nzc/cxrLLvxh/t/PFB2iZdjTHXfa5EZtvmJUv6av7JRyS1SbQReYqn6FwUDKnN9L6u3drh6bkY4Xkb2VIGqw2rjj6E/rfSuL6BXqL+0lYGYQQ9BbamdVyFM3pyazb+zCts99IIF1sM0nRG6C/2IljpaqIvPWJ1liNNbKqdklNKnWf2ob/0j5E2tZQfnRNNa5XP+6RM6Yz3BpuvBRrZnPssCilwsE0YPFZf8/is/4+3n/lbz/Lng0Pc9q1363Zn3aiWrE3+ltJH2Ha9O3byEM/fDup+kkEXgErkWXXuvvZ9vzvsBNZcj27WHzWB5k47xR8N18W5hru/hkCJXW/NPzbRQQ7e3Af36rJuRVZRTX3je65oXlBhfs3kHrDUpShCLyS7h/DCEmTZqh6K7RDYiXYvPI2Zix9HZMXnAXAknM/wuaVt3HURZ/ATmQ5EJXO4UwjJvpZOPKoyZimQTbj0NqWwbYMcjmXKVPq2b2rj1y/C5MhCBS+H3DHrasxTYOLLjmCIJDc+4eX2b2rj3MvmI+UikSYBRaFiSppCSLtYNQnEXUJxGApideChai5cv2QrD36wrdyTJAy4ujp/rQssCwzzioKAolhCHxPYloGgoAnbvk4fmkgRAYjlWVVwUWTmFaSUr6LmUdfztwT3hIjLq9JqzEHD2fCymrErlJ5Ofo0k5jZefgDG6uI/PrHyiwuo2LPA18Njx9pgfhiRcIaVedhuAdNlcIlYclHmaIqPboqUyVQqL4i1rxWnGOm1jzWiOePhKnG6bgobzwozdAMCwDbsHhw53qOap1Ke76fiak6UrZDTynPm+YtwxCCHf3dtCSzBEpy5tQFnDp5Li927eGBHS/hmGXGe5Qd9Ob5y5iUaSATvijSd0FJSvke0g2T8Ar9RA/EQPdOsk3TUDLACyeRfO9enHRjRdPLGRV/dVjlIGxIVtug74AyEpNxSJ43f+j+hoFSOvNHqgDHStE+sJU/rruJgVIXblCkLtHCBYv+DqkkL7c/wcmz34jrF7HNJFs7V/PAy9+nLtmC6xdwrBQ9hXYSVpq/P/1mXL+AbSZRhDVZIic0bF/hrhc1YkB1+4e91jAsJHuLNHzhAhInzqxyWCozewK/hJIBUvpYdor+jq0U+trJde/ESTdimDZ71j+EVxrAsnVcWousVThNSmGEEPjEuafwvptlPEjf/rnFzD3hLRx36b/iuwUsJxXrWtiJ0VV2Y6QszABr+Orr6Lz658iufFXq5XD9gdDcNaM+ifvEVvzNnVhzWrDMMsFSSYmU5crOQukBtXPH88w69sp4u7bZJ7Dmvq9TynVrp0WOvVL46NdJTP6sq0twwkkz2Lali97eog6HAutf3o9lGcycpcNstm2yd08/kybVcc75+rk1TYNLX7+YB+7bgOcFJJIWuQGNsMiozhPEPIXmn7455EiJ2q91NG9UeTpRo0f4/q9xDFOg8i5GQ4r+bz9O3xcfwJzWAHl3zM6XUYGARM7dWefMjedp2zb1ujouwGly0pu/gcDAd3PYyTq2rf49+7euYPnrv4xb6NPPBhLpe8ggTAWOeC1SQuQcRtfzarYw2UU4Vij4OEqDlaRWZoOSHsKwsbJzCQY2Dv09KBLG+FBBQadKK3/0841gB4bhRA/XGNCI4Txko0kTBUWF0mrNQfsgb340AI5Vkj9uS8kf3xwuiIvaWaZ+EeY0tHLdkaezvzCAJQz8sEJqEFdJhVQIz6Mgbdk4ZorH97xC3nc5ZfIiOgr9BEoxNdPIT15+gld693NU6zTcUMWzLJSlw3VWMht7ztmmabpphonlpKu2e61bZeioVnZL5d9KqjDtE4YqaRpYFQjT1IaF/N1p3+GF3Q+yu2c9Fy7WaMWGfU+TdcJJJZwYj5h0KkdMOrWqXVIG/PBJnfpoCJMoDTpyWGICblce74W9FTVVhueyxA6LYyL7iiROn036ncvD76wh/aARlzJ5Ote9i759G8l172T7C3ez6IzrAHj+3q/SvvFx7FQ9gVfETmapyi4SDDl2Faoa/60dHrfUR9JqIfCL4TM4/OhdmTKv3ABreiN1Hz6Nnk/fg0gkIRg+TFSVSWQaqJ4ixRVbyc5p4cU/34Tr9jF10Xm0zToegYEMM5qi96LY31HFw0nVTcAt9CD9KMx3aJ33OFwJ9PYU+OXPn+PMc+biukHMwXjy8W1MmlzHrNnN+L5k4qQskybPZ83zu6lvSDJrdjNSKs6/UFdTLuQ9rZpdfaJyv45TmuJVZ6G4pF4Ujz0rZ7hxIOIIRRbp5jz2yGaOWz6NdNpm5W+/QODldUadlSDXtZ1SrotVv/8ipVwXppNCBj7JbAvHXPzpcnhI6gUQiVeBoN84TNSHYetsAuQBonHheGEkJ+H1PFf5Aygw7Lry+cLsS2GmKev/j9/++k+20g9T6bEtuiKvF4BS2EdP0Qq0gyr0CkMg6hL4mzvxXtiD7C/F6dMjniMsrW1OqsOa2xI/YGNpH4DqK5bh+NEGsTBFQLkBqr+kY/NKkTAt3jx/+bC7mVUvGHhBgGNq0uJ1S05nQdPEqu1zfomcpwdWX0mcsH1esT/MlJhFrnsnQhh07lhNrmcnda1zUNKn2L+P3vYN9Oxdz6R5p5LINI+9T17lVjVh+1K/gCFBRETZYyMIqlV+RmTZ/f3b6M7vibf3AzcujiiVH35KvKBIwkprngWKnkJ77dBCNH+HBFx39W6CXb1aHKumCN6gtho6LCuSNtkPnBxm0QRgVNcwqpzkA6+IaSfZtOKX9Oxdz9zjr2HNfTfSMu0YJsw5kQv/4feaYKgUwrDw3Ry//EQlF6VWO0KoXAb4XiH+3jCtWMsiVT9Jh5VGCLFUpbGHPKTUm46i8Nt1uCu2I7JOHDKjYtKv3L+Sw1Z6+BWy1yxj09O/ZKBvFzvW3INpJznusn9l0vwzkBWFERXV2TQxT2bEu3AQVnEJpmkwb0Erp5w2q2qTfe0DBEF0H6FUCkgmLXbv6mNgwGXW7GaKBQ+pFNlsgrr6BHaYNWRUcMDGwv14tZsQoqIwX/Q59v0HI96R3s2Kp7Yza3YzEyZmQ5Vhgw3rOzjyqCmk04JFZ12PYYBXyulsS8vRCrjFfpxUPYFf0kijqatha4dFfwYdObzVu3UZlNdC6rMINY9aMngv7Klo96CFSbz9MO9y2MeG04Is7q/4Wqs3l9r/RJQtq4IiZno6fm4zht0QbTnupv9VnZb4YbQMuq75P1RRk3plb46Jqz+Bc9RkPeEYZnwtsqcAvqT/hgfp/dQ9FcXwRrBIgKirj7oPnE7Td68CV0KyWiejygYNjLK3OPYXRam4XLrsLWI0pjQcJgRF34sHWAOBaRgMeCWaEmkGvBK+LKMuUZgg75dYtX87cxpa2ZPrI1ABs+pbeaFzFzOyzWFfGqjAQ1gOHdtW8ZsvX0iqXiAMC9Ny+M2Xj8N0UpRyOZLZLB3bV7HpmVvxigGX/OMfmHHUJcjAxTSSw13Va8aqwieWQSWMOZLWSOVn+Xv9uanjWYpef8xvyCQaeWrLr7HNJBcvvj48tiRhpXlp72OYhs2CCSdiGTYZp7H6YJXnCQcEf2MHsjuPOaVeq0yPkDEE6OdrwCVx5hwSp83WE3oIcQ8WdwPikM3+rStYc/9/MmneqZzxrh/x9B3/zJ++90YuuP63tM5cVrVfNaep2nQfSgzTJt+zB6Ukuc5tyMDDTmR4/t4b6Nq5hlTdBHr2vkwi3UTgl6oQn6HHq7gnnq4llX7LMbgrtsdhjciVGNw78cSsAAO8SAnZEMw9/hqWnPthbv/sAu6/6QqOveSzLD3/41o0zDBJZlsp5bvjYxX69+OkGiq4CYdvxlGqHM4Z9EvVI2CG41wqZTNhog63pSsyjZYcOSk8nooLAR5qjZm/qYlBf4zhcgY7K9F3hiHI511WPrODOfNawm3075lMIlR9TVLKdRJ4OSbMPrH6wA2T2LPhEZqnLiXb3FT9WzivuE9vp+PyH2NOb0DlXyMy/oJQFNUOSfB+GLJVVdsAWkDOdqqjK8JABXmE0wyGrbcJTUkXmYf9959fLpgYHU9BYuLs+Bjjtb8+0iIAqUi9fokm1Bph/DIMFxEolNCMd+GANbeVxOmzMVoyelIawykieFR257GP1StHFci4kFWtF7sMZOubErT3h47MWE6IhqnzLnL/AMxsCmXwK8I/FZYNdU+WNE8h64SQWejQACxtmcZtG1fyVPsWCp6OnaZsh+39XVwwfTGATm0Mb3iqfiILT7ucRKYZr9iPaSfIde8k37uHOctOwSv2IwwT006xfc3vMcO01bEQsF4LFumICMPA39JF0N6PMA1ExtG6LYPQu0qrHOh86WGbSVbvfIAFE09i7a4HWbfnYY6ccja5Ujfz2k7g7AXvwpcupmFT8PrIJprZuO9pElaaBRNOJO/1UvLL1WGjc8TPWghR+690xFWYQQw7yZRXzdopS77+yEG/1x6sLSdF1661PPLT95NIN3LSm78JwPFXfoVSrot7v3khx7/hq8w94RpMO4kIV48wvK8uAx/Tcmjf/AResY/2zU/SvXsdLdOPYaBzK337NuLmuyn07dVkxjGs9uP3LuwX5/Q5mPNaCDZ3xQTlkQ+gU59lbwHaQVoSv5Qn0ziFdNNUZh79ep6/56sALD3/4wA0Tz2a7t0vxofo2L6KbPMMnFRj2I+HD300TMHuXb28/NI+SkWfIJA0NaXY9EonRyyeUHFZuu9KJZ8VT21HCEF3d76qUjEKlh49KU6RHinb7rVr40OMBo/tbknzf555egcTJ9bR2ppByjI5OggU0tfI6f4tK3DznUyYfSJecQA7mY3Ryl0vPYAQgknzT8d383G4Pfapkhbm1HrMiXWowmuoyrMAfBUXB65G6EQ5/JMJkZFBr4YIQ+VmYiLS7Yy/N5KTSE47CjM9AyVLRN6KMBPIUhdWNuIYVjuYY3G6D8xpEYM+R9s8gl9VyPgPJI03XaljaRWm1VHDGH1aryqy159K9vpThxxzvGaMov9RXgnrD7m3f8wZRHFsPucRtA+EB6g+V+W2tqEJttcfdVb8nSF03SGpJFfPX8bV85cNez4ZHiPat3HyEVzwD7+r2uaFB77JSw99hzPf/ZOq7398fQY1bDyx8oa+xuDlUgApg/7/eZTczU8j0jbOcdNou+/9cUimlpUh5ADLsFnf/iTP77qPd554I5Pr5/Ob1Tdw5JSzEULQmJ5Eys5S9AawzQTZhEa9JtTNJhHW35hUP4/XH/VPADFXpgoJMbW+SrCjB2FbowrKxd/7AUZrBufEGWHDh15DtK3vFti66k5W/eGLBF6Rs9//KxomLohJtGe88wesbJzC47+8nh0v3MMZ7/4xiYicLYavIR5N5tue+x2mncJJNbLm/hs5+72/4LRrvxdvt/PFB/jz995Udo6HPV4ZfRJhpojZksY5Zgr59fsRwhmd1KgAy0DmirBzAGGb8btX6G1n0ZkfoHnaUh792d/RMGkhM5ZewsxjLueRn7yLfVtW0DrjWNb9+b+Zdewb4hDAcOjQAZsi5uek0zYnnjyT7Vu7MUyBUtDZmWfJkZOYHBYxNYxyOYWjj53Cc6t2sXVzV5UMfRS1jjJj/v9W7bB4XoCSikTSYu/efp55egfvu+5EgkDi+xIZ6LpNsmJB46QaCMIFRxC4mIGH7+Yx7SR2Ihunzsey9JUWSF2otKj/Kzstw4RbXi0WvV9GOQuzzFn1EGYaSTt9T34Bs74ZFbiUPTVDb+M04nY8ipHQKJZSAYm2M2m7YPXIpw7D0iqsPThY/Xw4G3/KM5Qnc19qBdnRbkoNJr7sLmBkE9orNQ0Iq5j6W7tQObe8j1+p6DhOZmw4SWAZcfjHmt9as3PKRMPwtNu6tRMVfTfiwKlDXqozR7CjJ7zA2uTK6MUyhMCXAYYwMCo8XEMY+FIilaqqIakBKl0IzBSDi6ppRrswTLzSAIl0I4FXiJEUt9CH5aTxSgNhe8JJTgY61h8SUmXgASkiSf/4xK8FC9tp1CUxJ2Y15Bmhd6NcQ0TIBdja+TyXHvlRABZMOJHT5lzDju4XSTsNuL7mcFhmgs0dq9g/sJ2JdXNYs+tPJKw0DamJdOf3kE00sa3rBY6cfJaua1RJYhUCVfQI9g3o53KELBndOMASqAEPZ/k0zKiERq1CgxHHqTTACw98A9NKcNZ7f8GE2SfE4SKlJEoplr/+SzRPPRKvqJ+XcogtoBa7I5rM2zc9wZZVv2b+ye9g3olv4d7/upiX53+fI854P/nevaQbJlHs3x8qy46eOlzV/vCa7KOnIu5cGzt0o+1vGAIKHqq9DyORiMOshmmR79nN/JPezo4X7mHFHZ9k4pyTmXXsFWxfcxX3f/tyDNOmrnU2S875kN6nosDioUQsyrxkMYTPMvh6hNCpulIqJk6q46LXHTHisYfjZ420zWvOxjD0V15jhEht3dLFT3+4kksuX0RjOB5U6rMkEmYcrgsCN+ZlJTN6QZIIP+1ENq5JVHu+E2VBP6H/i/vbMocWBH41myH1gspSCDuDX3qZ7ie+h9WCDvWARlwkOhFIaf/FahYoAoQw9cJYenq88wLNlzEERjKFzOd1/zgplNBhbmDMujfjT3mWKuaVVGb+jGW/KoJtRIxMWPEqC9Og+7o7KD64CbMlrTN4oroZUfx6PBYNFOG+wjGZtOGTiKiYXI3VajRwyl291YcaFb4HhMDfFsbKh+HeVL5Y1jBF56xRyLGDtxfCiKX2o1oqlSqkpp3EMK145Ru1zLRTVddlJzXT27ScGBYcO69njNsdZlNegAqrrKpQMn2ktg12/qJsoej7U+e+GYC1ux+K9zGEwc6el2jv28ye3g1MbpiHAp7feR+VZN4jJp4aoi3hwxZxCksBsruAsMY4kIUK09asZkRSpyiKEN6ueg4MAyl9UnVtnPmen2InMtS1zkYGPpYTZutpmUqk9Jmz/Opw37JzYSUysQheWW9IYloJ3GI/q+76Ak66kYWnvZe2Wcs58tyP8NTtH8dJ1TPneN1XplVxzaNY9UQaOi0L28oLjdFMoe91KUD2uvh+Pq4NU+jvjCea4y79PHf/59m88Kf/Zvnrv8gpb/kW05ZchJvvZuYxV5CqnxBnGR2uiT26Gt8NhlQdRug03MrxxDC03ojnyWF70nbMmg6LUgrP03w60zSxrFFKYoyl/Qfh+By00zSGXSqPf/+969m7t59tW7o578L5LD9hOgCP/GUT7e0D2I4unbBjew/JpOZX2IkM6/783+R791Lo34dpJZB+iWS2la3P/44T33RjdKJxtUUVfT1xv1Z8xUAhAoXqD1CFXuzE0bSe9xWM+ibwXc1l8fQYJOodhGWjvAIqJ8FTYckeE2GauiZRBXBZvG8jyQsr5ScEpSe2YrRlsee3Vo1tw9n4kZaEBW5AsKcvzuQZdnAR+v/MyXVaYr1yuyp2uIgfBPu4aWAYGPUJrZUilSbnHmzRL4UuOTCC/kJE5Az29RN05vX2o6z2Ir6Bzuyw8F/pRA6UNIpUg0tRK+483N/D2cgv//D7h2eO70OuawdBUIqhaN/Nk22ZSXGgs4L3MmpzxrfdYTYhKlc7Y9yesvMXhFkvkRR/yc+TsNKknXqa0lrL2gtKnDHvbWNqTyWKU6nQq/KeRvwGp60OPQAYep+4UGmghry5MYJn6OKBzVOPjO9z7971PPj9a7j0Ew/rlWO4nUbWQkcmDFENdHTEWUGVDpVX7OeZX3+KXS8/yElXf5O2WcsJfJdlV3yZfO8eHv7xu9iz4RFOfdtN5XaP16J1Q1smTAUftCio3UH6XktFMJCjaeoS6sRMAOadeA0NE/UA2Tj5CGYcdRl7NzyEDDxMO8Wc5W8qH0XKKoflcDkugrI2SM2rGbQgMQyDxCiptMMhLY5TDolLKau0S8Zjw2WnRb9Fvw/mVgkhhox3tUjjh9JCsJF1a9vZvauXTMZhztyW+PeNGzrYuLGDTMbB8wIEAiepHfrpR15EXcsMAt8d1J+SqUsuoGnyEmBkpdxyO1RcJ8ucXIfRmilnw71aVnjDmJIKkbQx27KIBgNBI3VHf6bmtsF6X0dLEjbWAgtMtPaSacSf3isdMd2i/1uPYLSlkf1FzOY09tLJFP+0EWf5NOz5rTqUf2iclvChdH2QCve53ew9+hsjMqRV6CSovEfLne8kedbcEUukR2nMjV993diadBAWP1CDX5xAZy75W7qRHTmtdTAKr0UIrcFBoG+0v34fsiMf5r6HHJ7D8IJWvVRS6qKIhonv5jDtRDghhZkqobCc7+Z1ep5fBGDFr/+ZDU/eSrqhWf8mBIZh4XtFJsw5KTzPGEmJB/seikGfB3qA8flx5U3C+2MOilcnrDQKxZzW45jTehwKnTHkBkWqKyYPNcdM1Z4AlQpfzrESPvWkLNKRps/Qzq6asEIV2MB3sZwUXmmAzp1rkTKsOI5EYMSia9F1mJbDiVd/icZJOhwhTCtGH1b/8T9Y++fvcOR5H2TJ2R8MJ1QTYZiceu13AYVb6K043thv5JAQaiYRUwFGPUqYYWQaDsXufZz5/u9DKLF0zvt/pTcJpfyXXvCPugK1aSOljwxLbGjl3OoKz4fbhhsTxruAGbxv5JzkcjkeeughXNdl3rx5LF26lCAIMM2xF4KtRfCOzhEEAYZhYJrmiO0cvG3lcWod/2AtEvL72CfOwPclDz6wke986wk+cP3JTJ3WwHuvq84MuvWXz5PPeziOhQwCWqYfM+yxfbcAoVKuYdojOy9SQcpGtvdQ/y/nknnn8NIXr3ZTMgClybTKDxCJBO7KreR/9XxYQkV7irK7SOqyY0meM09TOiKqxYb9uE9pGQOEoPTQK8h+l+RFCwEwJ2lQQ9voz8LYnJbwoTJSNtbCNozWjJ7gR3rYQlKt7MrHMauRLB4wwjoZquRjpB36bniQ/C3P03TTlSROnY3MuxrtORCZfQGYxvArqaje0SsdyI4cxoSsDjWM0o/acVBa/KungPfiXqxZTUR37fCsKMqrAGEYMfxvWmEMNpmNJ7eIZGlazXoADx2Rura5TJx7HMlsK4FXBASGaeMWeuNS7GOefA72EiOtkognNR6LlFUhVlIdfGxljjHcMIxJpEYohIlC4oikjuuOiS9TY6PxJqgIUVZpHuZ5Guy4RGqdwjBxktkYUYs+y4cO9T5Mm+Mu/dfwWBp5iPRN5ix/E8IwWHr+P8ZOUfRp2UlOf+cPw2conOwO5pkPa0ON5whKEKvYSq+EYWnHXYSOlVKKxkl6kNSoigUh2jTYMT/cnI/K+3QoHCSjhsPV19fHz3/+c/r7+7niiitYunQpvu+P2Wmpha4opQiCAMuyMMIQdqlUolgs4vs+UsoYHbIsi0QiQSqVirf1fR/DMIa091At6irbHAQSIeCCixdS35Dktl+t5sMfPy0kmwp8PyCZtDXaEp7aToxcGT0aY+PzMcocGEWFQ/V3OVDSCSaj6DK9KiwChEwREpDTcahHdhbJ/996su8+A/uospZYsKOPvhv/gjm1AXthm0aUlSL1ukWkXrcIgO4tXdR9/EwA3DW78bd347/SgTW/tXzeUWxUp0UpFaMg9tLJTHzyQ+O9fH2cSL1zmJe0TFoKB5BwAAp29uKu3oHKhVC2ZejBaewLhqFtGcIJEVXn9NbtDXk05baNdrxI6EqYBqWHN4c3qRpGPbTEPhFPHIW+drat/j1WIkPg5klkW+nc/jyGabN55e14pQEMU4cNTDsZp+ste/2XOP7Kr9Q8vgzbbJjWsP1VTQI9yOuJFDDH4ODWtGQ4QVsVBTUjSHocXvyw7av6e+h9HcvKOQaDTBE73qOu7OPnSqAGStFBRzxP5f0JW4kMXHyvgJQ+gVfUYaTK84aDVOAVMKwEdiIToylKKZqnHUXztKPK1xyFUgwzDq3IcHIShlnmRI3BBved7C2USfCjmRDxe1eZIRYEJUwzUbGZdvCpbLsQFWjTX4eoqqDqPIdqsh58HNM0aWtrI5VKkc1qnZcDQW6iY0cIjmVZdHd3s3btWrZs2cK+ffvo7OykUCjEHBrbtkkkEjQ3N9PS0sKsWbNYtGgR06Zphe4I8TnUyFblcx8VTiyVfE46ZSY7dvTw4AOvcP5FCwgCiWNXcsL0fs/d8zVUUNROffjeVbUs1Clyi/1MOeIcpi0+n8AvYg4X7ou6OxL/i4QujYOYvP4GFj9fng8JC+/lPVjT6rCPmojszIFj6Yrz0+txTpiOt2a35qV5gea7lHxEwiL382fx1rXjPrUNa8kkct9fgb1oAt66dlJXLtUnOxROy2BP+4DU/gwRx8xHO9eQ75IWRjpVJrYG4epkLA96yNYfHAqqdZ6IzyL7injP7kJk7FD0a3SHI+6bkBTortyhi0mm7UM6OA02GfiYhkn37he55xvvJ9MIfhgVslNgOQnu/a+r9bguwU5qHpWGOUH6JQLDpNLj0HoxVix7XonoSCWrwicR6dQwrINDWhRaHbY+qVfZB4K0FDzMyfU68yx83vAC5EAJ2VMcprDnAZoQmsOU0UXpxj3R2SZGXZKgK68LkI7YLB0awjYJdvfpryrDSmPiu+rVUqZxqp6oR6gNNHg1CVFIIAAlEYY1lD9hVHN2gKrKyjpcNMxF1nB8g+09OnyWsEZVCwbi/omqfRuJhOYBRRNu4CEDX2sVWQ4y8DFMi779m3n4x+/ivA/cTqp+Ylnu/zCangvD1HTfp6+vD8MwwgJ/47PIQaivr6/JNalEQcbdzhoOS6lU4u677+bpp5+mo6MD13VHRIt2796NEILHH3+cxsZGli5dyutf/3paW1uHOC6HwmqhQ1EW0dnnzOO2X63mtDNmk0prhKWisgMAM4++VA+UQo+tQhhlFE6ECJ1pV1VJH1fb/wphx8Nh8TWG4441t5XCb9cS7O7FnNKgf6tLoAKJ++Q20lcfrb+rqFE48K3HMKc30vjtK+j99L00/+9VNH3rCv17fbK8IBvDazCm8FDVCsQ6sAfsgL3pQGl4La50FcZQx/GwjLSKiv8d8k/8zZ1a+KtCXn2sD6YKZdaDXX24j28hecFCzRq3D09Gggih3rq2OZx27Sexk3UhJG4gfQ8lfUw7RWUcttC3l7q2OQCYVqJqcomvQyle3PsoiyedXg5BCZ1qXfRyMUlVKknSzoR/q3H5LVUTnx+w/8LvH1RfxGYIREZnr3gv76P92G8emuNGJhUinSDY1U32+lNp+PJFUPShRibdkHtegfyI1jTsG0CLbIyCtIShVn9jh1ZcbkiW62mFNY1GMqUCZODz0sPfxU5kwwl8UHwqgrKVwjRt5pxwTYzGaK6TSQRvDkf8LL9LQTk9FLCcdIW2hah12qihAHgvtqM8iUiOBWkJ+ydpI5o0stKz60X273qW+Se9XTsiph0jKlF/gIVfGmDXi4/GYa2/1qQShVg2b97MF77wBRoaGuLvxmKRs9Lf38/SpUv5+Mc/ju/72Hb1M6jvmzHucady8o8cls7OTm666SY2bdqEZVkEQUBDQwOTJ0+moaGBVCoVnz8IAgqFAv39/ezdu5eenh76+/t57LHHWLt2Lddddx1HHHHEIXdcaiHAOgNL0dqWYfqMBjo6ckyf0YgMdJaLDMoL4K5d60C6CNPCslMEgYcKnZcgcHU2UeDipJuYsPREzQ2rpdny/6gJ00D5EnNSHemrjqLnH3+Pc/JMdO2DgNJDm7Q+1hlzNOk4YSH7ivR+/j7s+W0kL1+MMAzq//U8er9wP9m/PwVn2TT89fswW6Mq86O/g2Pu8YN5qKocFqnGx0cRGlZTA9qrl915XSdh1GNo2BjHxEg7NV8MFU20IgrgQenBV8akD1HTFGAbqO48pYc2k7xgYbk1hxh61tC9hVKSupaZnHDVDeM9ABLF9x+9DhDYpkPe7WNKwwKuOvYz3L32v1k86XQC6WEaNvsHtvPinoc5c/7bqw7z55d/yPLZl9FgTah9nhHaX9Un2cTB82KAOMUd9PEOuL8VuqhX9GwYCGHqQSwJoj6ln0P0ORRRzaPonCF3SnogTCozdETaxppcj7dmD0LYMZl0uGdDBXoA8F/pwN/WrctdKD3dj+W5MgyLdOMU1tz3dfxSLgzfRO6CirtNCJ0y7STrmL3sjTDMgDwYsRwSmrCTpOrKse5c9644DXmwY1DLqXOf21Uu4DYKkiSE5jMZmQS0aaRl94a/8MRtH2b+SW/HMEw2r7yNjU/8jMbJizjqon8mmdXxc2FaJLPZvxlcHzkVlmVRKBQYGBgY0/gQOS19fX3kcrnD0q7oPIZh4Lou3/3ud9m0aROpVIpkMsm5557L0qVLaW1tjcNPg61QKNDV1cXGjRt54IEH2L9/PwMDA9x000185jOfYfLkyeMmB4+1/dXIuv7+gosXxkUTzXDxnUxZoQMvsRNpAk+LzK39838zad5pNE5ehJvv0WrjpQE9bvqlGK1TjIJiVT6/hzHs+FczU+tMOSfNRPzkGexFE3UmZEMCc0YT3vO79Dqi4CHqEmAZZD94qs4MAmR/CXtBG5n3nRj7J9mPnVEeF8ZAD/iru4kiYWl9l9HuX5QeWvAQ9Ql6P3svPZ/4/dhufFR7aE8/mXctp/Frl0DJjyuHxm0ZNGAqBaVHNg9q8DgetDAkIeoTFB/eRDaEz6LUr0POaVE6nTbKFKlSMxyufUoP1qZpM1DspuTn+dBZP6Gv2EHSyuCYOjwQpfdW2jPb7iJpZyl6eqBMWCme3XEPx8+5orJh42x/SMbb0xc6t6NdxKhHHvTv8R5Ln18IE2GmwjaCCoooWUIIByNbR7C9E9mj0QSlJAaWXnFUnlkFCKOcoSMifQPbxJzdrBGaRgFeRVr04NZEoUehdWdK960Pa3QpnV44gsMSoQstM47jmn/fVBNVG8nGy/MwQiXZGUdfzvSjLo2/X3zW39M4OcxIqgobVfCjQn0Gb91e/LV7NVrmj6HyrCGgFCCyDvZcPTA6mUbSDfr5ffmxH/LMnZ9i+pKL2LH2Hjq2r+L8D/4GJ1UPSmlE6G8E20fXXywWmTt3LrNmzaJUKsXE1ZH2M02TYrHI9Olaf2S0fQ6kXRH689BDD7Fp0yaSySRtbW1cf/31TJpUrhYfZQhVIjSGYZBKpZg6dSpTp07lxBNP5Oabb2bdunUMDAxw99138773ve+whM2jNlRei1KKRKIsHBjJ+F919dEo6QMG04+8kHzvXrLN09m57j7mnvAW6sKEhF0vPkAp382CU9419kZEw1lIoI/0ow4oieTVYBEnJ5xHjYYUyfMXxD/L/iLemt36tzo9FhhpByN0WAp3v4j75DZA6ISYpEXhjtUkLjqC5Flzx6TRAgfhtAw3oA0HHUe/BR25aon84e5fROpLad6AyDj6QsfAd4jyzI2sUxaxqzH6xW0MJ5LS09vxN3VppyqquDq27tDbCqELUDkWwfYein9+hczbl1UNioeSKV/mFJg1uQjDmQxl/IWA+mQbhjBpTE3EC0r0FvfjyxJFbyC6KgA8v8BAqYu9fZtx/QJCgGUkGCh14QUFMOL4wvjbbxk0/vjNusq3LwepR4621Kb6Garps4zxGJpwoJ2LoEBQ2AXCAiSG3YCZnoXb+RRu16M0z/4MYqLmBhkJh6C0D+UNgDAQhoNhNyCsDLlXvkNi4nlYdQv0qiwEY6z5rWBHpOHhHYMYpVSaEF/443oy7zsBoyk95N0aNvQJEDq3YzYxQpbdGCzihiilOPVt39F/S4lpOVWTW2zhZRbveZmgfUDrWpRGz9xDgPIl1tSGGGnx3IGYx/Lyw99j4anv4YSrvkbH9ue471uXsnnlbRxx+vvwigPjduQOpUWISS6X47TTTuPss88+oONIKQ8pWjH4fq9Zsyb+/s1vfjOTJk2iVCrF4aAoI2iwRXwX3/dJpVK8/e1v59///d/p7u7mpZdeolgskkweACdsnNcymPBbHdLUSriGabH+sR8S+C5HnvthFp/9D6TqJ9LfuZWXH76ZUr6bI8/7CIHv6oy5sYy3UegymsAbXvvFaWVvEdlTQKRtgu09eBt1ZWejLoH38n6C3X2oko+/vQdrZiMIgbBN3FU7cR/fSv3nzq9IjACVd+n9/P0YDUmcY6ei3EDzD0ewA3ZaBj8M0ncxLFsP+IMcFhmEyoyWQ+qyxcj9ubKabiWnUAZlGXEDRGBQf8NFNHz1dRzQ6jtM2QIgUd0RtTJfSn95Rac6T8zq+hHjeJHKK2JiDkLhttWk33yMnpwqtjsUNhw0P9RpLKudlq85xisp+Tmk0unCj2+6led33U820UR/qQvQDo6JxZTGhSyZfCa2mcAK0QOB4Kip59GSmkaMkh7I9QlB6uKRpcr/2hawjeK6H2Ek2pBuJ8rro+noPyBKLcjtOZz5LUCgw0WmRWHbL3D3PYiZmYPb8RjpOX9HZu4HcPf/BbsprCOlJIQFNO1FEzEnNyB7CwjLqJ2qTfm5Ur5EpG38jfvJ37GG7PtPAjfQZNVRFhDlgx2+QoCDrXKC8N0ChmVXcWQq26cCLeXt7+whf8caPciHCqLl8G3t44Pexl42FVvoiSSRbsJO1NG543mK/fuZf/I7kTKgdcaxTJh1Ans2PMQRp7+PdOPkmHj5t7IoBBOFeXK5HIlEYszEXE02tQ/LxB+lJkeZQS0tLcycOTNELUavzxTxnBzHwfd9mpubmThxInv37sXzPPr7+w+r01LLSYm+rzxfND5mm2ew+dnb2b7mDxT62hno3ontpJlz/NVD9FtGbbMQoeCojbdmD6W525Cd+QOX6/hbmqkTHVKXLqb05FYKv12L2ZLBmJAl94MV5e2UQmQc+r70ALIrT+YDJ+McPUX/Zhg6cl8Kqp0WX+r50SoTnkezcTktwxHvhBCxjHz07+gzYlyDXmk1fPmi6uOFL4YMiU5DVj5mWLzKHF2FcKztrnqAA4lwTILOHMX71iPqEyOK4I1k8QQTquN6L7ZTvPslUlceGXuQNVeZB2E1J4Gqz1B1NbxfSkpk4GLYSZSSZBKNMbH2rAXv4KwF7wDg+4//A37gYpsJXtj9II9vupV5bceTd3tjmXeJIpNo5DuPvZ9zZ7yThTNOww/cUYmhtSzozGM0VmQP/c3eawFIDGMmjUtuAUDm++hf+1n9d2eA7JIgQZZ6MVLNKBWQXfBxWKCrCOe3/1LX3QCE3RiHiHRoTk/S9uKJmDMbCZ7pRySSEATD8lqEiAQMJSJlk/vhCpIXHoE1o7GsPlnj2T4UPKrx7Duc4xStSocn7upQ18B/PUqwt18Tjd2gnPU36DxVDosw8P0SdRccy46N9/HyH79HsdBBsq6NfO9ufDdPXetsjTIZJummKWxd9Rse+8UHyPXsxnIyqGD82TWH2iISruM4YybkRnY4J/1IFM40TUqlEv39/dTV1eF53hDi73AWict5nsfAwACWpTPQksmwrMhhDhEN9/eQZxVQgY+SAYWBDgI3j3QL5Lp3xk7LWPtahGVEjMYU+VueJ/ejFQc3rh1s1PwgjiFMgSoFTHz+Y6QuOoLURWNfYCqlOazOMVOQ+/rp/9qD2oHxAs0JDBTJc+bjLJ0cL15Gs3G9HVXoiQxQgYcwLHyvwL5NT9I2azlOqkFnsJi25kEaJp07ViOlR9vM5chCKBufiGpmhJkpVoLiQCcDndvw3RwgsJNZ6tvmYifrQrlzMSZPbKR2D7EIZXnwFfzNXUMGzAM6V6SEG0jytzxH8tJF4Jg1HYyDsTj27LuxOulghKWU7yGRbozvybbVd/HELR/mrV/bjmnYbNr/LCu3/Z5SUMALStQnWzl66vl4QTGuUjy/7UTqk208u/1ubDOBVFqgLlA+abOeK4/5FI1KS2XrlOgD6LekFerv/PXQgOEtdMq8bgy7icKen2O3aQlvbA8jXQcGGKlm7YgIEyVdUAFK+nj7HyV7xKfCYwmGVNUOJ+nEmXNxV+7UIbURHoUyIqERvGCPfvmbbnpD3NXDOeWHa0IYzkYKUdVqo3J9hGNR+MOLFH63DqMuEddpGe0diSpm24smwqJ6ghfzuMUefK+AaSWRvqurdzupOEPIMG1k4FHK9+CXBv6mKEulRWPNgY45h7otleGUmTNn8sorr1AsFvn973/Pddddh23bMRo0HCoUOaZR6Oree+9l9+7d2LbN5MmTqaurO6DrPVRWHot1+4t97UxecCYzj3k9M3k9AP0dW9j49C/YvuYPLL3gn2iYMC8u7DeSxX0opU7Fb06P+p6/Kk2F71nJj8M2Mu9q7sloCSuxppqeE5MXLCRx9jzk/pzuQyEwJmR1oo1U8eILRn6mDxBp0WJSEeteFV1+99ULeduNL5HINGOG33vFAexklpW/+1fcXDeXffJRpCWxQlQmmlh9N8+a+/+TravupNDXrqsRI7BT9aQbp7DglHey6MwPHPDKcSgcWAmZA0qRv3V1WO9EjWnAHOlcCLSXXZ+k9PR2Cne/RPqKI0Mo7NCm+EVhN33ucigo8F0sO8nW5+6kZ89LnPjGrwMgAxcvlFvPOA2cf8T72T+wHct08IIiGaehSotFqoCknWFm81LWtz9BwevHECZ+4JJJNHL/S//LSXPfRIr6OAPlgIahEDKtVa/pcKFTte5D2UEoYdhNKD+P1/UMdUf+GwCG3URp9z30FDtwWk8jPfs9OkNIBQgzRXHXLzEzszAzM3Wbazlx4fWlXncEuZufqlLIHI6bolHJcPXWkKTwhxexFrZR9+HT44l/OMfl1WA1EUE3QDgW3svt9H3hfu2wjvVeK8AykN156s4/CWyYtfgKZh19JZtW3MKzv/8CTroJw7Ao5XvjMaeU62LSgjM49+9uob9zG7d/dsH/Z9JWh+NMDefoRjyVc889lyeffBLP81i1ahX/9V//xRVXXMGsWbOqthvO9u/fz7333svjjz8ec3jOP/984NDzccZig98NMySQT154Nl5RayF5pQFMO0Vd62yOu+Rf2fLsr9my8naOed2nQ5kBbVU9KlUI1+j/YkfU9aHEYTUhdLheoTjUfqAyBIQlfABEyh732KLrMIVJCFPqq48ffj/WMWv8SEvoZea6d9LfsRUnVU+ueyf1bS107ngeJSWlXCfZ5hmkG3U8K5lpKYs2VXZoOMGv+PWn2bTi/zj64k8z/ciLSNVPAiUZ6N7J1lW/5pk7P41fynHUhZ/QKrDm+AaZwXB1fC1hyKZ433q81bu1GJw/vDbLSISuqj6q5LZYBrnvP03qggWIYVKvD8Qq27Bv81NkW2aSbpgc35/IeenZ8xL7Nj1Z0T6jQrNCxFWMK01XKS4rhSJg/8B2tnQ+x4IJJ1H0cmFdHYPjpl+id4qY34fyjVGqSkG06tojfQUxCvoWEXFDfpMwjKGTO+XHUn8XIEI11d7n/oHklDdgprSap5I+VsNR1B35FVSQj08jzBRuxxMU9/yBxuN/jgryCDPNEIcF4mJi1pwWkucvIH/baoymVIzw1bLK50p5AUZdkoFvPY45qZ701Ue/qh2XmmHZ8N0LdvfR86HfaXJfUr9/NeuCVRwLwkGw6GNOrCN5iYarXbcfx66nlO9CBT7phknYqTp69qxj4txTCHyXYn8HzdO1sm++d89flefzt7KozyIZ/wgZiVCQ4bhQhmEQBAGTJk3ine98J//7v/8LwAsvvMDGjRuZMWMGc+bMYeLEidTX1+M4DkKIOAy0f/9+tmzZwtatW+nr68OyLPr7+3nDG97AsmXL/qYOi1IKX5XLDphSUtc6K97GDgUYpQxABsxedpWOLiiJMEy8IEAZYCipJ1EDnRlrm+DI8QtkHqR5IcneCPW0DoUJwBACDANpCFRVOHf466vZx0JghYgKSpWHRUMMCQmNNm6Ne4kR+EUsJ81LD3+Pp+/4CnUtjfhegWS2jUd+8h4M0yHX08vS897H6e/QgmFaOjxyNXVrozz3/s6tbHjix5z9vv9j5tGXVZ0rWddG64xjSWRaeOH+G1ly7ocxrUQVojAeqybfKk3+UYr8Lc9r+Ctlg8/QiWOYyXPU8/kSkXbw1u4l95OVZD94ik5/q2BHH8zEolSAEBZ3/+e5nHDVDSw550MEfgnLSWFaDr5XYM/6h5l7fNkxUZThUADXLxCLVSut/WIZNn7IyYh+8wOXzR2raMvOYqDUjW06BNInZdeRsFKMJlcw/msL+2WQg4irxfrEmIsNDjqu64NhxPvHq8v4vBIhTPzcVgbW/StO29kkp16O9AcwrCzIEkZyEmZqMkoFIQJiU9h+C8Vdd1J/zH8hDBPpBYhqseFqC0NE6TcfTfHel8NaXqOjSnE/hGUxev/lXgDtuHhBVYbdoQxFHogNh3BGDpa/s5fu992Gv6kDkU1U81hGWjRI/e6qXJHEJYuwF08EX2GG5FDLyeAWemiasoS61jmsvu8/uOCDv2Xrc3fSsX0Vx7/hqwDYicyhdbJfZRY9KxFHJpXS3KJKZGQ0ErdpmgRBwPHHH09dXR233XYb27dvJ5fLsXbtWl588cUhCE1lqKtS/r+hoYF3vOMdnHbaaYc8VD4WG3ytdkUNLiXQSSCqunRJFFGQgYdh2nGSoR05W1GKrid1nb2Urase13BaDoTrV+MqhgwpAkGD7WAIgRcE5P0ihhAHLRzhK0Up0Gnatq8QYyAQj9jHEVpcoyfGs9Aat9OiFVbh6Is/zaKzPoidyDLQtZ3f/fsJXPjhu5kw+yRKuU6sRBa30IeTqkfJSgXOeHrQ/x94qMCLdRV8txBuK+LUsnT9JHyvGPJawl0PlNsS7qoCibBMig++QumRzRgNeqVbMzRUMXmCXikj1bAdXYW2hKTc3E+eIXnhQqy5LTF5snLbA3tpwzBD/cS4nlAln2X/lhX07XuFKYvOG7RH+VyONTR1TynFO074GgCm0C/w5IZ5fPycWxgodcWidpqvoSsGV0Kmh8KGrMz9cEIOpd39zV146/fjrd1LsK2boH0A1V/UUKNhINI2RlMac1o91oI27CMnYc1uxmjS/VQ5wdfq/+LO20lOv5rk5MtQ0kMYERFcQIiwKK8Pw2nC71+P17uahuXfx7AbwtVYqAkxjC61sE2UF+CcMIPEBQso3PkCRmNqxIm7FkIkHJPez9yD7MyR/ftT4muLVi+10MG/htVEV4Kw9o9j4T63i56P/Y5gR2/osPhVlZZHOq5GMSVGQ5LM+8OqvYbm/ADhZKORwmMv/Vce/en7uPUzc/FKAyw59yO0TD863G6caeCvMZNSYlkWPT097Nu3j56enirV2mw2y6RJk2LHIrLBzkfkuBxxxBF8+tOfZuXKlTzzzDPs3buXXC5HPp/H87y4XEBE3E2lUmQyGVpaWjj66KM5+eSTqa+vR4Z8hhHHPlXxGYTIajRpSqXFB8c5K1eez5cBL3fvpRT4tCSzzKpvQQojrhI9+J0xTDseV93A5+XuvXhIWo0UM5vboClJ4vQ5Fan65XkuCtsE46jHVcs0WCwwRDX3w5cBq7r24kp9LfMapuHKQDsuEdJc7gWqJ9ChvwkhkEqRNS3qEmkwDPK5AaRjjFryr7qPZdzHDYkU8xrakCHqUmu/ys+R7ICzh+xERq9UgFRdGzLwSWZasZwUljOtaj/TSuKq3ugounHhoJ5tnc3UxRfwyE/fyylv+RYTZp8Yxxi90gDbnr+LFXd+irnHX4PlpIiqGo/XorbHXWLoNNOB/30yhKvKpKJheQ7Rpxfowo2ImoNzZJobEZIn2wfov/Ehmr571ZDjHrjpvgy8YlyNVx9Xv1yr//g1Ji08k5bpR+OVcnERvFrXNvjfdcmWIWGwifVzmMicmi2RyjukTkvVwBEohGWi8i75362j+KeNuCt2aHVkx9RcCFMrzlYNuIHOuNGOjMBaNJHE6bNJvX4J9hItjqV8qVOOldKRpnBAyC78hP5degjDRgVRsUIDQgE+YYQr+7qF1C/9ari9j6goRiiMxPAhiHA1lr3+FNxHNyPz3ohIA9RyXAQiadP3tb/gb+6i/rPnagc8zMKqdI4Ppw0XMo3voSdjIl/+ttX0ffVBVH8RkXHG5LBUoiwiYSK7CmSvPwX7iAllUSo/fB98F9/XxNvJ80/nwn+4ix3r7qNx0kKmLbkwXjUjDKxk5lURQjvUZhgGvu+TyWRYtWoVK1euBMLQh23T29vLiSeeyMc+9rE4u2ewVfE+QsfFtm1OPvlkTj75ZPr6+ti7dy/d3d01CyY2NTXR1tZGa2trfJzBCrjD9n30tW0iMg4ibcffi6StvxshRDGcRWUvet0ir7vr2+zo3cfbFp/KLy54D6XAJ2VV8zUqn+coKrKv0M/Zv/kGXfk+3rf0LL5/zrWok6bTdt/7x92eg7HI9ejE4y23fZG9A91cu+gUfn7y2ygBoyel1zaJViLZFPTyzQ0rCKTkstnHszxdhxcE2KOUXoja1e8VuPT332Zb734unn0U91z+ITwZkBgnvWOwHXD2kFIKGXiYlkN/x1ZMy6Fv/ys0T1tKoa+du//zHF1V2Ekx0LGN6UddEh6j/HL4bh7TSnLS1f/JE7/6MA9+/y3aMYliroaBX8ozdfH5HHfZ52PBqMEplOO1KJ6ev2MN3oodiKwTc1mGbFsBdQpDZytkPngK3ortlJ7aFq+OB8/YcV9Vkif/uJ7E7atJv+noqhTog+UfRKsW0H2ayDTzytP/R/umJzn/738dtn/o9dU6XzzpV1xDZIH0ax7HMh0O9bgfT1ACsAwK977MwLcfx1u7F4RWYzTaslDyUUVP6+oEKi49jyk0ez1hYWQTYOq6Ut6a3eRvW03qiiOp+8hpWqQtnPT0tUb3w9eoQJSuHCODRaTbEX4jQx63AuWBsCoQFo2ESLcDIodnkMMoTJ36Z89vI3PdyfT9258QTSmt3jzCMzH4PSRQGHVJ8retxluzm/rPnkfidO1cRs4LRu2inQfrOMeIT60wkJTa4bRNcEyCPX30f/0hCr9ZC0lLc1jcYHwOi2Wg8h724glkP3Cy3iCccCPZhTnL38SUhWcDOjTdMGkhDZMWhseS8TMt/RL9+3uRccrz/3uhIiG0sm2xWIwLM0opKRaLeF4YAq4Y44JgeORJCF3kUUrdh9lslgULFgy7fXTMqLBiJEI3UgHHaJtoDan6igQ7uzUCPqBVv0XKJtjViypUH2csY2kk72AJg+Zkhl63jnpHp16bNRadlZ/lYwhaklkCpchGUh+eLvmhIv4cehjypMS2bbZ0t/OW+35I1k7gS1kJxGjwVpULT5rC0L+Hv0mlSFk2u3O9fOToc3nv0adTLJb05C8MKBRozAvyeZNMf5jRVShChZDjWE0IgRt4JJMJXt68lc/f/UsIAlrPNVm+fDK+CrAxxzRmGAiakml63Cz14xA/Hc3G7LTUWklFDd/98oMEvsu21Xcx69grMe0U8058W8x12fD4j5BRUbLQhBBxSKN+wjwu+sg99HVspnP7c5Ry3QgEybpW2mafSLqhLBl9IKXkqyBqqRCOiewpkPvek2HGAvEDMuwKN0xhNiZmqfvHMyncvprS09uHPU9lX8VhooRF/9cfwlk+HWt286BV/oE4LjE0FMPciUwz+7et5OnbP8G8E97ClCNC57FK50YReMV4ktZIwCCYMIztalg0jG+LER6XQ81YjwoCKkX/v/+Z3A+eBiEwWzOooqdRFtvEnNWENbsFc2IWkXZ0TNkLUHkX2V0g2NKFv7kL2VVEJC3MSXWoUkDuB0/jPr2Nxq9fhn3kpCoGu3ZcBlWuDq/dys4lNU1zhKKQkRACRJTBFb4bobOTmn4NZlpnEmFUh2z0ATXil33/ibhPbaP44Cs67b7oV+mvDLbBjovytGPsb+qk6323k3r9ErLXnYQVStsrFRYejdIQK44z4n1g5EhsLVRSa+2EvCEDVN4j98tV5H+yEn9Hj04BDWQV6XbY81c6ZyJsjBDUf/Y8vWCoyjzQ/eWkGnBSDeFEaSGlj/S9uMpzlBSQbpjCydd8nkSmKbyWGvfnNWpKlaX+58yZw7Jly8jn85imGVdsnjZNI+IR8iGEGLdGzGgmhBaXG1fbUXF4M3H6bBr+/RL9TnihkrdlIPtLWAtCBKdGqL3oeyH7o2I8RjsHjmmS811KgU8p8CmGTmved1FoJ6FyH4nCMcw4rKEUel/fw48QbhEukoZcjAABBUPydNc26p00rvSrWB0KRcK0MIUOs5cCv2p9EyhJ1k7Q1dfJLldnNynbiAvm4pi4JriWIrBDRyuhy4ocyFNshEhIJpNicssEPBlQH9aXGg8vRwFuEFAK/JgofCjswHVaQog1172TLavuYPrS17H7pT+zeeXtzFn+Jo553Wfi/bp2riHfswvQ0K1pJ+nevY4HbroCJ9McFlNT2Ml67EQmXq3KwMMr9eMXcwjT0lwKFK/72AMksy06k2gMMtxVDkEgwTDJ3fwU3ob9Q3gENfeVCiwT1Zsj8+HTEAJSly+m8Os1uCt2aIg7kDUdj3I8X7+Iwf4cvZ+7j+afXlMu3S1GHriHt/IKKQqpbVpxC8/e9TkyTVNY9vov6eMbJgReuU0ITHtsktJRNtKoA/khHOPjCQro/cQfyP/qeYzWDEhF0N6PNbuF9LXHkbz4CMzpjRiNqaqJuNJkbxHZmaP0yGYKv1uH+9wu7exMzOKv30/nu2+l+eY3agnpQaGiKmcy5PGY6RmY6RnoukJDs8HK2zsoFZCccnl4TRIhqtVr4+2lBMOg4SsX47/5FwR7+hBJS1c6HiFcVOW4oPRzHKpN5n/1HKUHXyF50ULSbzkWa/HEuKSF8oMISoudgOEc9uFuq0aXCEOrxCiSRlX0eYL2fgp3v0ThttV4L+3TqFeog4Rg1FBYJQKA0gOx7MpR/6lzNJLklye36lWx5jxEY4NhWBhOue5MVDAyVT+BZZd/ITpb1XMeSEmgJM5BQtl/K4ucllwux4IFC7j44ouH3S6y3bt3893vfpd0Oj0i4hLZaDyEymOP5pg6jkN3dzdXXXUVxx9/PB4BtjJwTpiBc8KM4feN0LdBi+qkNbLw3ZRMA6ahnYRMKBnRmEiPuI8XBHFtTSGqEe7hTIRjWcq0OX7CLLJ2El8GsdKzVArLMNib62XAK2EZJtOaGmPeigACFGnTYXeqnqnpBqBKSB6kwlAgosV3+B3jZ1EA5XtV9DxybglPBpR8b5S9hrl+ITD0pR4yG1eV5/KAWx4Qnrnz0whhcN4H7uCxX3yAJ371ITJNU5k49xSKuS6SmWb8UrnGRxQeclINTFt6MXaiTnNUwnCTlAEoGW8rDCuMPwuk7wJlXZLReqLyRRFC6DCAbeI+t4vcT1eOKmQVvwiWgcq7WAsnkHrz0Xq8T9pkP3IGXe+5teb5Bh8nylM36hOUHt5E/40PUf/PZ8dhokouxlhXeSJ8Km0nzc4X76d90xNsfOoXZJunc/o7fkAy2xpnaVX2gww8Xnn6l1pwTkTL12gDjTJ4pQEmzD6BlunHDDnGYTW9HEJYBv3ffEQ7LBOzGho2BZnrTib7gZMw28qVZSOkgSidTo8oYAqMhiRGQxJrTgvpdy6ncOtqBr79OMGuXkRjCtWVp+fjd9Fyy7WYE+uqEJ6h/AwDpQKocFgqn8FKBFL/rUXnhLCoVd4igpKjMJE5uZ7Gb1xG13tui3lTo6UAVzm8IRoIYDSnkf0lcj9fRf7OtSTPmI1z5lwSJ87AmtNSs+uVlOUq7DERMoIgKTs4UbjJGPTsALKngPv0dkpPbqP0wAb8HT0I29R1VwJZFUodS0goapdI2sjOPKk3H0P2+lMJ/ICi8hG+GIryiXBFKAMcw9KTU8WkVvA9bMPEQuF5RWw7hackgZQkLRupJKZhYBJpDr02UZcYdShqlHtgYIBEIqHflzBcE5UAACgWi6xdu5b6+no8zxvzOHSwJqUkmUyyb98+zjnnHN32IADL0g62V1HxG8p/O+YQNDIiev5i/dP0lgpYRlmFPBrnTWEw4JXIey5J0+bFrj38/OWn6HUL2OHCWP9PYRsmfW6R06fM4/iJs/RhxtgtSqn4/LPrW1lx9aeHXnvY3vf86af8auMzTM8288fLP0zGHp6RopQiYdoESh6y9OaqcS78rs8tYIah1/4w+1fUGO/+2jau8FB5gNQNffqOT7Bj7b1c+OG7MUyLk67+Bn4px33fuoxjL/ks8066FqAKDYn+zjRN45Rr/ueAG14JB4/UcZXOAOHk0P+1v+jq0RlHkzVrrDIr4+iEvJTMO5dhtmR0+MEQJE6ZSfrqo8j9+BmM5rSuq1BjgqmEtyPRudzNT2EvmkDqsiVxCui4w0ShA+mkGti66k4M02HygtM55a3foWHCPKQMhjoboez5gze/Da8YRz3CiwZZAqcO+jrg9Ld/Qjst0o2Pc9gfVF9LOburdzPwv0/pfs17iLRNww2viyWkVVRqQVAz1z++pGgSBhCQvuYYEqfPpvsjv8NbuQOjJY2/sYP+bz5C4w2XxEjckPCLUhiG0CEEYcbvgQj7JJoEwk4iHlmFg1QKoaVzazouSiqMKJto+XQab7yU7g/9VjuQoabLaI4LVL+jqhSSxeuTIBWFe9dT+MNLmDOasGY2Yh8zFWf5NKz5rRqpStm6D8c4BqpAIoseaqBEsL0H9/nduM/swN/SSbClG5V3EfVJjIYUKDVmdGXwdaAUImEju/Ikz59P47+/DqkUpmWSGTWXQVtlxoJSmh8AOlRgO2kCJbENE9swKfoeScvmD1te4DsvPMQ9l39ITw5j65YDttFQi+Fs8MKslkXhn0QiUVN6P9rPcRxmz55NJpPB9/0DbtNYLWq7lJJEIkE2m6Wurk6fMyqYa5nltOJhjiGEiOOY0ULiK8/cw8be/WQsBz+srRaZQGAIQWsqy8R0PRt62vnAQ7/EDJGPyCLOSkdfB186/U0cP3EWgZSMNegyeAwJBoWdEBq9SVo2Od/FNkykkuwr9DPbTuAGfhgyikYThSGM+Fmu1Yrou1oZOmOdXyLez5a+To3KC4OdA92A5gL9rW2cSIvOSvGK/Tz843ex44V7OePdP2HC7BMJ/BJOqp6z3vdznrr142xZdQezl+lMmWryZsTwL2FaCTq2P8ddX9WEOt8rDfViFRiWRSLTwhs+9zyJTPMQyfqR2xxuE2Yv9H/vSUpPbB0xLFR1cy0DlXNxjp5C+s3H6O8ikRwg++HTcZ/Yhr+tO07HHTFMpMJJ1Dbp/df7MGc24Rw1ZYgq4NicA+24eaUckxacSeAV6Nu3iXzPLhomzBtmF4lpOiy76vMEvgulcPJXmusj6hIwEFDq6WD6ERfqtgcG2H9dzzr3gxWooqfrUxQkDV+6kNRFR2jCrWONqUYF6EmyUjNBFT3MqQ00fe8qut76f/hbujCaUhTveRn3bctwlpb5LYP5W1HYCIY6ClUrEOIujWHkcoOqQ4JVxw4dl+QFC2m88VJ6/ukPoUNmxKncI01SVaiLiBy2UHSqIanRyo4cpV09lJ7crjknloE5uQ5zRhNmawajJa2dmISlV7K2qdEbN9DOel+RoDOP6soT7O7D39odkqBDjoplIjI2Iuvoffwgbs9Ynutqh0UjmrIrT+KseTR960qUY2KgMzh+/vJTpCyHoJLYiHZSIoTl4plLmJZt0pNN2Nc/f/kplk2YyeLmybGT8tz+HWzsaeequccBsDvXw192rY/v5+G2iJzqum71faywwU5E9OyMykuqDLPV+C0SmJs2bRrf+MY3hmwTBEGcpnxgYeyhNhJ/Rik15iKQ8e+DNnNMi4Rhaa5I+HxEqcd+GPrb1teJRGEJk6RlYwiBEzpLEhU+RyaY9gFN1oPHcqvGtZiWRn26S3lsw8SVATsGupld36rRvhrnLS94ok4oc8+itOpCGM6JnLCEaY34/lX+Fg2Xj+5+BTtsw1N7NzPgFcnayXgRMK4F9iG0cWL+Ov5uJ+tonLyIWce9gbnHX615KlZCr+wNi1Pe8j+Ucl3YKR1/E6JSKVWUvwMS6UYMy+HMd/+YutY5+KVcuKoXBH6JZLaFXS/+iefv/XeSmWYMy4mdoOE6bchq1tMhGHflDnL/+9SY6ptUvZyGoO5jZ2inxAvFzcJwj9maoe4z59J93R219y1/Wc7IkWVnqOdjd9Hys7dgTm0YNzG37ET2MXvZVSw87X08+rP388BNV3LudbcydfH5VaJIUdtMy6niHFWa98OXsN+7qHwOFGYyOaQw3yG3aOyxdZaJ+/Q2jGwC2Zkn/aajSF0eIlLJGkRsGBazHbydSNpaSbU1Q91nz6P73beCbSL39uM+sgln6aQh+5ZKJXzfJ5VKDamzIoRWAHVdl0wmE38X7eu6Lp7nxcXhBu8b/Z7NZmMCovICUpctQSQsev7x91r4MGlrYbyKwaJmNw6Cb+O/3ZDEaBuIZKqCj6IIdvcRbO3WzkmgM34ipyFGjQx9k4QpwhRzjW4J29QOTuScKV2ROoL0h4bYxuCwRKUcHAPZkSN16SIavnE5Iu0g/QAsE1MYtCazpCy7YnUsUEgMYeDJgLf/4Tv8/g3/xLRsE54McEwLAbzrTz/lG6e9kcXNk8n7LknL5jebnuPbax7i6vnLAUhaNi3J7LBtPVQmhEBKSTqt+RTRMzRWk1Liui7AmCov1zr/YERx8G+R7sqhtsHni855MJNhhDDcfPa1FHw3Rj+VUngywBQGacshYVrYpomBIFASNwgoBh4FX0s3WKapF8xCUAw85jVMAMA0DOQY3dhaz3zldXkywDZMNnS3s6G7nTonSW+pwKO7NnLGlPkEUmLWGHOHznd6+AsqUMSGRG39rcF9O/g7X0osw+DZfdtY07kTBViGycaefTyy6xVeN+tIAiUxxKHJfD0QG394KPz38it0LRYlJWaYWmUYpnYolCKRacYrDmAks0TKoeGRqj6VUvhujglzTiYTyv4PtkLfPrxif83fRnVYpJ4IZF+R3n+9L1y92xpZGAllifQguguk33wMibPnaqeiYoUfr4zPmUfmncsZuPkpnUJb8oaSV0U5lCAMoY+VtAk2d9H9od/Q/JNrMOqHOgdjeiBCeNQwTM581494tnkGj/zsfVz6iYepa5kVTtjVL04QivXlf70GI6FTT+0lExl4/gXqXq6ncPeLpK88GntiI+6G/VVE1cNl0WTlrtqF7CvpCbYuQeqKI8vXydhhTr1LJfoQDiJJCyUVidNm45wwA3elJlO7K3fGaqugV72JRII777yTVatW8fWvf73mOVavXs1NN93ET37yk/hcnufhOA5//OMfeeKJJ7jhhhtq7rtixQp++tOf8oMf/CBM16YKcWn6UZqej91FsLMnJrEO5t2MdO1xf4XbK6kgqE7RF5ahURUNCxHzV6qWrxU8F1nh0EgV82gqkzUq362xIgGC8BmwtXij7C6Qec8J1H/ufO3MBxLTMgmkpCWZ4Z2LTh7xuB96+BaS4Wq+sgmz6ltIhFmIxZCgnrYdZtQ1x9sEUmo10MNsvu/jOA4bNmygubk5lruP0oqllDHS4fs+nufheR6lUolCoUCpVGLPnj0ceeSRXH311UN0UMZig1G/yCLRubVr19Lb24tlWQRBcMATVGUI1TRNli1bFqY3Vz/HBzoJVo4LJ06aPex2e3K97M710lMYwJcBSdOmMZFmfuME6pzhExQiJ+NAwaZa1wnwl13r2VfoZ3q2GSGK3Lf9JT509DnUO8maDsZgE0LEfKzNvR3cu20t3cU8jqn5OVIpLp11FBnbGRIqHaxNJpVWafnftY/SVcyxtGUqHYUB+lF854WHuGjmkjg8Ntjh/Ws5LgdMxFVSghBx+mD5d6OMYIS/mVYS045k/EXVpxACO1FHKddFpnEKbrEfI6wQ7XtFEulGvFK/rvQ8WiPDDao6MOSk9H3xAbwX24eWvR8GYhemQBV8zOmN1H309KqmVw3GYSmAun8+C++FvbgrtiPqhpckj48fFZCqT+Cu3EnPR39H081viitiDkcGrX3dKo4Bu8U+ll3+Bbp2PM+Tt3yMC67/DZrYXNF7Iswe8iROfSOFX7+gnaU9JVLHzsH9wyb8B3fgtTVjnGaS+/Ez2mlxfYQ1vvTFMZsQ8b0KtvdoKWxhY06qwz4yRD8GS++P8SURoVNX/VzoUIZzykxKj27GaEzhb+mCqJzDoP1d16VUKsWqotF3TU1N9PT0jIh+eJ5HZ2dnGX41DFzXpbm5md7e3toDUei4JI6fTsst19L7mXsoPbQJoy6hOVYROZHRB/kh/SUGoTCDybeVDkrVgeL/q/h3xT+Noe/SSFbVboXODk2YqH4XkXVo+PJFZN65PHa2hGngywDLMNnUu5+PPnIbkzMNBErGTnkkcm6GNVjK8Hq5LX1uIV6JTsk0ApC1E/S5hYq21+YMHEqLEIzGxkaeeeYZHnvssZqTeC2LUArTNBkYGIi5IIfSItG5W2+9ldWrV1NfrwvdjXdyqhzDgiDA8zzS6TQ333zzuNOhx3oeN/ABQaACUpZDX6nAT15+knu3rWNHfxf7iwPkfZdA6gyxOjvBxHQ9C5om8rYFJ3DJrKW4gR+L0ZkVXJIDscFjuVQKx7Roz/fxk5eepDGRps8t0JRI83zHDn728lP8w1FnUQp8kpZdtX90rZEZaD2YrJ1g1f7tXH3v90OAQZOOS4HHhmu/RMZujp2WquOFh4pCpbdsXMkftr6AZZh8etlFrO3azQ3P/pGV+7bzn889wCeOuyDe9m/huBwQEVeIaq2HIbH5+O+w5PfAfkq57mjrqk/fLVDK9XL3jWejpH7QIPxQCoSBDDxAEXgFnTlUsaKraiPVNzbKzBn4/lMaURjFYYkHcoWGwAse9f90pg7d1KhEGfdJIBGORcMNr6Pzml/o4m+h/katm1nluBR9jKY0xQc20vvPf6DxG5fHA9K4Hoh4taq3OfHqb3D3jWexeeVtzFl+NUFpYEias5KS5KVH4O/t1fomPQVkvoSoc0hdvVSrUFomRlMqbvdhtfAaZG8B/FDyPWVrMm5F6YMDejkE1RNxoMACc3J9LOkvewr6PNEu4TlM06Snp4df/vKX5HK5eCUbEQj37NmD4zgEQRDH6aN9DcOgt7eXO+64I16lRoN3Mplkx44dMaw/ZLUZOi7WtAaaf3INA99+jIHvPglFPySRSy1dX+GEjMuRq9VH1X8cNhsyCNsmSInsKuAsm0b9ly7EOWqyDjWZInaIolDQgFfkDxtW8PhbP8fs+lbyvkvCtAikBu8NIbjhlCtj9U3HMENYX1DvpPjTjpdIWw67cj1Mzzbx2O5XDqkA1mjXDtqZ7ejowPf9WG22cvysdDYjZzeq4+M4DrZtk81msSyL2bNnx9seiA23ggdoaGhgwoQJpNNpuru7D+gc0b2ur6/HMAwSiUQVCj1aWyIbi4OuQodAKoUjTF7q2sOHHrmVNR07CZSMCbGmMDDDMWXAKzHQu5+tfZ08tHMD18xfzldPuTJG5ECjbwfyatQKj/phuPKGZ/9Ie74PX0n++dgLWN/bzu0bn+U7LzzEqZPncGzbDEqBT8IcKpcwfB/EkEAImsbQ6ZB+itoTOSEr27fx5RV3k/ddLpyxmMvnHM1Z0xby5x0vs6ZzFzeteYh5DW1cOffYmo7LX8PGhbREnyNBVpV/RwqVx17yWYIQhjXClXqcRdQ8jQv+4TehdsJwtRkEwjBjMbrBD3llO+LP0GEpPfgK/Tc+jJFxtOiVqP3gDwkLdRU0l+LKpVVhocHerkZlDJQbYM1upuHfLqL7+jvDNo5en0iYBqrkYzSnyP/6hdj5UVJVOWEjPxgVXrfpIAOP+rY5zF72Rl56+HvMWX41hjl8mrjqKaLy+v4IS5M+ZWcOa26Lvgb3/436LKO+UoPGgug+SymZPn067373u2vutnHjRr761a9iWVbNezRt2jSuu+66mvu+8MILfPvb3x7a1uh+R0RYIaj78Ok4J8yg/6sP4j6/W1eVTVnglVVe/1Zx5rGYXtWJkEsTLjLC8gsylPTP/v3J1H30DF1xvUYNpeiaEqZNS30rp0yeO+bzDwp2IcPFgVRDi9AdTouc3mnTpvEv//Iv2LYd66IMHiMq/4ucFsdxcByHRCJBOp2OHQGoLoZ4qMz3ffL5PNlsluuvv37c5xBCxCUF7rzzTjZt2kRzU5NeoygPFbggjHCRSvWidBCrXZipEZ/tGNGROm19Q3c7b7r3Zvbm+3BMi3orxelT5nFM63QmputwTIt+r8j2/m4e272RNZ27sAyD/133KN2lPD849+0YhM/dOF+nIfPEIAfh/9Y/zS0bVmIIwbEt0/nU8ot4uXsvT+3Zwr58Px999HZ+ecF7mZptjB2X6HiVJgHbMOkq5Tht8lw+dsx5dJVyOKHeWaAkLSnNlaoMDUXtcmUQk9H/7i+/oD3fx4xsEzecciUA9U6S7579Vt5wz/fYnx/gnx7/NQLBFXOPIVASKbXWzKsOaam04ZyUStPxSwslAybMOUl/FxJ1I7RGKYWTrGfWsVeM+dy1BuUhDktIvPU3ddLz6XvK+zKI41DrmKFMuLWglbpPa82AiE49eDVbtZ8T8hDOX0Ddx8+k74YHNbJTGirxP+jk2pEqBRiNSXL/twqUouFrl2jHJUJkRpqMKmB7he53gEVn/j2//9op7Nv8VHwPBrlq5f0HIRER6VgVD39cv3zSEJ2oT2oZfiFQBa1+azSlh0xc47UqgC6sWyLb+3VaZSAxWtI11WJd12Xr1q2sWrUqju0LoQm4dXV1PPfcc+Ryuap74/s+tm3T39/P+vXr+fOf/xzD7VF4qKGhgSeffJKurq54tR3B5VX329QDuvICEifNxLnt7eT+bxW5H6wg2NGNyCb0NShNgo0g3/GiL4fLqt63UKwwCqtqZ1mRPG8+2Q+dhnO05rVVLhQGv+ug1Us7e/bx7TUPMTFdhycDJqcb6CgO4AYBptD8O8cwuWLOMZiGEWYQQZ9b5OypC7hy7jExXL6lr4OV+7Yd/r5AxSGguro6TjrppIM+ZsR7ORxkWSg7Qo7jHHR7H3roITZs2IBp2Sjlg0hhjCIEV2kjhUlq/fvTT/6W3ble0rbDiRNn89WTr2BB08Sax/ZkwO82r+ZzT9+FKQxuf+VZTpk8l/cvOQ0vCGLNEhhMchhaAqCyPbUchPu3v8i/PPk7hBBkrQT/c+Y1ABzRNImbzrqGd//pZ6zt3MV7/vwzfnDOtUyva6YU+NgVyryxX6cUpiEo+R4z61o4d/oRw/ZdZYjLDXydZWVa/H7LGj71xG/oKAxQ5yT59plvYXZ9q0aXgAWNE/n5+e/h2vt/xP5CPx959Fa29Hfw4aPOKVe8/ivZYVMMK4dATJ1aCzFht/pGylA0btQjYljO6A5LIGPibc/H70J25BBpJy6ap5Sq6UNUDayWQcOXLsJsy9YMC1VeY9WnbaJ8SfYDJ+Nv6yb/82d11c+CF5NrB+8XtxutrWE0p8n96jkQ0HDDJTpUNFrmjor/DyhvU982h9aZx7PjhXvLjqNSFYhW2I76pCan9hXjMgfKDTBaMljTG8ecXnzQFjqH5rQGrRtiGcj2AbwX9pI4Y44OGY07LXyYydvQqFrpqW16VV/yseY0Q6IaLZFSctJJJ9HW1sbevXursoeEEORyOaZPn84//MM/UCwWsSwrrnALcNlll7F48eK4/kpUuyWbzaKU4swzz+TSSy/Fsqw4tDRcaDB6voRjkn3PCaQuXEjuJysp/G4dQXu/1mXJhuHTyHlRZZ5HpR0uR2YIhD2InIttgRtoZMUysY+bSubdx5O6RGesKS/QcuhW7efdChHa2XUt/N/rP8SAV6KnlKezmONt997Mp0+4lAWNE+jxXEwhsAxzCLiYtRPx4B1IiWGamIYRK6MeTqssshqRa8dzL4aMOSECczit8p4Wi8U4RXo8oUjXdUmn0/p6DRMVlDDsFPlXvkVhzx8xEy0oWQIMLdwoozlBgTBBljBS02k45ptEWZPDWUSYfWz3Kzy3fzu2aXJ06zR+deF7ccIQYpTSHD2gltAFcN847zhakhne++efkbES3LnpOd664AQytoMvA4y41IO+l344HuTiEgD630mzOmziBj6WYZIwLX6z6Xn+6fE78MJKzN85660saZ4cHktxzrQj+N7Zb+WDD/2KZ9q38uY//oAbT7sqRhXjooVR/0btESImlveWClWZdVHKc+X9dEyL7mKer6+6n5+vf5qC79KUSPO/51zLGVPnx+eJ+vTYtuncfvHf8d4//4z1Pe38+8o/8tjuTVy/9ExOmTw3ViE+3EukEZ0WqZSWHBaiNjFvOBNahCa6aZGCbe3B2BizpPzgY8BQTzYSguv56F24q3frjJxReCyDw0L1nzqbxKmzxqSdMuQ3U//d8OWLCPb0U3pwI0ZTqqqWTFVXDXFcfIymFLlfPodyAxq+fqneb1A6tN5XP1Bv+NxzWAmdnmlaui8Dr4RpJ2idtZyd6/7Istd/Ue9jGBhWOTWy+OBG3BXbQSrsxRMRDUlkRw4MgfvoFoLt3aiBUvlax3ynxmkhogRgHztVp6UXfWR/icLv1mqnZZh7roGmkeFiVfl3yUckLEpPbcN9chuiLols78dePl23wZdIU8Wox/z585k/f/64LmfLli3s3buXdDqN4zjU1dUhpSSVSpFIJOjp6YkL2Pm+zzPPPINpmhxzzDFVZMwhjkskWe4GmFMbqP+Xc8m8ezn5W1dTuPsl/A37QYFRl9BZQRG0HqiwEGkZkYuR+INwYGo5KZrLFqZHVwjuqZKP6ipgtKRJnjOP9FuPI3HW3Jj/RZjpN/i4g9+9gu9S76R464IT4nYUfY8vrvgD1y05nZn11Yq/xcDDFmVofF++j7/s3MC8hgls6t3P/MYJPLprI/sLAwfcD+Ox6HqiUM9rxYQQNVP3x2JV4SslwbBRvkd69ntJTH+bfn2FifJz9Ky4luZT/4AKCtphQT9P0u2OHZaRnKZAarHANZ076XULGEJw3ZGn45gWBd8jZdk1xQKVUpQCj7OnLeTMqQv49aZVbOnroKM4EJNYK/siUDJWrm2qUQKgso2OaeHJgK89ez/feeFh/FA36KYz38L5MxZVOwhBwMUzj+Sn572L6x/+Feu6dvP2B37M+xafygeOPJOmZIg6V4zGZcRH93PKsmuWoIje00BKfr7+ab7zwkNs6evAl5IlLVP4n9PfzHETZsQoTLS9bZh4MmBx82R+e8nf84nHf82929Zx3/Z1PLl3M7dc+D7OmDo/JspHJkMOkVTjcSBGthGdFiP0xg7Uhgy4NVYJ47WavJBo0AwzDHo++QeKD2zAaE4Nq1Jb1b64iGKR1OWLyV5/aozYDN62VnuGDLBSOxhN//16ut51C97zu8OMIr9mHZ+aiEtTivwda5DdBRq/eXlVcTggnqiVUiTr2mq0UT8kLdOOJts0LT6X9F1KuS79D9sg2NNH8oIF4AbYR00m94tVZK49DhwL95kdyPZ+0m9fps/pHEYp/2jO8wKs6Y3Yy6dT/OPLGC1pCne9SOKseaQuW6yFzBLWmJ+heDv9D+08Ji1kb5G+L/9J/+IHGK0ZEmfqlYzv+1iWwzPPPMPq1atJpVIUi8WqyUYIUcVDEELERMq3v/3tDAwMsGvXLurr6/F9n/7+/jgctH79eq699loKhUJcwM40zZgTU6v9Q75zQq6LAnNKA3UfO4P0O5dTemgTpYc3U3pkM6qnEGbdCI1cJe2QzFGhxQJDzjleixcQphFL/KMUquijSlosDUNgLWwjed58EqfPwTl+erxv/FwPek8Hv1dRKCdVAxEpheqhbo3CbMmQTBmtgn983jvZV+jn8T2bMISgPd/HGVPn87aFJ5RJjwfVI6Pb3zpkdyCmlIqf2fFYhLRkMhmNVAoBykNYNsWdt1Pc9xfM5ASU9EAFBMW99L/0byCLGkEwUyi/l+ziz2uHRfpxfbqa7Qw/B9wSpcAnYyeYFhbFTIzQdn1P9H2ZlK7Hk5Ki7xGEz5Sq2M6TAXVOkqf2bubLz9zNgFvS6dAhrvmZ5ReTtRNIpQhkwCO7N/Lfq//CU3s3IxA0JlJ8O3RYKh0EIQS2aeIGPmdMnc+dr/sAH3/sdh7ZtZH/WHU/t258ln869nyuPeLEKuRn8LUP5yRE75FpGKzv3suLXXtoTKS5et7RfPGky2hNZmNNo3KfaLMNE19KJqbr+dn57+aX61fwvbWP8L7Fp3LGVK0rox0WjV4ZQpCxE9Q5yZrv7IHaiLNQziuxqXd/zMSOhn+pZJhOZcYrtsgi5v7CxonjgvDHYzWRj0CnsPZ++QHyd7wQOyyD0zuHHCPUhVB5F3vJJBq+8jq9waCBcyQb4rgYGhkxGpI0fetKOt/5K4Jt3YjUoDDViI5LmFX05410vesWGr91Jdb0xljyn8rtpVa1rVyBRGjKzGMuxzDtWGQuWdfGpPlhCrchyLxtWdW1GK0Z7KWTAbAXtpX7q0ZhssNi4a3Kvud4Sn/eGKdB937uj4iESfKChXqzSMbfCNsyHNIS1SOK6DtJi2B/jp6P/hZv3V7MlgxBez+Zty/DCas9G6GA3Zw5c2hqaqpKcW5paeGuu+7i/vvv58Ybb6RYLMaryEjXwvd9li5dytKlS5FS4nlenCFk2zaFQoFLL710+C6o8XzXRBejbCqpIJCYzWnSb1hK+g1L8Xf2Unp4E6VHtxBs6iTY04fc2w+mgUiYoapwJcR+IPezkquCRlLcQCNZjok5pR5zSgP20ZNJnr8A+6jJGFndD8oPw2ymGBZdiVsm9JhjCIPuYp4n9m6KHZFASZKmze5cD7lcH3/a8RIdhYGwjoyO/+c8l+UTZjAp04BUMhaQq2WFMFStDvHq8LVslc56FPYcr0XlA2zb1os6odXD7ZYTMeqWgMyFSKogPft9SLcDYWYAgQryRAVHdYNGDodFT07WSZIwdS2pzX0dHDdhBsXAJz3MBBrIcubgjoFuLMOgMZGKM4gqeSy+lKQtm5e69/JM+1Zd4DBMj1YoPnbMuXEYcnXXHt774M/ZXxggbdkc1TqV/zr9zRzZMmUIohFZFMaa3ziBOy6+jm8+/yd+/OITACxqnlS1rUILywVK1pzr4u0i8CD891dPuZLWVB3Ts43xO+GHoTUvLOo4+GgGglLgI4C3LjyBi2YuoTGRxpfaJ1AyiItAlgKfLb0ddPbsY1fz5IrWHpyN6LSsaN/KOT/5NHbzZDy3SMzwNi0sw8D3SuEDFM0IBgQerXUttL/3Pw5F+4ZYLdhYF9gz6fvPh8n9YIUOx4QOS63JtcphMQ2UJzEaUjR+4zKMxqQurGiNjzsxxHEJs3DMaQ00fecNdL3nNuT+AV1jaJgieFUTk2HEoSJvzR663vErGm+8DGfZtHLcP3aShofUtcPia1VcJZm66DymLjqPqCiY7ClEnQKOSf0nzkL2FfVq2dWigMI2EdlEVTuHdup47+QwZmsRMeeEGWTetZyBm57AnFyvOUofvYv0O5aR/buTMJrLcKySCvwaBRMNEYrhldtbuOtF+v/7EfxNnRhNaWRPAWt+G9mPnqE3MA0MNN+gpaWFlpahxQVnzJhBc3MzkydPHvJbZAMDA2SzWW666SZeeuklvvOd7wDQ0dFBd3c3ALlcrqpgHWiy43DPnIhCPeHfEV1ElyoIS0uEis3WtAastx1H5m3HIXuLeC/swXuxHX9zF8G2LvwtXch9A2WEq4oXNRaLnKlwoZK0sOa0YM5qwprVhLWgDfvIyVWOL1CuQTSIJ1XZB7XfWX3Kfq/IivatZO1EOLDqbQ0h+M4F76bfK/LQrvUIBBJd8K7XLTCnoTV0WhSe9LXceeXlCr2SjI5nG+aIRev+v2SRhH+xWOT2228/oOyhIAhIJBLs3r0b27bxA83fMDNz8frvIznpwiH7BYVdKH8Au+nY+LuIUjDSmGyF7Tu2dRoNTpK87/KdFx7i3GkLaUrqLBpfBuFEq58d2zDiIpl3bnqOR3e/QsKwOKJpEpPS9ZrmIKqJuJHMf3OyrGJcFSpFk12PbZvOzWddy+dX/J7XzTySfzzufNKWgxfURjSikAoKil4JIQSfWnYRJ0+aQ1MizVGt0ygFvi4xELY/Yztk7UScYTTcfahELaVS/OOx5+m+DksdRKEd2xgekTIrCpRF115Lw6YtVce3zryGXrfAzFC40RIHz48c0Wk5a+oCvM/cDmjSUTHwyNgJHtj+Is917OSfj7uAfrdIxk6E6ItufFcxp/cZdAMP1oZ3WAz6vv4QA996LK4pFDksgx/uwfwXPXNLGm64BPuICXGq9HgclsiGOC6hzoa9cAJN372K7vffrjVcIkLlMI5L3E4j5F9kHIJdfXS961bq/uVcMtcco2H1CoXa4TgA2nGpyNiSEim1EyOEQDQNXTkZ9bXj1iMqsR5K4CXkttR98mz87T0U//ASRlsGfMnAtx+n+Mf1JC9ZROqihZq025AaliwsB0rIrjzuE9so/Hat5u8YArMlg+zW/IrGb16OOTFbxRsyDIMgKCs5C6Hl/NPpNLlcLq6cWyqVhsDlkY4GQHNzMxMmTIh/a2trY9asWQAkk8maUPuIz1zl96qiNET4vJAIybyBjBEmoyFJ4rTZJE6brX8reMj+EnL/gJbw3z+A3DdAsG9Ap7+X/PC/ADwfjBCdSViIhIWRtjFaMxhtWcwJWYyJdZhT6jEakoi6RLWGUxBWjo6cSGdoyHW0d0yFcLZUihl1zXzxxMuG3XYkC5SGr0ca9LwwFNDvFtne33lA53m1mFIqVtYd736V9yN6noMg4De/+c0BoaxC6IVAfX09mUwGQygM00QWdjLw8n+QnHQhys+DmQJZRJgp3I7H8Lqfpf6o/0C63RhO05ieFysMY5w8eS4nTprNH7etY2PPPt5478184cTLOK5tOhk7UcW9ANif7+fWV1byjef+jBD6eblmwfGYhlGVPaSU1oHpKuZ447zjuPG0q9iT66sivjYl9GRuhzXyLpi5mOUTZ8aTvC9DDkvojMsQKRFh+yOdxsrMnDOnLoj/TphWjNK4MmDnQDc9oWge1CbfV80JQodvIkSlshp2n1tkW18ntmkOH2YK72lU00ufs9oMIbhwxuL4nkTfHayN6LTsyvXw45eeoCWZwQ20Z9qYSLFq/3Z2DnTTlszSURzQok5KYRsG/W6Jdy86GUMYcR2Dgw0RDYHFQ5SEENHou+FBBr7zRMz7GJPDAnqC6i9R/8ULSJ4//6AclshqOi6uj3PUZJpuupKuv7tDV8C1hndcqo5jaCRIODott+/T9+Cv30f9Z86Njy0ca+h5hwkvCMPANEJidCAZuOkJXdrANMqZHuGqWynNU1B5l8x7TsCcUl8l8lZ9k8bVTcOb0siBDkeZNP3PFfQ2JMnfuhqRMDEn1hG09zPwrcfI3fwU1uxmrLmtGJOyiLSDkdL6HirvIXsK+Fu68Dd2ILvzuiBkYwq8gKC9H3vJRBpvvAx76eSa6FotZwQ0GhIREqNsoSGXET5j+Xye/n5dgsL3fc455xzOOUen0g/HDRiPkxyda8jzM0j8ET90HkwDkbIxUzbmhCz2kmqo+WBNqVDXRymNBpoGg1mPwznqw1nlQBtISTHwqkiIg7cb0iYUCdOKB+aRzhetME+aNIevhjoVr0X+iRCCRCKB53nDFiYcbMOFfnt6ehgYGEAIQSqVOqD+iBYCAwMD+L5PfV0WJUGYScykfgaVdPXCKtBOi7DrEWYobGkm4+OMNDbHIZDwp6+cdAUvd7ezra+T9T3tXPPH73Ns23QWNU+mNZnFMUxyfolduV5Wtm9jW38nScumszDA9UedxRVzjtHEXtMMxeVEvDj3laQxkaYlma1Zp6qyjb4MaE5m4vpHscMkytlAET9l10A3W/o62ZXrod8tUQx0yNIQBhnbYXq2mWnZRhY06tTtlmSGzyy/mLzvckyr5orVQkpqzQ2V25WkT9K0eXT3Rq64+7tMyzaR990RHY1qBmW1SaUo+R69hX7OnbWUP13xUVzpVwn2HYiN+DTvHOjmyyvuYVZ9S0h004zptOXgmBafX/F7LGHghd5W0rJpz/dx2pS5cfwYjEPvsAQyhMQN+r78AAM3P13FYRnV4VDoTKGeItnrTyHzjuV60nKGCsgdiA15OBxLOy7Lp2vE5YN3ogZKIeIS1CTnVh0nqlVkCEQ2Qe6HK/DX76fhSxdizWuNa78M60yI8vGiPo14IM7x01ElvxxW0RtojoxpgKEhfZEdXpxOf3/A3VX7cJHjkrBo/I9LcZZNY+B7T+G/0oGwDB0eEgJ/Rw/+xg7trMpQ7l2gs1YsQwuwJS2dep5zke0DGC1pMu85gbqPnq7VdscYDqx0RDo7R16FR/tPnDiRDRs28KMf/ShGZyKHKILNU6kU73rXuw7KUR7U0DICE/0+iFQ+RLZ/OKt4foY5e/lPQ+j3zxka+hnMyanZ7uGaUOlIGgYZ48DCNmO5v7ZhEijJcRNmcNyEGXqC+isoBI9mtdo+0vUEQUBHRwd9fX0MDOjVd4S61Brb9EQfTrC+H+sJAXz2s5+lvb0d0zSHFAyN9o1QnejfUQZcpUkpaWhoQEpJPtePbUFQ8BCWdkwMp1HvHzooVnoW7r6HwmuV8eoehn924udEaF2eOQ2t3H7R+/nwI7exon0LgZI8vmcTD+/eiBv4If/CxKnQP0li85njL+Yzyy+OneUh9yPkcvohMjfglUhbTkz2NiuyZ4XQqfdSySHOROQQ9ZTy3LnpOf64bR2b+zroc4v0uYUQLAhQaNTFNkwylkO9k2Jypp4zpizgmgXL+cRxF5T7ObymWk7oiP0X3i5PBvilPH1Okpw3stNS8x6UD8W0bBO2adESh9AOM9Jy0qQ5uNffdEAHjmorHAzKUvMFjdKQXZ/ez9xL/rY1msMyQkioan8pEQkb2Z0n/aajqf/ns/WkX0Mi/mCcrWglONhxSZw4g+bvXUX33/8a2V8KRenGgriEg76vM4vcJ7fRec0vqPvHM0m/Rcd8I6RouMGtqh/CYyZOnjnmaxprob5DYVXXLJUuWnn+Agp3rKH00CbcVbu04+eYYBuIpBU7YoR8HRVInb7cUwAhsBe24Zw6i9SVS3GO0nwUHRIyx3TfI2Rk4cKFXHnl8KtwpXS6tFKKyy+/nMsvv3yIHsfBoHmjmqjAIUIHdMj1mX/9ifhAr/NQvI9jOU7lhOfLAE/qujV/Xb3coTb4WfF9XztYtj3kt+jastksb3jDGyiVSixapDVwInRwuOuPJrlKZGbLli3s2LGDM84445Bcy3PPPUcymWTRokUEEgwrjdvxOP0vfYX/H3vvHSdHceb/v6vT5M2rsEpIQhkJBAgQEiAymIyNMcYJZ/vw4XgO9/Wd7xzvbN/ZOJ0jYPtsbIJtsjFZBJMEEgihAMpZm3did1f9/qjunpnd2aQA+H5+eImZ7emurqqurvrU83ye51F+DiEslCxixMZQ2vMgZmraqMrvD3B9KTm8YQy3nf9Rfrf+Ge7d8hJrO3exM9ejw/gbIuCWmEyta2FBy0TeNmMRx4yZHDmfDDXuwt+sIDdRpTdP/2dj9CMQh148N29Ywbeeu5eNPe0UPRcfFeTN0s/TMa0ICXjSpzOIS/Rqz16e3bOF69Y8zttnLuKTC88gZZVjEI32fQm5QLMaxvLp4y+gwUlQquCNjURCwCKEJiv/bv0z5NxiBO4OhgyrN9yd6+Fnqx/lnxe9iZxbimxsGoEadBVz/G79M3xg3lJ9HCLW/sEALJUvY2gK8fdl6frEbRQffmUA6XZIwOLrzMqyK0/83NnUf/N8/aKG5R/MhUQwkHMQalwWTaLxx2+h86O3IjtyiLit2xbGcRHVw6R/3VRRaz5ktkTX5++i+Pgm6r5wus6jE7jB1uS69C/Ll2AIvFfayd+8Cn9Hj+Z7tKSInzsb++gJUPD0Dto2XzPA0r/NmBqsmk1J0h88gdS7j8Xb1IH30h7cl3bhbelC7ulD9RajPEJhziJzQj3WzBac+eMxpzVhNmvEH51nVcdECe/dX5RSUfbdOXPmMGfOnEjlXUuDEJYZ7j5HqqI/2H17sBb8g1GHN7pUjgPLMAdwHl5PqRxP4VgKvdXC8Vc5HjOZDJdddll0ve/79Pb2VmlQKsuOx+OR6/13vvMdZs2axbnnnsszzzzDLbfcwsknn0ypVIqAe2V9bNtGShlFhU4kEti2jeu6UXiAQqFAMpnkV7/6FS0tLcyZMwe3VCAer6fh2J/hF3ah405FjsWYUybgNC/Rf5nDey3pOoU4vQK4KB0J+cpZx3HlrOPYle1mQ/de3nHvL9id7ePcKfP4wSlXIBCMSerEk/3jjcDQfIxasLb/u1dtLtLUif967j6+/PSdxEwLqSRt6QYWNE/g6NYpHFbXRHM8TdJ2EOhYRJ3FPFv7Olm1bxvP7dvK1t4Oet0C31n5AJt7O/jJae/AZGCcp5H0ndYGKeY0jeebS9487DUjkds3rjzgWFD9ZdiZVAjBK917Ae2GZfUb8A2xBGs6d2EZRhWSPNDFbYC2IAAs7oZ9dH3sj3hr9gwPWCpBQwVgiZ00lcZrLy6bIEaTUXl0jaj29qgELsdMpOlnl9H50Vvxd/QgUk5VHBcG0bhAYDoJtDOiLk7+T6txV+0kffUSkpcdqZvu+kGiuerMyFUAzjTw92Xp+/5jxE+fQfrDi0GA9/Jesj9/ipR1As6RbZE57pBqB/rJANBql71jRMzCnjUGe9YYEpccMbpyS74mhFaYS6LJRf8xoA7hOeGi4Pt+FHJ/SJAcnB8eG07+Vhb3/6vyRgB4tSSsTxiqf9WqVXiex9FHHz0gfH/l2HNdN0rqmc/n+exnP0sul8N13QhwxONx9uzZw8c//nHOPlt78Kxbt46GhgZAa2fGjdOcE8uyqgBPaObcsmULN910E2vWrMH3faZMmcKll17KggULovqELs+tra00NTVVtctpWTKCPqjtcFB5LDgzeoXDY6HXT0iyHpeqpz6WCIimktZEhrHJOoq+F2SHLpN5Qy/L8Pqk7fSfmkcsYX1CDsvdm1/kq8/cRZ2TQAh4+8zj+MC8pUytaxlRee35LL9b/wzff+FB4qbLTRueZUqmiS8df0HkmTTSsRzNV0JUkYJHq2MM9uoIISj6LvIQKCmHBS0GIiLOZN2iRoSUkb0Okx0GfqokQo3+ydbUrvgysJNbFB7YQPcX7kbu60PUxbRnjTHQrBP+DQMBi3PsJBp/eKnOrdMvPP6hWIz77+DLwMXHnj+exusup+ujt+Kt24eoj+vgaUatl7Ef8AhNJyUfoyGBv7OX7s/cQeHul8l84uQo1ooqeVpL0r9dJR8SBsWHX8FeMF4Hbit5mudy/GSwDAp3vKRBS9FDJJ3XdFKv7LfKNodh9gnjfBhh1FdZNg0FTIQQMKKCiafCe6W/BgohagKWMLqr1saVJ+qhQpnX6qc32kL4f0ZUwC/Yj/f3tQTh+yP96+V5HqZpcvvtt9PX18fRRx8dHRswxwhRFW3XsiwKhQJXXHEF8+bNo6+vryqH1oQJE6LrkslkFFfI9/0qLlYoIVjaunUrX/3qVxk7diwf/OAHicViPPzww3zzm9/kmmuu4dhjj63yYCoWi5RKYYj+yJgAgHR7AYkwdBDE3pf+jcz8r4Py9T/DoZa7c5VmU0lKQdBHRIWZHg1edJZnSWcxpxdXtHkobF+sIg9Sf81KaGXQZqP9tyBYgdnqFy89ri0SKP79+At59+zFum+VDKYsFY1v3QYRtEl/b04k+eiCUzh+3GG8+77rKXhx/vDqSt4z50QOq2uOkkaOVnwlybrFCMCoSgeNsNllhVjNNhpCx3ORSkaJSQ+WDAtapFKkg3gF9bHaKjpbGAcciKkmgi6VybG933uUvu89ph9a3A5C89cOdhb1ZyVg6c7jLJxA088u06H9awCWQyWipsYlcIc+vIWm6y6n8x9upbRiR6A98oaciKtMPGhvDWEbEI9TeGADpae3knrXsSTffSzmGM1qj8whkS+dHsz2/PH0ff8x3DV7sOdo11x/Tx+5m1YSO/VwfT/r9VGTD7boCyEiYmmYo0SYodYjfEkD4FJD+o+1CLjU/L1CUxKBooF1+rvUlkMOCCoXphoaL41hKz4JPq3X1tw5WqlVL8uyoqSboWdQJa+lloQmpBCcTJs2jSlTavPYwvJc1x0Q7bm/hMeuv/56xo0bx7/+679GWpgjjzySn//851x//fXMnj2bdDodmUnDuuhGuoCDl92I8nPYdfMq2i/xul/U758wALuqXyo3G2XAoikLcWvwhTqMMVIZoTUEBd2lPDmvRMF3I61L3nPJ+y4l36O9kGXR2MOIm1akiRityICvsi/fy0sdOyn5PssmTOXdsxdTCqI6m4ZREwwMEKUThx4zZgrvnHUCX3/2Hnblunm5cxeH1TXjKVkVU2U4CT177tuyhjff/WMmpBp0HifTDADe6NtqGyYZJ4Y9RATj0cqwJaXtGHdtfpH13XsCZnQAKILOL/oeXcVcEDVXDiAbDSeDoWaCxHD+vizd/3w3hXvWYmRi2uQzhKtwFTioBCxHtdH4i7dqUFAzj8/Qj2QovsOIJAAuVRyXII6LOaGephveRtfHb6Nw33rt0TIC4BJ9D8xc+D5GJg6+pPfa5eTvXEPyyqNJXrEQI/D+iTQvlg4Db89sJfnWI8n+6tkgA68AyyR2whQSZ8/S9a2lnXgNzENDia6Hjwjdt5WPECbKz6OUxLBSA8GJrwMkCqNW4k4f/CKYieqdm9eHMGyEEXvDLnAHLEqhhECEnwex6NH2V83nHrjgR0o0fWLt3/SWWmvVosm/dh3eaJqWoepTLBaJxWI88cQTrFu3Dikljz32GMuWLYt+C2WoOSOXywGQz+erAhvatl2VH2io/gi1LBs3bmTTpk1cc801GIZBsVjEMAxs2+bSSy/lySefZMWKFZx88sk1vY6UXwRSFHfehXS7sevm0f3c1WSO+DqGnYHAiyj7yo9wO56ibsF/YMTGlN91VebphRyU9V17+OJfbyNtO1FKB6l0cP0woJpSioLv4UqfhliSp3Zv4oI7fkDOLaFQuNLH9X1K0qfke1ESwq19nfzglLdz5azjdHyT/RkzwRiVSlHwXUrSZ0wiE9XTqeUBOpiIMnl2YroBqaQGW9KruNn+VdFX2sLRWcxFnsOjLU0qRdyy6c730l4I83oduHJgWNAStyxOnjCDXdlu4qYdmYYEAjNQx53cppPJRWqkEUpN7UqYh8Q2KTz8Cj3/fh/ehn06Um0pQP9D5BKC4KWVFYDlyDYaf/5WzKZkOSBbxb2lDFRgFTovIcAwBuZLGorDMKxULAih9kXYGjwY9Qkaf3oZ3Z+/i9zvnseoT4Avh/XYGaB1CeLUGE1J/O3d9HzlPvK3rCL13uOInz8HI+lE/YxpIHMlYkunYh89AdmuJzSjLo5RH0fm3CgIMjHrNSfiAoF5ZsCvUf6R4t6HEMLGbj4RgK5n3ofXs4bWM58DPw9WsgxwolgPFZNeYAISwgSrIumZ8kDY7LljIqlZ/0Rmzhei8t4oi9xBk3BcDgNYqgBFTfAQfFGV51TokSsLD/tP1P67qn+HAB6D1lUqZK4EBQ+Vd3VAvbwLBReVc7HmjcNsSb2mHnFD1rfWXKi0G3HIS9m8eTM//elPWbp0KaZpct111zF+/HhmzZqF5+mFKnSlH6wtIf+lMqs4aNOT67okEolhNwxSSkzT5MUXX6S1tZVp07SHTwicpJQ0NjYyd+5cnn/++cE9jwJPG2E4CDNJfstvUW43hW2/IzHp7QgzTmnfY8j8NmJjziC74Ydk5n2JcNtfTQfQnzmvxO0bVyFEOf+OqPh/+GEgIoLrjmwXr3bv7ddn5XdBokhZDqVSgY5CXxR7bPS6h/Iwj5kWrfE03cU8z+/bRlcxR0MsScFzI37oYMRfpTTnJHRtBnhw2zocwyJtx6LkjfttwgoUEnnPZfH4acxsGEveK1VFBB5pOZYw6XULHNmi898ZojadYzQyPBEXwY+WvX34CirN1B5JZWpqV2RZuyL7ivR9ZznZXz4LSmHUx0fuIaRUNen2uMk0/vjNQTyOigiygOf6WJaBOQi6lVLh+z6WNbim4YDNS0ppM5UvwRQ0fPN8zPEZ+n7wODim1gh5g5vCBvwdal6LmrAqEjbehna6PnMHzq9XEL/kCJKXHIHREARtCkCMkXQiQBOKkawOAvTaujxLkC7CrB2TQ/pFhGHRveKjCDNB6xnPBj+4OjtsVb1LCMOh69kP4TQeQ3LaB4MAVvHot1L7k/St+QqNJ94a2NQ1WFJ+DqTeaYWxGQ66OXHYotTg5ww43h9E9D9vqN+DgyI0nIuBfxuiH4Y4uONAQRBvRwfDUyUflS2hciX9mXWR2SKqT/8t+4qobBHVG3zPaYCiQrASgJTwOK6P7MjR+Iu3knjTHJ32IDZywuKhkv7mSs/zsG074k89++yz/OhHP2Lu3Lm8853vxDRNent7+cY3vsFVV11VBQzCgHK12lSVbblCKkFMrRgrlRL+tmvXLhKJBOl0Okgyqq8PTVitra2sXr168EYr/V4ZyUl4ex8GWSA979/Irb8WMTWJ4TRTan8Cu+l44m0X0vXUu1B+EWEO1HqGC3xTLMmRrRPZm+vDNoJ8QMIIlG8aDFiBqzNKe7rGLIuEFcMxTOKmRcyySVg2CdMhZTskLJuGWBKhYNnEmfSU8tiGhVLFwds2iBiBO31TPMXJE2aypnMXm3vb+fjy3/Pdky+n3ilTMHwp8SryCQmhFQWWYUbh8JVS/Mezf+aeLasxhODwhjEcO2YKSqkhQ/EPJWG4kq5ijg/MW8qbpx+9X+X0l5CAfKDrx5CgJeysgueG1FtAd2baieNJn4Lv6QdfI4fCkOTY8LfQ3GOb4JgUn9hEz9fux31+J0Z9sDMu+REXYzjAgtKcF9mZJ7ZsGo0/fDNG2tFAJtCwhK+jbZv4vmTv3j7yuRKuqxcqxzFJphzGjEljGCYyCFg2pCpZBQStKt5DoE0J6q7LqeZFaK1BEGU2AFyZT56COaWJnn+7F5V3EQl72Dg0lX0fEXV9CR4avCRt3Bd3Ulq1g9wNz5C49AhS7ziGwsOvoHJu2cQU1E8YgYrFEKiCR/y8OXpnOlhE3IMl0gXDwet6nn33H4dSEuWr0LqGMEHmoeWMPxJvuwjDbsRwGisKMKIdXCRKa+jym65Heb0atCid9Cv8zc++Qnb9HTQuDjU74QJuRov3aF+zUHsxshMP6IRDLgo0+bnkIT0d/0aFn77Ui78nUa5EeeF3H1x9jcp7yEIAHPKu/l4BJlSuVPHdRWWLyGwJShKk1jgS/FO+zlJNkCgyTBgZuSqEmaaNMEBixd8JG5Gwdb0q2/cG07TYto3nebz66qvcc889rFixgrPOOou3ve1tEaj42Mc+xh//+Eeuv/56Hn/8cc4//3xmzJgRaTwqgYfrumQyGf77v/8b3/cjr6FPf/rTLFmyhGeffZZYLEYymaRQKAyZyTlaFwqFKvfr/r8nEgmy2WyNEoL3ya7H71lNftMN2PXzkcU9mLFxuD1ryK6/FuX1ofysNt0C0u3RWlJ9FyrfixC0TEg3cM+FH9OZIyrvGCDtcNMhCEBM8GN43ECHtx9qLITJFEcbvyd8tqEG5OoFy7h3y0vsyHZx75Y1nP2na7lqzmIWj5vGxHQjTfFUTU5Kb6nAjmw3K/dt49drn+Sp3RsjgPKphWeQsBzNRRlBBOjh5GAGVrRNHbjRHCZ31HAyIpfnuGVHneBLiWkZ/OnVlSxomcDUuhZ8Kcl7LoYg8jTqr4Hob2YRQujJzTAQtuauZH/8BNlfr9AeMU2JmuagIQEL6Ei3nTkS586h4dqLtJdQPw2LJqX5PPCXDaxauYOuzgLZbBHP1XY82zZJp2M0NiU47oTJLD156oB7Dfg0BGZZ113Zg9E306z1e/lvJQQiiEmSfPN8rKmNdH3qdryNHVrbVPJHre3QYESCj45dErfxNuyjuHwjqXcdi5FwUEJokq5SVeRnQE/2loEI637IJ/aybcHP+zQc9x8YsbEoWUBggjBRsoCVmRuc5vXTrKiyrjgSXWcjPh7DCdwto8kgcAk3k5hJKtoXqqxkVKdBFcKq//fgfNWfCKq/R8crFmKUiiL6Rt99BbK8QKvou/5UQWh+5Um9aPsa4BH8rUGFXwYX4WcAJpQXfLq+Ps/V4CO6zg3KrQQjbr/zXD8oz0eVJMr1dKLNsNySp+tcqa2p8SlqHg+/B88jOE8YIgoGiQhcXCs1Q5X9TfkZKF/3rewt6qSg4bkM1F6+1lI5n7iuyx//+EdWrVrF9u3bmTp1Kp/73OeYPXu21iQHdZZScvHFF7Nw4UJuueUWvv3tbzN+/Hjmz5/PpZdeSiwWi841TZNCocDZZ5/N9OnTKZVK5HI5FixYwCuvvMK//uu/Rq7IxWIxclEeSioj5Nbqv1BbNLgYSLcbWeoAEYZ6MEC5+MXd0YaiYttbs5TKedgQBnXO/mWiDvtKD5eyB094V9fXLs/mfmpawzqahta2TM40ccMZ7+GDD/6a9V17eKV7D597/A+0JjJMq2uhJZEm48RxgkServTJukXai1k293SwPdsV9U3csvnOkss5d8oRUabm/QUGhhCUfI+GWJIbXv4rz+zZQs4rYglz1EBNKp1CY18hy7tmH89JbTOinEn7K0NeWQkGQiSX9UrUOXEe3r6OpnhSgxYlSQSuYpVk3FoMbyBK5iYsE5Qid/Mq+r7/GN6r7Zpsm7BHFJI/GqzBQo4pkF0FUu88hvqvnKPvW0PDUir5/Ox//sqmjZ2ccOIU5swdQ+vYNMmEjVLQly2yc3sPq1bu5KYbV7JlUydXvHNh9T2Dz1LJx3FM7rx9Dbf9YTVNzUlKRS8AWFq7ks7EUFKRzZYwDFG1phqGBlD/8u9n0tiUREqFERB0naMn0nzjOzQR+d51USLDSiJy+Jz6P7fK5xfyNjAM/I6cjhFzw9u0SWhsWr+lASgxGhLIzlx5UbUNvSt1Dh77e0QiTBCQnP5RDKtGXo/AZINhMwBKDPaeykIEcJQsgkqgZKBulgWURwXgqdCWhd+E0FQXC+1e/sAGrQ0s+TXBXGWMh6oia/E+Bv27/D0CO5XEU6UqFumy6UepEAxR/btSAQ5T5X+y+jcxBLgg5HlVAguj/F2EofxjJiJugYiXu7BW+yvbXHVO5bPofxwN4Gr9Vkui+6torKu+QLX/OoOVShFCp3WwbZvW1lYmTpzIVVddFXFGQvfmyvfb8zymTJnCJz/5SXbs2ME999xDS0sLsVgsys4MGmCUSiWOPfZYZsyYUXXfRCLBTTfdhOd5ZDIZrrnmGorFwU0fYZmNjY3s3LkzMKGX54dQS9Pd3R0BoWoJwIHbid18IunZn6W05wHMWAuyuBer7gjqjvgq3c9do4PKicBsZWcQNXgV/TeQo1lWaz19PbQHevBY0bqmeS4hr2SkUllHHQfG58jWidxxwdV8d+X93LnpRbb1drAz282ObFcEmqrrpt8vQwhMYTA2UceJ46fxiaNOZ0HLRHwpI57L/pLMtRs2ZJw4D25byz2bV2vtyH6QaH2lvY87evZxdOskTmqboUm+ByBDrkTahUyQc0vc/MoK5je3RQmZGmPJCMj0lAr84IWHGJ+s54NHnFRtBgINUITQOzOlosBepWe20nftoxQeeRXhWOWEh6ghF+Vqwq3SoEQqVLZE5lOnkPnHpdGkXenW7Lk+tm3y+PKNbHy1k0999hTGjc8MaHcq7TB2bIajjp7AkUe18eMfPMFRR09g7hFj8TyJZYmKyUB/zp03lnjMIh638ANzku8rEgmbu25fQyrtcMHF8+jrK2JZRjQfh8AmEfBJwqZGnkVjMzT97K30fne55rmgIpfvSBkwRD9FfWSbqLyHNbmBhu9fgpF0UHmX0lNbUfmS7j9XkrvxeVLvPkYvZJahd9kFl9T4Okg5wULyWkz0eoGRuW2IzCxU4MUT6juUdAPuSf/61Kpb+WULI2uGJiVh1+lPqy44YWRtE/Vx5O6+iCcxqsVPDPrH4LPoEOcNmJRCzUV/18lQRT5A4SfK3wW1AUUtMDEI0IpMOZUgZbgxcyBDql9fDErSDxd700D2BIuy8VqM5eElrHMYc2XZsmUsW7YMICLjhsCgsn2WZUW5gtra2njve98blWkYRgRcwmvC5J25XC7izIR8lkq35KEk/H3GjBk8+uij7Nixg0mTJlWBqmKxyIYNGzjuuOOAQTRZARjxul8EI46RmETf2v/AysxCelmk20G84UJK7Y+DYWPEJ2oeWkCkryqqoo2H6olKpTCFNh+lrBhpO048pEVUnFeLFjGAwxkBF0lrIs1XTriID8xdymM7X+G5vVvZ1NvO1r5O+kpF8n4JpbQnTsJyaEvWMznTxBHNE1g8bipHteo1ObSGVN5zNIAlfK19pQJ37xIp26ExlgwAy2BlDf6bryQpK0bOK0Zu5gf6hIbWtKD5K1fddwPP7t2CbZh8/cSLuXjaUWS9In7Qyo898jte2LedvF+ip5Tn00efFamAhKKsaQlMNO6a3WR/8TT5O9egciWtQfDliHgbVYhaSh2oreAibJP6/ziP5GVHRuHp+5tRVGDz3rq1m7YJGcaNz5DtK+HEBtpvpVQYhuDIhW0kUzb79g60zSqlME0DKRVTpzUxdVqtXQU8/OArNDUlWXT8pKG6e6CpK/AsQkDmmpNwjmyj58t/wV2/TxNpQ3NAjT6r0giZAuVKjPo4Dd+7BGtSgw4Yl7BJf+iEqjoYDYkBx6L69QOBh1aCBcYKsspaqepdVojWhQAq8lqoWjkugroaDn5+O35hN372VYSVRnk5jPhYvJ6XtMZguF1AUJQ9oxWRtLUpzRCDg5ZBNyej37WM+LJQ0zLUyTV/Uv3mnxE846FOCQH4AdqwRyuD8b1UqFkyBKr3jaVp6f/ehkDFNM0Boff7t68SzIQgpRbwqNS8hCH7pZT4vh/NOWGU56Ek1KQsWLAAgEceeYQrr7wyMm05jsOKFSvYsWMHixYtGqrVurzUYRh+ntjYMyjuvofEpLfpkAVeltjY03E7nyG/8afULfivIeu1v+OrloOFX+G5GTpqRJp6z2NTTzuFfA/78kEyysqNUUUZpll2Hw/vEa4tCh1oTiqFJ32m1DUzpa6Zt886jrznkvOKuL6vybhoTY9lGCQsJ4qdBhqsGIgBgGW071zISz194iw2vvurWEEwvv2cqar6w5M+LUEm7AMxDcEIzEP3bHqRkvR4/or/x+/XP8Nv1z3NxdOO0uojO86Grj3kvRIvXvkvrOvczWcfv5X35PtoSaSrSDcIgbt+H7nfrCB/8wvI7jxGJobIxMtagwrNwXCABdB8lZ4i5qQG6r91PrHjJpddphn48IxAKzJ1ehPPr9jOhvX7OHzG0CGTH1u+kXzeY/yEupr9o5QegL4v8X0dx8YwwC1JEkmbVSt3sntXL3v39PHKhn1MP7yFfN7Ftg0qNdy2bQwYaBpwBC+M6xNbNp3mI8bR89X7yf3xRYStVfDKLU86NcnOgDAE9d86H2fBeK3xCiR7wzPIfVkynzoF2VuMAEvhvnUUHniFhq+di78vi9mSGppLc6AjexCJXJ6VD5VpIghMj0oijLINW1jJgcAjuM6ItZJ79Y/kXv3jgPoKE4x4IvIqGk7MKY16AfQCHsqh6oBDJTXnMlHW0hxkhdrrzRmJRAGGQPYGnJY3iKYFyvNVOWJsOTPzSKR/H1dGxBVCEIvFSKf1whEmT6wEN5UB5Ya7T5jG4rLLLuOnP/0pLS0tnHXWWZimyfPPP89Pf/pTzjjjDCZPnhyBrwHlmAlAEW+7MDrWePxvAFDKj7z20rM/G/2u+6h2BuP9kVr8RCn1ZpSgyr6neZwh2XdsXR3XnvpWuvMFjmrVrryOYeF7Es8PsjybBpZl4Lp+BFLsIDJ5qJ3X66I2xzimFXgL+ZjC0N5L1uB8IF9JPCkxhBjgJbQ/gKWy/QnLYXKm9gb8QEUF1ptDSsR9bt9W/vHI00jbMd47dwlP796MK33ipk3csnh+3zYunb4QgJmNYzlmzBRWd+zglAkz8aTEDGzgsjNP92fuoLj8VcxJDVq7IstxRUIZrCFVpg5Tq75lR57Y0qnUf+t8rAn1VSTSWmjTDLQEJyyewpoXd/Oz/3mSo4+dwLTpzbSOSROPWygFuWyJXbt6WfvyHl5YuYuzz53FjJktSKmwKhIRhvUNNS6VrtOWpb2OHrxvA6mUw8TJDdx52xr+8ZMnkUjooE6WNYg5pwbwCM1FRkuKhv++kNgp0+j9r0fwNnXovhQE5jeqTGuYBipXov5rbyJ+6uFRHylPT1BGawpV1DEesj97ktjpM3AWjEckHMxWnVzQyMQG1K+WSeK1FBEBkRbyW25n121jAYmf20ds7EnBScHzCECMLOwmdfjlNBz3S7y+DRhWHcrvw4iPJ7/lN3Q99dHhAUvQTnNyAziVCd7eQAvzwZD/Q02pkmAjoHqK5Yzxr6EWaPBqld+tSrBxoGWGUiqVyOfzfPvb3yaRSOD71cHRYrEYn/vc52hqaqoZCK6/hB5My5Yto6Ojg5tuuok777wTwzDo7e3l2GOP5W1ve1t07iA1BILAjvpMUC4IGzCoP+bH+izp6XOFedC1dv0BSwgq9uzpo7srT3NziqZmHfdEehLDNvn1/6xg/rQpnHD25KAVChOBsARmsD50dxf49jce4t+/fk7V/Xq6C3z7Px7mw1cvZnxbXaSNATANI/IW0tycMH5YZYW1ecUU1evNYJvV8LfhgIxABBvBIN4Xmt8W7l32dw9Teb0RmNYO9PkNC1p6SgUmphuiBjmmSV+piCkMDARdxRzjU/XR+Wk7RncpIDsGGhblS4zGBA3/eR5dn74d94VdiHQM5fvlzMbUnvQrXzwlVRT+nkKJ9AeOJ/PZU7XGxa0NWCqlDF7gAx85gZ/9z5M8/OCrvPTibvbtyyJlqCK1qK+Ps3tXL29/50JOPnV6hJb7l9f/AfgBSdYwBH+4+QVWv7iL937gOI5fPJmv/fv9/PK6Z3jXVccGqlTNsalVnlJqoNYlTBooFYmLj8BZfBh9332E3M2rdIybVDliMKA9qboKZP7pVJKXH1nVRxH/IAi8BeBt6iQW7BTwJSqrd3zKl5FL4GDP6dApGgYQMKpv6xcxU62kpn8YgNym60EWaldK+RhOM8JwsDKzq8xNZmJi8IYNbc8Pm25kYpjj6vC3dSNsIzI9/l0OnUST6gg1D4MUojUtPQXNRbLNg65V2h8JTUKGYfDAAw/Q3t6O4zgjAhCVYhhGRKg966yzor6ybZsrr7wyCtHf/x0WQow4E3n/OerSSy9l4cKFrF69Gs/zmD59OvPnz686d+i2V2gKRBmwmYk2fagiBPz+mj5G0hbQtIDb/7CaPXv6mDipnvs3rWfa4S2cdc5MfKmwgB07u/EtyQlMJlsokow5CEPw3LPbefWVdurq4+zaqbXrf7zlBSxLh9A47YzDcV2frVu7cN0w31G5HrovynNsSLodSf37XyulwvNklQZ/SAnAEELv8XxPYTuDu73vrxyM5zfsKPWVxBJmRJ5RlO13Qojo91CEEBGjOuymMHiaNbOVxh+/hc6r/0Dpma0YDckoQWAt00hYngqc7kXMRHYXMFvT1H3xDBIXztMvji8H3TGVXy79t2EICgWPRMKmoTFBW1sdn/viaeze1RvxU2IxC9f1+fK//IXJhzUC4HkaYFSWU7Y/q4Bjo7ACEHLTjSt58L4NvO3tR3HCiVMA+IdrlvD97zzK/3z/Cd7zvmOJJ2ykVFEcGKNGPxAwyKPjQbwJTdJNaw3K+XPp+96jFJ/YjDANRNLW0W7bs6TefzyZfzhRB6irAEiht5A5oZ7cL59F7ulD7uyh7wePY7Sm8Dbs08G3IEpKOaQcqkk/3IUpD6WEfsuFoYPCmQmU14vTdAKZef8GgJ/bSqn9seCagdsUFQAa5XaBXY/y+jDsepSXHeSayrqgX2pAJGysyQ34GzsgZgZuvQer0X+7oiBKBzBkX9a6cAQnRJPvEJo+UZOATPkZ2aYOVCe1iVHUMBG9HtqXELQ89dRTvPLKK6RSKTzPG3E9tMZXuza3tbVx1llnRb/FYjEuvPDCIa4um4fCsgaTgfwPn6lTpzJ16tSqsoZLBzBYG8qLrKQycnV478rPA5VK/ollmdzxp9W8+MIuPvfF0zBNg2xfiW989QGSSYulJ2svrkTcpjGpTdJhiA+AseMz2LZJKu0Qi1k8+9RWDpvaRDLlRBteYQgScbvmBng4qQaL+hWofBbhOgJ6LXH6beLD776vuU36WKjtKm/KDUNgHKLULQfj+Q0LWpKWQ1epIg6G0oSdMJdDyorR5xWin13pV7CEg0tClazrY46vo+kXl9P5j3+k+MCGmnl2KiX0fMGXyK4CsZOnUf8vZ2LNaNEaF8uomfiwf2dX9pETLN4h2jVNg7YJ9VX3LRU9XE/iBVqL0B4ZYYmKh6gftgATtmzu4pbfr2L7ti7e+8HjOPa4Mvm2oSHBJz5zCjf8/Gm+/uUHeNMFs1l0wuTI5FSr3iEC7j9oIpKugtiJh+GcMIX8zavIXvc03to9qIJH4i0LqP/Xs4Jou6GpJOCBOxYoReyEKcT+dJUm5gaZr/FldUK5uPXaT+JKBaauYCyFZpuwCqEbtDBRXm90mXS7CL0SKgoLPnyE0JOMsNJ6hxeUI8x48P4O1caQXq9BsjmxIYhWXKENe53MDAekfQhkNJNTpbl2wG/6hIqCy+X3P8aAYxW7S1HxQ/9jEY1IVX+X6I2QrAA5AdgUjqU5275E7u1DduX1mC961QTz1zhlRVhPy9Lv2ec+97mDVmZomlFK4bruoOMkXMRM08RxnCpyb61rqs0pJr7vR6DHMIyank5DlVdZbvnztTHdabO/SU9PgRXPbOf8i+Zimga9vUUymRjLTpvOY8s3ccyxk0gkbTxPUgzWjpLrkbQdpFS0tdXR1qa5j4W8Sy7nMu3wZurqyiZnAXi+HBWeh8HXs8rjlUCooyPHc89u55RTpwfeqmUlgFWVAHegJnvf3iyrnt/BstMPr9qcv1FkWNAyKdPIHZtWsbB1Euu6drMn30PajlEMEk4dVtfMn15dyZunH41UkrWduzn/MK0aDNNiR51tmyhPe7E0/eQtdH3qdvJ/Wl0buATcDGwD1VtEpB3qPn+aJooKMSThNpTK47mcG5QJxaJPY2NCazdM/XtnZx7bNpG+xLJNsn0l4jGLUsnH9yV9faWAPKt5LfF4ObtqV2eeLZs7eeLxzax7eS9TpzXxmc+fythxGZRS9PWVME2DRMIilXL46D8u4b571/GnW1fzl3vWcfSiiRwxfxxjx2aIDQIQqsxEYVtDkm4QpC/51iNJXDCX3I3PU3phJ/X/fra+tnLnKbTOTJvu0LwiwIhZkXdQCHCi+DcVMpQp76CKMBAWyFI7wm5AeUHCLVlCer1It5NYy0l6caxgo5fdoKsK0/+3UlFWaFnYhbDqUH4WIz4e6fZorBNdO0T7AhBoTm7QJHJDaxVGpIYdqYy4mMoTB9c+RIfEIOepke+qqs5zwojB/WwsepAFgKLiMxhv+DV+CzWiwXmVx7WKt7zgaY1jOdqtMIWuh2VgTqgve3WFgeh8iezM63MtA0yBNbFBlxXMI5Utfq0n6sp+D/MIHaiEICgsfziujOtqM3FHR0cUydYwjJrX9e+fMN3AUOeEdRqpGSqs96GW0PTfvi9Ld3eeKYc1AJAM0phMndbMXbetobs7TyJp4/uSRMIKznEiwJDPuyQSNl2dee7/y3omTKzn1pte4D3vW0Q2WyKVqg5rMRrpr1Hp7i6QycQiQCKEIJst4bo+DQ0Jdmzv4bqfPsWJSw7Dsgx8X69b7e05bvj507S0pBAiCMVlGeRyLtNnNHPq6Yez8dV2bvjFMyw5eWpEX9CK0zcGeBl29Fxw2ALOve17bO3t5Pl9WzkvACSelPS5RZa2Hc6nHr2Zrz9zD+u6d+P5ktmN46p8xqGi0y1tKhIxi4bvXYxIx8j9ZoXOvhxEfNW7IlP/3V0gtuQwMv90Ks5RbQGnQw4LWCqlVPT5/KfvolTytAmG0LStd3BXf+gPNa8TAn7w3cdAaROOZZv0dBdYdPwkrv74UkpFn1jcYuXzO7jtD6s5bGoj73j30Sw8RjPKw0H8kx8+QUNjgvd98HgKBY9YzOSMs2ZyzLETefD+Dfz1sc089+x2PvQPiwPQMvjArtS6RKg7zI1U0m7MqasWkQrOd32fkvIRwbzvmCa2UQ5QhUEEYqoIvOiFQWdG1QGLBuMKHQpRfh5Zgj13L6xyHBJmgFNS4xh3wU6C1bbiwhpmmoinouhdfR3Z9dfV9GwWpr6vzndUW4MgKP9kttXpBdBT0aJfGynU+LOW1mHQ46LfOTWOVVayMq5K2Df9NRNUaGdC7/ER7qqqJtDugo7AG95Elb9iahAhbLP8GTMwLBNsQ49b20D0P8c2dCbyimOi4liY8kPELETwiWPq8Py+Iv+n1RpExi2IW1orNjZD8h3HINKOviYTI/u/K1Bdec1rCVNYCEi+ZYHeSNUA7fslI1SJh7+PZlEfSkb7voZamXPOOYeJE/Uc5nkenZ2dg9Z1KOm/gQTo7e2tmWbgYEutdWHwsa3r4bqSYtGPYmZpLyJIpWzyBbfMeYxZbN7UxSMPvkJDY4IFR7UFQMYm21fihl88w/GLJzNzVivf/o+HeOTBVzn51GkVddu/Nun6CHq6C3z2k3fwxX87k0mTGyjkPRJJm+t/9jRSKj72iaUAtI5JV0x9+qa+57PyuR18+RvnMGlyA/m8qzX9CvygfYmETXNLilisrC2D0WliD6UME1xOMj5Vzw+WXcHXn7mbi6YeydVHngpA3LRwg9TfX1t8Mf/w8G+ZmG7kutPfDZRzQfQfPJGpKOighm+8CaMuRt+P/4pRFwtvjOzMY06oJ/25U0m+85jIvIRtIBjIIB+qI23H5OOf0kHvpFK4rqTS5B5tFPtJCB6EEJiW9tf3PEk68KYJiUqnnDqdWbPHRIHqfF9iGCIy+xgBMTf8rndTksamJJdetoDTz5yBUtDQmCj78A82OAJzUXU9A/DiWGVQJwS+0PkebMwBxZR8L+o3LzDp1dJUaca3WQVCh58EDkAC0p1VN5cx5/wZgoSJAkODD2EhDIeyx8EI7h+YixpO+B3K7QbDoRq1lAeAiMxOQ/B4wmc5NoM5vk7nRrLs6gU7/FIJHmp8V0pVRKSlAmRUayKUUhFgUoFNuirsf1gvIcoaBkHEgao8bpiGThkhiLQUylMa9I5U02IaqLxL5h+XYk1r0n2Q0HFrIlBhBVo700BYgcbDMrTmz6o+LszKYxWfoxR/Vw/5u9aQvnoJcnefbl/KJvuTJzEyMYzmZNR/5tgMKu2AZaLyLubEevp+8DjW9Gbip8/QkY7jBwgghADfQ5aKo99iC4Hh7H9W8dHwB0I+jJSSyy67LDp+yimnMHeuTpkxXNC5oSQM5/+hD30o+j4SL6nRcCpqrjVCoHwXJX0MO14bxATvumka2JZBseiRquChFAoejmNGfyuliMVMWsakI6qB50kef3QTT/11C0tOmhrxGD989Ync+Ovn2L6ti4vfMh+F1mwc0LQpyp6wUJHNWlCuo1QUCx7SD8yk4aWGIJ2JkUw6CCFIViTJDUGZlIpCwWXXzh4Mw6C5JYlpvjEACwwDWgyhA98sGT+dOy64GtDJE0GTcUNgcvqkWbz8jn+LrtORA6uBxYDBFCzMypfUfeF0RNqh79pH9eQXt0heuZD0R07EmtKIkhVJFRnZQO5v65t2ePMBdtXA8ittiOPGa1NQ5ONPhfk92uWW561Qref7ivog43JY5khf1Jo7CUOAYeIpiSUMVu3bxv3bXqYhliTnlpjdOI7TJ83mRy88zKzGcaTsGE/t3sinFp5J3nNJ2noQF32PmGnR5xbZ2tfBnMbxZN0icdOOzH6HRnS4aMOuIzburEHPGs0uLXSTdJqOOxgVjECLOTaN8iT+li7N+wl35iFACEFBaKKoTOIXLNaGqU0VlQt39Bku+sF3XyiUIXCSMR1QyrGw4k6gjTB0VOmYDTET4ViImAmOpTUOtgGOiRG3cA0wEzZWOq7J45Mbyf3+ebr/5V7M8RkouEMvsFpNiSr5JN48H+uwgx/TQYWJEWFgOoL+L5NAm52TDt4r7dgzWokdN7mqvOKDr+Dv6cVoTkYgMH5GdTh7AG/d3lGY5oZsga6f9BG2jRhBLp+apfTTlhyqRaM8VxqUSqUoqF1zczPNzc0HVI+wXCllRNYNSceD7eLL3I0Ks5lfQqFjYfW7A0KYWIZdVTfNufIRpo0wbZSS0VxQPk8hpAumQ2Njgrr6ODu2d9PUlCSfc0lnYmzb2k1La5pUWm+gPE8ydlyGufPGVtWikPd425ULmTCxPjo2aXIDn/78Mh564BWkr4jHbTo78xGYCGXItmFgmdUAT0kVEW1DjYgwhE5mGogwBLF4dSJjpcAt+eTzmt9UyHuYlgC0N2sq5UTnLn94I74vufjSIzATBiDwpYevvAEb51APbRnOIdfIDJvl2RACT+qkcYYoewbFTAszQKh5zyVmWpGGtRKwDAlchEAZoFyfzD+ehEg6lB7bRPofluAcq9WTEdnWMmoO6sGk/72LRY9YzOKVDe3c9NuVpDNOlCCx9vU6L50deCWdf+Ecpk5vplj0sO0y6vY8HVROSqVj0hgC3/MQhqBU9LBsHa/FDwZpMYiJEmZUFgIKhSBFu6FJUqMhP/XXNkXXBZYKx7RI2TGa4yn25ft4uXMXp0+aTVM8ha9kRD3o/9zD6Ii/XPMEv1n/NPdd9HFSQRRGGez6D9Xg1CYwGQWX6tdgwED5WYRdT1CRytrT3/YTjTVZGl43azj92iMHLmIhaBlfR+OP36Lz2AQq1jD3Thm4iDJw6Z99OIg3JALQEoKXkHcR8YssgTIEZiJQW6MGzb7qUak3KotJeaiHsTQVIKTS2pempObqGGLki7ag7BafdzUwirq+wlQmqq8ZcHyQY5HX2kg8Lw1949iSqWR/+iS9//2INl0ZAhG38Hf1Ys8JFxmlF6+CB0pFiSSNxoTW8E5qGFjv0YphoNw8ZnoC2efvZs9vP4HVMB7lFqBfMLDqManQ5PICVkMbEz7+p2ixPZRSOS9XakAqUwgciMYnBC6hN1TIf+k/f4UpCQYCGjlg4a7VBqSP8ooIw0IpieEk6HnsV/Q9dxttV9+E8l1Ev6S+pu3guT5NzUnmzBvDX/68niPmj4806g/ct55jj5tEJvjbMg0KBT2P53MuiaRNLGZx5jkzASgWPHwpKRS8aF04celhFIsehazHJz5zMs1BDCzT0G/rsG2jou+DeaajPUddfZyuzgJjx6UpFbzIezUcu3v3ZIknrICfowPkOTGT5pYkQggSyTKYDkGQlHqtv+xtR5b7FVBITMPCHIZVMhrt2P7IiHSfVsXOOtyJf+6YczCDCoWmBaufF8VQu+Eq4GJpc1H6/cfD+4/XZbia4FhLuxJeP5xU3sMOVM2OYxJPWMTjNp4lB91QhjU3DMHjyzdx6unTAa2WCwFLSMq1BlFjhySmeNwimdKDI52ODXpedO8DeOjhdZahQzDPbhzH7MZxAOwc083v1z/LPZtXc+emF/ngEUvxVLkPVEUZr3bv5cZ1z/Bixw6+uOhN/PjFRxiTrGPJ+OlRtMSRqmz3R4QwItNQTZF2eINqkGI4AwLElbV7ow/aJcxEZLIKdxdRebZJ7PjJQ11+UCTMebJ1y1ba9+3jqKMXsvLZ52hoaGDKtKn4hRICgeHYWEMs8H7RxYzZPP/sc7S0NDNxymS8XBErHS+7A4/2cYVj3zb3y5yzPzJg4xNkcXdf3kPp8U04x01GduQwW9Nam9udxzl6ItnrniZ28jSs6YHmINiFdn/2Tuq/qoOAGfXx6izn+13JqLJgmAg7jrDjGiRJvzxmhUBYjjZRhMcMHbRQWEOM/9FWZwSbvMrPUCpTCNS6PoydM5J7K1X2kKqM+huWHeZB6l/fMAnv2t1P0FPYh2U4qCB1hyFMXL9IY7KNw1uPRZkWRrDhUkVNJi5sfo6eJ26m7Wr0TtQsly9Ledw9r+C0zQPg4jfP5ze/XMGvrn+G2XPGsPL5HUyb3szZ586M4moppQKwQRRMDiCXK5FMOtz2x9WseGY7i46fRD7vIgRBzjqdc+6lF3cxvq2OVMqJEhyu2/Mk3fk9VW0TGPjSJR1vZvbYE/GVj4HeBNu2wV8f38ILq3ZRKnqkMzH27OnjsKnVWs8H79+AlIoLLpqL4+hQHj3dRR59eKNO7lvyI/pCLuey9KTDiCcsLFsDM9MUWKaJxMcQJtu71rKtaw0xK4mUnh6/CKSS2KbD/LbTB127hlvXRrrujSjLcxTkRmntgIKaoYMH+17L1ti/ohCog4WIXEqrfx9Y9kgkQvlBDJZJkxv4+KdPHlUZL72wq5x/Iqi75/lYlsljyzfxzFNbydTF8NzqoE1hzJdNGzvZsb2H63/+NMWCN8BHX89rArfkc8U7F9LQUOa27I+EbTaD3BE9pQKNsSTXrnyAp/dsZkHLuRR8Vz9XVZ50LMNgb76P/3z2z/SU8hzRPIFrT76clkSao1snc/vGVfzwhYfJuSWuOeo0pte34ks5pLloKPPdgUkAXg1bc1TCo6VOZHFv1TlD1WM48fM9kedSmDqgahy7snqnPOA2YvDfxMDftYlRlrP5CkEumyWdTnP/A/fz0EMPcf3113Ptj37A4sUn8P7p76eARyqRZMOrr/DnP/+ZTCZDoaDDEMTjcXq6ezjttFOZO0dzE773o++zbNmpvHPKO/CkryeB/d0Q9Sf8Vv00/IK2PzIoVyMg8VszWzHHZcjd+DwibpG4+Ai8LZ34O3rKdcuWdLyWIIiiyrmIOlNrtkI37gMh4SqJsGP42Q5SC95JasHgps4hi1GqhkljpNcNPm8qpVBKItHjN/yvbH4L3Wr1oiSEgSGMARqfWo4B/aXWGjAYn2X58uXMmjWLMWPG4Pu+dqeWLoYZ49dPf55V2x+mLl6P6+vcUY4ZpzPfxZLDzudz59xO14pbyS//NfbY6TSccTVGLIURS2E1tISVCTpAgjBxd69ny5eXMPEzfyYx40QSMXjfh47npdW76ezIccqy6cyY1QoQeXVVWPurxn3oSpztK9HckuTSy+YPaF+h4HLfvesizbtUPmBy4zP/wrNb761qm2XG6Ct0M2/8Ur564XJ86WGZJoapY41dcPFcWseko7J37+qLNEBhBa94x8LoeQM0NiW45lMnkc2W6O0tRvsU0zTI511KJZ9sX4m9e7LYdjnqbskrErOSPLLh1/zyya8wJtNEwctiYCCEiSeL1CfG8uMrNiFqqEb7P38pfZTvRXOqMG2Mfs4eg431YTUt/c0xypMgZYX1rVbBSqu9rYGE2cpTwkmmKuW1Ujr0fJAoUBgHHra5ktsipcJ1fUT4tAbTtEgNJIpFj1LJH5CCPPzTdX3yuRKWbeAGqDX8XQjtQTRrditSKbo684Hpp9qioZTW6JRC76kDkP6TmykMGmNJsm6RZ/Zs5nsnX86cpvHsyfeSdYs0xJJR2/wg4+g3l745Km9bXyffeOYePnfsObx37pIB9zP6PxdR+W+Ynd0wz2BIMXVo7cald2hitl9EmDHsliUYyYnBTQ6ERKlf1sTkC7DrjwjqXCPl+0GKGul6HrZpsWrlSn74wx9y7bXXEovF2LhxI1/8ly/y61/9mrr6OhoaGwFoamwkldQq5jAV/aQJE7nw/AsYN24cX/va18jlcnz5y19mz549jBs3LrpXY0MjqaTuv4MPKcpy6PLtlkWpwCPRk9hzx2LPHUvhvvU6VILngzRBgNmajoIlAhjpkOANImnrVBhA6p3HlAs/IM8hEWlZlOchS32YyQakW6C4aQV+XzugMJINxKcsxIhn8HPdgemizNnZHyJurYlfKRVxEcoAxMQYke2tsmydC0kiMTAwAk/E4TTheqHSPJbu7m5Wr14NaE2ObdtIKVm0aBHr169n/PjxjBkzplxWMI7q42NoSY8n7TTiyRKgsK0kphI0NB+OfO7PbP/mm6k7/Ciya+6nb8VtTP3PtaBkpHWJ2uEWEbEkfSv+BCjMem06lFLPS5V8lf6JD5UabNOlj9l2WRtfKno4MSvStBTyHplMbMCGtC7RqtsWa8TztRbKMhziVpL6xJigH8riOGYEUEIND1DW+gcn9/UWSSRtREBFiMdtjlk0sUbdyzJ5SiPnnDe7GvQGBSadOlrTDTSlJlD0cgj0WPJkibr44Hn8qsaI9DEMc4CZVEkfYQwfm2dEs3r5ZipQAQ9mUe93nVSDulFGNv5BZaCpaX+BS39SbkhcGqkIMXD6tQOuyrLTprPstOn7Va/BpD8hdzTSX5OVdYuk7Bj/ueLPTK9vZWK6EVf6bOppZ0HLRM1PCW5hCMHXn7mH3bkeitJDKcXefB/b+jrZk+9lX6GPeidBynYwhMGXjrsAxzSrLQqq4t9wiET1+xyhVD1PK11x3Ccz55+j78KM7VcfalOStok3Lbmtoryy98FBt9VGGjyPvr6+KHy77/v09WpNj+/5lIp6F1YqlQbE84jFYkyapIMZJhIJLMvCcRwmTpxIsVgkl8tRV1dHoVCo2jX+LUs03k09P8n2LO7Le3Bf2o09ewyq5FN8YjP5W16g4dqLsWe04L3aTt/3HkPUxVAln9KK7XT/8916vopZyK488WXTSVx8RFn7e4BiJhvIr3+cPb/5JKVtL+D15ECBmYnhjJtJy2VfJ33UeQPG60jHby2wEHJBPOlimzEsUeYvlLw8O3vWs6d3Mz2FvfQU9lL0cnh+UYc4MGPYRoxUrIH6eCuNyfG0NcyiITEWIYjAjuuXMISBaVgV91Q16+66LrFYjKeeeopVq1axePFiOjs7SaVS0TXJZHJQl2/XL1B0szhmPNJGSCUpyiKF3j10PPYNUvOXMvkLyylue5FXPzGf7Iv3IuLpIH+RXhhVoQ8jnkYW+uj883fIHHc5zpjpSLeAYWvw6rl6o6oD5VVrlxzHxLaquSO6vfozX/DoC7KIF4seCp3eBSwKRY/urkKQXLdf27zqtvmmR9HL4fqFqnOVVPT2FCPQUix4JJMO2Wyx3N+Btde2zcjTKARfK5/fwdNPbiWVcvC8aoqEZRk4jklrayrwgq0GFp50KXg5Sl6eopcLNC0atJS8PINJJWARhkn37vVsXvknitkOnEQ9kxdcQGPb3BEBlxGt3vpmEmEYkZpVxK2BocsVevIoeJhj0ljTmqPrdO9qF8K+7z5K3w8fj6JRRjtzAKldJ/2dvaTecywN/3k+quAi4vvHvg/rX/k5Eql0PTZCQmVlU4PjnieR0cTWfwnQxyp3O4MTB/TxMPLu/oK0yutc6ZOyY9y56QUe3L6OX5/5Xj7z2C3UOQnu3/YyJ46fhiv9qh3xZYcfjW2YNAZE3Zhh4ZgWPaU8tmFS9D1253qwDKOK60QABNOfOJnUh07Qgb+UGhKz6Arrposg8NKIPJirdnbhyy8QwozItvsLWKrLN1B+Ae1m/dpEBQ5t+yFR0TCMKtLiUCr/YrFILBbjlVdeYceOHeTzebZu3YrjOPzqV7+ivr4e27bp7e2N3E4PvS7k0ErVMwk0KpmrB2oEvVfaI86N0ZLSY9TUYzTzj0uRwSKDAFypvYwgCLUwuvw/kQgB0kNYFsUda9jxvbeQWnAuY97+bezW6WAYuPs20fvk79j1k3cx/uqbSM09rYosOlrAUvleSOVjGja2GcP1i2zueIENe5/ipZ2PsKN7HXm3j5Kfx/NLeLKEVH70PonAFGQKC8t0cMw4jpWgMTGOmWMWM3vcEqY2H0VjcjwAvtQUcLOG5qWW1uWMM87gyCOPHNAWz/MGNR1rDZEZaYoQAiElZqIed/tq3J17aPygTrIYm3gE6aOX0fvU73HGzYryFxmO9tSUhT62f+cilOfS8pav6vIryLCWXcvEoT+7OvM0NCaqjgERz2Xa9GayfSV+++vncEthxGxtehOGYOHRE4j38+oRGBiYUb8LiABBZJILN5emwbHHTeKJRzfx5OObNcfFMYnFrIjTYpgicLgLAWTZHfrwGS20tqYxLaGtZMHy5rqSTF2Mp/+6let//jSnnxV411VZv0VkJjSEEWlaapkOq5+dQPoehmnRvuU5XrjvOxx+3NtJt0wh17WTlff8B3NO+TBjpy+OzhtMRq5yKPqQMMj+5K/0fONBzLY6VM6t1paEgGN3L+kPLabxexdH10U9B8i9fQjLoP4r5+Dv60PYVrlnFGAZqGwJe26gohuKXXioRYHrDTTblIllRpmMeLBueQDapfB8X0lsw+Sm9c/y3ZX38z+nvoPJmSZ+cMoVmIbB9Wsep7uYrzIPFXyPwxvG8OiODfzwhYdpjqfI+y5SaW8iV/o0xJJcc+Rp1XVFj3qlFEbKgdToCa+jbXclsKi6PuC3HKiGLro2IPUeDI3fSERzDRTFYhHHcSiVSlU715AUaZrmgMk9DNr1+9//nssvv5yHH36Ym266iU9+8pN8+tOfjs577rnnouin/xc0LVD9fFReZwUWhohCKCTOnY0RZi2vi0fmoFDM8QPLLgeX299eEqggHlLXfT+g+aIv0nD6R6rOsOrGkJh2HMk5y+h+4H9IzT2tVkE1pT9YCb970sUybExh0FPYy2Ov/I6V2+9j/d6nKHl5LNPBECZm8M+yUwiRCTYvYfDNcMxp3otUPtliF72Fdja2r+Tul75PW/0s5o0/hcVT38yMMdqBwpMuprCGBC5KKdrb2wEdbM6yLAqFAo2NjcggxtRI+xclEZaN39WB39dObOJckD4YJlZDG6Uda4lNPgqvO8+un7+f+pPfS/fy68mvfRh33xbaPnYzVsM4lO8hzKE3JmaQr+2St8wvexJZZa5j6Cl62hmHc9oZh4/g+TEq3mJ4bn19PAogN5hYlhHFXQklfBaplBNF560lEyc30NKaGmj6P0CRfgnDtNi88jZmLrmKtlnL8Ep5GsbNxo6n2bTi1gC0uAcJtIRiCGKnTKP1vg8hd/ZALAibbghU3sWaUM++i64LKQFVohc4PRmYkxpIXDRv2NuFJqnXYpdbKeGtbMfka//5pgGDVJ9zaOqzP5qhSglJuK70Wdu1mx+deiVHNLfhSp+i75E2YuQ9F8swkQEJD8peYpMyTVw87Sgc04xi7hSlR72T4BPLb+LtMxfRmshErP6QwBeaEFH7McnXShY5jNTawfX/fqBcqINZ3kjv6fs+v/nNbxBCkM1micf1AmuaJvl8nl27dpHL5QaETc/lcvz0pz9l8uTJLF68mClTpvDVr36VX/7yl7zlLW8JJqxUFIPj/4r0XxxFhRtn+LRiQbK7COh5lR5noky+rTgWpsnYbxHlGshCL+ljL9XmiVIOwkR7SnME00ddQO8Tvw3uPbow95XvTAhYCm4f96/9OQ+t/xV7ejcCkHTqiZkJTblVCoVEKqnrUMUsLJt1w/8LIXQcFOFEvJj27FbuXfNjnth4M/PbTuO8I65hStN8zXlR2ttkME1LCLAdx+HGG2+kr6+PZDLJ2rVrOfvss6PzRtgJKL+E8koY8bpo/hFOHFno1RpYF3JrHiB99IUUNjxOcfM6Gs58nzbJ9dNs1aqzUipKqBtyQsKYXP3pB74vh5zHQq9TIcTo8HC4r1c6g3MtwON5MqA/iJperZpbpKLI85XH3ZKO8F7Iu/iePPgbmmCDGUu10LHtBdpmLcMKNF/tW1cSSzVFdRlKRvx2RICj5EcLlDGurirMdRQMSqDNPhXX9Sus/HuQqG9QeR301/0nwaZAVfxa7bYPRCrrZgmD/7foTQB4QURbL4hiLJWi4Ls0xBKRJ1jowj4l08SUTO2AYUe1ToyIn+WbVrzsYfLIA6z7/19FT44m73vf+7Asi02bNrF8+XIgmFhcl3Xr1uG6blUyPIDf/va3NDc3c+WVV5LP52lra+OLX/wiv/rVr8jlcrS0aKJcX19fpGl5o6laDkS7ONR1yvMRVkX6iv7q/6GUufs7JiNeF5ipJrof+hnNF/0zIp4ZcGr38uvLsEF6ZVBTq9hBzEEKhWXYvLjjQW567itsbH8Ox0yQchoADWjKgGTggxc1voUN0SAHUGU9jGU41CdSeLLEk5v+wOqdD3HWnA9z/hHXYBo2vnQxDXvAM/U8rzz+gHe/+90RSfc3v/kNuVyuqp3DipIYVkx7a+U6IannLlnMYaabUG4eqznOtG+9CkB64YXkNzzBju9cTPttX6X5wn9GeSVEjajgUY9UgBI38BKtzPtT+WkOA3bNAzQcCCEGhMmIuiIcQlKSy7kDnnJEd6gBeAyzfCzyQhpZjUZ0lhkkUp527Fv4yw8voXv3WhLpFoq5Lna/8hinf/jmoB5DU0FGoWkJ7cYi6hnVV4SErXcphtBug/VxbSaKHnyN3WrQ4bKviL+zV/Njwt5W6A71JCITw2xJHbwcICOU/oMwvP8bHbBAWHcIuTSu9DEQEdAI+SvN8RTjkvV0FLL0uSGprexF5FfEPjGEoOh7pOwYG3va9e6M8rR3sLQQb+R+fa0k5LSUSiUsy8J13Uij4nke9fX1nHzyyfzpT3+KJv6w39761reSyWTwfZ9EIhH9dt5557Fv3z56e3uZOnUqc+fOpaGhQf/+Gr5Xw0mVB4q3nzySwUQIHftptGLo60TMirwdRy4q2sE3nv1xtnxlKfn1j1J/6gexW6chDAN332Z6Hv9fci/ey8TP/Dms7LAlV85FocZTALe98F/cturbSOWTjjXhSw9PlkIn5uBTRdyQ8H6D3VFV/V9pV9XgCqkkvl/AEAZJpwFPutz83FdYv+dJrlr8HZpTEyLNT1hngPr6+sjlOQTepVIp0iiOfB5QILTnoJFpw8z4FLesJDlGB3nzOrYSmzRf83QCIq50C+B7JA5fzNgPXM+O715E8oizSExbNKSJqHJs2kNEZn+jzGHxuM3UqY0DtDFD1S/8LZG0mTK1kRpL+CAyPLiMOIJSkqgbS9OE+cxd9hGKuS7i6RY8N0eyPrDRiqEtK6MALZFuKtp5iEysuuBM8BJUJd6rYvFoMQ1KT25h15xvovIlTdoMxZeIpIO/vZv0RxbT+INLoeRBvDqK4aGWKnWzOdBu/kYZnLVEVKilQy1K+Dzilt75XD7jWIQQrNq3jZaEtvU7gVraNAzMfvY9I5jg3jHrOJIBYj6ULq2vR1/X8sA4GGWOpjwpJfl8PiLKhiahsF6h108YWbRSMpkMruti2zYbN27kjjvuIB6PU19fD0ChUOAPf/gDZ599NvPmzdNE6xHkf3ktZMDzHmQn+XqICANCOqa2pIx4TS27PNstU5j4mXvYd+Nn2PU/78DryKEUWI0OiemLmfDJO4hPPRYl9cKpLx968awELFJJfvHENTyy/n9JxRoAcP1iFU8FFLbhAIKSn8eXbmQqqmCxhK2OmqnrYGAKE9uMYxqWBkJKRnFafFVCCIO6eCsv7nyI//zLpXxs2fVMbJgTARfH0ZqMxYsXA0RxWKAavISec8N3rwaFMruP2PQFOBN20vGnL9N47GXk1z9O34rHaH3rf5Bb+3DkXiuEgYinkcUs6SPPJTHrFNpv+X9M/Myfo74fTNNS+TnYsddTQo6lUoqp05r4f/92ZnR8OG/Uymtnzmrln//1jOj4gbavaswGz9n3XRrbyvQQJWXg4eWgN9yDa6tGDFqiB+SEGYV9/J3ai0iVfJ10zZWYE+q1psWpZkcHf6Bcn9RVi0hcOFd7BIW2ZDOw74VaG8/HaEjo3ZGoBg01O+MQDJzBOBNvlEE6Gun/sinAQ7KgRdtnfRT2EHpLQwgkcOFUzfhX1IjRcoBSS9Ua2mD1RHao+l0FGo7whZLBRH5g9zPN0WvnEokEbW1tXHfddUgp8TyPww8vk/qGik7qeR62bdPR0cH3vvc9TjzxRN7ylrdUnbN8+XKuvfZaPvGJTzB79mw8r4Q1yhAAh0JCPpQwBLKnQMdVvxvIM3m9RCmwTPytXRj1cZ1IcaRjPwQuvkdswjwmfOouvM4dyEIvoBCxFHaTdlOPdvpSpzUfil8RmSuCXemPln+AJzf9gUy8Gdcvoij/BgrDsBAYZEtdgKAxOZ7W9CQmNy1gfN0MmlJtpGNNOGYCQxi4ski+1ENXfjd7ejexpeMFdvSspzO3k758B6lYA5bhBCYnDQaUUpT8PEmnnn19W/j2/ZfzqdN/z8SG2fjSi9yiPc/D9/2I11Jp6jz//PNJp3UYg/6crZoihDYPOQmaL/03dn31BF69ehxe317SRy0mMXMJ2ZV3VvRhsDEJNl71S9/Frp9dRWHjMxo0BmaiWn19KOZ9o8ozaOjxPlwqh/7rU8BHHnbdqjXvht6zB6PNoflSCIOnbvkc+d49FHp28/B1VxEClFzPbh77zdXEkg0cf9m3DpamJRAFxeUb2Tn9a9p7CB36WrkS1VdEpBzknj5SH15cfZkvI+6KNaURpjSO7ra+1O6H/VRxUUd7EiKA8waZ7N7gYgmBK11QWiMjqcE/CiRUCRelh22Ygar5YPWz0En+agCWcJfQP17AoZBQ0zKcTXq0ZQ436WmVs9aAzZs3j//+7//G9/1g92NFO89KHsBQ9d+4cSPt7e0RYOnt7Q1s8BYnnXQSf/zjH3nhhRc0aPH9/ZgEDpUEQNGXuCu2H3CgxYMqSiHitnYKkKNcwJTSYMTXXk1WY1v1z77OgSQCLagwqs0P5WKq/5bKxxQWv3768zy5UQOWkl+I3FLD8WAZDoUgqvP8ttM4etK5zBu/jLF100bVBblSD2t2LWfl9vtYsfUuegp7STkNhO7V2lXXxPULxKwk3fk9/OiR9/OZM26mITkOGQQVsywLy7LYu3cvSinGjBkTtS/kXYUclxH1reXg57th1vFM+n8PUXzqD1ith5FZfKU+x7AqeEQhzUGXHT/8BKzmyeVAZzU2qmG/u35Rm5oOxuZJ6FJ86WGbcV1uLQ0P5bAOoefXiLg+QfnekNQUhWXGorFS2Vazn3XhwEWXcezFXwEUhmkj/bBy4d/VAHgwGTZhYlThwK88/ZETSVw6H5GwUQUXozVNx+W/wj5mEnVfOA1/Zy8IdAI20OcFZNvebz1M7vfPYzSnULlSOaGcrzBakqiSj+op6PgJvkQkbGRnnvibZlP/r2cNIO1GHS117pGD7Xr8/wexK4bAUL0XDtvYIVzi+r84Ya6Pp/66hQfu20B9fTzIlRGcD4EnQ2UZFeWgIvW1EP3PU3oDbRn09ZWYv2Ac556vI6Y+cN96nnlqG3V1MVzXr7SIVty3Rv2j4/qe7//w8SSTzrApGQbucmTVLrNYLJJIJDBNsxxfRQzcBYWT/MSJE6mrq+Oee+7hnHPOIZMpEz+ff/55stksc+boto5oN/taixCI+rhO5vhGEl9GgGVUk7kIAGVInPbKbuxCCDCtaKIWQuD3tWOkmmryJcK/Q5PLw+t/zX0v/5R0vCkCLFWm7UC7MmPM8VxwxCeY13YqlqGT25b8UsR/M41ywFCFDh9apZ1FkHTqOGbyeRwz+TzOnP0h/rLmRzz26u8xDDMg3npBzA6dDyhhZ9ja9RK/evpzfOyU66N6bdjwCmPHjuGee+5BSsmll17Kzp07mTlzJnv27CGdTpNMJkcHXAItTnLmKdTNPAXQsVgAZLEPb9/e6FzdIF2u3TyZKf/6JGamVa8jZjVxuJLobA+VC20/JbSCWkYMpfqZxYL7WkEYh4QzkMB9MKQy+/Vg1oUDlWgDGpk+ZZVbs/57ZBSQkeUeEpoYK10fs60Os61OFx5oP0BgNMQxx2YwGpORCUnmXP09TC41qR574QQNaEo6IWLIYcn9fiVmWx2xZdNRPUVtLrJNVFce67DAk6UGqShKlrZ6F6VntukgZd7g2Zv/Lm9AMQSJi45AOGa/CUP/3N1VYMP6fTQ3pygVvX7kUaWzdUOUQ2MA+YyAXOyryAvVskxCUl13d4Gx48oTwu5dfaxZvZvGpmQEnPZHfH/ki271LsesWtTCiTufz9Pd3Q2UVeyVYpomvu8zduxYPvjBD3LnnXeya9cuGhsbMU2TbDbLpk2buOKKKzjiiCOQqEGjj77uEqa0eCMpTgWjByxAGFQsmpQsZ5DpScPeXb/4AG0fvRGsar5RmcfiYxk2u3te5Q8rv4FjJfGlNwCwaO5KgfOO+DiXHvlPWBWLriHAGSKzsCclRc/DDEIaSKVIWBYCiVSKSY2zee+J32X+hNP59dNfoK/QjmMl8KQbBRor+QXSsSae2XwHD637Jctmvotsro+nn34q4rOYpslNN91EOp2mp6eHxx57jJaWFt785jdHxNyRzeVBDLBSFoWBEkZEgo5PW0TTeVcHDe+XisOKBYCldiTWSt7Qs1vupKewF8twkGo08WQGF6kklmHRkdseAZeQTSSVj2U6dOV388TGWyi62YOicdZmRa0RO3bKBdTFWyIX9UMlVSR7BmpTRpNfa0SaFiUVwjYjr5/oRqEa3RQRUKnMkGoky8hJSUXy8qNIXn5UzXsV/7qZ+NmzqPvMstp1CZIoDpgwXB8sg8I9a+n69G2YkxpRfSUNev4ub3ARepfkmMTPnqXHTpUao+zN5JZ8PM/X7oYVoEQAybQDQaCmQt6lVPIwhIiKkkonrkwmrUjDks3q3a4A3JIfhNnWEotZpNMx0mmHfN4ln/dGTGEI66Q3sKNbbWuR/IBIu3LOOeewaNEiABobG8uTeiBKaXdpKSXz589nzpw5vPrqq/T09KCUIplMcsEFF1TtYkekan49xAjGRqgxewPxyEZdl0DTooo5vXuWZXMQUqKUxEzURWNfm4dq81ggzHpsctfq79OR3UE63oTrF6vU/AKBLz3ec8K3OPnwK5FKIqVH3vfZm88GHoAqCGJu0Jaqxwl2vr6SQcTrgaAm63qkbIeeYpa0HWPRlAsZV3c41z70LjpyO7DNGFJ6mn+hQEoP24xxz5ofcczk88gkm3n7299es5tuuOEGPvCBD/DUU0/xzDPPsHTpUjzXixwhgp4YuqstG2FUg8K6E66g7oQrNDAJvIOgDFxQsgqwDDDDSQ/DdPj9in/j5d1Pk45l8PzSQRuTCkXaacSxEhp8hs9Z+thmgj19m/jOA28fltMy4vspiW3G6c738F9v/qsGLdLHOIRa16GIzIP9PZgMr2kJtCmlFdvx1u3VmowgbL1yfYzGBCpbwl27l8LDryD39GkTjgCVczEPayJ2/GSUkpr3Evi4RwOk6GnCbd5Fdmr/fL8jh5F0QGlmuzANHfK/1g4nNFHWxTEnNmJOqB8Yqffv8sYUgZ68bbPm84rMHZMaOOW06aTTYa6M8vjxfcmq53cCOrbA4TOaGTsuQ6nkY5oC31fEYiZbNnexZXMXtm1g2SYnLj1MBzm0DPI5lxmzysm+PE/i+5Ji0SORsFl0/CQ8Vw7vHlwJuBSRhuZA5jbN6TGQUjJ27FjGjh2LUoovfelL0bsQuo+WOUBGtJOdOXPmgDI9z3vjalj+L4kIgZeBLPay7qo6ZAGMAGsqD2QO0osWMeVLT6H8EsKIB2BX9StKP2s/MAtt6XiBpzf/iYSTwQ8ytoeLsSFMCm4fVy76OicffiW+LOEpg5hp8e9P3M66rt1MzjTSUyrQEk/z1O5NvH3WIj50xMn0lgpknDjP7d3CA9vW0pZqIOeVEMD0+lYe3r6eTx99Jv/0+B/5n1OvpK/Ux6TGuVx9ynV8+/7Lybs9mMKKwiJI5eOYcXb1rOf+NTdw8cJP8qfb/0BTQysnnbQUz/O49957aW1tRQhBMpmkvr6e7du3B9erfvvPwV4mUfN3WczSs/x60se9BatubFVfRt+H0zAE5zckxzMmM4GkXY8n3YMKpD2/FPCCRD9QJbENh9bMYQftXqGpy7H2lbVvb6BNwXAy/MxV0mH4c799jr4fPYE5oQ6VrwAFUmG0pHBXbKfjwd8Gqd0lIm7j7+ol+faFxI6fDCVfE9nCiTyqQeANYRqRx5GRciLuSrQGDEZoDN9t149SzEd8mb/LG1uE0B4ijhywgQpd8KRUHDF/HEfMH1e7DODzL2jvgJ7uAiefOp0FRw6Myf7IQ6+yfu1eYrE4qaQdpWyvlGLRIxaAY9MycF3JxElp3nrFUfvdxAP1PugPREJybq1zKr+HJibP8yIib2h6sqzXJo/S/+8lIEMppRBOkgmfvhWrbiy9T92EzHXScMbVlHasofep3wPDe4do0c/s2a130VtsJxNvqdCySEzDIVfq4uTDr+SM2e/D80tYpoMMgkqmnRh/OO/DVSWu69rNXZterDomlWJ6fSu/Xfc041P1nD5xNn1uMeCJgR+Mt5iVxPOLTGmaz1uP/hd+8cQ1mJZd9T6roG0rt93LxQs/SSZVT3dPF52dndx66620tBzQtv8AAQAASURBVLRw1FFH8eyzz+5PJ1dor3TkYWUlkV4BM9lAaccatnz5amZedxxW3Vj8bBfCdqq1oEoh7PiwYfxLXp6Cm8UUNp48eJoWAMFAb7GyOVBS9LLDlDByUUpHLC562TKP5o2qca0hw5uHEpqY1PDN82n45vm1zyt6Ouz1oFH61OAJD0OynVTRdxVyDypkULetSoBtCv3vYITg/rscUokWc0FNgFnpOeT7AR8ltLuIsgaiWHCjwKPCEGT7dFr3XK5ELGbpDKgpbeYJNSVSqSjRpc5CDpYlIi5MsEFGCK11AcjnXR0WexTvtmUP7rY6or6JCMQBJ6xCfTuUarXyXfm7RuV1llCbaJpkjrkEAK9zB173TuJTjyV22DFkV94VnDv0nKWUwjQspPRZseUuHCupTTER2dzAly71ibGcP/8TABFPwZc6F9nmnnY+sfwm5jSOo7OYY0K6gT+9upIFLRP0PYJ7HTNmCseMmcLuXC9HNI9nyfjD2Z3r4bGdr2AKIzpTKknMjOFLj6XT38ZfN97C6p2PELdTURJFX7ok7Ayvtq9gn7uBdLyeBx9+gBdfWE19fR0XXXQRSini8Tg9PT10dHTQ1BQk/jMMdFa/qBf69a8B0seIpUGAk2zSS4Gj1VlO21ycNrDHa42jmWoYvH/78VoGf2/LJjilRh5wMAzu98Yhaf1tyoiJuEPZvkOtyGDnjGjits2Ih/L3DeDfpXLcmaYxaOhr5Vg6fFYAaMJ8G45jYZoGTkxfaJllwCEQNRNdel5AbK2Yh0IgE4vtX76e/dG0VL13Uuqko6N5JxTgmANCBPS/x9/lNZAKk6Gf7cRMNeLnOpH5Hn2sa4eOyxKdPFRREoHJju617MtuxRRWRUg4hW3E6Mrv5uTDr2RsZiqeLEW8lFjAV/nK4otYsWcztmExIQA8751zIssm6kW9Lljsc16JpOWwat82Ogp93LVpNc/v28qRLROpNl5VBxE9fdZ7eWHHA4RpFwlCIxjCIF/qZWvXCxRL47jg/PM5btEJ3HPPPTzxxBMsXryY448/np/97GdMmDCBSy7RAM+yTLwqwnk1OFdeCSNRT37dcjqWv4ke08VWBhKJMB1krgs/C9u/eQ5GuknHYRFGRD0wnDhe925aL/9PknOW1QznH65rMStBzEoRt1PkvQIGOtO9qnjISlUHgjAC5r8AHZ1cGJhCVPWfQOAOah7SYNQxE0OOjdFIaB6KWSn6Z5D+W5CDsg07IDJfQNxtuf2qKHV86F79d/n/rwyqWQukrDqt3ruUs9MGO0FJ1d+1yhh48+pzwnL2J8fgcO2oJVUEeMMoZ0kfpYTB2v7mTEFKE++RCiWU/vuNsDvd31QeIVi2g9QKllNO0OckRrxLCwm4WzpfxPWLgReLXtBD4m3SrmPOuJOq7gt6wfzDq8+TshwaYkl8JamPJTCEoK9U5P5ta9mb6+XE8dOZ1TiWpOXQWcjx/L6tmIbBN068hIe3r+PRHRuC+1VLuPhNbV7I2LrpdGS3Yxm29rJBPz/TsNnatZp4MUOupGN0nHHGGdxyyy20trYyZ84c2traSKVSQ5gwB9iRtXnId/GzHfiWj6EMrcH1XDAM6peeqX/r3AmWXb5OSZSbwO/rQPml8nHKgEFSjiq+t2877dnt9JW6SJoGUmlwF3pYUXFuZb8bQmtzU7ZD0fco+l5FYE6BUj6pWCOOGa/ySop4e9KlO797yLExGomIuIUePL9Ys1sPtRzInDQsOjgQe3wog11fuQs1glDZB8oB+Lv8XQ61HOqxWenK723soO/aRxGZmE76N8R9ldSeWLIrT+p9x+MsGK+J72+gkPhDi4g+jKTzxnB5rjBJUvK1Y8L+zk9V/AFV8X2ElwfX7+ndjOsXgkVOm2AMYeLJIg2JcUxu0uHRDcOKzJy+UnQWcuyRveS8nTTHUzy0fR1Zt8j5h80n57l40qfPLUT3+/Zzf+Hriy/hv5+/jx19XZw4fjoPbFtbc1NuCBOpJPWJMUxqnMeu7g048QR+kEpASoljx3hlxwtcOvtdmKaIuFZnnXVWlJ4ilUpFXm21+7d8TLssx5G5nSTnXEHr+39CC/uvNFDSR9gxrYUh4IABvaUSaSfBh078Fpu6NzM508rv1j9NYyzFqRNm0l7IEjMtJIreUqFqQ1UfS1DwXJKWwx2bVjG3qY25TePpLRWwTQvXL5F2Grhz9fd4Ze/TJJx6fFmKnqnrF2hOT+SqE/5Ltz4ENPvZxnA4G8LA9QqMq9PRto1RZBY/2DLad2lENT3QCXqw66vV4HqC+jtg+bu8kWTAPi8ar2HG3IN0F2FVj30pAQN/Vy99P3gMY2wGVRjGK04qnbdraxfxUw+HBeO1p9/fCmhRgTlBKvxt3YhAwxKBl0MoRsDTkP1BhF69QEqM+gQiYY0+Im5YVBDxU/leEB0XlFfS8dZHVgIAfcV2fKXdikM8pXfkHnE7TVNyAmG8j7CatmHw4fkn8/1VD3L1gmWk7BgNsST78n28b97S6rsoxVeevou2dD0nT5hBRyHLvzx5O/945KnETCsg4Q5sv47u6tCYGKeJqoTcFxERyPd172DKlMnRNYZh0NjYCOgouCEHS8qRxkGRYJjIoiaqlrL7iCWawDAo7VqL37MX5bsI08bMtOKMn4XyPWQpp3M8BYQ4YcWCv6t1ev/zwiOs2LsVQyg+euS5nDVrGQCtnWM4LNPM/ElTq2rzxafup88tEjMtekoFGmIJvna8zgH0UmkG81qncFRz44BWPL3ldtbsepSUMNEwVJuFPOmSjjWzZPrlI+iL/RMVxKEZaJY6NC9dZcA+BsQWGl5edztMVGGjWiU24kaoft9VoE4eagdT+50bnYy2DPW3wSPYLzV82BdhnInXGHhqLpzSdR/msauhTxlWyuN16PTp+yPVfRaYlWIm5sR6jJZ0QHgfogCpifMEBPqgwge9nodMgjnASDnUffv8KMN1aCo46MBFld1KSyUXwxBBOgXKSh8hUCUfoyFO9lcrKD2zFSMT1xqsUXoomsl6/ZlqAE+r5c1MCyKWCs4YurxwSit6eaqjp5Z5LZZhYxpWlOunUtNiASv3baPk+6RsWDJ+Om4FYMp7JWKmTc4rsWziTE5qm4EvJRdPP4p5zePZneutIOHWMrfqOsXt1IDgayGvpTffxa9//SuU0q76tm3T3t7O6aefzrRp0/j973/PjBkzWLhwYVVCxcFFBETnIMFoqgV39wb2/O6fyK99BK+jHVUC4YDV2ERi1km0XPYNYm2zB8xR4d+u7+GYFjdteJZXe/fxk9OuZGtfB194/Bb++dhzmN00iWJpH135EjCVrkIPaTuGVIovH3c64AEWK/du5Ildr3LHqyv4/YZn6ShkWdR8MVBPzs0TM22Kns7VVPLzEXCufK5CCKT0cP1iEADOqHji/fohnP8qtYNSotxC2QxZ+TsDw/hX9oMG1yUMJznMMxheKsuv5OpU3W+Ea8brDlpgcM+HkV0cfBoGwjYQlgm2v38EhEMmoVueTzl3xcFATgdDyqNYCANxIFERBfsf6nyoGtYY1NEn5cSEpmkMrtUzdD4N0xSYo1hs+k8Oyi8izBj7HjyZ0u7lGLE0ShYZGk0MUrIwQRYxYmMZc/5WhKgRPNFX2o0/76KK7tAgJPS+y7k6F9frIP2j9I5EQk+wiOgcM0m/45iDXLOhJTX8KRT/uoXi8lehMakzzw/3/gbscGEYyGKWV/6xDeWXUIUCyod9t/w/vK4uYpM0CTbMMjwgnHtUnv7QC5cY+AMDNx3l4aKPFzyXuza/yORMUxCN1WRt526EgJPbZmAIQdqORYDFNAyKvsuMhrGYwsCTPqYwyon+avRByGOpfHsE2lwSjyU467SzGdM6httvvx2lFFdddRVSSv785z+TyWRYt24dzc3NTJ48eWRatoDTAlDc8hy7vnYa2DGaL/4SydmnYKQakdlOcmsepOO2r7LlS4uY/MXHiE1aECWp1MXoOSbkpfx110auOfI0ACalmzhv6kLu3/Yqs5smEbOS/HzN0zy8YxMfOuIkjm6dhK/AlRJPQsKCX699jvfMWcyMhrGcPnk+337uPkrKBAxsM4FpGCGlE0OYNTaMYVJME9uMDT2fhtkRK0UBJmAPDTpCU5hSCqSnUxlIqTNem6OFCJqjUzkfludvX89xXg5hJelb83XslhOJtZ4SzasjkTcEaDkgCU3DRQ/ZXUDUF1HZN1ZE3DDplWnHMQyngm3+BiAXhoAFjeh9rzDsFYOKAuGYtSMXH0gNKwCL5/pRr+lU6lAs+ggBxUI5Jkn/rvU8SSHv4ThmOSz//uDGANQlD3s3TstShJXUE8aIyhGUn7sCDFAewkoPXhGB1kAYQgPzIQCXImALG+K1x8PBJu5AcxmFu+ttr2ziL3+5j3lHzOO4Exfj5YuYllnmGyDwfQ/P9xH72VjTMrEch57OLu648w5amps56+yzkb4f7AKVzjifdzHqdQBMEUTqHZEGKySJKxCmTdN5n9UB5EybQB2McgvY4zRoCRMlGnacWg8wbGfcTuvdcb9duSFMSn6BgttH3E5XvYNmADIun7mIZ3ZvYke2i0Kw0DuGhSt9Fo05DMe08KUO1R9mfQ+vzXsuJekjURS8wNRVUYcwxHyu1B3waSpfMB2ywFQOjz36GKlUkldffRXDMLj//vuZOnUqe/bs4Z3vfCdPP/00L7/8MpMnT8bzfQxr5H2996bPY6QamfL11RhOhcdN00Rik+ZTf8r72fSFeey96QtM/OQdNYsSQlDyPbJukfHJeoq+R8y0aEvW89yeLQD0lvJ88qjTOO+w+RTDxH9KYhsGtmHw3efvZ0K6nnnN4/nLljWsat/GSx07uGjqgqg/9P9rz1f9KgSUE2Tq5vbbyPVLshmCMXffJjru+hatb/06RjyjwYihY/pE9xTBdYYJph1dW9y6it4nf0/Lpf9eMd6H4hkFWi/DDBQ5+j0JtZnhhlh7wYEs7UVF8WdGvhb+7YOWMK/R2DTOMRMxxmXKk8sbQvQDM0ybvo4tlPIVL/QboYqhilz6OMl6kvVthAGkRi5hHg/NwZDtOYS9H9lwB6tixQtq9eNnODGTT39+WRAOQ9HQqHcVlh0kRAs+l548laMWtgUZo0Pd/+jrIgy9E05Ofd9+t2cw+Vvmciml91YdHR0UCoUB7uFSykij0l+z6vs+6XSauro6PM/ToKV9Nz//zQ287W1v47ili5ExI+I7CNB5k7AOaAKTAfzp8wv87803Mmv2bM664E0oJSo0CeEd0d6N0dw6QsQrBCiJsByaL/zCoKcpJSEgQ3rdu6ltAND3a0iMwzSCxH6EC5qO4ZIv9bCndxOTm46IFgqlFJahibLnHzaf8w+bP0Q9dPLEyrcsBC0zGsbwT0efRcyw+I8llwIa8ITXWYZNycuzr28LtulUaFx07aX0aU5PYN7kIyi6BQ4/fAZSSnbt2kVDQwPZbDboMkE+n4/KHVYqQGRu9X2Mf/PXMZwEfq4bM1lPmBAw/LvxzI+x9/efC6pVWyOsCExqhokXeGgZhhEF1QMisBJG/zUNg735Xv7rufuImzb/eryOazYp00jadtje11W+fhT71VqgPAyHIAwDWcqx8VPTGP+R35Cce5rWOgX3cfduZPcvfkDzhV/QoCWIurz3xs9Q2vEyRjyt3bztGH7XTpT0mfipuxGJDMVNK9j7u6/SctG/DBOsVW/AlCxhGCY9Kz9FatZnMOPjAu1NAWEm6F39r8QnvhW7XhPFMWL7pdn/mwYtSqkovP9QeY1eb5H4GJi88L/v4ZWnf0uibhy+W2BgBMzXnmyjlMS0YhSy7cw88T0sfcs/H9Dde/7jAXq/9TDmxAbIHRyNl7br6kBzzz69jZ6eAo2NCerq47RNqGNcRbJDKVV0biXYSaUcUqlyLpX+8YdGChZCW6zyC1rdGbBpatabUPVqoKSLMBMIw0b5+YgoGWaixkz8TZPQPc/FsU1+9KMf8dRTT1FfX4/rlkOdh8nxKiP0SimJx+Ps3buXK664gne84x24rkssFiNmO4xtHUMmlQZAVHRxqI1Zv349a9euJZFI4HkenqfztmheSvm5Simr+DGgn+OSJUu0twqC1pZWGusbyveqfARSAub+KUZDIi8gS/mapwjDRFQkR2w49YNgDuRMhX05NjM18Bwqm+KU8rHNOJ25nWzqeJ7JTUfgKw8DMxpXhjAo+R6uHKidUkDCsgP33Br8BqVwTIvWhH7XxiXrgjKrtQCd+Z1sal9JzEpFnk2h6dmXHhMbZzFz1ozovqVSiRUrVrB06VJc142O709MJAAjnsbv2aP7y47pjNqB6UTYOgaN170bI1E3ZDm2YZK0bDqLWRKWg22YdBdzNMX1pkgATqDdCMnbj+18hR+98DBnT57LO2efEJU1u1FH83527xZ6A+8sOYLBFJrYyvPBAGO1/pCSwsbdyFx3cFgSBioUdhx7LOVEkQHIHHPFt8ulBLFp9vzyanJrH8EInrGRasRuaQzG70jMzfocP7cZZPlZhl5zfm4rys9VHd8fDuUBg5bX00W5it+gofF+Ew+1ukyN3GpTSUtRlF1L+x3XSLiIkUhiFARmycEs2eAO7b566KXM2jZVDNuNYxYt8EH6bqSqZiQ7HdNA5Urabb3kHxLyp07wZ3L/X9azds0eGpuTOLZJpi7GmDFpjjp6AjNnt5LJaLuo58ko0FwIeip3beYQgdeGkmism/FKpejg56N5MIZdR3HXn3F7VpOe+clBr/1bBCxQrveECROYNWsW6XQ6cmM1DIOenh76+voYM2ZMlLBRKYXjODQ1NdHS0hKdCxqYFAqFaCGrfHahNmb58uX85Cc/Ydy4cRSLRerr65FS0tvbG9VJSonjOKTTaYrFItlsFsuyKJVKzJ8/n1Qqhe/7FItFSqXSgHsdhI4B30N6rt5V1ni8SilUKUc4aWSOvxxZyuv9q5OIxmm4K53aspCYlaTgZTGE1vyF6niF4rmtf+bkw9+BWUEWrwQezhA8hf7zeZWHp1JIFEbATzGrXJN1nz2/7V56CvtIxxoDDyKBDO7vS4+pzQsB6Mv2kE7W0dPTQ2dnJwDxeBwpJfl8noaGBgANiobTSofnAPUnv4+OO75FYsHZpOadoX8OTwOyq++j/bb/pPmizwYNluW5Lqin6/vYpsm0+lZ+t/4ZPjL/FADu2vQiV846Tt8nluC/nr+PP21cyQfnncSisYcxKd3If590Ga2JDHdueoEHt60l65XwpWJyppFzpszjqJaJQBnwjEREBF8G/gIgC32YaZClvuCwWT5b+qgCA+bxSjMSpo2f76b78V/R+pavseunV+H37cPr2gGGoT3cnJE4HgTrsZmAWppKM3FgnMlADhi0VA5q3yvqCJ6v08QrRgo4+okCTCsWuOjt572HcCuN8i3FHMx4HDMeA1MxXNjuQypK4nslUBJp+EjDB0uAqYHWqDOKWhGj7ODXFQgHfl1djPqGODHHolBwye4ssW9vlpdW76alNcVxx0/m+BMnk07Hgh29qDAJVRAW9xNkjwakK+nqSdGM4XY+R8fyc7HqF5Ca/hGEmXhdgP6hEsu0UMB73vOequNhNukbb7yRG264gXe/+90sWbIkOt5fwozW4YJZq39CbckJJ5xAMpmkrq6O7u5uHn74YdLpNCeddJJeYKUkFouxc+dOHnvsMWbPns0xxxxDqaTzxqTTWosTmqwO7rMINlDSR9g2gsEn/Vp3DfkYlWMkzC/UmBzP5KYFrNn1CKZlB2u6wJMuKaeBVdvvZ/XOh5k3/pQo99BIx20tR4jK60xCjkwlmJGYhk221M2D627AsUItkI78aggDzy/RlJrApCCGTDqlNR3pdDoibx911FH85Cc/oa6ujgsvvBDQz9pTpWF7OwQeTRd8AbX9ZbZ86Uwyx55ObOqxGLE0stBHYdMz9D1zP5nF59F84ReDjg7nbRVYmQRmMC7fPfsEvvTUHXztmbvZk+/liOY2Thg3DYCS7/ORI07hshnHkPN0/SZndOqBP726krs3v8gnjjqDtlQ9AsHtm1bxv2ufZG7jOKyA81H1FAZ5JKrGj+WwCNrhJL/hcZQHvU/dTN2J79SaDaU0CRcRcGKryxAheTkg3O7++fuxm6dQf9qHaQj6pG/Fbez4wVu1FlCNhGAfcrj82ptd5bNfC3Q/OYiaFm1m+FuWwV7sQY8HGbC9LZ10fuRWmq6/HLM1HUUirbweoNjbTl/7PqTv4pfyowcGw4kwIlVczfZBYJLQHBsn0RAcEcEcG7qg7Q/uHL2tdn/EdSVuSWLbPrGYRSZj0t2tAzp1tOe47Q+refKJLZx+1uEcv3gKAL4vMc1q97r9lf47zwFqUxWSbBUiCKGe33YznY9fht0wn+ZT7gsAi09Nb6G/YRGUuSshCAjNPYVCAdu2I96C67o4jl5Mw/PDoGJDiVI6GaSUkrlz5zJ37lwA8vk8d9xxB42NjVx00UVV16xfv55bb72VxYsXc95550XHKxNJHnQxTJSbw8xMovfpW9n186uwmyYji32Db1ZC4qJpo0pZrKbJTP7iYxEnA8BXPpYwOP6wS1i1/S8k7Aw+XuS2qoLx97tnv8Tnz7pNZ4EO3J+rbzW6cdd//qv87kkX24xxy3NfY2f3epIVAdK0G7ZDX7GdI8adRlv9TNasfYm777yHpqYm2tvbozxDxxxzDI2NjTQ2NpJOp8vAdjjLhCpvAI1kPW2fvIP0kzfS8+gv6X7kOmS+GyNRR/ywY5nw6RvJHHdZYLLVnBBUOZic5mAoXOXTEEvyn0vezPId62mKp1jYMomsWyRl63XOiThWuh/63CJpO8aD29byz8eey6RME670kUpxxcxFbOjewxO7NnL6pNmUfI+4ZRNpS2qY7Mp9X9HOsJ5SEk62HXd9k7Hv/za5F/5M+21fpfnCf0YW+nSgvAGqXP0h3YImfJuw+4aPklvzEFP+7RmEYZJ/9SmsurGUdq7RoE6NVBExnO754LxnB0fTEiSa2vriPeS6d2JascBm/8aWkG8gfZfDFl6Ck6irmiBCGUDSUgo8qU1CSYfSU1sp3PUS7urdmMvSOgt2zILAFTdk1U856kJSjROw43VIX7uvlu9V6VGkP2tN4AJRfvaKKpug9F1M0x4UN2iyqsQwHfK9u9n+0l90PcLCquryxhXbMcnlXE5YPIWL33wETz6xmVc2tLP25b3YtkFnZ47f/vo5Xlq9h0svm099fTwyFx2oKXMggO2nYaso1u1+gb6Xv052w29JTXsLDYuuQ1jp/5OAJZT+2hPHcfB9n+7ubhzHYc8ezTewbbumdmO4/oj4GUHW61KpFPFiQjATEoE9zyOZTLJv3z5isRjFoo6NksvlovvvL3diWAkWUuW7mJlJJGYswUw2VO8I+m8yDDPwRNMqebNuzIBiQ4LwURPOZGLDXPb0bsI2YxF/RCoPx0qwpfNFfvb4x/jYKddhGlakcRl1SImoOdVjVQiBVD4yyGPzlzU/4aH1NwQgySUyCQSOCLaZ4MTDLwNg+rRp/MNH/yF6V2Kx8mb38MN1hNbBNHFD1FD/33fBsqg74QrqTrhi0LZEgAUiwBKCWMM0MAgC9/mS0yfOBqWTToZ94CtJoVSsundo8lk8bhrff+EhPnfsuTQGGrNHtq9jR193lJgy9MwK5/8wa/QAl3UEfhBLZ8C6ZDns/d3nsFsOo+mcT5I59lJ2/uBtGIkMDWdcXbN/wg/DjlPatZ7d138Yd98mJn3+IazmyXg9e+i+/4dgOnj7NmGmmzUnaLSuzyJsn0BF+Y3eIKAFQEoP0zB5/q6vsX3NcuLpNL5bPPiahIMuOiiRW8hy+VcXa9Ai/ZoZogcsVrZ27S3c8zJ91y4n+Y5j6Pn3v2DUxXCOnlh1nRH4vc9Y/C5mLH7Xa9nAQaWvYys71z4UgBZtE44mpTc0ZqlwsTQFqbTDaWfO4LQzZ7B+7V7u+8t61r28l3jcZuVzO9i2pYsr33U00w5vjjQu/bUl+7vjVNINYrQE1/s5vNxW3I4nKWz/I4Xt9yIsQeMJPyQ1/SO69tILPJDeYOkqFDrOS7DTVGKIQRCcE2Vor/qp3J6QMLtnzx42bdpEU1MTq1atore3l0wmU9trYwSaloiLZZrE4/EKrVf5esdxIvfr/r/F4/EI9BwyCTyGZK6L5OyTSM4+af+KCYnfSgXe7yauX6Iu0cqpM9/Nr5/+AjE7iR/lswmzKtfx7NY7+e7D7+b9i68lFWuIwIQhzP3icpXrJPGlh2U6GMLkrtXf45bnv45jJpDRAls2G+XdXua3ncaxk8/Dlz6OHafSWlapaQsJ04OH8h96fETaYq9Ue5FUCkw78roRVgxhmFUgae/evSilaGhowHECgrQIvKh8ff+EadNS3wiUgWTIFbp85rFkSwU+//DNCNvCADJOjI/MP5nWRCaKfwNgBJQEzy8NWC8jMKW8qmcQUjD23vR5SrvWMf4j/4ssZrFbDmPCP/2ZPb+8mr6nbiZz/FtRnht0WcgrsXD3bab9j/9G7sW/kJx3BhM/c08UnM+qG8O4D1wPQN+KP7Lla5fo30ZhHkJJlNeLUuM08dbPI8x49Vx5ADJi0DLU5Bo+tERmDOmmMcSSTUivOEANWsGXGrEIIQYMPiVHwmQeiSiEYeEV+yJW9WCdWrnAyO4C7gs7KdzxEu7Le8l8ZhmJC+aR/dWz9Pz7fTiLJxM/cxbWnDEYicA10TDwQyZ7hULFsgfP3umV8lVaFQDLSfQ7J6d3ZtJn76anGTv9RJT0gs4e2Abpu9jxDPnunVULhAKMQxDl9ZBKUP18ziWRtJkxq5UZs1p5bPlG7rxtDbZt0NNT4H++/ziXX7mQYxZNrAlcRi1+HqwkuVd+RPvD12ClIezy8NkaMYO6BV8iNfMTGHad5rcIawBgef0laL9lIBI2Im5pMDIUN0kphG3qqLs1NCX92/fiiy+yd+9eMpkMu3bt4oknnuCss87C9/0hEuMNUtt+50kpMU2Trq6uyBzV3d0dkTpN06SnpwfLssjn87iuG3FiBiv74GhfQgKpgfIlft8eiltWkZx7GrKURZgOhpNAFnr1rlT67PzJOxn/4d8E6ngAAyOWHNA/VuDufNqs9/Ls1rtYt/sJ4nYGT5YwhOajebJE0q7juS138fXeTVx+9JeYP0EHSlMofOkilfYoCq+pJUopFFK79SrtVi2EgWU67Ovbys3PfYW/brqVuJUuJ2+Mnr0GLgk7wyVHfjYsMSq3st/DNoacpsHHxDDjJPSaqfDGGkzCSK/S9zBMi56eHu6++26y2WxEHp82bRr1dfVkc1mWLl0acXA+NP9k1q5bRzaTxbZtXOnillw2btrIvHnzWOzV0Vio55IzL2RvbzetmXqAKsCilHYuyBa7yZa6dNbuyEIhAJ0gs+jlyBY7ScUakb6LYdqUdq3DTNTT9g83Rm1VXgkzUc/4D/0qyiIuLAfhUPWeCjuOWT+Wtmv+SHzqMfh9HeReuh+/rx3lFUFJ4lOPw6wbR/MFHx5Zv1ecI8w4ViaIPWSlwNKhG43YWAYNnDgKGRK01NoNyoCpblBW7RZ9j7hZHmx+oDYkOM8QGgEXfTeIpihG7JjrucUBpibDih2U3anevYzM6SrkrxSf3ELu189iZOLYCyeQ+eKZGCkH5fmk3nkM8XNnk799NbmbVqLyLql3H4uzcALKl5g1XqQnb/o0vlfUoClgsvtuESfZwKJLvjrg/I0rbmHPK08AMG7WMqYs0LEAvFKetct/StusZShlDw4wg+dkBInBgNBuhBFmQH1jq1rKEjTRdkKPE62+XXLSVA6b2sQvr3uGfXuz2LbJr294lkLBZclJUwcAFxilujyI3Bhru4jWcyYgzCQoD+Xn8PPbKe1bjtf9Al7vGrye1TjNi0FYaON8tafC6w5cQi1EzsXf2YOwTWSuNHScIwU4JrIjD171DqxyzrAsi97eXv7yl7+QTCaZP38+zz//PPfccw9HHnkkY8eOjbQx+6PxqpT29nYMw6BUKtHe3s7YsWOj3/bu3Ytt2+RyObLZLA0NDTX7PSTvlkqlqE6GZWLu71wjRBBh1EC5Rbru/yGp+WdhxDIgPTru+iZNb/pM+XTTxohVRy+t4pHoA4SeOJZh845FX+c//3IpRS+HKUz8INS7EQCXhFPPzu71XPvQu1g46VxOn3UVM1qPxzTsKsOmlD5S+agoeo0IyrEwhIlR4fWxr28ry1/5Dcs3/Jb27FaSTqjFKY9phcQxk/QW9nLFsV9mWsvReNLFMspE6+quGp2ZcDBRpRzKcKoXRyECU1zwrlsOyiuy/dvn0fLW/yAx/ThQPnfedTeTJk5g6VKdi2ndunX87ne/47jjjqtyow/B8D13380111wT3ca2be655x7mzZtHe64XO5igQhORJ31Nwg03v0phCugp7KWv1BnE3gkjpgf8LWGRd3vpzO8iFWvUwSOVwhk3k6bzdayZ3b+8Gqt+LM0XfRFZzGHEkjpsP6D8El47UX4r5btY9WNpvezrAHTe+126l19Pat6Z2C1TEHYc5ZXovO/7yHw34z/8v2DZqFJfMO8NMl8JIlOnn9/GnrsPx0xO0ZoWJRFWHaX2R0kedlXl0xr184VhQEuVdkGpKOwzaPBS9DyUECSCDsr37qHYvRdH+Si3CEJQkD4l36Pge4xJZHClR8n3I+Ay6L3RD81JNpQ5MsFAzvfsRgXs9AMThWHGcPM9WjsRHOt3ir5NcO/Y8ZNxjhqPiFVrJUTgPWO2pEhfpd3iVN4t54CpIuZq3kyp0Evvvk2ceOUPcAu9GKaFkhLDsPjr7z+BlB6GYUU7gRfv+w7FXBdzT9X2yg1P/oZi7z5mLnkPhmkTSzUHN5CRTXFAi5VEYKJ8v+oF0X0Rpm0fTR++/hLOTyHZ1nN9Jkys5+qPL+Vn//NXtmzuIp6wuenGVRiGweIlU6rIuaOZIPX7YKKUxEpNwUpNGXjSzE/i9a2n7+X/pP2Rs0lMeiv1R/8QYTg6XksYGKwGp+M1l6APrOlNpN51LEZzMshxNDRoEZaBzJYwGgLNn1HNlQi1KLfddhvbtm1j1qxZvP/97+fnP/85Dz/8ML/5zW+4+uqrsW17hDlmKm5fA2ju2LEDwzAoFArs2LEjIugC7N69m1gsRnd3N52dnTQ0NNRMyBemEojH41XHpSwN6nQ6vIT8FYGZbooOyVKe3JoHaXrTZzQPw7DKALICqFTVsdI0Zlj40mVS41zeu/g7/OCR92EIA1NYOj5LhcbFtuKgFE9uupXntt3D5MYjOGriWUxtXkhzeiIN8TEknDqM/vwswPWL9Bba6cztZHv3Wl7c8QBrdj1KT2Efthkj5TTgBaTbMmBROGaSnsIeTp/1Ps6ddzVS+RFg2a+NQrkThvzVitcTKjyHK6X7oYeoP30LienH0d3RTm9vL0uXLiWfz+P7PjNnzuSMM85g8eLFVdeGmrh4PM7dd99NJpNBCEFPT080dkxhEAu+C1svs5WABQjWMJOu/C66cruwzRh+tA7pNdY0bPqKnbT3bWViwxyk9DGCd8Xv68BMN5F9/k7s1sNovuiL1XwpwGpoo/Gcs8r5rQwL6RYx7BjZF+5h728/xfirbyZzzMVVbWw882Ps+N5b2PwvRzPtWxsj/s+gPasAob2iYmNOI7/t1kCzHAsAo4/Tugwj1oqSJe2kIOz9opCMWNNiCG0L7SrmkErRFE8RD+Jg/Hz1ct4z7yROePM32NexlT5fBvk9FQ2xJI2xJM11LXz7yds5YdxUZjaMpdctYAVZSvuLNmN4WE6CVfd+i/Ytz2HHM8ggAuFJ7/opsWSD/nukA78/8g71+L6HMgyS9W36J8MMbIYQqXilQpgmMldA9RYR8RiyqwCezqArHBORjqF6C6iSXz4es/TEXhdDOBVq8IhrK6kfO5Nk3VioG1tV3UzrdJSUKCExTIts5za6d69nyZU/iM5ZeN4XePKWz9Kx/UUSdWNxC1olOBItllKyajeiuTeBmvFvDbX0E8s28TxJJhPjw1efyI+//wRbtnSSSFjcdONKMpkYRywYF5FzR2ueiHgG0gNVJh1CsKMTBlZ6Bg3H/pT0rE/R+cRb2XvvApqW3oGVPnwAr+X1EqUUwjFRUhFbMpXYkqn7XQ4VPDDXdbFtmwcffJAHH3yQTCbDxRdfjOM4XHLJJWzatIlnn32W6667jve9732YphldM1Lp329btujw6qVSic2bNwM6oF0+n2fbtm3EYjHa29vZt28fU6dOrdYeBzvn7u5ubrjhBnzfjzyd5s0/gpOWLA1ijRyA2UhJvZHTHYYQBmayMWxNeNKI2lwGLjaedFk46Rzed+K1/OKJjwNgGw5uECPFEEbEM0k5DfjKZ1P786zd/TiW4dCcnkRDYgwpp4GYncIybE3+VD4lL0/O7aG30E57dht9xXZsM45jxkk59UglcQOTlAo2tYYwsAyHnsJelk5/G+8+/luBRvvgeO8NN7Pt+Pl7Mbr3RFw9JX3MZD1m3RgdaK11KrFJC7DHTMNqClMmQDKVQgjBzp07GT9+PACFQoEXX3yR5557DqUUl19+OS0tLRHIdl2XfD6PZekM7blcLopNlEgk+POf/8zWrVtZuHAhCxcuxPO8KtNk+LS3d68lV+qmMdmGh4sRuIorJJYRpye3l21dL3PkxLOq5mYRaOXMulaMhDY/qWIWGRC8lZLYrdOY+Jk/a44P4djR46G4ZRUIk8wxF6Okr01KQoDvYWZaaDjzY2z+4jJUsQ+c4TyDZbQpS8/+POnZnx/8TK9Xn+v1oPxqMvNIZMSalqLv8aMXHmZ99x5sw6TBSXLMmMkUfZdbXl3JO2adQOv0E9nU0skPX3iIcck6tvZ1cnzjJJZNnMn9HTt5pH46bz3ubbTaFq0jrOC6x65H+h6VXjZTjrwQO54ecSNHKuFiVPVeVHBD5JYCfd9/HOIuSAOEnqxld4HSk1uILTkMkXTKJEXLQPYUyHx6GfaMFvCljmcSlG9aMfZufoZNz/1BE5dDNKsUHdtXYVplNafq53YWPZe+dp6748vEUk2UAjumBmiDaVqC3Zz09eIZJV9TGJH56m8btGjThIHvSxIJm/d9+Hi+/51HA1ORwW9/vYJ/+McltE2sr9K4jLTs6L0wLAZ7hZSSIItYmdm0nrWKjuXnsffeBbSc9gR2w5ERcHk9pdwOgfIluH5NLtTgosCxwBBV7s62bfP4449z8803UywWueSSSzjyyCMplUqMGzeOq666iu9///s8+uijFItF3vnOd0bBxEayoPUHGx0dHezatYvx48fjui5r166lq6uLhoYGtmzZQm9vL01NTeTzeV599VUWLVpU5V4dknqz2Sy/+93vACKPpIsvvYSTlizFZ+T7o0FqXd44Be+edINQ9dLTZupRcPXCPrAMG88vsXjqm0nYaa574hP0FPaRcOrwpYdUZVda7aFiYJvxIDeRpCe/h47stoDjImuahyzDxjbjNCTGBR5DfqRdCWPHgMA2HDzpkit1cdacD/HO475B6IIdkYkPmHw+yOAMnmXXA9eRaGjFmXwkMteJkWwgt+Yh3PZ2UvNOIPv8HfjZDlQxh8xDqO+34wlOPvlkbr31VmbOnIlpmmzcqPkps2fPJpvNUl+vgUGlpuXSSy+tqsaPf/xj3deex7x587jgggsoFHQU3P7axNANfXP7yiAhol+mOwqBUiKwbjhs6XwxuKYC2AfjRZVyEa/FzLTU7B4RrCVCGNG56UVvofPe77L7+o/Q+vb/xkw1RucXt69h98/eR8OZ70LE09o8NGiAuQrQbdgo6aGUW8OFWyDMGMLQQNFuPAYzMTGsYHTWcONj2FmzJH1ipsUNa56gzy3yg1O0G9mN657hJ6uXc/rE2SRMi5hl47pFFqXTXHfihWCY3L7hGersOF7vHux8F8lSL6Xe3dA4nqJbwKqM3Be1qzyoS/keCn37tPdNYB6SUtKx/QXGTl+MV8zqLJRD8gL0MPArQhxrm18K5ebpfuinNJx1DQZlLYvMdSEsG2E6OjKlV8SqH0vJWkXsc3UkJi6tuoO3pZPd879N/cMfwaivTaxVSmkTUsDk1iRbwczF76Jnz4aKNhr4vsusE98bEHc9TCtGumky6aZJPH/315m15L0IYfDyoz/HMC1O/5CeaB/91YeCLhx+EZbSCzylyh4Wf6vmoUqp9i4xIo3LO951DD+49jEAcjmX3/7v81z98SXEYmWNx4ioZrXU9jXPM8BMoGQJhEXTSXey78GT6Fh+Dq3nbsCwUtRyr38tpWoRMY0qbcmIy6Bsyg0n89tuu4177rmHnp4elixZwqWXXhpFpvU8j9mzZ/Pe976Xn/3sZzz99NPs3buXc889l+OOO25EdQbd//7/x957B1pSlOn/n6oOJ91z8+ScA8MEhgGGASSDgogoIgiKedU1oKtrWHQNi7K66q5izgkXCQJiIkscYMgwASbnuffOzfeE7q76/VHdfcI99869MwPi97evDufc093VVdXVVU+97/O+bxAgpWTTpk3s3LmTFStWMHnyZL773e+ydu1aVq5cybp16/A8j+OPP5677rqLp59+mgsuuADHcSoi9vq+z7hx43jve98bk3WLxSLjJ06AABwL+qOoyhpQB9BIRh5W5YTTcKMlbNdorpNhuPRwt1/NZzlQH0RiWy5+UGTp5LP4xBk38YvVH2ft3vtJ2JnQ5OBVADRNQBD4CMzCaVyh5aBFRmPqr1AoVbqmfFdnND4uoOkvdpNNNHPJii9x8py3xmDm8AGWyntX1DWKuWPD2Eu+TnbVpfGx/X/6Gvv/8BWmfvYh/N42gu69FHY+x46r3xw/waBYYN7cOUydMpm1a9fieR4XX3wx6bR5JlEsmfK+HxgY4Jvf/GZMzu3u7o6PDwwMkEqlSCaTpZxZFXQLo5XKFXtYv/dBHCtFUEZ5MNopQaA9kk4d6/Y+SOfAbprSE4yJSFoxipbpJgbW3s2+33yUoLc9DAYXgkXLQRUHcMfPpeV1V8bJELUKcMfOZPLH/0rnn7/Gnh9ejkzWhcBaEPTvp/5V76Dp7I+FdR8uHEaZQ4fXgxB2WLfqczXK7zcbnCBHevrlaOWhvJ4waezINHEHBC1RXoXn9u/mY8tOjwt+89yj8VTAZfOP5YXufRQCj4RTUiHdtvU5HmjbwVdWns9THTuZ3ToFa+vzaMvYuCwnNSTBLYr7UuhtZ6BrJ5aTCL1uJDoo0LPvRcbNWonlpMpsbUOL9ou0/fbjYUCdBMU9LzDpY3/EzjSRe/pPtJz9UVM+EPTsZd+vPoQu5vA6tpOcuYLirueZ9JFbKG5bi79/D6k3nkDQ14O0MoiUhe7OQ9JGdebibLDYJa0JthVnh41QdOQFNPu4S4eodSQlbcuRZ3yUFx/+NU/c9iVA0DJlKYvP/BhaK7x8X0U46gP2ifINVyYKCKg1djh5/qObhyrJoAa4TJnWyAUXHsm1v3qCdNpl6+b93HbLWi648EiU0lhWbY7VaKfXSpu1DlWmRRAujSt+RrHtnlcEYIGRA7Bhy6AUU6OtrY2f/vSnvPDCC+TzeY466ije/va3V9zDtm2CIOCoo47ive99Lz//+c9Zv349QRCwbNmyIT17atU7AknPPvssUkrmzJnDsmXLuPbaa7n99ttZuXIl69evp76+nlWrVvH888+zfv16Nm7cyPz58ysAkFKKVCrF8uXLB91P5YrIlGviLrk2wrWM1nQ43k+ofYq4b1amie47f0T3PT8KOw1UHnru/wXCsgkGfNwJ4a4z8MEeJoJuDQ8t2zJajkmN8/j4GTfwl+e/yx3rfkh7/3aSTjbkkxhvHhVGJtWhGai2O2stI7O5xlAF7PiXnNeNFDZHTzuXC5Z8kkmN802wNHFo4QVGJWGx2ge/cycAXsc2nJapBN170WHeJ5nMYmfHkJi8CCHfXNY0049tbW3U19eTTqdZv349+XyeZDKJ1pq5c+dSV1cXj7t3vvOddHZ24rou6XQaKWUMciZOnDiI31U5N5j8QBvb19AxsAtHurHzSqV7ueEvdQ3sYcO+1Rw7/fx4fo5yKU366B9Q/Z2ofE+JcFxS2ZiozDGnxWRKF9JCBz6JyUcw/l0/xe/ei+rvNGuvk8BumlSZJXvYdTacO4XEcurLH8dQj6n0KR1EzHVSFQB3KDngDBEBi5MnzeWrj/+VTy4/m4ZEkh88ez/37NzAo/u2sLGrjYRlyIVtuT6uf/FxHMviKyvPB6BjoJfNvR3k/QJOVBmta5o7wDDZLWnRvv0J+jt3kMyOxfdyWLaNCors3nAvc1ZeFsaHcWs2suJFsV3Gv/cX8bG2334CVRzAqmtGphrRgY/2C4hEBivbysR//h2Fnc/T+ZdvMv4dP4gTSiEEdosJ+CSSTikEtGOBr8xkBpCwBoMpXVL7KRXwxB++iF8ciMGDOV/E4ElahqSUyDSx9DWfNgRdy2HuqsuZu+pyAHraN9O29THGzVyJlJZxf67RB4MqgglEp4IwoV3otWSFL4EY9vpXttTyCLJtSeArjjluKls27+e+ezeTzSa4/2+bWbhoHPMXjEUpTfnQ1GiiTfWh3D8GLlph183CrpsVnRne6x+vj6slqn8ikaC/v59cLscJJ5zA5ZdfHk/45YtWlDhxyZIlfPSjH+WXv/wl55xzDslkMuYIHOhekWlo3759rF69mhkzZjB//nwSiQRnnnkmv//97/n5z3/O9u3bmTZtGmPHjuX444/n6aef5r777qsALZEopYbwHgr5GP0eqnMAkbTNxmQo0KIxiUKLgdm8KMBJ0nL5N7GCMjOvtAxAkRbay2M3h6BlBBuxas8zrU2W5UD5WMLmnEUfYsW08/jbi7/mwU3Xsb9/FybWiI1rp7CkbcxB0T+qg1lGJPEwnQIyDm3hBXlyvplrHJlg6aSzOWXu21gy+QyACi+hlwWwVPdNrLmKNFshGRQgNGGq/s6Klztqei6X4+abb8bzPFasWIHWmnXr1rFlyxauvPLKinZkMhkymQz9/f089thjdHV1USwWSafTHHfcccyePZsgCEgkEoP6IdqIrtn+R/ygSMJKxakPSnWqdAtfvflGjp1+PnHFI41yugEr3XDAfqkAulobjUvgg1bYDeOgoZJXqbyCcZkW4gDWjDCbuNfD/kfeGnNyBj1vHf+nJNJBe11kj/xKBd9vODkgaHEtgxBfP2sprmXxrafvYsD3eNWkOXx1+gUUAp//euIOtNYUAp9fb3iEiZkGWpN13LNjPQO+x/h0PafUtzC/cRxj00YlWq5lqR7QMgRAW5/8PdIus/XpAMtJ0bb5EXrbN5NtnVFSlVWVU91hqjiAdNPkXnwYLBuneTIq1wuWhbBsY2YqE799C2qgC4Ag142dHQPKN0x/CANrleJZCLcsC6yCQWmMyrRlQkhmHPUGtA5QgU+6YTxbn7yF/TufYfl5n6e3YwtOIkPgF0tu0hFCVwGFXDfJTDMvPvRL8n3tjJu5Er+Yo2H8/AM9zhI/JwYtxiYtbTcGLdGL84+6mFZP6EKI2IX3zFfPY+3z++jrNQSwP966lpmzmnFdG98310gpsCyJZUU5iyrlQBNw+bHSeIyIiCYa7uHQcrwSJOoL3/epr6/n7W9/O08//XScOyYIgvh5lPNIIo3L5MmT+cQnPhHvXg+kaYnV66F254EHHqCjo4NTTz015sWceeaZPProo9xzzz1YlsXRRx8NmDDx8+bNY/Xq1Zx22mnMnGnyyJQ/CykliURlSAUtzUbCmt1M4oSZyJYUuhCUrS/m5Y6v0YAUKM9HNqbQmPer9YySe+zQ7TMbiJEs8rG3TrlJNCR4+0GRsdnpvHHZZzh93rt4etcdPLr1Fvb0vEhH/05yXg+2TGBLF0tGbs0ShEnrEbktK+0TBD5K+RSDPFJYNKUnMKFhDgvGncBxM97AtObF8WYM+LsCFiPhux87GpQtlFGeqap4V7ZtUygWmTdvHueddx47d+7ktNNOA2D79u088cQT2LYda14iMu6OHTv44x//yPz585k9ezZCCLq7u7nhhhs488wzmT59+iC3fqUVtuXSObCHtXvuNwCSwVqWqO+iqMMb2lazbf+zJoN3lJpBhCH9tYpNOzX7Q0gDUqo1weG6p1UQlhEWISwTFuOAUtKdaO1TbLvXmBmFMMGr4hwMwtQh9LyM6iVkAlVoo25+yMccQTbpETEBfa1whMWilomMTWVZMW56xXEpBJ4KSNoOVyw1D/prj99Ozi/iWjYP7H6ROifJTZue5Lqz3830+hYCrbDLQpnHDzR079294W/seeE+7EQm1EQYN13bTdO3fyub11zP4rM+HqcdBwa9IBXIMlw0+h67gdT8k8PWO/gd29n70/fijJlB87mfRBdziJRD7oUHGVh3L8FAd2x7jjxDBkmgUN15dI0IoZFUt7N58pEVx1unHYXWAan6saSqwneXo3OtFclMM73tmxno3oVWAe3bHqd16lEsffUnYxA35EQR/hQUc0Qj1PA/HCw7WXHOqCabV5hFqVotLaXA9xWNjSnOPHsu1/7qCerrk2zetJ+HH9zGSSfPRIXRLj0vIJ/zUEpTLA4RhyTIURkIqmRUq/5NCGnyDQV50GpQWnqBAKs0ib4cE/xwz3aod6jW+UKYXZVt2wRKMX36dKZPnx4vpuXkw/KyomMRL6X8nPLPWveL1O0dHR3cc889tLS0sGrVKsB4D9XX13PRRRdxzTXX0NTUxDHHHEMQBNTX13PWWWfx7W9/m5tvvjl2uY7Ct0f3jLQ4sbgSNNRf8Sq44lUj618ql4+BYjfPbv0zx8y8CN/rMTE5YvUrZicrLaSTHNV7V/2cot9syyVQvvHgTI/jpNlv4aTZb6G9bzubO55gT8+L7OnZSHv/DnrzHfTlOygEOXy/EHqtONhWgqzTQjbZSmNqHOPqZzI+O4vJTQuZ0bI0JpJGIMmOvA91yZ3/FadJ1NWAJj4QbyyKxWLMdwLjDVedpyrKNL5u3TpmzJjBSSedVFGaUopnn32W6dOnx+fG5iLlIa0Ez+66i11d68gmWykG+ZhTVK0pVjrAtVJ05/ayestNTG1eRLnnp9HSj4AmMcx7bagFg+kFB35+GrDQuoB0xzH+nCcOWI+h6xd6IB2KeSjeRakAR1o8tHsT67v2smLcdLoKA2ScBAKYXNcUu70JIWjP9bGpp43x6Qb6vEKsXp3TMIZ0aKutJn2V70i11jx7xzdRgY/lJEtTvBCooIiTzLLhwZ8xY/mFZFunxxyYWg8lbAnSSdJ5+7dB2tQtPQcdeAhpI1P1ZI9/C9LNGBNMqp78tqfIb32chpPezv5b/oMxb/7P2h0U1Tnl4B41uWQeqoUVqhbRKC5M4OVxklkKA114BZNavJjrxk7UmXYJUUHSFZakbcujPH/3dzjyjCtw002sufVzzDnurUycd3IIWIazDZq/vWJ/CYCF+YgOSdPyCpqXIqmeNCP35mNXTuOJNTvZsL6dVMrh3rs2svzoyWTqzKT72tct5LQz5yCEwAm5SZZV2k1rrUya9er71apD+Kl1gLCSQ55nznl5Jvh4HCq/ks8gJAg7bKNRVVeSKJUhDdQAFBpD6lTKsFSDQOE4DjfddBN//vOf+cAHPsDixYsrXJsj4FJLQyqlxLbtQdFpo3ODIODaa69l586dXHDBBUycOLECbGitGRgY4OyzzyaTyRAEAUopVq1axWOPPcbDDz/MX//6V84555yaMVsiyXt9JJ061my/DeV7rJh5PgMDXaRD2z2YWCZOMsVjG2/h3hd+STbZAgj29+/kTcs/y8zxR9Nb2M+je27nmDkXoUXK2PKHezajHAu1zJIRqFC6FJOptW4KrXVT4usK/gCen6cY5Am0j9aBAfnCQkoLW7q4VhLXTlckXdRa4Ssv9DKyBgGWcuDysoomHp+V3Lzhd1VKKRw3QV9fH2vXrqW7u5vt27dTLBZ54YUX2LRpE1DyAIrG8NKlS7n55pu54447aG01njudnZ1s3LgxTs5Z4easNY6VwFceD2z6XyzphB5e5fNVKWKuMc0Yk1vSyfLg5t9xytzLaa2bgtJBHPRvpJq56u+juWZo0WYsa0Xg9SGljbCSBIV96EIHWvsI6SKTE5FOFuX3IcrjiGkNVnKYNatSRuhzGdrwnASNIcM9bbs4oYbji8cZVbCnAhxh0Z7vo88r8NljzqGnmKfOSeCFXkimjhpLDmaUB34By06w4YGfsueFv+Eks+HEqpBOGhV46MBH2i4D3Xt4+i9Xs+ot3yUKmFZTw4IGpej409X4HTsY99ZrwkXHsKpluoH0vBJK9tq3su8XH6Dp1f9Cdvn57P7uxez/09dofvW/lExDYUcLK7Qlz2xh7P0fKN3bqt35FS9zFEG4zM0xSl4oLceYvCKzVwjKBrr3sOGBn+Dle1l27pXUjzHq7WNe/2XW3vsD9mz4G7OPu5T6MTMrzGbVdQAMcbcM2UvbiYm4BzXRvESalup+NOZYHf87kFQvAEFgyLkrV01nQ5hgsa2tj4ce3MLpZ87F8wKy9Umy9aUAYxVgOLS59m/8HkHfC4b1Lh0IikTxD4SwwHJBFdH+AFZmBpnZH6D/xW8T5HfH6vcIHAi7jrr5nxrxS3soUvHODeOyLeLJsBwAS0xM8KFFokFY+GGk3P3797Nx48Y4u3O12a78s/y47/v09vbGSQ4r7iEluVyOVCrFvHnzOOuss+Jro0Xl1ltvJZvNxhqYcrnwwgvZtGkT1113HUuWLCGVSuH7fkV9jFo+IOkYXsRDm6+nEORYMfN80ulGU0ftYQkHJ0zFcfSs8zh61nkoFBLJX9Z9n6I08TG8IE+d0xg2cmgV+KGYDWuZFoQwoCIKNql0EAcws6RNwk6TsA/stRRpUxQKK4ySaw+R9uPvqV2RCQlW1cY49NgcTgKlcIDnn38e3/cZO3YsDz74IGAAyqRJk+js7KSpqSk2TSqlaG1t5cILL2TNmjVs2rQJrU3Ooje84Q3xuZWmoQBL2Dy98w7W732YpFNHEIaeiNfBCAxKOwYuWgc40qWjbwf3vPhL3rj003Ggub+fGW6wCMfkNOl59jPktv6SoHc7OgDhgNOwiMy8jxmvoRp1HWk7DhinBUr8k/Hpev77qTvZ2N1GbzFPwrLJhZyVq1eV/NXHprL0FPO8/57f4CsVo0hPBXz9hAtpSKTCvBflZiEPy07Quet5nvzTV4yGRYW7Pcvh2Dd+lWdu/zpde9YhLAs3Wc+mx65n3OwTmX3sJTHgqWiw8hGWQ89jv4MgYNzbwsBsKmRXI+KATyrfi0xm6X3kOhpP+ac44M74d/+cfb/+CKqYQybqCAY6B/WR1toElBtBFuHq36PQ+eNnr6J16lGmv+1k9UXmYSXSTF1yHs2TFpk6h8H1UvXjOOq1V9K5+3ncMMjQ0A89Ai09RDFptFZYdhI7YpgfjNrkJXpXqtthWRLHseLvB7p39YtgWeb7kUsmMH1mM9u2dOK6No89soPjjp9GXV0iTAdgAJIQhtcSP1MdADb57ddR2Hc30m3E7+lCpgQyctsL+gkGFHZ9A8rrxm05jszsD9Dz9L+i8gNYmVZ00G8i4wYFZHIcdfM/+VJ03yCJNUVC4vdvIRjYZuqhPKzkeKz0FISVIrfjRoTlkpxwLjrIIawUyuvB79sAcRbfCIBI0D5WZjpWahLlxhHHcchkMiPyCorOB1i0aBE/+tGPqK+vr/hdiJKnzzvf+U66u7tpaGioAEM33ngjzz33HOeffz4TJ06sCOrl+z4TJ07k4osvZt++fUyePJndu3cPMmNJaaLLdvTv4Oanv4ZtuRw/801c/8R/cPTUc5navAhbDM6TE5FQ9/VuoXtgL/PnmzAEExvmhrFNXj6p1u5GAEZWtTVybR7sNSgQIlr8RaxNKb+2WsPzd1s0w6p7bQqV647rB4TE5hrawTKtjG3ZKA3HHHPMsK730diIPpVS1NfXc8oppww6NwI3tTSJt6/7YVl/G9K/JW3yXl/IEZJsal9D2m0IQaYg0D5JJ8PfXvgVq2ZcyISGORV0gL+fmPFBmNix65FLKbY/QN38T+I0LkXYdahCO8W2u+l54oOGwzLv46XIuFEpIwTsB5xJtNYxGXfFuOnccf4VFIPSrgRduokTdl5zMsPvz3lf5Xlh0yJTUcULpQKk5eAV+nn4uisoDnSG7swWxVw3c49/G9OXnU9xoIvV1/8LUtqGpOokefzWz9M0cSEtU5bGyaSqtRnZY95Uerm0rnANjty6RCJj6h7mAjEu1gJhu4x723fC3/w4pkJ5/wghjAdRVeePRCKE7SSzRrOk9SDPo+gcN1lP86RFIeFNI8tIVFormiYsLKvTEDbOsG7FKPohgAqwnCROyN0ZSf1fKnQ/nOkEIJfz6OnJ43uKfC7UfIXvq+8FsYu+EALbru1yGQQmEu6xK6exZfN+UgmLnTu6Wb+2jeUrJpsJxKrkWcTXWwYYt5x8V3x8528Fraf8DbfVxO8p7n+Etr8ey/jzuyraIJ0sdfM+QfaIzw3Zxpdl0g/dLbtWX4KdXWCSmgHFtntoPe0Rc4rfjfZCrkeQN0Bm68/Jbf9fnPojTU4RYZmypIP2e0lNfYsBLTqIx5ZSCt/3Kxa24STq53Q6zYwZM8z9qxbHaLEAaGhoiBeHKHNzf38/06dP5/TTT4/Pj8qJXE+PO+64IetgSYudXeu4fd0P6M61MbP1KM6Y/26STh2NqfE8tPkG7n3xl7RmpnLK3MtJu/WxZiZq3R3rf8Sx01/PY9v+wPb9zzLg9ZSFaH/5Fphq7VH1OyuECImTIyvPOD0PJrqXnYAWukZgsdHJqM1jIWF+zEXvJxVqzmNtduDFnkTlIhNAZE5EI0WJv1I+3iJxHKcChJQDGN/3K3gvkWmzEtAWsaXL6i2/Z/3eB0mFWpYSC07gqyKvPuKf2de7lQ37HkIKiwCzSYgIuV0De/jDs9/k3auuQYf5zGrTIl4mEQKtCsjEGAq7/4j2+xh37vZBpyXGnUZqxrvoevRtpKZegpWadFBBNg94dvkgV9rYrlxr8GXV9lQ9xHkQvrIVE5Hp5Id++2HatjyGm6xHo/EL/TRNXMSy134OrTVzV13OnhfuY8sTN+Kmm9DKp5jr5v5fvY8z3ncD6caJMZG3+gXVvgeWjZCSUowMjd/bFlZKmdDqSoEO4gEPEAx0YaUbTZjjMJUA5e1VitLusvyzoidr9EJ4nhCowACROI3AoLIMO1+rINTOyNg9GiEQwjKJF4WsUUbZ/cN6Fwc6Y3CktcZyEjGnxYA7FZ5bXYYO7/fSmDK01jEhNqquEOA4FsWiz0WXLEVrTRBoslnDbpdWqIlyBpvDlNKVmhKIPYIWL53An29bRz7v4TgWD96/mWXLJ8XamGrkXzGegjwI24Si9iHI7Q55SgqV221+K7Qh3Ra034t0GtD+AMrbb+pV7ETY2dICL5yXT8UbmgqszHQaV/w4/rnzoQvpuPdU7Ow8ivsfpf7IL4fnRxoth8Zjfl0731IoxjvKBl3SKpQvANHErtTwXgKR+7HRjg1OMhkBkQiwRGJZFpdddhn79++nubm5YmGpXrg9z8N13cH9LWBS43zOO/JfaM5MrDg0s3UZM1uXUfRz5LyeGLBAKcLp75/6KvXJMcxqXc7aPfcxa8wK9vfv5IW2R0o3GEZiYIBGaFE5FRyCHIrpKS7jQHw3wYgBSyUo0PF/BQdB4g3H9ITLrjHdpRQybbR0TWd8iIZXvTM8LQqgqfHaiOO3RPPigVJJ1BpHQoia11WYlbXGli4Fv58/PfftCi0LYXs9VWRM3XQWTzyV/mI3t6/9Hnm/vzTXIvCDIplEIw9vvpFjpp3PkslnxNq9v595SBirhl2H37uO5JSLAFCFNoTTEG+SVLETOzMdt/k4/L6NpQ3OSFkqoYzo7HiiKHsAtc6Jjhn0Xvu8SO8eg5swPfcjN37agJFUQ6g50NjJDCsv+jqJVEPs/rvigqvo2rOWnrZNWE4C20nSs+9F7v3ZOzj1Pb+NcxINAi52ebKu8MWTDtkVbwzrFWqAypjYsUo1TGGenHF0nC2zvLyRBLg7YB+XRyMdcj6QUA4Eq5IiWnai/OShywAKA52Uu+HaTopE2kR9jDQ4ozG7HA7RmH6ohh7ROLrr9heZMauFefNLSSBMYDgTQG7d83tpbx8ADQ2NSRYcMZZk0hkEXIyJQZPJuBxx5Hjuu3cTiYTN1GlNgwBOrTYDIE0UUWQSBMjk2HjHIBOmfiJKCBapQIVEhCYFIRM1OSUvz6QTeo35/QS5XSCkAVU6oOn4G7GS4xnY/GOUF5pCQw6GKrTR/djbcZqPBe0jrDpUsR1hpVHF/STGnkpq6sVGTSwqNVXVLs3VBNtaMpTnUYW5Q8pBx5VSMWAZaoxWgyEgDu7VX+jm/o2/IenUMVDsoeD3k0k0IpAMeMY1s85tQumAtNvA8TMvROmA9r6d3PL012hMjef8JR8nUB4Lxp8IQNHPs7H9saGfSC0tSLkG5HAPC80QQSSrf9VDnF/2l64mvlb70Qmz0Yn/qvpbiLJzoyJHO6+Em7FcJ4lEQ8WGzG6aGH+PPU1th9YLL8MdP9f8PcQGu1pqbWSGO7ecp2JbLneu/wmbOh6nLtGMH5hNptIKR7r05Ns554gPknLrSbn1nDj7Ldz6zNepSzbjBYXY2SWaD69/4j+YPWYFmUQj5dyzA9Xr8IvGBH7NY6WmUNj1e9LTLovnwUis5Dh0kMPreoL0zPeEnXSYEyaWS4WZ5wAParjzSiYho52Q0uLxP3yRdfd+DzfdUHJv9vMcd/G3aJ22PAYsKvBI1Y9j1SXXcOcPLopD4TvJOtq2PMo9P76Mk9/xCxKZpsoYJ1X3jr/bDo2nvDc2p1S/KCXA46JVQHq+cXfUKkCU8WeKue6Yf/PKlkir45Pr3mvMZCGxVVg2vpfDLw4Q51+qoSzSShlTUmhOOxwvhxBmx2wDe3f38Idb15FM2kgp6OstsGjxeFaumh66IBtNV6QdsSxJbsDjtlufJ5VyGDPWqIH37x/g9zc8y5lnz6O5JR0Dl0hUYMDy/IVjefD+LVxw4ZEcf8L0yt4aajyIkNsiJCoYQFqQ33Ej/eu/CjKBU78wxJOR+64qGxmlCf7vN1pCjZPTgJUqaRKEsOl6+GLshiModjxEduFnze+WAcPZhVdSbD8Z6Y4hv+fPFNvvo37xVwly20Br7Pows7KQQERKNuAgItT29vbium7tDU0N0VrHodBrab+ic8qfjWVZg/gE5YB1qPtEi68lbBpSY1FakU3aTM8s5r6Nv0Upn5PmXEpn/y4s6eIFudBTCAreAH967tscOfE0jpn+utgFuOjncO0UnQO7KvPGVD+RKkDmq8EE5PDMIX+vOCLKQYCoPEuUn1/dn0PoSV6CwRqZ1LRScU6jQAe4VjLMxVNrfhli3ET0lUQGpKxIeqsDzzzb2JyuwXKZEAYcNaHtncO24FebzgLlY1suO7vW8afnriFp1xGEvDCtNZawKPg5xmVncuKsS+Jrz1zwHh7dejP7B3ZhSycG1YH2STgZtnY+zY1PfpnLjr2aQAfYYQTiqA4vG3DRGmElUfm9JCedT27T92m/ayV1Cz6L03AkwskaTkv7ffQ+dyWpyRdh180axGkZqbzsGduEEDH3BODRmz7N2nu/h5uqj71k/OIAx77hP5m+7PyQoGsmOWk5qMCjZeoyTrj0+9z7s7cbsKAC3FQ9+zY9xJ3ffxMnvf2n1DVNjsm51RNCVA+tNQTFCgBSq75Gm2LCHoMuUzEqEBZ//u9X09u+GdtNjy7z9N9RDI/IRQVFbDdN95713PTFo4a7AMtJMdC1i3knvouVF32DwC/EHkeHVBdd2nm3tmZ481uWogJFpi7B44/tYN8+4wqulA5dasG2ZLTG8Owzu0mlHM45b2FFuWse3cEjq7dx9mvm4/sBrlsa7jLUbE2d1siHPnoCM2e1xOaIoXbnpTGkENJB+X10PXwxWkOx/T6s5DgIchT2/AWA7jXvpfHYXxtzCfCKAbU6AGHj96yjb+1VIGyEU0/Qv5nGlb/Dzkynb93VKK83vEDQv/H7oD2EnUU67QQDm1H5Pfi9z6PyexFuM4Vdt6BVgczsfyYi5RUKBdLpNNdeey0///nPRzyRaq1JJBK0t7fzsY99jBUrVuD7/pDq+6E0MCMV13VxwmBaCTvDcTPeUHG8v9BFoH0WhpqT6nu7doq3Hvuf+EGRje1rmNW6PP4dwLES9Be7oysOWH/HOvT3aiiJNNwBpYi4BjSoMEpu9D0I3aUDAu2b/ENhEkbzt0+gfQIV/fPCvz18VcQPiniBcacu+nm8IBd+z1EMchT8gfhf3uvDCwp05fbyxmWf4bR570BpH0tUP++hzVJh48yf5c/fcmqeqwoDCCcxKOja4ZBam/frn/gSPfl2Mm5DWXZshZQ2Ra+Hk+e8lZa6yTGgaUiN5bWLP8qPH/wgrpU0nClhYgb5QZG028A9L/6C+eNXsWLaeTFQfim04MNLaL0QxnrReNy1dK/5J7pWX4zKd8feQzI1jvT0t1O/6KrQoWswkX0k8pKBlqE6LgISXqGPh6/7GJsf+x1OqmQXDrw8x1zwFeauujw085S8BiLgEvhFJs4/hZPe+iPu++V7UH7BaFwSdXRsf5I7vnsBKy/6JuNmHY8KiU7l7r8V9bIHh1gesh1lLtvl5yllItuqwA95DWV2aBj8fchOq3HuS1GGKJ0b9bv5VJXnlF1n0giYiSpy7R1y13MIYtmSTBk3pa+vyMxZZjcrpKChIRmfFwGYqdOaWPvcPh5/bAfjxhsyc29PgfXr9nHMcVOBSjKm+ds808bGFI2NqYpsz0ONh/i79kG49K//Kvkdd9B4zP+QnvnuOA6LVkUGtvyMroffi924mOyCf4tK4BUBXEKVbHbx1fg9a5HJcQQDW0nNeGdsMrTrj0B5XfEl0mlAFdvRfj9KWCTGnUVy4vmo/B7QiqB/szF5RW69YTujvCyJRAIp5YgnJ6UUiUSCVCo1yIwzqDnDaHeHU+OX82z279/PmDEGVERRYAWCgt9PXaKZrvy+cC6BvsJ+EmX5oxwrEYcu2LDvYR7cfD2zWpfTX+wiaWcQQpCwM7xmoQmLUEvjIoSI42705Nv5xepPxAC5os5RQLrSX+ZTR94/uuydjsIYKlQIVOIkiFFG5/BTxeClGshUAhilgjKtSAhkqgCM+R6ENIHB6QBKv8uKT9tyKXj9DMTgbhQSW6ZUaMo6cGRVmUibflMRcfzQF/rquSMCEnes+zFP7PgLGbcBPwYsGimMx9D05iWcPv/dpl5hrCSlAk6cdTFPbv8Lj237QxXY0fH1v370M0xpOoLx9bPiSLkvL3CJFgvLeCEmxtB8/A34vRsIBrahlYewUtj1C4yJKHxGB1u/lwS01OowEzLeit2aH/rfD9O+9XGcVL1RoflFpGVz/MX/w8yj3xTzUsrLi8q0bJfALzJp4emc/I5fcP+v3ku+rx3bTWMn0vTv387dP7yEJa/+FAteZVwOlV9ESDtG4LXUaENpWqqPVZ9n2YbEaty0/zE0LTXxxnAcltAsZDspZOz6+NK00/eNd09nZ46dO7o5fpUhfiYSFqsf2kZ7Wz8zZjbT0mq8XsaNz/La8xfyxJqdbN7UgRCCumyCU06bzYSJhowXBZYrcanKvoe8GDgwVyeO2hjkGNjyUzJz3kpmzgfNRB+EpD7pkpn5HrzOxxjY9AMys/4Z6TYSmUz+nlJuCk2MPYXE2FMo7PkLXqEDgh30tt8PwiIx4RzS0y7FBMVLkZpaSi7ndT9LMLAd7fdgZabjthxH9VhwHPPuXnrppVxyySUHPXFqrWMezEjdpkcqEZBNpVKceuqpTJgwAQAhJK4sI6UDjnQJQj6Ya6VxrMoQ59HrlLDTtGQmAVCXaIqPZxKNzEgsG7SoDCpEQKB8nt51B335TrNAlb2sVZCs6uugkJ1l30r8mNJZ4bdyDg0i/FNUHI/+VyLaRiBEmEzR0injqsh4CjTdV8V5qeLARHmPLGkjhCQXavhqb4nKTS8S7eWx6lroe+IWNn9N0JcCOwA9kuGmjAeR1wHTvvBL6o+/FO3lB4X4H6lUzx0Rj2VLx1Pc+NSXca0UgTab2nKNv0Bw/pJPkHQyBMorgdqwDRcf/UU2dzxJT74tNhNFINeWDt25vfzkoY/w8dOvx7EScQbplw+4lHGZohQOysfOzsXOzq08U3lxgsSo/fGxEdb3sMwCQ+1IDVo0ETQjV+R1f/shT//1qxQGunBT9YDAK/SSyo5n1SXfZsK8V1WYj4Yy60TAZfycEzjtvb/j/l/9E527nsVNNSJtF6UCHr3p0+x54W8sO/ezNI6fBxhNj7TcijrWusdoJN/XTq5nL3YiY0xIopZapPwVFAyNGoZTrRyuMoZq39DqGa0UdiLNQE8nXhgHIbbPHEYJfBWbbv582zqWr5iMmwhTu2MItEFQel7d3XmkFCSSNiedMisGJ0ppvGJAT3cegETSJpEYvFgIIRBl3kIHJtiFE22Qw+/ZTsPSMD6RKiLCUPw6KIBlkZp0Af0bfoj2+8BtDDVUh7/PRiMlbZEHwiW3/Xfkd/wuTFhmghX6/ZvoffZKrPRU3JbjjKeUdPG7n6Fv7Zew0tORbjNmV/8Ufev+k+Sk80lNuwzt9yOdbPx8DhfQONwTsNYlr6KmpiauuOKKQb8LIeKF1WQsDoFtjV185KSQcutZs+02Boo9DBS7saSFJV36C53MHnMMZy54T+z6Wi1Ru5J2hmlNR7Kt8zlcK2G0FiPcIBwwQ3vVO6trfKsoS1cerz4/MjVV3l0PPcyHaIbWxjsm0AE5r6f2SVUFaK3AdlG5blJzVjHj1f9Gt7cfR7qV/TDUlKeNqT/o32+cLMAkxj0IqR6fWiss6VDwB/jFI/9KzuslaRtQEpNvrQR9+Q5Onvs2jpry6krAAkhh4SuP1ropXHL0F/nufe8JzUOBMakLia88Um496/c+xI8f/BD/dOL3ibRtg9a2GHC+VKLRXg9aWEbnF/SDDsyYE8KYoYWNGhSvSJsI42JkObcOaUYZitymtYpVbjL0qNi3aTXP3P5f7Fx7J5aTxE1mTRKuXA/jZh7H8Rd/m/qxsyoItDU7nkrgogKPpokLOeP9N/HwdVew9albcJP1SGnjJLPsePbPtG15lPknvpu5q95Oss6EWlah67K07EEL1YjBS3h88hFnkZu8uCIg3v9rorXCchLk+zponbYcoCLezaGKEKGCMTQN3XDd04wZk2H+grHkch6plIPnBaxcNY3pM4yX06aNHdx1x4vU1bkU8j62Y7F9ayfNLWlSaTc0MTj09RZYcewUliybiO8ppCXiuduyDjYgljBWlpj9Xr7rLe1kzaY0HF8yEdtx/14Sv0shCc7vfpr0jHdg181EKw+0ws7MJDHmZLz9D+O2HIfye7ESYxjY/GNS0y4nOfGcqjIVnQ+cT2rqJUgnGy5isuKdPVQ53DvG6nnF80zGc8epdB11Q5Pf8TMujK91rcE7cSkMIJ5QP4ePn349eb8fidGSRKr+lGu0foN5GnGtAMN/STgZ8l4fAhOwbnDcpUPs13IT8LCLmagEaUNqaGP9i/mvLL0DonRSjXuJaFuELV0c5eIFQ5GQB1dAEGo/bQfqx2AVBFZI4h3cWErvplYmu3YxT2riAuzGCSa1izV61+FyE3tsWdABlpD86pFPsbHtsUGmHUvYFP0cExrm8Yalnw6rVhmSX2uTudtXHkdPey1n738/tz79DbLJljhXkRQSLyiQSTTy0OYbaMpM5KKjPhdn+64FXA6/iJCjYmGF6S2Gu82Q28Fh08+UZNSgZbgdj1I+OgiwnETswrt/x9Osu//HbHn8RvziAG6qHiEs/GI/WmuOOOUDLH3Np7HddAxYIoRfDSZqARdDzvVJ1rVw8jt+wbN3/DfP3PF1/GI/dqIOJ1WPXxzgidu+xJYnbmL2sW9h5jFvJpkxPAkd8lGk7cQTQ7XpqLytleYk08ErL/rGaLvxH160UjFB+nAsJEppLGDXjm7+8ucNTJnayOlnzgnVxtGiL+jszDF9BhTyPjNntcR8l0huuek5Tj51FvUNg4mMSmkcdzDQGt0uPnypnAbsxiPI7/o9yYmvBSFK5qHQjJDfdQtO47yY56GK+9F+RG79+2hc4rEbmj+Sky+k77nPAgK7cSkAxd1/o7D3L9Qv/i+AONKv27KSwp7bsFITEG6zGf9Bjvzu27Cz80xk3TgG0mAN7KHI4VZxVz9z1x28UYLSPDNUbp3ot/JzG1JjaRjhfcslVvlbLo3JsTHHRYigzPRSS0SNb2V/DTILlZ9htEkmlPxgl3KzcLqluTGCIFLGQ9jwaUxcJ2PqUbHJJ+LNEP9dirxbzsFROsAWDn2FTjr6d0QFD99WIdF+ESvTyMDzd7HpG3PoS4fmoYpOJ7J2mSTCEVBzwd8PjWecwNTP3Bd7EY0WsFSf7wUFHCvBH5/7Nn978Vdk3MYKHoupu+n3i4/+Ag2psTVjrUSflrBRWnHhsivZ3f0Ca7bdRibRhBdm3Y7it6Tdev783HdI2nW8bvHHTGLGWsClrOzDI8poSbxuOh68ABmCmKFsAWW9Zxwail00LPsWdnbuiILNjRq0lHeACr1pEIZMJaUN0sYr9NO+dQ0vrv4NO5//K4WBTpxEHYlMEyrwKfS3UT92NsvP+wJTF5tdm1J+vAhW36f6/lD58kvLjl3cFp3+YcbOPJZHb/w07dsfN1oXyyWRaaFn34s8dvNnWf/gT5m5/E1MXXwuTRMXYoUaA6WMKktrhbRqB/uqVZ8gCJPI6VpB5g5FXkllQNQuIa2KyMOHU17c0M6y5ZNYumxiBTkWQCtdCuFvS4pFH9e1eeThbfT2FjjtjDns2tlNR8cA9Q1J+vuLWJbAtiRCCixL0tWVo21fP7ZtNCzTpjeNahdfrqWom/9JOh+8DLflBNIz3l5xXm7bb+hb/x2ajvsxMow4m5xwTuwWXPIoevlFa20CKeoAp3ExdQuvZGDbr1Fbfw4IrPRksov+AyszjYjTorUiNfVihNPAwNZfoosmEZq0G7Abj6TuiH8ffI/DODYO9zgbygxYS+taC8TUmhOic5QKappphDBh9IfrG8NHsKhPjQE0UtpI7SFMVifidzH0PCEm2+rw/yV+gaGT6MrfdHSsRNaVwsK1U4MWzOjvnNcbRm41c6PSGu35aAuQYGFjSQdL2khMTiJpOViWjSVsLBn9c0wagUAgscw/aZnrHBvHSZL3+pncGL0jw3uAaa0QToKgr53M0tcy84pfU8i148oESvnIZJbizufZ/KklTL/qCRKTjzCclUQGne+L+zAyCYmy4JojGW/l2pzomgiwPLLlZm588ipSTj1+nPbC9LwxC+3n3COvYOnkMw33RVY6nFQ/g+i3d6z8b9r7trO963mSdgZfeTEYCpRPwknz+6euxpI25y76MIHykcgKDcbh0n5WikDrAL/76SpwXI0Yy2gKWoN00V5X2YbvwHU7JE2LLAvIo7WibfMj7NpwL7vX3UXblsfQWmG7KWOS0YriQBfSdpmz8q0sOfuTZJomGYKutMPkUIM1GoFfLJFbI5W+kxy8KwhJdYFfZOzM4zjjn2/m6b98lRce+gVevgc7kcFJGrfqgc6dPPnH/2Dtvd9lwtyTGT/3RCbNP426lmlVbQ3VVcpHq7xBk8oPvUTMzloICykkCI2wD47A9Y8oh5tjYHYecNKpswAGARYwQKX8VrEqVunYq+ic8xYyZWojAJlMyT5dLAZYlnGD/vmPH6OxKUkiYXP1189lpNWPzaHSReuA9LRLCfo30bX6HfRvvAa7bg4ICPo2Udj3CNlFnyY94x1ENv/mE/4QlmMyPr+kBLmo3BrFlxYmC43CaVpOQ9Pywe0Nd1DmXIlGkZzwGpITXjPETfUgbeU/utQCN0O1rbxfD1TmUM9e6QBwqEs0k/f7ySofP1b1S7PoCwu8ApaTRNpJJAIpbKS0wuMSGYIFGV9jjhsQYYU7cEnCTtOb72D9vgdxZCImeWqtjelBFVk86XRa0pMQ0iJhpXHtJImGBmzPwlEOjp3Alglsy8VxU9jCwcYxf0sXW7pYVgJbW1jSwa3LYpPCRlLxhpftr4yGdSTLkzDcFNsxACrTjIFE4dFECq/NFC4sh+KeDah8L6lZlWkcRjuf1QKuEWB5Ztdd/OShD2NJNzatGS2awpYuuWIPiyaewhuWfioEjSWHk+jc6jpJIQmUT12iiX868fv8151vojvXjm05BpjEHkWKhJPhxievIu/18cZln4kBTXV/Hr75R6J1HulOYNyrHz/oUqJQEofdPFT+YLv3vkDX7ufZt2k1ezc+SH/ndvJ9HQgpcVMN4cur8fI9BH6RcbOOZ/GZH2Pi/FMBDky4DU0QMJgcVUuNKYSIeS5uMsvRr/sC05edz9N//io7190JegAnWYflpLCcFCrw2PL4jWx7+lZS9eNpnDCfifNOpWXqUlqnHhV7AxmvI6MiFxYU2u5BFdpITb6wRr1GZpf7R5ehdqqHVCYQ+IYLVaFhCT9zAx7F4mB36yBQvLihnbHj6sgNeKxfuw/fV0gpCJRm3rwxcWC5hGvT0Jikvj6JW8NUNGz9ysaZIY0psgs/S2LcWfS/+C28zscBjdO4hNYz/otE6wnheA4934ICCCvUcrxU4yPslyDMG+QpsKKUDDVbhVZFBu2CwszAusLjSaBrBj3TIGwMv+fArqb/vxdrcGLV6LsjjVfSqXPfzjHTXhcuaKUxFBlo7PR4dNCHKvSbzWNoTq/04BGDPH1KbsYWgSri2ik27FvN5/94BslUHUHIldChlqdQ7OT8xZ9gZuuyyjas7YfpGajap/Wf+7+k3rIUefE88Km5wgQb9+PvbadY9NBSIBpc7HljkMkkgVdAWHJ0YemljMPxD6y7l33/9Vqmffk5nNZpoBTphZOxsmMB2PXfFxD07mP6V9ZiN4xH+0UYQqs+lNQCOBFgWb/3IX5w//tj056K1wOTHdsL8jRnJvH2476BJe0w4WHt9aJ6fbOkja88JjbM5T2rvst/331pCFgssxGKgUtAws5w6zPfoCffxuXH/Ze5NnS/PrzmIgEECOGiCvvYeV3kfVbeDolwGlCFztKPCoTjEgwUyS76AA3Lvg2qAFbqgHU5KB21VgHCsmnb8ij3/eLdOMk6CPklyboWhLRQvodX6EEFPo0TFjB31duZs/IyLDthot4KUWliEFVEptDledOa39G54xnsZF0Y3E1wxGkfCiOyDgYIEc8lIgO3Tj2KU99zLZvX3MC6v32f9m2PAwLbTRlX5WwrWivyfW3s29jJ1idvYcbyN3LyO38ZmwGK+x9lYNP3jceEsCnuuwvt9+F3PYMqtiGcJrTXSd3Cz2Mlx4YD6PCRVP8h5DBpHC0pQVYOWts2AObIxeOpi/INlQWPmjt/DJ37c6x9bh9FL8CSYXZaIfB9xcxZzSRCDySlFJ4X4HlBGVlwZDJ4shJo5eO2HIvbcuzg85VvQEp0Tegme7i1VJUS7jxTZjMgMyPxhhiNx0TiwKf8nxxQqsdA9UKSdOpIOoOT/EWy/38/RfbYi3CmLz1oC7DS0Rylh7m8FCum6A1g2wmU77PjmM/T/LOLSL9hOWqgD2FbSDfF/vUvkN0zhgbmERRyCDsBfoCwHfz1e+n7xn3IphSyNWO040qjCz65/3mY5GsXkjr/CAhGuZBqXQrPbzkUd/cjM00AuBPmMeub2ylsf4bNXz2T4p4NTPrwTQawqCA2DY3knRzKRBgBlud238t3//ZuikEOKWyU9svWJ5PrTgjB5cd9nTHZqYN4LCMBLrZ08IMi88at5F2rvs337nuP0StVAZdA+WTcBu594Vd09G3n7Su/QWuduaclrJoa0YObj6LBpxBWmuyiLyBUEa29sHwb5feT3/l76ud/Cu0PgLQw6Uw0qthFYtzpYWNrx0GrloMCLdKyUYHP7GMvIfDyPHrjp3BTjSjlowKPYl8HUto0TlzI3OMvZ/pRF5BIN6K1HlK7IkwL43so5RnQ8shveeGhG0k31hN4eUAw74R34iQyZrCKoeyAEmHJGCDNWP4Gpix+DTue/TPr7/8J7Vsfi92uhbRwU40UBzqZvuz1nPzOX5p031H01cwMUpMvRHldyMRYE0hMuvi96xHSJRjYjkxOiAmL5TPIS7tAHX456PoepqZVz79amxgqSmlmhKTb8pD8WmvGjKnj3NctHFxYmRSLfqy9iRaJ0T6OWpo9hG3AiQ4oaRlkrFGJ6li9QEXlHVYRkarcItjaSbC3F9U+gEhaUfqg/5O/qxj+nz2jGWEN3mxVjy+lfbQXuocKQt6cRtguO37yFSbWNdI6fSlef7vJYlz+kLUxh5jo3YNRTeXYE8PsOUp1lNI2pibLIjmmmUQ6axYQOw3hxsKtq8NJpo02yDKJXbEl+Iruf74Z97hpNHzxrEF3yd/5Al0fuRk5oY7EsdPQXoCokQB1cPUMYNGFfhjI47VvQaahuONZrIZxeHs30vfYjfQ+dgNWppEpn7qb9IKTQ1NEpbfO4H4pv01tnkmgfRwrwZptf+THD34wJNzaBNovI95qLOEw4PVw6Yovc+TEU0YVvbaaZ2RbLn5Q5Kgpr+YdK7/JDx/8oNHQVQEXT5nkis/tuZev/PV8Lj3myyydbPreL+PRlPfBwa1TEq2LCLuO+oVX1j7DaaBu3seHLEHrYESmITgETou0bAK/yLwT3sFA926e/svVSCuBZbtMOfI1TDnyNUxfdj5OmBLcBI9zYsACw9t2IzJPMjuGutYxpLJjTRZjRM0EhdUvfyQR7ybwi9hOiunLXs+0peez/Zk/sv2ZP7Bz7V0U+jooBp20Tl/BCZd+FymtUtJF5SETrdiNy+h97t9oXP4Dclt/hQbS0y4FoOe5z1I/9eKy+8uKBaqSnxNFX/z7xuuIRRMvdub5HKTp4jA3p7r/pBQEgbG3l+cQEsLEbvH9oQO3uW7Jrb0aqZR+Hll7a3IcaiQ+rG7HywFahRTogo9sStHzH3fS/fnbDxuY/D85VAkBpWMx7omPILLJCixRsXkLf7akA4na7tGpqRmSYewpJwzjUEu0UkMmdBVVnweUyFuo6KGKHipvAFUc4RSJ9v04+3x5fi2V9/C3dlL3oRNqFp04fjraUwS7whgt6sATivEeKmClGhhY/zc2fztFfxYSKcHmT6yKkgub8iePZeIVt5KYMB9VDDVAYvB6UbPZNfgrUUwVWzjcvvYH/O/jnw85RBYqBCymDxSulaK30MFZ89/L6fPfFeciisoazdwQnRcBl+NmvAGlFT956MOhl5cVh/sXGJ5NymmgK7eXb9/7Dl415zJev+RfqUs0xZGObVlpHhv9XKXNs1d5lFbkt/yc4v7VJvyBKiJkiuyiLxiyrTYeZV2r30LTcb8NwYob8gRHdt9D4rRYtotSPsvO+Qxa+fjFHHOOfxsNY+eYxQ8dEm2tmrFXyh/CUBJ4efxCH36izmhawgV2uPpV3yeqqzEZmYVv6uJzmLr4HPo6trFz7e3sXn8PR5//JRKZ5gptUPSmCjsNSHI7bqDY8SDCaaJvwzeom3sFurA/bE/o+lVrBxXzc17ZEk1yfy9NS1xc1ctjEhya+CqqxoTmDLMr07qUs0hXjR0V6HBiOxxeVpUSmaheLi1bxZhPOcb8dfib9X8yWokdCjU4FkM9kHjOiHKwde1m678tNYn+KlRlguLefnZd8ybshvEoL2/U/UKCChCJDH7HNppf92+0vv7fUcUc8iCjvFa2w7w7/tZOUJqgzeQEIxpnphGxhlorA1p0zkPWJch+7FV0f/6vBLt6cBaMRaQcdN4n6Oin/8eP4K6YQvL0uehiEGtuhq+O8fyJg8u97Sd0PHszbd9+D9O/8jfs+rF4+7eRW3cfPQ/9hq1XLqPlvM/Qct6/la4fJjNyrffWmF5MpNuCP8Bv13yOuzb8lJSdxaRDCGLTiwqTP/YV9nPMtNdxyYqrzAasLB7L6Lq/ck2LgMvxMy9ECoufPPQR8zikHbo7W2EAuqLRqgjB7Wt/wLo993PukVdw3PQLsKUTeoYxdKTmA4qIx54Ukty2X5Lfczd2aixaFVH5LtKz3oudmWFACgLptpiAckO0bTg55DgtESHxqNd+ruKcwCsgbbdCswIHMYkLiRAWQspwxzByRDq4rjKOHxP4RYSQ1LVMZd4J72TeCe80dVeqZL5SvjED9W2k5+lPYKWn4nU+RmbOh7Gz82i/63iTNK5vQwxMKjgMoaZAKR8pbXY89xe6927AdjOlHEV/Z9EYDYuX72HSgtNpmnhERQqFv1u9qp+dFWk4Dq682FXaKsWXEBiPpNJfh19eVk1L+ZgPlCFClnFrzUlV3ysqW/Vb+Zxafd3/lTHyMuJzhuOOlEs41p0kmaPOC7Oxl99ck11Zj/byaL9gTEBCooNiaD4ybsCJSUeYcg5TEEgdKHCg+PA2REMS78ld5kBo/gmrhggJ7sIOP0N+Vd17j6Nw3yZyNz+LNXkV3kNbscbV4W/txH92D+M3fqqCZ6aVPjDvLPTulKkGaB6LO3EhagCSU5cgU/W4E+aROeIMms7+CB03f4m2X19Jbv39jH/PzwyvpSyuUEVba7y3kUnFtlw2dzzJL1Z/gk3ta0i7jSjlhUrrcsCSoq/QyfzxJ/CeVd8xa8EhhNivtaaVNC4XkHQy/OCBD5jM4lYyDmYnwwi8aKhLNrOndxPfv/+feHDTdZy14H0smnjyoDZH9xuZhO7LYdut9FQmvjEX52LrfuLDhmQLxAH0VL7inqMxmR+SpqWckBhFmAXzkljO0KTDQ5+8R6/Krx4ckdbDpBmI6i0rtQyRLddtwmk6Gu11AwLt96ODPMLO4jQdhdf91KB6ld9LBR5S2qz92w948aEbSTfU43sDNV+Wl1u0VtiJOnrbujj1PdeEoMX7u4OW8meWz/vkcp4xC41ydwLGbFIsBjQ2phjoLxJpy5WGrs7cQQOhA0m2PhknZny5+EwV71glbaH2d4b5fbi//6+MEZcRz4FwQNSttY7nICvTxPh3/HDY8w8kEdn0kMZgZMJK2OieAsWndlH/qdPo/fq9BLt7sCbUl7BZfYL8H9biPbWb5Gvmkzp3IcWndoEGa2IWf2MH9tRGUucswJ7bij21idwfnifY3YP32HZEQxKd87AmN2K1ZkaoKRQQagt04OGOT6MKfchUvdFCSQsr08zYS75Oas4qtn/ljdiNn2L8u38KSoFVm24Qm/XDsu0wLP9f1n6PPz9/DTmvj0yiCT8oAKWFV6kAx07SX+xiZutRfPBVP8W1U6GnkHVIG5nhgMvSyWdxxSm/5gcPvJ+2vm1k3AaKQUipCM/3ggKOTJCwUjy9807W7X2Q+eNWctq8d7J08llDgrhRiQ4I8ntNEErpooN+VL4NnZ6B8joBGXIAD04OamWq1XFDLXIvp3p8OKlGkHG9h/BdN58WWhWRbjOZOR+ke817qJv/SfrWfQUhE2Rmf4DkxPPIbf1VdJPa9w7fulR2DNnWMSSzYwn8PJErXM0XMwJSZfUuK3Cw6Oj2IwweFPFFwpxCAnCS2WHb8XKL5ylc1+K+ezZx3bVP0tKaoVDwKzgto5Kwn1NpF99X+L7ii5+7/bDWufxe/37VWWSzCbMJeWV06f/JK1wq51YVu/IOEmmh831Y2Vb2/eZj9D3+e2Z+bSNBX4dJ+KeM6eSQAUvEefNAOg4Dv12NNaGezFuXk7vhafp/8ij1nzkNnfMQjoXuK5C4cAnZj5yIznmo7hwDv30SkbDBC0ieMhtci+7P/cWYlUJTUvbjJzNw4zPITIJgfz/pC47EOnGm0eDYB6i7VnFguNSc45n9o/5SN0UB41QAKiC74g1M+9yfcCfMizq8dpFlzyCKb/Lkjr9w6zPf4IW2R0g5WZJ2Bi8EBZEoHeDaSfqL3UxpOoIPn/JL6hLNNbMvH6wMB1zmjD2Wfzn9en5w//t5oW11eG+vBEY0YZZun7RbjxQWj239A3PHrmTp5LNQWmEdNGgxbVJeN0I6iDC/lvb72HPzSVgZQIHywG2dHF5icqCNRg56Oz0arcnhBywH98CryVTDnRMPrChPiA6om/8ZnIYjyMz+EFoVSIx5Faq4H5kcH11dqmGNiSLw8niFPuxEHYGXQwiJtBMGzQcqjlJpckoYUFHwPRxpUU7lCcIX3bzzGpNpVeKHZdgxmq9STIRqbCGMZkyF6mQAr9BXpi17ZZCEo96zHUkq7ZBM2ggpqj2iR1ViZRK4lwhMhB3/fzjl/2S0UrkYSUQiM/S5kTq+rhkZnidT9aHHUK3yDmJESmE4Jg4UHt9K4b7NNH7rdQDUf+5MOj9wI6mLluDMjsjAIjYPIUA2pGj88uBghNoL0L5CSGEATa32BQrhSAh84mhxQ85N4e9hZPQo51PcB9IyQC/wySw+u+L3Wn1TbspZu+cB/rL2Ozyz626UCmIgEITRaKOyIpNQf7GT6c1L+PApv6YxNa4mYDnUNbG6nNirSHmMy87g46dfzy8f+QT3b/wtSacOK8xhFGldlFYIBL35Ds5Z9BFee+QVIWAZmdvxYNEmJD+QXfRlpFtKr1K38HOkp78DrIRZb7SPcKIN8ujv9/e1ARy0vHzLgdFG+AiZwGk4AuX14LaYaIrK60E4DTQe/UOTbE4MdqGrLCzk54SfSEm+dx9aKVKOG7fK14pc4KOBeidJn1coC8UtSNlmUvJVgCttAq3o84skLQdbSvq8AlZVSvuyShAlP3RTDSFR2oR5fmkzgI5eovrnch4d7QMGxBX8OKPuK1ZCcKgOYTf1f/L/TxnNZlCrAEElZ0UrRXmIqEM2yQfKgIrne+m+8k+0fPG1yMYUqr+Ie9Qk6t51LF0fvpnmH12INaHegIZo3Ec4or+ISFgEu3vp/+5DqK6cIeEqs7FCa3Q+IP3mJbgnzUT3FxFJB1GTjHsgjosxiwpqx8ARlo0OPDMXDwFYItGh+9G+vs08tu0PNCTHoLQq066UAAPokMOyn7ljj+ODJ/+c+mTrYdWwDG7q4DZGxNqkk+Hdq65hWvNibnrqavJePyk3S6C8kvmq0MWSyWdw8dFfMJqYsHsPDlRpwALl4zQcEa88Wiuc+gVQv2CINoye3/MPClpe+sWg1JFBhUurDLNYln+PgtCZ60YQETca71rzqnf8gmSmmR09bQThfdO2S1MyTcpy+fqTd/DuhSeQcpxQw6LZ2d8FCNK2Q69XwBGShS0TuXvHel7o2sd7F53InoEeUpaDolLVooIiiXQTuzfcy5N//HIYP6c8GuorR+yQyLfi2ClMntKI61jGC+gVUNUoauhwkk5H+URKv72cxNz/k//XxYy/oH8/2itU/Hb4xIzRwrVPknnnsThHTTJu9RkX7QVk3nkMQVsf+T+vJ/P2FWhfDVJfRgEOuz96C7IlQ/2XzjZ8lajGBZ+B3z5J18dupflnF+EsmmBIv4da83JNS7mWvVacsBrvoiVtAuXzqtmXUvRz/PrRT5NysvH0U66NsYRDb6GDo6eey3tWfYekU3fYNSzDlVN+zNxTobXmzAXvZVbrcn792Gd4cd+jJJ06XDtNzuthcuMC3nX8t0xk3jCC76HV0ZBxSxt4s9CZ7PEBpYlbg3TC3+SoI4T/g4KWl36yL2lYbPzeDeR33oRwGxFWGoKcGRRBP4mxp+E0LsXrXIOVno5MtDDSiLgCmLL4XGzb5cdP3cueXC/NyQyP7t3CN048nanpOnbsaWP6kWfHOTo08J3H76SrMMCWzg5mN4xlel0zK2Yeh5WehNexk/TM45kC1I7wYCTwi4ZXI61XijWoQrQ2sVm0MsHjxowZOjroK10ic1yk0TqUieGQbOFlk8Yr8JH/n5TJyBY683vQ24H289GFh7ciUoAfkPjiidgoE349MufYZizXf/JUdMGYloUlY5fn8naozhz5ezcx5o/vxGrNVMSPEQmbzNuOpuc/7qD4+E6cRROgGECqlqblIIj4I6ACDLpLWO8odP4Z899N18Ae/vDsN8MMy4UKzcaA38tZC97HJUd/ybRXBYNciEf7zg8CW+VclkAZLZg7GBTFpkUBflBk1pij+eQZv+fmp7/GnRt+Sl9hPw2pMbx71bdpSI2NY84cHnCl42zv8Swj7IrAccrrRVpJep75NIlxp5MYd0YYwj85ojv8g4KWl3nK1QpkAuk00Lf2KhITzsFpWEzg95qwxED/C/9NatrbSIw7zTCNrAODFg0U+tqxGsbzb0teFf/+rcf/wkDPXkjX4RR66OjbT72bwhISXwVcddRpdPR38v57ruV/jn0NgW92WW6+h56uXaACisUctu1WaFqEkPjFHG6qnmKuyyygxErOw9dfh0Hil0cKVKAIVImjU/46DeVcEIfGqHG8lifroL+FQPnFMO9UWSyHUMPiOBa+ryoIeyooZSoXQiCFsa8LYcVE9eE0cSOZLIY7rtHIkhoPXdW2cqjySjMF/r8sBwM0yxcPVcxBHJQyOkGi8r1IN4X28i/d61vGItdFD9zEIIcGHSiIQhKkHMNDKW9HoJBNKbJXnET3J/9I3RUn4Swch8y66EJAsKuHvh8+jLt4AqlzQzPCEDyXl0vNWkFyDYHJhUddSX+xizvX/4T6ZCtKKwp+H0kny1tW/AevmnNZzG2p9hI61DpAGZBV2oTusGSNewwm6AbKx7VTXHjUlSyf+Bquf+4qTp7zNqa3LIlTEBw+DXBps1+rPQDCjvL4JYwWZpTyDwpaXvqBaxZMExHXrp9PymnASk2gsPuPZI/44qCHIpPjYxPRaOpn2QmEkAx4RdKOy4aufbR5RZaNN9mOHTfFmLpmUydKD+y/n32A/b7Pn7c9z9lTTQj7xkwjt25fx5i6v/HhJaegKOOvRfdzQhKv5VaRRV9Zi1j5SyQtyWEKNTGKCiiwI+SvoULXJena9RwNE+YTJQUFG5zq16k0eXfve5FkXasxxw0TpbTkHqtL4T9K5vOhq4sBIp4KCDDkbEdaVZcNBeP+3xARkyIPbFqIemE01xx8vcSou760QAVDBoaLfrcaxvKSvSDl9bWrwEi0wFkhWRcIdnajegqlNkCcS6z+U6dSOHEGhYe2UnxwCzrvgW0hswmSp88lee4CZNqN47PUXkBfns3VIO/YMB7Z2479Gr2FDh7Z8nss6TKr9SguO+ZqprcsCYm5doWZRWttcimN5pUbwtMgAoDCkvjbuwi2dJI4ccageDaD6l4EktB59e203L2HT/z5BhjwUUUfx0lUDMuDBywmIq4Uks7Vl9D/wv8ik8YCJAAskG4zVnoKVmoyyYmvx+t8HHfsqdGdR3ynEYGWv4sdPkJlwxzjJYxzUmqneUm6Vl9C0wm3Urfws/St/Q+8ridpPOZnaL8fKzUR7fUepNI9BBHh562bn2L5mKlxu3sLOd515y+Ykm3mM0e/Bk/5fHnNXxDA9We/m889ciud+X4unruCvf3dvHfhCbx/8cn0ewUyTmLwM4tJcsNFzvr7yyETCA9BAr+IZbtsXnM9Y2YcQ13z1DhAoIm1YPPEH6/ixMt+iJ1IA4LCQCfbnrqVOSvfSqF/P4lMM21bHqO/awfTl57Pc3f+DzNXvInxs09ABR6WTNTcRcXtL/88wKOJnqTSmqybwpUWSmt6vXwIfirH8v9LErVPa03R70cIgWMlh8UH0TVKKzx/ACkss9us6Kuhrj1IyKdD7Va559owpwsh0H4RYbu03/Tv5F98CJluNL8JCeFxK9tK7yO/w8q2RhceTO1G3giGeDdD7UrTdy7AnmcyKgvXLrVFa1CaxAkzSJwwY+g7DAtY4HAA7uqya61vg80tJf7Ku4+/hkD5TGiYwxuWfCp2N45C8w8q70Au20PUsebvxQCRkuRuepber9zFxF2fNRndy0DrIA2uMiRbqyDo62ynAdBpu7SlCrVph0PLAoDyaVj+RezMDJTfZwjPfg7ldaO9Tvy+F+ld+0X87q1kF10VXjrytXxEoKXiwanAsNZfypdDSJRfxLITIYObipddBSbnhfILhy3aY20xUSYt18VKT2Fg0w/Qfj9e5xrql3wN6TSA02DOTLRSCrU9ssWhfEFKOgmu3fAoXYUcr5u5hLzvkbQdkpbN+458FfVuEltK/rptA+PTWd5/5MkAfPPEN/GLdQ8D0JhI8WJ3W+U9RvycXtm775EC58MBsE20YpfdG+6lrmU6dc1TTbTjsvfKTTfGnCDLSVAc6GbvxoeYs/KtJDJGM+YX+2nb/CjTl56P7aaw3bS5uMbEWP69p5jjtbdew7b+Ts6eMJ/vrv0M+WI/CWlXEavD4jCAxbZs1nbsoi3XR8KyWTZmCo608JXCsSxec8u3eLZjF8eMm871576fglfAkdaBwXZoIZAv4SbBqNVHT7IWRPwnG5FMgoKg0H+Aa6L4Gy4kXXNNvi822w1Xh6ivDwkAZgzxESGQDDdWzT38jq0UdjyHlW0xhNs4Y7DEa9+C0zqN1NwTTf1eyvlQA4Wg9njRoC1B6vxF5k8/QNhWhSkJSxiiLpTmdF0WgsCRIIi1CS/FJrl8foiCaNbyYKnFRZHCQgNJJ8OHTv55HI7fD8P6V5QfJnz0ntlN21k/RLak0YWgYg6pFGFi66RcdNGn8WuvJXXOAsPtKTOVRb0hMy5y/PBxtQaBS0di+RJ/YzuqIwcC7IXjDKl6JNGHDyjh9XaKuvn/ipC1WZUR57P7yY+g8nuAxSEpdzgWZklGqWlRyNDf/aUWGQY6i8wY5Q/GTTUCYDkjI+4ckljG/qb8PoK+F/C7nyWz4NPY2bl4Pc/Tt/ZLWOkZ5Hf8L8lJF5hrRjS5i5BzoAmA7z51J9v7u7nquPMBsEPzgWPZLB87zdRBa14z3UwKGzr3cvPmp+j1CrjS4ppn7mFSppF3H2ESkiWsg/W3ry0HBgOR9qvkDKgZvGMQETElOjbM/D/URBIEiiAwHBez4JtPKWuHgx49kDHnuKnGQWko4jJVEIMTADfdQG/bRtbc/Fm8Qh/JulbatjzKxPmnhnVQ+NFiGoLbQTs+SovinlwvO3s72T+uAA0WkjqESUJfU4oE2Fh8bs3d/G7dw4zLtrDu0s/TaCXiia6jAXYXffY3CGh0sXCHnkOrRAE5gur9wyFLVJ6NxDkI4BwZ7gZUL9c/+e80psZx3oIrhr1GobEQdBR2cvPT/8WkxnmcNee9Nc2pteRwQrehxqbWGmEn0Cpg/Lt+OvKyrJFlyj0okQIS1SbHqjrkPEjaMWAZpEm0BHiBGVACcKx4sawwNx3GeSuS6s3BUPnwonOV8tGBH/4eGmCFBCmxLMNzEUJil5FM4zqHbZIT6qn//FmIpG00IkO1KZoLEzZdV9yM7i+Wfq91uq9MX5u/hu+EaGpOWPib29l30ndBa1R7P2Mf/Gfco6eYIH7uoa7rkRY/IBjYil0322haYqcUAcJGB/0IpwEhk2hGb5YduaYltMP3d+4k17MHabkvmR1YCEng50lmx1Do32/4I2VuuW1bHiFZ14ryiwZAHSTR6QCVCFW6Ps1TjkLaddQvM/kjuta8B3RAYvxrSM98H8JKkpp6CXb9/PDaAyNGAXhakXIS3L93C4HSXH28AT3lL2ygFF2FARoTaROXxbJ5eM9mrn9xDa+dsZiU5RKg8IKAu3eupzVZF5YRNePwTF7V4CEIgtJLLgXa87CSNl7g46NAGLJYlDMokiAIkJYVvrshgCmPx6A1QaCQUtZW0SqNZcmaPGeldMXEE4GY6nJG2i+6LNR09fmBl2fLEzeRyLQwYe5JBF6BdONE5q56B/1dO8m2TsdJZE2ST0BImyf/9BWyj1zL4rM+QbZ1eghbS+VG8WcsIUlaNgnbwQ0BsPTDcOODhnr4gx+Aa9GAQ4OdYYyVQhQDSIIOfLBsHA8SvsDxQ9Do+ybg1jDvj1GLG15RakRL+sFL4AdYZaTnWqJ0aacvMCHWXSdFT89eblh9NVOa5nDu3A8SqAApSj0ccX4s6aCCItJJsL9zO//70Dc4asqxBrT4RWS4AFWLEJJikMN1Utz4+FXc+6IJHFYM8iH5+UBiaqHR2LZLT76DhRNO5D2rvlNhWijdLyKiWyivEBJxo4YIUH68sVOFfhP99iUBLCWTli4GFB7chvYDsyhHEW2FQBd87GlNOAvH0fv1eyncv4XWG99mzBmuVcHHwK2KQq40FH1E0qHn6rvxnthJy28vRRf8IQPPDS2msuXeevGRMGGutGy0CujY/hT1Y2fhphooD1+vtTZcIsuGGoRSU76HVcN1Ou610IvKas1Q9+5jR9WCns/95cAaR8GINSOR8lAPeNhzxjD24Q+i9vUhHAtRb9Lt4BwOKF4yagvLuLQLK1PDXBX1abkb9MhlxCMi8AvYbopn7/xvnrjtq9Q1j8Mv9jMS196DFq2xE2ncVEOY4NA08PZrzueljS2iEcIiCDySmXouvnonOsjh9zyP03AEdfP+lb7nv0Bi3Bkkxpw46OoRmTHQOEKS8wqcMG46J4ybHmomwoRaYTySpG3jhi9OEL4U3YUcvgo4Ztx0UmW7hTt3rOX5/bs5YeJsfK1wDuMiUw0ebLtq6GTN302ZeiyZRCmL4kAeJUL8p02/pBIJirkiKsy2rZUuRdAEhJSRI0JNDYtlSbZt7eSZp3bT1JwmkbBpaEzS1JyipSVD+ZjQWuN5AZYl49D/5ZPL0ADG/B54hdBUZDQrWI75lDYq8Gnb8iip+vFMmHsSyi9iJzJkW405SUhJ48QF7H3xAXMP4JgLvkzz5MUEgdkhFZRC6VJWQ601lpT0+wV8pVBa4ymzMehXHklRHbBOk7AcA3bCTvPQ5LVPXgdoS5QmNwFKQhB+AvHCMxyPI9AKS0q29e7nB8/dR52TwFfqsFAntAbXsukuDHDK5PmcPmU+ngpwhvG8s6r0HFaoUs4km2itn0BzdjLSsofNnSXC98J2koxpaKCxboL5Xcrh+yPs+o7cTjZ3Pk9LsZ2CPzAqs5nWCtdOky/2MrFhbljsYJBUPn/IMI9bfEwFCJmi56HfEPTso+msj5hxSQ0+w+HkI/oB3rN7jKuzHxiNiRCIjGsWxBmhw8CAR7C1M6pAWGcDWIL2fvK3Pkewrx+RckieNhvniPEm2zqg2vsJdnRXXDs6CfssfP5KBUTpUqS0SglzvQI3fmE5r/7IH5ly5KsNHcFJhv0EwrLp2r2OvRsfINe7j8ArYDkJUtmxjJt1PI0TFoRVHEZTFoZsoOAPojgMqrIOr3EsY0IbUdNL2u1hzyo/HG3ksgkDCA/ZJFRWj3KJN3wBQ8MMY+YcrYzae8h2UyQzGRLpRizb5aVJ+hc+RRGGm1d+aLs2R51UQzixvDTEQrOZkSYQW6YVraF+6f8gk+PQWmPXzaL+qO+YgaY8owUSEoQ9qgki2v0BFP0iju0OmgDbcn0xjyFpmei3Z01biELxyQd/T7+XR6HJ2AlWTZjFm+euQGkdR80dXauHlvJ2DQwM0N7ezsDAQCX4cCyCYxzkT0+hriHL2DFjcFwXpQwQC4KAXbt2MWbsGFLJFIEyacqFFKiERAJbt23lm9/8Jl/84hepq6sz14amsiDQWBZs3dLJtb9+kvHj6giUJp12cF2bTMZh/IR65sxrZfqMZlrHZOLszkEYMjzSvgznjhiN6WzrdB7/wxfJts4ATG6mXPceZh79JtKNE1nx+qviaxJ1LeS693Lvz95B4OVxk1l6929j9jEXA1AY6KSYCydjpVBWyYRXLePTDVhh3aLn2DCEB0lc57JPiTBLux58jqjx+3DiqxAo9u7nPx6+hcZ0PfnAG6F2Yfga+zogaTn09HRQPP78EmipMj+Xg8t9vVtCtby5fxQ6va1vK0U/R67Yzd6eTSgdhM/RGJCU9kk6dTSnJ8YaYq0DCl4fRT/K7zN8xzjSgIc3L/885x35MWQYxGtE7uNC4AcF6hJNPL/nPr52x5uoSzTFfVHd3mhsGg5LeTESlevByrbSfff3KWx/hqazPoIa6EYm68JM84DWCMs+DNoXbQCxBiuTIXXeEXRecTN1HzmR5Ekz0XmPzg/djLNwLCiN9/xegr19pR28LGlYio/voPtTfyR55jwSJ80k2N5F18f/QPqipaTfuty0z7WMKaVGv4yotioAC27/7gXowKeueRpesR/HTdPXuQO04sx/vgWNIpltQoabvnhl1xqE5InbvsSO5/7KnJWXMW7mSiwnie/l6W3byP2/+icmLTyDZef8W0xdqKUVjoALqZHNxfE7PJJma+IAfDrvV2qry0UKdN5osdA6jjIs0yXN3oHJzyOoTPlnuQVmWOb6wa3fIwctMaiTYeyK6N9Lm6nYlnZsqonkYOxg5RI/GF17lyN0uOvSynAadICVmlDW/xpp184JMvoHb+7vWHZMKhRCxKaCS+auwC2bxK2QNPbqaYt49bRFNUuMrj3cqmLP83BdlyeeeIJvfOMbXHzxxfi+H5tggiDAzSTp6uvlz7/5I9++5hrG1jehMVyBnJ/no1d+iauuuor50yagKA1A3zcajWKxyJYtWwiCIG5DJFFTEgmbpqYU6YwbZ4AeGPDo6sqxZ08fjz+2A9uxmDWnhfkLxnLkkgk0NxsSbBComP9iyqzsK611vOM64tQPcsSpH6w8rgKEtNjy5O/J97bhpBqQlo2TyHD6+64fsu9mH3cpdc1TAVBC4gC/3fAoW3o7SNkuQahRkUKQ84sM+B4p27jAf/eZe+n3CianFDq0qEn6vSKXL1jJhEyDiZb8Eig9o9FjSYnrJsm6SRzfqkilMNS8VOv3cnMNwJhUHduBpkQ6vN/w4/Vzt51KR/8OXCtVpqXSSGGTSTTRldvLFTcsLrufwrVSdOX2c/zM8/nXM27CU4XQHCOI0mocSMzzN+9eNtlKNtl6wGuGkqb0RDxV0rANvk8ZQdsdzNuLPIXciQsMSMHkH6JGidF4PaS5IHxoGuPSXLj7RZwjx5M8aSbFx3eS/9M6dE8e1daPaEjib2hDhNGsDXiy0IGi6xO3kVg1nezHXmWOrZyGHJ+l6wM34iwch7tiCrrgU2sjMeKqhgvmvo0PceQZH2XxWR+PvfmevfO/eeIPXwz7RRF4hXiBFZgNsrRs2rY8yhN/+CIXfPYpGifMr7zBvJMZP+dEbvj8YiYtOJ2xM4+LvQ2r+XSj6e+I3yekGNlabktk1gDDKOLwUCIaww2PYxHs66PvR6vRvQV0MSDz1uUmS3dkuqtRr9E6cwgrWZqsXwJu1chBSziQCgNd9O3vRgiBV+jHGkEQtUOReJIbAmCMujwFShndhSVlaBesKleDkBaBX4hV+VoHCCSB0mFXBFiWNUizMvqJoRzhlzQaMvx83cyl4eHKweOHHlwyhlHG+8ISclCdRlWPEYiUkoaGBt7whjfUPN7T2cXtN92GlQugHnS+AMkE9BVJaxsxEJpc8kVwKnchUkrS6TRK1QCm4WMKAkWxEMTB3VpaMhSLAfm8AS+ROWjd8/t4/tm93HPnRhYcMZaTTp7J+Akm9YLvK2y7ZMOu7jMVeFi2y96ND9K9Zz1zV709npwAigNd2MlsHEzO/DOaoxdX/5qOHU/jF/qQlkMi3cT8k95DpmkyWvnoEOh//9n7uGfHWhqTdRSCUHsQmtHq3SRp2+HF7n187P7rw/FgHpPSmqRl05Xr5bQp85iQacB/ifhldgiYF7dMYvWFn4yTcR6sokUg8FVAxkmwrnMP77/nWnK+R2GEyTpntCylOTMJRyaMBw5mI+WrIvt6N2PLBHPGHBPOFQKNwpEJegsdsTlGHASVtoJXpYNRL6xCCLygQMJOk/f6kAIcKxHWp+o+IdDwu3az9d+WgmWDCoxWR9qgA4STpLhnG9qDTR+bYbJBCwkqQCQy+B3baH7dv9H6+n9HFXNDxnoZjWjlYU1rwp43Jn5MwrUQCYv0W48m9RqzwPe1pOn/nvFo1L5CuKC78/jP76Xx6nMAUJ05cCTJk2ZCYDQ07oopYUyTQ1/onGQ2JtFH64a0nFIme4jNRmDmABku2olMC8nsWPZuemgwaAH2bXqYZHYsiToDHss37odloyjEgd+vgk/x8R20nfkDk9vJsUo8owj4CCDUooz563sQSRvV3k//Tx5B+BrdlSN9zgKYUG+4SRYlzXk1sXhEEvKJCvtKfMBh54qXWNMSDYDJR5yJZbu4qUYCv4DvH55BNpRE2gfLlliyNHGPSsq2d5YtSbgWCEGx6ON5oX2+vNxw4VAqwEnUheo/s8uyYjQ69II3ugZGiLQycFP0WVQKNySllh+3q7hEJjM0g8oY+n4Hb17TWlMoFOjt7a1Q3fu+TyaTYde+PQRCGU4FQLTrcqQh6YZgQThWKZJmUNJ4ROUAsWmosgkCGXoYuK7FFR8/id7eAnt297JrZzfbt3WzedN+w5lwLXp7Czz0wFYeX7OTY4+bysmnzqKpOW3yGGEIu4NVu2YyK+Z66GnbCICX70W7aaRlM2nh6Ybg6pj6RIBm7b3fxyv0ceQZV2BZLhpN994XeOK2/2DZuf9GpnGSIVZKi4Rt49ouqdAsaJ6feS7R07GEJB3v4swgjUBLX5k5sdbTPtS3shw8Z5wES8dMOcQSKyVh2UgRbh6GqW25NuwTZ9xY85yu3B4+dN0CWhun8vlz7xr2vtWk15FI+fiQwjqozrVCF1AVglvXHgpIhO+EkyRz1HlGEySkMfWE0a81msxSB1SAKgwg3SRgxq2wEwR97SQmHWHKORRvz7IpQkgba1I9sjUTh+1HCGN6END7zfsQCZvcbc8jw919tHsX9Qns2a0U/rYJd/lkZJM5XnxqFwiwZ4VZga0RahoOICrwyoBlafyooDwCq449UIVlcuUU873UtUxl1Vu+w9p7vkv71jUk61oQwkLrgHxfB73tm1l1yTVkW2dQzPXghBm2DxWwCGFAHpKyZJFV5YW/uyumUP+p0yBhGbdoWT6vl4kUEHoZaU/hLJ7A2Lvfh4dCIwl8jyBfwEkYL91ovh3k9XVA0Qhho4HUlIuRdpijbzgtpnQ5GE7siF2eZUhCnLLo1UxZ9OpR3+iVIjt2wx9uWUcQKM44ay6LZ40Mt/l+gG1b/OWP63n6qd3U1bm89R1Hk8m4KKVjc8NoJfALaG1UlYNMbRocIcJEiYcuQkgCL49luybWjaj2XxmZSClJpVJks9maxydNnIRjO2VmuMjWCZa0UKHpJ/BLGZt934+1dlJKNm3aRCaTYezYsSQSoX28vKK6VLTjWowbn2Xc+CxLlk1EKU17Wz9rn9vLo49sZ/eunpDPIbj7jhd56sldnHnWPI4/cbqpR0jwreiruK02dS3mvGRdKd363OMvBwiDzVkxyOneu4FZx7yZTOOk+NxUdiyb11xPvredTOOkONbUN064kO5irqS9COtYVD7vuuuX7OjrZOX4mXzzxIvoLPTjWnYlSFQB85vGAyUX+XI51FFTqV3QxoR1qJtIwFOKlO3QW8wzmgKjjUQcBE6Y/rctl+5cGwhBoD0Git0k7AxRyoX4U4g4tsao633QmtQyCZ9d0c8hhMCtkWvF8CDCDVKmifHv+OHB34/QPFSWWmLUIoTZhQPe+t2QTKN7CnhrdqBzHsXHtuNv6zRk00DhHj2Z4hM78dfvM9dHnBbbouHLr6b7yr8gEjbuqukE27vou+ZBMv+0EnflNHO+e7i8QQe3VZT9VwUedqKO5+++hg0P/JT5J72XcbNW4oaamGlLXsu0Ja+lMNBJvrc9jOnikMy2kkg3xWW6qShproqf20H1s8b01UDBeAkmnEHNiIm6SuGumGI0U6MQtX8AtbsXACfSNtpOBQro7+8nnU6Ty+VIJBKjsKRoEDZa+aSmXRpVmNopS0JydrETHYyMT1YuowsuJy1U4BH4HrZtdrBPrNmBZckRjTNhVo64jge+xiC/QsHniEXjGDehnmLRL2lcRihCCDwvIJlOsHvrFh67/1F8XzF/lmLurIXk+gdIJOx4511dadtJElkrdu/qYe3ze2lsTOH7h66ST2XHIITEHkZ9ezgNcG5ICnNTjXGkxOGk1kvo+z7t7e389a9/HcxpcV3a29vp7++PeSnlUiwWSSbNZB2DkarvQghWr16N1prXv/71JBKJsl1TdFLlp3HDNmYa25aMHVfH2HF1nHjyTJ56Yhf3/20TL77QQSrtkBvw+O1vnmD9un28/sIj42dp2+UaLfMpLZtNj/4vtptioHu3SbsgbQp97YybvYqJ80+Ncw4BzD/pPTx317fYufYuLNtBSJve9s00jJtLy5QlaKWwbdOeBc0Thux3V1p4KqApkWZB8/gDPKUDc0EORiq1CwJ5mEzBUV2rCbcjEVlN0hXmHXyx7VFsmaAvv5+NbWs4ctKpJujXEAGu/i4Sjq2834cUFilncHCwaq1t0NuOlW2l645r2PWtf2b+df1o5SMTafKb15B/4QFkXQuZJa/BSjWYPERhiAhhu4cGWMBwWULCZ+H+LRTWd5I8Yy4aTc+XjUYre8VJeM/vBdvCXTEF54HNeI/vMNeraKHVJFbNoPnnbyb3+2fJ/f5ZRMKm/nNnkDhuGmqgiEi7h0XLUqp7NdAs9YG0HPxiP3NWvo2pi89Bq4C+/dvZ+Mi1OIkMhYEupLRwUvVYdiJUg2gCv4CX60GpgES6Ea/Qz4zlb6R+zMw4avZBSWhC0H1FhGsjMoNBS2kdjry3jHmcQIdmutr3jgLdJY6fjmgwc+999/yNzt5u3NA8v2LFClpaWvjpT3/K+973Pm644QbOOOMMxo8fX5EAs7aULerSABekPQjoxyankNCeGHcmdt3csIiRxxUbdRh/aTkgHISE/pzHb369llTaNd4ZQ9wr2s1HHAQQ2PaB7WVKaRIJm46OAT700SbGTXSRloN1EGGRwz4hlcnS2NKM7yvSdfWAxE3WIa3hg0bFJNCkTV1dgkzGPSxWsbt+8GZkGPn3pTOyRRIGCLRdcj17wqiuRWwnyVCzRfkkalkWSikWLFjAZz/7WfL5PEEQVJlVNDNmzOCLX/wimUwmPg4mRsv48eO5+eabyWazSClxHAfLsigUCtTX1zNv3jwALrvsslhVGQ/k8ipWEdaNG3bp5QjCiVZKwbLlk1iybAKrH9zGX/+8gc7OAbLZJE8+sYsd27u5+LJlzJ7TWqFxkaHXzpjpx7DojI8QeAXqx8wy74C0SWaa4x2XIXMaIN04fh7HvOErtG15FC/Xg5AWk484k8bxUQyfULslTJ6gajAmhKAQ+Cb8BSJ2ee7zCiQtu8rl2XBO5OEYiDXksGgXakjEMRjt+lTLhGcJh4Kf494XfglAzuvlT2uvYeGEk7CEiTx7sNqVwy1RLw4Uu5HCIuM2DTpnEBE3jLskU/VYdSBDwnLXXd9l368+YrgufkBq7komfugG7MYJw5Y3alE6ToCYfefxI2ID6b4iIlvSjhqSqUYXA+wpjWQ/eELl+YGKtTkjKH1EZwkh43AFgWdMakr5ldpsrUubRSGxnCR1zVNwUg00J+tDAnMQk53NadKYilSAl++hmOuOI10fjo2D6isgknZMsi1fZCqepW2BXQr0p/YPkLvxGYqP7SDY04su+siMizWlkcRJM0mcMIPUaxeSeq3JU9fU2oyW0Nrayp133klzczMtLS0xp6WSz3LArW3pm9drcvb5xfBIpBm2EFYyTjCsVIHkpPMNxy8omOSJIxyjI9a0lH8aVbjxrhFCI1EooYa8YfRrNutg28YDIjfg1dZsVNxXm/KFjlne5t6DXSKjh1lL+2H4FgHJpEOx4OEVPQJfUSwaW1+x4OEmrJr1iWN8lJFAfT/A9wdrEUYsmpCgJNi59k5KbpOHc5sxhISTvbRcnEQdSnllL8bg+8fmAaViVWFLSwstLS2Dzh1OtNbU19fzP//zPxQKhQotjNGEeWSzWTZu3EgQBOzfv5+mJjOpxyrK8uFVY6iVk8essqB2vq+wLMHKE6azYNE4brr+WZ5cs5NMnUt3d57vX/MQF168hGOOnRoClxKHyU6kmXrkOUO2S/kDhu+mwn7CxknUMXHeKZXnKT8GN2jzKg+laSgnukaAJCHtmBRbS6LzXnrg+/eRWu6kgfaxpcuNT17FpvY1TG46Aj8o8MzOO7n2sc9y6TFXgZYHpXE5rPFNQonAU2++Ayms2OV5WFAVglStNVFC3ML2p9nz4/fTdMZ7GXf598hve4ptn1tO2/9+nAnv/RU68BAhB/GQgWfZtBQMDCClCWGwe8ZVNH3nDaTfcCSqO4fIJNC9BWRTysRp2WPMEKiySLhWJWE1qpewJEQuuAecAkfWDhUUYzCRzI4BwElkTEDSsrJUSAAP/Dyp7BhmhSEKdq2/myDfYzgw5byM0KPUclJMOfI1Ze1RsZdW1K6DkWBnjwEtLaF36hDOAqWNnKbvh6vp/a97kU0p7BktiIyLzCbQRR9v7T5yNz6DbEhR/4WzSJ46G41m0aKS52l7eztjx5p8UZZVakNpQ3VgTYuGEPhlw1+o+DTlBQg7XXlc2oBNFNr/sGlaqiXibzQ1p/jAh1ZhWcOYawRhoDTB7X/ewJbN+3Eci3PPW8ikyQ34/tB5jHR4L68YMH1GtIBVYv3qhxjF5RjU0PDhpzNu/B6mQlNJKu2EZQ/T6JjnZIJPHcygFKaA+J8QgmRdK0PildH+PtRNddV3YTwUdBiGGkSV7a7s8pBga9s2a9eu5fbbb6euro58Ph+fo5SqMPtE17muS6FQ4KKLLqKlpYUgMB5Xtm2TSCQq+rBcm5NIJGJ7ak0vogNI9cC3bRkD2sbGFG9/1wrumt7Ebbc8H5uEfv3zxxno9zj51FllwMUAPOUXS0CijNQtpMCqfgkBFeRj3g7aR9p1cbCr8smmur4qNMP0eQUCpZBCUIy8asoeja8CvDDGDRjAMuB7uJZNoHXNCeNg+u9wu8xHUlpIR16X8mtNNl0LW7rc/PR/cc8Lv8CSLq9f8q8knQzfuOst3L3hp/iqyCVHfxHXTuEHJjilNULwUt5+PyjGuYIOel8hzMJmS4fOgd1Y0iHpZtFaU/QHsKQNCJO4sbzNKgL3Oo7X1X3vj3FaJzL2rdegA5/k1CWMvexb7PvVRxhz8dex68fGHkiHU0TSMWp/rREJO46nIpKOIY6GsVnSb11O8tXz4mOqv4jaP1Ail1omiF88p0qJ6s5hT282EbQPgdMSua9nmqaw5Ymb6G3fQnGgCzfdwP4dz5AJww5E50X9LKUdB5DbufZObvz86Zz6nm/hphtQvhdrXaR08Aq93Pm9D3D+v/2JKYvOxi8OYLvpiuemcyEReARjRkjQhcC4i29sB0siXMu4f3uBSXWQsCvHRaDAtuj70Wp6Pv9X6v/9LFLnLcQaV8kz1IEi2NZF73/fR+c/XU/LdW/FPWoShVweJ+nS0d7Btm3bmDdvHrlcLjb3j+7dVwhho4qdtN21CoFGWGlAo7WP9rpxm4+jYfn32H//uajCPoTbAkEeVWwnM+cjpGe8M9a4HEhGDVrKJ7Rk0uHIJUPb5cslN+BxY0+eZNKmWAyQlmD+wrGjvresCoJT3sGFgs/a5/Yio2Rb0YVCEPiKZMrmxQ3tSEtgI9myeT+NTSn6+go4Zcm9DCfK8GDmzhtDXTZxQK3QiOpPqDHSkZlME3i5A153uMWsuyIELwpdVp9aEmk6Zs6cyaWXXhqH2Pc8j4aGBh577DGuvvpqvve975HJZPB9HxmS0pRSNDQ0ACWy7dVXX01TUxPvf//7yefzJJPJOAaM53m0tbXVjNMyUonGRPn3yHwUhfo/9fTZjBmb4dpfPkGh4JPJuNx43dMEvuK0M+fEpiIRqo0Hi9E25nfdgt+3CenUobwe6uZ+lPz23yKkg52dR37XzWSP+CLa70fYmWEnhMhNt7MwgK8CbGkx4BfxAhPeHsLEiNIapHVxQ1CUtB0TOVmIgw4RUEnAVXjq4MJt1ywb8EKX5xiQjaIuJtGhQ6ACrltzJfe88AtyXi9nzH83x04/H4C3rPgPrn3sSu7e8FP29W7i/MWfYO6440ZVz/J7Hoy30XCyr3cLUkhmtiwzGxenrnTfaI4QFl7Hdl5871SEBUiw680z9jq2YtWPNWEZcj1YqXrcyUfidxWN67NpwGGtM2BMONKULRyrFMk6/Iw8hZz5Y2H+2DgMv/fMbgZ+/QSyJQUIRNIAHpGwjYkjYRFs7cQ9fjo4lsmDM6QcQDsfvhfnfvwevEIffjGHlJYhbLspbCfcZEgTeDDWJWgdehBBItNE/dgGFrzqvTVzj2mteOymz5DIRBvpSq0WgEiNLpOWSIaeZR0DyHFZrDA0Q8RTKU9oqJUxD6kBj77vPkT2E6fEqQK0r0z/aU3oV489o5mmb76Ojj299P73fbT8/M0mAKWQrF+/njFjxrBhwwaeeeYZ2tvbkVIe1GZRCAHCQtppvO5nQCucpqMI/D6INgsyATJBse0eEuPOQPW9SHH/Iwa0aB/BSwBaqlVUvq+G2swjhKBYDEilHP7yp/Xs29tHtiGJ1j5/u3sTi5dOpLExSeArLFsaFXvZvco2tbGZptaEb/LRCHp7CvzgOw/jJuyw0wfXzLIkjmNh2/DQA1u5/97Npi3ldjkNjmPR3ZXjXz9zKnPnjzksoEVKB2knkLaLVZbr4u8pWgVYtmuCI8ULYW07aqQBqZaWlhbq6+tpbW0dHN6/TKLn5nleHEwukggYTZgwgSuvvHJYl+eRSDn5q7wtRktozIVHLp5A3T+5/PTHj9LfV6Qum+Dmm57DdS1OPHlmTM6tpXmIvAXQCoIcIjWB/JZf4jQuQdj1JlZBaK+F0lgur1vNugrY1N1OzvdIWg7t+T629e1nVsMYcr5Hynb445Zn+dX61bSmTHwXSwj80CNnzb6tZN0kfhAcNNelUvMjSdQIOnUoEgGs5mQdB1qEKsnAxvV33Z4H+P3TX2XDvocJlMeJsy7m0hVfMSBZB5wx/90k7Tque/wLPLXzdrbuf4blU8/h6KnnMm/c8XFkWwCNqgmMhRCxWenO9T/mxbbHSDv1+Mo7wA5UoLSHFxSxpWMySVM5Bjv6d1CXaOa6xz+Pa6VACDw/T2vdVM5f8nFU6PJu1Y9h4kd+hl0/jr4nb2X/bd8BTDJZHea0IuRtqP5OZJKXJZktKgy3v7vH7OT39JpFVymzUIYuuNa4LDpQJI6bRuK4aSMqunDPRkRdDW/BWEbGr4jiI5V7+kBELyiVVVFa7JEYcnCGiH1kNnolT5K87yGQRLxES0r6P3Ybwf4BE9ahanxV89IM4VNh1SUoPLAFXfDpfN8NaC9A53yc+WOov/KMOHM2IS+Ugo9q68MN+1Z155ANqYrouFprgo5+rJYMzpETKNyxwZimbYdcPs+aNWt429veRmNjIyeeeCI//OEP483lyDeMEq3yiNQ4xpzyN9Mnu24BHZCc9PqyfvNpPv4GADpXv4WmY3+N37+F/he+EXfDSOSQNC3DmWPAcAlSKYd1z+/lofu3kEo7WFLQOqaOvXt6+dOta7n08uWh95EeZPo50L0HHScK8qXD/IqVzE2zAR08gKpL0ioq5/CwTKRtXsKVb/4mx1741VAfqEf+lF5K0REQVViOIaVZdqUnT3m/lxO1isUiiUSCfD5PoVCgUChg23asaSkvo/x5OY6D65rdawRyItBSX1/P8ccfb6o23PMe4YOpZq6XtC4Wvh8wY1YL7/6n4/jhdx+mv79IJuNww++epi6bYNnySTWBCxhbrNYByUnnw6TzAVBeN3Z2HsV99yKsNEKaqKsHfARh2YHW2MCT7dvpLuYYm86ypaeDtft3M6thDIXAgJanO3Zw7bN/o7m+lT4vbwIKYnJTNSRSJMJ0Dwc7wMpNVZ35fh7dtxVbynCyLdtNMvxjqHXc1FORsl02dbeZZUPrIU1Fhk9l3Mo3dzzJ75+6mhfaHqU3307Kqec1R3yQNy77TPxcLGkTKJ8TZ1/MlKaF3PDkVTy3+17+/Px3eG7XPXzqrFtorTMmAikkrpXElrU1KUr5IB0e2HQdd677K80Z49k7XHQDpSFpQybRQM7rZaCoBp2fSSQQCG555jsoo+WnvwBzx82KQYsEpJOk4YS3mXKLOTpuMqCl7qjX0X3vr+h97AayR5sAjx23fInUnOXYjWGaglGAF0OxU2HQvAAVz6OqcuGO2hEo0m85iuKj2/Ge3h0mTQwLsiQ672FPayL7Lyeb8pUCr9pdvmQewhLongKyMYXqzBHs7gkrVmt0jcyTJQYeZeeX3v+q9lSVrbw8/V09bFz9G9x0Y5V5yKaY76W/sxsv1JKnQnJ0OdfS78lBTx5cq8L7RmA8Awe1Qgi8vl7cZZNACpMKIWGjevOo/mLludHmPZvAOWI8/T9aTeLYqQawRG1X2iSsFQKrJYPqzJG/bS3uKbNMsx3Jr37+S5YtW0ZjYyP9/f1kMhk8rwTKR24eMq7NBHkCrwfLqaew7y4IciTGnw3CBuUh7DQ6yJkxVtwPQND3Qgy8K2eXoeWgNC1xVYdYVJRSaG24BBvWt/HLn61BWoK+niLnX7CI1rEZvn/NQzy+ZieJpM0bL1pMRJa1LDnkPWrVAUocm2w2wdveuWIQx0YICHxNMmWzdUsn9969EaU0K46dypGLx9PfXwwXpuieIC3DpZkwIVtxj9FKef0jYtgrWWo90/K/y/32I2Bi2zapVCr+W0pZU0MSPcP+/n6c0NWut7eXZDJZMs1pjed51NfXxyammi/PKB9HdVtMvQ1wmTK1kcvfuYIffX81vq9wXZv//c2TNDWnmT6jKTYVVWhatEYIC+X3ImSKoH8jxb23k5n5HpTXDcJCFdoZNrgSVJRph+euadtGEGoWCoHPPTtf4NwZi2OTUJ2TpK6ukfHpegb8ZIVGxVMBvlLDu8KNQAKlkJbF5t4O3vLXH6O0HrxDPETRQEsyg8JEcjYy9LNO2hl2dK2jN9/B7DErOO/Ij7FsytlEyVOjfjTAxWN6yxKuOOU3PLrtVm5f+wNOn/8uWuumUvAHSNhpfOXRnetjoNgV16dcIjDz5uVf4PR578a1kiG3pVYVJb4qkHGbeGrn7fzpuW9xytzLOXHWJfQXO7Flgij9iB8U0ehY4yOEJFAemRrE3KC/EyvThMp1YdUbAJBd8UayK17Lrm++kbpjXo+3ZwOFHc8x+V9vN+6wZUTckYgUkqSTIWFnkGFaA60VjpUw8W6iZxO9Qwmbpu9ccMByjcklvDZxgAEZ8mEyl68gea5JRkjNDfHIXnwR8QYHVaoqL07ZHyI0YdSPm8sZH/glgZdnoHu30aIQBXzUWJbL6e//OWMnLWJAKX637iFe6OmgGPhkXZMD7tQfXzRk3Xqp/Xo2DNOeyCQUz8++QtiS+s+cxv53XEfHm39F+tKjcI+ajJxQj7AE2gsItnZSuH8z/T9cjU5YZD98IvligV/85Gc0tbZw0kknoZSK+YjVa+2IRAi0DsBKoFWBjntOprD3XoQUxjy++Gukp11qOJRWCgFxXws7y2gn81GBlgrGdxU5zuzCw91OOFAfuG8zf7j5eZTSDAx4nHbGHFadNAOA112wiN/f8CwP3LeZnp48579hES0tGaPOKssPUwv11eIrRByb444fXg3Z0JjirjteJPAVc+a1snjpxBG1/WBBS2Ud1cjh5MstoU1vKM3GcKi7WCyyb9++Aw72CPAsWbKELVu28OMf/7gC2Uf3UUrxlre8hWw2OzRoOcj1czBJ1wCXmbNbuOgtS/nFTx7DdS0KhYBrf/k4H7zihJjTJOP4QNHzNNl2hbTpf/EakpPfBIB0G+lb/1WETMTq0VrukOV1iRIFPt2+g9V7NtOYSNHn5WlKpLl181NcsfRUJtWZRe3tC1byhlnLTB4qjEYk73uMTzfwtjt+xi2bn2JipvGg+EBxv4Sgc37jeI5smcTdOzbQmEhRDIIhtSIjlfKgbwKT4LFWcDwITXrCwlceExrm8P4Tf8jTO+/kNYs+GAMPGWqaVNmCJIUdkm8lx04/n6WTzwzzFQVYYfyDlJPlqCknM6t1eVyvivtKC6UVc8cey9yxx464ff3FLtr7B5hYP4fFk04bVd9EZrBobIjQJVf7Rby9QNhPE/75Ojr//HX6HrkOp3U64975I1KzjjME3DBJIgz/3kZaCC8osK93H8UgT9HPI4VEaYVrJenK9eCHJk7KNhYUQ/A2FNFUYOKzDPX+VrXZgC0TnA7CRfowmyVNvUJg66S46KqNuGkDFaTlxnN0KjuGOcddesCiPODf77+eydkWzpth8l0VAo/btjzLjvZ23nrECXjFAo5lYi45ToLL/vQDQJhs6TpAYjSsKdvhv054I65twhpEBgENCCkGEXGFLU3smxNn0vK7y+i56k56/v2vxqRUDCJipiFMO5LECTPJ/OursMbWsW3TZqZMn8qrz351OM5lbK6P+IqRxWLEogOEdOl99jPooMCYs55AOE14HQ/R+9xnsdJTSYw5yQSTE1YMHnWQH/V6OGLQMhRQUMr8s20Zg5Ud27v4yx/X8/xze5FS4HuKs149j3NftxCtjSbmVafMQkrBH25+nifX7GL3rl5OOGkGK1dNIxGSj4JAxXwVUbWgRp1ai2NTLUKA5xlTVX9fIXzXBLkB40M4MFDEde3BD0mbsP/lLs+j6a+Kl1WHKrRXImCBuF6j8RyJQMiUKVP4yEc+Ept8aprutMa2TR9fdNHQu5Dqa4bUthxkPw7ipmgdA5clSyfymtcu4OYbniXbkGT37h6uv+5pLn/nirLrDeqMNC0I6HnmX5FOI6kpFwKgvC7qFnwGu24uuW2/MvdhcByeCrAWft6y+Snac300JzO40iLQij0DPfz4+Qf57DHnUAx8Mk6CjFPJLQqUwpKSlO3EJs9D0YtEJqK04/Kp5Wdz4ezlJrHjYYgnFGhN0nLY2tvB9579G460YvfvWn2ktcaWDoHymDXmaGaNORpgkCtz9bURgVZpRSL28rLiIHmTGufzhXPvBsw4sGRpYSjn0UTeQ+a3GjcCJJKCP0Am0Uiu2IMjoRDGpOgvdJGw06haiV7LvdHEYO8hEQYrS897FZOu+IY5VWuEnaDltZ+m5bWfLhU1guSI5cessOwJDXP48Ck/qJGE0qIY5BmbnWnaGD0jIWCIQGa17nWguSQ+x5LGW0bpykW6fBqNTFdxrJ/wfyMY7NX1SdWPrfG7NMkUg8IQ5imT+TyZzLBx/27a8v385wlvrDi+oGkCn3r497zVOQHhOIDEDpdaN5vix6dcNmw9y6Fa1PSaFocw2rC7dBKt170V78V2/BfaUV058JRxnW7N4CwajzXWkL11oJgxcwYzZs6ITf1R+QCFQgGlFI5TFtG8asAbsni0/hquirDr8Ps2Egw00XLqA7G2zs5MQ6YmkdvyUxJjTkJYUVwc0x/SbRiyn4eSEYOWam6AiUthtCqWZX7fvq2L1Q9v44k1OxkI7XDptMu5Fy3g2JXTSnZny7CTT3zVTJpb0tx843Ps2tnN7298ljWP7mDl8dOYt2AMLa2ZCjfkIFAxGTeq00g5NrEK3jb2RE2JQ+O6VuwSPXQHjLSnKgeY0horXHjREXdBxYkOK46H9QzCuC1SVLZVhWpwOYKJ4GBlpPbMckDR0tLCmWeeOajttbRxxgzoH/Ae5Yj/cGpaqu9bPi6CQHHaGXPYvq2Lxx/bQbY+yZpHdzB7disnvGpGGb/FkKiV103Pk1cgE2PIHvkFlNeLdLKARPt9aL+H0hRU9eJXPFfjEbSzr4tfrFtNS6qOjlwfXzjuPO7esZ57d27gV+tXc8GspSxqmYQXBIZjEpEOMQHp0tI1nkM17zhyieoWjbMzpi7kjIMsazjZ3N3O/zx1F7ZlxeTcWnWJxoElnZB3YVpuS4e9PZu4+vYLSIXuw4M0WiLU7GhVphWI+l7Sk29jatOR/Mvp1xEoP17IK8y6I/QeihZSWzohvSOMPm2nRl5G1eIkLButAtyJ83Enzq/ctPnFMICaQITRlwebMAdrTGNghiQIijQmx3HG/HcfoF4KOcI4GuX3Kv8cSZtFOH9rXeL8aRXEXktSSGxphyZBjSVsLOlgjYDDU71eGC8tOeh3ISW2HD5CeVEppta3Mjad5aP3/Y5TJs8LOWADPLJ3C+fPWAKA5/vY5VGJlaarMEDCstG6NGbAENSdIUBnrbk0BnphcD5ndivO7NrZx3XRN55a4dqrlKpwmojKnzBhAo7j0N3dHXsQVfKCJLZ0kNL0uxQSoQOw04iBfVipiQghCQrtSKcerRROwyIGlNHWeV1Pgw7Qfg9+/1a8zjWxZ9FIg/MdtKZFSkGx4LNzZw9bNu9n08YOXljfTj7vx2Bm3vwxnPf6Ixg3Pmt4AVLE6jmjklIcsWg8kyc3ctutRuOyZfN+tm3ppHVshtlzWpkytZFp0xoZOz4ba2DK6zSaFwPA8wKjacG4QZtjI+2FSolxaPU8WfYSWKJEaozqYZXxHKyqhVmVHa+egKLkeFF5LxVwGYmUt1Ephe/7MTqvVa+KRWAYD6Ohrjlcmpbq8uPiRMkU+fo3HMn2rV10d+dJpRz+/Md1zJ7byvgJWWMmCnW3qthJcuJ5howLJf5K6LJqdhODd9fV7fFVgGvZfPvpu+nI95G2E0ypa+Ly+SuZmGngvl0v0FvMc+XqW7jx1f9kEgwKgVXWCVZovjwco6F60SsGfgykD5XVIjCmsKybZEtvB2CSQqZquI1GUj4OTLJCiyAk7/mqyN7ejaScenJeDyPN4KxROFYSPyjSkBoX/xrXc4TzSmU9Kyf5+HMUGbir72sWUQsd+OigiIyirwoBtougNhgaam6ofqesEEwFgAoK6JjAXVIB2VaiwmR1oLKHkpFsaCrm9HA0W2FiSW1beNoj5/XhKxM7KdA+ea+Xoj+yHDYV9xAlgHQgjVD1Oa6UuNLl6uMv4Pn9u/nW03fjq4BL5x3Lq6cdQUvKaDZSEWAJy0nbLo2JobmNSquKvh62/lG9LAkWxt05UGULmjBgz5L/H3vnHW9JUeb9b1WHE2++k3MgzAxDHHLOkhEkCuasqIu6rgHXVWANa1xzBNYECkqQIEEyAkMcmCFMzuHOzeee0N1V7x/V3Sfcc9PMgOjr8/nAuXNOd6WurnrqeX7P70G4ZWW8EnMYrYGRFfy884y1+NBDD42pKioxc35QIl/qp5jIUfD6EcJCovH9AraVIsivR3m9WImy8lTc+leixIgd9+yPnwuQLhRunkkwAE0HXDbkeNSTMVlaog4WCj6/vvZpNm3qxfcU3V35OMzZcSwmT27kmOPnsH/on4xOqJWDJITAtiVBoGhqTnLxpfuz3wFTuPfu5axd3cWWzX1s3tSH61o0NiZD+nyXC9+6L+MnNKCUGjEcVuuyQhE95J7uAgiBlNDbZ7S/yocyFkUgSmQYBBopK/omRYwz6C4OMDFjHn4p8HEtmy8/8Wfet9eRtCezfOHxW7jq0LNjMjEpBNvyfTjSosFJ4odU765lsS3fT0LaNCVSVYrQ30OqNhIpY9fQcAtT5edoZNA9MVBah3wrFV/ugIia5y6lwPfNfDz1jD257pdPkc269PYWuPVPL/LeDx5i6hNWaPqciZ2ZiVYeQjqUAYtWuHmK2AxaWU/l+HiBUVge3bSCa5Y9RqOboquQ4wN7HUVLMs1pMxZy2KQ5PLV1DQ9tWM6Vi2/nigNPi+dSudzB/dvRkak9oAxlBdlRcbVZwILIYiKMW6ue1JtDYSsBwtOexaSm3fi3Y3+NFxSNG2Owp9dsHMKQ06WcBtZ2LuG//3IWSTvLG1Gq5oplG6uLViYaJ3IpSatqPo30nglRzki+ddXjPHTd+0g3TcIv9nPih/5o2GMrF856bRFixHYMVXd8f0UKCyHEILeWECJOUvjgL99B/7ZVjO96mXfsfzEHnftVBro3xNGOSvkx181QkWC17aj376GUp9rfioHHE1tWA4J+r8Ci8TOYmmkm0Jp92qfybMd6LGES3TYlUuw3bprhXRIWa/o6uXvtUhrdFK5l01PM41gWxcBg0ua3TiLQqupgW7f9OmR1qnz2tqwKdx4kuoxJqqschwdQgL333rvqeyckfXvT/A9x+JwLsKRDJQBeB0WsRDvuiu/Qcc8iGvf9DlZqEqVtD9L/0n/TfPBvABh3yqumLcoH6YIqIZwQglwngWg9GdNqJIQgCAw2ZPbcVhY/uY5UykFagnTGZfacVvbZdzL77j8FO+RdAV0VMlq7URhXkRnMefMnsOe88Sx5bjPPPbORFcs76O4u0NtbILepxFHHzDYKS6CQowJplR3QUb3r1nUjBEhbsmFdt7EA2cNEqdQdCPORCLVXuyaNuKdMvpOXu7fwi6WP8J2jLqAYePHJYX2uCz9kulzb1xm3Lx94pG2X7z73V86YtTcHTZiJVXFyvH31EjTwznmHUfA90s6uJb0aiwy1OO6IcjL6Ss2HZUkSCRvXtXZJPZXPPpq3Bxw4jWee2sjzz22ioSHBC0s2s/iJdSw6aFqc8VvrAJQfszjG+aNUAa0c0B46GIhqMV2otFBphWNZ9JUKfP5vN6PQlAKf3Vom8OG9jyZQiqyb4HOLTuEtd/yEBtflf5/7KzMa2njbnodQDHwSu1ihiNpY7+9dJUbhht5SIXS/SNJhwskdqS0C47Zmpox8cYW0Z2fgqWIc1fNGk/rWSjkIoDoaS0G1hFbnQj+d65+nNNBFcaCLwA9z9OgAWaNsD97Qx96OaheMVfWsh7u3b9sKOjcswfY82q12mmUTzc1Ng0JwonbtjAW6sg2BVwjxGyZxKtJGAMUg4O51y7CFhS0l969/hWSodF+9+E6Slo1r2eS8IrMa29lv3LT4UHHW7H14bPMqOvJ9PL1tHWfP3oecX6Lge+w3bjrzWyeF1vYRxiVUXMbYubrvV+0BFMpEoJXPW2tNQ7KNhuTQKVz0/CvQpU46HzoV7YOVbqJxn2+SGH8sWgfYmVn17wtd7qN5dmNyDwkhYkzJcSfsxvZtA+QLHnvsOY4ZM1uYMLExHEtdkXyuxo9YIeWBMo2Moob23ncSe+87iY5tOdas7mLliu10d+c5+9y9ohur2jSUVLlWpGHMXf5qB1IKbMdi1cpOOjpyTJjQEB8uRvXyh93YsrmPxqakIdiTke9ck21O4LgWEkHKjhQLESstScvBV4qSCkjaZcUj+j3juDy2aSUNToJt+X4CrZiabWFp5yYOnDCzqm+7UmpP2LUK5tgWxl0v0fQpFHy2d+TCiB52Ct8C1VbE8t+C40/eg5df3kYQmI31rttfZv5eE0in3ZCh0qrK/RCBzFLT32pcRTJBNjO76rd4QYwYJwV84uE/sHjrWsanGugs9HPFolNpTqTxlcJXAYdNmsPH9jmO/158B63JDJ965EZcaXHh7gfiK4VVcxj4R5H+cFOwpRUDi3ekH9F7ByYSZrhcPkIYWn7XTlEK8qN2J/09pfK984s5Bnq3IENcR6Z1GnIE8O1QIqWFm2oMc5AFFdaVkS0lO9KOyEIjhCTfuxW/lAPAdtOkGicMea/tZnCTjRR1DyVtsJIlrz+kzldhi8UOj0MktWtALRO2VgotBI1uki8dfOaoy41wi54KePf8wwHYlOvhZy8+zCf3P6nq2siCWm8t1lobvhu0cflU0jDUsY6NVuopx5ELv3bdN6zUQzFkayzp0rT/98nu+RmU34OVnIJ0m8PnboUYrAr3lTHTMdq8Q7ADWZ4rFYHzLtpn0HW+H0USjc41UPldBIz1fQO4bR+XoX1chgMOnFpVx1DMuJXXVJYfBBrbFix5bhOd2wfMCR3IF3wee2QNZ5+zV0VI6wibcwV46vvffaRqPGxbMjDgcflnjmbGtBYCpUKSr2oJtKYlmSFlO9VtDT8T0ubZjnU4lkV3cQClNeNSWV7t3sqRk3erurayv7ti46p6SQIP7ZdMivswjHK4MX8tlJrK8qI5NWNGM29+y0JSKZP+XFqDX7qxKl6Vz9GyJH5hgFkzUixcOIGnFm/AtiX7L5pKMhk+s5oulk8jClnhz8XOlF/YivojWv7PPPpHblj+FBPTjWwe6OWd8w7lzNn74CsVhwErrfn3/U/mmW3r+MvapbQk03zkgd+xrr+LT4WLnqozz97oakxPsYDSGkfKstKyg2XFljLpjnxSC3EcBii7syid10eUX8Rykmx8+a/85Xtnkmmdhl/M8ZYvLSPVMK6MoxqDaK1RvodSPjrwRjUUO9sO5ZewnCR/u+Fy1jz7JxCCaXudyvHvvz4ue1A7lW8SF2qFDJVMS9oxQ3Jlf3ZmDSqvAUaxWv74b/BLOYJSnvFzDmPczEVo5eMhcKTFcx3r+cYzd3Pdie9kQ3836SiFBmaPcKVN0nZwQ9eZCd036Tm25vvYXjRK24Bfii2Nla7Yuu4qtzy2EbV/vP6Pucf1+z+UW0xrPWjMa8XQegRY6alYROHrxn1uLGwjA+53iaWltuHRPhsE0UJpTqZSmuy6O0t5H21OJny57OKJuVJ0+WHV0xKjNoeXYlnGrfXYI2vitklpIo2efHwdBx86nUmTGgeRiI00gAO5MO2qqFBacl45b4Mu42WkEHHEEGge37yK9lQ2dhMB+OHGs2mghxOnzeOC3RaxeaAHTwXMbGxnU66HzQM9ALEypLWOffUmhfqOooprUPRKISwnJqmKaOuHGpPqOaLi1OQ7LFqH9UenDhG7EmfMamXGrNaay2t97pHi5cdkW1FZQ5muqbBY2EkDljvy2N149umNvPktCzki5BiqNz+qzaiVJxE9SGEBGPBKfOaxP/LLZY8xPtXA9kKOfdun8uVDzgJqcFYhF8v3j7mQM2/7AS93baHRTfKVp+7ksU0r+e/D3sxuzSaTbRXXyM49gddcukv5MArIIhsrLWNdest+eq0VpaAQujbq914g8FWJpJPFCwo7UN/fS8J5Ji1sN2Ny6OzsAUEQns6j/16/dli2a8g2hcSKw/eHKmNwG2ut9rALXdPhoWTxnz7PQM8mCn0FDj7vPxk3cxGBX0KG+YuyToLdmkzY9JRsc92igjCs+Iq/3czavi7SjotE0OcV2Jrv46MPXE8h8LCk4caRSL555FtI2W454CJQCEvir++m5z9uR/UUSL91fzIX7osu+THI9rWSoca1vFfoeMyENEpNbFERMiaSq71vNHXUkx2ytETlDkW4tqsO2rY9dEEjnZpjM3xg8AdP/G0NK5Zvx3UtGhqTHHPcHG7544sM5ErcctOLvP/Dh9bXbOv1L3x5jjh6FomEHYd/m4iqgGyDeQmlNKGoYDYpjaY5kUZr+N2rT9LgJCkERvHxVRAv3OfO2Z9fvfw4y7o20e+VYh6LAb/Iu6cb82KlOV3rMJx6B/P0DBrTwEdYNoU1z5B7/g4yC04gOfug+Pth7w03blE3yeAOtCnO8WOUZSmNAmoUZvMcHKdO2GKseNlxm4dTvGKFPPy75+H/Q3h9jN/3Ii77xNHMmtUcP+eR510UlRC5nqsVlkAr3nHPNdy2egnTGlrpLg7Qkkjz/WMuotFNxqex6HpLSHwV0JbMcu0J7+Cc23/MloFesk6CJZ0bqjI+7+we/Hq4/yKFrCe0IrrSpjkRppAYgkl56HfSmKzNuiRjPpbhJLK0ZBMtBNqvuwG+YUVrYxlRvkkz8Pdq+062Q2uFUgFCKBPS/HeW6rkVuulbpiAtByexHTcVJjCssDI40uIv65YxJdtCVzGHK+0Qp2YAtqfO3It92qfiq4DPLToVX5fz9FlC4kqLvF8aNKeTUSRduXHmY8Cj+PAq1PYBEofPNL8FkWWHnVdiRymDXFdysNJba1HZlZb4MVtaikWfUmnHWDFNZmHz944uFFobsGQqVe2uqOcDDAKFbVt0dOS447aXSCZtcrkSxx84lSOOmsVLS7ew9IUtvPzSNm7904uccfaCMNJpaGbYSoX/gov3rau4BRGwTwjakwbV3pSojvv/8bFvxZYW773PkI850qKzkGPzQC/TG1r52uHnEGhN1kkw4JdCi4pgeyHHss5NTGtoIeskq6KIgoFuk7QsejOi9lZG2QhR/Vv4k8w0xy+kDkoIy6bviT+w7qqrmfrJLUZp8YtDKi2Vyk5p86ts+eX7kekmU9aocQOGKEpIG3/7WlpO+w8aD7kA5RWRTsJ0JXTf1OapGuR79QpIJ0nvI/9Hx01foP0tV9N46EXx9yO5urb8/F2UNvns+ZvTmTVrOoHnYTkVRGZDKbRCoEr5upYdIQyWxZYW5+9+IE9uW0dXMU/CcvjRcZewsG1KzIxbO7dtaVg15zaP5/o3vZeL//JzOvJ9/Pbk97BX22T6vSJZJ7FTG3DlGBZHkYF5R0QIgR9mee4o5EJMi8QWMg6tNuH85npLSByrnr+7bMrPuE3kit1874F3xhaz4SwtSissYVP0czQmx8VUAruwlzGtwq6XEBsnRFzL30dq27FzZf29pfJdE1LiFfsp5XvxSwP4pQGKua7owhgkOyXTzBcPOp2u4gAtiVTM/2ULi2Y3FR9CQcRA3VrJuvUPd1XzPfxU23LIjHF/qt6QpTgMAomt7a+D4lI1VkKg+orgBQZn05gYdF3l37uibaO2K/mewnEt/nLnK9xz1yu0tKRC5WU0jTALScRwKzBMs2OZ7FprbEeS6y+x57zxvPeDh8RKSSRVm5Yym1sQKH73q2fo7y8hJcyc1cpRxxpw5Fnn7MX6dT0MDHj89d4VNDQkOOb4ufi+ill4606CcD3s6srT0pzECzE4cUiuZep2LYt71r3Eni0T6S0V6PcKHDd1T9KOy8qeDqZkm6uUrUc3reBPK5+j0U3S6xVISJtHNq1g0fgZOJaFrxSNbpKeYp4P7X00+4+bbhZ5LUBK1nx+X0qb1yATdkw6FY9NSL5Ue6qJ+jjnu5uwmyaEi37oN840404CmWmNLh7hIQWATdC7hc4//xW7GXSJ0a9JArQPdiv43ZA94CXzfUWbh1MWqn4P7yluXErPA6tpPLy6rBH9pm0z0GoFGkNGGOFmVCkPtfgRIYwZNEw0KSPq9QrLjlncjSUh0PCWOfsxMNDDl5+4je8e/mZOmLIbJb+Ea7vlUEaIT09aaxxpUQp85rVO5NcnvYt1/V0sGj8jVnR2RmqVvqEW2V0hUcRTbylPoEzyxChFQT2p5K6A8NmFc9TXAd2FrRSDPK92PFOeoqpCN4/29gplXSlwLRMB1lPoAIZSc8YuGkWgTRTOrpfaVr5RrER/f8VjZ6RsnQ0Qlk3n+iV4hV6k5WA7KXq2voryPSy7zFhsScnx0/YcttxAq4pko2WpOC8OEuO1q9h7fAWWJH/TElSuhGxMUrxvOf66buxpzXEeotdDYakSX4Fj0fW+31N8aBX23HbG3fP+uC2vlYw+eigc3vxAic7tA7HVJfa9D324MZuR0iSSNo5jobQm11MwvrDak/8QT1ErjeNY9PUV6e83Wma9cYkUlkhu+O1zvPpKB+m0QxBozjlvIem0S6nkM258lvMu3Idf/vQJXNfitluWEQSa408yYNdKfpl67XJdC2lJErWn/vDi2Y3tXL7f8eRKRVqTaTSapGWDNhFC6dBvCVAMfE6ftTenhzksIvnog9fz3aPq096rcCPTyhDmyUQGmXSRiUyotJQHVRX6EUIikw2DxlbEp9Nq0X4JlQeC0pBjUCWhWdOdtpC53/8z2O7gDb6iTVWvrpBor4DVMI7ue77P9pv/r0JZGqHeyjZHL27YH5lowG4FGVq8ou+H86sC6FIOVQCBDlsbAtHcoZkyozLXf/1NtLzp38gsPNlYp8KFTgthxtovop0kb1t4DCcvPIaI2sy13diqFBYYKy/RIuZaNr4KmN86ifmtk/BDhaXge6MfpDpSuUgWfI+PPng9xcC837qCaCw6aOiq5zdC2RXXCwgZgCVLOzfR4CbZMtDLO++5NlbuTD8tuooDHDNldz6y97FVvDQ68CDwwErQ7o7jc8ffhG0lsIRl3IhgwOMiil4MzHOoaHuE1yp6OVLCnHatMQJZ6/fUgIEzLjgx78SObyRDWZjK/3yjKAtvFOVpbDIYrG/Wq/Uv3EmxfzvJhnFYbpqtKx6jZ+srtExeQCWwvqT8muzn5m8DxLViN+9QnFr1vq3yGhQ8RNKhcM+r5G9bisy46IKP6s7T/40HaP72WfH+KGT12vZaKDF1y1S6/F/t+f41aMOolZYofnvmrFaOPHoW2WwCz1cjvjMCUErjuBZrVnWxbVsO25bst/+UGA8yqnc6BNTmCz7TpjWHbRr8kEykkGnr9b95lscfXUM269LfV+LCS/Zl1uzWOJOv7ysWLJzIOeftzfW/fZZ0xuW2m5eyvSPHmecsIJl04qiiem2MJld8TU1bXMvmzFn7DLovFy6gSmucMGTWEpJAKUrKN/lOQhN6KfDpLORoCLEOIn4h7Jj+P3LrTP/S00ZJqLA2CDeFt20VK/9tLlZLK7O+vhxhu2XABcZPK+yKvEGRuVxaIRdChWI61OPREQhVY6WayOxz6oiPdCjJPftnVI4d8tfX+qW1N4D2QJfyVd8LIQwNui5jY6oWXiERsuIbFSBsi77Hb8Dv2WysKiGVuHBTWA3jyOx1MtgOPQ/eRWbf04zSEgQIO1IKDG245STxtq0iv/opGvK9dKqAROMEEnMOwm6aGJJ2lS19Ucuif9vSIlAKhd5pCwtUzFnKCsZNK55hwC/FSRl1eJ3SOjSBy6ptSoSWpOi6uGwIXWK1ij00uUkcadFbynP9q4vj35Q2CeS6cz040uIjex9bBp6HCQGtUEGWG19l3gvLSJ39Hzvc/+6fvR/9nmOIGH93dImNCLgOnfUWDnrnWXFKgOj7HZHB4MdylmkNBDsFeBd1/9zpsv6BpMo6H/hYtkvP1uWsfOr3OKlGlAqQlk0p38NLD/2MQy/4VphWwESgJkbIpj0aBaL2t/i9LwWIpEPpqfX0fO4OCK356fP2pvCXVxj40wvI8VkaP2uScmovQDjVJH+VgRq7arxikcIcEkLXEIFC95cQTcn61+8iGTUQNyLdOuiQ6Rx0yPQdquyPf1jCurteoaEhyVvffgDJ5I6jniNsQ+2ksG1Bf1+RG373HM89s5FsNkFfb4GTTtmDQw+fGbqUzAIaMfIeduRMPC/gpj8sIZNxefSRNaxa2ckpp+8ZZ4GO1+ER9tHaCVMM/DD6QzLgl2hJpI15XIBC01PMh8WazSgVMjpGm1G/VyRpO1VJ5SrHoBKIK2uS6GkV5sgp9JsvAh9dHMBKNZY3KK3RXiHc1DUIC13KYdmt4Jeql6Jh+l7d77FHDwlpoQr9WJkWdGnAJPgYo1ThR8KxCvo7kS4E/durvtdamdP40A2q21+tA5MgTDuo0gB2wzh6//Zbcs/cye7/Z9wBdouxeplyytgLISSqmGPbbz9JadNLpHY/klK6GUsIBrYsZ/ufv0p6wfG0vfmLgxeemk9LSiwGL3hVXRjluFUublpr8r5HeypLzithh4qxFCbM07EsbGGR84uhQmPqMXgUc51rWUTcKVIYrqJ+rxhabcptKwU+KjyRNlVQm2ttEiq6lh1jwoS0zHy0XXoe/CX9z9xCYvo+eFtXUuhaR+oPPfid6w0wNN9L+1uuIjFtH0CQX/EonTd/GZlpNbiivg6aT7yM7N6nERR66M1tphl2SmGpfjZ23RxGO7WAh7cabpNIa1GU8j1kmicPavtQ5vnqNui6f+6YVBdQr/6R+//6WWsGPRdzKgDgmVv/i3zvZhKZVvxiDmm5OMkGVj75O6YtPJWp808k8ItVrqLKMiul0m1dpUBUHRorLT3aJDt0LUjYFO5+hZ4r7kT1FqDgk/nI4TR84mjsPcbR++V7yP3sCVRvgcbPn4DMJtC+iVwUEdZlELQhzK8udNiEsUE04r5GEU0rtxNs7kekHFRPAe+FTSQOn2UAwvbQ47IzMiYgbjlyQ49acRNCUCr5pNMuxWKEgdH09hRIJDKUSsGQUUh1RYOQooplN5JSKeClZVu49Y9L2bq1n2xDgt6eAkccNYvTz5of41ygPJAR7uXo4+bguBY33vA8iYQB7177i8XMmNnCGWcvYOas0Oc+QlOHwwZEvvyrDjmbtOMy4Jc4bFKYQbWm4MicfuCEmfFvQ2rkVX+XzfARVXJx9WJj8cr3UljxN7IHnA1+CW3ZxspS6/IIlR+ZajLKWqULcFT93rHoobgdoQttLKeDqtN9qJCofB/FdUuQGSiufQ5VzIWuM5MNt7DmGYLebaHlqdqNpf1i2N+w3NDM23jIRYPqLm1bicr3xtFbukQYfl7RB20sipt/9k6K65Yw/YpHsCL3V1TOluWs/a9D0F6BcRd8zTw/y6la+Co/a/8eNCYjjlrUtBC4HtKHtyYzvHTJfw26phj4bBrooaswwPzWSbElRmlFUyIdm8C7iwMmlFlICoHHq91b2ad9KqUgqFJczGsfui+rGmvcUJFCA5AQ0pzmgIaDL8CdtAeF1U8h3RTJGftB4NNw0JEkZ+6PKvZjt0yN3UHZqfuSfOv/ggqwGifQfc/30JtXYO0NUjg49tAuv9FKrVIZv4e7yEwfWVMHerYYBU6bhHnFnGHUjk5Vo6prlAewsUhVkcNYSIev8vWx1tRTJIPApDZ49vb/Zv2LdxlCu1QzB775ahb/8XMEfhEhJE/e+GmaJ+xOtm1GnA5hJMWlSmrq1brStSLCPEEWqqdA/48eY+BXT6NzRUg6NHzueLLvORjtBWTetgjhWPR+5a8MXPsU/isdNHz8SBJHmf1ER2VqbV40Kcpu89idNfoJMGjMAoOzKT6ymmBNJ9aUJoKNvRRuf8koLUoBo6cPGYuMLfcQ5azOY5PQBWKFGi0iDlPdUSr26gln+FUevH8FN/1+CY2NSdIZl76eAkcfO5u3XLBPeH31vbWKy2FHzKSlJcUNv32O7u48jmPF+Y9GK/U2lto6I/r9tO3y0X2Oi0/OVae10NXysX2Oq9vm2vrK3w8ey/6n/mQsO15A3+PXkz3gbHTgI22X4oaldN3+NWS6Ge17ICUEHsJNU1z9FFaKstVkBEtLvX6PSSLFYafcQpgXxpIUNy4l/+ojOG1pBl56gOKGF0nNPghdyiOSWTb98GJ6H34Ju4VBgGFnggjzHZbbJKSoBuKGrqXuu/+X5uM/FH4VmOkeUqIT+GhbI6SF17GG3od+z4wvPYSVaUUVczE1uFY+7oS5tJ9/NVuv+SDtb7k6JPRTVIZZvhYihMmB5FgWT25Zza9ffoK2VJbOQo6MY6xRrckM/7bvCTyzbR0vbN/I/uOnI0TZPH7zymd5cONy9m6bwtvnHRqXXSr4/OTFh7nmhLeTtFTdkOaRJJ77cXTay+Se/TOyoQ1EGCnmpsgvfxRV7KPh4Avi+wBEIo0zYW65QGmRnBu20bJ2Wcht9fu586fMKsthOAf6OlYaFlrMXOvetIyJcw9n8Ms5NMBQvxZaSxyFELn7hwIn6sp/vX6iy3QGcf2RVVgFWLbL0vt/yAv3fBs33UyhfzsLjruM2YvOJ9e1nqdv/RKppgn0d67loevey7Hv/Q3JbHusuED1WgyD18JBim2gQMo44SGA6i+S//MyBn77LKUn1yFcC3veeBo/ezyJI2YZC4djoX1F+qL9sOa00XfVvZQeXU3n85tIvWUhqXMWkjhoenQqMG1S2lhhbFl3Hxl+6GosvUojXBvVlSd37WJExkX1FhHZBPlbXiT15r1wF01DlwKEO5ijameVl2GVFq0V1IQ+jm6aC7DsuHHldb+MX9GVc3wH+lDZ8chSs/+iqTz71EbWre3GcSSnnz2fk960xyBrRL1PyzJZp+ctmMBllx/BrX9aynPPbOSiS/ZjytQm8nmPVMrZ4fe8yncamQkBX6lBIZ2V13pBgC1HP9HifoWm9Pyrj5J74W6QIDT0PXkjTcvuJz3vGABKm15i04+uxRkHqghEyrgHziSQKQzw8e8ioxvswaZeM+G67/0BakAj7ABdgq67vkXqg7+N78vudyZWptW4pPxS1UTMv/IwwcDAoMkZRwaFhHV9T/0JVeij4eALgdCNocBqNGRvMpk142c5hmFYYQDKoRjrjIjBztJJla00OyBR0k0pam13BisSaDUokgGIMSd7tU3miMlzSdkOe7ZMZHshxwvbN3LE5DkAzGxo45XuLQDkvBIJy+GaZY/x9La1XL7fCfzm5Se4evEdfHbRKXGdUeinrwOkFmM53wGinCE9DLnvefAXOG3TaH3T5VVXFzcsZcu1HyS76JyYFDHueymPdFMUNy6juH4JrWd8JlQwdy0r7lDugZ0RrY3SqwKf7WufRdqmzVr5bF/3bHwNGOVXWA73//xStq9/DjfZaNhk43aIUAGXBKU8iXQzyi/tOMdTWO+d3zk1ptEfxuca113o24aTaqLYv310Y7QzZ6E66z8QJ2QUluSFe7/Ls7dfjZtqJN+7hYUnfoLdD3s7gVdg4YmXk+/bytL7vkemZSrb1jzNfT+9mCMv/QkN7TMJ/KIpp+JwUW+9HrRO2RbaCwi2DuCv7aL02BoKd76M/9JWVH8Re04bqbP3Ivvhw5ENiRizApgIHS8gcdB0nF9fTO7Hf2Pg+ufo/9FjFG5einPgVJIn7Y67z2TkxAZkcyq+dyzWj7rXBgqkRd//3E+wuhMUJE+ag+rJU/zrCnr/+z5ar7nQtLkmskmHFsKdeS+GVVqEkFUL7FhlV5qEhhro2LQdKFpb05x7/kJu+v0S3nT6nizYayJKRcRTYkjFILLC2LZRXFpb07z9XYtYvaqTqdOagHKKgZ15eWI3W0X7KxWWep8RUHeosayvkCmwbLQK6LjxCvyOIg2HnYCwHfoevYOtv7qMqZ+8C7tlMonp+zLr6z80rpnABynRXhGZbqb/qT/SeesNcSjv6y+je6GqFMKQh6X/udvp+et1JKbPJD3vWHru/yV9j/2O3v3OovGQC1HFHOMu+OqQZa/4+DSCvgHqUVZrFYCQ6MBj6/99hKaj3kVi6gKUV0TYLjLl0H3fj8kt+QsNh1xIZsEJaL+E0zaD5Jz5bP/Tl5h6+W1l3AsgrAaUV2T7rf9Ny0mXGY6XUDEai3gqoBj4lJRfcaI2krJdsk6yIh9W9TgOeCXSjsujm1eglea0mQvpyPdzzbJHOXTiLD7x8B9Y2rmRk6YvAKDBSdJXKrB46xq+dPAZtCYzfHbRKXzi4T/wYudGFrROxq9IZSFCd8noX6Fqa4VwUmjfo/m4D9D91x+z6UeXGjeeVsYdKS2jyGgTLRdFjBkF3oxj521foeno90Qd32UAxddC4vc9pMbv2fIKfR2rkFFKDWnRvWkZfmkgzngcSe+2lXRteJFEuplgEIGZWSMs28VJNrIzCSOjGdaz5RXihKHD9wqtFU4ii5OK6h7FMxiDXlm7jtZaV4wSJ5GWg1/MsfhPn2f5E7/BSTSQ7+9gwXGXsf8ZX0BrhQxxKwed8xUEkhf/+j3SjRPoXP88f/n+WRx20f8yafejAAOKlnXcufXWKR0o+r71EN6zG1DbB/Ce34guBoikjTW1icy7DiR13j44exrGXe0HVUoHQsQWF5lN0PCJo0metYD8jUvI/+kFive8SuG2ZYisi7NwEta4DPZu7WQ/cTQyOXxKlqHaDKCLPiJhk7t2MQM3PIdI2IhMgsarT8Ff3oH33Ca8ZzfS/anbaPnem2PlKla2RrBGjUaGVVoGXnqQ1f9xNO6kFlShz0STDN1NhLDQfhGrcQKzv70uBj0O26zYv1bHDDWMkhFF7MTulJDifdacNj72yaNikG0thmXoMk0fytmpTaRUeXCHHcdRS72HVDvJ67lahlJYas1uWgXmNGPZbLnuQ+SW3IfdYtF21udwJ+1JYcVe5Je/wKYfXszky/6AO3427nEfqNvWoGcLqnjDiCy4u0yikNNIUYjKFvUByLULU6SwlLauYOuvPooqQstJH6Pl5I9TWPMsheXPsO3XH8OdsBvJWQcQ5Htj8LKu9fNqVXfiRhYZYTls+PY5CCfJuIu/ZdoRWhd1ySM191AaD78E4ZYBpsJ2mPjeX7LuqqNYe+WRNBxyIXbTBITt4ndtoPveH9Fw4Dm0nfNl054aMOdwEuGlvnXkeVx5yFnYUtIYEldFasKGXDedvR1sagyztOpomM3vkbVlSqaZV7q3AialxPh0A23JLFcdchY3r3qO1b0G1OxYFq90byHrJGhNZtheyNGWzDCjoZWXujaHSkswCK81Fql6b6UEKUlMWcCES76LKuUNyDxUWqx0U8VYu+H9Kv57y3UfITFpT9Lzj0P7HsJ2dsqq9VpK5TutlI8lLdYt+TN+sR8n1UTg5XESWbo3L2PLiseYMu94VOARsTGPn3UQTjKLk2gIv68+6EnLpjjQRc/ml7FCssUdkajUiXOPQIa51IZ+2kZhkZZDz+aX8Uu50Dqx6yxd9dZ5c7oPIsMdMjwIbFx2H8/8+ct0bVyK5SbxCr3sf/oVLDzxcqKowkrrwIHnXE2ycTzP3fHfWHaCUr6b+39+CXsc8R4WHHcZiUwLYCw4IBBSImrSoxgruLE+OLu30/+N+xFpF3v3cVgzWkgeO5fECbthTzFzWZcCcCTCHnx41VqXOVE8hTO3HefTx5J55yKK96+geN8KvFe34a/cTunBlTR9/XSjsPhBXF6dAayKVIzbHBjiI5GwGfj9c/R+5T7j+vEVLd84Has1jXXQdBo/fwI9V9xJ8e5X6P63W2j66qnItFs33cBY3VSRDKu0OO3TaT//cuzGcWXzeTy560w0IQ1WIpmt2uWHnZKhlUQFJiy6nuLieYalVlbgPqL8RJXXRQkPaxUWU019zEX5e9O1qBwwWJlyvqPhOvH3kdpx0n4RGQJgt/7mE3Tf9yMIoPW8K0jveQwAkz78OzZ++yz6n3uA9d84lQnv+inJ6fugvAIohbAsVDFnMBf5HvMY6yxoVfV6hQpf9o52xkIX+8FJGBZdAdoL+XiK/aEP30c4SUQFxX1t373ta9n0g4sorFxB01Fn0nTcBwGY/MHfsO4rx+Ft28TG753HpA//zuBblI8IcSU1Dap+5pH+FG6AG793AYUVjzP9i48j3RQ68GOlXgfgTp6HO2nPGC8hbBdVKpCafRBO23RAY2Vbyb/8IO7UvSiseJzCyueZedVzg8Z3pJe78pq2ZJa2Ggx0dN8Vi05lS76XqSGRm1Ubph92+IhJc9nY3w1Axk6wX/s0xqcb+P7z97N46xr2bp8Sl+2pIHY32aGyKUU5fcXOvjaVSrnWmu03XUHQ12HmgZs2811a6FIe5eXNXEQgU420n/tlhLQobniR7X/8Iolpexu3UOCP2YL1ekrtxmTZCfzSAOuX3o3GZGe2Us0ov4iX62fDsnuYMu94oo1Sa82hF35nxHq2rnycP//PsWRaG0EPjLWV5iNs5wkfugl7DOD7B699D6ufuQlpuTusMNWTKqur8tGBj+UkEaK81W1+9SFeefRa1r94V7y2uckmDrz4B0zf+zSU8hFicOiwUj4LT/g4LZMX8MSNn6Z/+xoS6WZevO+7rH/xTvY86n3M2PdsEunmuK7ALyKkXZWBOlI0UmcuQLamUbkSzvwJ2NPK9+kwr5+owVPWO8gKIcC1YsXCGt9A+vx9SZ+/L8HWfvyXtqLzHsmT9zDX2/XdRHH7asYSL4gVjr7vPUL/9x8x9ytN8zfOIHHkbLRvDnnpi/dDB4reK+8hf+uLqG39NF19CvbsNgMODjE5Q1mjRqPEjKC0zGT8xd8YtoDhZDQNUCrKCj30IEZA2BjnUQrYtq2fCRMbqsKeocIFE0Y6jVaEELHSU1aMKlwDb0Ar8qDJ5iTxe7ex9VeX0fe361EFaDnx7bSf/Z/mRKkDMgtOYNKHb2DTDy9iYOmTbPj6SbSe/hmajn0fMgw7jU/GlhPCQwaHM1bVOwzh2pgkilpKNqA8sMKTi5U1lgFBZBWpBqdGfc+v+BtbfvE+8i8vIb3nQia888dIJ4HyiriT92Tyx//Exm+eQWnDKjZ+8wzaL/waTUe8varM8gJau5AKtFekuH4JW375PtAw40tPY7dMjgGiEb8KAlSh15TiFRCJjOGECUpAkqB/O+mFJ9N4yEVxNFJfuoWue64NwbnhQmU5sYI23HtU+SwCrWL9UYanJBn+fsmeB8f3GDdl+d1RWpOwHHpLBZZs38Cz29ezvr+Le9Yvo7dU4O51y5iSbcbXinxFOLtr2bG70w8ZYAOtScX5U3bdiyOEILPPaXH2cSvbRu6ZWxlYei/jL/0efsdqAygPSgjLJXpp+x6/gez+Z9F42CV1XW5av3GsLbUbuAo8LNtl7ZI/07n+OYS0mDDncJom7sFzd36FVMM4Vj31e/Y44t00jZ8bR8apwKurDAghCfwCTiKLV+irsGaOtaXVN5RyXVhNE1F+sepQMahuL4+TjKw/u9bKAhVrk1JIaYO0CbwCAz2b2PTKA6x+5k90rn8Or5iLFYnp+57J/qd/gWzrtBjnUlVWbHGwUYHH1Pkn0jplIc/efjUrF9+AtFz6O9fyxI2fZtkDP2LawtOYdcC5ZJqnxjmLqtixA/OCas8nccSsctuLvhkOiYmUExhlYLQizH+6FJjIITRyXIbE+Nnl8qM9rYbxvVZRjiKahGOBa+Ov66b/2w+Rv+VFUBrZnqHpq6eRPGZOlftH+wGZSw9AtqTovfIeig+upPOd15P96JGkzl5gXFqBKrtmQ2/JaA9oMBIQVwXoUr7MJgrowEMVc8b1I23qMp4K438eumIdvyu2LVnxagdr13Zz7PFzY7dPZQf+cscr7LX3RCZMbMC2BStWbOdrV/+Vb3//LBobk3U7W2mNGa3Uulve6FK1WQ10k3vmVrbfciXF9a9AAM3HvZUJ7/6FWZSFMOnBfY/svqcx9RN3sOnHl1Jcv4ot1/wb3X/9MZM/eiOJyfNjEK+wbGQKCBPMxdaGygmmArrv/aE54YaJDXesL9JYS9ItFJb/DXcS5J6/w7DyDnQj3DSq0E/TUe/Abp4cL84AwUAPPX/9MZ23fQWvo4vkrN2Y/NEbDVFb4CGdBNovkZp9EFP+/S9s+t55FNe8yuYfvYP+J//AhHf+BLt5Ulxm5fOP55O0KG1+hY3fP5/s/mcx/uJvAcSboK51J8UJw8L7bTeOMrAyLXHbVaEvZCnWSJcqnAtUL3ajsbRYQla1o9IiVfA9FBpLCBJWtV/b5PyxWNXbwcvdWzh95kJ+9uIjZO0Ebcksd6x+gSsPOYvVfdu5eaWxBhUDj1mNbfR7BTblepiUaUJrzYrubZwyw+BebCl3aluqXUxTcw+tvkAIsGwSU+aTmDK/7v3t5/yX+TsERNeWK4ewEOwq9+dYTpG1bhzLdvG9Ai898BOk5eIXc8w64FzaZuzPSw/+GIBirpOXH/oZB537FZQKsKQVb7z1JTwcVLp9d3LJk7bJiSPtxLB91DpK9Pra0M4bC0uAlBbdm19ixZO/o3fLcrauehy/2B/i0UxyzebJC5h/zAeZtf+5AMMqLNGntBxU4JFumshhF32X6QtP5cX7v8/WlY8jLZv+7WtYdv8PeenBH9M2bV9ap+zNHke+h8Zxs+N2Rfwl1DCpi0Sd7XhH8NFDeH5qyx/UR6UMR0zCBkuABaq3QP6mJeR++ST+qk6ELXEPmk7Tladgz22LFZayFcmAi1Onz8ee007vl++m+OBKui+/hcLty8i8fRHukbPiQ6fW2ihTjoWQu8DSIqSFSGZZ99UTAMjufxbZ/c/GaZtmKowy8I5ZBAgRKygvLNnMvXe/yrFh3p8yB4vp1K+ve4pPf+44pkw1fr7W1jSppFMBaB2MUSkWfQZypVABqt8EQmXPDxRSSlpaUrv8RRrLgrVDZYRcHoWVT7Lxe28zfXKh5bR/Y/zF3xxcjm0Ul9TuhzP98w+x9TeX0/vwDSQmz8MdH4aFhs9UDfTgbQE10B0VMLhxymfjdy4j6AVh77DOYkQAykQsWWnovO0PdPzhD+YnF/xO2OO6g43SEvgQ+ou3XPN+uv9yPTIJ6QUHMvnD1+OMm1WhUGiE7aIDj+T0fZj2+YfZ9uuP0fPg7xB2AqvBRPpUKSyhxSJ2TagAZ+LuTL/isXKOppCd1Vwjyi4yVR4rYdmo4gCF1U+BCrCbJ1HavAJ73fN4nesorX8BZ+LuFFY+ibcd8q8+YtyspQLulPmmrhHes6Fcn5W/a62rOINqFXRHWgRasU/7VPZpnzpkXa90bSEfRjoN+B4tiTRHT9mdLz95Ox/c6yh+v/wpZje1s3vzhLjcWkDwWKSyjQYPYELcVCmPlW7C37Yab/OrgCEQlKkmo3wKwHJjxdowGkc4l8r2aPzerUPWXXm9UioG9o9GtNZYllXl1h7p+ir3Rmhlefmhn7F9/bNIy6F95iIm7HYkiXQzcw+5lKV//R6JTCsrn/wdsw54C+NmLoo336HWjciyVPVcdnbJq3hvajFo1ddFh9zX7mAY9TWZaWfDi3+hY+0zpBrG4ZfyuOkmmibswdyD38rsRedhu2njDkJUKXrDWSGkZbBQWgdM3etNTJ53PGueu4VXHrmGzg1LKA10k0g3s/Glv6I1JBvGmXVZSrSvCNZ1hxYuXQ5L1oxu8YwyKpvFaTSDUX62ERdMoLCmNiHcckJXkwZAQkKiCx7Bpl7yN79oople3oYu+ljTmsm87QAy7zvEKCp1ALZA/Jszbzyt11xI7udPkLv2SfJ/eoHSY2tw9p1M6swFJI6ejRyXjZWpXWJpAUNI5m1bRXLGvnTe/GW2/t9HaTr6PYy74CtY2bb4VD7qTVnH/4sllXZobTOuiYi/JRpppWDK1Caef3YTndsHCJRme0fOuH/U4IfmeQrXtXjisbX88HuP0j4uQ7Hgx3kZakVKQeAr5uzWzr9/9tiQgG6YfmhjdhvtK1f5EHzfR40R+Gfb9rCLnghDaTN7nUj7uZ+j5+FrGX/xN2k46Dx04JtFXtpVC7awzT12yxQmf/h6svufTWavE81zVCo2n6d2P5LxbzuT9Pxj47oGibSZ+L4vG0ZdYcFoowHqirGN6qAU5uxJGhCwVsZVVejHmbB72BY7XiDbTv8MxVWLSe15NOPf+h0TZhwBLSutb5ZhsbWybUx877WkF5xEZt9TjWJRyiOcRHwCEG4Gmah4GbUB2tpNE8yYWm5VFmcdKSyASIjyWFkOQe9Weh+6Jg5/bjnlctABnbd+JVy8bkNIm/GXfoLeR3+NsBME/dtpPv6D2E0TYpLAHZGhFJp6PmVLSPpKBRrcJD9Y8gB9pQKfPuBk1vd34UiL8akGZja2xcpPJnzvz5t7AE1uil+98jj7j5vOBbstIlCGk0Wjd9pyWbUgRkkpQyXOnbKAbISlSTYaMHSFBUFrHVqFB/vxw8JpPOySuvWW3QKmfsuysMZOUjWqxXgohaVzwxJeuOebOIkG/GI/847+AIl0M1oF7HXCx1j/4p3ke7cQeAWevvWLnPThmxHSHlTeiOvzTusQu0IJ2fkyTD8lSvkkG9o58cM3c/cPzkYrxaxF5zNlz+OZsuAkY/GAKp4VKniF60EOKueCkBKBjO+ftf+5zNr/XNYvvZuNy+5hw9J7aJ68gBM/dCOWnUB5HtJx8Dd2s+3EnyBSjgkdHs1SGZ2JlIZSyClkSYQjK5s8cjERv0p3nnF3vddEJgUabYGQgqAjx8B1i/Ge30Tp6Q2o7TkINHJSI+lT9yD9tkU4e4w31pEQmwKgS75xeUmDrQHiyCYcSfaDh5I8YTdy1z1F/k9LKNzzKsUHV2JNacLZZzLuoqlkLj0AkRo5qglGo7QUc6TnHcvEd/8Mv2cz+VcfY9vvPsmq/5jHlI/fQmruIYMUl9qKK4E9QpZ5VSIJAk0h76OUpr+viBVaWqQUFAs+WsNTi9fzzNMb4u/dhFWVGDHuUEhfvN8BU7jqa6caq02d/EZCCEqlgNbWFLfdvJQ1a7rNAxhhgTV9EIyWxDdehJTCtncsbcGIC0/4AradfQXNJ37EuEUqXDyVZcR/W04cadR4qMFVlF0uBlCd2ftkMnufbH4LvPgZR+MQbQhtZ35+h/q1o6K1qsharUhM34dpn3sYu3liRVudcjsDH3SAsBPIKJpHWjQd9c64zIh/RXlFhJT4XevwOqhIPBnWrYJ44xz0PMIFxO/QZUxLMYfTOpWJ7/n5jvU1tBC8Fqb0uI6Ksu1wXJvcFDMbDGPv1IoMzPuOM1bWygSGgVKcNH0+J0037hmlK983gSt3bN7Xtq9qI5E2oHEn7oY7cTfQKn7mlVI15yvXpLg8SdOR76hfR4Vyo7XmN7/5DStXriSVSuH7/rAKiOM49PX18c53vpOZM2fi+/6Q7/9gBcNE2AR+iSdv+gx+KY9WisnzTmDW/uegtUJrRTLbzn6nXcGD176bRKaFLcsf4dk7vsJ+p30u3kzHpLjslOyKcne+jKifUtqowCfVMI4TPnAjWisyzWFKFqUMpkZaFQqLqb9WOaksFwavoxEjbuR2mjr/RKbOP5Hcceux3bRRWEKwPwBKozoHEOlQaRlNn4W5D8dCNidBCPRAiaCjgIgJW0chWqNdC9WVD+s230WuOtmYJNjST/62ZVgTGrDnTSB50u6kzpiPM89YTuNopigqN1SEBlelY8Cx9gLs3dpp+vLJpC/ej/yfXqBw18uGQfe2pbh7TzIKS0XSx+Fk5NVESFS+BwCrYTwNi95Mw6I3s+XaD7PuysOZ9pn7Se1xZN2TbdyB8DMINL6v8H0VDpb53nUtOjsH+PiHb6ZUMouBwIB0E0mbYsHnY584knkLzMCtX9fDlV+8u5yBuWKgIjxMtiFBtmF0/CLNLSlefSVMTz/UBAi/D1RlH0YuWwhBEARYlsULL7zAli1bSCQSBEEw7KInpcTzPBYtWkRjYyNKqfh0WXutmXQKLBe7aWIc+qsKffQ8dA1Wto3GQy+ueiHjEyiYyDDLLv87dKsUVj1F/+KbyOx7GqndDqsCMVYuhFXRQyZeLrSE6NCNw+Cxqux65WmhJvK46nqtB0cPSaNg2c0Tw0gdUdVGwlTzYBPkuvC2rsDv3You5lCFPoSbQiYyWNl2nHEzscOFreXEjxL0bUOmwjDayAJT58QeP8fws+2cd5GYub/5KsrwXBqo6FhlZys6GPYxPFaZvlZYc14rqS67vEn/8IUH6Srm2ZjrJmHZONKis5Dj+Gl7csjE2XhhhmlLSrwgwNMBlpAkLNtYQQUoregujTUyZXD7KpV/3/fNCFa5fhJo7RP6lKvuk1Ji29XjWGVx8UsIJzHkOEdzbfXq1SxZsoRsNjus0qKUwnVdurq6yOVyVWXUK7tWsYhA4X+74XK2rvwbTrIBabkccOZ/xfdE2IoZ+57JHivfzbL7f0SqcQIv3PNtGsbNZu5BF8X5cV57hYUqJfXvKZXjKEOuqnSTOcxEVPzSchAM3jtgaBdrJLVWl/jvUIE3AGhFpsW4WCNAsA4VFNmYJPuhQ8GxoM6hu06FMSA22J6jcOfLUApwFk7EPXQGur8E9hiwhJZED3jIthA7V+GeEq5F81dPw913MqIpSfLoOYhMaKGPchpVRDNF+YcK975KsKEX2Zwkefr8KsVDCAGR1QWNM288zrzjyH74MAp3vmzuOWmP8OKw3J11D0HZLWDM4oaNccLbv4/dMoX13zyNmVc9j9M+swrIWH2qMZ/JlE1jY5JM1o2xJkKA7ymam1N89PIjyPWXYktLFHnwnW8+RH9/Ca01vqfo7y+a8mseVOUkKpV88nkf1402uOo4BiGhWAxoakrieYpE6FcbcqzC71NhHxoaEqPSCoFYabnjjju4//77aW1tpVgs1lVCoHxS6+np4Zvf/OaQSkv1gifLC174vPzeraz/+kdJzkjTeOjFg06a8d+1ZGNhSGhuyZ2svepqpv573igtfikuu+qFrYke0lqjcp2GaC3ZMKoxGosMthrZVcDcqmssm/zKJ+i5/ycUXn2M0uZX8Lt8VAG04YhDJMBuAmfCLJIzD6Dx8Lcz7sKvV9VZC4atZzqO6p34bmNVMVaScLxqALZj7etrvumEEtHynzpzL7JuAqU1c5rGobVJ6NmcSNMWAvMrc2I5loVTgQCM8GYtiXTMjmvLsbtWovIrFRDXrZyvowtdjt6fehsOwygslZJKpWhoaCCTyYzK0hIEQWxdGXSQq9OOyIIipc2zt/83y//2K1JNEyj0beOQC75N04Tdqmnjw9P7AWd8ka6NL8YKzpM3fppkpo2pC0563RSXGCejFeh6pw6ojs57bdSbQZYladI0aMCqQ5I5lGVlpDqGkggXo1RgyBSjORdGucrWNE1XnjLmfgEEW/sp3PESeqBE4ri5NHzsyB0qJxKtyu0SUsQhyemL9itfEyob9XhiovxDfd94gMJdL+PMm0Dy1D0H8blFVhdTnlF+ZGOS9Pn7mO8i680o5+josjxHXBPSik9+qjRA25mfxdu2kk0/uJjpX3gUA7IanEsosoic9eYFnHm2iSqQ0igWrmsTKIXjWEytiFOvlIaGBNf9cjHX/PzJcEGwSKcdY+2oEd83ZT3z1Aau/fliEkmbUrE+pkVr07Zcf4kZYULEoQYs6sO55+/NOeftHV5L1edQEpXZ2NjI+PHjaWpqwvO8ERc913WHdSnVN/2K2KUhpI07AezWaSbkVvmxxWDoMqWxnLgphJPCnUiZtGsIk2nc7tAlVVz3PCs/ui+ZfRcx44tP7hCz6/BtrHPaqVFYwCybHTd8hq7bv47XGWBlIDl7LzJ7z0RmWoybJ/AI8r34nRsornmarpdX0fvoH2g48AwmvPNHYaSSComi6j+vSjeCEAJVHCi75iLNfCf6Wvn5WksUHt2azHD27H2HvC7QZcxKvbZFY+JaNnu2TDQL9igWpaHcNJHS0dvby/PPP4/ruiPiw6SUlEolxo8fz+677z5I8R9KiR+yz0GA7/vxf8NZWoQQeJ4Xt7HW0jJozvilMAJH8tStX+TFe79Lqmki+Z5N7H74u9j90LehAj9UWMoZepUKsJwkR7z1R/zle2cw0LMZy07w4LXv5vCL/pcZ+52NCvyY6Oy1EhkqUOWIpPpjU/n7GOAYOySVa0Ns26y0sLHz71V0eNZUr0tyCIusVhqKfo1lWQwdJSQE2guQaRe1td98J4WxsGCie0TaLefKqRVNCMCt+S5hV0XrRIqL4TwKLZa2jJWNen2JCrXGN2DPaMGa3EjtE629p1L5wTOYHuEOY72uI6NwD4kyDiD+SoCTQquACe/8Mav+fXe67/8pzce8N3ZNDFNc5b9MIywZKwX5vIdtSVS4yAWBccWccvqe7L7HOAJfsXFjLzfe8Hw1j0o8SuYjCMnqPnjZYSaTdN1BMIMTBJpUOnzpRmE9qSxqNJph9IJ4nkexWKRYLI6otCilKBaLQ5qVy20ZZlPTGpXHcIXYLoLRgTmjk5x0EqgBTCJFGL3vNFxUY0vYaxAoMOyGE7qEtv7mE3Tc+E3sZpdx532QxsPfhjNuFlZD+6Dygnwffudacs/eRted36Ln4VvRymfKx2+JMzwP95wrLS0ykQ6DAaL+/2OE0FdKoBQl5Q/yYGltIoKcChfdULD0aAHytcKRg6MMaqV20YreEdu2CYIAKSUbNmzgK1/5Ci0tLZRKtfT01WW5rktnZyfHH388l19+Ob7v11hpxiaRkjySIll53XDtq5oztotfyvPkTf/BK49eQ6pxIoW+rUzc/SgOOue/TbmxwiUwe6VGhpwsmZYpHP3Oa7jnR+fhl3JIafPwrz9Ef9c6Fhx3WVjPrky+qWPLkNaKUr4X202HLhiLui99yNOSzLajglJYhq64dte8J8OtDa/JIUBUE7LV1jdIMUjV8gRp8ILQtVm/fPNJWSeo/BSVX1R8r7UB7CaGtujXO/gOiVEZYux00UPlSuiBwTnqhrJICyFi0G7V2rorLC1WwzgmvPPH4T9q8QzG+jLxPb+MQZBjO1GbJ+T7iuWvdvCpj99KsRhEEbcoBYmExaaNvcyY0cLc3cxm09iUJAjqg3aiZ26igCS77zFu9K3ZAVP8aK6rfFBGC5fxZz2pzJe0c6JNGLJfpP+524myEg/bVilNOGnjOIrrnkcky6bfsRyL6uimr4tERG/55X+j+97vYTcnmPjeX8YkbtE1WvmxFUQICyvVgDVlAYkpC0jPP4GN3z+fvsfvoPfha2k6+t1VHB/DSdX8eZ2sI6+FWEKQGiFiabTEcY60CIJgWMuIlBKrIg+XUgrHGcyp4jgO48aNG7W10rZtGhsNwdfrZa0ajVQqLKDpWPM0T/7xc2xd9TjppkkU+jtomTSfIy/9qaHZr3G9V2M3DL6ldereHPOua/nrzy/FL+WwE2mevuWLdKx5mkMv/E5MdFb3hRzj0AhpYblJLDuBVoq7vnvaGMoQBH6BVOMECn0dyNht88Z5PrtS6m32lZYXIQXUURTi+8MoHdmeiS0nIh0eLBtCA8GwUea6LgdKbbtGcygb4sfQSjPC3jKMkj9iHRUyQpbn0IRWETVS+Rn5C9N7HhV+p4bAtNRXBOzQVHTAgVOZMDEbmnPLZleNsXz4XkBbe5qBXAk3YUC7WlWGUpYNjVENliVxHKMU+L6K9w/PC7AsGTPpVraxltRuNH14I4tWAcICv2cLqz45+kVFCFAeuOPBbiCkRmdMB6HX2tIyZL2BZ5SWlx/E21qi/Zz30njIRSYlgJBgWYPCYk17DWhX+yWSsw6g9dRPsfE7HyS35C6ajn73qK0l0RwJgmBEK9k/u1iWFSsgowkXjtw3nufhOA4333wzlmVx+umn43ketm3HFsjRWCu11hQKBTzPi797I0lk+fCKAzx2/b+xfd2zZFunM9CzkaaJe3D0u64j3TQRFfgGVFpzUq2nuIyffQjHve93PPCLt1HMdyOkjVJeTZRMvcaMstHhOSvwCuR7emLCu7Gui1prbDdFvnc7pYgH6o2YK+U1EhG6fYRjUVq8jv4f/w3ZmjY5emotGdpgSlR33jDIZhMU/vIywZY+9IBnXDiVc1sDtkT3F7EXTKThI4fXzTe003i5elafOG3cju2To2nTyO6hEMRUtwLTOhMmKkJ/mDL+Ol2Dban3IKQ0BHMTJjYwYeLoAZsL9prIj37xlopyKlJfh9d4XsC6tT18+vLbKBZ9408HGhuTFApeaNExC2oiYbO9Y4BzzlvIqWfMo1QK4tQBlfKPpLAAIKShOElmaTvjRIM/GElzESZEWDhJvK0r6H9qcXmDH2v3K82ar7OoYg41AO6keYBR4GRi6GgyIQREGBQgMWUB2q8g1huleJ6H67rccMMN3HPPPSO6Mf7ZJFJSkskkV111VcwztGHDBjo6OnAcJ7YkRu9uqVRi4sSJTJw4MXaNAjzzzDOx0hJ9N1ZrpRwGi/SAi0PUAAEAAElEQVT3kEochxASpQOcRIZFZ1/Jw//3Pvo719I+fX+OuvinhkW1jsJCfH+1AhMpLuNmHMBJH7mF+398KYnGFo5+5zUmgiUGq4+gHGhtwJFRi20L/DBFha8gBenmycxedCbJhnYCz0TlDPmeV5AtlqswYd2lfA8TZhuWY7GDQO1yma8vaH2sMqh9gQLHwl/dRe6XT2JPa0YNlOp4EEK/rC2RjUlI2Pgvb6P01IYw5Ll2bA1ORHXkSJ60Bw0fOdzwqNTs9lWKrx9R/49y3KQB0ArHMs+X0LVVDNBW5PIbyzPQIGUcJr3DmJZ4IR9BRppsQ71wkeISAXKfe3YjD92/kmxDAt8LrSPC9MeyJNIySo7jWLiuxalnzCOTcWNmXSgDZufNH89l/3Y4jmMRBOahSin49XVPc8ih05m/1wRyuZIpVxoG3RkzW6vKGKrt/yhiwoGNi2/yR28c8/3d9/6QngcWx7wkYzoI7cIhG9MzCK+zmyZgNcHAsvtoedO/IRNpQyBnu0QTSxhggMFkaA2BH9+fW3InwgW7bfoOtblYLNLf308ikaBYLP7DzqGxSmRlikL6fd/HcRz+/Oc/c8stt9De3h6PR4Q76e3t5V3vehdnnHFGjF0BaGhoiK0z/yzjJ8BsEF6UEM9GCZ9Jux/FAWf+F6uf/iOHX/pDEukWFIFRWNAjvk6Vikvgl2gcN4eTPn87qr8YcpZ4DE/tT/zOCqvMwxFLeIiznBQaGDfzQE76yM1j7v9QopWqYvHdEanchAOv8IayrAkhsWqj1CKlM2ljTWpETsgiBrz6bhaBUQ68wAQGpB2sxmSooVC9NodKi3AsZGu6fH+NVLmohsr6PFyfonsiS48UhjRvJ2QoV1alDKuRDCy7n1WfPBZ3cibMNxTygQz2yphOSInyFHZTK3O+t3XYZG+V30cKh1cK2LY1R6kUUCoF1ZAAbUirLEtSyHusXtXF8SftRiYTua4ijgMTFj1ufJZx47OD6v3N/z3NHvPGs/e+k+v2OXIf1bbxH1Ki56MCVClvmFVHAuJJC13ox2poN/w8MipoR+oPXYkqMGFwO7KIiNGHwgFhojzI7HMaialfpu+J2+j4w+dpO/uKmEDONK1MCBWfEkPlu+fBX9J113ewMtBw8IVxO8YiEUYj+m8oi8A/k1RGdEX4lAiX8o53vINLLrmkCsshhKBYLPKd73yHmTNnAsalFATmlO/7/iC39D+sRP0OlFnsKzcJX6CFYvaBFzD7wAvAh8IjK0jsNx3S4UlWRpwyQ+CCQkyB1obwLMgXsZ7vJ7n3ZDQaKZ3hLa2CmDdE5UoEL241XCKBwp7Tjr9yO1gmaiVxxCwKD6+k49L/Y8qq/6TrP27GX7Gdcb9/F0F/DpF20KUAmUzQ/dnbyN+0BFTIolpRpVYg0jZ4mtZvnUPqtHmG8bVeDp5RLMOVlgxrDBmnXy+pTMpaCURNnrAbExZ/rJy7bbi+Dkf7H0UilXw6zr0O1V9Ce0FtCbFUYmq8l7Ya0jvHKpOxVu7vUVWi4t5SgGxKGrI6S6DzHsXH1pi8RdG9w5QRfSekQJd8rMlN2LNaR0xbMnyW5wlzmPi+LyHTTah8n6kvJPcyyGQbVGBqDgfMmOEzI4IQKxcv2zbstosOmsaig6YNex9AX2+R//zcXbELp9JMaoWJnoYSrTRe+CCDQA2KQHKcakTzP4fiIqo27BFvCcMXhZMMTX87WG8oIkqitoPDONpnYXBWEh14OO0zGHfh/7Dpx5ew7XdXMbD0XhoOvZjkrEU442djpVsNGaIKCAY68TrWUlz7LP1P/ZG+J25BezDu/M+Q2euEEBs0NlbXSIFWSlW5PP5/kKjvlQqH67pVkTsRfmX58uXk83nmzp0bW2CKxSINDQ07xSL9hpMIH2hJio+tpnjvcgCSp8/D3XeKweipAGFZ+Fu66Tj6J4xf/FHcfacYRcUN3e3OCFZtX5mEfF0lNh36bSY89XES+01Fe37dqJBYBCEBGgRruwzVfFMStX2AtusvoftjN6OLPv6GLiY8/nFk0oWNBaPr50D0GIXEivYHy+xWosPDnT6e7HsORg14xp0RidKQsOn++M3oXLFqnAZ3bOQhNmuEUQyev+vrFPq3I22TJ+jvsYZH0V2+VyDTMpW9jv9ofUB10sFK7jpKCGM5MezzI1K3e0ZJ7L3yHgp3vIQcl0UXvFFykBlLtWhIYE1sRBd9Ot/66/Jvo3hoWmlk2sVf303DZUfQdNUpUAwgtaNKS+s02s66Ysjft99yFW1nfm7oBo1isxFC4HkBjmPx2CNr+MP1z+G6NqWSPyj8OAz2QClNb28BL/S7RpYW31ds2dxXhlLEihGARoah1d3deXp6CvT2FHAcWfWe+L6isSlJU1Ny1LTCb1TRWiETEPR1sOpTc0d5V/nF134Ju4VyyPNoh0IFWGnwtq2sqHd0k9i0mzDRYD/TP/dQmPzQH1FxiJVXy0EHPo2HvRWZaqTjxs/T/+zf6HvibzjtFjLTgkykTY4WFaC9AsFAD/72AtqH5NyptJ76SVpO+piZW2LHLW9jRcb/I0utNaQ2WiIG9oduI9d1uffee5k/fz6pVFmpjpSbiDn6n0JCK0bftx+k/zsPY01rBkeS+9VTNP7nSWQu2g/V5yMaLIQWxlUQKSjhuKmBEtuO/iE675VPs0ojkjY60DRecQLpcxaacbYF9uRmpBtuhiPNP01shREJG9GQpP3Gt+PsNRHVnUcHmtbrLsLdfwqyOUXhr8sRDaESagtwIguA4erSKkBgo30fe944UufvPWTVPVfcMXL7RiuhpeLlR35B75bl2IlMmBDx9ZfI6lXK99E+c5FRWsJRrl0XdFAZ+h0V4IfjWaYWEMLBhIR65hMNOgDplDfIkj92o3YpQBd8k3G56KMrn0ft0l1pORECtbkv5oKJ8xFFqVaihtQrI5y/Wkoo+CGR3cgyfPRQYMJC/Z5NdN/3Q3QhR9Mx78Wduheq0MvAS/fT8qbL694rnOSoLRVRv/J5j0LB55TT96SQ95GVWnmFuUlKc2q34wEyl3R1DvCFz95FwrUIVH0/cCrlcPNNL3LjDUvMexLeq7QOAbk53vyWhZx34T54foA73OnkjS46MMyvqh+/s3/09wnzHlgZUEVMMkQYtZdIB4ZxNujrxtvavWMWFgtUHlQUuTQarb0yGsyy0YFPdr8zSM87hv6nbyb/0oPkVz6Bt3UFXk8HqmTee5lK4LTPoOGA/UntcSTZ/c7Ebp06yEz5/4PisSulNjovGr9SqYTruixevJjly5fz9a9/nY0bN3LffffhOA6lUonGxkZWr17NbrvtFt//Dylhs2XCpfjASvq+dj+N/3ki2fcb8OnA756l9yv3kThsJvaMFnNLaxpd8Mo079EhzLHIvPsgtF8GMoMxr/d988FyPhkwWK18aWjSsRFE5z1ka9pgFHIldK6IbE4hm0PlMmRPNVXpwZT00eNS2ihZUJUVuFKB1V6wwx7oocRNNuFmWrDddEi2+XdRW5DSQqsCtm02G+31oiuicYlaZiUp89tEba2O+CrrCrqGcysa05C/Zyyu6BC/mX7bAbhHzjI5kfwRXFS1IsKQZx0+VyEQ0kEHJeJNeyhcrtIIR6L6irj7TzHfOcO3f3ggrmUTDPSw8Ttn421dhbBd+p/+E1M/fQ/uxN2xMq2Gl0VXvxhVGYXHsNgIAW1taU44afdR36OUjoGzrW1pvvat06uUkVoJAoWU5UGurT9QmnQIJnJGMMW+YSXEZjhtM5jz3WXGUiCs0U/ECgVR+0WsBsN1I9wR/MQh0C8xfR/m/GCpoRkfLqpgxPoVTtuMsE81mXvrzKtBYG/LRgceMtlA42GX0HDYJeh8L6qYCxmCA6OUWA7SzSBTDcZ0CwQhaFfXEqwNqhSEGMwC/S+pL5HCsn79en7+859z/vnnk0qleOmll1i8eDG2beP7PolEgu7u7n9895COPjT565/DPXQG2fcfak6VfkD6wn0pPb2ebSf/FNmWRuc9Y+lI2gZbAGXApmOR/cChg6pQfUXyNy3Bnt0WXh5pSjsxJ6UoRxBFdO+1+IionuGyFSdsKPoE2/rRfcV4k4wwGcK1DMZnV1m0Q6voce/7nSGwE6PAiexqibAlWiGsBMJpxXIzgMBKNJtm1rutAvMCEPS/iiptR/s5kAm0143TcgDCzrL51omMP3UlpY6H6X3uE0w4bY05aQ5F7DeERIDr1Onzd6rLlRLoLXT95X9pP/lKwGeUxPsA5qBoD0/mOWJp3ff+AHfyfGZ86WkAtt3wabru/BYT3vFDhOUOabIfBDoaVYM127bluPPPL1EoDHYPmXKNm8crBUyY2MDhR86Mo4csS9LWlq5T8tjlHxXTUtVu28WdtOcuKnf451nFGeEkSUyet0vqrSy78u/AL6KHPEVqpOUiLTvMZm0i0YSQiFQjMibZqluZwUaNAQNU28Z/yWCJeGtc12Xt2rV85Stf4bDDDuPYY4/F9332228/9ttvv6p7vvGNb4yYcPANLxGuxwsoLV5H+t0Hlb8PwbiJI2ZRemo9qdPno0sBuuiT+8Gjg1wmWmsohFnHpQE+yuYUA7952oCeD5hqwlBda9dYLiJLd4SrtGrnd2gtSbsmpLb8FRFDqLAkA7cspXDXKyZqqrJPgUJkXNS2XIUbYcffoco1qKF95g6Xs0sl6GJgxffQyqe32BsqVXGEBEgXtCY98x3YDbuHlCHmELTtrnnYTXuTGH8cOhgwygvgtB6Etz0PykMVt+N1ra1fdyVYN/oI0wjUWkF10UROiqRdTfuvKSuwlsmlVKUEakBa1ay7vQm85evgZIhUDF0CUYcqSJe04dZCmLQBzsjs4yMqLd7WlbSe8RlTgVek9fTPsvln7wKguOZpln+wBZHImkypbgq/ZzPTPn0Pqd2PQPvFcrjsMBK1r6ExQVtbmscfW0vJC+rPXw2WLRnIlZg1p61KaTGAx51/W2uJ5v6RpJZ0ijAP0Q6WhnF2y2EjwXZ9vRUi7WpTeAi2q5f8rJ4EXhHLSfDKo9ey+pmbOOnDNxN4BZMDRZdNSsJyKA504ZfyRFmzhakQ5ZtknSY3TNhPogOVJNU44R9unryeorWOQ5cfeeQRfv7zn3PEEUdw6aWXxkkFo0ghKSWFQoFMJhOTzP1TiNaojhxyQhjRWGEulxMboRTQ8G8hSWfBp/9/Hx6EpxNCVFPAh5bg4l9XkDxpD7MN+pHSMop1UIPWATEd/zCajojaXHEvlmTbqT/Df2kr7qEzw86EbfYVWikaPnE0mfcdYtxC0WYnhdkYow0x72FNbTKWnLoH1bB9yrR1yDZWrEFqJAvpaylCoIMi0klT6lzG1r9+lnEn/oxE6xHoYAAhHLQOEMK4jrbefhZOy37YDbuHWBUJCDN+C/6L5OQzq4rXOkCGy5+QLrLOKyKsUAmIAk0i/TNMI1A7ylGocr01XmuNrBPRpVEIJH7vZtb8+35hcliB9or4PVvIr3wAVezHyrQy4R0/ZNNP3mYaogKE7RLkupj+n38jMWk+WvtmPEZhLBjZbiMlKtdtOuYkUH3byj9lmsnOOdikhVc+wnZRuW6sbHs0QiMWb8IiTWMPPHg6Bx48Nl4ME31UtgBYg04DOyb/FJYWIUZFPb9D5b5O9VaWDSZ7qpQWa5fcTu/W5YbbQUV5O0w6+sArMGX+ibRMXmCy3DoJcp1r6VjzlClQWnGGXADfK2BbDo/+9qMsu/9XZFqa8EsDIeOzj5NsRFo2pYEe86KGJ6HAK5JqnMDFX1sXmmV3rn+VYNVdJpWPqrZoMcT3oy1ziPuiiKlIpJSsWLGCW2+9laeffppzzz2Xs846qyqBYaUbKFJUKpXVfwqxZDlkWVDeSGyJv7wDf1Un9qxWgs298XyulXiuFDxE0iF/61L8lZ20/OhcU1bk0h7FkiWkhe1msN0UKvAYlJeoHNEQE4mWfwO0CdfN9xZNkr3KW5Pmedpz2kZuSG0fA1XFE2M7SWw3DVqVmX2H6F+Z/+vv61bUGMVAug04jZCefiHCrp/p3W4AYUW/lSeGcDL0v/w/eN3PokvdJKdfRGHjn0AZoK1R4FRd/VQXfHTeMxYUKIez9xbI/eJJg5kSRnkUroXqLeAeOM1Y+yqwR2qghEy7lJ7egD23DdmYjOeeCcUHYSfJLHwTOggjiYXESjWh/SI68LBbpuB3b0G6WbL7n03QuwWRyKAKfchESCqrRehqrwYo1x2vkQY/NfdQtv7qo0x898+RmRY2//zdpHY/AgBn3BwmvucXde/TShnT/Aibfu0JXSk9auSzEMR0/LtasfhHjfp4rdo9Urmv5XhVRaEEHkiLF+75NhtevJvmSXvgFXNxSgknkWXrqpc49t3fp2XyAnPiwpBiuammsJDqCRYthAeefRULT7w8ZhbVysdJNPDkHz9L5/rnOe69vyXZMI7AK5h8MGFI41g5XCqlcu7btj38+AlBJZ/GsBKZggIVn2ixaui+IzNwZZ3DYQu0Lpucdf1rI3I51y2DDa+//npuv/12pk6dymc/+1n23HPPmETOnIoV3d3dNDY2Ytt2lVXtn4XfRkiBNa2JYHWn+UKF4FVL4j23kcQJuyPb0ibxnDeCNaHoI5IOwaZeer5wF41XHI9sThn3y2gObeEUCLwC/Z2dCKCY746zw8dRHYUQQBuCgnXBq46oDBQNlx9N0JHDe2qDudYLEAmbzndej79iu+Ei8YLy3BMC2Z5B9xTMhiqFcRNlE6gtfTR+/gRSZ+9l3BHh7jTQu4Vc13q8gkdxoCts4zBWoco9pRSYax1rRNKyXStRhIeH9sDPrcJu2BPt9yOsBFp5CGGj/D5UCWNhGVSExs7Mxm0/AlXYipWcgNt6MKrUOdjqVSUCa3IT2lPIkKtM+8Zy7C3ZhP/yVpquOgV/TZcB3mrAD8pEdBU8ZTLMcdTz+TtInbs32XcfZBQWiIMUrHQzE9/zy2FHo2/xTTSfcBktJ36kTjfrp/8ZSkbIPaRoPOLtDCy9j1X/vh9aQ3b/42iNIoZ0gCoOYFiCRHQTwk2NuhG1J/SxWkr+US0i/5Idk2hDsyyX+cd+iMPf+v1B11z/ud1icqnYKKACVGAWhuhlK2+ORvHNtk0nW8OAu+a5W9m+9mn80gBrnr+NQ8//5i7tT/lkKOnv78f3B+ceMQoCZjMLVOh3ruhc7bWhYqOVRqQd4yfO+eaE5FrlU7wly1Eg4Xe6UHFijsz4YK53bZPnJEqO5lUDMKN+BEFAqVSKx/eggw5i0qRJHHWUcX8EQYBlWTHVfkdHBx/5yEe4+uqrmTt3bhzmvKvDnUeb4DHqyy5ZTyJl3rZJHDWbgZuWkL38aESFub1wzysU7nmVzXt+zWBSMi7CtcIwWMp4hECBZ56/6hpg+wW/InXGfNLn7B2fjrWv4vwvQzYpBOpn22aw7ymXkUg343t5nGR46rUkImljTzFKvjWlCZGwsSY3lhUWAxIz7eotVuBfzKe953hENoFsSJh2+8pc4ylyv36a5Am7GSKxfMgA61io7Y0mKWBYTmT52e2QSxjo2UTgFZi0+9EAyHo+kVCq9oSKdCx/l71CWAgbrOQkhLQRbnP4tcHMWXbauHdE5VZsXmJVGsCdcByJ8cfFv1jp6aFX450I6YB0kU7ZBRT1uf1P76huRjjfRDaBGvAoPrIa1ZEzypwlULkSzrwJWJObzDsvBfiK0tIt9P/gUVJnLMCZ20b/Dx/DPXwmzrzxZr6FVBSFVYvJPXe72fudJA0HX4DdOJ6uv3wXmWrEnbgb+ZcfAsDv3YpMNhg3kZsOSWtH/0xGsLSYMMVJ77+OlpM+ivaLJGcfHIeTBgM9yER94OtwE6Tebzs6if4RLCI707Z/hP7tShntwhL4BbyCITws5XtxEllUUMJykvjFfpQKT4nRDRXmdq/Qj5PIxLThpl4VU4lHZb5wz7d4+rYvs+8pn2HcrAO5/+eX0rPpJQ694Fs0TdzD3KcMwFTIHYsgirIZ9/X1ceGFF7JgwQL6+/txnLBtkcISaGTGhYSF7jSJ04YKI9RKG/OuLQnWd6P7isjxWawJDSbpWnji1bkSwrURWdcoLpZEjs+a8Q+UOSEnbWPxtSSqawCdKxnTc8k34a9KDwJPRspLImFoy2fNmsWsWbNit1GEb4nGfv369biuy8aNG5k7d248jplMZpfQ+Ef1+FphRS44EX1fW67GErswX1F0lkOTvuQActc/S9d7bqDxv05GJG36vnY/xcfW0PzNM3H2GG+wL10DbL/kt1VAWDDPAEviPb+Jzvf+nsQxc2j60snmFD3KSEcdulC1VjRP3IPDLvpu+cdIRywq04a3/gaRtBEJB5336HrfH8CWOPtNIXnMnPJ9joxftAis2/jpY4dsQ/7WpTR+9nicvSbWb2OgEE50sFDsdcLHq39XgSGNG2KNiOavsCQ9n78Tb+kWWv73bKwpTWas7OG1uqHWoB1RerQqEQyA1/00VnYuOshjJSejilsBhQ7y+DnQQT66I1bWnKY55F75FoX1NxoDgddNZs6HcMcdjd8N2s+hitvwewdb5nSFNdZEykaWH41sSWHv3k7QkIjdeFYpwIrwVsI8g/7vP0Kwpov0+fuQPN5QD5B2Kd79Cvk/PE/DR4+CFoHAJv/qI2z6yX+SmJxCFfI0HvZWU5TtGqyLsBCOAeLIZMMgdvKxjOuIuYfMwqlIzloUVmBAUUiLzN6nVFVae2/lZ71yo0/f30WgzTegCCEolUpxhtqxhYCLOENtqVQqb2T/hBK5R+RotW4h4hOjtGyElAgiLIQcfKrWZYUkkW4OvyqzU0ppEXg+2zc9x+ZXHuKlB39CKd/DEZf8iD2PfA8Ap/zbX3j895/i1q8fxdyDL2HGvmfROmUvEpnWneq7lBLf95kzZ05MZ19XVvTAmh44ahSRWUXwvvkgalsOObEB1bmFxJFpOG1BfEn/6deROHIWzqePjL/Tv19qFJikIRjTHd0mY+xACXngNJjXivf1Ryje8wrZu945qv4FQRArZ5USzeUHH3wQ3/d5+OGHOeyww2Jl57LLLouvqWTTHYtorbHDeZJyEtXusGE9YbvoNB5ZSQoeztw2Wn5wDr1X3MXWI42F0GrP0Hzlm0hfvH98i+ovGqxBVH24AfmrO8ld+xT5PzxH5gOH0vDhw8PNeWxriumbNGzQfpj9HI0lXAQWsilJ9gOHogOFLviIlEPmPQcbjER3HtmUrF6H6rgqVE8B2ZRky4HfIXH8XJq/chqqtwAagnXd+Ks7DXFdb8G4GwJllO2EFYe8mvZKAq9IBMCV0kKGPCcjulLBsA8/uIqmr55m2ExGGKraDTQKTce2RpXMryxG8ZCJNhoWvIX82t8BGu33k1/7e5JTzkAmxwOQnXc2diZUAkOLi9aacW96FR3kEFYmbJMB7wI0HXQJdsNuJPQpNO67Iup0ufv1ki4CqjtPsLYbtaUftbkvVlp0wQfXwprTFnKnWDR8/KhBvUocOI3EgWXWelXMgQXa95h+xW9oPPQiNv3orQS5bqxMq3E5Kg0G9R0Ocg1FyhgP5iO4h8IHJIwZiDDKIpKWEz5c9RDr/T0aS8s/TYTAEBKBDKMMt6ORyNff0GBMtpWMof/MEoEzR6e4RJ9heGV8vRh0iXSS9Heu44FfvoPerSuYc/DFzD/mgwaE6yR56pb/ZO3zt1HK9xD4RXY7+BLmHf1Bsm3TCbwCgV9k/KyDOfXyu1m1+Pe8eN/3WPnk70g1TaKhfRaHXvhtMs1TDCh4jBL1dWBgAIB8Pl+FCVG9BezWLJ3fuZf8n5cxecXnCHrzZoGvACyaqC3j9ul67/XgK1p/dTFCWqiN3Wx+3x+QNz2FbEohUjYDL6whc+AEmoEgVwBbki/mEM0pig++Sv+3H6L1Z+chLAcVlHDzOZK00tvbQ6GrhxSg+vIm14wa/O5HVpIo/1JlO6Ns2HfccQdLlizh0ksv5fbbb+eGG27g4osvxvO8nc7ZZAJcJP1egU5K9PZ0YzsOrrSxhGDAL5UPZoR4HK2YmG4iESUq3EVgYGEbV1zy2Lm4t07GW7YVNNizW7EmNcZJFEXKMblcIkwSxEpLsL4H7+n1tF5zIe4BU9F+ECetG+1mWrX2SssAXGt+tyY20PzNM4cowUjh3lfL/1AVG1KkD6bDA0TKKVs2tEYkbNp+fynOflMM7tGxYjr/iOKidh+xnOpIwSq8ior8pBXPKFI20g6yMWksCCHHjM57Juqq6pmG90tp3HJR+UqbtoftHymZX/XYhkDkzGxaDv19+RpVIrf89zTu913szMxBZQjpVJQTIO0sPc9cRpDfgLDSoAzZpkxOouuxCwCw0tNihaZ2rsZtDcc2eexcRMJG50rIlpT5PWGDXcKa0GCudSxyv36a0iOrwbGMW9kJ3cpeYMZDCqypLWQvNyH8OvCMAkOoyESKiQ4Vll1IlDOypSXwzIk03BiUVzB/Ww75Vx4ivcdRVS+9DjPlDodpqVRY8vk8L774Ypxg7Z9NhDCU5dlsls7OzthaMhI4OTqZPvvss2zfvp1CofBPO0ZSSorFInPnzqWtra0qqmRoEbGCEHgFpLRQgWcYMOuFtUQgQMtFOolydEH4co2fdTADPZuYtuBNTNztSJIN4/BLRol49vb/Zt2Ld3L2Zx9HBR5zD7mEWYvOp2vDElY9dSNOsoFkdhygR8x4PpREliYwyq0VkURZAlqzeM9uRD+7GTeTpPibZ0hfvJ+5UWnDkeGEkSkJibd4PerRdYy79wNY0kJ3F0hMbqb5A4fR+6V7aL7mAqzpLQT3rMRGIjEnWBIOziUHAuA2pPH/uJTGtx04qK1uMoFyHSzAagiV6SG6XRkRFWV+tiwL13V54IEH+NWvfsWZZ57JaaedRiaT4Wc/+xmNjY2cfvrp8T1ROZWfI4kQgkAFpGyHl7u38sXHbyPoz2O7Dqt6Osh5RRa0TqakfFzLJlAKx7LoKxX4/IGnMquxnUBp7F1Eeqa1wQloP0C2pEkcNrP8W4RHibhNXKvMewLxppk4YhaJI2ZV3ROVXRcHVVlGpFMMc6qt3Kwp+UQMp+bgilEGBjxkS6pKURGuNTivUeSekALZYhQj2WTmSurMsrWvMsdMbdRQvXZWKRSuxVATT0R6TqCMezTk75LZ4akSYsUkbIu/YjulZzfi7j/FYHDqtLGyrZUKB0ERjTKfOkDYaYLcKtOs/uVYqcmoUidCGGyKEJb5lBEY3VAvNO7zjfLmHxKFqsJWrNQk+pZdTffjn6Nxn/8xdTJ4/CKXWOnZDXjPbsSa2WoyMjcmEYEyfD9NSbxlWyg9vpb0hfuSOm0eyTftiUjayIxL96duI33BPriLpqGLfvifqmJcrjo0xlYVHa7H0X87LyNbWiynyrpSBjb6dN58Jel//0t5cCoSy0UU6ENt0BE/Q0dHB1dddRWZTKY+CPGfRLTWpNNpksnkiP2MIjASiQS//vWv/6nHRSlFMplk69atfPrTn+aYY46J89IMK1phOWYRjNw9kftHCBm/HvFh1S+SbZnGkW/7SUURQXzSnLbwVKYtPLWqiug3YdkEnvE5Ownj97Vsl/YZB9A+44Ad6Xb9LlVuzBJAoPoK5G98gdzPHyd96f7Ys9rovfpe/HXdZN51INa4bHyKihanoHPA8HU0JeITJ4Cc1GiUo/kTTIVJ25zwCbF3GNeESDoU7ltOsKWP3HWLybxtEarHWGJkmFVdeQHaD1AdA1gTG4a1qMbjGSpluVyO3//+99x9992cccYZXHDBBXiexzHHHIPWmmuuuYbVq1dzySWX0NzcXFXmqLEEWuNKm+2lXk4cP5N3HXwOyg+QtsUz29ayrHMzF+9x0JD3K62xpRy1kjSSxO0OLS5m3AUBCunaZl2NAJAaRMYtP1etzXopZIhV0IMUlkHjL0RVGWNyi0gBUYRI7YVReZaIsxSrngKqayAsqFw/gFCa/P89jVrbjeopIGyJKvoIO8QNJWxUR47Mx44gccSsMjneMOMYKWy5656i9PR6ZGPCgNTjrob9sCSqu4A1pYneq+41bi1PGaWkTFVs2tRXxN13Mpl3HGjGOIx4yv9xCds/9Tvavv1WGj52pMlCPUQyv/IYGloE7LRpkp0tt79xAcICu3EeQrpYycG4nrLVxBB69j7/H6hiB8JKG8UkxOcJO43X9Qxu26RYyRlWtFkj/Fe2IVvTDHz/UaypTSQOn2kyPCdsRDaBLhjSQn9tF71X3o01uQl/eQe5a5+icO9ygg09pM7ei+Rxcwn6esHF4FbC9Ve6qZgV3yRYdkybd1EY+oiWluLa5/G2rzEgGq3RgY+VbSU191Bk6MuPFRZpUdq6AumksFsmx98NVTYY03F7ezupVCo+if2ziu/7Ma5lOGtL5QLS1NT0Tz0mSqkYw5BIjIIwLloz7QS5rnV0bXyRXNd6LMdwTTiJLMovxZbBeOSENLTeQHGgGyeRiedm54YXyHWtI+JfEdJGWja+VyDdOJH+7avRKqBjzVMUB7qQlktMdhWGidpumom7HbHD4xCfZgFsSemR1RQeXEmwvhvZlKTl5+fj7G7SKTj7TyX3iyfo/cJdWFObsfccR+qM+XHODmtGMyLp4C3bSuLQGTGHRumJteBYDPzheWRDwkQP2JFrDdAamU2gOgcoPbWehk8dw8DvniN15gJkc8pgLSCM9MjRc8VdiJRD0xdOjC1Zg/pEeT4vXbqUp59+mocffhjLsvjgBz/IEUccEVsVgyDg2GOPZcKECVxzzTV86lOf4uCDD+bAAw9k3rx5saVx1IoLGksI8uFz91TAlv5efvPKk6zv72J20zimZJqZ1tDCgFfClhKlNa5lI3fxOxcd0gSCgADLtY1iJKpByeZihb++K6bNl9JCWsb6Ku0KhXw4AGOgCNZ3lan4R9C9Ro0riKw+R89h4oufAsA9YCrWpJBlOsTXaAlFNBw/m9LzG/HWb0doCLTGsW2EhpLnYaUc/I4+EkWPxGgaCsai6Fjkb1pC7ubFWOnGchRSpWiQTUmTmPLHfzMKfG33lEakHIKBXtKn7kfmHQcaDEtYlmhIYCdbEJGFZlgYTRl3EhQ2U9hwM8JOGWuuDhAySVDYSFCA3PLv4zTvi/Z6zWYuLLTfT2Lim7Azs4xyohUISf9L3yIz9/007vsd/NwKpJ0x80UHCCuNdFsG4USr3j1bogOFu98U3P2mlIen5OPsNQl370l1+yNb0zRcZta0zDsWoXuLyNY0hb+8TPHBlSSPm2vGClADPQjbRGyWNr/KK++YiTs5S2FVP9P+4/uk5lyEGwYvCHfnWOtHVH06bvo8nX++FbsVUBD0Q2bh7sz62svoYjkJn/YKiESG3gd/iTN+Nk1HvSv+bjjRWlMsFrEs65/aogBmUo8GJV15TalUeh1b+PpLpMQVCoVRhbjGyPpUE6ufuYlbvnJ4HMqstcJxM5QKvYbxtkp0jH1xklmktPG9PLaT4ulb/4sVj/+BdHNbyIgrwqRkRhl3kg1IO8GtXzsytuKIivYo5eMkslz8tXUxUG6souP/YWi2SwH27u2kL9kfe1ozWmmjNGiQDS6NnzqGoDNH6fF1JqInV0K2pNFegLPHeLLvPoieL9xJ8/+cgT27jcK9yynct5zs+w9BbR+IswPHobciNI1bgt6v3oez1wQarziB3i/fTde7b6D1t28tR6h4gcE9/HfZMjWcVVUphWVZrFy5kieeeII3velNHH300bS0tMR8LUC8BsyfP58vfelLPPLII9xxxx0kEgn22WefcLzHtj5oiBWQrz51J2vzPSxsm8LuTeN5fMsqVvV0cNzUPThz9j4oreNrdxkQN5REIhEfWlzXjdl+H3vsMXbbbTfa29sJUFiAaEnR9uu3Iqc1A9Dd18MLL77AEUccge/7MZdNXUUj2mxb0rT95hLsmS3me3vn+1K1KVoSqz2DDlScD0krhQiVMWlbJIHkf75p1OUHgVHmRhz70BLT+PnjybxjkclvNBR3kReUXUlDlSkFFH2syUbxEq5d5qjxFLpQijfnUXs4lIcqbkEEWcMOLkLcjLBoP+5aVGk7QW51bDVBOmivB4LwYFCpxEoQiTaElcBKTUHYDaCNpQ4doE1WXISdHjR2lc9LewEEGjVQwmpNE6zuMhGJe09CdeURKRMpSAjOldlEtTttnLEY2XPb8V/tMOWHeKOGg85DhDQT7W+5ksYjlyOTDah8D+kFJ5ry0k3VbdrB92tEpWXie65hysdb2fyzd9F8/AdxJy9g80/eFg6mHQJ0QfslSGSQqcayJjXKRkWbeUQ29c8uo+ljdM0/C7nWcDKWZy8tY3Y8/K0/4LCL/te4hGrIiXwvjxMyLUZ0/wZbEeIjlOGysMKTwTHvupaj3v5z47MPyiRPRnGRhmhOB1h2ohpSJqRRooQxSUs7gb+jSmaFpUImEzjHzqn6WUhRPu2FYrVmSJ1SkVtKG7eBBho+dQz2vPEMXLPYlDkuQ/M3zsSe3hxf3vulu1FbzMFDlwJk0qH/h48SrO2h9VcXoUsBjVecSM+X7qbjjF/S/K0zcSLXUhSNkCvFLqOh3EPRHD799NM5+eSTY+B9tAFXXhtR+ieTSY4//niOPvroOPJO1vDrjCRCCEpBQLOb5paNy+gp5fnZcZdWXdNdHOALj99KeyrLYZPm4KkAZwfD1+tJpIg/+eSTPPPMM2itOeSQQ9h7770BWLp0KePHj6e9vR0/8LFsi7Udm3huWgdntZmIonwhzwsvvBBbpaK+1Urk2tFaI9MO6YsM7sngNHaehLP2NE/lZqh1WWERgt5SgXvWLWOcnaZroB9bWGSdBBtyXezVNoWMnWDx1jVk3QR9xQKHTJ/LjOZ2AqWwhlnz4g1YadxF02DRtCGv3RGJyfNiDBCYF3y0IGfj1rHS02iY/4Uda4MOQlyLmTvJKW/G2/4YXY+dj1ZeuS1aIawkqtRJcvIZZOZeBqoEVqKirApF07HAIQY823uMx5rabPrZkIgB01XPOMJFSQElQ2qILbFCZVg4CTSKxDQzn3XgkVl4MpmFJ1f3SQXxoXFn5+GISouo4GGRyUZkIh0v7MKyYgyLlTGd0H4JdiCC4l/yLxlJKl+mCMdST2KSLIhfFMtysd1qq19UVm0Exd9DhBRxhEPut0+hn9gA2RDXY8vIZBAD65CYBSVQaM+wWTb8+7Gxm0djMrdWZm/VWpuwUymQ2QRNV58SM2bKxiSFO1+i9Pg6Wn7yFrNOhyfapi+ciL+qs8yuOeDFLqfI+jIS4F5rHbuBfN9HSlllMag0Z0ff+74fA9cji8xYMCbmbGuwKU9tXcOHFh4DQH+pgGNZFHyf5kSa8+cewL3rX6pSWnZWIjB9IpFgYGCARx99lPe85z0IIbjuuutob29nypQppFKpWHGLFJz+nj62Pb8Kwkh0y7IGRQ/WW/gHKRVF3xAC7kIm2KpTcuVmGLUrVOtLgc+q3g6ufnUxF+1xIOv6unihcyOnTF/ADDvge8vupcFNMq9lIhtKPeylShX3D72xxdYlKULLgRqVQlFXdM0/wughU8HYiyuPjUTrIOReqdcHA7Ct1yAhkzEQN4oGaj3splHVr8Os0vWscFXuItdCK0X63IXmuzBKqvJdjMuotNAlzbvo7jMZd5/JYYh6lCfJB4RhwfdLcWoVEWJZKmEiQ4KrRzlHR0bGRAqI1lWnUABVHKCw5hmE5RD0bye959EhaPefL8LlX/L3l8qJ7ZcGsN00D177bqR0OOLSH+EVc3GOEkKCsGhxKOa7yXVG2VCrF8boBVO+xx+vWkRpoKuKeK6qDZjFwXaNKfaYd1/H+FkHx9T+OyyaGA/gHjQda/7kkLRLowc8rOnN9Hz+Top/XcG4+z6A2tZvXDuCOMw5ZsQUJrpCo9G9RQZ+/TTei5vNNZZESJDtWbIfOwKZdmPwo3vQdBIn7RFzPKjeAv6rHTHnhurKY01vpvHKN5H9eLijOsNbAiufWYRJqbWuVI5z5fWVkXajxlwMIQ1ukhU925jTOoGsa55TIgQOrurtoDV0Y4+FNXck8X2fTCbDsmXL2HPPPclkTB3z5s1j6dKlTJkyhUKhEF+fThvleeWqlXTkuunP9ZPNZLFtm2XLlnHttddy0UUXDVnfoMU/6dT/fiel1v1QWb8Vsku3p7J8Yr8TWdvXySf2OQFPBXz1qbv4xL4nAHDDy4v5n8POrSo30ApbVifNC4ZJkGg5VpwVuExyONLeI2ILUT2Jx2ioYoYZvupxthAVANwxFDP4eYUKXf10IZHbScau82GfcVSulPHhp54VbkhLXri2EIWD61DRDC2AKqRGkW4Kv3sT22+5kglv+z6q0GdyFIbvenSgELZLxEg+2rk5stISDZSQEGpLkUuotOlllr9/f+wWKK6H+X9cjUw1oP06eRT+Jf+SnZR6EzvfswVpV5DKCUFl8sLIPTR979PIthpTcsShUD61RSR1DvOP+SBBGNavta5ZXYQxyQqLwC/w5B8/F+Npdib/UNS3CI9gzxlX98V05o3HX7HduHgq3Dx1yxKAr+i+/BZ0rkTjVW/Cnt4CUqC2D9D//UfYduJPaLv2Quy57WhfIVvTRtnxFf0/eATv+c3Yc9sRCSvkxNAmqd+0Jho+c/yo+1W5ANeCc2Hwxlfv+h0RrcGWkr5SgbfteQgfu/M6SkJzyIRZpG2XPq/A7Wte4LFNK/nKYW8GIDEIC7VzIoShPKjkqbFtm1wuV9FOs5B3dnZy7733csABB9DY2MhPf/JTPvKRj5DP51m4cCFvfetbY4xbPSVkKMVuZxW+0UplmwKlUGjW9nXy0xcfZlOum55SWUELtGJFzzZmN7bT5xVI2+4ghUUIEQOVh5LYnRMT7I2mj9VzcEh8UN0Khyl1DOMcb97DWZPCTyVcNBoVWlC11pRpHaJcfQGCoKoMEX7KkNleht9Flqkyf05Yly6XOqw1JErjESosxiITRgtV3GNl29Ahd4sMrd+Vv4t6Ze9aS0sAsVvI3OZOmce0z9wHGoKBLpz2GQS5Lpz2phGL/Zf8S8Yq9Sa05SRiJUQrn4iOv/KewC8xftbBjJ91MCrwQxrz6hclOn3MP+ZDo27PM7ddyY4CbwdJxelOF4pgOWZhkoZuXzYmUb1FdM5sWiY0OQTORUCbKIzUCxCuTXHxegp3vszEZZ+Ks7OiJbItTdN/nUzxrysYuOF5Gj97nHEzBQqRsOn79oPk//gCrb+6GHtGS1Uzg+05ut55Pd0fv5mW773Z5B8aIUS18rP2+9FevyOi0dhCUgg8xssk/3nQ6dy05nnuXPMiBd8jZTvMbmrn6kPPpjWZQWmFHOOpbziJXESpVIru7u74e9/3Y6tKpaVp8eLFzJw5k9mzZzN79mwaGhro6OigpaWFbdu2VfdtFwOFd5UIIfCCAMey+PkLDzGrqZ2DJ8wk5xf51UtP8NDGVzly8m6UlE+jm0QIQYOTrLaiqgBbWqzs6eCCO39K1k3gqwCBQGlN0nbYMtDLj46+mMOnzCW/oQu3z0e7ozg4aNBegDW92VgZX+dxHE5ZUdooJlSoJFIILGFcMNZOtjNSJCFSGIwiE0fKCVGlSFS2s/4Bw0QH+10bWfXpPRB2wli5I0t24BP099P/zC0mkrhSWbRdVK6TGVc+S2LqQnTgVVGrDCcjY1pCUyqWi0xGuQlCimI7idM+EwCHmeFPNsOqo8PIWPzV/5J/LtnRZ68CDyu0lFhuylhbaky/0e9a66EVlsgc7RUoawG1bTKuJMtJkO/bFuY3qmWE2UGp7L8lwZHlU0mEG7HKgEDhWjEb6iAJr5FNSUTSxluymcThM+PsrGBcP6q3UJFvRMRt8F7ehhyXHaSwAFhtGezdx+G9sGVwu9+AYrxuZj7s2TqJz7ZPrnudAZCWzes7u5lpbXIs9ff3M3/+fB544AEOPNAQ9S1btoxzzzWukcrM3scffzyWZbF69WqWLl2KEIKuri6am5s54QTjVtkVuZheS/GVUVhW9nTwbMc6fnDMxfFvUzItXL34Do6cvBu2sPAil4I2oemxFS68vhh4PL99Pc1umpIykaVKa9K2y6aBbnr7DYi858p7UL95HtGWQvWVhkxroJU2bs6t/bTf/h4Sh880OLJaYrzXQIayHCqt8VSAFAJHWnVD7bfm+9g60EdnIUe/V6TPK5DzSvgqwNcBfjiOjrSwhMSxLLJ2gqybpMFJ0JrMMD7dQHsyW5eKzwsCFBq3AoBez/o5WMJrE2majnkfhAELwnKMHiCEOXz5RarWU23wsKo4gJVpC4savaV6xKdVXPcCaEXQvYmBlx/G2b7OgG2JTraaoHsTQa4Td/I8Y3EZN6uqU8OJECa6QEpZZUL9l/z/IVrr+NmPbSE210b8LFop+revLWNaKuugDH5zEhncVNMgk3BsjnaHT5cQ4y3cdMiqOxaz9E6KLRGJ8B0ZzkwdZvt15k+g4d+PoeeLfyF98X7G1WMJ1NZ+cr99huQxc0hfekAFOM8U2njFCXR//Ba6LvsjqTPmm5T1QqC68xTuXY63dAtNXz89btMbXVSoWPWXCriuW/WkAqXMaVZK/LD/Vp3NZUdESonnedi2zdFHH82NN95ocFDHHMO4cYZzp3LeW5bFs88+y4svvsiCBQviiKnly5cza9as4ap6w0igNTawIdfF5EwzAJ2FHK60mN7QShBaE9K2y0iKvhSSRjdF1k3gKTu0tChStkvGTcY5pWTGhayLNbfdWP3qJPFEm+9UVz6k8n995u1QUXWBNkzRjmXFLsneUoFNuW6e69jAsq5NLOvczKZcNzm/RL9XJO97lAKfYuDjqYBAqwrrTHneWkLihuW60ibluDQ4CdK2y9RsC/NaJzG/dRJ7t01hQrqRbEWaBC8I4vchknou3RgXozVWupnxF39jJ8aoOvpzJBlRaem8/Wt03/VbnHFp+p+/A9UfkNknjMv3CgghWPtfBzOwbD12C3gdMPvbd5mbR+nbixIK7upU9P+SN75EE7VYLI7t+VcoHCuf/CPXLmuOsSiD6wiwE1n6O7pZ9OZPcfBbvkbg5auihka7SUVsl0or/FK+IgrgtbM4aK3NOaW/ZDhWRrpWiDgaIPv+Q0kcM4fiAyspPbzKAOgaEjRcdkSZEj4yBUsDsrOnNtP220so/nU53ivb0L0FA17MJkgcMp3Gzx1vwpxVGH76BnVVQBiNFG5uWTc1iDS0KlJIVN+3K/oUHcT23nvvOMwZoFAoxOzYlVbGTZs2kU6n2XfffePvurq6eOGFFzjggAMMl0mdw92uBtuGpTJWZTyyFExKN7Etb7KwtyYNALkj34/AhERvL+ZGNNJpjBXCVyp2DwVa46gATwWxqwNLEmzuo+ln55E6Zc+6VPvRuHS+/Xd4z20cEoi7q6RuJA5GKbCkxA6Vgq0DfTy8aTl/27yKJ7es5qWuzRRDwkrz3gtsKbGExJIyVnLMOAuEqMCGEBk+dazM+FrRXRigI99PoBTPdaznjjUvmOhC22Gv1sksGj+TQyfN5ohJc2gOo4UjhcipsFLXKi/xZ+CBkPQ9fj1B3zZSux0eJ1gOBnoQMsTD1j5wrRFueszh+CMqLeMv+gZtZ10RDpCJxpBhlIQMw06nfPIOVLEfYbloL487ZS+AmMp3OLEsi/Hjx5NMJv/pGXH/JYMlCguVUpJMhvNKDj6Z1L78Vji3Fp19FQtP+gSWnURHhEuDxKD5g6BEumlSeP+ORfrEdhUhaWifFQN9X0tLS5SJNfPBw2LuDZz6VslKjARgLC57jMfZY/yga3WgymHUlYpLoMAWJE/aneRJu9etJ9oYXi+X7lixLlIIPBXQ4CZ5cutqFj96A2KgCLZZPLXWRKn2lDZRKinbpaeU58sHn8HspnH4Su0yKv9isRgzP0NZmclms/HfQRBwwgkncN999/HTn/40DvOeMGEC5513HkDdTNn1wLimfzoEbRJ/VoqouKcSrFkuO/IaDo3DqBVHWngqYG7zeOa1TOTTj97EBXMXkfNL/OaVxzl3ruGe+cwBb2J82oAzh2IfFhX/RfuPCIGfojLOS2CAoVFSxIJvUldEhHNCmPDvlFMFeH+tpB6YPJpjTvisF29Zw3Uv/Y1HN69gVe928n6JBidprCOh5SVyqOiK5xgohV+uqUxKGcFSwv9XPltbShxhxWMWzQOlFc9sW8dDG5fz0xcTzG4ax9FTduPSPQ5mr7YpWMIoWbaUg/pTpcSoAOk4dN/9v+Rf/RvO+JnYLVNpPeMzZPc5tWpMRjNeI8mIuYfs5knYzYNpfrUKUAPdACSm7jVEQ+prUJG/V2vNxIkT+da3vjViQ/8l//+IEBZeKSDidLLtatNheW4pGsfPAeYMX2CNGHPk2C0ExiRq2pJIN3PB1cvj761RKOjDybAvtJShBaQJpjYNm2120AJgh0qIp8onHYEJfa6TcE/rsvUkzqIbhxRgFnzHqlJYovo8zxukNO0KEcJE4KRSqbiOkURp46Pv9ooc1j6V9x92Plt7O0m4CZRWJCwbW1gEWuFaNo60yHlFpBAkQ0CgLeUuw+xUWkeEKIdzn3POOfH3kZv05JNPrkr5ESkqQ2GxojLBYEqiE7IVEh+ORQKlzOY6BL5hOIldp+E78oGFR3Pbquf5w4qnsYXknfMO46AJswiUYrfm8VX31P69QxJaT+K8RhXYFh25MUdbfpyzaWztqcwMHltXQu4fKSwe2bSCHy55gHvXv0TB97ClRYOToMlN4odjb6JyBpc85Dei+ruKzErld1FX9yP6V9J2yDoJAq1Z3r2Vl7s285uXn+CUGXvxwYVHs984E3EZgaOrFJeQNTyKvnQmzGXSh35HadPLdNz4eTb/9B1kFpzIxPf8wpDQBT6iDqZwrPNs5CzPKly4qsoTIC3GXfg/Zjx8D+JziwbLGVJhicutaPi/ALj/EjAWFqXAsiSVFvB6kzxSXFRgIobqwWYrJfpdSAs5Bv9pVRk18zY6iu4q4Ga9lzf+Tgpzcoz4Eaj/ktctI+RnGa5fde8fJoturcIihBg5yeVOSLRxNzQ0jHm9kEJiA5MbW6u+D7TCEpJXurewZPtGzp2zX8zIGj/nXdT+WmtF1TwKJfq3UirmsgFiFlw5jKLtK4UKFbCob1tzvSzt3MSKnm2s6u2gp1Qg5xXxVEDGccnYCSZmGpnTNI49mycyu6mdjJPACn1opcCvUl5GOhHX/lYKfE6ftTenzyq7xUqByapdDHwsIeKNsHJsdljiDbreb0PdVEHEF9UvBbrgIRK24TIZ9v7K6odWWLYM9HLlk3dw44qnjVXFTZJwbRSakgqodcUJCC1K0WfkCqqwoiCoVUoj91D0fx2GRFdaayqtbr5S+BjXW8ZxIYzQuuHVxdy1dilv3f0gPrPoTTS4ySrixao5EI67SduTJrPwJIrrniM5Yz9yz9/Bmv86mOmfexCZakQrv4I8b8ee98hZnqUV87PUSmLaQnONPThUaSwT/F8uoX8JgFIaKQVr13SzeVMvliVJpmwW7DVxSJ/q4BxDI8uO+v8HKRa76oRI2ZoAxFwcgzbnqArvtcd+aW1I4IbaKGufRalU4qmnnnpN3mUhTNbzZDLJ2rVrY1r/keoSQhBoRdKyeTG3jTuXPU1LpoEBr8SpMxYwq7EdgM25Xp7rWMe5c/ajEHhkZOI1X5Pqzb/o35EVOpLK9AW14660MjgPaQGSpZ2buHf9Szyw4RWe3raOAa9oNiatYpcRodtHYECbUdTKjMZWjpg0l2Om7sHxU/cgFVoPK1MbDPfu1P7mVryb0Uk9+i4Cn+5SS8uOSFRnsnoPE00GkC/SDsaROLLUjosfjtu965bxqUduYmVPBw1ukiY3ZbA60X0Q58iKlILI4uXr8FNpVAS8rVBAiHlaouVIhAZRo95EIdNWhIup+LdGh8puGDWoVLzEtCTTeErx/SX389CmV/nmEedz4IQZdedCxQhAyOGmBrrRWjHuom8gbvw86//nFKZ95n6wbJOjaifSSoxoaamVqhOC8g1t7zCL2r8Ukn/JaMX3A1zX5tGHV3H7rctIpV0mT25kwZUTUUpjWdVzakfn1o7e/1rNZSEMn0fEmBpxeLwRZCQFL/q+v7+fb33rW4M23F0l0Rg5jkM2mx0V/k1gOEOa3BQ/euEhmn3JIVPm8sulj5L3S3xq/5MAYyJviHB6f88NtKLekRQCIUSF2wEWb13Dj194iIc2vsq6vk5SthtHjySs8IReW2aowESb4OreTl7sfIhfLnuMfdun8pa5+/OOeYeSsl18FWCJamxDrYWk8jcvCNg00IMjLZTWZN0E/SUTuDEh3ciWgT5AMyndFLu03J0l9ostJXV+i7xD8bXhR8LGW7KJvu88jGxNGXZoaVJliIRNsKYLa3xjtWu1jtSOhcFDWVyz7DE+89gfUVrTkkxTCnwCbeaZ1jpUMCSOlJSUT2fBkLGlw+fX5maY0djGlEwz7aks45JZGtwkWSdB0naxY2sV+DogUIoBv0TOL9JbKrAt309Hvp91/V2s7euk3ytSDDwGSiWkMBw5ScsxwOaKPhTDKKK2ZIZlnZs5/86f8J2jzufMWfvECmjFwJv/O8n4GchkA6pgQtLbz72SbaVPsulHb2XyR24IKf93PM/gmGdJ1YJvOVXf7WhZ8Fqh319feb3b/o88VvUkMnymMy5t7RmSSZum5p2gxn+dZGeeQbQZu67L0qVLCYKAXC4X59/5e4iUklKpxG677UZLS0tVssLh7mlvb3/NlJZIarEzw4+9jq0t41MNfGD2QYzLmnD3Z7atq7rOC0k0h2IEfS1kqINerdR1O4QEbtvyffzP03fz21eepKeUJ+O4TEg3EeiAQOthMBIV9YafjpS0J7NorXm2Yx1Pb1vL9a8u5tMHvIlTZiwAGOw+q3nWEefKtkIfX3riz9jSwpGSZ7atY2ZjG/u0T2ViupGbVjxDv1fk8wcaoOazHev52D7Hkfc93B2lvvArgLiRO9UMLLroI7LlsH4qxtpfuZ2+n9+P3diCypWMK1aY3EZyXBa1tR/VlQ8fxuBqa+dhpEz+cumjfPKRP5CxEyChEHjI0PqhtMmJJRAUAo/thQJTMs0cOHUme7dPZb9x01jQOplpDS240h4SrDxaUVpRDHxW927nhc6NPL11LS9s38iS7RvYPNBLo5siYdkxYDhSqvKBR8ZJUAp83n/frxHHCc6YtXc8/yoHJejbFrPha61Q+R7zfa6LcRf9D+uuPobOP3+V1tM+jfaLhoxuB2RYpUUpNWrg245KZIaOFrs3+iY8nHI1VlfDcDKasoZb5HakTX9viVod+IpiwUdKQam0Y66Q6BTDCJvb31tZjtxC6XSam2666TV/30YSpRTJZJJt27bx+c9/nsMOOwzf90fEq2itKRaLr7nSAsNjOwa1KwQLFgOPVb3bGZdtYn1/F/kgWlw1+Tdw2pEhcRKWxcMbl/PJR27kxc6NNLtpWpJpAqUoBKUy+2hYToyDqLAkxXWEn0priqF5P+skEcDSzk1c+pdf8J4FR/DFg04naTvxplxPcYkUmsmZZn52fDmj9uUP/Z6vHn4OjrT+H3vvHWfHVd7/v6ffunf7qndZzZasalu25d6NTXMDgyl2YkgooYSENAJJCL0XA3YMOGADxoAx4N7lKjf13suutu/eOuX8/jgzc+fevbvalWST7y95Xi/p3p07M6fMmXM+5ymfhy+//CD/tOIy+op52nP9TEw1UHKdsDZjHv0yrAYlKRdBtW7oRidM7KmrMsmiD16EEJinT6f1Dx+SUXre0LEr+ovoJ/rZzWuwP1domPy+eXDPRv7+mXtIGjJizPW8MC9T4CSed21sz+XExglcd8Jyzhg/ixObJtTUsrnCq/BNqXS3raoPZX8XaSKSn3HdZF7jeOY1jueqWUuxPZe1nft5bP8W7tz6Alt7O7A0g5imU/IJ7zQUbM+RmhXP48NP3MWEZIalrVPLGhefkbzpzf+CmsgAYLRMR9F8av/g97d+lvzW1X4ljz7kfETQoqoqlnV0aGisEuzm/qcBl+r61DItBM5zw4GF4b4fqdzqyb9ioomo8qL/xlLG6ylHAwbU0MFfQfFDcYPw57H0W3hulSrb88r2X1UBVaut7g7KG86PZqTfxyqhb4LnkU6nj6jReL0lCEEXQoTv/uifnySJDBxHX89xONZ7q4rqE5qBqek0+dwhiqIwLpEJWUVfR7qdMctIjp23b1zNp5/5Da4QNMdS0uwgA+5QFRXPB+2mr8av9IMIzGD+gubv/J2IT0OgeYrpBgoK31v7OGu79nPree9mXKKuJnCJfgZ09C917GVZ21QO5frZ1HOITd2HQAh6Cjl6irkyQdyxjBVVQamLYW/qQBuXxuvJgaVXhjzbLkraQuRKKKnImlZy0ZqTxC+Ze+TnMQwvUbTNhqrRkRvg08/cExbvRtJDgAwL7y8VmJFp5iMnn8fbZy4h7vuFuj4nTUCvr/ohlFq1061w/YV/dPOFADzPRqDg+SR9hqqzpHUKS1qncNOCM7hj83N857XHOJjrI23EQuCiKiq252KpOv2lPH+3+h5+c9kHQkCm+HNWfNapYXl1p5YTe6pxGdqemLuKxNxV8pFEKPvHulaMCFra29u59957qaurO+47QCEEhmEwODjIsmXLWLhwIY7jVHjN/7nMLUMcLiMSgJNgYtY07ZjZfKvtoYGMte2u64bcDnLBH95u+HprGCp2H7YbgoXhRFWhWHTRdQ3XLU+eQd+USg6apo54H8Mog5BoPRzHQ9MUtKoIGtcNnuHwE1GpVELTtCGawHLbbL/s0eXNqJbKfvrz7/iDvisWi+EY//+HlMdEnRnj6688zM7+TjwhWN91gFPHzQAqrAb/Y6QasHzntcf45+d+R1K30FVJdx+NKgl28gLJsiooh0AH4MBDmgwcn6jN1HTShoXtO4AqfrmuJyncmmJJnjm4g6v++AN+esF7mVbXNAS4RCUYR196+QF+dO67+PzKN3PXlhfJWImQwMzSdEquIx1RjwYs+td43VlwPAb+7SH6Pn6vZGkWovwwg8Ac10NtSyNyJcmKizwuRFX00JByBOhaSNg43Dxtey6WpvPNVx9hc287zbEUedcONSwAug9Y3jrzZP5z5Vto8Rf0ACQaqhZGb5WLd5E0DUHouyS4LH8vO2rj5od2kmqhKFp4vRwZ8rnZPtJNmzE+cNJZXDrtRD725C95ZN9m6nyHYQWZCqPoOWTMBM+17+TWDU/z4UXnUvJcLFXDK+UQTkmCkep+DJ6F8Px26OHx404u197ezi233MLkyZPJ5/PHdXELEokdOnQIwzBYuHAhruv6tnwZPv1GqO1rAYbgey3tSbATjoKUXC5HNpulUCiQy+Xo6+tjYGCAgYEBBgcHyefz5PN5CoUCpVIJ27axbTu8f+BUGE1nYBgGhmFgmibxeJx4PE4ikSCdTpNKpairq6Ouro5YLEYikSCZTGKa5hDwFJQR9GMAZIabZI5XX0dBkTEMEVq16H4unVjMKFNT+0AjFhsdKKgeL67roesqnidoP9RPoSBzmCRTJs3NyfCcKHCJfkbNIrXGSABWRuP3MZwE9/pza1mg3I7/CXU5VvGEwFJ1uotZPnvy9WFU1nmT5jI700rBdVB81fnkVANAGDHz59b4Vo/FIFT41g1P8w/P/JZ6K44jPBlx5y9aMn+Sgqmq9BTzmKrGyS2TWdE2jWWtU5mZaaHRSmBqOjmnRHuun409h3ju0E5eOryHLb3txHWTuG5gu265D3zfhowVZ0P3Ad75wK385rIP0hJPVfi4QBmEBzTw4xJ1pM0Yewa6efbQTn592c1869VHZRsRFD2HgutwVCouXwtrLBiHcn4eWlPS9DPcYwtATlcOtcFP1xHMhaOYX2pu9HxA5PiAZVvfYX6942VShiU1FX5ItSDQsOS5Yd5pfP3Mq4FyWHkFM3NFWa4EKIpGQJ6pKCpOdheqnkK1mkPgoigK6MM78TuDO1DNRlSzPrwm8EsRQoZeT0038fOLbuTGR37KvTtfI2PGKfnARfVNRXHd5L83P88756ygKZbCA1QrCVbyiH1Yu19lXY6LpmXq1Kn853/+J4lE4pgo9qt3sIFomkahUGDGDLnLCbQsUeR4vNTwURlOyxAccxy5sEW1PoF0dnayf/9+2tvb6ejooKenh97e3hCo5HI5hBDhpD+c6WakNtQyAwXAI/pP07QQwNTX15PJZGhubqatrY22tjYmTZo0JBJFCOnIWJ3vpxaIOZZ+liYPGcL8yINb6erKYZganjeMzVoBzxXEYjqbNh7G8pOY9fbk+d0963Ecb8RdsKoqXPqmeRhGeecnI45UXn3lAKuf3MW+vb309xdRVYWGxjjTZzRyznmzmTK1fghwCRbvu+66izlz5nDyySeTy+XCMRH4odx3332k02lWrVr1f4zO/wNFVRRK/txVcG1cVy7CU+uaKs4rODY5W4IaS9fDJIsKb7zFqHp+CjQhf9q9nn945rdkfMAifJAChI6drufRW8xz4eR5vHf+Ss6ZOIdYDUoKgJmZFlaOn8n755/OoWwfv9+1lh9teIqN3YeotxK+H4Ufhu07jNaZcTb2HOIvHvkpP7voRuJ6ZfRoAGIO5fr50ksPsKZjD3/56H+zsfsgP73wvfIc4RHTdbJ2kfGJDOOTGV6J+DiMqr89gWLJdzH94bMwP3zW2PvZEzJP1yjnuppzZYBd/Eo/vHcju/u7aUukpeOtvxgbqsqgXeS8SXP56hlvl5tVURkxVW3yF56NohrYfWtxszuJTbgCr9SFYjaR2/ZtjMblxCdfI7UrehLhFul/7RMIryTXUEXHK/USn3wNsQmXM7jhX4lPuwGr9VzwSqCVfX8URcHSdGxXjrXvnHUd+wZ7WNt1gLhu4PoUA66QeaM29hziyf1bePPMJdgDnfT87GMwCvZo2S4PRdPxioO0XPtljKYpIX/LaGTEszKZDOedd96obnSs4nkeKkUgQXbbt9Hik4hNfDPCyaHoCR9lqjXBxmgluK76HlGNSqDlAMhms3R3d7Nr1y62bNnCnj176Ovro1AokM/nsW0bTdPQdT10Jk6n00PKHOnvkaRWG6uPOY5DR0cHBw8eDPOYWJYVamdaWlqYOXMms2fPZsKECTQ2NlZoB4L6RE1Jx8t0FICW557Zw/ZtXSSSJq4z/E5IUeQ18bhBLK4jBBQKDvf/YbPULta6Bl97q6tceMmcUKsTlH3fvRt58E9bQAhicYO6OgsB5HM2Lz6/j43rO7jq2kUsXT4pBC5B36iqym9/+1uuv/56Tj755AoAGGhgVq9eTV1dXQha/v+gofj/i8hJ1iNtSh+GuvjwO8HqhT3QWvw5XVwC84yhauzs7+QTT/1KviNChPWTdZX+CXmnRMaM82+nXsl75p0WvsMl1ynnqoncv0w+JhiXzHDjgjN484yT+cKa+7l90zNYftSK65elKSoFx6bejPPQvk185vl7+cLKt+IID0Op9E3JmDHePfdUDmR7uX7OCn6+5QXu2PQ8/7D8EgbsAgOlIihw787XaE2kK2o22hknmLu8YhEUfVjHVL8z/QlGyAnD0IZllT4aCTRLf9qznoRh4AgPhbKpWgBxXecfll8S+ojUcmaumHeFAxg4A5soHX6S2IQr/HBhUIw0iubPR37fo6gkZvwlbm4Xgxv/nfrlP8YrtKMmpsifzUYUNdAcl+f6aLmGplF0HdJmjE8tvYjrH7gNFQWXcnCD5zMm37dzLW+euQThlMhtfBR0c6idT1QqO4QPfhTdxM32hGHRY7EPjjp6aLQPttaiHPgEBD4XFaKaaDhouo7nSFuqV+pB0f3F31eLlW14Y19Qh7NDuq4bcj8E0t/fz/r169m+fTs7duxgz549lEqlcFEP2pJMJsN6BD4unueF7Xu9d9xRABaYkYIFM3huhUKBrq4uNm7ciOd51NfXM2PGDKZNm8bcuXM54YQTQnNSoIHRdb1iR3EsACY424rpxBMG8bjua0yGv4+igON45HJ2+Hc8MbzqVu6ERYW/iuN4GIbGww9s5Y+/30hdXQzPE+TzdpkZV1FIpy1s2+VnP32JeMJg/oI2XMdD08sAbvLkybz66quUSiVyuVzYX47jkE6nGRwcDDWF1f40/9skeBeif78eMprxqCiSKyRtxHi+fRfrXvwdXraA4qeEUKuiLwQyS27RdfjkkguZlGoIGXPf6Gdaq13/9Ozv2JftpcGKU3SdCpOQoerknCLT65q55ZzrWdwyGVfIuciIELqNJNK/xaM5nuJLZ7yNE5sm8Olnfys1uop0MlUU6RxacB2arCS3bVjNmeNncfn0hZRcF1Mrk47FdZNFzZOIaQZnTpjNmRNmc+nvvsWTB7ZyzexleELQGEvy8uG9OJ7H22bKnESmquOJsaUHUgxJgDrSJcJ2wfHAGj3D72glAJD9pTwbuw+hKVoErAgszaAzP8jVs5eyuGVKCEThSA79/jysxlGNjH/In+c8F+EM+tc4fqSkipE5EdWoQ9Hi6OkTIH1CCHQQrr/5j/RdjXKD0OdzJ83h9PEzeWz/VuqtOCXXJVqz9b3tCOFhNkxgxtf3crQihDcs31stGXX00Ghf3GihwW71+eef59577+VNb3oTK1as8I9L5N/3/PW4zgCKGqfh1DsZ3PxlhDOAlphCqetZel94D2bz6Th962k8808V9rgjyXCalQBYBABkcHCQrVu38uyzz7Jjxw66u7vJ5/PEYjEsy8I0zSFmmmh21moT0+vtixOYn6KmIiBkVI2CmSgAKRQKvPTSS7zwwguhKWnRokUsXbqUyZMnh+DNcZzQr+GYgIt/alNzgkLeIRbXfSfbEUxjBJuismJeDOMPGpwrTWUqmlr2odmxvYv7/7iJVMrCLrnohsoVb57PvAXjsG2XF57dw+qnd2FZOqWSyz2/XMukSRnqMhLgBJLP55k7dy4rVqygu7s71LCUSiWamppYvXp16Cvxv12iDsuvlwQbhCONR+GbSwZcm7mZFm5YdjF93b0YpgFCZr9VFTUMDxXI87vy2ZBobqRx+npKtR/L7Ruf4b5da6m34j7pVxmw6KpK3ikxva6Fuy6+kel1zRRdG0szQiA/GjO7qqiYmupHEbncMO800maMv378TmDoBiZYqD/7wn2saJtOayIdgjxFkdEpApiSbqS3mKPeSvAvp1zO1155iDsvuiks919WXB5+94T0hQmcUkffYSP3oxDSDIS/9wlydwVyPNwNUBS29naQd2y0Cg2dP4oUQmdvR7ihs20tFwUi1wK4g1txcrv93wPfFw8t0KAEGhe3CHqC7PZbsHteprDvbmKT3oZn96BZLeH9akn1+LA9B0szWNY6jYf3bpZRTEHJQmZO7yvm2dnfJZOLFgbRY6kh9/XsAggPRdXwSgW6fvs5Wq75AsIugKKiGBZj8WeBUZDLRRf+0Ui12UVVVfr6+li7di0LFixgxYoV/qSmgvBwi900nPF7ep66HOGVcPN7w0Ho9K8nMe09pOb+Hd1PS3ueatZHwr1GX+dAsxJoSwD27t3LCy+8wIsvvsiBAwcAOfGapkksFqvQnNSSkfxTXk9NS61yo0Am+Lt68VAUhVQqJXehts3evXvZvXs3f/rTn5g3bx7Lli1j2bJloRnEcZyafi+jGWABkBBCcMP7lh+fho9CPE+gKPDIg9soFlwSSRVFVXjP+5czZ1450/HUaQ00t6T49a/WUldncfBgP089sZNL3zSvot+y2SwTJkwI/1VLPB6nWCwCR+8HVK0iHpscjddFqG+qeW2tsRP9bbg2FgqFUYKWo/cUCcD4aDQtnvAwVZ1D2T5+ve1ligODaIaO5wlimo7t5+tx/cUy6/scZKx4CBjeSC1LdBMUMMR2F7J8f90TIbOsEjlPUxQcz6MxluRH517vAxa52NS6d63FsRrIqIri5wayeevMxXQXsvzdask34gg3NBE4wiWhm2zqOcSPNjzFp5dd4hPLyfsHwOpfT3lTWObJzZP591PfTMl1wlDngmODApaqhyaW4yWKooTZyHN3v4azo5vke5ahtaTC48dDAo1dZyGL7bmhZgp8nyrPpcFKMMP3o1Ija1f1HF4LjDuD23CzuxBuEcX3Q1G0BAMbP4fZsIz4lOvR6+ai6AlKnU+R3307zee/SGH/3eT2/IzElHf4hYX/1ZQKEOuDqhPqW0kapm/uCtorx17Btekq5pgBqLEUA8/8jMLul1CsNDhFFCtJ46V/K5lyAVWA071XpgYy44zkuzqSHDn3kCLDPnt6ekYM6/U8D03TaGpqGgIaxo0bR0NDA4ODg0M6R9GTKIqKoicBgaKlEE4fWnIaoOAVD8vz1HgEqIywUx9mVxFEJgFs27aNhx9+mHXr1tHT00MsFiMej1ecGzjjBvV9PTUnx0NGAlBRIBOE1aqqSixWdsR69dVXee211/jTn/7EypUrOfPMM8lkpEoyGopeywY7XH2Cc4PQ4tdTFEVBeAJNV9m5o5uNG9pJpkwGB4pcdsV85sxrpVRy0fUysDv7vJns2N7Fq68cIJk0ef7ZPZx1zgySKSsMdZ4xYwbPPfcc27dvp1gshsDQ8zwSiQQDAwPMnj27oh5jkWh/1vKHURQ1MllEwYQic6IIMSqto7xCwQuvKSedrK5PoDGpbktQV094gEBVyvOBqqpMnDixAjjL+ivDXisXwLKZptZ7Gz2uqirFYpGenp4jczoJ2V5XeKSMGDMyzZTUOK6qMC5ex2ee/z0XT13Ayc2TyPkJ7DpyA6FvS7CAHj28OjoJtcGeh6pJGvgN3QdoiqXC0OZAAlPNv592JQubJ4UaltG+o9HfqudNy6d2v3HBGazt2s+PNz5DfSxByad3R8gw3bQR4782rubq2UuZlWmtMKkF1PCaIvPcaIpKgx/uHGhiLF0HcYw8LSOJJ0CD7C3Pkn9wA7ELT0BrSYErhssFOmYJxsegXcATHrqiEx01nhDENIOM3/bwfa4BJMPn4OZRtDiFA/cSm/AmvFI3hX2/JD71ev9aD6N+Cdb4y1H8iKHiofvpfelm6k7+JnpqJslZH6bv5b/C6VlD3aKv+P4lI4/msE5+tRqsZEV25+iYcjyXrGNLVbii0nP/18lteQG9vgnhFHGzg9SfezNaqsnvJ4FqJoKCwjJGWrtqyYigJVjoN2/ezGc/+1kaGhrI5XIVk2swyXmeR0tLC1/5yleGgJZgd9/f3x9R7QZOlI5/HwfQEG4O1WhgYN0/Iew+rAkBUvcizjoj7wKjgyFYcHVdZ9euXdx///2sWbOGYrGIaZpkMpmavja1NDX/r0rFTipiVoq2Och7c+jQIX75y1/y2GOPce6553LWWWeRTCaHzTQ73MQYfRbV/Civl7h+XdavO4RtexiGIFMf45TTpBo14HEByd0CsPKMaax77SCqptLfV2DTxsMsXT4pNAP9wz/8w6jLN02TYrFYM+psOImOV8dxKrhRFEXBcUu4wkFBwdDKqlQAVZEZq2230jylKmrFGxL4/QjhoalGuJjXulaOEa0mT0sIHiI7pGA8ZDIZ/uM//mPU7T5a2bFjB//0T/9EfX09pVJpePMQ0hej5DpMTNVzXstMaCn/vqx1KhdNmc/s+laydpGO/ACn+ep74S+08j5vnIQaaiEdIrsLWe7Y/BwJ3cLxZCbeUAujavSW8rx77qm8beYSShENy2gBS3XZ1fNn0Af/suJynm/fxfa+TmKaLnlcIvU4nBvgtg2r+Y/T3iyjh3zSxlq+NP/47G/5xqprKnPX+FWMOhePpb9GtOL5ZiC1LY0+rjGMOBqT00xERtIK6Irmg/Cqevqfnhj6PtW8t2ejaHGcwe0UD/yOzLIfopiN9L/8YaxxF8swZzeHNe4izKZTEa7U9KLFqF92G1brOQi3gKLFyCz+LvndP5XlaQmOREZXXSd3ONs8clOgKUqoTLAmzGPKZ54Pf2//8Qdxc71oqSZ/PCpl/5pjkFHNrqqqYpomyWSyQpMSiBCCzs7OYam+E4kEuq6Tz+elc5hhhPhD8eGuggbCRlF0hLDR03MRdnclWU5YZu2HPcTu6qd4Hxwc5N577+XJJ59kcHCQRCJBIpHA87xw4vt/QZtyrFLLzBNI4A8TOPT29fVx5513snr1ai6//HJOPfXU8LzhtC4jlXW0Uj1JyOcqhtjrgZA9d+P6dixLI5ctsfLMadQ3xMNookCC5IszZjYyeWo9+/b24QnYtLGDpcsnsWvXHjQNbNsJOXD6+/srQFsmk6Gvry8Ef+l0mvr6+qNqn+d51NXVYZpmCOxdzyFp1RM36nA9h4HCYVzhoCoaiqKSL/VTsAdpTYzHo+wrZLtFOaYJFkEPVdEwNIvBYjeuZ2PqcfKlgSHXuq6DZqgoSusQNuzAlyxX6qfk5qmPt/n2fPm749i4wpZalKp2qqjomsFAoRvHK1EfH4cn3ApNy3ASADrTNMdEcqkoCo7wiGkG397yNJc0z2JmYxuDdpG3z1pMo8+K++SBbfxx9zq+seoa+op5Mlb8DZ8LoiDD9Z/X73a+xs7+LurMmAQtwSZAkQ7DU1KNfGrJRQAhCBirOT8q1e+Z6oO+xliSTy65kL945A55X0FoJvKQZqz7d6/nrxeew4SkzO1U8hw29bSHWhlHeNRbcQ5k+3ihfRdNsRQF10ZXZKLAqekmGmPJ8L4jdxag+M61gCg6oNWYhxSZP0hJmohcCS9CKicKdiVr7mhkmIij4FudGQuzJwd9KHzT46Bd9JNEEpqOqoGlBCyODHPufYX+Vz9O3eJvA2A2riA+5Tp6nr2GprMeRlE0vMIhvz9kP1gtMux7YN0/kpj1ITQthqJZJGbcKMstdctQ57ATR+hbv24Hc30UHJl/yPF9jRTkBtHQNFK6FWpuvVIep+8QaqIe1Yjh5fog2Ph4rvx3HMytowItmqaRzWaZOnUqH/3oR3FdN9SuqKpKV1cXH/3oR4e1/wakZ4VCIbJ7Cx6cXf7U4v5gHCAx/Uacga04A5v885WIR2ZlhEK1ijOon6ZpvPjii/zqV7/iwIEDxONxUqlUuEDDUCfa0Ui12nq0crT28aOdgEZ7TjXQC55rKpXi4MGD3HLLLbz44otcffXVtLa2VrDuvhGT+5AdoKaEzrIBEAlASXdXjt6egvSnAaZMbQAkgZwa2d0pioJju5iWzvgJGXbvkiaHjo5BXNfjhReeY2BggHQ6zbp16+jo6OCss86iUCig6zrFYpEnnniCpUuXMm7cOHp7e5k3bx5nnnlmRd+Opm2madLV1cWHPvQhn2TRQfN3qVsPP8fOrpdJmK0sm/KXxPQURTeLpSX508bv8JtXv8R/XrsLgRtuAIYpCVD4yfOfZHf3WlbOuIpfvvQ5/vPaXbjCQVNqTwVCiNBBu+QWsPQEv3n1izy/+7d8/e1rcbwShmZJtbhuoFM70sv1pO/Cr175HJvan+YLb34ez7UxtNElxAy0u9Xmp5FFhgzHdIPd/V10JscxkzZ+t/NVDucG+MjJ5yF81X2rz0waJOt7ozcv0fICg+Avt62p8CNACJ/dVqe3mOfTyy9hYqo+9L8J7nMs72T19aavWXnrzMXcsfk5Htu3hTozFhKOOZ5LyrDY0tvBH3ev4/3zTwegr1jg1g1Pk9RNGaKNdN6dnGrg51tewNR0FP/+3YUs75t/OitiSZ+Nd4S6B65YnghzDKnxwHG6Rnv8XEGKoclrfFI5NXV06WlqaVqC+o5PZjBUTbL8BnOrz0bcmR9kQ/dB3jR9YUVSzuHGsmf3kZr/Lxh18yTvimqSnP0RlCCKSDUheGcDDZ3dj2rU4QxsxiscRIu14dl9CLeAFmvDbFqJlpjkXzPMXFHViWs795N3berMGLafxwsUPOES0w2a4ynpaKsEuZ2sciJEVUUJxqWmg/DeOE0LVL5U1aAl2BlWvyjhwPd376VSqfIhKQqKXud/1+h+/Dy05Ay02DicgU0oikFu23dwB7fh9G9ENer9c2s78AQ7skCr84tf/ILHH388XIBd1w2J1caqPg3Ki0p1jpUjfY61vOoyo74pwx0fzkY9klQg/ch1tm2HEUgvvPAC27Zt4+qrr2blypVh+6Oah+MNYKL3C0DJ4Y5Btm/r4tSVUyuOB+rHQ4cGcByZNiCdtmgbJxcjtYY6OIggmDwlwwvPqaiqwmB/ke6uHFdddVV4nqZprF69mmuvvbbi+oceeoi3ve1tTJw4MTxWLBbD3D1jbWdwjXSA1vn5i//Cnu61nD37XRzo28LXHn4n7zvt6zQnJ8vrPED4ZlA/vLXkFvjuEzeiqyaGZqGpBu0DOzhvzvs4dfpbKdlFFKHiuSK8VngCWxQxdIt1Bx7j92u/zqcuvGfY+qqKiqaWiSAdz0ZXDV7b/xC/W/tVJmROwHYLAOiqRbbUw9mzb2DhxPNwhYsW0JGPuoeOVsp+NA2xJLPqpX1oSqqRQiTSblK6AfdA8A697pWqXVN/DAT8HS+072JTzyFJ7OU7QQqkRmXQLjKvcTxXz1oKUOHAejzeweo5JHCwff/803nywDa8iqSGksdFV1Ue3LNR8sOg0JpI881V14ypXFd46KomnVmHq5sqtSdqXYyBrzyOd3g93WaJmKfWHE9CCBRdxd3Xhz65nr5/uh+1zkIcgXqh+nqRLVH3bxejT6of4sQbmLRm1DXTYCXYn+3FDHlj5DNNGib379nAXy08m1SQsyeyZobrkaojhBtqTaTmpWzBSEx7j/+tTOtf7hs/YaTZhF43X343MuADncT090XuaQyZt6N+Z6amcyDbxxMHtpIyLD8vlRK21/Y8GmMJJqfqceyi1PQZARCU7VZ0C6+YletVMSszOx9DosRARm98j8hIi3LNQnQdVVWxbbtqgVWpX/FjABpOuxsvvxctMYWBjf+OKPURm/pOms59CkXRUfQUihbzrxuaqAvKpos9e/Zw2223sX37dlKpVLgAK4oyqqSMI2lSgmujIcHRfEQBoKh1LAo2RlrUov1a6191bqHqukTLrm7DkcBF9e8BMEsmk2SzWX74wx+yfft2rrvuOnRdDwHs6wFcKl6kCFHdH36/kZ07urn8inmk62IV7evpzmHbMtdQLKZT3xCr6NNaYLWpOYmqyuSMuVyJwYESLa3Q19tPpr6OXC4XmoAGBweJx+P09fXJ8np6mDhxItlslng8ftRtF6JcJ8uK8fzu3/LU9p/xhTc/R8pqBOAXL32W25/7GJ+64B6/DSrB0q/IDRC6avDUjp/zV6tu5ezZ7yZnD3Dzz6dy4oSzZTkEGwwqHOE8/z49uQOs2fv7EXeBgX8M/i1czwXVoL1/B+sOPMrfnn83JTePEpBBQni+pmh4YowhrUctgTlF5WC2j/948Y+cPXU+v9vxCt2FXOif8cyhHWE+nD+XhO+b3+fPHNrBwWwf4xKZiqzNQTTKZdNOpC1R50cL6cdkFqol0fdY9xeaCybPY2HzRNZ27icWsKRS1rasPrSdXf1dzMy0MGgXSegGj+zbzH271vK1M69mY/dBnjq4DQWFS6adyLh4nc8aq2CoOvpoooekbQJiOoWHt5J9YRu5NLilEIMPPV+AkjRRDJX8PWt9qn9ldKhZCDB1vIEcqY+dBZPqQ+fesAjfpGdqOstbp7Fr2xpimuHzzcgIr6RhsaZjN3dueYEbF5xBwXXCJImCaqCo+RoJUQEuQJp4VLMRYfcj3Fy5jhCaidz8fvpf/SR6Zj7C7gdFk+YkZ5DkjL/0qf/L3GfV83bJdYnpKv+18Wm29Lb7TuBOtVMGJzVNCusM4A50yp2UbzJ2+g6y5b0nYTTJw+4gJE+cJS93HVCPzhP6qEDLWCVYUKPcJoFEY7TV2Hh5TDXBR5Gq2RRJFFUbGUIZsLz88svcdtttZLNZ0ul0aAoarXYleu/qxT6axTbIIRT4zRiGEbLixuNxMpkMmUyGdDpNLBYL/8XjcQzDCM1XAZ2+ECIkuwscg4vFIoVCIWTgzefz9Pf309vbS39/P7Zth9ojx3FCx+kgb1E1iAnaV93WaqmleQk0VPF4nAcffJADBw5w8803h47M1cDlSP18JIlOwkIIdF1loL/IKy8dIJEwee6Z3eza2c1NN59Kc0sS13HRNJXBgaKvAdKwLJ1UykLuuIeWEdSvvj6G6meVLhQccnlp9zV9n44AdANYloWmaaG/R+DHFYvFjokNV4nYkfHgkc23cer0t5OyGskWe0la9Zx7wnv50kP38cq+Bzh50oWUnFx551LWOVMXayYTb0NRVJJmhvr4uND8MxxgCBZFQ4tRF2+teU4tEZRdzQw9TibeiqnH0VQjdAhWFRU7cBZ8A80uwre795XyfGLxFTyy+VVydpFLpp4oOVxKBSzN4PTxM1k5biZQZsZ9o31agjKDJIJPHdhOTDN8R8gyYHE8j7QR4ypfyxIs9K/nhkFRlJDe/W0zlvDMwR1y940bAVMqg3aBx/ZvYWamBVPVUBWVWZlWGq0k/aU8//biH7lg8lxyjs2/Pvd7/mXFZUz0cz4F7T9S/YUnTT6id5DM595COjMH4eSIKxojxSgKx5XU/4ZWNQbL0XjDXCnPdzz0mX7qhxqBBYED8mXTT+TOrS/4bMpCXg44nkfKsPjPNfezqHkSy9umUXBsP4t2ZfuFECGl/RBrgm9tSMz4i7KpSLMqPtMLPkOp+3kUxUAx/P5VFFQtHq6rQXuF37yg34M63b97Pd9b+wQZMxEmtQy0booi23vZtJP8ciX4aH77v6HG6kJLSPNbP0dm1ftlyLMQ4Nqo6aAPjy7BLLxBoCUc+BFNSyAVC53f2OTsv4lcHCSKimbwrdTwBIDliSee4Cc/+UlIihdl8x2tdgUINRRB2GcAIgqFArZtk0wmaW1tpampiaamJlpbW2lra6O1tZXGxsZwAXu9KN2D+gwODtLZ2RnmQTp8+DBdXV10dXXR2dmJ67pYlhUuugHgikYNHalfgr4LQJ9t26TTaTZs2MCXv/xl/vqv/5q2trZhgcvRSPWL6roCXVd44rHttB/qpy4Tx3U90ukYmUwsqCgA+byD54kQ6FiWXuGEW6ut8bgRah9s26VUlP0TaJkCrVX1sSiwDUL+j4d4QrC3dwNLp0jiLUtPIIRHc2oyrmfT3r8jcry6nwW2WwzNMwBFJ4tlSKfTmJ5E+NEflTtNvx3Crbj2SBKYLQA8z8Hxo5FC85H/m+PZGJrlnxw8i9ETRB6NCAS6Is0pU8007z3pjCNe4wkRhucGNX2jLEbSDKPQVcjy8uE9JIyyaQgkaMk5Jc4YP4u5DePksXDxEcOvu0cpFfOmf+/TJ8xkYqqenF0K8+qU3UwETx3Yxvvnn05nYZDVB3f4TrdJfrj+Kd5xwvJwofv19pe5dcNq/nnFZaG2aFTPWQhQFUTJwVw6CZrHE4NhPKmOv0hAMXTjHDhCnzXhBE5umcyG7gPEddM3q8jrAmD3/od/wg/PfRenjJuO63l4CHSlzMJd7U5QWZaKEC56eo5fHy9iedAQwsNoWIrRsHSENpTJWYPNkuOHqsd0gz/uXseHH7+rgncnaLuuqOQdm3mN4zm1bTpQftdj0yrLjE1bQmzakpp1UGqYNEf7rr+hoKU6a3LwW/lTq4Ey1ZrnBw0MAMujjz7Kj3/845B7JGB1PdKuv7qjFEXxM00L8nkZuWQYBg0NDcydO5dZs2Yxbtw4mpubaWpqqnnP4L4jmoTE8DET1fWp/gy0NI2NjTQ2NnLCCSeE55dKJQ4fPkxnZyf79u1j48aN7Ny5MwRcpmliWVYFeDnSoImCkMA3KZVKsW/fPr761a/ysY99bFjgMtYFZyhgkVma9+3t5ckndhJPmNi2i2loXPnWBeUkjH4xgT+LrGvUJFSrXfIzzEKtKHiuKPPKhPdRa4YxVzMOH08pOrnQSVVQ3m3rmskT2+5goNjFa/sfJmHW+eVHCKtQQg4VT7gkzXqe3n4XA4UuXtn/AJPq52G7w4cLK0cIi6x1BcjJq+jk+Onzf0fRzmJoFjm7n1kty7lg7k3levpRCF4xi9A83/YvUHQL4RRBUSUBFUroyHc0EqjtA4bbI4njueHi80bmHgrHvF/app5DdBezpIxy1JDk/1DJ2SXOnTRH+hW4bpilF8rasuHev9G8l0N29v55hqrheh4nNU1kXsN4Ht+/hQaftwUINWpbettxPQ9dkb4pAsnXMugUOWP8LAqOjaXprBw/k0f2BUEWY+jlAEWqCl5/AZrByxXAlSZkJTbG8SKEz5CrymjgkapSI3oo3NQpMtIqY8X5iwVn8NeP34WmqNhEGYwl2WFHfpB3PHArf7/0Yt43/3QMH/wVXQddVVGpTbchPyPmI0Uh6ipBAGo8248SCuy/kdlPjRNYNzwfrFiajqHInEPffu0xvvbKQyGA9iLjQSAjoQolmxvmnuY/fwdT1eS77NrlpIeKinBtcO1KzZaioviZ1BECxUygjMJlIyqvG2ipNkNAeZc60jXV5oVoY6pfqACwPP300/zkJz8hHo+HwOhIvivRMoJdcvB9YGAA0zSZMWMGc+fOZdGiRUyZMiUkoAskMOcoiuJzKPgDKzLIjvdiVg2CqoGRYRhMnDiRiRMnsmjRIi699FJ6e3vZtGkTr776Ktu2bePQoUMYhoFlWaEmabTAJejbUqlEPB6no6ODb37zm3ziE5+goaHhmJxza5n/NE3Ftl1+9YvXKORskimLgYECV7xlAZOn1OM4LrquEQSECU+EE48SgpaRy1W18lstKI8Nwzf9vOUtb+GKK64AyuagZDLJLbfcEpqJjpeWxa85aauJfKlf/qVogMDxHEpOgcWTL+bti/+Bklvg6e13+vUO7NoyDDUwxziuTd4e4NTpb+OtJ/8d+3o3MFjsQVOPkGCOoUBMrdhADFWxe8LD1GO86aS/oeTkfQI5j7iZDp11hXBRfIc9I96AUnUnxazKSu65PoAZG3xQlHLk0La+Dn5zYAOl/kF00/DHcZkGP2eXOKl5IouaJ7G1t4MGK0FzPFUGLsegNRxtXaHcwufad1X8LoQIuXXqzDhLW6b654sh9wi+V8ylXglUg1p06SPNr8IrAWq4EDnCw1J1lrRM5on9W8rlIZ1oLU3ncH6Qlzv3sqx1KtedsJxT2qZz9/aXmJJu5PvrnuBTS2WI9q3rn2ah7xMx5nQJgZ+Kv9lQ4gYo2pigtnA8FF0le/sLDHz5cTL/fgnxKxeEx4e9rsa8FoAJU9NxPY93zjmF+3at4w+71tIYS0pq/8BFwk82WHBsPvn03fxh1zo+cNIqzpp4ApZW3qwHGpgAEAVrTOD0G82IHD7P8DcjNAMJghyRMoJLER6qkPfRVBUNyQT98L5NfOe1x3ju0C7SplUBWOR9ZNTagF1gRds03jX3FAAMVZWmJ2v4ZKRHfBa+5ue4a1qO9OKO5sWupWmJynAgJfq9vAOX/hsbNmzgxz/+cag9ONJiWcs8ZRhG6KOSTqc5//zzWbx4MXPmzKngqvA8D9ff+aiqFmo8yjdjRNNoxHhZ/nOMC3stBF7dniiY0jSNhoYGTjvtNE477TTa29vZtGkTzzzzDFu2bMHzPGKxWAgCa/V9dXkBcLFtm3g8zv79+/n+97/PJz7xiQqK9dFO9tFdXfRayceicM+v1rJ9axeZ+hh9vQUWL5nI+ReegOcJdD1wJpP3UlU17OcgLHrYrvUfguN4ZaATse8G1wVApXpsBekOjsWEEZXQP0CFeW2ns7XjOS5Z8FcUnSxJs54DPRsoOlkWTjwfgIbEONwwGVoZhJtajPp4m6y7HsPSkzQkZAqClNVIf6FTalOGfTyCklPwNTz4gFylYOciIco1YzVQUEP+lmifDBS7SFtNqGac0qGtZP/rI3QV2qWJS9PwCgM0XvpJeh/8NnrjZOInrEQx4iTmniW9LI+CvlQgEyOWXJfD+QHyuQHcgnx+hqqhqSq6opJ1inQXsgD8ZNOzLG+byhXTF1GMOEu+nlLdV5t6DpYp+/1jMseQzaxMC9MyUsNbHTUEtf3RAl+H6sUh/HRyEtRURJR4YdRKcF2wYJ7cMpmYbkrNRqR8U9XoKgyyb7CHZa0SWO0Z7KYjN8Anl1zIR5/8BR994hfYnsu4ZB03zDsNIEweeDT9BoDtMvDFR3EHS9T90/moSXNIfqEaF8tPVcHd34e97hDxKxccscxamtVKACOPfe3Mq9jed5htfR3UmXEKjnQ4Vn2tmaIoZMw4jx3YwhMHtrJy/AzOmzSX8ybPY0HjeB9QDBVPeGGW75DvJagbQZMU/5/qZ+cOKlaus+t5vNq5lwf3bOSRfZt4vn0XmqpSb8VxhFfRJk94aIom2aV1iy+c/lbJ2+La6JqBO9jFoVtvlJsLIZ30VV+76dkFqcWKaLsF0priFQZou+F7GC3TEK4zaq3qqEFL4ExbS4SQXBMDAwN88pOflB1Y9VADptAjLWTDLcrRwRJoRrq6uviv//qvitDrkTQs0ZdV7uLlsBgYGKClpYUVK1Zwzjnn0NbWFl4TLOSBT4iqqjJbqCMQqkDkbdSUxeCPnqP/H/9E5j8vJfme5XiDRbkD8ARoMv+NKDoolk7/5x9h8NtP0/jja4ldcAKUXEl0NIIM3QWJ0L5brc6Ngim5+JcjmNra2mhra+OMM85g3bp1PPzww2zYsAHXdYnH46Gz9Gi0LgFwSSaTbNiwgTvvvJN3vetdFZqraN1rtSl6v6D+AdjQNJXf/3YDTz+xi7o6i8GBIlOmNnDNO08etp8Ms1xuGbQMM26Rr5Fjl/l/NE1FN4ZS21dr74Jj1WDraEWIiE+CgEtO/BCfv/8KXt3/EIt8kHLXS59hWuMiThx/dvmaKnE9h758O2v23IcnXEpugf19mxksdgE+8dwInhoKCqqqY+pDTSqGZlWcWevqcih05e9pSy60nlNCrx9PcsVfYYybSNePbsSauIT6Cz9KYevTOP3taOlmBl/6HdbUxSTmnoVwHRijmUgIP6mbU2RV03humHnKqK5rjCUjzLJjKvKoJZzb/D7dO9gz5BxNUSm4NpPTDUxM1pdNWcL1AYa/s45Ench5wqZ0+AmMhqVDks2G41YPwLeMKAk+S13PyvDZ9GwQZTPHyc2TsTS9gvDOo5y/ae9Ad1jvKalG5jTI+fRrZ1zFnsFuFBSmpBuHtH/M71CgXPQEuZ+swenKkv7kWZA0KxbJYTpd9uvEDEomjru3V75P2tG/y1IrouJ4Lm2JOm47791c/8Bt7BnsJmPGKbhOqDkRQlASLmnDQkHhiQPbePLANr752qNMTjWwrHUqi5onMau+laZYkowZJ2VYJHRzdFFWSE3iQKnAoF2kv5SnM59lc287r3bu5cWO3RzI9tFdyKIpKvVWAld4If9O9LkaPo2/7bp8/eyrWdIyBdvzMHwTtHAdSgc2SPOu66CacezDOwGB0ToTr5hD0TQIlRYCRTPwcr0IJ/CfG702c1QzgRAijFQZ7nfHcSgWi2F+oSEFjQKwjFR+rUH0s5/9jI6ODpLJZAX/ypEAC0gzSpDg7YILLuCSSy6hpUXyOAQRN5qm1aZk18tOekpAUuR4OO09CNf3/QiOR+GyX7bXX8A92CdZGSPHR5JqLYSilpFzLbNKFORF+yPatkWLFrFo0SJefvll7r33XrZt2xZqDwICueodXK0+DTRUjzzyCHPmzGHFihUVzLkjtam6fa7joftq33t/s54H799KKmWSz9s0NCZ4z/uXkUyauK4XJmSUqlMp8YQRRgLZtks+bxOPj5yPJTtYCp11TVNGHUXPqaUFivbr8dC0ROtWdApMrp/PzWf+gAc2fJ/1Bx6jO7efTLyVdy7/D7nzITDXRFRE/udHzvlvsqUedna9gq4a3HT6t5ndsgIAU4sRhB9X1UC2BUG+1M/PX/xnHC8AOEoYAXTDqV9GVfRQC1MtA8Vu7lrzr+XIJkXxtR0F3nPqV1AFKLEUTDmBwpYnMNtmEZt5Kqqm4w4cJjZ9GXpdG3bXbtRYuqJuYxUhBJqi0V+SE+Oagzv59e7XKDo2b5l5Mqe2zSDvs7IWfX+Ekusc0Yz9ekhgqmrP9dNbzMsEiZS1LQpympjiR184QqALjyCcVXg2CDdMqCeT61l4di/tvz+ftssfwGq7QJqKtIAmQDpx9r3018QmvRWr9Vz/Ph5oGl2Pn0Ns4ltpOOW/EW4eVZdZfMcnMzTHUuzP9mD47MeyfhIo7vJBS18xz+RUAzcuOIOBUgFVUZjq1z/4W/XNEJamV1L7j0Yiw0Ibl0ZY+sjalRqi1sVQNAW3KwuOh2KMPUt59dygqxqO57KgaQK/uOQvuPHhn/BK5z4affI8x/N8pmCB40mygXpT0iXkHZv13Qd5tXOfdOJVFFrjaVoTaRqsBBkzTtIwiesmlqajRTRgrp/8s+jY5B2brFOkt5inp5ijPddPZ34QRZHpBnRVxVC1kIU4CGmOAhaAuGaQc0poisq3z76Wt89aUuFLJYRAz7Qx/QubK/qk/+mfIlyHzKr3HrH/hPBQtCMnQQ1kxFUl2K3PmjWL2267LTSVBItREB3T1NTEV7/61SELZGXF5M40lZID/2gn+WAxfPLJJ1mzZs1RARZN0xgcHGTmzJlce+21zJkzJ7x3hUaF2oDA2dIpY/01BZF30FpTuB2DqIkEXvsA7oE+3I4sSsKAkos2vRE1aZbXF11DMXVpB5BHRtV2RVEQJRfF1Oj//MNkf/Q8DT94O7HzZodanJFs1kDYtsCMBLB48WIWLFjA/fffz+9///uQMr3ambm6LtEyAiD061//mvnz54f8OFHtyZEmA6nl0OjrK/Cbu9ey5oV9pNMW+ZxNus7ifTetoKU1NQSw+BUCoK7OQtekf1Gp6DIwUPRBS3RdL4MNUOjtzfttgFjMIJEwo7cc0ubhzGbHItG+0XxQsHDCucxtPY2D/VuJ6Sna6mRunKKdBc2suEYhmDANVs16R437B0SIKq5wiY45ITx03xSwdMplfOvqTeSKfTK5oi+qolF0sv755VBXT3ihyei06W/nxPFnky31VYCaimsR4EiwfvjOTzLz009RbN/Kge9cQ/rUa3AHu1GMGF4xJ534jkEUFBzhkjQserD55muP8s75p5HSTf5r42oszQjNGMHErynq0WKkY5LAf6YjP0DWLko6+ABgI2nTTU1nmr/oq8IDRcfJ7iS7+Yu4hUOgGCiKRmLmB7FafGZmVGTKmYCgLNI4X7s3sP47qLFxWK3ngmeX6SastjKhZ2SDZKgq0+qa2D3QhaXpvplCgg9D1TiY7QMgY5V9ANNmpeau+m8gTLZ4NCLyDiJfGv2GPZgLUhboKqInL/lfjtIaWG2WC4DL7PpWfnv5B/mnZ3/Hz7Y8j6qoJHQD2wcuwjcDlnzndFVRiOsGKmZ4z4Jrs623A0d4uJ6HGzERQdm3KcrlI81CKrqqoisahqZJ9lrKEXKeD1Yi3VG5Tqoqh/ODzG8cz1fPfDunj581BLCEWudiFjWWGtrBVeIVBn1+FuH7JcWOryNuUCHDMEItRK1FPMjuPFo5kvmh1rlAyIkyMDDAH/7wh5CVd6R71dodFwoFzj//fK6++mpisVi4OFdrBmouso5H5yU/xOvJgyFteLgCJWWiz2hk8PvPMPCVx2X/xXTcQwO0Pv5BrDOmSycveWOEI0alYRmuXe7BAZwdnbIeEamlaaluS9SMBIQg5U1vehOzZ8/mJz/5CQcPHiQej48aELqui2maHDhwgD/+8Y9cddVVod+RNK1U2lRrSS5X4sXn9/HEo9s5fDhLJhOnv79AU1OC9960gslT6ocAlvJzlfdoakqiGzKiKJcr0dOVo7U1Jc1pVbuwoEs62gdxXYGqQjJlUpepJKR7o0X4viFBjqCpjQsBafrRVJ1opFDZGbPcH7ZXrNCmqIqG45WIG2l01ZR+JMhsy4EE11p6gpbUVIjOP1XihVR0kToLQcxIEjOS0byEQ9smvJAETG+YgEDQc98XqDv9eqxJJ5Fb9yBaslHmLDlKX4dym8r8GD/d/BxXzVrKhZPnAVBnxrl942qWtU4lZ5fIOyWa4kMzKb9REjzH3mKOgmP7mZHLEiQnnFInzSqqZuLl99H9+HlY4y6h4ZSfgWqS330HPauvpH7FHcTGX4oQtp/cd3jtkZaIoRoBM3lEe+cVCNKsQBk8aarKlHQDjk+5Xx6BcuHMOyV29nfyg/VPYfqmhVAbQ9nE7wiPhG7SU8zyrjmnsqR1Co7noh5N/6tKZBM4elESOoqmInI2wgvmZ44KuFabuyVw8ai3EnzrrGs5b/JcvvbKw6zvOkBCN8PEk1GTkRACR1T2qKooPp+L/B4aEwPgRSVWk5cL3wlXhGaeAKCEbacSrARtUHztl+cJ/vKkVXxi8QWMS9RJtuYqwAKA56LGUvQ9cRv5LU+iN00lt/4hEAK7YxteYQCvVCBz1vuJzzwl4lxPxb2Oi6YlerMAHNSKDIn+Xv3got+D64czZRxJgvs98cQT7N+/n1QqNYSWv/rcasBSKpW49tpruegi6cUeLKzV7Q2uHdpjKq1P/bUc4P5gV8elGfzWU/T98/1k/v0SUh9ciXdoQCbqKtioDQmE64XJo4QrjpRss6IN0b8VU8PrzeO1D6CNz0itD4SER8P1ay3tS/AZAAvXdZk7dy5/93d/x3e+8x02b95MIpEYEbhE7xPwwqxevZpVq1ZVhEFnsyX6evMh4MDXDOTzNp2dOXZs7WT7ti46OgbRdZVYTKe7K8sJc1p4x7uXDKthCT6D8OZxE9IYpoZdchkcKHLo0ABz5rXKEL4hz1T27949vXiuhxAKdZkYDQ1xPNdDfYOyUw8nmmoghOdneS5T5weiKmqoIQmmH0WRjrjVEpDKDRZ76MruQ1fNkE4/EPksPTzhSVATGX6qolFwBkmYGTRFRx/p2qoFUl6bJWHWoVtpROce+r/9Loq71rDnMyvQGiaSWnwFuY2PIewCdudu8pseJ3Puzf4dji16RwG6CoNcNElqqgZKBabXNZF1JKdMwjBJGLIf2xJ14Q72DSNoofyu9xTz5F2bmKbjRNKECKT5qCVeB9hoisHA5i8j3AKZJd+R9/BKJKbdQKnjIfpf/Rix8ZeOCFZQNIRXQrgFPLs3OBg5QQ2vF8LxF0dZz+Z4Olx0g6uEr2npyA9gqhpXTFsofV18ICSQvDIBXb+pahRcB9tzmOSTzGmKOmJm4eMmwYJv6ZL3xTn2MmuZu3VVDYHDm2eczJumL+QTT/2KH61/muYaIFlRFDQUP3JIRgK6nmBEDulRLJ8Rd7kK8OMNs+apisIdF7yXsydJK0SQXqLWuio86UTb+8j3Ke56CS3TKqObFJWeB7+Nlm6huGs7Rst0CVrsAkok2mg4DfZwMipNS3RXPpz/xGhCPgOW14A5djSAJYoANU0jl8vx5JNPVmTDHe4+1Q/DcRze8573sGrVKj+/i1ZRj+HuNaStE+rKvyEHhJqywHZRMzHUpIk6c6jmSclIdakS16UqcoS2R+sRfvcddouP76C4ehdK2iL/+/Ukb1iKNjET5sQ4Ur9G+yV6TNd1HMehrq6Oj33sY3zrW99i3bp1owYunudhmiaHDx/mmWee4c1vfjPFokMiofH8M3v48W0v0tySoFhwfR7B8oSHIoFHImH4ZHIaF182l/MuPIFEwvDNT2rtfiHYvQlSKYtx41Ls3NGDbqhs39bFqrNnoOtDnWt1XaO/v8CBA/3ohobrekzwn60nhKRtGAOofj1EUVR0pTJ7erBwlNwC2WJPeHQkCQDKsimXsXjyRbT37yBvD1TeVwgURR1WRR/co+jkyJZ6x3itnGryuU7s+hbq3v9Ter71Fqb85c85eMu76H30B9Sd9g68wgAID6dnP/ETJBmcoltjy8ZbJQJoiaXZ3NPBnKYJpM0Yr3XuJ+nzRXz7tUc5kO1jcqqBO7e+yGdWSFK/N/K5B8+0v5Sn6NokdBNBmVgu2Lk3WknwXBTVwCseRrWaAZlgD+GimI2oial4B/9whAJdFFWn1LMW4YKb2yuPq7o0Ecm7omgJ/3CdX08pjVYijHAKZkHPB1bdhRyaqnHa+BnDFu8Kj47cAOOTmfBYOcR8VF12jOLPGwFDrusdN5BavZ6oigKKEmYQX9Q8mbwr/USEIMydFpxf9Fxp1lGl74kEL0EwBaFpJyxvSKv870oQJh0BKoqkArA9F9cHlNWb9cDHaOX4mb7mxwsBS62NcKA1MSfMY+pnnq/ZJ7kND5Pf9uxx6d8jOuIOZ7+vBVxcNwgHHqqNCXweAnBzpEifQKL31nWdNWvW0NHRgWVZoU/GcNeHtjlNI5/Pc/XVV7Nq1aowEWD0/iNpV6rbihs4pwCuQCjgbO9CbUxgb2yXg2uwhJq2yN29lr5//CP6xAxezpZRRI6L2piQKdWHae8Q1O77rLgH++n75z9hnTUTJW6Qv2cdA19/kvovXS7NhJFQv4pnIG805DlWg4Agn5BlWXzwgx/kC1/4Anv37j0iSAz+Dq598cUXufDCC7H8nC6mpZPJxEinY5imUyZ+kwXLF9MTuK7Atl2mzWhk1TkzfRDj1Ux4WPt5K5x40ng2bTxMOm2xaUM7HYcGaBtfF2pqQIY5G4bG1s2d7N/XRzptUSgITjzJZxodI/p/IyXwITlr9rtYPElqDNVhsjRDsOAZeMJl2dQ3AdCbO8TSKZcBDBvxM1y5bzrpY5w35/0AocZltNe+ffE/UHRkzhRloAvFjNF6w3c5+O23kTr5MlKLZf1SS6706+7JSdGzGWvYs9z5S7r+d85ZwYf/9BNUXSOpm9yx+TluPvEsABa3TGF+o9zxfv60N7OkZQpQzvj8RkiwBhVdJ2RRjdopBNLHIGPGQDEQQHzqDeRXv4XCwT8SG38JAG6hnfyu/yI5I9BSDfNchAvoFNv/iNl0IqXDj+PZfahGBoHUQKGauLndOP2bEMLByJyI57mgamTMeOjLEhVNVekr5ak34/xo/VPMyLRw7qQ59BZzaIpK2ozhCY+33vd9rpixiPfPP52BUoGkYYXv3BsquvRhEu7x0+5Uz63BZ5AiouDYFSaw6vPGJzMM2gX6SgUG7QIKCoaqhWH6mqKiKYG2JBIQ4PuJBKDGFQJPuLjCw/Y8bFd+TxgmjVYSRYGsXQyZjcP6+3XK2iUaYgl0jpAqwr9WFHPYXXvQ6ycg7AJoOqIwiJZuxu7eB8chwzMcA7lcLY3LcIAkMBFs2rSJjRs3smLFCiZOnBgeH0mqO+i1114Lic2ivCLDSQBYTj/9dC699FIcx6kJWEazOIXn6f4gcT0UU8Pe2knh4a2oTUmKj+/A2daJMVta9vXpjcQvmStBSq6EkjApPb8Xe0NHTU/3IVoe1wPXk4ClM0v3DXeCgMwXLkNRFZwth8n990toE+pI/80qhCcQtotilEN/Q7A1gpYk+l3TNBzHIZFI8L73vY8vf/nLFIvFEfsqCk5N02Tfvn3s3r2LefNkttFi0aG7O4eqKRSLTnmCUvCBrvxnGBqxmMH2rZ184d8e4bwLZ3POebP8cVQVMTRMfebOb6WuzsJ1BaWiy0MPbuOd75Z00o7tIpAsuKWSy2OPbMc05ffxE9LMmCV9BqqB3/8UkTs3FSE86mLN1MWajwi8yzs+DcezcT2b+sQ46hPjQg3JaDYPQbkNiXE0RK4djRNAcG1jcqJ/P4/6VTciPAe9roWW679JkMbDswvgOShGIkL3PfZnIPxyi65DAwb/uOxS7tnzGgWnxIcXncui5km4nsfp42fWbG8tjeTrLY7nVpB6QeCzIFBQMFXNN+vYWOMuJLP4mwxu/FeKh/6Aoqewu1YTm3Q1qQX/Iq+tMinim/EUzcLN7cbufp76FXfQ9eiZFDseJj7xrUhHa1DUGM7gVrLbv4vRuAwjcyIIBzAxNd2fT8pPPxgjBcfG1KSZaH6jzCcXJKTc2H2Qf3z2t5wxYRbvn386rueFTrnH5V0L5tQjbHLCuVf3NS0BhcRxlOrxE2hHVFWpqdQRgKlq/O7yD9JZGGRj9yH2DHTRnh+gIzdAZ2GQ/mKeAbvIoF2k4No4nheOGU2R6QQ0VcXSdOrNGCndImXGaImnaI6lGJ/M0BZPc/akOdyz4xX++dnfybQMTqnSl0gp57WKPt8Rn4+ioCXqUTQdRZNOccKnKlBj6UhfHFs/HzMjbrBQqarKo48+ysKFC2lqagqPAaEpZvXq1fzwhz/k3//930cNWqDsd9LR0cGuXbuIx+OhVme4BTSom+M4NDY28ra3vQ1g2KigkaTWBCYig7z/cw+itaaIX3kiff/2IANffIyGH14Fjoe5ZCLmkokV9+v/3EPkf79BOvLWqntwb1dIm6umUlqzl54P3gOeoOmud6FPlGrVxtuuoedD99D/n48g+gukP32eDNtz3GDWruByqSW1dga6rmPbNlOnTuXiiy/mrrvuIpVKVfC4VN+j+v4vvfSyD1o8lq+YzLTpDei6Gs4NQggKBYfenjx7dveyY3sXhzsGKRTsMKz5N3evY8umw1z9jkU0NSVxHC809Qw1Uao4jkfbuDRLlk3isYe3U5exeOG5PUyYWMc5580KN+uO7fKru15j185uMpkYfX0Fzjxrhs+uWy7jfxJggehuR8XzOTq0UWg7gt901Qi1LkKIUWtZygBYRfiq6iBVQC1AUcsfS1X9OnseKlB3xrsRro3wXMxxJ/iLsTRboJs+r8Oxs0oHAHlOQxt/13xReNz1PDRVghrhL8D4wOCNfu5Bb5X83bBCFRT0rcmB9kfysggS099PbOJbsXteRHg2iek3oqdmRvq/2lwnwrDngY2fx2w5G6N+EYkZNzK4/jPEJ7617Mdi9xGf+k7qFn21fLnqJwpVtZpLj6Io2J5LyZWZn9d27WdiqoGXD+9hTccetvcd5roTVvD2WUvKphPkdHc8+lwUHDk3Fx2EptReH6Uzh9SylF7fjOM12zTMmh0Ag8ZYginpxlDjB9KcNmgXyTslCo5D0XWwPRfPD3MO+jIglTNUFUszsDSdmG6QMqwhJH6NPjeLgkKtfUd1NY84R2gGTt8hdBQ8Oy8J5Eo5jMZJeNkeaeYNW3r0ctSgJbrQqapKNpvl97//PQ888AB///d/T11dXQhcgnNTqRTjxo0L8wONVoIX8NChQ7S3t4eLZ/BbtDOjO37DMOjv7+eqq66isbExDJceq4ZliHnI8SQQ0DUGvvw4hQe20PTT64hdcAL2+kPkfr0WbWKGzGculJqPbBHF1KXTbkMcb7AImjIE2Yf1CbJU6eDs6SF72wtkb3se68zp1H/pTWgT6kIzkD6zicafXsfAfzzCwHdWU3xxH3V/ew7WmdNr3nu05jgoh7afd955PPvss+zfvz80y40GuGzfvh0A1xWk6yzSdRbDyakrp+I4HhvWt/Pc6t2sX9eOrqvU1cXYtLGDb3/taa5/z1JmzmoKQUUtE2VgRjr3/NmsX3uI/v4C8bjBb+9ex84d3ZwwpwXHdnnlpQPs2CFZdvv7isxf0MbyUyb7mqY/j5YlqhkbzXmqInfdo5VKFXC5/2qB2QqAXvU9iBhxkflNqusdbCZq2b9V1DArLMiJDqD7j18lNmMZyZMuLlfiODtCFxwbBU2Gaau67y8gQvr0qPy5NGyebyqVlYCqzW95kXeygILwSih6UnKwBPewByTCUS1EVVZv4dmoeors9u/hFQ6SWvp9hOeQXvglig+vYGDD50jP/yd5smrglfrkPUs9qGaDD2i0EU05QTiugsJvd7zKgWwfm3sO8Vz7Lq6atYS3z5JaT9uTmaOPS19L+zJqU0JqUeKji11WGuL+BvGN5+apKQq4vv9WwacGUH0NSsaMkzHjI109rAghc0AJIcg7NhkrftwcnoOoq1L7Vja/ew56PYgg9ZEANQaldpj0yS/5V/wZQEt0kAVakE2bNtHd3U1bWxulUqnmdY7jkM/nhyWpG06Csvbs2VM58dYY6FETR7FYpK2tjSVL5EsSaFnGAlhgqMMnPgFR/38+wsBXn6Duny8gdsEJCM8j87U3IWyXwe+uRvTmqfvnC1Ab/XwqQaZhTS37xVTX3fVwD/Zjrz1E/jfrKD29C3SVzL9dTPJdS6V613caE7YLCmhNSeq/8ias82Yx8KXH6HrHf2Mun0Ti6pMxV0xBm1o/agfd6jY7jkMsFmPFihXcfffdo+4vXdfp6+vj8OHDtLS0yGcuhqpF/c2trylRWLhoPAsXjWfNi/u477cb6OrKkUpb9PUV+OH3nuWdNyzhpIXjK/xTwr4TMrTadT0aGuNcdd0ibr3leWzbI5m2eO2Vg7z43F4UBWJxg7q6GAP9RZpbElx13SI0TQ1J5t5os0DQ10D4/oymDsP5FtX6uzYoOv75sUDWPUgnIUEuxGKm1BCV8tiHd4CiIkp5zHEnUGrfimLEiM1Ygd21x6cBVzDGzWY02aBHI5qqYkQAykhmtTcasASlmZqGpvjvSeVjwhNSExPXQdWT/uEE1aIa6fJlQSSZ34eqniK77TvkdnyfprMelb8JF0W1aDj1LroePx8tPoHE9PcjvGJI5a/4GhZfHRXyilSLEAJD02S4r3D5z5VvYWHzJBzPpT03wFdefpBzfv1Vbjv/3Uyvaw6ZfY8ZuFg6Styk54O/RtHVEFwPJ8JvinCFZCw33zj/pSNJAAhjkRQSwgeCQgTpRcsRbhU5qFDCcaP4PaAohH4x/iXlc4+DBAkQ2957C81v75R/B4BIAJom3/OJ0lUgyD12tDJm0FI9uAIg8OKLLzIwMMDll19Oc3NzhbNrIHInPHw6gOEkOP/AgQNhtE+tugTHAtAyODjI/Pnzh9RnrC9IxY4+b2OvP8TA15+kcP9m6v7+XNJ/fXqg30SNm9R/80q0iXUMfvcZ8n/YRN2nziH5/hWSGM7QwFAl8Vyw6CrlugMMfu1J8nevRT+xjeRfnkry3ctQMzHfJOXJnYSihMy8wtf8xC+dR+zcWeR+vZb8r9bS+3d/IH7ZXOq/esWYdq3R/gme7ymnnMIf/vCHIaHtw10fRHp1dHSEHD/aCInIAnEdD0WFpcsmMX16Iz/76Uts3nSYdJ1Fqejy41tf5N3vXcbCk8eHGpdqHxdNU3Fdj3nz23jvjcv5xZ2v0tWZJR43Qi2fbbv09uaZMbOJd7xrCc3NydqkdW+ABJrBgHgxYCU+XhK8E4GGLGBFDtI7BMAiOOa6bgXgCP45jhN+Bv9KpRLFYrHiXz6fp1AokMvlKBaL9PX1UZfJ8Lcf/xixRBKn9yDdv/kcWuNE3IFOEALVSlA6tIWOOz4i2TE9F9WM03r9N6Wp6DiAyOoxO1rN1hsphqr5/oBDMAtBxmSAwR0/QHGyoFkoiuF/mpIYTq7GCHsQo3GFxCteoJl2QTWoP+VnqFaz1NRoFsIroadm0XDKz3AGt4WlBiSBAlFRH9tzA/xSwfUhAEPRMH0H6B39nSxsnkTRdZiYquerZ17FL7au4d0P/hffP/udLGiaEJrpxiyRx+m1D+B258j9/OVwQxj424zoQqEoqClTmuGPIULteEpQiyCaCnztpfxyzPf3jsO7FIgQwieH84hNXTyK86Vj/bHMsWMCLdWq+MDcsnHjRl544QUmTpzIqaeeChy/rLfRxnV1dR2RWj44HjiEzpgxY8i5R9VZrgBdofTiPrrfcyeoCg3feDOJdyyW9XGlH4rQVdSkReazF5O4+mQGv7cafV5r0BgAvN48brYfka+i8fcEiqaS+dxFJN69FOPEcVIrA6FzrbA9MFSyP1lD/u7XqPvH8zGXT0bYLkIoYOkk37GE5DuWUHrtIPrMJsmUO8pw6KB/okAAoL6+nkmTJrF9+3ZM0xwVaCkUCvT29sqm+dFj0XMCH4KoBMDGcVwamxL8xQdO5Se3r+G1lw+QSlvYtstPb3+R9//lKcyd13pE4LLgpHF8ZEIdzz2zh00b2+nuzqNpClOnNXDSwvEsO2UysZg+LGnd6y3Be5RMJvnVr35FY2Mjtm3XBOPR71HgXutY9b9o/qkoWKkGMVHgMhyACcBL1K8s+i/YmEjtmVYuw3/WRusMxv/VnfQ9cRuZVe+Tdfa8iNNtzY46bn3+P1kMNdC0VEYOgST8yjolmgFFS6MoGqgmiqL7WZxNGbKsqCCCyEr/nQu6z7NJzvgLeV/PQVFNf+ExEZ6D2bwSs3mlvEQdamIJbpO1ZYqH6nnAFR5p06KrmOWGeaeR8P0Y4rqJ63kUPYerZy/FFi7PHtopQYvwGFuO5srKKLpKw23X4BZtFEv3x56MEFRVRW7yRBAuXG6DENIp1i3aErwkzKEarv+TESXqYyccm4AtO3TepQy4Fc04ZsACY8zyHJ3Mg0XItm1+85vfkM1mufjiiyscbI/XxB+U19/fP2ofFNd1icViTJ06FShrDI5a/ERa5sqpZD53Efr8NsyTJ4akREE6c6+/gL2rB9GTA10j/vZFKDEDe8thtPFSbWudNo3k3uXoUxv8e/tmK02VvioJE3PRBOkPU3TA0MI07DgeGBr2y/vJ/m4Nifcsw1w+WUYY+RmWRckBTcVcKD33QyK8o3geQV+apsnUqVPZtGkTsVgsXLBqSeDnZNs22Ww2PFZ93+j5wbGgjroueVNMS+d9N63g1lueY+2rB0mlLYpFhztuX8MHP3w6EybWDQEc1cClsSnBJZfP5aJL5+C5njSpaWWNX/T64er3egGZYGzrus7mzZvDiLgjmUGr73GkY7W0CrV8UYLPWv4pQS6uICt4cE41gKp1rFAolP3QPIGCoP/J20mfeh3dv/t3Sgc3Izyb5MJLyJx9E6KYBc1A0a3XHTxGn+sbDVoDCcpKGRam6vt5RH5XFUnK1l3IMjXdSGLqdaNaWz27z8cvZadp4dmgqChqlX+fqiOEi3CykiFXUun6V1XWs8tPtBf9VUEyENfH09ieF+YZEqLMVZJQTWzX5Z0nrAh/O1a/FgFYZ0wbctwccmRkCbmZ/gzP//9VqRg/uoHi50EIx0vV5/F4v44IWqp33MEkG3y/8847Wb9+PbNnz+bii6UTXdR3JPp5LCKECMNuR3u+rus0NDTUbM9Yyw47WlNJvEP6yAhHLoCKpmJvaCd72/OUnt+L15NHZEtSk6IpKLqKkomjtaawVk0n9ZEzSVx7cvnekSRdSuBP4Xigq1JtGZWgT9MWWqIOxQp2Q5G+NuUkIGzXzzA9dg1CLVCRyWRGBCvV17uuO6x/U62yorgmAB2BM/c7b1jC9771DHv39JJIGAwOlvj5HS/zVx9ZSSxWOflGF0zp4yJ9KnRdQw3sukImZ9R0NfSNCeoRRLvVYoE+3hKtdzKZPKoyxuJ/M9y5tY5Hj1UnERypzFqAybZtCoUCdXVyMbS792FOPonsK7/HK+WY8KFf4hWztN9+M/GZp2FNWSgjiY57nwfjQ/EHnJC7P8cGVT0uO8GjkSDctMFKENeNCop3ZG1xPQlaABw7i6H6RGzK0CVCCF+L4hWlr3YUrKq1KR/kpxZGCKFaUosTkcAPoruQlURwVakgXOHRYCVI6lKD4wpJOBfuuIX0eXGDd+sYFrDgCs33EezvyRGPGxiGBqbGC0/uZMq0Btom11PKlijkbTkPGCqKArmsTfP4NJvXHsLzBPNOnoDjuOhBGPRxliDK+ki3Pvog/ze+HmNd46PnHy2AGRWNf6AaDibvIKrkrrvu4rHHHiOVSnHNNdeQSqXCRS3YMQYTlmEY4SJwNBJdjI4k5Z22FvoHHAuAqu5gbE9ytaigqCq5X75K3z8/AI5L/K0nEb9kLvoJzZKbxRV4nVmcDe3kfrOOwR8+R/6368l88XJi586SdlStMiRZUZSa4dBAecQ5ngRFQZ+oQxeK6D3C9o+59eUFKpFIjOkZBmaB0Yrim4uifa2qUluSSJhcd/1ivvONpykWbRIJg107urnvdxt529ULfc1fpZYgkCibc8VxXcVxbDyv3O/RzN5jJUI8GomOrSCc/HjL67EA19LYVEulGVdhcKCP1tZW7J79dNxyPVrjZIr71pFafAUAqpXEmrKY4v51ErQ4RRTz+Pn3SIfeyPgIwLLnofhOj0FelDde0yI/660EMd0ga5ci4cC+5tJ12J/tlSdqcQmyhrsfEnioZgvj396NovvOuWpZ/zCcpi04p+XCtSHAUbSEz6Agz9mf7fWTOspLJChRcDyPhlgCU5NmmnAGEtLsqPr8LtGEkEcrweX9/QWeemQXxaKD43jMndfKkqUT2bG/n2RTgjYg53j87vebiMV02tsHifkZod/57iX0Fxz6+wvMAzxXIHRByXWPK2hQFYWi62Bphk8eGBhPqhsFRc8hLiQZ3PEm3PufUg8FBeMorTGjovGPZj0G6Onp4a677uLFF1/EdV2uu+46FixYEPq4QKU5JgAOhm++OBoJFpSxyvFaBBRF7sykJkMLfUTsdQfp/9cHUZMGDbdch7l0UuV1gJo00ac2ELtkLvnfrqP3Y/cy8MVHMRaOR2tOhuHLiqIgCrZ0uK0xmBRVReRtFDMeOt8GrLoiWwLPQERAguITeimKghIfferv4WSs16uqelTPrBokSv4VlwkT67jgotncc/c6NE2QTJusfmoXc+a1cuJJ4xAeFIo5CoVKjZznecTjceLx+JA26PpQu/3jjz/Oqaeeytq1a5kyZQqtra2j5hQ6Gqlllvn/mwjhkStIrZvRPJXJ//gU7bd/gMT8c+l9+LsY42bjdO6isO0ZWt75NYAIr8Mxly7/d0t4pX7QLUBBuCWEXUTPtNH/3F1YE+djTToJ4dphKPYbJcFzb4wlSegm/aUCuqLj+roMzSfJ29XfBcjs2lAbSFfvYBWzoebxWlKxYfCTKIbn+2Ye23PZ2d+FHpASQghoXOHRHEv52Z+jzNLqEBN1rV336PsLHD9M+cnHdzBjWjMLF4+nkLf5xZ2vcvLiCdSlLZqbZJRVOmXy7vcsBeC+321g/IQ6liyTc3Uhb5PwQ6SFIhfUWmHwxyp1frhy0rDwAn6UIe1SaPKzJY8mcOH/5Xq8rpqWXC7H/v37sW2bDRs28PTTT9PZ2YmmaVx11VWcf/75FZP6wYMHyWaz4d+lUolMJkNvb29F9M9YGxeLxUZ1bXC+67rkcjksyzo+turodbYHmkrxyZ04O7vJ/NvFmEsn4eVKKDFJ/FSh1/AkF0D8yhPJ/2kL+btewX7lANr5s8GWOYWEECgxY2R1XMwHhQ0xUBS0cWn/bz+vUfX5VX1SufsdbbPlufl8vuIeI0ng8xRE64y1z6ufla7LcbPqnBmsW3eIHdu6URSIxfQwREBRFdauXcfmzZtJJpOhs2gymWTjxo1cc801TJ06NQTXnuexceNGstksuq5TLBY57bTTeO2111i6dClbtmwhnU7T2tr6umhA/jeJEILs4KD87joIz6F0YAOJuWfhDnbR+cu/R9FjNFz2txiNkxCuTMKGEMemqlcUhJ/dtrh3LZ1/+DxeMYtXGMQcPxe7ey+TP/UQhW3PouqWD1qcNxy0aIpMrDc51UCdGWPfYA8xzfC1CRIYuMJj90BXVfOGzmlDNMOBHmQU81/ltYEbQMCMKsOI+4p5DmR70VUtzPctEOEufnJaskoXioPENB1UDbswQGGwk7qWmTh2UZqh/GtV32/paOfmgb4i1iy51sTiBrbtks/b6JrCU0/u5LwLZlPnc0Qd7hhk964e+vuKpNMWzz27hwP7+yXxJNIResAu8OrhfZia7oPDY99IqIpCwbFpS6TZ3NOOpem+CbDyPE8IHtm3mbRh4QgPldrsuf9v1kOOH1fIzOsLmydVREiNVkYELQEHy+bNm/nbv/1bWlpaQpPLlClTeMtb3sKyZcsqnHINw+CWW27h2Wefpb6+nlKpFDpltra2hpmZRytRbU8mk2H//v2jGtiBur2np6fCr+X47WSD3ZtPsx+QGXnCp0RQh5wtih6KBWpcl4y1Ea1I8MIOfPlx3AN9KHETUSwg3IClBzkBOy5qOkHp6f0ocZ3Bbz5F/t5X8LJFFF2T2iAzDoqCaiXwslnUxjh1nz5vTJNCrR3RaEFn8MxM0ySdTlfcYyxSPZHJRIoq55w7i00bVjNzVjPXvONkJkysw7YdDEMPUxCUSiU8zwt5Zrq7uykWi/595Lju7u7mzjvv5KyzziKXy4XtSqVSKIpCIpEYErb/fzJ2CZ5jLpcLjqAYMRov/3uE55Fe9lbSy94ani88F+U4ko4FAMScuIAJH/kN/U//lMLul2h9x9dwsz3yPFVF8XeWr4c/w8hV9DdZnoehaUxI1rO260BkqVRwhSTB2zPQHSbeq/Y3jErlu6NUlHMkKV+rBhUERfEXGHitcx95x0bzj8kaymsMVWOKn7E5EfQnYBcH2fL07Sx/63+g1zD5Hc2zDogkr3jriTzxwHZ27eohl7dZvGQiyaQp2ba78wz0F8hkYrz2ygHWr2vnhvcv56H7t7BmzX6uv2Epzz+7h5KvsVZ1hbWH9nPmLz5PQyJD3i1JQsRjlnIfxnWDxliSouugBoAwMBF7Llf8/jtlv6vjLn++egjk+Ogv5VnaOpUXr/k0rvAizNqjkyOah0A6YK5cuZJEIkFHRwcHDx5k+vTpYThx9e593rx5qKoa7nYD/5Lu7m56e3vHHMkTdGRLS0vFgjncQA/KKxQK7N69mxkzZhx/vwRfZWYunYw2MUP+7rXE3zQfzafXF75mJThXURWUpEnpxX0UHt6KMX8c+tw2+buhhRND6bUDuNu7Iaaimmm0dJPc+QWqWE3D3d6HktaJXzYfrzeHsimNoqflzkgInL6DCNfF7tqBXjcFfULG7xjGPP6CviyVSuzevXtUvkmBlisej4eA8Xj0ecBUO2deC2+7eiHLlk8iXRfDdYPdJKxZs4aVK1cye/Zs+vr6QgbfSy65BMuSu63AhGnbNieeeCLnn39+RTkBqHYcZ0w+Of8nw4sQYkgkWXKhdNyXmhD/vVbUMGvscfcf8kmwBtfcg9N3CK+UI7/pcfqe+jGlg5tIr7jKL/f1UYcPW6/Qx0Z+nFDfyoN7N1a8rq7wiOkGu/q72D3QzUJrosy+qwzvF1B97Gi0q1FH4ACgvNy5l4JjU2fGcP08RdJs5NFgxZlS1wKuzc4ND2HpBpqZpHv/a/Qe2kTn7jXk+g6iGfHQdD3+hLNRxwhSPU9gGhqUYOuWw8ye08J//2QNp6ycSjpt0duTB0XhircsoLklyeBAkf37+7no0jkkkyaXXzmf+/+4hXxuKL2AoWo0JDK0JtIyueFxHYdS0+D4TubRtTNc5+LpNyDq+o2vh0D2bVw3aI4A2rHKiKAl2FXPmDGDf/3Xf0UIwb59+3j88cd56KGH+PSnP83NN9/MySefXEGRf80119S83y9+8QteeeWVcNEYrQSAY8KECRUhocOBn+C3UqnEjh07OOecc8LfjtdEqOjSr8U6bSqpD53BwBcfpevaO0jeuALrzBlo0xvDMGVRcrG3d1J4YAvZW59H5Gwyn70YfUo9wnGlhsTfqTT95LqwjELnC/Q/9RO0dBOeXUDRLdx8P/G5q0jNvjY8r/vxz+Nme1F0yTPQfOGnUYhz4L/eRtt7vxj2iaKO3jwUnbQURaGnp4f9+/ePyi9JUaTzdX19PW1tEpgdbch5LZOWrmuhOjcIVy4WpZZPURSefvppdu3ahRAy4iwgujv//PNpbm4OTZmKojAwMABANptFCBFqWUbTR2OV/40mpqDNnucx6JuH/F/w7CKKZkJVTpSoXxbH0c9HURQ6f/EphGsz/gM/p+OOjxA/4UwmfuQeDt12kyS6w9f0HJcSRyehxsT/e3HLlCHle8IjphkcyPWxofsAC5sn1lxojrfUcrhe07EHR1RGdkmzg8OkZIa5jeOxCwN07ngGXdOlT44eY/wJq9i34QEUyuYmVdUYN+tMGKMPifA5Vko5lx3bumhpSHHBRScggI0b2qVW2hO0HxqguSWJFdO55LK5DA4WeeXl/Zy8eCKXXDYHRVEolZzQGR8hwVnetSk4NgX3eIIW6f0TZGaOPrOoOa8YBfGvi/x56iEEuJpH3rH9ex+djMqnRQgRRg9NnjyZ66+/njlz5nD77bfz7W9/m7/+67+uAC6BdgXkQC8WiyQSiZqkWWORyZMnh4BnuBe1bEqQPC0bN24MqeSjSRyPx0se8KqkP3omxoI2Br/9NANfeIyBbzxVZq2VhUm66JKDsWIK6Y+cITleXE+Cn+jkU3KlZsWwUPMZEuPPl463dhE1kaF0YCO9v7qF1N+/BS87iGImiTWfiZcaQE+30Pnrf6Yw+RXi805DdNp4uYKMjDDGpmUKzg367LnnniOfz4fJKoe7JmiH53k0NTVRV1d3XJxYq+30Ad1+EBYdOHvfdNNNtLe3Y9s2t99+OzfffHMYlRMAqGg0kWnK3XcymeSRRx5h8+bN7Ny584hcNEcSIcpEbsdbWxM4RVZEjI0wtyiqEjp7j3jeEbRnRyu6rqPpOoWc1LTYHdtp/+8PoyQbwLF9dYI0Ywi3JBlxhYdqxhl3438dIyOugvDZYEsdO8C1Gf/Bn6NaKdreewu9938dgOSJF6KlJK+Ior4+DtejqSvA4pbJxHXT96co/+b55pfH92/h2hOWo78B9SybCzwMVWN732HWdx8koVt+9InvhItCyXOYlmmmLZbAEXGWX/nZMZczWtE0hULRwRyvcfVVJ/PU4zs546zpFefs3tVNve/rF4CSrs4cG9a2c/LiieRyMjHr4qUTURUFBxfd11whArr8Yxh6Q1vp/y/KN43e/A3b1BynegQPPzBBCq/qmH8v/5ignFdL1JiIRut3OqroIUVRQrAQsGMuX74cy7L47ne/y49+9CP+9m//lilTptRcoAK/APUow9wCoDFu3DgmTpzIoUOHQjNFdQOjTriWZXHo0CFeeuklLrrooiEmoqMFLhU7f0WagmIXnCATJm49jL32EF77AF62JHdQaQttYh3Goonok3zzUQ2GWiEEiqkRBAqak0/AnHxCRdmJE1dROLBGlm8ZKLpCYsEZ4e/xE8/EnDxb/mFpKDF9TCGcUTt5YBoqFou88MILY+6refPmjen84aQasCiKEpqKgvH23HPPsW3bNurq6jBNMxynL730UmjOev7550ONS2BqDExB2WyWU045hXPPPZcf/ehHFAqFo3IaD8QwZMoAy7KG9TuolmjfDwE6wWRQ/gDbLecR8VmB5WQRMaF6kq9HNTRJOmgMDf8+0vfop2S7rDwuQVS5ZtEe8zwPQ9cZGBigs1v6j+jN05n4yfuHtj9wvq3qE7+QIeePTiRpGoDRPI3kdV/F6dlP5y//ATXZgKJqHP7F36FoJon558pL1OMfOTKSBGMj2NG3xNMsaBzPS4f3Ss4WHxy4wiOuGzx5YBud+UGa46nQkfH10LJE54wgWunFjt3s7D9MUyxF0XUIPGaC6KGlrVPDtrRvfZIDW56geepSWmecipWop5Tvl2HPEJqrNSM+5vlYDnV57v5DfWxc384ZZ02nWHQwDBmKff6FJ4TtCLK267qK5QczxP3PREJuXIqOg66DqqgkDYukYaIpynEP9/3/jwRP35XsylpMAhdUhLABgaKYlOcFmf/IFYJ4NOOzZyMUFSVi6hxpLIz57dQ0LfRxWLhwIddeey0//OEPueOOO/j4xz9eEakTyLGqxVVVxXEcGhoamDFjBrt37x52JxxtsOM4xONx7r//fpYvX05jY+NxAy4V1yqEzLjG7BaM2S3DXidsV2aIrsoFVF0f4ZQo7Hge4ZTCCAg1nqZ0aAte3o/CCCazbA9asoH+Z3+OsEtoqWafotuQUUujtMxU18FxHAzD4KGHHmL37t0V2bWr+6x6sdN1nYULF46u4BHqEEgt4BKtx4QJE4jFYui6jm3bCCG48sorgbJ5MdACBhFNgTkIpKYlkCOx/QaapmqAEQXrfX19HDhwAM/zKBQKIfCuznwe7btgXAaOwmEbAeEKSVKoKPKZClDrAmZaENl8mIhTqn3llaqpoWYsvMESWlMS0V8Mw+ur/0Xp9z0kUymqTHUf1llViVsWhmFgmiaGYaD53y3TxLRMLMvCNC0syyIVjxOLxzFNk/qGRnCKGKZF931fID5nFfFZp+Hl+1FiKRRNp+fBb5OYfy7WxPkIxw75U46HCLsAlkVu85PYh3fSdMYNFPe8ijVtCYd/9jfEZ68kufBiqe1Rx5aJ/niIqiiUXIeUYXH6+Fk8eWAbaSOGg6RG94TAVHUO5vq4b9dabph3Go7nYWqV2trjIdWbqSDZ3q+3v4yuaEPy1wgEuqJy/pQF4JZQNZP9Gx9G0Qw8t8SLv/lHppx0KZNPujT03xuuvNFKcHpDYxwU+Onta4KboaoKhYLD269ZSKY+HvZPMmWyd08v//2Tl7BLLqap0T9Q5OJL5jBthox6KnkOXQNdFF2HvGszPBvO/3IRDkJIsKJoKbzSPhRFRs4qhr85d3pB0QGB8BwMVSVfzNPqz8F4DuimnOO8UjmtxNFqWmCob0PwGexmzz77bDZs2MDjjz/Oww8/zKWXXhpOusfDBFN9j8WLF/Pss89WLCzDLXDB4tnV1cXdd9/NTTfdVLFoHC1wqXWtEk1e6EqH2Eg6TanK19QyHT/DLND+C+2VcgysuQdcm0DFrVop7MM7CTK2BvketGQD7mAXubX303Ltl+Vvqu6j3tEBxuEAy969e7n//vtDkBi0f7g+0TSNfD7PggULmDRJ8iCMxSQXrYPreriuCCcnw9CGTKaqKs1DkydPZvLkyRX3+vrXv85HP/rRYcuKxWKsW7eOTCZDqVQim82ybNmy0Hl3uHZW+2RF/XWC72eccQatrTLnVJRB2jCMIcAvuJ/jOJimSSwWCyNtFEXmTlETJhRcVEAxdbSkRenZ3aiOQFFVYqdNg7yDlrJQdQ1VUVBNA3ryOM/vI37ebOxHd5B471xUDzRdCzcgAZ+OruuoqoqhG6RiMUrCw9ANdF1DG6Mf2nDiFgbRdAu7YweJ+efJPotLPpDDd30KrzBA/XkfkIvM8ebKiDzO5EkXEZu2hNg0yW6dPPEiEH8+p+vqd+P08TOptxLSUTKI/qHsEHvPjld499xT0YLM8cPMhcdDPCHJ4F4+vIenD24jaZh+vaQEHDLzGsdzYuN4P/oLjFiaCfPOp2nyItpmrGTDY9+l5+AmFl74sYpcU0e9cQzK11Ru+sCpOM7Q0N0gwijIT1ZfH+ejn1g15FxNVcPEjZNTDXzq1CtJGxa2N/Se/ydSFC2BYmRwB7dh96whPvWjeIUOFD1JseNhAMzmyxGlHlA00OLomkneKTEp1Qg46LpJqfMptNRstFjbqIDLqGeF6M42qsYGuOyyy3j11Vd5+OGHWb58+RD/kWORoIxAVb9o0SImTpzInj17ME3ziP4CruuSSCR4+umnmTRpEpdcckkYml0NPqLljbZegVSAlzGS8VRMOP5uW43X0XrdV4ac65XytP9XOeGZaiVx+to5/PNPUH/uX6KlmxF2cdTpv6vNQYFpTdd18vk8t912GwMDAxWarVoDqtopcPny5aHWIzAPjsVE4nmSVC5qaRwCFJUoa64b5kgCuPvuu0mlUmzYsIHp06cTj8fDaKDg2Tc0NHD99dczODiIYRgMDg7S1NRUoQmprrNt2xw4cAAgBBgDAwO4rksmkwmjpQ4ePIgQgquuuqri+igBY612HzhwgDvvvJOPfexjlSfc+jK8f7H8/vP1sGwuHGzD3dWDYmmoK/1cLj9bR+mlfQjHxVgwDu2mleR+9lsSH19M9ts7Sf7NgiM+g65ilm9tXM3HTz6fnCOZWYXrkndKfOPVR/jHFZdRdGws3eDQYB+fWv1rTFVDIBc4S9M4nB9kcctk/mHFZeRLRUnWpWoEkTmKlaSw80WUWIr8hkfIb12NOW4OzVf9hzRn+ovacV2E/dBKvWEi7ff8C7ktT+Flu1ET9RT3vMr4v7pTnvYGRw/JMmUbDd+Uu3L8TGZnWlnXvZ+UEZNZlZHZlevMOE8f3M6f9qznkqknUnIdqRXz5Vj7rPp6Twg04JZ1TzJol6g342HUkIfAUjU6C4NcMHkeSd0gl8+TMGIUBjvJ9u6nafIijEQdS970z7x2/5fZ8Oh3mX/OB/FcG1Uzjrqu4XbM3yDqw8y71XMT1D434MqZmm7iP1e+5ajq9L9KSjtBi0HnLti9BU6cDX19kDkN9m0ENwdTzwKkyYj8Jtj5A9AL0HIlnuuiajqFA/eiaBapef/EcISJURnzViZ6w2CXO3XqVFasWMEf//hHnnvuOS6//PLQcfdYJbpABf4L5557Lrfeemu4WA0XyhwM0sAkcPfdd4fh20F+mWMxEQVlRD+P5trav9XuO3ewM9S0qFaSwu6X6PnDV6g//4PEZ5+OcEohYBFH2DnW0lAFi6rjOPzgBz9gx44dJJNJbNsetp+j5o1CocCUKVM45ZRTgLIWYbR9HGhYNE1lz64edu/uwTA0LEtn8dKJQ32AIhoeTdPYt28fTz/9NIZhcOONN3LgwAGefvppxo0bx6xZsyoIClVVZc6cOUPqEM1MHZwbgONCocDDDz9MKpVix44dbNmyhQsuuIDBwUGmT5/OqlWrME2TdevWsW/fPq666ir6+vrIZDLs2bOHD3/4w3zpS19i2rRp9Pb20tLSwqc//WkymQyf+tSn2Lx5M48//jgf+MAHpN8WAvI2vc9toeH9J6Oj0L1hN4nFTThJgW3nUAyDOsDLFeCKWahvPQFn82H6freeOtchZ7iY+SL5OMQAUbIluI4o4RRFOluaus5AIc/W7nYAEnqZ9j2txdnSd1g+Vx9NtiXr+O457wivD3LPbOo5xJdfflD2s6ahBikRgvJ0i4Fnf07pwAayax9AMSxarvminweoJCPhhhlrQlRmqK4l1efJMmVbEnPOZNp/rEPYRYKMyIoRL3PDDFN2NAv2WMs+kkQXVtt1iesGl0xbwNqu/fJ4cJ7/6XoeP1j3JBdOnl/hkBtdmI9Gqtttey6GpvF8+07+uHs9KZ9srFLLYjMuUcdFU+bLYz4vzrjZZ1LXKqP88ARC9Vh40Sd47lefpHvfazROWhgCl2MSxTfRD9PuWlqo4c4NWH2lv06lf9j/Cfi+ECh6nP5NP8LL7cNzBnD6NmC99Gmc/g3Ur/gxxZKGVypRB7h2Ebd4mP5X/oHUCR9DS85g4LWPUz+1QGzClaixNnLbv4eeOYn4pKsQXgmU4dNdjhm0VC/ygUZl8eLFPPHEE7zyyiucddZZpNPpIdqW4GUey0sVfYGCe5166qk8/vjj7Ny5M1Tlj1TPoExN07j11lspFAqce+65IaCJRpO8nmrWI4miKKEz4uG7/pbSoa2osRS4jlQNqzpeKYt9aCsHb3k3495/K2o8Q/O1X8RomChztehWuEtVfbryahmubQFgyeVyfO973+O1114bFWAJ/DoCEHv++eeHmo2jMRMGoOXFF/bx61+uJZU2aWpOsnjpRF8DUxsoPvjggxw+fJiTTz6Z+fPn43keEyZMIJlMsm7dOu6++25OOeUUZs2aFT73wP8laINlWbz5zW8OQ5+rncjT6TTve9/7AHjqqafYsWMH1113HdXS0tIShvgGPjRBKoGJEydiGAYtLdL3qb6+viKKqbm5WZptFAVd0yGuYcVjqDt6UMalMVwF7WAWsXcAxTJBSNdtzTDB8H1nDmVJrpiGLlQM00SPW+io0sVbehsOqbPu92dDPMmGnoP8xSN3UPJ3+Iaq0VfKh+d6QqAp8hkka2j1GqwEqVraPh9Iu/3ttL7721iTTsIr5si+8nv2feUSWq76PMlFl4b+LNVjR1VVLMs6ooOzEDIyzLKsIZotIQSKZgxhvT2SudgwjLDsar+k4cquTh47nFS00f9+7ezl/Gj9UxTcCM2DovjalhiP7d/CHZuf44Z5p4XalqM1eQ8nQULDL655gP5SnowZp+TJXDSeEBiKSp+d5+yJJ7C8bRq262KZcYTnSv8VpO+dZli4ThFNtzjl7V+imO322318I6BG3ggqozpPU9QKsP5/Ui3yvYkt/HcARG4P2a1fJ7Xoq+EZqhgEw5KZto04pY4XqW9dSaL1dHntvE8wuONWYhOuxM3tpumsh9ESU/BKvahm/fExD0UlerNgwZ81axbNzc3s3buXfD5POp0eAk50XceyrDGHv0Z3IYHd/4orruCb3/zmkPNq+bZEPw3D4I477qC9vZ23v/3toY9BdX6l4/nij0n8XVPjmz7tk8opZcc1IaSK3YghillQVczWmbLtTqmcq8WvsjvYSfVOIdqm4DMw++i6zq5du7j99tvZtWvXEMBSLdHFPgA7CxYsYNWqVeGxoJyx9GVgw08kDZpbEiSTJvX18Yo6R+sQyKpVq0ICuQCUBGab008/nWXLloVjL3jW1Yy3QgimTJkCwFve8pYK82TwPUgNsWHDBgYHB9m8eTPTp0/nkUceASQZ49atW0MgEtQxGGd/+tOfaGhoIJ/P09DQwMGDB5k5c2ZY71KpVHGtoqvYu3vo/8pjqPVxis/vwe3L4/UVsM6eiegr+Od6YAsUQ6Pw1A6SN50Cuoqzv4/BHz6Ll5W5f8Lw5yoJIlH6SwVObJrAd89+h59cTT7HgmPzV4//XPZfBAzbnouhamzsPoil6czItFB0HeI1nGgDoJCYdy4i2GyoKulTrsacspDDP/0Q5oR5GC3TK6KJgj60bZuurq6wn4bzOwoAaHd3dxU/TFQbER3TR6a5HxgYoKuri2KxOCJ9QwBaenp6wui00WzUgrI1VcXxPKbVNfGWGYu5Zd0TNMVTFF0b/AjSgGzuiy89wFkTZzOtrhnHc9HV0UVg1Co72jeKooTP/ntrH+ehfZtosBI+e6o/f0DIcnrDvNP86/3eVDU810ZR9dB3RdMtirleBjp30DxlSahlOV7zrKIoCM/Di7KIHzcRoKhow2jhjvquo7iXEALPDxcWlDf9kdAH/1OJ/O/7w6GgKoScLCOXM7ogvXDeL3Wjmo3kOx6m2LOGFODm9qIlJuOhgSgz3yvpuRT334NVOIQWG8fgjlvRUlILp2hxel94H+kT/w2z6VQ/iGR4aHLMnm6B2aauro5ly5Zx4okn0traGlL7B40EOdl3dnaGdOpjKSO4TxABsmjRIs466yweeuihMDVALW1ALb8Vy7L44x//yM6dO7n22mtDZl/btiujNmpc+3qCmApzR6J+5JP9pFfCdUBRqlTaso7NV31+iDNjtP4Bn06gDXnooYe45557Qj6W0WhYAp8Sz/NIJpNcffXV4aJRfe6o+yGon+NRLLjouotdciK/1lb3BpFrgU8OSLARqPQDQFPdD9WTfGByrAXWSqUSiUSCXbt2sWvXLs4++2weeeQRPvCBD4QgL5FIDEug6HleuAsPwvKj/RXU47bbbsN1HK5/z7uJCwN94TjqPysZZAdvfY74ZfOxN7ZTen4vStIHOAUHtS5G7levobal0ac04PUXpM9LyjriPB4sRgndZHNPO5946ldk7SKgkDBMeos5co4EPh7Sz0EIQcGxMUyNe3a8wsRkPTMyLRRce2iEiRAohoXwXOpOv75crhHDK+Wxxs9l3F/eUQ57jphIg7lk8uTJfOYznzkiM3MwLm3bpqlJ8q9En0n0PTmSBGVcf/31XHHFFWHeqtGUPW3atIr6H6mc8piWfXfjgjP43a7XGCgVUJXyeHSFwPIjiT721K/41SV/EfpkqGOct2qdW3RtLM3g2UM7+fyLfyJlWKFfjRAy6N30tW+XTj2RC6fMxxNeaKoSQqBqBk4ph24m6OvYSinfR6JuHOse+gZnv+/HeJ57TD4tkRaU26GqaK9j5NexarKEKPOWDNEUCvleecLDE8LXZiroqoamqMeEw4QQlFwnHB+qovqf0bXyyG0s/+aimo0Ir4Tdswa9bgGFA78lNuHKivYAeHY/Rt18YuMvZ3Dzl1FUE7PpNOJT5RxgZE6WDrx+gs4jIaejBi3RyTxwkg3U5MFLG+4c/Bd25syZXHLJJYwbNw4YG0tqtAOD66666iq2b9/O3r17icViowYuruuSSqXYunUrX/ziFzn77LO5+OKLqa+vB8qLeXR3XWuRO95SUU9vuMgfxR/0EvlHd6LV7YxNX1Zxbyjb5aMZmDdt2sTvfvc71q9fH6q/A63ASDvEwOym6zrZbJYbbrihIiHh0QK9YFgoioKiyggARR36HGp9DzRGQf3k/dQQWHleGfSoqiLvXdVv1WA7qlEyTRPbtvnBD37A2WefzYUXXsgXvvAFfvvb34Yh1iDTCezcubPiPkGkzvnnnx+ajAAeeeSRIWVdeeWVaJqGqRuAivPqQQZ/8CzahDryv3wN69SpeJ05P2rNb2ddjOLqXZSe20Pdv14ob+56qOPrSFy3mOLqXX4HV/Z3UGbQm02xJL++9GYyVpwfrHuSzsIgn152CXsHusNkeLpSNnsEqvS2eB0tcZlrqjWe5oJJkqdHi5wrFxbpt4KqhURuqhlHuA56ps2vU9kRN3iGQsgQ9SVLljBWqZ6ThpOR3vNAG/Z6lR2UL4RAVzVKrsOchjaun7OCL730gM+NYqMqUs9VCsxE+7bwsad+xdfPvBohPBxPoKs1OKBqlF1rgSo4NjHdYHvfYT742M8oeS6mquFGfOSktkeQNCw+erKMAnOFGGJ1fOauv+HMd93C4R3P45RyZJZdhZVs9Nta6Tpw9HOq1EIrikIx10PHjufQdAshXI6HxkW++w6GlaJt5sqjqmulJqu8LntC4PjmtjKQqAS4Bcemt5hjwC6Sd2zyTomCa4c+ZMFz0RUVVVHRVZW4ZhDXTeK6QZ0Zo95KVjhrB+IJgSuk75WhahVAqhboLR9T8ex++l76ALHxlxOfch09z1yNcIvEJ18Nosx4qyg+L87ka/CKnXiFQwjhkN3+PeSzc6lffpvsp+MZPVQttV7uwPEsADHhC+jvTM4880zOPPNM2Vm++WC0A6C6A4PcNjfddBNf/OIXyefzoTngSMAFCBPpCSG47777ePnll1m5ciWrVq0KI0CEkORjAXipBWCifTDcb2ORsIwjAboqVDzkekWR4dK++jUAK4ZhhKBv69atPPLII7z00ksUi8WKzMhH0rAE303TpL+/n4suuohzzz23QssR1MO2XSIbjCHNiCo3FVWhWHTRdQ3X9SJlSVZLu+SiavLFr+w3eY6uKxVgq/y8PXRdpbpbHcdD06pMA74aNtr2AOjt3r2bH/7whyxbtowLLrgA13X56Ec/yte+9jX6+/u5/PLLQ9NPNSh3XZe+vj62b9/OlClT6O3tZfz48bS3t9Pc3FzRZ4F2QHgeqAqpD52Be7AfkbNJXLcYY8E4Si/tl5Fqtpy07M0dFP60mfSnzpEh0gC6hijYkljO9TttSN+V215wbPpKeVzhkbHiaKoa7p5NTaenkKPoObTG0+TtEoN2kbxrMzXdyO6BrnACHbSLrBw/k/ZcP60+kKlYIHVz6DFNR3hyoamOHIrOMcMxMg8nAUAf62ITnXOgvJkZiwRAdbRlR88LuFE+tPAcHtyzkU09h4jpBo7nSUI5oOS6pM0YP9n4LJam84WVb0VVGBJRNFzZ0fnCEwLbc4npBtt6O3j3g7ezd7CHhG5g+2WCb5rSDLoLWT568nnSl8U3EYYrsvDwBHiO1Kp37lnDSRd+HLs4iOdr65ySDOsPtC1HC1wURUG4Nopu0r33VX79r5eQqA/ZIo5NpEUeuwCNkyZz3Rf2hAk9j3hpjQVffkpHakd4WJpe8Zw684Os7z7Alt52tvR0cDDXT28xR08xS3+pEIKWvGvjBqDFk0y0mqKihaDFJG4YJHSTtBGjMZak3kowIZlhTkMbJ9S3Mb9xPA1WogIkFV0HXVWHbDSC9sj5UXKAZbd9G73uROJTpLKibtFX6XvlQ8QmvhlFS+DZvX4XCn+f7aEa9aCoCGcA4ebREtPIbvkKqtXig50jr6PHZB6qHmS1NCdRE4LruuEu/Ggmkej9gmy+kyZN4uabb+Yb3/hGaJI6EnAJJJj80uk0nZ2d/PrXv+axxx7jtNNOY8mSJUyfPj30eQg0NOGuVFGG+MD8OaQWkAoAiqJoKBGtiqZp9Pf3s23bNp566inWrVtHoVAgkUiEjrPBvY5kEooCltNOO413vvOdNdMkCCEwjLH5MOn+ghaLGyH9tuZrRQI2y5EkoPgP6iHvqZLNluhoH8QuueiGSnNLkro6CVwDP48ocKluO0BHRwfnnHMO550nd5dBv/7N3/wNDzzwQEUfRk0+IEOkzznnHJ566qlwDBmGwbhx48IopkAjVCqVMHQDoYAiBLHzZw9tqOPJGdAfhtq4OjKfvUjW13YlJ5AnVeaKWdv5tnwrmWH41c59fOu1R0P/BcfzEAhufvS/w0zD/aU8t5xzPasP7eBH65+iLZGm5LmUXIeDuT5efWwfnpCTckwz+OLpbyXu58VSGPquVACXiHmheqMSzCNHE5V4NGr96kVnrDnTjrbs6Bxjey71VoJ/O/VKrv7TD2V9iC4gcqeeNmP8cN1THM4N8qXT30qTz5br+pE+mlLbcdgTHq6Qi4qualiazqP7NvM3T/6SA9neIYDFE9Is1V/Ks7xtGn+/VJosA80bkeeoALF0C+3bVhOvayNZP5HeQ5sw45J4zIqYwI9F0yKVznLcJOoncNKF12EmMnjOsaWNAXymXxXXKZKonwCMba4fsln2eXc0VUVDpa+Y57XOfTzTvoPVB3ewsfsQWacY5j0CCV51VZOaFFVeW6fFpJ9KpD6B2U4IaVrK2SUGS0UOiD5sz8XxNwRx3SCm6aTNGCc2TWDluJmcOm4GC5smkjBMv55S06+rlRG28tnKjXByzsdRVAuEQAgXLTGJhlN+hqKaIFz5W0VfqMSnvnNIH3nFwyhaPPgrbMtx17QEDyT4HE4VWa12r47UGctgre68IDR3/vz53HzzzXz/+98Pd/rV5o3qxT0KXgIuEcuyGBwc5N577+XBBx9k9uzZLFiwgAULFjBlypQhdumoViKYZKIamddbAvAQaLiCPqme2AuFAlu2bGHdunVs2LCBPXv2hFEY1Sy3w+0kqxeRALAsW7aMm266qeZYCMDD/X/YTOfhLKal4blixN2PAriewLJ0du7oImZJbVz/QJFf3vlqBdlctG66rpEdLHLm2TOYMbMJ1/UqTD+PPLiVF57fy+GOQfI5Byum09KS5OSlEzn/wtkYhhbWt3pMRrWHy5cvByTt/09/+lPe/OY3M27cOHRd501velPoeGmaZuhMGzyLpqYmPv7xj4/4TCWbrCnHlJ8zCFVBFB3JiqspiFwJtSHh92NZ16wkfIDtemWuoGhfjfBsDU2ynJ4ybjqnjJs+Yh0DOX/yXM6fPHdU5wpR6WsRyJG+D/f7WGUkLemRrjlWGUvZ1e+goWrYrsuqibP55JIL+Ozz99EUS8oFTZTPd3xT0T07XmFd137+ftnFXDljUaitAekw7QovvE5XVDRVDbFsR76fb736GLdueBohBLEagEXzgVSDleQrZ7zd1/y4oTbO81xUVSPXd5CX7/s3evavY//6Bznj3bcAoJtJ2rev5sXf/CN2URINLrniM+hG/BiAiweqzFeVaTuBc2/62VHcY3QSmjdHqGutMR6YYIJ+Wte1n7u2rmH1we281rXfN8np6IqGpso0AnWmNCF7InDElaDE9TzKxhdRoamO/i+fsYKhaCQxKzRlrhB0F3I8snczf9i1jrQR4+SWyZw5YRbXzF7GzIyMbLQ9F803W0WBsqIooFgyr5eioai6vwb5/D1ONmIiirgMu3lkdm8Nz+5Hi7XiDGxBtVorzn3dNC1RGcvkM9Lx0Ug1cHFdl8WLF/PBD36QH/zgBxQKBSzLCj38a4GjKHgp+ztITUEQ+bRu3TrWr19PKpWiqamJefPmMWfOHFpbW6mvryeVSg0BMgGICL7Xqvto5Ugq3QAsVe88e3p6wqzM69evZ9u2bQwMDJDNZjFNk1QqFdZzuD6qXrQDzUGgtenv72flypXceOONoVNktXYrAAEvr9nH9u3dJBIGrjO8AyMQ5tryPEEsphNLSFTv2C6PP7rdz31TuRZ7QoKcrs4sJ8xtCUGLpum4rsfPfvoyz63ejRXTsSwd09JBQFdXjt//dgN79/Ty7vcuJRYzKsZW0A/RvgmctT3P49577+XMM89k3LhxYQSW67oYhkF/fz89PT0VzzwYF9u2bSOVSjFhwoSw/23bJh6PUywW6ejoGDJOFEsvRwVYfs6Ua06uPC/IZaVFwHrKou4z0r8l83kZglqtcQm1GIoiNSRRJ+rgnKrnZKgS5EQ5O0Y691jU///bpLqvDE36k3x88QXs6Ovkp5ufozmWlBTzEeBS8lwyZoxdA13c9Mgd/HDDU7zzhBWsaJvO+GSGlGFhUDlfdeYH2TXQyR93b+CXW9ewZ7CblGGhKmpohoJyZJmiKDiey9dXXc2i5kmhWaisHZJzkZVoZME5H2Ltg1+hrmUmGx79Lq3TT8FzStS1zmTBuR8i19+ObsTRtGMNLw7mLLUqesifKRT5XXi+eU8EGydZ3zBBpvAYOnpF+V6K4vvKHBmwRM+xXcl1gwLPt+/iu2sf54n9W+gsZDFUlbQRI2VYIbARSI2MXSM+aPgDwSH/f6mOC3+IrHwhoDFUFUvTSJsxPCF49tAOnjm4nds3rubCKfP5wElnsaBRapdC81+0R4QXRvmE7Q0IHJPTkD5FEPi0yLkpHtYliEA06hehxtr8o0fWyr+xmcGOg9TSBgSmokWLFvHxj3+c73//+3R0dJBIJMIMv4EMZzKKmj0CzUOQl8a2bfbt28fu3bu57777SKfTjBs3jra2NlpaWmhubqalpYWWlhYaGhqOWo08Vsnn83R3d3P48GEOHz5MZ2cnHR0dHDp0iPb2dmli8H1YdF0nk8mEJrpo+0cyBwUS+CB5nkcul+PSSy8NHa+Hy+cU3M0wNCxTwzQ1XE0doimpFuErD1xXkB0oynkHBcsHG9XziucJTFPDtHQ0P6dT4DN4370beXb1bhoa4hQKDrmcLbUpnkDVFBoa47z68gF+nTJ5x7uWhGVHx1m0b6L+TU1NTRU8Lqqqhn9feOGFZLMyq3E02ahhGHzjG99gyZIlvP/97w+jmgLgOX369P+PvfOOk6M48/634+TNu1qtcs4ghBACASKJnA0Yg23w2T7j7Nf22Wf7fLbP2Xe2zxnbhxM4knM0WSAkhARCICGhLK1Wm9OkDvX+Ud09PbOzq11phX1nHj5iZnu6q6uqu6qeep7f83v4+Mc/HijDA5RHQHh9q8SMoq4IhzEHyqcqE3aCBOqWlll6vqqomNrw3C+aoqANN7HVIPf9R5BDtSqHr/VH0/dPvoLOXJr7d2ygJpr0LC6F8Zb3MCkAa1p28vTerYyJp5hdPZamRCUVZhRD1cg4Fp3ZNDt62nmts5mcYxPXTarMGLYH7vR3164X1oyAjG3xnWWXccHkBcFiXNo+IQSaEaFq7Gxcx+Loc/+Vp373Pjr2vkIkUY0ZqyRWMYZYxZigvaOh0EoryMDoIdexpYtUL88S7rpycVXVgytPI1ZYPHK+vX1dfOfFh7j1jRdJW3niukltNIHrKZtQULN88Z+5H+Xjhy2HXULh/4etLmFXkUAE0Uh49/DnNkcIQG48Kk2Zw6nPynPz5tXct+MVrpm1hE8ds4LqSDxQXAptHAgFULQYQrhEx13i/eagaNGy1mtFk2Dp+JR/8o653rGh34X/dUoLDFQ0FEUJXEJTp07ls5/9LL/+9a95+eWXicViAZ5msMmjnGtDCFFkhTBNM/juOA47d+5ky5YtCCGCiBvftB+Px6mqqqKyspKKigqi0WhALBaLxQJFwneX+a4H393kZyjO5XJkMhmy2SyZTIZ0Ok13d3fwL5vNks/nyeVyZLPZwArgh9T6QGP/Xzlei8EsK/6xcLJBPxT6uuuu49RTTw3CPgdNQOl9NDZV4ApBNGrgOAfP7+JZIL3+Lhx3XVEWzCsEmKZGRWWUZFJOPJGozq6dnTz1+BtUVEbp78vT2JTivAvm0DS+krYDfTz0wGa2bW2nqirK88/t4qiFY5m/YGwA2j2YNbBUIYZCiPX8+fO9OrsDrHFVVVXE4/Gisv0+bGhoYMWKFcFzKFIC/e8UQKFBzYaa8D0XU/D5lrwp4rsVD9XKFH7+vhXM1HR+c+a1/PPjN3Pb1nXBwmcLx+PkkMzEChDTDZJGhKxj82zzG+TdAkZJVVRvpy3BmnHDxHHdgExQUZRgkYtqBhlbzh0/OPlKrp61JFiMYZD505V4q2TtJAAmzDuH/a8/xfSl1+BYEpxrZfvQzRgo6iH30dD95iJcN8gqneltxcr2SiCwInljzFhlEM0U8MqUWV98Gap+pec7rouhatyz/WW+uOputvW0UhWJU2HGAtZd8JUVuRvTVRXVsybjHbdcB8t2pDLpgW9dTxkRftCAV5KieEoO0qWjqxKYa6iaZykpqDm+pdTfCfrKk6ooUnl1HX6w/jEe37OZb514KSc3zSijuAyMNlIU1bOyCBSlfLBN4VzNczEphLM8DyV/d0rLcHcl5RYU31VUW1vLJz/5Se666y4efPBBstkssVisCP0/nJfSX0j8f/51Ph4kFpPgId+11NfXF+BLdu7cWXSdf21pW4dq22Bt9V8UX2FQVZVEIlHk5goraeFyhurbcvXzyff6+/uZMWMG11xzDVOmTCnC8pTrSzlhy/675t0jD1E9HPEZdR//6xsSA2M5VNfGed8Hjqe2TmZzrq2NM3FyNT/94Ur27e1B01SefGwb8+ePRdMGn7TCfZPNZgMwt+/68RU5n2nXNAeG7/X09JDJZMpe51vCfCW5tE+DnexICBq1ks+35E2VQ7G0wMBNlKrIpH6mpnPjGe9mQrKan214SkaL6CaW6+CGFFmJfZAWk5QZRQstMFAId7VdpwCSDv3mR5F0ZtNMqqjhP5e9jbMmzi12FQymWHuWw2Mv+gogKf2F65JLd+FvO1RNHwC8PhwpVZoUVUXRVHZveIBdL99L28619LbvwMr2oCgqZqySiobp1E85nkkLL6Zx+rJgzh7J8wrPm2GFRVNVvrf+Ub75woNoikptNEneZzcn5MBSFCKqjiMEGTvv4YgAFAxVY2yikrHxCmqjCWqiCZJGhLgRIarpMlrI43CxvYikrJ2n34vs68imac/2sa+/i/3pniLXr6FqxHQZuZUPK1BCkBcyDLs+lmRL1wGuevB/+OaJl/Lu2UsDDFN4Xhr4DIbGrhYdV8vTdgwmf3dKS+lAPVhCxHLX+yDcSy+9lAULFnDrrbfy2muvYZomhmEElozS+5WWE/6EgsLiW0XCAFbf2lN6TWl5hyrlFJywUhTe9Zfu3gdrW7ic0oXRXxT7+/tJpVJcdNFFnHPOOYFFqzSnUDkFyT/mOC6l1VcUvx4+3uNQemWgKIrM+trenmbr622YpkY2Y7P81KnU1iXI5WwiEZ18ziYWMzjrnFnc+IvVxGI6O7Z3sHt3FxMmVkmrjjL0c5s3bx6PPvooTz/9dBHZmM/n0tvby5lnnsmCBQuCXFcAc+bMobm5mf/5n/8hn88PwCRpmsa1114bAHkLbSv06YEDB4JrB1N+35K/nfgKaENDQ0B6eChzQOl8qKkegRwKX116McfUT+Cra+7nje42Kswopoc18knKQPFcAzb5cuUH/5f7dV+x0RSVPiuHK1zOnzyfb554KRNTNWUVFr+e5eosXTNa4ApyrByLPEVGDVHlj6qFxVNY8pluVt/2r2xZdRPCsUjVT6NyzAwJ+gXsXD/9XXvZ//B/8frKXzPv9I+w8Px/Qz0EVuHwOf6i/tU19/OfLz5ETSSBI1yyjoXqWcN8jJChqNjCpSXTS0I3GZesYmZVI4vqx7OgtpFpFfVURGKkzDhxPVJEBjccyTt58q6g38rRnc+wpesAL7XtYU3LDrb1tLGnr5O843ihzwq2Z8XxXYMZxwrcjR9/6s+krTzXLzhlUNdgaV8MZq0eybkD+vogE17Rj4e6Yxi08DLljOY9/MXVcRweeeQRHnroIdrb24lEIgF4tFQpGum9/94WjOHWP9zPYfFxK7lcDkVROProo7n00kuZOHFioESOJAJsKEuFbbvBbmOkYdGDie/aWbVyJ7f8+SVUVSGRMPno/zuJ2rpEqM5SwcmkLX7yw5U07+1BAGedO5NzzpsdlDOYpWMk72ZYmSxX3lDXlf7uv9Nf/OIX2bJlC4lEYgBGqfS+4XuXk9Id01tlHF4ZvoWts7OTr371q8ydO7co2/mhSLmdqi2k+6G5v5sfv/wEf3h9NR3ZfiKaTkTTMVQdgSi4E0RxpImiSHyE6vF7uLjkHQdLOFiOw9F14/nwglO5csaxQAhQyuBjYbD3u9RVUzr/hMvwFaPNnftZesu3i/IdOcIlrpvs7evi7gs+xFkT5xalmgieh3B56rfvY+uqm5l8zCVMXXIVteOPJlE9Ac3LiWXn+ult30Hr9jW8/txvadn6NAvP/QLHXvwVycWiDn9O8tvj99ENG57iX5+7g+pIPLCuhEHNhkfW15vPUhtNcu6keZw+fhbLmqbTGK8oew/HtQFVMuaG2ioQaJ61wnXtgHxQDfGtlJPdfR08vXcrj+3dzMO7XqUnnyVlRLz8VgNB2Jqikrbz3HDaNbxt2jEDFJeBz/zg3w8iZU8akaWlePfsjJjkyS/Dr3A5crnSe3R1dZXFBRysjslkEtM0gxw055xzDkuWLOGxxx5j5cqVtLW1BRiUwbK2DqdzR0N5G2052ELoSxiT4vdbX18fpmkyd+5czj77bI466iiAwFJQjl9nqPuVm9D8qKJSRaXcRDYchah0QADs2dNFPucQierU1SeorUsEbiNZNti2Qyxu0NRUwZ7d3QDs2tE5aP1Ld71hF9lQUtomyT1TXmkc7N6lEo1GA36dQxmHb8mRE19pCVvRDmWeKH2//XcO5NxpKDIcemyikq+fcDHXzjmBW7a8wN3bX2Zvfxct6W40D7dielTwfgSQTxVvuS55N0/WlrT9tZE4s6vGc+2cEzl3yjySRjQgLwsvUEXWFG/+LHUXF7dDumps28Z1HQxjdK0swb1cG1Uz2LH+Hra98GeOvfgrHH3OZ4vcUK5jS0yLGae6aR7VTfOYvOgynr/l02x87IdMPPoC6icfJxWAIXLglD4n28P5PL9/O19/4X5SRhQrhBMBqQCYqkaflSNhmHzkqFO5bs6JzKgqDvntz/eyv+cNycSrRxhfORtdk2HdWlGdpLMpa/WgoBI1ksiQYpWs1c8dr3yfy4/5NyzHQlU0iYNBagMTkjVcPWsJV89awqsd+/jlxpX8ZcsLZB2bhG4GiqJvGVKQSvIXnruTBTVNzKweg+26HvuyC140UdH87doeZklF+KDxw8zfNCKlJfyShjlXDlUOpq13dnby7W9/m/379wc4jeEsEK7r8olPfIKlS5cGx23bpqamhssvv5zly5fz+OOP88ILL7B//350XQ8ArGE8iF+XcNmjZWU60jKYJcX/zX9+fthzPp8nGo2yaNEiTj75ZI499tjg3HDyQzj0SUYqolJxaNnfy4aX9xON6ghXcMJJkwdYNvxr5GQtigaDz2Ibro8QAl1XcRyXttZ+dEPFdQXjxlcOViMAmsZXor2wB4Curiz9/XkSifIDK/wODDdKrPAsXBwrh6pqqIc5cH33pP/5v+Gd/EcRaUW0i1y2/qf/zA72vEo3COXeOSFEEA7tCsHMqga+cNx5fGLhGTy7fxurW3awuXM/e/q72NffRZ+VJ23nsF2ZbDGmGTTEU4xLVDMxVcNRteM4deJsZlbUB/cIM+sOZnUMuzeHskbK8anjLzujbbkPy7YX/sLYmctZeN7nAbCtrAytVpQia49wHRw7hxmrYMnbvk3rjtXsePF26icfJ9mZD6K0hNugKSq26/DNtQ/Sb+WpMKPewu9lXvcUlu58hqWNU/nGCRdzTL1MzipB0BYxPcLG5id4btstpKJ1OMJGQSWd72HF7PcxsWYBrnACFtucnSGix3lq65+I6HGWz3gneTtDxEjgCpv9PdsAqTRpJW5oV7hYrouuqMytaeL7J1/BFdMX8bnn7mBD275Q/ZXAdRTVDJr7u/n2iw9x4xnvRpEgGBRFBaWMZdrHq7hOkCzVd98d6nMfsaXFN3M++OCD3H///VRXVw+abbX0Wj+8U9M0JkyYwHXXXRdYQsqJT4Hd1NTEO9/5TqLRaFF47VD3amqS8eX+gPLdHq7rUl9fz5VXXslZZ53FK6+8wtNPP83OnTvp6elBVVXi8XhQXyhgR3zLxHAtAW+2lCoqfr39nVH4n2VZgVWlpqaGRYsWsXTpUqZMmTJg91Q6eUKxxWwk4joCTYPt2zr41S9WU1sbx3FcFi+Z4Ckt0goi6y9BtbquousDJ8Cw5cQ/X1UV+vtzdHdl0XUV23apb0iW7Sf/PvX1iYBBt78vT3dXlkTCxHUFmnZ4+KTiiVmV0RIDjh8a1iGstP29vINvCQPGHBTmCn+DNBwppRLI5/P88Ic/xHVdPv7xjwdYGUnfTsB4GlV1VkyYw4oJc8jn83Rk++nKZcg5FnkPrCujSVSqYknqEiliqgEKrH5uFR//89d52xWXc8qyk9B8b8sQVtB0Ok06nSaVShXhd8L94M/zt9xyC2vWrOGDH/wgU6ZMKcLHjaZ07t3A1CWSksG2sujGwCSKiqKAoqGbcex8hkiimpoJC2nf87LX5uFEOsrn6zNK37/jFZ7au4XKSJSc4wRhy77C0pXP8Pbpi/nh8iuJaAaW4ysFENMjZK0+Htv8G/7phO+TiFQH99nbtZm7N3yXD578CxSKlUSArN2HqUX8Svm1I6LHB627qqhEvPnTcV1cBCeOncbd53+Y65/4PQ/u3FjkmlORmb+rInHu2/EKq/ZvZ2njFGxAy/Vj93di1Iwv6hu7cx+KbqKl6mi/8z/I7lzHuI/fgbByKEb5EPSDyYjfFv8h1dTUMH36dFKp1JBp2v1r/N16T08PTzzxxJAEbOHrbNsmmUyyYMGCkVZ1wOApTpznUlVVxUknncRJJ53E5s2beemll9i6dSvbt28nm80WhSaHgaf+9UNZMw4mB1tkDrXsUpeP397wztxxHBobG1m8eDGzZ89m0aJFpFKF/DClE8mo7oi8yyMRyUhbVR3zcgwVn+a3W9dV+vpy7NzRSX9fHkWBysoYk6fWSN6XkOIir1HIZm36+nJSERFQVRWjnPhtqayKBkpLJmPR35/3yju8pvr3CAMDt6+9lWiqnklHXxSYcd+S//viM3Vv3LiRvXv3Blm+Sy3Muq5jWRbTpk1j8uTJRQBvIQSvvvpqERYvGJNC0vDL3W3hnTJNk0bTpJFqhpL+bIZENMau5n2sX7eOFV6aCttx0MqA7kHyV5mmyZ133snvfvc7vvCFL3DyyScHx/3NkX9uPB5n586drF+/nr6+vqDMIyGKquHa8t4MQ/nw3TLCsVEDLMvw5zrNmz/+vOUFUAiiuBTPteJbWC6cvICfny6p7MNh40K4WE4Oy8nhCJudna8wpXZhQKa5u/NVFBQc18ZyckSNBLaTJ2rIaMjdna8S1eOcOvNaVKUwd0scTJnWlszlMq2AtKxVRmL85szruPyBn7Oy+Y3AzaUqSjAp5hyLP29dy9KGCWiqTvfTv2b/rz7KrJss3HxaKqu6ydYPjaPh2u9Sc84nyW5/gczW57zeFgV2mRGuKyN2D/kWiyVLlrBkyZKRXA5Ae3s7zz77bEDcNpT4Cf7279/Pl770pYOe76eDr66uLqJLL/ULh5UX//isWbOYNWsW2WyWvXv3smnTJjZu3Mi+ffvIZDL09fVJc6xhYBhGENlRao3xpXQwlvqCRyKDuSnkJ4Aqw/souO8sy8KyLGzbIRIxicVi1NU3MGP6NI466igmTZpUlKTP95X7nCwjqc+hKDWuK8jmbHJ5G9cJAQSVcBSA4JGHX2fN87tob0uTyUjlOJEwaWhIcuqZ01h83ISiXEMAVt4hm7XRdQ2BTTxZDIAsrWM8bgbHcjmbbNbyWzastgzWL4XjjjTxdu/nrz9/H2OmL5BKi+uiDJPI7S353y3++HrmmWd47LHHAgt1aa4uH8f3wQ9+kMmTJweEhL4kk8nyEZVKIR3JqlWreOyvfyUWi9HV3Y2iSvCmABDSLRAxTbq6urj66mtYvGgRqpfjKx6NUllRie7ds9QFW/rd/1vX9bLuIL/u/mcqlSKVSh1BAk5Zh/rJi9nzyv0cdfanMSIJbCuDqpkeEW7BlCuQSR11M05v2w4ObH+emSde67VreGPTFS6qorKnr5PVLTuIaoZH2ua54hWFrGMzMVXDt068DCDEJAwS36ZiaBEMLcIHlv2Mx7f8jq0HVmO5OQw1Stys5PqTZSoETdVxhYOumWTyvTy+5bdcvfhrPPjqT3jo1Rs4e+71gAewHgRMXOrm87+bmk7eAzZ/+8TLuPi+n9Jv5YNoIllbaal7pvkNutM9VCZrJNDYAEXT0WIFILGiF/pRjaXQElJ5FnYejIGkc8ORQwLiqqoa+GyHc0Nf404kErS1tQ1gZT2YqKpKZWXlQROl+ZaZioqKIldOWFkotbz44odBR6NRpk2bxrRp0zj//PPp7e1l27ZtvPHGG+zZs4f29nba29vp7u4OsCG+EhTOQRQGaIZNpKX1KifhevqTga8UFbt+XBwHXDeHY2URioFtu8RiUWpra6murqGxcQxTpkxh2tTJNI1tQDMK5kLf567rxpD4pFITd6B4uVlQTYZLClQqqip9pUIJa93yuGU53Pybtax9YQ/RqCEZb81CHfft6+GmX62lpbmP8y+aEyRWlO1yyecdDFOyeEYi5V9z/56RqE+SJcG5luUEdRmOlL5jrpMHIVD1SFGfqJpBqq6WeGUTwQ3fkn8I8Tc5H/jAB/jABz4w4HffffLHP/6Rp556KsCUlQLfJZC1vOXAP37gwAGefOopFi9ezIQJE7Dyea8cQAHhSmUiZkZIeFxT/qvuOA55Kz9iqoly5yuKwksvvURvby+KohCJRGhtbQ2suUdGZEumHncVW1f/iVV//n8ce8lXiYfYdwsVlHOAasbpbtnCqr/8P+xcH1OOvVz+PMzoIUcIVAXWte6mz8qhK2qIfVZgqDqduTT/MnsF45JVRdFOPiDfdi2e2nIzlptDVTRURSXn5ekRIkNUJHhk0y9xXJuEWcXJ09/BG60vsHrHnRw3+RKq441cfNSn+f2aL3DnS9/h4qP+hbTVQ87OFPXLgC4oUV58xSXn2MytGcvbpi3iJy8/QX0sSdaxA1I6Q9Voz/azoX0fJyVrcASILHQ99nPcXJ/EDhkx3DzBPCdsK7B6+YrNoVjwDwmIO1LfLBAsiv7gHU4lfctJY2Mjn/zkJ0dSVWCgT7h08Q3Xo5SZ1j8/lUpx9NFHc/TRRwOSGKy7u5vOzk6am5tpbm6mo6ODjo4Ouru7i9wwYTeSbdtEo9GyEVOlEn6QPmFdkXKkySzZumYSjUaoqWmgdswU6qsNmsY2UldfT2VlJdVVVRhG8TNyXcfjIClYVISbk2yQQV4fBRSlxKcrUBSjmGZZ8/EZ0s1xOO4jf0j5VpPbb9nAC2v2BPT7uZwd5AbKZm0MUyMa1XngvteorIpy0ilTyOUcNE2Cbx3HDdqj64Mou15V9SC1gILrCBw7FGHl5LwJWRlwqRACTTeDBGrggYQH0IX7OzsXK9eHYw09kbwl/3fFx/+FNzkg5598Ps+LL77IpEmTqK2tLcqaDoXNz2BjzD+eSCQwDINTTz01YFc+mIQ5poaLkfLPsSwLVVXJ5STTrc9Armkat912WxCan8/nSSaTATbxSIjqRdiMm3Mm8077CC8/9J+073mJycdcyphpJ5Cqn4YZla7wXH8H3S2vs3/L02xfeyu97Ts54e3/TVXj7GFHDgGBgvJ6VwtZ26LSjOF4iQM1RSXr2DQlqjhtvMzkXi4MWUEhYiQw3CiJSBWmFkVXzWAett08eTtLX64jcAnpWoQVc66nLilxJHGzkvcv+zHt/XtRFIWaeBO1iXHBHYaSUuVB8z7PmjCH329+PsC1+BtnQ9XoyWd4o7uVkwDXS3CkaBqKpsv7aZqcV732KmYcq3U7+3/5HtRENQ1Xfw/h2gFYd7gyYktLaQOH6yrwJ3XfTDqcQeFbdbq7u3nyyScHsIuWc7Woqko+n2fSpElMnDhxwMAvbUs50FgpniOcTLGiooKKigomTJgQhAP77crn83R2dtLR0UFfX18AUOvr68OyLDZs2EB7e3vAETOUpcVX2BYtWsSYMWOCRIfxeIx41KCyspKamipSFTUodgd2xzNEx58F+JYUB+E6WPksqh7F6duC0/MSsfGXE+4O4aUQ99OIh2tUrnb+SyZch+zeWzFrT0SLT5DlKCOIJispvKAMq2zcsJ/nn9tFZWWUdNqiujrKeRfOZdacehzHZd3afTz8wGZs2yUeN3j4wdeZPaeBuvpEqKKFeygHGbCBxUMJPNtBGaoW4WAbrjA2RQiXfa/9FcfJM27OCjTdJKycKIoWDOLRlCOFDfhfLwoDdcNyx4Zb3CHsDKHwfMJuZT8NRzqdxnVdtmzZwr59+7j66qsBqeD47nhN0wJ8yMHEx6319PQA0NvbW1ZR8N03hxoF6kcfdnd3E4vFaGlpAQp5uAD+/d//HUWRIOJYLMYPf/hDHnvssSPiHio8F/lsFl/yNcx4Fa8+9mNeuOMLRJI16EbMU2wErmNh5/vJp7uobJzFye/+BTOWvgsh3EBhGc6z9p9tW6aviN8EZNROxraoTdUwu6oRIQoKQXjt0VSdZVOvBGBb2zq6My04ruVxpKg4wqaxYhrjqs7zrnWZVCNxni/uup+uTEvgNoqblWxsTmM5WY4eJ5XWkSihst7y+c2rbSJpROnMpTFUVeYtQvK2ZG2b9qzEJrmOjRqByuXvKyqz5X8+AB7brrAyaJVjGPOeX2B37fNuOvJ375DfnPDgtSwrwJ+ELRu+xSHM7OmTlpVTJEotIkIIYrEY+/fv57e//W2RO8oHl/oWCP9YNBqltbWViy++mPe85z0Bl0a4/NJ2hD9LLTCKohSFd4c5XcJuIE3TgtxCfuRSqfz85z9nz549RKPRIcMefbdTb28vV111FY2NjUM+C6v/AC0PvJ1x1/agmnI+VtFQVA1DS4MKmeY76Xr+M4x/twgUDOHkULQIfa9/l+zuP6NGxiCcNIpegaKauFaH7AM1iptrITbpXSSmfxThWoCg7ZGrqD39V8QnvwfcfGB5GZaULhqiwOHyzNPbcRwX1xXEYjrXvW8JEyZWBaeeevo0qqpj/PbGNcTjBh3t/Tz/3C7Ov2iOrK+CTB7oPUv3IAu68Hzbihe5FDx7TWPtXV8k3b0fI1pBWJkRuOT6O1h43heoapyF61hyMnRsHr/xXeT627nmP/ehpeoZThTCQburZGz4IpWlcu+SbzMb5B3z/pPWKBGcF/4+PDkMDeAIi3RhMlD7FiOLACst81CuNQyDlpYWfv/73wd5wsL/FEWhtbWVhQsXBpuhaLQ44iWRSAR1GEr8+vmKQTQaPSxiO1/Cbfc3cQcOHGDXrl0kEglefvllLrzwQlKpVFBHvw5+yhPf0nwkpHQzrao6C8/9VyYddQF7Xn2EA9ueo7dtO/lMN4qiEKsYQ+WYmTTOOJnxc1eQrJ1URCo30mftMwj7Y8KD4sos9JpO3DADXpPS+rpC0mz0ZFv5xcoPsWL2+8ha/ZIeAZW8k+XOl77Dl857BNvNE9HjWE4WQ4uiqrp0K3kW3+7MAaJGkozVy72v/IC5Y0/BdR3UYSinwTzj/V0TTRSyeIezJCkKtnBI+2Bn3UTkofnn70Lk0lLxM2MIG/BCnRU9gpaoRdEN9NqJA/pguHLISkv45QgPiOAheNFC4agbKCgtpZp2GIjmKwSpVIrPfOYzwT18jT2VSnHrrbfy+9//ng996EOsWLGC3t7ewBLjd4bvN/WJ0YbTQaW/DYaDCe9OBmJNRNG1uVyOeDxelrJ9qP5VVZWenh4aGxu9aCYT4VrkWx+Xjg9ho5qV2F3rMSrBarkfLVKPEBJToegVGNWL5dDRU2iJ0onLq6edxs13eqgpjfyBR3Az/UTGLkPYaRQ9gZtrx81LJUY4WVAUtAQomm/dGOFEHjpdPnePFG53F9u2thNPmPT35bjoknlMmFhFPm9jmrrnanNZeEwTG48bz/OrdpNImLy0fi+nnjGNRMKUicL88GnAtsqTrwlPSbEsF19r0TQVXS9gXLavvZXuli3YloNjSSOJcCBenSLb18vMZe+hqnGW5HTQDFAUElVN6Gacg5pohinhd9ZxC+kAZDTBYLtvFVc42G7OU12UwIokEGiKga4aCFwUVFxhy7MUjZEoIY6w0JQjBaosFl+hFCKsxPljbuD5csdfDhgtijBQhyIjnWgdx6GiooLq6mr6+/upr68nkUhQWVlJKpXi9ttvp7e3l0suuQRN02hpaWH//v2BVda3II9k51/6ebhStMh6dXrttdfYv38/CxYsYMOGDaxatYoVK1Zg2zaGYbB161YymUywAe3p6Qnc8KMtpXO1EALh2FSPm0/1uPm47sdw7TwymR8oqi7du55VwbHznmX00IIL/ESC/vjxr1KUwsYpXFRp2Zqqo6kGC8et4IxZ7x1Qfme62TtHzoOGFkUIl4Xjzxq0Tm19u737Hpp113IcqaYohVnBr7EqBGZEWvYrT7qOMSdeg93fIUG2momim4x5743gRTC5/R3kmzfLtuf6UKKpQ9oEHJalxX9x7733Xnbu3MlVV11FbW0t+Xwe0zR58sknWb9+PZdddhkTJkyQneDtKkp3EYGZrIS0rnSHEIlEguP9/f1BOX7I7lAy6IvorV6DubSG0xflvvviW5pGmicmrBAahu4lILToeupcvHEnq66CYih0PHVV8LdrgVFdz5gL93sn2hIIFSpbpgx3SM39Iqm5Xwx+69nwOTK7fk/dac+UrZdqeOHRjizXK3HQNozkpdy5o5N0v0UyZZJImBxz7Div/YWMsr759ZjF41izeg+qqtDRnqF5Xw/TZ9Sh6SqRiLejU2QI9FCSy9rBomcYGmak8P7FKhvJ9ndy0mXfJF7ZiJXtw4xV8MqjP6B5y1Popqe0hdpoWxnsfHr4SN6DSPidNUPcBgJBzEiRzvUNsJAI4WBqUWriTQgEtpNHUzUURUVBJWv1krZ60BQdRziYWsw7LzeiCS6mp8g7mYOfeBgSVi79DY2mqdi2zCSraWqQnNNXauT5Dvm8Z531QagCNE3x3o9Dx0KPdKJ1XZdYLMZ73vOeAb/dfffdbN68mWuvvZYZM2YA8OKLL/KjH/2IhoYGstlsYNGNx+OjYjU5FClyZ2gauVyO+++/n8bGRq6//np+/OMfc9ttt7Fo0SJqa2sB+NWvfsWmTZtIJpPkcjmqq6sDS/NoSznYgqIZuI6c91TNQDWLrcFCCBw7j6KqaCHCx5EoLP4pFWYMTVGKVBa5QZB5nPb1d9GUqApo8cvfQ9Dcs5WcnaY3246u+e5Em7a+XUGdC9eqWE4OVzgEFh4hMLQIPdk2ujMHgnIPJuG6+PPJzt4Oso7lubQKaotMqKmR8vpTNaMoagIjXlVUnshnEJ57qPbyr1N5uoxsUsziLPcjkcPaIvlKy6pVq1i9ejXnnnsutbW1QZba9evXc9ttt7Fs2bJAaenv7w8GX1j8DtuzZw/pdDrQxsOLvI/zqKiooKOjg4qKCvbv38++ffvo6ekZYHr0O0RVVSZPnjyAUjvAYYRcQfJ6FyhQUhdeElcu0kE671JRPA1CJwxMPZxdRWG35B3QEjS+rQtQEG4OLVJPbv/DtD56No0Xv44WH49wMqBoKIoObq7gtlEGli1Tg+fBteUORFGwOlbj9O8m37EGo+oYhN0LiobV+SKK6rEaBsrK4AtcYVdW2GUId+DuL7x47NnTjWFq5HIOM2bWUVlZhhTK2z2PaUxR35Cgs0Mumnv3dDN9Rh2GoRGLGwGvS19vrmy9/JWsry8X7NYjEY1YzA/3BNfOY2V7mXzMJUTi1UEZr6/8Nfl0f6jQUP082urRkvB71NyzlabKGXzyk58CF3Z1vsLk2oXs697MXzf/ivcv+z7tfS0kIlW09e1mU8tKNNUgaiSxnCyu62A5WSZUz2dy7dFkrF5iRpKW3u0YaoTq+FhsN4fl5KlN1vPXTTfTl+vk4qM/SltfCxE9EXJ3KTT3bKGxYppHeHWE3ETeexOPm17WcYs//X4977zWp1rX2PDyPl59pYW3X72QAwf6qK6OsfGV/fz14S0kUxFsS7LGxmIG+/Z2c8KyyZxx1gw6O9KBBa/srT3rbk1NDT/72c9YuXIlVVVVI7Ka+uUARVGXkUiEe+65h5tvvpkzzjiD8847L5hTV6xYwfLly0OuDgl0/fznP3/EQKxDSXhB860o9957L7t27eLjH/84FRUVXH311XzrW9/iF7/4BZ/61KcwTZPPf/7zAfdMKpXiBz/4AY8//vgRC3kupwionnvCdcu5UZWy1hW/rGHd05tYJ6ZqiGgGrnCDqdZxXWK6wd7+Ll48sJumKVXYroOplaxV3jwaNyqJ6Al+u+rTRW6uvJ2mNjnBq6eLohQ8B4ZWnqQtYVYypXah3wtDtqFU2bNdF1NTWbV/Gx3Z/qKUBIrXrqQRYVyySh5zXVA13Fw/XX/9Gbld68F1EU4eNANF0zFqJlB55ofL3nckMipvTmVlJXV1dQOyHKdSKerr6wNLgxCCtrY2VFUdwNPiD4Sf//znvPzyywFpXWlYshAiwMkkEgluu+02/vSnPwXYljA2xbcGxWIxfvGLX0iafrtfGsr9SBg77XnpBIpqoqgmMNCn6Wu1KMWZdweTQ/V9H0wURUExKr17eC+Rx3qo6EkULYYSwpYIOz1kWULIdgshUPU4uQOPkW97GgT0vvxZapY/impWY/dtpfXB0/DXJkX3dLegjYO71cImej+SR1PVkCZW0FpaW/rRNIVc1qa+IYFuFBPI4d1SCEFVVYxUKkJ7Wz9CQFurVCKiUZ1UKkJvdxZFgc7OYktA4ZnKvzs7MsEEEotHSCYleFaep6CoGtneViLxanL9nUQS1d5iGS500G4eRRH8ee2X+PApv8KIgqnFeWjtf/OJiX+gSqnEjMvXs6q6El2JkFTj9OzfQ110PA9v+QGTa45iUs1R9Kb3YSbmE0uYqE6EiBbhlR0PUJecyLiGt+GiA9KClKiI4mQNMKCqqgpdKZ4gf73uu3z4lF8NOH4kZP2LzWzZ3IoR0dm1M8N9d2+lqzPDKadNZUxjHa9v6gVMamtr0DRYeMwUFh4zZUA5mze1cWB/L6oSoaoqwsFc/b51J+x+PhTxy/Gtw7fffju33HILJ510Etdeey1QjEcpXdgPN23K4Yg/ln3emFWrVnH77bdz3nnnsXTpUvL5PJMnT+b666/n+9//Pj/+8Y/50Ic+FMzz/hpwpFxD5eoa/i5d+4P331DuoIMpMz5odUFtE1HNwA7RWgjv94yd59Y31nLBlAX4k0W5euqayYdO+eWQ9QxnoR6qrqYe46KjPiUtY2p5Wopy/QQEKQlue+PFUPg2XoJNhbzrUB2JMbdmrHe+vK75hndhd+xmzHt+TmT8fBRPIbS7W+h84Lvs+OxsJnzhSaKTFiEcK6D2H4mMitKSz+fJ5XIDXkbLssjlcoEp0HVdWlpa0DSNiopCnDYUAFv/+q//OoD/xQetmqYZpAHwy/NdLrlcjlwuFxwrVV58k6qqy8nYSe/mwIOzGXtZf9F646T3cOCh2dSdthKj6miEa6GoEk/jWr3YvZtw8+14AeihK11QI6hmDUbFPBRvRzrajKdhSxCuBZqG1b0BAKtrLVrsAoST8/hThreSCieLokVx0nvofuG9IFxqlj9I99p/pv2vS6ledhd6cjpN7+iUeBbPytNy5yQKbJPFz17iVKSy8cufrWL37i5iMckjo6kKuZxDIhnBtl1UteDYyGRkSKjjCpJJuRBKOv3isv0szNGo4YVwF9xAiaRJdU2MnTs60XWV5n09g/Sl/Gxp6fUwNYJUyqSqOu69R4Wbah4NuE/DryjqQe0KSmDZU/0DB7liaBFCUBGtx9QL1qeEXoPruti2g7cRwrEFmiaoiozlbUd/AYC1Ox9iwdgVHDvxvOBax3UQrgoaqMJEcWV7bctGIIgYOo7t4tpucFw3ImSsPmJmkp3tG2jt3sPWlheY3biMvJ31OCZGd3GV7giV8RNSpCoMVFWh7UAPxxw7lu7uLGMaE94zdrx6WoBM4eC6HhxSVbAsl2TSpKuzD58mw/EYUIeytFiWRSQSOSwLh28tAdi7dy9/+MMfWLNmDStWrODaa68N8p6FXcjhT03T/qaJMf15VNd1Vq1axc9+9jOOOuoorrrqKoAgOe3ChQv56Ec/yk9+8hO+/OUv8653vYt58+YFuc2O1GYuXEdgwPyf7W3lrm+dyNkfvYeqxtkDsjiXLtqua8u9lFqMhSz3nvgL9lF145mQrOKNnjZMRQsW+ZxjUxNJcM/2Ddy69UUun76IjJ0nppuhskOKoWuVWTekAqSrxpB9WFpXV9hog1wzcFMuv2dsi5hu8OOXH+eZ5jeojsTJeRwt/j1s12FaZQPTKxtwHBtN03Fz/XQ/eQeTvvIQ0clSKRFCgBDolWOov+o7dD7yA/pevEcqLXb+b6e0hEPcwuJrgoUXwQ1YG32lJXyuECJAyJeTvXv3snPnTrq6ushms4AcLKlUivHjxzNt2rQh6ymEQ+/LnwXASe/EzaTpWvNPqNEGnPRu4lP+CaPqWOyufi9CxrvOU1yszrW0/fU0kjPfjxptlMcVDT9s2M210PPSDTReuh6j6mjpShqmZWa4IvvSm2RVE+HkyOy8GUWH/i0/IDr2AiijgQ/SIfI8LUq+Yw1dz1+N1bWD6qW/JNp4NvopD9Px9LkcuH8SydmfJzbxarTkDPms3OFPoH4+HyvvBIBbkCRychJQAv0vl7W98sWALNDFVZflaLoSKB/5nFRaNE2loSGF8JQZX2kZrE/27enxFB+orU1gGCpW3kI1tUAx8V0i4Uigg/WwnU9jRCuw82nMWKXkZzmE+VpRZFZeVdVo7dvJ79d8AUOLIIRLc+8WudDhBm4zVVWC70IIfrHyQ8wbezI1ibE8ueVmTpx6OYYWRVM1bN9dpzigCO96FZ+jODx+FVU+p5iZJGv1ccfL3+KKY79I3smwqWUls8csG3njRiB19Unq6pO0t6c50NLPmMYUTeN8q2MhzxiKMgAbB+DB4dBUjSlTq4O2ltuxhuVwMjX74ru+fUZc27Z573vfy9lnnw0M5JQK32+k7orwPUdL/Hr95S9/4fbbb+eYY47hwx/+MIZRWBA1TcO2q+TFRwABAABJREFUbRYvXsznPvc5fv3rX9PS0sK8efOK6nKkLC3+vOTYOc9yqyAcG92Mk8/20nNgK/l0NwBWrl9iWJCLqqqbqKruQQBEKOTZ9dJwDExeGb6v5TpENJ0LpxzNN194gHgsSc6xpBVGCC/ZoM6/PnsHY+IpTm6aQd6xURU1iCbyy/JxLOVkKItQUT/4z0QxKFa4CjnXyrVBAWK6wR1vrOObLzxIyoiQ9xQWfx7SFBXLdbh02kIAXEVBcx3USILai67nwO8+TP07f0hs6nGoiWpEPkO+Yw8d930LvWocFSfKNAZKmXxQw5GDKi1DdVKpohLGkIT/Dn9ef/31ZDKZQGnxJ5awO6f0vs899xz33Xcf7e3tRKPRoknGx5zkcjmi0Sinn346K1asIBaLhbhQhKdVq0SaLkTR4lgdq0nvvJVo04Wo0SbcfDt6ai7CzaMMovwpqo5qmqTmfx01Uj/gdzffQe+rN4Dqv3RHakfhgptD0WJ0rfswihaj5oSbaH/qXfS+9g1Scz4fWFso+2J7eA5Fxc130rvxy/S//kNQoPqEXxGf8h6Ea6OnZlF/9qv0vPwv9Lz8FbJ776R2+SMokXqEm5Mh9kPsqv25KZ3O09ebk6RvtqQTNwyVWMzA11l8UTW/bkNPbsElovBdCbmhJk+tJuphU9rb0uzc3sGkKTWB9cd1JSdMR3uaPbu7MAwV1xFMnylTG4zGhB+vbJIWwphcWGMVjVJRPISyfFtURE940QIKhmqyq3Nj4feSHfqertd44vXfMXvMiSyf8S4A8naOh1/7OdPrlzBrzAn4vadSeI5uEWar9BkIXm1+mme338JpM9/D0ePORAiX1Tvu4pFN/8OCplNprJg+rMl1JOKTDu7Y3sF9d7/G4uPHc9Nv1nL2ubMY21ThKS2+1VZl395uenpyQdbveNzEshxyWZu1a/dgRnVyOZsJE6sOWsfRWGQ1TeOxxx7jnnvu4YQTTuCyyy5j8uTJATYmTDo5Gn0WnkvDNA2D1e1g4vfBpEmTuPjii7n88ssxDCNw4fvKn67rOI7DzJkz+cpXvlIUhHDkxH/vpXKhmyG8pDeXx6vGouoGsUrJjGvGKkoLkWHBqgYK9HXsRrgOierxqJo+IDPxAMXFG0dXzjiW321aRXc+jVqCa1QVhX47x7WP/JZvnngJb5+xGCBQXvxsyuWaF95EhD8Hk1KFV4jy17jCxfXKNzzL009efoJvvPCArHeoPFcINEUjbeWZVT2GS6YuBIrJ8sZc9zN6Vv2J/nV30fvcHxB2BhQdNRLHqJ9C3du+ilEzIbB0HRFMS/gB+ZYS/ya2bROPx4PBkM/LRHPZbLYoIZh/PJfLBVwmtm0HIXx+QsKwCdXfedx+++38+c9/Zt68ebz3ve9l+vTpATLdl56eHnbu3Mnjjz/OH/7wB3bu3Mn1119fxA/jtyFSv1yWn9mHmwW9Yh56amZwnt2/04NY+A/CB2x63xUd1+4tr7RYvRKHe7BOPQwRwgVFRdFi9G36Jpmdv6fujOcxqo4iNW8V3Wu/AIpCavbngnYPJYoWxe5+CbPuOCoX3YBedQyu1ePxtGRAUak85kdEx10KroVi1uHafeBaOP0gHB+MOvA+/iKy/PTpHH1ME4apYVsu0ZjOrp1drF+7l0jUwIMUARCNGvR05yR5kefuKfdS+wqKZXngOqVA12/bLjNm1lNZGaW7O0tfX44X1uxh0pQaXFfy1LgumKbGS+v20draTzJpopgKc+aOKSo/dMeDPZoBsvGvP8CIpnDsHJoRI9PTgqqZjJS3xX93HdemKjaGeWOXB7/VbhtPgLcKqqrgug7b2l5kyeSLmdmwlEy+BxSFWWOWUhGt5dX9T9FUOStg17RDlkVdDWvthXarik5Ptp293Zu5YP7HaayYTn+uC10zOX7Kpby2/xm2tq6lPjlpUJP0oYivaG59vY2HHtjMOefPZtr0Wl7d2MLdd2zkiquOxjBU8nlp/dM0lV07u9i3twfdUDENjfUv7mXKtFqSqQgTJlbxxpY2ausSw1JaRkMUReGSSy4p4mFxXbcI7zeY+yEsQykfYfEJ6YAgeeFQysnB7uvPy8cffzzHH398cI9ydBe+Kyvs8hrJvUYuCniu+HR3M9teuAXdjHvWYIGqGeTSnUQTdWx59nckaydi5zMoqio5TrJ9TDjqfCobppNLd7L+/m/yxuo/4joW4+au4LhLv06yZuIAxSXcZl1VyTs20yrree/cZXx1zX3URhNkbBl54y/6uqKScyw+8uQfeWLvZj521OnM8XAhIBUYAV7mbiWY20bGnRTqmTIuIUe4OF6+JEPV8Ke61S3b+e+XHuOBHa+QNCJyWi56LwWGqtJn2XzkqFOpjSbIOzamFqY1EVQsvYqKpVcNWqeAoLSMAjicsTgiS4uqqkHIMRTAVf5nTU0NIIG5IAmFJGCyCqDI9VMathd+uf0sw9u3b+fee+9lxowZ/L//9/+CsOYwPb6Pj1mwYAELFixA13WeeOIJjjnmGE4++eSgLGk6lDgU4WTp3fjvxCYupeflT1Oz7G7cfBeqWQUIFB16X/k3QCF11LcwKhd4fWHjZNJYHc8jrB4vSscAN49ipLB7XsNJy/NGX3wNW1pHul/+DOmt/0PNKXdhVB2FcDJULvoxihal+4XPk9t3H6kFX8OsOaHoeimi4NbSYtScdD8oGoqHQlcMD2+kFcx3kYbTCzXRJbiu6epmVA8UjDowlNB3/yw7efKA1rz6SgurVu4knjQ964ucyFKpCPube9F0le5u6QIs5drwMQ7ZrE06baFqMkJJAmjBdVzMiM6i48bzwL2vkUxGeG7lTqZOq+GYYwup01/f3MojD71OImnS359n2clTqK6Jebv60hqPYKL1JuVnbv5/AbeL64IZhXhVA+4IXGtQ2Dhoqs7+nq1s2r9S7oy0KHu7Ng1Y7IRwUTWNU6ZfExyLmYWd5djKGYytlKG1eS83SX1yIo+89gs2Nj9Jzk6TtzNMrJ5PTaIpUIgc16IiWseK2e8PykpEqoLvcxpPCr67wkE9xJxUpeI//4YxSd73geNRNYW+3hxz542hsTEVPL9kSr6/uZzN0hMnFZWRyVhccNFcmY8qJPJZH14KiuGIH/V41FFHBZQQzc3NfO9732PBggVcd911AVnmUOITeA4l/jydy+UClu5ymBz/vYnFYgeN5gkwEqF5F+A73/kOmUyGT3/60yQSiWAe9wG3pXXVNC1QokZLFEW6gRTdpKv5NR7+8ceJV4GTl2NPeDQQsVSM52/9Eq4MksR1wYhAbxtc+NkKKhum8+I9X2HjYz+mbuIiVM1g+wt/wcr2cOb1twXjoNStEuAmPcvB/1t4Bs80b+XJva9THYlLxcWzRLneuXE9wh82r+GRXZs4Z9Jc3jnreObXjiMZpjQQwnPN+IpLcNfQ/wdKaCYIubflN1PT0RUN3bOsduXSrD2wi99tXsXjezbTk89SFYlhhZjrJTZGEDdMWjO9XDljMe+efQKO64YUFkmqpyiq5GlRVOQk6m36BYCLouoBbX+Ry9B1A1jDwcbisCwt/ovY0dHBM888EwwAf6fgg2sffPBBGhoaSKfTJJNJtm3bRiQS4amnnmLXrl1kMpkisjlN00in08ydO5eZM2cGg9bv4M7OTjo7O1m2bBmpVIqenh5SqVRZDE1vby+pVIq5c+fy4IMPcuCAjE8vuJtcLzIIOp+9DL1iNtVL/0zns5fR89KnqDj6u157NYQNyblfxKw9AeHmA2VHNWtIzf4AuZa/ktv/kHwgiirBqEKAalBx9HUoajRgjfVufrBuPrgIC9DINt9L16qrcfO9VC/7E9Gmi6RLS4shhEPF0f+FXrWQnhc/iNXxApH6U0NlhL4KX3GxUfS49N0Kh/S2X+Lm21EUgwKdqK/wyUgjhCuBvqpBZOz56MlpIBwIkYyFXz7LcjzzPeTzDvG4SV9fzmOtRc4gXt0amyrY9OoBTFOjeW8P2YxFNFa8a5fAXIX29n46OzNeZJFL41ip1PqL3EknT2bNql309OTQNIU/3LSOzZvaGNuUor2tnzXP7w6AwMlkhOWnTwse18BBM4JJ1rt2yeX/gWHGcRwLTY+Q7Wvj1Sd+MmQUw2AiSeA0Fo4/mzW77sbUJCPmgiapTDqiAF6P6HHW7rqf1w88R9ysLNMOQc7upzrexIrZ78d1HZZOuYzjJ1+K41qoqkbO6idmVrDyjT+TEb2AJL96ZNMv6Uw3E9HjAZtuWNL5bmaNOYFFE87DcS009fA4RcLPPZmKoKoKf/njS5x+5nSSqQg1tdIVMGFiFRMmVuG6gkhEl0kvhbTExRNSqWlr62dsUwXZrCV5MlQFwxgdxepgEl70/bkrk8mwadMmqqurg7YeTHp6egYF5PrX+/nK7rvvPm655ZZB2ccNw6Cnp4dTTjmFT3/600E9h2pDWKlyHIft27fT29tbtk7lykqn03R1dY1qwkQhhJfvBioaZnDqe7+CEUniOrZcCL1zHCuDZkTlwuotpJpuku1ro2n26fS0bmPrqt8z++T3s+Syb6KZcTY8/F1eeuBb7Fh3O1MXXymtpiW5xcJznSNcNFXlp6dezdvuv4HNXS1UmXEyTh6VwqJsCYeaaIK0neP3m1fz5y1rObpuHCc3zeDouvHMrBrDtMr6UGLF0ZE+K8e27lY2de5nXdtuntzzOpu7WhBCYlnCoFu/rgKpsLRl+jhp7HS+f9IVst1FWJzQd73Uw6EihIOwcggl7831EkukmHFZTomlZagxOawe8Qdae3s7t9xyC5WVlYGbSAhBKpVi8uTJrFy5Etu2UVUV13VJJBLMmDGDdevWsXr16qJKuK5LJBKhra2Nd7zjHUVKi3+en+dn7969ZLPZAAfj59YAAq3et8K88cYbRCKRwOojO1Z2nN23la7V70ZPTqdy8a8Rbp7KJTfRtfpaOp65iKrjbkQIB9SQlUG4wXejcgGVx94wnC4LSSGc9/DE89cnZ2COOYPkrM9g1p7gtdFPQqkhXIv4pHcSbTwHNVInFRrV9CwpxYulfDE8vgAhteC+Td/C6duJohsItzCxhHPrgEDR4uRb09SflfSUFovw6xR++YoBtQXcgb/e+ZMKwMSJVTKnUMJg795u9jf3MnlqTaCoQCGaaNeOLjo70lRUyKiE8ROqANA8HEOqIsqlly/g179cjapqGIbGs89sx7Jk5FEyaaJpKun+PFe98xgaG1OBK0Jq/kW9NeIndsz5/1b0zmf72nnl0e8z0ogy6Q+Xi+u58z5c9hyZht4Izq9LTkDgYmgRHNcuMi8rimTLjXqRdHJSkQqqDwLUPaubquoB460QgnFVs6lLTAhd49URaQmy7By1cWnNUg7CDTEcCU9g+bxDNKrjuoKtW9rQNIXu7iyGFxafqohSXS0tZf47p3rvTCSiE4nqwfdSc/SRVlxKwapAQP1QSrQ5mBiGwb//+78jhAgs3uE6+9br5cuXM3fu3CL3eDlRFMlAnkgkAov0cBSncF/58ICDmvS9zeo73/lOLrzwwoC3a3T4WqQpRQiXZM0EFl3w74dUyit//SGqZrD40q9jxqtwHYuF532OHevuYPeGB5i6+MqyYzf8Dvmhwk2JSv5w9nt51yO/5pX2fVRH4liuIy0tyLDhnGOhKSqVZgwXwctte1nZvI24btAYr6QulmRMLMXkilrGJ2toiCWpiyWpMKIkzQhR3URXVHQvktERLrbrkrbz9FlZevJZWjO9tGZ62dnbyc7edg6ke2nL9tHc343lOlSYUZJGVDKHC5ecYweJESWGRUFXdVozfZzSNIPfnnktKTMqlTPh4joOim7gpntAVVCjKaR5WQM/8soDMiuR8kE2Qgjc/k7URBXDSbw7LPeQ/zLPmDGDm266abjvwIhECBEMMj9J2PTp0znjjDO4++67+elPf8p5553HhAkTSCQSRRp/Pp+nubmZJ598kkceeYRFixaxZMkSWZamS2uCouPmu2T+nGkf9K7UUFSTmhNvJb3jt4CKFhuHm0UCiJDKi9W9kczuP0prRr4dRYsHlhvVrMbNtXk7Yc+NZiQRTob41A+gxcZJRYjDZLH06NX11CxqTrwDALt/O7l99xCfdn3AtaKoBsLJoUYkoFRGPpkIuw8nU7wbKpqsPeVlzPk7hl2lfX9RAlfRgOoOAhgLA8NKqfwBJk6upr4hQV+fxEE98dgbXDe1BkUB25YWG8PQyOdtVj69nWhEAionTqpmzJhCXSTY1mXB0WN5x7sWcdstL9PXmyMaM4jF5P3TaQvD1HjblUdx4kmTPWVo+Avtwab3dPc+YhWN2Lk+zFglmZ79EAK3DVeKrFZO1iOvkuW4wiZmpHBcm/5cFwB5O82kmgVBQrUh2xBMEMVEiq7HAZS3M2RtiVtyhcPcxpOHVWdXOEV8Eocq4esNQz6b2XPqeWXDfpr39WBZLoah0t+fZ8HRYwOlxbe2+Qpuuj+PY7uhYwODBP4W4udtG6oO4QCHKVOmBMdLcTC+m6u6ujqw3gxXcrncIeUFCpPlDacNjY2NQS610VMYC65zGT2Ux3XygSJ+sCsVVcOIpjjwxnOMm3MG0UQNrusEfTH5mEvZse4O8pkezFhFgG0Jty3cFl3VsF2HKRV13HneB/nYU3/mvh0bSBgRIpoeUl6ku8jPCB3VDVKGVAg6sv00p7tZ7yVg9P8pnrXGdxCVts5nVBEeuNb1LCWup4ToqkZE06iJJlAAy3WxXScIz1aQ4FxVUTFVjaxt0Wfleees4/mvk95GXDdxXGlNQlGDKKvup27EqJtM6ri3QcgS5eNXrPbddD9+A1qyDuE6knTOsalc/l706nHsv/G9jP3g7z2m3JLFoURGBMQdDATmR/OU85mWO17u+sHC/d7xjndQX1/PAw88wHe/+11qamowTTPQ0B3HwbIsuru7sW2bCy64gIsuuohEIhEqT4aymTWLMWskYlvYfWR2/4l8xxqE3Ytq1mB1rkXRk9Se+lOMmuOC6xU9gR6fhBqpRzEqcTJ7UY1KrI7VdDzzCWpO+akE5ro5AmI6JxNkTh498RYWuw/VSGF3v0L7kx8nNundKKaJdOdIbIrP9utbiWITrsKoWuiVUzzgIAT2zB6QuwnfDVSEQ1a8Y55LzIWDL90HFyEEqhfRU1sbZ978Rp54/A1SFRHWr9vHY49u5fQzpwc4EyEEd92+kZ07OgOw7bHHjUfV1IC/BQgsfscdP4HxE6tYtXIHb2xpp68vRyxuMHVaLccvncjEydWe+2rkE2fhfR3IxaKbcdRQNINmxA6pu4qsVlr5XXldcgIXHyVN/KYew3YtXNcLHy+9p/dMFUXxQqcHElX5LJtHj18R5Dcy9SiW4/Ex+e9F6P1QFB/LpB+UT2K4En4//aivhYvGsXDRuLLn+1FhBcuGfB7nnD+bqiqPcvwQnvOREH9R9OfH4SgOPsGmaZpF4cZ+ef58OxJOmTCv1aE+Lz9607e0D9YGv266rhOJREblHfFFbto0j0tpBMlbPek+sIUpiy7zCnMDHpfaicfw6hM/xcr1SqXFy9cVlsEUl7pYkt+f9V5+uuEJfvDSY7Ske0iZUSKaju0pLyCHkO26WDgoSGCvqUUDq0egjHgnF/duKFAk/C3AwSihkGWB61lUyikHqqJgqDpZx6Yj28+kVC2fPfZsrpkljQCOEPhEEK1//BTZXS9hjp1NZstKFFUjs/kp8s2bMMcvoO6yrwRhzYqqEpmymMyrj5Hf/zrV534K68C2Ap2/qknKfz1yUM/EsGxz4QcyFFBssN+GG1JXzmRrGAbnnnsup59+Olu2bGHbtm10dHQESbgikQhVVVVMnDiR2bNnF4F1w4NHauFy8nXz7XQ8cwFarInk7M971hAXYXWR3Xs7/Vt/hJ6aSaThDIRroScmo099v0xWeOAx4pPeKTsvNYuel74WstyUbRnKKJJtKYoShFQrWhQtCQTApjBZklb0qcXHo8XHF5VTPGFIhaf98WVYnVuLlZWBTULRwe6niM8Ghof+HqxQfzE59cxpvLR+H5mMRTSqc88dG9m5o4M5c8dg2y7r1+1ly6Y2Kiqj9PXlmTq1hiVLZdZQfwftD0dVVXFsm7FjE1x6+YKiJgUtF6AIGyEUvxGUQeKWVFeaeR1bWoTsfAbdRHJE+KeE8FTegUPol4HWgNI+FkIQ0eOMrZyBT2ioKyoME09S7nlJd5RLVUxGU8ndlzasTLFerUbVeuErX6oqSQsdR5ToTeEcRGHrhHzPxzR6+bLeBPzKcEVVVUzTDDIgl6Y2OZgMnOMKAROHGmIcBtsORwzDwDTNIKLzUN09o6Xg+uXsfe2v2Pn+YP5zbJlTS9WNAJnrk59NPOp8VM0g29dGNFXv1ccNmG4TVU1YmW4cyxvbQ3RNqeLiZ33+8FGncvakufz45Se4c9t62jJ9JI0IhqqhqSquENghgL7jWWCCtg2rB8Sgfw5WZVUBTdFQFUkYZ7kuXbkMTYlK3jv3RD64YDlNiSocP+eXoiBQUYC6K7+NoumkNz1J//p7McfOJHnMRcSu/h4oatHz1CobSR17KYqikm/ZQmK+TPIovLxQqBpastbrw6HnyTcnPesIpNRk6A+iSCTC/PnzmT9//pDX+2HWZSMChC1DhV/9D9zsfurPfKHk6kkYVUfjZPbS9fy7GHPhPlA0XLsPVU+S2fFbWh99P+PftQ8tNhYnvRNUsPu2ScXHzUrsiK+FaxE5aEZocj24+C4WG2GBsHoRii7xK4o24AWVmBFHhizrSc8SU9o3rmxrtoXY5MtITPsQTnYvihrDM6mUFKohnDRmjQx/DBSpQ5x4/C6ybYfa2gQXXTaPm3+zFkVRiCcMXlrXzJrnd6MAkahBqiJCOm0RjWpcevkCTFPDzufQzUihzXYeNB1N16UrxXZQFSEHiqJKXJSioiluoPH74joWiuaDrAe2XyCjDyJegjCf90HVDA9jdmTyw5S6BIL6ekBq1SOTGh1RpZkcgaaM3HUwGla4ASUKPOWk/P2K3I8hcRw3UIpH0o4joeD498/n8/T19bFp0ya+853v4DjOQZOq6rpOX18fc+fO5corrwywKKNVX03TiMViw04Z0NXVRV9fH9/4xjeCIIpy72e4fr6l6GMf+1gRP02pSPenwPWeq4emC/4VrJthBV7m5Xn8l++ga38rRhQcG6IJE8fOY+XkfsTfl8SrarnqWztRNQMnnwmYr8OvrmbGcBwrRKg5eNtkHcLcLPJ5Wo7D9MoG/vvkK/mnucu4feuL3LFtPfvTPWTyeUxVJ26Y6IqGi8yT5VtYwuWVv7NU3QvOosJ5ik8m51tTIUjWqCJJ4nqtLLbrENcjTEhWc+XMxVwydSEzKqUCZwkXQ/UxjZ5Fvq+dfMsW0i8/RN+6e6g5/7PE56+g668/o++l+4jPXo5RPwVz7CyZ8VnVcO0cHfd9m+ikRXQ++hMqTrwGzZs/3f5O9vzXuRgN02h45w8QQ2x6hq20vBnoev+zVHHxB0M41Dk8KHwlxQflwiD+Uv8aDx8yaF3USGiXKlA8tjk12ohZo6PFZFx9ZMwKmq4qgNCUQRJXHczcNXLx+0pH0Qnq47uCyt0tfMx3HRVPFgVFKNp0AZExZ4ysRsrAfE0HldAj8K/QNAmiXXzcBFxHcOdtr9DTnSMa04nFZPssy6WrK0PDmCRXXXMMk6fW4AjQzQhutg88s66f0dXuOYDT00JkvMR4+JEGYUNEvmULqhkHzcDpayfSNEfWx4jKiINSM6qqYcbitGxdST7djZXtQdVNXMdGUdWA7n80ZSgMgOJHsnl/jZYUWe+OKAPRaIgy6FDzLXCHVOooj1/fCpJKpVixYgWRSITe3t6DAmd9fKFhGKOeh8ifTzOZDC0tLWQymaLj5Vz3mqaxbNkycrkc2Wx22GkG8vl82fq7rgR2+lZOFYWYbhDVDDRF7u4tK4fh2pjCQXhWTcfKIoyIDKf16jbn1A+R7T2AoupoeoTdrzxArKKB+slLyGe6MWMVdLdsoav5VVR/PtD0ICNx+FV3HRulxHJwMCkdq4am4QiZVuKo2nEcVTuOjy88g8f3bObR3a+xsaOZrV0HaM31YaoahqphaBoaiqf4OGiq6qXIEBRQLb4o2G4eVzhoqhEA913heFmlpdXHEQLLdYJ/9bEkC+smcFTdOM6eMJeTx82QYdfCJZNLo6sqmqLieB4DBQGagbBypDc8QmTKsdRe+qWgFg1XfxerYw/96++T8+iEo2Sfajot//NeolOX0HDNf9P30gO03/4lEgsvIDF/BUokQf2V30RN1gJDM1T/3VlaoDw4zldeRjpYixrvLerJuf+O1fECHc+cT3LWv6LGxyOBuu1kdv+J7N47qDr+Zr8EggXd7sHusXHSuz3Mhx/mG9pVC4EaqZOA2CNmivbq42RxM9D+1NkQ0q0HmgkFihbBzbUSHfc2krM/g3ByJUqWv+AJel76FNndt+Ba3bIdZcyOckC6KFqc6hNvQx1priUf9VXUosJO2nFcliydyISJVaxetYvXN7XS2ZlBURXGj0kxe24DS5dNoqoqJgOzM510Pf8Xqk77QFBmx33foeb8z5Db/gK9q/9C4/t/Q+/qW+hd/Rf0ykbcbC/CdTCb5iKsLGbTbNRYJZnXHqf+qv9EWFmy/Z1ketoL3Cr+5N57ADufZuXvP0Q+6wS7N0WBSDIJjCzVwbC6LDQJ+tiGt+TIiqIoQTLX0RjPEpsjF5SmpiY+/vGPH3JZPjZkNOrlRx6dcMIJfPvb32bGjBlFx0vv6yso73nPew75noO5tWzPSmkLl5Z0D1nHlqyxqoYeSeEYBn1aFjUhI0RjkYRnTZbzjxCCYy/6ctG99m56lDHTlrH4kq8G5+3acD+P/eIqVC9izoxXkUt3yjqhBq7HbG8rRiSJqg+P6TxscQm3VVOkhdD2XEbVkTiXTTuGy6YdQ3s2zdauFl7tbGZj+z62dB1gS/cB+vI5LNdG06Jk7RyWm/MsUC5CFGwqrnBImNVEjTi9uU5sN42maJhaBFMzcdwcuqpRY8aYUTWGmVUNzK8dy+zqsUyvbKAqEvMrKjPbKSqxSHl3pdWxm7ZbPo9W0UD/uns85tuc7BdFQY0kUGMVuLk0Tk8rNRd8ltZbPo9eO4H6K76JcCySR59LYv6Z5Fu2yj5TNYzGmZRGJZaTv0ulZTSkXNSK3yFapJ7aUx8ns/tPZHb/GdfuAeGg6En0xGTqzliFFp9YGATeQqzFJxGbfAK9r3wB4eYIUh4HyEYdYfdRsfAH6IlJlHKXjF7jvJ1IdCyxyScinLQE/g6iMAjhorgx3HzH4Ay23rWxCVdh976Om+8EBGIwTgjv/zIy6nAXz8IevtTiMrapgosvm49lOTiOF5arqwHY1rFtNF3HcQVdj/wIRTcRVg43n6Z31R+pOf8z0kVUIbEZsdnLMRqm4fS2YjRMI7PlWVLHvY2uR36MasZRjShqvDKo2fi5K6iom4Lhhev5xEhjZy4nEq8hkqwd4D5y7BwIgRYQRY2e4uo4Drqu893vfpdt27YRj8f/pon0/lFEUSSztx8efKhKQljxlIku5c5+pAqoqqqjqkj5oPWmpiaampqAgfmQfGu3pmmDYmb8xLnl3Fy+sr1hwwamTp1KXV1dkeLS39/P1q1bmT1nDrppkjIiXD79WOK6gYO0qOzfupKIbjIu00du82P09s+ivb+LyqomqscfFdDD+/gTx87KNBoCmfsLyPS0EK8cSz7dhaYZgWs8WTOJ3rYdsj6qFigE3fs3EasYg24mgroOR8q6vIRA8yKTbASO6+IiqDVMahuncHzjFECCcrOORUu6m5ZMhjV7nyNPHJcYnbk++qwclmNjCQfbddFUg+1tL9DRt5Hl409hSvUMKswYrtNJTLVZ3HQcYxMVNMQqiWq6jP4JiSSxExiaQbZrH137Nsq51IuUch0LM1ZF/eTFaKkGai/6gsRSKgpmwzQO3PwJjPrJVJ/9Cay2nSi6idPbiuZhhGou+Fc0z4XuZnpJb3xEuuaTNdipOpo+cgvCySNUAwaxJvvyf1ZpKZXCZCFNbYoWJT75Oph8Xdnzi6iGVUm9btYto+70Z4d1P3nd6HdvUX1qFlN3+spDKEO2v9iEKSeZquN+fej1GkaM/SBXly3PV1xAhjmH+V4cxwOGaYWoHb1mAmokgdBMUDX0Gg947LoB54xe0YCb7qJv7R0kFpxNy6/eT+VJ70ZL1SKsHESTEgsDCMfm+Mu/U1wnQyYqXHRhwSR6MClSJoUYFYxTJpOhv79fTn6HsYi+JcMXP8rmcBSFIryDB8Q9VBnKXTgSCdfH58DyrdqlZWuaRnNzMxs2bKCzszNw9cTjccaPH8+iRYsGJbPzFb+vfe1rfPnLX6auri7AIGqaxs6dO/n85z/PH/7wByKmyYRkNbec+8+FQrLd3PXov0k3END5l/u5XTXI9nQxZfHFnPXhO3EdC03Vgg1D2Grvz8eaIa0KMmGitFYoQP2U49j+4m0y6g4VJ59GjabYveF+KhqmE03WevxNI+dZKnpOmiGxJRQvwHnXkZsdj8I/aUSIJOJMq2xgx64fsmTShUwfcyoym3mxx+GV3XeyvbaK8dUnsq1tLctnnEVdxTzW7byV3lwfJ46dhu2k0TVJjeG4kspfQUFTVUxNx/Xmvd2vPMDj//M+4lXV2Lk0mhEl19/N2FknceFnnkYoYI6djbBztN/5VfSa8dg9+3FzfXQ9dgNW+y6ik44hteSKwnvjKSzpzU/T9ciPiExcCMJF2Dk6//pTqs/6BPFZJ0tm44Osm/8wSkvxi+Ox7gqrYNMPf3pUwwMXdQfcvMTElFt4guuNw1jAh5ay9REiGHhl2w4Be6mi6AOAuEU4IicHwvHKGyqEKKgRaLFhT56+heSoo8fy7e+dL0P6ICD+8hvhl+crJaW7tuC463pAMwc30405fgFufyfCdeh+8n/8worwGG6uX5pBs31oyVrszr30rrmV6rM+If2vIYCf49jg2qh6pKjvHTvvWVjCMSzFouo+XXkhJljVTQnWDTf2EMRfVHwQ5ltKy5EXH0t3OIrC4YYWH4mywm0Ku+DDbfQVmccee4yHH36Y008/nUWLFhGLxXAch87OTp5++mkefPBBPvShD1FTU1PE/uuXZVlWUe64cN11Xae2tjY4ZrsuDgLhOmhGhL50L116IkhgqCcTRM0YqqoTTfhllvRFmb5Rin5Wgrm8adZpbHjke7y+8lfMPvmfMaIp9r72KM1bngoI61wnXwDrDlOK3LpWlrZd62Q4tfCpAyRNQN2kYyEUju+4DoaeZG/3Vl47sB5Nr2Zc7QnE9Ah5x8ERNhE9woHePTy14xH+6YTvE9VNotHJ3PHKL3n/if9Nngjr9q1kYt1SxlfNlhxPnlKklYRt+zOsbsaJV9UQr2zEzkulRTdjRJN13oly4+Xm0qDp5PduxBwzE2Flsdp3YTVvpv/lB0gtuQI324saTQUWsO7Hb6D2sv8g0jQ7uG++eTNtt3+J2IwTUbSDuzsPW2kZLW3/SEvpAFcUBZSDM0YWf2qgDR9gOZr9UYrvKa3PUHcqdb2Uq1spmPhQaj6c9vrnlFpOypUx2Pei81VNWsWMGMlFl9D16I/REtU4ve1Unuz520tQ9yKfxrUyuLk+hHDJH9iG1bZLsgDbMmWDL5qmQyghWDC56yPfIQvXIdffTT7b7R8ZcRm+hDP3DoeR9C0ZHRlNS8toPLNwOSMpu9y5g80Jvkson8/zu9/9jg9+8INB0kRfJk2axMKFC/nwhz/MI488wtvf/nZs2x5gSTpw4ADZbJYdO3awaNGiovu7rhu4l0BGueiqFgBzY7qOKWwUF7loWnlspNunQDUgr3WsbPBpxqu8sSKtrXY+jRmvCugKVM1AuC5jpp/E+HnnsOovn6Jz32voRowtq24iUT2BaUuulucewrgHOfYVTae/ax/3/udyL6GjBPi6ro0RreCqb+1AN6JB+wUurzY/wyObfskVx3yOpsoZPLbpBmaPOZHJtQvRFRUVMFVB0owT9erWkGxCU2QZcT1GNt9JZ/9uJtfMl1CBcL3KvCuu6+DkM9jeP4TAzqcLfewlb7LadpDduorq8/4Fu30HihFD0UwiTXMwx80FKOZhcWyEnceon1x0P6NhqrS6OHbAjTOUHLbSUgoOfEuKxe+XQ/U/+yRRvgvg/wr4Uu5aC3+PmPBLURB2Hs2Mktu5nrbb/g2zaR6KHiG3ZyNm0xyy21azf/sLxOecWjTZGPVT0eJV6JWNKIpKfNbJ1Jz7SZz+TrREzQCMSuGWh7iz9c7XzThjZ51EddM8efgwLC2lCt1bSsubJ6PhiilgRA5tPPvjRWJjpJNjJIkfS+sRTtnhnYGMnFQDq7FhGIwdO5ZXX32VY489dgAny7Zt28jn84wdOza4hy+WZRGJRFi9ejWNjY2sXbuW5cuXU1tbSyaTIRaT1tow0Z0vMvpF8ikJD8EvPOp+RVELn0G9CawhgVVEUYKIvniVxOxE4tWB4iKTjOosvuSrZHpaeOWR/0YIqBk3h6VX/BexigZcx5ZWnhAOZ6SiqpqHj5FKC4qKcG2MSKoQsCxcVFXj2TduoSfbynVLv0dlrAGA02f9E+t3P8imluc4bea1xM1KahITqE2M59Z1X2dB0+k8u+0vHDvxAgA60s0cNW4Fx0w4l3S+h7hZMaSiKntQQVHV4J/fv4VULp5SlUvLfmyajWpEJE2/EAg7L+n8Q89DODaKppM87nL2/+wakse9DUWPIJw8fWtuJbn4clQjgnAsFG1ofqlRtbSMdhje/xXxc4uEk0EeTPyXyc8xEs6u/ZZ4Ysp+jU5ZzPhPPwiA3dvK/hveRf0V3whMk+lXHyPzxvMA9G98lM4Hv4fT3YLd1Ux605N0P/FL6YedXDeiiJ9yC0TpMeGZfoUQJKrHc+Fnni6cNwo092/J/y4ZSOcwGiBabyE/DEvLkAuFZ6lUFIVPfvKTfOtb3+I73/kOs2bNIpfLBVaYZ555hosvvphTTjkF13WDyCNfYdmxYwfPPfccn/vc51i5ciU/+MEP+NKXvhSQ65VuygbOlQefO4UXybn5mRvJpTslu2wkAa5Dx54NbH7mRjK9B4gmamnduZZ4ZWOgjLiOTVXjbM68/laaX38C17FomLqUivppuK5TVmEZ6fgVCBwrG1hYFE9pUUP97yuJp0y/uuhaV7jEjBQnTL0iuLcfLHLO3I+wbs8DvLT3EZZOeRvT64/DdR2m1x9HOt8DyESq4bofqvjvijlhPpHJi+i47zseNMJF0UycTDex6SdiNs6UVhlVlW4f16Xi+LcTGTub/pcfxM33o5pxai76N6KTjpFt0Qr5044YEDf84re2tpLL5Q5KkPSPJKqqks/nqaysJJ1OD6tvwsC45uZm4vE46XR6RErP/3UJ91HjmDEy+kszUDQTLVVXcrYIBoNRPZ6aCz6HFqvAzfZRceI70Wsn0LfmNm+How7LRFlul+rzRAyKFxJCktYpijRJv6Ww/MNJ+H2w8g4dnWmpdJQjw9PU8rY4Berrk1iWg2Fo3H/Pa+za2cn1HzkR23LQy7hdh6qHnU/T27ZjwKLsOhbRVD2xVAMKgmw2S319PZMmTaK7u5szzzyT7du3U19fz44dO3j44YdZsWIFUFA4fOWlubmZG264gUsvvZQxY8Zw0UUXkcvl+OIXv8i73/1uZs+eTT6fH8DwO2Lxrn3+1n+hq7kTMwauDbEKk/Y9L/P6s3cVk8tVV+M6Fqqme65mh1hFA1MXXxkq0w0ys4eVFd9tNtL6KiHLhR+d6n+3XRtd1Xn89d+ybveDxMwUCgrd2VZiRhJTi3ncKw4V0Xreffx3sJwchhahrW8XVy4qThapKhqtfTuY0bAEx7XR1FEIkfeu1WKV1F/xjUFPE65MphgovaoKrktk4tFEJh5dfK5niRlOX44KENfnMbjhhhvYsGEDqVRqQERDeLEdys0RfilKK/+/sYzwgmUYBvF4PMjPMZgoiuSGiMVi/OxnPwswC+V28W9mW/5eyvD7wXVdYrEYP/rRjzHNAhmgm+3zz5ZHHEfSYgJG40zMEAgsuL+dA9eWybx83+0gdRhql1r624Cdteem+t+CBXtLRl8cx0XXNXZs7+A//v0RKiuj5C0nAKWrigSm53IOrhsC2HuYUV1T+e6PLgxA7Z2dGfbv7wVGhpJyHQtNN2nfvZ57vn0SsYpGHCuDAAwzQV/HXhZd+AUWX/I1cG0Mj226p6eHRCJBZWUlCxcuDMpLp9PkcjkikUhgdVdVldbWVm688UYuvvhiTjhBZqbXdZ13vOMdHHfccYGlJZFIYFnWgHliROJdu/TK75NPd+EKCSJ27CyGYWJE4tKaqqgowkHTdTQ9ZOVQNRzHJpeR1BCqZnjRLCKot+/CKhddNYwKDvqLEC66Fxl62sxrOW3mteTtLKYe5fdrPs/iiRcya8wJ2K6FXiZFx6v7n+b1A6sYk5pK1u6nKjaGl/f+lYXjz5blM3RizpGKEC4inyn3A2g6qhEtstLlHRsXiDquF6nkhYjo5rAAuL6MitISdmUkEgni8XjRy/eWFLAtw1mswudEo9H/U1iW0RJfaZH9A8EAUFSM2gn+SQCo0QRapeRpEfl+8AYTKLhWBi1WgZasQ41VokaSaBXSfzxY/iH5PKRZ1sr28ugNl6MZUc74wC0yjLKMz3gwkONbY+QfT/zIt0lTqvnO9y+QLiJFwbFdDEOjqyvDD777NNd/5AQmTqwik7HQdBU/UZ4CRKMG+byniBsakcihT+WKqmPGqjBjFTi6iUBgmAmMaBdmNIED/OzHP0Y34yiK3KR2d3fzk5/8BMuy0HWdbDbL9OnT+fWvf008Hqe3t5dly5Zx/PHHY5omH/nIR6iqqmLnzp1kMhlUVcWyLFKpFJZlsWnTJlzX5TOf+UzgTh+4sRsGVseLiJl54rXDantPWiYQFF46BGk90YknK4e8LpPJ0NvbS0NDwwgtLcOZx5UgetNXNAwtGuRC8snXCvOMvG9NvIkTp1xBbXICvdl26pLjiZtVuMIOyh1NURRV4lgGkXC/OK6L6QU0uEKglhDXhdc8/5rBZFRDnn2yJNu2g/j7t6RYhrO7fgvcfHDx+6bADOu5i2IVNLzrR/J7RGb6js1YRmzGMnksAIhJ8XdZlcvfGxyLzThRnmvGB39WQoYruq5N6/Y10m9+hPINHa6ELVPDFwnEPNRzy1nRSi1Pg50fviZ83UFrMUQZQ5U12M6+3HMvbUs5kViD8okHw+03TZ2xTRUDro/FDXJZmzFjklTXxKkue5eCIVDS3x/GpkYIXCeP61g4jqSBcDQL4VrYloUGvP/978eIJhkuXt5XZoQQVFYWFIAdO3awb98+kskkjuME5HrRaJT29naeffZZvv3tb2OaZpmxN4w2eqe7Th7hurz44ovs27cPTdOYM2cO02bMIt3XTTxZxZOPP8rPfvYz/vSX24BCWHd7ezuPPfYY0Wi0KJedqqpMnTqVOXPm8Mwzz3DzzTfz29/+doTWlqHOC4HrS86znJyXfDF0dnBP2S892VY27n+ShuRk+nIdtPaNY+uB5xlfPbfovNGUoXB9/nHLdTBUjSf2bGZvfzfXzFqC7ToyMqykLW+apSUsfmXDlX5LimU4/fLWTnxwGVobFx6LY0nyNiFQykRXFM4pTAhKKPfFYP2vBD5uDTNeiW4mQscO7ZkdCZdRqXuyqFxFHahvhDNSh0m0yhFqCZnCzrP/Ft0zvGnxXQX+ouB/+lJucQ9HkfiJBMPKRblnGHaj+mUE1fd+93EI4fPC+ISwlNswlLalnPh9bZpmUIfw+xR+Jo4j0DTFcwm76LpKLmcTiejk8/L+2YxFJCrxUrbloKiSVNHvAUVRDn8T7e3YZQ4rb9EMlWuaJpZl8dvf/iZQNMJ4Dj+lwJ49ezj33HNZunQplmVhGEagLKuqyvLly7Ftm0wmEygDlmWRTCbZsGEDf/nLX4ZwDx28kcKLxrJsuOHnv8Q0DZYev5T+dJo//uV2Fi5cyAXnnwdANF5BLFHJ7t27EUIETMCRSIQpU6YESpf/bvz4xz/mnHPOYc6cOYEVXHbdCIC4Tn5g13ttlRwwEQr2tILykozUYAR0FMX30zxX0RXH/Du7OjegKhrVehMCwXGTLmLWmBMBaa0ZDVEUBUfIrNSREB2E6+V/lr+7WI5DVDfIOzaGqvG7zc/zxJ7NXDNrCVaJ0jIS+Ychl3tL/lFEGaCwyEm9fHRF4e+CQuM6lseKOdhkpCCEg6qZOFZGnu/kPSKmmASgjWQiQ0YYHQlwbri9PT09RZwubj5TUE6EC4riJZlUZNJJxw6UEdfKykgAv1xVRzEi3rlqcK4QMorQj3YTQtDb2xuwv/b29hKNRunp6QnqFolEBrgC8vl8oMxEo9Hgb38zFFY4fEUoEokU4cV8YCcQ0A74dfAXIn+x1TSNbDZbFDYciUQGPIveXokdCbehVFzXxTRNOjs7B9D0h5+vEJJsMd2f58UX9nDskgnoulRGuroyOJ71ZPv2Dl58YS+XXbGAWGw0s3gPXxzHwTRjvOtd7yqrVDiOQyKR4LrrrqO5uRmgSFmxLAtVVfnqV7/Knj17OOGEE+jt7Q0UAyFkMsjLLrssuO5QxoGfI+y2227lQMt+vv71rwe/NY1t5L/+679oGtvIokWLsKw8liWjnlzX5YorZFROMplk8eLFReW2trZSX18fHNc0LfAo+NFRfp8MVW87n6Fkl4CcT1xsK4MRsgQLITD1GEIILj7qU8Exn9XWHweql55mbOV0xlZOL3vfwnmHP784rouhKWiajiu8IHRFQfPmS1d4rL4e5sqfR2ujCcYlq4uOHYq8pbS8Jf8npZylZCh8SdjiompGUQjiUBKrGBPgY8zY0H7wg0k44dtoKC5hi4Nt23zsYx+juroay7ZREWhVY1GMKCKflQqIcLG7msGxcXP9qLEUeAuIVtGAYsYCJcfN9uH2dwSEUVq8AsfKo+sGPT3dPPzww7S1tTFz5kxOO+00ent7efLJJ7ngggu48847edvb3hZQwLe2thYpGADV1dXouk4ymeSOO+5g6dKl1NfXk8/nyefzgaKhqmqALXjggQeYNWsWra2tCCE44YQT6OrqQtO0QFnp7OyksbERwzDIZDJUVlby2muvsX37di688EI6OjqIRCLk83k6OjoCi5Hfl01NTWSzWR555BEuvfTSIFqytN/9Pp88ebLsP22gFc51Zbhye3uan/zwWX50w6XEYgbxhMkX/2MFdfUSL7Dy6R1s29rORZfMg5jnmfwbGWB960Kp+P1TUVEREMqVcxH61parr756YCEhCbPphu4y7HquXr2a5cuXAwQYmqlTp1JTU8PatWtZtGhREEDyjne8o6iOPsxBVVVyuRyJRII//OEPVFRUBM8zDMYtZRAuVkyLx3K2r122Ivz8FAXh2mT72omlGgLXczl3anDMvzRgOle9LM5WSeGgq8agc19YAq4bRSnb0yoKLmAYEf605QXu37GB3614D47rYrsOGTtPQo9gaBqr9m/nq2vu454LPhy8q3nHJm0PtDSNVN5SWt6StwTffeCgqhrtu1+ip/UNdDM2CG+Lr9zoWNk+NDOGqunsWHcnqhfiN1xMiKJo2FaGirqp1E5cGNRhtEUIwfz584nHCwA4e/3t2B270VINOF522+jyD0HXLrrv+W8q3/Xzwrkv3YXdsVsqOVYWo24y2oILYM86ep/9Pakrv4u/a7zrrrs488wzWbJkCX/605/o7+9n8eLFrF+/nlmzZlFZWcn06YUd4e7duzlw4EBgCWlpaaG6ujrY+dbW1rJgwQISCbmI33HHHZxyyikBHfxdd93F7NmzmTZtGtOmTSORSOC6LuPHj2f8eJl/qrm5meeeew7TNGlvb+fiiy8OeEQcx6G/v59x48bR2NiIpmmsWrWKNWvWMGvWLPr6+gJXyGmnnQbAmjVrmDZt2rD7vlworz+Z67pKXX0iiAYyTY2Zs2SiuZfXN7PhpWb++UNLSVVEAhfS30oGs/L4CkAulwvcauXOzWazrF69mvHjx9PZ2RmQbkLBItPQ0MDSpUvLKC7D19QMwwisXL5yAYUQ7KHa51sF/QSZzz77LK+88gpf+tKXgjr5VBaPP/44juNw2mmnlY0mKlUS+jt3h6IShaeAqri2RV/7TqrHzilWSoZQgoLvwkvSGrJ2hMX1Ur0M2X+KgmvnULUUrpMniG0oOkdmPVKBzZ0tPLLrNQA0VeXm157n88/dye73fAuA/elu7t/2kofDKShM6iho228pLW/JW+KJa+dQzTgvP/yfrLv396TqwMoNvatVFIgmJeD3wf++dERQNyHAMKGvA446+0rO+MCfgzocCenr6yMej5PLZtCjMXr7MziuSdetX6PylPdgjJlOFLBi9fT09JICnFwW4Vjs+v1nqT73U4hMHhyHrps+zbTvXEBvZztpRyMFYGVpbu+hpaWFiy++GCEEZ511Ft///vd56qmnvDYLLMsCpPtGURTOPPPMonq2tbVx1113BX9blkVfXx/RaBRN0+jp6aGtrY1UKoVpmnR1ddHa2ko6nSadTpPJZIKF0OdGuvXWWznnnHOYMWMGjz/+OLfccgtXXXUVqqrS399PPi93gD09PVRXV9PS0sLy5ctZvHhx0ULhui69vb3BguiTq5VbTHzXVSlTbQGDI9A0yOds8jmbXM72+sfFNGVI9O9/t5aLLp3HvPmN2LaDrodcn34HvYneosF26r5yEY/HA6UgfK7PnvtP//RPtLa2YtsW9fX1FPAb0lpg21YR2VyxDL+hZ5xxBg899BDnnnsuyWQSkIpmT08PJ510EiAVG8uy+M1vfoPrulxzzTWBixEkjmfdunXceOONvOc972H8+PEBe6//DKLR6KAA92IlVX7v2r+5pB2SZNLO9dPV/CoT5p9ThK8bTEqtOoWM8ocuPh2DGauUdSjzrP0jCSNCXSwZaoXACSlbEU2nJp4aZv66kckRVVr+Fr7Xv7UMNqj/EfviUKTUJDrcyJFRubeXXXTC/PNQNZNIolryCQyqtSgy0sLOAgqaHvHOHWZ9hUDVTPLpTsbOOq2oDkdC/IXDjMZQ7DzVy64BoO+vP6L2tPfh9LXT9cgPcdPd6KpABVQzgpvLUzH/dGpPe39QlrPnJRQgUlVP99Zn6H3xHlKLLsTQ+4sWLSEEc+fO5ZxzzuGPf/xjgEkBBgKDQ/UM74ZVVQ3cQf7v9fX1gRsimUxSV1dHRUUFqqoWAUTj8TivvPIKEyZMYMaMGeTzeU477TRuuukmWltbGTNmTNFO3O8jwzAGJKP0OUgSiUSwSBuGMSTnEpQP55RgXXnP6to4AqirS6AoCrqusu7Fvdz+lw2ccdYMzlgxI+B2KRoLfvSQKE6JcSRlMLC433//+Z/fCSyF/vMJW5qmTJnClClTDnqfXM5CU9USojxlkNSkBfHrcc4559DT08ONN97I/PnzSafTvPrqq1x33XWBlc937VxyySUSP+JFLPnP/f777+eee+7huuuu46STTgqUUChYbHzemcH6ptBH8rNjz8veM/SOC9/Ka9Gx56UB7RnMchO+j2Pn2bfpr4enG3hYPjOaonX7ajQ96lmZy/e27TpkHau4iNB3xxVkbeuI6NNHVGn5R4x8GQyP8I/YF4ci5RSW0r4rVWJGC/+h6SbCdZl+/NVMP35on/uREOG6A3heRrV832dvS1Ixp7+T5huuIXnspbT85oOYY2eh10/F6WwOAUeleTe39zXsrmacvnYAsrvkBCscBzQDhI1r56mrqyMWi/HYY4+xZMkS7rvvPk488USqq6sDRSTsltF1nTvuuIO2tjZ0Xcc0TQ4cOFCUCThcd5BKzI033hjkuNm8eTNtbW3s2LGDT3ziE+zdu7dI6enu7g52734SP0VR6O+XBGLldsqRSIS7776bN954g5aWFubOncupp55Kb28v6XQ6uGY4CnXpAuY4Lpqmsm9vD3fetgFFUUgkTG746XNETJ3u7gzNzb284+pjOHbJ+ID5NlwGEKQAME0Nw3hzXEbhMWnbriS/C+npiqLhOALHdoKgMsMoWIccx8ZxXAxTQ2FwN2gk4tO5l8A/Dl7D4NuVV17Jjh072Lt3L8lkkmXLllFTU0Nvby+pVIp8Po8QgqqqKqCAo1m3bh0PPPAAnZ2dfOxjH2POnDkDosX8fnBdl3w+H4C2yykswksB0Nu2nf7O3d4Y9xQCRdIm6GaCzn0b6W3fQap2crGbeDAQk3BB0bCyvfz1his80jwl5H4aoXiYPt2IYUQrAvbu8qcqlPI1i6Lfj9ya95Z76AjLkVqA/pElvFsfrb4NJhxV9aKBhpdxdFhlI82nvq86jHhR8Hai2pGJHiqqh99nukF+/2Zafn09ycWXUb3io/S/+hjdf/0plWd8mMqT3k3zz98NgHAs1GgKvXocrX/6NFplI8LKkTj6XADcXD/muLmkjr0UO92DqptceOGFPPLII/z5z39m0aJFTJ8+fYALJizbtm3jmmuuKcI3VFdXBwtFqVLhOA6nnHIKkydPJhaL8ac//YmTTz6Z6urqsukuyikWfsTKYJLP5zn22GNZvnw5Bw4coLKykmw2y2233UZXV1fZCJqRihCCbNaW4csK5LI2ju0iBJiGxj13b8R2XI4/YSIgf//i5x4kl7NRVeleMgyNrs4M06ZLJe9IzzVh19ZwsTXhd1pVdTQNWvb3cuBAP/mcF10VnCwXPNt2SCQjzF/QiOMKT70ZTl8XwLSqqjJ58uQAPOtLKiUjdE455ZQgT5JfT5AWtdmzZ3PZZZcBBKHbpaIoMmQ7nBeu9L2Q0Yg2mqrR8sZz9HXs8hI15vAVLOE46Gac7v2v07rteVK1k6UCohZC8wdRHeT/VZV41Tj8DM6l1qhShJ1Scrz4PAXXtQOF5e/RQ/CW0nKE5c1QWAazSLyZ8ma6cop2Mwy++zqYtWbAb97ffvSQ6zoIx0JylBxqu3zQrilzbwyjXUf6WSqAopnUXfktYtOOx81nSMw9HT1Vh9OzH5Hrw80WKxZjr78Z18rS8+zN2G07EXaetju/QnTKcVSc/B6E66BFJbYnkUhwySWXANKykU6ncRyHE088kf7+fs47T/Jk+G6ViooKGhoaitwsjuOQzWYD8G1YNE1jwYIFAai4qamJMWPGsHfv3oDTJfweVlRU0NbWVnRPIURw/WDv7cyZM0kmkwEmAuC6665DCMEvf/nL4NqRiHQxyPqNG1/JJz+znO7uLP/y8Xv42CdPQtc1XFewY3sHD9y7id/euIaW5h4uumw+mq5w+duPCm2k/RB9QUWVzyJ7ZOcA6Y5w0XSV51buYOuWduJxA9t2PWOAj0+RlpjKyijnXTgniJSyLAfT1Ljrjo1sf6ODS942j/6+fBHjr6pKPpqDud3K108NLFkvvvgizz33XJA6pdQdKYQMt7/yyisDRVcIwdKlSwH43e9+x/z581m0aFGgQJfbLJWO11LAtR+JuG/z4whfAQ9rDIoirTFGlDfW/Jkpi69E1QpL8+DvmPBviJ3vl2WPAoREUQq0EX+PMipKi282++hHPzog51A5OZSX8c2Sg+3ASkUOTpknaNeuXXzjG98gFosFk6dt26RSKb785S8fkYXIJ8z6e7Dm+D7fkdTF7794PM4vf/lLVq1aRUVFBfl8PmDJvPTSS7nyyitpb28nYpoBN4ArBJZwcRTQwmBJIXerWhkyufB9g/u7LppnvQlw/a6XJG0UI3mE6wyZ2Xm0rUcD7u+7fFwXo24SRv0U2u/8DyITF5JcdBFGw3TUSByrdTuRCUf7FwFgte2g9S//SmzaCcTnniYjFtLd9D3/ZypPv162y7GC/spkMvz2t78FCvNDIpFg5cqVpFIp5s2bF+yGfcxDWDRNK6uwgATXPvjgg0yePJloNMq6des49thjA2BveAzn83lmz57NypUr2b17NxMmTOC5557DMIzAvRQ2+/vX2bbNE088gW3btLe3E41GsSwJHh03btwhM1WHlVLf7dPdlcE0NTra09Q3JHEcl6nTavnwx5fxwH2vcddtG3GE4NK3LWDJ0omDli378cgrvbYjlZa1a/bQ3p7mXz53Km2t/ZgRHQXI5Wzq6hLccesGHn14S5HS4othaEyeWs3xJ0wa8l5CEIo4OXibBALVS5Mwbdo06uvri7hgFEUhn89TXV3N448/zk033cRVV11VpGj42KWHHnqIaDTKokWLyGQyRKNRhBDYts1xxx3HggULgmtKpQDclm6ezn2vsvfVhzHjVThlcHLSRRSn5Y1n2bfpccbNOd1L5DiU9bWw2dKMqCTVPMyNo+IpUK7rHHZZR0oOW2kJ7w4Hm2RK5e+Zmt43+R2KVFRUhLAAxWRSYSrrt6RY/D7y3QPhAeonRQSoqqoalIUUCJQZ1WN6dYVAVRRcIQqgMW+HI4QgqhuoKOih3bfiAdJUzWDPxodo3vwkZrwS17ZKJhqZYh5FQdMHY5qUN8v2HmDKsZfTOOPkoOy/qagqbqYHNVYhrUCJKnnYywdi1E+h7rKveDs32ebsznXo1eOoPvvjRUUpRoTup35NbPoJCDsfTLKxWIzrr79+wK1t2+YHP/gBUBgjnZ2d/OY3vyEejxcpMoqicOWVVxadC9Ksv3HjRvbv3w/A/PnzGTt2LM8++2wRZwbIuSYWi3HGGWdw//33B7vuiy++uGzX+G6AY489ljVr1rB9+/Zgl53NZonFYgFg81Ck2FUiPzVNpbcvh65r3vyj4DjS1H/u+XMQLtxz50ZqauKccupU8jkHVVOK7P6KSlFk0Yhk0NOVsrt2v/h43ETXVeJxk4mTzAHnjR1XQWpzW/mSh2sRGKnlwHfBCpk+YKh5d+LEiVRVVQ1YnH0gbmNjIw0NMg9Z2Nrmn+Ovd+VcQsFzEC6gseW532JlezFjVQic4DxFUT1CPBEoDK8+/mOaZi1HUQZmlg7/Hba05Pu7CjiZw7EK46JpplSCRvSOv3mb5sNWWsIP6GC5Tfyd+FALz9+DjCRHi28p8HkBSieMAvhudHMx+X3Z2tpKd3d3kOzrb2Fx8XfKtm1TX19PZWXlsNsrd5zWILlGPHO0p+RaliWZKB0HXdPIWHmeb9mOpqosbZyK4e3yO7Npcq5NY7wCx3XRVJW4PnBS9WVXbweN8YpCQi9bKhbb197K87f+D6k6sHMETPZCSBLYaDKGEA7ZvhLCJG8xcR2IV0XJ9mZJVE/4+1FaBIFFRFhZelf/BUWPYHfsRo0kcXN96NXjiE0/AYQ8Lz73DNIvP0Tbrf9GZMICaZLubSOz5RlqzvsMAIoeCZ5hJpPh+9//PtXV1cF40nWdTCYTLAT+s/7nf/5nurq6BlDe+6Gn4U2EbdvMmDGDGTNmDGiWr/CENx7+LnvmzJmMHTuWtrY2xo8fH3CK+HgE/3x/bmpqahpUsenv7x+S62MoKTcmTFNj9pyGACPiKy6uK3BdwTnnz+a1Vw/wxpZ2lp82DTNS3pp5qO5F1xlISIaXsM/1aOeLyvPWMtt26O+Xm4He3hzRqLS0ZLI2qVSEvr58kNSx0H75mcva7NvXw66dnfT25NCNUEJIBVxHUF0To3FsxQiX4IK1MmyF9vvEt6TEYjEymUzZDbS/WLe2tvLyyy8zadIk2tvbi+Yon1F5/vz5ReWHLTqua6NqBp37XmX7i7ejm/GA9ykA8to5VF1mdnYdGyOaovn1J9my6iZmnngdjp0vAucXKRK+ZVbVaZh+Ioeb+0zO4zqZ3gP0te/0NiDlDQxCCJzQ/SzXwRUuedcmohne34KByJnDl1HL8lzOxBsW/yG3tbXx6KOPDhnf/rcQX/moqKjg7LPPHtHA99td6ksvLX803WI+m+hdd93FvffeS21tbVl2zjdD/GzLBw4c4KMf/SgrVqzwaL8HVxTCEq5zuf7zn4PmL0SaRku6h2+tfZBKM45AcPf2l/nY0aczIVnNfTs2sLe/i88eezaOcOnOZvje+kfRFBVHuBiqRp+V45/mnMicmrH826q7+PaJlzE2UYkjBKqn4Mw86Z+onbAQI5qSE7sikfmKqmNlutnw6H9jRlPMO+NjFEy1EszmOBZmtII9Gx9i6/M3E0nU+I05xF4eRRECRZeLdeq4y+l57g/0r78P4eRRdBM300N06hKptODxjcQqqL/m+/S9eCf55k0y0qminvq3fwejdqJ0fWl6YJXo6+ujurqaD37wg4NiinyForq6murq6kGr60d5QEGJDSs0mUyGiooKHMcJ2EyDSClv0XIch1QqFYAww7mG/AgQv15A0T18cV03iBLJ5XKH8wSKsC31DUk+98UzguO+iyfs6nnXdceSqvCp4v1XcfBw2JGIY/ltEcFGXciCAqWl6L31vuqGRjIpx0oqVQCjprxIp4qKKKZZvMT4itjSZZPYtaOT9S/uGzDm5VzsMHFSNfUNKay8TTSmMRIgrr8elZsPw9a4cs/R//1973sfnZ2drF+/fkD9HMchGo0yf/78Ab8VqiLr8spff0Cuvx0zXoVr5VA1EyvXS3XTfKqb5rH9xds8946NcB10I8bLD/8XY6afRGXDdKn8qPoAxcVnzzZjFZz9kbuH0TfDk+1rb+OJX72beFUTdr6/7DmGqlFhxoK/ayJxEkaEiLchq40miOum1x9/pzwtBxsoPsajtbWVm2++merq6sAH/bcWfwLNZDJMmDCBs88+u2wCteGUM5xzRrPNkUiEVCpFMpkMQjnfbPEBbdls9pB3oL4crP62cNEVlf/Z+AwnNE7jyhnHAvCn19fwq1dX8qUlF6CpKgnDI0rSdF5r2cEb3a1844RL2NHTTtKIkHedgBypMjT4EDKSR7guY6aewJipJwyoA0hrzIv3/gdmNMWsk95b9hyA/q69ZHpCi9/fgYvYZ/MVrkN0ymKiUxaXP89TROR3F9WMUbH0HeXPUzXZd94ikUqlSKfT/OxnPwt2n76LpampiSuvvLIoOqh0A+Nb4KLRaIBL8KXUWuu/c2eddRY1NTVBiCsUW1DCCRNVVQ3uGYvFghDrQEEewiKsqipjxowZqosPKqW783DixFKLiRCChjES6zKauBU//4tjZUrK8pVzFTuX8eob3ljIz3R/nv37e2lp6aX1QD9VVVF0XaW9LU1tfYI3trbR25v1rvfnCfkcj17YxNELm4ZVTy3mK0QH52kJW1oGk7AL8Hvf+97AErw+P+WUU4ZVv/Cz8L/7FpId6+9kx4u3Y8YqA84nGaFoM+noi1hw1ic5sP150l37pMXFsVB1k0z3flbf+hnOvP5WFKVMLrXQvYQQnrXs0EVRVBw7hxFJYOX6BlU2/CNZx2bd3tc55k9fI2NbdOcztGX6mHXzl9AVle58hj4rR9a2gsSIo7UyvWnRQ35HG4YRuBAsa/A48DdTwhOqv+P7e6jXcMR1XSzLwrbtICkZFADFQ73opebGoYBX5dDyBbCZG1BwH671bGjFr5Bma3dfJ59YeAZpK4+qKJw/eQEPP/WqrI8Q2OF6KArHNUxmSkUdUyrqBtzLFSLAtXg/hEKffQsLAcpfM6Kke5rlZAJYuf7CpK7ICcDOp4nEq7Hz/aOJ5T0sKd2JS/CsjfDI8QIRAkUzvISI3rWqjKASVt4zcUsGU0WPFELDlcLziUajfOpTnxqyPr5SMNSOWAjBpZdeOuCaUhFCUFMjrVnLli0LjpWS1IXv41t6pk6dytSpU+WudRjWwWg0GqQYOFRXd+k41PVi3AL4O3oZCSPvpZa99pDFuzTb107R81c8xVY1yPW3DwCQ+26sk0+dys7tnaxauZNY3ODeu16lsyPN6WfOYOuWNhrGpIJ0BMIjUVv17E5a9vciBFiWE7iFBvaPTG+gKAqJhMbpK2bJ+x96a71yC30Xi8WK2G3936GgNB9sPpMbxeKyXddB0036O/aw7t6vSmUkSAKpYeczVI6ZwdQlbwfgmAu+yDM3/bO0nCgSU2fEKml+/Qmev+0zLL3ivyQFg6YPcHX537Uh3N8j6B0AuaEp23fgj6bTx8/ii8veRlQ3yDk2pqpjalqQW0hTpEve1HQcr+2uGFmQy2Dypoc8CyFpvP1/hzLoxBCNPxT3iBCiaPH3j/1vEf8FDr/I/k7xSLTDN4+Gs+6G/x1u2UOJ35pxyWru3/kKV0yXlpa7t7zMZE8h0RU1cCWBnI47ctLM2Z7tI+mBzGzhkjQi5Bybf37sZiamavj6CZeQNCISxFsmcWIwcRuFcFkjMjgAXVWNI+HWPSTxJ+DCRCxA0SCSLF44vF1xWUZMvaDICOTiRpmJ3R+jg7lMR/Ke+Pio4VhzS90lw3n//WzSQ7l3y11zqK7Y0gVHWlpcj6+lsHhKmn95j1c3tnDvna/ymS+cFvx++BsreX2mpyVwNRREoGg62XQnVq4f0wNtgxLUqdRakklbbH6tlbPPmzXgTr6iM258JalUhKpqqSz09+Wl5WiQGjqOwDTCtT08Ke133/JWCnItVXgPJkWKpHfs+Vv/hd62HdLK4mODVA0n18fc0z5Momocdj7D5IUX07J1JZue+gXRZJ3MHG/nMGNVbHn2NyQqx7LgrE8FeLhRUVgHaYP8Ur6vhfeULCvHCY1TOKFxyrDKtTzckKFqRLTDVzn+ZkqLbxk4lI6X2n6xj8//9POaHEq9bNse4Mf+3yZ+X+Tz+QDjMlxrynDEB6FFo9HgGbxZIhDonkXjfXOX8bU197P2wC4AunNpPr5Q4gL6rBwZqwCO9XEsADWRxIB3LqLp/PCUtzMhVRPsCgZ7K2UmZo0wQK1cKLMoUgz4u3AL+ZNw2NUSSGmDFUUqNAcT5chjqIarHLyZeK7DvddAS0txpmDHEfzhphc59rjxzJvfSHdXhm3b2kej6gVRfKVlv2cpLNCPCddF0wzSXc3kM91SaXFdFE3FshyEK7AdSS2Qy9nU1MTp6cmR9cjiOjszmIYGSsFiYhgqEyZWAbD19TZUVWHWnIYR1ffg+v/QA62038slOZS3Ovi6VNaS7bl31tzxBfZsfEjiWDyFRdVM8pluxs9dwcwTr0MIF82QkYeLLvwSHXtepm3nWomhs3PS4hJJ8dKD30EzYsw97UOS9E3VB6x7b6ZnQFNVbOGSt+3C3KZ4XwuvEAoKMd0IlJ3efJb2rNw8Hs50+KYrLbqu09DQQEVFxYiVFn83ZFkWPT09RaGRvnVhKEDfUOVqmkYulwtMzIf6Evwt3Uq+eburq4uzzjqLc889l87OzgD1Ht5ZHIr4JtOKigpuuukmNmzYQDKZDECMb4b44YxjE5X85NR3sL27DUe4TK8qTH6Lx0xiQ9ve4O8xsQqe2ruFDz3xB7KOjalq2K5L0ozw3ydfiYvAKNkBHPw5/h2YTkYgiqLQ0dERvOdHygr3lgwtiiLpD8Kun9YDffzql6v50EdPJFUhF7HOjjSvvLyf6poY8+Y3em6S0XABhOrivcPdLVuk+0+ibwEQwkHVI/R37ibb20qyZgJ4WYn9lAJ+bfx6VVZGicXkOKquDuHEQpJO54nHTf78x/Voqsq/fvF0enuyRKJG8fvoLYSapiJcB8M0kBFNB2/VkL+WuEhLvw9XyikMPo7llb/+iE1P3hBSWKRlzXUsoslajrngi7IMV2LLXNfGiCRYdvVPePSGt5HpOYCmm7iOhRAOmhnjxXv/A9e1mX/GxxCugxASG1PO9X+kRQi5edSN4b2PUV1ulj63+Fw+YklLYeQwIojfFKXFB7r6/uMf/ehHh1SOD9zbunUrX/rSl6iqqgqiaLLZLA0NDXzta1877IcXNhX/b8G2QKHetm1TV1dHQ0NDEF462pJKpUY9jHu44giBrii82tHMI7tfo8KMcusbL+IKgaFqWK7DFdMXATKx18zqMTx48cfQFZWMbSE8ToS8I3eFEVUPbRGkDPbsh93av4PXJow50jSNb3zjG3/jGv1jiz8Pfv/73ycWiwVZnnM5m1dfacGyC262Hds7EUKw8BjpfpEssYcfbVm02KoSe9XfsUtaCvFxF5411sNwdR/YQt2kRZK6X4OX1u+jeV8PkYiO6woc2yVVEeG1V1toPdDPyqe309+fD9ITOI4gFjM46ZQpgUVpTGMK05TffUVtqDpL+TsYVAytsGx6+pesv/9rGLGKUCi5QNFMnGwvx17xn1Q3zcO1816oswwzdh2LioZpLLvmpzx+4ztxbQtF0xGOLTl4jCjr7v0q+f4OFl30ZYAi+oQ30+oy0tI1RcUVgskVEuwu+bMOHZ/1pigtpaasQ9nh+W4JPwqgXAP9cn3l5lDvczgP/2+9e/Xr7bvJurq6ME0TwzA4cOAAa9eu5aKLLqKnpwfDMIJspX5/lSYGC4vvdorFYsNiPj5S4t+2OhJnbvVYEkaEeTVjyTsO45JV/GHzau7c9hL/sugs8o6DrmoYqsYPX3qMjx19elBO1BvwXfnMAHNlwY1WDPbwfdbh9PFCCBCuFyoqAaq+EiT+xn6h8LjzOUzekjdXSq3BvvhPQlUVkqlI0WKw8ZX91NUnGDe+KjhnNNbswq5cujm7W7Zg5dMyu7j/zgqfgVFaAlq3r2bacW/HB5snkxFqa+MYhuYlTJRz80WXzEPTVXp7csTjYU4TgRkpJv3r6sxgWQ75nM2BA31EInqRciKEQDc0amvjfl5A/tY+1nIAWCHcINHpa0/ewNq7v4xuxHC9DRHCRTNjZHtbmXf6R5l23FVS2fCAs8HY1AwcO8+YaSey7Oqf8sxN/xwA4iXTNBjRJK889iP6Ovew9MrvYsYqcTxCx9JxfSSVF5cCgd9wRQFsDztmaBpZW2bxNoZgCB9MhlRaDkd7K7229PuhSKDVDsKo63eiPzkf6n2OxAP3qagPt07DrZt/jp/BFGQSOj9ZXUVFRdnyy+IdQuL/frjYmMMRv/WGqtGVTwcg26xtkbHz7OnrYmpl3YDrnml+g3fPXkpE0xFCEiJVRmIcP2YyWij6B8LvQElfq37Ei04YbV9UMQgiahTKR0e8WTIaG4a3ZHRkQP+H3hfHdrEdqQg37+th/Yt7ecc7jylQ34ffrSHG/8HmieB3LyFf+651WJlu9EgiWGiDsS0EiqrRumONxF9oUkmZNr02SNA4UvEBuUuWTmLHtnbuvnOjl7fIH3igeDmKamvjnHP+7L8LS0tpf/ruHlUzUDSV9fd/kw2PfA8jksR1QwqLESPb186khRez6KKvSGVR1YMy/bKEkFnmHTvPhPnnsOyan7Hy9x/0MkQbnqtIEIlXsePF2+lp3cbxl3+b+snHSbd/GZBuKbh4tET1HIsjLVf3Lb9CBC4jR7hoysi8GkOuUOEOsG17gIVD07QBO27fBHok3SulRFAF8JoTsFz+rVw7gc80NMAEgn4rhzIc0KIiXwpXiCCKw1A1TE0fVpv8fohEItx2222sX7+eyspKhBBks1na2trYv39/kE69q6uLyy+/nKOPPppsNssTTzzBOeecc1jREUdasrZN3DD5y9YXeKV9H++du4xdvZ1UxWPYwuWSaQs5rmES/5+9946To7rSv7+VO04OyllCOYAAkUW0AZMM2AYbbIyNcSAstne9a3vXeVmHXa+N12sMGCfARJsskMlBCBMkQAnlLE0OPd1d8f2j6tZU9/TM9IxGIP/ePZ+P1D1dVbfuvXXr3nPPec5zwPenin4bl6yiKqCqj8rn550E+DsBNTJuHdvEdUykyEslQp4tszsEoVq5Lv8cgucuy9j5DErK8E3E73M3/p915RCTYA12Xf+LbQvwt/+cHvrLGmprkyw6fCy27fZmU/bANJ3IYkSI/fCCEGFBWNffM+/93S+jZecqbDOLFq+AolQVPp+PRk/HHlp3vUXtuAVInoNt+9aTyO1DJltJKgwm8495SJKCpvmRR65jc+LSyZy4dPDoE8+LJoF87xXu/jbawh1k5bpZed8/sunVu9DjVb18KYGFJd/dytiZp3D8J/4XWVYCC1eU5r+QmkIoLhPmn42s3MKLf/wCttWDosZw7TyOnUdPVNG+5x3++quLmXPKtcxe+kUUzQiVTrGJKl6Xi38brtieiyLqjf98c46NIavIUl+wtBgbiiRjuQ6arHD103/kpLEzuGTGkeQde0hRRWVZWlzX7XcH3h+vQSmNf6Qmz66urj5lybKMaZq0t7fT2Nh4QFaikaincAt4nociyST1gf22g4mgoy+3PUJ51DQNTdPIZrMYhsGUKVNC2nxxXCgnruuyYcMGPvjBDx5QXQ+6BM1XJQVD0UL3jyLL6JJMl5mjPd9DTSyJFzwJBYm1bXv41dvPk9T0kMdFk2Xa81lm14zm5HGH+VlB7DyKFmPVY//OW0/+F8nqcdhmD1EuFjyfhC7X3cL931lUspqe55uNjYSGY+cOdq8MKv+nvLw/0se6JcZvoIykKwwfL6LJPLX8Xf72yg4+96Vj0A2VbNbyzwtYcAUOpD8RiQkHmif8cWlg9rTTvmdNwMbq+BEfkR26OC/f3czudU9TO24BrmOjRvh7yhY3h+d4eK6FrFXguGDn2pFkDTwHZCP43httJ0k+664bhuCWEz00QuJ5wWJb2Jeu42/SFVWndddbvHLPV2jashI9UV2osGhx8t0tNEw9lhM+eSuqngiTJ/ZZm4rGh1Bcxs05g6VX/p4X/nA12Y49aLE0jpXHtU1UzU8J8MbD32XnmieYf8ZXGDvrtOD2TmDRUQo3XMO0siqyjwVsTFTwvVXP8M0nb6WxejTdVg5ZkpAlmfpYirZ8D3nHDhQXH7RtKCot2W4+OGkOj517LVnbQtMV/rz5TaqMBJfMOBLbdUZOaREWFlVVefbZZ1m1alUB+PXkk09m2bJlYaI7VVXJZDKcf/75jBkzZkB8xHBEDJyWlpY+WqQA4zY1NYVKS/SaYhlJ4FJUKYveV5CWdZhZLnn81/5bOMBb53mgyBKVepz2fBZVlmnOdvPhqYv4zJzjy9JIRV/k83nOP//8fvOnlBJN08LkhIeyGIF59exJ87A9hxf3bPJN7/hWqS4zx+TAPeR5oATP56uHn8HG9ibsIC+GB0iuvwMohT3xPC+YAJzAnN7LYwCgqIJWvR93pesiSTKKZhQqPP8n/78WJ3AD7d7dyf13v4Uk+ZiWX/9yBbt2dHD2ebM5YvG4YE7tzUdkWg63/XollhXw1uCb2jVNoaszx0mnTGPhojHYthMCXkuJ69goqk773nW07FiFFksFu/TC+StUXvDYv+llvFOuRY5YLgdWjKLluEhyzHcpKHHyTc8iKwZGzZLimgEDrxdlbNcGPWPAqyP1FvcSbRD9BrDu+V+z6rH/8JMgJmtw7TxC21J03yU0dtapnHDZzRiJqv4VFvoqRlGLS+PUYzjtc/fwwh+vpmX7GxjJWhw771uAZQUjWUPzttd55tbLGTf3A8w66WoaJh+NFPSj69g+WdwBrHOu56ErKu35Hj46fTFTDYOKWJq8Y6HLCh1mjque+gP/dvTZLKobT6eZQ5MVXPwNe97x88AB6IHbvCFeQTpQfofsZhrsBMdxUFWVN998k82bN/PjH/+YPXv2UFlZSWdnJw899BD//u//zoQJE8hmfTpoEXY8Eu6FUpaa3bt3F7ifXNdF13U6OzvZtm0bc+fOHTS0NzpQokRpwxHhjikNTpXwPOgwBZ21FJqBfW00ysqq0G3luGv33zh29FQa4mla8z29GYqH8EKKvs9msyiKwqZNm8jn8zQ2NjJ69GhyuVxYV0VRwuiuvweeGkX20ehjU1V8Yd7Sfs9zAncP+P17yriZnDJuZv/nC/9qoIwsPPNrzDv9Bgo5LHpFKDrSIFNplI+h/4zQ/yf/vxPPTwwoCIVt28Wj1wJT6vxc1sI0Bdmeb1lxdJeejIVt+cpzqQ114Tzql7934ws4VjYgR/QKduW++C5SPV7F/i2v0LprNbXjF/pcIWWQnPnHHSRJIbf3MezOtaip6WS3/R7ZaMDIt+FkNoGkYDSejpqahufaSHJ/i2w589/IbEALgbY+rkRYV9585AfsWvskihb3LSh2PrxWVnRyXU1MPuJijrv0JhQtNqDCUthXXrg+CMXFdSwqR83gtM/fz4o/Xc+2N/+CHq8EFFzH8jmodP/5bXvzL+xas5zRM05i+pLLaJx2HFosFbikhg/XEN6CrG0xPZFi+sy+qU2ufvqPfGjSfGbXjB6wHIHtzDkWlvAjDlHPLNsmE4/Hqa+vR9M0xo8f72v+pklVVRVjx47tkwZ8JC0Z0fJc12X79u19ooMEyG3r1q1AIb12qToUI/oPxCIkXGfJZLKwXFnGcm2qjTS3nHtNWWV1W3nqf/1lHv7QF6k0eq0ejutiKFrZ/SlYT3t6evjd737HW2+9heu6pFIpLrzwQk4++eSS4LK/FxeCLEk4nkvesQu428Tu01BUVLl3jMiSRN6xffK4qItc4AFkOQDoRp6fFguVjZGQkX4n/k/+PkUwyo4dV8n1Xz2RjvYcX73+Ib7x7dN4/dWd3HPXKsaMqWDRET6mRdP8cazrCl+49rgBy3ZdLzy/+N2GYAzKCq5js2P1IyhaHDdgPu7FbbkgSeGiLcky+e5Wtq9+mNrxCxlMMSg1vt3cfiRkXLMFp2c7PVvuQdIq0SrnYHdvxLO6Cs5/P9+PKKZNUXUkRSbX3cK6537Fhhd/Qy7Tih6vxHNtXNcCD98C5TqYuU7mnHINi8//LuBj4AZTWIQUW+o9zwtAuDZGooqTrrid1U/8hLef/E9c10EzkgHuzsci6fEqPNdhx9uPsnvtctINU1nwwa8xccE5YdLFYfcJvQpHJp9B02LYju/W2ZftojvXze5MB7NrRtNpZokrOh5ekOnZn3/1SHoAOVC6hyNlt8K2bXp6egDo6OigsrIS27bJ5/N0dnZSXV1NT08PiqKg6/qIAXGjL5ssy2zfvp3Ozs4+1MuO45BMJlm3bh379u2jsbExdE+VGixCAZJlmebmZpqamsIQ4KHWWWT83LNnT592S8Hi2mPlcfGIKXq4+/c8j3YzS7WRYNFd3+dL80/i3MkLyTk2O7vbqDTidJhZEqo+5NAw0Tf33HMPsizzgx/8gFgsxrp16/jzn//MuHHjmD59OqZphjlZDlUrS2iNKqKhl4GEUoJqu2i3JL4bijqg9bl4YhERFAee8ITIIvB/Csv/30XstSzLQddVOjqyyLIPbj3tAzPYurWNhx9cw5x5jSGRm1CurQCIK/AWQvMWrqTBgLi+1UBlz7vP0bFvg+/i9KL8LC5avAI73x28d/7Cq+oJtr3xZ+accm1A6d9/JGjhOJfwXIvEpE/i2j1kt96KWrWQxNSrcbo3oaQOIz7hUr9uTg5JiQ1Q//fmvYmCYq18N5tfvZt1z91Mx771qEaqgJYfJBRNx8p1ocXSHPvhG5l69KX4lAheAVN2uYEUeIXgXJHAFQnmn/Fl6iYezmt//iatu99Bj1cgSX6eNMfOIyERS9VhZrvQY5XUTTwiKPfAvB7RWidU3cfL4Fu9k6rOyRPnMDbpGy0q9Div79/ONc/dxdMX3BAGkRSXM1wpW2nRNC1M6y7CaDVNwzAMamtrkSSJZLI3B8tI7SqjLhxZlnn77bfp7u4mmUyGi6y4j6qqtLe3s2rVKs4444w+ZRSLZVkYhsHy5cv5wx/+QH19Pfl8fsj1DQeXLJNIJApCsj3PQ0bCUDQUWebtlt3cu/E1P8plykIWB1EurbkMuqySUDXIZ8KQsLQW84FNZfan6Kt4PE42m8U0Ta677jrAX+yXLFlCIpFg5cqVTJ8+vQBIHY/HD7kFVfQr9NLQl+N2LO6vctpVrLBIklQQTTFskUrXqVwZbHcmzvk/GVmRkEaMZ0dYgv1n6f8momKEO0g8w3MvmMN/fO8p3lq1lyOOHBfUxf9PGwSIO9gYE/xCW9/8M1Y+QyxVi2PlA+uLSbJ6HFOPupRVj/8HimoQ8pBoBl0tW9nyt7s57ITP4DpmcHwAK7ZrIckartVO1/ofgeegVS2k6oj/BcDJ7iG36z5yO+9BTU0hMeWq0JXxfopIy7Hp1bt4568/p2PfeiRZxUjWBElURR4hFQnIZ9qoGb+AJRf/xCfhO1CqfUkKeVDCa4M5z7FNxhx2MrXXPMzqZT/k3Zd/j21l0GJpX3+V5RAAfOrn7kbV433cUwfcP/jjUaRGqTTiPHXBDYBPOxFTNfZlO3mjaUdvQAreoG70cqVspSWTybBlyxa2bt3K/v37SafTaJpGa2srK1euZPTo0Zimieu6TJ8+nXQ6fcBhs9FOFpaV9evXhy93wY4YQmzLs88+y0knnTRo6LP4PRaLUV1dTWVlZZivZ7j1FbiWqFvCch0UWeahLav5wjN3UBNLokgyt77zIv9+7Pl8atax6Iof/eIBc8ZMD01xpmOHCky0zoPVQ1VVuru7w7QE3d3dGIaP1Rg7diyvv/46AFu3bkVRFGRZJpfL9cuB816LJPWmDPjrX//KihUryrKCeZ7HN77xDVKp1LDGXzF+aiRluGWXmvSK34Hy2imFkU9l3FTcaEh1PdRECpk3S7PJSn7ITJ/jnufiei7KAZjUi0W4ont/6P2qKHIInm1oSHHE4nE8tfxdDl88Nnj+heVA6XFUPMai40YASTv2vcvutU+hhdwsHrLqZ3QeNf0Eph39cda/cAtmttPHQrhiTpDY9OpdTDnqY2hGqkDBKAkslTU8J0duzyPIRj1qchKy0Uh2533g5pD0OrTqw5HUFG5uP2bzC+h1xyNwMO+XiEU5WTWOfKYZVU8gKxq2lUVCRpJl/2+zBw+YddLnWHjW1wNA88ix1JZSegTOxUhUceQFP2DignN564mfsGfDs/5w8lzqJx/FyZ/5I6oeD11cB6qwSJIURlxu6mjiomW3kbNNHM/Fdl0+Pfs4/vWoszFdhxgamqyQ1IyDMn0M+kYKvMZpp53GpEmTWLVqVWieP/HEE7nhhhvo6uqio6MjjOAZN24c6XT6gHZ/0U4WEUybNm1i3bp1JBKJkqBXobTs2rWL5557jtNPPz0EEg80gAS/i2mamKZZcmde7Gro73g0QaGETzmvKSpt2Qz/+spDnDZ+Fr857ZMAfPmFe/nuq49ywZSFJFUDGYmUZvD2pf8alhsrQuvD4IueWPArKytpamoCIJVKhcc3btxILOZjNb75zW+Sy+UwDIPOzk4OP/zwsC/fb54WYWXJZDJ0dnaWrbANlk6+1DWDTS4HMgEdaPnFCwJQ4Ia0bbuslAoiFFJWdN8lMMD5rmv7vnpF63PuoWaNg76WJn9nJ2M5PgBeU2J4uAUgeAkZ2zVxPQddiYW7Qc/zUGUNVdHJmRnKM2r3umpK1C4s11EjVtjg03U9enrMgjbMntvI6tV72Luni9FjKkJOlwMTv4ytb9xPpnU7sXQDjpWFQDHRYikapx1PvKKBcbPPYP2Lv/GzDrsOrmOhxyto3v462958kGlHX+pbJJT+5gg/Esizu7E71yGpCczW15AUA0lN+scyW3HzLch6FXg2duda9LrjB3DJltMHpc8pnjtL/d37bil4rsOo6cdzxpce5ulbLqW7ZTtqAFj2XJdcpoXq0bM5/JxvMW6Ob9X3I3VGlla/uAyBc/FcF89zaZiyhFOvvoftbz3K+ud/jWPlOfkzf0AP0ggMT2Hxel3jImDS6w0acVyXd1r38OWFpzK3dgxfffE+WnLd/rFA+Xc9D9vtHevivRsJa8ugPC1iZzB37lzmzp1bcPyHP/whxx9/PKeddlq/15a70BZLKdP+yy+/TC6XC3fR4jyBT4ne98knn+TII4+kqqqqYAEuVQ9FUUI+kwNZrD3PCxcPUXfLdajU47yybwt7Mu385ezPA/6D/8biM7lzw0qe3f0utbEkO7vbaMv1sL27lQothhtkNZ5YUVv2wBPtz2az6LpOZWUl3//+97noootIJBK8/fbbPPXUU1xzjQ8M/s///M+CPhTPeyRD1YcroW/5AIHS5d5HfBYrPVFl9EB3TG5AZR0VweDcX/nC5acoCs3Nzfz0pz+lq6uLZDJJR0cHS5cu5dJLLw2TY7qRySLYfuH73mPIioaZ6wzNxQWTfFgvCdVIABJ2vjv0ywMl638oSBS7BgFmzXNIGz5za1euJdLm3uMJrRJNidGR248sKbiegyprdOaa6cjuY2zVrNAK058y6Xpuv5acYpEkCU1XcT0ndA/pusrkKTWoEQVg4uQaFFnmzdd3hUqL0q+CUFoKFzufKC7X3cymlXf6mYQdEyQJWVax8xmqx81n3Gx/AZ569MfZ+uaffZxEMPZd10ZRdNY88z9MWHAOeixdMEcXLtQ+kZps1FEx7/thndx8M67VAYCsVyPrNUV1dgaIHiqrg/E8semSwjQDgrtGWGujSoAXENj1jg3wJAXHylHZOJ2Tr7qH535zOe171wKg6Snmnnodc0+9LnQZSbJaEFo8EgpLb5P6AnQlWfaBzY6faXnCvLMYO/NUXNfyWXkDBWqoG17/JBlZUZEUDVmxkGUVWdGQgznY58WSuW7hKYxJVvGrt5/HEXNCMSWRJOG4fsCEKsvkHBtNUXBcd9hWmLIYcS3LwrKscDK3LIuqqireffdd6urqOPbYY2lubiYWi4W5bDRNK6B8H4oUTCzBQrp3715WrFhBLBYLXRjiuLin+G4YBnv37uWBBx7giiuuKOlOij7IfD5Pd3c38XicXC43bKVFluXQJRVtiyJJdFt5TMehNpYMOEI8Ko0EjufRlushqRl89YV7+caKB3E9F1mSydsmEyrq2PapHwzZJyjacOGFF/KnP/2JG2+8EcuyaGho4JJLLmHChAm4rkt9fX3J/h/Jl24oUmpBlGW53xTy4poDwXb0McGWUJBGytLS39gqt3zLsti6dSsVFRVomobjOKRSKZLJZAGmrJTkO3fR07GH0eMXD1rn7v3r8TyHutGzBz33UJa/7fwLsiRz+NhzSh5/t+Uldrdu4KQpnwp+cQGZtZsf5fbXv8xtlzQdvMop/nNvaEzxr9/1lQUxfisrY0w/rI7xE6qD3yGXs/otqliKx5NwDa1/8Ta6W7cXsrdKEq7rMOWIi9FiKWwzS/2kxYyZeSpb33jAB946ls8GrSdo37OG9S/cyrzTru+zmy/8lPFc/x5W2+tk3v0vJK3ap4gLXHKe3UVq5tdQ0zN94jk1Ocx5J5jjXQ9J7Y3QUpTANRaQ7pWytEhS3zlElkDWYji2SWX9JE793N08d/sVqEaShWd9nboJi/xyS7iDir+PhETLin4X7LeOnUfRDBSMEGw9LIUFv01Wrhst1o1t9uC6NlauC9vMAuDh4ngeO7vbGZOsosc2w2v7eD88F0VRScg+dlIP6luhx9DEXDjEbiqLEVfX9ZLMt9XV1YwZ42chravrm+tFWC2G8gCLzxVlLFu2LNxdWpaFqqr09PQwc+ZM6urqePHFF4nH4ziOg23bJJNJnn/+eWbPns3RRx8dupii5Qtg5wc+8AGWLFkyrCSLYhcci8XYtWsXP//5z/soVo7nkdIMNEWhJZdhQtrA9STa8z1ISNTGUrTmMvzzkWfx1UVnsLO7jbiq+TH4YQ6boVuqwA/Dvuqqq7jwwgsxTZPq6upQuZRluQ+G5UCtCgcq0XuKRT6bzZLNZgtcb/1dGzVjDuWeor2madLW1hYqF47jUFFRQSKRGBFLSyaToaurK1SMXNeluroaXS/PjCvLcgiYjrqHAHL5PKrk8fKf/gHbyiEr/g7aSNZw/MdvYte7r9C09VWOHr8Yy8xh5bpYed8/IssasqqT7dpP1aiZHHn+d9i86jFkWWVu42xyPZ0Y8QoymW5++ctfhvV/vy0unueF88A111zDmDFjgrlBI2t2kTDSPPDaT9AUncPHnkPWzBDXfMUua2WI60meW/cnXtx8NydN+RSmlcP1XGJ6Ast0kR0dPLAdi1LgUA8PRVLpyjWTMdvR1XhgcRn8GSJBVbwRWVICC0DhogoeH7/88DB6KJXWGT26ov9C+xEpUEgUVae7dTsbV/wRRY0RjW5xrBwVDdOYsvhiv37BwjLnlC+xa82TBW3yXAdVi7P++V8zcf6HqGiYFgI9o8+lYJGVNTIbf05q5tfQqhYW1C/f/AKZd39G1eJbwnv0r7wP0K/Buaom096e45mnNtLRnqOmJs7SU6eSTvvu8If/soYzzjwsZBZ2HJeeHot02sf7WZaDpiksf+Jd5s5rZNToCh64500uuHghp1z1J58GISB+kxU1VFiK632w584+Gy1VbJb7RiyJug0m4hwjUUX1mNnE0vU4Vg5ZNbCznaRqJ4ozcT2PeIC1lCUJK0ghkLVNqow4pmOTsUziqs63Vz7MLe+8wNhkFV1Wniojzuu73+XsSfOCxgyt7YNaWlzX5fHHHw9DY4WJLR6Ps3//flauXImiKOFEJlwTU6dO5fDDDx8yK270QQg8yvr163nppZeIxWJ9IoZOOukk5s+fz6pVq7Asq8DEr6oqd9xxB2PHjmXcuHEFigv04gKqqqoKEgsOV6IZqMXiqckynWaWxY0TaUxU8J2Vj3DLqZehyjI3vraMmKpywthpfPmFLiakaqiOJaiOFebHGa7GDL0Zr2tra/v8FnX/lbrf+yliQcpms8ycOZNZs2aFRHmDiQAcD6UNtm2jaRrr16/nO9/5DlVVVWE4/JVXXslZZ50Vpj8Yjohrn3rqKW6//Xbq6upwXZeOjg6+9a1vMXv27LAOA4lwQUZFKFiyJKHqBsd//Cb8yUsl39PGK/d8FQA9lqSidgIAEi6JinqOu+RnPl4hUUXrjlW889TPADASFSHjr6qqYk1g+/bttLW1hZZNUaf3UqLmdwE4F33iu9p6sXh16bGhwuF6Jranhd8hSTJWRWPFJAAUVUVyRTngSX6Yqe966qu0mE4WVVZ5cv2v+OPfvkHKqCJv95Q8F+iFvHg+/uVXl2yjMt6AR69bNvqOa5qC47goisS8+aOZN390We9mH6ud5+NL1j13M90t28KIIT9NlopldTLj2E/5rg7bRFZ1XMemdvxCph71MdY+9ytiAROr5znImkFP+x7efOxGTvzkLaFbsXieiops1GO1voqSnIqkxAAPz8lit72ObDRGOqj3GZfuwL4iRfotn3dY9ugGZhxWx6IPj+WVl7ax7JH1XPCReSiyzP593QXldHTkePShtXzik0eETMUALc0ZTNMfC03Nfrv1uK80CutScZ/3X++Rl2LFrvd7X09COeMFCN1JExeex8SF5/U5T/SOquokI8EhuqxSG/cxk6ODsOeGREWo1MypGcPZk+ZRG0v6OYoUlSWjpnDyuMMA0Ibo+h8UiCtJUrjgF++Cv/nNb2KaJj09PdTX14euHNM0w0VyKA+xWEsVloD777+ffD5PPB4PFY9sNsusWbM44ogjMAyD8847j9///vekUiksyzdJCmXq5ptv5h//8R9JpVIFC3YUZxBVNoY68BzHQdf1kBE4HDT4FPGmY1MXT/Otoz7El569i8Pv+j6KJLOju43vLjmXSj2O6TqYQXbQTjNHUtNxPQ8JQpK04e7yhQIo/h5sl/x+KyxQuIteuHDhkPMhDdedI1x8hmEgSRKGYYwonkZRFAzDCLAnvitzILxVKSmFrQi/B4yiue5mNr5yB7NP/iJOyNgp07Z3HWa2M1RItFg6vLZqzBy0mD/puI7dC1qNTH7xeJx8Pl+A/3qv8U+CwRp85aRUVJmoc87KENf9xSZpVIfHVcNfdDTFoMfqjFwTxfj4H67nIAXA3VLuZcezGV81i2+c+Rgd2SY0WS8rVDoV1EeibwSOKFu4OTzPw3M9ZEUedDEqdgvJikbTlpW8+/Lv0ROVOLbAsihY+QzVY+cx7eiP+9eGGcv9sueccg0731lGtqsJWVbxPBfXNtETVWxb9SBbXruPyUdc2CdKJWxLEAWUnv1vZDb+nM5VN+DZ3SBJSEoCNTWd9KyvB/ccWGHv19Ii+VmyATa920wqZYTEfEcfO5HdezrZtqWNKVNrURQJx/ECZVD259cSDMS6roSYC12TkSQlJN0bCCvyXrrW+3MZie/DmU9621FoMZQAJ3A7Wa5DZ1erT9QJmK7NTaufYeW+rbTne0ioOvuzXZiOTXu+h4umHc5F0w4veW/hTRhKf5XlHpo/f35ZhRXLUNxDxQ/bsiw0TeOhhx5i7dq1oTIS3WGdd955IX3+6aefzurVq3nrrbdIJBJYloVt28RiMbZv384vfvELbrjhhhAHEFVcBLnagUpUGRCfrufnw7Echw9PXcSkdC33BDwtH566iGNGTQHAcp2Q1liTFRRJJnDHjsgLULywHAqKyUASxYAIUkMRtj3Yzl7TBqcY70+ElU4Aqh3HGfR+Qy1fLLriPtFjB1h4mPZeVnRad65GlhUmLboAAD1eSdOWlWx+9S5mnngVVq6LTSvvxMpn0OOVtO9dR667WRQGocWgd0J2HKcAbJ7P58nl3rtkkJ7nEYvF0HU97Dvbtvv0nVC4DC3J3s6NvLrtQdp79qIpvpvAcnJUxhvZuP9V4lpFeE1BKWLzJKkldrS9YrsWqmJQn5pIfWoiQ5Hixa+UQhJu5pTBF6PijZ/4XPX4D7FNn8/Dw3cN+VFDNnNO/pIfbWJbYW4hWVZwbJ+3Ze5p17Pi7i+jxCvxnIA913VQFI3XH/4O9ZMWk6qdGLKu9sG1eB6yXkV69jfxXDPEuUiy7idJ5MAXe/H483kbI+a/A50dOWpqE8QMNbSauK5HPF6oHJWKzHJdj1hQTpiuQy5ULks9q0KF0cJ17SG79g8N6afOikq1keALi8+kIeFveD416xjWtu712xwo/qqskNZixBQN23XIBYkUoy+YpihDVligTCBuqUkBet0r0WPRxaYYQ9KfFA9YYSZ/++23efTRRwsI21RVpaurizPPPJPZs2eHpHMAl112GTfeeGPoqnJdF8uySCQSrF27lptuuolrrrkGVVVLWlwOVDko1UfC4qJKPor68IYJHN4wITwukiBOTNdSG/P97X08uSNQp5FYePsz/R5sEaZ+wzAGdZ8IKX6mpZ7xQM9bTPwHQ7nrb4dUrvRZoAPlQjMMNrxwK3vefQFJkuhq3sqLd3wRPGjZ8SbVY2Zz2PFXMvOEzwDQ2bSZLa/fz4IP/hM9HXtomHwU6brJACGxWH91F/QG06ZN4+KLL8ayrIMaIi9JvWlDHn74YV588UUqKir6fYahf15NsL31bW556VosJ8i3FXSfIqvk7AxT6yKsodGu9Twc1w7CpiU0xUApEdki4UcQAeSsbjRlcMXav395ynW5Y6R4jAvrx9rnbmb3+qcLmFxlRcfKdTJm1mlMOfJiPNdFDsz54TNWdTzXYcaxn2LXO0+w850n0OIVuI7pu4lUg0zbTlbe/zVO+eydocWo9HvngucEikqva8VzLZAKsxEP0MJ+j8iBQtc4Ks369btwXY+a2gS5nEVbW44j6/25NZ+3+fUvV1BVHefijy0oaK//vveWuWd3J0ZMDa04/SmEJZ+B6wYRN+XNV38PouAbIurjaX6x9BLAt5R8af7Jg16bkktbY4ez9pblHip3oRhOhfq8aAGOpbm5mdtvvz201giMSi6XY+rUqVxwgb97FOGitm3T0NDAFVdcwc9+9rOw7kCouKxatYqf/exnfPGLXwwtNGJBPNjmPA+f8th2HSzXQZZkVKk3380LF36ld6JVDrxOURdb9O8DlfeDu6VY8SpnXBVPLIVjzA1Bj++FGfdgi1AuLMti6tGXMmHBuejJGsDDzmew8xkSVWPYteZJOvdv7L3OdZgw/2zGzOw76fiLSP9pHSTJd6um02mmT58+4m0aSOrr60N3dX+cPEKJ6M61snjiOdxwyp1EydBEhN7dr3+bFVvuD37zrQjgKxTd+TY+/YdRALRkWvj6B+/lmMkXYjl59KLEl2I3rco6ilz+fDmS46/YLaSoOm271/DWEz9G1RN4rp/J2V9UbbRYmoVnfg0fB+GUdlPh98jh532H5h1vYOW6EOHMrm1iJKrY+c4TvPnYjSw882uhotS3TrJv2SlwwUlDtLT0t4b0utHGjKti4uQe7vj960yaXMO2rW3MnN1AXZ2vtCSSOuecNzu0xkBv1m3TtHEdD1XVkWWJFS9t490NzWSzdnifwR6VPyZ9i1PLjjdp270G1Uj4FipJivQo/XzvTzGL3vg9LMPzLUyubRJL1zNm5ik4roPpuhiKGmZxdkq8h5IkEVPUPs+11LMeMUvLgcpgFSquvMDE5HI5br75ZlpaWkLwrVBcYrEYl19+eRgpJKwlqqpi2zbz5s3j4x//OLfffnt4jlBqEokEq1ev5kc/+hFf+MIXqKmpCRWX4joeCIakoJzgUzDcqrKCKiv02GaYg0iY1brNHGk9VrLfhqqRhhEluVzo8jhQkWWZfD4fsh2/X4t9oRvRCbgYQOT4AS86L4aTmqr6k2YUJ/D3qLD0W2cPFD2OosXZ8NLtjJ11OsnqscjBwuA6NooWj5zu0bztNaxcF10t29BjaR/jMGY2YceJgotvFRwT4yyfz/dxQZayckXb0J/Vq9R3SZLI5/MkEomQALJcsRwf09OZayEWRA/lrG4q4w3krZ7eOtHrCnBdh7ie5h9Puw/bNbEcM7TIqCV3z4FCzdDAySNl4e1j3ZBlXNfh1Qf+hXymLWRrBd/Kks+0suDMf/azNkfcQlFLQtRNVNkwjUVnfZ2X/3Q9WqwiXIQd20SPVfD2k/9JVeMMJh3+YRzLD7+NlldYdv9z7XAtLeKQbdocc+wEpkytZsumVpaeMpWx4yqxbRdV9Te4FZWx3jkASCZ9JSsW632ujuPy0UsXUl2T4PZbXw3qVt7zcG0TWVfZ8NJvef2hn5KurcLKZ0L30t+VeB6yapDPdDF6xrGc+7VTkFyHeKCYel75Od0sx8FlaIl/S8lBVVoGklIKi/j7lltuYd26dQU4FhH6euWVVzJlypSSIczC7bN06VI6Ozu57777CnIUCYvLpk2b+I//+A+uuOIKZs6cGRJmRRH8BzqZSJFPDz8ngyRLuJ6PWZn+u2/yw+M+zMcPOxrbdVjTupfj7/shb37sG0yprKfH8pUaQTBXDhhXluWQ+v7JJ5/kqaeeGpJ7rhyJuvD6YyY+2NI7ZrzehHJliADfbdvaxrjxlYMml/t7E0mScO0cshpj77vPM3Hh+QABURyMn3cm4+edGYAzVVLV45BVndcf/i54Hooep6d9N0df/GM0I0m+pzcpXH8SzQt1sK1w0dxT5SoFsqyQ1KsAqIz3chIZaiL4rSHMfhst0cVFlXVmjz6xoDzPc5GlvsqZsOwId7PgVQpdIyVlZMddqGw4lv9cH/oWezc8h56oiriFfHLB+ilHM/e06/zrlF4+rVIKkBJEE01b8gn2b3mFjSv+iJGsDiKQfCuNohq8cu8/kqwZR/2ko3zekKLcRIO58spoYb+cw0Jk2Qfa4sGSY318kQgnF3WwLCe0tKqKzKaNLbzx2i4syyGbtZg+ox5NU2htzVJdk8B1hugSFxbzRDXp2mqS1WOxzR7ez9QEwxU/sk1HM9qJVzQGv/VzbmTMmK6NGmAzJcmHRvhRQgqO56KU5Q4sLe+50lJK+46y0P7qV79i5cqVBVFAIqzxnHPO4YQTTghdSNB3pyKwLOeeey75fJ6HHnqIVCoVLq6WZRGLxUJm0fPPPz+MTLFtO2TWLC673A4OXRiRv2UkYlphiFyPbZIQIaWyQk0sQbeVpzbmh44lis53Pa8gceJg/Sv4ag6WRJ/hey2+0uTvnJY9tp531zeRTOpYloNhqNi2i+v6e2ZVlenqzHPciZNYfOR4AJ54fD2Xf2pxWQyjwzVhlqrzSMmAfR64P4xENS/+8QukasZj5TOoWpxcpoVpR3+csbNOxbHzxCsaOeGym0sWk+tujiy2gz/j92IcFLsHBxLhrnE9l5XbHkR54fN051tD143r2iSNKt7Z8yy64lufZEkJacglfAXF8zwf0yJJqLIW8qpE768qOjHVt+AInIqg7bcdE1XpP0z+QDZIoj8KXJ+Be2bz3+5h7TO/RE9UBhYW4RZy0Yw0i8//HopqhAps3/4rGvvB/Lz4gu/Tuutt2ne/g2okcR0rsOyo2FaW5377WU696k9UjZ5ZMu/NgW4SBr0yMBDed/dqPvfFY3AcD11XAheQhCxJBXNXKm1w6unTyGRMDEPFdXoxmoLLZci6ZfAqOHYeK9eFlc9gm5m/K6Ul+rw81cY2Mzh2Ljxmey624xSkmYmOQyNijTQdG11R+e3al1nfvo8fHHN++Ntw5D1VWopfULFjF66dW265hZdeeqlAYdF1ne7ubo455hguvvjiAqr54okyWq7rulx88cVIksRDDz1EIpEIXSQC6Ot5HnfeeSdr1qzh4osvZvx4f1ETLqXoznGoL5ok+bsCWZIwXZund6zzdyWAKsnoisqq5h2MS1WRtS22drWQVA2e3LGWMclKemwTQ1HJ2hYT07UcVt2I47ooRbtZsbMT0SgFuY/6Mc33qWcJs320zcXXinPE78JS9X5QvJt5m67OPLbtW+o2b2qlqjpOLKbiOC7xuEZPj4lt9e52Uymj7IlIKIBACDYdahuFshzlNjkYEuYVAo4499tku5r8XV+AKXBdi1jSJ4GUFX8xce18uDOUJBnbzKLHK0jXTw0T5Un9gOgOZRHWkDmjT8RxLdp6dvshm5KMh4vrOmTMDhpSk5hQ45NceZ4TUdR6FzdNiTHQoms7eVbvfoXP3TmJnNWFLKu4rkNMSxLX0rRn9xcNN9/++tOL3qEyXu9T+g9zQSulsDRve41XH/hn5ABI64uHrOjkMq0cef73qZ+4uP8wZb/g0D0oMCmuY6PHKjjmoz9l+f9ehGvnEQBaL6D4z3bu49nbr+CUq+4kXTtphBWX8t+72rokiiIjPJZig3LJZYsKLKySBIuOGNfn+pdf3BZ+V+Qh1jU4XVF1VCOBqseBANdzyItvOXOdwILuuSDJSJIcYp58q4kWutujz1KMo+U71jG/biyNiQrygYLywp6NPLNrAz845vxe2v9hyHuitBT7SsWnmMg7Ozu5+eabWb16dUmFZd68eXz2s58tWWZ/n+AvphdddBHxeJx77rmHWCwWKjTCHZVMJlm9ejWbNm3i5JNP5owzzqCioqLkPcqR6Hme56HIMl1mjrMfugkvPOZRG0vx01VPceNry5Akn8+lJpbksid/42eFliQSqk57VytfWnwmPz/pY+RdO6RDFqJpWgGvyPvh6vA8D8MwiMVi7wlnh49hknFdj3POn8M5588Jjz368FrO+OCMMGNuVIR1RoQ/DnYP8ewFWV0ikRjkqv5FXFtOyPZgUo67TzWSpI3StP4FlkmtEFCqxzU8z2XsrFP9c10XpQh0+vcgqqLjeS4fmns9H5p7fVnXKLKGHXAlRXlWPFzw5D7vlxq8i8dN/ShjKg9DU2N4noPtWKRjtby+4zFe3nIvVx33C2zXCjJKh4US1/yQUbEYlCulNiWC8KyrZRvP//6z2LkMimb0Jr5UdfI97UxccB6zT/6CH7Ic4BL6syQWz6uyouLYJrXjF3DkBd/npTu+hGqk8BwTkHBdC1WP09W8mad/fSlLP/07Khqmha6iUuUeLOloz2KaTugKivqVFMULeFgELsvfbMmyRC5nk0oZRPeGudwQLdbB0LHy3WS7OlG0Fuy/C0yLr6TIqo6qJ/u4NcVGXNMMHt32Ns/tepcbj72AbjOHEVhcVFnG8+CM+3/CXWdfzUenL8YKFOcqPUFjXNALDF8OutLSnzLheR6aprF582Zuu+02du7cWaCwaJpGJpNh5syZfPGLXwxJpKK8L8Wm+1KWHMdxOPvss0kkEvzhD38IWXuF68S2beLxOJZl8dBDD7FixQqWLl3KMcccQ01NzZBfruiCJEsStuNQbaTZc+X3A03b93XnHCvkYxEOpaxtkdIMYopGt51HDuiSRTRRPGJyE/fp6upi//79uK5LPp9/X6J7BEB63759ZLPZg36/cBKVJUzTYfvWNkzLIZ022LWzg9de3UV9QxI8mDCpGsd2UTU59GuX8zjFPVRVZc+ePezevZv29vaQbn+o9RXhunv37i2bCqA/Gez+AkfhlUT0y2ECvFL3D3fVrgN4Ie/GSOMvDraIdtiuz5XhAa5nE9fSuJ7D/q5tZMw2LCePjExMSxHTUtQkxwJw/JSPcvj4M4HSSoW/IVFxPZeJNfOZWNOXy8p0cryy9QGOnXLxIPUc+iJerLD4yRBbeO72K+hu3eknzbNNkHxafsfKUtEwjaMuunHI9y9QclUd17GYsvgjdO7fyKplPySWrMOxckGUiYWqJ+jcv4mnbrmUpVf8lqrRs0LFZTgu90irBz3DdTxUXSKVMrjj96/71oCIq7i7y2T+wtEsOXYijuNvfqK4uFTK/37WObPC3ydOrh5CHXtdaVWjZjJh3ql+Nm07d4hbWgJskuKn82jfs9a31EV4pCQJbEADXtm3lf9961luPPYCUnrhpsbyHOpSVSEjrhDTtck55efP6k8OqtJSygoSxaP89a9/5f777yeXy4WKA/RaWGbNmsV1113XJ1JooJ2B+BTnCQXl5JNPprKykt/85jdkMpmClADCHZRMJmlvb+eee+7hiSee4HOf+xxz5swZcioC0fZoH1TqiYJ3Lk3f3WuFHmdrZwt7Mh0cM3rKgH0p+nDp0qVMmTIFwzAOquthIBGLezabZc4c3+rxnrGkeh4//+kLmHl/R6XpMm+t2kt3V55Zcxr46j+f7FuyFDkkkSpnmhSKRjqdZvny5TzyyCPDzskUxW4ZhkE6nT6glADliCTJSP1gdvpbNAp21X+HLqGohEqnrEEkBPmVrQ/w3MY/srdzE+09+8g7meA8nXSslvrUBBaMPZ0z53yJqnhjGBpdykUKIEsyjuvgegI3IpO3e0jqlWTybQhcjOlkkSWZ6OjTFGPATVexlMIOCIUln2nj6V9fQuvOt/wMzgHrba+VROeYj/4XiYpR4TXlKA9Rd7E4T1Y0XNdh4Vn/QnfrDjatvDNIDZBDkhVfcTESZFp3sPxXF3Pcpb9g9IyTgnw9fblpRtLqIsq59PLSDKzRvhTujeh14piIJPI8jw+eNbNsRcvzvJDj6LDjr+Sw46880Ca957Jnw3M89tMzSFVPwLK7C46JlscVjZk1o3mndQ/3vvsaixsnUhNLMiFVw6hEBTnb6hMGLYDpByoHPeQ59LcGC7+qquzdu5d7772Xv/3tb2EyRgGUFaDbWbNmce2115ZUWEoNtKiUWuBt2+bwww+nrq6OW2+9lS1btoS0/tHs1WInPXHiRKZOnQocGDeJqKPtOkhFZkopAIZZjoPjucRUjS8+cyf7sp08d+FXiCmanyVT6ksfLgDHM2fOZObMmcOu38EQgTsajpVKuO7Kxcnohsq3vncG1TUJXnx+C12deT549kz27eviycc3AH53u64X8LMoQ8rP5bouiUSiXyKzgfA/pRQC27ZHPOKqFLaolAhOo1L1i/79frgYD4aElt1A8fjNiht4fuMdLBz3AS5c+M+MrZpF2qhFlmR6zA72dW/lnd1P88S6m3ltx6Ncf/IfqE2OK8CchPOZa9OLsZBRZB3wfDCvFLASS0qoOKiyFuy0e/tWlOEF58oDRFSUsiKLUOV8ppWnbrmU5u2vo8UqfKyJ7HOpSLKGle3k6I/8hMapxw6MYxmkH4vvD7Dko/9FrquJ3eufxkhUFSguimpgZtp45rbLOerD/8HUoz7mj88I+Hf4VpeBxce49d8eWe6rOBG40iRFLXivRdRhuX0VxQB5rvN3YaSUJBnbyqHH0pjZdiRJHjAVRda2kIBVTTv41kv3M6lmND22iSLJpDUD07UDJX3km3/QlJZiv6vgX1m+fDlPPPEEHR0dJJPJMMpF5DDJZDIcddRRfPaznw2puoeisETvWUpxmTBhAl/96le54447eOmll9B1HU3TsCwrzJtUXV3NlVdeGWZrHs4CXCyyJPkPMVKE53khI66GwldfuI81bXv481lXk1B1HNdFlUuHYfu7YRnbtt83C0spEe634fSXpmnEYrEQQyJwMsKqJKS4Lyqr/OiPWFzDDtgrq6riCP1BCsIbhfVAkqSyMX2SJJHL5UJFY7h4lKi7aSQBuaJMIMQ1DWTlOliLxHup6AzlOYjonZc238sLG+/k2qW/Y+G4M/qcV5UYxZiqw1g07gOcM+/LfPux07n/zX/ns8f9wr9XiE+VQtdQf2IEXDACVwOUJpyT+oLqB7OAid+F8tHdup1nbvskbbveRotX4gZKg+c6KFqMfE8bc06+hhnHfrIg0d9wxkBhPeQg43OM4y/7FU/d/FFadq5CM9Kh0uS6NpKigefy0l3X0rb7bRZ96F+DLMl5ZEUfYj3Kf/dK5RMq2Rb/D//+0YzNwe7SX7vKt7AWzM+KCsOMkHk/RAneKVnR/T4Z4NyYqtGW7+Ej049g3Ef/Gddzacv10Gnm6LFNvvzCvRACH0ZWDlqPhrucAIfy/PPP88gjj7Bv3z5UVSWZTBaENLuuSyaT4QMf+ACXXnppeG2xwlLuS1a8wIv7OI5DKpXiqquuYubMmdx33320tbWRTCbD6z75yU9SVVUVAoVHYoKXILSoSJIfkKkrKoai0mOb/MPzd/NG0w4ePfcaZlWPwnbdANTkvzz9mZGLF/RDQYa7MHZ2drJ//34sywpdhvv37w9zDxUrwqLsbI9JMmVg5m3MvO+D7cmYoTvIslw62rN4HmiajGO7vtVrEBEWwpqaGiorK0M34nBEKO4dHR20t7cfMPZIWG1SqRSvvPIK69evL8gJFB33Qrn96le/SlVVVQHFwIGKuIeIsBquUjeYCHJJYEjEho5no6Lzxs7HmFq/mIXjziCTbyempfAjIoKdNv4k3WN2Uhmv56zZX+K+N/8dKL0JaurahuP1zSsjSTKmnaU6MZqO7H40xaC5eweWkw/u1yteEFHiuDaVsXqSRhVR1t5S493zgkgdVadlxyqe/91n6GrZ6ruESigskxZdwBHnfTuwuqgF5Q5V+vSDrOA6NrFULSddcTtP3fwxOvZtQDVSvdYe10aSZDQjyZqnf0Hrrrc46sIfUjXqsLAuB0OJLqctQQXC+5t71oHroI+eGVpbhmp9/Lu2VopxEaw5A40SWZKCNUrhxDF9GbG/9tIDlEjpNCLynq14mqbR2toaZrU1TTPMT5TL5TAMg8svv5xTTz3VfzEDS0LxBDwUKbbMRN0qnudx4oknMn36dO6//37efPNNenp6uOSSS5g3b15BWPSBvFReoLE6nucniIpQB+7v6eKpnev47boVjElW8ti511IbS4Yx7IVt72vWP1RfjKHWTyygJ5xwAuPHjw8tXCLL87x5fkhqccoF8ZlM+ZaZisre0NTqmgRasNvav6+bb37tMZIpg2yPyegxFei6UkA6VSye5wPFm5ub+djHPsZpp502zN4olGXLlnHbbbdRV1d3wNYWMZ67u7tpbW3tt78F6eBIKhbiXlFyuYMtwgIngMzliFAqUkYNu9rXAZA0qnBdB9e1cXF9l4AkochqmBG6qXs7SaPKL8Sjj5L79YdOoCWzE12N+aDl4r73PBRZQ1UMvvin6SVt5K7nEFdT7Otq5wsn/ojz5n8F08mFxHdQBLh1bSRkZEVj6+v3s/L+r2FmOwusG0JhMXvaGTXjJI699BcFbTjQ+ayPtVdRcR2LZPU4ll75e5769aV0NW32OVxCN5WH59gYyRr2bXyJJ246l4Vn/QtTj/pYGKI/0nPZQO0recyx2PaNhThdJtNv3Yda0RCE+/59Y7sORAZ7IqILeywTTVFwXN8lZ7tuoJAfnHodVPeQb5L3d0hLliyhurqam266iUwmQzwex7Zturq6mDJlCp/85CeZMmVKmACxvyihoUrUZxl1q4AfOTR69Gi++MUvsmLFCrZs2cLZZ58dLgbR64d7f1mSyTk2qqJw3XN/4sEtq6mPp+ixLbKWSaeV5TtHn8vn550E+My5UcKe4S4w79cOZjginseRRx7JkUceWfKc/qxupmnzo39/Bs/1yGRMbNvlmac20dGRo7radxvV1Sf595+cjSSFtANA+VFEwiKYzWaHFbIsST4FfTweHzIF/WAilKt4PN7vOcIqM5LKruj/fD5PW1tbuAkphfEpNY4Hc4MUlyE2OtXV1WQymbL5ckRY8ikzPsULG+/klpeu5eNHfp+4lkYuwT1uuxbPb7yDR9/+GZ9c8mP//iX2nN/50DO4rhUMplL18CO4XM8Z0JUkIeO4FlUJn21UV3oB+gUKguv6kVyuy5uP/IC3n/pvZEVH1WK4tlmosGQ7qJ10BCd96nb/uOv4WK4RmhP6Ki4armORrpvMKZ+9k2du/QTte9ejxytwrFz4wjlWHi1QZl684wuoeoIpiy8OgcFl3HlIdYRAQY9y78hyv30gJyrBbSlvYuhH/p7m3WIJQ7IlibKcOsEpmqyEkbCyJKFKPpHfwZKD6h6KKi6WZXHYYYdx/fXX84tf/IKmpiaSySRnnnkm5513Xki3X2o3PdL1ESLcRZIksWTJEpYsWVJwzkgMQA8vzDu0qH48tutSacTJWHna81nWtO7hu68+wlstu/ju0edSG09huc6wUnb3117Lsg6a2X4kReBzCneXfsSNAEj3mWhkidlzGkMOFvBBt5blMGZsJQCGoTBqVLrgunKfbVTJVVV12G4VoQSPZEi6qFs+n6ezs7NkO8Q5lmWFlp0DGQviWtd10TSNDRs2cMMNN7xnE7TYUIgIrIHco37bFVzPYXz1HL5w4i389pWvcMN9C5g7ein16Ykk9Cp/Y2F109qzh/X7XqIls5MPL/pnTjns0wAFxG9izIyqmNLfbQ+obdFsx1GFRZJlOps28+r9/8zONcswEtV4gbVIkuUChaVm3HxOufKPGInKEPQ60otpScXFtkjXTeKUq+7i2ds/TfO2v2EkagImVQEKkjGz7cw+6fNMPuJCfELEcq105YzbCL+ObYKiFZAjerYFshxagArEsfBs94BAGAVzl2OFJG2H+uwrSRKOlfMzgQeEk+XWWSj1Yp0rJkAdaXnPooc0TcO2baZOncp1113Hgw8+yAc+8AFmzJgBUBAKfbC01VLKiFhMhOn8QPIPCQVN/JMkH4tiqBq24/CpWcfyqVnHFlyzq7udh7eu5nuvPsore7fwu9OvYE7tmJD99oDcUkH93wvT/UjIQCHA/bH9qqrCBRfN6/e6EHNhuwXK/1CjAaJlvd8Sfa6dnZ0cd9xxfPjDH6ajoyN0aRaL53lUVVUBB6Y4FY9tWZbf8/EVdR9H61Ms4YIq+YrLovEfZHrD0by2/RFW73qS13c8Rne+FddzSWgV1KcncfzUSzhq4rmMrpwe3qsUpsVxrUHz4AzajuB6P3pI7pMiwFdYHCRZobt1O3/934vobNpEvGIUVt7PtgxSxCXUQe3Ewzn5M38glq4/aApLQRuiiouqha6iU6/6Ey/84Wp2vrMsDIdWtBj5TCsT5p/DURf9MFDQ3IJyBpayTKN+ea6DpOp4tond0wF4SKqOkqjyjzt2gFuJWgWkAzYQFFjGFG0ICtn7L4IA0EjV+oryYM8jOGy6Dq7nYbkOHn6k7MjDb3vlPeNpgV7LxsSJE7nmmmuAXo6UqOn/YJvXSpnJiyMuBjOll6qf53lYlhXiBwQA0rIsFEki79h+6LPk5ybRZJmxqSo+N/dEzp40j0uW3cqZD/6c+8++msUNE0Mw7nDbKBb6N954g7a2tjBq5e/BbCkUylwux7Rp05g0aVK/fDmWVYLh1vOtMEpgfVHUQ7/NQxXh9qmurqaxsZHGxsZBrznQBUyMZ+B9jVqLzitRS2JUYStYUCUFx7VIGdWcNP0TnDT9EwOW77g2sqT06SvxWTIa6ACl1LPx3T4uqZoJHHHet1l57z+Ry7Sg6slgRyyjaHHymVZGTT+ekz79O4xEVUmFZaRFlNvXVWRjJKtZ+pnfs/Kef2TDS7cTS9WR72mjcdpxHPeJXyIIDOUDtCj3EbGpkRU6X/oDHc/dRm7zq3hmD1rjdFILP0T1mTegVo0JFcKRFN/C4vd909ZXadmxyneJuX1B24eUBJYhPVbJ/q0rUfVEL5V/Cck7NlV6gmd2buDa5/9ElR5nf7aLjnwWF4+cbSNg5yPd6oNuaYl+ioUoyr3Rn2Wj3EE80CQcnciKJ7XBXuJo3fv7XiyyLFNZWRlmPxZKSzqdxgOMIFooKq7nYrku41LVPHrOlzj5gf/ki8/cydMfvoGEqoeJEocjQnG6//77WbVqFRUVFWHW7ENdBMvu/v37ueKKK5g0aVKY0BKivCdBfhyKdHvJTzLpmMUU3L1hjKVyZ/w9SHTsCvcfQE9PD7FYrF9lQiQDHW6bJUkinU7juu6QwLAHS3xLm4qiKKH1KNquAouF5wNjXc/B9dySuYBAWD+kEIfyXmIUSs2XkiQFDMYuE+afQ7xiNC/e8QW6mjajx6vwXId8ppWJC8/luI//ElWPvycWlmid+youKp7rIMsax3zsp1Q0TOeNR75L9Zg5nHD5LcEiPhyczeDjTVhumu76Ks33+ZgktaYCOVGFuXs9TevXk1n9GGOufxC9cWpocRlJcR0TWVHZ+ModvP7QT0nVVvo0/oc0qNfzXUKui6rHg8zg1gD4Hj+wpN3sIWPlWdI4mWNGT2Fyuo5JFTVc+OjNYX6h9z3kudQgG2zgFf9ebJ4+kJer+EUXxGTQO0mP5AtbqnyxkI4aNYqf//znA9azWGRJxlBk8o5NWo/xm1M/SXOuO+RpORD/oLhnZWUlDQ0NIW7o72GR9rzenD/RcHRxzLckeSVzDJUrA0UPResxmNWtHCleQEdSohE8wl3SnxzIu5ZIJPjWt751IFU9aCLa0t/cEh6XlLKTE76XCkspCceK5IdSO3ae+kmLOe3q+3j+91fRtHkFsqoza+nVHHnBDwB8ZeAgW1hK1ROK+itQSFzHZs4pX6SycRqJytEkKofGyFt0p4EqAYCiJ+h49laa7/sxWl0VNed+g4pjP45sJMlueoWW+75J95sr2HfblYz7p78eEOh2sLoYyWrSddUkq8Zhmz2Hfu6hwF/puQ6ObZYcP+IvTVbZm+ng/CkLOXfy/JBITkhS00snmhyB4ThkpSU6yEwnhyIpKPLAXCbFE3b0vKEuCv0pTUKZEBO3EMuy6OjooLm5mZaWFjo7O8lkMvT09GCaZkhuJ0lSuGuLxWIkk0lSqRRVVVXU1tZSV1dHIpEo2NW5rhvu/qN+/lITxUBt9zw/v5DjusyrGxseU+TSNNNDFcF7Ikj0/h6UFqEYCmK3qPgWFh94+8iDa1i7Zj/JpI7j9HV9FboLfJ6Wzs48Rx49npNPnYZtOaha/4uYcMEAYeTPUN0iIvJF4LoOtP8HsiiWkpFcdA/1sVNcv4L3DS8Mbx5Iit+5A2nzYJbgcjd7nufTw7uORapmPKddfS8v33UtDZOPZuaJVwV5prwDjhI60A1kH4t5EBI9bs4HAKFUDZdKop8VT5LwgkSXVtsu2h77MZICdRd9n6pTvxC0yyU55zS0usns/OFpZFY9S9fLd1Bx3GU+OHck8wIF1XRtEyvXjW1mfKUlcg9/jB0aGLk+IvnpKPycTR4IT4XnhWqjoaikg5xDpuOgK36osyRJOK5LU6adnG0VFzsiOuKwLS2u54bheYLmupwBONCkMth9xfnRAS9wDuJfR0cHu3btYsuWLWzZsoWdO3fS0dERKhhRmnhRbrHiJEmFgFpFUdA0jcbGRsaNG8e0adMYP34848ePD0GIxUDewfqv+G+hpDiui4tXGD0UWZTKtW4V93EpF9ehKKGJOVAC+wNZCpEDN4+mBXwQXjBWJAlZAkVYYoJj/rkyihKU2U/RvdYcN7T0iM/hiACZJxKJ9xxXJO5luxaqrOG41qAbjeLr329XULky4OapTKDlUNvb37tY/Lvjutieg4SEKivIg7y/BYu/eC8UDde10YwkJ37y1uA+bmCN6cscPli9gQBA6SEjox0g+3cpxUWEREuSfIBKlVQS/CwBBJmEsxteILt+HclFx1B5ytV+BJGs+ER4uW70xqlUf+Af2P3f19H9+l+oOO6yoACvqMDyZMA2SBKSrCBJiq+wRNKxKKrmW14O+dfKz1ulaDFQNFTAdSyuXXAKX5h7IuArMJIkoQXM46ok8/FZxzIxXQuAEpIlhkvZAcmwLC1CSbl9xZeZ2XgcSyZ/OJwQS0l/L9JQFJbo9ZIkhXgN4Vt/5513ePXVV9m0aRM7d+4M2WyjVhChYAy2gyqFhcnn82zZsoWNGzeyfPlyKisrmThxIrNmzWLJkiU0NDQAhJaX/nZVpdpfrLgo9J3wwonPzgMESbnKe+lFzqfi9h9qIpTJwSZdH8vgJ0A88+yZnHn28HIvDeReEnikZDLJc889x5YtW8jn8yFgfCgixqthGGzfvj1kgx5uUsnB7m/aWSRJQpE0bNcEQJF11EBRiSosQ1FcDnUZKYtSqflqoL6KHss7doCZkfrwLfnvtlzyPkOZL2RZDWb/YPcrK33OGciqI46Lc/QIpmMkXGLFfSeUrZEov9QVnkdoKelZ9yzIkFxwlm8tCBQHAEn3uYziM09CHx0nt+0NrJYdaLXjca1crxnAKy6///dtKG3obbeM2dOG69gHxz01kuK5yKpBPtNKPtPq/+R5qJKEqhZufKKffzjj02ERejDPKbKvFB+oDKi0eJ6L7VqFqGdJwnLyxLUUz2+8A1lSWDL5w+StDJKWwvN6TedekEBMkfv3sQ42eEsN8iify6uvvsry5cvZvHlz6AKJx+MkEgkcx0G4jnqrP/gupNjcLu6taVrI6GvbNmvWrGHNmjU8+eSTHH744Zx++umMGzcOoE/OooHaWGqiif4W9oHrhGFpAvk+UP+JRfHaa689IAr6gymS5BOUVVZWctttt7F8+XKqq6vJ5XIDXuNbY3wgrm25xOIanZ059u3tCjM+pytjjBtXieO4WJYTuPV8i8tAQFwBFBesyBs3bmTNmjUH3H8CXGwYRgjUHiwh5HBEV3uJ5nS59/u21reYWDMv/ByJBepQknLaMNw2l6NguJ5XALSP/i5LEru623mndTdJVWde3Tgq9Fi/QPvovawgpFQqPMFfrSUJnP5DTD38na5ICRLdKAl5ee9muq08tbEkRzRM7FOn4fRZqbks+r1UOYPfp7/3xAsXf3PnW0gKxKYIksro2iXjAXrDFLRR08lvXY3TtR+tdnxoqSm4T2TTOpCU2y9CYXEdk1EzTsRI1uDa1iFtcfHwkGUVK99N9ejZgI+v8sBXmEtsrD3PI+fY6LKCIsvEAoX1e0vOw/H8fi4OSBmKDHilJMloitH3osCiUpUYRWXctzCElNclROTTKDUABhqkpQax4HPZtm0b9913H2+99Rbgc3yIyAYRTSFcOyK3UbF7yCsalL07Gd8yI1xOquqbv1zXLcDAJBI+3XY+n+fpp5/mtddeY+nSpZx77rl9kj2Wamup3U+p476FxU+Q9s5TP0eSFWYv/UL4W+k+j+BljL7P8FASoYAKq9lgE0D0HF/5UHjh+S089/Qm2ttyZHtMFEUmXRFj8tQaPnzRPKqq+zLGlrqXLMuhYiH6Ph6PjxgpnBhDnueFCSJLRb4MJP2dJyHheja/f+VrxLQUR048j6c33I7nuRw58VxWbL2Pzx3/v9z35g+44ZQ7gd4+iI7r/xdFuBxHIpdY8fWSJIWg+f98Yznr2/cxPlXNN448C/B5K3RF5dGtb3PVE7cwvmY0D5z1eY5omIDjuSWBwVGFRxuBsFyhiPRxYQGXP3k7m5p3csLEOTx34VfC+kbb17txsvHcCFlcCZGQBswQ3Fc8JNlAGoLLss89xYa2fS/IoNaMK3mOZ1vIsTRKqhanC9xcd1CFA2XdKUM8n8sm39PKkot/QkXDtIN7v4MgPhmgGirMxZYW8Xdc7WtZ83878NQ4AyotWauLHW3voCmxiAVFwnFNUkYNtmPS3L2dpu7ttGZ2oavx8DxJkrAdi4pYHY0VU7CcPJpisKXlTW584jx+fMHrpGO1Yehb387pq715nh8y/fTTT3PvvffS3d1NMpkMlRDTNFFVNVz8bNump6cH27aJx+OkUini8TiGYaBpWgjajZbvOE7ItZLL5ejp6aGrqwvHcdB1HV3Xw0VWLD6yLJNKpTBNkwcffJC1a9dyxRVXMG7cuALFJZyIHB80Jhcl5YpaVARTZPibY4Oqs/X1B5BVjdlLv1CQtbVYSvXdoSiyLIeKaLl1DBVY20VRZV58fgtPPraeU8+YzvQZ9VRUxrBMh337unn2qY385paVfP6a44jFBk+CZpom+/fvx3Ec8vl8SGg3Uv0nFCHXddF1nba2tlDJLvcefc/z26EpGu25vezqWMe/fOBh/rr+Ngw1wQUL/onufCuG6mNyUnp1QX1EioThuqv+niRKUtjfhGm6DoaicvUzd7Bs2ztMTNfw+HnXhRNxqescz0VB5u6Nr/HKjrXMqJ/AN448q+BZJTSdylQ1oxIVoSLS35QdtdC807KbllwmxJuUK6qsYDo2Y1NVTK6ow/XcPlEeEtCYSNOaqqYunipZTu884iDJKpJc+rzicocqnucgDYiNHKhU/5ib70aSQDb6qaNYn7QYromPefEPIMwd2765KCivRF97HpIWw+nYy7h/fIL4jOPBMUEtY2MYYGckSSbX3UxFwzSsXBeyagwD7DESSlY5ZUQoECUZRdUHnENL/R1VwIFBsVyDyYBKy6am1/jin05mbFWSrJUpNBnikdKrWL7+Vh55++eF2rvnoatxWjNZTp91IV897V4sJ4emGNiOSWtmF65gQuxHIy/W3sBf4O6++24efvhh4vF4GMIrFAfDMMhms+EkPHr0aKZOncq4ceOor6+nqqqKdDpNMpkMd9KlxLZtcrkcmUyGjo4O2tra2Lt3L9u3b2fjxo10dnaG9PIiIsSyLGRZJp1Os2nTJn74wx9y9dVXM3v27AJStFBTpa8Fym+r64O3KHp5g89Yuj70Dw/20KMD5lDeQQ+nbp7noagypumw5u19XHvDCdTWRYCySaiqjnPYzHr+fP/bvLpiOycsnYJtu2glooXE8xk/fjzXX389hmEcdPI0QcE/duzYgjoMW4JJsTE9FfDz2Iyrnk3SqEKRVbJWF7ZrYTq+68234DkoqsLGjRt57rnnSKVS/09ZXMRGJ5fLMXHiRJYuXdovSWHv++a/jy25bnZm2jEUdVDLgeivhnia6nQNo5KVfc5xXJce2yRnl5dWw3JtDEXjGyv+wkNbVtOQSJMr49lIgB1w0bT3dPHNY87lO0efS95xiKtFIeH4+c56bJO8U8xp1Ns2zzWRZB2z+UW61/8I2ajHc3NIyIWlSSqSmsKzu8Ab+P3xcJHkGG6+ieSMGzDqTwzvM3QJ5lBF9dUPt3RbwjXadZFk35tQLFbzbnAovZ67IBlgt4JrZv07e0NTH/y5y1dyFD3uY5T+TmSoVpLo+UJ/OFBL54C9VRGr44RpJ1KbHIvpZIsGKNiuiSzJKJJW8FJ7uGiyQVe+len1RwO9+Tt8l1MsQpw8OJ5FKCH33HMPDz74IBUVFaFFRJKkkGm3s7OTMWPGsGDBAo4++mjGjBnTbyK5YhdRVElSFIVUKkUqlSpgGXUch+7ubtavX8+KFStYt25dgbXH8zxM0ySRSNDT08NNN93EP/zDPzB9+nQcx0UJXpLOps14rkNl4/QwrwgQfu/p2EOuu5masfPoTVXv969j5fC8Egyw/cj/K4tPsXiuh6RIdHfnqayKU1uXJNOdx4j1WqfMvE0iqbNg4RjeeWuvf12JxUIovZ7nUVtbyxlnnPFeN6egDgf6zHK2b/J2PYes2QH44FxdiaPKWphEEMBxHRQUtmzZwp/+9CcaGhrI5XIjmiOpWIQS/V5YAj3PCy1axx9//IBKS2+/BwBVWSVWghCy9I38j5xj0WOZZMMdfPQGIBNsIMp4xKJLTNfBcSxytk3OKaQsKLYHSARASVmhIVFBUjOYXT0aAKXEuJLwd76yJIUMpqUr4ysgTm4f+X1PoCTG49k9AQBWAlwkScW1u7Dbm9GqRyEpsWCuEvOXKN8Ly5TUBE7PDuITLi24z1BF9JWcqAJ3J05XMzRM7XuirIIHbq4TOeZbXHp7wq/f5BtXoVQ04tl5/CzPfQNIPNdBqfChEVI/1u7+RDwj8Od8DnH6lqgMZKUu9/wDnd8GfBsn1Mzle+c8e0A3CG8UTpQebmTR7RdaFUxoAnT7/PPP89BDD1FZWRma04WCkc1mqaio4Nxzz+XEE0+koqIiLEdgCKAXqyJJ0qAEXOKfUEbEvSorKznqqKM46qij2Lx5M0888QSvvPJK6GoSmBpd18nn89x88818/etfp6qqCjPXjR5L8co9Xybf08aHvvIMjp1D1X1sjPi+7rlfs/bZX/LxH+/BdewCF5AkySV3B/31398DyHJYPuyAuKiiIkZbWw87trczfkJVwTlq0G+vvLyNiZOq+71XgQkzwD69lyISMQ7lWUXHaPFb1IuRkEK6eV1N0JzZwe9X/hNd+Wa/DHpDr5PJJOPHj6e2tjZ0ix0sKU7gKUnSQctfJLBDqVSK2tra8H79nRu1tLiehxP8K3eEhgpAP+/oUEa6GriQPjv7eE4ed1hIONlfIVJQZ11W2Zlp41dvP09KM5hUURvWrVjKURU9zwsVkPi4DxMf19PvuU52F3sfGEfDmZuQ1EQZpUfv4wT3GSoQt/eY3jAVc8PbmHvWE596dOEZrm/Ftlp3YLftRq1NIicDV6ncuzFU6yahxCsoR/6e5tn/V6SM6CE72O2DJCm9IKuy8QdyidTsgz9cAQ5UVZX9+/dz3333EYvFChYUobDMnj2byy67jNGj/R2FiMyA3gRvQ5Godhi9NqrEeJ7HlClTuPrqq1m4cCF33HEHPT09IejXsiwMw2D//v3cfffdXHXVVWE5WqwiMmn37QtVT2Aka4dU51JtiIIsD2VMi4jSGWp0guP4BHOLjxzPL296iWOOm8isWY2kKgwsy2Xfnk6efXoTsZjGRR9d4O9A1b5jocCEKcsDJm48mFJO24VlURAh9s08LYUW0WhpjmeT0Co4euIFPLbmF8FxGc/z36dMJsP27dvJ5XIH3dKSTqcLwMeO49DV1XVQ7hW1tEyYMCH8rdR54Xsf9JzlOijBuMjaFnFV7/Xw9zdWB3jN+kFJ9FtvVZZxPY/zpy4s86peeXrnev571VNMrqhjfq0PSlXK2OyUkt65RPHdLq7pR+K4weZR1vHcHLJWidOzA88Gp2cbasUsXKvLd/d4Digx/1rPDd3dQWNB1pHk8oD4JSoIgSs3Nu0YOp/6C9m1T1N5/OV4jhXS9HuOiSTHMHetIb9jG8l5S9BqxgdtiGSCNnsgXhFyvPS71snDT4shrOsjnfvovZL3U1krI3qodwJ3PRfXtVFkLdzpvhfy1FNP0dLSEqajF1aPXC7HUUcdxdVXX40sy2Gq+lLp6ofji4O+uJDotUIZWLJkCY2Njdx00010dHQURCvF43Fee+01Nm7axLQpE/2yXRvXiQLAiu7t2n4yNPqarMvdqkXbe6gDLIULr1wwbq/ly+dqOeLIcSiKxF+ffJdnn9pMJmOiKjI1dQnmzhvF2efMKghvLhX9Ef0sR8oZT1EFNzQHl2hfdGyVGmfFZXZ3d4cuju7ubsx8MJY833oiuFnciFXT81ziepoZjUv464bbwvLE2Jg+fTpXXnnlQU31IMbi8uXLyWQyIUNzRUUF559//kEJyxf3HAw7FL2vUBZ6bBNVVrA8h+ZsNzWxZOAGHwAjVvSzJEnDSpQXxQEI7pfB1B7/XIcKPcbta1/GchyWjJpMQhtaSpCS9RVjXVYhUC5kJRY5Hrj/1SSS6n+GfwfKktXxDmpyEpKaLPneDHchjJ4Zn3kSarVM9xsPYu5ehz5mJm4+4ysYARaw4/nbcfMQn34scjyNZ+ULlBNJbLJldVDq/WHVWZKwLR8PY+czPucW71/y0SGJFxgitPJ5wkZaBnXWep5He3YvhpogoVciB0qM7Zi+8lLkm4ahTf79nSkmm0wmw8qVK4nFYuGkJgCMM2bM4HOf+1zBbl1cW1yf4friot+LI33E5GeaJpMnT+azn/0sP/3pTwsWKFmW6erq4qUXX2La1Km99QkdmaUnCLETcV3b34G4TlhmufUXrrXbbruNTZs2HbK5h0RfNjc3h8RrA+30C8HZvktn4eFjmb9wDO1tWWzbtwwmkjrJpD9eRZ6hA33RSimz4vcoy7JQrId7D2FRLHZnNjY28qMf/Sj823GcXneoAnG1gu58C7e+dC3d+TZs12Rz8+scP/VjKJKG5eQLxqdIYDpp0iQmTZo0rPoOVV5++WU6OztDvpp0Os0555zzntxb4OOKx0FolfRcFElmW1cLTdkuYopKxjJ5q2U3M6obcT0PQaZccix5vV/cQFm1PRcNxedaGcamo1xOC/9cjU0dTbzZvANNVjh74ly/3XiUGo3R6ghcouO5KF4hRYUiy2HTPCeLpMTpeP3zuFYn1Uf/Ec/JIMmVeE4OOwNeoDjjWqAYuFYXrS+cjV5/AtVH/d63vEiF7Rp8jh7ArRdEdsWnHEXFsZ+g9ZHfsf+urzDqM79BragPz2197Md0vXQX+ug0FSdcETRO8+sTKU/0yGAypHXF89vguS7xtI+V1Mt0Qx1qcshaWgAc1+J7j5+JriSYWncEs0Yfz7wxp1ARq8fzPBzXLiCPgwNHB0Pv5LJhwwY6Ozv7MJEqisJHPvIRFEUJF+eoReRgdGZ/ipmu65imycyZMzn55JN5+OGHqaiowDTNwCWhsnXbVjramqisrvcXt0Gr5p+gqH6Uk6IZwX3LJyISdd25cyfvvvtugaXqUBNhxi+XcTY61nyl1UVRJGpqC/3otu0iy1KBwjKcsREdW9E6CMyUYF+OSltbGx0dHXR3d5PNZsM8SsICI3iEDMPAMAxSqRTpdJqampo+Lqoot4twcxTXDwliWoqvnfGXkGLA83xri+NarNz2oP9bZFcn6vFe8LSI3GDFZI+O44SUBYqiYDk2puugSnIBXwj4VhDLdUObgwxYruunbJB6F1YJn5RNkkCVfMtJfzwt0b8d10VRZNa17mNLZzN1sRR7ejp4dvcGLpy2qA8Wp89YknrvLwebj7hIaCkr+I+pPDdg9LOUFI9nK+BX+e26Faxt28vSsTM4etRkIEql3penRVRa4HBigQJAP33keQ4SvuXEM1sLGq7Ex1C56DPIel3wu/+su97+F6zWbaRmfHnQtvcv5Ux8ErUXfIue9c/T+cIj2E1LSR/9EeR4JT3rnqX71T/jWlB/3jcxxs3Fsy0kVcN7L3BsUgDg1WK8+/JvqaifGiRSFAzHh7J4SLKCY+eJpxuYfMRFBUEk76UMqrRIkozlmPSYXby85V4eX/tLJtUu4JQZn+LUw67EUBN9FJchuWH6+z14iFu3bg2tKEI5yWazLFiwgMmTJ4dKgV/Xgx/iG10so3+LBevII4/k+eefxzTNcDHQdZ2m/U20NLf0Ki0D3wXPc3Bdh3x3M4oawzK7SVaNxbXzyEZ5oEVRN5EAMpFIFOzgo26LQ0GiOaHEwt4fY2zhc/ZC94/jFC6IAsMyEgpLcTkCcyUsfXv37mXDhg1s3LiRffv2FSgrpmliWVZBG4UFRaRYMAwjZHOurq5m4sSJHHbYYUycOJF0Og0QKj29IfReL27L83fLHl6osHh4KLKK41p051oACtJtRF2I75UbsZTCIMsyMSOGK/nZqoXq6eIVRLV0mFkq9d6IQA8QrbE8F0XyVQLHdUkE7SyIWykaB8XjQSzuj217G9NxyDl+2PFfd6xjU0cTUyvrQ1dLf5YWCT953O5MO57nY2OqjQQtuYyPkRkB+tPi+vtJ61Re3rOZP6xbQVLVuWjq4VQZCfKOHVpriuvrvz1+GXnHpiOfpS3fg674CpaHhyrJjEpWRt45f5zIWgVugIkSYcpKfCxVi3+N5zl4ro2kxOle9x90vX0TFfNvIDn9GvxcSWpBO8qTAc4L5gjX7EGrn8zYf3iQvbd9luy6FXS/8S08C5RK0BurqD/nX6g566t+PQ6AmXWo4m9ybFQ9wVtP/CTIpNwLAD6UxfM8FFUn35OhfvIiX2nxnD4Rxe+FDPjEXM9FkVW68y184qj/YMHY0/jr+ltZseV+bl/xFd7YsYxPHv0jxlXPCnMP9TcIhV9TovAhDeQeAn+3KtxCYoI2TZMpU6aEi4VQWt7LBThqdQFCC8G4ceOoqqpi165dIXBYVVW6u7vJ9GTCepbebQUKkBaju2Ubf/iHujA7qIcf22+bWQ47/tNBHcobMGIXLf6Jugsw56EiImeTUKZ0XccwjJIYpcIFp3dXGiZCjEixu3CoUqywiPxIqqrS2trKa6+9xmuvvcamTZuwbTuMVtM0rUAhiUavResmFBkRtt/S0sLWrVtZvXo1jzzyCDU1NSxYsIAjjjiC2bN9Ku3isN0CV2ikP8R3RVZZNP6DuJ5Lj9lZ0K5S76sT8FxENyOO4xTgToTlp1xlUBC7FfetqLvp2Oiqylstu/jrjnXMqx3LqeNnYrsuqizTYWb5ygv38utTLgsyyvpKxlstuxiVqKA+ng7LlWWFTR1NSMCUyvqwjOI2R+sulJE1rXt4eOtbJFSdMclKPDzebNrJr95+jh8ed2FIJFeqva7noSkqTbkujrnnh73GimDoVRpxTMceOLx4EClWWBzXRVcUOvNZvvnKg7TkMhzVOImPH3YUAPoAYE9JkrBdl6Sqs65tDwvu+m5YX0WWydoWE9I1rLj4n3zlz/Pw7AySEsdzcniuj73z7AxIMngenmcjq0k8XDpe/yJda/6H9PzrqVjwEx/IKylI0shY5CMt8f9XNPBcjHFzmfDPz9C96mHy21eDY6FUjiI57wPoow8rSIPyXknvc3OJV44qe/4+FMTHMGnoiU4SlaPf17qUpWZ6nocq69Qmx/GRw/+N02d+jr+s/hHL193CD5dfyJdOuo0ZDUv6ZI+1HTNkYbScPHE9jeXmgwikoOxB7p3P5wsGt1g0RMbdYlPzeynFk7VgOS1lArcsK7RyBK7NEhLsFuw8icoxfODaR8hnWlFUHdvMkqwZx/O//Qxm1l90hoJvif4DQrzQoeouEnl6WlpayGYFiVNfvoRyZEh+54iUwq8IZcGyLB5//HGee+459uzZg6qqxGKx0E0kSVJoXcnn86ELJGo5iuJVhMVFVVVSqVRopXNdl46ODpYtW8bzzz/PnDlzOOecc5gyZUqoPBUvwD4QVwp30bIkI0sqJ02/DICLFn0D8HFpXmiHEGyVfkZaEfEnkqMOxJp7oP5tWfLnA11VeXbXu/x27UtcMHURy7a/w6bOJq6ac4J/HlJoCZElCdOxUVSZx7a9Q0xR+ej0xTRlu3E9l7GpKn719nMsbpjIlMp6LNcu4KcpVWeB+/j+3x6j08zi4edLeWnvJt5u2c2dG/7G8aOnce6UBeQdC0Ppa/H08MLQ46aert6yPY+EphNXtGHvq0uOR683bP265+/mzaYdpDWDfz7igyQ1A8t1CjPGlyoX4V5yac1lwuehSjI9tklCuIs8F2QF2fBdP5KaQHL88GfZiEQ7ugrZnffSuerLON3bqV7yM5LTrsHznEBhGe5YKaPnPBc/sskBVSe9+ELSiy8sPMWxQlBun2tHUofxPPBcvOCfRK+C7pg9wSnlz+FDOf9Apfh+Qmmxcl3YZmbQ60u9XyOFgxmCbcyvfI/ZSXViFJ9a8hMmVM/ljr99nZ8980n+4eQ7mFp/RGhxcT0XNRJ5JL6njBocz6bcOpfib5AkP8me+B7W8H0ABRVjHITboLheSsSq0btElBbPc1H1BDVj5/Y5Fks34DqWOHPI9RWugHw+z7x582hsbAyxBIeSq0js4ru6upgaAJjfD6tQKYVlx44d/OY3v2HTpk2oqkplZWWomApXEEBVVRW1tbUF6SOiIcqe54VsyoKBubOzk9bWVkzTDBVgYamxLIs33niDtWvXcu6553LWWWeFVqloPUvlCwNo6t5Gzsowvnq2/54q/Yd27+vagus6jK6chmWbaKrO1q1b2bFjB4ZhhHVfuHBhSK443DBpkQzQcmzu2/Q61y88jfl1Y/ngxDlc//zdbO5oCqwlUX4nL1RgxiQreXTr23RZeTKWv8lJ6zE2djTxoUnzgcJw31KTZ86xiCkaP1/1NMt3rAXg+NFTOXX8TGbVjOLBzavZ3tXKP750P+NS1RzeMIGcbfXiPwIRikRS1Tl/7glh+3RZ4d2O/by8ZzNVEezNUKR48rddJ+Ryue65P/Hotrfx8Pjc3BM5ZfzMUGER15aaHz3PCzaVDqPSFVwyYzF5xwnJ5kzXpi6WBs+3TNiZLeR3P4SSno6T2YJrZ8jtegA33wSAndmCue9JrM530GqOpnrJXei1xyBI5gZyzZXRA2WfI6wonm2GQQxIEpKilVZYAEmPI+k9B76GBJfLioaix1E0Pw1OL/5DwrFdXM9D1YTFrr8REawptrDuHlx3koSE63rYjo8F1BQ5pDjx08o4qNrg/DvF82Y0Xc6B9m/ZSoswMwtfuePZnHLYFST0Sn794hf5xXNX8k+nP0BjxWRMO4euxnh8zS/ZsH8FSb0Sy8mjKjrd+VYq4404wUAarPpVVVUFaH8B5tu5cydQuJCNVKeUI8WYFjFp7969m66urjDsWTy0VIApEde43sC2JsF6a+W7UdQYdj6DnqjEtc0QlDvcequqSltbG2eddRZz5swZdlnvpbiuW3ZCxZGSUgrLhg0buOmmm+jq6iKZ9EM3hdIRj8eZMmUKc+fOZdKkSdTW1pJOp0kkEoNyvziOQzabpbu7m46ODnbv3s2aNWvYsGED7e3tIWA3Ho/jeR533HEHLS0tXHbZZQUYGdPOcfsrNyAFxHI5K8Ooiql8eOHX2Naymg37V3Dpkd9HlmT+vOqH7Onc6BPQeR6mk2Ny7ULOnnstr2y9H0NJMrpyGtl8Bk3Vefrpp7n77rupq6vDcRwymQw/+9nPDlhpsQMW1C2dLRiKyvy6sbTkMtTGksypGc1r+7cH1hI3nC9kJLKOiaYobO1s4cyJc7ls5tE0Z7txPY+GRJrvrnyErV3NnMh0zACkWrxgup6H7TrEFI273/0bN772OIaiokgy3zn6XADGJKv4/jHn8anlv6XTzPGp5b/l16d8gqNHTcZyHd/VIxaqwGJRHUvysxM/WtDOBza9yRPb1zBOri4b11JqgRf4E0NRyVh5rnnuTzyy9S1s1+WcSfP5xpFn+QtiBHwrrutTPr5FJeuYTKmo4yfHX1yyHq6TRVbimPufofWF61CShEjoluc+XLgTc0GtGkflwv9Gq1rgz2VBepIDk/IX67C/VL3PGtPfHGK378XppFfJGXY1A3dnrpOetg5kWcPOZ3qZzz2Ix1V0VaEnY+LY/UeVeR4oikQ8oeM4Lj3t1oAbfi/8bwARz0rqu/56HqiqTDyhYZkOmS47dOXJqobZ00Gyqm8yyr717l2vo/i799TSEi6xwQ3VIHxyyeQPk7O7ueXFa7j9lRu44ZS7wrP/tu1Blq9/nJokmAE4W1dAVbSQS6K/l1dMgOPHjw/dLWLxiMfjvPXWWzQ1NVFfX98nCdp7tbBF72XbNrqus3r1apqbm8NIHUVRME2TUaNHU1NTE1znW1Og0NzXa4rrdSEoagxZUZFF9NAIobVlWaaz03czZTIZdF0/pCwtURFuiZF6rgOZLkudJxSWPXv28Itf/IJMJhOG4AvM0kknncTSpUuZMmVKaYxIEP0j/kHvIiLcQyJ1xKhRozjssMM4+eSTaW9v56WXXuLZZ59l7969xONxXNelsrKSZcuWkU6nOf/88zGtPLpm0JVrJmt28aWTbqM9uw9N6c2hpCkx6lJ+5JHtmCydfjmaEuPdppU8v/EOvnjirfRY/phoTE8JsS8iqiSVSlFfX09NTQ2u65JIJHqthwfyXIJhl3csbFHXSMRNd8BZFJ0rbM8lpfs8IedNXsBv1r7Exo79mI4dzE8yWdvkgxN8a2UqwishPoUlQldUfrPmJb6+4i8Yikpbvof/PP4i5tWNDetzxoTZ/PMRH+DfXnmYtnyGS5fdyreXnMMnDvNZV12x0AVAXMd1yTsWbgDErdBjtOUzyAE+bfAu8fyyisapUJIMRWVzRxNffPZOXt+/A8dzOXHMdP5n6aVDsmaEQFwkzKANnWYOQ1Fwvd5zYkoMD0hMvoLY2AuQ9SpanjsDz2qn7tSVeHYPniThdG0gv+8Jcrv+TNPjC4lPvIjKRb9AjjWEeYUK6uV5DLgKF/eL5wZunF63i0gwGz4ABh6PJY9JMhXHX46b7UQKgd7DG9OC56V+4mJmnvgxYqk6HCuPJEu4rq8UvLu+ib2tPcye00i6IuZHOUq9LfDnCT+LfU/GZO2afaQrYsxYWIfjiGdagvNpiHUWY1FYWFRNpr0ty4Z1TdQ3pDhsag22JfI0KdhmD+n6yaKhfhleL0UD+PO14E3TNI1t27bR0NAQbrjeQ0tLXxEJEJdOv5zdHRv486of8ug7N3HefD+s7eNH/TtnzvmSP3F6IgOVv6OriPmx86XSskPvwDrssMNIJBLkcrlwsAtSrQceeICrrrqqj29fyEA7jOFIMRZBfBe0/U1NTTz55JPhggaED2/8+AnU1Pmx+ZqewAtcPHLEVCkSKSpqDDV4ccKXMfwckaaE7hegX7DroST9TcLDUVQLJnXHxrNNZCPRZ7yI84QCfffdd9Pe3h7y3TiOQ21tLZ/85CdDi5VwDwpFWoyVwVxbApAbVWoURaGqqoqzzjqL4447jjvvvJMVK1YQi8WwLItUKsVjjz3G3LlzmTbNT3MvyTKV8QYUWaMiVo+mGJi2jwlKGdX84dVf43oOH5z9BXTVH2M729fSnt1HZ66ZJ9fdjOnk2LB/BR+YdbXoMMCPXMrn85imGWbBjvq8hy3BY1MkOSRAE5ZI1ysEkopno8kKD21Zzb0bX2dMspJ4QHGvKT4fiosPiP3xG0+wP9vFtfNP4fCGCQUYD01WaM1l+PbKR/jj+leoNOI09XTxxflL+fTs4wrcL7brcM2CU9iX7eLnq56mNpbk2uf+xBPb1/Cl+UtZUDe+oC2SJIWYF831y9DlwckTexfzwnEolBXh7rltzYv85I3ltOYy2J7LBybM5pZTLisgkivn3YhGVgma/4SqoxZtjrzAheefKOasiMsND1lJIFctQKtaQHL6deR23kvHm9ez/7GpVB31e2Jjzy/pJipfJFQ9EYYIe56LosXQjFTE+jwUBSjSP4rK6Kt+W3hsGLnAfCiAjue6TD3qEqYedUnh8aCGr//iHV7fuI3zz/ogk8bLBc+hWJra4I7rH2fehNEc9/EFZddluPLuZovfP7eMDy4+jGMvnV7yHH9e7N1IFsM4ouvLY489xtlnn8348ePDiMsDkTKvLmFWDCqryD6w7IIF/8SaPc/x6Ds/4+hJ5zOqYiqTaxcyuXZhv6UO9FIJIGJVVRWLFi3i6aefJpVKYVkWjuMQi8V4+eWXGTVqFOeee26o7amqWvCyi/tE/x6KlFJUosdEOHZPTw+33HILra2tBUR4Ihv0kqOPRgxZ17HY+Mr9NG2dgJXPIElyoM95yIrupy2vF0R0RZaVETIgiX6PtvFQluJnGv09HEeu64PsopuQaPSG2Ngpmj8huQ6SovqZYfuxugjz5rp161i1alUYNi6sHddddx1jx44NrWpCQRkq/qbU+BJ1sW2bysrKkPn5xRdfJB6Ph8SFTz/9dKi0uJ4TjhmBbRHKScbsYP7Y0zhj1ufI2z1oisFfVv2Y1p7dXLDgn7j5xS9w7JSLWTT+TB59+7/J2xlRibA+UUWsGM9VrghAsvgnBcWPSlRiBmG3VYbvSt3e1cI5k31ciuBb8dvpccKYacyqHkVSM3A9j6Sm87Flt/CLky6hQo8Frh+XrG0yOsi67JfhBwf86d2/ceNry9jW2UJtLMnenk4unXEkPzjm/IBErvfdU2UFx3P53pLzcD2Pn696mjHJSu5+9zVachnu/uBn/bDiolfJ87yAzbY8C0uxJUjCt3QJZeXFPZv42aqneHrnemRJwvE8PjP7OH5w7PkokjwkhaWPBNUT9e3/PGHV6Gvh8DwHXBtknfiESzFGn037K5fR8uwFVCz8FunZ/xZYSIYDjHfobt3uYytc38qi6nF6OjrJdTUFtSh/Liu2SHlWkP1ciw3bYh9eJ8u4joWsyEQTRko4gIoutaDTik4LUI+EDSH9n4Nt2biOix7TyHe2EVPaUBwNsDBzFooq+32IUCBk8nmbG7/3FJbllLBi+ff3PA9VkenOmJxz3mxOPn0muZ4suqFi5m1iiRhm9x7iahtYTcAUctk8uubfzwvaqKi9lkvLsli3bl0YoDBp0iQqKyvZtGkTU6dOpaKioiBtx4FKmUpLCTNU1DXiWsS1NOfN/yo/Xn4xj635BVcs+U9MOxf0WV9TliobZQ+MU089lVdffTW0qAChMvDnP/+ZTCbDRRddFLqRxELT30I3FCl1bZRQTNM0du3axa233sqWLVtChUVomj09PSxYsIC5c+fg2P5gm3XyFxg7+3Rk1fD9p5FxJUkynudgJKr9+5fY8YyUkvFeYUNGUoon495PH+gmyQPjfcKutvNIqkF2w4u0//V/GP35Pw6o3K5atSq0hAiX31lnncXYsWPJ5/MYxvBxRv21UdQlylH0kY98JMS5aJqGruts2bKFlpZmamvrMOQkm5r/xj2vf5eM2Y6mGLT17OHChV9HV+OkjVpkSUFX4jy14Te0Zfdy5bH/DUB9eiJ/XXcbx0/9GHNGn8Tezi0j1qaoGIZBLBbDMAxcxyERi+ECNfEk82vH8u+vPc73lpzHH9a/guk6LBk1BQBNUcNFyfVcqowEVUaCv+3fxvIda2lMVFBtJHh4y1ukdYO9PZ18/LCjmFbVUPBsnYCMrscy2drZTE2gsHxq1jH81wkf6TO+xHUyPsj2B8ecT0M8zQ/+9hgT0jX8x7EfJil2+lL/72g5b230vkJRcT2Pp3au4/a1L/P0zvXkHN8UXxNL8o3FZ3HpYUf5Fj5veAqLG9y3vwW/uDzB0yIpCSQlWOhFvitJAYFhcPLIWiU1xz9Ix+ufp+O1b+G5JhVzvx+EPpe3/Ig2GIlqll75h4LNhSwr2FaOVO1EgILkskMpW5KkiFuo8NhA0reve/+WFY1lj71Ld3ceNQC0uq6Hpins228RT6VZvnwH1TXNWKaDqinkchbjx1dxzHETsAJFQ9biOJ6OJxmAhqprlEIJKK6GaauYlhRGgBXVFgkJ2wVZiVHXUAPIaEYSWQFV1/37qf79kA1AQdXiyGr/houuri6+973vkUgkaG5u5utf/zpLlizhgQce4JprrgkTCZfXf4PLwLmHgin+pxe9Q1zzORDE7i16EyUYfAvHncGsUcdjBNk9NUXvaykYQoUF2+2ECRM466yzuOuuu6iqqiKfzxdYMR577DE2bdrE+eefz9y5c0OtLmpuj+4Oy+2cKAYhWobAIHR3d/PCCy/wyCOPFOAcxMJm2zapVIqPftQH5PmIdpfGKcfQOOWYsu5f3H+yoha4lA5EhAvLtu2Qzv1QFtHvxYu6YGZ0Mq3kt72BpCUgdEcK8UCS8RyL2OTFYRK1/K63aXnwDkZ//o+955UwZ23fvj2sg2maVFdXs2jRIoAQZDtSmKrixRL8vEzC8jh//nyWLVuGruuoqkpTUxOdnV3U1tYR09J85dR72LB/BfWpicS1FJl8O6Mrp9HWsyfMRWS5eY6dcjGnHvZpdratZUfbO1TGG5hUO58VW+4DJBaOO8O/9wBRRsORlpYWmpqaSCQS/nsTjyPjj8fPzDmeW995kauf/iOTK2r5xuKzIld6oaVFMN5qsoKMRJeZJ++0clTDJPb2dKDK1dz97mvUxVJcNfcEemwzVCxUWcF2Ha6YfSy7M+3815t/5btLzuX6hacGlhE/508p65vs+a6i6xeeyvSqBjrNLHNrx5C1TeKqHrL4aqUsbUHZPr1/6b6RJCm8/7q2vSzbvoYHt6xmQ9u+IBeSjCJJnDF+Nv961IeYVlWPFbAHK1KhwlKOSPjuN11RQ1dYqTpFPz18QLSb3YNrNhf8Fu0rSTEC5USi8vBf4mR30/n6D9DSc4hPvDTEuAwkYg70PA9VTzD1yI/2e65r5YI59j3K0q6oYd1633cxXnwjx6MPvcPePV0YhooTROS4rkddfZJUSuOZv75LPu+DXTVNob09y3EnTOaY4yYEOCkVCQ8JF8Eu7BVZwnpdNBLf+LdTSyqfEr6ybhgq99y1itVv7mHOvAZ+8+uVbHy3hWRKx7YcP6egB6mkhm3Zkfv1HRvCBZ7NZjn99NP5zGc+w8MPPxxGTyYSiXAtFpiXkPbD80rO5+XIIAkT/cFfGa8v6Jz+JmdV1vjCib+mLuVrvWL6769S5VhCRBTOhz70IXbt2sXzzz9PVVVV2DGO45BMJtm8eTP/+Z//yfz58znyyCNZsGABqVSqT3kCcxBVRorrFFVMStVt586dvPbaa7z66qts27aNeDyOrusFCosw61911VWMGTOmgAzMtS3c4GUuvUj6L2rUBCfEynWF+Z8OBN/ieR6xmA9kFAkLD2Zm35GSUmNPZG/NbniRLf90LmoVeHkKLdweSBrY7TDtf14gPv04AORYGq0ueofSYzGT8V0lQlnWNI2ampo+4+dAFZawFiVcVWIMVVVVFeThyvRkwvfBsnNUxOp4e/dTnD33eupTE7ASPpC1K9+ME2Tm9Tw33Ij8fuU/MbF2PnE1heWaVCdGs3LrX8jbPZw0/RPYjgnEOVAR7fjEJz4RZkS3bTvkXJIkny32yjnHceWc48Lrosn+YoHCLkk+QFqTFf66cx26orB07Ax2ZdqZUlmPJEl866gPccIY320WVwvzkgn8xnULTuHsSfNYWD8+TErYn8Ii7qtKCrbrcvakeeFxYRXpsvJ05nvozOfC88XmT5UVUppBQtVRBhgjjuciSwqvN23nn168n2ojge25qJLM/NqxXLPgZM6d7GMb7EhYc7SPBxuD0bZ1mlk68z10B+6RwURYM1Ozv4Hn5At+63uu6uchkhTSc78PnodWc0RwcHAXavF649j5Aru9v764yGoMWYsNUNLBkf7WQvH1is8eRS5rhaSXjuNhGCpPLtvA1i2tXPTR+YweW0Eua6NpMqbpUFEZC9rqggGW6UTcPAPfP5kaWAns7s6z9p39zJrdgCzLtLb00NKSIZezsG3fbWcYCkPIFlOw2VUUpWAzLOZHETlbbJEecUuLKMgN6HqLC+/7t0xDejIi+kXuo4UOTaLWDYBPf/rTOI7DSy+9REVFReimEZE7kiTx2muvsXr1ampqapg8eTIzZ85kwoQJVFVVEY/HicViZQOB8vk8uVyOnp4e9u/fz6ZNm1i3bh179uwJzfPpdLqAKVRkrvU8jyuvvJLFixeHi40XxKPJqobM4NaS4r4FqBk7L+QZGG4kkVCsdu3axejRo+ns7AxzshyKIkl+dFZ1dXUYuVKoYAV9pBlo1TDlZ++iJKrwHDMyMQpQi4tS0YBr5ZC1GJ5tEpB6Fp5XJMUZqF3XDcOco/UcyT4sfndEm03TLFh0VCXCARSxzFXEfMIvYR09etIFHDnRD+ONpt8YU3UYly7+XsG9J9XMZ3vb2/49DhD9HbUaeZ7HMcf0tTKKnZfneVhebySOEtk4uJ5HU7ZbXIEb1Ks9n2V0soKplfUkNQNd9l3DOduKZDYunLPE3JTSYyysH1/AadIvvskT+BR/55qzfQVQV5SAcVdhTs1oTMdmWmUDTpBjTPRfl5WnrbOZnapG3o0QTRaJFliCLp1xFNs6W/nRG0+wuGEil8w4kounHUFKM8gHLiJVkrFdZ/hPyIMFdeNJ6zFm14wJ+6jf0z0PSfb5OmJjzg1+c4LfIs/ZtUCSkSQFSfYjE9WK2dQc9xcQ7lxp8IjA4kVN7UcxcXPd5PasQwpAsANj/yQfd+MGNP5DodIXLnzHQh89AzmWDtrSN7zc8zwWLhpTsphXX9lBNmux8PCxjBlbOmliIkj2mg6UmFLdVKzUlZp+ZFnCNB10XeGZpzbR0ZHl5FN9Zf6qLxwTPFMJy3JIpwzeWr2Hn//XC2jq0NeXYv1AfN+yZUvonRDu7urqaiorK4dMlzCopQUKI3yKB1jx3wLk1t/xqAw0YEtZdTRN4/Of/zzV1dU8+eSTSJKEYRih0gCQTqfxPI+2tjaam5tZuXIl4PO91NTUkE6nSaVSxGKxMEFf1J0kGEyz2SxdXV10dnbS3NxMNpsNLS+qqpJOp8PzxW/g78hFRMmCBQsKLCxSiO8pT6ITpyAoOvriHwe/ucFvQ1MKRTRTKpXi7rvv5o477hgRy8DBEsGM29TUxMc+9jEuvfRSLMsq0tgFgtDFNUGtHoOsD0yA5NlBFlpJYgAPZigNDQ1s2rQptLJ0dXWxefNm5syZU5BKItqXw1XY+7tOpK3YuHGjjwcJlPbKysoIB5B/bs7q4baX/4FR6SlkrS40JUaP2cGSyRdyWOMxIU0/wL7OTfzqhc+TMmrIWd1UxhtYvWs5p8+8yi/zANHfxQqAUOqjx6OJDLWiHbg4N67qfGrWMWGd9CC09IKpC7l342vcuubFSMi0QoeZ5ao5x1NlJIJ5SSk5rzieW1JhEXVzPDegylcL5kIlMtHGggn+f5Ze2qf9dmDWP370VH586mVUG0kmpHz6g/4sLsIS9KX5Szm8fjwnjp0RWouAsjM/lyN3fODK8LsbWI0Gso77x5TAyuIhKYXzkFBs/O/+gu5fFyhwruNbYMp4PwazHInNR887y9n85QvQ6vA3Ib3Y14JPSfJdN7IOKOCZvidZ/F7C6F1YhutDPawWmPrTR0kuOBPPNpECZarvWHdCV5GYpnJ5m66uPIahsnVLK2PGVpDLWaiqguO4PPfMZnw+Nz8EuaM9h6YpuG5pxSo6nkt1k2X5CsuWza0se3Q9x50wmUlTanAdl1RaD8swTQdNV4jFtaAvhv7eFz8n8e5++9vfpra2llwuRyKRYN++fXz605/mox/9aIn5fGAZ1NLiekMj2pEkqWBC7Pc8pIKQqVLllJpgAC655BKmT5/Offfdx65du9B1HV3XcV03NEmJnC9C28tms2zbtq2ATl2YtaLacdQ9JKJANE0jlUqF7iShrAjqdYBsNouiKBx55JFccskl1NbW9smLdKALmCTJPmqewnCzoZYtronH44e0wgJ++w3DKAC79ltnSfJ3tmYO9ISvmAjTeYTLQVI0f6cV/F6OzJo1ixdffDG8fy6X44knnmDOnDkFmcb7a8OA9abvGIye73leGFb/8ssvs379+tC1Z5om06dPp7GxEc/z0DXf8vOxxd9mX+dm5CDiRpEVTDtLXWp8ULYcKiOfPua/ae3ZFS7Inudx+PgzGVs1E/D5XQ5UijcfAx0vpfh5noehqHxw4pwQ86FKEq7nsrhhIosbJvZ7b8fzrSD9ubdVSelzL1EHy3HQFAVFkWnPZ9nYvp9VLTvZ3NHE9q42MlaOjG3heC4pzUCVZUYlKplSUceC+nHMrh4dRi7NqR3DnNrenXd/1ujo72k9xgcm+uH0puuyqX1fkAZlJNIu9t7P9lySmsG0yvqwf/s7N+yjEvhGoaRYnWuR9WqU2KgCxQUkSu0Shj+X+WUpFQ2kFk9BrWj0o4D627l7LsgqVtMWnO4W9EkzkOMV/nwQ9Yn0VwXXRdIMnK4mlHRtQR1KtUXTepVcx/EziG/Y0MS+vV0kkzpvvLaLY4+fhGGowbojcc+dq+js9BUV23ZJJDQqKmM4TnlPvGD8Wg6aptDclOH2W1+loSHF2efM8s+Dgues635dY0agUJZ1t1Jd1LuuinrceOONTJs2je7u7hCfJ+AbgxFvFsuglhalTIR34YXlnTbYQO1PcXEch8WLFzNz5kyWL1/OCy+8wP79+8MEgGJSFDtRca1wIUUXBnEfUZ/opwDyRt0/Ir191CojyzKzZs3itNNO44gjfH9tNB5d3N+x8wHbYqStUQdtYe8gK1oIuvU8D9c2AS8kLyrVPwP1tVC4FNlP1yYUvFKKYalnUfzcRNWHOuEI4izxwrhF945+F2UKbpCyROxCZbXXhSYrhcNSD3AUmjGgZVi0Z/78+dTX19PZ2YkkScRiMVatWsXvfvc7Lr/88hB7JSxrUTxUOX1Saoci2quqKrqus2rVKu644w40TQuj1wAOP/zwMNxQWCyq4o1UxRtL3itqDfU8j7rU+FCZKadu5R4rdV5/5/d3PPq7h0+uFuVtkSUZ23UxhRIaeZYeHjFF65OReaB7Rc8TvC+7utv43bpXWL5zLRva9pG1LezA+uISIQtEQpElZElGlWRUWWZcqprjRk/l0hlHcfSoyWQDC5+haAXYmeL6RX83XQdDlsnnujnroZvI2mao1CCJO/cvXnHHRI8FdW/NZThhzHT+esE/YAfswcXnlZoz+/SjZ4Ok0/zEfBJTr6Zy0c/xnCySmixZXn/WmnLE8zzfHeR5xKcfy5Qfbyr72l0/PY/WRx5k/D/fTGLmiWVfN1AdSs1bQoTCkstaLHtkPY7jkkoZbFjfxNPLN3LyadOwLAdFkbnsU0dgWg54hJaWZY+u75MMtr95V5KkMNu9pins3dPJLb9aSU/G5AvXHkdFZQzb8qOV3nhtF21tPei6imU5GIbKnt2d6IbquzeH0A/iM2p1Fr83NDSQTCZD/NpgbRhIBtRILCdHW8/eMNPrSIgU7I40xaAq3jhoZUstzNHInPPPP5+lS5fy2muvsWLFCvbs2UNHR0e4Q1dlBVXVwn2JWLw9YbOTgBJmN0mSQJKQFRnFCwimbJtcLodlWcRiMaqqqpg+fTrHH388s2bNCkFIoo4Fi7DroKhDD4v1/bO+ehCl73cdOySjC5WioiSNjuf6D1iWUCKWJzvw+er9ZDkVCklvYsugz4rOM10niGYIFgUkXFxcMZcWt8WvChK+0mg5fri3MUC2Vc/zQkta2dwnYVm9UV9OthM30+orMpKEa/agVo/D6dgPMv3eX7hkKisrOeecc7jllluoqKjANE1isRhPPfUUe/fu5cILL2Tq1KmhqzGqLBf3bak2Fn8XyRMBOjo6WL58OcuWLQuP67pOd3c3M2bMYOnSpZHJoheH5ls8/bEjdueKpIZWOlEfx7ULyB9BLMBqn11ksYhdVZSO4GCKRmH4pIcf15BQNPxdfN9rhqLYi+NuED58+9qX+Mkby9mdaQ95Xyr0GI1Ggriqocsqiuy/La7nYrkOWduiy8rRluvh3fb9bO5o5oHNb/LR6Yv57tHnElOL8B+R+rmOiawUssbqkgySTCJRQYeZJWuZfRjAXUckPo22zx//stKbibtPm/FzN9muS0Lrf8dbUE/X8n0qBTOCb0HxnLzPeqtVIimlwdu99XYi1hqHcjAu/dbJ8/CsrI9TkaTIHBARWcYzsyipWt+lo4Db0w6A093ihzwPtlCLsmWlXz6Xwr72sG0PVZVxHJe771rF2jX7uOSyRei6yp2/f50nHl9PLKFxzLET8TyPE5ZOKbhlW1uWhx9cgyz3nUsK2h9gJj3PZ9IFePP1Xdz5xzcB+MzVS5g8pcZ3MwX1/fP9b/PWm3tIVxjYtovjuFRWxYkZKrZVntIiSb3wCFVV6e72cWci55DneWGuQJHnTlw34tFDG5v+xlfuP4HRlWPJ2V3IJcKehiIeLqps0J1vZfboE/j22U/hDJK4DUorLqrqczY4rktVVRWnnnoqp556Kpu3bmH9uvVs27qV3bt3s7+tma6OrnChjP7zbBccF8nou6sA8GwH13TA8NstwL3jx49n8uTJzJ49m+rq6vC6YhdBOAEGCsb21Y/QvncdmpHEdWyQQFENXMcO+Fok8FxkRcPMddI49ThGTfOjKBzbYsdbj5Lt3Ef9pMXUTTyCYgCYUmQSNQKLjJMzyXR1gedhWiYVAR9Bm9kV7kKL+9pyHZzARKjIAbmV8EkHUmUk6LF7yDt2uPNTJDnkCJDwrSmu15tL2PV8DIGHRzrwA7dZXaHi02fMBMpnR0cHuVyu4PkMJpKs4OZ7kIwErQ/9gD2/+A+0UeBmey3UkgpaXSWelUXWEwVlR8ea67qcdNJJbNu2jWXLllFVVYVt28RiMdauXcuNN97IokWLWLRoEVOnTqWhoaGsOvYn2WyWLVu2sGbNGl555RX27dsXgn4F/09tbS1XXHFFWL8omE2WFOR+lLziSUKRVZSh5E6NiLD2lHL5HCwZyK7Qn+Ui+jnQdUAAqpX50etP8N1XHyWlGSiSzMzqBk4eexgL68czo6qRUckKKvV4iC+xXYcuK8/+ni62dDazunknL+zZxKv7tuK4Lv/71nNs7Wzh9tM+STKSVqBgQ6YWulzE3NG57102v/04Nyw8LSSdQwrC/SUZPe5j+aJ5cyTZz9ht5jpDWgB/ZetVTj3gT+/+jZxj4fSDmSjsVzfAq/STdFCEMHt2oNiUOCdQfCRZI7vzfnBzxCdcGv5WrhTOA96gODYAgnMk1cCzQUn5Lh7xORTxgmgzUZfiZ+m6PnhWVWU6O3Lcd/dqVq7YwaLF4zhh6VQ0VWbH9jZeen4rf773LdpaezjtjOnouh8ebZoO8bhGV4dgg+/b/uj9pMDqBrBzRztPLd/Iyy9uZcKEai65bBGTJteEFh8hZ549kyOPHo8RWFp0w3clvfDsFoxYeXOC4zjh3KyqKj/72c+46667yOfzXH/99QXrYjHx5ohbWgw1wYSaydSnJmHaB5790sNDlXV6zA7qU5P8H4ejVbsuXt63aKiA4+bxXA9JlpkyYTJTJk0GoLOjg46Ne2h1e2juaqO5qZmOjg4yPRkynd1YOriqhNnSHS5OqqISS8RIxhKk66upHddINXEaGhqorq4mnU6HeALo5TqJ4lvC9ob1tUFR2frG/Wx57V7iFY3YZhZZVujp2IMWq0CLpfHcAI9jJMm07mDBB/+RUdOOo6tlKy/d8SWat72OZiSx8hnmnnotC878Gq5rIwfWg/Z8j0/3Lflhlv4uS+HsSz/CeR+9CEPVGZ2o4Iq//hZFkvn1KZ9gR3cbCVUPM+3aQQr1KsOfjD0PMnaeTjMX8jgI5eOMv/w3/3rkhZwzeT77s10kVJ0uKxear13Pw3TsAMDo1ycZ+P0b4349dFnh5lM+wc7uduKqFkkkWfjsbdsOczeVu0B6rosU5KepPv1aUovODd1BnpVFqRpD54u/o/nebyMFoOb+xpz4fvnll6NpGo8//njIkxKL+de+9NJLrFixgsbGRmpqahgzZgyjg5xTFRUVJBIJDMMIiQ8dx8GyrBD03d7ezv79+9m1axfNzc00NTXR1dVFIpEowFR1dXUxbtw4rr766oJw+uLJ0h+DgUExsvsT77HvLvSC8Vs44YqdW/Hurrh/2traqKysDDFdI2WRHYr4bfYB24lEYsg7N1EGEI7XR7e+zQ/+9hjVRgJZkvjq4Wdw+cwl1Mb6mrcdzw3cnTLVRoJqI8Fh1Y18cOIcrnNsnti+hu+++ghqTxePbXub76x8hP847sPYIu1A8A5n2nfx1M0f4+TP/JFUzQQ8z/WPKSr7Nr/Myj9eyzduK92/XW8/QtvOt7Dy3Th2HlnV0fQEVaPnUHnU+QO2/fFt77DNax0wDFu4ciTFoHP118jtvh9JjoOsIWsVyHodcmI8SnwssdEfoi9PUqF4bh5J1sisuxHXbA6UlvyQlJZwU2jlkTWD1kd+SOeKO3txLcXtkSQfk6Jq5He+gz4W9v/hGuRULdiWj4MZaPx6LpIWx+7cS8Vxl1PzwRtCIHB4SuT9kWXfTbPqjV08+fgGtm5tY/qMOj5++eFoqozrenzs44tQFJmXXtjKE49tYMP6Zo46ejyHLx5LPB5sCHQfhFtctej76jguluWwY3s7K17azpuv7yKXsznhpCmcc/4c0mkD23JQgvtKku+yWnJsXyzYhvVN3PmHN5i74P9r773D7CiuvP9Ph9t90+SsNMo5CyQQAhFFWEnkZGMTDF7bGLD98+Kwy+vX3nfDz2ucvXjBmLWBNRizRgZjMCAwwhJKVkagOMozo8lzY8f3j+ruufdO0Ciw73o93+cRl7m3u6q6qrrq1Dnfc07dgP3vCx+1tbVcf/31AJx11ll873vfQ9M00ul00M6TNQsPhAGFlrGVc3nkln2DLuxk4bouqhwa1CLTIwAI4USK9EiLuadEF7AsE1lWKA7HMa79NbOe+RhcPFfE5slRRtg7m7F2t6AvmUiQrUqSwHYhrODubsPadozQdfmZkE1TuDr6HJpebSxQ/fpePhfc/jgX3P54XlnPfnU885b/b8afc1u/z77pxb+nu2U/f/XFNymtncT7bz/G+l9/mbJh0xk1aymmlSWk6sz/5T+zv6uFsBLyXCIFka/NTPH/zbuCby24BoBsVRGaolJUV81kqvsM2L229RCHky2oksy0imFMivXmPBwrlqkcM5KKEXVEqSGCzFc2vMg/r32J2qJy0rZJRThOayZBRAnR2Hmc+866gu+fewMAmaoiZDVEUV01k/ppRyHEYjCwSjHgseS8LGrZMNSy3u6H+rCpwgzfh6an8BTgCw233nor9fX1vPDCCzQ2NgbCi++O3dbWRmNjIzt37syL99OfCjnXZJkb0TkSiVBVVYVlWRiG4aUKEMkZb7rppsCDLVfd6i+WPfX0fhbosbH7dnLXdYPv8k9uvfvfF7h0Xec73/nOiYbsQ4UfYLKlpYUbbriBm266KSAtnyxcV3jO2I7DEztXCy8aXP7hnGv4yKT5AGRtS2gfPWKp//+5kXD93EeO6xCSVZaNmcmkshpueeUnGLbFbxu28Ymp5zGxrAbbdZG9aWcZaY689w6WkQraJHsHhVC4iFjFMDKWKUi4roMsq5jZBG89/nEc22TWFQ9SVj2BkB7HzCbobNrFW7//Fu7Kf+Wie/4DPVqG41iedtbTmbqCqNwvtS6vg8QBzWj9I1b3bmJjP4WdOQq4OEYrVnIvTqaJ7m1fxU5mAx5L35ox8Z0cqQWP6C0NagXoA44F6KR3v0PnG5sJVYOTof9MBK7wAJJCkPjTphPJVzn1gBwGsxm06tVwxRd6EfmFdsxBVmT272vjhee3cfhQJ5mMxbTpNXzsjnmUeC7MvlBz4y2zqKqO8/qru9m66SjHm7qZMKkSTVODw4SuK4RCfRGYxTue6M7yk39by97drUgSTJlaw5KrJjFxUlVwrRrK9WIV2aMdx/X2VLAsF01TiMd1brx5FhO8e/s6uOSuJ7quU11djeM4FBcXU1yc78KdSqV6Ob2cDk7oPXSqHHVx0ndwXBtZ7l2NRM9GMRgpS5IkXNNGCimYOxppv/8F5LKoSB8tS4KXoqvIukrJN65AGVuOnU2TNrMkU0migJM1kCIabiqLEtVJrNhO6smN1C2fmv/cIdG+5Pr9tN3zK4Zf90+4homkyAERN7ePCu3FfW1O4jRoe4uDi2tbKKEwlpHEyHQDInCcqsfAs1EroTDtR3bQsPnXLPnMC5TWTsLIdDP5gnto3v8u29/4HqNmLUX23s4nl9xJY7KLeEgPtBYpy6Ark+ac2jFBmyI2WKYgBCYyKYpCYQzHQlc1NjY18PlVv2RXRzOW4wgvDVlm6eiZfPO86yjWwsK844JuuRieNG1lMqBHuWvs2VxcOZYSPcKejuN8YuXPeezi25hUWouLy+Sy2qAdYYsgcWQykwpyyPQHWZKQFaWfSCqIN9gVMRtQNLAMXFkRi5eVY4JDAttEKarEyXT3q+zryyzpIrRrCxcuZNq0qbz55lusXr2a1tZWuru70XSdSDhMLBbLEUaEKtn/uz87uP/PD3udzWbp7u5GlmWKiuLMmDGDJUuWMHnyZBwHDEMI57Yt5pUkSyiKTCKRDU5p6bRJLKYVzEMXRZGxLJv2tjSyIlNRERWLmLfgWpZNNmMRi+t9tjG3rf8v4Ofrch2XUEgjFNICjpePk1U9OwjzZku6m51tx8jYFhcOn8hHJs3Py67cf5tEHYokeYZ08d+UZTCxtIY7ppzL19a+SFO6m/fajjGxrAbLsdGD06ZMOB4LTL6OZWLbBpoSwjLS2EaKsB8kzzaRFJWOQ5s5vuNl7vrXbI+nHECslPLyEYyZcjH//ukwiYZ1lMy4CnCgIKK2L1ScqIdENmhxZXjY1ZTMeyT/d9fGSR/DdW2OvzIR104X3NdHmXYG106d8LqB4LsbV93yMGVLPie4KTmmm8KnAEmsO64jPAmDOC0F13vvbE+oX2Fyc80MasWovLrzGyRuKCkNk0yauC5cctkE/mrZFPSwGpBkwQ1MNRddMp75C0by+u93M3FSFVVVcZH5WZbIZEzaWlMkurO9qhIRdgUPZf45oygpCXPBheMYN6ECRZHp7sogK77Jvn/+ou/hnE45VFXHuO0O4VDium7eQSb3vtzvcg+ThcHmli1bRkWFML/1a404U+YhSZL6kZL7R7Awug6yrCDTP7nqpNW4/qWaijK8BLkkAqZHeAwpOJ0ZMiv3UJS1UBDqWlVWUBVV8CsU4UHieidTpSKGXBwm/cJ2fBY+soRrWCjlUeztzehTa8XSEwrlzem+BvFEkGUF28wIYlzOwuFrBqQgLHSP6H9w20uU1U2jfNRs0WbvvtFzr2Pdrx6ks/F9imuFa+rookpueeUn3DnlXP7X/KU4rstlL3yX6mgxl9RP4bEd7zC1vI59iTYmlArOhRYKISkyuqLRlOrmzjefpDZazMvX3M/0imFkbYs3Dn3A/W8/i/zuC/z4oo+CKzYMxxMiRDkaSDChvJYJ5UIwcWQJW4IJ5XWcXTOadU0NvHVsD91mhppoMQ3JtkCICYVCyIpywrOW67iCLKf2zKug71UVOQJqqafWzCEu9xfLT+onV0lh2cGn25OioaSklGuuuYYlS5awefNmtm/fwaGD+2lsaqajo6MnBpBro6iap3GRBS+gD02Lr2XxbcBVVVVMnjyZcePGMW/ePEaO7NF2yTJoWu+HchyXL33ht9z/hUXE4zrf+F+v8f1HrkHXVU+9LE54m/50hI3rD1NcrGNZLqZhcdFl4xk1SnC0tm9p5Kc/Wcd3f3hNnrbG95DySXaFgt1gcLLvfeHiKEkSlpkE1xF8KVsnk+rESHfl3ZenPrcMYaYt1ClIEooqCJX+vuW4LmnLxHRsqqIiarDlOHlxUk4Gvkv1iHhZYDLtK7icme7CsU2yyXYAlJAekO+1aGlemY7rgmUQLR9FUeU4Vj39aWZd/iCx8lEoqoZtGSTbD7PttYcJlw4jVjUOxxbxcRRyeHu9WjEIuDaO0SrakW1BChUjiLgqcqQOSVKQ1Hi/nJZ8yIGQdioCi+u6QXoUrXYCWm3fWYk/DAh+j9LH5i00KOXlUT52xzza21LMnjscAMtyUHOCtrW2JOnuzpJIGKiKzMTJIprz/r2txOI61TVxamqLuPTyidTVCQ1G4bvg/33+4rGcv7iHxPuLpzax6g/7KC2LYGTtfg9nhVAUmY72NHPPGsEnP3NO4DbdH3Ij3/rw050cOHCAyZMnY5pmr6CYPr/lZNeDMxehiBzVsWOiyCFaEgdpaNvKWaOW4jh2oOr0cdILl6qIk9WESip+fmuva4wtR3GaulFqhP+3KxdoiwoXVRes/W20f/bXuFk7PyARQj2u1Jfh2g6SMji3yf76JCDaeZK5Y5tIKKhaFNm343rNk3Lcc9sObyVWPhItXCyyhnpEvYoRszAzCRKtBympnQyOhekIAW53RzMATakudnU0M7W8jhf2buaTv32EqvI6jqcTzK0UG6CMFNjxn/rgXVKmwW/+6jOBh4OqKlwzdhbFms5Nv3uMz868yBNmzPwo+V7fJs0smqxiujZfX/sSMVXjmxtf5fmr/poH3n6Wd4/uoSpWggt0GWnmVY3Ke/a8PjNtUGSRD8P/XpZAVjyVpve95SCp4HR1YxyGAw+dC66NpCkgyUgxDTeZBddFUnXsVDvFi273bNLpgtUyRyNhm4E6XSxGCrISwrayqEoIyzRwXZdoNMrChQtZuHAhqaxN49FDNDc10tjYSGtrK4mUSUdbE6lkgmw2DZIamHV8M1A0GqWkpISysjJqamqora2lvLyc2toezZRpWoRCKtu3NrJ2zQHiRTrJhIGmKxhZmynTqjn3vNEiyyteMK8ccqXjOKiqwo5tjfzqF1u485PzGT9B5DB48409PPHoej712XOpqS3CdhxfcRXMcU3TiMViRKPRgJfjC1h+/qoTCfK+l1FuCAF/bAvNqj58zZPruiiqius4RKvrkRQVx7bR9TBKtIWK4VP8GZQ/jxzbS6Q3gMko593WFZXKSJxOI83m44dpz6Yo06OkLfOkBZeANAu8efgDQrJCRNUo14XpJDdORtvR7YBE66FNVI9dwJZXvklH404iRdW0H92BFinBsS0kWQkSAxZXjeX6r29n59v/xr4Nz4EEVjaFqkfBdSmpnsCCr/8Lag5J1SflnrqqXgq4J5Iay8sf5NpZUKKCiHvKkT5OoiXB2irj2haubQyOkDsIuLYpMsEHXpS+yOsiyb2zw+e2R1GEt1D96DLqR5d581eQcrNZi/VrD7FjWyONx7ppa02RSptkM16EZU0lFtMoq4hSW1vEzNl13HWPME8WmnD74qBl0ibhSIhwWEXT1CD+Sn+xd/Ke2RVmo5Cm5AlXva/rqbe/+FSqqrJy5UomT57cLw/RdyA4Y5qWE6GvAbMdE9mL7fLztV9iZ9MqRiz9A7XF44Kw4aeCYIBkCdd2cLOWx1kAN2Mhl0ZIPr4OZXQ5cnlUCBr9BhjyPiwbbWYdVb//ZJ+XpX/3Ph2f/40QWGynh/dyKu32Pg9ufZHK+rOIlQp+RbL9MJYh8tqoWoS965+lu6WBGZd+DmSFdFcjxVUi5LLjWKiepiVSXIttZTAyneJZHIfqaDH1xRU9Ib5lBV1RKdEj3Dl1IXdOXQjALa88xvGMcEvLNcccTXZSEy0irIZoz6aIq7oIb64ozKgYjmFbdBlp7z76XJN8r4gbX36UUj3Cv196B7e++hO+u/kN1tz4JdGvXnK5pS/+iPZsKq8dQV/ZDpIn3bueD7UkSThdGezDnYSm1gSeE1JYCHL6tKlU3n41SAqumcbpzILlYK4/RmjWMC8wVBhJ0Xps0b3WbW8BdOy8ODm52Pb7bzNq1jI6G9/HdV3GnnUjmVQXerSYbOt7jB0zg7FjRgfXN+9bS/XYBRz9YBVH3l/J2Vd/jXSyk5Ae9+qT+s1zlRv7RVVFf0ycVElNbZzKqhgPfeUVRtWXcfdfL6C1NdmzqPVxevZi6/Hq7z5g2oxaxk+oJJMxCYUULrpkPO+uPsiqt/Zxwy2zsC1HnMy88chms1x11VVcfvnlwenOjx9jGAaapgWRbv257nO//HEVz6AGPBRfqwTknbr8hcwn2OZGzMxkMqhqiH0bnsPMdInEl64LlCGxjSPvdTJ86pIcU5gguR7b9TadjR+g6lGx8XsaL1WLMGrmUnF4cF0sx6Y8HOOC4RP4oL2JA90tfPGdX/HDxbcGAosfzt/1vOlyNzTwzJieltqPtPvItj/wm/1bUCSZMcUVgbk2JCvexgiNu98mWlrHwa2/ZcriTzHxvDuwjDTRkjp2r/k5zfvXBjytZPthDm1/BVkRQlw4VoFrW9hWluKqcaS7m5BVjUhxjRBmAMc2GDHtCuLlI4WpWlaQTlVwCc6BToHM76/vg2LJnDby9iBJRtaiZA9tI3tkB0qkuKcdviZtMGXaBq5jE5t1VUCyzX3GwFrUh2kj9ztFkT0CrdC+yLLEsWNdPPv0ZnZ90IJt2wwbXsLEyVXE4logXJiGTTJp0NKSZPOfjrBl81FW/WEft942h1H1ZTiOKK+wfr8bwp5pePm101h69dRBaVgkScI0bMKRENu3NfKtf3oLZQChxX83ZVnmgw8+CFLB+KEeQqEQyWSSTCbDe++9RzKZDN5vXwszcuRIhg0bdmbD+OeirwEqXGRNO0tI0XFch0ff+QzrDrzALWf9PTVFY724B6cuI+XVr8hI0R7pXgqHcLMW5rZjxD/rJVrzg4UMNFEtB2PrMdru/qUg3wqiDVgOUlzDfK8JLG+lPwWBJaf1eAZ4Xvne1Vz26WcYe/bNOLbJZZ95geLqcQBkkm1sfvkfUbUIk8+/GyWkY1tGEEyOnI1d9hZrxxNQUlYWOSRTHArTmhFCULeRpjWTYFxJFa8f2snfrlnBWdX1rDq6hyWjBI9HkqTAa2BBzWie+mAt65saOLtmNECQrfaJnWuoiMQZmRN+PHe9kySJrG3xcsN2/mHDywC8uPRe6mIlfOOcZfztuys42N3Gg3OXUB7uOWX2LDg95biGhaSppH69DW3mMNRxFbhpEyIhrIZ2Uv++ntJvL8dNGEjFYcytTSQe+SNqXQXF6kNImoZrucS/PBOAri+spvjLC/scGSkULrAWyNiWgaJq7Fv/LAe3vUysbASpjqOMmH4F486+GTObwOcouZ4KXJbE4r311e8w7dLPUzZsGpaRwnVMNr/8jyz57AocO42q9GgtFDWfxJ2blTwYm5w0E/51mq5SVS20iSUlYUpKwsiyRFVV3LuPAed9V1eG8RM8V09FxrFdFAUiEZXOTt+tXHAdbMdB01SUfrgcr732GpdddhlvvPEGixcvzjtR9UWGtSwLXddZs2YNY8aMoba2FtcVyUUTCdGvuq5jmmaglSkqKmLXrl1kMhlmzpyJ44Cua2CLjOeu570jyRpbXvkX6iZdFAibPeZtQV71oyJbphAG3lv5A4qrxokQAo4VvGv3zbyIVw/uoDHZxe8O7GDpSz/kE1PO48r66ZSFo3nh/PtD1jZZ07iPJ95bzeuHPkCWwHRtvjDnUoq0MJbrIltZ5FCYg1tfwrEtFt/xBL//4XIObHmR+lnLsMwMiqoRjlcKIT1n+3SsLKFwEVqkGNvMUjFqLsmOI6z62SdY9LFHKaqoJ5NoQVY0HNvAyiZ6tfGUxIqBlsJT1t6cGnL3INdMI+kx2l95mOO/+BlKKdidICkn95xKXCiKJj6+D7lqDK5tBVnh+6q7P65HDyle/J5Kmfzi55vYvbuF6dNrWXzxWIYNL6GkNJznhgxCo9LZmeHYkS7+8NY+dmxr5LlntvKpe88lFtd67cN9CU7iMDS4Z5YkKdBcD9YA4puIf/GLX7Bt2zai0Wie5sQPgLlu3bq8gK/hcJjm5mZuv/12brjhhjMfxn8gc4hgyvuLtkJI0ZfjL3wAACMASURBVGlNHuGnqx/gT4d+xy3zvsE1M7/oXdy7zJNB4T3BxEibSJEQycfWIoVDRK+fGRB2Xauf4Dg+BWJ8BeELx+F0ZcGw/chnQuDpyiBXxQnNGoZr2KBIgeByqs8AECurQdXF5uI4FiNnXBn8tv75L5FsP8SVD7xK2IsbEAoXY5t+xlg56AvLEMFG/ARiMT0KksLU8jpWNwqPr1FF5ZiOzeJhE5EkKNEjdBrpPJW164qEe47rcMP4efxm/1au/M0P+OzMCzmrZjRZy+TFhm08t2cjDy+6gZFFZZiOLcKiF3Rpxjb5hw0vU67HeOryu6gIx0iYWb44ZwkRRePLa37NBcMnsHS0ECYM20L136pc85DlIGlgbjiMUlsshBZHkPSkkIxU6gWt8ght5pajqMNLiX92PuaeJqSICrKEk06AJGEZB3GM2T2CrCThWiZKtKRH/ZADwX3QaNq7hlEz/4qxZ90kTrp71zDu7JvBdbCtbBAILLf94aJKymrHC8+ciBjnWLkwxUWL6wKNkuM4edwC6D/Ldl+qYMtyCIUUMhmLTEYIrqvfaSAcUXF8AbxwqfYGvX50GYcOCg2db6u2bIf2tjQzZtZ5bZEwLQtNU9m7dw+HDh5GD+uBgBGPx5k7dy6bN2/msssuY9u2bSxcuDBILOk4DuvXr8eyrCBA38iRIxkxYgQAu3btorS0lNraWiRJ4v333+f111+nrKyM3bt3U19fj2ma1NfXs3TpUg4dOkQqlWLmzJmkku3Uz7muz75q3r8OI92JFikNuA5WNkPdxMXUTVzc6/qOYzsxvc3cdmxh9nNsRhWV87NL7+CuN57kQFcLW1uO8IV3nuNfNr3GjPJhTCmvpS5WSpkeCaLbGrZFt5nlWLKTvV3H2XL8MAcTbSTNLIoko8gyDy+6gWvGzsZyHBRcpFAYy8ywY+UPmHXlV6ibdCFTL7qXtc/9DdVjFhApFtwzxxbEeUkWqTxiZSOYetG9ABze8XtC4Ti4LraZJt2VxjYzgucRKSFcVE358Ok588nJCzD4PwbemqwUVaOUQnzOuajlI3GtLJI8GMlFaM3S77+Nk+7oiax90s3I3yv96LM7dzSxZ08L02bUcvsnziIe74nJIzIs5x5WJMrLo5SXRxkzrpyfPb6BbVuOsWN7I/PPGRW8/4V1AsFvK/5zu+C0lEYwDDsQSgZ4fEC8+/G4hmUNzEnyzUIPPfRQkJPNT5Pja2J9GIbR6/Dlez2e8TD+rutieqGWc35AlhRUORSE+bdsg9X7fslzm/4PSaOdT1/wKBeM/6iXIVriZDyF+kKftsOMEFjMnU0kfrKWsn+9zr9YfPT3XF4wuchVU4hcNaWfqwZux6k8iwvYZjo48finJscyWPurB9mz9mkuuOMJqsacjZlNENLjxEqHBcQ8caoU9SbaDqLpcQgLcta/7/gjbzbuZ2fbUfZ0Hue+PzxDc7obw7H5/KpfMrakiktGTOZL8y7n5lceoyMw83hxOjyB6KeXfJwfbXuLp95fx3e3rESTFWZXjeTZy+9m6ZiZQUK1whDPIlJohGevuIfaaDExj0AY9z7vnXkhN4yfS020xx2uMhIX7qLkS/eBxB/XkNSCPnZcIWDmQFJltAWjkEtj6GflR5MU5UV72bkdKUNQca+JIuoMRYoprRMaqdK6KXQ27RL3OjbR0mGku5rIJgQh0TcSWNkkb//sbuIVoz3XVZfmfWv400vfoKVhAzXjFnrV9gigJ0Lvk5QQNtrbUySTBumUQTZrkc1aWP0J6vS4Li67eho//tFqXn35AxaePxrLdPjNC9uprSvivAuE2SLXFp7JZOjq7iJiRQKhxV+AioqKgk//O5+z0t7ejuu6hMNh2tvb2bZtG/feKzbaWCwWLGqmaTJjxgxmzJgBwGOPPcZdd92VF04gFosFXgnxorL++0pWCMcFT8cZwMTnw3VtoiXCVKt475fqZVmeUzWK3y77LN/e9Dq/2b+F9myK3R1N7OlsZsX+HJK2X1ZQpjAaOa6LpqjEVZ35NaP5m7lLWFA7RkSqliRcBN/mrcc/xrDJlzBs0oVYRpqzr/snzGyS3/9oORfc/jhlw6aJ8Q+eUWgDJUki2XGUrubdaLFyXMdC1aJc+9BbpDqOkmw/gmWkWPFPl7DswVcZMW0JlpFCLQyiWPD5Z4vATidhtUDx+XdSeuE9J13M3geGY7cO1g96EM3y2pVOmyQTBmPGlBOP6yS6s4QjIWRZ6sUfcV0X23JIp03iRTpjxpbz7uoDpNOeyXWAwfLrMwybZNJE11UhtAyK0+IS0pQ+18W+tDlAkKfv7bffZv78+YTDYbLZLK+++ipHjhxh2rRpnHfeef3Wd7L76aA0LZrad8K0tNlNW/IoOxtXsXLXE+w9vpGpdefz+fn/wdjKuXkcltPZ7KFA7WY7YNpI4RB2c4K2O54l9on56AtHC/JmP0xnN2Mh6SqdX32ZxE/WIVdEcTszSLqKa9nIRWGkSAj7eCLYRSVVBllCiuvUrLkPKTy47KQDPUOkRJxmbdug7fBW1v/6qzTt+SPnf/wxxp19M45toXh9Xj3uXLb9/tuku5uJFFVjpDrRoiU071tDKByjqFrwXTrNLBuaDzCxpJqaaDF/On4IXVFYPnomXWaGNY17CSshvjTvcrqNDJECrxnLi4CrSjKfn30pfz39AgzbQkIKAsL5RF9Flr3u6ZnVirchjisRvv37u1o4muwgrISC8OexkMbB7jZvMVe4deLZVEfEpqflmg49Val9uBO7yVNpByu2lBN/wftSU0i/vBMpquEcT+A6Ltosz6xk2iD1TuDVo8bOLS8fjm0GAqaZSQSmOFWLsn/9L0m0H6ZmfL4QooR0Js37BCU1EzGzSVzHJtXZyLSLPsuBLS+SbDvklT74LaLw3bFtcVLevrWR8RMqaW7spr2tJ938s09v9orPn6N+GZVVMT5z/3msXtXASyvew3VdRo8uZ9HiMUH/GIZFW2uGZDLNtGnTqays4siRw8ydO4933nknIAgbhtAA5HoQ2LaNpmlceWWPFvHYsWNs3749+Dv3+lwOjKqqZLNZWlpaqKmpIZVKEY1GA4+l1LENHD64j2i8GNsyULUoqhYl3dVEKFxM66FN7NvwS7RICSOmXc7x/etItB3yNmob4VZcSaLtELGyERzfvw5JUqisn0vFyDmE4xW4jhMILsNiJXxr0fXcM+08VuzfwrvH9nM01UF7Nk2Xkca0bfFeIIR5VVaIh3RK9ShV4TgzK0ewdMx0Lhg2UTy34wssYn69+ZOPEi2pY9YVD+I4dpDNfeGt32fba98mk2jxJkFO6gLPPCxJMrGSOupnX42R7gzmp22mKamdhKKGyabaUEIITQz0mJolKXgHRBRreVBEzTOFID3EmawztygVXC+UhJ1sR9ZjgWmwT7jBf0RRZ9CT3z+ElZVFKC2LsG3rMWbNqWPEyNKc6t1giCWPv6eoEvEinSNHOtm6+RglpWHKyiN5ZfZZn/ep6yqZtMlVy+aycNFoOjszaJrSl3K5FxzXDQQp/7M/M5hhGOi6zsaNG5k9ezaapvHtb3+bo0ePUllZycaNG2loaOCjH/1oQNjPNWGdUe8hSRI5TF7c9l0MOy1O4y5YjkHK6OR44iAH2rbSmW5meOkU7jj3YS6acDvhUBzLMVGD9OT982AGg9zFDUBSZFBkzO2NtN31LJFrplN036Ies1B/fBYvOE946VSUkaXIVXGQJJz2FEpVnPQL2zHWHaT04WW4SSPYPLEdIQgNQEwaDBzLQIuUsP3179Kw6T/pbNpF66HNlNZOZtmX3qGyfp7wEFJCQTjukTOuYsOv/44Dm1cw+fx70KIlAOxe8ySlw2dQXlGPaWZ4YM5lPDDnskG1I66FCQWLhvhQ5fxA7lFVI1og2IRyvL8s375e4JjVbWQo0sI88d5q/v6Pz1McLyNpGSiSjOXYIlw/IpFdIpvkY1PP4+eX3Ynp2CKlgEfAtZsTWLtaMIcdI7J0au4xtqdBPg9GkXFaUlgNbTjtachaOJ5XTF+bdx4cWzg69AEJ4SorLutx15MVlUTbQdLdx3s8v/x+MVIUV08gVibMIK7nNafHyikbNpVEa0P/bekHuS+244jMsdmsxZ82HOEjH5vDfz63jfVrD7HsmqkkE8aAsZX8MsrLoyy9Oj8+kWnansChMrK+lL++99yA/NvQ0MChQ4eYO3cee/bsQZZlxo8fH9iu8wmIChs2bGDTpk2oqko0GmXnzp185CMfyWtHITRNo729nQMHDnD8+HFqamoKTGYS3a0NdB3fi9Glo4aLaTmwkbYjW5l8/j10Nu1iwjkfJ9lxlHR3MyOmXU6qs5HO5t2B94xlpNjz7pNMveheulv2M2L6FbiOTUvDRipGzPLqEf2nehpFF5dJZbU86LnnH0q0czTRQXO6m5RlkLUtHFdkoQ4rISoiMeq8TM9KjneU5TqCeBushy7TL3mAmvGLvKfzF3Exb2Zc9oWetU9Wg01XkiRsM4sSCtO8fx2/+tr5nH3d36BqEUHq9a91bORQmHNvfphIcY1XjvcO57xHhmORtYWL96DguiANRuj2NVESrmPgOob4S5JxrG4UJQKO0ev604avJfBMt5IWQVK1kyh98KTdwUD1otBOnV7D3HnDWf3OAR5/dB3zF4xk7PgKqmuKiMe1wNxjmjaJhMHx5gT79ray7t1DHDncyTkLRzF9Rh2u4w7s2eN/emanSCSEosiUlUVO2dLhl9eXpsXno8TjceLxOKtWrSIWi/Gd73wHVVVpbGzkpz/9KXv37mXcuHGBY0FuOWfUe8hxbJ5c9yDdGQdVFtp5WRb7d0yLEdVKmVC9gBvm/C3T6oTNOGul0dXIKTeqEHk8GsfBaUqQ+uUWkj9dR/ze84h/8hxcyw68TQLia2E53kDrC0cLrUzWEp5HJZ4bclsKc9dxojfO6nVvUP/pmIdcB0XV6GzeTbLjCLaRwrFMETjK2xx9YqEkKzi2SbS4lpmXf5F3n/sirutSM+5cNv/un+ls2sV5H/0RgGeHd0iaWUr0COf96pssHzMrT6vi4pI0s5TqUZpTXUEWV39RfL+9EdvLuSJLQpOiSCIZYpAUEeHSXBQKUx0tEqalgi7wQ/3LksTMYePZfMvf0ZjqCoJySZJEyswyPF7Gshd/FCyUjv+qmQ4oMokf/ZHox+dhH+zAWHcQbb7nGi3LvQQYJ2USvXV2L1Of67hCUO3Ddi95Zqui+Tcy/pGL+hwvSZKIV9QDPbwUACPdxawrv0zLgT+R7m7Ou6eocixrnrmfcLxK8CmMFHqsPLhPOgUyev5cE3P7pRXvMWJkCVXVcRaeP5pnn97MxZeNJxbXRAr7QczN/3xuGy3Hk4QjKrblBN4CsiRhmCK43Oy5teg6qGooINNpmhYsVKlUih/+8Id0d3fnkXDHjx9PeXk5NTU17Nu3j+7ubiZOnEg2m0XX9TzzhCRJQRTbTZs2YRgGW7ZsYfr06YF3kb/J10y/gZoeegaZOdfQtG8N9TOX9e43x6Z+9nLqZy/P+z5aOoyJ5368n77uib0BBEKH7TjYrgi2ODJexsh4/yYqH47rYHh5uRTJ8yTKFe5UndoJi3AdGxc5cEOWJAkkBce2cCwDVY8KUmXuIcIbX1WPUlJTwYIbvnnC9vTFZZGAl5fdh+nYhD0zWu7hpC9IsgqDmse+44CNLEfzXKMVL5mirJVjWwnv6lMLLte7Wv/5PJXCIFQL+fyeMyex5Hr2SJLEzR+ZTSSmsW7NQZ57ZislpWHiRTq6pqJ4pnDbdslmLZIJg472NOUVES6+dDzLr50WeA3lll0I/xvbdikpDfPqyx/w2iu7+g/KWQDHFZFxOzsy3HTrLGbOHtYrVkvuHnjs2DFkWQ4CYR44cIDrr78+SJ5YW1vLokWL2LNnD+PGjQu0LX45uZ+DwQlnniyr3Lf4Z1hOFj/MsmlnSWRbOda1l73H17O7eS3fWXkrZ9Uv5+oZX6SuZDy2YyFLSi8C4ck2MBe+JiX13FYyv9lB+RO3oM0dHnx/4gLEh9OZRi6J0PG5FWT/2EDNxs+BKmMdaMc52oVrOzitSaSQt9GGVTHaunpaApgSCpPpPs5l965g2OSLSLQdpPXQZt5780f89lsXMvuqrzJn6UP47pi+LX7axfdjWwabf/t/yKY6KB8xk4vuforS2slB/BsVgiytjakuOj3OiunY6K4QCXz+yN3TFiF709c3y1z32x+zs72RIk1HQkJTVHRZIaSogizrQlgNsbujmXumLeIHi28hY5tBOUEXe2NsOEJtLkkSxVqYsCIybcuSFLTDBQw753RnOUhhldQzm7EPdVLy9csx328m8e0/oIyvRCmP4lp2b08ux8E+2AGA3ZJEioaQVBlJU/PilOS2MYi9Eo4jh+N9jqnjWKx//kvUTVzMkfffoKRaBK6SZYXulgbSXY3BRuLHz5l1xYP9jr+shLDNNMHD0//Ckwv/d+HqKPPqy+9z+FAn9z5wHpblMGNmHds2H+PxH6/lMw8szHGH7Kcd3u/X3Tij12++O+XmPx3h4f//bW6/aw4gTlG+oBIKhaisFJqsaDTKAw88wKOPPhpkvnZdl9LS0iCp5OrVqwNTkW33Ps37HkWNjY2sXLmSr3zlKzzzzDNs3LiRefPm5fUBQDLRga5pSLJKNtVBOFaJ67oY6Q5hBvIEYVWLYptZQaz23inbyhIrGYZjm5jZJGooIrQSroMSiuQJDr4nlyRJKLKMH/rQ9oLaOa5bsNmJdsoIl2dFltEKvEIoWA99zWrhOilJkufOLIQ2xzYxUp095XjV2maWTKKVNc98DlnVhAkssHe64rkdm3C8gllXfqXPNbguVtJT7CAOZY6VyBFABtrgfRN7nMSu72J17RCh/V0b1zFQwrVkG19BLZ7mXX2mzVN+eSfWkOc/65lrR+G4hiMhbrplFmfPH8H77zXTsL+d5qYEHR1pDI+rp2kyRUU6EyZWUj+mjMlTqhk7TjhmDGZ8/BExTRvDsGlrS5FOmwPmEcu73wFdV2hpEZw5UW/v6yzLIhQK8cgjj7Bx40bGjRsXfO+Ta/35put6kP35dHFCTossySye0HdeHMsx6c60sLPxHf6w5yne3v0U24+s5Ka5X+OCCR8FEGH8c1KOnwpjPRgg7yQY/9Q5xD+zEEmVcbNWQKztayBdx+ntROFdL1fHUcdVBAJPyf9eQvFXLkZSZJTqooHbcgoQzy4FdupIcS31s5YzauZStr/xfTa88Ld0tzaw6LYfIys6fhZRWVGZfeWXmXz+PVhmmki8Uti+PYElMB14fRtWQgHhtTwnwZuv7bht0oLgO/8k+eZ1XyBjm0j4WZm9KK30TDxZEi6wfpnxkN7vkiVLUmBeioXy3dmKvSSGRSEdyz8FWQ6EZZI/20D6xR2U//RmEUhwcjXR2+bRcf8LlP3oWjF2PtnU2xDU+jK6v/8O1oF23EQWSVdxurOEptZQ9MD5gZeRj/yFxBHxW3ICRflRSGde/jd0Nu9BQqJy9FlEvdg6ihYRcT1UTcwvxDLnu0of3PISmcRxJp53J9lUByILb7Hnuio4P5Lc2048EFxXCBOO7ZDN2tx4y0xUVQ4Y/suvm8abr+8hlTTzTmN9wY/l8tiP36WjPU11TRGGYXn3CaHGyNrMnlsHkkIymWDz5k20tLTQ3d1NQ0MD4XCY2traQLuSR+z0+nHv3r08//zzLFmyhFGjRnnB7XovOYqi0NXVxWOPPcall15KbW0tV199NU8//TSRSISpU6cGwegA1FAYSXJRVI2m3e/wwR8fZ9mDq9CjZUJgUUKBUKqEdFxXw7ayhLQY6c5G3nnqU1z70Eb0aKnIoiyHyM2W3pv4XBAgUpJRBrkE9LXu5ZWlar2uy5sTvgo+Vk5lfU9oddmLGVMxcjY3/cMuzHS3CITYR8RfMXfy81PljlmQhd3TrvZ7gvf6SC2eiiR5LuUDuX7nnKKN5tfJNr2OrFeIAHSShCSpSEqUUNk87/pBHDwHC4kgFpNrZXHVUK88Qb3h6yJ6a5BPFYXj6roiSemYsRWMGVuB6Wk0TcvJS14aUmX0sBpoNwSPrXdS077Gyc9RtHT5FC6+dPyApqQ+24wYOsd2KSr2Dyq9y/Df5c997nOEQiEef1zk1ausrGTVqlVcd911AVF/48aNnHvuud7znR7N4tS9h5BR5BBl0ToWjr2RhWNvZP2B3/D85n/kX1fdzYH2bdwy7+uEFD0g5J4REq7rihM0rtCw9Cmw+C8LyDGNwhXGt2q4ho259RjJJ9ZhH09618oiJogsiZN/TCP2yXNEULvTEFj8Vql6FFXzTGeOHQS6mnHp54gUVbHq53ejqGEW3fYIrtszSf3Tkg8/ZX0eizvnTVuxbwuarNCSTqApah7PwXFdOrNp7py6kMlltViOnefVMxgIc5SBVbgQ5HTP3s7jPLLtD7RmkoHKWQKyjkVNpJgNzQeYW51j9gGUESWUff9a5OIwruXgOi76BWOFC3NHRgiq/mnR827Rzx+Lfv7YfoRWt5dmJv+llwPhJ/c713UJxysDTxQgMOG5ti0y6SohXMl7fkkK1NCmkQyYfHpO+PXqsedQPfYc8bhqj7vjieZU3hgrMsuvFSdTxxERi13XJRbTWHaN+F5RZFQvaqbrur2EdpF7CI4e7mL23GFcfd10BsLatZuxLIsRI0bw0ksvMXHiRDo7O2loaAi0Lz6pDsSitGvXLlasWMH1118fqIR9d+jc5xLtcXjyySeZO3cuF154IZlMhjFjxnDDDTfw/PPPBxE1fS2NqodRgOZ977Jn3X8wasZSdqz8ARPO/ThapIRCSJKE6mk51/7qi4w9+ybef/sx6mcvp6RmYq9+LhyT0+Hj9Xdvf3/nmcI9E5LruoyYdjkjpl2eoykR46tqkUADOBj09YxKQZ19PafrukiK0EqVzX/S+87xvuvjHtfBD+PvWAnKF7104nYp+umtsbnz3CbIC6R4PEBOYPbKwyDIqoNB7rgGgqoiyPSuK/hpA4fIF0Ee/Tguuf3T7zh5dRUVhykq7tuJ5mQwkCAPUF4uzN9FRUWk02kWL17MN77xDQ4cOMC4ceNYv349paWlzJkzJ3CNPh2clveQ64o4La7rIiFxdv1yJtUs5On1X+Wl7d+lLXmUT5//b2hqpJfG5WTRp5kp1A8L2dNouK6LdbADMvlMS595rY4sRSoJk/jxuyIWi+uKf17oeNewkStixD65gDMhertAd2sjZjYZPJMveNhWlvELPkqy/Qjr//PLFFePY+aSL/aojxVV2L5dF0mWewks0BOmeXblCDYeP8h3Nr8hNq6c1tuuSzykcyTZzvnDJjC5rFbY6l0ZJ9dDoa8xQAg8iiwjSzClvDbQmhQKTiNipVSE4/xw61t5ZiTvjCeEGAkmlIg4FIrimaAuEQuwazsBB8m1HPRFYwAwdzQi6flpD1zHGze594kW6GU6HOilz5tnjo3j5auRJFkksVQ19Fg5erSUlmRbTpyWfJt4y4GNDJt8MYnWBs9zRfStY5tUjJwduJ0O5l0oPKmL1PJSXlRMx3GD+AhtrSmyWZtYkS4yu/ZThWHYHD3aRVtripaWJLqmkGtNsyyHeDzEggXzWbBgfp9lrFy5suepc9pYV1fHfffdRzgcDgSWwmfKXQBvu+02SkpKguBTtm0zefJk7r333iBYlW8HP/b+KtoaVtNycBPzln2NmvHn0bj7Hbb9/mFKaiZRNnwapXVTgg2/s/EDju1+m6Pvr2T0nGuZcO7H6WrZz953n0KPV1IxcjZldVPQoqWnfTA5kyjUCPpRmiF/IxHrgsOJTDWFZqj+6hy4LXJgspIGOogqYfC1MXj39MV4l2RAHriswcK71XUs1AroeO37dL3zM0FOlvqOEt0XnGwCOdI3F+5UUfhsuUKI63ku+dWJywQPplBLMpjxy10TTsWykYtC7U5f9fgRsbu7uzEMg5KSEu6//36effZZ3njjDWbNmsXNN98c3NdXf5wMTqhp8SEEE1911LPoS/QsipZtUByu5NPnP0qRXs5L27+HLCncvfD7RLSi02roiaS9vPbmbFbFf3cpimcPDE7UunhB4p9ZSPwzfUdKHUz9g0dPf81b/lXKhnk23ByVrb8YTb3oXpr3rsk5MeY8X05OosK2iccTz/f05Z8YdMuE14OoezBRPnPr3HLrQ8H/654Q5ecs+tSMxXxqxuJBl6XLKq6EiMGiSkGuJwBJlXFSBnJUA8fFTeRnO5Vyok7mlhk8Y1fv7KgDtSUYZ1lByTmd+VmEp118H5IsU1o7OUhkJ8lqMO+q6s/ieMN6PnjnJ4EbKgh+mGWkKKocEwgyA6rXC9rkQ1F6z39J6lG7Lr92GsNHFNPWmiKZyPbi9fgL5pIrJ9JyPMnbb+0r0ICAospkMyYjRpZSN6yYVCoTaHXEgiiEi0xGxLvJ5ao4jhOohXM1LLnP4HscifYolJSUBF4FvoDiOA7xuHDVNU0D1xX3Hz+wgfLaycy4/EEkScK2stROWET12HM4svM1mvauJl5ej6LqgEvroc3IksKi236MHi3FtgyKK8cwZ+lDHG9YT9OeP6LHyj2hZXBj8l+BQo1gbv6z3LWor2itA+FU1uD892KgMBae9ix7HNfq9r5xkGWVgbab01tfg0JE3alO7ASk9+3E7j55q5McAdcEnH7cCk8B/ZkaxT8Y6FDc1z2DuVbuY108FZxo7/UPJAsWLCAcFofY+vp6HnzwwROWcyqQTiCJDfxjH41wPHOBLCs8suqT7Dj2Nl+76lWq4vXYjp23CXwY6K9j/AR7PepVycsY7P3rDxKgyqfc2f23Jz9pWe5p2somCIWL8u4/mbpsx8FhYCnbBUKep9Cp1OG6LrbrBnlWCp/Tz8/iDNAGSRIpBII2iC8HfG4nZeC0plBHluYlTRywnQfaUUeXn/bLUni69YUY18vjIq4R+ZBOXJa47kyc7PubY6mUQVNjgvrRpb2Skp3cmAfD0gvHjh2jrq6OJ598kptuuinwDPLnXmG9/qlsxYoVzJgxg7FjxwbB6nKTJ4KYH7630YYNG0gkurnwwoty2uXgOg6yogbm0r76JRc+78jxEuHl5ic7k2Pylwq/D4229ch6NWqsHhEf58MXBP330DzegNl2EFnr4fP1igCY+3fBb0Ir5BCun4sUOk2T1f9w9Lf2+BnrffjJVE9ScOnzgtMSWnIbnfv/PoelM91Mc6KBCVXzT5vXcjLIMyNlLNCUXlma/ysnYt4GamaQVS2PQFt4jTjJWkgF3leDreNknutMSL+FZZxqG06mjblCaH/3DtS/J4u+ynJsU/C75B6uhhCGBeclIPUFEH8rajjfvfUMIPf5TNNBUaS8fCZ9td80e7QdvSHMrLIsoYYGnquF7fD7ofBa//uB3sMTnepc18GxDJDkwNyRe69rm7iug6xqgQDi2Aa4IKs9hFsfjm2JpIpKqNc7OYSTQ8/Y9Qju/1WCYF91n9lyh+ZFf/D7pq9M731ddxJ9+eEILf01KHfynC6f5WTQr2bj//HkG6g90Nt+XejN8JeOvP5y3F5C6IDX225A4P0wtBqnIgx/GPOx92nH9Ui3ve3Sp6pd63VA8ZKm+TjRM51IiBnM9SdqW3/9Ufjdf7c14n8Cgj50DM4YV+Vk67Yt4Sl0unUOggP0l44TrYeniQ9XaIG+BRc/u/PQgtA/+uuToT4TGOjFOFlNy/90nEho/zDqOp37Biuw/KWN458jTvU9/bDq/u9c7hAGhQ9faIETT1wkPoQgQkMYwhD+XHCyQsiQ0DKEIfxF4pSEliEMYQhDGMIQhjCE/xY4g7kshzCEIQxhCEMYwhA+PAwJLUMYwhCGMIQhDOHPAkNCyxCGMIQhDGEIQ/izwJDQMoQhDGEIQxjCEP4sMCS0DGEIQxjCEIYwhD8LDAktQxjCEIYwhCEM4c8C/xesbHSfpVu9DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_reviews = visualize(noun_list,'total reviews') # 워드클라우드 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "oCl98qooGoG2"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer #sklearn 패키지의 CountVectorizer import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "60qNGkhJHrum"
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer() #countvectorizer 생성\n",
    "vectorizer.fit(train.review_space) # countvectorizer 학습\n",
    "X = vectorizer.transform(train.review_space) # transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "Y142nKNqI7xD"
   },
   "outputs": [],
   "source": [
    "y = train.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LJkqVu8pIRjv",
    "outputId": "25395290-5163-4542-bc22-e4c81ef47555"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.100582\n",
      "0:\tlearn: 1.5583363\ttotal: 2.21s\tremaining: 36m 44s\n",
      "1:\tlearn: 1.4107678\ttotal: 4.07s\tremaining: 33m 48s\n",
      "2:\tlearn: 1.3050317\ttotal: 5.89s\tremaining: 32m 37s\n",
      "3:\tlearn: 1.2245799\ttotal: 7.73s\tremaining: 32m 5s\n",
      "4:\tlearn: 1.1611031\ttotal: 9.56s\tremaining: 31m 43s\n",
      "5:\tlearn: 1.1098170\ttotal: 11.4s\tremaining: 31m 22s\n",
      "6:\tlearn: 1.0669437\ttotal: 13.2s\tremaining: 31m 16s\n",
      "7:\tlearn: 1.0318838\ttotal: 15.1s\tremaining: 31m 6s\n",
      "8:\tlearn: 1.0013364\ttotal: 16.9s\tremaining: 30m 58s\n",
      "9:\tlearn: 0.9760143\ttotal: 18.7s\tremaining: 30m 52s\n",
      "10:\tlearn: 0.9542687\ttotal: 20.5s\tremaining: 30m 47s\n",
      "11:\tlearn: 0.9347636\ttotal: 22.4s\tremaining: 30m 46s\n",
      "12:\tlearn: 0.9184638\ttotal: 24.3s\tremaining: 30m 44s\n",
      "13:\tlearn: 0.9044327\ttotal: 26.1s\tremaining: 30m 40s\n",
      "14:\tlearn: 0.8915289\ttotal: 28.1s\tremaining: 30m 42s\n",
      "15:\tlearn: 0.8796164\ttotal: 29.9s\tremaining: 30m 39s\n",
      "16:\tlearn: 0.8687167\ttotal: 31.8s\tremaining: 30m 38s\n",
      "17:\tlearn: 0.8597768\ttotal: 33.7s\tremaining: 30m 37s\n",
      "18:\tlearn: 0.8519400\ttotal: 35.6s\tremaining: 30m 37s\n",
      "19:\tlearn: 0.8440594\ttotal: 37.5s\tremaining: 30m 35s\n",
      "20:\tlearn: 0.8373151\ttotal: 39.4s\tremaining: 30m 35s\n",
      "21:\tlearn: 0.8312002\ttotal: 41.2s\tremaining: 30m 32s\n",
      "22:\tlearn: 0.8257233\ttotal: 43.1s\tremaining: 30m 32s\n",
      "23:\tlearn: 0.8209174\ttotal: 45s\tremaining: 30m 28s\n",
      "24:\tlearn: 0.8166850\ttotal: 46.9s\tremaining: 30m 27s\n",
      "25:\tlearn: 0.8127779\ttotal: 48.7s\tremaining: 30m 25s\n",
      "26:\tlearn: 0.8090440\ttotal: 50.6s\tremaining: 30m 24s\n",
      "27:\tlearn: 0.8059754\ttotal: 52.5s\tremaining: 30m 22s\n",
      "28:\tlearn: 0.8029225\ttotal: 54.4s\tremaining: 30m 21s\n",
      "29:\tlearn: 0.8001731\ttotal: 56.3s\tremaining: 30m 19s\n",
      "30:\tlearn: 0.7975396\ttotal: 58.2s\tremaining: 30m 18s\n",
      "31:\tlearn: 0.7951103\ttotal: 1m\tremaining: 30m 19s\n",
      "32:\tlearn: 0.7927645\ttotal: 1m 2s\tremaining: 30m 17s\n",
      "33:\tlearn: 0.7908094\ttotal: 1m 5s\tremaining: 30m 54s\n",
      "34:\tlearn: 0.7887245\ttotal: 1m 7s\tremaining: 31m 9s\n",
      "35:\tlearn: 0.7864687\ttotal: 1m 9s\tremaining: 31m 8s\n",
      "36:\tlearn: 0.7844521\ttotal: 1m 11s\tremaining: 31m 5s\n",
      "37:\tlearn: 0.7832078\ttotal: 1m 13s\tremaining: 31m 1s\n",
      "38:\tlearn: 0.7818370\ttotal: 1m 15s\tremaining: 30m 59s\n",
      "39:\tlearn: 0.7804979\ttotal: 1m 17s\tremaining: 30m 57s\n",
      "40:\tlearn: 0.7789271\ttotal: 1m 19s\tremaining: 30m 54s\n",
      "41:\tlearn: 0.7776310\ttotal: 1m 21s\tremaining: 30m 52s\n",
      "42:\tlearn: 0.7766700\ttotal: 1m 23s\tremaining: 30m 49s\n",
      "43:\tlearn: 0.7755840\ttotal: 1m 24s\tremaining: 30m 46s\n",
      "44:\tlearn: 0.7742472\ttotal: 1m 26s\tremaining: 30m 43s\n",
      "45:\tlearn: 0.7732236\ttotal: 1m 28s\tremaining: 30m 41s\n",
      "46:\tlearn: 0.7722224\ttotal: 1m 30s\tremaining: 30m 38s\n",
      "47:\tlearn: 0.7712388\ttotal: 1m 32s\tremaining: 30m 35s\n",
      "48:\tlearn: 0.7703639\ttotal: 1m 34s\tremaining: 30m 32s\n",
      "49:\tlearn: 0.7694441\ttotal: 1m 36s\tremaining: 30m 30s\n",
      "50:\tlearn: 0.7684861\ttotal: 1m 38s\tremaining: 30m 29s\n",
      "51:\tlearn: 0.7676031\ttotal: 1m 40s\tremaining: 30m 27s\n",
      "52:\tlearn: 0.7665417\ttotal: 1m 42s\tremaining: 30m 25s\n",
      "53:\tlearn: 0.7657483\ttotal: 1m 44s\tremaining: 30m 23s\n",
      "54:\tlearn: 0.7651120\ttotal: 1m 46s\tremaining: 30m 21s\n",
      "55:\tlearn: 0.7644364\ttotal: 1m 47s\tremaining: 30m 18s\n",
      "56:\tlearn: 0.7636439\ttotal: 1m 49s\tremaining: 30m 16s\n",
      "57:\tlearn: 0.7629567\ttotal: 1m 51s\tremaining: 30m 13s\n",
      "58:\tlearn: 0.7623966\ttotal: 1m 53s\tremaining: 30m 11s\n",
      "59:\tlearn: 0.7617941\ttotal: 1m 55s\tremaining: 30m 8s\n",
      "60:\tlearn: 0.7612488\ttotal: 1m 57s\tremaining: 30m 6s\n",
      "61:\tlearn: 0.7606920\ttotal: 1m 59s\tremaining: 30m 3s\n",
      "62:\tlearn: 0.7602118\ttotal: 2m 1s\tremaining: 30m\n",
      "63:\tlearn: 0.7597138\ttotal: 2m 3s\tremaining: 29m 59s\n",
      "64:\tlearn: 0.7591501\ttotal: 2m 4s\tremaining: 29m 56s\n",
      "65:\tlearn: 0.7584318\ttotal: 2m 6s\tremaining: 29m 54s\n",
      "66:\tlearn: 0.7579972\ttotal: 2m 8s\tremaining: 29m 52s\n",
      "67:\tlearn: 0.7576446\ttotal: 2m 10s\tremaining: 29m 50s\n",
      "68:\tlearn: 0.7571767\ttotal: 2m 12s\tremaining: 29m 47s\n",
      "69:\tlearn: 0.7565732\ttotal: 2m 14s\tremaining: 29m 45s\n",
      "70:\tlearn: 0.7561645\ttotal: 2m 16s\tremaining: 29m 42s\n",
      "71:\tlearn: 0.7557511\ttotal: 2m 18s\tremaining: 29m 40s\n",
      "72:\tlearn: 0.7550566\ttotal: 2m 20s\tremaining: 29m 38s\n",
      "73:\tlearn: 0.7543737\ttotal: 2m 21s\tremaining: 29m 36s\n",
      "74:\tlearn: 0.7537514\ttotal: 2m 23s\tremaining: 29m 34s\n",
      "75:\tlearn: 0.7532780\ttotal: 2m 25s\tremaining: 29m 32s\n",
      "76:\tlearn: 0.7527971\ttotal: 2m 27s\tremaining: 29m 30s\n",
      "77:\tlearn: 0.7524710\ttotal: 2m 29s\tremaining: 29m 28s\n",
      "78:\tlearn: 0.7516563\ttotal: 2m 31s\tremaining: 29m 27s\n",
      "79:\tlearn: 0.7511369\ttotal: 2m 33s\tremaining: 29m 25s\n",
      "80:\tlearn: 0.7506804\ttotal: 2m 35s\tremaining: 29m 23s\n",
      "81:\tlearn: 0.7500224\ttotal: 2m 37s\tremaining: 29m 22s\n",
      "82:\tlearn: 0.7497401\ttotal: 2m 39s\tremaining: 29m 20s\n",
      "83:\tlearn: 0.7495344\ttotal: 2m 41s\tremaining: 29m 17s\n",
      "84:\tlearn: 0.7491797\ttotal: 2m 43s\tremaining: 29m 15s\n",
      "85:\tlearn: 0.7486787\ttotal: 2m 44s\tremaining: 29m 13s\n",
      "86:\tlearn: 0.7483236\ttotal: 2m 46s\tremaining: 29m 11s\n",
      "87:\tlearn: 0.7480724\ttotal: 2m 48s\tremaining: 29m 9s\n",
      "88:\tlearn: 0.7476381\ttotal: 2m 50s\tremaining: 29m 7s\n",
      "89:\tlearn: 0.7473536\ttotal: 2m 52s\tremaining: 29m 4s\n",
      "90:\tlearn: 0.7469135\ttotal: 2m 54s\tremaining: 29m 2s\n",
      "91:\tlearn: 0.7464764\ttotal: 2m 56s\tremaining: 29m\n",
      "92:\tlearn: 0.7461158\ttotal: 2m 58s\tremaining: 28m 59s\n",
      "93:\tlearn: 0.7457199\ttotal: 3m\tremaining: 28m 56s\n",
      "94:\tlearn: 0.7454100\ttotal: 3m 2s\tremaining: 28m 54s\n",
      "95:\tlearn: 0.7450451\ttotal: 3m 4s\tremaining: 28m 52s\n",
      "96:\tlearn: 0.7447238\ttotal: 3m 5s\tremaining: 28m 50s\n",
      "97:\tlearn: 0.7444484\ttotal: 3m 7s\tremaining: 28m 48s\n",
      "98:\tlearn: 0.7439785\ttotal: 3m 9s\tremaining: 28m 47s\n",
      "99:\tlearn: 0.7437149\ttotal: 3m 11s\tremaining: 28m 45s\n",
      "100:\tlearn: 0.7433404\ttotal: 3m 13s\tremaining: 28m 43s\n",
      "101:\tlearn: 0.7430279\ttotal: 3m 15s\tremaining: 28m 41s\n",
      "102:\tlearn: 0.7426521\ttotal: 3m 17s\tremaining: 28m 38s\n",
      "103:\tlearn: 0.7423339\ttotal: 3m 19s\tremaining: 28m 37s\n",
      "104:\tlearn: 0.7421679\ttotal: 3m 21s\tremaining: 28m 34s\n",
      "105:\tlearn: 0.7418314\ttotal: 3m 23s\tremaining: 28m 32s\n",
      "106:\tlearn: 0.7414614\ttotal: 3m 24s\tremaining: 28m 30s\n",
      "107:\tlearn: 0.7412001\ttotal: 3m 26s\tremaining: 28m 28s\n",
      "108:\tlearn: 0.7406681\ttotal: 3m 28s\tremaining: 28m 26s\n",
      "109:\tlearn: 0.7403382\ttotal: 3m 30s\tremaining: 28m 24s\n",
      "110:\tlearn: 0.7399891\ttotal: 3m 32s\tremaining: 28m 22s\n",
      "111:\tlearn: 0.7396504\ttotal: 3m 34s\tremaining: 28m 20s\n",
      "112:\tlearn: 0.7393520\ttotal: 3m 36s\tremaining: 28m 18s\n",
      "113:\tlearn: 0.7390046\ttotal: 3m 38s\tremaining: 28m 16s\n",
      "114:\tlearn: 0.7387480\ttotal: 3m 40s\tremaining: 28m 14s\n",
      "115:\tlearn: 0.7384630\ttotal: 3m 42s\tremaining: 28m 12s\n",
      "116:\tlearn: 0.7380659\ttotal: 3m 44s\tremaining: 28m 10s\n",
      "117:\tlearn: 0.7377620\ttotal: 3m 45s\tremaining: 28m 8s\n",
      "118:\tlearn: 0.7374092\ttotal: 3m 47s\tremaining: 28m 6s\n",
      "119:\tlearn: 0.7370250\ttotal: 3m 49s\tremaining: 28m 4s\n",
      "120:\tlearn: 0.7367699\ttotal: 3m 51s\tremaining: 28m 2s\n",
      "121:\tlearn: 0.7365076\ttotal: 3m 53s\tremaining: 28m\n",
      "122:\tlearn: 0.7362346\ttotal: 3m 55s\tremaining: 27m 58s\n",
      "123:\tlearn: 0.7359012\ttotal: 3m 57s\tremaining: 27m 56s\n",
      "124:\tlearn: 0.7354919\ttotal: 3m 59s\tremaining: 27m 54s\n",
      "125:\tlearn: 0.7351979\ttotal: 4m 1s\tremaining: 27m 52s\n",
      "126:\tlearn: 0.7349516\ttotal: 4m 3s\tremaining: 27m 50s\n",
      "127:\tlearn: 0.7346721\ttotal: 4m 4s\tremaining: 27m 48s\n",
      "128:\tlearn: 0.7343428\ttotal: 4m 6s\tremaining: 27m 46s\n",
      "129:\tlearn: 0.7341394\ttotal: 4m 8s\tremaining: 27m 45s\n",
      "130:\tlearn: 0.7338566\ttotal: 4m 10s\tremaining: 27m 43s\n",
      "131:\tlearn: 0.7335872\ttotal: 4m 12s\tremaining: 27m 41s\n",
      "132:\tlearn: 0.7332741\ttotal: 4m 14s\tremaining: 27m 39s\n",
      "133:\tlearn: 0.7329936\ttotal: 4m 16s\tremaining: 27m 36s\n",
      "134:\tlearn: 0.7327139\ttotal: 4m 18s\tremaining: 27m 34s\n",
      "135:\tlearn: 0.7324993\ttotal: 4m 20s\tremaining: 27m 32s\n",
      "136:\tlearn: 0.7322866\ttotal: 4m 21s\tremaining: 27m 30s\n",
      "137:\tlearn: 0.7319667\ttotal: 4m 23s\tremaining: 27m 28s\n",
      "138:\tlearn: 0.7317158\ttotal: 4m 25s\tremaining: 27m 26s\n",
      "139:\tlearn: 0.7313836\ttotal: 4m 27s\tremaining: 27m 24s\n",
      "140:\tlearn: 0.7311702\ttotal: 4m 29s\tremaining: 27m 22s\n",
      "141:\tlearn: 0.7309848\ttotal: 4m 31s\tremaining: 27m 20s\n",
      "142:\tlearn: 0.7307544\ttotal: 4m 33s\tremaining: 27m 18s\n",
      "143:\tlearn: 0.7304228\ttotal: 4m 35s\tremaining: 27m 16s\n",
      "144:\tlearn: 0.7301361\ttotal: 4m 37s\tremaining: 27m 14s\n",
      "145:\tlearn: 0.7299079\ttotal: 4m 39s\tremaining: 27m 12s\n",
      "146:\tlearn: 0.7297037\ttotal: 4m 40s\tremaining: 27m 10s\n",
      "147:\tlearn: 0.7294713\ttotal: 4m 42s\tremaining: 27m 8s\n",
      "148:\tlearn: 0.7292517\ttotal: 4m 44s\tremaining: 27m 6s\n",
      "149:\tlearn: 0.7290482\ttotal: 4m 46s\tremaining: 27m 4s\n",
      "150:\tlearn: 0.7288073\ttotal: 4m 48s\tremaining: 27m 2s\n",
      "151:\tlearn: 0.7285824\ttotal: 4m 50s\tremaining: 27m\n",
      "152:\tlearn: 0.7283059\ttotal: 4m 52s\tremaining: 26m 58s\n",
      "153:\tlearn: 0.7280642\ttotal: 4m 54s\tremaining: 26m 56s\n",
      "154:\tlearn: 0.7277422\ttotal: 4m 56s\tremaining: 26m 54s\n",
      "155:\tlearn: 0.7275590\ttotal: 4m 58s\tremaining: 26m 52s\n",
      "156:\tlearn: 0.7272527\ttotal: 4m 59s\tremaining: 26m 50s\n",
      "157:\tlearn: 0.7270637\ttotal: 5m 1s\tremaining: 26m 48s\n",
      "158:\tlearn: 0.7268347\ttotal: 5m 3s\tremaining: 26m 46s\n",
      "159:\tlearn: 0.7266164\ttotal: 5m 5s\tremaining: 26m 44s\n",
      "160:\tlearn: 0.7263308\ttotal: 5m 7s\tremaining: 26m 42s\n",
      "161:\tlearn: 0.7261703\ttotal: 5m 9s\tremaining: 26m 40s\n",
      "162:\tlearn: 0.7259140\ttotal: 5m 11s\tremaining: 26m 38s\n",
      "163:\tlearn: 0.7257128\ttotal: 5m 13s\tremaining: 26m 36s\n",
      "164:\tlearn: 0.7255041\ttotal: 5m 15s\tremaining: 26m 34s\n",
      "165:\tlearn: 0.7252372\ttotal: 5m 16s\tremaining: 26m 32s\n",
      "166:\tlearn: 0.7250598\ttotal: 5m 18s\tremaining: 26m 30s\n",
      "167:\tlearn: 0.7248542\ttotal: 5m 20s\tremaining: 26m 28s\n",
      "168:\tlearn: 0.7247265\ttotal: 5m 22s\tremaining: 26m 26s\n",
      "169:\tlearn: 0.7244485\ttotal: 5m 24s\tremaining: 26m 24s\n",
      "170:\tlearn: 0.7242558\ttotal: 5m 26s\tremaining: 26m 22s\n",
      "171:\tlearn: 0.7241008\ttotal: 5m 28s\tremaining: 26m 20s\n",
      "172:\tlearn: 0.7239380\ttotal: 5m 30s\tremaining: 26m 18s\n",
      "173:\tlearn: 0.7237809\ttotal: 5m 32s\tremaining: 26m 16s\n",
      "174:\tlearn: 0.7236285\ttotal: 5m 33s\tremaining: 26m 14s\n",
      "175:\tlearn: 0.7233630\ttotal: 5m 35s\tremaining: 26m 12s\n",
      "176:\tlearn: 0.7230859\ttotal: 5m 37s\tremaining: 26m 10s\n",
      "177:\tlearn: 0.7229357\ttotal: 5m 39s\tremaining: 26m 8s\n",
      "178:\tlearn: 0.7227773\ttotal: 5m 41s\tremaining: 26m 6s\n",
      "179:\tlearn: 0.7226111\ttotal: 5m 43s\tremaining: 26m 4s\n",
      "180:\tlearn: 0.7224612\ttotal: 5m 45s\tremaining: 26m 2s\n",
      "181:\tlearn: 0.7222967\ttotal: 5m 47s\tremaining: 26m\n",
      "182:\tlearn: 0.7221753\ttotal: 5m 49s\tremaining: 25m 58s\n",
      "183:\tlearn: 0.7220438\ttotal: 5m 50s\tremaining: 25m 56s\n",
      "184:\tlearn: 0.7218684\ttotal: 5m 52s\tremaining: 25m 54s\n",
      "185:\tlearn: 0.7217041\ttotal: 5m 54s\tremaining: 25m 52s\n",
      "186:\tlearn: 0.7215118\ttotal: 5m 56s\tremaining: 25m 50s\n",
      "187:\tlearn: 0.7213640\ttotal: 5m 58s\tremaining: 25m 48s\n",
      "188:\tlearn: 0.7212254\ttotal: 6m\tremaining: 25m 46s\n",
      "189:\tlearn: 0.7210260\ttotal: 6m 2s\tremaining: 25m 44s\n",
      "190:\tlearn: 0.7208715\ttotal: 6m 4s\tremaining: 25m 42s\n",
      "191:\tlearn: 0.7206160\ttotal: 6m 6s\tremaining: 25m 40s\n",
      "192:\tlearn: 0.7204818\ttotal: 6m 7s\tremaining: 25m 38s\n",
      "193:\tlearn: 0.7202617\ttotal: 6m 9s\tremaining: 25m 36s\n",
      "194:\tlearn: 0.7201160\ttotal: 6m 11s\tremaining: 25m 34s\n",
      "195:\tlearn: 0.7199705\ttotal: 6m 13s\tremaining: 25m 32s\n",
      "196:\tlearn: 0.7198612\ttotal: 6m 15s\tremaining: 25m 30s\n",
      "197:\tlearn: 0.7197303\ttotal: 6m 17s\tremaining: 25m 28s\n",
      "198:\tlearn: 0.7195002\ttotal: 6m 19s\tremaining: 25m 26s\n",
      "199:\tlearn: 0.7193708\ttotal: 6m 21s\tremaining: 25m 24s\n",
      "200:\tlearn: 0.7191889\ttotal: 6m 22s\tremaining: 25m 22s\n",
      "201:\tlearn: 0.7189569\ttotal: 6m 24s\tremaining: 25m 20s\n",
      "202:\tlearn: 0.7186850\ttotal: 6m 26s\tremaining: 25m 18s\n",
      "203:\tlearn: 0.7185334\ttotal: 6m 28s\tremaining: 25m 16s\n",
      "204:\tlearn: 0.7183901\ttotal: 6m 30s\tremaining: 25m 14s\n",
      "205:\tlearn: 0.7182545\ttotal: 6m 32s\tremaining: 25m 12s\n",
      "206:\tlearn: 0.7180482\ttotal: 6m 34s\tremaining: 25m 10s\n",
      "207:\tlearn: 0.7178954\ttotal: 6m 36s\tremaining: 25m 8s\n",
      "208:\tlearn: 0.7177593\ttotal: 6m 38s\tremaining: 25m 6s\n",
      "209:\tlearn: 0.7176264\ttotal: 6m 39s\tremaining: 25m 4s\n",
      "210:\tlearn: 0.7174560\ttotal: 6m 41s\tremaining: 25m 2s\n",
      "211:\tlearn: 0.7173213\ttotal: 6m 43s\tremaining: 25m\n",
      "212:\tlearn: 0.7171307\ttotal: 6m 45s\tremaining: 24m 58s\n",
      "213:\tlearn: 0.7170068\ttotal: 6m 47s\tremaining: 24m 56s\n",
      "214:\tlearn: 0.7169083\ttotal: 6m 49s\tremaining: 24m 54s\n",
      "215:\tlearn: 0.7167500\ttotal: 6m 51s\tremaining: 24m 52s\n",
      "216:\tlearn: 0.7166059\ttotal: 6m 53s\tremaining: 24m 50s\n",
      "217:\tlearn: 0.7164745\ttotal: 6m 54s\tremaining: 24m 48s\n",
      "218:\tlearn: 0.7163689\ttotal: 6m 56s\tremaining: 24m 46s\n",
      "219:\tlearn: 0.7162279\ttotal: 6m 58s\tremaining: 24m 44s\n",
      "220:\tlearn: 0.7161210\ttotal: 7m\tremaining: 24m 42s\n",
      "221:\tlearn: 0.7160082\ttotal: 7m 2s\tremaining: 24m 40s\n",
      "222:\tlearn: 0.7158968\ttotal: 7m 4s\tremaining: 24m 38s\n",
      "223:\tlearn: 0.7157370\ttotal: 7m 6s\tremaining: 24m 36s\n",
      "224:\tlearn: 0.7155674\ttotal: 7m 8s\tremaining: 24m 34s\n",
      "225:\tlearn: 0.7154420\ttotal: 7m 9s\tremaining: 24m 32s\n",
      "226:\tlearn: 0.7153283\ttotal: 7m 11s\tremaining: 24m 30s\n",
      "227:\tlearn: 0.7152519\ttotal: 7m 13s\tremaining: 24m 28s\n",
      "228:\tlearn: 0.7151407\ttotal: 7m 15s\tremaining: 24m 26s\n",
      "229:\tlearn: 0.7150452\ttotal: 7m 17s\tremaining: 24m 24s\n",
      "230:\tlearn: 0.7149526\ttotal: 7m 19s\tremaining: 24m 22s\n",
      "231:\tlearn: 0.7148502\ttotal: 7m 21s\tremaining: 24m 20s\n",
      "232:\tlearn: 0.7146899\ttotal: 7m 23s\tremaining: 24m 18s\n",
      "233:\tlearn: 0.7146077\ttotal: 7m 24s\tremaining: 24m 16s\n",
      "234:\tlearn: 0.7144660\ttotal: 7m 26s\tremaining: 24m 14s\n",
      "235:\tlearn: 0.7142554\ttotal: 7m 28s\tremaining: 24m 12s\n",
      "236:\tlearn: 0.7141269\ttotal: 7m 30s\tremaining: 24m 10s\n",
      "237:\tlearn: 0.7140263\ttotal: 7m 32s\tremaining: 24m 8s\n",
      "238:\tlearn: 0.7139204\ttotal: 7m 34s\tremaining: 24m 6s\n",
      "239:\tlearn: 0.7138391\ttotal: 7m 36s\tremaining: 24m 4s\n",
      "240:\tlearn: 0.7137487\ttotal: 7m 38s\tremaining: 24m 2s\n",
      "241:\tlearn: 0.7136801\ttotal: 7m 39s\tremaining: 24m\n",
      "242:\tlearn: 0.7134979\ttotal: 7m 41s\tremaining: 23m 58s\n",
      "243:\tlearn: 0.7133530\ttotal: 7m 43s\tremaining: 23m 56s\n",
      "244:\tlearn: 0.7133012\ttotal: 7m 45s\tremaining: 23m 54s\n",
      "245:\tlearn: 0.7131659\ttotal: 7m 47s\tremaining: 23m 52s\n",
      "246:\tlearn: 0.7130307\ttotal: 7m 49s\tremaining: 23m 50s\n",
      "247:\tlearn: 0.7129017\ttotal: 7m 51s\tremaining: 23m 48s\n",
      "248:\tlearn: 0.7128017\ttotal: 7m 53s\tremaining: 23m 46s\n",
      "249:\tlearn: 0.7127349\ttotal: 7m 54s\tremaining: 23m 44s\n",
      "250:\tlearn: 0.7126349\ttotal: 7m 56s\tremaining: 23m 43s\n",
      "251:\tlearn: 0.7125036\ttotal: 7m 58s\tremaining: 23m 41s\n",
      "252:\tlearn: 0.7124202\ttotal: 8m\tremaining: 23m 39s\n",
      "253:\tlearn: 0.7123073\ttotal: 8m 2s\tremaining: 23m 37s\n",
      "254:\tlearn: 0.7121900\ttotal: 8m 4s\tremaining: 23m 35s\n",
      "255:\tlearn: 0.7120495\ttotal: 8m 6s\tremaining: 23m 33s\n",
      "256:\tlearn: 0.7119760\ttotal: 8m 8s\tremaining: 23m 31s\n",
      "257:\tlearn: 0.7118703\ttotal: 8m 10s\tremaining: 23m 29s\n",
      "258:\tlearn: 0.7117885\ttotal: 8m 11s\tremaining: 23m 27s\n",
      "259:\tlearn: 0.7117184\ttotal: 8m 13s\tremaining: 23m 25s\n",
      "260:\tlearn: 0.7115739\ttotal: 8m 15s\tremaining: 23m 23s\n",
      "261:\tlearn: 0.7115101\ttotal: 8m 17s\tremaining: 23m 21s\n",
      "262:\tlearn: 0.7114263\ttotal: 8m 19s\tremaining: 23m 19s\n",
      "263:\tlearn: 0.7113378\ttotal: 8m 21s\tremaining: 23m 17s\n",
      "264:\tlearn: 0.7111933\ttotal: 8m 23s\tremaining: 23m 15s\n",
      "265:\tlearn: 0.7111254\ttotal: 8m 24s\tremaining: 23m 13s\n",
      "266:\tlearn: 0.7110162\ttotal: 8m 26s\tremaining: 23m 11s\n",
      "267:\tlearn: 0.7109300\ttotal: 8m 28s\tremaining: 23m 9s\n",
      "268:\tlearn: 0.7108183\ttotal: 8m 30s\tremaining: 23m 7s\n",
      "269:\tlearn: 0.7107205\ttotal: 8m 32s\tremaining: 23m 5s\n",
      "270:\tlearn: 0.7105888\ttotal: 8m 34s\tremaining: 23m 3s\n",
      "271:\tlearn: 0.7105019\ttotal: 8m 36s\tremaining: 23m 1s\n",
      "272:\tlearn: 0.7103508\ttotal: 8m 38s\tremaining: 22m 59s\n",
      "273:\tlearn: 0.7102640\ttotal: 8m 40s\tremaining: 22m 57s\n",
      "274:\tlearn: 0.7101651\ttotal: 8m 41s\tremaining: 22m 56s\n",
      "275:\tlearn: 0.7100334\ttotal: 8m 43s\tremaining: 22m 54s\n",
      "276:\tlearn: 0.7099244\ttotal: 8m 45s\tremaining: 22m 52s\n",
      "277:\tlearn: 0.7098544\ttotal: 8m 47s\tremaining: 22m 50s\n",
      "278:\tlearn: 0.7097239\ttotal: 8m 49s\tremaining: 22m 48s\n",
      "279:\tlearn: 0.7096495\ttotal: 8m 51s\tremaining: 22m 46s\n",
      "280:\tlearn: 0.7095493\ttotal: 8m 53s\tremaining: 22m 44s\n",
      "281:\tlearn: 0.7094504\ttotal: 8m 55s\tremaining: 22m 42s\n",
      "282:\tlearn: 0.7094128\ttotal: 8m 56s\tremaining: 22m 40s\n",
      "283:\tlearn: 0.7093624\ttotal: 8m 58s\tremaining: 22m 38s\n",
      "284:\tlearn: 0.7091455\ttotal: 9m\tremaining: 22m 36s\n",
      "285:\tlearn: 0.7090870\ttotal: 9m 2s\tremaining: 22m 34s\n",
      "286:\tlearn: 0.7090423\ttotal: 9m 4s\tremaining: 22m 32s\n",
      "287:\tlearn: 0.7089853\ttotal: 9m 6s\tremaining: 22m 30s\n",
      "288:\tlearn: 0.7089327\ttotal: 9m 8s\tremaining: 22m 28s\n",
      "289:\tlearn: 0.7088673\ttotal: 9m 10s\tremaining: 22m 26s\n",
      "290:\tlearn: 0.7087526\ttotal: 9m 11s\tremaining: 22m 24s\n",
      "291:\tlearn: 0.7086347\ttotal: 9m 13s\tremaining: 22m 22s\n",
      "292:\tlearn: 0.7085499\ttotal: 9m 15s\tremaining: 22m 20s\n",
      "293:\tlearn: 0.7085058\ttotal: 9m 17s\tremaining: 22m 18s\n",
      "294:\tlearn: 0.7084314\ttotal: 9m 19s\tremaining: 22m 16s\n",
      "295:\tlearn: 0.7083553\ttotal: 9m 21s\tremaining: 22m 14s\n",
      "296:\tlearn: 0.7082222\ttotal: 9m 23s\tremaining: 22m 13s\n",
      "297:\tlearn: 0.7081389\ttotal: 9m 25s\tremaining: 22m 11s\n",
      "298:\tlearn: 0.7080478\ttotal: 9m 26s\tremaining: 22m 9s\n",
      "299:\tlearn: 0.7079454\ttotal: 9m 28s\tremaining: 22m 7s\n",
      "300:\tlearn: 0.7078591\ttotal: 9m 30s\tremaining: 22m 5s\n",
      "301:\tlearn: 0.7078186\ttotal: 9m 32s\tremaining: 22m 3s\n",
      "302:\tlearn: 0.7077327\ttotal: 9m 34s\tremaining: 22m 1s\n",
      "303:\tlearn: 0.7076448\ttotal: 9m 36s\tremaining: 21m 59s\n",
      "304:\tlearn: 0.7076158\ttotal: 9m 37s\tremaining: 21m 57s\n",
      "305:\tlearn: 0.7075612\ttotal: 9m 39s\tremaining: 21m 55s\n",
      "306:\tlearn: 0.7074844\ttotal: 9m 41s\tremaining: 21m 53s\n",
      "307:\tlearn: 0.7073661\ttotal: 9m 43s\tremaining: 21m 51s\n",
      "308:\tlearn: 0.7072501\ttotal: 9m 45s\tremaining: 21m 49s\n",
      "309:\tlearn: 0.7071493\ttotal: 9m 47s\tremaining: 21m 47s\n",
      "310:\tlearn: 0.7071290\ttotal: 9m 49s\tremaining: 21m 45s\n",
      "311:\tlearn: 0.7070884\ttotal: 9m 51s\tremaining: 21m 43s\n",
      "312:\tlearn: 0.7070374\ttotal: 9m 52s\tremaining: 21m 41s\n",
      "313:\tlearn: 0.7069432\ttotal: 9m 54s\tremaining: 21m 39s\n",
      "314:\tlearn: 0.7069194\ttotal: 9m 56s\tremaining: 21m 37s\n",
      "315:\tlearn: 0.7068521\ttotal: 9m 58s\tremaining: 21m 35s\n",
      "316:\tlearn: 0.7067786\ttotal: 10m\tremaining: 21m 33s\n",
      "317:\tlearn: 0.7066638\ttotal: 10m 2s\tremaining: 21m 31s\n",
      "318:\tlearn: 0.7066143\ttotal: 10m 4s\tremaining: 21m 29s\n",
      "319:\tlearn: 0.7065740\ttotal: 10m 5s\tremaining: 21m 27s\n",
      "320:\tlearn: 0.7064787\ttotal: 10m 7s\tremaining: 21m 25s\n",
      "321:\tlearn: 0.7064078\ttotal: 10m 9s\tremaining: 21m 23s\n",
      "322:\tlearn: 0.7063153\ttotal: 10m 11s\tremaining: 21m 21s\n",
      "323:\tlearn: 0.7062265\ttotal: 10m 13s\tremaining: 21m 19s\n",
      "324:\tlearn: 0.7061738\ttotal: 10m 15s\tremaining: 21m 17s\n",
      "325:\tlearn: 0.7061268\ttotal: 10m 17s\tremaining: 21m 15s\n",
      "326:\tlearn: 0.7060585\ttotal: 10m 18s\tremaining: 21m 13s\n",
      "327:\tlearn: 0.7059240\ttotal: 10m 20s\tremaining: 21m 11s\n",
      "328:\tlearn: 0.7058862\ttotal: 10m 22s\tremaining: 21m 9s\n",
      "329:\tlearn: 0.7058277\ttotal: 10m 24s\tremaining: 21m 8s\n",
      "330:\tlearn: 0.7057569\ttotal: 10m 26s\tremaining: 21m 6s\n",
      "331:\tlearn: 0.7057155\ttotal: 10m 28s\tremaining: 21m 4s\n",
      "332:\tlearn: 0.7056825\ttotal: 10m 30s\tremaining: 21m 2s\n",
      "333:\tlearn: 0.7056140\ttotal: 10m 32s\tremaining: 21m\n",
      "334:\tlearn: 0.7055267\ttotal: 10m 33s\tremaining: 20m 58s\n",
      "335:\tlearn: 0.7054446\ttotal: 10m 35s\tremaining: 20m 56s\n",
      "336:\tlearn: 0.7053150\ttotal: 10m 37s\tremaining: 20m 54s\n",
      "337:\tlearn: 0.7052526\ttotal: 10m 39s\tremaining: 20m 52s\n",
      "338:\tlearn: 0.7051961\ttotal: 10m 41s\tremaining: 20m 50s\n",
      "339:\tlearn: 0.7050973\ttotal: 10m 43s\tremaining: 20m 48s\n",
      "340:\tlearn: 0.7050187\ttotal: 10m 45s\tremaining: 20m 46s\n",
      "341:\tlearn: 0.7049787\ttotal: 10m 47s\tremaining: 20m 44s\n",
      "342:\tlearn: 0.7049365\ttotal: 10m 48s\tremaining: 20m 42s\n",
      "343:\tlearn: 0.7048464\ttotal: 10m 50s\tremaining: 20m 40s\n",
      "344:\tlearn: 0.7047660\ttotal: 10m 52s\tremaining: 20m 39s\n",
      "345:\tlearn: 0.7047278\ttotal: 10m 54s\tremaining: 20m 37s\n",
      "346:\tlearn: 0.7046778\ttotal: 10m 56s\tremaining: 20m 35s\n",
      "347:\tlearn: 0.7045871\ttotal: 10m 58s\tremaining: 20m 33s\n",
      "348:\tlearn: 0.7045425\ttotal: 11m\tremaining: 20m 31s\n",
      "349:\tlearn: 0.7044807\ttotal: 11m 1s\tremaining: 20m 29s\n",
      "350:\tlearn: 0.7044135\ttotal: 11m 3s\tremaining: 20m 27s\n",
      "351:\tlearn: 0.7043456\ttotal: 11m 5s\tremaining: 20m 25s\n",
      "352:\tlearn: 0.7042828\ttotal: 11m 7s\tremaining: 20m 23s\n",
      "353:\tlearn: 0.7042371\ttotal: 11m 9s\tremaining: 20m 21s\n",
      "354:\tlearn: 0.7042060\ttotal: 11m 11s\tremaining: 20m 19s\n",
      "355:\tlearn: 0.7040804\ttotal: 11m 13s\tremaining: 20m 17s\n",
      "356:\tlearn: 0.7040060\ttotal: 11m 14s\tremaining: 20m 15s\n",
      "357:\tlearn: 0.7039104\ttotal: 11m 16s\tremaining: 20m 13s\n",
      "358:\tlearn: 0.7038180\ttotal: 11m 18s\tremaining: 20m 11s\n",
      "359:\tlearn: 0.7037328\ttotal: 11m 20s\tremaining: 20m 9s\n",
      "360:\tlearn: 0.7037027\ttotal: 11m 22s\tremaining: 20m 7s\n",
      "361:\tlearn: 0.7036629\ttotal: 11m 24s\tremaining: 20m 5s\n",
      "362:\tlearn: 0.7036541\ttotal: 11m 25s\tremaining: 20m 3s\n",
      "363:\tlearn: 0.7036250\ttotal: 11m 27s\tremaining: 20m 1s\n",
      "364:\tlearn: 0.7035768\ttotal: 11m 29s\tremaining: 19m 59s\n",
      "365:\tlearn: 0.7035510\ttotal: 11m 31s\tremaining: 19m 57s\n",
      "366:\tlearn: 0.7034849\ttotal: 11m 33s\tremaining: 19m 55s\n",
      "367:\tlearn: 0.7033485\ttotal: 11m 35s\tremaining: 19m 53s\n",
      "368:\tlearn: 0.7033201\ttotal: 11m 37s\tremaining: 19m 51s\n",
      "369:\tlearn: 0.7032247\ttotal: 11m 38s\tremaining: 19m 49s\n",
      "370:\tlearn: 0.7031764\ttotal: 11m 40s\tremaining: 19m 47s\n",
      "371:\tlearn: 0.7031598\ttotal: 11m 42s\tremaining: 19m 46s\n",
      "372:\tlearn: 0.7030716\ttotal: 11m 44s\tremaining: 19m 44s\n",
      "373:\tlearn: 0.7030371\ttotal: 11m 46s\tremaining: 19m 42s\n",
      "374:\tlearn: 0.7030128\ttotal: 11m 48s\tremaining: 19m 40s\n",
      "375:\tlearn: 0.7029090\ttotal: 11m 50s\tremaining: 19m 38s\n",
      "376:\tlearn: 0.7028628\ttotal: 11m 51s\tremaining: 19m 36s\n",
      "377:\tlearn: 0.7027953\ttotal: 11m 53s\tremaining: 19m 34s\n",
      "378:\tlearn: 0.7027453\ttotal: 11m 55s\tremaining: 19m 32s\n",
      "379:\tlearn: 0.7026951\ttotal: 11m 57s\tremaining: 19m 30s\n",
      "380:\tlearn: 0.7026246\ttotal: 11m 59s\tremaining: 19m 28s\n",
      "381:\tlearn: 0.7026033\ttotal: 12m 1s\tremaining: 19m 26s\n",
      "382:\tlearn: 0.7025568\ttotal: 12m 3s\tremaining: 19m 24s\n",
      "383:\tlearn: 0.7024999\ttotal: 12m 4s\tremaining: 19m 22s\n",
      "384:\tlearn: 0.7024967\ttotal: 12m 6s\tremaining: 19m 21s\n",
      "385:\tlearn: 0.7024657\ttotal: 12m 8s\tremaining: 19m 19s\n",
      "386:\tlearn: 0.7024172\ttotal: 12m 10s\tremaining: 19m 17s\n",
      "387:\tlearn: 0.7023699\ttotal: 12m 12s\tremaining: 19m 15s\n",
      "388:\tlearn: 0.7023446\ttotal: 12m 14s\tremaining: 19m 13s\n",
      "389:\tlearn: 0.7022846\ttotal: 12m 16s\tremaining: 19m 11s\n",
      "390:\tlearn: 0.7022530\ttotal: 12m 18s\tremaining: 19m 9s\n",
      "391:\tlearn: 0.7022162\ttotal: 12m 19s\tremaining: 19m 7s\n",
      "392:\tlearn: 0.7021553\ttotal: 12m 21s\tremaining: 19m 5s\n",
      "393:\tlearn: 0.7021109\ttotal: 12m 23s\tremaining: 19m 3s\n",
      "394:\tlearn: 0.7020403\ttotal: 12m 25s\tremaining: 19m 1s\n",
      "395:\tlearn: 0.7019753\ttotal: 12m 27s\tremaining: 18m 59s\n",
      "396:\tlearn: 0.7019334\ttotal: 12m 29s\tremaining: 18m 58s\n",
      "397:\tlearn: 0.7018880\ttotal: 12m 31s\tremaining: 18m 56s\n",
      "398:\tlearn: 0.7017682\ttotal: 12m 33s\tremaining: 18m 54s\n",
      "399:\tlearn: 0.7017099\ttotal: 12m 34s\tremaining: 18m 52s\n",
      "400:\tlearn: 0.7016992\ttotal: 12m 36s\tremaining: 18m 50s\n",
      "401:\tlearn: 0.7016551\ttotal: 12m 38s\tremaining: 18m 48s\n",
      "402:\tlearn: 0.7015757\ttotal: 12m 40s\tremaining: 18m 46s\n",
      "403:\tlearn: 0.7015489\ttotal: 12m 42s\tremaining: 18m 44s\n",
      "404:\tlearn: 0.7014991\ttotal: 12m 44s\tremaining: 18m 42s\n",
      "405:\tlearn: 0.7014594\ttotal: 12m 46s\tremaining: 18m 40s\n",
      "406:\tlearn: 0.7014371\ttotal: 12m 47s\tremaining: 18m 38s\n",
      "407:\tlearn: 0.7013861\ttotal: 12m 49s\tremaining: 18m 36s\n",
      "408:\tlearn: 0.7013340\ttotal: 12m 51s\tremaining: 18m 34s\n",
      "409:\tlearn: 0.7012883\ttotal: 12m 53s\tremaining: 18m 33s\n",
      "410:\tlearn: 0.7012655\ttotal: 12m 55s\tremaining: 18m 31s\n",
      "411:\tlearn: 0.7011722\ttotal: 12m 57s\tremaining: 18m 29s\n",
      "412:\tlearn: 0.7010565\ttotal: 12m 59s\tremaining: 18m 27s\n",
      "413:\tlearn: 0.7010302\ttotal: 13m\tremaining: 18m 25s\n",
      "414:\tlearn: 0.7010192\ttotal: 13m 2s\tremaining: 18m 23s\n",
      "415:\tlearn: 0.7010066\ttotal: 13m 4s\tremaining: 18m 21s\n",
      "416:\tlearn: 0.7009827\ttotal: 13m 6s\tremaining: 18m 19s\n",
      "417:\tlearn: 0.7009207\ttotal: 13m 8s\tremaining: 18m 17s\n",
      "418:\tlearn: 0.7008587\ttotal: 13m 10s\tremaining: 18m 15s\n",
      "419:\tlearn: 0.7008076\ttotal: 13m 12s\tremaining: 18m 13s\n",
      "420:\tlearn: 0.7007245\ttotal: 13m 14s\tremaining: 18m 12s\n",
      "421:\tlearn: 0.7006958\ttotal: 13m 15s\tremaining: 18m 10s\n",
      "422:\tlearn: 0.7006329\ttotal: 13m 17s\tremaining: 18m 8s\n",
      "423:\tlearn: 0.7006020\ttotal: 13m 19s\tremaining: 18m 6s\n",
      "424:\tlearn: 0.7005396\ttotal: 13m 21s\tremaining: 18m 4s\n",
      "425:\tlearn: 0.7005089\ttotal: 13m 23s\tremaining: 18m 2s\n",
      "426:\tlearn: 0.7004330\ttotal: 13m 25s\tremaining: 18m\n",
      "427:\tlearn: 0.7003123\ttotal: 13m 27s\tremaining: 17m 58s\n",
      "428:\tlearn: 0.7002493\ttotal: 13m 29s\tremaining: 17m 57s\n",
      "429:\tlearn: 0.7002196\ttotal: 13m 33s\tremaining: 17m 57s\n",
      "430:\tlearn: 0.7001977\ttotal: 13m 36s\tremaining: 17m 58s\n",
      "431:\tlearn: 0.7001800\ttotal: 13m 39s\tremaining: 17m 57s\n",
      "432:\tlearn: 0.7001328\ttotal: 13m 41s\tremaining: 17m 55s\n",
      "433:\tlearn: 0.7000966\ttotal: 13m 43s\tremaining: 17m 53s\n",
      "434:\tlearn: 0.7000354\ttotal: 13m 45s\tremaining: 17m 51s\n",
      "435:\tlearn: 0.7000105\ttotal: 13m 47s\tremaining: 17m 49s\n",
      "436:\tlearn: 0.7000063\ttotal: 13m 48s\tremaining: 17m 48s\n",
      "437:\tlearn: 0.6999788\ttotal: 13m 50s\tremaining: 17m 46s\n",
      "438:\tlearn: 0.6999728\ttotal: 13m 52s\tremaining: 17m 44s\n",
      "439:\tlearn: 0.6999463\ttotal: 13m 54s\tremaining: 17m 42s\n",
      "440:\tlearn: 0.6999119\ttotal: 13m 56s\tremaining: 17m 40s\n",
      "441:\tlearn: 0.6998880\ttotal: 13m 58s\tremaining: 17m 38s\n",
      "442:\tlearn: 0.6998742\ttotal: 14m\tremaining: 17m 36s\n",
      "443:\tlearn: 0.6998615\ttotal: 14m 1s\tremaining: 17m 34s\n",
      "444:\tlearn: 0.6998182\ttotal: 14m 3s\tremaining: 17m 32s\n",
      "445:\tlearn: 0.6997900\ttotal: 14m 5s\tremaining: 17m 30s\n",
      "446:\tlearn: 0.6997311\ttotal: 14m 7s\tremaining: 17m 28s\n",
      "447:\tlearn: 0.6997218\ttotal: 14m 9s\tremaining: 17m 26s\n",
      "448:\tlearn: 0.6996710\ttotal: 14m 11s\tremaining: 17m 24s\n",
      "449:\tlearn: 0.6996333\ttotal: 14m 13s\tremaining: 17m 22s\n",
      "450:\tlearn: 0.6995761\ttotal: 14m 14s\tremaining: 17m 20s\n",
      "451:\tlearn: 0.6995655\ttotal: 14m 16s\tremaining: 17m 18s\n",
      "452:\tlearn: 0.6995541\ttotal: 14m 18s\tremaining: 17m 16s\n",
      "453:\tlearn: 0.6994825\ttotal: 14m 20s\tremaining: 17m 14s\n",
      "454:\tlearn: 0.6994746\ttotal: 14m 22s\tremaining: 17m 12s\n",
      "455:\tlearn: 0.6994358\ttotal: 14m 24s\tremaining: 17m 10s\n",
      "456:\tlearn: 0.6994071\ttotal: 14m 26s\tremaining: 17m 8s\n",
      "457:\tlearn: 0.6993619\ttotal: 14m 27s\tremaining: 17m 7s\n",
      "458:\tlearn: 0.6993253\ttotal: 14m 29s\tremaining: 17m 5s\n",
      "459:\tlearn: 0.6993014\ttotal: 14m 31s\tremaining: 17m 3s\n",
      "460:\tlearn: 0.6992232\ttotal: 14m 33s\tremaining: 17m 1s\n",
      "461:\tlearn: 0.6991943\ttotal: 14m 35s\tremaining: 16m 59s\n",
      "462:\tlearn: 0.6991293\ttotal: 14m 37s\tremaining: 16m 57s\n",
      "463:\tlearn: 0.6991000\ttotal: 14m 39s\tremaining: 16m 55s\n",
      "464:\tlearn: 0.6990656\ttotal: 14m 40s\tremaining: 16m 53s\n",
      "465:\tlearn: 0.6989964\ttotal: 14m 42s\tremaining: 16m 51s\n",
      "466:\tlearn: 0.6989447\ttotal: 14m 44s\tremaining: 16m 49s\n",
      "467:\tlearn: 0.6989232\ttotal: 14m 46s\tremaining: 16m 47s\n",
      "468:\tlearn: 0.6989100\ttotal: 14m 48s\tremaining: 16m 45s\n",
      "469:\tlearn: 0.6988359\ttotal: 14m 50s\tremaining: 16m 43s\n",
      "470:\tlearn: 0.6988226\ttotal: 14m 52s\tremaining: 16m 41s\n",
      "471:\tlearn: 0.6988111\ttotal: 14m 53s\tremaining: 16m 39s\n",
      "472:\tlearn: 0.6988074\ttotal: 14m 55s\tremaining: 16m 38s\n",
      "473:\tlearn: 0.6987791\ttotal: 14m 57s\tremaining: 16m 36s\n",
      "474:\tlearn: 0.6986769\ttotal: 14m 59s\tremaining: 16m 34s\n",
      "475:\tlearn: 0.6986633\ttotal: 15m 1s\tremaining: 16m 32s\n",
      "476:\tlearn: 0.6986462\ttotal: 15m 3s\tremaining: 16m 30s\n",
      "477:\tlearn: 0.6986158\ttotal: 15m 4s\tremaining: 16m 28s\n",
      "478:\tlearn: 0.6985447\ttotal: 15m 6s\tremaining: 16m 26s\n",
      "479:\tlearn: 0.6985116\ttotal: 15m 8s\tremaining: 16m 24s\n",
      "480:\tlearn: 0.6984745\ttotal: 15m 10s\tremaining: 16m 22s\n",
      "481:\tlearn: 0.6984220\ttotal: 15m 12s\tremaining: 16m 20s\n",
      "482:\tlearn: 0.6984011\ttotal: 15m 14s\tremaining: 16m 18s\n",
      "483:\tlearn: 0.6983654\ttotal: 15m 16s\tremaining: 16m 16s\n",
      "484:\tlearn: 0.6983417\ttotal: 15m 17s\tremaining: 16m 14s\n",
      "485:\tlearn: 0.6983222\ttotal: 15m 19s\tremaining: 16m 12s\n",
      "486:\tlearn: 0.6983109\ttotal: 15m 21s\tremaining: 16m 10s\n",
      "487:\tlearn: 0.6982824\ttotal: 15m 23s\tremaining: 16m 8s\n",
      "488:\tlearn: 0.6982656\ttotal: 15m 25s\tremaining: 16m 6s\n",
      "489:\tlearn: 0.6982195\ttotal: 15m 27s\tremaining: 16m 5s\n",
      "490:\tlearn: 0.6981971\ttotal: 15m 29s\tremaining: 16m 3s\n",
      "491:\tlearn: 0.6981748\ttotal: 15m 30s\tremaining: 16m 1s\n",
      "492:\tlearn: 0.6981343\ttotal: 15m 32s\tremaining: 15m 59s\n",
      "493:\tlearn: 0.6981172\ttotal: 15m 34s\tremaining: 15m 57s\n",
      "494:\tlearn: 0.6980993\ttotal: 15m 36s\tremaining: 15m 55s\n",
      "495:\tlearn: 0.6980475\ttotal: 15m 38s\tremaining: 15m 53s\n",
      "496:\tlearn: 0.6980288\ttotal: 15m 40s\tremaining: 15m 51s\n",
      "497:\tlearn: 0.6980042\ttotal: 15m 42s\tremaining: 15m 49s\n",
      "498:\tlearn: 0.6979873\ttotal: 15m 43s\tremaining: 15m 47s\n",
      "499:\tlearn: 0.6979683\ttotal: 15m 45s\tremaining: 15m 45s\n",
      "500:\tlearn: 0.6979498\ttotal: 15m 47s\tremaining: 15m 43s\n",
      "501:\tlearn: 0.6979430\ttotal: 15m 49s\tremaining: 15m 41s\n",
      "502:\tlearn: 0.6978950\ttotal: 15m 51s\tremaining: 15m 39s\n",
      "503:\tlearn: 0.6978792\ttotal: 15m 53s\tremaining: 15m 38s\n",
      "504:\tlearn: 0.6978443\ttotal: 15m 55s\tremaining: 15m 36s\n",
      "505:\tlearn: 0.6978272\ttotal: 15m 56s\tremaining: 15m 34s\n",
      "506:\tlearn: 0.6978059\ttotal: 15m 58s\tremaining: 15m 32s\n",
      "507:\tlearn: 0.6977843\ttotal: 16m\tremaining: 15m 30s\n",
      "508:\tlearn: 0.6977426\ttotal: 16m 2s\tremaining: 15m 28s\n",
      "509:\tlearn: 0.6977193\ttotal: 16m 4s\tremaining: 15m 26s\n",
      "510:\tlearn: 0.6976682\ttotal: 16m 6s\tremaining: 15m 24s\n",
      "511:\tlearn: 0.6976486\ttotal: 16m 8s\tremaining: 15m 22s\n",
      "512:\tlearn: 0.6976159\ttotal: 16m 9s\tremaining: 15m 20s\n",
      "513:\tlearn: 0.6975539\ttotal: 16m 11s\tremaining: 15m 18s\n",
      "514:\tlearn: 0.6975144\ttotal: 16m 13s\tremaining: 15m 16s\n",
      "515:\tlearn: 0.6974859\ttotal: 16m 15s\tremaining: 15m 15s\n",
      "516:\tlearn: 0.6974642\ttotal: 16m 17s\tremaining: 15m 13s\n",
      "517:\tlearn: 0.6974441\ttotal: 16m 19s\tremaining: 15m 11s\n",
      "518:\tlearn: 0.6974298\ttotal: 16m 21s\tremaining: 15m 9s\n",
      "519:\tlearn: 0.6974133\ttotal: 16m 22s\tremaining: 15m 7s\n",
      "520:\tlearn: 0.6973976\ttotal: 16m 24s\tremaining: 15m 5s\n",
      "521:\tlearn: 0.6973737\ttotal: 16m 26s\tremaining: 15m 3s\n",
      "522:\tlearn: 0.6973664\ttotal: 16m 28s\tremaining: 15m 1s\n",
      "523:\tlearn: 0.6973559\ttotal: 16m 30s\tremaining: 14m 59s\n",
      "524:\tlearn: 0.6973467\ttotal: 16m 32s\tremaining: 14m 57s\n",
      "525:\tlearn: 0.6972724\ttotal: 16m 34s\tremaining: 14m 55s\n",
      "526:\tlearn: 0.6972327\ttotal: 16m 35s\tremaining: 14m 53s\n",
      "527:\tlearn: 0.6971774\ttotal: 16m 37s\tremaining: 14m 51s\n",
      "528:\tlearn: 0.6971508\ttotal: 16m 39s\tremaining: 14m 50s\n",
      "529:\tlearn: 0.6970775\ttotal: 16m 41s\tremaining: 14m 48s\n",
      "530:\tlearn: 0.6970730\ttotal: 16m 43s\tremaining: 14m 46s\n",
      "531:\tlearn: 0.6970208\ttotal: 16m 45s\tremaining: 14m 44s\n",
      "532:\tlearn: 0.6969955\ttotal: 16m 47s\tremaining: 14m 42s\n",
      "533:\tlearn: 0.6969361\ttotal: 16m 48s\tremaining: 14m 40s\n",
      "534:\tlearn: 0.6969183\ttotal: 16m 50s\tremaining: 14m 38s\n",
      "535:\tlearn: 0.6968748\ttotal: 16m 52s\tremaining: 14m 36s\n",
      "536:\tlearn: 0.6968476\ttotal: 16m 54s\tremaining: 14m 34s\n",
      "537:\tlearn: 0.6968140\ttotal: 16m 56s\tremaining: 14m 32s\n",
      "538:\tlearn: 0.6967921\ttotal: 16m 58s\tremaining: 14m 30s\n",
      "539:\tlearn: 0.6967868\ttotal: 17m\tremaining: 14m 28s\n",
      "540:\tlearn: 0.6967770\ttotal: 17m 1s\tremaining: 14m 27s\n",
      "541:\tlearn: 0.6967235\ttotal: 17m 3s\tremaining: 14m 25s\n",
      "542:\tlearn: 0.6966740\ttotal: 17m 5s\tremaining: 14m 23s\n",
      "543:\tlearn: 0.6966666\ttotal: 17m 7s\tremaining: 14m 21s\n",
      "544:\tlearn: 0.6965846\ttotal: 17m 9s\tremaining: 14m 19s\n",
      "545:\tlearn: 0.6965469\ttotal: 17m 11s\tremaining: 14m 17s\n",
      "546:\tlearn: 0.6964980\ttotal: 17m 13s\tremaining: 14m 15s\n",
      "547:\tlearn: 0.6964015\ttotal: 17m 15s\tremaining: 14m 13s\n",
      "548:\tlearn: 0.6963418\ttotal: 17m 16s\tremaining: 14m 11s\n",
      "549:\tlearn: 0.6963208\ttotal: 17m 18s\tremaining: 14m 9s\n",
      "550:\tlearn: 0.6962945\ttotal: 17m 20s\tremaining: 14m 7s\n",
      "551:\tlearn: 0.6962594\ttotal: 17m 22s\tremaining: 14m 6s\n",
      "552:\tlearn: 0.6962433\ttotal: 17m 24s\tremaining: 14m 4s\n",
      "553:\tlearn: 0.6962255\ttotal: 17m 26s\tremaining: 14m 2s\n",
      "554:\tlearn: 0.6961455\ttotal: 17m 27s\tremaining: 14m\n",
      "555:\tlearn: 0.6960915\ttotal: 17m 29s\tremaining: 13m 58s\n",
      "556:\tlearn: 0.6960774\ttotal: 17m 31s\tremaining: 13m 56s\n",
      "557:\tlearn: 0.6960629\ttotal: 17m 33s\tremaining: 13m 54s\n",
      "558:\tlearn: 0.6960578\ttotal: 17m 35s\tremaining: 13m 52s\n",
      "559:\tlearn: 0.6960467\ttotal: 17m 37s\tremaining: 13m 50s\n",
      "560:\tlearn: 0.6959893\ttotal: 17m 39s\tremaining: 13m 48s\n",
      "561:\tlearn: 0.6959835\ttotal: 17m 40s\tremaining: 13m 46s\n",
      "562:\tlearn: 0.6959743\ttotal: 17m 42s\tremaining: 13m 44s\n",
      "563:\tlearn: 0.6959558\ttotal: 17m 44s\tremaining: 13m 43s\n",
      "564:\tlearn: 0.6959352\ttotal: 17m 46s\tremaining: 13m 41s\n",
      "565:\tlearn: 0.6959265\ttotal: 17m 48s\tremaining: 13m 39s\n",
      "566:\tlearn: 0.6958852\ttotal: 17m 50s\tremaining: 13m 37s\n",
      "567:\tlearn: 0.6958480\ttotal: 17m 52s\tremaining: 13m 35s\n",
      "568:\tlearn: 0.6958365\ttotal: 17m 53s\tremaining: 13m 33s\n",
      "569:\tlearn: 0.6958227\ttotal: 17m 55s\tremaining: 13m 31s\n",
      "570:\tlearn: 0.6958096\ttotal: 17m 57s\tremaining: 13m 29s\n",
      "571:\tlearn: 0.6957857\ttotal: 17m 59s\tremaining: 13m 27s\n",
      "572:\tlearn: 0.6957659\ttotal: 18m 1s\tremaining: 13m 25s\n",
      "573:\tlearn: 0.6957503\ttotal: 18m 3s\tremaining: 13m 23s\n",
      "574:\tlearn: 0.6957085\ttotal: 18m 5s\tremaining: 13m 22s\n",
      "575:\tlearn: 0.6956913\ttotal: 18m 7s\tremaining: 13m 20s\n",
      "576:\tlearn: 0.6956792\ttotal: 18m 8s\tremaining: 13m 18s\n",
      "577:\tlearn: 0.6956193\ttotal: 18m 10s\tremaining: 13m 16s\n",
      "578:\tlearn: 0.6956024\ttotal: 18m 12s\tremaining: 13m 14s\n",
      "579:\tlearn: 0.6955969\ttotal: 18m 14s\tremaining: 13m 12s\n",
      "580:\tlearn: 0.6955825\ttotal: 18m 16s\tremaining: 13m 10s\n",
      "581:\tlearn: 0.6955610\ttotal: 18m 18s\tremaining: 13m 8s\n",
      "582:\tlearn: 0.6955390\ttotal: 18m 20s\tremaining: 13m 6s\n",
      "583:\tlearn: 0.6955338\ttotal: 18m 21s\tremaining: 13m 4s\n",
      "584:\tlearn: 0.6955144\ttotal: 18m 23s\tremaining: 13m 2s\n",
      "585:\tlearn: 0.6954961\ttotal: 18m 25s\tremaining: 13m 1s\n",
      "586:\tlearn: 0.6954722\ttotal: 18m 27s\tremaining: 12m 59s\n",
      "587:\tlearn: 0.6954328\ttotal: 18m 29s\tremaining: 12m 57s\n",
      "588:\tlearn: 0.6954193\ttotal: 18m 31s\tremaining: 12m 55s\n",
      "589:\tlearn: 0.6954080\ttotal: 18m 32s\tremaining: 12m 53s\n",
      "590:\tlearn: 0.6953777\ttotal: 18m 34s\tremaining: 12m 51s\n",
      "591:\tlearn: 0.6953546\ttotal: 18m 36s\tremaining: 12m 49s\n",
      "592:\tlearn: 0.6953013\ttotal: 18m 38s\tremaining: 12m 47s\n",
      "593:\tlearn: 0.6952580\ttotal: 18m 40s\tremaining: 12m 45s\n",
      "594:\tlearn: 0.6952542\ttotal: 18m 42s\tremaining: 12m 43s\n",
      "595:\tlearn: 0.6952061\ttotal: 18m 44s\tremaining: 12m 41s\n",
      "596:\tlearn: 0.6951772\ttotal: 18m 45s\tremaining: 12m 40s\n",
      "597:\tlearn: 0.6951499\ttotal: 18m 47s\tremaining: 12m 38s\n",
      "598:\tlearn: 0.6951397\ttotal: 18m 49s\tremaining: 12m 36s\n",
      "599:\tlearn: 0.6951001\ttotal: 18m 51s\tremaining: 12m 34s\n",
      "600:\tlearn: 0.6950877\ttotal: 18m 53s\tremaining: 12m 32s\n",
      "601:\tlearn: 0.6950636\ttotal: 18m 55s\tremaining: 12m 30s\n",
      "602:\tlearn: 0.6950457\ttotal: 18m 57s\tremaining: 12m 28s\n",
      "603:\tlearn: 0.6949795\ttotal: 18m 58s\tremaining: 12m 26s\n",
      "604:\tlearn: 0.6949667\ttotal: 19m\tremaining: 12m 24s\n",
      "605:\tlearn: 0.6949522\ttotal: 19m 2s\tremaining: 12m 22s\n",
      "606:\tlearn: 0.6949287\ttotal: 19m 4s\tremaining: 12m 20s\n",
      "607:\tlearn: 0.6948526\ttotal: 19m 6s\tremaining: 12m 19s\n",
      "608:\tlearn: 0.6947844\ttotal: 19m 8s\tremaining: 12m 17s\n",
      "609:\tlearn: 0.6947724\ttotal: 19m 10s\tremaining: 12m 15s\n",
      "610:\tlearn: 0.6947353\ttotal: 19m 11s\tremaining: 12m 13s\n",
      "611:\tlearn: 0.6947057\ttotal: 19m 13s\tremaining: 12m 11s\n",
      "612:\tlearn: 0.6946864\ttotal: 19m 15s\tremaining: 12m 9s\n",
      "613:\tlearn: 0.6946219\ttotal: 19m 17s\tremaining: 12m 7s\n",
      "614:\tlearn: 0.6945970\ttotal: 19m 19s\tremaining: 12m 5s\n",
      "615:\tlearn: 0.6945846\ttotal: 19m 21s\tremaining: 12m 3s\n",
      "616:\tlearn: 0.6945267\ttotal: 19m 23s\tremaining: 12m 1s\n",
      "617:\tlearn: 0.6945158\ttotal: 19m 24s\tremaining: 12m\n",
      "618:\tlearn: 0.6944971\ttotal: 19m 26s\tremaining: 11m 58s\n",
      "619:\tlearn: 0.6944534\ttotal: 19m 28s\tremaining: 11m 56s\n",
      "620:\tlearn: 0.6944245\ttotal: 19m 30s\tremaining: 11m 54s\n",
      "621:\tlearn: 0.6944007\ttotal: 19m 32s\tremaining: 11m 52s\n",
      "622:\tlearn: 0.6943427\ttotal: 19m 34s\tremaining: 11m 50s\n",
      "623:\tlearn: 0.6943335\ttotal: 19m 36s\tremaining: 11m 48s\n",
      "624:\tlearn: 0.6943072\ttotal: 19m 37s\tremaining: 11m 46s\n",
      "625:\tlearn: 0.6942964\ttotal: 19m 39s\tremaining: 11m 44s\n",
      "626:\tlearn: 0.6942756\ttotal: 19m 41s\tremaining: 11m 42s\n",
      "627:\tlearn: 0.6942636\ttotal: 19m 43s\tremaining: 11m 41s\n",
      "628:\tlearn: 0.6941911\ttotal: 19m 45s\tremaining: 11m 39s\n",
      "629:\tlearn: 0.6941832\ttotal: 19m 47s\tremaining: 11m 37s\n",
      "630:\tlearn: 0.6941592\ttotal: 19m 49s\tremaining: 11m 35s\n",
      "631:\tlearn: 0.6941494\ttotal: 19m 50s\tremaining: 11m 33s\n",
      "632:\tlearn: 0.6941411\ttotal: 19m 52s\tremaining: 11m 31s\n",
      "633:\tlearn: 0.6940804\ttotal: 19m 54s\tremaining: 11m 29s\n",
      "634:\tlearn: 0.6940676\ttotal: 19m 56s\tremaining: 11m 27s\n",
      "635:\tlearn: 0.6940548\ttotal: 19m 58s\tremaining: 11m 25s\n",
      "636:\tlearn: 0.6940249\ttotal: 20m\tremaining: 11m 24s\n",
      "637:\tlearn: 0.6940090\ttotal: 20m 2s\tremaining: 11m 22s\n",
      "638:\tlearn: 0.6939949\ttotal: 20m 4s\tremaining: 11m 20s\n",
      "639:\tlearn: 0.6939450\ttotal: 20m 5s\tremaining: 11m 18s\n",
      "640:\tlearn: 0.6939247\ttotal: 20m 7s\tremaining: 11m 16s\n",
      "641:\tlearn: 0.6939061\ttotal: 20m 9s\tremaining: 11m 14s\n",
      "642:\tlearn: 0.6938719\ttotal: 20m 11s\tremaining: 11m 12s\n",
      "643:\tlearn: 0.6938364\ttotal: 20m 13s\tremaining: 11m 10s\n",
      "644:\tlearn: 0.6938283\ttotal: 20m 15s\tremaining: 11m 8s\n",
      "645:\tlearn: 0.6938165\ttotal: 20m 17s\tremaining: 11m 6s\n",
      "646:\tlearn: 0.6937768\ttotal: 20m 18s\tremaining: 11m 4s\n",
      "647:\tlearn: 0.6937496\ttotal: 20m 20s\tremaining: 11m 3s\n",
      "648:\tlearn: 0.6937380\ttotal: 20m 22s\tremaining: 11m 1s\n",
      "649:\tlearn: 0.6937180\ttotal: 20m 24s\tremaining: 10m 59s\n",
      "650:\tlearn: 0.6936988\ttotal: 20m 26s\tremaining: 10m 57s\n",
      "651:\tlearn: 0.6936674\ttotal: 20m 28s\tremaining: 10m 55s\n",
      "652:\tlearn: 0.6936634\ttotal: 20m 29s\tremaining: 10m 53s\n",
      "653:\tlearn: 0.6936249\ttotal: 20m 31s\tremaining: 10m 51s\n",
      "654:\tlearn: 0.6936121\ttotal: 20m 33s\tremaining: 10m 49s\n",
      "655:\tlearn: 0.6936050\ttotal: 20m 35s\tremaining: 10m 47s\n",
      "656:\tlearn: 0.6935589\ttotal: 20m 37s\tremaining: 10m 46s\n",
      "657:\tlearn: 0.6935393\ttotal: 20m 39s\tremaining: 10m 44s\n",
      "658:\tlearn: 0.6935256\ttotal: 20m 41s\tremaining: 10m 42s\n",
      "659:\tlearn: 0.6935070\ttotal: 20m 42s\tremaining: 10m 40s\n",
      "660:\tlearn: 0.6934922\ttotal: 20m 44s\tremaining: 10m 38s\n",
      "661:\tlearn: 0.6934832\ttotal: 20m 46s\tremaining: 10m 36s\n",
      "662:\tlearn: 0.6934669\ttotal: 20m 48s\tremaining: 10m 34s\n",
      "663:\tlearn: 0.6934496\ttotal: 20m 50s\tremaining: 10m 32s\n",
      "664:\tlearn: 0.6934368\ttotal: 20m 52s\tremaining: 10m 30s\n",
      "665:\tlearn: 0.6934329\ttotal: 20m 54s\tremaining: 10m 29s\n",
      "666:\tlearn: 0.6934129\ttotal: 20m 56s\tremaining: 10m 27s\n",
      "667:\tlearn: 0.6933147\ttotal: 20m 57s\tremaining: 10m 25s\n",
      "668:\tlearn: 0.6932981\ttotal: 20m 59s\tremaining: 10m 23s\n",
      "669:\tlearn: 0.6932792\ttotal: 21m 1s\tremaining: 10m 21s\n",
      "670:\tlearn: 0.6932643\ttotal: 21m 3s\tremaining: 10m 19s\n",
      "671:\tlearn: 0.6932085\ttotal: 21m 5s\tremaining: 10m 17s\n",
      "672:\tlearn: 0.6932031\ttotal: 21m 7s\tremaining: 10m 15s\n",
      "673:\tlearn: 0.6931906\ttotal: 21m 9s\tremaining: 10m 13s\n",
      "674:\tlearn: 0.6931520\ttotal: 21m 11s\tremaining: 10m 11s\n",
      "675:\tlearn: 0.6930733\ttotal: 21m 12s\tremaining: 10m 10s\n",
      "676:\tlearn: 0.6930710\ttotal: 21m 14s\tremaining: 10m 8s\n",
      "677:\tlearn: 0.6930598\ttotal: 21m 16s\tremaining: 10m 6s\n",
      "678:\tlearn: 0.6930409\ttotal: 21m 18s\tremaining: 10m 4s\n",
      "679:\tlearn: 0.6930110\ttotal: 21m 20s\tremaining: 10m 2s\n",
      "680:\tlearn: 0.6929991\ttotal: 21m 22s\tremaining: 10m\n",
      "681:\tlearn: 0.6929718\ttotal: 21m 24s\tremaining: 9m 58s\n",
      "682:\tlearn: 0.6929601\ttotal: 21m 25s\tremaining: 9m 56s\n",
      "683:\tlearn: 0.6928772\ttotal: 21m 27s\tremaining: 9m 54s\n",
      "684:\tlearn: 0.6928678\ttotal: 21m 29s\tremaining: 9m 53s\n",
      "685:\tlearn: 0.6928106\ttotal: 21m 31s\tremaining: 9m 51s\n",
      "686:\tlearn: 0.6927851\ttotal: 21m 33s\tremaining: 9m 49s\n",
      "687:\tlearn: 0.6927331\ttotal: 21m 35s\tremaining: 9m 47s\n",
      "688:\tlearn: 0.6927183\ttotal: 21m 36s\tremaining: 9m 45s\n",
      "689:\tlearn: 0.6926958\ttotal: 21m 38s\tremaining: 9m 43s\n",
      "690:\tlearn: 0.6926322\ttotal: 21m 40s\tremaining: 9m 41s\n",
      "691:\tlearn: 0.6925923\ttotal: 21m 42s\tremaining: 9m 39s\n",
      "692:\tlearn: 0.6925645\ttotal: 21m 44s\tremaining: 9m 37s\n",
      "693:\tlearn: 0.6925536\ttotal: 21m 46s\tremaining: 9m 35s\n",
      "694:\tlearn: 0.6925380\ttotal: 21m 48s\tremaining: 9m 34s\n",
      "695:\tlearn: 0.6925113\ttotal: 21m 50s\tremaining: 9m 32s\n",
      "696:\tlearn: 0.6924785\ttotal: 21m 51s\tremaining: 9m 30s\n",
      "697:\tlearn: 0.6924648\ttotal: 21m 53s\tremaining: 9m 28s\n",
      "698:\tlearn: 0.6924339\ttotal: 21m 55s\tremaining: 9m 26s\n",
      "699:\tlearn: 0.6924143\ttotal: 21m 57s\tremaining: 9m 24s\n",
      "700:\tlearn: 0.6923910\ttotal: 21m 59s\tremaining: 9m 22s\n",
      "701:\tlearn: 0.6923764\ttotal: 22m 1s\tremaining: 9m 20s\n",
      "702:\tlearn: 0.6923716\ttotal: 22m 3s\tremaining: 9m 18s\n",
      "703:\tlearn: 0.6923600\ttotal: 22m 4s\tremaining: 9m 17s\n",
      "704:\tlearn: 0.6923458\ttotal: 22m 6s\tremaining: 9m 15s\n",
      "705:\tlearn: 0.6923204\ttotal: 22m 8s\tremaining: 9m 13s\n",
      "706:\tlearn: 0.6923092\ttotal: 22m 10s\tremaining: 9m 11s\n",
      "707:\tlearn: 0.6923036\ttotal: 22m 12s\tremaining: 9m 9s\n",
      "708:\tlearn: 0.6922785\ttotal: 22m 14s\tremaining: 9m 7s\n",
      "709:\tlearn: 0.6922639\ttotal: 22m 16s\tremaining: 9m 5s\n",
      "710:\tlearn: 0.6922546\ttotal: 22m 18s\tremaining: 9m 3s\n",
      "711:\tlearn: 0.6922327\ttotal: 22m 19s\tremaining: 9m 1s\n",
      "712:\tlearn: 0.6922167\ttotal: 22m 21s\tremaining: 9m\n",
      "713:\tlearn: 0.6921828\ttotal: 22m 23s\tremaining: 8m 58s\n",
      "714:\tlearn: 0.6921576\ttotal: 22m 25s\tremaining: 8m 56s\n",
      "715:\tlearn: 0.6921473\ttotal: 22m 27s\tremaining: 8m 54s\n",
      "716:\tlearn: 0.6921225\ttotal: 22m 29s\tremaining: 8m 52s\n",
      "717:\tlearn: 0.6921035\ttotal: 22m 31s\tremaining: 8m 50s\n",
      "718:\tlearn: 0.6920872\ttotal: 22m 32s\tremaining: 8m 48s\n",
      "719:\tlearn: 0.6920761\ttotal: 22m 34s\tremaining: 8m 46s\n",
      "720:\tlearn: 0.6920598\ttotal: 22m 36s\tremaining: 8m 45s\n",
      "721:\tlearn: 0.6920366\ttotal: 22m 38s\tremaining: 8m 43s\n",
      "722:\tlearn: 0.6919803\ttotal: 22m 40s\tremaining: 8m 41s\n",
      "723:\tlearn: 0.6919651\ttotal: 22m 42s\tremaining: 8m 39s\n",
      "724:\tlearn: 0.6919464\ttotal: 22m 44s\tremaining: 8m 37s\n",
      "725:\tlearn: 0.6919365\ttotal: 22m 46s\tremaining: 8m 35s\n",
      "726:\tlearn: 0.6919283\ttotal: 22m 47s\tremaining: 8m 33s\n",
      "727:\tlearn: 0.6919099\ttotal: 22m 49s\tremaining: 8m 31s\n",
      "728:\tlearn: 0.6918953\ttotal: 22m 51s\tremaining: 8m 29s\n",
      "729:\tlearn: 0.6918844\ttotal: 22m 53s\tremaining: 8m 28s\n",
      "730:\tlearn: 0.6918618\ttotal: 22m 55s\tremaining: 8m 26s\n",
      "731:\tlearn: 0.6918384\ttotal: 22m 57s\tremaining: 8m 24s\n",
      "732:\tlearn: 0.6918314\ttotal: 22m 59s\tremaining: 8m 22s\n",
      "733:\tlearn: 0.6917907\ttotal: 23m 1s\tremaining: 8m 20s\n",
      "734:\tlearn: 0.6917681\ttotal: 23m 3s\tremaining: 8m 18s\n",
      "735:\tlearn: 0.6917518\ttotal: 23m 4s\tremaining: 8m 16s\n",
      "736:\tlearn: 0.6917419\ttotal: 23m 6s\tremaining: 8m 14s\n",
      "737:\tlearn: 0.6917226\ttotal: 23m 8s\tremaining: 8m 13s\n",
      "738:\tlearn: 0.6917065\ttotal: 23m 10s\tremaining: 8m 11s\n",
      "739:\tlearn: 0.6916771\ttotal: 23m 12s\tremaining: 8m 9s\n",
      "740:\tlearn: 0.6916722\ttotal: 23m 14s\tremaining: 8m 7s\n",
      "741:\tlearn: 0.6915778\ttotal: 23m 16s\tremaining: 8m 5s\n",
      "742:\tlearn: 0.6915689\ttotal: 23m 18s\tremaining: 8m 3s\n",
      "743:\tlearn: 0.6915601\ttotal: 23m 19s\tremaining: 8m 1s\n",
      "744:\tlearn: 0.6915489\ttotal: 23m 21s\tremaining: 7m 59s\n",
      "745:\tlearn: 0.6915187\ttotal: 23m 23s\tremaining: 7m 57s\n",
      "746:\tlearn: 0.6915124\ttotal: 23m 25s\tremaining: 7m 56s\n",
      "747:\tlearn: 0.6914871\ttotal: 23m 27s\tremaining: 7m 54s\n",
      "748:\tlearn: 0.6914591\ttotal: 23m 29s\tremaining: 7m 52s\n",
      "749:\tlearn: 0.6914316\ttotal: 23m 31s\tremaining: 7m 50s\n",
      "750:\tlearn: 0.6914280\ttotal: 23m 32s\tremaining: 7m 48s\n",
      "751:\tlearn: 0.6914020\ttotal: 23m 34s\tremaining: 7m 46s\n",
      "752:\tlearn: 0.6913918\ttotal: 23m 36s\tremaining: 7m 44s\n",
      "753:\tlearn: 0.6913682\ttotal: 23m 38s\tremaining: 7m 42s\n",
      "754:\tlearn: 0.6913608\ttotal: 23m 40s\tremaining: 7m 40s\n",
      "755:\tlearn: 0.6913480\ttotal: 23m 42s\tremaining: 7m 39s\n",
      "756:\tlearn: 0.6913418\ttotal: 23m 44s\tremaining: 7m 37s\n",
      "757:\tlearn: 0.6913354\ttotal: 23m 45s\tremaining: 7m 35s\n",
      "758:\tlearn: 0.6913277\ttotal: 23m 47s\tremaining: 7m 33s\n",
      "759:\tlearn: 0.6913166\ttotal: 23m 49s\tremaining: 7m 31s\n",
      "760:\tlearn: 0.6912994\ttotal: 23m 51s\tremaining: 7m 29s\n",
      "761:\tlearn: 0.6912818\ttotal: 23m 53s\tremaining: 7m 27s\n",
      "762:\tlearn: 0.6912313\ttotal: 23m 55s\tremaining: 7m 25s\n",
      "763:\tlearn: 0.6912251\ttotal: 23m 57s\tremaining: 7m 23s\n",
      "764:\tlearn: 0.6912128\ttotal: 23m 58s\tremaining: 7m 22s\n",
      "765:\tlearn: 0.6912055\ttotal: 24m\tremaining: 7m 20s\n",
      "766:\tlearn: 0.6911869\ttotal: 24m 2s\tremaining: 7m 18s\n",
      "767:\tlearn: 0.6911767\ttotal: 24m 4s\tremaining: 7m 16s\n",
      "768:\tlearn: 0.6911627\ttotal: 24m 6s\tremaining: 7m 14s\n",
      "769:\tlearn: 0.6911477\ttotal: 24m 8s\tremaining: 7m 12s\n",
      "770:\tlearn: 0.6911204\ttotal: 24m 10s\tremaining: 7m 10s\n",
      "771:\tlearn: 0.6910976\ttotal: 24m 11s\tremaining: 7m 8s\n",
      "772:\tlearn: 0.6910858\ttotal: 24m 13s\tremaining: 7m 6s\n",
      "773:\tlearn: 0.6910829\ttotal: 24m 15s\tremaining: 7m 5s\n",
      "774:\tlearn: 0.6910652\ttotal: 24m 17s\tremaining: 7m 3s\n",
      "775:\tlearn: 0.6910629\ttotal: 24m 19s\tremaining: 7m 1s\n",
      "776:\tlearn: 0.6910071\ttotal: 24m 21s\tremaining: 6m 59s\n",
      "777:\tlearn: 0.6909982\ttotal: 24m 23s\tremaining: 6m 57s\n",
      "778:\tlearn: 0.6909768\ttotal: 24m 25s\tremaining: 6m 55s\n",
      "779:\tlearn: 0.6909684\ttotal: 24m 26s\tremaining: 6m 53s\n",
      "780:\tlearn: 0.6909636\ttotal: 24m 28s\tremaining: 6m 51s\n",
      "781:\tlearn: 0.6909579\ttotal: 24m 30s\tremaining: 6m 49s\n",
      "782:\tlearn: 0.6909341\ttotal: 24m 32s\tremaining: 6m 48s\n",
      "783:\tlearn: 0.6909233\ttotal: 24m 34s\tremaining: 6m 46s\n",
      "784:\tlearn: 0.6908806\ttotal: 24m 36s\tremaining: 6m 44s\n",
      "785:\tlearn: 0.6908657\ttotal: 24m 38s\tremaining: 6m 42s\n",
      "786:\tlearn: 0.6908475\ttotal: 24m 39s\tremaining: 6m 40s\n",
      "787:\tlearn: 0.6908073\ttotal: 24m 41s\tremaining: 6m 38s\n",
      "788:\tlearn: 0.6907997\ttotal: 24m 43s\tremaining: 6m 36s\n",
      "789:\tlearn: 0.6907891\ttotal: 24m 45s\tremaining: 6m 34s\n",
      "790:\tlearn: 0.6907689\ttotal: 24m 47s\tremaining: 6m 32s\n",
      "791:\tlearn: 0.6907523\ttotal: 24m 49s\tremaining: 6m 31s\n",
      "792:\tlearn: 0.6907350\ttotal: 24m 51s\tremaining: 6m 29s\n",
      "793:\tlearn: 0.6907191\ttotal: 24m 52s\tremaining: 6m 27s\n",
      "794:\tlearn: 0.6906978\ttotal: 24m 54s\tremaining: 6m 25s\n",
      "795:\tlearn: 0.6906907\ttotal: 24m 56s\tremaining: 6m 23s\n",
      "796:\tlearn: 0.6906785\ttotal: 24m 58s\tremaining: 6m 21s\n",
      "797:\tlearn: 0.6906650\ttotal: 25m\tremaining: 6m 19s\n",
      "798:\tlearn: 0.6906533\ttotal: 25m 2s\tremaining: 6m 17s\n",
      "799:\tlearn: 0.6906369\ttotal: 25m 4s\tremaining: 6m 16s\n",
      "800:\tlearn: 0.6905595\ttotal: 25m 5s\tremaining: 6m 14s\n",
      "801:\tlearn: 0.6905288\ttotal: 25m 7s\tremaining: 6m 12s\n",
      "802:\tlearn: 0.6905261\ttotal: 25m 9s\tremaining: 6m 10s\n",
      "803:\tlearn: 0.6905213\ttotal: 25m 11s\tremaining: 6m 8s\n",
      "804:\tlearn: 0.6905074\ttotal: 25m 13s\tremaining: 6m 6s\n",
      "805:\tlearn: 0.6904845\ttotal: 25m 15s\tremaining: 6m 4s\n",
      "806:\tlearn: 0.6904591\ttotal: 25m 17s\tremaining: 6m 2s\n",
      "807:\tlearn: 0.6904410\ttotal: 25m 18s\tremaining: 6m\n",
      "808:\tlearn: 0.6904236\ttotal: 25m 20s\tremaining: 5m 59s\n",
      "809:\tlearn: 0.6904132\ttotal: 25m 22s\tremaining: 5m 57s\n",
      "810:\tlearn: 0.6904056\ttotal: 25m 24s\tremaining: 5m 55s\n",
      "811:\tlearn: 0.6903940\ttotal: 25m 26s\tremaining: 5m 53s\n",
      "812:\tlearn: 0.6903784\ttotal: 25m 28s\tremaining: 5m 51s\n",
      "813:\tlearn: 0.6903681\ttotal: 25m 30s\tremaining: 5m 49s\n",
      "814:\tlearn: 0.6903374\ttotal: 25m 31s\tremaining: 5m 47s\n",
      "815:\tlearn: 0.6903035\ttotal: 25m 33s\tremaining: 5m 45s\n",
      "816:\tlearn: 0.6902849\ttotal: 25m 35s\tremaining: 5m 43s\n",
      "817:\tlearn: 0.6902658\ttotal: 25m 37s\tremaining: 5m 42s\n",
      "818:\tlearn: 0.6902575\ttotal: 25m 39s\tremaining: 5m 40s\n",
      "819:\tlearn: 0.6902084\ttotal: 25m 41s\tremaining: 5m 38s\n",
      "820:\tlearn: 0.6901762\ttotal: 25m 43s\tremaining: 5m 36s\n",
      "821:\tlearn: 0.6901697\ttotal: 25m 44s\tremaining: 5m 34s\n",
      "822:\tlearn: 0.6901620\ttotal: 25m 46s\tremaining: 5m 32s\n",
      "823:\tlearn: 0.6901312\ttotal: 25m 48s\tremaining: 5m 30s\n",
      "824:\tlearn: 0.6900905\ttotal: 25m 50s\tremaining: 5m 28s\n",
      "825:\tlearn: 0.6900763\ttotal: 25m 52s\tremaining: 5m 27s\n",
      "826:\tlearn: 0.6900695\ttotal: 25m 54s\tremaining: 5m 25s\n",
      "827:\tlearn: 0.6900466\ttotal: 25m 56s\tremaining: 5m 23s\n",
      "828:\tlearn: 0.6900331\ttotal: 25m 57s\tremaining: 5m 21s\n",
      "829:\tlearn: 0.6900158\ttotal: 25m 59s\tremaining: 5m 19s\n",
      "830:\tlearn: 0.6900019\ttotal: 26m 1s\tremaining: 5m 17s\n",
      "831:\tlearn: 0.6899778\ttotal: 26m 3s\tremaining: 5m 15s\n",
      "832:\tlearn: 0.6899678\ttotal: 26m 5s\tremaining: 5m 13s\n",
      "833:\tlearn: 0.6899606\ttotal: 26m 7s\tremaining: 5m 11s\n",
      "834:\tlearn: 0.6899395\ttotal: 26m 8s\tremaining: 5m 10s\n",
      "835:\tlearn: 0.6899249\ttotal: 26m 10s\tremaining: 5m 8s\n",
      "836:\tlearn: 0.6899199\ttotal: 26m 12s\tremaining: 5m 6s\n",
      "837:\tlearn: 0.6899100\ttotal: 26m 14s\tremaining: 5m 4s\n",
      "838:\tlearn: 0.6898991\ttotal: 26m 16s\tremaining: 5m 2s\n",
      "839:\tlearn: 0.6898919\ttotal: 26m 18s\tremaining: 5m\n",
      "840:\tlearn: 0.6898816\ttotal: 26m 20s\tremaining: 4m 58s\n",
      "841:\tlearn: 0.6898271\ttotal: 26m 21s\tremaining: 4m 56s\n",
      "842:\tlearn: 0.6898141\ttotal: 26m 23s\tremaining: 4m 54s\n",
      "843:\tlearn: 0.6898023\ttotal: 26m 25s\tremaining: 4m 53s\n",
      "844:\tlearn: 0.6897932\ttotal: 26m 27s\tremaining: 4m 51s\n",
      "845:\tlearn: 0.6897857\ttotal: 26m 29s\tremaining: 4m 49s\n",
      "846:\tlearn: 0.6897763\ttotal: 26m 31s\tremaining: 4m 47s\n",
      "847:\tlearn: 0.6897669\ttotal: 26m 33s\tremaining: 4m 45s\n",
      "848:\tlearn: 0.6897610\ttotal: 26m 34s\tremaining: 4m 43s\n",
      "849:\tlearn: 0.6897435\ttotal: 26m 36s\tremaining: 4m 41s\n",
      "850:\tlearn: 0.6897198\ttotal: 26m 38s\tremaining: 4m 39s\n",
      "851:\tlearn: 0.6897077\ttotal: 26m 40s\tremaining: 4m 38s\n",
      "852:\tlearn: 0.6896980\ttotal: 26m 42s\tremaining: 4m 36s\n",
      "853:\tlearn: 0.6896826\ttotal: 26m 44s\tremaining: 4m 34s\n",
      "854:\tlearn: 0.6896688\ttotal: 26m 46s\tremaining: 4m 32s\n",
      "855:\tlearn: 0.6896557\ttotal: 26m 48s\tremaining: 4m 30s\n",
      "856:\tlearn: 0.6896179\ttotal: 26m 49s\tremaining: 4m 28s\n",
      "857:\tlearn: 0.6896025\ttotal: 26m 51s\tremaining: 4m 26s\n",
      "858:\tlearn: 0.6895875\ttotal: 26m 53s\tremaining: 4m 24s\n",
      "859:\tlearn: 0.6895846\ttotal: 26m 55s\tremaining: 4m 22s\n",
      "860:\tlearn: 0.6895733\ttotal: 26m 57s\tremaining: 4m 21s\n",
      "861:\tlearn: 0.6895348\ttotal: 26m 59s\tremaining: 4m 19s\n",
      "862:\tlearn: 0.6895160\ttotal: 27m 1s\tremaining: 4m 17s\n",
      "863:\tlearn: 0.6894711\ttotal: 27m 2s\tremaining: 4m 15s\n",
      "864:\tlearn: 0.6894645\ttotal: 27m 4s\tremaining: 4m 13s\n",
      "865:\tlearn: 0.6894475\ttotal: 27m 6s\tremaining: 4m 11s\n",
      "866:\tlearn: 0.6894260\ttotal: 27m 8s\tremaining: 4m 9s\n",
      "867:\tlearn: 0.6894142\ttotal: 27m 10s\tremaining: 4m 7s\n",
      "868:\tlearn: 0.6894103\ttotal: 27m 12s\tremaining: 4m 6s\n",
      "869:\tlearn: 0.6894031\ttotal: 27m 14s\tremaining: 4m 4s\n",
      "870:\tlearn: 0.6893981\ttotal: 27m 15s\tremaining: 4m 2s\n",
      "871:\tlearn: 0.6893898\ttotal: 27m 17s\tremaining: 4m\n",
      "872:\tlearn: 0.6893802\ttotal: 27m 19s\tremaining: 3m 58s\n",
      "873:\tlearn: 0.6893510\ttotal: 27m 21s\tremaining: 3m 56s\n",
      "874:\tlearn: 0.6893424\ttotal: 27m 23s\tremaining: 3m 54s\n",
      "875:\tlearn: 0.6892970\ttotal: 27m 25s\tremaining: 3m 52s\n",
      "876:\tlearn: 0.6892885\ttotal: 27m 26s\tremaining: 3m 50s\n",
      "877:\tlearn: 0.6892352\ttotal: 27m 28s\tremaining: 3m 49s\n",
      "878:\tlearn: 0.6892318\ttotal: 27m 30s\tremaining: 3m 47s\n",
      "879:\tlearn: 0.6892259\ttotal: 27m 32s\tremaining: 3m 45s\n",
      "880:\tlearn: 0.6892208\ttotal: 27m 34s\tremaining: 3m 43s\n",
      "881:\tlearn: 0.6892049\ttotal: 27m 36s\tremaining: 3m 41s\n",
      "882:\tlearn: 0.6891941\ttotal: 27m 38s\tremaining: 3m 39s\n",
      "883:\tlearn: 0.6891843\ttotal: 27m 39s\tremaining: 3m 37s\n",
      "884:\tlearn: 0.6891123\ttotal: 27m 41s\tremaining: 3m 35s\n",
      "885:\tlearn: 0.6891067\ttotal: 27m 43s\tremaining: 3m 34s\n",
      "886:\tlearn: 0.6890951\ttotal: 27m 45s\tremaining: 3m 32s\n",
      "887:\tlearn: 0.6890711\ttotal: 27m 47s\tremaining: 3m 30s\n",
      "888:\tlearn: 0.6890450\ttotal: 27m 49s\tremaining: 3m 28s\n",
      "889:\tlearn: 0.6890169\ttotal: 27m 51s\tremaining: 3m 26s\n",
      "890:\tlearn: 0.6890029\ttotal: 27m 52s\tremaining: 3m 24s\n",
      "891:\tlearn: 0.6889945\ttotal: 27m 54s\tremaining: 3m 22s\n",
      "892:\tlearn: 0.6889858\ttotal: 27m 56s\tremaining: 3m 20s\n",
      "893:\tlearn: 0.6889737\ttotal: 27m 58s\tremaining: 3m 19s\n",
      "894:\tlearn: 0.6889656\ttotal: 28m\tremaining: 3m 17s\n",
      "895:\tlearn: 0.6889504\ttotal: 28m 2s\tremaining: 3m 15s\n",
      "896:\tlearn: 0.6889385\ttotal: 28m 4s\tremaining: 3m 13s\n",
      "897:\tlearn: 0.6889278\ttotal: 28m 5s\tremaining: 3m 11s\n",
      "898:\tlearn: 0.6889114\ttotal: 28m 7s\tremaining: 3m 9s\n",
      "899:\tlearn: 0.6888973\ttotal: 28m 9s\tremaining: 3m 7s\n",
      "900:\tlearn: 0.6888909\ttotal: 28m 11s\tremaining: 3m 5s\n",
      "901:\tlearn: 0.6888817\ttotal: 28m 13s\tremaining: 3m 3s\n",
      "902:\tlearn: 0.6888761\ttotal: 28m 15s\tremaining: 3m 2s\n",
      "903:\tlearn: 0.6888674\ttotal: 28m 16s\tremaining: 3m\n",
      "904:\tlearn: 0.6888564\ttotal: 28m 18s\tremaining: 2m 58s\n",
      "905:\tlearn: 0.6888438\ttotal: 28m 20s\tremaining: 2m 56s\n",
      "906:\tlearn: 0.6888325\ttotal: 28m 22s\tremaining: 2m 54s\n",
      "907:\tlearn: 0.6888221\ttotal: 28m 24s\tremaining: 2m 52s\n",
      "908:\tlearn: 0.6888145\ttotal: 28m 26s\tremaining: 2m 50s\n",
      "909:\tlearn: 0.6888068\ttotal: 28m 28s\tremaining: 2m 48s\n",
      "910:\tlearn: 0.6887812\ttotal: 28m 30s\tremaining: 2m 47s\n",
      "911:\tlearn: 0.6887759\ttotal: 28m 31s\tremaining: 2m 45s\n",
      "912:\tlearn: 0.6887673\ttotal: 28m 33s\tremaining: 2m 43s\n",
      "913:\tlearn: 0.6887581\ttotal: 28m 35s\tremaining: 2m 41s\n",
      "914:\tlearn: 0.6887540\ttotal: 28m 37s\tremaining: 2m 39s\n",
      "915:\tlearn: 0.6887408\ttotal: 28m 39s\tremaining: 2m 37s\n",
      "916:\tlearn: 0.6887014\ttotal: 28m 41s\tremaining: 2m 35s\n",
      "917:\tlearn: 0.6886942\ttotal: 28m 42s\tremaining: 2m 33s\n",
      "918:\tlearn: 0.6886742\ttotal: 28m 44s\tremaining: 2m 32s\n",
      "919:\tlearn: 0.6886696\ttotal: 28m 46s\tremaining: 2m 30s\n",
      "920:\tlearn: 0.6886623\ttotal: 28m 48s\tremaining: 2m 28s\n",
      "921:\tlearn: 0.6886597\ttotal: 28m 50s\tremaining: 2m 26s\n",
      "922:\tlearn: 0.6886397\ttotal: 28m 52s\tremaining: 2m 24s\n",
      "923:\tlearn: 0.6886107\ttotal: 28m 54s\tremaining: 2m 22s\n",
      "924:\tlearn: 0.6886031\ttotal: 28m 55s\tremaining: 2m 20s\n",
      "925:\tlearn: 0.6885891\ttotal: 28m 57s\tremaining: 2m 18s\n",
      "926:\tlearn: 0.6885687\ttotal: 28m 59s\tremaining: 2m 16s\n",
      "927:\tlearn: 0.6885617\ttotal: 29m 1s\tremaining: 2m 15s\n",
      "928:\tlearn: 0.6885392\ttotal: 29m 3s\tremaining: 2m 13s\n",
      "929:\tlearn: 0.6885142\ttotal: 29m 5s\tremaining: 2m 11s\n",
      "930:\tlearn: 0.6884988\ttotal: 29m 7s\tremaining: 2m 9s\n",
      "931:\tlearn: 0.6884688\ttotal: 29m 9s\tremaining: 2m 7s\n",
      "932:\tlearn: 0.6884660\ttotal: 29m 10s\tremaining: 2m 5s\n",
      "933:\tlearn: 0.6884569\ttotal: 29m 12s\tremaining: 2m 3s\n",
      "934:\tlearn: 0.6884480\ttotal: 29m 14s\tremaining: 2m 1s\n",
      "935:\tlearn: 0.6884427\ttotal: 29m 16s\tremaining: 2m\n",
      "936:\tlearn: 0.6884209\ttotal: 29m 18s\tremaining: 1m 58s\n",
      "937:\tlearn: 0.6884048\ttotal: 29m 20s\tremaining: 1m 56s\n",
      "938:\tlearn: 0.6883810\ttotal: 29m 22s\tremaining: 1m 54s\n",
      "939:\tlearn: 0.6883589\ttotal: 29m 23s\tremaining: 1m 52s\n",
      "940:\tlearn: 0.6883432\ttotal: 29m 25s\tremaining: 1m 50s\n",
      "941:\tlearn: 0.6883251\ttotal: 29m 27s\tremaining: 1m 48s\n",
      "942:\tlearn: 0.6883039\ttotal: 29m 29s\tremaining: 1m 46s\n",
      "943:\tlearn: 0.6882897\ttotal: 29m 31s\tremaining: 1m 45s\n",
      "944:\tlearn: 0.6882844\ttotal: 29m 33s\tremaining: 1m 43s\n",
      "945:\tlearn: 0.6882654\ttotal: 29m 34s\tremaining: 1m 41s\n",
      "946:\tlearn: 0.6882527\ttotal: 29m 36s\tremaining: 1m 39s\n",
      "947:\tlearn: 0.6882472\ttotal: 29m 38s\tremaining: 1m 37s\n",
      "948:\tlearn: 0.6882427\ttotal: 29m 40s\tremaining: 1m 35s\n",
      "949:\tlearn: 0.6882366\ttotal: 29m 42s\tremaining: 1m 33s\n",
      "950:\tlearn: 0.6882270\ttotal: 29m 44s\tremaining: 1m 31s\n",
      "951:\tlearn: 0.6882159\ttotal: 29m 46s\tremaining: 1m 30s\n",
      "952:\tlearn: 0.6882023\ttotal: 29m 48s\tremaining: 1m 28s\n",
      "953:\tlearn: 0.6881963\ttotal: 29m 49s\tremaining: 1m 26s\n",
      "954:\tlearn: 0.6881882\ttotal: 29m 51s\tremaining: 1m 24s\n",
      "955:\tlearn: 0.6881776\ttotal: 29m 53s\tremaining: 1m 22s\n",
      "956:\tlearn: 0.6881601\ttotal: 29m 55s\tremaining: 1m 20s\n",
      "957:\tlearn: 0.6881459\ttotal: 29m 57s\tremaining: 1m 18s\n",
      "958:\tlearn: 0.6881364\ttotal: 29m 59s\tremaining: 1m 16s\n",
      "959:\tlearn: 0.6881186\ttotal: 30m 1s\tremaining: 1m 15s\n",
      "960:\tlearn: 0.6880969\ttotal: 30m 2s\tremaining: 1m 13s\n",
      "961:\tlearn: 0.6880832\ttotal: 30m 4s\tremaining: 1m 11s\n",
      "962:\tlearn: 0.6880708\ttotal: 30m 6s\tremaining: 1m 9s\n",
      "963:\tlearn: 0.6880653\ttotal: 30m 8s\tremaining: 1m 7s\n",
      "964:\tlearn: 0.6880163\ttotal: 30m 10s\tremaining: 1m 5s\n",
      "965:\tlearn: 0.6880034\ttotal: 30m 12s\tremaining: 1m 3s\n",
      "966:\tlearn: 0.6879554\ttotal: 30m 14s\tremaining: 1m 1s\n",
      "967:\tlearn: 0.6879067\ttotal: 30m 15s\tremaining: 1m\n",
      "968:\tlearn: 0.6878991\ttotal: 30m 17s\tremaining: 58.2s\n",
      "969:\tlearn: 0.6878951\ttotal: 30m 19s\tremaining: 56.3s\n",
      "970:\tlearn: 0.6878677\ttotal: 30m 21s\tremaining: 54.4s\n",
      "971:\tlearn: 0.6878385\ttotal: 30m 23s\tremaining: 52.5s\n",
      "972:\tlearn: 0.6878312\ttotal: 30m 25s\tremaining: 50.6s\n",
      "973:\tlearn: 0.6878184\ttotal: 30m 27s\tremaining: 48.8s\n",
      "974:\tlearn: 0.6877949\ttotal: 30m 28s\tremaining: 46.9s\n",
      "975:\tlearn: 0.6877902\ttotal: 30m 30s\tremaining: 45s\n",
      "976:\tlearn: 0.6877734\ttotal: 30m 32s\tremaining: 43.1s\n",
      "977:\tlearn: 0.6877644\ttotal: 30m 34s\tremaining: 41.3s\n",
      "978:\tlearn: 0.6877572\ttotal: 30m 36s\tremaining: 39.4s\n",
      "979:\tlearn: 0.6877518\ttotal: 30m 38s\tremaining: 37.5s\n",
      "980:\tlearn: 0.6877326\ttotal: 30m 40s\tremaining: 35.6s\n",
      "981:\tlearn: 0.6877254\ttotal: 30m 41s\tremaining: 33.8s\n",
      "982:\tlearn: 0.6877216\ttotal: 30m 43s\tremaining: 31.9s\n",
      "983:\tlearn: 0.6877119\ttotal: 30m 45s\tremaining: 30s\n",
      "984:\tlearn: 0.6877094\ttotal: 30m 47s\tremaining: 28.1s\n",
      "985:\tlearn: 0.6876996\ttotal: 30m 49s\tremaining: 26.3s\n",
      "986:\tlearn: 0.6876928\ttotal: 30m 51s\tremaining: 24.4s\n",
      "987:\tlearn: 0.6876838\ttotal: 30m 53s\tremaining: 22.5s\n",
      "988:\tlearn: 0.6876668\ttotal: 30m 54s\tremaining: 20.6s\n",
      "989:\tlearn: 0.6876559\ttotal: 30m 56s\tremaining: 18.8s\n",
      "990:\tlearn: 0.6876430\ttotal: 30m 58s\tremaining: 16.9s\n",
      "991:\tlearn: 0.6876401\ttotal: 31m\tremaining: 15s\n",
      "992:\tlearn: 0.6876315\ttotal: 31m 2s\tremaining: 13.1s\n",
      "993:\tlearn: 0.6876132\ttotal: 31m 4s\tremaining: 11.3s\n",
      "994:\tlearn: 0.6875841\ttotal: 31m 5s\tremaining: 9.38s\n",
      "995:\tlearn: 0.6875701\ttotal: 31m 7s\tremaining: 7.5s\n",
      "996:\tlearn: 0.6875437\ttotal: 31m 9s\tremaining: 5.63s\n",
      "997:\tlearn: 0.6875401\ttotal: 31m 11s\tremaining: 3.75s\n",
      "998:\tlearn: 0.6875303\ttotal: 31m 13s\tremaining: 1.88s\n",
      "999:\tlearn: 0.6875011\ttotal: 31m 15s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x7f0b87ad91d0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "model = CatBoostClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "0Yh1ri1nvIUf"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "DjUS6p0c1_zx"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "id": "Oxa4JOavJGAT",
    "outputId": "76eb279d-b895-4d40-ff71-e3ca95a89b45"
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " 불친절함\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "#run model\n",
    "X_pred = vectorizer.transform([input()]) \n",
    "y_pred = model.predict(X_pred)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"여기어때서초크롤링.csv\")\n",
    "test = test.drop(test.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3607.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.806876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.878675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              star\n",
       "count  3607.000000\n",
       "mean      8.806876\n",
       "std       1.878675\n",
       "min       1.000000\n",
       "25%       8.000000\n",
       "50%      10.000000\n",
       "75%      10.000000\n",
       "max      10.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test.star==10,'label']=1\n",
    "test.loc[test.star<4,'label']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2152 entries, 0 to 3605\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   review  2152 non-null   object \n",
      " 1   star    2152 non-null   float64\n",
      " 2   label   2152 non-null   float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 67.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       1.0\n",
       "2       1.0\n",
       "3       1.0\n",
       "4       1.0\n",
       "       ... \n",
       "3598    1.0\n",
       "3599    0.0\n",
       "3603    1.0\n",
       "3604    1.0\n",
       "3605    1.0\n",
       "Name: label, Length: 2152, dtype: category\n",
       "Categories (2, float64): [0.0, 1.0]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.label.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = test.loc[test.label ==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>star</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3597</th>\n",
       "      <td>침구가 너무 푹신하고 일하시는 사람들이 전부 너무 친절합니다</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3598</th>\n",
       "      <td>깔끔 조식......</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>새 건물이라서 깨끗하고 좋았음</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>,신상 깔끔 조식....</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2019 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 review  star  label\n",
       "0     신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...  10.0    1.0\n",
       "1     프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...  10.0    1.0\n",
       "2      좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당  10.0    1.0\n",
       "3     강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...  10.0    1.0\n",
       "4     여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...  10.0    1.0\n",
       "...                                                 ...   ...    ...\n",
       "3597                  침구가 너무 푹신하고 일하시는 사람들이 전부 너무 친절합니다  10.0    1.0\n",
       "3598                                        깔끔 조식......  10.0    1.0\n",
       "3603  오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...  10.0    1.0\n",
       "3604                                   새 건물이라서 깨끗하고 좋았음  10.0    1.0\n",
       "3605                                      ,신상 깔끔 조식....  10.0    1.0\n",
       "\n",
       "[2019 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>star</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3598</th>\n",
       "      <td>깔끔 조식......</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3599</th>\n",
       "      <td>연박하는사람이 없었을까요?첫날 발렛 만원 둘째날 부터 오천원이라고 명시해놓고출차할때...</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>새 건물이라서 깨끗하고 좋았음</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>,신상 깔끔 조식....</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2152 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 review  star  label\n",
       "0     신라스테이 서초 오랜만에 재방문했습니다.위치는 역에서 약간 떨어져있지만 너무 시끄럽...  10.0    1.0\n",
       "1     프론트에서 친절하게 대해주셨고, 방에 들어가서 큰침대 사이즈에 놀랐습니다. 전체적으...  10.0    1.0\n",
       "2      좁지만 깔끔하고 침구류가 좋아요그러나 욕실은 좁아서 불편 ㅠㅠ비지니스호텔이니 이해합니당  10.0    1.0\n",
       "3     강남에서 숙박할 때마다 매번 이용합니다 한 번도 바깥이나 옆에 소리가 들린 적이 없...  10.0    1.0\n",
       "4     여러번 방문했는데 이번이 제일 좋았네요방 컨디션은 큰 차이 없는데 고층 배정 받았더...  10.0    1.0\n",
       "...                                                 ...   ...    ...\n",
       "3598                                        깔끔 조식......  10.0    1.0\n",
       "3599  연박하는사람이 없었을까요?첫날 발렛 만원 둘째날 부터 오천원이라고 명시해놓고출차할때...   2.3    0.0\n",
       "3603  오픈한지 얼마 안되서 재빨리 예약해서 다녀왔습니다. 깔끔하고무지 마음에 들었어요. ...  10.0    1.0\n",
       "3604                                   새 건물이라서 깨끗하고 좋았음  10.0    1.0\n",
       "3605                                      ,신상 깔끔 조식....  10.0    1.0\n",
       "\n",
       "[2152 rows x 3 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test1 = test.review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1 = test.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run model\n",
    "X_pred = vectorizer.transform(x_test1) \n",
    "y_pred = model.predict(X_pred)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9209120788824979"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7950743494423792"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_pred, y_test1)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "리뷰전처리",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}