Notebooks/Python_Tasks/09_NLP_TopicModeling_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "### Topic Modelling and Clustering documents  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is based on [this](https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/), [this](http://www.brandonrose.org) and [this](http://www.brandonrose.org/top100) posts. [This](https://medium.com/mlreview/topic-modeling-with-scikit-learn-e80d33668730) one added later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U -q gensim mpld3 python-Levenshtein pyldavis\n",
    "#!conda install -c conda-forge pyldavis\n",
    "#!conda update -n base -c defaults conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "import codecs\n",
    "import string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import gensim\n",
    "from gensim.test.utils import common_corpus, common_dictionary, get_tmpfile\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "#import matplotlib as mpl\n",
    "from sklearn import feature_extraction\n",
    "import mpld3\n",
    "\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models\n",
    "\n",
    "from IPython.display import display, Image\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "\n",
    "%matplotlib inline\n",
    "#%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specifying the path to the files  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = \"../Data/Texts/doccluster/\"\n",
    "outputs = \"../Data/Texts/outputs/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - [Documents Similarity](https://www.geeksforgeeks.org/measuring-the-document-similarity-in-python/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create some documents.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of documents: 5\n"
     ]
    }
   ],
   "source": [
    "raw_documents = [\"I'm taking the show on the road.\",\n",
    "                 \"My socks are a force multiplier.\",\n",
    "                 \"I am the barber who cuts everyone's hair who doesn't cut their own.\",\n",
    "                 \"Legend has it that the mind is a mad monkey.\",\n",
    "                 \"I make my own fun.\"]\n",
    "\n",
    "print(\"Number of documents:\",len(raw_documents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use NLTK to tokenize.  \n",
    "A document will now be a list of tokens.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/rsouza/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['i', \"'m\", 'taking', 'the', 'show', 'on', 'the', 'road', '.'], ['my', 'socks', 'are', 'a', 'force', 'multiplier', '.'], ['i', 'am', 'the', 'barber', 'who', 'cuts', 'everyone', \"'s\", 'hair', 'who', 'does', \"n't\", 'cut', 'their', 'own', '.'], ['legend', 'has', 'it', 'that', 'the', 'mind', 'is', 'a', 'mad', 'monkey', '.'], ['i', 'make', 'my', 'own', 'fun', '.']]\n"
     ]
    }
   ],
   "source": [
    "gen_docs = [[w.lower() for w in word_tokenize(text)] for text in raw_documents]\n",
    "\n",
    "print(gen_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a dictionary from a list of documents.  \n",
    "A dictionary maps every word to a number.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "show\n",
      "4\n",
      "Number of words in dictionary: 36\n",
      "0 'm\n",
      "1 .\n",
      "2 i\n",
      "3 on\n",
      "4 road\n",
      "5 show\n",
      "6 taking\n",
      "7 the\n",
      "8 a\n",
      "9 are\n",
      "10 force\n",
      "11 multiplier\n",
      "12 my\n",
      "13 socks\n",
      "14 's\n",
      "15 am\n",
      "16 barber\n",
      "17 cut\n",
      "18 cuts\n",
      "19 does\n",
      "20 everyone\n",
      "21 hair\n",
      "22 n't\n",
      "23 own\n",
      "24 their\n",
      "25 who\n",
      "26 has\n",
      "27 is\n",
      "28 it\n",
      "29 legend\n",
      "30 mad\n",
      "31 mind\n",
      "32 monkey\n",
      "33 that\n",
      "34 fun\n",
      "35 make\n"
     ]
    }
   ],
   "source": [
    "dictionary = gensim.corpora.Dictionary(gen_docs)\n",
    "print(dictionary[5])\n",
    "print(dictionary.token2id['road'])\n",
    "print(\"Number of words in dictionary:\",len(dictionary))\n",
    "for i in range(len(dictionary)):\n",
    "    print(i, dictionary[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create a corpus. A corpus is a list of bags of words.  \n",
    "A bag-of-words representation for a document just lists the number of times each word occurs in the document.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 2)]\n",
      "[(1, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1)]\n",
      "[(1, 1), (2, 1), (7, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 2)]\n",
      "[(1, 1), (7, 1), (8, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1)]\n",
      "[(1, 1), (2, 1), (12, 1), (23, 1), (34, 1), (35, 1)]\n"
     ]
    }
   ],
   "source": [
    "corpus = [dictionary.doc2bow(gen_doc) for gen_doc in gen_docs]\n",
    "for d in corpus:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a tf-idf model from the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TfidfModel(num_docs=5, num_nnz=47)\n",
      "47\n"
     ]
    }
   ],
   "source": [
    "tf_idf = gensim.models.TfidfModel(corpus)\n",
    "print(tf_idf)\n",
    "s = 0\n",
    "for i in corpus:\n",
    "    s += len(i)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0.4262583060688039), (2, 0.13529174589513532), (3, 0.4262583060688039), (4, 0.4262583060688039), (5, 0.4262583060688039), (6, 0.4262583060688039), (7, 0.27058349179027064)]\n",
      "[(8, 0.2640668814624488), (9, 0.46382576584030827), (10, 0.46382576584030827), (11, 0.46382576584030827), (12, 0.2640668814624488), (13, 0.46382576584030827)]\n",
      "[(2, 0.08327819508571149), (7, 0.08327819508571149), (14, 0.2623812867136573), (15, 0.2623812867136573), (16, 0.2623812867136573), (17, 0.2623812867136573), (18, 0.2623812867136573), (19, 0.2623812867136573), (20, 0.2623812867136573), (21, 0.2623812867136573), (22, 0.2623812867136573), (23, 0.1493798172489513), (24, 0.2623812867136573), (25, 0.5247625734273146)]\n",
      "[(7, 0.10934952319763026), (8, 0.19614512267839235), (26, 0.3445231800303735), (27, 0.3445231800303735), (28, 0.3445231800303735), (29, 0.3445231800303735), (30, 0.3445231800303735), (31, 0.3445231800303735), (32, 0.3445231800303735), (33, 0.3445231800303735)]\n",
      "[(2, 0.19143057263818053), (12, 0.3433775663260005), (23, 0.3433775663260005), (34, 0.603132667722237), (35, 0.603132667722237)]\n"
     ]
    }
   ],
   "source": [
    "for d in tf_idf[corpus]:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similarity index with 5 documents in 0 shards (stored under /tmp/tmpygbblrkb/index)\n",
      "<class 'gensim.similarities.docsim.Similarity'>\n"
     ]
    }
   ],
   "source": [
    "index_tmpfile = get_tmpfile(\"index\")\n",
    "sims = gensim.similarities.Similarity(index_tmpfile,tf_idf[corpus],num_features=len(dictionary))\n",
    "print(sims)\n",
    "print(type(sims))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a query document and convert it to tf-idf.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['socks', 'are', 'a', 'force', 'for', 'good', '.']\n",
      "[(1, 1), (8, 1), (9, 1), (10, 1), (13, 1)]\n",
      "[(8, 0.31226270667960454), (9, 0.5484803253891997), (10, 0.5484803253891997), (13, 0.5484803253891997)]\n"
     ]
    }
   ],
   "source": [
    "query_doc = [w.lower() for w in word_tokenize(\"Socks are a force for good.\")]\n",
    "print(query_doc)\n",
    "query_doc_bow = dictionary.doc2bow(query_doc)\n",
    "print(query_doc_bow)\n",
    "\n",
    "query_doc_tf_idf = tf_idf[query_doc_bow]\n",
    "print(query_doc_tf_idf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We show an array of document similarities to query.  \n",
    "We see that the second document is the most similar with the overlapping of socks and force."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.84565616, 0.        , 0.06124881, 0.        ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sims[query_doc_tf_idf]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - [Topic Modeling Example](http://www.cs.columbia.edu/~blei/topicmodeling.html)  \n",
    "\n",
    "Analytics Industry is all about obtaining the “Information” from the data. With the growing amount of data in recent years, that too mostly unstructured, it’s difficult to obtain the relevant and desired information. But, technology has developed some powerful methods which can be used to mine through the data and fetch the information that we are looking for.  \n",
    "\n",
    "One such technique in the field of text mining is Topic Modelling. As the name suggests, it is a process to automatically identify topics present in a text object and to derive hidden patterns exhibited by a text corpus. Thus, assisting better decision making.  \n",
    "\n",
    "Topic Modelling is different from rule-based text mining approaches that use regular expressions or dictionary based keyword searching techniques. It is an unsupervised approach used for finding and observing the bunch of words (called “topics”) in large clusters of texts.\n",
    "\n",
    "\n",
    "\n",
    "Topics can be defined as “a repeating pattern of co-occurring terms in a corpus”. A good topic model should result in – “health”, “doctor”, “patient”, “hospital” for a topic – Healthcare, and “farm”, “crops”, “wheat” for a topic – “Farming”.  \n",
    "\n",
    "Topic Models are very useful for the purpose for document clustering, organizing large blocks of textual data, information retrieval from unstructured text and feature selection. For Example – New York Times are using topic models to boost their user – article recommendation engines. Various professionals are using topic models for recruitment industries where they aim to extract latent features of job descriptions and map them to right candidates. They are being used to organize large datasets of emails, customer reviews, and user social media profiles.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Image(url='https://www.analyticsvidhya.com/wp-content/uploads/2016/08/Modeling1.png')\n",
    "Image(filename='../Data/Figs/Modeling1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 - Latent Dirichlet Allocation for Topic Modeling  \n",
    "\n",
    "There are many approaches for obtaining topics from a text such as – Term Frequency and Inverse Document Frequency. NonNegative Matrix Factorization techniques. Latent Dirichlet Allocation is the most popular topic modeling technique and in this article, we will discuss the same.  \n",
    "\n",
    "LDA assumes documents are produced from a mixture of topics. Those topics then generate words based on their probability distribution. Given a dataset of documents, LDA backtracks and tries to figure out what topics would create those documents in the first place.  \n",
    "\n",
    "LDA is a matrix factorization technique. In vector space, any corpus (collection of documents) can be represented as a document-term matrix. The following matrix shows a corpus of N documents D1, D2, D3 … Dn and vocabulary size of M words W1,W2 .. Wn. The value of i,j cell gives the frequency count of word Wj in Document Di.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Image(url='https://www.analyticsvidhya.com/wp-content/uploads/2016/08/Modeling2.png')\n",
    "Image(filename='../Data/Figs/Modeling2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA converts this Document-Term Matrix into two lower dimensional matrices – M1 and M2.\n",
    "M1 is a document-topics matrix and M2 is a topic – terms matrix with dimensions (N,  K) and (K, M) respectively, where N is the number of documents, K is the number of topics and M is the vocabulary size.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='../Data/Figs/modeling3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that these two matrices already provides topic word and document topic distributions, However, these distribution needs to be improved, which is the main aim of LDA. LDA makes use of sampling techniques in order to improve these matrices.  \n",
    "\n",
    "It Iterates through each word “w” for each document “d” and tries to adjust the current topic – word assignment with a new assignment. A new topic “k” is assigned to word “w” with a probability P which is a product of two probabilities p1 and p2.  \n",
    "\n",
    "For every topic, two probabilities p1 and p2 are calculated. P1 – p(topic t / document d) = the proportion of words in document d that are currently assigned to topic t. P2 – p(word w / topic t) = the proportion of assignments to topic t over all documents that come from this word w.  \n",
    "\n",
    "The current topic – word assignment is updated with a new topic with the probability, product of p1 and p2 . In this step, the model assumes that all the existing word – topic assignments except the current word are correct. This is essentially the probability that topic t generated word w, so it makes sense to adjust the current word’s topic with new probability.  \n",
    "\n",
    "After a number of iterations, a steady state is achieved where the document topic and topic term distributions are fairly good. This is the convergence point of LDA.  \n",
    "\n",
    " \n",
    "Parameters of LDA  \n",
    "\n",
    "Alpha and Beta Hyperparameters – alpha represents document-topic density and Beta represents topic-word density. Higher the value of alpha, documents are composed of more topics and lower the value of alpha, documents contain fewer topics. On the other hand, higher the beta, topics are composed of a large number of words in the corpus, and with the lower value of beta, they are composed of few words.  \n",
    "\n",
    "Number of Topics – Number of topics to be extracted from the corpus. Researchers have developed approaches to obtain an optimal number of topics by using Kullback Leibler Divergence Score. I will not discuss this in detail, as it is too mathematical. For understanding, one can refer to this[1] original paper on the use of KL divergence.  \n",
    "\n",
    "Number of Topic Terms – Number of terms composed in a single topic. It is generally decided according to the requirement. If the problem statement talks about extracting themes or concepts, it is recommended to choose a higher number, if problem statement talks about extracting features or terms, a low number is recommended.  \n",
    "\n",
    "Number of Iterations / passes – Maximum number of iterations allowed to LDA algorithm for convergence.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 - Topic Modeling Example 1 - Gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are the sample documents combining together to form a corpus.\n",
    "\n",
    "doc1 = \"Sugar is bad to consume. My sister likes to have sugar, but not my father.\"\n",
    "doc2 = \"My father spends a lot of time driving my sister around to dance practice.\"\n",
    "doc3 = \"Doctors suggest that sugar may cause increased stress and blood pressure.\"\n",
    "doc4 = \"My father never seems to drive my sister to do better, but i think he should.\"\n",
    "doc5 = \"Health experts, as Doctors, say that Sugar is not good for your lifestyle.\"\n",
    "\n",
    "# compile documents\n",
    "doc_complete = [doc1, doc2, doc3, doc4, doc5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning and Preprocessing\n",
    "\n",
    "Cleaning is an important step before any text mining task, in this step, we will remove the punctuations, stopwords and normalize the corpus.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/rsouza/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/rsouza/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop = set(stopwords.words('english'))\n",
    "exclude = set(string.punctuation)\n",
    "lemma = WordNetLemmatizer()\n",
    "def clean(doc):\n",
    "    stop_free = \" \".join([i for i in doc.lower().split() if i not in stop])\n",
    "    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)\n",
    "    normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\n",
    "    return normalized\n",
    "\n",
    "doc_clean = [clean(doc).split() for doc in doc_complete]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['sugar', 'bad', 'consume', 'sister', 'like', 'sugar', 'father'],\n",
       " ['father',\n",
       "  'spends',\n",
       "  'lot',\n",
       "  'time',\n",
       "  'driving',\n",
       "  'sister',\n",
       "  'around',\n",
       "  'dance',\n",
       "  'practice'],\n",
       " ['doctor',\n",
       "  'suggest',\n",
       "  'sugar',\n",
       "  'may',\n",
       "  'cause',\n",
       "  'increased',\n",
       "  'stress',\n",
       "  'blood',\n",
       "  'pressure'],\n",
       " ['father', 'never', 'seems', 'drive', 'sister', 'better', 'think', 'should'],\n",
       " ['health', 'expert', 'doctor', 'say', 'sugar', 'good', 'lifestyle']]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing Document-Term Matrix\n",
    "\n",
    "All the text documents combined is known as the corpus. To run any mathematical model on text corpus, it is a good practice to convert it into a matrix representation. LDA model looks for repeating term patterns in the entire DT matrix. Python provides many great libraries for text mining practices, “gensim” is one such clean and beautiful library to handle text data. It is scalable, robust and efficient. Following code shows how to convert a corpus into a document-term matrix.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Gensim\n",
    "import gensim\n",
    "from gensim import corpora\n",
    "\n",
    "# Creating the term dictionary of our courpus, where every unique term is assigned an index. \n",
    "dictionary = corpora.Dictionary(doc_clean)\n",
    "\n",
    "# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.\n",
    "doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 2)],\n",
       " [(2, 1), (4, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1)],\n",
       " [(5, 1),\n",
       "  (13, 1),\n",
       "  (14, 1),\n",
       "  (15, 1),\n",
       "  (16, 1),\n",
       "  (17, 1),\n",
       "  (18, 1),\n",
       "  (19, 1),\n",
       "  (20, 1)],\n",
       " [(2, 1), (4, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1)],\n",
       " [(5, 1), (15, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1)]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_term_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running LDA Model  \n",
    "\n",
    "Next step is to create an object for LDA model and train it on Document-Term matrix. The training also requires few parameters as input which are explained in the above section. The gensim module allows both LDA model estimation from a training corpus and inference of topic distribution on new, unseen documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the object for LDA model using gensim library\n",
    "Lda = gensim.models.ldamodel.LdaModel\n",
    "\n",
    "# Running and Trainign LDA model on the document term matrix.\n",
    "ldamodel = Lda(doc_term_matrix, num_topics=2, id2word = dictionary, passes=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, '0.116*\"sugar\" + 0.064*\"doctor\" + 0.039*\"pressure\" + 0.039*\"stress\" + 0.039*\"increased\"'), (1, '0.079*\"sister\" + 0.079*\"father\" + 0.045*\"driving\" + 0.045*\"dance\" + 0.045*\"time\"')]\n"
     ]
    }
   ],
   "source": [
    "print(ldamodel.print_topics(num_topics=2, num_words=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 - Topic Modeling using Scikit Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.decomposition import NMF, LatentDirichletAllocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gensim is an awesome library and scales really well to large text corpuses. Gensim, however does not include Non-negative Matrix Factorization (NMF), which can also be used to find topics in text. The mathematical basis underpinning NMF is quite different from LDA. NMF sometimes produces more meaningful topics for smaller datasets. NMF has been included in Scikit Learn for quite a while but LDA has only recently (late 2015) been included. The great thing about using Scikit Learn is that it brings API consistency which makes it almost trivial to perform Topic Modeling using both LDA and NMF. Scikit Learn also includes seeding options for NMF which greatly helps with algorithm convergence and offers both online and batch variants of LDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t4ApBJZ4LRedSkBISYC4FKfF5LixrOeUQhgJvrrX6/TSY3ChISUC5gQqVY/eXG3hLpfHaO9lFuwNKY1y6yAoujUuaS87z5RufXUAJVnAuqJgE0zK2tqZ22cGQWXExoR8Qr1cqJvL81tRqwnDMC+efsgfQSIjUDLBSjeBrDL6B8zXKNYI54iaWigKV6gSX6pNUR7gm/5OhIgXaFHxD41JBgW9yqfxAn6bUdeSPZAcWPBAKXREuBJPqiOzKIlLVkPoQdC+JoUhwTqUWh0HNkV/hoJohYkvNRb7LQf2xCZcqXRrDtCJScbGk6kLSziSlPoVxmgrQdTjRQIHDplR6pI7GJAGSfqnaQNqB1IrSUvvDyBDIBhKlmii1JyHnwQjh8i8sKaRSwVL6E0yAS1oIVSwFtUiyQn4dg7oJk8fOmKBBym6AkZyB+im5KucoJ6hLdZAhDRObmDaSNGtqjsB6pV/BT4IjB3utkPxDhWwEJ0GlIweTYpcV7oHCjjaFgozz5pJT9N7LrmnnvSwVOdhnr+VQUhJot13iXNDGAvXB5yKKSauiXC2KOG4HgM22A3PVayw7MI67jyqQv7rQpnDc/aWxjxZSFJYth6s+a6zori+1oEdBueE1F+yNcSSCWV9QKXNZw3G3S5EbwtdmEVCCoU3huBeVxgH3mr110jifkoByA20E1Ef3II2zF0MQCYYLKb6omATTIuv67vve8JrWDac3/EGuSYiAPiAEXqVZEE0gv2cHlhyhgb2LgcmHMKkj6FhYQkSp1CtkvywClif5f6l+yZ6xLeT35ddtjYByJ38ldRelUnAsXCtQDKiSig7nYN1SWpxmg2YldSxs28QCc5uQao0hySNUgD6EREaqLKDGCF2qZ7pUk6iBwWIkB7JhUkJOz5Bas8A6hnlJenTKpNamYamXCcUSSbP8JmhDBGxJUoFDlMp/2RTodG7hwpazJqo/yUiKbFCFCJitMNOJ1KDkR0hX6inHyhQlPyFcpTVwA3RC0OF0AgzCwjaQJkmSb1M5Q4EomLvUwsMomHc0bGELW9jCFrawrboIy8rIBzi8APuQ46XCjmtQ6hXyjJdqg/zDCF/7Hugpooi2phQTxzuoaRwrHUt2osWE0BFYflyFDlNwjUmNg2iaVCTAVANWHBs7yp7UlIgN70l1B/4oAxMoxLIrZR7CyvpFbdB9lFMQ/H1CKj1SVZHDRuX3lR2Oyv+BjctxyYGOI+BTcGVqnEiVSypeTIDWBbodBoKR68KjeFUJBruVYpHsX5cjgLYJFMqO5ZscqBXKQKaYRZRiCGoS/Ep2roPjkrrqPMYmVl/FxACTILKkygY6JNawThgY1RjMGKxXjEuFj6z5PcMWtrCFLWxhC1sJCtZkCnvFHxBvw2NBr3+2VS3feoYKOS8hfAmDziA1EqJUA01ZcAioR+DSgqglAmqTVCaILyXg1uJg3UmrWCONc6WYEcaQRsAFBm5qFdVEMTMJyiAqB6UQbOTYlbAtB0JYF4Rybisbmq5UKq5UEKlyMdCYhPwZAtVE0iiVKjAhyWEcZ6LUYAyOdAbaoQXfF5wwjmwN9EUn5EyqQRBuJRUoXQjD4jolGcQWCTIEkUOb2AkOQ1GwkwE9nMiuEIGxwERlSRLA7iXUZDEDryiOSCLhZ1IbY5SYGuEWp5bQuQpg0xEzkCVZm2HIopqNwJsZA+YSm4oMGPNAEzTAUSjVQJOBrmbIzmV3VDCpRwILQQd1nxrfWCQClxgXgvI85WvWV5TnC/CJP8gPyPBZqzzLwVeww/y54DxKcKEwgvy4pXxKClqwi1KS3UPBredFSX5JIJ+65V5zQcUi5YvKjRxflciTKgXlhg8lXnPxoYR7Rwt5UcILhV94rcB8wgLOBW10V/Fic/GSpQGlcX4MVs6jCcJV5L3CfSjhxQ6CfCYXlBv+XA24uUihMjebOONEYLlR8PkGlBs5TEDB5Eap0rggJVsujf0pCSg3goisohLMR54Xlca80DP1kcaazdyf5fyd/Y+Cjfu+DNKAFKGMQsS1TkEMElhlVOg2luc8hDLJgx3sUkRw736EY7iROgfHlgYKglRNGOJSC4EfS8VFKXMRYevWomUuCEoi8coMStpcqko4JiC8S7ICvi5ElMvfgu4C+pHyU6pRBFfuNeXABFegit9SnkzQEeU4VL4jlTOdg+9NQHC9/BT0FBsMXe6CcXyhJmhHNmLL1tKiFjH0aKUN0V3EhAgt8B5KvYwCAwRn3MAZnl5gIq3FkoiW21LRgzGRzYUNvQinU8okk5Z4eg7eidZITY/AjCQhGcuckv8ikUqGEhD9xYkNChYy5fSpyiaQqpnkhL3EzDlNTyC9MsPAgEXBnwpKYc6q4MGeb/7Lov3wwD+5R0q2cC6b3i+boMSfRZvYrfwetvP9kBub+wIPzFUemCf+892SuaDAc0FbylV0D1xF9/9c8N9rASfL72GVBhno/ZEb6H7KwJKWzfsjjTct9N43aVySLNWiKuAI+6pm+Tp1TghYUQtWTnTY6g0AMt00pSu4/asUPMjdgyAjTrBGlPZEwWemwtPBUSYKUiI/0DBYoSCfAgLXndhtx9mGbYwMyldGb7/7479dnBnmlFTtO3bkyS9Fk9VSCzJscE1SwiBcHUgS4CyDgWFmVPkEwcaFLegbPI1YJypUnjjj2hoYn0Cdi8hBCWOyH6mdUKRLtQu+woRSrpTeCGmHQiPp5bkzr/79TNeZWGXNno9/ubHtiMNn24m4cqLtCdHZyu0Lr/efeY2l5+v2H+t4+lfjyQZqIUIkkdhyHh9GEUljZvn6qe/dufJ2tKxadtjUfgwclkKM91y69PL/LUlvefC5/Sc/YwHVTpYgMoBMyVZmyJcrsxdf/tvxviu0qvnkC1+t2bEvDSY+CMUCLU3Z3vzvGT636iBJ4CLw/W8LLVg+c/GyYBWcS5A7k/8lWxQKAkWloxLkW7D8DVr5ty5/rIecaFP/K50XV7fWgiW8b6L5dbDJpiRYzvrJwXoIYsHyB3coeBsO4g0IOJccSkQAC5YoZHLwt7UUpMTHguVDiddq9+IqCiY37p8FK6AEK0lu+Ns4g0hj5PEQvSRYTq5ASdI4iNzgHkwoVRr7sHFLLFg+0hgHSExZt2CVRT5ID6XggnIVea1MRCoQSwW7q0ghileNM6oiNfaFt4BvcVCsoupbSn/AEGkkkAXaE44Sdv70P/zi+/8FoQU5WtnNo3s7OutrHqKIxDSpRXFOiMJT4DqTegfhyI2xVwQpU5VSexikDfI4BRQGoFuAFxMjkoAepEIDPkMwWRFkqNArSGwE9ye4Q+VHlAt4U8dLKTR262L3L/4aoXikctuRA4eXJCXYyWjgmopxl2oQXVzsfeN7XW99C6F0z41zbYdOtDQ0pFMMYtSUYY8pnlRSPDYxfOaVv57tfiOSbN9x8uPxCMYMwrxuD924/OpfyGXDtMhDD3/SjqggdrBsqXRCDDa6GMWZ+cXbp3400vsW0lsOHHuoY3/HTEolcjrsxSHkf9jCFrawhS1sQZuWyYgPVsMCaAPMGHVDqQlEo6uQcAjT5jaCiCDHgCUwKoIrKTgDjAGp0fAMVhYyDh49E4PSlFoa6b74BkKzjp66ONV369wbDW0HOEqmVUQ8IbbUnCxEba4hJ74b8CKwximDECrIJeQCLGGJSNpamEktpPRIPFJRZYqIaZsKWIqA1QsjTbOihGCmpS1uOfohB+2KUVvjEC1vmiIeibXv2dd9SqphszN951OLk1as3gLIKmFDpqPU97AhxPzdvpWxWwitSJqtmf7p/hstbUczNiYCyhxB8BUY7YStk+k7NxdGrsufV26rbmhqTFvgNMQiLawVxR0W4WlhW/KJM1D1GHHC80HF4rpkciZlEFtlSlpRO22bskklTMN4A2JN2MIWtrCFLWxhK65gWfYHmkWoEgQtTGwONifA1eRYB6AqcO1J7caSWgpS/xdF6MQKFxQp+AMHwQmCxKFbZIKXjnffuDQ1fBthUV7VwG1zaWFmqOu9pblfERUJk2GIROJEBUZpUi1R4fDKFwgR7giim4BAFKM8NTV45p0fzXZfT80v6NFY9Z7Ojic/XVnXkrGFwh7FOuKpmdGLl8+Xxypb9j/A45WCYccapjL7VCQX+Oj08qY9WmWTPXdrauz28PDtyrZ6plITmaAmhgRLhtjYyMDU1B13kix159bV9pPP88Q2YAtMkChrmVhJLY0N3eapaUz0+tb2SLIqjQA6C0NcvGuEkqxI2yIDep5CzQILF+ig4N9UvHKQXOVPGOMZi5mApIUpw0EA08MWtrCFLWxhC9u6guWil39A6hUYTyASXMQQoQppnShsJ6nMmIxzDXQmpoKrlKajQDS9dTVHBSMq3hxgQ5FgFLL5NCQi5uLY1dOZmTEjUd355Oem7/TcOPfa5OD1uz2X2x5sXbYBHEIIChRBxD2gQjjgWAo3AbQ1qe3FMMtM9/zsL/9j16kfIjbvOm3P/Kj31nsv/ON/2bDzyBLnlIgYTl278MZr3/yj2u1tn////28VZZ1pwAB1gpkUXCoAdDGG9NrGXc079g7O3ZoZGxgZulmz7wkbosGAYPklJpiBM7MjfUuz42v+6MHbl+ylKa2yYSXDNYjQAi1Kl4xanBjqvSpH0PRk+/6TsXjlktTjIEYfbIEuh6QmRjHXgOsAeQWM4hDaxqglOQaJm65DHEAjNE2qZpqD7o5xCNQQtrCFLWxhC1sJCpahfZDDQ2QZwbqw7YVpZGUgwjyWwNFEGgA6AU1KvqODyw/QC9yiOd4KlqWyESkXtnJrQQ4euAlRVKDMyMiM1D9YKlGz9+Rnf+vSz39049w7qen+ka4Lhx/+LDcc2CpClZKmqWRGqVUYoKSBkid7lp9FhX3q1Pe73voOFmbdrkNVLW1To4PTAzeG3/3BKap//rf/bUVTm1QDY+bKRPdZPtcX39EQjRJdB6ehruKeOIV4fRU6D8gU1Y0tjTs7Bq+8zFKz04O9GrGxoUuNUkeYwYg0vTA7NtKPkGmUbYvEo4vjw6mJoaWJO9t2HkYRSZGIKrDRuIZmF6cmem9A4FpZY137A/FYmWkC6igElWmrUKFSzdM1TZdKII0y4CWj1EIqgp0grmvCAVPFnGqy6RFNMh8XrxkZtrCFLWz37yJ+TwLo/smvreo5YJ3bsP0SKlgmu5/OH7xhBa29h4XjHEQRbKPFqZ6rp2+fe21ipFfXjR17Du956BOVbcctkrA5AJFrHACsLKjd5+Ybeo7FneJ6AK8AAVyYpRxjDBdjt29M9N9EKFa790G94XBd+2hFTdP8dN/gtfNjQ32VOw4tWwBb7pTukT0AtqeAoHdJqakJUyCdofmJkTtX30X2UnPHo1/4+h9Etu+bGx869Xd/dvvN7/ae/tHricTxz/xmdXX9cP+VmxdPS4oqGtvjFU0pS45vg94iiKUQVTETtoJ8ICSybefeSHJbZmlkaKBnfGy4pqZdal5U7VyBrbnJ4YH+HoQiTftPVJUnL45/30rPD92+VnHwaaHHLGYjpkEqQCY1fee2tTApf1az65BRvd0ykWRthgoNcZu7XJPftuTQtqBC2AwQSgW4ZqH8jqnwKVQNRwhby3D5Tc4Z1ESEAC/qPrWwhS1sH5m2lu4kcFYC1GppVbfuh8gV4O8bYau0uREK2C1Tq5BxnFAGhNbSbxwwZCLyNBcH4FlsMVUOKLWiwQ1eUYUz3JpvaGO8cH7xK4e9q1Q5X94A5yzwqtoVjPLsAVcfK157dtld4/UfiA3vZz33MCJkyxQsCPjJW0Y+FeyRR0Fpz9Lr8NwgI08hFDgPFHyACihK6OnZy2+8dOq7f7Yw0YNEWn5l4NKb3dfPPfuP/lXzgY8vQiIdQ8RSUOkUF1q1ay+pAo6CMnzU5iqAikGdPlCMxPLi0K3LywvjerTi+INPGSLWvLOjasceqWCN9Fwbv3mhvnVvWmUJqrgrYVFb9hZnUnXRTMA6t+T7ZRxNT48tTo3K6ew9+XRj27E5WtbUVvupr/xPbHm598xPLvz8b4YHb9TU1I0M9iwO30jWtrYff0JPlKeZQlIFcFGnuiLsQIWZLlUgUrdrd3ltw+TSyHj/remRnu31u62MnCpUXyTYTk3dXbzbTY14xwNP6xq6+NpPLHNhqOfyocxSQo8DExVoRcZc6eu9apoLkivtHUfj1bUrHApOA0wDdeo5KoQFwXWR0Xkcu/Fpyqamsi8JFpzbDliXkANTpoBXIWwfijUKp+6z5xooqaA98i1X7r8U76UIPNpU6fWc7+BSCtr77KMglPiUO0XBavUHYawXgkBwSnweVtGjHd9bQXuvuZTExlKfS5D16Y/LEES0BuQqLhHII/uHVKjyqRhlVCF9VZ1rtbQrxGZgVWAezmmT5J68JZ0LJa0QrIS5igcB6c9UIViqnAocKqtCaXwipIBiFtjniSrADxdY+QuNg9OCYzeyAy6LEFvLnan5U1t0c8nOlbbnQHNDhIVNLAYl0eAOL88fsAJgpkGVWkjTkjd8rACHVDjxOtSAwnoUq9QSCACGAxFByVkQuXy9ugmnkICOGcoqWFZoUwgwK4C3x63k6xyzGidOQRTgIXZBdlRqvnyfOgHQTgyIOo4dBcs5ZXGp0jig3CgqsoJL4+Ani9fzvfe5FD3ytFjsvgS5r8GNCHDTYRfyQGDqKlhQNU/DfKT75pkf/veF8WsOsgIYUNjC6NVXL/+kceeOzpq6+gxEUBkOcKiPEdVBcqcKMIFKpUItHFP+E+OEVNzGJ/q6zgphJlv2tB8+mTS0aGNzc+eDQ13v8MzU3a53O596oTxZxxnTEZbaWUrDcg9HGIEyMhCsRCMcV3C8qEeMRE15495dhx/BesJQRDe1H/ni7/6b7+lGz3uvjN06O6ZixhK1O5/61d8+9tizaUM3IJ5LU9oVOCupwxy5meQbGq5p3Z1o3j05cI3P3DGHe8oefjYtNCZ3gEER44vDvciciVc37t7/IBdmRX3T/Oit+fEhc3GivqaOa7ApCCWZhczYnVu2uaQlG1v3HkhWxZdXuOZsUQTePse+l4iQxgptxdCgTJBKegScKyWEpE5npyIG1ZTo0IgRicUMIZjrb3XLRob3mrCF7SN0vVbVL0wVjyl3uA5wM+qIVfW9TBXymrAhNkNJmvfVhwVYga66IJTqAPLHJABFGOFIvpTE6upA05VaCIXOONZhFlAaX4WHgI6okKohAuLe6QeTv2svA1BpwIummiQvygECUb5MqZtxTP3bhjIbxKm+r+Jd8RoLVSE1IA/iXjl27rWqFhxVCpCqjaYOCVWB1w0p9qGfQHUQcLdEuFsPjUPGGDa4Qy4Ey1gEkzUh7lb+RW5Bf/fyjBUsNxAWFkfbsi2WSt8fBUuo4sJO/WaIGcdOlTwH31LZJoVpm93Xr8xM9Lknt1srENT33q4zA72X26qesm3MwNwj1ysoap5HPHbitRkUxoGFpZQGYpvgj0sP3Do3Pdolv7Tj+CMr8Yq0ZRERbdnz4OWymqXMYtflU8f6uhv316XTNI1UvBfGKwgtUrg42ExqKoJxZAkcbdr7yd//Q4bMyvqO6TTNEPhyGmGjtv3F3/k/uo49c/P6hUxmua6u6fCJj23f27mIkuk0dKLBHUZhpmJkqhh2Z/NzKlWsqu07jt459xrLzA/fvjA9PoESTTYUaubm/MTtq+ckQyLVjSRRXV4WbW7dCwrWzNidnmtS6TJ5BKLaiTVxZ2Bh4q4ktqllb1Vd88oSM00Asoei15Jwy3Ics4uzE723zplamSTJYLC7JVd12wJBJbk3fTeVmoFnYSNmCmtFpFNw87OJhtybbbjrwha2j04D0S84U8W4pKbAlDJgq5NYZwBizCmXoo9AidVg238r8o2V8wGCPXQEwQyqGhggJUqhKUlKOaEggiiNAQQUV5YippALlXPABjgctKZOCGV8IvcuvsAuAAebY3xSd3gNNDkp23X1MgWhFBBdK1lqukCKVEE5QlIRBfIU1g8cZJSB4RDsbVCSTP4CNEXHSwPRMPJpyCNAXvLlkPBEhKeCJVzNDw5WC6mUMccGKbCpjlWwgCk3hnKWwsNkcBxjCqEfNuixclZEpcBDhpVjvsQqzyts96xgmUpjzcY2deHS83CE1x32AcrxqMotzhMFG5ZN3GKC4LCD/wmNINO0JqenGbOzloq7O5eX5mfnp+SSZCZgNDCFy27DysSFYX8Flr1YTiKcHA4i4uHiItUzZKeuXnwrvTxpxCoPHX5UkIplhmMk2tCwp6ppz9L0nZWJO3e7Lm9rfcDGMQuqM0udQ24PYao6gxTSCmGjSAVLRJPx7Ufkns5kpOBBVN6TbKiHw3FEq2o98vEdnc98QVWR1nWaXGHIsrkL5blaSdrZ7yDIuI10sHEnaWJH2+EL0fKVzExf9+Wp6fGqMqlgQcLf8uLsSO9VjPXtrQe1SHmivLp65yF0+pXU/OREf5d46DkLRwB3npkj/T1z01OSGU27DtbUtc5msGOglqNwYonVB3vj/NtXu/tA4MB+hLKKoAATG8Fk5L0phebGnZpACH6G00IDxrvJm2KtiNvaIuGbAlNGAUrvOeuNZfWT/TKHkiCwv2S1pBIptMJp1uLzAiB2vsNLBFPGq4OyjS+z2Yjyth7yKMKFigGX+0AY441COZswr7lkb3kvOHgfueFDCQ8wlyBgyigLG9qHkhzOi2BcLeiAyF60XsDlPuIReQOX56xwXqz0XkBp7FOcQw0BwZ4cdAChDCHKvOGUX4UisNQSYrUQv0AepffWKXFQl7HgHivEH4Qdq8qzUFkCO6qBCmWAzGeQqlx5uyi2OQd/AIZbKBh7LGV/MZ2q9ELKbap0FOI4RUDPkWfNqgTjG3c0LwmEHQSnlOSMqowo8K5CGTd5rQetxsLysg119+UfSZqtYrKIk5AuoHItaLGuM0eBHSKoh0aEEzCDFN4zsZS2RUEBknOEOA8VDSyUw0HkCwonBIypBHzNLf+/FrGmDBKyFzgMsSPGHUWNK4OeIpCCSqXq7TInhgxBFhZbNdVl4+zniF8faewjNwqeCwHlRs65sAlo+xyhh4rJjexzoWANDFRMgmnKTOOS4vyAbtw5tFDwBMr7NPslVaE9yjbrRPzB41KgAFA+DwAUHD+wQRLVlYSAspK15VUPifJEWZnc2zYVMVUH2qJyFUFxmIKUqHqG8Kmz7pVBS5XaoWh29O7w7YtyA25vP1pXu0sTOgV7DaqorjvQ+chI17vMtm9cfLvjyRfwtlbLhpsDpYYkSkM8CjcVqEkjt7bcBSkOlwnOiaHB7QrwspxliLnJKCY61sqV+kRMGwMSKAAzgOyCr6p6z5xAYBg4M+GeZSJdw1Sv2b4rUde0Mj+4PDo4PzFc3XpMPSQ+fnfQnB3TjciO1kPxRLWNorVtR7XKFnuuf/nu7dTCDKquklJRWJnZO7fN+Smk19bsOkTjlcIkDOrsgBYLCh7FTkVGllkgU6aymqu7DdaUmsXdoAL5JncELJiKLYLSVG5aoaua1htuvasPmmx8EAX/vbZCqO/Lgj3Qje/kvCR5PfhTQvL6wYVe+lNCCxGPA1BCCxFDC80F+84lf6/lcxX5ziVniE3MBXnPBXvMFwWTG/6UoEL8oSVSQgJzNX+sgj3gQksaB5gLzvsH9V4hm5bGXo/DDWAHPYpoAIUjdKUccJW7vepLUkqF8kTkeDpo4YcCZfazWOQYVtyIebJ61DrhXHj92EKr8DuuaT+ignad0E9rFZsHbuqYG4gZEJILvUoplqEQH+aA8gCdAIZDlCuESamtK2ORjYXj+coXAiVJMDBOgfmHAU+IZjC4eqtys0Q4JTFgIA5A09wp/U8d+5TAxPG7SWlNlcNPSRKuYQhKMeBMIE4gvq0xBcFIlRfPqcqmEIQ0LNZZp2KNV1+qpwPpX/KIsUCdZDrobbqctUWlFifZIw/5CCB6I2WKJML5p3IdQigwhjONQW67M1cOrg8XTPK+SWPqK41RAGnsdZTcozQu6VwoKsG0yuh9so1hx/vsVLlxlWHHegUHO2jnWjzacfjgtdqmpdH5VTXJDU7c3XGw/cBhLaJV6jwKQeLcJn7mZ6VCAlSV42snSqOXI0c1cu3KOwuTQwjF9j/wWNOO7UtQBwfrQlRVVu4++ljk5W+vTPVMDVxcmehu3L4jSsFdRlT+HFXVpqna6hyqHCowB7irgM9SbmrNsWYr9R9ue5wLZ9nDYocifsi9gYH5ShmLuJI3gjtfImAQjiOUaGjavf/oVO+7wlqeHOw6/MjHSSK2Yqf7eq8zOxOraNm2e095VdxGpP3gsYbmnXfneseGb68sjNTubONM8Kn5hbEehJa3tXTsPnIMR2lSUzFuUAuRQACZ5uB5obrmvTs7nzTlFIWtqSwTrvC/nBpBdGW25/wbC7PDhJJ4hFZFHVgyrEI7Q1tv2ML2UWvCDabmBCk0HA5OAOVLAliZiHIdcjccPpCDTSlTPG+QnL9RIUGenRYlmFPpTIn8mHBiVsGsRYAwqvwi4GszMIoDhg6MGIH6ryqLnKjaqRhC23UlwBkFyxzZGnZBVBX418Dgx52sRqjExoUl5bxyIOpKl8KO908daCo8BE5D7vjilFnEiUaXSq1hw0wVeVCI3yQoyThUkANLBJwdyvXJC/Fz/aVz8jhuPYju4nBiqmh6jSoZj9xAWlW+Q7lRhAqqt4AkTt2MB654J8LKaFvmIrTM+7Jvs5ZAlo3QMV2q1FbYiEQ0tR48+fyvn/reN9n8kHKuybUR2ba78+QnvkrKti1kbLipMEjBsLGjMnmPqG5ZXNW3kQvahoXOzcWJO+dOifR02bY92/cdjEQ1i1kaUhcgmzS27d11oPP6qVupxZGbF9+u3XsS00ob9DkVJQkEUaZiwcEMy5grfIRTwwdM08onT2xBANsKKtCAn51Dwgu3YY9pSF2e1hN3wRivENgVlrsFy5mXR8q27z50lpZxOz3YddGcGYs1NNuLU8NXzkoSKhtaE9WNmYydEjReUVfVtPvu9TcmR/vGh3uq9j7IBZ0aGRi92y07r23YWVm3fTFlQVVF5/YIdyZJOHeMoE17Dj//a99Ia+UAXQGaIYObmEKf1yi1J/on+wcXZsdVLJvFLcYzynWAQ5yWsIXto6ddgfhSERDg2IM0NCnChMqNQRxSbMBRRBGYsOX7PBuRuqBAcHxSBdKsXDSFLNwBnNMJXivh7/i9BHUOemVlkfqJ8hZC6Xn4FJIHofgHsaQWIoW5bXMKP6BceQnBTwJ5c+BmNORlXv6L42Ah+q5ZCAn3pCnoxHTtaYAF5CgjEBKmjGRQ1s2JFuOOXYErvyIGAcyUINUgaYop5QVUVhWKjLgLrQ2aaVrFujDbPTVtwgDoUBAHAQLnMnXV0yrUKHDt11VWGYfzSoEpcnAmSSZgxwkJxUrAjoWg0ppkqTznIMoNmXAaSw5SsF4y95wOJf4WKFhLLEhqMCoJqEE+Wl0g94zPtgcrPQO2qwPVYGNdqz3x/NeilXW9Z1+ZnrwbpcmGls7OJ15oPnhi3tZTcGVRRZilEqPShskGSjYQpjGqpIUyyqpLTDxCeq5fGO69AnHisejAnf6ZlR9C7h7SqDIkEd3isJ6RnVnsu37hwPRksqqSS9kCGR2wc6Cus1NNmQPCAgG5o/LvoKo0F44jW61nxt1SiuoOpyC7oCIi4RT2C0TDK7uasvY6li25lKVQQyuC6xEt3tShV+/KjHctDPUtjo/o1XXzwwPWSL/cU/UNrRVVjbYpbAZm6ea2zltv1ZrpmZHum7uOz0STFbND/TN3B4hW1rjnsK3FMyZcUzJEXmIIVTFkqjSPSibRYpF4DdNqbKJCr+CiCBnRGCl7W3SB0pi65HCTW6ZlmfA5yAUVJI8JCoEaUAjU4DE79MsP1IC9B/1oAjVIGRejkGvthE7JI1/HIPiYKgmrDC5YI9i08QqUD0N87Y677ugTWdoJX3vL/ZYTG+LiVTmV++E/jvvJebmma7mfOvqN8pFRZSFKaxDYJH9uCRKXKgGllGgW6FuMKJMLVcKWQVA8ZBESCIqSd1cp2LgpoKysg+9XbNk4BLtON5cQlyL4P4eQVUjNU547DAVgmYoZdxC5FJqCKuUPUSCg1Lj5g/A5U/VIwJuAqbzK6ojpkjxOli1kg6mJu84dREyOdSr/IE3YkpsmJRlb2LaqpeF4azHOUU+lGqxCboAonRPHK6sOXsCQQMATKFjrOJNMoBscl4bAURiFIa6bnGYI1NuVlGpQ2xYz9yArSRoHlBsfPaAGPwmmRYSVZXLKD2EMrmCtfwTRYZy66Qx4HfxtbVeqmwJo+XApiVc89PHPd558cH5uRkOxmurmWKxyUWoGoO8zBRxFKNQUVGlv3pQIaitAcoaVdiV1CLnq+i+fXZoakL+aHul+9W/+GKniN2g1QxVqDGZmnV/P3Omd7LlS8/D2DIpSDCCbgH8AqAq2BUoYqIzK0w/GYYVs4qRJCjB3QbiZqgro2KGVXUtZtzTBpWRicI1wGCIYXBqQTW2I3MSQCMBtjhobW3bv6rgxfnVleeLu0K3KffsHe65kVqaJlqhtaY0kypbkLYgC2uquvYeSVXUzoxODt688sjhCDTFx9xZic+V17Tv3HbGw8msiMKNDOiQCYHhNKbROXUWsUMUyUrXDNiiLsM+lniUMNxwUiiVK5hnC0rGtreYMUxVKRzZcXLPjCIue35tTsLxikXOu0EV3Rfbyxh4Bx/5Z3NmhkP7HpxclYiPOhc9e43mhNfdVwRIbQ58DghbhjabqIHPxUrB85hJE4OYsEi9KcjiPA3DVi3hRbNnjTWlU+Su86Fw2r2A5CJzYWrrx3qnx/lsq/EaT1z8Dbn5gyzLhLXn6WrbNG3YdPvTws6YATGM3CAIr0AGxUcFSsfIbJolXjVdu6AecBtx1QjpWfawqXCD3pbqaKpsOKBTKYIUjQoXVyn9QtDLaf+3sa9Nzs0IHtSuKWESFbptITzNKpa4iMhlmdz7yyaY9J1cg1wlK4ks9Tc/lau765Ks4pgCtIMTa1IhYU7Ak4ZqKjYJnxKF8LiQ5wh0aLqkAJq0hKUghLQBkKjgG5deYUxtDIzS1sPT6m6/i5ckymsKZVEXljgOPfx4nyy2pRykfoDzsarGYutv92jsv69y0KZ00SfPeY51HH8Ga5mioGG/w3ql4FccdRKRuFFHZnjbE0kIAvQ42PxWRJo8OMGtBxFgaAq/lKbbQdelU1+s/3bnj0IPPfQGVVy4raGuIfpOXb4cT4v5J45y1uglp7KNgvW/SuCgSFtKqy/X75N3nrh6zOhrOGjZL5ZKrwxCVyfKq+hZws4G5hYkKLGIYZ8AEA/nDGiwbSn3H40o9l+vJUPkmmga+s+Ge6yr/L0K0KMqYgGUKy5OBNg+mLGQQ3dKS3F5Zmro733+17IkXInGAJRBS/VfpshjuRRrKsmMTZ+OvxhuuzobYOKKSQdz8YA65wVKc6DbVnRo+OmTH6Ja6XuhqJ0jRYYHShuor23Yf6Lzx7ksrqdnRsZ4HY2Ksv8tOzVRta93V0ZmsTDCbGUr8lLfvrWjeNzN6Y7z/WpLNxVl8CBDqUbJ+V0vHUVQel9clQ5nKVeAquCn1iOEgzOl6JJGM4hhNg8tVk+w2VACB1LYMis2liArP40QjiWi8qiyJHMuysniT0NobtrB9dPyDoEmwhcWhi69effeN3YdPlJXXLM5P9F5+216YMpI1+x95XovGF2aGuy+ePfHCV5967pO20FW9e2XMUeFQG/Q1V/Fwb9FrOXpS14CsQrxqnxIQKeWkv6loiTUPoquRUfVbC0pIQMS9PABMApgI8lprRMnw8MKFn307lbabDx9JRPTBnmvjPRe4na5q6th5/OOWbY31XR4fvLH3wIH6qgfn0zrEGym3JQ3IFkfRWzWl5XgWlU/DPZ4Z1M6VAlIZzKBuLIoQHgGnAZG3XNORt8qbwVVvBoQelz3e0f76j95+87X/gdhKsnZvbXPT/qc+sWAZCjRB3uyJkVl4+42X3vrWHyORilQ2HP/cPznUvrO+MqocPzgLZ98FLXIAS+X/MlhqoqiMQR59muBlqgPOhaPkCucmTSDkVgWEYIOkZtNnXn1p5I2/mWo4cfLhR7ZtrzWA+7TMApFvbkXIWthAwVpYCWCkz1fgfBwv6wFXKllB7ROskv3XLbFOxCJcD5iNhQEGFwIGVwq2IJUEqzAGNMrAN8cNBUGy7hPGeXoz7F9kKVOpBlHkpDzCuq+evnP7qlSiqrcffuRz/1gka4XSjQhYnimgfyAmL0KXr7x17fWXRGau+8rZQ4N34y0dNgPnHoOcWw0jtyqy0qwY7MBVSBWymtCJse2EWSkEXjkE6ExMmYQoLGzMiLzkUKlU2eAWhFQ9eVHQwLYNUCRMCHkRq9y+L1rRkp6/MzVwa6Tn+sxQNxJmRVVLTf2emUW0rDJRKOcRK1bfenDg4mt8aX7g+tmm3YdHem4glNi2s5PhcnMZFFsbSFDZwICRYmdsByKApJlYSLNlJvc/hdwhVVmIgAeQpaQAy9gQRyB1P6alTLKQQgspSI9RsxYkW0HOz7HOwQAo6przWnKo0L+LGoP9LQVeQ6MAo+dctAqa3nAASlAQc3Kh0QO6oYSHr68oTsMm5iICOMSKUiICzyU/3ifIXLDvWCUtSFRsBZbKVS8PQ3CXafC95r1CwAmUETbTd3Q88vzX/udEWc1U9/nJW+dm5pZjkebnf+0bpKpxZujm/My/jyeql1a4hVejfVyhh7PtgchF53HsUa7JyvEHOi5CRycTq+5CJbNVXNaq09BxEzpXU6fWvsqGEyacE1BwIs3xCoRWRY4//cknXvx1irU3v/uno7fPcrbS0LT7V37rXwtGzv782z/+h78gsfLpFbSckUcH08FNggknAufy01GncLY5aPUjotwN6g6Ms1kn34RYKwVgwTkUIlMYVER5Q8z5peHpiZny6u2xmlruRImotCvAa+ciTqK79z4wfeDitZ/9V3k+LE11Xz79w51HO02tWjDIm7I1sXS37723XlFFTdLxaOzIA89Ub2tbXHERIhDO9su6eiBRh6tUiSx7ZWFyKLWcKmvcnSmvMDmOqiA7gMAHwC3AjNfk1VoQmkJWRmuqb1/cdnhb62FslC8uoxRc4AmxQV2zHEyxVc/l2iN9X6WxjwQraV97SWOvHRdEGhfds6vf1EyLCBdJShCB1/63qs978GqtLtTqS7yqQLlbTuF/OPD83ElcwNmRgsjFmAWFB0zCRCGGQ3g1JgZXlMi1AfFhRC5SU2GUrlelKsg61zyN06BZaAuz03cvn2HL45hGdh/72LFnvmrGqyUpVIFAQYKJKuFDKa9o2TPSdX566OJQ94XR3mu76vesQL8EMHZBNVR6oJOegZ1MXJgvI66X3Z2bKoHjmHBtZb1jbsUsyC4EqzsHjDl5e7CoXMOQLssVPL3NVMFDhupa9zS1tPXN982NDVx+6ycKONRI1LfFq5oWbCkJsQbqGWE0smPPgUvxZHpx5PLZNzMZy1qYjibKdu09ZuGkaQEcs9RNGXZiBSDkH0LpFZ/A329xB9fGEW7Kfi1sVWqCgKvfraZlM562RMZU84JbJVjgBF5NUlhbJKtrIHvBrL2Ds3Tq3Jcbv5MVY7qhONb6p/lDZ7/097Q7oRDq7/VBN65wkTd0AUpWYzOyifeaSy4lWV92L6D5e20jV4NsPbEhDX6dkry5oPVKR2usy6Mk6FyyTih/ueHDVS+54U9JDvE+c/GSYO5VoRBX8ykpOBd3FK/16TGX/DWQ+3w3LmnuftldFrmUBN5rhbnqLAhAHTAi23ZXtJZX1O+XaogWrxVElXahERyrpfFt0TpRtffBysY9GVuzAbOFy1se1dwyNELV21rzNyopqNwq3NFR3MEcE44DssmcihxEvQO+CsRUarWmuSoDUyHezgxVTXx5ZQVhC+NwnCKJeGtn0+HHaFUTMxGPVoIfTHYYjbGyainGKnZ2Nux7PFm5Y0VBZ0UgSF/e0kVG1baHcDOFQgDBU3BhXfV+ACizipjRXKYxNTxnYi0I3zHbaU5VVu5a46TWSSEmnER0zDPpH/3Nn904f+nT/+j3H/r4ZxctpNLfVcaTQifNMMUUI07iFche5mam79o7d3pvbdv7sUxGqliA2Xj63VOZheny6tqFmTuE6qYWnTNVVTVVUxfQsRQchYvMzVytC2amodmp0Vf+/I/GJ6a/9r/8YaSi2pSnO+NQR4QiuFEzCGtXUbVISnpdr3ju81/rfODxiupmVN2wAKkEUMfDdsLyIZMeMQbWR4dLwA1buAtJGcVwtsgqURrnrtVNSOM8oRdcgq0Jig07uhgluNBc/He9U8LFhVJwAg7X/kareR8eYQgY49yXGxQ77KpUCGVX4Fx3Z6olp7zuqn4OJcjAKKP2HoEbAQQNUuWco0LpJeqBUg8F1Ok7xpXzj1JdQzMDd/qvnwWs3WhVx9FHaaIuI6RSBa52p2gAU4HzsvOaHR1N7Z3TQ5dYaqr/6tttxz+mJ2vknjbcAEcs1m5r2KnrB9l3FgS/QywjxBaqjFzlHMSOuqpwzEBKCgV4p5yGoNVQ5RCXk9UE0p3cGLV1ZOc1TTsqdu5D196Ymxi88YtXlmZGSbSiYW+n0GNSKulUKnwAhkKpUdPcUlbblF6823v9QiZly17Kq5obWnZTLaIDbJVzG1SZhCpVU7n3VNlByAhSEfzOueiuVqiorVQux+AI6b2UCEkebDi1hIhCDstaFetLZW0BoI1JntlLotBLhFFOD67TFRdeVLlDZ78s5g1RTF6Ppy2wwn0ocaNIssbK+UnBHrDHfsn+csG55M8oz2axge14Pecpi1cbic/hav7EC27nosFHReWGPyUF5YY/JTnLzGcu+RKs4FPz4OqGJbc+Fs4exV0VHpQEmUsuYdnLgKzNpdA0C+61krjqCuNI7IHHn9f1CLMcRWPVbiElrryNWShmJJ949sVEWSWnmhQXBkU2W0ktLKZXUlFDh3pa8TIb6zZkvVmEZ5BlZdIpqkeiibj8Z2oljXQjEYsKW17ZLHM5k0wmNY0uLS7atoU1Gk9WJCIJi1mplXk7k5ZyKxpP6vFy240XUkRpQmU4ga+trLLx05/79craHZm0PDJsBYwKqpeF7BV5/nNtW0vrZz/zGziSXJwZj1NI9JHjmLrUF5M6uMdS8iJPtbiJqaSOLq/IMeTdNRYpj+q6lLB2enllacGyTCMaTcQSNJI0FaKgfBw6wQaRF9Tl1KIk1aJGNJJIxqIxeZDI7+umPdZ/ufsXP6RLy3pqTF+eptzAkQjXqA2HhoKYAFkszw6toqYxntBGe3pmBm4NXjnXsvdJACLV0MLcZP/5d/cc3JOZGZMKlhC2pQK7AHFHMl9ee9PLmaVlZlry2IxEY9F4uQkxZpJC00wtjdw6N3TlZzRWyVOjON2gcal7Ul2eiBnTNtPxeCJqRJYySynTLo8k5YKIxJNNuztWbL6ILFvTZecstSKPD01hHZkInqJtm2wlzaWqRfVorAKKDhEHidYpaxhIGm+0BBU8F0qQGyJra6Bge63giZC3o4ucCwXnslFEFJAwmhG5T85HNbYv3D5ftWo5xidQUIiKjKKIrlYAd66bFLv3IZ/ulAUJnnpcLh2Uud1/cXGiR3bQ2Hpk+67DVIdKClK/0RToGyEu7hz0aEQPnnyi+72X00vjvRdPv/CFkYrq2rSN6Grdqaza6WpJcUDa1cEKBqWmVElSvK5ROyj22JkdXlMwIVNEcxBQQSUDRFTFHOWDVBj3RnL7/uNXTm1LL45MDKwwlkrUtrUfPo4MLcpVTAOjhirRWSM1rN37pwYvLs9N9V07Iy9iLbs7t9U3Uc2xpIE8kBQ6hkApGQEVVUVEaHJKMSJHjAhkAhkKawWSXEiEaELTiaNSChahwtBxBOpNOOmRWOihPz1sYfuINaNh1x6hIqSk7NU0oo5ycKlJYUANJGi0uf2AgvBDCcrnJnrOvP73N69fi2bspaXJqqaWJ5//4s5jz2S0yEj/9e4Lr8/0d03cGWzvONC078DlC2cmR6ePPP3Jjt27rl04fXfg9srI2ONPfkJEjTOnX0vPjmqVVY987NNHHnzu2o1rv3j7ZevOTXn929354ONf+XpZ/R4wzwCeANbBuEUiCAR4tKI8VnaIcZyhSNNRREcqPw/pmFboOG0hWttQVVX9+ls/PvPuyxUrM4a8xyfr2h7/1PHHnk9gcfVnfz8xdffhL35NXqHv9t3qeuWl2empROvez37qyzX1dYPd5878/B/G7/Qyc3nFtPfsP/LoC1/a1tZpWhALnCD2yODFt9/80WDPrWjank+ltrfv+9invrSj4/j04ux7Z16+9vZ37JkhTPVLb/xd743zyYa9j33yS9H6JhW9runKkpYgNIq1pF5x6NiJ1Ex6ZuJW98V3jj31pbKmnVJUX77yrjm3/NhXPvXqD/5a3WtFVJfnAqRG6SuLA+ffvnHuzeE7PempEdldddOeR1/4cuvJ55CRmBwaPfeLH/e8/T1reYKz1Ksv/bkor62q39W6vXW4v2tk6ObinYEjH3suXr3t7OlXV+zoi7/yW8vLU303Lk4N9Ejd6enf+v2dR54a6B84/7O/Xxm6pYuVDNY6Tjz/3Ke+2NV79ZXv/608CTsffOKRZz9rYUNT9QqZazJAOIRy8I/Bskz2ISKH5fw3eMymU1IaalRpRJjLozcuvZlJzcsJHj7yaKKibj4l0lA4R+X2YZXeAfc1SP+ghDS3HUlu2y0VrOnJ7q6r7xys32uhqAWu9oD03nPMqeCcie17OqtrGyYXRzjLyDejVY3Vja0pS14LoXiUkx6SllImUVPbvI9ocWaa8laoGdW1O9oi8fIlk1lgEOSCMRvLW4eUSgDXIq92Ttg9s+X9Ul79yixAQHbAWMAOpypk2+A+dOI3MUlZsm9km071Rx5uo7CF7aPWlJ/HUr4CUKA0lOGW7TpRpCywbJNj04SMfoSjmjXaf+F7f/4f7tw4e+K5Fx944rn3XvnehZ99f/zmuRd+5//seOhZbKXGh252v/NDnp5ZWhwbXphb6b+5MrtYRj9pryz1XT83eu0txKz3KK/f17mtblv/8PXZO5df7r958fQ5WlNfWV42R+ypgUvTQ9eXROQzv/kvY7HajCrYBYVcFLYUBXBRtKzCSCzAeWYWiDZFsS14htsmzYBPQHvkxKOxuYGX//Lf2SuTrUc+cahte5TzzNTE9de/Pz7ev/fkE9W7E+1N9XfRoslmX3j6ieaqxJXXf/CDv/yPmaWFZ3/tN6tbtv/4r//8ve//6eCty1/8xh9saz1GWOb6udd/8N//r/mJsSe+8NV9+w799Lt/deXlbw7duvDlb/xBfVtnZW3F3MyY4BbW40a8LFlerceTUlZj0wLQamwgKZOJvUSNDLORFmlqP5zs7puZ6Ou/dvrOjdOHGhpXFqb6zr1Z1bavrG4PY9Q1GUiRnLalQjM/M3/pvSs6jxw+dHJ48HrvlVNTg1emRno/m6zasf9hIxGpq0peWRhDIq1p2xKJMhyvjMcq5PkweOPsnUs/F8x8j9Cq5l3zPV2J6h0RwWbmZ2+ef2t54HKyukFbnmOpdEtzS/VTH/vhN9/rufQKIvoDDxyPilk9NWrP3n34yReOPPSoZVo20UxVk0SEClZABcv+aHBI1VpyK1UKPrc4PzM8TJBOK5ubDx4n8TLm5HWAL4/YcvmAqxAi/qBKqIjEKpp2HHl4dvQGs9mZm9d3PWNjSpkTYPD+NAz1gsrrWqp2H54avEEBZy++4/DjJFJmAVAXYOhBcg2krUi9L1G1+0i8pm1l6oacRqxmZ83ew0yPWlAZFCIewRqNAR9Y41izuKbFEU1gbGuRck0YplAQWJysQutTBUBHTKwJmiTym0YFMxK2CtpXnwoXij/cS2EL20elqVAacC9BITCiYXUR4276HCQhUQihNeA+SvDK4vRP/+6/Dp7/QV3bkaPPfq7t4MMxQS+/9qP5id5Lb/5o156j7fuPpZaGRi6+spi22vcf/ZWv/8HQjQvf+6v/p3FHZ8fejt6e2xO332GplZPPfOLhL37dFuynf/mf3n7pT82lyRhiv/H1/1WrKL/yxkvf/aN/k1mYHDj/C/7Zf6In6k3GDcJU2LVKolPiXQpAkzsZ3sRel9BSFYHKOapEBo0Y9Sce/MzVN38weOON8YHu1Mx8Yz3u7b8+fOfmwvzE4LWLO1oOLS6lpybH2x44Wb+jY7S76+Vv/fHc8Pn9j7/Y8dTnt9W33u3qe7XrrYmeMxdO//Tp1kMz473/8K0/mew7s+vo88ef/krrjraZ0bGeCz+ZHzj33ps/fKH9yL6TH3vtx9+e779hxKpPPvWl489+YZppi7aQwlZTVQAZoQ7Ue4qSlCC0urHjgY+NXH3Hzsx0X3ht3wNPjPXdnhnqfuFLv5dI1CFb1d7AhHFNim2CSLxm5+d+79+WaaoOtD3+k7/5d299+08Wxnr6r19s3PWAnqw6fOLEuVfqU6NXY9WNz37x/1feenTZJoa9lJ68OXb9VZOxEw8/9+xXfucX3/urvv6R6ua9zW0dfV1Xrg1c1KS6J/U/ebiQeFVb56Nf+MdT47cWR2+ffvW7DS31b//iFw2Hju1/5vM8UptRcTBQCAXgW7FTWUeEkO/+CpYg5KMA06ecjBZz4NZwItn82Of/2fz0i1X1O2r2PLhgU06QRqA0n1DoJQzqPTmOVgXcFat5+JNfa2w6uGKZFS17Ab5AA6iTQvkJ3vJqM5CY63gVEBqlVz714jf2738ss7KsJ8pbjp00NcgcpHDRBKeeU5zdRKz14PFP/e4fTI/0YUyqm3ZtP3ByBRKBnSBTqN9JlJbFCERhbu98/FO/9+/lfa9+39G0UcYUKp6BnWBa2TtUu8pwA5dtP/mr/2zvyPPRytr6XYfTNtiooVQhxhbBG3M4RQg0+ssGNBoEnBP5buGAlATk6i810KiXTNgqoFEcGJAMBc5ryl0hYLxHTvI2qCsYEEehhJ/TKcgQzS32FdfR5ZuX+i69CwGaVIz131ienFke7WU6QWlzcujWSmo+RhsQihCsQs41I2Mkavc/dPTzellzx4JQ8J9OoQsjsUwj8m5b03rISNamZxeIbnMNmcjY1rgnWb8zszCup+YEW2EUEKHMheGYynWSF0ItVl25fQ/Wk0Sn8vKrg23NcCu/SSVQc8tWy3lZDMfKaqXWcqf7bGp+vPvK2fa2fcODV+Yn+qR6Nn3rYuS5z1zq7VpkeN+RRxmiZ957bXLoCoEax6meq+/dvXVzZnIEIYOb6cn+PppZ7r745nTvewhL1Wh58NZ7E4M3h4a7QT7yzGjfLbySiuhxCjVRBCXEptqyhdLcqTeNGZS1FeqSKvUjiBrHForo1R0HH323umVhtKv70umJwa6hm9eNRFnTvv3MkueYyh2A3D8KIbsqBZJG0LKZYpllgyy3bG8B4whjywuzmoo1AXeqyha3qL5MqHyYaZV3YBNdKPhVLRKnRk3LyU/pbXOksj5tzmHdcBPSHHRSRDNwq39y/+OfO/f3fzo1cPV73/wvZOfBL/6jL9Nknbzna05lIBVv7OaN0hyYKBECjeYCjVKOChVDLFQFofDLfJCxgj344w0WZFyQFO2sTyHdjTsZHlqi9sCTLxK1GlIWxM4r8HUXqQ2pmu3q9uNkhACwbcWOw0dbDsuFTTUwykKVnPUEZOwxdNG5oGI51uu4FRaYpvSm3Sd27j5hCc4oMSlLM0uHAqMg/Czly5PLW1JOEpV7H/0MVyWkpZhMM25D4WlMHSxigVQBQag4YSKc3N5+vKVdKMTfFZgXsII4aKkQ8wbRo7aUrZHyjkdfENyU29O2qWlDRDxAJUNFLCeE32eReC0Y7AFDl79gNsFVnx5wIUpQ6evTSycIMhd/wlAASsTGYqPCe2rCu0N/TqJS5oI9XqLSueojN4KsEFwiV31+sjkJhjclwUTgnnGwyQZckAVWiJNaDAXplHVI4RJjwtHqfVVBZBIoiifP6Inh3vTcHELRdIoN3r4t0F1sp3afeMa00xVVu1AsCsHYAFmgEpKRWLLsWKL2+GOfwFrEspdUNWQ1rkritDiPxBKGoafB3wDuSMJZREtWxKqmVZEPKLBB0fk3X+09+7MoBijsjIUadh564Z/+i2hlmYDsNhKRnfBVKFMI5wBUT1swDrCJhGqx5kMPx1/dvjR2u/vSqRMPPTA43K2VVdiL46O95ycHL/TeuJRs6Shr3ieF7VD/TcGW5Igz84s3rt9UFZxp20OftblobOuMYDTRf9My5+VAC8szXd3XmKUxk+1++DOC86qmgzrRNMsmAnwNqmqtYKqGPmCqqgwnqrLLOdTShtJlFKDVaW1L++4jj1wavbU8M/zem98dGZ472PlQvKZhYWTIgbtUtWsBgx0q7lvL4z1Xe7vODExMxSixR24o0GwwAVDBNCYPBff5ckIyKgELhlVHgdJBJYPtFJOX6H2x7QD/yDJzECkM7mGuDC1UFdXVtMS2k0+/OHzt9PCNd6Qe+fQTn26q2z5vq6w0VdLQKSfMFOwZ5biQGCkqN4qKrIDSeBMnCy5l36Fgc/Hba5rGUeCjJTiV92a69uvNs3/mVNiEkg9yKZMMpLZCQSZV+QkuaqqGOSRIqIRXlYsKGx5qQsnlYqn8YalKmJaDL0FJrn8Z3/OkPHnFlelb3tRSNoKLECEW/ANSTqDcMhTpYRq2bYEtKM6gUSmWbKhmqupgCxfkBSBBHagIFWqmzHU6Ymrvwew5Q5pTKFVlYzOpXmEKiCcckMY4FxbonBq3hJuDC7CvkEGoyumUBD0XfIVs7YIpaelu+fq831PDpeyU+zfW/R5uy8dCH9YVsuX0l7BCnOBKVRIYQmqiEBKwlqeDHDhmUypNBEpkZOy0qrWqNTe1f+oLv21F6oWwhQaolVQYRqwSQVbQKuyoUxQQSb1Dc4CanCovKpIDanQZYDzjTtk9hdGjzOlIXugMZTsDWz2x0PY9ndGoHsFQji/DSLy6CRlx20Ez51iVHuMKWAC5QOvKoiKVAZPYGYHLd+7bvu/EzbHb88PX33vt+yKdfuLjnz7z8x+MDN84/dNvr8xHHnju17RYtZlZEHJ2gPqs7T342CMvfsNmSIfai2A6E5G4qemW1O8gcUjsaj/y8a9+3cpA5QsHWJVoBo2VmWxGHTSUaVpaoxbcXaESrdReoxCLIjKyK42pKm4ZqFTLrWhFbfvRZ66detlK3b30+nfiTZ1NB04KUoYh38BNNqLCssFiYF099XfvfPdP5ieGdz78mY6TTy9q/ObpV6BLBYbtzNop6EwhMR/uxVCGRF7ZmZP5DsBBFlgZIGeAw6OUzAN3q/yZzrHB4IZvKeU40dAYqWoGBU5YA1fPTA/crmw5klbVrBkFNdHJmRJ8rUb1L4XIQh+INNZUobmPhLNTlQ5EsAaQKmpOIQcOMnV5BJQDovJNLIVoAnoVc3MCoeSTJpe9cK4AUHmAgjJmi/dDuK9Z35BKjoU0SqpKmlOsAY66grCTM9MAMQ+MULZCVSEu4gbTnURLKJeoSpqC7VwhLlC4Y2hy5zgAqFi4s5N3Q8AdFkzBrSKomcA1havKVfiWlKjMtfGpGH9V80sLfelhC9tHroEc5ASwYwSOAuDzKtCmQIZCi4I7mbxiMVxRW49i5Sg1vzI/ySyzoqEujcFiYwgVrCqEIWwDiq4Kx4lkwP0MUooAaoeZOlalDpXHMSYYQHRy7oBbIaVsqWAeqTspOz7oXlIbMA8cfyB2/AEN6noBWigUeIbwdhMT6M9CwgQp7qTKc40jwHxSSU6WYIZUsMobO/adGDjz08WZyXNv/Pyx537l5EMP37xwbmVo9Oyp1w889OKutjappEldqrau/i6RFNmLs1NRTBLV20x5mSU4DZGxUpEz6+qaNZqw2XxqZtZgduW27Us2uAQ1XZLloDrKk4QBzrQUp2A1opDAqGrgQEo3AFzLOXGDmBQqi0DMhW2jXR0P7Nh7pO/SgLUwt+PR3bW7dlnAcZsp7ClwQSDToGZ6evT8K9+aGbxQtbP9M5/7Yvuhx7ve1tBq8WZ5JlDMVstlY52LBLIT2M7AtdnUwTnh6NLy7EsrT62Ayzu1iAK9ggAQZMqBoNKt1AXt1IU3X6cC7e54sP/WxaGuX5x/8++e+9WGmFEtAKIUCk8DaDakGqijJizy4a+WJCvoR2UuoEdoEJXJVYacuviArQq2NnGwFjFl2MXvZZAuh9yyomBLdQruqfoMALkOn+L3V9pJ/cYAaiEiwlSADsyNIyQGoD4IJ/jUCU7nLjAY1HR3cHq5CqJ0kJa5Ak+T2yCiwF6VGxSumERBsqSIgvdVcDuGFDEcIPChIqlj2VOInJpj9FotzhS2sIXtI9YclEqigGRiUvDENQcjUd7lapKGVoZSNtRZNnT80CNP3X79aP+5gTt9V8///KVPfLUhUVnDpZAUNvjkdFpG6XzMUCXyUcRA25I6jkXkWR4RKGpG4rrjN+FxA9cm5fu0PBZxgD4jGqktS5CEwZOEGk4tDBaPkfIKI5URhBGdyr8dgxC4J5TiBkQmDGwYjiVOCn1cW04jllJewGRjyCMggSNHTjx54bUfDPWc0jX9yKOfaG7f17Tv6Njd65nlzKEHHt21e9eSLYx4+ZPPfPrGmZ9mxnv6Lrw5eOgfHnnm0zwSTWtU6R9alR5//GMvXH7rlam7lwauX+x6/XuPf+7XaLzSBMuUPHJERIvGzGRFsuquPFOWZ8jydH1ULMmLsqbgskH7QlF59lAtpuFkVF5qWSJGE3Fctbft0COP9116JVlZf/TEU3UNddSg+jyUT0SqOHRFPNqUjIyOmji9CKe1bsTi0Xg0Kih1guArYrSuIspJlIgyLVElj4vlmbEEW2qIsLQGmlA0ojlSPBbRGspjaZukBceRGJedORXfEItKblcaUckxhLqvXL18+tSLn/lc87Zt/+EP/9Xs8JUrp75z9PjDB4+/kEJaBpRXMB2qmYNpUyAU5j75KVip1EdEAQWkdNAqwGzl+D0VVodQ9c4hsh1UkSz4Mg7QeGCg0WA1w1p0sLEs99PssO77LOYc/UptA3mPgMseRJMC0juVFw2l92VUaBUAuUO5K2orXD+lLGKdgbNdwZMASKiqM68iEZGQtxlTFU61VdlULEWhKkNqY1UUBxGNYYBvpwoqGWx+2IGmgDsoVJgAaeeUAEIqbydsYQvbR8V+JZRXEASgYbPp6btDV8+kFqH4fWpp8cq7r9UdfDheXWfRCM/w8njDM1/53R8vz473XDr1g292X3+vZc/heMW2DI3Q6u0PPfpkCvG+nuup9IL8+dzo3cGuC1W7jmlSC7Ey0xO9U8N9nEEa9PDgreG7g9FYYuTObXNlSUqV+ZmZodu3Kna2373bOz83K+VZeiXVf/06L2/V47UUgiK4igbTHKFNsa1zk1krd8Z7xruvC9uUWsXk6MjV829UNR024pVQGZCgDHjlWKK5tbLt4FDf6Zrd+6paj86SSPPhB6+89d3yHceq9z2wmMHLaQB8rm9/+BNf/hdv/e2fzk8OvPTf/vczb3+nqfWAnqieo1pjy/6Hjz9dsfPIU1/556996z/NjvX8+H/850vnXm/c1Z6sqF5GyWh96/HHn6+IRNpOPnH74qt2auzsD/9ieW6qrLFjx/5jFbXb0wrBWaqO1E6NTPb03742Pzcx0N8VadoXjVW0dj5Us+NoLFbd1v4gXsLL1vjQrStL02OSjeby8tjlq8nYdj1ZUdnaOTrYNdnX94P/9p93d54fvdOrGVE7Mzt2+/rd3u5ocwdBiX2HTk7efMecvfPdv/ijPcefKavfVVVVNTw8pDiPRwe6JVcr6vYK3bAXF6aGeuZGRyTrTNPq6bmpNXbG9MjYYNeP//YvoonK+v2PJRLJI4986o3vdM0N973y7T/XSEVZaydO/L/s/QmUHMd5J4jHlVlVfd8XutG4bxIAb/CQKFIUL5tjWfLIlmT5b/1HtuXV2Dvj8ew8+c28fX47b9+ba9feN7ZXsi1ZkiVZerJ1UaJEShRFEgAJHiCIG2jcfd9HdVVlZkRsfF9kVld3V1VXAw2ZtjJEt1FXZmRk5Be/+I7fr8HgM1gpbbwHCI9iD1a5xg986H+XZtFFEQOzF8F/04J37Jtk8XeKvqTL/pLFhyXL/iv6tSV/Kzk1vAwU9TRoxEgJv81hzEzB+zrQytcGerMcRv58TaKfGOCiJHwEuCNQLJDwBQ80p0Nhh4JOlrqKFa+l6MDS6B9a2ZcS/uEbDIWnDvB9JZnpoe2PuUDTzwC+AzTLAf4KKZdRt1ET8yR7ynwffgtBQ4k/gWEBpZ0AUqxAmQcvH86u8ZjGEuG4mXOhxxkEgrCfEn7lQ1ImeLfUCteii43GkptY6uVNGdWKf7LinS06DVa8lqI9uY6OVfisLfmowpFc0pO1uhZS2fNS4bWUml3lr+WmWrBSNuGGLFhlD9dqn7XSMySAlx5mL/mZ4OXDL105fbqttadr0y2NnduvjmUmAtHc3qNEQmljGFlDa/um3ftSDR0iWTWfU3OTucyc74vqjk27W5q6Tpw83XfydHNbd/umW3lV55UpmWrpqa5qmk9nj7z52vjQcFv35o4N+zK0Pp1sntfk7KkTtfWt5h23oWeSJEl13amTp1wpOjfua163bSxNWHVHXUOnWb8zYKDAVhsrpCEL1WyG6djo+MsvvOBPp7t6dq7bdKdb03lhdJJUt9Y0dPhQmUNVgGYNqvAEZalbHniqsXdfmnHmuNnJ+VsOPL7ulvu9wDHrQgaMa2LDhu0btt6Sqqqnwp3KeDMz2eys4jUt3Zv31NV2eCTR0rupd/ceXtPoOKn5jDc7nc7MZFlVU8eW3VUtXQHlLc2tTLip6iZFUrMq4TR1trRvcJwaPzBrCoDZ3Hz25ZdemhkabW5ZN+m5TmN3sqapvrZBBaJ96529u+42Z7l89cobR15prGowd6GhfdvIpM6mGpt7N7R3b0jVtybc+lxOBKJ2+/57Gjt7KashvHGa1dV2bUwmapoam5WbFDVNOe3Ms2qebL46PDE1OtXWvall0x5J60ZmdH1rb6q6ZmR4+NVDh0ROd2+4pbFr10ia6apWs6K8dvBlL5vr2L6vYf2egKTGxqeTVbVdvXs4a+mf07y1QzQ2Slg4zWoFUwKWhhWmbhlrXMpkVW6NK19ZSvWErMaC0RJHKPes0U9/T/6zSCZAsk4kVkcqF4yJaS4UEmxy3+p2KoqFs/gpeK8UDwCCAxLjkEzgQFgbko/Mrg7+FWli3PTGULsQU/Dh345WVpkRNnAMamxcUE2kqPMDaMd8QUjOtWAGOTGgsMO8dIiOorQPZCTgULAwX92qDkK80VLiKyuhJAn1sI40IZkD36Y+19KmsIFKKMTtzblcaf1f8YYkbnH75+LAsuI4YCtAdzUjM1TLpOAOJ56SHqS+s4RISkwnMLYoqaXLzQ7Nn8vNzaZnHI/WJGsMoCDJlEEozMtSs+wagySIHyhf0oSbwhplEsgsJYGLtf1m4YHIGgdp5AQzNg0WaLPxY8wlKkhggjalQRBIzZJaOArMnUa1OHC3O9qWhlNPSk/nOFXmJwL4BfwsaP1Xc5owvU1pbaxWFlPKqe/xXFakqjI8mQX77iXm09xxZSJlDuib19BlkVS0GpK/MzOZybH5KUaTtTVNIpFyeYIahMRxNTCbU+Xl5mfTc9Mi0HXVDayqzndcY6sh7wrlBDNz0zLnJavqdCKlQCEQIig5DvyLroGyfhbyNATxzJ7YSRAqUjoQgQcEnswBYgWZc7UHKm3mLlCgelbCMRBRcGPCvez0hPllqrreSVV7fnZuaqLKcXl1XcATDmXwY+pNzk0b4NNU3eBoJwckZ4q4OsPNGmhWO4c5VVAsChStAWegRxIQlYN4Bq8yFj7wM8JMCOFApooIoKLcT5qxD+isJuaGEUETijgyEar4MazMj4OEZZ6y//JTtRoSoOUvS5FD3NiDX/zgJY6PuqGYimTrYiAVCetXqQMRLx1Y5R1kIsVpgfSaCsjRFaR7Q+o7BNtROUZSm8AEgn2LT3Yjl1aSZsOCQZQ6h+vIcStECvlkZvamAtQl4GGFpM8J06FWqy18tsrzOkJpUASoQx17rEyEDEZQA8Ion4SQnzGmBjMh9QkipnkHCWrMWAH8hNCkBFBF8dmBjFNKw2RUtorrpxVz/6yKB2u1k5NURrKyqjtb+eS/jglzHUQsRfmWrnOTUvG13NTTrblJuZELX1sLdh3jtmJ/Khy6pacOrQdYTmMJoazGB2lYzAlA8QahzC4L/f2oFSughshsWqHwUDEsyobSNUjZhGx3MDpgMzAtB9NEIYAAASSQoWCUS9jeSVtSiKe1JIQMtP5sSSFki5rPk+ZAAaoMWn5jlEjWxJYi5iWlQfFMYmKp6btEMygCZJ3QpAqYDMk8UzkwXyyFe+55bZNVqYsLRQBZEHCtktuSO7g6sHscIZ0VyoXKPwilzhuYCOJmwGSFyasGZEBZUgY0x0APJKHhJzkXqvkMBDH7WygX1MDT5RA1h/KSroE7nEkF4vocGegDDdUAphtm25zG7LME1cYOJynJYea5gzmySqoshw4ISpMSGYe0OQjmz+NQS4ViaubaOWywgcNRWoodWNTMNZtOJqGwyxwWhlGASYcVQFAoWjdD50idQM6fNCThm1ugze0zy42DBZsM820CwGEKFw4ObgwORQ3WVaGvx/zeiA2pkGTuZlvjlS9NzM8FeZHpAjYBtYzoZVUA6/qM1IpsjeV6YuaRwOcEn8wAcr4VCxSWxto9GHwBqD98ap4pM3chxRI2Fag6aI7jIyaBgkKr6mydP5XeocJPdQEayZNnlOQxU1hF6EDfgIwYIA4zzzhzpEDiLonS+mGCFDImgG8JwoYAyoBJwVyXRosF8hGY0E+hyNpsPICEQXKzYZE2vR3TE6kLJhHkEM2bntnOAHG8AVhgsvCZoR6AMGa5cIDRHu0BW0o0ospSLpUhciSVEY0uGUay7CVdzbxfciNUxddCFn+nPI2krmBKLH/WSGWP3o0QjZKVBvm6AVYZu6FXCWsqGdUyk63MqC4Z+RuxYHqlaX+dFqyY3ahkVK8TYCF24VhGg4VlApNVgdVYW0Z3V6M2jQEP4IkyWz3I/kTpGgI8gczs+wA9QZ4C07NQf82BqpNKgRQvoBEClUMKvfIMucmBI8ecUUstmIO5nRApoKiuYdmMlZa+5gZ0+ShLi34Z0I6VUF4HORFoqqVDkGyKMg8r8qBIRzMP6cVBTVHSrDboSuU4GHwP2MelMXQcWXk8jRDFYAvMdjVdNlbVI5aZnJsFAxUxACSZKwMuKUpzYCSpkFwoWC4NXMyCaVY4LsRVDtDl0CCXM+NIzaLjYF4HSPrAQCkfyCyYB+nuMGBmZFyEjh7ELw3Agko/j4VT1pcki1VKvoFKiufMQqB5jnk5NMI5LVxApKDGoTHD2IJMLJGyZVDSMWMliLH5GsoGAJz68FPHDFnGWHQOcRyzbHD0OQBogmRfBpI8CMXMRfkgoQaeLiAiNyBTE1u3zrBUEggWYWE1x7eaOddhjZesC5XbjaIm6x/LGpchGQ6/ST/9bV3skS6/W33HASwdKkFrFY6cplEaJ7WC0lgzp7CqMBrgEHcrEgk/h0fTJThOr4NzvCKApRdfW/4/rkOqFQznUb3ASgrPE3i78RlDMpLCPYTWkXMMQFUBImGhsjaxe0c8iGVxsNW2FPUZLSu+5gW8VxLHk65iY70mAOv6/E+VvFxzJvfr9vr8zJjcy9+gtQJY18HkfpMAViXs5//oAKuMab3Ba1mRU55ExdS2mAeqlNGhAy4sHT7v1NoSRS3HFbO+cuu10qHZpOgax4+1/YIuON+igQL5HR1+HAYTrLIGIgOsj8Y131qqPPEABggtwaX11nNtrT3SHtre2tdgEpEKkWBwAs5IUZ4fuQGx7odFJKt0YUTgsAyBHoY6bNesvq2OyHpA0IJZlurQGtuFAyEdsbkkVNnxsYr7UGxkyRdtBICyiEDfystQuzzZocYv4xjmsUN4NE7CgUJkQ+xY2WVO2eWLhOgHj8kQHVsVf3Qq4pCq8OwLS2R+y6pwWPAW04jcyq5KmGwSXiOl0eqkw+u1MSGtV1gXKgRYa8vkvtqe6IrR4aoBlmBMqoW3tV64c+GbrJgNUIupM/P3u/AvKQELC90RqqCb+d/qApBJVnpZiDDZ0sPC/C64FpV/yZb9RC+6Fh1dhWYFP9GlrWPRa8mPKi17aYWjxBf3pOBjXThEYWA0/FCTYsNIrNu88CUmYeV7EqLO6HnLd8Z+R+dFM6L/lswKVfr+0sWTgZR9WXRUyeKzLLk0VWLC0MUYeUlP6OKXRa9lxftLK7uWJU/Nkumqiz0py3tCF8/w5S9L9aTUSl50uhYeQRfb35XvSRm7UaYnZewGW7yxXbLTLP+sFTy5Sy2YKvay/KgWvZb89ZJi/tVS17KiBStqN1Yc1cqt8ZJT50dYLHQGcQWQLud7YgEFKN2oSEUn/3DxCJ0QPIj9bb4nrGDvmr8iexy7tSMFFtiCrrAntKDDtOCuWWhlP6JL7FXeCOrojsiok2g2eaERCEe18Im20so8lFuVdPHcC2Elpq+Gb9LwqhEdLn3G8/eCRmfh0UBZS8sK9sl5j4C9/PzI6BCcEbXwwwXMm7fqrGDBoiGIXVjUwlEp2H2zpbNC2n84ltknutjwztKwb2yxodDRGObvSOH8LG83ypssXZkFK7OyLLcby9eFYo+Vvg5rvBzkLOkYzGonFWeoxS1ucYtb3OIWt7itZRNeEAOsuMUtbnGLW9ziFrc1BVgyiAchbnGLW9ziFre4xW1NARbT8SDELW5xi1vc4ha3uK0pwHJpHCKMW9ziFre4xS1ucVtTgCV17MKKW9ziFre4xS1ucVtTgKWYikchbnGLW9ziFre4xW0tAVZtVTwIcYvbP4GmSSz6Fbd40sYtbv90AJaXiUOEcYvbO7uFRNVEFdBjx7H9n8Ww6xAlLP9oAUHErXQrZGuMh+pnP4HjcV/jEaUF5LMVjK1QSuhlW40lMjHXIfW8XGimzFamlJRRZVLPRU5X5lqWfHp9yr269AGL9qQSfZZSPSlzaWVe3ohKU5lrqbAn1yf1vKpRJTegRHh9o1qhiN2aTJhFR7C4yupiIuGw1TtCodWQ4bqock2pUxc93Y0L5ax2hpDVSz1XPiGXP/WlrFBpC6Y0SscwqyUTNRUiXc1AMMZKidAKLViZ9a7MfVFlr/r6lAhXnJBFNRFXtMaL6fItvTjq1uOMpYvNUfl1YVWqipVbsKKG8TrWOF3BtejSJousXtJlxcmvCxRKcIbC/kCh1KMGHWe6qnXhplrjtV3jrs8aL5kMlVhjFUkRaK05aDGB7jUKGS+isC/sieDUj2Fp3OL2TnUAgFiueap9CvptrpSKcJ8xq1mG8rigbkZK4Inr2/EWPRot++Wbejr6s9rqL7aPILKuOacBC1U4ULmTWpU+phhRobrdsg7S1bxcsfHVdH61N7Hyw658LhTJL1zxKVUs1Ak2a7wy0xal/BTqDa7xhFzb2fgzmJBrfGmgpYjaQji4VpURRAOtbiFFNaAIA9BlGP0fZRhv9ulu8FzMKjOaeWvQKUodWYVHyTU3Vti8j7M5oAytQHFflqiuYmWUBBeD4oVeqiL6fYtkgMpoEdIS6n6s2HFIaX23JcJbpIQCV/melLqWJT0hy16SilXzaDF1pMpHVRU7ICnWkyJahBWr5i0Z51KnLt+T5VqERUeVlNUiZKvvSflNVRktwkpGdcn9pZWpXxUdVVJai7BoT0DbVYNSrc9IwKh5qhMovJplds8UHpXFmS43oRkTisiWMUUBYUmtEc9G04OjonCIuOKGkvQFzzKo6lONgEujCxD8KKCXbBXs4zFb07GHeUlBrzucjUyHWt4Ieu0egBIa24lVT2kQHzcjKyyWIiTHaECpa0yxpo4iPqESVTO5jSosWxfEjM9X1Pcnq3SfXoegP1m9Ej1ZpdA8uS4l+jK/rfxE1x0iXHOd8RUHmVR20kpGY7U9We2oklU6pcl13dkl36EVX8uKz9GKvWVKu7Cs8xyK+btSM+UHmnmcKS6pWbh0KPDP87r8cVsT7wKGWAzAMuPLYHeq0IpqCW8yYn0EKB5vc7T0z/26hfHrwv0DTEtJQ7eWVtbNijrHZssQg9I1RQKAA8ygcmMSJMApVIRmGMECIwLORA5bA4pR7xhjVTywSluNb2NnJYPB5B5jPmNSwWdSGbBFzO5XRTkCywOvIuvF4xi3uL0TlyzzRAe49w9gZfIdHTRWce4w85IagEXQmEIQxqryx3ZzjUEWrFBaAopSgTa3AnaxZrTdEE0QdBnAWqb0AoT+OW1RFJWE1RcaVnvw/YH7CmKtBq3C8g+NxHj0JjixNDi7ISfIMdZCIaTSGKWF9V/bhE2pYyfWqixAmHDHBGVKspmM7+cCSpPmA09TYxEswMLYoS66TRduPI5xi9s70maCdx9SVwyWClzqrW9x96xLJDhkBriwT9WY2wJLW8xldzOGX2OimzGmghuUJTASDPnC4B2ApBbO7NJGYt9huG6zqMAqzLkG558OY4fcLPncjJQMF664rdnIEwy+mh2XwbNBwDnYjYLEzHDp52bSxvBqtQYYzavNaKNDU+SNc+kZJQhxzDBKBsbZfMIV53a3tRxgtdTHxjlucXtHPuEaC7E4o0QkGNu6zr164szffPb73KkRxJYIkbDCbWHzdB3x+QorTclK9XCrDaqTZfHVCsPI5aPrepWdL9kTLMPSUgX1NYkPf+SJobFrX/+7nyTcOqI5RgMU7lttGhZd6VoqHMabkeZQtCflp0SpE5XsPJYKLgSgtI0TwujAqu8rr3d914c//OgzP/jJkVf7EskaHLlSM6TMNKhkVMnqUwOWs25UMiFXlVyw4qWVv5aiSQphU1pXV9P3v//x8YnxZ595USvH5mNDcBZvhba5cNYHW2lPKr8WvZpHr9SorsqCXV8iDFmpynB5joYxAlRo5ans7ffsfvxX7mltSdEcbG4Zerh8bjYMTACcVUXj3iKbDuKVLG5xeyduTPHJN9snTXyzS9KBOz4y8+pLZ1zRSrSDOyvrMViGsuJ24w3wqzZ7VOXPN7dWP/KounJp4uALfVVum8G7GnwEikGJp7GwnP18u2QQbGquNeb9hFMxoJRjvSAlQcabmtlHJx4jp04OvPzTi6lkczxZ12rkJSVceTUN6t4DuYGBmVcOXtJ+VZRqXYB+MAFLaWET4eK20sBqCanrAsgYVE7T2er6locCP+3nZrMJQhNcUUFUwJSZ50JRvpjMZQFgZQJeYpdASqC/SmBjhZnHZVDkqvZ/pVKQK888ruRaymP81aL7VY1qqSPom4nuS526kp7cSJo7XX1PSNk6whu8lqKT9vpGtfJBhr2mAuvJoDwInCk5z7zlsGSN6wqXK9cWZmMBPMWq7Ngqrj3GolRJX1cnEy4n5v9qqpMpJwkWFW6N4pDrQgNId4mHHzb0DNKCw1TfAMJWhEOWu8+dVCIpHE6SCaeqKpFKJWOAtVYNmfFYVUo6grgJXlWVpH4KnavGaGCFMY1MCiUS5mo8VSvb4gLLILN+P6m8VALSqfxAZgPwyjoKyl98bgCWNv92VJ7rbdG6IBS3CVpL/5KoAlkvZtmxL6OvFXkZ/V1Y4Za9JCFZ10KF86LT5Y8QHTy/8hTtSeEFFV4FCUtYyvaELPyk/LXQwjejquT8VM3/hBQMnS64ikWXQ1Y3qksPuPxaCtBE8SMUjFXh9RYZVVJ8GlTak0pGtVRPSME4LxtVUqYnJPLhLBvVwp4suhHREQonSf5aSt3fJdOgyKiWur9k6WTWFYwq2E1b7M4Id4lwFOGScsWEhOR2ZU8CFpPl8yzitqabWKiG0xLd/+ieAUobm2xhzamE+8NpPPRmZKglXgpZGjBJzWwNYFbDCAmmBLW0AZDCpmMdgjUb+ZCVAXLcGVQKmumKvCIUg1Y2DS6fJEdjT3flGwZiS1soJgMYi8sNYGJUUC4oE2ibDQJjZtw5eLOLrgvCccDFLcz9WPyXIMFdIaOP3SNbDpMAv5YP6NqXCunMJL5pj8CiI9iXtICaCLxn+BNa8NL81ik4glNwXoVfyNMq5jtmvow/zB8h7D+PflK+J2xx58tcixP1VkbDl1/FdfFrWeiJPYuzrPNFR3VJT+zZCw+Y7/mSaynsyZIjONFY5Qe2VE/04nORsi9XO6r2/uJLKpb1REdDZ/dcUU+K39/8y1LXsqQnhTdi8RFIflTtf6Xur/0OX9z5SkaVRlNxyUtZ9v4Ch4qCpxsGRPMEdRwGyzrQ3TCOyFDirpSF/Ayx2VzT7SuOLSayQMYV+hARG0DYEGkcsBoeUbiOU4dJyHGNKe04E+1SbjckQCsCw6ghadjyMcWgdO32AdY5ZcaZ2aoXaV1XyJlH7bwFHzeWxdB4J7YaI0BVhGKZQLPrMJZgwsApQYCY0AxwQJnHbCF3kXVBaJ+WiswVjRvpxZEMtTh8ogpCJoVHWFGegiym8VxC6Vl4drIsCFQqClWJLIOq+FpUiSOQlbJM82dRxUaVLNNeWNKT5QekFfRkyRHU4hFQJe4vKdb58i9XNaq0dMfIavIh6UrXsrwntNjLSq6FLB55tcpRXT5DVry0/DsKyWyQHkAFRHm46EvkDmbwCZeIvZgy61fAmAqZ8UoP5vIOYJqRFd7BbQwNC5MZcuvkD0WjlzQKneW/XHgECR4eCAxFB0ePX7GeLD9d4b9L9WTp6KG7jynsXUFPaJSdxvSy2YLfKbzRtHhPNHA1A56GRcnYSEWZ5FoyAqlG5hYA2IUL1paOLMyIX/laQtSxfBrjJ1yxFUd1yVDQsqcr9dHyJ45W0JMlnacFt4blPdVIKaq1AnkWBkRCBlsFysxbpZBZBLjEkJypfE8WOoz/W34ty3sSbodoYefhvcJZwQoekMIJQ5dN+PKjas8i8TylljPAObrIqBad4UVHtbAnlmxp8Qy3LylEqvEUEmcm+A3tEaj1weKVchVmvTMFUcWVerLEbiwe1VI9WdluLB/Vyu3Gkvtb9OGqxIKVmQaFdsP89RGnMimIhspB87GZyb7E2EG0SQA/IfK00CjoUbguCPSAL1xAGRL9Fd8vc4Tyx6Glj0Mq6wm93p6s6gjX3RNSmsKfVtaTop1Z8e6s9ggVDt2KQ7Tm1/JPd1Sve35aM+FRS2cTeNSXLBlABoXNbEH3oZKWChNSLMGsEkoWbVFLS62FX8NyOKqjGGvEAB3SPhf8HGOVC1Ib4ZejI4Q/4ZItLPwLgeMiY1JwuvDshf8u05PCztP8y2U9KVQFiRIS8t+xAGvpteR/i/9maG1hdC3MBd+AtGfCxUDlafQpifwCZa6l8KrJ4pfRjbDhbPvb8CNabFSXuCDyP9ElR3XJR8WLAwumxEJPlozq8s7nbw0w3lLLgqXNZgDpWUPyUdTSVBxDLQhQAYJBynXZniy6Fl3kMkmJUV3c+fClTb6nBUH8/CSh+V/pwuV8hVG1h83fmtJrIi12f5cesNSoFvYk6nz4k4IZTu1LhlXFNNR40fnECWIDh1JE2SSMLRy2ZE+WXdeiKVGiJ0XtxvJRXXp/K7QbS+5vkc6XsBtL7m/e6BGygt3gBMGUwVBKEAMDoaoQCrtlWFuEaW3UPuCqqD0XnMc1BXGL2zsv4hJaNRUaJg7BFWM3Q+IGCBRC3J+BJrECZbyF4iC6ithCua3TivKD1/EFssqt2c3ryYoiZsjijL5lriBtyCxgPOR3gn8AjyYQOyHGgmR3Hd6T/EGibTHACWpzwMPXhZtzZNXC7K4wGkFDQemIzIhZobMoGknRDYH639Gv8i45HY0hC/fyLFpUQ/uf97bgkmSTdGj4Ch0uiKOjhZYWG57i42aJ2zEPSwlIEdSh20Ojjy/kZSUAAmA8Ma1lsZDZQu5hQfgQquQZDSV2bUQcQuOK0ryiNJ47IuBikb8lciiEgifwZ8HbFFFIsPx6mk/QjDBvwVgwFaUbmIdRs1D4x+Ia8z+ZzztbuA9Ly1eWbC6u359QbC+mLcKHLnGseZHEMuRZrW2O2IKyMABUUFxYACd0eEFkYVQLtjM6GlkN2udLgkJ2x6IURT1pbbPxrBdKR7cAi+zy57LbEhplz4Z4Mp9+qiyk4VpHgWd0E0XTNNox5bNVaZ6HYqH/qxjGEmYBD65wKFFCE/cJnDLTHKq4AuksnBggVKjMyLOihxMy5nyLW9zeifF/EnmiUZQQsBRwBeIn0tKwROJXUYJQ3Nb0DthohWQIYdHSKuQeI+A+VBYISeX7vgdG3eGQYUi5A8ywfhD4xjozLoRj3tASbpFSKqc9xbjLHYeEMiYiwOgit7AJiumR1JAHsKAocD5A3leoj6wDBNUc1zy7JKJnSEsqAbT4WgaBOVhCJBW38nShfCKq+3CMIitE7UBcZZZLaZbjIOeY/yeEdlxFlFAM3XfWVQNrmkK9EFo2eUctJKxYFAPDoxCEYjjbJq7Z8YSgiVmQmFTo1NJSST/wzaUmHBfCijSw2tCenzXdSPEUEdxyjnHTTz/IsRwTVIikBadmSMwYMo5nlwa9OSgXY5Z8WwgSjhCMpQp9FyhBDYy90HMm7cMDgTNzE7K+CjziCO46koUc/QY0MjN65jzMet4gGQ+uTpnvBliw55uFllPhcLgExMHMsntrGrFQ0SXL/5pZCo1HN0ZBIhWuhSUKg9gwaYNABjnzFVckmJme5mMOGfBmihJfcuFyLhQOClQsB4H2Ama2cGaKEgFUG8QnTBrwrNAK6ZBai2IYXeNmAfw4Cjy8EJvkoT+HIvY0ww4Ym4EHE37jm/sXSDPOTkJYdMQwwiYpE3B3cFCBEBV8bjDaENEElXCBeeYYiBdwdASMGtFdqGlFFF1DYdAQvTEsHiAQ1OZ2qO2sighcNUa+NSklQSSkiolG4xa3d2gDk6HgAUdjBVt/sNtmWbQKurYwCCWz4nYzli4z3gGzRSh2nQwTLew23VjX5vbUju2bBGGn+/oHx+Zhb0t85vjbd7Z1dbScOzs0MDStICnJLDz+xk2NG3rWjY6kz/cNebko3ABi/WaBkqB+hO6qQEsOmieQOosrpY2y2eWIYf4PICrsB4VF1UwNamZHrmdz66YNbZm0d/ytAYMTTIelZfyHLJdIE5zhBYCOolm6zNqZuf2O7p3b289dSL9+7JoBL4oiauc+LorCOqL0yu7IfPm/JcAPnRc6rA9g0cemn0iGb7qMrldNg87euk1bu7x0tu/UwOykJAI6lqhit+zcmEw5F44PTE5mJcjp8kRK7bi1o6Gt7sK1kcsXp7QP8S4EsgzlYGDFRdpt+x8hIWBlDDm5LTjgEKXUsH6jlwrdH3Z3YgCnt3l3a09P88Dg5OlzI1Jh3NNGPgErSHtd+Ib0/fn65uTuWzY3N7uEBeaIVy5Nnz1xxc86TCTNDaXW2YhdC4NWNK/tvmgZvzETEQlihhBY2+x26xeSKtfYWr1793ZG/At9A0ODaQDv2jNQdtPmlp51zVcujAz0z1qGfYO+ejfUblzfMTY6d+7CcDYjOUOgDN5XDIYhWoD5FErxQD4XZyGGtF4rmNQA6iFPFNyN2vpnNRqybMf6pi3be9JzmTPHr3lzCKmEhU0YjQcxpbDKWuNfvAOAnbWSBuKg38hMSoNbAtwnhHASNxu6kmm6CssLswmFM7XEDQfmB2CAEK9XWd436zuV4N0scmrBmIwNadzi9s7DVrj9t+ti3s6TyEluCy3DaEWY7R2P2drCq6XUIwWhLGR7Mmu32rC59X/5N4821dV++cuHPveF5zAy43V2NvzWJ9+3Z0/nt77x9uf/+pl0xsAhXt8ifv0333PgwLanv3Xi6rWRXM4gK24XLPDiQBo9hqy0FGbPLyEmGVHEK6AyNPc3UJz6uF2mDsNImUbXjVn5NYQnH3h4z8d/466+c2P/6Q+/lh3zCXWx2NEc0UYULfLRACQ0l8qnPHPgQO/v//5jG9a1Pf39i6fO9GfnzKEFoTnQsYXiWPBn4QSzIb2KHK+FA0ZtKabOF3CATwVgD1wyBldpdvftOz/1r5/MzmU/+yc/eOGZs4ylfO1v397z+//usdq66i/+xXPf/fYhTao9P7d5a+sn/90jHV1Nf/m5F65ceoNQB+JS4F2B4CHDvHp0ewQYuAE/mMYHCM4lbWI3OrkI8J2EQpL5uKn2kynvX3zwjl96/95vfvONvv/2rAog8gN9hFisGb0AY23CjLjnz+3du/5DH7337vs2NjU4kCap6YXz0z957tjTf//mwMg8YUmrcm0xlQ2gVZYHu2o/i039ZhiEtS+gRAMQoelnev2G7k/928fr6+U3vvrql7/w00zW9MmvqU9+/BPvu/vu7n/4+utf/psXZtOA9JM1+l9+5MAjj+z/+28cuvD5gSBnpyCEvokMi52phchQdgNeQATsFPjPUEyK2tJbpJahATe3wAVwBv4+8730/PS99+393T946tzp/v/jj745OJ3hrpDa05E/CHin8KYBilGac8Eks9ns4H80UAtClGa2B+AXg26Yvgh00oKDkYRDTNfGgWUjxRjRRwwebhQ4OJg1pg2AY1ni04HQuQhcFnVVPDalcYvbO9B5BTt9aXNKaRUhjmNJbZYy5VIS81zevJuwfGSjlFm8DxPj45n0xDVHwnwAAIAASURBVPpdDXfc1fHFL+YMIvGlv76nYf+t7e1tifsP9Hzja2Quo5RULY11B+7d2NhEx6cH0/NpppI2fgWl3RIpozCdHpGBwQncViYxsxoyUJS1IslmHcfwC1Q5QKwQttcQ5LPhmMa6RHtzcnKUOCLAknxcDSEQmY8o23iSWQ9yPDX/rge3fuJ3Ht2zq3P8Wlb6OcEDXNDAuSUpMNkCAqQ2W51W7Gwp/y2F/bc+V26rAadGR6U/s3tby44dzT/+fkYTN6Detq11O7bUVdc4992z8fvfeVFq6ge53g31t9zans2p0aFB3/MhKAdjJTBaZcmKCLUJPxC+4jZVB6o+A59T7iADl8LxhuQkFmA0lIPPUEphQISmjfVufR2rrWM83NTYnGZmH0h7Epnzt29r/1//zeP33N8zPZUbGsx5WdbaIXbf0trb/a6UcD//pRdmZmTEcad1lE9PF7gol7iyrn9+qlAFEtUIo2AVYknzEdDpT02Ozs4N796xft8dG77996/Oz3lmQGpq1V0HOrrb3Hvu3Pj0Nw9Nz3mMEdeVe29p72pn4xMD6fk5lzUT8JM6DKk2DeLBOhozN1EdRtlsPYjEsqhc01JwoavPTDyG7wfwWwnxXWPO6mt5Vz0bbRBcoDOKQg+ltIUPmEyGZ4HYtzSIF6Z6oHLgIYLSaYpPg4BTasDJmGYG+A28vzYX0oZv18IvGBYzAq6SXPkGTgmHVCXdGmQPBeIG9I6aZw8eQ8KLklQLLxt7sOIWt3cmwlJQJwyuBMWZL1UCjfRyq8yKcZLE7Sa4sxbdH1htBq+NXr0wdODOTXW1iXVdzf0X08b8trY7NbXwcUtjsnd9y9DosLmVNfVOQ4Mzl5bDg1PZTC7lVEP4Ss0TEdRVVVebrbznz81nIEFJC0z2AscAc/wkAxqpIMg6KVJT6+Q8NjsLAShMa5KOqxrqRKDVzPwcJ7mwt5j1A/4xFqB9RzcKs9tszXjQ1Mo+8Gvve+r9dzc2JH0PkA4H9G6+71PmmK84UHSmF7iLtWVov15vQEHjmO8OBZphwExcOt8/eHl4R29Le2dTfYs7Np1NplhbRyLlArFQV2dNW0fTlX4pOG1qTlQl6OCANzo0ZUCrSHAlDeJMG0BZU5twBM/mvPScwVaOBuJCYnvuOEFViirfV36uqs4Vrjs7r3PzVsszYCSbSPHq6oT0TIfmEcLaGCeu9gArELcBVAO5ail9kVL3PbLz9gM9Q0PZ73/r0Pe/d3h6JtPd2/CJT/6Luw9sfvypvUeOXnrppUuAaDEDKWS+LjMoN+ZrIaSweC/0cCMJFk+I1LWrw8ffPn3fHevb22rqG2tGhiYFZ1s3tDVUg2+lqy3Z0VZzuR+maFtHnZkPE5NBf/90EHCH2qI8n7BcXaPrmClqQO5sLhuYIXEkejhhsnKZTFAzAz3fc6sS1dXJnEdm5n0Ip4HDNZNI6erqKmPDssM5pjyCNZWYwy4BWpnzQAJdoK2vCKktGJFOEsJuOS+TqtKppNAySM8HAUJiFGEKkT84KqUtPilIm1/Lxx6ri+BkZitjeuHnMp6Zi1yDD49gFD4AWt3i1LkiF8SRhbjF7R25qJutmLQplTLBAxlgjknJdS5GVzfPjaWLrohmAfIyZGTQrAKqtalh9+4tl/oOt7RU79m7kZtNdaBqq5w9t2585eigk+K792wzu/XTZycGBmchxgDLe9DSwvbdtfm223d3tTWOT0y9dezi669eGbgyy5gLvP3aX7+u7t3v3iG94NK1K7tv7d23b/tLB89+4XMvCZLiwt+6ufHAPRu37e41a9Gxt052ddVbJ0aAWS8c877BfaXN7h9SyjURBkAIV/3yrz7w8U88ODGVO/rGxc3rW5LUQehlC9Iwf9lWKubL8G8AXOn8YhWyGzBgdQ8VoAAGzM+QyYHArOMbezrWrW8aenOgu6N9566NgBKkbm6p27VnQ9/lE40t1du29WZz+uRbV+fGc+BhgkQcr7vXvfvurbfcsrWuru7a4MjRN68cOXJlaiwreMr8nIngttt6b9/fM3h5cGZm8t4H9zW2tX/9a4cP/vicI9xEUu3c03b7gW1bt66fm5s7c/JUS0sVCR1CmhY+VrB6C6V1Vqa3bmu57737DYR4483Rr3711cuXJhkXly5dUvrbt+79vUQyua5nfapq0PeUdf3phcq7vJODrRXGsl5AcABhynfIZIW5SZCWTkRmRo1eNcCd1KVSmzZ2nj877jjs/nvvSLoukaS2NrVzV/frJwalCnbs6G2srTt7Znh8IOfQaoxFZ5ua2f47e/fdvq2zs21mYv7EsXOHX714tT9tAJPmTGi9fmPTe969fXp28syFS/fct3fL1q2HD/Z9+xtHgmxAeXbTlvrb7928c89ml/FXDx9Zv6ENYAey+RN0Ay2U7izokqj2rrqHH95q0MyVy8O929p27N6YS3vHj108/NK58SED8JLM+q8iH1pBYSRdK9hKF8hcGJ7IoHkS5KSxw0EYcodgoYyKG4s+JELwOMk9bnF7J67pyNROUDyMcs6wECyWGPnHALqWG0EX82Ep9/SpkbGxue7uup3bW78dpOsbGnbs7oUaKqnqWlI793QnXCg7vO2ObY4rLl8ZvNY/xh3XD9Kbeps+8psHHnlirxAu076b3PTwY3tf+OHFL3/huVNnBymvkspft672o79+N+didj6zrqc55ZLxibQysJvLW/Z2f/KTD919x3pfai8I7rt/tyOshAUAF8qixCeKid804l4wO37OGutrRwazX/+7F/v6+j71u+9vbxYS8qKg1Mt8blOOmBVj1JpGxfDXsXhF8iwUQjzofKBhiT44sCRk9zDl8bOnhmfnvB272rt66l45crGttXbr1k5IcqOka339zh1d3/7uK82tTTt39wou+k5fnBrPoBMhvWdf9yd++113370d6XelSGx9+NE7v/OdY1/54g8nBucYS3IW3H33lt/42F1jA1MG1q7b0DSbIQdfOquDTDLF7r9v28c/+dCWnW05M4K+fu9De1FxjijIb5eUOpjhH914TGjj1Jmb9Z/78bHR8S0HD50fGpM01WgeT+npyZlMEACuMcsqSKgoy+/E7abITqEwn36tPFj2DiubXmcZFixfu9RUGptBAu3QxOXzs9euzrd3Vm/f2fHss686CWfvvh5HUC+n6poSe2/r/btvHpLZ7J5d6+qa3KuXBkeGZoH5ieZau5xf//898MST+5xkkpPAYc4jj+16/sd9f/1XP+q7OEKJq2WwZUv7Rz52f07Pj0zM7djV7eXI6EiWiTeUzuzc0f7bv/vYXfdvMnjdbBIO3LMtldDaFkeH6IVpWzZLQg1Ly5rW3d34qx8+UJVMZHIyUZsSQrkOf9+je7+z5fW//syzU5OBNqfGAg5tqwjyDiRLOkz0GkQJdShVYyaDVuGZuGCQwOiEpdsQCdUQoi6lti9qqmMPVtzi9k5sMiRJggTWKsJdEGugNHZU/aMg3iLjDiEgqfjJU5dGxqYNMmhrq2lqTjS2pGrr3av940ma7OppaGmp7+pqmp0a7V2fcCgdG56eGJ/jtNpNBu//0P5/8cE7RkYzL7zw5unjp26/bdcjT97+4ENb5mbSw3/19Ni4b1YOwX3OdEtLVZ2qOndu/O0jZw8fvmxwVHNL4pd/5cDtd/WOT83/3Zd/fLV/+F0P3vHgu3cBSRroeGDVPtRIKEunBbkrCpOCTZcleen5c6+8eOHQy0c7e1skSQRIUhmgR8j8nFvSLmSsihYbdp1L1oLcaZ5KBCkFuBV64qaLntQnT5ydntnf3du6rrOhppZ3dNQkEuTCxf6mptbmplRXV2trS6qmLtfe7mhJrlwZm0tLkRLNbYn//7+6/74Hdl69NPv8M68NDva/++E773xg+5O/uH9sZPRbXz3iZcxZPEay3NFd6xuzGf/Qi2dfO3b15ImL3CVd66r/5Uce2LG77dz58a9+5QeBpx977MBdd26EZR8oJgMJWWhOSIYSrZ4Jlpoa8b/+xUPP/+BEdl57mhPzZRUw7u+9fVMiRdJpOTkxlst6BVxPIQXr2j+8NKx7yS/wVlEAvJ8EU5s4dRPJc2euXui71rNhW8/66to63dnV0NCYuNY/OjOV2bVnfWdXY1tbrZmHnZ1Jc4TB4dnZWd9YnmSVeuSJWz/wK3fNzckffPOV48dO7du748lfvPOh920fGZv93OeenZ5AbW/MuGpta6hpqj9xYvC1w+deO3jVz2Sra+ivfuTBuw5sSs9lvvH3By+cv/bu+/c+8p7dhETkvEgXAjPLcvVbznlqcwchh7ypLZGek889d/zo26f37Fn/xON3PvTw3jdeu/yjZ9+mFAAWtQxxWKcg7RYolHVYC08WMlOYvwJpB6GehBEn5Sapa6ArJxBKhxwsVMhhJRCzmMnEBjtucXsHe7LM2me2wsLzAxf4lFR5r3bcfqa3R1I2PesNjeQ8j7S2NG3d1rF7z5a6uvpDL5wYHZz74Afv6Olp2bmtY7A/01DlDF/JDV2aoAEPiH/v/Tse+YX9EzPzhw+d//xnfzg6lDv44jVjxJ/6pbvvvW/T8y+sGxu7zC1OIspxyOuvXfvT//6tyyfHAz+ZcpxdOzu3bmt3XfLlv/3J5z/zU7PWnDo1WOM673lklxX9ZciQhTE4hQQQkNdFGdBNBTn9+qGrMlCZHGEMldM0ygMAnpcovMJg7QN0FVh2TUyl5jfsbImcN3B0iRovBmA5KpDjk9mpCb+jg2zY0tW7oXbv3i3CcV85fK6hdvrxJ2/Zsrlj+/aOxg7qOOLcmemh4QwhBgrQhx7ev//2LSMD6R9/762/+eyz2Yw6eWzUrRK37N/86KN3vPKT8xfPTYK4LdQVQm77M987+ld/+cORST8bOE5VctftG3o2N+dywZ//z2+98NxZQUR/31Tdv/+lvbetU2Eqk423odcNGZGYttxMNMjRoatpSLKCEKoMcum9t/V+9GOPGEx29szghXNXIToJSBXyuDQOH13QKV1LJxaiEqBqQ8Km6E2FbMQYKdSMT4zODg5OmfebG+s6O6ruvueWpsbqo0evHnnlUkdbW2d7286dvYlErq29YaB/vq9vxFylL7Mbuus+8MH7leIvPn/ibz7zo8mRzOEXLvmB98EP3f/IY7f+5KW3Xjs4kORcQdMuI68evfLf/8vXrp2bVTKplbf3ti0bNrYnE/q73z7613/2vJdTJ968JLR+4hf3ExoSIysK+wdMc2NhJmk4NhrVK8mzP3j9//2f3x8Zybz28oXO1oYDB7Zv397zox++gWWZ5mABZmxRy8uBzyNKuK6R1whKVNEjbD2wSpFsTs7PayUgw11ibp5PI52gogAL5LWKSBEW4aAta82XG/clR1h+QF3s0zK6cKXWjyUdXvFayvdkza9FlxUPrGRUSx2w8mupcCSX/2T5qdeqJ2V0L+nqe1LJqC4/9WqvZcXO09Xf2fI9QW49jRtoVUbQWUV57nFbu6YrNcFZP3j72IUH7u/dsbNl27a27Vtb2+rFwLXp737r0Lsf3Lxxc8fWbfUbe3Y01NUdfWv0wtl+wRPznt+5rqajNXW2b+KNV89NDJs1ummgf/R7T//0/b98Z3WN6OxqSyUH5+czUN6gaRDoQy8dO/7WoPBrmWZOgm3Z2tbUkhgdzZ4/P5HNpnjSvXRpcqB/Ioy3ACcq8j4qAftwA5uskpLNH1Ys66GcJRMSt/p5PtLQ2aR1qHQTZUwvUMDf8DQDpyzE2pCUwgZ3uJieladPD23b1XHbvo0/7K7eua2puso9eWpwarzv4Ud3dG9I7trVsW5DQ1Wi6uSpo0OjU0y4BtSsX1fXWOf2nZ44dOjEfFoIUfXWW5cP/fToffdsrq1x13W2Xrk4nWfVmJzIvvDCmUsXsypVY0YhkSS7dm9OpZzLlyYvX571/SrKxIXzYyPDBoisw8QphnlNoPQZKhBinQDHFDfoP1LQMuorPb3vjq5//x9+tbuneah/7gfPvN53cZixWhsgJfhDasWvdZ4Kc+0YMS3dl+VbX3gXaCkYsH4aUMgC6p46Nzwz52/c2LxhY9Pu3W3JJJ+e9H/03Fv33rvrjrs2bt3SWJPs2dDTeebswKXLgxDRo35jk1jfXTs1kn7j8MmJcQPIGyYnZn/0/cOPPrqnraWpZ13DUaefIikUYzoXkIMvnjp9dDDJ25E4VN1y66ampsT0VOb0yeHMnDBQ/tLlkQsXrxG2X4Xc8EABgpQdgGMQIdl4p7Ic+gbQHDn09mD/HCUNY0Pe2NiU4KS+zlEoCqrRrahDvhrLpYClhGztDEAoqaMXhAZU2KSyQpNYKYGiD0UNuHAWmRNdwsYs1xgtAzt0USuVTxmjhYUPxe2YrvhlmB2gi4yMXvxtvXhe50luFq5FFyhVRcy7evEV6iXXsng4dKlNG12g2S4cAbq4V1QvjEmBAEWRgy8RIyt6pXSlRWO5fm6pL+tiL8lKE4ZUANMrud2V9KTMtdDFP6Srv5YVvUQVjmrR45fsCRZgwyJpwxM6H6kCYYa8DdFh9TiN3Vc3J/iyImDmxAuCI0feymTubahP7tzZvm5damaWXLs2ffHC6NBwZtMm3dvT0tBYk2pwZmdnLl0ZprSGE+kCkbVOidTk6AzUuTPYjOd87mXNih3U1lUThwMOgb0+nZvJDfTPBcQFRk0MyzS31TU2V129ODQzmaYsgUnDPJNhSEoLgUCUSdbUEsBD/CTgmiOlEbhVFLJAmHXCZu4wTHyhBnAhzSgsr4yFNhK4Rm3mtNYrk42u7G7BSJDgyOIF2V4QGuJT03NvHj37xFO7O9rrb93V1dLsDA2m+6/ODfaPTUznmhoTW7d2btnRWV1FBwdHRiemqdvgMO4mqIAAHRsenZbIAh8QmvWgo4Hn1TfUCAfYwiSklZHhwamhoXnNqyUUsUnOyLr2xvoafnx0cj7ja2Z6pANNMjm8CAG8pqHfCdZO1KEEgl+uFNA74cDBoLk8e9c9m373957cuat1oD/9lS/+9NkfnpBBwoIq5LILOVBJqFQUMYKF4OCGvSyYS0Cs6nC43QIydEBcSmCgUAtHHD/WN9A/tn1b577bNpkpmp6Wx4+NXhmYvTo4fQchPRvquroSVTV0YmpucGzazJ+kEPU11Q5gNDUzPRMAE67przOfptLT/rzXWF+fdLk3B/4jLtjo2HRfXz9hNVbnwHSho6Wqva1qfHxydHQC2Egd7qVJxpOhiw2gFWfwXSSfZ5E8oAaufxvxS8/qmZkAOeZdH6QR4AITCY5suwHAM0vLEdAwDl6w6Oi1QK7aEpxYBne7OQB6D1tToKh1nmG0UxQpW4BXAggtVt676cpWlOU4ZmGd0aEm4iInaX6LpIuDPF0Io0gekOmlOImRglHVBWtUHljqyP1IF0kW6QJYwqIYdkh6Fn6d6OjIqE0egia21BmiF8t+FoKxkB2S5d2fkfoWXbQL0WxhdmhauNJGerHRFRSBkyGJ4DJAuRJ0XvklLf6i2OJDy88QXUHHlhxe02UId5nL6/ovbTXOiqLdvo5zVYo1oxliVbqYA9mV6FrIp4hGYDFPRB23m+DD0nqRpYkeuBDkQphLSzE2kp2ZlHVV8rbbtje3tPSdGbp0YYjyxNHXL9yxv3vv/k1ulTM9ry5em8jMB0wwZLBGzVqpINuH+JwlObKcQV2/pFJi+q8BDAxYOjwvyGSDwN5pzlDvRRigbQ6FYihIf4gM2xTFUnymfA7UQVwJiLSwcOWycyqSdENmLAVkRRRXO6D30YgkqArNFHQJpAWZlpW4r8IEILrwuOpFIob2YyTfhox6gC22CiwjvYGhqXRGugn5vsfuNWN4+OWLo8Pp+Xny+huXHn9s9x137WxsTI2PaAM0/UDxBOSMRauBNmA0iDLzVSgiZHBVgFsPoBQ158hmsvPZecIkIw4GmEwfDOgkrsBFEsM8wCyPplbikhqxzmEmDrqztNIGZ0jkofdVurGBPfG+3R/72Ls2rG89f2bqy196+eln3pjNmdvi0gXIYF2CBQlYmHYUqZTTG5+mqNYHXZfh0Sw9MQshMVdSBQaYjo1ndhDywP23NTbXjwzOHTvWl/Gcc30jc2l/396tBn3Pz6hzfVPzOaUtiX/AQUQAK5cDUMsBaGnJjZkdI0R3oDJhplzOm53JMiIkMqqipAHgHg7qfaDIh4Sh+SJoK4sgzT1wpBWSDPOxNAsZPc3mwve0DMw8ZwaHaRaWdULIFQs1lJYhsLIwGEcTUihsxrxeg8cfGewZPGEg4QO7XYdzR3CDDBmnQoX0u5HIQ35RXDi3CGRBaJywxWGL5T6AMi8XxziizDuO1GwGYkoOlLAMJ6wgAEYVynzjF0jpAJ9dQnTBSqvBKJAItGKUNNJFVWG6n7IYWKFHNyxkZUDfSz1kXkVZN7glMlRWZajxDuDUt3BaocIj5IVSiVoHFLcDZocnrRoDoljbo4Bpq7YVkujmdUmtBqm2ezUllO0FPJwsxInUVk0EFDXGYKdJbGopx3sRMIJSA3DeMAMQD61Qw5Oj21qaqY+PLBpKpmlR7yMtEZlafkOXRuIK5Ml1gbm0LvNFaEeztfIdRH5fq/3J8mfkUS1uIbpafi1FL18VgHlagQOvmEOjXPC3kp6Q0s9akY7pvHosEvUpG/1hVpSBscgjADK2UYgnbmvbrOKZdQtELNzgjQAWdlwuzBcEc3Jz4u03h3p7N2/cuE44zutHLl+5OqSIOHTw6K99+M6udQ05pfr6Jk+cukpFUkPuSCZnNuTarFu55vYaB0iazGqmGxuruEOFI6anp4Osz4GUyVeQ++FATAqRnY+aKONj6YnRXEtjfZOBdd5AwkklXFLfwFi4TdRWrQb9WIzYjHXcc+PGHJ8gZdZApNiyefAa3ViBlQ208ALNy6KJW36OWegWbfdt4hFuCVS49mhbI8aQsxKjlpglRkwnnPGR4ML56Vtva9y0qYtRMTQ0NjYxl8nIQy+99dSTe3p6Gsw1nTo+dPnyOBfgHwoC6XmwkDCXtLXVXTxzjTOeFLyxKQGs4q4zMTHlB9JxGbWFjBqWaVgW0OFrlszB0cmJ2a62jobaBGG+ZxbNVIJU1TG7iqvQ6aQhhQ1sEaf2TWC59Hx/pnt9/a9/5F1PPbWvJpU6dPDyV77w/OGXrs2bm5aEIj5AbVIhvbwuUJ7WoVB06KyxDFn6BvGVzgdtEGNQK6EIETiIZaHwEc3meN+5mTtv0909bY5gl89OXbg46Pvk2NEL42O39/Q0mX6ODmXPnLoaZKmgXPpydi4XQJ0nb2iqcygcValcc2uzmxCum5gan/WyEgpPw1xw8yvL9I5JS0SNj2f7h9KNTcmurhYqz1MiqhJOdV0KnW2oQkDDolWclrB64QRFJULEdQqnJTCeKAMfQuloiAHaTLgoq10hOykcFDkTLC64YXyFuYeUhnzxZveDL8xTC560ALoTEBR/DJ02uui6IJYtk7SEr7dyfW/4G5DIp2IDrAAsoTdCA+MKhkpBPxI2WYqx0sexTS6USCNURNZaFQbVKFLWw63iWDaNtg+fA475msqy9sItlRz3TGhqAAeDWx6GCRzjmqAvTxu76eMG1QH2MIDSEhESR+YW3OphfgMqhDOLHXVIzYvCU3aaA3G/xM0i+pIt2VykzUtDVTMvEgHVqMgNvLZYecrxRHaTEISi7Ro7CchcWjFV4DiDTpjv+AZSS2O5mS1SpUV8iOWGt8wN1Qs2NSrzjlzcdBmj2xpRCGhm0YOOROyjfV5YHrKoi5VM1yiWU26G08qCRkWPQK/3wSlznLCOWaNMPRhsJHuBzZ7SkRM2tDc0VIuLvVhr7b4CGiEQrqPhkxctYzQfkUGjwLmBQ4cPHX/qA9vMbJ2ekxevTs+mPa7F8Pj80Eiupb3WZdz3/NMnLxOdMNDFdcS5C0N9l8e6u+rueWD3awevXugb7dxQ88sfeCCTldeGZocGR2XWLCqusTFBmCalbIU7yCMqcv7s1bnpvT1djbfdsfXl58/MZ0f33bN1zy2brD6No7kIOBPAqW354RH1cBY6U6T008Yk+7l56dUEJPCpsXI5z5uVvgeLgnA4rUWHWmDzswKtaOkIOUVvkgBvXojDqAV4TIvIaw/af9pm36MUMaYGIzcEXJRgqWuXp08cv3jX3R2w0g9nL16e9mE1kwNX05NTpKlJc0ZHR0evXh1xaAKXYn7q1PDoRHrDxsYH79t59vVLk5Ojt93d8+CD+0anvf6B6aHBSZTjIcyyQjHMq5cMdLYZy/nkrRMX73/vjs6exr23buw7ftDcr3vuvm3rtnXWBlFb/g9WXANtaWhYgW6S08xt+9p/+3cev/POrWate+nFt7759YOnjo+maqpTQmgWZHMy6/nmqjCExciCK2vRUxrKSN4ApQANaeZBsoWFU9LiOJRYAlV4dDZoV+aEwVJP/sK2luaqdFa9fXI4k5ZCsuH+mfGJYF03LE5ZJfsvT8gMJPlxg5DG0ifODm/f2X7vu3YeOXh+6OpES5v7xJN3pWpqzvVNXLg6FkiaMOCH6HAthqAdgGdkyNVH3z5/zwPb1m2ovWVvd0s9GxsZvfuBzXfdtydLQt8jgniDXi2Fu03Jx8dNYRoWQWchOkJQtlpGqkNY3ork6SDgbcN4koYy3qgmTm5YgQg8oBSVyqzSK42eHBDywQA78IuGazGqAeQpuBbZc3Do3aQEBgtWoO4CN+vo1wZQCriKhf5LWwIgVz4ay6cxUVQ2NRcJnm9FAgWqkwJ3Q/AGvItFI8jpL5QSnAOnrESZVKLQoWUsD8SoA+vfgn0oxSc/cmLDXgWsGIdeU2SMhcwFZEKmUcWDOZlkGGcXyDRjSWOsfhjm7gEuVIj1QBcKcBjeLlCwt4FZHV2YTafU4e+BhdB0DFS60T0Jlp2BAhamGHDqaQJiB7a4B0kEzV8HshrB2aboGifjcJJ/Yhf7uuhNmjkUdY1JATFx0TP+XGQdwXYfdxA2+sMsRTOJ6pIi5VkaJ2CtObwK1yq6gKYwMkIL8yi1pZlycr53ZWBoNqvrqtnw8OzpkyNUwk49PRu89nrfjl3N5rGdHs9Oj2WYrAFRXeGefOvS88+d/8Rv3Xv/u3e4btW1oYkNvTW33751cjL36qEzZ073C5pQyoCrhAIFNszHAwMhEOLJvjMDp473d66rfeL9+xo6amdm5vbd2rNpS0cOuAU0apUYS+CghA7YeeuygkU9yLZ1Vf3Gxz/Q1sH8wGuuq1q/oUoyde+713f3/IovVSDJc8+dffnZ814grKactrao7PPOlz7EoSpdNDcpC+WqmfWomZfADYE02WDxGE+n5/r7xyUO8tX+yb6+YQNGBXeHBuePvXX5oYc2mI6NjM7MzWQZq1XAmKoPvXjqzYf3Pfz45l/4pf2tHXUzU5mdezq6ezuGR7xnv390ZGje4DYq0wEugKgepzAmikFARY6/deXsqcn6u9Z97F+9d/eeTeZb++7e0NZRk817JXDjrcIFCgy0WdKln928vek//OEH9t/erTySy5He7uZf+/C7NXPMTDAjFfj6hZ9eeeZ7r2ZzAQ0jLxDXonkTirsh2EeG6JTeWA5WxNse5eMqgIY43nZvEO7V6IVL/XNzWQBYs/LN1y5omRBMzsx4bx/v33NLq7nOa1enJybn0KQIysTwtdlnnn592/Yn3vXQjlQqMXBtqqOz6va7ts7Ns+d+dOzihTHBXK1zzCBywlQYB8X0P0DQzvFjly+dn+zqqrrvvi3/6T//6sTY7O5bO3fs7kx7WSnBXyukWTqJNOuzdT4AMrNwBJd02NJwM6QA3zgjhVoCFMO11ltqKcZsJQOyaoGg4VqsDdzKTYfLL7MRFQYxT8ALmOSIXLyhu6t4E/rmRBas5fHsJhwjzlhtDmMPkTPAOuBcRCCoy+WrRKUvMPgYWla4HwuYRIeSubcQZJwevTI3MWD2bb5E/XhQ6QJVLUylE6SmsbVjI+Uu5Cko5ehQusDcMoGa7hq3qjoiaQGdUzwvhOcsxZhlxMe4jvnr48PHQ38m44r7mFKqUTucQYUpSm3BRRtoBxjYQJ+AA9ySGG4E7xe6cC37B+6VwVXmoOMGNyUKwTnXgblqb2ryCk2n4ZLM9+saqpq6AuZwrT2zx6RWyMxWqoLyOdNrRBNsJ1hI3aaWRdP0skSwtVGGt9JWxYLoizP3qPrn7LWxeu3mbiLnYSh2oqKcPLx6DHoz3DjGcoRr3NRC/hAmu9qNbMizQ0MyJ2LzMwjnbGIyd/Lk6P797em57JW+UZmDBN70nDzy2tlffGqvVu7bb1wJchDvwDgZM8vb1/72Jx0tTQ+9d8uDj2wxh8wGZPDy7BtH+r77DwfnZ1EKGvBQKpcxx9cywByUsBrNGR2e/eY/HGxqrd+5t+vJJ3cbizGf9oeG5xNVKT8XMk/b1BbMRwKriaVW1JN+qoo/9OiedR0OECiaParZpErVva55Y2+L+cbsPD17bvplftqYbyTaRocPI2HCSbkkRjDxNmZNwmR2VIWzmuU6VCKWof+bYawQiS7CBYNfuTJ9+fJMd2ft9PTctUsjgU+FkxgZnnr9tZP3P7Dh2rX0qdMjkKYB2VuSMT49kf3MX/ygrv6Xd+1rfeypW01fp9Jq4Nr8M08feenHx6XvUtCtSwS+k84oz4MFG05o40eaXr089c2vH6qpemTbrvoPfOg2X5GZWa//WrqhqUZ5XCjhQ3cVykJjRAok+JgBvQ21VV1tjbkMHIZzsXFTz+ZtJO8mSKfJ+QsZoFVVWMeJATQWhp2i5FoEI7hb5+WWv0qWWm0dLpi3FBK4QgyAhkJB2sYezPxNz3hmeLu6mtKZzLnT13JZSpJsZib75pHTv/DkLsact9+6PD0zz4VjBsrcn2yOP/OdN3q6W973xP53PbTdrJ45RfqveYd+evwH33ktO2NugWtuqx+ITMZ8mWEqT2DDe4JVz4zNf/VLzzU0Prl7T8v7Ht9hujo1GwwOps1N9bOM2HIKSJExRkxYOgkzSyAln3AViGxGu2Y59AVkisP0FeYO+pL4Pqx0uBKG1YdWbjJy5nNi5VtvGMGERQjI76xsEnWUNgAxSvyCCyqJygdAw2ixCI5gN2eJMkuftEx1YQ5+eLdd6JOUHgSnFRN+Qd5UaU99iFE5ij5A/BH9XphfCiwursy9+cO/+8m3PqNBlwqcrszmNmn7VCc69773N//gj5N1bTkDl6G8NswPcCIUHCX+SDwuTSDyQ4QqZXhyc98kxzNaCXhwRIF7DjnSdKgNifcZjUbI8wq5GgwQmDFWynrbNSYQ2qkEnvIA7J6PeDiBmzyfWtcxFgZrUuPQqaErf/snnx4/9SrHPKt1dz7y0U/9caq6PfDhORKY4OBAiFNDIQysu2uUDaUjP/bSiH8+QhXtUGVY7L0my7xQlfXsn3dMTNu0GYE4S/IQNcMiIaAEimOFMMagbWZLHCJcW3zL8wifhnkeCLpCNd0wswaV9cwD7CbGhjOf++xPXr1140D/yOC16Yiv2z17YuYv/uxISjiHD54mykVqRAnbMpWYHPD+x//5jSOv7L3z3s31jXR8NDhy6MzhgyemJo3dTMKSzvjAtdmvfemEwUVXLk4T7hhEBFkxyjzvVW+9fu1P/sc33/PwPQYf+EHw2qGL48Mzm7ZumpxMz8zBHpFjPgJsuhjLpwE7Ijkz7n/mT3/S2l4X6CyCdYFMSkjrA6KHzttvDATSUSCj5zHqgqNfaqzZKjPHACwAJYDKByzykYzCvRezYAO+h9lXNvPZdDDhVJ85MfjZP/9pT1fH2TPXZqYD4MwGZOAceumqw1+dm8kce6OfOq6iPnjMlMN0wvzkP/7RFx565M79t3W7CXX1SvblF4699dpZL+tgHwyCZK+/elkGYmBwcGra4ywJqbbMRx0e8dILb5vhevChfb0bk3PTwaGXzmUz3pZtm86eGpJYU4A5ODQMtMGGJtBJcWkg8+d/eaS5JUW0T9C/TGydgAZR7kCyUydAi9oyqkfgChODWFS7YxY/hX7RG86rgGUWSikRB8LY8/waHLLHIyM542R8IveVLx0+dnTQYNbRkazBhmaCmX4fPzr+13/+ppt0Dh0+N5+2Et8AeykXU6Ppv/jTZ8xP7r5vW10DmZogr71y4fBLxybHfQLJPrA8Xjo/+rdfeM3L+cODs4y7mFWizaEdkTp7evj//q//8OAjt++6pU1J8uqh84MDI5u3b0rPeRMzUgpQEdTIgmEHSOLyaYZpYHD2K397VAhxtX8c/IKYsffi81fGh9npE1cEr7IBJ9z+o2OWS1g1cMVkcm38CzRMm7OTHiAUIzZpMPLQYGIGQD+pdQkNRPrfnvFujn0yewBwFprHlNBQ61wwf3ro0unXD2fGR926xp17D7St3+ZDwHZFtwbDaKOP0B+KQiTkXZnRFAnNqtT8D7/xZz9++nMk8M2TkJsbz81Pc8etqe9QzNzvROet7/nYpz6dqm/NQs4e2EQ7PpAhANYRo+DwJKLHGsAPiQS5goBhnA+4UKQrMamA8ADeB2DoQOo+gUSuUK3APkyYsKCx0BkqL4iZIBlGfLNb0Rx91T7Xni1tNPbLWBrPzA9pAJYw9swP60ElhgK4gcCXTrzy7b/8z3NjF+anx7KZyU33PPnb/9ufJWrWmc1VYH1mxBwFS1d5IG2CqiZrU1mGdQB6IT7H8mwbmM4fLiN2U8/WMv+FFhBQFH7w88RHgDVjeMnZah7cta3uzME3//iPvuQ4rWbmG5MvjYGCRZ9zHRO8r/UWkdliFCr9+aZW5w8//bHLl07/P//XdxNuJ0YPIJ2WYwKRrVDRULcjqfAhQBckjW1QuNLg4xgIMMQgpGxd+7AmUkjikNKstOYhN/t+H7JUfaGkQ0VCYeYCuoQCiA/CNodLIqQV+6ZAhM4giTprtpgMt/TaEwR+SzDCDjQDlFqCD4wbYEYVtRSi4CYwWz+JiawMrRADLUB0sDBldsFJSGfgEr37AqoUdVRcUXJHjTaZQXoKw2J2Mw6KY9jHbBq92d17ev/tH37oS1/6+nPPnEmlWgA4mZ0uWBeMwqHODQXCr3lOHTTSUSoNbDR8DF8ITOTVSgSYqJ7ATYdUMmuMJHUhc4QoHuQ4ZG0IZFcNIwlmDKWvza+STBnkKhgzK4WnkE0AYhGOQopKrTyXSmHMOrxtBoFanV+MfGlMjrX8F8buEjgCZv+i1bd1e5TYjFzcF7ngE1C4VbZLdEGld5T6vAb7IrSJfk29+t1PfXRoePhLX/ie8qsAbmlrmDXySWFVHkScfUhmgj5VSaBgBzkdHtiAicQy1IR1fGpY1jCHB+5RIFwpETiZvZ6UjHNHYjIz3G4KQR1wtUPGjYPEWLbqC6suzQR2lMAKr8CjQCDhqgCWjhSGXrBeS9tKVon+CY7p7+ZZmceNY1IFLngPuFlrs6YnCkr4EkoJzEWF/QMiCFCftpt/rvja+YmUwggC8+cfeuz2j//BI69fmx9LJwVJoW1QKEmkLA9WUT+RSN0sqRyWxGGzwQuFsGX02sXvf+m/vvnC94LMNE/UnLz1wV/5nU93b9vnVxBwRDzDkbEe7itKVENRiBuQBEne/8gHd+y7kwErn/fiD7/2/Lc+v3nPgV/66O/RVL2xYE5jW01To8F3tY5VHgl3oAKjjMYo4hNALeUMJo6Ddx7TLhwSelrhKeKQu475koQFUGutnQB2SVks2zQfwKRD8hm81xQT3CDj3vw4yYhneerMzZqbPfjs309NTe7Z/8Cu/XdltHJhVwrfN49BgoWp+xwLLQShvRu2/fq//o91Ivf0V//yp89+NcmTNYmEU8V92GjYWW7MC9xen/EAg8UiH9BbA7QT+l/zR4zUnnj+AWdkDYmY0EDZQDstRwD285AIFNZMaidFqHBwu0SBZS9yvWqr4UBigLX2g49hnAUeOm2TecP1EfeyzBIi4k0hnMNuzBccNYx5RH4I+MaYKIkODgc4mSLOFix7Eo5ZKmTOAec+5HYIIqxvx3KCGrPJgScIqRZsqjTsqsCeI5KhycBDD7ZBNhq8+UqG+VIKk/Zs9m3eSa+sOwOyKhjEEpTN3bHlzhzDHlgojwxONi2GReXDkpSPNFjpXSoxz5Tbah7YtdowlYIk9yiMiEVLmhby5JlVP6xmSQaYR0Kt9CaxUM2MkmaY2YzDZmkpsd4bojMpiBjkpKVHZdTB+h/Iy8Bib0bM7jUArx9oExOrLmdOwW2hnVkE/RyOi4F4OkkN4FXzABFsBanO++XzKw8CZNMlyW3BEs4HpNggtmI+r68Na5aNMVkMumDAGFkTdLUQJWQq72TNT19ib2M4khqTVgwgdSB+ZGu6sIoJQriBgL1/WIMVBm4BPEIKqDALlJfF0Ii9L8LAcaha43jLlXIgZwvcHcxynoc1ORoOSWnC4Cnf1/YXwhwykJBzTFyzqbAJ2OiDYugggqRqBxATV0ECva7Ilor3R2qXIiubr22eFabF5f2D1ApdU7lmiIZG23yFxCmSOjqVdJJaQD0HIrtQQLH0Wity3s2ID2LpHCad+RAmg91cUgevvfC9I899jQQZCDzlJs68+b1nv9v9gd/awpJ1Si14sdjSNQYeM4ETRPLIS41+W47ZBYHmVc0bNrStB5zB5k69cQjgXV1H944HnFSTLT0OPMI8cB5CLhRmCADNvrmdCBgCipx7SK5nxtPD5zoAbOgYeyDs6agldrChTg7RSAoqkGam+BItC2wM0fevcEoriRwQcAcEYmHrBxKMp2dmDz79lcErFx1es33P3R70CitrFNi9ULfCwEcFkM6gT6emvae2qaE6qG75vg3ambsW5EjOepNACoOrcGOlfYaZFqoEol79rcScfWsQWainZdP9ljzjbK0yovDRxLhjVOScZ5KmC9nFPw9OlNA0wuomzOZehvEELFgG0A/RohDshlSGdKFmexEgXUYHHAZ6bdW4JiHjiF38kUMiPI5exF+k6YLhib4cnZKFNACLX+riPSk4Ap4dt4EslGUp2ROyrPM2RLJiT3Se046GpDWq6LWEjN1hT/CRDen7cBsRTkNMGNB5uVdgWrd5xlAyBxbD7KFsjifDbAGsY8NsAm1/wzBCFi5vFHdhmJYJtxp1OULWdQuNsJjZPngozBKVLFtvFubdgFHA7AbY1lEsibB8npEMnoUr6BBFkEKQ0chWR0ImAwRnOPQRuU3DvA3L3YD+eBVx0hTMCkoXv2R5KgbrK7MqPWGBGLr3Qg5MpUPSbobcBzZgEISJ2pDCCoCMy1CcALgRoHgJTyKZzfSnlrSK2OGy2juQfQMBAzNeAbKDhclHeLEgXWwL3dBOh0Ef5OE0FyaAWwzwr0LdXg7UX+DuwVpty7Ea1pAjvQCiAVCjQ3ttk7Cp5SaHGiutgT4CzxDCgDCiZHOICbPl9WFiiVr8rIWjqgueFFUw28Pk7pD61c5srIIJoRGgaIkk7lYqm5JISNIOFEAbCpEYXAWpzd83w+tC8lSYUYgpwdo6R6QdNKzZA2RMMMOFhfYfNxpYnIU8YvChoiHmCdkXkI7VovxAM4QpyqzbrjFp9gCSsDAWCwwAmMgMQ23+lwx5/zCgjN4z5DICvxGGb0kUwsWiQiz4QhciepeLWbAFI2CNBlmwCUXshmU+g0oRYMHillEq5wWe5yhig8YU5/NSG7sIYPmyqDSNXqUDpGA+wOMKydpY4kd8h+UkzEA5NzNyuc+gKyzpgFRxFWRHL56am0un3HqF3Ci2xpgsaFNbmgDzxMAxYdhUxEsDUotI06LA8wu2EG4fr6HI0YdTPQ05kgwptzBiJ1jSoZ5v8DRS6WH5pzaAhGFNI5BPwe1EYjHNk4jnclpiGjEkYJm9Jbe+Ys6k4/kG3zCfw96MWVe4lSvRBuSZbwbEsdlQrsFPviIeSmK6mqQETRgI7k/52TGiZxNcZ6VZPHkOwRhw46GlFYiuAluDAEaaJx00BkjLISXQnvkaEykRY/uYhesjlwiCayIXAayiJEzlb2W0otokf2UZ4JglxglvUpQKxS0spGuwFbMOLJtGhrm5dldUWEHIFumarU4qJ0+IVVSWh5R9qZd59Zek/Fc2qksPWLIntm5Go/sCyNikLRoEXiQ09FjeiiyQSufpwRYxs5baXi0Mp46qz/FZKyRMWVxzTBdn0dM82s0fIUpayCfcUKqX9oQuPn6BDotdimheppUt2kUuuRa6mOstwvplelI4poU/WXwtNNxOLPSEWZwLK7pgoFqDDwGaKch+tnddoenAKAfUSJuvYT4S0bhxChljkVNHCesPwcWaKSSGLEgC1dbKI/eDdR5Q6y63PzGGkFuQo7BcmlgSAZoXQ9Oo7Q84ycIKLPOxxKHWlUFRXBlPKBEeIA0i5pgaZMK1TVtiUXql1lEdZd6HXTArlrzEvDVFwg0YOknw8pR1o6E2IoYdoGdQ9RNWGlqmeBVyukmLfi2UodRSz2OoKRwnhhzaIQZh1idELbs9rvPIY0KtkgoiGuwDgCaJAtbosGEh1ynUQhFJIvIkG+ZTOs8IA/9m0XwQkI6BFeosJMBHKoSo7/8fe28CZldxpQnGcu/bcl+UKWVKSim1oR0khIRWdmwDxjS2ywa7sF3L1FS13e7q6Zn5vpnunm++/qq6v+5qu7qr3GO73LYLXAYbG9sYg8GAhITQvoEW0IKEdilXKZf33r0RMXFOxL3vvpfvZaZQKjNxZxZW5fpe3LhxI/5zzn/+30hUSBP8SBqofLLQfiiMAqwaq8otfjJgVgc+KYH7R9A1rHJ/gncYOt2AYazMbADbGEeCygXAq6JY8QRIio3JZsPGXjC4dGkVa4FHR7HxwPhxhzLhtpJjWHbB4BkuTssExzVPMNBGjyErIuXjRDKFmQoAc4pZBTDTi4+90cw0k0EqzERlAKaNPq4dJ44RBRmAPKbM9mIUwbFMhF1nsDQ4gsQBm2HeJjBgTyjcN3DRIssVAAHFoirCWI38PXwSc0pGgFJVnmpR7lxwuBqYqBzyO0UFFXMfPMSAmHt2cJq46/JUGbGNqVZlkSbKOQe7HmnuSqEDDTVpTWOvDSA6V0PCDhTIGek9DbJNeg25oQ4LPAMGL/sc+jRgrb139GD76WMt02Y0ti5iFHwV9HzFqHf86MG6uum19Y39EjpTYkR2XT5z4J1d9ZObZrTekoglQQdU9Xxw/J1TRw/2tV10Y2WTJs+ePffm1KSpfWhGoYMtbsA6pFPFhbPHTh7f037xpK9EbcPM1tlLG5pbszyufyuT7j94ZP/5I1u6e9oIzZw+vmvzyz+uapzb0LpIz4NRLCESlZyp0VijzHijCGw8NK18KH3q2KqFrdBRwxUlyO4gtjhBS95NMsz7i9DV6klgIQoiOdO2yEiowZTbQwacp6X8+4qKc9q+eI41DGxFIWF7M83J4EehFR1KNXRIYarrnKJBUsRDlgCG+BNpTHIYRpM6kIKboE/Qfkp7iIqrwDCVSjUi7QWjX2Tkpd+djt3AcuGFzeJkie9yE9QLjzhpgqkqfBj1I6uPDeEafzIEB5B7hoNYB3YyUISCfAIgJdT/wx2dIcNB2vwHHB/E6nESlGU3gTFkC5TRFUeAJQLlI3hGELwIPBHhDIXeW9jTOCATSNK4SMuFsMvoElLcSg3fwaaaEH4hkIHCnjDYDfAPZnrgr1Bg0wgRDUN2XFkXCixg6F2Z+mYdM2i89lAb2acyw4Bcb9rAgjWM1S0FXYvwxhCNUt/0iYHMpzQiEcqygrDeoVCBAJEHxyyoMPAG0aaVoObKpFs4pk9QXdR6eqD+gqRGTxWbmgIUg8kwazxoHEBpQHMENjMWv6ADghkEJnHmYSEoGjWIRrFPFtnuwrjOaAqg4CEBhu51rnB8AR9DXOhZp/C5h7KdCirOpoymDBwJ2sXQPIBB0QYSQ2gOThD+4FwTU/gWeEHcdOmhybx+OW7VN63aFOYXgyyQ1U3QoQiGgRzuog4EfFzTQSwGd9ejysM2NWvRbMdm1EQxv4vJWPMVdqYyk8/kRhMLNDYK+CpI0cOCon5apOG+0eulHytrGKWXouzVY0bpLcf0oipMauK6RI15VdRbljhlKVUsdzUw1Cb5f6kGhO+0IKmAkYoJpQC1xlKJZbfdcXj7K21n3vGwNa2iZsaq9Z9saKgCSbGY4gZeqULCDbUtx6H4vX3SsDxoXbStTA2I+saIG9Nvzt1YKuWQMuYKElckzknvpVPPfe+vZs2d97V/87dO2SRPqqRLT584/vzT33j4oa+0tjQydCB3s5lXNj33/I9/9MgX/3TlomWxBLna1bbxd8++9usfXXz/XeL36uuJVTQsWLbm45/98syla7Msri9PP3kJvSaz6T3bXvvNc9997/AO2d8NQ3WrZ81fcu8jn7v5jodkqqa3v2fbSz/Z9co/Ek//VO3b8sL+bVvXfvyLn50z262o1Ducg8+o1Yk2UanNGRGW4mBZTkiMJVMJJ1auoMtaGfhvK0r62eICcz8056VY+jYVHOpqAJdcsdBJGKYbFfWtY4RkoW3piNbrjIAP46aLAYXzDdu3yCWQAZ8MslZJiaRXqVcgxd6oqE/OMGd14PM1xEhwYWPfoKQp0PcmtfWJxcsaHaeKAR3BuOgY7ZcJlYaRrs/ioQKHiZ+qqCyrriaNmbKblze4sQqjkkGN5QviW9D+pCbVboo3zFKeAGGYLzFLJ43Km5KhbRiE7ehRA/URX5k/VowEVSRjYhHIBBh1A5NbUgiaoAGHI3NdMIG5fKgoxiSSHwy33ShOm6QLUsKwec3BqFZSyxXjIR0Dw1h0eQOmjflNZLgTNWQfizS4yZok+5BoM0ojfqZ1ZkV5pZrZWrNsWUMsUUmUDZnCwB8DN2pKn4b4gjKTCn1uqEQ0rvGigyZCSOTHYhdBpQOMwIPEmxE+kIbPwaRj4RczWTCUk0OpcRO4U2v2yQP1DWl7HOBHyBYzDjQgee8IED+UppHcpEdsjEtNGY1Y7hYztniM5LZIFdmLuEFu+GvyOruDcBJFMkknNYDC2sLFdULEAvcWm7Ng1lIPuyEwB27oNkYdQaIwkfGhdjGeRhqTYLYayQKAyAJ3DYX2JZZdEzgsKuPKR22jF6ZxATQJRPlmVWCwoUyDlKkUm6ZDxgy9lJmuB2bsyk0l1GYGzSsQrqJhNrWqyzJI4mLVlY5IwGkwJQZOQspEy8zyeIwmU7wcS79WcszmQs0NL3IuOFlP3BCKu4nMJAiHwCyjk9Hsxbc/8Pi/fut3P+ntvhBPVt6y7pMr1n7cg843vQXAVgQ9yLSIcIS1LZVGPspoGfgSOuaYfn6Y4Mg2BCJjlnmm/C196WcFSKAh2dJjYt7ihc2T69/bs2nnlpfW3PtZ3wPxqr27Xj2x46W3G6YtWLpMpcp00HT+1KE3XniyrioxZ/4i13GyVy//5tnvvvTsd/xsuvWmpQ1TZ2V6rpw/eXDfpl90d575zJ/9P9MX3J4hPAtJ2vTBrb99+rv/8cL549NmtNa23BlzEhdOvHv8wNbL545kfH/NvY9lhGpumtY1b9EHx/dm+3tr65vqmmZXT54MmfqMtAVrYrL42BasTKwBaxhInHBlji9JVg80K4XwsPJATUodAlChYhJjAUrUSBy5QRoU3qan5+qlyxcy2X7I74dGW5RFWJU0whuQzDq2GY5LwZcGumF9K8ewMa0GhhdA62rqGyZNgh1ShOwEGild0v8ZeNbYn4y2alwvwMSyZa1/9dd/CkS+/Gy2mkBXN2L2AxtSzll1TfUCWb5s+TRiZKUjEbRSNCdZEgjoBH4KMqwjkCAQUjT4A2qzv0FWRaGInfUJwZwAet2YAq7hUknzymgNYvUZc6wyc9pgAxd6VVCTIlK2mczYsdpjKFcVC0/NgHgelKQsH4WqXBRFhzdxNrMdXrz+v0Tcra1JfeEP73r40VUM+2NwULa/XeWJ5dLA7xVp99YEKvBJUwGYoiTAo5SEHrK52xLKUBqdJDnAOEEVnjE0z28/9FMPkl7Y1WDwrlJh2dS0iQbTRIJrjxrmRsdGoqUoRdX1p2jN9DJOq6qrhD/p9lVTrYB3hBhCc/ZiWF+lFh9wAyYxRWjI8NwKmFn0T3OlL5VLF5mMkwzWNw0Ne4N3YSTyKoamZfN6kVqoUgFOCl809LwjkcdG5RYFJQUFN6ryo1UV4ZDQEWgeUKYKDOMqK0t6ZU423S081N9EIr9RwrOKkSVI7jeoi1AhBwia4KQNw5XjVi7d8OmZSzb09nSmysurakFGIZOxNVDkC7ABziGwegQzkjPUlcYkBxC35wIRISYc7GWAPmcPWvSEFCbVLQGygIcDdN1ITuK1U27/2GdP/M3Bra88P3/J+rL6lrOnj+3dupFkO9/Z+fr6ez7VMO/WtN/z2su/7rr4wbpHnqibN79DqLe3bXrtl09JL/vwY3++5uNfSFY0cJk9e2Lvj//Hfzq6d8vGXzzz6UlzZOVk4qiLZ47+6uffuXDm8PoHPn3/Z/+ibFKrhn89F97/1dN/t/2lf/rN099rmrpo5pxl93zyK8tvW/P//dUfn/vg+LK7PvuJz/5ZhpZ7vNz34C5CIGziCYT0pk0Dk+GgFwEZKqIyNNsn/BiwrhwXg0QPM/i+aWAU8KdyhACWIQIBNY2R0x1dm3ft6bjSAal5RjIEukfC8EsRGRFfp4Evc8CyLf1lgceLvl8JBVpl9dV1a29bObVpKsFeWKbsWRB56NlgBTn10T/hLbcZDl5f+b6UVZVlk6rL1ES6agzuhr4dbm1t+XhaH2NT2L2elECqrKaR1IzU5U88BYXAlsTr68s/6mupdE5vjO/52X7Pz/p+Wpn2QaN8yRQbJJ3h8BtjxK+waZUZE1FmKGlY7+WxqknNlZOarAOFCtFm6BsjBxJrjPgSt/UyaXLuDiZ7AgsGaRzUUWCfm7wKJIKZaZaAcM/x3SUr7nxz4S9Pvb3ryN7N6z425ex7u84d3EOI23npxMHdm6bPXnzp9NEDr79U3zB95R0fSyRTfVe6D+1/62rHyalTZ6e4em/fGxnBsUOnd/KU+mMH6Km3t3afOVa/sE7P8ZF33z13/J1UdbK8Jn70vd3iyBEiRNz1qmriyerKiyf2Hz381vQ58+Pl5YmKOpDvI9xJpMqrG0g2JkA+UKAWBfIxTf0XM9uY8+cxSl2m8VaGEOFASw2QRiVS/4FZCrVYME/Uk+Qga80LIf913ke8dULaALxbpS8rrzxW5jHeC7ZqFuUEfgBKXN8bZgkpQyE34XkdHZe8HdvuWLmypbmF+6a1CRC4MP1cBHEHCTu8rClyhOHF6Ed9wzTBE9DbBerDwFPgY5+FY0sbxtlN5ZHCCwEmjcZ3dAyugn4UD0qlonNnmSwkt6hopBueWGINzR10JEgKBVYU0XuClcFAk78wIlBBBibM7BrPeJPzMvt6SDyOmobiu5ginf0JLXICkxJl6eB9I4tIXS/6U5GZZFTabjzKBgmCjPm+tL17BUkgQgOdYxXEasp2eUbDLJv/kpRQNfCRCEmcjET7VrDgE+m6J0G+JBe92bemuWGES0GyPKY6Ke5CoUJFzRsR/mFGSFrVEENzDicn1I/PQSkaLmIZXi4xnsmWwo4lu6B4oEoUOIJmvIBfpsJcjjQOcgV9N6XAHcl7HZlLbrHwxkWMsZmiJCIEPHC/G6EP85bK9kaZiWHGhQBSPbY7k5UWaHSSST4q+xWzLLoR268NbyBk5sQUtqPEoccP2lKow5yUw8sc8ClF0XaXkorJM+57+A++8+83v7170/wli4/ueT2T7l9290Nnj+w/tHfTPZ/49Ntv/vZK25lVDz42f/Fq14mn0xcvXTyrQczltkvPPfNj0PQ0TDy9bIVHZKy/3+vsutyadIiQl8+fzWYyJJt57flfS/oyUSBbhGqF2f60HlHq7KXzgmTKyip4nIOjqka3nLsxlqBY8mc2QWp3cuzG8Tkz9zYpeCLOWAyuN8b05zyRpBJoAQrKdQCw0FkD5HVRGIKOTBUN+6o1LAa12LhLOZM8xqtqa914edbWJeHtYsqS30fkXNPX7KX729ovH+u+zLe/lVjtzJw2HTt9WRoF/2C3RtkMgwDRb8hqJpqKI5OUBY+d+IhWE80OzvBIcmjcUdxlXZ09bW2XFUiySWb02yn61OVcuW3tggYJP9twBfNAjf0ZHZEYMzS3L9V9rAKpWKpKbLHKMlFogSpuCWGH0Y+jkYjFUBuhcXKjfrrb2tqwu8PwbYLzKfQ1YJGTNOxYJZETTeYuTtISLnQyv4tMRb5DSe48jEQSZuWbggu1SlHE+urQvPM9pFwN/b4fcm8Whh8bJrVtmZWqWCzW0FDf0dHd09uLkke0EHuQwgEoFbBLI/bmZtnT618R0dtkN56Apl7qcZRICEMWS1DttWtA0Ov2FLyeS9EnAef1dTVe1u/o7sT1wCwcp4E/IaFRzG0oQyzwIAkfYmoFcoZ3OSVuXAF4osNJx5CAEVcI/2Vuc7CNBaM6sYpYK+qy8ipZU5FIJjIaW8Bx4xjBLSYHu0Innf1onT6DdRfh0SsFdsP4UvZlBVDA8Ecett86Dp25+LbWW9Yc3LX99Uk1+7e90jx7wYOf/bNf/fjbx/btfelX/7jnzd+VV1esufvBrExmMySdET4IhbGpc5fUTV3UK8H6UChQS49RGpPcT5T1VUzqFop56Z7sVSm95qnzps1dlWVcOkxh/wTDX+5XvG7qkt4Ml3HZL4xfL0it+FkpPAn9HxRNrA3nA7uM9OeeD/I2HAwswKrMR8KpRnZ9wDCD/AZ6QtMM6o26GCJmjYY/HZnKr2GYCAHuQ74nGOj9Uddx466r59qjGtHCfi2KyK4X7aoo9WUuzjEShSnHradE49qjnZfIjs13ytubG5so6NW4Ingg0bGARbkvyjBkix3iH1GIZQqvDGTb9Cpx9Xro7uqhzEHpaPNkG3ZqAeM+DL5VLtuSR1OgAxo5h2OkrXJutZQUewVVkDMrlS4pDBJpqbdWg/aBDnItBT8iJfpYi76RyvF4sIeIc6euzs9kst3dPTp+i+7xymYvSo1hSO0PUvq5oIP+qEQeIAocQlZRyXYKWmIaP9QKCWyEAt6NJecYfdNkKun7pL+/v7v7qo4rS9Smhrxrw1khahjXQoq97HCyrSSCo2mJ/rDhzOpwLm3gFRV/QRCC4qS6siqd9a5e6Qtyqka0kRbcl2G0eJMSCnrD3NLJoGt++LnscOSRNCYdzuCHzIsPd22ogEYI8QpPxSppxhPZjKc4w94Li1Cxe6B4wcTx1e+JZqPtSQHIiR6pRGhQ5aLiu0+RLqw0XiGJuim33f3IM9/8f1/9+ZPp/p5V9z3RNHvVkpWnDu/e+cLP/kFk/VX3f7pl1pJeH1ZmLFlRO6nxBBFzblrwz/7wX8nUpIyHfR+u4ul+V4h0ItHLYv2ClhE6qWEydZzG5umPfenPyhumZqBVA7TouEjTbJq55X0E3Am8LCoZY2TqKVDt8oX1Y5TQGaSUkYxHSO+h3ILAju+0lEYgzwNbQ+ysFdhOzTR8pI40ZUHUmCHGm5aOyKyamEa/o48xDxYiwejHQ3Ey30C5QI6u2EFIh/el/RDBqZAqr2yi5GzbxX2XL/h73lq/7LaWyTPB5MMmuk09UIU1l1y+IFIKMUhWUsI+kggLAbqeZwnOEMAtBFlj/T8EWKZBOprHze0dMiTdkpzRUShhWGrfvJa6Hh2LkmnxY2CYAKsorCkJsKgxcKFA6US9dZhqjQwwQZ73V6wk+rkm2DqcayFRuFzslUMZUp6rq5Q85wa5/A8FsEwTS46UjLKxJsEjfQdsY8H30OExxnmuIFocYJHS6/P6AVaJl70G2Ud63aDkmkrkQ8AaijYjoBcA7deOCs5DzmiRBaOGHwCMBMCiH5YMYJoZBkrpqREEWHSIS4MmQmVa6DnoToAQhpAm20lRm1O5xluAFm80ciiVvw/wyohrgYxx0CKLSrvMqNOCaD9MmecL7iRn37x61pKbj+36VeO0ZUtW3KV4xezFq6fMvOnkgZcrJs1be9+jxC0X2IKSqKycuWDx7o1Vu998bfbC1QtvvTcZr9THWm+6Y/eWVy+dOrHs3gdrp9+kwLw1Pmfu0ropsw7t37tj62u33/1QrKJB8ISOKA7u33Zoz45lt907d+F6n9Cg7QV6p62IhhEvRCUQh6ADmrkeYgRMkRhgZBvMvQcJOSwCohYMZn0UmvAYobWgnn79p6BVyoN/OPSCMx89qG2O3OoVAhaUoQLMdZ+7RjoY9emYBriTJpFz7RcOXDzn7XrrruWsdcosUAeUUkV0O6xWchiG0sKtjn00q4SmvMZA5QQgovHrDXT1SSA2N1DITpIcWSGC1ZRpkI6exCTCdyyV5imRYBjObomBTT7gZh8aW0VaxAswR9FrUfnFOTKgmDEw31DkGMbIBwU4jcm7otZKMIp1Cgc2HBG4oqmOwQFWJKyP1k3sK0RJeDyop5DSWZOBSUE6jJGUnEbkJLFcztKQNyNMGtT+4tgZa/LrqrT+3CCH39AjGepaBv5OwZJWpYsnUZeEQTSHhzOrpMQtLoo1B4M1FFtNQSYWtULxycduL2O3i6s4n8tGh6dKOOTDNXhyLpwxmc8lKFWDUvncCDog5UZy379mhcWBs1p0MUR7Fg27gqFrJ7b2Yw8/MAiRzYOENdTLN2JKJbjsTuyjGdsXfwKM2ibsLBp+uBxLKYAFAkl7B4WU6yc3L799/YkDO6ctvH367AVK+NX1TfNvW3vy4FtL1t43uXURWCb52AjtOMvWfez4ge17fvfsj/7u/5618BeLF95Omfv2sZ0Ht7zqy0RF7dT1DTdxR8M3Nq1l/ob7/uCXP/jbn/3wm7u3vjpv6W3V9Q0fnDy0/62X289d7Ov3m2csrqoC6x4XRVkIirBRsMiEkw/FZ8Ch01D0jR4uR/tpUBFGNw1UyI9R6eovY4zgnTdCJRZHSmK58SODKGikzm4FMqAlU6gwUAVDVGL9QGg/s9ZA1wMqEGyiphtIDzrxZFVDnTqv1OH2S3Ln1ntvpXOmzzaKPhFHGEoiKSsZQVRUfYTXt9HBEkBvB4lmVI00ZgNWfJuElToWiUyxLV9FBKBlALhNr8mg1faiWcaSRfk8ugKxde3oNwOTSruS5FD8aUoHdF4XJp7oUAMrxdmiw/gyIr8Q2JpToxGOhDhbcFEBpV0OoGETOuzc7XBHUvALKKGYd1Dl9FFUGPUHsv6KDCdtPOyRlB68UnRAFSiAAKiGbUpZlFlNyuGOhF7zSIa+lmK/Q21WnATaS5TSSCKZ0hBG5rEhhiOIS4d9acNc0vlfIg01dwkge4n6zFY9kljv5rz+a3pDRlL01tAwSUCs8BnNLRZia672G2E5jQYGE1RFlKUVvcbCyHCupcSlIYEQ1U6NDRbKeBsRNQ7632Cogj7p1pC0RMDpSPn7USJUhnoUk8QDd0WR7jd2PNhZh/aSuCc5CO+dOUtXL1r97qKV9yqekr5ynFTrgpWNy+5ZtPrjJF6TzdpGHSFYRWXLQ5/7lxrP7Nn8i10bf7p38281xBHCc+vq1t3/mVtW38GcuJDC48SJl992z6M9vf1bfvPjw3tfP7L/TcYc4adZPLn4zo/f9fCnEhWpjA9G5L6g6X5FfD/Tj2o1wsg9ExGQQo1sndX+A+1gax/qZaAu6PWhHJuQoFQPYu9gpGhsMiSacxh7+qFOxOFnD8IeJqg/olQcKiriojIGY0YexRmJmlhQOpAcuIQsrUg8UdVcTy60n3+386Lavkm/9cymWVj2NvayNPD6CgBaMGhBUT1Q0o+iTFTgSEAFaFDb1DROtNVWNto7xpZfWraFUdVgVmDJ6N/QsOZlVZPpjarRY2AQPo0SGzmCsJCG2RfTqYVhj3Eas+NWqN+nJC1a8QmD4NG9lfjGAk3VbWqQRsV6KIlqDY1e4Thn/2HnzhjhIHUchEallWMa3emi5gDPFe7N/cXjXlobRuz8pXT8HTqWCkjDTQjrQuhCjDljsKOzT5Yad/lwGrE9Vtb+kOYhQXtr6GApuhuyiyka5aXnMltGAdE89blGRqssF2hq2TtiLHcpHYHu1mtLLxg7UhLspcRY7AElGVzcTHeARHMhVeKwdX5vfHONjmyasFmLV2548OuT5y1WrmvV+1AWywdoD8LHzHdqGm9a+8/+tHnaTElcEJwSqmnawke/+JdNU+f6yoVoCx0MQUjcY5OnLX3sz//9rJtXnT70Vm9bZyJZHZ/SPHnRLUtuWRuP16XB8FlmObgypyZNffALfzFj7uJj+7a2tZ3TCKCmrmnqnEXzb1tXWduUhjcXoLOcrLrj/ie6Oi7PXbBC+dQBo3lm1EGlvU+msmKiaOvIlPH8+TevlN5XG+YudpykyfsKJVVee5Kxn6KUkBFZi8pIMwfap6Zjz5bd9JBQWTqmoA4dLdhd97Im1nHWxGGUp5IVzbXqQtuFC93tm3dspsvl9Kkz0UqSBL9OTZ0y5Buj1S52gpnc30c0KauUE5gxmwKbdccLbWHDxnt0L1KgsIueUzgrgLOhLYEb2gCqJ9ORIOepgegKbUNwIFIyo8yMcR/NxdlhusVoNYI9CrPiB8aJD+64oNHttEA6f/TuJDM6ntbDTwUnE1yS5Dx/VGR0BmYeCQ42iNguzpAholAHx+4CgRwEUjAjWpOjEA4Ej10ouGl9gDEdxKzZgE1UMKrGmTyHAfvCrDxrDmOSWMZHGb4psUKAdtjjCGTZBAsLJVuxLYbbgKDgaVV0VKcdzYOpFfNTkaSgSVvhMpYovEPDncGGDUpa3kwulBnNwQd7D/oCWdt1qlTQcZnrn5RRZakir/PN1+XQtMqST1SpL8mw2YuFwPZ6KKJIAhblnJW7POOJLtD/AgIFR9IK6K8q6uA8gTOmQz0pXaHiinkAIKT+mT4ahDR9WVDYhj/H81nPbzyu49irvR2dyURZrLI6TfRbQBsghzQJkNOzYA+mEpKUO1zx7OUrl/QSaaycxFS8R5CsVB5U8eBBiClWFacuI/0+tDoq5kjLadJwKzADw1ofRZhgiN0xKhIxlnRpxie9GeRyUXCDBjdGplEazIUIDEsHTM6HJAAiYwxWv36Lk2dPPr/llbZMdsrk6TxR3ktIBtRNDcBSlNLSLU7XlC1TgcSAsM5misWovk1+pq/7UscF2dc/q65hw8q1Lc0zEDqgLhql4XEuI1EazSXzhtO0NfhavX5aZdEXLD0SU+gDyCzLeHppa3JK/Oq5D86BZSZYexhyA8PaHDZU2pRQaAWKxiPQAQpKAxKzCKbgNRLVyzyMBQEdA0cJa6wOwYzNqlgcqKCyHMiQm11XP4UUGHUaVAnBwaMV/sZ8km/vHWzJo7W92vQsQlJoMHD49OnT+vv7z527AJIlgdFvcISN0sCU8ewzKuLBIkc4axECVJHBCkfSQJl8JO718EdXIEoRnqx66fmpsmRz89SLFy9hF6Ez3iIZaQvugesLpSyAp2ZZGgNpK+RAx1fIps8t16FTmpqymfTFS23IkYiuyejTOqqmWlLlZLHCbCsxrfVBnyPq0GBIoHITa6MwzB2O1Wzj2ISBpXoElbV1bkPVlgM9XekySl1gy+SIGSUPAgdtLof0WRv4x0Ud1oryy4rYFJb46cDXGSRoLnwpifCCUKcPtBWEcZgBaVtpjCMMQRUQCbRl6WDFx4AeHe88Tj19ivs6evbQjIHjugC44nOahaiReGl9o8ud6kpPkkwazFwhs8mgk49J5mJGR1ChX+eqUL5LeHkjl+RqhhsjUTB6DQwyPEW608poqQEJC5NRjsVYVBpnLGMOCw046NGE3pv9GT+bIR7Q4yFqFehObVzIJe64PrhRmtYRWfpuDt8Oz/qFMJsmlZKE4J1KEuaPCozErzN9Zd2tkIEkOMJdX+kV7cZTlU2UtqsLJ9svq51b1yvaMq0FyUiIyRQNiKrWuVBZD1I5QMiAFFtypSwLi1g0XsusDv44DDUSZao9xjPM1D+lxu/cyu6ZHItMZzO2TkQCX1yby6OO41DuytB/ZeSS7NF4kgZiC3jGK0M8MNtojm5julut0QzkXfRHV1en7/tVVVUqcKJVqnihYdQz4oSUUN1RJE9nclRHFaTUwvWMdrTSANmurg79iNbU1KjIIlKjPGfWFSUviV7KOnQcJYFsCcg0i8Jk9sBHbyIRr6ys1NusEBAPmL1kvA1+6Ayz5V+pUZ9VwwHIFbL1/qCnV3/V3X2lr69XT2kqVVZRUaagoV7Z5LcK1ETVWM42tfrs5kgRlj0GUByYORjJhMYiKjhiCrd6R8nhFwLUUHH5MG50yVzX9QzDmpTCvpf1wWEVHHKUABF7ZsIPCegHUh0SFeZNadVsU0JkhYY3lAsr86nvMyBnDk1zzGcgoQleqZIzhe7V+AroSyV9qyOPBj7BCaZxmspKVCx2PEk50IkEOv4EVlqMZXABwh1CRSNz10DvCqGYQClTjo+7DAQIUDGW+caS3EBDIjw4r2BYBAvB0qjloxGzusYqT/HvAFoRPrZM6GlwbPcaHJMOrr40Xr+4hrs5VGnGqCqgGJi0Xm4AiH0gfrlOoqJuEj3ddunwpUv+trfWCzm7ZaaeAuxJwP6AXCOlUalTuXzENauKq+uvnQ1zAZceicBeYeiqlHbJ5pJe2MjCOtvbfvn8Ly5fvIBlI+ZwjrpqaFYs1MqVq1avXhuHBGzg5D+g21PRa3XspiEP1dLpDYMZ8sVEBg+XKhAOj5Rf9L+u6xw7dvTJJ5+sr5v02ONfrK6uFgJLmrDJ8vy6BhmTXTYySTQqOG3N5ogtwo1qogUhrAyMP/Ff2HU4p4cPH3n66R83N099/PEvpFIpS64d1VIWtaKMKo8eHixmlkNdapx29UInCaO9vVffeGPT3r17z547X1NdvXDhgrvuuqdhUqNUec2j4woehlW0YiM0XehjwkWlOb8v0+7ASGdnx1tvvbVz547z58/FYrEpTVNWrVy5YsXKsrIK3/fw0ONhSXHs4FUQGRitVqlsNwFQMAT6fFptJVzmrNSicJA7NHwVmWGWPGiJEiEZtggbuUaZPoL9d1A6Mfr8jJkaCmiPQ28eLC8wouHKWBHrs8iAS6lAkdU4ilqXeXCawcYGDbuYQHIJ9ZHEi/gFsJWvX86FDgKBVUiNe+A7+HMVo3oYrqePCySim1PINRb4yEUAEAcMYOVAA750JcA5B3UkDBDmRHA0GHegPVD4sNRcZh02sBAMFkECXo1ILDESF8qUID7pQL6NKdNgPixBuUFUZCCBYrRMkf5j5MSUpZLj33khqVaNQAaLgFuOnisZ2NuyLHZO6rfOwCfU0xOVrKipIR3k8umOS1ve2uJQ2doyE5a9NVYPvS/MDBgKG7v2EmGptUrIh59Vco0ykqG9BlDgqD2ZJOjLKhV2N2UymaPvvnvq5AnHcXt6e8+fO5NMlTc2TuacC19OmzaDmZmRwUsomydiIXk8yEwGkk6WzRN9hg3vywSVhYnrIIJrb2//9a9/PWPGjLVr15pIlNI8eGVSuHqTdV33+PGj/+W//OetWzdtuON+z/NM3g0zMdh4TmkRjxOiRrXkZa1xojLqtnucqgKr7VEamFVsN00LyhRSQVrqvffe/bu//69b33zjnns+HtY30NJjNLCMoQBLRsPUlcmdyZBFmpNAyP10XIEUQwdilPX29Dz1o6d++atfzGhpmTN7zoUL57///e9r8Pov/vnX6uobsM5O6HgavCmFBAI1ViODBgKzKudETQMWkxrFKhtaO1Gr18c56+vre+bpH//kp8/MmN6ycNGCtrbLG19/9bVXX/niF7/86KOfTiaTZtPQpxq14oVq7GbVCgfaWTPZTdM2yKTJHVOrR0gjR0zeVu9UlnHye/FhDJhA10va58VHK8SYoDFJfMZctJIxeY6svnmSu1iBkY6bgOwSNVoHDt7aDBaxNfTCP3Lh2+CCx4z5smNMycOwTOMvrpDxBULj6EitNxvmc5nFteVghidhkC6GvgJxfQzpsubQwQYbOFjAPRFex3gdEZ8zaIEUQFuUyHEV2KnvApQCuKdfW7+pA7havyO4xIAwFWVkJIoYeI5w064aj8c44EA/tFkTOSsqPaVMXne1XKGLYorIGAKALGFp0zKN7AdPsTg+p7GyylpGesnFs1fbX9m++eNxdlPrLGBiCZWznqYgwcrlR1imwRDVGVLW45TGXLAljGacGVKE6mvr/+J//efpTH8ymTh48OB//A//YfHiJX/0R39cWVHpe6Kmpi4Wi+Mmy1TQXsg4lpjBD56hdwoFLiHSeU0JT4MzGgj/CJMYJeZP4ZVAKJLazlIJMQJEIfpdrl698uKLL6xfv2Ht2jX6FXzfp9SaeZuz3mACz8u+8spvf/Sjp/rSfdOnzUR3dhIkvSwftkg6SY0ifcT0MalovZJFHNysSLptHlKEjMoqCw5H47uam8zfvbrxR//0lEYG9ZMaDFfC9l0Z9d8bPGshX80Uoe2JiFRwqnLjKESu4+u5VMzISQpfn/c///nP7rzjzq985cu1tXVXe6789CfP/tOPf1RTVfm1r36duTFTLBonKbhg8oVtPldBDCDN80kDKmHU4GEUYxT7HCENXCnXcTe+/vqPf/xPGzas+/ITX546rbmnt+/Ysfee0nvBUz9saZm+du16Q90zaSFpVRjHZlaNGqUlsGO8pw/1VDwpaML40gmohQGZZxDM6vRlfk8AlllZaYsfuVEQIGiO248RsbDdZNIG01J64K7MJagkmLQWnDYgQQUpK2VPpKDD1RoLYzN0VoYJG25dqsyShlQW6cfHFf7Ss46lgVwn0KskttEj81plIGFmZXWMf6hJtel3yeJI4DuegfTmhGIyeI6ySOZGbg2QusxzI0Z+31KYW0ZZ+SzwkvXIIckUEGaqlOkdo3rjYSPjSqMwswJTqmFWUkIEZKrfDohCgfG0Bh2xsgqHkIuk7XD3Vbr1TV+I2VOnx1QMK8EwnwL5wA7ackkrYJADJ1TR67ZeG52oASh2ENBx3/Pj2PUbzYTBWnHjsektrfqak6n41d5e5vDKqqpZc+ZUV9UKoVGUf+XKlb7+vsrKqkQ8SQGFiq7uDg1BK8oqwHRcUZ/4HZfbEolEeVk5YyybzXZe7uzs7JLSr66urqurj8VcZtKn+ObtHe0dHe39/T2VlZW1tbUVFZX6jS5ePHf+/BnfT/f1dZ0+/X5FRY0+n4KeQTtgfec8L/3MMz9+6qknFy9e9OlH//TJJ5/0AIepYFc15C1GIl1F9o4xVSizdWMrGwUgQAQia1SxKJubjObAbJZWGjoLTNEPfvCDZ37y9M03L3n8sceeeupHWKEVZosx+9YoJAGCxDGLAhYUXwppywZVybCCpYgcX0cHbqqd3d0vvPSb+oZJX/zDJyZPmabjk7q6KY899oV9+/e+9MpLDz780Nw5C1DaW6rxk8QyjSMgGiB9OzAVNpBGbo0a5bUaPPZG6UBx1+ns7tr61psVlVWPf+GJufPnp/v7a2pq16xZn816//bf/Zs9+3atWLEikShT2DEjR2XpDr6kpU1W4i7MgJ/T72V70zFozaHUg6McxcBLSyg7GUF+vz4K1cNEYb0mNxG+efIFi/xy4e8Ue9mBAmVqwC8MVK1VA14WdTvzSkhhGYJHf6fodYrIq4kbW5WwnBMf8+PC6nlSRIqAYKBsCnT0kSqSWNygEB4ZfbegwACJQYFQVF9+PFlRW0cznW1H2jvoWzvICtLS1MKIw4KWZRwt0IJkjhZCB+RFrBzmKDcwD2siciR4GQMCqKl6hiKvNPCmVb4P1gM8y/ysj3ki6fu+3rawHU/t3b/nyX/8x8cf+8Kdd96rN6++vp7vf/8fdGj+1a/+i5rqBn1/Pzh74Rvf+E8PPPDAvffe193d/fLLL/3mNy/29fVC8taN3XffvQ888FB1dQ12+8k9e3b/9Kc/O3fuLIU2C+/mm5d+7g8enz59xre+9S29RV64cPa1ja/u3bfvlptXfP3rf+m6rsrrMIWCZk9Pz/33f+yPvvLlTCatlBX+ML9j5RpI+B1CcvqIo9thHil7R84kFahgRXXmRnVg2GpiaoOsv7+vu/vqvffc9yd/+idXr3TDGoHvA+3TulWPagdWkNGzopIhS1iGPYbUjoqMu9gGh/vuu0dOnTq1Zu3aKVOmZLM+xB6+0OHK7atvP/D2/ldfe23unPnUZpHpuBm4ndUwqYydx4Xp3tF/iAJ+A1BCTWFNnyDLlt+6aMnNU6Y0ex4of0sfQGF1VbWe5N6eHoHJbBrcDkLHmK1nJBqwd8iq5OuN1vMFYy7a40jk4ij9dalpdRgjv+8f9Dp++qHfgo7FMG7ECjOtsqClhFY81jzHUSRDQvu/8MkeqadXhTuCyhnD2U8cK1KjcRFPpcqnE3JZqJNtbRt37Vi/gsyeOgOGAyiMuyhqzGyaERBh1NwdTWdsy50YVXbPNYSAWBfDziVg3xZS9VVAuA5kj2mOumRPOcW5M7mxUR8b+/fvW7N2fVlZ+dn3zm7a9LoQ3qWLn6uvbdRn8p5dO8+ePdPc3KQ3uJ8++8zTTz+9bs36u+6+S7/A5s1bnnzyKQ3UPv/5z8di7rbtW7/5jW/WT2r80peeqKmpPvLu4V/88rmeq/1f+9rX77zzjpq6ql/+4ufz5s5btWrNtKktnPNCGhah8Xji0Uc/U1lZUVVZceLEccCMLg3DhGJFZkpybMLRO7SCU5dExNDtf7SI/c6opTRRrBvEsAg0M1Hn85//Ql1dTUVFRUd7JyUulRxttBwFCXpCR12YlUQsTlRhiEiDNo1xF82g6pI6e/asFGL2rFYpg6cK5QPmz1/gOs6Rw4dtCVuqcaXUoPI8ZEI1QFqAH8fiIUIJP+DiQRa+vKzy4YcfQfN6iAlBtIg5emc/d+5iV1fXzJmtiUTSsIxprpGEjt2sBp34qDTHUA+IWiF3s/FSU+BipQfppFJk4mPiY5BVZrNI+hyMc6NliTGoMqZ4DMG9JDSX/x+5fZrmfU4LzzJIUHE3WTapnl6ml4+3tYltW+PrnLmtM+AEMmVdpgIBgYEQysR0KpITHm/olliBbuUmNKh1ScBoCqeClrYPCaWkVEPD5AULFhw/cfzSpQuzZ83+4IOTFy9dKitLHHnvyLx5C/Rv7Ny1Y/LkyS0zWg4eeue55342Y+aMf/Wv/3JS/ST97qtW3d7Tc/UnP3lm/fp1VbXVv/rVL6/0dP+bf/tvV962Ut/9tevWlJen/v7vv7Vu74ZPfeqTU5oaN218ff5NCz796c+4TkIpm47KZeI0PnZijY2TUZo+R17IcyUZLxHIsNwWxwR247zC8BKx+LTmqYJI3/esrTmq4Ut13Z5VIxZwqqJzNe600FF1qbu7K+t5lZVVJl8RiCCx+vq6WIxfudKdTqcTidTopy1H6I7QUX94UNCR2WAPYAkzFCvLuHEc58KFCy/+5jfJVGrB/IWuG/c8wRmqG44D2MpQhACVffVu5XNOE3HHgx5nYLA4SMPnQEEpuRicbFpMoIiJj0GWGegWMnB19D0/K7ECRVUJOEJHZ98xtYesgnIgUU4yWV5fJzuJOnGl64Utm9LZvptmzYO2AGSFCdReLrGtKyt/MZ5vAMAQvel4QsTgmaYqzBuSwTo3qaW6SJFKpZYtW/bss89evnyhsXHSgQP7FixcyCk5sG/fJ+5/oKOz8+Sp9++4c0Mikdi1e3dnZ+eaNWsuXDh/7uw5SDglEtXVNZcuXTp6/OiUnsZ3jxxqbm7y/czbb7+ttx7XdeKxpO/JQ4cO3XPPPcKHNgyBtUyF9SroJQwqR0hztNqYkFnnpgubylyeQ42n5jI6LheD1cE3vXmCoAoeNDzTQK1RGCmy/EynopRO7GVDZgf16a5AV9ZBBwRL29CfxOMJzh3f1+jLSyTURw1djdnDY/JmAqVEjWg0hgbGcorGXbejs/2H//j9XXt3PvGlLy9YsEgIq46rhHH+GdP6YNBsY7Yps0J8L5tJOyYV6zEJJG57pcVbcBylJhbDxEfpHIrJl0jioYQYYHYr8D6Wx5LVtKQUbWGgUT2ZquBKnekQR7s66fbt+nfmTJtFJdfYUDIetrYzdBoJcYvJB3mMjEszHRooduqpF5ILoUI/PlnYKh4GjEERMfRT09tZPB6fM2eO72XOnD7V2DDp7bf3r1m9Wu8HO3fv7urq2LdnT8/VnttuXeEy3nbxgpdO796+/fDBw+Z04cTp7O5MJpPdHV0pN9bR0dHb2/Nf//abYOGDXWz9/WkhZG9vb39/v+u6ZisSQriuEWIOHH3QnJAEVr80Uqi15rrWi4ESwiYevEHSVyRoBQiOK4JxduhZINE5UUWSghGB2QmkVXJLgWeqoqKCO7yn54oKXDMVmqL2ZzK+L5IJHaqklKK/N/5yNzowxLqGsfky9TZFrYUDcxx29sKZ73z72y+//MpnPvPZz376M7FYXGBWW69gsHPIi+JHG9Ga2y+N9iCoWOtdyQFld2gbIowbixFuZP6RoMWKindMAKyJj8EfEmX28/HSNaPCFmmU5AJZfOrD4e06ZVVTKDnffvGD7u5NO7Y7hM1snsGZy5Qh9zDzIARVKOvqKq3GyTidfEO7AqkxEygZ2VySr8MZCrAYAW8jVJZTT4aNoql5SsuMlgMHDpSXl/f19C5YsNBx+OYtbx45cmjf/r36my0tMzSWS6fTbjy27o4NrTNnW0sWCeo13OWzZ886dep9KcSSJYvXrbuDsRjk/LlVYpo8uSkWi0HuyigwUduandto84TRlTEiQU62aYXO5a6KO5dNfIQZgXBig5UcmOZxVGjkpqaPqayc7IWVbZyY0pJQAJZyTU2Ni1EEnPHEOvzpZ66jrc0Tsra2kXNHqYk5HG58CDpCigll1LWZjrsArcCzTw8ePPy973133759jz3++BN/+EQqVZ71BUedKV/4FEWTDMBFthMLQ+LR4e8Zu0kj3S/ttovaSyzcVs1RArqWMsxEDARYnE8sl4mPwZ8TY3LJYLkZ656xIywZUW9LijbiWBTcsvuJcgkvK6tuoqyz7cKJjk531w4dJc2c1sKk0QU2xmzIJrM0XHP8GJuxcTv5QbqOCGjLZ5TSIgF0gK6ANWqaiJS08o/m2iY3Ns2eNWf79rc8z6upq10wf0HW92Lx+Kuvvnb69Only24pKyvX219NTV0ikVqyaMmDDz3sC2jA5ZT39af7+/uSqXh7e1tlVVVz89RPfepTyWQFbJd6WEJdvdKrjyXXdaAJiJq+T3hj7GHMiXeHIpRQN9TQittPjEoyWBkK+9tQlZ6AAiUOrdASKpDyJ5jMzUEuasE4CdyTcqksMv589MYNFIDNYdas2RXlZQcPHfS8LI87xkhb/8b+ffv0XN9224ogcpj4uAb0atLtpjPAcZxMJr1165s/+MEPrvZc+dpXv/bAAw9C+RUsUwFPCRW65WD+i0btcowMHx0ta3XLIcUtzVqT6fdGkjtgRMFMEzpURYDpXuxVHCUmEvITH4M8HjJIQ1izYOPMScfKviTy3OpjJIMwKwGkJC6ITCsST5RPrmu8SOm7be1ix/Y7fNXa0sqVrRJCMAU6rFZ5XARdwOM1jWuiZSRaMit6hruViZpybVrR/CKq36HlUmBzqX8Yi8XnzJv30isvvb5x43333dc4ZUp395XW1lmvv/47/ZdPfOnLiWQqm/UWLV76q+d/uWnL5tXr1pWlyvVu1+eln37mmffff/+P/ujLU6dPb5kxc/fuvadOfTBr1lwk55Gtb2598cWXH3roU7feujwWS5SXVWSzWXQcg5SACEBT4KsDJ5bruBT0fbnGZLBZcf2J/nBMw6rn+SG4nPgYuOtLW2E1PVYM5w3MKV2XM+jL0vfaicVcfWjpyfd9n0Rs4CYmsMRmokwj2PSWlltvXfH6xld37Nhxz933w2bB6fvvH9+4cZOOK9atW4+kxgmQOty8oJUntnGx1GtVB2kvvPD8Sy++OHX6tH/12NdvXb7c4Y7n+y4FDoR+9pWQYEFhc65Bk5XKqbWoUUFYgV4/s8IXcBnQ8AhWKeAYB0wToazXLZLdS1jlWJKHdS/N/RvZ4QosRIrYgwR1m9w3C14h/8vw72nUCnTgK9BCt92SX4avELmKom89cCQ0f/DFr4WGB5kNd4a+lvyRRN+l5KwWHUkwYDrAZHbwkZQ0dRl8JKrwRgCmMhYm5jIclGngJdO0NzZ/a69fGTIP6MkizQfI01mjAqC3xHj5pFp6jvCjlzv8bdvuVnTWzFapUPccBi8ZGM+ADq00WuSmEjDUrBYsg/z1OcxZzftyqPtrsBR4KzFDX1YB50YFqDBi22Gqn0DAEb6Qpu6JsmEAgyAGmzd3fnVVzenTJxcuWqyDrmSyXMOp53/9XHPztBkzZmCxji9btnz16rUbX3+1prZ+w7oNnPFt23f8/Oc/v/vuuysqKlOp5P33f/zv/+6//f23/vsnH3qkvr7u+PGjP/3ps2VlFXWoKar/nTd3nj6cbl22vXXWnFSqHF2cc5KHehlduXLl+PFjPT1XHU4vX77U3t7OHHfz5jeqq6v1GJqamvRgsBAzcUSVeLioDG0du7q69C3o7b3KXX72zJmOrnapxMZNr6aSKSH0ZDZrSIxId6I4OPik4mMoSTKefPDBTx45cuhb3/pW2+WOOXNu6uzqeP5Xv9Tz/Bd/8c9ra+t8X+SbOE18DJZ9p0Fwrj9zHXa57cI/fO8ffvWLn9fU1a+auur9E3onOGq8R6ViTU1Tb7llmevEhER/OcosxgrPFDrKuWIaqjAiNR+QFEiFg7OFREu+cHSKKpUPJ+zCcphD8ns4B+nnHCi2SYt9WfQV6CC3ocTL0kF/ZzivQIY3kuFfC/2w1zLI39Jh/8mQlzbw+0Ne2tAjgQwoUPiA+gNFaNNZQ4uCqhtfHQ9YPRob+YQmAG2QXoIK8wT6ZT0Q2nbjifIpdeyMuniqq3PTW29KIufOnhP4cUHJ0Dp0BZuA0cRS9EbP6jXdbissxg1BhGm0Aw3PYRNZEHsHRmPYVaz/dVxT97d2KnBwoPP4tGnTZrS0agC27JblPmrlzWydNalh8i23LK+rq9ff0b9ZXlH+J3/yp4lE4pfP/XLja69Ryq9e7V23bt1jjz1WXl7h+/5dd96TzYinnnrq0MG/rqgo7+jsaJk+80tf+sr06dOFEPo76zesP3zk0Df+9ps11XWPPPLoI488kslkbMIfbYkvXbr4gx98X8MCtErMdnZ16tWlzzMjmvWJTzz0la982XVdoPQrMoEKCvKZxtrZYO1YLHb27Onvfvc7Z868ryFpNpvt6u7uaGv/5je+gfbP7gMPfOoLX3hcTyyeYWyc0drGSSOenVSoTKH8+bx58/7F1//l9773/W9/+9sVFVXpdH9lRcWf//mf33nnnTTU+J34GN4djuSwpV6QFy9e3LZta39/H2nvePanP0VSLCMIVvQmsGHD3XPnzqutjYvQrmTAZjmKJcKAe2xWCKAp8G9RXHEH+e8EnGGYDfVppOkobz+n/+01dcOeH1LM8jZn6T9s28no+Z2vkG6taEILiQHpjkjCKziXjDpm9EVsf0NEglLa9sycXF7+WxehwZQytyYFg4/kuuiA31ZhLiJi6xW5UvtqlFp5oTwJx0gb2cigfSurbSiKlBw/c+r5La9fzPY1NTazZGU//pibPhFFA9bMaOybRmTbQWThga8RanGBrDyEGJywOJg0ynR/b2f7RafnanNV7YbVq2e2zIDcFWR9mcEmKngqwDvStmWNowAwsMLT1ynKeHbpzOSUWM+Z02cVODVI82DLwBrZEBj6+3vPnj1bVlbR2DgFi0QghhBmwPQGl832NzdP5yymgXI623vu/KmKyoq62gZKYkg5V45De3p6P/jg1MmTJ9LpdEvLjFmz5pSXlyNHVTqO63ne6dOn9U87OtoaGibNmTNv0qRGzhzko/pCeBcvXTxy6Eh7e/fy5bfq48pUqdCbimDXYd+5c2czmX5YPJhb0YPElnh9WU51dU1jYyOCLVr0MRu12WfS5tyk9B0H4Kk+a8+du4BjG5taW0hXN0+ZRtz6Tp0/fzad6TO9BgihuFLW3Vzj5kmTJpn0VUjAKmH1eKNGa/YQPYdlZanm5uZLFy92d19lliquxsVzRpUx/we9I+UxBlaynZ3dH5w688EHZ2pqqmfMaGlqatTPnBDcSHaP2hwOe6vQ8IU2NTVlMulLl9qI7ekZ6xlGhze93aIsi37YWTrTf/7cuUwma1LtKDYGSSLs1VMazupdy3GccNkEq5Ua7GvlgdQoXgBwRW3LTkVtfbyhesuBq93pMkrjsFpg/NJG6iVGRf/ra5IOGlkUBQ4Dv1T5/xb8YoCnsJhJWFgnMYpjA6tZKm9ztciGGRNT9GE01U9JrOsyeslSe0JioCGwQYwGGkf6ueAKbHN8ZoybqKGHQo+ItahjQVFGn9BSYEKDw3kMrnZYlJKwsRrbQSQAsTwEV1A/jXyhgiGiExQ3lWlCfZozxBFY7cJPrMIaeDZb/XH0ebcqgpCr0astjo+3jz+VRsMf1dXhKLYlSUpo3qwOvE2lgHDBTzn2SujZev/M+y9sef1cJt0wudlNVvSjIO8AgDUaH1GAlWHShey+yppOO8pcxIQM15nXe6Wv/bLX29dYU3XvqtWzps3g1EH5EoKzpkwfLsMymhoexqcfdlYLHhxa7FnL+1KFK8QvY9klM1ONsSsaYBHHeJCH7XnWxQ+bhSGx4ftS/4ffYcAtZfb9XTeuf0FvcAY76AfAjcH2h/I/3GrVgBYoh/SS8PUX+nMNgNC22YJ7bLF20DZHmLQTICQThSIp1XEdSKNLGgqNRq9JL5MY2lYbtYZAcMAeXL4vslmPjgMrFaasvYu+PtdlU6eGAIsRwsZ6eOhtgDL9GvwxbjMr2FhuzBy5sUsK0O0oAwJDvaHWahjVuRBgNV28eKm7+wpj7jhMtwTLEE4ZVz8YwAqSRlTM87JBb+a47DeGlQDldc/LXLhwmUQaHcbDxGI/nl238XgsiAZVINdmcvLSUAZlTiVfhgmFscp4muIgsu9leW0dAKx9V7oz5QR6qClyr1RIji16LjiMKnLtFbjBqya28YxiIKUiEAsFJeymGpqA0uFU5kzDpzIvhxwE5VNsAYOzHwzqAkMNPPYNgINjX0fwwGzWD45LcXenxkAXMuaONWmWwt5GauqukLVEug7UZIygH6QHJcdmOmDxIMrj0no8E6OjH1Ebk8gKMn9r/sQ4xHEEWPodhMZDCBEN9sVbBduRb+pvChrzATcw+I7PhI83MrDyt6+sv84CXhSKSS4dZrNxBsYy+mGrjDT/mGFwlcp4LhN0p2bmqnPIko6axGgezMJVHdiB0JgKB6SwQA55SJ6qqKb0PL38Xle7s3Ob/uGc6a069nfheqS0VsRWNYAOXpkcbH1e26yS4dXRuQXKqCZBzR032lIQ1PFAWwoPM7sr6YArnbaHgek3pMzOj/4fnhPWrMSsyGwGOxf0cmM5N0DUevHMl9msMKAqHKAGZNlsFndJZmAc/gINohTlZWVA+hlYk4IvMxnPegCQnKkHdh5gPMvGBcfFSJPktksa5LQpzWW7x26YCs2vlfKznr6vJGi1ogOTCAWfjA66wuSFiSTDY5KErex0HHoRmioBtZG254Gxt172+si3lSwyxs4tQ1YbSGgAGXS/WW0mWiKAG82xBcNLpzO5bcFSG1S4KjBEDNPDLG9TpGODXE1TNkTwOFLUQQHsQcCOCvuJECooQoueC05Z/EYlXkkObOXciG3DPNqRm/B8yDIhDap6XCFUITbxYApDDCs+HIkqwmR68NUhe6VDbQQbPr6CKxFRwXnDjbcvtmNhtghaLhFdYbSvTxsftytHGvQDquD6RzFcCT4mnCCDYlhvCJ44Zsj8IEg3lB79uibLBRkzyIdxFehluETaRB/CtbhRYmS2fMUkdaUB/dJneF2KuozHUVMDQI9Ps0JmHJugc/QMCBT8ZjQuR9TSE28Ww/mJOTDLPqYGeZFbpEZt/zYMd5xBxbGrJM9sM+hZ95FdwVPldUQJqt7vaGc7tsVcZ97MWS6gaNOTQi3MUuNvy1dhCMITwKpBLiUkBIhtFsemMppnI07zRZIIGSBhplRBTs0SNAui8+gr5P9JsPUU+YXcUTp4NzvNhwMq776R8VA8srVjA8CD0r1pImPBCBUlY+iqpwrPV1r0hBibc4kEKqiQH6ahDtdYjmq4c5qbRiktOhwna3KQ+Q6kZqKO6XpnA25qXsMXG9PHqWCh0kLoX2ye1RguBgl4AQ2d0Z5KA9Z43E0q7jkID2wuebA5daA6UKI2N0jVo0hzWv6/NPIKiDT0ZiSibVQa00ih9KPHw2bMYq8TXe+e6Zkk0nKk4Mr1MUkdKgjodQtXn6lQQyMcK3l6Vhw8XLKM6oAkIx2Qj6PchVqf9CBNBYU5N8i1+QbwwMkA5zH0C4ANMMd2MxoTyreyScomc5Th7mDtXgMvCYAKpMNtLszY7UpmUxEaAjGfCKl/mUUIVcJqSQqbofABTEruKbx9GpriqVrmOiTT191xqT/d4zhOdVUdL6v0fOVBIMsdoV/c8zQQpBqZKX1FgkX0J0vcNVLs/hbWvFRgws4pyOxi4oHn/eKox0M5W8LI/yuRMtL3Qk9LLFHWVMcuKHq8o52++YYU3tyZc/RkMRBjwoIjLMbA+Tk/n6TyL5Vex6yS0nXwAWXxoI/UEgFEjOvN3jXZXEaD5GiQAKJkoutuhD/CzH8eeKQkr4mMTlimlE6n5GHp6OPCJibthmBaYlzMLIixUIbSfILQxD5xDScNTqpkypwRmCryfc/zBRJ3VEDFV6XPBccTTAYpIhYsfxGm+4PYOI9iHbDEw59GvzSfcySt6h/5NOeNbVKwlEO5DUi6Es44H2EKMrPsGApeHEgeQfIWr4lBygkAGjcsHCGzJw7tOPXuXkcAdQryQCDebaIPDY9isSktC2/bEHfj+DVkYzT+8NEdmyNcggEI4Fr5eKw5BICLfmXXgi1IAEqlx62xlgIkhAQNqZijOA0CWV8F2tdImlTAk4HslSNpQkJKLENNQMcxureatBzTjD46jlOrxy99SEL6hpUSUzwuxemjR3Zt+s2hg29d7jidSpXNnbP0tjsemrH4dshYCY7WrpC684GKDNcg0A4+nM/wNrHIjSbF7q9JLIa3AMShgx5VDbBQXxcVjj8SzwdBRpoiDnXcRPmUenKZqkNd7XLrZt+TN82YB57QWLrViyobFFUHQiIzD6GNy8BZDZfrwKJp2FXBI788yLMmInEFM66oVAkpfLylzNDLIHPqSLPaJo6qG4fjbRuRvgUOzTUdQHiWa6mZgFgDw5oAelLbw2ooIWrijL9xgNYALKMzbjxA7VyzSNZ5wuPnGlErh+750LUakile1tfHs9l7pbFdt4QL2BQGngsOIVZplEeeD174uJACWkR+dTTvy0ixQpocu3lL6Hsi1IHnrV9CUilGVFwqVyBjKfq3BaVXoM0aWrhVp9ev4EOdT3GXcGQlZvdt+e1LT39TX37ktArgAXGblt238ObliXi5n4UT1dTTrYQjkM2Zi1JPDs6XBpyCEKOHDUCQCsQYzEAYZKoY7xU939KLhgZWvkAf1CKAveB958JLSeSJwxQ4QiM1LBhSzuGk9G2nGPEduCgN2qAm6DFAiw5kw/zDb2977vv/7ej+TYRkoOdakfff4Nwp7gAAgABJREFUfnP/7u0PPvHV5Rs+yWkSnyPJcYIEM4Ba4oWq6B0ZOL1D319FA+MzgMDGxVd9FB5TSOEq0wUGZL00EfFEsra+rr9dvHvlqtix3aGxOdNbCRosehp0B6CoFC+KF5vGovM5cFYL/naQZ43nA8SABhrUWwxd1GRXwzo8VfmwUF7LmR8iQ1osj0lKf0ny8WT41iT/y8E1L4qnTQcdycDyARvGSFR+epEOqq5ndEBowBy3UZ6Vbba6gsGrUTVgJHQ40n0Dru5DXMvA+Rlk6uig4KYgQCg1kiGvJYc7bXMrTl0QKcvg6BHDxmqqVHq39JfDuZZoNmDIBV90JANfYfizeq3P2sA+qrxVESTfpZlqbHUURqSYFnbRDHziSk3yNa2QUutzmLM6/H2j6JZFr3FWC+5dyWcNTKelIboCjcgq+1MrMEosjlAB2afIaetUpPgNOt8M28q8OSAd5ThSHHv37ddeea77/Mny2sY77nxkwS2rMq7jKxnmMSGZFTnjTG8HyJBZ91JspkXZXf0dKPEASHSnTp85bekqRbIuJ13nznWeP1Ve2dA4bTZnCUF4y7yb6pPJVNLxXds3pgwQwV4bKplry3nEB7oVek8EkE8BBYpyYbVa9d4qAuKg6Z1DihUN/kSZtgLkihmAha+GLGUXOoDBI1djLL+/l2gEFUuymCPsKwCcc4F9T3yuYijOH6Pq9LGDv/jR3xzd/0pdQ8vNK+6Yu2hJx5Wuba+/curInmf/4T82TZs+75Y1wtevyeKUuA4UOvXbuZKNiFqIyaGAvhIn8bgLZLePSMRunhWFdWCB99djjCfL6usmtZP241e7X92xzXH5TTNbYoy6xHCGx9eVybBJlfIkdWMaEsZ5eVkZcleZ0U0NhESGA0dIiV17ZAEWHd5IrgdgDXICDTmS4QIs21GJnXmMMdfVq8gpL0+CIEWevsrAkQwfCgyODIZzYg0HChSd1UGWxCCzOvi10LwZVkHsj1tuPBFzYyyVjPsixYfQlB/y7B/+NJaa1SGX64gArKLn+pCw5loAVpA/oZzF4pBnraxMge5deNDTaDKdjA+ANfCth79vjCDAKtgTii0JbDgzTW2JhMs1niiLecwVnBktA2OSOEjng+Nn6SDPYqlrHXwxWsqslUOETm4J+RV57N2dT37nrw7t2kRILyHuyXd2Pv6//F9zV90jpG3SNh5kLFLUDEqEBt9wrIhC158Co2vJNcBSGh4lbl398Nyla32qXJrd+PN/fOEnf996062f+7P/M5ao0eNwEynFy7oz0sRUhqFFoDcf2wJA7wCpRYBVic9AQAF5XBI8dpVykUQoQXKJiWBJOyZqwKQa9vyDMAS1/HZlmIXIxNeDl1gHhB3HR8TV193+0jPf7bx4acUdD96y9p6MohxSb8yHbj1IO0kfG9sgVZTe/8Yb7+3ckqptfvSP/rfVqx8pq2nw/MxN89b+03f++uihXS88/YOW1gU0WSPRH1MIKRhQpajgLMzUD0r0USX2tmDhA0IXgug5F760omsfhTwzMOQgPUjNXfFwP3KIk4hX1tc6naTtUOdlZ/sWDcpnz2x1FQeuFqFDktLIMDbFQc7AIZ+16G/idDOoplMUD5YqlUw0N0+mSPxjA+0RCl+ADOocoD4Uo4wWe7vBZ6jUSIaEAnQYLMFS16IGPUWGvpaQb4t+GRragtticmosYlypis25KiHcQUp/ea3XogadxmHO6kBoQkfiWvLutQq2EL1UHQCpTl1dVVVVOaV0eCMZ/FpKjWQ410JK66vQ4c0qGWqFDz6Sa3rWBl8G1gEiHovLuEomYvlNI0WB9TU9a2TQyx+Es31NszrMfWOYz/Vw3n2otWFggjULoSTm9ijiZzzfjwnYklGvgMqAqEELTlsLsLJ+XiQb8kvIoGGLzP+pzE+OW7oCRQFhSA1Ak50U3tbNLx3a9VLw8t6Jo9teefEnk+evdMpqQXbMSvlYsyGZ42ARKzhhOiMQMfrUJue5gNJevLKhsbpBv0ScZisrG/SfxpLVtVNviiXqOCar076GB1avLIMiA1DCU5ZWo7CSZOzTAUwQlFSSkODCRkLMuFIinJjAvIKGYh666WLVENrQpG3eBhUGjkpcglANp3wpMIdIHQG6TR6n3OHpnvTuTS+dOXW0oXnmsrX3+EC5AmFYjXU9vEyzweu9vLer+4OTF2lq+vI7H1i2/lMqOaUrqyFhau4t69fdeejM8fc+OLz3/Knjk2avUB6qf9lR0Gxwc2ikKqzyCUCDRH+5+6vQsk9POyO+D/LaPupx8Y8IGwTFnVRWgZywAcAazSYSFY21pFOJE+2X/K1b0p6YN32OQ93wSRy4Z7OIOq0a9qySYly3os9aAQEx5LFIKyGrstAwrqN/JAEgd5AE5f+JjxuTPgwtOkAjkTvgXZ0FUQQW9JgrOnyx5P85uEAkaIkx+FQG6lzALHAc7oDrnJf1Jnx7RpAIQQOtZAdKRCChEkr2B8GAkaKaYAte88TCeSEtd5zrM5xT6UvPk4Ib4AUbPzgAQIbISk8WnAtOgZ4LLZHso8WK7UVj0pAVQpEvYurtDmX96Wx3x8XgQDEfoqP9fCabdstzyhgi8L2jkRIhQ8KvyZzYS8MMk8ZwguvLczzQxYJrdGmayAwhvoSTyCdCOJDlgpY8CsIVoMXbB5wvOJw4MKs0aMgC2ZkxQdBuRak4wIeMT4xUFtQsuZ5CqA36ni85c5gD8+4hP8nXa9dFEVJI0oFwtgskeE8onwrpOvoodKSPTwCoQ4BRZIqCzjhVHldpl+n/HP0GBPsZBZM+AmampL4gnojf+YlHb113z9SZrSRRe1UjNA4Nh3q0cxYsqaqs7Om9cvn8mclzbstyaQphKPUAv0JLCIjQQckaNO/+5jhJKKyEHuKUfCRSWJB4UyjIgTfF8ON8Zb3cyhNJt6rycjZ9pqNj99599ZV1UyZNkULSYsH4cDSr6KDZ7aISWUP7IhkTdwDo4FHEsTsi63mKGpal6aYYSyG+3+stNnTwh36ZeDwupEABMG6rB0opqvIdGf6nRqREb4DhQ0PzmJqo26DiJO57XsbLcsbUxHIdOSxgImm9QoWUvvCsslSOESQLlQonJn9Y8MrGDMw0emlMAQwZI9dFQ6s1o8QZ1VTPKxEmY+yGHG4oOI5ibQC0NMopq0xW1zfm/xqrqqmvrEjGY8BHwucwlCiMamMoGuqWqlDfHIjnHrMK0MbqDLUJNJLXX/nKFTzBeBKMuWOSOlxu2vraod2bb11267I1n8gqrs9duHSReXPzq62zFkxtndXjgehDOSenT+x74fmfzFqwZOXqB+KpSodxr797+5sv7965ufP0yXiqvHX2/BXr7p+2YLlgrp52ge1qLiFJfSuy/Yff2b7jzd+eOnFE+GLqrPlrVt+zaPHt2XhZllHR3/PmKy8e3flaR/tJRTK7d73c3X+ldcGqFavvd90Yhxoiy3BkfUlw1ksmKmtvvc0IifVJD1TOHEf4isc1FPN8knVcUlZVEUtAbdEoIsUhVYai79ffM0JDByEoUzk6kEeM9RGRBIAZ6AfyGCR6XcxJ6JmJERnXnwov25/RG5Keu1kzZzRMqklAiwEbP402kDk01v1QMucxxoulDUOFE0omWoRG+gYE+leByUyQElahg4P5RFIypr4+42O6OMmzqw9qqwRV/lkY7RnLDRbx05z4uM5NDjKFjhUgVmaZShmJEJHUYfyArBDWxF4x5KxKo3wlrTEK4Cyo3PA4jxHLwcIdAKjgKIFOiwopOn7Wl/kVyGiJcGBpg+YzwaIlwui/poABZTIKSlJC6meN3XLb3Qd2bDl5ZCdRGX3T65rnr7v3Eccty2YE2tBA5wMPOs8jbw00JjSEoaD5ZBn8pmMQMl4mM20DfgIongDC8tMaifhKZMFs0ie+lL07fvdP7+/d1Dp7SXn9VKi0uO7RQ7t/9eTf/rPP/HHT1Gm+T13u9l9pf+XZ//HGb382tfn/iDnUEeLC6RO/ePa/b3/lZ9nuDmBGSXHgzZd3bH75gc//2fL1D7JYhTQFQuZf7b208Tc/+d3Pf9h2/gSuc/7urjf2/e43937ysQ2PPBGra8z0dR/YtmnHy88SclVfxbH9O469c3D1x7NLbt7glCX1cH0K1SwfFe8V8ftgWiARo0dPHahq+hpWMZLpvbpj58aOrktTZiysaWwGcydfH8fCY1DQQ489eq0yDSTyHZlX5DLdkxrB+6aLUCrCSwL/0QgvVNQkMmRO5ZvZovorNh+gUbVeYZBLZERDU+Jl2jrberq66p347UuXLlu6zFGul/HJgKweifA5ZWTxf7hZJQMETQY+a/kzT6SV/fJdJiAPapo5MRuNtHzMYFltf0KHlNO/cTdIlRDzL8qFGNxUaPC/Ha0QNhBSlQYcBM5oNJTCgiSz+UwFeysdndh6qAkZk3kznEel7GxQ29gmzP/Hu2ysysMEwLhLogxiEjdecz9BHQrtJ9HPG33/9K3ggWC6SUwYoku+ne4Yzu3An5IBJKuxm+2QOiRREJ1gbya4fxHiCZH1shJ1BPDckegUx3ig91xwLjjZG4NkmTmDNbKB/mZo+5O+M2PubZ/7k3+3+ZXnrnacipdXrrzz0cUr7vEk87HNxK4PpfKRINThsHCmcaNjND0dSYjN1wAsR2YVRV9rcKJjWA+E49XnAphJTkaC1c3Cm5fNWbj8wJtvbnz1+U/8wR+D7Kf0dm3feObI5l3bps1fuZZVNulXPHrs4BuvPje1dd5NS+903PKr7Wd/9sP/vPXFp2oaJt1830NTZy7Kdl55/92dh97e8ey3/33STS1b/UkXFCCl8Htfe/knz/7gbxwiV6y8t3XOEuq4R9/be3DfW8/88G96GXvw8b+I8+T8JWucdPbAnl/39HW1zF7UMm9Z65JbCOdZATjN18BRGrl2Ix6rQCUVtnmu9ypOHGAw+r1733p562+f0+HIzRs+WdMw3c8SRxh/RaCI+YGN2ghAeSN5T4Hwb5qsUYeChgsxDONpiHtu9OqnkfSt2TsCReDQXdZ+TZRrHxUUKaF6J/KzXl9XZ8fV7u7GWGLN4qXLFi9N8ThYj8rxSWgyuhgaPvuBA7ndLA3GClEmCXreBpHJvoHcl8CcteDdo+LdUcvh8NcC9XZVerQqB6NH7VTOSTSwYJen2MxinoYgiRWR/h6lsyBQPBrkRg+c3lGaMaJUiPGjn8EpZNR8mYWtbFxWqWj+OjT7jMqb0nC5jhMamdl1YZNAbrL5z4QAgfwYDfYHRgLCx6jRsXITRcmQ+1LBDJOcX/iY+PtglCW5CejR0cGBSfbQ5xg0A4AARdGUTeG3i3447AaMXuVOX6NgwIH2oieLJxYuXzt74ZK+/u5kKpWI13rQCKgCET88oFn+JmqjIcWNz59xDzUqdtb8z7q0GVHVoB0VJBQckLYyXsUabfBkef2GT3zu8M4du9749e3r75o8fcHpdw+8vWMjIdnDBzafff/gvFuas31XXnvx2Wwmu2LNfdNnzFc+2bn1d3veeC5RWf3wF/98wz0Pl8erXUVOnTry5Pf/867Nv9700tOt85eV10/Xt+LCiWNbX3hWpvvu/oMvP/roE7VVjRr8tXddeu6Zf3jl5z9444Wn595869Lla+97+HOrV63+q/99f+/7vbff8eDnvvL1q1meETGQ2aEMHcalcTZCp3Ek2ysf7CYkVuhU38E9rz3/w79tO3v29rsf+diDn/NZ3FPGlJQ7qJJKDf35uoNEK7ps6RQ2+pRB/7qKNCvTsOg/WovfLHWGYlfoXKQs9d5sNSYhih0J6GkAlWqI87L9nd0dXd2dTbHEuqU3L1+wJOkmqLCGHmT8RdQmZyVVYC1oDc4ps7rNNl+scr27KtySCjDNjd5GB75v9EvznVJuPKWAl81WKkVG3b0uGImlWoXsIhq2aNmenFEdUsGRg8I8LHrTC9bA2Jz3gQCuspo4ZgEYOysWnrjj6EGLPCl5cUKAs8MpHYWn6ZqLWSZZgpZf6JTMCh46ZAvlxI/oKLIGC8KAgRBKr97Aj6hIhDbmyzgSKiiDO5hNwCpj0RskCEsets6NCmZo2B6lp9CYwIAagq+HkqhOJar1cPtVGAHS4kRis4/BDx1HBquCBpmU3N8p1F4EDpYLMoCQvwOBUaO/ha41YEor3JsWrVq88s79W17Ys+31h5pmnjq44+yxd9xUTd/V9n3bXpu/cO2Z9/Yc3PpqU8u8Ves/5jpuR2f7obd3pHvbq5vnnjp5+mdPP0U9fYpzxQWSnPgHR/eePXd4zpQG6qvj777Tdvr9RKq8u6P7ued+KiHpxCnnV7uuxBKp7gvvHT+4c+GSFcKt6mUxCe0FLENiaZLMImmKYA1Uj5YHuAUlsWCVcRTb0hMZY+LIvi0/+x//6cyxt5euuP/Tj321qra5FyCP8BgaAOP+zyi1bTwjcB+pspr4lquHiR70ghzrUr7IqboZ1TVlMn8iajwAjZmwGJP6f9m0RlfdnZ1T44k1S5cuW7Q46cTBuAj01Mg4ZCVQFXh3QYmWB5KjUYV567xZgFfCfcronhSkjkZ8z4oeTqX20+h2mfc7RA22I5voig5SR7yBZ0N4hBmDZ0tphbA1TAaM9pAGznP0k7HPrwS1+7CIH73j45b2E6L/cOqYFVU2Gx+XAV4xD9R4C8NMUiIoXucthtAxJ5K7GoNAJWo6Hi5jExsUwMHoHRnbZSxzzzxIRUl94DB0/6Us4qBBBx8kdsmRvBT38HX9yLWLLucC8wH+pEPKyakhtQshYWeYNJCw4IQhVUgYsQopVFVlw133f3Lv9pf2bntjwU2Ldr/1koY0933yc+/seP3wrq0X7zmy5dXneno673nk883TZ2WZ352+fL7tFGDBrnPbfvespwgXCUpdj2ep7Hfcckmdjo7LRAnuy8vnP8h6vRpN7tv8qjBFcYVWm1QIoUi8orOzO9OfLotV+TgkfWz0OiJNhAZYHNj6kG1xUC6WoE9iBuWPGAVqO5cyEVPvHXzrqW//9dkju+fcfNejf/yXDbMWZzzmgCGiAqUqJn19yYyg/jszkhb0upSDAa9xw60gNqRzQmG1EOarMdhT9BBS1sdEBWx8PQl2j8kpJihVDgBKUc/r6ujs777S5Gp0dcttS2+Osxh4NwZLRxZRcR/wptciiEmLKdkV/bL4jTCe5dRG0IZTGS1ghHkVlQ+tJH4YrwxrWKIKIvUbsseatzbvGwK7QUpX+qngnAchk6GT0YGFrRzAGsWi0oBgmtopsxVCm1qkYX3WasuMHhqIJgWjZ//Y1AcLOErGi9UeplR9FJpizDSahIqEdjGcUujt4WGtLcy1jLex///svQdgHMd5Lz4zu3sNvRKNKOwNIAn2ToqkSElU77IsWYn1t2MrthQneXJJ/NziOLHl95wn2yqWbKv3bokSVSiJYu+99w4QHbi73Z35zzczu7d3OICgBBKgjTMF4w53u3uzM/P9vvb7YWdLPIu3fGE9AndmeqGVHMMEPyFuvSfbBHoKeEuOJrGTilARFl0aEPHQ3epztRWrbiOcYBd00iXVgotigfBbZ4skGxPayczGpkZAcsZhMUWWbQ8aWTVu+uzNn362+EXfto0fDa6ccNn1X25pbVr1/pKXn/t/O7eszivuN3veIujho/zjzNAgyDdt2ryhoye32dyv8SGshTVTYywIWjbB8tJKzTI0ZPJ3ckdn1Igxk6ddyXwGlFJhHAXtQ+yjOIqMvJJBiPg5FCaWZFQQiXJgVLCptCKS2Exod1OlzsEXOfFTlqqhgzvWPvHgj49uXV42pPr6u/6xZFh1G9K5ndKorUmCL8BzFGSvZcOATLd+4Y1TuaPYGWXmqOf0EFlDTADCUXtWHcmYWFSpMyGndklHNMgv2TLP1Na2NtT1M/QZ46onVFUaGBjPNJFhFmV/oo6vl9WvisYOJoooZX2dysZ6dPDEd8cENLOwIhzSDInVhYMo+ouxs48p9Qx8vlagdEkTYmbtw0LynZowYgKNiTXrbL5e51WWjigVU9wD4atOoITrH3qVxy9wxMVrjfx+v8TW7StaLsyi7OCXi+MRH+WlImTFHV+dALm0EGMVzkOvBItOQybrpUOaGLQWy5+olgfu+ZoJ7oHXhejBuCAQIqnwoJS6oyA/BFXtto01ScwpYlnSACX3//SQj3S9ixB1SjRKO6YqpfFrLqEzy/uUJCNyRB1QNaLYEaQDgoPIAB4qkGlE/gDx+bGOdItxuAHjZDAayi+cc+nVW1Z++tG7LxDDmDhjXtnAUTPmXLnh06WfLn4W+wI3/v0/lVQMiWI/x6hF2cUDi4fs5qOUXrDwqq8EMvMigpfVgnxTmxGxgv60ZhuFEdNto3/FQL8/1dBTZ867qrBiqElRlJ/Xj8KRVtxsBlMzTIQiUaoTkhowRCUCDhF/KggcAEEctOlxdwlECnUk5HxBBxEjP0IhRk7u2/L0735+aOPHFQOH3/XNf66cdkkL4t8TFHI0W/nPVND2E0HhbquCNPxFughZrNwNWwgbPg2WheTIQLHC4w4M0AXxmhVhiSjScVQLnPQJ5P5C/K/R1vozta2NTUXBwCXjqseNGes3DGRaug/y6RzbWgj5CdB2kFhvYmIAtee6CGVNNfFxFKVjh842tldBormtbfe+3a2tLQ54Uu8QnjcrKioqLi6G+8YkoxxuH6dpXzLVecrJGwOLAQ5vSqKDzVEeikOrhoaGzZs3Hzp0iL9SUlI0YuTIrMxskkxBRYSMHEl11EetGodT5ThHIpH9+/fv3LnTsqwhQ4cMqBiQkpKSMOZ9g3bWaKXXwMsB3r9/3+rVa0dXjRkyZIjEWN739A1d14c3IaDV2tq6Z8+eXbt26bo+dOjQARUVgUCAtcNhPRvHkp3E2CUrwkBbbfhIAAHjKIirQe0JllIhUlcjSRdhxI0nd1kX9CypjQ6O0PU8Y0fHPIsDRTmS0qCo3tFosk3E4YwJkQDuI3OEoFkUyp0rBo2vGjd15fvPlg4cP6J6VktULxkwrnTouM3L9/UrHDl5xlURFmizoUY+aKSPqpz86eIXlr73Rm5RefWshSmp+dz3PtNw4rN33zi5/8DC6+4qHTEuSogfaRVDqkoGDNuwfvlzzz10yeU35eSU6XqwPly7cvk7m5d9NnP+tRNmXIaMgKBttxGBbgRIUZmiNg3UdFTPkqTe4NevA3267cP06MEdTz/yi81rFqenZMycd0V+/4p9h3ZHIb5F/SSUlVmE9VRZfM5hQlREYxyqC9aVm9XJXyFLJbVvgbCeSvZ7p8HKaeYT/ZsUXbjuFLVWZUEYU9M6IvT5EAiKw8KQaMSKRtrqzrQ1NhYEQlMqq8YOr/QzHUWAfdeGuKFQNpIlep6YHD6bwtwXHdWu5RkJc3uWufusYuteCAtuFtFO1dT85je/2b17lwaZZBoOh0EzKxDkb7As87bbvnTHHXcEg0H+fUmyzcuLsVB8CWr7Pc7ZLtVfkBPdhM5wgvm+uf/A/pRQSv/+/eXFJI1grV+//pFHHtm6dathGFEzyv8NHjTw7q/ePW3aNAzaVKxdihChnigf6bVQwM2t8EE+c+YMH8z3P3jftmwZCRg7duy9995bXFxCweVm7ety+h4dBVrUuoPwKtm5a8fPfvazjRs2fv/7/8YBlluO3TeMn8MZ8IZajx079vAjD69csQJysQwZujZ56tSvf/1r+fl5zFaVoxoI7PVkRE5xUYkyACdELTY+G6jJKXK0ykSJFSOq8DdJBMs06V/H3gOVSBRFNWQKjgqLYstkBjB5M26cbIkUbMJxVkp68fhJc7euXT+ganZe4eC2Vqan5ldNmL1j3bKps65Nz65oNJEFHGMQyqyaMOuSy29746WHn3zoFx+8/8bgoWM1rO3bu3nf5nWpaYVDqi/JHFTFfAZ/a3Z+6aVX3/nk8Z+/+cKjq5Z/MGTY2OyMfgcP792+aVmkLZJfOmTo2NnB9AC/ENsybduEiGOEoQijFrUErFK8kkxpdHOIaGjW0cPbn3nsgTVLX+UQwjTbPlzy9gdLPwFVQMJxmpVdOPwrX/9edslQG7izqI/ZYQKc9jrtpvgqkHPCRKIa5js4JN4ECqSu1iRUL1GPiPgFCVxhlfUSWx0kWS0I2kmpRNk2yAIIRaPhU3U1tL6hKBSaNGr0mOGjDOKzIQgpJMPFqrElDy+jDPfGWY0lwRCzNN3ms4Q5TbXObgU3JD01ddasWcOGDeUo6sCBA08//fSAAQPmz5/PnUIOqoYNG87dRDEbiGTNSiw9FhF7b+JJ2hh3/ti2nWCNRBxT7TqMuvkU7eTJEz/96U+mTZ32ta99TTd0TBPxAd839+/b/6tf/nLHzh033njT7NmzOSb7aOmHf/nLmz/7j5/+x8/+c/z4CVCx6LVhuAfKR3o5zJKhVD6YTU2Nv/v9b/kdX7Ro0RWXX8FfWbJkCX8ajUa+/73vFxQW2bblrdPqwwcdDanET/wXDvojkcjSpcsefuT3a9eu0XXDi6v6Ru/zDa8cYUxwfV39b3/34DvvLL7h+hsuueQS7g2+9957zz33HDeI9913b2ZmNmjL9YK6PYGaIMIhO2HdyL1tWjSq3F1p/yikdCjtYFLo2kVP6eq00iLGoQ9FwNyOUMCy/VLSTxO1LCLPAglUYVyNYZXTp8w/WDVtHtICiI+ioQ0YOW7y3OvHTrqEBFItkW3jHwwzFkrLuuTGu/yZ6R+8+dShrasObV0j9jZSPqRy4VV3j54xFxv+KKGWzQwSmDTtSg6Q3nzl0cPbVp08uBWYM5CRW1Q677ZrZi28xZeWaVLm04CHAYFkjl/jH6JCnwcjS/DeU8WOaeuCWPT0icPP/un3az58k6MFft62aPOhvRuRovkE3km70aaRVkOwumoqUWaLvLBOuoHeG0sODKlfBOlL2bLukv8oek8lduXin/N7vwUjAxMEHPxMJmOW6qUV0IrafoxgQKKRM3W14YaGIp9v0ugxY0eMCup+5HTiSWIG6lJ7CFq5XsaGjmUrrsJYgipbcEmwGGegqOLLzMi89dZb+aikpKSsWLHi7bffHjVqFIc4WVlZ0ajJtzVZ7QQctEyRbMgaCBtIeVWQifvs1EE23MbwX7iZ4X/loI3bGG6nXTOjcySlaVErGgmHQ6EQNFnI43AIblvHjx9rbmkimtgkPYXf8qTRaHTJ+0tWr11z//+6/7bbboO8AEPTZ04bNmzID/7tB2+/w6+80ufzeQuJ+uxZArqVsk+g2RAIfvjhyief/POCBZd997v3Z2dnczdhbPUYPvgPPfTQxIkT77jjTrdyKAFm9T2SBrH4WJ08efLxP/7xhReer6wcedNNN7/37nuU2t64b9/jnP0B1TCIQoHgG5++9vLLL375y3d++9vfTk0F2e8xY8ZEzeiLL744Y8b0efMu9fab9uhc9Xiy7h6mqAyo2NkYcQrMgFK8gySHnp6i/5UsEkH+xbB//lU3V46qzsgvyM7L0KCqCVlATcqxF1AjiIwS9pcNuOb2r6fkZAWDBhTDIzRo+IiSgu+kZeagEOAnbo18lgprFFdUXH/X1yfNnHli//5TJ46GUlMKysoy+pUWFgzWIeXHbTwCi8KwkZ4795pbBo0dd+rIvuMH9zHLqigfnFNYktu/3BfKpLaokeIGrLDoH//tN61tLf36D0ZpwB3qpzgEunmyAU7WnUAFe5adv+j6OxZdea0gdAe+VmEiIX4Jcog29oeySgcO1FJQgBFNqC5yi2oDOsS4u2IykBnElkZ8frCtWBV4OXSBbi0OvlBJHNe/AVZ9DkmhZk1GdPlN4LM5wAfatk7Wng43NeQHfPMmT5xYNcYPARWIE0KxleCKkbV0TMhlEop64zJQSArASRBz31o0FlJZAqe5GWBwBuAlKBv3Rp74U4GU2LJlyz7+eOmVV15TVVnFB8w0zZdeeikcjlx3/XWhYJB/pK6u7o9//OPECROmTZ+u6WTHju1PPvnkzp07OKwaNHjgrbfeVjlqlOxM1gz9TG3t88+/sGLF8qamptzc3CuuuGLhwoX8XL/85S9Xr159/NixJUuW7N+/n++bd9/9/+m6LsKyKk3AARbHbTOmz+APEWADSBcMBMeMGVtS3H/Xzp2NjY0FBQXe5u0+E9U+dyFSw6S+ru79999vawvfcMP1+fn5LS3NfLjS09OvueZqbq44kJ03bz4fTMuykNME1zeeSRGAt8h6+/btr7/++qXz53/jm/+wdOlS7q5Ydl9m8AuhK4lfuS0+XXPqvffe0zT9iisuz8xMj0ZM7plxx2DRFVe8/c5fPvjgg4kTJ6WnZdhU9e/04IA73FeSe5BKKjRNR8Ggnxo+G2rcme4gKtZxTEGPhO34ghDWTvm3g70/eeO5p8c/7gjtD9iRXgaOF+bp6kMEbMDPzsgvLSgcwN3xRtPSUFjIy4FoD0dBFnAJAwqzsZaaV2zRaEtbRGfYAmDjS8kt4oNohsOWrnGnHEp6mAYsnyiCdF/RoAmFAyaYkTZuxwyfz2RaxGSmGRUTSDdZlGqoDaQSSUFxZXFx5aixYWpHQ8EQlCHbVmuriTEQroqAo79s1Az+fsu26iMW3zB1oRFEBGc9IEDGDSOOYtsIpA0bO51A8lOgAbe7iiG4xcCkiUxqW21AJKtRWY8EkQMLu+yTuIMOAdbpnY0NPoWAEaJEt/hygL5lweQu5pSNpMy4Z1ZekIeqIuQ3i6NVrMk4mk/8DEBVe+RMQ11rI8SuZlZXjx02ijASbQGdH4MQ1VkrhtF22j80ptoCOp3eLF7lPGEYuzKqSQ/IPNM+buSFACeDMLXC07qkbGTeHUxW9csYIkOyf1DdOACffFOAaNJTTz2dnp7JARbHYCdOnHj66adamptnzZyRMXAAf9vWrVvffPONMaOrdENfuvTDn/3sZxzozJo1i3+Qg7Nlyz/7+c/+Y/r06fydhw8d+sUvfrFixYrJkycPHjxo3br1P/rRj7jT/6UvfYl/pL6+3rT4VG89c+ZMQ0ODnCSqOUDwMgSDQf7Om266KSMjQ6Ir0f6IWltbmpua+/Ur8Pv9HBAkVBz3GSrvtJFjwzehkycPbty4ccSIESUl/UUaFwg4OHouLCyaMGEiB7vHjh0rKirqjfSYvSx85c1kFRYW/vTHP54waUJqaoqYikgnmrdasW/Euo6unMQrPPMF/Id3HNq8eXNVVRUfZG77LGpLlv/S0rKhQ4asXr2G7yHcQ5AF4r1g0jq7vYP1QFLDMqNhw9IgMabHwR6XaDvOEOimheI7AnHXTCXu4Ck72xuSvtJeAuqczTVRbM+4OWxFRE+7CRlBojndRxTJ6nFR1Iw5AAO+MIMSm6iatRbKDFFexIGYxvyQgkW2rdkW9JrhMBP0rTgV8ZnRBrNGQ5IeHJo5QSNRdm1xOGXZMDsgoBBsaoUUoAkBUmxAyEV1pre0miLGxBEcgDtKgGJAaHfZWNXJQA9ZFACUJTpGMWWW6F3QJYG35HyCon0OvYBqQIuo70sE05GNk2BWlOxp0vuC49/KqEZtBlVAkGEVwqGGYEv35J5ZN1W5J5FSc0m13QiZpP4zEHK1N0BPnt+8aLS2rq6tsa446J82ZvT44ZWardEoUOHz283XtLDb2OWulIezpdzUWab3ObGJdWW9JKy1xKXnfFUAWBa2LSrTPImB2/gjyKVORNpZ6JExbUDFoKKi/tw1rz1zuri4+MDBfdt2bPEZvr3791YMrOCH+PDD90MpwTHVY06cOPbAAw+cPn36f/7nf6ZNm8ZHdsOG9ffdd9+DDz44fPhwjoqee+65xYsX33///XfccYeu67W1tT/5yU/++Mc/jh49+t///d/XrVv3rW99a8GCBffcc09KSgofc2/URP7CHVa+7booip8iHImsWbO29kxN9bjqrJxMM2q1N369KYDUw7ks2Hps7g/qzU0tp07VcNCcEkoFLhpB1UJtFAqm9i/u//6SJdxcte8S7QvGdAKz+HQdOHDg0KFDuV/DTSlSOjQ0qQxU3+Psk5XFYiXcHPIJeerUqbFjx3JHCwk+HQZ1xzQzMz0vL2///n2RSER5lqiHp6gUYRP6c6o0FfTWbG5eGDcmgvpG2hyp9khZB4ZA1wmLFaWo/uu4ehpPmYpsAXdjYt6/xn3WcwT5sUR5QfdQ7d+W8DPZuZJfiRRisATBOqVCGZowoQ/taJCqKBeYUg1wCdWYholoO8SCbUgQoQnYJF4GqgSot+J2QldEYlRS4nN8IVKCADhg5qjOKiIZTQmQczJbUorr8AGfaveUpUpi9giwxXGTBhEyuAAgRQVZI01dJBYaKViTvFM6Qn6REuOvU0FvpDEph2Tz76Ih2S7KL8HGFtGxU8zcbiTb36CEUfXcF0dCWbDkK6UWJLXZgAIEA4+rI9UlDs66p9Y9NkFFBEeSvAmkIQt7lAqQExcRVYi6wJrMNI+fqaFNDfnB4NQxldVVlQGiCwJemXhlsgeSj5pb1O3OSJZEr5i1GxOUdH52NKre6DGODyZ3tNbi3+DEooSSD1SWK7UiFitT8Ko6xP7FKta5p8idwimTJy9fvuzEiRP5+flbtmxJSUkN+ANr1qyaPXtWW1vbho0bOH7KzMx85ZWXN27csGjRookTJ0ajEf7x6urqOXNmP/zwQzt2bO/Xr9+77y4uLS2ZN2+uJLDhr1x99dVvvPHG8uXLOSCTRRV+v5//ImJRdgLmhOuxLE99FbRrLf3kkyeeeGLcuPGTJ0+SUbdeycXAYgIBPaejx4fHIbNgkUi0qbGRA1mfz8cEP7Est+LWKz0jI2pGGxobvA0Kfeiqiw9gZgJaSZJwm/sCq+cSvkIYJ0gKkpaW5sbGhrS0VO6b2dTlp6T+oJ+/GA6HGxoa5Dt7QUuLV9ZWSWRBQEXI1UlYpXncWoGxktgF3e/XOkkQdpQI7DzhgTtIEKJ2gtmdnK5z8uuEK2FOEAvsv44MG9jMCdRlIxHBwk4TvgBEqnwNqPAFTTkULVmCS0oToEZngttRwDZbdGIKuCXHmUiJRxsa6wDuIClrzSQhGVRXE9G9qRPISFL4K+AzmbkRCUcs+QQEwBIgF4swlmBT1Bx9HFD8wTFiJB/Ejbhp1UwOqQBgQZmrIB/ncAqqr/jX5bjK1JhPB6DHpCJVOy4AfDatfZaElQAmFCXY8PPvpDlKfxAis8RF24IMknWP6G2ijyiDTYx5ROCcP1kQRIQlSgRcNqPh+jO1jU0N5YHArInjJlZVBnWD2EgzHKiCHVJdHKPQ0pzrpckiVCjZ5Pzco9r5Sml/HFH6IevFBA+WmLiixN/xMRRsdzhbkrHI8F9CoRDHSRw8HT58uLCwcMWKFZfMuYTva5s2bW5padm8eTMHXnfeeSd3Mbdv38Fx1ZEjh3/60x/ZIAeFDcO3du0abnL27t3Lffpjx45yHPbQQw/x15FTFMw/sn///rq6OlkID8rZti3TKzhBuN2RnoAkF9Cjko+WfvSfP/+PQCBw7333lZaWqlRXb+S76mFopbxqEayWpotjU8Pnj0ajIhqPiVrxSHbD8bH1+fx9zYPnlNLyihHFBy37YlfnGr5CLKYYrIaUbxqGoXMgxXcIYHAVaQMhs8uE56DxGavGulfkB8VqUj3XwtIT7PPrwK0JQEE2lQHAYKKtLKld0KOmElRxmRKJU1uTUHXCPG4yjS/t6Zw+EXcNYHk/iztmFvXWZ7lXIk+nQVqQW1r4aQtmJo5sLMED5mWyZ9gJAQr4BXk54oYWsDC6RMnMiDCWDTaOOWdnTlQQ2ULJmiFpttU3I0wJRYhCKFHoAzSnKhllewpoCIsxrVtK/AgbjgwHf256jAxUV4g/2UIhSRNhG2FpMQj4MAXImA3RNUECjhPuSMJtShhG5nlF84yqG4mhGjYtZgKTOzIFPOT2Mywukq8JA8CiC9u7wY64gpaGuAqqhDYhhmO6ZlsIBPkw9kPLYGtdXU2kqbG/zz+runrM8Eqd+mgYQFgsMyKKrtzMqfzuGu0SwHIXBUlGNNr5qCaw5uJ2hLqo3dLzxPEgqokF3YpFlYg38+T+KYpFIB3Ftzh0Is1GRUVFv4J+W7duzs7OPnjw4GWXXcbh1J///GcOmzje4m8bO3YsfxsHSfz3UCjY0NDs2Jvm0tIy/o8js6amlqamZv5La2srIRF5Op/Pt2jRlSNHjvREWSQhOxLSs94Ustx1YcD4PstB2ysvv/6rX/0qJZTyg+//+7TJ0xhzhqN3mgwmVTJ6DPt5Qsuw+kMpodzc7Nq6021trdkkG3xoBmQZkUhbfUOt3xfITE/nd9nNxvZZ/S6iK0xU3QGTCQeHbqSvmu1zwCxR4sAElZ+dlpaWk5NbW3smEommpWFBfgzIJNwWbm5sTk1J4/9EbYNgKur5hCwT2uQKIEq18ohlA7OVqL+SkQVdCKiwZLTeALAsGkNezFm7HUWw2nvkNN4vp+2OgOIRVSdP3c+6L9L4M9KzRbBkOYrMFco328zrfGKX0cF1S2zHUMe64RTe9pooKHiiSYZfJbBYfGg04RJV2TGLXSFyro26xt45iBX/cfeZrS5V4UTmFJY5x8GuMqAV95XjRtV7mzqJtTBPEpeCaKItpzsVZV8uiykSctqCHz+m4IK72WPHIm4o5auxBY2iAvxikfTGWMfEpJbfirbU1UYaGotDwZljx48bPdrHJ7YpYj0asVncikm4WaxdbCIpwKIePPS5R9X7ZtZu6jLPWVxzyqTmKB97TYTUqTcmJLch5SMqMTUxEyiKVY3IjFL//v2HDhn62WfLNc3w+/1jxoyBqWJZH3/88abNm0eOHJGTkwMtAoFAWlr6rbfefumll8pACNxlw+B22rbtDz74IBRKmTlz1re+9W3+TuUnQKTExw8lK6s88TOXEUDR7RMJlPld0/WGhoY//elPv/3tb0eNGvW9736/qmoME/0TApz1TjYBiXOxR4EQ91z6hd9gOzc3p6KifO3atTVnasoHlIuGGxByqG9o2LVrN0fF2Tk5/I5I2kZ5N/toGjrHWJB/0Ai08sR0iBWlC3Di2HZSsYG+R0cQAaNYEYlovygcMGDg9u3buSPXr18//orYQKCU8+ix4xXlFVC46VUp7cnVHqsCcyPx2AbKcluopjoZJMhl2cI8YZbELsiyDtz+p/dp+3842dOkR0Dn8hSf7RTtnyZecPxbUaxgx/NOz1FQ8hcR6vhqY68nG6DEN6B23xQle72Tj8e/rq4v2fGTHKGDsepoVBPfI2cVkSyVydn5cWJYtbtNicrwYpObFCbaTjCkVg3CDEw1ZpNopKa2tq6hsSiYMn3C5LFVVTrjL1NRsiQCJh0Maftb473duOPJ+blHtStrLfZZNQu9sjOqJs2bRZQBuRjXkXiuqZI5VeLAN6zMzMwRI0fs3Ln71VdfKSsrGzp0aEFBQVFh0SuvvLJl0+ZZs2aHUlP4OwcNGsRtyd69ezlm4riK/wwGgxs3bnz55Zf5npifn8+3xb179/EDBvwB/gYOs06dPv3qq6/yj4jWNl38A74Iuat6qsokJzJc6r59+37yk5/87ne/W7Ro0QMPPDBx4kTkoTbt/QgA95hspSxSgOlhWzQvL2/mzJncOK1YsSIajUq2Mw5e+fCuWbOG4+Dy8nKJrvqQQdfCLaD27DbSSO1nsfjU3tc3hucU/JGlHY6ILZDkFRcX82l5/PixDes3cOivEbVR7Ny1a9/+vZfMnZuVlSWZ27yypD21yL26acxRvVVrH3Za2yORihFKvtXrmPRll/seZ7ElVDbdYRKjF0XnReyZOZZYIAkQO+auQSsRwVjoGWT8lyhIHyE/on7uTphtTXV1kcaGotTUOePGjR05wocEVRfQn6lVQTDDF/NGpdaug8S88TEZRZHdq9K+wsYk6N2R03YuP8b3rMpRVRwt7dqx5+qrrhN9Z3TcuAlL3l+SmZ5VVTnap/mjdmTG9JnPPfc8B0yTJk0aNWoUPx2HUz//+c85lpoyZcrgwYNnzJjBMdm7i99dsPAyjrHqG04/9uhjS5d+/KMf/XDY0BEZGZllZeX79h44dbKWozGoU9B0rzI1t/Sr16z59a9/vXXr1jvuuOOmm27iPuvBgwdljpSDg4yMDP7T3V77FmD7NBZVnRoQrL/kknlTp7z9h0f/UFE2YOLkSdxi7d695//95sG8vH4LFiwIhUIceKE+foGuR7BA/iESjoRlGLWlpYVPxba2lvr6etuy+czkHgVfC6ivpq0LESBRYAODqhEgHwJ9OENbcOnCd99997HHHisqLqkcOYrvznv37H388ccHlA/gewsfYW8taY8PsiJ3ZE4ySe7FRGQOwc0XuRvZRtcRkzvtW3h9j04hjyZ6BplIRsvZQgC9ywpr1t3L0ulHVHlehzdeVIZZTi23CE7ZdqSloa6+pbGxJJR6SfX4McNGBJihCS4DgGBYLg2KzwcSvFCDL4CsjEaL9KbsYVKVcm6iUXXvI1H/FGkLc7PqpghdVa8hQ4YU9CtobGgUHYKm3xfkEIqDpMqqqn79CkGZkaGKigHf+Idv/Pr/PPCd73xn8uTJ3JasWbOWH+173/1ufl6+puu33HLLoUOH/vuX//3hhx8XFRXu2LFj/759/MXRo8dy8JSXmzd96vRH//Dof/3Xfw0aPGjSxEnTpk0zTVNVrmikrqbuwQd/+8EHS1JS0j766KNly5Zxv1bML347rcpRlf/8L/9SWFgoAVbfo32IxfkJd5bfxP4l/b/znX/+z//8xb//8Iejx4wOBoIcuUYjkfv/1/3jx42X00CGXmzJ/9L36CR8hbFlWe+9+97bi9/mMIsP2t69e48fO/b0088s/2wlXx452dm33/7lMWPGtO/N7Hskcc2dSJDQ+oB5GIlEBw4ceN+9//SrX/3ye9/93tjRYyij27Zv57jqX//lXwcNGtR71B69evawu4q0PBV7LxWUA4RKGkrRVi/yiUkxlk5o36rre3QK4pk0dxo0nSpMn5RUtttcHwtB+wABqlUUxczPsAXJMFA9IkxGpBj3M83aupamptJQ6uxx40cPGxHEPsFIRgQNlOLvADoMii7SEJYswQL+NsEfihW8TMzEEqfiSVY5fP3rXy+vqPD7/TIxJC0rf0tOTs4dd9xZc/r0yJEj5YvDhg27995/GjxkSHZ2lmkKTEbwwssuy8vP5V7myRMnTdPmaOzS+ZdOmjQJ9D1Na/jw4T/84Q/ffOutvXv2HT58uKCg4Oqrr54/f34oFOKbI98oL7/icsNnbNy0cdu2bcOHDfdulPwNPp9vzuw5FeVlBPJWlvouou+WUrusrFyq9PStus6DWO4Q8fs4YcKEn/70J++8887+/fvC4fDMmTOnT5/Oca3MDLqfcouK+h6dhK/4Iy09vbioxLKhPKikpGTWzJngXBJMbZqVmRkMBvuG8VxdAhcwyXk4Y8aM1NRU7l8dPHCAP503b96sWbP4PiMD8F4ujB69cg/jgiqHVmXngtOJSe4rp2iDyOajJMd5clnfdOl7dGrlhfdhE7zn0MHXP3rvcFtbXmF/GkyPAOsXMzHzMeSH37vB6RBlppKTDJCWiTRblZUJlnbY6qgPW22RcOOZWtpYlxsMzps8ZcyIUQHNp1lMc7CUJKqzoJVWiJ5fpAALRoNSDbowQpo9vNhfmhFpbmkCcjYmKfsFBZjyoGJyH26Y3cvZ09HOJTv/ZYxSkCkBuzqHVo0NjbZpp2dmBAIBScfM9xSiIanX1NjY1NLSmpaWJutSvWyW/GdDQ0NbW1t6ejr/q/cCnEuiqjhLtVZitwsy4VJ7idHlZoCbBI4Im5tbgbKl16ABt4mB36Dm5mbLAs4zDlK9IcCEmXAhR9WdD1JEmY9ha2trJBLphcVMCRQnWIXOhSoZ8A1KH4dh58p7TziQyRi3hlJTUiyL8nXXS1ZQgjPgHWFZZVVfX89/ycjIkE9drlfvNtWDX0EwR4iYFTTnM8OfEg74Vm9ubY6GENH5FzJFDbvOlBJu0ocsd0iQpsEemgUUH6to/zQpvfXnEM1lntb1Tg6OOrgSt5s+6XfBnYYy2x8/6bfAnQqhkE6vpCsH7OiLJ+gI0a7VknfE2XTWU8cx/TMRMQLieAgzMJmA1iS7eGfKMF8EVEBxCWAs4SWYjAQE85NP9MsFGbWi4aba2pbG+tKgf+7ECWNGjjCA8z6iCcoFoGFFMi9IZZeHw0GWcCPouYwq8/CH4M87ql3UiaIJ3O5QdMPdZ2aLegbs0SCVjhZ1S67dLdW7K3mpERMUPxxWKvE2InvkBIkyZbpGcnJyZCpKnBagqmiblU2xOAMeme6mmXDGzMzMrKwstwgswaMVtCfCO6RejmOFFfqqW7oSGEiATXzoOLSST71S2d5b0zeqXYICoqZd8PzIigS+3RFBfonbA4W+x1nDV0mfygHPzs72eoMJb+tx/jaHKNUVzEHE5lsWFYFhyOdoCtxiKjgGcBz+UTurTml7aZqkbeYdPU36p88xBZPmm1inl4HOBsVQF5gBk56Ode3sXUScXTxgR1+8E77Vrnyvcz11AkrgRlDMEWC8p5KX4nzXNBGhWy6/qw3ykUgXrqOfWhxINdbVms2NZSmiqn3YSN0GMQI+x22mOufhIkFyEOhYBQ190htxTqPaHk59jlFFHTztjE9XcKgxQbfAPMNOkx0wCZTp+lOHqoIIGQhxDsI8dH9SXAgDEZQIE4qiBNYeWrkbaHsHNPY7i3XlOHIBrA8HfBHrlTDmfYwMn2Mwvf4GNCErZmkk6Q5d/rO+Uf3iE9UFTzF13Q6Wf0/qPWNHp15yIoGWsMOAAyYRWBg1RCShJvIQMcVJ5fSGiHffoxc7d6KgmsgABzzzAfhxSVvPx7RmjomHPo0QE/SOCPn4+aNhjq5aG+sLQikcXVUPH+knOgMaa001OMr+KkedkyRjL7voPGtBJcw8EC+mSOQqkX5xinGsAt2COT9W3YnchlHmqIy72KiTk55lW8Tt9Bd7vdFiF5Ul6wMBX2whMNVv49x8jHqletNFDrkuAkIWdycUG7EN+RzQdRH6ekjyyhC1byY/gh70/3XdvI63QtzrN0r8BbZ1fB4sgbTfVEhNM8G+zUDfQPKKyk0nLjvGvvgmBIcUHYDKxeEuAsg56siOtrU11Z2xGxuK00LzJk8cO3xkUNNF1lKzlawgliiAxO41vng1LiSzP1VUwtiHqaFjqXcda4LGjNCO5s65jzxSjrqzp7j3U9K9KH0CD1Ev/puyu31yKWd3xnD36L337MLDHt5m5AhSXRxbRt+jO91bR67W7erSkOEzAgjU6UBXXRROxHzOZDuEblnsAtzuL3yOBEoAL991XDaOxivqeAmXO9ASYx0AG9xFAHcuXzOOpjueSAquk2JPJQ4TmbL4y1Ccbe0Wk1eVpdv10phqldBkuR7BsgwLUm8mwlHEDPF1MOsef0SGxzimswRrpoiw0tZwuL6uljU2lqWkzBg9ZszgYT5E7CjSsGYhoDiHKlSmrkAKocfSUeii3PKxlGCSGTtma9DGJLAVzAySgIZwd1oX72yL1+++aEzN+Yph4T6MdRbvKI7hPlai57g7F80AJi6EXr1RQIMLiknA99GedVf4Sol9yWpR2eZj2aZFpVVCwgKCEHDHPRvnEWAxT1BFFEhLAkmiRGBUbS1FrFP7hyW2UkUFjt4dE9+PCAEOKlNCommS2QQoJpk6DdgmSgRRhdAP0bDbU8Y8ojYOITmm8Uo6TqmLypoILCtqHd1tQvZquovQQw2liuMEABbmkDlsnaL/i6qlwIhQL5TRCIvfD3leLAPVTGf83hEhPQ01MRTZDGqSgFhdqWZBhFLylcNXIkwqOTFvIfQXRsmUwnmoxewItE5w6MMHGRsYU6XawyzUbXxTfDQswG0g4ezDLIBopLWpqa7WbmouC4ZmTBhfPXKkzrAZpVK5T94xxb4IbT6qFfEiiVp2GsGSLSyCnsHQmCT4crRwlGYyhZtO2XkMtidUFbC/Hf/VMyWRKOaDekTcx2vTQYSPSWJrGhfsc/oZpZMGYyiq8PsGrJvnKiStRM+IS9Lr9jr0PT7vlAbLTNUGDKafAF2fbdmUUOIEO/igE9bxfD5fNVhUNJEDRoANyRF0AolEInl7DNUMBZa7MzcB1qOQMCbIoFJqTVBQiP/n88oG22IRkQ8VmovYxJK7W2RNBUcYha40EdUgfICoBkknCi0B8gKYOLpUdRNIhsUkgMGYcdPGTyBSVw6OEjlYDQk5ZwUbiWxxZ8ilEHcjKVBCI8kUdapzaGIL5RkKEkZQKSdeAcFpfiwbSaY1gQH5jbNtEIwUwt0Wv7cG2FJ+cgG8NOh2gaMAgrawyq8hgTEE7uoG58sRPaVS9pIDqQgmUUx0Aex9jIQEvaWFaBQLMTn2hYuBEIpi1MbsDIKA1jfcGqmvxU2NpcHg3EnjRw0b4UO6TamGpRiL4zAr1na3ibOjKvKL6UGEz0CxGH95J3TN7w9SZ9EI90q0W2LUlyE4LwBLEAxiDXICOiZCgbHPbHUEsKizEh2NJOX5wG8gioKZ4eN7bwD3ac5091zlmwQfYT6ugaDfE9uIF1vv2yLOZT5DEES00EPvPP/VZ5gE4ILGsaswscQJ73cyrLrfT5LdgO66GQKLOOEELGERQQ67EpZE82e1gtAdKbrwlbI1lPMLBkxpYyhQAYneWghnycwoETZW5tqkop1IbSkRFvF23cmASFggkIkTZnVUhsC8Iaiwdq6RxQVF/IxvvM5LAktSrMVCeI78MRVKtwS0XuCTuop2aU6US0hUY0nOrcnQlC3CcAQp+V5I0cFQabYgEdIIRO4kwBIoFim/kRGB6cR4fP6IRUIfB4wSh5ig+UsgFm0ACrSl9LMcXVuwYcVpWX+RyA2i6Ril8CNH207U1qCmptL0tHlTJlSPGmXw8QZUS8g5n4glS+ee04T/IouiizQNKDGPjIXKBCJ+bBs6IhowsxMo6qfOXaZYZV+6cP0MXTBOMNZlNpHeGhMjUq8JCXE0DRY603Xq3VS7Tpry12/jpavqaDHEDAw3R+CeSfMPw8gMLHk6+h7dudawkPS2maH7kOJwIsibxyBMVab0jXxXLRGEbHSdgOqghAeY+f0+UzOoQCBdWfgyRZig649R3H1IagnaUwHF8QNhtUXJaJPM3yg8wagpAk8M2T7GNBKL9+BkP6UTTyXVIXZIKaBbUqxlTaTYuJWxJSGlABu2U4wjaIIA9iCQvGa2agy3PRVctB1hlYxaibwkltk69R1FATVV8TkRlzWoCEYpUiVREo5dU+ZWjoFnJzgzmIVlMpd/a1ugOSLMI9WBbQVTmb7kd1KGJjAzRepQ4jPYpExCBJK0kIWwABpATyXSYiIKBgcEunViK5a8zm9T0vuLEt4pMTy/XGZRjVIfBf4PsUHSMOMXIZ8C0tXU9SenijrrdciL0aGoixmM2eFw/ZmaaFPTwPSM6eOqRw0ZSWzNjPB7qxGnVKsL3wXHT+n2H0mY8B1Z/KRHONdR7WiQO7oSsVWKCKKtW3xUGhvbTp88KTgrHLF2mHvUqexnXYY0Xp420q52sT3JGU7GOtEZfx5zObqSg65YEt6R22fJqigTroQlG1vSbu8i7XjOEga5/Z2i8W92sJNw4jg4KCwsaAuHa07XEKwzj7wiihU+onZMfiyxIvTzM/mhTkkKvRePnDFk8d8l4UraI37ShSvp8Ls41ts7FQX6pxD65xM4GAz265dfU3O6sblVI3oHN7T96ei5j2rS70LbrVnSjnOOJTp6nV1J+yN0cVQ/x1qjnheTzHC++2vEzs/PM03zzJk6EdDGKp+hyslYu12r/Q5G2621rnyX9hfWle+SMKqdsxJ2dCXtL76Lo5pw7zrcN2yRHBN8NXZadpYvI9Om1DZtS8AWLblTGXdhumkyz57eSaVFR2bAfTH2Bg4aDCqJIvklElvl3WIFSgAaiKq/0pCjpBg3Op4KCFnKKxCEjD9L/ISljwkn4n4SQHgLvEwNLD6WmTYwRDYFsGFRamJsi3CZrPxXDOEqoO3tBICIIBVJSFv80RR/0YEuQASaIEgGf6IAOYgumuokOnIUjFSsXBQlyG0aECUfEFOTGTe4k1AcJyJDopyZGoC8iK1iEZC25Me3CTYB5CGOOQKYBACkwlBGOcaiKApRJKjQ0qAeDcjP+de1ZPcZlnSbKNlt7SKHk+oKwKL6zQY1dGowHAASPlsHDTkRihMROkOcVHcyVbJAW3N+ok6fUo/RQOKYBqKWGWmtrYk0Nlakp88YN75y+Ag+qyJRE7xgIbsH40hULjXZ5EQdTM6OBqErNUYdUYuxDo6Du0Z11smVSFojqH7jzimQjdqsrbWNEtAXRNgFWMzTOtn36L7Eiyrmo4ahWzafk3ZbS4QQSxRg9OUJkz5c+6QWJjAJca+UcodQs2wWidrhNj6GfVp+3ZwiNAiyLGaatK01IhiJRTMSTrg1uG+XOKeHBaae6dDgZflSTG6FragVNS2Laph1xAEUt4HrOMnbuk7vmfzNmuBFlELaov4J8kpYliExiWuAxg3AhNDdwB0afk/OCRMFhgQ6kWpzUMEONUl80dZGo/Ui3a8TpSIi0iuUb4bEMFICwQyEDVGTbstyIicYJgaAyP5zpvKYgKMAYYlkJtbF1JRZPJk+xMAwBqfn8MiGz0MBujSGGpXuOxFMrzaFQ1Mht4tFjEeXZVJwbaJA3SflfDlgEmldqhhYbAiVYUgEQTzKj8PNtbu3bjixf0+0pS09O3fQ4BHFA4ZgI8jhsQWU5YDziGDtl1rJOMYtyzo152e/v0SUkMn8Lp9oJBpprqu1SEMLxj4kImmI+tyi/tiSj1Nz6vQp9p6SQPgK8c3Yam4qSUubM35C5dDhAX7vwG3QZEaSiMpBpWhxDmyf6NwpZDvHQ5/j1OgcP0KkDA7BQrgd0sYaEsJyokGCOG0YpC/w363ZAeATBMeCMD4TIaUF3pwmGNc0zc2/q7YVzP7WzZbKHXi2HCLHUNNkCw+DvLYYUCjHgmB336Obhp6JLjbYGvgIa0AyjiXPDSFuf5a8NbIWpW/IugRaRS+m0BHjw6lDQZGIe8CgagQnFL12aF51qD08PxcoEnmCDhf+J6jlGTP4b7qBRDYpamkQxCCkS0YOi3Sd+FI2gDKRBbUg1syNzZLFr3309p8ppOBEp6Dgr0eyugv7Bg6f+vd3/1Naek4bRL8IZm7MDN5AKFI9ATJ1KJKClMjCdMjWGcK2UaRKyJzUDNK5T0aYSEAqxKDbRGRvsOT+FUFaMIMcdVkAmrAgJQcAakJiF2JpGlRuaVDFr9TZRNQPUwLyR1C2kELsPbs2vvj0b3ev+7ThTA2lzG8Es3L7TbnkyqtuuiuUU9IKXwpZNjLA3moaaCIjG3db84JYo/xOEh8mAUBylLW0wLBiFUQURZW0HUNE0qqjjnKGagLYSNbSQTl9QVr6zKlTxg4bHhDnxJou7ZlH55z9LaxzKM6DvZNJ5T6g08dO7RtVVC3MHRTZtApdpY7Zx/EaPz2IWViyLJCTIKRM9L942VR67oKZQ4xvx/TIpDcAvhBhqq1ATUiEelLt0pM6k5aVukQu7IJdgIj2EemTxlS2mCx5hQsjriAKk0zFrNeTH3hGVRErq7Lh3rTxqKoqJrs0qewnFLuClMNAzC3EksnE3j3aved6Yp3oROaDZOc+d690Tbc1TXQXnd1F10WbWvdvT1iWsou7a4NkiagiYjQSbThz6mikpV4Lhfr1G2gYGRZxGAuUmWg/hUUvpOrh00SKkNtbWzKAaUizqV1z+tj+3ZupbYq5ZkJgBUYmAINA/Hpavm2HoYGVWaLxUheRDyrFUoHVAk4sg1si9kUFzROxAKdwyCLkWvg2axJp1CAHqYlsnSZsg0aI/K5Y7RyQ3iOih9FmNv+kheAs8oZBT6QsUBfrAYrRGDFFcZpOLeDglzzCVMNUMzA9dXLPsw//cu3S1wyfr6RocE5RRe2Z40f3bnr1iQeam898+e4f6Bn9olCiBl9GR3J7lUnIbpm2WMb5OZwMBdNGDRlR2tpChRvqrAuowZIKzTj5ijmHKiyhHijjiax/Sf9hAwbpfOhMW1b6U8XZQbFiMyR/1WwvSqhDuAOU6tT9slL9WpHriy4HBbqc4ZSUKNIwwDZL5EeEz9FzpNRCsYtIRhJlruK7IoQCBRUM/Ezh7B60wqI5U3iwCgLKGksxlqr3WtVg9XS2UFBHiL1HcvYwGb0GZHiBUkJY4XuK3Q4hxbBDFScP9WbSCboIUlW21IQSoyoAtQgTMzmwvWufoNKbj3M7YSYQ136KBnD+i97TuW3bYSuUFFPKKxQbFulVc0JepMvnxESeCMqooVbDFraIxeoPSUfKvPr5gI44VroqAsc2vxqbW0qz5fQH7zz31ktP1Z08GMjMXXDFLYuuvyuYVWTJimWMk2demIvRZUOiBCdy0jOhQEeGV0+3dezHlsHM7etXbVj/YWHF0IkzLke+lCgjA/oPIz7IplFR/U0kHgA1YIsQ6GkR+T0oJrbBWkH0B+qfVMhA1biLbkSlEsNE3aYGfpnIh1Ikya6YjICB4rB4P7VFMocpOTmHhduKhPcd2BKJhIv7VeTlFluyhp1yWAcBM1G0DH2QYhTMVcs+WP/pEo6cL73qy7fdfk8gK+9Mw8nXnvj9u6/94aM3nx45avrU+ddzEMdxmziIxlSa0UNA+4XikFIvCg6Ym5MzOSuLEE20BSmjIzgiYrXKX9QbY4qxlEL2WhNlbjYkFBTNlVyFqunzrz2CJWPATniAMZnLVo3DHpQq+WkdVS813RwFHaoAmbC7koqux2IHTGpFx8TyXG5YFcQS/XqSPZmhHo5xYOawngiRa4wTYgaxGdie+PdCj6uz/CT1kQP44MmFDUE4TH9YUvAIL8DdxFmsvpbhi2HpirVHNCLnKlU9kmJV9rICPA8rg/opU1iupllM/AF3h1X4wuFOJBikWYzThyLUCwnmRKGv7Op3lSqFnDpzWIcVIlHtHckiWOcLjTOZ8hLlIvwG29SH7ZUr3n3h8f9uaTzO/958uub1Z38dSglcdvM9RA8ypSIsVikm7W6Kq4CiytKBHAjbchMkVJ8wfs6UibO4J+fH0ecf+78b1n9SXjHmS3f8q5aWHSWMWBzUkChcBxFaQo66lC6Gjwh+BRGNEokYZlMLQeE48E8Bn7im6NtEx6D0vvhZCcQDoMZLUZ5KgCuC4baKKhPZ/MckFYRPBK0Motc1N73w6C+PHjpww83fvPrGr0RsZILRJKBEI/oKLUXahVrNiIVR8eCRAwYMuubGr6UXDGlBOLsw69Ir7ty5ac3unas3rl8+a96igBa0qCzAx1R8PdJNvitzG0VEPtoPTavIjYPIwClhMa26bjijQLFMlWgxKW6PnKS3SELgv5FWY+k7ix9QSujIjsa3LmCH6iNmxiR2oTIejFVeNRYexj3EiNP+nrlISwU2RLWAhFlOEq7Hhj7GI8c6MWm9onCYObIe7pASGZTvDpHKc5ywUhY+VuTucrlLq+8GWvDF4N/E0XUyFRruhRfvCaXQ+Fnq3AVo/pKdXFoP0mF53CoslznyaD/j3nb3vfIxWLG7geQzFGESLIkvpF5Zx9euI3TeON+YJYKUEO/Ric8KN+3YvFaiK/kwww2bVn8yZ9FdocwU2xY13VijLLZZxC9dWVMle78lS6lIF0EGhCMgzbI1aE3RseVkJy3b4LguDDXmyAfgAIrTLIZME6rvQcOYScIwwfAJFA66oHyw+O4U8GtRC0VtwHIR6FIkPg37NcnjjkwLhS1m85NrVHK8QlzMVvsxByE+n2o0svlBLGRSCODJ1o6gD4d10nzyQMOxneGWM6kBjUWRFQV8BoVjTs+B6sbRgpNmLRo5blpaalpGXnGTZZvQk0NyswszMrP5uyLRNibrGSk08dlu5qKb4A4WXGuilUpUlkFOk4laMuHZqxoLCbG6MXbgcqcJ6lJbsvhjT07xb6KoWMZpZYAKe8MlLu5VHetMxtvdqoFY8FpVvcQiQz24lSVgKdd0yav1LvleSELNVE2RDA+6VU5aL5Hea6+O4kQH8QWzRyhWQM0SI1tq9V5MXWzeyZnUH+g96ApjFM+VgFncXZAmnuKeJhv1zlJ3eNsv/96z/TIcK18TtQ1YUQ4Kv1WWeONO6wl11N2qBRL2ie1fk/keQXAOMMu27fiYMrZMSm1BzU4FdCIyW5eoeiYqdpnj0Su+dPEdgR1LlG6ICg5oIKSiVNqiIhLCT2hLmRpGa46eaG6uzczND2UUgNidPJHOTp0+HgqmpAYzgc9UNA1GIk1H9xxKCaZl5RVTw+DY36fjltpj+4/sb2s8o+u+jNyinJJyXyAtatkiuCnzCBAA8/tQY/PpI/sO1dfUMptmZ+XnlZT6MrKiEX4qjdrW0YOHjx7YbEaaMTbP1B7csXUNCWWnZRcz4hPJQTie5nJsYD09vzi1oIRQFAUCKpTmIz5kr9u9/ujRfQj7hlWNs4hh27GWcj4clqyV7ya74lgSLIJ4RKWtHETlcMdLJo1uc8flzgEDKlIfTpJVTidnW2F/3eErRavrFrR498zYwmeSJltzUkOxyIFwE0W+lQp62lhi0SPQqXzKBEB+1n2YuYWgrn/vUcpMboTcF+WuyjGWz+eD1UNt0zTdP0ns1cOD7/quKmyIFU5gohk61lCgQuEX2HK5w86cwnY+AXQdWFMsCxQfPODVudbziwxczrzENlilCc+wx5PvvZQiCaPEp2IgEOBDaoJTzrxeQW8CgkrEzRtqxQwlMlRhdOHrr7whq6Sv8OHlv0cikV4Jsd1mOMWXyVzecqaEaGJItgODq9PuR1heMitqCyp3i+GgL1Q2eJQeSLPCTcLXIZruHzxqXCA1RZIYSB0lgjGKXb9rLMA+EKesiMqyEzC3mkx/ysCPBWxSESpkRKAOXQSCCAEUaWjWpnWfvP7iH0ZUT7zrnu8zHILQlc7q6o6/8Oxjs2bOHTduRrSFEuAdjnz26VvPP/bI5Zddc+UNt2OUxo3U9jWr33n5z7s2fdZQe4Tovv5lwyfOvnz2FTdn5BdbFAJh/Bp9hOo4enj/jrdeeWr96k9qjh5CdjS/sP/YSTPmXH1b/4HV/KojTXVvvvT4Z++/cubMIcasT997ddOatZNmX3X1rf+gBw3QzcG2JGU1oBkQW9iywDwCv4PGiB1p27pp9faNn6z6+O3jR/ZUTZw3dsIcC2lRZOpwK0F+R8ox026sYKFCEw9ryMk1iaCoG1EBcGUrz75bcEXM06W27RTnUUdtiVCU6Cb/1WIs5SRR5EpeAphX9QoybMgXDN//GxoaolFTNZFR5pQzwn1LTU3hD0I0VUoQD6Y8mx3zbH/xRqQd/kLO8nTrpVR1tfokdmGW+iOLJVzAfxBooLm5afv2bS0tLbm5uaWlpcBDTWlCzKAjYHdB/FdVxqgcbMZHEIteF0JlTYYM3cYQxgU0WSoSoEK9fDDr6+sPHTrMb31JSVFOTo4t/D6nbJV5AwbJ8EG3jKrDxkyJyywoygZdxXrMOmOP7C1RK7dWjK+wxsaGPXv2FBUV9etXIKiuKcGkd24UAAehXR2E+FXemgk2I1Xewc5jqups4+lFVM70Ewp3lr127Wa/PzBo0CD39V4zK5jDZ4mJQjVyqxPNcJJloAMuxDiAFQri87cTyFyVCGEhA2mXzL/i9OlDS157LtraTLTglLkLrr35y+npKZbFqCF3LOmQkfgv6gSXmQMrhbGHTkIETAbSS5Km3seBiMjOcfBkBFkwyAJi7vm1wMjRo195qnH5klfmzr+icvyMiMnSA3j90hUr3niiNDc0fnx1ICWkE1xTU/P2i4+HG4+XDa7ISc/SCVq8+PVHf//LU4e2+VJS88qHNjfVH9y3bv+eLccO7f3yN/6lX/EQG1oBsaGxvdvWP/Lgf2xetdTQfVl5ZbpBTh/b+5cXd+7etfVr9/64atz0062kmRuWxhpmQ5SvtanRNI+1tbRwcBUIAPOVDXwbUE2ly3I6REzgbZc6O/hUbc2zjz6wdd3r/D3T5lx71z0/KCgbaDIU0EXxGFRi6m7JbXfo1kjnnbjxMbFWJOkSpqqC1WEU7J4VrKjGVEpBFGYTMYOwh2r/b+SBZfuECCD6uZ+gY0crSkVWbJG8Pnmy5uGHHz5y5KAcKvEhIpEWv2eXL7x80ZWXG3qQo3cKAktUKB2oWIPc12xwg4QyBDTnQkcMTGmiFiRztnDJC6NMvPi4DVS4os5SyU/bTY31GjFSU9Ph7bYiUZMLVHj/cMYjR468//6727ZtPXzkcGtLa3p62rjq8ddcc03/0nI3YRi/07mlTvjC54icIDoFsjvYWW1JRtAjNWJg9iXEFm21IjNA3/rLX95d/N6xYyf42OXnZ0+bOvXaa68LBFJEZkChK9u2nTSxi6fZeaCole3jNBZlFf0VcirGQzvc2wJXMrAqB4cbkfqGM489+uhnK5bffffX5s9fgOROqFpKknodPRnEknke6K9XERPCXMZrjOKpzy/owCKnqtIJS0MIkG9l4UjkL2+99cQTf542ZfK37/0nogdsu1elX7Ggt3SYBgW60jEO+A1b00Wvkfwy7oRI/tBtyzp/CFBGOCT9ZRTZoax+137pm8PHzqo7dTI9M2tE5ejM3ALueVMVpBAwqn1IDcecIJTQ3C+KnFQ4VzITOEKHFrMjLEzsKLaBUirCUEn54JmXXvnC4w8uefPZIcNHaf7MulOnN332odm0f+XHf5k5/7KiklHUjK5b9uGh7RvHz1s4sHoixcb2Tauee/xXpw5tmjR74aVX3ZJfXB6JNG9b98krTz22bMkLxeWl1932TZ+eaSO7of74Gy8+unnle8NGVl99098XDhypafjg9vWvPvfH3Zs+evnJBwsKitMy+13/5a+OnVz9xIM/OnXi0Mwrrl94+W3p2SXEHzAtm6kGJbEpSm4uKa0DiVNb04xoNMxRpeEPmZEoEPeBrYzaYoA0qpuxNkzK3CBxt0X/1V2ATL6VBLN3b1Of7RF7s+Op3v+mHlS2zXIrqlmUGSJspQnOEycgRZFG9OyMjLaWLJ/fOF1zauXKFbk5/UaPHsNdQzNqBwMBsKtOW7TIl1PJ+ihzylD9h2V1o9ORDPyQ2KZM6nkyAGTEtSgyKCKaOZAB4pg2CJ8hTdfJ4aNHHn74t5Ujq666+jpD9zOnx8Y1A/xfbW3NE088/uFHH1aPHXPVVYssy1q5YuVLLz1/8uTRe+65r6i4xLbbN2nJyaX1hBfrCcdjphgxPFEZzJx2ogsTqMDIYbTh+zzAptdff+2RRx8uLa248cYb+J+WffrhQw/9jjtxX7r9zmAoVahlME9K0WvDur0PV5Z9xemTYJXLB2mK3kx+746SXBdHDh965pmnX3ntRcPQI5FWCFqIpmZZfop6UymWox6i5qdD4pYY4u8RyhM3eur8oiB3XV3dO4vfeeqpJ44e2V85anjv3d1Vo5CkGhdMY3x7Mi1HXQasofS3Omq80qMmO187k9MfarlifhYKpuWNnzaXWpZOdGqzljDttmSW4E/yEysqMoQ2JRETOFdBY4ejZ9sOGWTqnCs+eOf1dcs/2rRuzfjp83dsW7929TL+5iN7t69bszw/b2j9yaPvvPpCSihj6qzLUlJzalqb3lv65t7dq7OKS6fMmFNcXE6RnubPrKqcfmzKoXdef/LjD14bN21+WdkEfs4tWzasWrbYH0qZPGtByaDhzPDx0e8/bMyEWfOOHt278pN3q6cvXHDZbRUVw9NS014KZWBiFJQNqZ4yvzGMmk3TAcWCdgJyglDdbIP9BPIIfmtNC6Vl9bvh7/517/bZqz7+y4pPPqypb7r1a/cPrprAv7JuYyHLCE4MhTAx7hPz+Ct4OM0EjM8CyJdqTnObMPHgltosOyf3q1+927KjwZTAhg3rd+/aXT2u+tvfvi8zLcuyqD8QMAyDwx+wybagJYZGWgBdFjhXIrpEsK7xJcP4OoFwGYds3Me0TOAiAZUYsVNa1KniwD6fj5/ftmxiaBDbATF2ZBi+aCS6efOW3NwCXeeGyRe1oi7JudypbNtav379unXrbr7xputvuCEzM5Of87IFC//4+OPPv/jclCkz+peWUVlXGTd7cU/lCGLQMNYDhxXflAqvX7i4GlY8JpJKFvNB3rJl85/+9PiAigHfvf/75RUD+F/nzJ7+wAO/evqZpwYNGjL/0su4+6r8zyT1Q+R8XGBi9DGmntV7oZWTvIYp2traumzZxy+88LwZjcyYPmvb9q2iMZLYlLWbF70lfOUEfbHbv9muU7NnLrp9ipA7h1u3bn7++ef37ttTVVllQ/ElRt3G2tjd4WLJOMUcajm+C5q2GRUOqlBaBhuNnEhhUoBFzkOpsKyrp24BGImRb1Duh9sW93dNPqNNBPLqCnd3w2mJcLl1qaEDdeI+IF7AQNkJclg2KikdPP/ya556+BerPltSMWTYhnVLT50+PGBQVe2Zo+s/e3/OzEXrVr6/b9em6glTpo6bHbSNU/UnDx7YiZAVaW169bk/o+efpWDjNJ1oba01/PWGU0dPHdtfXlFlUHpk746WpiZ+vg/fefOD95faMPJMI5oVbYqaUdtsPnBgS1u4LojzomETRHT4CETscDhicrTJsSekdTTJ6C43biCiEJLoRIWxbD1ojBg7YdToqjFjqn//q+9vX7v0nZfLRowaTbWADapJlkGJ7A/Q5GzGfboIF/UDO+RpyCG2cpgckcuBDmm4lNQ0Puf9QV8olIKJZhj+9LSMtIxMCrPM3r5j2569e8aMri4uKRHUIvaqVSuj0ei4ceP8/gBfgm3RyLsffVRa2n/I0ME+XT9Vc/rjjz/et+8Ata3i0tJZM2cWFRXZEIGA8qlwOPzJJ0u3bNna3NKck5M9efKkylGV3Il67fVXNm9d39xcv2375j/84ZGysvK5c+cSiF/Lii3lPfDXv/GNb1ZVVWVlZYPJR7iwsKiysvKZ555qamqS7BKSpqEX5Ai8v+BkcA/3TFCTmykdbsTSTz4+efLkvfd+Z8DAgS2tbRw2Fxf1v/HGm1cs/+zjjz8ZP2FySmqKjCupbHPyK+9G64uThb979xrz1Abxibd79+5HH/5DYXHBV+/55t69e7dt2ybzmwqKEQVdMLu4qhVwj8BWF2PJp3z3aGxsfOqpZ3bt2nH33V/lu8e2bVuRwzjc65A3djkXRShfZOSBeYmIVitgiUKKJE8p7CUpG9DTgsZ5ujwby5XtCnaBv60jqZMAJR9AtN6dDTgAJwziN3Q4pk/TUwOGLyQ0BJEhC//9esacBVctXfLihpUfDh02eM2ytwuLS/7um/e++Pxj+7eu37Di3c8+es1noEXXfik/r4gPZB222lqa+eVlpWenhUJRy9YAwABVQVpmztCMKYG0HN1IDYV0bJttzQ22aWfn5qWlppow0ppofEMBIy11eLVJWWpGtu7HwVTsD/gEt5YdMrS0kE8zMQX9PdGNJ8i2pPYO/y8Fg1CPAaKGVOhVQ3bOh/3V42dOn3npgd2bj+7aEq6tzSof1GZboEREsUcZrS+CddEHsKjTTurDumbIYgAq9JZcjAVrzLLBCBhUFcMDRwmF1J0FMgW4obHx97///Z133nnzzbfwNXn6dM2zzz7T1NQwcNCAosIS20a7d+18+ukn7rrrrqqqUbt37frt7x7cuXPXkMGD+bE/WfbpZ59+8u1vf3vY0KGmZdXV1T3zzFNL3l1cUFSck5u1adPaJe+//fd/99W5lyxYt2712nWrGpsbDh7Y11DfMK56wuzZszjAign6M9hhBw8ePGTIYMl7Zxg+vm3V1zds3LQpIz2rf2l/QY1jM9bnGHQaWgP1CP3E6WObt2yqGFDBH6KKDjZYM2qVlpRWVo3ZsHH9sWNHRowYITrgPPyjvcD69sIIlhvHCoWCt3zp1imTJxUXFuzbt48pqhhJ5OUq52B8sSGsHolduWPrDjUf4alTp95ww/Xjxldv2bgRChdkNCimsNSLsq/OxYtCUkaJhgIBv637kORrEilCCyvJu+QRrHA0VmUQJwuHEsueknol2PN+FKfgxJ1TW2pMypJ7MYY0DAToRGg/w4tevvyzHvysDhEfg4BmUcEFwQ1Mq8msCJbEDUJLhERNkpE/YObCm575/QPP/+E3x47sm3Pl7cMmzq3cd3D7pv/58yO/qqutHTlm8vAx0xrCxAb6eSM1M4MfeOacRVdff7eJfFFMozhKoEZFB7UcpvuCqU1RKE1Pzc7Duj6sauKdf/edQGqGiQ1RLwydieLaNL8/xFhqYyuLWJb007lZbItEI6ZPcHUJFR9ELcxMQRysa8isrzm2Z4eOaEFxaVpuSYT4bWg1pIaupWfn6z6jra3pxPEToYJBYWjss3VKDCjeYgJdM+Thjkp6m9o/7WQatH/akRfc+Qzp6Eo6OWAnV4I7nSFecQAc/7SL3wXFy/rgrn2XL/LVvO8UcVBgSGOgCKVJbSaMPP3DxGVBZszp6JNU2XKD45imtLSUQxnuNTY3N2Rn5x09enTTpvWaxo4dO1pcVEoIXblyBV8xo0YNb2hoeOj3v920ccN99903fcZMfqjVq1Y/8MCvnnjiz//6v/41NSXlvXfffumlF6677tqbb7nV7/efPHni//yfXz/5xJPlZYP+8R+/tXnLhl/818+nT51x0823pKVmQPgqtl26rEIc+CHbtjlWsywrEomsWrl89erVl1566YgRIyEPynpdcpsx1BsiMR5zBZtmc0vzqeMnyivK09NSbYGwiWgyTU1NLy8rW7d+TWNjgwpkiyo++ak+258UCrikl+XlFQMGDtJA5JtCfaEQH3ApDrAinuhzXz/PIPPVnZKSsnDhZT6fzh9UprtjaoksGQVmD1404WBC5pWQTUATzmbRqBWJ6oL2GfZYAAkioCEpDrwzQ34l3bTO18YBzrdk+JaZKiq7MARbl6KSEAEb2m0U+fw8JqKmaO23kG2CggwSOsVwC0FZEGmGnlI5fs7S8tcO712dnl0+espc3Z9bPWHuB6+/dPTABl8of/4VN+v+9DaTjycNpWaXlg1dRQJbt2y9ZEFr+aAhLQyZuo1p5OC+vY11jQMHVRopGVHTIsgoLh8cSs/cuXv78ZrT4wePsFEQpJ+wdezovqNHDg0uH5GWktcmxNYsKpW5SNTCEY6x+F0EcMw3QVDysQmLCrZ0A7ETJ44/8v/++8juDbMXXn3nN+43MkuwDR5Va2Pd/n07o+FwoDAzq7DYtiCugfnHGcAvnTn5WdQXCbioH1KYUtSl45gWYZKYr6Pbh4mXDgfL9rHU1NTq6tH7D+w9fuJ4Vlbu/n37OaxPSw1u2bJ5dNVYftiNGzf0LyvNys7+bNnKlatXTpwwceHChZquY0IWLFiwbt26V15+6cabbszLzV3y/nvcnF991TWFBYUcC+Xm5N10080/+MG/rV274StfubOgoMjQfWlp6Ry3GUZA8N4x5NFNlUaKb6+nTtX/+Mc/PnDgQHNzPaL2gssuv/POr+TmZPP967zJz3+eUFGcYj7reSvl3GvYBMJt4camJn5zDZ9fAikBBTAHvqlpaaZpNjU1ScDaOzkRemEoCyyiqFaUAmAgs86k7qdgXJRGpm8ku+wMIE+JG3YeogAA9iUsSP/BcpFYK2tCpWBP4i0mtVuYyK/LBnq+VwF3kNs5KFk3hZpL8jynrhFGVTWX7E0SbhByepSUqlms4QSrli41z5zTKGY+4vwuKKqIEsskCaEA0fstiMtFZ7hqNiLONVBHspZ4lCraPU28EqcnWJOSIuI3qolmPE2I5QCPDbTdkfKKkVOmzX1x76YhIyaOGj05EsX9CgeOHj/z6MFtI8dOGTl6EiWGTaFOPORPnTL90tXLlmxa8/FDv/nfU2cvKqsYwY984OC2v7zx3NGjx27/x+/PmnetQXwa8g0dNnbstDmfvP7MHx762e59WyoGjUlN73f6+L4li59du+rja67/+pe+dJ8/LVOU/EtpPWRrGqxfzRYsX1hTnEEcWokt1GYl/YoqR4zYt3XJsg9fy+9fPHnmFTm5/dtaW1Z++NbKD9/GyBg9fX52bhG/434g6UEiaigk+wTrvXs35dA5t8lpQnM4J3FcQ5H6CHa5jpxp4H3qeY+6WWo5OJPEOyXkqROuhMSmnLqSGFRw6LASriTphXmOEIswMU98yzu9sefUCd/FvfhOrkR2qbb7LrHpmnRUsYfbk8WvtaSj6sYdkVvsQbAn+oBRPEM3jkk8SxI8UC9XtxIqHGkoFKisrPr0s09PnjpZWFC8fsPaaVOmUGZv3rI5akaOHD564uTxS+beomn6tm3bopGooWvvvPOObcNU9fuDtbW14XDkwP79kXDboYOH8vLyVq1atXHTJr4TGoZ+8NB+y6IHDuxvaGiQ9en8qWnagl0k1v2OPCyHtk05CJg5c+bw4SNqak4eOnhg544dTz/99HXX3VBWPrD35Acd7kZZ6Ip7S3rQ4R5T84sxby4GZhEhEiVIMncsoy5JJMj6HglxwfgUFYtpRsgpDI0CzGls6Ht0xRmIf+oZaoZF8QNDroRqIvFFj+cKlfVyma8lrCASIzos0HJRSR1wnMQu6H4fic+BsEQG2OTZDBb/18QjYE9XZlzWxPEFbRW7VjoK8adjKEltVkdPPaTSBPmAVVTwUtqMf7WAHwl6QGwTbGIqW1l9AJvmb1q7vmrqpbl5BZQSLSureuqsVWuXz7vipozcAj40QZlTJXRY1bib7vqnp/7wwJrli9et+qB//4H8gk+cPGyGo2MnzysvLgvqWlB8jbTsgttvucc807Ji2eL921blFpYHU3NqT55obawtHjhk4KBBwRTDMJBBkF/XrYjJqMnhetBvIJDvUdWThqRGh8Ae3LlAVu6VV9967Mj+1cvff+6x/7vqo8U5BSXh5qZdW9a3tjVNueSqG2+5w+/3MRNyltBpKNXcZB0Wxp3epk6yVR1NA9bxbWrPuNj5qVkHsythgnXxSpJ+l7NeSdIDdn4lKNn8ZF24kg5XSgdXIhe45FGArchHsCR4cyjvYkONVTe8YuhQoiTYdRy5YdDLysuCocD+ffvycvL27Nl91TVXc+z17uLFJ0+eWLV6hWVFRo8eze1H7ZkayzK55/D222+LBn/on4pEoiNHjeI2u4k/mpuCweBHH34gCWKAsoOaAwdUZGamc0AmSGHkqamUQ3S3TJcvXu6woVDo1ltv5btTa2vLieNHlyx5909/+mNzc+u9930nIyPLsqzekSmINQli3ItiasLec9wcyszKamhsikYjkjVDFgpFo9Gmxkafz5eeniHyG45Qeh8yOFv4SmnzizQgiXHFyZ8yjNE3VJ8ngoWc/DRVoF/9SQwyuGW6In/BvUHIIZYWcEj8JNEY3/J0Q/MhjTgceYKmAQAG6WCr17m76TEqCXmlpGmmjv6a5Ag42QeZimLF9t7YXzq8EnTWK4H/gCWR5RaWVQyZ3b98hAVyhEC6CvzoFPgLIGTHsGWzwvIRl95497CqajPKR4daGioZUHnt7fcMGjHJZAa1LWmlTEp1XaueNi8jN++DN5/dt3Vla1OzYQSGjBg7YsyUmfOuKxowggNWCzgIcYSxkrLKr33rf5cOHLFh/QcN9TWRtpai0opBg6+aueDK4ZWTTeI3QZOZUBIcMmpqVnb/nH5lZpQiSPAhCwuKWCTEsSUBFqJtmBVXjPzKP3y3oGTAhjVLDx8+uGfPDkPzZ+cWLpj9lQXX3pKe0y8cNTWobta4TbOUaII3EnPWe8qSbePndKM7OlrXj4O66QjndCWdvN7J0djnvZJzPoKiD6IAVDTD5pNRUUHhOFkMppAYQw7VkEOcjh1RGlxcWDRk4OD169b5fH7+ysgRIzWNvPXmW+vXrd22dUtxcXG//H4cm2lYC6WmXHfddWPHjIFSRqIxmxoGN9ZGMBhYu3aNoelTpky5/vobDGBqICIISwKBkK75/P6AYDlkSgUcq2YLj/oYksI4FJaVbppRTdP8fmPgoAF+3+WrVq9avmLF7TU1OTl51nkj5ztnPzxmX3tHTE2VY0OcMj09vaio+PCRw40NTYWFJBoViuAEt7Q2nzxxIienHwdYUkdMluIJ0tE+gJAk1uJGVuL0ManruiPsain01bafy6g6uuMMOWLZ3r1OsXrI6L2Dw3oLuvKG3pjaxQRbMrNMIe0qc1BAjAdF7o72TOKurpvnI/SdmK2hKI6G2ysJ6hTs4i98RhgMYlps8vxrps65gtsG0xcK2wQKsSBFCuoxkJCjADmNlNzJcxZh4P4hUl4oK7ds1txiXTeiFn+rrBMHugO+bSGiDx4+YeDAysam2ppTp1OCHNXkY8OPdSNMNSB7lcygonUzvf+gG796/xUt/3Cy5oRlmQWFhanBdKz5w1iPQjyA8qsJpOf+wz//F0E20oPhsGpKsRGzoO8QQwW9MFKCXYhFsNZvcBXHfnMP33Zw347GpvpgSsaAipGFZWVawN8KKUFK5CylEKODr8sEoTImfaUXF7kbKKAUoGegsKJxxJBxJfVqr3L2L5WOpDKIBRYiJyd/0MDBr776clNjY1FhwdDBQ1pbWzPS0t9+660zdXXXXHNtakoanz9F/Yo4iIua0YGDB4XDQJFnGP7Dhw8fO3Z8AISpsrJzclpbWgoLi1JS0yk4C6S5uWHPnr35+UVZGQZfMwRporRCkxx4gnZB6efwJVJbW/PSSy/365e7cOFlHLEJenGIE+iGDk2SmLj5xL5HB1kLkWJm0CWQn59XPaZ6xWef7di1Y8jQIdKS+XzayZPH16xbM33GnJKS/mLSkJh4emf7dd9DrSlu4zWCubMhXBsmRCuoMzn7ooDnCk+Q4v+moiOHOk6gEvWzSTyTnIvJOm98vVAODWT8RN0G+IsWlBgJVMWIC7YZ0FUiCycXI9QxxecplM1cGl/lcBPGnGC7xHuEORIc+AtXY2Mnn8vXRaqhQzdNmPv9GhZNhXIoBCcPHybRUaj7QMoDmDyJRsBUaIbfB+uJry4sFY2Feojg74BYpm5k5aTm5A3gBwR6Ie3/Z+9PAOwqrnNRuIZ9pp7nSa3WPAshIUDMEojZBs/Y8RATD7GvY9/fw39zb+J385z37nVe8pzr5HpK7Djx7BiwwQaDGSRmgUASAgkkIdA8tdTd6vH0OWfvqnq11qp9+vTcDaLVInu5LfqcPmfvqtp7V3211re+pbEkImwRfQS0yK2xkIszLx4rS80pa7DH8TnkgXGqYYVfgWWJy3iyzAKsABMJBQV7jUiomI8Jl7aFEuQ2JKUG2UZ6ieKZ85bPm7/cfhAU2wMPmPIk4WrfsR9A55w9t8+UL7SnhNScs4jmfm5P9yBvRhF/zkeTtAxVhQtYlibUwzTcg3tMebHYwoWL7F+2v/jCRz78sfLySntXL1+2/Cc//bdEIrVixYq4Fw8Cc+HFF/76njs2bNy4du262tp6ewALiX7wg39pbT3x3//7V+fMmbN02bKtL7ywe8+uCy5YA1Vac5nf33/f7+974DOf+dyMGU2p4mRdfU1X1+lsNgMK8iAVwcKJEtrU39//xBOPdXS019TUXHHl5RhmNCrIbd783Cuv7Lj0srU1NdUjCY2ePYQ7UeQxFRiFzqEQ1zIWSOldfvmVD/zhgTt+9YvFixbPn7/IDt3p0+133nmHhV9XXXVVeXl5NpsrjNSMJOPOI3SF7hYdqp4IaQ1GTHv46KEkA/xjh12paF6arBPL+f8EFc2zC60EhjuUpxdh/oWA9ZfqsQ3RwAsR7VkSSs2nBVL00imoG2KXO+8Qd1zaEaGUhxtIV4okz8bSg/kvBU6icQg1IvyuDmt7DoQHsCIhC4fLYGajCRNf2bAjTDCzPU8iJpDi8BC6qSA8qiC1XbthMYI0oqDiGvr24FJKn7M4D0tegGwjHko4dCU4+f9c7TcdoB8eNoYGnkKES5ClqGmf6KE2JJQsczkIqGrl4eMao7onWPlG470XmAAilOhy8hiU6rRNDVAGC3GXkMwgAgMfhq9BSNV+OkDpUQnvAxzzNS2nHNNeQZuUFjMZltYoYJoPvBxNm8MMvg3YmC+HCyUUUMsHSRvoCbRkiGemsCVmzIaxMXUcCm9vXnh/FrRwjL6YwT0SwxpvJjyqQxoz3qgCgwlvNhow/H94uLCcsmFhdRK8CYS2t3wA94w2+XFzs9iCBQvr6+r60r0rV67UShelihcvWebFvPnz5tU3NKJullq0aOH73vu+n/3ip1/7v/766nXX2CV806ZnX3rppU9+8pO1tXWxuHfru9/92uuvfeMb37juuhvr6+t379n96KOPXHzRJUuWLFQqV1tbZbHaQw89fM89d86fv3DmjFkts+dAsXJXgoc1NjZ+9rOf/fa3//c3v/n3Dz70wMKFCxOJxP59+5/ZtKmpqdmeury8wvf96aQmoMdxmw+6WabCwULljuyznsv5dgA/9alPf+tb//j1r3/9yivXJZOJF7Y998IL2z76kY+tWbNmyEgWVn0uwFhnsBahYdO+nPNojaaBtQv/9u3bX9rxkoLSGmzHyzs6Ozsee+yxkyfb7CNTVlZ+2WVXzJo1ezrtASaL+81UFiZHuWxg7iATUFtsdeTQkaeffrK7uyuRiB85fKjzdMfu3bt+9K8/ZCImhHfBBRcsW7YsP7AUDR82l0+Rr1iHQu4sn5/kFEU1UbNQyd1gDS/uBL6HrQuecmUAcW80eC3Uw2rA5V+O+D4Vqcv/yeQZoqaAxc4LQqyojhWuKHzIEUY79YgtIaejcux9ReANZw7hFBEQjCo+UDOW87Cgm3EIT4f+Nu1qwBlGGJu7yUmHNTWd+iNKpod0Y/Ad4gKMRczJc4iUYyNcDW7MDCTZD+ZKlsAL2uMDmlMC6o6E3jzUssLPOu80E4ErhSy0U26E6CBDfyVIVEi3oHouXQ4+lB/VIZdpxFFlw24DNuZLPfhPhcvRkIs45NQjtqTwCOOeuvDl8L4MwUn525uNcocPyZMd3pL8J/XE+jKkJcPPPvao5jclxtHaNd3dRLhEV7AmR5XA4LfCXYB9tIqLy1auXD179jyLjYyjQ6HGj2E1tfVrLr1ixszZS5ae50NsTs6ZO+/KK65eft75FRVVgYLaAnYGfP9tHyyrKL///vvvu+8++148Hr/99o/fdNONdlcf+MGqlav/y1f+2y///RePP/6oPYJdkNatveYDt32wsrIqCIJEoujqq69tb+t6+ulNzz67+T3vef+8BQuzWcVDHeRYzLv88suKi4t/99t79uzdc2D/fs+L2QauueTyd7/7PYsWLXa6yaEoUTjVirMRHRgheIQtEQMar/DwTn3zTD5x117x9euvS8QTd99zz8aND9ttYTKZ+pNPfupdt7wrlUoFQX57ywsT5UITZ7JJho9UiPAcgFjuCcUqIHZncvzo4e3btuZyWYY4dtGSpX09fVue32KMqqmqXb7svHNGS2xg88Wm/qLgyYnggPwFJ9Tp9fR0vfTSC+3tbeiy0gsWLbD34ZatW+36mEgkm5oali9fOuCGyadxDmr8lN5VYdIjuFQUYhZI+2eCylg7EfOw0OYI68KvNvlvy+DK8D3mcBg8hqLpmW0GG+X43GFlxgbT/yZ+8BGVLXkUGHx7mKE8FSOMintqTktJleg5evCohMkJyDUBFzH0ZgXSEXUt4kmn0xYM2fUVnO15KQ2gn+tsJmNBUklpqdtOGJ3JZKSMxRMxAmNQSloKpfzu7u7W1tZsNldfX1dTU2vfzHOl7cH6+npOnmzt6uouL69obGy054IKzVQij5n+/vTx48e7ujpbWmbX1tYbnd9wGVdrghvbyPb29o6Odrttra6uqaqqtsiAEAAI5IR2ViJxebxA07vWyvO8lpYZ/f2ZY8dapfDCLAIWFk3gU9cqxvMsljwGtbiqu7untfWEbWptbV1ZWamUcfu7vU0KPzb89zM2qjReZsiRaE8P76LCZFFz8wx7U3V29lj0P+38PMAWBLpi1s/2p9O8oEQ5dADqnUMYq6ioOBZLTDeMBbXgJW9qaspmMydPtlGg04RLy+B+5i/KWx/RRocFyIk7bwXDFNesnR+g5inpz2ClPOecYDyVKkokEvlMDkRZonDZdgWOp+Rpg/gVJWfbCZar0qqqyrrqHbvTPdmUL2KoNGlnZlAb51A3ZxQl92QsEkeZVpgwssgGNkAK/c3CQN0AD6FJqNMOHkos5I755KGepwVA5eXlDIm6BUspecLs/FXMU5wQDKy9wispLiPVXyAjYpQb86hlZWVNRUU1SixiGiOAJJgNKc2qpKSsuLgUHeYIvLRtBviLlbYNEEXFpfPnw1/hiwFWxXLKLVpAsQKYc4uLS4qKSmbObOHcUTMpeyjMd9Nn+zEhhw8fsQIheZmNyNfVNVMIBFhezJ2IIZQbWFZmL3s5ph3YdwK89KIwQ76gY/yMj6rjoJghfj9j+JB933QMIJqwdrrBuziRKk6mShxrkIUijUy7cJV22WLTdb4WQzKRgaxSsB0Pq9PyqfINciLREMay/08k7HasKASueXDrIlw4abmJizYvYSpyfoNRIJswBZ5N55cS5JCz73mejMMkx3CXwz1kkivG46P4RzwAj5FFFtn0M8z/Qzk+TCSEOKJ0xT0VpD4YkmChhYyjfozOV6Zlrl5aONHiVlIBxLE/gdISM41B6RuKMkmqthUK/3EF6i3gbtK+ysuHoywWcFHtl2iVFxRXd5ks6NLRJM4cZg7ywmkf+QEWFEoZlsThrggxH9BOHZB8PPsgN78GDErgDKECrRlTyAvJCwogFrXrloKMHNBfwGQIaIaPOeSFdKupGUYnZcqHw5cBzkMBI9GY6QVREDXhuGm4t8FtiZxmTf5X7rTmgEsk2fTLHeJsCMfB5P/Ah3xs6hrvFJWxABzSY6CMnZ1YgEDj4oYFu7+8b9U4aQycbXiBmKYrrTzcMfdWjSjuNrWj8jhikX0HC7+SHw3ppZBWKEYTcvGml+pEZJFFll+bDFch5QcAlKHIPxb1dU4MyqdAFURI2GOSRNxdcq7hYZ0U50ICh5OmRB5cPUAiC8SrYJ3m2q3JsIeELRq+oBT/kA5GGgEakivsPOnihnlFW5Nf0FEtUBWu8fl5EqnEATMUiwRimeSukBewNGCzC04yYczZrU1jqJK+82AgDY7gJ4QO3EeENiO5Dd7aVsHIC0pztugbQyyc/AOG0DNQb7FJHKkjfOpGbBjJi/Iy+JA2mPydOW12MgQFAWPRQioxVcgujrQPIceK53kcCrxMr8Y7kCLyuxKD3kRH7zSD0YhzNE4h8KO6www2dUoK7qYS2n5pp4hJig148wKk5XhXu4vCBwqBEsCZsmdNujsVHzG8BQzWU4Qq+pA5BxVicPPrKueM2CwvCk5FFtn0NEphFZiKSjgFMVboVUeehaDdISp8UvxQ6wCYUgWOFVgwEPZgKi93uS9CUEVo6WqDEsOBMqVpNwlproSxVDiB24kGRUyUQHYSd3jPYTqPVJgxpCILnFADTHWIRTJJIhJQvJq+CjhNkGCTfZP020bIVC6ctydYWmDcWgIFcHbIEUy+7pFx0Qkdir8ang9aMM5GOfvYWv9sAqUFRugL5EHDfE7eLMVcSVf3JpV3hcrgXIdhRDZ6GQM2LFN23JYMb/zA1/mwq+YqRFE9AlYwnmZsHuy4lQ/GrvAxkb4MelNgypEWjnUXFsZmiAew0jr8U9o+AACAAElEQVQEw4XRzqMx4QoQbDI3JJtA1v4oB3QgFu5RbgYdlhs2+s0/wboaZgLPHRvpacUoNVLA0RtOe4CB3eOAEqY7g526lCc97IORoUZmQYzQTPi5HiO/fML3Bt4LmJUGMy3JSoBQJVHaDacahByrKw++9AUAK5mMFrLIIpuG/iseCjoY+0DHpPQ8lo/7Uf6qfbK9cL4nZxMDlnqMnvQhaXAD02eePx7KFPOBYBLOgKHDSgg3c8hQIN5l+YVV7chTTuo1LC+2FGYQjyDKyF0Cblg8UdBpJXfdyEfcChKPR5y5RvsrH/bmiJ8Z+4BhKTpyAUDoSCDcpYgFHzjLoImVj3yccc7IhsEgPsoxWVi0R5DmmQjDJcS44gN/dlVzCsZ/si0ZY1SHXw4+4jCG+V6U2y4omxpkmXHDMOZlHa1JYzd+sqNa8JLqpINzECmDoY8Y7k8NSfihHBKW2B3y3TfVknFHcsTfhx7BZbLA2EosT8zzMwUf68pOqiVm8A1pRh7JIV/nTgMzhC2OOTB0Zgi5epT9QOVp+CB4NJqKzgS7xsa7pUceVUZinTjj0Y2SSiQUl77kyv0VSBqxMII5ggdLB1GMMLLIpqOpUCKOQdKN0jweMLvHw1KTqMUC4g3EaHCi74aNnhKb98nwkRSchvCe8mJ1bJjjaDgUhOldDZSsdoQcM/Lnh24SR/eonH2Mq8Pi9JAXoBVTPoQHBlWkHXHb+pZj77BdA0M6mCFGZSTP+hYBY9jIxAcBQWD7gZIgRFj0tEx0BvdgMLDgDxUJI5+Gmo6bMYiza3Rp+sqF5tFbZKZH6wo8SNC84dJrmo02Iw2gHzPF+fG4uUUpf+ACAL3VtjuncoEGdBVgtpGFX56bA0eu/OZBHb7IIotsOs6bEMzjGkL8geE5xcqL4mU15VCtAJlAOcHimNak+LjV8szZyNsyQ5bbkRpgGBPT9gIQeAHWWlLGeKKipoKLAcWDkfbxUzPzjzuq7GxwP8yQq4laIo6iF08keJwny4vLJLD3puHFhlpqbmPAh6gBh8utCZBPNP2aDr4fmfJ4nJXXlg+qjMTP/mbgzNmUNt645CG7vYXfU2UJJVhgtI96HpRnYpAmi4XpRnkIf7MpEv+PLLLpubwbcGOhFoMyuZpyMac+FpdSMZdtYx/dBAIsn7OpyV1+s2Dl3DHUcEY2mYt2UqkIFyOMRnViS9QQPKhRzmOalvPTITZ0NeZGovaoabkbwNIfjq9E0hIMi5BM473LuWGkz4WlXSBk3NETHDuey6p4APU1kGZqpHBq82JkknsqFQ1jZJFNQ3O8dqw4yj2WSOeCA61+TJhAAy0EqZU8gSItAdfTeKFFd4Dh5xbAcnR2ULfHrH0GzHGOEy3jsmBpm54A66yLDQszkH6PaafIbZekiMTYdHQDOThiBsQtRwdh0+1eRa+Vq6WI2W7c5cVwPlRkJLKJPVmu/hiWc8Gi+Sqb48xLxuOUDYnMdwMeTT36g+j5fjT0kUU2TaGJsHt+LgOAU3ZRF0Gmn/MgEG6uV5zHNfOg8qZmnI2RkY96eW9dMIuSACnvevgpRFjdfbTGGcOn3SyEpVTR3YKZWdpFjSgtTheqOik+paJOJKfBGB8dR1E2nJ7iUR189bUpEOoULPQHGjNtq0sTg5CPg0bcrT7dTGhitRsSaDBDcjBCAKCnUrdjyL066XnjbE4UhE5hEwsi+QyqZsCrhN0jBFBzz2DNLMe88p2gy0gAK4gihJFFNg2ne1yOUCfBLuFYrBB+jUFZBtA3IP0V7muuaAkba+J01ZiAHS/OcFSJU0rboOTwIToIVKJzZMdFWAl0+q26xpULRXl0Kk2EIp8mn1Lmmq8HCuZM1eY6P1xmlD8NFIKdsoaR9JrIi4Q5bx82IAAnFjCFBZd0u0xXfyb1gpuRopiumqOZdvcqXXVBtaiwmS5bjxfW0HS+xLPReD7qpKTE0Ft6dHA1tRNFWGrIIFsANNsVyeJpx2nnCqc9BRvdUW9oL1rJIotsOs704eJIRVlcYIJ7rnI5vkB9ZPqFjz/B8ZHrpLzpyZMXwIvhOdJmHHYt59N0rc1LpJJWRV5CeiA1yxUhmXLW8/CU9SF/MmejBHVe+GMgtX5AJQL1OCR4YQuL/09LNxYbnc44Xe/VgbQ7J+LCC++DadB+PkpZYD6BWzr8AJ9ililngxJI7Mk92tOIPALjTh1vjE2MN0jXI7LIIpvWNjAlyTO5m3wrdqhT6T6ZDlfkP9SpJ9gqfu40O7o/p7gZk703+LQd2DFa5mH4M7LIIossssgiiyyyM2YeNxHAiiyyyCKLLLLIIjujAEuIKIswssgiiyyyyCKL7IwCLM2jUjmRRRZZZJFFFllkZxRgkeT74DfHKlY27APDC4m/4ZpBfMxi7+O2hI2SrWAmcNIhxx+xF2Mfik+gYXzCjR+x9D2b2MBOsNL4mxnVSY3GxFsy2VGd+MhP6kKM3RI+SsH2ybZkgl9hE3vWRrthJniB3kBfJjJvjH1lx+3LG3iE38C0cG7NYBPsCx/pX/bmnrVJ9WUid8i4gznBKeuMzGATn42HTAVsMjfn1MwbY9yW02c2fuvWuCmbjc14g8z4b5/JRTAzssgiiyyyyCKL7Ex6sIpSUbHnyCKLLLLIIosssjMKsCIl98giiyyyyCKLLLIzDLCyQTQIkUUW2eRsRIaOGJA9fpuYcT9mbG5RZJFFFtkIACusaMVHJ/HxgtmGTYwlxydGfhztdGbYqdnoL8c9wojnYpPvy9hcy7H7woaN5KRGdbSzj90SNh63ccSWsPE6PpGWsMnQKoefnU3s1MO5jW9yVPl4LWETYIyy8fo1qbtrtDvfjEdynxQlc2zG6Agtwd+MDOvJGiyHqLCEv6ECPmbsEjnnArqioslQyBXq6lHxHI2VXvjINOcJjupZmcFG5BRP/FmbLPY+I+vC2H0Z7emYyMrCRjngxNc4Npl5Y4wpa9xhNG/N5mjis/Fk14Wx5o0zOhvziS00o61KE7w32Oj3mxn3qfdKUsOLiA2vEMSGPdJjf3i0r4x4hBG/y4fd6MOHaeLHyf/1zfRlxAOaCXyYjzQbTqolo519sn1hw6o+8dGLGEzwgGekJW9gVN9ASyZ7BD7SvTduw8yYozqpu2viRxixp2ak94d/bNKjagBnMEn1ZaGoL9ec7z585LmXduayvhFSeQJqUKMEzLkrtBcIJpWZVVd36aoVZUUJLPpK1cdgBLxRR3Uidwib8hmMv4nZeLLr9xlZF8bty2jrwqRm4zc8qpOaN8ZeUscaRnPmtyh8YuvCaH18i9aFNzAbv5mWvJnZeMSpdeTjeNkoRhhZZJFNyrVjwQdnnjES/DpaMB4IcaKt7fndL3dlchnhdUJ1X4F7N3Uu+rCkYf3cBEZXar0mN3/F0sVFUhpl+yIslKQKrwpXP06/ROUwIjvDTxgLy3VH4ehz2Dyto+sXWWSRTXTzS+4rxYQwXBhjOFcWcIC3Stg3eSyeKC4p4haP8DgXb9QLcrbXN8NKhNK5DOvvY4Yr+6syEjxYoBtoO0vxQoFjYf9gzNv+sttrzceWSELNah26B6JlZVwIBaFm2J6M9FcYRCPwNjOhGPgY0Uw2LA4w0pPL8c6NrsxUAiwebb4iiyyyiS+2xrGQmAF8RbO/FkJxCQArkaiprqtKpgzAL8G4OBf7qBiLa9XTdaqvv98g68qA4wo6g/8IWtHAk2WB5Nt8Ch0ATHxgXWfDiUR80LWOlpWJjKodtNGYlwiymA65f4WfGYM0Nsa2yB5GEKkwGv2pA1hCetEoRBZZZBM3SbM1gA7EWgg+mJABkxnDM1xqIzh8iin45Rxd/yx25IHtmxB2kpTS8wS4ZxBO4apoKHESHBH/ASrmm3H107kbj8gmNapsPO7/EF7UG64ywiPYexYAVqCjMY8sssgmAT00gg+LMIQGaGWnEPhhGD5jXBnuG4Al9p0sM+IcDBdxZpQx2pgAg16QEamNRlSltVHoycL1EZILFWdvd4A1kYIhb5gU/1a3mU/jIRWjNJKPB8KGQysxJtqKVvmzBLAiDlZkkUU26fXBwirjAg6cMyUs0jJA8iAnjxDg2zLGEw6KnVtmcVUSCTASuwQUK/uLnSs5RA9NSLmyHyG2+9ud10JXlQ2mmg1P6TLTaSAmXi5wGuG/wYNsKPiM75hhHxiUL4yOVYN3JseoIh8PKEc2JQBLygjbRhZZZJNaHCAyKLQRIcLy7A/N/yYkwhvQatCunjw/5wCFYtg7hItcMDtPcnDY0V/BbwcOPOOEKhwp7W15rQFfwnrNB2kjcGOGkM+mheoZgY88CqEWhjSmaWV8lKFmnsBMAeiCG3EhOA04ISfqS+Hgo5dE24/hNeFIkxyx19FaP/UAK0K1kUUW2YRNuyghkWaNi5UB+mAeMx5EC8GnI3CWj4ObRwyb5ScliTmiaPwYnBUzgTL4bGwBWMl4knk5EEzF1hu7hjEJv8MCpmjZ447VIt7WMg2UzwbLvTahbgC8FkOXazMdCFjQWm3ylyP0NZ4j1wfi7PbBMh5sXpQA2V6uaX9iULzXonqt8tAq3ykpaD9AjixjlMvHiPRDzj7AMlpHoxBZZJFNfOvtQkEwk6Popl0P7MoASMpIN+9r0nGIjbzoTlwPczRmzxiclTfwlaGnA5yFa50GXQZapSFjEnSwCuNkodfmbSbTwIeBFqoXREoNbsmADIchAGu6hAgppMsL0u7OlQskGEWkMd5uXMAdUgkNppNQCQHj6iSYQV+kK6Wdb8vBMmOitIOzC7BE5MGKLLLIJr56uZR9gS4Lju4Mlz8mXNITCZ1DHC1zbvbRBy9WqM/g1i6NyAscVwaX7nwWITNvt9gLHwxWKHFSQSCK4qUEr0d0j+izfncCQNEW3XN0ZhEUnM4jXaDLgAwqjwtFaMowif8YgLQY+dNGYu4IwkdeeJ0IVaGfUfN8AHeE0HXkT5lagBWPRQg3ssgim6iRmqRdv4TmUIuQS+GI7RA7M5R/F/JANJ8eoaNJLnqOyc4xXoNEipgnYpLKEjLqlDQuTEZK7mdgEXdKnoYNVzoi2o0pSLUfFOEcKTBK3iZ0xmEhxeG9LDiEGcycNvllWwjJiIBlsaXvh54U/IgUjpJmnLNIC+Y27AO+Iz7s6g8unV0AAMYkpA8+7KjQ3zh/oudZKAK/KQVtHk8sZFKqB2N8xY2kcTJxBO6GHwoeDhwEpFeFfdOQewvuKu4YVvBM6UBxGmjOpQen8X3spyv7iYWq4LWWUoKXVXHquJOHdUDYDHJ1RTZlACuIKuVEFllkk8RYqE2gOWgWYOQI0gaBBWLBR4CMEfuxmDE5RtmG56ITB5oNUljgQDBcmQD+Q8mRwgkzoDdPTQhdmWHOIZC3EIY7iAL0GhhJC2hEXKB/EBuguT2vCgA5COOUIgkTwTIrAMxyE9ZUcTrdxMQxCHDt34TGmBMfJGVFGNjRdOz/Y57tovYVgmcNQMnCZ639js7O7u4ue5qq8spUSYVd5e0XlV3ODQuoEDZ3cpjgfSEikOSeBEeX0jqnADeE+pYgSy4lQFUJB2HKKF+xUOWCEC0vAJWGgCKOkrADZKGDjJFgpgaFkHzVbQAZPCTFqawfpHvT2aA/l80WFZclEsWQ4JoHhgPgKCSEYxN46BkiYMpN6BdyeRv0HzqFptxYPqilLpvPDryCux7hZgH2Y6gXFwJFreESK8ibMAPhafQSukFNxLytLz134ljrunXrPen5mXSQ6+/P+RWVtcKL6YDuHQv0Qd932/PPMR6sumCVJ+KBgj9hkFHSPcCNeOtqR0c2FsBSkUxDZJFFNknogWuFndo14icBQQzFArtCMxbnYS1kLmLMJDQ/5yJogsKcRmjieIMjROfT5TmUYiRcgUoNfIKgtNAdBex5YNdYUCVcvphAr1i6u+dI26HunpNBkE0kU8mispLy6vKKJs6FB1pjHrMrqzSkOGb/UYpJKRHiGEeRAoQjkX5j0aFtIZSMtH3QSPBhKKEhdaCFZwGagmoezO/tPdh2OFmWrKhuECZuDxPjxk/3bnr2sQ1PP9HZ037y9d2Llq35/Bf/qqq6Nhcoh5lw7bCDBCDKtoJJTG/gbadPdnUcizGVKqqsbJxNamE0irYDp061pttPFXGe0bmiyuqKmpkGlWvJEya5UpilaTD+bE1yrQxUXvKM7E93trUdyeYyEDczSiBQszgm0Mw30sIKaYJEnKeKqrZs27rhkd8cPHTo81/86iVXXO/7FpzBYHPHzTIBjL9AV5NGYQMYXOGKIFn4ZA+oLNrliAwFFTgHKCOJAgWpDoClBDHPhYJPKokw0LCcUHaw4/ZbQFVH/5KFpBbk+AiwhP2kUUhli9vh95kvEWvD4QScVuiEYHt3bLvzFz+4+LJ18aR34sixJzbc98Sj92Uy5ut/993a5rl2mEjl3f4TV6a0OPnzn37fz7RftuZaxpP2pL7h1BmkDUKSBvqSoxDh1AIsLlQ0CpFFFtmkMJY0RLjijpIEyz+TLEyLR7V3u3fOCUORknOrdwqxDnUt4CYMsem8u4KjvKqnw3DQ6P3jeQ8LHZuCWPksewQ9dkW3S7mFNHt2vfDwww/sPXioOJVKxb2e3t4TJ45cdtU1H/njL3ipFLNIQ2vpxTXFmOC4FjfYldnnglwtAQe/E1OAS8DBSAkHeAEAT6BnC0VhIXSkNOb1x+PeU4899t3v/O0Fay793Be/mkqmBACB3KYn//DLX/74pg988Pobr/unv/9bX6USRR6eQgs6nwZ0g6u4gKuslRZw5BMnjz54/507Nj/c0DDzE1/4q0WLVvpBoHiAQ8iPHjuy4fe/eW378wuXLl9347sq65rDBAJEC9Aj+K8mnhu6DBHnqnhS7tn76re/+T+5J2sbm6QQvt97eN+rTPHmeYu9ZIkKVMfxI9ls3+c+95XG+or21tdPHd+vdRrSWu0WwA4ywCsM9AK8NYH9JjjBNNL2IT5nUO5MgNsS1GQNBr3BnaiQDiUU4m0pmAuVGhD3x45L+y3IyMcv4O0gLOxDUIVS/xLDlVjAEtxWyuJI+La9FL7FxwEgZO32LoJ5QrWdOvrzn/xTPKauWnulUaaoqCiZkscO7ZaySOt+uLYQGrSt5B7co3re3Nlz57X86uf/Ul/TMH/hqiyOpoZyCgDE8YoB8mY8EhafWoCViEdB2cgii2xSGARXZpe2JHFrzKVEYjEvSDPkBEj4uYggGfrlYkzHBIvFWTIhEJsIE6bP0QKL5a4ZG0PL3YW/ZN6VZWDFA4Rl12smCKMKzxOv7n7xf/2/X8n4/p996a9XnndpSar45PHDX/vLL4psurwoxmJSYelHQfQwgDbgxrFQJ9BKOqeKkOBC4cR7w7gYCSfhWs65dqEwIekaYaHuRNy+lU14urgokUrE4zERT8Rbjx645+6fzWxpuv76d9RX119z/ftFLFFSXsLBVUUOPkILXGL6qI9YyL4XCHP+hStT8dzzG3++5emtRaUlX/zK/6hpbPah654dsYsvujBIt+3Z8fS1N1x/yVVX5rSngcNN9wk4Ru1tpNA3CKODgWfohmZekvGY7ujpvPXd712//gbfDw4f2P0PX9+Uy/p/+vn/X9PchYLJDQ/c+8Sjj1TU1S6cM3vRokXHjhwqinvxuEA3gkBlDYyqQmFyC7U0UsTQm0hXCf4EimcxCxiRD4UohVPo0uMxpJ+TNAcxrDh4MCm3L6RVYT8kOvgA0gHmhKNLi/B4nCHYMXFoBniXPJT/MKjHiwelC+Q/+eSDBw6//mdf/ouaqkalWX1d3SVrLnv4dzPaOruTSZlMCp6zPRA+Ju8mgXFWfPPNN7+45ckHH7h71ry5iZJKi7EV3HYCo832QosYHF/yKEg4lQDLguxoFCKLLLLJGDgYKJvMUWGQFu6iaOic0YTCBD93k+zQTYUuFFJroOR3EgFw0l/ckXsmsWoJZPPAYqwcMcl4gmWzuUc3bnx1z96PffyzV131zlwOYjnNs+YvW3XZ4mXLJE/4PnCewLM2QGmClRm8KjD8EjjmHAJsIiwSLCzeMnZdNwp4UUIFLr8fBepBUQlcXYLl+vnla66eNWNWWU2NFAl4m+v9e159+cUXP/nZ/1xWUtHb5Z+3dI32pFFC6UBapAFRR7gD4OwYsBQIujS4heyIiOKSypLi6t5E+7OP3nfPjLkf+cTn46XlfmDBi8UDPJUqKa+sTpVUKR1XvhLo7jQ4MOAKgwYAD0wZxybT4BezXbNvJ89fdfnFF189f+7iXAAI2B5KiNzc2Qtb5i+TUh47dOzkqc6a6qZ0xmIhj/MYQCaFpQZQUwqwEqKZQDufE1HgQQsBg4S2O8COwqiwxLCtJtq6hVv4FYMxN7wvCOlSzU0lHBqEQ0qN8NaTPqR8KA8ihxwLg8NXKNxpL1cA8BwOCxFZ9G3aI8ckP7jvwOMbHj5v+fnLll4Y5KBiuu27ykluYsx48EkV2EuukMSO0Ws7XKJ5xoIr195w1x2/vGLdtRdcfG3GcQMtWJXoOuROKiuavaYSYCk/GoTIIotsEugKZK6EwS058n5Q110puzmHDbJ9w3MgDFm+5y7EggghxJbs8uYHmKzlxBoElV+EQmMY+OFjlQMaIhVg8gCLkzq3UTwmc5ncwX2HhIkVlVTYk2Ry4K3K5cy6G95TWWmhiQm4lnGeEMLP4bKf8AD12UZkWdaXLgAJ7hfhJYTFQgCzLB7qZxaIGI9l7dUB9ryMxxn3oEcx5lnQlg2AQF9eWtOwqsbX5nRaA8sr8I8fOxzksmXlZSWphH0vGY/l0DsTBCzIwoWPxaQXY8IDSKVzWvn2hmABRq0snstkgqLi6muvvfWZ55+5+84fVtfWv/M9H+OyOFAAIpS25ze5wPi+hXlA7ba3jUwKz+MeoqwggL5z8LsA2tMAXCy8EGUl1Tfd9J76upauLmNPnc1qLM/Esxk/k7FIwjTMmLP++luKisoz6R76ikU4CQA4HDS1A5HzjR0KBCdARYOa5J6IxziEDi0MswOSQXK4vRKeiHnSNs9CNdtTaQ+ihA5Yf05zj8cSQOMXGj6vAgjx2k7ho6BsP4pi4Mey0Mh+HboZKIyfg3qEHbekPXIAF7HHjpdFncbCVnB22a7GAKWqPS/veH3PK++85V3xRJFCop0daN+COKRUKV/D6GgvHk9YcMvjTGS1n9VeLLlw0QrP/PyJjQ8tXXYZixVrUKrDZAgMY+sh2lmRTYUHi0est8gii2yS2MPk1UYdfqLccpQfEnabHzPMB+EGABLnrJOck0YB+C8AFIkAY0XchOloqLMKISzGR/cM0KfDRD+iohs3dHQWpIjL+ro67WeefuKhy664ds6C8/uzzGe8Zf4ii+XSOnvq5OGjh19vO942d9b8ZDLx/AvP9PV3VtU1XHrhNTPq5/ajwHeC8Wwu/eSmTa+9tiubzcycMfuKi66sqKoFJxZXMSl7Ozq3bdt86PDrff3p5uaWRXMXNs1ekMvmXj1w8Oix14QXu2TdzfajO3dv3b79WSPV7n17HnzwwbaOXuXn4gmQDKiubVqx6iIh+Ivbdx4+vE9zVVvXcP55F8dlCUaLES1Btp8QOn7xldfOXXL+9771t3f87Dszm2evvvS6LLg0NVKVqCY4yGnGgCTlb9m25ZWdu9Id3SWVJcuWLVu+7HzPS0IeI9xUEC/Mab+qrq62vpYpaUGhR3R1CP5p4i7lfNUwo7lpxgyl7bsG/XvAVn/hxWe3v7g9yAbz5yxZtmxlqrLU4iRu8SG4pdTr+3e/uHVr24lTRUXF569atXTpeVLETre3HT382rGjhyoqa2bNX7J9x5YTR14vLS65aNXFCxedt//4see2PXf65JG6qprzV1zS0DhPWMwN/i1AZvv27d3+/Oa2jrZ4UcnKCy9avGS5ETFIEeUs3df50qsvv/ryzlxPd11Dw5zFS+fPXxITSSTyCfI69vae3vHic8r4lbU1dhwD0JmwVxfSLimTUdrni3vHjh99Zc/Lnf3dga/mzpizfPnqlCerqurKK4o3P/PIhz/+Z1X1xTlAbyaUXFWOvR8VzJlKgJVMRsMdWWSRTQ55gAgigixw5CAHK+5JwamICiWjQ8LVOZ1Bg2QrZdFiPGGSSanB0eLq4pBmNiIJzMsai8ga6g64iZYiq04GHXg+EOAxXsJbf921z264Y+fWx//261/6wAc/fenl15aVVigeMybWl8nue33Xz3/0nQM7Xl685LyW+UtPtLft37+7u+3Ykyt/97nP/8XSlZfao54+ceQnP/vnV1/fe8matSrn33PXz3ZsefyTn/5C85xFOab37N5+549/1Nndu2DpeXU1tU89ueGR++/+y6/9XV9f5o7f/vSZjb+bu2Dp5desl4nitrZTBw6+zoy/a/fO7i5TW998uvPE4w/f3dI884v///9RXJQwwhQVyScefzDDsh/708+JYi8hkCzOhbL3Q4x7Mc+XJllS8a6rbz5w+MDvfvWvP/nxNxtmNM5ZsDLgPBaDwJqMi0TCvvDSnW13/vonz255buWKi2obal99befDD9517bU3vP8DHy0pr4kxLHpJ5flg9BQGJYXnsbjL01N29OxPAhxSEKYW3DPKkzGZzfbdffddReXVx04ee/WlbSXx4rXXvvP2z/7nqvomSGLws089/fC///pn82YvbmmctX37ticfve99H7rtpps/0NFx4te//sUzT95fVVO/cvU1Jzs7Xt6+Kdtx6rxVK69Yd/3egycOn2g9vO/lbNepiy+85OOf+fNlK1YzE+NB5qmnNtxx17+3zJ43o6l58/PPPbbxvg9/5OM33PCeRCp++NCBf/rBP2Sz/uKFK0xR6q7f3rVi/8oV//V8ZqRnnP5DwuOd7Z2792ytqKmsqqpJJmPMYkkuYx734iTgYOIxryiRKC6KPb/58c3bt69ff9PKpSuTSS+REA0NjdWV5Qf2vXSq9UhDy2yO0liopwIhSkzmFBG+mlKAFQSRByuyyCKbnGkQPQJiEnI7IPc/MI7XTsx2pP5gOt453UtMcNPagEAUaD6iM8pYGIHUK+0kwSYTBqBjciePjghUo39j2ZLzP/VnX/7Fj7+3a/vmv3t1z6oL1txw07tXX3RNeWVTPJ44f/l5TzU2HNmxZdniObfe9kfJ0pqO7vZ//9n3Nv7u5z+I87/42reqymvu/NW/PHjvL770X/6PW279mDbs1/Xl//iNrzY3N9z+qS+dOHXoB9/5Rm9Hx+c+/99WXHBpsqQ4mZAPP3RfEORaZjWfv2rJ5ifulHEV6MAu3qtXX7j96UX7Xt155VXrbrj+g0XFlcdPvLZr28N9p49VlRVLTL2rripPeGLdNTevXn1xLsv9HChj2YFQID0bVzrgWmd9lSqpff8ffebI4QPbNm346Y/+4XNf+D9rG1okJswpMK0y2d/fd8edd/zwto986oMf+mR5SfmRIwe/++2v/+zfvlNTVXXDO97PZZE2RHuCcKEhYTUO8UilnfvKDqBS9hqBw1RiloUKQL/MIoxly8+78qrr4kWJIwd2/et3vvngvT9bsnLlTe/8gNbBgb3b/uW7f9M4e/Yn/vj2WTPm7N13+d/831/++Y++tWTpirlz51yw4rznn/xNRWls/TXXNLbM7uxuvfsn39vw4K8tbnvX+/901vxFOuj91Y//98P33zd38dKFCxYVp0p37n7x29/8n3MWzPmTT3+iacbstVev/dqff+bH3/t/zlu4dMH8pff/9o5nN/72q3/1P9df98GMVjKVyvb2Qu5okIVW48ZEC96X7mvvONbcNDeRLPV98ItB/iMEGxWqRgAcz2bSL2zZnO1P/8ntn1i39qaqsspcLsjmdKqktLSyUqng+IkjS+zSDuIpmP0ITliFGg0i0hqdUoClggjQRhZZZJMykFIMZc0Vrn0clktXPQ1mdYkeGkmkbn5ObptJPtt2yQ/sDxaEQ66VQS+KXaxyUJvQjoAYv3d5CU1KsgR0Buscuq/sHwUkg/HYuvUfnDlr+WOP3rf5qYc2PfHQC88/cdXV7/vI7V9sXrCoqqqxsqpRxhPNLXOWL7so7fPmlrnsj9j+XS+++Nyju17ZVl1V/diG31aUl8+ds+jQgSOciarKuphg21/Y3Hri4KanH9q55bmP3/6F1SuvCpRI92SWnrc6mSpLpSpSRWXNjbOTyRKLnFRggoAVF5UWp8o4T9TUNM6ZtyDTF1SUnr/mknX3/voXjz/6UHPLYuEltm/dmvX1pZdc398nAmVAcQu1PgOgwgeAR0G0QWSz3PboQ3/ypVOnTjxw7101Fc2f+PRXpI7xIGYCaTu9/8DBB+77TXlx0dq1N3Be3NmVra5qvv6G9z618cH7fnf3BRetq62fBTQxQKQSFbIwKgtJfSaAvEoPoocB6JurAKOuiFxtL3xQCEssX75y6fKL/axqaZqzY+vWPXv/7ejhfRZd+f29Gx/+3cF9r1593c1+Jtj1yl4R17W1dduef3bnzpcXzF/W1DQjkUg01jVdfNFlvKh0vje/t/X4Yxv+UFFatvrCS6trG2LCpN9x2/NPP7Vvz87evi6Lw++7764jh/be8M6bstncnl2vliTjNTWVe3dtfnnPSy0t81pbTwZ+9tixA63HDlfUzbhh/Tt6urpy/YixMVEEtfKZ35/t7Tpdc15NKlnuZ2WA2mKBxUcBULWk4Ol01wN/eO7Jx5+6/sZ3XHjFNYyn0mmQwAXJCCmrqhvsjdR+ss3iK3tZPFTVCCSjkt3MRCHCqQVYPBruyCKLbDLoChwI5MDhJi8FzpirkGbRVc5AvpPLqToHpxhDlDLIlZNcAw5gIITkVBo1Cn0j1BJhNZqx693hJ2k8kJQGIpeQMYeENdJsBVUqEHGfueiCj8yZd+11t2x6/KH77v7ZQ7//hYVxX/yLvykrLrHLqwVz6UClMyodCJPj8+YsnzNnwcHXXzxx7FC6t6O9/Wh19cx7f/t7Liw84t19p0pr5xaV1maz2Re2PhtPiFlz5qHIqO2TnDlzfn3j7JgsCnyQFccqN5xq3WgQrQfPkB8EuWzWgpWYiK9df+vGDX/YuuXxW279o1Sq+pnnN1906eVVFfXpwGIczVxmJUEgdJIIrOTIZNbwFRde/qHbv/T9//XXv7nrRw2NM+fMnetB9EpIyU+ePLL/wKsXXXRBKlFmBwj44tpCw/r6+sb9r+/NpHs9wXIwxJKSClEpFAvAGMfsx4ArlWsSwsDXEfECf93ijv5sti+bU77wTKy2tsliJj+XQ9n8YPcrO+yF2bN7b1fnLyE9Q6jj7T0NsxcHXOR0gD4zL1Ask/GNB1SoqsqGsuJqFej+XC6dYXHtz2hoKS0pT/enc37GnnD79k2xhHh1996e3l/6SnqSdaVN/Zzz01rnBF99yRWPbfzt97/zj1s3P3fBmqsvu/KGObNmgQo7KWtgoiqJnwoULWOog4rcLNi5eMDTE8aohx+57/f33nXpFTdcdtnlSsQAy6JaiEbpDCmkEF5YklyEGqp4zLB+TjSFTR3AivIKIosssjfg3qGKJZSjBMpCAghJKgyACZPnHZ2TSYTgKEFlIoXUYE4MYe48ULjy0Y8jsZtxnWFuKLBeDCovYbgHRgqlHkygUBjfk8IrnTVv1eyWJXV1M7797a9tfe6RY0c+U7ZoOY45fMKXQqFgZFFRqihZTOmD6b7ebKZ3VkvzNddcIxKVkFMnglve997yVGl1RXnr0WP2uzLuOY10UIaKeYkkRJ8s3IhR6RodIkXhoRIpOFYkCj1otnDpqqWrLnnlhSf3vLK1vGzmqbb229eu04bFsHieRTgYh4JcOaBXGU+QjoErzhK/5rr3dJxo/fE//90vf/7dK6++Wul+zQMLQfr7u/2+TkwGBIAHaIMZz5NFqbhS4JUyjMQRwvLFpIqBdWoEJ4QKfh0JTHlgtUOrufEk+R9Rw4IbX3BPixiP2ROATgPEMk1PV3dJUeqC1RcuXroaKtdwtu666yyKqaxttEBWAzNKEMvQjksANYysgRoZROyQhCdEHNhktjleTGl9uvNEaVHy0osuWbL04iyUEtJXXbOexxNVVTW+ERdftu6zX/rLu3/zo2effXzL5mc2/OH3t/3xf7pq/c0gP8FIMR4OGY8nS0oq2to6+vq7y6sagwCF+UGJNIfCovZ4XmfHyWef3njZ5deuvuRaFEZDRyiEAHXbqVYV+NXVNbbHCvNewcfMPbhlcV8QrfhT68HiEZ6NLLLIJm0o4iMoaIMBiEGbYyyaolG6/NybY9BZpQOmssaHlDdgCVtIo9HxBLAr4C470qnZcz4qxEKkxp3akglFQrESNoRQodwQgja9Z8+O3u7ONWuulDxmF924l7py7Q33P/DzV3a+1N/bE8PqQwbVMgNY77nFQD196d50RojkjKb5fX3t0kv192cWL1uaLK7P2CUd8xBAzCnTV1xc2df3eltHu5K5AM5v0YOnDAApDuILWRABwOrC5JAELSoeMyBW7wFuNoEXL1m7/uYXnn1oy7MbjKxesXJVbV2TstAFXUo50EAA/1fMxGKKx4CaJygeBQcWIhErue22T7QePXzfb354968PVdbOiIHYpykpKvLiRa3Hj6b7+sqqGNRi5Dqr0z3p3trquniyWPAYdwKgQLEyIO1FZYy9MGPAEBjGAcbcCoOFaATdh3jFgH1vNN6QAlAcJOKVFpdnc6qquu788y8MlIcRTkBfWQu+IL8+Dp+VAvlR0vbFB+F8uOVJN8KeUmNlc5CZV8KLJ8sratuOHiyvqD5/1cV92cAXUlONRggq64QXu+76Wy++5Iq9r+y47ze/fObJR+65q2TukvOamuaiaxTgpYVuXixZWdF0sq2zq+d0owggH1OhMCpEo21TvKsuvz7bl/3hP3/nJ//2veqG2c2zFjuKvGG9vV2nO9uk5PV1jQyLPmO1ZxRzBxcsd6Uso5KEUzZJCnReRz/RT/QT/Uzwh5Y03MQ7cVFaRexi5uNLj0JpxtUJOUcnx4CRfjdpTzghTXJkSUSV2pViNFjZd5ThQqFShKG4OuMqh5Rme2RU+jYoHsb0zpe2PbNpQ0KqGOcW18TgdCAiWVxW1dw4A4vucTpXQhq7+Cc99trBV/btf232vOVz5yyYOWNBbd2sl/e8/NzzT+gg7YGgEhCqlEUBgi097wI/67/w/JNdnUfiXsZjuYSFGBwrwzCdFBCBwnx+bAx30V3PgOKGPYACYXNv+fJVLXMWbnzsD889/+ila66wB7BnwAQ141H2qMsvhcGDWCSUlbGgAEoe6kClSqr++BOfX3X52kxfV7qv0+KTIBfU17csWLTsdHvrqRMHQb4rAN9nx6m2zvaeFSsvKi0thwqAjKKNpAjKtYtCOs0L0CkzAXeiIYRgXZVmQSFeKtDoqhpBxUSLHOMytWjZ6ky6/9lnHmk/eVQqO1I+4DI/AOceyrzbIwV4kphiCSxxBMWzEXRxKDyo0UFBvdZFidSK8y/t6+rbtOnx1tZDDGtnQygSCGpwkCc3PbVzx84ZDS3XXnfLf/3Lv16yYvW+/buPHt6fiAm8QeyTFDDflFVULFywpKv9VCbdLY3ysC6jZPkaiqqoqPx97/vUumtufWnLM7/6+fd6uk/EQeJeeEyc7jjV2dlVXtcwo3mGh/UqFWjJYhKhe07dPRlNYlPz4wkZebAiiyyyyTl4sMKzC5pJdH0I1GqMo4MHo0bwMsDKMNM1kdAgmaqgX8b9n2joAaziwkM3jyeg2AgiLe7hCg88Hw/WLan5BOSxQ7GwsHQOd8EzWLDtcX3DDh3Y/+orzx85vLexukUrffLEqYc2/u7wsfab3vux+hlNCA+AOxT46aCvyyKKo20nf3vnj3v6e/7zl/+iubmlprr++ptu++VP//Hb//D1IwePLVl0QbK4Iqt1siQ1c2bjNTe+Y+vmp57a8EAiKa66an1N9UwmywIWa5rRmIrHe3t6M/2qP53LZXpkWUUu29Pd3a2DTH9PdxBkWQwEOLhmTTUNa6+89vvf+ptLl62aNXO2ZDEjfSyTJGIQa4ReCRDjzLX3nepN95/u7FHZdMJLgqvKgFesceb8T33mz/++/dSJkycsjBRazJo578Zbb/vX73/j0Y0PzJm9qLy4or2z47GHH61rmPuO99xWVlVhcYeMQWCPUZ0iRirqKqdynZ0d6XRfLpfr7DilcllpMYbEck1G57KZvr50fyadTfdZoBOX0vi5HvtGJpPu7QyymVi8ZN0Nt27Z9vjGP9wjdeyqy2+qrKsDryBj9fXNRZUV/b2n7Yf7+zMmly5JVAmlspl0ur+/rzcd9PclQWk+6Onry+T8XF+6t6utsb7xXbd8eMfzWzY+cq+9U6646rrKqjoZT1kM3tg4s6Ki/LnNmzpPty9cMKe0pLizq1sFZta8ObNmtTD0uqH3EOBiWWXlsgsveuyJh9MdbUnDAgF5A7ZT/X22Lek+C7vSmaXzFn/sk184euLAQ/f+qLqq4rbbPl9dVivjoqvzZEdPz+pL1pZX19qOxKCqNNynUPlHOrG6aPqaSpMf/+xfRaMQWWSRTQaYUMaYK8lH9fSOtJ166fV9GSHjpWXMi8cN1fZlVM9j+vWBEtOgQMsA8KEyzBCQgprJOtPvp3tmV1asmD+vKBYnmUdQqzessNziuAUXzeD/8pCKhVgAxeCh+p56bd/e1uNH9+3dvWv3rm3bn3vyiQ1HT7Suv+HWd7/nIyXFFRZYPffck6+8tKU/lz16snX79u1PPPZIOtP3wY/eftXad8R4sZTxlnnzikqL9r2298UXtr66e+fLL28/cvxoZXVNQ1NzTVV988y5nR1dL2574dU9u/bs3Pnq63ts51tmzz6wf9+jD/2h+3RHIlHkJctKSkq2bNm8/YXnE54XmFSsqLS2ockeXBqRiMkg6H/xpe033vS+FSsu1cJz1CiKRAkOogJSvPb6ngcf/H3b8aPpbKa4rLqpcQaJphkUK6+vrS8qrmw/3X/pJevraposumxsaUiVFr20Y8fBfa/vO7x/89ZN6f70hz56+3krL+ACSgMpiOkhHQrjaCickdvx4vaND93f1X4qmSzq7Q94vLi2vt6LWVTP+/p6nnry8Ze3bxFGe4mSqvqZ1dXVr7/26uOPP5zr6+YyHi+urGtsrm2sb5rV3NXV+dLWl3a9svvlnTv27N0dKNUys+Xg4YMbN9zf3dmWLCoqq2xobGhua297+JF7Tx47HIsVl1TWzZw1u6uzc+OGh44cPCi9hOaJ5tlzmltammfP6jzdtmPb1l2vvLJr587de/YERs2aNbOkpGjXrh2v7t3devLYi9u22Oalikve80cfXbJkFdOeoTAyOM1sR4FytXPni4FWF1xwiScSivNjrccfe+zBYwdfi8fiyaKqhsbZs2fPTZYkjxzce+TI8b5+2+a5ZZVFjz95/9btWz/8sU/Omb1Uo6AtG8gcNATqI4Q1pXvRDVuDaBQiiyyySQEs9GBprO8BLB+7qm16ZddPHnrwtEgUNzWzZHFMcygWB/KG04+D5QQXQjX6AiNFywCcU366q62/9fjaObM+duONVakiBVLawqLJmAJ3VCAcWtLjpWZpXsDPIqoaJ7EGVH4UVDnHdHef7ulp72o/2dVx2q6NZRWVtfXNtfUzhRdHBnj6u//4P+696yfrbrjxsnU3aR6rrqyyKKGqusFjCYOeCi50Jtd9qvXQ8eOHek53FpdV1s2YVVk7I5kqjUGwT3WdPnXy2KHjJ4/aFtQ3N1XVNZWUVfX39PWebmcmC7HdRGlZWWVPT0euv8sTMa29WEl5RU29/d32PiGDLVse+uG/fP9PP/2VlRde4YPKEnTBBReZ49v19na0tR0FmrxiJaW1VVW10DKOLDRlYpzlsn3t7e2VFQ3JRBGXgeFZP0gfP3Hi6JHDuSBXXVHd0NBcU9MAzHcMORqqyMxINN9gqFZ1nm7v7WyLAd2f53xWXF5noSQVos75udNtp3KZzhiK5JdUN5aUlPd1d3R3nPSECpROFNuTNMZjnjGZ7s5Tp44eO3niZE7z2sa6hsYZFeVVPT093V2tTEA6ZVGqprqy0ff91rb90vjGeF6qrLK6TivdYUdS+cBOl/HqxsZkIsZN0HP65NFjh1uPn7SotKauqQ4OWC6F6O7u6uzpbD/V2tnRblHsrNlza2obDU8IEKVHOpdQHih+ca6zd9zxwwfuu+fLX/7qhRde5Wvem+ltbz/Kdb9ROhkrra5sSqZSuaCn9dThbGAbkJjRMKvj9PG/+fqfN7XM+dyf/beS4lrlikzTHa6pPjc7VyVTzlXzlMkT3swgpZYRYgIjvuSDNmlvij7HwzaMcXA2ekuGO+Qn0p6J92XsQ4324XxowIx3QD5mT/Pv64ltQnjB2c1kumZC6ui4xx+jJSP2ZUhLRhyTsUd1UiM/xoV4Y2fRwy7lpEaVj3R/jj2qww84/HQjtmRSYIm/oZvfIE8bU8XIIwPiAwKXQu4DDQs0yHNOgHt6TeqEruxPjgs/RH8WVJUZU0BWR8420qad/ACu7Sas9qwd+QZDVmPe/OR6cQclt1eYMI+1hlGt1ZjysqryirLZM+doX0G6PY/Zc/hQDNgOJiXbQ1tmzV6wfv0tOd9g1R7IFPMZKHECkFVCeqUzZy5tmrkQU8sk82I5bQLsszayrKqhsrJ+3pJVWmJ6INSqY2VlqarSGg9KIGuLiS1uKC4pRRYdSKjb1RsSEkFMXaX7u597dlNpUVl9w0ytqZAiqoSTCoV2MvclZRWlZSUk8s+0pxXR+pGNxFlgdCxe3NBUqqEGn0L+ekx6FTObK5tnLlA68DgQ+hU+B0TjA2U1fGm/i+mNwEWvqK6vrqlDCj2GqZHohVLnLOElGupnCDEDlN01JCIKIUtKqyrLKpGNZO9PCxeFD7mbqfKKlqryxgVLTQDqDphqx3h5RaqsqoaDxpvtYRyilF6iZdYiLoQdad8ged3jM2cuIFKdAraWUUp53KusbCmtmrFwKdWB9gg+B0yXVDSUVNQ2z5wPYXMIrAptRxxvKlfywPDAXjMBRPer19+0c9vW399798JlKxPJimSquKVlAVD9MJuVadzYeCUzmpeAQLttpO8/9PADfZncTTe/u7S4CnU3GMnS0cUnb6viw1fbCS7fE18XJrvGTWqdHbvxZ3w2Hnf6ZWPPxl4yZQo2b2aUadqM/tKM+cnJzvWTPfhopxujL+yN9sVM+DiFL0U+mjCZ45vxlkM24X5N9tQThMhjt8RMoCVm8qM6qZEf40K8sbPwYc2e1KiOeH+OPaoTOb55c8+dmHxf6JUgBVEFHiyI3EgorQsrVICAI2CQ/SUQsEw3D1ae7xxjITZkrrxiQW8hixAStyT34jyZslhHEkKKI7NdYoU8SuPng67w8Juf68L5F2CcIGoaRYXQnwU5hfSb9OKSONwMeTkMw0YsYf+1Z7Z/TCZTTIJ2uQAIAIs4krg5SDnhmppA7QfSNIAix5JJCLORPoHtdRyoUogYUdubPg1KBzAIFiMA4QrOjNJJ2mMiBkmL8pWdrz+28f51a9/R1NTgJUCfIOAkmYCpD1j6z1AskCcYVEAWICzglnfU8gAGvDKhLJNBFQwOUq0CA7NJLnUQ2A9Juk6BkxnLO06lwCrWhOcBcXJKjoNL4YWf8qTQvsZgIuAVSfoEDAhWJL5vxwQpSRh3VKgGyQEhofqZyPt8BPjm8veHQOEDbiRPCiDaQwkbe3jtKhhIuqFQ3szeMhQ8N9RWhnw90JsA6jPha3s4AK54iUGrFwKsIMbgAZoWs2bPuf1P/9OP//X7zzz7xC3vencmgxmkkGOJvYZzC0SuTnvhtf17du1++aMf/+SFq9fYK6bCOLTzV3HaKhg9oNhmJrnG8TM6Gw9fCN7AnG/e6KnFmC/fGB4YeTb2ICP1DGCjaRjEiFoS2bS9EOfy/RmWiKEqZ5q0H+2mGgr3m2w+RY5RijqbzjpYBZE74zOWoLnWgApDDDtiXKkcreBHodNJAOwCdKJDiosw44YkC06IaYcKs/PsUTR6khSqTSri2KNvQ2N8R6M+BIsDr6irp7c7yPntHe2dnR2eV8qE5wMmUAy0Jl1FFFI99WGZNxKXVIAuCnGOQYqZQYSiMbFNgM8H1Z64g5foovU1wjSDjDOAQqo/k247fuTHP/qn1lOtTbPmWAgIwv2MWGsCGwH+JouHJAJGDesKtxgQ8QqHREYJ5V5sQwTQqmCMUcsUm4PVbiSu/FCOSPBAI27HXEEFZYQ4SUBxYMpDO1GKAZC7dky4QKOWGF00XwUgLxYgTIUvwHekphuTKfgSeI4wz1HCK3C7BqgoYkc+cFcBABAVpAxQagPuAQNuWUYlDOjCBnQPIJGO7idDPk8M69rriOwxE+4zwFmFMrNcYQUEg443jb480Liw1873EVWLFStWv/e2D7V3dvems4jMIA8V1DQo8ue7PAlA0UK1d7Xf8s5bL7l0LVOepjsJO0siI4w6wiIVrDPuFRovRMiy0YBHFllkkzDKsJPkwQJ1IqC5a1zI4swUGZ2F3HpO4bHprYJFIAKsBDwosCoqbhLOHcJjoHokQPAyCymTEK4DdIQdF1j+BuOFfLzhUqEqax5kCRMCLyj44rIXOcghoPADQ9KSiyiqnkzvli3P9Peklyxaeexw64ZHN665aG1FWQ0jqEfpdRiq5KSLgZIZGdRSx+gavBsQunKyZega0siRQy0uXP+5Z5yMqnLiXRpFNOXru/b+9p5/P3Zg/7Xrb1yxfKXyDci/k6YnMNhFXlJWaOqXu/jhADDta3LpUGaEwaqFzsmCw6hdDqJgwMoCD6gLXRvKRTXOC8QBzaFH0F4Ci32gNCQqOIRRW9KSB8AvKLHRUAlyxQIcJiBwhaQ5jjFIxR07CVGbEJTm4LxDxqEshh4oHGIIrKJ7z4dxciIS0A5klRM4xTfQc4lQ03HMcZgBXePQSBwrBe2RwsmaAMgzVK9SmtUrr+jP5lTGFSqECKkh5CQcf9AOo4ByhYsXnVccLxImmeuHLyokG0DrBQUfkcQGdYVcbkpkU2NepDMaWWSRTXYD6JShKAbB3ZpNqpQGg4Z2gffMtBYZDalnJFUF2l2BMWGRHPCRSFSDknZVQ9ILJ71s1yeXlgUhJT0kRDhsrEJhsEJuHQ8DizwfQoWAlhZhLMZpZlk0wVUsxpYtXbxw3lc8HssEgUzEi1IJowOMrEGwknhhpJ9PmlHcoIjEYPo9kcWMKxlM8A1AGjq/ULiHJJMQhbgXdngUm9k0+wMf+LAX+1BNVX1xURXGNyUR9UPtSkN6n47ExAYgDI0BnRKp6pzgnXTcf0JZSGYDnOS0GGQ+6RLBDYlA2b9LInRhN2wLJafuOg0CdNoALQzzXA0KiWh6E84qnTC7h5WK8N3AwVxXUJLE+QkJkpoWMy6ERLVrNCPxe0bUQk33AIRTSUkWxUiBz4ZNEg7PcOc8Egj20InooCBqoXHjOWScz7sF/pkWIl6cigeAwbRLYqV4JfqNEcwq/LooSZXBmIIuKfbasdcMqYZRySpSuIgW/CkFWLGEjEYhssgimxTAUshGgt0/kmLsguAJqKUnkIcLutvaxVHEdIaJJI+OQJBwUyhfCYt5nJk06JPrRIIlExKXOGk/EFNUr0Wi04JLM2lUN7o/Lcysd7wfC68k8JXioqS4BHhQ2kIwdDhpcjrxOJcFsViIBtESKm3DNHOa52GIKP8vAiGJypmwWAcOXgjPkEqF4M7TZa8jFJmJV9ZU11VyqqcIpXNI4kIaV8Q67xcJ0SEBGsRcZqDfImwm0uGNzKNN9z6Nu4gxRGlsGGcHLhBS3hy8BbcU8avgH4HACqJvKGEOVfxQ4l0i/8u5o/B9h7gcaKPTA7dJGIeyHCTM++GcW5BR2ixm/SHiBiqbppI8SNfiVEJHkkDrAPlQOGdpSNHBBhsTZkdwV8cmlDShPiFFDBhjISPNQeSw3g146IhOaNx4cY9U24WriI3lBkDe1oiCixPZFAKswI8GIbLIIpscNAmgErJdSIxCnwOQnH1QMwehUa0AZmEAJBbGxabbvpkXeNc4ZospKFBNqyFPYp2XHBC+IV4T5LT9G86UsLwbDIoG5Gow5PUaN+FjgFqsRxsOqM8i7P8C56NRWLJQWLwAQM4Yil3ZtzGVjpZUKjoNWf4GXRcafSoGAmQUg4KoH8JcTUxzlg/bQSiJuOksgF4CJkCtcgwBY7IaUJ2UoLApVStkaoAGLgI4Ka7tOhAYhjOE8TghOZS9x0ozeR8Tczqu9m0FDHjt8LdBuStjwZykSJtyeHcowELKVIhTlENH5METJkR3wp3GhLhNYzYGpwgeohuPCHUI2cARhNm7EoNziMAwZEkZeFhJGjlUEJEk7xgR7CFGidDUoz0HRi1tN5DGxUnAgpqtucNZmuWDxaAyT76xYOCmoO8h0jagGC/QCwXIDmFYQK4zg+cDghwTynMuPBwdBfHovPIcxzLPdkwDdCCKyH811QALclgje+vnc3SV01RAG5mCaXcQ83DIB8J3XIF/Hb5kgw5iBqe/UuXQwid7hJYMa+GQhg2cNHyThyUr8p+hE7Hw/fx53Ywohh4hv4s3g4+f/9YIXxl8RqEHt2FI10w4gxd8gA8eYR7uJU3By8KrwMNmjHhdhr/5H21XiKuv4lizWKMaAJB5ZDIetwigK9vv9fVVxouggB3HgAoRj0M8YwrxTf4KhwrqbzUwNJwN5Fe5eCDDVDiUuMIsfw+45TrTn+7q6okFOhlP2L8FSmERQGQLaZQSJ5aUcZSfUdjDPK/c7votlGGhl2rwJzlgVApIEXJwNHGmXFYd0L2FAdmlwFXXwRREWDgBjsACT6EpcMwp7IwAwph7Kgg7UD0+C2ywfjIDQAc1dZDVBC8BnmmUrXcPgXZZgtkA0higJcRNRwiCef+oMqFNQcY9ggSEEoq8K9r5goBWTt5C4CNp454oFFxFnxmnaBjF4UJFDPd45uc0SszKe81CaSeNoTPAmUoXjKrw4VQynKUMEcW0piBs3iUHNaoDHs4IAOOAoYaR0nB6ACiGvisYL3CKaRcjDIN7+DAEOOFI9LAFLm3PedwIECp3H0L6gkT0pokoxrmLhYYTUoDRPV5QM51wnRsadCCbgocKeWJGu6CqvYqSY5oCuFw1J+qbm7E1Hzr3jjFjD3fDmv+QU98bAFg6KpXzlq9HA/81g9/RPHREuz/yAiUAd/uKgZcDX9Xufbcq4ZZoqMKJ29pJPkZLWIHykhgswaQHyVPTti2fKMMLvsjDvw9eUYaGRPhIg8LzYQPNB/UxzwgudI3TK8o64i4nZ2gcgQ+4Cvhol8HwwkNyWuiJXOOuAjoIUHPS6NGWzImGfd6GJsKbgvb2tPue1TLzgmXLT7+882BHh8+8+rIK5kGFOgxghelVA5VinIcjf6mmQADRULVlxn1cZWMMr3PIIIqFig32/upL951sb4vnMktmNK48b1kqmQAXEGghCO5EtOC+EVST0d1JYzbfODEHusf44M+bvHarVJSUR082eJVQWywsmm2xoHZ/AhiiudQuQQ0LzjlRfeOeZs4IVginz4nAIB/64ij0xUMqkMMzAnGAcTQg4pRTviP4jRRxtwCqKUKCPD+fYNVorumFppKEnCjkBCIwLYK7GwbzJyl9EYhNtvkoJ4pl8yDorGmqIacTd2gifKZxNsKvcwCI0uECxV3WZzi8HFnkWOOIXHPO24VIRrAwZioMISs2kJDB85/E+YFh4p/CHuNMSzHUgfJHOOKEUSk6F4ZHkWjm4UW1l8DHitT5ACgW/4HvYWc1lWYKw5Iu81S5S+NuE56f31HxAZMWNFUNB28jXFBD2RekeSVDVyI9cUqE35dDJjS3+pjRZuyR57wIPIwHsKJahG95NMXFFYx78Ch8Dr/D9I4zHEmj4CfwUaAMbcp9NlTqLb8a4Tbco0pwTAPF0bgK/8JxY7nb6sAcIgsxGT6uRAfQHhItQegO5ympkcApWYC+b/vgeuFjh6F8rR3s4Uj7ZUQ1oN5I5G7m91lmwC1U+KzqUKbTOPFGFlYfpYkdd4g8bComHGmcA4UKIRd6GMDrrrnjOuBkhTlseFCnwEcZVXnvVQjDtGNSCOGWK/I/SB1KHotw5dOGssuR52CIvwBsWhW6IUJBo9EVvPnb/K6WITeFWMh2KCtLit+x9vJYzHvohe2H2o8zk6svr/VkMnRd8EBrWs8xYQrIuFkQHyJxBGOmZMjspc8y048MZqSxI5kd++IDJIDKxH193R2n21J93asb6t5x1RWLZ8+JIyAQ3MNF2y3UcbdAMh0SpMfZYZn8Cs5HDCJipM8iCxHyZQR6/jTIUoVuDW6Q0U6qrvDYKWS2C3pMiY1EyzUVlXb4yRHLAbm4wtWSCzcrwRdkqLXBcEPmZhyGtRc56VAAvpKgqWVCNrnJe1ZIHYHO7yGJS5MvDVEoCRQIUlYQVFdHU7ElfMqpapCIK3CnBdLokIztQCdHFn4ezeQfOmLjg34XirXCoYnkLiU+tsZ9wOl7SXymtcOgeHLtaOaShkaH8wYgUxVqYgl0ummcu518l9tbCiXdBEnpigKHilF5ZqR9YRCVWgp/5nGsbaBEgJhTGGK7I6rSjmjlECXLBwqd/EThXSTdhtax3dxUZo8ZAxaaNG4Y0M2IC49gMQLegrMw84ELlue5mTxwLNiODyLVFfzOC6IEEcQa14NldDQKb6n7yvnngRBpQO8Gqk1xFe4NDW2ASUIabn2PuWxhxE6umgeyOAeulPE0kVPp+YNdJ/EnaGsV5F1CBbVA+MBbrqgXzY3ayQxiOVCFPmR4KI0o9BxDVhEI6mkjPU1UUSI6gAqL1B5XYUoSD6MfZqjWiMjn3YAQH27ZGFEekGKAvgDcVVNikslLJUIHBbVTYTcl+eqRCYOTIf2XptkC9kWIrrTbTrppyEgnKkmZU66pJr+thH06iFxjgABHiDwPUKAO98FmYJbhZqT1kg/r/dvsrg4Tqwj7asgpszesqoonr7v4IjtiD2/fdrCzNWl4fXk9j8VynAWgCMRw+scEN8Z8CGpBPCVhzvg8PTzcOPBOQMljtr14J5AcQwY3Hva6pdM9XR2nvN6u8xvq3nXFFYtnzSZFbMOcK8M4p5dx2326U8e/3g4kjflJJ2xkBrvzwik6v7CZgS0TLd/0qdCXFCpjGEasKWbycR+TTyTEuF64x3FCq24T4g7j3Ot6wEWMoMUNJz1rBcjRhMR88oKhRLlbsw0FiMNxxJqFEFy0eAA5UZBuCiFBFOzCtmunjqABgOG0EGKAvH9Ouv2kcQflFNNz+QGUSac0+bAQAtNfEb8I0rKifSyq2cP0jIIJAslmOoydSYI3nKQ86KTISMNpSDFyHUHHhHZXz+2l8dAUwBPIdsctIaX2EXAymKqpkeoPclnceVQ5JiHi1KipoFKIYAlPDuQVuHGi/ync64abRBIVAd1ZhYKxMQh7o6/LuQVprXD1jTRiRJKTd+3X41S8cFmwJgoSjg2wTASw3mr/laF0aw2VEdCTDk5w0CehTRFKquC8BLsbrpD2CXjGA/Ij3eiKBJe5mzHtIxcw5mar8InVZqCMBwg9DiQkDYQhOOa2GEMSyTDTCYpxKENZNMQkcE+tI4vgB3FOpMwj+1QHNMO46YRwFgRQwJ0kiSFgYR4v5DcLrp1rWqIqDqTxo6yMCVATm6Madbh8UKoP5EeBl04rvEsRxeFGWEHFXcR0ODUbymkzxiEtZgZwDhXhQiiQX1ooLTycjDwY+AAeBC0pCIDygCj8CD4OLywKQ91lPJ8LZNiwEGVegvDtfmdrKo8SUo+R7mxHrrK4+Oo1a+yk/tDWrd2d7SeNKK6q4tID9pKQOVws0M9hNwkQqos75i1/C/Y1I7iRNKqJxhnvx5sdCTEmjB6qbF9vpqOtqKtryYz6d129blFLi6CiJBhgUVRyBmnjTjzJjLSVGGsumOhnzHifGVsS20zmFKP9MpmiB86XIhmCK3hq0I+EglOYXmofcxAfR7ToQ7qbkZoI2xwfNB4ACR19T+TLw4NYeBCA41BLifpXhjaPBoVscTMmIVhpFJa0ETRzhpL8tI90FY20w5GehOPFYwC+/H6L+9G3hhuqkFruRFaRnc+dkjwxwrAeUOgRRD1RQjr4HqaWuuS9MC4bOIkEnFw0TtlCCSHigMewmifIt3N6KqD5ys3gyjl5Q79gPt1QI86VinBmvuYSMcFwy4CRQERQmoTOkDsGU60UhOuonJWdRAPsE3lyaUJ3xSURTQ7mswwqOOYybQfCA5GNBrDiXESj8Fb6r1wGrkUGHrd7NoXVGaCYLO64UDYOgYgK7GQihUT3sZ2dUNPGTk1K4FOH8QBPswCmfAjhYagOVAA5ohNIDEbWbQDTGLzFXfKOpkoVVKeW6nHkk3vzTxCeCyYhSagJoRg5fyTshcCFD6nIKNgiHGOSK5w5PWQJQDpwWGrCufbD5GTnLRMUMxAeth48QnCKWODDxGnQE0UgycNiF3EXGzSYDg541HYfaJ8SqnYwrCKMe3Xbd42DxHW4IacHX3DK1MEaHZykL9H7TtMRdFV5MZ3LZez3Yl5cBbT3h8GHHnMPQQS6Z6CDfIA/40I6Ixa0IpT3Nr+xBdKQ3TgTowOjVUKZ+pLid152edzwR7a+cLSrzd4uFeWVCS+eJllNSv7HuytGX5qq0cpnDtobrdSu+PgYZISIQfJj0N/f29PemuruPr+p8b3XrZ87c6YHJHKC2hBCEtrt3MXg6mciipPQUqupwiPU68OkRxaX9qGxG0tlEa2yMxsTOQdJ7X3CE3EI6qXt8y9gVrDTiZ1b4pKq0JhAK7ut9CCzE9AAzJ54bEQMmnzw9vMxFmSCTCqRyga4fyXaFJRatBMpzUbcZVyaIJtLHzt44Gjr/tZjB0+3d15/3fvnzl8WYLKm3fUFlKuJ/nXHbRfhHIlRUk48N6c8ihM5zcsc5xhXmxIfCDuDCy1jMGNLwI92axFS8wWFVwXMWtIkYoZqCAUkGUElj2ACBwTpUyolbiKp1jWqUYDymZNUJU+dRaYu8MtDvVIk0du5FEAveN5iwvRn+2OxlKaNEecuTRPnNOl2pBB91GHl7mEAa3D12OimnwjAYlHi5lvswALPMJRdFU8/+dhLL282LIM1GLyYTATgDLY7iVx/uvPCC69cu/ZmqOvq2JhQ5Qof6P+PvfcAk+u4zkRv3dRxenLOg5wzQORIAswRTJIorSR7Zfnt2pblsM9+cth1Xtt6frIty1awRFGUKIEUSZAEEQgQOc8AgzBIgzw5T8d7b9Wrc6puT0/EAAQorTj98QMxg+7bdetWnTrhP/8P213ksUQzssgaAx8ONHFrEoqppKC4BMSRqQLi6TYFI2BEhdyVTJFDxibZNYzhDFJZQykM3sWQmU+jEiBLknU4WTJDz0ZiJOBjwqdJSi8xRHi6hTfiCGRzpDf84c5tV6/WKUrUcWy/L/PBDc8UllTygNLmJg7F7LhDc+lC7dZ3NoFOhqJbxDt+8vQlS5Yafj/0MFELoj7iulECRqG4FNSKIN5Dqj3RNSQS/Co208ijAFxQjXurSnzHzq1vv/HDwvyKFz/zpbLiqphlococRNWiSqo5ybZw0j9XMQANOqCZgP0qWyAmutiT1WvBO40ZBFTlzfJ6H1q8hP/ynePHm9sa+bPwpWcZhsciACfWsZWOYYpAY6hRgn+5exPWhz52f+MI50hKIzLJfmlJhiIaj/REWpvTIj1Ti/KfWrt6UlEJgeSGLHKpKBenSbETMnRy7JNu61wkkGCa1dXGloa33n492tvuUeIE6nC6J5i7bsNjxeWljgUKQFs2b+robHv82Rd13cc9iJa2ll073m9rvsnDHkfTZs9dsGjBCk31OpDTx+wPdRHnmMdXNa3x5qWfvPLvN65fffDRp1eufQikbwT7pkgoATu6BGNB4KkqbR1N772/afv215uu1emONmPa7PETp/Bvg8gTn7RNHAz8VMgziWVDJZWqAEkxl1hBQBcUSa7FkBFfIpO4SW5ouL59+zs93W26k3Asq6i0fM0Dj/lC2RA8U0enqgFXdA4f3HVgz3bTUEG0m3jnLl4xc/Z8YL9iCPmDmFnkzyjRhVOpyNKr6F1FsligagfTBtQdVOTYMaOvCBwtJYaqtrY1bPrp986dqVm74al1ax9RdQ/MqoYVEkpS5EjF2SOgaGSoZU76Wbix1y0dLCoLr2Ove5nGgvwwqayqqLtw5JUffDfS1WwG0tc//GzAn813RGvrze3vbfL6Ag888ETYsgEbSbG/mQkkODDRUGxfpiINjA4DRQZgFb0IbNWiWA6DEh4PksA7s4kUmBBwcFHEYTKRRKVIiFCEEJoaErWtypoPpSI5RsSVFQnRcHSBqEUcqI0i9oJ1WYxOlFtc0Biqi4g6AJTemGqaxsSJ42trdv1803cUx/YGMgNe3zPPfoFpXkWowHInLNb93rs//dF/fsOx46YZfPjJL4wrr/AYBsV2cMrcYo8AtUDFRpUS8wzy/Srm7B1HNoxjpgv6ARQhcYJ9BTxAVg0j3NO95e2f7d/2mqrnLlq0urykCuiDhJ4Fd9EcG6VIiFDxEC3pZIgiFBmo0PwxIbZ/wcuaUoluk7zj+IABSAIiyU6Gz3P/0sWOxnYcO97W3sLXqS8r19TUuOg0E/4vczHX98YlTepMp3JC2CLFqKqigm4zFo700LZWT7Rnan7uYyuWTywtJbato1iNxEe6mCaaUkVLZv7pmIclnA4mIZ0OCOuRYJp/yuRJb/z4u3t3vsmjqYpxM7/4G3+Unp5uJQBU3tPV9rOffqupuXHxqpVFxeO4Wx70p4+bOH7rmz+qv3r5pS/+X5VlVQQu5XCvR8XuSpE5BeeciLYWeuLk4Tc2/Wc03KgYbMXqDQrxYsUX0UuyeRXMhM6dEMp9L5qXU7RyyZoTh3c1xS0zkOY1gpA2p45sJ3RkNQHAElQIl4PpQ24xDaGgBG/SkX2xVJBYYARLXYceAP5OKD04adL4l7+95ej+LXyJFZWMKywpmXvfOsR7EVQQV9vaGl579Tt7dmziX5idXfzcS18qLSxAMyNtCIAusOuPMgmPSmK4uFUzANtPHBid4BUTs4/KSUImVLLsU69XPXfu1M9+8p3u1gtENZcsWRXwGfgFDmL9EIghVcupS/I15FZMMXpsLLAYZQaLjGGw7rXpEcpSanlFxYrlyzb/nDtYlt/UP/9f/ktebiU/ki7Vn7vRcD2/sAQLLCIPwwTsQ5G9wjSJTVUVUUSXLczI4cJErV3sfv5AG29cOX+xtqp8YlnpRJBZEPsFNivFPDN2W2NLsKDmo6Im77bO64pQxFcE4h02HoIPMLjRBQ5AIckeauoSLVJBz6cyZNdzsLuHMLfzB/twmKbpZNbMmQ1XF739+r85ihULNx0+uG39+oczsisoMhfy3d3cfOXQ/u3UCSuK5fH4li9eOG3qlGgCFSMgvUTx6orbmqnLhnFAyDuNN6+cqztbVFIyftxkSiFLJW4C81oAAEMD5wjX1Gt6586e13L9Qm5hRWlpMbiyQv8egSEYNzsUO5+pkmx2lj6UUClxLY4r3uF2Hn4ibAeeBVRQUIO/BTExglSYyp+EnUj3GOsWLeQz/uGxmpb29piiZWTmaJonShSLMRNbzS3RczCw3vrR01eynMekhozsZBBZ2AQRiQ1YSvFIT29Ha3pv97SC3EdWrphSWaFRBykDXKrwJOw7VXiG9TVyMTJ21kh2AUE6KoS+Ta93xYoVphK+VHegoeGi16tMmFCenpEWjTuarpypO37zxoWu7q7jxw+Wl47n0aA/GKwoL/H4yPKVK595+tlgWnY8AdAqDVCdbg+l4N2S3+YUFZcuWLKqrena/IVLVE2zLMyJCl42RCBgrU0VcSTfyAFfWnnpuNz0fAQBarqqIZ0YiDhrii46L9HborqmdHe1nD5d7U/zT5o6m2h+FetowPOKNhgcddngIQygSmQvJ3xvwB9YtmRp9f7tR/e/wd/UcON8zbG9M+csUT1+GBsCMs7WVVdX70dKrkRmVnDVihVlJWVRy+2OlqeAIzs2RXVC9FarNBHvPX32bLg3Mm3mXE9aSPYDKKLZE6rZeI5QUR+1E6yoIH/x4mWX63PnzV/o8xgCr4EtC+BL6iiqBJLTioSqqcl+8+GytINJh8ZeQxpJz5hUzj2N8+FY1gS5AI+ZDdOjGR6wR4YvMyMvMyPbtpTysklPP/25cePGa5riNXSUSFA8XtXUFESjK3HuJtgg0qBhW7ZBFP4uQwqPwivhYB5dAffFsWM/f/17mzdv+p2v/NG0aZMtR+PxJAG9CIA5cCORsKgNCTXCAztugAyVn3ZKzGbY+U0MBZCmBK6J9T8Yjm6DHi2wrGhE1+EdCuJYNBt6wxxFDgTEIkxTF+3PyIin2CCfzwTLDmNSz0tTVdMMBDPzHKu3t7Pr4oUT586fXFVYroIGCdM1++Txw5FIZ3FJ8fVrl/msmT6TB3wGnw6omKqGBndBhCgIP6FtYlvQ224YEIz++Cf/se29dz77a78+d97MhAX8kAoyCekaYFmpjZksMDEKtRS/L/35jZ9dtXRVKCMrI6uQ30zAr3MXTpUpciBmiPPZA0ytJrCkjk1Fp49IamnqwMdN0WH9lQ8aVDeepW6jpko10QSuooejY3dAQUbawyuW+FR926HjrW1tMUoCmTmq6Yly3xmKG4SvHw+5u3I6KWotErFLhaKzJoRIiJJQ+diohy/7SDjS1hro6Z5ZXPTU/cunV5YZFMk2DcSnQGCjJ3mCqIAqKv2wKKIZdewl8N2iYs9QSkcwL82eNX/GzLncwbpy5dLFi3WTps/jm0ZTrVMnD3V0Nifiseqj+zc++Wndo2t+Ze+ek9cbGz7zud8syC/oidimB4wJGC5TFS4yZPJtwDQBQaeqzpqzsCj/Lx0rmldSqugG36P8aXEjovHdbhBLATIaqKBxPwUIaIB9VSWStZWPzTBVX0AxPB4wSrZiOUoMdTR1lQQ86jvvbPnmv/zDynXrZs2f4/F67BgQg0L0qGmaIZuiHSChYuCg0STDnjAJzOfRvB5vIJALnLXRnqNH9zz0xKeKK6aABBMhiWjHoQO7An6Px8xva7zBbZeP361u+AAVAVQcpo6wVBU5tPjAHNvCKIQbbX7l07Wn/u5//0nAn/YXf/2PuWk5YJ65WwvgN0BDWgnchibaIgdKIVPGT/nq7/1Zb7ijoKCcDwrsFjZEUki/adxn5OeHoeo6smlZAIBDDtskKGJshd95FGqPTd69PIkw2SIiaKLZDt8KePwSqiYSzIrRuG1xy7B6+f261x+JQYZJwyRKV29XT1d7LJ4IBtP8oSyfN40HIhYW3zTCuns6Ir1dlF+CsWAgkJaWrmi+eMJSIolLl08e2LO5p+N6uLezs63dVjTu1QFM0k7Ydqw3HA54MwIZmZZjxZ0YH0E0GnYcLSuv2OGxjG0l7HgiEYnFIqGMXO4L9rT3hGPR7OxcU/PwgM+hsY6Oro6OZr6VA/6MUFq2bnjidgLA6tAiZLc1t3Z1d8QSsbRQiMeLpunTNBOqeFSVtDTIJpWIk+z0/PyC8ceOHmpquHrsyIH7FqwlWhp3glrbm05WV0+YOLmz5QZ3sMAKWNxF5H843EHVNdbV3RqPh2OxKGHU5w+mhbIJ8VuWFY/Fr1w9tW/Xu93tV6K9He3tTQ7zarqJWXub32kkEk7zhALpwfb29mgk7ud/9/j4lfOy87rDsa6OTn9aMNLbEw5362BreKzIPVrDHwjFnXhPIkKBPMxIC2bomsSwQcbRHiJ/8qvfRkjlH7baR49rp5RLk/EtPw9DpnflooWOpew8erS1o7VXgVohMQyKKQHM2lK9P8nt3TnyAZvu9kFgnoyvvgQO0Ys7Mxbpjre2hnq6ZhYWPL12zaSyYn7G8ocuqJIcUeAmKdrLmPqlgx/32MvljBAoSNG7x3+0FOLzZU6cMmvXzs3R3s6ztSdWrXmK6N4b1y/X1h5JxLmbzeov1NbXX6gsncz3WPWhg3n5heMnTI3HIRbiRkbjflK0p7W1o6en0zTNYDAzGMwGdAL0yPHoKRoIBayo2t7c4cs0iWHyx8dDwnB3Z0+0ozvR49d9PsPPw8I4pQmmZITSHR5discIfOix7nB3e1tLIhoPeoKhULbm8UW5BXQSLTeaPvzgvdaGc4nYYm4u+Go1iCdomkQlVjzS2trWG+lKWFYwPdPnS/eaPh6VIs4T0Prg/xHAF1i2U1Y83hvQq6v3njldfebM8dLiiYoD0Pf6S3X1F8/On7uotvZQGybGEtzA2YQHpOAiUaujraM31htPxEyipwfTfaEQ97UsR406dm9n64G9O8+fOjR56tzuznafP43p3AXVo1bcsmKxWCIzM0fT1e6uLm4S00OZhurjFjRoBrnx7ezoDKWrsbiViPVoHhUTjhp/g88bDEcjCSfmOPw88vgDmSomDsmYc/URHSw6Fn/d00gfWTFtoaAKACK3DQ103ChRaXPbjVOna+fNXmboPtu2TY3GI12HD+5+461NLe2tSiTSE4nNX7hk47OfqRo33aGalYjuP7hr+64tly6e7bx+RaHx/NLSjc+8sP6hZyNRZ8e2d7e89+rli2f5ifLm66/t31OTlZW/4bHHmm7ePHxo18VzJ1qaW3/rv//xo49trDlVvXvPu+dOHb1yrm78zHl/9bf/2h0O79m5o+bI3ssXTnV1tD7+zGd7wtEPtm3LLCj+i7/828Kc/M6Wm9t3vLNlx3uJaDTS1cFU44H1jz3+2PMZubncJHR3tG97/9133nmbOyj+gNcmalZG9nPPf2b+giXQRY2FIyHUAH6LDZ7k0sWru9q7T58+fujQ3vX3n584aQ6PB8+cPHH1WuOjD6/ZuvnHwnRTBy/AbZAd37f9g9273687U9vZdJM6kezC4qefefHhR563bWXH1nc2v/dK442L/L3vb9l88uT5rOySpza+EO7u2fXBe+fPVV+rP7dmzUOlleO3bnuzozv8/HNfzEnP2L/3g3PnT3a2dXzxy7/7+BPPXq6/9vqbPz13uob7VNyY3rdgzec/96W6i3X//G//xK3f4kX3P/XEs2imqaYR7jRI8jJs+ZQnjej2+ZXeVZIazCVVS/5dgNSgqZBJqljsUHfSPZ77ly3mgfGOY9Xt7S1Rlfozczyqaak60pEo9C5X2fqRSIlSO4JTxMOC5oxEpCfa2urt7ZlRmL9x/bpxxYXEQsgvkqSrSN+guiz/KV7l2Gt4Q6dQqYyI/HUUxe+IYk6ZNi8vv+Ta1XM11UdvXr1cOX7y5fMX2tpbAunp4a7Om9cunag9OmnC5Atn648eObR69aqsvMJY3AGMqRK/dOHi5rc3HTl8kDmJro62vMKSp57+1KpVD2q6/1xd3a6975+vPVh/7vTkqfP/4Gv/EMjy8EDs1Jnjr//sR6fOnNG9nu7ONp+hZWRk+jLyM7KLnnziqZK8DIHZsG1r34G9r29+f//eHVZ3Rygz95mnPvXos58hqrl//57tWzYdObSb39jhgwea/uRrHm/2M08+u2j+3BtXr77x89f279+nsITh9duqPq5ywguf+lxZ+Tgm1juP5bAu6UBaiF8ssGr56uvXL7W03Ni1450l8x/ISMtLxON79uzWDXPRfUtPVB8WBWeHR54WTFp3b8fW994+fHDP5cvnO5uvabo+fvzU5z716SXL1mlaoO503datm3Zt/5mixG/cqP/6P/6d5s2YNmN2YV5W9fH9dWeOd7S0bvzUZ6NWYut776anZ//+H/y5STwfbN92pvbwhYunJ06e+ju/92f1l66+9c5rV6+cslnc1EOPP/rCk088s3PX1ldf/WF6ZvbDDz+5ctX9uq4zlqQ2c7WJBrHLkTEw1i0crLHpuaeJc5IUxqcEMUTiJHac+OWr5zt7e/Yf3l17+vTUqbO9gZCuKvFYx5uv/+Db3/qG6U/77d//48y00D//v3/95mv/383rF//HH/1NYcnk3kjsxMkT/kBww/qHG65cOLxv67naPd9svJaelb940bqpk6d8sJUHf3Hd4y3Izx0/YXwwkB30e9p058yZo/Vn9ihq0FGiqq54vUZj47XD+95nTqQsOk5XHQNoqey6M8cu1R1UVePgwQ/SM7K7Wq+VVVb5TbO7o+lb//a/N/3s5Znzlnzlq79/5crFb/z9X3zrG3/W3dnx+S/9lt/vffvtV7/x9b+dNn3Ob3z5N6omTDhy+Oj3v/29nq5OSLgBuh1rekB64AA3MoFYrWr8jKnTGriDdfbM8bMXjk+aMp17brUnDmdmZlaOm6poHncTQw+XqqnxRPjY0cN+n/ehhx5uvH7t4J73Lp89+K//dDk9PWvt2ocmTRm3+Z2EbYV105uTkz150uRAKCfo99uReFPTzeqjHypO17FjoUgiFu7qsCJWRmYaY7G680fP1h4wPV7bifKDdcrE6RmfS//6P/7Zrs3vcXuycNY8n6naFv/m7lUPPPD0M8/6jBCI8io8hhZMjBKYQ5MUjOSuuwu/jPUgjNKlnplQgMPmU1fTRK54bMCHU4OFfDr3sSzm7Dpx4kZHMz+QMzNydZQxSSiubgdLQWL1AdRTtDfJyEd8UvNDfli0OyDXgqIpLvE3967CPbHWZm+4Z0ZR/lNrVo4rLQBSJiZk3gT/IvBeSvqksdeo0oWK4HbSXLY5lN6jqqqVV00uKK26dvX8qVPHr185U1ZedvpkTVX5uAkTK7a8/fPu7ta6sycTCetiXU08Fp8+Z5FhemMRx68ZJ88c+vu/+39O15x69KkXntv4qbc3v/aTH/7b2VOHHCexYf1TfGN2tTYdPbTLijaXlBWpmmNqysHD+7/+l1+5fOHMCy/99hNPf/qVl7/15k//VTM9n/213374sc/kF5S0N10XbUGW5Zw+dXr85JmL5s/bt2vL9SvHvvOdhlB25sOPPz95wrh9u7RIb6uqksyMtIkTJ2hGRnp6kA/1O9/9+ls//8nDjzz9+JMvZObkvPHWm/v37LHtGBDjU8GAagMBiKYjM5fNJ2DazIVl+7ZxB+vIgV0NVy9lz8hrb2yqPXFywpQZufkljuhkwukSakHt7W0namtKSkpnzJh65mz10T07jhx8s7XtWk5BwZTJC8pKi8rLS9o7m/gXBYPGuMoSI5BfUJCvq9al8zV1J8Ep3Ld/a25uXrSrJTMjw+M1DKZ2hZsPHdkWizQUFmdza7xk0fKMdN/f/t0fnq/e7w8WePSNhmHH4x2ERZ584otLl99PiImpSEGzmpQgIIMo/1xQ6thrOAfLMMeS3Pc2tBNYIX6EGBpUnoSsRiTW+5NXXza9mWfOVefm53s9PGAAIquTR4/9+Iff6uq48qknv7pq5ZqAx1t9ZOmpmg9rjn54YN+OFz47NVsPffWrX9G9phfU7Z0f/eCbf/4nv9PZ2XCi5ujyZfcvu2/hti1TDh94z+cJPPzgo48//pmYBZHl5IkVhw6/V39mt+4hHkPzmsqcWTOvXV1+8INNPV0Rn8+ja0Z+bvqGBzacPr7nUt0hj9fz4oufXrnm/r/5y/+Zk1vu9+nvvrnp9Z99x2vqTz3x2II582ZOmbj93Z/s/bB+5wdvrn/4kaqqqq3vv+EkmidNLJs/b2Z+fmlxfmlHS095WZXP44kRyxAOpqDg8yiqCcB7nz99zdoHt+94s6OtYc/u99ete7Crs7u6+uD6DY8XFZWKqo6CODDDryccJ5SR8Yf/44/8pmrqQAb9ve+U/PVf/VGst+3k8SNr12xYet/it6rGnTy8w+PxrV/70Gde+s2oTRMJxxxX2dSysvrI290dXWtXrvpvv/3HOw/s+tHLP5o2ZVpJcU71sV1na/dpKvF7PYYJIreV5RVf/vJvNV45W1d7YM+ezZOmjP/wwwNz5y/4zEtf8PtCiTgVOmAaS4oFu5mrT4yD5cYOyRDW5dRWJEWWFHdWBV+4lHrJTvc/snaZ5tXfP3a8saO9i+gZ6Tk+zcAUL3ZdEEl0LsWYXcLqpJ64MpyLxVydQUJcGVwmpe6g5o7AFFTFYZT1RMKx1pb0aHhGceGTa1ZMqyzVmAOScZqK3hXKnjhMMIXTsRrgKJcDE/x8wB8LXAQasG+IrubsnJzZc+47eWx3LNp27uyRqskTz9SdWrZ4XtWEit07d3W1N186f+ZC/cmaE/snThw/fcZsvpN4+NfW3PDyD75Ve2x3WfnkF595/r4Fi61o15bNr7a1Nvx80ysLFy5dMH9aZ8fKw3t+1nCj2WMaaX5PIhHetWvzlUsnNM2zauXqhQumX7+8Yvf21zq7Wphjjasaxz39XtNEXCnz+Xxf+OKXVqxeF+7p/Ys//b1Nr3033Nt4oe4EdTbOnj1x/pzZm1+HGuX8OQt/67//rgb1NeXE0YN7dm3zeaxZMyfNmDkxEMze+PTzPjOUm1dgeDTqUMnljMGH4QVELF/WuYWFS5avrak50N3evHfP+7PmzKm7UN3e2rL61389SZcODTrcyplagtLKyoq//qu/8RmaaZLu3t7/9Sdfef21f29pvFx3+uT0yfOLinJXr1z53X9Pawm3FpcUff4LX8gvnpygxI523rhYU1e737aVFzZ+ev3Dj37729+7drUhLzO3qCDvkUc2HNr3xqXzDfzY8Po0j4ff1/zf/NLv/OnXLrQ0X9u65cceH92+473Hn9r44IMP2kyHVBqCFqXmq0KGr4ePbZARHSym2GOzcE9jfSaPH8UlWodjyOMx71//YFpGYf6R/Por9ZYDJEKJRGz3nu0NN67w59LV0v76Tzfx5V1Xd4H7Zlai+8LFc/wk4i4Aj0l6u7tbOjsptbxer6obUPHo7bYSiQQPmWw8nlSdMi0acxIOsDrZLjsV6FOpYAJshIYBzB1I33VEskNOG7nMCSJBTb8v4+FHNgaCWT1dndu2vWVbPQFf7qVzF15++YdEsZubO/hnG29caWq8OWnihOyMDH5r27e+1drWsvi+lfMXLHt24zO+QEYiZiNYHLCajiCnRHp2QIU5yuSpcyomTOEO1sGDH9afP9HU2ByL9cyYMwduyp1BEKhnSKSjKn5Tj0d6+PHMAzivx8vHT2mip7vbitsC7Y5bXnMcLRYHnD7/sGXHGEtge6DCLa9N9elT52180ZOVnWdZIDDnmgkKvQRMScSVivLJG5/7/N/X1129WvdP//S/Zs5e+tJLnw+YoUgMdVYAemUT1W1jdE1k0qNmv+p9ZVKWSHG5DlNVzFhSbFOQW2vIzIbup02DPu+aRYv4at91/ERLe2s3ZdkZOaZhCsJpoeaksj6zTURLZ1KcbRgPS4L7FJLgT5ooXkaMPh0CxUJmDkwj272RcGdba1Zvz/Si3MdWrZhcDj2DUIV296pDhSAAgfOS/yRpa8eOkVu/qHSsNUVK6TiMaA5jpumdMXNuMJgZi4YP7t+ZV1yciEcmTJk9bvy4/OIJXe0tVy+fOXxoe/2lc0tXrA+lZUUtYBY+c/Z4zfFDCvbxHjp0+Gztpcs3zkXjkCM7d76uo6udsipFiBdD2kzTGAlHetuamyAk00xDNeyE4uE2zBdsb288e/ZCPA7kCLKXWpKrEyvGH65/xow5m998NZ7oDUfD8VjUtvy27Yi0DSF6PEFVRnlM5/H400IZbW0Xvv/9b9aeObFo4cqZs5Y+t/H5QCgDwLVSFIAIBVgGRKk2jyoNr2/W3CXZecVNNy58uGvzY489WFuzJ6+woLJq8vnzZ1PcFD5fAJc3NdWjK92d7d3d3XxbBIN+mFtqRbq7+SX511iA1hKntpGw1bilWPxzQMqjoSom0TSfRtJWrny4ra3D60uDQ0FqFUGs76h8WLbjqHNmL9nw4MaXv/9P1cd2X7xyacOGjY8+/hy3iBY08lA1GTYJUtLBW6CfUMfYa7gSoTU2Qfc2hQX0oCrVKfcviA3uD2xdw/TPnb8kL7c8O7/kyPGj/ETgpw7fpfUXL1CW0IjJbVBjw01+4OcWVT3+7Jeisfj0mQvthJWIxY7u23n44IcXr1zR/b7enhbKbA1rVJZDItAPaImUvUVZjO89RERYCSFohjIKDmwgdKeS1RjHshOqZlg0gTAxOCPjtt0TcyZMnGt6fZcvnTp/vk6crF1dXXF6kxuOmXOWTZ6+kFI1J79Y070PP/Zc/eX665dPfrB106F928qrpj7xxKfX3f9EUM3jFs9B5nRHtBgB+xC3B7ZDWSgjb+F9K09VH4h2db7x01eaO7smT51RUTmhvblFAmeZCo0BcaKBpER0795dR4/sOXe53uMLdLc1AC4ULxm3WdzhvhR8RCXQuRGxGLWQkp5/L5XplRh1InHL48uau3C5oanxcI+g88ZuG/h03AGwKvfkli+/v+bo7rfe+EFL0/XSkpLcnMJoBCT+gXfQwR4pWxlS844JosBPSuZ8SM1XV2wYUb9UeqGoXWk72V7/g4sWG1R9v/p4U2ezzZy8rPw03YwrJCp6sZCKQ2hsW9gcEkMW/hAdSVkb1JoVFiUsAn6dEqKqiTQmDmLcLeZwl8sKd8fbm7N7exYU5D+5csXEsnIVzkDmMpDKzkAHWYeIRNeNuVajda9ACwwIpAR3mBAPhr1pqGpZSWVF5dTW1hu1p45z21dYNKWkYorhS1+8eO25k/vbW69tfuMVry+fOyI8LkLVSnrzen1vZ4uCOn/tHW1tLb1xytY/9JRFbd0T8vgzwgkhQS+FE+0E9XgCxSWV3LhSJ9HcCp5WpDfc29PDj7mJU2cA6ZOlOFZSOJIbTDvOwzBL8QXTNU2KZHH3hTtXMQjVbFCC5x+ywKTEFZabX/rw4xu//70rV69c4P/t3LZ52rQlzzz7xQWL1/KbEitNQVUaFe0vchcq3B5XVk2bPHNB042LDTcvvPKf/1J36dLaRz4VTM/mUSGVfg8Exg7aqe6Ojg/2f1Bz7FBjU3MgI+1C3QkRvXArbds04XAbxUdoozX3WpYZS6AOqMNsQQcEUK5YOGblF1bkFpRy+xjlps0hinB8+UFkQxzCEopuhh55dGPNib0njh+kbS3jqyYG/ek9UZCzQo4SVRIZsuHIyMmYg3VrBytFSYsNEh4io07T0JTpZsNcgQ3/I+v/wNidlOL6fd0I9zLgxwGMkcog8XBlqB9HeS9Y4YJ8jcJdLFBggVUr2ccTcTseswvySh/aUIryLVQgHTEip+vXP7Dq/idjCWBD0IScFY++HGfzO6/9xz//TUtTw/pHNm545LHGhisHd+9gTlxThdC7gj0ykNyWNMMUxBqIC2ABugJkbldFToAIcXadSH0XWaORjHNMI6qJaWIquLDT0kPPPf9cxfhZlqOjAAOyJKuqTZ1Vqx/MzSnY8tamvXt33LhZd/rk4etX63ko+sRjLxHFBx1YDE0QEjyi/Bb3Nanh9c6cfV9ufuXNKxe2bXs7Lavwyec/F/Slt9FWTLkrUuBUJwZV3vzZD7/5L3/Z3dm8cOnDj2585lr96SMHdnH/EoT6NaHLLLRLgKYdz1YUdURVoiSVEaQzqO7KnagaMnuhtVKJy/zO75e7lcFQlnh6Oz54f+nKx+bOXxGJOaI3TbKVKoqSKk3orhCisEFLeMgFk6I7odwB3+ZwS5fdat8pQylgqHeU8HeFhNnAiwMjmuysF5VDJmoO/IjLCvjWLbmPn5Q7aqrbulo6GEnPymUGP4jVOFM8KtCh6Sj5pyP9mqmQEfWgJeKKv99LwM0CTkmClI6KoI2kfH1b4d5wWwuPSGYX5z+5evXkslKCyV6pW4fPkShOn4y3q38waBqHfEBDWjAxq+weW7DBZx4dZiTKHVmw0S1IqcyeJOwjSR4Lfrzn55fMmjX/+NEd8Xj0Qt3ZteueDgTSdY9/weJlP/p+MBbrrK09+cgTC0rLxgGXlSpUkwWkSSkszv+N3/iSqoaAvhRAnMRSNY2ZsbiFsavQ4+IOg+PxBDc89PjFU0cP7dv62k9f7orFdm7d3NUbWbv+6Scef95xhNB0UtwdSFcsJG2j7ohFfh2Be5I5BEifgeMNTGXQH9z4/EvFZUUfvPPmkUP7Ojpv7N373rUb138/EJyzYBn3joiQ1aSiO0JxpTFYRnbenHnLD+/e0tvb/dbmNyZNmz9n7n0qCi9Sob8MZlE1NbU70vXtb3/9569+m9vpx594ac0DD255Vz1buw/4QHVViosLvkMwXiqW1V0ma6KJ6XcNvAEky9LQaYpI1QN/tIMmDPqNAmlpphngv07Eut/ZvGnugmUFpeNiUZBsoyhvrUorR4ZSvyQpFmCU3sIAV0EZxltgo/McBp/XIxzfyjDm944X/wjuQd9IdK9XvxuO2hiZ1rC2XyiTgzKVStDJUgVkwTQNj1dncRWUGlBgnqpGRWXlgT06pXZN7fEVax4NBjIcG7cUKiR3dre+t/m15qZLZWWTXnzhsyvXrNm374DOIzYS0w0eRDHTlA8Z2tx04vOqCQjDNF3RPTo/p7RIT0800hn0A0+eYQjKFkDJ8H/VDCVhGMCFgswSHlP3GzoP5rgP4k9Lqxw3oa35cmdXe925UxOnLfQwn3BoVBQsdZzE6dNn0gM5f/iHf3vx3Kk33n7l9Td+0N3dfuDgrvUbnkwP+XlAyJ01B8tqHlPRDOjR13Wi68rUybPnz1v69vVLvZHumfNXLFmwSgcVM3QR8WD2aFrApzQ3N296/eXW5itFReWfev6zjz++YetWXQSNhq76PCq/dyn3isr4fo9OBRhbpYbuQRQcv6zuNQ3+xSpxwErjoY77gRsrzaPDXrBR/nTfob2X6q+tWPnIkcO7r146+dOf/sf4CeMzsgoSjpB2dHMeKA5G+u1tMsZznJwMpInSpOwlQ3FM4EVjeZn+x9et8Ho9W/cfauxut1SSlpmlG8CfYAFwhaB0kmKi/LklyG+FG5x06VIsoCCB5Z8yKcsgAsqFermwHCyTKdHebqetNRAJTy8ueuaBdVPKS3TsUhOuGZbvoX4E51Kfdf0/16Zpv5DHLYjAVZReoalBBqXBtIzps+YH0wq6OhsyM/IWLloWCPipplRUjZs2fd7xI9szM3Puu29FMBi0QL0QzEJpSWVGRl5jtKO5ueFcfd2COauAlAb9dh1RkEjiZ4iQiulMMzTTp1VWVfFrpofSyioqT509WlZV+NCDf738vvWZeRVxeLuimUAAKMMwQ/V4IZev64ZUQlaZ6dFMj6JJgjuIxrhhQU5CJd7Tc77u4vw5K9YufrC25vCrP/3W1u1v3rx54XjNofmLl3hA/4bqKNHKbYJpctMCPN78Lx6Psmzpmvd/Pv30yT2ReGLO7CWTJkzhhsfUTSRcA64vboE9Xu3aufNvv/79aKRlwfyVL33ms9PnTDtz+qhYkrpmAoLKo3B76EYFMcNwPD6IGDWkwcIGRs3QufEzIL7U+J/UywdgakIyQdWp16N6vcABHYv1bnn/3YAvc/bclSeqDx4+tPOdzT/67Be/wkNL7izqaNyplLe/ixn5T5aroDs2kSTVA/5MHhXJGK4f1jTlzQN+7HeFPohEavmg71IkJYhnQ41EUfre01eMGGokA66Q/MiAexkwsNHfyxC3lnKsDnkvMiGEf0LOBt0oVD1wmO3QOGipMyAoB8140Gj2LF62fveu7dcvn377rTdKqiYuX/qAx5vGLQGzqGH4w+GEFYvDoWHFW5ubmhram1taoLoH0uhxVUnweCiUlsP3UU931/Xr9Z1drUw1DYM70VpWRr5pBBOJzlMnqy9erTe9vra2m3GEGsRjMXBKUFEOtCmw8J6wE8iaCdW89FDu6nVPnKw+2tPd/uqPv5+dXzR+0mxN8+qq6cSdQCDDchKbNv0oOzPvq7/1B4sWL6A63X9o16XzNX7uGREGNT4kL8CsAgCI43bCsnlkC5FUZihv1tz7tm99KxGPrVn9YCCQCVgtxXbshDyhASWjRMMRisiDhJVoaW9ubAq3NLc7iO1J0LhN4c0ZaelE8cSjkctXLrZ3NmnU8Bh+n8dE0V409Sjlw5La+/zSCaTaY/gteEZwX+zUmerXfvLK2lVrl9636E///A+OHdq+a/ubc+csfOqZL6qq13ZcD0u6WYPyQySlsJRcLYMX88irq9++SxH/GnJ9Dt4LA+K5obdk/5EMuRdudySpLiaq4VDBJiWyEpRhRgBmPN303r9wEV/Y7x4/fqWjlb8nKyNb0Y241NAEeGAMC3U6JpMsNwbvL6+MSVt8Dg5CqlVUxUxo0MEFKVJF6Y30xNtbgr09s4oKn1y9akppCaS4HPmYKBNSnpoj6GOVYWzgkI9pyFkd4bn0K6cOsm/DXeGuWLAhVsgoLNgQV+g/+OQioZAuQctGmODBckGn3JNJWKxy/OSqidOOH2qZNmNRXkGpA6wrND09c8F9y48f2VWYP37y1Fk26OBApZHa2pRJs6fPXtjcfOn61Uvf/d43dT2Qm1uMeFNCEyyQmc0jQqzmYobFtlTm8Ee4a/u7hw/u+sJnvzB30Yqmtk6/V88LZRiGJxLu0f1pyCQiUjOI1HCg5K8KvUngPmdglhUAOAX96boeshLdV6/Vt7c1+EM2d38aG6+//PJ3Hnzo0aceeWLtAxu6os0HDu9MJGx/0AdgPcjPQj4KBaC4peHXilv8PxrjN1tWWjV15uIztYcK88uWLF5LiIGWLWE7CYwJwfJqVImHI5hWU6KxcFNzQ05DWUdHh5huR2SeKMSCfN56uxqaGm82Nl4OpGWq4DPyubCEqodNAZrm9sWKp2BRYU5B/suGb2b2hx/u2Lf/0H/9wn9lNPa1P/ndppvn33j9lWkzFy9evA6xh5ptOYqrZUVueSCO0m7cwmSNzoKNbLJuPZJhvnTks54Nc4UR95r2+V//GkOAysA/hZ4rZQKdPehHfBtV+v8oPsXkb8Q+6/ej+xGacv3Ur0u9jhxJ8lP9fxwwkgFXGHCdfoNP/kiGGPxI95K835Qf6a3uBVWxKIRY6sWLF3Z9+P6h/TsS8V5GjVBGnm4AahKF1Qm6CmpedkEwEGpubr1+9fLhQ3sPHDlUU33k6LGDx2uqdc07cfzEazevXjh3uqO95VL9jfP1Vy/V1zc1XItG2hkzcosqMjMLAqb31Inajo7r1663XL3e0NLSyndjdnoOtyOnT9e2tTVdunSluqb29NlTV67UN9+8Hov2UMVTUD5O1TynThzfufWdpqar3EQEgjnpGbmhYIhB76NRXFDMozK+o+svnt23b8+R40dOVh85cuRgXV1dUXGZoRs/ee2HV+vPcbNy9sKlbe9vrqk+MXH6nBc//cXyskk8HHJAWRkOQiuROF1bs+P9zSdqqjMzSwuKJvBYNi3oO3zwoN+f/qUv/743kNnW3rFv9wc7tr0dCbczRQsG89MzC/NzCxobbl6uv9TR3nbp8tUL9fDiJiYa7nSYJ7ewPDe3IOD1Vh891tnV3HCz/fqNxrbWzkAw2NTSuHXLW6drj1Enpukhfyg7lJFlejzhcPexowe2b93c2HCZMdUfys3JK+Qm9vCRA//6jX/oDYd/7df+W3Fp5fXrN2qOHbASkfrLDZqZnpWV5/f7qfvQKf4n17l8+qT/rsEqV9/qHX6v9Vu6pP8VBu+1/os/dVsl35C6qof4cfBIBm89lvK9w4xkOLtBsRwBhxZJ3qAA5qB0LxyNfo9RlJ/Pffr21qaucC/3dPymh+iGSNoKzjjNrbraDAn8MQ5HkBsZkOEXypQG0fjxEsNsBAsrjJkAAIAASURBVD90E5GentamjEjv7MKix1etnFJaqiPtNaVSqi05ckU8JjacDWQD57zvTsnANw+2YGx4C0aHXwbDjaSfaR2dBRvwfEdvwQaukGHPBQG8wmlUXC9GKC0Dtik9PXSh7tyZU7Wfeek3p06dG8MQkwc/sd7w7l0Hli5bu3L1g5rhA2Ur9H6CgWBRSWFHZ2d7a9uZkyf37d9/vOZo9fEjx44cv9HQXFpS1tbesmPrO8ePHLCtsKr68wvKDE1/+40fHzmw9VRtzQfbd3y4c8f77775zltvHDt2+GbjTTPgVXX10MG9u7Zv6WhvpMzILhhXWDy+s71t29a3qo/up9wv0QM5ucU5ORVBv+/ixbobNy40N3VcvdFys6Ep5A/GorHXN/2ko7M1Gk2cqK15e/MbN2/eXLLqgSeeeik9PR9cdlAPZBrRe8Ldhw5+uP39N69cuVxYWpGXV+rzpPFId8/ubTNmzn3xxS8lFO16Y8P2LW8c2PcBc/hqNdPSC7OzS0N8li5caLxxtamp6fLl6+fqr1y9ernxxhX+jboZKiyuDKZlek2jq6O1pqa6u7Pr6rWWxoaWzq6elqabu3ZuuXL5PDZIZvkCmaH0bF03uVt2/frlndvfPbhvVzzeTTRfRnYhdwr37N39nX//j9Lyqhee/XR2Vu7pMyfqL57q7eq82dgVSsvNyszWDA8T7iI+TUpHPG2ZMtCMDGs32EjH9ygt2Mgma+BeYwNH0s/Sjs4aD2d+hzAR/UaiffHLXyPIND7UnyP8x0b8cbg3kGHewEb8pTLikNhQV2B3OpK7fi8s6fRev3q1sen6lKlTFixaOmvOIoV5/YGM4uJS0KaROlOKoXsmTpg+ZdqcsorK/MJiwzRNQ0/PzJg4adLUKdMzMrKrKidUjZ9cWFDCd1peccny5aunTZ8dCmUWFFemZxUUF1eWF1UWFJbmFZbl5Bbk55cVl5ZXlI4L+EI5OfklxWWZOXl5eYU5OQWV4yesu//B8VUT09Pzxk2cmpaVFUgLNTY0ZIUy5i9cNn/BsmAwx+NPKygs5L4IXzEBf2jG1DkTJs8oKa/KysrRuC30ebPzcqZOnzGuaoLH68vKyszJzbx2o/78pTO6pi1fueH5F359xrSFRDEBrwyCYA7/vWNZ58+ftSxr1ow5wbQcPp6s7IxQKOgPpC+cv4rfONNVbjq5WaksL1+0YOmcuUsCgRxfWlZRYfmkSTOqqiYVlZSnpaUVFBUvX7lu6tTZwVBOSem4jJzCgoLSirLyovyiwpLxOXkluXnFZeXjCgsLGptuRPn5OmvO/IUrCwvLmWoWFJekpaf19HRcunQuFAosXLhkzvyl6Zn56aEsj2FeunSeD2jegiXjxk03jIBl0bKyigULl4+rmqYbgZy8gszMdKRKUoSkjrsS8MyHdjUEdo9qCQ23dMmotx4b5uKjX97sdvaacqu7SPmg8HcQ1SJyWOL3mkupg2gV5vV4CgryiO20NzVFeqM2YwGvT+VOEvfFEWJuCHVDIgR+iZs4JEmssqzDy9qhKoRQEsQxFScW7u5pa8kM984qLHhq9ZopFeVeCOmpaJRVZIEcrw1QnFHO6uhn7LZmlY1qVm/DQN01CzbqE0H8VSXEbXMQqkmiOq8bXp+vuGTcylUPBINZCHZjGveMDF9+fvHipSuLiispgMrxQWBaqiC3cNbc+yoqp5WUVgRDQV0nAX+wtLSK+ygFBQUNTdd5rDV16vSFC1dOmDhd003+y/aWhtoThzs6W9raGtvbGjo7mto7musv19XU7O8Jh6vGVba1tuTlFs5bsGL2/KWB9NzsvLx4LNLV0TZ7+pwFC1eVlo3zBIL5BaUFuUXFJWWFBeWFBWXZeUXF3LSUVWZzC1dQoJna5Ut1V65ezMjM2vDIxqef+i8lxZOYALWiFVeJGg73nj1TmxFKmzZ9ptcXyssvCQUyMjOy0tODy1asKSudbCvKtRtXWpqbZk2bsXDR8ulT56lGMDunqLS4YsqUmeUV4wsKi32BUMX4yWvX3F9WNiEji/+iPCu/KC+/MOhPKymuKOC2vaSKW3Ie3/Ib7+7uCvj8c+ct5paKHw2GGeC/93i93DW6fvNqc+PNqVOmLFiwcvyEaYbh50+kvaOjuKhk7vz7SksrIZghdMLEyfMWLi0srDA8gaKiEl/Az1yEm0Sjqbc8bdmojcZHt2DsI++1IXf0La0xu72B7a+xfmH9R8NixD7O77330ARxuIDumkNUS4OzA05nxwYkNgCRsBULWOlQygpUCyE6jYUT4Vgkomiqz+/njhb8kwNCgPwiVqQzaiVMf8g0A7ZtRXs6dO6aeQNUNQ3gGKfxRCQWT3j5JtHBjmkOQKtUYkXiXZFozDC93kCaoeosEe/pbdd8pu7j20knDvEg3hLqhggTFsZSobrKcKDEcWi8J9qdiPGIyvD6vbruUQFhjvV6Bm3wkWjY7wkEAiFN8TIQu2JMmlvbrSY4OjAQ8muptgKMUrriQIqemNzjt/jvHKYyaojrMhZXVAu4lBBEptiJeCQc6/EF/R49mKBOuLdL0TWPL0iI5mHMUJRYIh5O8MnxeTQTilKMfwn8nmC9yoJahoFwa1uhlq4qhgLtjRYfEkqVgZagZjgOtj3zUSq2pvABwzFu8SAXKrlIlZQ8U5RUpWLi5mfumKnho6/PXwpWAexFQiixRHqrItTQZH+3gFRB13xCVTuisR17D3xw5HgrfwTZuZlZeVHdDOP+8RJiY33XhJ5EBT0rVWCHATilEpEkFpyi6NfxR2pTYveEO5zWFqO3e0Fh0ZNrV08qLzcp0/lbCUvtDaSuFvStUCa/PFwNtxzJHQz1I96dWPYq9oigtwrS32DeoJMUdfv4bzSSAP1savCdZWvQkKvx/1CSne8ph29D4KFjGnamqEK4FHpinISd6I102TTm83InLU1TPCi6bAE1v0oMXEoO35oGZLBe/v4/r1/3YGXVpBiQmemJWPjggR3vvfv6lGkL/u//+fcTx02lNuBBsW2HOahxo1Lb0KDnNKE4FiD1vSbTNdVhdjwcCYNerwdMk8FvSWUJ0AfvsK1EwJ/m94YIMS2ZY+1TgkelUm5boMfZBrISkzADLLASIQpfgybYFMVSQTcHrBzU8MTbFAONnhWLdFvU9gTTTM2wY4meSK/h4wbXy69DmA5aiNTujUe4Qfb6fRToRcDSoyGGxlhKdYV5kNKZEZrglp8fAKCMQ1X4Vz7b6AnCVmKagWw2mm5jEV+z4TZ5oKIKbgeWsi7GWEVvv4twpN69AY1OI/w+2S8zyuu4G7Lv98NdYXC70whXTr3siIZg6JEMObDRzAYd1Jbo/smkNYf8FATXGlCYQ8aR+1Eakf0pIvzW4OQBNhU8nIiRZmYEPNmCOIsy1CxlIETLPSHTk274CN8J3Evj2yotrYBI3AOP+6FmoRlpQUOxoXHaFsLsKE6s8XDK9KXz7Qc0w2Bd9FCGh/s5NgzAUIlGBZwCD0QEb1OJBAawGAalxEz35TI/Wig47SgTjdlQ9zfSvNy1Eu3CiuMg9EyoZ0j8MFxXBWcMK2uq5Lp3oE0QJEyw4wyVqUVDH7aDgYww9Hc5Fv+bo+lGKN0T5JeOo3VNT/NYqiJy2RS6j0GPOujBQgOFjn1gNeVf4QiLjeyXCIVHGVmC5wDA4QH5A5hO/gmNOTo8IKhEQUjK/Vp8wI6o5UKMjnxN0twwlQxEcQ5AI91yKSabv9Sh1ufgJpehVtottgAZ9aa45a4feSTujwRgIVrqtkBAlSPbtbAzFg5ZlZ8WWT7v/UuX8vnefvhYW1trp6KEsnIc0+RPE4hdCdopl0bBpSMFNgcbThLIG4IYEz9GwKJRfjIp4e5Ya3MaVAYLn35g3YTSEqDexw5btY+9jMgmX9EuNRoH4tazSkb3mEa2KiNb0eFWyGhs6QgWTB3iId6GNdbc587kxQgq3SlyE4EdQG9GoJ4ILhA8wXUhrS779kT7JsKHgHIA+m10g+gZIR8aMZsJnjL4BhN1jWDL80fIbWL95fOvvvJyfm7Bk0+9WFAwMZqwVU0zverkqRM/+GB7ZlZBXlaRSqGRAhSQpeMn8e7C0BCA6INhgg5nbBMKBL3wLVjaxjIZ0VQtPeSFdwILg0ZR5BpVDeSjR2sOHRcKULOjzr0QPQBFA78jGi4BBKoTagIcVHCyYhMz0MVA2sj0+bI9hLtg/GcegwfTMgKOKopVYJWAckIx/V5DUQFF60ADISik2hDPwoj4TNuisQSsqaExUG9E8hGkhBPt7NLIEgvu30MdQxpoqVmvyH4sMpqlSG7VfT96C3ZbKZjhNsUIO3E01niE9sDbs8agwitWs0yykxQ0mfwlGeIrkw3pShLP5f444Aqpri9LifMx3En5VyKD/tT3MNIfOEyGaIZMdlVLuJk67L3Ikav9u6/dj/Tdi9p/JGQQUI4MnF75EeZOfvJeFIFwF14CNyFULGAEH1JhC1WZ8zBQyM0CNwB47Si6GwSjDeRt1ARjqSMMFvSqCM03ReAxdGT2w6QkD8JkpzNookuXCXSx0GGAv2PYb/KdaKFxwRwbuEJAFIGXEkkYvGUet1Eq4N8AY2U4MGjuwp0odKwVt0uMgcsH8pYIniEuuaq4bxnnwfGGBgIa+JmgiRCsf5B4gPyRgG0kFGjCQeQpRWPL7x1iYpABdIR4P+hGwycltIffOL8Wtywq5MOAd4xfE/kiXYIG149x0LujMg/FJxlObTSCqkuTKc27pUGmy6MLNLVcJ2AdXQJZZYgWZZJs82YpqzRlp8hFq/Yluvp+TK7G/itcSe4vlrKYlZS1mkRZqoP2mtJ3TZIER7O+gYmvTn4TIYOobwZYlpSRkBRDkfJd+BDhXnTEE4suP4bUkOh6CZp1+EPn0bdDs72e9cuW8Q/uPHK8pRM0oc3MDGp4gcgNq0a2dMhxDWLk4sCpRBxZG5T5YojBY+GelqbcWHRKfsEz3LsqKebHEKa/ICkLbLd8UxG1zzahz2argrtjANx1kN0Y0N89YFaTEFcl9e/KEBaMpJDT97Ngw1tjOe2DLViqNe4/huFGMrC9XR1o3/ruNGUkI1hjBuYNzn8e3WEdFjYWiMwrqlwMaLkwKiOS1AFNnbwkFYVeXaq1MrRGDCWoNPRORGQketBURbLkQY8zZjDB9bpy+VxzY61t51+7cSUvt8rQSSQeudnYuGPnzvTMvLUPPJKTmWsJLmKwh1RU+EG2izGxgCgVttkWN0EwnnLEhEBwh0zJjoYNQX06yESEd9D2iq4til6jVVKJJFZguE6JJcio4IYc12bi9cGmYiitwjK3KTe6ACEUKB+bIS8zFVkytJGKhp/nHhyypoKwLXiuNtwRovfl/hCriUKFADCJVFWFz8v/sxkeBMhSAUcNxO6q5IyQRZUBK3yIM07tv3SThoUMazcGvCdpwVKvMMDEKYP22oCuiyEs2IgjGejkDLbGA0YyjDXu58INssbik4dqxzRM73WREPwA4dxoLvE3xCxqskcBAigVWVQSmjD0TIhOqLAJJF2xWPwUI39dALuAIR1oBcBd4q4Upl+gTQZ8KcitQ8iDH9ME7AR9PCKotpDUHf0tQWYg8cREKL1IPm4wQIYDTlVCE0gyyKGZyO+XHJhGpQCZ08eG47ZdqMLHwiqp8IoIEQ4imiQpIoqIZIr0LgzDPjBTfLB83/Pj2dIY7WtdYmKiCM6MIr5B1PUUkUpDqnXsnmGqw5L+OzhUckuAYycjTgZ5RZE3Q7SBgNgyZF5ATjIbZ0kXTH+SuzzpYSRb0ccy54NLhIrkGtNwkpChA7v3Wb/Z0h18L8FNQEh7PPb+ngM7jh5rcijJyvFm5jCo80LY7TAZfbslSLFGcV/JX8Llnd6entbmQG/PnJLCJ9etnFxRplNRuII8BJBdi2RlyijQq1dsKfIz9vooNA1Mw2Na6gwpiiVBBiqm6MEMOUK8sn8nJCb1JXeUyGChjXHZHgikjhGfxZLEHAK3ykQEyrijReuvnfrGP/z5vj0fTpk2b8G8ZcGMQEtzS0NjSyQRf+TRR9c/8AhhBnWEn86oSyKL1AYEdRRVoE9THbcDjH8rEdl/BRP0yAFPRJIZEm4qkWIBolePJAlEJG0b2FoRSxOh5swNNMr0gJIQQ8dM8ExJjSciahUwJwbDqFeQ+cHnoIQKA9Cw65XK5j6mQ8KL2yxVw3JsAv03FQjnINoWOucaRvd8/Lbw/vAgAGcRNqUo7HJ3DXwtTTaQqmMG7a6V8w/UWresrg/aSiOkxO8ugmQECaR7AWgYPdHobVy/b9uB7yC2qyqVn0XwyMBHER61pVELzIiqMxC7gZSxKugz0QoASxD8RRe9aoTaWK3iVsAALwf8FVtFW6FAyC7S15D+Eqx94NbwT1loP7S+sg0SlGIhjtsNx+3IgiwPAX4Y8NJsoEfn11G5r2bAJucDo/hWUNdREYgMP8JFVCYpTB03mwH7X0RswExBkloqUoaE9YXhPJRTqSryXBSDQeIgt44oclL5S7Aa6H8KuIPw8MCFowJ9LuNuKmBtDJm9iNBVxdQh/l3FMcgSqLCTMteFnBKqSI0g9If12wpq/yoJ6ePb/FhhOnd3p9ztjYxmmqECoOoCU1if5AYyIlKiSt0dyCHxh2zrpD0c37Jv3wfHjt+wFZKZnZ2RRwyPBZJeDNlg+xwsl2FRnjYghRDujrY2BqORmYBqXzm9stzAgF3mKmGrUZdPsh+RBpUn7i1u/FbG8JfZgt3zkQgwAWRJBPEJZrapyFSiNdHw7LcxXY6WEIpeQE4gvGS0jTr+nkHdChL4CNzEjyZLkG6CFLwUbDalioNUyfwCsWvXLh49eOT8xXNdnR2+gBEKpVdUTpk1577KikpgOUDvRFA7YVMRddXJgacY86MMCgMKlWsVTaIODMnMlv4KxJUUgFCaXMxogrCUoKIjJZNrkkyEuYr/iqgDSOUoGTxrVE4T9gNoroam4y5yxCxCrsoCD5CgGDSPPBhyxXE7DGknB4oZRMPY0IKSPNEh/8ZAEgepM3SwvaqtgoqUJntOxAOiqGHFXS08WxhLVl4YofcGIHivTdbddUVu606HIRp1HGXk2qeaXCFuvsUZaGGGpHn9aNHQ3bzabX3dyN/Obv8h4DmCIRi4DZDMVkWpkCEzFgR8TMAVBFsQZRI+BGAtyEIx20HmIIbhP2x50VUOOV5hmYDIHKFQAqWoOkJ6TYRRNjwdG8GOiCMiWOwXDCmiGQsMHTdSILrLQxnMlesOYJMgSAMZXIJ4VAf+j7tSw2K+I5R8ZQgn6n0EkRRUgCpc8D7+jVJZXGOAf8BLQc5a5JngiBQ1NcgmoWPXl5RgEjeK3hCVIAcRNRL0PpkBQaKGN8Kw8iehtrKtRzAyqSisyqhMg1OX3F51e2oJlrPASiIeH1094RswDZUb3YwjYXQADTHrozNmH3fe4KOtz+Ev+FEvJVIS0smFE0UkJFIz9sL9F7UeB6FRlDo0w2euXLzQUtj2YzU3O9rbqZaflafqBrD3uOB0kUUQMiw2pTaDlEBHuDfR2pIR6ZlaVPDI6hVTKso123aPeXCxcR0jWxFTUhaY5BYY0ab14Yz6CnnuW+kv2IKx2/n2ezgYkbrGjU6I9F0EfonvMV3kuCV3DbQk6GhNoA2GCh9DwChw7wvSDNy8ujzsCU1RC2CCkh0NGiwzG/Y7v6hRVjG1uHhKNBaxEhH+jx6v3+P3E6JHLVuV6VOkU0AfA4iPhUeFqTJVyg8qqKKJquW4/cGHgTBVc4iuoel0iEw74ZWA8APHSNEx0ojAF6ZMNRKDMim0pWDhlErdCERlYF2PiUhOpr0Q+a/otiZRguB2EkkJoEp1T0p1Fe26ozFNsESgn4atQwRlB5FCl6EfxmdKRVwFziUGpWoS/cb6VKeFMWR3bzV+bCbrF771BgPToeOGDvWmvvfRvgYJ+Xn13lqTX6B3xe7JUsC2cEwk4bZi8jCGIEORdHyO8Ehgj6Gn4Bp8iS5iWDHDE0l3hE2HrcSkMi5lruyX8Nuwguae9yJdTUU+iSZxqCIrLyBZkFQikv1DxQIKuIDC71JsLP1TVar7OjIJgM6McB5VYQFFpzIuJ8JSwTiu/o7IOwhHj4mGMFEccFIUV0Tw1A/igKZJkUkONwWPZhxHJQW0KZO2UcyVeK+oAdiugHCy4UDMCHE9N0eA0MBLZFJMgwhQiFsHREOb/DQdAFFKUcv5RXlX7B6c0x8tvMMTUcQMqqwd9y0J17ZQl7BBEU4SUOY6Vqbf+8CS+7izte1IdWd3WxuhgaycuCl4uwF1Q/Ec408pAr1pxKswq6eHtLdmhDvnF+c9sXb1hPIyjdnAvytxEcAUIsyYALJS0u/G1YHzMERsypKxJVFSYXHqL9iC/ZIcNsTV1yYSqAOugNiHFOM8iH3c7U+QH02gF5i7VBSXFVPEmkyKzbA+m5XiYAkHDnarI3q1cStbFsDXfZ6Az+sF8BZwMvPlZ2EJT8RcYOJkw5+kkXADNzWpX8wkNAxMNxWpbEUaOkyjIvEnE7YO011iwGof9I2lYsOl0oBopwbbrkpvDO2twGWoLgwNV2OShIQKOD4aXnRfBQtvX1grErMikUZVCZEiwmhrTHqIDD6OgHy3k1eGO+jIalScN8lzn5F7pUXBPi7X6hflXdEBMh6k+ow1IN+CYkwqZECRUMvWFeS6dqskuPgMR6HamMT87didfgTMsvFQdS1HnwAIoh+lqIgiPREGCTCBvJY5ABdVzGSBMCU5wFzD1F8ligwSjXKV9JInHlNkQ6NL3DiAMlrkqzQ3cnNBVi47bx9ydoi+Kvc4SjqFydWWQrYrHSAiM2z9vrzfadeHO5R9xEQ6Xyzp2/WXA+x7swBcSSy7fBLJ+WKpwEWBzCXuTMt+oL59lJoQHoMsjGCCXPxav6aR/g9HtDIJNJ3sBdK0zmj8nd0H3jpwsJnPfVaOPytP1Uw/AOuwEiXIt4Hlw7bDPbGWJjPcPac071Mb1lZVlCqOPM8Q/kOImuxTEZzv/ZaoOnz9gVDpD4jfJUjyefd1LXFjKDsTPsFGLiW9JMIc1rezUqYGjnO3rtYHoScpXddUcVHTKSYrufVSDzMhtaoI90pWEAWyyrVQIvGVlD+SppEIOzq4uuTiFUTaXcqcJQ3YQB4P0WqHreDSQetXd061CX1dq4S6UqVM6ffWlBlzrSCRnUaK6vbNukKnSh/OPOkRYeWPuv4ncrtJ+pEkdNTBGVNljCNxcC4MlowZtI8aUYIvTFnyhBazfvREn7VjolhF+xHMy9JNSjMkGYPFj73GXmOvu/ECtgxEKXBXycamCx0ibGzs0vW2mP3Org+3Hjl6Q9HUzNy8jByPBrXqhKphPpZ5GItGe6JNN/2x8PSiwo3rVk2pLFOh6Uoo+0IaRUuNHaVZUxVlQAfo4E5saBshbKAuTUoCn8ozbizOHHuNvcZesjCUop7GiG7bNNWLJ0QZkumBSaonkSYnY31TY6+x19jrLjhYIruFFViRVqCyOV1hlhM0jAeWLNUU7f2j1Tfb2iOMmBlZquGxAQcDmKrezk6no8UXC88sLHhs1apxpWWJuK1L/4gZ2Jxqk/6ZFpYqsDMk5sxNbjoDzFwqO2lKKmXMGI69xl6f4JebU+wHMBX/ohO3YIP5U1VIig8JRBigtDw2rWOvsdfY6yO+dEWAD+HvqiCPTgE2EYdmerzrFy/SmLrleHVbR3vUYd6cXN0wKLM7e7tpR0t2uHtmceHjq1ZOrijVmGywgB4FUVxX1f7MEMkM/nB9Ce4vmar0FUtSK+yp9tC1nWMPcuw19vpEOlfYa6W6pB+pbFsIcjc9pP+7k4CXVKo6SWkkTJNoOhjMY5hiie6Q0WAoTlhlxMbOWzIp3JasxIDQ9rZoGsgoBkZGPfjhWlhHM7EDmBCH+3H0szqa+72tWVVGJPBVbuf5Krczq8odPdkB7yFD3csdjGSU96vcqg95yFlV7mj33da9KHfEvDzoCknwniRSAroj5IFTBKuoTpk33bdh1QLVIFsOHGntaHGI4snKSiRivS1NObGemSVFz2xYM7Ws1MTuWtHiriETE0UxNSQeQax1P34fMqq4tF9UmqwOqkryB4rQFvKxWbCR3/BRaBpG80Wjv5fRrJBRLsh7MavKHVnjoRQLbntHfwx2Y4SD9S5a4zs+B4ec1bt4xt3xrN6W0euj0tDJcJZZIUdOJvqZFKnuyCRSMfXyTIp40jHQQcoMs0GGegCG9xPhxg/CCSOLlvJJhOsNtSo+gUtilC9ULEJsLpAqwo86elcI/6REV4G7CEg81LZI4r2dez44dqKV6MTnsxxbC/dMK8x9bsO6qRWlhkN1LC46mkzCK4psaEsuQno74F0mUdSii2KIvnVBq4bCVGOP8Z5solTJyLFNdAc2eSCB/y+k1/lXfaFK3mwXoJly5EG3IErlKLK1Bwwc9HRSaZuQekhFeklsuiXJg9Tl4v4EL1/RAMOGcHTZJxCU0d+RQo1FyeJOPqmrol/U88neLMN6MBTaAVlS1EIVLOuo+oH/7ljIieYoNqUZfs+Dq5fxd+06dqK7p9PL6ITC/KceWDeptFixQQ9aiK+hTgEGiLQva5/stVJE/IjCBrcKkJEhiVKaFIdBUia3vRc65iUrAZWtcmOO1j0yrWOb6A7mEFsQ+5kjqWozNod3z4JBS7OKkrnA9cOoBfLkTKg+YRJelz6TTFaJjndVuFYoooqyborguU0yB0giMvbJtijU7epnKS0BUoGcfMKiTSmsQ1J3Mu2Tl//krYokh5dQImOf9M0yXJDN+qsjCusEvOoUxNCxb59JigzHyvCZG1Yv5389cPR4bnbuI2tWT68oBYpFit6UhtJqqJQuuK1Ux21nZy69D5OsECO7Q7KTnVHq+laiw13ucCEqpSiSschNrYydXHd1EylJQj2mJOVoxiZ5VAY5SQzRx2iTejyNzdHd87EEgYcgChL83n3kJDw6PFJDU9IQwGDrQtwZrnKUvRO8x0ickVSoHLGIO4Ike2q1coQ3k0GZoOF+vOUVRh6JcquC9DBXYKmuRf+7IsrwZW9lqL8rtyqNDzf+kWdVuVXpXRlGG3zkqRtqkpn7v37XEoR7ox/Jba0Q5Vb3csvnO+QFRx6JcgtAA2PDDoXc2U4ZbuWzW2GwRoNIGA0443ZHMppZ7bs+VVRNULlr0i2l8hxlQkFHLCsQzmQO8LIbno7uSO2p09kZmVPGj9MZihigIifFnkGTkSSznNr/MdxeiVBov5Cki4aOlagLYn4tRYLZUcgon8vdtWDK6CA1I6D07oo1Zrc5klufC3j2JD2BpMMgjMktz4XR3IsyzK2xUZ8Lyu3YDTIK4zyaWVVGN5MshdCX9R8cIeR2rfHtngsj2427aI3v7Iy73bWhjLTyBcmsTC+ocumypKymoqfUDCFGQ1VgSpJqvdx2CJk8kkolg1Giogyq/5DhEhzDv+GWHxkpb3IHqZaPNpIh3kOS6uJJAXp2L0Zye6O641TU7cfhSXxvf3Jz6XeSO+2wInc8kruxPIYbyWhXeErFaPSr4l6s/1tCg29rad2tWe37pSoQTsjlKX4rhLqR5BiluIVKIbLDaqpiJxJZAd/S+XNRZA30pIiKIk1Sxk24sqRP1T7lO1N+uhU2kEipS+pS22Ba2iGoF8AITQYTAn4xaJHf0x06SrtBbjUScjeM6h1fZ6SRkEH5Xvn02MdvN8gd2QRldP1V98yCMZfRl5B+VkhRyKhM0UcUDSQfeYX8Us7qMKbC1SAXAhGUSB0neAI6iBQlpw/ljvBQFDoAQvIIdJIIS02Dq/1Ia4dNq3xsJ+vd+ro7EntOlsGwDETIkF3bt9tc+X+iEGbKCUaTNSA2MIK6t+rgt/zXeypGTpK10r5/Yf2o7D+upfvLv+/czARxRaGYjPWEwwJClQpq7Ko6qpugIghF9UonoUG0qApKZEh2qOCBoRC4gspHtzTCt7pThjQNiEAFS0gpSpBTWTUkLEWLUB3kDZCPUXH2lrv+45SdvrsrivT5Ckl98I9vOsk9NlnKPV8kLIVLhLp2SBKh/DIIJP9qiD0TJon8BFCAgLQEcUShkP9G13Wt74lQlJUAxn1AtXs8mj9AdKFtS4aO0Yh7nqqD/kx9Dx1qDlSlj71USflsH2d8ypvZMD8OeYXkL1NHwgYNTFH6aV6MfC+DycGS+n+66LpEwAbBNkxKkuNhSW0GtR/HjgyBWUoGjCrJioQw8gLWyZKZ8eSt3XJWRXGEpv4oRbAI7X8vav+bYgOfCwhsManGI5AthPbdmnSsqCJlK1Q8cCji9jSNUHdwqfOZdMCSKeyUDE8ygZ0UD5NToaSEZELEggxzL0r/WRLvpq7bQ6VOjgAiEiY7P1xeANJ/VpOCHm4VlLhoHiXlXsRsCDFo5rKZqEL2A5K9qjB0lPRbior7jJKumatlJpiAXTXiYVYFahf3XUcZausNsD1EtgATNWUeCEmV5Rs4q0r/bU4G/ciG2vXsliPpmzpiu0eBxtycFhMicIC84nMSjbFwhDpU0YGbXQM9cKkhjuK1EmPONLUvlzxM2eh2XlQh7sTzOdINLRAwDZ0x1sfdnvwadRi78ctjwdSUAY/eGg94vsrwdmO4FTLcvdz6XBDCqmLPgqIzd6KJ2ER9X82S9iOZrZR2Q+5HJpeW+NSAGxRblZK+UvIA6zrcrLJB1ni4c364M27wBYec1eS5IGxUkqNSquK4uH9VaGP3nSMpY4IeXJwHiuGC0F8nyfzxIOPsnhQpNRkl1fi46UWWchAMPL5HsBt0mEkYpd1QBi3IIS7I+haJkvp3V+FMlQ9dZLwJSzHg/UaSMqvD3YviNvzFEyQSti2baUKMSWodcd8gdY0zVyJXVTweNS1drT9/s73NImDZqGh3xuz4GF63n6vJjwRNscsr8/wB5eyZBtsyHZCoSgG3EZH0I9JJQDpEcS5oDkPNUebmb1E+nQghesetomAJZdQtNHCOgjQ8D+s1lGITmDkEW8N2S2pSjurU4ZeSzgcjA1xNITiPC4PKbi3qeAxlyrSiWKzrzJk2TfUzMgARkIxGFVU0Ywl55SRUiWEnGRNTga4brDnaR0TE3JZYSkaBHGduezc/iTUcAHXTD0QoPifxoKi+ySdNFZJl6PI4ckej4qorL02cvoqFlE9VZYqXCpuHHpmTFtDHT8q7eaOh+WacEo8QqB60KqTFRvFtYSKpSqVbjj+iqr/80XGrj8KxEJLdSXnHEfPYoDXOIBEDUCJR53KkC41GODXh9jHtHOaKQOP9osa5wIzzCQDvip+sGWme8ZOyHcUOh/kdayp1PVjW1x1IccZVBLgP34yebKYa3Q2KpmmhD60pmZlma0tr/fkOVfWAUrgqnyPg6MfEcu7N2oAWK2r7/eqEKUUtza03rvQoxIesYywlP0OFNDPryxOTZBMDgRYt6kCCUxUSy26jlmsHYO1pyi91b13SnYMcL94tX5mGazcUoVWfVJDtczOlh6Kq1MrODVaNz7l4/lpri6NpBpS6ZZAv5XCpG37KuYS50d19QAekflG9HY8V5ZeLhkcsAUej+LiJcGdUmQnoA0+rmCwAveX/n733jq7rOu8Fz96n3IYOXFQCIECAAHsnJVKUqEJ12bLjKj3Zjhw/j/PSk5lMMm/lj3lrzZvJTBJPJn5xyYt73GTZlmz1LkqkxE6QIEgQhejtot56ztl7z/6+vc8FSIlFjsJoPRNLpkng4pZz9v72V36FK2DU4kUOzLDgmjAKcZiIK5wy8lJbltPcWlZYRGZmfA6qWNhXxy9LLCFxCpUEcAzFEYP7/Ef/8vSxw+MmiXLiq16LJX7tWeb/aF/5hgbwLWn2E5+6s7Yu+t++8tP0QsigDlEHGCxYio7MSD0SyhmUY48Q0msT7rfaKtj2ATNlC84ZOErV6W4Ga11pMZIrxgNQA6I+nuYWwuzkOvNUDgTf0cpDXGc47zyBLviWYKpQwfExVbpFi9tePpNczrLAlC9HbUI9L1dS4vzBH398ZOzcV//+2Ui4UiwR/xdoiEkMtIfDJ8RFZTL8aFSLsAlD+fkKi8FrccxyuAqF6tVxCiS3ByVXWoa+njzh5hFmMJbSccHUplGw0dAEmsHnhIAbHN7E52hnJ98MVsa+CVdXHf8G2kbBhuWGLd+TqRMgji9mMu42NpQ9+jsPvPjia6+90k1JIeZd+FngFSFBMtUFgG3HGK4j/KlcFSa+MRa0zWxOlLksUx+HwCvqmk1+HFNQcqWZFzZ+ZLAx8Xfla8kd7eNdgRkcDrr4krL22n0FHrRAFMTrgObmMAw0hZ9ra6/7oz+9u6AsxhiDw0L3NnT7eLEVKoRv5FsZ5Eob9yp2N1exFliMVPiO7ezf1/HPX30uGi5HKJY2a8fEml4Phu9/aEXRWeZna2qLf/f3Pv7m/v1P/uKIY5ZgH4ahppCpcSxIxOL6vFf7xYQkQH6H+gweDDvIlJvbZEtKCCaDJJfRFaIKXWJR+QH6UpFXrVgfPyDkDpxa3Ak65pBycdxB5oXBXDXpZRbhesnt29s+97n7vv+d5w4fGg6FCzkELkFV5YZJJzfy6RrI/aILghUoqXCMoTSInJCZYJDMD8o/QPkpCKsQrqIH1p0cbOEX4znWZ8L08c1T6AhSXRjrVpfWopIXlGHT0BLm5REFvhAFhZHPf/G27Tcuh4rQYABkQOyCfCaL0AvuJ6j9qZqQyGNTzM6mpseT4DaBV5nAkUbEOzy6fnPDAPae5GlJrezCAk9lWCKRTc1yompcTLAgnZC3Ul5Y3TCAM4wbprqvMONAqR+CrQiZCRHdQVGHPzwDEUy7q+nWyRUXNJ7b8ItyP3jqFxVMFzSFFrOrdwtr71rK682ueh6KW49dJZV5QAbC5LuHet93mSvSKZ5OssnxdDSSDaaEugWFDVrIHU0lwAYJKMPWQx6GKQIcpr/I01SNbbiSKkliGAiupn+1ZCCHR+8SShJH5I5KQ02V/kFHQriGlmESSOfA2R0e3yawbKGngVtIYPsCn5YruRMZ4+CNQXySO4V5BbFcJivmFryp8axJbNzTvhpnKYiPof+JY1ah6kIoHimMm0FgBTpm8DNPtTmDlBReJMiPofKhV4VPwfIdojHTsUZ7+/Ngdsr/nY4YXRYLdcHVwBmagkSer3PxlOdBJYLezQF81HiXOo+8l/zpKt6TgM2CgkIUcPRGJpVLTM6nnQjXgxyVFHJxvYH1b9PCggXhZUOhSCYj5uUmmsiGzKxcInIT4dSLBkteC6FAgg7FGEQXrIBgp3JVkDCPIDgG2/umCjXywXCsMlR4xKKLfuBOGNjc2KrHUKD65/DGs9iZ4UrsFrNJnUHkr4hQbXBOvVx6esbNZsTMbGZyMhUKQYeH4HXEa8iVeolYnIWp2OdhsweWNwwZVOOMcKKup2oRGfTdAAX/vk0slW6q0YqmI2N+yIO2gJo5qJaBEkZQR1swdFWZtvqw4kq9JMj0fdd1c5m04AwRQlgEqhEIMSzbphenzHDcm7Yp/9+XP7btkEnCmHah4Q7HZyD8N72PJQJcLpr/w3W1HUIt0wxbdogQB9MkTI8M7PtQX+bN+GsWlOaGHvmpZYv5iipBWPACwUwZ7zfOp6DLxYmiNF0+vdKnsB6+6bmSuQQ/IC5EZVw6wRLCuGC4j73kYJfDPlUZD1EFv/ymacqPb1rEoqYdNq0I1boearlAZ8IQnmH6mJ3KK2Phgr9IDZtc2Bsni13v/OUykNB1xV6FGsRS7EhoSBgsb/z0mNwAatqGklc+AtIdRlRnziCB9o4a6youG1niHSU3LeMU6hyqMj/swch/QMksI7y8EnZYLglKbcsOUxLGLAK7k0bgXUUpzkMJji8p19NbPLnx45safceXoH10JaRgY5i1iyteCg0yU1ePYm9MLUBiBndWLJn/kmu+k/Ajy/VjwhtD1CeWdXJ92NBydxzbCWmA4zV6V8LI60bgcgZQodza8j+BGoJBAwCDwHWd0fd3QRDV0obz3LLC1JSbyLFkMLEikGuYDEEuJhagWDUh+QEnOtjitSiaJKElpeqbU4yiFNla8ntQU+VZFbgHiTAMQj5wFbyhKzEVgYLRHzGxr69hpNRYHGPl44WaAwAPlwvPdiKGZRnyVLdD1AlTQUieqkny81XNvMV/E5pPTXT4DU4qHY6EClkfrJUvFOgMsSPc1Dg7hbxStxgGASZqT4mAZpzvw4kAmqKuDJTcCqlxudAK56FnAteZ2tQK2SYDSPYiHMjiPr+wLWsIH7mGXFgCpxIaXSJUqxCa99eFygytQwCpDJXHLEM5V6FyX5jdYmLCKGZG8BhctwQfgg3MfIYKZ2reA0J3htSNVzMItQtU70Eo8U5sZpDLtq9g4RMNCYLkXSiNRT0DugikIi7B6NbfI8RfYhWOc0aC5Q7H9qse2UNiaJmUMZR9xCWreteINhNYDFHVHEXkESxMzhTUAt8hJ8Yit57oyZt6PNGLFeFSKr3kWr/zKti+VFHtMfgifgoG9EuGgKpS4Uiv4RiVAmFKsrRbInTPIqh5sGOFdaCuV+SzQrnMNIhcXjUm1BMhdCu4u9jN1IworlYF7keq5eV0vikuuEG61WQEKG4VcEFk3MQul8GpIJcreEQ+QcUtLnBWorXIl9C0/t3aV/pCKQFqRWQAXDNFEAS8VeZxSH2ta9ktEsGhQ7S2jX6TqgGgEKl4J/j17Or9Da1MKICUrEohtOLUGM4/DvESuBCMCuw5w2SfBvhUsTgiVG5LIp8B4wbEaZeGtcMzKaIE5mpM6FyDfrAuhE7fLzh2BNYhqvAmuinN1NDECijsXMmBG2oIgkA0QGNhkDKQkKvDlibe4OUIwK0IVOC6Kl6SuOlLFBTYxPjAuZaoUQlEUTPP69GKjFi/4UGTj96+Dt4aa0EUzF2NfnSdS64kIKwuBgz2ZCJr+PL8C1YgwfGkxdiSaReDLjzmxl5YIYKxZyH/Kk8l0CBFoSwcwxJKftMTLQgCJs3TOZSlBir4QILjmdDb8RFkBy5p1MTuE94+yI7VdInqygFODgHtEKqt7LCtISxfr2yqEOrYj/WNABZ/6aPUVhqzCGBSwouMYiMtLwhKVAajWkTaLkkHqKWi/fK9MjWdIkQNGTkJCHmwDLmGNykSvdKjFcECC3DLaGPpY6Jow5OaXLeCTDyuqHx+aN9riLfuPRuB0CDkBJxYeldTBexW+RW/bDzkOHizNCWRqWY4N4H6j11vKFOw280hhi9KK0AXS+Pr8uYq+dxUV5NBuIF+pA5aEOioygIR8gSZKSTZPJAfgPQLsysmVCtPvieLw8BCIfoBVoazYnVgYBNe7kXlLCwUQh5OChsFCWCKJnRlpuPmJVaFWhImnkl4+DAAYGFjzyd0kbaC4PKLvX7+jb84fgoT5+OKcZsv/mSw4kznNDJv5z7V3oDXKr3S55O8aY5YUpfANz3cOzayEXxCCLmuMf7+diKwhcKAfOFDsY/EIBnMTIAicfRu82C7yFug/CKgW28KVZjAllMjf6KgdHD4cd9ULV+Me4Yv96qV5w5CaWQAHusDdRN1lR1QiBBlpTDXHsoByKABpzpV8BLYzh7Scsw8GkLhsCB/hEKeITsX5ycqY1InkkrjCLJpsG6jSCMQAYMHk1RTEDUOQbiqiUEd09gP0HhQQygcrqdGED1ljo5RD5sMWNCaHoE5MpTDCpWAfQxKmGpbUFP+ALqcRCWhPkEVvkuU8vADeVmZ7kDASoXfVtROiGnWEs1tgaQLGe+RZ6TQxpBH6ZkiXaSRA9aU/qYXbbq7oo5cBGhTBPcoyCQxMVkiypkAMmP5ACsYMak5sJm3lMGUAfDnPEgZoNhQCQvRzRrlXmRAkAFg8OXfmQLriKDvo1IyNZkPgE0IP8Lu+gWS9ALXl07DVctYAe8ELEhCFNBY97TUGxK6JlTHj4lNTuUeoE4pMzi/VYKGU0Ubnk8gal1LhhAjoMsq5qtAsLnyTaGE8EWhreCvhNArGe0q1I7It2dxISsGSR7MqK83zqUMZiLg2xCBZrTgXItXEuVuIFQowm1Edf9KdS6DkMS0ggB4t8DMUAU/ggUR3mDsqXHFSqQkL97AAW8rApFMCIbQPsbmmk7gKBZAwXsL3jySeuAis8vmmpg5KUU75AHhrREK66XeHVet0mtLE9Z+SpBfE1yfAMrD7hUMCW2B2TDR7jfXsKjTeSa8EXnKIyhOlf5EEY10bw03NxHXW1jvb2iF9rbqVJsqjkJBBMcOnDuM0gsaruqBKv+AMgp6nSZFUojS/DDhJONcOS+pTIIGmiw0kD8gV6ZH/DuMvBisfJyGqu4zonYJU7kWhknYxdREdqURtOZN7d+EuE5gY1IlO4LFb9Afp4Gkm0BGgUwV8rwjFRYoknp16wCKaZi6UT3CwmcyroxSutYjVWooqLLiNmlLQKHylcBBSHXJgfqtYzYn+szC+p9ggFdlHuD9L3Pa4glL8UIpQSxI41QOhe/Fsh164VuUZQEEOssCIpTSVzYRSUs0SJmYWn/5N7uNLYIRIa50hAtq46xgbqTkR9ShwHEIpKDa0OegWqFEPQ9XBA2hufdU6AEUDVhvCjpAkYamovnlr39+dkZxOoVHAtIa8ta3NBhP5plXIoAhLFUhRwg/Mi0Ax6AyA2zgU+yA5rm6+LbU3wGmrDleQq8YrMIQZMQVdlWjroCpoRMshKBBY0p7kQS1FwqzEQX90i4PqiGmVbIudxlMJXeheXqaRYjFSX4qhkQwNQCCYoPmgd+YFuvgYWLfTifHmN4wgQm1nsb6qiZEyL+KhExN/02o9fSFVLmBGdwAikAjinxInKVySyepahqsgLdKEAhzXKwYTc1cVfsaR68I8qNXkszPWxRjwh+8T9zaOnNANQh1Za+lp66J/UkULxIi3yEiyisCvwXXkFomwNgMg15jdJgCrlHbNM08WkW9T2oKhZkzLMMgv/Hx8H0Ordjo11UKx52uE2zV5NTjHIWaIYvjfKpEnpQyH0NmRL71jnuUykw5Pz5kIi/9QskH1eeQKNy+GjQIDUaAbh30Y5hPkUotLLwyIhAnhKxCtcIFz6uFodETCT49bjphKGdiaOPjfsMXgkQU/upxXT1qth32EfHqK/yrnn18oCZZQh1rBnbXEKGHfgxKm0peNctQ9DKojal6GNe566KxUPBXTo2lAg/vFr6QP69SToBowuTI0pNUSFgNC/5YvJlaBoNBtppXZNClo5pYWmo4RvjVeU4tmSW/yz9/PX804+p8mq7ynRj/Cv8svc+VngJ2SBhMDAArTRE0zRG6K5esx4GCbkPWbOiWFg66XbgxWH4AKgbKFa7GX+rov/CTIlYTEmzOr3BVDR64dqvEhgBVRnV75E/cQHMUsj6i2mO4j7S3kqHFTYUW2oYUQhg5gD+YDp4oRAGtsINnYsaiiCUcPyaD16VqmqgShUDHBQ5TUAcApQAuPzeuepUvCqbk8yhY9kIyR7VeWJDGGIHFnG7GqYbhZdyvtEQMTDlVXocjAJy+cgywgLgKSIoUQLFKHtTg6rWJhlyR/DRfyKvIsH9sOibM8yDFFKpJJRTaVCZ1CvzJuQLAISYAlgSMkpT5iqoRGfaQoYtM1IwVjw5ZucMwjJpENdXgQvrIhtLjjSWCtciJwNMHmzyLveh3WeoIpdQtUENlBQa6OPBAiQv1PuE4y7OaL3FVL2nsdfW+mUvuGpSbcOstrlAyAh2feVBECL2YuSqX6bU82QTKDqlecN7JQoFeVcJtat4FF1fw73tXh8drHMHe1Yvw14vGxqVXxRWtD6/iXMDJnm7/E6VngjVIXrmGogG43qP5hYS5hNDBgqHqv7x/IE2rmNQQTCyLKFMSoSkiKHSAdZzxDjDi1XsRGlex2q/GRO/iq6oRYgTCDGrKcN/Pys9EzagSSsT4aQqhhg6c5r3s8IMpojJF1d7gHCdaNAgjiVCzVnUGcE0sUHGPgyQ5XEPKVEufwZxVtdFNRKqiFgInV+lFaLwffruXiCF5N0Ctg4qkBxRYFMjmocQRvpL8YcD1ExZQ+HDOiQNALkMQYqgC42axyDALZIUvEfREvrQlyq70ItCW5ftLtcJwFK1KeASKGQoPDKINlF5wkfKDjaWb7Z1/Gu/YjRf98zL+We/65MYl/v5rv5P39Ax5QeyLZgn69BeLQJ4Ad4mCQ5S6lOKEzYghMwynPjxHLQ+B4g4WJZiTiTTsGFkeizBRIlkqhghTg2OWaHxf6qMJrUGNb0BHKqhzIOESOcPI4KQpJAxbJUJCT49EYF6JKpkyA4A5NGaNpl+7rFQGqMRkOpfDTrEQgcyn0hcVKFBFtTxjfh0SsmghBBtSplYZA1BQNuUx5eyOkgQ5YjHKLIPa2FaR38vJlI6a8vLIRzpqmplHQOl+0AX8DvKOMthYFHIPpoUo4qLUqGSw9qjpMi7zoQhAFPMNSMrUUJGqXgrKxrl+2iBuWVlhUUEs5/qJmaSbZpYVQTwIXaJMz4IXXNwfYtFwdVGSnchVQTz48DxEjShqX8lXlsEua0LmZnPDJkgsFVBJ5iDvlGGCO2Txugeyq8aiwfilNpdYsmpw9SGrCuelnPhcZOWnI/Li87DCpRFiXPUu/rX3bL5eDOAGRNMYVJZPMXtVM1TIOl2yVKb1mgCBAiqF7gTQvL4G0XKyS5OCq49g5NeNP+9bBPtXR2PjvUfjq15RgSyx7q/nO0skz/xSlAO5gj1CXRijGTxoqYD2CuYb8t8hQxaEoGCeQ3ahiUQkGyVsNcybBEpqRASzgvdwVa9ihV/uV668KpBnDMQZE5IkGXBYTV2h3KfDo2nogmCXV2sHkkXUqEozF4ukfGxQFp95xKViKtE0JTmDWkLokg50toBeIM8rmYM4QN0F8EAOFDRRRY9jGUkWGYbk6haScdVb4+qv6kWHMuxUmS3BzBTQBRpFToQLqp7ELyuPFcRibo5PJxZkGJd5KmUORdCNDMRQMsMqsBT0HyfJF33AC97hIhQXszL5DAyGOxyNvtDbBVBB5lKvDqCLa50IjkBdQ5HPccSzpIX2Gw440B8f4f+GbhWpyR8VijiI0z04u3xeU1Ny5+2ristJ17mJfa/1ZZPQpjJNtnFL07adjefOTr/xxtlk2iUIu1u9tm7n7tbZafbyC2dmJrI4N2MAIVDAzsCH+0oABmOJXJYeu8sELptLrVxdeeudq7hwX3y6e7BvDlsnS84GhPgoNB5Foi5jnhDJm2/bdM8DNw0PLjz+41dGhueV7pLF9SxStaG4tlOiRNPWkUPINTsahduM6mUVd+5tt2PG4aNDh98aAMg798MRceNNbStX1XYcmzqwv9NjsiyzbcvYeGPLlq1N5/vS+17smp9xicw7TNUoNNUQ6fL6K0Q73Oi/iIAxoyh38rKu2dB0w83L52fmn/9V93QigxUd9NAsjlhZKC9MKC0IY2x+eXP57j2r6uri4XAok/NmZ+dOHB04+naP58mMx0GRAaWhxZVeKFf4T6QY6GYkbF8dDJnv37Bj1Zat1Tk/+/obPWc7p00iL0WupDR88951FeUFr7/ac7Z7lMHbJAWF9q49qxsaK08cmzj4Zg9eaAVEVceKuApnPcNYjLcqcdATOTyK3Lvu3bxiVWnvmYnXXupPZzi5lvsb+kCmEYx2ghZVMGMGAI3Q03gUgb12b4wrAAYWppxr8roGLCI/Ip9b6RLr+nDv/cttAY5oBMxpFKyjJFgBVHEK5V3Ztbtp9fpqOBlJXsNAd57lw6Ym5198tiuT9G/YtXrD+qqR4YXXXz47k8jKKk5QPffHcEWUNLlB2QcTkIZzPV+w1A23rLnrgRszudz//X/+wluQRZJJ1dBcmZIHvGtTB38URYbFaebFB1BPSxWQ0IJyHOP2u1YtX1HOclBJUoXMRQS9L2tF0+rtnXzphZORiH3LrZubG0tPdY4deKMnnfUMNRsXHyAkdpAJ4SBFsZhg4gPtDJ8lV7RV7di5qq42HotFPNdNJGYPHjp74kg/c6NExGDQBKA9XyF9QOMDjwuyqIj/7q+o8TUiMCTUOo55fI2wHNvKl7iKyK/EhmQ1i95eDNXbeAC/0SQ3ZtDfdAFjohWamMLooJ6SUrtDJKbKRCnnfiwWuvPuzeu3lbx9KHHq+HeG52TFwEIFxscfunXvvfWHDsycOzc53z0MUydL3Hn/joce2fTCc92vvdyBFz9w0cEY7ps+IKLIUgO6d8R2kscgMtTwAey1iX4qnGWbV1R97nduYbl0T+f0QO+0rOp83QlRLpTw3BxVHUANDTQHvDvu3vx7f3R3eUXRgbeGC4qoMeQbhoPN60BOhaq2jckWrfs0zovnaWGAOBXFJZH7H9y6or3w8Z+fOfZ2Pwf5d16zrOzTn7ltw6aq/a9PHTt6yl2ApK4iXvCph2/dsavhiZ+c3v9qh16XWixUeaIgZjXQfSHGpetqPRNUYDDGqY9C7N6adQ1feHT3+cHpQweGpyeShNhC5I9Kgm150PFmPLluS/WXfv8jG7bU2aaRyyDVltDjx8fq6g8+9YsjbgboC0ImZpQpTh9Tx7PGvhNVRzESgP6BM+03raj6D5/bLWzuk8jZky+ahu36mZbVzV/80r2FxTIgFnb3DMu7LTP0ZcsqP/eFvfHKgrHRp6GOpGbAr8AJJUrtB/fiErJmyscs0GUGBqNAKrNA3WPLu/WOTffe2/D882cOvjGQTnsMVZ+uxdmBkd5UXgcwk+BGgIjADJCj6zxccGqbtm0oHDO5Nu0rrjXD5MWyLGDAw16E6T/Xo01FOhN5B9frX+9baMWlqaCpVHeStY0byeOyhfB23tT08MM7oMnJlYY3hC9EuMO9OfTW4MFXenJz2R07Wj/z6KaDB4aOHumdnnYpBcFgYRGVMmvMLMzLxXuld1wyCF/1r19kaPjODYLUN2ZYmRtvWvEfv7S3bV3l+YFJ22KwRUxL9au4Nm9lKKyHICStHaLKXUxMUcyawT6DhNJEbpX8v713rb9772qPI/dQ4SZw4cuK3zHEk7/qfOn5Y6GQtffuTXtvbfzxj04cfqsfpJQJMuOpIfLF7KUviFgi8yP+7TFYBnMAZiK3KrfkJ/WFt2n78k9/Zu/Onc2WvPOeYduEOsYtvRt+8J2XXvjVyVzKV7MXE4FpRKMtYLrAkGN9mTyH07wtCDTbLeCUmzh5hIhsQgeLL+pgKQSz6hwKTebkKrviWp+JKw49v05K1lNXQgPlJkG0Pt4i4kjmXpY1Njp6/MTRDdtvral1KuLhoYF5mfIUxOja9SXyUTXL7HhVqLdHZj/cDvPWFUUhavT29MzOJQgtAkQOzLvlMSw3kecwjpZ2IIwZDL8FzruYoZy7IEogBxVkLZVMugvKl8Rj0MRKM5aWKwyJpa5SB+U6s14cg2DzyifEKykN3X3PrY88ckNTY3R2ISP4PCdZZngmDWsSL0MjUYpSm0q/Q6kGadqeoIG/JmRw1BgZGzl9+nRr+/bKeLiwNDQ9Icsyr6oitLwh5thGXYNdXlUwOz8rn7KkLNTSUibf0OmzJ6dnpi2rBFAB0B/ijLhE+BggHLmafcQVQJGLlHmDKdUBCoApmAPKCsbmppJK8ISRMQyXQ2RwGU9BzspceWERgCU/Csu7EOJ1lU+Rqa4v/OKXPrRzV934TLrj6PmRwYmVbQ2tK6tWthdRc8fMZPb1F08TLgMeJDueSRZ3RqBjog331GJRLqAWPXr06MT4yqbW6vr6glCYeK7vC7+xMVpXa7rcaGyOxArs5AzsxrJyp6G+YHJy4ezZLnl/Tc3083EQIkMDA7wrCRGBSvFKpgu2vYdqEChXBhgU+QNZuKN1O0G2gXAx7ZK3MO37WQzRHoEZLmMkANQCLBQwCxrTCs1FBi9hIkSYa4URGVhAigQVVxW5TgCCkyMJ4mKXtHf0iS7iIwclIICRjXx/DhsWStv7GsHJNfgXm2iMC1/oppZ2bVokaV0gLnf96/05JXXHNbj72jJuqc0D3IThoVT32Wl5jAmfVVYVlFbE5ufc8akF15cFhNndNeNmUMJA7nRI3XOCpwyWESpxZjJltrUOn+Dotyp3FEMbOU/T92GYaMt0gpI8h8Q3QOaFgF0SVlJyUVpy6xHqA1/fQ+VlGU1kNWDxpdJ1QvEbXQ42LWDvIx9DCc23/DVUWgm/oYV54J3vFZeSm27e9JnP7mltLbcsY9FRA8UHEa8NBzRXZEN94RToXcnB8EBiGLYuQwlWJcAg429v39ypnhniykjCl9UVFxWGpxOZyUQqB4ZutKd7AfSZQCcljZsibYgMgEINk6g2rqwR0e1KuaAhJJehCoZP0MiEgjKPzYy87D43ZQSG88XGqhtE+YHhI/+Jl5cYnjpBCZEJnhVoQyvFa9RbhLqcI6tY5pcWR9K6tsWBDjMFTyRAQmN+zvy6hpJPPHT7rlubmesdPzbQ2XG+fnl83eblq1eWfOGLt09PLhx4eZDSCBabFE8/1bkSgfLk5fJnFGbXY0KqGu5MBCx9WEkW87XaBhFLeBTQvcCnBnVGijgUGrgiwfNZi+v8NzrDUqp1aO0Es1VYvFq9zlDHDaV0eibZfW4c1TR5Y2PVqZOTvseWL68riUKL0LHNhhVVR44O5pK8uLSotCySSRlTI1nPNYmqx7hXUBKqaSyOFxDLJ2MJ7/zIbC7NiOkgP0EUhIz6qgqTmBNTEyUVxTXVBTPzXvfZOS8HTSibesvqSiprYtThAwNGNGxYgQkowOtBIkYJ2XEeCJ4irc9rai549NG9t9+6Tr7v6dm0CSA8S8U5LpBzwpWJtAlwIgPhlprjRWhA3eO4rLTHMjVm59KDgzM+k2/DWbGienKy27ZFdW1BLAJjcLk2V69r7us9xnxRWhoujVm5eX9yNJlziSVrEubLRKEkHq1uKKmIGDQnRsbT/eNzOaAbOcClF15RAa2prpSp6tRUoqqqvKLSGR3LdJ9bQBEoX+Zw9fWlMp2V+35qcibkcMw2BFN9R/mmqfwg1AKysoKLCpvSG3et2bS9fnbG/cG3XvrpTw9lFmgsat52Z9sf/vG9rc2l69esOHloYG7GRSEJ1BDlwiaBVJeSkVFWExoPrSaqtKdnNJFIr2ghJUWR6rrS3t7pwqJIc2O5GmKWVRTVLCs7PT1p2TReXRKy6PRodi7hQW4Nr+JZVFTUFlbXRgstnk3R86PJyamUfGXAasJcn5WXh2vLSmdn57NeuraxLhIh/X2psdEskCV5rjBmNzZUF5ZbmWx2ZGjYoUqGS+uM2XCHiR5FokqQTLksTouLwvW1Jcl0cnp2uq62vKgsks3ysYG5yamsDIU46FMNcFhiFlPUD2Fclp5lah1dGjTgMGVBDIkV6KEZKC4DJoX8omnc5UH37woYEpcAiCy1yV8q8Yp4aCOQiGNKsTD/kRT7dQk8+vrX+zbtQV+qQJ0DJQWI7h6jnhEz7Z/9/Nizzx83If7MP/rorQ89csep06Nf/rsnxkfTlNpuTizMe7BjkDRrCq+0wClZUx8rC7kZMjAwk5jKcOIArwYbRYzKXcxr40VVNTEnauRydGJkdmJswRMRgJ0CWICXV0SqqgpmZueSuVRtQ2VB1Jmfcof7puU+Ki6L1S6vKI7R7Kw31De/kJRhy+J6x0Nwi0VpTV15eaVpUj6bECOD88kF6BNRuXcoyXNUgeHBcGRlUN/zlrcVf+7R227e3RqJsoXZ+dKKIhOnATIjdHAeKB+KjmwUGcoaOKHMGgzFLqYir6tM4MhXilYoKuYb3/nmqz/+4RuydjKs2f/yXx7atXPdK/vOfOOfXsgkZSrppNPEZyFKbFsJu4hcedxsbi6Pxey5BTbQN5NKuoI68KbBcYxavrwtflVdUUV1NBTyUgtsbCA1OSUfE4ISGM4CXlNfUFRsT44vUGLW1Vc7lpgazw0OzDDfr6osWlZfaYeNqanM0MBCNutRqAmpqrBk/lpU4tTVlZcUQxY3PekODc2mczKXCim+LyLVOFHmP6gPyGlu5y2bduxcLgvzn/zLwX/51svJBWY4/p5bV/7Jn3xYJpR33LWp48jYwjxgOnAsrbSDsS0A1vLY7r90fWgGFsLaMoWxoI+IMUWAj8cSmFjgaKIBsJr3RXT/W2g57SAbu94V19WCltxQA1QNNiSm6h/Bd82J8dzERKawILJ6TfOLL57wmXvj7vXRWJj7PFbobNi0/JlfHk4vJFetb6+Kl/X1TMqFK0QIA0t6xcryvR/Zvm1XW2tVhKWNQ0cn9x/qePWFjrGJnGlEfMOtrS3+4hfuKioIH+/oXL2pff36+LETE3/1l49NpVK2w27es+r2vZs3bamLFtHO08N+Jmdb3PfUXiOKR2EEeTPN+/1aZMPm5tv2rkrOp5/65f72lcvXbmg0uNzRlqq4TAbIUl/76C2BlOtJOKGLeD3NM1ZyfsMjmZlErqEh3r6q9o3Xj5eWlG7c3BYOh2RWVFYZ27Kt6Zkn37Yi1qp1TaZpnz4+Oj6apFYIU7rsus11u+/aAsXH8kKR4p1d4796/tCzL3TOJz1DBkrmt7Ut//zn73NzuTPnurdua1+7Jv6Tn3b8H//7444ZLYhReSnuvGfL2nVxmUGc6Rw3iWfbmgQuNDg/IAxiO0/WGAXFobbVK6nJT3clXnmhZ3xYZmnFmfnUC8+89elP7amuKSwscWLFoem5tFK5Qsq0JmbimZD3tdLARRqgdphv9vUmN20WDY1VjY3xrq7Blta6TVtWITeKNbfEm1fEO44NFJVEN25qdbOi48RgOil/z5abPxT2b7y1/bb7tq1bV1MRpqMD/v6j51575djhQ+eZFyYygFv+7l3rP/7g7q6uc1meveGmDaUl9B++8sZPf/iW74tlNbF7H9i6a/fqlvbi+ZTbcay7ZUWZ0OB88BongRCGxmUK+YzyI3ibtq747GdvH51KjE2MbN28qrG+6PxI+sShvqeeeOt0Z0IYts+VXIcWuDDh0NKF82UHhRjvzUVicj7OKP0PRewhhGjxoovzp6X/vAyI+6LHiEtMlBe703nTAqKnLjS4LHm+ko6WxmWxGte/fs0ulqEt5IjWkdGYY0VIkfdibp5NJzglshhNzy1k5Y8yrhgfyQ4PuZSCH6FhmjL9Qm6qKI8XPviJm1auXtnQEEtM+QfePPP4T948e06uW4cD25BZjnvjjpZ777px/ca6sjJrdo6fONr34jOH97/Zn3I5oGKEv2tX+2cevq2/b3AikVi1ubWhvnikb+6px1/v7OrfsWfbzpvXNzVEZobTzzx//GeP70/MyPwJ8zuWq6srumPv+q3bWtatrwqHxfD5zOuvdT755MGB3jmTxFBEFUHrnGreB0qXuG5uw6aWex5Y5WUzj/3ohap48d333RT0ThArIgIDLMUNCazjsWhRF00s8geFCGiwSsoeYNgz04wz1+Q+sdMyWZGPTCe9kZFMZhYCvGHaaJ9gKX51Y1P8kc/ftWHT6ni5MziUee3Vjp//8MDYhCurIdMyDU+ecdZte9fvvnXT6vWVRQVidMQ79FbvM08fPnx8GFJJMGvNPvjg7i3bW8+eOV9YFNu0eTnLkSOHB1586a10KvnhD918w40t8mYd7xh+7ZVTLz9/KpNFgjn0Mfz2leW3375x047WtpVFnIn+npkXnz/y1DMnphKeTxUmBet8gtNf0BciTsgpqyyKhsXQaPJH//LK2KBrhwpzycz+fWcP39T9wIOblzWUOBHqzzETyPF6QI1OYpiWk7zo9iVwKFz3EgKuEcphYiqpvmFdIDqQl3PJY+RJPuMSwXGpCMzXo8mSKC3IZbSDQk7o3LnRkx39d+xd1dxUEoN5EGtfXSlvaDKZjRVH6+vK4hWx6cmR9lXxeDx86sT50eGE4BYT2camkt/9o/u33rI847PZGa/Aodt2Vdc1RyOO8fhPjiazwuY8FiWNzZHGxqrVG8qjRU5Wpv2eB47FInfDjWs//cjtbWvLmPCnF3Ir2uvKCkOy2MikArXx/Jkh9OhdM9INk3Hn7bcHfvXEK8cPnfrzP/9dSy4VAUgjFLZTkU5pxKv+l7nkgoilGEARpPe4662urr7hkbGNGxvrlxWHHRoJ05bWKvlj1+PFxaHKquJ4RSSTzaxdvyxaYA0OjgwPJSyZPInshg0Nn/rtW9ZtW05tMZfMFEXsbbc0tq6rdGzrpz+FppcMOcVFdntb1AmXbb0hbkdlIiljh28Jz6bezTdveuSzu5tbi1PZzPyCu2ZtbXHEtixtnoFegCLPDdcdCZumfe+xnzx3pKMgmwyNjzHHKbTQtFBuG8sSJqU5lkuznK95IELJzGsVM71zLlAv1EaOEBmt1187cfe9LQ3LCmtrIiZxK+OR5Ssq0umMzJDiFeHGhng0wqOx3Jp1dbJ27usdSqcBOSJI9va7Nv72791evqxofjY7k+Hx+tCdVW319YWZVKajY5YLGRR5ZdxesTLS0LQmXAiBY3omzeXl8LJlpYUf+q2bPvzRTcXl1kJKvnNyyx0bwqCkLjRgHgitljIY0gxi8H2HQ6qknDa3h5vtRmo1p1O5rPCWLY9VVa+jtjk5+/zoaNIkYcK06cYFIvuXRRQE3Cd9uYS4eCEFe4wL8s7vX/ywd0uefo1/5iVqhD6ySLCg82J3yvqJiLx82PWv97OFJYx34wiLfB0nI5clj3Xg0cmA5Mi/Y0OUOo4TgnaJAi0zhIFDG2JZfVW8uirpevNurqIu+uGPrQ9H7b/5v56YnWPUNIWRu2FH0x/80f1NjWWpVGZqMhMttO68a1VLUw3nz7y6r5OKiC9y8XKyen2stbU9mc6lMjIvYRvWxeurbp+cXIjX17gsSzlvaSn+bP2uHPO+/603hHBkHK6Kh3/rt2648951FeWR5EIqscCXNRQ98ujO0urY1/7+WZkRmk6ICS31gvwolpfylFvvzJnZX/z4mZee3ffZR+80tEEgoTIImVQZdSjx4UWJYKJBH0HpQS7YbEFVTFHkRV43Aogqy7AcZdEqr2nIsXgIu36G6Xu+GjjKtLZ97YqWNWYy5ac9v3llUV3DDpEz/unrzxsMOtHU9h74yJZHPru3tDw8O5OeGmVV5aGPfHRDw/KKf/j7X506JXOssMFZfU2krbWwrW29oFY2nQ3FzM3bKsqrd5RVliyrLWdezrHsLdtqQ1F7ejrz5uu91AL0cFNzxUMP3bprd4sdJbKglQnimrXVK1feEYmGvv/Dt2eTKMMgfNRFF0qWVgAMgj7z1JHe/l750sk5Ho5GsPwj0Ug4Eop6zHBd6I6jthcBJImCBiCqVUFbr+AIRDWS9B3oArVKucUXISOBPKX2HiSCXxx8Fjnm12PA1cwEtK9vaGR4vr9/wjBWxcKhmprikpJIvCo2Mj7b0zWyfmtbQSy2unV5f8+Z6mpHXtjx8eT0bFomZpEIvf3OtTff0jw2nfraN144+EpnfU3Z7//hPctbKnfdtKbr9PhbB4YUkl2m0aEoVBEvPnfiB997dXQoMzeTKilz7vvQhrbVZQvzyX/86nP79nW0tdf+/n96YN3qmrxiJuokKKUpX7HQle4tZ/YbL/W8/VrX1MSYDSbF4Gms+wfwMAtLpkB0Ubyb5Sd5V1yLPTq8MDWVYq5RVlS2vCFeWBKR//X0T7lpsbwtXhCNtLct7+zqqIo78q1NTMzPzbiOEykqprfuXbdhU61J2Xf++c3nnnh9eWPl579479atDY98ds+ZU1NHDo+Ytmol8miYJjP+Yz/c//QTJ8aHs7YVildF7ntgffuq4u4zU3/zdz/qOz+1dVPzf/qP9zW0luV9qMmiCKvqP8GcjHnG2bOTZ7oHTTMi/AiYzsJ+zN10y9rKygJB6cTk7Mx0VpAI6lTI3/O1Bq+4JGpVZVg+c7pODy4kc5Xl0XhFWWVFrGVFLbWNE0cGY7GCZc3VzQ0N1fEiO5qtqHBSKX90bDaXAxxmfWPZAx/d3NxQ9Nxr3f/8T88lx5N7b1v3uS/saW4uu/vebf29z84u5Kgq/SkvLA1NTs1951uv7Xu1azrhU8rbVsfv+9C6snLrwFs9X/3aU9Mzcx/7yM6HH9pdVOQovh4cSBr9wfBSmAiskzFYFvpGOETNkHXixPA//O0vJyemHvzEzR99cPuO7W2vvXJseHjChntAtW+p6pCSwOybXO44JRfZrRlK0X8RbiX0kwpyrU54xD0ryStEU4iLtUTzwfAaK99f/7ogDSNLtDLyOcSSR6BfmC9voe1Yx44NffUfnxweGX/kt2/7Dw/fvHpdTUNzfP74FGO8dlnhw5+8rbW57GTHwP/35ad6+6dLK0L/8198aOuW1pvvWn2iq292EpoTDHFXdsToODDwta88Q032x3/2ic1bayvrS94+MvTlv/sxyfHPf/6e2+5as2XLsh9/z2ZZy7a8LVubbr21rSLuvPRi51e/8rjviu03rfqPv3vP/Q9u7DzV/4Nvv0l4KQhV5rUPUUVdLrhQJHRg3+kTR86ODk35rs1luqaBHKZqnyCvVkHUrpJpoV069OyeqF4s1Z3ivDw1OG0wlArzsbWswbqhsP3y02e/8/VnufD+9C8/fNPO1q3blj3588KxETfr5m64ufH+j99YWum8+srJr3/1ucRYun1txX/+q49t2VKz5+a2wd7xzILcwsCqKy2KTE5nvvfd51567siN25r+4M8+Eq9qznniJz/Y96uf7du2vfV3vnTX5q3LOk5Wv7W/W26+WJjsvql1x67lVkR857uv/+ynr0ec0IfulzXznoc+e9Pbh88fenuccMsCcXb0IsHPCCKIzOzpmTvfO+nYpuHaFKYuWWImt27fvn5ri+v5J4+fT8EAxGIK4AUXxgzEgwPtxkuQHy5eZzq5Uhh37bJBATmk/tPZG9f0uOsx432KAnBECXN0LJdKG+UVBa3N8W1b2yviZROTyV8+ebi7a7ixIbZ6lTxQy5bVViSmvNNdo4yZPvPj8eiHH7zJzbD9r3a99szJoX7/7f2D//zfn81ksms3NDS3VoO2tVLXI0LWbwcP9/z1Xz9+7NDU+Ajzfdq2pqGktCAWMY4c6v7FTw6MD5HXXuh66meva0l0VA8XSjJ5SUKolDw5B8mrsZGc78WoKPDRS5grs2StRxro3Onsnl6JRoPKoMT0PHt4SJaIrGlFVVNz+ab1K8riJZ2nB3/6+Buzs8mW5or1a2qaGsqKi6P9van+3glCwjLRaW5bVt8QLwpbPd1jP/ruS8N9/M3X+77/vecW5vz6+tj6jfVWmHhU7hMGYg4Of+21U1/7yosnjsxNTICqQ2tbbbyyRIavx3728puvD4wM8Oef7Xj1lWOqBPEDMV/KNUVMIDkUaUnyPUcEL2K5EO4b5ovZNVurPvP5uwqKIp2npw693ZvJItWW0kAo3bzigYuh0EpnjKHBrIxmLU3xlqaKG7avkdfo2PHBZ54+4rs5aPLVFqxeuUzWc91nJycnkgArcTM33ti+eUtjX+/sc08eOvnW+GCv99hjbz//3OHq2tL1mxoraopRyQ6c28BrzTK+/rXn/uV7bwycy80lWLQwsnZDC1BpfPdnj71+5MDIcD/7/jef7ekaVgM4iEiUcXUhtEoqR8dlQ1lFynp3bib9vW8+f3T/QP/Z7BM/2ZeYnGpeEZIZMGc+CcRMNXZY8cCM98KwUr8X6AWSAHxBtb2XsRiv/k3/AwiaNg3RnprXvz5w5a14t0JX6OC0ZIiMgjNkfj7z8x+/fnDf4EBX+tjB3qnpheIiu6w8QhHk3rqiZsf2euZn3zpwfHomGYrG0ln30MHTmWxm6/YVy5ZVep4MlJZSWpmcSj799PFjR8ZOnZ46cLA/McuTWf8bX3/s0IHhjo6J4x3Dbs4oKIgWlUQ5yxUXR9atay4vC1Pmv/ziSyBz6Vj9/f2jw9PUNNZtWB6PFwrGQFsY3roCLlJl304tmphID/ZlfDcGRBbTNBY5VXKfsoBERN/7iJosnXNply2+tBDUHVzlG6b4veOj8z/47vMdx8e6T08fOzTkuryo1CkuCcmQEw6bGzc1t7dVpFPJ1984mM3mwoXOxGTi7UNd8pzasr2lvKoI7WCF0jQ8cfzsE4/tP3N65u0DfQvTPGLbpzvGfvLDV/vPpH71iwM9Xf1lMTMWC1khyjy/qrp03foVBYVkaipx8O3D0UhEXqjTZ86NT82XlIU3bmgK2aDdxQ3TgzLMVJ7+CnxGjZDgEe6GDW4LIIylt+xovufB3eFCa2Bw+pWXTsBkwERXMpWtEq0uyi1GlGIPuao+C8kXi3nBBiHPZWvRJpuhJYmlRAgpoUrn+vrXvxKjJbeLLYv+zsGhdW0ralpbywoKCopj5uycv39fz8YtrTu2L6+utm/ctaqpuW56Ktl7dthncvcwy2EVVaab9vtOj2UWfNOOyTt2/ET37My83P/FFeWRWDjtZhki5jzDONU5Nj4uK6YynES79fUV1XWF2Yzf3zPDecS0Ctx0Zmbc81LKuwvNeWig6ajPQmUfQZWGNbgKeg7QuUyZ5aBDjiItGkrIDxhtMFpjZtDEusJ+lqs9k8sdPHry7vvamlcULW8oaG2vKI6S4dH5F58/9tFP7qyrKqqtLdh984Z4RcmRw0Pnzo1a8ipwvzJe0NRcKZ/gTMeQnzOoHc4yd2g4PTycaWmOrlrbVFh6bDwx76OfYC7HTp9OTEwbphXGwSerX1FZWhmZns6Oj7uMFNhW1GV8YpzJp0LvZ8Fw6E4Z+sFCHqlk7KnS2AOxdUgzZGR0126u+YM/u7+prXpgMPmLn7556sQwNQoYaMkq/BoAcLXsvLhchkVNmsum9x04sn17TUt7xYqW4hUry+Q9kZnT6VP9H/vUzprySFNTyYat7SHH7usZm5iYR6FRr6jMcGxjcmR+pHeKkhB1QrPz4x0d5z7ysZs8Lipra86eS5hIaCUmGRlNnzw9ls3ZESfMPS/kkJVtNbGYNTYyPzXuWmYhpaHkbGpuUjPhGHaeMNDwwAIWVgKDNYAafsTILuTOdp4Hi24a8lzTlyUuNSyTW7CgTIqMTZTb0RRAQd4Dh1015NGBUqc1ynoRcGW2Qa+I53p/e1gCbTihi0covV5zftBCq/acCCJP8KeyJlOtabE4mh8enj9/fkYet7bjpJM8m/KjMTsWlbHLcxx7RUsj2LxSc9fOjavWrpchRi7lkgIRoWZBVSReUmDyKaaAFIYxPDhx5EgXlfvQYpPT03MLSZqKTE8I04oZppFM8dkZEQ6Fisoiw6MzBQXhtrbKSBhOgv/w8P3pHETXsGVWlZd6OdbW3lJVUzk1NhwmMYb7TigvMDTKQNPrMAHZ4SyYYBh5sSVU7gIgFUV4hsmQvXIV57W4SPpcKBS8ofzCtB8RGKuiorGGQkL2IZM7cvbc8Pj4rGk7pi3mFjKZHIuEzWjEEsytKC6pr64TnhG2Qx+579a9twv5IIP4heVR3zBqGorsgrBHs6YBvHj5pGOj6emkS5xQxidT054dyXWfHnfTlh0qkOmm5yJildi2ZWdkgVsYbW+rkqlKWVHh73/x464BbGgnRO1IWEbyzRtX/7Lw8GQiQ+woo6YFAt0yorsCDRoNSEttGcYZ90zH37a17ZHf2btxS3xuZu7733rzzMmELFzlj4CPtkTAHenwDOASwrzSQtTGJwCtsEx1BJjK1RfwayS44kQnfGTRXEhcDynvS5lFbXrm3Pnhwcn2lTWtK2uKSgrmk7yzYzSZ9EfH0qm0ES8vu/W2LaUlkcH+ufHxeVktgYq3hVuIUj/H0AkdFAmoafu+vIUsHJE5kw1HmkFNkyRT7uiYPHEdhvIk1CcFkXBFeVQwb24maSJp1aQ2922W09bpIpBFX9R/UYbqIn+2qbmxjzBDlDIAdqmPc0SqFGm5WAT9XvliEMP1/XPnhtIptyTmrlpT19xaNZbwBvpnkwt+f/98S3NVXW1V2+r6wkJHvu3h0YSghfJ8D4d4RaXtWCQxloDdbqI4jDBmZ9Ke65SVFdoOFgdI9kklc6Pj0zD51Jb6ZqwwWlRsJ8bTuawnP6wHvtqW7wF1JvCdB/QVMzX0gwpNxEHXHKDH+TxbUEx372n5zBdubmuv7e2f+9Y3Xn7ulyeJH6WWAMt/+Hi2MCweJAmXvRayyhIZzzt+rCuXu7MgZm/fsbKkJNTdO98/MD03601MZYqj3tatq9dubpanRWJibmHeZYYDxHGFkxfUg9Sac8OD8suwZKCRITYWi0FiLBTtl46OTSaTORmnfLw68rfLimOFRc75c/JSuMgOlrlLyM/RQMBfK7gwJGkgIweFHpWJLgL23Bwg9HGK5xgirHh/lomyzpSBcgSgpUCIxgwcl66+gxWAdFHgQ0kUUZo3CCOazkeu2l7m8s4k5OLVueRPGqBXFklEQlx3HPxAzQfRGg8ECDkJNqwqaZUNt+quC5TPJiSZTKYyOaDDct/3CJNHeCEBDLfhWyYtK4vaIYP7pKK8MFbsq/LA9sXESELuHE8uelmJBaAZuXeS8wuUhg20rZEvnMswwKcyjg7CBNSSTXAWU1zq8vJIQSycnE/XxEshmIJOisFz2cRIKjltm4YDQqmqqkGkoal0rJCjDnuc5Awge4tFoUE4willPNgxyjfsavS4aKBfmfcGwiIKOUtLnJDRXwGCoseJK4it+FtTUwvySsBUwfCYL3MlqBRNx2SGb4esktIC4KVzs6qqpJQz3MKgdTM0MDI7nYXHo1caFmyG68n4bPkURNZBedHnuZSPyGNG81BHrtjc1LZIvDLshGk259XWFjIUbJBXMp1KTo6kMhkPjVDQ5Fu5fsnUCEaRngrmIEPIZGnKd+1Z85FP3rJuffnoyNS3v/nK87/sYJ4Nt1Hb4DsYsXxFuFQub+ZVBS2tHiMUU0rXltBYlMeMWILrhEjmo0Ed1/iu6ynW+xIMTC9rjY2xXJa3rGwoLi0cm84dPnLKY0bXyb6x0YmVq2rhxM3ys2em0ykus3DfzymHb8Z9WbvbppXzFPufWZQ7psVcF/yVmA15gUDFD1yzSFaF0OK6LJPxi2JWrCAC2i1Ai/Nlsm6HjXRW8QZNJbiiEM35UwbBJzg+RpIEBX0XHzWSlcwtAqGVe7UW/cbldcUCXzVEiJlOWqOjfllxbsPmleXx8iOnRnv7R11GXn350K27G1evqTNsY27W6O+bc3Mw5/KYyHp8IeWHbLuwuAjpMEQu05AN6YJ8Yhk5mc8tDv6G8O7lPvWUEzxyQ2SEyPpeznBsO+IAq4hQhzLfthiK7FIT2SLaM1FxQTjRRglQKfq+u1CzrPgTD+2570PrKqtjh98e/9Y/v/jm6+eY52BjHzIBkN8iWJpd2TU2sNgzrPlZMjHul5daN+xcX1AU6dnX2y8vRZofO9K9uqVi245V0ZLw0HCmf2BePhh9I/WdYsINhSyq2k2AtmIhWYgRM5fNwrhYxnfQdDF8zwOdTplEyYeBihXJykvh8UhByAlB3iivGBeuHcU3rTNUFHJBe2kFYNEwKpCfQaICRHUTdUBJ4EcO+b9uWBFfe4FBYwvX0q+j3kgCO2ttAMIY8328AEuC1Tv69O/886K/vOuvkEs/g2ZiETwSlHj+9a8PWAnLtGSbCJxRtEE3fsvHha30bGQsY6g7pOobIxhAy29zlkpnZXGQTKW+8Y0fnTg1bliyUGEO8cpLitIZr/vcPJdRmAWATYrja6oUA6ky7oBsj6pC01T29JBJccsT5nyGyySlq3vga199ZnYO9pxJWGHUsh06nTAGz2eI7XClTawiMOCfqNpSHEUnkY+LUknq9aGFQ9GgVPG/eTDPu+JeIxcyBkTgjoYsEq5FdPGqUKILT7jIItDyYVw7+HNDBOoCkCnIBCqddX1ijE7O/e3ffn94dB4yF4PbjltaGp2dzowMyeQTCJs0KKMwXlA4hpRaH9HVLg8cHRBRJW+P3H1M3h9qi31vHv/RD/f5bgRiu+UWFAG/eXSEz6az3EQJG5AGJKijyrSVGPdkBG9oKH7wY9vuuX9TzbJY54nz3/zGi/v39XnpkLwHnHvYV7JR0D9/kFHBTTSd51csEVWNDlY5XMsDctRPhoRxCcsATj8ZmJXGFUww+fUJ4fuSXaHbOSk6cmTojjtay+OFlmWn0+nzvaM+Fz29MP1pbqj0uC9ziNOnz6fSrgEVg8jl+EySOaaoa64Kh+lC0pcrrWHZsuLCWMi0EqOJ7ELaNiz0XSIoMUJB7RPgWHKVuSMjE5OTyXhFeV1DkS/SMrkKR3lDUwkk7BmVRphKktJQtsQ4ulbWyxB6GMgr+n7G9Vx5EqMUjUwjctzPyJcwADMQgcEJiLDwgEFNrpDoy6KQOPOz4q39ZzZuuKE8GnIcc34mNTYy6zPacbwnk+axClk3itGh+a7TQyYInMAzT0zO952fKi+vW9leTZgvco5NaFVFSWVVOBQ2Tp/sSSZyDgnZYOAJB7MlEPIOSibQ6RrtH12YTdVUx8rKHMJSJCcKCsWy5YXUgSaXhZqi8gr4RFszQuQEnCRUdYwn12+u+uwX9u7evVpmvT/78Zs/+v7+kyfHQWDHyto8FCIhCAWQqcraB5xRg77xpdcDXmeLROem6ZHj5x+4r8UETwdjYmx6amJOZkrdnYOM74gUOjLIjY/O9PeNC+V6TIy5lJ8TRkFppHZZ+ckTE7JUC0fDjY3LgAzE6XDfiIn6nlS5NeFNpdyiMpkkhud653qHN26uiVcVFBVT30/JYruhoai8MpxHyWGUIeiMaij1BHChp5bQEjv5zybPE3nTXAVbQ01nqtRoiLa0p79meUYCE2/lcomIVeRWLQnq79kV0fh1HAADkqyaomhx8etfH5ThgKL8qo2x6Diq7IwwoCnvKwNNY7AbIqguoEwtAAFEetPJ5Uh39zD0+E06P89PnpiUlQjn7pr11fft2Z1ccIcTB8bn5/LQLoJ0IA82mmlo9LICVhs4JEfMoNb2NDIZ1t2TWNFaUVhefLZ3YrDfky8ZLRIf/a1b2ldVnzwyMNDXAwcv2ughAQ4VT7EhDG/Vd6G2oTIQZWURjbtAMC/lZQwrbOP02lKccHI16t+6auB6mypdYAW6VpIyiulMta8jiHmCrh/FtEeFIKHzK9Ve03BPa3om2TcyIugK7tDRqdTRY2OOWUBpds/N7Xfu3Xn27MjgYGeGMwuFFE0FyGWmzWSkskHV3IS+E2TBmBgxLS8hq3vEfSYzZ86Nbdi8zIoWHuucyC5E5GeuqjJl0VteGX3+xc7eoWFmgcktBZF65I8arqEsn3lu8/bGD3901223t4Uc46WnD377a690npziLGxYHnM9OxQBlWaOgxnwh/UMnQRSEkhTkauBs6nuKbTjaP4AsEIhuiTyI6MAyOrClvHa8tQ8QryjvX796+rDgLzeFnRw7Y6Orpn5rVVVBZmscfLY+GxKcNtKZfj5/vSmzdy2QUDu/PmRbMq3ImHimIlE6oWnj3760zdu2rpiz91jr7x0RG7Rhz93R2VN+ZnO6bNnzkMvwjRRMBeZDkzhV5iy0TvZeW5iaud6u2Lz9ubb7lzVcap3+7aNe+7erqVp8Rdo0E8HJrPqXaFpAKFuXWNheZksFPxwxCupCAnOCgtJ+5qqSKFtG9H5OXdkYDbjynwcRJJE4AR22WiIwm/ESiaTJ0+fE2SX5ZjTM6zn3EIyDX2SVNI4fWZ6d3W9Q4x0dkqGPIA3gVYbOXt6sL9/fNu2+jUbGu776OaXXz7W3NJw/33bDZMlZr3jHX1ezrVMaC9r531ZTPpoBA9DPvN0Z//E+Nyyhtg9923r7R0cGpu5dc/WG3auUW8Nmi0m037YQnX1KFWgRNCpa//il+5Yv77W93n/uZG+8/3LV5Y3rmyQBZTMO+cS7tmOkdQCQ21OJveMahcTcmkioaGJwCa1p6aTJzu7PvzhNlmbDo+4Z7sSDMCadHTcnZxwG5tC8r0sJFOjI1MyBICgqm0dPdjb3TW2vCl+y96No5OJgcGJHTvX3vnA9nTaOHGsb3JimhPLQEsMQ8nqMBTcI7L+I8lU5vDhzo9+bHtZaeTu+7fKDFxGuU9/Yu+y5io1/sJyDfz2OQ5hEdvKlK+ZmgILFfuU6j/A01wVR3x0iaW+owT2aGDoD0vxPUUNkidhkuDuoC+mZcpMll5Ddy41lsT2gTAt+fqoHkjeZdZ4/evfpW5FP32lyi4XOVVND/k9Bge0qcVC1DyeY0ue+h64JDAsCBhXs18QnJJr3ew+N3KmZ2JNW+VvfWJvJmt1dQ5X19Q8+ujem29tP3R4/Iknjxg+jMl4AAYHl3duc+VSiagM6Osz0OjCRoav2CEytqRmcx1He/bcvKKxqfp3vnDfYz96PZPM7bhp3YMf2blqbWlqnmXc04B8NgLcocwSTCWiJ9+dX99QVlkR8Q1Z1maqgMJihEP2us21mZQsD0Ozs27fwJTBbXqZku6C3aXlRmEHqxRT+VVB0DOFVhPFJIEwKKsYDCIBnMrY4gYFSQmLEAuTSsQjmCSXc7s6ehITW5ZVFX7uM3dF7NdH+hOr17Z94Ut3t7XVMB6mZhc4KnJIZZg2F/UtiO44IhFc1VBwp+CGBAb6IOtHxscWjhzt2by5afuW1kceuf35Z46EqHPv3ds//MCOeF300LER1xs2wcYbfh96SICbAfFTGdh33bbq4c/cuWp1eTRq9J0b7jp9vqGpvrFlFfTMLDYzmzl6cCCXhQQc7VGEJhEob0pN+TOusN3xJ6ZphpR+D0xFYDn6FKTo80UZ4Szf8NMi8TAhQnYVyhcvgTaI9+7b9D9Y5qShGeQdQ4cL/o7tEOgKyAplJpmZmHTbWw3PFUePdnsZubjMVMo7ePDsXXe3OKXR0aH5xGSaI0KOCppJimd+eWL7trWNy4s/9ulde+5YU1gSXdlSnUh4T/3qaOfpIW6HuAdwAUDXyfOahmCDOnLT+/L2TCWyT/zsjZUrK1Y0V/4vf/7xvsGpinhFc0NxzmU+HJpUDYEwL4NOB65zqKHkXg2HrE9+as/mrY1uhsvtsLy+KBwyG5vin3zoTtejIdM+dXzg2//9+fQ4aF/KrWdS5REj3v1iBAuHojaufM2ZeW9yxlteay0s5LpODfs5YtlOKsVeeeXwzXuWySs7OpZcmM8JHiHYTZmfSz/71LHVaxs2rKv6wz+9984HNobDBSsaK0yTf+d7rx493m+GoyCfCj1tU8Aqd+BMpoDalgt4bCz55BNvL2u4Y9sNzX9e9vDkVLq2trK2rlBxBZnQ9tzKbodybXXgM6+w1PnkQ7ds3FiXyfFUyi0qKX/w4/dSajLD4ZiNdp9OfH3kZ/PzM3JfUcWby/ew3nEVljaxZJ1NAVvBJiYzyaQoKTJGhmbOnByyzai8jD3dE51dA00r1mSy4nzfTDotsyQHkU1O75nJ557u+u3P37T1hsZ4zYc9L1dfH6+sKjp2cPzJJw+kMh6xLZ/6EPPgfw646FkuMX1oXxvmieP9Tz918JOf2n7PPZsbV9TIz9WyoiYaM0EDQmGygPqAaY7KjeQJIuMQ4kJgiUHAU0IvSnudqrGZCdAWSwhTCRQJ9AwnwfThPVUkeftxxR/UoBo4JPNK79dkg3MlCad6V1x5YGlsDAbD4EjTQqPX7Qjfv6J0CYBRvGu1FkwHlT6jrChlAESIKKVy28sImAE9Keg/w7zbsWxZh5qYBKmMX2YGEDHhHoLwgQw+A8Mz3/3OK3/xZx+9YXtLRXFZYma2uLCobWX5TCL99JP7R89POlaB5zEbCPwKmWNim0c+sXxywGmYKLYpkysZDB0rjFLssCR8Txw8cGb/9uV33rXqU5+8ad3qpkzaq66pqKyLHTva/8ST+3Ju1jYLsJigCMMAIDjBEYJlW/c9sO22O1YtpLKC+PGKAvnaZaXFX/xPH/c9WhR1znT1/9f/+rifhgEGIZe5aBdOCAWMKbWhKSKwwA/UtC01AofQYRk8y2WSIssyT14leQVDODbD0SSGCcptW358rE/l8nes0MH93U/99OhnfnvX3tvXN9bVZubTlVXlja3FZ88mnvrV24mJWZPECHdMwwLAvIkYLYYub7b8jkUNhDkAvwt5LXA3TawPjXTKeOXlzq0bVuzc2fTFL9y1Z+da+cOGusqyCvv5Z48c2H8KOm3ozkUwfEGn24fysKAotHP32g0bymV5Ji97LFr4oQfvNElYfjR5NJi20dc3eK5rOJ32Q/IeYrdcwRsUg9hYzLcut7m5ihTonIq1p1DYZHmJLd8jAchZ+0KA2xuk6hTx7tr/X893tQ6NVtEQv9mTv0XNQ6KDwhLecGCerXpEkLUQ3zXePnCuujyUS7LuU0M868i9yT165tTQyZOD1VUlb+47N5VIW7aNyYqsmaNnOhNf/ptfPfTI9uaWirra5bJKONM19Mwvzzzz5NF0yhK2LZe33Hq9/TOptDc773p6wALIPNso3Pdqdyzy7KcfvrGmtmzd2sbxiZk3Xj8XCZtOiGSTHvZZAjKOMvBGPyQ0JTEcm8UiXtjy5fNNT04tTHBPwBAu5JgwGgelYQrdXFlfUDSKI3TJZdF27cG1IBpSCd8DVOT46Pybr3Ty7XXDgwv93aNeFpJ/N5M9fWKos2NEPvuRg72ob8dhCwPGsvDEoeGvffmZhx+5qbWtYsO6hkwqNzQw9uwz3T/44f6cG7ZkOUfFQoqf65nwuDG7kIUcU6YXUA/C8O/ZZ08WxCJ337++qq6sorJ8fCjx4otDlZWFE5MLOfkLYDdm2Zqfo627lF/L9FSqq2sM1K8A6wSOQbCNaAr7+NTNZDgzwYgQD1yq0OUKMcm1YY5YguzBxg/WRzi0t01raGDmrf3dq9oqersGhwfnKY/KBFG+6IkjvStXls3NsqOH+wRURyauMdvzyM8fOyRD4O17W5uWx+XtmJtLPvvsqcd/cKjrZMI3wvI9yxpuPOGePTsxMDib9Yi80zayFywjkpzNfvebr8oScfeetpam6lQuN9Q/2jGRq62LTI7Ocp9D3UZx1QK2TyUPFkeH/oUZr/vs2OBgmnloMilsP+cMnJ+JRELzs65BHa5wGuBiL7SnDaFXBN8GsSTgBuQ3kjJcRhEqWeD6wPGwgkeKd2zFqwG5L+3ov/Ofi7+LoY6LoDOLhEKlNkq0NTAxAil3pUNBjOt4ive18R9oWL3zugaHEG4iotxKBJ2aTA0NTU1NTvkuA5FJcNNVXmbm9Hjm3OnxkcF5P2eq1n02RQe6ZwsKU8l5HxBOwDFzXn3mTNR49sGPbKxrKK1ubMhkcsdP9f/yZyeef/ZUzregHWWIuZnM0EBieGgWlGsoAIoySXdmcnZ6kucyQLaVoWphzh0fSYyOzLgZgBfJ9TIynPzaP76Qmve2ba+tb6owbTo/k37jte7vfvuN3s6ZkBUBLiCczyqMmEr0F1eZTBpY1MlB3kCom0r2nlmQi7AwTHlYfl9eBVcmKTJkUd18WkLJWGyc5P+5SF7TrCRc2LI6okA8N0dH54YGpmYSswZW7VzkKDqxC5+MDizIqi8xnoJCh8IGn5/LnO8Zn55OuVkKosym7WXFd7/1ai5Lb97bUtVQHAlVpOfSb+w788MfHXnrUK88UqA68sXo6HRv38zEeNrgUWryrMv7zk8QEgXXHQ/HjyIyMpDqG0gkZxc0bZI4XV0zf/+V5xIzN61eW7NqTY38/tTU/BtPDH7vO2/Mj8mDVCaHnqlMbpT5gmJDGtZsYn6wf8yEYSvDpJe6IqXkiWSgSsnqFn7D53kEXdC+Vpaoi0fY5SCjBMM55MQEmlbQF+Vw9Biko9NfjDGQdwGoTt7dcJRalvirv/inowfGKS0EVQntEKl4/YT8pocTod0gIevM2TT3yGfvr2sIf/n/+UlqLgpDBRwDMSx2TbSpkw8rLqaV5QWyJhgcTmc8QE8RzkK2F6+xolFnZjo3M+Vx4cDDVdoCLnC5sgpr1dqG2urymenZzjOD46MZngkZpuOj0mw0zOOVYVmQTSWy8/MaSm6oibxwTeouayhc2d4UilnnzvaND88WF8dk1jI26qbTiDEMaKRcG6sytDj143G7qNhUc3iELcDwEGksUKslF9zEuOf6YKwJ+niQfcAiY366pNj+X/+3Tw+Ndv3tXz8TDdcIPd3HuRE4c6KNKc2Wl9OK0oiXJSPDbsY3FezHcdyq2oht0ampzMIc2DnzQBFJwHJNl8ftllV1sv5Lzic7T/VOjPiZnEMc28B0L1Yo4pURWf8kJjPpBbm9bE3cgNXuOpa3fEV5a3u9DGJdp87NJJKlJYUyGo+P57wsYi0RwM3zVr6cyy0QL49FCwBogVYWajKP8mM4Ek2nzcmJnOtpQIOCf2K/GHCZK5ri/9PvP/jsMy+89FyPYRShm46Bfu2GslNmpm9ablU8XBKJzM/6w5OuDCW4Eb3iEl5eFTKYOT6aTGdAUll1jXBVuCHHbWgqaV/TFLLM/vMj3T3jCwn5QaMMsLFcrqrSUrO0LJTNeJNTbi4HYUXlPHAjebYgJlraaxuaaucWZs+c7JXVVHFRJJnmEzI7BdcJ6NcrfWOYseIMRd78wlIWr7Q91xgbSXk+ZF22xatrQ+EIHRtLLszKvN6GETUuJaRT0qsYpi0WbRq4gD6pAs4WWE6r1lb/5X9+sLKmdHoG1E7FtauecEMoJqNcBvHw97/94n/7f58OhSpRx0ag45gIOLjXu1fvd2hF8z/fT9Y3lv/Rn3xq32uvPv7jY7ZdChqcMJajaDCsNaBMwy2tsMornHTKHx+VJ6mlJ97QqfcrSuyy0kgy609MpbMuLLKiAjNeHpZF1cRUaiHJ0IHq/2fvPQDsKK504a7qvmly0GikkVCWUBa2MAghgsEYsAU2YBxwwGDvruE5YO/6bf5tr3ffe15ne9eBxesFjMgIMMkEAwIkEFkoohxGGs1ogibe0N31nzqnqrrvnTtJmiQx16x2burbXV116oTvfJ9MGTk8NfGUIrAwFZXFjc0tW9/Z3XAo6bmFMg0My973yivYuKp4e7t3qL4j4zmwQirLYhXlEBz6DQ0dKdmjbJUVR6uqYqmUD9/NuIhyknrh6YJCNmNm+bSZ1dFYrHZfw7tbDrU2M5LFUH67LIjL9JWleEalGzBufKy8DFZUGqeZTLzI1j7cpCHeaOtw6+rbfS+C6tEe9R0SWRfBFeGcU+7R5SuWfPkvP/qf//H7V18+HIuXcUUjTkK6cF1YM+FdEybGSkoiLS3u4XoCAkijDx+MORZcUaF8q+uI7AeUbY5lZU5lZdxPi8MNyc4kqk/DD2ZS8YQ4ZXr5jFMnFhTG6g8e2bxxb3OT50m+i0hELmzY7CKlZYnGpnTTkZTs04pJhcfC4kh7s9sMryBFaOX4REmZ096Sbqp3XR92GYHa9qmycqn8ccqU8WAudu84uHM73D/wD2OYPEJYvkB+ZKzDwFYWcayK8bywyI5I50mOnWepxneOKIauLlF/MIVXxEK2CMlV5RRj6CL3aMQQ/JeGbf1/3fThc86f39IMr0W5UjOUWQfNg6X+kc2gHF+Bf+WdYrbSlLeCtARlPsaUni3dMm5TYEs84hIDJGnAUXbSUVlGwg6KeAvMocYOuQnDHZdpAtnilfbsffvSlpVCXyWqaEDxG9yW7EJH6t0Xn9vL2F6hGDfiXIqqy1IYPEsm2d69HZgsg6/bAa2Z/DcCs3P37uTePVulLyajDwdmNn7PUUSOGtLLNCsf+ozO4UOZurqkCXvCCEpGvahodGxVy9FixpZx+2XuHkE80iYwcSqslgAAgABJREFUHVwJtZHG4KKOHO7EVhzK7svjp9LO7l1daHDg9YjauAhNIbf8gsP1mcNHajmrRT8Dvhx1ImRMZbW+o81ta+0gGXzGbBGI2cOBoinX3ra19d3tm9CNlKwKjY1d+FM019US4iaCZtxzrUOHunCvDcLAkDy+harytmKTJv4rLB06CO1CTlZkhULVe5kcloxjqraESErbS8dqD7i1ok2mpqUQj0f1/JajoqmlE8WtbEtyhhrdC7jJ0VSGb9/WuWPHJvgFhJM4YGYIwYGlO97U6B1plMEuR5SDmhLkB9rx1k73jTfq33yrHttP5ZHrIKDEe8EUg5DqHaQubjS11tGjfnNLBx4zQuybrgtzrxOFY+V3CedLAF8/qJOyXleR+j0rkFFT8pZa1RNRGTDjI0pwbbgWt00svvJCbeScII1WhcFHt535vctkjT2OEV9FiwiXIlfsswZr7Ls2IXQYiYFjZBNtOOLVN3QwLBJqq6byr/WN7uEjR3FBO6jyxTra/ba2dmmiJLYhojT8bCfjw3xO7d2/E/vo5f3nvEgm0ZWcqN3Y6DYcaZOrkkcdfLH5SLrxSBeGqrgAhdXS4jW14NJjtqUFbGw7Ds7f5g0tmzdJr8rzSUzYIbU7yzK6dB4hzrGdQ75Vfzh5uM4N7dGWavYgWyBpIuKcNCzUBiR0nQtxpHDuGZl9R7peTsE3pvFUrR0Lq9izYkVrazP7D6QZ2XZLj67coaz9YAYPdiIeMkrF8OYWr7GpHa0Bx749tJ+ReDLtvrv16PbtR5miPAbzHnewC0Y6gMw5dChz8GAax9AG8+m5/qHaLot1yewduZsWrzuUPHQIi5Fyl5EjKEkZrfjRJveNV+rffLVeVQMg8LMd0phX7DrMlEvkCs4IUXco7aPNZYohTevYKApVnDCM52T4cFS4Tv/lN2JKx4G4NaSn78CuztC1I+EiW0icWhZuxoEXPQgcPRbFbgip0yP7CT30GyQyV6btmWdp3dr38oOkbQWCqlXOVfaVqNYDS9Fay5g+AI44lm1Qb6EihcxbqGRRt5Q47LBRA0ChAoaXWyJxLCJR6b7/SOcl6lJIqA1FCOYgegSQcQdduHxv43e98MvKpVLM3aggbzP0MUgumGDfMKM8I1TII/lOA0xRRIRxFjlT2o6S8ZVDYVt+7mds/K/7CNNQSEp93TsU5FZE78gF26YupPyxNhNZhIbSs1DsBCRKSJRUQSOKoH4EpvurLcNMk3saeLaqdSHPbXUwx4Ja7mjGcz/GOS3Rnr4e1Lcjhl2nRwxdMBWtqD6m+YxjWd1vRJ9gEDNxBOlt60kpCMWVQcfP99UAp4Xru8rpG7YHMr1xz/MicWoA8Ul3Fs7HReMawdZyjygExx6Dalo1tIWwfMQwq/ZFRj4ELqKQaYW/YvknPLf1EguXg50Qx5nIWjXGwnDK8WSvSqTX88yOm7XQ8r5ivuv4BqaaZ832sHykaxLLWVGmbU1Irif5YxFK3oR5TJBsRvYVcRU8c+niSOqaXNWn0BnSEy/PLVHD5YWNLRJ9ha5CmCFSbkVgpXv6FaH2AhLHCts3K99GySPGYaHtzMu7eWUNIN4y0YPmaH4DK/ppwaggqYVXpbukJLa08pojfGNpEdmFmUlHgkq9CEcgGqnYIpM8dhhRexBCWd/zOSwYUQf9Yo6ZK6n/Rg0l6ITaCrPW77Bb9BpR94ZhZH0H5GxAM2fA58lQRlzSQREKUqZDKepnBNjBjh3lJQrr2E+jLwR5P76etcKOs5rRfeRlBwy5lsjugqyAgiuUI6V/+z8rxFANBRuwMRmSB1I7IGSGK/kuWeyVkZ6DgHlbuVgCoxiJymXDWKmS4ZPcj2Slx5FkhmSoiWUJ8YokJsLGKJmHYPhtQjogSR3HphCVWfZk6IYZnjDBMevnfO/TbPZlbK3j6R9lAzZ9rBfjwEwWCgsLBDvyKCghKhUVwFO1QvXWiOM+9yG4zP4enw0sgjsuA9vnmci0IAoA2MIBl0nylXlIaqPBpI5iiqSKrWzVcTlRSmLx0fPTnpUUIip8B0XWONIMejZCeN/jfcqUCfQsj/uezTykZLMg3hV+Bp65qCZFIIHjXJMnhDWkhDUTLhEEiIzsBpEkcYqIxqLGLFWpOnnHgSqSrsjYvqTPkGUzyUDn2lbGY7oXLWhLYe/5IEWJdmEPMwV72GYvkfUS2Y4gMMGpX5qz4UM7SUvnMUo12tge6bmuSNpWCt9CKh/lgfliDOE+2KYV0YKubFH2VO8ErCLHz3hYu2MKpsLf43QZmL8mMipGmAOmqimSrIWaxIWXkW0+Pqpe+Rnhpccm2GANPhYjM75r+34G0wmSEoy7WMDBwMtRdGsaKs8RK8KoBshEosgqKQVny8WCl6T4RwVvodFH7/HxFZiekfBq8Dwj8XQ0Fi8uZQ73LEfaBmIH12VcdpIPhcRMsEzGKy4W8aiXiAuYOYVxz6dufiIKJnK6k9rBEsQPiBjSRLEbiXqFhby4VNi2bDzyVDVpzMHKmjwIhveVMhq2NmEDs51xrYJC2U+rqq3+sI0XdbBjK7kU4ZS3M56wiktEPO5aRCBucZW4YmP0o0MxJbis3nh+HBdRotAuKZOtzZ6UuRRKzEoVrNh7fpsXSLmFeHom8TwqvyWTfH40LQoK/YjjF0hD5MdwAo89BmuiWr5XVMxjMaxho5K1UFxXCKvbvMkNZcq46hdjmXhClJRFNm/Z1dyUZCyiuJWDFtCxjcG0dFIlzJs5syaWsLduPphxbXQoAk06dlKPGNMVQARQ+jGHz58/uTPVumXzYWYXhCItn5pcxEnuYFEDv/Smiopic+ZOqj1QX3eoU6IKmNCzwiJI69g6Yhp7QeAXhLwQvAZRq55XVhadO686mfI72hyZhx+mEqFPorcSmitcO+JWVSdqDx7evr3OsWOexQ3X+5gxHDLTSi1VEJ9E58875dDhI/v3HmV2dGwRdVs+undIyQIJZkI9wgj76fFVZbNmTti6ff+RpqTDI2PZ1sH0boUfd/jsU6viRdGmI64lGaEd7GhEcP7mTZnQp6VADoe9gaXjMdkgmiji5kaG4XVj+SvLsLSQW0qeKbKKdANmn/xjZbj+dcpeDYwIjZJ9XNCpE2xWqClBNIgy3uY5U2IseRXeIXw9eQzzjCn8cCy7HzzY0tkRZyI2XKMmwRU+4mOFSEfBwRqfKCjhBmRv4lFrrDg4lPbEUkgMZN2zWM6+M6aVmxMTWNm0b1x3++M/HrPssVEairlKAmGHGzqOHgEHIKZ7E7BEGIkELpQvZHeMQ9ohjidDbmqJCO5bqJV97EGbgaBSgeRP54wadbkxEO8ZE4BCbUJtk0jfJZkgtDgdeZ3due9PzoeLJVHpa2LTmY2d0GHz1w/xhfeUj0UqaIqsk6kmfXxNKbNZER6LogbFsGHcJSTCov5PR0qxgK2kfo3s8HXs9g3Rg/QlcBFJollbUl46LMSrNrYH5Tx0cCt0KsS0sqF6v8zH+mPDNjQeFipfMGZLOgpHqRwhx4WD6ALtYCGzv4eNnMjaI1qPtrquh5lGqfLERNi5ymFJzsukHP5kOHrP4Vnu/rQ7tVdPT/s8Qt7fyjmZ/lxL9lOiH0AQrI2tYfF4DCLc9s5WS7FxIJCAyHOEyJbkFt3aVHo/k7zn3+eo9n5pPZ2J1dfQdTsTSXpE3EVIcsd4IhHzLLezo506lhU9FEnd57lN3X/d6t9Pd+fpPs5RZX2dSd6TF2HKBxKVogIH+ATxeDyd7kqmXcuIQPtcsWahskOv97eXmS96PZO819In+3nesRromfRnVI3UoCCBeUmm43El9CeBzYpIRhLHRKIFdoz7DvJb+GIYIwbUZENVRrnCeWdnV1dnF2fUu+JbCgXAe74jvay74bdg3X9rsKyxGLgt7dWCkcogIwFj4XC7oCDemUqmUxnfDhjTqE6oF5HV7WR6v5berVB/rsUaiN1g/TDO/RnVXg7oCxYwhmvuQqKR9p1YNBaLpro63UxGM2/1eSZ579ExW2OWz2gcpzXuzx6X90wGNDes3mY+KWfboqiwkPKFJM+IktzyA47nqmwL0jdLmIztS7JnmNeeax050tze3i45MORdyWD527EY61YkzDuCPW14vTwVfVnzvA5kP81T3p/ufdvo5WyVYLKUdcIhrK6ujkTsQ4fqXJ+k2wQxlam70K94t6cz6Y85OH4Hqz9TMP+ZIPW2QIJVK2LbNTUTk27XoYN1UnFTRv6UBvXQCjBFJtmHgzWgM+nPqPZnP+vPmfSxXwpl3yTSMRGNTZw4oamptaW1hbSCmSD0LsnU+qyPM+lzIfTpYPVn6bF+rzWr16ky0FFVHhbRdcpEEfJGexzbxhBBApF3YUFxTXWV53kQ+Wk2sd736V68kN6fZp220DINUuzZkvR/rR3tdXWHpUybZODxJBmWiCjA2LFYsGNwsAZkwXqfQv23xgOaIf13wXv7abWIMBcTj0RramqOHm1vbG4k9l7sHuTBBtfHtLT6IaNkDfBarOO2YANaa33bQCQzVmSJTIQLJ9z3veKSkvHV4w4fPtLe0Wlzu98Gbeis8UD3uGO0xgMcz4FaYxx54fOIXzOxxvdjntTydZGcBxExzHWyU+4kpkbZRRm1uS7YNY+YyBW6EC2Oj3SKmvBasUvmOVHtUPdgEI8tqu6nd99nzNRjpEJEodl+QH7bjaVBCrYszxNOhGVgjKXWsk9KGpSi9ZkY7FzLaMpgGTYoqZIiMxL4p59yXUcQFRZNIJIXFX3dpkGJmax+DMIxjKrVn/lJ9N5SfdJFbU8hpNKyj3mZ0KzwmM/yT/5hy2B1v0zR77zgYGSwSGAI1xpHJTeZkJDiHKgmxHxYSplMSoaFtk6R5j9m9wvs/TK7u3q5r6geYUVszxAKJjKuy4mCSYYKAgkxmZG1zn9HmGUFUqWjLIPFWCCoOsIZrNwl5hO9L2MZZMGARZR2PUm+iwlgXEQIztJaXv22YHl3n0HIYIlA3kQcUwarP/tC/gPyrKfY56+2ZQpZPFhF8LbrC9jQPc5GIoM1FPWEY8tgWb3a837WE4J3peyJJGxnSp0JBXZ8vbwcJ8JD4aQk0yfTIauJWgFGUelyrp0PunsqAGd6f+1xGbFRVyBk+QupygvgBBhkvcVcNLLSHbWlyqONlKwqMcu4ZLyXA8fNEPi9Zo0GxbcfkfwVQfzI77al4IDATARq3UhWWskMQk1hXG80guUP9o+nQGj19ffwLWgCl/rId8WJ0Zhq7FyXixlTEpa26CG2GKz8VS8byJBmJ/pzJpbGLyr1JIsK7sR9T099MFwMG3LtCI9EJN/kMNKMMlVL8aX6JnrMFlHo+nSqEqClpKx7soGBdDUbQgvWfwc8z6+z/rok/bfGA81f5VlrJLxFdI2StlEZFGLTVItInb8tep3eAy0Q9jNPkneGCzZoBcIB5a+CMVSvMgriiDJAEK8cjqmkdSdw0ODl4vrMX42gNe7l1wejQIgZQrn1RSwfXAA7EpGiWih2w9FVchzDiq/OT7K4yW94AntopHmzibCMEvRciW9xz/dtjMalW0b3T+0swqLIj45m/DFqG6XtJnvU8j5lPYWWPX8l7xFy3xIG+yHIQWK0lNW8FEoIz/fUWz3+tDBoAMTkKu19fIPTjhrIo6B4cy+Bc/5rVCfFQuuHsIrcV6qUeiqEz1OI7EhccH1dXPt5LKtJxwqzY7O+RjXvZzhB0rBg6kvnwsd6IWbyVSM18QqbQc293oHfX5U/UNkxSylLoZUj95irRmXl8zAzMuqpOQJB5izenzPpa3CEIhOVQYyHOoRaF1qdCzPSwIiP4FYPh+1zZPq8Tdl/C70FcLWYWdjdNaU31n1K9H4mbMBnkv2iLMCFcqCUIdcgNoZejYX2SKBK23BSpiuVC8l3CVPXJu07PCXLDyTk6DOwc8lAwlPuM1HSC8ZsY5Vz8PnHbsFMj51gqhdfvaISZdibLzhx7rCQ6TBsh+ozloEyCRUEhEyH+qq6FpbdrtPnGunzWno9jlC4CkS4I5pFMpGhRaGcopbNIcRjPtPan52l91ma78OChctwehHpmofm40FhO64XEeufGelzX+j90qxsRUxFxMf0OmLmW5yHN5R+nwkbyJlYAzRZxjJTs4uvNYxw3zT2y0dXgoc0GY/rng50buQ/Gmlm63P0UGwCmQ4piwjBgR8CdWJGkbYqxcBtaSU1PV04NovBI8IkqztXCxc3fmyl41KuEKWjkKhPVtCEn+8emOq51pMZ+gdTinnCUiqP3EcVTGODOKPV7PN+hMpMC3Qit5NvoVAOVRfVbNF9l+wYT1UPD1O+m6Az9DzjUalgP6c6okWh1S8LkeVvcW4+MAiDTqGSQcdI39LnXHWwKFUcIxusiWsG724LM8boxSkqUzlKEABgDwe5NqhILVhoQHRiVQzaCQktSiGUuChm7oS2CJqIIHTGI9GFRg0YSjqeBg2dA8mVrfeFYTktYfYEwXQLIf5FW6sU7MZpj3JdnhSioOBleIbJV1OL5DQ95bajg6X0w3DQGApwUlZAhWcalcXVSrQosytIBX5Q7qBpFENGFAJ2+NL2kq1QVGIsO5gn6yFo7vk67sXbDaPLQnUMxo4BkDe4hpqh8i3KEWKUolKAWlY927SOyBlaRvHdJwAzWmIUrJQLyhqRB+vmTDDto+idghM+dPQ0wJqQCr0nKT+qk1E2U1kPDBj01jq6unfR2HOlm+VLRLsS5iViSEdYgVy0UKG89ntlOkJQasIWtBExTJzDClZ4CZryfrZWhLSM2BlqqRSCKqYFSSwROFiCWdYwDhidA9e3zZgSKdPIIQD1OfKD+Sok7W13sELl0ZxBZwEnK7NCBK3HcLZmreLZ8vDqDeZiqAShTow+g1/3PJWNow1A3rXsTZQd35LWkbCVrU7JslMhISeaDdq6JPE6VNLgmEEUtm37visUTMY3hQbcqnWeVWZehVrRg0hjFFYXzPV5mZ4VIpQoGlYzQYVc3QHHmVnwymW3GeNWiD93yE8vlJDJUZkWoV1TFzvIZxk+TZpwTCKQ4k7ZRAV5IVglgS38QAkDnULf9836sjHfrFmaB2FUQ4kndaKYJlW9SzpVreZ7iBraClLy6I3pnn6BCAdb1SI4z5f7HE4LnXODWXZEy6xBMq3HERSQaCZVto3JUyPOiTptsMLXY/WuQq5nT6mBUeNfhRKmqiBEviAtpWDz0gnaUXfy1I5hspuYZKHMJjhYjmN3S4wTi4MsdiPsyldhoy6rCVS116tRKKwhrl/czhXDLsNtTITVOHX+wAqlVAdx5vcecgUFwayPYYKNccdBEachm+/H4giGXIEs302rBevaJhNCG3RhZTtP8uoch4sA1UoAMwoUTmgWH6a3maCegNubChg4N3uGgh8qZE8wJ1mQrRi+RctGdsS4ryaQMNaMM9t2hDILKuU2slaMmaynqm4w5vBIxFYVt2EznQZOwLGywsI7lsDQ1NS8aXMVmAW0wF/RS1i6Zrqgz/KPqzBBmtB51d4jO56NUhcUx1L7PZllShfnglF0FtxH/AO3aRdDuKCwTAxMsWXoF/moXPsj+evcJwVqpsvIlM3kGmiiHIFuucCxR/4pLalPKIKxtHFSIZVFVSaTIBh1E1Ftu1ix4JIHS+5CoeXjBAXEIDnMSVZHmDxE0B0hP+DK5ihPf8U3Bgal7pk0Lox5nrQq4L4ZO0DrVpgcv2p9EMbRIqBK+I8+XRCVldEbJ8uXITGz3Hw4lLtS9gjOraWlNRqNxeMJkY18GtmbF5pS5B1KNL3reu3tHTDyhYWFsVg8lUozjeahyoAI2lhEJpMJw7TwLmDZl9la4/tENAGKa1C1YBBagxwE4XH0GDo7u5LJZCwWKyiQcj0wIc04ZBs+YfUc6p00JkwTy1uqPmARqIFwk7ylpcm2o4UFhcda0B5sN5AKvaZwiA9P8qEM32xVGB8DAgl+V7J2hfwsMIee8cXQQnJjcjh3sHGUqzQqmdWczu3AvlJxlHXfmM0r+ttCU+3pvJVK80mAjS+RINJ/EtnpdX1MCcdFR1CAGYEXCwoK0SzYOSnzEEx/7BGem0GaV/PiyrFtbm6JRCJyMEN75diQ9cMwaQw07keOA5NTJJNgvVPxeLygIOF5NoYEOsQZTW6rBjLqs8ciH3lHdJIOWofcSSRQ4TRnIMhC2I6z4e0NL7+y1vcyUjMa1qXteF5GkjkIq6So+Nxzz5s2c/oTjz/W3NL6sY99DMbI8zyhoX+MvD3VNUT4wHDbSBCVMZZ7ATmmKewwWapWS4kcW39RhD1fxkLdHjo+xjyH+9xzz65du+6SSy5duvR09AUhBuUj5i8L05EkiLjMlDEdJ7Jz5861a1/asmUTzMJZs2affvrSxYuXyHw/Kn0y2S6i0IDwv40bN73++qudnR0qqGUKaTd9+ozzzvsg+Geed+JaOktnZ5nCm2FwDjcunU6/8MKLr7/+2sGDh6qqxi9atGj58uXl5eXga2ICj3VDG9vvnQ1CbckY2HC5ePkbb7z+0EMPwiy67LLLbTuCiEkmhqFE2JfpYga/JkFQiMEaAfQFohW5Z7CqoZKGzNjv27f/+eefbWtvwWwQh5ASnBU0qtLdOeus5TD9lKUjx4zl7WA2pSYh8kV3YSMWtmZEXkgGlDK1CoaBFGLdDiNMjQvOc8uWjQ8++OCcOXNXrrwMPAOKOrP9uTH/IGsrNazARDBPrq2sCnP21ltvPPTQQwsXLvrYxz7OZdujrxKWY4++DJIgGBOiuyPRyNGjTWtfWrvhnQ2HD9dNnFizZMmS5ctXQJDsuv7odFhN65QvCNHNlOlCH8fJzgAT4YjuVPEV5iDcVs9tfmDfvuefez6TSYJ31djU2NbeVllRWVRUCo7UhKoJixYtns1n/+nJJ3bv3v2hD32osLCAHCxa/8xg7bBREV8k54BTckVh7ZH8XOcbshwvPdcNdsrXdseUzHwdh2UZI1Mbov0DHpEIrATxpz899fOf/wy8kPe///3kXaE74mMkOgLOMpKNMVWysQx6A7wre/PmTT//+c9raw8sXLgATOQf//jwY4898s1v/jU4ELoEIExnGgzmyy+vW7XqjokTq8EttgIQEoMbsmLFOWywAecjtUZVDdzyufSuUnfddee99943ceKEKVOm7d279+mnn3z77be/8pWvVFZWpFIuFrJNarA/XdInwd5AqUrdx0LeN+wDNnv9jdd+/ON/37p1cyKRoMBDx5JsBINa4zvTRNbv8BypmqE/EWGwS8IwXWWj6xobG9euXdvQUAdLrKOjvaG+oaSkpLy8Ei0HnzRp8sKFC1VVnpNR9a3stt0gp05REA86WghTaEBdOqQkGR8PxftVwzb8u2fP7hdeWAMh4qmnzkfPy2csW+YMjwouNdg9sCQ/+emP3nzjtU99+gtgn8GYiBBb4VjqJV89gfxOtY1w6sVgDBbRWxve+tnPfwJGJhqNEn5DtRWOlQj7FSqrapJtO0ePNt9yyy2PP/H4rJnTJ06cuHnzxieeeOKqq3Zde+0X4/G474tRODk1Nodx1cLLmb75cnpkcybIIp+q/vHAbTe5Zngzk3aXnXXWtJnTJKskY3feuer5Nc9f9Ymrzz5rhee6ESc6sabGsmUzheu6NppE4oiC3/I8V7FyYgM2uXGOozroMQdmqBN04luVtxFur1NKGGcLDZ/SuDCFoVe9gRrOTgB8DcBSyXSyXLyhoeHee+95/PHHYrGERjZk8W4M+70UuqFKcHJ1cZjgMsEmgkd/882/BY/hm9/81vLly+DTmzZt+n//7//+53/+sry8bMGCRSRqJBuCkMYMvt3W1lZWVv73//CP8VjU9Vym5HR5aWkZbKga/H4iJ5gVQ778f9h/zJ577rlVq/5w5rKzvnT9X0yYMLGlpfnuu+984IHVMERf/OJ10WhMt41mZ8Jy/z45Q3BaBaSOle7qeurpJ+++686M5xYVFWNRDkbAE2Lkt1hFJSZMzKDArYj9HOYzIfaPMJo08MhhLs2cOeumm76ZSiXj8ejLr6y75b9uWX722Vd87ConEoNPVlePR+unzCeNP8Z+Htooufw8D1a37fsKv2Xpz9CHaZEa6DS1rxD4FR0vGi0PNqd9+3bfedcdFRUV8+fPw35BA+wKPCc4TjKZfOyxR++8+86W5qbKyvGGO8Wc3phP0GuuImsRuenM439+6q677+ro6CgsKpAte9I/9nRcPzaS/RxYOaSZTObBBx9evfqBK6+84nOf/1xpaemhQ3W//c3N4GOUlZV96lOfGoUmWieAaPko2JjGK0lclgP+jy4ekjPFFWUPSRkhD6CvmV2koyP8ynHjx0+otpHv7c/PPRuJRqdPn774tCVUbPQyGYQiSDY+sCLtbe2HD9eDiYBNvaioSIdhxCMjYF42NzdnMuni4uLS0hLY/2hSghVIpVKFhYXgpTU3N4HpgQ0yFovD+aRS6dbWhnQ6XVJSWlBQgHhS3wAUOjpa8YAuHK+kpBi+orGpggg28OqlzwcB389//tN33tlw9SevhhO75+67Pc8zSXjdhDfMIQglryxDBau8LHBOHf7qq+tffPGFr3zlxosvvpiQqsuXL//KV77y//1///zCC8/PmXMqY7YnW7WJGEeCR8DBgnFbtGhhIhGnMrbvCYTHCZlVFFnV2BMzeWXpofJhJhw92nrfffcWFhZ86frrZ86ckcl4NTWTvvCFa7du3fLkk09ceOGHZs+e4/uutn3vFbFeij3CYERYd7///X//8Y8Pnf/Bc5csWfybX//WlRV/j0tmz5EfFhGm0GO6HIfEtcMwWWXw51s+94IAVchaqqbK9RUIAd8uLi6BJQYjHE9EG5uOgDHEusZpEWnKeDqdbG9vhylXUFAk2Uplv6bo7OyCwyYSMe31SgAoxHjRaAQjSbelpQVmMsxnMIlgGDGJJb0x+ANi1JaWttbWo+l0CgxgWVlpJBKBpd0MkURLEyzr9o7WI0caIpE4nJilYQZowX24Brjvv/nNr//0p8cWL1nw2Ws+ffttt7uuHwrExxyCvndTE3/AHnTLf9388B8fOnP5mUvfdxr41p7vIQbUEWJstAb2cBx77979Dz74wPz5C6677vpx48aBDwDRy4033rhp08aHH1594YUXVFVNwD2ajSrXUDtTIWOl03Kw6hxhiIpwMaqynCVEDhm7YLr/EIIvz89Ynuw+9jKudKdcD9wqWQnEXdzCUqQ8xo6d29c8v2bTpi1gMWbPngUDN2/ePEx3W2ApNmzYcN9999XW1sKWD57TsmXLrrzyqgkTqmOx2HPPPfvww3/87Gev2bRp84svrgGf6X3vO+3aa68Fc7N69eo1a15sbmqeNXvm5z73hSWLF2Pzn/QbXn1t/eoH7q87XMekMYqsWLHiYx+7oqqqynVh58BmaWmokAfKsurr6+ErN91008WXfPjRRx/xFBxJRXu+bwZrRPYX6SLJIFaWKSE85Y1HGtetW1tYXLx06elgUsFQUhl0wYKFMAtfffW1Sy+97JRTTrEUr6VwkEwjmewqKSoCV7W17eiRhiOwaZWXV1aUlxPdA0MtihPWqAomDC2KJcFnjIO7vGvXrnPPPWfKlKnptAs30XWTsP+dccaZt912++uvvzZt2lTqLnxP1UHC3hXSfNhdXV1NTY0f+9jHvnj9tdu2bvUwjYFsuUI3i4x4F2Eocx5eFkwMy9yyNOUB4USFASpYBOPTpwbWTEaUErLqpdMZDHy8VDolGQUlAD799NNPrnt57Ze/9Jdz5swFu9jYePh//ufWiTUTP/OZTzvIEL9ly9b777//6qs/uXDhAjBrT/zpiT8/8zQEmGAhyysqPn755eecd37EkWEwuGsvv/LKg6tXNzY2gL2KRKLnn3/+5ZevBM/sJz/5yVtvvdXU1HzPPfc8+aenVqw4/9prv4iwV49cLKJshYiroaF+5crLvvCFzx08VOtlOQL+GKS9H4kWi1qFIo4Di+hw/eFLLrnky1++fsfOHZpnVFCOfMxhHZDnmsmk33zzzcbGxpUrV44bV9XVlYSX0ym3pqZm+VlnPfbEE+vXv7Jy5eXE1zDaLLfQVkLTXQfJS8e0jChuXEliLjd3m/lWQOqVRRSqmcq4YitX6WVF1ETYJ3gRLPjjTzxeWVn5iauv2rd336OPPnrzzTf/7d/+bU3NBAjFXnzxhZ/+9Cdl5eVXXPHx8vLyTZs2PfbYI83NTV/72tcmTZoE333zzdcLCuI1NZNgxHft3PHY44+4bmbBvPkHDuy/5JIP1x2se/yJx377619957vfOeWUKW7Gfe7Pz/3Hr34BB//kJ68uKiwBc7N69f2dnZ1f/vJfJBIF2ESm2nlkzdATs2bN+va3vz35lBrw7cADC7UFaaLvEU3MUG5fd307zS3NO3ftnD1r1vjxVZmMT4MPF1VUVDx37rw1a8AHbQKvwvKJ9A71c32R6krCDXlpzZq1a9fu2bsXjlYzseacc887/7wLChIJL8yjdaImsGgjVOCcbdu2wsYzd94CSdymaWlgo5o9Zw5ESDvBCGbtKO8JAFZoe6C+PNmkA9Pmuuu+NH58ZVlZaTrjUiaQ53A/DU/PjsiiSLNC5kZkMVNL5FzWKQ7H5EJpArnYbMONEkL/Gx4melNX5TTTAjwBH6iwqOC11149/7wPzpw5Oxp1du7c9cwzT82cNWPlyksrKiogQF2//mVweuBGQCD0h9tvfeSxR85dce6ys5alUsmnn37m5z//GRzooosuggP/6ZmnfnvzzdOmTf/kJz9VUFDw+uuv33XX3V1dndd89tNnnnmmm8kcbWmZO/fUuafOnzFjdjC4ioALQi8PAtQbbrhx/PhxxcVFe/buzhriMW+gn4sIx9TzRDye+NKXvlw1fhzcuy1btyoGJCkY1p34eQyM1YeDBZv7rt07CgoS06fPgLEl+CBsWI7Dlyx534MPP7jt3a2XWZcZZqJRc+YBO5IkznSIad7Q3zJD00CcBSwg/RMhyGO2AfSF8bp8xSqsBRtEiCwhmeoaVznui1+8tqS4NJ124bfuumvVvn17pk+feqC29u577mpsrP/7f/iH5WcthyN86EMfKi0t/Z//+f2Zy868YtKVjkSMNnd0dFx55RXTpk0Fw7F/355HH/0jRIc33PAV6VGh8Oett9564MCBGTNm7N2z5+57VqXTXd/6679euHCh8K0LL7wQ9tSHHnpw2bKzli9fDnGmCNK78mLLysoqKspkHdNzDQMfeVemiDqid5KFWisZmFEIW0+ZPDmRSPhewKwBTysry7q62tvb22SfleR/ZUyh1vz2jvbt27c2NTVMnjx5xQoJknvxpZd+9rOfHqlvuvrqT8TgUCdkfZA09I3WZiD1cfhwPThYlRUV5m7STRxXWRGJOK2tLel0uqDAJPBZKE2rUH8n6zbDglYA+Wc8VjB16jRZz3c9mxNnB1f89spJyG0QGaIdwpBs9LLFa7p3fzg734XQ7XmW0A0klqKwURLOXL1LSHPsidGgQMmp7iPQc9qUaROqqzdv3nTeeedHo8W7du2EhVxUUrBv375x48alU13vbHxn6tQp1dXV69evv+/+ez5w+unf/vZfl5aVwQ+ceeYZ//t//+87/nDb6Uvf39XVdc+9d9mcfftv/mbW7NkQeZ1//gXwS6tXrwab+YlPfCIRj7/55lvLz1rx0Y9ehhLjRCXKjY6Oj+ZiypQpYBtc19WKydZ7J8YYBKfbUGEKKxaLT5s2AxaRm/Gk7KiSkbNy1srwxSontufqHzlyBPZ92JepF49elEDGCdUw3K1HmyEa4TwyKieGMCGhlqRSHTGOaafSHS6cWdwIm+j2EyXroWQIGflYgbY9U7hs5itudmlpykrLzz3nHIiVPdeTnum0qalUR0d7uxNxtm7dtPGdd+bMmVtYWLB3717wlmKxWE1NTSad3rp1azqdohriGWecMXFCTTrtgV1YsuS01998czqYoilTMxmvMJGYOnWq7dgtLc3wk1u2bNq5a8fi006LRCK7d+/2PL+goKhm0qTW1qNbtmw5/fTTQyhOpJnHXB1SACJPs08MNQITHiEHciTXslGPlR4fOPjJZDISsSWzjuYEJxbNoqIScK3A/lqWYoDl2A6e9tyKysriwyUrL/v4JRdfDCGvdGQvuuQXv/jlqlW3zZ4ze9lZZzElpHViLXuFVAuqOXqjSCa7wK2MRgndIkU0UctMJBIF4GDBACIrGDcbCuuW3zlZNwbKOQsEIZp2Uik3bUuxUcvX4k7YUREmyht6WjhNzkw0AcxQnSolOtXG7QtXiAhJZgyTxSeNL1u5UmH/j7wo2zCyMtX0S3AnRS6hhDkn1EycM+fUDRs2dHV2wjrduXP7ktNOg8hn48aN4EvV1R1saDh8/nnnwwC/8MLzsIXMWzC3rb21sfEIHCIai02qmfj8Cy/s3be3vr5+39594EvBcICJA9MVT8QhcGpuatq2besZZ3zAE4rigXhEsynNRHgnk+TDtm1pn1E3bluGxMEaayTM5+ZrrDKKl+hOT1UYkXALIsJlRJQfZjEcG9I+Hx5sYZ2dkgcr4iDeEpttJY2cXVxcAq+kUikIj+Px6Oh0vEkyFR0j3wrF6g6JUBGzH5eCI6pth/uqrhhYFvmvh1G/xnvKP8MblFH8le8UxAvBPbKZ7YoMvB2NgDHnmUwaLFZDXd3Rlub9e/f+8Ac/IGoiOLdOOb5WV1trKtlpSeBRbMa0aYlYvKszbdsRcCMSBYnqiRPBelgu9zwrGoVN0+7s7ACXqL6hvr2jFYzXd7/7PdtmqHtjt7W123a0qakRnI/CwiIN5xRG0wRJwTD5L8N3TnUlhKapyxixFUEScXo8YbnG4vHS0rLOzq5MOmOxQAYa/oBxs20HLpAyV74nRwj2TScS++pXb2pra5sze07EiXo+xKzWrNmnXnb55Vu3bXnl1XXvW3paNBr3xQlr7yjKsX3SfqGspG3DfT9K1k8r7/JkKglOPLhZSCTrGy5s5qvdHbMQJ3ngLbRwFVKgmDo4wzXMdcLG+BbDE3bTMlNCXMxSIno+/W0gCijyZkuhHBbwUQ116l9qWaqx4H62nI86MQ+HFQHvDJ+aZiCBRApoc4uLy2fOnLN23dqGI4diTmTPnt0XfPCCzZs27Xh3eyaZeuP11+HDpy1ZKHx3797dmXTq6aefWrfuFSRZkHiL2oO1sRgYsaa6ukOdne1bt27+znf+Seoa4aAcPdoKQX9TY1Oys8vBFxE45stuNkq0UHKWeSSLjOJRmgrN0gKZuuKpM+ZjiZaeopSAfo/MMrY72MhGKyE15G6N5a6O4WEzXlZaChM02d7JBdLky6K2D8swBY6B7xcUFCbAeo9ORmgNctWao0oZXErlRCJG6s4ioXgskHHbscJ6CUq6UGA3imRVDguJGUtpad4mlIyVRDsORcOmFYh+OpXOuF7q1Hlzl75vqYVuDUG2HceeOnWKJGXRaprETUy/ACeHnTg2no6NjbKy4g0/nEolYT9dsGDBwgWLKb3oSaC3ze3IjBkzwLPzlECy4XTQsouB3KzQKiu+FdCispFazL6aSgSZFAWJwspx444caYLYt6q6WmSQm58xuPCmppaCguKiomJZyfaUy0uw5alTpiLrFfiWGdKQ9LzMpEk1xcXFBw/WZjJuLMZOVBLzwOk3oiB+dXU1TJ76+gZkVZEUshb6ES3NLTABKioqke/HgLhFj5rpJ1v6XXfpq6ZiH7cHD9+yUcWY9l1SJGTGxwiH4EO2QygvT3XRID8JcRQIpeYkDQdEZxGH+2wYRXQFZ4YAxBE2y5LJ0XGYIUwObbzo22vjKGFjc+fOLS0peeON16uqxkOAtHjxYpiczz777I5duzZv3FhZUTlx4iQY6WQyWVhQeOYZZ1WPr3Y9DHiF7K6KxSDUnL59x3YYnsWLT5sza47Ey2FVAWyiY9szZsxEsIct21ak2baNfr32DLilR1mpQWtBZULPhtJXY3t9n5OVcEDSh6VkoR5S0opww+RHSvZx6EvtJ3BiEP+FFQHWO+O6jY1NsgtNFpXQhvuivr7e4pGysnJu264rRplpVR4PJmXAbWJcJ7GxqgdTITAOlibrDDS48s6voOoQALVCmS6dbUZtQhb6hPoMzLeystJ4vGTWzNlfuPbaaDTuui7nNkzEjo52bnMwKCrbTfUMDGyN6KtKnWnjRt1+ZaXl8XjBgvkLrr/+OuleeAJ8Ozhse3snkRRnTW5tOKlKqO+zh8NCwb3lBzR9w3q/VGdcFrzXQudg3KL5Cx948IEDB/bPnj0rg6cGLmlra+u2bVtnzZo1blwlEgbaSKwPp+91drQfqD1YOQ7+V2l5PhI3yL2is7MjnU4XFhVzm5/w9lSYSScfixYtgsnzzjsb0ulMJBLVnj3funUb3E9ksmBIk2YFrP5MvDfAJ0TeozQZyceytP6BJGjAGUVeFya0RI4a+hBnI1mQGWKm7RHpskXgATAUKRguyxkIJQuL5VEgVqbI15yBXIWhetqhhYJ1Z8+SvSnVa55bM3369AkTa2bPPhUCoQfuX/3008/sr61dftbZ8USBm8pUllfVFRy+4IKLzj57ebIrJSlzbDvZBXM5FYtFSktehShr8aIln73mc5iLkqRWbjrTlUza+PBcyS2hKpeSaEIwlU2hkEKTPFu6thVYa6HFOu2QNz72yBOomLXDdFOhUBulj0wxlgldaBGFeLDHqoQ95rBhdUWcyNx5c9N337Vl66aPfPRSJj0rQdv7G2++UViQgM1db9Zs9DmIvooOZeHP4SHfiQvXV/9lfNiYXdfLuL7rIfu6361GJnKsDK1ZnvddHB6OSvS07GVcBRs9eKmzZ8895ZQpr6xfX3vokKQk9eFlH57+8Ic/2rDhHRmmYuQK1hQsho8hNtUM4JgeZqiUlL2U3YRXZOWrvKLq5XUvS29XcLg54F0988wzP/7xj7Zv3w4+ljk5jVaV3nEk4sTjsCNHHakjy52oDX/G41F4hXM+EvcpLH5vITLGIuL14uKis1ecE48VPP30n5qbmxIJedq2zV955ZVdu3aCRa6uHg+r+NChA2+++To4YXBbtm7b+n/+z7/+/r9vSSY7i4sTEceKJaIZN/3888+1tBw9bclpsWhMDFPFZajXqLyrYMrAhVq69PR33tn4xhuvR6MObEvxePzw4foXX1wD4/P+9y8lVeNAM4STsIF4b8TevpL4Q0XLaBQGJwazPRK10e8Usbic/PCwMVEc5hMflmxktnejEwOEKfekbfIsVwT2amj/E7LULi2hD/YQGWhCTnmuudMItqApF2lWVG+1X1paduqpc7du3/7i2nVz584dN27cpEmTJkyc+PAf/9jQ2Lj09KUQFzHHfv/SpUdbj657+aWuZJePGjhtnW3/fdt/3/L737W0Hj117qmFJcXr1q1rbGkC7wpOLeNlnnzmqR/86N937dnJHR5PxCNxJ+2mJGeXrBywQNZHjyq8CPcdbAfcetjSUDqWowYr/BdHfTD/ZNflPB47w7RLLWgRwc6SUIvIoeJ7DG5CHNErTpAMHpZFdALHyD7KGCxYsHD2qXNeXPfipq0bYwUwK51YIrZz947nX3hu5syZp3/gDFSEE2zUmWtFxw6TwnUt8KDAbvjyP2msnFB6OwR40MU8kZVrNi0/odKMLJL6CsDBfC0tyJFq1A8LbOEWKMHl4GDNnDX70ks/+rvf3fxfN998+eUfLykp3bFj+x/+sAomZllZOUOmIl8tdZ2UxRKeChpQGwIBGfSaNWfuvAsv+NA999z125v/6yOXfKS4uHjTps233XYrGLLy8nLNtWqFNZ5raw8crquTv4Og0c5kJ5zDupdfhgOCAZo8eUpZWeUw2hoWaudkJqOoabngzolFi5ZcccVVq1ffW1hU9OGLPgxLGK7x9tv/cOaZy84774MYwrqPPPLIAw/cd+GFF99ww40zZkyfMGH8gw/eD4c4a/my6urqjvaONWuef/DhR84+ewX8R7muk2KRKtIKWJSf+tSndu3a9ctf/rKlpQWWZXNz80MPPXToUN1f/MVf1dTU0FxlufVt6+TuItTeklnRDIZl//69YAlsh2/evDmZStYePLR+/XqE8TmTJ59SWTkuLPQ5ggWZrLjDGoqzyfGbTHeqYsOiJKhJ9hhe9MAYqm8hAlI1QGlpGka0xvbSpR+4/4F7U6n03LnzwD0aP37CvPkL1r38/NKlZ02ePNnzJBXLBy+44Jk/P7X6wdUVFRXvX3p6JpN59rnnVz9w/xVXXllQmJi3YN655577yMMP3/K7W8DWFRQkNr6z8bbbb5s2dXpJWTG4oNOnT6kcV7HmhefnzZ9bNW5cPJaIJwpDZMLyZjY1NR44cCCTSYH/tX37ux1t7YcOHXr11fWOI9HEEydOGD++eiQy9ydM+krn/2TaEgLdffv2gQcOntbmzZs6k8mDBw++/MorqIfi1NRMBk+aoC9j3lUfNQikyC6vqLjms5/9wb//4Gc//ck1n71m4sRJ9fWH7/jDH2A0P/3pz5SUlHneqLTPjEpfagsJWRE5YxxwvUNiVVSihxnCIdQn9JSVv39XFezBoMjg0lcdyqYPBcbCU+qswYNUa8h8ffzjV7iue++9d61f/1pxcUldXd2UKVO//OW/mD59uud6WjgxSKGpH1INPOhteZaPIw5vQVQG9wA+/9BDD770wouFhUWHDzfMnj37+uv/AhwL2ZNsBUVxpDVzn332OfBFUqkueNrW3tZ69Ojq+1f/6U9Pw89UV0+48cavLVs2Hr44jFVzstZcV0KF0gjBGhZce0FB0Wc+c41ts4cefnDNmuccO9La2v6BD5zxl3/5V5NqJkvuBsaOHKmvq9tfX18HF1hVVf3Vr34NjPXjTzz26KOPgPU8evQoXM0VH7viqqs+ATuoIXM/CRwsAh37vjdv3rybbvrGLbf87kc/+lFFRXlbW3siEb/++i9ffPElCMXL2VpZdpXqZNUiNP6VbM9Bod8tv/nNrxsbG7jNk12dsO++uv7lnTu2+xJMWvzFL3555cqVmMQeMR/LkKpQrc6Gk45wm9mChWLBAJkg8v0bfsvKfre7VcslRVM6XdiVyx1fZdWzZ4jqPNUIeE/IZLO0Ukxx2HBiJGX2nDmnlpWWFxeXzZ+/MJ1OJxIJeCUWK1qy5LTS0nIa6qqq8d/61l9D0PnLX/7nhIkTM+nU0aOtF1500dVXfSICF+/bn//c52EE7r/vgT8/80xBorC+4fDcU+ded/0Xx1dVZTLp8dXVF1zwwXvvvfe73/nnCRNrPv2pa84++xzkPlVXxDnbsmXzzTffDFYC27Ik2WzDkcZ3330XLi0SgeDkM5/4xFUj7VWP6iiFEMYwXNFoZOvWbb/5zX82NDTYjg2LqKXpyKvrX9m1YwcYGdiAPv/5a1euvAx3Ln/Mx+o9LUiOBIqjr/jmTZnf/f533/uX71dWVBw92lJRPu6mb3zz7LNXUHf/qNMgJ3lQdEtgWshSGYtwIhyl3NDeHW5O6IZJIj8aF0Wl/EDt/vb2JLWnITrIVxRE6lK9PXv2gps5Y8bMcePGm/gYjBGExZ2dnYsXL45EokgXZsNEhJhp5szZVVVVsBE6Du/qSm7evBGWd2tr28SJNYsWLZky5RRMvdoHD9bu3Llj3rwFlZVVJCxYV3fowIF9EK7B1y2fM9uuOwyf2TNzxszq8dWy9mFbHR2dmzZt3LFjR3t7e03NJPj1SZMmd69BUAPIvn17DxzYL+SWK1PnYH1QDkxeRDwenzVrzrhxVbqeyPJ5Q0rTRigyQn/ChKpoNHrgwCGaKwOfB552sLgVqvdb6gcEVjF5R8fRrVu3bt8Oy9ifNm3G7Nlz4PI910VErL9r187a2gPgU86YMQtBSH5bW9u2bVt37NgJf4CzBeM/d+5csM6eoqOwjl99jFI/uJPA6fqOzaZMmZRKpfbvr4P7rlzGQXZFBTbtIN2XrZTYTYUebm5d3UHwISBYh5s4a9asmTNnxWIxV2o5kdLzUIEhzKzwMcpIJGITJ1Y3NbW0tLSS1stIOVjU5YupeJlQOXz48I4d76ZSafQbBLLbkxS8AEdm2rRZkyZNykG4D3WYEVpQBMFCcnlfQFwxZXJ1V4eX7IQV4Az3NkWISCdTXhFvbWuorW1wbEclt5in+djkFmHb1pEjjTt2bJ8wYcLkyZNVxz4uC6RZcTdtese2nfnzF+AklPkP+DCMM5g+DXfjED4dPly/ceMmsKvwLVitixcvKistdbFHx7Ht1rbWTRs37tq9B0zc5Mlg4paAK2bp7r+Ojo6dO3du3LgR7uz5558/Z84cEtq3tOJPQ0M92FXZEoQar7QW6MbbdgRC3MmTpxg9pZHLaQSLCHaKeDwyaVINBIeNjc2oyThiW6ysyVjExiYXUX19/Y4d22CoqewuYcSIutKLaCbsQWbtGLnu4a+/wRiWlJTAtDxYe7CtvQvF5UZb+KdAlhiQ+Lt37wSvoL6hoWpcFcQhM2bMwFZNFc6MKhwbcpaLGGeTJ43nPNZ2VHARE0z1vchpvGdbJsgwmdy35cXiorg8Unswx8ESloHCo/MRleVmGyYZkSDoApwFPgoqcnQqaUzfj0QkICaZTIKtwQZMBQRx3Qx8F/Y/MAWYMZITNBZzYrFoV1cK3iL3H34FPgzbNryCrAqWE5H9NfDTXsaVzgXz8YBxPKALH4ZvZTJuruimYn6RIUhUZu/CBQKms2V+Op3ChoUe7+aQOlhB2gyVIDWtjlzfsDoiUYf0HmPxWDqZ9j1XUHylLsqGq4Zxwhsk0xXwgDEBUxuNxuCmwBjCUEvraglfte6fsA6WROkZB0uYGlIsFkH10DR4mSgJl8Z+HzvMRDAUC3V0OliKokEvXx+Vv2DysCA9bSrFcnWn0xl4DLMhy3KwmCKBQ0K74lMmj+9sQwfLcobtfPzgpITtuBVViaOtDQcPZjtYwdwm35THYnEYOphvaAxtajAi4EgiEYfJ2YXodcmiJbt5wPp56sOkzCPDSwcmbTqZsmRAHJFaZJ7itYIpLrFWECpk3HTGjcaiHj50l7gF52Y7jgdve4rkIgwDkiRD2JOIr/ndEnhymaTT3ohvYKPTwcKoDK0xaalZVgTcqFhEY916W0Qjx9cw+h0sJUJFFAVC8iuB9Y7CXzB6sJ/CYMIWLxXuLE6Y91FFWEgUchFm10yuskW0tUUuskBlTzDHyoY3oBgFtkb3vC2qr6LFhmmEBiK0SeMAJJNdGj5FBJ4ce/ra9SsWZoy8ri7SiuepVMZXEvBSNhBchEzGwzBLAQjwlTSSSHBKQIBtct0OJPCxSQMETE1XVxdHTQ04se65K+T3wvXJBVqTTA8oKxPDDWfRl2elTY3rwomxCfNsSLTQ2ZmEcBOuu6ujS6UMyWYzhhflazoWiWEFf9R1JeSCcyedBq8rg82dXG0hJ25NTMk8hyBrFjVLS0tHdx9pLNKYC7GR5Cyoh7632nlUyhptmJS4tLyM1+l2iEDyxQ+Vz9jIj4/ic7SMHA2m31CIng2TewXrTlFEKBxWuJjerbyIRg9CMtftJHIZVWRkSsAVPoEazzanzhUui/6dnRldI2HSm8AgBewkOEi4bVvJlKvspwLGwrf8js5OLlcwzO2UkMl8Tj2O0qP33XQyAx4gl9VwPzxW9DcGn5kedmJbc8IZhqwxbvfsAVSJFmmR4f8wkM9kL6IsS073Lpy4GusizLfVCtPZhcUlkUymyEVJJtOEbsxX8R9F5hUZsJRelqXCeJU3lvGYCcIUUAv/v8X7OKZi0xbUkBz0nmDCStkDEYqiQnPLePFcKytLP8HGigCdoyZi4No0yJdNjw4Tun9Rg5QCo6eg9Hj0vJVv47pwZTx7T80MY6jRXRdPBfKEv5YpLN22qbp/8X9GTJJ8LKEGLUA0k9C15Unad7QDQmnpCKWjdsL5C1rmOTxGpLdG1VVVBPQ8YdvMMGoyZr0394xsOLZP/oLqS1EgS85If8EyGgIjOVCBRpRZtZw5DkV/rB/rtBd8Vd7lZuWuPhwReuZLavvwu92PhpkNX/NeEIBCmEIBD6ye0OOtfH0ZlGu9QPq0VrSzDNZUNbuYNmOlzoMZKVlSER5TQFvhCZdL6iBwrjxM9nS/6FAUJ7JmiDIgvn6TS7bSk1g86riiFQWRyVpExCurvVOhzdLY6PXtYanCqxHhVVUvUluxaJ5Ly8UURny0TUnfgmhGwjS5I4IAFdeX4wk3ZGeUbDOuNINQz2NTNFJCs2kH9tv4QGgv8iuKZ0lHa0oe4+4YXqKsJLbKmJlf1zbLuIb4RPi65JWvM1ZYwTH6eeuPyzEfyBFYt++G/2JmhAWRoIbRxyxLU88kXqn7ifCDKoVNbDdSztsP/JMTD+duoMWKjkgnaDBDKd+TPB1CUV5hAZENyj09hjkwsl57TgSuCtqCdT8fZpkkF3FNjdicIEfZyoVay+qBaw1XWy/Too0WEXGRUr7OrfV0f8noaZPkMwrjhLFUSrRVMd3qLsXQpDbOZUCbpMmWTAokrGyjXzGZE5LKyp1podBXWN0drHCMGthHITTX4OjIHh27aR3kFU3sbPkWNTNbU+gDIycG0vNPjyYXRZjhQmUqFvR4K3o2S/XdiRzWkVFz+gJDG0uyznvSCbQDn8RijtRVUVq5dD1yQ/IkdFhkX4vo7mllz/TsJp1g1fbphVikEIhNmJaqhgVKfDzXhAVxlmF6MT+KsYPcXIUmsgqfMGOhVsfBassXRkxVGB5kMRhzOKRIrB1QReuuW3/RgXBR/oJR5TRkRyl7pW6Dw7mvCBvJhfXQjPNBgnsLIyXM8vBBd7c1g7LAuze2IouuUoVDvRXaz1QqdJitish2BsQgzYrjHzLEq2lnSqc0NDFYdogzAtsAhnfGnTHyqbKFweOhckxO8qk/XYTd3Y7eughpBmEgifj/kDaoilrznDwlkkI1DysrEMSEmKJ41j6OCI7HmBW4tqo2Su1IXGTDfAzvgrIGgTIuxcW6kyM893iQ8RVaSt5UN7XDJRQ9TMhVHNG9S5c4rFDHRXcY03BOV7Ik3BRtWFZecFS4LsZHz3KoxSj0rtT+RqeKwYNverz0nk39ATjJBR+tAT8nkVfh6Qo7UgxLQJZUew1FiSRIiCTsvprOUqDQpIfCtpgZthdcjzxI+JH0vdbG7DU0YYo22kMuI2nVkKEP5XrCuRwDONXeYcAgr6m60LOymG2rPkcqOGo8fs48083LxxESC7pMSwkmBBTJNNJZriE7Dh+LGWC+KefT+iFeMdNYkMv0SkArdL+kcg46pL7CwyrK5hDZ2XE9PCW+YaEKJOmGU1sfF4F0tnKLs72uQQ6G0M/iJnmqBYTFkP1o/lmBN8dXuQ/16wTD59kh7zBuViw0CCK8eDS/Ey0iFtZjGE5PFE0tV+BynLe0vogflWHXGx+2QcN42pKICunBhLI/WLuwLD8k1BoyelnGyg4UmUwWiomwN2MZ0iwVxDNOtXtG+GTk1Qg5dwYiRwsZNx2OeXvLlkEUTS9mtAq6r+0w8WGWxkZAi4E+jZL0ECPmWzGhxfgFcWTgRksXxbqZ1uHKbJpFpAU3RIinQ8t+6rz68J1YblSPe5/SRFI7hWrOFaOSq1910TLGuvvPFD/glA7EyEeRe8VM5sJm3EMWD6NzYzndZgCzQsVjZjNywHgQDuImkZu0Zf1LMvTxKcI+9PBV1vPPqVS7KQXR8xByYqgmBUXYSHxNtSnZ5adkDwM6QnZMsoa9f16EPeN8Dzs8O7NftYciZiKzLwFi2OkDe6E8Q2akZEWohsyGLD2D/Q6WGKGaoNnSqGMLuwlwSrBgNoZnBWMjmMk6pmk3BNtCkFNWaSEhG4aED9ZKas/bNnotciD5sGylLJujXUMTsFRHTWw8a183EECrd5PYg0Wzu3/AzruSe3gxZDODkM4+7ls6ssVBLd0jCM5PdozbXAeYOsxmI3uq9qhZR2HZunCoT2gZhhUPrFnYbKi3xUEfydGNAkT4OJdLkKvRRv471cvnO8IPsuhU5dEJZMmPUFlRXlJcTM2FGueSs4G9tx++qo7BtE3EY7bDJ06oFrmCQojOO7nHQRiOa2FzG4YiGnUmT6oxnKmBa3HS00Rr4TpBbAjRaHlZWUGikLFcXW2fBe0x79kHNWSp0lgg3mDjG57wPUlaEHG6pJqWyRANdYnQJJpQGl/hOUVxcaHFamxGbU1+iBbYHzOEQ5XWhKkgF1GktLQkFo1lLyKm6N3GhiurZUHPedKbRMQNDB0Y5PHjKkrL3NGKuGUnqC2UFBLMSiTiyaSplChUg6RJCu9+OHuxQVnWEmU2oiCRcCNeqBnCFKF9a6w/ghwshWTyJdU0Z4WFBQSGMJ1uCE0S7OTux1FE/gosFnG4pNwuLGBWkKYhZIewTva2JGGiapnslkxgLIZSJCLfrBjbSVGvFPEAGtfCDW2QZXmcycQF4hM4y/Krskc8z7/dn1r53rXyPSUYqDC4CG4JcPWKC/OSxo0FnINvTmRlVrIny9hV6vrJTDBnVs4iEkGF4D2+jATLcTUNwSzsR5IezWbxRDTiR8bm6iC7AJawLe5EHNGVVgkEoW29xeD1YLw9iRqQ5Em+L6T8scW6pJi7ywnSr4CxBDX1tNeZV8nr2LzR7t/tfvAcO8h6fdofvzjvz+W9ijyHUkoYjLm+W1RYZNt2e0ebpVEViIXUJDXMt3ITFr0cP+dMBuTm9ykP0vsgW/370exKighvMsK2i1zPbW/vtB1uulN1s9+ApsqAz6R/T495VLvnS7pPfrr3UjeX27zYKU6lulKpFFeU38y0Hmh/gvV6tlavy6Gn+zugeLH3a+nPifX/ZuU5guo9EZQKolniqGQ78yJONBEv4A6T8ctw0jQqnLftC9+2JbIznU53dXQh5Y32wFQ+1g+h74fagvX+gbzLPO/9PYa1NqBr6c8M6eVMTIJQ3vLSktJ0OtXV1cWYau0MmpRUxwY/Tgs2EGucYwoGlJg5trUm+pHv6e5gKbSf7/vxeKzAKehMdroZV9Md97kvDLU1Hro9brBGtU9rbJr9eXFhocN5BGICsAk2eVnyA6ZEGHxDdaPInDwhl/1QQOkbZ1kRMfQWTR5T7J8bR1o9Pz2GD1i9fqCfga9xXclzkAhCW4qNCMOeICgXiCaYOh+Zz/o6oBhgkN3PYewzpj+GUc16EfMLmhAVszIe6UJw1XmrUnrYsq5ofUJ9Yv2+O32fSb+fHvOo9pQpoeUjU5m+TO5ihphbYYkMoaYMoXo4jQAXXAxgwveZuen/0hMDHJPjvxE9fJhZodYZn3xTpJ9mqlPbUBx4lkJC5R6Ehy6hJ3ar8FdYX36D6dgTZAN9oVHDWA3E/ImvuGEED9GMDYMFEwO3YGLglnAQz2SgC82iTgvdCi2V0yBaC4SbqF+ZLA3Zk4GZ1mOY4b3b0mMY1UFfa+R2emiBONMUzCSTx5HVFlsi5Fs8a+MWA9m4h3RUB3GPGyILlvcp01R31EKAu77QRHdMOFKaNGRZNM+KJ/VNVR2XogVODDpC2RQmhgytfKI8qJ8B2f1ZTtzANBG1fFtOaX4ylwdxhjE1rdQa970Qph3phFC6zaKs3kkJSVNNXrLCLvvKbRHQlSlC56ALiYIU2x9bREIYbmPDr8sCVKjCkHs+ulyE18oNgv1+Z1DyRq49bYGKrgpCTZt5zIoY0Rt9kp7FKEHLBRvj4xzkWeH70s9GvglpYAMNhqAtwhKKQHtswAh8IYi5lvqmlQaiIJZO7bGq4G5syAZv6H2SThLYOu5KqRzE7DPCYHEe9gyYNitIkmRprQgVQ2Knv6CW5TFtcLMXcJzeir2ejLOKay2TsBEnv0nk+H+0sLF/XMInCKBmmW6xk38oNI8vN8wHasPGpigixyYTyMbWEC0hERojriS7BDNUKhTZceIdyR41McAMSv+TKIbWnlKOMkaSW5WgBmpOYjq6RDh2Kwc/VsGg1Q64MAwvks9CpnVs5LX5ZViwJnSasLimSZMW2Pdtx2Ga8zaXaXjscfz5BcXsn4VVp4DRkSiZcPpFMFuqmDKHK9CIFcg4sGOwUyf/3qDjA8MOoRKwjGcHxCf3+iYoWgBiV2gJPeWMV/EemDlh4Tqm0dI8eCVYRmPdZ1aAv8JchBAh54YynySBZLOIDAb5sG0OipfXkj9t28hhyzQXqHK9DDvWmDEc9ChFsYsJXf0KYYzeW6a1/+ZXLSJNHasZWYXptGRZtP9jj8GbrDRfwZkFR1ZwM9yW1M4Rfo7X5FmEIpIhWkhShTQYmL6dObTUI4iIzDnC8CDlFRGg5SOFq1zvJpy1Qnx0wc/psnifVzEUiEhrIAPbO7qzp0EWLJuAIWz8mC6VWbkqE8eDM7X6jenupXV/QKPaz7OFW00gRytLi9pEKSIkaMREN+bVAbVl9H8W9bIwjw1nmndwBjSqNCwepoLIsBDLfMjTskwaCfkSSeVh+CJTcxpEy2yJkE5TGO+l7/WJZMH6bY1HoG8JKWaNM61B2SJUZBeGe4zAcKzfdkP0+6IG0SZYQ9u3xDxmeqqCQjvLK5vZc4gynNZ40Pe4gXbYiAH2LfV8KJKOEVqdTw+wQMStIzx1GD+wK4jVIilgrgyLqXYJ5HjPJ8Zk9frK0CEig9MODYffDe46WKeqTapqgFIKqSY1qAWomf4MswZA1ZL3THI8M7/XVOIx4C5zHjlDlzOqVr5BFqFQXkHQ6D+VzWM99TENCBHZy5n053r97BMY0DD2/ysBpywThtVZWIGSpqCeBy361P04fTrix4lWzvtz4jiWnt/DuuvXygqmhY6uhekKoAQSkfLJh4UV1uHyr5QCgKQTl62ECPjhllJCsBS6lRRJTkgL1m9r7B8HR9GxXQsROAWk9zjcvhU2rcEi6r4Rin6MqujHWjv+hdPLmRyDxe7Z/NJWpMvqAWGlymkZCktf9LYb9ed6B8saH4NpHZC3IPptsgZkwXqIwxjV+6SR4sL3NDCIMc9RLMUKzw5GxBPyRR/lPsgl822LCw2FkHaPM0LKhXCp2R6f0QbM0Q/I50caaI5E7IjgtoXUnUPMciwA8egPCP0iQ50JYWyf/g3f6Fzk4RbMZa5T0HUWuAU9+sgItNLsh0yhuwVTiF1FasHMdsuPKUTICXBZd6OQmxIKiOaM6yNCnlB//P2ctqy8XVfhM5FDIQNOI3OGJLY4OEKEWUHA5fJZzz3t3edu1pnQgLNsnu3gCDrBIEKiu4HRsUx9J08QiYwAvZtarq+393BH/bzqGg0QuHJAsmYFjnMoVsl7f/uffOpbMKTHNaiFGoUIoC45OMt8a6F7HoKFTG3+uZrvCHagMMOoh1DI1nK6n8wTKs+nJZeZNWTUBlmjKrkAmdrJKXBC0XTV/ynlZ+VU5nr+M9ZDwkYE2TCrm/py+L4IpUZhhTQB1Wz2WW/0cSFfXVvI7ss88O/zqeVqsJvKRWcbgfANtY7Vgg1EY18oKyqUsKMQGopBCZjsRcRYLnmx6GbBeM9nwgZ0LfpGEKsyN33B4YiBhbL6WNkQJnGtd6jjSdjkPX+biiRMs8BYRo2J+XoAlRXmgglmHVPCpp/7woDTYEzksxtZ+FU95Cx8f/vDYjOgYezdguU5vnL2lT6ej9GYzq6gzXdsO0u8WiCZPlyLLXs6URwINaE0/ZUvDaIq7gZnrM2e+oRhfgtuZI5KK1JzCqPvzvT+qOYfVzJYAZBF6MCFmadq62TcmA4RYF+0iCkppRpdLmYOhSdly593fepuZaTEIHyNA2Gs59UYuvfcZ0o70deSQj4LVzaYFRKx6P2Aff2cWkvdcgCk/SpVyXyiSOBI1i8CgWc6XhjCcgw/3eOZZKedtSi/1KM0MaaK9QXj+aZ1n6WTkPnSexmnqenJHKyNejS+wnhx8vTCs870doZdM6H0WWgq9Bmt2D2cNuu2WRmPwQ9Mkp6OiMdSsysf06jd141gPQ9g7z5i4D5ZOuyCEYMJI38SWRwVMYvInvwi71rI+3P9UXnJdXRY+NTCaS0UKEcudQRCOSSa0vuqZD0QMbB+b7FM3zncQjFphRKIJi2JHaJMKE1qpvACpuExNOV0UZiFVZJCQC5C6BlksioTBAkxrdFjmyglb7ErmEUqqmL5lzkdAdGRrudbPglbcXl5RmU3ay2zvtbjoJiRHqYrWeuQoJQigpHTQ+9LOVX44z2TPo9gZqqR4Jebhuwg8+R8QZSgFE1UCQvNOGVc5uzWezZ4wyiC38iOqVAUWOj8n4W2sbtoOuv3bw3KMHZ7StkH0zcsbN3AK3vwOQvmc77Jyfoyj8e4qfVgwXL1HqjoigrrUh2OMYfcXAoInJDMg/T0OZYOVT1Rz2u1S2J0BMfYvn37li1bPCm5J2wHHDQmfRQUPy0sLFy0aNGECeNffe3Vtrb2s5eviEZjvpEetUyGCLNgvjCi7r7QcEaV71T+ksouKHV7dSWm0iyYZlqiPZMxI3psJIaNRjRGn4EAJwxHZ2fHOxvf2fD2242NTSWlpfPnz1+8aHF5eTlDbqtwU0CPGUkWSk2FIhds6WC6sjoUBYyghEGhBUzEvfv2wrXs2LGjsKho0cIFCxcuKiouhv3TrOoBVSsHekIid2ZrXk1T1jnW39ZTgvmmswuFHw/U1m54Z8O2bdsSsfgCvF64d67rKrggiZhoOuOuZNeWLZtrD9YKpZBtEYNZ1InMX7BwxozpnpaQPL7bonNCIs/2Rh24hrmiB29gKO6NYLq/QDPa+WANag8efPvtt2EA44nEwgXwmFdRWeG5OtrCNA3TkMuhofgUPaj2MUJi4f84wR25n3MOg8Uc2JOhxXmLsC/ULTfNjTaRztPJ1dc3vP32W52dXfLucjCHXDJKeD5lABfMXzBj5gxaqcLM5HCcGYiu0/9nKk7UQ+NrtXKVj6CuboPZ0PEnTXPpLfm+0KFoWDeXsCEQTmfS6T1796xf/+rB2gOO48ycOet973vfpEmTbc49zzeZSjZyIgNaYZBbeZwG7CLkYQ3C4TxPs4hIicGHuO7Qobq33n5769at8Xh8AWwhC+ZWjatyvaBZA2kkYfL6zBoqSgmhoGm5MmRyD/O5ZThZjY89mqjvkTtKaKONW7PNM25m7Utr39327ocvvnjKlCmWoqMTKj4YNQ+hiPs4sj5K2kPyBrQrYjueS0kw34ia4l4pA9tAnpeWnS+beCKRyOuvv37Lf92cTCfBX+voaE+lUgUFRTC9Mhl3Us2kv/mbb50ypebWW3+/e/feJUuWVBUk/DSynMkF7KGwjCACxlCbg4WEcpxYGUM1ilCEL7LFloi7knYLswEHxwwF3qHJBBYEBflhPHhjU+Nvb/71Y48+Cl5IYWHRkSMNra2tHzz/gq997evTp0/v8y4yFXiaVInIHncudPI+i9Bn8GyQwTxhXM/WrnvpP37xy6bGptLyslQqueqO2y+66KK/uuErleXjhkV+fABZqGMYCqEdftxgfLh9MAl/8bNf1B46WFFRnk6n77jzjvPOO/er/+tr1dUTSHUfcYQ678B4c3PTzTf/dsOGtyEGMJPE9dyykrIbb/z67NmzkIvvePWfWFAQ7R4+ySQnLTEmQm2Gw3BvWNCXJxDiBGsN3IJf/PyXe/ftI6/07rtXnXHGB77+9a/V1EyRBk8Y6kwx/NPGJMwYUiOiYg0sXlOi6BNC0R1qY/UMGRHZ5QA/qBRIL4qTqyl8k7bXeHwpbc737N5zyy031x0+DLtCKpkCexiLJwoSBZhnsW78ylenTZ8m06u6q0uTogVWi26QmqtaXs+sWXPL1CrQN0SjLyQjNEcEyOHDh99688258+ZNnTqVDB1jVtgeQnQNK+WB++///f/8PplMVVWNa2trq6urmz179k033QTBsMxo+v7IeldWuA0kn88sgqYQdsz25BgjSGZcTxmxgHu6afOmn/70pzt27KqsqPQ8984773j/+0/71re+NfWUaa4XylEIfzhGTDOEGRHsvHvTcDulA4yiYVRhuJ586on/+3/+LwQqp5122rRpU11XsFEpK6YRAKpkInOXQjk56DhxJ+AXENR2wHTfYagtVr0ov5XJZCDiueHGGz3fhQV5//33vfTSuo9+dOUZHzgLzDSYllOmTIG13dTUDAseRsp2mJAlCLl0UdNb0XL6kp+To6230fXxcgba/AsfIOcstGGQPLiK/+hvekppOhF6wCvGb6NPwt8ZN33HHXesWrXqwgsvvOaaa6qrq3fv3vXYY48/8sij8EPf//6/lpaWhn8x76TuLRxRCX2WWxsd7FCPChRbtmz50Y9+2NWZ/OpXv7poyWKwm3fdtWrVnatiiej/uuHr8USC6mijY46yY/US5Nl7vh+J8u3bd/zoRz9qqGv4yo03nH766e0dbatXP3DPPffYduTbf/NtcKF0Okro+866urr27d/3gTPOuOTii2EO6whDxGLxBQvmwSuWNRjqmiwcV3cz/azviTNUcXcIPQZOy969e372s5+Bc/BXf3XDsmXLuro6//jow7ffdhuET9/+m78tLi4xO7rJCg/jBAlKmSado+xRACBi+bqYWbeyaU+fsbJ7iKz832Wkii9Jco2WBRU1VUpf+FOmTrn2i9d1dLTF44nXXnvt1ltvPXfJaZdeemk0GoVPL1q0hMwXgvQVep9iPJqiYUcKr9fjtgNfhG8RwN4iVnvlI2PuH1w5ZtPr5gZFo7HNmzb/v3//wTe+/o1p06bh14XxrjTtJF/z/Jqf/vyn1eOrpaFYtKixsXHNmjV33XXX9773vZ/+5GdLFi9JByHuaDURLAcAxIZvVoampePY+w/s//GPf7x58+Ybb/jqihUrksnkY48/+rvf/Re89fd/908V5ZWe8HSaYKgXUXZKj3WrtIzuhxmfWCzW0NBw9z0wJe9sbDxSWlIGMTAb5YqtSq9et+TomglNFyeo+DNh9hhh5QddSPZiz501a9acOXOQQEts2bL5zTffXrbs7E9cdRU4WBRjybYb20HLYsOT9vYOx3aisSgZGmlFPB/ehCPA087OTvhiQUEBxwdpZDDDk8Z5KpWioSeTJIX/8BGPx/WsVbfHwQMmIZBMpWGXtXkEcWci5y7CMbdv3/3Qww8tXrz4H//xH8G7gtenT59++ukfaGxseurpp6+++pPnnnvuaL6vmuwE0zCu9+ijj+7etevf/u3fPvqRy8ifqKmZcPDgwdtvu/0jl1y2eOGiVO/O4gnzkAV5uN6nnnpq46Z3/vmfvnPVVVcxJEk65ZTJhw7V3XXXHSs/+tFly84iaYiwXfNceM0/fenSK664QrtTKl4XrkAhFOskfpjKOwwgxIjPPvvnl19++e/+7u+uvvpq3Iz9yadMOtLQ8NBDD156yUeWL19BI2NyhyO1FlTfM0cSFMbC8KTs7aNPvWerL52T7t9VDrogCnnGvLD2poIzsOrq8R/9yEfhpQJ83HffffPnz4dpmUgkyND5UjhfTjAYZxPp0R/hYJL8YC6dMTlX29rb4fOJeIEKC6VOrAVvkuUGh9h1M2gzsb9RWlw/7aba2lpdLwM/K7KdDzKbzc3NDz30sPDZP/3jP5+5bJk0etNmnHHGGWAqv/e97z777LPz5s0Ph6Njj26zURV/YNjBgX7yySf//Odn/v7v//Gaaz4Dr8D706ZPbWlpuueeuy/60MUf/vAlYfszgovohLBOlLvasGHDD//9h6+9sf7jH/94Z2fXurXrwmmR0T+AWU6T7CbwHYtlo+5NQV/0BvqVvpRFaSQKyHwPbYmK0jTMpLb2wJ13rnr2mWfBxzp7xYrrrrsONkL4oJT2Ztbu3btXrVr12muvg481bty4y1ZedvnllycKEhBGP/3kU48+/th1X7wO4oPVDzwAH7jgwguuueazBQWJxx9/YvXqB5qamsE0fO7zn5s1cxYmJKRt2rFzx+1/uP3tt95OpZLjx1dfecUnLrroIrB0ah+l4iduq+CEgR1cduYZVVVVlM8Ac1ZWVr5o4eL1L6/ftWsXOFijelUoKjm5X+7fv3/t2rXgIJ55xpnkv8JNqJ4w4YILLnjppZceeeSRuXPnwtV5J7iP5SvwPq+rO/TSSy9OnDDxnBXnwFOYinB74T5e+KELn37m6YcefnDxktPisZhy99FHh69nMi7sO4lEAbze3t4OowGvg9cuM9LvDbENCsEhsDl08OCzzz5XWVm54pxzotFIOp2B6y8pKb3wwgufeOKJp59+ZunSD8DIGB9rRJdAlqsw7LzdQoSwhT2gZxkYNPgY+P1hrxSxgAwMESxA2Dauvfa6mTNmgI060nDkzlWrIKi74sorMd0uIa133333lVdcsXDRonQmCWsZ4vf9+/fBkeeeuuALX/gcxIHEcA+PHdt33HrrrZs3b4QYcsLEmquuvPLiiy/u6ur653/+p9dee+3IkYbf/e6WRx75I5iv66//EmJJAwsGkSqY2ctXXjb31LngkpFBKIwXgH0YN278zh074TjFxcXmKsYeeWoGuIgczg7WwiJ6try84oILPgihfldXEt4tLCi46KIPP/zQQ7C+IEoBz9WEKJzzEV5BJ4KbBStl46aN3/j6N6+86uO/+vWvqC5FS2k0bsciJw2nQi/CZZM77mR/3DMN2aKX7AlOF9Rcs3Sm2lQiFL8Z/NvW1nbvvfc2NDQsXLzw3Xe3/+GO248ePfoP//APFZXl8PVNmzb+y7/8y4ED+z/4wQ/CNH3jjTf+/Yc/OFRXe8MNNxQXF+3Zt/vRRx+Jx2NtbUenTDtl27Ztv/7Vf3i+N2fO7If/+OC4qspkquu22/+nqbnxO9/9TmVFBfz066+/9v3vfw8cLzgg7Bbr16//l+9/t7HpyBc+f62uJ5pZ7oOJ+d53vxePR6lwaSkYhFd3+BD8Bc7KCRJzyFiqtra2rq7u/PPPLygsNMA1cLLALpeXl7+09sVvZL4BJuBEt20UW0MQf/DgwT179p7xgTOKigpNChOC/gXz5k2cOGHtunXJrq6Ezm4y/UhnUpyxTDrz5z//+bnnngOvtKKi4uyzl5911vLxVeN1eCpOekm5iBOFAdyxY8eiRYvKy8o9BceWG+60adNqJk5Yv/6VdDpNCRgxsrpGBvQqOT9kLppbw3l/fEIy0y/6IvfUWHZmQlpFyw76R1XlLgrx2/333798+fIZM6bZ3N6+Y9uqO++YM+fUSy69tLS0FDYP8KjefPPNq6++GuzPvffe9x//8cuampoLLrgQ7OeaNWve3vDGv/3bvy3DhNO6dS//679+H9wg8J/ASL788ivf/e53waiuXLmyoKAA4lL4UfCMYbHD37pdUZg9Hib8N77xDVhH8GEZYCDyGt5sbm5ua2udMm1qPJ4Y86H683Ci0b3797377rtLliyBUQXfWloniVv2Jk2aNHXaVLihHR0dRUVFBsdiajIjsKeIIBk8mq0brJTFixb96le/+v/ZexM4qaozb/icc29VV/VK03Sz2zTQ7NKALAqKEDXinqhZxuT7shh1jDNq9sXJ/OabyftmMzE6OiYxM1k0cUzUqNGMqKyigCgqi4BCswnI1vtWVfee8z1nvbfWrmbpLpx7JoPdVdW3zj33nOf5P9v/mT17Viwec7kTR0WcCtM74CeNQqazF5WFhVTWzNtpyFqWxvINg/yUVn5jEit2I+zzP2sER/RfMTCnurq6vv3tb9fW1ra0tAKcev6F56+97uOLFl0IR/qhhx7asOG1u+/+6cc//nEQQ01NTf/2b//2hz/84ZxzzrnqqqtAQLS1tWzevAn+asaMGaASbr75JhA9l19+OSCwuXPntrW1/vM///MTT/z5M5+5YdSiRaAv4cEADvv3f79/yZIlGBPAHN/5znceeujX58yaDdfs6enx+yPhG6PFUe7C17EkQCpb33ln3bp1I0eOmDZtGjtDWuYRjFpaW9ra2sAmNq5U7kp0aU1NTWlJyZHDH/R0x0Ckfih8MHyHdXR0wv0OqR5i8XRIpmMurLqmZlBFRWPjbvOsvdRgxpyE097R/tifHoNjDIKvqqoKdssLL7xwySWX3H7H7WNq6xJx90Pswze0VphgUMlg9gwdWhMpKmK6xAQj7sEFlb9v/wFYwLKyMj90GMCJy6xv6ezBmPSvv190s4AJWHwl/BjLy6fwbGtkeLqMWrVte9KkSWDngGl+4YUXwvKCVt67b0+4KLx//75Bg6bHYrH169fX1Y0ZO3bs5i1bfvazu0eMGHnvvfdKGw+sxH/8x3/8xS9+MWXKFEC9DzxwPwi6Bx98EC4FEwLL6lvf+ha8O3PmTEBaYM2CCP30pz997bXXpuRgSdUOkwE0IOMPogCehUPho0ePLVu2DCY7Z/ZskBI9Pd0Bfur1EBE4RM0tx44dGz58OABayYMrdWV5eTnIlq1btnV1d3ld3gtAsPiTCAvWhJ44aVLItsHYc5yEPOzYT3tYePJZ9LuhKjGQCK4b7Mu9pj50pbmiFB+LL3QoS5SZrkn0gSpm4oleuqghtiovH3TZZZeBpICFGzZs6Pz557W1Nre2toCB9dZbb61cuXLy5MkAfUBwAN4qKSkBYAQ/gEyBV0TqVQ+YfVOnTgVhMWzYsAULeBZhdXX1ggUL4N1Ro0bPmTMXTAdAZvBdYDHAH06f3jBz5ozOzg74Fvjk4sWLd+/e/cbrryecBFEN2U0JD3LioGoT8gAAngNA9osH/+PokSOf//wXQA76kyQK3Gfd0w0L0yOT2DwQiRHoyKJIuCcW6+hoY2d4CypfM32ert7V1Qn3axHLX80AsCkajYLGgqdvtqi5cUDSsXhPa0vr5z//RYD1P/7Rj3/+83s+9alPPf30U7/5zW/hmqZs4kM8ZEwZdktHR0c0WixawIpNJHYOrF60uDgWj7e1txmtMIBr4pEGMQ/U9L/xz7y2OLqtheiTr2u0VPWM8HJR4u1TPmDXjRo1CgTdhg0bwNoEw2Dbtm11dWMB4rz51puWRQ4fPrx3794pU6aCCPrbs88B8AXwBGobQDB8+Oyzz541a9b69et2796zceNGEJsgMMH8g8cHHwCbCkTczp0733jjDfjzoqIi7qEMhUDA2naIpcUwZOBSCj2YMOCteCz22GOPLV36/Cc/+SmwYxOJOAu6+PciiOS/BPBTR0c7qC1R9SbqrjChFA5RpLS0pCfe09HekWLjDYj7ihima4wLNhXCmAH8yAj0j3QRkglCeCK9kLYoU9xmwsSSVhU3xFw1ScJgczh+BxdlmtJdJFQx7cYSqX3I49PAmosK+/ad8ZuJ30DB19fXS9dfyA5FiqKwCQE2wdnet2/fkSNHQCJ8//vfl7mfsE0BXcEnAeh0dnbKcF5DQwPoUVB+8O01NTVgfo0bN05aYDLJHTQCCBr4GS4oSo4P33XXP3HSEUpB0ICFB7bFvvf3d3d1S6+4SXzG2KvmCofD8Mmf/OQnL7300i23/P3VV18NU4KZaExW6CoTlhFuARYNHh4JSW8qn7e0U0OhcIQXjaMz2j3j5YoiFokUwZMHdeW6Dty4sRHhkQHgho0ULY7Ke+VVEZyWhMGTnzBh4vf/9f/ALpo5a5Zl8W0/uKryxhtv3Lx58/Jly6792HWzZp0Dm/PDbn9TOP7hojAoBrGArqLtEm/JYVtWSXGJPwV7QCSaV5ui2DZhfk7/hgh1YzEuM6lg6pMSn6oOYqZBhQjJq75ZOHn+DFUNrp4yeervH/798ePHATNt3779iiuu2LJ1y6ZNm2ADb9z4BtiTsCdh677zzjtwhF955ZXGxkaZCAWCCEAVvLV///4DB94HXLVnz55vf/vbUlvD5ge5B5+EF+EtCfJEZr0QdIikLJY/UAXHBKTiHx555N/vv2/x4o/cdttt5eXlp4QK7kNtnxheIQpSqKSkFKQuPEQUKuKYlRN08xMEyxiybNBNpoxmQFZV8auxNHhYsMYzUvzihsHAz3plAqyFtUV9fHQ8zULgJ5HMIHs5YduX0WjEB5atH1TZDvbe09fESvCQrBXnPBPQtsAg5mAlnhCkMETwQ/CLgqCJx3uGDRsGCk8KC9iIgwYNqq2tnTlzpoy5WlYIJIhZVpmVLFNDkEj88lc7g3wB82v40GGDB1cJM517I8rLKyZOnHz2tOl+A4J5R4U/zKJw0Y4dO37wgx+sXbv2ppu+9IUvfkElKAxYTmKfT1F5WVlpaWlTU5NgcRdyWZhTLS0tPT3xykGVJSXF6ENTxsIYgIOysvLm5hbHcU0sxiJWe3tHV1dnRUWFzM0S9yv8CqCYsVVVNfhSHjvGYs84ApC5VVVVYLi//vqGffv3zp07V5SrfjjNboORXEbLy8rhrMGGicfiJcVlgsAPThPuFKOirAJMF1NKNuD2ImO+Jtr9GuGQIIpSrGSFrz1eao6qr6mXrtJWgVcClt7UqdOi0cibb74JJiLYgfPmzYMVXrdu3Z69e9a/9hrs2IkTJsAfNjU3AfYdMWJEZWWlEUEXXHABbPgRI4Zv2/aO4ySGDx8+WKScipxRNnXK1MmTpkyf3iBAoNTlMk+A6MZHOF1i2CG7pbXlV7/81W9/+5sFFyz47ne/O2rUKIm2g2K33kwUJFepomIQPKZjx47FeuKlJYBNKZwsi9PBdHV0dILqiaYdon5eWIwxO1OdhKxXKFZwekmQp6sQmUaK0pIJ6c9Qw5lO5QlOpozxSWnZkRDlrlDgNA2Y8M6H2BKd2GRpM88QAvkeDhctXLjwzjvv9PMvSPgvEoZwCsOCnz7Uv9bSzwQIoygSveTSJbd86RbtqpOZVQQMRFcMpBJRZftLUTXtuKtXr/7hD3944MCBr3/96zfccEOkKCprIY3b/+TWHPeD/hk2fHh19RCwjLt7eqLREplWZmO8a9cuALIfWTw3FAqfuZVBfrNb3gWA8pEjh+/Yvq2rq0ua3UiYBbt37z1+rGnWObOKeGoR9ag+LL4J4/FYd3c3fN5nexjWRyLI2PCH1/I2Bi1JJBLDhg0dO3bsjh3bW1qbwSAxlHMHDx44frypYfoMzuGUarWfdvHko5kyR16lPekcLG4Q9ttj4r0IhcgSgIUJEk4jDVPaDRpKZqo6BLhS3Cqqz2nTpg0dOmzp0ufr6yfC7m1oaIA7efLJJ1esWL6ncfeMhgaAUPBcBlWCXBx04403LliwIKbBvnTwwwBAVlxccvXVV99ww2ck/akAfJxoVGVeOjzLnotw2VsipbeCFqRwI42NOx944IG//e25a675GEjgoUOHcgksuoIMWCJ2hi3BCg8gKLLZRMIBHFxXV7d165bmlqbq6hqJCuBhHT585NChgxMmTJQlRwZjBbD1JNXmgIDUPGfLRL9HC2HLskU7HNMe1xIhWk0sirHJNBDRMcFN55MiyNd8kKkmRxkahquf+cGnyKs6VM0HEdhhdXVjRowYuXHjxta2VrMLd+zY8ctf/nLLli3YN/xeQZNPk8KCCH8Oe33woEFwwfbODukAsy0bTMYH7r+/cdcukR4ruOy5PUolumppaXn00Ue/971/Aoh5zz333HTTzWVlZVjwlILR2Rd0la3mEp9+9yRfutoxtfPmnfvee++9/fbbMp8MEAbAypUrV8ZiPdddd104HEqrwsjGBnQqrf9TZzV6V4P7BdE2f/6Cvfv2weOW/BqcU8B1V69a1dzSfO211wp1xVPam5qaumLdcA3BVP7Yv/zLv2zatAnQAzxxbliEQocOffDKK6+MGXNWXW0dbMvTYdWcvpXpO8bi/wVTe+TIkYsuvPDAgYOvv/46nClbDLAr1q9f33T8+KVLLjMlhObk9o+oMu7ypIZjonU61ULJJ6/64X+Gd1QEJ0kSzY0XDMSyj4/hH2ci11WwX4luisOGDZs4ceKGDa8tXfq3qVOnjh49GgBuRUXFb3/7uwOHDiw4/wLYkxYh06c3tLW1v/32W/LPBFBjzzz9zMO//0Nra1t9fT2YkfDIEvG4EowEwzUf+tWvGhsb+cEPh4lOt0gVX2JteQcSN7Fi5fK77rpr7dq13/jGN+FEjBkzBj4cDoVB7mUVWcwUdPan9xBl5+Vg/on1W4KpJPqRqWyjRo9evHgxCBBYSfhViJMQvPX662+8//6Biy++qKJikPGv9x8mNVE2hs6UvFudxc3/w8kxQXPbkkSTiAbBRAooqcQHvrQ5g46XkgvL7CpVlSMOHBhCtuO4yQidufxtl7hId8yQPUs9j5apXZbee7WLfIaH6WUou0Uj0+pE/JuIJxoaZl5yyUf/9Kc/Pfbfj4FNVlxcfOjQoXvvvW/Xrp2zeIqMpdeRafgl+tsgRQChu3d5jbpmzTrnvPPmv7Rs2eOP//mjH/0oQIo9e/f+9O6ftre3X7hoEWeB4t0SKTwjeUcHDr7/q4ceeuyxR6dMnvzFL34RVM7WrZsl3wQ816rB1UOGVPUN6TC/XlDZeQyd4q5PLAU5CzEeLYpeecWVr7766n333VcUjowfPz7hJFatXPHiCy8sWXL59OnT/akVzO+T9Jnjp3THYd1BknnBE5Q5YJE/xpLbCRRtOBy59NIlK1esuv+Bfy8rLZ04aSI8uDWvvPLXvz5z0UUXzZ49m9f4tLY89NBDmzdv+eQnP/mxj13NWwVb1vNL/6e5uenWL3+5dnRtqCh09MiR3/3utwBM77zzjvH19XGut06h9Y2S+/x7rcBO+a7I+8Ew1U6D2BdffPHyFct//etfV5RXNsxoAHi64fUNTzzx+LnnnXfOrHOkiaIbJPRH5oZpw62aeTNRWiNdVzrBQbQn7s8Vc5Hs7yap5ahNpT2Z2hWcaSAgWdcNb763dUE9zD9v/uOP/+mDDz44++yzYWGHDx8+c+bMhx765fSzZ0yYWA8XCIVDV1555dNP/+WPf/zjxImTJk+e7Druq2vX/+THP54/f/7HPn717DmwtWcvXbp03tx5CxcuKgqHG3c3/uhHP04kEos/stiyrdFnjR5cNXjjG28sWrRo0KBBSCS8I18QAEyOv/zlyQcffBAe7k033TR37tx9+/bt2rVLfqC8rGLo0KG67iGpNRlTDSn7z3ugW4Ehr4O+kXlMNU9SZMvJ3R37LRwEqn/JkiWrVq0COTO4cvCsc2bDir2x8fWHH/79nNlzzj13PiAEAF79UTrjZRar1VCdchg+A0iwdHdiXrJLeclaV0cnPPCeWHdrayvYEh8c/mD3nt1OIgFGSGXlYL9zvTBcmmJDCpY6zvbLROdJ1WAFbsu106QwVn1osWF7l1WIvFG4rC4U5HUWFh0aYzGns7PTSfCWkq7sxUi4sysWS3T39FDqoTdAct3d3a5IE4pGir/w+S+2NLc++MCDL77w4uAhVe+9uwMuddttX57e0ACf4fnKTsJ1BLckfBci8BWdnd2JuMMd4PA/iuH9ro7ueNxxXVQ5aPAtt9za0dnxo5/84K/PPl1WVr5jx3bQqV//2jc54EgkxN4jAlpSQGtr1qz+3e/+q6uzc/v27f/3/35fZfMAmHPdSCR644033nTjTa5YrbwAAFPtyXiIgIpm4L4HcKpVEUt2j/HSlenTZ3zlzq/dd+/Pv/qVrwBW6Onp2bd3LyjLf/iH26PRYuW+4ig1nTxW9AE+dbtNCR8qsviopYtYRMreCTfJMbfKpRUPiUyaOPkrX/naPT//2de/8fVx4+vg8e3evWfGzBlf/cpXQbXAlojFYmBZvvrqmhkNMzh1ucWuvvqqzq72Rx55+Oabv1RfX19eUbF7VyPosJu/dMunP3UDqEDnFGb4GrmvlgKOBO+Jbha6v+loVONfKdBgjyfGjh1/5x1fueeee+666zv1E+rhrO3ZvXfqtKl33vnVmppqyU5pctyRj0D9tLlipeJMbljLZA6daEeIWQppTL9IT6ZzrsQpYcqK8msuifuF6UhAXoFIScQSxu8uHznsW9icJSUlpWVlDQ0NsDlhl06bNg3emjVrZlVVpeMmQCxOnDjhW9/61n333XvHnXcADuvu6gbpNGnSpC988XMlPI+y+NZbb+3q6vrXf/3/Jk35c0V5+fYd28Ha/+Y3v1k3ti6RiNXV1c6ff96zz/71yLEjY8bUXn3lNXPmzobXlTFqWWC+AqTetes9mMivxfD7AxYtXvTd73y3qqoKJG9aXAJj1UZzAPWwpPnEuk8RTzbBsq1+f04Mq7rSRCI+tm7s1772tZ/effc/fe+f6uvHOy7du2fPxIkTv/HNb44eNTJTK6TTEnDX6IpKhkv/aohMHteXxlh4+Epid5imbXW0tYNSXrFypYxU7Nm9++ixoz+++8eVgwbBYsLCfvnW2+rrJxROugtmfqVmeihLP5UUaS4+utf1K1v+kvjXCqPiCru7uz2R6JGmBGFSQTNeW8gRFi+lWb9+3bvvvrvg/PPHjR0nGkLwjwCy+dvf/qeltfmaaz4mc8ZBgYGptGLF8oULL5xQP8EVDUoPHfpg1cpV27Zt6+joALE+f/4CsNIiRRE7ZG/ZvOXll19ectmSurq6eIwTtb/zzta33n7z3HnnjhtfD18Usu3t23eseWXNuefOmzx5ChXNTQ8cPLh82bKdO99r7+gcMWL4Beef39Awk2fkGAe3hJWY7dixY93atT2xOBEBVBGDEDiSslDInjVr1uw5c0TDCZbs7GHpup+qFveU171bFtyLL/nB3x+NpWRupLml/J/xd1Jjye/SFCVDeaSXACKFJXpt/Wu7GhujkShYwAvOXzBy5EjehVB35yCY4AySi6SFrnCWr2bZ74V5+oYgzrBHaXtHB8H+zqwZby23maU/wwSDG8+HoSZsDepn7bq1YMfDZphQP/H8888/66yzZKfnzs6u1S+v3r//fTD3Z8yYIWomMLy4afPbsET733/fwnj4yJHcJ3DObMlaLl26rJeZ5H5M3r1I8xHbFmjGmGjeRDBO8j72vqoouWNxxl9zbJikQVVzYsmEJzIRGT8F69e/tnPXrpBNJk6cNG/euXV1Y4WXm6pUU+WvybhXs80kfcVywGbzV0S7SUTYW7ixJLoillVaWtzVnojHACfb/QawRHaeyEzi/PdOWWXYod0dHZ1YNCiU/dwZJcbDByKusbFx9erVAIlmzpwJv5rAB4gmsHmeX/o/FrEuXbIEtiuxeCX18uUrZs6YAVhKxgB45S+lW7dsWbPmZXgX/mp8/QTAPePqxholvX///mXLl+1ubOzgIm7EwoUXnD29IRwOycaJ779/YP369W+/uRFE1yeu/wTs7oQTV8YJ5pQQq1atam5u5l/E+0MJ7h7RGhZ+HlNXt3gxJyV3uPMydZUJ5S3WcT9GjeQJAtxgWQTwZTyeiHX1cAlgijdVW2zcbx4sKVuQzmGWG3bneztfXbd2186d8HAnTJx47ry5YNJTV7abSyrFPU0eQOUD0qjT58FCxiECIq6ktBj2DCjTgqrf0g4sBicCLIpXX331vXd3cNoYoaiJSAcXG5UNqam+8MJFNdU1hUXTwGQ3ZFB5JXDUutth7cMW1QxeoK2O7PGlnvAmK7qlaBiXloe6etodJyY+LNqLCgXPu2XptHSRy2L39MSkVGQqmohFASCKx+PidSJ8/bgoXCRq6R2kWfjAAuvq6nbiTqQ4ClcCS1oKLMviMW2en04dLnKZC98C14RXzJ+HxDDMLiD4eJzWAlDYk4jFItFiAGrcLWEClCKHQ0YeZVjXFmyqOlKORJdoDl/AxAQrn5BULvzk1CVf0E3og2g0qgGWqeY1Tot8NBDOosB8Liss+V1x8gNWZowMVLe3t8G/gGvF4jPt+1FNM5JKYo0RwTLfWnI33Iy/4tTDwt0OWFAY0/b2Dn2YGTJxE5ZRN2e9oG5VLI8hGOJEcrWJRDq443BXVye8L/OuRHmgBGF8B4JyBkPT1DfIBDX4VfCAwJEok9WpYnYOTjv46TPxAaMcOMMzcAAcwMRAucKOkulDyv5mLL9V7RVvpQOsDJNXsXYmpbw5BdiyuV8NcKfQYcWS/URsZpYWX8gxE9anHZLxXpjXcl7yyXjXCeFQWUlJZ0ci1g1S1+43Je8KpivMQjx1kyRKK4sc1t3R2Uk0wBJsN8TkhchktSLRo0nKKKzqoZSWDYeL4KdYIm6kH+xGkF1mf8rvhddFGww4OxhOsbTXZfaCrNoBSdjV1QWnG96FD4vGmupcS3ar9nawimWnQqL8pjIKQ0R2qeiFz+uOZBcdprIvYlwyJ2RTTpJSOo+RKsDup8UXxItYBteoxa0UAFhxDbB8ZsyJ5x2cMMBKTgsW6bycFqurCxa7pKQU1tA8/f6LqTIV2sD+zncGA1IK27IYAFZnR6EBLF1sp+yQUCgszlFSfyHNnk1hDxRitxw+UQYyiiZwVzts3ZBFLTVH5uKju2J+mUKlJxyjUBEurQh3xzriiR5ZEaNQg8iGsGypsJEfoat4s+hrQQj21S6Zkhbdz1++gJESTEgyiOjjIjq9yJxSmcupif4VvNCbW5E1qB5AkrOLUpu3jFAiSU1PBjeTnSi+/BL1IqWukGDyXmAhXJzkf/KDKp2IIUkHhVkVjUZA4yuAhWSZJfNHjPKLhvlBFUnTnanhJZl2qR4Bj/+ykB2SzdHk8hrAoeadpP+M0xv1hvNyOWyE1wHr+CXH0qVlJTAZ48GSlavCfc2QqrU3C0tz4glm9rDfza4go6zusohOCvY8csyLKkiSJ8PyJ//WklXu8MQJqBqx50mS/wpnd+n5vZLUt0qYly4qEEmkMQL6DgxHDbD4izorS/wtZr72oCw/x0/KJsmIbFInb/IkZQ0UExVySmFiFXiV7irp3+2jZsiG81BvFkXSs8Kq9g0MOCqNPfknYSvMje/WuAsqjFfq9GtYijLCD4/tlg0Ku7inq70TSbJmXY0Nu8j4e1LI0z3uZi+YqAp6JLL36WBfUobM9uVlrR5BpfFgpVABpVjz8uIyh1WRMGG92Rj251xzmOVSwzkhPyy/VO+jAW9AKXYooyREUgCWqnjCKWj+NMeDEErpzmnwFuEDG2tW67wB551Stj8VAAssKAOwCite6Ds1KQ4/lvwWNkUuBQawsIUAYDkx1NHq8gAeTwiR+VSuV/YsSmWIyrhiQllQjrmQMbm5gpQsWRJIYZMBL9zmHBRpQkuMDaWKSq7FTIMAqdsETIX/d4X8cGWbOaozRZD2dFIhGiS/g9gZQhSr2le4lRCSveSllCA8lEupo3e4IiszwUHmxQlT3DlYCDWvjkbAR5J8vnBqYgtT/mmtWUgy3b2KiOO+lXPgZKmR9gNOi8BLe5rKZUeuQ9XuFBhV541otIczhcEzzyH3lHz/MqRdHhI3CLOYOT49Kz1vBnwkBR/y+GrV25rvQLhPTuAuyX5FRpHj8vskAl/6M3Gxorw1bSglBBfURq7PJBDxKNg6NKXpc7ZFwNknT3RaAdLJcljtB+Ytl1wrvjdSwWW2r+51k2RdN98DkJX7MmWYYeLlWKpESayeHfZme/KbNvfEfH+l07yEUJEZY1y/SnyjsohYHyyVU5jf4jnU+Kbj0VXMVHqWoe/14yqsjTbEjAT2VIn+ADOlPMJEJDKR3lRzG1Zkf5G//LA0J/xKKPlJY1OE6Jdw5kPS0ybPD9GWqvSNeZNEHvBjUnIPFIs+ZmnVuCY+NwCK1l/I5vdjuW5yKJDloHPqLzzIklYSmSACKzRCGsx0V/Ukz6nPqFa4SgOOwsKHeoExU6WDPvLQ5F6EYL/Y2KZS5djCdaR8yNopzvTzob7WlaqJpNlWipZPZtZq7hATIRbZC/IjMjdA5jMIDlINxTRjlnJIqM/xAAAqUklEQVRAGNcCQYqpiyCvPs34wzSGUoRf/FKE+8bUszO2mt/N6DnetKxPckImUd74XdNS9KjMW0FLI4UB9hSG/IAfIaQa9yiPiE8vmU86CYyYW2PalcTFKE9N01Q+mKQFvDzlezI5WDiJroMwtWEEqsO6e5IPOPvq6PLLwVJQXmJJCymWarkhKWdZ475Y49gkomOJrnLlk7NEe0bde0DtBoyUG4KIsDDpTfD0noNlAhbMs3Il6lImR9qusDw93LccLJzpX5RtPRXEZz6zR+4QJKhLZDNlDscJUmuD89ir6JTmYOkHjY33RFtHktIY9rDFG8zj/vRgiSRh2F7MopaUPfoU66ItheGRFna+pcOeSjNdyAShqwZGMptQ5JgwpI1znN5TJYXM2q91Ujy7phtpxoCsuY4hZ0q5fjpTtsYxbEAQgib5YdpVr1g7WKos7T90lbLsKarE95mBmaE/LcAgZBV19SRiwSW8KwonhA33fcqGR0ZgDeiqZpdkktYFhSzuZefiSm9V22cAyUCu+B8oJeqRnyh5LJISBOwhuruhDvUp+9izRxk20Af7ZIzOS1AWkSRZ8KEYnJqPw7xv4JIIGZNLE0d4ZeRYEf5hkzarftOoHfsiYjoNRU3bf0iwVr1eem8m0xx7BI7CfUJVdRHTniJzSZzNuM9p0/f+K/KH2rDn51GRXK4cFERGujLU78XCJ+q1ynQFLJlh5dJS1ZqJJcckTZgYZ3KD5d6/Op5lAswaxFjIZIdgE4ixmG/bi6ep4n/62SOqspE0LkUp2dw5MEHO1fCCPEbmMpMF5fsj/67I8Xx7fTT5XUEENCTzrjmq2DuzPB3LI5rDzBdPz2cmOb46f1ecbH5KhX9IIRnmdVI2kbL+IvcRid9meXjlKu+PwAR3g/LCKstQ1QIoOceQyRFiPh3LY9nCa0mQ18/OGJ9JyjtdnWe06dNVvv/zGmklfTCdtzkbeaM3K60WEKb9R/Gq+V2YhFhYdyzCIuwu9ghHu/3uylCRD9mSBLEU7Ou3ZxChAx12859uw2EJi+YiXHBNSoRngrBMVZZ+p6BOqyiwCCE/xbYUn5S5PDTBtLZlzDZMCsoDJ7JppLtaWNhYbmttNVCJhUXluTbzMEPJ/VD9/02t8NaJy9gLEvus1RTLTwExdcC8a/qzn43Pk2mXPUOe0kXY+y6N4DBKmoCf8cWn/hjqxXRTnmqRC686vyoHWqYqrtMnnrwVNkEpP3eRhzpPo3KixhcmaxWxpPoXHiZTdUO0GsUnupmZvlmlPEzWvEeCi1Mdjf6SepN3LIwDYxhzQ8E1FvLJnjiN0ZEvJ4O7Z6kfv2UuCD2tbmy1MdQRwD76SGKS7nlaABugaJD+L2XeDhZuHku1f4OthWh/ICzhoee0M9K5B99pCZOO+nahyghlzGR2Yp9uS4s0ahXH/F1BU1JPdLJi0gnJp2bK6wOWfEEfUvVyP9I7IJncrGypXaj/adsY8hgYTQhJ2feeFTsQsS5pjaXmBvmbpaL+Tb3PKpOZ5xPyB44LkbCBmZhQhu6ZqAAadvWml5jpPSqWWfcbZLy1q5XsFVcNniWPKpGIReVSyaCPeBPjPvggctm0JDlUnA7HfK4azHoz9XHyuzhN2rE00IeTw1X5ixMTykQyTCcT2+xC6FneVz/CqTBBpBh3iUiIQswSKpIk5a+LYNkpse3MOcwAgrP9inXGFEtC9WrbE40ST/64aRpvLJpCitaQLvFTvXImE+ES6Oc9gdPOGkqrq8AqGavfAxpUZS8g2alLmrQif1TkKIkkIYJIfyFSLDhbxKbm9jUR/iyZ6ycyEGR6IxFS1PSNz3bUsEmVSBIOaf4P7KNN6XOQJc0XZS7lXTZH9UKuwgaMT2xWJ7EhZLBSuATEwxeJH6LJKtIJKQOC/rwcEJxx3VTMGFsD51LB2l1CdQ4o0RnJloi4F16bXSKPHMmxq4WnhBRkQzNGBGm7cMBb8rAQnU9mZ9DDki2J8qz1aDQSZiHmV2Yyz8VLCM4nGyOXIz4/i1Rld6Vp0LS6pHwyH3sF8SwvkCTppo1Lhhe9Y1xSUpIkRRnu96zCbBV/KG/2qRMRiLqqh59mi1jEJmVlpZ70YT7CLXwKbQfcd9XpYztO2hL4VIEFUxgnc4cjRZFwOORbCpTi8+hX8dtnE6if5kV9qBmOlcyyZLwaxibUQqLCF/VbPpBQ4UqZ8zpT3gg5FA6VkBJJverH5vkktbAzttXlQHivfK4+EW8jtl0UIbxtLh6oHVrQK5b5oOuEacr5+GANOXeP67hnaFPEwjxBmocY2SGL0yIRE9xTJlNKDpb0dnG9AE+iq4OGwkRntBh8Q5EiKiFZbj8lIRol2cepv+bPX0CzWFEsUxJunzRMGltPEnxkWXKN9bQokkwAbkwSy5DkOlKcqfFfNi6lbOnkKBOHUPqqopwETumfYZkAa25YlhGlmcCdjK6wRMzhVffESkvTYTlz9tNvKq+v7gu40MFFhrJTJOSeCcqjCECnBlKWwILsA9tZdl2ORc5mReDs9kz6FbLlp+d5OvKhXcD53UvGQ+pnF6Mqo0m84qoV5IwSjuPShAwb9RsntYzWgTayqUNlZlVXT7y9rQ1bFmUkSQTkE7/ALLskYX5D/tTdH0uygdkZprlMbrtt2+XlZT09se6ublkWgFJwFkPByLQaqjSP82BFouXlJW1tHfFYHBdeDtYZvvIcLgwqL0XIcjnfjWtTojzfiNrJ0lXUFSFLlsg4PU48lhAVViLaw7lGmQ5t9D/7/in5xhQCoYx8iTrun5pwnwaVJEkDM+FLipjfd431kpoqWdKb4qTJE6M6eQIloyt8Spc0Rcr752nouFJm4mV1+Bx1KpOHCFp8kepOkmGxollKA805iEYzzsT3pX0zx1hm1ZL6uLPNhCZTqVEfCb7vXgx0U3kFNA1wmzVMf77pxGDp+phmag+Ak2eC0+5ac2T0noNB89urKQeH9XYvGY0EmnoSOfucbPqhqz5FxqcIqrL+SyCR3G6iB46s5QEp2tnVdfjwMYuEqFeCL1Mm6Kl0HuI+ol+UA5BnA0/J7VQKF6NwiFBUFC6OFne0dRw73kSInWyq4UC/51g7zd5FK8oHRSPRpqbWzo4uwad9hoDFMwM9U5tYkVA4HCrWKbhEp4Qjm4SIz6koOH5lE2VMLGwTpNxXgkDXYh7A6u97yC8JoNePZfToZLA2FeVCHh4xVYRGVKpbUkjesJbj07cBT86JjE/RdChKasYkX05aQB+UOfk4jydg8cm5mRXHDzsls/Imprut+uodfUARIzwQkoN5PRKzOlmQ4W8cILGqjxJWucOyYkwzpPYbi7NghBX95HlWhWsJ1zTPVSdUSRhlZ4qGeChT3WUKAiB5ObmyRb1xSqN3bTl79MO+8iCKsz/HFH8nluKcFRxQYUiSLsrsA7EPmGIdzMimG4wUdGXIOxSjHFLVg4yxQg21GY8rRb5quIxe8IKaus4Z4HCK54mqRFGRh2e7yc09RMjWoV4uKdaWJnzMGaAbZNrcxydtKbK8ULGRmHmVmfvIHFII+U6XrmIFAtK8QTLMKlmXs1Q33MnPv2/Ly7JuLd3S5JTBTf/EME51XQ2UYuj1q/HACrIkChHVZUpCCMKS+SNO/xBgijG5r13RVkby2Oo+OUp9qeofnVPga96gHIH5xrKZwUw4E9hAXhd+RZfDl4ZgU5+L/U5F5QnDOY89UybA6REIJ7cPsK4SptQ02xDFDhkyN4KR5TTLdn6cYl4unSpTwwWahOWjOVMkB9gnn1GmOFKBTFxZOlgQYYtVN4z/zMYuTT/ppEAfADsV4IPljVECC+kMMNVOxedZ8LjPnJ3fb1FCmfAlOSJcwX+I0xWCH1Zrz4siVpOlZF4TXoRzlu9r7qd0PIQzfaGS7X5vKPUFXi2sRX9v5reiQihUfwbuFwPxQ4iuUGYCflx48JT5/qfNFp+14cvkoJq3uWCmznQfOCkmELU0G6b8jx3s02AEIxjByGhQK24enLt1r885iZHmO07Sbpj5bfFMl5DNdzJXAPjVjfm8P67HsEr1w5piXhBC9xZBl621mSaKDlDLSewUdEY51XDhzQf7Ool53qzkzh+4YFeTaFJGPxEm/GYXVm/tYAQjGMEoAIlJmZLzgD8s0c7XR7DR258n9XPUpdy5qUAUpR5Nrgcj+ehEnFTtofpDmU5euXxmXviaBXG3k8LhSSXJwRqeALpivoOAdYMW7FEgp1b2FMbUfdw7vK+IpA9FRNZvUoxsHARGghGMYAQjFbVoZSljhUx1f2AZVCxT/+cBF5mTqI1xnMKNmV1d62aLyWQhzF8xomtJVFhC9iXzKMRMu27ZIkv4qDJ6p5hu4oS9Et20ZqzB8B5NhmXRzPsm54ZkXLxgSfMAqSkdclL6OHlurMKKbjKvH5F51F5bCpzUizAYwQhGMILBh0i60lxclGZooa3dQ5aNTWWWUgwCTxFMeJcSKiu2WUpDQP157HvFfEAybCqRLbEV1bnsHleKqgJg4hqGI4PIxt6qBRI2zcJ02b5uC23bIbiy6zrwP6SCi4odN1nzBSP9SfkdGHwxZUOURMI1nYeS6BJxgFxzYJQkXy/W7VcsC1bVTiQSqvOMzFDsx0riPqNEJLj8RGGGsnQo70VIg2ccjGAEIxjp8Ml4IFS/vzTIEYvHu1s7AKNw1CIZA1XXP45UotFIJFIss6NSWv7JH2SfOOS1tNMUtaqLrGoiIjt1M509y2EWI55cZ7LJIPEat4mG9YK5VfqzqO48yUKhEKCBlpbWI0cOx+PxqqrBVVVVvDSeys7KFqVMd83wp78EI6ntoMHKgK5aW9uOHDli23Z1dXVZWbnjJMQaEgPLdOcfHGCsNFjCkltkIkX5g3FT0/Fjx44NHTqstLTMdKsuUHAlqUSYtKRULYv8FXaFHTzmYAQjGMFIHkRqUMobumsFmZwBQgjes2f3n//836AMRJ9C0fid8p5pImpnXX755RdeeCEhFlOEedzbIdvuyuTXdN+GahSuk7GoDpRINEawaX0om4dzLlRXOAEcx+nq6izifZnCKqELU8mJirVyAq3f2Nj4yitrdu58d/fuvT093UOHDl24cOFFF11UUVFpiPJluw/JlMhQgAiSnViKL4M//e7u7pUrV65Zs3rfvn3hcFFtbe1CPi4ErUqpF+EyXowAXaXZMSKpXTT+lOaJOBr48OEPfvOb/4K9evvtX21oaBB+rIJNcpNZj7wrGu+Wp/hTuE2DSRAiDEYwghGMzNa1kuqWR+iZmivCa7NdSl2APuTAB+/v2LZl2PDR48ePIxj+yNHdHhROQshkliCpdwnxlIbPNSIFtjKCFVMk/zARf8/dUZYlfU7EpaCQmGVZ77yz5YknnrjsssvmzJmrkvQFk5RXh4jxwYMH/+M/Hti4ceOsWTMXLjy/tbXttfXr7733HtBnn/vcF0pKyhzHxYqeHvvaGwSwwDx7QXQkqG8TCeeZZ55+5JGH68bULrn00s7OztUvv/z6htdjsfjFF19iWVy3Sg9lgKtynDFsHLQMtjE3UXbvbnzkkUeeffapwYOrE4mYXG0JWQozao1lBJMxjaaULwuG7bpu8JiDEYxgBCNlWIrWk3MeUN0PxzOkuQSlY8eOu/OOO13XKYoUvbxq1Y9/8qOLFn/ks5/5TDgSYS6KFkdDIZuDLd5PUcY+SCjEw3CO4xDZTZq/EnKchFQktg1gyTaKGbQ4z5HiDWG53wvM5OKiCOX9ERlcB64qMVBRUbilpenVV9fMmzc7Go04Do3HE37GW9BfoPhffPHFbdu23XzzzUuWXFZaWgpX3nvVVffe9/NHH31kzpx58+ef77rUI9/CSKXMB1FCDaalIxHWIhwOv/Hahj8+8sjZZ0+74/Y7Ro0aCSB78eLFP/rhj37zm/8cM6ZuypSpwu+ifJNBiDCrB0sX3MJx6OhoX716xRNPPgE7c9rZ01uaW5lq44Ckm7YA+SV0ij4TdHkuZrwFLzeDMMyZBjQNwQhGMIKRwbomKgNXYR3liNCdK2XKVNgOlwyuAjMVsFTFoEGAlkpKSodUVxeFIyBwnYS7+e3NLS2tU86eVlFRAX/d09MDEKekpGTcuHFS9ra0Nm/dshV+HTp0WMjGR44eXr9+/fvv7weYNbZu3Lxzzy0tLUsk4oRjL7urq/uVNat27dzR1dM9csSoOXPmjhp9VmdHx+qVy9auX+s4PW9s3NDT01171pgZM+eoQKHqT8XnPnnylNtu+8fzzz+/vLwMEF4oFJ46deqMhhmvrFl+/Phxc1MpKiRAVwpaY5kXxyxCerp7lq9Y3tLW8onrP1Fbe1Y8FoNVmjJl8sc+fs3dd9+9YcN6QN6AlAP1mg/GEueMwYaHdfv5z38+bfpUMFFeeWXNihWrkC6/kGx0BbktdMKiJKLDivyXu5sxsQNAHYxgBCMYmWWnYkDARLSy1oQIqo+3hCKOw3vpuI7rJBL8Ywy5DksQB4tPvPPOtj89/qdvf+vb58yZTSkD5PTrX/9q9Oizbr/jjkhRBMTvprc3Pf74n2+55dZRo0Zu2771F7/8RWPjrrNGj+7s6vrLU38579z5t9xyS01NDaX06NEjv/v971auWD58WE1RJLL0+aUvvLD0y1++ra5uzHN/e3bL5i0dHZ2vrVu/+a3NH7noolkz5yCZqaUqDhnAqblz5wlPDJ+wZWEAAEeOHnn3vXcrK4cOHz5chSmZopkIRgoSwLrBox2yd+7e+fbmtyZNmnTWWWeJELHoUE4JvDJy1Kj161+7/PKrqqqqhEfQCxt5XbmC4R0yVbnhuk4kEv3sZ/+fj156cWVlxZo1LzNYVSKSGrHmhCs8uMLrSETzRMzTsLjHV/LSSc+cTVlQRRiMYAQjGJ7Lhie268Il4eznDaepZEAkUnzKNjiy8RjAKszfJ4gSQDRMkKgTl3EGh1G1o1pam97b9V7DjIZwuGjP7l2bN2/s6mxpb2kuGToqFk+8+frGaDRSWzuqqen4/ffd17i78Y7b75gzZy7o7Oeefe7h3z5cM6T6Szd9Cabw6B8ffu6Zp2747GevuvKacDi8adPbP7vnZ79/+OGvfw3+75svvvTSw7///bXXfWLBggsqygeJHHjeO1EwpmIq2SYoDym6rtva3g7T7OrqBogGyOyKK64aO3YsaDis22r7MIDrJ9r+344GsGDttsjxpuNHjx2ZPGVStLjYdbHLLM5g5qLKyiE1NdX79+9PJLpFQZwrvBqcH0sABCraWRrqLBKsqre2jM2YMWPOnNnhkN3R1c587HJwoohyH6ICC7J6fay8sLqaNwNjyw4cWMEIRjCCkSo1lWUt4AkvDNLEnrIhGjY070xQT8mgoek8o4IF8DcjR44YO65u65ZNly1ZErZDe3bvdhJOc0vLjnd3jBp5VltrN/zQMOPs8rLSp55+et36dddfe90VV1xBiIWJ9YXPfXHrpq3PPvfslVdd2drasnzZS6PPGv13f3fD4ME1juMsueyKvfv2PfDAfZdddvnVV1+9Y8e7th0aPnzU1KnTXJcmEg4vZJMkqRjrCAsrKors3t141/fuam9r6WhvhY9cd90nbvi7z1RUVDiuq7AhMdlbQQlUEgKQ3OKWZXV0dbS1tZaVldmhECBXDrGR5TI3Eo2WlZX29HR1dABEcGElRc2oqCtjmHn0H8HCmrPGywelNzjKOU1EJhNfT48mXcXdsI+6t3Agt2igzdGzZqijpsUWQrYVBImDEYxgBMMvNbnQ5P+Bfx3ETGf/JMWgKKkMJSg2NYOKB4vnxqPq6pr68fXr1q7t7Gx3nMS77767aNHiAwcPbN269aLFFzfufq+9vWXKlCnUpa9v2BAOhSzbXr58eYKnqJNoUTTmxABa7du///AHB5uam+vGjn1t/XqX+9RQJBJpampKJNydO3e2tbXJaBR8RXd3t/RDyYBUMs8WfIBGosVzZ81p7Ww/8sEHBw/uf3PjxrLS8iVLLhsypIZKHlPmU3/JPRCDodLUKJN1nYoHXxR6Ep5IRCybFzFIagbmdVcSHhgC+ECiVxJgLH3UNFeDwCZUWgIq8d3zqPpsH1x4t6CeM++SQ7AlvN3C0Y1tHNA0BCMYwQhGsgeLMV/bM4lVcIrparoze81dETM5Wjz9CaRtNFo8fvyEZcuX792/DyDRgUMHP/3pT23ZsnXPvj0t7c3rNqyzQ6HJU6a4DB3+4DDY7u9se2fvvr3UARFtgYBu72gfX19PqXO86XhPrOfQ4cOPP/E4k6TuiMXjsfHjx4VCtuPEdd4UVc12GPWccXqCFub8TIMrh9z2D3dQxlpbm98/sOeJJ5+4/4H7u3tin//c50OhsCgrV52qFV1DgLCMs0VsCJfSsrLyykGVzU3NiYRTVATr43JeDU48C/i2O1IUKSkpxZzKn2l/KHd0+roasQBgmaOGVKsBhE2OI07DUt6GLCzIr2WA7OYuHcYSEnJsZYssyGAEIxjBCIZPbjKJVpAkDBT6MVOnWWzihhbn59RCV8bjhH4lkyZNBmW8du2ro0ePCoXDU6eeDZbtH/74yJatm3bu2jFy1MjKwVWCdp1UVFZ++oYbxo0b5yRcqUXCYpRXlG3bvs0OhS++5JKFFyzkcxFy27btcNgC3IYtwgl3FOWhUVeGm4eIDFziOi6xLNl4B16prKysGVoF+u2tt95asXz5NVdfM3z4SJcmgga12bCAoHXlrJiVlYOrq2v27d/b1d1ZUTGIFzogSghqa2sH1FUzdFhRUUQ8AqJwg+oAE6TkZF9er+OUz42qW6WjAsb5go5XVIjwlHaqbByM4YzaNPBgBSMYwQhGsirFHreopAzU6CXZhYWVI8uPvFQTG5G8xQuLzjqrdsSIES+vWcPzserqxo2rj8ddJ+H85emnDh448IlP/l1RuCgWS4wYMWrv3j3RSGTK5Mk93TH4xlAodPDgoY6O9iF8VEeKigCzTZo40XV5ojq829raeujQobKyCouEpKeJc24hQ3joj7OgPXv2LFu2rKGh4ZyZM6mqfKeY2WWlJfCl8XgPYy5XCoAKVQKMur2AB8tgbsqZ82kiEa+tHTN16rTnlz63f//+ESNGym0C4BV+3bf//euvu760tEyQmRHdXSeFhjzIzDEOIGw6eYpDRrHiwxCBWBWAE00MDG1WgW1GypDOdqRYe5fFCaO2HQpa5QQjGMEIhk9iimwZWQ3E6RmIpOFJMbhNF2eRcKNCdF7nWkKI4PJmkUh0+vQZa9e/euzYkdu+fHtxtKSmZvi4+vpVy18cUl09ffp02wpTG11wwcIVK15avnz5nNlzo9FiUMyHDx++9957QEl/4xvfmD69YejQoS+vfvmyy64YNXIUQlYsFnvsscfWrVv71a9+rbq6uqKioqSktLm5GfFGuRYgMJ8HhUv8Q4cO/vd///Hll1d975+/N4GHHXm2fiwRX/vqWsBeV115TXl5hevSpPwtL1gTDOkRpBIwwTNdvPgjr7y65qmnnqqrHVs1ZAh84PixY8//z/PhcGjevHklxcWOeAQKOuh2hMmbJBhewwC5UQmx4DwRziAmUtV4y2ciW23yLMPC69wkktz5bViY2JZtIUtm48sUTtvFAU1DMIIRjGCkuCuYoTa0EA8SMmM8+6xo7ZzgvzkUcdeU45iMcukJI7Y9Y8bMIjsUjkQmTpgUTzg11dVTJ09+cekz9ePrq6uqE4kEfNV55513ySWXrFq1wiJcQ8fj8ZUrV23ZsunWW28D8DRo0KBrrrn2v/7rP3/4gx9ddNHFZWWlb7755rJlL1188SW1tWPgS+vqxtbW1i5d+kJl5eCRI0cOHz6ipmao6tws6AWmTz/7hhtuePTRR374g/8zddrUMWPq4MXtO7atXrl6Qv2Ea675OOAzuI7f9RVsg9QtodxRDB7ZrFmzPnH9px977NG7f3r3ueeeC4j2tdc2vP32m//v5z43YcLkhOMa3yHWIDVgcs+xsPF4DLY0YH1YYyeR2LRpc1Nz0wsvLt2x/V0wBgYPHjJ79tzBg6sKLXdN5geoputIdXo21olN3QBgBSMYwQiGX2gyinCIWlQQYGGRtYSU3z9DNohwFLHS0rKpU6fW1AyTvivu7eAdo3lqSV3duDlzz4uWFNfXTwDlEQ4XTZ129jmzz1206COlZeWCqJIVF0f//u+/PGTIkDVrXmls3A0q3LZDN95400c/eqlMl77yyqsjkeJnnnn6ySefBPs+Hk989KOXAWYqLS1NJJzBgwfDB5599q/wLgCyT33qhuHDR1KakOFCmExJSfEnP3l9dU3V3557duXKFSUl6y3LchLO+ecvvPba6/jEHDfgu8rPj8V9lvAsrr/++mi0GGDuE088Ae9Eo9Ebb7z5yiuvLCoq0r7ApOhqgK6yryqi1HnnnXfWrVsrDpgbj/XUjh69Y9u2d7fvhHdra8dOnDgZTgct1KxxzsfhgJFlMezVmuKWfT3B0w1GMIIRDP8gjBCeCo4c7BCLFQ8qPtp87ODBw4JRkmDtkNAdmrkqjcViHR3tEVC8xcWyz6DgGpQUObSlpVnklQ+SgUXXdVpbWouLS+DDTJUicgKqnp7uo0ePHj58BD48bNjwoUOHSrgG71oWcRynqen4Bx980NPTM2RI1fDhI0QytfSLYFBRra2t+/btg5mMHz8BtJFCVwIx8pommziuAzM5cvhIa1sT4LwhVTVVVdVlZWW8ZTWlXtePgYMCMv2Gk1xgwDFuJBIaOXIE3Nfx4808fjRwEFAupuq6LV6AH+LxWHNz86FDh0IhG7A1QFtAV7xntvgMHmDedibm65aXlw8bNuzggYPtHd2AqgvNfSXnyXhZaztsbBGH5a1mOG+D6AIK62xZ4fLyinA4XFAgVZ6rkIVHDa8psYudDpfwYlEifZ0cYHW+7wTCNBjBCEYwkkxqUVbPqwiZAz+FBxUdO37s4KHDyCIoGWCZYj3LsrFs+so01QEHWCpdXuQ7I8A3vIyPUYJt0UjDFf1AZJSESOJv0CacmpLjKiS41yUvF5HUSrZti1438nt5VpDOspLVavIVJhOwlDISGSGinknk3VsCH/CL8O+ivNEPFV2MqUFXBQWwRo0a0dIy8ABLduBGSbFCmTZkXuEvUJeJagOMBj5jKDPAKqiaBSY9PZg3nOHb0ZL7XLbHMeeC71jB9IYKDWDBsAkeNaKmNFxCO5kEWOZ92wqKCIMRjGAEI1WbKp4rDlgUSmIZNZPo48ErBrliEC1RZIWeyM3AinSU2+ImdMDhkcO713F7l7+FHKyEMgdVMroE/zJT2c+pC6nsdeY4jlQxVHdA0znCHtoTEM31uKVVITnBIune5ZQQ3P/CIzHCa8VvQfR6kQBx4NWWdP4wk/4sp8S5MBQ/mSpsHBhWb77CHGUTV5CNUUU6ajNefymK3TxWJDwAkzMpQFkWt+CYOplMWCQcm/J6EV6XJ44UkqCfCFo3bGlO38KLYsstK2YvzrBmwrJpwIMVjGAEIxipQp8qYCQlOkAQijWtoOgow5WsaJuvKR0s0X6MKtCDdcMcoR6EC4oJUEN1Gx1xdZU6z5SzA/sBk7LXmUqaZcz04ZEuMaQxFdFIy6Aro4eMeMdMMzli5dMyrjokvGWKoXTAPQS8al/QpErPoJ68VsNYKmSiNJggT+qfwv0k95WEWZzWQnCkcdcabzMofJNyz+D+1e96obgrUm5YpoE4Q8jkD7JCazdt9i1GXh8/8SIRLkOL6dOhrI0CbC3AsHFpY0FzIlvlwC8BR0MwghGMYKTJfap63kh/BBMeJC3aKZWd5aTdKhm6eTo8QiY6pP1dGgYIN5FoBUsEEhPwgepOK8TE+JhsByw/qNuw6ColxDwyU2SCgPpPdDxFITPJwCRVkuyYJlukJZG7eyxXzM9CjwawVkuCSFnFqXUw8UUGXcHpquo8NVpgpx9woyQAyrDi7E7KYWeyFZ1cTqRRQf/5UHS/JlfgbeOLNMUZ3gIXkCWjiViFp8rsdHFELITMu/Ikqc1cUKLCMHQhxY5nQsnEJiSoawhGMIIRjFSFJZlCVYaIrXxARCUuY1/+MkO61b/ftvZzkjKtlLGPAUEm7aSUmqkmNaktgTOwgJuv03+iAoFIBSglSvO97ifp0r+aixSeX8C4r5jOyGG6dZ3Xwkj/1g9aNOV388QNmvI9l/7GqSx1wwjWedGmhckSVyYbERReix6cvG5M2zVqdzLfeqOCo3PXPm1hD8iURh7qRLIfPBJiIwgRBiMYwQhGssoiMiqFqXRNYZnQ6lLtYBFkpETF7pJaEmb3MLBMn2Fpipj10W2R/gPr21/3JxbId2YqAkqZl2DmykeiuIcUuf5AIgZWWMunlsIVQUseGZSLRXkhBZEVb5SJft6s4K2bPm/mgbMDZEt0xlLtHw4dbZlEFoxgBCMYwfCZpjLZlieu818d3ik5HC4ilnIGuTzmxhsUBrxRp0FpMVFEiCiseSiEGbYtuwgWHxMdSsI6+Y0GD0ADTdiqliHApeodAABWiK8hgjWMhMOaYyIYp2YA6g8TKyTqW7kNQE2rHFF0GgtoGoIRjGAEI3lwaIVFNR9PEmFFJSFm0a5YXHi1mI7BBOO0uARUzo1oQIctUhQJJxJOIu74qDtxsrMD/69HWEx39eP/SCImqjg8WChkRyJF3V09okA12Len1BQTrqvicJETp25MIFym+lMFACsYwQhGMDKqecFCJYr9eGTFYmD8I5yxBivIsjjlWktm32MZ5HKxLDZAmbBUsPi+VUvanJLqgNsKnGBNFDwQFrj7Tvmic18rL4KJ8Z6lyEWijJfHuEF6BCHCYAQjGMHI5BTgspP7q7h+oiiWcEDPg6IiPGHYr9BUOMYS8SqS/C/yZZJk/JWqeKR3Baz/Vn7GFW/5f/VTLGBkyta8C5IsM6EpzA3G+5H81dlmkvFeWNpMzJWpnnDGe8k4E5Q0Ycx8tALq4rqbn3R0UcmU75tYPvciP+P6ZoK9FHo1n9yraq6Akyefz6qmXzCfVU25QqZdoemvvMRw1Uo5fRuQ3mbih2wo572kzCRl8ih5V2T86ow7vNdVzXYv2DcTlOn50uRn1+tZS9nS/nsRexSDAWCp6KwgB3ap3LoULLLuffFAlgYjGMEIRppnRNDyyHb4mKluhFjUiWOtDWXuOvYXyWNTHa97Q/sgWwqC0z+oKnSN2XQ7afOz3z+RFBTz17DpyzHfTLyfk2eSDmy867MsM8k0+UywNHkmyVdIiehl/VXrM4ZTVtXrs630W9oMkpYuy6oi36/ZfEE5VlV9S/IVUlY160xMx/D0DZO+qti3jCn3kvyrLrBLWQ/pw/ItY8pXZ59JXveSfmss567I+NVZHlMvM8lyL94uYVlPdsZt0OtMMt4L/yoqll9Q4BEi6fHEewCw7CDlLRjBCEYwUodgayQSZQkz1jIS1h8oxCgjRkmO1zCUC6OkIA+W6d90gJWCkLJ9Xba3cgGsnPeSDgVYH2eC0vwj2WaS6Qo4TeFmVsa57yXjt/fpXtKv0KdVzbgrel3VXm8N5eRvZ3ncGsriw0K9bciUC6Ls94Xz2Pwokw8rfSb53BrKeV95nrWM8xe9KbHiSeHtE3kDU0yJBl+AtYIcrGAEIxjBSB2uT8AST7Ozgmw2EoxgBGNABq8jUCkDHFD5ARb3YCXDunSomPGVbGg9m5mV58A5Daz0X3NPLNsrKPsHslkq+VwK5zExnPfkcabngvqysDngPOrLTPK83z6tao5F7tOqnsC9nMCTzW3x5L+q+R+BHL9mO2vZNkyfvrpPOwT1RUrk+XX5rGo+W7FPYiHT1zHMLJZiuupQTMqdSwcX8+IGfZNgfb2XPM9a/iKlTzNBJyfBcB8nkE1Q6GZ/Jh8Go74LtBOQxjk8WH060adXGovkdqpjZlgUtLET0gsnoH1OeFVP4FtOuY7ru/g1yMrEJgVxC9PxRpt5O5Ul57TlQEss7X5YchYayj/WnjzpHHR7LIvPM2WGOe4l21en30ueM8Fpk88xE5Zp8vmsasphzubIRZmOPcs5w2yvoDxcwSi7QxtleS4oJx9iNnd9tlXNdkiyrWrKg+jrqqIsV8h/VXMHTXLMJM+zhnpzsKMsRxvnQTmZYybZ5AbKOZNe7yXH0csoN3LPJON5zDUT0blPFAjpUJTq4SGSO1xPxvKcC63yT0CCob5IsGxpTDnkRu7ni3NK+9w7pE8SDPdRL/QqKHTeW4Zdw/Jb1WynPk+9cAJyA/VdbqTL0nzkBmW6xZPeu6zveqFXaYzzlsYphyvbTPKRG/moEtSbXshxrPokjVUnK4JMg3KNuXQTJ7uPKVi5veO9+s4zVjj3m7v9BCYfRANO4Qr357cHD+5M3zYnKWpOek5YkzH7slsUd4CXj6XfYf/L5cZJ3m/f/xyrNkCYsZPurZdbbpyRj5K3oWaemv/fxGWR+wmejqcpLmjpVpW+rHuQEv9/AAAA//80YRwPc3Wa+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(os.path.join('../Data/','Figs', 'nmf.png'), width=700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_topics(model, feature_names, no_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic %d:\" % (topic_idx))\n",
    "        print (\" \".join([feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = fetch_20newsgroups(shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\n",
    "documents = dataset.data\n",
    "\n",
    "no_features = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The creation of the bag of words matrix is very easy in Scikit Learn — all the heavy lifting is done by the feature extraction functionality provided for text datasets. A tf-idf transformer is applied to the bag of words matrix that NMF must process with the TfidfVectorizer. LDA on the other hand, being a probabilistic graphical model (i.e. dealing with probabilities) only requires raw counts, so a CountVectorizer is used. Stop words are removed and the number of terms included in the bag of words matrix is restricted to the top 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NMF is able to use tf-idf\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, max_features=no_features, stop_words='english')\n",
    "tfidf = tfidf_vectorizer.fit_transform(documents)\n",
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LDA can only use raw term counts for LDA because it is a probabilistic graphical model\n",
    "tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=no_features, stop_words='english')\n",
    "tf = tf_vectorizer.fit_transform(documents)\n",
    "tf_feature_names = tf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned previously the algorithms are not able to automatically determine the number of topics and this value must be set when running the algorithm. Comprehensive documentation on available parameters is available for both NMF and LDA. Initialising the W and H matrices in NMF with ‘nndsvd’ rather than random initialisation improves the time it takes for NMF to converge. LDA can also be set to run in either batch or online mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_topics = 20\n",
    "\n",
    "# Run NMF\n",
    "nmf = NMF(n_components=no_topics, random_state=1, alpha=.1, l1_ratio=.5, init='nndsvd').fit(tfidf)\n",
    "\n",
    "# Run LDA\n",
    "lda = LatentDirichletAllocation(n_components=no_topics, max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying and Evaluating Topics\n",
    "\n",
    "The structure of the resulting matrices returned by both NMF and LDA is the same and the Scikit Learn interface to access the returned matrices is also the same. This is great and allows for a common Python method that is able to display the top words in a topic. Topics are not labeled by the algorithm — a numeric index is assigned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The derived topics from NMF and LDA are displayed below. From the NMF derived topics, Topic 0 and 8 don’t seem to be about anything in particular but the other topics can be interpreted based upon there top words. LDA for the 20 Newsgroups dataset produces 2 topics with noisy data (i.e., Topic 4 and 7) and also some topics that are hard to interpret (i.e., Topic 3 and Topic 9). I’d say the NMF was able to find more meaningful topics in the 20 Newsgroups dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:\n",
      "people time right did good said say make way government\n",
      "Topic 1:\n",
      "window problem using server application screen display motif manager running\n",
      "Topic 2:\n",
      "god jesus bible christ faith believe christian christians sin church\n",
      "Topic 3:\n",
      "game team year games season players play hockey win league\n",
      "Topic 4:\n",
      "new 00 sale 10 price offer shipping condition 20 15\n",
      "Topic 5:\n",
      "thanks mail advance hi looking info help information address appreciated\n",
      "Topic 6:\n",
      "windows file files dos program version ftp ms directory running\n",
      "Topic 7:\n",
      "edu soon cs university ftp internet article email pub david\n",
      "Topic 8:\n",
      "key chip clipper encryption keys escrow government public algorithm nsa\n",
      "Topic 9:\n",
      "drive scsi drives hard disk ide floppy controller cd mac\n",
      "Topic 10:\n",
      "just ll thought tell oh little fine work wanted mean\n",
      "Topic 11:\n",
      "does know anybody mean work say doesn help exist program\n",
      "Topic 12:\n",
      "card video monitor cards drivers bus vga driver color memory\n",
      "Topic 13:\n",
      "like sounds looks look bike sound lot things really thing\n",
      "Topic 14:\n",
      "don know want let need doesn little sure sorry things\n",
      "Topic 15:\n",
      "car cars engine speed good bike driver road insurance fast\n",
      "Topic 16:\n",
      "ve got seen heard tried good recently times try couple\n",
      "Topic 17:\n",
      "use used using work available want software need image data\n",
      "Topic 18:\n",
      "think don lot try makes really pretty wasn bit david\n",
      "Topic 19:\n",
      "com list dave internet article sun hp email ibm phone\n"
     ]
    }
   ],
   "source": [
    "no_top_words = 10\n",
    "display_topics(nmf, tfidf_feature_names, no_top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:\n",
      "people gun state control right guns crime states law police\n",
      "Topic 1:\n",
      "time question book years did like don space answer just\n",
      "Topic 2:\n",
      "mr line rules science stephanopoulos title current define int yes\n",
      "Topic 3:\n",
      "key chip keys clipper encryption number des algorithm use bit\n",
      "Topic 4:\n",
      "edu com cs vs w7 cx mail uk 17 send\n",
      "Topic 5:\n",
      "use does window problem way used point different case value\n",
      "Topic 6:\n",
      "windows thanks know help db does dos problem like using\n",
      "Topic 7:\n",
      "bike water effect road design media dod paper like turn\n",
      "Topic 8:\n",
      "don just like think know people good ve going say\n",
      "Topic 9:\n",
      "car new price good power used air sale offer ground\n",
      "Topic 10:\n",
      "file available program edu ftp information files use image version\n",
      "Topic 11:\n",
      "ax max b8f g9v a86 145 pl 1d9 0t 34u\n",
      "Topic 12:\n",
      "government law privacy security legal encryption court fbi technology information\n",
      "Topic 13:\n",
      "card bit memory output video color data mode monitor 16\n",
      "Topic 14:\n",
      "drive scsi disk mac hard apple drives controller software port\n",
      "Topic 15:\n",
      "god jesus people believe christian bible say does life church\n",
      "Topic 16:\n",
      "year game team games season play hockey players league player\n",
      "Topic 17:\n",
      "10 00 15 25 20 11 12 14 16 13\n",
      "Topic 18:\n",
      "armenian israel armenians war people jews turkish israeli said women\n",
      "Topic 19:\n",
      "president people new said health year university school day work\n"
     ]
    }
   ],
   "source": [
    "display_topics(lda, tf_feature_names, no_top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 - Tips to improve results of topic modeling\n",
    "\n",
    "The results of topic models are completely dependent on the features (terms) present in the corpus. The corpus is represented as document term matrix, which in general is very sparse in nature. Reducing the dimensionality of the matrix can improve the results of topic modelling. Based on my practical experience, there are few approaches which do the trick.  \n",
    "\n",
    "1. Frequency Filter – Arrange every term according to its frequency. Terms with higher frequencies are more likely to appear in the results as compared ones with low frequency. The low frequency terms are essentially weak features of the corpus, hence it is a good practice to get rid of all those weak features. An exploratory analysis of terms and their frequency can help to decide what frequency value should be considered as the threshold.  \n",
    "\n",
    "2. Part of Speech Tag Filter – POS tag filter is more about the context of the features than frequencies of features. Topic Modelling tries to map out the recurring patterns of terms into topics. However, every term might not be equally important contextually. For example, POS tag IN contain terms such as – “within”, “upon”, “except”. “CD” contains – “one”,”two”, “hundred” etc. “MD” contains “may”, “must” etc. These terms are the supporting words of a language and can be removed by studying their post tags.  \n",
    "\n",
    "3. Batch Wise LDA –In order to retrieve most important topic terms, a corpus can be divided into batches of fixed sizes. Running LDA multiple times on these batches will provide different results, however, the best topic terms will be the intersection of all batches.  \n",
    "\n",
    "#### 2.5 - Topic Modelling for Feature Selection\n",
    "\n",
    "Sometimes LDA can also be used as feature selection technique. Take an example of text classification problem where the training data contain category wise documents. If LDA is running on sets of category wise documents. Followed by removing common topic terms across the results of different categories will give the best features for a category.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Example: Clustering and Topic Modeling applied to film synopses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this guide, I will explain how to cluster a set of documents using Python. My motivating example is to identify the latent structures within the synopses of the top 100 films of all time (per an IMDB list). \n",
    "\n",
    "It will cover:\n",
    "\n",
    "<ul>\n",
    "<li> tokenizing and stemming each synopsis\n",
    "<li> transforming the corpus into vector space using [tf-idf](http://en.wikipedia.org/wiki/Tf%E2%80%93idf)\n",
    "<li> calculating cosine distance between each document as a measure of similarity\n",
    "<li> clustering the documents using the [k-means algorithm](http://en.wikipedia.org/wiki/K-means_clustering)\n",
    "<li> using [multidimensional scaling](http://en.wikipedia.org/wiki/Multidimensional_scaling) to reduce dimensionality within the corpus\n",
    "<li> plotting the clustering output using [matplotlib](http://matplotlib.org/) and [mpld3](http://mpld3.github.io/)\n",
    "<li> conducting a hierarchical clustering on the corpus using [Ward clustering](http://en.wikipedia.org/wiki/Ward%27s_method)\n",
    "<li> plotting a Ward dendrogram\n",
    "<li> topic modeling using [Latent Dirichlet Allocation (LDA)](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 - Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "<li>[Stopwords, stemming, and tokenization](#Stopwords,-stemming,-and-tokenizing)\n",
    "<li>[Tf-idf and document similarity](#Tf-idf-and-document-similarity)\n",
    "<li>[K-means clustering](#K-means-clustering)\n",
    "<li>[Multidimensional scaling](#Multidimensional-scaling)\n",
    "<li>[Visualizing document clusters](#Visualizing-document-clusters)\n",
    "<li>[Hierarchical document clustering](#Hierarchical-document-clustering)\n",
    "<li>[Latent Dirichlet Allocation (LDA)](#Latent-Dirichlet-Allocation)\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But first, I import everything I am going to need up front"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 - import three lists: titles, links and wikipedia synopses  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 titles\n",
      "['The Godfather', 'The Shawshank Redemption', \"Schindler's List\", 'Raging Bull', 'Casablanca']\n"
     ]
    }
   ],
   "source": [
    "titles = open(os.path.join(datapath, 'title_list.txt')).read().split('\\n')\n",
    "#ensures that only the first 100 are read in\n",
    "titles = titles[:100]\n",
    "print(str(len(titles)) + ' titles')\n",
    "\n",
    "print(titles[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 links\n",
      "['http://www.imdb.com/title/tt0068646/', 'http://www.imdb.com/title/tt0111161/', 'http://www.imdb.com/title/tt0108052/', 'http://www.imdb.com/title/tt0081398/', 'http://www.imdb.com/title/tt0034583/']\n"
     ]
    }
   ],
   "source": [
    "links = open(os.path.join(datapath, 'link_list_imdb.txt')).read().split('\\n')\n",
    "links = links[:100]\n",
    "print(str(len(links)) + ' links')\n",
    "\n",
    "print(links[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 synopses\n",
      "  On the day of his only daughter's wedding, Vito Corleone hears requests in his role as the Godfather, the Don of a New York crime family. Vito's youngest son, Michael, in a Marine Corps uniform, introduces his girlfriend, Kay Adams, to his family at the sprawling reception. Vito's godson Johnny Fontane, a popular singer, pleads for help in securing a coveted movie role, so Vito dispatches his consigliere, Tom Hagen, to Los Angeles to influence the abrasive studio head, Jack Woltz. Woltz is unmoved until the morning he wakes up in bed with the severed head of his prized stallion.  On the day of his only daughter's wedd\n"
     ]
    }
   ],
   "source": [
    "synopses_wiki = open(os.path.join(datapath, 'synopses_list_wiki.txt'), encoding=\"utf8\").read().split('\\n BREAKS HERE')\n",
    "synopses_wiki = synopses_wiki[:100]\n",
    "\n",
    "synopses_clean_wiki = []\n",
    "for text in synopses_wiki:\n",
    "    text = BeautifulSoup(text, 'html.parser').getText()\n",
    "    #strips html formatting and converts to unicode\n",
    "    synopses_clean_wiki.append(text)\n",
    "synopses_wiki = synopses_clean_wiki\n",
    "\n",
    "print(str(len(synopses_wiki)) + ' synopses')\n",
    "\n",
    "print(synopses_wiki[0][0:627])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 synopses\n",
      "\n",
      "\n",
      "In late summer 1945, guests are gathered for the wedding reception of Don Vito Corleone's daughter Connie (Talia Shire) and Carlo Rizzi (Gianni Russo). Vito (Marlon Brando), the head of the Corleone Mafia family, is known to friends and associates as \"Godfather.\" He and Tom Hagen (Robert Duvall), the Corleone family lawyer, are hearing requests for favors because, according to Italian tradition, \"no Sicilian can refuse a request on his daughter's wedding day.\" One of the men who asks the Don for a favor is Amerigo Bonasera, a successful mortician and acquaintance of the Don, whose daughter was brutally beaten by two y\n"
     ]
    }
   ],
   "source": [
    "synopses_imdb = open(os.path.join(datapath,'synopses_list_imdb.txt'), encoding=\"utf8\").read().split('\\n BREAKS HERE')\n",
    "synopses_imdb = synopses_imdb[:100]\n",
    "synopses_clean_imdb = []\n",
    "\n",
    "for text in synopses_imdb:\n",
    "    text = BeautifulSoup(text, 'html.parser').getText()\n",
    "    #strips html formatting and converts to unicode\n",
    "    synopses_clean_imdb.append(text)\n",
    "\n",
    "synopses_imdb = synopses_clean_imdb\n",
    "\n",
    "print(str(len(synopses_imdb)) + ' synopses')\n",
    "\n",
    "print(synopses_imdb[0][0:627])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'  On the day of his only daughter\\'s wedding, Vito Corleone hears requests in his role as the Godfather, the Don of a New York crime family. Vito\\'s youngest son, Michael, in a Marine Corps uniform, introduces his girlfriend, Kay Adams, to his family at the sprawling reception. Vito\\'s godson Johnny Fontane, a popular singer, pleads for help in securing a coveted movie role, so Vito dispatches his consigliere, Tom Hagen, to Los Angeles to influence the abrasive studio head, Jack Woltz. Woltz is unmoved until the morning he wakes up in bed with the severed head of his prized stallion.  On the day of his only daughter\\'s wedding,   Vito Corleone  Vito Corleone   hears requests in his role as the Godfather, the   Don  Don   of a New York crime family. Vito\\'s youngest son,   Michael  Michael  , in a   Marine Corps  Marine Corps   uniform, introduces his girlfriend,   Kay Adams  Kay Adams  , to his family at the sprawling reception. Vito\\'s godson   Johnny Fontane  Johnny Fontane  , a popular singer, pleads for help in securing a coveted movie role, so Vito dispatches his   consigliere  consigliere  ,   Tom Hagen  Tom Hagen  , to Los Angeles to influence the abrasive studio head,   Jack Woltz  Jack Woltz  . Woltz is unmoved until the morning he wakes up in bed with the severed head of his prized   stallion  stallion  .  \\n  Shortly before Christmas 1945, drug baron Virgil \"The Turk\" Sollozzo, backed by the Corleones\\' rivals, the Tattaglias, asks Vito for investment in the emerging drug trade and protection through his political connections. Vito disapproves of drug dealers, so he sends his enforcer, Luca Brasi, to spy on them. The family then receives two fish wrapped in Brasi\\'s vest, imparting that he \"sleeps with the fishes\". An assassination attempt by Sollozzo\\'s men lands Vito in the hospital, so his eldest son, Sonny, takes command. Sollozzo kidnaps Hagen to pressure Sonny to accept his deal. Michael thwarts a second assassination attempt on his father at the hospital; his jaw is broken by Police Captain McCluskey, who is also Sollozzo\\'s bodyguard. Sonny retaliates for the attacks on his father by having Tattaglia\\'s son killed. Michael comes up with a plan to hit Sollozzo and McCluskey: on the pretext of settling the dispute, Michael accepts their offer to meet in a Bronx restaurant and, retrieving a planted handgun, murders them.  Shortly before Christmas 1945, drug baron   Virgil \"The Turk\" Sollozzo  Virgil \"The Turk\" Sollozzo  , backed by the Corleones\\' rivals, the Tattaglias, asks Vito for investment in the emerging drug trade and protection through his political connections. Vito disapproves of drug dealers, so he sends his enforcer,   Luca Brasi  Luca Brasi  , to spy on them. The family then receives two fish wrapped in Brasi\\'s vest, imparting that he \"sleeps with the fishes\". An assassination attempt by Sollozzo\\'s men lands Vito in the hospital, so his eldest son,   Sonny  Sonny  , takes command. Sollozzo kidnaps Hagen to pressure Sonny to accept his deal. Michael thwarts a second assassination attempt on his father at the hospital; his jaw is broken by Police Captain McCluskey, who is also Sollozzo\\'s bodyguard. Sonny retaliates for the attacks on his father by having Tattaglia\\'s son killed. Michael comes up with a plan to hit Sollozzo and McCluskey: on the pretext of settling the dispute, Michael accepts their offer to meet in a Bronx restaurant and, retrieving a planted handgun, murders them.  \\n  Despite a clampdown from the authorities, the Five Families erupt in open warfare and the brothers fear for their safety. Michael takes refuge in Sicily, and Fredo Corleone is sheltered by associate Moe Greene in Las Vegas. Sonny attacks his brother-in-law Carlo on the street for abusing his sister Connie and threatens to kill him if he abuses her again. When it happens again, Sonny speeds for her home but assassins ambush him at a highway toll booth and riddle him with submachine gun fire. Michael\\'s time abroad has led to marriage to Apollonia Vitelli. Their euphoria is shattered when a car bomb intended for him takes her life.  Despite a clampdown from the authorities, the   Five Families  Five Families   erupt in open warfare and the brothers fear for their safety. Michael takes refuge in Sicily, and   Fredo Corleone  Fredo Corleone   is sheltered by associate   Moe Greene  Moe Greene   in   Las Vegas  Las Vegas  . Sonny attacks his brother-in-law   Carlo  Carlo   on the street for abusing his sister Connie and threatens to kill him if he abuses her again. When it happens again, Sonny speeds for her home but assassins ambush him at a highway toll booth and riddle him with submachine gun fire. Michael\\'s time abroad has led to marriage to Apollonia Vitelli. Their euphoria is shattered when a car bomb intended for him takes her life.  \\n  Devastated by Sonny\\'s death, Vito decides to end the feuds. Realising that the Tattaglias were under orders of the now dominant Don Emilio Barzini, he promises, before the heads of the Five Families, to withdraw his opposition to their heroin business and forgo revenge for his son\\'s murder. His safety guaranteed, Michael returns home to a father saddened by his involvement in the family business and marries Kay the next year.  Devastated by Sonny\\'s death, Vito decides to end the feuds. Realising that the Tattaglias were under orders of the now dominant Don   Emilio Barzini  Emilio Barzini  , he promises, before the heads of the Five Families, to withdraw his opposition to their heroin business and forgo revenge for his son\\'s murder. His safety guaranteed, Michael returns home to a father saddened by his involvement in the family business and marries Kay the next year.  \\n  With his father at the end of his career and his surviving brother too weak, Michael takes the reins of the family, promising Kay that he will make the business legitimate within five years. To that end, he insists Hagen relocate to Las Vegas and relinquish his role to Vito because Tom is not a \"wartime consigliere\"; the older man agrees Tom should \"have no part in what will happen\" in the coming battles with rival families. When Michael travels to Las Vegas to buy out Greene\\'s stake in the family\\'s casinos, Greene derides the Corleones as a fading power. To add injury to insult, Michael sees Fredo falling under Greene\\'s sway.  With his father at the end of his career and his surviving brother too weak, Michael takes the reins of the family, promising Kay that he will make the business legitimate within five years. To that end, he insists Hagen relocate to Las Vegas and relinquish his role to Vito because Tom is not a \"wartime consigliere\"; the older man agrees Tom should \"have no part in what will happen\" in the coming battles with rival families. When Michael travels to Las Vegas to buy out Greene\\'s stake in the family\\'s casinos, Greene derides the Corleones as a fading power. To add injury to insult, Michael sees Fredo falling under Greene\\'s sway.  \\n  Vito collapses and dies in his garden while playing with Michael\\'s son, Anthony. At the funeral, Salvatore Tessio arranges a meeting between Michael and Don Barzini, signalling his treachery as Vito had warned. The meeting is set for the same day as the christening of Connie\\'s son, to whom Michael will stand as godfather. As the christening proceeds, Corleone assassins, acting on Michael\\'s orders, murder the other New York dons and Moe Greene. Tessio is told that Michael is aware of his betrayal and taken off to his death. After Carlo is questioned by Michael on his involvement in setting up Sonny\\'s murder and confesses he was contacted by Barzini, Peter Clemenza kills him with a wire garrote. Michael is confronted by Connie, who accuses him of having her husband killed. He denies killing Carlo when questioned by Kay, an answer she accepts. As Kay watches warily, Michael receives his capos, who address him as the new Don Corleone.  Vito collapses and dies in his garden while playing with Michael\\'s son,   Anthony  Anthony  . At the funeral,   Salvatore Tessio  Salvatore Tessio   arranges a meeting between Michael and Don Barzini, signalling his treachery as Vito had warned. The meeting is set for the same day as the christening of Connie\\'s son, to whom Michael will stand as godfather. As the christening proceeds, Corleone assassins, acting on Michael\\'s orders, murder the other New York dons and Moe Greene. Tessio is told that Michael is aware of his betrayal and taken off to his death. After Carlo is questioned by Michael on his involvement in setting up Sonny\\'s murder and confesses he was contacted by Barzini,   Peter Clemenza  Peter Clemenza   kills him with a wire   garrote  garrote  . Michael is confronted by Connie, who accuses him of having her husband killed. He denies killing Carlo when questioned by Kay, an answer she accepts. As Kay watches warily, Michael receives his   capos  capos  , who address him as the new Don Corleone.  \\n \\n\\nIn late summer 1945, guests are gathered for the wedding reception of Don Vito Corleone\\'s daughter Connie (Talia Shire) and Carlo Rizzi (Gianni Russo). Vito (Marlon Brando), the head of the Corleone Mafia family, is known to friends and associates as \"Godfather.\" He and Tom Hagen (Robert Duvall), the Corleone family lawyer, are hearing requests for favors because, according to Italian tradition, \"no Sicilian can refuse a request on his daughter\\'s wedding day.\" One of the men who asks the Don for a favor is Amerigo Bonasera, a successful mortician and acquaintance of the Don, whose daughter was brutally beaten by two young men because she refused their advances; the men received minimal punishment. The Don is disappointed in Bonasera, who\\'d avoided most contact with the Don due to Corleone\\'s nefarious business dealings. The Don\\'s wife is godmother to Bonasera\\'s shamed daughter, a relationship the Don uses to extract new loyalty from the undertaker. The Don agrees to have his men punish the young men responsible.Meanwhile, the Don\\'s youngest son Michael (Al Pacino), a decorated Marine hero returning from World War II service, arrives at the wedding and tells his girlfriend Kay Adams (Diane Keaton) anecdotes about his family, informing her about his father\\'s criminal life; he reassures her that he is different from his family and doesn\\'t plan to join them in their criminal dealings. The wedding scene serves as critical exposition for the remainder of the film, as Michael introduces the main characters to Kay. Fredo (John Cazale), Michael\\'s next older brother, is a bit dim-witted and quite drunk by the time he finds Michael at the party. Sonny (James Caan), the Don\\'s eldest child and next in line to become Don upon his father\\'s retirement, is married but he is a hot-tempered philanderer who sneaks into a bedroom to have sex with one of Connie\\'s bridesmaids, Lucy Mancini (Jeannie Linero). Tom Hagen is not related to the family by blood but is considered one of the Don\\'s sons because he was homeless when he befriended Sonny in the Little Italy neighborhood of Manhattan and the Don took him in. Now a talented attorney, Tom is being groomed for the important position of consigliere (counselor) to the Don, despite his non-Sicilian heritage.Also among the guests at the celebration is the famous singer Johnny Fontane (Al Martino), Corleone\\'s godson, who has come from Hollywood to petition Vito\\'s help in landing a movie role that will revitalize his flagging career. Jack Woltz (John Marley), the head of the studio, denies Fontane the part (a character much like Johnny himself), which will make him an even bigger star, but Don Corleone explains to Johnny: \"I\\'m gonna make him an offer he can\\'t refuse.\" The Don also receives congratulatory salutations from Luca Brasi, a terrifying enforcer in the criminal underworld, and fills a request from the baker who made Connie\\'s wedding cake who wishes for his nephew Enzo to become an American citizen.After the wedding, Hagen is dispatched to Los Angeles to meet with Woltz, but Woltz angrily tells him that he will never cast Fontane in the role. Woltz holds a grudge because Fontane seduced and \"ruined\" a starlet who Woltz had been grooming for stardom and with whom he had a sexual relationship. Woltz is persuaded to give Johnny the role, however, when he wakes up early the next morning and feels something wet in his bed. He pulls back the sheets and finds himself in a pool of blood; he screams in horror when he discovers the severed head of his prized $600,000 stud horse, Khartoum, in the bed with him. (A deleted scene from the film implies that Luca Brasi (Lenny Montana), Vito\\'s top \"button man\" or hitman, is responsible.)Upon Hagen\\'s return, the family meets with Virgil \"The Turk\" Sollozzo (Al Lettieri), who is being backed by the rival Tattaglia family. He asks Don Corleone for financing as well as political and legal protection for importing and distributing heroin. Despite the huge profit to be made, Vito Corleone refuses, explaining that his political influence would be jeopardized by a move into the narcotics trade. The Don\\'s eldest son, Sonny, who had earlier urged the family to enter the narcotics trade, breaks ranks during the meeting and questions Sollozzo\\'s assurances as to the Corleone Family\\'s investment being guaranteed by the Tattaglia Family. His father, angry at Sonny\\'s dissension in a non-family member\\'s presence, privately rebukes him later. Don Corleone then dispatches Luca Brasi to infiltrate Sollozzo\\'s organization and report back with information. During the meeting, while Brasi is bent over to allow Bruno Tattaglia to light his cigarette, he is stabbed in the hand by Sollozzo, and is subsequently garroted by an assassin.Soon after his meeting with Sollozzo, Don Corleone is gunned down in an assassination attempt just outside his office, and it is not immediately known whether he has survived. Fredo Corleone had been assigned driving and protection duty for his father when Paulie Gatto, the Don\\'s usual bodyguard, had called in sick. Fredo proves to be ineffectual, fumbling with his gun and unable to shoot back. When Sonny hears about the Don being shot and Paulie\\'s absence, he orders Clemenza (Richard S. Castellano) to find Paulie and bring him to the Don\\'s house.Sollozzo abducts Tom Hagen and persuades him to offer Sonny the deal previously offered to his father. Enraged, Sonny refuses to consider it and issues an ultimatum to the Tattaglias: turn over Sollozzo or face a lengthy, bloody and costly (for both sides) gang war. They refuse, and instead send Sonny \"a Sicilian message,\" in the form of two fresh fish wrapped in Luca Brasi\\'s bullet-proof vest, to tell the Corleones that Luca Brasi \"sleeps with the fishes.\"Clemenza later takes Paulie and one of the family\\'s hitmen, Rocco Lampone, for a drive into Manhattan. Sonny wants to \"go to the mattresses\" -- set up beds in apartments for Corleone button men to operate out of in the event that the crime war breaks out. On their way back from Manhattan, Clemenza has Paulie stop the car in a remote area so he can urinate. Rocco shoots Paulie dead; he and Clemenza leave Paulie and the car behind.Michael, whom the other Mafia families consider a \"civilian\" uninvolved in mob business, visits his father at a small private hospital. He is shocked to find that no one is guarding him. Realizing that his father is again being set up to be killed, he calls Sonny for help, moves his father to another room, and goes outside to watch the entrance. Michael enlists help from Enzo the baker (Gabriele Torrei), who has come to the hospital to pay his respects. Together, they bluff away Sollozzo\\'s men as they drive by. Police cars soon appear bringing the corrupt Captain McCluskey (Sterling Hayden), who viciously punches Michael in the cheek and breaks his jaw when Michael insinuates that Sollozzo paid McCluskey to set up his father. Just then, Hagen arrives with \"private detectives\" licensed to carry guns to protect Don Corleone, and he takes the injured Michael home. Sonny responds by having Bruno Tattaglia (Tony Giorgio), the eldest son and underboss of Don Phillip Tattaglia (Victor Rendina), killed (off-camera).Following the attempt on the Don\\'s life at the hospital, Sollozzo requests a meeting with the Corleones, which Captain McCluskey will attend as Sollozzo\\'s bodyguard. When Michael volunteers to kill both men during the meeting, Sonny and the other senior Family members are amused; however, Michael convinces them that he is serious and that killing Sollozzo and McCluskey is in the family\\'s interest: \"It\\'s not personal. It\\'s strictly business.\" Because Michael is considered a civilian, he won\\'t be regarded as a suspicious ambassador for the Corleones. Although police officers are usually off limits for hits, Michael argues that since McCluskey is corrupt and has illegal dealings with Sollozzo, he is fair game. Michael also implies that newspaper reporters that the Corleones have on their payroll would delight in publishing stories about a corrupt police captain.Michael meets with Clemenza, one of his father\\'s caporegimes (captains), who prepares a small pistol for him, covering the trigger and grip with tape to prevent any fingerprint evidence. He instructs Michael about the proper way to perform the assassination and tells him to leave the gun behind. He also tells Michael that the family were all very proud of Michael for becoming a war hero during his service in the Marines. Clemenza shows great confidence that Michael can perform the job and tells him it will all go smoothly. The plan is to have the Corleone\\'s informers find out the location of the meeting and plant the revolver before Michael, Sollozzo and McCluskey arrive.Before the meeting in a small Italian restaurant, McCluskey frisks Michael for weapons and finds him clean. Michael excuses himself to go to the bathroom, where he retrieves the planted revolver. Returning to the table, he fatally shoots Sollozzo, then McCluskey. Michael is sent to hide in Sicily while the Corleone family prepares for all-out warfare with the Five Families (who are united against the Corleones) as well as a general clampdown on the mob by the police and government authorities. When the don returns home from the hospital, he is distraught to learn that it was Michael who killed Sollozzo and McCluskey.Meanwhile, Connie and Carlo\\'s marriage is disintegrating. They argue publicly over Carlo\\'s suspected infidelity and his possessive behavior toward Connie. By Italian tradition, nobody, not even a high-ranking Mafia don, can intervene in a married couple\\'s personal disputes, even if they involve infidelity, money, or domestic abuse. One day, Sonny sees a bruise on Connie\\'s face and she tells him that Carlo hit her after she asked him if he was having an affair. Sonny tracks down and severely beats up Carlo Rizzi in the middle of a crowded street for brutalizing the pregnant Connie, and threatens to kill Carlo if he ever abuses Connie again. An angry Carlo responds by plotting with Tattaglia and Don Emilio Barzini (Richard Conte), the Corleones\\' chief rivals, to have Sonny killed.Later, Carlo has one of his mistresses phone his house, knowing that Connie will answer. The woman asks Connie to tell Carlo not to meet her tonight. The very pregnant and distraught Connie assaults Carlo; he takes advantage of the altercation to beat Connie in order to lure Sonny out in the open and away from the Corleone compound. When Connie phones the compound to tell Sonny that Carlo has beaten her again, the furious Sonny drives off (alone and unescorted) to fulfill his threat against Carlo. On the way to Connie and Carlo\\'s house, Sonny is ambushed at a toll booth on the Long Island Causeway and violently shot to death by several carloads of hitmen wielding Thompson sub-machine guns.Tom Hagen relays the news of Sonny\\'s massacre to the Don, who calls in the favor from Bonasera to personally handle the embalming of Sonny\\'s body. Rather than seek revenge for Sonny\\'s killing, Don Corleone meets with the heads of the Five Families to negotiate a cease-fire. Not only is the conflict draining all their assets and threatening their survival, but ending it is the only way that Michael can return home safely. Reversing his previous decision, Vito agrees that the Corleone family will provide political protection for Tattaglia\\'s traffic in heroin, as long as it is controlled and not sold to children. At the meeting, Don Corleone deduces that Don Barzini, not Tattaglia, was ultimately behind the start of the mob war and Sonny\\'s death.In Sicily, Michael patiently waits out his exile, protected by Don Tommasino (Corrado Gaipa), an old family friend. Michael aimlessly wanders the countryside, accompanied by his ever-present bodyguards, Calo (Franco Citti) and Fabrizio (Angelo Infanti). In a small village, Michael meets and falls in love with Apollonia Vitelli (Simonetta Stefanelli), the beautiful young daughter of a bar owner. They court and marry in the traditional Sicilian fashion, but soon Michael\\'s presence becomes known to Corleone enemies. As the couple is about to be moved to a safer location, Apollonia is killed as a result of a rigged car (originally intended for Michael), exploding on ignition; Michael, who watched the car blow up, spots Fabrizio hurriedly leaving the grounds seconds before the explosion, implicating him in the assassination plot. (In a deleted scene, Fabrizio is found years later and killed.)With his safety guaranteed, Michael returns home. More than a year later, in 1950, he reunites with his former girlfriend Kay after a total of four years of separation -- three in Italy and one in America. He tells her he wants them to be married. Although Kay is hurt that he waited so long to contact her, she accepts his proposal. With Don Vito semi-retired, Sonny dead, and middle brother Fredo considered incapable of running the family business, Michael is now in charge; he promises Kay he will make the family business completely legitimate within five years.Two years later, Clemenza and Salvatore Tessio (Abe Vigoda), complain that they are being pushed around by the Barzini Family and ask permission to strike back, but Michael denies the request. He plans to move the family operations to Nevada and after that, Clemenza and Tessio may break away to form their own families. Michael further promises Connie\\'s husband, Carlo, that he will be his right hand man in Nevada (Carlo had grown up there), unaware of his part in Sonny\\'s assassination. Tom Hagen has been removed as consigliere and is now merely the family\\'s lawyer, with Vito serving as consigliere. Privately, Hagen inquires about his change in status, and also questions Michael about a new regime of \"soldiers\" secretly being built under Rocco Lampone (Tom Rosqui). Don Vito explains to Hagen that Michael is acting on his advice.Another year or so later, Michael travels to Las Vegas and meets with Moe Greene (Alex Rocco), a rich and shrewd casino boss looking to expand his business dealings. After the Don\\'s attempted assassination, Fredo had been sent to Las Vegas to learn about the casino business from Greene. Michael arrogantly offers to buy out Greene but is rudely rebuffed. Greene believes the Corleones are weak and that he can secure a better deal from Barzini. As Moe and Michael heatedly negotiate, Fredo sides with Moe. Afterward, Michael warns Fredo to never again \"take sides with anyone against the family.\"Michael returns home. In a private moment, Vito explains his expectation that the Family\\'s enemies will attempt to murder Michael by using a trusted associate to arrange a meeting as a pretext for assassination. Vito also reveals that he had never really intended a life of crime for Michael, hoping that his youngest son would hold legitimate power as a senator or governor. Some months later, Vito collapses and dies while playing with his young grandson Anthony (Anthony Gounaris) in his tomato garden. At the burial, Tessio conveys a proposal for a meeting with Barzini, which identifies Tessio as the traitor that Vito was expecting.Michael arranges for a series of murders to occur simultaneously while he is standing godfather to Connie\\'s and Carlo\\'s newborn son at the church:Don Stracci (Don Costello) is gunned down along with his bodyguard in a hotel elevator by a shotgun-wielding Clemenza.Moe Greene is killed while having a massage, shot through the eye by an unidentified assassin.Don Cuneo (Rudy Bond) is trapped in a revolving door at the St. Regis Hotel and shot dead by soldier Willi Cicci (Joe Spinell).Don Tattaglia is assassinated in his bed, along with a prostitute, by Rocco Lampone and an unknown associate.Don Barzini is killed on the steps of his office building along with his bodyguard and driver, shot by Al Neri (Richard Bright), disguised in his old police uniform.After the baptism, Tessio believes he and Hagen are on their way to the meeting between Michael and Barzini that he has arranged. Instead, he is surrounded by Willi Cicci and other button men as Hagen steps away. Realizing that Michael has uncovered his betrayal, Tessio tells Hagen that he always respected Michael, and that his disloyalty \"was only business.\" He asks if Tom can get him off for \"old times\\' sake,\" but Tom says he cannot. Tessio is driven away and never seen again (it is implied that Cicci shoots and kills Tessio with his own gun after he disarms him prior to entering the car).Meanwhile, Michael confronts Carlo about Sonny\\'s murder and forces him to admit his role in setting up the ambush, having been approached by Barzini himself. (The hitmen who killed Sonny were the core members of Barzini\\'s personal bodyguard.) Michael assures Carlo he will not be killed, but his punishment is exclusion from all family business. He hands Carlo a plane ticket to exile in Las Vegas. However, when Carlo gets into a car headed for the airport, he is immediately garroted to death by Clemenza, on Michael\\'s orders.Later, a hysterical Connie confronts Michael at the Corleone compound as movers carry away the furniture in preparation for the family move to Nevada. She accuses him of murdering Carlo in retribution for Carlo\\'s brutal treatment of her and for Carlo\\'s suspected involvement in Sonny\\'s murder. After Connie is removed from the house, Kay questions Michael about Connie\\'s accusation, but he refuses to answer, reminding her to never ask him about his business or what he does for a living. She insists, and Michael outright lies, reassuring his wife that he played no role in Carlo\\'s death. Kay believes him and is relieved. The film ends with Clemenza and new caporegimes Rocco Lampone and Al Neri arriving and paying their respects to Michael. Clemenza kisses Michael\\'s hand and greets him as \"Don Corleone.\" As Kay watches, the office door is closed.\\n\\n'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Joining the two synopses sources\n",
    "\n",
    "synopses = []\n",
    "for i in range(len(synopses_wiki)):\n",
    "    item = synopses_wiki[i] + synopses_imdb[i]\n",
    "    synopses.append(item)\n",
    "    \n",
    "synopses[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 - Importing the genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 genres\n",
      "[\"[u' Crime', u' Drama']\", \"[u' Crime', u' Drama']\", \"[u' Biography', u' Drama', u' History']\", \"[u' Biography', u' Drama', u' Sport']\", \"[u' Drama', u' Romance', u' War']\"]\n"
     ]
    }
   ],
   "source": [
    "genres = open(os.path.join(datapath,'genres_list.txt')).read().split('\\n')\n",
    "genres = genres[:100]\n",
    "print(str(len(genres)) + ' genres')\n",
    "\n",
    "print(genres[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generates index for each item in the corpora (in this case it's just rank) and I'll use this for scoring later\n",
    "ranks = []\n",
    "\n",
    "for i in range(0,len(titles)):\n",
    "    ranks.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.3 - Stopwords, stemming, and tokenizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is focused on defining some functions to manipulate the synopses. First, I load [NLTK's](http://www.nltk.org/) list of English stop words. [Stop words](http://en.wikipedia.org/wiki/Stop_words) are words like \"a\", \"the\", or \"in\" which don't convey significant meaning. I'm sure there are much better explanations of this out there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load nltk's English stopwords as variable called 'stopwords'\n",
    "stopwords = nltk.corpus.stopwords.words('english')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next I import the [Snowball Stemmer](http://snowball.tartarus.org/) which is actually part of NLTK. [Stemming](http://en.wikipedia.org/wiki/Stemming) is just the process of breaking a word down into its root."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load nltk's SnowballStemmer as variabled 'stemmer'\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "stemmer = SnowballStemmer(\"english\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Below I define two functions:\n",
    "\n",
    "<ul>\n",
    "<li> *tokenize_and_stem*: tokenizes (splits the synopsis into a list of its respective words (or tokens) and also stems each token <li> *tokenize_only*: tokenizes the synopsis only\n",
    "</ul>\n",
    "\n",
    "I use both these functions to create a dictionary which becomes important in case I want to use stems for an algorithm, but later convert stems back to their full words for presentation purposes. Guess what, I do want to do that!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here I define a tokenizer and stemmer which returns the set of stems in the text that it is passed\n",
    "\n",
    "def tokenize_and_stem(text):\n",
    "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
    "    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n",
    "    filtered_tokens = []\n",
    "    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n",
    "    for token in tokens:\n",
    "        if re.search('[a-zA-Z]', token):\n",
    "            filtered_tokens.append(token)\n",
    "    stems = [stemmer.stem(t) for t in filtered_tokens]\n",
    "    return stems\n",
    "\n",
    "\n",
    "def tokenize_only(text):\n",
    "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
    "    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n",
    "    filtered_tokens = []\n",
    "    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n",
    "    for token in tokens:\n",
    "        if re.search('[a-zA-Z]', token):\n",
    "            filtered_tokens.append(token)\n",
    "    return filtered_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I use my stemming/tokenizing and tokenizing functions to iterate over the list of synopses to create two vocabularies: one stemmed and one only tokenized. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalvocab_stemmed = []\n",
    "totalvocab_tokenized = []\n",
    "for i in synopses:\n",
    "    allwords_stemmed = tokenize_and_stem(i)\n",
    "    totalvocab_stemmed.extend(allwords_stemmed)\n",
    "    \n",
    "    allwords_tokenized = tokenize_only(i)\n",
    "    totalvocab_tokenized.extend(allwords_tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these two lists, I create a pandas DataFrame with the stemmed vocabulary as the index and the tokenized words as the column. The benefit of this is it provides an efficient way to look up a stem and return a full token. The downside here is that stems to tokens are one to many: the stem 'run' could be associated with 'ran', 'runs', 'running', etc. For my purposes this is fine--I'm perfectly happy returning the first token associated with the stem I need to look up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.4 - Tf-idf and document similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='http://www.jiem.org/index.php/jiem/article/viewFile/293/252/2402' align='right' style=\"margin-left:10px\">\n",
    "\n",
    "Here, I define term frequency-inverse document frequency (tf-idf) vectorizer parameters and then convert the *synopses* list into a tf-idf matrix. \n",
    "\n",
    "To get a Tf-idf matrix, first count word occurrences by document. This is transformed into a document-term matrix (dtm). This is also just called a term frequency matrix. An example of a dtm is here at right.\n",
    "\n",
    "Then apply the term frequency-inverse document frequency weighting: words that occur frequently within a document but not frequently within the corpus receive a higher weighting as these words are assumed to contain more meaning in relation to the document.\n",
    "\n",
    "A couple things to note about the parameters I define below:\n",
    "\n",
    "<ul>\n",
    "<li> max_df: this is the maximum frequency within the documents a given feature can have to be used in the tfi-idf matrix. If the term is in greater than 80% of the documents it probably cares little meanining (in the context of film synopses)\n",
    "<li> min_idf: this could be an integer (e.g. 5) and the term would have to be in at least 5 of the documents to be considered. Here I pass 0.2; the term must be in at least 20% of the document. I found that if I allowed a lower min_df I ended up basing clustering on names--for example \"Michael\" or \"Tom\" are names found in several of the movies and the synopses use these names frequently, but the names carry no real meaning.\n",
    "<li> ngram_range: this just means I'll look at unigrams, bigrams and trigrams. See [n-grams](http://en.wikipedia.org/wiki/N-gram)\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:388: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['abov', 'afterward', 'alon', 'alreadi', 'alway', 'ani', 'anoth', 'anyon', 'anyth', 'anywher', 'becam', 'becaus', 'becom', 'befor', 'besid', 'cri', 'describ', 'dure', 'els', 'elsewher', 'empti', 'everi', 'everyon', 'everyth', 'everywher', 'fifti', 'forti', 'henc', 'hereaft', 'herebi', 'howev', 'hundr', 'inde', 'mani', 'meanwhil', 'moreov', 'nobodi', 'noon', 'noth', 'nowher', 'onc', 'onli', 'otherwis', 'ourselv', 'perhap', 'pleas', 'sever', 'sinc', 'sincer', 'sixti', 'someon', 'someth', 'sometim', 'somewher', 'themselv', 'thenc', 'thereaft', 'therebi', 'therefor', 'togeth', 'twelv', 'twenti', 'veri', 'whatev', 'whenc', 'whenev', 'wherea', 'whereaft', 'wherebi', 'wherev', 'whi', 'yourselv'] not in stop_words.\n",
      "  warnings.warn('Your stop_words may be inconsistent with '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.71 s, sys: 14.3 ms, total: 4.72 s\n",
      "Wall time: 4.78 s\n",
      "(100, 564)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.8, \n",
    "                                   max_features=200000,\n",
    "                                   min_df=0.2, \n",
    "                                   stop_words='english',\n",
    "                                   use_idf=True, \n",
    "                                   tokenizer=tokenize_and_stem, \n",
    "                                   ngram_range=(1,3))\n",
    "\n",
    "%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses)\n",
    "\n",
    "print(tfidf_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms = tfidf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'d\",\n",
       " \"'s death\",\n",
       " \"'s father\",\n",
       " \"'s friend\",\n",
       " \"'s hous\",\n",
       " \"'s mother\",\n",
       " 'abandon',\n",
       " 'abl',\n",
       " 'accept',\n",
       " 'accid',\n",
       " 'accompani',\n",
       " 'accus',\n",
       " 'act',\n",
       " 'action',\n",
       " 'actual',\n",
       " 'admit',\n",
       " 'afterward',\n",
       " 'ago',\n",
       " 'agre',\n",
       " 'air',\n",
       " 'aliv',\n",
       " 'allow',\n",
       " 'alon',\n",
       " 'alreadi',\n",
       " 'alway',\n",
       " 'american',\n",
       " 'angri',\n",
       " 'angrili',\n",
       " 'ani',\n",
       " 'announc',\n",
       " 'anoth',\n",
       " 'answer',\n",
       " 'anyon',\n",
       " 'anyth',\n",
       " 'apart',\n",
       " 'appar',\n",
       " 'appear',\n",
       " 'approach',\n",
       " 'area',\n",
       " 'argu',\n",
       " 'arm',\n",
       " 'armi',\n",
       " 'arrang',\n",
       " 'arrest',\n",
       " 'arriv',\n",
       " 'ask',\n",
       " 'assign',\n",
       " 'assist',\n",
       " 'assum',\n",
       " 'attack']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terms[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "dist = 1 - cosine_similarity(tfidf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  8.00612667e-01,  7.62919309e-01, ...,\n",
       "         6.40317667e-01,  6.70731044e-01,  8.24020109e-01],\n",
       "       [ 8.00612667e-01,  1.11022302e-16,  7.33522706e-01, ...,\n",
       "         7.33496310e-01,  7.71593583e-01,  8.93000317e-01],\n",
       "       [ 7.62919309e-01,  7.33522706e-01, -2.22044605e-16, ...,\n",
       "         7.24210289e-01,  7.49546661e-01,  8.58322258e-01],\n",
       "       ...,\n",
       "       [ 6.40317667e-01,  7.33496310e-01,  7.24210289e-01, ...,\n",
       "         2.22044605e-16,  4.51669244e-01,  9.17444019e-01],\n",
       "       [ 6.70731044e-01,  7.71593583e-01,  7.49546661e-01, ...,\n",
       "         4.51669244e-01,  0.00000000e+00,  8.68338789e-01],\n",
       "       [ 8.24020109e-01,  8.93000317e-01,  8.58322258e-01, ...,\n",
       "         9.17444019e-01,  8.68338789e-01, -2.22044605e-16]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.5 - K-means clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now onto the fun part. Using the tf-idf matrix, you can run a slew of clustering algorithms to better understand the hidden structure within the synopses. I first chose [k-means](http://en.wikipedia.org/wiki/K-means_clustering). K-means initializes with a pre-determined number of clusters (I chose 5). Each observation is assigned to a cluster (cluster assignment) so as to minimize the within cluster sum of squares. Next, the mean of the clustered observations is calculated and used as the new cluster centroid. Then, observations are reassigned to clusters and  centroids recalculated in an iterative process until the algorithm reaches convergence.\n",
    "\n",
    "I found it took several runs for the algorithm to converge a global optimum as k-means is susceptible to reaching local optima. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 72.1 ms, sys: 0 ns, total: 72.1 ms\n",
      "Wall time: 111 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "num_clusters = 5\n",
    "km = KMeans(n_clusters=num_clusters)\n",
    "%time km.fit(tfidf_matrix)\n",
    "clusters = km.labels_.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.externals import joblib\n",
    "import joblib\n",
    "\n",
    "joblib.dump(km, os.path.join(outputs, 'doc_cluster.pkl'))\n",
    "km = joblib.load(os.path.join(outputs, 'doc_cluster.pkl'))\n",
    "clusters = km.labels_.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "films = {'title': titles, 'rank': ranks, 'synopsis': synopses, 'cluster': clusters, 'genre': genres}\n",
    "frame = pd.DataFrame(films, index = [clusters] , columns = ['rank', 'title', 'cluster', 'genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>title</th>\n",
       "      <th>cluster</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>0</td>\n",
       "      <td>[u' Crime', u' Drama']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>4</td>\n",
       "      <td>[u' Crime', u' Drama']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>0</td>\n",
       "      <td>[u' Biography', u' Drama', u' History']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Raging Bull</td>\n",
       "      <td>4</td>\n",
       "      <td>[u' Biography', u' Drama', u' Sport']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Casablanca</td>\n",
       "      <td>4</td>\n",
       "      <td>[u' Drama', u' Romance', u' War']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest</td>\n",
       "      <td>4</td>\n",
       "      <td>[u' Drama']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>Gone with the Wind</td>\n",
       "      <td>0</td>\n",
       "      <td>[u' Drama', u' Romance', u' War']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Citizen Kane</td>\n",
       "      <td>2</td>\n",
       "      <td>[u' Drama', u' Mystery']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>The Wizard of Oz</td>\n",
       "      <td>4</td>\n",
       "      <td>[u' Adventure', u' Family', u' Fantasy', u' Mu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>3</td>\n",
       "      <td>[u' Drama', u' Romance']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                            title  cluster  \\\n",
       "0     0                    The Godfather        0   \n",
       "4     1         The Shawshank Redemption        4   \n",
       "0     2                 Schindler's List        0   \n",
       "4     3                      Raging Bull        4   \n",
       "4     4                       Casablanca        4   \n",
       "4     5  One Flew Over the Cuckoo's Nest        4   \n",
       "0     6               Gone with the Wind        0   \n",
       "2     7                     Citizen Kane        2   \n",
       "4     8                 The Wizard of Oz        4   \n",
       "3     9                          Titanic        3   \n",
       "\n",
       "                                               genre  \n",
       "0                             [u' Crime', u' Drama']  \n",
       "4                             [u' Crime', u' Drama']  \n",
       "0            [u' Biography', u' Drama', u' History']  \n",
       "4              [u' Biography', u' Drama', u' Sport']  \n",
       "4                  [u' Drama', u' Romance', u' War']  \n",
       "4                                        [u' Drama']  \n",
       "0                  [u' Drama', u' Romance', u' War']  \n",
       "2                           [u' Drama', u' Mystery']  \n",
       "4  [u' Adventure', u' Family', u' Fantasy', u' Mu...  \n",
       "3                           [u' Drama', u' Romance']  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    48\n",
       "0    29\n",
       "3     9\n",
       "2     8\n",
       "1     6\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    44.344828\n",
       "1    52.833333\n",
       "2    59.125000\n",
       "3    51.666667\n",
       "4    50.187500\n",
       "Name: rank, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = frame['rank'].groupby(frame['cluster'])\n",
    "\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "\n",
      "Cluster 0 words: b'killed', b'army', b'soldiers', b'war', b'family', b'men',\n",
      "\n",
      "Cluster 0 titles: The Godfather, Schindler's List, Gone with the Wind, Lawrence of Arabia, The Sound of Music, The Bridge on the River Kwai, Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb, Apocalypse Now, The Lord of the Rings: The Return of the King, Gladiator, From Here to Eternity, Saving Private Ryan, Unforgiven, Raiders of the Lost Ark, Doctor Zhivago, Patton, Braveheart, Butch Cassidy and the Sundance Kid, The Treasure of the Sierra Madre, Platoon, Dances with Wolves, The Pianist, The Deer Hunter, All Quiet on the Western Front, Giant, The Grapes of Wrath, Shane, Stagecoach, Mutiny on the Bounty,\n",
      "\n",
      "Cluster 1 words: b'george', b'marries', b'family', b'woman', b'friends', b'john',\n",
      "\n",
      "Cluster 1 titles: It's a Wonderful Life, The Philadelphia Story, An American in Paris, The King's Speech, A Place in the Sun, Tootsie,\n",
      "\n",
      "Cluster 2 words: b'mr.', b'singing', b'music', b'perform', b'film', b'bar',\n",
      "\n",
      "Cluster 2 titles: Citizen Kane, Singin' in the Rain, Amadeus, The Good, the Bad and the Ugly, Nashville, The African Queen, A Clockwork Orange, Yankee Doodle Dandy,\n",
      "\n",
      "Cluster 3 words: b'marriage', b'relationship', b'love', b'father', b'new', b'new',\n",
      "\n",
      "Cluster 3 titles: Titanic, Forrest Gump, A Streetcar Named Desire, The Best Years of Our Lives, It Happened One Night, Annie Hall, Out of Africa, Good Will Hunting, Terms of Endearment,\n",
      "\n",
      "Cluster 4 words: b'police', b'car', b'killed', b'father', b'asks', b'says',\n",
      "\n",
      "Cluster 4 titles: The Shawshank Redemption, Raging Bull, Casablanca, One Flew Over the Cuckoo's Nest, The Wizard of Oz, The Godfather: Part II, Psycho, Sunset Blvd., Vertigo, On the Waterfront, West Side Story, Star Wars, E.T. the Extra-Terrestrial, 2001: A Space Odyssey, The Silence of the Lambs, Chinatown, Some Like It Hot, 12 Angry Men, Gandhi, Rocky, To Kill a Mockingbird, My Fair Lady, Ben-Hur, Jaws, The Apartment, High Noon, Goodfellas, The Exorcist, The French Connection, City Lights, Midnight Cowboy, Mr. Smith Goes to Washington, Rain Man, Fargo, The Green Mile, Close Encounters of the Third Kind, Network, The Graduate, American Graffiti, Pulp Fiction, The Maltese Falcon, Taxi Driver, Wuthering Heights, Double Indemnity, Rebel Without a Cause, Rear Window, The Third Man, North by Northwest,\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "print(\"Top terms per cluster:\")\n",
    "print()\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
    "for i in range(num_clusters):\n",
    "    print(f\"Cluster {i} words:\", end='')\n",
    "    for ind in order_centroids[i, :6]:\n",
    "        print(f\" {vocab_frame.loc[terms[ind].split(' ')].values.tolist()[0][0].encode('utf-8', 'ignore')}\", end=',')\n",
    "    print()\n",
    "    print()\n",
    "    print(f\"Cluster {i} titles:\", end='')\n",
    "    for title in frame.loc[i]['title'].values.tolist():\n",
    "        print(f' {title},', end='')\n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is purely to help export tables to html and to correct for my 0 start rank (so that Godfather is 1, not 0)\n",
    "frame['Rank'] = frame['rank'] + 1\n",
    "frame['Title'] = frame['title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Rank</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>It's a Wonderful Life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>The Philadelphia Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>An American in Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>The King's Speech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>A Place in the Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>Tootsie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#export tables to HTML\n",
    "html_output = frame[['Rank', 'Title']].loc[frame['cluster'] == 1].to_html(index=False)\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(html_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.6 - Multidimensional scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import MDS\n",
    "\n",
    "# two components as we're plotting points in a two-dimensional plane\n",
    "# \"precomputed\" because we provide a distance matrix\n",
    "# we will also specify `random_state` so the plot is reproducible.\n",
    "mds = MDS(n_components=2, dissimilarity=\"precomputed\", random_state=1)\n",
    "\n",
    "pos = mds.fit_transform(dist)  # shape (n_components, n_samples)\n",
    "\n",
    "xs, ys = pos[:, 0], pos[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#strip any proper nouns (NNP) or plural proper nouns (NNPS) from a text\n",
    "from nltk.tag import pos_tag\n",
    "\n",
    "def strip_proppers_POS(text):\n",
    "    tagged = pos_tag(text.split()) #use NLTK's part of speech tagger\n",
    "    non_propernouns = [word for word,pos in tagged if pos != 'NNP' and pos != 'NNPS']\n",
    "    return non_propernouns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.7 - Visualizing document clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set up colors per clusters using a dict\n",
    "cluster_colors = {0: '#1b9e77', 1: '#d95f02', 2: '#7570b3', 3: '#e7298a', 4: '#66a61e'}\n",
    "\n",
    "#set up cluster names using a dict\n",
    "cluster_names = {0: 'Family, home, war', \n",
    "                 1: 'Police, killed, murders', \n",
    "                 2: 'Father, New York, brothers', \n",
    "                 3: 'Dance, singing, love', \n",
    "                 4: 'Killed, soldiers, captain'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wNjbm0qVLnDt3jvr16xeovmLFihEUFMT8+fPp1KkTwcHBlCpViqpVq/Lxxx/ne/7Tp0+V7QX29vb8+OOPuLu7M27cOKXMb7/9hrm5OadOnSI5ORlXV1dat24NwOnTpwkNDaVKlSps2LCB8uXLs2PHDgBiY2MLdA/ixZKZfyHecPGPk/lpwRHOnb1LenpG9g59fQMi7l4mPiGGC+ejWLHsHBdCr9C/f38AGjVqxHvvvYeDgwO7d+8GMvbANWrUiC+++AIPDw8gYxQ4NTUVHx8fBg4cqHS4AUaPHk2FChWytSVzFt/b25uRI0fi5ubGtGnTspVZvHgx+/btw8PDg6ioKGJjY+nbty/16tVT/kAvWbKEpk2b0rRpU06fPp3rvWf+IUlJSaFly5a4u7vTrVs30tLSCAsLy/N+hciPXmkTSnzfEUu/EZh96YnpWDfMvvTEcv8ISnzfUTr+QgiRi7SEh4Us/6jQ13jy5AkODg44OztjbW3N0KFDCQwMZMCAAQC0aNGCBw8eEBcXp/P8x48fEx4eTpcuXQAwNDTE2NiYvXv3snfvXhwdHalfvz6XL1/m6tWr+banU6dODB48uFBbCvbu3cvs2bNxcHDAw8ODpKQkbt26RUBAgPIdxt7eHnt7+wLV17FjRwDs7OyoU6cO5cqVw8DAgHfeeYfbt2/ne36vXr2AjImamJgYZcAj85lmtnnFihU4ODjQsGFDHjx4oDwfFxcXqlSporRh3759fPHFFxw+fBhzc/MCPhXxIsnMvxBvuM0bQ3kQnT3ITe2azXn8OJq16ydhbFyCdl6fsnd3gvL+w4cPmTlzJikpKYwePRpPT098fX05cuQIQUFBnDhxIsd1HB0dWbFiBa1btyYmJgYLC4s829WmTRsWLVpEw4YNsy2ZGz58OLdu3WLVqlUAREZGsnTpUoKDg1m+fDkVK1Zk69atBAQE8OjRI4YMGcLmzZuz1f3pp5+SmJiIs7MzdevWRavVsn37doyMjJg8eTIHDhzA1vafPdvP3q+Xl1eBn68QelamGPdxfNXNEEKIN4aeSalCli9Z6Gtk7vkvalqtlgkTJjBixIhCnefq6sru3bvp27cvKpWqwNfasGEDNWrUeJ6m5mBgYACAWq1Wfs58nZqaikajIT39n5VsSUlJ2c43Mcl/UFur1fLDDz/Qpk2bbMf9/f2znV+9enVOnz7Nzp07mTx5Mi1btmTKlCnPdV+i6MjMvxBvsNjYJELP3ctxXE9PQ5NGfRg84Efs67Qi+MwW/rocRUpKxv59S0tLrKysqFChAjExMURHR2Ntba1EztUlc2l9+fLlC7R0K7O8kZFRnuWqVauGoaGh0pbr169z9uxZmjdvTteuXYmJiclxznfffUdISAjR0dEkJSWRkJDA0KFDadasGevXryciIiJb+WfvVwghhBAvjmn9TqBXwDlGPQ2mTp2L5LpNmzZl9erVQEZn1MLCAjMzM51lixcvTsWKFZUJhuTkZBITE2nTpg1Lly4lPj4egPDwcCIjMzIStGzZkvDwcJ31TZ8+nZIlS/LBBx8UuL1t2rThhx9+QKvNWLl55swZANzd3fn9998BCA0NzRa7YODAgZw8ebLA18iqTJkyREZG8uDBA5KTk9m+fbvOciVKlKBEiRIEBgYCKM80s80LFy4kJSUFgL/++ouEhIQcdURERGBsbEz//v0ZN25cnis5xcsjM/9CvMEu/P8e/2fFxkVialIKPT0NxsYl0Gq1pKdriXmUETAm64i0VqvFwsKC27dvk56erjM4jq5z8pPbqLe+vn62IILP1lulShUaNGjA+vXrAZQ/Ls8yMTGhe/fu+Pr6YmlpSfXq1fn999+ZNGlSjvYVtu1CCCGEeH6akuUxdepC/Mk/8y1r6tQFTYlyRXJdHx8fhgwZgr29PcbGxixfvjzP8itXrmTEiBFMmTIFfX19/vzzT1q3bs2lS5do3LhxRvtMTVm1ahUWFhZcu3aNUqVyX9Uwf/58hgwZwueff863336bb3u//PJLPvroI+zt7UlPT6dKlSps376dUaNGMXjwYGrVqkWtWrVwcnJSzjl37hzly5cv4BPJTl9fnylTpuDi4kKFChWoWbNmrmWXLVvGkCFDUKlUyp5+gGHDhhEWFkb9+vXRarVYWlrmWKEJcP78ecaNG4darUZfX5+FCxc+V5tF0ZLOvxBvsCeJujvGkVHX2bbjGzSaYqj1NLRt9SHp6Wmkpenu+Go0GgYNGkSTJk1o3Lgx+vr6L6zNZcuW5eHDh3Tv3p3FixfneN/S0pJ27drh7u6Onp4eLVq04Msvv9RZV9++fWnTpg3r1q1j5syZBAUFYW5unm3Jv/h3IiIiaN++PRcvXiQ+Ph6NRsOJEyf4+OOPUavVNGjQgP/97385zjt8+DDt27cnKiqKYsWKvZC2+fr68sknn3D//n3lS1vPnj1lgEcIIV4DVv0XkHzzTJ7p/vTLVMOq/4Lnqj9zZj6rUqVK6eyIent74+3tDaBEtwewtbXlwIEDOcp/+OGHfPjhh9mOhYaG0q1bN50rGrNmP1q2bFmONtrY2BAaGgpkxFPKjK1kZGSkRMvPysjIiDVr1uQ4HhcXh62tLRUrVszxnr+/v/Jz1ms8+97YsWMZOzZnesWsZQCcnJw4e/as8jpzMEOtVvP111/z9ddfZyv/7DXbtGmTY2uAePVUeX1JcnZ21mZGfBRCvH6OBoaxeUNogct37laXJm42Ot/L3At24sQJli5dqvOPkfjviEm8T0j4Hh7FRaHRGvPNJyvxPxCARqPh3r17lChRAkNDQyVSsp2dXbbzP/74Y+Lj4+natStt27Z9rjakp6ejVue+O83X15cff/yRWbNm0apVKwYNGsTZs2dfyB5QIYQQ/7h06RK1atXKt1xqXCSRq8ZmpP3LGv1fT4OpUxes+i9AY2b1Alsq/mt0/W6qVKpgrVbr/Iqa9FqRmX8h3mB17MqyddMFnUv/n6VWq6hrVzbX93/44Qc2b97M06dP810mJ95ecUnRrAmawpk7e0jX/vNF7Xp0JL8eGUO/hl9Rtuw/v0f6+vro6enlqOevv/5i0aJFzJgxg7Zt2xIWFsaAAQOwsrIiLCyMLVu2ULFiRQYPHszt27extrbG2toaHx8f6tWrh52dHba2tgQGBrJv3z4gY6/l7t27s61M6dSpE5s3b6ZZs2Y8efKEEiVKABAVFcXQoUN5/PgxtWrV4ueff8bHx4fr168TERGBjY0NS5YseUFPUQghckp9FEH86S0cOh7E4YsRzP7xN1Zs2s2QIUOAjNlpX1/f56o7OTmZli1bEhgYSHp6Oubm5pw9e5Z33nmHgQMHMm7cuByDtPnJbEvmjHleYmNjCQ8Px9LSEktLS+W4xsyK8u+v4fKpAMrGXSAt4RF6JiXRt2vL/fj0bB3/W7duUalSJVJSUrh27RqmpqZYW1sXqs1CiLxJ51+IN5i5uSF17ctyLuRuvmXr2pfFzNww1/c//vjjAuWAFW+vuKRo5uzrRmR8mI53tZy+vYOIuIuMa7UBM0MLzp07R1RUFLVr185W8vTp0zg7O1OpUiXu37+vRBaOj4/n0KFD/PHHH2zYsIFGjRphYGCAn58f33zzDU+eZMSkuHPnDkePHsXExIRhw4Zx7do10tLSqFq1ao4tKeXKlSMkJIR9+/bRokULZZnk7NmzmTBhAo0bN+aLL77g2LFjQM6sFZmDBUII8aKkxkUSuXIM8ac3Q1oqcfe0JERouf5ZFRb5mzGwe/t/PfttYGCAVqvl6dOnXL16FQcHB4KCgnjnnXe4dOlSjs/poqTVann06BFVqlTJPchvcStKNHBXXiYnJ0N89sB5mR39x48fY2FhgZWVrAgQoqhJ51+IN1znrnUJvxObI91fVqUtjOncte5LbJV4E60JnppLx/8fkfFhrAmeSvdaXzF69GjWrVuXo8zGjRvx9/fnxIkT3Lx5k6NHj1KxYkVq166NWq2mQoUKXLt2jRs3bii5ix0cHJQOeo0aNZR0Qf369WPNmjWkpaXRp08fnW1q2LAhU6ZMYceOHUrn/9KlS4wfPx6VSkV8fDwuLi5AzqwV0vkXQrxIqXGR3J7ZVOe+97WXUrh0Oxq3ujYsXLuTWbNmATBo0CDCwsJQq9Xs379f2f6UkpKCl5cXKSkpWFpasm7dumwrr+rVq8fZs2e5cOECI0aM4NSpU3h5eWFqaopWq6VPnz6Eh4dToUIFVq5cSWBgIN988w0ajYaHDx+yZ88eihUrRo8ePUhOTsbY2FjJGz99+nQOHDiAWq1m6dKlpKamcuXKFTQaDUZGRsTGxvLkyRMqVqxIeHg4NWvWJDk5mYiICCXve36uXLlCtWrVuHv3nwkNMzMzbt26RXp6OmZmZs8d6E4IkUFS/QnxhjMtbsAHY12xdyiHWp09wr5arcLeoRwfjHXFtLhBLjUIkbHH/8zt3QUqGxy2i959ejB37txsWwAyBQUFERgYyO7du9m0aRObNm0CdGd2OH/+PEC2LBNZ9/k3a9aMw4cPc/jwYZo1a6azPT169KBly5bZ2lKjRg2+//57/P39CQoKolOnTjrbIIQQL1LkqrG5BrzrVV1F9ZKwokUyZc4soly5cqSkpHDnzh0OHTqkdLYzaTQatm/fTkBAALVq1coRqM7FxYVTp04RHBxM+/btuXv3LsHBwTg7O7Np0yZq165NQEAAderUYcOGDQAUK1aMbdu28e6777J//342b96Mi4sLu3fvxsLCAsj4fA4PD8ff35+ffvpJGaRISUnhnXfeoXz58piZmVGlShWKFy+u814TE1I5GhjG/r1XORoYRlxcks5yarWasmXLUqZMGaysrAgPD8fGxoaaNWuSlJTE06dPC/cPIITIRmb+hXgLmBY3oP8gJ2Jjk7hw/h5PnqRgZKRPXbu8l/oLkSkkPPse/0zpqVp2fRPJg5tP2Tk7EpdeJYiLTCX41Ck+//xzAGbNmqWkRLpy5Uq2/Z41a9bk+PHjjBkzJkfdDRs2ZNGiRbRs2ZLy5cvrTDmkVquxt7cnNTU11+B/VapUyZFSaeLEiQwfPpzY2FjUarXs7xdCvHSpjyIyAt0VQHzwJlJj7qJfohyDBg2if//+VK5cma+++kr57EtISGD48OGEh4dz//79HJltGjRowNy5c0lJSaFEiRKo1WpOnjxJgwYN+Pvvv6lfvz4Azs7OBAcHU6ZMGWU1VIUKFYiJieHu3bs4OjoCKOnlLl++jL+/vxLJvVy5jLR8xsbGuab1zZSYmMoq32DOn7+LNv2f42q1iirvFKffwAp5Tk4kJSVx48YNICMw8dOnT19YBhkh/gtk5l+It4i5uSFN3Gxo2cqWJm420vEXBZaQHKPzuFqjot2kMngvqUT7SWWwqmZAtSYm/H54Jv7+/vj7+ysdf8iYcV+5cmW2Oo4cOYKNjQ2rVq0CMtIBZaZa+vXXX9m/fz9169blnXfeASAwMDDb+SqVir59++Zom7e3N8OGDct2LDNVkaWlJZs2beLAgQP4+flhY2ODj48Pnp6eQEYgKxsbmwI9GyGEeB7xp7dkj3Cvg9J1TkslPnizssVp1apVREVFcerUKaXsnj17qF69OocOHaJbt245Vi/VqlWL8+fPK51jW1tbVq1ahYuLC1WrViU4OBjIWJ1VtWrVjOvrWJGVmd7tzJkzAFSvXp3WrVsrn/krVqzI854y47xER8ey+c8bnDubveOfUUbL39fi+GnBEeIfJ+dal6GhIVWqVKFGjRrUrl1b2RIGoKenh4ODg/Jf1nR7WcXExPDzzz8rr/39/Wnfvn2e9/C8/P39UalUbNu2TTnWvn37HGn0nteFCxeoXr26EiMHoF27dvzxxx8FOt/Dw4PCZHIrbPlnbd68mYsXLxZZfeLfk86/EEIITAxKFKq8cTHzIrnu0KFDcXd3JzAwkC5duuR4f8qUKdy8eZMGDRoUyfWEEOJlSUt4mG+ZciYw5mA6f8dqSUt4xOPHj2nZsiWurq7cvn07W4T+hg0bsnXrVtq3b6+zo6tWqylevLgym+/k5MT9+/exsbGhc+fOXLhwAXd3d86fP0+3bt10tqdz584cPXqUNm3aEBMTA2TEZClbtiweHh40b948Wx57XczNzbl8+TI7tl4hNjbvZfoPohNZufwYDx/qflYVKlQgLCyMK1eucPXqVWVgAcDIyIiQkBDlv9wGdJ/t/P9bqal5D+hUrFiRmTNnFtn1sqpTpw5du3ZV6t+8eTMpKSm5xsTJKi0t7YW0Ka/n8Wzn/0VdRxScKq89j87OzloZnRFCiLdfTOJ9JmxtonPp/7PUKg2zOx3F3KjMS2jZ68vf359BgwbxzjvvoNFoWLNmDaVLly7w+R999BHfffedzlSJ+alRowblypXjyZMn9OvXj7Fjx+os5+vrS2pqKp6enkyePFlZfSGEePFi9i8kcuXoApe3GvAjJVqOeoEtKjq6cqlnFRubxKzp+wucinjilJaFXq1oampKfHy88jo+Pp5OnTrx6NEjUlJSmDFjBp06daJ3795s2bKFGjVq0KpVK9q1a4ePjw8WFhaEhobi5OTEqlWrUKlUBAcH88knnxAfH4+FhQW+vr6UK1cODw8PHBwcCAwMpE+fPnz66ac62+Tv769svfjss89o1aoV7du357PPPsPDw0Nn/Xp6erRt25bg4GDOnj2Lg4MDN2/exNramqpVq3L+/HmMjY2VayQmJuLo6MjatWvp3bs327Zt49atW3z22WekpqbSoEEDFi5ciIGBATY2NvTq1Yt9+/bx+eefs2jRIubOnUv9+vUZMmQIFStWZMaMGbk+Yw8PD+rVq8ehQ4dITU1l6dKluLi44OPjw99//83169extrZm1qxZDBkyhOjoaCwtLVm2bBl37tyhffv2mJubY25uzoYNGxg6dCgNGzbk4MGDxMTE8Ntvv9G0aVPS0tIYP348/v7+JCcn88EHHzBixAj8/f358ssvKVmyJJcvX+bMmTP07NmTO3fukJaWxpdffkmvXr2ytVnX76ZKpQrWarXOhfoFe0vJzL8QQghKGJfBsZJXgco6VvL6T3f8YxLv4391BcdubMC9XT027VjDoEGDCrzsMtO8efOeq+MPGdsa/P39OX78+Cvr0MfGxuLh4YGHhwfm5uZ4eHgwePBg/Pz8ClXPrVu36NKlCx4eHri5uf2r+AyZe5J18fX1VWYNvb29uXZNdxC2rPz9/SlevDjnzp2jTJkylC1blnr16nH48OF8z5s8eXKe7clsU3BwsFIewM3NLVuZsLCwHHnWQ0JCOH36dLZrFYaHh0e2WbS82lnQZyVyMq3fCfQKGF5LT4OpU+cX2p6X6cL5ewXq+EPGFoDQ8/cKfY0nT54oS/67dOmCoaEhmzZt4vTp0xw8eJBPP/0UrVbL7NmzqVq1KiEhIcyZMwfI2NIwb948Ll68yPXr1zly5AgpKSmMGTOG9evXExwczJAhQ5g0aZJyvadPnxIUFJRrxz+rSZMm5ehU51a/lZUVSUlJxMXFcfjwYZydnTl8+DA3b97EysoqW8cfMmItzJ07F3d3d3r37k2lSpXw9vZm7dq1nD9/ntTUVBYuXKiUL126NKdPn6Z3795Axgx6v379sLW1zbPjnykxMZGQkBB+/vlnhgwZohy/ePEifn5+/PHHH4wZM4ZBgwZx7tw5ZUC6SZMmdOzYkTlz5hASEqJsNUlNTeXkyZPMmzePadOmAfDbb79hbm7OqVOnOHXqFL/++qsS6+H06dPMnz+fv/76i927d1O+fHnOnj1LaGgoXl4F+94i/iEB/4QQQgDQ22katx+G5pnuz8rUht5O015eo14jcUnRrAmawpk7GcERI64nEX43iQlbmxBzugJOld4F4MMPPyQkJIT09HRWr16NtbU1ixYtwtfXl2bNmnHixAkleJafnx8zZszg+vXrREREYGNjw5IlS/j777/p27cvZcuWJT4+nt9++03nktaswa98fHxwc3PD09MTb29vJa5CUVMCiyam8PUMX+rYlaVdO0/8/f2f65rDhg1jwYIF1KxZk/T0dI4ePVr0jSajo92/f/9cA0dmynp/l67co0KFiqxZs4ZWrVqh0Wh47733+Pbbb2natOm/blNmp76w+4FDQkJITU1VAriJ15OmZHlMnboQf/LPfMuaOnVBU6LcS2jVy/EkMaVw5Z8Urjz8s+w/U0pKChMnTiQgIAC1Wq0ERtTFxcWFihUrAijxAkqUKEFoaCitWrUCMpbJZwY3BHLMMOfF3d0dyB7D5sqVK7nW36RJE44cOUJAQAATJ05k9+7daLXaXD9nOnToQIkSJXj//fe5cuUKVapUoXr16kBGqsiffvqJjz76SGe7R4wYQc+ePbMNbOQlc0uBu7s7cXFxynaQjh07YmRkBMCxY8fYuHEjAAMGDFACAuvStWtXIGNbSub2lb1793Lu3DnWr18PZAwuX716lWLFiuHi4qKki7Szs+PTTz/liy++oH379kXyOfxfIzP/QgghADAztGBcqw04WbdHrco+NqxWaXCybs+4VhswM7R4RS18deKSopmzrxvBt3dk2xpxNTCB9RPvsHvdMe6X20ZcUjSzZs3i0KFDTJ06lV9++YXU1FR8fX05cuSI8qXnWY6Ojvj5+XHr1i1iYmKYO3cu8+fPZ+PGjURGRuYoHxUVhYeHB7Vr16Zly5Yv7L6zin+czCrfYGZN38/mDaHs2XWFzRtCmTV9P1GR8UrQrhUrVuDp6akEY4yKiqJjx440b96c999/P1udt27dwsrKSsn0oFarlZlvPz8/GjVqRKNGjZTVBLqObd++HScnJwYPHkxKiu4OxMmTJwkJCaFly5ZKQMq5c+fi5uamzDydDbmIXd1G2NVpwBeff8meXVcIPHSDkmZ1Wbt2K0+TM+q+e/cux48fp2HDhnTr1g0PDw+qVq1KjRo1aNGiBfPmzeO9997jt99+4/Tp07i6uhIfH8/w4cO5dOkSPXv2pFq1atjb2zNhwgT8/PwYPHgwmzdvpl69eiQkJAAZ8TDMzc1p1KgRV69ezXY/ixcvZs6cOfTr1w+A4OBgOnTooFxLq9UyatQoWrRoQbt27Xj06FGB/o23bt2Kk5MTw4cPz7ECIXM5NWRsWckMHifyZtV/AfplquVZRr9MNaz6L3hJLXo5jIz1C1feqHDldVm9ejVRUVEEBwcTEhJCmTJlSErSnVLQwOCfDAN6enqkpqai1WqpU6eOEkPg/Pnz7N27VymXNdhgQTw7+59X/e7u7spsf6dOnTh79iyBgYF5dm7VanW+g5m62t2kSRMOHjyY67N51rMZHTJfF/Z5ZMp89pnPHTKezQ8//KA8mxs3btC6desc16levTqnT5/Gzs6OyZMnM3369Odqw3+ZdP6FEEIozAwtGO76E7M6HqWP81d0sv+MPs5fMbvTUYa7/vSf7PgDrAmeqnNFhK2bCV1nlsWqqgFhN6+zJniqMis8efJkIiIiiI6OxtraWolMrUtmgK7y5csTGxvLjRs3sLe3R09PT3kvq8xl/9euXePSpUvcvHkzR9TuohT/OJmfFhzh3Nm7OZbypqdrSUh4yk8LjvA0OTXHQMbs2bOZMGECBw8epHjx4hw7dkw5NyIiQpn5unjxorKNADJWMuzdu5e9e/cyZcqUXI9lDrZMnz49z1k+BwcH9u/fz4ABAwBo06YNgYGB7Ny5k/jHyQwc8AGujUbQu8csoh/c4vHj6IyTVWosS9Vm27bt7Nq1i5EjR7Jy5UpOnDhBdHQ0u3btwtnZGSsrKyW7xIwZM6hfvz76+vpK/vTu3buzfv16fH19sbW1ZcSIEYSGhgIZAcnat2/Pzz//TGRkJHfv3mXLli3cvn2b7777jhs3bmTLbz58+HDGjRvH6tWrgZy52rdv3461tTUHDhxg9OjRLFq0qED/zt988w0BAQFMnTo1x7Ps0aMHGzZsID09nbNnzypp4ETeNGZWVJp0GFOXHjm3AOhpMHXpQaVJh9GYWb2aBr4gdezKolbnnQYwk1qtoq5d2X99zdjYWKysrNDX1+fgwYPcvHkTgOLFi/P48eN8z69RowZRUVHKZ1RKSgoXLlzQWXbTpk1MmDAhz/pat27No0ePOHfuXL71N23alFWrVmFra4taraZUqVLs3LkzxyBcbu0OCwtTtuesXLmSZs2a5Vp+6NChvPvuu/Ts2VPpfA8cOJCTJ0/qLL927VogYxVD5v79ZzVp0oQ1a9YAGYMwmYMWBX32bdq0YeHChcoA7l9//aUMhGYVERGBsbEx/fv3Z9y4ccr2J1FwsuxfCCFEDiWMy+BhO/BVN+O1EJN4nzO3d+dZxqGjGcEbYjEttYOrB8oTePgo+/btY/Xq1VhYWHD79m3S09OVL4HP0pVu6/z58zg7O+f65TPzvOLFixMTE4O5uTl3795Fq9Xmec7z2LwxlAfRiXmWeRCdyLUbkTRvkfGlM3Mg49KlS4wfPx6VSkV8fDwuLi7KOeXKlSMiIgKA2rVrZ8slrlKpMDMzA1BiI+g6plarMTU1xdTUFEtLywLfU+agipGREZs3hnL33k127v4egKTkBB7HP1DK1qzelNCLflS3dWHud5Np27YtdnZ2nDt3jubNm5OWlsbVq1fx9vbm3r17lClTBgcHBx4+fKjkT2/Xrh2zZs3i+vXrBAUFcffuXUqUKAFkRDTXaDRUqFCB1NRUbt68yZMnT+jYsSOJiYk8efKEqKgoKlSokOe9ZF7r/v37rFmzhj179pCampotHSdkpE9LTk5Go9GQlJSkLN3V09PDxMQEExMTLCyyD/R17txZ2SecuaRZFIzGzIry768h9VEE8ae3kJbwCD2Tkpg6dX6rlvpnZW5uSF37spwLuZtv2br2ZYskNXG/fv3o0KEDdnZ2ODs7KyuKSpcujaurK3Xr1qVt27a0a9dO5/nFihVj/fr1jB07ltjYWFJTU/noo4+oU6dOjrJ///238lmUl0mTJtGpU6d867exsUGr1Sr/b7m5uXHnzh1KliyZ7zUMDQ1ZtmwZPXr0UAL+jRw5Ms9zPvnkE2JjYxkwYACrV6/m3LlzlC9fPtf6HR0dSUlJYenSpTrL/PDDDwwePJg5c+YoAf8AevfuzXvvvceCBQuUJf26DBs2jLCwMOrXr49Wq8XS0pLNmzfnKHf+/HnGjRuHWq1GX18/W2wDUTDS+RdCCCHyEBK+J98sCCXK65MUl0ZKSgppeom0aNECe3t7ADQaDYMGDaJJkyY0btwYff38l7d+9tln9OvXDysrK0qWLJnjnMxl/+np6dSqVYt69epRsmRJunTpwtatWwv0hbGgYmOTCD1XsGBckffjScyy11er1VKjRg369++vzBRnDTRXuXJl7t27p0Rn1mq1Sjqq9PR04uLiAPI9lpCQwKNHj4iKigLg4cOHGBkZKZ1aAH19fdLS0tBoMr76ZA64pKamE3ruHqVKVqCFx3uYmpQiPT0NlUrF7TsZgygGBsYUK2bMpUtnmTTpS6ysrDh06BDFixfH19cXc3NzPD09GT58OH369CEqKoqUlBRlub5Wq0Wj0WBmZsapU6eYP38+CQkJysz9s8tqK1eujLGxMTt27GDbtm3s3LkzW8dfX1+f5OR/cqM/O3hUo0YNBg4cqAQme3Y7RJ06dTh69CitWrUiMDBQGTxIT08nMTGRmJgYoqOjs51jamqKmZkZ8+fP5+uvv0YUnqZk+Tcmmn9R6Ny1LuF3YvMcOCxtYUznrjlXNxVE1kj/ABYWFtlWFmX1+++/Z3udNcjljz/+qPzs4OBAQEBAjvOfjcsREhLC//73vxzlsq5egox98VlXYuVWP8Dt27eVnydOnMjEiRN1lsuUNd1jy5YtOXPmTJ5lIPt9ZG55iouLw9bWVomBkFv5rJ6N71K5cmUOHDiQo5yrq2u2VH9Z67OwsFDap1ar+frrr3N8tjz7PNu0aUObNm10tkkUjHT+hRBCiDwkJMfoPF6+tiHla/8zW9VuUkYGhC9/HMq7dcZkKzts2DBGjhzJiRMnlJmTzC9BWb9E+fr6Ahkd5OPHj5OWloarqytlymTPrnDlypUc7bG2ts6xDztrlPjnzQpQmKjdWi2E3cier3vixIkMHz6c2NhY1Go1S5YsyRa88LfffmPs2LHExcWhVquV4FRTp05VAmNl7uvUdeyLL77A3d2d+vXrU7ZsxtLh77//nrZt2+Lq6qpcp127dnTu3FmJRZApMeEp6elamroOYPfe+aSlpaJW69GpffYlvU+S4khKesyFC+mo1Vp69uyJpaUlPXv2RKPRcOfOHcaMGcMXX3zBxIkTKVasGNbW1tnqGDhwID179mTp0qVKACtdypUrR/v27alQoQKmpqY5skI0atQIb29vQkNDdeZr79ixI2PHjqVFixZAxh79jh07Ku+PGzeOIUOGMHPmTExMTJRZus8//xx3d3ccHBxy/M5Bxize5MmTlRlVIfJiWtyAD8a6snljKKHnsn+OqNUq6tqXpXPXupgWN8ijltfT25Q21czMjD//zD8opXg7qPLaF+js7KwNCgp6ic0RQoi3m62tLV999ZWScudFnZOX3bt3k5aWluvSx8K4d+8ev/32W75Rg319fZk1axZly5alSpUqSic3L7Nnz2bAgAG5LnV+WfyvruCPoC8LXL6P81c5tkz873//Y/PmzTx9+pTly5crUZlz89dffzFs2DASEhIYOnRojkB5L9P+vVfZsyvnYENu2rxbg5atbF9gi/I3ZswY5s+fX6BgWG/i/b0oqampaDQawsPDGT58ODt27Mj2/s6dO7lw4QLjxo17RS0UrwtdudTzomTReJKCkZE+de2KZqm/EM/S9bupUqmCtVqt8ytq0mtFOv9CCPEC/bPH8yEX7yWyxP8aqSqNstw3P2fPnmXevHk8ffq0wOfkJT09vUAdoqLm6+tLamoqw4YNY9SoUfTr1y/PQEavqp26xCTeZ8LWJvku/YeMrAizOx3F3CjnrOmb6mhgGJs3hBa4fOdudWniZvPiGlTE3vb7K4w1a9awcOFCEhISWLBgAU2aNFHe27BhA//73//YsmULpUuXfoWtFK+Dwnb+hXhZpPOft9fjm5UQQrxlUuMiifipF9c/q0LkytE82DiFtT9/TYf4jTy8cIiEqIy9fR4eHnz66ac0aNCA3377LUc9Gzdu5P333ycxMVHZ4+vh4cFnn32Gs7MzCxYsoH///tSrV489e/YAcOLECTw8PHB1dVWW83p4ePD5558zcOBAfH19WbJkCQCTJ0/Gzc2NFi1aEBMTw7Jly/Dw8MDZ2VlJQeTt7c3IkSOzpUXLFBYWRv/+/YGMpcjvvfceDg4O7N6de4A8BwcH7ty5Q8+ePWnWrBmtW7dW9nHXq1eP/v378+233+Lt7c21a9fYtGkTLi4utGjRgp07dz73v8nzKmFcBsdKXgUq61jJ663q+MOridr9Mr3t91cYvXv35tChQwQFBWXr+AN069aNwMBA6fgLIcQbTDr/QghRxFLjIrk9synxp9ZD2j+zxRcfgH2pdBobRbBmVCNS4zLyt/fv35/AwECWL1+eo64zZ87QoEEDvLy8lLzmkBHV+MiRI0ybNo3vvvuOXbt28dNPPwEwZcoUtm7dSmBgIKtXr1ZShHXp0iXbPsUzZ85w/fp1AgMD2b9/P+bm5vTq1Qt/f3/279/P3LlzlbJZ06Ll5uHDh8ycOZMdO3bwyy+/5FouICCA6tWr4+vry6FDh+jZs6eSSujOnTv88ssvjB8/Xim/ceNG1q1bx4EDB2jbtm2u9b5IvZ2mYWVqk2cZK1MbejtNy7PMmygzandBFFXU7pfpbb8/IV4HaZHxJP5xhvhFx0j84wxpkfH5nySEKHIS8E8IIYpY5KqxpNy/lu3YzTgtf8XA0H3pPE2HKg8jaLtqLJCRpktfXz/HMvdr165x/vx5vLy8SE5Opnr16so+/cxzatasqQTmevToEZCxVSAzuFd0dLQSAf3ZvNx//fWXMruXGS18z549zJ8/H61WS2RkpFI2a1q03FhaWmJllZGrOiYmJsf7c+bMYdWqVXh4eFCvXj3Gjh3L+fPniYuLo0uXLkBGvmITE5Ns502aNIkZM2aQmprKpEmTsLV9+futzQwtGNdqA2uCp3Lm9u5sWwDUKg2Olbzo7TQNM0OLPGp5c73oqN2v2tt+f0K8KmkPEng8Yz9J+69Cavo/b8w6gKGnLcUntUSvtEnuFQghipR0/oUQogilPoogPnhTjuN7b8HMJioal8voZI/cn05c0Ea0qc450nxl2rhxI0uWLKFly5ZARgTv9PSML0+Z5zyb4gvA0dGR9evXY2JiQkpKipIm7tnBhRo1arBt2zZGjx6tnD9r1iwOHTpEcnJytkjpubUxK11tyWrcuHFKpPXg4GASEhIICAjg119/JTw8XGcbISOF0JIlSzh69Cjff//9K8vra2ZowXDXn4hJvE9I+B4Sn8ZiXMwcx4pt3rql/s96m6N2w9t/f0K8CmkPEnjY73fSbsXkfDM1naTdV0i5eJ9Sq/s+1wCAnp4ednZ2pKSkoNFoGDhwIB9//PFrEy8mqylTpuDu7o6np+dznd+kSROOHj1apG0KCwujffv2hIYWPOaJePNJ518IIYpQ/Okt2Zb6Zzp0R8uAmv90jquVgKCIVNIeR+com2nHjh2MGfNPyrjatWtz+PDhfNswbdo0OnTogFarpVSpUmzYsEFnOQcHBypXroyrqysGBgZs3LiR9u3b4+7ujouLCyVKlMj3Ws+rRo0aXLt2DS8vLypVqpRnRH8fHx+OHz9OfHw833333QtrU0GVMC6TI5r/f4FpcQP6D3J6a6N2v+33J4pObGwsnTp1AjK2Tzk6OlKlShX69etXqM6dm5sbgYGBXL16lQEDBrB161a++uorfvjhhwLX4ePjg7e3d7b0mc9avXo1P/zwA/r6+tSqVYvFixcXuP6Cunz5co4UkI9n7tfd8c8i7VYMj2fup8T3HfMsp4uRkREhISEAREZG0rdvX+Li4nLEpnkdZKYmfV5F3fEX/10S7V8IIYrQg60zebBxSoHLl+76FaU7TnyBLRJCCFEUsmZv0TMphWn9Tnh06ElgYCA+Pj64ubkVuvO/ceNGOnbsyKpVq6hWrVqh21SQzr+LiwtHjx5Fo9Hw6NEjSpYsWejrPOvZiOrPdv7TIuOJ8vwl+1L/3GjUWO4fgZ6laaHaYGpqSnz8P7EDrl+/ToMGDYiOjubmzZsMGDCAhIQEAH788UeaNGmCv78/Pj4+WFhYEBoaipOTE6tWrUKlUnHq1Ck+/PBDEhISMDAwYP/+/RgbGzN+/Hj8/f1JTk7mgw8+YMSIEbm2KS0tjaFDhxIUFIRKpWLIkCF8/PHHeHt70759e7p3746NjQ2DBg1i27ZtpKSk8Oeff1KzZk2ioqLo27cvERERNG7cmH379hEcHIyFhYVyr3m1f+fOnXzyySeYmJjg6urK9evX2b59e65tzTrzn5SUxKhRowgKCkKj0fD999/TvHlzGjVqxG+//UadOnWAjODBc+fOpVatWowZM4bQ0FBSUlLw8fFRBsReNYn2n7fXb12MEEK8wfRMShWy/L//EiaEEOLF0ZW9JXLlaK5/VoWnd68owVtXrFiBp6ensr0pKiqKjh070rx5c95///0c9SYmJtKzZ0/mz5+vdPwzU6DqyrJy4sQJ6tevT58+fahfvz4Ao0ePpkKFCnlmRUlISODEiROkp6crHX9vb2+GDh2Ku7s7U6ZMybW9uo7du3ePyMhIrly5wp07d4CMTu/169e5cOFCRnaaZ/f45/mA00n2u1qwsnl45513SEtLIzIyEisrK/bt28fp06dZu3YtY8eOVcqdOXOGefPmcfHiRa5fv86RI0d4+vQpvXr1Yv78+Zw9exY/Pz+MjIz47bffMDc359SpU5w6dYpff/2VGzdu5NqGkJAQwsPDCQ0N5fz58wwePFhnOQsLC06fPs2oUaOU4LrTpk2jRYsWXLhwge7du3Pr1i2d5+pqf1JSEiNGjGDXrl0EBwcrsX4K6qeffkKlUnH+/Hn++OMPBg0aRFJSEr169WLdunUA3L17l7t37+Ls7MzMmTNp0aIFJ0+e5ODBg4wbN04ZaBGvN+n8CyFEETKt3wn0CrijSk+DqVPnF9oeIYQQzy+37C0ApKWSFh/N7ZlNSU9OwNHRET8/P27dukVMTAyzZ89mwoQJHDx4kOLFi3Ps2LFsp1+7dg21Wo2Li4vOaz+bZeWrr75i69atLFmyROkYWlhYoK+vn2dWFF9fX+bMmYOtrW22TCwtWrQgICCA06dPEx4errO9uo7NmjWL4sWLU6NGDWXLVkpKCjY2NlSuXJkHDx6QHptUqOdc2PL5SUlJ4b333sPOzo4ePXpw8eJF5T0XFxcqVqyIWq3GwcGBsLAwrly5Qrly5WjQoAEAZmZmaDQa9u7dy4oVK3BwcKBhw4Y8ePCAq1dzH6h45513uH79OmPGjGH37t2YmZnpLNe1a1cgIxBvWFgYAIGBgfTu3RsALy+vXFdo6Gr/5cuXeeedd6hSpQoAffr0KdTzCgwMVNL21qxZk8qVK/PXX3/Rs2dP1q9fD8C6devo3r07AHv37mX27Nk4ODjg4eFBUlJSroMV4vUie/6FEKIIaUqWx9SpC/En/8y3rKlTFzQlyr2EVgkhhHgeurK3PCvl/jUSonbR9KP/AVC+fHliY2O5dOkS48ePR6VSER8fn6OTb29vj6urK5MnT2bmzJk56n02y0pcXBwVK1YEyJH1JK+sKA0aNGDz5s0kJibSvHlz+vXrB2QEhwWws7Pjxo0bOtur69hff/2FgUFG8MvMQK+Ghoao1Wr09fVJTU1FXcg4GYUtr8v169fR09PDysqKadOmUaZMGc6ePUt6ejqGhv/Un9l2yAgamJqaM05PJq1Wyw8//ECbNm0K1IaSJUty9uxZ9uzZw6JFi1i3bh1Lly7NUS6zDfldX5fCtP/fqlChAqVLl+bcuXOsXbuWRYsWARnPZcOGDdSoUeOFXVu8GDLzL4QQRcyq/wL0y+S9d1O/TDWs+i94SS0SQghRWLllb9HlacQl0hMeKq+1Wi01atTg+++/x9/fn6CgIJ17oqdPn87ly5f5448/crz3bJYVMzMzIiIiSExM5Nq17AMSmVlRhg8fzvfff5/tvcyZamNj42zpWs+ePQtAaGgoNjY2Otub27GnT58q96mLQUtb0BSwm6FRY+D571K4RkVFMXLkSEaPHo1KpSI2NpZy5cqhVqtZuXIlaWlpeZ5fo0YN7t69y6lTpwB4/PgxqamptGnThoULF5KSkgJkpMjNXN7+bIBDyEivm56eTrdu3ZgxYwanT58u8D24uroqS+z37t2rpO8tiBo1anD9+nVlFcHatWuV906ePMnAgXkHqW3atCmrV68GMu7x1q1bSse+V69efPvtt8TGxmJvbw9krEr54YcflH//M2fOFLit4tWSmX8hhChiGjMrKk06TOSqsRlfHLMuFdXTYOrUBav+C9CYWb26RgohhMhTbtlbdNKm8+Svo0Av5dDEiRMZPnw4sbGxqNVqlixZkiMwn0qlYsWKFbRq1YqqVavmeYkvv/ySDh06UK1aNSpVqpTtvbyyonz88cdKR7J79+6YmmYE1jt06BA///wzzZo1o2LFijrbq+vY+PHjuXz5MleuXMHExERZjZCVnpUphp62JO2+ku+jM/S0LXSwP4AnT57g4OCgpPobMGAAn3zyCQDvv/8+3bp1Y8WKFXh5eWFikncqwWLFirF27VrGjBnDkydPMDIyws/Pj2HDhhEWFkb9+vXRarVYWlqyefNmoqOjdQ58hIeHM3jwYCUt76xZswp8P1OnTqVPnz6sXLmSxo0bU7ZsWYoXL16gc42MjPj555+Ve83cvgBw69atbIM+urz//vuMGjUKOzs7NBoNvr6+ygqD7t278+GHH/Lll18q5b/88ks++ugj7O3tSU9Pp0qVKnkGFxSvD4n2L4QQL9A/0aEfoWdSElOnzrLUXwgh3gCvW/aW1NRUNBoNCQkJtG7dmiNHjjx3Xd7e3kyePPm5MgyA7ojqz0p7kMDDfr/nme5Pz7oEpVb3Ra903p3z18327du5fv16tkCC/1ZycjJ6enpoNBqOHTvGqFGjlFSGBREfH4+pqSlarZYPPvgAW1tbPv74Y8aNG8eAAQOUWfu3nUT7z5vM/AshxAukKVmeEi1HvepmCCGEKKTXLXvLkSNHmDJlCo8fP1Yi9L/O9EqbUGp1Xx7P3E+S3zPR/zVqDD1tKT6p5RvX8Qdo3759kdd569YtevbsSXp6OsWKFePXX38t1Pm//vory5cv5+nTpzg6OiopCefMmVPkbRVvLpn5F0IIIYQQ4hmpjyK4/lmVgi3919Pwzndh/5mVXQWZ+c8qLTKe5P1XSY9NQm1uiMFzLvUXIj8y8583mfkXQgghhBDiGZK9pejoWZli3MfxVTdDiP88ifYvhBBCCCGEDpK9RQjxNpGZfyGEEEIIIXSQ7C1FIybxPiHhe0hIjsHEoAQOFdpQwrjMq26WEP850vkXQgghhBAiFxozK8q/v0aytzyHuKRo1gRN4cydPaRr/xk4WRs8DcdKXvR2moaZocUrbKEQ/y2y7F8IIYQQQoh8ZGZvKd1xIiVajpKOfz7ikqKZs68bwbd3ZOv4A6RrUwm+tZ05+7oRlxT9XPWbmv4TMHDnzp1Ur16dmzdvsmjRIlasWAFkpDRcv349AB4eHhQmkLm/v3+RR/W3sbEhOjrn/fr4+DB37lwApkyZgp+fX5Fe90UICwvj999/z7dcREQE3bt3fwktEgUhnX8hhBBCCCFEkVoTPJXI+LA8y0TGh7EmeOq/us7+/fsZO3Ysu3btonLlyowcOZKBAwf+qzpfpenTp+Pp6Vng8qmpBchG8QIUtPNfvnx5ZQBGvHrS+RdCCCGEEEIUmZjE+5y5vbtAZc/c3k3sk/vPdZ2AgADee+89tm/fTtWqVYHss+i52bt3L40bN6Z+/fr06NGD+Ph4AHbv3k3NmjWpX78+GzduzPf6d+/exd3dHQcHB+rWrcvhw4cB+OOPP7Czs6Nu3bp88cUXOs+dOXMm1atXx83NjStXrijHs65WCA4OplmzZjg5OdGmTRvu3r0LZKxi+Oijj3B2dmb+/Pn8+eef1K1bl3r16uHu7p5vu1esWIG9vT316tVjwIABAGzbto2GDRvi6OiIp6cn9+9n/Jv4+PgwYMAAGjdujK2tLb/++isA48eP5/Dhwzg4OPC///2PsLAwmjZtSv369alfvz5Hjx4FMgYJ6tatC4Cvry9du3bFy8sLW1tbPv/883zbKoqW7PkXQgghhBBCFJmQ8D05lvrnJl2bypk7e/CwLdxsfXJyMp07d8bf35+aNWsW+Lzo6GhmzJiBn58fJiYmfPPNN3z//fd8/vnnvPfeexw4cIBq1arRq1evfOv6/fffadOmDZMmTSItLY3ExEQiIiL44osvCA4OpmTJkrRu3ZrNmzfTuXNn5bzg4GDWrFlDSEgIqamp1K9fHycnp2x1p6SkMGbMGLZs2YKlpSVr165l0qRJLF26FICnT58q2xjs7OzYs2cPFSpUICYmJs82X7hwgRkzZnD06FEsLCx4+PAhAG5ubhw/fhyVSsWSJUv49ttv+e677wA4d+4cx48fJyEhAUdHR9q1a8fs2bOZO3cu27dvByAxMZF9+/ZhaGjI1atX6dOnj85tFiEhIZw5cwYDAwNq1KjBmDFjqFSpUr7PWhQNmfkXQog3WEziffyvrmDWsjGUrWBB02autGrVigcPHuR6zpgxY/KtNywsDJVKpfzhvn//PhqNBn9//6JquhBCiLdUQnJMoconPo0t9DX09fVp0qQJv/32W6HOO378OBcvXsTV1RUHBweWL1/OzZs3uXz5MlWqVMHW1haVSkX//v3zratBgwYsW7YMHx8fzp8/T/HixTl16hQeHh5YWlqi0Wjo168fAQEB2c47fPgwXbp0wdjYGDMzMzp27Jij7itXrhAaGkqrVq1wcHBgxowZ3LlzR3k/6+CEq6sr3t7e/Prrr6SlpeXZ5gMHDtCjRw8sLDICLZYqVQqAO3fu0KZNG+zs7JgzZw4XLlxQzunUqRNGRkZYWFjQvHlzTp48maPelJQU3nvvPezs7OjRowcXL17Uef2WLVtibm6OoaEhtWvX5ubNm3m2VxQt6fwLIcQbKC4pmsWB7zNhaxP+CPqSo9fXUd4lhbqj7lGpsZZlK37N9dwffvihQNdwcnJi06ZNAGzZsgVHR8ciabsQQoi3m4lBiUKVNy5mXuhrqNVq1q1bx8mTJ/n6668LfJ5Wq6VVq1aEhIQQEhLCxYsXCz2AkMnd3Z2AgAAqVKiAt7e3EmiwKGi1WurUqaO08/z58+zdu1d538TERPl50aJFzJgxg9u3b+Pk5JTnBEBuxowZw+jRozl//jy//PILSUlJynsqlSpb2WdfA/zvf/+jTJkynD17lqCgIJ4+farzOgYGBsrPenp6ryxmwX+VdP6FEOINk18E5Su3TuN3eQlxSdF8+OGHNGvWjKZNm3Lr1i0gY2kfZOwrHDlyJG5ubkybNi3HdWrWrMmlS5cA8PPzUwIQRURE0Lx5c9zc3Hj//feBjKjIbdu2pUOHDri6uir7J4UQQvz3OFRog1pVsN3FapUGx4ptnus6xsbG7Nixg9WrVxe4A9+oUSOOHDnCtWvXAEhISOCvv/6iZs2ahIWF8ffffwMZ+/YznTx5UmcQwZs3b1KmTBnee+89hg0bxunTp3FxceHQoUNER0eTlpbGH3/8QbNmzbKd5+7uzubNm3ny5AmPHz9m27ZtOequUaMGUVFRHDt2DMiYWc86G5/V33//TcOGDZk+fTqWlpbcvn2b8PBwWrZsmaNsixYt+PPPP5UBgsxl/7GxsVSoUAGA5cuXZztny5YtJCUl8eDBA/z9/WnQoAHFixfn8ePHSpnY2FjKlSuHWq1m5cqV+a5AEK+GdP6FEOINk1sE5auBCWycdI+Lfo8p55LMmuCpzJo1i0OHDjF16lR++eWXHOe0adOGwMBAdu7cqfNaNWvW5MSJExgaGiqj9RYWFuzbt4/AwEDi4uK4evUqAMWKFWPbtm28++677N+/v+huWAghxBulhHEZHCt5FaisYyUvzI3KPPe1SpUqxe7du5kxYwZbt27Nt7ylpSW+vr706dMHe3t7GjduzOXLlzE0NGTx4sW0a9eO+vXrY2VlpZxz69YtjIyMctTl7+9PvXr1cHR0ZO3atXz44YeUK1eO2bNn07x5c+rVq4eTkxOdOnXKdl79+vXp1asX9erVo23btjRo0CBH3cWKFWP9+vV88cUX1KtXDwcHByWI3rPGjRunBBhs0qQJ9erV4+7du2g0OQdg6tSpw6RJk2jWrBn16tXjk08+ATIC+/Xo0QMnJydlS0Ame3t7mjdvTqNGjfjyyy8pX7489vb26OnpUa9ePf73v//x/vvvs3z5curVq8fly5ezrUwQrw+VVqvN9U1nZ2dtYfJhCiGEeLFiEu8zYWuTHDP+EReTCA9NokHPEvgveoD9u8WxqGyM+fkuHAk4QUpKCrVq1WLZsmW4ubkRGBiIt7c3kyZNwtbWFg8Pj2z7+cPCwpg8eTJjx45l+PDhTJw4kYsXL+Lh4UGNGjUYNWoUMTExhIWFsXz5crRaLfv27WPmzJn4+vqiUqkYNGjQS346QgghXoZLly5Rq1atPMtkrlLLK92flakN41ptwMzQItcyr4Nx48YxYMAA7O3tX3VTCuzHH3/E2tpaZzyBwvDx8cHU1JTPPvusiFr2Yun63VSpVMFardb5FTXptSIz/0II8QYpSARlh45mnNkSR2JcMrv9dnD48GG++uordA326tq3l1WDBg1wcnLi3XffVY79/vvvSoRlV1dXpd6sdeU1sCyEEOLtZ2ZowbhWG3Cybp9jC4BapcHJuv0b0fEHmDNnzhvV8QcYPXr0v+74i7ePpPoTQog3SEEiKJcor09SXBppqVqKGerRokWL5/7SolKpcuyjbNGiBQMHDmTz5s3PVacQQoj/BjNDC4a7/kRM4n1CwveQ+DQW42LmOFZs86+W+ouXx8fH51U3QRQhWfYvhBBvEP+rK/gj6MsCl+/j/FWhcycLIYQQeSnIsn8hXgVZ9p83WfYvhHgh/P390Wg0REZGAnDq1ClUKhVhYWHs3r2bHTt25Cif1+hyXrnpvb29lai9mTZv3qxEsM3qxo0btG/fnmbNmtG8eXNOnTpViLvKkBkt/1V4WRGUhRBCiLzI9i7xupHfyfxJ518IUaRiEu/jf3UFx25soGqtSvzx50oANm3ahLNzxqCrl5cX7dq1K1S9Bc1Nnym3zv+wYcP47rvvOHToEFu3bkVPT69Q9b5qLzOCsvhvSouMJ/GPM8QvOsaW8T/h1qgJHh4efPLJJ3mmblq6dGmB6tdqtbRr1w53d/cc9X311Ve0bdtWeX369GkcHR2ZOXNmtnK6BhCFEC+PoaEhDx48kM6WeG1otVoePHiAoaHhq27Ka032/AshikRcUjRrgqZw5k5GQLqI60kY2jzhh9VTMXK4ztnz16lTpw4Avr6+pKamMmzYMIYMGcKtW7coW7Ys1apVA8DOzg47OzsuXLjA8uXLcXBwUCLUb926lWnTpuHk5MTFixcJDAwEYO7cuYSGhtKqVSsGDx7M7t27uXTpEt27d2fcuHFARj7ecuXKUaNGDQCKFy9O/fr1iY2NpV+/fsTFxeHg4MCCBQvo3bs3v/zyC+vWrWPHjh1s3ryZ9u3bs2XLFlJTU/H29iY0NBQfHx/at2+Pn58fkydPBmDGjBnUqVOHjz76iLVr15Kamkrr1q05cOBAkTzr3k7TuP0wNN8Iyr2dphXJ9cR/Q9qDBB7P2E/S/quQms6D1ARm3VnB7+8Mo1STuvxkcoZff/2VkSNH6jx/6dKlDBkyJN/r3L17l+LFi+vsvB87dgxjY2NiY2MxNzdn9+7dzJo1Cy+vfwa80tPTs70W4m1ka2vLV199Re/evf91Xc9mcykKFStW5M6dO0RFRRVpvUL8G4aGhlSsWPFVN+O1Jp1/IcS/lls6Hz2NCj192Ll/AzEGxSiRZp7t/ZMnT6Knp4efnx9ff/01T58+BSAyMpKlS5cSHBysdP4zffPNNwQEBBATE4OHh4dyvE2bNixatIiGDRsydepUvLy8mDx5sjKgABmdjnLlyuVo/+LFi+nVqxcDBgxg2LBhnDhxgoYNG3L8+HFOnTqFgYEBKSkpqNVq9PT0iIqKYt26dZQuXZrWrVvTvn17fHx82Lt3L5CxsuHo0aMkJiby+PFjjh49iqen5798yv/IjKC8JngqZ27vzhb9X63S4FjJi95O096ICMri9ZD2IIGH/X4n7VaMcmx//CW6m9fHOF2fpN1XGFjBkqFp6xk5cqQyGBcWFoaPjw9du3bl/PnzeHh4MGnSJFq1agVAamoqAwYMIDw8nAoVKrBy5Uo+//xzDh48yLBhw1iyZIlyvRs3blClShUaN27Mjh07cHFxYfHixZibmxMfH8+PP/6Ii4sLEREReHp6KgOIkydPxt/fn2LFirFx40Y2bdrE8uXLiY+P5+uvv6Z169Yv+3EKUWixsUlcOH+PJ4kp3Im4iotLY7Zt21Yknf8XQV9fnypVqrzqZgghCkk6/0KIf21N8NRcZ6ErORgSuPQhTYeW4saxkGzvXb9+HUdHRwCcnJw4duwYANWqVcPQ0JAKFSoQExOT7Rw9PT1MTEwwMTHBwuKfzm3dunUBMDIyyrWd5cqVIyIiIsfxv//+W0ll5+zszLVr13B1dWXHjh08efIEe3t71q5dq7S1dOnSWFtbK+2BjKj4ZmZm2Y517dqVLVu2cODAAWVVQFGRCMqiKD2euT9bxx/gfupjahmUVV7rhyfw5PF9ned37NgROzu7HLOLmzZtonbt2vzxxx/MmDGDDRs2MGPGDIBsHf/Mst27d8fJyYkPPviAvn374u3tjZubG56envz444906dKFxo0b4+vrC8CZM2e4fv06gYGByvLjXr16MXjwYGJjY+nRo4d0/sVrLf5xMps3hBJ6/h7p6Rm/w4HHVmNb1YULl7fyIDqObt074uzsjL+/PwMHDuTkyZOcP3+eb7/9ljZt2jBr1ix2795NUlISixYtwtHRke3btzN16lTs7e1JSUkB4Nq1a7z//vskJyfTqlUrJk+ejIeHB35+fmg0GmWFwMSJEwkICEBfX5/Vq1dTvnz5V/mIhBBFSPb8CyH+lZjE+5y5vTvX960djLCoUgzLqsV4kBBOXNI/SwSrVKnC2bNngYwv8Znyyhefnp5OYmIiERERREdH6zwHMmYlnt1PXLlyZe7du8eVK1cAiI+P58yZM1StWpXg4GAAgoKCqFq1Ko6Ojuzdu5cyZcrg6urK3LlzadKkCQAPHz7kzp07JCYmKtdIT08nLi6OuLg45Vi3bt1Yu3YtERERvPPOO3k9xudWwrgMHrYDebfOGDxsB0rHXxRaWmQ8SX5XcxwvoynOvdQ45XVSegrqmKekRcUrx/Lb7/v3339Tv3594J+Btdzs3LmTWbNm0bNnT44ePUpSUlKOMk5OTtle//XXX8r/lyqVCpVKxZ49e/Dw8KBjx47cvn07z/YJ8SrFP07mpwVHOHf2rtLxB4iMuk4ZK1tKlajNZ5/8SFpaOv369ePIkSNMmzaN7777jl27dvHTTz8B8OGHH3Lo0CFWr17N3LlzAZg1axaHDh1i+vTp3L+fMWg3adIkfvvtNw4dOsSFCxe4c+eOznYdOXKEgIAADh48qHO1nCiYe4lxLL90jNmHt1DTpT5N3Jtibm6Oh4cHgwcPxs/Pr1D1ffzxx7i5udG4cWMWL15cpG3NupIyN7nFa6lSpYryewcZkzGFSQ+YV0BnUfRk5l+8Ve4lxrHn5gVikhMxSNGy/ss5pCYlk5SUxI8//si8efNYtWrVq27mWyUkfE+2ZefP0jdU02x46f9/peXi3QAg43XDhg1ZuHAhLVu2pHLlyspsel4+//xz3N3dcXBwoEyZ3Du6bdq04f3336dHjx7Z9igvWbKEMWPGEB+f0YGZO3cu7733Hn379uXXX3/F3t6eRo0aAVCsWDFcXV1xcXHh8uXLynELCwt8fHwICQlhypQpAEydOlVZ6jx9+nQAzMzMMDQ0LNIl/+L1sX//fr766ivS09MpXbo0S5Ys4ddff2XAgAFERUWRnp5O/fr1CQkJITg4mKFDh/7ra/r7++Pn56fMov/+++/8+eefqNXPP5af/P97/J/VwrQmQ++soKu5I8bqYix+eJi2JrVJ9ruqdMzPnz+vlH92AA5QBtbatWtHUFBQtm04Wd27d4+KFSsqM/qLFi1i3759Oco9e581atRg27ZtjB49Gsj4cprZ6UlOTsbV1bVgD0GIV2DzxlAeRCdmO/YoJoLo6DD+3DSVtLQU7keW52lKAnXr1kVfX5+aNWsqf/sePXoEwMqVK1m9ejVqtVr5/1CtVmNqaoqpqSmWlpYAXLlyhQEDBgAQExNDeHi4zsH2zz//nEGDBlG6dGlmzpyJiYnJi30Qb5noJ/FMPr6F3TcvkKr9/8/WUa1JUqkx/i6S9bu28+M3c/Ou5BmhoaFER0crcY4y/+1fptzitZQvX57jx48DGQOyxsbGhaq3sAGdxb8jnX/xVtD1QRu/Pxi98sXoOWAQU53fJSpMZoBehITkGJ3Hy9c2pHzt7BFXPUaWxsyqGL2beyvHMr/sZ5X5x83GxkZ5P/PYu+++S8eOHQkPD2f48OE56shcdty9e3e6d++eo+4qVaqwffv2HMd37tyZ49ihQ4eUn7POQmb+kcuqdevWOpcX6+vr07NnzxzHxZspc1/u3Yj7zPh6Itu2b6diRUv++usvnj59yvjx4wHYt28fqamp1K9fHwcHh2xxK4rCkSNH+Omnn9ixY8e/6vgDpMfmnGEHsNCYMtaiOX1uLUGtUlHXoALvl2lGemwS7dq1w83NjYYNGyrlXVxc6Ny5M59++ilNmzYFoHPnzqxfvx53d3fKlSvHF198QXh4eI5rbdmyRTkHMmahvvnmGypXrpxn2x0cHKhcuTKurq4YGBiwceNG2rdvj7u7Oy4uLpQoUeI5nogQL15sbBKh5+7lOP7XtWO08RxLZet6AGzc+hXJyck8jkumVGl9nZ31n3/+mTNnzvD333/z3nvvARmr0RISEnj06JESlK9GjRrMmzePcuXKkZaWhkqlwtzcnLt372JkZMS9exntadGiBe3atePrr79m+/bt9OrV64U+i7dJ9JN4Ou9YSNjjBzneS9Wm8zApgc47FtIk5SkrVqxg9uzZ2NjYsGTJEqKiohg6dCiPHz+mVq1a/Pzzz8q5RkZGXLlyhWvXrlGtWjVKliwJwKpVq/jxxx/R09Pj559/pl69ejlisvj6+tKoUSPs7Ow4deoUs2fPxsvLi8WLF7N06VIaN26cs62FiNeiUqkoU6YM9+7dY+PGjXTp0oXk5GTCwsKYPHkyq1atwt/fH39/f8aOHUvXrl1RqVTY2dmxYMECpb2XL19WJmu6dOnChx9+WNT/PALp/Iu3QG4ftKpi+iRdu8Pm88c5/yCcBXXf5ebNm3Tr1o2wsDC2bNlCmTJl8PLyIiUlBUtLS9atW8ft27cZMGAAVlZWSrmKFSsyffp0Dhw4gFqtZunSpdjY2LyaG37NmBiUKFR542Lm+RfKw/r161m4cCEJCQksWLDgX9X1og0fPhwrKytZNvkWeHZfbujF/ZSxdOHH/52krn1ZOneti2lxA7y9vZk8eTKLFy/mwYMHHDx4kPfeew8/Pz86duzI559/DmSksLtw4QIRERF88cUXpKSkMGzYMAYPHoyHhwdOTk4EBAQwcuTIHCsGrly5wv79+9m+fTtGRkZERETQr18/UlJSsLe35+eff8bf359vvvkGjUbDw4cP2bNnDyYmJrz//vtcuXIFIyMjVq1aRcmSJVGb554WqaVpLVqa1sp2TG1uyLSR05g2LXs2ie+++y7H+fr6+qxduzbbMRsbmxwrsEaMGJHtdc2aNVm2bFm2Y1njCXh7eys/P5sGcMqUKcqKHCFeVxey7PHP6vqNU9R3aK+8Ll2qEieDNhB6/h7uHrpXzri4uODu7o67u7ty7IsvvsDd3Z369etTtmxG7I6ZM2cyZMgQkpOT0dfXZ8OGDQwfPpwOHTrg5uamrBDo1KkTT548AeDPP/8ssnv+L/jy+FadHf+swh4/IOnOVfo5urFixQpat25NTEwMs2fPZsKECTRu3JgvvviCY8eOKR3zqlWr8tFHHzF48GAePXrE4sWLadiwIQsWLODIkSOEh4czevRonZMbkLFdcebMmaSkpDB69Gg8PT357bffOHLkCCdPnsy29RIKF68FMn5nNm/ezIkTJxg7dmy2yZOszpw5g4eHBz4+Pjm2jU2YMIFFixZRs2ZN0tNzrkYTRUM6/+KNl9sHrXHjOqQ9ekz0t3/wyMyEGR8kEh8fz6FDh/jjjz/YsGEDY8eOVb5AT548mQMHDmBra5ujXPPmzQkPD8ff359Lly4xa9Ysfvnll1dwt68fhwptWBs8Lc+l/5nUKg2OFdv8q+v17t37tY1+/Kyi3pMnXo3MfblZl+cmJDzEwsKG9HQt50LuEn4nlg/G/rPEfPjw4Uo0+sxOq4uLC/7+/uzatYstW7ZQqVIlhg0bxtatWylevDitWrWiX79+APTv35+vv/6aVq1a5ej87927l48//pjSpTO2z1hYWLBv3z40Gg39+/fn6tWM/fvFihVjy5YtzJw5k/3796NWq7G2tmbhwoXs2rWLRYsWMWHCBAxa2sKsAzqX/uegUWPgaftvHqcQAniSmKLzeJ8es7O9bubmTTM3b1JS/j8Y4P+vgsv686+//pqjno4dO9KxY8dsx6pVq8auXbuyHXv33XeVgLeZMjPXiMK5lxjHrpuhBSp7LTaSCtUysiWUL1+e2NhYLl26xPjx41GpVMTHx+Pi4pLtnL59+9K3b19u3LjBkCFD+OOPP6hcuTL6+vrY2NgQGxubrXzWzrWlpSVWVlZAxpaP6OhoKleujEajyRFLBXLGawkODs620utZzZs3Z+bMmdjZ2SmrU3StUnF3d+fQoUP069cPLy8vZRsKQHR0NDVr1gRybvESRUc6/+KNltcHrUqjh1lHV8w6upJ4/CJ7VqzFq3pd1Go1FSpU4Nq1ayQkJDB8+HDCw8O5f/8+tra22NraUrt27WzlLl++jL+/vxIQRWZy/1HCuAyOlbwIvqV7tDkrx0peEpBOvHF07cs1MSlFfPxD5fWD6EQ2b8z/S9/169eZP38+W7ZsAeDs2bPKF/To6GhleW7m/l5dX4BGjRrFkSNH2L17N15eXjx48IBRo0YRExNDWFiYktEiMwNGZtaM+/fvs2bNGvbs2UNqaqoyo6RnZYqhpy1Ju6/k235DT1v0LE3zLSeEyJuRsX7hyhsVrrx4+fZk3eOfj3StlqDIW2RuCtRqtdSoUYP+/fsrnfHU1H8mVR4+fIhKpaJkyZKUKlUKlUqFpaUlN2/eJCUlhfDwcMzNM1ZW5heTRavVYmFhwc2bN0lLS8sx6w8Fj9eSSV9fn/bt29OsWTMSEzP+XpqbmytbSTLbkpaWpsRFcnBwyNb5t7TM2EJXvXp10tPTZQDgBZHOv3ij5fVBmxodi14JU1QaPdRmxmjT07mb+M+oqFarZc+ePVSvXp3ff/+dSZMmKSOTz35IVq9endatWytBSTLT5ogMvZ2mcfthaK7p/gCsTG3o7TQt1/eFeB3lti/3HRtntuyYRa0aTSlWzJhHMREcP/qIlKcZEZD19fVJTk7Odk5iYiIjRoxg2bJlGBgYAODo6Mj69esxMTEhJSUFff2ML/i6gudl0mg0rF27ltatW2NjY8OOHTvo3Lkz3t7e9OvXL9fPsRo1ajBw4EA+/fRTIPvnWPFJLUm5eD9Hur+s9KxLUHxSy7welxCigOrYlWXrpgs6l/4/S61WUdeubL7lxKsVk5yYf6EsElKz/42YOHEiw4cPJzY2FrVazZIlS5Qtpg8fPmTIkCFARjyH6dOno6enxwcffEDTpk1Rq9VK9gddMVmepdFoGDx4ME2aNKFZs2Y53i9ovJasxo0bB/yzRatEiRJYW1vj6elJtWrVKFu2LCdPnmTixImkpKTkCIb89ddf895776FSqWTP/wsknX/xRsvrgzbldiQPF25BVUyDSk9N8Y5uPH3mS3zDhg2ZOXMmQUFBmJubY2urezmrg4MDZcuWxcPDA5VKRZ8+fZRgcyIj5/y4VhtYEzyVM7d3Z9sCoFZpcKzkRW+naZgZWrzCVgpReLntyzU2Nqdxw95s2JIxg2FoYIpXq7E8epSxT7ZRo0Z4e3sTGhpKt27dANiwYQNXrlyhf//+AKxZs4Zp06bRoUMHtFotpUqVYsOGDQVqV6lSpVi5ciX9+/fnl19+wdvbm82bN+d5TseOHRk7diwtWrQA4KOPPlJWHeiVNqHU6r48nrk/I+1f1i0AGjWGnrYUn9QSvdIvJ+r3oUOHmDZtGunp6ejp6TF9+vTnityfNeBVpubNm7Nt2zZMTU1599136d69O0OGDGHp0qUkJCSQkJDAgAEDlKCNnp6eStCqTL6+vsq2DiBbrvSC8Pf3x9ramnfeeYfdu3eTlpZGu3btCn1/4s1lbm5IXfuynAu5m2/ZuvZlMcsjNod4PZQwyD/KveXEjM9/s85uuDRyA7IHLd60aZPO86pVq0ZAQECO44MGDWLQoEHZjk2bljMmS9btIpmd85EjR2bLhpRVQeO1PFs3ZHweZq6UXbp0ab7lM1/XrFkz11gBouio8srR6+zsrA0KCnqJzRGicJZfOsak41sKXH5mo04MqpUzqqkoOjGJ9wkJ30Pi01iMi5njWLGNLPUXb6z9e6+yZ1f+y+EztXm3Bi1bvdl74tMi40nef5X02CTU5oYYvKSl/pnXjQy/x8ClX7Jl21ZKVi3P48ePuXbtGo6OjoWuU1fn/7PPPqNDhw40a9aMzp07U7FiRX788UdGjRqFt7e3MluW2cF/EZ1/Hx8f3NzcXnga0Mxn+uheNH1+nYCqpBEhoedwdHSkSpUqOYIqvihTp05l7969/Pjjj8qSZm9vby5duoSRkRGQsc+8WLFiedbj4eGRLfDjqxAWFsb169eVQbR/Q1c8kWeVtjDmg7GumBY3+NfXEy/WvcQ4Gq2bXaCl/xqVmhM9x1PG2OwltEyoVKpgrVbr/Krb8TqQmX/xRmtTuQ5TT2wr8AetV+U6L6FV/20ljMvgYTvwVTdDiCLxX9yXq2dlinGfwne0n1fagwQez9hP0v6MFQdbY4LppLUhucsfxPz/ioPMjv8333zD1q1bMTAwwNfXF2tra53HpkyZwsGDB6ldu3aO67m4uHDy5EkqV65M3bp1uXnzJpARf8HBwUHJ2PA8Ms+tVq2a0lH18fHh+vXrREREYGNjw88//4yvry+bNm3C09MTOzs7ZZBBV6aZwYMHc/v2baytrbG2tsbHx6fQz1QPWEcveKyms+kD9m/Ykesqjhex1/bAgQMcO3Ysx/HVq1fnu5e4KOjaCvO8wsLCOHDgQJF0/k2LG/DBWFc2bwwl9Fz2VUZqtSpbJhHx+itrbEbbynXZFnYu37JtK9eVjr94JaTzL95o8kErhHiRZF/ui5X2IIGH/X7PFmvgfmoctQzKQmo6v69dw/Jln+Datx2ffzmBAwcOcOTIEQIDA5k1axZTp07NcWzKlCmcPHmSw4cP8/vvv+eIXO7i4sL69euxsbGhQYMG3L17l8ePH6NSqZRYDAUxZ84cZTVASEhInmUdHR2VlF6JiYl4e3srM/9ZVyU8m2mmUaNGGBgY4OfnxzfffKOkXyvsM1WkppP+6AlrWo1nkcFp0lRapkyZgpeXF40aNcLR0REjIyMePnyIkZER586do3nz5sTExHDs2DE++OADhgwZwsSJEwkICEBfX5/Vq1dTvnx55RLPDsZs3bqVc+fO4eHhwfbt2zE1zX0Vibe3N4aGhoSGhtKqVSumTp3KqVOnGDFiBNWqVePRo0cAOvOh7969m9mzZxMfH8/YsWMZOHAgPj4+3Lx5k/DwcN577z2WLVuGRqMhKiqKESNGsHLlSgwNDZX0aM+mwjx79myOlJmLFy/myJEjHDt2jP379+f775Ef0+IG9B/kRGxsEhfO3+PJkxSMjPSpaydL/d9EXzXqyPkH4Xmm+7MpXpqvGnXM9X0hXiQJoyjeeF816ohN8dJ5lpEPWiHE88jcl1sQsi+38B7P3J+jk1pGU5x7qXEAdDV35MfSPbjnH0pYWBj29vZARuqpa9eu6Tx28+ZN5ZiuFFY2NjaEhYURFBSEs7MzdnZ2rFy5knr16hWq7ePGjcPf3x9/f38cHBwA3amt4J/MC5kpvXKTNdNMTEwMN27cUO4l8xr50fVMs9Ki5acrO9nU8DP8/f2ZM2cOkJFtYtKkSXz//fcAtG7dmiNHjvDnn38ydOhQjh49ym+//QbAkSNHCAgI4ODBg9my39y7d08ZjJk+fTqzZs1i9OjR2NnZ4e/vn6Pj369fPzw8POjatatyrE2bNgQGBrJz504Apk+fzubNm1m6dCl37twBUPKhHzx4kOLFi3Ps2DHc3d3x9/fn+PHj2VLxVq9enb1792JpaYm+vj5bt26lQ4cOnDlzhv3791OhQgXOnDnD9u3bsba25sCBA4wePZpFixYBGSkzt23bxrvvvsv+/fsZPnw4AwYMKJKOf1bm5oY0cbOhZStbmrjZyGfJG8rCyJTN7UbRwcYejSp7N0ujUtPBxp7N7UZhYSRZU8SrIZ1/8Ubx9/dXlmNmBhHJ+kGbdCSUhICzSvnob/6gXaU6r8UHra+vb7YZHshIefLJJ5/g4eFB06ZN2bNnz3PX7+HhoTybsLAwJajYmDFjcj3Hx8cHPz+/bMf8/f25fv36c7dDiLdN5651KW2RdyCn0hbGdO5a9yW16O2QFhmfEVzwGS1Ma7I+9jRxaRmz3KmkkxYRRyVTS86ezfh8DwoKomrVqtjY2OQ4VrlyZSWtlK4UVgBWVlaEhIRQoUIFnJycWLhwYY6c2s/D3Nycu3fvkpyczNWr/9zbs4MC+vr6pKWl5Tj/2XJVqlRR7uXcufxXuOX2TLOKTXvC1eRIOi75jFbNW3L37l20Wi1WVlZUrFhRKZc5YFGuXDnq1q2LgYGB0r7PP/+cQYMG8dFHHylpvQCdgzF5Wb16Nf7+/mzcuDHHdTNjAcTExGBtbY2pqSnVq1cHUPKhe3h4sH//fiIiIggODsbT05OWLVty8eJFpb6sA0BZB2Gy/vzo0SMuXbrEmjVr8PDwYObMmTx8+DDbOZkDMkLkx8LIlIXN+3K853hmNurE5/VbM7NRJ070HM/C5n1f+fdR8d8my/7FGyEzaNGTUxdIuXGPtMh4li5dqqQ9yfygrflXLGcjb+FQvxXmxYz4xeoIP3n0KXAQppdt8eLFWFpa4u/vT0JCAm3btsXJyQkLi+eLih8QEKDkd82UmZ6woPz9/XFzc+Odd955rjYI8baRfbkvRvL+Z7IK/D8LjSmfWbbC+85y1KjQU6kZU7o5Jc/H0bx5c5o0aUKxYsVYvnw5ZcuWzXGsXLlyODk50bRp01xn8xs0aEBwcDCQsST/4sWLRdL5HzhwIIMHD1YyxOTGw8ODCRMmcOLECaytrXMt17BhQxYtWkTLli0pX748NWvWzPP6uT3TrMz1jCirMeOPSsMoObo1+t3rolKpcuzzzzoQ8exe+RYtWtCuXTu+/vprtm/fTq9evQB0DsYU1rPXMjc3586dO5QsWVIZUNGVD71Lly4sWbKEChUqKIMEQLb7yu2eckuFeeTIkQIP3AjxrLLGZhJkWrx2Xs8ekRD/79mgRU8S/iY54Qbf1x/MuagzNHNtyo+LfsbOzg4AcwMjmpSryrB6GYF4lqn1AHLdC3jlyhWioqKoXLkyv/32mxKEKSkpCUNDQ/7880/09fULtA/QxMQkRzkTExN69OhBcnIyxsbGSlqtTBs3bmTr1q0AmJiYMGjQIHbu3KkEscqv/pIlS2arr2/fvqxcuZJWrVopx9zc3AgMDOTEiROMGjWKGjVqcOXKFU6fPg3AihUrmD17ts5AVN99992L+Yd9y2RmODgWeIrJ7/3M5WtnsbWpy6lTp3BxceHGjRtKrl5dwsLCaNiwIbVq1QLgl19+oUaNGtnK5JcOzN/fHz8/P2bMmFHgdr8OkbPfFLIvt+ilxybl+p6bSTXcTLIHgkuPTWLChAlMmDAh23Fdx/L7/2Dq1KnKz6amptk6c5krtLIGons2vZW3t3e215n/H9WvX1/p/GbKGqAvs24bGxudabsyr5M1Vdavv/6KRqPhm2++yXOgAPJ+pplUqBhRuik9bi1G47MOu8BGSn7wgurUqZMSf+DPP/9UjusajMlLv379lBn+rPVk9eWXX9KxY0eqV6+u3L+ufOhdunShU6dOODg4UKJEiULdD+hOhWlmljNOUN26dZkwYQK9evXKkQpNCCFed5LqT7y2dAUtOprwNwEJVxlv5UXHsJ/Z4T6RUqv7KlGLfX19mTVrlrIHMSQkhOjoaJ4+fYqxsTGpqak0a9aMI0eO4OPjg76+PpMmTWLUqFEMGjSIy5cvc+TIEX799Ve++eYbKleujImJCaGhoUyYMIFdu3YREhJC48aN+d///seWLVuYOXMmdevWRa1W5yhXtWpVrl69yqRJkxg+fDhNmjTJ9qWxadOmHD58WHm9Z88eQkJCaNiwYYHqz/qFNzOYUocOHVi2bJmSmiqz89++fXsWLVpEyZIlqVy5MtHR0fj4+GBubs7HH39M69atWbduHfPmzXspKajeBnFJ0awJmsKZO3tI16YScTGJ46tjqN3SjH7ePbmyRZ+Agxl7ZvPr/D+bSqwgskblls6/eNMk/nGGuK/88i/4/8y+9HypWQheF4MGDeLGjRuYm5vz559/YmiY+2CTPFMhhMhJUv39Q/b8i9dWfkGLANJuxfB4ZvagO7qCMOW2FzAzfZSDg4OyN/HZYwXdB6ir3PXr15X6dAWeMjAwyBa9+c6dO8rARUHqf5ahoSGurq45olsDxMXFUbFiRUxMTLC1/ScPeUEDUYns4pKimbOvG8G3d5CuTVWOl69jwJ3QRIJvbWf7oVVUr5mx7LVTp05KpOqPPvpIWXKsS0hICM2aNaNhw4Z8/fXXQMbA1pIlSwgLC6N58+Z07949RwyJZ3344Yc0a9aMpk2bcuvWLSBjq0mjRo34+OOPAbh7966yZDc1NbVI0lcJURAGLW1BU8CvIRo1Bp62+Zd7Cy1fvpyAgAC2bduWZ8cf5JkKIYTIm3T+xWupIEGLMnfhJfldJS0qPs+y3377LUuWLMHPzy/bcsDM5Zlnz55V9iY+eyxzH6C/vz+BgYFKZyy3/YJZy1WpUkWpT1fgqS5dujBv3jwAEhIS8PX1pW3btgWuX5cPPviAn3/+OcdxMzMzIiIiSExMzBaESfYzPp81wVOJjA/LcVxPo0JTTMX9q8kYlUnhxoMQAHr06MGGDRtIT0/n7NmzOQaD9u3bh4eHB23btqVGjRr4+/tz4sQJ9u3blyO9V2RkJGvXrlViXuRm1qxZHDp0iKlTp/LLL7+QmprKb7/9RmBgID169AAygnklJiby+PFj9u/fLys+xEujZ2WKYQE7n4aetuhZSpCs/PyXn2lM4n38r66gZScXfHfNIibxPu+//z5TpkwB4MCBA8p+/mdt3rxZGVDXFQg3P/fu3WPmzJnP1e6sgYwhY0tJboESd+/ezY4dO3Kty83NLcexzODIQggB0vkXr6mCBC2qoF+CoXdWcDXhHsn5DBRk7gUcNmxYts7/pUuXaNmyJU+ePKFx44ygLA8ePKB169YEBgbStWtXOnbsSFhYGC1atKBFixbs2rVL5zV0levcuTNHjx6lTZs2OqMEjxgxgvv379OsWTPatGnDhAkTsLS0LHD9umRGZn7Wl19+SYcOHRg8eDCVKlXK9VllriyYPn16rmX+62IS73Pm9u5c36/kYEjg0odUaWDEg4Rw4pKi6Ny5M9u2bePw4cO4u7vnOKdVq1b4+/uza9cubty4wbvvvkuzZs24dOkSkZGR2crWq1cPPT29fNv57bff0rRpUyZPnkxERATR0dFUrlwZjUaTbfCha9eubNmyhbVr19K7d+9CPAkh/p3ik1qiZ10izzJ61iUoPqnly2nQW+C/9kzjkqJZHPg+E7Y24Y+gL0kqeY3lW+cyYWsTgv/24+8bfwEoMVh0ydr5LyytVkuZMmWYNGnSc99DQXl5eeUa9yU30vkXQmQlAf/Eaym3oEVNTKrSxCRjhv7nCn1zlM8tCNOQIUN0zpIOHDgwx0ynl5cXw4YNy3ZMV8T8zGBMWa+pq9y2bdt03guARqNRZv6frbug9WfKunc7697xwMBAIGPbQXBwMAkJCbRu3RooXCAq8Y+Q8D3Zlvo/y9rBiDvnkrCsWgz84rl4NwD7mg0wMzNj/vz5ua7ayLRw4UK++OILPDw8cHNz49nYLM9G5dblwYMH+Pv7c/jwYfbt28fq1auxsLDg5s2bpKWlZVuJ0q1bN/r160dKSopkeRAvlV5pE0qt7svjmfszVntlHfTVqDH0tM3ozP5/XBeRv//SM83cfpV1FZZV1WL8dTiBqk1SiEm+z/nwWOKSojl16hQ9e/bEw8MDPz8/NBoNHh4erFixgt27d3Pp0iW6d+8OZA+Eu2TJEqKiohg6dCiPHz+mVq1a/Pzzz/j4+HDz5k3Cw8OZOXMm8+fPZ9WqVTRq1Ag7OztOnTrF7Nmz8fLyYtGiRfj6+tKsWTNOnDhRoFgrT548YciQIdy/fx9LS0tWrVrF6tWrSU1NZdiwYQwePJjbt29jbW2NtbU1Pj4+xMbG0rdvXy5cuMDy5cs5efIk58+fx8PDg/nz5zNx4kQSEhKwsrJi3bp1L+hfRQjxOpPOv3gtqQsZPbuw5f+Ljhw5wpQpU3j8+LGyDFI8n4TkmDzf1zdU02x4aeX1k5THAPTu3ZvJkyfnm66rXbt2jB49mtq1a1OsWLECt2v16tUcP34cyIiGbWpqSosWLZS82xqNhsGDB9OkSROaNWumnGdmZoahoaEs+RevhF5pE0p831FJ6Zoem4Ta3BCDt2xZ+sv0X3mmurZflbYpxoNVMTy4+ZTS1vokPU7ix+0fExYWRpUqVXLUYW1tjZeXF5MnT6ZatWr4+Pjg6OjIihUraN26NTExMcyePZsJEybQuHFjvvjiC44dOwZA9erVWbZsGWFh/7Th4cOHzJw5k5SUFEaPHo2npye+vr4cOXKEoKAgTpw4kaMNK1euVAbqL1++zOTJk1myZAkdO3akT58+LFy4kPXr1yvlT5w4gYGBAX5+fnzzzTfK1rDIyEiWLl1KcHAwy5cv53//+x8rVqzA39+fv//+G0tLS3bs2JFjQFkI8d8hnX/xWjJoaQuzDuS79B947qBFWWe9Mz27cuBt0qxZMw4dOvSqm/FWMDEoofN4+dqGlK+dfSDKY2RpqlTJmE1XqVT07ds3x3k2NjbZVmt4eXnh5eWVrUzW301dWQE8PDy4ceNGtmO6gveNHDmSkSNH5jiur69Pz549ddyVEC+HnpWpRJ4vYm/zM81t+5WeJiOOzf2/krF8x4CkuDR27dxFaQsHIGecG12eDYR76dIlxo8fj0qlIj4+Xtk+oCuQr6WlJVZWVhltjIkhOjoaa2tr9PT0lCDEzxowYICSqSXzs/7SpUsEBwfzyy+/kJSURJ8+fTA3Nwfgxo0byqCug4ODMhhRrVo1DA0NlUDBWVWtWhU7Ozv69euHk5MTn3zyic62CCHebtL5F6+lzKBFSbuv5Fv2bQtaJF5/DhXasDZ4Wp5L/zOpVRocK7Zhw4YNSvrG183w4cOxsrJSMk0IIcTrLq/tV6Ur6/NXQAK1WpiSFK/Hrm+j6NipOADm5ubcvXsXIyMj7t27B5Aj0K2ugLv9+/dXOvupqamcP39e5xasZ8+1sLDg9u3bpKenc+7cuQLfX40aNWjZsiXdunUDICUlhdWrVwNQpUoVZTA/a526BjYyjyUnJ/Pxxx+jVqtp3bo1/fr1o0yZMgVujxDi7SAB/8Rr678WtEi8OUoYl8Gxklf+BQHHSl6YG5WhW7duBAYGUrp06fxPeskWL16sM/aEEEK8rvLafmVVtRjadNAYqDEtrSEpNo0qtTM6usOHD6dDhw74+PgoAXbbtGnD+++/z6JFi3TWN3HiRGbMmEGLFi3w9PTkzp07BW6nRqNh0KBBNGnShN9//x19ff0CnTd8+HA2bdpEy5YtadGiBadPn1bea9iwIUlJSbRs2ZJz587lWWelSpXo1q0bV65cwd3dncaNG2dbnSCE+G9R5bXvx9nZWRsUFPQSmyNEdmkPEv4TQYvEm0dXoKlnWZnaMK7VBswMLV5ew4QQ4j/A/+oK/gj6ssDl+zh/hYftwBfYotylpqai0Wg4ceIES5cu5ZdffimyOr/55husra3p06dPEbRUiLeTSqUK1mq1zq+6Ha8DWfYvXmv/laBF4s1jZmjBuFYbWBM8lTO3d2dbfqpWaXCs5EVvp2nS8RdCiBfgebZfvSo//PADmzdv5unTpyxfvrxI6hw6dCg3btzA3NycP//8s0jqFEK8/WTmXwgh/qWYxPuEhO8h8WksxsXMcazYBnMj2UsphBAv0uIjHxB8a3u+5Zys2zPc9aeX0CIhxOtIZv7/ITP/QgjxL5UwLvPKlpMKIcR/VW+nadx+GJrv9qveTtNeXqOEEOI1JgH/hBBCCCHEGydz+5WTdXvUquzzWWqVBifr9hJ3RQghspCZfyGEEEII8UYyM7RguOtPsv1KCCEKQDr/QgghhBDijSbbr4QQIn+y7F8IIYQQQgghhHjLSedfCCGEEEIIIYR4y0nnXwghhBBCCCGEeMtJ518IIYQQQgghdIhJvI//1RXsCF3Ad8u/wM29Ce7u7nTp0oUHDx4we/ZswsPDCQkJ4fTp0wWq083NLdvrzDp08ff3Z/LkydmOhYWFceDAgee7IfGfJgH/hBBCCCGEECKLuKRo1gRN4cydPaRrU3kSl4bfvGjafl6OhtXfxdlsIE+fPmX8+PEA7Nu3j9TUVOrXr1/oa2XWUVCZnf8WLVoU+lriv01m/oUQQgghhBDi/8UlRTNnXzeCb+8gXZsKwO2QJ9g2NUFjmE7wre1sCvsck5L6eHt7c+3aNRYvXsycOXPo168fo0aNIjQ0FIB58+axYcOGPK+XWceDBw9o3rw57777Lp06dcLf3x+A4OBgOnTogKurK/Hx8SxevJiVK1fSsmVLrl27RuPGjWnevDlff/31C30u4s0nnX8hhBBCCCGE+H9rgqcSGR+W7VhiTBrGJfSU15HxYawJnqq8Hj58OOPGjWP16tX069ePNWvWALBr1y7atWtXoOsuWbKEESNGsHPnTp4+faocL1asGNu2bePdd99l//79DB8+nAEDBrB//34OHTrEiBEjOHjwIBMmTPgXdy3+C6TzL4QQQgghhBBk7PE/c3t3juPGJfRIeJSW7diZ27t5mvYkR1lXV1eOHz9OWFgY5cqVw9DQsEDXvnHjBvb29gA4ODgox+vWrQtAhQoViImJyXZOjx49OHfuHP369WP37pztFiIr6fwLIYQQQgghBBASvkdZ6p9VJQcjrgUm8PRJOgCx91KIf5jMw4QIAPT19UlLyxgcUKlUuLi4MG7cOHr37l3ga1epUoXz588DcO7cOeW4SqVSftZqtdmupa+vz/fff8+yZcuYMmVKIe9W/NdIwD8hhBBCCCGEABKSY3QeNzLTw7GLOXvmRKEFDEzUNBteitT0jOX5jRo1wtvbm9DQUH744Qf69euHh4cHf/zxR466QkND8fT0BKBDhw7K8WHDhtGtWzeWLVuGnp4e+vr6pKSk5Di/bt26TJgwgV69etG1a1d+/PFHEhMT6d+//79/AOKtJp1/IYQQQgghhABMDErk+l5FO0Mq2mVfwj95zgdUq1YNgMOHDyvHVSoVvXr1QqPJ2d16dul+pvT0dA4cOIBaraZdu3bY2NhQoUIFPDw8gIzAgJkCAgKUn3v16pXPXQmRQZb9CyGEEAWU+iiCmP0LufHHl7jVq04ztyaYm5vj4eHB4MGD8z1/6dKlub6XV55nIYQQL4dDhTaoVQWbH1WrNDhWbJPj+OHDhxk6dChjx44t1LXj4+Np1qwZDRs2xMHBgQoVKhTqfCHyo9Jqtbm+6ezsrA0KCnqJzRFCCCFeP6lxkUSuHEP86c2QlmUvqJ6G/v5mHD1zAY2ZVb71uLm5ERgY+OIaKoQQ4l9bfOQDgm9tz7eck3V7hrv+9BJaJP4NlUoVrNVqnV91O14HMvMvhBBC5CE1LpLbM5sSf2p99o4/QFoqafHR/NC3Hg0bOOHq6srZs2cBWLVqFY0aNVKObd26lfPnz+Ph4cG+ffsYNGgQzZo1o3nz5qSnpyt5np88eUKfPn1o0aIFvXr10rnfUwghxIvT22kaVqY2eZaxMrWht9O0l9Ogfyk2NomjgWHs33uVBs5uXL58HYCtW7fy2Wef5Xt+5t+nf8Pb25tGjRrh6elJ165d/1V93t7ehIWF4evry5IlSwpyio1KpTquUqn8VCrVRpVKVe15r61SqXxVKpXN857/qsmefyGEECIPkavGknI/9y8p6VpYdvwe26Y2RdvhW0aPHs2WLVtYsGABR44cITw8nNGjR7N9+3bs7Ozw9/cnJSWF2bNnc+jQIbRabbZIzkuWLKFjx4706dOHhQsXsn79evr06fMyblUIIQRgZmjBuFYbWBM8lTO3d2eL/q9WaXCs5EVvp2mYGVq8wlbmL/5xMps3hBJ6/h7p6RmrvatX7UzP7iP59JOv+GXJN+zcueOltWfVqlVUq1aNCxcuMGTIEA4dOpTt798L1l+r1V5TqVR1gKUqlaqZNq8l8G8pmfl/A/j7+1O5cmWaN29Oq1atePDggc5yYWFhBY7y6ebmlu31oEGDuHjxIgDvv/++kirkwIEDfPrpp/j6+hIcHExYWBgHDhwo9PWeFRMTw8aNGwt93o0bN2jfvr0yW3bq1Knnur4QQhRE6qMI4oM35VnmYRKUN4XkkC1ULGFAbGwsUVFRVK5cGX19fWxsbIiNjc12jr6+PoMGDaJ///5MnjyZ9PR05b1Lly4xb948PDw8WL58OZGRkS/k3oQQQuTOzNCC4a4/MavjUfo4f0Un+8/o4/wVszsdZbjrT29Ex/+nBUc4d/au0vEHqFC+JqmpycybPx2LUvXp0KEjzs7O7N27F8iYVR85ciRubm5Mm5Z9ZcPBgwfp168fKSkpTJ8+HQ8PD1q0aEFYWBiAzmO61KlThypVqnDr1q3/Y+/e42q+/wCOv76n0kXURrmTYW3mUkqj6ykkcs/cLzFszOyCueQSlstctt82l1loLLK55tqEUy5zKSKXoZFLoaKLSvfz+6P1VTpd3G+f5+Phwfl+P9/P93OOI9/P7f3m2rVruLi4YGdnx7x58wA0Hrty5QoffvghXbp04fLly8XqLO+91Wr1WeAKUFeSJDdJklSSJIVJkjQIQJIkb0mSVv+3SsD3v2P1JUk6KklSIPDOf8e2SpL01n9//kGSJKsSb/oSEZ3/l1hS+m1Ul1bz95WNOLo3Z/OOAAYPHqwxZciTatmypdyRTklJ4dq1awAcP34cGxsbPD09sbKyKtL5fxKP2/kfNmwYCxcuJCQkhMDAQLS0tJ64LYIgCCVJPbG1+FL/h7ytB7GpkJ2dzbkdKzAyMsLExISrV6+SnZ1NdHQ0RkZGwINczbm5ufTt25fff/+d+Pj4IgOZ5ubmfPPNN6hUKo4cOcKoUaOe3RsUBEEQSmVsUA1lo0F0/OBzlI0GYaRf7UU3qVy2bDrDnYR0jeccbAdxJTqM+nUd+GTY9+zdu5cFCxbI59u3b8/BgwfZuXOnfEylUvHrr7/i5+fH+fPniYmJQaVSsXjxYubMmcPp06eLHStNzZo1uXXrFvPmzWPGjBkcOnSI/fv3Exsbq/HY/PnzWbRoEZs2bSo2Efqo9wZigepAqFqtVgKtgE8KnT+pVqvbkj9AYAyMB74GegBV/ivzJ+AhSZICaK5Wq8PLuunLQHT+X0IpGQksPziKSYG2rAubyuHLf3D2ZgiTAm3ZcWI5GdmpgOYRritXrtClSxdatWrFlStXgPwlpA4ODjg4OHDixAmN97SxseHYsWNkZWVRoUIFeRaqoPPv7e1NcHAwy5cvZ82aNbRp0waAq1ev4uHhgZWVFTdu3ABgzJgxODo60qlTJ5KTk1GpVEyZMgUAPz8//Pz8WL58OXv27EGpVBIfHy+3Y/fu3SiVSqytrVm9enWRNl69epUaNWpgbm4OQKVKlWjRogURERFyZNTZs2cDyO2FB/uCNm/ejI2NDS4uLuzcuRO1Ws3IkSNxcXHB3d2dxMTEJ/uLEwThtZObdrfMMgoJ+r8n0X+XmmGzfmXWrFloaWnx2Wef4eDgQL9+/Zg1axaQ/7O2W7duHD58mDZt2mBnZ8f169dp2rSpXN+IESPYvHkzbdq0wcXFpcSf24IgCIKgSXJyBmdO3yrxvJFRNQwNq3D1+ikmT/HEvWMnrl+/Lp9v0qQJAPr6+vKxmTNnMmPGDHR0dPjnn39QqVQolUpGjhxJSkqKxmOliYmJoUaNGvz777+0aNECAAsLC65cuaLx2OXLl7G0tERbW5tmzZoVqetR7w3UAm4CVpIkBQN7gcaFzp/57/dYwIj82f6TarU6Bzj937ktQGfAAQjlFSH2/L9kUjISmL/Hg7jU6CLHLx1M4/qpDHKyrlF7QS6Hj7eWR7jOnz/PnDlzmDRpEnfv3iUkJITw8HDmzZvHt99+S2BgIKGhoSQmJjJ06FC2bNlS7L4WFhZ8/fXXnDp1iubNmxMfH090dDTR0dHUr19fLjdixAjeeecdvv32W6Kjo0lNTSUkJIR169axceNGbG1tSUtLIzQ0lN9//51ly5bx4YcfFrvfiBEjuHbtGr///nuR446Ojri5uZGTk4OTkxODBg2Sz928eZMaNWoUq8vc3ByVSoUkSTg7O/PVV19p/Gw3bdrEH3/8gZmZGWq1mu3bt1O3bl2WLl3Krl27WLZsGZMmTSrtr0cQhDeMVsW3yyyzrkP+OHr3hhKmAydibGkJ5G+nGjx4cJGyCxculP9cOEcz5A+OFnj4Z6MgCIIglNfZQnv8S3P0+J/0+WgObh0b8fW4B7FlNO3D9/PzY+TIkQQEBPDuu+/i6urKTz/9BOSvfDt79myxYyU5f/48V69epU6dOjRo0IDw8HAcHR05efIkn3/+ucZj9evX59SpU9jY2BAZGVmkPk3tKYkkSe8D9YDrwGJgGBADXCxUrPCHJ5G/TaC5JEnHgKYAarU6VZKkFOALYHKJN3zJiM7/SyYgfHqxjj9AI/uKtOxljGrZHaKvXsbv4mxCVNEolUoAuVPctGlTtLW1sbCwICoqisuXL3Pq1CmcnZ1LvW+FChUAOHz4MNbW1sTHx7Nz506qVSt9aVPjxo1RKBTUqlWLqKioIiN11tbWhISE0KpVK7n8w4GtHhYeHs6MGTPyl8/+F4OgQI0aNYiNjS12zZUrVxg7dizp6elcuHCBuLi4IvcoiOXh5eXFt99+S05ODl5eXpw/f56AgACCgoLIycmhdevWpb7X11VS+m0iYoKIS7jJ7C/9MNR9i8hTZ7G0tKR+/fr079+ftm3blru+a9eu8cUXX5CYmEhOTg6enp4MGzbskdrk7e2NUqmUv99liY6O5vLly7i4uDzSfQShLIYtuhK39ssyl/4DoKWNoVW3Z90kQRAEQSjV/fTyZYlpUL8lAX9O5PJVG4yNjUstW7duXf73v/8xYMAANmzYQPXq1VEqlUiSRN++fRkxYoTGY4X179+fSpUqUalSJVasWIEkSXzzzTcMHjyYrKwsOnfuTK1atTQeGzduHP369aNatWrF+icWFhZl3hvwlyTpHnAP+FitVqslSdoMbAUigKRS3v4CYC1w+79fBQKAb9Vq9T+lfngvEdH5f4kkpd/m5PXdpZax6FKZ8I3JWHY5i5NLe5YvXQHkj3DFxMRw5swZcnNzOXXqFA0aNKB+/fq0bNmSDRs2yOVK0rx5c/z8/Bg+fDh37txh6tSpeHh4FCmjo6NDbm6u/PrhTnaDBg3kgCFhYWE0aNAAIyMjbt68CUBkZCTNmjUrVk+B7777Dl9fX2rVqsW7775b5Fy9evW4desWFy5cwNzcnNTUVC5dusTKlSuZMGECSqUSe3t71Gq1fE+1Ws3Zs2fl6319fTl8+DCLFi3Czc2NQYMGMXbs2DI/m9dRSkYCAWHTOHkjSI5i++GXoJCyuDXbgMDdG1g09+dHrnfYsGH8+OOPvPfee+Tl5XH48OEi5/Py8lAont6Oo6T022w5+AtHQk9wOnYPCyevoWGDRmhraxMQEECVKlXKrMPT05MpU6bQsOFjZ34RXlPab9XE0Ko7qcf+LLOsoVV3tI2Lr04Snq3k5GS6du0KwMmTJx974NLIyAjL/1ZtuLi4yIFvn9SXX37JwoULi8WoiYiIIC8vTx4wFwRBeFr0DXTKLNOv13cA2LbqSzePJtjamwFFV6GpVKpix3bvzu+reHl54eXlVaROTccKFK6jsHr16sn3Ke1YgwYNOHr0aJFjnp6e5bo3EK1Wq4stRVar1SuBlQ8d8y70Z89Cp4ovZc5fIbC2pJu+jETn/yUSERNUJJWIJsY1dchIyUXPCPL0k4uMcLm6umJqakq3bt2Ij4/H398fExMT3N3dcXR0REtLCxcXF6ZOnaqxbhsbGw4cOICBgQEGBgbExcVhY2NTpEyTJk2YNGkSvXv3lqNvFtayZUv8/PxwcHCgUqVKrF27lsqVKxMbG0vHjh3ljlj16tW5e/cuPXv2ZPny5bz9dv7S2u7du9O1a1csLCw0jkD6+vry+eefk5qaH/dgwYIFuLu7M3r0aBo3biyvYOjRowfdu3cnMDCQt956C8ifTT5y5AipqaksXLgQJycnxowZI88Wf/nll3Tp0qXUz/91UdL2EoA8dQ6pmYnM3+NBTs6HrF69mrlz52JmZoavry/x8fF8/PHH3Lt3j/fff58lS5bI1167dg1TU1Pee+89ABQKhZxZQqlUYmNjQ2xsLOPGjeOLL74gIyODrl27MnnyZO7evYuHhwd6enpIkoRSqcTPz4+cnByGDRsmrwZ499136d+/PxmZ6ehWS+P93vfZs/QWty9mErxnP/WsKlKpShoXDibRunVr/vrrL8zMzJ7Hxyq8pkwH/Ejm1ZOlpvvTqdYQ0wE/PsdWCQWrltIyk/D+dSgWtdrTydUDlUqFt7f3I9dXkIbxcRWsMnt4ddsPP/ygsXxERAQ5OTmi8y8IwlP3QdPqBG4+W66l/wqFRJOm1Z9Dq14vkiR5AF8BXV90Wx6FVFp6Q2tra3VYWNhzbM6bbceZHwmMXFh2wf90bTaOjh98/gxbJLyulh/6jPBr20s8v9X7Nl29qxG7xxSXpv346quvcHV15Y8//mDWrFn07NmT1q1bM2HCBLp16yZvmThy5AgbN25k/vz5nDt3To5SXhCEZc6cObRu3Zr79+/LnXxnZ2d27tzJTz/9RK1atejfvz/t27dn0qRJREdHF+v829rakpp1l+/39yZgXhhWPYxIS8wl5kwGtZroEXUoDSSJeg2r0fStLuza/hdvv/02lStXxt/fHyMjI6ZMmYJKpaJChQps2rSJL7/8kilTpnD9+nV8fX1p1qwZH3zwAZ06dWLLli38+++/8goR4c2UkxJH3O9j8tP+Fd4CoKWNoVV3TAf8iHZl0xfXwDeIplVLkJ97e8/sdE4ci2TR3J+5fPkysbGx5Rq4hPwUuAcPHpRfx8TEMHToUHbu3MmUKVOwsbHBysoKT09PMjMz6dKlCxMmTMDb25urV68SExPDmjVr8PLy4tKlSxgYGLBr1y6USiXBwcH88ssvrFmzBn19fRYuXMjo0aO5c+cO1tbW+Pv7P7fPTxCEN8Pvv4VzOuJmmeWaWdRgwOBXIkvdY5MkKVytVlu/6Ha8DMTM/0ukoq7xI5U3qGD0bBoivNbKs72kwM2UKOo3qgXkp2RJTk7m/PnzTJw4EUmSSE1NLbI6pHBchsaNG8ud/gJWVvn/uWiK03D58mU6deoEIM+EaYrdcOfOHTr0tiMuIZZ78TmkJRXdPnIlLJ2cDDWXDqZytu5qdHIrc+bMGTkApaurK5cvX+bgwYMUHvxUqVTs27eP3377jZs3bzJlyhQ6derEn3/+qXGVi/Bm0a5sSs1RAeQkxpJ6Yiu5aYloVXwLQ6tuYqn/c/Qoq5YsLS1ZvXo1rq6uJCUlMXfuXCZNmiQPXP79999FYr1ERkbKP6/69evHiBEj6NmzJ8OHDycjI4M5c+bw2WefMWPGDBwcHHBzc2PgwIFAfrCpVatWsXnzZkxNTfH19ZWz5hTYunUr+/fvR19fH7VazYgRI+TBTUEQhKetW48mxNxILjHdH0CVqgZ069HkObZKeNFE5/8lYlGrPevDZ5S59B/yZzgsa7d/Dq0SXjfl2V5SQK3O49+EcKDXf6/VmJubM2DAALkjn5PzoK6CuAznz5/n/fffR61WF4ntULDXf+nSpcXiNBREcW3cuDEnT56kffv2GBkZyRFdIyMjcXZ2ZoXfL7z1QSofOlZj388JoAaFloT6v+dsMysDtCtIZGeoMaqWh4fNUOBBAEozMzNsbW2BooMLM2fOZO/evejo6FC3bl3u3r3LnTt3SEpKonbt2o/xSQuvI+23amLcZuSLbsYbq6SguIXFpUYTG5vOGKf81K/lGbgEzcv+BwwYwNixYwkKCgLQmH4KHgxsXrx4Uf758nBskxkzZjBy5EgqVKggp34UBEF4Vgwr6fLZGDu2bDrDmdNFo/8rFBJNmlWnW48mGFbSfYGtFJ430fl/iRgbVMOyjlupy7ELWNZxw0i/9Ej8gqBJWmbSI5XPyEkr8nry5MmMGDGC5ORkFAoFvr6+RfbUr1ixgjFjxpCSkoJCoaB3797F6tQUp2HYsGF4eHiwevVqdHXz/yNq06YN8+fP5/jx42hr5/+4qtZYi1NLk7hyPFWu7+06Ohxbn8TtqEyqv6uLVgWJuLD7XA2/T3rUb0wYN0UOQGlubs62bdsYPXo08GBFQeEUNlWrVqVLly58+umndO7c+ZE+L0EQno1HXbWUnpUkvy5r4LIk3t7ezJo1i9mzZ7Nt2zaN6afgQUff3NycI0eO0KlTp2LBTS0sLPDz82Pt2rX4+flRu3ZtMjMzy/v2BUEQHplhJV0GDLYiOTmDs5G3uH8/G319HZo0rU5lI70X3TzhBRCd/5dMH6sZXL97ptSZDVNDM/pYzXh+jRJeK+XZXtLVO39gybqnMXbW+bNjhaO0bt68ucRrzczMCAwMLHa88Iyam5sbbm5upZYpcOjQoSKv086c5qN5xZdZd5lWjdhzGcScyaBFNyNadDNih89tappVLRKA0tjYmHr16mFnZ4euri6bNm0Ciqew+eijj/j6669ZunRpie9VEITn51FXLUXFhwEfycfKGrgsvOzfysqKvn37Ehsby7x588jNzeXXX3/VmH6qsC5durBt2zYcHR0xNDRk586d8rlPP/2UK1eukJmZyapVq9DV1cXT05MzZ87IuakFQRCeBSMjPTmav/BmEwH/XkIpGQkEhE/n5PXdxYIZWdZxo4/VDCrrVX2BLRReZUnpt5kUaFvu7SVzux5+qVaZqC6tZl2Y5owVmvS1noWy0aBHvk9iYiLDhg1j48aNj3ytIAhPnwiKKwiCIDwOEfDvgaeXbFt4airrVWWE3WLmdDlMX+tZdG02jr7Ws5jb9TAj7BaLjn85JaXfRnVpNX8e+Y5WSgvsHW1p1aoVz2JAa+XKlcWO+fn5YW5ujlKpxNnZmdu3bwPIqe/KUjhQ3tNUsL2kPF7G7SUWtdqjkMq3aOlxY2P8888/dO7cmS+++OKRrxUE4dkQQXEFQRAE4cmIZf8vMWODao81Y/mmezgN1Jmge+i+K9HYxZhmNVypUe/pD56sXLmSoUOHFjs+fvx4hg0bxtq1a1m7di1fffXVU7/343iVt5c8j9gY7733XpGUX4IgvHgiKK4gCIIgPBkx8y+8VgrSQIVf3yE/IGrrSty+lEl6ciYRsTtZdmQwO/7aypQpU4D8GXo/Pz+io6NxcHDAw8MDKysrbty4QVRUFK1bt8bZ2ZnZs/MjR/v6+uLg4ICDgwMnTpxg+fLl8l7Rgsj0xdqVkkLlypXl15mZmbRr105+3aZNG7Kysli+fDmtWrV65oMElfWqMr7dRqzqdio2i66QtLGq24nx7Ta+tKtM+ljNwNTQrNQyL+vghSAIj+dVX7UkCIIgCC+amPkXXiua0kC9a1+RtLu5bPOJw6CyAufPcknPWYU+xfOapqamEhISwrp169i4cSOGhoZ88skneHp6olarSUhIIDAwkNDQUBITExk6dChbtmxh9erVGoPVzZ8/Hz8/P65fv87Ro0fl47q6ulSrVo3r16+Tm5tL7dq1USgUrFixgkOHDnHs2DFOnjz5tD+eIgq2lySl3yYiJoj0rGQMKhhhWbv9S//QXDB4IWJjCMKb5VVetSQIgiAIL5ro/AuvjZLSQCm0Jax6GGHVw4iow2lE7rpHPYtjvJtdH8hPAVWQ771x48YoFApq1apFVFQUQ4YMwdvbm/79+zNgwACqVKnCqVOncHZ2LlebCpb9R0ZG4uXlxYoVK+RzPXr0YMOGDeTl5eHh4UFCQgL16tVDW1tbTkX1PLyq20te5cELQRAejxj4EwRBEITHJzr/wmujpDRQ9+JzqPiWFgptCf3KWqjVWWjrqzl7/gSQn96pWbNmAPIgAOQPCujo6LBo0SKysrKws7Nj586dtGzZkg0bNgCQnZ1d7DpNjI2NuXv3bpFjHTp0oHv37gCMHj0aLS0trl69Sm5u7jOf9X+dvKqDF4IgPB4x8CcIgiAIj0d0/oXXRlpmksbjd65mEfxjCtoVJBRaEk6fvE3Ft7Q4sTOejh07UqVKlRLrDAwM5OeffyY9PZ0BAwZgYmKCu7s7jo6OaGlp4eLiwtSpU6lTpw4eHh74+Pjw3nvvydfPnz8ff39/srKyWLRoUZG69fX1MTY2RltbG11dXQCGDBmCra0tTk5Ocrm5c+cycODAYvmkBUEQ3mRi4E8QBEEQHo2kVqtLPGltba1+FmnRBOFZeF753wVBEARBEARBeDVIkhSuVqutX3Q7XgYi2r/w2nge+d8FQRAEQRAEQcjXqFEjAgICih1XqVTUq1cPpVJJ165dycjIwNvbm+Dg4Kdy34iIiCKxtEryX0DuCgCSJHlKkjTsUe4jSdI4SZL+liTpoCRJ3o/R1JeK6PwLrw2RBkoQBEEQBEEQnp2cxFiS9i7lTqAPB5Z7YduyBdu2bdNYduDAgahUKmxtbeV4WU+LhYUFH3/8cZnl/uv86z7OPSRJqgx0UqvVrdVqtT3wv8ep52UiOv/Ca0XkfxcEQRAEQRCEpysnJY7Yxb25PK4+cWtGc2fTNNYvmU3n1E3cPRtCWvz1Eq+1sLDgxo0b8uvY2FicnZ2xt7dn1KhRAOTl5TFs2DCcnJzo0KEDAEePHkWpVGJnZ8eqVauK1KlSqZgyZQoATZs2pV+/fjRv3pyIiAi5TFZWFn5+fgB1JEla+N/hDpIk7fzvlyRJkr4kSeskSdonSdJ6SZJ0Ct0mF6guSVJzALVanQggSdLBggKSJKkKfpckaaEkScclSSp7VOIFEZ1/4bVSkAbKqm6nYlsAFJI2VnU7Mb7dRpEGShAEQRAEQRDKIScljus+DqQe3wC5DzJrnbsDzd7Oo7V+LAEjW5GTEqfx+tDQUN599135ddWqVdmzZw8HDx4kJSWFS5cusXXrVkxNTQkJCWHHjh0ATJs2jcDAQA4ePCgH0NYkLi6OlStXsmTJEn777Tf5eIUKFfD09AS4rlarx/53+IZare4IxADNgGFAoFqtdgFUQM+C69VqdRrwBTBfkqSLkiR1K+Oj+h2wBwaXUe6FEdH+hdeOSAMlCIIgCIIgCE9H3O9jyL4dVeTY1RQ1F5Pg4z15ZOVB/buxdPh9DDVHPdj/v2bNGg4dOkTjxo3p2rUrp0+fBuDOnTuMHDmSpKQkoqOjiY2N5eLFi9ja2gKgUOTPT586dYouXboAkJCQQHx8vMbsVw0bNkRPT49atWqRlJRU1ts589/vMYAx8D5gJUnSJ4AesK5wYbVaHQQESZJUBfgL2FJwTiqe6/uMWq3OliQpr6xGvCii8y+8tkQaKEEQBEEQBEF4fDmJsaSGby52/K9r4GMr0bpGfv/30715pIRtwjTpJtrGNYD8Pf/ffvttsWvXrl1Lt27d8PT0pH///qjVaszNzTly5AidOnUiLy8PhUKBpaUlGzZsoGLFimRnZ6Ojo1OsLoDCffCHM9n9d03hTnrhAhJwAdirVqs3/leXfBNJkvSBt9VqdQyQAmQ/OCXpAuYPNaXkNHovCbHsXxAEQRAEQRAEQSgm9cTWIkv9C4TcUGNp8uB1Q2MIi80hNXxLmXW6uLiwcOFCunXrRlpaGgBdunTh5s2bODo60qlTJwBmzJhB586dcXZ2pk+fPo/VfqVSCVBDkqRpJRRZDnSXJGmvJEn7gBaFzukCq//b4x8C/PDfcT/gIIW2CLwqpIdHRwqztrZWh4WFPcfmCIIgCIIgCIIgCC+DO4E+3NlUUr+5uCo9ZlGly+Rn2KJHJ0lSuFqttn7R7XgZiJl/QRAEQRAEQRAEoRitim8/Yvm3nlFLhKdBdP4FQRAEQXijJCcno1QqUSqVGBkZoVQqGTJkiMayfn5++Pr6AvD9998zZswYIiIiWLFiBQD29vZA/tLSnJziS2NL4u3tTdOmTeXX8+fPp379+uW+Pjo6mgEDBhQ5tnv3bjlKdnkVtL+wzz//vNRrvL29C3JnC4LwmjNs0RW0yhkmTksbQ6tuz7Q9wpMRAf8EQRAEQXgjFGSBSctMwvvXoVjUak8nV49ydWT//PNPDh8+zPr161EoFFhYWDxxe3R1dbl06RKNGjXi8OHD1KlT54nqc3Nze+I2Afz0009FXhcE3xIE4c2j/VZNDK26k3rszzLLGlp1l4P9CS8n0fkXBEEQBOG1lpKRQEDYNE7eCCJP/WB2fn34DG6lpPPrqqWs+OU3tLS0WLJkCc2bNy9y/YEDB7hx4wY7duxAoVCgUqkIDg7WGMW6sF69enH79m10dXXZsGEDlStXLnK+e/fubNq0iUGDBlG9enUSExOB/PRWo0aNIjc3l9GjRzNgwAD++ecfPv30U/m6rl27ApCdnc3gwYP55JNPuHLlCjk5ObRt25aBAwdiampKdHQ0W7dupXbt2gwZMoTr169Tt25d6tati7e3N0lJSXz00UdcvnyZZcuW0bJlS+zt7Tl48CCenp4YGhpy8eJFAgIC8PDwQE9PD0mSCoJoCYLwBjAd8COZV08WS/dXmE61hpgO+PE5tkp4HGIYVxAEQRCE11ZKRgLz93gQfn1HkY4/QJ46h3v37+I9ZwI79mzG398fLy+vYnVs2bKFgQMHoqen90j39vPzIyQkhF69erF+/fpi521sbDh27BhbtmyRO/MAU6dOxd/fnwMHDvDTTz+RnZ3NpEmTWLZsGSqVSl6Wn52djaenJyNGjMDJyalI3ampqfz55598/fXXbNy4kaNHj6Krq0twcDDm5g+yU8XGxrJq1SoCAwOZMWNGsTba2dnx119/4evry7Bhw9i1axe5ubmP9DkIgvBq065sSh2vAxjafFR8C4CWNoY2H1HH6wDalU1fTAOFchOdf0EQBEEQXlsB4dOJS40u8XzGvTx038ph4+lvMTMzIzk5uVgZb29vli5dSkRERLnvm5uby/jx43F0dOTnn38mNja2WBlJkqhRowYBAQG0adNGPp6YmIiZmRk6OjrUr1+fuLg4EhISeO+99wDkJfihoaHo6OhonIVv3LgxCoWCWrVqkZSUxJUrV2jWrBlAkS0LDRs2xNDQkFq1aml871ZWVgBcvnwZS0tLAFq0aFGsnCAIrzftyqbUHBXAOwuuYDrwZ6r0mIXpwJ95Z2E0NUcFiI7/K0J0/gVBEARBeC0lpd/m5PXdpZbRq6QgNSGXsCu7OP3PcYyMjIqVqVSpEuvWrWPo0KHcvn27XPeOiIggLS2N0NBQPvvsM0pKrTxo0CA6deqEjo6OfMzY2Jjo6Giys7O5fPkypqammJiYcPHiRSB/Dz5AmzZtqFu3brE9+pA/sFBArVZTv359IiMjATh9+rR8LioqirS0NGJjY4ttS4AHAw3169fn1KlTAJw8ebJcn8HjCAoKwsHBAaVSyddff/3Yqwz8/PwwNzdHqVTSoUMHQHNww0eRm5vL119/jVKpxMHBgaCgoCeqr8CWLVu4e/cukD/QFBwc/EjXX7lyhU6dOuHk5ISzszPHjx8v97WP+5n4+fmVGSvD09OzSFDKR7lX4c9EeLlov1UT4zYjqdJlMsZtRoo9/q8Y0fkXBEEQBOG1FBETVGyp/8MkhcQHroZsnRFDv379mDVrlsZy77zzDosWLaJ3795kZmaWeW9zc3OioqJwc3Pj2LFjJZazsbFh/PjxRY7NnDmTfv36YW9vz2effYaOjg6zZ89m+PDhKJXKIp39mTNncv78eQICAkptz4cffkhGRgZt2rTh9OnT8mBDnTp1GDp0KJ07d2batJJzeQ8bNoxffvmFDh06oKurW+b7fxRJ6bdRXVrN2gOz+WbKGAI2/YZKpcLExIRff/31sesdP348KpWKXbt2PZV2Ll++HBMTE1QqFbt378bHx4eEhIRHrketVhcZDHrSju6wYcNYuHAhISEhBAYGoqWl9dh1PW1nzpzRuOqlLKLzLwjPhlTSSDSAtbW1Oiws7Dk2RxAEQRAE4enYceZHAiMXlrt812bj6PhB6WnuXmU5OTloa2szb9486tatS9++fV9oex4OxHgxNJXcHPigjTGWddzo9sEkBvb+mKCgIFq1akXTpk05fvw4c+fOxc3Nje3bt/Pdd9+Rk5PDtGnTimQ78PPzIycnh2HDhsnHCgIZRkVFMWrUKDIzM2nXrh1TpkyhQ4cO7Nq1iwEDBmBlZcXIkSPp27cvmzdvlq9v164dgYGB6OvrA7BixQp0dHQ4ePAgX331Fe+//z4//fQT1apVw9nZmY8//ph79+7lH/f+jmkjx3L15g1upt7lSvJNZs+bi62tLTY2NtSrV4+ePXty9+5d1qxZQ1paGjo6OuzcuRNjY2P69u2LoaEh77//PkuWLJHbdPXqVby8vPj999+LfLbe3t7Y29vTtm1bPD09adKkCZ988glDhgwhPj6ed955h1WrVsmfyXfffYdCoWD48OH079+flJQULCws+PHHH0lOTpaPVa1alT179nDmzBlMTEzo2bMnMTExvP322wwYMICPP/5YboOnpyetWrXi+vXr+Pj4yPc6evQoEyZMIDs7m2HDhjFkyBAGDx5MdHQ0CoWC3377DRsbG7S0tOjcuTPLli2T6yzY4qKtrU2NGjVYsGAB1apVK9f3zc/Pj6ZNm8pbWYQ3hyRJ4Wq12vpFt+NlIGb+BUEQyqFgZurPI9/RSmmBvaMtrVq14lkNkHp6ehIV9SCqrqa84uVVUN7Pz09eLlzg4VzhpS05jY6OZt++fY90b0F4kSrqGj9SeYMKxZf8v04+/vhjHB0dOXjwIN27d3+hbdEUiDE9KZeKb2mRp84h/Np2fjrQn/sZaQDcvXsXHx8fduzYwS+//EJeXh4LFixg3759qFQq5s+fX+we8+fPR6lUMmnSpCLHvby8WLFiBSEhIZw9e5YbN25QsWJF0tLSyM3N5cyZM4SFhRXrJGZkZMgdf4DatWtz8+ZNevbsyYYNGwDYuXMn7u7uzJ07l0mTJhG8YTs6x26zq/UkMkMvUzcqj8mprbC5Z8LGGUupVbEKbm5u+Pv7M378eE6dOoWdnR2JiYk0a9aMatWqMXfuXN5++232799PpUqV+Pvvv4H87R83b96kRo2yl11v2LCBX375BVdXV0JCQuT/Two+J4Bx48axfPlyevfuTWhoKOnp6Rw9epRly5bJxzIzM6latSp//vknBgYGxMTE8M0337B//36GDh1a7L5t27bl8OHD3L9/Xz42bdo0AgMDOXjwIP7+/mRmZnLjxg1CQkLYt28fdevWxc3NDQ8PD/r06VOszuDgYIKDgxkyZAgjR44s870X8PT0LPZ3+vD/iYLwuhOp/p6igvzBcQk3mf2lH4a6bxF56iyWlpbUr1+f/v3707Zt20eqc9iwYWhraxcZ9XwcK1eu1PhD+WWrUxBeNg/PTJ0JuofuuxKNXYxpVsOVGvWqPpd2WFhYPHFecT8/PwYMGPDY+boLOv8uLi5P1A5BeF4sarVnffiMMpf+AygkbSxrt38OrXpxfvvttxfdBJmmQIwGxlqkJT7Y4x979woJ9/NXqJqYmGBqmh9QLCkpiYSEBM6fPy8/V8XFxaFWq4vEOhg/fnyRmf8CFy5cYODAgXJdMTEx2NjYsGXLFurVq8etW7c4fPgwdnZ2Ra7T1dXl/v378gDAjRs3qFGjBi4uLsydO5dPPvkEQ0NDKlasyPnz55kwdjy5/8STej+NplXyf24206vNzntn8DRqzf9u7ONm399QV3nw/dTR0SElJYWEhARq165NXl4e+/fv5/r16xgbG1OvXj3Wr1+Pvb09TZo04b333mP16tUcPnxYnkU/evQofn5+/Pnnn4wbN474+HjOnTvHokWL+Pzzz4ukjbx8+TJr167F2NiY7du3c+PGDbZs2UJ0dDQHDhzg9OnTGBgYsHjxYiB/O4uWlhbbtm3jq6++wtzcnJ07d+Lv78+tW7fQ0dGha9euTJ48maioKEaPHs2dO3ewtrYmIyMDR0dHjh8/TpcuXcjNzeX06dO4uLiQk5NDv379qF69OidPnuTSpUs0bNiQHj16lPgdcnFxwcfHh9zcXK5cuVJsNcfkyZPloJj+/v4sX74ce3t7tLW1WbRoEQAjR44kJiZG/rfxv//9TwS0FF5rYub/KUjJSGD5wVFMCrRlXdhU9kYv4cMv02k66jYm9QwI3L2BevXqPXK9ubm53Lx5k+jo6BIDBZVHXl4eK1eufOzrS/Is6hSEl4mmmSltXYnblzJJT84kInYny44MJiUjgTFjxuDo6EinTp1ITk4mOjoaBwcHevbsiYWFBevWrcPV1RV7e3vS0vJnsmbOnIlSqcTFxYXo6OhS26JSqZgyZYr8Wq1WM2bMGAICAoiPj6dLly44OzszatQojdcfO3aMiIgI2rRpw5o1a8p87zk5OfTt2xdHR0d69uxFqCqKmTMW4uu7Cn19gxL3F+/duxelUomjoyPdu3fnzp07xVYxlOVJg3IV9vDn9rDLly/ToUMHlEolHh4exMfHP7V7Cy+esUE1LOu4lV0QsKzjhpF++ZYPC0+mpECMdZrrc+lAGtkZ+bOxkbtSqNLkPsn3bxcLYFi1alWaNm3K3r17UalUnDp1qkiZ0pibm7Nu3TpUKhXh4eG0bNkSW1tb5s+fj52dHWZmZvzxxx/Y2NgUua579+788MMPAKSlpeHn50eHDh3Q1tamfv36zJ8/X15RYW5uzsz6PdhYfRhB9b/ArVJjABSSxJmMGCz06+BsaI7qfBiciZMDGzZr1gxTU1OcnZ3566+/iIuLw8nJibZt25KUlMSpU6e4d+8ev/zyCxMnTmTp0qW89957rFixAn9/f+7evcuXX37Jp59+yoQJE/D39+fmzZs0btyYsWPHYmJiwqRJk1iyZAkqlYr69esze/ZsTE1N5dcFWwji4uL4/vvvcXd3Jzw8HMgfOKlSpQqdO3dm06ZN6OrqMmPGDH766SdMTU05evQoe/bskWf6q1WrxrFjx7h79y55eXmEhoZSsWJF1qxZQ69evfj5558JDQ1l4MCBdO7cmUOHDslBGnNyyh60MzU1JSEhQeNqjkOHDhEaGsr+/fuLrY7IysoiMDCQli1bEhgYSGhoKFu3bmXmzJnl+g4JwqtKzPw/oYLOgaY0QnnqHFIzE5m/x4OcnA9ZvXo1c+fOxczMDF9fX+Lj44vsByu8hwvyU/golUoyMzP5+++/sbW1xdvbmwsXLhAfH0+9evVYsWIFERERfPHFF2RkZMijrX5+fuzatYvU1FScnJyIjIyUgwQNHz6c5s2bc+TIEb788ku2bdtGVFQUa9asoXnz5hr30D281y4rK0uu08vLi3bt2j2nT1wQnh9NM1Pv2lck7W4u23ziMKiswPmzXOb+/ilpaUaEhoby+++/y0skU1NTCQkJISAggICAAP766y9mz55NUFAQDRs2JCYmBpVKxfnz55kzZw6//PJLudv25Zdf0rp1a/r06cPYsWOZNGkSrVu3ZsKECfz999+0bt26SHkbGxssLCwIDg5GW7voj/49e/bI+yijo6Oxt7dn8+bNNGzwLu7tx7H81x9Y8N2vmFaxJcEkC5Oqefz4v9/o5N4dw0oPAn/Fx8czc+ZMtm/fTqVKlbh48SJZWVmP9qE/ReVZzjl8+HCWLVtGo0aNOHToEF988QVr164tUsfjrpQQXg59rGZw/e6ZUtP9mRqa0ceqeI574dkoKRCjvpEWFl0qs2tePJIEVcwq0LyTASdvFI+qr1Ao+Prrr2nTpg2SJNG4cWN5drosPj4+DB06lMzMTHR0dNi4cSPW1tb8888/2NnZoaenx7Zt26hYsWKR6z755BPGjRuHk5MTubm5TJ48GRMTEwA8PDzo1asXN2/eBGDC8DEMad2Fezn3USCxsEZPAG5lJ3M+8xZ9r/mSpc7hnQomOCneZeTwT+jVrw9aWloMHDiQ3377DUdHR1auXMlnn31Gt27dcHFxQaFQYGZmJrft1KlTmJmZYWdnR0ZGBu7u7ly8eJGtW7dy5swZFAoFzZo1k/fXDx06lH379rFgwQJWrlyJJEk4ODgwa9Ys6tSpQ8WKFYmPj2f//v2YmJhgZ2fHBx98QL9+/fj111+pUqUKtWrVYtiwYfTq1YukpCT69u1Lamoqurq6ODk5ceHCBeLi4gB499130dPTo1GjRnIAv1atWtGrVy+uXbvG/fv3+eWXXzh9+jRVqlQhNzeXTp06ceXKFfbs2UNgYKDGVJYF4uLiqFq1qsbVHN988w2DBw+mSpUq+Pj4FLmuYHb/8uXLnDp1Cmdn53J9dwThVSc6/0+orPzBAHGp0cTGpuNi2Y/Vq1fj6upKUlKSvB+spAf2zZs389VXX5GVlYWvry+2trYANGnSBC8vL0aOHMmRI0do3rw5KpUKSZJwdnbmq6++AvJTBa1fvx6AwMBAOSXL3bt3mTlzJrm5ubRo0YLo6GhOnDjBihUr+OGHH+Q9dHl5eXTo0AE3Nzd5r112djajR49m8+bNNG3atMw0L4LwqippZkqhLWHVwwirHkZEHU4jctc9bpkdoH2TLwGwtrYmJCQEeJBnu2bNmjRp0gSAmjVrkpiYyD///INKpZIfasqzZ7PAxYsX0dPTk2egzp8/z8SJE5EkidTU1GKzVWVp166dPNPj7e0NwLlzF7hxTZfMtJtUM23IrbgoDAyMuXv3Bs6OH3Pk+J/8b9F+vvjaWR4A2LlzJwMHDqRSpUpA/kNfYYWDRhUEkkpLS2Pw4MFFAlAVKAhApa+vT926dXnvvff48MMPuXPnDt7e3ri7u6Orq8uoUaPIzc1l9OjRDBgwAE9PTwwNDbl48SKTJ08GICUlhQEDBjBnzhw++OADID9QVvXq1WnUqBEAdnZ2TJ06ldzcXD7++GO5Dj8/P/r37092djbNmjWTZ8zmzZuHtrY2d+/eJSgoCB0dHT766COysrIwNjbGzc0NT09PZs6cyb59+1AoFKxcuRIzM7NH+vsRnkxlvaqMb7eRgPDpnLy+u0inUyFpY1nHjT5WM6is93y28AiQlplU4rm6lvrUtdQvciw9K5mDBw/KrwuePTp27EjHjh011uPp6VnsWEEdDRs21JgBICMjA8j/mahpUkNbW1v+ufuwjh07kpqaKr+ufCqJVbUGFSkzzsSVxXdULKzRE4eK+T93Bl9fRUeDD+g38isM+lpy9epVatasCcD06dMJCgqievXq2Nvbs27dOqDo6ihLS0s2bNhAxYoVyc7ORkdHhw4dOhQ71q5dO/T09Ni4cSM9evRg4sSJQP5E0+bNm3F1dcXb2xsvLy8aNWqEUqmUV00ZGxuzc+dOIP+zDw4OxtjYGHNzc/bu3UtgYCD/+9//8PDwQKlUYm9vj1qtZtiwYfLsvYuLi9zuqlWr8sMPP7Bjxw5q166Nh4cHANnZ2SxatIiLFy/St29ffH196dKli8bPGyAkJIS33noLLS0tzM3N+eGHH6hRowa5ublIkkRmZibu7u7Mnj2b7du3F7m2cArLli1byjEbsrOzS7yfILwOROf/CZQnf3CBmylR1G9UC8h/+E9OTi71gV2tVrNv3z45p2/hvMKWlpZA/h7gqKgoKleuzNixY0lPTy8y2lpSNFMTExM5MmqDBg3Q09OTOyQl7aF7eK+dILzuSpqZuhefQ8W3tFBoS+hX1kKtzsLQVGLvoV2M+2oSYWFhNGjQACiaZ/vhJavvvvsurq6ucsquR3ngePfdd+nbty/jx49nwYIFmJubyxGqgRKXSuro6JCbm1ts5l+ThDgdLl06jUmVZty6HYWxcQ0UWlqkpSVSo/q71K/XghMn/2bLJhMGDM6/782bN2natGmJdRYEkho4cCDDhg3j6NGjHD58GFdXV0aMGFFkpr5wAKqTJ0/yxx9/cOfOHVq2bMm5c+c4efIkU6ZMwcPDA39/f2rVqoW9vT29e/cG8jvyP//8MyqVSmPHv6C9BQ/ZBapVqyan7iqoIysriz179qCtrc2AAQO4dOkSABUqVGDr1q34+Piwd+9eMjIysLW1ZeLEiXIQqtOnTz/RCg/h6aisV5URdovl2DzpWckYVDDCsnZ7sdT/BXgTAjHmJWdoPB6cep6hbz2IJfCubjWOpF/B9b/yERER9OrVC319fXR0dFi1ahXVq1fn7t279OzZk+XLlxepb8aMGXTu3Bm1Ws3bb7/Nxo0bNR5zd3enW7duDBs2TE4bKUkS3bt3x8PDAx8fH8LCwjAyMpIHRMsyZswYebWFu7s7o0ePpnHjxlSoUKFc148YMYLhw4ezZMkS1Go1c+bMYdiwYXh4eLB69eoSU0q2bdsWbW1tqlevLt9f02qOHj16yNsP/vzzT86fP1+sLhMTE9zd3XF0dERLSwsXFxemTp1arvYLwqtIdP6fQHnyBxdQq/P4NyEc6PXfa3WpD+zHjx+ne/fucr7hiRMnEhkZCeQv8erYsSOnTp1i4MCBLF26lAkTJhQZbQWKLFUtqRNS0h66oKAgtLS0yM7ORpKkYuUevlYQXjclzUzduZpF8I8paFeQUGhJOH3yNoZVtIk5q8bBwYFKlSqxdu3aMgfJLCwsqF69OkqlEkmS6Nu3LyNGjChSZvDgwejr61OhQgW++eabIuc+/vhj5syZw7x585g8eTIjRowgOTkZhUKBr6+vxtnlwg9/BTMtGt97WhZ6Ou+RcGc76/6cSEWDt7Cx9iDhzlXupSawePlAqrxdh7eMa3LmtA0pyRlUNtKjRo0apeZz/vfff+VZOmtra6Kiorh48SKfffYZ8OBnVkpKCuvWrePIkSNA/h7YqVOnkpyczNdff82BAwfIy8tDR0eHxMRE+b3Wr19f4+Dnhg0bGD58eJGOP6Cxvbdv36Zq1apF6rhz5w4jR44kKSmJ6Oho+ZqC1Ry1atUiKSmJmzdv0qxZMwA5OOOTrPAQnj5jg2ooGw0qu6DwTL0JgRgVRnoaj2+uVzQ6vZdpxyLlu3btSteuXYtdFxT0YOtD4VUQNjY2xbKwaDr25Zdf8uWXX8qvC1aoFThx4kSxez6cPhDyU+0V/Dxr1KiRPGjbsGHDIqkWoejqi4JVZZAffLa0e5S2qrSkc5pWc/z1119FXhduQ+GtBEOGDGHIkCEl3lMQXiei8/8ESlu2pklGTlqR16U9sG/evLlIZgClUsmmTZuA/CW+bdq0oW7durRu3Zrk5OQyR1vr1Kkjj+yW5lH20NnY2NCtWzfGjh2Lg4NDeT8GQXgllDQzZWZtgJm1QbHj42YOL9KpMDY2lh9qCj8sFX4Y8vLywsvLS+N9Cj8cFSioo+DBr3D6qsK5qB9WUP7hhz8AMzOzIg9f3t7eHD4YjSSdoYv7hCJlo69F4NF1OvXqNgdgU+Cs/LRYkbewtTejY8eO9OzZkz59+lCpUiWioqKK7Jlt0KAB4eHhfPDBB4SFhTFs2DDi4+M5cuQITZo0kffXV65cmSlTpjBkyBDWrFmDlpYWCoWCpKQklEol06ZNkzMOGBsbEx0dTa1atbh8+bK8Qqnw4OeQIUO4fv06W7ZsoVu3bvLxevXqERsby6VLl+Q9/6ampmhpaRWpY+3atXTr1g1PT0/69++vcQBUrVZTv359IiMj6dixI6dPn6Zly5ZPtMJDEF5XBYEYw69tL7PsqxqIUbdNI5izD3LKkUpOW4Fu2/LNtguCIDwJ0fl/AuVZttbVO/8/LOuexthZ5y/rL/xQX9ID+5w5c4q8dnNzw83NDW9vbwYNGlRkYKDgXGEP73UrHMCq8IhxwZ8LdwA07aHTtNdu4cKFGttelsGDBzNhwgQaN27MqFGjqFq1qrwndseOHeWq18/Pj0GDBhV5wM/JyWHQoEHExMSQmZmJl5cXnTt35vPPP5cfvAsolcoy4xWUVJ9IcfhmeBNmpkpyP11zB/XyleO0sOgkv67ydh1uxJzl/v33gfzlk1OnTqVTp07yctPCuaSHDx8uB41q1qyZHEh00KBBrFmzhgYNGshZRApijYwZM4aff/4ZS0tLkpKS0NXVRVtbW46BMnPmTPr160dubi6fffYZOjo6xdotSRLLly+nT58+vPXWWzg5Ocnnfv31Vz777DMyMjJ4++23NS7Jd3FxYdCgQWzZsqXUz61bt2589NFHtG/fHkNDQ3R0dMq1wkMQ3kSveyBGLVND9No2ImP3hTLL6rVthJaJ4XNolSAIbzqptBRy1tbW6rCwsOfYnFdLUvptJgXalrtzMLfr4Scevfb29sbe3r5I5/9VUbDXMmBVIMZGbzPx81mMHjEWbW1t/Pz8mDdvHmZmZvKe3dIolcpiUcu3b9/O0aNHmTVrFmq1muTkZIyNjUu8vqzOf0n12dvbFxkMKYmIEv7qW37os3LNTFnV7cQIu/JFmX4VHD4YzZaNZ8pdvptHE2ztzZ5dg14hOTk5aGtrM3LkSAYNGlQs64IgCA+kZCS81oEYc++kcbf/WnKvJZVYRquuMW/790OrSsUSywiC8GQkSQpXq9XWL7odLwPRM3kCLyJ/sLe39yvX8U/JSGD5wVFMCrRlXdhUbusfZNvedXyzqTVXEsPJzM4PxnL8+HFsbGzYvn07jo6O2Nrasnv3brKysnB3d0epVNKrV68S85UbGBhw4sQJYmJikCRJ7vgXRJfdvn07VlZWDBkyRF56GxUVhaurK05OTnz77bdF2q2pvuXLl8spDiMjI/n9999p1aoVdnZ2nDp1CshPYTNy5EjGjRuHs7OznLu3Z8+eRQI3Ci+/PlYzMDU0K7XMqzwzVZIPmlZHoShfTA+FQqJJ0+rPuEWvDnd3d+zs7EhPTxcdf0EoQ0EgxjldDtPXehZdm42jr/Us5nY9zAi7xa90xx9Aq0pF3vbvh56bOWg/9MitrUDPzVx0/AVBeK7Esv8n9LovW3tSKRkJzN/jUeTzqWJWgTu/JxEfnY66SiLXM9OIvHCC6Oho6tWrx5AhQ4qkGmzUqBEmJibs2LEDtVqNJEka85W7uLhw9uxZunXrhiRJrFmzBnNzc/n8nDlzCAkJITExUc7n6uXlxYoVK6hTpw59+/blxo0b1K5du8T6RowYwerVq1GpVHI6sEOHDhETE8Po0aPZvn07CQkJeHl5Ubt2bb799lv279+PjY0NmZmZcpYF4dXwpqYIMzLSo0mz6pyOuFlm2SbNqlO5hMBWb6LCQbkEQSif1zkQo1aVihgv6kJuXCqZey+Rl5yBwkgPXbHUXxCEF0B0/p/Qm9o5KK+A8OnFBka0tPNnFG9fzMTkHV0yUu4zZ/lXcoqth1MNvvPOOzRt2pT+/ftjZWXF119/XeL9Pv/8cz7//HP+/vtvpk+fTkBAgHxOoVBgaGiIoaEhJiYmAFy4cIGBAwcC+SkMY2Ji5M5/WfXFx8dTr149dHR0MDMzIzk5GQBTU1O5jn79+jFnzhxiYmLo0aPH436Mwgv0pqYI69ajCTE3krmTkF5imSpVDejWo8lzbJUgCMKrScvUEIO+li+6GYIgvOFE5/8peFM7B2VJSr/Nyeu7NZ6rUk+Hi6FpvO9iSEaqFru/+5vhA8doTDWYlZXFV199hUKhwNXVlf79+2vMVx4bG4uxsTEGBgaYmpoWyRkO+Xvw09LSSExMJD4+HgBzc3N++OEHatSoQW5ubpHo3SXVV1DGxMSEq1evkp2dTUxMDEZG+XmIC+/zf+edd4iNjeWPP/4oEnRRePW8zjNTmhhW0uWzMXZs2XSGM6dvkZf3ID6MQiHRpFl1uvVogmElzXmYBUEQBEEQhJeL6Pw/RW9a56AsETFBJQZDNG1QgVv/ZKKtq8BQV8H95FyMzdQaUw1+8cUXDB06lNzcXN555x1MTU015iu/evUqPXv2lKN9Pxzhf8KECTg6OtKiRQuqV8/fo+zj48PQoUPJzMxER0eHjRs3YmhoWGp9hdMmfvbZZzg4OKBQKEpMidixY0eCg4PlwQFBeFUYVtJlwGArkpMzOBt5i/v3s9HX16FJU7HUXxAEQRAE4VUjov0Lz8yOMz8SGFn+dIBdm42j4wefP8MWvRhLlizBxMSEjz766EU3RRAEQRAEQRDeKCLa/wNi5l94ZirqGj9SeYMKr9/M+JIlS9i8eTM7d+580U0RBEEQBEEQBOENJlL9Cc+MRa32KKTyjS8pJG0sa7d/xi16/kaNGsWePXvkrQOCIAiCIAiCIAgvguj8C8+MsUE1LOu4lausZR23Nzo4oiAIgiAIgiAIwrMkOv/CM9XHagamhmalljE1NKOP1Yzn0yBBEAShRMnJySiVSpRKJUZGRiiVSoYMGUJwcPAj1XPt2jW6d++OUqnE3t4eX1/fcl2nVCoBOHHiBJaWlvj4+Ggs5+fnJ2dg8fT0JCoq6pHap4lKpaJevXoolUq6du1KRkZGua65fPlyseP29vZPtW2CIAiC8DSIzr/wTFXWq8r4dhuxqtup2BYAhaSNVd1OjG+3kcp6VV9QCwVBEISk9NuoLq3m4PXf8P51KFt2rqdp06Zyh/hRDRs2jDlz5qBSqQgNDeW99957pOt3797NnDlz8PLy0ni+cOf/UZV23cCBA1GpVNja2rJhw4Yy6ymp8y8IgiAILyMR8E945irrVWWE3WKS0m8TERNEelYyBhWMsKzdXiz1FwRBeIFSMhIICJvGyRtFU7OuD5/BrZR0UjISAFi9ejVz587FzMwMX19f4uPj+fjjj7l37x7vv/8+S5Yska+9du0apqamcodfoVDIM+HBwcFMmTIFgG+//Za2bduyfft2pk+fTrNmzcjOziYqKorly5djZGREamoqly5dYvfu3WRkZLBs2TKys7OJiIigTZs2DBs2DIAFCxZw5swZ2rVrx/Tp04mKimLUqFFkZmbSrl07pkyZgqenJ4aGhly8eJHvvvuO8PBwPv74Y42fi4WFBSdPnuSLL74gIiKCvLw8/P39qVu3Lq1atcLS0hJ9fX02bdrE5s2badu2LQsXlj+7jSAIgiC8CKLzLzw3xgbVUDYa9KKbIQiCIJDf8Z+/x4O41Ohi5/LUOaRmJjJ/jwc5OR9iaWnJ6tWrcXV1JSkpiblz5zJp0iRat27NhAkT+Pvvv2ndujUAsbGx1KhRA4Bz584xatQoIH+JvLe3N3/99RcAbm5utG3bljlz5hASEkJiYiLOzs40bNgQT09P7O3tadu2Lenp6UyaNImoqCimT5+Ov78/FhYWBAcHo62tzd69e2nfvj3Lli3jww8/ZPr06Xh5ebFixQrq1KlD3759uXHjBgB2dnb8/PPPQH4HvyShoaFYWVkxZswYDAwMCA4O5pdffsHHx4eEhAS8vLyoXbs2lStXltspCIIgCC870fkXBEEQhDdQQPh0jR3/wuJSo4mNTWeM02wAatasSXJyMufPn2fixIlIkkRqaio2NjbyNTVq1CA2NhaAxo0bo1Kp5L38kiRRuXJlALS0tID8lQGGhoYYGhpiYmJSrA1r1qzB398fhUKBJEka29mkSRMA9PX1Abhw4QIDBw4EICkpiZiYGACsrKxKfb9r1qzh0KFDNG7cmK5duzJr1iz27t1LdnY277//PgCmpqbUrl271HoEQRAE4WUk9vwLwiuuYK9uE6uGbA9fSlL6bQC+/PJLQkJCSr125cqV8p8///zzZ9pOQRBeHknptzl5fXe5yt5MiSI9K0l+rVarMTc3Z9GiRahUKsLCwujatat8vl69ety6dYvz58/L5XNzc4H8ffIpKSmkpKQUOZaWlsaNGzeIj48vdv8lS5agUqn49ddfUavVAOjo6MjXA8UGBczNzVm3bh0qlYrw8HBatmwJ5A80lGbgwIHs37+fxYsXk5SUhEql4sCBA8yaNUu+d+E6Hm6HIAiCILzMxMy/ILyiHt6rW9E8hUUrp7LDYQGWddwIPXC41D2oeXl5rFy5kqFDhwLw008/Pa+mC4LwgkXEFN3jXxq1Oo+o+DDgI/nY5MmTGTFiBMnJySgUCnx9fTEzM5PPr1ixgjFjxpCSkoJCoaB3794ATJ8+nXbt2gEwc+ZMACZMmICjoyMtWrSgevXqxe5vY2ODo6Mjjo6O8jF3d3e6desm7/l/mI+PD0OHDiUzMxMdHR02btxY9P1HRJS65x/grbfewtDQEBcXF5o1a6axjFKpZNKkSRw9epRp06aVWJcgCIIgvAykgpFsTaytrdVhYWHPsTmCIJSHpr26ybeyORaQRLsvTUi4ksW/+9U0fMuWuwlJmJiY8Pvvv+Pv78+uXbtITU3FyckJHx8fLC0t+emnnxg5ciQHDx7k6NGjjBw5EnNzcy5cuMCJEye4du0anp6eZGZm0qVLFyZMmPDi3rwgCE9sx5kfCYwsf4C6rs3G0fEDsTpIEARBePVIkhSuVqutX3Q7XgZi2b8gvII07dU1qq5DemIuOVlqrhxPx6BGJsaNU9m3bx9KpVJOW2VsbMyOHTv45ptv5FReTZs2leuZNWsWgYGB+Pr6cu3aNQDmzZvHjBkzOHToEPv375f38wqC8GqqqGv8SOUNKhg9m4YIgvDYCrb97TjzY/7vQZtxcXFBqVTSpk0bDh069Mh1RkdH4+npCSBn6ShMbBF88yQnJ6NUKlEqlRgZGaFUKhkyZAjBwcGPVE/Btfb29ly8eBGVSiVnfykQERHBihUrAM3fP00KYspo4unpSVRUlMZzkiQpJUn69qFjFpIklbwkrGi5A5IkhUiSdFCSJN3/jrUoV6NfILHs/yWgUqkYPHgw77zzDtra2gQEBFClSpUnqnPw4MFMmDCBxo0bM2rUKKpWrcrMmTPZt28fO3bsoGnTpjRt2rTM4Ed+fn4A8n8EwotX2l7dWk31iDmTQcyZDKrUq8C2w6HY77MlJyuPvn37YmRkVObfeUpKihzMqlGjRgD8+++/tGiR//PMwsKCK1euULNmzaf4rgRBeJ4sarVnffiMci39V0jaWNZu/xxaJQhCeWhK0ZmRksue/91hwg8DGOI4Bylbt8ROz5MQWwTfHDmJsaSe2Epu2l22TO2NYYuuKDv3kjO3PKqCCaejR4/y3XffMWDAgGJlLCwsSs3E8qyp1eoIIKIcRacAnmq1+l9JkioD2YAF+X3rE2VdLEmSQq1W5z1+Sx+fmPl/gQpGbP++shFH9+Zs3hHA4MGDWbdu3WPVl5f34DvUsmVLjh8/DuR35gpmcI8fP46NjQ2enp5ldgKFl1Npe3XrtzTg9I4UDKtqY1xTm2adK/Htik85cuSInG6rcLAqTZGzK1euTGxsLOnp6fKDQ4MGDQgPDwfg5MmTRfb2CoLw6jE2qIZlHbdylbWs44aRfrVn3CJBEMqjYNtf+PUdRZ4FrkXcp5G9Aefu/MX8PR6odTKxtLSkV69eODk54erqSkpKCpDfCevXrx/NmzcnIiICgGnTpuHg4MCcOXPkOnNychg+fDgWFhbs3p0/6VDe2Vjh1ZWTEkfs4t5cHlefuDWjubNpGnFrRnN5XH2ybl4gJyUOgNWrV9O2bVs59kp8fDxdunTB2dlZfubUxMLCQk6/Gh4eTufOnbGzsyM1NVXjaoA5c+bg5OTEhx9+yMmTJwHYvn07VlZWDBkyhOzsbACioqJwdXXFycmJb7/9lodUkSRpqyRJQf/9XuG/41aSJG2TJOmQJEmGhVcDSJL0x38z+3/918EvLB1oJ0mSvlqtTvmvIz8CGC9Jkr8kSdqSJK2TJCn0v9+1/6s7UJKkQGCqJEkL/rtPVUmSNpf7L+gJic7/C5CSkcDyg6OYFGjLurCpHL78B2dvhjAp0JYdJ5aTkZ0K5AdDUiqVuLi4EB0dTXZ2Nm3atMHR0REPDw9yc3OJjo7G2dmZnj17yrP0kB8g6dixY2RlZVGhQgV5YKCg8+/t7U1wcDAqlYoOHToU+YeXlZVF165dcXNzIzAwUK5zzJgxODo60qlTJ5KTk1m8eDHbtm3j0qVLvP3226jVaqZPn86xY8ee6+f5pknLTCrxXJV6FUi7m4uZtT7vtzEk+ng6Xw70wcXFhRMnig9E1qlTBw8PD/755x/52NSpU+ncuTNDhgyhTp06AHzzzTdMmzYNW1tblEoltWrVeurvSxCE56uP1QxMDc1KLWNqaEYfqxnPp0GCIJSppBSd6Um5GBjnp888HHSWFjZNGTduHH5+foSEhNCrVy/Wr18PQFxcHCtXrmTJkiX89ttv3Lx5k2PHjnHgwAGcnJzkOu/evYuPjw87duzgl19+eS7vT3ixclLiuO7jQOrxDZD70ERTbg65qQlc93EgLzMNS0tLgoODuXbtGklJScydO5dJkyaxf/9+KlWqxN9//63xHqGhobz77rsAVKhQgW3bttGxY0f27t2rsfwXX3xBSEgI/v7+LFiwAMgfEAgJCWHmzJncvp2f5crLy4sVK1YQEhLC2bNn5QGGQuLUanV74DDQ479jWWq1ujOwE2jzUHlPtVrtBPwB9H7o3DdAC+CMJEm/SJKkAJYD89VqdX+gO3BOrVY7AmcBj/+uq6BWq7uo1eoZQAspfxbOA/hT45t/BsSy/+dMU6A2gEsH07h+KoOcrGvUXpDL4eOtiYmJQaVScf78eebMmcOyZcvYvn07+vr6TJkyhX379tGoUSPi4uIIDg6WcyZD/qja119/zalTp2jevDnx8fFER0cTHR1N/fr1i9y7QoUKbN26FR8fH/bu3UtmZiY2NjZ4eXkxYsQIIH/QIC0tjdDQUH7//XeWLVuGq6srf/zxB3fu3KFly5acO3eOkydPFhuxE56usvbq9vn+wXJ8l8+q0tfaC2WjQQB8+OGHRcquXbtW/vPBgweB/DzY4eHhpKWl4erqCuSn7lKpVE+h9YLw7CSl3yYiJoi0zCTOHrvO5hUH0NXRo0WLFsyfP7/Iz0hNIiIiyMvLo0WLFqhUKoKDgzXNHmh05coVJkyYwB9//EFcXBx16tQhJSUFXV1dlEolgYGBcn57TfcrD19fX5YtW8aMGTNwd3cv1zWF+fn5MWjQIHn1T2W9qoxvt5GA8OmcvL67yCyiQtLGso4bfaxmUFmv6iPfSxCEp6+0bX8GxlqkJeannWxoV5Hq5mqu/32V8ePHExkZSUpKCt27d88/37Ahenp61KpVi6SkJK5evSpntLCysuKvv/4CwMTEBFNT0/x7JyU943cnvAzifh9D9u3St4tk344iLX4XDl9+D0DNmjVJTk7m/PnzTJw4EUmSSE1NxcbGpsh1kZGRODs7Y2xszJIlS7hw4QJNmjQBkL+LRkbF48usWbMGf39/FAqFvGJVoVBgaGiIoaEhJiYmAFy4cIGBAwcC+d/XmJiYh6s6+d/vEUBL4BZw5r9jMYAxkAwgSZIWMF+SpKZAZaDIzLxarb4FjPiv874UcH3oXg14sPw/DLACblN0S8ABwA7oTPHBhWdGdP6fs5JGbBvZV6RlL2NUy+4QffUyfhdnE6KKloNY1KhRg7S0NEaMGEFMTAy3b9+mUaNGNGrUiObNmxd7qK1QIX81y+HDh7G2tiY+Pp6dO3dSrVrxpZsP/8O7efMmlpaWAPLWgMJ7vq2trQkJCWHcuHFMnTqV5ORkvv76aw4cOEBeXh46OjpP5bMSNHvWe3UPHTrEtGnTuHfvnkhdJbwSHt7/mpGSy18/JOA+oQY2jToSs8+AX3/9lU8//bTUeiIiIsjJySl3Z7yw+vXrc+XKFeDBCqtTp05hZWVFWlpasY7/o94vLy+PP/74g4MHD6Knp1fkeFm56wv4+fkxYMCAIuUr61VlhN1ieeAkPSsZgwpGWNZuL5b6C8JLprRtf3Us9NnzQwINWhlQwUBBbk4O129FoV+hIqGhofz6669yZ6jwlj+1Wk29evWIjIwEkJdVayonvN5yEmNJDS/f6vOs2PPkpd2VX6vVaszNzRkwYIDcd8jJKfpdbdq0Kfv375dfX7hwoVzfsSVLlnDy5En+/fdfhg8fDuT/35eWlkZiYiLx8fEAmJub88MPP1CjRg1yc3ORJImlS5cWrqp5od//LbhtofOF98JaABXVarWjJEnDgSJLXiVJaqhWq6PUarVakqR48lfTZwO6/xX5l/wO/w7AGigYUSm8z98fmAMkq9XqNI1v/hkQy/6fo9JGbAtYdKnMya0pJFY4i5OLPSqVCpVKxerVqwkKCuLdd98lJCQEDw8P+R9JSQ9+zZs3x8/PD0tLS6ysrFi6dCktW7YsVu7hf3j169fn1KlTwIP/BArv+Q4LC6NBgwZoaWmhUChISkpCqVSyatWqIlHjhWfjWe/VdXJyIiQkhBMnTtCtW7fHaKEgPD+a9r/m732tiJZuHuHXtpPVOIQ/N+Yvd23VqlWxPawFli9fzvz58+nfvz9QfC+iWq1m5MiRuLi44O7uTmJiYpHrTU1NiY+P5/jx43zyySccP36c8+fP07hxYyIiIuQ9i7Nnzy52P011q1QqunTpQpcuXfD39+fYsWO0b9+eqKgomjdvzoABA/juu+8IDg6mVatWtGrVSo6+rFQqGTt2LC1btmTFihUcO3aMiIgI2rRpw5o1a4p9jsYG1VA2GkTHDz5H2WiQ6PgLwkuotG1/+pW1sPIwImhhPNu/vc3BVYm07dGSqKgo3NzcSt2SWaNGDaysrHBwcJBXAQpvntQTW4sv9S+JOo/7Fw8XOTR58mS+/fZbXFxcaNu2raZl94/FxsYGR0dHVq1aJR+bMGECjo6OzJgxg+rVqwPg4+PD0KFDcXFxoWPHjqSnpz9cVRVJkv4C7IFNZdz2AtBQkqTdgI2G8wMkSToqSVIIUA8IAo78d/wnYAvwgSRJoUBTYOPDFajV6otAXfK3FTw3Yub/OSptxLaAcU0dMlJy0TOCPP381BqSJNG3b186duyIj48PYWFhGBkZyZHYS2JjY8OBAwcwMDDAwMCAuLi4YktwNOnWrRs9e/akffv2vPXWW0B+AEE/Pz8cHByoVKmSvFzc0tKSpKQkdHV10dbWxtbWtpyfhvAk+ljN4PrdMxpXkRQQe3WFN4Gm1VTpSbm8XaeC/Ppu1jVu3Mn/2VuwhzU7O5vRo0fj5vZgIG3EiBHk5OQwbNgwVCpVsS1RCoWCunXrsnTpUnbt2sWyZcuYNGmSfH1BoNV///2XCRMmMGbMGAwMDLCxscHc3ByVSoUkSTg7O/PVV18Vud+2bduK1d26dWuysrLkQYoVK1YQHByMtrY2N27c4PDhw1SsWBF7e3t5ma6bmxtt27YFYMCAAcyePZt27doRGhqKhYWFfL0gCK+esrb91fpAj1ofPFgZ5GJtz8zRy4uVK+jgm5mZyfGiNG1xKjwQULD1TwwOvL5yC83kl2Rdh/wJx88tFFR5L3+QuHDMsc2bS1458PB3pyB9IBTNKlZwrKD8r7/+WqyugoHxwho2bMiuXbuKHCvcNmC3Wq32LfRa9d8v1Gq130PHIX+QQCO1Wu0NeD90+F/AodDrh5fyy/cr5A6wi+dIPAE8RyWN2NZsrEfNxg9+WLt75f9j6vqxEx0XFc2nqilo2++//66x3sGDBzN48GD5dUFADKBIig5N//C2bdtWrL7FixcXOzZjxoPO5ePkkxUej9irKwglr6YqvPcVICdLTXpOIsn3bz/SHtaHt0Tdvn2bgIAAgoKCyMnJoXXr1kXKF3T+1Wo1+vr6pKenExYWxuDBg7ly5Qpjx44lPT2dCxcuEBcXV+Ta8+fPa6y7pC0B5ubmVKxYEchfvVWwraDwFrAmTZqgo6NT7m0BgiC83ESKTuFZ0qr49iOWf+sZteTNIEnSDmCvWq3Oep73FZ3/56isEduHGVQoHvRCEAqIvbrCm66k1VR1mufvfW1oa4COnoLIXSnUs9bj5I2gUvcX6ujokJmZKb9+uKy5uTmDBg1i7NixAHJ6oQI2NjZMmjSJTp06AVCxYkUOHz7M999/z9ixY5kwYQJKpRJ7e3vUanWR+2mq+9ChQyV23Asfz8vLk1N45eY+GPR4OJWnjo4Oubm5YuZfEF5RBdv+wq9tL7OsSNEpPCrDFl2JW/tl+Zb+a2ljaNXtWTfpabrz0Kz/C6dWqx89cu9TIKYDniOLWu1RSOV76BIjtkJ5ib26wpuqpNVU+kZaWHSpzK558WybeZv7yXm872JIelZyqfW1atWK33//nc8//1zj+S5duhAdHY2LiwsuLi7FlheamJhw7949rK2tgfysKzo6OlSoUAF3d3dGjx5Nr1695ICshe9XVt2lmT59Ou3ataNdu3ZMnz69xHLu7u5069aNjRuLbT0UBOEVIVJ0Cs+K9ls1MbTqXq6yhlbd0Tau8YxbJDwLUmnRO62trdVhYWHPsTmvv+WHPivXiK1V3U6MsCu+zF4QBEHIp7q0mnVhU8tdvq/1LDntpSAIwqsqJSNBbPsTnomclDiu+ziUmu5Pp1pD6ngdQLuy6XNs2ZORJClcrVZbv+h2vAxE5/85K4hMXVagtvHtNoof3IIgCKVISr/NpEDbcu9/ndv1sFgZIwjCa0Ns+xOehZyUOOJ+H5Of9q/wFgAtbQytumM64MdXquMPovNfmOj8vwBixFYQXl0qlYrBgwdTv359jIyMWL9+fZG86+WtIzg4WGN0ZeHRiNVUgiAIgvD05STGknpiK7lpiWhVfAtDq26v7FJ/0fl/QET9eQFEoDZBeLUU/FtNy0zi0o2bfNSnBwvmfc+8efPYsGEDAwYMeNFNfGOJtJeCIAiC8PRpv1UT4zYjX3QzhKdMzPwLgiCUICUjgYCwaZy88SCqfOy5DGLPZPHJuH5UvetEz659sLW1RUdHB39/f/r3709wcDBaWlr07NmTxYsXExUVxTfffIOOjg4jR46kWrVqzJs3D21tbe7evUtQUBB6enoMHDiQmJgYatWqxZo1a0RU9nISq6kEQRAEQSiJmPl/QDxZCoIgaFBafA41eYRf287Zzdt4v/F7hIaGolAoUKvVtGnThv3792NjY0NmZibVqlXjo48+YuvWrVStWpW8vDxCQ0OpUKECW7duxcfHh71795KVlUXjxo1Zt24d3377LRs3bqR3797P/42/gsRqKkEQBEEQhLKJVH+CIAgaBIRPL3Ep+aWDaWz79jaJSYm4DK7P4MGD+fLLL0lPT6dfv36sX7+ezZs306NHDyA/R3zVqvkzzwX52Zs0aQJArVq1SEpK4t9//6VFixYAWFtbExVVcqRdQTOR9lJ43YSEhODi4oJSqaRNmzYcOnTokeuIjo7G09MTAF9fX6ytrdmxY4fGsvb29gAolUpycsqR61sQBEF4pYiZf0EQhIckpd/m5PXdJZ5vZF+Rlr2MAcjIPsfPy4+w+PsVbN++nd69exMbG8sff/zB2rVrAZAkiTt37lClShXy8vLkYwXUajUNGjQgPDwcd3d3wsLCaNiw4bN7g4IgvJQKxxfJSdNi/lR/dm7fTeXKlbl3794TDwr+8ccfHDx48JGDlAqCIAivBzHzLwiC8JCImKBypY8D2LXgFg4OjuzatQsnJycAOnbsSIUKFTAyMgJgzpw5dO7cGWdnZ/7880+N9XTr1o2zZ8/i6OhIZGQkHh4eT+fNCILw0kvJSGD5wVFMCrRlXdhUAiMX8oPfNLTfu0RA5CRSMhKoVKkSlpaWzJs3Dzs7O1xcXLh27RqAxmPTpk3DwcGBOXPmALBhwwaOHTtG+/btiYqKwtfXFwcHBxwcHDhx4oTGdu3evRulUom1tTWrV68GYPHixbRq1QpnZ+cSrxMEQRBeTmLmXxAE4SFpmUklnqvZWI+ajR/MmrlPMqVrs9F0/OBz+ZgkSfTr109+bWdnx+HDh4vUo1QqAeTluADr169/soYLgvDKKSm+SHpSLm/X0SL82nZ2bd7HrcOV+aBxU65fv86hQ4c4ePAgc+bMYfr06ezbt6/IsWnTpnHs2DEOHDjA2rVr+euvv+jZsyc///wzwcHBJCUlERgYSGhoKImJiQwdOpQtW7YUa5ujoyNubm7k5OTg5OTEoEGD2Lp1K/v370dfX5/SgkYLgiAILx8x8y8IgvCQirrGj1TeoIKR/OclS5awefNmunXr9nQbJQjCa6mk+CIGxlqkJeYCYGqVTuev3+XGjRs0a9YMeBAbJDo6utixq1evysesrKyK1X358mVOnTqFs7MzPXr0ICkpSWPbwsPDadu2LW3atOHcuXMAzJgxg5EjRzJixAji4uKe9O0LgiAIz5Ho/AuCIDzEolZ7FFL5FkYpJG0sa7eXX48aNYo9e/ago6PzrJonCMJrorT4InUs9Ll0MI2s9Pw4IWduhJCTm8WpU6cACAsLo0GDBpiZmRU7Vq9ePSIjIwE4efJksbrr169Py5YtUalUqFQq9uzZo7EN3333Hb6+vgQHB2NsbAyAhYUFfn5+KJVK/Pz8nuTtPxW5camkrztJzPd7cGxsjZOdA0ZGRiiVSoYMGUJwcPAj1VdwrY2NDRs2bCj3dREREcW2QezevZuvvvoKyI/tolQquXXr1iO153FMnz6d1q1bEx4eLh/Lzc3l66+/RqlU4uDgQFBQ0CPX6+fnh6+vb5Fjn3/+eQmlBUF4GYll/4IgCA8xNqiGZR03wq9tL7OsZR03EVVeEITHUlp8Ef3KWlh5GBG0MB5JAklL4utx/ciMMcLW1pYKFSrw22+/Ub16dZydnYscq1GjBlZWVjg4ONC8efNidZuYmODu7o6joyNaWlq4uLgwderUYuW6d+9O165dsbCwkDv/n376KVeuXCEzM5NVq1Y91c/jUeTeSePet3vJ2HsJcvLQAv6gN9xT0M3wDns37mDWT/Mfud6mTZuiUqnIyMjA1dWVnj17luu6iIgIcnJy5KwtAG5ubixevJioqCiOHj1K27ZtqV69+iO1Jy8vT84SU1779u3j77//LnJs+fLlmJiYoFKpSEtLo0OHDlhZWcmZaB73/j/99NMjtU0QhBdLKm2/lrW1tTosLOw5NkcQBOHlUNI+3MJMDc0Y324jlfVKf3gSBEHQZMeZHwmMXFju8l2bjSsSX+RNlXsnjbv915J7LUnj+S7RS9jhOJmfml0h+tYNYmNjMTMzw9fXl/j4eD7++GPu3bvH+++/z5IlS4pca29vz8GDB7l79y59+/YlKChI4zWLFy9mzZo16Ovrs3DhQkaPHs2dO3ewtrbG399fru/MmTNMnTqVO3fuEBQUhL+/P7/99hsA//vf/2jRogW9evXi9u3b6OrqsmHDBipXrkzz5s1p2rQpTZo0ISUlhdDQUHR0dPD396dmzZpy/fPmzSMwMBBdXV38/PwIDAzEy8sLS0tLtm/fjqGhIQDt2rUjMDAQfX19AFasWIGOjg55eXnk5OQwbNgwvL295Xg0ixYtAmDkyJF06NAByJ/5Lyhb+PNav349Y8eOJSAggNzcXNq0aYNKpcLX17fIezUzM6NHjx5IkkTTpk358ccfH/crIAiPRJKkcLVabf2i2/EyEDP/gvAGU6lUDB48mPr16wOwdetWOUL9m66yXlXGt9tIQPh0Tl7fXWR2TiFpY1nHjT5WM0THXxCEx/Yk8UXeZPd89pbY8S+Qey2JrOQrWPayYfXq1bi6upKUlMTcuXOZNGkSrVu3ZsKECfz999+0bt1avi4yMhKlUklUVBSzZs0C0HjNw4EPR4wYUaxjDNCkSROMjIxwd3cnLS1NY6BFPz8/DAwM8PX1Zf369QwfPpwbN25w+PBhKlasiJOTE6GhoSgUiiJBFm/dulUs2OPSpUsJCAhApVIVaUdGRobc8QeoXbs2ERERVKumeeVaVlYWu3eXnPK2sFq1apGYmEhGRgZHjhzB0dGRhISEYu/1888/R6lU4u3tLYJFCsILIjr/gvCGKZxH+tKNm3zUpwcL5n1f6jUF/0kXzk3/JqisV5URdovlzyw9KxmDCkZY1m4vlvoLgvDELGq1Z334jHKlFn04vsibKjculYzgS+Uqm3P5Du/XbgBAzZo1SU5O5vz580ycOBFJkkhNTcXGxqbINQXL/nNycnBzc6NPnz4arykIfFihQgV5kKAkZmZmmJmZFQm0KL+f3FzGjx9PZGQkKSkpdO/eHQBzc3MqVqwIwDfffMPgwYOpUqUKPj4+8vGHgz3OmDGjxDbo6upy//59eQDgxo0b1KhRo0gnvPCfC29fKI/27duza9cu9u3bx/DhwzW+V0dHR0JCQujfvz9ubm4MHDjwke4hCMKTE51/QXhDpGQkEBA2jZM3Huwxjb2QQezFLJYfyqLqHUd+XLSU1NRUxowZw6BBg/D29ubq1avExMSwZs0ahg8fTlZWFsbGxri5ueHp6cmYMWOIiIigcuXK+Pv7v5YrB4wNqqFsNOhFN0MQhNeMiC/y6DL/2+NfLnlqck7Gwkf5L9VqNebm5gwYMEDOgpCTo3ngRVs7/xE5MzNT4zXZ2dn4+fmxdu1a/Pz8qF27NpmZmaU2pyDQYkEgwezsbCIiIkhLSyM0NJRff/2VmJgYgCL77F1cXHB3d2f27Nls376d3r17A2gM9liS7t2788MPPzBp0iTS0tLw8/Nj06ZNHDp0SA4OGRkZKXfWHzXOQM+ePZk0aRI3b96kWbNmxMfHF3uvubm5zJw5E8gPHCk6/4Lw/InOvyC8AUrbv37x4D28L6zApOZGDuw+g4G2sZzPGeDdd99l1apVrF+/HltbWyZOnMjIkSMBOH78uPzQ8vvvv7Ns2TImTJjwPN+aIAjCK62P1Qyu3z1TZnyRPlYlz+q+SfKSMx6pvDotq8jryZMnM2LECJKTk1EoFPj6+mJmZiafL1j2n52djaurK8bGxhqvmT59epHAh7q6unh6enLmzJkSg+BpCrT41VdfERUVhZubG3Xq1KFWrVrFruvatSv3798H4M8//5SPawr2WJJPPvmEcePG4eTkRG5uLpMnT8bExIQ2bdowf/58jh8/Lg94lGbRokUEBAQARYP91a1blytXrsgxAzS9VycnJyZPnkx2djZt27Yt816CIDx9IuDfa6hgiXJcwk1mf+mHoe5bRJ46i6WlJfXr1y81Ou+tW7dYsWIFXl5e8jE/Pz/mzJlD7dq1AZg4cSLt2rUrdu3nn38uor6+pJYf+kzjzFLsuQxizmTQspcxN//J4NKOCphWfIfTp0+TmJiIt7c3tra2uLq6MnfuXJo1a0bHjh355Zdf0NXVRU9Pjzt37vDZZ5/xzz//sHDhQn799dcX8A4FQRBeXSkZCSK+SDmlrztJyqzyp++rPLUtBn0tn2GLhLLcSk8h6OpZkjLTMdY1oNK1RH6et4C8vDy0tLSYOXMmAQEBj/QM6efnx6BBgx55hYLwZhIB/x4QM/+vEU3Luj/8EhRSFrdmGxC4e0OZDw/Vq1cv0vEvMH78eIYNG0ZiYiJdunThgw8+KBJtNi8v77E7/o+TxkYov9LySBd2alsK9kNM+N9gf1o2d5CPF/zd1K9fn8jISDp27Mjp06dp2bIlDRo04K+//gLKXnIoCIIgaCbii5SfbptGMGdf+Zb+ayvQbdvo2TdK0CjhfipTjmxl99Wz5Kjz/75y76WTuGQr/RdMZV6bPujmqImKinrkZ0g/Pz8GDBggnh8F4RGJfzGviYJl3eHXdxQLHJSnziE1M5G2vZpj72CLg4MD165dIzU1FaVSSWpqKr/88gvff/890dHRDBgwoMT7vPXWWwwdOpQ9e/agUqno0qULXbp0ISgoCHt7ezIzM4usCmjTpg1ZWVls374dR0dHbG1t5eixrVq1YuTIkYwbN+7ZfCgCUHoe6cLMWhqwe8Et+g/uI+dzLqxbt24cOnSI9u3bc+vWLXR0dGjZsiX6+vo4ODiwdu1aPv3002fwDgRBEN4MBfFFOn7wOcpGg0THXwMtU0P0ytmh12vbCC0Tw2fcIkGThPupdNuxlO3RkXLHHyDz9GX0WzcmKD6KbjuWkqktYWlpib29PQBz5szBycmJDz/8kJMnTwKgVCoZO3YsLVu2ZMWKFRw7doyIiAjatGnDmjVrXsj7E4RXlZj5f00EhE8vdb8gQNMeCj5sZMk797vzyy+/4OPjw+TJkxk+fDh37txh9+7dXLt2rcx71axZk4iICOrVq1ckFYyPjw+6urpUq1aN69evk5ubS+3atdHW1mbBggXs27ePvLw8OnTogJubGwkJCXh5ecnbCYRnIy0zqcRzNRvrUbOxHgDvKQ15T2lI12a95TzS3t7eclldXV02bdqEtrY2I0eO5J133gFg8eLFz6ztgiAIgvCwSl5tyD53u9R0f1p1jank1eb5NUooYuqRQKLv3Sl2PDcpFZ3aJgCc23OApjN86d++s3z+iy++YNKkSURFRTF9+nT8/f0BGDBgALNnz6Zdu3aEhoZiYWFBcHBwueIUCILwgPgX8xoo77LuiG0p7Dy7ktqVj9Dkg/zUMO3atWP06NHMmDGj3EunYmJiqFGjBqA5FUyPHj3YsGEDeXl5eHh4kJCQwPnz5+XgLnFxcajVakxNTUXH/zl4mnmk3d3dSU1NpWHDhkXyIguCIAjC86JVpSJv+/fjns/e/LR/hbcAaCvQa9uISl5t0KpS8cU18g12Kz2FXVfPaDynMK5IbtI9AAxaNYZGtbkecVM+v2bNGvz9/VEoFEXSCzdp0gQdHR2xzF8QnpDo/L8GyrOsOzM1l5vnM+kyvRqNMttwRhUHwLJlyxg4cCC//vorPXr0KPNeSUlJ/PbbbwQEBHDhwgWNP4Q7dOgg56kdPXo0Ojo6NG3alKCgILS0tMjOzkaSJPED/Dl5mnmkg4KCnmbTBEEQBOGxaFWpiPGiLuTGpZK59xJ5yRkojPTQFUv9X7igQnv8H6bXtAF3l2xGv+X7KAx0ycnJJSYtST6/ZMkSTp48yb///svw4cPl44UHAgB0dHTIzc0VM/+C8IhE7+s1UNqy7gK6FRXo6Els//Y2B/ceA+D69esEBgbi5eXF6NGj5dyrmsyfP582bdrg4eHBlClT5Jl/TfT19TE2NqZq1aro6uqiUCj4+uuvadOmDc7Oznz55ZeP+hY1Skq/jerSav74ex4WH76PvaMtRkZGKJVKhgwZQnBw+aMBA/K1SqWSkJCQp9JGPz8/fH19SzyfkJBAnz59UCqV2Nvbs2vXrqdy38IK8kgXdiEkFXVefqaPiMBk0u7mDwyIPNJPpuA7OWfV5xgY6nM19iIAnp6eREVFlauOlStXyn8u2AOpSVnfLU0kSZL/XTzK9X5+fuTl5T/IPcp7Ka/C71kQBOFRaJkaYtDXEsNPW2PQ11J0/F8CSZnpJZ7TqmxA5a723PlpI/HfrSPp97+w6Ogin7exscHR0bHUzFSQvxKxW7dubNy48am1WxDeBCLV32tAdWk168Kmlrt8X+tZKBsNeoYterY0ZTWA/FnrPbPTOXEskkVzf8be3v6R8sja29tz8ODBYsefJBuBn58fOTk5DBs2TOP5vn37MmrUKBwcHMjKyiI8PPyZLKcvCAhZEBdi26zbuE82RaH1YCTd1NCM8e02inRSj+Hh72TsuQwOrrrLe8rKDBnZh5Bld5gx/VsaNmxYaj15eXk4OjrK38OSvpNQ9ndLk4YNG9KsWTM2bdpU7uvz8vJwcXGR91Z6enoyZcqUMt/LoyjtfQqCIAivlt/O/43Xka3lLu/TqiuD3xdbCYVnR6T6e0DM/L8GLGq1RyGVb9lTWcu6X3blyWowf48HmTnprF69mrZt28qdm/j4eLp06YKzszOjRo0q9T7R0dE4OzvTs2dP/Pz8SsxWMHz4cCwsLORjW7ZsoVWrVjg7O8urB3bt2kXHjh3p2LEjhQfbcnNzuXnzJg4O+Wn1KlSoQOvWrcnJyaFv3744OjrSt29fcnJyUKlUdOjQgc6dO2NnZ0dqaqrGY2q1mpEjR+Li4oK7uzuJiYnk5eXx9eiJ7P8um0P/g/h/c7hzNYsdPnFcPJCGatld6igcGGGzin49PXF0dGTMmDFAfgfTw8NDY/uFfCV9J82sDLgansbx6G2ciVVxL/MuycnJdOrUqdhn3Lt3b9zd3VmwYAGRkZEolUoiIyPJyckp9h0rbNu2bbRv356uXbuSlZXFyJEjOXMmf5/lDz/8UGxGpHr16hgaGnLx4sUix8eMGYOjoyOdOnUiOTm5yPd/wYIFxaIqL1iwAHt7e2bMmAHkb/WB/IBM33//PRkZGfLWn5kzZ6JUKnFxcSE6OpqoqChat26Ns7Mzs2fPZvny5UXesyAIgvBqa1/vA7Sl8nUxtCUFbvU+eMYtEgShgOj8vwY0Lesuyau+rLs8WQ3iUqM5E7sfS0tLgoODuXbtGklJScydO5dJkyaxf/9+KlWqxN9//13kuoIOiFKpJDk5mbi4ONavX4+np6ecrUClUjF//nwA7t69i4+PDzt27OCXX34hLy8PHx8f9u/fz/79++VOfe3atdm5cye1atXi9OnT8v3i4+MxMTEp1v7NmzfTuHFjQkND+eCDD+QOXIUKFdi2bRsdO3Zk7969Go9t376dunXrsm/fPkaPHs2yZcvYunUrpqamHDxwmNOHr7Dy6xO838ScXwLmMv2rRVjV7UgfK28CVm+kd+/ehIaGkp6eztGjR0ttv5CvpO+kpIC6LfSJPnafjJxUdpz5keXLl2v8jI2NjdmxYwfffPMNTZs2RaVS0bRp02LfsYeZmpoSFBSEra0tmzZton///gQEBAD5g07u7u7FrhkzZgz/+9//5NfHjx8nLS2N0NBQ+vTpw7JlywDk7/8333yDhYUFe/fuZeDAgQC0b9+egwcPsnPnTgAqVqxIWloaubm5nDlzhrCwMKysrDh9+jQxMTGoVCoWL17MnDlzCAkJ4ZNPPmH//v1MmjSJESNGFHnPgiAIwqutukFlOtRrUq6yHeo1oZpB5WfcIkEQCojO/2uij9UMTA3NSi1jamhGH6sZz6dBz0B5sxoA3EyJon6jWkB+asLk5GTOnz/PxIkTUSqV7N27l9jY2CLXFHRAVCoVRkZGNG/eHC0trSLZClxdXbl58yZqtRoTExNMTU2pVasWSUlJxMfHU69ePfT19QHkrQJNmuT/B1hQroCJiQnx8fHF2v7vv//KWRSsra3l/dWa6nn42Pnz5wkICECpVOLj48Pdu3e5ePEitra2cpuMDaphrF+NDh+MRtloEBW09B/5vkK+sr6T7zkbcn5/KgDnboZy/sIZjZ+xlZWVxusf/o49zNLSEgALCwuioqKws7PjyJEjREdHU6NGDfT09IpdY21tzb///ktiYiJQ8t97wfdfk4LvRMF33cbGhi1btlCvXj1yc3M5fPgwdnZ2/PPPP6hUKpRKJSNHjiQlJYWPPvqI06dP079/f42rGQThebuVnsJv5/9m7oGtvGfTAltHhyeKH3Pt2jW6d+8ux3Ipb2wNpVIJwIkTJ7C0tMTHx0djuWcdg0MQnoZZrbpgVqlKqWXMKlVhVqsuz6lFgiCA6Py/NirrVWV8u41Y1e1UbAuAQtLGqm6nV34/d3myGhRQq/P4NyG80Gs15ubmLFq0CJVKRVhYGF27di21joLOe9WqVWnatCl79+5FpVJx6tQpJEkqEnm2YDDg2rVrZGRkAMgPZw+XK6ClpUWNGjU4cOAAANnZ2Rw5coQGDRoQHp7f9rCwMBo0aFBiPQ8fMzc3Z9CgQahUKg4ePMjs2bMxNzfnyJEjRdpUECW3sEe5r5CvrO+kbkUFxjW0ifs3kzx1DhWqZGr8jAvHlCj8eZf12Z86dUr+vUGDBkiShI2NDePHj6dPnz4ltmvYsGH8+uuvQMl/74Xb9PD35eGoy7a2tsyfPx87OzvMzMz4448/sLGx4d1338XV1VUeVFu9ejU6OjosWrSIVatWMW3aNI31CcLzkHA/lU/3+9Pqj7l4HdnKz1F/kzrSldihDhjUrc6GXdupV6/eI9c7bNgw5syZg0qlIjQ0lPfee++Rrt+9ezdz5szBy8tL4/nCnf9H9bjXCcKjqqpvyBb3kXQ2a1ZsC4C2pKCzWTO2uI+kqr4I0CgIz5Po/L9GKutVZYTdYuZ0OUxf61l0bTaOvtazmNv1MCPsFr/SHX8oX1aDwjJy0oq8njx5Mt9++y0uLi60bduWGzdulKue8mYrUCgUTJo0CScnJ1xcXOROfWl++uknFi9ejFKpxNnZmaSkJLp168bZs2dxdHQkMjISDw+PcrUToEuXLkRHR+Pi4oKLiwu7du2iS5cu3Lx5U97TDZqj5A4fPpyAgAAcHBzQ1dWlVatW5b7vm6o838km7SuRFJs/QODSzbLMz7hOnTp4eHjwzz//lFn3nTt3cHV15eDBg3Kqzv79+6NSqUoNdtm9e3fS0/OjMbds2RJ9fX0cHBxYu3Ytn376abHyZUVVtra25p9//sHOzg47OzvUajUVK1bEwsKC6tWry9/vVatWERgYiIODA61bt6Zfv36P/J4F4WlIuJ9Ktx1L2R4dWSwlWY46j7sZaXTbsZT07KxHih9z7do1TE1N5Q6/QqGQs3YEBwfTqlUrWrVqJa8m2L59O1ZWVgwZMoTs7GyioqJYvnw5EyZMYMOGDcyZMwcnJyc+/PBDTp48ybFjx8qMwREVFYWrqytOTk58++23QP4KgdGjR+PmVr4tgoLwNFTVN2Spcz+O9JqIT6uufNPCFZ9WXTnaayJLnfuJjr8gvAAi2r/wynjTshoIL7+X8Tt57tw5lixZws8///xM7yMIr7KR+9eyLbrkGCbxs3/HZPIAqqsu0d/Cnq+++gpXV1f++OMPZs2aRc+ePWndujUTJkygW7ducpaWI0eOsHHjRubPn8+5c+fkwQGVSoW9vb0cJ8PNzU3eHhMUFERiYiLOzs5ERUXh7e0tZ6tJT0/HwMCAqKgopk+fjr+/P0qlskj2ja5du9K9e3c+/PBDjh49Su/evVmwYAF16tShb9++zJ8/nylTptC+fXv69u377D9cQRCEl4yI9v9A+ULEC8JLwKJWe9aHzyjX0v9XPauB8Gp42b6TBw4c4JtvvuG33357pvcRhFfZrfQUdl09U66yUclx1GpYHygeP0aSJFJTU7GxsZHL16hRQ44n07hxYznmBeRvb6lcOT+wWUE8DYVCgaGhIYaGhhoDwK5ZswZ/f38UCkWJ22MejsFx4cIFOThnUlISMTExQMmxRQRBEIQ3h+j8C6+MgqwG4de2l1n2Vc9qILwaXrbvpIODQ7EsFoIgFBV09Wyxpf4lyVOrCYu7Rq//XhfEVhkwYIDcmc7JeTD4V69ePW7dusX58+d5//33UavVcryMvLw8UlJSAIocS0tLIzExUWMA2CVLlnDy5En+/fdfhg8fDjyIwaGtnf8I9/CggLm5OT/88AM1atQgNzcXSZJYunRpkTgegiAIwptJdP6FV0ofqxlcv3um1HR/r3pWA+HVIr6TgvBqScpMf6TyaTmZRV5PnjyZESNGkJycjEKhwNfXFzMzM/n8ihUrGDNmDCkpKSgUCnr37g3A9OnTadeuHQAzZ84EYMKECTg6OtKiRQuqV69e7N42NjY4Ojri6OgoHyuIwVEQg+BhPj4+DB06lMzMTHR0dEqM1fEmyo1LJXPvJUKOH+aTNd9S/92GSDpabN26FSMjo6d6r4iICPLy8uRsKgXu37/PuHHjOHfuHNnZ2TRv3pzFixc/1XsLgiCUROz5F145KRkJBIRP5+T13UWWWyskbSzruNHHasYrH9xQeLWI76QgvDp+O/83Xke2lru8T6uuDH6/9TNskfCs5d5J4963e8nYewly8jic9i+haZeYWLMjem0bUcmrDVpVKpZ4fV5enrxyovCfS+Pn50dOTk6xQZpJkybxwQcfMGDAAABCQ0OLDO4IgvD0iT3/D4jOv/DKSkq/TURMEOlZyRhUMMKydnux1F94ocR3UhBefrfSU2j1x9xyLf3XlhQc7TWRagaVn0PLhGch904ad/uvJfdaknxM7vyb5mc/0KprjO+Ht9gevBtdXV38/PyoW7cuzZs3p2nTpjRp0oTdu3djY2NDbGws33//PR9//DH37t3j/fffZ8mSJSxevJg1a9agr6/PwoULGT16NHfu3MHa2hp/f3/53o6OjoSGhhZrp6enJ1OmTKFhw4YolUpUKhXe3t5cvnyZ2NhYatWqRcOGDdmxYwcdO3Zk2rRpeHt7c+HCBeLj46lXrx4rVqx45p+nILyKROf/AbHsX3hlGRtUE9H8hZeK+E4KwsuvukFlOtRrUmq0/wId6jURHf9X3D2fvUU6/gU2JJ/g2P1o6uq8zeScDgT/s41D0Sc5ePAgc+bMYenSpdy4cYPDhw9TsWJFdu/eTffu3WndujVjx45l0qRJcsaHv//+m61bt7J//3709fVRq9WMGDFC48x/4Uk3V1dXYmNj2b17d4ntt7S0ZPXq1bi6utKtWzemTp2KtbU106ZNA/IDPnp5eTFy5EiOHDki0vQKglAqEf1FEARBEIQ3yqxWXTCrVKXUMmaVqjCrVZfn1CLhWciNSyUj+JLGcz2NWrCp3qf8ULMX17PvYp5mRG58KtbW1kRFRQH5wRMrVnywHaAgyGNBxgelUsnevXuJjY1lxowZIshV8AAA4D5JREFUjBw5khEjRhAXF1eu9v31119YW1uTk5NTJHBj4QGCgmwONWvWlP9saGgoB420tLQEwMLCQm63IAhCSUTnXxAEQRCEN0pVfUO2uI+ks1kztKWij0LakoLOZs3Y4j6SqvqGL6iFwtOQ+d8e/7LU0Xmbs/djyQy+RFhYGA0aNAAotre/4LW5uTmLFi1CpVIRFhZG165dsbCwwM/PD6VSiZ+fn5yV4WG2trasWbNGfl2QLcLIyIibN2+SmZnJpUsPBiwKDwpoGiA4deqU/HtBuwVBEEoilv0/Q40aNWLWrFn06dPnqdS3e/ducnNzcXd3fyr1RUZGMn78eDIyMsjLy2PcuHF06VL2LMeNGzfo1q0brVu3ZsiQIXz88cf07NmT+Ph4Fi5cyIEDB6hbty7vvPPOU2+zIAiCIDwNVfUNWercj1vpKQRdPUty1n2MKujjVu8DsdT/NZGXnFGucqbalbAzaIDzlEHo16nCb7/9Vmp5TRkfpk+fzpUrV8jMzGTVqlXo6uri6enJmTNn+Omnn+Rrp0+fztdff42vry8VKlSgUaNGVK9enUGDBjFkyBAsLCw0Zn4oyfnz52nTpg1169aldWsRmFIQhNKJgH9PUUGwr7TMJG5eTmH/n2cgT6tIoJfHVd7osuWVlZVF27ZtWb9+PTVq1CArK4sTJ04U2StW0j3Xrl1LSkoKn376KbNnz6ZFixa4ubnJ5729vbG3t6dt27ZPrb2CIAiCIAiPIn3dSVJmBZe7fOWpbTHoa/kMW/R0iectQSgfEfDvAbHs/ylIyUhg+cFRTAq0ZV3YVAIjF7L0t/lkvHeAc9f/Jj45BgClUsm4ceOwtrbmxx9/ZMCAATRv3pygoCAAjh49ilKpxM7OjlWrVsnXfPPNNwwaNAg/Pz98fX0BmDJlCvb29ri4uJCUlMSqVatQKpVYW1vz119/AfmRYz/99FPs7e2ZMaNojvEjR47g6OhIjRo1AKhQoYLc8W/VqhUjR45k3Lhx7N69W6539erVpKamMnPmTL7//nt+/vlnli9fzoQJE9iwYQNKpZKsrCz8/PwYO3YsY8eOLdJmQRCEV5FKpaJevXoolUqUSiWBgYHyuf/9738olUrMzMxo3rw5SqWSkJCQYnWsXLlS/rO9vf0j3d/Pzw9zc3P5/seOHdNYbsuWLdy9e/eR6h4yZAhKpRJjY2OcnJxQKpUkJyc/Uh1lKfzeC/Pz8yM8PFzjOZVKxZQpU55qO4Q3j26bRqBdzkddbQW6bRs92wYJgiC8YGLZ/xNKyUhg/h4P4lKjixxPiM7Guqc2ce8m8fnC9iyfrAKgf//++Pj4ULNmTc6dO0dubi6ffvop7du3Z9q0aQQGBlKpUiXatWtH//79AeTosn5+fgCcPHmSy5cvc/DgQXnPV+/evRkyZAjJycl89NFHuLq6AtC+fXuWLVvGhx9+yPTp0+X2xcbGyh3/ffv2MXPmTKpXr05AQAAJCQl4eXlRu3Zt0tPTcXNzIycnBycnJwYNGsTEiRPlCLYJCQnyqPPPP/+MQqHA09NTPlbQZkEQhFdNwWquv68cx9G9OT8t+BVjg6KpG7/44gu++OKLMmfgVq5cydChQx+7LePHjy8WNfxhW7ZsoUmTJrz99tvysYL/IwrvFS6s8EBzcHAw2tolPxY8vBqsvCvSNL33vLw8PD09y7xWEJ6Elqkhem0bkbH7Qpll9do2Qsvk1Yrx4O3t/aKbIAjCK0Z0/p9QQPj0Yh3/5FvZJF7PYufcOHJz1CRUv0pAeH7Hu0mTJujo6PDee+9RrVr+Q2RiYiKQH6ylYM99QkIC8fHxwIPosgUuXryIra0t8OCBLigoiP/973+o1eoiUWYLIsPq6+sXqaNGjRpERkYC4OLigouLC0qlEgBTU1Nq164NQHh4ODNmzCA7O5tz58495qckCILw6kjJSCAgbBonbwSRp84h9nIGMTczmBRoi2UdN/pYzaCyXtVy17d8+XIiIyNRKpX89NNP5OTkMHz4cI4fP87cuXNxc3Nj+/btfPfdd+Tk5DBt2rQiW6k0+fnnnwEYOnQoHTt25LfffmP37t2cP3+enj17kpaWxtWrV4mJicHf358+ffqQnZ2NiYkJf/zxB1paWhrrjYqKYtSoUWRmZtKuXTumTJmCp6cnhoaGXLx4kX79+rFr1y5SU1Px8fEhMDCQffv2oVAoWLlyJTk5OQwcOBA9PT3atWtH1apVi7z34cOHY2lpib6+PpUrV8be3p7GjRvTv39/srOzadasGUuWLCn/X5YglKGSVxuyz93WmO6vgFZdYyp5tXl+jRIEQXhBxLL/J5CUfpuT14vnZr1y/D6Ow6vQcaIpnadUIz0plxNXd5GTlyV31jVFbLW0tGTHjh2oVCpOnjxJrVq1gOLRZs3NzTly5EiR6+fMmcOuXbvYunVrkfIlzfa0atUKlUpFbGws8CDa7MP3++677/D19SU4OBhjY+NyfS4lRbgVBEF42RWs5gq/voM89YOfi5cOprF1Zgzew1fw5eJ2pGQklLvOESNG0LRpU1QqFU2bNuX/7N13XBRH/8Dxz9EEVCAqWFDBWIgFhYCIgnggIoqiBnuvPJbER2MsCCq2qDH6i8kTNQYNFmxRYy8R8URsESJWYkIUGyqgAgLS7/cHuZWDAzF2nPfrlVeO3dnZuQVvb3a+852HDx8yf/589u3bxw8//EB+fj5ff/01YWFhKBQKFi9eXKyOxYsXS2H/N2/eZNy4cezbt49Ro0YxefJkLCws8PT0JCQkhMmTJwPQqFEjfv31V6pVq8bevXsJDw+ncePGhIWFldhWf39/Vq9ezbFjx7h8+TK3b98GwMnJSZpSZmJiwr59+9DS0uLOnTsoFAq+//57FixYwLFjx/jPf/7D0aNH8fPzK/beVZFlS5culc5ZrVo1Dh8+TEREBKmpqWqZzgXhRWlXrUiVkP7oe1oVnwKgo4W+pxVVQvqjXbWi5goEQRDKETHy/wKi7xxS+3KocuvcE5p1fBo69oG5Lnf+SCct61Gp9c2ePZuuXbuiVCqpUqUK27dv11jOxsYGCwsLnJycqFChAjt27KBLly64uLjg4OBQpk56hQoV+P777xkyZAi5ubloaWkxduzYYuV69OghLWFT1s6/XC7Hz8+PM2fOULdu3TIdIwjvKlVoeELSXb6cEEylCh9w8fxlbG1tqVevHgMGDChzMqZhw4Zx/fp1oqOjadGiBTKZjIkTJ3L27FnmzZsnlYuOjiYqKooRI0aUWJdcLkehUEg/x8XFUa9ePc6ePYu9vT3379/H3Nyc0NBQKepH0BzNBdDQuSIte5v889NDNkfNwtfp+391DlNTU8zMzABITk4mKSmJmJgY6e8kISEBpVKp9vBWU9j/wIEDmTNnTolJZVVRY+np6fj6+nLnzh3u379Pw4YNWb9+PatXr8bT05Np06ZJx1y9epVBgwZJbbtz545aXYVf//HHHygUCunvp2bNmvTq1YvAwEAGDBjAwIED6dSpk1qbCkeWqTx48IAxY8aQnJxMXFyc9FBaEF4W7aoVMVnqTV5CGllH/iI/JRMtY30qvIOh/oIgCC9CdP5fQHpWssbtXWeqzwlt1c8EgDF9xkrzKSMiIqT9qtcODg7FRmQKf3kvPD9y/vz5auVmzpzJzJkz1bYVnm9fuB4VGxsbDh8+XGx74bYNHz682FzNwu0oPN9MdQ4nJyfCw8OL1SsI5UnR0HCAVhNAS5bNvS8N2X1wG0sX/u+56tQ0/7qkf7s2NjZq28oy/9rOzo5ffvkFe3t7du3aha3tu5PV+nUoKZpLk3O3DpLy5D7GBtWfXZjS1+quVq0a1tbWHDp0CG1tbXJyckqM2lJJT08nKCiI3r17s3btWoYMGVIs6kr193Do0CEaNWrExo0b8ff3R6lUMmjQIOrWrcvs2bM5ePAg58+f58SJE1hZWfHNN99Qs2ZN8vLykMlkrFixQu1vS/W6UaNGeHh4SMuY5eTkkJuby9KlS8nOzsbJyYlOnTqpvRctLS0+++wzvvvuO6Kjo2nTpg0bN24kKSmJ4OBgZs2aRdFViJKTkwkLC+OTTz4BChImFr5PPUvLli05e/YsAE2aNOF///sfbm5uzJw5k5YtW3LkyBGWLFlS4lQIlaFDhxIYGIilpWWZzy28XbTNKr1T2fwFQRBeNhH2/wIqVjB5rvKGesavpiGCILxWJYWGA+Qrc0nLesTiwz5k5Wawbt063N3dpVHbxMREvL29cXV11Rhto0lUVBRdu3bFycmJtLQ0tUzoLVq0YODAgXz11Vfs3bsXOzs7hg0bRk5OTrF6PvroI2JiYgAIDQ2VRprj4+NxdXXF2dlZapNCoaBTp05q5y3vSormgoKw/z1z77Nn7n3+PJ5O2qMsJvmP01j2s88+K7atTp06+Pj48McffxTbp6Wlxeeff0779u1xdXVlwoQJxcoUDvs/fPgw/v7+TJs2jVmzZrF+/Xru379Px44dGTt2LCtXrlQ7tlWrVuzevZsuXboQE/sXEfGxzD+6g/9MnsgPG9ehUCho1qwZhoaGzJ8/n+HDh+Pm5kbnzp3JyMgo8Xqp1iOXy+W4urry008/sXv3btq2bUvr1q3p37+/xveuelgQHR2NUqnEzc2Ny5cvM2bMGNLT04udJzk5mR07dpTYjmexsLDgxo0bPH78GFNTU1RLGJ89exYHBwe++eabZ3b8BUEQBKE8ECP/L8DGvCNbomaX+GWxMC2ZDra1O76GVgmC8KqVFBpeWEJaHPHxGbjZ9mfdunV4eHiQnJzMwoUL8fPzo3Xr1kydOpVTp07RunXrUuvS09Nj165dzJ8/nyNHjmBs/PRB4u3btzl58iQVK1bEycmJY8eO8ejRI1xdXTXW9dFHH3HmzBn09fWpUKEC8HTOtY6ODgMHDpTmXBc9b7du3Z7jKr17SormqtVEn/7fmhfb3m1EO+l14SgoVee2sI0bN0qvC49aqyI7OnfuTOfOnTWef+jQocUy43fo0EF6HRpasI55z5496dmzZ7Hja9euza8nwgk4vYuDNy6Tq8wlI2QNyqbV6XBwOZ0smvHLrweoZlAQ/lx4OcOHDx8SHByMtbU11tbWXL58mbVr1wIQFBTEwYMFkRLLli3j448/BgpWn+nUqRMTJ07E398fMzMzvvnmGzw9PYmIiMDZ2ZmlS5eSnJzMl19+yciRI+nWrRv6+vpcunSJY8eOMWvWLGk6wapVqzh8+DByuZyff/6ZlJQU+vfvL7XFxsaGoKAgqV2F2wIFUXW//fYbpqam9O/fn1OnTgEFD+KqV68uRdrMmzePa9euER8fj6WlJUFBQVy/fp2+fftSvXp1kpOTNf5+BEEQBOFdIUb+X4CJYXVs65SekVnFto5nmcNDBeFNSM64j+KvdSz46TNqmFfD2aUN3bp1IzMzU2P5oUOHEhsb+8x65XK5tH74smXLNI6KQvHRvZLKvWnPExp+NzWWeg0LOo21atUiJSWFmJgYpk2bhlwu58iRI2Wa36xatcPc3LxYB8TKyoqKFQsSVWlpaVGpUiXq1KmDqampxrq6d+/Of/7zH7p06SJte/DgAT179kQulxMRESG1qbTzlkflNZor6Uka3fetYG/cRXKV+QDkJaehbVyJXGU+WzZv5kNba8ZNGM+9e/cICwvjxIkTzJkzhwULFgAFeQjWrFnD8uXLWbt2LUlJSezevZvw8HB27drFnDlz1M7ZqFEjrl69yo0bN6TcAXXq1JH2Ozg4YGNjw5EjR6QcAx07diQiIoL9+/er1eXr60uHDh1QKBSYmpo+d1scHBw4e/YsZ8+exdHRkdzcXK5fv069evWKXStbW1tCQ0O5efMmycnJLF68mKVLl7Jjxw4ePHjw4r8MQRAEQXiDxMj/C+prN5tbDy+VOgpoVsmSvnazX1+jBOE5aFrWrJZDDtZ97nP/mJL1m35i1LAxL3SOI0eOlLp+ODzt/Kvm9WoaPX0blBYaXpRSmc/fSVFA739+VmJlZcXAgQOlpGmFV9ooiabVQVSKrruenp7Oo0ePpKVCi2rZsiV2dnZ07txZWr5z48aNdO/enaFDhzJgwACNa8MXPW95VF6juWac3k3cY/WOq5ZJRfKSHwNg6NiE3AbmhB6JZlBcHM2bNwfA3t6e2bML7l0NGjRAX19fehB07do1zp8/X2KEiSoKRU9PD319fQ4fPiwtUVuSkpamLep522JnZ8fcuXOpU6cOEydOpFq1ahw4cICWLVuW2AbVw7pr165ha2uLjo6OdF0EQRAE4V0lRv5fkJF+NSZ32I5d3S5oydQ7N1oyHezqdmFyh+3PtSa0ILwuz5q7nmn8N5sUX9HWxUnqpBbNDB8cHEy3bt3o2LEj3bp1Izs7u9RzOjs7AwWZwlVzmJctW6YW2puYmCiVCw0NxdHREUdHRym8WS6XM2nSJFq2bMnq1atfxqUos5JCw0uSmas+h3n69OnMmzcPNzc33N3dpaXUXoapU6fi4uLC7NmzqVGjhsYyMpmM1atXU6nS0wzXbm5uLFmyhO7du2ucc/2+KI/RXPcyUjlw41Kx7frW9ck4dZn8jKyCDXn53ElLpqJZVc6fPw9AZGQk9evXB4o/CKpXrx4tW7ZEoVCgUCiKJY9t06YNy5cvx9bWVppXX7TzXzRBYUlJDksrV5a2VK5cmYyMDLKzs9HR0cHOzo4VK1bg4OBQ7Fya6j5//jx5eXlcvHhRY/sEQRAE4V0hRv5fAiP9avg6fS8t+ZWRnYKhnjG2tTu+E18OhffXs+au3/0jC9N6Su5cKLkMFCzf9eOPP7Jo0SJ27NhB37591fa3b98emUwmjSIC+Pn5sXLlSj766CPy8/O5efMmN2/eZMOGDWrHBgYGSuuLe3p6SknqBg4cyJdffkmHDh1KXfLuZStLaHi3wIJ/9/Y9TXCyL+hgFF5945dffin1+MIZ/lUPSEB9pQ3VtsLzx729vfH29tZYp6WlpcZrq6KpY6PpvOVdeYvmOnTjshTqX5i2kSFG3Zx58N12kMmQaWtRubMjv2cl4erqSps2bdDT05Pm0RdlamqKl5cXLi4uaGtr4+bmxowZM6T9tWvX5tGjRzg7O1OpUiW++OILrKys1Orw8vKie/fuxZYwLKpGjRo8fPiQnj17smrVquduCxTkuqhcuTJQEAlw5coV7O3tSz0vwBdffEH//v2pXr061auL+7kgCILwbhOd/5fIxLA68oaD33QzBKFMSpu7/ldEOvf+zOIDc10s7A24/GsiKU/uU7Wyucbwb9WScTY2NtKSWoVpCvtPSkrio48+Aih1iTqZTIaRkRGAWkbuZs2aoaur+8zl7V628hoaLhRQRXNtjprFuVsH1X7PWjIdbOt40tdu9jsTzZWcVXK2/gqNLTBtbKG2LSX7CX5+fvj5+altVz1ksrS0lB5kDRs2jGHDhpVY/40bN6TXSUlJxeqaMGGCtLKBj4+PtL/o8pba2tocOnTohdqiWkYTCpb7KxxJoDpf4YdhhR/WnTlzpsR6BUEQ3geqAc70rGT+ir7LjDHfcevWbUxMTBg6dCgBAQE0aNBA7Zjo6Gjy8/PVErD+G8HBweTm5j7zQbFQNqLzLwjvqdLmrjd0rkjL3ibSz7oGMo6c24pbk0Hcu3evWHlVmPD58+elMOFnMTU15c8//6RRo0bk5+cXC+1Vyc/PJzU1FaBMIcKvmio0POrm3meWfVdCwwV15Smay6SC4XOVN9Yrfb69IAiC8P4omhcKIP5qJrrG2Yz078yaJbtLPDY6Oprc3NwX6vzn5xePXBNejOj8C8J76nnmrjd2q8Q03yV0dv9bYxb5Bw8e4OHhgb6+Pj///HOZ6vzyyy8ZNWoUMpmMHj168Omnn2oM7Z01a5a0rFnRLN5vSnkLDRc0Kw/RXB0tmjLrzB6Nof9F6ci08LRo+hpaJQiCILztVHmhNH3XsbAz4GTY7yw6+Ak5ebV48uQJ/fr14/79+5iamrJhwwZWrVrFgwcPOHr0KLVq1WLw4MHcu3ePyZMnEx0dzZAhQ1i8eDEXLlwgICAAgHnz5uHu7o5cLsfBwYH4+HhpuuedO3fw9fXlxx9/pFatWq/zUpQrstIyONvb2ysjIyNfY3MEQXhdFH+tY1PkjGcX/Ec/+7kaO0LvazhWamZSuQkNF8q3MUc3sifuwjPLdbVszgrX/q+hRYIgCMLbbtWJcRqjHOOvZHLnUiZ6hlpUrqZD+l9V6ddpHNWqVaNfv36sWLECExMTsrKypO+Hv/zyCwkJCdy9e5fffvuNLVu20KtXLw4ePIizs7O0xKunpycnT55ELpezYMECWrduTXBwMDdv3uTMmTP/uuMvk8milErlsxO9vAdEtn/hnaJQKLCwsKB9+/bI5XI2bdr0XMfHxcUxcODAYttVmeWfdW7Vk8lnneNlJ0hTrXm/c+dOHj58+FLqtDHvWGyFipKIuevFqULDF3ifpJ/9XLo1/4J+9nNZ2O0kvk7fi46/8NaY6+iNZeWqpZaxrFyVuY6ak0UKgiAI75fS8kKpfORaiZijaTxIv8OevTv55ptvkMvlrF27loSEBLWyTk5OnDx5kmvXrjFgwAB27dolJVFV5XYyMjJSy+2kWhIZYOXKlfTs2VOM+L8EIuxfeCeo5t6eun4WF68WfPf1j1SQGdGrVy+srKxeOJnI20615v3OnTtp1qwZVapUeeE6X9bc9fcpE7wm5SE0XCjfqhlUYqfXGGac3s2BG5fUpgDoyLToZNGMuY7eVDOoVEotgiAIwvuitLxQKhUqamFSU4c7lzPpMLQmrrY9pOStOTk5bN26laysguVkzczMuHv3Lubm5jg5OdG9e3fGjBkDlJzbqXBC54CAAHbu3Enjxo1xdHR8qe/1fSNG/oW3WmpmEqsixuK3uw2bImdw8tpWLt89ht/uNqz//QtGfzaSPXv2ADB+/HhcXFzo0qULKSkpaiP1wcHBUvbm69ev4+3tjaOjI9evX1c735kzZ5DL5Tg5Oallhy7K2tqa/v3706JFC6KjowGYOXMmbdu2ZcGCBVK5vXv34uLiQps2bTh4sOAJqqOjI//5z39o0aIFP/30E5988gnNmzeXkuY5OjoyatQobGxspGOcnZ25efMmBw8eZMCAASxevBhXV1fpQ7Jnz57cv3//ua9vX7vZmFWyLLWMmLsuCO++agaVWOHan9O9pzHfsRtTPvZgvmM3zvSexgrX/qLjLwiCIEjKmheqWcfKJMfn0sSuDr/88gvt27fHzc2N33//HUdHRzZs2CBFr9asWZPmzZtjaWlJYmIibdq0AZ7mdurQoQOzZs3SeB49PT02bNjArFmziImJeSnv8X0lRv6Ft1ZpiUbylblE3dxLdPJvZN1pxtmzZ0lPTyc8PJwNGzawcuVKWrVqpbHehw8fcuzYMaKioli0aBErV66U9s2cOZPdu3dTuXJlOnTowIABA9DT0ytWR0JCAmvWrCEqKoq1a9dSvXp1fvvtN44fP87GjRv59ddfyc/P5+uvvyYsLIz8/Hw6deqEp6cnDx8+ZM6cOeTl5fHxxx8TFxfH77//zurVq/n22295+PAh8+fPJycnh08//RRPT08A6tati6enp7ScSlZWFkePHsXBwYGsrKx/tQZ1eVvWTBCE0tUwNGJI49ZvuhmCIAjCW6xiBZMS99Vqok+tJvoAGNfUxTekLk2aNWZsjwXFyh4/flx6vXbtWun1nTt3pNceHh54eHioHVd4ydfCEaaFl30V/h3R+RfeWpujZpWaTR3g5p2baOXL+Pvvv6XQf3t7e44dO6YWFqRUKqWl4aytrdHR0cHGxobY2Fi1+s6fP4+3d8G816SkJBITEzE3Ny923gYNGqCvr4+5uTnJycncuHGD5s2bAwVzlH799VeSkpKIiYmRspQmJCSgVCoxNTWVOur169dHX1+fWrVq8ejRI6BgCTwzMzMAkpOTS3zv/fv3Z8GCBdy5c4dPPvmk1OtUmvK0rJkgCIIgCILwYmzMO7IlavYzQ/9B5IV614jOv/BWKkuikdxsJRcPPMahVzzVzU349ddfAYiMjKR+/foYGxtz9+5dAC5evCh1zi9dukReXp7GNeltbW3Ztm0bFStWJCcnB11dXY3nLrzGvFKpxMLCgosXLwJw7tw5AKpVq4a1tTWHDh1CW1ubnJwcZDKZ2rFF6ylpm4qurq4U6v/hhx8SHx/P1q1b2bhxY6nXqizE3HVBEARBEAThZeWFEt4+ovMvvJVKSzTyV0Q69//KQplfsP58FQttZDUSMDAwoG3btlSuXJmNGzdiZGREfHw8nTt3pmrVp5muzczM6N69O4mJiYSEhKjVPXv2bLp27YpSqaRKlSps3769TO2tWbMmdnZ2tG3blhYtWgAFiUo+//xz2rdvj0wmo0mTJnz//ff/8ooU6NixI2PHjqVXr16MHj2azp07ExoairGx8QvVKwiCIAiCIAgqfe1mc+vhpVKjcEVeqHePrOjIYmH29vbKyMjI19gcQSiw79K37L64pMzluzX/gs5NP3uFLXo7LV++HFNTU3r16vWmmyIIgiAIgiCUI6mZSeUiL5RMJotSKpX2b7odbwMx8i+8lUpLNKKJod77N/K9fPlyfvnlF/bv3/+mmyIIgiAIgiCUMyIvVPkjRv6Ft1Jyxn38drcpc6KRhd1Oig8hQRAEQRAEQRDUiJH/p7TedAMEQRNVopGyEIlGBEEQhJdJoVBgYWFB+/btkcvlbNq06bmO37lzJw8fPnzu837yySfI5XLkcjkfffQR/fv3Jy4ujoEDB6qVu3fvHvPnz3/u+gVBEIT3mwj7F95aItGIIAiC8Drdy0jl0I3LnI2NokUnV1Yt/RZjmS69evXCyspKWlL2WXbu3EmzZs2oUqVKqeWKrvKyY8cOAPLy8nBzcyMgIEDjcTVq1MDf37+sb0sQBEEQADHyL7zFjPSrMbnDduzqdkFLpv6cSkumg13dLkzusP2dSDQiCIIgvL2SnqQx+mgIjlsX4n96F1v+ikJx508cty7k89O/MPLTsezZsweA8ePH4+LiQpcuXUhJSQEgICAAZ2dn3NzcuHnzJgcPHmTAgAEsXryYmzdv4ubmhpOTE4sWLQIgMDCQYcOG0bFjR5KSkoq1Z8mSJXh6etKkSRMAbty4gY+PD3Z2dty+fVuKBjh58iRTp04F4OHDh3Tr1o2cnBzat2+Pi4sLPj4+0vKww4YNw93dneHDhxMYGAjAokWLcHJyktotCIIglG9i5F94q4lEI4IgCMKrlPQkje77VhD3+EGxfbnKfPbEXeBMah4tbj/i7NmzpKenEx4ezoYNG1i5ciUeHh5cu3aNiIgIaSTf09OTgIAAGjRowLhx45g9ezZt27bF09OTQYMGAdCoUSN++umnYuf8448/2LNnDwqFQtqWlpbGsWPH2LRpE9u3b6dbt24AtG7dGj8/PwB2795Nt27d0NHRYe/evRgYGBAQEEBYWBhGRkZUqFCB0NBQFi1axJMnT7h37x5hYWGcOHGCiIgIFixYwIoVK1725RUEQRDeIqLzL7wTTAyrI284+E03QxAEQShnZpzerbHjX9itO7fRzs3h77//lkL/7e3tOXbsGJaWlrRp0wZ4Gr5fWOFjbGxsuH79OgB2dnbFyubn5zN69GhWrlyJtra2tL1JkyZoaWlhbm5ObGystF0mk9GiRQvOnTvH7t27CQoKIj09HV9fX+7cucP9+/dp2LAhDx48oHnz5lIbTp06RVxcnLTN3t6e2bPFFDpBEITyToT9C4IgCILwXrqXkcqBG5dKLaPMySXtcCR36hljYl6dqKgoACIjI6lfvz5WVlacPn36aXmlEl1dXSncvn79+tIx586dw9LSEgAtreJfwZYuXUqHDh1o2rSp2vbCDxWKrtLUs2dPfvrpJ3Jzc6lSpQqHDh2iUaNGHDt2DB8fH5RKJfXq1ePixYsAXLhwAQBLS0vOnz+v9l7Ko+SM+yj+Wse+S9/yzQY/6tStjVwux8nJiZiYmH9Vp7Ozs/RaoVCUmJtBEAThbSNG/gVBEARBeC8dunGZXGW+xn0Zpy6T/Xc8yvx8KrazQauOKfer6WNgYEDbtm2pXLkyGzduxMTEBAsLC5ycnKhQoQI7duygY8eOjB07ll69ejFlyhSGDBlCdnY2Xbt2xdzcvMT2zJw5k48//pjDhw8DBSP+U6ZMKfU9ODs7079/f2bOnAlAq1atmD9/PpGRkRgbG9OwYUNatWrFypUrad++PbVq1eKjjz6iRo0auLq60qZNG/T09Fi7du2/vIpvp9TMJDZHzuTc7UPSssHxVzMxs8um/xdNqZftxcqVK1m2bNlraU9+fr7GBz6CIAivk6zoE+TC7O3tlZGRka+xOYIgCIIgCK/HsugjLD53uMzlp3zswfgWbq+wRa9Obm4uOjo6LFq0iLp169KvX7833aRXJjUzicWHfYqtFhR/JZM7lzJp2duEx1eNqJ3lgU/33kydOpWcnBxGjhzJsGHDkMvl2NnZER4ezujRoxkxYoRaPc7OzkRERAAFI/+hoaGMHDmSgIAANmzYgEKhQKFQEBgYSIsWLbC2tqZZs2ZMmzbtdV0CQRAKkclkUUql0v5Nt+NtIB5BCsJ7QhX6uPXUImxaNcbZpQ3GxsbI5XKGDRtGaGjoc9Unk8nYtm0bADk5OVSpUoXg4GCNZQuvU71mzZoXeh9F661evbq0LvbVq1eLlQkODiYoKOilnVMQhPLDpILhc5U31jN4RS159UaMGIGLiwsRERH06NHjTTfnldocNavEZYL/ikhn95z77Fx2BYPGd5g5cya7d+8mIiKCkJAQsrOzARg4cCAREREaIyISExOl+86ECRNKbcvt27f54YcfRMdfEIS3guj8C0I5l5qZxKqIsfjtbsOmyBkciVtOqwkZWI+9j6mFIbsPbsPCwuK567W2tmbfvn0AHD16tMzzRV9m5x+gQ4cO0iiLlZXVS61bEITyraNFU3RkZfsqpCPTwtOi6bMLvqXWrl1LeHg4e/bsQV9f/00355VJzrjPuVsHS9zf0Lki3jOr47OgJmuX7SE6+hze3t64urpy7949EhMTAWjWrBkVKlRAS0tLrbMPYGpqKt13vvnmG6DkvAxWVlZUrFjx5b9RoVQve8Bj4sSJODs707p1a1atWgXAZ599BsDQoUPVEnG+Dunp6Tg5OdGrVy+17YW/YxXOTVEWL2uw5HnPK7xeovMvCOWYKvQx6tY+ac6jSr4yl7SsRyw+7ENWbgbr1q3D3d2dkSNHAgUjG6ovRGPHji1Wt5GREU+ePCE7O5udO3dKI0klrTENBUtRXbx4EblczuHDhzlz5oyUeEm15NWQIUNo164drq6u5OfnExsbi4eHB+3atWPevHmlvt/o6GjatWtHq1at+PLLL4vtL7wWd3JyMufPn8fJyQlHR0c2bNgAFNzER48ejbOzs8h+LQjlXA1DIzpZNCtT2U4WzahuaPSKWyS8qOg7h4rd7zTR05eRnZGHpVUN9u3bh0Kh4Ny5c1JOhsKd+cKd/ZIYGxtz7949ACm5ImhO7Ci8Oq9iwOPSpUskJSURERHBqVOnpA73d9999yreQpmcP3+etm3b8vPPP6ttf9kDLEL5Iz6RBKEcKy30USUhLY5L8UextbUlNDSUmzdvkpyczMKFC/Hz8+Po0aNUrlyZU6dOFTvWzc2N0NBQ7t69S61atQCkNabDw8Np3LgxYWFhUnlvb2+sra1RKBR06NChWLhlVlYWt2/f5tixY4SFhaGlpYW/vz+rV6/m2LFjXL58mdu3b6u14fDhw8jlcjp16oSVlRUKhYIzZ85w+PBhnjx5IpU7d+6ctBb3kSNHMDY2ZsaMGYSEhHD8+HG+++47cnJyAOjYsSMRERHs37//3156QRDeEXMdvbGsXLXUMpaVqzLX0fs1tUh4EelZyaXu/ysinT1z77N3fgLNuxjRd6wHXbt2xdXVlb59+/7r85qYmFC3bl3c3d25cuXKv65H+Pde1YCHgYEBV69elUb3P/jgA6D4CPeTJ0/o168fbm5u9OnTh5ycHIKDg/Hx8aFz58507twZpVJJeno6PXv2pF27dgwbNgyAvXv34uLiQps2bTh4UD1yJSUlhS5duuDi4sL48eMBmDJlCps2bVJbaWLVqlXSAMvFixfJzc1l1KhR2NjYSHWWdh5NevfuTbt27fDw8CA1NRUoiPwcOHAg1tbWbNq0CS8vL+zs7KTvZ8nJyfTq1Qs7OzvOnj1LdnY2Xl5eyOVyevfu/cxzCq+WyPYvCOXUs0IfC7ubGku9hgWjHbVq1SIlJYWYmBimTZuGTCYjLS0NBweHYsd5e3vTuXNntS9MmtaYbtiwocbznj9/Hm/vgi/USUlJJCUlMWTIEAYOHIiFhQVz587l6tWrDBo0qOA9JSdz584dateuLdXRoUMHadT+ypUrTJo0iYyMDK5evUpCQoJU7s8//yy2FvejR4+kZbfq1asnlW/WrGAk0MDg3Z3fKwhC2VQzqMROrzHMOL2bAzcuqWX/15Fp0cmiGXMdvalmUOkNtlIoq4oVTErcV6uJPv2/VV9twdauORPDvlbbVniEX9NovyrZH6A2HUDTqGvhssKrVdYBj/j4DNxs+7Nu3To8PDzUBjxat27N1KlTOXXqFK1btwYKluucMGECw4YN49GjR6xatUr6PlFYUFAQ3t7e9OvXjxUrVkh5kWrXrs2yZcsYNWoUFy5cICwsDA8PD3x9fcnPzyc/P5+vv/6asLAw8vPz6dSpE56enlK9q1atok+fPgwaNIiRI0dy5swZ5s2bR2hoqFpEpK+vL+vWrZP+Zh8+fMj8+fPJycnh008/xcPDo9TzaBIcHIyhoSFBQUFs2bKFUaNGkZCQQFBQEL///juffvopkZGRbN68ma1bt/L5558THx/P6dOnSUlJ4T//+Q/Lli3D1NSUffv2FVuqVHj9ROdfEMqpsoY+AiiV+fydFAX0/udnJVZWVgwcOBA7OzugIFN0UTVq1MDd3Z1evXpx7NgxAGmN6Y0bN+Lv71/sg75wKKWtrS3btm2jYsWK5OTkoKWlRb9+/Rg8eDC+vr6cPXsWKysrvvnmG2rWrEleXp7a8UWtWLGCqVOnIpfLcXZ2Ljbvcs+ePXz66afSezQxMSEuLg5zc3OuXbuGmZlZsTYKglD+VTOoxArX/tzLSOXQjcukZD/BWM8AT4umItT/HWNj3pEtUbPLdP/TkulgW7vja2iV8Kq96gGP/v37079/f65fv87w4cM5evRosXpjYmKIiorihx9+IDMzk379+mFsbCwNKJibm5OcnMyff/7JuHHjgIJpIQkJCcTExODu7g5AQkICSqVS+i7y999/07lzZwDs7e2JjY0tdclQFVNTU+l7TXJyMklJSaWep6i8vDwmT57MxYsXSU1NlaZ3NmjQAH19fWrVqkXjxo3R0tKiVq1axMTESPsrVapEpUqVSElJoX79+lhbWzNgwADs7Oz4/PPPn9l24dURnX9BKKeeFfpYVGZuutrP06dPx9fXl5SUFLS0tAgKCpJGyQv76quvAKTOv6Y1pgtzcHCge/fuTJo0idmzZ9O1a1eUSiVVqlRh9erVeHt7k5eXh5GREdbW1syfP5/hw4eTlZWFrq4u27dvp1IlzSNwXl5efPrppzRp0gQ9PT21fTY2NsXW4p4zZw79+/cnLy+PcePGoaur+1zXTBCE8qWGoRFDGrd+080QXoCJYXVs63gSdXPvM8va1vHE2KD6a2iV8Kq9ygGPhw8fIpPJ+OCDD6hSpUqJnWUrKyvat2+Pj48PUJADKSQkpFgySCsrK06fPk2zZs3Iz8+nWrVqWFtbc+jQIbS1tcnJyVE7pn79+kRFRdG0aVMiIyMZOXIkmZmZGttQ+Lii533WeYqKjo4mPT2d8PBwfvzxR+7cufPMcwDExsaSnp5OSkoKRkZGZGVlMXHiRLS0tPDw8GDAgAFUry7+3b0povMvCOVUaaGPKt0CCz587Xua4GRf8JS78HJ9v/zyS4nHFg1lHDp0qPT6999/L1ZeFZq/ZMkSte2FcwIAhIeHq/3coEEDDhw4oLENlpaWUr0Anp6exULYCrdr/vz5avtsbW05efKk2rbC77+05E6CIAjC26mv3WxuPbxUagi4WSVL+tqJpK7lxasc8Hj48CHDhw8HID8/nzlz5mis09fXl1GjRrF8+XKUSiULFizQWG7UqFEMHjyY9evXU79+fdasWcPnn39O+/btkclkNGnShO+//16tfP/+/fnxxx9p3rw5jo6OJX4/qVOnDj4+PsW+7wCkZibi3qcFDZrUJe7PePoP6kPIus2cPXsWBwcHFi9ezJo1a9i8eTMA//d//0dsbCyenp7UqVMHc3NzQkJCpGSDtWvXRltbW2Mbhg8fTmxsLMuXL8ff359jx46RlZWFubk5ZmZmKBSKYtMWCnvy5AlffPEFV65cIScnhxYtWqhdkxchk8mGK5XKZ2ZGlMlka4FFSqXyikwmWw4kKZXKmTKZzA3wUiqVk8pQhwJwVyqfPpmSyWTfAJOUSmVeSccVqcMG0FIqlcW/XP8LstLmXtjb2ysjIyNfxnkEQXjNkjPu47e7TZlDHxd2OylGQARBEIRyITUzic1Rszh366DafVBLpoNtHU/62s3GSL/aG2yh8DIp/lrHpsgZZS7fz34u8oaDX2GL3h6pmUlsjpzJudsF0RHxVzI5HZJMk/ZGDBjam6u7dAk/eoKff/5ZLcIzPz+/2GoVDg4OnDx5Eh0dHR49eiQlPyyL4OBgcnNzGTly5DM7/35+fjRt2pSBAwcCBQNDLi4uz//m/yGTyaKUSqX9P68jlErlM9cjlMlknwKPlUrlWplMtgHIVSqVQ2Uy2VQgTqlUbilDHQqKdP7/RduHAjpKpfLF12FEjPwLQrklQh8FQRCE95WRfjV8nb4nOeM+0XcOkZGdgqGeMba1O4r7XTkkcj1oploBoWgUTK2mFbh9KYOom3tRHEvH/qOCPADBwcEcOHCAtLQ05s+fj42Njdpx6enpnDlzhtatW0sd/6FDh2JgYMCFCxdwdXUlOTmZU6dOMW7cOIYPH87QoUMJCAhg1apVPHjwgKNHjzJq1CiioqLo2rUrDx8+5NChQ2pTOk+cOKEWOaHq+N+8eZOhQ4eSlZWFt7c3U6dOJTExkREjRvD48WMaN27M8uXL+f7771m/fj0GBgZqEacymcwbsP6nUz4fUAKqJxABSqUytNDb/Q0YIpPJNgHZPO03twS2ymSyZYANBavnDQDuAb8AFYEEpVKpWtpgkUwmcwFWKpXK1aoHAkAA8CFQi4KHCSNlMll9YOM/dVUCRgC+QFWZTOYKDAHWA+bAHWAQ4AxMBXKBKkBHpVKZRgnEUn+CUI71tZuNWSXLUsuI0EdBEAShvDIxrI684WA6N/0MecPBouNfTqkGPMrifRrwKGkFBG0dGTp6Mu7/lYVB9RyuP4iW9pmYmLBv375iHX8oeDiwePFiGjZsyA8//CBt9/Dw4MSJguiBESNGcPLkSVavXq12rK+vL5MnTyYkJAQAPT099uzZQ+fOnTly5Iha2cKR6R4eHjRr1ozbt2+zaNEiZs+ezYkTJzh69Cjx8fEal6betWsXR48e5ejRgqWsC9W7G7ioVCrlSqXyMBAIePzzX9H5HNFAi3/+Ow/clMlkloClUqm8Dvgplcp2wGzgP0AdIFGpVMqBPoXq2UBBB31IsQsK55RKpTtQVyaTmQBfAP8FPgHM/imzClisVCoHAD2AK0ql0gW4DPj8UyZbqVR2BfYD7TWcRyI6/4JQjhnpV2Nyh+3Y1e2Clkw90EdLpoNd3S5M7rBdhD4KgiAIgvBOEwMe6p61AkIdG30i1jykXksDHqTfITUzEUBKeqhJy5Yt2blzJxcvXmTNmjWkpRUMMKtWNKhZsybNmjWjQoUKz1w5qegqCCX59ddfsbe3Jzc3l7///puPP/4YKEjkfP36dWmlBrlczpEjR4iPj2f27NmMGTMGX19ftWWfNVAqlcpUpVKZCuQV2ZH9z8s2QOQ//3UG7v+zfYpMJjtOQeRALaVS+TdwUSaThQATC1V1SalUZgH5FHfpn//HA8ZAPeDCP/kALmkoXx9Qzf2PBBoUqecOYFLaGxZh/4JQzonQR0EQBEEQyjvVgIfI9VDgWSsg1LUx4PaFTEzr60FoGlfuhgNVi83zL+yvv/6iYcOGGBoaYmBgIG0vaQWAwnR1dcnKytJYrmgOujZt2rB+/XoGDRoEPF19QbXygYuLC+fOneOzzz7TuFJDTk4OwcHBbNy4US2Rs+p0hV5ryWQy1XquxbMXFoz4DwV+BKoCc4HtMpmsKiBXKpVtZTJZB2CATCarAPyfUqnMl8lkv/7zEKDo+Upriwy4TsG0hEig6T/bc4AK/7z+G7AD9gH2QGwJ9ZRIdP4F4T2hCn0UBEEQBEEoj8SAx1PPWgFBV1+Ldr5VpZ+f5DxGxtOfFy5cyKBBgzA3N5e2TZw4kUePHgHQs2fPEpde1sTR0ZGhQ4dy6dIlaTnEksyaNYvPP/+coKAg9PT0aNiwITVq1GDKlCkMGTKE7Oxsunbtirm5ucaVGmbNmsX169fJysrip59+Ytq0aYWr/00mk+0EllAQsn/4n+0zNTTlN6CtUqnMADJkMpnZP9seAWkymSwMuPBPWQtgjUwm0wauAaWGHJTgayDkn2MfUdDxPw0Ey2SyZsDnQE+ZTBYO3AUWAU7PcwKR7V8QBEEQBEEQBKEcESsgPFU42//bTCaT6SiVytx/HiCcAJxfZKUATcScf0EQBEEQBEEQhHLExrxjsXxPJXmfVkB4y334z6j+b8C6l93xBxH2LwiCIAiCIAiCUK6IJZ/fPUql8k/A5VWeQ4z8C4IgCIIgCIIglDNiBQShKNH5FwRBEARBEARBKGfEks9CUSLsXxAEQRAEQRAEoRwSKyAIhYnOvyAIgiAIgiAIQjkmlnwWQIT9lztHjhxBLpfj4uJCjx496Nq1K7GxsWplFi5cyJ07d8pcZ3R0NL///vvLbqogCIIgCIIgCILwmojOfzmQnHEfxV/r2Bj+JZ/7jWP9ltWEh4ezaNEisrOzi5WfNm0a5ubmZa5fdP4FQXgT7mWksjbmFAuP7+Ijh49p49IWY2Nj5HI5w4YNIzQ09LnqMzY2xs3NDVdXVwICAsjJyXkp7XR2dpZeBwcHExQUVOZjk5OT2bFjx0tphyAIwsuk+gxeFn2EaWu/p3bdusjlcpycnIiJiXlp5xk6dGixgap/Iy4ujrCwsJfQIkEov0Tn/x2WmpnEqoix+O1uw6bIGfy4aQkmNol8edSDVSfGUaNuFWrWrMnXX3+Ns7Mzs2cXZPJUfcgGBwfj4+ND586d6dy5M0qlkujoaNq1a0erVq348ssvAVi1ahWLFy9mwIAB5Obm0q9fP1xcXOjXrx+5ublMnjyZixcvcvjwYWxsbAAYMmQICQkJyOVyJk2aRMuWLVm9evWbulSCILxDkp6kMfpoCI5bF+J/ehf/iz1F2hgP4oe3xbBuDbYd2IuFhcVz12ttbU1YWBhHjx5FV1eX77///l+1Lz8//18dp8nzdP5f5nkFQRBKUvQzePG5w6y+coLHLerQeJYv/vNms3LlSqn82/DZlJ+fLzr/glAGovP/jkrNTGLxYR+ibu0jX5kLQEZyHoYm2uQrc4m6uZfFh33IycukY8eOREREsH///mL11K5dm/3792Nubs6FCxewsrJCoVBw5swZDh8+zJMnT/D19WXy5MmEhITwyy+/0KRJE8LDw2natCnbt2+nTZs2nDx5khMnTlCrVi0eP37M/fv3MTMzA2DgwIFERESwdu3a13qNBEF49yQ9SaP7vhXsjbtIrlL9C2WuMp+Hmel037eCjJxs1q1bh7u7OyNHjgQgMTERb29vXF1dGTt2bKnn8fPzkz4T9+7di4uLC23atOHgwYMlbnN0dGTMmDF88cUXz3wfCoWCgIAAoCAaIDg4mLi4ONq2bYuPjw92dnbcvn2bVatWcfjwYeRyOYmJiQQFBdG2bVvatm0rRVy1aNGCgQMH8tVXXz3HlRQ0SUlJQS6XI5fLXziKRC6X4+zszJ9//snBgwfZt29fmY+/d+8e8+fP17gvODj4rehMCe+n0j6DlSjZE3eByaGb0THQRy6XM2XKFAYPHqxx8Khr166kpKQAMGnSJH777TdiY2Px8PCgXbt2zJs3T6q76ECVpnI//fQTcrkce3t7fv31V6BgQOvTTz/F09OTVatWsX79etq3b//Kr5MgvKtE5/8dtTlqFglpcWrbDE20SX+UJ/2ckBbH9QfRNGvWDAADA4Ni9aj2mZubk5yczPXr1+ncuTPt2rUjJiaGhIQEtfJ///03H3/8MQD29vbExsbi5OTEyZMnuXbtGgMGDGDXrl1Ur15d7RwVKlRAS0v8uQmCULoZp3cT9/hBqWXiHj8g7PZVbG1tCQ0N5ebNmyQnJ7Nw4UL8/Pw4evQolStX5tSpUyXWoaenR05ODvn5+Xz99deEhYWhUChYvHixxm0ASUlJ+Pv7s3TpUrW6Ll68KHUoFy5cWGrb09LS+Pnnn/n888/Zvn07vr6+dOjQAYVCgUwmY/fu3YSHh7Nr1y7mzJkDwO3bt/nhhx+YNm1aWS6hoIEqfDn4eiTDVixg8/7dWFtbo1Ao/nUUiUKhYMmSJXz11Vd4enri5eVV5uNr1KiBv7+/xn2i8y+8SaV9BmecukziwhD++H4ztxp+AECPHj3YsGGDxsGjrl27snv3bgCioqJwcHDA39+f1atXc+zYMS5fvszt27cBig1UaSrXp08fFAoFR44c4euvv5ba5eTkxK+//oqvry+DBg3iyJEjr/ISCcI7rdxk+1coFISGhkpPB4cOHUpAQACxsbHk5eWVeFN2dnYmIiJC477AwEBCQ0Ol/aWVfZ2SM+5z7tbBYtvr2BgQ+k0S9VsbomegRcq9HG7dSSQt6wHQUGNdMplMeq1UKlmxYgVTp06VRjSUSiW6urpkZWUBUL9+faKiovDy8iIyMpIGDRpgZmbG3bt3MTc3x8nJie7duzNmzBiN5xAEQSjJvYxUDty4VKaysSkJmDeoB0CtWrVISUkhJiaGadOmIZPJSEtLw8HBocTjs7Oz0dXVJSkpiZiYGNzd3QFISEggMTGx2DalUomZmRm1a9cuVpeqIwgFHbfc3Nxin62qn5s0aYKWlhbm5ubF5rheu3aN8+fP4+rqqrbdysqKihUrlum6COqSnqQRcHoXB29cVhvFnHVmD1kpiSQ9SQNg3bp1LFy4EEtLS4KCgkhMTGTEiBE8fvyYxo0bs3z5co3129jYcPv2ben3PmTIEDw9PcnJycHU1JStW7dy69YtBg0ahJmZGXFxcezatYvc3FwCAgJYs2YNPXr0ID09HTMzM7744guio6Np3749I0eOZNCgQa/lOgkCPPsz2LB1U4w+cSEvJZ29363GxqgGdnZ2AFy/fp1JkyaRkZHB1atXSUhIoEePHowePZomTZpIA0dXr16V/q6Tk5OlBNRFB6o0lTt79izLli1DqVSqDU6p2iAIwrO9851/1ZqVp66f5cbDGyRn3MfE8Omos6en5wvV//DhQyIjI7G3t3/Rpr400XcOSaH+hRkYaWPbw5hDixNRAhUqaqGlreTKvePYNnMsU91eXl58+umnNGnSBD09PaAg1HXo0KFcunSJpUuXsm3bNlxcXKhZsyZTp04FoGbNmjRv3hxLS0sSExNp06bNS3u/giC8Hw4V6aCVJl+pJDLhJr3/+VmpVGJlZcXAgQOlL4K5ucU/J1W++uorunTpQrVq1bC2tubQoUNoa2uTk5ODtrZ2sW0ymey5opeMjY25e/cuUBAZ0Lx5c6D4A1ddXV3y8goiturVq0fLli3Ztm0bgJSQUERN/Tuq8GVNo5iFp5C0ycnG1taWdevW4eHhoRZF0rp1a6ZOncqpU6do3bp1sXrCw8Np1KiR9LOOjg579+7FwMCAgIAAwsLCaNiwIWlpaRw7doxNmzaxfft2unXrBsCtW7cwNTVl37590kMiGxsbQkND0dF557+iCe+Ysn4Gy/T1yHuSxUO9dOnzSdPgkampKZmZmQQHB9OvXz+g4GHmN998Q82aNcnLy0Mmk7FixYpiA0Wayjk6OnLs2DGysrJwcnKSyqraUPjzVBAEzd7ZO0tqZhKbI2dy7nZBRzj+WiZ37mbit7sNtnU8ycnLBJ6OwowcOZJhw4Zx69Yt6tatS926dQkMDCQlJYX+/ftz+fJl1q5dKyWsU/nss89YtmwZ69evl7adP3+esWPHkpeXx6effsrAgQM1bhs6dCj6+vpcunSJDh06MGvWrJfy3tOzkkvcV9tan9rW+mrbPqhZ8HPhkSmABg0aSGUCAwOl15oemBw/flx6vWXLlmL7C8/nL7yMoOqcRV8LgiAUlZyV8Vzl03Oz1H6ePn06vr6+pKSkoKWlRVBQEJaWltL+ixcv4ubmhlKpxMnJialTp6KlpcXnn39O+/btkclkNGnShO+//17jtufRvHlz4uPj6dy5M1WrVi2xXI0aNXj48CE9e/Zk1apVeHl54eLigra2Nm5ubsyYMeO5zis8VdYpJJm3/2KBvCDaoqxRJBcvXsTV1RUTExOWL1/OoUOHAEhPT8fX15c7d+5w//59GjZsSMOGDUuM+Khfvz7W1tYMGDAAOzs7Pv/885d8FQSh7J71GZxx6jJZf91GmZNL5S5tyDvx9G9Z0+CRavuiRYv49ttvAZg/fz7Dhw8nKysLXV1dtm/frvFcmsp16dIFFxcXHBwcMDExKXZMs2bN8PPzo0+fPhq/qwqC8I52/lXJ7orOef8rIp17V7PYxWpS7+bzxbSnN9EzZ85QoUIFQkNDWbRoEU+ePAEKwjnXrFlDVFSUxs5/w4YNOXz4MPHx8dK2GTNmEBISgrm5Oc7OzvTp00fjNiiYw7Ry5UpatWr10jr/FSuYPFd5Qz3jl3JeQRCEV8mkguEzy5hOHwiAUXdnHBwLlthTPdAE+OWXX0o8VpV4qijViifP2lbStK/C24cOHSq9PnDgQLGyGzZsAJByBABSxxFg2LBhDBs2rEznFUr2vFNICnd6yhJFYm1tzdGjR4vVdejQIRo1asTGjRvx9/dHqVQCxSM+VLKyspg4cSJaWlp4eHgwYMAAafRSjPwLr1tpn8EVPqpLjcVj1LYFjJ4k/Z16enpqHDwaO3asWgLWBg0aFPtsLPwZrhoo0lRu5syZzJw5s8RjjY2NCQ8PL/E9CILwjib805TsDqChc0W6zqhO1xnVMW+ux75L30r7rl+/LoVdFu7gN2jQAH19fSnhnSZjx47lf//7n/Tzo0ePsLS0RFdXl3r16pGQkKBxGxSfw/Qy2Jh3REtWti8FWjIdbGt3fGnnFgRBeFU6WjRFR1a225KOTAtPi6avuEXCu+p5p5CcvX9Dbdv06dOZN28ebm5uuLu7S0nJnqVVq1bs3r2bLl26EBcX98zyN27cwMXFhdatW2NqaoqZmRleXl507969xBFRQXhVxGewIJR/79xj5ZKS3Wly5W44VQxboqdVkXr16nHs2DEALly4IJUp6Wl8Ye7u7sybN0+KFjAxMSEuLg5zc3OuXbuGmZmZxm1F639ZTAyrY1vHk6ibe59Z1raOJ8YG1Z9ZThAE4U2rYWhEJ4tm7Im78MyynSyaUd3Q6DW0SngXlWUKSeEoktofFzyoL2sUSdFojMIRH6olGgvTFPGh2la0rgkTJjBhwoRntl8QXjbxGSwI5d87N/JfUrI7TfKVudx8VBD216pVKzIzM2nfvj0XLlxAV1f3uc47cOBA7t+/D8CcOXPo378/zs7OjBs3Dl1dXY3bXqW+drMxq2RZahmzSpb0tZv9StshCILwMs119Maycslz5AEsK1dlrqP3a2qR8C4qyxSSwoz1Xl50niC8y8RnsCCUb7KSRrsB7O3tlZGRka+xOc+279K37L64pMzluzX/gs5NPwMK5uzp6OiwaNEi6tatK2UefVelZiaxOWoW524dVHsgoiXTwbaOJ33tZmOkX+0NtlAQBOH5JT1JY8bp3Ry4cUktdFtHpkUni2bMdfSmmkGlN9hC4W13LyMVx60LyxT6ryPT4kzvaWIUUxD+IT6DhfJGJpNFKZXKt2fptjfonQv7f5FkdyNGjOD69esYGxvz888/v+SWvX5G+tXwdfpeWu4wIzsFQz1jbGt3FKH+giC8s6oZVGKFa3/uZaRy6MZlUrKfYKxngKdFU9FBE8pEhC8Lwr8nPoMFofx650b+kzPu47e7TZlC/7VkOizsdlJ0hAVBEAThPZP0JI3u+1aUutyfZeWq7PQaI0YxBUEQyjEx8v/UOzfnX5XsrixEsjtBEARBeD9VM6jETq8xdLVsXiyDuY5Mi66WzUXHXxAEQXivvHNh/1CQ7O7Ww0sal/tTEcnuBEEQBOH9JsKXBUEQBOGpdy7sX0UkuxMEQRAEQRAEQRBKI8L+n3onR/5BJLsTBEEQBEEQBEEQhLJ6Zzv/KiaG1ZE3HPymmyEIgiAIgiAIgiAIb613LuGfIAiCIAiCIAiCIAjPR3T+BUEQBEEQBEEQBKGcE51/QRAEQRAEQRAEQSjnROdfEIQ37siRI8jlclxcXOjRowcPHjz4V/UEBgYSGhqqti04OJigoKCX0UxBEARBEARBeGe98wn/BEF496hW6UjPSiYnXYvFgSEc3P8rlStX5s8//yQ7O/tNN1EQBEEQBEEQyhUx8i8IwmuTmpnEqoix+O1uw6bIGey+uIRlwbPQbRzLpgvTSM1MolGjRhw8eBC5XI69vT2//vorANOnT8fZ2RlXV1fi4+PVyqxbt046x+rVq3F3d2fEiBFq587JyaF9+/a4uLjg4+NDXl4ecXFxtG3bFh8fH+zs7Lh9+zYAAQEBODs74+bmRnJyMj/99FOx9giCIAiCIAjCu0R0/gVBeC1SM5NYfNiHqFv7yFfmStszkvMwMJERdXMviw/7kJqZRJ8+fVAoFBw5coSvv/4agBMnThAeHs7Ro0epWbMmLi4uKBQKTp8+zQ8//CDV16xZM0JDQ9HT0+P06dPSdh0dHfbu3Ut4eDiNGzcmLCwMgLS0NH7++Wc+//xztm/fzrlz57h27RoREREcOXIEY2Njje0RBEEQBEEQhHeJCPsXBOG12Bw1i4S0uGLbDU20SX+UB0BCWhybo2ZhmuDOsmXLUCqVJCQkADBlyhSGDBlC1apVmT9/Pr///juzZ88mJyeHK1euSPXZ2toCYGNjQ2xsrLQ9PT0dX19f7ty5w/3792nYsCENGzakSZMmaGlpYW5uTmxsLH/++Sdt2rQBQCaTAXDo0KFi7REEQRAEQRCEd4kY+RcE4ZVLzrjPuVsHNe6rY2NAbEQ62U/yAVCc3cMXkydx4MABdu3ahZZWwceUm5sb69evx8zMjL179/LVV18RFBREaGgoJiYmUn3nz5+X/l+/fn1p+6FDh2jUqBHHjh3Dx8cHpVIJPO3gAyiVSqysrNQiBpRKJQsWLCjWHkEQBEEoL1JSUpDL5cjlcoyNjZHL5QwbNqxYEt1nmThxIs7OzrRu3ZpVq1YB4Ozs/FLbqlAoCAgIeCXHGhsb4+rqSps2bThz5kyZ6pTL5f+qLSXZuXMnDx8+BAqSFkdFRb3U+oX3m/gWK7zXXsbN7qeffmL58uUAbN26lXbt2gGQlZVF69atiY6OZvXq1S/c1qFDhxIXF/fMckFBQdjb27Nv3z617QsXLsTFxYW2bduydu3aF27P84i+c0gt1L8wAyNtbHsYc2hxIrvn3OfkhiRcOrfAxcUFf39/qWPfrVs32rZty4EDB2jXrh09evSgW7dujBw5Uq3zHxMTQ/v27Xny5AmtW7eWtrdq1Yrdu3fTpUuXUq+jjY0NFhYWODk54ebmRkpKCl26dCnWHkEQBEF41yVn3Efx1zoibq0l8Mfh7Ny/BWtraxQKBRYWFs9V16VLl0hKSiIiIoJTp07Rq1evV9TqV8fa2pqjR4+ydetWFi9e/EbaULjzP3ToUOzs7N5IO4TySYT9C++lwtnmA38cjo15R7p4+KBQKAgMDHyuulq2bMmSJUsAOHv2LIaGhuTn5xMdHY2NjY303/PKz8//V6PMW7duJSIiAn19fWnbgQMHuH79OuHh4eTm5tKjRw9atmxJkyZNnrv+fyM9K7nU/bWt9alt/bS93Zq78dO3n6mVKZpob/jw4QwfPlxtm6bf3dChQ6XXv//+e7H9GzZsAJAeAgHMnz9frczMmTOZOXNmqe9BEAShvCp8z/wj6g6/rD6OFjpUrVqVoKAgJk2aREBAAA0aNHjhc8nlcpRKJTKZjCpVqrBjx46X8A5KFxcXx7Vr13Bzc3sl9Y8cOZI//viDLVu2YG5uDhSM6A4ePBgtLS2GDh36XNevU6dOPHnyhHPnzmFra4uBgQF9+vQhNzeXkSNHSuUOHjxIXl4eXl5exepIzUxic+RMAn1/ousMM2n70vXTuPxnKqmZSVy5coWffvqJunXr8uTJEyIjI0lMTGTEiBE8fvyYxo0bS4MfAAYGBuzatYvY2FgaNGjABx98AEBubi6jRo3i7NmzLFy4EE9PT/773/8SHR1Nfn4+ISEhPH78mDVr1rBkyRJq167Nnj17SE5O5uzZszRs2JAFCxZQqVIlvvjiCwwNDYmKiqJr1648fPiQQ4cOkZqayoABA8jJyaF58+YsX74chULBokWL0NHRkcpJ7z81lYEDB7JgwQKaNm1a7PokJydLr1XRhrm5ucycORNPT09WrVrFmjVr1AYZzpw5w9SpU8nJyWHkyJEMGzZMShasUCgYPHgwv/32GxcvXuSrr76iY8eOyOVyrK2tOXv2LMOHD8fT05ODBw8SExNDz549SU9Px9nZGblczqBBg7hz5w7m5uasX7+eiIiIYu+vUqVKZfobEt5fYuRfeK9oyja/KXIGfrvbcC/1b1IzkwBYt24d7u7u0k00MTERb29vXF1dGTt2rFqdTZo0ISYmBoD4+HjkcjkxMTGcPXsWBwcHKcTst99+kzqYRkZG3Lp1iwULFtCuXTtatWrFuXPngIIvPlOmTGHw4MFcv36dVq1a4e3tzbVr14q9nw0bNuDo6IiTkxPnz59n27Zt/Pbbb3Ts2FFtvvvWrVv54osvgILEdxMmTGDbtm3s3r1bapOBgcHLv+D/qFjB5LnKG+oZv5qGCIIgCGVW9J655cRXLFm4DKthdxm4wJoZc6a9kqVZjxw5gkKheC0dfyjo/KuSwD5Lfn7+c9d/9epVIiIipI4/FHT+/01dUPBAX6FQSCP0Bw4c0FjO09OzWMc/Pz9fLQEvKNX2K5V55ORlMn6ZO2d+O8PYsWM5fvw4VapUITk5mYULF+Ln58fRo0epXLkyp06dko6tX78+tWvXZtiwYTRr1oyTJ08C8PDhQ+bPn8++ffukBL0LFizg2LFjzJo1ix9++EH6LhUXF0fTpk05efIkJ06coE2bNuzYsYOtW7cSFhZGp06dANDT02PPnj107tyZI0eOUK1aNQ4fPkxERASpqan89ddfGstB6R3/ixcv0rZtWynaLz8/n6+//pqwsDAUCgWLFy8mNzeX1atXExERoRbdMHPmTHbv3k1ERAQhISHSv40BAwZw4sQJZs+ezZIlSzhw4ADff/+9dFzfvn05ceIEwcHB1KhRA09PT0JCQpg8ebJU5pdffqFJkyaEh4fTtGlTtm/fXuL7E4TSiM6/8N4oKds8QL4yl7SsRyw+7ENWbga2traEhoZy8+bNZ97stLS0MDQ0JCUlBW1tbezs7Dh79qzU+VdRPQiYOnUq/fv3p06dOvz3v//l2LFjhISEqGWR79GjBxs2bGDx4sUsXbqUHTt28ODBA7U25+Xl8e2333L8+HFCQkLw9/enZ8+e2NjYcOTIEbURhLt371KrVi3p59q1a3P37l28vb1RKBT07dv3X8+fKwsb845oycoWaKQl08G2dsdX1pZnuZeRytqYUyyLPsK0td8zYerTm29gYCAKhaLYMUqlEi8vL1xcXMjLy/tX5/3ss8+eXUgQBOE10XTPvBX9hIZtK6Kjn0/Uzb38EjeFih/oSscUniY1fvx4oKDT4uDggJubG/v370epVDJmzBjc3Nzw8vLi0aNHz2yLXC5n0qRJtGzZUppGd+LECZycnJDL5WzZsoXc3Fz69euHi4sL/fr1Izc3l+DgYIKCgoCnn98KhYJOnTrRtWtXnJycSEtLY9WqVaxfv5727dsDMGfOHORyOW5ubsTFxREXF4erqys9e/YkODiYIUOG0K5dO1xdXYt14MePH4+LiwtdunQhJSWFgIAALly4QJcuXaQyv/32G9HR0bRv357169cD8PXXX+Ps7Mzs2bMBiI2NxcPDg3bt2jFv3rwy/c4OHDhA586d6dy5M0qlUnr/Rds/dmZPVn1+mpPrNV/73GwlB4Ji+KhNFT7++GPi4uL4+++/SUlJYfXq1XzyySdUqlSJHTt2EB8fz6pVq3B0dGTixImYmZlx/Phx9uzZg7+/PwCmpqaYmZlhbm4ujah/9dVXtG3bloCAAOLj45HJZFSoUIGwsDA+/fRToqOjiYqKwt7eHn9/f+bNm8fQoUOlgY1mzZoBSHU+ePCAnj17IpfLiYiIID4+XmM5gG3btmFra6txxN/a2prjx4/z1VdfcerUKZKSkoiJicHd3R0PDw/u3r1LYmIiFhYW6OjoqIXknz9/XhoounfvHomJiVIbKlSowEcffUT16tWpVauW2t+9ra0t2traWFhYlJhU+O+//+bjjz8GwN7evsTrIAjPIjr/wnujpGzzhSWkxXEp/qj0YVqrVi1SUlKIiYlh2rRpyOVyjhw5It1UVOzs7Ni4cSNWVlbY2dkRFRXF1atXady4sVq5a9eusWzZMpYtWwbA+vXrcXFxYeTIkWp1qm4m165dw9bWFh0dHZo3b65Wl+rmo6uri6WlJSkpKSW+r5o1a6rVf/v2bWrWrAnA6dOnOXr0KNOnTy/12rwIE8Pq2NbxLFNZ2zqeGBtUf2VtKUnSkzRGHw3BcetC/E/vYvG5w6y+coKfrpxkzNGNJD1JK/HYu3fvUrlyZcLDw9HW1i71PCWN9Hz33Xcv1H5BEISXSdM9MyM5D0OTp59xqhVaVFatWkWfPn0IDw8nIyODM2fOFBu13bt3L3Xr1pU6eStXrix27vbt2yOXy9Ui7QYOHEhERISUs8bPz49du3ahUCjo1atXiSOjmhQdLfX19WXQoEEcOXKECxcucOfOHRQKBd9//z0LFiwoeK8JCWzZsoVBgwZx+/Ztjh07RlhYmNr0vLNnz5Kenk54eDh9+/Zl5cqVzJs3D2tra/bu3SuVc3BwkB7UDxo0CICOHTsSERHB/v37AfD392f16tUcO3aMy5cvc/v27Wf+zmrXrs3+/fsxNzfnwoULavtU7ffu2ZHDO07jPas6H7Yy1FhPdkY+tZvr8yA7jozsZGm7ajrGunXruHr1Ks2aNcPLy0saBe/YsSO5uQUPiqpUqSIl1C2aWPfBgwcoFAqOHz/O3LlzpQS8dnZ2/O9//8PFxYXs7GyysrLQ19fHwsKCoKAgfH19Wbp0qcY6N27cSPfu3VEoFDg5OZWY1Bdg2LBh3Lp1i507d5Z4LYcNG8a6deuoWrUq1tbWUjTK+fPnMTU15caNG+Tl5UlRm1DQid+3bx8KhYJz585JkR4lXQeV8+fPk5eXx40bNzAzM0NXV7fYQEL9+vWlxH+RkZFSQuOS6hSEkojOv/BeKC3bfFF3U2OL3eysrKxYunQpCoWCyMhIunXrpnaMg4MDK1asoGXLlnzwwQfExcVRsWJFtS8FGRkZ/Oc//yEoKIgKFSoASHPSfvzxR7UPbdVx9erVk24KFy9eVDun6uaTk5NDXFwcxsYlh8r37NlTiizIzc3lm2++4ZNPPuH+/fv4+/vz448/qt1AXoW+drMxq2RZahmzSpb0tZv9StuhSdKTNLrvW8HeuIvkKtU750qU7Im7QPd9K8jIKQjhKzpqNGXKFI4ePcrIkSM1jnwFBwfTp08fvLy8uHDhAsOGDcPd3Z3hw4dLeQqcnZ25c+cOffv2BQoiO1Q5CIKCgmjbti1t27aV8hZYW1vTv39/WrRoQXR09Ku/SIIgvDdKumcWXppV5dytg2TnPQE0j04WHbWNiYlh8+bNyOVy5s+fLyU2K0zV0So8n1w1eqq6PyqVSqpVqwYU3DM1nbukjlFpo6V//PEHCoUCuVzOmDFjSE1NBaBFixZoa2ujq6vLkCFDGDhwIAEBAWoPdEsanS0LVZtUU/CuXr3KoEGDpKmEd+7cKXMdmt6Xqv3hl7ZTqZo2WtoyTOvpaazHwFib+39mk3w3m9jESLV9DRs2ZPny5QwaNAiFQsHFixelUfA6depw6dIlXFxc8PLyKjGi8IMPPqBSpUq4ubmpJSd2cnIiLy8PY2NjateuLQ2gBAYG0q5dOz777DP69OmjsU43NzeWLFlC9+7dSU9PL/U6yWQyVq1axYYNGzh27JjGMtra2nh4eLBr1y4+//xz2rdvj6urKxMmTEBHR4dhw4ZJUxJUZs+eTdeuXXF1dZXu5WXx888/06ZNGwYPHoyenh4dO3Zk7Nixag/GunfvzuXLl3FxceHixYv4+PiUuX5BKEwk/BPeC6Vlmy9Kqcz/52b3dB7X9OnT8fX1JSUlBS0tLYKCgrC0tJT2Ozg4cPHiRezt7YGCm3fRUf/t27dz9epVBg4cCMDmzZtxcHDAxcUFFxcXjW354osv6N+/P9WrV6d6dfXRcG1tbcaNG0fbtm3R0tJSmz9WlJeXF+fPn6dt27YolUpGjBhBs2bNmDt3Lrdu3cLb2xtAY0j7y2KkX43JHbazOWoW524dVPt9aMl0sK3jSV+72RjpV3tlbSjJjNO7iXv8oNQycY8fkHn7LzrjgZ6eHrt27WL+/PkcOXJECskMCgpi8eLF9OnTh0GDBjFy5EhpqSATExO2bNnCmTNnqFChAqGhoSxatIgnT55I5zA3N+fRo0dkZmZy+vRpXFxcSEpKYvfu3YSHh/Po0SOGDx/Ozp07SUhIYM2aNURFRbF27dp/lVRSEARBk5LumXVsDAj9Jon6rQ3RM9Ai5V4OuhXyeJheEFmmGp1s2rQpkZGRjBw5Uhq1PXnyJEuXLsXT05PBgwczadIkAHJycsrUpqIPqGUyGQ8ePKBq1ark5+dL5/by8iIyMpIGDRqgr68vPTi/ePEirq6uxepSKpVqI62NGjXCw8NDisbKycnhzp070kOHvLw8+vXrx+DBg/H19eXs2bO0atVKev+q5LSFR2c1UZ1TR0dH4/uzsrLim2++oWbNmuTl5ZXpAX1po8Cq9usY5vE4KZf8fCVJcZrzNVi1q4h158rsX5BIpaoF0zpat26NpaUlenp6/PLLL0DBdAxbW1tpFPzx48fY2NgU+y4REREhvVbtKxwJoeLq6iot11s48e6iRYuKlVU9HC+c1LfoIElJ5VTbtm3bVqx84bbOmTNHet25c2e1cqNHj2b06NFq2xwcHIrljih8LQrXXfj1V199Jf0dQMGATc+ePYu1bcuWLWo/F05UXPj9CUJpROdfeC88K9s8QLfAgs61fU8TGjevDRSM2KqobnaaWFhYqN1ot27dKr0u/OGsCu9T+fHHH4vVVfhGUb9+/VLXmR0yZAhDhgwp8fjCpk+fXiy0f8aMGcyYMaPE+l82I/1q+Dp9L2WOzshOwVDPGNvaHd9IqD8UzPE/cOOSxn0yXW2UuU9Huf5Kiif0+LFSR1f+/vtv6UtC4ZEf1VSO69evS1M4bGxs1PJHQEHo54EDBwgLC2PUqFFcu3aN8+fPS19aVVRfbMU8P0EQXraS7pmFl2ZVAhUqatHOtwq5+QWdyFGjRtG/f39+/PFHmjdvjqOjI1OnTuX06dOkpaWxZMkS2rVrx/jx46XM+hMmTJAeQKu0b98emUyGtrZ2iUnMFixYQNeuXalQoQKjR4/mk08+Ydu2bbi4uFCzZk2mTp1KZmYmixcv5uzZs2qdq6KaNWuGn58fffr0YcuWLdSoUQO5XI5MJqNfv354eHhIZR8/foy3tzd5eXkYGRlhbW0t7WvZsiXBwcG0bduWypUrs3HjxhLP6eXlRffu3dWy8xc2f/58hg8fTlZWFrq6umzfvv2lZHI3qlgVq3YV2TXrPjUbVyixnH4lbVzHVmXp9BBarv2kxHKFR8FVSx0LgvD2kpU2P8Te3l4ZGRlZ4n5BeFco/lrHpsiyd3L72c9F3nDwK2yR8LZYG3MK/9O7NO7LS3vCoxW7qDqpIMzwwf9tZZSvL8YJ6cybN096OCSXywkICJCSNFavXp3BgwczcuRIaYkn1RJMZ86cITg4mBUrVrB48WLS09MJDAzE2dmZiIgIbt68iZ+fH3fv3iUsLIzExETGjBkjjVDk5OSgq6srlY+LiyMwMFDtQZUgCMKLEPfM8is54z5+u9uUKRpSS6bDwm4n39jDeUF4WWQyWZRSqbR/0+14G4g5/8J74V3KNi+8XslZGSXu065kgL69FUkLQ0haGIKBnRUYljxSAgUjX5s3b6Zt27ZUqFABR0dHtf2tWrUiMzOT9u3bc+HCBXR1ddX2161bl+vXr0vHmZqaSisJuLq6snDhwn/5TgVBEMpG3DPLr3chAa8gCK+OGPkX3hurTowj6mbxOWZF2dXtgq9TyfPnhfKltJF/TeY7dmNI49YvdM7c3Fx0dHRYtGgRdevWpV+/fi9UnyAIwssm7pnll2oZx9JWQDKrZMnkDtvfSB4eQXjZxMj/U2LkX3hvvM3Z5oU3p6NFU3RkZfso1JFp4WlRfF3g5zVixAhcXFyIiIigR48eL1yfIAjCyybumeWXKgGvXd0uxSI8tGQ62NXtIjr+glBOic6/8N4QNztBkxqGRnSyaFamsp0smlHd0OiFz7l27VrCw8PZs2cP+vr6L1zf2+BeRiprY07xadASqtWqQRuXtsjlclJSUl7aOVRLhak4Ozu/lHrXrFlTpnLBwcFYWVlJSTx/++03oqOjpeUX/63PPvtMqr/wsmGC8CaJe2b5pkrAu8D7JP3s59Kt+Rf0s5/Lwm4n8XX6XvxeBaGcEtn+hffK25ht/mW7l5HKoRuXOXviJJtnLqZRg4bI8vIJCgoqtvygSnBwMIMHD5aWAiosLi5OSmanSV5eHv7+/pw6dYrc3Fy8vb2ZOnXqv2r7hAkTWLJkCdra2tK2oUOHEhgYqLa04sugUChQKBQEBgYy19Gbiw/ucPXsObSrGKFjZkJ6xEXIz6eiSwsALCtXZa6jd6l1fv3112zfvh1tbW3c3d0JDAz8V22Ty+WvdNnFoUOHEhAQwIcffkjv3r3p27cvaWlpWFtbS6sSwNPVLkpbQijpSRoBp3dx8MZlcpX5ZP11k2y7D4n3aUsni2bk6D39Xebn52v8G3uVynLONWvWMHz48DLVN3nyZLXs3MHBweTm5kpre/+btqiWFAsODmbgwIGv/RoJQkneh3vm+87EsLpI1igI7xHR+RfeS+XxZldaJ8z2sR7/97/vWPX9co3HvkinY9WqVVSuXJljx44BJS81WBbffPPNvz72RVQzqMROrzF47hnCrfwUMDOR9unItOhk0Yy5jt5UMyh5maXU1FT27t0rLd336NGjV93sF/bf//6Xtm3balxPuCySnqTRfd8K4h4/KLYvV5nPnrgLhG7bRdP7ueQ8yWT+/PmsWbOG6OhojIyMCAkJ4dGjRwwaNIjq1asTGxvL1KlT+emnn8jIyODQoUNUrFjxme04c+YMU6dOJScnh5EjRzJs2DDkcjkODg7Ex8fzf//3f4wYMYLHjx/TuHFjli9/+u9g1apVXLx4Eblcznfffcf58+f53//+h7a2NsuXL6dFixalnnvVqlU8ePCAo0ePsmHDBsaOHcvVq1cxMDBgw4YNnD9/nqVLlwIwZswYpkyZgrW1NZcvX2bt2rXY2Njg7OzM0qVLiY6Opn379gwfPpwNGzZw+PBhoGDZs4MHDxZLDikIr0t5vGcKgiC8j8TwgiCUA6pO2N64i+Qq1cOGc5X5HL9+hQP3/yTpSZpaqHTh0OX27duzfv16/vjjDymsedmyZQDcuHEDHx8f7OzsuH37tlr9O3bsYNKkSWp1AvTu3Zt27drh4eFBamoqDx8+RC6X4+rqyvjx48nOzsbLywu5XE7v3r2lY3Nzc7l+/TqtWrXC29uba9euAdCtWzepQz1hwgSioqKkc+bk5NC+fXtcXFzw8fEhLy+PuLg42rZtW6zdw4cPx93dnaCgILX3YaStR9KxKKodvESzYzfobNGU+ncyqb8pkhuLN1BVvyJPnjyhX79+uLm50adPH3JycqTjtbW1uXfvHufPnwfggw8+ACh2vVX/nzRpEi1btmT16tUA7N27Fzs7O4YNGybV+9NPPyGXy7G3t+fXX38FCkbhR48ejbOzM7NnF8y11fQ7mzNnDnK5HDc3N+Li4or9zSxevBg9PT3++9//AhAYGEhoaCjZ2dl069YNT09Pdu/eXey4wmac3q2x459x6jKJizbyaPU+kjLT+DMrmX379pGTk0N6ejrh4eH07duXlStXApCWlsbWrVuZMmUKmzdv5tdff6Vz584cOnSoWN0DBgyQ3qvKzJkz2b17NxEREYSEhJCdXbDmeI8ePdiwYQMLFy7Ez8+Po0ePUrlyZekBDYCvry/W1tYoFAqaNGnCt99+y/HjxwkJCcHf31/jdVOd/+bNm/j6+jJ58mRCQkLYu3cvdevWJSwsjE8//VR6f9nZ2ezevZtOnTqRkJDAmjVrWL58OWvXrpXqdXBwwMbGhiNHjjBkyBAsLCyIjY3l6tWr1K9fX3T8BUEQyqG8hDQyNp3jzv8dxqWJPe2c2mJsbIxcLmfYsGHk5eWhUCik70JldfDgQfbt2/eKWi28y8TIvyCUA6V1wrL+vEXu/UdoT+rDjNPFO3OqTkdoaCg6Ojr06NGDlStX8tFHH5Gfn8/NmzdJS0vj2LFjbNq0ie3bt0sdRoDMzEyN89aDg4MxNDQkKCiILVu28OGHHyKXywkMDESpVHLt2jVMTU3Zt28fRVcdWbx4MUuXLqVVq1bSyGuvXr3Yvn07w4cP5/z582pRAjo6OuzduxcDAwMCAgIICwujYcOGxdrdunVrtLW1CQ0N5csvv5Q6iQB6enoMHToUZ2dn3N3dCQ4OplrjLJYtW8aoUaO4cOEC4eHheHt7069fP1asWMG2bdukTP0VK1Zk2bJlTJ48mbi4OL766iu6d+9e4u9s4MCBfPnll3To0IERI0awYMECjh07xqNHj3B1dQWgT58+DBs2jJSUFHr16oWHhwcAHTt2ZOXKlbRq1YpZs2bh5+en9ju7cOECd+7cQaFQEBMTw4IFC/jhhx/Uzr99+3a1TrDKzp07cXBwwN/fH19f3xLbfy8jlQM3LmncZ9i6KUafuACQHnGR+1X1uJ+Ryt9//y2Fx9vb20vRIk2aNEFLS4tatWrRrFlB/oVatWppjJ4ICQmhQYMGwNMHK+fPn8fbu2BKRlJSEomJiQDSFIaYmBimTZuGTCYjLS0NBwcHje1OTEzEwsICXV1dLC0tNeYrKBr2X1hMTAybN2/m0KFD5Obm0rp1waoQhacENGjQAH19fczNzUlOTtZYDxQ85Ni8eTN5eXliNQhBEIRyJu9BOo/nHSHzyF+Qm482sJU+8FiL7pUecGT7PrSrFkS+KRQKnJ2d+fDDD8tcv6dn2ZZzFN4/YuRfEN5xz+qEmU4bgFngMFK3H+PAjUvk5OcBFOtwqyQlJfHRRx8BSNMAVJ0zTR0WfX19MjMz1bbl5eUxefJkXFxc+N///kd8fDwuLi7k5+czYMAANmzYQP369bG2tmbAgAH83//9n9rx165dw9bWFh0dHZo3bw5A9+7d2bNnD8ePH8fFxUWtfHp6OiNGjKBdu3Zs27aN+Ph4je1W1QuozW0viaojqjo+JiaGb775Brlcztq1a0lISFAr37FjR3799VdOnTrF3Llz1fYVvd7NmjWjQoUK0jXW0tKiUqVK1KlTB1NTUwAOHTqEXC7H29ubW7duFWuXgYEBUPx39scff6BQKJDL5YwZM4bU1NRi7+2HH35gwIABxfaV9Rod+md6SVnky+DgjcvUr19fitiIjIykfv36AMhkMqls4delLUVbmK2tLfv27UOhUHDu3DnMzc2Bp3+/VlZWLF26FIVCQWRkJN26dVM7XnVOU1NTbty4QU5ODnFxcRgbGz/z3Lq6uuTl5UnnGTx4MAqFgoiICL788ku1djzr/RWuq127dhw/fpzjx4/Trl27Ml0HQRCKS864j+KvdSzd+DnNPm6IY5tWTJw4Udq/ePFinJ2dGTBggBR1VXRbTk4OrVu3plKlSmpJR0sycuRIRo8erXHfxIkTcXZ2pnXr1qxaterlvMkSZGRkSNOgXF1d1SLmVAIDA19pjhmhuLwH6TwcsJHMQ1cht8h9NDef/EdPeDhgI+2c2pKdnU1wcDCTJk1i0qRJREdH065dO1q1aiXdY4KDg/Hx8aFz58507twZpVJJcHCwFOEYEBCAs7Mzbm5upT50Ft4PovMvCO+4snTCZPp65D/JJleZz8PMdLKysrh48aK0v3Cnw9TUlD///BNAyjxeWoflk08+YcmSJdLP4eHhREdHS+Hd48aNQ6lUkpeXx5w5cwgJCWHJkiVkZWUxceJEQkJCOHjwIPfv35fqqFevHufPnycvL09qZ6VKlTAyMmLZsmXFRkIPHTpEo0aNOHbsGD4+PlIbi7ZbVS/AuXPnil2nwtdB0/FWVlZMmTIFhULB6dOnGTt2rLT/yZMn3LlzBwAjIyMpTFupVBa73kXrVl3r9PR0bt++LY1cL1iwgAMHDrBr164SO5BQ/HfWqFEjPDw8pKSG69atK/ZeW7RowfTp0+nfv79ahvlnXSOV5KyMEvdpkpL9hJYtW2JgYEDbtm3ZuHFjiV+On9fs2bPp2rUrrq6u9O3bt9j+6dOnM2/ePNzc3HB3dy82daVOnTr4+Pjw119/MW7cONq2bUv//v2LPcAB9bD/w4cP4+joyIYNG/jss89wcJdz4PfT1LdrTqUPjLG2acHYsWOlh1HP4uXlhZOTE9OnT0dLS4vmzZtjbW1d5lwcgYGBtGjRQqqjqODgYI1f/kvyPKGmVlZWuLq64ujo+MzpIoLwOqRmJrEqYix+u9uwKXIG51K20uq/WdiOT+Lk5X2cigwnISGBo0ePEhERQfPmzdm5c6fGbTo6OuzcubNM+VHy8vK4e/cucXFxxe6Xly5dIikpiYiICE6dOkWvXr1e1dsHCj4bVfeCLVu2SFPuhDfr8fwj5N1MLrVM3s1k8m4mo6WlxdChQ1myZAlLlizBysoKhULBmTNnOHz4ME+ePAGgdu3a7N+/H3Nzcy5cuCDVc+7cOa5du0ZERARHjhwp00NtoXwTYf+C8I4rrROWceoyWX/dRpmTS+UubQCw69IBZ2dnOnXqJJXz8vKie/fujBw5ki+//JJRo0Yhk8no0aNHsVHSonx9ffH396ddu3ZStv9x48YRGxuLp6cnderUwdzcnN9++43p06eTk5ODu7s7N27cYPjw4eTl5fHhhx9iZmYm1fnFF1/Qv39/qlevTvXqTzNK9+3bl4CAAGmUW6VVq1bMnz+fyMhIjI2Nadiwoca2tmrVihUrVtC+fXssLCyoW7eu2n65XI6fnx9nzpwptk/1XkeNGsXy5ctRKpUsWLCAVq1aAZCVlcXgwYPJysoiPz+fCRMmAEhTCQpfb02mTp2Ki4sLH3/8MTVq1ACgS5cuuLi44ODggImJSYnHFv2d/fe//6VGjRrI5XJkMhn9+vXTGMLfvXt3rly5wuTJk6lcubK0rWfPnnTs2FHKW6CJSQVDjdsrfFSXCh89vXYVna0BMNYriFL4/vvv1esxMZFWkig8l1/TCgOq1QdUIiIigIKpK2FhYWr7Co9kmZqa8ssvv5T4XjZu3Ci9/uijjxgyZIjGckOHDtXYrl9+PUDA6V20/nkRue71SZMlo9vcnMx2tnSs3Ri/5u7F2mxpaSm9H9W2CRMmSH83UPCQp3///iW2W5MlS5bg7u6Ol5cXt27dok6dOkDBQ6HSVm3Q5HlCTU1NTTl69ChPnjzBy8tLmoYhCG9CamYSiw/7kJAWJ20zNClYeSRfmUty1n3Wnvkc99uTpM8cd3d3QkJCqFixYrFtvXr1UrsXqXz22WfSah0q4eHhyOVysrKyOHXqFG3atJH2GRgYcPXqVWJjY2nQoIH0Gevo6MhHH33EpUuXCAwMpEuXLixYsICDBw+SmZnJypUrsbW15cSJE0yZMgVdXV3GjBlD7969iyUZLfy5feLECRYtWgSAmZkZbm5unD59mmbNmuHj44O+vj4ymUyKEhs3bhzNmjXjm2++oU6dOty7d4/169djYGDAkiVLuHHjBgsWLKBSpUp88cUXdOrUqdj5p0+fXqweHx+fF/6dlhd5CWlkhv5VprL5jzLIS0xT23b9+nUmTZpERkYGV69elSIQi0Yqqvz555/S32DRgQPh/SQrLazS3t5eGRkZ+RqbIwjC81obcwr/07vKXH6+YzeGNG79Clv06uzfv5/Lly8zefLkN92U9969jFQcty4sU+i/jkyLM72nUd3Q6DW07PXStOJB+vELZMfewahnO7QrG2JZuSo/e4xkYI+e5OTkYGpqytatW1m0aBG2trZ06tSJPXv2EBMTg5mZGbm5uVy4cIFNmzbh4uJCXFwcu3btonbt2gwbNoxbt25Rt25d6tatq7acZGBgoJSzws/Pj27dujFhwgRsbW0xMDDAyMgIZ2dntm7dysSJE2ncuDHfffcd1atXx8jIiIULF5KWlsb48ePp27cvjRo1wtjYGHd3d+bNm8fw4cO5f/8+pqambNiwQS0JobOzMxERESQmJvLJJ5+wdetWJkyYwJYtW8jNzcXDw4OwsDB69+7N/fv3qVChAtu2bcPIyAhra+tiKyAIwotYdWIcUTf3atz34GY2v21OptMUM/L+aIR9TR9Gjx5NbGwsX375Je7u7qSmpqptW7NmDfB0mVRV3hFNxo8fz8SJE8nOziYoKIjFixer7d+4cSMrVqzg0aNHrFq1ijZt2lC/fn2OHj1K1apV8fDw4MSJE2RkZGBoaEhsbCyzZs0iJCQEFxcXduzYQbVq1cjPz2ffvn1cunQJPz8/Dhw4QHR0NH5+ftK52rZty/Hjx6Wff/jhB0xMTLhx4wbm5uYMGDCAjh074ufnh46ODgcPHmTevHl07NiRXbt24e3tza5duzAwMECpVDJ48GDmzp2LpaUlSqWSvXv3Fjt/27Zti9WjKS/Q+ypj0zlS54aWWsY7bjm7LcfyyY2VHPxhM9/d+BV7e3s6duzIZ599ho+PD3K5HGdnZzZs2IBCoSA3N5eRI0cSGBiIXC4nLi6O3Nxc7O3t+frrr6WH7Eql8r18CCCTyaKUSqX9m27H20CE/b+jUlJSpFGywllBQ0NL/0Ap6ubNm/To0UP6ECmaAf1lkslkbNu2DSjIzl6lShWCg4O5d+8e8+fPB9Qzowtl09GiKTqysv1T1pFp4WnR9BW36NXYvn07X375ZZnXYxderRqGRnSyaFamsp0smpXLjj9oTrZp2Lop2lWNSPpqE0mLN/P37RvMjtzP3r17CQ8Pp3HjxoSFhdGzZ0+2b98OFPx9Fw4p/vzzz6lduzY///wzn3/+Odu3b+fMmTNUqFCB0NBQrKysSmxTXl4ev/32Gx9++CFJSUn4+/tLyw0C9OzZU/os3r9/P15eXri4uEjTWX744QcpAaYq1DQoKAhvb2/CwsKQy+XS8SqJiYm0a9eORo0a4efnR82aNcnIyODx48ccOXIEd/eC6Ifg4GCOHTtG79692bJlC0CJKyAIwr+RnHGfc7cOatyXmZbHieBHtPOtCkD8k0skPCiYrpWamoqJiQnGxsZSLhTVtrJSKpWEhYUxZswY/vvf/2r8Tta/f3+OHz/Onj17pBVFqlatSt26dalYsSLa2gURCuvXr8fFxYWRI0dKU4eUSiXVqlUDCnKJqJKMyuVy5s+fz8OHD4u1p7Dbt29Ts2ZNtdwuqoSkTk5OnD59mri4OGrWrIm+vj6zZ89mzJgx+Pr6kpCQgL+/P/PmzWPo0KHExsZqPL+meoSn8lMyn12oSHnV9Z0zZw5eXl58+umn9O7dGz09vWceb2Njg4WFBU5OTri5uWlMZCu8X0TY/zsmOeM+0XcOkZ6VTOCPw7Ex70gXDx8UCoXaCFBZjRw5km+//VbKEn7y5Em1/fn5+f9q7XdNrK2t2bdvHz179uTo0aNSsq8aNWpoXFJLKBtVJ2xP3IVnln2XO2E+Pj4idPAtM9fRm4sP7mhcaULFsnJV5jqWzxDwkpJtynS0MfJ2wsjbiYzTV0j7NZL9+vokrNzJg/sJ3L9/n4YNG9KhQweuXbvGkydPuH37Nh9++CHh4eFSPYUTVsbGxnL9+nUpAaaNjY3G1RomTZrEBx98QN++fTEzM8PMzIzatWurlXFzc2PhwoX85z//oVKlSlSsWJHjx48ze/ZscnJyuHLlSrF6Y2JiiIqK4ocffiAzM7NY3g1TU1OOHTtGaGiotFTjJ598wq5duwgLCyMgIEBKBHrx4kVSU1Pp0aMHUPYVEAShLKLvHCJfmVtse36ekqPfP8Cxv4k0BaDah9rs37yHmf5zCQ0NxdHRkZYtW7J8+XKmTJkibSurs2fP0qNHDylfyLRp07h48SLW1gXTnx4+fIhMJuODDz6gSpUq0gjsw4cPuX37NlWqVJHyzixfvpxz587x999/M2rUKKBgEOXBgwdUrVqV/Px8KcmoarndwsvPArRu3ZpNmzbRr18/EhISOHLkCDNmzODUqVOcP3+eJk2acO7cOTp27IhMJsPBwYHJkyczYsQIoOBzJjg4mI0bNxIcHMz48eMJCgri5MmTLF26FE9Pz2Ln11SP8JSW8bMfhuy2LMgntMNiNHpVKuHkZKt2byiayb/wlC5NfQHVAJsggOj8vzNSM5PYHDmTc7fVb2pbomZzLzWD1MwkANatW8fChQuxtLQkKCiIxMRERowYwePHj2ncuDHLly+Xjr158yZmZmZqWcJVI+9yuRwHBwfi4+P5v//7v2J1aKo3MDCQa9euER8fL52/MCMjI548eUJ2djY7d+6UvvjFxcUREBAghSQBxMbGMnbsWLKysujQoQMBAQGv5sKWE+97J0x4M6oZVGKn1xhmnN7NgRuX1KYA6Mi06GTRjLmO3lQzqPQGW/nqlJRsMzcpBW2TSsh0tNEyMgSlkvSLf5NvWoVjW3/G399fGpGTy+XMnDkTNze3YvVoSlipWh6xcEKnwlRz/lU0PbzV0dGhXr16LF68WPoc/uqrrwgKCsLc3JxGjRoBxVcyaN++vfQArmgnQ8Xd3Z0FCxbw6NEjfHx8pGzpH374IVFRUVIi0B9//FFKkPlvVngQhJKkZyVr3H7tTAaJ17I5s6lgv0MfE6o3qkDjj+vi7OxM3bp1mTBhAnp6eri4uKhtA+jduzcRERH89ddfTJkyhW7duhWb8//LL7+o/fuTy+Xs2LFDrfOvil7Lz89nzpw5AFSrVo3AwECio6OZOXNmQfscHHBxcVFb3WbBggV07dqVChUqMHr0aHr37s348eOlz48JEyao5dsIDAxk7NixrFy5EplMxrJly9DT02PkyJH4+Piwbt06KlSoIJUfMGAAcrmcTZs2ATB69GiuX79OVlYWP/30E4GBgZw+fZq0tDSWLFlCu3btNJ6/aD3CUxXaN4QFYcWz/Guio0UFd805jATh3xKd/3eApsQ1KvnKXNKyHrH4sA+5ua2wtbVl3bp1eHh4kJyczMKFC/Hz86N169ZMnTqVU6dOSWtPx8fHU7NmTQCuXLkiZS5XJcrq0aMHrVu3ZtKkScXq2LZtW7FtQLHzFw2Xc3NzIzQ0lLt375a41jaAv78/q1evpk6dOvTr14/bt28XG70SnnrfO2HCm1PNoBIrXPtzLyOVQzcuk5L9BGM9Azwtmr6zUSZlVVKyzZxbCTxcsQuZng4ybS0+GN4ZgKigw3Tp0kUtKWXPnj1p3ry5xtH2olq1asXKlStp3749tWrVKpb48nn4+PjQu3dv7t69CyAl97SxsZE+twsnwJw8eXKJyS6LGjBgAD/99BOff/45+vr6UmfIysqqWCJQQXjZKlYw0bi9QZuKNGhTsdj2IWN6Il86WG3b1KlTmTp1qtq2rVu3Fju2aLK/BQsWqP3s6empNkrboEEDtRFcFR0dnWIDJj/++GOxck5OTsUiNIu2obCKFStqnEpTtWpVjcv7yWQy+vTpg45OQfeg6LGq5IHPOn/ReoSntM0qoe/ekMyDV59ZVt+9Idqm4nub8HKJf5XvgM1RszR2/AtLSIsjPj6D8e0K1vysVasWKSkpxMTEMG3aNGQyGWlpaWod7po1a6qth65aF1xFtca3pjpKqleVbVR1/qKdf29vbzp37qxxOa7Crl69yqBBgwBITk7mzp07ovP/DO9zJ0x482oYGr2ziST/rZJWPDCwbYiBbfHRmnk71hW7Rk2aNCE392k0V+HwTU2rIPz444/o6OiwaNGiYitSaAr3VK0kUHR/586dSUt7mkV6+PDhxfJpODk5qXVUCkdnlXaewvXo6urSu3dvoGC5zsLlih5beAUEQfi3bMw7siVqtsbQ/6K0ZDrY1u74Glr19jt+/DhTpkx54bwbL6ue8qyyf3tyrtwvdbk/7bomVPZv//oaJbw3ROf/LVda4pqi7qbGkpGdLP2sWpd84MCBUke+8JdMCwsL7t27R0xMDI0bN5bWYldRhYtqqiMiIqLYtosXLz4zfLNGjRq4u7vTq1cvKXxVEysrK7755htq1qxJXl7ee5mZ9N96HzthgvAmdLRoyqwze8q84sHLSLY5YsQIrl+/jrGxMT///PML1/cq+fr6YmZmJkWYCcLrYGJYHds6niVm+y/Mto4nxgbFl/B73TQ9FHvd2rZtqzGPyJuqpzzTrlqRKiH9eTz/SMGyf4WnAOhooe/ekMr+7dGuWjxSRRBelOj8v+VKSlyjiVKZT2xiJNBL2jZ9+nR8fX1JSUlBS0uLoKAgLC0tpf2rV69m/PjxpKamoqWlRZ8+fYrVq6kOTdvK6quvvgIotfM/f/58hg8fTlZWFrq6umzfvp1KlUTokyAIb483kWzzXRpNW7Vq1ZtugvCe6ms3m1sPL5UaNWlWyZK+drNfX6MEoRDtqhUxWepNXkIaWUf+Ij8lEy1jfSqIUH/hFZOVllzH3t5eGRkZ+RqbIxS179K37L64pMzluzX/gs5NP3uFLRIEQRBUkp6k0X3fimcm29zpNUbk3BCE1yg1M4nNUbM4d+ug2iCKlkwH2zqe9LWbjZF+tTfYQkEQXheZTBalVCrt33Q73gZi5P8tV1LimpIY6hm/moYIgiAIxYhkm4LwdjLSr4av0/fSEskZ2SkY6hljW7vjWxHqLwiC8CaIkf+3XHLGffx2tylz4pqF3U6Km5ogCMIbIJJtCoIgCMLbR4z8PyVG/t9y72LiGkEQhPeRSLYpCIIgCMLbTOtNN0B4tr52szGrZFlqGZG4RhAEQRAEQRAEQSiJ6Py/A4z0qzG5w3bs6nZBS6YerKEl08Gubhcmd9guEtcIgiAIbxWFQkFAQMCbbsZbJzU1FS8vL+RyOY6OjkRGRpKcnMyOHTueu65Zs2axf/9+oGA1nSFDhgBw7do1fHx8Xmq7BUEQhHebCPt/R4jENYIgCMK7QHWfSs9K5q/bd8nMyXzTTXprqK7N5p9208SpBv4TvqSSXlWePHnCgwcP2LFjB5988skz68nPz0dLq2D8pmXLlpw9e5bOnTtz+fJl8vLyADh79iwODg5lrkcQBEEo/0Tn/x1jYlgdecPBb7oZgiAIgqAmNTOJzZEzOXf7kJSkNv5qJvF/ZmPvtgf9HDMMDSqxbds2jh8/zrVr1xgxYgQffPABd+/eZdeuXVSsWJHExETWr1+PgYEBS5Ys4eOPP37D7+zFFb02fzxI4/6pLBKrHaFNUy/62s1m1apVHD58GLlczs8//8y4ceO4f/8+FSpUYNu2bRgZGdGiRQusra1p1qwZ06ZNA8DBwYGVK1cCkJubi4mJCY8fP+bs2bN4eXmxYMECDh48SGZmJitXrsTW1ha5XI6DgwPx8fH4+PiwYMECKlWqxBdffEHnzp3f5KUSBEEQXiHR+RcEQRAE4YWkZiax+LAPCWlxxfYpycdmcBa1qupidrsTW7ZswcfHh40bN2JtbY1cLufUqVOcPHmSWbNmMXz4cI4ePYqBgQGlrUj0rtB0bRo5VyT9YR6758UTarSamCm/M3zI99y8eZMNGzYAEBwcjKGhIUFBQWzZsoVRo0Zx+/ZtTp48ScWKFaW6zMzMSEhIIDExETMzM5o2bUpUVBS///47s2bNolWrVvj5+REbG8usWbMICQkBoEePHrRu3ZpBgwaxdetWLC0ty8X1FgRBEEomOv+CIAiCILyQzVGzNHb8AZT5cHpjMg9vnUEn9zIjB35GlSpVePDgASdPnmTKlCmEhYVx69YtateuzezZsxkzZgx6enrMnTuX6tXf7altmq6Nlo4Mu0+MsfvEmNiT6RzddgVTw/+T9ufl5TF58mQuXrxIamoqPXr0AMDKykqt469iYWHBtm3bsLe3p2nTpvz666+kp6dTuXJlfvjhB0JCQtDS0kImk0nH2NnZAeDv78+8efPIzc3F39+fhg0bvoKrIAiCILwNxEQvQRAEQRD+teSM+5y7dbDE/Q9uZJObpcR7ZnU+lMt4kv0YgDp16hAWFoabmxsXL16katWqANjY2BAcHIxcLic4OPh1vIVXpqRr8zgxl/zcglF2AyNtlEolMQnhZGanAxAdHU16ejrh4eGMGzdOGpEvaX6+g4MDK1asoGXLljRt2pSff/6Zxo0bA7B8+XIUCgU//vij2si+qi4LCwuCgoLw9fVl6dKlL+/NC4IgCG8dMfIvCIIgCMK/Fn3n6Rz/YpRgYq5LQmw2+xcmUKmqNvf0/wagTZs2JCQkIJPJqFy5Mo6OjgCMHj2a69evk5WVxU8//fS63sYrUdK1eXAjm9BvU9HRk6GlLaPdf6qgb6zkj7ux9OzZk6VLlxIbG4unpyd16tTB3Ny81PM4ODgwf/58GjZsiEwmQyaTScn+HBwccHFxwcXFReOxgYGBnD59mrS0NJYsWfLib1oQBEF4a8lKm99lb2+vjIyMfI3NEQRBEAThXbLv0rfsvqi50/jn8XRyM/Np0qGytK1b8y/o3PSz19W8N6q0a6PJ+3RtBEEQXheZTBalVCrt33Q73gYi7F8QBEEQhH+tYgUTjdv/PpXOH2Fp1HMwVNtuqGf8Glr1dijp2pTkfbo2giAIwusnOv+CIAiCIPxrNuYd0ZIVn0VYv3VFvGdVx8BYW9qmJdPBtnbH19m8N6qka6PJ+3ZtBEEQhNdPdP4FQRAEQfjXTAyrY1vHs0xlbet4Ymzwbmfvfx7i2giCIAhvE9H5FwRBEAThhfS1m41ZJctSy5hVsqSv3ezX06C3iLg2giAIwttCdP4FQRAEQXghRvrVmNxhO3Z1uxQLc9eS6WBXtwuTO2zHSL/aG2rhmyOujVDe3ctIZW3MKRYe38VHDh/TxqUtxsbGyOVyhg0bRmho6HPVd/PmTXr06IFcLsfZ2ZmgoKBX1PKy+f3337G1tWX+/PnStiFDhnDlyhUAxo4dy8yZMwEICwtj0qRJz6wzOjqa33//HQCFQkFAQMAraLkgFCeW+hMEQRAE4YUZ6VfD1+l7kjPuE33nEBnZKRjqGWNbu+N7H84uro1QHiU9SSPg9C4O3rhMrjK/YOMYDzJlWhguSWDbgb38b9HXz13vyJEj+fbbb/noo4/Iz8/n5MmTavvz8/PR0np945cHDx5kwYIFeHo+ncLTsmVLzp49S5MmTUhNTSUjIwOAs2fPSstsltbW6OhocnNz+fjjj1/9GxCEQkTnXxAEQRCEl8bEsDryhoPfdDPeSuLaCOVF0pM0uu9bQdzjB8X25SrzeZiZTvd9K2iTk826detYuHAhlpaWBAUFkZiYyIgRI3j8+DGNGzdm+fLl0rE3b97EzMyMjz76CAAtLS2cnZ0BkMvlODg4EB8fzxdffMF///tfMjMz6datG9OnTyc4OJhffvmFzMxM9PX1+fnnn9HV1WXs2LFcvXoVAwMDNmzYQExMDBMnTsTQ0JCBAwcyYsQI6fznz59n7Nix5OXl8emnn+Lo6MiqVaswNjYmLS2Nnj17AuDg4MDatWvp168fenp65ObmAgWd/969ez+zratWreLBgwccPXqUUaNGERUVRdeuXXn48CGHDh2iUqVKr+x3J7zfRNi/IAiCIAiCIAhlNuP0bo0d/8LiHj8g7PZVbG1tCQ0N5ebNmyQnJ7Nw4UL8/Pw4evQolStX5tSpU9Ix8fHx1KxZE4ArV64gl8uRy+XS/h49erBhwwasrKxQKBScOXOGw4cP8+TJEwDMzMw4dOgQbdq0YceOHezdu5e6desSFhbGp59+ysqVKzlw4ACLFi3i6NGjDB8+XP19zZhBSEgIx48f57vvvsPCwoKhQ4eyZMkSqeMPYGNjw/nz5zl//jwtWrSgbt26xMXFERcXR7169Z7ZVl9fXyZPnkxISAgAenp67Nmzh86dO3PkyJF//4sRhGcQI/+CIAiCIAiCIJTJvYxUDty4VKaysSkJmDco6AzXqlWLlJQUYmJimDZtGjKZjLS0NLUw+Zo1axIfHw9AkyZNUCgUap1/Ozs7AK5fv86kSZPIyMjg6tWrJCQkAGBrawsUdM7Pnj2Lnp4emzdv5tChQ+Tm5tK6dWsmTpzIvHnzCAoKYvz48Wrnf/ToEZaWlgDUq1dPqrcoPT09AE6ePIm9vT2JiYns37+f6tWfTuN5VlsLa9asGQDm5uYkJyeX6doKwr8hOv+CIAiCIAiCIJTJocJz/J8hX6kkMuEmvf/5WalUYmVlxcCBA6XOsSpkHsDCwoJ79+4RExND48aNUSqV5OXlSftV8+dXrFjB1KlTpaSASqUSKAjbV/2/fv36GBoaMnjwYCkJX05ODrm5uSxfvpz4+HhGjBjBgQMHpPpNTEyIi4vD3Nyca9euYWZmVuJ7a9GiBcHBwYwaNYoHDx4wY8YMfHx8ytRWXV1dsrKypLIymUx6rXovgvAqiM6/IAiCIAiCIAhlkpyV8Vzl03Oz1H6ePn06vr6+pKSkoKWlRVBQkDTaDrB69WrGjx9PamoqWlpa9OnTp1idXl5efPrppzRp0kQahQd48OABHh4e0px/PT09xo8fj5ubGwATJkzg2rVr7Nixg7S0NKZOnapW75w5c+jfvz95eXmMGzcOXV3dEt+Xg4MDx48fx9DQEENDQxISEtSiCEprq6OjI0OHDuXSpUtqDwwE4VWTlfZ0yd7eXhkZGfkamyMIgiAIgiAIwttqbcwp/E/vKnP5+Y7dGNK49StsUYHg4GByc3MZOXLkKz+X8G6RyWRRSqXS/k23420gEv4J5VpKSiYnI+LY9cvvfGzriLOzywutPQsFS9CMHj1a+nn//v38f3v3HVjj2T5w/HsykCFREkWQqFUkxA4ZThIihJRatWpUKTVaWnvEelHj1Ve1KBoqWsQKajZOImpXYqUqSNKkRiIiImSd8/sjvzzNkSFUqbg+f8lz7nM/9/2c4VzPPa7GjRuzZs0avXL+/v6cOXPmb/ch1/79+3F1dUWtVjN27Fi9aXBCCCGEEC9Ce9sGGKmKF0IYqQzwtm3wD7dICFFcMu1flEip99PZsfUCF87fRKvNmd3SVj0VAwMVCbensHvXfhYtnvfU9WZnZ3Pjxg2ys7PR6XSoVCp27NjBDz/8oKSlgZy8rgMHDnxe3SExMZG5c+eyb98+zMzMmDdvHitXrmTEiBHP7RxCCCHEqyYlJYXevXvz4MEDHj16xFdffUVsbCzvvvvuy26aHhcXF8LCwl52M56LSqYWdLC1Z1f0uSeW7WBrz5umFi+gVTzX311ClFQy8i9KnNT76Sz/31HORdxQAv9cWq2OBw8yWP6/o2SkZ7F+/Xratm2rTBFLSEjA19cXd3f3AgPr0NBQZcOWY8eOERYWRlBQEAMGDCAsLAwnJyeGDx/OZ599hp+fH4cOHUKr1TJkyBDatGlDhw4dAJg3bx5t2rShZcuWnD17FsjJXztu3DiaN2+ebxbBnj176N+/P2ZmZgB8+umn7NixA0DJfxsdHa38x7d7927c3Nxo3bo1+/btK/SYk5MTH374IY6Ojsoxkd/NtBTWRR5j5OrFWFWphHMbN9q1a8edO4WnORo1alS+Y3l3LH6e4uLiaNasmd45k5OT2bZtm/J37vukuHLfv0/j8XPm9ccff2BkZKTs4vy4gQMHEhUV9VTnE0K8vnK/lwf/ZwpvtmrEjz8FERYWRunSpQv9HhLPz2wnX+zKViiyjF3ZCsx28n1BLRJCFIcE/6LE2bHtAncSi96M5k5iGr9F3n6q3LMA27dvp3v37vTo0YPt27fj4uKCt7c3AQEBuLi4kJiYyJQpU1iyZInynJ07d1KxYkVCQkLYs2cPAGPGjCEkJISAgAAWLVqklO3Xrx9hYWGsW7dO77w3btygSpUqyt9lypQhIyOjwL5ptVoWLVpEcHAwGo2GhQsXFngMICkpiblz57Jnzx5WrlxZjKv7ekl8mMpHhwNw2jyfKcd3sunKGTKavkX8IBe0zWvz7Tr/Qp+7bNmyZzqnTqd76p1+Q0NDGTJkiN45iwrE/ylFnXPbtm0MGjSInTvzrxPVaou3a3RJkxu8fBn+M+sij7Ft/094eHigVqvx9PTk6NGjxb5pk3tjaf78+cTHxxdYJjw8nF9//fV5NV+Il+Lx7+Wfb19l04E9NF8znVFHNrNm/ToOHjyIWq0mISGBnj170qZNG7y8vEhJSQFg6tSpuLm5MWrUqCJvmu/YsQMnJyfc3d0JCQnh3r17dOrUCTc3N0aPHg3kfK5yb+b/5z//AeDBgwd0796dNm3aMGjQICBnR/uSdLPdysScHT7D6WzXMN8SACOVAZ3tGrLDZzhWJuYvqYVCiILItH9Roty794gL524Wq+ztW6nUqFEHKF7uWZ1OR3BwML///jsAt27dyldnxYoVqVq1qt6x33//ndatWwN/pX35/vvvCQgIwMDAQC+9i729PcbGxkq5XHnz3gI8evQIQ0NDvTK5AWNiYiKRkZG0bds2p5+3b5OQkJDvmE6nw9raWkljI3ll9SU+TKXLnm+Ivp9/dD9Lp+VM7BWib/3JkIfDmD1xCuHh4Wi1WgICAqhevboyxfPUqVMMGzaMWrVqcffuXSBnhskHH3zA/fv3qVevHl9//TV+fn7ExMQQHx/P3LlzGT16NGXKlKFdu3ZMnjxZOXdsbCwDBw4kPT0dX19fPv74Y2bNmoVOpyMrK4uRI0cCsGrVKuUH8JYtW7h37x59+vTh4sWLrFu3DkdHR1avXq3caPryyy9p0qRJodcjLiWJ7r17cSv+T+7+eYt6dd8mJekuKpUKa2tr+vXrx9WrV5VzVqpUiTlz5lCrVi0ADh8+jKOjI0FBQQwfPhzISZPk4OCg5DfOLTdmzBiWLFmCkZERhw4dYs6cOU98vQYOHIi/v3+RZR6fduvv74+Dg4OSbipXcnIywcHB/9i04cSHqUw9vpN9edJlZd9P4+7XO+m7aBoLPN+jdJbumWZCTJw4sdDHwsPDycrKKvJ1zqXVavN9DwnxshX0vWzaqgHZd+9zc0EA31nsoG6/Trh5uLPlhx+BnM+5qakpq1evZtOmTXTq1Ilff/2V0NBQNm3axN69e/VukGu1Wjp06ICXlxdz584lNDQUExMTtFotixcvplevXvTv358hQ4Zw4sQJGjZsiEajQaVS4e7uzqeffsqqVavw8vJi6NChys3N3JvtmZmZjBw5Em9v75dyDZ8nKxNzvnHvw820FPbHXORexkMsS5ngbdvghU31F0I8HQn+RYlyMc8a/yfR6SAm+m6ev4vOPXvq1Cm6du3K7NmzgZwf2efPn9ers6Afy3Xr1uX48eN06tRJ+UH99ddfc/bsWa5evcqHH36olM17IyCvDh060K1bN/r06YOZmRn//e9/6dq1K5BzIwBQ2mJlZYWDgwP79+/H0NCQzMxMDA0N8x1TqVSSV7YI044HFRj4px27yKPz19FlZGI6pT/TjgexeN48TE1NOXToECtXrmTu3LlK+VmzZrFjxw7Kly+Pra0tgDLDpFWrVkyYMEGZYVKnTh2+++471qxZw7Bhwxg4cGC+12XBggXMnDkTV1dXvL296d+/PxMnTsy3w/HQoUOJjY1lw4YNQM4Nn7Vr13LmzBnWrVtH1apVCQoKIjQ0lLt37zJ48GBlKUleuYHq1sBA0ks9wsC+CukGmUTZW1HtfgXGd3uf97p2Q6fTERMTo5wz79rLhIQEKlSogJ+fHwMGDCA5OZly5coRFxfHL7/8gpmZGQMHDkSj0RAcHMw777yDkVHx/3t61pkDha0PzZ3B8E8E/4XdVEo/dw2TVvXZnxDF5T3fsMNnOI0bN1YenzdvHvv27ePRo0esWLGCxo0bs3v3bmbMmEHDhg3JzMxU+jR16lRSU1MZM2YMjx494p133mHy5MmsWrWKO3fucPjwYdatW0f//v2Jj4/HxsaG77//nrCwMGXW0vDhw5VlSkL8WxT0vawyMsTC1xkLX2fSjl/iys6feWiRMx09Ozubzz//nPPnz5OSkkLXrl2JiYlRbjg6Ojqyd+/eQm+a29raYmJiAuT8/3716lU6duwIQLNmzYiKiqJs2bKMGzeOtLQ0Ll++zO3bt/n999/5+OOPlecBJfpmeyVTixeym78Q4u+T4F+UKA/TMp+qfHp6lt7fReWe3b59u/LDAHKm2RZnWrWvry+7du3Czc0Nc3NzfvrpJ1q0aIGbmxtubm7FamfFihWZNGkS3t7eJCcnU79+fTZu3Ajk5I91cXGhZcuWQM4PjbFjx+Lp6YlKpaJ+/fosX768wGOiYDfTUtgbc6HAx0xbNcDiXTfurtlDdlJOObND5zkRGkZmZib16tXTK5+cnEz16tWBnOAeKHSGSe5Npx49euDn50ffvn3p16+fXhB29epVZeTW0dGR69evF6tPtWrVokyZMtjY2JCcnMy1a9eIiIjA3d290OfcS3+oBKoZt+5SyrYS2SkP0GVk8eiPW9zwaMKnKxazdfMWzMuYcP78eeLi4pQftosWLeLChQuUL19eCTLLlSvH+PHjuXPnDlqtlh49erBnzx7S09MZOXIkLVq0IDk5GU9PT722FDRLIe/MgXnzcjbwXL58Od9//z0mJiYsXry4yFFuPz8/XFxcMDU15dNPP8XU1DTfDIYtW7ZgbW1drGtcHIXdVMpOTsW4as55Lh08gsPM1fRt31l5fMyYMUyaNImoqChmzJhBQEAA8+bNIyQkhLt37+Z7HevWrZtvNHLo0KHKTaItW7ZQv359fvjhB+bMmcPWrVt58803ycjIKBFTkkXJU9j3clbiPQzLmaMyMsTAwhRVmVLE30/iVloKcZFXePDgAaGhoXz77bfEx8dja2vLpUuXADh3LmfDusJumsfGxvLo0SPKlCmDVqulZs2anDlzhgYNGnD69GmGDBnCN998w4QJE5T9gHIHEo4fP469vb1y019utgsh/g0k+Bcliomp8RPL9On5BQDOrfrg4pxz9z/vdOHt27cX+Lzc4CKXt7d3vml7eacU+/n5Kf9+fAO/b7/9Nl/9Go2mwH/n6tixIx07diQmJoaePXty584dKlasyMyZM5k5c2aBZZ90LG97Czrn62p/nunYhTHv6MT9nUcx6NeO3QeP8NvJXzl48CABAQF65SwtLYmLi+ONN97gypUrAAXOMDl//rwyQmRsbMySJUvIyMjA2dlZL/jP/fHp5ubG2bNnGTVqlFJvXsbGxnrpIB//4VmjRg2aN29OYGAggDJynJd/5DGiK+RcB6OK5ciIuUnZjk6kHb9I2i8XyYiKp/xHvjzMMOfo0vWcOnWKzz77DEtLSwDat2/PihUrlOB/+PDhfPrpp3z44Ye0atWKBg0aYGNjw7lz57hy5Qpjx47l5MmTPHz4UK8diYmJBc5SyDtzINfOnTs5fPgwJiYmxf6BvXfvXhYsWIBarc43g+F5KuqmkkE5M7KT7wNg6lQfalflj/AbyuMFLRUyMDDA3Nwcc3PzfDcorl+/nm80Mq+8N5GaNWvGmTNnePPNN4u1JECIl6Gw7+XMP26T9M1OVKWMUBkaUG5gB5LX7aNT13fY+u06oqKi8Pb2plq1atjY2FC5cmUcHR1xdXWlfv36ylK7gm6QT5o0iTZt2mBmZsaMGTP48MMP6dOnD99++y0NGzbEycmJ5ORkRo4cSf369SlVqhQAH374Ie+//z7ff/89NWvWZO3atS/6cgkhRIEk+BclSgOHSgRtv1isqf8GBirsHSq9gFY9X7a2tpw4ceJlN6NES04vesNIAOPKFdDeT4PMLAzLlMLDw4OGDRvmKzdt2jR8fX2pU6eOMgOgoBkmeQUFBfHVV1+RlpZGv3799B4bP348AwYMICMjg86dO2NjY1Ng+ypVqkRSUhLdu3dn1apV+R63trbGx8cHNzc3DA0N8fDwYNq0acrjqZnpHFj2HSqzMgC8McyXh6cvk7joRwwtzbFa0JO73+3l9qz17DM0oLt3J+WcPXr0wNjYGHt7e1JSUtDpdMrUWTMzM1JTU6lTpw5nzpxRZiLcv38fV1dX+vbtS+fOnXn48KHynMJmKdStW1cv8AeYOXMmw4cPp1SpUsyePZs333yzwOuT1/Dhw5kzZw6rV69m9OjRytTc562om0plHGqS9PV2TJrXw8C0NFlZ2cQ/SFYeL2ipkFar5cGDB9y9e5eEhAS9+goajTQ2NiY9PR346yaSj48Pp0+fVvZmkHX+4t+qsO9lk8a1MWlcW++Y1bhe9G/iRfXq1QtMr+fn54eRkRGbNm3i2rVrQME3yLt27aosscv1008/6f1d0EAAwNatW/X+lpvtQoh/Awn+RYliaVkG+4aVOJdnxKww9g0rYWFZ5gW0SrxqypU2LfB46berU/rt6srfVp+/B8D4lf/Nt94x94dey5Yt8+2wbm1tnW+GSd6ZIr169aJXr14FtsHW1jbfD8eC1q4bGhqyf//+fO2xs7NTZroMGjRI2Yn6cQ79fLGupX8Trfzwd/Sm2Jo622NoaYaJU31iT/+pnFOn0zFo0CBUKhUWFhY0atRIr57x48eTlZVFWFgYfn5+6HQ6Bg8eTHJyMh06dKBOnTpK4A8UOkuhoEDV0dERf39/Nm7ciL+/PxMmTCiwf3m98cYbfP311/z555988MEHrF69Wm/WxPNS1E0lQwtTLN5x4c6yraBSoTI0wHHMSMLX5bxPCloqNGHCBNzc3GjSpAmVKunfyPTx8ck3Gunk5MTAgQO5cOECS5YsITAwEDc3NypXrsyECRM4evToc++zEM9LYd/LhbEsZVLoY1OmTOHYsWMYGhqyefPmv9s0IYR4ZUjwL0qcLu/aEx93r8h0fxWsTOnyrn2hj4vXW3vbBsw4seuJU/8hJ6WRt22DF9CqF6uwQPXxKbZvDO6IYXkLzP/IWaLwrDm2hwwZQrdu3Vi/fj2lS5fWe+xJsxTy+uijj7h+/Trp6el89913eo8lJiYq+3Y0b95cOc/KlSvZtm0bqampTJgwId+sifLlyz91fwrypOCldD1brOvZKn83d2rNVx+OAwpeKuTr64uvr34O7dwbO7Vq1SpwNPLIkSPKvzdt2qT3mFqtVlIGCvFv8zy/lxcsWPA8myaEEK8MVVFrIps1a6Y7ffr0C2yOEM9H6v10dmy7wIVz+rv/GxiosG9YiS7v2mNetnQRNYjX3fDDG9kVfe6J5TrbNeQb9z4voEUv1rrIY0w5vrPY5ec6vSO7PT/BzbQUnDbPL3bwcqLnREmXJUQer/v3shDi2ahUqjM6na7Zy27Hv4Es7hMlknnZ0vQb0JRJ0z3p0s2e9h3r0qWbPZOne9JvQNMSGfjfTEthXeQx5h/ZydstmtDazRVLS0vUajWDBg3i0KFDxa5r0KBBqNVqypUrR5s2bVCr1dy7d+8fbP2z0el0yohw3mnaeTdXUqvVeikbnyQlJQUfHx/OzfiGe/N+IOP6DbLvpXJ/1y/5ytqVrcBsJ98CaiketVrNqFGjlL87depUaPq5gp77T2pv2wAjVfH+iyipsx+et0qmFnSwLd6Mow629hL4C/GY2U6+2JWtUGSZv/u9LIQQJZlM+xclmqVlGVq72L3sZvyjcvOw78u7mdhwLx6pDDBdfJvAvbv5asGip6ozd7q0Wq3m0KFDejnXc9MWPW/PUu+NGzcoW7Yse/bs0Tu+du1aBg8e/EztWL9+Pe+++y4ffPABN+8nMyV0Kz8nXMWwc2uljJHKgA629sx28sXKxLzAPhS3P3/88Qc6nY7U1FTu3buHlZXVM7X7eb8uuYFqcUbZJFAtvtlOvpy/E19gur9cErwIUTArE3N2+Axn2vEg9sZc0JtFU9j3shBCiL/IyL8Qr7DEh6l02fMNu6PP55tKnKXTkvToAV32fENaZgbr16+nbdu2DBkyBICEhAR8fX1xd3dnxIgRRZ7H39+fXr165YyInzvHrFmzUKvVeHh4EB0dTWZmJp6enri5udGtWzeys7OJioqiVatWuLu785///Ifo6Ghl53qNRqNscNeoUSP69evHF198wYkTJ1Cr1Tg7O+dbr52VlUXv3r1xc3Ojd+/eZGVlMX78eA4fPqz0CXJ2yj9//jxqtZqDBw8CORujNW/eXEm5WNR5TE1NOXbsGImJiVQqW441Ph+wpWVvqm87y/gmXvR4ZEXl1SFETPmK0yE5m+g5OTkxfPhwPvvsMwYOHMjIkSPx9vbmzz//xN3dHRcXl0KvcfPmzTl58iR79uzR22l6zJgxtGnTBldXV2JjYwFYtWoVTk5OfPrpp0o5tVrN+PHjef/995/qNS0OGWV7/nKDl852DfPNrDBSGdDZriE7fIZL8CJEIaxMzPnGvQ/He05krtM7jG/ixVyndzjRcyLfuPeRz44QQhRBgn8hXmHTjgcVOYIIEH3/DsFxl2ncuDGHDh0iNjaW5ORk5s+fz6RJkzh8+DBly5bl2LFjRdZTrlw59uzZg4GBAfHx8Wg0GpYvX868efMwMjJi9+7dhIaGUq9ePYKDgwkJCWHYsGEcPnyYSZMmFVpvXFwcK1euZOLEiUyfPp2goCDCwsIICAggIyNDKbd9+3bq169PaGgoDRo0YOvWrcyZM4d27drppcrz9fXFwcEBjUZDu3btAOjXrx9hYWGsW7cOoMjz9O/fn+rVq+Pu7k7btm25efMm1iZlecvSmpEOas5u2s0RTQgajYaFCxcCORvJTZkyhSVLlgDg7OzMgQMHsLKy4uDBg4SFhZGSksKVK1fy9b9r165s376dn376CR8fH+X4vHnzCAkJYcaMGaxcuZKsrCzWrFlDWFgYPXr0yFfHhg0bnvo1fRIJVP8ZErwI8fdVMrVgQL1WjG7kwYB6rWT2kRBCFINM+xfiFXUzLYW9MReKVTbq3m1satUAoEqVKty7d4/IyEgmTpyISqUiNTWVFi1aFFlH06ZNAfjtt9/QaDTKmvPKlSvz4MEDhg4dSnx8PLdu3aJ27dr06NEDPz8/+vbtS79+/ahfv75SV96NRvPmao+IiFB2L09MTCQhIUHJY3/16lWaNGkCQLNmzThz5gwtW7YsVv/t7e0xNjZWpsUXdR5jY2OmT5/O9OnT+eGHH1i6dCkfffSRUjYyMlLZMf727dvodDoqVqxI1apV812rO3fuMHz4cJKTk4mOjubPP/+kdm39fNR16tTh4sWLmJmZYWHx14/XL774gp9//pnMzEzq1atHYmIitra2GBkZKfU/fr6nfU2LIzdQvZmWwv6Yi9zLeIhlKRO8bRvIj+2/KTd4EUIIIYR4EST4F+IVtT/vGv8n0Op0nL4dS8///1un01G3bl369eunBI5P2hQvN3CuU6cOXl5eLFu2DMjJuR4UFESdOnXYuHEjU6ZMQafTYWxszJIlS8jIyEkBd/DgQW7evAnA+fPn89UL0LhxYwIDAzEzMyMzMxNjY2PlsZo1a3LmzBl8fHw4ffo0tWrVKrStKpWqyL+LOk9MTAxVqlTB2NiYihUrotX+dY2trKxwcHBg//79GBoakpmZiUqlyrfWPvfvjRs30qVLFwYOHEjfvn0pLLtKt27dsLa2Vv6+c+cOGo2GI0eOcPDgQQICArCysiImJobs7GzOnj1b4Pme9jV9GhKoCiGEEEK82iT4F+IVVVge9sI8yErX+3vy5MkMHTqUe/fuYWBgwOrVq7Gzs3tiPY6OjlSqVAm1Wo1KpaJ379507NiRuXPncvr0aSwtLalduzZBQUF89dVXpKWl0a9fP8qVK0f16tVp27YttWrVolKlSvnqnjlzJp07d0an01G+fHm2bt2qPNalSxcCAwNxc3OjcuXKTJgwgfj4+ALb2KJFC7p06cK4ceMKfLyo84SHh9OzZ09MTEwwNjbmu+++U4JoAwMDxo4di6enJyqVivr167N8+fJCr5WHhwfvv/8+O3bsKPKa5u7wHx0dDcAbb7yBubk5Hh4eNGzYEAAjIyMGDRpE69atadOmTYH1POtrKoQQQgghSj5VYSNRAM2aNdOdPn36BTZHCFFckoddlGS5ywyS09O4EfEbG6YtoNZbb2FpaUm9evVo27atsvwir+joaK5du4aHh8dLaLUQ/w65n58bibfxHzeT8mXMuBhxjsaNG1OjRg369u1b4OenMC4uLoSFhT1TWwYOHIifn98Tb0SuXr2aFStWMHPmTGX/E41GQ/Xq1Xnrrbfw9/cnKytLb4PXJ6lbty5VqlTh4cOHTJ48GV9fX/bt20d2drbeHit/15dffsn27duJjo7G0tKSN954g5kzZzJlypSnum5+fn5s376dcuXKUbt2bb39bPIKDw9Hq9UqS+Get7i4OLp06UKrVq2UWX7JyckEBwfz7rvvAk//nvDz88PFxYW2bdsyZswYbG1tqV+//nN/LYQoiEqlOqPT6Zq97Hb8G8jIvxCvqPa2DZhxYlexpv5LHnbxqigodWX6b7FkNKpGvbFDsT4axUb/9YUGLtHR0QQHB0vwL15L/0Tq16f1rGlHN2/eTFhYGGXKlFGOaTQaXFxceOutt56pLdbW1hw+fJiHDx/i4+ODr68v3t7ez1RXUcaMGcOYMWP0AtxntXjxYiUzz4ULF7C3t89XJjw8nKysrGIF/8/yeoSGhjJkyBBlvxvICf63bdumBP/PatGiRcosOiHEiye7/QvxisrNw14ckoddvAqKSl2pQ8eu6HNsfRRLXFwcQIGpFFetWsX333+Pp6cnAKNHj8bNzY1OnTpx7969Ao9FR0fj6upKt27daNq0qVK/EK+SF5X6FeDQoUM4OTnh5OTEoUOHAP20o9evX6dly5b4+vpy7dq1fM/fsGEDTk5OODs7ExERQWBgICdPnqR9+/ZERUUBkJGRgb+/P+PGjVOWcO3du5eOHTvSsWNHdDodDx8+pHfv3nh4eNCrVy8yMzMLbG9qaqrymL+/P6tXry70cz9o0CDatm3L4MGD8fPzIykpCbVajbu7O6NHjwbgk08+ITs7+4nX6d69e/Tp04dGjRoRHh4O5MxwcHV1xdXVlV9//bXI5wIF9nHVqlUsXLiQvn37Kv2BnNF1jUaDRqPB19cXX19f9u/fj4ODQ7525IqNjcXDwwNnZ2cWLFhAamoqs2bN4r///S9fffWVUm7VqlUcPHgQtVpNQkLCM/Vt06ZNnDp1SsmM87SvhRDi75PgX4hXmORhFyVJcVJXxoZfwsymIkCBqRSHDh1K//79+fnnnzl16hQPHjwgNDSU9957jxUrVhR4DHKCgy1btjB27Fi9PSCEeFW8yNSvfn5+HDhwgAMHDjB9+nTleG7a0YULF7JkyRK2bdvGnTv6bcrOzuZ///sfR44cISAggClTptC9e3ccHR35+eeflc1cS5UqxcCBA1m8eDGLFy8GoGrVqvz000/Y2Nhw7tw5Vq9eja+vL8HBwajVagIDA/XOlZCQQJs2bahTp06BKWcf/9yfOHGC0qVLc+jQIerWrQvA2bNnUavVHD58mC+//BKApUuXYmhoWOQ1gpyMMGvXruXrr79m3bp1JCYmEhQURGhoKDt37mTWrFn5njNu3Dhq166NkZER9vb2BfZx6NChfP755wQEBBR67oyMDIKCgujQoUO+duS1YMECZs6cydGjRzl8+DApKSlMnDiRzz//nJEjRyrlhg4dSrt27dBoNFhbWz9T3zZv3szw4cPzbcJb3NdCCPH3SfAvxCtM8rCLkuJJqSvTjl0k4Ysf0KU9IrtBNe4+esCdO3fo3r07arWasLAw/vzzT73nPJ4eMioqqsBjAPXr18fAwAAbGxuSk5P/mU4K8Q95Xqlf1Wo1P//8c77P0uNUKhUWFhZYWFjoBcG5mUauXbtG48aNMTIyUjYtzZWQkICtrS3GxsbY2dkpI9zFkTsFPvdzGhkZydKlS1Gr1axbt47bt2/rlbe2tiYkJIQtW7ag0Wjy1ff45/769etKex0dHQFwc3NDq9XSt29fNmzYUOy2AtSqVYsyZcoo9V+7do2IiAjc3d159913C/yuWbx4MeHh4SQmJvLo0aMn9jFvIJ13H6+8SwIeb0deeb8THR0duX79+j/Wty+//JKxY8fyxx9/5HusOK+FEOLvkzX/QrziJA+7KAmelLrStFUDLN51AyBlRxinbsUQuzEqXypFY2NjZTpuzZo1OXDgAACnT5+mZs2aBR6Dwn9AC/EqeNGpX7VaLSkpKQB6099z15bXqFGDiIgIWrRooZfaFXIC8piYGDIzM4mPj8fS0rLQ8+T9PEP+z2ndunXx9PSkW7duAIVO+2/bti3z5s3j7t27escfr69GjRqEhIQAcO7cOaV/uaPYjo6O9O/fv9D2Pq6g+ps3b67MUCisvWZmZnTv3h1/f/8C+7h582bS03My+FhaWirX+Pz587i7uwP6aXSL+n7LTaPr5ubG2bNnGTVqFFeuXMnXpie9FsXpW9WqVfnmm2947733OHjw4BOv1eOvhRDi75ORfyFKiNw87KMbeTCgXisJ/MUr5VlSV3p4eLB48WK6dOnCgwcPgJyRwaNHj9KrVy+aN2+OiYkJrq6ubNy4kY8++qjAY0K86p5H6tc5c+bg4eFB27Zt8+17ceHCBSXDxpdffsmMGTNo164d7dq1Y8aMGfnq/+yzz/jkk0/o2rUrb775pt5jhoaGfPzxx7i6utKnTx9mz55daDvVajVz584tcAo55ExF3759O56ennh4eBS5hr5v37589913hT4O0LJlSx49eoSnpyfnzp3D2NiYkydP4uLiQsuWLZWN/Iq75v9x1tbW+Pj44Obmhru7O/Pnzy+0bJ8+fVi/fn2BfXRycmLDhg2MGjUKT09P9u3bh6/vsy3vGz9+PNOnT6d169ao1WpsbGwKLFepUiWSkpLo3r07SUlJz9y3li1bMmLECAYMGFDkjdaCXgshxN8nqf6EEEK8dJK6UohnJ5+f5ycrKwsjIyMWLFhA9erV6d2798tu0mtLXgvxvEiqv7/IyL8QQoiXrr1tg3z7VhRGUlcKoU8+P8/PBx98gJubG2FhYXTt2vVlN+e1Jq+FEM+frPkXQgjx0uWmrtwV/eS1nZK6Ugh98vl5fh7fDV+8PPJaCPH8yci/EEKIfwVJXSnEs5PPjxBCiCeR4F8IIcS/gqSuFOLZyedHCCHEk8iGf0IIIf51JHWlEM9OPj9CCPEX2fDvLxL8CyGEEEIIIYQokST4/4tM+xdCCCFKqJtpKayLPMb8Izt5u0UTWru5YmlpiVqtZtCgQRw6dOip6st9bosWLQgMDMx/vps3mTt3br7jarX6Wbvw1Hbs2KHkIffz83umPrq7u9O6dWtOnDgBgL+/P2fOnPnbbRs4cCBRUVFotVq6d+9e4DV8klGjRv3tdgghhHg9yW7/QgghRAmT+DCVqcd3si/mIlk6bc7B4V48Uhlguvg2gXt389WCRU9dr4ODAxqNhkePHuHl5UX37t2Vx3Q6HW+++SZTpkx5Xt14Jjt27MDe3p7y5cs/0/MdHBw4fPgwcXFxfPLJJwQGBjJw4MBnqit3dqVKpdI7PmbMGFxdXfWuX3EtW7bsmdoihBBCyMi/EEIIUYIkPkyly55v2B19/q/A//9l6bQkPXpAlz3fkJaZwfr162nbti1DhgwBICEhAV9fX9zd3RkxYkSh50hLS8PExATIGV0fNGgQ7du35/Tp0/Tr1w+AVatW4eTkxKeffqo8LygoiKZNmzJ06FBcXFwAiIqKwsvLizZt2jBnzpx85xo9ejRubm506tSJe/fuER0djaurK926daNp06bExcUpZWNjY9m3bx99+/Zl4cKFAM/cx+TkZOXfuTMIhg4dSmRkJJAThG/evLnA+vJek8TERL16Fy5cSKlSpRgzZgwAHTp0AGDKlCl88sknAHh7ewPQs2dP2rRpg5eXFykpKQDKdRNCCCGelgT/QgghRAky7XgQ0ffvFFkm+v4dguMu07hxYw4dOkRsbCzJycnMnz+fSZMmcfjwYcqWLcuxY8f0nnf+/HnUajUNGzbkvffeU47XqVOHAwcOYG1tDUBWVhZr1qwhLCyMHj16KOUWLFhAaGgoM2bM4NatW0BO0LtmzRpCQkK4ePGiXjB/6tQpHjx4QGhoKO+99x4rVqwAIDU1lS1btjB27Fi2bt2qlK9evTre3t4EBATw+eefAzxTH11dXXFzc8s3iyHvVP2ffvoJHx+fQut7/Jrk2rp1Kx999JHetbt8+TIxMTEkJycTHx9PtWrVgJzlBiEhIfTs2ZNNmzYV+ZoKIYQQTyLT/oUQQogS4mZaCntjLhSrbNS929jUqgFAlSpVuHfvHpGRkUycOBGVSkVqaiotWrTQe07utP+srCy8vb2VGwBNmzbVK5eYmIitrS1GRkZ6jxkaGmJmZoaZmRlWVlYAXL58mf79+wMowW/VqlUBuHr1Kk2aNAGgWbNmhISEAFC/fn0MDAywsbEhKiqqyH7a29s/dR+PHDnC6tWrOXbsGI0bN1Ye8/DwYP78+QwbNgxzc3PMzMwKre/xa5Jr5cqV9O3bl0OHDmFhYYGzszMhISGUKlWKMmXKcPDgQVq3bk12djaff/4558+fJyUlha5duxbZTyGEEOJJJPgXQgghSoj9edf4P4FWp+P07Vh6/v/fOp2OunXr0q9fPyVwzcrKKvC5RkY5Px/S09MBMDDQn0hoZWVFTEwM2dnZnD179q9zarWkpaWRnJysTIevW7cuS5cupXLlymRnZ+utj69ZsyYHDhwA4PTp09SsWRPQX0P/eNYiY2NjsrOzlb8fL1vcPg4aNAhnZ2eGDRum1+8aNWqwcOFCJRgvqL7z58/nuya5GjVqxOTJk+nTpw9BQUG0bt2aTp068cEHH1C2bFmWLl3Kpk2bCA8PV2Y9fPvtt8THxxdYnxBCCFFcEvwLIYQQJURyetpTlX+Qla739+TJkxk6dCj37t3DwMCA1atXY2dnpzyeO+0/MzMTLy8vypUrV2C9RkZGDBo0iNatW9OmTRvl+Pjx43Fzc8PR0ZE333wTgLlz5zJ48GDS09MxNjZm69atmJubA9C8eXP8/f1xdXWlbNmybNy4UW8tfkHat2/PiBEj9JYbPE0fcxkaGuLl5cXOnTv1jnfr1o2ePXty48aNQut7ki5dunDp0iU+//xzFi9ezN27d3FxccHc3JzPPvuMunXrkpqaSlRUFN7e3lSrVg0bG5sn1iuEEEIURfX4HfO8mjVrpjt9+vQLbI4QQohndTMthf0xF0lOT6NcaVNmdu7Hf+bMVaZmq9VqDh06xJw5c3BxcaFt27bKc9PS0vj444+5fv06KpWKRYsWFTpted++fWRnZ+Pj45PvseTkZIKDg3n33Xf/dn/Wrl3L4MGDgZxNzsLCwor9XHd3d3bt2oW5uTkdO3ake/fuDB48mLVr1/LgwYNipUvLe/6/y9LSUpk+PnPmTL2AuLiK0551kceYcnxnkWXymuv0DgPqtXrqtjyrrKwsjIyMiI+PZ+jQoezZs+eFnVsIIcTrSaVSndHpdM1edjv+DWTDPyGEeMUlPkzlo8MBOG2ez5TjO1l49iDjN3/L7cpmTFu9jMSHqU+sY+bMmXh5eaHRaNi0aROjRo0iIyOjwLLe3t4FBv6QE/xv27btb/Un19q1a5/5uU2bNlXyspcqVYpff/0VyNlA7vE13n/3/Frtk6fZ566V12g0eoF/cZ77NO1pb9sAI1Xx/ms3Uhngbdug2Od/HgIDA2nTpg3vvPPOS08JKIQQQrxuJPgXQrw2bqalsC7yGCNXL8b3o4HcTEt52U16akOGDMHFxUVZ/5ub1m3zxh+In7xSKffw18uYujcmLikB3x0F3wDImzLs6NGj9O7dm08++YQKFSrg6enJsWPHlBRnAAMHDiQ6Ohp/f39lavOsWbNQq9V4eHgQHR3NqlWrOHjwIGq1moSEBKX+2NhYPDw8cHZ2ZsGCBUBOOrT3339fLw1brlWrVilTzM+fP09WVhYffvghjo6O7Nu3D4Ddu3fj5uZG69atlWO5WrRowcmTJ4mOjsbe3p579+4BEBERgaOjI6tXr8bV1RVXV1d+/fVXkpKSUKvVuLu7M3r0aIKCgpTzHzx4kBMnTqBWq3F2dua7774DcmZSjB8/nvfff7/IvjwuOjoad3d3unfvjr+/Pxs2bMDJyQlnZ2ciIiIAcHJy0uvv4+0pTCVTCzrY2hd5/lwdbO1509SiWGWfl/fee4+QkBBOnz5N69atX+i5hRBCiNedrPkXQpR4iQ9TmXp8J/v+fzO09CuxpMdH47R5Ph1s7Znt5IuVifkz1a3Vagvd2OufcPnyZb3p77lp3R6du4pxjSpk/pmIcRUrMmNvY9HFlTIOb3H5+BmmVQoqst7cTdGWLl0KQNWqVZU1zYU5d+4c8fHxaDQaIiMjmTdvHpMmTSI2NpYNGzbolV2wYAEzZ87E1dUVb29vZXf3xo0bs379ery8vEhOTlbWkA8dOpT169ej0WgASEpKYu7cuWRmZjJy5Ei8vLxYtGgRwcHBaLVaOnTooORGh5zgPzAwEDs7O5o3b86NGze4f/8+KpWK+/fvExQURGhoKHfv3mXw4MGMGjUKtVqNn58fOp0OlUqljNZDzjryoKAgypYtS7t27ejbty8AXbt2pVWrVvj5+RXaF/hrrTzAl19+ye3bt5WbKq1ateLo0aPEx8czcuRIdu/ena+/27dv12tPUWY7+XL+TnyR6f7sylZgtpPvE+sSQgghRMkhwb8QokTLHRkvKBDK0mnZFX2O83fiqX/4OpcvXESr1RIQEMD9+/dZu3YtixcvpmrVquzatYvk5GROnTpFxYoV2bt3L6mpqcydO5egoCCCg4MxMDBQpmb379+fihUrEh0dzc6dO6latSpTp05Fo9FQqlQptm3bRmJiIiNGjCA9PZ127doxdepUvfaNHj2a8PBwLCwsCAgIYOHChZw7d45OnTqxe/duJa2bNj0TnVaHuVsjHv16BZWhIRnXb3BjzDLQ6TC2q0TApRg4+ztqtZrmzZvn9D8ri4EDB3LhwgXS0nI2isvdFyA353pCQgIPHz6kbdu2+XZV/+2339BoNEpQW7ly5UJfh7wp2xwdHbl+/TqQPw1bYRvIWVtbU7FiRQBlp/jIyEhl34Lbt28rQTuAnZ0d0dHRnD59mtGjR3P9+nW+//57GjVqxLVr14iIiMDd3V2p383NjZCQEPr27at3cyJXREQEvr45wXJiYqIyqyHvvghF9SVv4B4dHU2jRo0wNDTk5s2b2NraYmxsjJ2dnTJD4fH+Pg0rE3N2+Axn2vEg9sZc0Nv930hl8LdveAkhhBDi1STBvxCiRMsdGS9K9P07vN2pOSHLv+bQoUOsXLmSOXPmEBkZSXR0NA0aNOCXX37h7t27qNVqoqKiKFeuHJs2bSp09Ds1NZWQkBB++OEHtm7dipubG9euXSMsLEwJoocNG8aaNWuoVq0avXv3Ji4uTslvfurUKSXN14YNG1ixYgVz5sxBo9Gwe/du4K+0bukXrlHG4S1K1bYhdf9JMDTA3LsF6RejsRrbkzv/24pZV1cs72Uzbdo05syZg4+PDwkJCWzevJkKFSpQp04dfvjhByAnkI6KiuLkyZN89dVXrFixgvnz53Px4kW961anTh28vLxYtmwZAJmZmdy+fVsvzVqumjVrcubMGdzc3Dh79qyy4V5RKdvyPvZ4OSsrKxwcHNi/fz+GhoZkZmbqlQGoWLEi4eHh2NjY0LRpU0aMGMG4ceOoUaMGzZs3JzAwUGl3dnY2s2bNAnJuTvTv31+vvsaNGxMYGIiZmRmZmZkYGxsD+inuiurL43KfZ21tTUxMDJmZmcTHx2NpaVloXY/3ryhWJuZ8495H2QTyXsZDLEuZ4G3b4IVP9RdCCCHEv4ME/0KIEit3ZLw4Aleu5cKcbyFbS7169VCpVJQuXZrg4GBGjhxJUFAQiYmJfPbZZ0RFRSkjvoWNftevXx8DAwNsbGyIiori999/V9Y45wZxly9fVkaYk5OTiY+PV4L/vCPlzZo1IyQkJF+bc9O6PQqPIutOCg9P/UbWzSSy76Vi8Y4L2tSHABhVrsC9gIMkR15j+vTpSm72ChUqUL16dSBnpHz//v2cPXuWJk2aMHv2bHx8fLh//z7Xr1+nZ8+evPHGG3rnd3R0pFKlSqjValQqFb179+aDDz4gKSmJ7t27s2rVKsqXLw/kpHgbMGAAGRkZdO7cuVhpy6pVq0a3bt2YO3duvscMDAwYO3Ysnp6eqFQq6tevz/Lly/XKNG/eXNn0r3Hjxly6dIkWLVpgbW2Nj48Pbm5uGBoa4uHhQZs2bZg8eTKZmZnKbIIWLVrQpUsXxo0bx8yZM+ncuTM6nY7y5cuzdevWJ7a/OAwNDfn4449xdXXFwMAgXx/yytseV1fXYtVfydTihe7mL4QQQoh/L0n1J4QosQpLe5b+Wyzpl6KxeNcNgOzUhyQt386q7ZuoEpdKQEAA/v7+zJkzh23btnH48GFGjx5NQkICP/30E/7+/mRlZTFkyBDCw8NZs2aN3uh3fHw8U6dOZcOGDcoO7126dGHRokXKWnidTsd7773H0qVLqVy5MtnZ2ahUKmVE+NSpU3zzzTesXbuWDRs2EBcXx8SJE/VS3q2LPMbksG0kLd9OhTHdAXh0/hpZt+5iXNVa6WNh/atVqxYajYby5cvTrl07jh49ilqtxsnJiZ07d7J3715sbGyoU6eOMk1fCCGEEOJVIqn+/iIj/0KIEit3ZLwgaccvkXH1TwDKdmqFqnQp5g4YQcfWf6Vhc3Z2ZsuWLVhaWlK1alWsrKzy1VPQ6LeXl1eB5WxtbXF2dqZ06dJs27aNuXPnMnjwYNLT0zE2Nmbr1q2Ym+esw27evDn+/v64urpStmxZNm7cmK/O9rYNGL/6S4yrVVSOlapdldQDpzCuaq0cMzAtg2GZUnz70QSaNW6iHLeyssLPz4/w8HCmT5+uHJ8zZw516tThnXfewdHRsdB1+EIIIYQQ4tUhI/9CiBKrsJH/wsx1eueVmyI9/PBGdkWfe2K5znYN+ca9zwtokRBCCCHEv4eM/P/lxeWnEkKIF6y9bQOMVMX7mjNSGeBt2+AfbtHzN9vJF7uyFYosI2ndhBBCCCGEBP9CiBKrkqkFHWzti1W2g639K7kLem5at852DfPd6DBSGdDZriE7fIZLWjdR4t1MS2Fd5DG+DP+ZdZHHeKtWTX788cd/5FzR0dH069fvH6kbwN/fHzs7OyVzhlqtJisr6x87nxBCiNeDrPkXQpRos518OX8nvsh0f6/6yLikdROvs8SHqUw9vpN9/5/6EiAz9jYPKpsxbfUy2r7T6ZW8+WVqasr27dvp3r37y26KEEKIEkJG/oUQJdrrNDKem9ZtdCMPBtRrJYG/KPESH6bSZc837I4+rwT+AA9/vYype2PikhLw3bGMxIepqNVqRo0ahZOTE6tWrQIgIiICZ2dnnJyclEwcv/32G2q1GrVazZdffklmZiaenp64ubnRrVs3ZTQe4MSJE3z22Wc5bUlMpGvXrkRHR+Ps7Iyvry9OTk5cv36dpKQk1Go17u7ujB49GoDdu3fj5uZG69at2bdvX76+DR48mG+//VbvWGxsLB4eHjg7O7NgwQIAOnToAMCUKVP45JNPAPD29n4el1cIIUQJIyP/QogST0bGhSiZph0PKnBWT2bsbSy6uFLG4S1+P/Er0yoFASjpNV1dXRk4cCDTpk0jICAAGxsbXFxc6NWrF5MmTWLFihW8/fbbaLVaVCoVu3fvxsTEhKlTpxIcHEzt2rUBaNmyJZMmTUKn07F161Z69OgBQFJSEiEhIZw5c4YFCxbQo0cP1Go1fn5+6HQ6tFotixYtIjg4GK1WS4cOHfIF7OXKlaNOnTqcOnVKObZgwQJmzpyJq6sr3t7e9O/fnzp16nD58mViYmIwMjIiPj6eatWq/VOXXAghxCtMgn8hxGsjd2RcCPHqu5mWwt6YC/mOZ926S2ZcAolLNqPLysbozTfY63iBKtpsGjdujKGhIba2tty+fZu7d+9iZ2cHQI0aNbh9+zaJiYm8/fbbABgYGJCamsrQoUOJj4/n1q1b1K5dWwn+AVxdXTl69Ci7du1i06ZNJCQk4ODggJGREY6OjkRFReHm5kZISAh9+/bF29ub9u3bExkZSdu2bQG4ffs2Op0OlUql15fRo0czc+ZM5e+rV6/SpElOuk5HR0euX7+Os7MzISEhlCpVijJlynDw4EFat279XK+1EEKIkkGm/QshhBDilbM/zxr/vB7++jvlBnpjNbYn1uN7o733gMzsbJIePSAiIoLs7GxiYmKoWLEi5cqVIzo6mszMTK5du0bFihWxtrbm999/B0Cr1bJ//37q1KlDSEgI3bp14/EUyX379uW///0vlpaWmJmZAXDhwgWys7OJiIigZs2aZGdnM2vWLAICAli8eDFWVlY4ODjw888/o9FoiIiIyBf4A9SuXZsHDx4QHx8PQM2aNTlz5gwAZ8+exc7OjtatW/P111/TuHFjWrRowdKlSyX4F0IIUSAZ+RdCCCHEKyc5Pa3A448irmLu2UT526hyBTKu/EG2VsuWLVv45JNPGDRoEKVKlWLWrFn06dOH7OxsPv74Y4yNjfnPf/7Dhx9+iEqlomvXrnTr1o25c+dy+vRpLC0t9Ub9AerUqUNsbCxTp05VjlWsWJEuXbqQkJBAQEAAJ0+eZPLkyWRmZtK2bVsMDAwYO3Ysnp6eqFQq6tevz/Llywvsz8cff0y7du0AGD9+PAMGDCAjI4POnTtjY2MDwN27d3FxccHc3JzPPvuMunXr/q1rK4QQomRSPX4HO69mzZrpTp8+/QKbI4QQQoi/I3dvi1NHf+HH6QupV6cuhqgYNmwYvXv3ftnNA3JS2Tk4OFChQgWuXbuGh4cH0dHRTJ06Vdl4L6/09HQ8PT0JCwtDq9ViaWnJ9K3f8WX8aZK+3U3ZDi0xrmqt/5zfYkm/FI3Fu24AmH9zgAu/nMTI6PmPe3h7exMUFESpUqWK7IcQQogXT6VSndHpdM1edjv+DWTkXwghhCgBHk95l34lloymb/FHN2faVarDd1+uo27dusqa8Zdp4MCBAGg0GoKDg/Hw8CiyfOnSpdHpdGRkZHDlyhUcHR2xvJWKkcqArBt3MKpSocjnG6kMKF/G7Hk1X4+Pjw+enp6UKlXqH6lfCCGEeF5kzb8QQojXnkajwdbWFk9PT9RqNT/88EOxnhceHo6rqytt2rTBxcWF9PR0wsPD+fXXX//hFudQq9VkZWUVmvIOIEunZe+N3/izpS2btm0tMHVddHQ0rq6udOvWjaZNmxIXFwfA1KlTcXFxwcPDg+TkZKKiovDy8qJNmzbMmTMHgGrVquHi4oK7uzuBgYHK9Pc9e/awbNky0tLSKF26NElJSXz33Xds3rwZPz8/Dh06xKpVq/j+++/x9PQEICYmBisrK702aDQapk6dSqNGjYiIiODUqVMMGzaMK+cu4mn1FqoypUAHSSuCSJgfQNKKIHTZf12DtOOXqB5+k19CjxAZGcmIESPQ6XQMHz4cDw8PfHx8uHv3Lr/88gstW7bE3d2dNWvWKNc3r+TkZLZt26Z3bM+ePXz++efK33Z2dmzYsOG5vzfS09NxcXEBcvYiKFu2LNeuXQPg/fff5/z5889Ub16jRo3623UUV3R0tHITKJe/vz+rV69+7udau3at8u/cz8zjNBoNRkZG3L59G4BTp06hUqn48ccfsbW1Ra1W4+zsTGRkJIDeaxkeHq68Z4pS2LmfRnR0NMHBwcq/+/Xr91TPHzhwIC1btsTNzY3+/fvn28PiaRX0mRBC/HtJ8C+EEOK1dTMthXWRxwiMOkOjDu4E7NrO3r17CQgIyBekabX5N5ebM2cO/v7+hISE8NNPP2FsbFxogFfQ85+XwlLe5ZVQSsvucycwMjJi9+7dhIaGUq9ePSWQSE1NZcuWLYwdO5atW7dy9uxZrl27RlhYGD///DNly5ZlypQprFmzhpCQEC5evEhcXBwpKSmEhoZy+PBhKlT4awS+VatWHD9+nJMnT6JWqzl27Bi//PKL3mZ0Q4cOpX///vz8889KG+rWrau0Ia8WLVpw6tQpzpw5Q6dOnbhx4wadjapgVduOR7/+jlGVClhP7IuRjRUPz1xWnlfHpTmlf8vZMG/r1q10796d3bt3U716dYKDgxk5ciQrVqxg7969LFiwgMOHDzN48OACr+HTBDpP894ojryzHyIjI3F0dCR3aWZkZCT169cv8vl533+FvReXLVv2TG37t8sb/D8u73dAtbdrs37LjwBs376dZs1yZgn3798fjUbDwoULWbFiBaAf/Ds6OvLBBx/8w73IkTf4f1YBAQGEhoZiYmJCeHj436pLgn8hXi0y7V8IIcRrp6Ap8unx0Thtnk8HW3uGjBzBrl27KF++PIMGDaJChQp07NgxX1BoamrKwYMHqVKlChYWFgCsWrWKO3fucPjwYT788EOWLFkCwPDhw9m3bx/h4eFotVoCAgKoXr06Tk5OODg4cOrUKebPn4+3tzcrVqzA39+fNm3acOLECTQaDSdOnGDChAlkZmYyZMgQBg0aBOQEL7t+PUrC6t2QlU0Zx1qU7ehE8sZDlGlUC4A7X+/ApMXbxN5LxMnFmWu/X6F8+fKoVColdd3bb79N3759uXjxIjqdDmtra6ytrfH19VXaHxwcTP/+/YGcH/3x8fHY2NgwYMAAKlSoQPv27ZVr8+233/LTTz9x7Ngx5syZw/r16zl37hzly5dn9uzZbNmyhRMnTnDlyhXlOfXr1ycmJgYbGxuioqL0rnXz5s1ZtGgRmZmZBAYGcuDAAY4ePcrgwYM4GHuJ8IvRJFyKQfcondKNalHK0pyssItUM69OZFw8zs7O/PHHH+zevRs7OzuuXLnCli1buHr1Kubm5owbN47Nmzczc+ZMbty4QatWrcjMzNRrw6pVqzh48CBqtZotW7awdu1agoKCKF26NP7+/lSvXr3Y741169bRv39/5Rp+//33hIWF8Z///AdDQ0PS09MJDAykfPnySp25sx8uXrzIsGHDOHXqFN7e3pibm6PT6ejdu3e++vK+/yZOnIiDgwO1a9cmLCyMgwcPAuDp6cm+fftwd3cnLCyMgQMHUqZMGS5cuEC7du2YMWMGJ06cYPjw4dStW5fLly/nu4nRs2dPbt26RenSpQkMDMTCwgIHBwccHBy4ePEi69atw9HRkenTp3P48OEn3qzINXr0aMLDw7GwsCAgIIDly5fTuHFjOnTowK5du4iMjGTUqFEMHjyYW7duYW1tzYYNGzA2NgYgKCiI8+fPo1armTJlCgATJkwgWKPB0qMJ0Q2sSYuK4+76fWjvP2TK4vlcrW9F9LkIGjRoAEBoaCiHDh0iKysLjUZDdHQ0Cxcu5Nq1a0ycOJFJkyZx9+5dwsLCuHjxItnZ2ZiYmNCoUSMuXrxIp06dWL58OampqTRp0gRTU1MGDx7M0KFDOXTokDJbZs6cObRt2xa1Wk3Tpk0JDQ3lo48+0ruxsGrVKo4ePcqxY8dYs2YNMTExdOvWjejoaHbu3EnVqlWZNWsWwcHBGBgYsHbtWiWd5ePu379P2bJlC7zOZ8+e5dChQ8pNLMiZudC/f38qVqyonC/vZ2L48OEkJCQwcuRIZTZESb2hJMSrSkb+hRBCvFaeNEV+V/Q5Zl0+THTcH0BODvZNmzYVOBr8xRdf8Ouvv2Jvb8+wYcPQarUMHTqUzz//nICAAAAyMjIICgqiQ4cOzJs3j5CQEGbMmMHKlSsBSEpKYu7cuezZs4eVK1eSlZWFv78/R48e5d1331XONX36dIKCgggLCyMgIICMjAwADsRcJHnPMSy6uGA9uR/pv8WSffc+RlWtyL6bgjY9E23aIx7+cpH0uNs08m3HiBEjqFatGl26dFGm/cbFxVG/fn3+97//YW1tzR9//MGlS5eU9nt7e+Ph4cEPP/yARqPhzJkzNG/enMzMTP744w927tzJRx99BMDNmzcJDg6me/fulC9fntDQUFJSUnj48CEnT57Ezs6OyMhILl26RJUqVZQ+5k139/h05Hr16nH+/Hm0Wi1BQUGMHDkSExMTThwJY5xXN4a/25tVOzbx9lu1aFK6Al+r+6B7lIH/6jWMHz+e2NhYmjdvztGjR4mMjOT9999X+nn9+nV27drF0qVLSU1NpXr16syaNYtbt27ptWHo0KG0a9cOjUZDdnY2wcHBHD16lFmzZjFv3rynem9s376d+vXrExoaSoMGDZSZDjqdjr179zJs2DBWrVqlV2dBsx/OnDlDs2bNCq0v7/svLi6OlStXMmPGDGxtbYmKiuLy5cvUrFlTCZZztW/fnrCwMH766ScAZs+eTVBQEKtXryY2NjbfZyF3lkPPnj3ZtGkTkPPZWbt2LV9//TXr1q3jxo0bnDx5kiNHjtCmTZt8dTzu1KlTPHjwgNDQUN577z1WrFhB9+7dlb7lzuRYvXo1vr6+BAcHo1arCQwMVOrw9fXFwcEBjUajZE3w6f4uJp9151jQPrJ0WlJ2HMHiXTdMnR3ISkoh8MAezhul8Sg75+ZPREQEn376KYMGDVIC6YyMDL744guuXbumN7NAq9Vy/fp17t+/T7Vq1QgODlbac/v2bUaMGMHRo0fx9/cnIyMDPz8/Dhw4wIEDB5g+fbpST79+/QgLC2PdunV616Sg2TJ5Z+ycO3eO+Ph4NBoNy5cvz/e+hJz0lE2aNOH27du89dZbBV7nwjx+vryfCV9fX+X9smnTpn/NBqNCiL9I8C+EEOK1Upwp8n/Ex3EhKxnIGW01NDQssFylSpVYtWoVUVFRqFQqDhw4kK9M3g32vvjiC1xdXZk6dSp//vknANbW1lSsWBEbGxuSk5NJTEykevXqGBoa4ujoqDw3IiICX19f3N3duXnzJgkJCQAkpz8kKyEZY9s3ATCuVpGsxHuUrludR79eIWHOerIT72HSoh7ZiSkcDNjCkiVLOHHiBJcv/zU9PndUEqBKlSpkZWXx5ptvcuHCBTw8PLh37x5z585l8ODBeHh40LFjR9LS0khMTCQ7O5uqVavy3//+F8iZmtywYUNat25N5cqVuXr1KuXLl8fExIRffvkFZ2dnLl68SFpaGufOnaNXr15Fvh4ABgYGlC1blooVKxIREcGWLVuIiopS0t7tCtzGaCdvYs9epE09R8qXMaNChQqYmZnh6+vLn3/+yQcffEDp0qWxsrIiOjqaw4cPY2trS5MmTfjtt99wc3MjMjKSDz74gGrVqmFtbV1oe3L7CNCsWbN8MxWe9N64evWqcr3zPr9x48ZAzlTygmY/nDp1irt371KuXDkMDAw4efIkzZs3L7S+vO+/unXrYmaWs/Fh3759+fHHH/nxxx8LDNLs7e0BMDExASAlJYWqVatiZmaWL91hdnY2n3/+OW5ubnz11VfKe7tWrVqUKVNGeW/HxMQo16xp06aFXtuirlGdOnW4du0aDx8+JC4ujrfeeovIyEiWLl2KWq1m3bp1yrr9wvyYepXYR/fg/282Zf6RQMq2UNKOXQQDA5LX7yfDoRpnE3JuADZq1Ij//ve/elP9VSoVJiYmWFhYYGDw18/punXrKikhMzIyqFmzJgCxsbHcv3+f/v37Y2hoiK2tLbdv30alUmFhYYGFhYXe94y9vT2lS5fWq7sg9evXx8DAQLnGv/32GxqNRhmJT0lJyfec3GVNXbp0ISAgoMDrXNiNuMfPl5eJiQkVK1YkNjaWEydO0KpVqyLbLoR48WTavxBCiNfGzbQU9sZcKLKMLjOL1IOnie/qRsLD+0X++I6KiqJWrVqoVCqsra3RarUYGxuTnp6ulMl9/p07d9BoNBw5coSDBw8qMwMe/5FtZWXFH3/8gVar5dy5c8pjjRs3JjAwEDMzMzIzM5WR2nKlTTCyLkdm9C1K161GZuwtzNs2pXTtqjw8dgnDN8yx7OmO4RtlSfvlApO++4qhTT306gCYPHkyZ86cYfr06YSFhVGzZk2cnZ2xs7NTNvcrV64ce/fu1bsGDRo04MiRI0DOpmmQs+ldREQECxcupHbt2mzYsIEVK1bw4YcfEhwczMGDB3nnnXeoVKmSch0ANmzYgIuLC2q1Ot9mewCHDx8mISGBuLg4ZTQ1MzOTc+fO4eLigr+/P99++y3x8fGo1WpllLZcuXK4urri5eWlXPNly5YRFRWV75o6OzvTqVMn4uLilBssuYyNjcnOztbrI8Dp06eVIK+4742aNWty5swZfHx8OH36NLVq5SzRyK0zIiIiX525sx9yA+jca7tr1y5UKlWB9eV9/+b9d5s2bZg/fz7Z2dlMmzYt37XO+74EsLCw4M8//6RcuXL5bkqEh4crI8e51//xOnQ6Hba2tsrGhGfPns13zsfVrFlTuWmS9xqr1WqmT5+uZImoW7cunp6edOvWDSDfco287cjQZrM/9pLe8Jdx9YqYtWtK5pV4zLyak7L5MMY1KhN/OJzk9DTKlCmjLAXJG/CmpqaSkpKit4dC7rlUKpUSNFtZWTFu3DjKli3LuXPnaNGiBTExMVSsWBGtVqsE6LnvrcfbnFfe9+Dj5XQ6HXXq1MHLy0uZbv/4tcirXLlyJCUl0bp163zX2dLSkhs3bgDoveceP9/j7enTpw/jxo2jRYsWhfZBCPHySPAvhBDitbH//9f4FyTt2EUyrv6JTqvFrI0jBtWsCYn/az36vn37yM7OxsfHRzm2YcMG9u7dS5kyZbC1tcXPz0/ZxfzChQtKMALwxhtvYG5ujoeHh/JDuiBGRkYMGDCA1q1b06pVKyVAnzlzJp07d0an01G+fHll6rOXbQPKdWxFwupdypp/wzdy1vGWeqsyGdf+VP5+o6sb68f68YPBbL06ALp06UJgYCBubm5UrlyZCRMmcPToUb22jRo1qlhreCtVqoS7uzutW7emVKlSytTl1q1bK6OdZcuWxcnJ6Yl1Qc5I5fHjx4GcDAQ+Pj64ublhaGiIh4cHn376KVFRUXh7e1OtWjVsbGyKVW9B13TChAm4ubnRpEkTKlWqlK9fSUlJdO/enVWrVhXYx1xPem8sWbKkwOttbGyMt7c3jx49yrfpYe7sh9xR+aZNm7Js2TLs7OywsbF54uv3eF0NGzYkKyvriaPLANOmTaNz587UqlWLatWq6T1Wt27dYl3/ypUr07RpU1xdXWnUqFGBZZYsWcKPP+Zsurds2TJMTExwdXWlbNmybNy4EYDu3bvTsGFDLl26BORMhf/www/5+uuv0el0zJs3j5YtWyp1tmjRgi5dujBu3DiSHj0gS6dFlSf6t+jiwt11+9GmPSLzxh0qfNwVAB06IhLiuHjxIiNGjOCTTz5RbqpMnjyZUaNGMW/ePMaNG1fgCHuucuXKceTIEapWrcqWLVv45JNPGDRoEKVKlWLGjBnKcoRZs2YV/gL8P3t7eyZNmkSvXr1YsGBBvscdHR2pVKkSarUalUpF7969GTp0qF6Zvn37YmpqirGxMT/88AMVKlTA399f7zrn3uzp2LGj3kaej3v8M+Hp6cn777+v7GMghPh3URWV4qNZs2a63J1khRBCiFfdl+E/s/DswWKXH9/Ei9GNis5B/0/IysrCyMiIEydOsHbtWmV/gMIMP7yRXdHniiwD0NmuId+493lezRTPmUajUTZZexHGjx9Pjx49aN68+RPL5r4nHzx4gJeXV5E3Fv7NXsZ3QHZ2Nt7e3soGiyXZ69RX8epQqVRndDpds5fdjn8DGfkXQgjx2ihX2vSpyluWMvmHWlK0ZcuWsWPHDjIyMvKNKBdktpMv5+/EF7mXgV3ZCsx28n2ezRSvsOnTpxMTE1OswB/g6NGjTJ8+nfv37+ttTPeqedHfAUlJSXTr1o1hw4b9rXpeBa9TX4V4VcnIvxBCiNfGzbQUnDbPL3Tqf15GKgNO9JzIm6YWL6Blf1/iw1SmHQ9ib8wFvf4ZqQzoYGvPbCdfrEzMX2ILhXj5SvJ3gBCiYDLy/xcZ+RdCCPHaqGRqQQdb+2JNke9ga/9K/ei3MjHnG/c+3ExLYX/MRe5lPMSylAnetg1eqX4I8U8qyd8BQgjxJBL8CyGEeK2U9CnylUwtGFBPUmwJUZiS/h0ghBCFefL2rkIIIUQJYmVizg6f4XS2a4iRSv+/QSOVAZ3tGrLDZ7hMkReihJLvACHE60rW/AshhHhtyRT515dGo2HAgAHUqFEDS0tLNm3aRJkyZfTKrF27lsGDBwPg7+/P+++/X6y0eOLVId8BQpR8sub/LxL8CyGEEOK1kBvoJaencSPiNx5dimbpgoUsWLAAGxsb+vXrp1fexcWFsLAwANRqNYcOHcLISFZMCiHEq0SC/7/I/2BCCCGEKNESH6Yy9fhO9sVcVHZ5T/8tloxLMaQf3oh7vbr079GLb7/9Fq1WS0BAAOHh4Zw/fx61Wk2HDh0IDw/H09OTIUOG0LBhQ0aMGEF2djYjR46kX79+DBw4kDJlynDhwgXatWvHjBkzXnKvhRBCCH0S/AshhBCixEp8mEqXPd8UuLmbDh27os+xe9cp/rfyG4YNHMyhQ4dYuXIlc+fOxcHBAY1GA8DevXuVkX9fX18CAgKwsbHBxcWFXr16AdC+fXtWrFhBy5YtJfgXQgjxryPBvxBCCCFKrGnHgwrd1T3t2EXSo+IxrlyBVT/vYsOa78jMzKRevXpF1nn37l3s7OwAqFGjBrdv3wbA3t4eABMTk+fXASGEEOI5keBfCCGEECXSzbQU9sZcKPRx01YNsHjXjezUh1xcvoOYX89z7ugJAgICAFCpVEpZY2NjsrOzMTIyoly5ckRHR2NjY8O1a9eoWLFivvJCCCHEv41sWSuEEEKIEml/njX+RTEwLQOljXH3cGfPnj3K8RYtWtClSxeOHDmCj48PXbp0YevWrcyaNYs+ffrg4uLCxx9/jLGx8T/ZDSGEEOK5kN3+hRBCCFEifRn+MwvPHix2+fFNvBjdyOMfbJEQQogXTXb7/4uM/AshhBCiRCpX2vSpyluWkrX6QgghSi4J/oUQQghRIrW3bYCRqng/dYxUBnjbNviHWySEEEK8PBL8CyGEEKJEqmRqQQdb+2KV7WBrz5umFv9wi4QQQoiXR4J/IYQQQpRYs518sStbocgydmUrMNvJ9wW1SAghhHg5JPgXQgghRIllZWLODp/hdLZrmG8JgJHKgM52DdnhMxwrE/OX1EIhhBDixTB62Q0QQgghhPgnWZmY8417H26mpbA/5iL3Mh5iWcoEb9sGMtVfCCHEa0OCfyGEEEK8FiqZWjCgXquX3QwhhBDipZBp/0IIIYQQQgghRAknwb8QQgghhBBCCFHCSfAvhBBCCCGEEEKUcBL8CyGEEEIIIYQQJZwE/0IIIYQQQgghRAknwb8QQgghhBBCCFHCSfAvhBBCCCGEEEKUcBL8CyGEEEIIIYQQJZwE/0IIIYQQQgghRAknwb8QQgghhBBCCFHCSfAvhBBCCCGEEEKUcBL8CyGEEEIIIYQQJZwE/0IIIYQQQgghRAknwb8QQgghhBBCCFHCqXQ6XeEPqlQJQMyLa44QQgghhBBCCPHc2Op0OuuX3Yh/gyKDfyGEEEIIIYQQQrz6ZNq/EEIIIYQQQghRwknwL4QQQgghhBBClHAS/AshhBBCCCGEECWcBP9CCCGEEEIIIUQJJ8G/EEIIIYQQQghRwv0fW+ETZ5MOGOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1224x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create data frame that has the result of the MDS plus the cluster numbers and titles\n",
    "df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=titles)) \n",
    "\n",
    "#group by cluster\n",
    "groups = df.groupby('label')\n",
    "\n",
    "\n",
    "# set up plot\n",
    "fig, ax = plt.subplots(figsize=(17, 9)) # set size\n",
    "ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "\n",
    "#iterate through groups to layer the plot\n",
    "#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label\n",
    "for name, group in groups:\n",
    "    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=cluster_names[name], color=cluster_colors[name], mec='none')\n",
    "    ax.set_aspect('auto')\n",
    "    ax.tick_params(\\\n",
    "        axis= 'x',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        bottom=False,      # ticks along the bottom edge are off\n",
    "        top=False,         # ticks along the top edge are off\n",
    "        labelbottom=False)\n",
    "    ax.tick_params(\\\n",
    "        axis= 'y',         # changes apply to the y-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        left=False,      # ticks along the bottom edge are off\n",
    "        top=False,         # ticks along the top edge are off\n",
    "        labelleft=False)\n",
    "    \n",
    "ax.legend(numpoints=1)  #show legend with only 1 point\n",
    "\n",
    "#add label in x,y position with the label as the film title\n",
    "for i in range(len(df)):\n",
    "    ax.text(df.loc[i]['x'], df.loc[i]['y'], df.loc[i]['title'], size=8)  \n",
    "\n",
    "plt.show()\n",
    "\n",
    "#Saving the Fig\n",
    "plt.savefig(os.path.join(outputs,'clusters_small_noaxes.png'), dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clustering plot looks great, but it would be better without overlapping labels. We are going to use D3.js (http://d3js.org/), a browser based/javascript interactive. We will use a matplotlib D3 wrapper called mpld3 (https://mpld3.github.io/). Mpld3 basically let's you use matplotlib syntax to create web interactives. It has a really easy, high-level API for adding tooltips on mouse hover, which is what I am interested in.\n",
    "\n",
    "It also has some nice functionality for zooming and panning. The below javascript snippet basicaly defines a custom location for where the zoom/pan toggle resides. Don't worry about it too much and you actually don't need to use it, but it helped for formatting purposes when exporting to the web later. The only thing you might want to change is the x and y attr for the position of the toolbar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define custom toolbar location\n",
    "class TopToolbar(mpld3.plugins.PluginBase):\n",
    "    \"\"\"Plugin for moving toolbar to top of figure\"\"\"\n",
    "\n",
    "    JAVASCRIPT = \"\"\"\n",
    "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
    "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
    "    TopToolbar.prototype.constructor = TopToolbar;\n",
    "    function TopToolbar(fig, props){\n",
    "        mpld3.Plugin.call(this, fig, props);\n",
    "    };\n",
    "\n",
    "    TopToolbar.prototype.draw = function(){\n",
    "      // the toolbar svg doesn't exist\n",
    "      // yet, so first draw it\n",
    "      this.fig.toolbar.draw();\n",
    "\n",
    "      // then change the y position to be\n",
    "      // at the top of the figure\n",
    "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
    "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
    "\n",
    "      // then remove the draw function,\n",
    "      // so that it is not called again\n",
    "      this.fig.toolbar.draw = function() {}\n",
    "    }\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.dict_ = {\"type\": \"toptoolbar\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "text.mpld3-text, div.mpld3-tooltip {\n",
       "  font-family:Arial, Helvetica, sans-serif;\n",
       "}\n",
       "\n",
       "g.mpld3-xaxis, g.mpld3-yaxis {\n",
       "display: none; }\n",
       "\n",
       "text.mpld3-text, div.mpld3-tooltip {\n",
       "  font-family:Arial, Helvetica, sans-serif;\n",
       "}\n",
       "\n",
       "g.mpld3-xaxis, g.mpld3-yaxis {\n",
       "display: none; }\n",
       "\n",
       "text.mpld3-text, div.mpld3-tooltip {\n",
       "  font-family:Arial, Helvetica, sans-serif;\n",
       "}\n",
       "\n",
       "g.mpld3-xaxis, g.mpld3-yaxis {\n",
       "display: none; }\n",
       "\n",
       "text.mpld3-text, div.mpld3-tooltip {\n",
       "  font-family:Arial, Helvetica, sans-serif;\n",
       "}\n",
       "\n",
       "g.mpld3-xaxis, g.mpld3-yaxis {\n",
       "display: none; }\n",
       "\n",
       "text.mpld3-text, div.mpld3-tooltip {\n",
       "  font-family:Arial, Helvetica, sans-serif;\n",
       "}\n",
       "\n",
       "g.mpld3-xaxis, g.mpld3-yaxis {\n",
       "display: none; }\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el184151398290075598401098903595\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "       mpld3.draw_figure(\"fig_el184151398290075598401098903595\", {\"width\": 1008.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-0.6970340174700527, 0.7129748768455497], \"ylim\": [-0.7099271132517121, 0.695432645589847], \"xdomain\": [-0.6970340174700527, 0.7129748768455497], \"ydomain\": [-0.7099271132517121, 0.695432645589847], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 0, \"tickvalues\": [], \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": false}, {\"position\": \"left\", \"nticks\": 0, \"tickvalues\": [], \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": false}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el18415139829126811760\", \"lines\": [], \"paths\": 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\"labels\": [\"The Godfather\", \"Schindler's List\", \"Gone with the Wind\", \"Lawrence of Arabia\", \"The Sound of Music\", \"The Bridge on the River Kwai\", \"Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb\", \"Apocalypse Now\", \"The Lord of the Rings: The Return of the King\", \"Gladiator\", \"From Here to Eternity\", \"Saving Private Ryan\", \"Unforgiven\", \"Raiders of the Lost Ark\", \"Doctor Zhivago\", \"Patton\", \"Braveheart\", \"Butch Cassidy and the Sundance Kid\", \"The Treasure of the Sierra Madre\", \"Platoon\", \"Dances with Wolves\", \"The Pianist\", \"The Deer Hunter\", \"All Quiet on the Western Front\", \"Giant\", \"The Grapes of Wrath\", \"Shane\", \"Stagecoach\", \"Mutiny on the Bounty\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829005397008pts\", \"labels\": [\"It's a Wonderful Life\", \"The Philadelphia Story\", \"An American in Paris\", \"The King's Speech\", \"A Place in the Sun\", \"Tootsie\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829005397872pts\", \"labels\": [\"Citizen Kane\", \"Singin' in the Rain\", \"Amadeus\", \"The Good, the Bad and the Ugly\", \"Nashville\", \"The African Queen\", \"A Clockwork Orange\", \"Yankee Doodle Dandy\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829008123024pts\", \"labels\": [\"Titanic\", \"Forrest Gump\", \"A Streetcar Named Desire\", \"The Best Years of Our Lives\", \"It Happened One Night\", \"Annie Hall\", \"Out of Africa\", \"Good Will Hunting\", \"Terms of Endearment\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829008124128pts\", \"labels\": [\"The Shawshank Redemption\", \"Raging Bull\", \"Casablanca\", \"One Flew Over the Cuckoo's Nest\", \"The Wizard of Oz\", \"The Godfather: Part II\", \"Psycho\", \"Sunset Blvd.\", \"Vertigo\", \"On the Waterfront\", \"West Side Story\", \"Star Wars\", \"E.T. the Extra-Terrestrial\", \"2001: A Space Odyssey\", \"The Silence of the Lambs\", \"Chinatown\", \"Some Like It Hot\", \"12 Angry Men\", \"Gandhi\", \"Rocky\", \"To Kill a Mockingbird\", \"My Fair Lady\", \"Ben-Hur\", \"Jaws\", \"The Apartment\", \"High Noon\", \"Goodfellas\", \"The Exorcist\", \"The French Connection\", \"City Lights\", \"Midnight Cowboy\", \"Mr. Smith Goes to Washington\", \"Rain Man\", \"Fargo\", \"The Green Mile\", \"Close Encounters of the Third Kind\", \"Network\", \"The Graduate\", \"American Graffiti\", \"Pulp Fiction\", \"The Maltese Falcon\", \"Taxi Driver\", \"Wuthering Heights\", \"Double Indemnity\", \"Rebel Without a Cause\", \"Rear Window\", \"The Third Man\", \"North by Northwest\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.7.js\", function(){\n",
       "         \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
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[-0.6571281053667809, -0.22291124891927902], [0.21432887407542456, 0.06311819353210596], [0.131973677315417, 0.29277688006002694], [-0.46196612360055384, 0.010909109984507162], [0.5693559443578058, 0.34543891297214707], [-0.3040407996114277, 0.45907281590832394], [0.645928733710947, -0.16515458906587963], [-0.1620924143367659, 0.48545066257040115], [-0.3413661174233606, 0.03926735017760567], [-0.5644884469294672, -0.35747624056074984], [0.27325324166181514, -0.2971129618042276], [0.6688323858359598, -0.079465816372304], [-0.33469022514630004, 0.2972323945292012], [-0.6040791993821621, -0.03824059518501709], [-0.33229452158443495, -0.06761294535175161], [-0.3442186462039003, -0.1441120072449543], [-0.5517283460064781, 0.02943501097205763], [0.33502653892029244, 0.3951945807921433], [-0.46942289625506506, -0.09253966807222425], [-0.572632828326994, 0.30697813718797895], [-0.5848482605235175, 0.11009315264971938], [-0.21172440381001423, -0.055029811609290276], [-0.20714085007332295, 0.00989764991743565]], \"data06\": [[0.7974270353302613, 0.22841550159430954, 0.18242580328673044, 0.13643610497915135, 0.09044640667157224, 0.04445670836399314]], \"data07\": [[0.7820660522273426, 0.015329899435859717], [0.9910394265232977, 0.015329899435859717], [0.9935995903737841, 0.015329899435859717], [0.9935995903737841, 0.021461859210203577], [0.9935995903737841, 0.24834437086092717], [0.9935995903737841, 0.254476330635271], [0.9910394265232977, 0.254476330635271], [0.7820660522273426, 0.254476330635271], [0.7795058883768562, 0.254476330635271], [0.7795058883768562, 0.24834437086092717], [0.7795058883768562, 0.021461859210203577], [0.7795058883768562, 0.015329899435859717], [0.7820660522273426, 0.015329899435859717]]}, \"id\": \"el18415139829007559840\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829005395712pts\", \"labels\": [\"The Godfather\", \"Schindler's List\", \"Gone with the Wind\", \"Lawrence of Arabia\", \"The Sound of Music\", \"The Bridge on the River Kwai\", \"Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb\", \"Apocalypse Now\", \"The Lord of the Rings: The Return of the King\", \"Gladiator\", \"From Here to Eternity\", \"Saving Private Ryan\", \"Unforgiven\", \"Raiders of the Lost Ark\", \"Doctor Zhivago\", \"Patton\", \"Braveheart\", \"Butch Cassidy and the Sundance Kid\", \"The Treasure of the Sierra Madre\", \"Platoon\", \"Dances with Wolves\", \"The Pianist\", \"The Deer Hunter\", \"All Quiet on the Western Front\", \"Giant\", \"The Grapes of Wrath\", \"Shane\", \"Stagecoach\", \"Mutiny on the Bounty\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829005397008pts\", \"labels\": [\"It's a Wonderful Life\", \"The Philadelphia Story\", \"An American in Paris\", \"The King's Speech\", \"A Place in the Sun\", \"Tootsie\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829005397872pts\", \"labels\": [\"Citizen Kane\", \"Singin' in the Rain\", \"Amadeus\", \"The Good, the Bad and the Ugly\", \"Nashville\", \"The African Queen\", \"A Clockwork Orange\", \"Yankee Doodle Dandy\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829008123024pts\", \"labels\": [\"Titanic\", \"Forrest Gump\", \"A Streetcar Named Desire\", \"The Best Years of Our Lives\", \"It Happened One Night\", \"Annie Hall\", \"Out of Africa\", \"Good Will Hunting\", \"Terms of Endearment\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829008124128pts\", \"labels\": [\"The Shawshank Redemption\", \"Raging Bull\", \"Casablanca\", \"One Flew Over the Cuckoo's Nest\", \"The Wizard of Oz\", \"The Godfather: Part II\", \"Psycho\", \"Sunset Blvd.\", \"Vertigo\", \"On the Waterfront\", \"West Side Story\", \"Star Wars\", \"E.T. the Extra-Terrestrial\", \"2001: A Space Odyssey\", \"The Silence of the Lambs\", \"Chinatown\", \"Some Like It Hot\", \"12 Angry Men\", \"Gandhi\", \"Rocky\", \"To Kill a Mockingbird\", \"My Fair Lady\", \"Ben-Hur\", \"Jaws\", \"The Apartment\", \"High Noon\", \"Goodfellas\", \"The Exorcist\", \"The French Connection\", \"City Lights\", \"Midnight Cowboy\", \"Mr. Smith Goes to Washington\", \"Rain Man\", \"Fargo\", \"The Green Mile\", \"Close Encounters of the Third Kind\", \"Network\", \"The Graduate\", \"American Graffiti\", \"Pulp Fiction\", \"The Maltese Falcon\", \"Taxi Driver\", \"Wuthering Heights\", \"Double Indemnity\", \"Rebel Without a Cause\", \"Rear Window\", \"The Third Man\", \"North by Northwest\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.7.js\", function(){\n",
       "                 \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
       "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
       "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
       "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
       "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,\n",
       "                                                target:null,\n",
       "                                                hoffset:0,\n",
       "                                                voffset:10,\n",
       "                                                targets:null};\n",
       "    function HtmlTooltipPlugin(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    HtmlTooltipPlugin.prototype.draw = function(){\n",
       "        var obj = mpld3.get_element(this.props.id);\n",
       "        var labels = this.props.labels;\n",
       "        var targets = this.props.targets;\n",
       "        var tooltip = d3.select(\"body\").append(\"div\")\n",
       "            .attr(\"class\", \"mpld3-tooltip\")\n",
       "            .style(\"position\", \"absolute\")\n",
       "            .style(\"z-index\", \"10\")\n",
       "            .style(\"visibility\", \"hidden\");\n",
       "\n",
       "        obj.elements()\n",
       "            .on(\"mouseover\", function(d, i){\n",
       "                tooltip.html(labels[i])\n",
       "                    .style(\"visibility\", \"visible\");\n",
       "            })\n",
       "            .on(\"mousemove\", function(d, i){\n",
       "                tooltip\n",
       "                .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
       "                .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
       "            }.bind(this))\n",
       "            .on(\"mousedown.callout\", function(d, i){\n",
       "                window.open(targets[i],\"_blank\");\n",
       "            })\n",
       "            .on(\"mouseout\", function(d, i){\n",
       "                tooltip.style(\"visibility\", \"hidden\");\n",
       "            });\n",
       "    };\n",
       "    \n",
       "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
       "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
       "    TopToolbar.prototype.constructor = TopToolbar;\n",
       "    function TopToolbar(fig, props){\n",
       "        mpld3.Plugin.call(this, fig, props);\n",
       "    };\n",
       "\n",
       "    TopToolbar.prototype.draw = function(){\n",
       "      // the toolbar svg doesn't exist\n",
       "      // yet, so first draw it\n",
       "      this.fig.toolbar.draw();\n",
       "\n",
       "      // then change the y position to be\n",
       "      // at the top of the figure\n",
       "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
       "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
       "\n",
       "      // then remove the draw function,\n",
       "      // so that it is not called again\n",
       "      this.fig.toolbar.draw = function() {}\n",
       "    }\n",
       "    \n",
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\"labels\": [\"The Godfather\", \"Schindler's List\", \"Gone with the Wind\", \"Lawrence of Arabia\", \"The Sound of Music\", \"The Bridge on the River Kwai\", \"Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb\", \"Apocalypse Now\", \"The Lord of the Rings: The Return of the King\", \"Gladiator\", \"From Here to Eternity\", \"Saving Private Ryan\", \"Unforgiven\", \"Raiders of the Lost Ark\", \"Doctor Zhivago\", \"Patton\", \"Braveheart\", \"Butch Cassidy and the Sundance Kid\", \"The Treasure of the Sierra Madre\", \"Platoon\", \"Dances with Wolves\", \"The Pianist\", \"The Deer Hunter\", \"All Quiet on the Western Front\", \"Giant\", \"The Grapes of Wrath\", \"Shane\", \"Stagecoach\", \"Mutiny on the Bounty\"], \"targets\": null, \"hoffset\": 10, \"voffset\": 10}, {\"type\": \"toptoolbar\"}, {\"type\": \"htmltooltip\", \"id\": \"el18415139829005397008pts\", \"labels\": [\"It's a Wonderful Life\", \"The Philadelphia Story\", \"An American in Paris\", \"The King's 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create data frame that has the result of the MDS plus the cluster numbers and titles\n",
    "df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=titles)) \n",
    "\n",
    "#group by cluster\n",
    "groups = df.groupby('label')\n",
    "\n",
    "#define custom css to format the font and to remove the axis labeling\n",
    "css = \"\"\"\n",
    "text.mpld3-text, div.mpld3-tooltip {\n",
    "  font-family:Arial, Helvetica, sans-serif;\n",
    "}\n",
    "\n",
    "g.mpld3-xaxis, g.mpld3-yaxis {\n",
    "display: none; }\n",
    "\"\"\"\n",
    "\n",
    "# Plot \n",
    "fig, ax = plt.subplots(figsize=(14,6)) #set plot size\n",
    "ax.margins(0.03) # Optional, just adds 5% padding to the autoscaling\n",
    "\n",
    "#iterate through groups to layer the plot\n",
    "#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label\n",
    "for name, group in groups:\n",
    "    points = ax.plot(group.x, group.y, marker='o', linestyle='', ms=18, label=cluster_names[name], mec='none', color=cluster_colors[name])\n",
    "    ax.set_aspect('auto')\n",
    "    labels = [i for i in group.title]\n",
    "    \n",
    "    #set tooltip using points, labels and the already defined 'css'\n",
    "    tooltip = mpld3.plugins.PointHTMLTooltip(points[0], labels,\n",
    "                                       voffset=10, hoffset=10, css=css)\n",
    "    #connect tooltip to fig\n",
    "    mpld3.plugins.connect(fig, tooltip, TopToolbar())    \n",
    "    \n",
    "    #set tick marks as blank\n",
    "    ax.axes.get_xaxis().set_ticks([])\n",
    "    ax.axes.get_yaxis().set_ticks([])\n",
    "    \n",
    "    #set axis as blank\n",
    "    ax.axes.get_xaxis().set_visible(False)\n",
    "    ax.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "    \n",
    "ax.legend(numpoints=1) #show legend with only one dot\n",
    "\n",
    "mpld3.display() #show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncomment the below to export to html\n",
    "#os.chdir(outputs)\n",
    "#html = mpld3.fig_to_html(fig)\n",
    "#print(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.8 - Hierarchical document clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import ward, dendrogram\n",
    "\n",
    "linkage_matrix = ward(dist) #define the linkage_matrix using ward clustering pre-computed distances\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 20)) # set size\n",
    "ax = dendrogram(linkage_matrix, orientation=\"right\", labels=titles);\n",
    "\n",
    "plt.tick_params(\\\n",
    "    axis= 'x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False)\n",
    "\n",
    "plt.tight_layout() #show plot with tight layout\n",
    "plt.savefig(os.path.join(outputs,'ward_clusters.png'), dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.9 - Latent Dirichlet Allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#strip any proper names from a text...unfortunately right now this is yanking the first word from a sentence too.\n",
    "import string\n",
    "def strip_proppers(text):\n",
    "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
    "    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent) if word.islower()]\n",
    "    return \"\".join([\" \"+i if not i.startswith(\"'\") and i not in string.punctuation else i for i in tokens]).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#strip any proper nouns (NNP) or plural proper nouns (NNPS) from a text\n",
    "from nltk.tag import pos_tag\n",
    "\n",
    "def strip_proppers_POS(text):\n",
    "    tagged = pos_tag(text.split()) #use NLTK's part of speech tagger\n",
    "    non_propernouns = [word for word,pos in tagged if pos != 'NNP' and pos != 'NNPS']\n",
    "    return non_propernouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.34 s, sys: 5.36 ms, total: 2.35 s\n",
      "Wall time: 2.35 s\n",
      "CPU times: user 276 ms, sys: 0 ns, total: 276 ms\n",
      "Wall time: 276 ms\n"
     ]
    }
   ],
   "source": [
    "#Latent Dirichlet Allocation implementation with Gensim\n",
    "from gensim import corpora, models, similarities \n",
    "\n",
    "#remove proper names\n",
    "preprocess = [strip_proppers(doc) for doc in synopses]\n",
    "\n",
    "%time tokenized_text = [tokenize_and_stem(text) for text in preprocess]\n",
    "%time texts = [[word for word in text if word not in stopwords] for text in tokenized_text]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1836\n"
     ]
    }
   ],
   "source": [
    "#print(len([word for word in texts[0] if word not in stopwords]))\n",
    "print(len(texts[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = corpora.Dictionary(texts)\n",
    "dictionary.filter_extremes(no_below=1, no_above=0.8)\n",
    "corpus = [dictionary.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.9 s, sys: 0 ns, total: 22.9 s\n",
      "Wall time: 22.9 s\n"
     ]
    }
   ],
   "source": [
    "%time lda = models.LdaModel(corpus, num_topics=5, id2word=dictionary, update_every=5, chunksize=10000, passes=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(3, 0.118864946), (4, 0.8807635)]\n"
     ]
    }
   ],
   "source": [
    "print(lda[corpus[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics = lda.print_topics(5, num_words=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.005*\"kill\" + 0.005*\"night\" + 0.004*\"arriv\" + 0.004*\"sing\" + 0.004*\"love\" + 0.004*\"friend\" + 0.004*\"home\" + 0.004*\"film\" + 0.004*\"prison\" + 0.004*\"go\"'),\n",
       " (1,\n",
       "  '0.004*\"run\" + 0.004*\"first\" + 0.004*\"arriv\" + 0.004*\"kill\" + 0.004*\"friend\" + 0.004*\"home\" + 0.004*\"famili\" + 0.004*\"father\" + 0.004*\"polic\" + 0.003*\"away\"'),\n",
       " (2,\n",
       "  '0.006*\"shark\" + 0.005*\"water\" + 0.004*\"boat\" + 0.004*\"discov\" + 0.004*\"escap\" + 0.004*\"ship\" + 0.003*\"home\" + 0.003*\"kill\" + 0.003*\"plan\" + 0.003*\"name\"'),\n",
       " (3,\n",
       "  '0.005*\"love\" + 0.004*\"father\" + 0.004*\"home\" + 0.004*\"want\" + 0.004*\"marri\" + 0.003*\"famili\" + 0.003*\"night\" + 0.003*\"ask\" + 0.003*\"meet\" + 0.003*\"final\"'),\n",
       " (4,\n",
       "  '0.008*\"kill\" + 0.005*\"ask\" + 0.005*\"meet\" + 0.005*\"car\" + 0.005*\"n\\'t\" + 0.005*\"say\" + 0.004*\"go\" + 0.004*\"men\" + 0.004*\"two\" + 0.004*\"famili\"')]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.show_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics_matrix = lda.show_topics(formatted=False, num_words=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " [('kill', 0.0052970056),\n",
       "  ('night', 0.0047856886),\n",
       "  ('arriv', 0.0044878903),\n",
       "  ('sing', 0.004409983),\n",
       "  ('love', 0.004379427),\n",
       "  ('friend', 0.0041808505),\n",
       "  ('home', 0.004079715),\n",
       "  ('film', 0.003986474),\n",
       "  ('prison', 0.0038908517),\n",
       "  ('go', 0.0038217062),\n",
       "  ('two', 0.0037241052),\n",
       "  ('shoot', 0.0036297035),\n",
       "  ('say', 0.0035889305),\n",
       "  (\"n't\", 0.0035420356),\n",
       "  ('show', 0.0034370415),\n",
       "  ('first', 0.0034121103),\n",
       "  ('ask', 0.0033789123),\n",
       "  ('day', 0.0031848776),\n",
       "  ('call', 0.003166708),\n",
       "  ('order', 0.0031576154)])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_matrix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/pyLDAvis/_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  default_term_info = default_term_info.sort_values(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el184151398289516849287746094863\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el184151398289516849287746094863_data = {\"mdsDat\": {\"x\": [0.0025737866040435996, -0.027570489311839953, -0.051732835452777876, -0.03369069331569158, 0.11042023147626584], \"y\": [0.009960976720353561, 0.04029281175338135, 0.049709234715641606, -0.10196863942289221, 0.0020056162335156703], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [35.03066189043921, 20.915307803645568, 16.28150493678333, 14.043184465175543, 13.729340903956357]}, \"tinfo\": {\"Term\": [\"shark\", \"sing\", \"water\", \"boat\", \"love\", \"ship\", \"alien\", \"affair\", \"prison\", \"murder\", \"escap\", \"polic\", \"mother\", \"patient\", \"tribe\", \"song\", \"window\", \"discov\", \"perform\", \"film\", \"marri\", \"stage\", \"droid\", \"first\", \"shoot\", \"plane\", \"marriag\", \"lake\", \"home\", \"town\", \"porter\", \"juror\", \"motel\", \"briefcas\", \"ransom\", \"mous\", \"trade\", \"trunk\", \"landlord\", \"penicillin\", \"union\", \"emperor\", \"tag\", \"stash\", \"verdict\", \"stamp\", \"gym\", \"certain\", \"passport\", \"waterfront\", \"consiglier\", \"loyalti\", \"extort\", \"shorter\", \"dockwork\", \"commando\", \"palant\\u00edr\", \"doorway\", \"beak\", \"imperson\", \"boxer\", \"gladiat\", \"squad\", \"smuggl\", \"booth\", \"bridg\", \"murder\", \"vote\", \"guilti\", \"car\", \"tower\", \"trooper\", \"kill\", \"testifi\", \"meet\", \"ask\", \"build\", \"men\", \"n't\", \"offic\", \"drive\", \"say\", \"ring\", \"explain\", \"death\", \"famili\", \"talk\", \"goe\", \"look\", \"walk\", \"reach\", \"bodi\", \"go\", \"die\", \"two\", \"apart\", \"call\", \"wife\", \"polic\", \"come\", \"doe\", \"arriv\", \"day\", \"way\", \"want\", \"friend\", \"work\", \"next\", \"help\", \"also\", \"monolith\", \"plantat\", \"lifeboat\", \"anticip\", \"drill\", \"ape\", \"neglect\", \"nobl\", \"millionair\", \"mediocr\", \"accent\", \"unnam\", \"twin\", \"stockad\", \"cruelli\", \"elocut\", \"sinner\", \"epidem\", \"gentlemen\", \"penniless\", \"household\", \"yacht\", \"astronaut\", \"requiem\", \"magistr\", \"sled\", \"graft\", \"excess\", \"allianc\", \"lancer\", \"pod\", \"ranch\", \"opera\", \"error\", \"necklac\", \"affair\", \"class\", \"marriag\", \"oil\", \"disconnect\", \"disguis\", \"disapprov\", \"marri\", \"boot\", \"love\", \"comput\", \"relationship\", \"father\", \"financi\", \"war\", \"final\", \"want\", \"wed\", \"home\", \"young\", \"refus\", \"front\", \"career\", \"board\", \"becom\", \"famili\", \"fight\", \"night\", \"end\", \"brother\", \"later\", \"soldier\", \"show\", \"men\", \"day\", \"meet\", \"life\", \"come\", \"ask\", \"go\", \"friend\", \"goe\", \"run\", \"say\", \"kill\", \"two\", \"warden\", \"droog\", \"choir\", \"launder\", \"poster\", \"concert\", \"9th\", \"bouncer\", \"foxhol\", \"refuge\", \"abbey\", \"gala\", \"fundrais\", \"expatri\", \"presid\", \"black-and-whit\", \"bridal\", \"rate\", \"graze\", \"gospel\", \"chaplain\", \"symphoni\", \"indio\", \"cach\", \"demo\", \"gover\", \"farewel\", \"avers\", \"presidenti\", \"porn\", \"sing\", \"song\", \"parol\", \"bandit\", \"singer\", \"violin\", \"soda\", \"gold\", \"platoon\", \"stage\", \"campaign\", \"perform\", \"audienc\", \"rock\", \"prison\", \"gang\", \"costum\", \"studio\", \"film\", \"danc\", \"love\", \"solitari\", \"shoot\", \"night\", \"town\", \"show\", \"arriv\", \"letter\", \"gun\", \"kill\", \"friend\", \"home\", \"first\", \"go\", \"two\", \"appear\", \"say\", \"n't\", \"order\", \"end\", \"soldier\", \"next\", \"call\", \"day\", \"later\", \"ask\", \"life\", \"wife\", \"away\", \"doe\", \"come\", \"headpiec\", \"code\", \"nuclear\", \"shrimp\", \"galley\", \"witch\", \"mule\", \"angel\", \"wolf\", \"stammer\", \"snake\", \"pirat\", \"doomsday\", \"buffalo\", \"castl\", \"shell-shock\", \"airstrik\", \"brandish\", \"archaeologist\", \"elud\", \"chicken\", \"feather\", \"discard\", \"20th\", \"duster\", \"dust\", \"jewels\\u2014but\", \"token\", \"encrust\", \"aloud\", \"chariot\", \"poss\", \"fleet\", \"cavalri\", \"stagecoach\", \"footbal\", \"tribe\", \"wizard\", \"idol\", \"winter\", \"plane\", \"wing\", \"robberi\", \"blank\", \"bus\", \"mother\", \"speech\", \"bank\", \"ancient\", \"run\", \"team\", \"first\", \"command\", \"polic\", \"post\", \"search\", \"attack\", \"arriv\", \"away\", \"father\", \"offic\", \"famili\", \"becom\", \"friend\", \"attempt\", \"life\", \"home\", \"learn\", \"kill\", \"live\", \"hous\", \"reveal\", \"help\", \"follow\", \"open\", \"two\", \"day\", \"also\", \"meet\", \"go\", \"doe\", \"way\", \"droid\", \"exorc\", \"reservoir\", \"alien\", \"roulett\", \"scuba\", \"deer\", \"shark\", \"skin\", \"lightsab\", \"harpoon\", \"toy\", \"hull\", \"quid\", \"quo\", \"stormtroop\", \"comeback\", \"planet\", \"estuari\", \"tooth\", \"tourist\", \"lamb\", \"bifoc\", \"salt\", \"profil\", \"chum\", \"raft\", \"mothership\", \"hydrotherapi\", \"compactor\", \"courtyard\", \"script\", \"ward\", \"cage\", \"barrel\", \"patient\", \"boat\", \"uncl\", \"water\", \"beam\", \"demon\", \"lake\", \"strang\", \"creatur\", \"ship\", \"beach\", \"swim\", \"window\", \"discov\", \"escap\", \"mountain\", \"larg\", \"plan\", \"destroy\", \"name\", \"appear\", \"call\", \"home\", \"station\", \"report\", \"friend\", \"hous\", \"away\", \"use\", \"kill\", \"two\", \"help\", \"come\", \"night\", \"polic\", \"order\", \"go\", \"ask\", \"sever\", \"play\", \"arriv\"], \"Freq\": [108.0, 104.0, 140.0, 111.0, 281.0, 133.0, 45.0, 82.0, 161.0, 225.0, 230.0, 256.0, 186.0, 47.0, 48.0, 48.0, 100.0, 222.0, 89.0, 230.0, 193.0, 63.0, 27.0, 296.0, 223.0, 77.0, 87.0, 41.0, 400.0, 175.0, 39.68645151523159, 30.05434395222604, 25.22116624282727, 24.273708165642343, 17.524033934597643, 15.602729462978832, 15.594597056890864, 18.259661004501304, 12.712392504705678, 12.712392504705678, 12.712391313144346, 12.712388930021682, 12.711744295341303, 12.711085361924944, 11.748942482889744, 11.746686857289081, 11.744497959122985, 11.73271937536041, 10.785496035757804, 10.785496035757804, 10.784213915765035, 10.782936562017591, 10.781333912026628, 10.760842631807611, 9.822042439257876, 9.82204184347721, 9.821916729537394, 9.817982789801215, 9.81661249426991, 10.680318108581412, 13.233040462290058, 27.163809194771847, 34.940810667495306, 15.608925581902998, 19.429043805007232, 50.81272217620523, 159.04647034859886, 46.43344354171892, 43.42246814799225, 200.71496021651942, 26.2006606379728, 20.433692059862214, 316.6494165726987, 29.158814117870953, 202.53225329950544, 221.96959275450305, 68.84872831033209, 170.61305151905972, 193.80569660165347, 142.4713470044493, 116.69122172410502, 185.99612737461553, 43.33072269170665, 98.3296238392492, 125.97201649347832, 163.63521116643926, 104.12529236359198, 141.43140946919854, 110.52366214193377, 96.98486585040139, 72.49576867534172, 77.75342819349915, 172.1456472362163, 121.23423518428508, 166.7414113755397, 103.61044254344702, 142.33514677800548, 114.42556794327567, 117.79229112191068, 138.38406285738702, 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11.582493296231773, 89.43066777922184, 11.444579699479341, 117.18926975809606, 18.926363510374227, 50.14379985090908, 107.20427242281056, 18.92718307800481, 62.433040036784654, 76.68783105847051, 95.51900235769685, 33.253700495971266, 106.29426778077084, 58.44403363548675, 69.05007541717123, 43.45116789376415, 28.86295797819943, 39.60680379638017, 74.86228400144275, 84.42172084454864, 71.2176553653618, 81.125641800582, 63.8191053347967, 54.89057620686615, 66.4000660895457, 58.13127182990786, 60.377905905798286, 71.6862773067982, 74.57705170023277, 77.88384203781577, 62.69612124620141, 70.47674346154835, 79.84531780124065, 74.44503870584843, 74.13001170640167, 61.396634162053324, 62.83231175892496, 64.45294983398165, 62.171842694629866, 59.986334704520324, 22.324960537602486, 18.63454583040145, 13.098831836756812, 12.176212653211937, 10.33097096324834, 17.712191369139664, 7.563098459681405, 7.56309735205679, 7.563068553816788, 7.562978836222938, 7.560858288896684, 15.866823409969907, 11.678191397373158, 6.640467646078068, 6.640362421739601, 6.639921587142657, 6.638392511361047, 14.021538522646306, 6.568804887271624, 5.717837386287038, 5.717837386287038, 5.717837386287038, 5.717833509600884, 5.717660720160875, 5.717484054034713, 5.715565648200777, 10.423966018335785, 4.795196604062161, 4.795196604062161, 4.795196604062161, 83.91688399035402, 38.90948371626457, 13.099036747310668, 23.247988434630457, 23.25963843033562, 8.48577856258013, 8.485651185749354, 25.210205252605704, 26.107733415737094, 40.70026904201022, 22.307728113835523, 50.471423264499954, 32.94303112404585, 22.163796725557994, 74.03841866462233, 36.41321018321193, 11.253653281396295, 21.3873009194046, 75.85800223036932, 38.39391547126804, 83.33544309422825, 12.176406487519639, 69.06906968846275, 91.06613125016061, 57.57562564644596, 65.40293411283209, 85.39937308063904, 31.50216435155971, 52.85290479765797, 100.79590461701763, 79.55675956617384, 77.63226622735112, 64.92852076804672, 72.72265809244975, 70.86542415077481, 46.52815114909012, 68.29320522834016, 67.40084967216285, 60.08577771593002, 53.28578221936446, 50.09490178322689, 55.52301115310647, 60.258797541858144, 60.60454478081593, 54.443546785892686, 64.29680257537704, 52.44591920896193, 51.32809115556789, 52.18257924139264, 51.85050009018935, 51.59889208254676, 22.924138497619147, 20.19483391488221, 13.82645496290848, 13.82645496290848, 13.823179059112768, 12.006915389908784, 9.277599342941587, 20.19504027102682, 8.367826690384174, 8.367826690384174, 8.36725156816632, 8.362192976565767, 7.458052604797979, 7.458052604797979, 7.458052604797979, 7.427066223444218, 6.548274220125438, 6.548274220125438, 6.548274220125438, 6.546010512326097, 5.638492969395333, 5.638492491719073, 5.637630286068553, 5.633040772555983, 5.633040772555983, 5.63103787599497, 5.62953176274506, 5.629469187154912, 5.628806172505095, 5.627948743617182, 17.23764336311227, 14.736401489624779, 10.780406117775158, 11.09837323139887, 8.367934645219087, 10.187444602290622, 31.11621185070793, 9.458226888800914, 11.097314700805214, 11.097176174689617, 40.28520194225972, 15.646451194413384, 15.64624961503138, 8.750739623761856, 25.744637210634178, 56.82079877294203, 24.47307829035624, 27.472990296175958, 11.140963802153223, 71.88409419015002, 21.106523004597452, 66.98560438653419, 38.924405453667426, 58.48968880684987, 18.092622727160112, 31.940526309525445, 45.52873450530687, 64.73070493743968, 57.075709754099, 58.680495633824194, 53.62696358022334, 59.955302752674726, 51.447498766214714, 62.02286903656255, 49.1838024311036, 48.52607661284416, 60.02041958053001, 40.985402733618436, 64.14755011558682, 42.25220399834004, 45.11785267151412, 41.24350459219672, 45.35012561932516, 41.13631786071122, 39.666423935590714, 48.445032147752904, 46.51938921302691, 42.41318854100787, 44.48810339648992, 43.70072477685288, 41.27676614558008, 40.05396077517174, 26.481080464534145, 18.318500077494626, 12.876106715459208, 42.906095377757254, 11.965931240251047, 10.155900076415989, 10.152003420644695, 100.85203420622136, 9.24894223762492, 9.248937567615606, 8.341982530830126, 8.340829038529659, 8.339003998889892, 7.435021423032538, 7.435021423032538, 7.4350172200241555, 7.4349565099030785, 23.907597112108466, 6.528058447231224, 6.528058447231224, 6.528058447231224, 6.528057513229362, 6.527378960876091, 6.527378960876091, 6.526965198050903, 5.621092202423392, 5.621092202423392, 5.621091268421528, 5.621091268421528, 5.62108473040849, 12.806174259989438, 20.11949664131153, 22.853641390152216, 15.594404254695014, 9.249071129881976, 35.55144341400952, 68.27828586677704, 17.41184578930894, 81.62184743850469, 9.24897959769943, 14.687609866229922, 26.943917615593264, 24.68017356489606, 18.31390478833001, 60.32287643290884, 24.66781298424474, 19.150310928416324, 38.93234335670621, 62.06717347964708, 62.01609852178576, 18.1081217619267, 34.844512348141784, 54.558299128325984, 29.627768232129863, 53.72415327274747, 39.855421133646075, 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\"avers\", \"away\", \"away\", \"away\", \"away\", \"away\", \"bandit\", \"bandit\", \"bandit\", \"bank\", \"bank\", \"bank\", \"bank\", \"bank\", \"barrel\", \"barrel\", \"beach\", \"beach\", \"beach\", \"beach\", \"beach\", \"beak\", \"beam\", \"beam\", \"becom\", \"becom\", \"becom\", \"becom\", \"becom\", \"bifoc\", \"black-and-whit\", \"blank\", \"blank\", \"board\", \"board\", \"board\", \"board\", \"board\", \"boat\", \"boat\", \"boat\", \"boat\", \"bodi\", \"bodi\", \"bodi\", \"bodi\", \"bodi\", \"boot\", \"boot\", \"booth\", \"booth\", \"booth\", \"bouncer\", \"boxer\", \"boxer\", \"brandish\", \"bridal\", \"bridg\", \"bridg\", \"bridg\", \"bridg\", \"briefcas\", \"brother\", \"brother\", \"brother\", \"brother\", \"brother\", \"buffalo\", \"build\", \"build\", \"build\", \"build\", \"build\", \"bus\", \"bus\", \"bus\", \"bus\", \"cach\", \"cage\", \"cage\", \"call\", \"call\", \"call\", \"call\", \"call\", \"campaign\", \"campaign\", \"campaign\", \"car\", \"car\", \"car\", \"car\", \"car\", \"career\", \"career\", \"career\", \"career\", \"career\", \"castl\", \"cavalri\", \"cavalri\", \"certain\", \"chaplain\", \"chariot\", \"chariot\", \"chicken\", \"choir\", \"chum\", \"class\", \"class\", \"class\", \"code\", \"come\", \"come\", \"come\", \"come\", \"come\", \"comeback\", \"command\", \"command\", \"command\", \"command\", \"command\", \"commando\", \"compactor\", \"comput\", \"comput\", \"comput\", \"concert\", \"concert\", \"consiglier\", \"costum\", \"costum\", \"courtyard\", \"courtyard\", \"creatur\", \"creatur\", \"creatur\", \"cruelli\", \"danc\", \"danc\", \"danc\", \"danc\", \"danc\", \"day\", \"day\", \"day\", \"day\", \"day\", \"death\", \"death\", \"death\", \"death\", \"death\", \"deer\", \"demo\", \"demon\", \"demon\", \"destroy\", \"destroy\", \"destroy\", \"destroy\", \"destroy\", \"die\", \"die\", \"die\", \"die\", \"die\", \"disapprov\", \"disapprov\", \"discard\", \"disconnect\", \"disconnect\", \"discov\", \"discov\", \"discov\", \"discov\", \"discov\", \"disguis\", \"disguis\", \"disguis\", \"disguis\", \"disguis\", \"dockwork\", \"doe\", \"doe\", \"doe\", \"doe\", \"doe\", \"doomsday\", \"doorway\", \"drill\", \"drive\", \"drive\", \"drive\", \"drive\", \"drive\", \"droid\", \"droog\", \"dust\", \"duster\", \"elocut\", \"elud\", \"emperor\", \"encrust\", \"end\", \"end\", \"end\", \"end\", \"end\", \"epidem\", \"error\", \"error\", \"escap\", \"escap\", \"escap\", \"escap\", \"escap\", \"estuari\", \"excess\", \"exorc\", \"expatri\", \"explain\", \"explain\", \"explain\", \"explain\", \"explain\", \"extort\", \"famili\", \"famili\", \"famili\", \"famili\", \"famili\", \"farewel\", \"farewel\", \"father\", \"father\", \"father\", \"father\", \"father\", \"feather\", \"fight\", \"fight\", \"fight\", \"fight\", \"fight\", \"film\", \"film\", \"film\", \"film\", \"film\", \"final\", \"final\", \"final\", \"final\", \"final\", \"financi\", \"financi\", \"financi\", \"financi\", \"first\", \"first\", \"first\", \"first\", \"first\", \"fleet\", \"fleet\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"footbal\", \"footbal\", \"foxhol\", \"friend\", \"friend\", \"friend\", \"friend\", \"friend\", \"front\", \"front\", \"front\", \"front\", \"front\", \"fundrais\", \"fundrais\", \"gala\", \"gala\", \"galley\", \"gang\", \"gang\", \"gang\", \"gang\", \"gentlemen\", \"gladiat\", \"gladiat\", \"go\", \"go\", \"go\", \"go\", \"go\", \"goe\", \"goe\", \"goe\", \"goe\", \"goe\", \"gold\", \"gold\", \"gold\", \"gold\", \"gospel\", \"gover\", \"graft\", \"graze\", \"guilti\", \"guilti\", \"guilti\", \"gun\", \"gun\", \"gun\", \"gun\", \"gun\", \"gym\", \"harpoon\", \"headpiec\", \"help\", \"help\", \"help\", \"help\", \"help\", \"home\", \"home\", \"home\", \"home\", \"home\", \"hous\", \"hous\", \"hous\", \"hous\", \"hous\", \"household\", \"hull\", \"hydrotherapi\", \"idol\", \"idol\", \"imperson\", \"indio\", \"jewels\\u2014but\", \"juror\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"lake\", \"lake\", \"lake\", \"lamb\", \"lancer\", \"landlord\", \"larg\", \"larg\", \"larg\", \"larg\", \"larg\", \"later\", \"later\", \"later\", \"later\", \"later\", \"launder\", \"learn\", \"learn\", \"learn\", \"learn\", \"learn\", \"letter\", \"letter\", \"letter\", \"letter\", \"letter\", \"life\", \"life\", \"life\", \"life\", \"life\", \"lifeboat\", \"lightsab\", \"live\", \"live\", \"live\", \"live\", \"live\", \"look\", \"look\", \"look\", \"look\", \"look\", \"love\", \"love\", \"love\", \"love\", \"love\", \"loyalti\", \"magistr\", \"marri\", \"marri\", \"marri\", \"marri\", \"marri\", \"marriag\", \"marriag\", \"marriag\", \"marriag\", \"marriag\", \"mediocr\", \"meet\", \"meet\", \"meet\", \"meet\", \"meet\", \"men\", \"men\", \"men\", \"men\", \"men\", \"millionair\", \"monolith\", \"motel\", \"mother\", \"mother\", \"mother\", \"mother\", \"mother\", \"mothership\", \"mountain\", \"mountain\", \"mountain\", \"mountain\", \"mountain\", \"mous\", \"mule\", \"murder\", \"murder\", \"murder\", \"murder\", \"n't\", \"n't\", \"n't\", \"n't\", \"n't\", \"name\", \"name\", \"name\", \"name\", \"name\", \"necklac\", \"necklac\", \"neglect\", \"next\", \"next\", \"next\", \"next\", \"next\", \"night\", \"night\", \"night\", \"night\", \"night\", \"nobl\", \"nuclear\", \"offic\", \"offic\", \"offic\", \"offic\", \"offic\", \"oil\", \"oil\", \"oil\", \"open\", \"open\", \"open\", \"open\", \"open\", \"opera\", \"opera\", \"order\", \"order\", \"order\", \"order\", \"order\", \"palant\\u00edr\", \"parol\", \"parol\", \"passport\", \"patient\", \"patient\", \"patient\", \"patient\", \"penicillin\", \"penniless\", \"perform\", \"perform\", \"perform\", \"perform\", \"perform\", \"pirat\", \"plan\", \"plan\", \"plan\", \"plan\", \"plan\", \"plane\", \"plane\", \"plane\", \"plane\", \"plane\", \"planet\", \"planet\", \"plantat\", \"platoon\", \"platoon\", \"platoon\", \"play\", \"play\", \"play\", \"play\", \"play\", \"pod\", \"pod\", \"polic\", \"polic\", \"polic\", \"polic\", \"polic\", \"porn\", \"porter\", \"poss\", \"poss\", \"post\", \"post\", \"post\", \"post\", \"post\", \"poster\", \"presid\", \"presidenti\", \"prison\", \"prison\", \"prison\", \"prison\", \"prison\", \"profil\", \"quid\", \"quo\", \"raft\", \"ranch\", \"ranch\", \"ranch\", \"ransom\", \"rate\", \"rate\", \"reach\", \"reach\", \"reach\", \"reach\", \"reach\", \"refuge\", \"refus\", \"refus\", \"refus\", \"refus\", \"refus\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"report\", \"report\", \"report\", \"report\", \"report\", \"requiem\", \"reservoir\", \"reveal\", \"reveal\", \"reveal\", \"reveal\", \"reveal\", \"ring\", \"ring\", \"ring\", \"ring\", \"robberi\", \"robberi\", \"robberi\", \"rock\", \"rock\", \"rock\", \"rock\", \"roulett\", \"run\", \"run\", \"run\", \"run\", \"run\", \"salt\", \"say\", \"say\", \"say\", \"say\", \"say\", \"script\", \"script\", \"script\", \"script\", \"scuba\", \"search\", \"search\", \"search\", \"search\", \"search\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"shark\", \"shark\", \"shell-shock\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"shoot\", \"shoot\", \"shoot\", \"shoot\", \"shoot\", \"shorter\", \"show\", \"show\", \"show\", \"show\", \"show\", \"shrimp\", \"sing\", \"sing\", \"sing\", \"sing\", \"sing\", \"singer\", \"singer\", \"singer\", \"sinner\", \"skin\", \"sled\", \"smuggl\", \"smuggl\", \"snake\", \"soda\", \"soda\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"solitari\", \"solitari\", \"solitari\", \"song\", \"song\", \"song\", \"song\", \"song\", \"speech\", \"speech\", \"speech\", \"speech\", \"speech\", \"squad\", \"squad\", \"squad\", \"stage\", \"stage\", \"stage\", \"stage\", \"stagecoach\", \"stagecoach\", \"stammer\", \"stamp\", \"stash\", \"station\", \"station\", \"station\", \"station\", \"station\", \"stockad\", \"stormtroop\", \"strang\", \"strang\", \"strang\", \"strang\", \"strang\", \"studio\", \"studio\", \"studio\", \"studio\", \"studio\", \"swim\", \"swim\", \"swim\", \"swim\", \"symphoni\", \"tag\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"team\", \"team\", \"team\", \"team\", \"team\", \"testifi\", \"testifi\", \"token\", \"tooth\", \"tourist\", \"tower\", \"tower\", \"tower\", \"town\", \"town\", \"town\", \"town\", \"town\", \"toy\", \"trade\", \"tribe\", \"tribe\", \"tribe\", \"trooper\", \"trooper\", \"trunk\", \"twin\", \"two\", \"two\", \"two\", \"two\", \"two\", \"uncl\", \"uncl\", \"uncl\", \"uncl\", \"union\", \"unnam\", \"use\", \"use\", \"use\", \"use\", \"use\", \"verdict\", \"violin\", \"violin\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"walk\", \"walk\", \"walk\", \"walk\", \"walk\", \"want\", \"want\", \"want\", \"want\", \"want\", \"war\", \"war\", \"war\", \"war\", \"war\", \"ward\", \"ward\", \"ward\", \"warden\", \"water\", \"water\", \"water\", \"water\", \"water\", \"waterfront\", \"way\", \"way\", \"way\", \"way\", \"way\", \"wed\", \"wed\", \"wed\", \"wed\", \"wed\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"window\", \"window\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"winter\", \"winter\", \"witch\", \"wizard\", \"wizard\", \"wolf\", \"work\", \"work\", \"work\", \"work\", \"work\", \"yacht\", \"yacht\", \"young\", \"young\", \"young\", \"young\", \"young\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 4, 1, 2, 3]};\n",
       "\n",
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       "       new LDAvis(\"#\" + \"ldavis_el184151398289516849287746094863\", ldavis_el184151398289516849287746094863_data);\n",
       "   }(LDAvis);\n",
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       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el184151398289516849287746094863\", ldavis_el184151398289516849287746094863_data);\n",
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       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el184151398289516849287746094863\", ldavis_el184151398289516849287746094863_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "4      0.002574  0.009961       1        1  35.030662\n",
       "3     -0.027570  0.040293       2        1  20.915308\n",
       "0     -0.051733  0.049709       3        1  16.281505\n",
       "1     -0.033691 -0.101969       4        1  14.043184\n",
       "2      0.110420  0.002006       5        1  13.729341, topic_info=       Term        Freq       Total Category  logprob  loglift\n",
       "5516  shark  108.000000  108.000000  Default  30.0000  30.0000\n",
       "1627   sing  104.000000  104.000000  Default  29.0000  29.0000\n",
       "1394  water  140.000000  140.000000  Default  28.0000  28.0000\n",
       "1670   boat  111.000000  111.000000  Default  27.0000  27.0000\n",
       "395    love  281.000000  281.000000  Default  26.0000  26.0000\n",
       "...     ...         ...         ...      ...      ...      ...\n",
       "285      go   40.006375  403.020443   Topic5  -5.9942  -0.3243\n",
       "49      ask   39.660805  441.325007   Topic5  -6.0029  -0.4238\n",
       "585   sever   33.893362  186.456855   Topic5  -6.1600   0.2807\n",
       "472    play   33.285633  178.621669   Topic5  -6.1781   0.3055\n",
       "47    arriv   36.368564  377.242216   Topic5  -6.0895  -0.3535\n",
       "\n",
       "[430 rows x 6 columns], token_table=      Topic      Freq    Term\n",
       "term                         \n",
       "8017      4  0.940099    20th\n",
       "7747      3  0.963067     9th\n",
       "3509      3  0.963161   abbey\n",
       "5706      2  0.960502  accent\n",
       "18        2  0.755974  affair\n",
       "...     ...       ...     ...\n",
       "732       1  0.304018   young\n",
       "732       2  0.359858   young\n",
       "732       3  0.105476   young\n",
       "732       4  0.105476   young\n",
       "732       5  0.117884   young\n",
       "\n",
       "[904 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 4, 1, 2, 3])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n"
     ]
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "pyLDAvis.gensim_models.prepare(lda, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.10 - [Enhancing TM Models](https://datascience.blog.wzb.eu/2017/11/09/topic-modeling-evaluation-in-python-with-tmtoolkit/)"
   ]
  }
 ],
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