Part 0.2: Text_cleaning_Tourism_data_regex.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_Basics/blob/main/Part%200.2%3A%20Text_cleaning_Tourism_data_regex.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# We first need to import dataset that we will use.\n",
        "# The dataset is available on Github. If you run below codes, you can read the \n",
        "# dataset directly from Github without downloding it your local computer.\n",
        "# Remember: You can upload up to 25MB files to GitHub\n",
        "\n",
        "# Pandas is the library that makes reading a dataset easy. \n",
        "# We can start with importing Pandas\n",
        "\n",
        "import pandas as pd\n",
        "# Dataset will be stored in a Pandas Dataframe\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/DataScience-in-Tourism/Chapter-15-Introduction-Natural-Language-Processing/main/travelsomeday.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Check the first column of the dataset\n",
        "df.head(1)"
      ],
      "execution_count": null,
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              "      <th>author</th>\n",
              "      <th>title</th>\n",
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              "      <th>source</th>\n",
              "      <th>user_id</th>\n",
              "      <th>profile_image_url</th>\n",
              "      <th>user_statuses_count</th>\n",
              "      <th>user_friends_count</th>\n",
              "      <th>user_followers_count</th>\n",
              "      <th>user_created_at</th>\n",
              "      <th>user_bio</th>\n",
              "      <th>user_location</th>\n",
              "      <th>lang</th>\n",
              "      <th>coords</th>\n",
              "      <th>Unnamed: 18</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>https://twitter.com/_LoveCaribbean/statuses/1252926931634458624</th>\n",
              "      <th>https://twitter.com/_LoveCaribbean/statuses/1252926931634458624</th>\n",
              "      <th>2020-04-22 07:47:13</th>\n",
              "      <th>_LoveCaribbean</th>\n",
              "      <th>RT @grenadaexplorer: A tropical treasure to cherish forever 🌅 For exclusive #TravelTips and island insights visit https://t.co/wFawu512ad #…</th>\n",
              "      <th>A tropical treasure to cherish forever 🌅 For exclusive #TravelTips and island insights visit https://t.co/wFawu512ad #Grenada #TimeToLime #PureGrenada #SpiceIsle #ILoveGrenada #DreamTodayTravelTomorrow #TravelSomeDay #WeBelieveInTravel #GrenadaDreaming #CaribbeanDreaming https://t.co/64Kg2mPH3l</th>\n",
              "      <th>Twitter Web App</th>\n",
              "      <th>1356684936</th>\n",
              "      <th>https://pbs.twimg.com/profile_images/474150473876770817/aNbE9lsf_normal.jpeg</th>\n",
              "      <th>8047</th>\n",
              "      <th>1099</th>\n",
              "      <th>3019</th>\n",
              "      <th>2013-04-16 07:55:23</th>\n",
              "      <th>#CaribbeanDreaming The diversity of the Caribbean makes it a wonderful holiday destination whatever your tastes or interests.</th>\n",
              "      <th>London</th>\n",
              "      <th>en</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "                                                                                                                                                                                                                                                                                                                                                                                                                                        id  ...  Unnamed: 18\n",
              "1 https://twitter.com/_LoveCaribbean/statuses/125... https://twitter.com/_LoveCaribbean/statuses/125... 2020-04-22 07:47:13 _LoveCaribbean RT @grenadaexplorer: A tropical treasure to che... A tropical treasure to cherish forever 🌅 For ex... Twitter Web App 1356684936 https://pbs.twimg.com/profile_images/4741504738... 8047 1099 3019 2013-04-16 07:55:23 #CaribbeanDreaming The diversity of the Caribbe... London en NaN NaN NaN  ...          NaN\n",
              "\n",
              "[1 rows x 19 columns]"
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        "As we can see from here, the text data is at the index. We need to convert indexes to columns.\n",
        "\n",
        "In order to do this, first we need to give a name to columns in the index"
      ]
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        "# Real data can be messy. In here, we don't have a column name. \n",
        "# Let's name the column\n",
        "# For lazy naming, we will go with simple names with numbers. You can follow any\n",
        "# naming method you prefer\n",
        "df= df.rename_axis(['id1','url','url2','date','username', 'quote', 'tweet','as1','as2','as3','as4','as5','as6','as7','as8','as9','as10','as11','a12'])\n",
        "\n",
        "# Check the name of the columns\n",
        "df.columns.to_list()"
      ],
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              "['id',\n",
              " 'guid',\n",
              " 'link',\n",
              " 'pubdate',\n",
              " 'author',\n",
              " 'title',\n",
              " 'description',\n",
              " 'source',\n",
              " 'user_id',\n",
              " 'profile_image_url',\n",
              " 'user_statuses_count',\n",
              " 'user_friends_count',\n",
              " 'user_followers_count',\n",
              " 'user_created_at',\n",
              " 'user_bio',\n",
              " 'user_location',\n",
              " 'lang',\n",
              " 'coords',\n",
              " 'Unnamed: 18']"
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      "source": [
        "# Check a random row to see whether your naming is in affect or not\n",
        "df.sample(1)"
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              "      <th>url</th>\n",
              "      <th>url2</th>\n",
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              "    <tr>\n",
              "      <th>420</th>\n",
              "      <th>https://twitter.com/TrulyMadly7/statuses/1251600725588746241</th>\n",
              "      <th>https://twitter.com/TrulyMadly7/statuses/1251600725588746241</th>\n",
              "      <th>2020-04-18 15:57:21</th>\n",
              "      <th>TrulyMadly7</th>\n",
              "      <th>RT @theCuriousPixie: Come and take an armchair tour of the Cotswolds in my NEW post @LovingBlogs @BloggerLS #TravelSomeDay   https://t.co/4…</th>\n",
              "      <th>Come and take an armchair tour of the Cotswolds in my NEW post @LovingBlogs @BloggerLS #TravelSomeDay   https://t.co/4BNHMpM9Ua https://t.co/BxQWIs9WKr</th>\n",
              "      <th>Twitter for iPhone</th>\n",
              "      <th>958849177</th>\n",
              "      <th>https://pbs.twimg.com/profile_images/3112148464/5bd987a8cfce2f358679502759e02d2a_normal.png</th>\n",
              "      <th>29070</th>\n",
              "      <th>1859</th>\n",
              "      <th>4017</th>\n",
              "      <th>2012-11-19 18:07:28</th>\n",
              "      <th>Megan and Rosie in #tmk: Tooting, London and Skye. We travel, photograph, write and parent, in any order. Next trip: My home.</th>\n",
              "      <th>London, England</th>\n",
              "      <th>en</th>\n",
              "      <th>NaN</th>\n",
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              "                                                                                                                                                                                                                                                                                                                                                                                                                                                      id  ...  Unnamed: 18\n",
              "id1 url                                                url2                                               date                username    quote                                              tweet                                              as1                as2       as3                                                as4   as5  as6  as7                 as8                                                as9             as10 as11 a12      ...             \n",
              "420 https://twitter.com/TrulyMadly7/statuses/125160... https://twitter.com/TrulyMadly7/statuses/125160... 2020-04-18 15:57:21 TrulyMadly7 RT @theCuriousPixie: Come and take an armchair ... Come and take an armchair tour of the Cotswolds... Twitter for iPhone 958849177 https://pbs.twimg.com/profile_images/3112148464... 29070 1859 4017 2012-11-19 18:07:28 Megan and Rosie in #tmk: Tooting, London and Sk... London, England en   NaN  NaN NaN  ...          NaN\n",
              "\n",
              "[1 rows x 19 columns]"
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        "outputId": "bf6cadb0-ecd9-4e5f-c700-efaf2c611138"
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      "source": [
        "# Sometimes we may get issue causing from indexing. In order to prevent it, reset index\n",
        "df.reset_index(inplace=True)\n",
        "# to save reseting index, you need to save it:\n",
        "# df = df.reset_index(inplace=True)\n",
        "\n",
        "# Check a random row\n",
        "df.sample(1)"
      ],
      "execution_count": null,
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        {
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              "      <th>guid</th>\n",
              "      <th>link</th>\n",
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              "      <th>author</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>source</th>\n",
              "      <th>user_id</th>\n",
              "      <th>profile_image_url</th>\n",
              "      <th>user_statuses_count</th>\n",
              "      <th>user_friends_count</th>\n",
              "      <th>user_followers_count</th>\n",
              "      <th>user_created_at</th>\n",
              "      <th>user_bio</th>\n",
              "      <th>user_location</th>\n",
              "      <th>lang</th>\n",
              "      <th>coords</th>\n",
              "      <th>Unnamed: 18</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5797</th>\n",
              "      <td>5798</td>\n",
              "      <td>https://twitter.com/clairesturz/statuses/12739...</td>\n",
              "      <td>https://twitter.com/clairesturz/statuses/12739...</td>\n",
              "      <td>2020-06-19 10:10:51</td>\n",
              "      <td>clairesturz</td>\n",
              "      <td>Mount Roraima: The Lost World of #Venezuela #t...</td>\n",
              "      <td>Mount Roraima: The Lost World of #Venezuela #t...</td>\n",
              "      <td>Revive Social App</td>\n",
              "      <td>874023301</td>\n",
              "      <td>https://pbs.twimg.com/profile_images/613107654...</td>\n",
              "      <td>42181</td>\n",
              "      <td>19532</td>\n",
              "      <td>24749</td>\n",
              "      <td>2012-10-11 13:32:59</td>\n",
              "      <td>Helping you plan your best future travel adven...</td>\n",
              "      <td>England, United Kingdom</td>\n",
              "      <td>en</td>\n",
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              "       id1  ... Unnamed: 18\n",
              "5797  5798  ...         NaN\n",
              "\n",
              "[1 rows x 38 columns]"
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      "source": [
        "# Checking random five tweets\n",
        "df.tweet.sample(5).to_list()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"Bittersweet! After years of construction, #LaGuardiaAirport\\\\'s Terminal B is now open and it is STUNNING! But who will use it anytime soon? https://t.co/D0b3lB7kNm #TravelSomeday\",\n",
              " 'A #Yorkshire #Foodie Adventure: Finding the Best Food in Yorkshire #travelsomeday #armchairtravel #staythefhome https://t.co/vvCfeRQfyI',\n",
              " 'Chios, Greece. #travelsomeday #home #agean https://t.co/O5H7XUBGYT',\n",
              " 'Lucha Libre in #Mexico City – Your Guide to Mexican Wrestling! #travelsomeday #armchairtravel #plannowtravellater https://t.co/8tJq3coDbc',\n",
              " 'The Best Things to Do in Mexico City #travelsomeday #armchairtravel #staythefhome https://t.co/qx4sOOMfoE #luchalibre']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "ckzuwKkfWdDu",
        "outputId": "3dcd173c-3f36-4b7b-a29b-f9912c79add4"
      },
      "source": [
        "#We have two columns as a text. Let's check random 10 of them and decide which \n",
        "# one we will work on in the analysis process\n",
        "df.loc[:,['quote', 'tweet']].sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>quote</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>RT @clairesturz: Semuc Champey #Tour in #Guate...</td>\n",
              "      <td>Semuc Champey #Tour in #Guatemala #travelsomed...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5883</th>\n",
              "      <td>Lake Atitlán #Guatemala – Your Ultimate Guide!...</td>\n",
              "      <td>Lake Atitlán #Guatemala – Your Ultimate Guide!...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5507</th>\n",
              "      <td>How to #Travel Around the #UK on a Budget #tra...</td>\n",
              "      <td>How to #Travel Around the #UK on a Budget #tra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3177</th>\n",
              "      <td>I can’t wait to get out and explore my favouri...</td>\n",
              "      <td>I can’t wait to get out and explore my favouri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1858</th>\n",
              "      <td>RT @RoarLoudTravel: A66 I want to go back to C...</td>\n",
              "      <td>A66 I want to go back to California, it has be...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>807</th>\n",
              "      <td>Will #Europe keep its borders closed until Sep...</td>\n",
              "      <td>Will #Europe keep its borders closed until Sep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1043</th>\n",
              "      <td>RT @always5star: Happy #EarthDay everyone! Her...</td>\n",
              "      <td>Happy #EarthDay everyone! Here are a few of my...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2634</th>\n",
              "      <td>RT @clairesturz: The Photo That Cost Me $500 #...</td>\n",
              "      <td>The Photo That Cost Me $500 #travelsomeday #ar...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>RT @clairesturz: If you love hostels, now is t...</td>\n",
              "      <td>If you love hostels, now is the time to #adopt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3827</th>\n",
              "      <td>RT @exec_tours: #TravelSomeDay Why play at Ata...</td>\n",
              "      <td>#TravelSomeDay Why play at Atalaya Golf? Two g...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  quote                                              tweet\n",
              "64    RT @clairesturz: Semuc Champey #Tour in #Guate...  Semuc Champey #Tour in #Guatemala #travelsomed...\n",
              "5883  Lake Atitlán #Guatemala – Your Ultimate Guide!...  Lake Atitlán #Guatemala – Your Ultimate Guide!...\n",
              "5507  How to #Travel Around the #UK on a Budget #tra...  How to #Travel Around the #UK on a Budget #tra...\n",
              "3177  I can’t wait to get out and explore my favouri...  I can’t wait to get out and explore my favouri...\n",
              "1858  RT @RoarLoudTravel: A66 I want to go back to C...  A66 I want to go back to California, it has be...\n",
              "807   Will #Europe keep its borders closed until Sep...  Will #Europe keep its borders closed until Sep...\n",
              "1043  RT @always5star: Happy #EarthDay everyone! Her...  Happy #EarthDay everyone! Here are a few of my...\n",
              "2634  RT @clairesturz: The Photo That Cost Me $500 #...  The Photo That Cost Me $500 #travelsomeday #ar...\n",
              "183   RT @clairesturz: If you love hostels, now is t...  If you love hostels, now is the time to #adopt...\n",
              "3827  RT @exec_tours: #TravelSomeDay Why play at Ata...  #TravelSomeDay Why play at Atalaya Golf? Two g..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHpJV9SYXjQc"
      },
      "source": [
        "It looks like quote column is mostly retweets. I will go with ***tweet*** column from now on, and apply text cleaning on it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBnBXqQAZ1m4"
      },
      "source": [
        "# **Text Cleaning**\n",
        "\n",
        "In this step, these text cleaning steps will be applied on tweets:\n",
        "\n",
        "\n",
        "\n",
        "*   Make all text lower case\n",
        "*   Language Detection\n",
        "*   Expanding Contractions\n",
        "*   Removal of Stop Words\n",
        "*   Removing URLs\n",
        "*   Remove html HTML tags\n",
        "*   Removing Emojis\n",
        "*   Remove Emoticons\n",
        "*   Stemming & Lemmatization (and the difference) and Tokenization\n",
        "*   Spell Correction\n",
        "*   Removing Punctuation\n",
        "*   TF-IDF\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqTAu8uf_dKy",
        "outputId": "7dc74af8-9f58-4dc1-ee2b-73b6e2187775"
      },
      "source": [
        "!pip install -q wordcloud\n",
        "import wordcloud\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "import string\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "stop = stopwords.words('english')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qm0Hy4OAC68n"
      },
      "source": [
        "# **Feature Extraction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9nTDz3mCs11"
      },
      "source": [
        "# Here are the feature enginnering scripts that can be done before data cleaning\n",
        "\n",
        "# Number of stopwords\n",
        "stop = stopwords.words('english')\n",
        "df['stopwords'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
        "\n",
        "# Number of punctuation\n",
        "def count_punct(text):\n",
        "    count = sum([1 for char in text if char in string.punctuation])\n",
        "    return count\n",
        "\n",
        "df['punctuation'] = df['tweet'].apply(lambda x: count_punct(x))\n",
        "\n",
        "# Number of hashtag characters\n",
        "df['hastags'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
        "\n",
        "# Number of numerics\n",
        "df['numerics'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
        "\n",
        "# Number of Uppercase words\n",
        "df['upper'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
        "\n",
        "# sentence count\n",
        "df['sentence_count'] = df[\"tweet\"].apply(lambda x: len(str(x).split(\".\")))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "xnnwbqDzDqrb",
        "outputId": "1157b309-7959-41b5-aa64-17f9901742a6"
      },
      "source": [
        "# Checking first five tweets\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>id1</th>\n",
              "      <th>url</th>\n",
              "      <th>url2</th>\n",
              "      <th>date</th>\n",
              "      <th>username</th>\n",
              "      <th>quote</th>\n",
              "      <th>tweet</th>\n",
              "      <th>as1</th>\n",
              "      <th>as2</th>\n",
              "      <th>as3</th>\n",
              "      <th>as4</th>\n",
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              "      <th>id</th>\n",
              "      <th>guid</th>\n",
              "      <th>link</th>\n",
              "      <th>pubdate</th>\n",
              "      <th>author</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>source</th>\n",
              "      <th>user_id</th>\n",
              "      <th>profile_image_url</th>\n",
              "      <th>user_statuses_count</th>\n",
              "      <th>user_friends_count</th>\n",
              "      <th>user_followers_count</th>\n",
              "      <th>user_created_at</th>\n",
              "      <th>user_bio</th>\n",
              "      <th>user_location</th>\n",
              "      <th>lang</th>\n",
              "      <th>coords</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>numerics</th>\n",
              "      <th>upper</th>\n",
              "      <th>sentence_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>https://twitter.com/_LoveCaribbean/statuses/12...</td>\n",
              "      <td>https://twitter.com/_LoveCaribbean/statuses/12...</td>\n",
              "      <td>2020-04-22 07:47:13</td>\n",
              "      <td>_LoveCaribbean</td>\n",
              "      <td>RT @grenadaexplorer: A tropical treasure to ch...</td>\n",
              "      <td>A tropical treasure to cherish forever 🌅 For e...</td>\n",
              "      <td>Twitter Web App</td>\n",
              "      <td>1356684936</td>\n",
              "      <td>https://pbs.twimg.com/profile_images/474150473...</td>\n",
              "      <td>8047</td>\n",
              "      <td>1099</td>\n",
              "      <td>3019</td>\n",
              "      <td>2013-04-16 07:55:23</td>\n",
              "      <td>#CaribbeanDreaming The diversity of the Caribb...</td>\n",
              "      <td>London</td>\n",
              "      <td>en</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>https://twitter.com/carolcruising/statuses/125...</td>\n",
              "      <td>https://twitter.com/carolcruising/statuses/125...</td>\n",
              "      <td>2020-04-22 07:44:47</td>\n",
              "      <td>carolcruising</td>\n",
              "      <td>RT @clairesturz: Backpacking in #Cuba on a Bud...</td>\n",
              "      <td>Backpacking in #Cuba on a Budget #travelsomeda...</td>\n",
              "      <td>Twitter Web App</td>\n",
              "      <td>319114596</td>\n",
              "      <td>https://pbs.twimg.com/profile_images/630068953...</td>\n",
              "      <td>26683</td>\n",
              "      <td>660</td>\n",
              "      <td>1227</td>\n",
              "      <td>2011-06-17 11:47:16</td>\n",
              "      <td>Carol Colborn is the author of Carolina: Cruis...</td>\n",
              "      <td>Phoenix, AZ</td>\n",
              "      <td>en</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>https://twitter.com/Frommers/statuses/12529251...</td>\n",
              "      <td>https://twitter.com/Frommers/statuses/12529251...</td>\n",
              "      <td>2020-04-22 07:40:04</td>\n",
              "      <td>Frommers</td>\n",
              "      <td>We likeee!  https://t.co/2g1HMCpsVn #DeltaAirl...</td>\n",
              "      <td>We likeee!  https://t.co/2g1HMCpsVn #DeltaAirl...</td>\n",
              "      <td>Hootsuite Inc.</td>\n",
              "      <td>66244710</td>\n",
              "      <td>https://pbs.twimg.com/profile_images/463423099...</td>\n",
              "      <td>69152</td>\n",
              "      <td>32598</td>\n",
              "      <td>153703</td>\n",
              "      <td>2009-08-16 21:44:37</td>\n",
              "      <td>The Most Trusted Name In Travel since 1957.</td>\n",
              "      <td>Everywhere</td>\n",
              "      <td>en</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>https://twitter.com/clairesturz/statuses/12529...</td>\n",
              "      <td>https://twitter.com/clairesturz/statuses/12529...</td>\n",
              "      <td>2020-04-22 07:32:39</td>\n",
              "      <td>clairesturz</td>\n",
              "      <td>Experience Buenos Aires like a local #travelso...</td>\n",
              "      <td>Experience Buenos Aires like a local #travelso...</td>\n",
              "      <td>Revive Social App</td>\n",
              "      <td>874023301</td>\n",
              "      <td>https://pbs.twimg.com/profile_images/613107654...</td>\n",
              "      <td>39950</td>\n",
              "      <td>19520</td>\n",
              "      <td>24799</td>\n",
              "      <td>2012-10-11 13:32:59</td>\n",
              "      <td>Helping you plan your best future travel adven...</td>\n",
              "      <td>England, United Kingdom</td>\n",
              "      <td>es</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>https://twitter.com/grenadaexplorer/statuses/1...</td>\n",
              "      <td>https://twitter.com/grenadaexplorer/statuses/1...</td>\n",
              "      <td>2020-04-22 07:32:02</td>\n",
              "      <td>grenadaexplorer</td>\n",
              "      <td>Who can\\'t wait for Grenada Sailing Festival 2...</td>\n",
              "      <td>Who can\\'t wait for Grenada Sailing Festival 2...</td>\n",
              "      <td>Buffer</td>\n",
              "      <td>17083471</td>\n",
              "      <td>https://pbs.twimg.com/profile_images/703782166...</td>\n",
              "      <td>4070</td>\n",
              "      <td>856</td>\n",
              "      <td>1882</td>\n",
              "      <td>2008-10-31 07:22:26</td>\n",
              "      <td>Travel &amp; Island Guide for Grenada in the Carib...</td>\n",
              "      <td>Grenada</td>\n",
              "      <td>en</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id1  ... sentence_count\n",
              "0    1  ...              3\n",
              "1    2  ...              2\n",
              "2    3  ...              2\n",
              "3    4  ...              2\n",
              "4    5  ...              3\n",
              "\n",
              "[5 rows x 44 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UT6-BbyQC1CI"
      },
      "source": [
        "# Text Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Km_eY9XauUd"
      },
      "source": [
        "### **Make all text lower case**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhqQdkrbXisS",
        "outputId": "56c84e65-7482-4c58-ecbe-acb1be06b1c9"
      },
      "source": [
        "# Apply lower casing step on all tweets\n",
        "df['tweet'] = df['tweet'].apply(lambda x: \" \".join(x.lower() for x in x.split()))\n",
        "\n",
        "# Checking first five tweets\n",
        "df['tweet'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a tropical treasure to cherish forever 🌅 for e...\n",
              "1    backpacking in #cuba on a budget #travelsomeda...\n",
              "2    we likeee! https://t.co/2g1hmcpsvn #deltaairli...\n",
              "3    experience buenos aires like a local #travelso...\n",
              "4    who can\\'t wait for grenada sailing festival 2...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1bxlS8kFZgr"
      },
      "source": [
        "### **Language Detection**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVjp6VhMFbI3",
        "outputId": "9f84a1a7-2198-43c6-bb92-485a7beed628"
      },
      "source": [
        "!pip install umap-learn\n",
        "import os, re, nltk, spacy, string, umap\n",
        "\n",
        "\n",
        "pd.set_option('display.max_colwidth', 50) \n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "!pip install spacy-langdetect\n",
        "from spacy_langdetect import LanguageDetector\n",
        "nlp.add_pipe(LanguageDetector(), name='language_detector', last=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting umap-learn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/69/85e7f950bb75792ad5d666d86c5f3e62eedbb942848e7e3126513af9999c/umap-learn-0.5.1.tar.gz (80kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 4.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.4.1)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.51.2)\n",
            "Collecting pynndescent>=0.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/af/65/8189298dd3a05bbad716ee8e249764ff8800e365d8dc652ad2192ca01b4a/pynndescent-0.5.2.tar.gz (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 9.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->umap-learn) (1.0.1)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (56.1.0)\n",
            "Building wheels for collected packages: umap-learn, pynndescent\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.1-cp37-none-any.whl size=76569 sha256=f8715ed35a9c139615b74678243ffa2dfec1067d83ecf53fd4126535e54da9e7\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/df/d5/a3691296ff779f25cd1cf415a3af954b987fb53111e3392cf4\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.2-cp37-none-any.whl size=51351 sha256=48355c28d1c1ad241f2b4bbbd8c2f52c918612dbbbc9aadb554e9209309e3f7d\n",
            "  Stored in directory: /root/.cache/pip/wheels/ba/52/4e/4c28d04d144a28f89e2575fb63628df6e6d49b56c5ddd0c74e\n",
            "Successfully built umap-learn pynndescent\n",
            "Installing collected packages: pynndescent, umap-learn\n",
            "Successfully installed pynndescent-0.5.2 umap-learn-0.5.1\n",
            "Collecting spacy-langdetect\n",
            "  Downloading https://files.pythonhosted.org/packages/29/70/72dad19abe81ca8e85ff951da170915211d42d705a001d7e353af349a704/spacy_langdetect-0.1.2-py3-none-any.whl\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from spacy-langdetect) (3.6.4)\n",
            "Collecting langdetect==1.0.7\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/59/59/4bc44158a767a6d66de18c4136c8aa90491d56cc951c10b74dd1e13213c9/langdetect-1.0.7.zip (998kB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 7.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (1.4.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (20.3.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (56.1.0)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (8.7.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (0.7.1)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->spacy-langdetect) (1.10.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.7-cp37-none-any.whl size=993460 sha256=a9ebbb185c9e0c57fdd5930050cafd0e3fcd37f5a71cc6952b987c9e4663fedc\n",
            "  Stored in directory: /root/.cache/pip/wheels/ec/0c/a9/1647275e7ef5014e7b83ff30105180e332867d65e7617ddafe\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect, spacy-langdetect\n",
            "Successfully installed langdetect-1.0.7 spacy-langdetect-0.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WycDcMR-FkML",
        "outputId": "d320440f-5ea6-4b2d-8337-2b999a135c4b"
      },
      "source": [
        "def detect_language(tweet):\n",
        "    try:\n",
        "        doc = nlp(t)\n",
        "        language = doc._.language['language']\n",
        "        score = doc._.language['score']\n",
        "    except:\n",
        "        language = ''\n",
        "        print('sth goes wrong')\n",
        "    return language, score\n",
        "\n",
        "languages = []\n",
        "scores = []\n",
        "for i, t in df['tweet'].iteritems():\n",
        "    l, s = detect_language(t)\n",
        "    if i % 500 ==0:   # depending on the size of your data, you can change this number\n",
        "                      # This will print out every 500th tweets\n",
        "        print(i, t, l)  \n",
        "    languages.append(l)\n",
        "    scores.append(s)\n",
        "    \n",
        "df['lng'] = languages\n",
        "df['lng_score'] = scores"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 a tropical treasure to cherish forever 🌅 for exclusive #traveltips and island insights visit https://t.co/wfawu512ad #grenada #timetolime #puregrenada #spiceisle #ilovegrenada #dreamtodaytraveltomorrow #travelsomeday #webelieveintravel #grenadadreaming #caribbeandreaming https://t.co/64kg2mph3l en\n",
            "500 sometimes sesha is draconine, othertimes he looks like this. 🇹🇭 @thailandinsider @thailandfanclub @gothaibefree #thailand #bangkok #watprakaew #ww #tt #ttot #travel #travelblogger #solotravel #gaytravel @ilovelgbttravel #travelfromhome #travelsomeday #armchairtravel #picoftheday https://t.co/llfjacahsf en\n",
            "1000 happy #earthday everyone! here are a few of my favorite photos which celebrate the natural wonders of earth! share your favorite photos that celebrate this great planet! 🌟🌟🌟🌟🌟 #earthday2020 #wednesdaymorning #stayhome #travelsomeday #wednesdaymotivation https://t.co/wlczwhaqmm en\n",
            "1500 the only #coronavirus change that we hope will stick around https://t.co/2g1hmcpsvn #travelsomeday #deltaairlines en\n",
            "2000 how to date a woman who travels #travelsomeday #armchairtravel #staythefhome https://t.co/yezjvksqsv #traveltips en\n",
            "2500 one of the best ways to see luxor is from above! #dreamnowvisitlater #luxor #egypt #adventuretravel #travel #history #divergenttravel #travelsomeday @experienceegypt https://t.co/8fhyif9amn en\n",
            "3000 #travelsomeday fun things to do in western massachusetts https://t.co/x0zllar3my #othersidema @othersidema en\n",
            "3500 what you need to know if you\\'re planning to visit a national park in the coming months https://t.co/fr0nqllxai #travelsomeday https://t.co/bkopsmnbxg en\n",
            "4000 a pasta making cooking class in #milan #italy #armchairtravel #travelsomeday #staythefhome https://t.co/t1lmxufm5s #fooddrink en\n",
            "4500 #portugal is rewarding the countries that speak #portuguese, or have a large community descended from portugal, in its reopening measures https://t.co/h4xzptlrey #travelsomeday en\n",
            "5000 finding a campsite for your rv could be unusually difficult this year, thanks to an increased number of rv vacationers &amp; closed (or partially closed) campgrounds. we\\'re here to help with an easy four-step strategy: https://t.co/o7tyudq0xr #travelsomeday https://t.co/rtjwfgjeyb en\n",
            "5500 the most colourful cities in the world #travelsomeday #armchairtravel #plannowtravellater https://t.co/vkfjcdx4in #colour en\n",
            "6000 7 of the best free views in #london #travelsomeday #armchairtravel #plannowtravellater https://t.co/ywcwq9jekc en\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "FJ8m3W3jFbPK",
        "outputId": "5f86d4c2-b0b5-4076-a908-b494a6bd45ad"
      },
      "source": [
        "# Create two new columns for storing language detection outputs.\n",
        "df[['tweet', 'lng','lng_score']].sample(10).round(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tweet</th>\n",
              "      <th>lng</th>\n",
              "      <th>lng_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5441</th>\n",
              "      <td>bittersweet! after years of construction, #lag...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4135</th>\n",
              "      <td>someday soon i’ll be back here again. #mexico ...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4461</th>\n",
              "      <td>the opportunity to see incredible creatures in...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>will #europe keep its borders closed until sep...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1947</th>\n",
              "      <td>the best afternoon tea in #york #travelsomeday...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1387</th>\n",
              "      <td>when will we be able to travel again? &amp;gt; htt...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3094</th>\n",
              "      <td>#tikal national park, #guatemala “it is a stra...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1510</th>\n",
              "      <td>the best hostel in munich? wombat’s city hoste...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2250</th>\n",
              "      <td>@rickgriffin @always5star @_sundaysunsets_ @gr...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2099</th>\n",
              "      <td>2019 seems like a lifetime ago, doesn\\'t it? d...</td>\n",
              "      <td>en</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  tweet lng  lng_score\n",
              "5441  bittersweet! after years of construction, #lag...  en        1.0\n",
              "4135  someday soon i’ll be back here again. #mexico ...  en        1.0\n",
              "4461  the opportunity to see incredible creatures in...  en        1.0\n",
              "802   will #europe keep its borders closed until sep...  en        1.0\n",
              "1947  the best afternoon tea in #york #travelsomeday...  en        1.0\n",
              "1387  when will we be able to travel again? &gt; htt...  en        1.0\n",
              "3094  #tikal national park, #guatemala “it is a stra...  en        1.0\n",
              "1510  the best hostel in munich? wombat’s city hoste...  en        1.0\n",
              "2250  @rickgriffin @always5star @_sundaysunsets_ @gr...  en        1.0\n",
              "2099  2019 seems like a lifetime ago, doesn\\'t it? d...  en        1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7Tb-xoQFbVp",
        "outputId": "d361d6ef-115c-41cc-abd5-2db74080d045"
      },
      "source": [
        "# Checking numbers of tweets for each languages\n",
        "df.lng.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "en    5877\n",
              "no      79\n",
              "it      24\n",
              "fr      13\n",
              "es      11\n",
              "da       7\n",
              "ca       5\n",
              "sv       5\n",
              "sl       1\n",
              "af       1\n",
              "el       1\n",
              "nl       1\n",
              "hi       1\n",
              "id       1\n",
              "Name: lng, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "Eed61q2gGkIa",
        "outputId": "b6e18bb9-125c-4277-c2fe-0513f3fc1b21"
      },
      "source": [
        "# Same table with above one just with dataframe format.\n",
        "language_counts = df.lng.value_counts()\n",
        "pd.DataFrame(language_counts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lng</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>5877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no</th>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>it</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>es</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>da</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sv</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sl</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>af</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>el</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nl</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hi</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     lng\n",
              "en  5877\n",
              "no    79\n",
              "it    24\n",
              "fr    13\n",
              "es    11\n",
              "da     7\n",
              "ca     5\n",
              "sv     5\n",
              "sl     1\n",
              "af     1\n",
              "el     1\n",
              "nl     1\n",
              "hi     1\n",
              "id     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ac6aoeWkGsT6"
      },
      "source": [
        "'no' stands for Norwegian language. \n",
        "\n",
        "For more info about acronyms of languages:\n",
        "https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj5ebE0NGuUx",
        "outputId": "2cb47aa9-3fc7-4088-fb7f-70e7f9350724"
      },
      "source": [
        "# Number of English Tweets\n",
        "df.loc[df.lng == 'en'].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5877, 46)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vo9755i5G2dT"
      },
      "source": [
        "# Keep only English language and drop the rest\n",
        "df = df.loc[df.lng == 'en']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrmuA_tPGeN5"
      },
      "source": [
        "### **Expand Contractions**\n",
        "\n",
        "Contractions are words or combinations of words which are shortened by dropping letters and replacing them by an apostrophe. \n",
        "\n",
        "Let’s have a look at some examples:\n",
        "* we’re = we are\n",
        "* we’ve = we have\n",
        "* I’d = I would\n",
        "\n",
        "Note: This step needs to be done before word tokenizer because NLTK word tokenizer has in-built methods for dealing with contractions. However, NLTK word tokenizer's approach separates contractions without expanding. Expanding is a better method than simply separating."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXUxHXygNwCE",
        "outputId": "61c960e4-6da8-48f1-e070-fb6eb7fe5acf"
      },
      "source": [
        "# Install contraction library \n",
        "!pip install contractions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting contractions\n",
            "  Downloading https://files.pythonhosted.org/packages/0a/04/d5e0bb9f2cef5d15616ebf68087a725c5dbdd71bd422bcfb35d709f98ce7/contractions-0.0.48-py2.py3-none-any.whl\n",
            "Collecting textsearch>=0.0.21\n",
            "  Downloading https://files.pythonhosted.org/packages/d3/fe/021d7d76961b5ceb9f8d022c4138461d83beff36c3938dc424586085e559/textsearch-0.0.21-py2.py3-none-any.whl\n",
            "Collecting anyascii\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a3/14/666cd44bf53f36a961544af592cb5c5c800013f9c51a4745af8d7c17362a/anyascii-0.2.0-py3-none-any.whl (283kB)\n",
            "\u001b[K     |████████████████████████████████| 286kB 5.3MB/s \n",
            "\u001b[?25hCollecting pyahocorasick\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7f/c2/eae730037ae1cbbfaa229d27030d1d5e34a1e41114b21447d1202ae9c220/pyahocorasick-1.4.2.tar.gz (321kB)\n",
            "\u001b[K     |████████████████████████████████| 327kB 9.9MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyahocorasick\n",
            "  Building wheel for pyahocorasick (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyahocorasick: filename=pyahocorasick-1.4.2-cp37-cp37m-linux_x86_64.whl size=85390 sha256=b86c82246cfb503a9e3d34668e252a84616a3954cb93670685d940dcac0a999d\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/03/34/77e3ece0bba8b86bfac88a79f923b36d805cad63caeba38842\n",
            "Successfully built pyahocorasick\n",
            "Installing collected packages: anyascii, pyahocorasick, textsearch, contractions\n",
            "Successfully installed anyascii-0.2.0 contractions-0.0.48 pyahocorasick-1.4.2 textsearch-0.0.21\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dc0NipDDMWtc",
        "outputId": "50cebc89-45ba-4dde-83a1-fa91a1eea376"
      },
      "source": [
        "import contractions\n",
        "\n",
        "# Example text \n",
        "text = ''' She'll be airport in 30 mins. We are supposed to catch the arrival, aren't we?  \n",
        "          I'd love to welcome her personally. It'll be an awesome vacation.'''\n",
        "  \n",
        "# creating an empty list \n",
        "expanded_words = []     \n",
        "for word in text.split(): \n",
        "  # using contractions.fix to expand the shotened words \n",
        "  expanded_words.append(contractions.fix(word))    \n",
        "    \n",
        "expanded_text = ' '.join(expanded_words) \n",
        "print('Original text: ' + text) \n",
        "print('\\n') \n",
        "print('Expanded_text: ' + expanded_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original text:  She'll be airport in 30 mins. We are supposed to catch the arrival, aren't we?  \n",
            "          I'd love to welcome her personally. It'll be an awesome vacation.\n",
            "\n",
            "\n",
            "Expanded_text: she will be airport in 30 mins. We are supposed to catch the arrival, are not we? I would love to welcome her personally. it will be an awesome vacation.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqrXtrIRSRB6",
        "outputId": "9f7e4708-2e58-4727-8914-cd2b9b1adbfb"
      },
      "source": [
        "# Apply expanding contraction on tweet column\n",
        "df['tweet'] = df['tweet'].apply(lambda x: contractions.fix(x))\n",
        "df.tweet.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4886    this interactive map shows at a glance the rul...\n",
              "3866    11 tried &amp; tested tips for backpacking on ...\n",
              "1503    i found these #wildflowers growing in an empty...\n",
              "4453    is #barcelona safe? and how to avoid the barce...\n",
              "2137    what to do in sigulda: a day trip from riga to...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PM4s3rQFV7_b",
        "outputId": "a89f1bd7-514b-4d55-dbe2-a7f4e960924a"
      },
      "source": [
        "# Checking first five tweets\n",
        "df.tweet.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a tropical treasure to cherish forever 🌅 for e...\n",
              "1    backpacking in #cuba on a budget #travelsomeda...\n",
              "3    experience buenos aires like a local #travelso...\n",
              "4    who can\\'t wait for grenada sailing festival 2...\n",
              "5    the best hostel in barcelona – fabrizzio’s pet...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l05k9waN_Ow7"
      },
      "source": [
        "### **Removal of Stop Words** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "Zw4duFXWs34o",
        "outputId": "739bd04d-1705-4cf0-8b2a-c7d2754ccdd1"
      },
      "source": [
        "# before removing stopwords, we can count count them.\n",
        "# Remember that we already did this in the beginning. But it could be done here as well.\n",
        "\n",
        "# Save numbers of stopwords as a new column. It may help us to use undertstand the data better\n",
        "df['stopwords'] = df['tweet'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
        "\n",
        "# Checking the tweets and their number of stopwords\n",
        "df[['tweet','stopwords']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tweet</th>\n",
              "      <th>stopwords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a tropical treasure to cherish forever 🌅 for e...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>backpacking in #cuba on a budget #travelsomeda...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>experience buenos aires like a local #travelso...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>who can\\'t wait for grenada sailing festival 2...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>the best hostel in barcelona – fabrizzio’s pet...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               tweet  stopwords\n",
              "0  a tropical treasure to cherish forever 🌅 for e...          4\n",
              "1  backpacking in #cuba on a budget #travelsomeda...          3\n",
              "3  experience buenos aires like a local #travelso...          1\n",
              "4  who can\\'t wait for grenada sailing festival 2...          3\n",
              "5  the best hostel in barcelona – fabrizzio’s pet...          2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1J57igbcCZz"
      },
      "source": [
        "Removal of stopwords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HrfXgqHc_Syj",
        "outputId": "30721d75-486f-4356-8d54-e324937aa2d1"
      },
      "source": [
        "# import stopwords from NLTK library\n",
        "from nltk.corpus import stopwords\n",
        "stop = stopwords.words('english')\n",
        "\n",
        "# Remove stopwords from tweets\n",
        "df['tweet'] = df['tweet'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))\n",
        "\n",
        "# Check random 10 tweets\n",
        "df['tweet'].sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5594    bittersweet! years construction, #laguardiaair...\n",
              "936     new post: back nature luxury stay fish hotel c...\n",
              "5778    banff jasper itinerary - best places visit ban...\n",
              "3998    kava anybody? fiji famous local kava ceremonie...\n",
              "5240    best afternoon tea #york #travelsomeday #armch...\n",
              "3574    know it\\'s law wear high heels acropolis? rhod...\n",
              "865     brick lane street art tour shoreditch #london ...\n",
              "3204    el santuario de las lajas – #ipiales #travelso...\n",
              "594     lying meadow #flowers chatham, ny. 🌼 #hudsonva...\n",
              "2429    travel planning made easy: essential travel re...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5xmxi4VQS2Y"
      },
      "source": [
        "#### **Adding common words from our document to stop_words**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhyCQuQYQR8R",
        "outputId": "302a598f-2af2-4a0e-ce39-355adca999b0"
      },
      "source": [
        "add_words = [\"also\",\"im\",\"ive\",]\n",
        "\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "stop_added = stop_words.union(add_words)\n",
        "\n",
        "# Re-running stopwords removal with additinal words\n",
        "df['tweet'] = df['tweet'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop_added))\n",
        "\n",
        "# Checking random 10 tweets\n",
        "df['tweet'].sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112     evolution bread... wow! life exciting moment.....\n",
              "2633    experience buenos aires like local #travelsome...\n",
              "395     virtually travel explore new zealand article g...\n",
              "5249    last #cancun pic. razorous limestone lining tu...\n",
              "5639    exploring catalunya: #adventure campdevánol wa...\n",
              "1795    oops hashtags work added fact? #srilanka #trav...\n",
              "5146    best things #guatemala #travelsomeday #armchai...\n",
              "5859    japan amazing country can\\'t wait go back. the...\n",
              "2397    visit #prague winter #travelsomeday #armchairt...\n",
              "465     visit #kansascity complete without stroll arti...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kVKfDIHDrmE"
      },
      "source": [
        "### **Remove URLs** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKRT8ArDCBYC",
        "outputId": "da1e99b3-87b8-4044-efec-ace24674f9fa"
      },
      "source": [
        "import re\n",
        "\n",
        "# define a function for removing URLs\n",
        "def remove_urls (vTEXT):\n",
        "    vTEXT = re.sub(r'(https|http)?:\\/\\/(\\w|\\.|\\/|\\?|\\=|\\&|\\%)*\\b', '', vTEXT, flags=re.MULTILINE)\n",
        "    return(vTEXT)\n",
        "\n",
        "# Remove URLS with newly defined function\n",
        "df['tweet'] = df.tweet.apply(remove_urls)\n",
        "\n",
        "# Check first 5 tweets\n",
        "df.tweet.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    tropical treasure cherish forever 🌅 exclusive ...\n",
              "1    backpacking #cuba budget #travelsomeday #armch...\n",
              "3    experience buenos aires like local #travelsome...\n",
              "4    can\\'t wait grenada sailing festival 2021?! ☝️...\n",
              "5    best hostel barcelona – fabrizzio’s petit revi...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KX8Jp-ikr9f_",
        "outputId": "5e718fcc-98b3-4036-b7cb-471d97db93a6"
      },
      "source": [
        "# Example\n",
        "sentence=  'NPR can provide useful information https://www.npr.org'\n",
        "remove_urls(sentence)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'NPR can provide useful information '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q98Z5yJN_tsf"
      },
      "source": [
        "### **Remove html tags** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6w9YSdez_vsD",
        "outputId": "79f9ef28-c23d-4b94-8104-3305e5ecdaa6"
      },
      "source": [
        "from bs4 import BeautifulSoup\n",
        "\n",
        "# Define a function for striping html tags\n",
        "def strip_html_tags(text):\n",
        "    soup = BeautifulSoup(text, \"html.parser\")\n",
        "    stripped_text = soup.get_text()\n",
        "    return stripped_text\n",
        "\n",
        "#Example\n",
        "strip_html_tags('People and neighbors are for the most part very friendly,</a> even in stores</td>')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'People and neighbors are for the most part very friendly, even in stores'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vj8Fup5-ANMB",
        "outputId": "14d0c4db-6f54-4540-9b13-7f2008756bce"
      },
      "source": [
        "# Apply HTML tag removal on tweets\n",
        "df['tweet'] = df['tweet'].apply(lambda x: strip_html_tags(x))\n",
        "\n",
        "# Check first five tweets\n",
        "df['tweet'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    tropical treasure cherish forever 🌅 exclusive ...\n",
              "1    backpacking #cuba budget #travelsomeday #armch...\n",
              "3    experience buenos aires like local #travelsome...\n",
              "4    can\\'t wait grenada sailing festival 2021?! ☝️...\n",
              "5    best hostel barcelona – fabrizzio’s petit revi...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEnOiQKWOVUf"
      },
      "source": [
        "###**Removing Emojis**\n",
        "\n",
        "Eventhough emojis can be indictor of some emotions that can be related to the tweet, we need to remove the emojis in our text analysis because for now we don't have a strong method to utulize emojis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "jTY42KPWO_Zt",
        "outputId": "4ec4698a-98b7-4d13-db94-6cb869dedfb4"
      },
      "source": [
        "def remove_emoji(text):\n",
        "    emoji_pattern = re.compile(\"[\"\n",
        "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags \n",
        "                           u\"\\U00002702-\\U000027B0\"\n",
        "                           u\"\\U000024C2-\\U0001F251\"\n",
        "                           \"]+\", flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)\n",
        "\n",
        "#Example\n",
        "remove_emoji(\"Have fun with NLP! 😃😃\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Have fun with NLP! '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijrm-k45PYcy"
      },
      "source": [
        "# remove all emojis from tweets\n",
        "df['tweet'] = df['tweet'].apply(lambda x: remove_emoji(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CO8yws0TPezN"
      },
      "source": [
        "### **Remove Emoticons**\n",
        "\n",
        "In previous steps, we have removed emoji. Now, going to remove emoticons.\n",
        "\n",
        "What is the difference between emoji and emoticons?\n",
        "\n",
        ":-) is an emoticon\n",
        "😜 → emoji."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAVVLr5DPoN5",
        "outputId": "b2f38853-6eef-47eb-eff2-052ebe8276f8"
      },
      "source": [
        "!pip install emot\n",
        "from emot.emo_unicode import UNICODE_EMO, EMOTICONS\n",
        "\n",
        "# Function for removing emoticons\n",
        "def remove_emoticons(text):\n",
        "    emoticon_pattern = re.compile(u'(' + u'|'.join(k for k in EMOTICONS) + u')')\n",
        "    return emoticon_pattern.sub(r'', text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting emot\n",
            "  Downloading https://files.pythonhosted.org/packages/49/07/20001ade19873de611b7b66a4d5e5aabbf190d65abea337d5deeaa2bc3de/emot-2.1-py3-none-any.whl\n",
            "Installing collected packages: emot\n",
            "Successfully installed emot-2.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Cj8BoEFhvZBv",
        "outputId": "410cac32-f049-4876-8ad0-ec7d895dec61"
      },
      "source": [
        "#Example\n",
        "remove_emoticons(\"I used to play Super Mario games :)\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'I used to play Super Mario games '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZNz5lKIPxvD",
        "outputId": "3f51a5b8-9e1a-4afa-8188-dd59cd98b925"
      },
      "source": [
        "# remove all emojis from tweets\n",
        "df['tweet'] = df['tweet'].apply(lambda x: remove_emoticons(x))\n",
        "# Check first five tweets\n",
        "df.tweet.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5125    things #evora #portugal #travelsomeday #armcha...\n",
              "3282    hostel etiquette : stay hostel & piss #travels...\n",
              "949     dance life #cancun.  @ilovelgbttravel @vivoenc...\n",
              "5318    \"soon come\" say #grenada. visit  top #sailingt...\n",
              "3686    #budapest street art & cultural walk #travelso...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6yL83gfScGY"
      },
      "source": [
        "### **Stemming and Lemmatization**\n",
        "\n",
        "**Definition**: Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma . \n",
        "\n",
        "Source: https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jwXEJktWI2E"
      },
      "source": [
        "### **Tokenization**\n",
        "\n",
        "During stemming or lemmatization process, tokenization will also be applied.\n",
        "\n",
        "For many of the techniques we'll be using in future notebooks, the text must be tokenized, meaning broken down into smaller pieces. The most common tokenization technique is to break down text into words.\n",
        "\n",
        "There are different types of tokenization. For further info, you can check these articles:\n",
        "\n",
        "> https://towardsdatascience.com/an-introduction-to-tweettokenizer-for-processing-tweets-9879389f8fe7\n",
        "\n",
        "> https://towardsdatascience.com/benchmarking-python-nlp-tokenizers-3ac4735100c5\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDQ-z4GZS73N"
      },
      "source": [
        "#Example (adapted from datacamp)\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "wordnet_lemmatizer = WordNetLemmatizer()\n",
        "porter=PorterStemmer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFsgLNxNyuJx",
        "outputId": "544f1a09-fe95-4339-ca25-5dc51be47d82"
      },
      "source": [
        "#Tourism specific words to be stemmed and lemmatized\n",
        "word_list = [\"tourist\", \"booking\", \"rating\",\"itinerary\",\"recreation\",\"amenities\",\"attractions\",\"sightseeing\",\"eating\"]\n",
        "print(\"{0:20}{1:20}{2:20}\".format(\"Original Word\",\"Stemmed\",\"Lemmatized\"))\n",
        "\n",
        "for word in word_list:\n",
        "    print(\"{0:20}{1:20}{2:20}\".format(word,porter.stem(word),wordnet_lemmatizer.lemmatize(word,pos=\"v\")))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original Word       Stemmed             Lemmatized          \n",
            "tourist             tourist             tourist             \n",
            "booking             book                book                \n",
            "rating              rate                rat                 \n",
            "itinerary           itinerari           itinerary           \n",
            "recreation          recreat             recreation          \n",
            "amenities           amen                amenities           \n",
            "attractions         attract             attractions         \n",
            "sightseeing         sightse             sightsee            \n",
            "eating              eat                 eat                 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "ZtGtbTOySd99",
        "outputId": "9d13e43b-d9db-4cbc-ac4c-cc417f5685ec"
      },
      "source": [
        "#We can apply just one of the stemming or lemmatizion. For this study, we will go with lemmatization.\n",
        "#Still here is the codes for stemming in case you want to use stemming instead of lemmatization\n",
        "\n",
        "# porter=PorterStemmer()\n",
        "\n",
        "# def stemSentence(sentence):\n",
        "#     token_words=word_tokenize(sentence)\n",
        "#     token_words\n",
        "#     stem_sentence=[]\n",
        "#     for word in token_words:\n",
        "#         stem_sentence.append(porter.stem(word))\n",
        "#         stem_sentence.append(\" \")\n",
        "#     return \"\".join(stem_sentence)\n",
        "\n",
        "# df['tweet'] = df['tweet'].apply(lambda x: stemSentence(x))\n",
        "# df.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nporter=PorterStemmer()\\n\\ndef stemSentence(sentence):\\n    token_words=word_tokenize(sentence)\\n    token_words\\n    stem_sentence=[]\\n    for word in token_words:\\n        stem_sentence.append(porter.stem(word))\\n        stem_sentence.append(\" \")\\n    return \"\".join(stem_sentence)\\n\\ndf[\\'tweet\\'] = df[\\'tweet\\'].apply(lambda x: stemSentence(x))\\ndf.sample(5)\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Igk5DrHhVyeP",
        "outputId": "d3d1e287-5e62-45d6-e5f3-dbbabc6c3397"
      },
      "source": [
        "# Lemmatization and Tokenization\n",
        "\n",
        "def LemmaSentence(sentence):\n",
        "    token_words=word_tokenize(sentence)\n",
        "    token_words\n",
        "    lemma_sentence=[]\n",
        "    for word in token_words:\n",
        "        lemma_sentence.append(wordnet_lemmatizer.lemmatize(word, pos= 'v'))\n",
        "        lemma_sentence.append(\" \")\n",
        "    return \"\".join(lemma_sentence)\n",
        "\n",
        "# Apply lemmatization function on tweet column\n",
        "df['tweet'] = df['tweet'].apply(lambda x: LemmaSentence(x))\n",
        "\n",
        "# Check first five tweets\n",
        "df.tweet.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1021    happy # earthday everyone ! favorite photos ce...\n",
              "1275    see @ tpcdanzantebay fit landscape . ‍ @ villa...\n",
              "1660    latest @ fodorstravel , hotels look like covid...\n",
              "3203    break : european union release plan summer tra...\n",
              "5600    castle sintra : fairytale palaces , garden his...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBExBJBbFLri"
      },
      "source": [
        "### **Spell Correction** \n",
        "*Reminder: below explanation was taken from somewhere and needs to be parapharased*\n",
        "\n",
        "\n",
        "We’ve all seen tweets with a plethora of spelling mistakes. Our timelines are often filled with hastly sent tweets that are barely legible at times.\n",
        "\n",
        "In that regard, spelling correction is a useful pre-processing step because this also will help us in reducing multiple copies of words. For example, “Analytics” and “analytcs” will be treated as different words even if they are used in the same sense.\n",
        "\n",
        "To achieve this we will use the textblob library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNnDCA2qFKyW",
        "outputId": "56563a48-4e9f-4a73-e2d0-a08c166da895"
      },
      "source": [
        "from textblob import TextBlob\n",
        "#Example sentence\n",
        "sentence = TextBlob(\"I havv goood speling!\")\n",
        "print(sentence.correct())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I have good spelling!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeDHVwaO1Li4",
        "outputId": "2bf5c231-aae2-44f7-8bd3-773c38274f14"
      },
      "source": [
        "# Example sentence related to tourism context\n",
        "print(TextBlob('In Chicago, a lakeside is a goodf lcation to relax').correct())\n",
        "print(TextBlob('Minesota is briming with natual and cltural bauty').correct()) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In Chicago, a lakeside is a good location to relax\n",
            "Minnesota is brimming with natural and cultural beauty\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6JqNEGXRzJD",
        "outputId": "356b4bc0-08cc-4562-a3b2-d93a12ee8c02"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5880, 46)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HOMme5UmwDpl",
        "outputId": "a8781abe-62ef-4939-dfc5-54c11a1e6b3e"
      },
      "source": [
        "# Apply spell correction on tweets. (This process can take a long)\n",
        "\n",
        "# Running spell correction on first 10 tweets can give us an idea about how much it will take\n",
        "# Our dataset has 5880 tweets right now. We can use consider the ratio for execution time\n",
        "%time df['tweet'][:10].apply(lambda x: str(TextBlob(x).correct())) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 43 s, sys: 83.1 ms, total: 43.1 s\n",
            "Wall time: 43.2 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     tropical treasure cherish forever exclusive # ...\n",
              "1     backpack # cuba budget # travelsomeday # armch...\n",
              "3     experience burns air like local # travelsomeda...\n",
              "4     can\\'t wait grenade sail festival 2021 ? ! exc...\n",
              "5     best hostel barcelona – fabrizzio ’ s petit re...\n",
              "6     discover ancient history karay complex labor e...\n",
              "7     discover ancient history karay complex labor e...\n",
              "8     discover ancient history karay complex labor e...\n",
              "9     get know big easy house , virtual tour , music...\n",
              "10    discover ancient history karay complex labor e...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uSBe8U5SSHN",
        "outputId": "219476b8-32ca-47da-aa09-3f4ba0cc3490"
      },
      "source": [
        "# Apply spell checking on all tweets\n",
        "\n",
        "%time df['tweet'] = df['tweet'].apply(lambda x: str(TextBlob(x).correct())) \n",
        "# Note:if you are only playing with the dataset, you can you to skip this part because of long execution-time\n",
        "# This part can take 6-7 hours depending on your computer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 4h 38min 25s, sys: 32.7 s, total: 4h 38min 58s\n",
            "Wall time: 4h 39min 28s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a68plr_xhvX9",
        "outputId": "c1fca16e-60a7-40da-9d62-76d79d1b2c95"
      },
      "source": [
        "# Check first five tweets\n",
        "df.tweet.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    tropical treasure cherish forever exclusive # ...\n",
              "1    backpack # cuba budget # travelsomeday # armch...\n",
              "3    experience burns air like local # travelsomeda...\n",
              "4    can\\'t wait grenade sail festival 2021 ? ! exc...\n",
              "5    best hostel barcelona – fabrizzio ’ s petit re...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX4z0Fb--10J"
      },
      "source": [
        "### **Removing Punctuation**\n",
        "\n",
        "A reminder: if you remove punctuation earlier then html tags and URLs, then it won't be possible to remove them probably later. That's why, removing punctuation the last is a wise choice.\n",
        "\n",
        "> Reminder: if your NLP algorithm can utulize punctuations such as VADER, you shouldn't remove punctuation.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrTpn_NQUgX8",
        "outputId": "cc55d571-6bb0-4e6a-ae86-a2f04a92cb92"
      },
      "source": [
        "# Remove punctuation\n",
        "df['tweet'] = df['tweet'].str.replace('[^\\w\\s]','')\n",
        "\n",
        "# Check first five tweets\n",
        "df['tweet'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    tropical treasure cherish forever exclusive  t...\n",
              "1    backpack  cuba budget  travelsomeday  armchair...\n",
              "3    experience buenos air like local  travelsomeda...\n",
              "4    cant wait grenada sail festival 2021   exclusi...\n",
              "5    best hostel barcelona  fabrizzio  s petit revi...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EMoGwyS_y5Ft",
        "outputId": "b1d98e63-d787-42b5-fe3d-d6b62991b6cc"
      },
      "source": [
        "# Example\n",
        "sentence = \"Do you listen to music a lot?\"\n",
        "re.sub(r'[^\\w\\s]','',sentence)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Do you listen to music a lot'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxWEoW9WsdDE"
      },
      "source": [
        "### **TF_IDF**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqUr8SRLsekn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4b3053b-3621-4602-b6f2-57a1b4a5f023"
      },
      "source": [
        "#import the TfidfVectorizer from Scikit-Learn.\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = TfidfVectorizer(max_df=.80, min_df=5, stop_words=None, use_idf=True, norm=None)\n",
        "transformed_tweets = vectorizer.fit_transform(df.tweet)\n",
        "\n",
        "print(transformed_tweets.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5880, 1969)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1PqzEfKvSzJ"
      },
      "source": [
        "dense = transformed_tweets.todense()\n",
        "denselist = dense.tolist()\n",
        "feature_names = vectorizer.get_feature_names()\n",
        "df2 = pd.DataFrame(denselist, columns=feature_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDTbEAtJyTJN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "915f7c4f-a1d8-4a6f-a3f0-260065951cdf"
      },
      "source": [
        "df2.head()"
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      "execution_count": null,
      "outputs": [
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1969 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    10  100  1000   11   12  ...  yourewelcome  yucatan  yuri  zealand  ziplining\n",
              "0  0.0  0.0   0.0  0.0  0.0  ...           0.0      0.0   0.0      0.0        0.0\n",
              "1  0.0  0.0   0.0  0.0  0.0  ...           0.0      0.0   0.0      0.0        0.0\n",
              "2  0.0  0.0   0.0  0.0  0.0  ...           0.0      0.0   0.0      0.0        0.0\n",
              "3  0.0  0.0   0.0  0.0  0.0  ...           0.0      0.0   0.0      0.0        0.0\n",
              "4  0.0  0.0   0.0  0.0  0.0  ...           0.0      0.0   0.0      0.0        0.0\n",
              "\n",
              "[5 rows x 1969 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0Y0ruNDyUrB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41f79491-a7ac-4bbf-a6f3-b7f20103a83c"
      },
      "source": [
        "transformed_tweets_as_array = transformed_tweets.toarray()\n",
        "# use this line of code to verify that the numpy array represents the same number of documents that we have in the file list\n",
        "len(transformed_tweets_as_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5880"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Us5zshUWWe-P"
      },
      "source": [
        "### **Vectorizer**\n",
        "\n",
        "Vectorier is a function for converting tokens into numerical columns. \n",
        "\n",
        "We can do this using scikit-learn's CountVectorizer, where every row will represent a different document and every column will represent a different word.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7QkoYMgWvWz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "2693945f-5ef8-4d7b-fef3-6e99a3da82f4"
      },
      "source": [
        "# We are going to create a document-term matrix using CountVectorizer, and exclude common English stop words\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer()\n",
        "data_cv = cv.fit_transform(df.tweet)\n",
        "\n",
        "# Create document term matrix (dtm) by using count vectorizer\n",
        "data_dtm = pd.DataFrame(data_cv.toarray(), columns=cv.get_feature_names())\n",
        "data_dtm.index = df.index\n",
        "data_dtm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5880 rows × 5012 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      02038725000  10  100  1000  ...  zygoma  अरकवल  सof  ফর_আসবofমর_পডয\n",
              "0               0   0    0     0  ...       0      0    0               0\n",
              "1               0   0    0     0  ...       0      0    0               0\n",
              "3               0   0    0     0  ...       0      0    0               0\n",
              "4               0   0    0     0  ...       0      0    0               0\n",
              "5               0   0    0     0  ...       0      0    0               0\n",
              "...           ...  ..  ...   ...  ...     ...    ...  ...             ...\n",
              "6022            0   0    0     0  ...       0      0    0               0\n",
              "6023            0   0    0     0  ...       0      0    0               0\n",
              "6024            0   0    0     0  ...       0      0    0               0\n",
              "6025            0   0    0     0  ...       0      0    0               0\n",
              "6026            0   0    0     0  ...       0      0    0               0\n",
              "\n",
              "[5880 rows x 5012 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMm9bfRMYhU8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "580f7330-eac2-4e8d-b124-b28b8f13d6d5"
      },
      "source": [
        "# Count vectorizer with different parameters\n",
        "cv = CountVectorizer(max_df = 0.90, min_df= 0.001)\n",
        "data_cv = cv.fit_transform(df.tweet)\n",
        "\n",
        "# Create document term matrix (dtm) by using count vectorizer\n",
        "data_dtm = pd.DataFrame(data_cv.toarray(), columns=cv.get_feature_names()) #Document term matrix\n",
        "data_dtm.index = df.index\n",
        "data_dtm"
      ],
      "execution_count": null,
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6022</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6023</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6024</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "    <tr>\n",
              "      <th>6026</th>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5880 rows × 1690 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      10  100  1000  11  ...  yourewelcome  yucatan  zealand  ziplining\n",
              "0      0    0     0   0  ...             0        0        0          0\n",
              "1      0    0     0   0  ...             0        0        0          0\n",
              "3      0    0     0   0  ...             0        0        0          0\n",
              "4      0    0     0   0  ...             0        0        0          0\n",
              "5      0    0     0   0  ...             0        0        0          0\n",
              "...   ..  ...   ...  ..  ...           ...      ...      ...        ...\n",
              "6022   0    0     0   0  ...             0        0        0          0\n",
              "6023   0    0     0   0  ...             0        0        0          0\n",
              "6024   0    0     0   0  ...             0        0        0          0\n",
              "6025   0    0     0   0  ...             0        0        0          0\n",
              "6026   0    0     0   0  ...             0        0        0          0\n",
              "\n",
              "[5880 rows x 1690 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPKJ80fzjfN2"
      },
      "source": [
        "# Let's pickle it for later use\n",
        "data_dtm.to_pickle(\"cleaned_tweets.pkl\")\n",
        "\n",
        "#Notice that the pickled data contains only tweets, not other columns."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwZynJP2lAZ_"
      },
      "source": [
        "\n",
        "**What Can You Do With pickle?**\n",
        "\n",
        "Pickling is useful for applications where you need some degree of persistency in your data. \n",
        "Your program's state data can be saved to disk, so you can continue working on it later on.\n",
        "It can also be used to send data over a Transmission Control Protocol (TCP) or \n",
        "socket connection, or to store python objects in a database. Pickle is very \n",
        "useful for when you're working with machine learning algorithms, where you want\n",
        "to save them to be able to make new predictions at a later time, without\n",
        "having to rewrite everything or train the model all over again.\n",
        "\n",
        "Source:https://www.datacamp.com/community/tutorials/pickle-python-tutorial#whatfor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1so2ttI-RFDT"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "# **FEATURE EXTRACTION**\n",
        "\n",
        "For the feature extraction, we will apply following steps in order. Depending on your study and data, you may skip some of these features and add your own feature extractions.\n",
        "\n",
        " word count: counts the number of tokens in the text (separated by a space)\n",
        "\n",
        "1.   **character count**: \n",
        "2. **average word length**\n",
        "\n",
        "\n",
        "Features that can only be obtained before the text cleaning\n",
        "\n",
        "\n",
        "* Number of stop words\n",
        "* The number of punctuation\n",
        "* Number of hashtag characters\n",
        "* Number of numerical characters\n",
        "* Number of Uppercase words\n",
        "* sentence count"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHZucMsyBHGV"
      },
      "source": [
        "#Let's save cleaned data\n",
        "df.to_csv('tourism_tweets_cleaned.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oKxtT1puHU6K"
      },
      "source": [
        "# **Word Cloud**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJAcKH8kHZOF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db8c637f-6df0-45bd-ff81-4fa82a8304ca"
      },
      "source": [
        "#Word cloud for the all reviews\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "text_for_cloud = \" \".join(x for x in df.tweet)\n",
        "print (\"There are {} words in the combination of all tweets.\".format(len(text_for_cloud)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 719175 words in the combination of all tweets.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJUMbnWaLgJC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ff5c7a5-1691-4773-ea28-b9d183e2921e"
      },
      "source": [
        "# Check first five tweets\n",
        "df.tweet.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    tropical treasure cherish forever exclusive  t...\n",
              "1    backpack  cuba budget  travelsomeday  armchair...\n",
              "3    experience buenos air like local  travelsomeda...\n",
              "4    cant wait grenada sail festival 2021   exclusi...\n",
              "5    best hostel barcelona  fabrizzio  s petit revi...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nu5OfXcMLDtK"
      },
      "source": [
        "#Libraries for visualization\n",
        "from nltk.corpus import stopwords\n",
        "from os import path\n",
        "from PIL import Image\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZI-numALOiY"
      },
      "source": [
        "# During text cleaning process, data type can change at somepoint.\n",
        "# Make sure that your reviews are in string data format.\n",
        "df['tweet']= df['tweet'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gON-anAHobu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "76d38f68-e8c3-4902-e31a-87d094f4d5e8"
      },
      "source": [
        "# Create stopword list:\n",
        "#stopwords = set(STOPWORDS)\n",
        "#stopwords.update([\"drink\", \"now\", \"wine\", \"flavor\", \"flavors\"]) # you can add new stopwords by modifying this column\n",
        "\n",
        "# Generate a word cloud image\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "wordcloud = WordCloud(stopwords=stop_words, background_color=\"white\", max_words= 50).generate(text_for_cloud)\n",
        "\n",
        "# Display the generated image:\n",
        "# the matplotlib way:\n",
        "fig = plt.figure(figsize= (8,8), dpi= 100)\n",
        "# figure(figsize= (8,8), dpi= 200)\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5ocfutR5KCZI4w3E1va0WQcPj4NYWzU5AvEpRqvJcakfT7AS8+8NrtLshjZZPd6uJ42i29rr4gcf2fhcEhA2foOFx7fYWQcNFyKrq1xjDYLeD5zvsXq9ec129EYGNls+Hf3JIllcezw9+fIOda4/zBXav9fjzf/PRZr8Ag50Ojus8Y7v2ANbUvD6sNRQ2JS2nzJEk5ZiOe4ums88o+Q2B6tP17rDIj6v7oN4lKUdk5ZLSZizzMyyGjnvIqjijsAmFiYmKEYWN2Q9+iqEgLobkJiIzS7Jyybb/EaHextic3KzIzGLtNXwxxpaUJiEtp5Qm3byemRVRccEse4ASDoEaVJEbCnITUdiUqDhnFf+S/fCnuLLFOPmclnOAq1rMswe4qo2vuizLEzKzrNZbLgj0gG3/h0jhYKk8fcYWVE8GS1JMiMohUX6OFA6+7uGpDnExYpE/xNiSwsZk6YKB9wFSOAyTfyA3MQBaBAS6j192MTansMlTtrf8j1jlZ2tP6dPXvLQZACUZi+yYwibsh39GaTOm2Vd0ndsAzPMjms4+gRqgROUJfB6ulnx8uMcyyZBC8P7+Fk3fw3c1NwZdWr7HMslI8wKlJPu9Fq5TFX24SpOXJYGr6YQ+Qgg+Ptyj6XsYa2kHPlutkHbo4TmabhhwZ7ePlJKtVmPTwmW/2+Zf/+gOvtZ0G/6mcvcqfNdhv9fmX350m9IY2oFPvxngORohAv7xuzdYJikNz2Wn0yRwNVutBo5WbLcbpEXBTru5Od+r2P4+YChIzBJPhmx511kVM0qb46kGEoW1BiHersLM1yoA/cChv9Pm9gf7eIGzETbBWmwBBA2Pzlq8ALRtiLUdgGc8YnvX++xeq4ozhACp5CZfz5h1L7718U0nIGx69LZbSCW/sQLW9TSOq2j3wk0/QLE+X3/boTdoYq2tzr/ub9Ro+TRaPod3dp46V7sb0uoE7F3vrXtBVe/Z3u+ytde5skefHz4rToOGx83399b5e/aZ69HfadPbbj2V39fsBDTa/jO2v9/l+DU1bwK7rvRLWeQPcWQDVzaYZw+wjqHj3iIqhlgsnmwxz44obEyod7HWYMgAS2YWxMWY0uZExTm5WWGCkrKcM04/RwhNXq5YFqc0nT181a1CSxZAoGWwCYM+j00I2hqUcNDCR6xDoHExZpEfE6otms4BQkhKm5GaGbldsSrOOY9/Sc97By0CMjMnLitP5yT7ko5zE1c2mGX3yUwEwKo4obQZPfcOjry6xUUloGdE5bBavxA09A5JOWWcfk7TOag8l9kxodpGCs0k/ZLCZmjhEugtKOxaHC82EZJL2133NvP8iNwsn7nmhYkpbUZuVszzh0TFGVv+RwBk5YJIDilMwjj9La5sEqjne1cv0Upxe6f/tT551X13q9Vg0Awxxm4iM5tPkQ+DVuOJ50L1vlbwuMhwr9va/Lvpe2y1wsrGutfgpZ1BK6TfDNZh5BcX4frrsO12u3HFzxw64bMez8B1aIceptdGKfmUyHsV298XrLW0nAGeDJnlF6QmRgsHJd7OiNxrXdXOQY88Lza98V6Wb8yzuMKtfFV/PyEEWqtXLka6FHlPv/jqIup5XrdXPs/jZJIruXLvtcevpuaNUTWLt4R6m6beJzcRSrhkZrnOGxOPj1sLk8wsEULR996vBFY5QaBwZZNSZYzSzyjJCfUOjgiIyguW+SmuaiGQ+Kpb5bUJWYlN1cQzLRp6Zx1SfT6ODHBVG1c2CPSAQPc3Y6mUdGk512k5B7SdGyjhrG2fkJQT4nJMYROMLXFVk4H/A5b5CfPsiLScgnsTJTzickJmljgyRIsQLcMqZ+85xXK+6mExrIpzsnKOsQUd9xalTUnLOTvBn6CER2lzLIbcrNAyRFOFwitPXkFaToiKCxDiadu2JCvnIOQz1zw1C5JyTFJMScsphU2x1hDqHbb8HzJNv2JZnJKbFUJoHBnwKg+S593iX9SH9lUF04uOf5PeNynElc/g34ftPyS0cOk4O2QmIRGLTQjYrp05b2OV9GsVgP2dyjul9Dcnz17yTcdd9fPnvedKIfctbb+6aHs94u+b3vMq16Ompub1cFnJlxQjSpMRFUNc1cKVLXzVIzNLTuO/Y5bdx1dVRMNTbXITM0o/rULD62KGS29eVq4QQq6LQkDLkIbeI7cRpc0RKJTw0MJDUhUplDZjnj0AF0J9tZequh+ojRcwKoYs8oc0nYP1XiRaeNW5ZeV1KmxV5JKbBLMWYGBRwiXU28yye8TluCpAWReyhHobmxsKE68LOipPY2ETkmJMVJxXaxearnu7CtuaGGsNuY0pyhRj83Vod0BUXGxyBEO9jSO8TcNcJRyU8KqoDIpQb5Gb+GnbMsDXfdJy/sw1z01EUkwpbIyh2Ah1LT18ehQ2obQpge6jpf9KobpXvfc/fvn1OEne9P3/u7T9h8RlH8DLQqaG7uJIDwQ40nsrr9VrFYDt7rOu5Zqampo/dKRwCFSP3MZkxYrSJigcfNWl6RywzE+ZZnerggLVRgqHht4jKs+ZZndJiwmObCJRlOv8Nb2ugK2EXY4jG/S9d5nlD8jNCiXcSmytRdpl6HdVnOOp7nMF4OM1a1zVJl/n/TX0LlLotfArNsLzMQItfDzZIVTbaOFX51hX3laew2t4soMSDh3nEIFgmZ+gpIsrm2gZkJQTchNjbUlazkmKCaW+RmkzSpOipY9jAiwlAoGnKu/eKj8nKaeUNsERAa5q4ckOFoOjmijhgQSPNo5skJSTp2w7MqTp7GMpn7nmYl15qwnW6/eQwkGg0MLbVAK3nUNcWT/Hal6dqtHzhNxmWAxKaDwVruvJ3/jkj2+FeDw79xupG8vV1NR8LylNVhVgrEM61hYbb1iVX5auK0gNSvh4soWh2IgeQ4lE4ak258mvmGdHbPkfkpVLomJIxz0k0Fu4qkVhko04cmVrIwCzcklhIoSQm9YuL6IwCamZA6CEiytb6/YvOdaWSOE+9gCahNys1nlzJcbm+Lq/EUNpOaMwCUp6aBGghENps43n7FI8uut9FyYmKxdI4aClhyMb6xYwOYWNMLZAoPBVF4SgNOmmaMRi8VQbgSI3SywghUIJd9Pi5rL9ztdtlzat1vS1a25ssakkvvQAVoUeLqVNubf4j4DlWuMvcJ8Q3TU1L8syn3A/+jXT7IzcpHTcbZJySWZSftz9V/Tcvd/ncl7K3fh2ZibW1NTUvEUo6RLIwRM5fpezGwSuamJtYxPevQz1KFy0DUCuj1+/7qkOvl6uqwIFrmqs++WFmz+Pv2+LjSVPtda5f49zDl+Elj5a+uuqYQvItZB6toBES7/ysD1p94mQlac6eOvQ9uY9wkfxtfcgUDgo5eCp9tNGBEjroPE2IfVLpNQ4hI/Ps7atXyDEnsy3vLSthY+y3jPXHFgXXTz9elauSMoxvuriyMZGeNbUvCpCSLRwcaSHFJKWHlDagqiYY2z5uKj0LaIWgDU134InPee/6y/1189lraUsHjc+f5XzmNJs/iinaoxuSlM1Jv8OZkNvptJYi5ByU/F4+bNLvukaXjaLL9ZFZpfX5fd9Q63E2NV5uFcJh6uSv1vOAUq4zLK7CCSB6hPqbVz1ZGHHi/LKXk2gvGw+27dJVH/e9XiRDXHFY+d12n7eua56PTdV1XPD2cPXvU3D6pqaV0ULl567i7Me/bbr30IJRWEypFDr5udv12erFoA1Nd8GWwkrocTvLgBN1VT8UqAVWcGjL8/wApe92zvf8O7HlEXJ9HzO+YMh47MZ73x8iNKKydmUgzu7tAetbz7JG2B6NqMoSlq9Bq7vop1KwHx93y8iSzJGJ1O+/OV9brx/wMGd3U2/0D80tPDXlbwtBKzDpN/UdLjmTeCrDkp6qCcmjNTUfBsc6dJxtklNxCh9xGxxgRKanruHK/23TvxBLQBral5IluakUYqQEsdRuIFLmZcspyvOHgxpdhu0By3CdoAUgrIoiRYJQoDjOziexhrI0xzXdxBSVjOsrV2PTrSMTiYspxG93Q6NbggWTu9d4Dc82oMWFovWlW0hBEVeEC8TpJI4rka7DqY0LKcRX/3yPvPREqkV1lhWs4gHv3lI0PTRTjXg3Q3cSjwBySolT3O0V028UUqR5wVlXlIWJdaCdhR+WFWzlUX5jO3ntbowxmKKkmgRUxYljU4Iay9lkZeMHo2f2rcfVlWeWZwTrxJcz8HxqmtYNVMvGD2a0NvuUGSVJ7DIqnW63no8WGmqYKd8Ne/p75PLsWXOWzQT9PuKkt46jF1T87shhcJTIaFqk+glcbkkVG067g6O9N9Kz3ItAGtqruCyOXi8iBk+mlSTYLoh/cAlTXJO7l7wt//3z7nxwQF3/uQW14Jd0Io0zjh/MAQJ7X6TzlabsjTMhws62y0cR5PGOdaYSiCVhs9/fpcHnz3kB//4PQ5/cI2w5TN8OMLxXbYO+hhjCJo+g8DFGksSZVwcjXB8h0Y7pD1okac5k7MZn/z1Z7T6Df7kX/6Q9qDJyVdnPPzilMFBD6UVpjT097poR1WD68dLFuMFzW6DZq+BH3pE85h4kZBEKdZawlaAc00jpbzSthBXt32ypSHPCrIkB+xmRrUpDfEyeWbffuhhSsNisuTswZB2v0l70KTjtXF9Fz+svIfGGvK8wLMuSZSSLBM6220EkMV5NWPc1W+tAKypqfnj4zKlpen08VTVl9IRHp4MEHUIuKbmD4cyLzk/GnJxPGZyNqOz3a6ajFMJmyIvSKMMayphgxDkaeWZK/KC07sXzIZz/slf/gRrLZ/9zy+rkKxS3P/NMXu3dujtdKqB8GlOnuRI9fTEgHgRc3L3nLP7F3R32vzk33zM+YMhk/MZSZQyHy5wXIeP//mHOL4D1iKVQDsa7apNc3CLZTpcEC1izu4P+fiff8j2tQHHX5xQZAVFVvLlL+5zcGePgzu7/Pq/fsboZEJZGMJWQHe7zWK8JM9ysiR/xnZnq4XjXRE+W+f7XRyPyNOc9laLdq+JVJUX9Ov7tsaSrFLSKCNPCj7560/p73b5J3/5k+c2wR0ejzm9d847Hx8Szav9XX9/n/5e97V/Jmpqamqeh6EkMwmn8ZeMshM86aOFgytD9oM7BPq7ScF5EbUArKm5AguUpSFZJkzOZ5RFSdDwgF2UozbTbrzQo9EOkFIQrRKmF3MWkyo8/OirMz7+Fz8gbPkIYHJaCbcHnz6kPWixfb2PtRKlJUorGu0QL3jcm01pieNrokWM1JI0Sjm5d1554DyHyekUx3OIlwmOp3F8B893CZo+zW4D7ehN11nHVVgL04sZq1mEF7g8+M1DejtV+HU1j5mPFjS7IecPhoxOpkgl0VoRLxMujscsJkuyJHvGdqMbXikAhRRIJUnjrAo1Jzllaarr56pn9m2MYTFZMRstWE6WHH12QhbnXFZuXoUQlVi/OBozOZtyfjRk53DrOyl4qamp+f5irCEtI6JizjIfka7zeh3pM/CuEdjWW5ezXAvAmpor0Fqxe7hFvEg4/uKU8cmEsBVgLfgNj2avQaMT0N/r0NvrIIRgMY04vXfB7GLOdDgnjVOMMfR2OvzwLz7g0//+BQ+/OGU1i1GOorlunN7shDR7Dbau92l2G6zmEdZCZ7vNBz+7Q7xIKIuSJEoZn84YPpzQ7jfRrkOzG1Y5gq6m2W0QdgK6O222r/efmoxzcGeXoBmwmCxxvEpUzoYLrr+/zzs/vkmeFQDMhgu0o2n3m3ihx/X399GuZnI2Y3w2JVmmz9i25uoWoUIItKMImk8XOCitaHYaz+w7S3PGJ1POHlwwOZuxnK7o73Ve2IF053AL7Wh+89+/4OJ4hDUWN3Dwrpi1XVNTU/OmsJTkJsFTIV13l8wkZCamMNkTrZjeLgVYf02uqbkCYwyL8ZJoHgOwnMWs5hHwuJeTMZaTr865+8sHVWh0mTAfzsmSHFOa6kSWtTgLydPq9Z3DAWErQEiBkAILxMuEL/7uHmcPhps1yHUbGKke59jtXO+zc2NAluaURYFUirAZ4HrOpsVKNXz+6bw8qWT1R1atMLzAZf/2DvPhkl/+v//Ag988pMgKenvdquhCVGsTSiKlXNseXGn7sqDk62RJzvhsyvmDIR1arzkAACAASURBVEefPeLzn99l9GhCkRUgeGbfl/l/q2m0LkAxm3Mtpysujsec3K2u971fHREtErSj8RseySpBacX2jQHeulimpqam5veFEg5Np4cnQ6y1pGWEIzz63gGu9HnbxB/UHsCamiupevEZHM9hsN/DWXvYNk15A5edwy2UVkSLBGsMjqdpdELCdhX+bA+aBE2/qpj1HMqyOt/19w9odh9XgLZ6Dfq7HbIkJ4sz1FaLwX6XZq+JlIJWv0mZF7i+w96tHbzA4+TuOUIKejvtTYWvcjSDgx7t/uN+cmHTZ+/WDn7Dx/Md+ntdGp2QRjvgxgcHjE+nTC/m1f46DXo7bbavD/AbPo7vVKFkLSmLEr/pkyf5lbavwpSGIivxQhc/9Cjy8okK6Gf3LaRYVz43aXRCysKwfX1QVR/nJUJAs9tAakmeFZjSVGFqV1HkJWHT59q7e/iNuqVKTU3N7xeJwpdNHOkjhUQgcKRPqFpIqd/KL6X1KLiamiu4FIB5mpMlWdUGxtP4obduxVKSxinWVH3s/NCjLMqNMLGmmlMQNn2klpR5yX/+D/+NIiv483/3M4KWv/GcpXFGnuZgH7eOSaMMqSSu75Cn+aYdy2VrlcuQrdKq8nitiyjSuHrfZS5hnhVkcYYXVl6xPM3X1bhy3Y6l2DSddjwHx9WVB9OYTSPqy2bSlzausn3Vzc2YqiF1Fq9z/9ZCWDsKROUhfHrfDnmSURQltrQYY9CuJmj6GGMp1nsRSqIdheM5CGA2nPOf/8N/Y3DQ4yf/5sd4gVNXANfU1PxeuZw0M0yPGWWPyE2ynnUdci14n1B3vvEcr5GXUpu1AKypeQFm3a5FSbmpqv36z7FsRJC1VXNjgdgcv5yuGD2aMHo0wW94vPfTdzZFJJfYdU/Ay9Dri7j0oAGbX/Nv++1ys17xcg2tX6ftjf2v7ftV1jS7mDN8NGb0aMLgoM/tH914riCtqampeVMUJictV1ykR8yLIaFqo4RGS4dt7xD/qUk/b5x6FnBNze+KlBL5gkxZ+bUfXiVa8rRgMVmxdb1Pe9BC62fHTQkhEOrlRMuTxR2/Ky8r/N6E7Y19dcW1eMk1pXFGluRce2+f9qBVV//W1NR8JxhKYrNkVU6JijkN3UFJZz1h5u28L9UCsKbmDdPqN3jnx9VYtqpn4He9oj8eersdWv3m+tq+nTfZmpqa7wcCQW5SlsUEmap1H0Cfjt4C9fZN/qkFYE3NG0Y7+rmFEjW/G856XFxNTU3Nd4lAoqULCEqbk5oVhdAYDIbyu17eldRfmWtqampqampqfgekkDjCw1chngqRKCQKLfRbNwLukroIpKampqampqbmd8Bai6FkWUxY5hNSs8KVIU3dpaG7ONL7fS6nLgJ5E8zHS8anU3o7HcJWUM1cFYI8K4gWMVqrTTuNqsWGwfWrENWmB5oQCAHRIgELYTtAKYm1VYuNsqzaYFgsrufg+lVLjzTKiBZVY2LtarzQ5eJoRJ4V9Pe6aK2QumodIqUkT3POj8c4rq7mtboaubYTLRJMWW7Wetk2I14mJKsUqFpzeIGL0ooiK0iiFClFZfsFzXaHD8dMhwv6e128wEUqgetVrTmstVwcjygLQ3envXkdIF4l5Em+Dus9DptetW+tFWVRsprHVSsWXbViqVqMCOJFAli0U41Iu2xlctmbLk0ygqa/aZcyOZuxmsf0dtr4DW9j+3L27cvsu6ampqbm+4nFYmw1Dzg1ESDITcIyn+CrJg6/VwH4Uqi/+qu/etljX/rAP2YefXnGJ3/9KWErIGj6uL6DKQ3RIuH8qBp6L4TA8TXxMmU1i3CDqglwGqePm+Eay9m9C1azmEYnQCpJWVTTJ1bzmGgRs5rFVXPc0MOUhvloycm9C+ajJXlW4IUuv/wvn/Lgs0c0OyF5WvV08wKvmk27TPjkbz4lXsS0+61KrEpBWRiGjyYsxkuMqUSS0pX9yemU86MRs9ESa6qGx1JANI+5eDgmjTMQVX+75wmhz37+Fb/6m08Jmj7WWrKkamKstMIayz/87W+5OB7RGbRwXAe13vv0fM7kbIZZ94zTrvPcfQshiFcpZ3cvmA4XJKsUN3Cr65iXDB+NieYxZVGuc8QEWZIRL5JKxJ9M8QJ309fvq0+OuPurI1rdBl7god1qrctp9NL7rqmpebNctgj6euDKAuaKaJYFrH385/F5oCirFk/wrLvE2mfPt3lt/fKL7gPW2qdt8/g9l+t/XvDtKtulsRSX04Ve8vZj1tfq64Y21/CJNUE1+9xY+1raO30fKW1BaiJO4t9yknyJI1ym+Tnn6X363gG+bP4+r+m/f5mDag/ga+D+p48Yn0yRSnI8PEFqxQ///D2GDycMH4557x/dIprHnNw958YHB3S32ygtOD8ekacFna0WrX6DaB7zyd98hnIU2lGc3h9y6wfXcH7icHr3guV0RRZnPPzqnN5uh72bWwhZNfcdn06ZDRdIKfnTf/UR7cHVPYfGp1Pu/uoYqSRplDI+m/HBz96hv9vh9P6QNEpZzWMefXnG7R/doL/b4eTeBaOTKZPzGb3tNsr55ia7ZWGqHm3HY5bTiD/91x+xe7iFuqJNx3K64qtfHVFkBUVh+OIX97l2Z4/DHxxwdn/IahY9s+9HX50zvZhTZAWn9y8QQtDb7TCcrrh4OCFLMubjJckq5cf//EOa3Qbz0ZJ7vz4iS3N2D7cwxj73F7LIS84eDLk4Hr/Svmtqat4cyyhlGacoKQl9l0bgUpSGRZQynkdsdxs011+Ak6wgzYpK6FmLlpJ2w8NYy3yV8OuvTtFKcmO3x1a3gedqisJQlIY0K1hGKZ1WQCus3jNbxiyilMbabug/f950mhUkWUFelEgpcLSi4bsoJSiNZTxbVY3ifYfAdRBSXGm74TukecGXxyMeDWfc3OuzO2jRbQZESTU9J/Qc0qzAWIujFUpKsrzgYrqiNAZHSdpNH1drLJbRLAJLtYfAxV1HYH57dEGc5tzc79MMXNy6cO2VMJQkZRX23fFusuPfYpw9YpiW6xzAt28WcP0//BoYPZowOZtx+OEB0+Gc1SxiMV5Ws1+NYfhwzORsxtmDIfu3d9BO1bIiX4dVqxmxlizJOT8aMTjoEbYCkmVCNE9IVxnRsvJcLacrFuMlzU6IdqrxMlJJgobP6GRKGses5jFB8+pxWMtpxIPPHnHzw2u4vku0SFhOVjiuJlrELCYrFqPKTpmXKEduwsnz4QJTGvznnPsSSxXm9gKXLC2q+a5rEXfVupIo4/iLU3rbbVq9Jqf3chbTFYvJimgeX7nvIitYTlesphHz0QK/Ud30Z6MlDz57hOtplrOIaJEQLxMcVxMvEqJFjJSSVr+JFzgv/H0U8Er7rqmpeTPkRckyThlOVsyWMY11KkYjcLFrQXf34RAtBa6j8F2Hi8mS8TzCUYpVkiEEvH+4jVKSVZxx/2SC52oGnQZlGZAXJfNlynC6JEpytJL4nkPDd8mKSpSNpiseZTP2ttq8c23w3PWeT5ZM5hFaK9KsQEnBO9cGlKYSkssoJSuqytDDvR6+67CKs2dsB55Dlpecjxd89XBEu+HTaweUxvDwYobrKG7t9xnNIoqypNcOyfKCZZwxmUfMlwlCCj68uYMMBHlhWKwS5quEorR8cHObQacBwGQRs1gl7A1aBHVl/SsjkGjh4EiP0uYk5RKg6gco3s7rWQvA10C0iDHGcPOjazie5vxozGoa0eqGtHsNvvzkAefHY4qswPEd/IaHNbYKJz5xniqHwLJ9rc+tj66xmq4Imh55ltPqNZiez3j01TmtXoPuThvtVgIwaPrc/OF1hBQMH01IVilpnF3pscrWgqa/16E9aJElGQDJKqW71WZ0MmV0MqHdb9IeNPFDj92bW8TLhOPfnjA+nRK2AtapjFdjwXE1197br8KweUGZl8Sr9EoBWOQFi9GS6+/ucftHN8jSHGsti/GSsB0wGy2e2Xer32R8NuWLe+e4vstgnW8YL2Iujka0B02UknQGTaSUmLIKbWxfH9Dshtz88NoL+8ZpR7FzuEW0eIV919TUvBHSrODByYST0ZwoztgdtGiGVU6VkIJllPLl0YheK6TdDPBch7sPR9w7GbM3aPFoOCfLC3Z6TdoNf+1pq4SWUlUed5oVnE8W/OLzh8Rpzke396rEfmuJk4wsL1lGKX/32TEf3t59oQC8+3DE/ZMJB9ttTkdzsqISZ7NlwlcPh7SbPrNFzKPhHEcrBp0Go9nqGdtQTRvK8oIsK6ov/FKSFyWf3jujGXjc3Ovz8HxKlOQoJTkZzpksIlyluPtoTJLlvHMwwHMNUZKRFyVHZ1M+u3/OoBNuBGDN74YWDh1nm2UxYZZf8Cj+gqbuse3fwFm3h3nbqAXgS1IWJcvpirMHQ45/e1Z5ofKCOz++SW+nQ1kY/u7/+QfGZ1OEEFx7d5fuVoskSpkNF1hj2D3cqrxicc58UuW1TS/m5FnJu396szK0zsGoJi6sPzCWagZrmhMvE5rdECGqNQkpWc1iPvkvn3J+PAZree9Pb2GNZXg85uSrC5SWGGO58/Ehfuhx7f19Hn55xt1fH3NxPOLDP7tDZ7vNaroiSzKSKKXVb2KppljMhguWswgLm/zEF7mzpZIUWcHn//MrVvOYyfmMmz+4jtKS4aPJJp8P4J2PD3F9h/07u0yHC37+n37FxdGIa+/uMdjvsZyurtz3ZTFHvExQzuVsW0tvp8PhhwfMhnPyrEC5Gr/hbYo9LqdMiPU1ztOcxWTF2f0Ljj57hJSSdz6+wcE7uywmy1fad01NzZvBdTQH2x2iNGe6iDkbL2k3fK7vdFFC4rmaRuih1eMvvcZafNfh3RvbeI7DcLakNBYpBe2GTzP06DQDdvtNPFeziqsvw9u9JkpKbl8b0G765EXJ2WRZpZdMlyzilDx/cV83Yy2eq7l9bYDraEazFUlWMF3GLOOM92/u0G+H1T22KFlE6ZW2lRI0Q49mWK13p9ekvQ5Jw+PcQrMWqlhoBi5xknN8PsVYy1a3ie9poiTn0XDOZB4xmUfEaU5pzGv/v/q+cplO1HP38GVIYXO0dPFVA0e8nbnjtQB8BcrS4vou/b0Ojqs3ybRbBz0QVSWp1oqwHdAeNGl0QoSSdHfaYGHv9jZBw6u+VZaGRjukzKscEQDXd9i7tU2r10S7mv5uB7/hoxzFclrljOzd2l4XnliieUy736xy5/IS13cIGh6Nbohcj+xqD5qbeavWQrMTcvMH15icTklWKY1OSLvfxPMdHo2WuK7D9vVBVbiSl0SLmKIo8UOX7Wt9/MCjPWi+sK9Ru99k93ALBChH0tlq0eyGuL5DGmU0O43Neqy1eIHH9ff2NnmMQdOn3W/QaAecH42u3PdsuCBLc3ZuDFCOwlJVETe6Ibc+us7pvQvyrCBo+jQ7IY7vVOFjrQia3uaX0dqqOjho+nS22ptqbGstxthX2ndNTc2bQUqB52q0kpSlYRSt2Bu0gEpAZXlJXpTMVjHz5eO8PUcrdnotoiQnyfLNjGnf1Ti6evwlWY73RL5bK/QIfZdBp4HrKKbLmNEsYjyLWKxSsqykNN/cFc1aWx0n2My5lkKglaw6EqxnjIPYFGp83TaAciWeq1BKVvssS7SqwtxFaTg6mzCcrSpHAZVYVkowmkd4jqYZeiglma0SLiZLllHKKskoiqorRVFWHsZVnDJbJoxmEa6j8dbzyt9G4fI2YqyhtDlKaALdJjdp1RC6jHBlALw4DFxas4kwiSeyBnNTUpgSTznoF80l/RbUAvAlkUrS3Wrxo794nw//7B2kkihdFWvs3txi61qfIi82OXmOqxFS0GgH/MVf/qQ6x3pclUCwddDjL/7dT6oWJlKgHI0Q0PvLn6AchVSKD//szqba9dFX5zRaAf/2//qnfP6/7pJnBcNHY25+eMCHP3tnI1qkkrheldsWtHz2bm0DoJSsbACtfpPyg/1NEYTjaYYPJ3z1yQNu//AGP/4XP+Dz/3WXZJUyHy4YXOvR3+lw/b39dXWufqET7PDDAw7u7GKN5dJj5voOQlatWv7p//HTat9Kop2qMjls+1y7s4spDUIItKNI44zjz08Ir9j3vX84BuBf/p//G8dfnDI9nzE5m7F9vc/tH93gxgf7YFm3b3EQEoKGv3kAXN6QHU/T2+vy03/7MX9amup4R6GUwvUdulvtl953TU3NmyEvSkbTFas4w1jLYpUQJXlVnLGKOR3NeXA6Ji8KjLH02yFleRlNuaLKl8prdjZe8HefHvOnH1x/SgQ+GYApS8NsGRMlle2XaYgrgEWU8L8+PWKyiNFK0viBg7PdJi8KPr1/znQRk2QF793YptsKOJ8sn7F9ibEQJTn/cPcUi+XOtS167ZDjsyn/8X/8lovJkp1ec1NhnBeGOMnQUiJFVUUcJTnTZYwxFvuE4y/NcobTFQ/PZ5yOFhSFQQpBM/BwHFXf8l6S0uasiilJuWJVzplmpyyLKQL4uPuvcN29F76/EnoGJSSOVCghKK1hnidM05iDsLOeNPL6qAXgS3I5tN5VctPX7xKlVSVm3Ge/MQkh8MJn+/8oqTb9775+rkvkOhHXLQ03PzwgWiYc/fYUS+XZ6+91abSDqs3J1yKTl7kiV40gE1KgL+2sQ6FhK+DOjw/BwtmDIY6n6Wy36O601738qv1JKTfi6XlUbWXUlWviOfuWUm76BF5eQ2Msh8/Z960fXGM5i3h095wsyWn1m/T3OjTaIdpRT+3v8fV4dq1CCJQSqODZXywlK3H6svuuqal5MzhK0muHKCXZ6jZI84LtbnNdCOLx3o1tQt8h9F16rRDP1Xx4a4e8KHEdzf5Wm1bDo9sK8N3qnviP3r9Gmhf4rqYRuDhKsb/VZtBp4Gi5icyEvstHt/fI8qpLwUe3d9npt1643qrC1+PDm7tVJERKOq0AayyuVuwPUpK8wFrYXRddKCWfsX3Jrf0+oe/gOZp+p4FWklv7PXqtgFWSkeUFjcCl1wr49P45yzjlzz++RZRkWAvTRUwr9PjJB9cwBuI0J0lz9gZtHEfTbQX87KNDoiSjGXhVKFrJWvy9AhZLaQtW5ZxFPiI3WTUJRDpgqx6BUjy/k8QyzziJ5txfTvCkouVW+fJZWVLYkqQs2Ama7AYv/uy9CvUkkD8AytKwmkVcHI84uz+k0Q3Z2u9xcGf3tbnn86xgNYs4XeclNrshOze22Lnx/ETnN82L9h3NY8anUx5+eYYXunR3Olx7Z/cZcV5TU/PHQ3kZOtWySnP5HTHGYqxBym8+nzHrKIv85rDo//fJPWbLhH/2J7cJ/cfN44V43IcPxDNC70VchpTFWlDCOvdvHUW6XP9/+9V9hrMVH9/Z42K6YjqPub7bZafXpNsKvpXtmm8mNynLYsoofcg8H6Kli8WgcLgR/oCW03uhADxeTfl8NuTvRw8BaOjqWdZ2fQZeAyEEB2GbH/Ze7Elc81L/ubUA/APgMmewyEuKvESqyoN3WQX8WmwYizGGPCswhfn/2XuvJ0myK73zd4XrkKkzK0u3hhhgQGJsOMs1GzM+cPmyf+8+ccmlDW05IBcDNWhZMit16AiX9959cM+oqq7MEkAVGtUdn1kB1pERfu5xj3D/7hHfWaZntf/dBYlf5rezjqoyVHmFkKJOx/t6dVNbYYXvMZ59Xr2Ne9+bHO9N3vvNwTlZXnJ3f6Op5XueAL7ucV5l/7LXjgdTjgdTTgYzlBR0WyF39zeW+oh/iu0VXg3nLNYZzosnzKsRfX+XyhXkJmU92COQ8UvP+eP5iMfzMefZnKN0wkk6Y1GV/Gx9j3+3fZvfD4/o+CG/3LzxOstZEcAVVljh5ahsiXElFotzFoHAVxEShcWSminOWTwZ4MkAgaByFcPikMzMaet1Et3BlxEOQ25SCpsR6RZaBEhRRypKW5CZGVp4aOmjhY9x1RvZVqLejIyKYzK7oKM3CFR4pcaWc47KNf45gxASJTSeqNP9xlWkZlbXwYqgmdUpsM7gsFS2qH1RLRyWcXmKLyN8GaOEQl78Q60epis8h/Esw1hLNwkvFb9/l8iKktmiYDCZ17WHoc9aN8G7pPRmhbePRTWhdDmx6lC5ktJmxKrzyvq9SZFxms24Nx2wqAoKa8hNRax91oIYJQTbUZsPu5uvs4zVLOAVVljh5cjtnIWZ1uklVwLQFzu1mKmti5oLmzUaV1v4MqS0GcfZfUbFMfvxJ2jp4csQ6ywLM2ZUnrIh9kmUQuBRupzMzJiWA5TwCFVMS6+R2Rmpmb227VjXtS+j8oRJeYYXBWipXyqympopuZk3JNPhyYCut4kQUNicaXWOdRZfhvS8LaRQFDYjM3MKm1LaHCU01hlOsofEuktL95BCEcqEUCUgmk7OFVZo0P0OReND3yP0PTZ6K32/vyTq8XqWUCWE1OfeugrJ0wjwy9DxQxyOw8WEthfQ8UIMlifzCfenA/52Y5+tt1j/BysCuMIKP2ic5084yR6Q6B4LMyEzMz5u/x2J7lG5WsZgXJxwnN3js+7/xmZwHesMlS2oXD33uhYsqOuiUjPlNHtIJFv4IkIpzWn2kFk1RAuPSXWOFh63kp9wlj/mPH/y2rYvCODrwuE4zu4xK4e0vD7j4hQtPYL2L1BCU9gU4wxn+SPm1Zgfdf89gYwYFSccZV/jsKwF14Cn81ELs2DkChbVhO3wBonusCJ/K6ywgsVQ2PS5tPywOGJSnnE9+YyW7L3yGJHy+LC7SWEMxtUqGp/2tvnp2h5tLyBQb5eyrQjgCiv8gFHYlNzO2dTXMa4kNRMqV6drUzNlbsZMqwHD4ojCpggh8WSAlj6e9ElUF1/W0Q5BHQlz2GUa1QGzakhm5uxEd8jsnNIWpGbGvBq/ke0/BZmZU7miJpnVmKrR5XK45Rqm5TnTakjlCjwXULkCB3gypK3X8WRI3tifVGdYZ4lVGyU8hPjLpvdWWGGFv06UNuc8f7LMZgAMikNm5ZCd6M4rP59WJcN8wTfTAQ6HJ+uU/W7UYSd+u5G/C6wI4Aor/MChpcd6sIcvI4wrGnI0ZVSekJopi6omZtZZlNAopQlkRCAjQtV6prZF1LIHwsdiMa4CHKWtpxx0vU0KmzKvxhQ2pXL5G9m+SLFYZ5b1g8aZ5Y778jo8h5Y+PW+LzMxJqwuSOWdaDknNlNTMMLZYivEKJG1vnUR1WPf36jFhDQGclkMsFRvhdQKVLDsqVyWAK6zww0Zpc4bFIYXNlq9Nq3MKm2HdyyfHAMyrgkfzEf/95D6J9tluSF+sPaD/Tta8IoArrPCDh+DbaczSFSzMBGNLLpO+rYnanMeLP7Id3qbnb5FWNXE7yx9jncE6Q6Ai2t4a0/Kcr6a/YlaN8GTAdnCLqRw0xOr1bFfN68PimPP8Mc45dqO7bAY3UOKqOhvBohrzzezXjMtTtPTZV5+QmjmpmWJcyWWNcOKZ/30Wu9EdQDDMD9HCI5AxvgwRrArsV1jhh4xItbiZ/AT3DNmbmzGZmROpzis/LwTE2udOe53duMPt9hoAiX674s/PYkUAV1jhB4y2t44QEi18ItWm7+8RqTZa+PT8bXCOlu7T1n1a+ukutOttYp0llBFSKAQCKRQt3WMrvEms2gQqQiDpepto4TGvxrS9unki0m36/g6+il7btmgijF1vAykEieo1nckvhxQKX4Z0vU0CFRHKhJbuUfl1xDHWXYwtCVSClj6J7uK7sBnfVMOTAT1/m1DWelznxZNa1mFV/7fCXyEKkzIuj/BlTKjateC9eNowVdmC0uVIFEp4KFFLihlXUTYRLNl0zDsspc2ZlKf1mDPVwVfRlc1X1lmsq7CYpbD/s7YzM2VejYhVt+n6r2mIcSWFTZGiXpNuSiycs5SuwLoK52z9GaHBQemyRmBZo4X3Up29dw0tfTpyvclKGByuzmzoclkm8zIoJFrUepRpVXCWzes/hHWDyLvASgZmhRVWuBQXKdeLyZTfTrE657AYBHIp9/KqY9XHeXXd3Ktsvw6ss/xh/N+wGD7r/MPyIVdDPGfjVetfYYX3CaPiiD9O/it9f4/N4DZSSALVIpQtwJGaCdPqHF9EBCpZRqgKu2BanoIQeDKipdcwtmRanfH19P/FVxE74Uf0/T0CdXmXsbElmZ1R2rwhZ8/ahtP8Po8Xv2Mv+pSev0MgEyyWzEwZFYf4MiRSHSLdRaLq6RrVkMLOMc40n4lxwKQ8prQ5voyIVAfvoh75O6jJuOBShcsom7SvJwICGSGEqqduvWTDOC1yHs1H/NPxPcZltjzeLzb2+ce9D990OSsZmBVWWOHPQ93YcTUkry+B8qpj/bnvf/Hz0PO3cLiGdL64zj/Xxgor/DUjtwuGxROm1Snr/nW2wjuMyiPSakLpMhbVmET3uR7/mNRMmFdDpuUZgUpo6XWc6uFwWFdRuhztgjr69pLffGpnPJ7/FtlE/S5s70YfL7U8v43K5pQ2wznDcfYVAsnd9t9hXMW0PGVSniCEJFZdHI6FGTHIDyhdTm7mLKox1+JPWQ+uo4X/0vW9K9RdwBlH6decF4cEMkQLD1/G7EZ3iV6hYhBqzbWkyz/ufUBpLQ5HaQzr4buT81kRwBVWWOFSvGoXXf/9NcnfG+7I384OXtDxNgGHRL5wzJV48wrfbzgUCikkWTUlVRMyM2WQPyazMySKaXUG1PW1xlVUNmdhxlSu1r/sst0InnsI6lKRUCYvTbVWNmdUHtUd9DpY2i5semXUsLAL0mpMZmeMikMcjlvub0nNlHF5jHWGULYIVIJCsTBDTvP7KKEpbc60OmPd7GNchRL+d1KYYZ0lNwsW1YRZeU7eRCM9GbIeXCNy7ZfeLpWQJNoniLs4oLSGs2yOvmyI/VvCigCusMIK31u0l3WLK7K3wg8JDocj0WtsBDco7AItAxZmzKQ6pbQZoWrhy5BARVhn48V/FQAAIABJREFUSJo620H5hIUZA5Kt8C6BDIlUi0DFxLpL29vg5b+nurTi27YzO2um7byIRTViXB6TmjGpmdZzdJ0jM1NSM2Ev+oSOt4kvEwSCwmaMyyNC1UELj1h1UULjMHxX1WoOQ2kzAhXT87cpbFarHdgC52yzrqvPm3WWzFTMygLjLPOq4F/OD7ie9Nh+ywLQF1gRwBVWWOF7iTeJUK6wwvcJAoFCMymPye2cQX7ARnCDJFhj3b/OpDwlM9NmlKEmUDGFScnMDJyrmzHQzQjFi9+RY1qecZh+zlpwnVC1XmJdMC6PnrMdyITc1DWGw+IJWng4DJvBbTI7JzNTjKuW+qEAse7RMlNOs/sMigNi1V0eayO4TdZoh0oUWgZoEX5nZR1KeLS8fi0x5QbkZlE3n/nbTRPIy+9FwzzlcDHh0XwEgHWO+7Mhbe9y0vw28J0QQOdcPaezNBR5RRB6KK0Qor5pl3nFdDzHDzzCOEDpOn1jraPMSxygtUQqCQ6qyqC1Qsj6PTgHQiCloCoNVWlQSqK0RDXzELNFwWy8IG6HBKGHVLUNU1mKrKgPIQWer0jnOeksJ+lEaL8uJNde/SWzxjIZLjCVIWrVx9JaYYxlPknJFjlxKySIfTxf175bh7W1/0IKgqDujjLGUuQlUgqUVmitsNZijUN5EoHAuto/IQRCCsqiwhqLVLVvF3MnF9OMLC2IWyFeoF85j/Iyv6WUOKDIamFLpVXt9xXn3NUXl/kke2/8XmGFFVb4vkHLgI631XT15ngyWDZ7rDV1cuPyGCU8Yt1Di4CCWug90b16PnbT4Q80Hf7rlDYns3Osq16xAkHliudsBzImNVOEkEvyaFwt9aSFT6jatc4nFi3q2d+x7GC8bY7N1xRmjkJhXUWoWnU9Y3FIYeco4RGoFlpePRbyXUOiCGRMqBIi1cJiaekea/4engpfWXJSWkNmSjJToppu4FBp/Lc8/eNZfCddwM45ysIwGcw4PRyyvb9Gp580JFAwOBnzm3/6iq39Ptc/2CZuhSitqMqKk4MhAEknIm6FWGOZjha0ujFeoCnzmhgIKQgij8lgzmQ4J0oCknZE3K7z8gffnPC7//E1H/7kOts31olb9QVKZxlHj86pKovva3qbbR788ZB7f3zCJz+/SW+zg/YU7W6MEILFPOPX/+0LZqM5d398nZ0b67R6Meks54//6x6Pvjzm7o/3uXZni/WdLmVeURZV7f9whtaKrf01hIDFLOfk8QA/9Gj1YtrdmCIvyeY57X4tP1EWFc5apFYEocf50ZhsURAlPkknIozr3cJXv33EwTcnfPCT66xtd4mSl+8iLvM7jHycg+PHg1qSo1mTc+7Sc16TNsvnv37w3vi9wgorrPB9w8W4xrrLvU4H19IqARaDdVUj1F5LtOhG7sW4isrlXIi6ezJECol1lsrljaCxwJPBlQ0ddQfyf2E7/IC96OPnbNc2SipbNHIvGiX8RtS9if45ixBqKcPknGmm89TNXF7T5GGdxVDW70egpd80qLy82/ZdwTpDaXNKm2Oo8ETdBKKEqqWyXqE0kFYluamwOJSQOOc4Sqe0vYBrSfdNl/PX3QUsBMwmKQ/+eIgfeASRT9yqdxvWOLJF0Tz0a955fjTm5GBAVRqyRUGRFdz98XWcc9z/4yE3P9rBDz2e3Dult9EmboccPTxjPs1YNP+299e486NraF9jKks2zylLs7RxejBkcDohnWYMTiZY5/ibv/+QqqyYjxcMT6dMBnNm04xPfn6T7loLUxpmowXT0QJT2do55zCVYT7NGA9mtR+ujn5NRwtODgYcPjyn04vZ3KtrlIq8Ilvk5FnJ4YO6MPdv/uEjJoMZhw/PufXxLmVRcfJowOZ+H6UV09G89m2WsZhk3Pp0j/07W2hfUxYV2aKgqp5OSrgKV/mttaLIK/K04PxozHS04Gf/8CFKS77+/WP2727jB5rD+2f0Ntp01hKsse+N3yussMIK30dIofBV9JyQ+oW4uUIjhULz/OZYomraJ/QLck1SSHwRNdJJ7qUEy5MBfX+Pll7Hb5o+LmwLajLkfUsXTwsf5Twu6uSei5aJes3P3s0v1qe+RWH+GnQ552bEpDwHIJARse7S8Tbwxcu1/DypqJxlmM0ojKG0lkVVEL7DCOB3RgClFBRZydnhiN2bm5TbFSRcyVvPj8d884cDtq6tMZssOLx/Rn+ri9KSwcmYVjdCSslXv3vMBz+5jtSS+58fUuQlprIcPxzgnOPana06dXwJxoMZJ48GVJXh+PGgjmj93V2ElDgHVWmYDucc3Dtl9yJqKMVyHFQY+2hdTyQQUoADax1B2KRBrWM+TTk5GHLvXw/45Gc30Z5CSMgWOdPRgnSR8+jrY9JZzsc/v0k6zxkcj+n0YqajBfc/PyTuRkgp+Oo3j5Baks1zjh8OaPVitq71Ud6biWFe5XdZVEyHc9J5zvGjc+796xPufLZH0okYn82IWyHOsTznnfUWQsr3xu8VVljh+wFjLJWx5EWFEBAFHlJKpHz3hODZjeYFccmbza/va+Ql+pllVU+LUKpO9b2rjvSrCNGVrwvx0qk29d9fvlZPhKz7N4h0540I2dU1u5fP5flrIHvPoxbDL2zGuDhhbsZ4jQB9qJJXikFb51hUBQ9nIzJTLv17VyLQ8F0SQCUJQo9WN0a/xoM7TwvSWc729TU2qm5TQ1jhhyGf/uI2p0+GnD0ZMRsvkEqgPcXgaEJVVQSRT9IOCSIfY8yVyeykExHEPge/r8dMbez0CCJ/WZu2d2eTMqtTmcZYyrKit94m6UQIKdi81idqhUglaHUikk5Eqxuzvtul1Y2XdlrdiFsf73Hj41029/pIKZkM5hw/GjAe1FFGZ+uauY3dHta62r/DEek8x/M1pqzJWtwO0VrR7if165VdzjR9XVzldzrPOXp0zvh8xuh8Sp4WWGPprrf40S/v8OjrE44fnTMbL1BK0OpG4Hhv/F5hhRW+H0jzkrPhnIeHA7SSfHp3hyTy8eW7f8Q5B8ZalJTLmdDH51PyvOTmtTV87/k1GGM5G8wQUtBtRwSeRqm/NjLzp0PLgK6/851O5fguIBBo4bHm76GFx2H2DdPynLP8MXvRB6/8/LTMOEqnHKdTOn7IdlTXSX4Pm0AgneeMBzOGpxMOH5zi+YooCahKw+hsyvnxqB6lEnpcv7tDEHm0uhFnh6M6ajRcsHN9ne5aiygJePjFEePBjFY3IooDgshnbafL6HRCnpZIIfB8jedr0kXG8GzC2eGYg29O0J7i+t1thKijedNxShj5+GFTUNq8fnowIs8KpuMUpSRh5KN0vcvM05In907ZvblBd72F0gqpBKYydX1b4NHbqFu5tVbESUAUB0sb82nG+Hxapy9Ls9w1xu2Q3kaLr37ziHSW0V1LCEIPF2g29/qk85wir1Ba4gcapSWzScrwdMrJk+EyMurfrBtdLtsRSyUv9Tud54xOp6SLnLIwWOtwDoLQY32nyzd/OCCdZbT7MWESNNFP3hu/V1hhhfcbzjmK0nB0NuF3Xx7ie4qt9TZa1VmbojQYW0cGrbW0kxCtJNY55osC5xyeVvi+RjeZodkip6rM8nVPX05kLiJ5Z8M5j4+GbK63WevEdFoh56MZo0lKrxMT+LUWXxR6IKCoDIu8xNNqGcOqjKUoK2zTKOecIww8Ar9+RM/TnEVaAALfV0SBh1aKsjIs0gIhBb5WRKH3netbSiGR4odXe30xMm9ejUirKV1vg5buLZtDXgXjLFlVMsgXFNagmxR8rN9dY8t31gWczXMW04x0kXN+MqHdi9m7tUmW5swmKdm8YHgyJWlHbF1bo9VL2L6+xsmTEdPBnLKoiFsh/c1O3SUsBVordm9u0OpGhJHPzY92UEpy+mSIH2jiVkAY+QzPpswnje2jMe1ewu6NDfKsoMwrtFZ1VLJpPtBaEiUBpwdDskVOlhYEkU/UqkOzfujhnOPgmxNa3Yjues3cfV+jPcXJ4yFJJ6a32UZrhR94mMo+16Fqbd367ocerU5UkxYl8QOvSbU6wjhg7/YmURIgleTuj/d5/NUxk+Ecz9dErRDtKWbjBYtpymKacfJkRGe9zda1tYYEvXhzKPPqUr/L5qbpBZqkHZL3k7o7WMkmmlo32+zc2Hgu0ve++L3CCiu833CuTrcenU34zecH/PKnN9nst4gCj7KypHnBIi1YZAUAoe+Bg6woORvOKCtD4Gs2+i0CX1MZy2A8Z5GW+J5irZfQScKlQsVltg+OR/z3f7nHjz/cQ93cop0EjGcZx+dTdjZmaCVRSrK71UUrWZO2rCQK3HMp4/PRHGsdxhiK0iyJbFkZhuMFZ6M5VWXpd2J2NtpY65gtck4GU3xP005CwsDjO+Z/P1hYZyldxrQaMK9G7MYf0NFrV4pffxsX4yinZc6iKshN3Wn9vYsASilo92M++cUtrt3Zwgs0URwQJgF+5HE38tnc7SF1HWVrdWKstbQ6Eft3t5tmC0d3vb0kEz/9+w/If36TsCFm2lPs3Fint9GmyAqklMStEKkV3fUWn/2b21z/oG5iCJOAIPI4fTJiNkn5h//0U4ZnM+aTlNHZlP5Wh3/3H38K1ITFGMfaZnsZVfr0F7e5/dk1lJLPEaFbn15j89oaUtW2lZSs73TprLewxj7XoXrr4112rq9jjaUsKoSUdNbqL06YBPzyP/xoSYaiJEAIwf7dLTZ2upSFQUpBqxfjBx79rQ4//98/4ZO/vYUf1kTK82vJlstw/Pj8Ur87/YR/84+fNdItFaY0rO/2ljV+AkHSibn96d6S9L5Pfq+wwgrvP5xzCOp6upps1Ru+yTzj4HjEFw9O6Hdiru/UjWcngxmPjoaUVcV4mjGepvzbn94kCX0eH48py4rZIud0OOMnH+3x0a0tQv8yYuUwto4CZnlVr+GZzWZRVpwMZgzGc4yx/EPo0e/EWOt4cjImCur/bichg9Gc//HbB/TbEdY6Hh+P+Nmn+wA8OhyS5SVpXvLgyYDb++tsb7Q5PB1zMphxNpyz3kvQSvEqseEV3h2UUISyzW54FxNUBCpGi9eP3nlSESqNEgLjHEVDACtn39WSvxsCKITAD7w6Pbj+bYXrOlLU7r3Imv2mZhB4IaXX3+y88P64FRIlQXODEPUw5mdtbzxvO2mH5IsIEHhena4M44BOPyHpRE9rB8VTPwB6G+1lt+uzu8R2L6bVjeou40a/Loh8gujFc9LqxiSd+r0XWncX0Fotu2afhfYi4iR82pXV+BconyD0Yfv1WsfjVki7+6Lf7V6zpsbvi3OeLXLG5zOSTkirF9PbaOMHT7/o74vfK9TaU/Oq4MliRCA1a0FMywtRQmKcxTq31KdqeUFTU+WYVwWFNQRS40uFlorcVCgh8KTCNN2CtcaXpGo62pSUeFLhy7o4Pjclh4sJodL0ghhf1uky52obxlmEEETKw5N1Km5e5ZTG4CuNJxVKSEprqKyhcnVE2ZOKSHmIxsfLbFfWMqsyZmVOZir6QUysfXyhmFY51lkCqQlU3RWZ2wpPKvTFuaG+FVxodj0L5xyls4yLlCeLET0/oufHtJrdvHUO4yxFs+6WDtBS1aUYb2i7sBVpVeJJhScVQdM1OCszDtMJPT+m44X4TZfhZdfba2zPqhzrHH5zXRE059ZimnP7dF2Qm4qFKQikXtq/8M3iyE2FA1o6QACWF/2+uK6X2S5s3c0vEWipsM1xL74nCPA9RRh4hIFHpxXSigKErLVK80YvNA49eu0IrSXjacqDJwMCX7HISmaLnDyvKEvDvYNzAk9SVpbZPCfNyjpK6F0y/1YIfE/hexqtFa0kIImDmii6+v7Va0fMFjnjPCXL69pxJURdQ14ZKmPrjFhRcTacsdFLiEJF1aSt86KiMpbJPGM0TckaHVxPq2Vt4WyRAY4w0Fzf6b3V+8MKr496/olhXo2ZVueNZE7d4bwd3nrlLOBAKTbDFn+ztodxDl/Vv4t3NQUE3rNJIEKINw5vizfosNq/u0UQ+Tz4/BA/9Fjf7rKx23sasXrJYYQQlxbyCiEQr1ng+ybvXX5Gvror61V4ld/fPn3pLOfJ/VP6Wx16G208X79AyN8Hv1eoH7An2ZT/5/gr+n7Mj/t73Gge8BeipLMyZ1SkXE/W6Pn1Q/g8nzMuUto6YC1ISLyAcZHiK03XD8lMSeUsAgiUJq1KDhdjAqVpeQHrQYJAsqgKfjN8zFqQ8JncRfkhNCThPJ+TmZrYbIatJSkd5gumZUbbC+l6EaH2mJQp87IgsyXOQdsL8cI2QsC8Kl6w7ZwgMwVH6YQHswGDfM6Pervsxl1aOuQknZDbio4OWW9sD/IFHS8k0h6pKWtiIiSx8l78kQC5KXk4G/Cfj77gk+42n3R3iLSHcyzP7bTMyUzJjWSNqCGlr29b4EvNpMg4y2ZE2qPjRzV5Agb5gn8+vc8n3R3utDfQfnjl9VZCUFrLWTajsIa2F9DzY6QQjPKUhckbMgb9IGZNJAgB4zLlNJvR9gK6XkTHjyisITclma0YFylCCMKklvjITfWC37K5rpfZHhcLjK110dpeUBNHZ+j6Mb4AKQRR6JNEAUnos9ZN6HWeZmFC32N/u8/N3TX2tuqNYZoXnI/mtJMA1TRiKClJ85Lz4Ywk9gk8TacV4nuKqwIwUgji0KcVB8ShR7cV0Y6fZjeiwOfO9Q2UEhwc14L5xjqkkoSBRku5JIBQ1/3tbHTodSKGkwW+p7DW0W1HHJ9POBlM6bUjus26NtfaLNKCe4/PGIznRIGHde4lPbwrvEtYZ0jNjEfpv/Jw/ntKm2GdJdYdEv1/EqrWS7lIIDXbUZv1MKnle5rAi1rNAv7LIIoDtvfXaPfiuoEg1PjB9/8UvanfSTfi9qfXlk01qwaL9xfWWaomwmedRYu6k3FRFRylY74Yn2CcZTfu4RpidlGfMsgX/K/zR/ykv8cn3R0+nxzT9gJutdb5YnyClpJbrXUeL0aMiwWZqRjkc0Ll8cvNW/T8S0LCwLhIOViMOM4mOAfrQULPjxgXKX8cH9e1Ntbwu9Ehd9sb7MU9fnX2gLNshsXRakjpMJ+T24rCVi/YHhcpx+kEB5xlM46zKR90NimtYVbVZOfJYsw/Tb/hH3c/oufH/H70hOtJn64X8eXkhLUg4XrSx12h02Wdo3SG1NQitrrZtEzK2r8vJydEyuNaUke5K2evtP3b4QE3W+v0/IgvJses+QmbYZvj7IxZmZFWJef5nO2ozb/duHWldthV1/s4nfLV5BQpBAtTcJbN+Pn6dXyp+eezByyqAikg0SGbYYueHzGvinpCD44vJydshi1+tnadUbHgyWLEF5MTtsI215M1AGZlfqnfJ9mUry+xHSufP4wOWZgSXyquxb3lxuKn/Wv4V3x/noMAIXmOoK/3Eu5e3+BsOKMoDWFQN2i0WwGf3N7mbDQjzUqUkgS+rhsrXnKPc85hjOXrR7WO6Qc3Ni9Mv7BFXWQFaVZycDyirCx5WfHZB7uUlV1mb5/9jMNhbf2+LC9xrToTVZaG4SRlPMsAwXxRMkuL72oM7gqAcYbMzGjrNa7HnzGvhlSuJJAxWvi8Kj2/aLIhvzp9xKhIKW0dQfzZ+jX+/c6dd7Lm7z+7eQNoX6N9Xad7f0B4U78vUugrvP/QQuE3adSwiSBpoUhtyawsmFU5vtT0/YhAakprOM/nDIsFZ/mMr6en7MV1ZEUKwawseDAbcn92Tj+I+aCzxVE64TidEEjFWTYj1gFZVWL0y4ub52VB2jzwt6I2OLg3O2Mn6pDogHRRMCkzonzBcTphkC/wpCRSHoWtOMmmDItFk6p+3vZxOuHJYsTt9gax9msihCAzFeMiY1SkHKdTvpgc87cbN1gLEnypGBUpg3zBvdn5MuV5VSRaNylnKQSx9mnpEImgsIZ5mTMtM2Lt0/VCtJRkpuQsm71guxfEqCYaNilS7k/P8TqKjbDFo/mQeZkhheQsnxEovUwXv8n1Hpcp38zO+KC9iS81sypnXGRIIThKxxTGEGmPRAcsqoKsKjnLZ6wFCXtxlyfpmElZn7dpmTNr/l2Le7S9ACWu9ntyhe1cVZzmM9KqRAlBakq0kLS9sCGeT9FuBdy9sUHr2Qhc6NPvRESBRxL5y9f7nZi71zdIYp+yNPi+pp0EhL63PMY8LdBa0u/Ez3XrXoZ2K+SDG5tN+rdmcFtrLZLIR2tJtx1hjKMVB2gtqbRhZ6NDURqSKEArSTsJ+ODmJp2mkWN3s0u/U0dBz0dzAk9zc28dKQWlscwWdbq8lQTsb/fotiPWuvGq3vk7hBACLXza3jq+jPBkQG7mSKFfSxInrUpGecq0zDjL5szLHAfcaq0ttSbfdof3igCusMIPGKHSdP1omRrdDp/Wm0gh2Iu79P2ayAlgUCx4ko45y2acZlPmVU5lDaHSfNzZ5t7snN+PnjDM53T9kLYXMC0zTtIJfT/GV5q2F2AdLzzEL9DxQzy1zlE65jyfMS5T7rQ38IRimC/4uLPN3c4Gha0QCEZFiqcUvSAiUT632xtIBKfZlNNsRm7KF2xPy5y0KrnVWifWARZHoDTzKuc4nTJoCO7ClBhn6XgRf7N2nd8ODvhicsK0yhBCkGj/Uh8AYuXT8UJaOmAjaLEePK1r9pXmZmuN/aTPrdY6UKdsDxajS2yH/KR/jS8mJ9yfnTEsFghRX7tBPmNSZnR0SKx9Yu1TWbt8YLzu9c5Nna7djjok2ie39UiqtCqItU+iBX0/4k57k9QUHKdThkXKXtzlk+7Oss7yLJ8BkHgBdzubfNDZ4lr8tC7tMr+vsj1piG3i+RTG8NX0lBvN575NcPc2u+xudJ/LxPc7Ef1LNrWdVkinFXJ7f72OyTSpZIAkXuP6Tr8OpDWvv+qRe22ry95mF2PrXLEUgk/v7iz/vr/dY3/7mdq8bszeVvPfz0T8rm31luv/edMAcnw+5ZvH59y9vsHf/2yL33zxhLwoGUwW7G502FxrcWtvbdlpvMJ3B0/49PwtcpOSigmVrUfnWUwzVeXl36RFVWcKftzfZSeacZrNsc6xHiZY6jrYt40VAVxhhR8whBA4ajJ2sBjz6+FjPmhvPv17M5ZJNO8trWGYL1g06b9lg5CQdPwQ6ywn2ZQ1P2YtSBAI9uMehakY5vN6fJNsHurWcJhOeDgfcprNsM7xaW8HTyoG+YLcGApr6iieqeiGIbda65xmU46zCYfpmA/aW+xGHR7Mz5eyCfVa63VdT3osqvIF2ztRh9xW/OrsIcfZhGG+4EbSBwSDJnX8LInSsiZ7BkvpKnaiDl3v6gHvz75unePe7AxPquW5Fc25lc+MvcptealtTyh6fkRmSsZFxmbYouNF+FJzu7XBwWLEuEiXTS4tHdR1j+mYB/MBljq9/Glvh1j7l17vjhdyI1njm+kpqSk5TCf8Yv0GSZDU5Mhd+FX/vy8V+3GPtCr5r8dfcpiO2QhafNTdZlykFKZCIpDfOheX+X2V7W4QcZRO6iYbKtKqIJQeO1FnWef47Pn+9qV42bVxzl1atwzw7ZKrV0VdRNP1oZR85jy96jOv91orDvjJh7vkZcWXD+o0+XovYWutTeBrlBQIrZDy3U0TWeF1UZd4+DJAiR6+DOnbXRyO6BX1fwD9IKozAVWJw1Fay7TMkeLdTT1ZEcAVVviBw5eKvbi3rA0zru6y7DQpurb3dBSRFpKuHxIpXXeuCslG2EYKQag8un7EdthmP+mxFXYQwLW4hwAeK40Sks2wRaJ9SmuQCPp+XA8/p9ZWu+A+vSCicpZIeUTKI9Y+d9sby9RuSwd0/WiZhpyX0TLCJRA4B4n2yUz5gu2dqIN1lqN0ghaS9SAhaTpV+36MxREqD18qul7U3NjrDlclJNeTPv3g1fpesfK5kawRyDot7ZpIY8eP8KQifiaCGEh9qW0lJZH06fsxO3GH/bjPWtMxfRENO06neEqyESSEWpPZEi3qNHGo6jmqF+f3suvd9SPutjc4zWe4qqDrhfT8iEQH7Md9XFNb2fbCpktXL9O15/mMUHn0/Ii+Hy/Jq68UgXpaKnKV31fZ7noRu1EXT0oKa8hMsYxI6z+zMP5lD+M/hUhdbJLe9nM69DW3rq1zfD5lMF6w1o1Z77XotJ7+Jr9PU0TeZ1gMpclwWISQJLr3RtNQYu3jCcVM5Mv6v3ZZ3+O+rTLwtiCuShVcglV56QorfA9hnaOwFcbV4iKh1AghsI3sx4XciBAC0zQRLGVeGkLhN00HpTWU1qCEQAmJEvXUhcpZSmsRAlTTvQq1+v3FzU4JiZZ1osO4umPU4lBCEEi97BatrMVi69FLjTRKaU0jC/RUUNU51/jhXrDtcBhrKV3d2SoEz62pjm66WvZEabSQWBz/99EXHC4m/IfdT1gPkytr7S5grKWwFbbp2q0JkauPD0jEssuvspbKmRdsX9goTEXlbH2eGukZ4xyVM1TWIURN0H2pcDgqW5/bujZJomVdy+bgkutdn/PKGmwjr+Q39Y1lI2chEKgmYnxxbo2r5WHqv8klSbaNBNDFd6D+ntlL/bbOXm676Yq+WHPlzLKGEd5+PdRfI1wTZTdNWl8IgRRile79K0RuFgyLY0qXo4XHenDtlfN/n4Vr7pPzsqjLIJwj0X69GVRv3Nv9Wj+OFQFc4bVQFRV5Vo9rwzmkUoRxgNd0Cx98dcRsNGfn5iZxJ0L7Gmcd88mC2XhBlISEjdg3QJmXLKYZZV5323XW20gtMZVhMpjVeo2hRxgHqCtGMVVlRTbP8UMf3cwJdtbWD3QlSKcZi2lKd6NNEAfLm2aRFkxHc4QUeIFH0o7qkVJZyeR8ShAHtHoJSkmMMWTznPu/f4wxhu0bm3Q32sspMN8nXHR0XqR9X4YLgnKRGn4VLt4PT+9ML/uca0gE8Fy68G3avuo4374nCiGYVwXH6YSMOaIiAAAgAElEQVSzbIZA8FmTTn1dEnJR7/iqNV9m+1V41o/X/czTNT293n+u7T+HkP0ptldY4a8Jpc2ZVUNOsgfMqiGb4U18GaLQdP3NV5LBrCo5zWb8bniEdXUWwFOK/bjLrfbam/7GXuuNf5EUsHMWMM1/CcQyLLr6kb8vWExTzg7OcQissVSlYefWJt31NkIKzg4GnD4e0O4l+JGP8hRVUTEfLzg7GCCEoL/VZe/u9pKADQ5HmMoStUKSXkKRl8wni+Z1gxd47NzaJG7XI+K+jTwtOHl4Tm+rQ9wOGZ/NULoeU6d9zfhswvH9U6Tcr2czhx55WjA5n3H2+Bwv9Gj1klrv0EE2z3ny9TGd9TZB5CNCD1MaFtOMr//lAVVZ4Yf1pJnvIwGs0wxvoN34Bsf+U95/Ve/l27L9ste/DWMt8ypnPUjo+hGBerOZq6+bwvmz0o9viAsxYqi1xrRqorzWUpYGpeoonVKynlHrXD1jl4awNVJl1lqm8xyAKPTwvbo2zTlXN/s04820Vst5uxciyBd2pazFkRECJcVyHu7Tc+IoKlPXD0qxXGs9Dq2WQolCDyVfPfc7zQqyvCIKPTytlhtDYyxFZVCytqHkUxtlVUeYpRBopcjyklmak4Q+nldHSpV6SqSLso4s+7567tpf6rd1OPs06iuFQOs6cl6Whuk8Q+ta7Drw1DKq7Vx97itj8T2FkvW1KUuDtfXITaXqSHFlLFLUftVlFs3mqjnXl/ldVobZIsfTijDQy9dXuBxSKEKZAI7UTJmW53giQEuflu7BqwigKTnPFnwzOcc4S6R9jLNI4GZDAN82/kI1gAW4FJwBEYCIgVUI+33C6eMB/99//j0b19Yo84qDL4/4u//0M+J2hB++KAnjrCOd5+RpQT4v+PJ/fcP2rU327mxzejDg8JsTjh6c0t/qsn1zE4fj+OEpB18dYSvH4HhEOsv45f/xM/bubBPGL0qGzEcLPv/V19z8bJ/eZpvP//lr1q+tceOTa3gCpoMZD/71Me31FnE7xA88Th6dc3TvhPMnQ3pbHZRWOOuQUlKVFQdfHZHOM3pbHbRXT0cwlaHICqx1aL0aLfdDROL5fNDZqlOXQqK+Bw/C6SzjbDQDBEnk0+vEaC1ZpAXHZ1NasU87CWknIWleT8ToJLUES1WZJSlM85L/+fuHOFdr4O1udohDn7IylJUly0vGs5R+J6bXRNtH05TRJKUdB7STgLiRXhFC0E4CKmOoqjrt6Xsa4yyngxlK1sLP7ThAa0VZGf71myOkkHxwc5Mk8pep/KtwcDLm/sE5H97cYrPfIm4kYtK85HQwIwp1I/BczwAuK8PZsG5SCjxNrxPx8HDIb798wqd3dtjoJ0spl4s1nQ7qOcM7mx0Cv548Y6x9we8kDkjzkjyv6nPqHIGn6HVjitJwfDbhV79/yFon5vb+BrubHbRSzbk1LLKCySxjZ6NNEgVY5zgbzUmzgnYS0E5CfK2YznMCTxFF9XUxpk7SB74my6tL/Z4tcn79x8esdxNuXVsjjgL0qt7wStRlEJq+v4snQ5Twmte8Z4JeVyM3dQnEftJbCqUXpp7C867wTgmgcwZchq2+wZqHCBGCCBAiRuo7CNllFQV8P1DmJfNJyu0f1YXvZwcDyrwim2V4/otfI1MZRidjzg+HDI5HnB8OSboxDsfweMximrJ7a5Pedpf+VhelFZOzGUf3TgiikCItavJVmmb284vwI5+tGxuks4zx2YTTgwGdjQ5h7KM9jdISqSTO1mKqCEg6EVErpMhKRicTvMBj7+42ylMoLVG6rkGzjUK/8jRxOySIfYSUrO32CKKrpT9W+H5CCUn0Pam7youK47MJk1nGdJFxOpyzvd6m34kpioo0q+fOPjkZkUQBP/9sn9PBjPEs5ebeGpNZPT93a72N7ymKomIwXjTRpLoEoygNw8mCJydj5mlOKwloxUE9BaWxMZ5l3D84Z3ezw6d3dzk4GYGD2/sbHJyMKErD1nqL4SRlusiYL/IlSfz0zja9Tt1wkmYlUsomavhq/4tm1m/ZEC5rLUdnU4aTRW0jK0iigE/vbJMXFaNpyulghucp+u16dm9eVoynKYPxnNkiZzrL+PSDHbbWWggEp8MZ03lOEgdIUY/YzPLL/X5wMGAyz4gDj8F4QRR6/OSjPay1zXSSOZ6uJ/DU56/kfDTnycmYyhjaSUhl6ikjWVGSZQVnwxlf3j/hw1tb7G52uH9wTqcVsrXW5uB4hNaS9V7C2bBe/7f9jkIfax2LtKAVBRj7THfWCpdCIFHSo63XCFXy3OuvMxM48Xx6fsSprrVAc2vYCBN6QfTOWNI7jgAanJtizT1M+VuE6IJQCAKEXEfIDisC+H7AAThH3InRnqLVzGouspKyqCjzkjzNWcxSWnmCtZrB0YizgwGj0wnpLKOqDDjIF0U94WGvT7vfImqFSCEoi7ou0FQOIQVJJ76y/g8gbkfc/vF1vvif3/Do80PyRYH2FEEcYCoDCKSUFFlJkZY460i6Ma1eM8bqbIryFKaydQrGglQSU1myeUarGxMmHlE7Ikzq8L0f+pemo1dY4X1BWRoOTydMFxlZUfHwyWCZjpyl9WzcLK9lR6LQ48cf7TGdZ5wMZsSRz+HphLPhnHYzkgxBPa5N1RM1lKrTh8NJyv0n5yyygs/u7tYkzbmacKQFs3nGH74+Ii8qPrmzw3C8oKwM3XbEvcfnVMay1ks4Op9weDJGKbmc3rG72VmSqz8X1joeHQ2XEcYnJ2OSJOD6Tp9FVnA6mHF4Oql9E5LtZn79BdGdzjO+eXTO1nqbXjsi8HQTXZ0zX/RJIh8p5aV+f3x7m0eHA8azjJu7azw6HhJ4mrvXN/C8+t5X2TplHPoeUgrmaU0Av350SuBr+t0YKQWVMUxnGfOs5Hy04DefP6adhGz0W5wNZ+RFhRSCLx+e0m1H9Doxj45GjCaLF/y+sL3C66NuirN4MsAXIRKNcRXGlYhmvvnLUuiJ9lkLY1ppQKgzLI6b7T5bUeuddQH/BQjgAkQLqW6AK3BkOHLg3YU1V3j7EKJO637163tUpeH4/im3frRPEPsMj0cc3T/l0edP6mibcWzurzMZzFhMs6a+5eI4gu2bG5RfHPKr/+tfiLsxO7e2+OSXH7C+2+f6x3scPzijyivCOCBKQoLo8t2T9hXttRZFWjAbzdm+uUmrl2Aqw/hsyvHDMx598YSyrDCVodVPGJ9NmZxP68jBLGU+8nHWspimnB4MePL1MUpJ8qzA8z10E910xjI6nfAv/+UPfPSL22xd3/hLnfoVVnirUFrS78aMpguOTickcT0xQynJaLLg8HTCcJIymWdNfZxjf7uHpxX3DwacDWa1ELWvaSchWilaUUAceWyvd/C0Yp4WAGz2WyglubO/TisOqZr06OlgymkTfSqb+rMLuZPff3XEyWBKtx3RinzSrORkMKXVNHJ1gjq1Zq1DvoWUpHWO4XjB6XBGO/YJfE0rCnDW0e/U2mxnwxmTWQo4bl7rg6vr565t95ZRSEedDo8Cjyj0iENvqSl4ld9Qb67bScind3coK0OalxSlqVPdSUgrCuh3Yzb6LbxmQ6ykZHejS68Tcfvaek065zmHpxMG4wXnozmLrKQylijQfHRri5PBjD/eO+Z8NKeThCSRz2SWXur3VfOPV7galSuYFGc4HJ70aek1xuUJw+KInfAuse68VM/PUctG/ai/y4edTaxzRNq7cqzj28A7JoAKIdoI2cbZAOdmgIeUG3Ut4ArvFYQQdNZaRK2I/laHjb1+E22zXP9ol6Qb011v0+ol+KHH7u0tehsdqsqwsb/G5rV1ENDf6i5TrF7g0Vlr1Q+l7R5C1jassYRJSKsfo65ogZdSIn3J3t1tvMBjbbdHf6dbdxAHHts3N/ns7z+i1UvqNLOSBLFPf7uLc47N6+vE7QjP11jr6Ky1+PBv65mLvY02YRIgZC27cOOzfdaGc6JWsBqDt8J7DSHA04qiMkzmGRv9BK+phZ3MMkbTtKn5q2VbcNBKQuZZwWiyqCNz3Rjf03haUeq6YSTNSg5OxssIGUDge4S+JokCfK9unhhNFoxnGVlRExTbREb63ZjhJOXh4QDf07TiepRaOw5ox3XaFcDXiqD5zY7TjPPxAmMsrTjgxv/P3ps92ZHdd36fc07uN+9Wt1YUCjsa3eyFTbZIihOSZjQTipHGY3s8jvG8OMKPjnnzk5/lf8IRfp0nO8Jhh0djibItBaURySalZrN3dKMbO1Co7e439zzHD3nrogoobN0sNJq834jCkpWZZ8vlm7/l+1trY6ngSGHkstRESU5vWLl0b9/rYynJ6mKDMHAJXJsoyavnh121AZVlzRhDlOSUpaYoKkkhIaA7mFAUeuaalkIwiVMGUwvgrXt9lJLUfJfeEeOGypJoW5JG6BH4VSWXSjaImWV2NEnY2huyvFCfrmGV9FLzXWp+9S7N8oK9qUv6YBuWUiy2Q7b2RmzuDHEci8B3sC1FM/QZjZOHxp0XJf1RzG5/UpFIz+HUWptacF++Z47DyHTCdnqTTMcoYRFabSZFn7gcs+huPPH4cZ4SFTkaQ912qdvHz5GOlQAK4SDUEkZ3AQWmQKgW0jqLEDXmiSDfHEgpcXyHs6+f4uTFtVkihBAC169I1YN46a1zVUyQNkh1P4MsbNeotQJOvbKO0dNMPynwApdGJ2TjpROzcyOenBl54TtnufCds4e21RdCXl4Iefl75w9td3yHZqfOypklLEsdcjH7oceJ8ytHtvHK9y+gtUYXeu4CnuMbDaMhzQvKWWaonLkz06ygLA2OrQh8G9+tsp1tS2JbFYELPIe1pWbl/qUK4rEtRW8Y8dn1bQLPrurequqYg+7E+xmyVUJF4Dt4TtVG4FWkJIozltphZT2UkuVOnTQruH53D60NrmsTeE6VTDKMGYxi4iTHthSthk8j9JBH6DOWWjOaVPGL/VHM7a0+tcBhqVPnxHL1UXj9Tnea9WoT+A7DScI4zlBqqktYmlliiufY3NkaTJNckil5UvRHCcNxQncQoWRVC9ixLdKseHjcCFzHqrKjRUUEHUtVFT6mhNKxFYNRzLXbezRCv8qCtiS2rbAseWB8hiwrEFTW2TBwquxgJQmn7vIsKzix3GRh6jY+udJCa/3QuKMkoz+KGY4T0qzAdSwW27VppvVxX6HfTBQ6pZ9vMSn6aFPiqmCqaVmVI9zXJ30URnnKvXhElKecqDWnOpzVcV9V+PxReC46gFr3MeUexgwACyHrSLmCkMGXPeUczxn3ru9w4+PbXPzu2UNk72n04o7a96jr7igtsqdp41lhTCW7IJ6xfJKZCvRWZafmsatzfDPRG0b89N1r+J7Ncifk7taAwHc4tdqustxFFReXZgWOY7G22EAIwdbekL96+1M2Vtu8/tI69ZqLbSnKUrPXn5DlBUpJWvUA25Ik2ZRkSjGLg8uLksEoJptKlSRZQb3mzayG4yidZqTa1HyHmu+S5QVxmhPFVeywbSvaDR8hBGlW0BtWFkDPtWnUPDzXnlroDt+jWhuyvJgmv6SVyzNwqQcuaXa/DSEFnmvTrvsUpZ5KvmQURYmSkqWFcDYOEDMplsV2iO9Z5IWmP4xI0gLHVtRrHq5jMZwkM4mW/XEvtUO6gwlCCBaaAaNJQlHqGRkupnOrTUXKW3UfqMZdao1tyZkFMElzBqOYUhuKsiLznVaNhWYVr90fxQyGMb5fzW3gOcRpTpw8PO5Sa+Kkqjksp9bGZuhjT8vOzfEwKiHoe2Q6QSDwVIgUCikkodXGlo+36PXTmJuTHn+/c4sF12ej1kIIwbJXZyNsPfbYI/D1C0FXWcAxutzElNsgbBASsFDW2WkW8BzfBEwGEYO9Ee2VJn7tN08Db445flswHCe8+8ktXMdioRmwtTfC9xxOrrSqJAbHeuhDZzhO2OmO+OL2LqfWFrh4egn5wIdQlVFb1dh9UtD6s35M7VfE2H+tfVXh6Se18aBgeFlqhDzs/jQHXLj7fXlSl77sR+S+LuLT1PzV+16Vp/Ce3O8TD417jmeDNiW5zjBUCYiO9J6pFFxUZGxGQ/5+5xaOUnTcyoK4XmtyvvHMMecvAgFMMOUWRfY2ZfYR0jqFED4ID8v9R0i19qynnGOOOeaY4ytg33p1c7PH3e0BrbrP+mqLs+udRx5z/c4e3X5EuxWw0Axohv5z7PEcc/zmIysLRnnKZjyalccEWPJCTtae2Vj2olQCkQi5jLJB2d8CWUegEPKZTZpzzDHHHHN8RSgpqdc8zq53WF2ssnaDI8TcD2KpHdII/QPJEXPMMcevE0lZkJQFnrJm9a4B3CfUG/8qOOY7WQAWAgcjHKAAk4FQVSTy3Mo8xxxzzPFcIWWVeOA6Fu2nPKYWuNSevNscc8zxJdHPYjajIXGZ40gLWyomRVVicckPj6XNYyaAEiFsjJlgis8pii+qzSLA9v8FQs4fKXPMMcccc8wxx2837kUj3t27w53JgAU3oOX4XB3t8tbiBi81l46lzWPXAUSEKOsCQvgYM0SI/SogT/vtOcccc8wxxxxzzPGbi6bjsVFrEVg260GTthswLlI8ZaGNRvDrV584/mAOoUD6CNME4yKEjRCN59L0bwKiOCOOMxzbwmAoikpWwRgoyxLHthCiKr0kpUApiTPVlVLqcMZamhWMxwm9foTvO3iujedaCFnpQ3V7E2xb4XsOrmvhuTbO1xTvo42ZltYxlKakNBpHVqWYAHJToI1GCokSCoGgNGWla4ZkevT0bNXvMl3gSnu6f5XpVhpNrnMEotJbEgqNQU+l8Pe3a1OJv0rkQzdh1UfNpIxJygxfuXjKwRbWbAwaQ2HKSvdLVvFWpdFkOkcKgRJq2nYl3qqErKodYABT3fyIh8a9Px+TIiEtMwLLw5bWbHuhS3JTTIuSV+PbH3emcyRidh5DNQ5t9HTuBLZUSI6e89la6IJU52RljiUVnnRQsqpdmul8NjYlJOX03JZQ05HdH9+TdLKeN7Q2lWC51iAElq1emOzIaBQz7kdE46pk4eKJp/+gNqYa135mqWUr5Asi7ptOq/pEowTLVixvdB5bDvLrwmQQMR5EVZ1w38V5QgzlHL/5MEaj0WQ6odAZSthY0sYSNuKI98aDWHADToVtHKVoOj6+ZbPshTQcjwMJ8L9WqD/90z992n2fesf7KMCMKfMPKPN3QAhMeYcy/xhprc8TQZ4Ct+50ufzpJmlesNcdc+PWHoNBzPbukBs392baTzdvVVl6cZyjpJwJhe5Da8Pu3phfvn+T/+vP32WvOyGOs0rhPsq4dnOX//ij97h5u8skyigLjW0rarWvp2JLaTS5KUh1xiiP6OZDXOlgSwuBoJePGOYRxugZERnnESCwpEWuS3JdUJgSjWGcR2zGe9jKwpEWUlQ3ZKYzdtI+qc4BsKRFYQomZUJWVqrsSkiSMqM0umr/wQoDaGKd8unoBh/0v0AAjnIIlEdhSjKdk5Q5vWxEqjMC5YEwxDrlXrI3bVtgS4tM50RFjC1tDBXpKnSBBiyhHhq3JSuCfmOyyeej2/jKwVE29nR7VKbsZX0SnaEBTzmVfprO2E565LpACLCkItclURkzLiLGRUJUJtjSxpLqyDnfb3uQjbkT7/LF+DaTIsZVDpZURGXCZtylpCLmrrSJy4RMZ9jKRk/XuOpDRURfJJR5SRJnxKOEIi9wPWcmgP5148and3n3xx/z0z97h7LQnH/j1FMfq7UhjTPicUIapdiu/cKQrN07Xd77T5f5+V/8is1r21x88wz2C1h559pHt/iH/+8DhJS4vkNQn2dF/7ZDU5KWETvpDbaS6+RTPUBbuNP3zeM/sjxVvVtuTwZcHe1xbdSl4wZs1FoselW43DN8gP5PT7PT8dcC1qOK/cplpDqLEZsY3cegqeoBvxhfni8qojhje3dEOlXPj+IMJWUlJpoUZHmJZUmiqCpFlKY5qysNlHV4XoUQhDW3KqAuJWsrTU6fWqTV9JFSVudVkqXFOufOLNFq+gTB11euLy5TutmAu/EuBmjaIRrNuIjZTnpEZcKkTIiKhLO1NZp2yPXJJgtOgwW3wd14F0faLLpNHCTjMuZ6tImjLFxpEyjFdtKjmw2rcxUxSigu1U8zLiI2k10KXRJaPgtug0E+oaY8ztZOPNxZYyh1yaRI6OdjclPOrH7DPGI76XIv2aNh11h0q4+eTBckZUpW5mzGu4DgzdZFBvmYe8keZ2pr5LpgK+my7LVRQjHKo4fGve4vY0uLQpckOqOYtl0aTS8bspsO2MsG1K2Ajgstu8Zm3KWXDSlMSVSkKCG4UD9JNxtyY3KP3BQoofCky7iI8ZRDocuH2u64TXbTAVGRMCoi7ia7lEZzwl8kKbPqR6fcHe3SsAO+3bzIdtojKhI2aiv0sxGjImbZbVO3XxxReGMMeVbQ3exz4/JdiqzA8R2W1tt01lqErephnExS4nEVpJ2nOXmWs7DaqkoIIhh0x0SDiCTOaLRDaq0A13dIJglZkhPUfSxbgRDkSUZRVOURvcDBGEjjlFF3gjGGZqeO6zuz2tTt5SYnzq/w2S+vkSXZU48tzwrGg4gbn9whGsYgYOnEAp211kzgPU9zJoO48jIUBckkpbFYp96qIaRgMogZ9SekUYYXODQWQtzAochLJoMIv+7jeDZSCvK0oMgKtDY4no2yFWmUMh5EJJOUZqeOH3ozC1qtGXD21ZPc+OQO8Th9SBg+iVKiYcyoHyGVIKj71KelJ40xJJOUyTBm3J/Q6NQJmwG2Y5HEKckkpchLbNvCchSj3gTHd2gtNZBSUOTlY+c8niREw4R4krB9a4/JMCablocz2jx5vUOvOtcR623Zc4/YNx2Vl8XGEg4Gw256i930Nr6qc6b2GqH9eCu9kpKG7XGpucRa0KDUmrbrs+AGx+Z5OOarzlSiiMJHyqVK+NlECNlCCIuvUFzktwZFoSuLnDYIAWWhKbWmnJYkMhiUkhSFpiz1zPJnP/BFL6WgUfdpt2q0mgFnTy9y/uzSTFg0jjNazYCN9QVeurDyVIKjx4lUZ/SyEbeiLQLLZ9FtIYVklE+4Mr6FEpK4TLmXdAmtAFva9LIRhsoid3V8h47bZMltVSWQdM5u2ueEt0juFKBgK+lyO97GEordbIAxhhP+Er1syM3JFrkuqFk+wyIiKVOW3EfdwGImGGswuLKywBljiIqY7bTHtcldLtVPT12wkBQpozwiLlNuR9tEZcrLjVMkZcZeOqRu1xjnEdcmd6lZPlKII8e95LaxjpAJMMYwLmK62bCy9DkFnnIwVOPeSrssu236+Yh4OrbNuMtno1sA1CyPBaeBptKjGucJljzctiUtvhjfwZU2jrRIy5zMFGhjiKYWxKTMuDa+Q9MJeb15nqhM6WZDfMvjbrzDKI9o2eGLJQhgKhK0d6/PZ+9cA1ERkyIv8GrujADubfa5d20bL/TIs5w8LfDrPq5fvQB6WwP27vaIxwm9RsDCapOTF1YZDyKGe2OanYKwVRGU7tZgRjSlEKRJxtatPca9iowMuxOWNzq0luoIIeistjBaE9S9Z7JKFnnJqDvm2oe3GPcibM8iTwos15oRwFFvwhfv38SbfgBOhhGnlKTWCBDAqDdm8/oO8TjBdmyanZD1C6uURcn27S7NTp16u0at6TPcGzEZxjieTa0ZoCzJ1s1dBrtj0ihj1J3QOdFi+WQHIQX1do16u8a7P/6YcX/CvuNrv3pPb2vAvRs7xKMU27NpdEJc30FZiiIr2Lm9R297OCWBEe2VJksnF+hvD9m92wPDdL4Mo35EYyHEshRu4BCPkyPnvNmpapN3Nwds39olzwr27g2IxwllUYIxaK0fud6T6XrnCxUpdFz70HpbtpoTwN8ICKRQKFGt5bjoEZdjHOmx5p8nfELOfamr8JjQdvGUPQthEsf4dDzmq85FqhOUeoAurmKKqwgZINQ6EDC3/j0ZUgpsu4pZY6o4L6VEiCoqQAgBpiKHUla/e5wKv+AJsQQv0JtYCcWqv8iC0+BUUFm6dpI+W0mXQHlYUtGwatjSwpE2F8KT7GUDPh/fppeP6bhNapY3rcdoT0nLfbI0LCbspgNCy8cSCk85s99ZUlWkRmdcHd9hI1ih8QgrlRKSUPmEVvWz6DapW/f3rVsBZ2onOF1bY9lrI5EM8wlbSZd+PmZQTGYVAhbdJtpottM+u2mfuEwrC58pjxx3aTRHfUhJIVl226RlzlbSpZeN8JTDOWOIyoRSl5wKVvCkw05aWVQLUxFeWyg6bpNVr8O4iOhmI7bTHrUH2s7KnEE+5kxtjTPBKuMioWZ5aAyDbEQ/H9PLKytfYFXVY9b9JRxpcX1yl242xBYWnnJx5Ivj5jNAmWvyNCeNU1zfwbYVjmMfqgN9+R+u8vb//Ute/t55WkuVJctoM7WclQy7Y8aDCDdweP8/fYKyFf/q3/0R437E3avb7NzpcvLCKgurLT56+wpKKS58+xQjYPPaNj//0XucfXUDqSS/+NH7fO+fv8Ebv/cyjvdwGMLTQhcleVqQxRlaayzbwnYtrAMfjHc+3+I//C9/xUvfPcPqmSVsx0KXGjE9fjyI6O8M8XyH6x/f5s7n9/gv/90fEdQ9tm7u0r3XZ/FEm/NvnOLqh7fZvdPlpe+eQQjBZBTzix+9R9DwWVpf4Bd/+R7rF1b4/X/1vcpC+AhXtNaGLMn47JfX+OjtK3zrBxexPYtxb1K56qOEvbt9fvU3nxCPE86+tsHHP7+ClJI//Lc/5NrHd/j8V9dZO7PEvRu73LqyycaFVZZOdpgMIlZPL9Hd6h855698/zzxKOGTv/+cqx/c5KXvnGXUG3Pjkzu89N2zGG0o8kevdzSM2bqxy72bu5y8sMri+kK13pbi4ptnZvXQ5/hmw5iSTMcM812G+Q6+CqlbHfwSj/EAACAASURBVAKrgaue7OGIypxb4x5/u3mVvXRCNhWD/uHyGf544+Vj6fNz+ewQoolUGxhKhAyRcnEqAfMCsY0XFK5jEdZc8mkdSttSjKPK7RT4Nnle6SkGjkUcZ+yTgUe9IBzXotUKuHuvj8Gw2KnTqHtYtqLVCuj1Jnz4yR0WOyGtRvC1xQBCNQZX2vjKwVNVPzxls+S2iMuMTBcoIXGkRU151JTLdtpjJ+njTWPwBJXVaphP6GUj7sa7WMLCVy41y6dhByRl5UKzpJpZ72yhcJVDmuX08zGX1Gka9tFaTPvxa1JISq3ZSno40qZlV/VNlZD4yq3i86ZEZ99dnJQZha6SQwwQWB4tp86V8S3iIqFph5WFDevIcSshGedJZelLe4SRjxSSFa9DqnNyU2CorIFhHmAw1CyfSZFwJ96hmw1JygxXOXjSwZrG9jnSxlMOkzLGlVXbyQNtB5ZHyw6Jy5Rrk032sgFyagkdFhHDfEKmc0pToqeuvNDyGRcR/WyMMdBwarOYzBcFQoDj2QShT60ZENR9FlZbrJzqUDsQ65XGGUmcsbjeZu3sMq7n4IeVRU5KgS41SZSSTCrLkuNaFHlB2AwIWwFXP7iJ61Uu1N07PertGkEzYOf2Hrev3GP71l5VdlEI7l7dYufWHsO9EQsrTaTz5ebLdi380KPWCLA9m2anwfLJDo2F+9d2nhUMe2PqCyGnLp3AcizqC2FVOxuJ0WZKInN2bnfZvL5DMklZmFrbrn5wizTOOPOtk/R3BvS2B3ihx2QUc+uzTe7d2KVW98mzgrtXt7FsRXdrwMJqE/8RBFAIUEpR5CXjfsTeZg/Hs1laX8CyFeNexPWP7yCVZGmjw9rZZXbv9Bj1Juze7bG3WVnmFtZaDLtjirSg3g6xHMXeZo+yKNnb7B8550sn2vR3R2RJTnu5ycrpReIoJWzVZi78x613rVWj1gr49J2ruFOX8/56h62gOscc33gY7ieCVOX+ZFULGPlUTCcuMqIip2Y7TIqMfGoRNBi0McdS/vCYCaAGkyGkjxCnQdQQwgFhAfZ+EcXj7cI3HEHgsNgJGU9SbKtKypDdas7arYDJJMMAjbrHYBDhOI+3DtQCl5PrC+x1x7O4wX1R2BOrbXZ2h3x25R5ar+DY1tdGAOWUZBTSwRL3L9PQCjgfnuR2vM0wn2BLi0C5BFYVd2VJCRiW3DZNO8RgSMqUSZFM4wqH1K2A9WCRRadJaUpuRduAIVAegXJJlEdoBQSWR64LHGnTduo0rMfrVtrSwpKK7bRLaPm07DpKKBxpUxpziOToKVF3lU1o+VMSKXGkTWC5GAyecjnhL+EpFyXkkeO2pUU/GxFNY/N20h5NJ6TjNImKhFwX+MolKbPZQ6jjNEjKjJvRFpMixlcudTsg0zlBUmURu6qad1fa2E6DmuVxO9451HbDCtgIlrkb7/JFtM0gn9C2wwNZ0wJPOtStYGaJrbKBFYnOaNohy94C1guW/CGEqFy97YBGJyRs1eistViZWsP2YTuKxkLI+TdOs/HS/bKWRV6SJQVmmkWcRhl5kqNURZ7ayw2yJOPv/5/3aSzUOXFumSRKaS7WaS6EfP6rG3S3BgghGA+iSrjZr2LsomFMa6nxpcfmeA5hK6DeCSnzkvZKk+VThwmgsiR+zeXUpRO8/L3zs+261FU8X1m94NI4I00yyqLEaE3YDDjzrXU+fvsK416VyZtMUow2NBZCbl+5x9bN3anFLmWwO0JaEoRg3JtUfXjELSaEwHYtGp2Q1lKDaBiTTFIc10ZIyWQYcefzTU69vM7GpTXWz6+we6eLAXrbAyaDCNux2biwRhZnbF7bZu1cpa12+8o90iRnuDc+cs77uyPu3djFdi3Ovb7ByYtrZGnO3c+3cH0HXerHr/dSgzzJefsvfsXeZp8T56q+NxfrT72WeuohqMJMpusk7xOLUldEYb8+s5ges//v/VBKISqionUVJ2wd8BjN6h7r+woK1oHs8ELrGQl5sA70kX3WetpvDtWHLo9o2wBFqUnzAqUktpJYUk5JlTm03z6eVG/6eaOq7yxxpIenKgNXFWzwdBbeXFdz8kprhUWvxk48JjealuNjpkoJv248h0ogElNuoovPMSYD2UCqNaQ6N80CfrEW8UXDUqdOWHMpSl3JhShJnlemYcuSlKWZ/bsoSoQQ+I+RJOgs1HjrzdNkaYGUgjD0cF0LDLzx6jpJsoIxhjB08b7G7Lu6FeBKB230IbdtoDw2gmUW3SaFKZHIikBNr6ML4QZr3iKucvCVi0RQtwNeaZzhZLCEPbX+ecpl0bWo2wEn/WWgInCB5eFIm6YdooQkC9d5o3WBBaeB/YSSPGdrJ1h220ghCSwPJSQdt0nTCdFG46v7ZPpMsMqKt1AlHEyzYBt29fbzpMsPOq9VAeLTcQjEkeN2pEXbafDd9iVeaZypLHPKw1UOHbdJaPus+h200ZU8i1Asui1CK+BMbXUmOdOyQwLl0XGaSCGwhYWjbDpu9YJSQrLoth5o22YjWGHJbZNOLX2+8qaucI/clFWm75REV1IzTF9U1fk2ghVc9eK4fx/EfojFUTXTpZQoSz30DdvbHnD1g1sM90YVQfzD04z6E0bdimBIpbA9Gy9w6e8MufrhLVpLDRbX2yDAshULK01qDZ8L3z7NwlqLPMkJ2zXq+1anrzyu6u+jxiWkxLKth2ILo1HCFx/cZG+zh1KS13/vEpajGOwMq3mQEtuxcX2Hcb/P5b//HMtWrJ1bxrIVypKEzRrn3tjgxNllNi6dIEvyKpGkE+L6T/7YfOm7Z1k+2WHUm3Dto9v86N//DX/83/1jpJI4vkNZVi5uQ2XJLPMC27ZQair9JPdDaPaDYarXs2VZj5zzstAMdkfTRJ9pgldpKPIquaW/PeTWZ5uPXG8hBbZn01pqMNwbcfnvv6DRCVlaX3jq9cqKgjjLSfJiGg8uaAc+nmMhgN4kIitKPMcmdB2kFIySFNeycC2LOK/kpiwlwcAwTtmbRKw0Q+qei5peEFlRMogTkrx6PyzVazhKUZSa7dEEISB0HHzXxlaPvw4nWc44SUnygobv0gp8Sq0ZRA+3neYFd3tD3r15l9VWndOdNiuNkKIsSYuC0HMRQF5WpNKSEvcFi5uUSDxVY8k9RaAaxOUYV/qEVhtXPtkF3HZ9XKWIiryS0CpLBllcPTOOiScdPwEUFogA8DB6F8wQrYegJxh1AqlWQPgI8eK+BL5OeJ6N92vUmPJcG9expl+THEr2aLdqM22wrzsJxJbWTMrkICypsKRPoLz7X0XifqBsw67NiNQ+HGHjODYt57AL11KVy7duBbPz7LftW89u+azbAaHlzzT0hBCH4goPIrQDaub+l93BubakYsl9WCLpUeN2lcRVDxcLV8qZEkIfJe5/bXvKwZU2huBQ21Wc5GE5i4Ok9ai2H+yTONDGflLMwe2DfEJSZqx6nSr79wB5f9HguDatpTq7d3uM+xFloVk/vzJLlmBmBTnc/8oilLG32cd2LVrLDbI4m+0rpMDzXdbOLbN5dZuPfvYZF79zlpVTiwgpWTzRJh4n3Pz0LsPuGKkkWhvCdg3btUjjjP7OkBuX79LbGuAGLld+dZ3lkx1qDX+WtfooKEtSb4fcu77DtQ9voUvNyYurrJ6uLGL7ROnBVTGmiosc7IwYDyY0lxpEo2S2tpWbVrJ0ssNwb8w7f/0h5147xcalNSzbotmps7yxwM1PNxn3I3pbg2kcYhMvqBJp+jsDdu9Wrth4nHDl3eusX1ih0Q4pdcmoN6G/MyQaJQz3RvS2h+RZQWupzsalNbr3Blz98GaV0HFjlyIvaS038EOvSiqZLtfB+00KQedEizIvj5zzeitg5VSHm59ucvPyHSxLsXl9h8kwpixKyrJ87HojBG7gcPqVE3zx/k0+evszXvvhJVZOLz71tXhvMOb6bo+sKJBC4tkWUTPEs21KrelHMZM0JysKzi11WAh9oiynO4lJspxxmrHcCDnZbqIx9OOEL7a7OErhKoXv2AzihO44Yms4RgpBw/fo1AK6SczeOGKSZaR5gQDOLXVo1bwjSWBWlAzjhO3hhEGc4FgSZ+raN4Yj2y5KzTBOub7bx7YUa806BkN3ErM7nrCx0CItCrrjmE7o0/A9vr7gpKNhAG1KEj0hKocApDpC5yWO8rE4+l2wjyrAQmAJSd3x0EDNdmg53jc1C1gANlKtIZBAhi7vofUdTLmJtE4DbyHVMswJ4HODeIz5Xsonpom8EKhcG1+9n7+u83yZ832Ztp/1GCkkRyWJ/jrbfpbt++7qC+E6baf+QsX+PQiv5rJ2eokrv7rB5rUddu50sWw1I4CWLXE8+yFLmeNVcV6fvXONvXt9ht0JO3d6tJfvu/vcwOHCG6fYvLrNlV9d5wd/8iYrpxaRUrB+foVRb8LPf/Qe3a0Bll1luP6jf/kW9VaN8SDi2ke3+PBnV+jeG1AUJb/6m4/57j95FcdbeSIBtG2LpfU2Ny/f4eOff87eZp8yL2cEUCqJ6zmVe/YAlFWRuKvZLW5evks8Thn3J0glD5BAwcmLq+zc7vKTP3uHl757lo2X1rAcxeJ6G11qPvzJZ9z5fIsvPrhJkRW8/L3zrJxaJBrF3Pliiw9+8imb17bJ04J3f/xRNdevWORZzpV3r/PpO1dxPIciL1haX8DxbJqLDV7+3nn+6n/9Kbcu3+XGx3cosoLl04ssn1zg7hdb9Lcrt7pSCtu1UJbEmCrec+PCKv3d0ZFz/uoPL3Lq5RPcvnKPL967SW97SDJJSeMcY8AL3Ceutxe4XHzzDFffv8Vnv7zO7/9X32f5VOepr8Wbe31+cuVG5Z3xXDphwDjNUEIwyTKkEAzilBt7PSyl8B2Loiy5vtPl6k6PKMt46/RJznTalFozTlKu73ZZbtRoBR6+Y7M9GHNjr8/mYMRSvUboVoTlbm/Ip/d2WAiDiiAOxtRc95FWwDjPubHb505/SJRlrLcb0xi2yg18VNvGGAqtSfKiukZVpT26N474YquLb9vsTSKubvd468wJQu9Fo38V+Ut1zE5yg530Fh3nBJNySFyOCOwmrny8nMukyBhkMdqALRVLXkjHrdFyvGPr8zETwAJjxpjiOnpK+KT9yrRZA6QYs4MxTQTHU+x4jjnmeHGw5LZoOXUccbSF90WCV3NZPr3IP/7X3yceJ3iBS2ftvmX21R++xMalEyysHra+1po+p14+Qa0RkCUZXs0ljTMc1yZsBQgBtmOxcmqRP/w3v8tb/+y1yko2JW7Skpy6tMZ/8d//M/ZLAAghWFxr43g2DRVy6XfOs3Z2hR/+i+9gOxb1do3mUuOpKlJYjkVnrc3v/NEbXHjzDN40MWEfpy6d4N/8D3/C2tnlQ8e5vs3a2SW84Du89sOLeDWvsoAVJSfOr8yIcGe1xe/80etsvLTKiXMrVZzelHi1V5v803/7Q7KkEl4XUlRSLLYibNY488pJFlaafPcPX0NrTb1do7XYwK97GO3x6u9eZOPi2sySZ1mKxRPtadyixw/++Zu89sOXZh+5fuji1zxe+f4FTr+yTr1d4/y3T7G43qa5WCVpnby4SmuxTqMTHjnntlORxW//wSvTpBiFLg1aGxZPtHB9h7LUj13v/di59kqD06+s01gIsZ/BhelYiobnYinJUr3GWqtBkuf0JjFbw3FF1oRgoeZjK4mSksWwxuW7O/SjhFOdFp3Qn1lpPdui7rmHYvy6k5i0KHj95AqdMKDpeziWIs5zoiznjXaThZqPkpK0KBgn6YwkHkSaF9wdDGn4LpdWF2nX/MqF+5i2fdemEbjUPYfFsMZivYYlJWutBgi41RuwPRwTZxmOpfBfMPcvVELQSTnBkQHL7mmWvTN0s7vspuUsGvBxxpV70ZDLg212kwl12yW0XO7GQ77VWuEf+8fDj57PLAobIRsIdRIh21UiCAaj++jy3lQTcI455vhNx74czDcBlm1h2Rb+JW8WK3fQ2re0vnBkHJfj2tiORdisAaYqs/aA21FZilozoNZ8ODZICEGjU6fRqVcJFsaglJqdQ1kK13forH65SkpSSfzQYy1wWJ26IQ/2rdEJaXQefuEoSxG2agR1H6OXEUoc6U3wQ686/5kHCtgL8Gsep19Zpyw1utQoJSt3sxA4Sk6tefVH9n1xfYGFtUoDcT8GczYuR3Li3HKVfFBopJIz2Z6qVF6lw+b6Du3lh0Mm/NB75JxLJVne6LB0cqGSxJlu24cx5pHrPe5Xbuutm3tYtsW3fnCBxkLt0PFPgpKVG9WxFHXfpRV4bA+r2OHAcSi0xhhmMX8Ao6RyQzc8FykEpTHkpaYoS6KpW3hnNKHuudT9ilwqKYmznGGczlzNllI4lkWUZUzSnFJX8eiPSsKQUuBaFlobRklaXbNS4lqKtDi67er3VVtJXjCME+wwoBV4FLrks3u7jJOMmuvM+vSiQSCxhI0tXUqTk5RjAGpWE/UUHk6NqQT9y5yW4+Epi0EaMcyS+8+fb1YWsIUQDYTV4GEFOgGijbSevoblHHPMMcfzRhUP9+wPXqm+ejjFs5CEZ4UQAqGevX9Ciq9cEq/SLH32l/i+FYnHzIsQAvUVEmWeNOeP+v2j1nv71h6f/sNVPn3nKhfePMP3//jbNBYeTXKfBa3AZ6Pd5Mr2Hv0orgigbaGN4fLmNp5t8dLqIle2dtkZTdhYaDJKUu72h1zf6ZFNEwpXmiHLjZAoy/np57fwbItTnRa/d/E07cBnqV7jg9tbdMcR2hheO7lKKzi6/F3gOFxY6fDujU3euXGHVuDz5qk1Xl9fpTeJjmzbs6u4dK0113Z6APzg3MmK8FkWSZbjKMV6u4Fnv5jhYpawadpLjIseg3yHu/HnhFabJW8DWzo86Vmw4Aacayyy5IWcri+w4PjspmNqtoPGHEsiiDgqC+wReGa1SmMSTLmDMSkIC6lWEeKbYwGYY4455phjjq+CUW/CYG80yxJeXF+o6i8/A7nfHU3oTmKkEASOTeDaJHmBMWArySBOyYoCJSXLjRqebdOfxDPOMYwT6p5HJ/TJS013ErM9HOM7Ni3fY6lRIytKxknG7niCFIKa67DSCMnLklGSMUlTsqIEBOsLDequO40ZP4yi1MR5TnccM0xSHCVZCAMWan5V0/6ItpWUTNKMO90hQjBr21KS7iTiL97/jKbv8daZdRbCAO8FdAFXXMoQlUPiYkRhcizp4KkangyxniB2PykyduIxlwfbpGWBRFAYzfl6h9cW1p5VB/CpdjxeAqgjdHkHU97DmAhpv4QQIaAQMpy6gueYY4455phjji+DmTbgfuwiX95VuE8H7rsc7+sDmqlu79Pq75lpp8y0X097XKk1UO0fZzm744j3b22y2qzzndMnnkqD8OtAaQryMsGgp3qAPvIZNU6HWcIn/W12kzGZLlkPGpystThRezhk4Ql4qgk6XhotbKRqUxSfUuYfYYgRoo4QLsr+Foin10GaY4455phjjjkehpj98Ws63yOUA35d53ocDhLFvUnMvcGIc8sdFuu1F078+SAKndHPd8hNiiVsOu46zjMSwNB2+U7nBOWUgKtpgYDjwnOQgXERag1lMhCNqdXPYV4HeI455phjjjm+Gn6d1rD7pxIPbP8SsaJfkpUebKvpe1hS4jsWnm2/kJa/fUihcJXPINlmXPTITYYjPRQWTWcJRz4+/C0ucnaTCR/17jHKEwpTWUIvNZf57uLJY+nzMRNAObUCrmJwqWzUEoQDvJiBnHPMMcccc8wxx9ePhu/SeIoKMS8CpFB4ssoEj8sRo3wPW7hY0iG0WvAEAhiVOVvxiM8G22zFY6KiKvPqKZvvdNaBb1wWsAAchGiAUmBKhHBB1uAJqthzzDHHHHPMMccc3wQIBEpYtJ01bOmhhD3dZiOewhUcFxkGw2sLa6zEY7ppRImh49WOLQv4mAmgBpOgi+vo8maVASxchAiQ1jkQTb4JVSfmmGOOOeaYY445HoWq0olFaC3gqdqB7ZU+4JNQsxyWvZCG7WFNS3dOigxHyGMrl3nMBLDEmBG6vEaZf4AQTRAKgYuQHYTc1wf87YMxhlz3MCZDChclA6R4Pqbu/QwvbWJy3UWJGkrWELzYMRa/CdiXCsh1l1JPMFRxHgIbW7WQwkPOhdGPHXk5oDQRUjgoEaDk0ZpmxwVjSkoTUeoYbVLYvw6EhS3bSFH1p9A9tMmQwkGKGkp+M9xhc8zx2wZDVS/bljY2DpawEc+QwNFwPDxlM8lTCq0ppoLbrrKOLfnlORDACESIVKfAZBgSDPcfeL+90GyP/wNxcZPAvkjL+z415/xza91QMM4+5s7w39P2f4+W931cax0xj808Zmi0Sbk3/j/oRj9GmwwwONYKa+F/Q+h8C9dafuJZ5vhq6MZ/Sz/5GYF9kab3Fg33jefafqHHDNJfMEzfY5J9SqEnSGFhyw7rjf+WuvsGAovtyV8Q5Vemz4jvUXNeeq79nGOOOZ4Ohc4Y5jvkJpu6glexn8GoIxFYUuIoi7brYyvFqm6w4B7fx+kxE0BVyb7IOka7GDMGbKRchOdk7XpRYTCk5SZxfg1L1tEmfu49KPSQSf4ZNefS1ArxzFKPT9eSMRgK9pOABPKZvox+0yBQ+NYGdfd1Cj1mlL7HJPuMvOxjTP51d+8hGFNO169aO5AvpKX4wX4+Lu4mL3eJsi9QMqTU4+fXSaDQI+LiOt3ob8h1H0s2sFUHgTX1BHjse0ay4h5RfhUl65TP/RkxxxxzPDUECCEZ513icoI2Gkd6SKGoWa1pNZBHIykLemnE1VEXg8GeVsqpWceXL3HMBNCuXL16D1BAiZALSOvCVBD6t5cEvAiQwsaSLZSoIYTN8bnjNYUeYiiRwkMJf0okfvsghEIIxVLtT+gE/5Ss7HFz8D/Ti3/6dXftkdAmI9c9pHBn6/cihm6UJqbUo0pnVPjTfr54yMsuk+xTesnb1J1XWan957jWGqAoTYJnb8zDAOaY4xsGJSx8VSfTCXvpbUDgSBdbuDjSfyIBnBQZtyZ9frZ9nZrlsBJU5QIDy2a/jvWvG88hCSRFiCbKfh3EmwjhV+RPPFwEfY7nB4FFzXmVc+3/EUct4agOguMpsK1NSi/+CaVJCOxzBPY5HDUXAQemwb0vHpk6iKS4zW70/+LbZwns8wT22ReSwEfZF/STnxM456vrTJ79urt0JPKyT1buoGSIb58ldF+fxv8KQE8tgHPMMcc3CQezgC3hYEsPicKSNvIpPF5CQGA5nKt3WAsanK1X78hvsAUQKhIYgx6CsDCirEylynk+zc/xECr3ncBRC8+BiBm0yRlnn6BNgqM6L6Sb8+tBtQ7HleH11VGFBGTlLv3kFwgUrlrhuEIFvjyq/qTlJoP0H5DSw1WrX3OfHo3STCj0ECVcbNnCUZ2vu0tzzDHHV8S+5EvdWkAJi0JXsYCuDFBPYdFXyFn2b1xk7CaT6hdelSByHDh+BiYEWm9RZr/CmBFCtpDqLEq8iVCPH9R+xuT+T/WYnxYW5KD15HBtwP3jDHq6j6RKOjGzs9w/dp+Z77czO8u0pf39HjxWHtHm/nn0gb7uH3F0X++3pjGmONSDg8fA/ZqMh/t7eF72Z+ZRcVrG6Gnm6f2W9uO69ts4vP/99gzlASvh/nyYA+e4P77qOD0dV0JS3K7GN43T0lMSeH99xAPj0xjMAUvT07R3f14MB/+/Py/327k/tgevEx6xruX0b3Von/vXqH5gHQ6snbjfg68DB++jB+cFjr6PjNHsr1+h+0T5FULnWxhKjCnQh8irmMV0Hly/w9cmPDgvj17vh++RR63V/bnX5OUecX6VhvsmhnJ2jd1v9xH3BGZ6Xe7P0+HrBY66Jg7O5xFzKQ7+/+B8Tl3VJp7eS+Lp+2me7hnxpH4e9Yw4/Ky9Py/3973/DHzUM+Zp2tu/DV7cj5455viyqCyArgowaMamjyM9QquNegoZGCUkBthNJlwd7aFNda+9tXiStaBxLD0+/koguEjrNCDQxRW07qGLK0j7IrD02KNLMyErthim75MUt8j0DlqnCKFQokbNeYma8wp157VDxxlyovwam6P/jbr7Oi33e1W2Xf45aXkXMLhqldB5hbr7JkoExMUN4vwaWdlFCou0uIc2MQ33u0jpU+oxo+wjBALPOsWC/wf49saBNjPycsg4+4Bxdpm0uDcN2jYoEeDbpwidV6g5rxyyuhk02uT047fpxT8lKW5hKFHCx7VO0HC/Td15AylsKvJhpkHkNxil75GWW+RlD21SpHBxVIfAvkjdfZXAPvfQnA7Td9kc/+8Yk2FMAUDb/32a7lt49kkezgI2aJPRT37GzuTPWQ3/ayzZYJi+R1zcINc9wBDYF6g7r9Jw30SJGoXusxP9JVH+OVm5zSj9AENJPuxhySZq6uYKnW9Rd9+YHufP2tuL/5p+/Dar4b8GIRil7xMXNyn0AICa/TJ19zUa7psIHAxFldSSfco4+4S03KLUI7TJZ3NZcy5Sd17HtVYAyPUe4+wyO5O/oOn9Dgv+72HJ1qE5KE1EVm6zPf4zhLBZrv1nOKqDEpXOU1LcJsqvMEo/ICt30SZByRqetUHNfonQfQVbLfD1unkNk+wy4/xTJtllium8SGFhyRaetU7L+wGutYYlQgDG2YfsRn9FrrtE+TUKPWQv/mvG2YdYsolAYskmdfd1Gu63Z9mp2sTkZZdh+j5xcY203JomGIESAYF9nprzMnXnNZTwMOTkZZde/HcM0/dYqv1LQucStmodGkGu+4yzj9ie/Efa3g9peb+LrRYYJO/QS/6uiqvLr5CVu+xM/px+8gtsWRVQd9Ty9Fr5Dr596tC8GFMwSt9nkl0mKW5X0jBYuNY6ofMtWt73keKwt6I0EXF+jXH2MZPsMwo9QgiFJZvU7IvUnEuE7iscXPNu3fGG8QAAIABJREFU/LcM03fJyh3i4hZpsUmpx2xN/k+G6S+n/VwisM/T9n8f3z5Q+skYjMkZJL9gkPyCuLiFoUDh4lrrs/vn/jNif872GGefMMkuE+e3KM0EKTwctUTdeZWa8xK+fXq2b5zfIMqvUeoxQlgkxU0EFg33O4Ak111G2YdYMiSwz7Pg/wGOWjzQXpc4v8Yo/WB6r45Q0p+O6yKh88p0/ufkb47fPBg0mU7YTq7Ty+7hqZCe2eSu+ZwztdcJ7cfH8XmWxVpQ54fLp4nL6qPQUzZLXnhsfT5mAmiAAowGYSPUaqVvZcppTeAnHG0KCj0iyq+SlncoTTy1ImkQgqzcpdATPGsVJeqoaakVYyprQDf+MXnZRaCI8muk5SZ52cNQIFDkehljUjSStNhikLxDUt7Bt06TFpvkukehxxUpEIq0uEOhR0yyz/DtMzhqESk8DAVpsc0g/SXj9EOS4ibaTLNehaBggJQubrn6kPtT65ikuEku9gAozKCyCBhNlF/HmBxL1vGsU1hT0mFMRl7uMc4+pdRjtEkwFBhjyMpdkuIOYLBlGyXrhwLKK4tcRmkS0mKTcfYxtuoQ2OdwzRoc8aViKInz6+xEP8K1VnHVOlF+jUzvUuoRxhTYsjVL9KiOqYhcaZKp1lkyJbsZ2iTsWxq0SWdElNmRBVF2hd3oL3GtVSzZJM5vVOttxhhT4KjFqj2jp++TalxpcY9JdoXSTKYSKwW56ZGVe8TFDaTwUdJHiSrANi979JO3EULhW6eoOcH0RVqh0H3G2WX66S9w5ALU/qSyOZmsyuDN3q9e7MXOdIwlhR5T6BFpsQUCas4lXPX1SLtok04lR37JKP2Q0owxJp+uk5iuWUHdvI4x5ey4yoKWVms31aozJkWbdJqxLpHGRZuMfS3D/eNKExEXUzJhJvfvWSAr98h1D0d1cFmdZuoa4uI2veRn+PZpbNV8iACmxSaj9AN68d/hWxsHbItF1U9zv5/VNZbOsma1SdCmONRPAE1OWtyl0COksMn1oCKrpiQu7lLoIbZq4VunsVX18C70hKS4RS/5CXF+k7zsUmUeV3OZ6x6F2Sc+y1gyPNDPhNIks4+vfSvlfj9Lk0ytgYf7WZqYuLhFLrpVO2ZYXdtGExU30SbFkg18ewNL1Kcao13G2WW60Y/Jyt1Kc1JoQFLoAbnuUZoIIRwctUCpI5LiLv3k55R6hG+fJsqvonUyzVSuY0xJVtwjNhFJfoeafRElQqRwKPSYSXaFXvK3ZMXu9LrKKfWYohySFlsYkyFEFUbwNM//Oeb4JqE0JamOiMoRqY6p2x3ifMwo3+P/Z+/Nuiw7rju/XwxnunPOmTVXoQozCAJkg5RIWRTVWr1a6lav9rLd/QH86s/gB38Fv9jLa/m53bLcvVa7ballqSmKotgcBJHEXEAVqgpVmZXDne8ZI8IPcfJmZs0oVBEgmH8soFD3nnMiTtxzInbs/d//fbLxLM65h6on+LXFoYRES0msArSU80jJr1kpuApnh5jqA6y5gQpeRQWv1QLQD29aCI2UCVq1ifTXaQQX0LJFZYdMy4/YmvxfVHZIM3yGZvD83AA8jFH+Dxg3ox2+wlLye8T6FMZO8BmpIVp25iGY3NyiqG6zGH+bRJ8hq66zl36PQC3RiV5jtflHjPKfszn+E9LyCo3gPJHawLqUafku14f/K1KENINnWEy+Q6Q3kCIkN1sIFFr27iJ4F2aXafkhveQb9OI3aIevYF3BrPyIzcm/ZZj/GCk0K80/RMtmPS4BSjYIZJdu/DVidQotW2TmJuP8H9ia/HukCIn1SZrhs8ja2AHoRq/RDl/GUrA7+0s+2P0fH/nXtK5kd/aXtdf1FRaD30GrjvcYoNCqg6hL/AWyy3rrX2JcTmG2ubz7PwGOjfa/ohU8T6i891eKACnCey4IlZ2yM/szmsELtKOXWWp8FyUaGDeZC+YeSH1IhAi99y04QTN4jlCtIEVEWn1MP/0B29P/m1htEKpFmsGzBHKRJDhDpNYpTZ9x8VbtETlQcS+qbcb5mwgkkT5BpE8hUHVY9DK7s79iVl5mtfnPaYbPEsgehdllL/0+e+l/ro0DS5R8Pgag37B8yF76PYydsdH+18T6BEomVHZSb1ZyArk498oCNMMXifU5HCU70//EeO8tevG3WG78Y1rh83jRcHnXbydQSBGjZZt2+DLN8FmUaGFdRlp9zO3pf2Bv9lc09HlEFBDrk4RqjUSfJlIrTIvLRPoErfD5I/eRlldIy6sEcpFYn6wNakE3+nodmi65Nf433Bj+7yw1fp/F5HdoBs/5PtX9vFNo3dgpw+pntMLn6cW/xWr0hwgRUZhtNsf/llH+M6TQLDf+6dwALMwWw+xnbE3+Pa3wBVZbf0SsTmJdSVZ9wtb031FUWzhnWGp8Z24ALsS/TTf6Gs5ZBtkP6Wc/YFZ+xFLyXdZb/828n0IEd80RpR0wS6/Qjb9OL/4GnehlnLPMqo8P5gip0fIP0bKN9/i+x176PbZnf8ZS8nssN/4JcXCq3ji+w+3pf6A0uwgh6cXfrFuyZNV1BJJGcIlIrTEtL7M3+x6N8CKd6KtstP87dmZ/wTD7CWl5jVCtEageWXWdQfZDNid/ymrzj1lu/gGxOklp+4zzt7k9/Xd+M4BhKfkO8jgJ7BhfMlhnyMyUSDZYjc6ylpxHi5DCzhCImj5xfwMurUpuTAb85c0PgAPe34sL6yyunHkqfvNfiQ6g1OcQsouUHYQIEELzKGEAKSIitcZi8jtIQh96EgHWrRGoRUbZT6jchKy6QaxP3/MaWnZI9Dm68ddIgrMo0cJRgvPeOSWbWDOojxZo2aFdZ+Wposle+j1CtUQnep1meJHC7KBkA+NSKjshUjArP2JSvINxM3rxGywlv08SnK0NNkVQh0mkCO+qOCBlRChX6UavsxB/yydJYFEyYVK8XfOarmFsNu+jkg0SfZ7lZkwgeyjZQhLMF6lB+ndYV5KbTRJ37kh7Qmgkql6o9zMPHw0CSaCWaQaX6CW/RSC7SBF5r6YAQXDIeyZRooEQIdamSKFw+DCglm206tbXPMphOtpXRSBX6gX6m2jZRQpdtycQhHNOosDTAlrhS8T6FIHq1SFlRSC7lGaXQfq3VG5CYXZoBJeQQtW/91dIy4+ZFu9gku+AOvAv7YevI71BI3gGUfOeCrPH3uyvAUcnep1u/DUidaIOw69Q2SGFuU1WXWdaXGYx+d1HHucnCedM7QEr6vFcqBdtv/GJ1CrWVQRqsX4vPSSB1+905fyZ9RVrmmjZmRt9R/l4IERIoBbpxd8AIJA9hAhwGEK9yrj4JbPifXKzScON5wlJsT5FK3yZafkeaXkFa4tD84RjVl2hMLdphy8SqrU551ASIoTCuXL+PEsRo2Tr0DO2/9+jmXhCaEK1RCt8maXGdwjkEkIoQrVKK3qbafEB0/IKC4d0AqfF+0yKt2kEF+hEr9MJX0XJFg5LoBZIq6tMyw8YZD+kHb0yp2F4CZ3QvwOyiRDhgbFc9/Ne4+nPDQn1Kp3oNRaTbxOoJcChZItp+A55tcms/BjjZvUZlnH+c7LyOt3oa3TjN+hEr6Jkk0itomSL3GxRVFvspX9DM3j+kHEsCNQi3eh1KjvGYdnhPxHpDTrRazTDi4zzXyJFROUmGDdDuSaj/O/JzZZ/F6LXaYUvoUWL0K0AirS6Sml2GeVv0ovfIODYADzGlwtaBrT1ErlJGZa3mU1GOCydYBktwofyXgtTYZxlJWmRVRXGOXJTkZvqEMv4Cff5KVzzEAQIXS8WCmfH4EqcmyLkIuIhBohf1BMCuYhxE3KzWYc67YFwsTMUZm/OM7oTgVqopUcuPrTCgkDX4dbTKNGgsiNAoeUCzfASkVpHywWUbOBcWYcyIS2vk5bXkCKiFb5AL34DIfR8kQro3rdNJRK0Wqj5jJfmnztnaQTnGVtvsBwOHXvB2CYhyxg7o6puzkPOxo5BSKzLfbj7SHj16N1++kdKEKk1z+MKLj5QzNkv7ArpXO2lq009obwR+ghZUQJNrE/UvLGLDz3a6xp6D0hlxxRud37/xqUIEWLslMqO2Q9Ba9mmE71KXt0kLa9S2j1Cu4IUIcZNyc0WaXWNbvx1GsEz7I9ZZYeM8jeRIiJQS/UzWMx7U9kJQgRk5WWy6uZTc+E/DEL4ZyVQi1RmSFp9DAKsW0PLNoFamev6He6bEBKcAHGQiONTFB78+wmkHxPZo3ITcnMbRzkPAe8nMZR2gLEHwsaRPkE7eoVR8SZZ9QmFuV2LI8vaU3mDys1Yil4hPBRO9/3kaD+FrI2rBz9jkgCtFmmGF2mFL8w/17JV00Buk5ZXMC6bf5dW15gW7xPrdZwryMyteX6QdTkOi7FTZuWHVHZ46Hff36j4lg+SXR6hnyImUH4OOloJRJIE5yjNsKYgFPXcWDKrrlDaPsuNf0IrfI44OHlozEI60VfZNf8fk/wtSjuYc/k8L7RLEpylshP/vHDATwzVKlp1kSKuNxY5zpVMi/fJyhvEegNjp2TVjXl7pd1FioDC7lKVE4zNHikcdoxj/DpBokhUCy00hc1IzZhEtVkM11FCP/R5F3XZt6WoyQ5TpmVOpDShfHpm2tMvBWdHmOLn2PIXCLkAIkSIBBV9C6E2eJAR4vDG3SD7IZPibWbVR1R2iLE5YCjMbh2C9XyYeyGQXWJ98pHq7Hqyd12tQqg6GUDUnrtG/ZlAoGsunZ/5PZ9mSqQ2ar7go5dTkyIhVqdQ4ijR04etfEWA/YVlPi6uJCtvMMh+yLR8n6y6QWmHc29PaXaRYVgbP09OskMISahW6/Dt05+8hdBEeq1OongYvNzMtHifYf4TpuX7FGbLezGcxdgJpRlQ2ckRo1iJFu3oKwyyv2NSvsusvEIolwn1Cml5naz6BOsKEn2ORnCgK2dd7hNw7C7j4k22p/+Rw2PiNwiej9YOX8VbCZJfxbgdRiC7tMPn6USv0U+/z/Xh/1J7My/SiV6rvVTnETyaV/5h8AbQhGH+E59cUX7guXU2qzN199CyhbX5nC8KEKlV2uFLaNmlNH1G+c/pxq8jUEzL9yiqbRQhnejVJ1YqT4iw3tT17vwGKSKEUPUm76CflR2RVh8xKz9gkP3oDuqCq39z/75au1/y8rNpJioRE6uT6ENUDt9L7+0UQvh+OotzJcZO59VNYn0SJY/OLVIERGq93uSOMXaErecfQTSfK/18p+tzotoTfFDJxzlTZ09bn3BSvsOkeIu99G+OeJPBYGyOJaehLxyal44NwGN82eDQMqKpO4Q1Ja10BfYRSt/2wgTrHDdmQ9KqJDUVz3VXWEtaT0119VdUC1iCbHsjxk3B5fAIWnCT4l1G2U+ZFG8jRUw3+tp8YnbOsDv7q/qa9zdz9jk1j1Z6bF/a4u7P5/In8y8PSyYYnLNIEdQerk8RVhWqDgnd66fYv85BW46KQf4TxvmbTIp3iNQ6vfib83Epqh12079Cyhj3BI2/feyHsn4V2F/gHqW90vYZZv+Fcf5LMnOTWJ2gFTznzxWSaXGZfvr9Iwke4BfDQC4Q6RMEcoFZ8aFfbFWXafkepR3QCM4R6jWU3OcGuprAnxKpdRJ9niQ4Nec/3ol29CKfh/EH/vmSJD7sJhdJww9r79uUfvoDpsV7NMKLLCbfrjmTn+23TcuPGGY/YVK8jcPSjr5aPzMakPTTv6EwO9w5FkIEaNmhGTxLVt1gmP/YG6ZCMcr+HgGE+uQ88epJQCBRMrnrmdj/1s8ElsPyQ97Lq3wGrT4z57LedbZQRzzGn62fqt6A3m+O2Jdo2VcVOEiskkJzt2i38NcSot5Y7svWiNp7Ku+49kFo+pCPuP7Ty/AYl6NFm0Z4gVifRst7y1aEauULkBV/jGM8eRhXkZsps2pIYTMaqoMSGi0D1APKUu5DCkEziLjUWWE1blFay0rcZCFqPDVv+a9Aidnh6wGv4OwQsHWm6cNvaFq8w276VzhX0It/m6XGHxCpNZRsYF1BVt0gLa898BoPqwl69Nj79enB4VKBD/dam9a7Yj+pPsqP5kNm95qk7w3nKobZjxnlP8W6koX4W/SSbxKpNaQImZaXmZTvPpLH89Oj1nv7VHV8D3P87tYae/jZBxqFD0JlBuzO/jNZ9QmB6tKJXqMVvkColxFodmd/ySh/kztlbrykUEKsTxHrU8zKj2gEF0iC80yL9zF2Sjt8mVAtHTKObP00KGJ9ioXkmyzE367Dz3fDG+efV+UMH2Zshc/T0Oep7DeZFO8wLn7BKH+TYX6Fafk+sT5RS4R8NlHiWXmF7dmf41xBO3qFpeS7RHrN8wZRVGbIIPsRdxuAEikT2uFLVHbEOP85S8l3AXyoXSY0gvOe73qXwSaO/vkp9j1CaB69Ao4PM2vZohU+x0L8O3Sir9z3aC3bT+Z3F6LmTj/iPCYE1Mf6cOudNBBXG4mm3rSGjzAGhzUc7/WtIlBLtKNXWUy+TaLP3qdvqh6XYwPwGF8uWAypnTA1A2bViKbuouQ+L/7h80BhDdY5VuIWJxtdQvV0KnMdxlM2AEOkWse6EZgK54ZIuYDUlxCyxcOMwMJsk1dbLCTfohO9SqLP1J4EUUvEjI7wuT4v+Hq6jTpEPahlZh49DPzpYMmqG1hXsBB/i1b0ErE6URPt/e6/Mn2QX5RqDWLOd/JCtg93hT8OjMuYlR/WBPZv0AyfJdQrdbjeS5OUZve+XNFEn6YZXOL27D+SVdep7JBZ+SFCBHTirxEcCRMKH7rX6zhnKO0AJRtHyPwcPfop3PGnhxABWnXpxF+lGV5iqfH7bE7+hGH2U8b5W2jZvcsAPCzU7eV9H/z7laZPWl5lIfltOtFrtRcvwGfBGSo3rrm1d19Hioh29DKz8gq71V+QVle9Tl/xNsuNP6AZPoe8z3t1WBjcYR/az8eDQMsuWvbmclL3+8390U9/Ar8TUkRo2UPLdi0JdYNG+MyRY6wryKtbWJfVmpzNz+T5FShCtYSxYwqz6Y3k+74Lnht8jGN8GSEQlDZnUvWRuUKLgFDGdPUyqAeXv+3nKbv5lMpa1pIW609J/PkwnqoB6He/EUKuobRFqhMgWki1BqLJw72Aftdp3azm1VRUNic3m7Vw661aZuPzRRKcISnPMcx+xjj/BaFaphFcQMkWAjXXLwQfArkzE/jTwRtUzlkqN8a5AkuJsylpdZVB9mNKO0Tdxxt1AFfzJj+9V+7T99eHuUuzy7R8v/ZW+jCe98DoWk7ksxlKAp8N6uUman04O2RWXmZc/PIuLuVhRGq9zvIVzKqrDLMfU5g9mqEXsD06noJA9ujGb3iB5fwdxsHPaYTPzPlk1mUYO8VzQjpE+l6lyfarWLg7/v/JobJjCrONcxVC+CQnL+Gi6+fIHCRU3fN9lLVR0akz0q+S6NPeq4lP8vISK7VhJvzz6RMEMsBi7IzS7jItLpOW12rZpbvv0xsSK8R6g1AtHdK5TOtkoPMP8ILJOvu3QW42ycprJLUywH4UQMxD0Y8DPzaN4ALN4BJp+RGT4pdEen0uN4SzGDeZ358X1n7Ye/hkIYRAEtIMLlGZEcP8pwRqqZbm6WDsjLS6yjD/qc9QjF4nUF0ePhfft0WECGmFL2HslEnxLuPiF0gZ18k64uCdrD2Okd5A8DQiFMc4xucHHx0IAYFxJbmdUgmNxWIP8YjvByUFpTVcHu4wKFJmVYkQ0AliluLmQ89/HPxKivFKtQqPIYTrJSuWyM0Ws+ojonIN66paZuFHdT3NpzMwnwYNfY4ivE2oFpmWH+CmJb3kGwRqGUEwF9tV9QKl+GwGYKhWyc0maXmFWflRLY1TMsp/xjD7Gc6ZOXl7Hz47MKvFZj1RvbR7+IxZn2mcm02U9ZwoJTufYbE81Ns6fBWpdSo7qA3kFRwF3qPSQcteXR3l8Q1AKSJifZLC7jIrP2RWflR7iCcMsh8xLd73XND7eB8CtUQSnCGQi+TVTfbsX+NcSaTWam1AecfxiyzG36aotpgUb9PP/pbS9om1r+BQ2TGlHSAJSIJzcwPQuhJjZ7UY97Tm4/nElNL2KcxWzdurNTDlZ1OB99VRPqC0AwSSUK2hpJfuyastCrOLEr5aw50h7H2JFl/54dy8GoenYSQIAi/aLBeRyhuAgewQ6Q1K02dWfuTFhF1JWl1nkP0dhblde5vuJfvjpYN81ZbnmZUfUpo9lGwR61NE+gT38h7t9zNQXSJ9grzaZFK8TahWvUFUGz+BWkSKzzaezeASZbTHpHibSe65yY3wIlq0am/wbl1lZV8y51drAFJ7bNvhy5Rmj+3ZnzPK3wQEkV6nMl6/clq8QzN8jsXkW2jZmyeNPA6kCOhEr1KaHYbZTxlmf491hmZ4ab4ZKMwuXkZqsX5ejg3AY3y5IIUkEBGxahCpBhKFRKGFfgC97AANFRJJzVY2YVoVFLZCIDjTWvj1NgAfF53oVawr6Gc/YHf2l+zNvlfvLNe8h60uHfZ5E4qlbNKKXuR0979nlL/JtLzMzfH/ga+d6zMKG8EzdKLXjlRbeBwIEXhdRBGyl/2Azcmf1rIcsQ9jhs+iZRsljrqbfTm3HzPMf8o4/wecKymMX6wG2d8yLd9DyxaBXKAZXGKj/a/v47X69JAiYqnxHVSWMEh/xNbk/0QQoERMN36Dbvx1tOw8UsHs+yFUS6w0/4hB9kNGxc+5Pvzf6uztJok+Syt8nkAuPiCjWKBki1b4IsP8p0yK92iFz5ME57iXYapki2b4PCvNPyTSJ5iVl7k9vVx702Qt3dGjE712hLyfV7fYS/+aSfEWaXkd6zJyc4vKDrk1/jdsT/8fL3yuNlhIvsVq848ee0z270sgmRbvkZYfYWpv375cS6iWWW78Y7rRa0Rq7Z5XiPUZ1tv/ikH6Q8bFL+ukrKju42/Rjr6CVj5c0QyeZa31L9hLf8Ag+xHD7Mf1WCyRBGeRIsTa/IEhx0hv0Iu/yebkT+pEklfrzdSDQ4fN4DnWWn/MIP07BvmPGeY/8QaaPkcv+W3a0cuf2aCO9Dq95A0QMCneY5j9hL30+3heqPd0J8F5OtHrfNbs38eHoBm+ALXkUlp+zNbkT7GUeKmrFp3odS88H30VLdufyQAUaJLgHIuN30XKhFktPL4z+3/xHsIALRu0wpcI9VqdcHKMY3y5IFFEqsFGctHrAdopoWzQ0j1i9fB5R0lBL0r4yuIGDoikmkvDPC18oQ3ASJ+kF/uMtdxsYu3MLzz6JI3gAiac1t4uNRdBBk80jtQaq81/TqLPEtaeuPtBiYREn6KX/BYAUsZeJFavstr8IxrhhTrkpUj0WVaa/5RW+OIR7axQLdOJvu71+dRaXRe2roEqE2J9mlifPgh9IuhErxHpE7Vw8VHDRIqYZnjJ69HZ2fx7gaQRXmQ/9LJfcskbmRcI1WodevQkdFkbgkJIr3GoNjCBV+RvYOnxjf1R88Fa2fIhmjnRXiCFphk+x3rrv6UVvnQHH+7hkCKgGTxHXjoGBko7RitLHDbBrWFMA5BUxmCsqTMoX2Ix/mOK4gypjIi1RYqDxJpJljNJCyZZTiuJaMUhzfAlsrJiamOEmBAoTRL6zFIpEl/6CkWk1xBISmMYpzm39sY044BWommHb6Bkj7TYJA4u4cx59sYprTgk0ArnHNO8oDKWSCfE6nlE3MSYLoHYQcgUY0EIL9Kd6GfA9ehPUwIpsUIRqmVifZq8VGwNBjTjM/QaMaFWta6dpqo6pFnCRObEoUbXhOBJVmCsJdSKQCmkEJTGUBqDsT6UHCpNHPpX25iILF9C2ItEqolSXi6ksoI010yyNQp1jobqIYXGOsM083qGoVaEWiNFj0C8SieSROokhRmB87yvQK4c2WyEeo0uXwdkzaUc18biKs3w2boUmjdE7pdB672A6/W706Abve7f4YdQBCK9Ro83kIReN3De9nqdxHPgdWqGz+EwRHUlksMQSJLgLA5DrE8S6wMNPSUbRJzwtYjlEjO15nm/roI6wSHWp0iCMyh5b85PrE/42ts17/RBaEdfIVBLxPokgTzKz9zfWPrKNMP5fCSE94Y2uYhAMFUfkFWf+DrHIiJQizSDS7Wu32J9X177cDH53XouFUg0SXCa9dZ/TSt8aS603QyeZbmR1zWbF/zcIlok+jwy8aHfrLpOZQYcGIBtGsEzdfj3afGjj3GMzw++/KmhsBm5nQGC0mZMyj6xahE8hPYgEMRKc7a1MHdrWSCQikGe0gxCAvlk+bNiX6j0EfBFySo4xq8ZnHNY59ibpNzY9eK4zShkfaFNVlYIYLHVICtL8rIiCUOkgLwyXNseEIea08tdAuV3RMZabu6OuL475OOdPc6vLnJ+dZGFZsJwltGfpiRBQKcR02vucw3v9jpMs5yr2wO+98uPOLnU4YVTq5xZ7iGlJCsqsrJkkhVMsoIzS11aSYR1lhu7Q6Z5QTuOWWw3iLRidzKjEQa0kog0L7HO+t2b1kyygq3hmCQIaCcRi60EIQS3hxN+8O5VTi13ubSxTDuOkVJQlIbNwZiiMkSBYqXTJA4DjLXc6o9Ji5JWHNJtxARaMZxmTPOCvPQ8024jZrndBAGjWX5X2w5vSH6yO+TazoBpXvDCqVXWui3iQHNjd4ixjnYSsdRu4nDsjmcsthJCrUkLz3FTUpKEAUo+WU9XUW0zLd/n48H/jBQJ5xb+h7oe73H1iGMc4xhfTFS2JLNTrk1/yXZ+naXwBFMzIjVjvtL7PRaC9Qdmv6dVQW591DCQEi0klXMMihmDPOVks0s7iJCPxpV/JDf7F9oDeIwvBypj+Whrj/40pagM13cGdBsxJxbaXN3aY1aUfOPSaTYHE24PJ5xZ6tFpRFjrja0kDFhoJrSTiHGa88GtHax19Cczbu6OWem0mOXl3Dia5SW74ylnVhZ49ewGUaDR6u73wTqJei5zAAAgAElEQVQoK0Nalr4At5QgBNOs4FZ/xAe3dlFScGKxgwOMsUyLgqIy3B5O+fEHN3jj0mlOLnZ458YWq502670279/aphmFnFjscG17wCjNycuK3fGUXiPhH108RSO6dwh0bzLj1t6Y28MJSgqWOg16zYTpeMr7N3cQQpCXFTvjKS+cXKXbSPjpRzcYzTIc0IpDlttNdtoziqoiL81dbW8OxuyOU6xzbI+mTNKcSxvLFJXF2JLC+HHf7I/57ivPECjFL65t8uzGMqHWXN7cYWOhw8nFp5OlVtgdxvkvoK6/nOhzhzQYj3GMYxzjiweLrwUc1rWAV+Nz7BU32clNzQF8MF1tXOZcmwx4d3ibWGl6oc8VsM7rGsyqkvVGh1PN+ysPfFo8tgFona1V4F2deXZ3qRMv+WE4kHwIPkc9tGN8XnBAXlYMZxnjWc5glhKHGqUkozRjMM0w1jHNCvbGM1Y6TVou9MZOVSGEN9Scc0yynA83dzmzskA7iQi0RApBVlZc3e5TGZ/ZvDOe0msmFFVFqO/tNldSEmqFkoIk9B4yVYdUp7kPL3caMb3a05ZXhp3RjME0ZWc05cPNXZ49scLJpS5SSsZpRlEZPt4ecGKxw8mlLjf7I/qTlFArdscznIOsrIjC+8mZ+Bd+nOUYazHOsd5tM8kLPtza5fzqInGgmeUlo1mOdY6twYRpnhNqTSMMSIuS24Mx/Wk2DxcfbvvWYEJ/MuPcyiJJfTxAWngP7HCasjUY8+HmLt+4dJrFVoNQKfYmM/LS8PF2n24jRkv5aNvMBz0bbj/7ucK6EutSZsWHjPI3ffmz4GJdA/zLO28456iMZTjN2BvN6DZjWo2IRhT4yd86nHMUlcE5aESBr/5hLbPcZwoGWtUUBf+uhFohpJifK4R/3vOyoqwMWkkCpdD1uzGe5fTHM3qthCQK0Ere11tRGUtWlGwPpmgl6TRjGlGAUhJrfX8rYygqQxIGhIHvV1aUFHXfAq1QSlJWpg5ZS6zzfXXO86Gsc6R5hZT+e61rykNl2Bn6trvNGK0kk7Rgqz9mod2Yj0+gFVr556Y/njHLSnqtmCgM5p8XZUVaVIRaopWa33dZGWZZicMhpag3kf6cWVZirEUIiALtqRhSkBUlxjq09H1VUmDq8TfGktf3vk/PONZC/HJBINEiIJARxpVkxvNqm7qLeoTqYIU1TKqCUZGxaw1b6fhAF7DZpV+kREo/UQPwsWfV/Vh3YaeU9t7aamDBZWAn/t9HSIU+xpcPUgh6rRgp4OPtPqFWLLUbBPWEvj8PunoBwPlzAqVoxSFxcLBPyUrD9njKUrvBcydXOLfqDcGiqtgajNkdT8nLilYcEQeaqp6A74Uk1HQaMa04YqndYKXTnE/yWklOLnV5Zn2J82uLNCNvKN3cG3Jrb8zOaMqsKKmspRkFvHByFYTgnRu32R3PwDkaUcBgmrE9mjLNC+JA04zD+aJwL3QbCWeWe8SBZpzl3NgdkhYlRWnYm8xYajW4tLHM2ZUeQsA49RzBXjNhrdvi/NoiC62EtKjYHk3u2fZollEayzMbi1zaWGJjoU2oFeM055O9Ibf6Y/bGM7KywljHQivhaxdOMskK3r+1zSjNUFLMF9rPBoelrDPRb9el/H7GMP8piT5LO3qFzzvJ62nDOkdalFy+sc2f/Zd3efvqJrtDz+OtjCUrKyZpwe3+hM09Tw2wznljfnfE5u6Y0TSnMpairBhMvKfdWkdRVmRFSV5643E8y9naGzMYp2TlgYTW5t6Iv3vrY27uDMmKB1dp2jfA/ubnH/Ff3rnGzZ0heVFhrSMvK2Z5QX+ccv32gHGaY63DWMveaMaN2wP2RjOyosJZx2SWk9ac06oyZEVFWpRU1pEXFVt7I/aG3kttrX9n0rzk55dv8sH1bSZpQWUdN3eG/MVP3ufyjW32RjOGk5Ti0P19vNnnZ+/fYGc4PfL5NCu4uTOkP06P3HdWVHyyM+T61oDN3THT1PN+q8qyuTfm2taAT7aHjKY5ReXXtf445XZ/wnCaUlYV1jlvSOYlw2nGze0hg3HKo7OujvHrBC0CusEKSmiG5Tbvj3/MoLhNJ1gmkPdWPrgTDR3wXHeFbhizl8/4ZDoklIqvLGwggWl1P1vrMfv8uCfWygvsZdeZmQEOS0P1aOklOsHaXLAZBNbcwppr+CogLaRaQapTCNH+VFUlrKvITJ+02iO3Q1p6g1j10DIhMwOsK9CyQSAShFCUdoYSIUpEWFfONeCkCJD30BOrbE5qdtnLPyBWPVp6nVgv1nppFZaKymZULiNWCygR4ZwhM30cjkQtIoX2hcJchnGlT1IRCi0i9GfS//v1hrVQVJZJXtBOvIacMY52EjOYZfztux9zsz+iKCsurC8xy0vSsuT67pDKWEpjeeHUKlGguLC6yLXtAe/cuM2V23s0o4jTy10uri+zM5oyznK0lARa0YpClLr3MyaE8Ltz67i+40PNF9YOiPZSiCOJJ0VVsTdJyeqQ8T4rVklJtxFTGcPedMZiy4dttZScXfYJM/3pzHsGpKQVh6RFyeZgzLUdzyc01vH8yVVwjp3xjNIYitIvHoWxtOKQC6tLfLI35Op2n83+iBdPr7HcanD1dh9rLYj9KUagpODM8gLTvLir7ROLHW71R/zwvWvc3BuRlxVnVxbIipJBHaa3hxYprSTNOKQyFmsdG70OrTh6Ih4MR0lR3WaQ/ZB+9sO61BosJr9LN/5HdRb2lx/WWorKkObeCNnnVY6mGTd3R1y9uUenFbOx6GVlysqQZgVlZbi1O2I4zfjWK+ex1vHW1U2eO72KVpKPbu6ysdyhGYdcubnLLC+ZpgWDyYyz64u8cHaVMNBeFrT2vj20r87VxlpJFGiUlAjhDbPt4YSrN/fIy4qVXgtXewRnNUd1NM145+MtLp5a4fkzq3x4c5co0JzfWOTKrV0ccHq1x9bemNE0I8srBlPvQf/qpZN0W34OdVD31c1LgTrrGE4zinKX7cGEVy5scG5jiUDL+fH7r21eVly5ucu49qJf2+zTaye8fH6D2/0x/UlKXlRs7o2RAn7r5XNs9yfsDKdkRcXOcMJomvHNl85hneO96yOyvGKS5gwmM14+v8H6UofRNOPa1oD+eMZKr0m7eSx/82XF/ny4EK4TywaVK9EyJFZNgrpm94OwEDUwzjEqMlaTFmtJm9xW4OD7W1dYjBqsJ0+WdvP4BiASifLlT8yQSbVLQ3XJzJjKlTR0j0g2akPL4ewEZ7dABDi7Cy5HyDWEXAQR3KfO5QFKOyU3IzLTZ1x+wri8iUw0WkYIpyjMiMwMcRg6wWlC1WZabRHKNpHqkFV9hJAEsnlffTtLSW4G7Obv0g5O+XNdF0tFYcZkpk/lcq9nJZsoEeKwTKvbGFegZUwgmlhXMi5vYmvxZ+MKErVIKzhRi9J+uT0ad8I6x3CWYp3l9FKXKNDYOpu214wpqzZ7dZi0GYU0wgAp/Rh1G/E8uQGgFUdc3FhmczBmnLo5N7DbiAnW/Pk74ymBUiy2GkSBfqDqRBxoTi11kTW3zjlHqBWdJEZLRTM6cN0HWtFtxrRtRCMKfRvNpE6G8EbgWq/FqcUuy50mSkp/bSmI9nzYa7nTJAkDplmBkpKlVoMkDOrFyS/AQggWmw2cg1lREgeaVhJxcX2JndGUrMhoJRHdRsJSu8mJxQ5FZWjUHk0fhhI0wpBJnt/V9sZCGxxsDsYEWtGIAhpR6D21zWQ+Bt1GRDvxn4daYa0lUIqzKwt0G0+mHi/1Im5dhXWFlxTRp2hFL9EKn//Mpel+HSDwBp8SnimURAFx/dxlRcVwknJ7MKaVhCSxfzemaUF/kjKe5dzaGXF9e8CrF08QB5q8qNgdTSlKw4c3d2g3IrSUfHRrD2MsxnovVqsRkZdmnmH+qJBC+FApPlO8lUQoJSnykvEsZ3fkvZdn1xcIAkVlLcNpymiasTee8cH1bTqNmOfOrFBWhryouLk74sqtPRpxyLn1Rbb6E7Z2xwRasjOcEmjJNFuhldzfgHL1OJbGsNUfc3riNzVa3c23rSrL9dsDrHWsL7WZpLn3jk8zBpOUvdGMNC/ZGU78HIKfHw4+nzKZea/kJM354Po2gVakecnW3phTKz26rYRxmrM3njIYp5xc6dTz0W/W/P+bAusMpc1RQtMJV9AipLQZhc2AfSrG/X/7pg6xkWMxahBKRTuIMDi2ZmM20zHLcfOJ6wF+JgMwkDFL0Tmk0JQ2IzNjZtWA3fxj1pJnWYkukOguUp8BEWCKN3F2C1t9gKs+RurzqPBrCLkEDzEAJ+UW/fwDlIhIqz0m5S2WomexrqK0U4wrmJkdttNfcKb1X9ET5+nnl2noVayr2MneIlI9FqNnffjtXr+Dc1hXUlkvleGNV0FpJ4zLT9jOfokSMYvRvnSDF12dmW0KM6ahV1AEFG7GVvpzlAgIZJNhcZVeeJ5EL6FqOZnfJFTGcnW7T6AU/+IbL/H29S2youL2cMJqt8mZlQWqyoDwYppeDsVnBq/32jjnvMimkiCg14i5sLboeTj4xUgriXNwcrE75+dopR4qObbYbvDdV57B1jypWGsSAlpxODfG9rHSafKt589hra09CY5IHzy3L5xa49LGsl/MpURJwfpCm+VOkxdPrc15WFpJvwDGIefXFmv5Hc9zAugkEeu91pwLFWm/aFzaWOb82qLvK8xD6L/93Nn55KKkmHs79vmEd7a93muz3G7ywqlVz3GqDTzn4OL6Us3F8vcU1eF3W1+/k0Q8d2KFdvL4pcMOQ4iQWJ9kvfUvWW3+IV42RM2rw/wmQAhBM45o19y/5V6LhdaBgd2IQs5tLHLh5BInl3soKdic5dzcGdEfz+hPZvMQ7EKnwSsXNri6ucfN3SGTWY5SEqUk233PSUoiz3eNg4DK2E8t7xCHmk7d14VOwtpCCykFaV4igLXFNq0k4pkTywRaMZ5lbO5N2BlM2B5MmWY+nBpqxTMnl/hke8hbVzbZG005vdqjnURM05zbgwmdRuSpIEmEc8zDwHeh/nhjyXs7J7McKX2ItxHf/RxZZxnNchZaCc+dXqUylllWsjuaEQaaKNBcvrGDVpLlboso1LSJ6TQjrt3uk5eG1YU2jThgME65uTOi0/RSTr1WglYKYy04WOo0Weo0uXBimXbj2AP4ZUVpCwbFFg5HIGO6wTLDcpud/AanGs/T0r2Ha5nqkJcXNhBiv2winGx0sc5zS9UT5kI/tgFoXElhZwyKT6hsxnry/Dz8RL1wZqZPJAG7hTOb3hDkPHPrSwis3UbKFoIHW7alnZKZASvxyygZUdopSoRUNqN0u2TVgLTaITPDuTeuG56lMBP28g+YVTsEskUgk/sSypWICGSTQCREqkusFmpvoV9NY9UjUj3awUm0SGqRZ4VivxRWXcjLWYzLiVWPTnCKwowRQlHYCZFU9ww/f5mhleTc6iKTNOfK1i7WOnrNmJVOk2YcEWlFWBtrd+6Q9D3Ct0qCdgefHz5HyaO1Rh+229733tk6PiTqkK+8x4u6b9gd5u8dvr7XzTt6nqrDyHd+LqVASu+ZuxNCOKQM/PbikMSKlOqe9/0gGZZ9b96d9yFrkry44x7udf1xmrM1nLDQSljvtT3h/wlJv/g2NEpoFA+ulfllhTjEg62M5ePNPQIlObO2UH8PgfK6j/vvQ5oX9MeeS2fMAac0CjRL3Qa/vLLJNC1YWWjRiAOSKOD0Wo/d4ZQ0L1FSEAaKONBMZt7Y+mRnSBIFOBwXTy6jlTzy/B3tr8A52BlOeffabc6sHWiDKikJtKzPF5TGMhjP5oafrb3dUkq6zYRPtkds7noDqttKEFKw0msxnuWMpxmi9jg2ooDSGPZGM27tjthREqUEz5xcxjqfrHH99gAhYHNvzImVLlGgGExStvbGXL/dJwq9YbbSa7HSbZIWJT97/wY3d0e0k4iLJ5eZ5V5rc5LmtJsRWh8kqFjnk0Cccz7pBmgkIec3FhnNMvKyQklJHGriMGAkcpQUyHrz9aQlk47xxUFhUzazq+R2ihSyjohOKV2OdYZHqQYihSC8wyOvn6Kg/GMbgA6LcSWlTVEiYDk6RyBjJBqHZVTeprRjcCXOpThXovSzCLWMEDFgsWYXa64/covgvMElQrSMEUJRuYxpdZvCjCnsGOMKnLMoEdMJzrJj3mJUXq/rUGq0vH/oSskQLROUvNtLJ1DEapGGXqEZeAFb6wzWVTgs1pXkZkwoW55r6CwShRIRSkZIVF0F5DePAayl5NxKj63BhOu7QzpJxEqnxWIrmS8wnzYqcj/D7nHDK9Jbn5+p7Sd5vHpC9/egcbrXN/c6vjKWaVaw2mnRbcZzPcZjPFkkUcBKr0VRGSapJ3uHgaKZ+Iz4MDiYk/YNojgMsNbRTELiMPDZtVLQjEMW2g1Or/boNHyW7jMnlogCxe5wRhh4OkMUel1H5xxxGFAaS1Z7E90D1h0lJYudBllR0R/P2Fjq+D7V3rZGFM73+VL4LNp2IyLQirI09NoJUgiSKKAZBzSTiBPLHVZ7LQTek2et48b2ACkEi51knsRUVqbOLHZMs5LKWOJQs7HYmWcfB1rRjP2YDKeZX1gDRVZ4aSQpBSeWO2z1J2wPp/4aUcBip8FoM6MoK3rtZO4Bz4qKySxnmha0Gj46sK/ZmUQBl06vcOP2gEmaE2pFu87ibsQB1nkR+2Pj78sNi6V0OZmZ1fZR5ctvygQp1BdSyeCxhaCdq0sc2wKEQIsQcchS3TeMlJDgSsCAiOCQXIw3iErg4RzAYXGNfvEBWTVkVm2TmT3OtH6XQDbo55exzpCZAYPiCpc6/4xTzW8BjpuzH3M7+zlNvcZS9CxL8fMPbGdcfsJH4z8nEA264VlW4pepXMq4vEVpJjT0CouxDwGXdkZW9bk2/T7T6hat4ASr8SsEssX16fcB0CJiUm6yFD/H6ea3kSL8jfMA7gtB+2QOU3OIFMEDpCaO8cWBqRMUlBRI4UPbx7/bk0deVqSZTwIJAs/NrIydZ40HtVTJ/rF56TNp979vNyLCQNeGUYExlkArwkAhhZwnFlXGS69EoSYONMZa8sLUfDk5D4HeyyO/D2Mts6zAGE+daMQhUop5opCUoqZyiLlsjLG2zgh2JGFAs6YRZEXFLCu8PEzgveiVsZSV7y/CG7xJzYusKsss98lCgVJEoecUZ0VZJ7OAsY5mHBDW95cVFUVVoZUiCrznfb+Nss7iDbSiEYd8780PGU5SXn/2FJ/sDBlOMs6sLvDJzpDtwYTXnz1Jmpfc2B5wenWBtYU2C52EovTVeITwxnygFJXxGduAr6ojj9+bLytKmzMu+1Su8Fxe1a4NP0Eok89U6vQx8HSFoL0MTMpucZVZ1cfW3q2mXmIjeREtQ2/guRRbXa2zgEucswgRocKvI9XqI3chVj0WwotkakArWMe6kk5wqs7o1XU2Z8ZC9Ayd8Mz8PAGelBmcJtH3Lj11GKFss5Z8FYEglG2k0ASiQUuvYdQCwaHyThJNoJosxy/QsacIZYtYLeGo5n1uBydpBSdo6jWUCPn86oN+ftj3aCkp5zvqY/z6YD9MfoyniyjQNRfzQDA20OqehdPmx9ZHHjbUhBD3TJaIpL6LDiDqzZhO1NwgexQoKWk34nlodD9b/l5eLq1kzeFz9+yvD5cenRf2NfyS6OjdCyFQoSQK755H7tb79KH1A6rF0THZ1wG9s0+rvRZaSvrjGdY6Oo2IbivGOkugJbO8pCwrWo1ortkY1UbznX0N5fF895sCJQLawYKvAgVo6Z83h70npeiLgMd+OitXkJoBW+n77BXXKMwMgOX4AqvxJZQLgArnhpjqfUzxY5ybgKsQso3U50Ct8KgaX5HqEMo2neA0wBF3aivYqEWnwb/0AusMxvqM3Uj2aAcnSA7VC35QO+vJawci13gLPrhHJQIlQxQhcXK0Nu6s2kaLmJY+wWrylf1ePfK9HuMYx/jNxD6/7pGPfYzre0PMesZynYUtkPMIzn74SiIRYv9zh3EVpc1qGk6AEtq3L7wJtX/dg7nOHfxTJ9Xtt2FcVUtkSQRqXt7K04pytAiRwmff3m88nLP7rGv2a5nv/10gsHgPqRRy3qf5fXNw3/sRGess1lWsLYcIXfDJ1pBGFLHUbbHQCWk3Ne1myLXNAQBL3QaL3YRGojCuPBLm+/S/zDF+3SEQnss8/+kdpc0obUGsWuhHEIM+jPttmJ4kPkMSSEFupiyEp4hUk0m5i8PRDlYOyp64CmdHSLkM4RteBsalQIwQCQ8rjXJv3O/4o58blzMqr6FkxHLyIqFqf8q2Hr/KgRYxvfAcDb30Kds8xjGOcYynC4elsCmVK7D1RjeUMbFs47BkZsqk2iGUDSLVJJZNKlcwLne4MXuLhu6yGJ6iHaywv8hF9Qa5tBlKejqQw2CcobIZmZ2QqC6J6uBwpNWI1AyJVJNINomUP39S9bk5e4fl6CydYJVQxtxvDq1qDjp474uWEaXN6+hNQlqNfN9Ug6AOwd3rvhPVqfueMi536NstaBguXVinFfQIdUDGACEF3U7Iy801AIS0aF0xrabkdkpbLxOpxn37e4wvNyw+KnqYVtcvNhmVO5xuvkhL9h5w9t1wQGk9ZSp4SrSxxzYAlQiJVcdr38kYgaS02SHBQwFCe7FnpZCyhTMKZ4eP3fSDrOA7vxMoAtkiEmqu2fdprOjPYnErEdEOTqFl43gneIxjHOMLhdLmbOcfY2sv38wMaeslVpNnMLaitCm5mTIstohUkxPJC3PKz7D0MhfdYB2HY1b1GRS3WI2fARz94iYtvYSWEZXLGZfb5GZGIGNCmQCWyhYUNmVmRvSLT2gHq2wkzwJgbMHU9OnYVdxDKkelZshe/gkWQygbtPQCqRljnSGQMZXNKV2OKQq64TqJ6rCbX8dijtx3EEdMqz6pGZPbGSNzCyEkK40NhJiSuYKsmsy9n6vxBaRQZGbCTnkb4ypCmdBUn26BP8aXC6XN2c1vYtxBRZm94haTss96cuGh5xfWUFlz5O83pyM6YcSp5tN5th7bAAxkTCtYprQzBNTl4IZIEczFjp0LEHIF3Axc6L1/zuFchpfqeIrpzTKiE556atd/EJQMacq1z6XtYxzjGMd4EEqXsZVerqUqOtzOPqKIZixEp6hsTmFnGFeylV0mkBHr8UXAh1yNK2tedYBAMKl2uZm+SzdcwzrLdn4FEMSqRW6nbKUfUtgZ68lz7IeJCzujtCmFmXJj9jYr0XRuAH4aTKsBm9kHVLYkVi164RqZGVPaHIejqRdwOHbyj30YOrTcyt5Hi+DIffeiDbZzL9+RqA6ZmczDyaNyi0m1h0AyqXaobEFTLxDImHG5w630PZQI5/0/3vD/5qK0Of3iVi387DGudilsVudIPBizsmByqNTbtCz4h72bnGn1vngGYOUKcjPBOoMUAa1gmYbuEdQpz/VRODeEuryTkEveI4hDyNYT6P4xjnGMYxzj08PS1EusxReoXEEoG5Q2ZVL1mVV9ZtWQzI4RwpdRC2RErFpEskFD92gFS3WBe3HE6LlTVaIVLCFYYTk6Q6LaGCrG1S6TcodJtUtup1RUPA4kEiX+f/bes0uOLD/z+4WPSO8ry6FQsI1uoN10TzdnluTsUlySos7hrl7sSi90jqRPQn0P6c1K2iNSpObsalf0nBlODzntprvhPcqn9yYyvF5EZKIKKBSAAtBmJx+cxmlkZty4cePGjf/9m+eRkSUFL7CpmfdIqwsookHfrlHQ1ojL2dDTKUiYbg8/cIgrpQPXbXsTxm4PSVBY0M9EGvcmAiKmN2DgNNGkUNVKl5KhcRjlNKaVBXQpRV47EYWr5/h1hSElWItfithNQoy8HhNvNEszOAo7oy4Phu3Zv03X4X6/SVJ5deThxzYAbW9Ex97F8c0o5yKG5Y/QxDgZZTHKAbbwvR0CvwuBHRl/NkFgI0hL873SdwxBEDBybOrjERu9LhlNJ63pJFUNSRQwXYeNXhdNkmafJ1WNhPp4daHteYwcm61+j4FtkdV0kppGTA6lx2rjIfXRkIxukNY0EoqGociMHYfKcACAIcsUY3FAYOhYz3zuOeb4dcdUrSAmZ9CkOCIyXuBiuj3GbjfMlfMdAmmqny4jiaGGuuNbjN0uCTmHLCgook7PruP4JiM3pMGYQhUNZEFDl5LIosbEGzJ2u5heD9s3o2KQsJLYCSaY3oCx22PgNNBEA1lVkVAO51ATwqR7WdAwvR59t0FeP4EuJrC8Ia5vY7o9HH+CIaWQxNBgffS6A3xkUSMIfAZOE8sb4ROyWsiihiJquH54TbKkhuOANHvvGVISQ0p+DXdtjm8zZFElJebxAm9WRKqKBp7sPNPmwJAVMqrx8N+SwlIsTUZ9dRuLYxuApjegMblL36khChIZdYW2tUVczlDSzyAGMkFg4rv38N27YTGItE4QdAn8ZqgKIhWYJ8x+d+AHAS3T5Jd7O/zZrau8kS/xWr7IqUwOQ5apjAb82c2rZHWD1wsLnM5kWc/kDjXCJq7D3nDAf7p7k3vdNpeKZc5kcywlUiiiyM93NvloZ4NLxTLnsgXWM1mKsTiV4YCfbN0nCAIW4gk+WFxBFAQ2+t1nPvccc/y6Q9hXGbu/0tb2TWzfBCJVnEPSdPpODQGBE/G3UCOPYG1yl6HbxnT7lI1z8Ejb01XeDzwsbxSlDMFDbfSAiTdg4Dbo2dXw/AjE5RyCJCEd0o/QA6igSjEsf4wTWBhSkoxSnvVz6LaYeCOSSpF4ZLA+et0CIimlSN+ucXfwS0Zuh5icITACUnIREYmmtRFRmIlRLmPYe0EQv5UEv3N8/Zh6v93AxonCvoqgEZNSCIIUsYo82d45nSpwKvlQ+9zxPWrmAF16vurh58GxDUBNikcVwAk0MU5BP8WUK3pWii9oiNIKAmIpFIAAACAASURBVDIBDpJ8Ad/bwnOuRdq/x6kCnuObgigIFGIxVpIpFuNJvlde4q3S4swDKIkii4kU53J5frB8IvLCHW6AGbLCSjLFUjJ0jf/W6hoL8cTMA7iS7LCayvDh0iqnMzkMReFGs87QsfnB8gniisrYsfm4ssOl4gIryfQzn3uOOX6doYsJTsXfQ5ViyILKgn429GZJMZZjF8IcuiBgOfYGsqAhi+FzZEgpziQ/JAhCz1hIbquwHHsdx5tE1bUuSaWEKhrESEUSWGIkqQmqGGMl9sasEvdE7C0MOQUIGFKKJeMCSbmIIuroUiJUUXhCrnhSKbIm6oiCjKtZLBmvkVHLKKKBLGqklQXcwMLHJy5n0cQYpxLvH3rdqmiQlPMUtJN4OMiCSlIpznIJM+oCAQGyoKFF6UtZVcEP3Nn4zPHrjbAKeELVvEfLrqCJOrKgoIoxFo3TGPLTvcQ+AZbn4vo+PgEp1UAVXx2H4PENQDFGRl1CdCRAwAscdDF5gOsJQUOUlvERIOgRauoaiNIiAnNR7O8aBEEgrqiktdC4Wk6mOJFKz0hgB7ZFSlMpxxOcTGeRjuA0UySJtCSRUjUmhsHJdJasHspDAaRUjYymcyKVZiWZwgt8WuYY1/d5q1Qmoxls93v83eY9zuUKLMQTz3zuOeb4dYYsquS0hwVySaUw+39VNPbx+R1U6lFEnbx2Aj/wmXLoyaIaHiP7kUftaG+YLCqk1YXoHMEB76MihIwSKeXphP0AupRAlw7PJVdFnZiUjlwMD89x9HWnCJTHrzuQAhJMOWSnivcCijh/h83xEH7gY3ljxm6fodPCisK+4XOzjBEkj/R3Ob7HwLHYGXZxAx9ZFDEklaxmoEuhqfay32nHNgBVMY6kqAycOm17m13zChlliby6tk/yREOQVhD8Lr57B8e5iSBmkORTICaYe/++u3g2NsZna+dZ2goA1/dDzVJJRhFFBCGUpJq63uezaY45Xg4OC/0+/O7xp/ao3x/dzqvEMYiyn3gd89VljqMR4M14JzPqwqyYyPXtSKji6Iin6TrsDLv8/d4dAFJR7t/r2TK54olXMgOlP/7jP37W3x744UOuP9DEOHE5S1ZdIakUokpgcR+rvYAgxBGkHKJYQhALiGIWQZjvoL6L6FsWjfGYievQnpj4AciiiB8E1EYjRo5NYzzC8UMtaEN5cg5DZTSgM5nQNMeMHCfUKJVkupMJLXNM37boWROmsk6u73Or3WSj16FujojLKmdzeRKK+tznnmOOOR7HdN1+krfh0e+f9vvjnONl4HnP8aTfC4f8mWOOxyEgiTK2ZzJ2+5jeAFXUyWqL5NRyyJd8xFwc2BO6tsnYc5AEEZ+AieeS1+OcSGR5TkWQ/+VZfnTs7NVQNifU9pUEhZicOZALMZX/CYIJBA4gIQrZiP4leGK7c3z7kVI1XssVkASR7mTCxHXxAh9DUTidzRFTVNoTk7Hj4Pr+kW2V40mWEimGtsXAtrC9sCowb8Q4lcni+B5928LxPVaSacqJJAPboj0xCQK4UCiSN2LHOvccc8wxxxxzvAyISGhiDF2KY0gJYnKajLpAXl1CkY42/oCZ8yOvxdFkGT/w0ST5lepJC4/yNh2BAz90/Amm22dj9Akjt4MhpRAQSColVuPvhMZgYBN4TTz7Mzz3JoK4GErACRqy9gGiNCdL/i7C9X1sz8X2PEDAkGXkSATe9jxs38PzAwxFRhGl2XeHwfZcLM/D9jw0SUKTwrZc38fyPBzfQxJEdDnUHfWCcFcUBCAJAookoogSwjHOPcccc8wxxxwvA37g4fgWjm/h4aIIYRGIJEgHdKKfBM/3aVtjvmjvsjnoMHJtzqeLnE+XWE/mgOfyAD7TD19AC9jF9sfIgooianiBgxc4qP6IgKnnJQB8EAwEcQFJOYcgJCKJuPhxTz3HNwxZDJO/dTnk7xL3hU0MUUQL5Mc+fxJUKTTUfDmknZgWgSiSFIWV5QOfy4Amywfy/o577v24125xs9nkXqeN6TyU8skaBqupNO8sLlFOzMnLXzY6psk/bm9RGQwQBIHfP3OW5dTTSVPn+G5jq9fjVqvBnVab5WSSH54IK/c1+dV5O54VfhCw0+/x040NdFlmJZXirYUy8TmrwHcSm90uN5oN7rXbDO2HShspTWc5leKdxUVWU+mXdr6R16XvtADQRIOYnCalFFCFo/n8fAJkUYy4/wwEBIp6nKwWe2WpEi/2tAmgyyk8PCZeyBofVgFHCDyCYACCjCgVEcQCgpgEJBDmD9N3HaIgwCET80mfPwmCICAd8vvDPhf2ffc8fXoaGuMxV+o1frmzTds0cTyfzsRkKZnk+8srrKYzcwPwFaBvWfx04wFfVquIosA7i4tzA/DXAJXhgF9sbfE39+7xzuIirxdL6LKM9oKvpJeBIAjYGwz48c3rpDWd95aWOJcvzA3A7yja5pjrjTr/tLNNfTjC9T3akwkFI8b3lpZYTCRfkgEoIAoStj+hZ9cZeT0UUSetFNGl+FPJoF3fxw8CYpJCWtHRZRlVlNCkbyENjCoapOQSY7eD59tM3D45bZW0sjjjfAqCIZ79BYHfBnwCbwcQQTCQtR8hyN+MVu8cczyKM9kccUXl7fIiHdNkbzjgP9688U13a4455viaEcz+YqZSMs9b/+5iLZPh90+f5VJpgZZp0jLH/IebNzCd40kQPgkCArKgkFOXkAWFyuQ+A6dF09phyTjz1ONFQcD0HK51qnTskIx90UiFBNGp/FOPPw6ObQAKgogkqkiCiiIaGHI60oachuaEkPNPPgOB+cjBCoIYe5F+zxFhq9djYFnoskwhFiOtz/Uoj4OUrqPJMsV4nKFts9nt8vcP7h8IB88xxxz/5UMEllJJ/tsLr6NLMsupFIb89bMJ7Pb7dCYmuiyTN2JkDePpB83xGJKqhpqWyMdjDG2bxmjMzzY2MJ3BSz1PgI/jW4zcLqY7IK0USMiZWXHI0yAJIjFZZcFIMvEc6pMhptsioWjfPgMwiKqABQQ0KT6Tx/EC9yHdjZBAUt+JjhAACfDCqmBBhSA4Vrhujoe41Wyw2etRise4VCrPDcBjQpUkVEkiqWlYrovn++iSPDcA55jj1wyiKHIineF/ePPtb7Qf9zptbjabFOMx3iiW5gbgMaFIEookkdA0srpHTFaIvQJ6MD/wcYIJA7fNyO2yGDtDSs6hSc9W7yAKof7vaiKDF/jYvkfDHDJ2X9076NgGoONPGHlthm4TP3BJKQsQGYMP87MsAq9KEHgIgo4gFfG9Kr67gaRcQBDzhEbhHMfFnXaL640Gb5fLTNyX69KeY4455pjjm8H9TodfVfZ4q1zmVCb79APm+EYhCRK6mGRRP42nuWhSDFl4dkNz7DrUzCE3ujUSiso7+RU0SSKrvrpo6fE9gHi4voXp9cPqXzGOAIiCONMCDgITz71D4A/DQhCvSOB38P06knySObv68eH6PhPXZXcwYHfQ53whjxfMee/m+G4hoon/prsxxxzfGri+j+W6VIYDtvs9zuRyuM9O1zbHN4RQQNFj5PYYuC38wAOIdKdPPlULWBQEDEmmoMfJqAZLsRQxWX2lVGYvUHIVMqI7/hjT60dSJ5AKHAraeriyBxN89wG+V4XAwReTgIgg6KGR+BRenDkOwvN9vCDA831GjkPbNKkPhxEZs4fpOAxte/Z7gZCyRRLFA5MoCALcqC0/CFAlEVEQZ+37gc90vRGEMDfh0Tam/fGDAC9KlPYfWaREQUASRaSIxkUQhBl9ixf4WK6HIAjIohhJux1uCPhRnxzPJwCU6JrEfb8PggAv8PH8h/052A8BSRCfmx7meRDqm7JvHA/2QxDCp2b/uISfC8/cxvRYWRQOaJwG0X2wPQ9REFBEESe6P0EQzM4nRRyLU1m9h+2JjzHNT9uc9uXgtQiz9sR9ND2Pjgcwm2vePmLu6X33guBIPUA/GoPpXAv2tTttR4za2t+P6W8sz8MPQp3aKbXQk+6dHz0Xru/PnpuXqSk9Hc/pfPaDw67l4fN2GPP/tI/TsVSjCkFv3xhNIT4yNgHgeB5eEKBK0qyt/fNA2ve7IMrlmc4n8Qnz1d83T3yCA/USghCa9/vn3xNGB8fzmbgOnn/4PJOiCv8DEpGz8QyOfOYeXYcOuy/Te78fYnR+RZIOneP723iRNXV6L/zAZ+y4s7W9Y5pMXJfJIWv79PhH5/T0OvwgQIm+f9JzPG3j0Xm+/5mbjun+1f3RufX4OuQCTx+7afvhfGPWl/3z5Kh16En9+CbgBx6mN2TbvMHW6BqOP8EPfGJyirj8R+hS4sj+GZKCZsjk9Xj47AXg+uFzqIivJlJ6bANQFGRkUUdEjm5QGKf2A3c2UQQhjaz9MPQAEiCIaUABQUQUn03we46H2B30uVKrcafdYqffpz4acbvVZGjb/OWdO3y2u0tSeyivF1dU3lks873FJS4tlGefB8Bne7tcqdd40Onyr167wPlCnhuNBtfqde512gxsGwFIqCrnCwXeWljkncXFg/3p93nQ7XKn3aI+CherkRPmheqyTCEe47VCgbdKZcqJBLGIRsHyPL6qVvg/r1ymGItzsVTid0+fIfEEmoW2OeZeu8Pf3L9LEAR8uHKCi6USi8mHO6qBbXOz2eBmxPfUmUzw/ABFEllIJDifL/BOeZFiPP7E87wovCBgYFtcqdW43Wqy2e0ysG1c30cA4qpKzjBYSia5tBCGdeKqeuCF5gcBQ9vmcq3KrVaT+50OQ9vG831USWIpmeJcPs/3lpbIGbFZLsvYcdjsdvnT61dZTqb4rbU1Ptra4n6nQ9+acGlhgYvFBS4Ui/x8a5MvKpUZzc3rxSI/WF0j+0j+qBcEszG90WjSmYQvI0kUKcZinMxkeLe8xHIqdWh+khcEWK7Lp3u7XKvXedDtYEXH5wyDtxbKZHQDPwiQ9kUO9qM3mbDV63Gn3WK716M5HjO0LWzfRxFEMobOcjLFu4uLnMxkKcbjs3ObjsO/v3KZ+502i8kU/9WpU1wsHU4+7/g+G90OH+/s8I/bW3y4usq75SUulkrHmwyHYOQ4bHW73O202eh2aI7GDGyLiesiixIpTaOUiPNueYkzudyhtEMd0+Tj3R1ut5o4ns9/d/ESQPjsNursDfoMbRtVkijF45wrFHhvcZnlVIq+ZfH/3bnNrWaDf/XaBeqjEZ/t7dGdmCwkElwqLXChWKJjmvzN/Xu0zTGqJHE6m+PDlVXOFwqP2eljx6E5HvNVrcK9doe94QDTcfCDkNMsqaoU43HO5vO8USxx8pBQpu8HjB2Hn29usNnrstHtYnsekiiSVDXOFwpcKBa5UCg+Ju3o+j5bvR73Ox3utls0x2O6kwkjx0YWRWKKQiEW41JpgdeLJcqJxGNcg14Q8FW1wueVPT7d3T0wC5eSSd4oLfB7p8+QOSK/+kXX1NpwyFe1KnfbLbZ6PRqjEbfbLTrmhL+7f5+vqtUD+d2aJPF2eZF3l5b43uLSgb58Wa1wuVrlQbfD75w6zXtLy9xo1LlWr3O73WJgWQSAIcucLxR4c6HM+0vLB9qoDAY86Ha4025RGw5pR2s7QYAqhWv72Vyed8qLlBOJ2XvH9jxutZr8+yuX0WWZ1wpFfvfUafKxw8OYvcmEjW6Xv71/j7414cOVE7y5sMBq+iEti+m64RrUbHCn2aI7mYQCAaLIQjzBmVyOdxeXWEgkSGnfnLysF3hMvCFJOcdq7HVGbgc3cNDEGLKg8jQt4LHnUBn3+ayxTdc2cfzQg/h2fpnfLJ96JX0+tgEoIaOJcTLqIrKohSXQok5cySNOFeYEHVE6CZJLMNUDFr55nqfvKhzPp29bdCcT2lE5e1iwEDBxXYa2fcAD4Pk+Y8fBeWRXGwQBleGQy7Uan+3tciKdxvY8frmzzXavS3M8ZuK6OL6PEhVHHMaTdK/T4ZPoZWQ6Lo7vHfA63W4F7Pb7tMcmv33yJCfSmdDTR2iw3Wo2uSu2MF2HD1dWMWT5UA9BYzTmV5U9PtraJKPrfH959cAivTcYcLvV5BfbW2x2u7THY9yoD4IgsN3rsdfvUx8O+XB1ldPZHAlVfem7xcpgwPVmg3/c3mK332do2biBH3l7grBgSpa43dLIGgZr6XS4m408owGw0+9zrVHn451tNrtdutaEIHKqCEK4Cdjp92ibJu8uLnE2n0OXFRzPo22afLyzQ84wEAWBW60mDzrhS7llmlSHQ9oTk8u1KrdbTZrmmM1ueL9XUmkUMTdbyFvmmK1ul19sb3Gz2aQ2HM68Cgih8b/Z7dIYjXl/eZk3FxZIqBqyKM526PXRiOuNOv+wucGtZpOeNUEWQ1WXduTZSKgqzdEYx/OQpcfvfds0+WR3h1utJtXhEMcL55gXea6CLtxqNWmNx/xg9QQfrqyg7OPNqg4HfFWrcr3R4HQ2y5lcHvUQj4Ttedxutfjlzjb/sLnBmVzuUIP0RTCwLL6oVrjWqLPd6+F4Po7v4UbeoSAIkBoC7fGYgb3Kj9ZOou5T2QGYuC6b3Q6f7+3RnUy4WCrh+D6f7+1SGQ7pR9KMbuDTsyw0Wea1fBE/CJg4DjebDX7y4D5Zw8D2PB50O+z2e9zrtNkbDGiMx9iex912i8ZohOv7POh0SGoqpXictK4jCwKe7zOwbW63mnxRrXC1VqM2GmK54UsLAcToZZcbDpHFw9cQgMZ4xOeVCvfaLarDIX3LirxYHrbnszsIN7uqJHEilT5gCLm+z81mky8qe9xut2bzw923DgUE1IZDOpMJPzp5kmIsfmCOAJHXPcDyPFzfo2NOuNdps57JokoyPzp58sh7+6JrquN7DPat7U1zzMRxCQiwPPeA9w9Ak6Vwbfe8x/pSH4242qjx2e4eGd3AkGV+ubPNg06XxngU9sPzQAgLJJaSqcdMk81ul493wrV9YNs4njcbUz8IIkM1vK4frZ3kTC43G9Ox43Cn1WLsOHRMk/eWlsN5c8ja3pmYfFHd4xfbm/hBwLuLSweeutpwyL1Om19sb3G/3aYxGs28rEK0Du30e9RHIz5YWeG1QoG4oh7haX51EAQBWVBJKnlU0UARNSxvhCjIiMLTPXim69C1TAbOhOZkxMgJDfWTidxD4YOX/M56IQ+gIaVZjr3JxOszctsklRKGnEacNRty/sE0z2ee6/MiSGkaZ7J5ikacVmFMfTTiL+/eoWWOeWdxkYul0oFFRRFFyokk5eThuQdutIh/Ua2wO+jz+d4eJ9Jp3l1aIqGos8UorRuPLZgAN5t1Pt3dZWhbrGdzrGUyZHUd03WoDAd8vLPDzzc3uFKrUU4kKBghTY0qSTPP3xfVCvfabXrWhIyuYxxqAI74fG+XgWVxJhd6v5L7vHhfVSv85zu3+Whrk7wReh1PZXOoksTAsvissssvd7b5ycYD3MAnriicyuYOJZ9+EVyu1/jz69e4XKuxEI9Hu9LwZTNxHfb6A7Z6Pa7Uqvzz9fUw7PpI2OXzyh7/x+Uvud/pkNZ0LpYWWMuk0SSZgW3x2d4uP9/a5KOtLf7Ht98hFzNYiB+8NzeaDfqWxZsLC5zJ50CA260mt5oNvqpWWU6lOJnJUIzHuR4Zm2+WFkip2swAfNDp8OObN/hocxPb87hQLHI6myOj61iuy1e1KtcadX65u0PLHJPRdU5FhjWEL9TbrSb/668+5167jSQKXCwtsJ7NklRV2qbJlVqN+90OcVnB8X0yxuMeltpoyM82HtC3LGKKwvlCkbxhoEoSXWvCV9Uq1xt1bjYa2H7Yz0w0x2KKwtl8frZR2en3GVgW2chA3g/bc7nRaLDZ66JIEmdy+ZeeeN+dmPzT9hY7/T6iIHCuUGQhHiemKHStCTcbDT6v7PGg06EzmfDWQpmsYRz64rQ8l9poxM83NxnYFrdbTd5cKPN6oUhMUdgbDEJSWUVFEg9ea2cy4T/fvsVrhSKvFQrIosC9Toe/uHOHm80mJ9JplpJJMroerguVPdYzWdYzWS4US8iiiO157PR7/NXdO/zJ1Sv4QcByKs07i4ssJBIYsoztetzvdLB9b2Y4HIa7nTZ/eu0KoiBQTiT5weoJJFGgOR7z+d4eX1QqPOi0iSsK0traAQPQ9jy+qlX4olphZNucLxRZSaXI6Dp92woNmd0dtnv3IoMuQ0LVDqxnkiBwvlCkFE/wwcoKI9vmy2qV/+2Lz5/7lXXcNTWhqKxnsuR1g9fyBWqjEX/34D6b3Q6XSgtcip6d/X0uJ5JPJKd3/YCBE0YSRrbNZ5VdirE4b5fLJFSVgWVTH43IGbFDw4t32i0+3tlmaNssp9Ksl7JkdT2sTB2N+Hhnh19ub/PZ7i55w2AxmSQdGbZ5I8brxSKf7O5yp92iY4aRhsPmcWts8unuLh3T5GQmy9vlRUrxh1Wz15sN/t9bN/loaxNDVrhQLHIqmyWmqEwch0/3dvm8ssdPNzcwXYekqnE6l0Xi6zcAFUElo5awPBNT6OP6Fn7g4eNFMnBHT6axaxMQcDG7SNkY0piM8IOAvB7HJ5htqF4mjm0A+pEUXG1yG8sbokoxBmYDXUqybFyKXJ4WgdcgCKxIDaQMzGlKjoukprGezc68PVu9LklNY2jblKMw57l8YfZ7UQhDsY+GTaYIoryK3UGfvGHwP7/zbrTwGyiSiO16WJ6HoYQ8VI/it9bWOZsLz5fQVBKqiipJeH5AZ2KynEzzTztb3G622Oz1OJ0bkolChTnD4P3lZbb7fXqWxeVaDV2WD4SI/CiEWB0NudNukTNirGeyJNQwMXbsODRGI76oVrjfaXOptMAHKyu8v7RCUlMRBQHb83hncZGPtjb5D7du8unuLhldZy2Tfen1563xiJ1+SMnzg9UT/OHZc+iKjCgIeEEY5hrZNn3L4vViES3SN4YwPHgzMtBqoxFvLZR5d3GJD1dWSagqoijgeB5vlxf5fG+P/3T7Fp/t7ZLQVP7o/IUDy11a1zmXz/NbaydJqCqbvR5/cvUKu/0+qiTx3tISbxQXmLgO/9dVn0/3dqmMhnStCa7vhR6Eeo1PdnZYTCa4WFrgn588RUrTwvsb+LxdXuRyrcqf3bjOzWaTv71/j3/zhkFCVfGCgO1+j5vNJvc7bUrxOG+Vy/zBmbOzuWW5XrgBqFT4h80Nxq5D5pC14VQ2x//0zrsEAahyGCbVJBlJDO/t6WyOlVSKX2xtUR+NuNtuc2Hf2L5WKLLV7fHp7g73Om2u1ut8sLJy4GVkex7dSejxmbguFwoFivH4S5clKyeS/NuLb2K5LrIoktI1dFlBFkUcz+NCochKOs0vtjZpjcfcajV5vVg6lLLCDwJM1+F+t8Pb5TL/9dlzFGJxEqqCLIbeoYAAQ1ZYeMRIUCWJlXSa95aWeW9pie1eib/feMB2r4csCpxMZ/jDc+exPJdfVSpcq9dpT0xqoyFn8wWCIKBvWfz13bv8qlJBk2V+7/RZ3l1cZD2bw1BkJEHEj1Ii/CAgremP9WMKEYGYovAvT5/ljWKJfCw00Ee2w/tLy/z4xnWuNxp8VatyKpvl9eLDsLyhKPz+mbN8f2kFhHCTHFfCdcjxParDISupNP+0vUXPmnC/0zk0VKhLEvmYQUJVcH2fjmmGeXHPeY+Pu6bG1dAAdDyPrjUh3evxeWWXPUmilAhD6PvTFwRAVxSMJ8zRaT+qwyHFeJz//uKbrKRS5IzYTGvddB0MWSFrGI9d54crqywnU7O+JVQVTZbwohSV5VSKX+5s88nODtv9HruDPilNQxRFUprGO4tLbPV6POh2uN6ok9F1zubzB/pneR6N0ZAbjUZkuOVIaRqKKDJxXRqjEZerFa436pyNNv4/WA0lA6fX8OZCmY93d/jzG9f4slohq+ssJZOo0jcRaQzzslVRQxIyqKJO1l8Mn8On5P8BZLVwszdxw2fX8X0GjoUovLpCuRfQAnaY+ENGbgc/cIkrBTr2HrY3ZtF4PXRZBj5BMImoYMZAEGoBIyGICYS5HNxzQZdl9OiB12QJ03XQ5TCxPalpFOPx55LRCiDKvxIoxeP89sl18obxzO7zc/k8Z/P5Qws4xo6DJslUh4MwPDQc0ho/JARP6xpvLpSjF/eQy7Uqq6n0AQPQ830aoxG7Ub7jhWKJU9nsLIQ3Mk3utFvcabXoWxa/s36KH66uHchV9IOA09kcY8fhJxsPogWpEeVdiUcmdj8vHM/H9jw0SQ5DZok4GS0kmJ6O6TTU+2iCv+k4UX5nE8t1ebu8yI9OrvNWuXzgHOuZLJok8+leuPB+vrfHj9bWD+R+plSNk9ks5woFikaMciLB3967S300JKPrnM7meKdcxvZ9fra5QRAQ5k3ZDo4X5lTdbrXY7vd4b2mJf3Zijd9YXT0wVqcyWRKaykfbW9SGQ35V2eNfnj7DYiIZ5mV1u9zvtGlHIaAPV1b5cGX1gNcj9BQpfLK7i29ODh3TYizGD0+szQp/HkVcVfGDgF9VwpDo7qA/85QIwFo6w+lcjrSuz8Lrb5fLGLI8G/++NQnTBAYDFFHiUqlM3ogd6vV+EaR1ne8vLx9aUAWQNULP5dVajYFtsd3rsZbOHNpWEFbDIIsip7I5fufU6SPn836KqGku6ZlcjjdKCyzEEzzodsP8NEVlOZXirXIZ1/cZ2TaaFBqU3ckEL2IfmG686qMhy8kUv7m2xgfLK+SekOt1FBKqylo6ywcrK7xZWpgl87u+z/lCgSv1GjebjTBvcjw+cKwiilwsLSDAofdrKZnEkGU2ux2qwwGV4YDu5OBcE6JCBUWSiCvhOylrGMeKEBx3TdVkebbhMBSFietiyAqSKJJQNQrPubYT9UMUBbKGwQ9WT7CcSj1zRempbJb1bPbQOWV7Hqoo0RqP+cXWFo3RiMZozPl8gER4P98olvhkd4c77RbXGnXWMpnHDMDmeMTOoE9lOOCHJ05wtnI16gAAIABJREFUNpfHUMJrHtgW9zptbrfCvM7fPLHGD1dP8MHK6oE21rNZfAJ+snGfnX6fK/Uaf3juPAn16w8D+3g43oQAH0EQicuZZwr9ThGTVRRRYiI5gIAiSvTtCRn18YjFy8KxR8gLHGx/RFzOUdDXKevnScklZFGbrk4gKIhSliDo4Tlf4jlf4TmX8d3rEAxf4mXM8SK4UCjy3tJy6Gl6jok2rd49DGH4OU5W16PCBouR8zCPJaFqnMnlWUqmIIDLtRqV4UFmdtvzuNlqstnrIAjCzOCc9rBvW1yuVWmMRyRVjXcXl1hNH1wkBcIFNR+LsZbOYLku9dGIYZTX8jKR0XWKsTi10ZAvKhX+cXuL6mj42HkOG2HTdbjZalAdDokpCu8sLh5YMKeIq2pYRBK9KKeFEfsJq+OqQikWR5MkNFkmq4chU0NRWE6lwsITQQi/l8IcM9sNc3ymuXBbvR6SIPBaoci5fWM+xbTd1VQKURCoDIb0JhaW5+H5Plv9HpXhAAE4ny/wWqHw2NwqxmKczubIx2Kzjc2jEKN+PmleZjSdpWQCVZKwXJeuaeLuG++MrrOaTnM6m6M5HnG9XpsVKUxRGYS5W31rQjEe6oMelfB/XAiExteTDIuEqrKSSmEoYfi0bZrYR8zRmKLw/eVlXisUZrm1zwJZFCkYMRKahigIJDWNmKIgCgL5WJxc5JlSRBFNllElOfLGh5WaPWvCdr8XPneaxveWlljPZmfe/efFYjLBD0+sUorFD9xnSQg9gznDIKlpDCz7UGJ25QkGNYAWKXmkNA3XD+hPrK+F3P24a+rLxplsjt9YWSWj689l0M6YGQ75LjRuYzMP5tC2GdrW7HtDUTiVzbKcDA3O640G273egTa8IOBuu839TgfX9zmVzfFaoTi7jyPb4XqjTnU4xJBl3lwoHwiBz84ly+QNg7V0Btf3qQ1HDCz7sbz3rwOub9N1GjTtPTp2DTd4vnkWSslJJFWd1USGt3JL/LC8zulU4ekHHxPH9gDKok5cymO6ffp2LdQEDhzicjayeqc5fxqCtIgU2CCkIq+fygvYnnO8ZGR1g2I8/lhO2tMwcmwaoxH3Ox0aUfWd6Tg4vofteYyiylxglrg7xXTXfTKT4UQ6w512i91+n8Z4REbTUSQpNACbDfYGAzKaznomy9K+fEbL9aiNRowch/5kwp9cu8Lf3L93qHj23iDMvxvYNrbnMbTtMJz4AuP2KF4vlvjDs+f46cYDOhOT/+fGdT7e2WEllWIllWY9m2U5laL4yIsOwtyh1jh84Wd0naSqHnodoiBgKPLMezZxXVrm+EBISxbFKPQszsY5pNMIqyL33+cpjUIQ/fGCgNZ4TN+a4Pg+f33vLjeajUNDTT3L4mq9TmdiktF1xo6N5bnIgkjfsmaJ6xldJ6sbj80tKTIw0pqGoRy+FE3vVVis0Kc5HjN2HCzPxY2KnEIPxAgjrYQ0EdGxUwqRQizGBysr/OTBA6qjITebDWRRpBSFJCtRoYjj+5QTSS4Uiwc8qi8Lru8ztG22el22ej1apsnItpm4YaHWxHXpTSZsdrskVA0v8I8sRJFFkVI8QeaQsT0KohAWI003byHlTXi8LktocjjvQgoXYfbd9PEd2Q4t08RyPXKGwXIqNTMgj4OYolJOJNH3eWUfnj/snyxJuJYV0gbtQwAMbYvqcMj9TieauxamG3qzLc9lbNt8VasCIVWU/3Jrew7FcdfUR/GipmNa11mIhxuk5+nH2HFomWPudzrURyM6ponpOtieh+P5jBybu+0WwGP5nYIgIEsSJ9JpzmTz4do+6EcRCCNKIwm402qy1euS1nTW0hlW06mZkepEqSgD26ZnWfz45g3+cXvr0I1ifTTiXqdN37LC944TrvFP2lS+KoiChCYZ9CZ1hm4HJ7BRRR0JmbRaRBWP3lT2bJO+E6ZMZFSdrPbq5XKPPUKKqCEJMqoTw/T6DJwGabVMQi4gClLE+SZGXsAlAmG/HIoMfP3ainMcjpiikFS1ZzbJp0Sld1qhCsnVeo3OZMLItmcv5mk4tG2ah7YxXYrWM1nO5nNcrlfZHfTZ7vWI5cO5MbAt7rRa9CYWq+k0S8kUGf2hl8H1PQaWhe16DG2Hr6q1aME9vN+SGBoDOcOIeLle7pvgZCaDLst4QTArbqkMhlxvNCgnErxeKnEh8qiV4olZIYsgCHh+wMi2CYKAmKKgSNITQxiyKJJSw3w80w35wSzvYYjvIW/bNNQc/icKwmMeqOl3RE57PwgYOTamE8rh3W412er1jlRszOg65UQCSQzzvgIBJo4bVYQKs9SFw5oQIy+P+oixO80Rqg4H3Go2uVqvsRnpXk9cF9vzQrkk12PsOIwjr85hdzSrG7y/tMLVep37nTaXazUKsTiFeBzLddnr97nbbmPICkvJJMvJ1Ev32liRoX6r2eRao87ddpuBZWG6LvbsmfGYuB49yyKuqtNb8kSIQkizEnvOF93UqJrmuIVzQIhe3Ae9adPvgJkxOq1K9QIfXZbD/LYX4CnTJCmsEj2kChweblIe5fhzopf9rWaT6406V+t1BrYdbhDcaEx9D9v1aJrjGcfh0aP6cvC8a+qT8KI9NWSF5HN4IT3fx/I87nfa3Gg2uFKrRdRLDrYXMj24fvjc7Q+l7+/n9EyrqTQXikWuN+vsDQZsdLucz4c50WPHnhnsK6k0y8kwP1GY9SPMNbRcl4nrcq1e544kHbkO5QyDQjw0mp5UcPQqIQoSuhgHAkxvwMBpoQgasqiSkDPwFANw4rm0JiNakxGLsdRsA6SK8ivTon4hE1lApKifIqedIAg8JEFBFGSEWXq9AKgIYgFBzB44cm4AfnswI9B9xkViZNvsDvr86bWrfLq7C0Lo/bq4skohZhBTVGQxTOL+2eYGP3lw/4ltncxmuFAsod2+zU6/x9V6bUahsDcYsDcYRIULy2QN/TEjIojSDXKGwe+dOXOop+lRrKZD3rqXneOlyWG46V9fuMAPT5xgp9/ncq3KjUaD260mN5sN8hEn2R+9doH3lpYPuZ6HGRRH4bAF9+Anh4zB9GX+lPEJaWfC/LLfWD3BqWzuqS+QlKaxkkqFBrAfRMZCcMC4eB74QUB9NOTnW5v8719+gQ8UoirGhXiCjK6jyzKW67Hd7/Gn164e2beLpRLLyRR3Wi0+r+xxrlDgfKFAdThkdzCgNzF5f3mFk5nsKwnZtUyTz/Z2+XdffknXmpBUNd4uL7KcSpEzwmtxPJ+OOeFPr13BfQZVH0FgRm7+fBCeyMoQkos/R0vCi3upJDEM8z9vzl3fsrjbafPvvvqSW80mgiDwdrnMO+VFCrFY5FUWGNk2f3HnDtcatRfs6bPjedfUV9kP6Tm8kKbrstvv8+ObN/iHjQ0Q4Gwuz/vLyxRiYZGXIkqMbIePd3f48c3rT2xrNZ3m0sICf3H3NpXBgK+qFZaSSQQhjMjsDvqAwPeXlynEYwfmUcBDCp+EqvIv1tcpJ5JPvY6FeDwq4Pr6JWYFBCRBJqsuoog6kqBEnykIz5ALGI9yAG/3m9QnQzr2GAGBpXiaU8nH04FeBo5tALq+jeObuIGNHamBuL6FJsYpGWeQUAAfAhPf3cD3tgCHIPARBA1JfQ9Benkkq7+OEPb9/aINPU8rLXPML3e2udVs4gUBP1o7OcvRSKoamhyqDFSHQ9LVo3c96ahqazWdpm9ZXG/U+dHaOqYb0nL0JhNW0im+t7T0mHGniCLpqCpUiPLVTmYyxJSji4sSqkpS1Z6Yv3hcTD1sIUGzSt6IUTBinM8XQmOwXmWn1+eT3V3Ws1lK8TjLydTM25fSNCRRjDx6YS7dYV5Ax/PpTSZhuDUqANJlmT7WIb16/mtIqGoU0hNZz2R5f2n5qSFRVQrDkaokMfFdYoqCLock8VMPXUxRHptnXhCGkyzPO2CQ+0HArWaTr6pVNns9fnPtJB+srHCxWCKjGzOvYdsMCVMf9SDuhyyKxFWVU9lsSCjd77HT71EdDrneqLPT7yEIAufzBU5mMq9ETeB+p82vKhUedDu8Vijyg9UTvFEqUYyFxOSKJDKwbDZ7XXRFfoz37UkQpi68rxGaJJNUVSRBZOK4dCaTF8y5mhqkz4fKcMAnO2GhgSbL/PbJk7xRLLGaTpOMPOS2F1YCJ7SvueDwJRjGLwNP2Ao+Ed1JuFG50Whgug4/OrnO2+VFzuTyJDV1ttbWhkNut5tHtpXUtHBtT6VpmyaXazX+2Yk1bM8LU0fMMHUkNC7jB547OVoPDVnG8TzO5vJcKJaIP4XEP6bIZHX9lSlnHAUBEUlUSMo5dCl+4PNn0QRWRImUorESTyML4uyZeJXz6PgGYGAz9rqYXo+h26Lv1LHcAUmlRF5bQxRlwA0LQNzbePanBMEQAhdBTCLKJ0Eq8u14TL57CHcWYZJuQDAj6fw60DFNvqxUaIxHlBNJ/vDceU7ncmT3hWdHjk11OHyqPrEhKxRjcc7n8xETfpuuNaE+HHGtXsfxPcqJJBdLpceoGzRZZimZJKGqTDyXuKKwmkqzFFXLHe4tfMjH/jIlvh7FNOxZjMd5M6qo/Ot7d/mre3f4q7t3udlsciaXoxRPhMSwokg5keBBt8PAtmhPQub9R685JPe22RsOmLguSU0jZxiHUoUcB3KU4J3RdQQhTLIuJxKcymYfk+Haf/37x9QWwjzGad/b5pjWeEzeMA4YK44Xhm97UWL+foUWLwi412lzv9PG8X3eXVziD86cYzEKNU9huk7IM3eEy3QaSjmdy3Gum+dqvcZuv8/9TpuvalWqwyFxReVcPs9K8vkqLZ8V270et5tNTNflQrHIf3P+POX4QVWKnaCH7XnfSPjqeRBXw82NLoeG6navx9C2ZhJ6XxfqoxFXalW65oR3Fhf51xdepxxPHNisdEyTnUH/O6WTPs3XFQQBgscl/l4lBlZYWFcdDklpOr97+gyXSgsHlDxsz6M+Gj4mm/codDmkujmbz/NpxAnYNk2CAL6qVjEdl7VMlksLC4+pEKmSNFMYGdp2WMCWTLKaCavin2Ud+roRei19FFFDFUKVNC9w8QIHQRBnwgRPgiyKpFWDC5mFA2NrSK9u8/ICT6uPH7iM3S6WN0BCQpdT6FKSUBHSh8Al8PuIYgFJ/T6S8gaScg5RWkcQDL6OXIz/UiGLIrGID2/mEXJfblXrkxDALNk+3KnpaI/wLk2cMG9jq9t9anvJiDcqb8TomhMedELuqCn334l0OuLROrirS2s6b0bEob3JhI+2NrnXaR95rqlKwMtGqEUaaew+8p1AWDk3JdMVBQHTcRjZzsx4NBSF14sllpIpxo7Dl5UKt1uP77CHts3uYMCVWhXP91lJpcJw10vKEVEkiXP5AmvpDJ7v80W1wuValaNGzNun5QlhccdqKk05kSQAbjXD8PejCfxNc8yDbpeWOT5AUzLF/pyvmKIcqt6y0+9zuRqS3T4Np7M5Lka8enuDAZ9X9viyUmXk2KykUiwlUwdIhl8m9muqGrJCQtUe8+7WI8LzR2lKvm2YVlbnYzG61oTP9nbY6ob5mV8nphqxAaG2cToqHtuPgWXxZWWPymDwhFa+fZhWPyuiOKtcnjiPPx+vAlMam4BQP3waXdgPx/O4FXF8Pg0xReHNhVAKdFrgdL1Z53qjRkLTWEtnyBkx1EdCtnFF5WJpgaVkkpHj8PFOqAZ0FA7Tcv464QY2XbtOx64xcNp4gUvPqbM9voHpDp6qLCQJIglFZSWeYS2Znf2X045XXf8sOL4UnKCiS0kUUQcy6GoSxzfRxHhYBIIAgowgJEGSEMUEgScR+NNy8Lkk3ItAkSSyhkFG1/EJuNlsspLaQxRAl5XZZEtpGhlNJ/USX2yGrMzyqTqTCR/v7tA2c+SMGJ4fGqM7gz43mw3apvnU3diUN+rT3V3uRV6ZnX6fljnmvaVlTmayh4b4YorC2VyO10sl6uMR1xsNDEVhaNszD0VIBh0mrfcsC12WWUyE+reSIMxeItNk4ylBanU4nFW8DW2b+mjI3sCYia9rEa3KNDl94rpcrYc6zYVYnJSmEZMVZEmcUWjcbDZ40OkgCkKYsByLzfKeDEXh4sICG90uW70uX1YrIZGuZc1Cw44XKit8Wa3QMk3eXCjzwfIqae3x3MjjQpEkTqQzXCgWOV8osDcY8LONDURBjIhyVWRBxPY9zIgbThJFMrrO68XQSyuLImuZkH+vGIuz1euFTP6KEql4yFheWER0uVbFjXSO90MA8kaMbJQYfrvV4pPdXU6kUwgImK5Dazzmcq3Kbr8/k7Y6ChldZyWdZj2TZWCHsmxb/S6leILXiyWyxuMGxMtCVtcpxOJIohhpDm+zmk6jimGYsmWOQyWSbhcz4tD8tkKVJAqxGO8vLTNxHO53Ovzt/Xs0xyNO5XIYEbm1F4Q8glNd3/VM9oDG64sioaosJpLcajWpjUb8YnuL1YjyxfMD2tEG4267TX9iHboOBZFhHlaWh2kXru/TGIeSY7bnM7AtqoMhvh9EFdMixhEE+y8KRZLI6DppXUcUBO62W3xR3cNQHvLABkFAStNI6/qBwrgXhSaF62NCVRnaFp/v7TFxXErxeLi2WxbV4YBr9Tqt8Th85I547kI94AJfViv8qrLHtUadvmVRGQ55f2mZ07ncodKMuiyzHpF+b3S73Ou0+fsHD7A8j3ykgS6JApYbFgL1JhM0WaYYi3OhWJyln0zX5okbFrWZrhvJ8jkznsvGeMTeoB+t7aGayZNkSY+C7U+oW1vYvokkyCTkLCO3i+kNKWirTz1+5IaMAKIgYMgqsiBQGQ/CSJIej/JzX65n8wWqgMMkR8sbgQAZZZmR2wqTxwU1lD4JVASpTBCM8H0NggkEEAQ2CDJzKpjjQ5MkFMNgKZUirijcajZRRJH6aERa1wkiktjT2Rzn8vmXagCmdY03SiWuNxvcbbf4j7ducKlUZjWdxvE8tntdtvt9gJmX8Kh5G1MUzuRyLKeSiAh8UdmjNTaxXJdz+TwnM4eT4RqKwrKS5nuLS7RNk59uPOCnGw+41WxGIelQf3Jg29RGQ3Z6fdYymZmwvcRDtZHKcEB9NGLiuAwdO9Tyjaprm+OwetP2PDRZJq4o5I0Ya5nMzAA0XYePtjb58xvXOZ3NsZhIko8S0T0/VE64Wq9TGQ5IaxrrmSwnM1nkyOAwIuH0aWjyWqNOfTRis9dlJZVCkySGtsOVWpXKcIChKLy/vMxvra0RV1XGz5gz9jTIoshCIsGl0gKVtQE/2XjAJ3s7bPS6nMnmWEgk0CSJUUQTsd3rkTWMMPcqlQ6NVUFgJZXmQqHIhWKRO5HGbms8Zi2TIaXpDG2Lu+02u/0eMUV5jJpDFATW0hnWMxk+VVR+Vdmjb1m8VS4jEhJX3223MCOjXYooX47C9AVxaWGBz/Z2udVsMrRtLhSKvFUuk1RfnZD8UjLF6VyOX1X3uNFsMPE83loICanHjs2ddkhj4ft+lLj/7U2NEQWBlKbxL9bXGTsOW/0ef//gPtebdS6WFmZGvhPJhpmuS0JR+IOz516qAZiPxbhQLHKlXmNvMOD/vnaVtxbKlOJxHM/jXqdDbfSQb1YWHi+GmG7+dvp92pEneuK63G2FqQem41AbDrlaDyvHp6kd07zlVwFFFMkYRuSR1rjf7SBuCXQnoVymQEjbdCab42w+/1INwISq8lqhyI1mgy9rVf7y7h22+z1OZ3M4nsfuINQADz2FYV+PYqDUZZn1TJaVVBpNlrlar9G3bEa2zXo2y5lc7tCjw/SeFG+XF2mMRvx0Y4OPtja5225xJpefyUEOIgNuq9djOZnk3aUlTuey6LI8M/7qoxGV4QDLdRna4brVtywsz6VlmtxuNSNlFZmYrJLVddYymec2AF3fouvUGLld/MBDk2KRMkh4f0Th6PZakxE1cxjydOpxdEnmaqdCyUiQ02LHUqZ5Gl64CjilhPI0kiCjSykCgkcSekVARhB0BGkRpDII2iNVwXMcByLwo5PrxGSFn21u0LcsfrG1SQAzvd2kqnHiCWoCx0XeiPHByio9yyKt6Wx0O/zTzhaf7oaesZwREvy+v7TMtUaNv75390iPhkAYNjyRTnM6l+N2K3wxF2JxzuTyM0miJ+FiaYGkFl7nrWaDrV6PLyqV/5+9N2uW5Mju/H7uHnvknnere2tDVWHtbnaDZLM5XGaGs8hGMtmYyfSup9H3GH0BvesTjB5kehyTGc0k0mRDUsNms9lAgw10AbXXXfPmnrG6ux487q0q1C2gCkB1o5v5N4PBKm9knHD3yPAT55z//6CNAcF5X9jNJOHdjU1uDZ6wWvPm4f+ff/kJ/+/9uxhjqa1pHhwLamP5ZHTCyWp5rnXWCgJ+99Iu//Pv/j7tRkw38nyudnu8PdxgWhR8cHRIbQxC0EQMPRLf54+vXOX7O9v87s7uc+KsEvhB44j8lwf3+Hh0wqPZjDuTMdY6tmQ7CPjjq9f44e4ev7O9w2aSvpa6q2u9Pv/+7XfZbXf4+fERvxyN+GR0wofHh64tm5JEnkvLvjkY8L2t7WcKtAXw1nCD//D+7/EXd+/wwdEhD+YzHs3dG203inhzMORPrl7lcLnk58dHz6Q+lZS8vbHhIo11zZ3xhNunIz4bnxI2HRu2Wik/2rvCZprwv3/4Af4LZESeRjcM+dHeZe5PpnxweAhCsNvu8L2t7WdqEL9pvNHvo63TLfy4kdZxrfkkseezlab84d5l3tvc4j99+DOmRfGtro4OlOJar8+/e/NNdttt1/t2NuODw0O0tedSM5HnsdVKudbvMfiKQtEvwqWmb/CscNHce5MJf3HvDr5UhM3z7zubW/xwd4+/uHuHn+w/fu63ktcV+/M5/+nDn/Fhc+1nLeyyqmoIC4ccLOYEykM1upr//Vtv8T99//1vdDxPQwJ/dOUKvpT85b27nGYr/vrBfSyOLNCNQlfz/A07ob0o4vf3dpmXBbHvc3cy4cePHvH3+/uESjXi713XxnM65f/46OdfqrenpGSv3ebdjU0+GZ0wyQtaQcDN/uCZ3vUX4e3hBqkfcKXb5aNj1w3mg6MDau1KbQJPkXg+/Sji7Y1N3hlunhNAKqPZX8z5808/5c8/u+3KdIw9V5godc1n41MmeXYu2J36Ae9tbvIffvf3X9i68EWIVZsb6fcpTY5AEKkWUiikkKTel6/To9WUn53uU+qalh8SKY9xkZF6wXm9/zeNr9ELWKNtfU5vrm1FpqeuHkPGDTnBACVG72PNPuAjRAgiRYguiLOSzTVeFWdvsjf7AwKpSIKAw8WcWV5QGYOvJIM4Po/KfP6717pd/ujKFfpRzLsbm8QXMDRfhNj3XQRqd49OGHL7dNSor2si5UgDb/T7fH97h0HsNML2Oh32nhJx/vz1CODNwQb/9uZNrnZ7lFqzkSTcGgy+dGN2kTaf1A/Ya7e5MxlzmrkIosVF1zphxEaS8NZwyJVO99wBPBPF3UpTbvYHz5z3/Z1LF1hzb7Vbaes8SnPWVeOdjU2nRr9cODHaqj6XU3EOaMq1Xo/3NjcZxPEzBICz9dx4ihV6udPh7mTCsizR1uIrxU7q5vZ7W9sM4vg8DRU0PUP/zY2bdKMzSRbfCUFLxR9evtKo7W/QDZ9Eg9/b3GJVVdzoD9hpP3nguZ6qPr5yEcHLnS6n2YplWWKsJfQcE3QQJ9zo97nRH5yLRZ+NZRDH/GBnB4RryfVgNqXQGiUk/diljC93OuftxMZ5dt6FQgrXwuqdjQ1M0zVgfz5nVVcN+zviSqfLd7Y22UhSxk2bsivd7hcyBc8iC2ng5m2rabG102p9IZP466IbRdwcDPlXb7hrfDCdsqpcuicNHHnp3Y1N3hwOOclWjLOM3U6bXvhs5D7xfd7e2EQ3/Xh3250LBcM/DyUESfPikvo+b29snHc7kUJyqz/gf3jnXd6/dIlLrfb5s2AzSfl3t95koynmP7N1xlp/czCkH0Vspin3JxMXRdc1WhuktuSjJd0JXN1O6AjfpeW0oecFvNMZkA0zbnaHjlWMoMhKVvMMP/QJQg8/9Lne6vKnO5fJMNzqD6irGmshkordVpsf9DaJKtgNEioJKEGkPOKl4UoV8E67j76s6SofdVpiTjKW/QwpBRpD2JSFnIxmjB6P6W60ubnR4/cv7SEE6NqQLXKEFAShTxD6DOIEYwyrWe6uB9j2I350ac9FkMI25SRnlhmiOCCIfJSnKPOKMi/xfA/PV3iBhzEGo928lHmFEIKk7cSRzzr4PJ7PmDRsa1+6WrFNFaFyw3KWEcY+XiOmPpQB30kHhFcF7w03zl9cp6M589MF3Y0OcRriBe74qqhYLXL8wMMPXJT8+5vbmGUFDxdkQhJ2Ywb9FtvtNlfbHb432GRDhhwNJ9za2ORKpwPWspytqCuNH7j1k0pSFRVX0jb/5sZN9lptlkVJKwh4azAkFor5eOnmwvfwQ8cyLrKSk8djoiTgeqdDeFlxqdXm9ukp4zw77+YTN3qLwyTmzYHLFp2VcQjcc3mj6Tj0NH5ne5uLEHpOZP9lXiY/D1+G9INLWJz+aSCjV2oFl3oBwzAh1zWx8kl8n24QsRm3Xls3GfEKYrjPHFiZnEI/285tP/tHLJY3Wj/ClyHWllgzoS7+ClP9PUIOQfgIEeGF/xrpXWadBl5jjTV+VVhVrr7zf/vx3/J/3f6EH1za5X989z3+/dvv/Lov7bcKZVGxmmX8+M9/xsnjCe/88CaX39xhsN2lLErKvCJflcxO5iSdmO2rG1hjmZ0ueHzniFYvoTNo09tos5gsqcqa7kYboy1V4chTXuChPMXh/RPqqiZpRXQGbcLEvQD89C8+Ynw05b0/uEVvq8tqnvGX/+f/R2+zw60fXMfzFEknpjNwLz6Pbh/wt3/+M975/ZtcfvMSaTd29aZS7f2lAAAgAElEQVTLnIN7J/iBR7uXkPZSPF9RlzWPPztiOV0BguFen84gJYwCDu4eMzmeYYHeVofeZocwDpidzBkfTYlbEa1eSrufUuZl4xhWzEYLpJJcemMLP/AQF5QCWGvRlebo4Ygyr/ACj8F2l7jlSn+O7o9YzTPa/ZRWLyVKXQDgk5/c4dN/uMe7f3CLrasbJO0Ia2E+XnB474RWN6HVS2n1EvJVwf6dI/7mP/+U4aU+b75/nd0bW3iB59Yvq1jOMuajBZdubNHupRhjOLh/wnKW0e6ldDfahHHA9HhGmASk3YQiK9G1BgthHJAtCw7vnxCnEa1uTGfYRkjB+GjKj//8Z2zuDbnxvauk3fjcwf1txbJyHWxqa0i9gJb/JHDzFWr/XuoLX3lGV/WYo/z2M5+NiruEqt1E/gAM1q4QsoVQVwGD0wIsgV8NY3WNNdZY4wyLsuTnR4ccLV3P5d/Z2nbRizW+WdgnEa2qrJBKIpWkrjSnhzMe/fKAowcjNi8PnIMiBEVWkK8KjDb84m8/JWlF/Oi/fZ+DeydMjmbc+sE1xkdTTg+mXH5zh7rSjI+mFFnJYrJkdrrke3/8Nns3twmiCwgazZaYzXMO7x5z8njM1pUh3/lnb+EHF2+FD28fcPzwFCFgPllhas27P7qF0YaTx2PKvGJyNOXwwYgf/je/g1KSzz54QJmVzMcL9u8c884Pb5J2Eh5+ss9yuiJbFkyPZ2xd3eDdP7jJ7HTJ0f0T7v3jIzb2+uxc22z0HS+e2tlowdHDESePxggBvc0OnUGL6cmcux89PHeSf/n3d7n23mWuv7dHED4/H0Yb7n70kOnJAiHg0aeHxGnIuz+6BRbqSlNkri5Vea67V7bIOX54yr1fPAJj2bw8cA6p1mSLgiqvmBxO+eivf8l7P7rF5Td3+PRn9+lutNm5vsndjx4SRD471zY5uHvMfLKiyAqmx+5F4Ht/8jZp5/W3QPs2IlSufWehayprOC1cF61IeaRfom37VfE1XGqBFM9+PVApoUyfqgEUCBEgRAshO1gzBhRCduAlhBHXWGONNb4pnDHUf3pwwChbMYhj3t3c5NILShPW+OoQUuIFLgXo+YpWNyGIfIwx5IuC+XjBYrJk79Y2URIClsV0xfRkzmKy4tHtQ9J2jK411liKvOTo4Yij+yMmxzN2rm8yHy+4+9FD/MBjNc85PZywmFymKuuLHcCmw470JJ7vsZisCJOQbJEjOxfXJ57uTzi8d8yVt3Yx2jA5mTM7XZxHK7N5xvhoxmw0x2iDMYbFZMlyumI2WjA9mVOVNbrWPLp9SLbMEcDxo1OCOKDISrJ5znyyZHoyY+vqkLgdIb6grtcYS11q5pMluqrRtWG426fKK+599Ijtaxv4oc98smQ2mrOcZnjD51ORxlgO743IFhlX3trl6OEpZV4xP12StCM8XyGkJIwD0k6M9CTVvGY5WzEbzV3Urp/iBx5VWTM5njE7XXB6OOXOhw+49MYme7d2MMaymCzZv2N5+Mk+g50euze2OXwwYnw4xQ89TvbHtLKSfFU098M/PRhrWdYlj1cztDHnUb/NKP32OYAdf4uWv/HMZ4vqBItBnTt3AULuIOQYdI21jSagdwshWqzr/9ZYY41fFc7kPX78+BHTvOCt4ZC3hhtspa9W7L3Gl8PzFe1eStpNqCvN5uUBaSemyBxbvbvRIe0mXHt3j+5GG2usi6TdO2EymjMfL/EDD2th5/omypPc+egho/0xSinCJGB8pDm6f0J70MLzFb2NDspTGG1eKDFrraW32eWN7109r+ubj5cE8cUbbLbMqcqay2/tkPYS/MAjXxQEkU9vs8P+nSOyRc721Q3STkzSihhe6nP88JTJ8Yz+dpd2PwVrGR2MKVYFaSchacdEaYiuXE/yVifh+nuXuf7uZbauDr8w5dfupyhPcvxoxPED5zBfffsSxlimJ3Pe+r032Lm2SVXWKE8xG81pdS9wcJuaPeUpbvzOVYwxzokbzUnaLkWddmJ6Wx2Gu0865Cil2Lm2SX+rw9V39pBSMD6ccnjfRWpPDyasFjl1pYmSgFvfv8rDXx7wyd/fYXo8p7fVJWlHzMcLRvtjOoMWYeSTtmOsttjPSwL8E8G8KvhsPuIv9z8lUh6bsXsuWTbZS18P4/wrO4BCSKQVaFuirSt2D1WKRD1pYdLEsYXaRYkQZd4DETsGsEhYO4BrrLHG64CxrjvO7dMRo5Wr0bo3nfCzwwMez+d8d2uLf3n9jabjyfo59E1DnDUJtrCcrvjk7+5w9Z1d2oP0/O9KSZSSSCmpdc1imrGYrtCVfsYJSNoRYRIyPpiAheFunyD06W60ufr2HrPTOVVRo3xFlDix8NPGITm8f+IcnO9eIUpDpFKMHo/5+V9/wsNP9tm6OqTTT8mb1Ob+Z8cEgY/Rhhvfu0p70GIxXfGLv/2U6cmcIivZub5JEPlMjmfky4KyqFCewlowjQNTZiV5VpyP1/M9Lt/a4ejhiNU0Q3oSP/BI2hHLmdNKPUuTyy9h9RergsnRjLqsKYqK5SSjLGpavYTdm9ucPB5z/PCUowcjrr93mVY/ZTFZcfRgxKPbh/ihh641e7dcTebsdMFP/u8POXowIogCrr69S9yKqMdLrHE1hbf//h6X33pCipPSXatqyBJVWTM9npMt88YBd+unPEV70EJrw/hgSmfYojNsIZVk59omdamZnbq6R+VL4lZEmVecPBpzcOeExWSFtdatRT9F/Rp6/P4qEUqPvbTLVtTiWttJ5HSD1yNOD1+LBWwwtmJWHVGZrGEbxoQyxSOCp+RgpOzDWvZljTXW+BXBWCf38MHhIb84OcFYy73JhKPVgmEc84OdS/xwb49W8PLs9zVeHZ1hi+V0Rb7MKfMKKSVRGtKuE4y2KP9sQxeESdAQFxJ0rekO2ygl8UOfpBPTHrQIk5DdG1suArfR4fp3L3Nw55h8VRCEPq1ugucrVvOMIAqI0whdaepaozzF5t6AfJmTLXL8yB2fdBPm4wVCCjqDFkIK6rLGaMNgu4uuNacHE3StiVsRnUGLMi9ZjJek3YQwDpCepMwrFpMl0+MZQRzQ3+riBR51pTHasHtzGz9w6U4/8BxJIgmds1XVKF/ih1++JRtjMcbQ6qUMy5owcizjpB1z9Z1dRvsTZqM5fujR6iW0ugmL6QrP90i7CdZY6kojhGDzyhChJKcHrmNT0o7obLQJ4gB/VbCxN2gihRlGa+e0dmKkEsStJ46J8hRxJyKIfcLYxwLdjTZSSaIkpNVN6G122Hljk8FODymdA4gQHNw5wvMV/S1HZClWBdbac+JH1azFt7xD4teGFJJAKdp+SOoFhI2cjfcl+oFfB1+LBZzVM+4u/yvLekysnEJ/29/iSvo+ngy+UoPvNdZYY42vi1JrlmXJ//rXf8Vf3r1DXtckQcC1bo9/fu0af7B3mbc3NvCkfG0SC2tAkZVURQ1YgijADzy01hjtthPPV0jl+qSWeYWutJNFMdY5Fa3IybXUmnzlunl4vocXuIibrjRVWWGNberVfKSSGG0p8xJdG1QTbZPKOWlnzoS1Bi/wiJIQow11qSnyEqXkuSSJMYa60tSla8UmpSRIAj75uzt88F8+5gf/4l2w8NmHD9i9sUVV1Hzyk89470dv0tvqcPsf7tFrCBCDHZdG1ZVGSIEXeASRj65d7aA1Fs9XXxrlMtpQ15oiKzHanDtZqiHZ1E1doBAQRD5e4LmoZFFRFzXSU/iBwgt8dK3RVU1V1i4q6ynCOEBIgdHmiQ0pCZvoqq4N1rrPzqRkdK3P5/ZsLGESEjap9TOms/I9fF+hfIWuDXVVUxU1UjrbQRycs5yLvHTr7Sn8RlLmtzlaPytzHi4n/PXRPbK6PI8Ef3+wyx9vv/Gqp3upifrKDmCuFyyqE47yT1jpCQKJthVtf4ub7T/Cl9HaAfyWwlpLZebnTap9kSCEh7Eli+ohhR6TeNuEaoAvW4ChNitKM8eTMUpEKBGhbd50fomwuIeCxbCq98nrEam/h9eooPsyRTY6YKWZYW2NkhFKBIDE2AJjK4zVgEXJCE86Npg2OZVZIEXgSglE+MIHgbYVlZkzK++ihE/sbRHKHlJ4GKuxaFe2YDJ81cYTMRZLZRZom6FEhCdipPCpbYZAoUSIRWOtxmKbcWhKM0MKDykCPBEhhKQ2OfPqPkoEJN4WUoRIoS6ccyVdsXOpF2ibo6Qbm2siXmJsibFu85TCd2shBMZUF9o2tqbQU0o9oTIrYm+DQHVQIqTUMww1nohQMkIgqW2OEj6SAEuNtU44W+K7Tj6/wdDGUGrN3zx8yL3phFobIt9jGMe80e+z02qf6+Ct8XphrUuLCiEulDV57ljbHPsSm7219snO1Bz+Zd87/84rHP/5a3r82SH3PnpE0o7RWrOcZVx58xJSCR59ekiUhAgpWExWbF0ZsnNtg7SbIJV85nq/qkNzpqdIk0p/5m/G9fJ9lTn8ojm3pvm7/PLzfRXbr7p+v60odc2sKni4nFBoV1ZXG8OlpMPNzsaXn+BZvF4ZmDMTkddBo8n13NUFCrV2+77FMLZG24JMH1ObHE9GSKVQwjkE0/IO8/IuG/HvoESIL1sYW1OaBav6ECk8QtUj9S5TmCnG1iRqk9pmaFMgZcC8esC0+BSEcA6WNbSCK3giorYZWT1C2wIlAmJvAyk8svqYyqwwtkQgCVWP0BuAhdLMyOtThFCEqkvibYOVFzopxlYUesoo/8A5nfj4vqvDOXNia7tCm5JUeCgVuvHpKYV2NmJvi0gNyPUpUvjEatg4UBolAowtqW1OVh8DAk8mpN42ihBjS2blHQLZIlAdfOFaEl0058IqarMi12Mqs0AJn1D1CWSHTB9T6TnaFggkvnLOH4ZmDp+1jYXKLMnqYxbVA7L6mGH0Hfd7lJLCnFKZJQJF6u/iiYisPiaQbXzZojIzQDjnWirEb7g+p5KSWEr+7I1XfnNe4xuGEAKhXm5XeFnH4enjX3XDedXvXHRNg+0unqd4ePuAqqwZXurR3+kSpyGtXsqjTw9ZTJZ0N9oMdrp0hk8xzb+BDfIsWnfh36R4peDLl835q5zvq9heOwwOvlT0w5huEGFpVAvKjOg19gX/ymcOZEzH22JVj9GmJK9nDMIrdP1Lz8nDrPHtQalnzKv7jQNhibwNAtlFEWGtwdji3OkQQmEx1DZH2xxjS07yD4i9DdLWHovqkXNcgoB5dZ/SzOgFb7nzUJPVJ9RmSa5P2ZMhUvhMi9tIEaJtzrLeZzP6Ab5MOVj9DZVZgJAEskPsDQl1H21cZFDbklV9QORtcCn5IzyZoC50UizG1tQ2x7MRQnggBLXNWdUHnBa/wNiSdvAG4CKWtVmhbUFhpoyLjxmE32Ezfp9p8SmejFBBwGnxC0DQD99iWT2m0GMMmqw+QgiPS8kfEavhK8250ZrT/EPcO7NhWe/T8a/TDW5wvPo7VvURFkMgO0TegEJP3fqY8jnbpZ6xqh8jhEeuRyzrA7r2FsbWVGaJNhWr6oBx8TF7rT8j9S4xLj4m9S4RqSHj8hdEqk/Hv4F9EYVyjTXWACCIAvrbXdJegjW4NHPkI6WgM2wRpeF5eta/QINvjTU+D20tpalZVq7zU6YrPhofcCnp8Lvh69FG/Hq9gIUkUT0IBR1/h5Y/JPUGiKeYwGt8uyCFT6A6ZPURpZlh6ppYbRCqvnN2ZIQn4+bfCRZDocdk9QmZPiWrj84d/FB2GyLQHZbVYyw0aUkPiUfsDSm0R6EnGFtRmRWL+jGD8F18UnI9cmlko6lNhrEuRRnINiAp9Ji8Pm2khQK0LTC2xtgKmnTlc+PDx5cxXhO9jFQfKfyGrV4hhMKXHRJvG08mGFuR6RG5PiGvT1nVh7T9q+dRyNqumJafsawfEcgeSkQUesyyfowSEbVxqVtjq6YF0MvPuRQBy/qAxN8hVpvn81Q0KVxjS4Twz9PnpZlS1OMmevqs7cKMyfQJ/fBtjK0ozRwpPGqbU+sJhT4l0yOW9SG1WSFFQOJtom3GrPyUVbWP16Slf9Ojf2us8bpxxti9yLmTUv7Wd61Y45vHvMo5yhbsr1wHmdoYbs9OCOTrYz5/LRZwbSon/qxSfBk3G//a+fs2w5MxidhiUT6gNhmFntL1bzRNq1N8keDJGE8m57VuuR6xqg+fckxcQXTL3wUsR9mPqcySUA3wpKufk8Kj5V8mkB1qvQAEuokkejIhkC1y7fozalu6+jcCfNmh5e+dp0wrs3D1hSJGiRBPfHFDeSUDfJviCTcGTyYInvyAYjVsbFxGCtU4t0fOAdSn1CbHWI0UPu3gGrPyDqfFP1KbFaHq48sEQ02p53hSI4XCF0783EVQ6/OIpTZ5cx3RhXMeKA9NgRIhsbdBXo8apy1DCg9ftvFkROpfQgq/mY8ltcmes+3qJysC2QUFuXTp69pkrOoDSj2j0BO0LbAYfJnQC95klH/IrLqDtoU7n/ynqcK/xhprrPHrxKIqOczm3F2cooQjpy3rksq8vq5pX0MH0OXux/l95vUJ2la0vCEdf4dBeBX1NcsL13g9qE1Gpo+baFpFoV1E6QwWS6UXjPIP6YVvkXjblHpOZZa4Vn5P0oOO4OBRmRWeTBvSw9m6i6f+cwhkm45/nXl1n9osyeoR2/HvEaouAoWlfuZanRN2nUrPWekjJH7jGLVcavcFcIL/LqV6nP+Ufvh0n1fB0+UujjgxoTbZM+MTSALphDiz+ojE2yFSA0CQertok7OsDxEIhJB4MkVTsaoPWVQPnVNGzTD8DkrEFGb83JwrGdH2r1LoU/aXj8n1iF5wi8TbYl7epSZ7bmypf7HtxNumNitO8p+R1ccUekInuIoQPrkeY231zNoJVBPhtWhTEXtbznlcY4011ljjV45+GOPLLa61ByghsBb2VzOGUfrabH51BxCBEj6BTJAoFvqESmcYa+gHe7CuA/xWQgiFJ2IS/xJKxiRmQaieaDQm3g4mrPFlihIBEtWkS2Os1YRqQOxt4O4A5+BZa4jU0EXV8Em8LQAXrVOCdnCdUPXOGbWFmVDpJYHsEntb+LJFL3wTazVKRkRqiKHGt208EVGZFaHuIfCIvU3HHP6ComUlQrrBLbQtGiKIQghF5A3xbYoSTxjqSoTnEUdjSyJvk3Zw1RVG4xOrDQbhu8TeliOfAIm3hRCSQDmHKVBd/CZd7suUbnALsERq4Fi9wrtwzj0R0/GvkesxpZkTyBaJv0OgunSCG9RmiRQ+kbeBQDoGtIxcpO9ztoXYBCCvRygRNg7rhmN3+zVgSMwOibd9Pg6X6rXn0drIG3xzN9oaa6yxxhovjUj5+FKhrQGclmns+YSvkQTylWVgrHWF64VZMiv3OcpvM6uOSb0B7/X+LYH84lTdGr8+uLVz0TaB9xwDzP1d4/o9q+azs+jYEy0mY2sW5QMerv6Szeh9htF3G/LIi50zd7894f2/lFQAT3+n+d5LlBk42RbjalK/RNbkovF9k9f0RXP+Tdl2c2vcv58a70Vzbq3B2prD7Mcsqofstf6MSA3O13uNNdZYY41fHZxUmGZW5lTW7b9tPyRU3rkY9Csw5F+vDIy2JblecFLeQZuKjegm2/FbhLKFJ15P4+I1vjk8XRd38d8lz95Dgs/fU6v6kNIuGIbfJfG2X4E88FVqRL/Kd+R5lPLlzv+qNl7t+BfP+Tdp+0Vr8Ozxtc1YVo/xZMIges9FEdfkjzXWWGONXwsyXXGwmvHj44cYLKkXoKTkjdaAt3tbr4VZ8TVZwMKxLVVE198hkAme/O1z/rK6IqtrBOBL6UKyTZ9LYy2FqREIEt9HIhyFu64QgCcVgVLnvUm1NS4WYy2R56GExAKrqqQ2hkApfKXwG+ZPXlcsq4plVeIrRScICZRCG8uyKgmUO38gFUIIamNYViXLqqQyhm4YEikPJeX5NZRak/j+ObsoqysqY/CEJFCyuSZBXlfkuiaQnrsuKamMobYGbQzLqsIS0A628WSL0miWVUWoFJHnnc/FReP2GtvLqqTQGgEEShEqD/U5XaqnI2u10RRaUxuDFILUD5rPDauqRArRzEmjUG9tQ1h6Mm5fuvXI6gptLanvo4Sk1JrHixmh8uiGEVK49VNS8Hgxx1jLIIpRQqCkfGbOP7/eorG9qkosEEi3rgKojHatFJv5CJSb3xetty9lQ/R4uXtNPDUfz9gW7vPaGApdsqwrYq9H4nWBgFVdUWhN2syRu1a3dqYRi1VSEinvmXtNW4sUgkh5+FKivqSX6RprrLHGGs8ir2tmZc6kzKiMYa4KaqNp+yFv8XrkEtV//I//8WWPfeZAgcKXMR1/i7a/hS8jJzr7W6jiPcpWPFhMmRa5c9I81yKoMppMVxytlizrinYQIYWg0Jr95ZxMV24DVorKGGZlwaIqWJQFq7oi9J7k/PcXc46zJdY6QcgzB2aUZXw2HfPh6JBpkTcOjGRZldybjdHGooQk9DykEGR1xYP5lF+MT7g9GRF6nmt3hSDXNZMiY385J/Z9Qs/1QT3OlozyDGMtnnBOhLaGk2zFo8UMg8VrbCyrkllZMC5y8tpHig7dsAco5mXB/fkEiyX1A6SQFFpfOO4zh+dgOefxcs60yAHwlcITL27PldU1x9mS0zyj0DXtIAQsy7rk/nxCVtdIBIHysNZS6Pq5cfvNejxezBjnGS0/wJOSaZHz5/c+ZVLmtIKAsnFMPSn5iwd3uDub0AtjSqOxlvM5z+v6ufUWAkpT87C5bxDuc2thXhYsqpJ5WbCsKzwpiTz/hesthHP+XvZee6FtnO1pUTAvNaWOnQPot6iN5Shbsr+YkzTrI4RorrNkXhUsq5LaWhLPP7/X7s8nnOYr57BKhScV3toBXGONNdZ4JUzLjFmVI4VESUFlNLmu2YxaXGsPXJ7o5f2r/+VlDvoaKeCKwiw4yD5mXh07bTag7W/zRuuHeE2bq99krKqKu7NTpoXbsB/Op1xpd9lO2kyKjEeLGbcnI7aSFpfbXQTwcD51G3LtnA6D5Yfbl5mVOZ+MR3TDkKyuOVjO+ZPda+y1upSmpjDOsfnJ0WPe39rlVm/I7cmIeemieY+XM0wTRcvqmqyuqK3h748f0/ID/uzKDR4spzxezqmN5mi14DTPeLM/YFlVjPOM25NTjLVsJ22shWmRc3c2ZtVs8qd5xrvDTd7sDamNIddOlPIXp8dc7fT40c4Vfnq8z/3ZlMq4SFE/jJkUOdtpi9oYPpuMyVrOMWsHIY8WMz4cHT437n4U82gxY1VXTIqch/Mp39+8xGaSXniTG2s5WM7ZX855tJjRCUO2E6euX2jNqnJRzDuzY4pa82dXbuArycFy/ty4patU5HC5YFLkdMPQsa5w9XWLquDBYsrhcsFeq813N3bQxkXaDlZzjldLUj/gD3Yuc5pnHK0Wz613JwgpdU2pDY8WU/5m/wF/duUGkefxtwePSHwfi+XxYs73Nra50R1wbzZhUT273sZaxnnGSbZ66XvtRbZ9pfirx/fJdY0EUj9gK1mxk7bZiluM84xfTkakvnMAI8/jo9ER4yKjG4TsL+ckfsAf715lnGccZyvyuuJgtaCoa/755Tfohuv2amusscYar4puEFEazXG+ItMu+7aXdtmKW69NWO9r1QBm9Yx5fcykfEhhlk0/A422GtX0FvxNhraGSZFzkq2YlwVH2ZJ+lOBJF/0Y5xkHywVbSes8KnKwWvDLyYhAKkb5isoY3ukXnKxW3J2NeXewSaFdxGha5nSriHlZMilyjrMVH46O2Gt1udLu8tl0jC8l3TBqZEVcoeisLJiXBfOy5LPpmJYf8Kd7hoPVgvvzCVfbXXwpncNoodSaaVFwtFqQeAHtIMCXkmmZ84vTYySCwrj051aSspd2WFQl4zxjXOR8PD5BSskPsRwsF9ybjRECtpMWvlQcLOd0ghBPSmZlTrsMKGuN9S2TIrtw3L5UjLKVs1NkHK2W5HWNJ+SFN7tzABccrBZoawilR9xE4JZVySjPmJUF92dTxkXGjy5dIca7cNxSCJRwEdFpmZNVNXVw5v6BFC7pfJqvCJRqIl8u5exLxbTMWVYli6rkwXzK/fnkufUWwLwqmZY5j5dzfnZywPtbu7RtwGfTU97o9ok8j6NswTjvMI1ybk9GRJ73zHoDr3yvvch26vvcnY6pjEuFSyEY5xlSSDailEJrpkXuXi6MwVrL/nLOrCwYRgnTsmBZVayqimlRcJItWdUuKnl2/Isit2usscYaa7wYgfJo+2HjCNZ0g4jrrQGbUfranqtf2QGsbUlhlvSDy8Sqw7w6xmJo+5vNBv5Ut+3fUHhSMoxTDlcLHi1mtPyAYRTjSYUUgnYQ8tZgyJv9IXutDkoIJnnG/mJOL4pQQpKGAQJBbS1SSG50h2hrmJcFnpBMipz95ZxRtuJwuWBZVed1Wqf5ijc6A7473GZaFPjS1QseZ0tOshWn2YpJ4Zyps/ReoWtu9YYETd1f6LklVkJwud1lI0p4sz9ECcnBcsGj+YzY94mURy+MCJVH1qQ0T7Ilh6sFs7Js0qGC2PPohTGBUrzRHZD6AaNshcESKEU7DM9tnuGicQsBW2mLw+MDRtmKjTilF0YvTB8aa5sUueX3ty/TDSMSz3dz3kQQT/PVeWrYNOz2i8Z9ltZNfJ9W4widwWLZiFPeG2yxqqrm/Bml0XSCkO8Mtyh0zazImZU5h6v5hes9LQoeL2eM8hUn2Yqsrs/rIA2Wy+0u20mLWVEQez6LquQkX/FWb+OZ9T7Dq9xrL7LtSUk7cBI6/TDmVm/AqqootUYKQeJ5tIMA1TDOLO6FoxdGfH/z0vm4C103cxdyezLCWNhJ2yS+W4811lhjjTVeDZV2zN9rrT5vdjab/c2lg18XvrID6MuYtr9JbQo84WNMTWlXSLxGguK3YyMQuBTjotbXDwMAACAASURBVCppBSGyqcc6ixT5UjW1T27T3ExSdtsdpkWOxdKSAYnvEzbF+b6UKCvOad2FrjnJVo4EYk0jF2IJpOJS2mZeFfz1/n1uT0bspC0XASwKpkV+TiY46906iFw69qfH+zxazJgWhXMGG7KCJ+UzBJNWEHCzN+C0yMjrGiUEYUMqmZY5s7KgNgbzjICwQAr3nyckXkNiWZYlWVXxeDFzdWcW3lNb1NZcOO4z9aFMu3R2N4wQwjl6F73tSCHYSVs8Xs7524OHdMOInbTFre6QeVkwLpzjd042ObveC8Zd6Jp5WXKwnPNgPkUI6VhXfoASgqPVgp8e73N3Nma31aYXxkTKY1oW/OToMZ9OTgkbgsblVpfSmOfWe1rmnOQuRaqNafr9PoESjkTiSXk+Pxet99Pjf9l77cW2HblGNnPiSSc7U2nNpMjOI8he86LxRrePweI394UnJVJIsG79tDUsypKgIX9Y+9vw2vftQFnXLAv34qWkpJc48lGlDZNVhpKSOPDOX7ZqY9DGklc1WVnRSyLiwNX4TrOCsq6b432UFBRV7Qg+xmUJQt8jDXystazKivEqIw58Yt8nDnyKqkYbQxz4bu2b+0rgSifOrqmfREjp7pG6OX9Za5ZlSSsMSQKfvKrwlCLwFEVDrvOkQkpBWWtmWe7IXJ6iFYUvjH5UWrMsSn55NAJgt9uhn8ZEnneh7VYYYLHM84JFUZI044t8j1VZIQTEvo9u5tJai9cQ+KbNNYW+R+y7l+tKaz49OkUK2O13HLmtqW2eZQV5VaOkcHYCV+O7aGzHgU/keedzUNSaqtbu99a8SCvp1vsi29oYxquc8XLFZJWx3W3TT2Ji32OaFRR1fW7Xk5JVWeErFxCodUPqamytSVvfEghBris+nY3ItQs+9MOEnbjNTtJ5LSa/sgPoiZDU86hMjkRifUthFgQy/VItuN8UaGOZFjkCGEYJkfIw1rKqKjwhaQUBomGdnmEnbVMZw73ZBNNET1p+QD+K2U5ahM0DZRgnpH6ABSLPbaBew7LthTGB53G90z+v8wJLqDxC5RF5Hq0gIPV9cl0ziBKUEGwlLbK65tFiSl7XhEqdM3LP0puJ96R3ZTsIeWewyb35hHGeEShFN4zcQ9/z6QSOQVzomo04QQjoRxGlrt1DKgibiGDkHohGO/IFsGrSiInnXzhug2WSZwRS0YtiAqkaNm1N7HnPRZKkEFxKO+R1zQfzKaXRRMpDdyyeVKS+T+L5DWvVEjas5dT3nxu3sZairpHCzXleuyhYJxBsxS1WdcW4yBACEi+g1zibh6sFJ9kKbQ2xF9EJIq52HCP48+tdGU3i+cRnIp4CWn5ApDx20rZj2ipJP4ppBSGpH/BGt8/BYv7cep+9cLzsvfZFtreTFgJBJwxJvQAQFLqmNi7hHCqPUrviY4tTpz8TIm37ITTkl3m2YtU47mdObK5rSl2fE1rWeHVY65y4aZZzPF9irCUNAzpRRK5rZlnO6WKFBeLAZ6fbRgLLsmKa5WSl+91FjaOwLEtOF6tzB2CznZIEAQezBVXt1nhVVvSTmKvDHrU2LIqSw9kCIQTdOOL6sMcky1mVJZe6HYrKOadx4BF4jmz1aDzD9xRx4J5PxsKiKJlmOcuipNIa0XUvHvvTOZ0oZNhKGC8zhBB0ohBjnXO2P50T+R69OCINXyz6XmvDPC/46PERQggizyMNncNzke0k8KmNZpYXHM0WSCHYaKfs9TqMFisQcKnbZrLKqWpNHHhQ1xRVzfFiiTaWyPfY63WacxkenE6QUjBoJc0Lpiarao7mCxa5m6PNdoqvFIuiYLzKmK4K99tPYzZaKYezBZNVTl5VyCaSbzoW2Tjqn7cthWBVVhzPlzw4nXJvNOb7V1wGRknBZJUxXuVIKdjrdejGEUezBWkY0EtiJivXYejMmVwrf347cLbnzaucx6spi6pkGCbYPt8+B9BVSUkCGeP7Iak/wFiDEBIlfjs2gFxXfDw+YTNO+eHOZX5xekyua46yBZtxyk7awjQyHmfYiBM6Qcit3hAATwgSPyD1A/ZaHdJmc+yGEUGTut1N241ciUVbQ9ps1je6A/ZaHSqtzxmeiefTi2IXmWucnTMW6Xai6Icx7w42zyNpqe8jhZOAMdY+E05OPf/cRm0Nsknx+lLxB9t7jXyLRZ9JtwjJ+1u71KaJ6inX9/lqxzTSMZbLrS5COCZz1DiH1zr958Z9bz7hg5NDvr+5wyBK+MXpMcuq5CRbciltP/dWKnARztTf5lZ/iEDgS0nkebzVH3K90zuXfBEIOkGIEILYC54bd6g8tpKUf3XlBpXRbv6alPmfXr7eRDecoxg20jS/t7V33pNRW4vfpJDPUuefX+9WELAZp+draqyl0zB3/7vrbzXpUskPt/dQ0kVSh3HCtXbvufU2uGjEy95rL7KthOBfX70JuIeNL9X5uT0p2UxSfndr17HKlUeoFH946UrTFQXe7A/dNUjFx+MTTrMV//baTU6yFQfLBaNsRex5awfwa8BYy/50zoPTKY8nMzbbKaHntui7J2MejqfEvs/JYkle1fzRrWsESnE8X/DBo0OUlNzaGmKB8TLjHx7uO9a71jwcT/nu7ja7/Q4/ufcIISANAu6cjLk27LHVdnWgWVWjreWjh4fEgcdur8PD8ZTD2YLI9zmYzHk8nfOd3S2GLQUWHk5meFKy1U5RiaSqDYezOT9/dMQsL7i1NWSr7aKLP32wz7Vhj3Yc8dnJKUpKrg16jFcZJ/MVh7MFmx3nNBlrX+igGCy1cS9zZxFtKQR5VV9o20U3nUO3LCt+/uiQGxsDLnXbfHbiiGLtKOTjg2Pyqua93S2O58vGATMcz5aUteZfvP0Gu/2LN+RFUXLnZNw42JqtTkonjliVJT97eEBZu3KLh+Mpu70O71/d5Sf3HnP/dII2lm4cMWjFTLOcSmtKrZ+zXWrNvdEEXylGyxWPxjPe2tmgNoZF4aScRsslP390xD+7eZV3L23yyeGIQRpjjOXDx4ckQcC7lzbOS2XW+PXDE5J+GPP9wS6x8vlkeszD5ZTNuPXlX/6qNr/qFyubkes51lp8GRKrHuI15qp/HQiUx7V2D4NllK3wpKQbhvTDuEnrPj99vnQyJnGzCZ7NiOd5TSSuOe6pSI57Y7bnx59FTyPPbcL2c5/7SjWp4mc/P9PqS3z/pSjjqtFsO7PxtP1WEF5o40x370WIP7f5+1I989nZuDvBmdMkmJU5sefTDSM6QXhhSsJpTgqUDEj8APvUfMWec8Q+f63AhTWFUgikUnTV81uLHzw7tzTOTysInvscQCln47n1lt65g39m8wyBetIlpxU8Ycv7qBeO4yK88F77AtsXjfnJdXm0eZa9337q+p5ex+3Esb6nRUFlDJ0wpB9Fz63/Gq8Gay3H8yVZVXFl0GWjlTJsJSgpmGY50yzn6qAHWI7mSxZ5QeC56Hns+7SigN1emyTwOcwLHk9nvLW1wUYrYbrKqbRhvMyYZjnbnRaX+11OliuEEKyqiskq53SZMVquOJov6MSRiy7HEVlZ8eB0yv5kxqIoz2uMa21Q0vXY1ta6UgBrqbUh8BT9NGa316YdhRR1zaqsKGrtop1ljafcC2orDFgWJXlVcTJfEnmKa8PeC+fKl4o0dCnc2PcbZ9kjr+oLbdfGcLJYcjRfcjJ3/99oOdWBYZowyXI+OTjh4XhG6LkX2GmW8+B0Sux75LXr5FM1KeKLarN8pRgkMcfzJad5gbaGYZoQKMXhfEEvjtnrd5jnBdZaTpcZWVVjjHsJ68QhrSBw0cJlxrKsnrM9zXIOZwu+s7uNkoKj+YJAKYqqZrzMGC0zjudLDmcLVqXTE73UbbMsSz4+PObxZMblfpfY99fp328RCl0zKTPuLycoIXm3t01lNZfTF/8Gvi6+OgnElKzqMYV2aV8vDEEIJBIlgt+KFHDsebw73OLBfMLj5ZxuELEVt9hMvrg581nE5FXwojqXF53rVT//InyT53pZ9MKIH2xd4s50zKTI6EURW3HKIE6+8Htn1/P5++ubvNZvas6/ybV41eNf59oBXG53CZXHp9ORi/zELTaTFq0veUFY44thgbxym/1Ot0U7ikgCH4Fo6vVclKrSmlVZUWmXulfCpXc3WgmX+65PtLWWstaEvkcviegmEVIKF+Ezlm4c88bmgMP5Ak8p8qo+d4ymWX5eq4a17HTbGGv5hwcH5/WBke/hK3lOIjLWsixK2k2tnQX6qatLu9zvEvkeJ4saX0lqrV2Ktqzc+ISg0ziZ4KKXke9hzIsjVIGnaAWB0zT1PULfQz7llH3e9jwvOJ45x2i0zFg16XIpBFcGXfSJ4acPDsiqit1u+7zecVWUmOa4pCFJGWMwBoraZQWWRUnku5rMYSslOp1Q1jWzPOfasEc/jam0wVOunrOXRE7Sq6pQUpBGAaFSXOq0ScKAo/mCrKovtK2Npao1SeDKSHpxhO8pp0c6nTPJcsar3DnCTer45taAnz8+4pdHIyqtXW1iuP6tfptQGqeZ+2g5ZSduc6uzQT+Mz2vXXwe+Rg1gQKQ6HOefUtuK2uaAJFJtBuFV1NdrMvKtgBKSbhAS9YZc7fScGPIXRFDWeHkEyqWr46Er2D4Tml7jNwOJ57PbajOIYlfEr9STmsM1vjKkEFzud7hzMub/+fgO3SjkyqDHD65cYthKWRQlP773kMkywwK3tjYIPcVosXJB6ae8/jQMuLk5ZH8y55eHI0aLJT+4sstON8VTrm7z6ZcEC8yLgkVRPJcaPHP4jhdLfCW51G0TKEVWVhxNFzw4nbIsSrKyQl7dpRM7gXZn44kVXyk22y1OFise/OOnPBpPub7Rx1rLeOlq1xCCRVm6KBlfnKJ0pRpwNFvyX+885Lt72+eZls/brpvo2aosXQbhqSxCK/z/2XvTJzmONL3z50eceVfWhcJ9kOxms6dnRtJKo7WVmcb0aT/sv7sfds20Y9qVRq25Wj3T3exmEwRxVKGOvDPjdPf94FGJKqAAAiAww57JxwgWEBXhr7tnZuQb7/E8IVIKnk1nHPQ6bHdaSCm41uuwKAqeTWYXHLEAYx0niyWPxxOy0tfQ/vHNa/SSmNFyRVZ6XtLJKicvayKtubXVZ7zM+IsvH3K2XHJ7OOAnBx0eno6ZZvkra7vW61C101ds73XbTLOcXz075mSx5HA64/b2AC0l41VGUdfrbMX5e8o3ofj60t1um63Wmx+0N/jHR6pDDlpdQqXWjZbnKu4/OB5AKTShTEhUj8KuMK5GILHOfLDJVZWhbuqhtPLpyrq2SCnQWrJYFVjriAJNECiEFFjjw/N1bSnKijgKSGKflprMMs4mS7b7LVppSBD45ZdlzWJVsFj6G1+/l5LGAWGgkY0023kWvjaWVVby7PkUay1bgxadNCII9ZW248h/8LKiJstKolAThpowUJSlL8KOQv+ka5vOM6V8WH6ZlQAEWhKGjZybtRwez3DOMRy0CAO9Pj/LS8rSIKQgCjVR6NeXFxVZXhIG3q7Wiqo21LWlrr2qhVKSJA6QUmCtu9K2c47F0u/TfJnT76W004go1KzykqoyhIEmChVKKYrSd8EFgZeuc+eddVoipVzL2G3wh4fzmqtNyvfDQgjBdrvlU6lAHGh6ia8d3e20fJH/MqOfJARKMmwnKCmRUtCtIt800aAdhTzYHTJeZSzygp1Oi4N+h2Er5YuDPbY7LQIlubXV97RWUcTNQY9+kuCcY7fTphOHXp1HSnpJzE8Odom0ZrfbJm7un6045LP9bYqqppfEtCMfldtut2hHUdNp6r/CIq25tzNgtIiZ5QWDNGGv26YdhZTKYDs+u3K936UbR2vJyNchUIpP94YsipJW5AnMQ60YtlNaUfiSbcXNrR7DdkplDPu9DjcGXV9eohTb7RZ/cvMa250W+902SvqoqhDQT/2etKKQThyhpaAdhfxof5eyrhm0kiYip2hFIdcHPdImpb3f6xBqxZ1hn24cMV3lDNsJ+12fmn6ws8Vup4WWgmG7Rah8M825hOjLttMo4BM35GyxopNEXOt1OOh1SMNw3cGcVRU3Bj1ub/n04XmGSQrBzUGP3e7Hqyvb4P1gnZfWfLqcYJp670hpduM2B00TyIfOrArn3vyEdQGXTrSuprYli/qMymbr46FK6QXXkOL7f7HP5hnzpa+ViOOAJApYZeXaWXl+OqeqDa0kpNdJCENNUdZkecUqK1ksC4aDFrvDDkLA7x4e8w+/O+SLTw+4ttejlYTUxjKb5xydzHh8OMIYx4/u77Ez7NBtv6pqkBcV03nG//c/fk9VW37y6TVuXOvTbsUURcXqJds7W22MtZyNl5ycLUiTgH43ZThocTb2XX7Dfou8qKgq0zi3CmMtx6dznIMkDtjqt4gjX2/zl3/zEGstX3x2QLsVEWiNsZbT0YL5IicMNP1eSrcTU9eG8XTFaLKklYR02jG9TsJkljFf5uRFjXMQR5qdrQ5aS8qqfsV2oH1h99HJlKdHEx4/G/PpvT1uHAzotmNOzubM5jlp4s9vpRFn4yVhoOh1ElZZiTHeeU/ikCDYOH4b/GHAXogWndeEXnwqX9fvXki7e0qcC/RJzRM9eAoV6xzG+kYtJV887VvrEMJHrjwtkh9HIjwdk3O+1ObCeC/jvGHo5cjBmsKqIem/NN/mfxdtiGZtVVPjp6RECa8BXTfpzIuOxbt8OVn7oint4nXGWipjCdS7aUqfN8Qp+XoZyfW5zR687Zz9fvq/+9fmu7+IK+MfrAN1mRHjQ9k+f40FeNqdi+trmtjObVjnU8Z/9/iQ3xyd8Oc/us9B/9VGuw3+aTEtMh7OR/znw688rVfg6/C/GFzjz/buvKsU3Fud+D26gBVaRrSDbdyFqJ8UGsGHeWMdHs/4/aMTqtow6Kds9VqMpyustQSB5y/KiorT0YI/+tF1trfaTOc5Dx+fMp1lHOz1GPReH+p2wMPHZ5yNlwgcp6Olv5HY3ddf48AYS1E2hbvNB7wsa05GC77+9rJt5yDLK4qyJi8rvvz6iP3tLv/rv3nAt89GVJUhCjWPnpyxyiru3hwyX84ZTVeUpWE6X1GUNf/6p7fZ2+4i1auva5aXPDuecnI2Jy9qBr2UNAkoCsXvHh6zynxdze8fnTIctPjiswP+/stnPDmaYIwlTUJ63YTZ3OsBl5V5xTbA0+dTBDCeZhweT7l5MMA0EdGyrJnMVvziVyM+//Qan97d5eG3p6RpiHPw+0cnBFpy+8Y27/DQscEG/+TISk8TBD6KpKSkqOuGbF1wMl8ihKAVBqRhsKaKqpqMgMPRa2r5ACZZzvF8yfP5nH6ScKPfpRvHFHXNaJWRBH6cVug56epGd3yRl1jn2Om0vjNyPlpllLUhDjStMCRsHipHq6yJloW0I88bWRk/fmUss9xTlOy0WxR1zeFswS+eHLLf7XBr0GOv2+a3x6d8czbms70d2pGv9+4n8TuVcAjBlaThUggC9d1O3KvjeXWft7nqHb9Im3m90+neubriNvchbb9t3XhR1Xw7mhIoxc9uXqOXvJ5bcYN/OhS29kwarT7LuqS2hswaqobb9mO8Yt9LCq60GQKJkgGBiFmZMdZmpFojnfre4cosL5nMVmuHazbPUVJQVoa8qLh3e4c4CiiaFG4UabK8pCh8DUS3HZPEwevn4ZyPXC1y7twY0kpC8qJ6He0UAEr59KrW3vHrdRPCQGGMI8+rV2xba5nOM8bTFdNZxtOjCaq5eQdakeUVTw/HPDmc4Jzj3u1tJvOMb5+OCAPNYplTVoYsr6iNuUQDcg6fwpCUpVnvV7+bEMchh8czgkBxbafLyWjOKiuZzjJGkxWTqe/+S2Jf6D2eZSxWBVlWvmK7KCsePxtx5+aQbjv26XEpqSrDalUwacb89tmIg/2e5+WKNHle8ejpiCeHY7b6LcJAXSrU3uBVWGfJTbmm8gllQGYKDvMRg7BNqmLAEcoALXy3b25KSlsRSk0gNVp4ihevJ2wpbY0QglRFvqHAWTJTeC5E4Qmmpbj6wc04S20Nh/kZpa0Zhl1aOiaSXs84N9Urts/tBet5+GoW1VASrUyBEnJ9Ps05Fu+M1LYm0ZFfX3O+w5GquKEcgtoZxuWMeZXRC1u0dUKifNdyaWtyU6CFQktFIPR734+O5wtOliuMtWylCXEQMMvzNcH4oigpa7/eu8MBlbE8Gk18d64xTLKCT/eGbLdSxqucSZZzsljy1fEpNwY9BmnCqvQSe5PMc9AN0oQHO0NWZcm46fqUUtCNY4bt1z/ULouSs+WKcZaxLCoqY7i3PWCn3WpoQkqO5wtWZzU3t3rcGw6YF4Xvjl146cPtduoJwq1lusr56mS0TkGfkyJ/O5rSjeO1M/z5tV2GrfS1Sj4v43Wvxbkj9654l6and30fvM/7Rp6H6z6S7TeN8/Lvzkm1kyClHUeeIHzjAP7gEElNS4d0gojcVGTO0g0i2jr84dUAFnbJtDxCCkWiuuhgl7PiW2pXcF3+lEDGfN8+RCmlr3GLJZNZxvHZnM8/uQZCcDpasD1okcQhWV5irWO+KAB8yjLocOfmkCR+QRlyTk+w/gMsVwU4x71b26zyktF4+cbQeBhoOu2YVhqhtWR32EFKwXJVXml7lZWcjZY8P50xmqxYrEqq2iAE3DwY8PRoypdfP2cyW/nIXRxSljWnowXddoyQgm7H/7QNxQL4n+f/jiPN9b0+Z+MlRyczjlYzdrc7DHopi2XB/k6Xu7d8jU6WV4ynK6QUdNoxYaC4vten3Yo4HS2YzjJm8+wV21leMZtn7AzaqB3J2WRBmoQUZc3J6ZzJbMXZZMkqr6hrSxRqHtzd5fffnPCr3x2yykpfM9iKXru3G/j3p3GWcTmntDWh1GyFXY6LCf/l5Bd80bvHQTLEOssg7NDWKdZZptWCcbmgEyT0ghYdnVLZmtJWlLZmWi4IpCZOhkghKWzFaTn1EnIqphu0mrTjq59ZYw0rk/NXoy+ZlAv+1eBTbrZ2CQLv3L1su6UTlrXXGO4FLSpbUzvT8CoG1LbmuBgTyYC2TunoBIdbz3VRZSzqjINkSKpjjLOcFBOMs1xPdoiUV6zI64IvZ0/4evmUH3fvcLu1Ryx9DVxmCo7zMYmKaOuEbqDBvZ8++aPRhF8/P6Goam4OevTSmNPFCmMdYdPV6WlTliRBQFZV/LeH33Kj7zk8f3N86kmFgf/59AgpJLWxHM08zUpZGx6NxyzLCuccD0/H7HXaXOt1GC1XPDwb89ePn3F/e4vP93df+zoBnC1X/O2TZwghmGQ535yOG4WLkNqYNcn0zx895V/VB9we9BktV/zq6IR/ODzmZ9f32Up9Y49znnYkrytfhy3l+p5eW8vZcsVoteJotmDYTpuavU1a8YeGKNDc3R78U09jg+9AL0qwOL5dTiisoTQ1n3S32U3aPzwt4FU94Sj7NYZ63RG8qsekqt88jX3/CZ/Xw6VJwHSeURQ1/W5CEPin+YePz8jziuOzOX/8k5ts9VMms9zXSlyqK3HM5hmHJ1O+fTr2qhW14cGdHbYHbU5Gc/7Lz7/iyeEYpSS3bwy/c27OOs7GS/7m7x9z99aQuEnvvGzbWsdsmbPMynX9xznSJEQrwfHZjE4rZnurg1KCna0Ot65vMZossZUlCgPSOMBZx8nZjKdH0/V4n97bpd9JmM5zstxLf80WOXlREWjFwV6PZVbyX//6a07O5gwHbR7c3uHweMp0/uq6drba9LvJK7bVVpsb1wZ89c0J00XGt8/G7G53CQPFZJ6RFRXuAmWDlKJpgIFVXtHvpm9Mx2/gMakWHOcTTgvPBdUPO3SDF7RDs2pJaSqeFyM+797hTusalaspbMWyzvjt/DG30j1+NrjPtFryNDvl8fI5O3Gfa7HnXXyWnXFaTMltyaxaIhD8Uf8+g7BDKF69JZyTYkt8HZiWqqlJs2SmfMX2F707fLN6jhKS++0DvlkcYZzhRmuXo+WIebUCYF77nz/p3kEIwbic82h5hBCC3ch/YUkEDsFpMWVlcrpBihRtT4eBpXLeaRSARGKc5fHqmHE1JzcVk2pBV6f8Uf8+qYqaaOO7QTWE45HWnK0yZnnBXrfNLCtYVRU/HXinKVCSvK6Z5QUOuLnVJ1SKZVX7n0XJ6WLFg50h+502i6JgkCSe6He+bChRNEkYkEZeacI6SMOQe8MtHuwMuTnoEarXO1mrsuLJZOZlzxwM0oRAKbKq4mSxZNQ4qouipG4oZAD6ScynO0M/t24bAaRhQC+OaEcR2+2UnXZrLRMYas2DnSGTrLW+31e1IQk2TUEbbPC+aOmQL7aucbczXCs69cJXexE+FL5HF7BCibCJSAmcs8SqQ6r7SL5/+heg2465vt8jjgK0VnTbMTvDDnEUEGjFaLKkrg1b/Rb9bkK37ZnOAy1Rl4qIfbSvlYTs7XRJk7ApnBXsbncQQnAymtNpxyRxSJqEBPr1N1kphHd+Qo0xdt25227F3u4F20oJeu0EKQR1bQgCxfU9nyI9jybu7/QYDloc7PXQSjEctDB2myQOMcbSSkNaaYTSfs6DfkoU6UtF2EL4/bq226OdRnTbMUGgONjvczpaMJmuaLdiBr2Ubidhf7dHGPhU9qCfkjTO2nlzxsu2W2nEnRtDTs7mZIVmb9s3ybTSiK1eStWK6LUTokizM+wAvnMbvBO8u91hZ2vTefZdcM5Ru5p5nTXpW8dO5Dv5HHgHRsCkXLKoczJTsKgzZtWSSbXg0fKIVPni4dyUzKsVk2rBfrJForw6ykkx4fHqmEBoxtUCgY+YdV3KVbcEn4bWhDIgkSGdICVUAbWzTKvlK7bP17EyOYfZGY+zY0KpudXaY1TOGJdzbqa7TCrLoloxq5YoqVjUOdNqSVsntHWCbjSsJT5iuaoLClthnPWpXaFQwqeuA51kYAAAIABJREFUWzomVgHGGZ7lZ4yKGVooTsspxhoKWxLJAP0ewlfnOqyhVjwZe0qOH+/vIoVgtMooarOuERQXUn/nUma9JGo6+L1cl7GWVVVhXeOAiYYg3ZWsigotvKzZeS1cqBTDVsp2K6WXvPnLQEpBqDVVw0+XNNx0xnkS5PEqZ95oDJsLD6Ox1my3W2y3Ujqxfw1DpTzDglJUxrIsq3Wdn+9YTdFKcjSPEHBpvA022ODd4Et+JP0gphfEVNZwmi/JTU3vI9l8bwewG+xyr/NnOGdQIiBWHTjvXvtA6oI3rvU52O+vu9GcA608u9NWL8WY4brbSSmfFkkTT96JeFEkq6Rk0Ev5ky9u8bPPb6w78qQUHOz12N/pUhvrjzfdbm+uA5T8yRc3Pe2Lc4Rarcdyzl2yHUcBP/3RgSdxtb677iKD/P5Oj//9P37hI4dNN2C/l9LrJNy7ue2LP6XX0BQCkihkd7sDfihkczxNIoYD33GspXdCpRTcvNbnYK+HNdaPLyVKCn7yybUXahrCdxxevH+/bJvGwbx3yzdxiAv7dH2v51+fZn0XHT+AMFDcPBiwt/Nx9Az/OaEbtJBCcJxPOCmmrEzB7db+ef0C15IhvaDFosoIpWZRZxwXY8blgrNiyrLOqS40ZXWChLuta9xp7bMT9REIZtWKk2JKV6doodZO2+tkobRQtHVCSzd0JFGfREUs6oyTYsppMb1kW0nFndY+z7JTfjN7xKRasBdv0dIJpa2preFWukesQo6yka8HtD6yuJ8M2Q573G7trbMISggSFVFZc+GYXDuKHZWwHfXp6JTclIzLOWfljI5OiWRAqmKss9/JKfc6eKdK0Y0icI7aWna7LQap10H+5dMjRiufEv7Jtb0XZOVcLgNLgoA7W32eTmf87ZNDns8WaKn4ZHfIveEWj5XXdQ0bp6sTRcyyYj3W22CQJHyxv8tXpyOmWU6kFZH2451z4J3XY17C1SVrvtvUWh6ejgD4X25ff/uN22CDDd4avva7ZlGVGGdZ1iW/OHvKzVafvaTzUWy+twOoREisJJ7sU/ru3w+cp5ZSrvuJX3YpnQN5gSNKrCNhr87hvEBYKgEvpU+88+PWjtTbrME3XQicFOiLlAoCXr6NisY5lVKcsytcsiGluLSO8xGc5NXjjY2Xj/vf+aYSjbxEMXC+PvfS+tQV3cTncM5dafvlOZ2PJSWXUttCiDWVjJSSzz+5Rr+brh3DDV6PzJSMqwW1M5S2IrcllavXv5dCoIRcv6drVzOtlmR1jrnCyRFN2lYKuW7y2Il6LOoV02oFOKSISVRIKK++HazfS814srFvnGV2hW2JoK0TBIKjfEwvaNEP2kgEW2GH0pT8w/Qh49Kv83qyjRSSWb1CCYm6MNfCVCxNxvN8zGkxxTnLg851DpLtpgHGkduS3y+eYpxhO+xxLfa65LPKj6elJFXxe6V/Ae5tb7HXbRM2klrWOnZarUa+LOLGoEdZ14Bgv9umNo7/9Nl99rpttFT865vX6cQRoVJ04oi9bodPdwuK2vjIXrtFN47ZaiXcHQ5QUtBLfIPFsJWShr5T97uifwCdOOT+zhbDdkpRe8WH/W6HSCv+9NZ1qtpQWcOf3jzgoNdBScFup003jjHW0o4vq0Nst1v8+Wf3G57AkFBp7m9vMWyl9OKYJPB8df3kRZfzBhts8O4YFxmHqxmPlxPAP5B/sxjTCT5e3fz3SgF/CK6/98WHdDZfOG/vc93bXfiuNt6r80y+HHP4sLbfpfPMOSgrQ6cVcdBwLm46z74bDotz0AlSamfITEEoNU5o9uMhqYoIpGYr7NDSCZEMaTWdsbH2qd+t0PNeRiqgrb2U0MXavp2oj3GWw/wMgF7QeisHqRe2iU2FbN5jsonMudC9ZFsQqYCWjukFLa4lQ3ZiXxs8DLvU1jAqZ1gsqYp8A4rw3cYKQaJfOCEOR2UNkQyIVYhx1jubzuEEdHTCVtilsobCVCghuZYMEUJwlI0IpGYQdoiVl9F6H+x1XpQu3OhfTsZ04wjn2mtuvnNsX+jUbUdbL/YwgZ1Oa92QcvEzMWwlWC5HDjtxtE7Jvg3iICBq0rlrTWn85/P+9taa0/Ci7V4S00uuHq8bR/z0YA9jfbpaCu9Q7nd9RCIloJ+85uINNtjgrVFZQ24q8uY+JoUgVvpK5o8Phfcmgt5ggzfBOa+IAudRzncjiv2XCu/geGfG4lBIYuUdosKWhDJACkllax+Jg3WHrXUWiyOUmlh6Z8nSqK9ItXaAamswzlBZH7VSQhKp4I3dpQCFKXE4Ihk2BLOeXsY0VDPnts+pWCpbk5sSLdW6Zs84S+0MtTW4hhYmatZ3XvPoo3aqOeYaO772TwpJuKaOgcrVlMZHSAOpCaVubNTrlLGWikieN2l92Pfgy5Hv73PNVffi95nvm8Z5n/l+n+s22GCDt0NWVxSm9vd94ZW3jrI5nSDieuudqwDf6kO6cQA32OAHBuccttFRfR0331XXrCM+b/EF/aGcjfe1/S7nv914fkR5haNzjo3jssEGG/xQYaxt7vtirdozq3ICqd4nDfxWN7uNevsGG/zAIIRAvSON0rsQ4Z6f/6HwPrY/pCt2Fe3Uxtn754t/qmikcxaosW6JIECIFJqouZ9ThXMlzhUIkSBEBMgP9JBT4FyNEDHin6D06oe45/+YcK5q9l/jX9OP8BoIEA3Pr3WOumk4e9/SlbfBpip/gw022GCDPzAYwH7nWR8SjpLanJIV/5Wi+gcc+aU5GDuiqH7DMv+/KeuvcM43WX0I1OaYsv4tzmUfZLz3ww9vz/+xYO2MyjzC2BHOFR/FRmFqZlWx/nOaL/jbs6c8nI8+ij3YRAA32GCDDTb4A4FzFdbNKauvESJEqz2k7CMIgBrnDI4S5zKk6CBEgo+oLHAuQ4gEKWIg8M6UUAgivHNj8A5bABiMnSGERhD6yBsKISTWrhBiiXMlQgSNslROUf2a2hwhRYQQIQiJo/ZRLFdhXYYUMVK2AYG1GdYtECJGiggIgbKJIlY4HEIopGhj7Cll/TVSDlD4mlcpWmv71s1wrkaK2NtG+qghFTivMStFgpSJt+1yrF0gRIgQEYLotVG1H+Keg2rWvWiOSYRIm30EYxfNfOImEqtxrmz21yvbCBEgRbuJ4Fav2hYS52qsnVKZJ9TmCLRAyBDnIqyd4jjf87jZ87yZX8BlhzlAfEck73k252j1Qp1hVZf8dnr8UXWb39sBdM5iseAcFoN1BodForwM3EcMW26wwQb/fGGtw1hLZQxCeA6+i9RGHxPGWqx1vnHpApXSx0yBOefWaw3U26eW/iU2ZjhXYswZefnfkaIN4Z8QiASEwLkl1i6wdol1CwJ9EyUinDMYM8LYU6RIUGoXJQcYO0KIACW3sW7uU3xohKiwLqc2hwgUUrZRaq9xDBKfBnQOXAXC4ZzB2gVl9WusXZLG/wEltxBorMuwdolzC6xboOQOQqQ4KowdU5tjpExRsoeSOxg7wZoJ1q08x62M0eoaxoyo66cYdQNnVzgsgb6FxOFcSW2OcS5HihSlthFojDnBuqV3BIVAyy0Q2+AExk0w5hghQqToo9Uezl2drv6h7rmf1zHWrRAi8mtA41yBsadYO/X7IbeQsoO1pxg7x7kMh0DJDkIrnAXrVq/Ydk7g7IrKHFLV32Lsc5Tcwsl+s+cnWLdAygStriGIMPYEKboI2cZa78xJETQp4zf7RJMiW1PAgI8ITsps3eT2MfDeDmDlCnIzo7QZmZmyqE4pzIJUD7jb/rcEYqP5usEGG7w7lmXJ6XzJ47MJkdb86GCHJAwJ9cevfVoWJWeLjF4S04qCtU0HWGs9X+gHtlkZw6PTCXGguTnsv9O1xvlmIfUvxAH0xEA1toks+YiNwLkVtXlGUf4DjoJA3cG/agbnFjhyrJ2QVf+dKPwpSfivKarfIGVCFITk5S8BQRT8iKo+T/V5x0oQkMb/G0rtvGFODpA+uiUCBLJxUJ5TVF9S22NCfQcpt3CUFOUvvYMgFGX5a5QckkR/Rl7+DWX1FQ7jJQ/lAKPH1PYER01tnmPd1xg7oiX+E4KAsv4dQgRYl2HMIXH4r5Ciw6r4L40NgRRdjNpFmWFTT1jiXE5tjlBqlzT+D0iR4KOQP/w9t3ZKVT+itoc4Z9FqByf7WGfIq//ZzMFgzCGBvk8YPGBV/Ddqc4TDomQXJYcYO24ipeUrtq2bUdVPAYGxJ1T1E0L9mY+IssBRUJtnlPmXtJL/hFYH5OUv0eoGWm1TVL9CyS0CfR8lvjttfb+7zbX0RbdvaWs+Xe0wjFpvuOr74b0dQOtqSpuxqE5Z1iNWZkJlM5QIsa7Gue8OeW6wwQYbnOM8EnY0mfOrp88BwV6v3XRCO/KqZpkXaKWItCIMNLWxGGsJtVrLkTnnPGG2lCyLEmO9Wk+gFEoKjHWebsdY8qom0l5/1zpvY57ltMLzNA5UtWGa5TwZTemlMVutlHYcoqTEWMuyKAEv5xZqdUGC8jKstZTNfG0zx0ApamN5OpqRhJp+K8E5CJQkbjTPq9qwKEq0koRKEWiFtdbr/o6mBEoxbKe044hQ+9TYy+s+J2HPyoqyNp4nUmui4A+sCkjoJjUYIEQLJbdAhGDnWLvyKU9ClNpFiATnKmp7ijGn1PaEqv4Gra8DEilbOJdTVl9R1d+gZB8hImpzTG2eIIRP8wkR+bovd3UkxqdpY5/qRKHksEkJWqzLcZQIpI9EiTY4Q22eAYJAPcCYE5zLMHaEsSOsmwISIbsIobF20tQTSpTsg7XUrsC5rHFSviYMPkeJLrV7grULnKgahypHkCBkhHM1tTnG2DPAIggxdoqQaRMljK5+uPkB7rl3tP051s6pqdYRw6p+hFb7KLmFMYdYt3xpbzUCz81p7BnGnOEoXrFdmxNq84ww+AzltqjFKT71nGHtuLn2OWX9NYmdI5RGyl6TLh9T1Y8QOlyn5L8L3TCmHUQNTZe/RyUqIP6IPIDfY2Qfhs3NjMpmBCIiUBGJ7noy24bGYoMNNtjgbWCdIytrvj2b8POvn/DvP7nNXq9DEgaNU5PzZDwl0gG9NGa7k7IqvX7usJ3igLyqEIBWikDB2WJFXtV04pBemhBpTVHX5FXNqiiZrHK2WilRoKmtpagMeVU39Vf+DlbUNU/HM/7i1w+5u7vFjw92iAMvUVlUNSezJdY5WlHIoJUQB1enqytrma1y8rpuuvsE3SRGScHz2YJQK3Z7XkmkFQXs9zvewSxLjiZzokDTTSJ6aUxVG07nK/7yd49pxSFf3Njj1lChpaS29pV1S+n3cLrKmaxyAi0ZtJI/OAdQihglukjZQck+Sg4B1bRaCLTaR4o+ob6PELpJnT7D2OdNunAJrkaIgEh/Qll/RV7+AutmKNlDig7OZdTmBCV7CKGR0pNeOyw0pU7nESaHRRAhpZ8Tzvh0oAjXKUAlh2i5S6AfoGQHY6dYu0CpLUJ9D+fmPoVqR4BEih5CRATqFlIOqM3zpgM18tEkN8S6DF/Lt8TYGUpuoWQf62YAWLdEkoCMkdLvx3nEzxjvAErZQcoEKVrQlHNd9aX9Q9xzKVoE+g6VeYJ1z6nrcbO/PaydI/V9Qn0P6+Z+n+wUgU93S5kQ6DsgAox5jrFjrFu+atutcC4jUAdIkfhxRYx1y2Z9Y2p72qSUa6TsEId/RF7+HUX1K6xbNdKvbyfj5pyjtoZZeS7nKegEEZHS63KPD13q8d6f/kAmdIJdCrsk1X3aeofczFAiQIsIsWkw3mCDDd4R53rEWkoC6R0agLyuycqa2jh+//w5AsGf/+Q+R5M5j8+mfH5jj0Ve8GQ05d6uV7x4Np4hhSSvKk7nK356c5/9fofJMuPhyYiz+YqDQZd+mnCuDz7Lcn53dEYahSRhQKAiT8lQW7Kqwjm31sB+NpnzZDSlqGsmy5ysrPg3925wfatHfIVjNVsV/I+HT9aRwqPJnHu7W9zd8fNdFSVPzqY8Gc8YtlN6acyj0zGjZYYUgvEyQ0nJn945WEcO86omDjVa+fqtk/mC3x6dvrLuvV6bs/mK49mCRV4y7KSkQQAfL7v0kWGpzROy4udEwY8vHL+shuSosG7U1MJdTMMppPTa5MYcotQ+Sm4DEKibTTr1OV7Y0NeF4Upqc0hVfUMlFM7VROHP0GqXN0d4Ls9JEBDoWxg7Zpn/X9TmEKV2iNRPqerHmNcP9MpYUvSI9CdU9dcUTR1bEv5btNyhFL8FLkfQhAgJgwdYO6O2J039XdA0b3yXlN8PZ8+FSJoIZ4ZzJbbpzhUiIdT3MPaUZfGfqetDwuBTtL5LKb4Gt3xlVYG+2rZWB1i7Iit/Tm2eY8wpgb6LIMDYs6Yb+GKXt1o7045qHYV8W2Sm4mg1469OnmBxtLTPMtxtb/FZf/ejBNS+hxZwgJCKjt4FAW29TWhSnLNIof7FFCVvsMEGHwYC3/ARaU0cBHSSiFYUIIRgkZeMFiumq5wno9k6XXxOmPp8uuB0vuTZeMbNrT5FXfP18Yj7u0MirVnmBdNVThoGLIuSRV5SVDWtOCQJNUKwHmtV+t/Vxn95aakItNcnTsKAThwhheRsseLr4xGhVsyzgkVRsiwqamPgCgewqGsOJ3P2em06ccSyKJllRZOutQRKkUYhZVWzyAvyquLZeM48L7i/O2S0yJhnBZNVTj9NmnSzIA40vSRGK8k8K65cdysKcc6RlRWj5QrjLP30u7WFf4gQIiTQd/0XsJB4XrgIpbaQNllzxZ2fq+QeQnRRMkfKLoG+2Xw/RSg5JAgeEKjrTZpSoPUBCEEt27BO3fovdh+Fu+Ujf7J1gQ9OEKgbTaSqcQaFblKcAd6x0M2cAgJ9G2FianuKUjtouY+SfQJ1y3fligAld5GyhUAiRdREvhI4bwCRfe+siJDanoIVfh1qByl7hPpBU0voGyEcVdMpm2LdHGk6TVfvwbrr9Q9lz4Xw+6nVHgKFscMmmpgSBg+ozQnGjdFqH612m1q8uyi37eendnyHMRopEk8385JtrfbxTu9zlKyQooOSA4TQaH3gfR03RMk+Wu6CEIBvVhFIAn177eC+DfK6ZlbmTMqMylrmqqC2hk4Q8Sn8sBxA8DfsbrC3/ncsO4DbRP822GCDd4YQkIbe8WvHIbvdFoOW15kdLzOejmeMFhmTVU4caKxz7Pc7SCH4zeEpR5M5lTGEWlEZw3iRsXO/Rb+VUFQ+EjJZ5QBsd1J2u20e7A1pR77w/dyZ6sTRumYOIAk13SSmHYdsd1psd7zO7zwrOJzM6acxSkoGaYJsagyvgsNHOHc6LT7Z22aeF0SBZp4X1May1+vwxY19lkVJZQxZWTPLcpxzPNjbIlSSw8mceVbQjSO6SUQ7jhi0U/Z6Xq+4qK9ed1HVXN/qMc1ynk5mHE5m7Hb+MMN/QrRJo/8IWJyzvkMUQaDa8FKfkBR9kujf+3PX300vXttAf0KgH5yPDICSeyi5SxR8wYvolmzO7xDo+xdns/5bHP6by/MkRqubV6xAo9VttLqJwyBQaztR+MdE66hSM7YCLhyT9NHq4MLvrxOu6VTU+nga/8cr5+lhmz+X1/c6/BD3XOoeAdfOR10f9+Pfe8V2Ev27V8Z4EfN0r9hWooWSO0SBuTCfFzb8/jXNP+v12YZiJiXUnzbR4bdDab0M3I1Wn1mVs6gKSmuo7cfjPXxvB9DYivolQsRR+QRw7MYPUHxXOHmDDTbY4AUuZw2E/685tswLZquc2tqmHsZ/ISZBQBqFjJuo1rVBlzjQSCG4u7PFo7MJvz065Wg6549uXmPYTpmsMrzu8Qs5OmPt2qF7dDoGoDaWJPSdwL4L2PHoZEykFff3ttjptri3u8V4mVEag1aKJAyuTP82K0IgeHQ6YbLM+eZkzIP9IYNWQqB8E4sU6zgKADvdNtNVzl9+9ZiT+RIh4PbOgHYcUdY11jmOxnP+9tEz7u8O6cTRlevuJBHj5YpVUWGtY7oqWJXVh38R/xHg3xMC50QTtXo9RdD6XN+36s+9mI4Vl9OXl6Fe+p146efbzPP1xx2ymcvLSjZXXffysYsp13PH7+X1vWme587Md9m9OOcf1p77+0DAet0XdbVRV9j+rtftVdvuwnFxac/Pj7+wYe2SyjxByjZx+LOmlvDtmQt6YUxpDSf5isxU5KbmeqvHbtL+KNE/+B4OYGlXzOuTS8dOi69RImA7uot6TT2BcZbK1pyVMwTQ1S0iFazF3c/F5bWUKPFCwL60NZWtvah88zvwwva5LSmMv5mlOiIQei0mfxHWWSprmFQLAFo6Jpbh+tzCVJdsCwS1M74qQEhMU88ghX9jnGuQetF5iFTQfLFs0t8bnL8/yoaI1TTpG41voMpx1A1lRNA8UYN1no5AIJtjnlDU2gW1PUPJTkMAmyCExLoCY0YIEaNkC9B4AlPb1KhY38WH5rvki6627ddgXe47HQnxN0mDw4BrnoKFaH7X8IbZMwBfHL9et486nBPHChHiCWE9l5mjQBDQjiW3hj3S8MU9JI1Cttop1jmUEISB9h2uWtGOQ4btlC3gxlaPNPJO2P39IaezJVlZ0Ykj+q2YfhpjnUVJue4UPodxjkgrhu3WujnCOf8nCTQ3hz1CrSjqGudgu93ik/1tno6mlMaQRiGdOCJ4A12NAGprqa2lk0T005heEnN9q0sviRFCMGgl1NYSBZqDQZck0JzMl2glaUch/TQmCX1h+LV+h7yqyUvvDHaT6Mp1t+OQyTKjHYfs9zpNiv0PMwV8jjc7Ei+d+4qz891XfJyk20ULH8bG2zs4l696H9s/pD1/01w+lO3XjXP1njucq9Fyp0lHx+/kCyQ6pB9atqKUVV0SSsXt9oDtuPXRyKDFVaLpr8GlE0+Lb3i6+uWlE8blE1pqwM8G/wehSq8cpDAlo3LOX5z8TySCn/Xvsx8P6AQpAsFJMWFlCjo69Q6aCpux54zLOW2d0tYxqfY3r3m14jAfcZj5L5x77WtshV1a+tWbW2EqptWCn4++BODTznX24yHdwM/1OB9fsq2FYl6tCFVAokJWdYEAQhV4GglnqKxhWi0QSK4lA7TQGwdwAwCcM9T2DGtnWJcT6utI0QUsVf0U4xZNV10fJX0KrzYjanuKFAFSdFFyC+sWjcTUXxCFPyEOPmtqhTS1OWGZ/79otU8UfObJTUWAczVV/azpTmutb0hvwlW2HSXGjijrJ4T6JloOAY11y6bIu8RTSijP2eUstR2xzP8fAOLwp4T6FlK0PdeWLZvOxTFabTc1Mq6hXDhBqT6OLta2L1GqVLVPhdhzmhcpibVGSl+3V1Y1Dt88opQE50mdz68RQNCMZ+05b5s//5zo2TXam5Wxa+dQK8/7Z52jrM2aviUKvANmrKM2tlFaeDNp9ZPRlP/zF1/y+fU9/vj2ATjnu5W1pKrPiaBlM55PSbsL66A5Fipfs+iAsjaY5ndRoJGI16+7WUNtDErKdTPKBhts8P3hG17O08U+ZfyuvkBtDcuqZFJm5KZmN2nTDiKCKwJa34G3MvzeEcCWGnAt+fHlY3qIFiFSvH5YSxOJM/Wlp+9ZteJpdkptDYWt+Lo+5Fa6y07U53k+JrO+IPLb1QnbUY97rX2e5xPm9YrS1jzPx0ghuZnurm/ur9r2EcBFla0jiwKYVUser05esT0I23w5f8Ju1OdmusuT7AQlJNtRj7xxZJ+sTugFLXbjPh/7iXGDPxwYM6W2z6nMM5wzKNHxkS9RNxJQGbU5Ji9/QRz+EVHwKbV5TmUOMWaMUgMCpVEMvNKAy6jtGaErX3p3u8YJqzh/RqvNCZV5grELrJ1j3Jw0/FNfiH2F5JN1xWtsbzWpjTlF9SVShF5OSnQo64dU9ROk7GDsGFxFGv27huer9PxiF+4DzhXU9oSyeoixs8b5G+AlozKsW2HcjKL4kkDdpBX/2aU5BlqhnWwSLpfTa1II4vCljIMAIeSlWr7za6S64om+cdpCKQmvuH0pIUhC70TROFZSSqRwaxviwlhXIQkC7mwP2O22fONJc5UQXKJjeTmCqKQgQL2ybgHEjSPqnFuv4bXrFgKpBe4fUVllgw3+pcCnxt+//yGrK07zJb+ePMc6RyAlv52dcKe9xRdbvtbxQ39i39sBjHWXWPuW7vNJtfV206n0+mF9OlURSE0gtY+0ScWkXPC7+VO2ox5KSA7zEanyUbivF8+IVEg3aHFaTLHOsh12+Xb1nMLU7Mb9JiL35mJJgVinb6WQpCpGCcmkutq2AL5dHqOF4iDZ5rSYooSirRMWdcZpMeXb1TGfdW4SSJ/+3fiAGwAYO6GsHjZanylCRggUXnNyhHEzantMVvwNWu0Q6fveWTOnXtaJCiW6oF7U3OB8R5xPqb7+jVbbU/Ly7xEiwNgZlXlGqG+h3R7iKoUeZ15jG7y+ZeX5vOzMO5vCUZsjyvprouBTjDnD2BFR8HnTTXjuwDRUDmi89NWE0jzG2pXvsBMBDouxE4ydYuyEvPx7XGBo8WevTFMI8U4fr6tlrbyTXFUGU1uU9hFD2UTbnHO4JqonBIShxlqHbSTicCCkQOvz+h8wtcEa74QFoaYsKxaznLQVEgQ+5a+0Igk1d3e2aIcBRVa9YruuDXVlCEON0rKZL2sHz9Q+MhiGCpyPii7muY+GJgFhqJHSn+/n7K8JAuXHa2y8vG5jrL83qhd7AC+6oqvKd1tLJVAN3YwxluXihW0p/VhXwVqHMdbL68nzaGvzGuFrLb/vuoUAYxxK+eiLtfaFQ4y/5uK6VeMg53lFtiqIk7DZP4G1jrKoWcxzwkgTxwFBY8PaZk7GvyfOr3EOqrL2NEFKNusQr133+b7XtUFK6UsSlJ9vVdYsFgVRpAkjvd7zDf55ozA1o2LF1/Mz2jqkFyV8OTkhEIqfDPb9ve+HwgPocblbKVbnXcCvn2S5IRRgAAAgAElEQVQgNe0goaVjEhWxFw9QQnLipkyqBffbB+zGfSpXo4RkVM4YVwvuhPs8aB9gnME4y6icMSrmdIKETzs3yEzBos7emCsPZUAn8Cnki7bLwlxpe1ItMe4FK5Nz4MSL+EtbJ9xt7XOntb8ea4MNAKybU9vnRMHnBOp6QwcRYuzIR+fMCaZ+3qRRz0leb2PtnKp+Rl0fomSfCC/6rmQPKTsEchctt3nTk6a1cyrzFCV6ICRaem3QF8Lkl/Em2+AZ96XsIi41dnlx+Tj48Toi6FPBoklpd5CyTaBuIFANISsEah90QBR8ipKdRv/ziKo+9DJXdubrJj8WmpTqZLRkPs9pd2I6nZgkDalrQ1UZqrJmNs0IQsXuXo88r8izkiL3DRRBqBhstQkChTGW+TRjtSoxtWVnr8vzoyl//fOv+dHnB+zsdnHO0e2lRHHAQb/DZLTk2dn4FdvT8YrR2YK9az3anRilJMY0c6oMi1mOtZbd/R7OOsajJX/719+QxAF37u+yt98jigOqqqaqDNmqZDbL2N7u0OkmOOteWXcUBywWBVpJWu2IujIY453dKNJUleH0ZEYQatI0pNWOUUqQZyW/+uUTklbEnXs7pGmEfE2aytSG+TwnTkLiOKAqzdqRklIyn2Xfe91KSZaLglYnQkpJlhUEgUY3CjEvrztt+Xf3yfMpX/76Gfc/3Wd3r0eahpRFzbOnI/7ur75h/2DA7bvb7O33EFKs57RcFGRZwd5ejzgJscZyejKnqmrandivQ8qr140jigKyVclotCCJQ9JWRKvt5zSdrvjl333LtesDrt/cIk2jxrHd4J8zTBP1u9PZ4lrSZStKWVYlrSBcZz4+NN5fC9hm5Gbun3plRKL6yO8oMgf/VKnwnFpa+kggQCRDdqI+s2rJss44zafcSHfohR22ox6lrfhq8ZTn+Zh+2KIbtNiKupS24tezRzzNTvH6hK+vaTy3LYRgUWc8XB5xLd4iksHVtoM2g7DDrFryi8nv+XZ1zDDqXhhPEqmQSAWE8g+LUX+Djwtfd7ftmfftBCUHhPpO4/CcYO0cSwlr+gbb1NX5Y14tYN48+StE001WmWfIuk+ob2Fs3qR7n2HJESIi1J8iRUqgDjB2Ca5siF7PGziuwtW2AazzzSe1eU4hWiA0cZD4eQvZ8IrFDdfZuTaAFz43dkpR/YZQ3VrfvYQIESJGynOB95rajDBuiqPwjSWvKeH4ELDWUmTeoZuMl3zz9TE3bw+5/2Cf2TTj5PmU4+dT2u2E7d0uIPj24SlHz8ZUVU0YalrtmNWy9NFBYzk59ryEaSvCGEtV1synK85O5/5L/mzJZz++xv71Abay1EV9yfYnn11DAPN5xqOHJ4SRJgw1SRqyXBScHs85fDYmjgMGW20vXm8dRVExPltQd2Os9VGyoqiYjJYcPp1QFBXtTozpt3xUK3913bfv7vD02zPCSHP9xhbPno5x1rGz12UyWrBYFCyXOatlSRRpPvnRNdqd2PMlZiVSCaxpOmVegywr+ebrY7Z3uvT6Kc+ejIiTkK1hmzAUH2Tdk/GS3315xGefHxCGmm+/OWVr2KLT9STfL6/70x8dAKyduao0uIa+xxhLtio5O53T7aVrG/mqZHS24PCp7xLv9lKM8RHKotnbs9MFv/31M378xQ16/fTKdff6KafHc5aLnOWyYLUs6A9afPKja4ShxtSW5aKgLGofdd7gXwSOshlfTU9Z1AWVqTnJF2Smeqn/+MPivb2W2pas6jGFWRDKFjqKQAgkEiXCN4ashRDrCOA5Uh1xu7XHST5hXM5ZmYJIBexGfWpnOM4mPF6dsKpzhlF37RQ+y0Z8vTxkWi7pBS2UVLxJrl0IQaoipnbJ0+yUftB6re2duM+izjjOJzxaPWdpMrborFPYkQqwzq47mDfY4Bze4btLVv4N1j1dSyidc2T6rt3+/8/em3bJceVnfr+7xB65VdaOlQTZ3eyRNN0azxzP2OMX8w38Tf3efmFbZ8742DMjqSU1eyEJAiCAQm25xx73+sWNSqBQVSQAAi2omY+E0zxRGfHE/WdExhP/Fa32XN4cLcasQLjKWWtLxCu3p2sMO6I1c5r2CE/fcnMw7RJLgzFz6vYET99FyS0C7wuq5huMWblxTzJFyuuLQG7m7vLzTIa1Da2Z0LbnWF0hcOOhXFPWECVSF14WrmWClClte0ZVf4uW427SQIAUcReGvnyPCnyk7KPVjgs/fyA0rWG5LMhWJfNpxm9/8wSlJJ98ustyUfD86YSHXx/z81/e6kJv8Oy7c/7w5TPAMhgljLZSqqpBa4VpDbNZRpKGpJ3YQLiQYFW1FMWSr//wgu2dHsNRQp5XV7g///kBQkqKoub4xYyDwyGjrYQo9slWJcdHM7763XPu3N9me7ePkGKtkU3rQp1B6CGloCxqJucZjx6eYK3l518cIqXE3LDu23fHTM5XKCWJIp+HXx3j+5rt3T7Pn005PVmgtOTkaIZSkv3DIWH4di2+qqrl9Ni9UFRlw1d/OGJvf8jWOEVK8V7WPZtl/PF3zzi8PSKOA548PgNcONYYe2XdFwLwOlw8uprGoLTAD3QnImtOTxZ889ULBoOYrXEP2XkF5/OMPK84PZ7zj795zM7egCgOrl13HAc8+faU5bIAITh6OmG80+POvfGNc6Q3+PPHvCo4KZZYa1nWJQjwpSZQ6oNVAb+zANTCJ1R9ToqvaWxNYwtAEqoeW8Fd1Pcc2peaXw0fdEPeHRIV8kmyz2E0pjEtFrtu03In2mUvGFEZN6MzUj6+1BxEY7b8Pp/3btFagycUPS9eexVv4v716HMa0yCFJNURSsgbuT9Lb3En3qW2rs1CqHxSHdG3MabLO7yoVN5ggwtIkeLreyi11c3BVEgxACFI5f+C7eZaQuW6y+PjqQMXeu2amEqRro/nqdsM4v8VVzTgBrMr2SfwvnCd/JGdZ83tI2UPX9/tGs1KpBxwc8uEm7hdODf2f03gfdq1q3FiMgp+jbUNUiQuv1DtIkXUeQIFSfA/dcUpaj1j01f3sKrBZQJ7azvFwb/DDZRvsba6tO73japsOH4x4/x0ydnpgtWqpKlfpnn0+hH3Pt3l3ic77Oz2XIFGqOn1Q5SnODgcMRhErJYl08kKay137+8w3knp9SMC/6Un9PDWiCD0yJYlnqdYLgrOThdMJ6sr3EoJgsAjTUPUa0UgYexz5/429z/d5eBwiOcptJb0ehFJGjAcJezs9PE8RVnUCAE7e32i2OfepzuEkU+RV9eu2/M09z/d4eR4we9/+4yzkwW7BwPSNGC1Kjk9mZP0QrSvSGL3wm6+x9t3HZIk4PNfHHB2suDrPxwxnWTs7Q9J0gAp38+6X/bTe5nHiXXf9/n5itk1Nr8JYeST9kKSJGC0lbK11VvnDHqe5uDWFju7PW7f2yLwPaaTFUfPpsymGefnK/K8pm3NjesOI4/pNGM2zYgTnzDyiOOAtssj3eCniX812uduOiRvauquniHVAX3/w7VremcBKIXGlxGRGlCajNY2CCTGfv/N5faVDP3LP/JaKrRURNb9yLxa8ea2v4yDX2yPVEAo/a7j95tVtUkhGfmpS5TuWjoIIW7kTmVEbEN4C44NNhDC9d2TRN2W9dWLVCF2nY/30sEvRISwPtDHtRB4+YIkZYyUcddq4KLxqYcSTghePQGfy4Nev6dnlpA3cgt8UD6K4aV9lBi+sr9Gcrntk1bbr5yr7BLyr/7cCKHRautae3wINHXL5DxjMS8oioa2MZcEjfYUSerysYLO06WkRGuF52vCyCOIPFbLcu19mk5WVFXDalGyezBw0VABUewTxQFh5COkoKxqJucrlovL3NZairxmMc+ZTlccPZ/gB5okCTDGebHiJCBO3LEu7KQ9iZSSbFXy4mjGzu7L6yAIPZJuH6Uky/n161ZKsjVOOT9b8uy78zWv1ope34mgsqhdOxlf4wcebWOYz3Imp0tWi4I4Drl9d4veK8UVr8LzFTu7fV48n3JyPCcIXJhXSvHe1u35mv4g4sXRjKYxnLyYs7vXJ25aptfY3BhDntXMJhlnJwuef3eO7ymCcIxS7vuWUrCY5ZyezNnZc7aVUhBFLmcv7gRxWdacny3Js5qqrDGt6QqCrl+35ysGw5iiqCjy2rX/8RVBoCnLxgnJsyW+3xWg3B2vC202+PNFqDR5o3iRn9Nai680RdsghaDvddrkYykCkUKiRcBWcJfK5Fw8lHwV/agf8JtcnTdVALrt79bQUr3GdRO3fIvmlxts8BLihv+Gm8Yl/lCz5u+b13mV++2u2R/iflu8+bnebI/3DWOtq9YEgkATJwG+r0G4ClfPUxhjL/0WaE8RdJWgnqdc7z5fM0wDtKf4p79/QlW1bI0T4s6rFUWeqwaVLoSouyrc6hpuYyyrVcl8ljGf5hw9mxLHAYe3RoDA85SrkH2tEOBCOCwXBQ+/ekGSOuGmtcL3NNpTP7huKQVxEqC1pMhrdvcHbI1TELB3MKSuWx5/e+p6H4YecezTtob5NGMxzzHGVUqPthKSJERdcwlprUh77g9ta9jdGzAYxu913XHss3cw5MXRjMnZkuk0o67b77f5snDcs5znT6fEScDewdA5Bbrv7fxsyXePzxiOEqQUaC3xfdVVgdOtyVUNCynWwtt5K69ft6cVt++OsViePjnH85TzAiYB0/MV82nOYuam1YShx/Zuf13hvcGfL/Km5nk25/89eYLFknoB1lr+YnTAQdz/IArknRtBuykHLUW7oLYl1ho8Gbp/Itp4yjbYYIOPDlXVMJ9mNI2hbQ1lWTMYxoy2UsqipmlcpWYc+3hdQ8DpZOU8NVIQhhqlFXXVorXzbM7nuasO9pxnp21appOMrXGCVK7KNQw9ROe1al/j3hqnrvXHomA2y/F9TZz49PvRuuq0bQ1R5K+9kuAKGM7PlrRNi6cV/VGMlJKyrGkbg9bOgyaEuHHd4+0eAIt5zuR8SRQFxInzcJVFTZ7XZJlrgB8EHoNh7Aoi8orpJAMsYejT64f4gXdjKxiAyfmK1bJYe9DCyHtv6wZYLUvKoqaqWkxrGI5i/NC73uZbKVXdsJw7ERiEToD1+tE6l/L8bIlAEISafidYq7Khbc26WAVckct8lmONpW5aqqJme9cVfly37iD0KIqKPKvI89r1mIx9BqOYum7JVi69QGtFFPv0+tHaI7nBny+O8wVPVzO+mp+xaioa05K3DX+5dcB/3P/UDe97c131Rh98ZwFobENjKibVd5RmhRQaTwSEqkfP23VhpY3XbIMNNvgI4aorXzZPfle8/Pl0Pd5cDcj3H+99cb96PNdS5YeP97bcF73rXA8yt+1jebl/fd2X+gu+9j28q82NMevej9+33/dx/9A+H6NtN/jTY1bmvMgXfLM456RYsmwqYuXxr0b7/PX27SuN4H8AH3YSSGtq8mbGd9k/sGrOidQAgIG/R6xH6Osazm6wwQYbfARw3pQf/7B9+Xss3rhH6/vifpfjvS23E0zvdl4fGq+v5fvO9V1t/qZ5d+9ip4/Zthv86dH3Q1os366m5G1D2TZ83t9mN0o/virglobK5iR6tH77amxJa+sbR7FtsMEGG2ywwQYbbHAZRduwqErypmTkh9xNhtxNRww+YBXwO6eVCiRaBgQqRYuA1lZo4ePJH1cEssEGG2ywwQYbbPBTgu3+TwuFEpLGtBznC6Zl/sE431kA+jKmr3fRwqe1NXk7J9ZDBt7+984C3mCDDTbYYIMNNtjgJXyp6XshO2HKsi75/06f8L99+4/87dnTD8b5zkrNYjC2oe/tE6oetfkZoerhyYBVc04oU3wV//CBNthggw022OCfGa1tqU3Fd/l3FG3BTrBD3+sTynA9g76xDUVb4EufRCdYLGVbsGiWhCogkCGBDKhMhcEQyACL7WbKW6RwTatXzbIbo+rhywDdDS94mj+lMhU7wQ6hDNfb8zanaAvXjkcGhMr1Fy3agmWzJJABvvTxpU9ta2pT05gGg0EKSaISlFAYzI3ci3rOvFmwqOf0vQEDr08gA3JTULT5em2+9ClM4SqkZUBrWwyu56cSGnVNO6nGNORtxpP8CRLFdrBNz+vhCc/tb1tqU5ObgkQlhCrEYsna7K24jTUsmyVKSDzpE8gAKSS1qXmaf4cSmu1gG1/6157nPycq0zCrc56splSmZT/qoWLJXtT7YJzvngNoa0qzorUVSniEXh+LpTYFeTun1TVg0TK6NPFjgw3+pcFai8FQmYKsWRCoCF+GeMLnpePe/ci7dkg+AonF0pgKCyih1rOyW9uihFx/5uUMXdk9aNyUGinU+keqMiV5syRQEZ4MkF1zZWMNja27/QVKaKSQWGupbQWAFMp9HmgxWGvWjZclL3+wb+JubUNtampTAoJAhd3fBI2pEIj15y0WY836QWcxF22rr22k7hqyt+u8YWejiybtL20ohXLnhSJrl9SmIlLxmkcJhejW3XR5yBK53g8ga5a0tiVUEUro7vwsrW1obYMnfSRqfY6tba9wm85+SuhXvvuX63ud+4KjsVX30PXXPQ+NbTvr2G4ftX5If4zX2p87jG2pTMWz/BnLZokvfUIVrUXHslmSNRnGGvregFjHtLYlNzmzesq0tqQ65SA8ZNEsqEzF2B9TmIKyLQlV0F0jLfN6Rm0qpJDsBLskIkEgmVTnrJqMnnbiSFhBaUpm9XQt9Pp6gC8DsjZj1SxZNAskklgnjP0x02rKvJlRta7JtC99Wr9FC017DbdnfUpTMKtnnFVnPM+fczu+TaB8hJBkzYpZPcNiGXkjxsE2s3rmhip4I5bNoruvQgIprxVWrW3I2own2WM84RGqgFi5ljt5m7NolhRtgbUGHWgCGdDY5gbuKRLFlr/Fopmvua0tKU3FvJ5hbIsvA3bC3fWxTsoTPOnT9/roG4TqPycaY8iamkmZYbAMg4hE+wz86Id3fke8swCsTcG8PuZZ/k9UJqent7vWL+5BU7YLai9jy7+L3FQEb/AvHLUpOS6e8sflbzgM77Eb3mbk72KtWQuIrF3RmIqhv4MnfYxtmNZnGGuIVUKk3fSbrFkSqhhP+jS2wljrJi3IgMJkrJo5gQydUFFumseinvL18h85jD5hHOwRyggQtLZhXk+wWDzhk+o+UkgMhkU9xWIIVdJ9HgqTU5uSxtTdyMMekUoRiBu5y7ZgUp9wXh4jgP3wLonugRBMqxOU8IhUQqxSGltTmpxIpUghnSCyzvPhRMxlGAyFybsJQk5cWmuoTUWoYmpbMavP8GVIKCNClfA0f8h5dczd+HMiFaOFR6xS1MW6mymtbQlkSKQSZMf7onjCql1wK/qURF94HxoWzZSsWTDydwlVjOJCCBVXuKu2oLUNkU6xuO/eWtsJb3WFWwlNYxtm1TmNbdgKdvCEm2pUmJzGVE7MYQlURKJ6H+219ueOC2Fdm4rG1k5II2hMy6ye8Wj1iLPqlNvRHXqe88pUbUnVuu/w4eobEp2yF+xzUh4zr+eEKuS4eMG0nvJJcp+8LTirzrDWMm/mzKopvxr9au2teh2VqXhRHHFWnlG0OUN/RKgiWtvyJHvEqlnhq4CT4phIxcSDmG+zhzzJHmOsIVIxPa9H1q7AWqrufnyVO5Ah3+VP0EKzbFacVaeMgzHGGsq2oDY1WZvxcPUNd+N7DP0RR/kzpJAE0udR9ghrDfeTT/Dl9WNRL2xbmQqp5PoFrDIVk3rCN8tvKE3JneiOiy5iKM313M/yZyihiHTE4+wxxrbcTz7hrDxl1sww1jKpzrEYfOWz5W990OvmfUIKQaA0ZdtQtS2WirKtPxjfjwgBW/dwkSnGNiybU4xtifSA/fAXVGZF3swx/g+PhntTGGOp6oamaxAqpcDzNEE3rLuqGuaLAs9TBL7G9zXWWtq2awJaNVRVw6AfoZSkqloePT6lqlv2dvoMBhG+rymKet3JvaoapHRTAqQU5EXNYuGSMn1PE8cBSnUDwTvuMPAIQ71uyvrku3N8X7Oz3es621/vEW1bQ123PH02YTrL2N3pMxrGJEmwXkPbGtc8VEv6vQiwlGVzhbtuDE3drge3t8ZgrbvAtJYslgVNY9z4o8DD66YGPD+aMZ/n7O31iWMf33OXSFHUrLLSTTbwNVHk32jztjVUVUPdNdUVsO6OD9zIvVwWzOY583lOHAeMRjFh6FFVDctl2a3NW49MstYShh6tsevxSxdjnN43jHVemUU9pfL31x6r0uTM6nPOyiOU0PS8YecFayhNQWUK5vWEx82MB+lf4EmfZ8VDtv0DAhVxWj4n1j0ilXBenVC0GYXJKNuMgTfmVvQpWmqMbSmNEx8XXqdVM2NeT5jV52jhkeoBsUqY10sm1QmtbahMSWUKDqP7CATf5V9TG+cZ9GVITw+JVIqhoWzLS9wH0X3m9dna4zapT5AItoMD6s6jVZmKef0CY1se9P6Css05LZ+zG97CWMN59YKhv0OqB3jXaIjalLwonpC1runu0BtjsOTtkkC667tocwqTEcqYO/FnlG1O3ixZ1BOm1QmGlrvxz7CtZVFP1p+v2oI78WcM/R200DS2pjIlhpceRxCsmjnHxTN8GaKFRsiASXXMspld4Z5UxxQmY0/cJWsWLNsZI28HYw15u7zCPfDHCASz5py8WRGpGKGd9+1p9hCD88zM6wkDf8zd+PPue/u4rrU/f/kHSmgiFRGoEINl4A3cf1tDa1t86THwBoyDbVLdw2JZNAsm9Tnzes6snq09rX2vT2tbjornnJWn1LZGCY/KzDgpjwlkQG2cyDTWec2vgxaanu6zqBfM6imTakKiEwZ6wLxeALAX7FO1Fca2a8+jQOBJj1Sn9HSP2tSsmiWrdnWFO28zzspT7iWfkOiUWT1de82ch3HJrJ4yr+cuDI1kK9hm2Sx4lD3irDwl0Qm+DDrP+HXr8AhViC8DYpUw9AZo6XXXmfPgRSpiO9gmUs6z6ryqsyvcI3/EqlnxJHvMWXVKrGJ8GbBqM07Ls3VoWAlNay+PevyYESjNbpjy19u3aYxBCoEUgnGQ/PDO74gfMQpO4cmQ1NtBCMmyPqWlxr3Fp93bZvNa++gfB2MMk4kbtk0n7JIkYG9vQFnWLJcF55MVUkmS2Gd7nGKMJc8rFsuCoqgxnWjwPU2eV/zx6xdkWYWSruM7wPHJnOEgJk0DZrMM7SniyKc1lvk84+R0QVm29NKAO7fHGCPI84rTU3dDJknA4cEQ378QmWekaUivF6K1unZcklufpShrHj0+49GTM/7il7cIfE0c+06ArUqWq5KyakiTgF4adoLwKvdqWbJYFuxs92iNIcvKtfgUwNnZkqKoUUqys92j33XAPz1d8N2zCVHs4fsarZzons0yTs+WBIFm0I+IIt/ZdXHV5stVyflkRVnWWGPRWrG93SMKPay1V7iTJKCs3ND24+MZj5+cs7vbx/cVUghWq5Lj4zlaSwaDmMODIfNFTtMYtscpWV5RVY0TpqH3QQTgOixIixIengicp9sUzOtzjorHjPwdhv4OEuHeXJsFpXHesyfZV+yFd+h5Qxb1xImNdsHT/CH74V208HhRPKEyJRbLeXVMYxt2wltIe/0LQ9kWLJsZ0/oUXwZIIRgyZtFMeZo/ZOTvULY5x+VTenqIxfBd9k0X7gzo6xGtbVg1c3KzwloucW8F+xyXTylNwUBv0Zi6EwKWypTUpqIyBaflM5bNnNvxA2pbsmymhHVE0eY8L74lkBE9Pbh2DY2tOauOmNcTAIo2QwpJZUpndwRSaCbVMaFKOmHZrkO3s+acrFky9g+oTMFR8RgpFHmzYlqfMvC3Sb0h+toHk/uBrUzBvDmnaA9IdB+Nx2l5xKw+u8K9ahesmjmx6nFeHTOrz0lUn6LNruVO9ABf+RRtxqKZUpqc0MbOy1c+QQmPvjfktHqOxXAQ3l0Lvo/pWvspQAmFUhGB9Gk6gfRqR4tYJ0QqZuyPSXRCbWoWzZxp5cKzRVvQaufwGPlbtNbw9fKPZG3WiRQXrq+Me3kSQnbpCAqLyy+sTEVpCvI2c6JIRqQ6wVcBjW3J6ymjZgS4PHwl1Fo8lZ1nXyIIVIAnfIbekL7XZ1pPqbvjv87dmIbGuheRQAbEKnYvTKZhWk9Z1AuW9ZKyLTthpdgNdmlMze8Xv6OxDb0uX/CmsKqWmlCGBNJHd97yV22b6pRQRoz9bbTULJsl03rqbPsK90XYurHP+Wb5FbWpSHW6zrV017Tp1hcikZ2Ad2t3L2oZvvTR1v0mfCzpDYHSBEozDj+c4Hsd7ywAA5kgvQPKdkkgU/rxXvfmuOLR6r8y9G+x5d9BSe+HD/aGKKuGL3//nKKoGQ1jnj6dMBolDIcx3zw8YTLJCALNydkCgeDf/PoexlpOThb84Y9HRJHPvbtjsE6XNq2hKhuapkUpJzYWi4Lf/MN3fP75Hne8LR4+OiGOA3a2e7x4MScvatrW8O2jE8ZbKffubVPV7Xr71w+PMcYyGv7SzRh9KzhPVlU11JUbAi2lpG0t02nGw0enPPz2hHt3x8Th9toDdx338cmcx0/OnbdvUfDku3MePNhF4Lx8bWtYLgtevJjz61/d4/PP9tbewlfRtC3Pn015cTzjfJIxHqfrdX376JTT0+UVmz96csaXXz6jNU5s93sh83mO9hRlN0rpVe7dnR7fPjqlNZaiqHlxMieKfKy15EVNUTZUdctvf/eM8VbKwf6Qp8+mrFYlnqf47uk5i0XBZw92Cd7a5m+Gi5BeKGP63ohUD9a5XJ702QkOGQf7bAf7aOGR1WecV8esmgXLZtZ5ngyxSrkT/4xpfcpJ/ZSsXbjZpsJj0UypTOlCctLlfr2aH/c6et4QQ8u0OiVrlmAtu8FtalNRtCuG3i8QvsTQrkNbgYwQQhKrlJ3ggMbWLJs5s/ocgbzE7VrQGogAACAASURBVMThAk/63Io/pTQlRbtCIJ33q5mzbOes2oXzCGIY6C1ELDgunjKtTylNgZYa/5rwFlwIPEUgXdL3WXVErHpsBbucFM+6UHCCJ3wiFWOsCw95MmAvvENQR5yLYxpbsWimTKpTIpUgEKR64B6uN3hXACQKX4ZEMlnnzVlrWXbe1de5R94OgYw4Lp4ya85c6F36rJr59dwYJGpt11dnHjtP0Yjb8adUpsSX4Tr0/bFdaz8lGGtZNEv+sPgD95NPGHgvX15eFQsXRQp5m1+xWyhDPKGZVlNCFbLlb6GFYuANuBXd5rQ8oTY1gQq60K9gUp1zWp5wXp1jaPmUB4z9bef9ajKsNWTtitIUSCEZB2POy3P+bvrfWdQLBt6AB+lnnFancE3UcOANGHiDK9yJSjkMb3FUHDGvZ5xVp6ReSqpTls2SsntReBW+dBGgrM0Y+VsMvMEPCikLGFx49g+L3/FJ8gAt3T0nXmuk3tqWZbO6wi0QRCpyTozyjN1wd8297W9TtiWn5QkAUglC5Qp4zqtzTspjl0JBw4Pkczzf++jyAP/U+FFPSykUiR4T6aELBWMomjmtrUnUFrEaInh/BrbGslqWKCXZHvc4O1tijGG5Kjk9W5LnFYe39inLhuWqYLEsEELQtgbP0/T7Edvjl2HYKPK6+ZWSrVFMEHhkWcVyVVBVDcZYsqxGSUnbGoQUFEXF+WRFXbdorfC0YjJZcXq2dP97ugRcOPet7SnlOsQZBB7DYUwUe4CladxIoij02Rql9AcRQrhw6nXccezTSwOOj+ecnS85O1/y2YNdqqrhyXfnhIELE9dNS9P9u+5ykEIQxz5aK/K84ux8SRT53LkN01nGclVcsfly6byUUkp8X5OmIWXVMJ3lTGfZFe4sqzh6MWNvt8/eXp/jkzlBoGlbyyrLWCwKJpMVJycLdBc+H/RDmqbl8ZMznh9NsdYNndf6/XswhBAI1PqtdVFPmOlThv6OsxESX7oKwAuh40J4Mypb0JjahXi6IoChN+akfMqynhGphECGaOnT90bM63MqUyIQaOHhCZ/KlCyaKfP6nNPyOVpqdoJbNKai7hL/yy6Pru2SnxPdY95MaEzNqlkw8naQXfGGEBItPPfQN65gI9UDalNe4nbH6VOZguf5t0xr98Nqu7DshdhobNMVloCvQhL6lObi7bzvCglu/KEVSBReZ7dJdUKkUkbeDrWpXBjU5O5TQuPJAC08jG04q16wbKaUbYYWmkT36HsjKlNgrPOOaOFearJ2waKZMq1OiVQMAfS9EbWtWDZz5s2E0/I5UijG/h6xTsnb1RVuT3gYDLP6jMbW9L0RnvAJVXItt0CQt6tOZJ/hl6Gzr/SxuNzBQMadB1dicN6Zj+1a03g/mWK+w+jQeZVUQKgCtND0vQGhDLGA1zk1JF0Bh0owWMb+NqlOu6IaSap7fJZ+TqACBt4QT/qkusft6DY93aOxDZ7wSHUPT7oQ6d34HtvBNol2Akx3YentYIdAhZSmZNvfRgnJtr9DIENWzZItb4tEp0Qq4iA8INEJCkWiU0IZEKgQiUAKeYVbC83t+Dbzes7A67Mb7LIX7NNfF3bWtLZhN9hjJ9wFXqYEWGsZ+2PG/vYP9v/1pMe9+B6VKQlVhCc9tNQMvSGJSlz6RacCA+lzEB5QmeoStxAChWLkjfhF/wsG3oChN0QgGHhDpJD0vT4AkYqIumrpVKd8mjzAYEh1SqTCj8bz98+JdxaAxjbUpsBXMRKJFgGGFqEFI24T6yGq+wF8n7AWwsBjPE4ZDRNaYyiKmryoscBomJDnFbbLjZNSoLVid6fH7l6f/f2Xb3NSBUSRR61kJ3JctZvWiqYxZKuSsqyJIicS0yTg7GzB0YsZw0FMrxeilPOwnZzMmc9zlsuCIPCw1oWsm7alblrKqqEsG6LQrHleh1KSOA6Io4Ao9Imjixw89waUJAG3bo04PByyNXJu4tk8v5Z7PHZJ4F9++YzTc5df5fuaum5ZLguaxq23l4Z4nnLVk50oq+uGsmio65Yw0AyGMZNp5pJrJyvSJMRaN5T9OptbrMsf7PIe9/cGXdg3u5b74jv0A832OGU0Sogin7YL+U8mK2aznCwrO6EKB/tDrIW/+81jVquS4TAhDDw878P1oJRCEaqIZTNjUp0y8Mbu2pcenr2c/2KtxdJ2uS8xqe6jpefyjLRee762/F1i1cOTPrvBLQSCSXWKlh6BCvFlyLKZUrQZlSmZNxOiOmXk7XZJ0qX7IRUvvbeJ6jH295nWp6yaxdq75EmfUMXuWlAhSmh0V8Ax8ndY1JNL3KGMGft7nFZHPMm+YtnMSboik4ssYE96zjsmWxTKiTnh09gGLTy2/cMul+8Gm+Le0i/Ekt95WoeeKyrzpM9J8QwtPAIZOU+sTlCN5qw6Im+WXTJ6RKxdYv5x8YzKFHjCJ+i6ECybhcsdbFdMqhNSPSTRPco2p2xdHtysPiPRPUbejiu6wF7h9mXQiW1DpBK2/F209Eh1n4Po7jXcilU7pzQ5WbtiWp3S00N6YoQvI1d5LMS6AtlVa4uP7lpT6qfT2/VOfNfljtN21dySoRzCawESLTW3otud/Y0rgHzld73n9fjL4V9d2sfdLxE7QSekus+7a9+n711NlQhUQM/rsde9WFwI8ZG/xdAbXeE+jG5xyK1r12atvZY70q+cEy+r9Uf+VvdyZ9ccrsNAA9YSKRe23fK3fvBZ70ufB+lnay/+hW0D/2p0IFQR95NPrnBfYBxsMw62L+3T83qkOmU/PLiyvlCFVz6/wY+qAi5Z1CccF3+ktgWJHq8vAGsNpVlS6RUD7xB9TfXfu0IIODmd81//+7c8fz5hZ7vH1ijh9uGQ45MF/+2/PWQyzfB8xaef7GKNZTrLQIhrL1BrLJPpir//zRM++2wPz1dsj1NevJjx6NEpz46mfPZgj3u4weNV3ZLnNYOeXXsXlyuXb9e25pKjfJVVHJ8sePZsipCCtmn54heHbI/T781TM9ayygq+/N0zHjzY5fBgeONnsxu4w8Ajinym8xxjLIcHA4JAo5XkZ5/t8eJkQZ5XaCXddq2YTDOOjmY8enwG1nkSvbtjZtOMxbIAC6tVSZa5/Kzt7R513V6x+XJVXnuug37E1ii+wj0axty5s8XZ2ZJHj8549nzKg0932d8fsFgUrDKXu/Hq+nxfo7UkW1XEsc/uTu+DeP9excjb4dfD/7kTJkHnAe8TqhhjW/QrFXAjf5dIJetctcbWDLzx+u/3ky+4FX2K3/UOU8Jjy98l1QNuReVa1EghSXSfu8nP2A4O0NJ57lyLDicy+t4Wxrbd+fS6Ks/7bAcHXVgPJ9wQfNH/HwCX7+TJoPu78wJu+/uXuJWQ63M6DO85z5bUpKpPogfsmTuYrhIWXEj6QhgKBInucRjdJ/yefqC+irgT/8y92QH70V3Xz0yFjMQOqe6zF9xBdmLQkz6H4X3G/h7ginOEgJ4eIpD4YbAuypBCEuseSmj63hZf9P8Nta27NhQxXpe0/iD9V10BhOdEngpu5Kaz5V+N/n3njQvXbVq88M4VbpfvNOSL3l9Tp13LLBWhhccXvV93Hk2fW9GDTgS46+pju9bku88M+BcL9RbRK/GW9nlbx8hFK57r9ntf3Def02VeY1tOyxNaa/hZ7+f0vf5brcfFU97Uttev+Yf22ODN8O4eQFoaW2BsQ9WuqNscgyFUKVvBPVpbs2om9Ly993m+gHv4DwcRnpZsjRLC0GN/b4Dve8xmGWHoE0Ueg36EMRapBEkS0O9dnqknENy+NSJNA+LIx9OKKPS5f2+b6dR5q+LYFVV4nuZ8MkFKwRc/P8AYV4Qyn+f00pC7t8cYaxmNElcRG7p+bEns8+knO9jub76vf9D1vLfbp2lbks4DKKWk1wuRStA0hjB4+Sq6tZU6b+Nr3Krzat6/tw3WuiKMyAcLn9zfIe2F5HmNpyWjodvP8xT7+wOkEgwHMXHsr6t+t4YJ3LPs7PQYbyUIAbvbPZSU19jchb+1cuc96EdrcRb4+gp3mobcuzvm7HxFGGbEsc/BwZBeGrK/P2AwcN/jcJAwHqcIcTFIXdAaw2iYcngw/CDFH68iUOG6KhDc9eNJH4+rLziBugjRdX3XXguf9a5903e5WBcCyv2/C8+lnZfpVVyEGQMZdmFD+crfnGfvdW5f3TxX0kp7hfvlOfUveQZ8ws7zYS9tvyh2cBXJKYn+/oeDEurSulJe2kUphW8DYtVbnxNArFMim1w6V+geLFYTyuRiw8vtQuFf42lwQtin540ub7+BWyDwhM+Wv3v5QELdyO3fwD3wX4q0RF9u9vqxXWs/NbxNePBtQ4nvEnq8Ufy9R+6b/nZlu3AvKtrThF2Y9UPZ609h258yhH3zEulLH8yaCbP6iLJdsqxPmVZPqUxO39/nF4P/xKR8SmVW3I7/ikC9n6qW5bLg//qb3zMYxPyH//Ezt4ALN6/omsoa55mT8s0vBGOsy4WSrugCnPfLWlDKPdyWy4L/829+Ty8N+Otf3eO3Xz6jqlsO9gfs7fZJ0/CduG/CRfsaIcSNbWNe/ezbcFtrLxwu68TbH7px2tbQNAbPk2sbvW9uc9GqRl5uGGyM876+ur1pWr57OuE//5c/8q//8g5f/OLwyn4b/OkxqU44rY5I1YCeHpBeIz422GCDDTb4oHijB+E7C8DGVFQm47R8SNkukF1idmtrGluR6DF9b5eBd3ApXPFjUJQ1v//9c+I44LMHF2/glwXgBd5GCFyIkgvP0sW2V49VVQ2Pn5wxm+fUletDOBzG3Ls7Jgi9dXHC23J/3zm96fHedt3XfedvwuHaB1wWWe+T+6ZjXbf9+fMp59MVZdlw63DE7k7vjc9hgw+Hi76DWri8RP0euwBssMEGG2zwRnijB+E7h4C19JFCEakBCo2SPloENLZkVh8RqyGJHn9P9d87cCrJwf4Q7alrH/Tv+vB3oub7j6W1ZH9/iO9rTk8XxHG8btL8IUTHx+Ymf134fQjuNw5B4LyESRywt+O8rxvh93HAl8GNLV822GCDDTb4ePDOHkBwnpnaZOTtnKyd0PN2CDtB+KoAfV8P53f18L1v7texER9/erz+fWy+gw022GCDDTYAPnQIuDU1lcl4UfyBsl3iq5jK5ISqx63oL99b2HeDDf6lYFoUnBYrTvOM1PPZT1Ii7dqbNLZlWVUoIRlHMUoIKtNyXuQoIYm0JtYeQghq03KaZ2AtieeTeO5eypuGqm1ouvFGfT8g0B5V2/D19Jxnyzn3+kP2kpSt0FXeruqK8zwj9nwi7bm2IEJQNA2nuTvXsm24lfYZBCG+Ule4LZZVXRN7Hp6U1MZ046tcWsA3s8kl7lEQURtDY8yVdQugaBtO8wxfKhLPI/F8KtNSti2J53XTLcw679OXiklZULYNodLEnkfQtSX5djbhRbbkXn/IMIjwlaJsGr5bzvjy7IR7/SEHaY/tKEF26z4vMjypiLVHqDWNNRRNQ9m2tMaAgGEQEiiNAM6LnKptiT2PSHtoKWlMS2MMVduyrCsi7TEMI9ob1q2EoGzbG7ldfzlJoBRl23atOa4WjLXGUFzzff9z2jzUbntW10xLdz2HWtP3g/W19rY2v4n79XvsIOmReB5l23KWZ90169aXNW7yU+L5GOtsYyxoKfFvGse0wQZ/HviwIeDW1hRmyaqZYGxD4m0zqZ5RtRkH0S/X+WIbbPDnjqptmZUFZ0XGi9WSr6Zn7MUpqe9Tm5aiaTgrMrAwCCK2oph5VTIrC07zjNoYIq253x/hK0nRNkyKjGXlZvb+bOT6Vz2cTSjahtYatJTsRCnDMAQLTxZTfnt24h68QUhrDdOiYFrmTIoCIVYM/JA7vQFZU7vzzTO+mZ2vBYyvFK01V7iFEDyaTzlMewRKc5ZnhFoTKk1r7RXuYQBZXTEp8yvrbqxhVVec5SvKtiXSms9H2yyqktM843avj7GW8yKn5wVObFnDWZ6xrN0Yq9u9AXtxihKC8yLn2/mErSAi9XwnAE3L0WrJ3x4/Q0lBPwgYRzHTsmRa5EyKHIMl0h53egPmZcmz1Zyice1sPCUpkx6x9lwvyqIgb2qEEBymPQZByKqumBQ5s7IAYCdO6Xfbp9ese1YWTMviCveyrjjOlhjjhMowDJmXJaHW3EqvVuIaaymb5qOyubGqO37G0WpB7PlshRE9P3DrfgubJ9rHYK9wj4KIZV1yXuSX7rGe7/ooZnXFSb7iRbZgGET8bLTNeZGTNzW30j7LqiJrKlIvIPV8bpzHucEGPyH8KAFYmRWJ3iJQCTvBA4pmTm0L19NL/FRGiG/wU8eyLvnb42fUbUtjDc+Wc3yl1g/Vb2cT/v7kiF+Od0l9N9f10WLK4/kMay3Plgtaa4g9n2EQUDQNdWt4OJvw+8kpg8BNIPjfH39NY1p8pRiFEftxxn7SYy9OKduWsmm6lieCujX89uyYaVmQeD4PZxMGQcAwCPlqes5xtiTUmuNsxbwqqE1L1bp/r3MHWvN3J8+xQKgUf3v8nM9GY+71h06QvMZtrOG8yPny/PjKusumIatrGmP58uyYZV2xn/Q4Wi35p7MXBEqxrCv+6fSYX+0eoKXk69k51ljmVcmj+ZT/cHiXnh+Q6KsFJhaLsYaybcmbBhAo4ZorfzM948liTur5PF8tqIwTQw9nU/6f508w1hB7HqMwYloWaCGZlQWJF1C2DQ/nE/7t3i0+G445K1b80+kxT5YzfjneYxy5tjSTG9b99eyc767hPs5W/MPpEUXTMo4i7vaHHGcrdqKEw7R/5RfUAs1HZvPWGJ4sZjxZzDjOluwnPUKlnXf4LW3uS03e1Fh7mfv+YMRvz45dc/1X7jEL5E1N3jQ0xvC3L54xCEMeDLd4Mp9yWmRE2uPb2YSTfMW/3jkgumYNG2zwU8SPKAIJSdSYvJkzr16QNRM3Ak6PusKPjfjb4KeBujUcZyv24pT7aY/zImfgu357xloCpTlIe9ztDdmNU6QQnOcZT5czEu1jMQRKYaxhVpac5RmnxYqTfMW0LKiNwZMSJQSe9ugHAXdSN//yIjSZej6pH7AbJ/T8YC0+BfDZcIuydQ/IsyLjRbYkb2p+vrVN3bY8Xy3RUrKoSxZldYV71w/55dYO87LgUeE8TJ/ZMT3PDWB/ndt+z7qnZcGz1ZzTPOMkz6jbFmOtEz+9Ac9XC06yjGlZoISkNYbH8ymR8jBYAqVAONFxXfKKRBBrj57vE3se21HMKHSzQ2dlSd7U/HK8g5KC0zxjUVbkTY2WEi0U4zDmMHUesWlZ8GK15Nd7fRKdsqorWmuZljlVa0h8n1tpn3v9IVsdx03rnt/AvawqjAVfSRZVyZdnJ4zCiMTzrv0FVUI4e39MNheC7TBmXpY8Xc45yVbE2uPT4dZb2/y8yDju9n+VO69rjldLDtP+pXvMWMtpnjEtc07zjNMiQ0uJBQ7SHgj44+SUZ8sFxrr7LNh4/zbYAODd27t7MiDWQ3wZYzAs6hM8Ga4rfzfh3w1+KnBeJ4sUAl8pIu3hvfKQiT2Pw6THQdpjK4yQQtBaS922rOoKIQSx56GFZFVXPF8t3IOyqqi6h7WSLk8w9X22gpiDtEekNMbaLrfP6/KaxKUzk0LgKUWoNZ6UNMa4nCsLWioCrQm1RgrBsrqeu+8H/GJrh6ypebqcY63FV4q4y7W6nvv6dc/KgqPVkrM8Y1VX1KbFWstWGK+9X0fZAikEgVJ4StEYQ97U1KYl9nwCpbFA1TbkTc2yqpiWBavajYMMlCbSLm9MCPGKaLEIunUrTdCFvAUQa03Pd6Hig6SHFs5Wbl6vwFcuR1MCjTGApecFHCY9DhMXFv6+dd/EbXG27PkBtTE8mk8IlWYUXD8+T0m5zkX8mGwee05w207EnRVZV6j1dja/yGN8nVsIgYEr99iFaHyRrTgvMufptO763k967EQJj+dTpmWOlvLKvbnBBj9l/KgBjwLJTvgpW8FdrHUzWqXQbz2aZoMN/iUjVB73+yOOsgW/n5zwdDnnF1s7/ByXuyegm/D6ErfTAXld8+18ihNqksTzmVclkyJbi483hbGWVV3xm9PntNbw+WjMrbTP0+Wc/+PRV5znOXtJyr/dv03VtjxezPi/v3vI89UCgC/GO+RNfS23lu7cGtNisNzu9em/Mtnide6fb9287mVdMSsL5016JU840h6pFzAtc1pj+GQwIvI8FIK/HO/zaDHpQoROSPhScVZkPFnM+Gp65gpSsAyCEE9JLJbGtPz+/ASAv949ZDdOWdU1/+XZY86LDF9p/mJ7n6ypr7Vpz/cZhSFPFlOmZcFZnrMd3WUnTniRLS86kF7Z77p138RtrOU4W5J6PrOyZFVXDIOQcXTz+LyPzeaJ73Oar5gWueOrKhZVif2edd9k86Efsh+nPO5sfsE9jmIeDEYcZctL99iD4RbzsmRZlq5h/CvXbaA0nlQsG1fAdJD28OTm2bTBBhf4ETmADa1xieISCUIyr19gsWwFd1EbEbjBTwSR1jwYjhiGIdMy5cFwi93YVWYmns8wiKja1iWfd9iNE7SUbMduSo4L6fnsk/JXOwfrB/yqrthPUjyp+Hf7d1DShThHYcQwCLsRYYr7gyFKCqLub1pI7vSGpL7PtCyp+y3DICT1fG73BsSex3mRc6c3QEvJQdLrzjG4wg2sPWmJ5/H5aJtR+NJD9Tq3FIKtKCLQ+sq6b6d9ws5z8+lwCykEPT9ECkHiefxq55DWGrajmJ4fIBF8PhozCkOypkYJyUGS4itF6gV8Md5hFEYMgpC9pIeSbmTWXpzyH2/fJ9aeq4QVziPkK8W0LCjbAYHSbIURnpR4UqGlIPVdnmTiebTG4inZeboq6tZwtz+k14nfizD/RYXq9637Ju5IawLtRM5h2ud+f8i9/nBdWXsTPiabe1LS8wNu9QZEnkdW1wzDECXEW9vcWAi7z7zKnXo+nw63GIbRpXtsHMbuZafXxxjL/cGIYRChXhV61rITpXw62CL4AbtusMFPCe/cBqZsl2TN9NIHjorfIYTiQfrv8eTNM0c32ODPERdtUeQNTbNfh7UW0/23uPgnhNve3ZfqLTwW1loXXhOu8OHinC7C0/K16SrtDdtf567blqJt+M9PH1GZlv905wGx511zvMvc33+eV7m/D87bdNlOb2KPi7VcfCfvwt12s63f9Hv9vvN5W+4fPt7HY/OLFjhaykvX7fvkvukeu+46L5qG7xYz/ubZI/5ye49f7xz86O/wzxmNaaltS21afOm8p5I/jb1q03bpFvIS5z9n799/4fiwbWBWzZSj/MtL286rx0RqwMvH2gYb/HTwpsLkVdz02H5XgfC6EBC4woHrP3v99te5F1XJw9mE7ShhEAQESt1QoPDmYvUm7ptwIQTeFtfZ8W2534dYe1fuHz7ex2Pzi3zTm4pX3gf3TffYddf50WrJtCz4q+09DtP+e/0e/xwxrTKOihmPVmd8kuxwNxkTaZcS8KExKVdUpmHgx4TKw+smiF3kV7/Ndb7Bm+OdBaAvI3re7qVtFosvIySbJNsNfnp4X2PxfsxIwx/Lcd12TykGQbhuDK2kvPK5Dzm68H3u86fi/pDHeZfj/SnWLYS4XrS9R+632R57mm1iki694sfaP28qirbGkwpfaTyhKE2NQOBLTWsNBjdXXkvpUima0gljqQikXldrt9ZQGdfYveeFSCGpTIMvtavE7gqEBKCFIm9rirbucjE1uvt8Yw3t/8/emzxLcl1pfr97fXaPeXpzzplAAgRBEixOXV3FapZ1qRcybUolWctk2mrR217qr5DJtK7etHWb1C2puqraRLG6RoIFkiCAxJRzvnmMOcLnQQv3F5mJfAnke0AmQTC+tFy8GPz48Xvj+ufnnvOdLIMsy4tcFH0mJv9J2/IpJCpJ83O5Ozlk1xugiFxxQBYR80ns4ycRuqJiSA1dfrbfkzivKNdlfr7HRO6TfmsyT01wk5AojamQp5akWUqUJux5Q3rhlLZZoaZZlLR8ZzFIIqZxmI+FVNDlk6Lpc3w2zkwAS1oTR20Uf+XJt/V4hYwMKeZ5FnPM8VVBWTdmeW9zzPGbgo5d+kKOk2YZcZYwCF36oYuhqNR0m6pmMQjzzidNw8FL8splRQi0TCFME/a9IVJIbFWnZeTn4ycRbhwyjQPcJOScbKAKSTeY0jBK6FLBjUMQoAqJKlL64ZRB5OIoBlXdpqwajCIPL4ny7i9pgq3qLNkaaZoyjYMnbOvy5O3cOEtw45Dboz164ZTvta5QNxwMqZBkGf3QpRtMsFWdhlGirtsMQw95gt9qphCmMXveEE0qlFSThp5rZJ7ktxRG3hUmiYizvAPOsZySF0fcGu9za7TH12trXC63cdT886PI58AfYSgaZc2kZZSQ2Xyb+LT43EwtySLiLCBMXFRpoEpjXgU8xxxzzDHHVwJuHHBncsAk8nHjkMNgzHmnySvVFW6P9vLImLLMzWFekX253GHHG9APXcIkphdOAPhu6zKKkBwFY26P9lGEZNWukwGDyOPGYItrlUUMqXJ7vM+yVaOiW2xN+yRZQkLGx/4uK1ada5UFbgy2ceOAqm6z7fapahZV3WLPG3EYjJ+w3TLKs4KlR5EBaSEZpQo50xxNishckMZMYp/3+ptcLnf4dvMCdycHKEI+4ffGtMsg9FCloB+6pFnGtxoXkEKc6LcQApkJ9v0h0zigrjs56ZUKCSlRES2URZ5rnKXcGe/TC6YEacyhP6amW3y3dRlHNWZbx3M8G85MANMsJkoDhtEuQTIFQCAwlDINYw2FuRj0HHPMMcccv9nwk4iNSRc/jSDLc+WaeokojVGlQpjErE+O2Jj2sFSNl6RCN5iw6fYwpEo/dJEI/CSvaj7eSq5oFnXDQZcqgYhxVJ1+OM3tTbvUdRsnnyaAVQAAIABJREFUM9h2e9R0h5ZZYs/NiVI/dDnwRkghOOc02cx6uEmIl0TsegO2vf4TtpMs4aRbviIkZrG9a6sGNd3GkBpxltALpvSCCb1gyvq0S023AYEmVfw4fMLv42jhlXKHceQzjDxGkfdUvyUCCmLnJzFRkpBkGVqxvawJBU0qVDQTS9VJspQdb8CRP0aTKoNwWvTZjvLcwXn62alwZgIYpxFeMmBr+i5+MqGqLzIIt7HUKlV9ASmsOf2bY4455pjjNxpRlnAYjIjSBEc1cFQDU9EQQnCx1GLXG3JjsM0wcllR6pRUAzcJOfTHVLVckqmkPuwMpEqFFbtOx6xwsdQGQBMK32ic51e9de5NDnGjEEUoGFJjHAcs23Uulzp4cZT36w7zoolFq8KrtZV8WzUOCZKYXjh9qu2ToEkFTVqUNAMpBItmFUvVGYYe+/6QAz+PKE6TgChLUKXkcqnN+rT7hN9RmrfDPO+0MBSNbbfPNA5QpXKi31mWC61bik6oJIXUSP5aSTUoayZl1aRllimrJl4S0g+mHAUTKpqJqWg4qjGrDp/jdDgzAcxISbOUstahoi3SNM4Xu/dnrdebY4455phjji8XLEXnSnmBXW/IKPJQhUSXCuWCWO15Q/a8AS2jTMsoIRAsWzWmcUAvmBZ58QJH1fOCjRM0sFUpcYQx69KyZNcoaQaGonLOaTKMPP7+8BZ73og1u8E5p8ldeQA8WXizZNXIyJ6wfdL276chyRKGoYcbhwV5LCSVEDiqiYAn/G6bZYI04q3uPXrBlDRLOec0kQgGkfuEjSCNGUUe226ffW9IlMa8Ul3mnNNElQopME1CPhhsE5Rjlqwa55wmAL1wWhSsSEqagSbn0b/T4swEMExdxtEBaRajSoM4CxFCFp1AvtqIk5QPd/fJMrjUbmBq6nNtLxQnKYfjKV4UIYSgU3ZwDP2zvzjHiQjjhGkYsjcc03c9ojihYpk0SzYLldKvRSw2TlOmQcjheMrheEqUJFi6RtPJz2k+3nO4YcjA9dkdjvHCnEU0SjadskOr5Dzx+S/jPP9NhKloXCjlEa1eMCn6F5fyFnUIqrrNsl1n2aqxZNXyNdqskGUZO0qulVvVLBzVIMlSqpqFLtVZRSvkNE4TeUWsJlUulJpUNRtTapx3mhwGYwbhlKpm0TAc6rrNmtPAVnVAUNcdbNXAUjSWrRq2oj1hW/2M/LiOWSFI4pnkiioUarqNrqjUUycXKy/8MxT1RL/bZt7TuRuMsVQNU2rUdJu8GWL2hN+QRwFruk2SpZiK+phcT123WXUaBRnMZvmDipBYno4qFRbMCqaizaVizoAzC0HveTe5N37z0UMhhKCmr3Cl/E+KYpCvJhWcBiH/y//9Y+I05V/9wfdZrJapmM+vSnLiB/yXm/fYGYxRFMEfXr/ChWb9udn7qqPvetw77PEXN27y1v1N+p7PaysL/O6VC/xXr16jWfr0NlzPA24Yce+wx1/dvMtffXSXgeex1qjxg8vn+aNXr3Kp1fjsg8zxlcZ6d8A7mzv82bsfsdEbgoDvXzrHj65f5veuXnzi81/Gef6biKxoeZdkaREJEyiPiq0X7x0LGEtEIfeSkmS5Jq5AokqZV7gWt1IBM2mWXBQ+48e7HzCNA/5w6RVKqoFEkpKRFrYFAimOizTS4m+RH3NWBftQcuWTtj+tSva4x7Ui5COi6ccSL4U4fFGgAZAU/j3dbzEr3gBmbfo+6XdWHCuXvcm/IwvukJKRpOmM/KlCPiYpc2xDLY43rwKe4fkKQVe0BS6Vv/cJixJdcVCE9pUlf8cI4jifmFn2CWr8xcPQVF5bWeRSu4EQgobzxS7ccZJwOJ7yztYel9sNri20vtDjf9lgaxrnGlX+xdeuca5R4x/urFM2jFP33/0ioSuSpVqZH167xEqtwj/cWSfNMqIknee2nIA0y4iThFv7R+wOJ3z7/Ap1x/rsL/4Go+FYfH11kYpp8NaDLW7tH6FISZKePD9e1DzPsow4SdnoD7ixvc+3z6+wWq9+Ycf/dSPXNywESsTjrwMoPNmJJU+Ekk++LsSJwsqT2GfPG9LQHZatGqbUkMjZ5yWCTDwuhP1oRE957NwEgifFkz+LHKnyyc+rQslJ2idsA7Pt15P8luIkwnmyLmdOCE+OTioIpBSz2fpwLE7v3xxP4swE0FZr2GrtizyXOZ4CTVG40Hp+Eb9pELHVH/GP9zYxVOUrTwANTaWtlWiXSzQcm53BqHj6/fVBVRSajk3TsTnXqHI0nrI3mvxaz+nLjDhJGXg+N/eOuH3Q5aXF1leeAJZNg7JpcLHVIE4zRl6Arj59W+9FzfM0yxj7AfcP+/z0zgYXmvWvFAE8xhcl9H4SkizXyVswK1R0C00qj33/aSLbX4TtZznWaW1/kVTspOPNyd4Xg3kSyBwcTCas9/r0pi5BnPy6T2eOOT4TQRyz0R2wMxwz8oOnRsHmeP5I0oyd4YjN/pCB5xMl8zXktChrJtcqi0gh837FX/EdtDm+HHjhBHDo+ewNx9w+6HIwmjANw7xKydDplEu8utxhtV6dNe0Oopjd4Zi90Zi+69Mu2Qw8n7uHPcI4oWaZXGzXubbQwlRV1rsDhr7PyAs4HE9IM3AMncvtBt2Jy93DHs2SxdVOi2+eW37s3Lb7QzZ6A+4f9Rn7AWGSIoVguVbmUrvBpVaD8iO5fpMg4kG3z682dhj5AWGcUDYNlmtlXl9domabs0Tr0/odJQnDIsJxc+8QL4qomCYr9QqvrSzSLj+Z9H0wmrA7GrM3nGDrGqamcmv/iKHnFwUrda60m1zpNAmThO3+iLc3drh72OVBt896d8j/+8FtHhz1AFislrnYqnO53aRsGqRpyu7w2MaYZskmTlJuHxzhhhGmqnKx3eBqp8m5Ru3UfvemHlv9Ie/v7HOl0+Q7F9YAECLfZvKimFv7R/z1zXv88NpFXl7qYKh54vTA9flo74CdwYje1CPL8i2zlXqFlxfa1B3rsQb1zxu/XN/mzXsbvL66RJKm3No/Ik5TbF1jsVLm+lL7sfE+C7qTKduDEbf3u/RdDy+KkEJSs00WK2VeW12g5djIwu87B13+8v1bvLzYomKa3Nw/YhoEgKBqm7y00OLlxTampqJISRjH/OX7t/CiiNdXl7i932V3NP7UeR4lCdMg4sPdfTZ7Q3pTFyEEVSufu1c7TRYqZQTw5r0NHhz1+f1rF2mXS4RJwt/dfsDI86nbFq+vLVEydI4mU+4d9ZECvrG2zM29Q97f3ufeUY/17oBJEPJv33qXum2iKgoXW3UutRpcaud5kxM/YHc4YW80xo9i2mWbrf6Irf6QMElYqJS43GpwdaFF1TJJ04yD8YSN3oDbB10mQUgUJ0gpaDo2a40qr60s4hg6cZJyMJ5w+6DLZm/IP3v5Eiu1KsdDGiUJfhTnfvk+/+yly9QdC1VKkjRlvTvgVvHbOC7yWi5+dy8vdpBSvLAetqfxG/IikzsHXd7Z3OX+UY8H3T5b/RH/1zsf8osHWwCcb9a42Gpwud1AV9U8r+yUfj/runb1N3jnQhESRZkXMczxYvHCCeA0CNkZjHhnc4f17oCxH+RVRapKxTTQFEndtnAMHUWInKgMRvxyfZu7hz2uL7UZ+wEf7h7ghRGdSokkS1mqlom1lJv7h9w/6rM/mjDxQyZBgCIl3724Rt/1+HD3AF1VGF0JeHVlAbXou+hFMR/tHeaL2WGPcRASJgkSuLbQQlMUlquVxwjg2Pf5eO+Ird6Aw4lLEMVoiuRco4aj61xbaNEuq2fyO80ypkHEem/AP97fZHswomqZvLq8wEqtciIB7E5dPtw54BcPtmmVbdplh/e39zmcTMky8KKIsmlwpdMkSTN6rsf723vcPeyxN5ow8QNuHxzRc/Ny/audJqaqslqvUsYgzWB/NOHdzV1+/mCblxdbqFJyY2efsedjGzphkhTbmLVT++2GIevdPn/27sf83tULfH1lEV1VUIQkyzL6U5d3t3b5d794j9V6lSudFrqqMHR97h32+NndDTb6Q4ZuQJZlVCyD1XoFQ1VRFUnNfnFbhB/s7PNv3nyb/+Ybr2CqKh/sHhBGCZoqaTo2UghKhkHNNp9oYv+sGPkB690Bv1jfYn80yStDhcDWNTrlEhXLwNY1SkY+Zzd6A/7Nz97mh9cucaFZ54Od/SJ6lmKoKiMvoGIarNQq2IZOlKT8+KM7HIwmxEnKR3uHbPVHnzrPh17Ag6M+b93f5O5hj4HrI4WgbBos1SoAmKpGxTK4sbXH399Z53K7Sckw8OOYv711n83+kOVqmeXi8/cOe/z8wRaGpnJtoc1Wf8R723s86A4YuB5JmvLO1i6WpqKrKkmaUrPMGQGchhHr3T6/WN+m73pcX2qz0Rtw97CHF0ZcajdQhGCtUaNq5cnqA9fj7mGPn93bYOwHBHGCAEqGzpVOi3bZYaVWRYr8Ieft9R3+9vZ9LrUbtEsOhpZfDz+K2RuO+dvb9+lOXL59fpWKZZJlKUeTKR/s7POz+5t0Jy5eFCOATsVh4Ho0HJuabWHr2ueYic+O0/htqApxmrI/mvDe9h7r3T6H4ynTMOLDnQO2+kMAvCimZBizorUoOb3fz7qu/SYTwDnm+HXghRNAS9NYrObJ7oqUNEsWbhhxY2uf//Pt93l/Z5+GY/Pq8sJjEZt+sTDVbJPX15b4r79+fVblJIWg5ThMwxCgkNFI+R+//03e2dzl724/4KO9Q751bpk/vH6FP33zbbb6I3pTl6plMg1CPtw95G9u3ac38fjD61dYa1QpmQZuGKJIia3rOMbjC/HQC/hwZ58fXb/My4ttVKnwNzfvcXP/kJ/e3UBV5IyondZvTVFYrJb4o1eu8vWVRX7y8R0OxtPPvL5+FLPe7ePHMe1yiX/5ndcpGblQphSCmp2X4JuqwvXFNkuVEr/c2OHdzV1uH3T5g5cv8ftFRaGpqdi69oQEydgPuHtwRNnU+cbaEv/qh99DKZqAK1I8JklxGr/b5RIXWg1URdJ3PTZ6Q1brFRwj18+6d9ijN3F5aaHFQsXB0nPJofe293jr/hbdqct3L67xzbVlkjTl7Y0d3ry3wZv3NoiShB9cPn/K2Xp2ZFkeIbl32ON7l87xr//5PyWME27uH/EXN27yq40dFCn5/uU1bP1sEi+OrnOhVadsGjiGTtnQccOIv7+zzt/dfsCN7T0cXeOV5YX8nIoTu3vYpWqZ/A/f/QYVy6A/9fjL92+y0evzd3fW+aNXr2IXY55lGYeTKT+9u8EfvHyJP/n2a586zz/eO+DPb9xEUyTfPr/Kdy6uEsUJdw57/PijO7x1f4swTvjB5XNULJOGYzHwPPquN0v2douHBj+K8MKIncEYU9PoVEqUDJ0fvnSJK50mP3+wxUe7h/hRxJ98+zVW61WEEDgnzFmAg/GE7f6IhmPzOxfW+G/f+DpJmiIEGKpC2cy/I4CyafLyYptO2aHh2BiqghtG/MWNm9zcP+TdrT0UKTnfqHOhWadddoiSlL3BmMOmy2o9J69Dz+fdrV1UKbnYqlO1THRVoTdx+auP73L/qI8mFf7733mdTtnBjyL+8oPbfLh7CELwTy6fnxHZ543T+H2xWcfSVH7nwgoXmjV+/mCL93f2uXfU57/79mu8trIIMFs/jiPEI88/k9/Puq7NMcccz44XTwB1jYVKaba4VEyDIE6Y+CENx8YNIw4nU5JPJCtHSUKcJFxqNXh1aYFL7TpkEGcpUZxgatqMAGqqgqlpnG/WOBxPqTsWZdNgoVLiUrtBydBJkpSRF+DoOv2pxy/Xt4mTlAutGq8sd1iuVbB1jSCOi8Rpgf4JIc2SoXOl3eBqp8W1hTwattkbcDiZsjsc0Z96Z/ZbFlEyo5RHrxqOzdgPPvP6plmGG0V5xeDKIlcXmpQMnSzLCOMEReaVZaLYhnUMna3+iHrxxL1QLn3mDSdOUyZByHK1wtdWFrnSaaIqeTVilCToj2ginsZvW9eoWSYXmnXSLOPm3iENJ48OpmnGvaM+Q8/nG6vLNEsOUgiyLI8Q3Tns8sa5ZV5Z6nCl0yTLsjxC1huw2RvScuwXSgAhlzpYqJS41Gpwud0spA7g5t4hA8/n1v4Rb5xfwT6jxF/J0FFkhXYp14W0dI0witnoDSmZOgPXp+/6T3yv6dhcbNW5utCkapp0py4XWw3Wu4P8Jhs9rlTr6DoXmjUut5tPnedpmuHHMZu9IR/tHPAvXnuJb5xb4kqnSZykKIpksz9kdzjixvYeb5xfoWqZNBx7pm1n6iolQ6dk6gRRTBAnjPyAnWEe/e6UHTRFydMRspRWyaZiGaiKZK1R5eJnSOUEcYIiBdeX2lxfarNSq5Bl+XxO0nT2+xZCULEMDE1hsVqmahpoqkIQJ7yztcfeaMLheMrYC5Di4Rxfq1fZH0/Y7g8fI4Dvbe1h63oeUS8ig+Mg5FcbuxiayrfPL/PSYotmySaIYm4ddPlw94Bfrm/z8mL7xRHAU/gtRC6FUikI7YOuQ9UysTWNpWrlxHNOs+zMfj/LujbHHHOcDi/8V6MpEkfXMTWVJM3oTj0mQYgQgmbJJstgEoQntK0RMzmUq50mmqKgqQqWps0WoWNUTINWycbUVEqmTqfssFIr03AsdEXBMXRUReKGEXGaMvR8bmztUTENXl9dyrc8TWMW+auYJhUzv9E8imbJ5rsX11ipVbA0DU1RWKiUWKyWmQQhbvjwRnp2v8+Gc40ab5xfoWaZaIqCrqqUTAPrC9pOEkJwbaHFa8sLMyFsU1Mpm8Zs+wtO57cQAtvQeHW5jSolH+7uMwkC0jQjTBLWuwPcMOKb55ZpOvl34yRhZzBmdzhmqVbB0jW6U5ee66EqkqVqmaPxlN3hi6+o1RTJtU6LS+0GejFXO2WH19eWiNOUzf6AOD17wryu5hFaQ1MJ4pj+1GMaRmjFA0OYJLjFQ9GjuNiq87WVBaqmiVFEec83a9i6lgsGJ48/fD3LPE+ylLEf5ELWkylXO01eXmyjKQqWrrFULfO9S2sEccLt/S5hnFC1TJoFAdzqD9kfTqjbJu1yCV0tfHJddoZjLF2jVbJR5OfLh6taJr9zYZVzjVrxu1CwdY3yJ37fpqZia1oeAYsiBq6PF0ZYxRx3wxA/jnP9MyFYqDi8utzmsMihO851G7g+H+0eUjENri200BWFNM2YBiF3DrtEScK5Zo0wSehNPUZ+QM22MFSVW3t5ntuLxLP6fRZ8EX4/73Vtjjl+m/DCI4AjL2BrMOTt9R22+kOmYYhAMPB87h/1eGWpc6Ku3nH0SFMln5UypUiJqsiZdpMUAk1R8qdEcaySlItrkuV5KQPPR1UkFctAPuNNRlcUao71GPmUQiBFvtg9SubO6vdpoUhJ2TSwNJXPea98KjRFoWIZ6KrymWNxWr9tXePV5QV2BmPe29pj7AdMw5D90YS+62HpKi8vtalaJnGaMg5CgjhmdzDmT998G8fQ0YpogBfFTIOQaRDy0mL7+VyMT4EQYBY302NoiqRsGCRpSpQkfB6+35u63O/2eXt9m6NHcqn2xxMORnnC/EmHt3WdsvFwnh+TGMgrOj95Us8yz9MsYxIEhEmCoar5Q8EjrZlURaFqmmRZVjx4JVQsg2bJZrM3xIsiarZJxTJZq1dRpWAShIy8gJ3BiB9cPke7iPqeFVbxYHIsivs0ZFnGwXjKrb0j3t7YZhKEs1y4zf6AIE6o2uZj83ahUuZrK4v8h7c/YKM3IEkzhp7P0WTK0PNplmzON2uoisQv5mWcpPz8wRY7wxG6oswi2pMgYBJEuFH0Qqubz+L3aRDEZ/f7Raxrc8zx24YXRgCz4iZx97DL39x+QBQnGJo6U6PXRwq7w9FTQ/lSStRisfisqslj0vfJv2ev5WLp5BrOGUKAIvNFKL8BPptPUgp09fHWNY8STAoF9c/j92khBDOy+7y0kqQQ6IqCIp8+Fmf1W1cUVutVqpaJH0ccjV0UIdkajNBVhYVyiUZR0ZukKYrMZRMMTWGlVqFuP05UBAJVSl5bXXwu1+LTkGW5wn36yM0szcjzzopuAmc7bn5t39/Z5+2NHaIkoWToszy8JEsZFsUXJ0FV5BOtC4/H8ViZ/1E8yzwX5MRKFgVMSUEMj6lWlmVERbRTyvzBrFq0Jftw94BgmhDEMa8sd3AMHSEEEz9kbzRm5PmYqkrFMh/XRjulVIY6S394uo5YmmUEccJb97e4c9AlSuLHqpwnQUBv6hW+P0TVMvOooioZ+8FMuaA39ViolGiVbMqmMVuH8t9O/r3zjXpRef3wnKSQaFLOtpKfN87q90N8dg/4z+P3i1jX5pjjtw0vjgCS3/je39nnP/7qA/74W1/jhy9d5PXVJTLIoz1eQOnX0PNUVxTqtkWYJPRdjzg9brvz8NzzNjt84gZ0/P/pC9KX2e9jHOcEZll+Izip88RpF92z+q0qCg0nr/SrmCZ7ozHdqctWf0Sr5HCx3ZiRPqSkZOiYusZytcKfvPEa15c7NB6p9s3Iycev46aRFQK50zCcXdMwTmYFD5amnSmilWYZcZryj/c3+endDf74W1/j+5fPcbXTJMvgr2/d48+ij2f5Zp9ErtD/7PaeZZ5LKSkV2/9xkuZbhVGEI/PxDeKY7sTNK4KNvPq7ZOg0HZtJENKbumRpSt22MDWNNM3oTV0ORhPCOI8qPl7YIR4nrRlPzNuzjHkuNxTxVx/fZej5/PEbX+Ob55ZZreU9Tv/D2x/w1v2tWZT5GLau0S47tEsOQZxwa/+Qjd6Q3tTjtZVFOuXS7GFHV5VZYcTXVxf5n77/LTqV0ixCebzeZGQvTALmrH7nEIV2XbF+cPIa8mX0e445fpvx4nIAi+hanOQJ1xXLoFzIUxyNpzw46nPr4IjBC855AajZFm+cX6bverx1f4sH3VwHMC2053pTj/3xhPAsAqdfYr+PYWkquiLpuy4jz8ePYo473CVn3aP8nH4vVctcX+pwMJ5wY3uPm/uHLFZLXG43ZjREkEeTLjRrLNUqvHlvg492DvCieDZ23cmU7f6I7sQ9mx+fA2GS8v72fl6pGsdMw4idwYg3722iSsmldgPtDNpfGXknjGPB3bqTF/DEacreaMy9wx53D7uP5aA+bxwTu5VqmfPNGh/sHPDO5i5RkuKGEZu9IX998x66qvLKUgddUbEKiZo4SQvJppCSobNULbNSr3AwnnI0cVmslp/I8VKkoGTqJGlKdzJlEgSz63G8JX0WHJPrKElQpchziVUVP4rZ7o+4d9Rjoz84cS3QFMlLC21qlsmN7T1ubO8x9n2+eW7pMdmmXAJI52srCwRxzE8+vsvucEQY5ykBEz9gfzxho9A4fBH4PH4LkRNgIeBwkheJhPGTY/Fl9HuOOX6b8eJyAEW+ADQca1ad+4v1be53+/hRzNZgmBcQfEpro+eFmm3yrXMrjLx8y+nnxTaIqWnESYqpqdQdC0fXZlsiz4wz+u1H+U2z7+Z5RLf3j9gfT5BC8osi58vSNM63ajQ/Z2/gumOxUC1j6xr3j/r8fx/dwdRUGo5Np5JHNUztlEnWn3O8FytlXl5s89O766x3B4RxQrvksFQtPzRRRB2uLbQKkjHgRqFtJ4UgTlPiJKVqm6zWH2onDlyf9V6faRCy2Ruy2R+SpClBkvDTuxsslEtULJNzjeqJciLPjrx4Zas/5G9vPSCIY7b6I0aezzfPLfPKUnu2Xe0VWnUDz6c7cbl31Kc39Rh6Pj+/v8XBaIpj6KzVq1QsA0UKFsol2iWH9e6AKEmomEZOeqcuFct8orfn84QUAl1VuNhq8P1L5xh4Pr/a3J3pDO6PJvQ9n+uLbb6+toiuKUXhkIKmSBSZ9/w0VJWaZRJUSrPo6blG7QktPENVWKqWaToW690BP3+wzVZ/lGsgVkp0ys5jckTPCoFAk5KVeoX94YSPdg85HLuYmoofRUyC8LH8yUehSoWrC01Gvs+b9zYZej6lhRZXOk2q1kOZElHoIn7v4louIN0f8Nb9LW5aR0ghCOIYTVGoWSaOqc+0R7f6Q/aGY9wo4oOdfXaHYwaeh61rKI+Iq6818jZsp5nnhqqe2W9FiDz6WXYwVIUPdvYJ4hhDU+mUHTrlEq2Sg6rIM/k9xxxzPB+8MAIoyLcAri20+d0rF/i72w/48xs3kQgutOqs1iv84PK5L6wK9jSo2xZvnFshTlJ+eneD//z+LfbHU7wowlBVvnVumR9eu8i5epXKKbWEz+r30PP58Ud3eHdzN+9u4vmzp++be4d0yg7L1Qr/8ruvf24CuFTNydbldpNfbe7wn258jKYofOfCKj+6fpnfubB6agL4ecd7oVIiShL+03sfsz+a0CzlfXLrJ4g5v7aySMOx+fGHt3l7Y4f/4xc3mIYRuqrQLjn86Pplao/cgLf6Q/79z2/woJsLhg+8vHOIpkh+fm+TlXqVV5Y7/PEbX/tcBFCRkrV6lb7r8b/+5KdMgpCKZfDq8gLfPLfE62tLM8mcgevx5zdu8v72PjvDUd5Sq5C3eG9rj8VKmQutOn/8rVd5fW2p2EZbou/6/OcPbjH2A0xV5UKrzkotv7Zx8uL7G7+02KZVsvl/3v2Ytx5s8u9/8R6qlCxWy3xjbYnvXFzltZXFGTlVpKRqGkxti4ppoioy304tOYRxTJpmrNarTxBAS9O40m5wf6HFzf0j/u1b7+bdciyTP3rlKr979fyZCKAiBY5h8N2La/ztrfv86ZtvA1A1TS60aixVy3zz3BLxCcUZqiK53G5y96DHrb2jmTTPUrX8xINjxTT5g5cvUzYN/svNe/zHX32Qi3kXHX9eWerwo+uXWWs87Lf+y/Vtfvzhbbb6I7pTl2lRTX9ja5+ffHiXC606v3/t4owAnmaen2/Uzuy3ImXxkNfmfGOPH390h3/3ixtoqsKPXr7ED68g0DJvAAAd/UlEQVRdomrlY3sWv+eYY47nA3FSrsZTkK1vdNna7nFurYnj6ICg5BioWi5tMBi4RFGMZelYVi61EgQxURyTJClSSrwkZhAEbBz2GU494jiP0NRKFs2Kg+eHGIrK5aUmihD4UUxv4nI0dfHimFeXOyd2dBgOXYZTn2EQkMgMy9BYq1dZ3+/zYK9Hs+qw0qzSrjnc2NwnThKWymVkmmHqOvWazW5/zM3NA9585x6GrdFql2nVS/jjkHgc8u1XztOs2cRJxq3uEaqmcH2pQ+BGTEYevb6LNCWZLpjEETXdoGXaWJZOkCUMAp8H+z384hqZqoqpqliqiqIqlC2d5VoFTVHyiFBvQL+QTYmSZEaWjiVXbE3jYrs+u9ENXZ+j6ZSdwZjVeuUzddFmA1vkqj3oDuhNXdwwysW1S3YeZSk5mJpKmmX0px6Hkyk7gxHXlzqPReROwsD12BtNHnaqAEqmjqPreZVqBpauzvw+RpLmW4cf7Bzk5Ka41ifd1NM01wjL28C5jLyAOM3b+JmaymK1PBO2hVxU/P5Rj0kQ4ofxjFjn1eK59E/NNjnfqFE6YxTiT3/6Nv/bX/+M//n3vsNLi238KM41ElWFmm2xVq/SdKxZ/qUbRtw/6uUt3cKIKHmYh3ospVIydM43azM/DsdT9kcT9kdjoiTNt0UNY9YuK80yqpY566ixOxzz9sY2l9tN1upVLD3PQQzjhO50yuHYxQujh1XWScIvN3bIsozrSx1sXZuNUX/qcjRxOZxMWalVOF90ekjSlCBO2OwNOJq4jPy8GMXSNBqOzXKtTM22ijxEkcuCHHRxi4etK+283WKcpryzuYsbRvnDTq3yWBTteN3aGY7Z6g8Zej5JmuYde2qVxyKAXhQxmHrsDscg4JWlhafmRx7Pp73RuLi2uXyQpiiUzHzO6krefrBVch7b2s2K4ped4YiPdg9QpUKn4vDKUueJYqfjQp7uxM3bXE49vCie+VCzTRarZZqOPXsI2egN2B2MmYYhYZwU+qR5cYuuKpQMnYVKvgUPp5/nn8dvgO7E5UG3z9ALCOIYKQRL1TKL1RJNJ48AnsXvs65rc8zxW4xnSqI9FQH81XsbvPf+Jq+8tIxlagRBzIULLcplkzhO2d8fMhx5JEnG2mqdcslke3dAXOSDhGFMo+7QblfY2xvi+SFSCoIgxrEN1lbrHB5NCMKYhU4F1w3x/BDHNrAtHdN8MgoVBBGTSUB/MMX1QrIMFheq1Os2SZLS7U7o9qYkSUq7VWZluc5k6jMaeQwGLhlQLpmsrjTwg4jNrR7/8OZtVpZrvHRtiZWlGh/e3D3Rb8vSmU4DBoMp+wcjHmx0uXyxw8ULLUolg8HAfcz20mKVnd0BUko67TL9gUucJDP/dP2Fq/LM8Zzwpz99m//9b/6Rf/1H/5Tfu3rxxNZ9c8wxxxxzzPEc8EwE8NSMI0lSev0JUzdkb2+IbetoqkIYxYRRwlF3wocf7fC7P7jK2mqDG+9voWkKjm2wvnnEhfMtbNvgo5s7eH5Ep1VmfbNLpWzSbJbY2u4xnviYhsr2Tp9eb8rLLy9hGief6nDk8dHHO7kO2SRge3fAt14/j2GoJElKEMZMJgEffLTN1SsLLC1WGQ497tw74ONbu1y51MEuWjFkaUYcJ/hBRJpmxTaVeKrftq1z684+uqYynvjs7Q9ZWqiSphmeFz1hu90us7HZRUqJZWncvrNHmma8/NLSieR2jq8GnhRWmWOOOeaYY45fL05HALNcpmNpsZZH59yQDBhPfAZDl/7ApdebMBi5hGGct+kJY2o1m5WlOuOJhyIlk4mP50cYusraapOpm7c4m059ajUbVVV4sHHEwcGYNM3QNRXtKVs2rheyvTvAMDSyLEPXcnFi348YDN0iCjdmMHTx/byyLE0zDF2l066wvFSj1SjlCeiGimMbWKZOtWpTrzuoqvxUvw8ORlx/aZl6zaY/mGJZOlEUMxy5DIfeY7ZVRbK8VKM/cPn41i57ByOqZQu72C6fY4455phjjjnmeBE4dQRQUSTtVhk/iDnqjhECptOAg8MR44nPeOIThnlOB4U2lyIlhqFiGBpSESRJSpbmWk+6rmLoGkmakiQZrUYJXVP55TsPcN2QasXGMFQ07eRq0bwdWAp+lGurWTqaruQRu96E/sBlOPYIwogkeRiJMU2NTrtMp12hWi3EiXUV09QwdLUQhn74+ZP8juM8T0sqAk1XME0NVZVEcUK3O2E48h6zLaVkcbGGH0R8fGuXJMlo1h0MU0P9NVQ/z/H80CrZvLrcoeFYT4guzzHH80KapaRZyjTxSLMUUzHQhIYiJIVCXy7UnSUoQqKK/BYQZwlxFqMIiSIUJJKUlCyj+O5xJDsr9CAFcRYDGRKJFMcdPTLcxCPNMizFQAqJFF+Nh9s0S0lJISuKq4REQSJEntvopy5ZlqJLCynkTDczyWKSLEEW11bMrm062x2QQiJRyMjH7/hq54LxaqHTmhbX/NHPFx2tKDr6IMlI83uXgCDxiLIQXZpoQkcRanHs/F+SJblYvsh3oFIS4jQuBMuVR84pQxH5OnY8Dyg6bSVZTEaKmM2DfLyPbRvSQhHq7PUwDQgSD0MxUYWGKNToTradkWQxXjLN+YLMvyOFkufcfsL2o+L0+XglSJTC708TgE+Js6hoGvFwLDKy2Xkc2zlGrneZkmQJqlSRKLPxTklnvwuBeGy8BQIhJJJc1Pwk2y9Kt/bMSWePnl4YxgwG7owQPfohIWB7t0+3N2b/YMS1KwvUajaapnDUm/DzX97j4GBEs1miWrWwTB3Pj/C8CMvU6bTLaJ9Cjsolk5euLLK902c88dE0BUPPCeN4HOB5IdkJ3T2EeJqwbUaapmxt95FS8uory0/127ENLpxrsbnVo9ubsH8wol5zqFYsRiPvCdtCgGnkP4TpNKDTqdJolOaip19B/NOrF/j62hJ128Q6rYTOHHOcEUmW4CY+7w9v4ichF5wV2kaTkmoTpTFxFhOmIeN4iqPY1PUqKSmTeEo/HOCodv5fsfGSgDRLKak2SUE+sixDkyog6IdDMnKiZys2UqhkZDyYbhGmERedNWzVQv+KEMA4iwhSjyTNi9kUqWFJB7Ug1zvefeIsZNm8hKnYqEIjJWESD3GTCZbiYCkOujQJC3KUZAkZGbo0cJQKYRoQpUFBIjIUoVJSKyioJFnCMOohhMCUNoZikWUpfpJrnCpCxZAmURaQpDEIwaZ7h164z5J1nqa+SFmtk2T5PIizCDeeoEqVqtYCctI2iLro0pidb35OIZZSygMfxTxQhIIqNKbxaEYyTcVGF3nh3GGwQz86YNm6RFmtoou8iGsYHbHp3mHZukhNa2FIi4z0RNtxFjOO+tydvo8uDRbN89S1NoaSF4J+0rYqNJIsIsliwjSYXfeyWv9UIfsoDRlFXVSpoUsLS7GJ0pA4CzEVhzRLidIAVeoFyUxJspgg8XGTCRWtjq2UEQi8ZIKXTjGlg6lYqEInTH2iNCDOIhShokkDSzpPtS14MUGDUxHApcUqqioplUwsM+Xa1UUaNYckzZAybwEVBDGXL3Y4t9oEcu5Tq1osL9VZWqzRaVfQNRUpBY5jcOF8i4VOpTimjqI8VPdvNBzWVhufWhzhOAbnzzWpVCx8P0JRBO1WGdvSuXSxTRBGxHHK+XMtlharSCmoVi10XSWKEhz7cZmPcsnk9dfWQAjKZRNNVZ7qt1Qktq0zHLp02hUunm+zulKnWrVACKIofsx2DjG7LgvtCosL1Xlro68gKlbe13aOOV4kckHnmFE0wU8CkiKaFGcJo2jCYXDEKJ5QUm0MaZCREaYRQRrgJj57/iFVrcIrlasc+l3CNGTVXqIfDnETj6ZeZxwnTGMXN/HwEp8kS7jknKeuV1GEJEhDgjQkIflK5b8OwiO64S6GtIiynBStWlcoazm5iNKAMAtmfqekebQr9ZnGQ/b9DdrGCgvmKnv+JmHmo0mDcdTHUSqcd17iMNhhGg8xFItpPEYiOe+8RJQGjOMBcRoRZgFplrBkXiAl4dDfJskSdGlQ0Zv48ZSMjKrWwEsmjOM+7XQ5r8AmxU0mDKIjBuERluJQ1ZrF+YeEqU+Y+vTCPRShctF5hXHUZxh1WTDXCNOAUdyjqjYRQuInU/zExU9dgtRjybxAQ19EFSpRFhIkXh6leyQwFKcxfuISZ9FsfsRZdKJtgSDKQibRAEspkRZRNDeeMIyOnrBdURv46ZR+eIibjHPCLT5dyWEQHjGOB/hJfhxVaKzalxlFfSbxgFXrClEa0I8OqGpNFKESpSG9cJ8oDTAVh5JaIUx9+uEBQeoTpyGH6Q51vU1Nb7HnbRBnEZrUceMxtlJm1b7CJBowiUdP2LYUZxa1fJ44lYXFhSqLC9XZ37XaQ/25hU6FNH0YihZCcHg0xjA0lhZrvPbq6uw9P4jQDZVq1ebV6yuz1wHCgrDZtkG7VWZxoXqi+OgxLFPDMqt02kXvSHHc6kpw9coCaZq3JZKP9K2tVW1q1ZOPVy5bvP71cyRpOtu+/jS/qTszcnfsN0CrWX7C9nFOZEZGuWyyuFCh1Sydqi3XHHPMMcdTUbSrTLMEARhSRxEKcRozjidsewcMoyFXSheRReTKS3zc2MdPAu5NNmgada5XrjCOJ4yjKSXVYcvbYxq7VNQy/WjIjneAIiSjaMw4ntLU65RVB0X59bW0fN4YRV12vAd0zFW8eMwgOqKmtbGVEqp80u80S/CTKUHiMo3HrLs3kUKhaSxyEGyRZBF1vcNRsIOvTlmyLnAU7DCK+7SNFXrhPmmW0DFXGUU9DoMdalqTSTxiGHcpqVWSLGYv2CBOI3Rp4KVTwjRAFwZlrZYTpixDLbZ/syzFS6Z0g332/HXW7CuPbM36eIlLmAbs+5skWcKqdQUvdRlER1hKiUk85DDYwSjl0ccd7wFSSPzUpRfuYyllKmoD5ZRpL0Fysm1dGrNrKRBowkAimSQDtty7T9jWpcU0HnEU7DCOB6zZVxHi0/tH98J9uuE+UkiG4VE+Rvoi/fCAo2CXjrGKl0w4DHZQhYYuTbxkwr6/SUbGmn0FgSRIPba9+8V2u8ZBsD2L7u75GwA0jUUOgx0cpULHWOMo2GUQdZ+wrQsTRfmSEcDPwicvsmlqXDjXpNV4nOSoimB1qY5WFGw8iqPuhNHY46Wriyx0Kp9K/h63/fTXzxJhk0I8c8PUpx3/k7aTJGV3b0gSp7z+tTXqdeeZ/Ztjjjnm+CxoQqVUbOMmWULLqGMqBkGSF8DV9AoVzWHNXqKilXJtz3BIN+gziEZME49Kmmt7LpodNNln3d3mKOyjSw1TMQiDiG7Yw1ZyHcuy6iBFntf2VUZGvu3Z1pfxVbfIv0sIUv9EAhhnIYOoyzjqMYp7BIlLUuTwZWTYaqWILuVjE6U+SRZjSIsV6yISyTQekmYJXpITu4a+QEmtIgM5i5opqKhSI84idr37NPQFKnq+JXm8lVrVm1iqM4vE2UqJBfMcC+a5WQRwmozoRwdM4xFuMs4JIxk1rQVZRj86ZBh1CVMfVegEmUcvPMBSbQQSR6mgCvVM8+BptjVpYCslDMXGUSvU9AaK0AgS/0TbWZaQASW1hq1WWDDWcNRP16qdxiNGURdTsVGkiiltjvMk825Tx7t2j0azBVW9hSFNOsYaumIyinq48Ziq1aBtLJOQoAmNSTwiziIqaoMl8wJRGhS5smOGUfcptl/Mb+m5EkBDV1lZrmNZ+mPvSSnpdCoo8klmbpoa9ZqTy6x84nunsf1Zr5/1eJ/HthAC29YxDPXU/p0WWeYX/2OEMJCyVLwekKYThNARQgeMPGKQTomTTaSoIGWjeF/mP6hsUiSvaghhkKZD4vgeirKIlBVAIoRBPp0SICHLYrLML45zbNsjzcZIYRafN4EoP16ygRRlpGwiZRVx/GP+hG3xlLB4/tkQiIF82+uhjexUtiElyyKEKL5LhCDPeyo6JJNlEVnmIYSDEFZhwy1eMxAzO5CkfZJ4G0XpFMd/fuM+x283pJCo5HnGbuJzEHRp641ZlEeXKhIdSzHRpIafBIyjKaNoQpCExGk8u9FVtBLTxGU/OIIsw9EqaFLFVixKqkOURmRZhiF1NJGrMIzjKYNwxDRxKak2y+YCqq7MEt5/s5En7I/iHn7iEaQ+Qkggv5mP4wFeMsWWJVrGMkJIJvEAN5kQpfm27YxEZBmq0LBUB0Oaec5fEa1TUDCVPH8sSNyCCOkY0mQU9wkSjyD1UFDziJhQ0KVOmqSMogEdY42yWkeTOlIopKQMoyM0oWMped6ZIlVMbAxpoRVRNj9xmcajfNs2jZEy981SSkRqyLZ/nyD1sNQSmtSBjLJWI85CkixGEWpR1CHwkymTeMgw6tINdpHIfOs0C5nGQ0ZRj16wl0fUDAMvmZxoOy+cUZFCIUg9+uEhVa2JJvUTbatSQySgSh0pJIZinUjOH4WpOBjSnhHx/Dg6pmKjCZ2DYItJPGQc9YmNFXTy1B5NGJjSxlQspMgLTWy1TJgG9MID3HhMWaujSxNZRAj74QHTeIwhTXRpYCtl/MR7wrYUX8IcwNNCL6RWPglFkTQbpRO/87TXvwpQFMlC58nr8UUiy1IgIU2HpGmPLAuQsokQ9uz1ONlFCgspK0jZIcskaTYiDN9GVc6jaUZBwFKyzCdO9oEIKWykbJOke/jBT9D1N1CVC4VvLYQoF6TTJc0mZOkIKRsoig2kpOmIJNkglU7+ulwkywLiZBvf/wmqcg5d/3pxrpxoG2xAnHAziQt/fSAhI0KRdaRcLN47jW2fNB2hKB2yLCXNhghRKhJzY9LMJUsnpNkIVVlGCIMsS0jSHml6iBQOUrZQlDYAaXJIEP4MXXsDVdWRn7EgzTHH54UqVMI0Yn26hSkNanqlSNrPyeHx7ycjm5E+TapYiolRzE9TMdClRpAE1PUqLaOBIhTqeoU1e4ltd48ojdAVnf+/vXv5jeM4Ajj8q+6ex774pmXZjhzJjmID9i2HHJIAOef/zSE5BAgC5JBbckgixTZivU1SorjkcnenHzn07HL5kkhbUiyxPkAQsJidmpkd7ha6uno6tgIR9qb7DP0Bz5s9CnEMbI8l129/0N9uAoTU8N34PtM4wacppamw4hj6XUZ+n32/y5YUdN2A2vbmP+yFlFS2S9EmSIUp58mSlQLaDtDClMe6f50pMBj6bpmUEtuTR4zCkESisp15Elfbbn6fGLpuQN+ttN2vuYN1e/KQ0tR0bG+eVDmJxzq0c7eyx4mjtDWFqRDyMVW2xscphZSsl9coTElhSj7s3OS78QNGYZ/C5PKoFZcTXz9k3z9ne/KQju3Rdysc+gMOwpBRGLIzfUJluqwW7+Fjc2bs2ZV3UnAYDng8/pbadum6wZmxiza+E5dLvxdYD3m13Gw/1weICIWpqEyHgVvhwO3xZHwvz79MDTHFeTe3Mw5jjlKo0lRsVtd5Ot1iZ/qYcRixVKwxcCs4U3Lg93hw+DVDv8tG+T49t8Rm/SFGzKnYs67s1+1STwJ5nQeiXo0YR/hwnxDuE+MzjFnHuY9x9gOa5g4xbhPTmBieYMwadf0rRPqEuMN4/Kc2AfwMY5YJ4RHef0NKY2LcJaZn1NWviemQ8fiPOPcpIl2Cv0dZ/QLnbhHDDt7fxYdHOHcD5z7B2Z+Q0gEh7BDiI5rmLsas0O38jpRGNM0dDse/p3C3c1LpbhDC1pmxnbsJ1KcSwBC2GE/+Qk4OK7z/lsJ9QlX9kpSmxLh74dhNc4fG/4u6+g0pTZg2/8S5mxjpktI+0+afpDTC2Ru44lOs2SDGfWLcwof7NP4uhfs5nfq3ADTNHcaTP7cJ4M12hPNtHw1RP1YpJZ41e0zCBCuGQdFvE7kmL/sh0LE1ViwhBYbNAdPY5KaF0FDbis1qLc/XDhOeTndzcmhqOq5DSJ5xmHIYxqQUscax5PpYsTSp4XkzxEdPaUr6rkttq3bpi7f7nr87/DsPDr/iVu+LXE4lsezWcvk1Nuz75/iUz7tj+xixHIZ9QgrEFGjilJ4b0HPL7De7WHH03NJRQmdqRuEAgIFbYRIP8XFK2XbJ+tQwDRMiHhAGLj/ybxxGGLHz5VIGxQpd20cwjMKwXULF0LE9atNlGqfzpUpq08GZnGwc+CHTeEhKkSY1GLGsFpvz89vzTyFBaeu2VNku6RJzo4cRQ9cOKE2FTw0HfsgkHlJKlUfZ7OzeOeDADylNToq7tt+WuMfnxPYM/TNiCvNrJsiZsQtTzUdbQeajcy8yjWMmYcwkHuYlcUxJ3y7Nu76nbVf27HNxUhAI7fFYKtNFROZdwU0c06SGRMz3AYZ/PP8rhSn5uPs5ITWUpmapWMOn6ZmxjfzgpWBez5NA1I9bYkII90hphDEDrFnDSC+PUIV7hPAEpCLEpyCWlCZtCfO0GHdo/F2EipT2iekZMY7IZdaESCeXhNOIGIekuN9uN4LkczlZeuSldZ4R4xYxPCWE/5LSiFyqLeblY5FeWyK158Y+Ks2eOO80JYbHGLuBMRtAk0fq0mg+Gnrh2GmfEJ6Q0rRNQLdJ6RoJMy/zAm2pvENKnhh3CHGbGLbx/hus0eeVqv8PEWGtXJ53fc7Kr90zltOyYlkpl0gpLaxRdvTbUduKDzrXTrwnl3wHrn28ocz+ExyWjn03u99r22G5WGe13KTvlo+dt7VuvjTJotJU7fpvHLu2y+X6fJueO6oKLZujjtWO7YE9/szlns3jMLIwcrcYd4WNY/F7bomuHbRr2OX156pzPp+eG9BN/TPvA2cK1sprp95jxdGxvfl7aJdXK8VSlqfjWLG5W7k4/v3Yc0t00+Cc2I7VcnNhHb18P58XO1ecLi43bVT0WJrvB6DCUZp6XrZ/2XqWuQTcJ6Xusc97Eg7puWU6tsdquXksRilnx77IyOWroAnguyZNCeEx1n5AWXzRztErSGlEiNuEuIXICiIdjPTbuXMpr6hNYjZ/LidtQ0J4iDGrgGtLyZaYpkDC2uvt6NczBEuMQyBizQbWbFAUtzFmhZQmhPgdPtwjhB1ifI6R3DltTBdjljFmgLHrbZlXzo3NCyfHJqy9Rll8SYzPEekQ0wEhPiaEJxeOneX5fqmd07g4AO7sdUT6FMVtRMp2dPEh3j8mhMekOGznCKb5cZ38l9L3n5+q1EWICPaC64ktTna/6PZXzcCtYskJzOXmiJtX8nOeY15+T/mzfb33watIWF4W++R1fBOxL3s98nuOH6cVx0Z5vZ2TeTqJ/D4xXhVNAN8xIh0K9zNCeMTh+A8Ys4Jzt3D2Y5y9BUkIcTs3U4jDmEFODsN9vP+aGPdIBMriS4xdp3CfEeIWMAE6OXFEgALf/BvPf2j815hqE2PWiPEJJ7+kEokY9+YJ4unZBAlSwPtvEKkp3GdY+96Zsc8brTxfvHTs3BCyzGT6N2J8ivcPcO4WyKyb7PgXcUqeEHZJ6eDE/iMx7uL9Pbz/ivzn1lAUX7Yjj/qEEKXeFl03oDL1S5sKlFpkxLJSrr+xxZ0vQxPAd4xIjXM3SExJflbqjIDFuRuIOCR0ECmx9npbwgyIVFj7IUa6eaRNwJoNKD5HQh/SBDGddkSvoShuAxZSg7XvY806xgyACcw7dfPckvyImxWcbR/bIyXWbEI7yVekmxMsctcxJIzZoDgjtpwzOTaf908xZh0osOZa24HczaXa9tE+F4pt13Apz2cU6eDcRxhZRkwPomBMiZjOwj5cW2qvSGYdIzXOftQeWUJMD2tv5Ovzhrq7lFKvVmkqMC9eVFipk4wY6oVS/o+JNoG8Y9K8lBs4GvGy5Fx/9npot158PUE7uTgnNrOh6shRCVTa7eXE/mlftwv7Atq5GvmYwsK+ZB579ozLvLRKPq68PM3ZsY/mvpwYZUyzbWfHPosjHCXBl4mdl7LJr5mF8178Mzh5frNznF2n2fVYjG2YjfxdxTKaUkqp1+5CPy6aAL6zFuefLZYs08JrMy+6V07uZ3H7k7fEy+65xdint02zh6zPJwG/KPZlXS72USJ9fML1D4mhlFJKvQGaACqllFJKXTEXSgDf/tU5lVJKKaXUpWgCqJRSSil1xWgCqJRSSil1xVxmGRid1a6UUkop9Q7QEUCllFJKqStGE0CllFJKqStGE0CllFJKqStGE0CllFJKqStGE0CllFJKqStGE0CllFJKqStGE0CllFJKqStGE0CllFJKqStGE0CllFJKqSvmf5tS6hh/5F6GAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6HNmhaLNNnl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "a08dddc6-bbd0-4f6a-b87e-1f84ea00cb64"
      },
      "source": [
        "# Create stopword list:\n",
        "#stopwords = set(STOPWORDS)\n",
        "#stopwords.update([\"drink\", \"now\", \"wine\", \"flavor\", \"flavors\"]) # you can add new stopwords by modifying this column\n",
        "\n",
        "# Generate a word cloud image\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "wordcloud = WordCloud(stopwords=stop_words, background_color=\"gray\", max_words= 30).generate(text_for_cloud)\n",
        "\n",
        "# Display the generated image:\n",
        "# the matplotlib way:\n",
        "fig = plt.figure(figsize= (8,8), dpi= 100)\n",
        "# figure(figsize= (8,8), dpi= 200)\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBYCKUp8NjYW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "6573c176-8eec-4b5b-9f42-1d6076862acc"
      },
      "source": [
        "# Create stopword list:\n",
        "#stopwords = set(STOPWORDS)\n",
        "#stopwords.update([\"drink\", \"now\", \"wine\", \"flavor\", \"flavors\"]) # you can add new stopwords by modifying this column\n",
        "\n",
        "# Generate a word cloud image\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "wordcloud = WordCloud(stopwords=stop_words, background_color=\"black\", max_words= 20).generate(text_for_cloud)\n",
        "\n",
        "# Display the generated image:\n",
        "# the matplotlib way:\n",
        "fig = plt.figure(figsize= (8,8), dpi= 100)\n",
        "# figure(figsize= (8,8), dpi= 200)\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9Xq97IpFIJL48kgBMvBBKRQ9YWQqZiWHStg10p83vuXZN8d/+N0NOTjwf/q3FdoEnezFrRWsYjYUbN/VGAPzT/2LAH/+vK370Y8utWxqt4ego8IMfdrz7ruHvfC/jr78fKwMHA8Xv/aOC4RB+/GPL7/79nLoO/Mt/VXF8ErCXHAbwWYhAlglFIRS5sKoCXRvnthZbWQZ/9IclN24o/v2/7+i66KUKxKK5shSuX1OYTMgz+C//aclf/lXHv/43FdeuRXH05huaH/ywYzQW/skflPzkw467d2Oz1b/72znvv2/48Y87PvjA8B//XsG//FcVP/vY0j6nQ0SWwa1bmv/8PyvZ33P87UeW0UiiJ1LB1auK3/hOxu/+bs6HH1oE+IN/XPB//J8Ne/uO3/+9gsXS0zSB3/6tjOUycPeeoygUDx449vbdpWsuIrz9tuYP/0nB/oHn8DBw547G9lH35+33D39kuX5dUxQwmwe+//3z+71aXdr4K5FIJBJfEEkAJl4IawOrVWCxCJyceGYzz8NHjv19v0n2z7LoxTk48Pz85462DewfnH5dScy1y3Ph5i3FN7+p2d5WdF1gb8/xwQcZN64LH39s2d1RvP225q/+Q0eewxtvZLz3rqYohNUS3nvPUFWBmzc1beuYvWLOnvdQ14HlMjCbx/Dpo8cuFpG0a29WFHHGCHfvOZomeq7suntDLyJHY2FrS3jvPcP9Tx0hwOFR4MoV4XvfMzx46CgKeO89zcc/t3gP77xteO89w513NKtl4PYbmtu3NLdvK/b2hYODy+e1DilvbwmCZv/A8+RJzL0zJnotd3cVh4eeBw8cRRGF4c6OkGWaW7cUP/qxZ3/fMxgIi2Xg+CQwnQraaLa21KVrnufSrz3s7XkePfYcHwea5nS8z9vvJ08c3/lOxu1bwkcfnd/vlDuYSCQSXz5JACZeiK6DkxPP4aFDqcDxceCT+47Hj09jkt7Bqgr87GPLn/35edeV1qB0FEzjsTCZKIpcMDqGQn/yoeVb38p47xua6ZYwGkVv42zm0TqGlieTGGJ9621NWQp1E9jZUTzZ868sAJ2D+TxwdBSF0PGx59NPHffvn84rhJirdnzi+bd/1p6rbs0yMDr6q8pS2NpSlEUMmXsPd+9abt1UfOtbhj//C4VzMdxZ1VEs3XlHc2VXkefCrVua6UTwPjCdKMYjxcHB5YUs1sbcwid7nuFAuHFDc/9TT1UHtBbefFNjDPzFX7T89KeOyVR4521Nlgk3bsS1fPzY8cMfdfzO7+Q8euT4+V3Ltz4w3LiuuHFNLl3znR3FdKr46KeWH/3YcnTk+dnHluUy5hd+1n5/+LeW73w7484dzXa/30W/34tFEoCJRCLxZZMEYOKlCEAIwkVtIUMIWBueaaGSZfDrv5bx1lvRm/Qf/rrj8WPPtz4wtF1sutq20RMnAt/5dkZZCj/50LJaBYbDGJr9yYeWgwPHX3/fUhbQWdh74lgsP79giI1fo7dv06D0DM7FfL2nO0q8847hG+9Fb9nHH1v+5geBt940Gy9W18UClaqKIdLFIvCTn9hNXmTbwt17lr/9CL7//Y62DWhNFLWzz04W39tz/It/UbGzE3P1/vAPcr7/N4r/6/+uadvT6mal+0KegRAOieveN7xdF5tsevTKqWf0sjX/tV/PGI1kI4BjKD3aMEb4tV/L2N1RF+53XE84mcdcv9/6rZyigA/7/U4kEonEl08SgIkXJoToLbt2Fb75TdMXQFjufeJwLorDKCTOf4iLRBGyu6t4603N3p5nOJRzosP7GEo8PAz8nd/KuP+p4wc/iB4l5zx371nee9dQFMJ0KiiBbh5Y9B6n3V3h+nXNW33Y81sfGNoWnjxx5/IUL2NVRS/f2mumDdy75zaCxIeLr1HksLUVCy3aVrO/78/1nQ0BTo4DP/uZ5c47hvnc8zc/tBwcRPFz774ly82m119Vg+2il8za6D27cV3x3nuayUTx7ruGTx84Hj3yhBAYDVVfiSuMx4qbNzWf9Pvx6QPH229p3v+GwZjYwmd7S/jpT+NcN0ef9il3Z+c3mwcePnSXrvnjx57pRPGN9zTex8rpa9cUXRfnP/iM/Q4BHj10PLyl+a3vxf3+y79qWSbvXyKRSPxCSAIw8cKEEEXae+/CP/wH+UZsPXpcxX6AAZx/Vih5H8PHSuDXfz2GcrWOuWFnxdIn9x3Xb1j++T8bcnIS+JsfxHywxSJQVx3f+27G++/nfPvXM/Icfvozy+MnHq3hrbc0//Af5PzmdzNu39b83u8VIPCnfxpDit1ntM47PvaczAL/+D8xfPvbhjfe0Pzrf1OzWrnNHC5qbLxYRDH1zjuGW7c083lgd1edPTKTvX3Pn/+7jn/+z4ZUleJ/+eNqk0P4/e933HnH8L3fzPnGexkiMR/xj/+k4skTx+6u4j/6Bzm/+/dyrl5R/L3fybA28P/8vw3eC2++ofmjPywp+1YrVRWYzaN4/MlPLFtTxR/9YcY3PzA4G1vQzGae/b4djPdR/zkfNgLN++jl+/5fX77mDx86RiPhj/6o4P33DfN54I03FYeHAe9jjuRbb+rn7vfde46tLct//V8NODry/OBvOqrU/iWRSCR+IaSj4BIvxXAoXL2qeOstTbUKHB56PrkfPU7jkfD++4a9/ZhHt2bdc+7aVcXt25rlMlbQjkfCT39qefTYb3rO7e4K794xPNnz/OxndhNONiaGW7em0oehYT7z3L3nNu1Qrl6N7UqGQ+Hw0LO3H/P67GcftUlRRE/e229rAOazmOO4WsU2MN983+B84KOPzse3h0NhZ0fxxm2N84G2jfN6/MTx8cexECTPY9+7O3c0zsGPftTRtnFMSsHt25ob1xX082rasGmXkudxXld2Y97d0VEUb3t78drDoXD7VszrW8/xyZ7jwYMojK/sxr1ai7sA3P8kFrjcuaPZe+I5mXnuvGOo++KW6TT2Izw69peu+boNzLvv9m1/LBQlHB/FIhqtY2/E5+33dBr7Sv73/92Qv/h3LX/yJ9WFDxCJRCKReHHSWcCJL431sWlw6jF60e/T+nJv2ougVBSDzn05x7WtzySHl7u+1qd5hK8iYEROC0f8S4ogpUD3585fJnZ11LWvtGafteYip3mDT79+0X7nGWxtK27d0rxxW/PGG4q/+UHHn/7p63fCSSKRSPyykQRgIpF4LdndVfz238n43vcyJhPF//a/V3z0keXoKP2KSSQSic/LL1YArl1CL+u6SCQSXzuKAq5d01y5osgy+PnPHbOZf27T60QikUi8GF+uADwbJwNUnqFGI9x8QWialxxqIpFIJBKJROKL4EUFoHrpK4ugihw1KDd/zZUrDD74Jno6eenLJRKJRCKRSCR+sbx0GxjRGnP9GmowOL3IdEp24zrN/U+/0MElEolEIpFIJL54Xr4PoBJUOUCPx6cvDUpEKV7I55hIJBKJRCKR+Ep5+RxAEaTIkXVfCUCVA8zONt3jJ7j5/EsZaCKRSCQSiUTi+Xz5VcBKRREogmiNGIOvKoK1Lz/aRCKRSCQSicTn5kUF4KsdBbcuBBmOQITgLL5unjkDNpFIJBKJRCLx+vHyAlAEVRbkt25RvHsHX1X4rgMfaH7+c+zB4Rc/ykQikUgkEonEF8YrCUDJi5gHaPSmFQxK0z58yLnT3hOJRCKRSCQSrx2v1AdQMkPoLHb/AN/E9v2iFCKpDjiRSCQSiUTideflBaBzuNkct1qBKFRZEnygffQIt6qS9y+RSCQSiUTiNeeVikCCtYSqxs3neGsJbYs9OMBX1UteSVAbDRrweKT/d8Cj0CgUgYDHAUIpIyDQhhoAjyfg+6sJGkPov8NvXlcUMiDg6UJz5vX4fgCLBc6cdveU7UBAoTfXjt8drxLwz7VtMHgCju7cvGXTOVF6G34zVi0ZXWiwod3Mff1nPS6H7ceq0GS9XbdZR+mvy5nRxrUNaEz/9bh+oZ97JgWGjDY0eOzm9eftoMHEYqCwHlM489X4x2I3+3SR7XgfqM2artdjvRdaDD74c68LCo3BSIYgdKHdjFmT9feH6/cibGxryTCSYUOLDZaz90/cY4fbrJvq99Wfu9cuXnPp1/20J6brx3PZvfb8eSu0xHmE4HFnvvYqiAJtFKIlZmp4cJ1HaWH79oCucVQnHQj4LmDbfr8yIR8YXOfjXxtQWsiHmun1cvN97crhXQCBfKARkf79ntAP2xQKnSkI8fque/79pbRgCoXSEh80V44QQLSQDzT4gLMB1/n4uoLJtRJCYHXcnbOdlQptFM5Gu0oLW7dKbOupTjpCAN9/TRSIEpQSTK4IQLuyhADaCPnQEFzAdh7XehAwhWZ6vQBgddzRruxmfk/b9i49KCcSia+WV8sBzAy+bWkePCTUFaF7tdYvCoUhR4iCz9KhMAjQ0ZJJTkaBxWJDPGN419wkBMex2+s/ei1d/zWFppRRfDV0dDQbsbOtr2FDx8wd0NECASWaUmJD65WfnRMKT9t2WIzkGxG5FjYArp/L07YBjBgGMsGGjiq4XtgqjGQozOYabahwBAwZW/oqI7XFidtn6Y+pw6q3plGiyaXEB0sVFv0mZgzUFEtLFxq60KDRaMk2QlkT+zZGOevIpUBhNuu3FpMjtcVQTTi2ezRhtXn9MjSagZpEQSeOOlT44Db7kUmOkYyVn2OJ6QJP2/Z4DBlKDKofZ0echyBkUjCQMa3Um/mdtT1UE7RoZu6Qxq8IeAZqTCDQhbq3EXrbA4ZqylBNWPpjVn5OR4sEhRZDIQVtaHBhuVnbUoZYus2+CnLhmis0RjI02UakxzV0KFHP3Gvr61w073jv5Az677HS0YQKd0awvizaKMqxISs1ogVvA9WsIxsoPvhHV5nvN3z6gxNECfXCYg/6/RoYdt4YUM076llHPbeYQrFze8C3fv8as72GBz+ccXBvha8cSgmTawVKCVX//rWYHG5llNMMbwPLw5aq6543ZEyhGF/JyYcG2ziOPq1xncdkwtb1Au8C9cJSnXQ4GzCZ4s3f2CL4wCd/fXze9nZOOTY0C8tq1pEPNB/8o2ssDhse/GCGs4FmYam6Dm0UOleYXDHezfEhcPSJx/tAPjDsvjnANp7VScfquEUpYbyT8c1/eBUE7n//hIN7qyioL7Ddrtwr7WEikUh8Uby8AAwBQkCPx2Q3ryOi8G2LXyzoDg7wy9ULXyqXAVv6KgqFo6PyCzIpUGgcXfSZSEYuJUt/wsrPo2ASRalGFGpIG2qO7GMGakwuJRpDJgUez7F7TNt/mPLUOSWhn0qphijRZz7EFUM1xfTCZW176U+YqB1c6FiEE8Z6G4gf5qWMyKR4xrYNHYJiqCZ0oaXzDTZ0FDLsBWmL9MLgyD1m5We9z0yf8SZGz1QuJcNe7NS+ogrzjadL9x6wqbpCILBv71OqEQOZMPcHaMkYyJiVn8V1V4ON5yyXkhO3z8Ifb7yF5/2FlzNUE3KJ1xqoESEEWvcpSgy5DBirLTyB2i/7/S4ZqukztrvQMNVXCCF6A0s1YuFPOHH76N4Dp0SxI9dpQsW++5SR2qKQAYKilLiHimOUaAjRe1uqMZlcY99+isczUlubtRyoMXVYxocAhv39M6AJK7rQbu6D03uqpA01x+5x9OZdsOZDNWWqrqBE4fHY0JKFgsavaEL9zL1mJGNH33hm3nN30I8979d2TBtqarvkrJf6ZRnt5rzz2zs0Swshesoe/WTG4rBFlDCYZlx9Z8TWrQHHDyru/tURV94eMphmIHDl7SHtynHvr48R6T1hA4PSbfRoBdi6WXLl7WH0Ag4Nw2nGJ98/ZnXcMdrJmd4oyUrF8rCL47gEEZjeKNm6WbJ1a0A965g9qYHAzpsDtm6U5APNYCtDRLj7l0fM9huQvg7tzA082Mq4+s6QfGQoR4bhTs6jn8xZHjYoLRQjw86bA6bXSo4f1XzyH44Y7uTs3B5w7b0x872G4wcVIQRMrsiHGm0UV94ZkRWan/x/T2hXDmUU2UDjXfRKhhAutf3owzm2cSljJpFIfGW8Wh9A14fMsgwznYJS+GqFq6qXEoBG4oeboPC4zYfqOnAYPR2KgRrRhZaGFeuQayCQUSISvWlDmVCoIcvt9CgAACAASURBVB5PIUMCjoXLsFzmYQhAQEuGFoMWjQ2CFsNYbaNEnbNdsUCjUaIYqDEjtYUNHctwTKlGlGp0ge34QRs9hwGN7j2JGSO1xcIfAVDKkExylChCH68KeFzocMECghFDLiWFDKk59cwZyWLYVjKGaoogHMrDGLoUQyHD3us12XgSJ2oHj0djKNWoF9b6jAB8MTRZDFdjyGWIPxOSzqWIAt3X2NAR8GRSXmgbAkM1pfErutCQy4BcaowYDHk/v5yR2kaHDNynlDKikCFVmOPxqP5W1mRopXvv2YihmnIsTwgEJmqHlZ/jcUg4DakbMgo1IJOCys5xuF64RwEYCJQyQgVBedMLwvyZNc+kYKSmIMQQevAUMsCLownVs/cahqGa0Pjq3Ly1ZORSkEkZxyZDfO89/qyQ/PPIh5pr747Y+3hJu3JMrxfM9nLaykWPoA+EAKOdnK5y5EPNlbeHmEKzf3fJzhuGfKgZbmc0S4tzcTS28TQLi3eB0U7O9W+M2b+7RKko4kb3c7wNZIWKHsCJQWvF8qi5dKyihOn1kun1Eq0F23q62hE8TK+XXP/GGNcFRrsZyige/e0cfdwS/LPrU44NNz+YEAKYXLF1q2S+19AsLaJiKDz4KJBt5ynGhnKSMdzJ2bpRsthvYvjXQzHUjHZyyknG7ptDRjsZH//5AfXC4mwUwa7z1PMO34/vItumUDFsnULBiUTiK+LVcgCdwx4dEboO3n6L7PpV8lu3aO8/+IyA4VPXIRCCx2NRGCbqCkt/jA0dE7UdQ7zBxjy4YDcf2G1omLnDGGoTRUYUG6UM6UIbs+NC+5n2Ywi5g3DqV1FoSjUE5JxtCCz8MYUasqWukssAF2Z0dBjJLrUdPUHdOXElvYdt6We4cBpaFoSONnoLQ0sdVljaTV5kFxoW/phFHxYGNt6sXEpyKfrcNaEOS/AwUtMoXsTgQwxFDmRMRxQoXagJuH5EL1fFHdfGMdY7BHwfonSIxFy2lZ9T+yWrcLLxVl5mWxDqsGLhjzGSY0Nc10KGFDIgl5JMctoQ80yN5IgIc3vEOjfU48kkp1Dx/bkaoEX3V4+h86PwGOctuRTYcPpw0PiKhoqFP6aj7YVXvE4XWlwfAlYIhZQUavjMmse7qcUHT+0XVH5JLuUm3/Hpe219Nzw9b4XqBWH0+lo6WupNXumrIhLdYyePamaPa4qxwXeecmJQWpjvxRBwMTLY1lMMDcOtnGZlefjjOUqE0W6+EYDVrKNeWBYHLbMnNcHHXLdyknHysMa7gM40SgvKCPt3l4x2cybXCnbeMBw/ujxnWATGV3NE4O5fHsWwaeUILjDczti6OaA6iZ7HZtn2+X9yoQDMBprt20Paym7CtrZ1KC2IEpaHDY9+MiMf6Xj9aYbOhHre8fijOY8/mnP0sCa4wGArZ+eNAcPdnOFOhik0ogTbeKqTlmre0Swdxw9rgg9sP8d2IpFIfJW8Yh/AHLOzTXb1Cm61wv3s5yCCPTx66cutRV4milxyTkJH5ecohExKtBjWmWsWiw+OXEq29XWGaroJp1V+DioWMUDAh/gdCk2uokjyvQdl7o9woUNL1odvSyAw5xAfonDJZXDOdhdaQCgYMlAj2tD2uVqBxq8wkl9gW8jVgFKNerELc3+4EVvrgpEQToO9YeOZjJ5I8ULTi55AwIfzRQ1rD6BCb4SlAF2fr5ZL9JLWvvcahsDSn2Akj+FSAq73LuVSUMqIoZrgtUW8Yulnz805izl6eS9+/Zm50Yt7t/Fa2dBeaDsKqPVanC/qWXv/lJxvM9SGCoVmx9xgKBNENEt/jJYYrl2vB/1fFzqqsOjzBTMGvUd0rcbOF+/E/678AlQUm+vXbOhQyly45mvC5k+8lurF59P32vp7n573+mKCwkiOCx0hBGTjFf88BHbfGjC5krP7xoDquKWeR++dd31hhvUEH0OY8/2GbKB593d22bk9wLaeJz9d0K5cLCgBtm4W3PrWlP27S6q5Zfak5so7I/KBZutmyexJTVc5hts5utDoLHoC84G+fJQBZk8atm6WvPN3d6hmHbPHNXs/W7A4aDl+WDGYZohA4xxdFUXVaKdg+/YAnSm8DTHUWjv2Pl4w2s4xpcL7OM+2srjWM7la8OZvbnPlrREnjypWJx0jowi9N891YeOpK0aG4XZOlsfCFDmz+SHWgjDayXjrN7bYv7ukWdpLbLsLxerXGYWmkCETtU3llzRhRUsNrEvaVF9Apuj6ByJBkUmB9A/s6yI0TYbH9tEBBZufNL9JQfH97571w3lOyUhtUYcFTajPFNApMvJYHBXig9ymaI7eNu5MBCQmroic/u5ZR2wus73+HZFLLCKqwyr+3BNzpj1hMz/Vr0QsMFuXkMmZ3zlPI30OeLxRPa7/DtX/d1zD+NnicFjGskUmJasw33zmnC3wW897/f71egxlihFD5ZdYus1ngiFGTNpQb37fr+f9tO24foLl1OlzPvL3rO31HFTvIFqv6/p+EYlXiZ839rW9137RvJIHULRG8gxVFnSHh7ijY3xVv/R1XLA0foUj/oBkvqAJFVVY4L1joCYUlL23KObPNWFFRtl7VsDR0dH2oT3PUE1ZVwnHHLy4lT64WKknMUHfS8y0W4tCTUzE72hZ+GMGyp2zvf4BjoUAUUysvXBVWIKXZ2yv60p9cIjEkKzqf+jqsMQHi8fFH4o+TLqxERqM5FGEhiiULF0cbzj9IfchihKHRQWFCw7fiwkXOmyw2GBZ9d5Gj2XmDxmqCYacEFxfPesxRKHjg+/Hqvtb+WK0ZCjRtKHeFH5E0Qyu9665cHpjd6G50LbDUvvVJlQcizC6WPUbLJboha1YbMRw7VcopRmoMUp0/wPpULj4vVjEC1ZiGL0jek8HMiYjj79w+r+un+RatkF8MFn5GYEQHx6Ci+PC9p7hZ9fchq6/n128Z9Zjx194r7lgqX31zLxDCGiV9d7uul/DGDJ2vHoRSOjdj+XI9BWpgWbpWB63zB7X1HNL8LEwJLiAbTxHn1aMr+YxFJsrqlnH8qilqz35wDPfrxERhtsZ6lNhddyyf3fJ9HpJOTF4F6/XLB2j3RhaXhzGYyO75vJ5eB84eVSTFZqd2wNMrrC1R5Qwe1yjtbD71hDRwuo45hMGH3P0bF/ZXIwNOlc0c8ujD+dceWvIcDuGeauTWJxy8rimGBlG2zkA7cpRzSym0GgTPYquOx2ns55mZWkrUFkUgbafh/eB5VHLcDtnfCXn+GF1qW1bp/y/p1F9Pu+OvoGwj/P2XHFVRtHn+IaNONK9cDJkBAn9z6xnIKMYkcD2Asrjg0P3D6yZlCDxd3UdlgQCRvKYouLdJmoTbeeUMgToPfxRQBlycilj6g6e2q+gTxfRYvoHtkBLTReiuLjItiaLkQsZMFJbADjvNi3Vchn0efKOVZj1cx5sPn8yKeJnaLC4C343KBSFDE8LzUL8mdVkfYRJU8gAJGBDSxUWfXHdlOA8ThwhBKqwIBAL9mIeeeyKUfnFprCyVDFi0/XRIdcLp1zKmCblPSHU+F44ZVI8YzvmXWtCWKFEY8ii6O4fgp+2bYmf86UM4+/I4Fh3Xljnh0MUgy0NVZj3BaOv1732VSAven6viIQz/0CMQYwmdJbgTm/WlzLelzusN2v9Qe77sGAUIOtK2/h6rLDsH7slepniDbCuyz2tdrVn26T0wuS0ncZpaw7pc+88py07LrIdrxVbkqy9W+sng5exvbYb8xMD6kybkbXHaN0yZG17/VyyDjWefYLaeKL6VizrH0aI+W1Af3W3GdOphyyK6PU8dF9p7Dfzu/zGvG7eJqPgyD1ioCZkUrDys00hRVyLs0+lcqHtdXh4be+0ACac8bLF+fngN2Hx0yftaCeKzmhh/X6gH0vov8eg+tfjveZZt2CJ63Q2VL+2oTZfO31CfXbNn74H1k+E67k8e6/FX0pPz1uL4Zp+C4dl5vaZqF0QNiH1dTX1y3L9G2O++5/e4uM/O+DRh3OUEprK4VpPPtS4ztM1nqzUMZfNepQSdCaxdUvfHqbp8+GUFvKRRinBu7CpbNW5wmSCaIEQRZWzHqUV+UCjMyGE2Falqy8XgUrHFixZufbGBdqVRbRgMoUp+n1x6/YwAW2iDZTg7WneoDLxWsoogg90tcPZQD7QG09eDOU6mlX0Jq5DxK71m7YtpoiVwaLi1wlQzTu8je1vipGJwrGfn3dxTKY4b9s+R/x+XcnImeqr3NBvc+AeMfMH1GFBLgNGssVUXaGlYulPWPhjIJBJEb+utpiqXR7Yn2JDxxV9m5k/xIaWLX2Vyi9owoqBmvSV9zoWxoUZB/YBDstAJlzVtznx+6z8jI6WgYwZqEnsBBA66rBk6U9679EuHhs7BagxB+4BAFf1bWI7KX+ayhPavtpfnbN9aB8xVjtkEh+IJzoWle25T2KRILEtWHxPwUP7Uww5U32FmTtAiWKirjDzB1R+fmHOe0bBFX2LXAZAYBVmQCyGW3cVEIRSjbGhY8/dZ1tdY6SmLP1JL8gy9twnrHOjYzFdQaFG7Nv7zP0xHsuuvkkhA47dXu/tswiKbXWNqd7lyD5hFeY4OqbqSl/Id972WLbJpeTYP6GUEaWMmPuDTdeOp20v/AkB38+x5Mg9oQkrFJrr5k1AsHSMZItVmLFnP0Wjmejd1+pe+yIJ4WwZ3OW8Qgi4L/7Y3SG7cR3ROibsAM3Hd7GHL34WcOgdp6cvdGe+Frgo5yl6CzdvOnctt3FTn8fjNh6q8/ZD/DANz75+Wb7VRdd6WduB80LDPfVDe5Hwiu7vS8azfu8zbwgXioXLhN1mP8LzW3Osqf2KIH6T52b7sPjlTzThUttnx3l2Hc890V64391T8z6zc8/sa2/7mdcv5rLRXrbmF7377PgvutcunHeAKsSio9i/Morb7nOGCpqV5eDuktlew+rk/LrV89M176pTG56AbQF5dt28C9Qze1p520/VV44uFuyew3lP7WIPzRcJf3oXaCtHW7nTGHuAYAOtdbT1s3tgW79p+/L0tWzjz10HoFn0837K1e1tiKLuKWzjT6/z9JfD6fXWxSXnxvSU7cR5XO+9d7hNO6T1A3n8etd7UVZ4PBn5psit6P8qDC5U1H7R5xAPKGVIwyp682XEuq+nIH0xmIZLfq5s7+0XiYVtgUDFos/7HnDiD7BYslCchhv7h0CHownxB2Hj6YJztpXoXgTBLByTh0H/EBm9RGsvYyEDMskRNJaO2q9irjPl5vuflyMcuyQIoBjKNEYysGdylN3mwXn9/x7fR1yEUnSfXlMwlAke23vITrvaXmb9bKoTAhKiI6OQmLf/tG1LiwrCUE0oZRQ9bv0D9EW2T3vKxh+ssw4DTRa9fn5BoQaxj2cfjXnd7rWvglcQgPH0j+zaVQYfvI8UJWL6G/745KUEYOKXm1WY4bGM1FYM54eKJlRfmTv7VwWPZ+GPGMh400Jovbafh2ZhefzhnNXRs0L0M3ne+8MFAYBL3h9F0SsooIu+5VWE1OVq/4u5zvrLF30aJuH3XNZ53i7YTdX72a/VYUnl5zR96NNscriLczm5lpa5P2SqrzJS05gC1IflYgQn23jz1w3fo2dOofuQoxKDhC6mGIUG8Bgpo0SRdYRB9REMNh4cheCDJUhM6ajCqViMkSB51nYfFWjXPVwlIwA5BUXfIcL04k+AJtQEPLtyk1KNo/gL/rm/d30flRJgIBOaULH0MzIVO0g41q2rzjhh+jGtw6JnG+87Ys6h7dOH2Mi3deOuGF6OySunxJZeGgk2xrkusF37FV4cW+paLLLr8+1Cv+fP2oazT2TrFJt1xKwLDUt/QikDfIhRJUv32t1rn6fDw6vySkUgalDi64bqo48xW1NUUYAIvq57V0D6Tfd1YF1Y0fhq85SXxN8XQfzltgwnMe8mhC9kXduV4+DeCtul8GPi9ST6qISx2gYCJ/7g0veqvjXW2ZN3IIqdlhYtUaxUfdqEw7HwJwzVJLZnYl140JH1TdeHMgUN4oVj16GJ/UpBnYoXVF94uGAsO5s8rxVzbOgYXzBWFxyLcLLx5m1iRqGl8gtKNeKGeYeBjHF0zDlES8wNVM/Mz+FC9IIFAquweE67M4hPaFECxnWLIqUKC8QLAzWmlFEfZfKb06c0hh19I+Y5ojgJe6xCDOkO1Ai1Tl/pu2SUMmQoEwYyjukfTvBh1udQjhmqrZhW5WHef3ZIUBfaNpjN6V0xX97TUjH3RxfaLmTAQCYMZYLSGnzMOZeYH/RLca+dLab5RfFKjaB93QCzKPicQ49HSJ6/ci5g4peT9XF0n3VaSOLl2axt+OLWdh1STSReV9rQcOSfbAqt4sNQR8Oq7/hwmjKxboslQBUyljLbeGwCnpWfbyITbZ8+sQoznO9oJBb4NWHZCypHHVYcuSd01H0kwyPEIr2lP6GRajMegIU/JpOCjgwVYiWpD56Z3weI7ZtCjQ0trdT4YGkkf8Z2FeZ9a6oyHk+58TrGh+zYImtJ9Gad5lbTp90s/PFz2545HEs/OxPetDT9CUkrwPo4vliIFqt0V36OlyjKOqkJBNo+TBp8iAUTonsvYc26Snjhj2mljvOmZd25ogpzxEVP6fr9VVjgvH3G9vq0rZk/wOP6wjpLCD626brQtmPpT+ik2dwnlnjy13ovV37Rp311m/m8TvfaV+EBfLUiEKVQWYYMSszONno6RY9G1H/7Ed3e/pc11kQikUh8DViLla/iQ/Ei1hW9r0Mcf928/pp5Cxs6nrhPXotx/bLyut1rXwQvWgTyylXAKIUo1VcDx4pgt1wR2i+2miWRSCQSiUQkp9ycoNWGhlU4+aqHlHjN+BKrgAUZlIg2iIBfVfjq8yWnJxKJRCKR+GxiAUTsMWpfsGNDInERLy8AlcJMp6jhEFGK9tEjwiLlgCUSiUQi8WWzbluSSHxeXq0IZFWht7Yo3ryN5Bl+sSQEsAcH+NXqSxhmIpFIJBKJROKL4pUEYGhbQhddz6os+/zTgJulXIREIpFIJBKJ151XE4DO4Y6PqT+O/163fgk2tZhIJBKJRCKReN15eQEIUQAulvj2fAJqaFJeQiKRSCQSicTrzisJwPX5v+tQsGiNGg4I8kKVx4lE4peIePzV+leFx4enzt3sj8by4bSTvRINCCGE/pilELvoSzyeCeJRU+sjmOIJC7o/M/T09bVtraJ961tC8Gds8MxRTkoMWgxKDC60OO/68zjpx6r6ObgzNs7aDpvXtZjYpHUzJtnMY334VDzrO54jqsT0zW9PC+OeN29Bb+YR7bvP7Ee2Po0C6BuFx6bAut8jv7l+YN3l7Nl5y+Y6cd/ieHI9RBA63wBhs3+ZKlFisL7ZnOt6fs2f3e/1XggSTwkKZ+4DpF8OeaH9Xp/T+qL32nNt93sRj1ALm/tAiY73jLeb1zYny272L5w72z3aUP2+uv5osl+dfnKJX21eXgBqjd7eRjID1mJPTlDDEeV7d2jufoI9OEingSQSv0JolTHMtgBwvqN1q/4DFUQ0mSrRSlN1s83JBYUeIaKwvsP6mhDAqLz/kFWA0Pka6xsERa4G5HpI55vN62dtD8wUAsy7fVoX204NzBQfHJ2v6VzN+tD10owZmCkDM2Xe7lPZOdbH0wO0ZOR6QOtWNG4JgFEFuR7gQof1Dda3CEKmS0ozwbqmH1cVj3xSOVoMIlFQtS4eVaWU2Yyp6mb9eNSl8wbIdEFpJkDAuobWrXDh8iOhBE2mBhiVA0JtZ/hgEdGUZoIgtL7B+jqeRyr6mXm3boWIItdDcj2g6k6wPvZv3SnfQInmpH6IDx4XLJ2vmBbXGZgtTpqHdL6J635mzZ/ebx8cgqLQI4zK6VxN5+NpGKfrEYWb9S2dry7d787113vBe+2zbGvJUMoggPUNjatQoshUSWmm1HZG6yoCHi0GrbKNWA7Bx/sft7nX4l4EGrvC+uYLPb0nkfgyeWkBKFqTXb+G2ZqCCG6xQIzB7O7QPXz0ZYwxkUh8JQgDMyXXQ4zKKc0Y6zuOqnsYlZPrIcNsGx9cFBUIuR5SmglGcowqMCrnpIliYpJfw4V4IH1hxsybPRbdQRQoKsPoklF+hcrOOK4fMMy2yPUQLYaB2SKEwNIe9Z6u6G0bZNsoFAfVPUTieLXKKM2EYbZLZedoVaGVYWi2MKqIgsDV8fvNlExHQZWpgtrOmTWPkV40ackYFttY33JY32OU7TLOdxHR+OCigLAFjVvhQsfATAnB07ka61tyXbJV3Hpm3it7TKFHZKok0wWFmVB3Mxp3eReFTA0ozIjSTDY26L1ia2FTmDFbeshhdY/WLcn1iGF2ft7RLwi5HjDOdnG+wweHDx4tGUYVFGZMpoa40HJSP0RLTqZKCj2iNPFM15PmAfRzenq/a7vY7GuhR0yLG8yax1TdCdPiRvQ84ij0mNrOOWkeMjATMj14Zr+NLjCSv/C9dqltO2O7vEWmBwhq8zBj1ILWVRhVMMmvEoLHeYsNDcNsm4GZYkNLpkoEzVF9HwgbgZnrIbkZcVR9QufrL/2nMpH4onilRtCqLNCTMZJlqLIkOEewjuB98v4lEr8iCPQf+GOUKAZmi85HAWHEUJgx4/wqlZ1R2xmBQK6HTPPr+ODJ9P/P3nt8R5Jk+XqfmbkOiYBK3dVVraanZ97jhuSCCy54Dv9scvEOD7ngvNPD6WlV3aVSAhkI7drMuDAPT8hMAAVUZlX7d04uMgLhwtzd/NoVvxsRNx4VYzXD8IC0XmCtpudPKHVGVi/xpHuB+zJiFD5oPVCJv0MgI3LtXui2CRk6j4yHkgE9f4dAJSyK13gyYBgekNdrQDSeJolE4cmAxN9ByYBKv2q9O/1gD08GCASxP0IKxaY6aQ0hJX0GwR61rZjlz4n9IcPwAWApTU5Zb5BCuX6kdUUgYywWJXyMqPFkdOl5lzol9oaN4eKTeGOMqc+EoM8TqoTEG+OrqPFQudCzFF7jGQuIvSGj6AHr8i21yQlUdOG8t6FdTwSEqo8n50hx2qvrwsOxP8RazaacOu+lEI2nsY8vI9L6BCUCBsHehetdm9IZ9TIk8HqMo0et17Uf7GKsptAbev4YgWBTTukFu3jCv3C9PRHc6F67at+lTukHe4Sqh8G0186Ftitk40XNqqXzTlpB5A3oB7usqxNC1cdXMevyLUIIEn+EEIpQ9Yj9MYv8NdftrNXR8SlwYwPQVhXFV19TvXoFQmLSDFs7l7fJu44gHR0/FSxQmYyQhMR3npGi3jT5VwHGVmyqE9JqzqaaY6zGE0FrKIJ14VEMojEgsmpBoTcoEWBsjd94m5w3p4/fhAdB4MsICyyLN62Xx1qD34SLAxUTqBglFEI4w9CXMbP6JUW9wZchtXlXqFbUa4w1rMoptSlciFf1UdJHm4ralNSmRApF6PUIVJ9AxvgqwmqX/6dN7Yxgq8nqJYXe4MnQ5ZpZTW3Li+bbJecthKQ0Gb6KibwhtSna0OJVOE9lyKo4otCbNhQbqQGxP2rD6J70m5y1y8/bXVuDthWVLdqcyu2xVqZgWRwhUM5AVRFSemhbsy6n1KYk8cZI4Txsl11v5zEeEqiYqPGUba+rC12vWRavUcJDmwolfQIZo2194Xo7b+v177Wr9m2x7vqiqG1JWs2w1qKkD4Cx7tqevQaC2lYsiyMqr6Dnj0GIxoDNGAYHeDIgr5ZtfmRHx4+FW8nA6DTDlBVCijMGYEdHx08La13ifKDixvhzBQTb74ypmhCimwO0LSn0GiV8t0C0BmOcwQPuJatNibYV1hqk8AhUgq9ckcE2yR8slSnwZcggOCDyhoDzTrmwW/wuN6sxdrSpKE1O5A3wZYgv47bAot23rdG2bAs+Cr0hFM4IdMUCGm1qPBESqKjN9UNsG8a7Qo3t32lT4YkQJfzWMJXCwwQ16/Jtk/d38by3xQ+yyWvL9bopINiWllzE5ZeVxP4IX0UUOiOr5njSJ1RJk9umtmULV5731qvmq5hI9al993lazcFa51kNJkReH4t119dopJRNWN4ZVdZqKpNder1pQrTO+PLOHI8bR01tyubesW3hiRTqwvW+6b32vn27MpHt9atACBQBnvTxVdyEzF2oWZcVQrjiEnftyia/0F0hixsrKTxKm1953To6PlXkrX5ljQsH+AEiihBhiPB9kLfbXEdHx6eJM8oUelvhKM5W32pbY09VRZY6ZVUeN0n3uvWqueKKoqnYtWhbtdW77iXvPqtM3njRLHm9RNuaUfiAyBughNeG2IQQrcem0nmTnJ+RVnMib0DPn7Sh3daws/WZ6lxtazbVlLxetQbd1ijZvsxdEURBrV1V7OlzMrZucufcNl3IOCCQcRPeDRqv08XzBlDCd7lo1oV+BTiD85yxtKXQG4p6Q+yNGAQH9INJ471yeYAG3XgSs3Z/20KOs1XJzoj2RNjmdgYqRgjZFlYMmlCpQFKZ3IWcrSHxd4i9AVIoalOS1YtLr7exNVJILIbauiIPVxxhmjGum2teN+NoyK643mZrcF/zXrtq3y63rzxleGqMqZu/95rxcF7NUPVcaN/qxnBurr2tMNYV9yjht5/bxst9esHR0fGpI66bsyCEaP9Q+D7+/h7B48cIT2HKEr1YUB29Ra9WXR5gR8dPAIHkoPcFSngsy2MSf4xAsqlOGiOjag2YrYEhkC7/SvgIIbHWUpkMi5MYqRrjxJdhK6+ipN/KlKhGasSFS32UdP+cXImm0imykXjZ5qoB5PUKoP3NtmpzW1W7lRDBWmpbtme4rfLcGhtbI9SXUVMp6opNjK3J63VjOAWt1Mu20tZiXXhaxchmTN5V+sYXzlsJn73kM2pTkFVLkmCMNhWbakZRr9G2On853o2tDBsJE02pc6SQ7jPhzlGiKPQabarWq3r2vHF5kSrEk2Fj/DrDyMm9uDF1Vc6asglzd6ebswAAIABJREFUK+G3n1sspU7Z5iBevN7WFU0011UKRanTpjAmaSquS3wZNlI6FQKFkt6F600jw3L9e+3qfbvrI1pjvLkLWiMwUFHrHaxMgSdDpJCUOmurqbWt6Ad7jKOHLIvjNh9wXZ40of713T2EHR23wFp7LU2+WxmAKIW3s0Pw6CHeZMcZgXlBPZtRz+bo+RxTlKC7ziAdHT9WBJKd+DGeCMjqJaHXByybctYYVheNlO+7x8vDaDf9/C65n314MmQSPWkMrIzQ66NtyaactZIl/7j8ENf1+9HzJwzDA7J65aRovB6bckZer1pJm46Oj8X9GoCACENUr0f47CneZAcZR24FvVhQfPU19WyByboHoaPjx4wvYxJ/RD/Yo2qqdtfltEt2/55s8ypdSHVIZTLSasGmOvnYh9ZxDZRweZf9YN95GE3Kupw2XtGOjo/L/RmAQiCjCG+yg3+wj14sMXnuJGBwOoEiDKmnU/RiecvD7+jo+BQ4HXa0Vrehu47vj0S1Ie5t2PHuvaod98E2NWAryL3NFTzdJaSj42NxXQPwdr2ArcVq7cK+8wV6tcJWbuKSUYTaGWPr7kHo6PixYzFtAUfH3WLQGKM78eAfIdscQq07g73jx8utQ8AdHR0dHR0dHR2fFvfnARQCEQb4B/uET58glNfKv2R//BPVm6Mbb7Kjo6Ojo6Ojo+OH45at4CK84RBvd4KMY4TnenMW335790fY0dHR0dHR0dFxp9zSAIwxeUH+5Veo8QgVRSAENi+cYn6nA9jR0dHR0dHR8clycwPQGMx6ja0qZBiAFGAMMopcJXBn/HV0dHR0dHR0fNLcrhdwliHrClsFLv+v1tikAmPA86DrDdzR0dHR0dHR8clyu1ZwQXA21CslwvOQYYg1Gj1fdJ7Ajo6Ojo6Ojo4fmPurApYSGcd44xFqPMakG2zV9GYMAjAG4flOGzDv9K06Ojo6Ojo6Oj41blcEEkdOBubZU/R6jakqhAWdppg8xw9D0DV1ZwB2dHR0dHR0dHxyyNv+UK/XFM9fYq1FDQZ4exP0ckl1/BYZRQjfv8vj7Ojo6Ojo6OjouCNuVwSySRFBgAgjmBtMmiJ8HxEEqH4Ps9lgyq5faEdHR0dHR0fHp8jtZGBWK0zgw3hEdTTHlCVCSoLHj5BxRP7l3zFpeg+H29FxCnEuz/V04ZGUYAHsu8+FQCgFCKzRYCzNH7ltCSdpdG4nTurI2rPbv+m+4ey+r1MkdXof5//+in2AAMG7cxECIRXWnpJo6gq0Ojo6Ov7huXUvYOH7Tvuvrp3+nxDIKATApCm2rpsXbEfH3SP8AOn5zqgSAqs1OksBi/A8vP4QrMWUBTrPnR3nB8RPPkN6PsX0iHq5wBQ5QklUMkAGAdVqjq2q1kiSYYRK+piywJQFtipvvm+jAYgePUN4HuXbN5gix2p9xckJpB8gPA+hPKwx2LrCFLn7Lgjx+kNM89n2cyEVQilkECL9gGq9RIURwd4h9XqBzjKsrpxsk9bOEO3o6Ojo+Elxf1XA2x1UFbqqznymu6KPjh8If7SDSnpYXSPDGIwhf/Xc/d8PkFGMihOEkOSvnmPKAuH7+KMJMgioVwu0ks5bhsDrD5xRVZVos8Yag9cfouIEGUQIJdHZhvLtMf54goqTa+9bp2sQApX0kL5PNZuCkMDlBpiQCn+yh/QDrNHIIELnKcXRK4RsDLw4wQ8jbFWRvfoOISQq6eEPRyAVpioR6Qbh+3iDIWCRQYiQimp+Qr1Z/4BXq6Ojo6PjU+PWBmBHx8ck3D/E39mjWpwQ7OwDUM1PMGWBDEOE8gh3D5z3azmnrAqEUI1XTb3rWmOtK2Tq9Ql296lWc0xZgNZEDx4jgxBTFAT7h+h0Tb1aER0+xhuOrr1vnd7M2BKeR/TwKdL3qZcLgoMH1Osl1WyKUB4iCBFKEj14jFAexfQNQiqCnV3iZ5+js5Ts5XduY40TXoUxXm+ANxhhyoJ6tbiza9HR0dHR8eOjMwA7fqQITJGTv3qBrTUq6SGUQvX6eL0BqtdHJX2E54OQ2Fqjsw2myLBVSbWYOUMPXF5rVaKLvA39CimRUYKtSvI3L5ynUEq83gDh+zfa9+1OT6CzlPz1C1AeWIOKEmQco5IeXtJHxr0mZ9F5+63WVLMp1WpBNT3ClEXjiRT4+4dYrSmPXzfh6o6Ojo6Of2RuLQPT0fFRaXLv6tUCna6dMScEKk7wBtvQbdjk6QHW5dHZWrs8ujByhpUQLp/V95Fh6Ay4KHbbb3LkZBAgpAJgmzN7o31LiQxCVBSjkj7ecIQMw4uFJOdPUUqE5yNk85hai4pivN7QGYO+D0q227HGoLMNerNCZ5sm97A5XtPk/DW5gjTn09HR0dHxj0nnAez4EXOqirdB+gEq7jVVvZf8RAhUb0Dysy/Ivvs71WKO1+/jjyeEe4cu784azJuX1IsZ/mhC/1f/jIoSypNjqsWM8MEjpOdde9/SD/CHY/ydXbz+0HkGtcHkOVZf3TfbG47pRQneYER5cky9WRLsHbQG6qUG5MUhwVpL9t3XSD8gevwMU5boPMMUWVcR3NHR0fEPyq2rgM996WQpjOleKB0/CP54F6Ek5WyKinvIIEBnaetpO11JW7x52YY9/ckeKoyxdUW1WmCKAhn4eMMxKuljy4J67TxoMoxcFXAYucKKPKU8meKPdxDy+vt2BSghwXjShI+Lpio3vfR5kWHE4J//h7a4RPg+Js8op0d4g9G7fYQRVmuKNy/b6mChFKYsMXnabCtE9Qau2lkqvP7AnV+6ea/x2dHR0dHx4+S6VcC3MwClPON9kIGP7PXQqzW2KG54qB0d94AQ71+MbBcsd7Gt7/v355BBSO+Xv0VnKenf/8IFl94d7KOjo6Oj46fJ/cnACIEMA1Dvcoi88Zjw2VOyv35JfdwZgB2fAB8yjq5r/F1nW9/378//XGvKk2NsWXKp8XcH++jo6Ojo+MfmxgagUArvYB8Zx+82MhziHx5QPH9xpwfX0fGPiDWaejFzUjUdHR0dHR33wM09gFI4odt+/91HceQqFu/yyDo6/lGxFp1uPvZRdHR0dHT8hLl5DqAQiDBo+po6ZBTj7Yyp3hyhV6t7OdCOjo6Ojo6Ojo73c79FIABStr1QhXIdFkyWuR7AHR0dHR0dHR0dPzj32wu4KQSRSa8R5K0xecF1jcmOj4MUHkr6KOlf+M6YmlJnXFl00HFvCCRSenjSaRBqW6FNTXctOk4jhUIK9/waW6NNhbHvxL4/JYRQeDJACIm1llrnWLqc1p86p+9RcU6n1FpDqTOsvbwHescPz+2qgKOQ4OFDwp9/hskyTFWBsRRff009Pbn7o+z43gihGMYPmPR+xt7g8zPfWQur/A1/O/pvVLprE/ZDE/oDRvEjHoz/iaJaMV1/zTx90V2LjjP0wj12ek/Z7X/OKn/N29XfWOXHaFN+7EO7QBKMORz+hiTYodQZz0/+jbxaYWwXIfops71H9wZf4Mmg/dxaKOsNfzv6b6yLo494hB2nuZUBKILQ5QF6ChlHyNgJ5ZavXt1KnyzwegQqIfASNsWUol7f+LA6PoyxGm0qKl0ghcRXCUm4gxQeFoMUXXuwj4GSfmsEpmrGOn/bXYuOC3gqJA7GjJPHGFuzSF8gheRT9KcoGdALd+lH++TVCil9xEcsE/RVTOAlBCohr9dk5eyjHctPGWsN2lTU2snBeTIkCSZ4KqSo1ngq/MhH2HGa2xmAvoetauq3U0QcI8PAVQF/oLfpVSTBhFH8iGH8gBez33cG4D1grSYtTqh1zip/jZIBg+gBj8a/I/D6H95AR0dHx3WxFmNralNQmwJrDfYjhqojf8goecwwesDJ5uvOALwnsmpBvS7ZFFM8GRAHOzwa/44k3P3Yh9ZxCTc3ALVGL1eIIMAbj5GRa0dVvX6NTm/TW1SQBGN60S6+F3eej3tEm4qiXlPpHCEkUnjUpsAn/vCPOzo6Oq5JWs74dvr/omSAsTV5tWzyFT8OkT9gEB0Q+D2kvF3qe8eHMaaitAZtCoRQ1Kak1J8T2dHHPrSOS7jVk2DrGpvl6NUKU9fYsqSeTjFZduNtCQRRMCIJdtz/hLzNIXVcA4tBG4OmAqDUKcbWXfFOR0fHnVKbglX+5mMfRkvo9emFu7g3TudkuC8sFmtrTNNn3JMBxtRd56JPlFuHgE1ZUrx8hc0zbHX7xF4hBLE/JPaH5F3ot6Ojo6Pjjgn9Pkmw06UXdXSc4uYGoLVgLarfx39wgBASU5aY9ZpqOsVs3l+5GPkDeuE+vXCXyB/gq4Sd3hMCr4dSIT/b+x85HP3mwu+OV1/yZvEnzkseBF6PfrjPTu8pxtY8P/k9Qggib8AoeUwUDPFVBLgQaFlvmK6/dvlw5mzfYl/FRP6wObYhgZfgyRAhhJMyMAV5tWRTTFllbyjPVGkKpFAcjn7DOHlCrQum679zsvnmvePRC/d4OP4tvozIqhUv57+nrM/KsQgkod+nF+4xiA4JvR5KBQgEtSkoqjWbYso6PyKrFu/d312SBDttorevYpQMkEK2xSZlnZJVC9LihLScXVmt6BLGJ819McFXcbMdQ1VnbMopq/yIrFygT10zgURJn8PRb0iCCUfLP+OriF64RxyM0KYmK+dM119hsQyiA4bxA3wvRuuS2eY7lvlryjrl/H11l2Meen360QH9aJ/IH+LJoCnGSdkU02tJvijpE3g9euEucTAm9Pp4MkRJDxDUpqSq3fbWxTGbYnrhfIbxIY92/hVjNZtiyuv5f154Bs78Rigejn7LID5EyYBXs/+PefriXuQ8PBm24x35Q0K/jyeDJiVEUOuCol6xKU7eO+ZCSPrhAaPkIeP4Mc9nv2ddHOPJkFH8kH60T+AljTyJodYFi+wl880LKpMD4MuIRzv/ghCCo+Vf6IV7JMGEOBhR1Snr4i3T9VcEXsIgOmQYHyKFR1lnnGy+ZlNMqfTl0RApFL1wl164TxKM8b24qZYUp56bDZtiyjx9gTbFFaFTixQ+o+QxoTegF07wZAhYalNRVEvWxZRF9hJtSqy9eM3ubMyRJOEOo+QRk95nF75PixNezP6dst588N65i/ncVzH95h0TByN8lTBMHhB4MUr6PJn8Kzu9Jxf2vUhf8vzkv184Rk+G9KN9xsljAq/Pi9nvKesNvpcwjh+RhBMCLwHAGE2tc04237LK31DpnNPP9vcdcykUk95nHAx/TW1y5ukLjpZ//uCYPhz9ljgYY6zmxezfSYvphZzMwHNG8jB+QOQN8FSIELKdyzfF20vnlo4fL7dLhtDuARG+jzccgpSYLEVn2QcNQCUDIn9IP9wlDnaaCuCe0w2ykiTYIfR6F353VTjBa6rN9gZfYK1muv6mmZgPGMUPCbwengqRQmGtbV66J2TlxcksDkaMkyeMkyenfuc11WsCMBT1mk0+RSBY5W8o6rMtu3qBOxaASmcs0pdoe/kLXgqPXjjhwei3SKGYpy95vfgD4tRfuxd/n3HyhFH8kEF0iKcCd1zCvTSqOqMX7uKrCJm9IC0X96q1pKRPoBJ2es8YJ4/phbvN8Sg3zliM1RhTscqPEUjKenOpARh4PXrBLjv9ZwyiQ5Jg3J6btRZtKnqVM8hnm2/ZFCfv5FGEQAqPcfKESf9naFM0xuReE+6BvFqibYW1hp3kKb1oj9DvI1Eo6WOxzDbfnpGnuLsxF4Ren1HykL3BF/TCPYLm3jamptYZcTB2VZLN2F2Fr2KG8UPGyZN3+xVe++IAN1ZpOcVfxxijKer1mfMKvISd5ClKhayy15ysv8FU9aUGhhASX8Xs9j9jp/cMay3T9VecuTnvkNDvM04eM06eEAVjfBkipYdAtsoClc5IixOU9LCppajWF17WAknkD9hJnvJg/FuW+RvA0g/3GcYPSMKJGzvpnmtrdVNR+7qpURV4KmC3/3N8FVHpnF6wSy9y95Q2JUk+odYFoT9gnDymH+3hKzfm20K4efr83HEJfBXTC3cZJ0/cSzYYunmvDUnaZo7K8WTIMn9z5aLJk5FbLHkJkT8k9kenKm0tZZ2S5EcIId08VV3s0HRXY44AJT1Cr0c/3EMIiZIBoefy7RbpS94s/+wWzB+4d+5iPlfSa4ysSfvMRd4QKXyQgsgfNcbyWao6vfT+ltIjDsZM+p/RC/eYp8/bRd0ofkAUjPBVhBCuHaqxhrxesS7e3sOYu5Sp3cHngEUIxXT9d4zRlxrXUigif8D+8FckzVxztPwLp09UCg9PhYyTx4ySRwyjh/gqarX8jDVoU9IrdgmzHkLIZjH+6ckPddyM2+UAak09m2GrCp49xT/YI3j4kPL5Sz4UDNamIq+WgGFdTJFC8XD8OxKxQ1mnnGy+Yl0cX/jdIn3Fh2YPT4YMogN2+z9nGD8gK2dsiil1YxgkwbiZqKtLb95BdMBe/3P68SFltSErZpQ6xVqLkj6D6IBeuMcweuAeWqE4Xv11OyoYW5NVc7Jy7ib4ZoV3lf5V4CVE/ojQG7Ap3rIp3qJNdWZlFnoDJr1nPJ78VyJ/QFXnbIq3lPUGYw1JuEMcjDmMD53ncj3i+cm/NV6t+yH0BuwNfs7h6Lf0gokziotp85IReCok8Pr0wwkmrMmrBYvsovg0wCh+xP7wV+wPvgDcZLfMX6FNhZQe/XCXcfyYSe8ZodfnePVXZ4icI1AJB8NfUdQp2lRk1ZzYHzFOHmMxGKuJ/SGb4oRal0x6T9nt/xwQLLPXbc7K9vzuYsyl8NjpP+Ng8Cv2B7+g0hlZOWOZv0EK98IcJ08wVmOtfa9EQuj12R98wSB6gJI+aTGlqDfUpkQKQRJMSMJd9sJfEHh9pFQcLf9K3ngQLIai2rDIXrKTPCXyhyThhNoUlPXFvsOedJIjoT8EYJm9pKzWl3qS7oIk2GGv/wWD+BDTVKyXOsWY2nn1on3iYMwgOmgNgKPll9SN1+4yBII4GBOohEc7v6OoVmTVklV+hBSKwEvoh3sAaJNjrUGcMsLjYMSj8T9T1jnauMrGXjhhp/cMIQRKBvgqYp1PiYMRw+iQg+GvMFZfMACl9OhFe/x873+mHx2gZNBEE2ZuPrQWX0WEfp/IHxH4PbQurhxv5/HzCf1h652pdI6nwsYzPyEJd0iiPb45/n8uNQDvasytNaTFHGO+ZJOfoFRAP9xjf/BLomB47XvgPLedz7XRlPWGZf6GrFogUOwPf8GOekpZZ8zT5yyy5xf2tylOPnh/q8aDG/lD9ge/ICtn5OWcRZ3iqYDQG9CP9pwcii44/876vmNubO28u/kRw/gBsT8k8ocU1Yr6kneapyJif0TkD9CmYpW9odLZGWPR92JG8SOeTP4rw/gBVZ2TlifkpSvccXPFmL3B5/SjPZJwl++m/8bmEgO348fFLXUAA7ydMf7eLjpN0X//GoSgPvlwaX2lMzb5MVk5d14P6bHb/7kLr5icZfaG2SVh0/IaorihP+DB6J/IqxWv5v/BOj+m0hna1kihGq+Jz7p4e6mHbJUfYazBW/+NWueUdYY2FS7cojjxB+z2fs7B6FcMogPScsbx6ktOP+RpOWOVHzGIDoj8IYPokErnZwyMLb1wQj/cRQrZ/u6doehCyqPkIQejXyOFx2zzHcerLynrtAnrWHwvoh/uczD8Jb6KGSdPWKQvLglR3x2BlzBOnhF6PbJqwYvZ78nKRav9tO1qEXhxY/CvLoTEpPAIGgNoFD9Em4qTzTfMNt9QVBus1QihnJHUe8Le4AvGyWO0qVjnb5tJ7PTkKhDCY50fM9t8ixRu0n84/h2DcJ9l/oYXs38nKxeEXg9P+kTBkDgYoaRHpUVzXHcz5rI59knyjEF0QG0KjpZ/YpY+dx4/pJucg1HrSX1fAVRRrzle/o355jkWJ6pam7K9j4PG+/Jw/Dsif8A4ecps811rAIJ7hqbrr0mCHSJ/xDh+RFlvLjUAA6/nPC4qoawzZpvvLni775K0nPFm+Wemm29cV5p603puwaWOjOLHbrEYThjGD5muv6Z+7/taMEmesSlPeLP8i5t3qkXzTIvG05uQNmF4iz2nVOc8zIv0hTMapeLxzr8y6X3GMH7EPP2Oo+VfyMo5g+iBu+b+gMgftCHmLcPoIfuDL+hH+1Q642TzDfPNd2TV0oUurfNcOaMypqhXaFtfGTL1lXu23q6+dIZOMXs3z3kJD4a/YRg/aEKhk0tTHe5yzN8950XjndOM4sdtaPQ23HY+3xrrebVsvPaSQbSPTR5Tm4JN8Zbp6uIisnpPOsQWJcPG8Jvzav4HNsUxeb1G6wopJUqGBCphkb28dNF/F2OeVysW2Ut64aR5Th8zM99cagDG/ohB5FI41vkxi+xlO0+Dm6f64R6Pdv6FwEtYZm84Xv6FvFq24WslQ+JgxP7gl22UbJG+bMPVHT9ebuUBFEohAh8ZhVQnJ+jZHJNdvRI/jTZVMwEDCJTwmxeZab2Dl7nOr4OSAf3ogHn6nNfz/ySt5jcKha7zKWkxazwy+kKOhBQeEsWk/zNCf0DsXyxtz8sF6/yI2pROeiB+wDJ7dcEAEgh6wS5JOMFiScsZm+JtG46TQhJ6PQbRAeP4EYvsNdP117ya/4Hzq8qsnOOriL3BL+gFEwbRoXux35MBqKTfhmqLasN0/RV5tbo0BCqEQODCCKfxlMurGcYPCP0+881z3q6+5Hj15ZkXpxCSyuRNKNWFKPqbPVb5UTNBOVyFc8m6OObt+m8IBKE/4HD4G3wvoTYlrxd/otY5STihF+4S+v0md9Frj/OuxtxrJs1+dOC8RMVbjld/42TzTXt+AolSIUqGDKLD91ZjF/Wa6earJmSpL3gqBJJNOHVaZ/Ehg2j/gkexqjMW2Sv2qi+c9mbyiFV+xDJ7feH8Qs+FqjwVsM7fMs9e3mtnkqxcUFSbU8/e+fNTVLpg0v+MwEtIwsm1JKOiYERazXkz/yOb8urcvMuw1lCbkmX+iun6awSScfKYncYb7cJpf6aqnfewF05cjqeKkEJhrG3mEMEwPmQn+Rmeipilz3mz+COzzXeX5mBuBZM/pJtXm4rj1ZdN15jT5+VSD0K/zyh54iIRXp9K52ee0bscc4tpNf8AgqrXzuu35bbzubH1mWIPgXItyLAYU5NX61u/Y6T0SMJdVvkRr+Z/IKvmNwqF3sWYl/WGZfaK/cEXzQL0Mevi+NIczTgYMYgPkEKSV0tW+ZszhqLvJfTCPXb7P2eVvWG2+ZZXiz+cMRLBRVg8GeCrXzGIDhlEB2TlvDMAf+TcXHPFWkyWUb16Q/qHP1IfvcXkH145/RA46YHXrPI3Nzb+AIytqE3ppFEumXyNralM1uRsSbymKOA0Rb1uXjQpgZcwbBLozyOEIIkmxMGYSudk5ZysXLQTppQ+g+iQONjBAovsVRMav3hclc5ZZC8p6zVK+vTDvXsVd3YvxgKBIPCiJhRzWfjSYq25tF+pyyF8Suj3qeqUt6svWedvL7wwXHjphOPVXynrtfOqNS/g0xirT61a3ctTm5JCbyjrlKrOqOqt7I12Xllbu1Ce8Btv9N2NeeD1GESH+CqkqNecrL+mqFZnzs9iqHXOppiyzN6cWhhdMea6cJ6qS16qWwO4qNdoUzsj5JzchW702LJyTm1K+o2X+uJLXTjjIX6EEJK8XjUhxvt7zreiwcZWl3q9LC65Pq+Wzgsro2tJRqXljGX2mnVxTK2vt0jdok1J2oyVOwZDrUt3P+ntPeXCadrWrt+tNc672/TBFQiUUE2IfodaFyzTV5xsvr3UY+P2Yz9o/OXVimX6krScXXpeWTknLedgLZ6K8L34wlx1X2N+V3zf+fw+MKZmnR+zyt6QltP3PrOX/v4OxrzUKevimKJeo1TAuPeYQF3uaY2aReh2fkyLk/aYBdLlt4a7KOGxLt6yzF452ZZzaFs3hXhzhHC5+pc5QDp+XNwiBNwUf0x28A8PEEq55FWg+Oob6pOP1wvYJcHPKOv0lpOFSwCP/TGh3ydQCapJ/pdCugcm2iPy+41RJy8kDW8LBNb5MaP4kUvQDkaNALNbpbtCmAGRN2yKSY4ozuUJSqGIgpE7Bum7CjQVs9c/28fXbc8n9PrEwdgl7zfVbvdFUTuvnxoGhP6Qxzv/hXX+lk15Ql4uyKtlU/F39UtMqcC1CJJhU/V4cqWHqdIZ62JKbUqSoEcv3Lvg3doapafH0FqDMRUI68IszYRrmy4FzuO21Z4UdzrmnnLFTlJ6lGXa5i5dxFLVG/Lqw4U7AoiDcVOh7pLhlfAbUW+Jp2KG0SG+it25XujMY7FWsylO2BRvmfQ/ayu5N8XUGcRN9XPsjwi8mFX+tnlpfLhS+fuipCLyx0T+wJ2fDJDSFbq44o4hSbDj2krV28Ks92Ep6zXFthDohsdvrDPQjTGnPtNtnq45tU1rTROytS5NZntPNcURvufE1rNyTl6vvncCfaUz13VBl5ecl0WboqmYt0jpNfeJuHAJ737M747vP5/fPbYxpFyB1e2O6fuOuVsMlmyKE2J/TOSP3JygepTapWlsU2wi3xUarfOpK9w4HZYWLkriFtOCQXSAQDRFX2eNU5fS0qcf7ePekxFKXXRsdPy4uIUBKJFRhL+/R/yrXyDCCOE5D4KeLz6qAWhMTVGtbzm5CsLG5T5OnpAETqZmWwkspXs4PRWcW5VdnBBrXbDMXhH7I5LQrbCyctEagL6K6UeHBF4fbSrm6fML+VXbHLLt/rfVqB96Cdc6b8Kt9/eyLqoVb1d/I/JH7PZ/zm7/CwbxA7JiziJ7xSp/3Sal17q8NBdGCo/I66Ok34Zsan35ddOmcgayqZHCJb5fNHCtq4Q742FrPJDU514gFnO6NZVwvpG7HHPZ5JdJodC2omw8c5f+3pQXErPP4+QjeoySJwyjQ+Jg3FTqOQmJbWeXbYXru/D4xbLGtJiyzN+w03uyqeKHAAAgAElEQVRKHIwZRoeuU4Oum9W980xL4bEppk2y9/0af76KiYMh4+Qp/XCPOBijZICSfnN+rmrbb8Kr7tQ+bIyUOqPU2a0O3xUP1XDqulhrXBjecDatwdrm3tvuyKU/uBfx9j53xsNNPZGXUZuCol5jr+gEbJpUAXckEikvhm7va8zviu83n98PBkNZb86kn9yEuxpzazTr/KhJPzloCtOGlJnL83RRiX0ibwjWqWhk1fzMNgQQqLhdnPSjfXrB5IMLpUpn7rnotJ1/9NyqCETGESYvyL78Cm80RIYhCIHJ87aU/WOwDYPdfGXmEsIPhr/hYPhL4mCHSmfk5bLNNdOmwlpNP3L5EqE3uHJrtSlZpK8Yxo+afIlDp+vUVDc7OY7HBF5MUW8aA/CiQKmrNJYYWzFPX5BXqw+GHIx1ydiu0vp+cKGxGc9P/juL7BWT3jP64T6D+JB+tEelf0FWLZltvnVae9mrC9sQiFa2woWJr054N1Y3oXmDEAJP+k424QKWy2Yla+21u53c1ZgLZLOib+RsLskpfXd8zqi46hil8BjGD3ky+a8kwQQlPfJyyTp/6wzn5p73ZMBe/3N6ket4cBVpNXN5qrogCoaMkkdMN99Q6QwpPQbRPkmwg8U21en3qfvljKTd/mc8HP0zcbiDtYa8XLIpXrfFNwZN5A3YG3zRhJ6uZ4gYUzkv8C3fVtZeedWuNc+JZm4RQkIja3QXldTG1G1R0s253zG/K24/n98j1mJMib1kUft+7nbMjdWs8mP60QFAoy25xzJ/0yoKjJKHbfXvMnt1ifSZQMp38lOr7Ii0PGnu0avvK4txFezlx3P2dNwNtxKCdjl/S2fwaY3q9xBBgNX647Z8se5letNj2IoHO421PSqds8xesUhfkFdrF1o0NQb3Eh8ljwne460xpiYt5+TlAm2rVpB0m94dej1GySMEkqJakRazC0m37nwsWCcuuk3Wv6xi8+wQuPDAfSbnuvy6qg1rVvWGVXhEEmzDEQMG4T6eDPBkiEQ2Idxz8hGY5uX6LmR22YtaOHOqTY2/2sNpv/+q9M7G/F0ml/MEqQs5WNdDkAROZHen94xaF2zKKSerr8mqRSMhUWGsxm/00mIzfm+/U61L8srplCXBDkm42xQJZCjh0wudWHJRrZpE7/ur/lXSoxfuOc9m8pBK56zzY04231BULm1CN89eP9xjlDy6kP/5XuyH8+k+sIErNnv9bbo5Cbd4lupO8ui23u3b3PD3PuZ3xS3n8/vGjf3Njumux9xi23zess7aBgYShcFJCg1jp1+a1+45vjTFxr5byKTlictVrjcfWKRY1+P3HueFjh+GmxuAxqBXK2yeI+Im/Ctw6eb6tiu1j/uAB17CpPcz+pETD56l37ZVeufph/sf3J7FUGnXBaOoNkT+iNgfNaKvTgy0Hx40idqzRl/u7IpyK4JsrMZgyKsFy+wV6Se06jK2bopX5oj13wlUzCB+yG7/Mya9Z4ziR66rh/SoZv9OXZyq2rXa6Zz5phGO9ZvKyYsrayFUo0qv2srM81XFd8FdjrnLP3Q9MGVz/FdVULrwrWpFhM9/txXBDb0+y+wVR8u/8Hr+R4w965mMmmP/4LFhqeqMefqizRNyxUhZ06ligpQ+m/z4TvLV3oeSAaP4kdM9kyEn6294vfwTx8u/XPhbX0Zt3ubHnjOui8E0OYOmEdd2At53w+3G4Kc+5p8idz/m1hWLlEuyctbkDU6ahgqCQCX0w30slqyYUVwayXCRie2cUVQrltkbNsXb96ajdPx0uN1S1FpMXWM2KdXrI4q/f032xz9RL24adrRnbrQfNsDwDimccrySAbUpWWVvKKrLekYKvK3C/TUkKPJqyTJ/jUUT+gOGTTjYqfYr0nJ65cPmko2dfp5EEgcjQv8jrMKvibWm8Zy+5MXs93x9/H+zyo/xVcxO8rRt37RFm4qsnKN1gZIesT+8UgjZU2GTK+NTm4q8vJn0wvXP4e7GXJuq7cShhE/kDa70ynkqxPeSS8PaAifUuh2/rJw3bZwu696hCFT8XkHpLZXOmW2+IysXeDJgFD9knDxmEDvZmqpOmaXPqerry6bcBiEkod/DkyHGatbFW/Jyfunfug4PvSb/82PNFjfDyY6sGsFuj9gfX3gWfmh+6mN+kfNtHn947mvMS71hlj6nNkXbmnAQHZKEk1ZsfJm/RtuLaSwWKKp16+EP/QFxMLx0Idrx0+TmBqAQiDhCJgkyjrBVhV6tqGdzbHnzl/K2bRhYFy68hmF1H7S3fKNHeN6T4pLjx23y/XWOM6sWLLPXaFMRej2G8YOmQ8gYcMrzm3x6aThB27oRM12BEPSjA1eu37Z7uuwcZNPJ4H4f4Dav6dztY6x2XQnytyzzN1Q6RQhJ4PUQ57weTvTb5b64Pq2Pr8h5EY2w8WN8FVNr97ub6Lldl7sc80pnbMoTalPhezHD+AGBumjkCeFal/WCyXvCtu8qAY2tXTHJuVtmKwQd+D3UNTxM2lakxYkzwm1FP3Jhp364j5IBRb1mkb64dbL7zRAgmrngCi/mtnOJr5JLCxo+VYzVVDpznRp03vZzvp6O4X0+xz/dMT+Pe8c4+SQl/aZ/9sfg7se8rFPm6XMqneGriEF86FoeBhOEUGTl9h10Sc6iNW3nKmM1SbDTtL2MrsixduewrVbu+PFz86soJd5wiL+/h39wgIg+7G14L9a2zcpDv3cqVCbO/bs/rNWUOm/a8ahGHDg4s38lA8a9pwzjB66n5DWOKS+XrLLX1LrA9xKGyUOG8UPiYIgx2lVYlidc5ubXpnL6V833o/gho/hR0+Zru/93/wQC1VQou/7FH7604szYimuv/LbSFu8Mo3P/2nw+2ObCnT/Hqs6Ypd+R1ys8FbM7+Jx+tO9EmZFsM/+U9EiCCfuDX7i8tHrDLP32XvJP7nLMi3rTCIK7l/6k/xlRMD6zHVfxF5I0QtJKXJTusdAI7LrFlesUEZ7KmXT/euEek/5nhF7/TDuzq3D3/IasmlNWG5JgwjBynSOkVM4AzF7dScXq+4/DtkVWopGX8M49eyAYxg8YJ09cKsCP6uXjjI+0nJFVczwVMowfMuk5UeiL85xoe2rLM8/RHR7RDzDmFxdM4pY5sHeBbRsQ+F7UCHW/m2d+mHfM/Yx5WW9YpM/dQlqFjZPhIb1wghBOeHqdH19ayOaKvKasi7doU9KP9hj3nHC4k3i5eG8q6eM1aT3XuyfO99bp+JS4XRFImqFGI8InjxCBj1lvsBbq6RST3qxbgLsJT0jDOcP4kIPhrwm8XiuK7PLDvCYh/2I16V1Q6pxF+oJR7Ay0B6N/IvR6TUWVwfdiYn/MKHF5WGl58t4q4PbcmtX/ppjSj/boh/sIBBbDKn9NUW8uFd1sfo02FYv0BS/n/8Fe/wvGvSf8Uv6vrPOjpjq1bNuu+Som8kdoU/Ji/nvn2Wn1y0Tb+9STYZtz1m+6RUip8FXY5JkN2m4TtSnaSeu0ATeMH/LZ3v9EXq0o6tW7CjZr2nZYvXCXfnTQ9t48H0p0IeAFJ+tvXJ/l8ICH439uJHPmrhewUMTBDoP4kCgYscqPOFl/3QoZ3/10cndjvpWwmG2eN4UVuzze+ZemhaAzMH0ZkYQTYt+1QfSJLzkk00ixTNnrf8FO8qzpI+yq+4RQRMGAQbStwC4QqGsPTVrMWGSvOBj+ijgY46mIvClMMubqyuy7wjQpF8PokFH0kL3BF3gqJPKHri2jCon8QbNwGpOXS0Kvx48tH22evkDJgNDrk4Q7PBr/C/1w3xngtes3vm1vFgcjsmrJ0eJPlDq9sdjwh7jrMRdCEnr9M3PLINon8GKkdPmvo/gBgYrbqt7aFI2w+eWCyHdNXi1Z58cM4wdM+p8Bzjhyz5DTa8zLBSeXtCG9C+7rPjdNTnRazIj8Eb1wt3nH2Caasbgwf589Lids/d3Jv7E3+IJ+uM8XB/9LK/pcmxKJbPteO+Mw5MXs9865cUrbVCDdO6aJkLn5e+zSW6RCWkk/OmDbm90Yp6npCmB+mPug4yy3MgBtWWIrNynJKGruLYteXmxFc40NssreOLHUZvLzm5dQW4kknPTIfRmAWhes8yMW2cvmpT5kp/eMKBhhrUbJAE8FgG3c5Yad3oeFli2Wutl26PcZxg8AQVrOmi4Sm/fe9Bb38j9e/hUlPPrRAYP4kMBLqHWONnXbT9mTPkoGZOXiQmhJNsnnO71nxMEIifuNG+u47R+6N/ii6ZDhHs60PGGVH52aRByeDNvK5tqU7yZyawBB4MX4KqaqM5bZK6arv19oS7dtHTVPX7QeD1/FTHqfUYauVZJo2uEJoUiLGdP1V8w237Vhyet4um7KXY25KwTK277Ern3eDqHXJ6tcs3gpJJ4MnSGdpZdW/dnmnltlr1lkL/CVCycr6b/zJngxUohGM60iCSZEwfBa55tVc1b5a/YGnxN4PRCC5eo1aTn7QSZkbWvScsoie9WEvmLGsRPg3i4oPBUhBE7Q2VSI+CE/Nl9CWs6QG0XsDxlEB4R+n53eU/p6v9WRlE0xlK9iZPb63tI57nLMBQJPhq2mpJtbVNt+Tgp3Prv9n7uFoq0wRpNV80Zi6OQH0flLixNmm29dj3IVszf4xdnCCAHz9Pm9GYD3d59btKnZlFN69W7TU1w5z+DmlXuPfuAdk1cLjpd/QQrlNHDDXXwVU4b7jTi8q15XIkCpAGsN3jkdVtEYids0kq2mYeAlRP4AJX2sNUyad9C2FV7eFJ5k1fzeow0dF7mdAag1ej4n/4ozZeS2vl0V8CJ9jjYF1hpGySN6wW6jb2SodUVRLfHk9ww1vwdtK7JqwZvFnyjrlMPhr10PxWa1UlRrVsUxR4s/o01JEu7QD3evue2aZf6GXrTPpPcZ4ISiZ5vnTXP291PUa6pNTl6t2EmesDv4gl44cd47GaBNRa1zSp2xyo+Ypy8oqrMq9VL4RP6IJzv/hVHypP3c9b91bvzIG/Bo/C9n1onT9VeIxX+2K7QtebXkaPklg8gJmfaD3TbHT5uSUqeNd+9r5ulzltnrKyehVf6Gsl6TlXN2+5+x03vKMHnYNJQ3bePz6frrRpbn/vQNt9zFmIPL1ztZf01Zb6h1zk5TaZ6EE2pdkJVzZptvOdl8g7GGcfL40uMpm7B3bSsOh79iFD9mt/85bvIvSMs5b1d/Z5G+IAqGHAx+dW0DMC+XrOQRZb0h9AYIYFUc/WDV5ttr/Hb1N2qdczD6NYNwn17kjOSqTkmrOUeLP5OWc+LAdUFR9zgf3AfGVKzzI/5+/H8x6f2M3f7PGEQP6IUTPBW67jSmpjI5aXHCppi2i7G75i7HfPuSfzT+V3Z6z059vg35urDhg9Fv4ZQgzzx9ztHyz2304L5Z5W9asfVx8oRBdEAv3AFEW6iTFlPuq9r5fu/zrV7nHrv9z5FCUdgVy+zltebLSmcssryVP9sb/IJ+uMc4eYySAbbx2JZNNGuZuQXiaRUCKRWBF3M4/A2Ho39qPxdtapHLfzwY/fpMzvsqe81L8R/oddkZgB8BcV09IyHE6TYHCM9DBGdbwdg8x9Y3FcgEEK33L2g8gVJ4rdaVNmUbBjuP65AwcDkPSNcjsdHuuymeilyIJhi7ghSpsNYZNZXOnFekcdf3QxducyvGq8dw6xZPwgm9wBmNeb1inR9R1tkFKY+rtrJtPRb5w6YDhN/IouhG9b+mrDPKeuO6OpySUxFNaGkU30zPK6+XpMXswvacmv1OU3EaILdtpqC5XhW1KcirVaOa//6CDef9S4iCYRNKcn1Ut9sqmnO6KJfjQtuu5d6AtElo3uYHxsGYQXgAwhk6y/w14PLoYn9EEuy0K/+q6eN6V2N+Gk9GRMGQyBvge+7eNlZT64KiXpFXa8Cy03vWhMZn5OXyTOWeFB6+F5P4YwK/3+YLWqupTEFeLijrDUq5cwv9QaMx9mE9yH64zz89/t/ph/uUOuWvr/8PZptvf6ACEIeSgXtOgp0mB9fD2qY/ty6anrwFngzaAoqT9TcXnnOBIPSHTcHWDpvibaNleFlV/0VEm+/7BCV8J3NUzdux6If7JOEOwJk5yVdx0/nHicjP0xeuM825hY8QisjrE/oDAi9pO7mAC+eZpqdwUa1Jy5Mzmpeh1ycKRvSCCUW9YlOcNM/ERSMx8HrE/oh+tE9WLsiqOXm5PHM8dzHm2/Ha6T29VlrMlqJetwUI56MLdzWfn0dJnzgYE3quzaeQW6F2F0bNy0U7R5xGCo/IH7hx8hI2+VuyanGrQrS7us/PE6jELcajfQSSqlmclvXm2mPnvM8JsT9yuZJNSN8J6eu233VZp66jzbnwr5I+w/ihm1evSalT0uLkwvY6vh/W2muFDm5lAArfB+mU7W1VIZRCJjEmy9vQcEdHx6ePFB7D6JBfP/rfCLw+m2LKl2/+T5bZxRdhR0fH/SOF1+RLVk3v3nfi9y5i4wrrtkU1W7+qwHUb2bbJdGoWNRYaZQDRbGvblci2BZeu4827fWyVLmpTok19qezUO1x3Jl/GaFs1Oq11+53zAkoEojkep3G4VStwbTndvp2Q9XbR865syDbHJYXCYlz3pOa8pVDNvrfH+m6R805z056LinmIM/veKjq881y7cXr33WX7vnzMdTO2p89Pb7dy4fya+Ol7xvfmXNcAvHkIWCnUeIzwPahr6sUCmfSIPv+M4pvvqKfTT065vaOj43JcUcIugUqo6pTlDyb90tHRcRmh6nHQ+4JNOWVTzdpCD2OrVkdQ27KJvNAaPUr6xJ5rOiAQrMsphXYe4sgbNt5GQ6GdV9BYTaCafuWmomrSsJQMGEePiL0hy+KItJpR6KtVF5TwSPwJe8nPWJdT1uWUrHL1AFJ4LgIlQ9fCsnKRDSEkkTdw3kqTU5kcrOtgUpsSbWtUIzdjcWlaToM3oTbOyNx6DAPVYzd+yrp8y6baFgg6YzJUPQTSRWtM2hhnglAlCKGodU5tSmwjQ6ek1xiHgkpnlMZ5ea/a92VjXuoUiyHyRi68b/JWZN+TAZXJmxxwry3YuUyn8YfgxgagUAr/YB9vNAQh0Os1wvPwJjtUrzqvQUfHj4leuMtO7xlKBiyy15yk33W5OB0dHxHX7zwgUD0sFl8lpNUJWbVkGB5Q6pR1OSXxxy69pV4RqMQZWcIn9oYIIUmrBVFTiOPJgFAlKOlzkn6HkJKwUYXYtuxc5K8p9LrxqEX4Kr5W20IhJJ70CVWPTCxbr50zmgYk/hhrDaXeQJN3rkTgQuvegLH3iJPsO2pTMAgPXHjd5PT8HSpTUOmMWA2aY3X/inrNsniNsRopRJues/UZ+jIm9HpE3qDV4qxMTqBC14gBhZIeXnjAsjjCWs0wfNAUtwi0dalHgc7aTlVn9/2GyOu7MP65Md96/tS2wYTwmGauGLAf7LIpXdpI4o/J65Uzrj+Sz+zmHkAhkFGIGvQRvo+MIqzW2FpjzafXt7Gjo8OxrTSEbe6Wzyh5xCh+4ESwyymr7A21vv+k/I6OjvfjDMGQfrDrcgRNST/YJa0Um2pG5A1AOJ3Qnj/BawTct143eGcICZzHzensvkDJiGF4iLEGX4Yk/tgVv5mMd9bI1YLVF7A03izdhq2V9Im8Hv1g1+Vx18vG86UIGh3EyBuwEz9mU55gbE2oehir8UzIIDhgU7kq8Z6/63QTBSTeCE/4bKop1lxub6jGIN0agACiMZRH4SFZvXQqD8EuhU6xVjOKDpHCQ1tNWa9dKFxG+CpkW8Tybt8nbsxVdGHMt21NlfT5/9l7kyDLsvs+7zvn3PGNOdc8dlWjq4EGiAZAAqQIQpTEoBiyTVuyFgzbCy/stcN77xzaeuOFIhwMe+GQ6EGWLIcokiAJEAJBTA0CaPQ8VNeUlXO++Q5n8OK8fFXVnVVdmZ01oOt8EYXh5Xv3DO++e3/3PzbiuakldQ2AVDXQUYkSMZ30mBeaD7CuPmoOLABdXVO+f5V6dRWExI4ns8QPWzzatlGBQODwZHGHpfYl/MXZB5l3suPEKmdrdJX+tPCze1KPo4FAgL3SL4UeMtE9lEx8rUCZfcQid8fileJw9Mu1qcDKZ6W2pFF00mM+O1gP0K4mlS3yyNcfBUdpxj5WzTlf/9WWRKZgogcfm5yhbTV141aUekSpR7NriLHGJ+fVvak72/ix4y6JapBGDdQ0ibA2Bb1iddoNZY5E5YxqX0ljz1ppbIV2NdpVDyxEXZkxAuGtdDJDRJJRtY0SEbHM2alv+RI3UzfsXhtYqNG2YFTt+C5WMp8mN7l7xt4rrebgI3sey4xE5SSqQaIas/jL0gzZLVZpJovkUWfqzrfoI67zeRAObgG0FjMYYEYjhBDYqgYbCjgGAk87SdRgoXl2+oQaEakU62p2xptsDN5hWGwE8RcIPHF8kkeicoQQPj7XTGalWCKZMp+dopkszDKRa1sSiZh2ukwedxCoWXwZ+H7jvuuWN9YYV1PowbSbky+5ZZ2eJmlMEzJE5N23OO++feB8/WcS1SSPOkx0f/Y3n8xyJzFEyYhENYlkMouD20u6KPSARrJAqppoV01jHw2lHiEi7zp2zk3jIGuUiEmmlr69BAxbbU8tcF54KRkjZTSLdazMeGYVTVTuM5CdmVWBsE77YxPP1q5EfO/YtqYyE2KZfWTP1dR9v2cJ9Ikl0/7wjOiI4ygZ3VXk/cldcw/VFNEZC3ba4svaWVkYp3UQg4HAU8pem8M4aiBgVvdrZ3yd7eHVkPwRCDwFWOeLyHtLUoNYZjisr69a98jjLp3Md1bR0zaqhe6TRW066d7rpc+cFRI5Tf7Yy14VSLQtGVQbU9dxikXPki/8HPx/N+I5alt8jABkVkonmZaRKcxwKpY01slpFrPHZ87KWSmsSk5mwlO7CqZiclL3qW2BdZpR7euS5lEXi5lmJ9dTIZlOhWCLNCpn7l0lkmm5LjO1nDpqW8zc54nKvVXR1TOrp0Cgp0LS/++CQpdTYXtnbG0rJnUPkciP7jnefe9LOpW+bBg+29g6jZyWOBvVO7MWn0+Kg5eBkZJorouIYpzRmMEAmeckp09T31pF7+6GOMBA4CkkUhmNZG4Wq+KcRZsSbQtqXTyWzh+BQODB7JU12StJIoSknmarKhmjRDztxeuTFWozQUyTGrxFT06TLsZ0sxM043n65QaRSkhVk1G1TWmG/ngi9oKEaa/iaTZqLH0dwD038scJFSkiUtXA4cVjZSbIaVtAgcRMrYsASsTTGoPTfugoCnOn1mMsc19w/66SMkrE07VHPit46nYWqGnCTDbLlta29O39hG/ZKVCAncb6+dhEJSPktGVmrSfYqesWsVfLVs8aJTh83OKHx57N6UN77tvgRdM5+DI7hR5MxV/EidYLAGyM30fbR1Xs/RHVARRJTHrhPKo19Yv3B4gkIT62wuT1N6hvrwUBGAgEAoHAI+N+HUvufb2THKMRz3mr2DSTdVzvUOgh2n68xf9uN/LDz4v7zO3x4+cPj3Y+D9c9RomYNGrSjBfQtmanuPHI5vXI6gAKqYgXF4lXlhFxjC0KMAaEREyLQwcCgUAgEHhU3O8+e+/rY72LcTXtdBnnHKUZUTxEYsedox30fv503f8fT0zzw40Ry3QWH1mZ8UN/7lFycBfwtA6gauQg78oCFgK928MVIY4oEAgEAoEnzV4Hi2jaU9g6PS18HMI9HjdSREQiniawmI/prvLJeLSt4JLY9wKWaloDUHsRGKx/gUAgEAgEAk+MhxWAH1/me7+DVzW2rHBaIxsNVLPp3b/iocYMBAKBQCAQCDxBDtUJRKQJ8fIyyamTU5evQOQZxTvvojc2j36WgUAgEAgEAoEj4+AWQCGQSYJqNonmuqAUIk1IThxHNhqPYIqBQCAQCAQCgaPkUIWgkRJbFFS3VqluriKzDNVq4erau4FDLGAgEAgEAoHAU8vBy8BEEcnx46i5OUQck0YxQimQU2NiEH+BQCAQCAQCTzWHiwFMEkQcgXPIRu57DA6HYB1EEWj9CKYaCAQCgUAgEDgKDtUKTrWaoBQYi7MGHAgpEWmCMwaz2wuWwEAgEAgEAoHHzCPrBAKAkESdDmpuDjse4Wpv8RNJAtYiohgzGISi0IFAIBAIBAJPIYdyAcs8I15ZJj17BjMcYusa4cCMx9iiIE5TMBodBGAgEAgEAoHAU8ehCkEDmOGQ8sYtnHOodptoaQHT71NvbCKzDBHHRznPQCAQCAQCgcARcXALoHOY0dgngqQZ7FrseIyIY0SSoFpN7GiErapHMN1AIBAIBAKBwCfl4ALQWuxggE1imOtSr+9iqwohJcmpk8g8o3jnPex4/AimGwgEAoFAIBD4pBw8C3jv/8cxMstwWuMAoRQyjnHOWwSd1r4sTCAQCAQCgUDgsfCwWcCHE4BKIeSd44ssI+p00Ts7wfIXCAQCgUAg8IR4WAF4uF7AWYrM89m/eHGR7PlLqE77wIcLBAKBQCAQCDxeDtUKLj5+HNVqzF5T7Tbx0iJlmh7p5AKBQCAQCAQCR88hCkE7sBZn7J1XqhozGvm4v0AgEAgEAoGnFBElRI0mcXsOWxaYqsBWFc5ZZBQTt+dw1mDKAluX2KrE6fqjB5ISGSXE7TlknMze73SFc44obxE1Wv71qsDUJU5rZBQTNdogwGqNmYwAh4zThx/7KPbhUDGAUgB3XMwyz4i6cz4GcDI56jkGAoFAIBAIfHKEIOnM0zx9ibkrX6LYWKXYWqXc2cDpiqjVZe7KlzHFmGJjlXJ3nWpng7q/85FDySQj6c7T/cwXSedWmGzcpNxepx7u4IyhdeYyzbOXKdZvUmyv+eOM+sTtOdrnXwAh0cMeoxvvAY5kbvGhx34Qj7YVnHUgBSKKQEqwDr27G2r/BQKBQCAQeHpxDj0ZUY/66GLMZOMmk7XrmGKMsxYQmPWCIAQAACAASURBVMmIcnud8c33MOUYU+5v2HK6ph700KMhQsWMb75HPexhqsKPMx6gRwMmt69RbK9j64J0/hgyzZisXsPWJSKKaZ66QLFxCz3sP/TYR8HhBKCUqFaTqNvFOYfT2tcC1BpnzBFPMRAIBAKBQOBosFWJmYwwxZhqd5Nyew2m3lAdJ/71/jbF1urs9f1w1mCKMaYYI5Si2Lo9ded69GSMngwpt9epdjcQQhKd7iCkYnTjbcxkTNJdoH3+CuXOBnWv/9BjHwWH6wWcZSQnT5BduoQdj7BVDQ4mb7+NXt94BNMMBAKBQCAQOGruJ7IOIr4e8hgChFQIIXwNZWv8O6QAIe7/uUfEoQSgSBNAYCdjnAOZJggVIaLYL+IRq9ZAIBAIBAKBw2J1TT3skcwtI6SiHuyix0OcNejRgKjRoXX2eerBDvVogH2AK9aUE6JGm+bp56j7O9TDHnoyxFYltpyQrZxC5Q3vdh71UFmD5tnncbpCSEW1u4UpRoca+5Nw8DqAU2xVYXb7OOM7gThtwNmP/VwgEAgEAoHAk8SWBeXWGjKKSbqLqKyBiCKcril3N/BJGUuovIWM4gceqx720eMBSXueuNVFJilCSPRkRNXbJspbxO15VJxS9bbRowFJd4FkfhmZZj7+bzQ81NifhEN3AomXlkjPnkbNz+FqTb22RnVzFdPrPaq5BgKBQCAQCHxihFTIOPGeS+dmJVoAZJx44SUkti6wWoO9f36DiGJ/LBXjdIWtK5zRCBXNxnDGYOsSnJu2zs38h53F6hqra3DuwGPvxyNvBafabaKFeUQS+zqAuz3McIgLmcCBQCAQCAR+GdiLvfuIFhK+2t1BQtruFwInxDSs72GPdYix7+LRCUAhEEmMSFJkkmDLElfX0+LQ2peICQQCh0MqhFJgrb8ACHnnIiCED7Nw+PJLzoFzvnTBfuEXUiJVhIgTH2xsjD+uc/5vSQLWzf523+MEAoFA4JeGR1cH0DmwjqjdIjl1ChFFPh6w16NeW8MMhgc+ZCAQ8CTdeZKFZV9XSkpUmmPrygu2NMOMBjhrUXljWn6pQI8GmGJyb7V4IYiabfLjZ2leeJ5qe4Nqd4u6v4MzmqjVoX3xCnoyptrZpO5vo/u76NHgyS0+EAgEAo+NQ9UBdNZiqwpbFETz80TNBqrdxAwGQQAGAp8AmTV8YHCSI6Yll5w2iKnFTqcZzhhUloO16PEQPR6yn2vBGYNQirg9T7W1ji0nuLrCWYNAELU6vt1QMcZVVajhGQgEAs8QhysEbQymP6Ca3jDilWWixUVEmh7l3AKBZw4Zxai8OQtMFkohpPSuYCGIVeRdtVIipYK9elIf7sPtHGY89KUNhj0mtz5gcvv6zJ0sZIQe9Clu32B07Z1QuikQCASeMQ5dCDpaXCBeWcYMBkzeehuMQe/sPoIpBgLPDs5Zb4nbE2TO4axDCF8w9O6YXecszlo+No73vn8Poi8QCASeVQ7nAnZumvRhsGWF6fcx/UGwIgQCn5C9uL69JA+na2Sc4PCFS/3rgK5hVh/qAa2KjPZFSlsdkoUVzNgXJ3XWYMoCleWkS8cx4xGm/FAcYSAQCAQ+tRy8ELRzuKKgWt+gePc9XF0j0hQRRR9qZRIIBA6KqyvfS9IYbF2hxwOsNVhjMGNfKNQZjR4PvFgTAp8uvD+2Kqn7O6Qrp2hdfIF0cQWZZjijqfs7xN0F2pc/R7ZygqjRfHwLDQQCgcAT5RBlYEDECfHyEvHJE7iiBEDmGcW776O3th7ZZAOBTzsyzVBZExGpaYkWjYgSAJyufYkYBM7UCBWDs9PM3v0TOGSSErU6yDidtRmyZeGzhFttZJIhhECPht4CWIc6noFAIPDLzKMrAyMkMkuRrSZRp0M9WUfEMcnxY1SrtyHov0Dg0Niy8ALtqI5XlVTbG/sWO613t+9YEEP9v0AgEHimOFwSSJqBNtS3b1PevIVqNIiXFsHY+1fCDgQCT477/SadIySDBAKBwLPHwQWgtZjhEJEmqLku6dkzIAT1zg62LIP4CwQCgUAgEHjKOVQSiJ1MsOMJTmtko4GIIvTW9pG6rgKBQCAQCAQCj4aDJ4HsoRQijhHyThaiLUsI3QQCgUAgEAgEngiPLgkEEHE0rUdmcQZk3iBaXKBeX8eGVnCBQCAQ+ASILEXEsa83W/o2hSKKiBbnkZ02ZqeHGQ5xE5/RLtIEmee+1WFV46raf14IXFWBFL6jDgI1P4fqdtBb27iyAgG2uGO8EHmGiCJ/HF2DdYg4QqgIlO/I4+p6VgFDxDEiz0BrXK1x9QNqaSqJTFOiY8tgHWa3hx2PcdqAlAgpEVGESGPspPRzx1fZEEniWzbWNa7WiCTxZdnqevpZb4xx1vhkzYZvF+m0xlW1D89SinhlCWcMZrc/W9++ez7tLiTSFJHGuGq6NmO8BogiX5VgrxtRUc7G2HdsIZDNhv+XZ5jewHsS68p7EpXCVRW2qsFavz5j/DyURAiBQ/jvKYSaHQkHF4BSIptNf6LsHWR+jvTcWex4HARgIBAIBA6HFIgoQnU7yDTF1TWm18cVFpHExCeOEZ8+SfXeB77I+aTwoirPiBbncbXGjkborR1kI/fhSb0+MokRcYzTmmhlieTsaZACO54glEJvbGHLEhHHqG4bmSS4qsYMhrha+9eyDOLIh0GNxuz5umSeobpdnNHY8QSz0wO7f1a9UArZapI+dx5qTfm+80LTOkSaovLMC7EkAbeDqWuEkshmA9Vp47TG9PqY/hDVbuKMxfT7XiBGyoslGyHimGiu47sHlSVmp+dFcRQRnzyGq2rspPBiEbv/nhuDSBJUp4Vs5P714Rg7GiPbbb+/SQwObFFgdnu+fWWSfHRs55BJQjTXJVpeJFqcp7x6HW0t1hlku4nMc9Aavb2LKytUp40tCtykQOWZF+q19q0wgwA8Eg4sAEWkiI8fQ7Xbs9dUu0W8tITMQi/gQCAQCBwOmefEy4vIuS5CKUy/jx2Poay81ymOEHGEc9ZbroRAJIkXIkqSnj2HHY3ROz2ipQVks4kzxlsO2y3qm6uIqTVQddtESwtEc13GP3sNWZYkJ4/7mppxTDQ/R3n1GnY4Ir9yGdls4BzYyQTTG6A6LW/9iyJEpFDzc5jhiOK1t7CTYv9wKCG8NSuOfRtHa3DOIZKYaL5LcuYUIk3QaxsYKbwgThNEHCHzjPjUCepbtyneeJv4xDGcsbi6IjlzCpSkvrFKNNdFtZsQKVSnAwImv3gT0+sfaM8lGcm5M74mqQA1P4fe2KK6cYvs8gWihXmQ0u/HYIhpNhCR8k0hPjS2TFOipQWwFtVpEy3OU91c86Fkmbe4RnMd4lMnKH7xJvX6JsmZE5jdAXq3R3LmJHY8oV5dw4WGE0fGgQWgs84/Fd3VfN4VJViHHYckkEAgEAgcDt9etEQphWw2EEpiegMYDKeuT//PjsY+5lwIVKuB6nRQrRbRwjxGecFiJwUiS4mOLREtzCPimPr6LZyx4Cx2OEJkGSzMeRdvpIhWlqhurOLKIXQ7yNQXYReN3Fvlao0rK6/j2m1ku4mQEqe1t9xVlRdAUu4rAJ0x3u2sNa4svcdM66nI23NZewucLSqEVKh2G9VpIzttosUFb51zDjspkFlKfOIY0fICrqiotEa2m0Qri7hKI/PUWxfjeOoCP8CeA9HivBd3uz1Uu+0tpK2mt/6lqXfz1jUY662UrebMXXz32LLdJJqfo7q5iukPUHMdnDWIJEa1Gsh2C9XpEC8tUuaZd1EPR4jUW32jpQX0+iau1sH6d4Qc3AWsNfXNW/e8JNKUqNvF9Pd/wggEAoFA4ONwVY3e2iFaXEDOdxHNBvXapu+KU5S4SYkrSuxo4oWUFKh2m2hhDtlqIvMMOxoDAr21DTjSy8+hmg3seIKtKt9H2xj0+pYXII0crENmCTLLsJMCMxggm01weHdwrbHGYicT9MYWQklUtzsVTMrHItYaV1TeKsl9qmtqg50U2KLETkrMaOJj6qZ/Nv0+riip1zfBGGQjR811UXMdLwKz1MfdGUu9tkF8bJnk3GlklqHLLWxRIqLIWz7V1DAzmUxj8OTU+qkg8m5fIeV999xUNTJN0Ns1envX74fwFkM33QtX1+jNbVxd+/1o5Igs/cjYMooRSYzpD2buXVdr7xZemEM2m8h2y1tzpcKVFfWNWyTnzpCcPjF1NTtcWT6uU/GZ4OBlYPbDmGkgq/749wYCgUAgsA8iiVHzc97apiJkq+Fv/rM3CESWkpw/Q7SyhBAC2ciReQbCJ3ns4eoapw0yTbBlhd7ZBX23Ve5eiWYnBfXqGvHyAvkLl0lOHgfw1jDz0Zg+Zwx6bROz0/PWMCm9NW1S3Lc14wwpUfNdsucvIlt39eB23GvhUgrVbCDT9E43H5iWYyvAOaL5OWxZYvoDcA6zvYPe2PIJK0rNxLOI/JjR8iLxqZOkF88h2y0fw7jPnru6pl7fRDUb5J99geTMKWQco3d275voorf2H1vv7mJ2e6QXzpJduUx8fAWZpl6stluIJEbcvT5rsWUFCEQcYXoDzHD04D0NHJhDZQEjhM86UgoAmWVEc13qra0gAgOBQCBwOKzDaY3e7Xk3YC+7J7HQ9PrUN29ji8K7H63D9AberSsFbjLB9Ic+CcM6/08pbK+P3ticJlEMqG6uYssKoQ31+iZ2OPLuWW28JbGRz1yxZjCkXr09zQyuMIMBQkhsUeGqCpGlqPEEpsd2dXXfJBC/CIO+ve4tZbWeZcva4WiW5bsnAl2t0Ts9xHiCiCPsaOStg9PjmMGQ6uZtzO4uemvHC8Cdnk/MGE/8lo4nfr/wFtZ6bQMRKR9baYyPRdxnz/feqzptH/+o/VzscIxe2/DvMca7pLXB1QZX+37lHx7bGUMtBLLbuZNEMxj4929ugxSYXg8zGHihDndEt3Xoza2ZWzpwdByuDmCkkGmKzPLpE8g86fmzjF97nXr19qOaayAQCASeBabGhfvWlVXSiyR71/3rw21IhSBaWqTxxZcor16jfOf9Bwuzu48DB4s1O+xnpPBzepiPHbTN6kHn9KA9P6qxD/K6lGSfuUR88jiTn/zcC8MQ//dQPLo6gFISdbtEi4tEC/O4skI2G0QL88gkOfDhAoFAIBC4h48TavuJpg+JAzXXRTZzymvXvXXsYcTfPsd5pJ+x7uFbcR90jIO+/0H7c1RjP+TrIomJFhdwVUV59RpmL44xcKQcXAAKMXsacNMija4opvWSHlAAMxAIBAKBh+HjbvYPZTHzLk+z2/fJCE8jT5OmedoElgAzGPpi0dOC2IGj5XAu4L2U8qkIlGmK6nZ8VfOn9YcWCAQCgWeHWVLBAaxsgaeHw7jVA8DDu4APJwDvsgLutX4RSexbvoRewIFAIBAIBAJPhEcrAAOBwLODlAgVzWJ8bTF5fO2YpERE8TRb8fH1AJVJisMF11MgEPil42EF4NHUAQwEAp9aZBwTz82Rn71AfvYCstGclYB65GMnKcn8om8WH30oZFnKe2ujHRVCEM8vkswtHOqzj2ROgUAgcMQcrg5gIBB4JhBRRDy3QH7uOWxZUPd3cbr2/UujGNVo+DpiVYWtfesqEUXYuvKN4fdE2l68cJb7emp1ha01WANSIaZ1RWWSYLXGFhNf9DeKfHeAibxj/VMK1WiSLh/HlgW6t4uejHz4iRB+jGl7Lvugmmx7x1cKISTO+Rp0zlqiThchBHroa4/5dlnTVpdSofKGbydWV772qRDIJCVd8cWD690dzGQ0S4zbd92AiBNf5NdarK6DxTEQCDw2ggAMBAL3RUQxUatDdvIM4/ffxgwG2D1RkyRErY7vgFBXVNtbyDhGtVroXg9wyLzhRZC1OCBqtrx71Wj0cIAtJog4QWU5MssQUYwZj7BlgZASGcWorIEZ3ikCK1RE1GyTn7mAngpSUxaA8Alp7c7MbVz3dryY3Md1LKQimr5XCAFSYccj9GiIajSRcTz7u60r6q0NL/6yDNXqwNRFXA96fj+ynOzkGS92p+LTGTvdp/Y96zaT8WwdqtXC1RV6NMIEARgIBB4TQQAGAoEH4sBb+YydxuF5YSiSFJQiXTmBynPMz15B5V4ETdwHCKXIjp+muH0TZzTJ0grOGISKiOfmKVZvUG2seWveygni+UWqjTXMZOxFlHPINCM9dgJbFdiywBiDEMJb7eIYhPDxiDiidodk5ThCKlSjQdSZY/T265Trt313hg8h05T8zAUAbFEQLyxR72xhrr7rLZJxQjQ3T7KwjK1KzGhIPDdP1JnD1jVRq42IYtx7b2OL8dT6GfvyWNMYyajZJDt97qPr3lwnaneJO11k3kAPeti6IqTQBQKBx0WIAQwEAvfFaT1tT1VhJiMvzqxFZTlRp0vUahO1O0Ttrm8BVVWY8ZCo1SFeXCHqdL2rVUriuQXfP3XYx9a1t+7lDWSc+PZQVen/NhWAWItzU/etkNNer+CMxlYVTtfYosCMfBsvmTdIFpeJWt7KuOdWFuo+lzkpkVkDnEMP+zhdI6REZRlI5Rvd7+541+9U9KpWB9VoYkZDzHgEzqKyDKEibF3i6tq7pfeOFyf7rlumGa6ucNYik5So3UXlzf3nGQgEAo+AYAEMBAL3xekaW5Ve2I2GXpwBKsuJu3OovOHj4VQEQqCHfViH7PQ54u6cF1LG9zsVceyF5KCPabVxzosfhMAUE2xVUm9vepE5m4B3pwopfRKIEN69WhY+7rAqsXUJziGjCJXlWKlw1mCKibdYcv+kDCHwwnPQQ4/mwWhkkiCkxEwmlOu3UY0WMs99JnQcI4TEjIcIIZBRNJuXnUym86mxZYmzdmpJ/Oi6hVLoQd+LVimJ2h3MaHjfeQYCgcBREwRgIBA4MDJNUVkDIe/NBnZ1jSkmqMwLpnpny1sQjaHaWCfuzpMsHyNqdZjc/AA96KFa7ZnL9+5IPZU3/PuXlqeWPIUtC2xVek0nJfHCIrmpKVZvoIdDirVV4u6cd8Vq7cXYPu7f2XwdxHOLyDT3LuitdfRw4BM79mZzV/ygHgwAQePcc6hGE4Sg3Fz3onVvTp0ujQuXKFdvYMty33Wb8QjVaKKyHKT0MZBJelRfTyAQCHwsQQAGAoEHYsYjihtetOyhR0PE1vrU6jfwFrCy8EKuqqg21xFxjO73MMUErKXa8NY0lWWYyQTd2/V/E8IXkZ9a9/Zw1qKHA4qbvheoGY2msXVgq4ry9k0AbFmCdZjJiHJ91cfjTQWgGY0+tji91TVm5EVf3dvGlgXlxprP1HUOPeghigm2nKCtzwZWjSa2KnHGYEbDqftYUW2sIbMMWxSzVpn7rduWhc8yHvRxzqHTjHp3+9F8gYFAILAPoRB0IBA4PHsdgQ7yfji6gs57dffuLvXykGOoZpv2Z79AtbXB+L23DjavB637IHOS0v8zj6/IdSAQ+HTzsIWggwUwEAgcnoOKlqMWOXvtKA8xhtM11eaat2Ae5ToOMqe9jipB/AUCgcdMEICBQOCZxOmaanvzyRdfDuIvEAg8AYILOBAIBAKBQOBTQugFHAgEAoFAIBDYlyAAA4FAIBAIBJ4xggAMBAKBQCAQeMYIAjAQCAQCgUDgGSMIwEAgEAgEAoFnjCAAA4FAIBAIBJ4xggAMBAKBQCAQeMYIAjAQCAQCgUDgGSMIwEAgEAgEAoFnjCAAA4FAIBAIBJ4xQi/gD6EiaLQUdeWoCou1j3Y8KSFOBAjfF76uHISme4dGSIgiQZIKZCQQgK4ddeX/PZE5CX9eJalERQIh/Zx05ajC9x0AlIIoESSpRAiw1vnztnQY89H3P43neSAQ+OUiCMAPcea5lH/y3y7x6g9H/ODPh/S2NUY/uvEWViJe/HKDKBbsbhl+8cMR5SRcwA9LdyHizHMJX/zNFsdPx0Sx4J2fF7z24zGv/nD8RObU7CiOnY55+TdbnHs+pdGSvP96yRs/GfOz74fv+1lHRbByKuGFX8n5/NeaNNuS3rbhnVcn/Pxvxtz6oPrIZ57G8zwQCPxyEVzAH6LVUXz2Kw1OXUhJc4GUD9VT+dAYA5ORZTy0lBOLO2KLo1SwdCLi+S/kNNuf/q/bGkdZOAY7BiEFx88mnDifMLf05J51rPVWmUHPoGvHsdMJJ84lLB2PUerRnl+/jAjhreJnL6ecupA86ek8FrR2jEeW/o6h1VWcOJdw8lxK3tr/N/vYznPhrZMnziVceCElTsP5Ggh8WggWwCfMzobmh385fCTH3ruRPvdizq/8RpM//hc7jAblIxnraaG/Y+jvTHjrpxNe/s0mX/9HXcaDR+zH/xjGA8u1Qcm1t0ve/tmE3/uDeaoyWP3uh1LQaEm+/FstirHl1tUK9yneLqNh42bNxs2aV74z5Pf+YJ4zF9MHfuZxnedSQpJJPvuVBsvHY/7dv9imV+7jkw4EAr90fPpNQs8wUSI4fiZh5XRMq6uIovD0Hnj6aXUVpy+lLByLyJrhEvUkyRqSUxcTlk/ENNrykXtEAoHA4+NILYCNlqQzr5hfichyH/DuHBQjy2DXsH6rohjfeZRPMkGro2h1FULCZGiZW4podSVKCQa7hp0NzdaaJs0E7XlFkkqyhqTRkljjGA8tO5uapeMxWUNSjCxrNyt2N+88pSoFnYWI7oKisxARxz4Q31rH7oZhZ1Ozu6nvsTLkDcnKyYRjpxOkEggBxcSys6HZuFWjazdz1x503VEsyBqCY6cS2vOKKBGMeobdTf/eD8ccSulvis2OotGWTIaWOBEsrEQoJagqx+6mZnutZjiwKAVLx2NOnk8493zKc5/NOXYm4cWvNJhbjsA5drem697w645iQasraXX8Ho+HhkZb0V1UKCWYjPw+b69pirE90LqFgGZbMrccsXQ85uqbxT3fjxBerJ467/fj6hslo4HBGh/s3p5TLB6LaHUUcSJBwLBn6G1pNm/XjzRGcz8uvpjRbEvWbtR05hXtOYWc7tFgR7OxWlNO3KGtVkL6/eouRMwvRySpRCpwFsZDw+6WYf3mvefJqQsJK6di1m7UJKlgfjlCKoHRjmJsWbtRM9j1Lmjwe/rcZzMGu4bRwLB4LCbN5QPPc6mg2VYsnYhptiVJ5r/v8cDS29Zsr2uqwhHFggtXMox2XHunxGhHu6s4ezmducLXrlfEsaDZVcwvRQx2Db1tzfGzCWeeSzn/mYwzl1LacxFf+noL66CcWHa3/Dk7mlq78qak1ZE0uwqroSwtCysReVOhFOxsanbW/dycgyiCRlsxvxzRWfDnugCMcYwHlu0NzeZqjXMQp4JmW7J8IsY6+ODNAl3f2fO9hLFzn0kZ9gw33q0w2uEAJWHxeMzcYkSzIxFCUNeWYc+wvabp75jHatU8yLr3kpaOn0k4dTHl3PMpF65kRJHgC19r0t81mNqxu6XZ2fBrAby7+ADrPtB1rf9krfiBwKeRIxWAKydjXvxyg6/+TpuVkwmNtsQYx633K954ZcI3/9Uua9erWWZtdyHi8ksZl17KiWPBtbdLvvj1Fs9/PidvCF5/ZcKPvjXke3/aZ/F4zItfarCwEnHyfMKZSyll4bj2VsGPvj3kt/6jLifOJdx8r+RP/o8dXvmr0WxeSS659LmML/x6i5d+rUF7ThHFgrqy/OhbI3707SGv/NXgnhvq/ErES19t8vmvNlg8FqMiwe3rFT/4yyF/+f/sMuyZWbbdQdedNyUnzib8zj+d47NfadCai3jn5xNe+c6Qv/hXu7Ob2+xLSgRnLqVcfinj3PMZ194u6S5GfPUftMmbkp0NzSt/NeR7fzbg3V9MyHLJF/9Ok9/6j7usnEpodfx6j5+JqSuHs45X/sOIH397yI++5dfdaEmee9F/FwsrEdferjj/QsoXfr1J3pBcf6fkx3815Ht/OuDW1epA65ZKcOJ8ype/0eI3f6/DH/6zNX707TtubxULWl3F3/snc7z4pQb/y/+4xvuvFxQTSxQLzj+f8hu/2+HiiznzyxEIePtnE/72r4d899/1GQ8ffbb23fzeH8xz/oWMP/mjHT73lQZXXm4Qp5Kb75e89uMx3/o3u6xdr/fN3nwYolhw8lzCF369xVd+u8X8ckyaCbR2fPBGyd/+9ZA//793GQ3vxIz++u92+Pv/eI4/+aMd5pcjvvKNFllTMR5aVj+o+JM/2uG1H40Z9vykTj+X8N/8D8d54ycT3v1Fwd/53TYrp5IHnudpKjl9MeUb/0mXC1dSFo/FGON4//WCV78/5nt/NmDrdk3elPz+f73AqG/53/+ndcZDy9nLKf/Ff7fCYFfzxk8m/Mm/3KE9F/Hc5zK+9Fst3vzJhJ9/f8Tv/OfzvPilnOWTMWkmMQZe+tUGDti45V2kP/rWkPdeKwBYPB5x+aWcyy/lTEaWrbWaX/v7bc48l5Kkgle+M+IHfzHge3/ax2jImpJzl1O+9I0WL321ycJyjFRQjAzvv1Hygz8f8O3/r4c1Ph744pWMb/x+l7py/OE/u82wd+dESzPJmUsp/9V/v8LbPyv4l//zBpOhxVpHkkl+5TeafPE3Wlx8MUNFgv6O5q2fTvjenw149Qejx/rgcpB1q8hfo37jH3b48jdaHDuVkDX9w8HFF1OMgVHf+O/i20Ne/b5POtlzFz/sug9yXXv7Z5PHt1mBwDPCkQrAYmJZvVbxg78YYo3DaP8UfeGFjAsvZlx+J8Nax9p1/xjtnzS9m7LRkowGll/8YMxPvjMkjgWDnvHWlMIhJaS54OSFhMGO4f/9X7d4+estFo55EfL6K2OuvV3y8tdbrJxKaHYmTIaW+eWI8y9k/OpvtwH49r/tMRlanHNEiWDrtmbjVvWR5IulYzHb65rv/vs+5cSR5pLLn885cSbmS7/V4qd/PWLjVn2odRdjy/rNim//2x63rlZ87teajIfGu2j38bAI/MW1uxhx4UpGWThWP6j4v/75/AFYkAAAIABJREFUJlHkhezulvFWTAtl4XjtRxMmI8vzX8g581xKez7i+98csHajAgcbqzUbt2rsXSJFKsHS8Zgzl1ImI8e1t0ve/NsJcXzHAjjq3/nAw657/aYfq79tyFuSpRMRCysRO1PrY6PpxWeSSjZXa0Z9Q11Zslzw0q81ee6zGVlD8jff7DPcNQglOP+ZlIsv5EyGlrd+NuH2tfqjG/eI2LOwXX4p54O3Sn7xwzFRIjlzyT+YvPRrTeJkwgdvHS7e0lkYjywfvF1SlZay8KVi4lRw5YsNLr6Yc+2dkg/eLNle93fTKBZ05hUvfLHBtbcK/vUfboOAlVMxz72Y8/xLOXXl+NlfDzEGpBRkueTspZTJ0PK9Px1QlQ8+z1/6apPnfyVHRfCjbw/Z3dCz3++FKxnF2PLWTydsrWvGA4tzsHQiZuu2t0p6607M0gn/uVZXMrcUMR5aBj1//v6HP+5x+3rF5Zdyjp9N2F6v+f43Bzjnk6U2Vms2V+9811L4dZy6mDAZWiYjv5bvmj5RJNjd0ty+Xs9+30bDsG9559WC7XVNOXFI5cXelZcbPPfZnJsfVNx8r2I8NFx/t0RXjs684sTZhFsfVIym1qjF6W9lPPC/jWJsMcZx4mzC5361wdlLKdvrmp/+9RYO6MwrLryQceXlHBy89XN/jXocHGTdo4GhGDt+/O0hOxuaz3wh58T5FGsc3//zweyhYHO1Zv3Wne/i+JmDrfsg17VAIHD0HKkAHA18wPbGrZretmEysmQNSZIITl9MOHk+Zf1mPRNC4MVNe06RZoJR35dBufl+hRD+RiukoCr9RVIK75q9+kbJd//9gJPnUy5c8a6yv/mzAUrBV/9Bm/acotFWFGPLyqmYF34l59ipmLd+XvD9bw7YWvduw6zhXc26dh+1IAnvkv7Jd0as36pptCR5S3LqfMKVlxu891oxuzEedN115V2wu1tj+ruG7qIia6iP3d8slyysRBjtuP5Oyas/8FZOFQmSTFCM/E1X1971trW2J/AESyccP/zWgHdfLR44RqPt3brlxPLOzye882rhXbSxIE4Fo7tuWAdZd29Ls7VWM9gxzC9HrJyO2d3SOOPHvPRSjnOOq2+WDPsGo6HVlXzuVxssrMSs36z5yXdG3Hy/RCpB+k/nufxSxue/2mRnUz9WAYgQSCWIYsF7rxe8/uMxKhL8xu92+NXfbnP5pZz+jjm0ALTWMdy1XHur4OZ7JTsbGl07soZk5WTM6edSf5Nd0zMBuHczzXLB+i3Nd/+4h64dn/lig6UTMcfOxAx6KT///giMQ3AnSchox99+9/7n+ebtmigSfOaLORevZLz+ypif/IcRH7xZECeCr/x2my99vcWVLzUoxpaN2zU7G5qs6d2nRjuS1D/gdeYVzba3SLfnIjrzit6Wprft3bqv/WjCqG8Rws/v+rsVf/mvew90l0oFc4sRpvYuyVd/OGJ7XSOANPehInu/b107etterL3tHDvrGiEFx07HnPtMytxyxNlLKTvrmvWb/gF0Z0PT6irOXEoZ9s1MAC6f8O/duFWzdr2inib2rJyK+eo/aLO9pnnzpxO++8d9rHGcfi5l5XTMibMpzsH1d8vHJgAPsu49gffOqwW9bX9+OUDX8N1/32d7bX9Bdth1P8x1LRAIHD1HKgCHfcN4aBDClzdxFkYD4y+QN2rSTJDvE9QtBGyva/76T/r0dwz1NE5p779nT+/Gv6+3palKy+6m5vZ17yoY7GqyhmKw6y1UecMHLB87k3DuMynvv1ny+itj1q5Xfm6Arv1c97u53Hi35GffG7G9UVNOLELC9XdK2l3FyQsJyV3lEA677oMihD/2az8e89qPx76IMCAqRznZfx2HGWMysrzynSHvv1F495/worUYc49QPsi6nfMZz6+/MqbdVZy+mPLOzwuscTRaisufy1i9VvHOzyeUYy8Askxy4YqPU/ubb/ZZv1VNs2cd779R0JlXfOFrTX72N6P9F/MI6W9rfvq9EavXpnMq/c1rYSXmyss5KyfjQx/bGujvaAa9O/9/73xdu1HT7Pq42TS/95wqxo5f/HDMe69NGE9vsDsbmndfnXDppZz2vEJ8yML8MOd5PHXRnziTECeCH/z5gLUbNVXlqGvHe68XNFqSr/+jLms3En74LT/ugoxYPhmD8C7F29crpPRlXdJcMrfk4yfff72YuaYPw56YvXW15G++OZhZ4gRQ1/ce1z986dk+GAMCL45Wr1Z0lyK6ixFR4t/gLNx8v6TRklx8MWPtes3qB/5hY/lEzOmLCX/73RGr01p9UsLCcsSLX2rwf/7zTV778ZhiWt5pe0Pz2o/GfOFrTS58JiPNHl9CxUHXfVA+ybofx3UtEAh8lCMVgO2uYvmkd4s0WpIoFuDg/AsZyydiRn3zkSwyB5SFZTSwDPv2nir2H/7hOwdVaakrf2Ex2lEX3u3jg9UddnrhlwoQPratPad466cT+jsare893v2uLaO+D4SvSx/M76zvDGKMI0nurQ94mHUfBl07xkPDqG9miRh7e3hUF8m6dIwGllHfUBVuNsB+Yxx03bub/kbw8m+2OH0hIZp2x5hb8skO77xacO2dkqp0xIkgb0nypmLxeMzf/8fzTEZ3khgWj8Usn4yZW/IB7Y8bXTt6W5pybGf7Mh5Zhj1v+fok2atSQXc+4vjZmNMXU5Jc+HqBDq683KDRkvS3NeJDQxjj2NnUDHp3Au2NdhQTL6ijaceIu3mY8zxKBO05RZx6a3l/x1BMrD8vHIz7ht6WIW9KGi1vyd7Z0DQ7iuUTMSoW1JVj/Ya3JFalD81YPhnTmY/Y3TSzB7fDYJ0PRxgNLKOBmf2o3ew/7hAngu6C4tSFlGNnfJyhkF6QXvpcTl07ttfqe4Tyjfcq5pYiXv5NH6sYx4Ks6ZOaGi3FrQ8qNlf9Z9LcW1AbLcWvfK3F4krMZORDTrJcsng85tjpmGHPIB9jDcjDrPth+aTrfhzXtUAg8FGOTAAKAacuJjz/+ZwLVzLSTCKlvzgvHYtnAmG/C0xVOqrS4uyDf+3O+TizveB6h7dImbsyFR3ANJRO4N0IcexvXKZ++KtJVVkm4zvJBc5NrV8OhLwTq/dJ1n1QtHZMxg6jH34dB71+1pWjnNiPTWA4zLr7O4Z3Xi34yt9ts3Q8ptn2rvqFlQiEoLelZ3GJWUMSJQIpYX4p4srLOUbf66q3FjZuaX/Tf8zsPYDcMx/j0NqhlPhEN/ckFVy4knLpczlnn09R0yx053xBXlP7MT48grM+47e+q8agc96CuGcl+zAPc55L6eP3/Br9uu++MRvt25YpJVCR/53ubGiWTsRceCElyQRb65rb1yqE9OtbPO4FYKMt6e9oxp8gy9NZKCcP11av2ZE8/4Wciy9mnDiXzApxR7F3h25veCvZ3Vt1+3rF8TMxi8di5pYi2vP+nG22FWVpfQjENBM2isWs3d/JCwmdeYUx9+5XXTs2b+vZw8zj4DDrPgifZN2Hua4dNTJPQAhsWfsfwHQqQkkfe2S9gQHnfKqzc/5iJ2B2w3GAFMhI4bTBmek5LQTIvWvh1LJs7jyoiFj5MQC0BSVReYKtNa4y4O4aGxBJhExjbFnjanNHJUuBiNR0rtbPb296UeSLvjn8vKZrFJGcmmCnr99PcUuBamZgLbbUwHRO0zFEpJB5gqs0ttYfGlv54989p33WvbdGEUm/xwDG3ZnXfuu768Impp9x2sy+C5WnOGOxVX3v2A+77k85RyIA98oGfPXvtTl/JeNvvzvivV8UPkDYwZe/0eLzX2tSFY8/lqMqLJOxI2s+XJzdHs75G9mDeJrXPeMRnNeHXXcxsWzertnZ1MwtRpy5nLKwHLF8PObqGxM2Vu8kpdSlZTKwaO144ydj/u3/tk1/x9xbQNn5G8yTCBJXkY9djO9ymSWpJG8oysIe+juXEjpzir/7+3PEieCn3xvx/usFvS2/MX/3P+1y5mKKMft/sc4d7Fr2MOe5rhyDXYOzkDQFaS5RfTu7YSe5pNGWVJWlKnyP4631mjOTdFqSRTIeWm5f98lWi8d9OaDuYkQUCcZDS3HXfjnv5T/yU1cqn6jwj/7LBW5fr/nxX424+nrBZGRpdSV/7z+bo9H+6DVi2DNsrWl2tzTNjuTilYzjZxKcg3d/PmE8MLMH0GLkO/oY7fjWv+nxyneGfu/uOW19j9+t+8TSHTWHXbefLB/7ReyVfnra1v3QKEnj0gmEFEzeX8dMSi+sANXJUY0UMyqx4xKrDaqZ4rTBjisvSJQvd+OsReUJ8WKHenuI6Y9xxiKTCJnH/r1TMal7Yz+GEMQLLVTDF/6ud8eoZkr7pXMUN7coV3fAWMy4xBY+9CBd6ZJfWGH83jr11gBbTMMP0ph4qY2d1NhxiZlUU0EVkax0kEmEMxa9O/Zr1Iao20AkMc4YTH8yG+MjW5QndL54ATOpmFxdB2OxRY0Z+zjnaK5B68UzFDe2qG7v3jW2Il7qzNZgJhUYu++6zajAVZqo0yDqNgDQw8LPq9KoRkrUbWKLCjOpsOMSoRQiVohIolo5Aqg2+7jaoPKU1mfPoAcTiuubd43NQ6/7087RCEDpnwDnV2LaXcXW7ZrVaxVba7UvQBx7F9LW7cdfRHR7XXP7esXJ8wm9Tc3NqyWjvi/VEMW+mbq1HKqK/tO8bvAPPGXhT/j2nJzW6vMZgXvWoMM8+Bx23c56F/OtqxVSwoUrKSsnfemRd39RsHFXdqcxMBlbbr1foWKfPXr7es3GrQpdQ5oJkswLsMdpSdmj2VFc/nzO7qahv2MQEo6djjn9XDKrj3YYxLSUxvLJmPHA+pizaxWToaXZUaSZ9PXVbj3oKEe7H3VtGezC5u2ak1nClS81eOunE9Zv1qgITp1POP9Cxtaqt+Aa60VTObE0Woo4lcRJ5ZMHnLfuvvjlnLwhGeyaqfX/znjebe1IMy8sk0xQl97aKuQdgXhQlPIxqcfOJNy+7hM3Vq/5LiNpI6bRUvvG6hrtrdfvveaTXi69lHHibMLWmubdu+ItwVuzdrcM779ZIpUvFfPuzYKq9FmvSe6TpICP9XgcFYddN0x/h9NyTFkuvKVfTa3Kd11DnsZ1PyxCgGokCCm9FUkIZJ6QrHSReYJMItJTi1S3d6m3B2SnFzHjkmq9R3JsDhzU2wOoQeYp2dllAGxZ4SY18XKH9NgcpqxQeYpIIsZv3PQCbK6JamZE3Zx4vsXotRuAFyjxpATniNo55a0dihubOG0RSYRq5cgk8lZ6IFnpelGTRqiVBFvUTK6uIbME1cmJWhlRt4lMI4a/uO5rVTZTkqU2KIkZFthJBfcTQkqi2jkyTzCjkqidoXtjxu/cxhmLiBRRO0emsX+KBeKFFtFCC5nE/nWguLqOc5aou8+6jUWtdFGtjKidEy+0KK5tUowr0sUWqpWjmikyS6h3hkzeW0M1M+L5JsmxLrao0bujO2ZsJVDNFFsbhPJljESeEM01H37dn3KOzAIopL8RV6U3y7Y6EhUlLKxEHDsT05lXqCfQiWL9Zs3VN0q++jveWnXrasXWui9wuycgfPmIj5aC+TgOu25fCFoSRb4WYt5Us5vd/HJEHPtEmMknrG9njbfc6MrRbPt4vZ2NhGJi0ZWP9SomB78Yf5Lv2zmfZJA3JReu+GSJnQ3N1TfLj1gGyokv8XL+Mxmf/1qT0cALrbqwNDpqmsUNt69BMfGfTTIxK0rdnotIc19LrtX1BauNdlSFuyfW6DA0Wr60xerVilHfl/A5+3zG8bMJb7wyZv3mnQtKnPiYMaWgu+CTN6RyNDvT7zvx33cxsohp+mtV+gQLKX0Zjc6cYmEa99jsqMfakcFoGA8t198pabYVn/vVBka7WRbxhRcyTl9Iufpmwc2rJTifkFKVDhUJpPLCbbBrMMaxdCLm1PkUh08Q+HA9vLLwyQlS+v06fjahGHnrYl37EIW7Y4UfGu/xoSq8uzGKBXOLirQhOf1cyuLx+18O/3/23uO5rizP8/ucc/3zsIQhCTKZTG/KdGeZNqPu1nQoJIUU0RMzEdppM4rQQpvZ6g/QZv4F/Q+zm5jW9LS6p9RdVd2VmVXpyKQH4YEH4NlrzzlanIsHgASYZDJdJe8ngsUsEO8dc933/uywp7jx/pirb4RceytkbtFl82HG3U/T03FrBrpbOb/7hxGdGZfXfhRxsFcQj2yyVL3l4nr2RSiJNSq2+xjVJV5gz916w85Jabv+zqyDKmz5qKN1P9N5/hzrttnDCq2h0XG4cNHDGPvdeWZKa7f5Uuv+LuM0QuqvLFGMU1CacGXeennzAv9CBzVMMLmi9tIFVJxRHAwxxiADl2ChTdEbke8P0EmOP98murZAurFvRdFUnWzzAKM14aVZQOBN1QlXZkke7FIMbM1D4bm4nQbR1Xkbm7vTQ+ns8ckKQXBxBq9TJ9sf4C90AMi7fZxOHX+uBQj8C23cZsT41iZGafy5Fv58G4QgdyT5/he3JBWug9MMCa/Ok+8NSNa6Ewvko/jzbcLLc2TdAd50A69Tp+jZhL1gefrxdQ8TgkszSNfFqQdEK7MU/TFiXRBemsVphCAFwcIU2XaPbPMAtxURLHSovbpMfG/HruEJp5aM/C+17u8rX4kAVMo+rD/5Z1sO4y//9RSqbG7e69oYhd/9ckwy/uZjtbbXcrQa4gVw6VrA//S/Tlu3nbBlDW5+aDPPth5mz2xU+LLrXr7i8wd/ZusVLlzyWFjxcR1BPLIFUfe3cx7eyfjFf+xNXH9fhjw1rN3NuH8j4fIrAX/xV23+7H9ukyaGj3414sb7MXc+iZ9ZZD7P8TYG1u9lzC16/Iv/McD1BNtrNoh+/EgsXxJr3v/7If0DxVvv1fiT/75N1LCiPUttHbLPfjPmYPf4c6++G/HDP24wPe+ycNnnwrJHoQwrrwS89ZM6uxsZt34X86u/GTwmPJ6FeKR4eCvl1Xcj3vvzJmFNUuSGtbspH/7DiNVbxyVgLl7z+dlftqw4XvZZuupjtO0ocv2dGjsbGaufp/zqbwYMDhX9g4L3/+uQ629H/NW/nSFNDKOBordvrY29bjHJkP8m+fAfRgx6ind/XudP/ocWf/FXHYyxnSQefJ7wm78bsnb3eN15asrOD8YmBGBr0Y0GinrbYX+noLuZP2bB7e8X3Pkk4fo7KS+/GfJv/88F8sywvZbz0a9G3P4oZnvt2d/Yi9ywu5Hzj3/dZ/mqz7/532dJE/uSNOwr1u5m51qTR33F579LuPZmxPKKj9Y2gaa7nT92LDZXM/7Lf+jx7s9qrLwS8r/8H3NlSSsb73zn44QbH4zLODxDVJf8/L9rcfW1kJkLLstXfWpNB1UYFld8dtZsqaXf/P2Q22UZp2c5z59n3eOh5v5nCQ9eDZmed/lX/9ssqrCi8KNfjfj8t/HkXH/WdX+XEa6D066Rbh+S7fQQroMuFDLySR7u4dZDatcX8aab6M0D677UBpMVVhyezDYUApMXxA92KAZjQjVjRbnS5AcjopU5nFpAvtNHjVIbkyYg7w7IdnvI0MMUCtkIbXzdGTg1+0KV3N8Bba2GTjMCrVGjlHBlDuk65Ht9dJxRlO5ptxnhzTSRi1Nk24cU+0/aFEHRGxHf3Z64k51mZGPuzkCGPiJwSTf3UcMYkxc2drFQZ65bJxlFd0i4Moc3Vbeu6kEMCNzpBm49tG7nJEMlmY0TFAIVZyQP90hWd8m2D8+dD4AaJiRr3Wdb9/eYryYJpAw0v/W7mDw1XHk1wCmz/ayLrCAeaaTDpHYe2HIjmw8yPvzFkPFQnxvXBNDbV9y/kdLdKthZz9Ha8OBmYmvLlTeyPDf8+m8GrN3NJtaGLLWFiD/+1ZheV7F0xSeMbAZckRu2VjP6+8exKvs7BX/7H3rc+SRmPNDock6qsHO/8cGY/Z3Sxfcl150mmv1tWwR5NFCslXUPtTKksWHYs23ajmKslDLs7xTc/DBm2Nd0d55OudiHlOLuZwlBZAswB5EVKgc7BfFYTW7Dk3369ZioIR8TY1/F8T5icKh4cCvln/7fIUYbNlcz+gfqMUGmCluwWpYZsNay6OIHgjTRdLcLdjZy4hN1wsZDXRYPt2L03meJLaGSWcvD4V7xWGzSlyEeaVZvp9QaDp1DhR9IDrsFG/cy1u6kDE6UNUljK1az8qH78E4Kwro609hau3r7xSS5Ih7ZgujDnuLStWAy3tHc00TjOIKDE7GPtz6KcVzYfJCRnrCujAeKtTv2AT3sq8k19kznecnuZm4LqLswt+zTaDsYbdjbLFi/l/LwTnqqnMvORsY//Kc+YBMpjoqF727ktuTTvuL+zeQxa2yW2hZgH/9qxOBAMbvoohWTtmMn40AHPcXq7RT3bweTdZ6H0TZe9Dd/P6S7HTC/bEvSDHuKw67d26NjMOyfPv+zzLC/k3PzwzFK2Vi2u58mx5nyJxgPNOtJShjZ+MaFSz5+aJMA0tgWOz7YLSjK61trm1G+vZZNil07rj3n00TbTO29/NQ+Pct5/jzrLnJ7zt78cIwqDLOLtsXgeGDb8qWx/tLr/rL3ta8S4TmlK7KODDyC5TFm/QC0QQ2TMq6uZV2JcWpj8eIMpxYSLE5hcoWOM0yhkYGHUw9xmhHebMvG4sU5QgrM0ff5rk00ARvDnFtRKUOPfH84SRwxyuDUArypBk7Npzi0yQ0y8q17eLqBv9BB5wV66xCdZDiuxJtr47YiK1gzu5+yZnAbob1vx/YaFkIcu7t9F1kPJm7aMymTOGTg4c00cOrhJGZPBJ6N25u4YjPS3CaDmDTH69St+ziwMXcmzTFSPrZuYww6L5C+i1MLrQU11xijUSN7LGToTYS2yQrQGlMo1DC17tzSlSt8F7cZ4XbqCN9FjRLSjX3rrn6WdX/PEV8UAD75RSG+269sFRXfEP/u3y+zcMnn//6/tli/mz320KyoqPj9wKmHePMtOj99BacZkW0dMPhoFdUbEyxNW7fpTBOn5jP6aJXBbx9glKLx9grt964TP9glvrtNfGcLb6pO9PIi7Z++QnE4IlndY/C7+9ReXsSfb3P4i89wpxtEK3PED3bLJIw6jbcu40Q+2W6f4Uf2+1s/vgZSIqTEm64z+O19Br99gDdVp/HmJRpvXSbdOmR8e5PRJw/tXBc6+Bc6OPWA4mDE4S8+Q0Y+4aVZGm9exihFunHA8OMH6FzhzzTxl6atS3Smyf5/+Yjxrc2z96kR0v7JdZx2DbSx8XkPdjn4u09xO3VqLy/Q/sl18u6A+P4Og48e4DZrBItT+Bc6uK0QowyHv/gMneb4C1OPrbvojfDnO9Rfv4g/3ybf7TG6sU78cI/gQofg4kwpuguS+7sMPrxnv7tMJonv75Dv2hdOt10juDRL56fXMUqTrHUZfrSKzopnWvfvK8aYp4oRqgRgRcUz8u/+/TILl0sBeKcSgBUVv7c40lq1phsIV9pEgn7prox8nNBHlMkWRT+m6I3BGJyWTV5QcYYeJahROrEmetMNdK7QcUrRG+M0QqTvke8PEJ6LU/NR44xgaYr66xdJ163/0Z9vkTzsUhwMmdSVEQLhORS9MUVvbOMCWxFuK7Lu0FFC0Y9tskdpUROORGcFeXdA7ZUlwoszpBsH1nrYDElW98j3h+gkt0kVgf1MttdHDc/uFCVciTvVQHo2W1x4Lmqcku8NTqy7js4K9Dil6MUIz0GGHk7og+uA1uTdAf6iFX+Prhulia4tkHcHqFGKP2+zqdPNA3Sc2XEiH6MMapxSHI7sej0X4UjUOLVWQcqyNDUfb6oBxli3cS+2Vkzfe+p1/77ytALwKy0EXVHxwvHtJHhXVFR8FSiNHqek48fDB1QeT2LQHo0ZUf0Y1Y9P/czkiuJwZDNRT1AcjE79ji7H0klu/5QlU3Sc2RjCcTYRmjwSLmnSnHw3n1i6jtBjW6bm0dZWJi/KMVLAoF1p55AVqFGCist1f1EN3kIfj/nonLKCIhuWwvXkZ6x7vBBjTtZ1Mrk6c90Yc1ziJbaudp0VmFzZ2MhCkT8ytk7yMzN4TaHOPEYAKldPve7vO5UArKioqKioOAsz+Z+vnLw7YPjxKtGVeWTgofpj8u7gtIB81qEfEarpxgE6LYiuzIM25AfD0xavLyOAnnNO5657mJD3xoSXZwlX5in6Y4r9IXl38OXHPo8XXPgdUbmAKyqekTd+HBHWHW5/bBMovo06hBUVFb/nONImPDRChJSYvKAYJhM35leBcK2L22lE1sKWFTYjt/gWmxOct+5CIT1bAkb4HiYvJkk3Fc9GFQNYUVFRUVFRUfGC8bQC8Mt3rK+oqKioqKioqPi9pBKAFRUVFRUVFRUvGJUArKioqKioqKh4wagEYEVFRUVFRUXFC0YlACsqKioqKioqXjAqAVhRUVFRUVFR8YJRCcCKioqKioqKiheMSgBWVFRUVFRUVLxgVAKwoqKioqKiouIFoxKAFRUVFRUVFRUvGJUArKioqKioqKh4wagEYEVFRUVFRUXFC0YlACsqKioqKioqXjAqAVhRUVFRUVFR8YJRCcCKioqKioqKiheMSgBWVFRUVFRUVLxgVAKwoqKioqKiouIFoxKAFRUVFRUVFRUvGJUArKioqKioqKh4wXC/7QlUVFR8N5BRhKzVMXmGTlNMmn6zE3Ac3GYLYzR6PMYUBRjzzc7hRUQIZBThNJsIIQABgBoO0Ulsj0PF14oIApxmE5PnmCxDx/G3MgdZ/kFKQIDWqPEIPRoB5T2iXrdz/DbuERVfKZUArKioACB8+WUa77xLtrVJcu8eyd2739zgQuA0GrT/5E/RWcroo9+R7+9jkuSbm8MLinBdoldeof3Hf4rwXIS0jqH+L3/J+MZnFAcHoPW3PMvvN/7SEu0/+VPy7W3S1VXiW59/48I7uHiR8OpVgssrOFENpEQnMcP332d7D3N6AAAgAElEQVT42w9BKcKrV2n86MdkW1sk9++R3L79jc6x4qulEoAVFRUAOFENd2YGNRohg/CbHVwIhOPiTnVQcYxwXYQQfG32PylBWEsXWr/QlkajNcX+PvHNG4gwxF9YILr+Ck6jgXC/o48Ix7F/G/PdFadClJY0vnCe0g/wZmcxSUIeRcfn5jeA8DxkrUb40jWC5WX0eIzq9TBKgTao0WhyjcgowpudRY3H3/w9ouIr5zt6dVdUVLxoGK1RoxE6SdB5jvkaRZkMAkQQYvIMk2WYPP/axvrOoxTp6irp6iqyXqfx7g+IXr7+bc/qfKTEqdcBgc4z64b8DopA4TjIRgO0RmflPL+DLxoyCPDm5wkvX8ap1el9+CH57i4qSZBBgBoOvpP7W/H8VAKwoqLi20dr9HBA77/+vRWCg8HXKsqCi5fwl5bIt7fJdrYp9ve/trF+rzDmOylSTiLDkNrrb4AxZNtb5Ds730rM3BfhNJvU334HNRqW59kOJsu+7Wk9hggCvJkZMJDv75M8fDgRfUrKKgb0e0wlACsqKr4TmKIg39n5RsZyp6cJL19Gx2PEQSX+fp8Qnk+wfBGTZ6h+j1x+N4tZyDAiuLxCvruD6vW+UbfusyBcF6feAAw6idGjUZXc8YJQCcCKimehjOsRJ2PIjtAao/W57hLhuiCEfaMWwn7HiYeX0RqUshaYo3GOxhDC/tF64hqdzEEbjFZnjzuZqzxK7iwHM8dzfdTiczS2656a3+QzSn11+yLEZF9OxuSZQoF5gttJCITnlXFK+vGxz1qf4yCkA1Lgdjq4M7PIMET6PiIIHp/ryX0p9+MLx3t0b07+3sl9OW//j/YDzs+CFgLhOHBknXl0X4/GPOvYfVfiHZ90vhhzvM8nfv9ozU4twpudQY3HNn4tCMq9ArQ6+1x71utAOgjXOZ7HecfBdcGAUcXkuj06n2W9jjc3h07jyTx1Oea514QQII/PgSeez0+zn+eNI6XdTyHsNVCr2b0BROAjjH7iPAX2/mPXemJPn3SPOBr30XMTMErb6/3kOOVanu4eqO2xenRvyrHEqWsBME++V78oVAKwouIpEa6LjCLClSt4i4u47Q4y8K2oSzPyvV3SjXXS1dXTLikpEb5P++d/hHBd+r/8R/ylZYLlZby5OYTjotOEdG2NdPUB2fY23twc/ty8DQxXClmr4c3Okd6/R35wgHAd/KVlnGbTBvDfukX64P6JyQpkVMO/cIHw6hXc9hSyVkM4DjrPUL0+6foa6foaxd7eqc8JKfEXl/Bm5/BmZ5BhiDGaYv+AbGOD+PYtdJJMbp4iCHBbLYKVK3hzc7itFsLzQBt0mpJtbZZrW31M1LntNq0/+mO86Wkb2C8E6doa408/oeh27TiP4jj48xdo/dEfkT54QHznNuHKFfzFRdypaRBQHB6Srq2R3L+HOjwEoPb6G0QvXUM26gSLS7jtNs0/eI/aG2+iS4tH0e2SPHhgP9frTcYLLl6k/Ud/zOjTT8jW1wmvXMGbX8DtdEBA3u3aNd6/hxoM7GEvA+aDlSu4nSmcRt2K1qJAD4ekm5ukaw/Jt7bsPoYh7tQUzR/+CJPnDP75n1DDESbPTq3daTSov/Em/sWLDN//DdnmJno8tv/caOBduEBw+TLe7BwyjDCZtZRlm5uk62vke3vfugh0p6cJlpfxl5Zx2+1SxCl0mqCGQ+Lbt8m2ynUZg9Nq0/jBD/BmZ3HaHbyFRTytcKem0HEyEWDp6irJ/fsk9+/Z89NxcKIIf/kiwfJF3E4bEYYIx7HX7P4+2eYG6eoqatC3e1hvEFy6ROPHP2Z88wbx55+jhsNTYkGEIW5niuaPf4xOE4YffIAaDHCnpmi8+0Ocdgtvehp3agoZhvgXFtBxjNEak+Wkqw9I7t8nfbh6emOkxJueofneT3A7HZxGE2M0qt8n394hvv05Rb//mMDy5ubwl5YJVy4ja3WEdNBpQr6zQ7axQbL28FRGfbC0RO3Nt3BaLdxOB296xrqC5+dxO1MYrdBJSr6zbe8tqw8en+fCAq2paXuPiCKMMRQHB2Sbm8S3Prf3wJN75rr4Fy/a4764VCaRGHSSkD58SLq+Tra1CUohfB9vbu54PkrhNps4zSbJvXvo0RDheviXLiF9n3x/n/jGZ2Sbm8fj+T7e/DzBxYv2XhlFGG2tnNnmJtn6Ounaw2/9Wvg2qQRgRcXTUr7xOu023tQ0wvdKK51ENjxkGOK0Wujx2Mb7HLlRSutFcOkSbrtD3u3itFq27prj2NIbJkCGoc1+LYPc/aUlomsvk+3uIKTEm+og5Et4gwE6SXCa9ju82VnUaES2tWljjIRABoEt6XDpEv7CYrkAe6OTfoCpRcgoeizLU0iJOzWFCAJMmiHDwM7JkQQXL+HU6nZ921sToSOEsDfb6WncVhvhefaN2xW4gW9rhwUBqt9DDYenYvuMMZg8R+cF0vMIL11GSEn64MGxAHsEISWyXid6+boVOFrjzcxYK4aUCNex9czKB/3xYPrYUioEOA7GlJaDMs7pLIuPkBKn1SJ65VVMoZCejzszg6xF4FhLyun6acfni4wivLm5U8f2qN6a02ohpEAPh6jJC4PAW1iwe7C2RrqxjuodC0Dp+3jzVuD5S8uMP/7IHtbSkuMvLhK99jpOrYbwfYQU4Pu4U9M4zSa4rs367fUma/5GkRIZBATLF6m9+hrCcyfCX7gu0qnZ/Vlbe8QyaC1L1lJkEFJaK5DSVvwVhQ1fVKctx6I8zm6rZY+Z71lLsJCIWo0gDPGmpzBpSlrkVqRhkGFIcPESajSmODhAJ8mp+D2nXidYXiK8coW82z0+X0xpjVfWSnVkZTNK2bkphVGF/Z0zhIfTbFnxliQI30O4DkJ6OAsLuO0OOo3h4UNbmocygzeKCK++RLC8bF/yyhp+Mozwl5ZwGg2M1mQ72+jh8Gia9jw/mpNWiCOLnyrKnxfnWtXczhTSD9BpggyOz+1g+SJOvYGOx2Sbm6i+FdUyinCnp6ldfwV3dhbpBxOroeN6BFeuIms1jFKow4NJWajg4iWCpWWy3R2cMMSdmrYvzKMhOk1xGg2cegN/YYHiYJ+828VkOSKw53ztlVdxp6ftvU5IhGMQsk64soIMQ3SRo8rj+yJSCcCKiqdFa3SeW6vW9hbZxgZqNEJIgTc7R/2dd6m/8Sb53i46Tc+MZ3Onp2n/7Ockqw9IVlfJt7fsQ9HzbNB1KaoAa/VbWLBWwb098p0dam+8ib+wQHzvHsn9uyAEU3/+3+JfWMBttSmObp6tFs0//EO8uXmKgwPGn35Mtr6OGsc4rRYy8G227SPB88LzCC5dIt/dZXzjBulHD9BxjKzVaP70ZwQrl+0DrMgnczVaY/IclSQUqw/INzfRSYKMQry5eZrvvYfbbJJtb5GurZ0SdqrX4+D/+WtwHLzpGeb+9b95pkMSXr6MDAKS1QeMP/uUvNu1Gb6Odd+dXN/45k3iO3eQ0qHzL/+S+ptvMvzgA5I7d8h3dyZrsQ/Es11D4Usv4dRqxKsPSD7+iOLw0I5XuvZPxU4phS6L+qYPHpDv7aGTGLfTIbh0mdZ7PwFjyPf2MJub6Dim6O6hDg/wZucIrl6l6PdO7ZcMAsKLl5BhRHF4SL5/gI7HVuTMzBC+/DLNP/hDBv/8TyS//S35fndi0Wq+9xPr6paC8SefoL8FAShcF3d6hvDaNaJXXqH3//3Civ3BwL5AdTr48/Oofu9U1qzq9xn86pcIx8Gbm2f2r/4VatBn8Otfk22s21IlcCy0yuNnjLEW1zSh2O+SbmxYS59SeLNz1F5/g/rb71Ds76NGQ7LYxsDl+13yvb2JCMm2tk4JQLfdJrh02Yrpw8NJElG+t0fv7/8O4biEV1/Cv3CB9P59hh+8T7axbi3Npcv4rHMsWFhAjUeMb9wguWlrMMpazWZmX3vZZq0XxUQAysiKvPpbbyN8n+E//xP57i46S3GaLRrv/oDaa6+DFIAhKQVgtrlBvruLEAJ/cYHaW2/jX1igODzg8G/+sxVE5kh0n7Y2itIinu937T3ik4/Ro5G9R/zhewSXL1tBnhcTAehOTxO98iq1N9+i6PUYfvgBRbdrX2xnZqj/6Mf4CwtgDOObN1E9a7V3m03cmRmSO7fJNzdxul3qb76JMXMk9+4R3/gMtzNF5y/+An/uAmlzjeJgH6fZJFy5TP2HPyTf3mFUXgtC2Bfc5nvvUZuattfCp5+iSyv8i0YlACsqnhKjFCQJyb27CNc97lYBqNHIuiump3DqDWRUO/s7soxsd5fk/gPStdJVfFQvrLSGnfhtTFGQbW2RPnxY1up6CeG6JPfvk62v49Qi1HiEkFYw0u/httqEV+0bdb6zzeDXvybv7qFGI/swLAUDSk1cnxOURvUHpA8fMv7kY2uxUwrR65E9fGhvoJ0pZBQdz7IoKPp94hs3MFrbNakCBg5qOCS8etVaYDpTtrzEY5tyFGuk4Bkr/1nL55YVcd09dJpOrC4Yc3p9SmGMQUttY8Uw9mdFjj56uJ+w5Jw5Xum6TW7fpjg8RGcnxitfEI7QeU6+u8co+60V20mKUYXdHwP59esgbDxivrtbfkiTbW0joxrhyhWSO6cL7YowxF9eBqNJH65hUmu5EK5LuHIFt9kmvn2L5O5d0lJwqJF1D3uzszitNuHlFZJbtybdHb5prEXZAQwmTVGjEcWgjxiPKAYD8r1dVL9/Ovv06NooXzYm50xR2Jeyk8fv5LHTGp2mpKsPyXZ27TWb52Dseeq224RXVhBhiNNoTD6mRmOSu3espXV5Gfnxx8cuTSFwW22CpWWKw4PjY1eOdxSXaYrcir2jeWZPmOdk3BHZ5iajTz6mODjApCmi3yedfojTaCKjWpmwYXE7UzaEIR6TP1y1oQvjsX2ZHA5JOh2E7+MvLJLv7BwXdy/30Qh7ntq4OhsX98T9BFsbcDAgW1tj/NFHqNEQUxSIXo907SHC9XDabXs/KvFm54heukbe3bWu7/v37bUpoOj3kK0W4eXLRNevk+/tTQSgMfYcSTfWKQ4OcKemiV55BZNmxHfukO9sW7fueGwt/1GI6En8CwsEly6Tb2/ba2F1FZ0mIIS9V8/NESwvE718nezh2iQM40WjEoAVFU9LGZxuSpHhdqYQrjNpmyRDm0hgXZ7+mV+h05R8d5d8d+fc0iPC88pftg+9ot8n3+8i4zE6y5B5bj9/cGBFTpLY8QPfuiubDfzFJYSU5N2u7SpwwpWjnpDhZ7RCDUc2duhEPA3YEhHuzAze3DzCP7E+raG0sti4t5mJKJoEipdu7cnazthbG5x97tTORI1H5Ls7NtPyyCJ57uJKwYd9sGCYPPSeNhhcDQfkOzu29EgSP3m8UgToJLHWrWZrsi9HbdeE6yJr9UlhY6M12daWzVK+9jJOy8asmSQp24W18GZnbdza2sOJwBVS4i8s2ELevUOcZvOE6x+cZmMyrgzD84/D140xkzg/NRrjtDv4CwvIwEeNx+g4Ju/3zhbgRxYpo8HKd+vWfzRh5JHPoBQ6z3A8t4xPdY+v2TJ27agu5BE6HpM8uG9jfTtTuJ0OOrbzk1Fkk4g6HeI7t8n3dk+PqfVkbuUkTocfPAE1HJLv7ZJtbJyyJhcHBxS9Q3vthcfzdBoNwpUrqF7PWldnZnGnjsdwajWE6+DNzuE0W4/sjS4TZ/TkejjaryfN0xiNHg7Le8TGqX8r9vdRs3N4c/PIo3uElLjtNt78PKPf/ZZsY4PiROa9Ho3INtZx223qb71tj9HJl6ostdbubteOXxT2Prq9TdE7xG217XUgxWRMd2oaf3EJ1e8hwxBvfn4ynnCkTfwKQ/yFxVNC9UWjEoAVFU9LKWaCyyuEV2zSgWw07MMDgYxC0Ma6c88pTaGzjHz/nOSGRzBKWbfTkUApRZzRGpNaa9JRbBRgM1wRyCDEbbXQcWzr6T2Lq09rG6t3hnXoyLIhPRfkidg6x0HWbExecOkS/oULNhbJ8xBC4tRqFP3epLvHV4ke2xitb6pWmRqNKHqHpbXyyQjHwZuaIrr+ik1QmZ21D2QpEY6LrNfJ1tdsHOiJDOhsewvvwgX74JqxiQT59jZuq4U/N4eMookFZmKpkdLGQi0tIS5eJHrttUeyaGUZT+aQ7ewgHHdiJf0mMXlOvrtLuvoAb3qa+jtvU3/rrWPL0IMHpKurZ8eefRlKkR0sLVmX7OKiDYGIInvNBgHC9+3fJ65ZHceka2vUej38MsFCj0fkeW4TnaanEa5jXwZOJlE9J2o0tG7TR9ZulLJWNtc5FdcqfB93agpvZobwpas03/sJJ19JhOOAYy2u0i9jVJ8381Vrin7fJsY8gimsC/5kbKf0g8lLhxoOz/ycGgxRw5E9FlFkRe5R7GSWTyyR9rwo9yNNJvG69h4oyoxkUYafzNmuNi9fL++Vk12x15zr2Gxvz/tWroXvApUArKh4StypKaKXXyFYWkIEPsmDB7YhepGDNoQvvUSwtIzRmnNlzpHr5SkEBJQukPKGftwZo7zhnXfDkmWJmS/RJusoKeP0DXPyr8elLk78NFhcInzpJfylJUyeM/788zJWSSGA2ltv2YcPwPk786UwyrrXvs6uIafHU+eXZzmJEIRXXypfFJYoDg8Yf/oJJssxWuPU69TfegudPl4YWCcJqtcj39nBaTRtbNbeHu70DN7sLEXvcJKYMMkGFQLh2bCEfGvLuvzLzOBHUcOBjYP7th54SpFtbDAoFN7qA9xWG1mvEywtW1fhteuMb9qMzuctBu40mkTXr9vkhFaTbGsLfe8uOi/AaIKli9alqNTj5WHynHxvD296hmB5iWJ/j6LXw19aQkYR2e4eRb//lSYQmKKwsZmPHZuzXcbCsS+l+dYW2e4Oxd7euddCev/+V3bMTVGcf484mhtMkq1s4o21Hp5t3bVlYGz7xxP3rSMX9JF1cjLGI/fAo7Iw5YuUcBzQmnRjg3xvl6KsBHAW2fr6Cyn+oBKAFRVPjdvpUH/nHeta3d0lvn2LotudxNwIx7pannwzKW9uT3W/OeOmbx75+yyURhfFJPP0Wd9urUv06X/fu3BhEmie3LnD+LNPUb1eGeMjcOfm8BcXv56brPkCMfxVo80J194TEIJgZYXwpWsgBPmtm4xv3ZrEtnkzMwSXLp09b6VQgz7p2kOcqIZ/YYH45k28mRncmRny3d0zrZ5GafRoSLqxTnzzxunYtJO/V8bFfZsUh4cUvR7pw1W82VlbFunyZbw5GyumxkPUYGAf3Cf36BlDBZxGndrrb+A0GqhBn+T+PbKtrUlZF/1OQrCycvaLkjHkuztknSmi69fxpmfId3YIFpcQnke2uWG/57yad/ZLnmVbjq39T0vpss3390lu32Z888a589FHsZNfAUZrzNPeIyYxvgbpemf2lxblz21GvjplATZn7aHhCS99x/G/+e4O8c0bJPfunTu9b/ta+DapBGBFxVMio4hgcZH41i2Se3fJt7fLrD5jM3nDEKcsRfK18RQGNJ2lqOEQb2oKp9GcJHx8XSLJaTZxp6Zs5uLqqrVClA8hUdZhc8JvtsH9EzkZ2P5ogeavEHdqCul7NlNyzQaxHyURCNfDqdXPtXCp4ZDk7h0aP/gh3vy8dWnNTOM2m8Sf3yQ/PDj9Aa1RwwEyCnHabYw2J0rLPMJ3pd1bGb+abW1NYlWjV1+j82d/jjc3j3dh/5wQhhMK8AuOn/AD/AsXyLtdkjt3rPgbDCaWbBn4NvnDOftRmO/u4jSaNN55x8a/zs/jLy3ZZKAHD2xC1Tlrm4jVo3l+Deg0Iz88tKWkgsCWsTlPkH4bx1xr210kjjFFjtNsnkpiOcKWc6mXyVLJcaLPM2PPDTWOUaORTVZznPOvhXKOLyqVAKyoeFq0wagC4XuTG4sMAltweXEBf2HBxuh8nULnKe6JRa9PuvoQf2ERb36exrs/sHX7SuuT8Dz7xi0lajScFBH+0pSB+TIKbSKM4yA9D7fdsbFv09PgfIEoFsLWrLN+o7JsxdeEMZgsw6gCb3aOors/KVdx1PngzA4bz4pSNvGiXi9runkIR+LNztks7UYd1euf+VEdx6Tr69Ree92GHlx/BXd6xmaFb28fz/doSUVBurZma+wtLZOX5Xrs9xt7TMIQISW6KFDnxU2eFFQnuzB8Vbiudfn6PghQcXz8sHccmxTjeYCAs8IkjpI6kgRZZrcWvT7FUdmXMiFpsjZTXrOea+NSXXcS8+cvLBBcvFTGRp69Th3HNgHhYB+naWtBOo0G+fa2zbI+z/1rjC1sHcc4UQ3vwgLFwSHKGR3H8Z6oP/llKXo94s9v4i8s2vJN29s2PCCObUhcENj9FMLG350oM/WNobUtuL2+bmMVr1yhGPTRY1sBwalFBFeu4LTbJPfvT5Lbnod8b4dsfa3cl8sUh7ackslzmyxy5B3BWqO/i72kvwm+9wLQCyWu75CnCq1M6YErC+JKYf/blF1wThoGJAhpH0pGGbQ2eKEDBlShJyZoU76MCgHStcVJjTbWSyRAOgK/5mK0ocg0OtdfeG67gcRx5ak5I+x8pStQhcGoE18iwHHtDcxo7Ge+5NgV56PTxAbQ+z7ehXnr1lTa1py6ehW307HC4uvc5Kd4HqtBn/ThKtHLL+PNzVF/+x370Nrbw2Sp7YTguqA02ebGcwtANRpRHB5OulD4vd7EHR5evWo7ieSPP+iO2mMJ30d4ns2q9jykMWXWZWzr1imNGo9O1WF7LoyhGAxQ/QHe9Ax6ednGLJZuIz0aWcvTcwrAot/HTRLc2Vn8hQWM0pMaauHKio13OjOOysYB6iyj6Pfx5i9Qe/11nHqDotej6O4/dsyMUqSrD2xW6OUVgkuXQdokBWMMwnNx6nWMspZCfWRZKwtIC99Hej6yXrMFo7EZpO709KTOo8nzSbjDl8XWAZzG7XRscfDBwFrRtbIZztPTmDxDDwe2C8oZY+m8IN/ft7F5Fy+hRkObyWk0ajRGj46Fjskz8r09W1Nzfh5/YRE9HpfFk6/aTjtpdu6xNkWBGg5IN9YJli7iX7gAQlL0ejb54wnuX52lduzQFr5W5ZpQBWo8Rg2Hz12LUfUOGd+8gVd284hefdWKwH5/Iq6E72Py3BZm/jYEIFDs7RLfuUP9rbfxl5aJxmOKXg8hBE67bZOXEMS3bpLvd597vHxnh6Rex19YxL+wYLu+7Gyj0sQW7q/XEY6c1HWtBOD3lPmXm8y91GTzsx7jXo4qNOkgR0hBUHdJhgVGG/zIReUalWu0Mvg1l7Dp4YUOcT8nGxUsvNoCA/2dhCJVFJmefMYNHJqzAUWuycYF6bDA8ST16YCVH8+QxwW7d4f0tmLy+MkJALNXGrQWIrY/7zM+SMkTjeMKah2f+kxAbysmHRRW6AFe4FCfCWzMSqqID3OkK77U2BXnk3e7DH71S6LXXrcV6i9etkHboyHZ9ra1wITR11tV/im0pclzit4hg3/+NeHKCuGVq9TffBPhWkuAKXKKwx7J/XuI3ee/BaRrDxGeS+2116m9+hq1V1+zczjske/uYNLMWgAfecj6i4tE114mvHIVWa8jfd9aC42m86f/jS0XMo7RwyH9f/wH297rKyJdXUW6LtGrr1J7/XWi11/HZLltdXfrc9I8Rz1PAoLWxLc+B62IXnudxo9+TOMHtsVb3t2zrsg4geIJY2hNvruLPz9PcPFS2dZrHZ2fIYSVIt/bY3zjM0ya4i8uUnvtdeQPfjgphmxS23JLHR4ev+j6PrVXX7UZsktLCNdD1usgBLU33yK8chWdZajeIenaQ/q//vXz1Q8sXaPe9DThyhVbTshxENgMeT0aMvzdb4/Lq5wlAJOY0UcfEV2/TnT1Kq2f/ty2LktT4s8/J7l318bmGUPR7zN8/32iV18huHSZzp//RVmLMSHf2SZd36DoD564Jp0kpPfu4U1N4ywvk26sW5HyxNi/snj1+7+hdv0V245s4UIpKEfEtz638YjPWYtRjceYhw8ZeB7h5cv4i0uEl1dsy0FjqwUUvR7JvbvP/ULzPOTd7iQG0V++SP2dd22msAFT2HqZ6doq8e07ts3bc5YpUr2erXlosC9cV1aIXn0V4UgbA5sk5Lu79neeId75+8b3XgAGdY/WhZBhN6U2HeAGkq0bPRxXMnetyeaNHirVzKzUGe1npOMCx5c0ZwOilk+RKYpUkccQNjwcz1r5HF+S9HK6D4Y050JqHZ+o5eFFDnmi2LrZx2jway7txYikn7O/NkY+hWvLr7nUOj5uIE+5JqK2x9y1phWemSYbK6KOR2MmoHUhIo8Vo4OUpF9MrH/POnbF+ejRiGR1FQwUe3tlGzJl61htbwNlDT9tg7KPP2gtKONPP0UEAfl+F5M9qRaf7S6Q3L2HGlrrmjEGioLxjc9sU/mxtY6o0Yjxxx+hRmPy/f2y+Kx1cWabtjWcGo1xynI1SGktOcOhdQuPji1J2dYWow8/oDg4PPMtvNjrEsvbVsDsHnc5sQ+Ye7ZfZ7tT9nUtrOtsZwdxVGKjjFObbEsck3e71i33SBkOOM5I1nGMOhFrZZSiODxk8E+/tnXHzuiN+kUUB/vEdw06z23dMd/HFNayVPT7p9vVaU2xt0f/l/9Itr5OMRjYlmRfQL67az0OWV7WY3MxeUHetdmkMrCZ0TpNz31pSNce2qzZrW3ygy7Z5tbpbiMnMFlGsbfHOM8pBn28qenSMoYVPeVDrzg4sIW6YVLSI9vasg/oR9y+phRsejy2ddiecZ8fozx26doaJsvscXdc6/FNU9vzdm+XfHf3XIuvtWZtWIvfcIg8KhmSF7YLxgnrqE5T0vU1DMbueRgdJwhsb2HywopQrSl6Z2eKGqXsMS/PiWxzk6J7dg3Pk+gkIVt7CFpRHB5YS3j583xv75TVqTjYZ/j++6ocA5gAABUMSURBVPaa2dt9TKwVh1bEFQcHZNsnihaX3W7StTV0nFD0Bzg1G3JwtE41HJJtbZ8rctVoRHr/nk1oi8dPLKmUbe8w/OB9ioMD8u7j5W/y7j7i9m2K7t6pedoXwkPiu3dQwyHF/rydI/a8zXa2yXd2bHZ6GSdbHBwQ37qFLDsuGa3R4zGj3/3OVl/I8/JeecDwgw/suX14aK9NrVB9m/SjR6NJRxXhONainSR2r3d3bYHoFxTxtO4qIcTvpUx+6aezXHp3ir37Q6K2T3sh4tP/vIkfObz8R/N8/B/XSUcFL/10lr37I8aHGc25kOZcgBc4HG6M6T6wP1/50QxR20MVhlrbo7cVc+sXO1z+wTRTF2sYZWgv19DK8NnfbJIMcurTAW/+yyWG+yn3frVLbyv5Qivc5R9OM325zsPf7jPYScjGCukKlt/scO1ns9z7dZfde0PGBxmzVxtMr9TpLEaMDzIO1sds3xogJXSWa888dkVFRUXFMSII8Obmaf3s50TXrnHw1/+J5M6dcwVjRcW3jTHmqaw933sLIIDKNd37I8JmhkDguAIvco5zyUQZ7wd4vkN7IWJ0kLK10Wd0kJEO84klrjkX0roQsv7JIYebMUZDbcqnNR+RjHJUrklHBQJQmSbu5STDnPF+Sm8roUi/nBleK0OeKJJBgSqOtfhgN8ENJO0LIbWpAFUY9u4NyWL1lY1dUVFR8aIi/YBgeRkZhjb2r9tFjR4vZlxR8fvG914AGoMtytryrAvXlxgNeaxI+jmt+RAhBY3ZkMONmGRQkI2tC7U25SMdKwyz0nKWJ4rRfoqQAj9y8UKHItPkmcKPXIwxk/jAItNIR6MLgxs4NGYChnspeXK2FU46VpjWOh6N2YDOUg2VaYoswfUlQd3GJbYuhGTjguSEMMVA2HTRys7ZKIPKnn7sioqKigomvblt9wwfb26W8MoVhBCkaw+fvbtORcV3lK+xYNl3A11opISlN9osvdmmvRBSpIrBTkp/K2b+5SYv/WSWmZU6Qd0lGxfs3BkQtXxe/uN5rv18jpmVho2fM7D/cMSNv93GCySzV+qETY/+TsL+wzHSEUhHoAtDOiqsxa3UZ60LIZfenSJsnR/c6niSxkxAezFiZqXO0hsdOss1vEBS6/i0FkKmLtVYeLXFzEodL3Bs7GHbxyDwAoew7h2LwmcYu6KioqKCSXknt9XCu3DBtn68+hKmyElufX5+7b+Kit8zvvcxgLUpn/q0DxqkZ8u6HK6P0coQtXz8moMbODiepL8dE/dzpBREHWsxVLlmfJgR93NqHdtoOk8UtSkfowzDborrO/iRQ9jyMMb++2AnQeUax5O0lyIcV1Kk6olWOCEFXihpzIZELY9sXNixezmOb0VgYzawruB+Xo4t8WsuUdNaN4tMc7A2fuaxKyoqKirAabcJLl6k/vY7tt4nNokhvnmT8c0btk/28ybDVFR8jTxtDOD3XgAecZTY9uhyz/v5yX9/2rJuQpyo0vHoZ8o4w1P1+74qBDiOrWl4Zpeqr3PsioqKiu8RTqtFsHyR2ltvI1wHPR6TbWyQrK6Sn8zAraj4jlIJwIqKioqKii9DGQMI2LJK5qiHd/UYrPjuU2UBV1RUVFRUfBnKloAVFd9nvvdJIBUVFRUVFRUVFaepBGBFRUVFRUVFxQtG5QKuqKioqPjacZwA6XiAQakMrXKEkDhOgOuF5NkIpXLAIKWLkA5CSAQCYzRFkSKExHVDao15tC5Ikx5FPkZrZX9XSKR0cVyfIk9QKgMBrhviOD5K5SiVYfT5dfyOx3ZsOzWj0OW87Pg2M1jpHK1zMJw5ttY5QjhEtRk8v06a9MiyIapIkI4PRqN1gZAuAmzvXqOR0sX1agghMUZRFClGKxDgeba1n1IZWueYMusvqs8hhUOSHKJVNvl5RcWTqARgRcXzIiWOb/sCm8L2CzXmOGBcOC7S89F5bh88k0ByYQPNjTn1+0efOUorN0aVDcsNQjpw1DN3EqdkQEjbS/fR7xLCjiOl/a6jB5+UOF6AUeXD7eTY0pmkv0++/zyEQHoB0nHRRY5RhX1YPWlsIez6jr7f2N6fCGED7cu9sU9FjtctxHEwfjkn4XrHY2t1at1C2iD+yRjn7rk47oFblgU4+V3njY20D/xJf1FV2LFEOYY+GkNP9uLkPMqn/vl7+71BIB0XL2jgeTUr8rLBRAB6fo2oNsPIGIwZonWB69VwvQgBSOlhjGI82rMCzK/Tal+mUAnGKJRKERhcJ8D1olJouhijrUATEs+r4fkNjNGkSZ8s7Z0726OxwZ4TWhek8QEgcNyAIGiCkGiVk6Y9jFY4jv/42LlCOj61xjy1+jz9wwdonaNVRhA00aogTft4XgRIVBGDELhOSBC1cRwfrRXj0S6KDCkd/KCJlC5KFSTjLkqlgCCKpnEcn7yI7X2oEoAVT0ElACsqnhM3qNO8/BrFuE9ysA1ao/IUnacAePU29QsrxN0NssHB5OfSdfHqHYzKUVmCylMwVux49Q6O51vLRzxEZQlGFbi1Jm5QAwxFMqZIbfN2J4hwghq6yNBZgs5ThFNaMqSLG9YwWpEPDzFG4wY16gtXyUeHpIe7k7EREjdqlqJGUcQjjMrPXriQSM+nNneJsDNPvL9B1uuSj/vnjw1Iz8dvdNBFgc5iijRGui7S9VFpjDEG4TgIISbCygnrSNdDF5ndi1JoB+1ZgtYscXeDYjxAF1m5twFerWkFQp6i4tG5ey6ki+P5SNefiOsiHqLzDDDnju0GNbzGFH5zCpWMSHt7qDRGuB5+o4NKY/sni5FegJASlcalWD9aX4H5nteUcxyfqD5LELZxnIAs7aOKFBhhjMF1I2qNBfI8RqkUrQvqzQXCaIosHRCGHQDyfIxSOVJIpOMhdFaKeztGEHVoti4ipMN4uINV2BLXDZDSw/NqNFpLDHpr7D9BANabC4RhhzTtEwRtwNAtUjy/TlSbQascz6vhejUO929T5DFe0DhjbCZWQSltlyhjDFJ6NNuXyLMRWTYgqs0ipcN4uEsQdkrxCbX6LAhI4n2McXCcACFcovo8YdhhZ/ND4nH6tR67iu83lQCsqHhOhHRwwzoAfpHjBjWy4SHJ/iZGa4TjTkSEKC1MXr2DW2sgXR/pehitSLqbUH6XE0S4UQM3iBhu3gPACeu4foQT1fFqTeLddbTKcRsdnKCG44dILyAbHJAebOP4IW69hd+YRmUJxbg/seUJ6eAENVQ6thY6BNIP8KImXqMDGIp0XArP8wRgufaojtfokPa7VkAJgeNHeI32Y2MLx8XxAqQX4LdmwBhGm3dxwjp+c4rkYAchHfxGh3xkrSvSD3GDCFmuL9nfIut1MbpAuj5OECGPLKZC4EYNvFoLr962gmvcR8VjvHr7zD13a03CzjxiYrnJyP0QnWflPoWnxi7GfdxaC8ePcMM6QWeOfOCRDQ8Rrm/FpB/gN2dQ6Yjh5j38ehvph6SHu5Pjm496qHSM4fstAI3RFHlCELRwvRAhBHk+Jk1KtycGQelGLcPSXSdACoc06eG6of3/0pu4cLUuUEVKlpUWQydEShdtFCbPSZMeqkgnVjPfb+IFTYKwQzzae+J87VguWTrAdUIcN8B1A4Kghe83GfbXAIPjBjhOAHDm2MZodOnq1ionz4ZW+AqB4wQox15XjuOXL0vS7oV0CIImjuNTFAm6tDAGYQvPbxAETYKwhZTV47vi+ajOoIqKrwjpuLhhnWhmCeG4ZP2utaydgd+ewW9OU4z7+M0ppBdQjAdI17fCCKyAak4z3lvHaEU4tYAQAiesE05fIBscIMd9wpklKy6lQ9CaRTou+fAQN6oTtOeozV9itL1KPjp84vwdP8RrWouWjTkS5MPzLSXHrlPrfj62ZgncIDpzbOl6yCBCuh7h1AKOHxLvreMEEUFrFpXGOEGNaHaJ4fodMJpodhljDI4f4LdmMXlGMeqj8jOEkxC4YQO/OY3X6JCP+9YqKM7f86A9R33xJRAClcYU8QDpeBgMjh9itD41NlpTm7uIURpjFK5fQ3njcg+D0iXuU5u7SJGMGG3dxylFqc5SgvYsbtSYWAi/72hdkGUDQtVBChc3DEmTE+eVMWitkMIpRY09r5TKGA93kdIlDNtlTJxGFQlaZaginQitI7J0SJHHxOMuYPC8GkHQLkVgHcfxJqEB5yPQWlkB6Ib4WIFnPyvJ8xitCxw3REiJxDtzbAClUpTKyj/Wuikd69IWQuC6IdLxJw0JlEowpkYQdtAqI8uGGF3geLXSOmgtj1K6p0IsjqyMjvRQUkLlAa54CioBWFHxFSAQ5OMByf4W0vXRRY4TROhzgs0dL0BKh+RgG51neI020gvBaIpkRDi9gHR9suEhOkvRWUo+6hFNL+CGdSuAUhsz5NfaCMdBF9atqYt8YtVTaUy8v2WtZsPDEzFoj1MkI+jtWctjWEc6LllvD5WMzv6AVugjN2cakw+tReuIs8Z2wzpBcwo3tGOI0mKYj3rE0sFrdPDqbaQXYHSBEA5+cxqVJxhtUMkIrYrjOMjH5mTIhodI17PfVWti8pS4K87dcyEd63rWinzcJxvs43gBbtjAa06hsvjU2ML1cKMGycHOxNKq0hghpbXuRQ28qIETRPYFQAjS3i5GFYQzi3i1FkYr664/z7r6PUJKl+D/b+8+mttI7iiAv06TEJi0SpZdW2UfffD3/yI+uVxr0zK5TIgTO/jQDZAUKYksydqV5v1OlEhiBmDAY//7DbJZXC0TIu2VywAImGyCvDxENflpX6QYbA0hdl/fx/ZIxuCTl4c4OPoZm9X7O+8L9z5HSAVjqli62O3D/KwAY0ocHf8ZWT5HCB7L5S9AcwMIidnBOxhTQekMTX0JO7TI8umDY9+ldI7ZwR8h5X/RpjJIls/w4tVfUVYn6LpVumcSQigonaffHbF4onQOY6oH4VUpA5NNUZTHMNkEEBLr5b/RuKu4d5joExgAib5Y3OyvTAYzmUNmRdwDmEaUuogj22x2DD+kkZAd4J2LY8lyGlcF0gqa0AYqi2NN22z37cDgBkiTQ5kcQx3Ho8F7uK6GKieQJjULXSpjhIDgLFxbw/cNQtofJ7SBKiqYagYED9d38O4ivk+IuHNKZ5B5CaHMp++5d6n0IaCrGVzfwLYxBD527DjansVVMq1TByIG1UGuUBy/SaPpJgZZ4WDbbSzS6LgvMTgLBB9H3MUEZnKAbH6C4Cx6t4CQIu6zEwIyK6GKSdzMb/tHH3PvBnjvENwAP3RwXQupb/dffnjsYHu4roFUGtnsOO29HOJor5jE8KzzuA8yLe24toYzBcyruL/SttsH5ZsfVUCA87Ew4YND1y7QtnFFOHiHvl1hvTzFMNQYhhohBDT1JYRQCMGj79YI3sKmgoMXHtvNGaTUsSEb4s9B360hh/ZeqPZuQNNcQ3axLNS3a9Tbi8+cL+CDT3sSY9PXDk38uQo+FVni93PfbdLb4cGxd7p2Ef/IsPF3QvAOTX0FZzsYU6UV0g2s7ZAXh1DKYLP6TwrBAsqU8G6I5y0E+nYFqTPY9Fh5b1FvL6D7dWwZp/Mh+hwGQKIvFEKAtz10NYeu5jDVPBY32hq6nO33owVr4Z1Fv13CNhtIk6N6+acYIIYWtq0hBKDVfL8ytitmCKUhduOrXSNViPhkcnOOAi+RTY/iE4wd4PoOqohtwNgKvH1CUFkBU82hJ/MYdLyDbdYA4vh014iVOr+zEvPp+y+kRHH8GsFZ2LZOG94fHjuGqSy2cN2urRiDqh/afYjtV1fwQ7ycRXN5GsfFxSStnA0IzkFX6bGdHu5X1IZ2A51PYsPZu9TSjSF22K4gTfHgMZc6i8HOxSARzzuVb5aXKI5e3Tu2bbboFr8iP3yJ8sU7mGoWx8whjbiVAYK/14jevS1NDtvV6Dc3P3z5Yyd4i75bYeg3++/ZHWsbrFenWK9O733Oenn777a5RvvBpHxx/Q/sRsW7VW3X9A+ObW2L1eIX3K78PSUYBQz9Fsubf94LUw6I9+GR23K2/eitbTfn2G7OASGRav1othdpL6LA3XmtySaQUuPq17+jmv6ELJ/DmAptc4Pt5uzRY/fdgOu0gkj0HHwtYKIvJJSGqeb78YxQGq6r921YnVfQ5Sy1SBvYdgupTFrNywAZV5fiiPcNyhfv0C0vIJWCrg7Q3pxBSI3qxVt0y0t4Z5HNjtGvrtCtrxGGLl4ORWcIIcB1NVxbQxqTVqFUHGM6uz8/lRXQ1RzwDq7vYNv4xCa1iaUHZQAI9Jvb1vLH6GICVUwRvIvH7mpIkz16bFVMoLJYBBDaACGgW14huCGO66aHEELGhm7fACHExykr9iUN12zhhi7uuSwnKcz1sbSyaxSbHCorY/N26DCsF5Ame/QxlyquiCKEuDJr+1gqQQwrHx7b2z7dfp7avXGEbNvt/mOFEOnSPz26ZdwTls1PcPiXv6G9PkN9/q84ch/d5Tr21/b53ZrO/wApNdar03th9VuoJjH0CakhhYRzPbabc1jbfvLahUR3PfW1gBkAib6qZz7B7Vb5kvLkLYqTN+gWF+lyMAfolpcQSqE4er3fS5bNjtFvFujX17DNJgWJr/Tkemel4nmfJ/bX7Xvaxz73XJ9z/0QqpjxyHcOveeyP3dYH/6+yEmZ2iPLkLdrrMzRX70cx/v0eZdkUQkh03RrfOqwqle/3RXo3wA412mbB/Xz0LAyARN8hmcazxfEbCClhmy3aq/dxxFxO0h65Erat0V6fYdjc/NanTE9QHL2GLicYmg1svb5XliEi+poYAIm+Q0LKVAKpYnHBDamh6iGVhspLCKXhnYXvmv2Fj+n3TWW7r9uAsHvVEiKi/wMGQCIiIqKReWoA/HzFj4iIiIh+KAyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCPDAEhEREQ0MgyARERERCMjQgi/9TkQERER0TfEFUAiIiKikWEAJCIiIhoZBkAiIiKikWEAJCIiIhoZBkAiIiKikWEAJCIiIhoZBkAiIiKikWEAJCIiIhoZBkAiIiKikfkfVDLDXEriVdEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zi7BIP5peOXN"
      },
      "source": [
        "**Check frequency of the most common words**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtRradN9NWeK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66f23440-cfc7-4674-85c0-481ae4ce4bd6"
      },
      "source": [
        "# Frequency of the most common 20 words\n",
        "freq = pd.Series(' '.join(df['tweet']).split()).value_counts()[:20]\n",
        "freq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "travelsomeday         5880\n",
              "armchairtravel        3203\n",
              "travel                1917\n",
              "staythefhome          1532\n",
              "plannowtravellater    1217\n",
              "s                      662\n",
              "visit                  606\n",
              "best                   583\n",
              "trip                   377\n",
              "world                  371\n",
              "things                 368\n",
              "tour                   358\n",
              "get                    309\n",
              "day                    283\n",
              "traveltomorrow         281\n",
              "plan                   267\n",
              "time                   259\n",
              "ttot                   251\n",
              "new                    250\n",
              "gaytravel              243\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XXaKjRsgjpj"
      },
      "source": [
        "### **Checking requirements for this notebook**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYFillrDf-hf"
      },
      "source": [
        "# Save list of required libraries and packages for this notebook\n",
        "!pip3 freeze > requirements.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Efk1LLfgd3m"
      },
      "source": [
        "# show the file's contents\n",
        "! cat requirements.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OhOJF3uDpoM",
        "outputId": "9f0807fd-76a6-4c23-a0df-b52914593d83"
      },
      "source": [
        "# Import the os module\n",
        "import os\n",
        "\n",
        "# Get the current working directory\n",
        "cwd = os.getcwd()\n",
        "\n",
        "# Print the current working directory\n",
        "print(\"Current working directory: {0}\".format(cwd))\n",
        "\n",
        "# Print the type of the returned object\n",
        "print(\"os.getcwd() returns an object of type: {0}\".format(type(cwd)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current working directory: /content\n",
            "os.getcwd() returns an object of type: <class 'str'>\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}