Project1_SentimentAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we are going to perform Sentiment Analysis. We are also going to work on our own data. Before following this notebook you might as well look at the Warmup Exercise for this project for understanding web scraping and for pre-processing part for this exercise. We have used www.foursquare.com for scraping our data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reference for web scraping was from following site:\n",
    "\n",
    "https://www.nicholasrenotte.com/how-to-build-a-sentiment-analyser-for-yelp-reviews-in-python/\n",
    "    \n",
    "We follow the same style for text-processing and working for sentiment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day 1: Scraping Data and See our Fruits of Labour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "from urllib.request import urlopen  #urlopen is a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Opening up connection and grabbing the content\n",
    "url = 'https://foursquare.com/explore?mode=url&near=Helsinki&nearGeoId=72057594038586161&q=Ice%20cream'\n",
    "req = urlopen(url)\n",
    "site = req.read()\n",
    "req.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#HTML parsing\n",
    "page_bs = bs(site, \"html.parser\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets play around. Just like in our first warmup exercise, lets try commenting out the code snippets and running them. Feel free to add more cells and do some extra experiments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# page_bs.h1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# page_bs.p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# page_bs.body.span"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets grab each product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "contentHolder = page_bs.findAll(\"div\",{\"class\":\"contentHolder\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(contentHolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(contentHolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#content = contentHolder[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#content.a #Is actually the title of each div "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#content.a.text #Lets get the text only and remove all unnecessary tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was the title now lets get some more stuffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#content.find(\"p\",{\"class\":\"tipText\"}).text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import re\n",
    "# content.find(\"div\", {\"class\":re.compile(r'venueScore')}).text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title: Jädelino\n",
      "Reviews: Chili chocolate flavour to burn you and matcha tea flavour to extinguish you? Yes please! The best ice cream in town!\n",
      "Ratings: 9.3\n",
      "Title: Bus Gelato\n",
      "Reviews: Great Service good coffee and excellent artesan icecream\n",
      "Ratings: 8.8\n",
      "Title: Helsingin jäätelötehdas\n",
      "Reviews: One of my \"must go\" places in summer! I've tried every single ice cream they have and love them all. Try it! :)\n",
      "Ratings: 8.0\n",
      "Title: Helsingin Jäätelötehdas\n",
      "Reviews: Great ice cream. Comes at a price though\n",
      "Ratings: 7.9\n",
      "Title: Helsingin jäätelötehtaan kioski\n",
      "Reviews: Best ice cream by the biggest scoops I've tried in this town!\n",
      "Ratings: 7.8\n",
      "Title: Fazer Café\n",
      "Reviews: I have been coming here for many years great coffee food and ice cream.\n",
      "Ratings: 8.0\n",
      "Title: Käpylän Lippakioski\n",
      "Reviews: Good coffee brilliant ice-cream nice hippie-like atmosphere:)!\n",
      "Ratings: 7.8\n",
      "Title: Magnum Helsinki\n",
      "Reviews: Excellent Ice Cream with a speciaö twist\n",
      "Ratings: 7.5\n",
      "Title: Café Vanille\n",
      "Reviews: Cute kind of authentic place. Nice Finnish pastries and sandwiches. Not too expensive with friendly service. Free coffee refills !\n",
      "Ratings: 7.7\n",
      "Title: Robert's Coffee Gelato Factory\n",
      "Reviews: They have fabulous vegan ice cream too. Try pistachio!\n",
      "Ratings: 7.3\n",
      "Title: Jäätelökahvila Paja\n",
      "Reviews: Ice cream pancakes\n",
      "Ratings: 7.1\n",
      "Title: Helsingin Jäätelötehdas\n",
      "Reviews: Maraschinos on rusinoit buu!\n",
      "Ratings: 7.1\n",
      "Title: Ciao! Caffé\n",
      "Reviews: Good coffee and excellent gelato made in the premises !\n",
      "Ratings: 7.1\n",
      "Title: Munkkiniemen lippakioski\n",
      "Reviews: Free wlan and tasty icecream!\n",
      "Ratings: 6.7\n",
      "Title: Chjoko\n",
      "Reviews: Cute chocolate and ice cream place. They have also vegan oat milk ice cream (raspberry is yummy) and sorbet.\n",
      "Ratings: 8.4\n",
      "Title: Robert’s Coffee Gelato Factory\n",
      "Reviews: Worse lunch buffet than in esplanadi. No take your own ice cream on lunch. Boo.\n",
      "Ratings: 6.1\n",
      "Title: Sinisen huvilan kahvila\n",
      "Reviews: Take an ice cream and enjoy the view :)\n",
      "Ratings: 9.2\n",
      "Title: Ciao! Caffé\n",
      "Reviews: Very tasty ice cream with yoghurt :)\n",
      "Ratings: 5.9\n",
      "Title: Kahvila Sävy\n",
      "Reviews: Try the new \"Twitter McFlurry\" #delicious #doubleshot #affogato\n",
      "Ratings: 9.1\n",
      "Title: Sokerileipuri Alenius\n",
      "Reviews: Great portion of ice cream.\n",
      "Ratings: 7.3\n",
      "Title: Ciao! Caffé\n",
      "Reviews: Delicious vegan ice cream. Ask which flavours are vegan.\n",
      "Ratings: 5.8\n",
      "Title: Brooklyn Cafe\n",
      "Reviews: Try the super yummy chocolate brownie with ice cream about 6€.\n",
      "Ratings: 8.6\n",
      "Title: Jäätelökioski Senaatintori\n",
      "Reviews: They sell also Mövenpick ice cream including Maple Walnut and Pistachio flavours.\n",
      "Ratings: \n",
      "Title: Munkkiniemenrannan jäätelökioski\n",
      "Reviews: Summers best ice cream!\n",
      "Ratings: \n",
      "Title: Pingviini Jäätelökahvila\n",
      "Reviews: Mixed vanilla-chocolate soft ice-cream 🍦👍🏼\n",
      "Ratings: \n",
      "Title: Spice Ice\n",
      "Reviews: Local ice cream chain with stalls in many shopping centres. The jet black salmiakki flavour is my particular favourite.\n",
      "Ratings: \n",
      "Title: Pingviini Jäätelökioski\n",
      "Reviews: \n",
      "Ratings: \n",
      "Title: Jäätelökioski\n",
      "Reviews: Beautiful girls. Expensive ice cream like everywhere. Great location.\n",
      "Ratings: \n",
      "Title: Pingviini Jäätelökioski\n",
      "Reviews: \n",
      "Ratings: \n",
      "Title: Pingviini Jäätelökioski\n",
      "Reviews: \n",
      "Ratings: \n"
     ]
    }
   ],
   "source": [
    "#Now lets loop through the ResultSet objects\n",
    "import re\n",
    "\n",
    "filename = \"Icecream.csv\"\n",
    "#Had to do some searches and find out about encoding. Try without errors = ignore and what happens\n",
    "#https://en.wikipedia.org/wiki/ISO/IEC_8859-1\n",
    "f = open(filename,\"w\", encoding = 'ISO 8859-1', errors = 'ignore') #The filename is already given, which is open here now\n",
    "headers = (\"Title ,Reviews, Ratings\\n\")   #Headers or column names\n",
    "f.write(headers)\n",
    "\n",
    "for elements in contentHolder: \n",
    "    title = elements.a.text\n",
    "    ratings = elements.find(\"div\",{\"class\":re.compile(r'venueScore')}).text   # venueScore have additional words with it like positive, neutral and negative\n",
    "    ratings = ratings.replace('?','') #We do this so as to find out the NaNs easily\n",
    "    \n",
    "    \n",
    "    while True:\n",
    "        reviews = elements.find(\"p\",{\"class\":\"tipText\"})  \n",
    "        if reviews is not None:\n",
    "            review = reviews.text\n",
    "        else:\n",
    "            review = '--'\n",
    "        break\n",
    "    review = review.replace('•','')  #This character wont be decoded properly\n",
    "    review = review.replace(',','')  #The extra commas in review will create problem while creating csv file, as there are only 3 commas in our csv file\n",
    "    #Lets also remove the name and date as we dont need them\n",
    "    cur = review.find('20')  #Will work for now\n",
    "    review = review[cur+4:]  #is creating load for the compiler though....we can think of other options as well\n",
    "\n",
    "    #reviews = elements.find(\"p\",{\"class\":\"tipText\"}).text\n",
    "    print(\"Title: \" + title)     #I am printing to see what is really happening\n",
    "    print(\"Reviews: \"+ review)\n",
    "    print(\"Ratings: \"+ ratings)\n",
    "    \n",
    "    f.write(title+\",\"+review+\",\"+ratings +\"\\n\")\n",
    "    \n",
    "f.close()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how people scrape data from various websites and use it for their personal purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of problems while scraping data. But for learning purposes, various sites offer data in ready format.\n",
    "\n",
    "Also refer the following sites:\n",
    "\n",
    "https://medium.com/@ODSC/20-open-datasets-for-natural-language-processing-538fbfaf8e38\n",
    "\n",
    "https://opendatascience.com/25-excellent-machine-learning-open-datasets/"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now lets load the file that we saved and do some stuffs from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tranq\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(r\"Icecream.csv\", header = 0, encoding = 'ISO 8859-1',sep=r'\\s*,\\s*') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Try without sep argument and see what happens\n",
    "#df = pd.read_csv(r\"Icecream.csv\", header = 0, encoding = 'ISO 8859-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What difference did you note ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3  \n",
       "1  Great Service good coffee and excellent artesa...      8.8  \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0  \n",
       "3           Great ice cream. Comes at a price though      7.9  \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title      0\n",
       "Reviews    3\n",
       "Ratings    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Refer the Python Basics notebook\n",
    "\n",
    "# df['Reviews'] = df['Reviews'].apply(lambda x:x[x.find('20')+4:])\n",
    "\n",
    "# Had we not done it above, we could have done it here too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3  \n",
       "1  Great Service good coffee and excellent artesa...      8.8  \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0  \n",
       "3           Great ice cream. Comes at a price though      7.9  \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing Data, Some Fun Stuffs and Getting to Destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Magic command to display the output figures\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import normal\n",
    "plt.hist(df['Ratings'])\n",
    "plt.title(\"Review Ratings\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there are no negative reviews at all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean/Pre-process our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #We can use our own stopwords like this, but since NLTK does that and since we will need NLTK for some other purposes too, we import stopwords from NLTK\n",
    "# stopwords = ['a','an','and','the','of','this','that','which','in','is']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review_lowercase'] = df['Reviews'].apply(lambda x:\" \".join(word.lower() for word in x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review_nopunct'] = df['review_lowercase'].str.replace('[^\\w\\s]','') #Removes non words and letters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why do you think such steps are even necessary ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_lowercase</th>\n",
       "      <th>review_nopunct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>one of my \"must go\" places in summer! i've tri...</td>\n",
       "      <td>one of my must go places in summer ive tried e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "      <td>great ice cream. comes at a price though</td>\n",
       "      <td>great ice cream comes at a price though</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>best ice cream by the biggest scoops i've trie...</td>\n",
       "      <td>best ice cream by the biggest scoops ive tried...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \\\n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3   \n",
       "1  Great Service good coffee and excellent artesa...      8.8   \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0   \n",
       "3           Great ice cream. Comes at a price though      7.9   \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8   \n",
       "\n",
       "                                    review_lowercase  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my \"must go\" places in summer! i've tri...   \n",
       "3           great ice cream. comes at a price though   \n",
       "4  best ice cream by the biggest scoops i've trie...   \n",
       "\n",
       "                                      review_nopunct  \n",
       "0  chili chocolate flavour to burn you and matcha...  \n",
       "1  great service good coffee and excellent artesa...  \n",
       "2  one of my must go places in summer ive tried e...  \n",
       "3            great ice cream comes at a price though  \n",
       "4  best ice cream by the biggest scoops ive tried...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Title', 'Reviews', 'Ratings', 'review_lowercase', 'review_nopunct'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take one row for example to see what is happening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_lowercase</th>\n",
       "      <th>review_nopunct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kahvila Sävy</td>\n",
       "      <td>Try the new \"Twitter McFlurry\" #delicious #dou...</td>\n",
       "      <td>9.1</td>\n",
       "      <td>try the new \"twitter mcflurry\" #delicious #dou...</td>\n",
       "      <td>try the new twitter mcflurry delicious doubles...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Title                                            Reviews  Ratings  \\\n",
       "18  Kahvila Sävy  Try the new \"Twitter McFlurry\" #delicious #dou...      9.1   \n",
       "\n",
       "                                     review_lowercase  \\\n",
       "18  try the new \"twitter mcflurry\" #delicious #dou...   \n",
       "\n",
       "                                       review_nopunct  \n",
       "18  try the new twitter mcflurry delicious doubles...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Title']=='Kahvila Sävy'] # Without using sep=r'\\s*,\\s*' in the read_csv() , I got keyerror Title. Can you think why it happened and why this solved the issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets recall what is stopwords. You can convert it in Markdown type cell and try answering what is stopwords. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\tranq\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Before proceeding, you can try doing some interesting problems related to lambda expressions\n",
    "\n",
    "#Code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review_nostopword'] = df['review_nopunct'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_lowercase</th>\n",
       "      <th>review_nopunct</th>\n",
       "      <th>review_nostopword</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>one of my \"must go\" places in summer! i've tri...</td>\n",
       "      <td>one of my must go places in summer ive tried e...</td>\n",
       "      <td>one must go places summer ive tried every sing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "      <td>great ice cream. comes at a price though</td>\n",
       "      <td>great ice cream comes at a price though</td>\n",
       "      <td>great ice cream comes price though</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>best ice cream by the biggest scoops i've trie...</td>\n",
       "      <td>best ice cream by the biggest scoops ive tried...</td>\n",
       "      <td>best ice cream biggest scoops ive tried town</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \\\n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3   \n",
       "1  Great Service good coffee and excellent artesa...      8.8   \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0   \n",
       "3           Great ice cream. Comes at a price though      7.9   \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8   \n",
       "\n",
       "                                    review_lowercase  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my \"must go\" places in summer! i've tri...   \n",
       "3           great ice cream. comes at a price though   \n",
       "4  best ice cream by the biggest scoops i've trie...   \n",
       "\n",
       "                                      review_nopunct  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my must go places in summer ive tried e...   \n",
       "3            great ice cream comes at a price though   \n",
       "4  best ice cream by the biggest scoops ive tried...   \n",
       "\n",
       "                                   review_nostopword  \n",
       "0  chili chocolate flavour burn matcha tea flavou...  \n",
       "1  great service good coffee excellent artesan ic...  \n",
       "2  one must go places summer ive tried every sing...  \n",
       "3                 great ice cream comes price though  \n",
       "4       best ice cream biggest scoops ive tried town  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wordcloud\n",
    "Word cloud or tag cloud is a visual representation of text data. Notice the different color and size of fonts. \n",
    "\n",
    "So what's the intuition behind wordcloud then?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19a93a69ba8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check documentation\n",
    "#https://amueller.github.io/word_cloud/generated/wordcloud.WordCloud.html\n",
    "\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "wordcloud = WordCloud(width = 1500, height = 1500).generate(df['review_nostopword'].to_string())\n",
    "\n",
    "plt.figure (figsize = (10,10), facecolor = None)\n",
    "plt.imshow(wordcloud)\n",
    "#plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cream         16\n",
       "ice           16\n",
       "coffee         5\n",
       "vegan          4\n",
       "try            4\n",
       "great          4\n",
       "excellent      3\n",
       "chocolate      3\n",
       "good           3\n",
       "icecream       3\n",
       "tried          2\n",
       "place          2\n",
       "town           2\n",
       "nice           2\n",
       "service        2\n",
       "delicious      2\n",
       "ive            2\n",
       "tasty          2\n",
       "take           2\n",
       "free           2\n",
       "flavour        2\n",
       "lunch          2\n",
       "cute           2\n",
       "best           2\n",
       "yummy          2\n",
       "speciaö        1\n",
       "kind           1\n",
       "coming         1\n",
       "extinguish     1\n",
       "pancakes       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pandas series has funtion value_counts() which will give us with the number of occurence of each words\n",
    "pd.Series(\" \".join(df['review_nostopword']).split()).value_counts()[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_stopwords = ['ice','cream','ive','take','tried','try']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['review_cleaned'] = df['review_nostopword'].apply(lambda x: \" \".join(word for word in x.split() if word not in other_stopwords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_lowercase</th>\n",
       "      <th>review_nopunct</th>\n",
       "      <th>review_nostopword</th>\n",
       "      <th>review_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>one of my \"must go\" places in summer! i've tri...</td>\n",
       "      <td>one of my must go places in summer ive tried e...</td>\n",
       "      <td>one must go places summer ive tried every sing...</td>\n",
       "      <td>one must go places summer every single love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "      <td>great ice cream. comes at a price though</td>\n",
       "      <td>great ice cream comes at a price though</td>\n",
       "      <td>great ice cream comes price though</td>\n",
       "      <td>great comes price though</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>best ice cream by the biggest scoops i've trie...</td>\n",
       "      <td>best ice cream by the biggest scoops ive tried...</td>\n",
       "      <td>best ice cream biggest scoops ive tried town</td>\n",
       "      <td>best biggest scoops town</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \\\n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3   \n",
       "1  Great Service good coffee and excellent artesa...      8.8   \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0   \n",
       "3           Great ice cream. Comes at a price though      7.9   \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8   \n",
       "\n",
       "                                    review_lowercase  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my \"must go\" places in summer! i've tri...   \n",
       "3           great ice cream. comes at a price though   \n",
       "4  best ice cream by the biggest scoops i've trie...   \n",
       "\n",
       "                                      review_nopunct  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my must go places in summer ive tried e...   \n",
       "3            great ice cream comes at a price though   \n",
       "4  best ice cream by the biggest scoops ive tried...   \n",
       "\n",
       "                                   review_nostopword  \\\n",
       "0  chili chocolate flavour burn matcha tea flavou...   \n",
       "1  great service good coffee excellent artesan ic...   \n",
       "2  one must go places summer ive tried every sing...   \n",
       "3                 great ice cream comes price though   \n",
       "4       best ice cream biggest scoops ive tried town   \n",
       "\n",
       "                                      review_cleaned  \n",
       "0  chili chocolate flavour burn matcha tea flavou...  \n",
       "1  great service good coffee excellent artesan ic...  \n",
       "2        one must go places summer every single love  \n",
       "3                           great comes price though  \n",
       "4                           best biggest scoops town  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets see the cleaned review in wordcloud "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "icecream_mask = np.array(Image.open('icecream.jpg'))\n",
    "#icecream_mask  ##This image would be displayed as matrix of pixels [R,G,B]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computers understand numbers as an array of RGB pixels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Height and width parameters wont make sense here\n",
    "wordcloud = WordCloud(background_color='white', mask = icecream_mask, contour_width=3, contour_color='violet').generate(df['review_cleaned'].to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/1LQNdE4hNUeZEmCKPR9CGqhFlf0KGtmXhv3KNOOQbWnwKsQefcoylVmWbj/MNztsm8DtJU/2DTm5eg04wP8tfLzHISkRAXK3EJ+Y6VD7vrvfQKvTsqX3HDKVefzvsPEDeW8/vf9UUVg+6H8U77R4nL533gBKcjY7jtdOrUClNQ25LmJAtfa81Hik2eW3wXvfRq4xtYzkqgqMZDAnoyWS5Zn8HLeTLbePWW23pvssqnuZfzvzdtM7VQ/b9y4ytallUyqWMKFOX56ubxpRVcT7534iKv2izWszu8UEi8c/bW2fb5w9aQhKsqLjRzbbvGkh2WRpHq7el4er97XZbmWnuWb3dwlpYyK/pTHH1jJ1Tu0V1oleYaauAaXHbBXQhFYBTWzKWRqdTmkUWWYLqVttCpVnUGluWW2nUifo20tqWW13t9C05lUKlSeJSxlObsFmcvL/wtO9JSF+U4gIfM+uMf6IOsvE3h3oMutrm22XbT/Msu2HiZ7/Eh7Syq8oJleqeePnrey5YP/L3+kbtxndxfaKQUJ6NlNWrOdWhu2o10nL/gTg6LxpeHuYXx1yBUX5nkI9/C228ZIU/25UWg1SsYSRNbpyLvsGO5JO8kKjocjUcjYnHmVMrZ6G9Coqrd6imSLP5lTWNdoG1aeOj+kScrC7eZ+/ME/XLR2X+Zsl9hqOm+Y6moJ16DTGPgQiSTXEXo+Wi9KUmivj+13HDZ/rhAXRtm4kEQHOO0pWr3aWwsItZGRNIyf3U3JyPycs5Gc8PXtT1d/sSqJUWX4oW+NGfgpLYkzzuiQWpnM6yzgnjVanY+HldSaKU7I8k1dPLkOrs57747/kk1yXJfF9l9ecktVRnjm6iHy1bedgS0oTgI+bJ7/e2G1WaQL9D/vHuP8sKk7J8kz2pZ41e6wk/yWf5NUmo/Fxs26dEKg85KpzeLnbn97AU9qKXP5GrUm12k6h0kcxOaKUVWXy5QdJSJ+Ij2cvaoYsQ+JE5Ygz8UnM/Wu34bObRMz4Hm0J8fMmu6CQs/HJnLxu/OxZsGk/74yxrbiP6NSCuNRMcgoKSUjPxsZty2GmfLveRDYAPy8PagYHUDMkgHyFkuhrt1Cq9YqBj4c7g9uZOmKXZvLydSRlGfvy1Y8IZki7JqjUWm5lZrP15BWj42/+spUlz5RfioUcld6yaq/zda4qnxAPf4PlKFamT/FxIScerU5HkzsO5GKRyPBMsWZtqihcopJLfF9B4vuKK4aym9lrt6PSaBCLRfz2ygSa1TTvvOgIIpEH3t6j8fYeDeiQKw6QnvE0Ol0B4IaX1yBCg7+jqitRgf7OpTmYGr2EHmEtDNabQXtmodCqeDzqU6DYqnNNlsQzRxeaVUSqeQazvOPL7E09y4Bq7anva5xvKEORyztnf+BSbgLXZEkUaBR4lwqP/7fPJ0afi6xM/lJvNveybRUpSWzebaZGL0FxZ4nof3X7M6b2AwRIjX/Y0Zkx7Ek5Y3WsRZf/AmBu66fpEWZchiEmL5HnjunN/i8c/4rlnV426V/NM5ggd18GR3ZiSsOHTI5nKHJ55KD+/EYeeJ9NvT4yuTYClYuHtCkK1WVupD5G/YhteEhtP+AAQv2nkppT9L3WYi6FXkbecmRy1/lhVQXi08YClClP08FLNwAI8PZk5QtjaFojzKSNVqfj9Z+2suNMDAC/R50h0MeTaUOs+6LNGT/QZJ89y3f2sPXkFROlaf9HzxPka/3+nJojw8vdulXo96gzRkrTlIFdmDbY9FznPzGUxMwcBs/R+5TtvXCdDm98xYnPTO9PrsDS8pwt6viE0zaoPkczrpCvlvN7gj6h6qDI9gAMq96ZzYlHyVLK2J6s930aZca/qaK4a2sMXE/RL0f1bdHAJUqTKSI8PXpRI/IsIcHLATWFhVtISumOLN95x8aKwM/HeUvFk/UGGLZLKgclwz4blFKGStPEvxZTGj5kojQBhHj4M6f1U4bPZ7NNsyu7kq+vbjIoTX5uXjzTYLCJ0gTQMbgxrzd71OZ4EpHYRGkCDKG1AJdyEyz2X9fzPbNKE+ivTdF1Vmk15X5tBBwn1P8lAHQ6OQnpT1psp9Vajh5MyZ5ndn9aziIAfDzuvZQb6blLUWnKFtW47NmRZpUm0D+wP3isv9G+wzHxZZqvLCjVGhb9Y5wz7PEH2tlUmgDCA6xHmsnkChb/fdDweXjHZmaVpiJqBAcYKTRFlq3yoOjemqXMt6t9SVeNpn56C1J8QZrBqlREiwB9brrLubeIk+n9mWtYWR0ob5xWnHTqK7YblUKVOcnZ6UzIK1QAMLit4/4J9iBX7CM94ylu3W5IRuYL+Pm+SGS143h7DSc75xMSbzdGra78B1t6poxHp35ntG9Ib+eKUjb1r0Vjv2LzZ7+IdobtR2v1dk5AM4SUWP++kF2+N7fTWXrfgpYBdfm798dlHu+Hrq9bPDY4sqPN/rZylkxuUOyvU97XpoifunzGo7Uqt5TM3UKA90ia1tRHDanUCVy8WcPs3/UUU+W4ac1YfD37kpG3jIs3a3DpVj1ibrc39NHqChCL/agTvs6lMmfn/0ZG3nekZH/A7czXkCv1llWFKoZb6c+SnP0+aTkLyMr/FYUqxqVzN4w8iNStJqk5n3D1dmez1ypL9ovNcUQiaF3H+gubn5exdfZsvGMBQ67k6aV/kJJdHKSzbfYk3hrVxyVjj1u8hnxFsQ/oJxNs/3aXPWtc6mz7afv+zyIHV1fG1ekF6K3nlijQKAzbJaNsR9bsCsDBtAtkKY0DnIZW74ivmycbE49wI9/6cndF4PRSnSrjCaQhaxG52ZdhVae56dIiv2H+PiSkZ1OodC5Kxxp5su/IztE7LkqlTfD1mYyvzxMABPi/ja/PJG4ntyMnd8Eda1TlUFCo5MmZP5GdW2C0v20L5xxLg0o51ZV04rPkWFfk3OcszkZZOcqQ6o5nvi5Nda8Qanmbf+MFCLQjJ4kt/NyK30gr6toIOIZY5E14wNtk5C1FozXvmCs2Ez0nFnlRO+wHLt1qhE6nQKdTotakGI57e3QiMsi0WHNZuZ1p3o9Qo80ht3Cr0b7wgFl2Lz/aRkN86mOoNLettkrKegupW018PftYbNOubg2Lx6oipZW2miGuc0yOT8t2uE/HBjWMavCduJ7IoLa2/8+O+lk29y9+9mh1OrNLdxtvmQ/oKoqeSyrMAjB6rogQ0TygNtdllsscVSROK046bQbKtIG4hx9AJLH+pVYkNwGdClf6Bj3ZuwMf/7WLNYdOM7KzcxaW0txM1L/RiMX+REYcwc2tjtl2RekKCuWm+YUOnbjGG/M22DVfyWzfruLtqYNp1cS5pHghHsaKU8lq8SHu5qMkrBWAzFMXsv7mQeLzU0goSCVfLUemKkRpJaLMlaQrih9qQyLLnny1S4j1HDFSM2U2LLE/9Rxx+cmcybpGrrqALKUMpUaFzA4HdoHKJ9R/KqH+9mUsN0ZitvivJXw8upktflu/2n9m25trW5biuUXWNUtIJZFmx8/IW0ZK9lzc3eoZlYQpTXb+n9zOfJWEtCeMyrKUZmKf9o4JXoVw0u3HLuxNMeAhdaNj/Zoci9Unptx68jJvj37QZr+SDthLYv5mYr2+huW4ZHkWHmIpQaVeGAdFtmd70kl67XqT7qHN+LTNU4hFIpLlWTxx+HPkGhW+bp6s7vKqUb8i69aJzFgARtc0Xn58rHZPZp7Su8mUNfN3WSlDyZUgdNosVBmP4x6+10IrLercT+8oTSANWuLsdCYMbNOYL7Ye5NKtVPacv8aDLRvY7mQHUreGRIRvRySynaVaLA52yZyuZGgf5xNfFiUeM4ejTn8zTq3gTNY1NDrbdcnKi1xVsSXOWafFkpS0wDnL8cwY1sbv4WSm9QeSgMDdTJbsVwCC/SZbLQnj41GUT896OFu98Kp3r7WX2qGBths5iSOWLF+v4vtXXqHCokWoNEURbb8nHOD3hAOEewRQoFEgU8uZ3mSkSd6k15s+Qq6qgMPpl4lKv0S/PbNxF7sZBRN90uZJIr3M/08z71QXaBlY12h/C/9iQ0ZTv8pN1+G04uQecQKd6hLK9IdQpg1CGvIbohKhpjrNLZSp+vVOkbQ50qCViFyYWDLQx5OoOS/SZuYXvLx6M0880I6x3dtQN9zxcNciatWw3wxoqW3XtvVYPmc8y3/Zz4WrSWg0FaM49OzUgPdffqhMbzeOrmeb49WTyw1+RQBP1RtI66D6tAtqYDS+pXxMrqQo7xLoLWNlPT+JFcXSFlqdjhmnvjFcm1reYfSLaMewGl0I8fA3yLY58bAheu9+IOtjvVk+6F3nf7cCVQ+lWu+f5+5W22q7orp8YpH1Ze5qgeVfj6+8aFXbum+WI5ROl7Dh6AU2HL3g8DhanY60nHwiAm27F+zvN5/EwgzeP/criYUZyNRy/KReDInsSNugeibtPSVSFrSdRKo8m21JJ/jrZhRqnYZ6PhG81Hg4nc3UvCvCX+pNrqqAap5BPBhuXFbKT1rsxlDkD1VZlCkdgUjaTG95Ul9FlTkR99C/AdAqo1FnPW9o5x7yB9hhwXEUjVbL8I7N2HT8Ir8cOMUvB07Rr1VD6oQF4Sa27oT7ko0wVWeRSMS0blqD5XPGU1CoZMXag6zberJc5gK9GbhDy9rMf3OU7cYVQJFiIELE1MYPM6bWA5UmS8nouRR5FtU8K++tdcvto0bX5qdub5hV5HSuTiQjIFAJSN1qoVInkJP/l1Hh35LIledIzdW7K/h5W3dw9pDevUWB/bxcl5NLJlfYblQO1PAKYWVnx1IYhHsG8mS9fjxZr5/dfbb2/sDq8aISLY4eAwj3CLDZxl7KnMfJPeIEypQO6FQXUGU8hkjaFk2+vpieW+BnSLzG2BjBOVq9Zj7Xxq5zsXb1N6c4pWdMxMd7LF5e5sPFHcXby53pk/oyfZL+xlHap+nQuvK3ulQWSzpOpWVA3UqVwatEDqTFl9czv619RSrLg+VX/zZsL+k41aL162yJkgX3A4Kl6d6kQbXdxKUMI6dgIzkFG5G61UIqqQHoKFSeQacrXrapFfoDfl4DLA92l1OgcF2QR2mf0r6tGtC1kXWrniX8vYUccc7ikgSY0uBfUGU+gVYZDUp98T2ROKjclKbyolD+H1JpS5cpTqWpFuZPcprlMM27nZLZwgOllk3AeRYyb9uiQK1Ao9PaDOk3x7GMK5zJvk4bF9YrcoTCEiVkrF2bIxmXKkIcAYFyRSzyom74eq4k6osaq9Q3UalvmrTz8+p/TytNoK9F5yp8PYyVnQYRIYzvaVr2SqB8cYniJJI201ueUruj0yQj9hyKNMh2XaGycG5h2QsvmsPHx3KNsLIypHcLVq87XG7jVzYlHQ0/vfQbi9s9b5SnA+Dj87+yK+WUQ+P2Cm/F/tRzqHUaphz/gpWdZ9jdd1G7Kcw8/R1anZZXTixjWuMRDKve1SihJ+jrHx3PuMKwGqbFn11Bl9CmHEnXK0WfXvqNrztMMzouUxey+PJ6u8rCuIqsj7OcsvjcD35JRedYxL18ruWFRBxQpoi+e4W9F1yX708sNrZUnzBTzkWg/HFpFURpyHpUmU+hlW9Fq9iN2MN2raCqhk5XYLuRkzSsazkH0L3G+ewbPHf8C95sNhYfN09i825zKivWYaUJYFj1LuxPPQfoS6h8e20r9X30Dpd56gKu5N7ireZjzfZtH9yI5xoM5ZvYfwD4OmYT38VuZVBkR/yl3uSpC0kqzORYhr5GWHkpTsOrdzUoTuezb7A58TCN/WoiFolILMjg88t/kq+WMySyE9uSjtsYreyUVgwcIejdoDL1v1soUpbuh3MVKD802vILELqQkGK7kYDLsak4KZKcW9pQZZr3J/GIrPxs25aQSMJJz3iKyIiochm/d5fGeHlKKZTfu4kN/3twPkuvbmLjrSjiZMk8f/xLo+OvNhnNyJrd+fD8zzZrwxXROaQp/z34KQP2vAXAmhu7TdpYUpwAxtXpQ4fgRqy+vp2o9IsotCo2J1as5a9HWAv+e3A+A/a8CWASOecl8TBcG7lWafe1sQdzlqWyWlDudQtMyfO7189VwPXUCQs0SlSZnS8nsAylsEoS5u9DWq6+pIlCXTE58ZSNGncAACAASURBVASMEdmI4tEpU8qecbkk7hHHXDqeK8nKfhuV6jwK5XEkkuqIzFSkiaxWNmvAghX/sfE//UPxXnYOF6g6lFSc7odlNoG7n93nrvHK6s2Gz6c/fwWJjUhpMC3Qe26R4y4drhgjt1DOoI9XGUXBvT6iFxN7d3B4LHNjD56zylB2LNTPhzWvjicy6O5N2VBFsZi/xqbFqSorOq5Glr/asK2xUSbAWaZO7M2TYyo3B4XA/UXR0lqRsiQoTQIC5Yu/lycvDOrCgk3FhX4XbNrPY93a4Olu/bF7MyObnHwFLWtHWBx7xrAH+PDPnQCk5+Uz4Yu17PnwOZtyXU5MY9PxC7w5so/9JyNggkt9nCqDeRv28Ofhc6g0jlV8Nudc7kgCTGfx9nLH24V5PQQEbFHSR0dQmgQEICE9m6X/RpGcJSMpOxeZXGmw4JSmyALl6+mOj6c71QL98PV0Z2z3NlYrVkzs3YF1h88Tl5pp2NfpLX31jPAAX6oF+hER4EuBUsmFmylk5xcHhkzq28mi4gQwplsr/o6+xMk4vXN4el6+Qc5mNcKpFRqAXKUmKSuPhPRsFCrjJT1BcSobd7XilJ0vZ83B05UthoBAlUdQmAQEiknPy2frySsO9ZHJlcjkSlKyZQC0rhNps9TXD9Me5cXvNnLhprETd2qOjNQcmWNCl2LFlNHM/GkL+y4a+w1fSkzlUmJqmcYWsM5dqzjdzMhm6CfFS2vDOzSjea0IAr09mbXmX2qFBPJkn/bkFipYtTsamVzByM4t+HjswEqUWkCg4hGUprsDwf/s3iPY15vfpk/gRmoWq/dEs/7oeZt9xnRrxYhOzW2283R34+vJI0jNlfHjnhP8tM92hYrn+ndhVJcWdskuYBmbzuGumkinvopWvguJ7/O2G9vBd7uO8dXWQwC0ql2NNa+MNxxr9dpimteM4Pfp+pxMuYVy5m3Yyz8nLjGhZ1tmjbJdFVpAQECgIhEUp3ufXediOXk9kZ3nYsnOL0Sp1uAplVI/IphaoQGM69GGtnWrOzX23gvXOR57i51nr5KcnYfUTUK1QF9qhQQysE1jujSuRfUgfxef0T2NRefwClOcNPnfoc6d57J0BA/NW01CejYjOjVnzrhBRsdavbaY6sH+bJ/9jNH+brOXIpMr2ffhFIJ9TWvn3UyMxN9vOgH+b7hERgEBAQGBuxe1Us0X038m9lwCt2JTkEjERNYLo27TGjw1eyTV6oQatY85Hc/L/eeaHavzgFZ8tPalihBbwDVYVJwcr13hJDpt2dZzS5NToHeka1W7mtnjskLTNPdD2zcFYNPxixbH9fTsU3bhqhDN3ltMs/cW892B8k+qKCBQ3mR9nHVPJqS8V8/rbubc4atM6vwOO38/zI2LiaiVahSFSm5cTGTv+mPcjhP8iO5XKs7HSevaiLWixF9e7lKzx3MLTUtXPNKlJX9EneXXA6d4+sGOZvuJxYGuE9IGP68/ys6oy8TeSHPpuEJ+qLuPe3GZ5l48p/KiKlyjTtveBuD4kE9cNtbhwR/jJpKUebyyyjG75ShG1urkUN/Xhy8AQCwR03VQa3qP0vePv3Kbq6cTaN/H1A+pcds6/Jv+reHzvz8f5IvpPzkrvkAVxU7FSW1/UwvotHll6l8af09P5EoZGXn2l0jxdtenAcjKt1xkVqE4htStcZnls4ZKpWHF2gOs3RxdrvPcL1xMSuXcrWTGdmpd2aI4REnF4l4pY1I6X5Srz6kqKBgC9iOyvNpRpUmIKX7Rf++nF+g6qI1T4/iHWC7oLXD3YrvkSkpb0OYiEgfhHnGieL+TpVhcRb/WDVl78DS/RZ2xaD3S6aBE3VmOX9NX5/aUmrdSVQvfRXrGk0jd6uHh0cPlMgPk5BUy9Oml5TL2/cpzP29Ao9XedYqTq8ugVEVKnpOzRYVdTVWRwxnuFiueK6xWlcmu34tLMjmrNAncu9j0cRIZdKuq9ebQsX4NAG5n5nI1Kd3oWFFq/l8PGheULcr5VDMkwOyYsvxfkEgiSU0fw+3kDiQldzL5KwtanY53Fv5dpjEETMmQlV9hZgHHsPVArypWtaoih6NUdYXpXiE/1/KqhICAHSVXrC8n2Rslp8qaila+zT6p7GBgm8YMbB3DjrNXmfzNX+z7cIrh2LLJI5ny7Xrmb9zL/I17TfrOHT/IZB+AWOyPh0d3PDy6u0zOkkx+8xeuXDetZv321MG0blqDWpHld1OsWmqvQFXmbrFqOMvdfl4ll0Dv9nOpisgLFORlCy9jApapMOdwkdj1a72PP9AO0KenL0n3JnXo16ohu87Fmu3XsFqI2f0B/m+5VsBSlFaaAvy8eOuFQfTq3LBc5wUQ21Eg824ku8A0CECgbJTXw/he8eOqCgjX0nWcPXSFU/suceXkDdKTski4YhzINDjUtAacj78Xf13/slzlKsxXEL3zPBu/3UVyfDo5GXl4+XrSsmsjmrSvy7jpQ832K8iTM7reywD8eHIeEbXNP+9KUnSOvgHerLm4AHcPU3cWpVzFkX/PsGbRFrJTcwEIigjgqdkj6dSvJWKJ5WdM0fjjZwzlybdHWmz34ycbWbtoKwBPzhrB+Ncesil7ZVBxipPEuaRe1mhfvwbt7yzZleaLp4az7sg5Nhy7wLmEJPy9PPl47ECbKfIrkq2rp1bYXP6eHmh1Otp+tMSkrp9YJOKxjq14f3g/m+PEpWcx9KsfzB7z9XDnyKwXkYit27e0Oh1dPlmGTGGaMqIIHw93Dr05BQ83069os/cWm+zLLpCb3Q/wdI8OvDGol1WZXMnd7ENT3tyr16UyLEAVOVf/nXPIURUQ5uHP0s6TqOcbbnR8wsElXM0zjZy25evUa8cHFGqU7Og3m7dOreFkZpxJm9o+oXze/gmTOQEyFHnMPv07JzKNVz68JO680nQIUrEEldZ6HdMDm0/y9/d7rLapSFISMniq49votKZpFPOy8jm87TSHt53mh7kbqdeiJsv3vWfUxtvP07A965HFrDo+x+p8Wo3WsD1v/XQTpUmlVPP51NXs22Ca0iY7PY/3J3wNwBNvDOeJN4bbPsF7AKcVJ5G0mYMd/JydymnGdG3FmK6t0Op0iEVVa7EqKMA0AWd54uUu5f3NO80WQ9bqdPx2/CwzBz6Aj4flAsSHYuN5+TfLPloyhZIXf93IoscesjiORqtj2trNVpUmgHyF0qzSJCBQ1ShSlEtagCpLeS6Pea/mJZGj0i9drej6LLW8TS0YU5sM5GZBBvGydLJV+exMOufQHBdybhmUJm+JOz5unqQp9FaNhPx0ph5fxdYHTVcEXjj2PXGy4nxKoR5+5KoKKdQo+fTCJsI8/A3jWOJ/bwxn1PPGL42r52zgwCZ9MJQ5xaM8LfgRtUPoOaw9Bzbr5+87pgv1W9YkolYoOZkyVr6/DnmBviBx3IVbZseIrBNGUnwat+NSuXjsGs07WzYYnNhzwbDdqE0dk+MfP7mcY/8V/z9HvziAWo2qodVoibuYyD+r9gLwy2d/oyhU8sz7jzh8zncbTj+Z3EO3ONRe7DUCqdR2/Z3ywF6l6WZipM02tWq4Jh/V/0Z1cck49jLzT73589CbzxPs42XYr1CrafuRvmJ3x7lL+WLsMAa1aGTSPyO/gMk/rQdgSMsmLHrM2Ey8+cwl3tu0k/1Xb9Bx7lIufTTdrBy9P/+WDFkBErGIr8YNp29T0x/02mNn2HHR/DIrYDR2VkEh3T/9hkBvTw6/9YLFPhVJVbOqLNx1kPVnLiKTK3B3kxDq68PI1s3oUb8OLatbrsAOoNJo2HnlGt8cOEZ6fgG5hXIaR4TSKCyE6X17EOFneQl+6f4jHLwWz7nbKXi7S3moRROGNG9M57o1bcrsaN/mH39BqK8P+6c/i0arZevFGBbtOki6rIDmkeH0bVyfKT0725x34a6DnLp1m3OJKai0WuoEB1I3JIipD3Qxe62KlKV7ddnsal4SEw4uoY5PKMs7TybM03zJjh5hTYw+O6o4TY/+kWWdn6FTiPH9IFdVSL+dH5MmzzV5AT6XnWBQmszli/r84t/8Hn8YW/iH+JqkDfD2K75HVq9naukqb2avmmLx2LCne5OZksOEFq8DcHznOTr1b2XUZv6m13iy/Sx0Wh3vjV/CumtfmB0rP7eQj5/8BgA3qWm+rZyMPIPS9NqSpxgw3tT/d9pnE1i/7D++fe9P/lyynYmzRiB1v7dfeivM8UUkDkHs3rWipisXXJkcM6DED7OiaFE9wkhpAvBwc2NA82Ifq2/2HTXb98udUYbthY+arq0/3KYZ7zxUXANQrlKbHacoAq5b/dpmlSaA8Z3bsPqpe/+tpbzJUyiY/tdWvj10nHRZPnK1mly5guvpmSzafYhHVq4hT6GwOkb/Jat4dd0WLqekkS7LR6nRcP52ChvOXGTAktVm+xeq1HywZRdf7T3MyZu3UWk05BTKWRN9hv/99CeJ2ZYtAGXpm5InQ6ZQMn7178xcv43bOXkoNRpO30pi0e5D/Hsxxq5rdTw+EblajUar5Xp6JruvXOORlWsYtPQHq9eqpMJcWcqzK+e9mpfEC0e/B+DbLs9ZVJpcRWmlCcBfWny/ui4z9hHdcFO/dCQWic0m2XypyWAXS1h1CI4ojgy/GZNscjy8ZjBtH9BXypDlWHZ0378pGqVCBUAXM2kX/v5+r2HbnNJUxKgX+hu2/1sbZbHdvcK9rRY6iCVrkkZzm+TU/lQLd906eF5+xTo1+3l68Nuz48we+2rccJ5avY6jcTe5nGyaxXzN0TP8eUL/1jGkZRMsGfDGdGjJu5v+A2D2xh1mFSx3NwlKtYYjcTc5lXCbdrVd7/tWEdwNUU0d5y8DYGKXdswe1AcAjVZLXEYWC3cdZH/sDfw8PMz2fX/LLn47cRbQW2wXjh5Ci8gIgrw92Xs1jkPXEhjRuplJf61OR9t5egtmvyYNeLZ7R9rV0v+Pv9p7mO+joun71fccem0KoT7eTvUN9fXh0AxTh12AXou/o0agP8vHPkzfJvoH8Zd7olhx6DivrNuCt/sOTr01zeq1mtCxDfVC9P/X2LQMjsffYs6/e5nay9RKXJX//2XhWl4KEw7q/xe7+7+Hn9TTRo+y0dTf9n3gmiyFhn76EltJhdn8fUu/lDWlkXnfTA+J+Xx99xoZydlm93+4ZhoP19D70Wal5RIUZqr4fjn9ZwCCwv2Z/b3pb+qXz/SuGdYcvwFEJR4KS99cQ99Hu+Dpbf7eci9wb4ZauRiJpDpabRa5ea6LosjIynfZWPbQLDIMNytf/q71a1k8Fh2faNju0dB0DdwcpxJum93fv5neuqXWaHnqh3Wk5VXsdXAVRdm+i6jKtcaKlCbQ5zhrGBbC8nEjiHrN8nJAkdIU6uPNmqfHMrRFE+oEB+Lv6cnDrZoxf+QgutWvbdJvbfRZw/aysQ8bFB+Al/t044eJYwB495+dTvdNl1n+zuQrlfw+aZxBaQJ45cHufDZSb30oUKos9gX9tSpSmgAahoUwvmMbol6bwpDm5VtRoKqQKs/h5egfAGgbVLfclSaABn7Wl4xBr1gXkZBfnLuvrk9Yuch0t1PSyXvPn+ZXEoroP7abVeWoZgPb/5+QSP2KjEatJfZsgp1S3p1UmMVJp0lGpzqL2HOgy8Z87/cdtK4TydB2TfE2Ez45ZuEvXLldbEF5rHtrXh/eG08n11/zC34lKNA1GXF/2XiU5x9/wCVj2UPbWtbf6LrUs6w47b58zbD9zsYdvLNxh835knLyyFcoTZzEFz46lJf7dmfwl6tRqjX0WqCv6xTk7cWqpx6habW78yZYlS0P6fkFJtYdgAAv8w/E+MziN9hDVpSr0obHozdu8tG23QB8PmqI2T7takbiJXVj95VrfLRtN+8N6etw30KV2qhvSSL9/fA2U79yWMsmvLZe7+f378UYBltQghy9VvciD+2Zb9g+nXWjQuaM8HTMDaKkw3eEV8XVF61I3hq9iHNRMWjUWtuNLVC3WQ1uXErk2/f+pG3vZtRvUewjeP7IVcO2LYfuWo1t+//WaVKdjCT9vSMhJomWXU19Ze8VKszipJVvRZX1vMvGOxwTz4ZjF/jwz51sjr5ocvzZFX8ZKU0Af0SdpdOsJQ7PpdPp31Klbk2dE/YO898cVWJM07xO5YmnjQg1S8WSobigsqMozUTwAdQJCeTSR9O58OGrBj+nrIJCRi37hWbvLWbSj39x24ovy/2Ko1atIgtJj4Ur6Dh/GR9s3W1Xv1+O6TPsOxqJ+uvxMwCE+/kyvJXl38rjndoCsOZOe2f7mosQHdLCslUo5I5CtOXCFdN+Ja5Vk48W88HW3Zy6ad5qeq+zqMNEjg/5hACp/noN3DW3kiUyJcSjOEo7XX5v3SveGLGQwaHPcXr/ZTRqLU+8MZxPN8xgW9oK/k3/1vBnD/M3zjA4fc8eU+wgrlSoDGkEPLwsR1IXUZBnO5N6yWzr/kHO5W2U5dwdGdsrzuLk4iK/x2KLwzD7tzZOILn/UhxHYvSmQolYzLtj+nHhZgp/HtYvBRQqVWYVhZzcT032aTRpKJSHAPD2frhMMvfs1IAeHRtwKFpvwXlt7l98+f5jNKgdWqZx7aFAZX2JotDKEoZUIjE8pOaMHEignW/fvlZSG4D+wbx0wsP8EHWCDacuEpOiN78fvpbAYyvWcvBNy9aO+xVHLFuLHhlKy+oRLNh5gDyFgrXRZzgSl8CYdi2Z3N18fUeArEL9zcvf0zEfhaJ+wd7WAx+C7hwvmaXGmb7ZhXLCfH2MjvtZkTnAy5OM/ALy5KapMEpeK4C10WdYG32G1/s/wGPtWzl8Le5mHgjXK64LOjzB1GPfk6XMp0CtwNut6lyD2iVSItzIT6N3Jcrias4e0iv2IpGIKXMeY+QU2/n1LBEQ4kfnAa2I2nqarLRiBfPov2cNis4DD3ewOU7qrUybbdISi9sEhjmXfig3U+ZUv4qm4pzDta4J4y/iv7N6M+MjXVoS6md885y6ciMAPZvWZfmzowztjsQkcDMjm3kb9vDRWNMlw/yC3032iUReuEnqUi38ACJR2Z0NP3trFE9M/4G4m+lk5RQwccYPtGxcnbHDOtC3exPbAzhJ9A3z+T6KiLpmeU36gUZ1Dct1UomYfs1cm0T0qe4deKq7/se75dwVZv65lYz8Ao5cT6CrGT+aexVbOXgcXQ4Ui0RM7t6Ryd07suNSLOvPXGBPzHUW7DzAgp0HaBQewj/PTzTpp7mTeM9Ri1NZcqW5Ks+aOStUESV9ZMzNX3St1Fot0/74m/2xN4yu1coJo6nmXzHV7qtC8EG7oLpEDfqYTtvepvd/H/JEvZ680tR8tuqKpoZ3MEOqt2Xb7dN8E7OTJ+ubqk7W/t93Awu3vGE1/5K9zF71PA9V06/2FOYr8PLx4PNpqwF9vqfXvn7KYt9Gbepw9Uw8iddsr45kpuQA0LhdXYvLdOlJ5h3Zizj49wmb81QFKmypztUWpyyZXltuYKF8CsCz/Y3ztvRsqndsLl0UuIjq1U6Z/EVGRBEW+ptLlKYivpv3OEP6tDB8Ph9zm3cX/c2szzby/R9RXL2Ryu2UHOQKFUqV2uE/c1xKSrNqVTp83bLiVNJxfO8V08y+ruShVsXK45lbpmG2pRHd8bRx5T2yMpy8SydPdDUDmzXkm3EjeKVPd4PF8GpqBuYuW9HxHLn1VAWlCfLSW4My8q3X+So6XlJVcqavOb+j3ELLMmcX6O8Zfp7WLaFuYjHfjBvB/unPGl2rl/6s2ALdVcVvrk+EPv/eL3EHK1kSY0bU0ltN1ToNGp2pH9DPcfsrWqQyUzJbeECoZauNzIFaehK34sf8sR36VRdFod7qOmB8d6OIuNIMnGBf3daScvd71HLaoUvHrde2LYs/V0Vi0+KkSKpfEXI4jPzO0lO1QOMv1/5LxQ/29vWMy7G0r1+DtYfOcDU5o/wFNMPI574hzYopcv+xWPYfi2XVH2XLg3Fo3UyTfQq1mhFLf2bH9Ekmx6au2cyJO5FzLcwk+ftf13bcyMhizdEzbDt/hfeG9SXQu/ydZUvnnDJHkRw5hXKOxt206uRui9IKS2Vkfy5vpe3FXl14sVcXms/5Eo1Wy4qDx3i+VGLI8R1bsyb6DBqtYzexSd06sO1iDGmyfDaevcjI1uYT3hb5Nj3WvlWZ+rpLTHP3bD53mXeHPGiyH/RLewDDWtrnqxjq483R118gVy6n02fLOZtoW5F3FVVFaQJY0P4JhuyeR7oijwG75vJj9xep7lUsX6ZSxr+3z5CvlpNcmE2+ulh5fePkr/hKPYnwDMDHzYMeYU3Mlk5xhg7B9anpHcytgky6/vsOAGEe/sjUcgo1esWgkV+k2VIwVRVRiXJVi176gU83zDBJJvnpcyvZu/6YQ+M27Vify9HX+XLGz3QdXJyvacJM67Xghj/zIEvfXAvAkPApPPP+I4yZarxas+bzLfw8f7Ph84jnTIM2GretQ8zpeG7FJvN4yzf49fxnRsevnoln3uTvHDqnyuSuTUdQFK1VUKp0x6Zj+vTxEjMp8d3u3GhVauu1i8yh0xWi1ZbNCdGa0lQR3MzKISnH2PKnUKuNouZe6GM+o/m0B7sZtkct/8XiQ3XLuSu8tNbym/nEVX+y4dQFs8d0Ogz5ogAeaFTP4jjm+HTbPofal6YoxUDpNAMVQcm5K+KhGX7HN0hnxlTXKLzY5y42zf6XjNY1qtEkQt/3p6OnLLaT3wk2GN222Opalr4lyZXbzo/W3cHlX3/P+yeizhILO/wPD4mUbGU+06N/MjoWJ0tl8aUtfHt1F5tvnWBX8nnDsT0pF/j71glWxu7my8vb2Jd6yaVyfdPlWVoHFf8/0xS5FGqUeLt58EHrR6nnW3lRuhq1ltyM4nu+Uq5CKbfua1qSC0djeanfXGJO3eBWbDL7N0azZOavDitNUGw5KsiTs3HFLof6jr6T3FKn1bHy/XV8++4f/PvzQbb9fIDlb//GT59uMtxHLCXJHPtqcaRsRnI2c59ZwT+r9rLz98OsXbSVl/t/wu24VPpasVZVJUTmbpwl0BVZnDwirZvYbKHKegGtfHuZxyli1pp/+efEJdrVq85P08Ya9rd6TV/odcmkEfRpYWwt+2HvCRb+vZ8gHy/2f+RYhF9axuPI5bvLVHKlx5jPne7rCCUtTkWFb8+9/wqzN+5g8xnLN66T70yzGl13NTWDaWs2k5BpfZ0asFhyxVIh3tL0bFiH7yaOtqvtjD+2su28aaRUEe8P78e4Tq3tGqsycaVfS3xmNs//tom+jevzdLcOhhD7xOxcdly+yqc79MsYp2e9hJfU1PD86rotbLuTaVsqkbDk0WE0iQjD10PK6VvJnE1MZtvFGLa8YOoj1eQj/f/4wcb1mdKjkyEf09f7jrAy6jiFKjW7Xp5EzcAAp/r6eXgQ/eaLRv2af/wFmjv3shqB/kzv24PhdyxLX+6N4tuDx1FrtXi7S00SYFq6VgCrj5xg2f6j5MoVBHt7cXim6yKDBaoui1/9ie2/6Jcm7Y1gAxgcaj4xqyWmzHmMUc/3N9qnVqr55p0/DDXgSjPtswkMm9SHTyZ/y/6N0Tzy4gCe/ehRm3N99sIqdv95xPB52KQ+TPtsgt2yTu76HrdizVtdI+uGsTraevSlTqtjeI0XUatMDRc+/l48/8k4+j/WlRG1pqFUqHhy1gjGv2bdIlbOWFzDrDDncJGLi/x2b1KHf05c4lTcbfZcuMaDLRpw+oY+fLhOWBC9mptaK66l6N+eQ/wcL7Drah+tisZNIuaTUYPYfuGqSXoBsUjEuM6trSpNAI3CQ1j3/AT6LlxptUhvvVDLD/4QX29D2RVLvDagJ0/3sB3pUcSnowdZVZyqCrYUI1dbmq6nZ3I9PZOVUdGE+/kiQl+WpAg3sdis0gQwb8RA8pVK9sfeQKXR8Pxvm0zalEwUWZLejeqx72oce2KusyfmOuF+vhQolYbvjJ+Hh1mlyd6+S8darsA+e3Af5v67l5nrt/HJv3vRoU91Afrv+cfDBpjtV/JaeUnd8Pf0JKdQbrBwuYnFfDpikMV5BQRchZu7G9M+m4DU3Y3zh69y7fxNJBIxrXs0pl3vZgyb1AeAZh3rs39jtN3jDnq8h5HiNHBCD4fkWnHwA47vPMcPczeSmZqDWCQiMNyfiW+NoPOAVjb7i8Qifjw1jx2/HuKf1fvIy84nOCKAyLphzPr2WUO9wBoNIywWL64qVJjFSZ23GI1sicssTgArdx3jy62HTPaf+fxVs1E6rWcuRqeD5/p34elelx2aKyd3AVpttsuK/ApULSoqiqkio6W2X7zKkRs32Xv1Oil5+YhFIhqHh9A0IoyPhvXHzY4K7wVKFVsvXOH7wyfIzC+gUKWmaUQozauFM2tQbzys5AdbfuAo+2NvcDYxGR8Pd4Y2b0z/pg3p2cB29nlH+xZZnGYN7M0Tnduy6ewlvtwTRWZBIc2rhdG3SQMTXy5L1yqnUIFSo8HHXUq/Jg0MfwICApaZcmAd/92K4fr4tytbFFdh0eJkW3FK1pu7Pao5pmiURpO/EnXuJy5VnLQ6HW1mGld9DvL1Yv+H5s3pRct4304ZTU0f59ZSBcXp3qG0ElORylNVcv69FyhSnGb268mzPTpVtjgCAvcd95PiZHOprqwKUxFij364+dvOUOrQmCIR5xZOR6vVEZ+eRfUgfzwsLD0o1Gqe7KNf/unWuA43Ex1TgpJSuqNWl28ovkDFEvRukFE27opSZgSlqfy4uzP3CAgI3A1UnI+TWz0kbo5FSdmLWCyiXniw1TYebm7MHN7L+Tlc7KMlULkUWX0qSokRrEwCApWHRqvl1wOnWb79MIVKNc1qhjG2extGdjaNzvxp30n+PXWFi7dS8ffyYMbwB8y2E7h/uWvTEZQVf79XHGovcathu1EVpM7KBSw+aeoH3SwE4AAAIABJREFUtiD6ACM2/QJAvkpJnZULuJJlnBj0SlY6dVYuIF+ld8zt/tsKHvtnLZP/28Dg9T9yOi2JxScP0ebnJcw5useQqbf7byuQqZQ2243Y9ItF2eqsXODS61BVcLTenICAswjftWLazvySBZv2seXtpzm14BVmjXqQfq0amrSLuhLPgk37aFevOqcWvMLW2U+bbSdgnQRZNr3/XkbT3+fzvz1rzbZ5+dBG6q/9xGzy0vprP6H+2k/MtjdH/bWf8PKhjWUX3E7uW8UpwP8th9p7e412WNmqKqy5fMbos1an46+rFxjXxPEQ/ZisDA4lxnMpM5Xnd27ii5NRSMRivjsXzbqrxflb3j2002a7p1q0N5EN4K+r5vM8uZLSy3QVMZ+AQEVSkRbVu4FqgX4E+3ojEkHrOpH4eZnW3tt38TrVAv147eFeiETg6+lhtp2AZaJSbvDQvyu5KctGqdVwKDmOFw+uR+1gQt2qjEuW6rTyLWgVB9Eqj4NOgXv4AVcMW6Xw9hoGXsPKdY7zMbe5nZLDraQslCqNIanYC084v8T4avvufHHSOBP5tN1/k5Sfx/imjitOWYpC4ie/zlsHt7P28lniJ78O6C1by88c47HG+rDUwXUbsbjPUKvtRjVszqt7t5jMkZSfZxinPKmMh4rwIBO4V6nqy9EdGtheNbicmEaHBjVcVjvxfuTlQxvZPvQ5qnv7A5CtLKT9X4tp+sd8YsfNqmTpXEOZFSetfBuqrJestlFlPgGANPiXsk5nF1n5+rwtQXaU7KgKrF53mEPR17hkIblYWRSnJ5q1Zenpo1zJSqdJkD4z838JsU6PV0Q1b1Ofr6IlPYC6AUF2tZOKJUayZSv0mZ+dUeoE7l8uvvtqZYtwX1OVlwTVGvssHWqNFrWVItEC9jGpSWeD0gQQ6K5/Dt/tRZdLUibFSVu4CVV2UYZoCWDhSyfyQivfhaZwAxKvUWWZ0gSdDn45cJLPNpmW2zi3sDh7tUKt5tlv/gLgp2ljSUkbQlDAHNzdixMtpmdOIjR4lUvls4RWq6PvhC+cKv9SkocnLycjO9/wuXSdujAvH9YNH8+ozb9ybMIL+ErdUWo0vNvVfD2vklhLVWGuTpimRPtgTy+72pWWbfzW33m364O0D69uU77KpOjtuuQDoyq/bQsIlCcV+d3vPnsZeWaKOT/ZpwMzHy5+yRw5/yfyShSq3nUuln4f6uuhtapdjS+eHm7SLktWSExSuqEdwK73nzWap8usrylQGJdOmTthEA93NF9f0VF0wK28HLYnXCWlQEZcTiYKjZoCtf3lWlzFXw897nCfiY1NkxfX8QsiPq/qKteO4rzipCtAnfsRANKgZYg9eqFIbmm2qdi9G1r5LnTKI+BixWna9xuNCvtawsPNjVNxtw2f1eoE3NwaGbVRKi3XyHIlBYVK3lv8T5mVJoBBvZqzZvNxw+fUjDzCQ4ytPG3DIslXKVkXc57O1WoiFUsY3dB2lEhF/FBLy3YxI5VfhzxW7vOWhSJlSVCaBAQqngGtG9GuXnXa1atBiJ8Xp28k8cK3G/hp3wkjxWnjm8XlgFrNWEy/Vg359PEhJuOVbDdxye9UD/Y32w7gfEIKBQoVbepG8v2LY5CIxLzx81beWbudIB8vHmhWtsjxHy+eZMHJA+QpTRXDuwUfqalPWKC7F/EIihOq7FfQabOQBn2N2HOw1bZizwGQOwetfBeYr7TgFN/uPGpQmuqEBtKvVUM6NazFC99tsNrvp30n6NdIhQ7LZUPKC50OBvzvK5eNN3Vib3ZGXSY1XV8S5v3F/7B8zniTduHePrx/eBcR3r6sHjTaYBEC8JG64yt152jSTcOSGcDCEwddJqc1SsoGGMlWVlyZo6miUxgICAiY8uFY47I5PZvWJdTPh/S8fAs9XENCejbjv1jDJxMGM7xjM8P+RU8No9WMxbz43UbOLTJfo9Ma+xLjmLj9T1eKWqnkKuUEuBsXxs5SWC+zVRYyy3FsSzitOGkVeodjsedQm21F4hAAdDrX1Xv7PeoMS7bpZZg3YTDDOjSz0UP/Azt4+QZ/n7jEqA4zuZ3UComkOh4e+iziWm0OGVlTrY4RErTUaZkzswsY99JKk/1zZz7MA50aIpHogxwdLQb83PiezFmyDYCzlxM5ciqOru2M33y2jXqSrmtXkFIg44EadU3G2DjiCfqvW8W7UTup4etPcn4en/cewtGk8q8ZVFK21zr0LPN4JZ1UBSVHQODe47ON+7hyO42ryekoVGqTpbPyYM/5awAMbtfY5Fi98GDiUjMdGu9mXg49/1zhEtmqEt9fPsqM1r2N9iXITAvDh3r6AHAmI4n2obYd94val2bZhSiz+8uTMqQjsD/qQKfVXzSRKND56Upx6Eo8AM1qhNulNAFUD9Y7rKXkyPDznQSARnObgoL1FBSsR6crNGxb+isLf2w5QX5hCcdoNwnTJ/WlT9fGBqXJGZrWjzD6fC0+zaRNqJcPw+o3QWIhWqRRYAijGjYnwtuXfJWSXjXrMbphC+r6u+5/ZomSsj3a2Pxyr72Up5OqoIQJCFQ+j3+5lp/3n+T0jdvUjwimU4Na+Hq6tiqFOawF2ukczFl/PSeTEX//XEaJqiY/xkQTl1esRGYr9cFapZ89rUMiAfj20mGj/WczzVf0KGpfml9jTzotq7M4bXESezyAVr4drfIwYvduVtuqc+fp+3g97Ox0JhyPvQnAE73a2d2nKMpO71joZlRyRadTkZTSmerVys/P6ecNR40+7/3NcbOuOerVCjX6fDD6Go+PNC5oWqhWsSM+luX9R1gc54s+D5ns2zlmkmE7atwUw/aLbbrwYpsuhs9F6QYcaWdOtkifsmVoL0/lpqJLswgIVCR3w/f7ZkY2Z+OTTZbEur29rNznHtC6MQs27WfrySuM6GTsCH4j1f4XNh3w4F+mKw/3Cl/1GMXwf7838pEdXKspX3UfadRuZN2WzD62jR23YowSWwZ5eNEtoi6HU26YbV86CeaGgU/x2Zm9Lj8Pazht5hB76KOyNDLbX1itXL+MJPbs7+x0JqjU+hBTa9XZS1MUiRHg7WlyTCSSukYwOynPPCHpWTKTfb9cOk2+Skn/2lUvC25Vlq0kVfmBIiBQVu6G73e+3PySnExe/s7UkUF+9Ghah1W7j6NQqQ37i4KEwwN87Rrn+wvR5SFelaF3ZH22DXmWWr6BuIsldI+oy7Keo3ETm6obW4Y8w4SG7fCQuOElkTK5aRf+z95Zh0dxdWH8ndVk4+6GQ5DgHtzdvZS2uBUKBb4ipUCVAoUKVlqkxd2LO8ECBGJYCESI62Z15vtjySabnXWJML/n4WH3zp07dza7M2fOPec9p3tNRA1HN9qxT/b6FMEOruCzOajm6IbP6rREIzdfjf0thdEeJ7ZgOAiWHaTZMyFJCwfLdohyGyWLByVPgyz/O1DSGEUjwQOL11LDaIZTL8ATka+ScTXmFXqEqa8503EqUlGwuF2dYNrtLMKRtt0SjOzXzGJjZ2aXBEm23bsZQqkEWaIi9KtWR+NSXXkw5vQ+RGemVci5aaIy3FwYGIylon+/6/h5AFBkyTnY8pUPw590bo5tF+9q29UsbJw0GO0W/4FmCzaotC8e2hkj2jTSuf+3dy9jU9QdvY/na+eAjv7V4MizgY+dgwEBMtZnU/uhytcB9s640m+azn2CHVyxsnkvrGyumsW4vFkPLG/Wg7b/xb5T1Nrp+loSk3ScWDZ9AMwEJX8LecEvynZJetksOxY4TqtMOZQaLWsGIvJVMi5EPceCgR3gaKvuRSpLTqFIuS8d3l7qWlCWwtvDckaaVFoic1AgEaNAKsH8Zu0xpWELLXtZH1cbW+RJxBVybgwMDBWTrVOH4qvdZ5CZL0R4vRCMDW+MIrFM945m4viij7HrSiR2Xn0AkqIwoHk9vYwmANgT/1hnHzZBoGtgDXzbpjvcbekDoisDbc+uRGv3GljddGR5T8XsmKwczvd5CXnBL5ALD4CSJ6lsI9jeYNkOA8fBPLE8pZneozVik9Jw+elLtF38By4smwhPR3pXaUJaNuZsP65830/PYHJL4mCv29AzFhcngfL1o3HaVd3Lkw2d+mGDbh1OBgaGKoSpsVQtawbg/NKJau3apAD0lQnYMXOEzj4udraY2bsNZvZuo9eYpcl9XxlBE3t7j0Ir7wCDx7UWaaI8sAgC7nz9YlFtOZYP2i8PzFKrjm0/G2z72SBF/4EiswHIQLBcwbKhFxEzF1/0Dcflpy8BAL1X/YVO9atjTPsw5fa45HQ8S8nAV3vOgiQVC9GB7pbPEtOHvPwii43t6W5agHVlozIEtTIwMCgoq7j/oSAltQseBzg4VWijCQCupcWjkUuAXobTjR6LrTCj8sEEOQKawWy6gy0YAbZgjMWNJgAI9nTB5smD4eYggFgmw5mHcRi3Ya9y+9Cfd2HRv2eURtOBL8bi5KIJGsd7k+SDzCz19dPibdm55vsinLr81GxjHfnvkcr77u3NI/1fWWBEKS1Pyut09HSfhJ7uk8p7KhWOD+GzMbeh8yH+XvfGR2nc9lO7Xrg+bLLG7RUBKSnH+rhz5T2NCoFZPE7lSetaQTg0bxy6r/gTYhn9OjePw8aQlvVR29dD53i2Nt01bhOLbxs9z7I8S0gzyzhyOYk9J1SzNBqH+ptlbAaGYiIvx5T3FCosH8pnU1pYlsFwYrI0X/P1KYEFADkSIabd2Y5/203F5Ii/EJ2bDH+BKwYFNMXwIPU40Vl3d+FJzlsQBIFGLoFY01S9qsS4G5uwofk4OHJtMfbGRrwuzIS3rRO6eYdiSq3OSCzMxJ8vruJU0iNISTmGXv1VZf+HfVaovA87uUT5urqDJw6G04eLXHkXi+WPjyBPKkK4V22MCW6Npm7Byu1CmQTjbm7CwfCZmHF3J57kvAWfxcHmVhMQZOdOO6a1sJrhRMmegRRdANue3qNjCq72Atz7oeSPI5bJIJOTsOMbvr5arCJOh0wWb9T8igmt5YOn8QrtKJKk8OBJIprUpw9U15du49ZDLCkxGCeObIeawZ4mjVlZYJborMe2FaaJv1ZVKJL6ID4b5jdmOvfTkjRuo0vV10R0bjKanFqGiJ5LwWWxcfjNfayMOgYASuPp9/gL2PzsMlY3HYl1zUaDAvBfyhOEnVyCRfX7YkSQaob71DvbkVqUixOd5sCOw8e1tDi48BSB6YF2bljecBAW1OuDNmdX4ED4DNRwUBVdLk2xIVXagCpL2MkliuN0/x9YBAunkx9j5r2dmF2nu8rcXuSnofHJpTjf9Uu48e3xMDsRAy7/gi7e9fAzjRFoLcy6VKcNUnIfsvwfrXIsPodjlNEEAHJSs2w+AdMCustKECz++TjiXxnveXoY/VbFaAKAQT30y+6oKpirBh2DdgpyrF8PqjIQ/zCB+WyqKBSZBkr2EqDMU9M0vYj+e1LD2XANot6+DcFlsQEAgwKaYlRwK+x4WVJbdPvLGwCArt6hYBEssAkWevk2BAD8FndBbbxnee+wudUE2HEUBXrbe9ZGfWfLrFzkShSfw5IGA8AiFCZIL9+GWBTaF7/FXUChTFWTq66TL9z4isSvMBeFoyEi46VF5qYvVjOcKDm9jHpFIzPzE7U2ilIEctvZmZZW2bl1bWxfPV75Pje/CBPm78DwGVtx4JR+svHRz1Lw06ZzaDt0NaYv3aOyLSTAHU4O5iuQW9FhnoIZyputXx8s7ykwmAmKzIA0exLEKdUgTqkGybtWkKR3hTi1jrJNmjna6PE1ZdQNqGZ4lve4aqoZfTNqd8VbYTYiMhT19MRyqdLIKE19Z3/kSdUTk6bU6oSaWrxI5mTHqxsIcwlET98GKu39/BsjT1qEna9Ua899GapeD7dApj070dJYL8aJUlezrmgQBBcy+RuIJXfB5zUHAEikT5CT8z8ALNjZfWTyMWoEq8dZJaXmYO22izhwJhIh/upPH4fOPkTCm0w8S0jD41h6d68Nn4uVX/QzeX4fIsVZPowhRk/aW8OKlxpCYlwKom7F49yeW8hOy0V6UjZsBDx4+rshsLYPBnzWGaGtTFOU/+/fG4iPTMCdc1EQ5osgEUshsLdB9QYB8Kvuhf6fdYJ/DW+jxo65V75PvgzmQ5reBxSpXuezNKTktl5lxujQVM/OmW/4wy6PpXrrtmErKl8UyUu8Y8Xeo9LYaZAHsGVbr3JGoUxMO7di8qWqRpG2vuWFnoaTTP+uGqDkySbtbw38fRNRWPgP0tJVa+oRhAABfprXpw3lxoF52HP8HjZsv6zS/iY5G2+S1ZeNft5yXut4Alsezu2cZbb5MXy4zO/3E2Luv4JMollQUFf22IEX62BfSkusLA+vxWLhoDUatwvzRUiISUJCTBKuHlEkPtRuEoIlf0+Bu6/+xm0f7ymQvy/NVBaJSIr7l6Jx/1I0jm29pGw/k7FZ43jz+/2EqFvPtB7TmM9GJBRjdOh8CPMVN4xxC/pjzPy+WsfRdlx7ZwEOPF9n0P4fCnLhfsgLfgXbfibYghKla2nWeIXRRAjA97oDEOrfX3nhZsjy10OaOQYsXlNw3fYbdGwPWzukFOartXsJ9CvVUprYvBSE2Jc8hD/NUdyfajuWFMJ9nPNGbb8nOea7jxlLE9dgnEx6BDlFgk2ULHrJKIVcQ2NXVU9ZRVRL17lUJy/YBHFKXciF+1TapZnDDPpHSsyXkQYAq49fxerjV5Vq4PpwPTYB+24+xtvMXI197OzGwNVlHWxtusPWpitcnH+Er7f5pfxH9msGD1fDfzBlCQlwx8ZVxruPGRQw3iYFr+OStRpN5uBFlPoFXRdxD15h8Yj1evdfO3u7RqNJE7Y6RGlfx1nm4c9GwEfnYSVJKWf/ua6ltzoUperJ6DzUfKWtqhqy/FWg5G8gy1+pbCMlt0GKrwGEDbiuO2iNJgBg201SbIciZtdQ7Ln03h6hzPAYqv9Snqi8P5X8CAECV/jYKnQK7Th8Nc8NoNvbow0+W+E8yZaYFtPXxqMG8qUitXM4nfQYdhw+WrlXN2l8a6DTjSQv3AJADnn+j2ALhivbjfnimJPtlxXHH966IZzt9AvafpiQjE3nItAzrDZ+Gqe+blqMnWAE7AS6FWRN5chmRYbh1j03cPrKU6Sm5+m970eDW2Jo7yZwc668kvwMFY8fj86DXK5qcOSk5+OrYSUejN8ua86WAQCBDgNkyLRu2LJU8bQ+4vNeaN2zEeo0q6bWLz4yAbO6lVRCT4hJwrvETHgFag+mHVVvHrLTSn5LX22bjHZ9m4BgqT+7Jr14h98X7cH9i0+x+vg8rePSfTbTO6qmYhv72cz4cTRObLsMwPCl0XVzdipf12gYiGnfWy/bqLItcXNdtkEu3A623cfKNlnuMgAAz+MsCLZ2AUoWr4nRx+7oXw3PcjLV2p9mpmFQdf3kCIqp4+iDFqeXw9PGAW+FilWKB72/UW6/0WMxfoo+pcxeAxRG07Cg5vhffeNCOtgEC6vChmLi7W3gsThw4QmQLRHiTq9lyj6X38XiYmo0Ct4HeCcLc7Aocj+8bZ3wSfVwOHBtYM+xwZiQ1lgUuR8ro47BhSdQnkNkn29AVEgfkyo6DSe2w0LI838A23EB7XYWX7+aGZT0MShS/UtjTdzsFU8SL97pMw9NT93mDwv7bGRbfDqiLbYfvI3HsW8R8TCBtp+tDReDeoShTZNqaBxasRVmGSonwXX91NoyyiwfV69v+nev4+AW6PVRezRqV1tjn1qNgxHaqgae3n6ubLt69B6GzdRe0LO00cTjc9G+f1ONff2qe2HVvtm4uP82qjfQLg1C99mUxRyfDQBkp+fBRY96lhKxFNePlTzE9hzbzizHN4TKZDyxeE3UjJ/iuCZdRpMSwt6omN1qTq607a/yDM/q7e/fGPcyXyE6Nxl1nXwxKKApWGWKpM+v1xtJwhw8zk4EQRBo7lYNX9Xvr2FE/ejj1wiH3txDbG4KRHIpajqqBpRfT4vHsbeRyvdFcglOJyvq8w0LagEHro1ybuGetbEocj9Si/LQ0asuhgU1rxRGEwAQZd28ZdC4UZyieELk++gXHCnLXQC5cL/e/XXR4Iu1AICTiyboXUblQtRzfP73cfC5HNz7Xl2U602SD81eqgT4VY7sQIaKS75YjGY//q7SxmGxcGf+VNjxKl5tp4zkbIxtWPLgpC0OyBKs+nQTrh1VGAdu3s7454l2WZPS8T5DZ/TAZ18PsdjcysY0mfLZbF66H4d+Vygz+4Z44s87K0AQ2m8kX4/5DbfPllQOsPbfBqj8emqStNag5O/A93kOfRLNxal1AEpi1L0saBv9d/f1J1/qtX+ORIiO577D4Q6zVGKcGCyCxh+f1eQICLavtQ6lkajEVACAgK85g8Df9zkC/FI0/qsq7LsXhTrL1qLFd7/r7Dt22z7UWbYWZ56aJgBqDFVRY2nwln/hZGuDyAUzELdkDuKWzMHTr2ZXSKOpItD/s87K15mpORAX6R8TcnSzumZNRWXSN8OUHrjkV2n4ccqfOvcpbTT50GTsWoNig6ky/FYl71q9lxooiQNjCxRLm9LsqfoNYiZdJ5V5ybXXsWOoWBhtOBHcuiC4BuhPEOVfePbYvWgAQICrk8Y+UmmstaZTrjx4owh07VxHdyBeq2qKJYzbrwwP6jWF4gtxZbgg64uMJJGYnYOmAX4Q8KyXAlyZcSiThSbVEbzOYpdc1qQSGe5fNF9dSEvT5+MOytfXj+un7VZMjzFtzT0dg6gcHifp+/9LvkMsm/4A2CBF5yDLX61lXxKy/B8ULwnjAqxrudCXCtkV+9Co8RjKB6MDdnjuJw3qz7IdAC7X+OKz7Zf+Qds+at1u6FKrLxBJICsV1Ll8BH09OifHhUjPGAWSUk8ZLaaqeJ2uPnsFAPi0bTMdPYH+Devg10u3cC7mOb7u28XSUwNQtYyl0qTlFwIAPO2ZoH59YXPZBvXf+eh7TGm/HPnZis/6q+G/AAD8a3ijz8fhGDSlq9nnaC7CBzbDt58pltt0GYi7fjyufN2iWwOMnKM54cXSlDaaKvLSHc9LPamJ4ASD7/0Y4tRQyAt+h7xA4YUnuHVBwAaUPAkUWVLhgcXvCK7rFqOOf2bgBPQ5ul2tbt3yiAu4kvQK27sP1bCnAmeeQK02HIP1sZoAJsFyA8EzXFq+GE2yA3lFhiuI1vCmn0du3vfvXxEgiKqtwF0gVribvRx038CdbRUBfQUi87uoNVEsSln8urLz8a6DSMsvwIsMRcbUngePsefBY+X2jSMHoFPNksyypJw8dN7wJ25/MQUkRWHX3YfYcScSJEWhZ91aGNs8DKE+qjUJMwuF2HX3IXbdfYgiqQwda4ZgYMN66Fqb3quYWSjExN2H8SY7F7ZcLhr6eWPDsH5WD8+USWS4fPgu3sSnIDE+FXnZBchJz4dEJEVBTiHEIqnuQUrh5u2MVftmq2TkAcDb56nYtHgfntx+jh5j2qJFtwYaRihfCBYBilSEl8bcfYm6zdUzDgHgwr4SiZfyCArXRKX8vRK2YPHbghTfUDZR0hjaIF+uy28ADDPmi2ETBFa07oqhJ/9V23b5LSOmWlmwnnK4iUT9PAcAsOPKfdyKT8T12ASD9nexs8W3o3uiXZ1grf38fOPBqgDLipbG0cYGGQWFyCkSwdFWe/p4oURx47LlWffrYuwFuCJm+awe2BMAkFMkQp+NOzCwYT3M71Jys9P0N7gQ9wLLTl1Aj7o18XXvLkgvKMSft+7jmz6qnr/Lz15h8p4j8HSwx3f9e0DA4+KXSzcxfd8xtAoOwPZxqk+ydVeuA0lRmNu5Laq7uyGzUIhDD5+izoq1tP3NzaHfz2HPulPIyyq0yPi1GgfjTMZmPL39HF/0VQ3IvXHiAW6cUCyD2drb4N8nP+rUcLImy3ZMw9djfwMALBy8BrujV0PgoDq/X+f/g5SEEpXrNn0aW3WOVRGuq0LWgZTcAiW5A1IcAYoqBMEOBIsbarYC9c29/NHRvxqtoRS07UfcHjEVPnZV/x5Umak0hlMxH3Voio86NFVm1e3+fDT8XLWn7TrY8MFh6xfOJRbfgK1NT5PnaW027y4RzZs0SvfTp7u9ABkFhXiS/A6BrtqzEuPeKS7QrgLNatAVhYq6xOf+fmmO/X5d2YbDUbZpY/npizgyaSxqepR4Sce3bAJ2qWwroUSKhcfOor6PF3aNHw5bruJn3SYkEAuPncXhR9EqYwolUpAUhfo+XpjctoWyfUSTBqi9Yi1uJ1g2li3rXS42L9WsuszhceAT5A4bAR/OHg6QiKR4dD3OqGOFtqqBdWcX4fDG87hyWF3ItqhAhE9bLsG/T38yanxLUNoTJi6S4NLBCJXYJ0Ahy8BgGVi81gCvNdim6xNr5NeO/fDxuQO4905dybvb4W2Y27gdPgnVLKPBUL6Yx3Ai80BKrkNedBwojg8iHMC2HQAWv61FAsM7hVbHpacv4GjLh4ud+ZbVMjInaN1eUWOcth8scdvrYzhNbt8Cc/afxLwDp9G7vmYtHQCYuecEAOCTthX/h1zRPE2mUt3DVcVoAqBiNAHAjP3HkS0swu0v1J+Iv+/fQ81wmrH/ONzt7XDwM3XFeXd7O2QUWMYLVMzo0PnK1y6ejpiweBC6j9Yc2Bx77yU+7/m9xu26qNM0BIu2TMSiLRMBAN9P2oprx+4plcWz3uWip/skTP9hFPp9qp8unSVhsVlgc1jK+W2Y94+a4VTaUzd3/XgwVC4ceHwc7DMGK+9cwtYnd1WWBPMlYiyPuIDlERfAZbGxrFVntPMNRohj1bq2VWZMNpwoeTIk6V0ASqy2jRSdBQgb8DzOm12OoJavOy49fWHWMXkmqMJWNrrWVcS9kBSFR29T0cifvsjp8/RMSN+nyuoysBjMTwNf3cVnn6S8AwDUXrFWY58iqUzpiXr/IDCmAAAgAElEQVSS8g65RSK9+5uTsrXefvnvf/D0pxcGLEa71JzhLNz8GT77egg+bvaVSnmZzUv2VwjDCQBadm+Im6foM60K80qq29va2yB8oO4ED4aKyeIWndAloDpGnt5Du11KyrH4pkLbq4NfCKo5uaKtbxBcbWzhb+8ELosNPtu4eCt9sNNQJuZDx6QrIyk6BWn2DOV7guUGsJwBkACZA4rMBigRJGntALDeC4yZh/7N6mHHlQdqaqmm4OVhWKZgZYbLZqNvwzo48TgWI7bsBgCwCAKOtnywCAK5RWLIyZJMxPCawZUqfb4ixjkZg5ON7tib3PcJEns/GamxD4+juLjKSRK5RSKE+nhiaa/OOvubm3WztytfB9fz02k0AYqgbnPj7uuCE8m/oyBXiKHVPwegO4vNmizZPhWfNFuMlNeKZfIrh++iw6DmAIAV40syjL87OAc2gopXPb5yIgMpeQRSfB2AuiOgLBwH/UQrS/MyNwsxWek4+jIaMVnpSMzP0XvfK0mvcCXpFf6Ktl65M32FOT80jDacKDIL0tzFABTpmWzBCLBsVEshkEUnIC/a+z5TgQRFZoNgmedmFujujDvfzdDdkUEj3w/sATseD3vvKbK7SIpCjlA9S7FX/Vr4dgC9hENFomwWXlUxnnThwOcjXyxGmJ9u5Xs2iwUHPh8iqUyv/gB0qlcbQmZqSYHtes30K+YZc9dy2Ub2ZTSiKIoy6/kaC0EQ6DW+PbZ9cwiAIoOuw6DmyMnIx6MbJfFedZqGlNcUrYa1fseStA6g5PqHYhhjOHU6uNXgfRgqHkYbTrKsiQCZA7b9DHAc5tL2Ydn2Bcu2L2T5ayEv2ABZ1qfguh8yerIM5oXDZmF5vy5Y3q8LErNysDPiIXKERZCTFJwFNhjZrCFqerqjAtxHdPKhGEl0TA9vie/PXcXv1yIwrX1Ls/d39dIsGGsoZCkvJpev+/Jzbs8tnNpx1WzH14WhRlNp6QBzM3xWT6XhdOdcFM7vvYUrh+8pj8excpZrVYUisyFJCwcoRdwYwfYHCB4o2UuA5QQCHFBkDgBFyALbfhZYvLBynLF1eRT5Gl/M3oVZc3qibqgfpn5WomhfL9QPK74fjllTtyOpVHHqBf/rj249S5IcLl+MxoG9EYiNSaY9hkDAw7SZ3dGzTyO1bX27/wiRSIqDx+Zg+dKDePwwUa2Pf4Ar/v5HVfl98idb8eL5OzRuEoyf1o3ReH4Xzz/Ft98cAQCcv/qVxn6lMVo5nJIrJs8WaJ5QMWy7sQAIUPLXxh6OwcIEujrjq14d8dOQXlgzrDeW9umMWl6Vw2gqpmxG3YdiSI1o0hBudgL8fi0CF+PV4/6epqSp9QeA369F0I5Xtj/BMt+XwM2rJIMzLjJBZ/9t3xw027EtgbuP9b5j9y48xeNS3qbWPdVvMlURS/+OyaKj740mAhyH2eB5XgXP4zwAgOMwHzyvu+D7xIJl21exA1UAFr+jRedUEXmdkIH1a87AyUkA7ntR2uinSdj423kkvc2Ci0tJlvDmP1RLHUXcfq5iNAns+HD3KEkaEwolWP3DCa3Hj41JVhpNtrY85RwA4O2bLOTmCFX69xuoiFl+GJmgYtSV5b8zihWX4BD9SxYZ/8jyXnKeYHvp6AgQLA/FEh3BBJoxWIayF9cPxWgCAAGPi5tzJ6P9ui2YuvcYbZ+4JXNU+u8aPxxzD53SGCBeun9ZxjVaiK/+nIQ6zeiFGbXx2fIhWPHxRgCKbLnE+BQE1lJfMjy76zq2Lj+oVP/Wl6JCMQYFzYSbtzOm/zAKYeF11TSQitn980nsXnNK+d4YD86nywbj+0klyy/jGi3EzkfGZwCWZfLK4di0eB8A4PKhO8p2Dz9X/G/rJE27MRiAvPBvAADP8xIIdqDqRjLj/Qs2uM7rAef1EKdUg7xwO/g+qokOVZ2jh+9h7Ph22LDxYwBA1/BVAIAb1+Jx9PQ82Nnxle3ZZX63C/7XHz16NUJY4yDasYvHehr1FqEN/Gn7fLVgL35aNwaNmwSrtI8csh4Z6fkYP/oPHD4xV/mg17d/EyS8TMeRQ/cwa+p2HDyufk3bt+c27t15CQ6XjZ/Xj9Xvg4AJhhPBqQVKngxQRYAulW2qEBSZAxbffOq2JEkhLa8A3s7lLxT2NlX/AD9LYKmlAmtQldTBy5vzMybg0KNo/H4tAtnCIrgKbFHN3RVLe6pnijUP9MP5GRMwc/8JxLxLV/Yf2bQhetatqdbfO8gdqa8VN5H0pCx83vN7DJ/VE87uDpBK5SjMFSIhJgnpydn448pSjXNs01tVqHF6xxVoP6AZ+owPB9eGi9exyXga8Qxndpbokg2b2QP7N5w16LPITM3BN+P/AEEQaNWzEXyC3eHh5wqSpJAYn4LXMUmIvf9KZZ/Rc/sYdAwAaN+/Gf5edUTls5nb+wfUb1UTrl5OEIukSH6Vhux3uUh7m4WN15YZNH63kW3w18rDkJRRT+8+uo1ZPYEfMhSpuAapGU0AKDJPw14fZlHeNm1rKV9zuGzIpHK0al1DaTRpQ5PRBAAcDgsyGYln8SkaDScAakYTAAwa0hxbNl5EQYEICQnpCKlWUlGh/6CmOHLoHnJzhWr7AcD5s1EAFOfl5KS/TqHRhhPXdQukGcMgyRgMnsdprX0l6b0AkOC6mi8wbuO52/jjP4V20fLh3TC4ZX2zjW0oI2ZU/YC/5+mZuBj7EpPaNzfruOYwmCqT8eUisNXqzSnGz9lRr36l4XM4GNW0IUY1bah3/82jBurV9+/73+LsrutY+/kOZdu+9WcMmh+gWPZbtnMalo9T1AOTSmS4uP82Lu6/rd6XIHA6fRMA6G04lV1apigKt05rL6DK4XFwPOk3o4LC2RwW/r7/Lfr6TIVMqriZRt95geg76kumbj7ahWbpsHcW4McjX6jpWI1b0N/gsRg0ofnBk5KoF1pm8VqAlNyh6V21EQh4qFWnxDscEOCGVy/T0La96TI1Pr4ueJOYiYwMzXVia9ail2bp0Lketmy8CAB49VLVcAoMKimq/CYxEwGBqpp4L18owhKWfD3IoPmaEF3IBsdlPSRp4SAld8DitaDpIwcpOg9K/hYEy920w5Xh3ou3ilmwWOjeSP0JmcG8PEhMxprz181uOAGmB3ZXBoOpKtBjbDuc3nlNzVNjKK17haFRu9pa1cCr1w/A5FXDle+d3R2Qo+WiWoyNgI/OQ1vixslIiIt011bk2/Lw+5WlJmfS/XzyS/yxaI/Jnw0dxiyJMugPwXIFJS8ARWaBYKnKY5Aymu8oZVjtxNJcHPKp0fuWN3ZlyhKx3ns89fE2FfMmMRMPHyTgxvV45OUWISMjH2KRFIWFuuUfgqvRxyCx2SW/XZJm9cXN3QGZGfk4cSwSU2eUFPiWy0sSVQz13hptyUjS2oCSK/RVpJma9WOKocgMiFM0XwD4PoalHD99qxD9G9QiFPY2jI6JpUnN1X3TYqj6rDu7yCzj/HDkC4P674n9We++X260/s2pdpMQs302uhgwUbP+FoPhsO1GQ5b3PeT5a8FxWqG6kSoCyDyAVVLWi5RGGn2s6k7GF7ovb2xs6HX8Sgdpa2L1Dydw5uQj5fu69fwQGOSG1m1rwsaWh393XEd+vroUTmk8PbWXVtPEb5smYMzwX3FwXwRq1vJG1+6K1aniTDq65T9dGK/jJDe/KJ0hSN+XIzBnuRUGzRSIdT+9GwvjMWJg0EzZGMYeY80XK1pZMaf8CIvfDcD3IEVnAMelAKEwEAhuPVDSaEgy+oNtPwUE2++9JiGDoRQbTX7+rvh0UkeEd6yrsv3gvgidhpOxuHs4oE27Wrh2JRYnjj1A1+71UVgoxq0biuD+Xn0Nl5Yw2nAy1ENkbppV98et+NdIzCzfwOyy3Dgwr1yO23boao3bZHJS7yLHmkipQB6nihTT1GT2OoTXr4Z1E5mYEwbLsHDwGuXruevHo1qo5uBZBsMhOCHgOq8Hwa2lNJoAgOd+ArLcBZAL90OW+z+VfYwRv/xQuXO7JN5v+79TaftkZ1m2PuayFUPQNXwVnjxWFDBfvGAvJBIZBgxqhs5dQw0er9IqqLWvG4xb8a8R+ZJeUItBwdbr97D2wg0s79cFQ5uUBNCP/nOvQeM8T9esg2FtXJa4qGk2MTBURZ7efq4SC9ZpqG7B0g8Bcz80KTWaysBx+hbyopMAVZKVxXFeDbbtYLMevyqjLeC7mNLxRpaGJCk8iVIYUL36GqeFVmkNp3HhTeBiZ4tF/57BwB934MiXH5X3lCok227eg5wkseb8dRXD6UFi5TY4K4K3qaLxx5UIrL9wEwAwpUNLzO7Sxmxj33zxGp9uV6hY31gwBa56LpE/SXqHYZv+VWvvUCsEG8fql9FHR92lCv0pNouFJ1/PNnqcis4XfX9UvnbxdASXUQsHYE2vMxt87ycWPkbVJkSHsOSp48bHjBlC736Ncep4JLZuvKgsHF6jpu4i6nSY51dI5oGUXIe86DhAvbcuCQewbQeAxW8LEJbRWurbtC5kchKrDl9Eh2WbMDa8MSZ2ocvu+3C5+eUUjdt61a+FtcP0066ZvfcEzkZ/WIJv+kDgw9DSKX2eHAMyUOr7eSHmG4WsglAixamoOCw5es5s8+KwTFuCrgiIhGKsnv4XajQMBN+Wh+y0PBz645xS3gAAWvVohK//mV6Os6xYMA9OlYe6oX7K113DV8HJSYCgYHekpeUhNUURajPu4/bY+fc1i85j7vzeSHiZhn17FNInxizRFWOy4UTJkyFJ7wJQ6umEpOgsQNiA53EeBNvX1EOpkJFfiLTcAkjlcrStHYwLUc+x/tQN/H3pPvzddNfW2jtntFnnUxlxEegfWG9vU3FU38sGhobNXIvL303BN3vOI+5tOiZ0a4ahbRvi7/P3sPH0LYRV88PG6aqu9ecpmTgfGY991x8jTyiCh5M9avt7YO1n/TWWmZn860HEvk2Dgy0fbesFY9GwzuBryCjJyhdi95WH2HP1IUQSGdrXD0G/FvXQqaF+hW0rGq2rB2JUi0a4FPcSjrb0Sty6EPC4Bn3ntDGqRSOceByLr/t1Mct45YlMKsf14w9w/bi6ZlAxczd8bL0JMTCYmc8md8a2LZdAkhRyc4V4/Oh9yTY2Cx9/2gHVa+iuQGIO+g9qhuinSQCAXn2MrzdokuFEik5Bmj1D+Z5guQEsZwAkQOYoFFkpESRp7QCwwPd5bsrhVOj09Wba9rwiEaLfWiY6XxMtw4KtejxzEeKm/1Obj5NxqaDW4uO1ezGrfzvcjEnAyj0XsObwVfRuVhe9m9XFoZtRuPz4BTqWMlqGfrsDwZ4u+HJIR3g42SPyZRL2XHmIxrPW4uEGVeHJNYevYsfF+/B2ccCUXq0R4OGMhy+T0Xbeb+Cw1Q2na09fYebGI/Bwssc3Y3tAwOfitxM3MWfLMbSoFYCN04coNVAqE0v7dsbSvhUjFb4izcWSdBvVBlO/HamxbAyD9ZEL90CW+79yT5CyFo0aB9EWv9207TPa/nR9R45pjZFjWms9jqYCuyf+0x6I7+HhqHdx3o6d6+H7lUfx8acd0LhpsF770GG8HAGZBWnuYgAAi98RbMEIsGx6qPQhi05AXrT3fQonCYrMVtSsq2KsWTy0vKdgMHW8PRDopr+SsT2/4nic6Nz034zrgYbBPhjXqSm2nI1ALT8PLB7ZBSRJ4cKjZ4h6napiOO34YiQaBpeo4Dat4YdeTWujz9fb1MbedVnhCdjz5Rg42ys8Ju3qBWP35UjkCdWN9CW7zqJeoBe2zR4Om/cxKS1rB2LJrrM4HhGNU/di0bdFXbX9GD5MeDZc9J3QAXfOP4EwrwgyqRz1WlRHgza1MGpu7/KeHkNZqIqTYcxgGDeuKRItevTSr7qCJow2nGRZEwEyB2z7GeA4zKXtw7LtC5ZtX8jy10JesAGyrE/BdT9k9GRLE/WzYeUoqjoLp/bQ3akUR6bqX9AQAPo1rIN6Pp66O1qJsst1xUZQ17Ca2HI2Ap92V8S6sVgEGoX4IuGdalZgaaOpGL/3S7wSmRw8TokniSQpsAhCaTQV88f0wfhozR61cXIKinD5O/XYshVje+B4RDRW7j3PGE4MSnh8Lmb8NMbg/Vr+thGZQiHYLBbivvjcAjNjoKO8NQwZjCMuNhkrlinsDw8jxTSLMUEA8/0apUD3D55tNxbygl9ByV8bezgGHfTr0sCi47vZCeBmp38RREuiTYqA9z7myElQsrTBYhGQU6oigiRF4d6zt9h+4R7ScwvwLrsAIqkMgKK+WVm8XNQTHIK8NHtPw2au1bhNJJFp3FZMen4hrsS/ws7bkXidmQOSohDo6owAVyeMaxWG5sH+4NIsExZTHKeVIxRh3oFTeJCYDIqi0DjQF82D/DG1o/a0djlJov7Xv9BuKw72tibFWXRl8XK0x+V5E608m4pB8d+4KgTIVyoYj1OlZN1qRU3d0vX2jMX4GCdCUeaEYOsO6iJYHoolOqLiLPcwVC5Ke5j0yajRJvgZ9zYdI37YBQ6bhY3Th6Cmn7vS0NJk8DjbqceYOAlswCoTSS4nFXokO77QXYaIjouxL7Dw0Fnki9STLV6kZ+JFeiYux71EdQ9XnJg5XuM4ng72tMbGrReJuPUiEesv3sTQpvWxYkA32v0JgkCb6kF4mZGFHGGR0qgsL9pUD0K2sAivMrLKfS6lqfnTGlAAns+n97pbktvTNGfMWoryPF9zIE6pCRAc8L1jlG3SzGEGjUHKzBery2A5uoavAt+GC7lMDpmsRCfq982fmDy20YYTwakFSp6sqOVD6MiUoQpBkTlg8ZlSAQyGY26xy19PKMomrP6kL5rV1E+FOU+obsgIxVKQZbxT7PdP/3RLgfowY/cxlB4y1NcLLUP8wWax8CAxGU+T30EklWFo0/qaB4FCd6mYqR1boqanOzILhPj+zGXI35fwOHD/iUbDiUUQ+HN8SSaiJj0ma1F6LmKZDGHfbCi3uZRG3TdZtan85ysHKLlKCym5X05zYbA0YpFqQeaBQ8xTpN5ow4nrugXSjGGQZAwGz+O01r6S9F4ASHBdtxp7OIYPGHNrttyISUC9QC+VYHEAyMjTLPuflJmr1rbn6kON/becjcDEHoapPI//a7/SaNozcSQaBRjvUj4X/Zx2jLGtwnDtWQIm7TwMQLEk6OFgp3M8dz36WAs+hxGBrIqYs/6cJgiWG6BBe43F76TXGJT0MSgy04yzooekKBTJpMgVi0CWk8nqb69b2qeiom+mnTGYcAVig+OyHpK0cJCSO2Dx6IQn5SBF50HJ34JguZt2OAaz8vO56whxd8HgxrpFwDILhTjzJB7Ngv1R28vdCrOzLD4ujkh4l4VcoUi5RCeRyfH1P//R9icIgKKg0h8Adl2i191xdRBg8+kI1PR1R8cGqsZZzJs0+Lo5qowDAA/fpODOq7fK96YYTbrGaF8zWPn68dtUdKlbObWlypuo1HflPQWrYo3ztbTxxPO6q3Eb1/VPvcaQZk8HJdLuLDCWqIxU7IiJxLPcTDxOT1GLzbQ2rz9havLRYbQlI0lro8wukGbqjuegyAyIU6pp3G6KJkahWIKDt6OQKxRDLNMv/mFev3Cjj1cV2HL9LtrVCNLLcHKyscGKU5fQPMgPOz8ZbvCxKlJRXgDYt3As2s7/DR0W/KFsIwhgau82uB6doNY/Ys0sfLntpEp/ANg+dwS2nL2j1v/it5PRbfEWfL75GO3xDy8er2Y4LT9+Qfl65UD65TNDCHTVT2riZUYWuoAxnPSlxk9r9G4vjgMa+s9uPExOwbahgxEeEqzc/tf9B1h18TIAwMPODremTVZu67d9J2LS0nHy449Q26PkYYXuOE39fLF3tO5r8OzjJ3EyNk7j9hAXFxwaNxoOfL6y7Wh0DL44qW4k0M1jcsvmmB/eXuc8iil7Xago1wddEGzTH2pKE5megoHHd5p1TAbLYkJWXcVIyfzu8CXsvxUFqVyuu3MpPnTDyRA4bBYcbPh4m5Nn1P4V7YJoZ8PD9L5tcC4yHq/TcuBib4vvP+6NsGq+2HImQq0/j8PG2on9MfnXA4h7mw4HAR/tQ0PQKMQX1bxckZiRo7bPiWUTcCwiGpvPRCCnoAguDrYI8XLFwmGdEOLlqtY//l2G8nWTQD+17YZSS0/PoIy0XnHNqkD7kGDl65yiIqUXpnR7WRp4e+Fhcgpi09NVDKf7SSX1ItMLVZeJn2cq5DNquLuptC/p0gnZwiK8zMpCxJu3yBQKoQ8SuVzFaLLhcGDH4yFXJFJ+B9zsBCpGEwB42tspz03X+Ya4qH+vtVFZi3UTLPOVEPvy+hnsi39stvEYrIPRhlNFUE1d+M9pnHwQa9A+Pi4OGBfexEIzKj82776ufD1plPmD8NksAmn5BWYf1xyUVvoO8XJVU/5eN7G/2j4Te7SkjUG6u24W7TEIAtg8U13odO4gegOcz+VgWLuGGNZOP6G14iBzez4PIe6mG5rBZhiDQZ2/hpYEqd94nYjx+w6otZdldKNG2PngIY5Gx2BSi5Lg1DNx8Rr3KX4QZJfJ2hzfpLHy9cSDR3DppX7X4UmHjgAA+tSpjV/60denpHv4bB0YiNaBgQD0P19DqAgPVQbfy1imlw97W5CLYad2I7nAuIdRhvKl0gYdvcnMUTGa+jWti3oBXnAW2GDRv2cQ4OaM8R2bIK9IjG0X76FAJMbAFqFYMaJ7Oc7acmw/eFv52tyGU7awCDlCkdE1yhj0h2UmTR5G26fiUN3NFbZcLl5kZkFGkmp/m0BnZyTmqHstzUmWsAgA4Glvr7GPNl0whhJYNp3B5ZiWYTru7H7GaKrEVFrD6adjVwEAtjwuTi6aAA/HkqyfRf+eAZfDwog2jQAAE7u0AEUBDeetxZE7Tz9I1fGU3HxkFqq69QvEEjxJ1hzwKSdJJOXkYe7+UwCAYU20p8AzmE5ekQhJOXnwc67YtQEZ9IdFEBjRsAH+vv8AJ2PjMKBeiWp8c39/dK1RHd9dvoKHySkI8y2Jn7HjmU/3bkqrFph17AT+uncf1VxdMCi0HmyY7ESjIFhuIHhuujtq4GpSAl7mZunuWE6wCQKfhDZDS++A8p5KhaXS/nJepCrSQbs3qqliNBVTViSPIAB7Gx4KRBJkFQjhal8xVLCtxdbrd/HPnUcqbQ/fpGCoAdo8I5pZVp38QybAxQlvshWSBw/fpDCGUxWjgbdCKDguXRHLli9W6ILV9/JUbotMURhOyXkKZepQL/OVOOpduxaKF6GX/HceP165hj+HDEITP9OXnRj0h6QorLhzsbynoQaXxUZDd2/MCmuD5l5+sOMyYtXasJrhZO6K0snZCjdneN0Q2u2p2eqy+F8P64Z5O09i0b9nsGmSedboKwtL+nTGtA6t8NO5azjyMNqgfZsE+mJGp9Z6Z2qVpaJl1VVEvurTCVN2KeJQ5h84hT4NapfzjBjMSf96dfHFydM4Gh2DLzu0x9a79wAAYxuHIcjFGbU93PH3vQeY0LQJDj19CgAYE9bIrHN4Pn8usouK0HPbdmQKhRj+r6LOoh2Ph0+aNcHstm3MejxTqKrXjI/+24/47Ayd/Rp7+mJO47YIc/eBE181RKLG3z9DSqrGo3X0r4bt3VVjMGUkiSeZ7/AqLwubou4gJitd4/EmhDbF/5p31KBwxVAW63mczFzfh8NmQSYnNZZfKKvoDAAB7goxr2Jv1YeGm70A3w/qgbEtwzB007+o7+uFud00x0OxCAL2fB7q++ouq6ONqnbxswThNUNQ28sdce8yQFEKmYBq7oZlKTFUXIpvSO8KFAkWj1JS4SYQIMhF8TAS5uODvY+jkCkUKr1SxZ4oc+Jia4sbUyfhwvMXmHX8JOQkiUKJBBtu3saIhg3g7WC+jDFTsPo1g8oHCMuf+/WkBK3bCQBH+o1DmIdmyQMbDgdSiarhVCSTqvXjsFgI8/BBmIcPBlUPRWJ+DgYe34VMkXom5uaoO0gtzMeGjv30Oo8PHasZTuaWL6jr54nIV8m4Gfca/ZvV02uf95UmkJ6vWSH6Q6C+rxfCawaDpCi0qRZY3tNhgGIp+cj0ccr6cn3WbwcAtAwJQFiAD0iKQmxqOp6lZSI1N9+qxW0pSvU3k5ZfAHs+HwIe16BxcotEyH4fpCyWyZBRUAh3e8MUySkKKJRIlO/lJAWhRGrwXMqTtIICXE94jS87lGgeTWjWBHsfR+Hfh49x7tlztA0KRKCzcR5eXXBYLPSoVRNxX3wOAPh4/0FcT3iNdhu3YMfwoWgT9GFdE+TC3ZDlfgWwHMH3vAsQ2r9LxXqExqyeaJOz9BLY487IaTrHcObbIF+iWgIqvUj3PS3QwRkPRs8AAPQ88peaB+rYyxgQBIFfOvRlPE860NNwkunfVQOUPFl3JwMY274xIl8l4+SDWHzauTlq+pTo1rBZLMhJEruuRWJs+5L03SV7zgIA6viaL3agsjKgUT0cfvi0vKfBUIaYb+YgJiUNQzb+A4oCIl69QcSrN2r9QizsjaIrEFxMh5+2qLUNDKuH7wb3UGnrsuZPJGvQ/rr98g3a/7hZrT3mG/XEjdl7TuC/6Ge042QUFKLpyl91zsUSOJbRPNKFA5+PfLEY+6MUv7vS0gI13BTBxgefPIGMJDHazMt02vh72BC037gFKfn5eJiSotFwMvR8TcEa5VeKkQsVS5Ycx691Gk2mkJCnWbNqTuO2+LxxW73Gcebb4k2+agkoOi+SNs4MnAAZSaL636tV2o++iMbRF9GMYrgOdFpD8oJNkOWvBsdpFdiCEtXo8q4o3a5OCHgcNiQyOU7cj8GcviVPb82q+yPiWSJ+PHoZCWnZGNehCRLTs/H8/RJd02qmCwyW5m2qZVOJdUGRhsvyt6keCLcPLEC+slDXxxMnZ36Mq/GvsPfeYyRm5YLNIhDi5oIgN2Jab8wAACAASURBVBdM79RKb4FLBssR6GxYHa9QL0/cTnyDmLQ0APQ195JyFYZmA29v0yf4nnyxGC8ys1Qy9krzKCUVKfmKUIpqLpqNFUPP11isLoopTwIAsLj6rVwYS6yWGKOJ9fUvPmvDVv/eFEolND21w2GxUMvFnTbmKqkgD372TIKKJghKey0cSvKuGSgyCwTLFTyve8oN2sqnaMOcwpkPXibhn2uR+KRzc4QGqMYDfP73cVyIojfWzC1H0Hboat2drMiNA/PKewoMDB8EtVavVYmn9HZwQOp7I6S45Eoxp+LiMevYCeX7sttr/rRGuZRTdlumUIiLL14iJT8f+WIJUvLzcfe9criTjQ261awOex4f3g72cODzMaJhSQbs6+wcdNm6TfnelsuBA98GeSIRRKVKVG0dMggdq9En22g730KJBPliMZZ16YxxTcK07q8P1g4MF6fWBigp+F4PAZZuY8HYpbr5109jX3wU7TZDPDyfnT+Ec4nq9zZjvUQ/3b+KXx/dVms/0Gc0mnv5GzVmFUHjiqVOjxPbYSHk+T+A7biAdnt5VpRuUs0PTTR4j74d1RMto35Va+8RVsusc2BgYPhwWdm9K74+fxGS96rbxUYTHQ28tAd7B7u44FU2vbfleWYmFp2hL0KdKxLhQJTqsntpw8mWx4WzjQ1yRCIAQJFUhiKpahWAEBcXnUYTYNj5Gou1S7EQhDMoKh0UmQ1CD8PJWNKE9JUXnPmGCQvTeZxMYUqDlrSG00/3r2Ff71FmPVZVQafHSdMGQ61uWe4CyIX7y6VUC0lRYBGWC3er7B6n+HcZSM0rQM77wF1t9G9UV2cfBgYGhsqCLHcx5MJ/wbLpBa7Lbzr7G+txarNvI5Jo1MJnNGqF+U31r526+OY57IyNVGs3JS4paNuPtO0feKyT8R4n802h/NJcLWk0VWayCouw8WoEdtxW/xFqwhqGU+nAUGsGiTIwMHx4sPitIRf+C1J0FqTkAVg8zbVMKRl9koI+aIpDcubbGjSOJRTfQ9288DRTcxUJBlWM/gsQXMNuoAT7w1CoLa/4IkO9XlK5HG1+3Gj2eVRV4ToGBoaqCcumDzhO+ZDl/g/SzKFg2w4BSzAYLF5rRQdKAlL6BNKssQClWO7kOBjuiSnQYDgF2BsWdO9tZ34nxJzGbfHZ+UNq7RffvEDngOpmP15lx2jDied+0rAdytHjxKDO8cclBZLndWuP5sF+8HI0/W9kjviE0kZXeRlgx24/xdJdqjElD3/98GocVgWm/XYIN2Ne69WX+Rt/mLAFIyHL/R8AQF50EPKigyBYLgBhB0qeAqBEcJLF7wi2/SSDj0EnymwM9hYohxLqSi/R8zQrjTGcaLDaUh1bMAxsgWESBgyWY8OlWwCAPz8ajLbVg8w6NuNtYmBgqGzwfV6CFF+CNOtTAABFZgMoeQgkOLXBsZ8Glq1x6tqOPD5yxCK19jQ9xCtLU9vZ/FIkvvaO8LFzQEqharD/rZREzGzU2uzHq+xU2iK/DKaRLSwCj8NGqxCmAjYDAwMDoMgS53lcAimJACm5BZD5INi+IDg1wLYbBy3xwjqx4/LoDScN2Xaa8BTY07ZLSTm4LLZRcwMAAUfdk1XWkGJQwBhOHyieDvbIKCgEm8Uq76kwMFiU36fTF/S+EvUSszcdtfJsrIOcJHEjOgEAEF7fOM29DxWCEwQ2J0hF8Nkc1Hfzos2qu57yGvPQnmYPevzsHeFr74jkMmPtjY/C2DrG62jR5VBlGOgNszRFhWJIpXII7PjgcI03Ek2FuWt+oIT6ekIokSIxq3xVzxkYyouqnGwblZCKWRuPYtbGqmkYVkZ87eg1op7nGK5vWN1JveTSzRT94vg0kVem/h0AFJUSSC0vxCIpvluwDx/1XI1BbVZieIfvcOdafLnOyWSPkyxvBeSFf+ndvzx0nCxNZVTq/nlob5x+Eo++v+3A4yWzyns6DAwMZmTdkWvlPYVypSJm9/YMqom/ou+rtZct2KsPfUPq4FpSgkrbyVdxuFzzFTr66xYyLUtCXjbtkiG7HJ8uigrFGNRmpV59v5mzGzcvRsM/2B1bj8628MxM9DhRsmcGGU0MFQcWQaCGpxskMjnWXbiJjIKK5ZJlYGAwnqiE1PKeQrljlNFESSHLXQRp5jBI0tobXJNVG3U1ZK4ZQ5gHfd3Bfc8eGzXeo/QU2nZbjuWKHmuDJEl8++U+vfuHdw8FALxNUK+7ZwmM9jjJhQcgy1VoWXAcFoDgtwDBov9jMlgeQ71erX74AzlCRaDixqsR2Hg1Qq/9Ypcz6doMDBUdOUmW9xQqHXrVX6UKIU5tABBc8L3jDBrfSUtpldf5OQhycNZ7rDouHrTtJ1/FAfpVQVNh/vUztO2hbuYz9gxhaLtvISwUg8fnYNrCvug5uCkAoGejJbT9O/ZqiO8X7rfa/Iz2OJHicwAAglMTbPvJYHEbg2B76/zHUDEoNpoYGBgYqiKG6MmRRaVjwdggWG70HQk7xf+U1PiJ0fDfa+MVycty1oixxHL6WKZqNLFU1kBYqFi+/HzZQKXRVJEw2uNEihVr6DyPU2abDIP1YDxHDAxVk+MR0eU9hXLHkJJNcuFOyHKXgeAEg+d+VCnWrJcHykDquXoiOitNrX3VnUvoE1IHvgaogtd388ITmjIpky4cxj89R6Cdr259vnvvkmgVw4sZV6ex3vOxBJ37NCrX42vChODwYmdV+aUEMjAUkysU4W78Gxy5+QQp2fl4m5ELqUwOZ3tb2PF5aFrTD/7uzhjVIQx2NuZR3v3r3F08fpWC+8+TIBRL4OYggJOdLQa0DsWYjua74JyLjMf950m4/PgF8ovEkJMk7G356NigOprX8kePJrXNdqzS5ApF+Ou/u3ienIEnie9QJJaCRRBwd7SDt6sDWtYORP0gL7SqY5qAaqFIgg3Hb+B5cgZi3qShSCKFq4MA/m5OGNUxDG3rBsPelm+ms7IuFAUcvf0UcW/TcDs2EXlCEXIKReByWAj0cIG/uxMGta6PZrX8YcszPZ6koEiMi4+em2HmhpGYnoNbMa9x5NYTpGbnI1cogi2Pi4YhPqgX4IVpfVuXm/SJLuOJFF8FAHAc5lm8wkXP4Fq0hhMF4ODzJwaJTQ6tWZ/WcAKAcWf3YWydMKxo3U3j/oVSCUaf2avR2wQAtVzML7ZZFSAo7TLwGjdKs2eCFJ0Ez/MyCHag+WfGwKAH5yLjMf9PA8v/QJGKfvLrT+HrRp8iXLbkir0tH9d/mob8IjHaz/9d7+Nc+WEqnOw0xzZoIlcows+HruLY7ad69WexCNxYPd3km++5yHisPXINyZnqejO6OLVc8+dZFmPOb3j7Rlg4zIgADg1cffJSLV3fHCVXNp2+jf3XHyMj17CEC4IA+rcMxfKx3XX2LRRJsPbINZy6GwOh2DzLRpe/nwJne8MKzo764R/EvFE3BDSxYFgnjOpgvNaQIehbIFycGgpQRWoZ3+KUaiDYfuB5XlNrB4zPEO915G9a4wkAXn9iWA28pbfPY3v0A7368tkcrUaSOeZjLopjmc48WkHbvnTtaLTpXFevfUxAY0qh0R4nrssvkGZmQJLeHXzvWN07MFRYTj+Jx82Xr3HvdRLEMjkuzvm0vKekk7cZuej79Taj9/92fG+9b/IA4O1ijyGrduBFimGaKx0W/IE6/p7Ys3CM3vtExCVi8oaDBh2HJCm0nvsrHAU2uPrjVIP2BRSf55RfD+JtRq7B+xaj7+dp7PntufIQp+7GYuOMwagX6GXMFC1OoUiCP07eMmrfYu9UTmERfpk8QGvfApEEB64bl0FlDv44eQubTt82eL8f9l/Cmftx+H3aILN5fk2FYHmAkicClAQgLD+nPb1GouE/62m3JRXkwc9e/+vSN6266m04GWo0He471qD+lqAgrwj2jrqN+av/PbHCbEowaamO47wWkrQ2kOX/DLbdRyBY9JH+DBWXs9HPMGe/do/NhO2Km9xf44dYY0o6ScnKw8dr92rc7u3igDoBnnAS2CCrQIikjDy8TFU1eLqE1TDomDwOB9GJqm7xBsHeaF4rALX8PCCTk7gR/QoRcW+QlS9U6Rf7Vv8n8sevUmjVrHkcNiZ0aw43RwEIgkBmnhB/nbsLsVT1YpgnFOFlaiaqeWsIbtXAx2v3avWQhFXzhbO9LQR8LmLfpCMxPRsyeUnmlpNAf6+apvNrVScI7UKD4Wxni5epWYh7m4ZLj1+o9MsTijD110O4YoRxaA00GQMeTvYY0KoeXBwE8HC0Q65QhNN3Y/HwVTJIUtWxfyWq4mvd0RlNLBaBMR2bwNfNEc52tsgtLMKxiGjEvklTKXD76GUyFvx1Cr9OHWjROeorR0Bw64KSJ4KU3geLZ/m6bJqy66o5ucLD1s7g8ehUxE1leM0GaOLpa9YxjWHDyuNY+MMwEFr0pPJyhNjysyIrUGBnnSV9nYaTPgFy8oLfIC/4Ta8DVkUBTFP568At3Lj3AjHP6bVXLCWwWWfZWuVrNouAnKRfmbXhcnAp7iWOPorBgEZ1afuYA31F63ot/VOt7efP+ullDGXmC3HuQTx4HMNi80obTbsXjEHdAPU03b4tFJ9NfFIGhn+3U2Xbn//dwafdW+g8zkc/71F5HxrkhX/mj6btO6V3KwDA5A0HERGXqGwfvHIH+raoi5Uf9dR5PABYteeCmtHk7+6EOQPD9fpM91x5iJZ19Fuun7f1BESSEmMvNMgLayb2h5czff0tAMgpKELHhRuV73OFIizecUbv87M2/VuF4t6zN9g5bxTcHAQa+w1r1xCAIlbul6PXVbZFxCWiZW3Nn6mXs73GZcVpvx3CzZgSFWlzLD8WUyiSYNDK7Sptzva2uPz9FNr+I98vy524E4Pl/5yDVC4HAFx/+gqv07IR5Fn+ApVc5zWQpHeBNHMM2IKx4Dh9Q9uPLDoBae7/AAAsXhOTjtnONwjXk1+jS0B1TGnYEi28/I0e69bwKfg7+gGW3T5v0pyKsePy8FP7XmYZy1i2Hp2NmaP+wJWzUbhyNgodejRAkzYl16L8vCJcPPkIEVficOVsFADAN9AVG3Zb54GKKblSjrxJzsaSn49j654bGo0mXRw8HanyT1+EkpK4iPUj+uLeouka+7Z8Xwj4TsIbo+ZoCLqMJjphv/pB3np7kNwcBMqLuTE4CWxojabS1PJTD6g8e193iYBbMeolE36fRl9nrTS/TB6ARtVUnw5P3InRuR+g+DwP3FBf8tk1b5Ten+nIDmEI8dKdtnwr5jXOP1RNlf592mCtRhOguDHTnd+deMt/H41hwdCOOPH1J1qNptJM6NZcLcD//vO3lpiayWw+fRtpOSUK03wuBxtn6P6O9m1RF3MHh6u0/Xr8ptnnZxSELTiOXwEA5MJdkOX/AlJ85f1GOUjxdcgLfoM0ZxZAFQAEDxzHZSYdclrDVhheswG2dRtiktFUzMf1TDPkirHj8vB396FmGcsU/IPdsXjNKOX7K2ejsHbZYeX7tcsO48f/HVAaTYAi7snO3vB4UmPQaTjxfV6a9R8DICySYN63hzBy1p+4eMswEbWypKbnYc2fF5T/0jL1q2b9xQGFjMS64X3QvV5NrUHFXetUBwBcirPs30+X0fQsOQPjVu9WaeNx2Ng1f5SGPcxLwxAfXP5BvyeasgG+8UnpuKvlRv/4VQqm/qaaFjwivJFegeU2PA62zx2h1n7vme6b77jVu1E6P4THYePgVx8ZHCSsD8aeHwBsnzsCs/q3U2mbtP6A2eZmTuxseGAZWKrih096q7w/cku/oHlrs/2CasmQmz9PRx1//UQSywaFn4uMx+bT+gnvGkv2imy99JxYNn3Adf4FIBwgL/gF0qwJAABKngpp1keQ5f/8vqMj+N6xILgNTJpXW98gs3t1Xn/yJfb0Gmn0/v1C6iB63OdmMeTMQdPWNbDn0kL0GtJMa79+I1ti35VFCK5hvbhHxuNUDsxdeQC3HpjHCOnVMVTl/Zkr+mm4xKamg0UQCK+pu66Ro41i3bhALDF8gmaE7mYyoVtzqx1/bKcmeheGbVzdT63tTbrmgsrHymjvsAgCn/XQvbRXmrLxNYduRmnoqZkJ3Zqjuo9h8VHGYMz5jelUvpoy1iS3sOIJ1NIlYBsqMVD2O3rlyQsNPc2DIWVXWLb9wPO8BIKt/tsFALb9TPA9rtBuqyi09gnEzDDD47T29BqJXzv1t8CMTMPZ1Q6zlw7A9EV90aFHA/D4iugiDpeNVh3rYOqC3pi+qC8cnfXz7poLk4v8MhjGgVMPEBWXrNYusOWhX5cGcLS3xZY912n2pKdaoDt6dQzF6csKo+LPvTcwvE8T2PC1p6XnFIngaieAQI/09cL3y3qONtZxg2rin0uq2SOdGlbH1D6WD+YspnuTWnr3DfRwhreLA1KzSzyAyVmaAzjLZkh9MaQDPJy0L2GVZdVHPfH55mPK96fuxmJg6/poUSuAtv/rNPUncUt9nvFJqjWkjDk/Plf9cnUn/o3G86vMiKUy5BaKjJKysBRbz6p6h4yJTxrYur7K7/jpa3odInNiiPFEsFzV5AcqG/OatMe8Ju0BANeSEvA8NxNPM9+hUCqFLYcDZ74t2voGoZG7N9yNCEYvD/qNbIl+I1uW9zSUMIaTBkiKwvP0TMSnZYKkdNd96t9Ad9B0Vo4Qm3erG0Wr5vVH++Y1wGYrnt4MMZwAoGmDQKXhJJOTeBj9Fq0aa/ckudsJkJKXD4lMrjNQOjpFkRXmY+CNztJ0ali9vKegFXtbHlDKNikU6e+xaxhseN3HhiHq+0S+SNJoWDx+RV/Y0xJEJagey5jzo0Pb+VV2dGjsWZ3IF0kq76t5G16Ow7kCGYIfAu39gtHeL7i8p1HlYAwnGsLXbcG7/ALdHUuhj+E0ds5fKCwquXkumdkbPTvUM3h+Zenapg5WbjitfH/pVrxOw2lyeAssPnoOU/45gm06ZAZm7jkOAPiye7jWftamTwvLZfiZAxahuoxBGnAjbBBseF1HVwcBmtb0x/1SsU1n78djcq9WtP3LBmrTeXTMRVk1a2POD4BB58dgPgpFEpVMPQC49PgFwmas1bAHA0PVhTGcyiCVyw02mvQlN79I5b05jCYA+D97Zx3eRNaF8TdpkrpTp0pboFAKxd0Wd3eXRRaX3YXFd1ncnV3cHT5cixcpUGihSt3d0+j3R2jaNJNkJtbC5vc8PE9n5t47d0Iyc+bcc97DZEp6jOKTsxX26e1bB39cvYcXX+PB5fPB1CP2Ot0MCRc/8Ju6VY+gwTKqqoRDdaZyJldaruxkgYxKEgRUBEGpkpGnnt8UleurTqRk5+PRx2hk5BUhNi0bxaVc5BaViJfkKutxVTcqZtL96AjYNyHghkDI/QgIigE9O9AYHmCYTNN4SRYd3wc6w6kScy6KxCDfLJqu0ZieOrWUe+MmQ0KK4iwSAyYD839qjS33n8N3taSKbUV9J0AUyLu2v+IyEDqqHhszyZgFecuDKZVirhwsNfdQSMtRz4OXyvVVJV9TszB83UlwePyqnopaSMpSXlH+e6E0tT4gLJY+8E25hV+4FwDAtNgOumEfLc5MBxkWTfwXn4Ji1VlyRSY6w6kSZfE8mg6E9q+nubiM4hJyD5OpbZvBycJcLE0gi72j+qE9iew7HVWPPov8T7ryQ12TJTB4AsVxgmSgcn1VxcYLATj95IOUIvj3zI9iAMpCwL4pZTTRGLUAmj7AT4FQUP4yys2dByZND3SDnpWH0VGFFBeWau1c1f8upGVyS9iwNdV8ELS3h+Y0J+TJ01eml29t9PIVie/dD4tGTlEJ+AIBLI0N0c3HS1NT1KEhsvIJ3phlYKjPlPDYUOlLFVNDfbV4hzQ5R3Ugq56hl2MNzB/QDh4O1jIFP6tzvJARQZbukqGdMLSdXxXMRr3wC/eCV7ARgB6YFlvkepP4JVfAy50Pbs4vOl3CakZGmva8ot+N4ZSTnoe0uAxkJmajlM0Bn0f9DbbrGMXBzbamxsgopFbVXBk0mTFjbKic56BM6FJVyJZO0aF+iku5EtsmhrJrN5ka6kuUWilka+6NzcrUSEKaQVmoXF9VQGQ0nVw0EvVcq2dRYrKYEdQizCkqIWj5/SFgi0qV6JlMV7gEp2fYH7zc+dqYlg6KFBVoT/usWhtO5zb9D//+cUZxQ5KQMZwWdW6LX87/T23nlEX41zR0bauerLCUdElLu7EvubphmkJbBlNSVh6crM21ci5tk1fMplQ4t4zAMMnMpyZesgP6G7o7Iia1PJGgstaSOmnm7SxR708b16dtVp+WrBU2sLUvlo/4qYpmo17qOtvBSJ8pYbgGhsX/EBmNAp5IyoVhSs4gotEtJZbudFQP+Hz1hAOQodoaTnsXHMOV3Xe0ft7OtUVel8DYBLRw01wc0vtQ9dXZevUhVmK7rqd6NHKqO5/j039Ywyk6JQv+BOrj8sjIK0RBiaTXqLaTjcz2XgQ19TRFnUr1/ZS5PgCUrk/bPA2Jkdie379tFc1E/dBogLeTDT58LRfvrWgIf8/Q6DUg5EuLEsvpobG5/JdY/suJqp6C0lRbw6my0dSgXV241XOGSx1HMFgM6FGsbl+ZbY+IC0zS6TT81qUdxh0X1cGq52ALL5sacDCTn3E0t2MrSucP/6qem86Vu8HYeOCeeNvRzhxDe5Er+JhdVIJ9T17hWCD54sBhq9RXaZ0qJob6KKzw4Fx58i7a1HOTW2fve8HT0RpRyeXLPJsvPcbJRSMpjbH50hOJbaaentySNMPa+WHDhQCJfSUcrkY+z+6Na+O3w+VJCMpcHxHaLLkjj8/xaVKSC1W1jEglxpEKv/Rpjcnbz4u3q7uEAlkYpovBzZ0LAfs+6AaKPYRCgWK5F1kEJEoa1x1q/neTbl4/Va1Oa1VSbQ2nigz4pTumbRqj1jH3PiNXXDI0JR2h3zLt5EHGcKrn7YDQiHIF5Xch8fCvr9qy2o4jjyS2e3UkX3xywL4TSMv/fvRZ2vi44XZQ+Y+tiM3BzTdhGNRatYKb1YFu/rURlVxuzIfGpVFeinwaKnlTbl3PDQZystCIdLC09Xkqc31ED2p516dNVDUi1CmrQBTIrQ4a1XKU2ldcytXY+bQF3bAX6EVHwCvYDJZ+K4Amu+6ZUCB6uaExait1rnF3z0tsh4yeA1NW9YrTq0wWuxjx+bmIzMsCh8+HtYERLPQN0NKhakNCqpLqcdeRw6S/RmDogt5qH9eQqf0f+/6/RmHSr8fF3qZZK89Bn8XAveOzxeVWyLLjyCOcv/FOSo16/GByMQeX3oeKjaaFXdqiqZsT7BR41aqateN7IDwxAzFp5W98a07fx4BW9SlXo69uTOneHPEZufhfhWK/fVYexrudc0n1zy4olnr4bpmiWGvG3c5K6vN8EvIV23/uR3Lm5OnT3Eel6+uz6rDEPmVqpWkKZxsLpfum5xZi0F/H1DYXV1vJUihFbI5apCb06HQYspgo4ZTHObVZuBsH5wxBY0/qy67VBz0wrY+hNK0lSlPrg6bnABqzAejMegD0IBSkQciLhaBU5NFlmC0H6CbgF+4jHs1kGukzP06KQW/3Ouq4CLVy4NNr7A95g8wScolS/raOuNBrFPSUuA9ffrkMhkaqfz+7+y1TeQyyVHvDqVhD6ccffvtFI+PKg0YDlszojnELj4r3lXJ4GDHnEIb29MfgnoqX2D5HpuDGwxBcuRcsdczdmXzMyoMwUVVyT1trTG7ThHQ/MuSsydFIgDidRsP0Xi2x+NANif2/HrqBjZPUb1xrm597tJAwLARCITLzilDDXH4hTjaHJ1HctwwyxiTR5/n401dSNQyp8nOPFrj7LkLsnSF7fQAw98A1KcOwOgUmW5kawdzYAHlF5Zk9AoEQdLri/4O5B65JxW6pQuUMvhtvvmBoW/XIBozp7I8Dt8q99QKhEPMOXMOTDdMpj5VbWAILE0O1zEsVOOmtIOSnireF/BQI+SkQsIljbHn5q+WOR8VwisvPJd1WWwSmxOOvNwGU+rxLT0avq0ewsnlntKDgiWLpM9RiNGkbtRtOERGpmDf7ONjs8reSVq29MGxES9SvTz4DpsfEjrh16BFOr78KTikXU9eNUvdUqwRPNxtMGNISh8+/FO9LSs3F1kMPsfXQQ8I+rQdvUjjutuVD0LSBK+l5PI+OA4uhh6vTR5PuUx3o6u+Nv889RE5heSr0vfeRaPjLVnRu6IkVI7sQpk7nFbMRlpCOg7df4X10Mro19sbacT20OXWF1KxhjiZeNfG2Qi22n5YewJx+bTC4TQOYVoqZKeXycCcoHMtP3JXYb25kgNO/kvu9dPX3RgN3B3Rf9o/E/mZzRWrymyb3RjNvZ8LP9FV4PN5FJeGfO6/BFwjwYZf8+LeaNczxYvNMNJ69XeL6DFgM3PtrqtT1AcC1wFBsvfJU4v8bAG6umqRyiRgqtQMVQafR8PvQThJxXM3m7cCKkV3QpzlxaaWzT4Kx58YLCWNLHVQufr327EMcexCE6ysnqjz2jF6t0KWRN4asPS7el1/MRsNftsLV1hJ7Zw4k/H/h8PhIyMjFkftvxS8Hvw3piOHtG6o8J1UR8jMAqPclgSxPkmMx06/6vAC4HtqgdN8v2RkYdusMmtnVxKEug0gtQTq7qS+5w9rGFFkZ2inBRFOgJ0TpztK5w1oAQH1fZ3h62sHM3BB5ecW4diUIQiEwYmRLTJ7akdIEZ7VehoggkdAYg8VA24HNYGVvAVvnGmDqM0CnsMTVYwK1c2ua/lP3ISNbtRgjd+caWDWvN2q5UMuQ6rr9MDILi/BuqXo9b9rScAqKSsKkbeeU7t+zaR25htO1wFApg0SRYVCZoX+fQERShnh7WDs//D60k8J+qTkFUoYMWRh6dLzdPodyv6CoJMzed0WlWBuyn4+q17dxUm8p44CISy9CQ/F+wAAAIABJREFUsPrUPYXtqKDoGv1nb1NKMbxPcx94Olhj65Wn4n0B66Yp7ZGZu/8aAj5FU+pD5fv9Kjwec/dfk1i2o0p1MZy0SWXDhE6jIXzsfLAIaoXyhUIUcEqRUJALpp4enE3MYczUjHcmIicTfa4dA5uvvoD/rxMWyV26iwhNgqmZIRycrWS2ocLcMQcQ9jFBnSVXZE5ebR6nxARRnEQDPxds3S7pxRg0qCk2briBM2cC0fmnenD3sCUagpChC/rgwK8nkJ6QBR6Hh0dniLPhyFDdDKdT2yfi8PmXOHXtjVL9DfSZOLxhjFSRXzLUc7TFrZAIxGfnwsVK+fiMylgusxQbT5qksacTvJ1qaFR7qKqwtzRFo1pOeB+dRKmfpYkhNpOIayKisacTTiwagQFrjipurCLKXh8AHJg9WCkZA23x2+COWHuO2HMsi6Ht/PDbkI4IT8xQ3Jgkf43rjtGbTkvodKmT5rVdcHLxCAz8U32xWf9FBEIh4gty4WlhLd73T+hbvE9Pxo2YMCnPhbWBEUbW8UMbB1dKS2KKWPLirlqNJgBY//YxljTtIPO4dz31/o6dXKyR/b15nMaP2Y/efRth8JBmMtt07rAWNZ2tcPS44jXgc5uv49+lp8menhR32CdJtRt55ByOjhkEZqW3gNT8QrTfflC8bWVkiJcLyK9ny0MoBI5eDMTHsEQpXaYyDA2YGNCtIVr5e6CRirXuBEIhfFZuA4uhh4/LZqs0VlVz9P5biTd1eRgbsNDaxw3rxveUG39SlR6niqw//winH3+Q26ZzQ09M7NJMberU+cVsjNtylvRDt6u/N/o290GbetRTq9eff4T/vf4iITNRGaaeHo7MH0b5+qrC4wQACRm5OHD7lUS8GhH9W9bDtJ4tYV+huPKif2/g3vsIAKp5nMq4GhiKFZW+x7Kg+v0uI6+IjVEbTyExU3HJC4YeHdN6tkSf5j4yS8/8yBAtha1o3hkMOh3r3z5GIZeax9eQwUTwqFnQ11PNB6LKEp08PC2s8WDgJI2MrQVkPiDUZjj16r4Ra/4aDP/Gsm+enTushYEBEzduL1I4Xnej0RCquUgmGcMpJCUNg/45hdcLp8PcsDyug83jYfC/pxCZLllS4fjYIWjmql714qISDjKyCpCSngcOlw8jAxY8XGvAytwY6kwe6737GKLSszCtXXOMbu6HGiaKg3SrK3HpOXgTkYCbb8KQklOA9FzREqiliSFMDFlo5u0CNztLDG/XkFTAbnUi4GM0Xkck4P6HSOQXs0EDDaZG+mhX3wONPZ3Qo4lmsnLi0nNw5N5bxKXnIDwpA2wODwYsBmzMjeFoZYY29dzR0MMRPi6qGWylXB62X32G8MR0hMangcPlo4a5MZyszTG0bQO08nFTSmW8qrn0/BPeRSchMDweBcWlYOjRYWZkgLb13dHIwwk9miiX0k6V4K/JeBuViOuvviC/mI2cohLoMxhws7OEnYUpOvnVgo+LPTwdrRUPJofwxAwcuf8W8Rk5iE7JAofLh4WJIYz0mWjiVRNudlYY1s7vh9BdUxYiA6Wzcy08SKC2rFoRD3Mr7GjfG7417JXq/z49Gf2vyxajNGXpo7WjK4Z41oeVgRE4fB7iCnKx7f1zJBcp9vDETVys1LyqAZo3nMaN2Yf+A5pgwEDZGVqdO6yFU00rHDuh2EvTzaA8uLVucy9se7yS7FRUYvrZa3gYEY3wZZJvX/7rd6OIw0FrD1dsG9QTsdm5mHTyEvLZpdgztK9Ycby6UWeFeguHUhXArK5164pLuTj14gMOPXqLolIOnK3NUdfJFhtHKVfxfPaRawj48hUf15NLr6dKUk4+uq39V2JfyMaqEyPVoX2epcRgzANRCaqgwXNgZSBbb0gHNTgZnaFnMgt6Br0AmqRhx8kcACG3PIuZaXUIdP0OSp1HU54dANjYtgeGelHXYGt5bh+SC/MJj21u2xODverL7b8h6Al2BwfKPL6+TXcM925AeV7VAJmGEzXxIDn4+bng2tV34HH5hMe53/Y3buxGadya3g5Yf3uJqtMjTWw2cXxOEUfkQt02qBfMDAzQwNEeM9o2BwA8iFD+beG/QHUzmgBgxM7T2HbzGfJL2OALBIjNyMG9T5FVPS2ZsPT0YGVi9N15y3Soj/jC8tT15GLiB50O6gi5nyDkxYCXuxgQVtItErIljCYA4GZPhYAj21CoKn59dhu3YiMo9UkszJNpNAFQaDQBwOLG7fBXq64yjx/5/I7SnL4H1BYcPn9hT3TusBbduqxH4ybu8PSyg7mZEXJzi3Dxwhvw+QJMmtwBI0dTK03SaXhr6BtqT+chLjsX1saSb3LvE0V1jPTodJgZlKdYTmjRGOvuPcGD8GhAuZhcjVOVJVKA6mk0AUB0WhbMjQzwfBV1/ZmqwMbMGE9W/AwAqL9IvV5EHd8HI70aYf37ABgzWKhvpdyyzPeEpvTgKsMr3AkAYJivAOgVEmUE+ShNbw0AoOu3AdPqGITcYHAyB4CbNRL6Dl81PjcqCIRCTHt4BdHjF4JBUBWAiNPh0nqAZTSylVaKl8XoOg1xPvITPmSkSB37kq248kZluvstQ/tuvvh9w1BK/WKj0jBt0C7JuU3vhOGT24GhRl06tXmcAKB330YAgKC3MTh7OhAH9j/EubOvwOcLQKPTKBlNzG8y/h+ffFHnFBWfl06X+tJd/RgGAGhXy42wT5EKKbk6tA/vWxVtf/fqm52lQwcRwUPn4cXAmVU9Da2hjQxdIU9UqojOkkxs4pecAYRFoNEtwbQQPYxpTPUIiWqSgyHks7Sj8mQngLSimLX3d+tuai1/nJJILSM0IjQJC8ZJy5yc2PsQ88YcUNe0AKhZAHPe/B6YN78HStlcxMZlIj+/BBbmRvDypv52dD3vCCKCvmJW62XoZjAKWwNWoE5TT0q6Tcrg62iHN/HladJn3n3E6aBgWBsbYddQYrcSkQaHjupLRoHIHW9jqt5geLJveTp06FCMtrzVQl4sAIDG8JLYz8tfBwBg2twB6BVEPWkGgFC9oqUVmdGgBfxtHdHFxVPqWHxBLnYGv8T/voahhEf8wr7u7WNMb9Cc1LlCMmUXm+/rUZfchL/hY2WLi71HYeB16SSsS1GhGOhZj9J4EaFJhGVU2nWtj3kr+8PQWFJgc/ZIUQkcI2N99BzcFLYOFvj4NgbP7oci8nMyHt/5hPbd1FOHUyMlV/QNmKhd20Hlcbwbe2Dm1nHYPe8o5nVYBX1DFpp0bQALW3PYOFmBqc+EHgX324BZ3RW2aeTsKGE47X0qKi8wzN9X6sGYWSQqB2NeRVXQdZDnUMBbvIiIQ0hCKgq/iTyeC/yIc4EfxW12T+yH9nU9xNuRqVm49zECZ15+RH4JG7ZmJqjjaIPt4/oSZjeWyVesufQQl9+EwIjFxJL+ndCzkXT2VNlyG1GAd/1FW9HdrzY2jVYuUL2Mz4lpOBf4CTfeh8HO3AQtvVywdIC0HMLCEzdxOzgcwevnSBX+lTfPJWfu4GN8CuKzcmHEYqGDjztaermiXxNipWwdOsiirWU6AKJgcCFxbC4A0Og1Km1bSJRoUSd7O/VDTzfZ2ZYuphbY2KYHljTtgJZn98k0nngCAakXuXyObAOwpgn5AtxlNLYl9uK/SUukbDjJ4sndECTFZ2H32RmEx9fuH486vqJM974jmqNvs1XglPJwan9A9Tac1EHFrLoySks4eH71rdJjkjGcFnRqgwPP36D2Gsk4kjkdpJcZDwcGAQA6eHlIHSMiOj4T4xYcwYo5vdGhuZdSwpWVuXj7PULCk8Xby2bJ1ycqY/O9Z3CvYYmBjRR/mbOKinE7JAJN3Gqith01hfLqwuDm9dG3segNKq+YjX6bjqFvYx/M79VG3MbMUDLlfcDmY3CzscTv/TrAxswE72OScPL5B/gu3op5PdtgUsemEu1pNKDV8r0Y07YR9k8eiMjUTCw+dROLT93Uegbc6osPcC7wIzxsrbBrQj+EJ2fg9Itg1F+0FbO7t8LUzuTeSIk49/IjVl96gAYu9hjbzh+17KyRWVCEwwFvcf1dmFKGUwG3FPcTIvFv2GskF+WjgFsKYwYLjW1qoo6lDWb5toEBSa2arcFP8SI1Fp+yU8Gk66GlvQvaOXhgtLfiWpDuJ/4GALweNBs2hiKPZD6HjZlPryAyNwMFXA5sDI3R370e2jl4wN+m/EFRyC2F79ktoAF40n+GwgfPl5x09LwhypQ0YxkgeKjkd4QvFMDz5HrCvjGjf1d4LUSklxRi3ftH+JydhtiCHAiEQriZWsLZxAIT6jRBczsXMOny70sh2alY9eYekovzkc8phbWBESbXbYZernVhqa++2nPaysilMxtAwHkNIe8raAzRvZxffAoAQKNLSzQIBYr1qqiyqsVPGO+j+PtZhqW+IcLGzkMhl4N6x7dJHV/49Ba2te+lcJwCDrF+mr2RCUzUqFJ+MSoUf7bqSrkIsLGJAfqNbIE+w5uDwdBDwK2P2P33dUSHpSA5PhuOLtKq42VGUxkXni7F6K4bERedjkvHn2PgmNYqXQugZcNp04YbSE7KwZbt1bs+Wj0HO4SmlLswe/h4E7Z7GCEKDmzhRk6M8nFgBIRCYOW26zAzMcC+v0bC1Uk1uXl9FgN3n5bHgXVtWxct/RUbcgefvUEbT1dShlMpl4c1Nx9hTItGWNqjgyrTrTIqGkVlXhUDJgM15CzXnfxlOPxcyz2nTTyc0LNRbXT7+xBuvA+TMpwK2RxYGhtiehdR7ammtWrixLP3iM/MRVxmLlxrqE+hXR4vIuJwLvAjWnm7YvfEfmDq6aG5pzOGtmyAJkt2YtedlyoZTvdCogAAx2YMA6PC0nm3Bt5IzaWu3CsQCtHq0m4UciVv4nkcNh4mReFhUhSOhgehl0tdrG8p2wv3NT8La989woPE8uzIUj4P9xIicS8hErkcNqbXa0nq5p1aUgAbQ2NciQnB8td3UVBhbnEFHGz/+AzbPz6TMGBMmCLPsxDA5ZgQzPKVf4O++PWT+O9+bpr30v0WeBOXvoaAK5D0rkTmZSIyLxMPk6JgytTHx2HzZY4xOeCCxOcLiAzGZa/vYP37AHwYOo/yw1Ee2vA60Vj+AOc1+MUnwTBbBqEgE/xCUUwT3WikdAdhifQ+EqQWyy6vRcVoqogJkwVjJgtFlYQznyXHkuovS2/ISAWjyZDBlPKElfJ5iMnLllBIJ8OuszPgULP8O9BneHPcufIOUV+Sce/aO4z75SeFY7D0GWjS2gsPbwQjJkL20iQVtGo4PX8Wgfx8cl+61ZcWang2srk0meDHUomC0lLsG9YPRiwmbEgKR1Ys7JtfyIapseqifr07+eL24894H5oAAFix9TpuH52l1rR1RwszmBro4/6XqO/WcFKGikZTGU5WIi9CRApxqZf9kwdIbF+aPwbN/tiFFefv4sh0ahkiyjLj0BUAwIEpAyX2GzAZ8Hd3wruYJPD4AgmjhwpNPZzwMiIOv566hQW928LRsjz+w97CVE5PaXZ9eo7NwU/E27N928DZxBwMuh6yS4txPvojwnLSUcTloGEN2Vk+FTWOjJksdHOujU5OtVDK5+FQ2BuEZqdh84fH2PzhMb6O/l1hEOunrBSE5aRj8csbAIBernVhZ2iCEj4X4bkZeJeRhJn1pb3QLiYWiC/MxZbgJwoNp3+/vAYA1Lawwepm3aSO69HoYsOslM9DRG4G+t46omDm0lyL/Yy5z66KH5J6NBp8LO3Qwt4VDBodbzISEJKVCjafh9kN2hCO8SwlBuMfngNfKEqsGOjhi4Y1HGHMYCKuIBc7Pj1DIbcUnifXoZOTJ/7tOITyPCtSVrpJG0t2DNPFEPITwS86DH7R4Qr7f4Weyc9qO4+sDLY/mqlWCuzN8BlocXYv8it4jzJKipSKKypDlZp4I2o3wKHQIKn9z5PjKBlONDpNwmgqY9eZ6RjReT1uXw4iZTgBQKdefnh4IxjvAtUjHaRVw6moSHZZhco079lIgzNRHVN9fZjqU4ttqlyN3cpCPQJ2DX1qig2nohIOImPTUdtDPeU3yjBmMZHPJv//9yMgEArxJjoRhwPeIj2/EGl5hWBz5Ndzcqkh+UM3YDJQ08ocMRmazw4qoyxrkIg6jjZ4F5OEr+nZ8HZQbtl1bLvGuPsxEnc+RuBeSCTWj+iBTvVqQZ9J/XZyNFx0g9Wj0bCv/SD8VFMyQHdinabIKCnCvOfX0M+d2CuTxS7GvOf/E28/HzAT5qzyl5KBHr64EhMibnM47A0m1mkqNU5FniR/xZOUGHiZ18Dv/p3Q0UlS4PZsVDAaERhyg2r5YmswufI/ZQz1VJyppa/HgI2hciVKfn15Q2w0sfT0EDhwltSSGofPx/I3dzDIgzgGZN7z/4mNpt6udbG5VW+J4+5mlvgt8BZK+Tw8TIpSap5EaCvOiWn+F0pLrkvs0zOZotZzxOZL3wOMGEyMrK1app4xk4UhXr74N1QyjOVLjvK1DxkqeA09zYmNo4RCakucZuayn492jpaIDpOWPpCFhZXIuVFaonwR84oobTjNnH4EPXr6oXcfkYGzdcsthX34cm7oOpRnaK/GEt6s/z34pFbDKae4BKn5hVIxQD8yYckZGLz1BBh6dBycMgjeDjXEZT/k6SgZ6UuXkzA10Edarmw3fUXkGT3qwOLbNeQWK7fcAIiMwQvzRqOolIMb78Kw6ORNAKLg+OldmlNaBsxkizIcWzu4SxlNZdgYGuPETyNkjtH68m6U8nnQ12Pgcf/pEkZTGf3d62P7x+eILcjGmrf3YcxgYZgcg+VOQgSsDYxwtw/xw1NW39m+bcSG0424L+jlSpyZdCaqvP6gIiNOFd5lJImLt+5vPwhdnYnDDlh6eljXgngZNJtdLP5/Chw0C3YEBlx/9/ro514fHt9ixM5GBcv9fMmgVQ04mik5XSZhAVg2jwA69Viuj5nSAeXTGjRXybtTxqyGLaUMp9epCSqPqwwtZcgYhFE05PLzimUeS03MhqFR+edWVCg/y1HfQHRfZrPVIx2ktOEU9iUZ8XGZYsPp+rX3apnQfwVll0mIMDORfFAkp+XKaKkc+5+IlhRcLKlnWXyv7Lj9HACwZUxvNK1FvhZhIbtUysDMK2HDlGTmZYGGvXo5RSKDydJI9SBeY32WKG6qVk2cfRGMk88/YMftF5QMJ5aeHjh8Pl6kxiIoIxGNbajXfSz9Zhh0qelF+FAvo5uLN/aHihSfn6fGKnywT1DRoLn49ZNMw+li9CfC/ermf3HlhYZlGU2KuB5XHkMp7/Ot6KMg8/nKo7qWagLNFDQGteXoMnJLpV9W/JSsL1cZoqD8dDkxVZrE3pj488ktpSbhIKtW7ecP8cjNFhnyn4Ji4dvYDUlxWYRtyygqEJ27orGlCkobTpu2jJTSZzp5eobciQ3sJx39/71z4LlIbGxqa2o32WYN3TQwGxFRsdKW/ZobD3HyteQa+7OoOEq17LYMUS09/nviWXgs6tW0Q6d6kks0mQVFMnqIOPrkHWZ1K499uf4uDIlZeZj2Ezlj4sCD19QnW4nO9T3xICQKqbkFUjFHZ16KvgNeFZbprE1FLvGQhDTCuC5FeNha4ff+HZFbzMaN92EQCIWgk3T1B/Sbht43DyObXYzBd46DQaejS01v7GzbD3o0ai8X4+vIrpMJAC3tXMWG0+34cLltzVkGhDFMZBjt7Y8TEe/wKCka/3x5jcl1JYUVt318ircZiQCAn+u1UOocZDkWLh1rQpWDX15R7qPo81VEtTOY1EA+QQabm5nmrjOTLdtjo0mMGExYGxghq9L5kygs1enp0cHnCzCg5Rr0Gd4c/Ua2BJOph4Bbn7BnnWhJ1djEAIsm/gsGU0+i1BuPx5dSCb9ySvS792tKLgNeEUobTo383aT22TtoJ2uoOrH54TMA1A0nEyPNaT+VlEq7Iy2MDEGjAfJrOsuGTqPBxUr7/79a1XOpgIOFGWIyspFXzBYv0XF4fCw7d1duv5PPPkgYTrvuvgAA0in6515+VNxIAZM7NcWj0Gj8ce4u9kzsD9a3mwiby4NQCCkNKl9n0bLuoYA32D6ur3h/SAJxBsrbr0lo4iGt1xKaKGpP1mgCAAcjM1zsNgYdr+4HINKfuRUfhqYX4tDPvR5WNOlCeqzBd46Tbls5s6wy1gbKi6MOqdUAJyJE9bkuff0kZThd/hoi0VaTlMVVqpIqklFS/rJQJtegCK6AjyIuRy3LUD8KlWNcAZEMhabg8uV/xzWJKUtfynDiUJhPr6FNce30K5QUc3Du0FOcOyQdN7hk0zAsnXZUbDQZGutDj07D+cPPMGJKe3G72Mg0PL0r+s35NCSXAa8IrQaH29iYIiODerpyWlwGds05gujgOGSlKB9ke4ctrWhaVZSoaa2ViFKCAOZZHVtiVseWAICQ5DQM3n8K9R3tML8LcRYNIHoAmuizUN9RvYHm3wMX541Gi2W70XrFXvE+Gg2Y2bUVnobFEvYZ0LQeuvl5S8VAdfH1grO1pNF5cMpAzDpyTart6dkjsO3mM4l9nxPT8Nvp20jJLUBJhfI+9RdtBVNPD/YWpujVqDZ++Waw+Trb4+OGufD7dTv8f98hMdbkjk0xp4fk/3lv/7pYeeE+HoRES8zHwsgAzT2d8SpKMlZi/N5zhNdf9hlQxc3UCjGjf4dAKMTPjy/iUVI0ckpLcCTsLY6EieI2ng2YASdj6aXiymnY6sKYIR2rRpYG1g5Y2rgz/gp6gC850nW6yor1rmvRE7XMqKVnK4sqD+iypVCqcAR8qFeb//vGjKUvtVyVVJgHKwP1aV9VRJ+k7pkmMCT4/cgS6yRixm+9MXled/RvsQYCgWTcZ5PWXpi3sj+sbc1wO3gNju5+AAFPgAlzuiDsUyLmjt6Po7vuS43ZqZefWjScADUaTg8Clihss+/ARHB55INfM5OyMarWLFWmVW2JiKFe+JAsNlbys2/qO9qhnZebSD/Hg1o9Im2jCW+TpbGhQkFKEwOWzDZEy247xlfw1JAQu2zp7Yq3a4m/2//+PFhi26emHa4tGqdwzMoEr59Duq2suRChKTFPOo2Ggx3Kr/1teiKG3BV5kdpc3oO6lra42WuSRJ+KHo2bvSahrqWtRuZGlcl1m+GvoAcAgEdJ0eKsvJtxYeI2qgZPUyFPjkK0IuwMTZBWIoqXUVZ4UwdQy9waQelJEvtepMTDVw1xTjkE8VOaXAZUBjrFpXeWPgM3369S2G7czM7iv+v41hRLD1SkVm0HLF47uHJXpdFqcS0LS2PY2JALrBPwBVg9XLMxUXxB1WX5pWXmq22sgiLJm6JdDTMZLcupY2+jtvPr0KEJmtjWxPY25bFORN6bioTnau5lRBWuxJQvzVUMtNYGLiaqL6/7WqtePksHYGck/UIbqKbMt3fpyVL7nE2rLpmHaFlOWzVdF68djM1HJqNxS0/4t6yFPzYPx47T09R6jmpbcmWi70KkfC2PsWAw9dCqbxP0nd4VJhbGYDD1MNX/Vwj4Avi198GENcMAoRAJESk4vvoC0hPKo+xvl5wArVLcRdONe5DPLoWlkSECF5R/qJVLrXwP/L3njsR213aKizNOaNUY4WnEIo46dFQX+rr5oITHxW+BN2W2KcvMW/jiOhpYO8BDS8tfipjq0wIHPgfiWuxndHH2hh6NhlvxIo/TfL92WpnDiqZdMOnReQDA6/QENLOlHuPxZ/PuuP9NLfxrfla1+Xy/N3q618bNWMmg+YcJ0TgT8RHDvZWPdSvl87DwqfTv41688npaGSXKB5YLISpGXBlbQ+0t3NZr5Iq/9lH30pOl2pZzr2g0AcDhz1ux9ORs+LapA/f6znCu7Qh9Q5GbnmXARN1mnqjb3Atdx7TD0bBtWPhPuTG0fca/UuOXFUBUn762YvQqSRA8fhUpoyV5wqJTpcZpXE/x8pulkSFauKsnUE6HDk3yMUu+0N1wz4YAAL5QiOlPLikcT9mYHapUDPy+GRcmzjSj02gyhSbVTUcnT/Hfc55dRXS+/LRtIipKEEx/colwWUiHYupZES8j7/1IPWuxIpeiQpHNJv4/ORn2gXA/ILvcCgCJEkNUySguJEy+sNRQLFdVoFaP08B+25AnR7SqDDLxUBXZ+WwNbJ2l33JMLIxRUshGbobkshddj44uo9vCr70PZjRfgluHH+H2kQDcLjkhbvNygWzXXQ8fb2wbpLhAIkDNQ7Vidk8s31quTrtk41X4eDrg4DrpgsZkEAiFmPTrCYl9M8e0h7Nj9Vrb1qFDFmVZWj6WdqhnZYfmdi4wZrCQXlKI6PwsiXT6Ud7E1QRWNe2K4Z4N0fPGv4jIzYT7ib9hqW+Ivm71YMrSR05pMZIK8xGQLCq30MSmJs53G6Pxa/M0t8ba5j2w5NUtsacJALa17gtHY8XL6RUp5JYivaRclyetpBCmTBaMGPKz1mgAfvfviL/fPUJqcQF+unYABnoMNKrhBH8bJwiEQnzOSUNEbiZSivNhb2SKlwN/kRrnZq9JGHznOCJyM+F/fhss9Q3RqIYTfKzswOHzEZGbgej8LCR8C3zXdixUVWXfUsHDnLguaWx+Dm7EhKOXe23KY/IEAvz2/I7M40te3MUfL+8havxCiRqCWexi9L52TGa/vFI23qcno5Gt7DJHsrgRSyxF4Vfjx1nyVZvhlJiQTcpooopHAxd4NyHWXijzOLFllHKxdbbG5LUjsHXaQQgp5OGrQxyQiA4tvOFga46U9HI9i89RKYhJyIS7M7XyF9m5xVi3T/oH0+cn7bzJ6qhaElNy8O+p5/gal4Hk9DxwODxYWRjDxtoEbZt7wbeuExrWk+1RbNt/o9zxDQyYuHdmrrqnLZPPOWn4nJOG89HEcgztHD2w1L8z4TEAqGtpC1dTS8QViLJuc0pLcDT8rcz22qKPW13CbiyyAAAgAElEQVQseSVZVYGMEOWukBd4nBSNpKJ8FHBLpQogt7i4E4AoW86EyYKjkRm6unhjSl3pxIWpPi1gyGBh+WvR/YLN5+FlWhxepsWRvo66lra43H0sul3/B4Do8y0rwFxd+B6MJ1nMfvw/JBflY0p98rI22ewSzHx0TWE7gVCI7lcOY4JPYwz18kURl4N+/zuO5EL5cbYfM1OVMpzepCUS7nc1Uy7eLjuzAC8fhaGwoAQCEsllI6Z2UOo8VFCb4bTkt7No2MgVm7cq5z2RRbuBsoUDLWzMkBiZghQ5GWrdx3fA1mkHAQBHVpzD+FWKC626W2vmx6enR8eFPVPQevAmif2j5x0BALRr5om2TT3RrZ2P1LIeAKSk5+HoxUA8eBGOYoKaO56uNqQLB1MRvqxI2CrNZFTpIMeDp2FYufl/hMcyswuRmV2IL5Gi0g5PryzS5tSUImLkYrxKi8eR8CAkFOYioTAXpXweLPUN4WRsjsl1m6FzTS8YkZAHCOgn8iJf/hqC56mxeJz8FbmcEpixDGBvaIoBHvXRxt4NdbSYeWfClNZrI5Mm/jk7VSySKY98Dhv5HDaSi/Lhair7vjXG2x9jvP3xNT8L694/QnReNuIKcqBHp8HDzBruppaY06AtalvIThrxtrBBzOjfkc9hY/Xb+/iUnYqv+VnQo9FRy8waHmZWmFm/lVY/3++NOQ1bYfuHF1L7eQIB/nz9CH++foR1rbuhsa0TvC2lX6YFQiGOfH6H42Hv8TUvm/R5I3Iy8fvzO/hdjneqMqtePcA4H3/S7cu4EUPscerroTj2tgyhQIjta67i9iXqAq7fleGUmVmA0WNkawIpi74coUhTa9HaO09B4dUywt4oroxcx84GrhoWenS0M0dymrSK6pPXUXjyOgqbDt6Hg6057GqYwsiQhezcYnyNz5TKnquIsSELq+f30eS0q5SKJRi+5zdLVfgSmYK/tssOkq6IuZl8r2nX9j7IKyhBXGI28vJLUMLWjB6SIph0PbRxcEcbB3e1jTnAoz4GeNRXqq8mlpiUGXNPu4FqnwcAeJhZ40B71dKyzVgG2FSpyG914Hu4J0yq1wT/hr5FoRz9sbKlt0a2jjBnGcDJxAwlPC7i8nMRkZuJAgIF8sq0d3LH46QYlebKV1YtmYCaJuawNiBf1P7EvkdKGU3aQm2Gk529BQwMlReNk0VSlHRhxDJc6jjh5f/If7hhrxW7la9OHU16PAA4PnYIpfYAcH73FKRm5GPQ9AOEx0s5PMQmZiE2kVwgp4WZEW4cmkFpDgELZFf+ZnO5yGeX4sK7ENwJjUQpj4fNg3uic51aMvtomu/hpqhppi6SjGc7un08PFylPQRsNhdsjnyxuWXzpGP4rtz+gM377qk2SR06qoDv5YXKXN8AwaNmo9aRTQrbvieQGCDD8W5D0c7JDXfjIzHl/mWlxiijx5UjuNV/PKm2hVwOelw5Qnjs1ybUskhP7n8EALCsYYIFqweiSWviAuBVhdqy6lq0qIWXL1TPEqtMbIhsnQuXOtTWX0sJlrdUpZlrTTRzpV6Y1N7GDP27+kmVv6B8fj83HN5APdDV3sxE5j83a0s0cLLH6j4/4d7ciWBzefjljOK1dFmUeYvURXW/OWoDFotBaDQBovgkCzPyb3c6dHzvqPseo0kYdDqppWdlaefkBgDo6kLN2KhnbYf61pJVIj5np5MuFjz5/iVCGQJA5AFThjW7xlQ7owlQwXDi8wUS/6ZO64ygoBj81HEtAgOjkJFRINWm7B8VQl6E44sMT1GHIS3Ff9899ljhWFb21auW3qKpXfDs/EIc2zIew3o3Jt3PxEgfW/4YjKfnF2DrssGwraFctW4ymBno458xAyEUAgeeql6AVod6mDO5U1VPQYcOtZGzJkcl48dymeV39UL1Zew8/N26m1rHbGzrhNAxkgkdcRMXY0cHxSEcN/qNw81+47CtvfQSbNMze+B6aAPWvHqIhwnREjIGeaVszHh0FR6HN+JlSrzM8c31lSv541mXenC6NlB6qa5r53US2zQaoK/PhFAILP1Ndi0rgLocQeD1INRt5im1n8Eqn/6hZefQsGN9KdmC+LByiXvHWtWz5lotlxqYPb4jBnRtiJDIZDx6GYHM7EKkZRaAy+XBwIAJ95rWsLc1R+9OvvDxtCcMHtcUTd2coEen40FYNKa2baa4QyW+pxtadaVyMkBNe91nquPH4b94jxhZ2w8rAx+oTVfseLchhEWV+3nUxf34KNyICZOKW6LTaBhQy0fsafKysJYZH/VP6Fv8E/oWDDod5iwD8IUCqdp7RNS1+vGqVChtOHXoRK7au7IMntcLF7beAACc2XANE1YPI2xnZmWC/OxC5KTlYozXbLjXd0aLXv4oLmDjaiVF7Z/Xk49fEgiFiMrIQkR6FgRCxV6yvr7kMwZk4exoCWdHS/RoT71QqibRZzBgYWiAkOTqWdLiv0B2bpHEtqmp5qqq69DxoyEv/qli4om2iRg3Hxw+H34nd6CYQhHcitzuP0GhcbKzQx/sJOF5AoBj3YbA9dAGmcd5AgGy2OSlh273n0C67feC0obTsuX91TkPKbqNbS82nOQx/Nd+OPDrSfF2TEgCYmTERXk2ciN17uziEnTbfRj5bPLqqeownKoreSVsZBcXw1RfdoajDh06dFRn5BlPVenxYunp4eWw6TgR9gEbg56Q7tfeyR2zGrbUiEenrIyRqhB5wH4EaAqEIdWXj6gEHwJCUbtxLRgqeLvOTs3FCLeZMo+zDJhYcnwWWvZRHEfE5fNRf+0OynMNX/bj6hv5rt4BLp+PEU39sKK3LrZGk2TnFCEsOhVfIlORmVWAL1GpiI7NoDRGv25+WDi9K+VzV8yqU1UAMzYhC7+vvYzEFPlxK072FhjYyx9DFfw2Kwp20uk0PL60kNJ8YuIzMXb2YfH28vm90UVOTcfU9DwcOfcSN+5/ktnGxFgfF/+ZBiNDxQ+Hp68iseTvK+LtgIsLSC23VxYqVaTNVbH92f1T4GgniusMeBGOZRvkJ3h8D7pf6qTMkKpKj5MsBEIh3qYnIb24ENnsYtBpdDibmsPV1AJuZpqfp0AoxNwnN3A1+rPSY/zetD2m+crWYZTHwxvB2LDkAoxNDPDX3rGo06BKyoPJTN2qtkV+AaBhB3JLVlb2FmjZp7FMaYKN9/5AnabSMVJEXPskKo1gZ2qCsc0aoYePN/To1bakn9JseyAtwlaZ9IJCvI1LAvfbm8eQxspp4+ggz6MX4dh28EFVT0Ml5q04h7fB5FSpk1JzsfPfh+jYyhs21rKTHPRZDJR+02sTCKi/z916FCr+29hIH+1ayM/UGTPrMNil8pdOCotKMW72YSyd21OuSntVkZZRAEc7C6zffQfX7xErsv9Xqe5ZeHQaDc3sqGdrq/P8W9r2VNpwogFKG00A0KmXHzJS83B4xz0sGP8P9l+ahZpu1KpraBK1GU5XLwfh9KkXOHT0ZxgZSb+Bsdlc9Oq+EX8s74+OGoiPWnl+vvhvHocnEThOhZ2PXwIAnsyVrXP0I7DvCfnCkjQaMKpZQ/g46BSBdSimTLm8DCNDFjzdbOHfwAWmxvpITMnF5VvvJdoMnLQPfbo2wOIZxJlG6/8YiLnLy5NOwqPTUJtkssfrD7E4fbk8I3TrqiHQl3F/2LL/vtTcGHp0DOzZCNZWJkjPLEBUbDqCQ0Wq3qkZ+Zi19AyWzeuFru01G/dJlU9fkhDwIlzCaLKyMEadb8klHz8nIq9AVBxWVVmU743q6mmqTjDodMRNXIwulw8hIieTVB89Gg1vRsykJHZJRO8mK8Hjil7Y+XwBJvfbTrrv7eA1Kp2bDGoznILexsDY2IDQaAJErn8AePkiUiOGU0WUNZoAIKe4BCw9PTXOpnpiyFKsI2JuoA97c1P82a8LPG2kiyzrUD8/ta2LBj7Sb5rpmQX47a9L4u2VC/vAxYm4aKiFAtVwTdO/e0OcvPQKzo6W6NCqNqaMaiv1YO7RqT627L+HsAoCt7cehMg0nPx9XWFva47Ub3Uebz8KIW043X4YIrFd10t2sdHKRtP4Ya0wvF8TGFeqYLDz34e4cOOd2Pu1btdtuDhZoY6nPak5aYO0jHxcuxsMQORl27d+FNwqZR0np+Xiz203wSNRA+xHQ2cwkePegIkISIzBlAeX5MY9TW/QHKPrNFTZaAIgNpqqK2oznD59SsD4CYrVQYOCYtV1So1gZ2aCjMIixQ2VYP6fF7BsVk9YmmtGnLC4hIMuY3bg+QXF8R/vl0pXQNdR9ZibGRKWSzE0kDR0XZys4OVePT2A08a2w7Sx8u8Fdb3scXDTGHQctBm8b9puPDkabzQasPPPYRgyVaS2f+H6OzT2dUWb5oqX4O89+SL+W15c0cY9d8V/02k07Fw7HA3qEi+XzJrUCbMmdRLHFHG5fExZeLxaxQmVGU2PLy8EXYZLydHOAnv+HqnNaUmh8/xUfzrUdEfkuAVaO9+4mbILelcH1GY40ek08AWK31q4JOvKVRWLOrfFL+eJi6iqyqsPseg9aQ9G92+GKSPagKEmLSahEOgwfIvcB48OHdWRuVN/wqa9dxU3BGBvay6xvXzjNTy8MF9GaxGpGZIV4P/8tR9hu9OXX4sNDQC48M802HyrhSmPE7smYvQvh8Tb9558kRt0rm02rxwi02iqLiiSCZDXRsePiTYK9aqC2qKe7ezNERqiuJq3ra2Zuk6pETrXFtVjC4yVXepFVU5ceY3pf5xWy1hZuUWY9+d5ndGk47vErabk0pGigOyKS2ZcnmJ3/p0KQeFWFsZo2diDsN3TStUJyBhNAOBa0xq13MrTwR8+DyPVT1s0a+hW1VNQie9NEVzHfwO1eZx+W9IXE8bux9evGTh8dKrEsYyMAixbeh4AsHCxdHFRshTmFOHo6gsIfRGOr5/iIayQXXOHfVJOT/LQaTTsH94f445fAADUc7CFl00NOJjJL2syt2MrSuf5HJmC1oM3oU9nX/w2nbr0PpfHx94TT3D2umoVpHl8Ac68/Yjc4hKUkngQLejSRqXzyULnrv9vYlFp2ZrHEwBy5MK2rByCnxeXFzuOS8yCa03i+LuQ8GT8c+qZeHv7mmEyl+o+fUki3E+GCcNa4Y/1VwEAz14pLiSuQz5lMgG6e4GO6oraDCcXF9HNKz4uE2tWXUYDPxfUsDFFTnYR9uy+j1I2Fx07+aCOkrVn9i44httHAsAuIi9K+frWexR8U1w2MjEkpePUfNNe5JaUy8iHpqQjNEWxYjZVw6mM/z34hME9/eEpo2CrLH5ecgrhX9Ok9ld+g5fHrZAIbLj7BCl5BaT7aMpw0t0kdZDBx9sBbs7WiE3IAgDcfhSKn8cQx1NV9DYBkAqMVhfuLtUnTfpHQZ5YpQ4dVY1adZzuP1qC/117h+1bbyPgUXlApqWlMcaObYPhI1vK6U3MikGbEXjjnVLzsXa0wrIBm8TbZLxSpTw+DJmaqVz9/MJCvP+cgF+Wn5XYP27BUQDArSO/wMxEvtjnym3Xce+Z9HKAtaUxls7sgeYkXfM5xSWYd16kzM7Qo8PO1ARJuaJ4EDszE/D4AmQViWT1jVhMTGjVGONaNCI1tg4dAHDrYQgCXkTgxdtotY67fc0w9Bu/BwBw4uIrmYbTldsfxH/Lkh8gwlhGZrAsZGU36lAOncGkQxkWjDuI0A/x35ccASDKfOnbzx++DZwRGZGK3NximJoaoFv3BqDTqQcocku5ShtNAFDLzxU1vR2QGJFCus+H3zSbbdbIxxld29bF3adfpI6NW3gUq+b2RoM6ToR9P0emEBpNAHB8y3iYm5JPQ7/+UTQOjQa8+nU6jPVZYoXwxwtEGlbvE5JxPPADboaEo7CUAzND7ddHC4tPR2hsKga1a6D1c+tQDi6Xj0s332PX4UcaGd/Kwphyn/YtvUm3NTT4MctE/FfQLfv/NymisBqlKhpRDnd3t4G7u2r1cz4++YJFXf8Ub9PoNLQd2BwLD/4M/W8lDroZjFI4zsTVw7B6+DYAwJ2jj9FtXHuV5qUOVszphRVzemH2qnMI+hQv3p+eWYDpf5xGIx9nrJjbCzZWogDVrJwi9J2yl3Csc7smw8negvIcjr8SvY3fmT0Bxvqiz9PCyAAZBeVSDI2cHdHI2RFGLCaOvnyHkU394GpN7lzqunnN2nEZfL5AZzh9J+TkFaPvuN0S+yzNjdCisQcG9GgEFydLiQDv0PBkTPuVenyipbkRcvJEHtGbDz6hZ2dfiePnK1QRsLc1xx9zycdWFpdwKM1FftUqHdqmoriljv8Omal5WjtXta0lcriCSjAAbA1YiaUnZomNJrJ4+buL//76kVwZCG2xfflQzBwrbci9/5wgXr4DgLEV/i5DT4+OSUNbKWU0AaKlOhN9FlysyvsbMIjt6KltmwIArgRTk99X1WiKTMxEdj75Ktw6qp4Ne+5IbFtZGuPyoelYMrsH6nrZSwlJKkvXDuUiurcehkodvx1Qvq9np/qUlLHZbGpV6tml1AwtHZpH523671FcpL3foUY8TlwuH/KKB7NIxBt8DowQ/73g4M+o24xcrbnK2FYI3HxzJxjTN5PrJxAKEZWRhYj0LAiEilP9+/pS126h0YCRfZtiSA9/7D0pmSGXV1CC1oM3EfZTNhOvIkT/P67WFkjIkbbaXaws0NTVCeeDQjCnE7kgeHXcuHZcfKryGDq0x4Y9dySyynb9NQJ+9eTX28ovZMs9LotfJnTEx8+J+BKZig+hCTh+IRBjBrcAABw59xIR0eWJExOGU0vcEFB0IQUGxVBqr0OHMsTm5yC5KB8pRYUo4XFRypfUROzq4gVnU3MZvasnUV+SYe9kCRM1VDsQkNCRVBdqNZzOnHqJBw9C8TVafhbag4AlpMc0r2GGLqPaqjo1AEBhLjnvRXZxCbrtPox8Nvk1U2UMpzKYTD3MHt8RD1+EIyO7UG7bdb/2R1uSBYvlYWVshPjsXGQXlcDKWPSldbIQ/ehKeTzoV/I+cfgC5JUo95BTlneRinXBdFQf3odIap8pMpoAIDdPeY9iz86+4rp4dx9/FhtOj56HUx6LxWKAo6Q4b2RM+f1OVlUABkOyjBOHy4Ohni6WSodseAIB3qQl4lBoED5lpSKlSH72s5uZ5XdlOF07/Qp71l0HS5+Ba69XSBwb1WVjFc2KHGoznB4+CMXBA49gZMRCvwGNcfVyEHr08gOfJ8Cjh5/B5fKxc/c4eHlTq+XUc3In0JQILCciP1tx2j2Xz0fLzfvUcj6qXDkwDVwuHx1GbJU65lbTGv+sGy1VekNZhjXxxca7T7Hz0Uus6N0JADC+pT/Ovv2IvnuO4/zUkTAzEC2rXHofiuDEFNSy0Wz2UJtZu1BCIICYV8RG46nSnwkAjO7SGPOGyC7vMW/3VTwJ/kp4zNiAhYBtM+QmLryPTMKMrRfBkaNx1aN5HSwa3hHmxtoPnq9OJCZTjyv59/Rzpc/Xv3tDbN53DwDE8gQA8DUuQ/x3v25+pMaaN/UnrN91W7ydmpEPexvFYr0x8Zk4fiFQvD1jfAfCdg6VVM+DPyehRYUwAiKUNeR0fL8UcErR5fIhhUbSj8Cx3fcBAJxS6e95Vnq+1L7qhNoMp4CHX2BlZYydu8fB3sECt24EY+EiUUDmlJ87YeP66wgKioFPPeKMMVmYWZFT8CUDi4TRce2TKNvMztQEY5s1Qg8fb+jRtRMKFh2fidXbbxAei03MwoRFx3Bm5yS1nKtznVrYePcp7n6OFBtO7jUsUdfeBl9SMzB4/ylsHtwT+Ww2ll0TfcGbuTnLHbO6ZbMEfo6TaTQBQBGbg7m7rmDd1F4wIsikSs7Kx9RN5xUu3dx6FYaopEycWT5G5Tl/z+izGCil+LBPy9DsDbLXT76KGwHo1Lo2dh16hKJikZf5n5PP8Mfcngr7HTz5TGK7c5s6hO0c7SQNp6DgOIWG093H1GIKdXzflPJ5aHN+P3JLtePZf5uWhC3vnxEeO9V9mMbP36FHA1w/91rj59EEajOc3gbFYPmKAbB3EAUbm5uXr1laWRnj7/XD0LnDWsTGZmLZ8v6kx6UiJUAEp0KgZ+0mtRS23/n4JQDgydwpKp2XCulZBfhz1y2JDDsiElJy0HrwJjSo44S9f45Q6Zxu1pbo39AHk1o3kdh/efpo1FmxFfHZuRhy4JTEsTIDiwh1CNY92ykpBVHmZTI3NsDDrdMpjdVl4X5xYHnXprXx9xTJh+DNwC9Yc/w+nofEou3s3Qg6ME9qDEdrMxxdMgL330agZ4u68HSSFDrMzCvC/D3XEBqTisjETBSzOYQG2H+FTm3q4NbDEPF2WFQq6ngSe5jTMvKx+M9LKp9z/LBWOHL2BQDgYiXpkpkTOqCulwOpcYwMWbhyaDq6lGXgBoTi6atI3Dk9R2afD6EJePoqUrztYGcOJlOPsG3luM4zV99g+vj2MuvITVl4HGFRqaTmrioXoj7hfmIUnibHopTHg7OpOTzNrbGlTW+YstQTzK9JsktL4H9mBwAgdtyvVTwb6ggB1Dq8EXwtp2c2sXPC82TihKk7cZHo5uql0fP/srQPflnaR24bKppM3f2WqTol0qjNlcLn8WFUQTjO1MwQBfklUu3I1LOryCcZukVkqRhk7tVI/hseIMo2Y+kR3/w0xZj5RwiNpnkTO2HuRGlj5WNYEm6r4W103YBu8LKVVlOe3akVDFnl3jlbUxOsG6A4GL06pQCXGU0dGtbC2snSnoOeLeri1xEdxdtsGZ4SH1c7zB7UVspoAoAa5sbYMqOvePt9pPJlO6oSgUCI1Ix8ZOWUS1EIBULEJ2VTyjBr3VTyxWTZ+quIS8ySasfl8TFx3lGJJTVl6dW5vvjv4M+JeBlU7mHs2t6HqItMDAyYEiVZiks4OHDiKQoIAth3Hw7AnGXlQrZ0Og3LFEgeVC73svTvK0hNl07GuPUwRGtGU1ReFhY+v4nbcREo4nLAEwoQk5+DewlRMrNsdaiPEh4X0x5e0brRpIjzkZ+qegrQpxiWQtNiMWu1/TLq1XfGixeRaODnAgBo2swDhw8/wew5kg/cfIrBoPFhSfij3wb8eXUx5Tl9eRWJ33r8Ld6esHqowj52ZibIKCxS2E4dDJx2AGmZ0ksVvTv7Yt7ETjDQF31x+nT2RedR2yXarNl5E2t23sTOlUPhX99FrfOa0b45ZrRvrtYxtcm5R+VV7jdXMGwq079Nfaw5JoqRWX30LtZOUbw0U5ka5uVijMHRKWjtq9g4r0qOnnspUb9NFqUcHkbN/Jfw2NZVQ9HEz1Vqf/uW3ti/YbS4llxqRj5G/3JI7nmeXF6Ek5deYf/xJyRmL419hdihykHhyghlBlxcgEs332PrAdHy9PELgRIxTLJ4fGmhwjZ3Ts/B1EUnxAbjs9dRePZadm07Lw9btG3mhUNnlI8Dk8fx8PdYFngXgCgj60DHgQBES0ZhORlg0rX7Almd0UQYQjGPi7rHiGM3tcW8Rq2x9b309+tefBTORHzEcO+q08+r6UatlFENOzNkaEnLSW0eJ7+GLgh8UX4TaNrUAzf+9x7Pn4s8PmXeJ6ea5AKM3X3LjYE3d4LltJTNqqHbJNLumfqKLVgfe1sUc6jpuFCFy+Nj9/HHhEaTiZE+fp/eTWw0AYCBPhOr5/WGCYEGzpxV57H7+GONzpcM1SWuCQDeR1H3/ARHJ6t83lKuLpjXx9sBBiTeFGk0YNzQlqDRgGYkywTJgkgbSlZ2GxkG9mwEa0tyRpe5qSHWLJZtnFdEn8XAlpVDSLX1q1cTW1cOlbnUqQ7uxYuWGe2NTLGrXT/xfn09BvxqkFvi/K+g7qLDAqEQPz+4rLbxlKWLi+wM7XvxVVuw2tyS2m/YWEG5MnVCk6e3BNHyK2nu3v6ETj/VA4Mhssd6dN0gkRlSq5Ytdu4eR9oFt2zAJry+9V68bWJhhPW3l6KWnytoNJqEcnhZHToeh4dVQ7fizZ1gCaNp6vpRGDRHsUdBIBSi7p/bcHTMYLRQEAxNFaEQOHcjCDuOSJeioNNoCDgzT2b19jL2nXqKk5dfSwUs67MYmDm2PQZ1V66enCoxSpWX6NR1g1E2xqnlzJ3gKGHEPN0xU2aMUn4xG2cffkBMSjZiU7NRWMJBQXEpSrk8scGkKMPvv8bBk0/x6UsSvkSmgsPlwc7GDE72Fpg0sg3q11au2Le2ufv4M94Gx+HZ6ygUl3BgYqwPB1tzDO/XFK2b1iJlJBLBZnOx+0gAPkemID4xG1weH+7ONeDlYYuF07qQ0rpTFe8Tm8Dh8zGvYRvM8Wut8fNpCm3FOKnT6+R2aAOlh6u+HgM+1rYwYjBlxiUd6jIInZ0Vx/FWZtHTWzgnY2kubiL1lZ6q4vTBx4gOS8Efm4era0iZa39q/XV27S6ZwbJzzzgc/ucxXr+Ohq2dObbtGENp3XLhPz9jqNM08XZhbjFmtlgK8xpmaNFL0kA4suIccjPy8eDUM4mAcABo3rMRBs7uQeqcdBoNXjbWmHzqMia3bIK5HamJ58lj3przeEOgXl7DygTLZ/VUaDQBwLSRbdHU1xWzV0kqq5dyeNjyzwN0bFEbVhbULHVVY5Oqk7eJw+UpZTQBAIfHB9EnN33LRQRFJIKvRYG1H4EpatJf0yb57FKMPXwe/Rv6YHxLf3Rt70M5VooMBgZMLJjWRe3jUoHDF0lssP5jS3LKviSqq5RLKZ9HymjyMLdCeyd3TKjXGM4m5uJEAtdDG1SeQ0Wa2teUaTh9T4yYor1yahp9rfH0tMNf6xTHFcnC3NoUp2N3Y/PUA3h7t3y5Li8zH3eOSi5PnV5/lXCM1v2bYvmZuaTP2XzTXuR+E3rc++wV9j57Rapf+DLprKzKVDaamEw9zBjdHkN7+ZOeHwA09nXB8wsLcfa6tPeqz+Q9eH5BcbxFRZz/nVgAACAASURBVKqT4aMqLCYDTIYeuN90l+TFOFXGuJK3aerm8wgKL09mmNqnBfy9aqJJbWeJEh6yNKZ0aIYnkbFo5+WmsfGFQiGMmOrRS6tO/B0UgNTiAgRnpCC1uFwnaP27x1j/TvJ+6mRihueDiL28Mfk5WPT8JhIKc1HK58HW0ASrm3dBC3vFsZaq9L0bH4lrMZ9xLyEKnubW6OxcC/MbtoU+iWQedSWuqHqvzC1lo+VZ4rqjZTwePAVuZtq7Jw/18sWip7e0dr4fgWqfNmFlb4G/ri1Gf5tJKCmgpm8xZ/ck9JwkO4WeiFwtqmMf/HsUvNxsle4/rHdjXL77AQlKCA9WRh1yAtUFCxNDZOSKFNg7NKTuui6jzGii0YAFQztgRGfllkF1qI/47FxMPXEZYasUv6gog5mBPq7O+DH1uPaHkHsJlMfSwLs4GxEMXoUyVLmlbAy/cxptHd3/z955R0dRdmH82b6bTe+NFCBA6B0EETB06YKAiAUFG0VRFFQs2AugoOCHFEFQRJoUkd6RXhNIICEhvWeT7XW+P5Zsstm+O1uZ3zk5Z3fmbTM7mXnmvve9F78NNv2iLFUpMXjXGrvqVkjFmHmsIXxFRnUZMqrLEMXzx9TWlv8v661F7r7Hbbx9BRKVaR/a93oMcKloskSRqA5x/pYDwT5skCacFAqVU+fld1VoV/jcvZKLW+fuYPeqQxBU1kH0YAk1nUFHaFQQBj3TD899NBF0K6a9jGGN5chRPnlzJAb1NR4oz1a2LNcGxBwybTnENmZ197SAlaawdaHu88O649stx0nrf+07k9CphXf45Pg63x2yvCKQwjhN/X+SNnwNAHi3a3+82qG32boagsCzh7bidEkeAGD78GfQJSIWdBoNBaJaDN61BqeKc9Fj64/YP+oFhPP0neufOfin3XVnndiNvXm3AQA9IuOxdbjWt/VGZQnG7NuIDx6sDLSEJ9znllwxff2+3bUfXu7Q04Wjscz++3fwUrvulgs+ZJCmdIYP+QZ/7ZiL0FDTq1HSBnyBlFbR+Hn1dLv7SemajJSuyRjzmmNJbt3BtHG9MGNyX6t8mWzl4G9zoFJrMGDyUqvreMKNxBzB/jwIRFLUiWW4lFWA7q2tc9af/HgXnXB66uON+GX+Uw6lQxFJjOcsJAjgw/X/Gt3n7bz+x24cycxB5idvoqRWiOd+3YayOhG6JcRi3XNPGq1zt7wKBzLu4I+LN1ArkyEywB+p0RH4cfJomAqxsmDnAVwvLEF+tQB+bDYGtEpG3xaJGNtZ369o/vb9yCytwN1ybVyoNh/pT49eXzRbL79ikaAOacvW4r93X4GGILD5/DVsPHcVGoLA0HatMK1XZ7SNabD2Nm1vUveO+GRUmtlzVCWWYMZvO1FYUwsem4WOcdFYPmmUyWP1Zh7b8T8UimrRKTwGO0dM0wvc2cw/CJnPvIXVGRfwxaVj6L71Rz2RpiYInWi69+w7NtUFoBNNx8bNRHIja0yn8BhkPvMWHtv+P5RLzef4NIYnWKDq2Tv6WXQId94KSnu5UFpACScjuCaXSCPKXBRnwRN5ZWo/p4imepgMOpYtsm65c1M85QbSmK6tGtLzLN1qW8iFmDCteTmnuApTPt1k0rH73wtZeHvVHrNtrd1/wWiuug/W/oN/zt22aVzexpX8YoxZ+RvyqwWQq1Q4ey8fc/7cC5Xa8HyO+mkjfjx+DlViCVRqDYoFdTiSmYOXftthcP40BIGFuw5i17VbyK2sgVpDQCiTY8+NTCzYecCg7T03MnWiyRbK6kQY//NmrDxxHiK5AhKFEjuvZhhMyadEhiHc3/q4T4duZ2PID+txq6QcdTI5yupEOHQ7GzM2OR4N3RMpFGnv2y+kdjcZ7Xxq685Gt18oa0j+bGvdxiQbmcLiMph4Iqm1xbpNcUew3rw60316omgCgEqp/Um4HcFY/rqmXDt/D1vWnMD+7ZdQWeba3HYu93HytcSVq89cBADM7NvDzSPR0qOjYWBCb+XbV0Zh4S//4ODFLGQVVBh1wl44NQ0T+hsGadv75YvIKarCU59sRFm1ED1f+cGgjCUGd2+FQ5fu4Hp2MR55bbnRMpMf74zj13JQakUCaW9k9pY92PP6s4gK1OaMrJXK0OurVej46XLc+lh/0cWfMyajU7x+/J/CmloM+n4dJq7+Xc936KXfduJszn2kfzgXzCYvEyW1huey3qep3jJkrY/TU7/8gR0vT0XLRhHy1RoCjCaJnfe8/iwAbeaAR742n+RbolBi9pY9aB8bhW0vP623r81Hy/D39dsY0ynVqvF5G2Obm15hyGc2LK5QatS6AJobM7WpcKL8TOcdNVX3VHGexTFNbNkR629ftliuMe54UdycZTweIdNFuVDNEe3nj1KJodXOnNhzJpMGfgWpWI7OvZrjq9Uv6O0rLxFgztM/Q1CtH6i6W5+WWPzjNKcaJ+px2S8mkWj9b2LjPM+y4QhLjp7GkqOU34Wz+OQF+6dkW8SF4YXhPQ1WyzUlMdr4Nfnp9GGYOKCTyXoLnn4c8ycPRIfmvhss8IU+3XSiCQCCeNopT2OJj5uKJgCID9FG9r5XafwGTKcbWiBiggLsGqsxFCq1nmgCYCCabGXH1QwAwGdjjIcTqN//MFMjlxp8DuVaFyalcd06heXFOoFekE8PAAqEAqPb24REuHgkhvBZxs+h1IwjuzORirXuEY+mtTPYt+zjXQaiCQAun83G9g3OibLfFIcsThfO52Dhuw35miaON/9WHxsbgmU/PONIlxROwpPm+xvDZjKMJuC1llnj+mLWOPuC+7GYDCx4+nEseNr8ysyvZj6Br2aaz1PmrUztaSgcE0KDkV9t+BDQEAQu5BVi7elLKBeKUFonguxBTC2lWn+qbuWU0ZiyZgvafvw9GHQavhk/HGltWoDLItcI/lS3DpYL2ciKY9pE4GNXbTK6/3xuAWRKFenH4q2Ymp6zBmsWhnAY3nGer1UYT1g/3I6pRrLhmwi/4S7hVM/ISYbO8lfP5QAAFnw1EQOGa2cb9m+/hB8W/411PxzEU9OdHz/OIYtTz14t8Oa84QgI5FksGxbmjxU/PQt/F4ZFp7AOT0rOS+FZNE72XE8wz/j/8OiffsPzv27Dqew8ZJVVgkGnIdzfuJWBy2Lij5cmI4DLgVpD4K1t/6Dfd6tRWEOuD2QAl1xrhFpDoE5m2QqiUBv6xD1MBLMbngmhHO3nKiv9ZRrXtcaaVGuFVcoTMCVCos1MYboblosT3lviXlZD8ut60QQAw8Z3c+k4HJbqI0d3wcjRXfDSC7/gmyVPm11VZwv16VRoNBpadW+O5acWk9JuY9QaDRgeML9cT/qdYhSX1aKwpAYKpVqXMubVZ5ybxsMTLU0UnkGdVIbAJkKpRiI1KPfK5l3IrqjCT1NGI62NfuyspivW6uGymLi48DUAQE5FNf64eB2DvtcmBSYrTpMj1g5jMOg0+HM4iAzgY9+s50ht2xvYnpOOJ1u0N7pPrGwIh8Ju9MCd0a4n9uZlml35ZqruY7GWk2bvzfWOBRoipfFwMUEc9xsTqmWG/9MAEMKxbBRxJQtmrgcAJKVE6W2nuXgpK2k2zuYtoywXsgOCIMBkkq9615y9hOUn/sOHwwZiQpeGG8GUX/80U8s5rN/2H85cysHt7FKj+50tnCgoTJFbVWPgu1Rca7iC5eL9IiSGBRuIJmtpERGKD0YMxO7rt1EnMx7+wVNIjY7ApftFEEhkCPZz/0PPFbQJiUBmTQXW376Ecc3bGRWkm7KuGqkJvYTBGoKwqW5jCkS1aOYfpLdNQxDYnecdwonHZEGoMLy25Wr3L5gyJWo9zX+sTqC1Wg4Z495gxKQJp/fetz61ha3MX2d9gldrWfvfJchVKiw5elpPOF0pKCa9L2NoNAQef/p7XWoQexn90ipUCRoc5WxNt0JBYY6XftuJv2ZOQVKY1ipZK5UZXZUW4sdDhVCMWqlM50CuUKkxa8tuo+1evF+EHolxBttD+X5mhVNEAB8VQjHEcgX4HPNO/87i56lj0fXzH9Hvu9W4+eEcg/2Nz4Gv8O/o6Zh3ei925GSg+cZv9IJYFonqMOjvNZCqlAhgc/DPqBcM6o9v0c7uuoObtcShgmz02/4zekY1w5/DngYNwB1BJYb8vVa3As/TieT5GxVO92qr3TAafUxNLfeKJjfRvSNUV2pX2/Ye0Abjp7k3KbXHe9UFhvojJtn+tCSmeDutH5YePY230h412BfG90OHWOssaMfv5trct0SqwIfL9josmgBg6GNt8fvui7rv5VVCRIaRtyqJ4uFGpdHgqdV/YMnEEeAymboYS9N66b/xDWuXgjWnL2Hmpl1YOnEEZEoV5m7di2wTsZemrduK2OBAvPpYL8SFBEKuVCG7ogp5Veb97dLatMCWizfw/IZteP6Rbgjh85BbWWPUid0ehA9Em1ylMung7cdmgcmgQ6lWY/qG7eiXkoRQvh+qxRL8fuE6Vj49xueEEwB83nsoduRoVww+uX8T2AwG2HQmRMoGMbB64HgDq1B93Rq5DMcKc2yv+8hQ3BFU4r5QgAtlBUjdtAR0Gk2XuuS7viMw95T5WGxk4OgCmnj/QOTUGv4/FIg8N7ZhvJHfw13s3KRdlOFqfyZjeLxwav8oOalJmvJk53Z4srPhUkcAGJqago+GW5fjrvWntiV4JQhg8DTjMYHs4fVn++Pw2UyUP1DjHy3bi1WfTSGtfUt4S9oWCvu4+v4sFNbU4vkN21EuFKFbQhyOvPmiQbm3B/cDn83Gvxl3MGLFBoTyeVgyYQS6JsSiw+LlBqvqts6cgv3pd7D82FnUSeVg0GmIDQrE5hefQrcEQ0tUPR+PTMO8QY9i2rqt+GD3IXCYDIxo79iqpNI6EQYs+UVv265rt7Dr2i3d96Y+V+kfzoVcpcKcLXux/uwV1EikCOXzMKl7R6Q0CX/gK/CYLOQ99y5KxHWYfXIP7gtroNSo0So4HIt6PI5+ZvyReEwW1qdNsKtuJM8fJ8a/jH/uZ2FP7m0cLcxBUmAIBjdLwZudtS++rhBOjtI/PhknigxftHdkZ+DbR4e7YURaxCZ8rwDg6dbkvJDYih+fA4lYjhFdPkKX3s1x/UIuVA8MDb37O0cT2IJLhBOhIUCzM3aKmgSrjK2YWjVEBtv3XzG63Y/Hxqi0Dgj05+GXLbbFherWPgH7j2vfBG/dLYFMrgSX45rs7vXJMyl8l/iQIBx+w3KapFf798Kr/XsZbDc2ndUxLhod46Lx7lDb/fesScQbFxxotYN5dKC/Xc7oHCYT/3tmrM31PIWmqU2sJYYfiG0P8sW5su6IxNYYkWhcJNt7LLZQ/3Jor+Wpc4TxeG8qE1kNXMU/eXdM7nOX4/qkFx/D+uWHoNFocPlstm57SlvjOUONxXVyJqQJp4P/3sTXX+3Blq2zEBGpTXex+bczWLe2IVXGsOEdMf/dkTa1e+fSPbKGaBVtoiLQ2UggPzL4efMp/LazIUM5i8nArGf7Y8KIrnrlbBVOU8f00AknlVqD7fuvYupY1yWLpKxNFBQUvo6jL4jdIk1bUu/VVqN5UKhD7dvDwfy7ePvUPy7v1xKTXnwME194FFMHfYuaKhFYbCb6PJ6KhV8/ZbT85/O3AABCwlwT2oE04XTpci5at4nRiSYA2LhRKwDSBrVDVlYJ/t1/A8NHdEL7DtY7nNWU1yL/dhESUk1fdGTy90znBejcffiG3vcFrw7FsP6mUxhYS3yTyNf5xa6zAHlq4EwKCgoKZ+CM+93Xl0/if4+73nr5+YXjJve5OxQBnU7H5sPzUVkmRHCoH9gmZlEUchVat49Hq7ZxaNPRNc7spAUxOnf2LiZN7q37LperoFKqMW58d7z3wRhs+O0VAMDSJfutau+D3+fqPr8x4GOyhkk6WYveRNYi68z8tcKGWBl0Go0U0QQALJb+qpIrGfmktGsrNZ/WeP20nTcfg7eO+2Gh8bXlzdfZw0j9C6KjomlIQorR7f/m3UHnzSscattWNmdeM5mLjgZgx0j7plTJhE6nIzImyKRoAgA2h4mX3hyKGW8NQ7/Bxv2WSR8XWQ1JpQqEhjaYyc6e0c6bjh6jPw1VbmUW437je2L4CwMBAOJaCQiNNcH3vYdWzZ0T9woApDLXhclvfCMh48ZC4RjUw9izqf//oP5XvIfGYtdRXulo2oWiRi5Fkci656OjbL17E++fPWhy/+PNWrhl6tBbIE04xcWF4n5eJQCgrKwWX3z2N96YNwwJieF65dRq6x3h3lj1Eka/ok2kOczvGbzSfQFZw3U7IwY4TxnX1hmPAussfOlh7c0PNDLH/dOU0aRF76bQ0ti52Jf+Zx4GyPrf6hYZh79HmV7Y0Gfrz/jh2lmnBcXMrKnAmD2/Yf6p/WbzAK4b/KRT+vcVSBNOqW1j8e/+61CpNPjfqqPQaAikDTIMzR8QYJuX/uvfP4/3N80GAOSmF2BG53ewesFm3LuRj/KCKogEEqv/GiOUuzc6cWCA8+aP2WzXRZmgHgCehbeKvocJ6jfyPsi8z5laXVfP0iunMXD7GtLF073aaozY9avJZMP18FnuCSzrTZD2hH134SikDfgCQwd9BQBo2TIKfn6GP8DANOssLfW56pqSn1mE/MwibP/e9pUAB2SbdZ/f2XUAR+/k4K8Xp6BjbHTD+JavRc/EeHw9ZqjN7dtCbZ11CS/tIbmZ6+LI+NpDoHFcKm+PUeXt4/dlfOH6elhobCmsx9FFMbtHT8PEfX+YFEdFojq02rAUgDYGVrfIWDzZsj0SAoIttq0mCFyrKEZWTSV+u30Vt6vLzVqXGjOmeSqWDxhl7WE8tJBqmjhy/D2T+zQaAv8eetfAkdldXCkogj+HrSeaAG0erkqx82NC/HM8wyAMgb3sOnhd7/uQfuQ4nduLN6+0a+qz5c00veF7+/EADdeWu64xMvpt+nu4+/fx5v9XV0LmOeoUHoM7z81D4rpvLJaVqpQ4XXwfp4vvmy03/dB2h8YU6x9IiSYrIW2qzmJHdJrHiCYAEMoV4LPdZ5K8m1dOSjtqtQZb9l7S29alXTwpbdsDNXXnmfjSg5FMZ11H+neEpr+Hu38fZ5xLX74XkPV7vd9jACntOEoY1w+bhxqPkURhiMemXGk8reYMYoMCUFBTi7yqGl0CU2fTrlUMMu5o55c1GgJX0vPRtX2CQ20OnrYcckWDuXfG5EeRkkR+bj9rqbcGUG+xnoMv/Q5Nj8XV1xkZfTVuwxPEhTPPn7utaZ7OzA49MbNDT/TYshLlEpFbxtAlMha7RjovfqEzEatkKJRUoXWgfpzHrfmnsTxrLyK4Qdj26Ltg0Mi1EbnM4uRppEZFAAC+PXLKZX1OHtVd7/sHS/bgTq79lqdrtwr1RBMAjBvqntxCjfHmlWmehiserJ7w8H5Y8eX/FV8+NrL5Z8xzGBjf3OX9vtqxF/4a4brcpmRzsjwD088vR7lMoNu2IfcofsjaAwIEymUCfH3LsSlMY9AIwqzbGKnBk6ZOWYnSEoFZXyhXIVep0PFL8gKOWRsEMzuvAs+9vUFvW1x0MJ4a0VXn89R3wnd6+89se1v3+dbdEuw7mo5dh/T9mgAguVk4Ni173saRU3gyrrCoUNZBCgrPQKRUoO/WnyGQy5zaz4e9HsezqV3AopPjPlMs+BQAEBk4C0y6dfcSofQYFOpCBHD7g820b+Zl2n/L8GhEW7zcsmExV99D74JNZ+JY2ueQqRVIO7oII+N6YGHbCbY2bzLBrkun6iRi94YAaAyHyUSzkCAU1NS6tN+WSREG24pKBVi27ii2/XsVyfGGK+J2HLiGvIIq3M0rx43MIqPtcjksfPaW44597jStO/oAd/e0gDMEiCuOhRJNFO6EEu4N+LPYOPvUK/gt8yq+vHjCcgUbifMPxDNtOuPFdt0tF7aBirrVAIAw/2lWCyeBZA+qxX8hJngBIgNft6vfcpkAKQEN4R3kGm3w59Fx2mTjXIbWjzldQG42DZcKpzoXB2a0xOFZ+hnf62Qy9Ph2FR5tkYi1T493Wr9ntr2NLXsuYcWG43rbC4prUGAkz9ySXw6bbc+Px8ah3wwz0NuDN/s7UDdfx3G3+HQGvvJgbhoqw1dDGvjiMdkCn8XGKx164ZUO2od/RlUZThXn4ddbV1AiFtrUVrfIOHSOiMHCHv1Jsy6RBY/dERD/hRrxLruFk4rQwJ/ZEBvyg+ub0DaoGd5sM1qvXL6EnMVY9dgtnF5/9VcMH9EJI0d1AQAsW2pdDjpPJpDLBZfpGi05eVR3bNlzCRXVjjkEJjcLxydvjiRpVPqQ/cAx15a3Ptwa3+SNOS7X7/MmGi+T97axm6LxsXjrcZlyjPf2xRjGVhg2XjnprcdFFu3CotAuLAqvdOiFWrkMB/OzUSYRIb2qFBKlEnUKGRQaDQLZHPCYLLQICkX3yDgkB4WidUi45Q7cBIOujUml1tjvYxnJDUKxVFu/VFaDc1V38FabMQblmDRyRaPdKiHzdjEUCpVOOO3dfZW0QbmTjnHRlguRxK7V2sTHa7acwf4TGSitsD5P0bPje2HCiK4IC+Y7a3gUVmDuweWNN3xfEX+NMTV2bzw2Y2NuLDR8BXcckzdcD0EcLiamGGbk8GZUmiq7685vMw6zL6/GhtwjKJdp3W7Gxvc2KNd01Z2j2C2cvlv6NFJa6YuMzX+8Bp6RaOH1jB/zvb3duYyoAH/USF07pfjS5L54cVJfbNh+DjcyC3H+Wp7RcjwuC+OGdkafrs3RpV0zp4/L25Z5u4umb8j127wddwaadCbefjzGxu/tx2QMV4onX/q/9RZkykwAAIMWZHcbXUNbAIBONKUExBot1y7IsbA/TSFtVV3agC8srpYbNeI7SCQKq1bVbf/hH5zafh5f7X8PXD7H2mHocefSPZzadUH3/cXPJlusoyYIEAQBJv2hjdRAQfHQ4IvCkMI2KNHkONfztS/ybWJPgcNMMluWgAp3S0dCqsgAAEQEzEBsyIdOGZeG0EBNaMCi22Uj8oxVdXw+BxKJwqqyp3dexO0L2Zic9BoGTHwEb6x8yeb+AkL52PrdHt13a4QTg0YDaCbPFwUFhQ9BPSwpAOo6sIUq0SaUCoynirlbOhI0M+EhCRDQECIQREP8wVD/SaSPsR46jQ46ycEvARKFkzVWpJ9XT4dSpbGqvVvn7gAApEIZirJL7RpTTPMoTJo/Gn9+u1vXZtverexqi4KCgoLCd6AsTfbhx+6EAF5/1Ih3GexTa2wL79OxWQ5oNPelPrMXl85HBYfwERERYHO9XsO72N1nh0fb6D6nn86yu52HnXGnF2FT3iF3D4PCBL7mHExB4WyoyOb2wWN3QELYCrSOOYjYkA9Bp/FsbiPIbziaR/5GumiSqZXQENYZZxzBY3PV1cPisDB+znC76/dolILk7O5LeOptKvszhe/gi4KJsgRQUHg+XFYquKxURATMwO3iR6BQFVrl40Q2cy6vxuXqHN331T1f1zmDZ9UVYfr55fi9z9tI5BsGn7YXj/eAjm8VAzqDnGFWFNq/7JGCwhPxtbdmSjRRUHgfXFaqW/olQOhEUxjbcDarPgzB0bIbpPZLqsVp5Y+HsWPHRRAaw8V4dDoN+w++CybTNhEU15K8uEoCG+IkURhCo5zmPRZvD+5YjzePnUKLt1+DzoSAmaVaXkwofwLqpK535Xj/+ia0C0rA6p7ayON9D71rtNzfhefxQvM00vol1eK0fdsFEBoCdDoNcXEhaN06BjGx2uigGg2BzxcbOpNZQkJimhaNEUFHYT0MJ6xOoCAPX5y2o/AuqGvQPP3++h9+uHbW3cMgHS67Leg0P7Mr6pzBrdoCPBFrOe+eQOlYho6mkGZxmv/WH4iKCsL/fpmOgEBDZzFhnRRjRy/D/Hm/49ulT1vd7p3LOdCoNaRM10UYSaBLYT1KjQrTzn2OYmnDlOfUxEGY3nyEXrn/KjPwwc21etuYdAbeaj0JQ6L1L/KRJxdCqtZP/vxu6tMG5cjG16aEfO14LPGwHa+34Am/hydbvAqEtVh65TSWXjkNAHiqVQc8ldIBPaLi3Twyx+Awk9ChmesXX0nVCoSw/S2WC2KRm2GDNOGUfrMAX30zyahoAqDbfvNmgU3tigQSrP9oq1UxmIyx/Yd/dJ/7jnHuw9jX2Zh3EGt7voMEv0gAgEqjxtAT83GpOgsru78JAJhzZTkyavOwv/83YDcKOvZx+q/4+vbvSIvqqrNczbmyHNHcUKzpOV+vn7Rj8/Br7n78/sgipx2Lp95Y7cXXjscSvni87nrgk9GvpwhZb7N4bb1zE1vv3AQA0Gk0LHvsCYxt0dbNo/IeJiT0wZLMXegZ1gpcBstg/+mKWwBA6jQdQOJUXXh4AJRKy8sAw8JtD0ew52f75k7lEgW2Ldun+96+bxszpSks0T4oWSeaAK0VCQCyhA1iOKM2DwD0RBMATEsaAgC4VK19KymSViKjNg+TEgYa7atMZvwG6G03Rkv42vE0pT6PH4V1uOtcOdqvuwSTsXG7W7zZi4YgMPfEXiw4cwBXyovdPRyvYEKzPqiU1+H1Sz8b7Pv13hF8cGMTwjmBGGHFdJ4tkCacln4/FV99sRuVlUKj+wsLqsHlsrDEymm6KQvG6j5LRTKsemujUadzc4wOfQHVpQLdd8ri5Bidg1vaXbeFfywS/CJxsuI6AOBsZToAYGCkYYyuxuKMwrvx9lV/rhR+jR38nYGpdh39jTzF2uQpY3CUP7KuY9zeTUhc9w2S13+L3fduQ65WWa74EBLC9se3XV5AibRG5xg+88JP6HvoXfyScxD+TB7+fux9gxd5RyFNOEVEBoIgCLw8Yx1ycsohkylBaAgIhTKsX3sCr72yHh8vfhLRMcFWtTdu1lD4NZr22/XTAcwf8plVdeuqRfhi2gq9bQwmw/qDT0iP1gAAIABJREFUoTAKYX3qQhP1AdqDNSU0M2tLzPXiCzfGxvja8VA4hrOFJtmizN3WRF///9EQBGYf34Puf/yEqxUl7h6Og2hQXrcCpbXfQakm71j6hLfB9n4L0CMsBYEsPwCAP5OL2a1G4q9Hja+ycxTSZFjagC90n2e+uMZomQXvbDG63Vi6lqDwQOwo+wXDeM/ott08nYmh3KkAtGEK4lvFICohHHQGHRWFVSjJLce9G/kGbbXq1hzLjn1k0/HYAkEoUSP8ETLlDYilByyWT4n3TjPs5eosPJ88zK662aIiFEjK8UrL0QCAxyI6YlX23zhafgVDonvolS2QlOt996Q3WoqHA0+45sj2eWraFhnHGLIoxO3i6WGgTiHH2D2/AdDmU30sLhmr08aBzfAMg4CGkOFmQQoAoFOCoR9zRlEXqNSVuu9ltT8AADom5IJGggzhMdj4vqvt+WzthTTh1Lcv+TngaDQa2vdtjfQzht76RdmlVuew+3TXfDDZzgiSrkZJ1euQyI9Do/H9GFG36/KRLSpCS39tUDGVRg0ASA1M1JXpEdoGF6szIdcowaE3OOttzD0IAOge0hoAEMkNQY/QNvjj/lE94VRv1QrnBOn1TYkmClfhCUKgfgzOdhgnyynck1eyeRJ9YxPxX0k+NIT91ns1QeBY4T302PITRjdvi08fGUTiCO1DLL9odn9j0dSYirrViAx8zRlDcio0wvwP6DGBj6a2mI3Komqb6sz8eirGzx4OGp28kGMEIYdAtA6VtZ/a3YatFqeiiqeMbg/wG4dA/hS7x2EL404vwrupU7Dizg6Uyhp+B2PhCK4LcjDv6k962xg0OmaljMfouD5628ee/gBCpURv29xWEwzKOfPG3LhtT7A0OBNHj8+Xz48vH5uzoM6Z/WRUleG5g9tQIRWT0t6ghJaY2LI9hiW5PpF9bsXzqJMeAZeVitYxB/X2ZZc9CbH8AoAGa1SV6HcUVmun0RLDf0Kw32jSx6QhCNAdC9pssrLXRDT89dZSvLHSOlNcXMtovLB4Ep6cO4JU0QQA+WWDHBJN9iBT3oREftrgr6puqUvH0TO0DVb3eBtxvAgEMHnoHtraQDQBQKfgFpiaOAjJ/Bhw6CzE8SKwtuc7BmIIADb0Wohkfgy4DDbieBEYFdfHqGhyFk3b9nZnZmtwdGrGF89PvXj2xWNzNtQ5s492YVE4P/k1bBgyATym4VJ6Wzmcn42Xj+7Cx+eOkDA625ArcwEAfpzOBvvqRVMAd4BuW5j/FLCZ2pkKoeyk3f2KVTJk1RUZbN+afxqPHV6Icae+hNoJSX+9xuLkbkqqXoJI+o/lglZgq8VJrryF/DLj5lhv9ZfyZHztLZqaRjGPr/3exngYr4FzFbl44fRGdAyJw58DjL90V8hEGHf0ZwgUEqgfPAtvjzPtDzvi0I8IYHFNtucoJWIhph3YirsCcvKqDklIwcRW7TEkIYWU9kxxoyAZBKFCQthyhPDH6bYTUOFGfjK4rDZoHaMfVqhS+CuKahaByQhHu7irdvX7SfoWFIgrsKbXbN22AUfeg1KjxpDozrhdV4gCSSVW9XgVHYOTbG3e+y1O7kSjqSNNNNkDh2U6IJpGU+vCkXgWzrJEOfsB4wk+NGTj7cfky6LC238be2kXHAM+k40nkwxDntQTwfXH6RFv4+bYD104MtPE8ANwePyL2DfmOYTzHI92fTD/LmYc3olPzx/F7eoKEkZoCq2UoNM4eluF0hMAgFB/Q3cTJkObyUOjsT8dSrawBD3C9KcmlRo12HQmPuowBb/2ngsA2Fd8ye4+jOEMj2mfoqhyCiSyE+4ehklKq+cgNnyDu4fhc/jSg9QbjsWdFhFn9OtJVixPGENjXHVuAlhcXBq10KqynpZ4t31YFC5P0SauFSsV2JebhXdO77d7CmhNxiWsydCKBxadgf+ljUX/uGQw6eTYTrisVpAq0iFV3kYQtO4bSnU58iq1lrmIgBkGdTSE7MEn+89+nrgcb7dpiPl4uFQbJ3D9A8HEZbABAEdKr2Nh2wl299MUyuJkAU8RTUxGlNHtEvlpEITCxaPxDDztgWAt3jpuc5BxTI1XaHm7lYTylzIPdW6sh89i46lWHXB20qt4p9tjDren1Kgx/dB29PpzFbJqjK92sxUuS2v1EcnO6LZVCteAIEwH7lSrtYuM6DTjadqsgU1nQq5R6r4fKLmCtkHNkMTXD6LcuAwZUBYnExCEHPfL+ltVls1sjrCg98BhtQWLmaTbfrcwlrTxhAUuQFnNmwbbCUKKauEKhAW+RVpfFMZ5GP1EXEV9PKD6c9z4M4VvYe1vOu/iNuwvzMDZJ+bjw6t7kCkoxUutHsWk5G7ov38p6pQy7Hz8ZST5NyRvVxMatN+lv3hnUnI3fNx5JKnH0JRee79+MJ5X0CbI+Euuo8TyA/B6p954vVNvAMCRghzsyM7A3txMu9qrlIoxZOc6AACbwcDghJZYMWA0GHasRIsL+Qw14h0Qyy/iXrk29qLwgdGhWeh3RuvUSHYCAPw43ewZPgBgYGQH/JJzCF1DW+CTm1twtjIThwYuNihnTSJgW6CEkwmqhSugVBkG02wMjcZCs4g94LA7On08PE5Pk/tkD1YtUDgf6mHuPB6WkBAUtvHG+b9woTIPAPDxtb0QqeQol2lTe804swn/DJ4FFr0+ECQNKYGRyBVVQqUhfzWVMdJrilGnlCGSG+A00WSMtGYtkNasBR6LS8KO7AycKzUMPGktCrUa+3KzcLFsFca3aIeFPawzGtTDoDfkoBU2maUJ9Z9oUF6lroRUoU3Ay2PZn0N2RGw3zL68GhNPf41ymdbf14/JMSgXywu1uw9jeJVwEtWIsWHxNmSczcK9m/l6uesOyDaT2le12aX+DKTE23+R2gOLmQwepy+k8jMG+yTyUza11etZ7bHFRgShViSFWKoAk0HHmfVvAACkMiUGzFyB3794Fi3iw3X1cgor8fR7G3F89WzwuCwIhFKMnbcGc5/uj2WbjyM8mI+icu3F+9+GN/ViaOw6dgNfbzgCGo2GiGA+Squ0N75T6+aC3SgdzvRP/sA7zz2ONbvO4dSVHHDYTMgVKgzvk4qPXxlu03GSTeMHO/VQdy7U+bUeVwlNdwnaee3S0Ck0HstvHcOqrJM4WpKJ2+M+gpog0H7XYvx4+zjebJcGQBtVe3faq9rxKiTos+9b0sfT+L52vboQk0+sxbFhbyKaF0h6X9YwqVVHTGrV8PL+4bnD2HDril1tlUtE+Pnmefx88zwA4P70d6yu2zEhD+W1P6G0VnvOg/1GIzbkfRjzCLpf+SoAAkxGOKKCDGdSrKVraAvMTx2H4+XpGBvfG5MS+untv1ajDZMwp9Uou/swhtcIp1VvbcS/vx6HTCy3us6F/VchFGiDi/n58/DIKOtMggRhbj6UjujQZVaPgUw4rFSjwskW7uRXgM1kYMm8sejZXhtHY+/JDFTXSSzUNI5UrsQ3G47g5Jo5YDEZ2H0iHV/9ehg7jt7AhLROunJf/3oETAYdx36ZDSaDjgvp9/HJ6n/x45aTmPfMQL02v1h3COVVQhz93yzweWycuXYPwYF+9h+0E6DEk/OgzqttkHm+zIkjsvuxtr1OofEAgO7hiUAWMDg2FQB0U0qlMtdmbeAxtQ7HV6sLMOPMJqQERrpNNBljce9BGJqQgn15Wdicec1l/dLAQFTQHITwnwRBSMFhmU4K78fpDB6nI/jsrqDRHIthNTa+N8bG9za6r2NwIo6nfQ4WyUl+PV44ffTkEpzbZ596DosNxaJxDfOr1lqlygWmVbbW0uSeNRh8bhoEIuN5AK0lIsQfCpUaB89l6YTTyMfaOdTmF7NGgvXAajS6f3u0SozAcx9uRt9OyYgJD0RZtRAagsCOJS+CydC+ffRsn4hfP5mKkXNXGwin2PAgbFzckKOwb+fmDo2PbKj8XBSWKBL/hzj+Iy7pi0wR78mitV6cdA5tprddrLT+ZZoMorgByK6rwNMn1qFTaDy29H/Rpf1bQ9/YRPSNTcQXfYYAAC6WFeLfvDu6lXXOhM2Ms1gmJvh9p48DAOg0Oug08tfAebRwUsqVdosmAGjRKRHxrWJQeMe2TMxiqbnIq+5buMpkNrNcyAIhATzwuCzsOZmOkso6DH2kDUb3b+9Qm3GRwXrfk2K1zpoFZQLEhAfibr42fsjIuautaq91UqTlQm7Ek0QT5Q/keQiVRThc/BaeSznr9L7IvBZdfS3ZK/iYTngQ2oJQKceMs5sAAFKVd6xo7hEVjx5R8Rjboi0O3L+LFdf/c/eQvBqPFU43Tt7G/CGf6b7T6DT0G98Lb//yMjg8ral0KHeqxXamL56ExZO/BwAc2HACQ5+z7PSm1hhfohkdusqaoTsNNpMcy8vx1dooq2dv5GL38XR8vvYg2jaPxvqPnzZbTyIzPoWpVus7Ydb7ntEafafTaTiz/g2rcgfxOI6nH3AmniRSqBVonsflyp8sFyIJb/3NvXXcAPBfxT0s6DAU16sLsf3+VXx58wAWdhjq7mFZRYfwaHQIj8bb3bS+QAK5DCeLcvHVpRMoEvlmovovb21DkaQKP3Z/mbQ2PVY4NRZNIZFB2JK/0q52+ozprvt8cvt5q4STMTisDgjwG2NXXU+lT8dk9OmYjDeX7MTZ67m67TwuC3weG1ezivScw1dvN+5ftX73eXwztyFJ46rtZxAfGYwe7RIAAP26toBGQ2DRyn34/HXnLgt2Je629jTu35OsYJ5A03PT+DeqkmfiTu0u3BNqU0C0DByBXhHGw3kIFPeQJzyGrNodUGiE8GOEI4STgsdjv0JT6/Otmj9wt24fBIp7AIANd/VzLj7T8jgYNDZZh0g63ixmXEnLgAhMTOqKiUld8W6HIei592sMjG6F3hHJ7h6aTZRJRPj84nH8V5KPcon90bstodJUg0ELcNiXyV5OlWegVmmfD68pvCIA5vTPJ9tdl9bIwlFyr8zudrguCDngCnYdu4G7+RXQPMjLpFJrcDWz0KBcj7YJWLfrHNKztdOcQrEMF28ZD89w4nI25AptoLO9JzOw7fA1TBmu74jPYNBx+Pwd3CvS5mCqFclw614p1u46R9qxuQt3iZamy/cpTFN/jghosDf/Rdyp3Q2VRgqVRopMwXak12wyWu/v+8/gevVayNQ10BAqiFSlKBCfwqGiN6FusojkStXPOtHki/hCYFKyCWBxEcL2w9zzW5EnIie/nLPJqCrDiL83oOeWlfg75xbpoklDSFEt2oLbxb1xo6A5Mgo7oU52lNQ+bEGkklkuZCMea3GqZ+CkPhgyzfFoqQBQWVRtd10upwcpY3A33dslYNHKf1BYJoBEpkBwAA/vvzgEg3q11iv39dzR+Pjn/Xh3xR4oFCq0bxmDcxvmYfJCw/Qufy99CW8u2YnMvDLERwVj3jMD9VbUAcDZ9W+grFqIeUt2oahCgGB/HmIjgvDhzGGkHp8rrUBNhYs7LFDeaCVwxbRi0/ZDFoWgWHIBh4reQKxfT6TFfgv6gzdgFSHD5uzHcaXyZzybclqv3ohmvyCCq794QqQsxva8CdhX8CJGJ2zUbX+mpTZ+Tb2lyRU+Tq7C3dZVaxl1ZCVESjlKpQ3TTn/mXsa2vKsIYHGQFtMGn3VtsI6PPPwTiiW1kKobRHDqzk/AojMQwwvEE806YE6q/uKVppx9Yj5Sd36C4Yd+xM0xi0hLY0IW6VVlmHF4B4rFQqf2o9GIcL9qFurM+ghryaucgVrJv+CwWqBNzHGr+5hx4Uf80nOW7vu3t3dYrKMmyI/n5fHCKTqZPEdhhdz+sOuOhIX3JOIjgy36MtVjLG7Sli+fM9gmV6qxcqFhkLOmRIUGYPPn08yWWffRFKvGZg5X3NwbP0ga9+eqvo1NQVGYJ6PmdwBA36j3daIJAJg0Lmigg4DhDbapaAIAf5Y2I0Ct4r6TRup5eIsvXXad8US2akIDgUKKfLH+y3OO0Lg/q1KjRr64BiUS65Kov9amP1ZmnsAn1/fh0y7kxgyyl/tCAZ4/uA33au03GNhCXtVrEEqPWVU22G8kaiX/Qq7MgUx5G1xWqlX1btXqx0/cVXje5nGSgccLJxaLYbmQlUTEh1kuZBJ70yuSiSeMwRDCg8ZFZs40c205O86NL+Ou81Qs0UbY92NGGOxrHTwemYJtqJFnI4TTEH+GgAalkqvIqNkMiboSElU5VBqt6V9jNt6b7+Hq321pjwlY2qMhMWvzgHDcHveRXhlL3y1ha/l/Bs8yun126gDMTh1gU1tko9SocSQ/B68f301K1PRwHt/qsipNjU40NQv9BqH+k1HvA3g933A1eLDfGJQwv4ZCVYCy2hVIDLfOh3l5t5kG27Y9+q7RaOH1jDhumILFUTxeOFWVCByqr2i0Eiw6yfCGaS0aDbnOZfagVpOTkJHCu6l/gFFCTYslK4g154nL0IbUkKn1LQy770+DQJGrV47HDINIaVuIk4cRb5ne8wWulBfj+UPbUCt33J+HRWfg58fHYEC89au4ayV7dZ9D/a2bNeCwWkKhKoBcZb1fYLfQFgbbYkhOp2INHi+c9q05gpEzB6F5xwS76n/1fMPS4CfnjrB7HFLFBQTy7XdSJ4M6yVa39k9B4avI1NqHfL2AAoAjxW9DoMjF47FfoxlfP5VD0xVzzsCZwsMdfmYU5FGnkGP6oe24VFZIir3/xXbdMTGlA1JD7TMulNf9DwAQFfSG1XV4rNYQSo9Bpsy2q09rieQG6fLYkYXHCycAWLdoCz772/qcOfXcPn8XZ/9uiJTabVAHu8cgV9ywuy5ZSOWeFbQsOICH8xvnuXsYToG66XsP1v5WDBoHasJ4lOkqWRYAIIidqNtWJr2GQFYzA9HkCpwtmii8E5VGgzkn9uBwfg7kapXD7Q1JSMGElPYYmpjiUDtqjXZFIYeZaKFkAzRdaA613f2eHPSlxTJ8JhfAQyKcPtzyhi5w5cUD1/HN9FWY9f3z8Au0zkn7ypGbWPjEV7rvXD4HLAcCK8qVtyCU7EaA32jLhZ1AnfhPiE0s6SQrMCaFFko0eSeWxMaw+J/wT8FMHCyag7TYJWA0WlVXIUsHQAOd1nBL5DCCIVFXQq6uA4ehTfehJpQ4VrLA7Dh4zDBIVVVQaiRg0e3LsejM2FzU9e1drLxxDjuyM3BX4Hi4gxAOD2NapOKT3oNIGFkDDHoQ1BoR1IT1Li1SxS0AAIdpOP1mdb9WRJHf9Aj5L/eetW6yEX3H9kDP4V1034/8fhrTWs1B9rU8EIRp46RKocKisd/ivZFf621/9sMJJmoYQqNxjW4XunGqTCBaZ3Ifj+v6N2IKCm8jnNsWncJeQInkEvbkT0OJ5DJKJJex+752pWe7EH3fjCT/x6HSSHGk+C2IlCWoVeRhb/7zKBKbt/wm8LXhUw4WzUau8BBKJBeRKzxs1RgbiyVK4DzcbLubjin7/8TXl046LJqYdDrSmrXAxSmvkS6aAIDF0K40lcivWlWegAoS+WUAgJ8XxkikmRMh8IBlXNNS5qC8wLGLZnf1enD8rI/YKxCtR4XAeBLClPhiq9u5WxjrcBsAQBByZBcZj0rLoIcgOfY6aJ5rPKSg8ChqFXm4LdiG7Lp9oNHoaBEwDL0j5xste6P6V+SJjqJOkQ8uIwSPRX+CSF5H/JbdHxpCaTJWk0Ijwr+Fr0GoLAKDxoY/MxojE9abHBMlmCgA4POLx7H65gVS2grmcLFx6ER0Co8hpT1zSBU3cadU60PcKSEfjaPqX89vhqSINQjiNaSluVXUC0p1MRh0f6TGngeDHmh336cqbiGjNh/54gpozMRs+qqzYSgdC5jMD+bxwqm6VIAlM1fj0sHrdtXvO7YHPtxivcMaAKg11bhXbDzxbUzYOvjzrAvaSJZwKq95F7Xi34zuC/Z/ARHBn9vUHoVlqBVBFO7Al647T475JFOqwGV5zsvmL+kXsT07A7eryx1ui0GjYfmAURiSkAI2g7xwPpaoDzsQxBuCuNAvwGJE6bY3Fk5yZQ4ySwYAACIDZyEm+F27+yyQVGLymW+tKntm8NeWC+njvcKpHnGtBAc3nsTP840LiMaERgdj+PSBNk3PNcWU6AEAFjMBSdGWU4WQIZzqxH+irOZNk/ttFWEUlvGlh5c7oc6jZXz1HLniuNILyzD9l214onMbfDQuzWL5P/67js/+NvQTnT24D2YM7AGGCyN+/1eSjx3ZGdh69yYp7QWyOdg4ZCK6RJp+bjkboewkcsufAwHrnNbpND46NMt0qM+0ox9gSHQXzE8dB7oV/k42YlI4eY7ktgA/yA/jZg9D+76tcevcHexedQiCyjqIasQAADqDjtCoIAx6ph+e+2gi6AzHTqIf9zFIZCeN7lOq8iFTXAWX3cXofrKorP0cNUJzmdZN/q4UDuJrDzJ3QCUftowvX2fOjm6/63IGxHIFtp6/ga5JsRjVxXT06RUHz+Lno8ajTK84dBb3q2rw5VPkpn8yRk5tFXZkZ+DH6+Tk6KTTaPi+/0gMS0wBh+Hex3kA9zEkR27EvXLLmSnYzHgkha91uE9/Jg9p0Z2cIZrM4jUWJ3dQIVgEgcj8jxsZ8h0C/Z4EjWYYudRei1NZ9VzUSXbA3DJNOo2PxOjTYD4wh1JQUFB4Gs4STrkVNRi55Ffd94yvTFvlxXIFen5k7gVUyz9vv4DE8GCL5Rwhcd03DrcxuVVHPNWqA7pFxpEwIueg0lShqHoRJIqrUKgKQaOxwKSHIjLwNQT5PaGbxiODQUcXISUgFtOSByKQxQOTZnx6sk1gvK1Ne7/FyR2EBS20KJzKa95GVe3niAn7BVx2D9Bo9oc8qBNvQZ1kG6Ryy8lBI4I/pUSTj+Cr0zUUlvFkPyAycNaxXbxXYLnQA9aduKT3PT40CD88MwpCmRwz1+2AQqV9Qf3p8H/4ZrJhfk5PoW9sIp5K6YCxLdq6eygWYdLDdGlUCEJu1LBAFnG8MNwQ5GH+VdOLLwC7fJxMQgknM9BpfogOXYnS6tfMllNralBYYb0/VV5pb2g0Iqg19iVfZLNauT2KuT4E9uT2B53GxhNJ1i279ibIFjZN2/OFKS1fFwAUnsWh9IZo0+EBpnOqrTl+UW+Kbu9bzyM5ouE6vfrZHLRbsAwAsO9aJga2bY7hHVs7YcS2w2ex8URSa3zbz3PFnDU4UzRNOP01ahRCPN88DQMi24NNt99wYQuUcLJAgN9YKNUFqKq1HKHUWpSqfLvrMhlxiAv/g7SxkEG59DyUGiEAQK6uAYfhew9QMkWBMxMEu8N65e2iz5UYE82+giuvvcLqhkjQraLDTZb74z/91diNRVM9DDoNao3WKyW9oMztwokGoFdMAn4d/CR4TNcIAW9FoBDhrTZjMSK2u0v7pYSTFYQGzAaDHoSKmg+sXjHgDFjMJCRFW57Gcz0NU8F0E/PL7saRm7o3PdzcMVZnpgVx5vG4Q2R607VkK648tvI6ke5z1yTTK8lKa4W6zyM6GRdEY7u1w/aL6QCAwxnZmP/EYySN0nr8mCyMSG6N7/qN8MolPznlUxDiN9rqBL9NEUj2QK0RgMtKhR+nC2iw7jmyvd8CvHJxFRL5kWgXZF8+W3vwWOFEaAjQ6J5zCQXxnwWb2QaFFWPd0j+X3Q2x4Rvc0rclInm9wKIHgEnng+VAIDMKClfiC1OkDytKdUOgQ3+u8amgGrFU7/uk3p2MlosJDtB9FssVJIzOemgAekY3w/rBT4LPsj5Is6chkp0Gkx5is3CSKbNQWP0+xPKG6VQmIxxtYy+CRrMsT9IF+ZCplZh54Se0DIhBAJMHNt14vaVdX7RpbObwWOE07/HFuHXuDngBXAyY+AjeWPmSzW2U3CvD820b8tQckG12aEw8Tk+kxBdDJN2NkqpXHGrLFuIjdoLH6eWy/uxhVLLx0A2egje+6T/MfkOuOu6H9fx6O5GBfJQItNakijqxwX6CAKb9/Kfu+7zh/dA92fgqND6nQbDUSWUkj9QQHpOFtYPGo09soldal0whfBC+p7T2O0jk16FUF4PJCEdM8HvwYxuKVoJQIqvEMP2LSl2JGwXJCOD2Q/PI3832eaD0KjoEJwKwPrkwGXiscLp17g4AQCqUoSi71K42YppHYdL80fjz2926Ntv2buXw2Px5o5ESPxoaQoz8ssFQqvIcbrMpTEY8EqMOg05ZcEjF28SIt42XwjlQ14E+/ds0x5ZzWv+lnZczMG/4o3r7p6/ZhtyKBmvii/1N+8DUCzAACPKzLom8vdyf/o5T23cnak2tLnq4DuUd3C0dCR67PVpGbQOd1uDIn181V/e5U0LDKkmR7DRyyqdAKDtlsc/FHSzHjHIGHpvktzG9GiX7tZUOj7bRfU4/nUXGcHTQaXwkRZ9FfMROMOimHRRta9MfseEbkBxzjhJNTsJV0zM1n9Y41Bf1oPRuyL7OqGnFBtrHN4RiqRZJDPZfyLE+XEFjR/PG1icK8pAq0lFU/aHBNgDgsfXTm/lzG0SwXJkNW5CplWbz1ZGFx1qc6mFxWBg/x/7lmD2GNpgIz+6+hKfeHkXGsPTgcXqheewNvW1S+X/aCOPK61Cq8kAQcmg0QtBoHDDoIaDT+eCyu4HJiIU/byhgpTOcJ0AQauy8Z/wNbnwL67JjN+ZKxSfIF+6DhlCaLMOi+2NUsuU3EEt4myMwJZ4oAOo6aMq47u1w/PY9HM7QPli7frACA9s2h1pD4ETmPb2yv73ylNm2jt3O0X02NZ1HYR1MRjjaxek/A9QaEdILU1Et3opmYUt02+UPZmoCuPrWQgAI4Y9DjXgnyutWolnYUrN9zrm8GperG37D1T1f1zmKZ9UVYfr55fi9z9tI5EfYe1gG2CWcFHIV2BzXaK74VjEOp0+pp6KwipR2rIHHeQQ8ziMIxCSX9ekyaDQEc9pArhZAqrJifLgpAAAgAElEQVRvGrWeEvEJ5NXtsliOyyTnoqemPChcBXWdOZe5Q/vqhJNcpcK/N+4YlBnaoRW6JpkXQ42TZySFU7+ZI8QEv2ewjUH3N1Fae+KNxXnyY3dDjXgnZMq7ZvsjQOhEUxg7AFUKod7+1oHa3/5o2Q280NxyPkNrsUuRjGk9HzUVQpP7T+y5iuFJpkPg20Jcy2hS2gEAQUUdaW09zNBAx+Pxf2B44n6Mb3EVwxP/taudWnkW/it9AwBAp7ExMukYxre4qvc3tvl5JAWOQ//YdWQegtNwdHrOXW1TUDiDvcW/43CZ8RejOqUAx8r32t1288hQjO/ezuT+ID8ulk59wmwb9fGbAIDLYuK5fl3tHs/DDpMehlD+RKP7/DjmzquhDOE/KC9X3TPY15j3r29Cu6AEnBn8NXb3/8Bkub8LjecptBen+DgFhpiO5Gorkjqp5UJWotE81Kn3PI4bVd8BAFj0QDwWuwZshmGeKDqNja4RHxrdZw/OtgKELApxah/usGLUizVKtFHYypHy3ThdedDoPoVGjt3Fmxxq/9MJQ/DZhCEG24d1bIVdb0yzWP/M3Tzd50HtW4LF8B6XCU+CTvMzX8AgJ675ZzGNpnXS1xCG/muNuVVbgCesCH4pUIoslrEFp8y31VaTN8g7l3OgUWtIma6LiA8jYUQUZHC98htUSLU5pEYln3DzaLwDZ4kmX8uV52vH44mQcY7DOJGkjGVc93YYZ8byZI6ezZvh4uJZAAA/NhWl2174nG4Qyk6hWvQnQv0N3VMkCq3fk1h+HnxOL0gUDT7BBGEYAkKj0c4O0Wmmpvm0SNUKhLDNlwGAIBZ5xhzASRannWvJexCKBBLcuWLeXGeOwrslus/RSeQ5h1E4RqX0sruH4NWQaf2xZCXzxfQgZPOwWePIuBZEylrLhZwMl8WEH5tFiSYH4T+IM1giMExNptY0uPXcr3wNEsUNVAobXC8UqmKDOvVTdCyG+dXqcX6huC8utzi+lADT0eXtwWqLU1Ofpad7fGiipJaYRHKW5wPAguFfYlflWpvrySUKzB/8me77mNeGkjYmCseoVdQ7cvpSCDgKT4HK1ed8zJ3jH7MX6z6LVHV63wGAIDS4J8502tgoXEtU0FyU1n4HlabKMJbTA/y5j0Ak+w93Sxv8zhj0QAgkfyMRP+qVLaiaDwAI9Tcfp2lJl+kYeeJTHC9Px9pes/X2/XrvCH7NPYJwTiC+6GR52tYWrBZO6068j6tn7uDXb/ZBKDA/7/jE1D54Zp5jGZ2DIwJ1ztxSkQwEQYBGs+0hu+GTv1BdKtB97zaog0NjoiAfFt3C3DiFUSjrj2ncIZqo30OffEk2lBpt+hI1oUKO6JbRcgl+LVw5LAonwud0h1h+yeg+NjMOCWHfo1ZyAEU1HwPQAKAhkDcYNeLtKKx+F6H+U8FhJkAoO6nLCRvEG2G2zxC2P+g0GjLrCjH6hNZIsiRzF+RqJfIeWKI+6fC0yTQs9kIjDJy29DC689Wh3+CLTa8iJCLA2G5SqK2sw/Nt5+k5h3d4tA2+O7zIYt26ahF+nLseJ/46p9vGYDLwj2ijU8b6sCNVlWH//WEArI/jtCNHG9SUBjrGtaCm7XwRSlRQvH/zRbDoHHzcbqW7h0LhIsrrVkAkvwiFMhcMeghiQxaBz+lhsnxB9XxUi7YYbGcz45Aae85IDUOkagUWXt+IrLoi1Ckl8Gdy8ULzQRgT3ws8ht1BTU1aauySYcmp5M4XGiMoPBDPfzwRK+c1iJ2bpzOxeNIyDH1uAHqNMB5NPP1MFj4Y8w2kIn2Hs3Gzhzl1vBT2QcD5UV4p3IOvJdGlYoDZTiwvERVyx2K9UXgXkYGzYYvbf3zIlwbCicduh8TwVVa3wWOw8X1XbT5bNaEBg+bcpCh2WZxcyVtpi5F+xrFUKX8WrEJwBJW+xFnYY3G6Uv4J8oS7bKpDQeFOKAua7YhVQojVQkRynP+yTUFBMuRanFzJl/sWYunLq3Hsz7N21e/YL5USTR5IrP/jOuFUKb2CcB4VeI7Cs6EEk+3wmQHgM53j0nEkI8dyIRtJa0f5XFFYxuMtTvUo5Uoc3nwa37+2xmLZuJbRGPJsf0x+Z7QLRgYQhBI1wh8hU96AWHrAYvmUeMPll96MPRYnACiXXsDp4pcBaHPRjUg6BAaNa1Aur24HKmVX0D3yM4N9FBTeiDutV+6YctQQamQJb+Ca4DwCWcFI4rdCu0DHXpbaLVhG0ugayPiKnIwXlhjKM77K64D0N5f0b4ozuy9h14+Gz7APfp+DoHDn+TTby+cZf+F8ZRZ2PfYe6ORPz7ne4rTsnS1485vJpLXH4rAwfPpA9BrRBVePpePoH2dRWVSFyqJqaDQEQiKDEBYTgscm9MKolweT1q951Cipeh0S+XFdwK6HDaVGDJm6UvddpqoAk84H04rVcpG8no3aEWFv7gCEcjsilNsRIDQQKLJQp7gHqaoUPCY5wfIoKDwBd1uvXC2ePrs9FzWKSr1tzye9iU7BvVw2BgrruHHKMEzErXN38chI00I3p3wKQvxGI9R/il19CiR7oNYIwGWlwo/TBTQrk97fFOShU0iyM0STWZwmnM4dSndKu6HRwUib8ijSphhmVHYFBCGHQLQOlbWfuqzPogrj2b0D/MYhkG/fheoI9SvijPHPfcP0B4kBo9AtcrGR0loL1X+lb6BEfBJqQo4K6UVUSC8alAtgJdk9XgoKigax5ErBVKUox5e356FdYFe8lDwfsbxEAECRNA/fZS0AncbAkk6bXTYeY/RJSUSXxFhM7t3RrePwBNr3bW10+66fDpgVTiLZaWg0dTYLp/TCtnoBMuvhsTugReSfYNDNW7mqFSL0i7QvarwjOE04iYXk5ZjzJPLLBkGhIn9u3Rwy5U1oNIZRdhWqXLcIJ7J5JPp7CJV5SK/6ASJFHkSqAtDAQAArEf6sBKSGvoxAdoq7h0lhBMph2rtwtaXpWPleqAkVXkiep7c9jpeETsG9cF1gf/LVG1/MtbqsXKWGRK5AVkklcsqrsOLgWUgUSgBAqD8Pz/XrCj7H7mXrPkNQWAA4PDbkUoXe9uyreRbrylX3AQCltd9BIr8OpboYTEY4YoLfgx+7k0F5glAaFU0AIFXcxP3Kl9E88nezfUZxgyFUul5rWBROO9eewIndV/D93w1zvyve+8tsHYVcCbXKt5aZl1S9BJH0H7f0HR+xHfllgwy2q9RFbhiNc1bBBbCS8Eg0+T4LFM6DEk0UljhffczkvmmJc3BdMNXuthl066dn/Nh0+LFZCA/go2+rRDz7qNZ68tSPv2Pv1UzsvZqJG1/MtalNX6VFp0TcOndXb5uo1nzQawBQa2oNo4Yr7+Bu6Ujw2O3RMmob6LSGnHH5VQ3Ct1NCQUNfstPIKZ8CoeyUxT6Xd5uBsSe/QNeQ5kj2j7JYniwsCqfD2y7g3m19Z+Z/frdvhZs3oiEkKKqYBJnCfUEaOay2JvfVin9HEN98WHoKCmdQL5io+EbegTviakWwY1Aiyze6L1fsWJgZMtg662mdk3m/z/6HY+/NAIfp8YvNnUpyh2YGwskWooLmIpCXBg4zGUp1OXIrnodUkY6bBanolNBwLQhlWlEdwp+gV9+f+yiY9BCoNDWoEP6CiIAZJvui02iYnzoOL5xfjiR+JFID4xHIMu5f+2qKY9lMGmPxCvnmz1nITi802L7+1Afw4xuugAK003TT+3/u+Og8gJKqF90qmiwhlh6ghBOF26HEk+fjjmCkqYGdUSLLh0qjBJOun0h3b8kfLh+PMfgcNsRyBWolMhzJyMGITsb9fB4WAkP87a7LZIQjOuht3XcGPRitog8ivTAVTRfpqzViAMYT+QbwBqBGvBMyxW2z/Y043uA7e1dYjLtC0yvWXSqc+IE8dOpj6F8S3SzMZJ3AUD54fI5jIyOR3z7bDgCY9sGTNtWTyI5DIjthUx0WsxkY9Egw6CEQyw7bVNccgfxJqBP/abBdLDsEifw0/DjGneXLpNchU9dArq4DhxGIOL9eYNJ5pI3Ll6CmnuzHV86ZL18D7jimUbFPo7l/a8y/Ybj8nkPnYlZL88niXcGkXh2x7qQ2x9qa4xcfeuEUlWgoZKyDjjYxhtNrDLo/mIwIqNQVTfZohRSNZvg8CuGPR414p8Xpum2PLrBzrI7hNJskP9BzHs6bPtsBwDbhRBBylAus+1HYzOYIC3oPHFZbsJhJuu13C8mLlstj9zYqnABAKj9vIJzu1O5Cdt0+VMgy9LbTaAz0CJ+DlMCRD6WAGkyfCDqDjgNKw3Ppiw/LxjhLFPjSefO1NDGeQLvAbkjmt0Ku+I7e9jdbfY4obpybRtVAML/hPlgseDjDyjSGa6fRg0kPAYNu3FrFZjYzIpzMtaU1zGgI875VMTz33HvsEk778yw78a7Y+5Y9TXsM94rbmf3R/Lj9ERn8pZ5QciaB/EmorF0Mtcbwpi4Q/YKwQO35vly5Euk1m0y2QxBqXKhYhgsVy9Ay8An0iVoIGiiHyIcBXxI4zoTs8+TLVixrmZNiPByJJ1ArachrqlSp3TgSz4BrwgXHHCxGDJTqMqg1IqPiSaHS93MztZquHvoDK5SG8MzV+U57YgaH2T9P6gmYV7oMxIX/4TLRVA/bhJN44+CbTUWTHzMC4dy2iPHrjnCufv3sun24UEGtZKOgcCYPs2DyBm4UlOg+B/FsFw2+htoO8chltQSgQa1kn9H9qgdBksVybfgJueqebh9ByAzKqx880xqvwvMkHu7lAyaQyEwvoQ0LfAehgW+4cDQNBPiNgVR+xug+Ahrsvl/vR0DDcymnYSpiPAENbtVswaXKH5Ep2I4e4XNBpz08lwKNZjKSvkNQlgXPp7ETuyt/r4f5mqhTCvBN1nyIVcatDMs6b3HxiBrQEAQu3mtY/DQgtbnbxuIpFN8r/z97Zx3e1PnF8W/c6u4Ud4o7wxluMzZcNjZsY2zDhstg2IZsMAa/wWDAhjvF3bUUCi1tqbulcfv9EZo2jd94m8/z8JDc+8qJNPd7z3vec0zuE+azBi8z2iO14DuIpInwYPUCg1YbUlkeknLHqtolZH+o0beA9x+CveapHcvl7gQAuDE7GjV/Kj8Pq14cRAovF6VSIdypLAwOa4t+Ia0QytIdj00Usz1OUqkMT2/F4+ntBEgd0M1poBafVriC4zrP+XgYn3TN0tAo4TrPpfNuo0icBABo7jsBesrsgAQyGnuX78RL41Wf9BIAQKZYx9FanS+OzoArdsn2lEpLsOH1PJ2iyd7M3n9G7XnrWmF2ssRxSHymPX2EPujU8li1nJLfkJA9HLFpUXiV2RNiqVKYujE7aPSjkD1U3qiKlHmuOIzWBufe+OoEPru1Fk8Kk1AgLoVYLkW+mIv/JV7EiJtrIVVYXpcQdjPs33weu9efgUKuKUw++KI7Js4dZLU7e1PIzzD9x1JXELYyANt+r4nJaKPz3KP8bQCA+p7DEeUz0ajxGnp9hJdF/+FJwXZEuL1ntB3ZybkYVWsKtj1eg/kDf0JeeoHa+dXRC9CyV3n5gg2Tt+H0du07DE8L/gGNob5Nec2ELYj+64rW9oO+6oMZW8rzeiwa9jNkUhlib75CaRFPrW2XD9pj4X+asXbuPm4Y4jkG/ErZ7XW1NwWXeHJcKn82rs/K+mxKWIwiSYFVvEqWLvI7rXeHar+j7s2zt7h8gNiNdLPwBKQXLkJ+qWYJnUah90GjBAEAhJKXKOQdgyf7fQBAfNZgzcSZ79CXwwkAzmc9wYGUGwhj+2JX+5lgUsqvJdGZj7H9TTS6XpiHxU0/Re+g5oRelzYIC6dda5VZtBu3qYWWnetBrlDg0bVXiHv8Fof+uAwKhYzxswfq7B93PwGntl+Cf7gvxizQ3O325ulboqapkZVkuttRFx4c4lluLQGZpLtwrkCaDwDwZtQ2ejwveu13fQsMtNTOqweJKMopRvcRndB+YGsUZBbiv3XH0ew99Viqxh3rI+baC7Qb2AodB7UGr0SADV9sRUFWEQ7/cgqfzB6q1r5MNH39+xcIrhkAXjEfCU+TsW/lYfSb2FPDjrh7CaAxqBi3dASadG6A5Oep2DxjB64fuqPVbiFPCBqThrGzBqPpew0Ntq9OuPIxuahIRS8dke9F5cK+jsykbrpvTKsLBzcQr45BIjEQ5rMKdGooSkX3IZYkgUL2Roj3ApVoAgAmrSGCvRqqnvu4jUBBqaawrujF0sWp9AcIYnpje9tpaqIJAPoEt0B7v/rod2UJTqbft79w+m3hIQTX8MPvZ78Hg1Ve32fUN30BABO6rsC/v1/UKZwmRX2P1FfliarunnqELXfUE2ZOaTevcje7wqA1hTt7iL3NMIixVaWVbZUoYPpyJgCs//x3nJerl9/5cNYgjXZ9xnZDn7Hd1I4dyNiOhUNX469FBzSEEwAERvpj4OTequfvfdQBE5Zrr8tXnFuiZkdUt8bw9PfAik834H8L9mH8MvV+/BIBzst3G93eRdXCJQ5Nw5z3yp8RhAyB6Us/tuSTds3ww8CuoFGM/+2sihRmF+PSfvPDNgI8piPAhPbhPmvgw/kYRfyTEEniQaOGIMxnFUhGyJOnRUlY33KizmzhZcefFiWbYJFhCAV7PL7xGoPGdFYTTRUZNEZ7MsYyclLV70JSX+vO9ukoMOmOXTmbRfUBABRLjPfUFYmTlX0pPoTmrN08klC/MvzD/SAVS7Wey07ORfZb4/N+VKbDoFYgU8iIuR5nlfZVFWsKCkeIMSqzwVxbHOG12AJzvw9RXu0tZInloVLImNyjHRYO6wkmrfpsjtHG64eJmNFlkc7zbp66VzssAYfRBqHeS1Ar4B+E+6w1SjQBgD/DExK59mtIRfwYHuaaqAahb0teVhFqNdSd3FHfOQD4YcdXWDFqE+QyZSHg2TunEDHDpuiLL3IEmvmMw9XMBXhRuA9u1EA09PrYYJ8XRUr3aEu/yYTmbNXbODH54vZrfN1pvuo5lU6Fb4g3eEXaUz6c5O3FgZ+PYlRN5feC48lGr9HvYdpG7bFbJLJm3BmDzUC9VrUQd9e4mkumtnfhvFhSHLq8V+qczlSPDyWTKOgb9BFmPhkBX3oAorzag0JS9+z0D/6E0Fyxq2YabmQjkmM1y5LZcxxD5KUXoDiPiyObzyHhabLWWOWKzNr+hU3sMpXNrb/AxLubsLP9DPgzPDXOp/LzwKTQsamV/lgpUyEknHwDPfH2dZbWUiwAkPwqU+vxMjoPa4ttD1fh9J+X4BPkhU5D9YuS5cd+QFCkPxFTkZmYjQXD1hLqWxGylrTwjkQ4pzPYVH/wpbm4n7vRoHB6VXxE9TiMbdyWz8qQjKgk/uZJMr7vuRhUGgX9v+iNcUs+gbuPMsfX+s9/x5kdlzT6MFh0jFn0MXwCvXDur8uIu5eAY5vPwifQG5/NH67RXtcfvVyugCn7E0xt78J4HEVgWMqGiuM4ymtzBM5nH9F5Ll+cg0s5mjuWiQonR2Jy67kONY4l8Q70RNu+losPsiQBTC/IocD4OxvxS8tJCGP7gk6mgScVYn/KdRxMuYkVUaMQzCK2qqILQsJp9sbRmDFoPe5ejMWKv79UO5ebWYStS46AQtV/UY1oEIov12rWL6oMiURCq15NCW8h9wux1BtGLA7Isui2gUJi4KOaR7ErvhMUkGNXvFIMedIjwab6g0ZmQyLnI4v/EArIVf2a+YwDiWS9tf1vuy6EWCjRiIUCgIw32Xr7DvyyDwZ+2QcA8PZFGiY1mYmTf0Tjn7dbDc4rKBUi4XGSRqC6pdo7Apa6YOvLZ+TMosDatrvydmliz5xMLqzDrpfrQaVZ5xqRmv8tACDcdz2h/p3Oz1Y9HnvnF61tZj3aqfX4zd6rCc0JEBROdZsqtw4+uv4K80dvRYvO9aBQKJ/H3E0AAIyaaZlKxBwvtll5d5hulik2LJfrr5ljC2Ra8l2oQ0IYp6NaXqZicTKK38UyVaaR1wi08LWuC1bIF2k9XpBVhBe3X2s9p40ajZT5VfLSjNsBeOvYfchlcjTp3MAq7R0FS4gDQ/1tMYe1sKZ4qk6CqaRAmerDw8cxMzm7sB66YpktQQFPeUNNVDh18bfPjS7hiLgzyRvw6PorLBq/HY+uvwIA0Jk0tOvZGAu2TbCYgeF1g83qb6lcUgLxPXhwRlhkLKKU8P812KZniHJZ8kn+DiSVnkeJWH1HCwlktPWfibqeA0EhWUZU6uOjWYNw4OdjmNFxPsYu/hj+4X54cfsV1k36HbWa1UDiM/VgdrFQggkNv0abvs3R5YP2oDPpiL0Zh+jdVwAAY5douvXZHiwM8xmHIdP6omXPZkh+noJN03fobO/u42ZSe0fF2TJe29pD4/I2EUOhUODY/65h29KjGucq1ikVi6SYO/I3AMC6gzNsZp8L23GWv9twIzuyqvlYw42sgFlbCVp2qY+DMSvx/L6y7kxUh7oWd+l5+LlbdDyiiMTP7G0CBKLbRrdt7jsRzX0nIo13GyJZEcTyUjAoHgjjdAJdRwVra/DZvA9w/9xTvLzzGnP6LlcdH7vkE7Ts1UwtaBwASCQg+20uTm47j5Pbzqude398d4yYo5m6wM2Lg5J8LvYuP4S9yw+pjnf5QPuOno5D2uDqv7eMbu/CMngv8K4yu9Gq0mupzOKJf+LepRcG29EZVLx4kKS3jUwhA0/KhQfNS3WsUJyH58UPEMmph3C2dUqcvMnJR3xWPuIycyGSSOHrxoYHi4H2dSIQ4etleAATmbJuNB5fjsXtk48sPra9GDS5l0MksXZESAZKktgtsEcslAAAKFQyKFTzxNj7TGXiynNCzYym2ohP074rMMhnK9zZg42eV9c4dcNMT79QwjuA7ELtu0jo1FqoEXTD5DGrAouG/Yy7px7hrNhxYyucOU7IRfVi/+bzquTGoTX90fH9pmjWoS4WjFVWJqjocQKAfpHK36TPfxyC4ZO6aYx3MG0HbuadV8U+ieRCzHk2DnQyA2K5chnfEnFRYqkMe289wdrT14zu4+vGxpnvx4PDsPxSlIArxNNrL7Hjx/1IiXP8dDtlUOlU9BvfDdN+sb4nR67gISZVGRoRFZFq9fkIoFM1EvY4iQRi3Ip+jgdXXkIq0V4LZu7mMUSHB51JM9zISNZdWmhSezajM/giTSGSVfCVScLJkugSTQDg773KhpY4HkTqEZpCVV6WIUJVfT/MzZLt7Jzac1Mlmr7fMBI9hhmuE9a6W0M8uPISFw8/0Cqcnhc/UHv+y+sf8XH45+jg2xOp/ESsf21+ouOTj+Mw+8AZww0rkV/KR9tFW/DN+53wefe2ZttREZY7E+0HtED7AS3Ujh/aeAaPLj7Hg2j7rmAwWHQwOQzUb1MbEfVDMGza+/AL1b+RKo/7FzIKlwAkCiJ818OrwrXwRbrp6XoUcLzatsZCWDiN7bQMxQWletuYI5wsSZOOptUf4rD6aRVO9nLAKRTaA6wBgEL2BovRHtHpyhiDPqEbCc1RKslAOv8O6GR3RLr3BMn8+s9VBnOXZarjRdgZqcrLb8bw8Koy+WvtxqFGiSYACAxVfrfzs4q1ni+Vlqg9zxKmoZW3MkFyAFN/vj9jOPowFgsOntd5nkwigU2ngScWQ9f91S/nbkIik2NKL+sv1X8wox8+mNEP77O07yg/zf3L6jYAILSKk13yKxSQAgopMgpXqAkniSzLkuY5PISE0+b5/6G4oBSd+jbDt2s/BduNaWm77IqX23jkFs3Xei4xozHCA06CRq1pM3sS0nXPFRZwAiRQkcl/oLONPvKFcTiZqh7Mfy1LmUH209rRNo2HckQs5V2xt5fGkvM7mhCsyq/Nljy9rdwRPWxCV6P7eLzLycYrEWg9H8AIRaZQuUGlbGmOTi7blGLejShPJMb8/6LVjp34dixqBRhOQZOQnY+N0TdxMfYNAGDLhdsY0SEKPhzb5Ovz9HNHcR5X47i5YSnWxJPVD/mlf7973MvO1tgXQsLpya14RHWogx+3jre0PQ6PTF6I9LxPERlkm4KwJbwDes/TqZrBlUXiRDzI3QyBLA90sgf8mA3Rym+qRju5QoJr2Yt1jn0lcz76hP4CPUu9VR5LJky0pzfD3vNbm+oseCyFVKIsXUFjGB8mweMqBZO7l/aSHHXdGyNTmIKE0ljczLuAYGaE6lyOUH+iZEP879pD1WMmjYr1IwcaJZoAoE6gLzaOHoxWCzZB+O51b798D7MHGi8azaFWswg8vhRrk7ksRZjPCniylWmG3JldtLapG3QKNIpxyaolshzEZ2mvZ+voEBJO+dnF+HqV82zbJkJ4wCmk5gwCKiSLLEMiTUFCWgR8PL6Bj8e3VplfJHmJlOyeetu4sz/UOFaW+LIiWYJHeF64Fz6MehgU8Zfq+PXsZap0BQPCt8OP2RgAkMl/gIsZ3yGTfx/H3o7CkBrGBdXbgyVHfrC3CUZj74u7vee3BNoC7avC63IE6jQNx4sHSbh3+QXeG2hcpugrx5S7yFp11Z7/bFjoWDRwj8KWhGVgU9ywoumfqnNH0v8ibGt0TDx+v1h+8/pw2XRC4zxcNh2N5ygD3nffeISm4UHoH2VaaAcReo/s4nTCCSDpFEyAsroG24SarjRKkCWMsguEAln8Q7xRnK8/vsnZYdJbwMtNt0dNASnyS9aimPeP3hgkImQXfI2U7D5625BJHPh5al9O1EWBSD3hZIFQmX/Lh1FPJZoAIJjdGi39vgIAFIn1bzd2YTyFywqrtNfHVliqUK8LdZp3qgcAuHX2GUqLjUv4W1LIU+urjYYezbG8yXYsb7pd7XhX/wEYF0ms3tyt+PL8bxQttSpNoWL/u29ss7urVtMIw42cDArZ1BsY513JICScVv0zBSun7cLt6BhL2+NQ+HstA8lAksicwu+QkF4T8WkhSEivjbzi5eDyD0IqSw2vGjsAACAASURBVDc4vlSWCaH4AXKK5iIluzfi00IQnxaCEv5/gIEdB7VD40GlBGocZ1F8MDzyIMbWvaX2z5ehvIt6nP+Hqm2JRFlQMoStuSOikVfV9ijaA+8F3kZ5R5xVENhKGDq7h8lRBfTomX3RrldjCHgifBQ1H/nZ2gO+ASAtMQdf9ikvWdFzuP5gcg7VHaRKF8oor3aI8mpHyNbzz8uLcY9oH0VojDJGdizf+XYxNsGssYylZtNw1GpWtcQTk1bb3ibYDEJLdT4BHvDx98Cyyf/D4h2T0LxjXa1BbYbq1TkDgd4bkFUwxai2CoUAhdzfDLZLzmoPubwUMrlx5UMqQ6fpvrtr6fcV3Gmau1XaB3yHU6mfI1vwpMJRZXAmhWS9lPoujMcSF9OKS1n2yh9lqzInziygHNX2SfMG4+4F5RLShPeWo33vphgyvnx5JvFlBpLjMrDuu32Qy5RhDCGRfja3s1QkVj32czevDIyfe3l8VqlQrKelZanZJByJz1IMN3QCmkUk2X0ndn5+KeJeZiAlJR8ymWaIjTZGje5EaC5CwqlfBffqognbdbarnCzNGXFnD4U7e6jOZJZEkEiJ/7HUCLoOOlW3sq/jMUDrcT9mY/gzGyNPqG1d3fkFrrOhayeYo15QjcGZbbcFZYLSkd+nsFoBWPH3l1g7cy8K87i4dvIxrp18rDo/td8atfZbznyPWg0N/zauePkN8kTKLetlCS/3pmyBTC7FmMivTbbTnclAIU8ZmF72P1HyuOXLkiy65fIHGqL3yC64+M9Nm81nTUgEMxtZIvHlurWncfrUU0J9bSqc3hvYwnCjKkaA9yrkFv6ozGNhJ2jUSJ2iiUpmQSrX/wOiACBXOG/SsaqEtl1ult5O78gX6OqKM2SRb9mlPn479wPGdlwCsUj77x2NTkXfEe2NEk1Xck6qRFNFuJIivOLGYAxMF07BXu4qwZSQnW9y/4pU7B/ibbsSX1Vtqc5eEBVN5kBIODlKYktb4skZA0/OGGTkjQZPeNHGs5NRJzQJJJLuu6EAZlNk8O8hvuQk6npobvHMEcbo8DYBMgsHt7swDke/gFYlHEGwVFxC1XbckfDydcOxV+XeJbFICplUBhbHtMLge95uxsPCGyov08wn5YXSewQMwSsusTjZkR2bq3I4VQwUJ0LF/uO7GJf40xJ4OkgdVmfm793lHrtVqz9Bm7bWqX1YGdcajYmE+P2NYN+tNp0zzP+QXtEEAAEsZYDko7zfUCzWXAq8m7NW9ThfGAdFBc8TT5ptIUtduHA8HDEQu0wsOaJo0gadQTVZNAHAm9KXOs9VLPxrKj0a1QaNUh5X+zIjh9A4ManlnjAOg46ejatPgHNV4Nm7GDEymWQz0QSYUXLFEBt+2I+ZP48w3NAJcWMNRt2wwZAreEjJ7g2JNNnic1ApYagReAFksodR7aN8xkMoK0Rc0UEcfav7fe8d+otapnAamY0k7nl40Wuimc841fFC0RvCtruwPo7gQXEWHO19cjbRZA5iuVDnOYmceCC2B4uJU9+NQ5/VOwAAH27cCzKJhAtzJyHQw3C1g4zCEmw6fwvHH5ULu4tzJtk0xgkAzgn+tul8VY3Y58rd61Om2jaTudU8Ts9u22Zbpz0hkziIDLqFMP8joJAts7OETHJDiN8u1Ay+Y7RoKqO13zS95znUQISw21bY/UBCW39loP/j/D+QL4yDVC5AEvcCzmd8o2rjQj+29mg4ogfF0XC9R45BIDNM57lXXPMK3YZ6q/8+yhUK9PrpT4zZ9i9uJ6Qgl8tTO69QADdfv8WfV+6jz8871EQTmUSCO8t0j5oL7QjEMUjO+xxxmd3wLLU2YtNb4FVmd+SUbIFA/MJi85DeXZ68fczbWWnyvAYqyxMuJjSwziycTFhnUp8+7Zdi/6lv4eOr+45hWK/V6PF+U0z/vj9R02yCQHQbEmkKhJKnkEiToVCIIJdzQSIxQCF7g0zmgElvBSolBG6s9wFYrkYRV5KGu7kbIJDmgUyiw4/ZAO38Z+lsfyv7J8SXnNA4zqEGYkDEn2BRfC1mW1XE5f1xLOxdF9CZyXybh+AalksvIJVLsOb1bOSLctDSuxPuF1zFpFrfI5kXjwvZRxHJqYev6y4lPL5cocDK45ex7zbxAOFFw3ri43bGZ7x2oR25vBRv82egRKC76HJFPNkDEOlnXtjL7O/348GDJIwe0wnjxr9n1lha0Ok1sMpSnUgghkxqXB4FU+GVivDyueHkkvaGxegAFqMDPGD7RJLutDD0CjFetLYP+B4yhRiJ3HNqx/uEbXSJJiNwXaAdD9dnYjpSiQzTB67DZ1+/j17D28DDiLv4Ue0XAwD23Fms9TyVTMMXteZg+YsZuF9wFQDwZ6Iy6DyYGU44c3gZZBIJPw7pgTNPX6GIr3tZUBeh3h4u0WQhEnNHgycyvth8Mf8U5Ao+yCTtdQ6NoXOX+njwIAl3br+xhnDSiUHhNLThbIgEYrWcTP3M/LKbS1KCK5jZkpBJVHQJWoQuQYvsbYrT4vI6mYY1vUKuz4EY+7ecB48rxPblxxBZPxgtuxiu2Zafpcwufmj7FXzweTetbXzpAapddSn8BLhRPeFDN64QrLHcXKgsEbXz2gNcin2Dx28zdLZlUKmYP6Q7+jarBw7DlfzXEgglcSrR5MnuhxCvhaBTtS/TiqTJiMtQJlWNSW2I+sHnwdST1Fkfgwa3wC8bziI+PgsvXqSjUaNQYi/ARAwKp9BIPyS+1P0ltAc0uuWWtVy4MBdXPA0xXALHsUiIUZZgojNpaNrOuN1l/sFeyM0sQtyjZKPaR7DrEDXPKCa81xoT3muNpymZiM/KQ0p+MbhCEWgUMrw5LHSuF4l6wX5gUK22L6paUsg7pnoc6bcN+mJjGdRI+LqNQn7pHgByFPKOINhrNuG5J07qhp07ruKbGXswZGgrTJ1m/UBxg9+eLWe+13pcX1ZwuUyOAbV1x9RUJDEhG0f/vad6/sfGaNAZmjsbeFwhYp8ps4wO/6S9UWO7cGELXALAdFzvmeMR+0BZ0LvH0Fag0Y0TFm17NsapPTfx7A6xzUBiuQh0suWDsqMighEVEWzxcV1op4Cn9Cj6e0yGMRuKwnx+AoXsjpyS31FQuo+wcJo4fjuSk/MAADKZAocP3cfhQ/eN7n/x8lxC81pFdpMpZKNzfuTncnH2eHlK/0vnnuttHxLqjQ9HdjDLPhcuXNgXVwC34yHkKxPhengbv0OJ484EAAh4xJLo7k/dhjE1ZhDq68JxkMmVS7amLLkxaHWVfRW6i0kbokw02RpCwsmY3XIBocb9ILbpUAfRdxZCLJZiSPdV2PnvVHhpCUqk0aigVoGiwS5cVHdcS5uOiaePG/Kzi1GUX2p0H16JsuwJy42Y16hIbJ8LnwvLwqY3A0/0EHI533Djd8jl3Hd9owjPO36C7QLCK0JIOFGMEDBbo01zvdHpVHTt1RgMJg0slitgz4WLqoi1PU0uTxZxmneqi4uHH+DCofuYOGeQUbvqzr9bFmncqiYA4HreOX3NNcgWOlb8rAtiRPhuxsuMDsgqXg0/93EG24ul6cgoWgkSiYEI3y2E5yVapNdcHCpCrm6DYJBIroSL1kYqywZPeBHFpTsgkxdAJi8AicQGi94WDHoz+LjPMFjipVRwHDzhFfAEFyBXFINOrQMmvTUCvFdCX06q+LQQUClBqBn8CBJpMlJzBoNOqw1v9yngMHsDAEr4/6GodAek0jQw6a0Q4rfL4GvKL1kLgfA6hJJnIJHoYDE6gsPoBk+3sUa/L3J5CXKK5oMvvAy5ohQkEgtUsi/cWAPAYnQCm2mfu5uqRFlxY2vtQnQJJuI0aVsbFw8/gFwmx5GdVzH2O8O58sRCCQCgaXtl0PfhtP9Z1UZrcOnFG1XJlqm9XGEgRCjbQSeTl0KhkIJE0i8tUvKnQ6EQIdhrLuhU2+yEsyRWS4DpwjF5k9EAcnmJ3jZ0Wl3UCLyq9ZxCIUZCeqTBeeqGab+TjE9TVlNn0ptDKH6idi4y6BYEotvILlTfWEAme6Bm0D2tmdT5wqtIz/tUry0cZg+E+O3R00KG+LRwvWMAAIPWFBGBpt1Ru3DhTPwy+wDOHbgDAFj4xwR06NNUa7vn995g1Yy/VekIyjYLrXz5DeY1/MXo+Va8/AbzTWhvDb7YeRg3XysL/causm+qHUcmp0S7Z4gEKkgkOqgUH+Rx/wZPdBcA4MbsAAa1FmiUEMgVPEhleSjkH4PiXVF5N2Zn1A7YZzP7CWC7BJgKuQIksstr5Ihw+UfVRBOD3gwMWiNQyH6QywtQwj8MhUIID/ZnOkZQIDN/kuoZjVoD3u7TQCF7gC+6DZ7gLKQyZdHMvOLl8PP8UactQvETsBmd4cYehJxC5bJuIfd38ITRcGP1BYfVD/nFayCVpUEuLwFXcByenFFqY/CF15CRNxqAslSNG6sfOKyekCtEKOJuh0ii3GjAE16C8h5A+/eyoGST6jGL3gYenM9AJrtBKsuAWPIKxbx/AACebqN1vh4XLojgaEuLn07vrRJOS7/YiUatamLmmhEIqxUAAMhOK8DLR8n4+es9KLvpptLKPcxuVNPKRLEoxJMfWgqekHjNvOpEZtEqk9qXCm+jFLf1nL+BNzkjwGG0QZCncbvwHQVCHqcLh+5j3ax/sPv2IvgHKytc7998HrvWnla16fNRW8xco98ToA25XIFL52Lw6+pTEAkl+PXPCWjYROkG5JYIUFTIQ7iOkgBSWTaSMlvAy20C2Mwe4DB7mDx/VSUt90MIRLcAAOEBJ8CktzJ5jIT0mqq7BV0epULuNuQVLwEAUCgBqBWs7lUq8zhFBJwFg67M2JtV8BW4fGUeEBKJhTqh5QWG32b3gFgSBwatISICL2odi8N6HyG+2pcIytpQyL6oGXwPJBJL7bxUlo6kzDYAADazG0L9/tH3Frhw4VQQEWZXjj/Chh/2q5bh9DFx7iB8OLn8d/Zp0V1EebUzeq6dSeswoaZ9L5o9ftqO7GJlQLyjepwUcgWK87kQCyVQyO2zEJRFamG1saMiUgn1exGbjg3rz6J9hzqYOKmrha2ysMfp0fVXqBcVoRJNWan52PvrOXA8WGjbvSEuH3uE6P/uERJOc2bswZN3+UQqk51ZhCljt2PHgSlaxZNApLxTKirdiaLSnWAzu4PD7Akvtwkm21HVKBNNAAiJJgAq0cRmdNbZxtv9C5QKjkIofgqZLEdnuzLRpLSnjUo4sRnqwX4MWj2IJXGQyNTL7Mjk+arHAV6674Ro1JqQSJMgk+eDyz8KD476d1KuEOjs68JFdaTb4JaIqBOIqf3X6m1Xs0GImmgCYJJoAoBW3rp/S2yFI3qcFAoFTmy7iPjHSXh0IQYF2cWQy6xTxsxY/sqcYtf5tXH/fiISE3OQmJhjDeGkE0LC6d7FWHy9qrwG27T+ayGVyHAifiUA5R/UztUnTR731tU4PHmQhN4DovD9giHo0169+GOd+sqEZpvXnsHqTZrLJoXcjWrP+cLL4AsvmyycBKJ7GsfIZDcwaI1MGscRCfQ2rfByGTJ5+bbhEL/delqSEOL3DxIzGgOAUbWI6BVyf3BY6llfqRRl4GDluKzcovkV2gTqHNvX4ztkFUwFAOSXrNcQTnRqHbizh4DLPwa+8IrBJUYXLpwNIsuAtRqFquKWJGIp0hNzIZPJERLpZ3SOPmMwVWhZg1KR4win73qvQMyNOHuboZVgL2LJIq3JxQuxAIBGjW0bYE5IOAn4Ynj7l69l87jqxRUj6gYRMub8mWdo2CQM3y8YorddqpakVwqFBGIpsey1lUnLHapxjEFvhoiAsxYZ39YoFOU/DExGW0JjCMXl1cdJJKbethRy+Q+1SBwDFkP/jyMJ5ektKvZVoj31hVD8WOtxTVu8VI9lcu0eMD/PBSqPVyH3N0ikKfDgfAwO0/qp+124sDbm7mCk0amIbODKwm1NRHwxfpu122FFk6NSUMADANSqadnah4YgJJxCavghJSELTdrWQk66cg192oqPVOfpTP1b2XVx92Y8lq83vLxXVKSZZKtUcAoKhbY1eVddO7G0PGaITjWuBlVlJFLty6eG5443KJwqYkiUAcolQ4m0fE28LI7JcD/tMRtUSgjqhqWhhPcvsgu/RangJEoF5R7TEL/dTiWi7BVwXPkC7WiBz9UNR0o0uv71PKTyE9WOTar1Axp7tNTa/ujDWMz/LxoAQKdSsGXsEHSsW0OtTeM5ust+ORPZb/MwpoFjxlY5Omw2HQKBGAyCmoMohFJx129RA9H/3oNUKsOfK4+D485Ej6HlcTPZaQWEjGEyaRDyDbtNPT01l34kFcRBRWhUw9vMqz4yC4xBdKek5XdYKmA4aNV0yPDgjECI3y6QKt1PZOSNQXruJzr6OR72FCqVL9Yu0aQdW4ga7wXeqn/25HTmAQ3RBAB/Jv6M05kHtPb588oD1WOxVIb1Z25YzT57IuAKMW/Qanub4bREvvM05ecZn+3eEhDyOH237jP0i5yJQXW+AwAci/tZzcu0b1M0IWMGf9gGm9acQat2tbUqyNvXXwEARmpJs15xKakilYONqyM0ak3VY4k0lZCYpJtQg0itH9Xy1dDJJDdQKYGQyrIB6N7hRwQOszfqhKUAUMa6FXB/VcbKia4jJbsXIgKjQfB+w6bY+2LpKDY4Eo7kAbIV2xJ/QlzJU6yN2gNKpaSI2978hPPZR9A/WPOmhFoppY1UZombP8djaMDn9jZBL/Va1sRnc4eiaef6Ws9nJOYg5F2qCnuwcNFQDBm0AVeuvMSCRZohNtaC8BVg2oqP0LJLfYz9rr/G0lzZ8p2pDP24LfLzuJj1lWam6H/+dx3L5h2Er587+gzUrG1Tlj+oMhQ9gcPVBTKpvHSCUPyQ0BhMWvl7Llfor0dUcccbg96E0HyGYJhR38hYWIy2CPXbq/JAiSQvwK+wO9GFJi6xZBxV6X06tP0K1s3SnsYjhadcCagsmgBgcOhInWNO7V2ewZtCJmFa745mWul42CutgCGodCo++W4QVhz7HptuLkWHgS3h5qW9/M6E1vMx/8NfsPunYyjK5drYUsDNrTy0QyCwXZA/4QSYA0Z2xICR2r/MZbsxTMXLm4Nl6z7F2mXHVDvqvp60U+38vpPa14J1CScaNYKQLVWVrIKpcGebrswrZu3OyBuFMP/DOlrKkZ5X/oNYUbRZkgCv1UgSKD2bYukbwrFbxuDvvQo5hUrvqkB0W286Bm1Ul1ifqv76zMVe74+1ytuUceHQPSTHZWLWOs3EuRyqG/gy7cso3jTt+fgAoHeTuiblVDIn/5K9YqXmDf7ZqHafzh6CJp3qo3VvzSzuAzzHQyqWqh07J/hbo11hTjFyUvJwcd8tPLv+EkkxuvMmDZ3SBxOWfWyUbWcLtuPWycdIiEnBlPeWoCC7WHUuslEo6resiZkbjS97RYSLl+fi7JlnGNh/Hby82Ni4aTRCw3ysOqdD1aoDgHad6mL3kRlYMvtfxMdlglsiAMeNgVETu6L/UO2BhACgUAi1HifBcLBxdYBBawiR5CUABcTSBEJLaBSyL2TyfFW+LG0UcrdBJH4GACCTPYmaa5CKKQgy8yehRuBlve0VChFIJM1t1LqOV0RUYRmYSjEuEL0iVfWCaa+5XJiONT8ffon2314AaOrZFpdyjms995KrPbzCVJyxvGlhdjEeX4rV26bjoFYY8cMg1G+t+6aQwaJrCCdteAd4wjvAUzXW7qWHcHL7RRTnaXqJDv5yGuOXfAQq3Th50HFgC3Qc2AJj5g5BYW4JLuy7hZTXmbh/Pgbn9tywqnAqKODhVVwGpFLlUm5RER9jRm+DuzsTwcFeBnoDv28bT2hehxNOAMBi0bFq4yjDDSugUzgZKDZYXYgIvIhC7m/IK16Ot1nKGDEWoxPo1Nogkz0gV3DBE5yHVJahKsJbmVohMcgtWoyi0j8QnxYCGjUC3u4zQCG7QyC6h1LBWUjfJar04HyCQG/r3slFBF5AWs4QiCWvEJ8WAgrZG+7sYSCTPSCTFUAqS3tXbkVZSiUs4JjGGPklq1HI3QqABAatEdzZw0Gj1oBCIYVQfBc84WVIpMkAAE/OGHhydC8vuHDhiFhb0OZV8DJEZ5d7okkgwYPmhX5BH2PW089Qk1Mfddwao1RagjRBElL5iZhU6wez54/wNXyB1Ic7iwGuQGS2Habwy9Qd0FW1wzfYG6tOzUZEQ8O5idy9OeAV6w+d0MaYhR9gzMIPkPY6E8tHbdLwQA3wHI/un3TEnL++MjiWTCpH4vNUHNx8Di/vvUHOu81hobUD0KaX9lqHluKjDzZqPc7lCsHlal+FsgSEVMVP0/QlQCxn7uYxRIYnBIlEh0KhqbzLsl27ALzdpyC/ZLVqW75AdBMC0U2TxvD3UgonAJBIU1RLWJUJ9NafddgSMGiNEB5wAm+zldmLZfJCFJXuNNBLFwqIJLEQFeu+C/T3WkRwbPt4ZGw5nzN7m6rLUqo1EPBEahmtz2T+q7Ptm9KXeFP6Uu3Yn4k/Y0Pz/WbZ4MVmGW6kBzcG3ebC6eUd3TkH5+6eYpRoAgA6i26WHWH1gvHr1cUY7DNR49zlA7cMCqddK47iyO8XIOQr3z86g4aPZvRFo7a10aF/c7Nsc2QICadrJ/UnH+R4sFDnXX05ffTvvByTpvXG8BHKPD93b7xGy3a1QaOZnnuJTPaBXKapvEWSWLhjmMnjVVXqhL6FWPoGfOFFFJf+DZm8EHJFKcgkDliMjmDSW8Hb/Uu9Y9QNywBPEA2eMBqlgrOQK7igU+uASW8Ff68VIJFsl1ODTmuAumEZ4PIPgi+8AZ7oMuTyIpBJHqBSguDO/hBs5ntg0Bpq7e/nuRC+HnNRwF0PkfgFhOKHkMmLQCLRwaA1AJ3WAP5eyw1mP3fhvFQ3wTSwzizIpHLM2TQGXQeV1x8b1X6xyWNVLgNirggiQk1/8+JZgr08kFlk28Dm4nzt87l5stG0cwOjx/HwNj+GlMGi42jOdnzdbTHevlAvbfVB0GQcytqms+++dafAcmOiaad6GDi+G+q1iESwDZNRXrxsn2zmhISTtuBvqVSGuEfJuHk2BtdOPsb42QMNjiOVyuHmXh6DtOC7/dh/6lv4+LqZbBOV4g+pLE3juFjy2uSxqjp0am3Q3Wrjq+dxEMpEONJpi+rcv6mnse/ZVAwN7YWxkUrBeT33Ada//h8Od9oM0ru8TFmyBjiXn4frecq15e4BHTDQo5tW0ZQjysfkBwuxq+1qBAS+wunMq1h8ZxbkUKCjbwtMrztaI6VATPFrbHuzH/niQpDxGfwY3hhKv4N2vs3BpmjGrZ3IY+BhYRBSeN3gSXPD57U+RlvfZip79UEi0eDrMdv4N5AA1e3ibAqW9Ma5Yq0MI5Mqxc6OlcfVhFN+VrGuLg7Jo+XTAQBUsnnpQdwY5nltKiJXKEA2I+jq/XHdTGqvzeMklchANdH5wHJnYu5fU/Fl23lqx0sNLAOuOvIt9v9yBnH3E/HTTeVKROdBLdGkQz0M/rw7yBTHT91CBIsFAFGpFDRpWxtN2taGXCbHN0M2GLW7LiTMMj9yTHoLrWU4eMILFhm/KtInsBOOZ1xSPVdAgWPpF0ElUXEq86pKOF3MuQ0AKhGy9c0+nMu6gTBWEOY1/BJyhQJb3+zDmcyrGFljED4M66t1vnsFz7D1zX508G2OybVHoFBcgqMZmp/PsJvK2nJf1v4UgUxf8KUCJPHSsDH+b6xjh6KWm3oequE3p8Gb7oH+wV0xpsZQZAvzsCpO+Ud8qOMmkElV84/X2alKeY0qCjZnWfpr16ux1efgSooMtnGnEYtRYlAtc/maO7gbPue2Mbr98bTHWPDkMM71/A5BLOUGmO3xV7H5Vflv2dDwllgSZfpKx+c/jTCpvbu3ppOhKLcEfiGmf/dqNg2Hu7cbuIXquyC/670Ca8/P19qnedeGaN5V3Zufm16A+Cdv8W3fVYh7mISzBdtNtsXRsUrkdJcBUTi+67pRbXf+fgnrt44ze0431gCd8S0iyUudSzXVmb5BXXA84xKyhfkIZPribOY18GVC9A3qgrNZ5Z/f0yL1+knnsm7gzzYr4Esv/8H7vdUSZAnz8NXDRUgsTcUPDTQTu21J2Kvm3QKAIaE9tdoWwPDF+0Hl2/47+rXEyBqDNdqdyrwCBRTY0Wal2vF2vlEYd28OFsduwtImX+t5F5yDiiLD0S/IxmKN1+EI740j2KAPQze0x16tAZ1h3KWBXyrEB010L5csjNW/7A/YZ3mvIhG+XiYFmO96o4wLLRNNALD51QUwyFTc678IApkY7c8sA4lEwuJmpqV+IZnorXL30Vyqy03LJyScAGDL7WUa5V9ibsRh15KDGLvoQ432b+MycP6fW3gTk4LHV8vj10gkErp/1A7dP2pPyA5HxyrCKTvN+DvJ509SsOj7A/DxUyrnHVsugM4wHCPz9ewBas+Z9OYgkWha65FlFXyJMP8joJCtm9vB2Qhi+YNDZeEtPx2BTF+8KFEmq6vjXgPIUhe+blTlH6hIrkwyVlE0qcZjKvOyPC56oXW+mhzDcW9l5IjykSsqgD9D/2d2vyAGXnQPjeOeNHd40T3wmpts9JyOjPcCb7t5aJx5+ctWtjvr+6MNY0UTALA4DJBIJJ07xCbUnKVxrERSiBT+GzwpuoPPa1l3idwaZAnUlzRFMuU1Z3hEawAAi6JcPntaqDtXkqVgsDRTqfCKBYTHC6zhhzpRNZDw9K3a8VsnHmoVTpM7lm+Y8Q3yQv1WNTFoUnfUaxEJjod5QfuODCHhVLZGXhl+qRBpiTlY++1eo8Y5cmE29u26jn//Ls/GfP70M6P6VhZOJBILPu7fIr9Es+6PWBKPxIwm4DB7wYPzMdxYhuOvqgMkkNAnsDOu5NxFW59muJH3waEFawAAIABJREFUEB40N/QIaI8diQdRJOHCi+YOAOgX3AUAVOJKH0KZ9gyuUV7GBT0e6PALDqdF44sHCwAAbAoL3QLa4vNa6knZ5Ao5nhW9ggIK1fKeNnhSAThU5/8jrkoXZ1vizMKPCLZ8vSQSCX5BnsjN1L4k19RT+xJYJwCNPVthS8JSu3ucTEWmUL/+zXp4AE28wjCnifo1Kbk01+q2+IVqfs5Jz1O0Jss0llE/Dsfij9S9ksmxmvHDAHAm/w+TvWSWZM/fpu0Kr8yo0cRKshESTgPraN5FVIZhxDZJjhsDk6b2wrjJPVCQx8Woob9i+76v4EVwp4A7e4hW4VQGT3gBPOEFMOktwKA1AoMeBTLJDWSy4fnk8hLwhOcJ2WULOMzehPrVca+BewXlYrW+e02QQEIdtwi85iahrU8zZTu3GrqGMBpjY43oZBpGRAyAN90DF7PvIL40Gaczr8Kb7qEWP6WAMi6rlls4JtfSXYSXRdGf4NIZsGfcjDOLjjJPnTXFhCMJM3t4JTkeLJ3CSR/13KxTjsnaBLI8kFyap3p+Mzce85po3ozTSKbvDjcVlpvmRpn8DNM/i4rUjjL+t75MNCnkClzYfxvcYh6Gf0XsWkSE/+28ZlZ/mwqn9wa20HqcSqPA3YuNcd8PAJNt/E4FKpWMgCBPRET6wd2DBU8vYlu/adRIo9oJxY+VgeQ84zxjACCRJiMjz7qp482BaKHbjr4tsCbuT6TyMwEAQ0N7AQAGhfTAqcwrCGD4wofuqRJQTT3rGhyTTrZMOoL3g7rg/SClpyuVn4kZj5fjXNYNbG+9HABAIZHRxLMuYosTUM+9pr6hnJ6KF2ZriihHEgGWoqq9Hm2UfW7mvFaipbK6DmqBMAKFXoskBYTmszc/Nh2MSbd3os+FNaCQyZAr5PiohqZnraGn6VUGTCWysWb4w8OLMWaNGRDua1L7vj7q8axlwmne8A14dOWFQwaH0+lUhJlRloWQcLJWYktff3ezx2AxOkLgKsRqMtnCfFBJVNR951mq714TmxP2IEOQreZtor7LxJ4nKoQfQ/1HOlOQAwBo7mX5QPxwdjAAIF+kfjfVxqcpnhfHW3w+R8eaYqAqiqcyLJ36AHAcYWavz23ENGIehtOZByxsiW1o41sTQ8Jb4Fiqchd3fY9gre2aeodrPW5JfIM0Y00zEqyXMbsy8U+UsVCN29XB5JWfYEbPFapzHfo3x6Mr2uNdLcXIUfqLP8tkcmRlFePt2zwkJSqXTnv1boI5cweatcToUPVIVm8abfYYYf4HEZ9mfaVf1die+C/6B3cF7Z23yIPmhhJJKf5KPoKv6qgX7xwRMQCfP/gRoaxATK6tXCL7LeEfZAnz1PI/EUEil2Dqo6Vo6d0IHXxbgE6m4WXJG1zOuQsA+DRC3SU+OKQndicfw7CbUxHl1QA9AzpAppDjeclrPC+Ox7yGkxHBrlrfB2teHJ1tS729cLT3x1HsqMzMJ/q319fiGJ/ssTK7rj+EO4uJfs3qgUUn5uV+npaN6BjljdfQVo1QK8A4L8TSqOFYGjVc6zm5Qo77/ReBTrb+5TUgQrNQslQis/q8ZSwd/Ru2XFmA2s0iNM5Fdalv9fknTOxqUvv8/FJ8/OEmtGoViT7vE48DcyjhZCn8POchr3il4YYuVOSI8tHAo5bG8VxRAeq4qf9RfBLeH/+lnkG6IBsLn5fXCiKBhDGRpm2/1YSEXFEBzmXdwLmsG2pnegZ0wPCwPho9ljSZgR9jNuBpUZxG6gSKDeIMqiqOejE2B0u9JnvucnQmPGnahQiNTEdX//7o4NuD8NjRzxPw5G0GVp24ghOzxiLQw/TEyZ4sBnZcvQ8AKBEIsXh4L8L2lEEmkUGvJrnjCnNLEF5Pu8eN7e54G3J8fd1AIgGbN51Hu/Z14OlJzEZCwmn9d/vw4OpL7Lmz2KzMoAK+GCwTYqGMxdt9GrzdpyE1Z4DWpJgu1KmcW8nQcQA42HGT0eMHMHz1jlURGplqdNsyGnvUMbmPC/1URdFkaRzhPXI0z1dlFjf+zSrjXn2ZiCdvlXGdPJGYkGgCgPAK+Zv+uxeDoa0aoXkN+3moL+2/hR4j9C8/GcOD6Gdo3acZob4Zb7KNblu/RSSuH3uAnp900Dh38cBtQvNbm/ETumLnjqtYvuwo1qz9lNAYhFTPi4dJaNquttnp1D/o8zMO77+ren73xmtILOhmDA84iWBfxwtMc+HChQtLUHlptbrwNNU6cTwPk9MNN6rE65Is3M9PQjzXeMGhK74m9rZlSoSd23WVcN9XDwynnCmjy5DW+HPRIaS80tycdHBzNGEbrIl/gDKWOsOM3YeEPE6FuVy0NyNPRBlSqRw1apav0ZpTq047JLixBqh2nMnkeRCJY1AqOAOpLBsSaSIUEEOuEKr1ksk082+QQAWZ4ph3dS5cuCCOswfDW8r2l4+S8e3wXzFiai90HdwSkfW1L8EQYemLaSgU56kdGxc5E1Fe7QiNd+NVsuoxnWrecrwXm4kivvIacDH2DSZ2Na78SotTCyHXkviTBBIeD1yit05mr5GdcX6PZnWNk39cxPRfxxln+Dta92mGB9Hq+Q+vHb4H0ugtmPe37vx22uAWlmL9l38a3X7olz1xYsclfNGhPBFm2S67yIah2HpzsUnz2wKBQJmwtLCg1EBL3RASTgGh3gaL/xkLg2mZrevGQCH7gc3sDjazu9522oLL6fRGiAg4ay3TXLhw4cKuPLymjA/cv+UCgiJ8LSac7hdcU4mmAGYISiRFEMr4+Ct5Az4Mm4hOfqbvysspKb/oRfqbJxzDfb1QxFd6sLKKuEb12fnmuqqg7yc12sKTzkaRmI8DyfeggAIb4y7g6wa6X1dU14ZahRMR2vSJ0hBOAHD14B2ThdN/609DLNSsvqGPzZcX4NCWaOxZfUJ1zDfYCxui54LFcbwcek/f7QR005IDy1gICaef/pmC0e0Wo1n7OqhRL4jw5IDlatW5cOGMOHqMSnXA9d4ruXz0IQCgYctIvP+JZWqMHUzbiZt50Vqzg/+VvAEH03YQEk4FvPIb9xq+5n1+QZ7uiHm39JfPM+wQ+On5SexPvou7/RaCSVG/8Z/bZCCEMgnanVkKrkSAH5tq1tcEgN6juuDIpnN48+ytxrnctHz4hxmfS2no1D44v+c6Ep4ka5z7MOQrzN8zDS16GC7oHHv7NQ6sPWGwXWVYbkyMmj0Yo2Zrf62OxMMHSbh6RXmDMHhIS8LjEBJOXr5u8PDhYMHYbdh9e5HhDnqwVK06y0IBYLstnS6qL67dWS4chcJcpbclsoHlluhec3WX0OodOAxPi+7qPK8PCpkMmVz5G80TaS/xZCxiWflvva6aexW5k6eMAaosmsooO34vL0nvOJ2GtNIqnC7svYFPZw8xaEdFeo/qolU4cQtLMXfQauxP3gwvf82anmXsX3MCe1ce0XneFCFnS3buMC6WSyqV49bNeKSm5gNQJt3u1z+K8LyEhFO/yJlaH1fGUCZaS9aqsyRMelMIxU+sNr4h7ua9wVf3/qe3zaP+y21kTfWg5ekfVY/v91sKig23E1cM8HV074fLQ1Z1YbsxIeCJwGBabqdziaRY57lARijhcSN8vZCQrbwIxqSZFygel56jehzkaTgJc7agxKhxs4S6XzsAjJw3DLuXHdY4/tfig2jUvi6iujYyah5A6XX6/bu/tZ5TyBX4JEJ9yY7OpJm0JLfsiOEya9pYOWEb5u2cTKivMezdQyzZ9dnoH5w3Aaala9VZCirFvOVHFy5MxVm8TtVBMDmDgLUGNeoFIT+7GAU5+i/4puBD90emMEXruWQ+8Yz/4RWEE1cgIjwOAGRXiJcK9fE02D6A6YG3vDyD7QKZuj08hjj223mThBMAUOlUSMVSo9qaIpqad2uEmk2IZUHnlQgI9bMmi5cON7swMSHhRLSmkU4jLFSrzlIw6M1RKrBfIHg7v9pqHiWxXIqdb67ij/jLdrPJhXWx9oXakp4il9epajJvy1iM7bQU104+Qa8P2qJNd/NLJ31Zex5WvvwGP8d9jx4Bg9HCuwOKJYV4U/oC/6T8DjaF2A7q8V1a4fKL8m3zBTwBfDimJzMUStSFxsiOzQ32WdXyI3x2fSu+vLsLS6OGIaCCQMoSFGPh08OgkMj4tc1Ig2P1GtkZF/aqJ/oNqxtEKAfT4cxtmNT8B+S8W46yFKvPzCXcNzfdujeEFy8Tt80cHCpzuCVq1VkCR/M40clU+DEc471x4bxYOmt2VfXMVMXXZAwcDxamLP0Aa2buxZ8rj1tEOHnQvDA6cjr+TFyDvSlbsDelPFEtg8zEhJrfEhq3eY0Q+LixUVCqDOZefvQS1n02AKY6EjZGly/1MGlUdKpbQ09rJY08Q/BZzfbYm3Qb/S6ux+haHeFJZ6FQzMeexFuQKeSY3qAXarn5Gxxr2PS+asKpYds62HB5IUhk0z0iDDYdc3dPxczuS03uq4vAGuXpgk7/dRW3Tz/Bsn+/Vh3bueSQ3v55Gc7hSTcVkoFgOMORclUQseQ13mZ3UzvGoDezazqCgyn3sPL5cdVzV4yTZbFnjJO1sZbAqarCqboT/d89/LbwEFhsOoZM6IoRU80vQwIAeaIsPC9+AA+aN2py6sObrllnzRQKeAJ0WbZV7di5HyYgzIjltiK+EMuPXsKZZ69Ux07OGoeaJqQ2uJ2bgF2JN3A7t9zz1SOoESbW6YImXmFGj/P4cix8g7wQ0ZB4zFdF9q48ojV2ylRY7kwczSlPIL145GbcOfMUZwvKj/X1+RwB4boDx/MyCnE6d5vZttgJnerVJZx0UDmXk0s4VW2qsnBy4cIY9G30MQZLh3AYw7Tdx9WW7MogkZQB5M3Cg+HGpIMvloAnFOPyy0TI5HKN9o3DAvHvtM80jjsrT6++wJpJfyA3jdiy3cGM3+HubXgZta/P52pCqjIzeq7AxovzCdngAOgUTg61VOfChQsXLpyXArFm1QVD+NANL2npYu2n/fHFzsN4mKReKkWhAN7mFeFtnuGyGjX9fbB1nLnFyR2LqK6NsOPpz/gkcioEXKHhDu9o2rkBRv84zCjRZAxsD9sW+n39Ogt799zE2+R8ZGUVgUQiISjYE5GR/hg1uhNq1w6wyDwOJZz6tDd+bTb6zkIrWuLChQsX1YuNx4nFG1Vk2YvpJvfRlhzTWJg0KnZP/hinn77C2tPXkF1sWhmNHwa8h7FdWhGa+3T6M8QWpSNXpDvb+M8tPyY0tiVgsOmqpTZeMR+vHiTi3tkn4BXzIeSJQGfRUTuqBkJrB6Jd/xaE5tDnbQKAETP7ExrXFPh8MVauOI7bt7Tv0kx5m4+Ut/m4dlWZ+PK9rg2waPEws+Z0KOFkDM1b10REpHlr4y5si1guxcrnx/Gam4WEkmxQyWREuvmjiWcYvmnYF2yKeXljHhckY3fSDaTxCpDKL4BUIYcPnQN/pjs6+tdDM69wdAmob/R4Ff2z1rbdhQtHoW4zYlvOKzK5lvouJxqZjnxxNo5n7EVzrw5o4N4MQrkAGYK3uJEXjS9rzzN7TgDoH1UfvRrXwdht/+KZEQWAa/h5YVirxoRF0+3cBMx9/J/BdvYUThXheLLRsmcTtOzZxKbzNn+vgdXnmPPDAcTGphnd/trVOAiFEjDNKPfmVDFOr15kYP7Mvfhj71eqTOPWopi3W+05hewHN5b11bMuLBHjdDn7JWY93GvyGBXjf3T10zZ2sZiP7hdWGhy/iVcY1rUcCX+m8TsHFVDgvejl4EmNz+HS1CscuzpqT8ZW8TWaYjsAnOsx2yTbHZXqEuxdXV6nI3Ag9Q/cyb+k1auUyIvDpvjFZnmc7MHymOP47+199AxqhAkmBoJXNRRyBaEdgJYiKTEXkyYqixJ36VIfX07piaAg7ZsD0tMLsfW3i7h1Kx4kEgl/7pyESP1OGJ0vzKkiYOs3CkFJsQAH/7lt9bk8OWPU/tlTNDkjfJkYX93/y6i2z4vSMNXItgAglEnw7cO9JokmABge0dqodqbYDgBT7/8Fkcy0wpiOhrMk4LQU1e312ou4kqc6z0Wwa9vQEstxNy8RALC+9afVWjQBwJioORbPG2UKly7Fqh4vWjJcp2gCgNBQbyxd/iEAZWmdixeeE57X6ZbqAODE4Qf4YobphSFd2I7O55TxanQyFeNqd8GXdXuqnU/jF2B17EnczH0NAEjgZiNHWKKWTE4XHc8tUXvOptCxtd0ENPIMAbnCbji5Qo49SbdwI/cVIti+GBJmnFueiO1dopfjXr8lGmM5G1UtuaW211NVXpszINdT8zOZRzxzuD3JFeqOaaputOvbDGOi5gAAPP3c0XtER0xa+qHN5j/zrkTbx5+0MyqHF4kEfDKiPQ7sv4PTp55i4qRuhOZ1Ko9TGQq5Q60gutDDro6TNYQHAISxffBr69Fqxw6m3DM43tkMzVqGJ7rPQhOvMDXRBABkEhljanXGH+0m4ou6PUy03DTbpQrnLwpdJiiqirCo6FUqS9jpwrbUcWsMACiSFGicO5S209bmWIRgluE8UdWFaWtGYu6OL9CqR2MU53FxcPM5zOq/Guf3EashZyrcdzsGa9QwPu65bHmutNT43YaVcSiPk0ymmV+jjFcvMvDvu4J+vQcQr2rswnZ8GNEW9T10V1onk0j4LLID/klWLr3uSryOSXW6gU7W/rU8nPoAy2OOqp639InEH+0mgmzErYYxnixzbAeUgeS6bHd0tIkmS3ufbO3NKstw7sxC0J4ewPiYVDy8GoeLhx8gO60AJBIQGO6LGvWC8On0PqjVMMTgGKNrTEeUVzssiZ2icY5KomFUjWnWMN2qbO8wHh9c3YxLWS/RI8j87OrOTtdhbdB1WBvV8w0zdmHb/ANYN1VZqN7QzjtzqFc/CC9i0yE0ofZeKU/0rq/u33dDONSvfL9OhoOVGUwaPvysgw2scWEOJJAwofZ7Btu186ujEh8SuQwZ/EJE6ihVcDT1gdrzJc2GGyWaTIWI7QD02u6MWPpiXSZkbIkzi6Yy7PEabpx+ihVT/tI4npqQjdSEbNw4/RR1m4Zj4wnDKQyaebZFJ78+eF58H8US5efvTvXE9LqL4c8gfvGyF34Md2xpOxqjbvyBfqFN8U3D9+HH0L5ZqTom0p25cSymrxuFzd/txa1Tj606V/v2dfAiNh0vXqRj6DDjQjGex6QCADp0qEt4XocSTl17NdZ5ztuHg/qNQtGzb1MbWuSCKF/U7Y4glpfBdi19ItWepwl0i4/nRepbTkPZPoTt04c1bHehpCoIGW1Yy7Nl6/dLwBNheOM5RrWNj0lFv8iZaNK2Ntb8q99z9GHYBHwYNsESJlqcSy/e4GVGDgBgai/DN+VRJxeoHp9Of4bT6ZrhA2U8HbjMfAOdiNIiPuYMW4eEpykAALoZW/6NYeSojti54youXojFD7MHgkrVL1QFAjGuXonD5190x4hP2xOe16GE0/zlH9jbBBcWop67cYWSmRT1PyyJ3LhYIZLunaJmY23bXVRNnH1ZEAB2rCxPeUKmkNG0bS0MGtsFoZH+kCsUSE/MQUp8NvZuPKeKNX1+T7PkiTNx8nEczsUoN3oYI5zeD3HdvFdGIVfgp0l/4PaZJ5CIpACAqT9/hu4ftbP63P0HROH0qaeYPnUXxk94D23bad+tefFiLP634xoAZTC5OTiUcHJRdWjqbX4yPX10DqhntbGtbbuLqoezCyYA+OvnUzi1VxlHOmBkR0xb8ZFGm7K4ppHfvA+FQoH+Nb9V9R33wwDbGWtBsotN2yXnKEktHYHvB61BzE2l6GzUtjamrx2FPiM7WXyent1/Mtjm9esszJ3zr1Hj/fDdPjRpGo5x47sQsqf6LcC6sAlsKsOq47vTrFcDydq2u3DhiDy6/goAEBDijS8WGi5JQSKREBCiFIwPr8VZ1TZrwhWK7W2C0xJz8zU8fN0wfEpvrD87xyqiyRo8fvwWf+++Qbi/w3mctv5yDkf+vac15QCZTMLJa/MNrmO6sD8MK+8u87SicLK27aZS1XIruXBMkuIyAAAjZ/YFnWHc38DoWf2wbtY/SI7LtKZpVsVUj5MxLHp6BGn8AuzoMNEi40klMhxYewLxj5Nw+8Qjg+3PCf62yLyGsOaOuYqYE49kDRzrCgHg8P67AJQiKSjEG27uTHBLBMhML4RcrsBPCw5hwU+aLmQX1YtSKfEcHM6GPXajubAMlop7skX8lJefO/Iyi8BkGR/QWyawPHw41jLL6vDElvc43c9PQjrf/L9ZuUyOn8b+hocXYsAr5lvAMufk8y+629sENRxKOM2ZvgeBQZ74bfcXcPfQ9ChwSwT4oM8azJ7+N1ZvGq1xXirLRlJmC3i5TQCb2QMcpulJD104B69KnPcOlwgub5MLazNkXBfs+OkE7l1+ifcGtjCqz40zyh1lg8cRixVxBPSXayVGlqCYcF+xUILjW89j+9x9FrRIP3P6r9J6vNvHHdB3XFeb2eEsOJRwSnididGTumkVTQBUxxNeaa9+LRDdAQAUle5EUelOsJndwWH2hJebY26DdUGcN9wce5vgVFSFHV/OiKXec1t8dt2HtsKeX87hTnQMuEV8uHuxDfa5+67eV/ehxuXQMYUNZ28gv5SPiV1bo6a/ZuqRuIxci89pKWQK3cmcDfFVu/lIe23bG8P4x8koLeJpHE9PyHIJJy04lHBicxgIDTecm4fjpj14t5C7Ue05X3gZfOFlk4WTQKRZ+oNMdgOD1sikcRwNb7rhH0JnwZwfpuqMNeOlXLFYzo1voCeOvlyNfpEz8XHz+Vjx95do3rEuyBTNmNLLxx5h97rTEIuk6Dm8tSpI3FIMXLcLSbnKMi1HHsSiYUgADs4Yqdbmg417LDqnLqJOLgCHysCtvj+qnlsDEV+Mwb6WiYkylTXn5uGrdvM1jtuzgK8j41DCqU37OoiLTUfr9vqrZms7r1BIIJYmWMSOtNyhGscY9GaICDhrkfHthQeBgGqxXGoFS4gRwfFFCq/8D7lYzIdnFRKD1sRWRW5tJZpcHjTr0X1IS1w+9gjzR2+FhzcHXQe1gF+wF6QSGbJT85H6JgcvHyUDABq2jETXQS1xdOc1SEQS6Fr1+vgrzZqP+sgsKlF7Xiai7EUo27rfNV4xH3MGrLbqHPqo1SwC4fVDkPp/9s46vKmzjcN3UnejRqFYcWtxt+EMHzbY2BiuG9sYjG8bg8FgQ8aQwWDDhru7e9HiWkpbCnX3Nvn+CE2b5kSbCqX3dXFdOee8ltCc/M7zPvI4ROlaQkwi1vbvrg9bflCkhNOEKV3p0+E39m73Zc4fg/Eo64iFhSmJians2HSZvdt9mf3HxzRs4qXUNyH5IFKpUL0ao/xf+DtCRWsXhePEjFSs1ITex6Yn0+fsH/m9LK1Z32w0H55eIHcMb3tiDpOrd2FIhXcjBLYoUBCiqUTUvJt0Kf+V0rm46ET2qwnbfngzgB8//1vj2LoKp7kDOvPlfwcAMBKLmDegi079DYlQ9m9jkRE3us1Q2UcXq5RUIqWP2yh9lmZQpq4by7gm/1M6//vwlfy8Q3NpnfeJQhVOHZvMVHlt3GfCYY7Tv9wEwLErPyqcT069KNjeyrxoeePrQ3x6ChGpiiGzAYkRWBubUcrMRqexjERi+TZXy2OzaO1ajUX1hyi0yZRKOPTKj5/u7Mzbwg2MrYkFZzpMp8Hh7JvSwoeHWe9/gVVNvqCclXKF7IvhT7gZFcCa5+f4rFIrJlbtWJBLfm/ILZbyWzyVCDPD009HcZOfdKhVmftzlYWcKnRpm5uaUxfp3Ke8tfK9Jieu5raEpsSpbQNw/fhd/tfzd53mdi1XCkdXe2ycrPE9fFunvurwqltO8PyVg7e4dfo+Pm1Vl0R73yhU4dSsVVWDjZWRGSp43tjI1WBzFATnwh6x9eVVEtJTCE2JJSE9haRM5XDZLEuQWCTG2tgMV3M7rIzNcLOwY4RXWyqoqJnWu2wDdgRm+3CdDX3EyKv/UMu+LHYmFsSkJbEj0JfEjFR5m6alvLgcYZht0LwiFokob+1MQEK2Y2hEajy9z/6Bi7ktVW3dMRUbE5oSS1BSFLFp728Ib0GRO1VCSfqEd5Nh331Y2EvQi3yo862Wm91+1ljwydrEXCvhtGTiGqRahPWVqezGsFkDqFjbE/eK2TsHnSyUo8vzg3sXH5cIpxwUqnCa8dsAg42Vmi6cudbM5N36z34S94bL4U+1bi+RSohLTyYuPVl2Ihp6lqmvUjh9X6sHj+NeczcmSH7ueuQLrke+UGpbz7E88+t9zN2YoCIjnAB2tZpEuiSTZkd/VnASD0uJI0yLm1UJhkXI+mOo3EX5MW4JxQtPJ80FudVhY2FGfHKq5oZvMRJpTsC8q/UEjW3ioxN4E6A6MrB++9qM/2MopSsV3MO/raM1cVEJSuf3LD3KJ//rU2DrKOoUmxTcUqlwQkSRuMR5ODermwxndGX1Znk7EwtWNh6GvaklNe3LFNDKtMdEbMT2VhPpVVa7MOg+ng3p7qFdbpoSiga5RVJxFk0lFjr9sbfMWxUBazNTA61EN/atOKHymthIzJz9UwpUNAFUqO0peD7hPU6+KUSRcg7PCxJJjOB5I1HenkYKmuFebRju1cbg4yYkp3Lu9nM2Hr/Ji5BIJFIp5UtVwtPVgUHtffD28sDMVPjPoePYlYhxZFi3xoztLeyI3da1OsNTu/DvQVnmd6Ouipq8wfCFuDhYc+j3kQSHxzBszhYyMiX8PLwzN7v+AsDBSw/49++bzI48Q+2K7iyaqBjd2GjkIiQSKddXyxwV//7vKtcfhVLfrTarvpNZL6VSOHT5AXM3nsTMxJjfx/XAp7KHxs9nVHpXfB8G8vBlGH3Pr6WShxM/ftYJO2tzlX2MRGJudv2FBsMXAlCnefZNZ9Pxm1y8+4KbT4IxMRKtvafsAAAgAElEQVRTvbwb9auWYfiHTRCLC3hv4R3mXRNLrbv9BsCMqT1o27KaynapaRl07C37uzl7cIrS9ZTUdE6efch/W68QFZOInY0FlSq68OO3H2Jhof6HPuhVFCvXnOV5QDhvQmNxc7WjYjlnpkzqjJ2KHHkA7XstID09U76exMRUjp95wKbtV0lLz8DJwZpPBzWjWeNKmBgLB938vfYc9x6+4tGT12RkSvBwd6CshwNDBzWjamU3tevWF6EcT7rgbm/L6xjDl11Rx/Vjd1g/U9iHdOhPH/Hx1J4Fup4s2vRrgt/ZB4Uy97tEsRFOqpBSdMLpCwuJVEq3KatITFb0lQoMjSYwNJoLd/yxNDflxKLRmJrk359EWHQCgaHRDPt1CzEJsq3Fr/7cIxdCP/2bne7h/B1/jvk+pmMjZT+4hORUouKSOHZNVpQ0Kj6JzSduMqh9PeZuPMHOM7Jsxsmp6Yz6bRu/jOgqOA7AyzfRLN5+lnN+/vJzweExBIfH0GPaaoZ2bsiwbo21e38xCTjZWXHoykMWbj0jP5+ekcmNx0HceBxEzxa1cHXUzaG/hHcHrwouPHsRxr7DfmqF0/lLTxSOcwvEwSNWERGZvWWSkpJOaHgcA4atZMLID+jQVjin3OivNvAwV/LEkNcxhLyO4abfSyaM/ICuHWurfQ8SqRSxSMTAL/4mLj5Zfj46JolfFx1i/2blbajExFTmLz3KqVzFfgODIwkMjuTi1WeU9XDkv7+Hq51bF27+IluHsThvGyf6WpwOvvLjQUwI4amqRddv9foLnj+786rKPoO+66HXegyBazn1Tu8FTd9mv+BRzok/N48p7KUoUGyEk1hsTaaA1SkjUzkvxfvEPweu8tee7IjDEd2bULmMM6npGdx4HMz9F294GhxOUkpavoqmLPpMX0Oj6p60b1CF9UeuExwew5wNJzjv508bHy/a+nixYu8lXkfG8f3fB0lISaVPqzoKY3y7bD/XHgXy4+edyMjIZM6GEyzYcoYXr6PYdfYObXy8aFKzHOsOX5OPIyScrj54ybiFsqc+S3NT2tbzokXtiviHRHDOz5/HgWEs332RO89fs2hCL41OqA8DQnkaHMHMNUdxtrfGu3Jpyro4EBOfzN4L98iUSEpEUzFn2fzBdOq7iJt+L1W2iYxKYPaCgwCMG6FcFirLalXfuxzzZnyEiYkRyclpXL3xgp9+3csv8w9gYmJEmxbKf9OPnr7Gwd6SqV92pVoVN+ztLAl5HcPuAzfZtuc68xYfpmO7mmoLpZ8+94jfFh+htLs9I4a2pG3LagQFR3HnQTA21uaYmChbm7r2XwzARz3r07OrD55lZFaggMBI/O4G8uffJ/l0UFM1n5zumBkb5n41rUcbRsQ31Lr9y8QI+p9bTkqmUPobRVQJp2Przwme92lbE1FBe7vnoGbTKoU297tEsRFOIpE1ICSchMuzvC9sPXULkBVNnj+2B628s5OHdmlSHYDtp/04fVN7h/S80Nq7EvPH9UQkgma1K/DhlFXsOiuzEM0fJ3vSquLpzKAZsurevg8ClYTTtUeBtPauRI/mMsf/BVvPkJqWwa6zd/i4Qz0mD2gDQF2v0vJxchMVn8QPqw/Ljw/8Nhxby6xtuSqM6tmMZmP+JC09gwt3/Nl84iYfd6in9r1dvh/A5XsBVCztxLaZQxWuTezXkpPXC+YzLqHwMDfPLpCbnp4pKDKOn3mARCLFxNiITu0Ug1di3vqS9Ormw5djOsjFuoWFKW1aVKWshyNBr6JYvvo0zZt4KW2ZLZozkFo1PBTOl3a3Z9yIdmzbcx2Ah09eU7uG6u3ruX8cJi0tg3+Xfi6fv0a10tSoVlrj+58wUtF3srynE+U9nWjbqjoWOhQPLkg8nex1cjBf73+JlMx0PnCrwTCvltQyoA9o58/bGGwsfTC3MsPU3IS0FM2isCDYeUk5r1RRoNg4h5uZVBc8H5dYcIUSiyJRcbIbcePq5RREU076ta3L8q8/KpD1LBjfU34zdsthfWlRp6L8deUyznRsKHuavnJf+cndysKUBeOzfQAm9s0uMJolmrLGUcWHU1YTFZeEqYkxh+ePzCGasrm4fCKerrItlJxbb6o4ffMZlmamSqIJwNrCjJ4ta2kco6CInhVd4pCcT3i/dbD95odtgtf/+ucMAHN+7KPkczTyy/UAfDW2g6CFc/WfQxGLRYSGx/HH8uNK133qeKr0P3JztQPg+q0AtevPyMhk25rReoX5RwvUOwOwtTFXua53jf3BstxJCxsMMqhoApmPUWHjWk71fbMEGcVGOJkYlxU8nyl5v38cTN/erK49CuTO86K7bVmlrOKX1d5G9oOSlKqcw6qcq6IzqJuTrc7zpaXLfN/aeFfC2d5asI1IBG18lLPUq2Ng+4KL3Mur+HnXHK/fFXp/KPsbuH03iMBg1bW+GtQrr3AcGhZHaJj6dBrm5iZysfVSxzpipm+tX2lp6v0+q1V2w9VF9+8UwIhJ69lnwKSMRRGjQtxKKwiMTYuHwM1Pis1WnbVFN2IS/hW8lpr+UKVFqrize84whsz6j+j4ZIb9ugVjIzHzx/Wgaa3yGOXRqdKQ5BZOFmYys75EopwcrkJpReFkrSHKCGRCSciHS5PQaVCtLOuPXNM4PoCtpbnWjuSFTc7yKDmPC4KsrOI5RV9xEnFtWlRl6KBmrNt8iXHfbGT/lonya5evPQdkolyc6wf4Wg5LUJafkzrCIpSdks9ffsraTRd55h+m5+qhnrdwBml1nD7wLVt3+rJizVkWLD3GgqXHABg9rA3dO9fF2kp1aad3jX7lGrHuueoyNO86Jlr6ur4OjuLHcRsIeqGYi2rE153pO7SF/Hhw+98Y+U1ntqw+x4sniq4zm05+h6Nz9s7D/i1XuXPNn8jweJISUwl4KktsfeTOL0rz924yk+QkxQdrYxMjDtz4WX5849JTpo9ex/Lt46lYNTuqMy01g4/bzaXPp835eJTu1UWKjXAyN/VGJDIRrFf3Jmo0ZZx3YyTOW9jqu4irow3/TB1In+lrAMjIlPDln3uwt7Zg+dcfKQmWwkIXM76pAU3+w37donXbpJQ0LM1VizQHNaHe+UFexUZO4VLQ9eXya5uwMIVgTnp08Wbd5kvExSvmlzt68j4APnWUxUliYnYSRm0sPi6lFAMNJBIp//tlt/zY3MyEVs2qUMbDAXMzEzZsvUx8gnC+u5wYG+n+/RKLRAz6qDHJKensO3yb6BiZi8CKf8+wfvMl9mwarzLdybvGpxWbsTfoJqfePKSdW/F7IE9NUbbyC/G/0et4FRjJt7M/wrtxRWKiEtm48jSrFhzBzcOR5u2zIz8X/bSHzEwJ/x2fgqOzNf8sOsbOdRdYu+Q4k2dmJ9bsPrAx3QdmP3x2riPs4/Tk3iuSk9KoXteTeauHITYSMXfKNi6cuM+1809o2FLm5F6vqWzHYP+WK0z6KTu9zcWT90mIT6FTnwbafzA5KB5/yYBIZIGjzWQi45QrTKelP8U/pBZW5u2xteqPtcW7WVpAXzxdHbi+ejISqZRvlu3j3O3nxCQk8/HPMsfpRjU8+WFoR9z12PLKSaZEormRCnTJbWSix409J0la3hhyk5aRibp0qpaFlEhPX7KsPgVt7XkfatiVcsre/g0IjKS8pxMHjvhx+vwjrK3MmDejr1KfnGJp25rROs2XkpJOz8FLAZkz+ObVI5XabFYTAm8ohg1pwbAhMmtDdEwin47+l7j4ZDr2Xkj1Ku6sWFQwJUIMSe6CvSJEWBqb8tX1TRr7ChUILspIpVJCnguXL8vNq8BIOvTw4YPu3gA4udjy46KP6Vznf8yavEnBSmTvaMXaw1/Lj0d83Rn3Mg4snb2f0VO6YWmtvUUyJDCSiR//xbezP5LPDfC/hYPwf/yGsf2WMmvZpzRsWQWRSISVtTmHd15XEE6/TdshW7OzflHOxUY4AdhY9hQUTlkkppwgMeUE5qY+mJnUwMy0LmKRNWKxlcaxJZI4ElOUnTGLClbmHTS2EYtELBzfk43HbrD/0n2eBUcAssi1ob9s4tgi3W7WuUkuIpEYmshpNfrxs47YWqlOcpkTKzXWpneZwhBPxQlVn51nGScCgyM5dPwOY79oy9FTMmtTh7Y1MRWwvlQsr7/11+9eEClvv3+DPxLeLs5t/cpvHOyt2PzPSH76dS/XbwUo5ZcqKviHRREaJ8uZ1dRLOHN2TqRIFWp5FidiwuNIT9Wc+zAiVOaLV72u5s8LoGY9ZQuro7PsQSE1NV0n4fTQT1YurHaD8krXsrbj7t96Kbc6depdj10bLiGVSuWpHqRSKS6l9U+OXayEk4lxea3apaTdIiXtFiRu1Hrs9IwAQiKUo6WKCpXLaO/4PbhjfQZ3lJUqOer7iOl/HyIqPgnfh4E0qq76i5CRqd6idPNpsNZrKCoYGxvp7ABenCiOgqkwtuuEWLFwCH0+Xc7WXddo2rASd+7Lvh9fjmkv2N6zjKPcN+rUuUe0a6U6gWZuYmKzE1W6utgJtsnU8P3ND6ytzFjwS3+tfLYKi98PnuPcY1mtzvtzv1K6/q5ZjfLC8Q3ntWoX8TaIYcnsfSydvV/hmljAd9bFTY1I0VzjWIHYaFnkpmMp1dai6BzJY0d+25WQoCh+nrSRGX8O4daV5xgZifljwyjdJs5BvgmnsMRETjx7TlxKCplaVH8e18QwTrUWZs1ITr1kkLHeBzo1qsb0vw8BcM//tVrhFB2vul5RTEIyL0KiDL6+/OaCnz9dmxQ/P4USCh8rKzPat6nBgSN+XLnmr7kD0K9XA9ZtvsT8JUews7WgvgpHbb97QdStlR1JXNYjWyTeuB1Aw1wReweO+On+BrQkMTEVKzXO30XV0pRFgkDk7vvKnfOPNDcCHJxkuzQ/LR5Mk9baC3xDkOVMHhkeh2tp4YcjByfFSOleg5sybdRawl7HcHzvTRq3qabglK4rBhVOEqmU/x07wda7d3XuayjhVMZ5B0+DNSdqK0EZBxv1BZH3X7xP58bVaFxD+WbeZ/qaPPk4FTT923qz7fRtjl17zLNXEYK5l3KiKirvXaeoWGcMSVF6L99O6MSBI35s2eULwAet1Yt0G2tzpk3uyq8LDzF5+laV7ep7l2Ph7AHy4xrVSvP95G7MWXiQzTt9OXT8Lh6lHYiOTuR1aCyA3JplaGJik+SZw0HmlG5tbUZ8fAqpb1MfGBuLmf2/PqqGKFTexBZsnTp1nN1xhdYfFV4up2tHhQV2mVx1BrMEy/zvd7DjorIDt0QiEbQ8GYI2XepwfO8tfhi7nqVbx2L6NgJbKpUy66tNOLnYMmSMYkZ+78aVkEqkTBu5llcvIzjslzcrokHf2Z+XLuslmgxNKbvvC3sJRYaRv29j/8X7gtekUth9Lvv/q3nt8oLtapTPrtA949+jStcfvgwlLrFg/SfyysieTeXO8P4hkRy/9kRQ+EXFJfHt8n1MXLxb6VpxoCiJjPymKCT87NaxjsY2nT+oRU0NWbqrVXZXOtfpg5ryIIvYuGQePArhdWgsRkZiRgxtpXFMfcmZLR1kBYojIhPkoglgwS/9adKwYu6uRYIEHYNFQlPiOB/2WOX1xIxUrkQ8V1uSxUxFCpXj/xXNNAfebWsqnSvv5UpCfApbV5/lyf1XBD4P4/bV53w/ai0LftiVr+sZNaUrgf7hTPniX9JSM8jIyGT211u4dOohY6Z2w1ggY7+NnQWvXsr8evNa1sagj9BLLl8BwNnKit86d6JVhfKGHF5rHGzG42AznqCwbjJfpveYm4+Dufk4mJ/XKAuenDStWR4XB2HT5fr/DWb2+uPsPneX8JgEGgxfqNRmTK/mdGxUld7fC+fSKmrYW1uwf95wen//L0FhMUxbeUBt+7pexdeKmVs8FRdn8aJiTXOwtyQ6JolhQ1qo3HrLzfIFQ/Sa6/T+b9VeP3twisprJ/Z8rfKaOpwcrdWOW9SJS9btoe/Xewc4/eahSt8nK2MzRl1ZS1ePOvzq00+wzfA5g1j21Tql86osPvlNWko6oxpME7xm62jN2AXK0ZArdk1AKpEya/Im9m25SkpSGo7ONvQd2pxm7YSLUBsKz4rObDv3Pbs3XKJ/qzlIJFI69PRh9b4vKVNeuFDxsq3j+KzrQpq1y7trRr7sPVwZo7/TlSEp6yIrpJmafo/4pN1Ex/9VyCsqeC6vmMTNJ8FsPXmLVxFxvAqPQQpYmZnSom5FmtQoJ1gANzfTP+3AyB5N2XfhPjvO+BGXlIKTrRWfdGpAh4ZVsLcu2BxGhmL3nGEAHLr8AN+HgVy6F0BCchpW5iY0rOZJzQpuckf64k5RsMgYktyCqbAEVFZOo6GDmhXK/CUYlltRqgs45+RKxHOV13qMbs/NU3e5vP+m0rV+HmP44+xPeHi5CfTMH7o7DFN5bdGZnzBSkTtPJBbx4x+D1Y698YSwqG7WrrpgYkttsbW3ZOiE9gydIBxskZuD232RSCRMXzBQ7zmzEEnVO27r5O9eab7MEvH8m8l5WFL+kSmJIDXtLgnJh8nIDCU9wx8paUikik8cmZnhSn1FGCM2KrpP4RXdC+dJpYTiQXHN4p2TwrCkPXrymlFfyfKlvctWmYKmwY9LC2yu5LTsLTWhqLrc1D84gwxpptpou7oHfsBIJOZmt59Vtnlw5SlftZ0peM2tvDPrHipb9nPTyUI4L9bRZOHi5kIcW3+OBaNWqbyuy1iGJisBZl4EVhZZmcZ1GEvlfl7x83ZVg5G4FJbmbbE0V59iXci53NS0Bp4uR9T2a7pmJd83b83yG748isgWX27WNuztPxgXq+x8UYGxsbRev1qhvwiY1qI1I3xk2UwrLFnAoo5d6VW1Oot9L/PH1Us08SjL5j79AXgVH8eHWzZwa8Q4tesqilRcJLsp+H9VNEX2+0ZRE0tFZZtNV1JS0zE3M0EilTJxyibuPniFWCRi8bxBhb20d4qcYqaoUdXOjfsxrzS2q2CtPi9XjSaVEYlFSAXKSr0JCKerzWcMntaTwd/31nut6nhxL4jRDdX7A3/wcfN8mVuIR3eC+HLISoVzzm52/PbPF3qP+fOkjVw+/RAAS2szlm0zzG9l0SlWVkyYeuoY/tFRXP58JM/GT2aETwPeJMQz/3K2019iWhpD9+3E2dKKhR268HDMJA4M/AQpMOfCWYXxHkTIak5dCwnGytSUO2Fv5E7M98JCqeNScOZcQ9K+UiUsTUw0NyzhvcXQoqkgRFinPovo88lyOvRawN0Hsh/Xj/s3pk7NMvk+dwkFQ33H8gAEJQmnXwlPkUXp1XPU7M/Wc4zqxMWZGZmsn7WLw2vOkGbg5MK/D1/J2CbC5Uxy8sUvAzS2MRSOpWxwL+OIpbUZltZmVKnlwYpdE3Avq3+pNDcPB4yNjbB3tGLJ5jFUqqYcVKEPBt2q81myjLjUVFb26kl7r0p5W1kh8uK1DxmZimnnzUzraGVxMhEbcW7ocIXzFZYskI07QeZ82XztKkLi4+THWex78ohJRw+yb8AQaru4UmHJAhwsLLg5fCwVlixghE8DVt26zoxW7Rha14cv9u9mcO26tCuff9EqaZmZDN21k839+ufbHOrm9l6+jAcTJmpsW2PJn9R1cyuUdRZViouTd1FBGyvY+G838uiprJBpq2ZV6NnVWyHfUnGlbad5lCplw/aNY1Ver1OrDIsXqPeHyaLm1EXy1z/2/sAga1TFzN0n5a+12aoDmHNvP1sDfDERG/Ftja64mNsQmZbI7/cPkZKZTufStZlXT7t70ZMb/kxo8ZNWbc0sTekxqgMVapWldstqfFLlS8F2WdtrEa+iCAuK5OTmizy88oznd7TzzwJo278pU9cJ/3++JxTMVt2M9u2YfPAw3xw+wpqP+uDjbhh1V9AYG7krCSdtaVDaQ2ObkPg4wfPl7WTZVR9HRlDbxRVXK2tCE7MzoNZ2kaUFuPEmhKF1fbgXHkpd1/y1OF1/pdkknZ9zp2RoTv8PaN3ufaM4i6fCem/q5l36u3bC4H0k6JV+wQcDGmtO4ZAXcgonbfm2Rle2BviSLslkzr39Std/qtNLoJcwVepr/+CbmpTG9kUHNbYbWn0yyfEpxEbql6PKs7oHE5d8rlff9wGdhdPCCxdVXrM2NWVWh/b8cPwEH23cDICnvT2tK5TH0sQEYzUJsSa3KLi9VE2YmXqTknZbr74eNtpnI/Vaquz8ZyQSEZMic1b/pI63fIuvlKUl3atUY/2d2+x/8oi+1WoSlpiIk4X6pJX6EJ2SwrKrV9j54AGxb9eS5ZMEMLphQ75t0RLv5cvYMWAglZ2cALgUGMiQnTsY17gxXzeT/X/W/2s50Skpcl+mnOOAsI/TL2fP8CAsnCvBQWr75FwnwNXgYKV1TmnRUqHvmls3OfTkCXdCQyllacnK7j2o5eqq0GbBpYssu3oV/68mE56YyIBt23iTEI+XoyMfVKrEpCZNtfkYC42clpHiFilXFCiuQjS/SUrSvb6bpWnR3M43ERvh9+EspEg5EOxHVGoCdqaW9CjjjVikuwfM0eQN7PjjEKumbTbI+t4EKAc4aYu9sy2rbs41yDqKKzoLp2VXdKuwHRgTw4ZbmkVIURJOxkYF4zf0bLx6x2jvt9ak1wnxNHSXWbLqu5fm+utXPIrU/4uhiejkZA49eYKFsTFpxsZkSKU4WWSnG7AxNUME1HZ15UF4mFw4XQuRWaduvMqumxedkoK9eXYR3Y6VvIhKSeZWSIjKUjyHnjwBwMLYmOSMDNysrQXb5VxnLGBiZKS0zpwExsYy68wZ+fHr+Hh6btrItFatGV5fOeXAo4hwhu7aRXiirDbSvbAwXFSspajwPommghYwJYJJP7LcQdzV1StTgYNV/qc5sTQ1IUlPZ3QRIrqX8TbIOj76siv//rCNzIxMg4ynD6UruTJ7r/pcYCW8Z1F12mJuYpgvgiomNWrKYt/LvIyNoZyd6ptJ87LlqOjgyKZ7dxjTQFaSZmS9hqy8eY3j/s8wymP2U1VUdHDg0oiRAHx1+DBvEuIFfYc+9fZmw+3b9KxWnUyJhFXXr9OnRg12vbUAZfFJ3ezPc0WPHgC0/Gc1r+KEtyxzzr330UP5sbp1Vly0kHru7ip9nBZfvsziK5c5+ulQudAD6Ld1C3POnWVYvXqIc32e3xw5wqnPPsfKVDjLb1Ek5497yQ+9dhTn7cz8pnOPBaSmyrbJIyLiadtpnmC7alXcWfS77pGFFZz1dwzWFjd7G/zDikadzUPxa/mh93x887G2oBAisYh+X3UrUGfwdxmdhVNRsgzlF/ltcRpZryGHnz+l17aNjPBpQJ9qNYhPS+NycBDH/Z+xoddH8rZ1XFy5EPSSLxvLkuc5WlhQ3t6BgJgYvBydVE1RINR1dWNauMzydT88jKT0dOq6uSkJp7puhR/5t/3+PQAF0QQy8XcjJIQHYWFKW3bTW7d5p0RTCfnLu5oiIT/Zv/NL7t4L5uupWzAzM6Fl88oK10UiEVZWZowb9QHGxrpvYdlbmWtulEdszFUXKC4MZu3+hnM7rzJ7SMHlspp/bDq1mmtOhFyCDJ2Fk6GK8RZlTE2q5Ov4liYmHP14KIefPWHP44cs9r2MmZExjTzK8GFlxT/eoXV86L19k4J1aVS9Bkw7dZyvmxSuiHW1tiY6OZnTL/zZelcmTAbWqs2cc+cAiEhKwsPWlnYVC79GVUi8zEkyt79UFr6vgpWEU9OyRTMaqmMjxaR5x3x/LKSVFA+0FUIlgkkZExMj6vmUw7uOJ8Eh0Uz/rrtBxtU2us0QuNnZ4MdrrdsnZqTyx8NjTK+t+F43vrjM7/cP42phy6F2kzHSw9cpi1Z9G9Oqb2OSE1IY03g6r/3D9B5LFS6epVjhOxsrO8P7yRZ3SrbqDMjlz4VLzeROO5BFF68qdPFSL9K83dyV+g+sWYeBNfM30kQXHkdEcON1CKUsLTExMqKWiwuhCQk8iogoEtamnKja3lSflaOE95mSrTzNeFVyITjk3fSp61GvOlXcheubCXHyzQO2vfTlC69WuFnYAbDq6VmWPj4BwJvkWGbe2cvPdfOeuNLC2py19xdw7+JjZg36k5hwYfcGnca0MWfqmrE07uKNSJw/7h7FnRLhVIJaRCKQqFEVImTO3JFJSfzQpg0AIxs0ZIOfH2GJCXzqrb+/mK4uXOrW6WZtzZuEBJ5+WXBPskWJ4vzjnx9baMX588oPxo3+gHGj8zffUn7RpnpF2lTX3iq+7rkssjxLNAEsfXwCM7Exvl1/IjkzjSaHZyESiZihQ1oCddRqXpWtgcsUzt05/4g3AeE8ueFPdGgscZHxpKWkIxKJsHGyxtLanGqNvHAp60TTD+shNirJd20oDCqcPtm2gw+rV2VA7do69w2KjeXOm1AkUgketrbUK118q9G/S9iYmhEUG4tEKlVyngao4OBAYGwsAA3f5rCqX7o0W+7eJTY1hVourkp9dJkbUDl3boLerkOIAbVqs/jKZV7GxFDOXvfonuJAcRUD+RVBmPV5FcfPrAT9eZOseJ9JzZRF5PXxlJXKsjCS+UX6RQfl6zrqtKxGnZbV6PhJS574h/LHqlOERcaTkJiCTy1PqvuUp1dn4QfXxKQ0eg37i5TUdEQiOLfrG43ztew9HwBrKzP2rhmDqYmwfHjiH8r5q884fPo+0TFJODtZM7BnA9o2r4qdjfooyUfP3jDi2/8A2LtmDI72sjJl2/bf4MoNf27fD8bV2QZnJxtGDmlJjcruiN9azZJT0un9xV8kJqXp/J5srM3Z++8YTEyEixnnxqDC6VJgIPFpqToLJ+8ly4hPVc7xUcvVlb2fFE5CORcHxTwWRmLtTbnFiR/atGGD3228/sjO5DutVStG1JfdJD7x9ubn06dpUa6c3EfI0cKC0y/8ARTKqlwOCmSZry/xqam8eetz1Gn9OgeqAFsAACAASURBVNytbbAxM2VJtw+V5vZ780ZhbhDO/VTH1Y07oW8UfJhyrnNS06YMrF2bpqv+Fnyfxb1mXnH/8S/u76+os+6/izx+8po5Mz/S3PgdJ1MqUTj++sZWatmXYWqtbgrnAxLyL2VMFr63A/j65x1K5y/4PuOC7zMWrDxBk3oV+P2HvgrXrSxNmTe9N5N+3KaVm4IkRz29P37uLyiafG8H8L95e0nOVR7m1ZsYFqw8IV/Lz990x9JCc9DNw6dveP4ynFUbLyicD34dQ/DrGOavOM7aRUPl5y3MTVg5bzBDJqxBKoX09Ey1QigzM/v/cdGMflqLJsiHrbrAmFieRERw8PETTj57ToZUQpsKFehatQp1BPxd0jMzBUUTwL3QUKSoyXuej9hZfVoIsxY9jMViJjRuwpFnTwmIicHOzIwqTtkiMsuHSZuM6f7RMVwKDFQ49zQykqeRkQAsUbzvYCwWs6lfP3pv3kRQbCzWpqZUcBD+gdzUrx8rr11j1Y3rZEqleLu5KawTZM7syz78kL0PH3Em4AVmxsY08vCgfaV3tzxQCflHliWrRJRp5uLlpzx9pl+1hXcNVwtbAhIi5McXw5/yfa0PldqZiLT/IdaHa34vmfLLLvlx57Y1qVHZHQtzE7YduMHTtw7lV26+QCpVdn3wqeUpf33vUQi1qqne5bl664X8ddVKwrsIU37ZJRcjlhamjP6kFXa2FrwIjGDttsvytcxYcIDf/tdH4/uLiEqQi6ZSjtbUqe6Bh7s9t+4G8fDZG5o1UN5eLVcmO2r69OUndGxVXeX4V25qfk+qMGitukrzhSOWsqjh4sLWQQMUrBATDxzk4KPH7Bw8CO8cJVpq/PEnqRkZVC7lxJHPhgoNV0IJ7yUlUXXaYQjRo88Y76PYysrndProd4W9lHznWuQLhl/+F1dzW4zEYkKSYvD7cJZCm7oHfsDbwZN1zUfk2zqytplaNPJiztRegj6hWW3sbS3Z/vcIzM0UM7G37rMAiVSqdvstMSmVHp/9RVp6BsZGYk7vULbO/7b8GPuP3wFkW2RCazlw/C7zlh8F4PiWSUprAcWtOoB6tT2Z+31vLMy1zyDfZ/hKwt+Wmlm/+DMqeCrvFsUlpNDr879Iz8jExNiIU9sFfV9V2mwK1FvsQVgYP588pXDufqhMFXvnqmv3bcsWADyNiOR5VNFITlZCCSW8W+TV90lfAVTcs7bnxrmU9qWmCgqJVEpobAKhsQkk65kZXIiGThXoWdaH0JQ4QpJiqGorXJO1tkP+pTOJiUuSv/56VHuVgTRl3B3k7U+cf6R0vV4dmdUpITGVC77PBcc4dfExaemyJKfNGypb52Pikjh6Rpa7z9TEWOVaurXPduERWosQM7/prpNoAujZqa789d6jwolET5x/SPrbDO3NG+m+45AvUXVnRnxBWTs7hXMJaWnU/XMpO+7dZ17nTvLzL6OFbzCf16/H3Teh7H34kBVXffm9S+f8WGoJJZRQABSV5JEFYQ3KPX5Ree/5yeIFgxkxZg1JyWla+a8YmlfRcay/cJP/Lt4SvD69R1s+bqbsKH320QvikmX1OLv7qN7Wyc3Mun2YWVd4u0kilXCt60+YivMvaP2PVdkGiFKOqstADRvYjJlviwKv3XaZD9sr+h/Pm96bnp//RUJiKjMXHaSdQBLM35YfA8DR3oqZ3/YQXEuWsJo8SnVkZU5BJbQWIexsdS+5M7RfE14GR3L83EN2HrrFlyOU17Tob1lh51KO1vz8te65xwz+PysWiZREE8gKADtbWcnrfmWhbi+wV43q7H34kIsvA9W0KqGEEoo6hSEaiopQKSrryIlUKiUiIgEAGxtzzHV8qs/N4aN3adWyKt16yQI5XJxtBS0PWzaMydM8uUlKS+fbzYc489Bfr/6uttaMXbsHgBfh0Uzs2CzPaxKLxJjmIfmlNpy/+kz+Oms7ThOhAjmgTE2MWTijHyO//U/BWVqIP38ZII9gU7WWuUuPMnfpUa3Woklke3roX27nx6+6cfzcQ0AW5VelYrYPU860NUtUvCdNGFw42VuoTpFfxtZWSTipw8lSltE0Od1wZtYS8k5Kcjrb/7vEw7vBvHgeRkJcMg5O1jg4WjN8wgfUrOup1x+jOpISU1nz1ykCnofz5GEIqSnp2Dta4e7hQK/+jWjdoWaBztegqRdW1oYr1XD66D1u+vpz+dxjkpLSsLI2w93Dgf6fNKNFW+2fhEtQT1EUMQXFmTMPuXsniJMnH5CQkCIvvjthYkd69VIucq0Lq9ecVTgOM0CiRm0Ys2Y311+80rt/tdLO8teH/R4bRDjlN2npGXILjyGo7pUdtPUsIByv8tmfid+DYPnrciqEjL5rSU/PBDUGJV236FSx54gfU8Z2lB9f93spf521lakrBhdOMW/NnkIE5sqzoyqaLgvzt45qyRmG+yMpQX/2brvGqj+Pk5am/P/xJiSGNyExfD1qnVZjaePQHB+XzMo/jnHsgPA+dVREAlERCdz3C2L29J2IxSL2nJ6KuYX+X7j5M/dqNR+AWCyi+0cNGPdNF73n699pATHRyg8TsdFJxEYnMfO77QCUcrFl04Ev9Z6nhGxybp0ZcuuuqG7Jde70m+xHSkeWLTvBrp3XAPhv4xjc3VXnPysMp/Br/sEKokksErHi897Ur+Ah/+2oOXWRqu5KBEbGsOv6ffo00O0hLFWi+vfJLB+260xNjDExNpL76MyZmvckm8ZGYjIyJXz983Z2rR6N0dtkmd/N3g0g6MidRc61TB3fCVtr7bbXLC3zd0t3QI8GbN13nf3H71C5ggu9u8i2ar//VWZhzIswM/j/qrrszZFJMoc23+BgGpUpwwsV/k1ZxL0VVlYmhlGeJehPZqaEZfMPF+icowatICI8Xuv2EomUsZ/8zcyFAynjqXsB5CcPQ1SKJlXz7d12jZ79G+k13/6d1wVFkxARYXGcOHSH9l2LTqmdd5HcosmQFDXBBPC/6Tv0Ek0AnTrVlgunw4f9GDastSGXlmf+OHpR/rqMox2/DexCXU9hR21tefxau9xLaZIMBl9YyYuEcNIlqj/f3NF2hsLO1oKIKNlWa8vGXnker2mDipy/+oyomCR8bwfQtL4s1D8xSfYb3KaZ6tJgOddStZKbgsWqMOnV2Ztt+68jlcK+Y35y4ZT69sG/XfNqeo9tUOFkJBaTKZFQe/ESPvHxZmg9H0yNjNj/6DH/XL8ubzdoyzalvhkSCcZixX3hdTdkjn5NPItmsdX3hcN7b7Fo9n6FcwM+bU7PAY0o5ZwdTSOVwvRJG7l+RTE6w9jEiCYtqlC6jAMNmlbCXcPe9cN7wUwZu4HUXInUTEyNqNeoEo2ae2FrZ0ngi3CePwnl0tnsCI3gwEiGfbSMVVvHUK6C9l/g7q1+1Ws+gGEfLcPG1oKdJ77Ver6uzWeTIfCD1vKDGlSq4oqbuz3RUYkc2nOToABZzpjfZuxh8dyDWs9RgjI5xU1+CJ2cYqywhdSiRUe4fPmpwjmxWESdOp5YWZtx8cITtf29vFzp2as+e/fcYON/l+jWzRtXV2X/1cJgxamr3H4ZIj8+OmWY3mNVcSvFkzey79j5xy+Y1r2N2vaHX91h6q3tWBmb0dy5MmdCH9Hbsz4ZkkyOhtwjTZLB+uYjqW6XNxGnjsF9GrF49SnNDbVk1rc96DN8JVExifz4+36Ob5lESqrsflja1Y7vJ6i2qudcy387rzLja+WcVoVBGXd75n7fm+9m7+ZZgEwQZwk8Tw9Hpo7vpK67WgwqnAbXrcP6W7dJSk9npe81VvpeU2rTuGxZrgYppqK3NTNjpe81xjVpLD/3OCKCQ09kX+yS8iuFy4a/zygc2zlY8sV45UgFkQjm/DmYLWsv8O/y7C+1sbGYH+f103q+Wd9tVxIxFpambDk0GQsB866QsJvxzVaWrhuhlR9STHSizvP9s/QEcbHJ8nPxcckkJqRqNV9GeqaSaBKJRfQf0kzpc+37cROmjv+Pm74y59fc6yxBNwpqO62wRZPf7UAO7M+OMBOJRPw6tz916nhiZia77X/Q7leN4/j4lGPvnhsA3LwZQJcudQXbRWlpOXV0sNKqnSZuBWSLJkcr3SOvcmJnme2XG5+s3n0E4OjrewBsbzUOD0sHGh36WV6T7svqnfjJbzdXI55TNx/TEbRpWkUuVgJfReXJkRrAyEhMpzY12LznmlwwXbkhSxDZpV0ttXVD2zStwvJ1Z0lPz+T0pcdFRjgBNKmfnSRTIpVy9orsQaJru1p5GtegwumnD9oxtXUrai9eQqbAlt2l0SNxtZaFTj4KD2f/o8d08JLlUOi7cTMLL1xU6tOrRnW+aJA3x8XCQkoGqWm3ych8jUSajFSahlhkjlhsh5HYCTPTOojegTrLubfLth9VXwNo4GctWP/3GTIyZFEaKcnpvHgWRgUvF41z3bj6XGm+Hce/xdZO9c2xS08f2nWqxXfj/+PBHZkofxUURe928zT6Ut24+pxpEzbqPF+Xnj58OXyNfD6A3u3m8dvyT/BuUEFl3/S0TAZ3/0Pp/NErP6jsM3fpEEA58WUJulMQgqko5HCaPDn7b3rjxjG4qfFPUkeLFtnh6WfPPlIpnPoOXKpxLBsbc/btmKTXOnJzIyDbt6lf47xtXzvkEF4xSap9dLO4HC6LIvOwlP0t2Ztayq+VMrNmWaNPqHvgB57Fh/Fbvf4qx9m15AhPb76gURdv2vZvqtOaSzla4+Zsy5vwOAaP/xeXUjZsWzFC7puURVRMEjfvBrL/+B0Wz1S9FoCxQ1uzeY/M2JGSms7cZbLouM80rK2UozW7V4/mw6HLkEik9B2xknGftRFMbXDi/CNOX3rM7O966vJ29UYsEmFqYkxaegZrt17mv11XAZmVLC8Y/FfbzNiY4198zsHHjzn8+AmJaem0qlCe7tWqyUUTQDVnZ6o5a95Kmdq6laGXmK8kJB8gNe0ucUnbycgMA9SFeIoxNnLGxrIvZiY1sbHsXVDL1BohR3Bt8GlYkWuXs8NUzxy/RwWvdhr7rV+pGJ1jZCRWK2KyMDM3Ycbv/enfaYFO69R3PoAZv/fnsz5LSUrMfkpds/w0i/9VLZzOnriv5NfUrLXyDUaIKtVL8+RhiOaGJRQKOQVTUcoerq9oAsXcOyEhMSrbzfpJOadRRkYmV6/5c/b8Y+bM7It3HU+BnvqRmiOSy95SdSS3NiSkpMlfmxprLpOSIVG8p9uZWhKbnoydieJ9wy9afRqdC7uvcf/yE05tucSBv0/SuKsP/Sd3U9snJ79+35ux0zaRnJJOWEQ8PT//i5pVS1O5gjOxccm8CY+TlxWpXU1zSaycbD9wU+7fpA12thZYmJvI1/LT/P0cPfMAd1c7TE2MeBEYwctXUbwOVV2IPb/4oGU1Dp+6x75jfnr7++UmX8wd5eztGdu4MWMbN9bc+C3Pv5nM9VevWHr5ClIpDKpbhw5elTASF2hyc52JSVhFbOJG0tLV+wsIIyEjM5To+OUAvIkaB4CxkSs2lv0oZfe9AVeqH/duKX75tU0z0LZTLQXhdOLQHT4fo144LZ57kIf3ssNfRWIR/+3T/gnV3sEKSyszBSEz98fdTJ2pWpDmnA/Qeb5tR7/mwxZzVI6Xm4W/7FM4ruXtyYzfB2g1359rvmBQ14VER2mf0kMdRTUK7F2mqH2W7drVMNhYEWpSDLRoVlnwfJtW1fju66607TSPXj3qMWlcB4Osxd3ehlfRsvW8CM+bhe/6i+zvbO2yyvVUc+Pt6Mn1yOw6Z82dK7P88Umm5apXF52WlLurnIOrT3H/cvZvxr2Lj7l38TH/TN9C3dY1+O3INI3r8CrvzLHNk/j7v/PsOnyL2PhkLl1/zqXryhnAu36g3dZUx9Y1OHb2AX//dx5A7lCtDcc2TyIxKZW+I1aSmJQmuA7IW34mffh+QmcCgiJ5+PQ1AP175H0Hq0jtEzXw8GDtR301NywCZEpiiI5fTnS8ZhO1rsjE1FIyMoNwtJmMqYnwTakgiI1R/PJbWWv3dGdto9guPi5ZRctsLp1RdLquWacsTs66lXJo2qoKJw/fzR7z7CMy0jMxFqh8LRQAqut8pqbKXyFV8wHy7cssGjXTPiJGLBbRqHllju6/rdMaVVHUfuTfdYri5+leWn9rU27S0vL2tH7V9zkYSDiVK+UgF045hY8+pGVkv68aHpqLvTZwKq8gnJo5ezHm6nqalKpEW7fqxKbL7nXlrFRH2vqdfajyWqdPddtlGTmkJR/3bsSf/5zi8fNQAl9FYW1ljrOTNZ3a1KBBnXJU0jLSrdsHtTh29oH8WFdfICtLM3b8PYqL156zabcvMXFJJCSmUa6MI54ejnzSt7HWazEkfbp4M/utcOr2geaM5ZooUsIpP5AiQWTAknyp6fcIjfqK1PT7BhtTFfFJe4lP2ouR2IGKpfN/PiEsczk7ayOAAMLDFJ9OXbSIxsltSRk5Ufeb7KgvOyoIp5TkdLasu8iQ4co3o8N7buo8vjaomk+IAZ8212nsnv0aGkw4lWA4iqr1zkSLrSdtcXGxzVP/mFjt7h3a8GWn5lx6Kktk6B+mfy3T24Gv5a9tzM34spPm7+OYKu0IT4knQ5qJsciIRqUqYiQS8eX1TfI2VW3dWN98pMoxzu64ovLaBx/rdk8AsLYy4/uJ+ueTy6JebU/O71bvw6rNWjq1qUGnNrpbO6t5ueV5fiFOXsh+KK8oUPRXV4r2PpiWpEsSiUlVVvCHX3Zh9/MGPIpehVRqmL3NoNCuBSKacpIpieZ15GgkUtWm3/xCl5D+nOT2xXFx0z2MuVxF3ee2d7DCzt5S4dyzx28E2754Hqrz+EJoO58QIh0zrHvokS+qBPVEz4outGLA+U1EZEKe+uf0cXRz1y8VQcZbi07ZMobboqlZxpUq7tk/gEuOX9JrnEkbsqNxezeoqZWPE8CPdXpiLMpuu6HFSFq6VEUsEuNh6cCaZsMxNyrJP1gUiIpJxPd2ACBLHmoIDGpxWnblqt59c6Yi0JXb4XNISH9J2zL/AZCY/orjQb0xEplT1rozD6KW8yBqOX0qCReA1IagsB6kpF3X3DCfSEjeR8KrfZRzPY2piXbOxIbATcDUHxuTpCQWcnN0n6JVZNy3uj8NCaUC0AbvBuU5eyLb3Hz7+gvBdndvKvpvVamuX9oLbedLiNccsaMJfT+T/KIoOUHnBUO8h6L4ORzYf4uvvtK/QPqc2dk+ef36qY5Eattpntpx6tQqw/y5A/VehxC7J30izwy+4uRVVpy8yvA2DelcpwrVS6uO4E3PzGTi+v2cf/JCYbv+uw/1T/BZ1dadpY2G6N0/izot9U/KWIIwI6dsRCKR/Ucvna2dP6kmDCqchNIJaEtehFNs2hPcrbL/6J/F/odEmk67MluwNa1ITNoj4tOEf8y0QSKJL1TRlJPg8H6Ucd5eoOLJ2Fis4Jvzy7Qd/P7XpyrbP330WumcR9mCcwi0sFTcXszpLJ6TuFhFC56+okTb+QwhnIoSRSHsvoT848GDV1y48BgAKyszGjSoqLJtqVLCvoFmpsa4u9nx66yPMDbgtmEWZRztCI7KjtRafeYaq89co2HFMpR1zLaQXXkeSHh8IjcCXnEvKJTUXGW8WldX/d4KktotSoSTockqblyvtifVKxsmKWmR8HEyM87bMuLT/PF2zo5CeB67BQBbU9mXoZbjJC6/0a/O15uoicQn7dCpj0hkjrVFJ0yMK2BmUg1jIw9EIjNEGCOVppEpiSFTEkVq+j3SM16SmHIMqVS7xIaZkghehrbFy8MfkShvYbjasvvUd4wfupqXL2TZV/1uBNCx0UxKl3GgU3cfrG3MiYlO5NnjN1w+91ihr0gE02YVrMO/vYN6a1gWibkEjiYrWl7nS9IhvPddoChaWPShuLyPnMz4uQ8zftoFyBJdduhQi6nTumvd/8aNF0z5dov8eM3akZioCHgA2L5xrP6LzQNHpwwjPD6RNrP/Vjh/zT+Ya/7ZTuMn7z/n5H3hKK8bsybIa9tpw8PYEGLTk2lSqpJ+i1ZDu4FFv8hwUWbvUT9srM3JzJSw6/At7j3KdhnRlMdKFwwqnDRZjeJSU4lKTubIk6dkvs2F0atGdX7vor8pGcBIbEamRPFHycEsOxrA1Eh/p0ZtRZNIZI6lWXOc7KZialJVq8SWNsiyzUqlaaSm3ycy9leS065qJaJCo6fg5vinVmvLK2bmJsxe/DFDeixWOB8SHM2av9Sn/R8/pSttOupWNDOvpCRrJ0JNTY1JTsrO4aJvVm5t58udnK6EEvKLli2r0qSJF1euyFKCHD9+j1KlbGjTpjpelVVHjmVkZPLjDzvx9VUUGU5O1ip6FD7ONlb80KsdCw9fIDE1TXOHHMzo014n0QSwOeAKe4Nucavbz4hFhv1Ol66kOaqvBNXsP36HxwK+q7rmsdKEQYXT5Ba6RQO0W/0vex48pHm5cvSpqX++kdJWH/Aw+i+cLRpyLex7TMTWtCy9Qn49Pi1Aj1ElBIX10NjKw3krlmYt9Rg/G5HIFHNTHzycs2v4JaacJCTiE5V94pN2kJx6iQruBbOF6OJmxzHfH7l87jE/fbNVY/vRX3WiS0+fQvHJicrlEKuqDIqLm51CuoXc/Qw9n72Byk2UUII2zJ7Tj0EDlxH2NsJ18+bLbN58Wandkj+PseTPYyrHOXRYuyinyKgEho36lzg1kbenj36n1Vi6MrBJXQY2qYtUCg9CQll77gYPXoURGBkjLzxvJBbRqGJZapV1Y0KHZhjpGJiRxfHXsuAgQ4smAHHJw1We8KlVlpDQWJKT03ApZUOtah788GVXg89TqFt1f3bvRq8NG5l56nSehFM5mx6cDxnJ0cAPSc4IpbrDKIzF2T9S0akP1PQWJjZxEylpqsPVxSIrnOy+z7NoUoWV+Qc4288gInYeUqnwjSgjs2CzSMfHJXNg1w35sa2dBampGaSlZeDkZI21rQUdutahZt2y1KhTeIWZc5dscVLhf+HsaqvgjxWZq5+h57O1028r8F2iuDiLFxeWLhtK/35L9O7fsmVVzMw0R4fFxCYxZsJ6taKpIBCJoKaHK78Pkv1YSqWQkJKKFClWZmZ6iyWFOcj7GCXkD+M+a8O4z9rk+zyFKpxqubryVYvmeXIqB3C2aIiP83ReJZykgu1HVHMYrnD9RZxuPkoAYdFTVF4zMS5PeTf9wl91wd56JPbWIwl404z0jADBNlFxi3G0NUz9J3WkpmbQt/3v8uMjV37QOou4vty7HUgtb93KNESGxyvUjwNo1V5YlLfpUItLZ7N9svS1OGk7n1ChTKlU+LwqXgXpn7OmhPcPJydrTp6aRmJiKmPGrOVVsOa/Hycna7p2rctnn2ufiHHS15sIj4jPN4uSvohEYGOhufC2Lgyu0JS/n54x6JglvFsUunN4aRvdMjWrooLtR1Sw/UjwWs+KVw32jGAkdsKj1GYDjaYdHqW2EPCmieC12MSNONpOhHx+Ctq85rz8tb2DVb6LJoD7d4J0Fk65RQxA9dplBNvW8jFc7Sxt5hPiVWAkZcppn5vppX+YPksqMEqsTUUTKysz1q0bxdOnb9i+7SpPnrzh1atopG+3scRiEd4+5ahW1Z3Ph7XW+fsdGlbwNcgKi0Hlm7An6CbP48OoZKO5cHkJxY9CF06J6fo55OqCkUg3P5vwGNWV6iuWvqvyWn5hYuyJjWVv4pN2K13LyAwmLHoaLg5z823+B3eD2fRvtnAaPyXvGWqF+Gx0W9auOC0//mfpSVq3rymYS0oV82cp1oJr1roqDZsKlzUpJVBe5U1IjE7zpaYqF0FWNR/IEl5KJdnJY5b+fpi5S7XP//LPMvXO+CWUoAqRCKpUcWP6/wxfmd6jtAP+b6NuiztikYiljT5h4Pm/SJNk0NezgVKB3ywmVe+o09gv7gVRoVbhuTkUJhGxiXw8YwMxCcnyvEvX/5lcyKsSplA90R6FhzPz1GnNDXUkXRKPFInmhiqISfhH8LyVuWHqLOmDm+MyrC2EndxiE9fn69zpaYriYMOqs/kyz8fDWtKhax2Fc0P7aO+fEROdqBAlB/DTPPUhqHmdr3+n+QrnymjI7D0+VyLQm77+zP5eu63kHyZvISggQuv1lVBCQfHXn5/iVcmF4ycLpzRUQdL62K/0P7eMNInsvrgz8Dr/Pj8v+E8VzXs2EDy/96/j+bLmd4FSdlYcWzQa31VfFfZSNFKoFqdhO3fL0xLkFSkSguIPcyv8FzKlKbTxWI+juayYX1zaM0QiY2xMyudpDlsrw2Qdzcv8CcmHCnzeMp5OiETZRXFf+oezbcMlvBuUx6uqu0G37YaMaM3xQ3fkx1KJlKiIBBxLqQ+HTk1JZ4ZAtJ+mkiZDRrTm7IkH8tIS2s4HMOObrUpCTVONuvZd6vDvspMkJmSnzzh74gHT52icjqsXnmhuVEIJBcA/a88pHBsZiWnZvApzfjvAmvXnad2qGsYCEWJffKad39SeG4UjwHrV15w65XC7r/M8T/OeDbi4Vzki2vdwSR3KdwGRVKhEfDZqL+am0vyFei1iYrOmTGrWVK++MqTsel4PAHMjJ1IyIxWE09HA7iSmB2tdcuVpsHLpDXNTH8q6HMzDGg2D0NoAKpfJ3wi7ly/CGTHgL736GhmJWbZhBBW9tMtR8u2Y9fjdCFA4Z2ZuwuaDX2Fto5z089gBP+bP3KtwzsbWguUbRuDqrnnbLTNTQpemv+g036olx4mNVsw8vmHvRK3mS05Ko2cbxa1VkVjEwKHN+XxMO6X2f849yKE9N+Xma6X1+P6occ4SDM/7HEHYb/Byvfppmygzq5RKQXN/bsFZO7YtOMA//1N+2DuavKHA1lBUafCFTEsU8ladyqfuQvdxApjQVNjxWVtCEs8AUM6mO/VdZrLruY/CdWfzBiSmgBS6NgAAIABJREFUBwv01B5Tk/c7FX65Cs64utsR+lp3J9DMTAnjP13FkOGt+XiY5vQN0+f05cvhawjJEUGWmpJO/07z8W5Qgaatq2Jnb0lQQAQvnoVx7qRyuonpc/pqJWJAJuxKl3XM03zGJkZaz2dhaYpYLFIQQlKJlM1rLhAcGIVXFTfcPRyIjUni9NG7PLib/bfr7GpLeGicVvPoyvssBHTlfS83U1iZwosT3Ue2FxRObwLCcSuvX3H1okCrsUtISk1XED1CQujo1UfMXnecM8vGI9YltPgtz19FcPL6U3ac8SMuMQVne2uqeDozf1xPpUjl71cexN7agimD27F42zl2nPFDIpXyYbMaDO5YH09X3e57hZIA097cHGcrKzpWVu1Eqwt+EXPxcf4fFWyFS3uUsmhAQPyePM1hYaq6wGVx5+i+22zdcFEv0ZRFRoaEtStO4+BkTZeePmrb2jtYsXbneCaPXMu929mFeDMyJFy/8pzrV4RLJwDYOVjy07z+Okfjrd05ntjoJPrl8FnSZj6AhX9/pvN8R678wJa1F/h3uaKz9/mTDzgvIMwA5i4ZQr3GFena7BeF2oGGJEsQvMsCqiDeQ9bYRfXzSkjIrotobV0wpZkMiYWp+txRJkZG2JibUsHZkZT0dK6/eKWyrbGRGLFIRFpGpuD1jrUrU6+8B/0a1c7TmnXFwsacpt3rcXm/Yr7AodUn8+PWSTTvIewHVdTp1boOm45l5/uTSKXYWpmTnJpORqZEvoW778J9+rapq5doAhjw43rKuTnwzaC2lLK34vbTV2w9eZuGwxcy4aOWDO3SUKH9kSuPOH3zGY1qeLJ4Um9CIuOY8c8Rdp65w4W/JmJuqr0cKtCSK/lFamY0ViaqQ8BForwXlxSLhaMmijvhoXEs+EUxUs3MzJhGLapgZWWGkbGiH0NaSjppaRk8ffyG18FR5N4JXrP8FG061NQqo/j8FUPZuu6ixrIuOVm1ZYzeGbrtHCz5fEw7neZr3b6GzqIpi4GftWDbhktaFf/1KOtIvcay2oueFZzxf6pcViCv5BYD+SUKCsKyVRBCpihbnXr2yN7qOnlqmpqWmhnQfykAixcPwU2FVfXTL1bRtXMdBvZT/RsQE5PEX6tO8/GAJpTTEERxfeZ4rdYWGBnDuHXZ2/RWZqY0rexJv0a1KedkT2kHW4zEsntUWkYmT0MjeBYayfITV+TFgeOSU+nmXU3n0itZpEqUI2uzMBOrH/Obv0cyocVPhOQqE7Jw1Op3VjjVKC9zycjKUfcsKILaldyJjkviSWAYNSq4AfA0OJxerfQXq2umD6J2xeyivfWqlKFz42r0+O4fjlx5pCSc4pJSECXDz1/ISrzVA87des6pm085c+sZnRtrv6tUJLbq8oqtqRfRKfdxsRD+0r6My5u1CUAqzZ8n/KLM2E9X8SxHZu2e/RsyZnJnnZzBExNSmfDZaoIDIwFZJNrcH3fz83zNjvZisYhBn7dg0OctSE3N4J+lJ3j+JJQnD0Pk2crdPBz4sG8D2nWqpXE8bdB2voZNK2Fjm3cxveukLNHq8UN3uO3rz+XzT0hJTsPK2hz3Mg4MHNqcZq0Vv9ArNo7K87zqyBIdRc2Koi0Fte539fPRlYgIWWb8w4fv8PkwYefuoOAoDh72Y+2Gi6SmptO0cSUmje+Iq4usTuiSv06wa88NWresymcjVmNpacbB3foVXs8iOS2dLr+vAaCJlydLPu2BpRpLlamxETU9XKnp4UrPerIktbP3nWbTpdu0nLWCW79MxNRY80O2FCljrq7ncvgzte1MxEZc7zpDbRtreyvW3JtPP48xxEVlJ+BNiEnkizrfMnPX13h4uWlcU1GiU6NqzPvvJOduP6e1TyXWHvalV8vaxCelsvH4TWaP7MqFO/5ExSXRoWEVvefJKZqyKF3KDpCJMiEWTeylcPzTsE7/Z++8w5ssuzh8ZzTde7LK3nvJ3jIUZCgoIiCIOPATRXGAMlQQcQICggMVFAERRUFk771X2bRA9266svP9ETrSJM3uIvd1eZm8z2xp8p73POf8DnvO3ODIxegHz3CK8OrOLek6GgSMQSQwdEsn5x+3ew2NNtfuOSobxY0mgClvPmKVyjXo6raNfb4nn8wu0qC6ctH6eDN3dzFT3rSvGLS96yXL03j59CyWcQIu6a6NrT2METUG2r1e/0dbGUgjVFUeFIOjqnHrVuniq7FxRR64o8dvcSv6V9aveRmAEydvAzD3/eG88fZvnD1/1+gc1vDN7qLv9W8mDLfI6CnJe0P7sPaILpPth/0nebmf+Xjb/+IucjTlJt5idzoG12Vf0lVGRLZHpVGzPf4SCo2K1d1eoKm/4Y3dFF/sep+ZQz8jJTat8FrsjURe6/kBG+NtS8opLwQCaFI7nOv3UujVtj7nb8bz6sge5MuVfP+Prlbitbv2a35ptFpOX73Hmv9OkZKZS1JGNjKFae8fQKNa+rFj3p66kw+V2jrHiNMNpzylkgPRMWTK8gnw8CTYy4uONR1bqbhZ0BSuZfzA5ttd8BDpXMDnUheQJb+GFg1CgZju1VbatUae7CD+3s84YruVgtRk/QDkoGAfq42mAvoMbKlnOGVmVE4jNMw9mD+6LkeLlqisG8y+vMhpa93Ji6O2l2M/J7ZQUWN4XJQ90dGlG04ikZBd/74F6I7lnplQ9J1b3KiaN/dxBo+w77Oz7th5fth/EoAIf1+bjKYCGoYHcyMpjaU7jxLs48WTnUp/gFl8Vae1dGTQ+wD03P4xc1vpPBnz2jyBQqPioX8/5Keuz9MmyLJj/MimNfjlxiK+fOl7tv9cpJOXnZHDQM9xiN1EfLV3No3a17PlRyxzRvZuxfI/DzN5aGeS0rOpFqzzPManStl/9hb/HLZPbuL63RTGfLAGsUjIsjefoGHNUPy8dU6TgkB0YwT7O6bQutMMJ4VazZh1G7iQlGSg1dQqIoLhzZrybLvSg4StoUu1RZxKno1Mff9ISH6lsK1bteWEeLaza/7SCv5WRW5dT9R7X6e+7aUF1CaCMisrAgR4iBxb/6o4ibIUtibsZUp9yxXFnYnLaHIBkG0mFq9mjaDC1wEBXjRralw6xcvLHS8v+z4/R28Ueay6NrSvdFKHejW5kaS7bxy+cces4ZSh0H/w8xDpHw9KhGK0aFkbc8xiw6mAN1Y8z+G/TpKTpS91olKqebX7HCKb1mDK5+No1L4u3hW4aHjTOhHEJmcZ9eRcjkkkPsW+Ej3L/9TVt1348mO0b1z2SutOMZzM6TldSEzkQmIiH+7ZyxMtmvPpIPuPOiK8ejCkjk6FXIsGtFqHBIUXoFLHkpG9nEDf8kvDTZeWnbZJTrZc7709Ipd/b9QXeqvfqHKd2Zc1a+78hbe4YiQjBM4KdMkUuDBLeJgfqWnZhe+1Wi3xCZkkp0gJC/Uz6G+r97qAwzfuFL5uEG55rUdj1AryL3x96FqM2f5eYgkydVGpsAa+4ZxKi6ZDcF29fvsSr5QcahJ5noKUuHSunbxF36e7cu1UNNdOGWbz3r0Sx7uDnVdeqzSs0ZeqHuKHWqPh6KUY3Ep4Azftu4BGq6VX2/o27+XIpWia1Qk3mCMtq2xOMxxuOC05ctTgWutqEfhK3MlWyEmQZpOcW/TD/XHpskMMp+IIENpV81YgcEOrNayhJ81bX46GkxppnqHmB4BQ4Pgnj+BQfeXsaDPxDaaQy1X8s/Gk3rWWTiquWxVQapRcyrpOp+DW5b0VoGJnjrkoW3yNiMEW0LplLXbsvkxenhwvL3d++Pkg8QmZvD59LSMf189uysmRk5srNzGTZShURbEsaTn5ds1VfLwpyYLi1PMJI10eXfi+kV8EOxMuGxhOpVmH21btJSUug2snb5Ian0HMZft0BisqZ6/H0jiy6LSiRqg/cfe9Tc3q2P4AHRHkR0xCOtJcWeERnUKl5oMfd9i3YQtxqOE08MefuZmWhgCY0bsXkzq0N9lXo9XS6Iuv0AJ9vl/F3uefs3ndLMV1/CWlR+drtCqEAst+3GC/t0nNmm9wXaG8gVabj0BQ9t6A6ISHUKkTjLaFBnzk8PXadND/EkhLySY5MYuwCH8TIwwpqYsE0KBJNacGec+9vJiLWdcAcBdKkGuKSqL4iL2YXG803UOK0nyPpZ1j6c3V5Kv1jyHaBDRjVjPLUqJNseLWr+xMOmy0bV3nJbgVS1UefWwqymJpzbuTjrA76YjemD+66qs1r4r+nX8T9qEtIfA/tvZwRtSwrrioKVyeJueQl6dAqVDh7e2O2M1xnnFHEn07hRkzNxS+7/ew6XIkM94ewkMd6xXGLonFQl59+WE6dqjLr+uOMn/uE8QlZDDltTXcuJloch5LqR8WzPVEXd3G1YfO8MYj3W2ea/WhojCMuqFBpfTUMaPFYJ7Yv5QLGfdoFViLqU3603brbNbFHKe5fw2ic3WBz883MF1eZtErq2zeb2Vi9X+n+HTKY4Xvx/Rvx2drdSdDI3sXPRxeuZPE7O+2kZieTb68yGnRYdKXuIlFhAf5MqhTE14a3hWA3+aOo9f/ltJ3atF3okAALw7rypGLRUats3Co4XQzTXdOPLVrl1KNJtBVmH69W1e+OnyEu5mZdq17KP4lBtcxrb2j0GRxPHE6Pap/Z9F8vl7DjRpOACmZcwkLXGjTPu3BlNEkEHji4/WY0TZH879nv+flNwbSx4LU/28X7+Tfvwzjwqa84VjvYkkKjKbvOywgUOLPodRTfHd7Pb5ib5a0nY1QoK879fm179Ci5ZnIYdT0iiBLmc2KW2s5lxmFRqsx6G8NTXzrc1l6kw6BLekY1Io8dT7f3PyVTKWULQl79IybFe11JV/kagVTzsymR0hHJtQ1LuhawNYE3RfQpLpPUsurGjG5cfyXuJ9f7vyFRqvhiZpll4Xowjzb/jnH2ZPRHNx/FZVS59mY+8kouvVsbPOcCQmZpKXmlNrnkpVZrDK5kpRkKYsW/acntNqlS8NSx/Xr0wylUk1ySjaPDmxJSIgvAO9OHwyAXK5k+Urd93SL5qZ19yyhWY3wQsNJqbYvhrL4+OY1zcdyNvAN54nIDjQL0MVwCQUC3IVi5BoVl7N0QpyN/SIYX88yQeiqSoi/N6lZubRqUBTr1rK+7nX1EH/8iwmz5skURCekG8wBoFSpiU3OJDGtKGHJ21PCyyO6sevkde4kZRDo68nHLw6mdYPq/LDlOEonx9U6pVbdremW15f5cM9efj5z1qoxJfknuhduQm961ViFp1jf/Zecf4JD8TrdG0tr1YHpmnA6BDSsaVqp1tGUtpfaEQeQiB2jwF6S2zeSePPFn/QK0trDqHFdmfzqww6ZyxjRubFMP/+xgcelQEbg6cjHGFnzkcLr/ybs44foDXzReiZ1vIu+yLOU2Tx38h1a+Dfig+bGtWZu5dzh7QsLbZIj+OTqCs5mRLG+yxKDNplazjPHp9EvvGupweEfRn3NpazrrGj/EUESfVHCkUdeQYvWwENlD67sOtt5f/o6jh8xrvlT0nCaO+N3Du+/xs4j71s095IlO9j812nzHe1k2fJnadKktO/EskWl1tB/4Q8kS4uMxiFtm7DwqUdKGaVPSnYuved/W/g+2MeLXe8+b1eGnqUM9Bzn9DUczQNYQ8/kWavtj9MOolFIiN1z9Ki+gjxVAgfj9YUBo6V/cDhhCkKBhI7hxj1Ipii9Np2WlMzZaLWKUvrYj0abQ2L6q6X2cZbRBFCvYThfrJzgkLne/XCEU40mALVW95QhEZbMctG9zytxHHcq4yIBbn56RhOAv5svAW5+3MiOcco+QySBqLQqvaM5a5BrFFzKuk4L/0YGRhNAEz9dynLB78NRuIwm69FotCaNJmP06qsTZrx907KYQk/P0kuTOAKhUFChjCbQlVF5bWBXvWtbzl7l7XXb+Pf8tVLH5soVzNq4g4EL9Y/LXh3QtUyMJheVn3IXwLQ3uwIgwL0pfWqu4VD8S+yJHUPP6t9xImkGiXkH8XWrQ//IP81PUoLa4XuIS32GPNleo+2ZOd+TmfM9Xh59CPJ9DU93x9Wyy5XtJl26GJniVKn9QgPmOmxNU9RrGM6OE7O5fiWeGa/+SrbUskBMsZuI5q1qMXvhKIcobFtCA5/agC72Z1BET0QCEVezb/HV9R/xd/NlVDFvk0ar4ULmVbRoeeKI6YD/XFW+zRlu17JvM/NiUYyXWCAmSOJPrrog1dgqh24hV6Q3UWvVtPY3bty39m/KFektoqQ3aelv+zFQcVxGk/VIs/IZN1JXrkQiEbNl77uF33f9u84zOqZP/+Z8POdPflt9iPc+fNzsGpMn92Hy5D6ALpNt7a9HOX06mvN2CkwKBAL69GlKl64N6XvfmLOE0eO+IamEBlxIiK9TigIPb9+czWeucOLWvcJrW89dZeu5q7z1279WzdUmsppVteqUGjXfXN9DkkxKrsq0R35RhzFW7cNF5aDcDaebqcbPNa0l0L0F3ap9w7648eyLm4hUcYNQz4foHPG5+cEmCAv4hJjE0uvv5cn2kifbS4j/TLw9HjbjqSodmeIMubKdpEsXm+3rIelAgM/zNq9lLY2aVmfD9je5ePYOO7de4NK5u+TlyZFm5iESiwgI8MLP34tmrWpSq04Ijw5vh7uH85+GS1LXuxbRufcYc2wawe4BJMl0cRDzWryJp6joTF2LrnRCPe9aTK432uR8njbqNUXnxjLn8mJEAhEDwrszOnIIPmKd+NryW78YBH7bgimzS3O/RWBPaqkLu9m/O4q8+9ljb8wYbNVD4r07aeY7lUAgEPDM2K48M7Yr/fouKLy+br11SQ4eHmJ8fDwQWPlUu2PXpUKjKbJWMFqtlnux6aSmZrN5y1mGDXGcbl8B3z43go//3seG4xdsnmNou6bMGWG5N/xObiovH19NXJ7tGaeteth+n3BhG4MPTKW2VzWWd7CvdiM4yXD66cwZJrQzLzj53clTrDrtuPP5II+W9KrxI/vjJuLjVose1e1TC3cT1yLA5zkyc8xnQKRmfUxq1sd61wQCNyTihohFNRAIJAgEYrRaJWpNBmpNBgrldcD6GngCgYRaYX+b7+hgRCIhbTrUNci4q0iEewQTnXuPtZ2/QlSKjpdIIKS5fyOism5QzTMMX7FjFGULmHXpS5QapdE4o8T7xpyttPBvhEToxrnMKIbX6G/Qfj5Tpx/T1M92nRQX9rPxt2MAjH2uB/0GWlfM1BbDqTjt29fl9GlddlFoqK9dc1nCoqU72PzPWfZuf8egTauFvoMWcvt2CtOmOibbswA3kYg5I/oxa3hfDl2PYdovW5ApzR+BS8Qiejapy5djBhcWAbaUJw8sR6ZWcnbwBzYnj3y24z2bxrmoGDjUcPp4YH9mbt/JR3v28dGefawYPpQedergIdZfZuu16/xy9hwnYnXZHt+NGG5sOqPsixtfSqsAD1EI1b37EJ+716Bv7xqrLV6ngNCAeYT4z+ZmXB2rx2q1SuTKKOTKKKvHmqJa8Cp8PF3ZUqY4lqarOyVTK8wesc1s8jLPHJ/G8ydnGA3UzlHlFnqJrMXXzZt8tcxgjvlXlnE567rJcR4idwIl/pxOv0R+HZmel6wAsUDMhy2m8e6FT5l7eTFzm79W2CbXKLiWfRsBglINRxfOJzVFJwhZp26omZ6GePvYp6w9YECLQsOpLNi2/aLJtgLH1fZdFx1uOBUgFAjo2bgupz8qPSbUEbzUqA+LruywK+PWReXGoYbTky1bcuTOXbZc1QXnvfTX34gEAjpH1sJH4k6OQk68NJvoDH0XZ9/6ltffSZeZ/oDa07c0BAIJPp6DyMn/zyHz2YpQ6OsymswQ4OZHplLK+BNvFl5zE4rpHtKBYdX7U8urqPCmh8gdkUCESqvig8tLaBfYHD83Hy5l3eCy9DrvNnnJqOGk1mrIV+uOYORqQ6FUgK7B7fgrbifzryxndK0hBLsHcj37NmcyLlPbqwZ38kxnZT4U1IrtiQeZe3kxE+qMJE2RQbYql0ciehX2aehTB9DJL2xN2EukV/VCOQKAodX7Wf5Lc+EUAgK9SU7KIqtE+QxL8A+wT9S2fv1wu8Zbi1gkxFyqjLVHfxWVifV7IEDAs0e+4+GIZrQKjERswmvV3L/86026cDwONZwEwOIhg2keFsaiI0eRq1SotVoO3zEeqLh06BAeaVS6cGVJrJEUcCTVgnXHdbfjW6LW2OdGt4X61a8iFBqWLqgKfHX9Cy5LLTNyP265kDB34zeFc5lRZCqltPJvQv/wbggEQjRaDXnqfL67vZ69ycf4vctSvSfFDV2+RqlR8um17/g7fhdSZS5N/erTJ6wzkV76mUTGgsh/j/2X32N1gai9wzrzagOdl3Nc7RE8WWswMy58xidXV+Dn5kNj33r80XU5So2K0cemmvwZX6j3ND5ib06lX2TO5a+o7VWDRr6Gx6N/dF1ObH4in1xZQZoigxD3QFoHNGFZvQ/M/yLtwCVNYBkDBrfil1UH+WH5Hvr2b4FPKcrbBezfrfNOPzW2q5mepVO3XiitWtUiJsa+Y2FLGfpYW9ZtOI5CoUIi0b+tyOW6o7ORIzoYG1rp2HjnJF9d2Q7AufTSg/DPD3G8OPGDwrXsO7xx9gsAwjyCyFBIUWpUbO2pfzrw5bVf2J10Ai+RB75u3iTJdPfnXzvPJ0Bi+ph6S/xBVt3ezPvNJ9EusKlVe3NKjNMLD3VkZIsWPPnbOgPvEkCL8HBGtmhutdFUEagdcZDUzLlI8zaY7+wgqoesrrJGk6OQKnP48voPDK7Wh+fqjjJoz1Jm89vdf4jOjaW+j37JFzehG+81NZ/1Y60ukrtQwpdtDGMZ3IRis3ONiRzKmMihZteo6RnB0nZzrdqXI3AZTebp2qMxv6w6SF6egsWf/Ws2S06alc/KpbsA6Ny9dLFJS/hqUdkViR45ogOb/znLS6+uZvSTnejbSxf8vHvvFX7bcAxfXw9GjuhoZpbKwapbBwF4oWFv2gXVIcLT9d3sDBr71uaF+o/TNaQ1oe6BqLRqhh2cxsWsm7T0L5Lh2Z10AjehmA3dFiJAQKYym5Npl40bTfednpti9/DD7b/4os00mvhZH7PrtKy6IC9Pdk2a6Kzpyw2RMIDwoEWEBy0iT36IuJSnsDWtvDTEonBqh+93GUwWsjv5MLmqfEbVetRou69YV3uveJkTF7bhMposo2HjCFate5nnRn/Dvl1RHDlwna49GzF8VJF0SbY0n93bL3Ls8A327dJ5m6rXDMLHx7x3qiIRHOTDv39N43/TfmHBp1tY8OmWwraOHery03dllwHsbBr7VSMuL4NXGruOw53NsBq9C1+LBSJ6h3XgdPoVPcPJQyRBplaQIssgzCOIADdf+kd0NjpfoJsvq2O2cCDlDKsemkO4h20Fol13ETvwcu9O3WrHkeauJ036hUPmFAg8qBa8Em+PvsCDEdw7pPpjtA/sQKIsgRxVNgn3/58iT7F4jhB3XY2pk+nn6RtmeMyxKU4Xn1Y8xsmFbWR8lFFhjKeSRYgryr4KqBUZjEgkRK3WoFCo2LcrqtBAAvji4y0GYz74xNBjWllY+tVY4uIzOHz0BgIE9OrRmLCwqvXwN6fVMKTK/MJadS6cx47Eo5zNuMZl6S3yVXLy1DJG1tKXjni90TMsvr6W5058QKuAhsxqPtmkjIyX2JP1d3fwU6cPCHW3/bvCoSVXypPrmT9yL3sbWYobJvuUVXyUWpNKdt5mVOoEZIpzqNVJaLQytCgRCjwQCgMQC0PwkLRFLK6Fr+cIBIKy1zyq6FzIOseSG4v0rpUW41RScLI4PmJvJtUdRc9QxwmVPqi4YpysJzMjl2dGfI1CYTpVfugTHRg/qafdgeHlgVarrTLB3+ZovWWWxX1dMU62IdcoeO74B9TyjuDtJs8SJNEZ3/OjfqC6ZygT6xqGMai1Gs5mXGXupZVo0bK5x1eIi2UWDz4wFQEC5rR4kbmXVjC+zhCeiiw1y9PkH3QV8ThpuZRmmE5eXoiEIQT4TCrvbTxwNPatx7jaIziTeZmr0lsAVPMMI9KzGlMajDWa2u/CegJnBRp4eioC9nrCnGkQBgR6s/avqezfHcWGtcfISMtBoVAhdhPRsXN92naoy4hRlTcGaPT4FQwe1Jrxz9gX1F4ZGFjdOk0uF9ZzKfMWmcpsFjWeXmg0AWQopFT3NC7vIRII6RDUjEXtpjPzwlIOppyhT5j+ZyrSK4KOQc0QIGBNzFZzhpNJnGI4Pf7rWs4nJFo1xp4iv0cT38BDFEyvGj/j7VaD/+4MZlDtrQDI1Gn8G/MwzYIcL/nvouIxvEZ/o6KQLlyYw9keNP8AL4Y+0YGhTzg/u+zwIdNaYbbSrbvpZJ4uD9Xn900n+HG1LnB60oSePDKwJcFBPg7fR3nzabsny3sLVZ5wT13s0ZHU84VxTucyr3NVGkNzf31h33V3t9MpuAV1vXXSD0myNNRaNV4i0zp+W3ouZvnNDTx9dAZftZ1OhJWxTg41nDRaLe/v2GW10WQvWfJr1Pd/Gm833S9OrS2qp+YhCsZPUp+o9G9oEji5TPflwkVVpCJ6m8B1dFic2bP/cPicu/eYLlXx+qsDmPJiX2bM3sjZc3f44acD/LTmEAs+HEmH9nUdUpPUxYNDTc8w+oR14Ntbm9gctw+5RkmmIpvn6g1DqszV67smZitrYrYS7hGMRqshRa77fnoouHmpa7xUfyRb4w8x5+I3rOz4vlX7c6jh1GzREpTqoorsjzdvRrCXFxKRc4OcZeoUgj2K6iDJ1RkoNFlIhP4AdAr/jJ33zBfMdOHChXlcBoplGNM0qspIJGK++ERX91GrhTPnYnh/7iZkMiUikZBuXRoy9/3hLiPKhUVMbzKe6U1KqxSio6Suk6X9hAKhxWNL4tBPdYHRFOrtzaeDBtKzbh1HTm8SN6G24alrAAAgAElEQVQfMrV+BlaW/AahnjqXuEhoX/kCFy5cuLCWp4Ysou+AFrw6/cFT+xcIoH3bOmxa9z+OHr/FRwv+5sCha4yduJKfv38esfjByBh2UTVxyuPQsZdfdMa0JqnnN4or6Suo6TOw8NrhhJcZXu8kACeS3i3T/VRVrmZf4WLWBU6kHyNLKcVD6E5Tv+Y08W1Cn7DKqWmiRUts3j0OpR4gNj+WBFk8OapcxAIR4R7hhLqH0T2kJ419m+DuBAP8dMYponNvcyHrPFKllFxVDhKhBD83f4IlwTT1a0bfsIfxLOW83hKSZElESS9xKPUA6Yp0ctW5uAvdqeddn9redRhefQRCV207h5KTI+PvTaf4e9MpALr3bsLAwa3p3M1+cUtz7NhpWGzXFAqFivx8JbduJXHnTio//XiQ/PyiAir/bHkTLy+JVeufOh3Nn/+c4cjRmwCMHtWJ4UPb8feWs/Qf/DleXu5s/fP1UufY9tM+fpn/J6nxhkfDkU1q8MKCp+k4oJXRsWsXbubnD/9ALBHzd8p37P/jOMumrSbHSPmbZ2YMZ9zMEQiEpbvC5oz6imP/Gs/MnjBnJE+9OQShyFW/7kHAoXIE9T//ErAv0NsWpIqb7Lo3it41VhPk0ZJd955AqriNj1skbkIfMuQ63ZTyKtdSWXj+1ITC1zU8a/JB83mAzrg4knqIH2N+MDm2fWBHHq8xknAPx9XIslaOwBrUWjVXpFH8GPMDWcpMs/0lQglvN55BHW/rVWaNcTL9OPtT9nE1+4rZvgIE1PKK5Lm6k6npWdPqtU5lnGTFrWWl9vER+9AjpBdP1Ky8GkIVjf5d5xm9/tTYLgwc3IZatW0T33M2CoWKXTsv88UXulJCDRqGs3LlcxaNzcmR89/OiyxbsRuAOrVDeGJ4e4Y82qawz6SXVnE7OoW9200bd0q5iiFB5tfcnmu8cHuB4QTww7lPmdTm7VLneWRCb15fZnq907svMXPop6XO0XFAK+b9Od3MjnWc2nFB771AKKD9w65svQqGSUu6ShhOxsiSX2N//CTUmnwaBoynceDzuAltq3T/oFDccAL4vsNPJMmSeO+S5U+vXYO7MaHOJIdUDneG4XQx6wLLbi5BpTWtp2OOZe1W2ux9WnpzCecyz9g0tolvU6Y3tvzfYn/KXtbe/QW1Vm2+czHaBLTjfw1M19JzYR0njt5kx78XCuvQFcfdw435n4+mVdvaFS72Jzs7n8mTV5GSLOWll/oy6slOJvu++savXLocC4C3tzvfLZtAtWoBRvsqlWoGDPncpOG04aut/PD+egCEIiFbM1bpeXKUchWPBU+i4N41d/3rdBnSTm+O4oYTgLefF29994JBv9H1XiUjKQswbYSNrvsqGcm6Pr1GdmLmz6/ote9ed4RPJ60A4KWFzzDifwMN5ijJQM9xBteWHvmQhm0d82DmSFQKFSd3XGD5m2tIvmu89qGnjwdj3h3GQ4PaUKe59Q93FRSTn8gq61f0d2/M0LqHGFH/NC2CX3MZTTaQpczii+ulP2WV5EjaYdbc+ck5G3IADXwa4ia0T2z029vfoLVBGzZFnmKz0QTQM7SXVf3X3PnZaqMJ4FzmGWRqmdXjXBjnoS4NeP+jx5n27mCat9JXmpbLlEz/3xqeHbWM5Ps38IqCr68nz03sCcCvvx4tVbyzwGh6/X8D2Lj2FZNGE4BGU/pnZ828TYWvpy2fZHD85eYuZvLHTxe+Xzz1x1LnA5i/ebqB0QQwfWVRpnV6onHPc4HR1GVIO2b8ZChr0290kXbVb5/9jTxPYdDHEhJuJ9s0zpnI8xWMbfg6c0d9ZdJoAsjPkfHD++t5scMM9qw7gkatKcNdlj0O9Ti1/XoZUrmclcOH8XCD+uYHuKhQlPQ4FaeVf2umNpxmcP1Ayj7W31uHXKN/o13Q8jNC3Y0LlVmKs47qMhQZvHVB97M082tO64C29At72GjfZHkSMy8aPhlHeFRjXosFFq85+/J7xOfHGVyv412XHiE96RXax6BNrVWzI+k//oz7A41Ww/cdfrJoravZV/jy+mdotEVfXr1D+/JItcEESwyPh1Lkycy4aHiUMaLGEwyu9phFazqaB0Gd/MtPtnJgTxS5OXK96z4+Hixc8gyNmlSM8kD9+ur+zgcMbMk77wxx+noDvXWZVHPWvUbXx9qb7LdjzQG+eOl7wNBbVNLjZMqbVHy9qYsnMPj5vnptf6/cxbI3Vpud44f317PhK512YO9RnY0aWHprGvE4DZrQi2nfVJyafn8u/Y8Vb/1q8/gnpj7CpPlPIaq8iQBl43Ga+7Duj276tv84m5DgyKldlCPPRI4zajQB9AztzZzmHxhc3xi7wdnbsplASSAh7iGMqPEEbzR6y6TRBBDmHm407idRZt3ftzGjKdQ9lPebzjFqNAGIBCIeiRjM560WMSbS8kr3P0R/q2c0+Yh9GVt7vFGjSbePMCbVfQGxQD9X5J/4zRav6QyqstEE8Ma7g9mwZRo9+zTF3aPIC5qTI+OV50zHE5YXt24mOX2NzBRp4WtzMT+dBxd5kGS5cpP9qtULs2htY4Hjl45cs2hsvVaRha+jjpku+1Ua8RXI4/Trgr/sMpoA/liyjbcHWf5wWZmwOqvuy0OHTbb5SCR81P9hZu3cxchffwMgMiCAXnXr4OXmhlho2k57o3s3a7dSyO2s9VzL/JH+tf5AbORI7q/bndBoFVYFh6vVKQiFAQ98Dblwj3CzGXNh7uE8WWs0G+6tK7x2OuMka+78zLjazzp7izbxSUvjNe2M8UjEYDoGPsS7F9+yaa0vr39mcM2aOCk/Nz/6lmLcFSdbJSVDoZ+FtKjN12bHdQnuSufgLkw+NbHwmkqrIk2RZtLgciZV3WgqQCIRM2v+E4Xv5TIlTw9fQrY0v5RR5UNMjOmjGkdxamdR0LS7Z+mZfH7FVMlP7bxA9+HGS9Z07G88864kapXhsfbRLUVH6wWeKXMk30uzqF9JLh66arJNo9aQGJNCrjQPnwBvAkL98PRxTgmpO1FxrP7QMQKqlw5f462BH/PZ9pkOma+iYLXhtOzYcav6383MZM3Zc2b72WM43cpaR02fAUaNJoBGAeO5mvG9VXPeSeqFWqM78/Z070SNkN8QCMqn1pk09zeSMt402tawZrzT1hULxLzfdK5FfQeEDyJHlcO/CUXV3ven7K2whpO1hNh47Lj+3lqipJf1rj1WfZhTpA20aJkXpe/9e9oKT5UAAR4iD734pm9uLeX9pnMctkdzlPURnb317exFrdaw8MPNHDl4HblMWW77sITAQOfHieZkGnp9LCE7I9dkm7e/bUWT83NkKMrw30R7P/YrNyuP3WsPFx4RmiOkeiD9x/Zgwgf2Z8Qe+OM488cutXue4lw4cIVz+6Jo07uZQ+ctT6qErK1ck4G3uIbJdg+R9Te9AqMJIF9+nNsJbfD1epywgI9t2qM9eHsOAhOGkzNp6tfMKv2gVv6t9Qynqo5Co0AiLP2p+EzGab33QoGI3iaO5uwlOvc2aQr9p902AW1N9DZOba86XMsuevK9m3fHop/TUZRlAeHyLh2zYslO9uy4REa6/k3f3cONeZ8/VU670icxsShgvSwMJ79g22rb+Tphb54+HoglYlT3g+LnrHvN4WuURKvVMrbR6+RZ4XFMjc/gt0//BoGAMe8OQ+Jh+ynJ+s+d8/29bNrPfHPiY8RulTbeSQ+rDaeKIDVQEgFCtJiO4hc4IDVeo5GSlfMT0tz11Kt+AaGg7LL0RELTT8RJGW8SHviFU9btGtzdqv4NfJwv7FeRUGjkZg2KkobMs7Un4u9mOuPIHjbH/aX3vm1AO6uP2Zr6NdMznDRaDWczz9ApqLND9mgJZekBKsu19u+OYse/FzhxXxSyOEHBPnz29Vgi64SU2X7MMW/eZvbuKZJQePIp03IEjqLTI0V6T7JcOR7epj2zWanZha+dpYHUsX8rjm7VHdeVFqjuKAZ5WXYcaIzfFm7mt4WbeffnKfR5sotNc9w8F2Oy7YnXHmXceyPw9DU8edFqtUzp/D63L9w1Ovbu1XjGNXqd36LNhw1UBqqEHIGXuDppMtPHgemyi1bNp1RFm2zz9uhfpkZTAQITNm5O/lYoxWi0h2qe9mf15KiyzXd6gKjtXcdpc8fm39N7b4sYqZfI8FgjU1G2npmMjzKqlNfp8sVYFi38l3mzNhkYTWKxkI8+fYq1f011qNGkVmts+i8/X8GJE7dYsWKPntHk5+dJ167OfzDy9iv6+zvxX+khHgUGDeC0eJ/KeLy0cMI3Tpn3hU+eNmo0AQgEAr4+9CGj3zKdhWtK7qEyUiWO6jqEz2Pn3RFkKW7Qv9YmvbZ8VRJ3sv+2ar6M7JUm2yKCbCsKaC8BvpPJyDb8QGg0UjJzfiDAZ7KRUfbhCIXuO3l3aO7XwgG7cS5pijQS8uO5ln2VRFkCMo2MHFUOSo2CXFUuCo1jYh1sUf62lJIK6P8lbuO/xG12z5ujyrF7DmsoKy+Qs9cZ0G0extReHnu8PQMHt6Zx0+rOW7v/QofO9/vGV8usvpx/iC9ZqdnMH7+M7IxcA4kAgI2L/+W7mbpkFJ8A5z3IDp8ygG/e+gWAFzrM4PMd7+kFpZdEIVPadVTmCLRaLcNDJ7N4/1xqNzMdwlKSvRuOGr0uEAhYfe0rs+PFbiImfvgkAqGQ3xYaz8i9ExVn1Z4qKlXC4+TrVgeAbEU0J5LeIT53Lwm5+ziVPIsdd4cD6NWxM4dMccpkm0BQNrEeJfF0N31Uki8/5pQ1HRHXkq+ueNlBxTmYeoDlt77mnQtvsujGF2xL3MrZzDNckUZxL+8uibJEslXZBjpVLlyYo6TR1K5jXWZ+MIKp0x9xqtHkaOrWCy3ToryvLppQ+PrraT8bZLupFCq+f2994fsXF45x6n7CInWewDtX4pjS5X2j2XcZyVns3XCU90dYnq3rTPJzZCx4tvQySyW5c1/EtCRNOtYnrJblR/7jZz1O4w7GdRz3rDOdlV+ZqBIeJ4DH65/htnQj51I+JjZnR+F1d1EwQ2sfQmBFAVO50rA0Qnnj7dEfd7emyJWGtc1y8u33KjgLeQVUoFZqlLxy9kU9rSMXLhyNu4cbPXo34e1ZwypcORVziMVCnhrdmccea0doqG+Zrt1jREe2565moPd4tBotj/pPNNl3m/QnpxfWXXPlS2KiYnmx40xSYtNL3U/zLs45zvQN9CGiTiiePu4k3U0lOyPXbAB59KV75GTmWuyRO7v3stHrI6c9atVehSIhSw7ONSryue6zfxj6cn+Cq1VuuRGHGk4L9u0HYEZvy0tD7I+OJk6azZjWlmltmEZAPb9RhHi0JSnvKKAlxLMDAe5NEDjIsSYWlW8NHokJw6kiIxJULNs8WyXlq+tflGo0VfOojo/YhzCPMCRCd/zd/JEIJXo6VZWB/uEDaeTb2O55wt0jHLCbB48N/7yOVynBzc7kww+fMN/JCGI3ERER/lSrFoBEUr6f3Q83vsG3M34j9oah2Gy1emFMnDPK6UZTAXWa1WT09Mf4e+Uu8rJNGywDxvV02JphkSH0ebILT7z2CP4hhsbr7t8Oc37/Fbb/vN/kHJu+3s74WY9btF5BaZmS1Gjg2M//ub1R9Btju/xQRcChn4zvT+lSr60xnE7HxbPs2HEHGE46/CQN8JM0cMhcJQnwsT3jwRF4ezxMdt4m8x0dRL463yo5AmP4u/k7aDeOYdo5w+K1zf1aMq2RebmHymY4eYm8aBtgWJ+rLHkQSqeYoryMJoBu3RuV29qOotMjbfSy7KxhzDvDGPPOMIv6llZKpTgTPxjFRAdoJZlj2JQBvLDgacRmDNd+T3ej39PdeGPF89yJiuOF9u8a9Pn14z9p1K4unQeblyXJMFErsW6LWkavm2PMu8NY+4lhrNNfy3dUesOp3GOcQrwrT/FdiZv9T+/24CYu28rZjggK9nXzc8BOHMO9PMNU2eE1HrfIaKqMSFVS852cTOCswAfSaHLhwlamfDHOrNFUktrNahDZxHjc3IE/LBOtVspNF3G2hU6PGjfWYi7fM3q9MlHu5ygRPrYJnpVErZURn7uXpLzDaLTG/wAeCv/EjhVEeHtYVvbCWXhIWpfpelezr9hdqNeZWWTWsvjGl3rv3YRuDKk2tEz3cDrjJO0DjZeGsJcGPg25mVNUJ+tw6kGerjUGoRXxfRWRquK12vLXGXZsPc+Vy/p1C0UiIQ0aRfD50nF4eD7YJZ4edLZk/mjz2O/OLuSPJdv49p21etd3/3aYt1e9ZHa81lgKqB006VifVj2bcuGAfnhJWaqxO4ty9zjJVI6xcv+78ygnk2ZyN3srsTnbjf5nD0KhD6UUS66SxBrx0JSGWmuYbVJRkCqlZJZI12/p75jjYWu4lHXJaXM38NE/olZoFMTkxThtvbKiKnitjhy8zuJP/zUwmkCnuXTtSjxPD1/Mjq3ny2F3LkwhVUQRm73efEcH4eZuny/j0YnOqUpgK/WLFT+uSpSrxylLJisMKLeHsynzkaszqO7dj/ZhH+BmomadPbiJKr/2hLXsSd7NwIhHCZIEWdT/j9jfnbwj20mSJxpca+prubidzMbswIeCOnEivchVfjB1P3W969IztLdN85XG4zVGcjTtCFnKoliFL69/xtK2Kxy+ljWUdz248qZ/13l670NCfQkJ9cXTy538PDlXo3T1JnOyZXw2/x+uXU3g1TcHlcdWXZTgbOILaLVqavo6vwRO277N7Z7DlEBledF5cDv+XGqf06IiUm6Gk1qjYeyG30nONV2c0VJS8k8Q6tmRzhFO1NCoYNlhZYEWLUfSDll0nCVTyziUelDvmo+4bNOYS8OYIrZSa7nLeFfyDvOdjNAtpLue4QSwKW6jUwwnoUBE95CebE34p/CarQafo3lQjacTR28Vvm7UpBpDn+jAwMH6R+5ZmXkcOXidLxfo6oT9/ccpuw0nrVaLoLJpIFQwchTXUKjTcBM6p0RSSVp2K98YWmcQUsOyh+7Khs3WwI+nzzBv7z6jbfU//9LodVM8bWdGnUydQrvQ2XbNYQ61Ot2p81dU/orbxOBqjyEwc0z5xvmpKDSKwvcCBMxo8p6zt2cx1T1r4CP20Qt43520kwHh5m9QH0XN5Y6NR17N/VryYr0prLy9vPBajiqHb24tY3K9FxE72CAfUeMJPcMJYOrZV1jS1joxPNDVqRM6oM4jVP74JFs4dvgGs97SHfOMn9STcZOMp6r7B3jxyGNteOSxNvzy40F+/m4/hw9co1tP22+kr039hcuXY/HykvDjTy8QYiSd3Rzx8RmMG6vzVg4Z0oZpbzxi834qI9fTnVMD1BQdBjgmdKBeq0iTNePKmpoNq6aUic3fig/VrMnQpk0csonZfe07l/UURyBXO7fmlFqT6tT5KzJr7vyEppT4pWR5kp7RBNA5uAvhHhXnQyNAQBPfpnrXShbgNUaGIt1mo6mAjkEPGVw7nXGSz65ZnqyQq8plb/Jui/pW99Q/Vs5T57IvZW+p/4Yl+T12PW9dmGZx/9J4EI0mgOOHdYH6IaG+jB5vWfr16LFdAThZzFNlC1FROhXovDyFzZ6n6tUDaXj/xrd37xVUqgdLMDZTdrJM1/MNdEyilDNL0LjQYfPjbvPwML4a/ChfDdapimbmy2i/TPdUfeqVl0sdK0CAj7sEsdAxT7M9qn/LtpiBdBJ8TnVv5wTHabUyZIqzeEjM62E4C6WqbJ8igiRBpCvSOZCynwMp+wmUBNLSvzUNfBqQo8ohUZbIgZR9RsdOqvuCRWscTj1EuiKNfHU+aYo08tV5pCvSyVfnG9ReA5h58R0C3ALwFHkRJAnCQ+RJkCSIIEkQniIvuof0MLnWi/WncOqUvurv86cmUN+nAWMjxxMgCUSlUXE1+wrXs69yMPWAXt+mfs24IrVNVX5Gk/f54vqnegbmrZybPH9qAgA1PGvi5+ZHiCQUhUZOijyFZHmSnoesc3AX+tDP7FofNp/PZelFvrpe9MT8y52f+eXOzwCEuodS26sOniIvslVSZGoZ13OuuZTUHcyh/dcAeGZCd9zcLMtsFN/vd/jANV5/xzrF5uIUT5AKDrb9hvzcpJ7MeHcDublyNv5+gtFP60o/7YzWPYR0q7mNEwljUBZ7cG0cPINIv/FcSf2A2Owi7TMBQh6uq69OfTvzG25l6Op/9q9burhvwZr1A6dSL8D4PSY681tisr5DpTEtpSIWetMr8jBCgb7O1p6Ydqi1huKWSk1m4dolqe0/kUZBb5e6b0txlJinVuPY7DgXhjjsnCDAsygoLdDTPtFEa/EQheAuDuZY4pt0rbaYUM+HEBr50awpu2IMueJ8uRpOCuXVMl3vlQZT+fTqAuQaOQAZigwOpOwzaSwV0DfMctmGH2O+t3pfmcpMMpWZJMjiDdpKM5wECBAKhAYGwq2cm3wQVfpRb9fg7lT3rGGz4VTfpwGvN3yTT68tMNoelx9LnAPL+jX3a4m70L3w3644KfIUUuQpjlvMhVFy7itM+/kbxtdZOra8aVJMG+h2dLJB+8WUt/WMJoDraZ8iEnjrGU0AWjSoNNmIhc6JfdSi5maG+WK0Kk2ugdFUEcjOyCGijn3yLwA5WdbHDWvUrocma6gSEc+bbhUZM0cSDJWhC3i8/lm71knPXoy/zwS75rCHjJxvynS92l51WNZuJXMvzyI237xomVggZkV76w2hsuTb9qs4lnaU76NXWtRfIpSwvN23gC5Y/r/Ef8lRZdu0diPfxqxo/z07Ev9jU9xGq8dbGw+1rN1KVFoVU89OMThKNYcAAc39W1g1xoU+tWqHEH0rmSuXY+nZ17jHwhQ1Iy0vqloa9eqF2TXez6/oIfj8OUOPt7sojP51owABMlUCB+/1RYuaqNT3ifQbT+PgGUCRtygqdTatwswbN9aiRc2Bu7qKFQJEPFzXuOzHPelvJOcZT/ToW+eM3vuCPbsJA+hd+6gDd2ucc/uiaNjWfpHjmEvGi/WWRlaabd9pDyoONZy83MpHvM1eg6gkImEAao3hMZFKneTQdawhK+cn8uWmFGCdmz0zt/lHAERJL3Mx6wIn0o+TrZLiLnSnmV9zmvg2pU+Y+SMkY3zf4ScH7tQyOgd3oXNwF7KUmWyM3cC9vLskyZNQazUEugXQKqANDX0a8lBQZ71xAgQsavO1XWuLBWIerTaER6sNAWB/yl5icqOJkl4mV52LQqNEInQjwC2AYEkorQJa0Se0n81B2mKBuNDwu5d3l/8S/yVJnkR8fhxKjRIfsS8eInca+zYhwqMafcL64S6seE/jlZFnJnRn3qxNbPztOGHh/ox40jDWrSS/rz0GwHMvOibkoGZNx8WXZWYaejLahC8tfO0hrlb4OsSrV6HRBBDh/QiJudtIyz/ssP0UJ095B4VaF7MY5NnFZL9afk9Ty+9pp+zBXtYt/JtR0wbbPY8xIcustGz8g017+s7vr1w1UMsbhxpOF1971ZHTlRseknbkyvYYbdNoshAKy77+WmbOKpNtbuI6ZbKHZn7NaebXnKdqVcwvHmvxdwuwOBbLWfQK7UOv0LIRravlFcnkeuYVhF04hs7dGxES6ktqSjYrluw0azht+fM03y3XJQA81NUx9TZzc63zNJaGxorYGV+JvofNTaRLS1dr8hy2n+J4iqsjFEjQaBWky445ZQ1nk5Nl/+/GlCr38mmrmbH6FZPjrhy/YbLNHhytRl5RqBJHdY4mLHAh0QnGy2LcTmhN7fB9ZWasAGi1chSqmybbQwM+KrO9uHDhDKqizpO7u5i1f73GgG7z0Gi0hUKYkXVC7gtgSsjPU3D2dIxeQO+YCd0ROShQ+Nq1BIfMAxAWZnndSV+JvpSCSKA78tPinOoCQoEH3Wpu5+C9Pmi1KnZGN0UgENMmbCnBnt3tjm91NCHVA0mNN8wE//mDjTw7Z6RNc6YnZjKl0/tG2/b9fowDm06wLedno+1/LTOtU6eQKZF42HaadOnwNZvGVXTKveSKo9FoFahN/Gcp4lJUwrVaBYnppuOonEGa1HTaulDgg5e76YBoFy4qCwU16aoSAgF0KuE9uhuTypmT0Rzef40zJ6MNsqAmvtDbYevn5NgngBobW6RfFxFhuRCkUFD2YRse4gj83IvUt7VaFWeTXmL/vR5kK8o2scYc7foZjx9c+8lmm+ecP3YpGclZJts1ag3rPvvHIBA8M6X0YuC3LtyxeU+3zlcMPSlHU0U8Tlo23Wpntpc1sVBCgTcarfHsBJniFGnSTwn2c0waamnkyfaTkW06kLlG6O8IyuFLyhyD/HRp//9JbS9a6QhG13+NzBQpIrGQzUnfFqZ7m+Kx0MkGVcIHju/JtKUTTYxwzNiSFPz+tqR9b3bPVYGq5m0qzrzPRwOw+ocD7N15idi7+mK6QqGA+o0i+OqbZ3G3s1aZMTIz8wgIsD6zTy5X8sa0Xwvfj3i8gxWjy+dvtlN1XdJFSt5e4rI3kpK3B6U6g2NxIwBd/FPzkHl4iKuXNo3TmfbN85zccYGMJENDZ6DnODr0b8n8vy27vxz++xQr3vqV5LvmtQZ/nL2BH2dvsGqv6z/7h7m/W6/pplap+fXjP60eVxmoEobTvfsFfMVCbyJ9h3Av+19q+PQjVxlLmuwCvm61aRtq3IVpCj/vJ8nMMX3TT5cuQqPJIjRgvl17L43svL9Jynit1D4ektaltj/oFGj/icQii4QAp6+YTMyVWKRpOexcexhFvuWeyukrJpOZksXdawlWjzWFq2xG1WH8pJ6Mn9STE0dvkpWZR06OHD8/Tzp3a4i3j2MD8gMCvMjM1MXMLFm8nVmzR2Dtn9KqVQdIS9PpIbm7i+nQwf6Mr7Ii1KsPoV59uJP1E/E5f5KjuA5Aev5Rjsc/Sa/IQ+W6P6FIyKOT+po0LE7tvMi0PheS5pkAABKcSURBVB8yYFwPBk3ojUBo+I8XffEe5/ZHseKtX0yu4+XnSZ7UPmmL49vOkZaQQXA16x5uTu+6hDTdtJ5WZaZKGE6X05bQPOh/NA6cBEBczg7ahc4pbN91byT74ibweP0zpqYwIDRgPkKhP+nSRSb7ZOb8SFburwT6vkqw35u2/wAlyMnfRmrWXJSq0iUAvNy7O2zNqspvNxdb1b/XEw/RC10Q78kdF0i+Z15dvPjYAqwdW5Ly9tRZQ0aylDENX2fxvtk0alunvLdT4Xmoi2MCv0vjh1WTGTd2BXl5cvbvv8r+fgv45deXqVbN/HGbVJrP4sXb2be3KNNq5bfP4WFjnEtpGNPbcyS1/SdQ238CAIk5W7mYMh2FOo30/GMEeXYufbCTGT/r8VI9MlHHbhB17AaLXjGdGGSOTYkrSbmXxrjGtlcB0Kg1PNv0DbZkWvedNGuE8dqxDz9T+e9bVSLGSa7OwN+9YeF7ZQnV2AYBYwHro/sDfU1nIRSg1SpIl35BatbHKFUxVq9RHI02D2neBhLSJpk1mkBISMAcM31cuHA+Z/ddrrLZM5WVgAAvnitRG+/Z8SuZM/sPcnMNRVEBFAoVFy/eY8zTy/WMJoBatRyjK1USsdAyVfOSIpu2EOFTlOqfJT9vxUjn/W3XbVnLaXMLBAIEAgFhkSFWe66ffHOI3nulXEV+tuXxcqV5uToOrPynJFXC4yQWeqHSFP1DabRKUvJPEeqpO5MP8zSvn2IMocCbQN8pZGQvN9s3I3spGdlFmibubi2RuDXG070TErdGCHFHIPBAixK1Jh21OhWZ4gwK5XXy5Aet3luDmjEIqsY/n4tKzg+zfy/vLbgwwogRHdi/7yoXL+oewtRqDYcOXefQIeuKsP+xqfRwAXsI9e7HlbQPAUjLP0KwZ1eDPnJ1CkdjHyt1Ho1WbpUauOS+PEJpuIkCUaozUGqySJcdJ8ijk8XzW8qKEx9zYNMJ5j9jnz5cSZp1bshn22cWvv8vbzW/fvwnqz/aZHasb6APk+Y9hYeXRK//8LDJBEUEsOb6olLjLl/u9J7JIsPdhnWg96jy9fQ5girhcfKT1CNDrl8DKS53V+HrbIXtWQHBfu/YdCQmV14kO28jyRlvEZs8jLvJg7iT1Ju7Sf2JS3mKxPRXyMz5wSajSSQMcRlNLioMaUbSql3YzuED19i0/oRD5vr0s9F2jW/dOtKmwHJLcRcVKZtfTplBjsIwff1UwliUGtPZYgC3Mr7mVML4QhFMQ7TEZRcZ+MGe5jORAz2KguGvp1lekNtaej7+kMNjGedvfguxRP8e8czMEXh4l25cBkUE8Mm/7wDQY4ShwyE9MZOJLaaz8u1fkabn6Hmaj245w/xnvjZpNAE8+lzZaNY5mypx920d8g677o2ihnd/gjxa4iepx+2s9STnHcVN6EOG3Lb6YgACgRs1QjdwJ6kPCmX5a1LUDN2Ip7vhU1lF5e61eD6e8A0xl/XLAPgGejN10bP0GGFcL+ux0Mlo1Bq2pH7PyZ0X+PzF7w0CDT293dkQsxS3EplIU7rN5vZF/aPO5p0b8sWOmVRUjO0ZLIt1eq3PR1w7fdtk+7RlzzFwnOGNYnjES8jy5Ky/vYR545dx8ZDh33eNBhHMWfsqkcVqlu1Zf5RPJ39r0Hdqrw+Mrl+Z4rUqAmt+OMCtG0k8/pRtnvLiSCRidu+ZgVKpZtrrv3DlimF9x5LUqBnEoEGtGDPGtAK3I6nhO4q47N+Rq5M5GjfcaJ/6gVMLiwEbQ4uGDNlJ9t81/5Ab7NkdD3GE2X6tw5ZwMfkNEnO3ka24arTQb9OQOdT0tc84BZ1H6PaFu7zc6T275hFLxGzJXGXSENucqiuJtenr/7h85DoxUbF4envQZ3QXeox4iLBiR7KRTY3L8iTfTWXT1/+x6ev/rN5fhwGtrB5TEakShpOfpAHtwz4kwF33h90udA4H4yeToyyyfP0ljexao1boZuLTniNffsSueexBJAqtVEYTwIxhn5MWn4FQJMQ30JusVF1NpOyMXD6e+A3bTBhOAGqVhswUKXNHLzFahLJtn+YGRhPAxDkjuXLyFtL0HA79dcqsTklFYOKckcTfTuLejUSkaTkc2GS5x6Gk0RRcPZD8bBl59wvFfvXKKqOGU/HxBUaTp7c7Xn6epCXoSg7F3UxkxvDP+fVq0fFOUEQA7Yvp0JzerasL1vShBnj5euDCPkzFINmDm5uIpcueJS0th/XrjnH3Xhq3byUjl6sQi4V4+3jQpnUk9RuEM2yYeWkXR9I0ZC7uojBuZy4z2t48dAHVfYYTnbkSjdb478bfvTUSUXApHicdDYPepLaf5fIgzUM/ITF3m8X97aFeq0gmzB3JT3Otr2MJ0KF/S56f/7RF3qvHXx3E468OMtuvWeeGRB1zjKp4277NzXeqJAjMBHW6Ij5LkJP/L8kZb6PWpJvv7DCENKhxC0EFrOhtigIdojrNa/Lljpl4+Xoabe/7VBfe/s6w7EmBJpLEw43hL/dn4tyRNrmzZ49axInt523yOI1vPp3ke2k2aTHZMxas08E6f+AKzTs3NHDNA/yyYDO/LPiLL3bMpHnnhnptBR4ngE/+eZs2vfSfqHMycxkZ+T8AtmWuMpoSXXyvS/bPcWXVOYBBPT5Grdaw84h1EirlSZ8BCwHYu+Odct6JYzh5Kpot287zwSzjHjBno9Vq+WnO70Qdu8GFg8bFO338vWjYvi7vrHqZwHDnlQEb6DnO7jlmr3uNbsOs0QGrEJi84VQJj1NZ4uP5KJ7uD3E7vg1g6AVxBjVD/6hURlNxXlrwtIHRBNCxfytO7rzAoc2njBpOBShkSp77YJQzt1jpad3T8AihgCdeHcgvC/7i5vk7BoZTcUoaTQA+Ad6Fr+9ciaNO85r2bdSFRaiNeFddlC2nzsRw5Khz6rdZgkAgYOKHTwKQfC+N5LupxN1MRJ6vxD/EB29/bzr0b1kme+nzVFf2rrfvpKUSGk2l4jKcbEAkDKFhTV3MTmrWR0hzN6DW2K7ZY4gQb48+VAv5sdIHgbfp3czo9XdWvcjIWq+YLEpZQJMO9ZyxrQcGTx/d0VlpAdwNWtc2O0+My3AyyfvT15X3Flw4mJdf6MPLL1SMQOawWsGE1QqmRbfG5js7gXd/epnp373AkICJBuWBzOEX5MPvcd84aWflR+W+K5cgR3mXMykfkC67hAAI8mhNsEcbmgVNcdqaIf6zCPZ7l9SsueTK9qBU2Z7BJxC44+nelfDAzxGLqjlwlxUPH38vAkL9zMYf1WsVWUY7qvxs+X4vty/d5caZGHIyc5Gm5yA3Y5gC1Glm3iAyFmPmQsfxI6YLcLtwURUQu4n49L+ZLH39J+5ExVk05pHn+jBhrm0Fiys6VcZw2nSrLQAChAS4NwM0pOafJiX/JFczvqOGdz86RRhXMrUXgcCN0ID5hBq0qJEpzqNSx6PVytBochEIJAiF/ggF3nhI2iAUOu9suqITXC3ArOEU5MSz+6qARq3h0fuK+QDunhK6DW2Pf7AvITUC8fT2YMnrxiuiFxBa07ymjQsXlnD0+C02bDzBlasJRNYKYsXSZxGWiI3rM2Ahf/0+lT37rvDfjovci00nPMyPH7+bpNfvyLGbbNl6jpOno/H19WTOe8No3UonGDlkxCJkMiW7tr0FwCuvrSHqSnxhjJVGo+XRYV+y7e83kckUTJm6hh+/m8S772/k6rV4JG5i2raJZMbb+kKPcz76iwMHdYkSdWqHGOypOJ98tpWz5++Slydnyot9GTSgZZUukdSqRxO+Pa2TZVDKVfy5bDvpCZkkRCchFAqp1bg6tRpXo//Yql90vkoYTvdydGmRPm616FdrAyKB7nhCpckjIW8fJ5PeIy53dznsTISHpB1QtlkqlQVZnv213B501n2+pfD1e6tfodOg1khKlMYwZzi5cAxvvDsYPwdoHs19t/IKis6ctRF3dzEikYgbN5OYO+8vPpg13MCgSErOYsmynYXvY+7oF6jVaLS8N/sPAKpVCyAjI5fXp69l9JOdePH53jRuFMGZs0Xe/es3kgDIy1fg5Snh7r005HJVYX2+2LgMtu+8xImTtwkN9SU5WcqOXZcZ81RnatcOKZynZ7dGCICYu6WHXmz59xzbd15CJBIiFov49Itt7Nl3lc8WPGn176wy4uYu5sk3BpvvWEWpEobT+dRPaB3yDvX99fU0xEIvavk8ilKdzblU5wmYubAeaVoOcTcTy3sblZqTOy+wer6u1tVrSybQY7hhAKZa5TpiKwskEjGPDG1b3tsod0pm1fUZsJBnJnzL2p9f1Lv+ymtrTGbgSaX5PPnMcmbNHErf3kVJC9t3XuKTz7Yy7pmuDB/aTs9wUqnUfDh7BBv/OMn4sd3Yuk2/pIpKpebPzafZs/3twmt/bj7DK6//wpY/Xy+81q9vM/r1bcbWbefZuOmU0f29/Opqrl5L0Nv/7r1RzFvwD7m5crzNiEy6qPxUCeVwtSYfP4npwpmltblwLqZu3Gf32S5K6kJHWnxm4ev6JmLBShPGdDQalbrM1qpo+DtQXdvD0/HFdMuThIRMg2tdOpv+Tj5xKhq5XEXvnk30rvfr0wwvTwmnz8TQtLF+DKivjwdduzTg0v34mwIPVHEeHaQvvtioYbhNmllXryUYXGt9v+bclavmBUZdVH6qhOFUx+9x0mRnTbYn5x8vw924KM7z7WcYjWNaMFGXadFvdOUS9KxI1GlWpOx74M+TBu3bftrPG/3nO30fBfpOvy8uG6HAikit2o4rghtWheL6TIX8tGphurht1BWd8VMyNkosFtKkSTUuXLxHSIgv3bo2JCExi1u3kxk2tC0ikZCTp6KJuZPKhYv36NFdX/S4RYmsUC8viQ0/kQ6BQMD4574r/O+Nt9dRq2YQ+RYkY7io/FSJo7rWIe+wNaYvt7LW0b36N/hLGt2X4L9MYt5Bbmb9QrdqxlVpXTgXWa6M0fVfQyAU4BfkgzStqL6RQCjgrW8nO3S97WsOkivNJzU2jVxpPtfPRANw52o8H41dSmiNILz8PAmtEUTzzg31SonE3Uzk2LZzJN9LJy87n8z7Kudn9lxi/vhlemPb92tJSI1Ag7G50nxSYtOtGpuZImXPhmPkSfNJiU0rVPwGSt1zk471mb7yeT5/8Xs2Lt7Gjl8OUr1eOJnJUhLvpAAwYsoA/ly+w6G/45J8/Of/27u3kCbDOAzgj4claZmUpDMKxTyUomhnooNWKiUuQ5Du0sIisYiKDooW0kGIltBFGOyiiyIiPERXYSk0zcFMiGJNLdNl2tSpmSNxWxezzU/NvtlKnM/vany8L3thbDzft/f9/8/gYvoNKKvUSPHNgu/SRViwUIKez9YSCK7ecsXZhSrDI6WiTkPOBdPXV55aTPRKlFc2wmQyw8PDfm8/OmqGRvMFslTrX6L70+KhrGtGh64PR4/ssI17XK4GAKSnCfeWOnPftsViwT2Fc3+7aO5wieD0plcOAPhh6kN1Rybc4A7LuOKUEncfvNYXw2jST/omp4eq/+ta55NlUj9crTiDa9l30PZWZ2u3Ath71TmbPFcx5fWh/u9QVgk/66xLGYLgpH7+FnfzH06aq9f1Qa8TVoovvJ8nCD9/M7dd04myCw+mXPef1rz74FbcPK6A2WTGYO8QBnvt/fyyijKQeXrfPw9OcQlrcbL0EG7lWQPSxJ6C5JhzhbLZXoJTBQX5OTR+w/oQeHt7oaZWg12J9jpw1S/eYdg4gnXxwQCAyHApXiq10DZ3wdvbuq8oMGAJmlu64OYGRIT/uR/dTISHBULbzP2Z85lLtFyp7zo147lbAuVOXAkR0fyTkFQCf//F6On5BonEA15enhga+oHt2yJwqUAmOFWXkFSC3GO7kHFg+mrSv9q4SKV+GOgfxrBxBGmpcTh1Isk2JjG5BBaLfVN6RVUjSm8/Q/KeaJw/az31ZTSOYK9MDkVZNkKC7UVjPrbpkZ2jsM2tq29BY9MnfP06iHZdH7q7BxAbswoBy33h4+OFnMP2p1o1tRpcvlIJAPD09MDo2P4+V2k5QwBcveUKww8R0eyKjlqBlD3RePhIhXeaToStDkBRvmzGtY1uXM9EeVUjGlSt1jpOBTJs3CDsJDDxvj9qbN/fmnFPZcV6pWrFk6dNgmsNqlbb6/HBaeeOSLzXdkGl/gCdzoC42FXYvCnU4fekucklnjgREREROdFvE79LnKojIiIi+h8YnIiIiIhEYnAiIiIiAmAoNsBQbJh2DPc4EREREQnN+FSd67Z6JiIiInIQ/6ojIiIiEonBiYiIiEgkBiciIiIikRiciIiIiERicCIiIiISicGJiIiISKSfp2l0nJ2TZM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure (figsize = (8,8), facecolor = None)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.tight_layout(pad = 0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do spelling correction, lemmatization, sentiment analysis, summarazing texts, translation & language detection, text classifications and several other interesting stuffs using textblob library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lemmatization:\n",
    "\n",
    "Now Lets recall what is lemmatization. (Double click the markdown cell to enter your answer and press ctrl+Enter thereafter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\tranq\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now Lemmatization\n",
    "from textblob import Word\n",
    "df['review_lemmatized'] = df['review_cleaned'].apply(lambda x: \" \".join(Word(word).lemmatize() for word in x.split()))\n",
    "#Stuck with syntax ? See the PythonBasics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what could be the advantage of Lemmatization for text processing tasks ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_lowercase</th>\n",
       "      <th>review_nopunct</th>\n",
       "      <th>review_nostopword</th>\n",
       "      <th>review_cleaned</th>\n",
       "      <th>review_lemmatized</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee and excellent artesa...</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>one of my \"must go\" places in summer! i've tri...</td>\n",
       "      <td>one of my must go places in summer ive tried e...</td>\n",
       "      <td>one must go places summer ive tried every sing...</td>\n",
       "      <td>one must go places summer every single love</td>\n",
       "      <td>one must go place summer every single love</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.407143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "      <td>great ice cream. comes at a price though</td>\n",
       "      <td>great ice cream comes at a price though</td>\n",
       "      <td>great ice cream comes price though</td>\n",
       "      <td>great comes price though</td>\n",
       "      <td>great come price though</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>best ice cream by the biggest scoops i've trie...</td>\n",
       "      <td>best ice cream by the biggest scoops ive tried...</td>\n",
       "      <td>best ice cream biggest scoops ive tried town</td>\n",
       "      <td>best biggest scoops town</td>\n",
       "      <td>best biggest scoop town</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \\\n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3   \n",
       "1  Great Service good coffee and excellent artesa...      8.8   \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0   \n",
       "3           Great ice cream. Comes at a price though      7.9   \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8   \n",
       "\n",
       "                                    review_lowercase  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my \"must go\" places in summer! i've tri...   \n",
       "3           great ice cream. comes at a price though   \n",
       "4  best ice cream by the biggest scoops i've trie...   \n",
       "\n",
       "                                      review_nopunct  \\\n",
       "0  chili chocolate flavour to burn you and matcha...   \n",
       "1  great service good coffee and excellent artesa...   \n",
       "2  one of my must go places in summer ive tried e...   \n",
       "3            great ice cream comes at a price though   \n",
       "4  best ice cream by the biggest scoops ive tried...   \n",
       "\n",
       "                                   review_nostopword  \\\n",
       "0  chili chocolate flavour burn matcha tea flavou...   \n",
       "1  great service good coffee excellent artesan ic...   \n",
       "2  one must go places summer ive tried every sing...   \n",
       "3                 great ice cream comes price though   \n",
       "4       best ice cream biggest scoops ive tried town   \n",
       "\n",
       "                                      review_cleaned  \\\n",
       "0  chili chocolate flavour burn matcha tea flavou...   \n",
       "1  great service good coffee excellent artesan ic...   \n",
       "2        one must go places summer every single love   \n",
       "3                           great comes price though   \n",
       "4                           best biggest scoops town   \n",
       "\n",
       "                                   review_lemmatized  polarity  subjectivity  \n",
       "0  chili chocolate flavour burn matcha tea flavou...  1.000000      0.300000  \n",
       "1  great service good coffee excellent artesan ic...  0.833333      0.783333  \n",
       "2         one must go place summer every single love  0.214286      0.407143  \n",
       "3                            great come price though  0.800000      0.750000  \n",
       "4                            best biggest scoop town  1.000000      0.300000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from textblob import TextBlob\n",
    "#Calculate sentiment on whole review\n",
    "#df['lemmatized'].apply(lambda x: TextBlob(x).sentiment) #Gives us with tuple of polarity and subjectivity\n",
    "\n",
    "df['polarity'], df['subjectivity'] = df['review_lemmatized'].apply(lambda x: TextBlob(x).sentiment[0]), df['review_lemmatized'].apply(lambda x: TextBlob(x).sentiment[1])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sentiment function of textblob returns two properties, polarity, and subjectivity. We can see that for 'Bus Gelato', polarity is 0.833, which means that the statement is positive and 0.7833 subjectivity refers that mostly it is a public opinion and not a factual information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_cleaned</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>one must go places summer every single love</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.407143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "      <td>great comes price though</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>best biggest scoops town</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  \\\n",
       "0                         Jädelino   \n",
       "1                       Bus Gelato   \n",
       "2          Helsingin jäätelötehdas   \n",
       "3          Helsingin Jäätelötehdas   \n",
       "4  Helsingin jäätelötehtaan kioski   \n",
       "\n",
       "                                             Reviews  Ratings  \\\n",
       "0  Chili chocolate flavour to burn you and matcha...      9.3   \n",
       "1  Great Service good coffee and excellent artesa...      8.8   \n",
       "2  One of my \"must go\" places in summer! I've tri...      8.0   \n",
       "3           Great ice cream. Comes at a price though      7.9   \n",
       "4  Best ice cream by the biggest scoops I've trie...      7.8   \n",
       "\n",
       "                                      review_cleaned  polarity  subjectivity  \n",
       "0  chili chocolate flavour burn matcha tea flavou...  1.000000      0.300000  \n",
       "1  great service good coffee excellent artesan ic...  0.833333      0.783333  \n",
       "2        one must go places summer every single love  0.214286      0.407143  \n",
       "3                           great comes price though  0.800000      0.750000  \n",
       "4                           best biggest scoops town  1.000000      0.300000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets drop the unwanted columns\n",
    "df.drop(['review_lowercase','review_nopunct','review_nostopword','review_lemmatized'], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why even there are negative polarity? From the histogram of the ratings above, we see there are either neutral or positive reviews only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Title</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>review_cleaned</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jädelino</td>\n",
       "      <td>Chili chocolate flavour to burn you and matcha...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>chili chocolate flavour burn matcha tea flavou...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bus Gelato</td>\n",
       "      <td>Great Service good coffee and excellent artesa...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>great service good coffee excellent artesan ic...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helsingin jäätelötehdas</td>\n",
       "      <td>One of my \"must go\" places in summer! I've tri...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>one must go places summer every single love</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.407143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Great ice cream. Comes at a price though</td>\n",
       "      <td>7.9</td>\n",
       "      <td>great comes price though</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helsingin jäätelötehtaan kioski</td>\n",
       "      <td>Best ice cream by the biggest scoops I've trie...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>best biggest scoops town</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fazer Café</td>\n",
       "      <td>I have been coming here for many years great c...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>coming many years great coffee food</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Käpylän Lippakioski</td>\n",
       "      <td>Good coffee brilliant ice-cream nice hippie-li...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>good coffee brilliant icecream nice hippielike...</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Magnum Helsinki</td>\n",
       "      <td>Excellent Ice Cream with a speciaö twist</td>\n",
       "      <td>7.5</td>\n",
       "      <td>excellent speciaö twist</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Café Vanille</td>\n",
       "      <td>Cute kind of authentic place. Nice Finnish pas...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>cute kind authentic place nice finnish pastrie...</td>\n",
       "      <td>0.353571</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Robert's Coffee Gelato Factory</td>\n",
       "      <td>They have fabulous vegan ice cream too. Try pi...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>fabulous vegan pistachio</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jäätelökahvila Paja</td>\n",
       "      <td>Ice cream pancakes</td>\n",
       "      <td>7.1</td>\n",
       "      <td>pancakes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Helsingin Jäätelötehdas</td>\n",
       "      <td>Maraschinos on rusinoit buu!</td>\n",
       "      <td>7.1</td>\n",
       "      <td>maraschinos rusinoit buu</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ciao! Caffé</td>\n",
       "      <td>Good coffee and excellent gelato made in the p...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>good coffee excellent gelato made premises</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Munkkiniemen lippakioski</td>\n",
       "      <td>Free wlan and tasty icecream!</td>\n",
       "      <td>6.7</td>\n",
       "      <td>free wlan tasty icecream</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chjoko</td>\n",
       "      <td>Cute chocolate and ice cream place. They have ...</td>\n",
       "      <td>8.4</td>\n",
       "      <td>cute chocolate place also vegan oat milk raspb...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Roberts Coffee Gelato Factory</td>\n",
       "      <td>Worse lunch buffet than in esplanadi. No take ...</td>\n",
       "      <td>6.1</td>\n",
       "      <td>worse lunch buffet esplanadi lunch boo</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sinisen huvilan kahvila</td>\n",
       "      <td>Take an ice cream and enjoy the view :)</td>\n",
       "      <td>9.2</td>\n",
       "      <td>enjoy view</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ciao! Caffé</td>\n",
       "      <td>Very tasty ice cream with yoghurt :)</td>\n",
       "      <td>5.9</td>\n",
       "      <td>tasty yoghurt</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kahvila Sävy</td>\n",
       "      <td>Try the new \"Twitter McFlurry\" #delicious #dou...</td>\n",
       "      <td>9.1</td>\n",
       "      <td>new twitter mcflurry delicious doubleshot affo...</td>\n",
       "      <td>0.568182</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sokerileipuri Alenius</td>\n",
       "      <td>Great portion of ice cream.</td>\n",
       "      <td>7.3</td>\n",
       "      <td>great portion</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ciao! Caffé</td>\n",
       "      <td>Delicious vegan ice cream. Ask which flavours ...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>delicious vegan ask flavours vegan</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brooklyn Cafe</td>\n",
       "      <td>Try the super yummy chocolate brownie with ice...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>super yummy chocolate brownie 6</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Title  \\\n",
       "0                          Jädelino   \n",
       "1                        Bus Gelato   \n",
       "2           Helsingin jäätelötehdas   \n",
       "3           Helsingin Jäätelötehdas   \n",
       "4   Helsingin jäätelötehtaan kioski   \n",
       "5                        Fazer Café   \n",
       "6               Käpylän Lippakioski   \n",
       "7                   Magnum Helsinki   \n",
       "8                      Café Vanille   \n",
       "9    Robert's Coffee Gelato Factory   \n",
       "10              Jäätelökahvila Paja   \n",
       "11          Helsingin Jäätelötehdas   \n",
       "12                      Ciao! Caffé   \n",
       "13         Munkkiniemen lippakioski   \n",
       "14                           Chjoko   \n",
       "15    Roberts Coffee Gelato Factory   \n",
       "16          Sinisen huvilan kahvila   \n",
       "17                      Ciao! Caffé   \n",
       "18                     Kahvila Sävy   \n",
       "19            Sokerileipuri Alenius   \n",
       "20                      Ciao! Caffé   \n",
       "21                    Brooklyn Cafe   \n",
       "\n",
       "                                              Reviews  Ratings  \\\n",
       "0   Chili chocolate flavour to burn you and matcha...      9.3   \n",
       "1   Great Service good coffee and excellent artesa...      8.8   \n",
       "2   One of my \"must go\" places in summer! I've tri...      8.0   \n",
       "3            Great ice cream. Comes at a price though      7.9   \n",
       "4   Best ice cream by the biggest scoops I've trie...      7.8   \n",
       "5   I have been coming here for many years great c...      8.0   \n",
       "6   Good coffee brilliant ice-cream nice hippie-li...      7.8   \n",
       "7            Excellent Ice Cream with a speciaö twist      7.5   \n",
       "8   Cute kind of authentic place. Nice Finnish pas...      7.7   \n",
       "9   They have fabulous vegan ice cream too. Try pi...      7.3   \n",
       "10                                 Ice cream pancakes      7.1   \n",
       "11                       Maraschinos on rusinoit buu!      7.1   \n",
       "12  Good coffee and excellent gelato made in the p...      7.1   \n",
       "13                      Free wlan and tasty icecream!      6.7   \n",
       "14  Cute chocolate and ice cream place. They have ...      8.4   \n",
       "15  Worse lunch buffet than in esplanadi. No take ...      6.1   \n",
       "16            Take an ice cream and enjoy the view :)      9.2   \n",
       "17               Very tasty ice cream with yoghurt :)      5.9   \n",
       "18  Try the new \"Twitter McFlurry\" #delicious #dou...      9.1   \n",
       "19                        Great portion of ice cream.      7.3   \n",
       "20  Delicious vegan ice cream. Ask which flavours ...      5.8   \n",
       "21  Try the super yummy chocolate brownie with ice...      8.6   \n",
       "\n",
       "                                       review_cleaned  polarity  subjectivity  \n",
       "0   chili chocolate flavour burn matcha tea flavou...  1.000000      0.300000  \n",
       "1   great service good coffee excellent artesan ic...  0.833333      0.783333  \n",
       "2         one must go places summer every single love  0.214286      0.407143  \n",
       "3                            great comes price though  0.800000      0.750000  \n",
       "4                            best biggest scoops town  1.000000      0.300000  \n",
       "5                 coming many years great coffee food  0.650000      0.625000  \n",
       "6   good coffee brilliant icecream nice hippielike...  0.733333      0.866667  \n",
       "7                             excellent speciaö twist  1.000000      1.000000  \n",
       "8   cute kind authentic place nice finnish pastrie...  0.353571      0.807143  \n",
       "9                            fabulous vegan pistachio  0.400000      1.000000  \n",
       "10                                           pancakes  0.000000      0.000000  \n",
       "11                           maraschinos rusinoit buu  0.000000      0.000000  \n",
       "12         good coffee excellent gelato made premises  0.850000      0.800000  \n",
       "13                           free wlan tasty icecream  0.400000      0.800000  \n",
       "14  cute chocolate place also vegan oat milk raspb...  0.500000      1.000000  \n",
       "15             worse lunch buffet esplanadi lunch boo -0.400000      0.600000  \n",
       "16                                         enjoy view  0.400000      0.500000  \n",
       "17                                      tasty yoghurt  0.000000      0.000000  \n",
       "18  new twitter mcflurry delicious doubleshot affo...  0.568182      0.727273  \n",
       "19                                      great portion  0.800000      0.750000  \n",
       "20                 delicious vegan ask flavours vegan  1.000000      1.000000  \n",
       "21                    super yummy chocolate brownie 6  0.333333      0.666667  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try language detection using textblob and translate the review and then try again\n",
    "\n",
    "No. Not even google can detect! So no luck this time ;)\n",
    "\n",
    "So I will rely on ratings alone then\n",
    "\n",
    "Now the question is would you rely on textblob?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "blob = TextBlob('maraschinos rusinoit buu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ro'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.detect_language()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #No luck\n",
    "# blob.translate(from_lang= 'ro', to ='en')  #Shows error :("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Let's try this\n",
    "# blob.translate(to ='en') #Not even now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['chili',\n",
       "  'chocolate',\n",
       "  'flavour',\n",
       "  'burn',\n",
       "  'matcha',\n",
       "  'tea',\n",
       "  'flavour',\n",
       "  'extinguish',\n",
       "  'yes',\n",
       "  'please',\n",
       "  'best',\n",
       "  'town'],\n",
       " ['great', 'service', 'good', 'coffee', 'excellent', 'artesan', 'icecream'],\n",
       " ['one', 'must', 'go', 'places', 'summer', 'every', 'single', 'love'],\n",
       " ['great', 'comes', 'price', 'though'],\n",
       " ['best', 'biggest', 'scoops', 'town']]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_corpus(data):\n",
    "    \"Creates a list of lists containing words from each sentence\"\n",
    "    corpus = []\n",
    "    for col in ['review_cleaned']:   #This is a dataframe with review_cleaned column only\n",
    "        for sentence in data[col].iteritems():     #This is now a generator of tuples. The second parameter has sentences. what is the first one ?\n",
    "            word_list = sentence[1].split()        #Split the sentences in a comma separated words within a list. lots of demos in python practice\n",
    "            corpus.append(word_list)               #Now we make a full corpus combining all the wordlists or the documents\n",
    "            \n",
    "    return corpus\n",
    "\n",
    "corpus = build_corpus(df)        \n",
    "corpus[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What pop corn is being popped ?\n",
    "\n",
    "Let me think in detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.iteritems.html\n",
    "#type(df['review_cleaned'])\n",
    "# sen = df['review_cleaned'].iteritems()\n",
    "# next(sen)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sum (len(list) for list in corpus)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above function it is obvious that corpus is a collection of text documents. (Try len (corpus). What is the result ?)\n",
    "Each processed review is inserted in a list now, which is a document. So corpus is a collection of documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day3: Some High Level Stuffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "unable to import 'smart_open.gcs', disabling that module\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.00054435,  0.01496308, -0.00855039,  0.00156847,  0.01448958,\n",
       "        0.00406978, -0.00241559, -0.01291232,  0.00943908, -0.01131773,\n",
       "        0.00658548,  0.01429454,  0.0110494 , -0.01212319, -0.00916778,\n",
       "       -0.01130225, -0.00614717,  0.00563962, -0.00128897, -0.00635055,\n",
       "       -0.01367303,  0.01209151,  0.00520681, -0.00087466, -0.00131598,\n",
       "       -0.01517965, -0.01182015, -0.01454729, -0.00060454, -0.01167786,\n",
       "       -0.00513625,  0.01082508], dtype=float32)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models import word2vec\n",
    "model = word2vec.Word2Vec(corpus, size=32, window=20, min_count=1) # Adjust min_count if you get error. 1 being the minimum\n",
    "model.wv['service']  #Lets see the word vector service trained in our model.wv (Is a numpy vector of a word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without understanding the details of neural nets, we still use it for our advantage. Also refer to the Appendix section to understand TSNE and PCA. The input to this neural net: word2vec is a text corpus and the output is a set of (feature) vectors.These vectors are used to perform operations on the vectors such as vector lookup, distance, similarity etc. (We are going to see one magic that can be done because of these word vectors by using TSNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "def tsne_plot(model):\n",
    "    \"Creates a TSNE model and plots it\"\n",
    "    labels = []   #90 words corresponding to their tokens. Why 90 ? There were 120 words in corpus, we found above. \n",
    "    tokens = []   #Each token of length 32 as we defined above\n",
    "\n",
    "    '''The vocabulary is in the vocab field of the Word2Vec model's wv property, as a dictionary, with \n",
    "    the keys being each token (word).'''\n",
    "    for word in model.wv.vocab:  #model.wv is the mapping between word and vectors (keyed vector) we discussed in above cell also\n",
    "        tokens.append(model[word])   #This is array of the features of the word\n",
    "        labels.append(word)   #And this is list of course\n",
    "    \n",
    "    tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state = 0) #Try varying n_iter & perplexity\n",
    "    new_values = tsne_model.fit_transform(tokens)  # 90\n",
    "\n",
    "    x = []   #Are these the x and y coordinates ?\n",
    "    y = []\n",
    "    for value in new_values:\n",
    "        x.append(value[0])\n",
    "        y.append(value[1])\n",
    "        \n",
    "    plt.figure(figsize=(12, 12))   #Make figure little bigger ?\n",
    "    for i in range(len(x)):\n",
    "        plt.scatter(x[i],y[i])    #This will give scatter plot between x and y\n",
    "        plt.annotate(labels[i],     #Too many parameters. Lets just check the documentation of matplotlib.pyplot.annotate\n",
    "                     xy=(x[i], y[i]),\n",
    "                     xytext=(5, 5),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')\n",
    "    \n",
    "#     print (len(tokens))  #This is how i found there are 90 tokens ;)\n",
    "    #print(type(labels))\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tranq\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne_plot(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flattened_corpus = [item for sublist in corpus for item in sublist]\n",
    "# len(set(flattened_corpus))  #So, yes, there are 90 labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x =  [1,2,1.5,2.3,1.7]\n",
    "# y = [-4,-3,-4.5,-3.3,-4.9]\n",
    "# labels = ['a','b','c','d','e']\n",
    "\n",
    "# plt.figure(figsize=(6, 6))   \n",
    "# for i in range(len(x)):\n",
    "#     plt.scatter(x[i],y[i])    #This will give scatter plot between x and y\n",
    "#     plt.annotate(labels[i],\n",
    "#                  xy=(x[i], y[i]),     \n",
    "#                   ha='right',\n",
    "#                  va='bottom')\n",
    "# plt.show;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the question is what is TSNE ?\n",
    "\n",
    "Did we represent each word (that had so much features) as a point and did we show the relationship with other words ??\n",
    "\n",
    "But i dont get sense out of this graph. This was just a toy example. We can't keep playing with toys. Let's go to bigger project next so that we can make sense of this T-SNE stuff "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feeling dizzy now 😵. \n",
    "\n",
    "Take a break. Get a cup of chocolate or an icecream and see it again\n",
    "\n",
    "Wow! It isnt that difficult now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did we perform manual parameter tuning somewhere ?\n",
    "\n",
    "And what is PCA now ? [Hint: Dimensionality Reduction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tranq\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('affogato', 0.4602969288825989),\n",
       " ('tea', 0.4192280173301697),\n",
       " ('one', 0.34965670108795166),\n",
       " ('good', 0.34959790110588074),\n",
       " ('cute', 0.30896663665771484)]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('yoghurt')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Web Scraping and HTML Parsing\n",
    "\n",
    "- Regex & python string functions\n",
    "\n",
    "- Exception Handeling\n",
    "\n",
    "- File Operations\n",
    "\n",
    "- Processing dataframes (pandas) and playing with dataframe\n",
    "\n",
    "- Sentiment Analysis finally (Using textblob library)\n",
    "\n",
    "- Some interesting plots\n",
    "\n",
    "- Taken a small dataset so that everything can be transparent and it would be easy to follow\n",
    "\n",
    "- Experiment what data science cant solve for us and what it can be effective for"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Where to go further from here: \n",
    "\n",
    "I am now sure you can follow the part 3 from the link, which is about sentiment analysis of Social Media during COVID 19: \n",
    "https://www.helsinki.fi/en/news/data-science-news/new-webinar-ai-and-data-science-against-covid-19 (Third Video)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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