position2vec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# position2vec: профессии в двумерном пространстве"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цель этого блокнота - отобразить множество 149 профессий, которые люди указывают в анкете на кредит (на самом деле, больше, но я провёл предобработку), на двумерную плоскость. \n",
    "\n",
    "После этого можно, например, определять сходство любых двух профессий и вообще делать с ними все преобразования, которые возможны в векторном пространстве.\n",
    "\n",
    "Начнём с импорта библиотек и подключения к местному серверу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pyodbc\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 5000)\n",
    "\n",
    "from matplotlib import rc\n",
    "rc('font', **{'family': 'Verdana','weight': 'normal', 'size':8})\n",
    "\n",
    "import sqlalchemy\n",
    "import urllib.parse\n",
    "quoted = urllib.parse.quote_plus('server=olimpus;database=wrk_rsk_anl;driver=sql server;Trusted Connection=yes;')\n",
    "engine = sqlalchemy.create_engine('mssql+pyodbc:///?odbc_connect={}'.format(quoted), encoding=\"cp1251\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скачаем с сервера:\n",
    "* обработанное название профессии\n",
    "* флаги просрочки по кредиту 90+ дней за 12 месяцев с выдачи\n",
    "* логарифм дохода\n",
    "* один из уровней образования (от \"начального\" до \"академика\")\n",
    "\n",
    "по 50 тысячам клиентов банка. Мы будем пытаться по профессии предсказать три остальные признака."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52077, 4)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_sql('select b.bad_90_12, a.APP_C506, a.app_income_log, a.APP_educ_raw '\n",
    "                   + ' from Dale_ML16_VARIABLES_app a inner join Dale_ML16_sample0 b '\n",
    "                  + 'on a.app_id = b.app_id and sample_idx = 1 and rnd < 0.5', engine)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформируем отдельные переменные:\n",
    "1. position - столбец из n целых чисел - номеров профессий\n",
    "2. bad_labels - матрица n*2, в первом столбце единички у хороших заёмщиков, во втором - у плохих\n",
    "3. educations - матрица из семи столбцов с дамми-переменными образования\n",
    "4. logincome - столбец логарифма дохода"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "position_raw = data[['APP_C506']]\n",
    "enc = MultiColumnLabelEncoder()\n",
    "position = pd.DataFrame(enc.fit_transform(position_raw.as_matrix().ravel())\n",
    "badrate = data[['bad_90_12']]\n",
    "logincome = data[['app_income_log']]\n",
    "bad_labels = pd.concat([1-badrate, badrate], axis = 1)\n",
    "educations = pd.get_dummies(data['APP_educ_raw'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52077, 7)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "educations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos_num = len(enc.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Импортируем из библиотеки Keras (https://keras.io; она у меня стоит поверх TensorFlow) классы для создания модели:\n",
    "* Input - принимает данные из матрицы\n",
    "* Embedding - сопоставляет каждому номеру профессии свой вектор\n",
    "* Dense - обычный полносвязный слой нейросети\n",
    "* Flatten - слой, превращающий тензоры в двумерные матрицы\n",
    "* l2 - функция для ридж-регуляризации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Embedding, Dense, Flatten\n",
    "from keras.models import Model\n",
    "from keras.regularizers import l2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим три слоя: для чтения данных (профессий), превращения каждую из input_dim (150) профессий в вектор размерности output_dim (2) с небольшой регуляризацией, и для сплющивания этих векторов в строки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_layer = Input(shape = [1], name = 'input')\n",
    "embed_layer =  Embedding(output_dim=2, input_dim=pos_num+1, input_length=1, W_regularizer = l2(0.0001))(input_layer)\n",
    "flat_layer = Flatten()(embed_layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Над полученными векторами (они соответствуют профессиям) построим простенькую нейронку (5 нейронов скрытого слоя), и три выходных слоя:\n",
    "* один нейрон с линейной активацией - для дохода\n",
    "* два нейрона (\"хороший\" и \"плохой\"), нормированные к 1 - для кредитного риска\n",
    "* 7 нейронов, нормированные к 1 - для уровня образования."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dense_layer = Dense(5, activation = 'tanh')(flat_layer)\n",
    "out1 = Dense(1, activation='linear', name = 'out1')(dense_layer)\n",
    "out2 = Dense(2, activation='softmax', name = 'out2')(dense_layer)\n",
    "out3 = Dense(7, activation='softmax', name = 'out3')(dense_layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы понять, какой точности может достичь каждый прогноз, построим и обучим (на 5 итерациях) три отдельные модели:\n",
    "\n",
    "Модель для дохода:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "52077/52077 [==============================] - 2s - loss: 9.3855     \n",
      "Epoch 2/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.2245     \n",
      "Epoch 3/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.0862     \n",
      "Epoch 4/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.0705     \n",
      "Epoch 5/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.0659     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x45d93ac8>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = Model(input = input_layer, output = out1)\n",
    "model1.compile(optimizer='adam',loss='mse')\n",
    "model1.fit(position.as_matrix(), logincome.as_matrix(), nb_epoch=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вот такая будет погрешность (в процентах) у модели, определяющей доход. Тринадцать процентов - довольно неплохо!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13111062045087518"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp(0.0616*2)-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель для уровня риска:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "52077/52077 [==============================] - 2s - loss: 0.5279     \n",
      "Epoch 2/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.1821     \n",
      "Epoch 3/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.1636     \n",
      "Epoch 4/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.1612     \n",
      "Epoch 5/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.1602     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x457d2f60>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = Model(input = input_layer, output = out2)\n",
    "model2.compile(optimizer='adam',loss='categorical_crossentropy')\n",
    "model2.fit(position.as_matrix(), bad_labels.as_matrix(), nb_epoch=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель для уровня образования:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "52077/52077 [==============================] - 2s - loss: 1.2947     \n",
      "Epoch 2/5\n",
      "52077/52077 [==============================] - 0s - loss: 1.0353     \n",
      "Epoch 3/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.9900     \n",
      "Epoch 4/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.9768     \n",
      "Epoch 5/5\n",
      "52077/52077 [==============================] - 0s - loss: 0.9706     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x4783bd30>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = Model(input = input_layer, output = out3)\n",
    "model3.compile(optimizer='adam',loss='categorical_crossentropy')\n",
    "model3.fit(position.as_matrix(), educations.as_matrix(), nb_epoch=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Понял, что модели претендуют на потери 0.06, 0.15, 0.95. Что ж, будем взвешивать потери в общей модели так, чтобы все три достигли этого уровня.\n",
    "\n",
    "Построим общую модель, у которой на выходе все трри переменные, и обучим её на наших данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "52077/52077 [==============================] - 2s - loss: 14.4049 - out1_loss: 11.0224 - out2_loss: 0.3232 - out3_loss: 1.4993     \n",
      "Epoch 2/15\n",
      "52077/52077 [==============================] - 1s - loss: 2.2075 - out1_loss: 0.4970 - out2_loss: 0.1599 - out3_loss: 1.0935     \n",
      "Epoch 3/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7915 - out1_loss: 0.0929 - out2_loss: 0.1588 - out3_loss: 1.0825     \n",
      "Epoch 4/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7701 - out1_loss: 0.0749 - out2_loss: 0.1584 - out3_loss: 1.0797     \n",
      "Epoch 5/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7623 - out1_loss: 0.0689 - out2_loss: 0.1583 - out3_loss: 1.0783     \n",
      "Epoch 6/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7591 - out1_loss: 0.0667 - out2_loss: 0.1581 - out3_loss: 1.0774     \n",
      "Epoch 7/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7566 - out1_loss: 0.0656 - out2_loss: 0.1580 - out3_loss: 1.0764     \n",
      "Epoch 8/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7542 - out1_loss: 0.0649 - out2_loss: 0.1578 - out3_loss: 1.0755     \n",
      "Epoch 9/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7529 - out1_loss: 0.0647 - out2_loss: 0.1577 - out3_loss: 1.0737     \n",
      "Epoch 10/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7489 - out1_loss: 0.0644 - out2_loss: 0.1574 - out3_loss: 1.0716     \n",
      "Epoch 11/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7442 - out1_loss: 0.0638 - out2_loss: 0.1570 - out3_loss: 1.0682     \n",
      "Epoch 12/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7390 - out1_loss: 0.0629 - out2_loss: 0.1566 - out3_loss: 1.0638     \n",
      "Epoch 13/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7338 - out1_loss: 0.0622 - out2_loss: 0.1562 - out3_loss: 1.0572     \n",
      "Epoch 14/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7294 - out1_loss: 0.0615 - out2_loss: 0.1559 - out3_loss: 1.0506     \n",
      "Epoch 15/15\n",
      "52077/52077 [==============================] - 1s - loss: 1.7258 - out1_loss: 0.0609 - out2_loss: 0.1557 - out3_loss: 1.0446     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x4e7a6f60>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_common = Model(input = input_layer, output = [out1, out2, out3])\n",
    "\n",
    "model_common.compile(optimizer='adam',\n",
    "              loss={'out1': 'mse', 'out2': 'categorical_crossentropy', 'out3': 'categorical_crossentropy'},\n",
    "              loss_weights={'out1': 1, 'out2': 10, 'out3':0.1})\n",
    "\n",
    "model_common.fit(position.as_matrix(),{'out1': logincome.as_matrix()\n",
    "                                       , 'out2': bad_labels.as_matrix()\n",
    "                                      , 'out3': educations.as_matrix()},nb_epoch=15, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ура, обучилось! Теперь создадим ещё одну модельку, берущую те самые двумерные векторы профессий"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_embed = Model(input=input_layer, output=flat_layer)\n",
    "model_embed.compile(optimizer='adam',loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдём все уникальыне коды профессий и соответстующие им названия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_plot = position.drop_duplicates().as_matrix()\n",
    "x_plot_label = position_raw.drop_duplicates().as_matrix().ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, какие числа найдёт модель для 149 профессий:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 2)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repres = model_embed.predict(x_plot)\n",
    "repres.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Без регуляризаторов картинка выглядит кометой: клубок профессий и один хвост. \n",
    "\n",
    "С сильной регуляризацией всё сплющивается в одну линию. А вот со слабенькой l2-регуляризацией картинка получается вполне читаемой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим довльно интерпретируемую структуру: слева довльно низкоквалифицированыне работники, справа внизу - менеджеры, справа наверху - эксперты. Аналитик (то есть я) находится где-то между разработчиком и доцентом =)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dO5b4+Hj+9re/sWTJEk6ePMnYsWMZNGgQq1atwtfXl1WrVrFt2zaeeOIJ7r77\nbkqUKMGZM2dITEzE39+fXr168X//93+8++67bN++nYYNG9K/f38cDgffffcd58+f591336Vhw4Y0\naNAAHx8fDhw4wJAhQ+jevTt+fn7uGnAiInlJIU5ERERuipy6a82awV133UW7du1o3rw569evp169\nenzyyScUL16ctLTsRavj4+N55513OHHiBKZpUrFiRbZt2waAaZqcPn2a+vXr88knnxAZGUnXrl3p\n2LEjISEhnDhxArvdzieffEJ0dLR7n9dee43GjRtjs9mIjo7G09OTdevW8fDDD/PBBx8we/ZsAOLi\n4ggJCeHzzz+nU6dO+Pv7Y7fbAejQoQPFihVj48aNzJ8/n4YNG1K0aFEWLlxIkSJFsFqtdO/ePR9G\nWEQkm0KciIiI3BQzZsCsWXD4MNSpY9CtWzcg+6H9sWPHEhISgmmaPPzwwzRr1ow1a9bQvn170tPT\nOXDgAABt2rTB19eXIkWKMHPmTPd+33//Pc2bN+fpp5+mdu3azJ8/n8WLF1OlShX+9a9/YRgGkZGR\nPPDAA9SrV49+/fqRlJREhw4d8PLyolatWgwePJjjx49z8eJFrFYrqamppKenU7x48VzXERUVxbJl\ny8jIyKB69eoAFC1alHvuuQeAYsWKAXD8+HFsNhsul4spU6bQqFGjPBppESn0TNPMt6/s04uIiMid\n4IcfTHPgwOzvfyYiIsKMiYlx/xwWFmYePHjwqsf19X3TnDhxnmmaprllyxZz2LBh7vd//fVXMzY2\n1gwPDzeHDRtmNmzY0ExOTjb//e9/u8/Rp08fc/fu3Wbnzp3NX375xb1fjubNm5umaZqnT582mzVr\nZmZlZZk//PCDGRYWluv9U6dOmcHBwbm2bd261Xzuuef+/KL/gmeffdaMjY0127VrZ1arVs0sXbq0\nOW7cOLNevXqmp6enWa5cOfPJJ580N2/ebLZo0cL08PAwhw4dalosFrN48eKml5eXWaFCBTM0NNQM\nDAw0H3roIdPHx8ds3LixOWnSJLNGjRqmp6enabFYTNM0TU9PT/PixYtmr169zOTkZDMlJcX09vY2\nTdM0d+zYYfr7+5u+vr7m4sWLTdM0TYvFYl68eDHX9V9p3++//9602Wymv7+/+fLLL5utW7c2a9So\nYT788MNmy5Ytb8pYidwpfstE15SjNBMnIiIiN4WPD7z99l9rO3ToUPeMFsCUKVPw9PS8YtsZM+DL\nL/vx88+ERfsxAAAgAElEQVQ9WLduIV5eXtStW9c9CzZ06FC8vLwAmDp1KhUrVsTpdAIwZswYihYt\nStOmTalduzYRERH06NGDrKws7rvvPlauXAlkzxYC3HfffZQrV44WLVrw4IMPcvfddwOQmppKmzZt\nSE1N5Y033gDg5MmTdOvWjV9//ZVRo0Zd+4BdQWhoKCNGjGDQoEHMnTuXb7/9li1btjBt2jReeeUV\nKlWqxJkzZ5g9ezalS5emUqVKHD9+nBUrVtC4cWPefPNN2rVrR4UKFVi+fDl16tThySefZO7cuTz5\n5JOMGjWK+fPn5zqneUm5p9deew1vb28Axo0bx9KlSylbtiwBAQF06tQp1345Y3alfUePHk1kZCRV\nqlThyJEjlC9fnujoaC5cuEDv3r1vyliJFGYKcSIiIpLnSpQokevnnA//VzJkCMB9DBnyKT4+Vz9m\nQEAAAAcPHgSyywq8+uqr2Gw2d5uHH37YXargUnFxcUB2MFm+fPll73t7e7Nq1apc23bv3n31zlyn\nZs2akZSURPHixfHx8eHYsWMkJiZy8eJFXC6XO3Dt2LGD1NRUTp06xXfffcfx48eB/41rVlYWzzzz\nDBkZGRw8eJBu3bpx7tw5PDwu/+gXFBTEf//7Xx544AFKlChBhQoVgOyQWq5cOQAeeugh9zkCAwMB\nOHr0qPsYu3btyrXv8ePHqVKlCgDly5cHcofFwsrpdPLUU09RpUoVXC4X/v7+bN26lczMTLy9vVm2\nbBlz585l3rx5XLhwgWeeeYawsLBci+hYrVYcDkc+X4nkN9WJExERkdtazgzfHwW4K/n9TNGNuJnH\nupqkJAgNBajDc8+9SM+ePalUqZJ75c2TJ0+62x47doylS5dSv359Nm3aROXKld3vmaZJkSJF+OCD\nD/Dw8GD//v2sW7eOc+fOuUPWpWJiYrDZbMyePZsRI0a4w5a3tzeHDx/m4sWL/Pjjj5QtWxYAu92O\nw+FwBzyAmTNnXrZvcnIyACdOnLjZQ1Wg9ezZE7vdTvny5cnIyKBv377ExMRQsWJFEhISCA0N5auv\nviIhIYHIyMjL9s+L30W5/WkmTkRERO5IY8eOvWnHypmpu5VmzIDFiwFex8PDn5EjR1K/fn0aNmxI\n8+bNefXVVzEMA8MwGDNmDK+99ho7duygTZs2LFu2DMi+jbFIkSL06dPHvZDLww8/zGOPPUZKSgq+\nvr4AfPvtt7Rt25a0tDSCgoLYu3cvPXv2pHjx4u6QEBERQdeuXXG5XAwcOJC77roLwzAwTTNXkDAM\ng/79++fa95VXXqFPnz4ABAcHM3r06Fs+fgVFdHQ0sbGxeHh48NBDD7lvBa5evTpHjhxhy5YtvPHG\nG6SlpXHq1CkALly4QFpaGkWLFs3PrsttxMjPqW3DMExNrYuIiIhkz8RFRMDRow7+8Y8Epk8Pv6b9\nw8LCCA8Pd9/G+FfklGfo2bPnNZ1Lrs3/ym84+f77GMaPH8+kSZNYvHgx06dPx2az0bdvX4YNG0a/\nfv34z3/+g7e3N/7+/sTHx7N06VLeeustypYty+nTp93lMOTO8NsfR65pilW3U4qIiIjcBnx8YNEi\nePDBKIYMCb3m/a92m53T6aRSpUrYbDZq1arF+++/T506dQgJCWHatGk8/PDDmKZJ//79CQoKonPn\nzrhcLqKioqhRowZWq5XKlStjt9s5c+YM7du3x2azMWzYMPc5/P39sVqteHt743K5mDt3Lo899hhN\nmjRxL6QSFhZG06ZNadq0KdOmTQOgXbt2WCwWfH19OXjwIKNHj6ZZs2aUK1cOm81GYmIiY8eOxWq1\nEhISwpkzZ4DsYvJWq5UqVaoQFxeH0+kkPPzaQm9eyim/8fHHsHDhQgIDA1m/fj0Wi4UxY8ZgsVgo\nXbo0tWvXJiQkhJCQEHr06EFGRgYAnTt35ssvv2TFihX5fCVyu9BMnIiIiMgdzOl0EhOTPfsTHR1N\nVlYWK1eu5LPPPmP37t1MmzaN9u3bk5iYyPDhw3nvvfd48MEHOXHiBBcvXqR3796MHz+e5s2bs23b\nNh566CE6derEwIED6dWrF40bN+af//wnH3/8MTabjQ0bNpCZmUmxYsXIzMzEYrHw1VdfuWcKy5Qp\nwzPPPMOiRYvIyMigWLFiLFmyhJ9//pkXXniBgwcPEh4eTnR0NLt27WL27NnMmjWLNWvWkJyczMCB\nA+nduzfz5s1j/PjxWCwWTNNkw4YNvPLKK/k93FeUMxM3ZEjuZzsjIiLw8/PLtfiOFD7XMxOnZ+JE\nREREComcP57nlHPIeQ4rMTGRJUuWsGbNGjIyMnjmmWcu2880TZKTk2nTpg0A9erVY+/evfzjH/9w\nl4vIOf7mzZuZMmVKrue6AHdJhgEDBgDZJSFiYmI4deoU7du3v6y/iYmJ2O12bDYbFy5coHXr1kD2\n6puXng+yZ7ji4+MpX748CxcuvOJKnPnlauU3tEiJXC/dTikiIiJyB0pKgkGD4NChq7fZvHkztWvX\npnr16oSFhWG32/nqq6/o2LHjZW0Nw6BatWps374dgJ07d1KtWjViYmKwWCzudqZpMmrUKBYsWEBM\nTAylS5d2v/fBBx+wZ88eFi1axO7du9m+fTsxMTG8/vrrV+yfj48PrVu3xm63ExcXx3PPPceFCxfI\nzMy8rG3Pnj2JjY2lVKlSfPvtt39xlPLX2LFjNQsn1+X2+ROFiIiIiNw0Oc9hHT4Mdepkb8uZ+XE6\nnQQHBwPZM1hly5blmWeeITAwEJfLxcSJE3MdK2e/fv36ERoaypw5c6hfvz4PPfQQ//znP7n//vtZ\nvHgxO3fuZOHChbRp04bHH38cHx8f0tPT3cfp1q0b99xzDy1btqRy5cqkpKQQFBSEp6cndevWvewa\nGjRowMqVK92F3YcMGcKHH37oLjx+6UzWihUrSEpKwuVyUbt27Zs3kCK3IT0TJyIiInIHutpzWJc+\nc3ajfn+suLg4YmNjb2p5B5E73fU8E6cQJyIiIlKI3MwQ53K5OHXqFN7e3gBkZmaSkZFBiRIlbvjY\nIoWFQpyIiIiIiEgBojpxIiIiIiIidziFOBERERERkQJEIU5ERERECqX09HQ6d+6M1WolJCSEoUOH\nYrVa6dChA2fOnOHxxx+nZs2a1KhRg1atWuF0OmnatCktW7bE19eXI0eOMHDgQL755hsApk2bxsqV\nKwHw9/fHarXi7e2Ny+UiIiICu90OQFhYGPv27SMqKop58+YBYLFYiIuLw+l0MnbsWH755RceffRR\nVq1alT+DI7c1hTgREZFC4o8+sP766684nU7Cw8MBiIqKIjo6moMHD9KjRw8gu6bV+PHjAWjcuDEW\ni4XAwEB+/fVXIPuDqdVqpWbNmpftm+NKH2RF8ktkZCStWrXC4XAwYsQIMjMzcTgcdO/enffee48v\nvviCUaNGMXz4cNasWQNk/+6vXbuWUaNGMXfuXHr06MHixYsBWL16NSEhIQCULVsWh8NB/fr1/7Qf\ny5YtIyUlxf2zaZq89tprvPnmm+7i6iKXUogTEREpJK72gTU0NJR3330XyF13K4dhGPz444+sXr3a\nvS0+Pp7Y2FiCgoJYt26du53D4WDUqFF5c0EiNygxMZGmTZsCcOTIEXetunr16pGcnAxkB6pLeXl5\nAVC9enWOHj1K06ZN2bp1K3v27KF69ep4eHiQnp5OsWLFLjvf888/j9VqZe3ate5t586d47PPPnPX\nvoPs/1YTEhJo3rz5zb1guWMoxImIiBQSV/vAWrduXZKTk68Y4CD7Q+zEiRMZOXIkAOfPnyc8PByb\nzcaiRYtIS0sDICsry90+x/r1690zfydPngSu/EFWJC8lJcGgQVCyZDU2bdoEQNWqVdmxYwcAO3fu\npFq1alfcN+f3e8uWLdSqVQsAm83GgAED3DPPMTExWCyWXO0Bpk6disPhoFWrVu5ts2bN4oUXXsjV\nrn///lgsFiZMmHCTrljuNApxIiIid7i/+oHVNM3LZh0ANm/eTK1atShdujSmabJu3TpM08RutzN0\n6FB3u5wwd6ng4GAcDgdWqxWHwwH874Nsy5Ytb8XlivypGTNg1iw4caIfK1euxGazMXHiRIoVK4bF\nYmHRokU8/fTTV9x32bJlBAUF8cknn9C7d28AunfvzvHjx3n00Uf55ZdfGDhwIO+//z5Wq5WdO3cS\nHR191T+SBAQEUL9+/cvenzp1Kl9//bX7Nk6RS3nkdwdERETk1sr5wNqvXz9WruzBwoUL8fLyokKF\nClgsFkqWLMmiRYvYtm0bH330ETt37uTQoUOULVuWgIAAihYtysCBA4mPj8cwDBo1asSrr75Kq1at\nMAyDrl278uKLL+Lv73/ZuRMSEujWrRtpaWmEhYWxe/du93tX+1ArcqsNGZLz/T58fD79w7ZPPfVU\nrp87derkfjY0x+HDhwkNDQUgIyODgIAAdzH1uLg4YmNjGTt2rLt9zmImVapUcR//0vcDAgIAWL58\n+bVemhQSKvYtIiJyh0tKyg5yQ4aAj89f389ms7kXISlM0tPT6dWrF8eOHaN48eJ89dVXNGzYkPT0\ndHx8fIiOjmb27NksXryYu+++m/nz52OaJmPGjGHhwoU4nU73CoN+fn7Ex8czb948EhISmDdvHu3a\ntePMmTNkZWWxZMkSHnroISIiIlizZg0ZGRk88cQTl+0bHx/P/PnzmT17NgsXLnTf4jpu3Dh27NhB\n/fr1ad++fa6ZUbn5nE4nMTExl4W4Xr16MW7cOCpXrozL5eLUqVN4e3sDkJmZSUZGBiVKlMiPLksB\ncD3FvjUTJyIicofz8YG33772/ebPn3/zO1MA5CwAExYWxs8//0yXLl2w2+0cPHiQ8PBwUlJSWL16\nNXFxcSQmJvLWW28xePDgqy4Kc/bsWRYvXkzFihUB+OijjyhWrBhLlixh2bJlvPDCCwAsXbqUAwcO\nEBsbe8V9U1JSWL58OQkJCbhcLlJTU3E4HNhsNmJiYjSzmQcCAgLcs2SXWrBggft1kSJF3AEOoGjR\nohQtWjQvuieFiJ6JExERkSvKCR2FzaULwNx///2Xvb9v3z527tyJzWbj2WefJT09/arHMk2TSZMm\n8dJLL7m3TZ06lcDAQKZMmeJ+jvD8+fN4eHjkeibx9/vu27fPvVx9kSJFKF26dK62IlJ4aCZORERE\n5DdJSbBrVzVWrtxEzZo1SUlJuSwgVa5cmSZNmrB06VIge0GXK7UD+Omnn3C5XNSsWRPTNPn222/Z\nvn07MTExrF+/no0bNwLZAa1s2bIkJSVdcd+c827btg3IDm2nT5/G09NTAU6kEFKIExEREfnNjBnw\n5Zf9+PnnHqxbtxBPT8/LblMsU6YMgYGB7iXku3TpQuvWrXE6nbRr146UlBRSU1MZO3YsGRkZjBw5\nktTUVAzDoGrVqqSkpBAUFISnpyd169Zl+vTpPPjgg3h45P5Ydum+Oedt06YNvr6+FClShJkzZ15x\nVUMRufNpYRMRERGR31zvIjC/V1gXhRGRa3c9C5vomTgRERGR3+QsAnMjAQ7u/EVhOnfuzJkzZxgw\nYEB+d0WkUFKIExEREbnJ7vRFYfz9/Wnfvv1VC2KLyK2lECciIiJyFU6nk0qVKmGz2ahVqxbvv/8+\nderUISQkhEaNGrFnzx4uXrxI9+7dsVqthIaG4nK5iIqKokaNGlgsFjp37gxAWFgY+/bt48SJE5Qq\nVQqAHj16YLFYsFgs/PLLL5w5c4b27dtjs9kYNmwYTqcTT09PSpUqxb333kujRo1ynevixYu8+OKL\nTJo0CafTSXh4OE6nEz8/PwDatWuHxWLB19eXH3/8EcheIKVDhw488sgjOBwOAHf7gwcP0qNHj1zb\nclitVlwuFxEREdSsWZN169bRuXNn93FFJO8oxImIiIj8gZ49e2K32xk5ciSQHYI+//xzFixYwNSp\nU1mxYgX16tXD4XBQv359Pv74YwCGDx9ObGzsZSUIJkyY4K4j9u677xIbG0tISAg7duxgzpw5dO/e\nHbvdTlZWFt999x316tVjzpw5vPvuu9x7772UKFECh8NBvXr1WL58OVOmTOHLL7/k1KlTAEybNo1V\nq1YB2TXpYmNjGTRoEMuWLQPA09OTFStWsG7dOiZPnpyrb6ZpXnWhlN9vnz59uuqfieQThTgRERGR\nvyBnMTZPT08AqlevzpEjR9i3bx9169YFoG7duiQnJwMwefJkmjVrRvHixXG5XADs2rWL++67jwoV\nKgDgcrmwWq0sXryYJk2akJycnOtYhw8fBsDLywvTNPHy8nIHwHr16rF3714AOnToQJ8+fZg9ezbF\nixenZMmSwJVr0v3f//0fhmFQqlQpsrKyAPj222+x2Wx07drVfb3Hjx/HZrMRGBhIYmJirrE4evQo\n+/fvp0mTJjdtfEXkr1OIExEREfmdpCQYNAgOHbp6m82bN1O7dm2qVq3K9u3bAdi5cyfVqlUDsmfi\nNm7ciLe3Nzt27ABg5syZjBgxwh0IXS4XDoeDF198kaioKKpVq+Y+ltO5k/j4B7l0Iu/IkSOcO3cu\n17kyMjKYN28e7733HgMGDODs2bNs3749V026119//bL+79+/310wvE6dOtjtdj788EP3+2XKlMFu\ntxMREcHy5ctz7fvmm28SHh6uGnUi+UR14kRERER+Z8YMmDULDh+GOnWyt+XcTuh0OgkODgZg4cKF\nlC5dmp49e2K1WnnggQd44YUXWLhwIW+88QaLFi2iRIkS/OMf/8AwDPr370/x4sUxDAOXy0WnTp3I\nysriwoULLFiwgDJlyhAaGsqcOXM4ebI+O3fW4P77YcyYMaSmplKlShWOHz+e61wjRoxg+PDhlCxZ\nEsMwePfddwkNDeWLL764rCYdQGJiIq1btyYtLY3IyMhc13apkydP0q1bN86dO8eECRPYsGGD+71O\nnTpRvnx51agTySeqEyciIiLyO1erF3fw4EHCw8OJjo7Osz4UKRJBhw5+2Gy2m3Jcf39/4uLibsqx\nROTGqU6ciIiIyE1ws+rF3Yw+lC6t2S4RyU0zcSIiIiIiIvlEM3EiIiIiIiJ3OIU4ERERERGRAkQh\nTkRECqRXXnmFtm3bEhQU5C5yLCIiUhgoxImIyG3F6XTStGlTWrZsia+vL3FxcTRq1IjmzZvz9NNP\nA7B8+XI+//xzzp49ywMPPICnpyc//fQTLVq0wN/fnylTpgDg5+fnPq7VagVgwIABBAQE8Oijj7Jt\n2zYAXn75ZQICAmjYsCFjx47N4ysWERG5NgpxIiJy22ncuDFr165l1KhRxMXF8fXXX5OQkMD+/fs5\nceIE8fHxzJ07F4fDQYkSJXA6nUyaNIkJEyYQFxdHfHw8R44cyVXDKuf1jBkzcDqdTJ8+nfnz5wMQ\nFxeH0+lk2rRp+XK9IiIi10LFvkVE5Lbj5eUFQPXq1Vm+fDmhoaEcPnyYH374gbS0NNLS0txt69Sp\nw88//8y+ffuo81tV5tq1a7N//36ysrJIS0ujaNGi7vYLFizg448/JiMjg+rVqwPQp08fKleujJeX\nF23atMnDKxUREbl2mokTEZHbQlISDBoEhw5BTvmZzZs3s3LlSrp06YLD4cBisQDZIW3dunUAbN26\nlfr161O1alW2b98OwO7du6lSpQpDhw4lODiYTp06AXDmzBmio6NZs2aNexYOoGTJkvTt25fp06fn\n4RWLiIhcH83EiYjIbWHGDJg1Cw4fhu++W8bGjRu57777WLBgAWPGjOH999/n4MGDAPTt25cnn3yS\n//znPwQGBlKzZk2GDx9OWFgYmZmZtG3blnLlytG5c2c6d+4MgM1mo0SJEvz973+nRYsWVKhQAQ8P\nD06ePMmsWbNYs2YNX375ZX4OgYiIyF+iYt8iInJbSErKDnLNmjn5/vsYxo8fn99dEhERueVU7FtE\nRAosHx94+2148MH87onI7cXpdFKpUiVsNhu1atUiMjKS/v37Y7VasVgs/Prrr+6VWO12OzabjbS0\nNJo2bUpqairffPMNvXr1AmDo0KFYrVY6dOjAr7/+CkDr1q2xWq1UrlyZuLi4/LpMEbkGup1SRERu\nKwEBAQQEBOR3N0RuKz179mT8+PFER0dz6tQpypcvz5w5czh58iT33HMPhmHgcrmIjIwE4N577+WN\nN95g4MCBpKWlsWjRIr7++msyMzNxOBwsW7aM9957j5deeony5cszb948zX6LFCCaiRMREREpIEzT\n5Pz58zRt2hTIXsm1aNGimKZJZGQkffv2dbf18/PjxIkTWK1WSpQoQXJyMnXr1gWgXr167N27F4Cs\nrCx27drFe++9x8CBAxkwYABRUVFYrVZatGhBcHAwZ86ccc/W1a9fn169ehEVFcW8efMAsFgsxMXF\nkZ6eTufOnbFarYSEhDB69GiaNWtGuXLlsNlsJCYm5vGIidyZFOJEREREbkOXrth6qSJFirBp0yYg\ne8XVixcvcu7cObZt20ZgYKB7ddfY2Fh8fHxYuXIlqampVK1alR07dgCwc+dOqlWrxsWLF8nMzKRG\njRoMGDCAWbNmceDAAVJSUmjTpg3r16+nXbt2fPbZZzgcDsLCwpg6dSoLFixw92fZsmWkpKQAEBkZ\nSatWrXA4HMydO5cJEybw4YcfEhwcjN1up0aNGrd+4EQKAd1OKSIiInIbunTF1t9KIAJQokQJvv32\nW6xWK3fddRerVq0iPT2d0aNHA9mLJJw9e5ZXXnmFVatWsXXrVgYPHszixYtZuHAhFouFkiVLsmjR\nIrp160bnzp05duwYy5cv59NPP+XYsWN0796dCxcuANn1Gnfu3An8r/xHjnPnzuFwOOjUqROmaZKY\nmMjgwYMBKF++fB6MkkjhpBAnIiIichsaMiTnewA+PtnPiT711FMA9OvXL1fb//73v+7XdrsdgJiY\nGAD8/f3x9/cHYMaMGbn2W7p0KQAvvPACr732Gm3atKF79+7A/wLbli1baNCgwRX7OGvWLJYsWcLK\nlSsxDINq1aqxadMmatasyYkTJyhVqtT1XbyI/CHdTikiIiJyG8pZsdXH59aeJykJkpJa8tJLY3ji\niSf47rvvAHj77bcJDAxk7969hISEANmzfJcKCAigfv367u39+vVj5cqVWK1W+vfvf2s7LlKIqU6c\niIiISCE2aFD2bZsDB2aHRoCoqCguXrxI796987dzIoWA6sSJiIjIbc/pdBIeHg5k3x4YFRVFjx49\nLntv3LhxBAQE0Lx5c/bt24fVanV/Va9eHYBNmza526xduxbAXTMtZ6XF3r1706BBA3ettePHj9O9\ne3esViuhoaG4XK7LVmM8d+5cXg9LvhkyJDvA5dy+KSK3P4U4ERERyXOGYbBlyxb+n73zDovi+v7w\nu6CIJsFe0Khgi+WHvSFtd1GKiDUxKiriV0VDjBprLAgaYxJjIUSNBRSwxZKoUSEKS1GjqFGxRGwI\nqLGABey0+/uD7MgCJsbYktz3eXjYvTNzZ+7M3d05c875nCNHjqBSqQzC9FQqFQkJCSQnJxMbG0tk\nZCQVK1ZEpVKh0+mIjo6mWrVqAPj5+bFjxw5iY2NZtGiR0scvv/zCo0ePWLJkCcHBwQQEBDBw4EB0\nOh2xsbE0a9aM6OhomjdvzsaNGwEM1Bi///77ZxpXQYn9zp07K7lowcHBeHl5AbBkyRJsbW2xsbEh\nPj4ef39/7OzsaNu2LeHh4QDcu3eP6tWro9VqlT40Gg1Lly4FQKvVEhwcTEpKimIA+/r6KrXe9IZs\nweVPoriwTU9PT+mFk0heY6QRJ5FIJBKJ5KWTl5dHYGAgI0eORAjBzp070Wq1jBkzBoDTp08rtdBM\nTU0pW7YsUFQd8dixY7i7u9OxY0dF5j4tLQ0nJydFJbHwdgXrpTVt2pRz586hUqkoX748kK/GeOXK\nlWcaV2GJfYC7d++yevVqVCoV6enpfP/99+zZs4fdu3dTt25dJk6cyO7du9myZQsLFy4E4MqVK3h6\neioiJXr27dvHuXPnyMjIUNpUKhWpqamKAVgQIUSRPDaJRPLPRxpxEolEIpH8A8jIyGDLli2v+jCe\nG+vXr6d3796YmJgAKHXE5s+fD0C9evWIj48HICsri3v37hUx4ACaN2/O9u3biY6OZufOnQBUrlyZ\n8PBwRowYwcOHD4tsU7duXY4cOQLk10urX78+QggDNcbGjRs/07gSExMV47N69eoIIfjiiy8YP348\nAElJSTRv3hzIr/dWqVIltm/fjouLC/3791fCOJOSkqhdu3aR/tu2bcvo0aMZOHCg0iaEYPbs2Uya\nNElpO378OFqtlr59+z7TOCQSyeuNNOIkEolEIvkHcOvWLTZv3vyqD+NvUbB4dZ06dXB3d3/iui1b\ntqRy5crY29vj5OTErVu3ivUoTZs2DTc3N9RqNWPHjgXyPVNt27ZlzJgxDBkypMg2PXr04MSJE2g0\nGhISEujVqxdQvBrjXx3fsWP12LIlvxB3eno6Fy9eJC8vj0aNGgFgaWnJ4cOHgXzj6/bt28ycOZMf\nfviBiIgIpa9du3bh6OiorKenX79+ODs7U65cOaUtPj6exo0bU6lSJaXNysoKnU7HunXr/vI4JBLJ\n649Up5RIJBKJ5DXm9u3bSiHlCxcuULNmTaysrOjXrx8TJ04kNzeXadOm4ezsTEhICAsWLMDU1JRG\njRoRHByMnZ0du3fvNuhz/Pjx7N27l6tXr7J8+XKMjY2JiopixowZyvr6PC1ra2sGDRpEeno677zz\nDosWLcLf35/9+/eTlZVFhQoV2LBhw1ONpTgVxNeF56HGmD++O1haDsDCIpNy5crx888/c+bMGW7d\nuoW/v7+Sn7d+/XqMjIwIDAxk6dKlHD9+nEaNGnHu3DkmTpxI//79sbKyQgjB0aNHiYmJYcyYMURG\nRmJkZKQcr6OjI25ubhw7dozdu3cTGxuLr68v9vb2xMXFkZKSwrRp0wgNDX2OZ0sikTxPnkWdUhb7\nlvq1FioAACAASURBVEgkEonkNSYoKIjhw4fTunVr/Pz8CA4Opnv37kyZMoUdO3ZgampKz549cXZ2\nBvKLOVtYWODn5/fEPq9du0Z0dDRffPFFkWWFvV3Lli2jV69e9O7dm0mTJnHixAkAhgwZQq9evRg3\nbhx79uzB1tb2T8fyuHj1Uw7+H0b+uN5i1KjNRWq7mZmZERwc/Pt6oxhV4CQUFGSBfIPyiy++UAzK\nGTNmkJGRYZAfpy/6DSjXxMHBAQeH/KLgcXFxANSuXVsacBLJvxAZTimRSCQSyWvMuXPnaNKkiUFI\nXZMmTdi3b18RQY9Hjx5RokQJg3XT0tLQarWo1WoOHToEwJ07dyhVqlSxOWaFSUxMZMGCBWg0GnQ6\nHWlpaQDPJALysopX671R165do127drRq1QpXV1ccHR157733AFi+fDkdOnSgbdu2rFixAk9PT3bv\n3k1SUhKQrwSZl5fHsWPHlBIGa9asUZZptVpat26teCHt7OyoXx/u3fPC2DiJlJQURY1SrxSp0+nQ\narUG/c+ePbtI6OZ7771Hnz59lPejR49W8uwkEokEpBEnkUgkEslry9mzcOTI2+zadRJ4nBt18uRJ\n7Ozsigh6nDp1ipo1axr0UblyZXQ6HV999RVhYWHcvHlTUXosiL7vwoZdgwYNmDRpEtHR0ezfv7+I\nMfF3REBeJEIIZs2axVdffUW3bt0YMmQIUVFR1K5dmz179tC/f39+/vln9uzZw7Jly4psr/dI+vr6\n8t133xEXF8fixYvJyclBpVIRGRnJihUrSEhIMFi/uD5UKhV5eXkG+9ErSv78889s377dYLsyZcpQ\npkwZ5b2ZmZkiACORSCQgwyklEolEInltCQiA+PjBJCf3ZNMmU5KTk7G3t6dz586o1Wrc3NwQQvDO\nO++g1Wq5du0aNWvWJCUlRenj1q1b9OvXjzt37vDJJ5/Qt29fUlNT0Wg0pKSkkJyczKBBg1i/fj0J\nCQmcOnUKd3d3zp49i52dHcOGDeN///sfAQEBAIohMnXqVExMTGjfvj1NmjR5Jefnj1i2bBnVq1fn\n66+/RqfTFfEcHjhwgDlz5nDv3j1u376tbOfh4YGpqSkJCQkIIbh165ZSk65WrVqkpaUhhMDR0ZGL\nFy8yd+7cPz0WIQTLli1jyJAhzJo1S2mztrbm22+/fQGjl0gk/3akESeRSP7zZGVlMWTIEMaPH8+2\nbdv45JNPXvUhSSSAPsfKnFGj9mFikqIIY+gpXEOsd+/eQH4elH49fb6UngcPHnDq1CnlvVarxcHB\ngdOnTz/xONauXVuk7dNPP1VCA18Xzp7NN3ytrWHYsGHcvXuXzz77zGCdAwcOMGbMGIYOHcqPP/5I\nhQoVlDwygDVr1mBpaYlWq0WlUlGhQgUuX75MtWrVSE1NpUqVKgBERUWRm5uLVqulW7duBh7MguUK\nAO7fv8/hw4fx9vbm008/Vdrj4uJ4//33sba2VvqVSCSSp0GGU0okkv88JiYm1KpVi7Fjx+Lh4fHM\n/SQnJ+Pl5YWzs7M0BCXPhReRQ7Zx40aD988iQf+6Fo8OCMhXv9QPcd68eRw8eJDw8HCmTZuGWq2m\nUqVKNGnSBDc3N9zc3BgwYIBSS+53hTiDPv39/enbty8ODg588MEHGBsbA/nGr0ajUb4zTp06Rdeu\nXdHpdHh7eyvhm2lpaTx48IDJkycr+9D/r1u3LoGBgfTv35/c3NyXcYpeW8aPH0+HDh2oU6cOW7du\npVu3bgbF3/39/ZWHFl5eXqSmpnL9+nW6dOmCVqtVjGMvLy+DvMazZ8+i0WgwNzfH2traoJaeRPKP\nRv+06FX85e9eIpH814iJiRHTpk0TV69eFW3bthVbt24Vtra2Ijs7WwghRK9evcTFixdFu3btxI0b\nN8ShQ4eEp6enEEKIUaNGCbVaLbp16yYyMzOVvoQQYuXKlSIoKEgkJyeL/v37G+xLCCFsbW0NjkP/\nftCgQeL8+fPixo0bokKFCkWOd9CgQaJHjx7C3t5efPjhh0IIIfz8/IStra1o06aN2LFjhxBCiPfe\ne0+4ubmJVq1aiR9++EEIIURYWJjo0KGDsLe3FwkJCeKTTz4R7du3F1WrVhUajUb8+uuvynEUPG59\nW1RUlNBoNEIIIdRqtcjNzRWfffaZ6Ny589+9DBLJv44zZ4Tw8cn/XxA/Pz8RFRX10o/Hy8tLpKSk\nvPT9/hMZMGCAePjwofDz8xNz5swR3333nRBCCB8fHxEfH29wDfXndcyYMSI+Pl4IIYSHh4e4deuW\n8n0uxOPvTP02+naJ5HXjd5voL9lRMpxSIpG8EsTvogNz587F1taWkydPEh4ejo2NDSqVirfffps5\nc+bg4+PDvXv3WLVqFYcOHSIrK4vo6Gg2btzI4sWLadeu3VMp7BVHYW/Cp59+SoUKFYpdd8qUKbRq\n1Yr33nuPS5cuMXHiRKZPn86VK1cYNmwYrq6u3Lhxg6ioKO7fv49Go6Fr164sXryYvXv3cuXKFby9\nvdm6desT6zYJIZ5ZCEEikTz2XBbmVXkOZ82aJcMknxK9YirA0qVL2bp1KwBNmzbl3LlzxV7DxMRE\nJk2ahBCCzMxMRTm1YF6jnmf9nZBIXldkOKVEInklLFu2zKC2VL9+/VizZg3r16/n/fffB/JluW/c\nuIFGo8HMzIzz58/TtGlTIP+H/fz58wCEhYWh1WoNal7t2rXLIBQH4Pjx42g0GlxdXZUfez3Hjh3D\nzMysiLKfHr0oQv369bly5Qo7duzAxcWF/v37c+/ePQDu3r0LPFaWS09P5+233wbA3NyczMzMYvs+\nfvw4Wq2Wvn37Km2igBBCwTZra2uDNolE8uf4+vq+kvw9c3NzJfxSUjxnz8KQITdRqR4rpnp7e3Pk\nyBEAEhISqF+/frE5hw0aNGDBggWKcqqlpSWQn9cYHR1Ns2bNXu5gJJKXiDTiJBLJS+XsWViwAN59\ndxhqtVoRHahVqxa3b99mw4YNdO3aFciv9VS/fn22bNnCzZs3qVu3LkePHgXyf9jr1asHwMCBA9Hp\ndEycOFHZj5OTEzqdjvnz5yttVlZWREdH4+Hhwa5duwyOKzAwkIkTJ/7h01ohBMeOHaNevXrMmDGD\nH374gYiICGW5SqXi2rVrZGZmYmpqSqVKlbh06RJ5eXn89ttvBrLuBffTtGlTdDqdQW6SXgjB0dHR\nYN24uDj8/f25fv36U5xtiUQieb0JCICgoD7ExMSj0WgICQlh7969rFu3DrVajYmJCW3atAHyFVHd\n3d3R6XSsXbuWyZMnM23aNLRaLV26dOHevXsGeY0FvXevax6nRPKsyHBKiUTyUgkIgM2boXVrOHhw\nHj179iQiIgIXFxd69OjBoUOHMDEx4e7du8yYMYNt27Zx8OBBRo4cyerVqwkLC0OtVlO2bFlWrVrF\n4cOHn3rfZ86coV+/fjx48IBvv/1WkfZWqVQMGzaM0qVLP/GHfujQoTx69Ig+ffpQvnx5OnTogJOT\nE40aNVLW+eyzz3B3d+eNN95g7ty5GBkZ4ePjg62tLSVLluSbAnFeT9rPtWvXsLCwIC0tjczMTIKC\ngjh16hQajYZTp05hYWGBubk5PXv2JDY2lo4dO7J06VKGDRtGYmIiFhYWODg4UK9ePYKDg8nJyWHE\niBF4eXmRkpKCWq3GwsKCa9eu8euvv+Ll5cW0adOoU6eOcgyjR48mISGBsmXLEhYWxuHDh4mKimLG\njBmEhISQm5vL4MGDn/q8SyQSyZMYNQq+//4hsbGnFAEfrVZbRHl1+vTpTJ8+HYDU1FT8/PyoUqUK\nW7ZsMVjvSeqtBdslkn8DqlcZI6xSqYSMUZZI/lvoJcBHjSqquDd9+nQ6duyInZ3dqzm4J1CcofOi\niI2NVQym0NBQrl+/Tl5eHhMmTOCrr76iVq1aREREMHXqVOrUqWNwszN48GCl/eHDh5iampKVlYVa\nrebnn38mJSWFkJAQJbRMp9Ph5eXF1KlTqVu3LgCHDh0iODiYRYsWsXHjRpKSkmjXrh2RkZHMnDlT\nGnESieS5c/36dYPcwcLvC5OTk0NaWhrm5uYv4/AkkhfO7x7kv+QuluGUEonkpfIkyXQhBLGxsa+d\nAQevLgxHCEFWVpZBHuC5c+cwMzPjwoULAGRnZxusryc+Ph53d3dcXFyUQsaPHj2iRImiARj9+/fH\n1taW4ODgv5R3KHlMbGwsFhYWaLVaGjduTFBQEFZWVri5udG6dWtOnjxJSkoKAwYMAPJztGbMmKG8\n1mg0uLm5kZmZSWxsLDVr1lQ+E/o8Tb10emxsLL6+vkC+hDo8ll/Pzs6mQYMGpKamEhISQnBwML/+\n+istW7bk0KFDr+DMSCR/TmGD7c/EYEqUKCENOMl/HmnESSSS1wKVSkVMTMyrPoxiCQ4OfqIX7sGD\nB7z//vtoNBo6d+6MtbU11apVQ6vVkpiYyOjRo9FoNHTv3p07d+4QGxtL+/btcXZ2xsbGhqtXr5KZ\nmUnHjt14+20tn3+eH3LZt29ffv75Z44dO8ayZctYu3Yt48ePp379+gwbNgw/Pz/c3NwUI6swn3zy\nCStXriQqKoqKFSsC+XWs9AZBQYNv9erVxMTEsGLFCurVq2cgKPBHeYevipdhMEVGRmJjY4Otra3i\n6Vy6dCm2trbY2NgQHx8PQIMGDZRaYQMHDsTBwYGcnBw+/fRTypQpg6enJyqVihYtWtC3b1+Sk5Np\n06YN4eHh3L59m27dupGenk5OTg4jR45k6tSpjB49mkqVKtG0aVM2b96MSqXCzc2t2HNR+AFDQEAA\nJiYmynshBL6+vmzatInWrVs/3wshkUgkkleGzImTSCSSv8GyZctwcXHBy8uLK1eukJ2dzdSpUwkN\nDX1iSYQ2bdoQGBjItm3bWLZsGaVLl8bY2IPLl3tjYtKdGjV+Y+3atTRp0oQ2bdpw4cIFfHx8cHV1\npVevXhgZGbF7924AA8W9gjf0Xbp0wc3NjXr16vHw4UPOnDnDl19+yY4dO4qsO3r0aEqWLEm/fv1o\n1aoVpqamz5x3+LIYOHCgEnKanZ2NpaUlW7du5cSJE8ybNw9fX1+lJEN4eDju7u4cO3aM9PR0oqOj\niYiIIDQ0FCsrKypVqsTWrVuJjY2lRo0aAMycOROdTkdeXh7Ozs40bdqUjRs3smfPHvLy8rh58ybZ\n2dm0bt2aNWvWKN7LiRMnYmFhQXp6OvPnz6d37950796datWqsW7dOqZMmUJkZCTffPMNe/fuJTMz\nkzNnznDlyhU+++wzMjIyMDIywtXVlaCgIFQqFffu3WP79u14eXn94Tm5evUqSUlJtG3bVmmbMmUK\n1tbWimqf5N+NPhx7/PjxeHh4cOfOHVq2bImZmRn79+8nKyuLChUqsH79embMmIGdnR12dnY0adKE\nyMhITE1NGTx4MHfv3qVevXosX778VQ9JIpE8AWnESSQSyd8gMTGRkSNHAvly4ikpKcqywqGJu3bt\non379kq5ggYNGihG1ZgxXahfH6pW7Uy9em8B0KtXLxYtWoSRkRHe3t7Mnj27yP6flLg/efJkJk+e\nbLDu3r17i2y3YsWKIn0GBAQYvHdwcMDBwQEAT0/PPzwfLxu9R7FcuXJA/jm9evWqolA3e/ZsJk2a\nxMmTJ0lMTCQ6OhqtVktOTg6urq4AuLq6snbtWho3bmzgxdLXrFKpVFy4cIEWLVoAYGRkRKVKlThz\n5gxvvlmLDz+ErKz8bbZv385XX31Fbm4uOTk5QH5oa5kyZRBCEB8fj5GREeXLlyc3NxczMzNatGjB\nwYMH8fDwoEaNGkybNg1jY2MqVqzIqlWrqFatWpHxFv4PMHfuXLZt22Zw3WfNmkV4eDhr1qyhX79+\nz+/ES15bhBAsWrQIDw8P3n//fUaMGMG5c+cYMmQIvXr1Yty4cezZs0d5kFPQe/v5558zduxYNBoN\nV65ceZXDkEgkf4I04iQSieQZ0Au0lC1bj/3799OoUSNu3LhhsE7dunWVJ9n60ER9fSOAAwcO0KRJ\nEx4+fMitW0f45puG+Pgk4OIyiOvXrxMdHc2sWbPIy8tj9erVXL58WfEU/RfRn3Nr6yevEx8fT+PG\njRWDaeTIkVSqVAnIN/BcXV2ZN28ekF/X75dffsHY2JiuXbtiY2NDZGSk0tf9+/eBfCPOwsJC8UgK\nIcjIyGDnzp1cu9aRrVtBXyN+5syZjBo1iry8PD766COcnJwAlOtmYmLCiBEjGD16NA8ePMDOzg4z\nMzOOHj3K+fPnWbRokbLPqlWrsmXLFlq0aKGUlPD29ubBgwekp6dz9OhRTpw4wdq1a1GpVPTu3Rtz\nc/MisuqrVq3CxcWFxo0b07x58797Gf6RxMbGKuI8np6eaLVaIiMjCQsLM1g2ffp0YmJiyM3NpVu3\nbuzYsYMjR47QsmVLhg4dytKlS6levTrJycl069aNgQMHMnz4cDZv3kxOTg4uLi5ERkZiZ2fH7t27\n6dmzJ82aNSM+Pp4LFy4ghMDCwoL169czYMAA7ty5Q7NmzZg/fz7+/v4G3rINGzY801jDwsLIzs4m\nKioKgBYtWhAXF2fw8EhvoBX03gohSExMZNasWQAy50wiec2ROXESyUukYC6PWq1m6dKldOjQgbZt\n27JixQry8vLQarWcPXuWS5cu0aVLFwIDA9FoNJQrVw6tVktMTAxLlizB3t4eR0dHUlNTSUlJwcrK\nii5duig5QZIXS0AALFwIN24MZcuWLWg0GoYNG2awTuvWrSlVqhRqtZpVq1YxfPhwADZs2EDHjh3Z\ntGkTgwcPZujQoaxduxaNRkOpUqVo06YNo0ePJiAgAFNTU0qVKsXXX3+tePz+KRSXb7Z//34cHByw\ntbUlIiKCr7/+usj8njNnDnZ2djg5OXHp0iUAFi5cSIcOLVm4sDFffbWDjIwMBgwYgEqlYvPmzWzd\nuhUnJycGDRrEnj176NWrF5mZmWzatAk3NzdWrFhBYmIiZmZmyudPb7CpVCr69etH7dq1lWP39fWl\nY8eOODk5MX36dCpXrkyXLl2wsbHB3t6eCxcuMHPmTK5dm0WNGhpyci5iY2NDhw4dCAkJITo6mnLl\nyrFz50527txJqVKlqF27NidOnGDMmDEcO3ZM2b+fnx81a9Zk3759dO/enfLlyzNjxgxUKhV169bl\np59+on///ixfvpzIyEj27t3L6dOn+fHHH9m0aRNnz57F19dXyVcMDg6mVq1aeHp6MnjwYExNTYmJ\niflXG3DHjh2jdevW2NraMnz4cK5fv06XLl1Qq9UMGTIEIQTbt2+nVatWbNq0iYcPH7Jz504ADh8+\nzKpVq0hISCAuLo7c3Fxyc3N58OABkG/MP3jwgISEBB48eMClS5cwNjZm+fLlVKxYkT179pCenk54\neDhJSUns3r2b48eP8+abb3LkyBHKli3Ljh07+OSTT5gwYQIREREsXboUDw8PRYzmwIEDAAwZMoSo\nqChq167Nnj17nnr8Z8/Chx/CpUv5ocaDBw9WcluPHDlC/QJKUvqHR0II5s6dy7Rp0xBCoFKpqFcv\n/6EUUOShlEQiec3QPxV+FX/5u5dI/jvExMSIadOmCSGE6NOnj7hz544QQohHjx4Ja2trIYQQZ8+e\nFZ06dRJ9+vQRycnJyrZ2dnZCCCHS0tJEt27dhBBCnDp1SowdO1YkJycLd3d3IYQQx48fF4MHD35p\nY/qvcuaMED4++f//CgXnwL+dmJgY0bx5c7F582YxZswY0a5dO+Hi4iLu3r0rcnJyRNeuXZV19fP7\n6tWrylw+cOCAGDFihBBCiClTpoiIiETh6BgiPvssSCQnJ4sBAwaIlJQUYWVlJZo1ayaOHj0qBg4c\nKIQQ4sGDByIjI8Og7+c9toLXMSQkRISEhCjv9cf3orl//75IS0t74ft53Xn06JHy2snJSXh5eQmd\nTieEEOK3334Ts2bNEra2tqJ///5i/vz5Yvny5aJkyZJCrVaLcuXKCQsLC7Fu3TphYWEhsrOzhRD5\nc1EIIapWrSqSkpKEEEJUrlxZpKamCiGEqF69urh+/bpo1KiR+Oabb0S/fv1E+/bthRBCmJmZiRIl\nSogrV64ox7Vy5UoRFBQkhBBi+PDh4tSpU0IIIZYsWSJWr14t/Pz8RFRUlNK2fv36px6/j48QIET3\n7vnz8urVq0Kj0Qi1Wi0+/vhj4efnJ6ytrYWDg4OYOHGiEEIIPz8/8fnnnwshhPDy8hIpKSni0qVL\nwsnJSajVauHj4/NXLoFEIvkb/G4T/SU7SoZTSiQvmdDQUGJiYihRogSHDx9mzpw53Lt3T5GBr1ev\nHhYWFpQuXdrAM6AnKSmJhIQERdBCX2y6cE6Q5MWiL5Ug+WMK5puVLFmSY8eO4e7urpRPKExycjKN\nGzcGoFmzZor6ZnJyMlOm1OLq1f3k5uavK37Pefvggw/49ttvOX36NO3btwfA1NQUU1PTFzYua2tr\nJUcO4N133zVYXrt2bUJDQ1/Y/vWULl2a0qVLv/D9vO5cv36dSZMmcfnyZc6cOUOJEiWUuWBubk5q\naipJSUlMmDCBmzdvAlC+fHn69OmDu7s7gYGBVKxYMf/GqEQJsrKyeOut/NxU8fjBMw8ePMDT05O8\nvDwyMjJ48803qVSpElOmTKFkyZJKyGLTpk3JyMhg8ODBSt5rQfQqsA0bNiQhIYFBgwZx9uxZZfmB\nAwcYM2bMU49/1Cj9fwfq18/PXy2YL+vv78+nn35qIISkL5wNhvm0P/3001PvVyKRvDpkOKVE8hIo\nHOoSFxeHs7MzI0aMKCIDf/r0ae7cucO5c+c4d+6c0of+JsLCwoK2bdui0+nQ6XR8+eWXBsv1OUGS\n1xMHBwdF7v7fSsH5rs83GzRoEJBvmG3fvp3o6GiDm8WC8/vEiRMAHD16lHr16pGbm8v9+/eLGCv6\nud6oUSN69uxJvXr1FOn/rKwsJadN3/fzxMTEBDMzM+V9mTJlKFOmzHPfj+TPOXsWunSZj719H6Kj\no1Gr1RgbG7N//37OnoUhQ25QqlR1Spcujbu7Ow8ePCAvL4+cnBwOHz5Mq1atEELg6OjIo0ePsLW1\nxcnJiaSkJMBQybV06dKUKFGCR48esWTJkt+VZY1xdXUlICCAqlWrKttUrFgRtVrNJ598UuSYC4ZQ\nm5iY0KZNGwCmTp2KWq2mUqVKNGnS5KnPwZPqb+p5VbUuJRLJi0N64iSSl4A+f+ryZTh6NIy9e/ei\nUqnw8PCgc+fO1K9fn0ePHpGXl8eoUaNYuXIld+7cYcSIEezatQt4/CNcpUoVHB0dUavVAPTp0wdX\nV1fi4uIUEYWwsLBXMk7J8+XBgwcMGpQvclK6dGlu3brFhQsXaNy4MQsXLqRz5840a9aMixcv8tVX\nX6HRaPD19VXmT9myZQkKCqJLly6Ympry8OFDIiIiKFmyJF27diUnJwdra2vCw8OxtLSkTp06XLt2\njdu3b7N//34GDBhAVFQURkZGilCDl5cX06ZNo2zZsjRo0IAbN27g7++vFGkfP348VlZbCAl5m8uX\nwcpKpagiqlQqfH19cXNzIy8vj4YNG/Ltt98qywCqVq2q5MyVLl2aFStWMGLECI4cOYJWq+Xq1atU\nrVoVR0dHTExM8PHxUcottGzZksqVK2Nvb4+RkRGrV6+mTJky8gb2X05AACQkODN58gTCw4NISUnh\nm2++wd/fn5MnZ3D5chM++CAQG5vzaDQajI2N2bZtG3PmzGHy5MnUrl2bOnXqoFKpCAgIYMGCBeTm\n5nLp0iX+7//+j86dOyv7srS0RKVSUbp0adavX4+HhwcqlYqMjAx69OihzOe4uDi0Wi3jxo3DyMiI\n2NhYzp8/z/jx4+natWsR6X9HR0euXr3K119/zZ49e7CzsyM7O/uJ0v95eXkcOXKEW7duUadOHb7/\n/nslGqM49MXhJRLJv4i/Gn/5PP+QOXGS/wjPmj/1tLys/BvJyyUgIEAEBwcLIfLzelJSUgyuc/Pm\nzUVeXp5IT08XLi4uIiEhQYwYMUIMGDBAbN26VXTu3FlERkaKZs2aCSGEsLKyEhEREaJJkyZiyZIl\nIjc3V5ibm4u9e/cKCwsLsWHDBtGpUydhbW0tsrKyhEajEbm5uUKIx3llgwYNEufPnxdjxowR9erV\nE0Lk59ZERESITp06iYyMjOc+3zUajcjLyzN4L5Ho+aP59iLmov4zoefcuXNi0KBBf7hdTEyMmDp1\nqvj888/FunXrhBD5eXFdu3YVGzduFEII0aFDBzF//nzh7+8voqKixJw5c0STJk1ESkqKGDNmjEGO\nnxBCxMbG/mfyayWSfzvInDiJ5PVE5k9JnoU/qkEH8NZbbylhW1lZWUodtPT0dJKTk5XaT+fOncPe\n3p47d+4AcPPmTcVbVbNmTY4ePQrAhx9+SN++fbl+/Tq3b99WQswAgzzLY8eOYWZmRs2aNZU2T09P\n+vbti5mZGWZmz3e+r1u3zsCbtm7duufXueQfzx99vz7v796CeWZ66tatW2y9xcL8mfS/p6cn5cqV\nIzMz86mk/8ULCBOWSCT/HGROnETyL+BliShIXi7FyX0XvHHTv75w4QKVK1emVKkGGBtrsLNzIy4u\njnbt2pGbm0vt2rWJi4ujXr16HDhwgAoVKhAZGUlubi4XL16kZcuWACxfvpzDhw9z4sQJKleuDOTf\ntEZHRxsUnA4MDGTixIkGxxIcHMyRI0eIi4t77uehSpUqf/j+ZRMaGoqdnR0dO3ZkyJAhSpkES0tL\nmjdvjlarJTo6WgkxBUhJScHLywuAhg0botVq0Wq1dO3aVWlzdHTE2tqajRs3vpJxSZ4/UvpfIpG8\nKKQnTiKRSF5Dzp6FxMShJCUNIDQ0lAoVKjBv3jwDj1RiYiKurq7cu3ePpUuX8s03DTl58idOn75K\n+fJbMDExYcGCBbzxxhtotVqSk5MZNWoUjo6OuLu7M2XKFGxsbBQVv1mzZpGdnc3Dhw+5dOkShPWz\n4QAAIABJREFUKpVKuYnUo1KpGDZsGKVLl1baVSoVpqambNy4EXd3dzZs2MDbb7/9ck/YS+LkyZNs\n2rSJ2NhYJe/uxIkTREdHM2PGDGxtbRUFwILnreB5rFKlShGPTpUqVYiKiuLRo0d06tSpiNql5J9J\nwXxoK6vH3u6lS5fSsmVLWrRowdSpUzExMaF9+/Y0adKEjRs3FincPnHiRAYPHsyMGTNo0qQJ38jQ\nDolE8lfjL5/nHzInTiKR/AN5+PChGDdunOjcubNwcXF5IfvQ1336o1JNheufnTkjhLm5nUH+z9Pm\nj6nVaoO8M0nxBAYGik2bNhm0abVaIYQwqPMlhBDvvPOOUqurffv2wsvLq0i7viaera2tEEKIlJQU\n0aVLlxd2/AXr261cuVLMmTNHtGzZUtjZ2YkePXoo+V4uLi5CrVYLS0tLERsbKxISEkSrVq2EjY2N\n8Pb2Vvp77733hFqtFubm5uL8+fNKzqQQ+XOq4HnJysoS9evXFykpKUrNtJMnT4oWLVqIgwcPvrAx\nv0r+LCev8JyRvDoKzvFhw4YZ1PXTX6cnfQ6aN2+ufA4Kzm8hhHBwcBAxMTHC1dVVNGzYULzzzjvC\n2dlZyZMUQoiBAwca1JmU/PfgGXLiZDilRCKR/Ak+Pj788ssvAMyfP5+FCxfyyy+/kJGRodQKO3bs\nmKKquGbNGgA0Gg0eHh7Y2NgopSA0Gg15eXmEhIQotZk0Gk2RfY4aBT4+j+s/PQ3168PRoxsNZMaf\nNn9M73WT/Dl5eXkG742NjYtdT+9xi46ONrgOBdu3bt0KQFpaGmq1Gmtra5YvX/7iDr4QFSpU4MCB\nA8TFxVG5cmUl1M/c3Jzo6GilNETDhg05dOgQe/bs4cKFC0pIX15eHtHR0bi4uBSZP4VVQQMCApQ8\nTch/iOzr68umTZto3br1Cxzlq0NK//9zKDjHk5OTSU9P/8N1Cn4OWrRooXwOCrJhwwbS09NRqVTs\n2LGDTz75hAkTJhAREQHkX/8DBw4onzuJ5K8gjTiJRCL5E/r378/q1asBCA8PR6fT8eOPP7Jnzx5O\nnDhBSkoKvr6+fPfdd8TFxbF48WJycnKA/PyxvXv3sm3bNrKzs4u9aSuu7c9u/oBi88+eNX9Mp9Nh\nZPTifhIePHjA+++/j0ajoXPnzlhbW1OtWjW0Wi2JiYmMHj0ajUZD9+7dFQEWV1dXJdcsLi6OW7du\n4ejoiKOjI9WrVycpKYmQkBA0Gg2dOnXCyclJqQ33vNHnNllaqgkLC1Ou75o1a5QaX4V5klFcXHuV\nKlWIiYlhzJgxzJ079/kdeDGEhoai1Wr54osvuHXrFh988AEajYbIyEju3bsHQHZ2tsGxXr9+nf79\n+6PRaDhx4oSyXu7vldfF7+GiZcuWJTk52aAPwECoQ8+UKVPIzc3F0tLyhY73dcbX19egALfk1VF4\njleuXJkvv/wSjUZDSEhIsevoPwf674OCn+379++zdetWevfurbQV/uzn5eURGBioCFhJJH8FacRJ\nXgtiY2OxsLBAq9XSuHFjgoKCsLKyws3NjdatW3Py5ElSUlIYMGAAkP/Dpy+YPH36dMUDMmfOHDQa\nDeXKlUOr1bJu3Tq8vb2xt7enXbt2HD58GHgsItCyZUt+/vnnVzZuyT8Da2trDh48yMmTJ6lfvz7Z\n2dnKj/H//d//8dtvv3H79m2qVauGkZERtWrV4vr16wBK7aaaNWty69atVzaGV82yZctwcXEhOjqa\noKAgvvvuO5ycnNDpdNy9e5esrCyio6Px8PBQam3pvUF6QZDDhw9jb29PVFQUzs7OSt9dunRh165d\ndO3ale+///6FHL8+tyk8/P9499130Wg0ODo6EhkZyZQpU4CixviTvCxpaWlotVo0Go0ibKJn3Lhx\nZGVl8fXXXz/X4y8ssKHT6Zg4cSImJiZYWVkRHR1Nnz59gHzDLCsrS9lWCMH8+fPp0+dxMW2AEydO\nFClIPXToUKZPn46bmxvnz59X2gsKdeiZNWsWxsbGiudaInkV6D8bvr5F5/iECRMUj/STPgc3b96k\nVKlSRfpduHAhY8eO/cMIh/Xr19O7d28DD7VE8rRII07y2qC/sZg0aRKQX1R1+/btrFy5knnz5gH5\nN0WpqamEh4cDkJCQQHJyMrGxsURGRuLt7U10dDRNmzZFp9PRp08fAgICiIuLY8GCBYoMtF5EwN/f\nn19//fXVDFjyj0Kr1eLt7c3AgQNp0qQJO3fuBPKFLho2bEj58uW5fPkyubm5pKamUrVqVWXb7Oxs\nLl68SJUqVQrmBCv8F8IYExMTFQEVc3NzgzGfP3+epk2bAtCsWTPOnTsHFPUG2drakpCQQPPmzfnp\np5+U7fUS7Q0aNODKlSsv5PgLhrcOGDCA3bt3ExUVRXBwMGXKlAGKelUKekojIyM5c+YM9vb2LFy4\nEE9PT27fvk1aWhqDBw8mLi6OJUuWYG9vz/Hjx+nevbvy4Orhw4doNBoCAwMNVDAHDRpUrCeyYKiu\nWq0mLi6OCRNiWbjQl1WrbhIUFMS2bduUc7xixQrc3d2Voun9+vVTvAcqlQqVSoWzszNTp06lR48e\nnDp1CoBevXqxfft2NBoNP/30E2FhYTRp0oTdu3ezfft2GjZsqIy/sFCHvu9Vq1axdOlSpcyFRPKy\n0T+gSU8vOscL8qTPQbdu3fjf//6nrKPHwcGB5s2b/2HIbJ06dXB3d3/OI5L8V5DqlJLXDv0Nm96D\n0aBBA65evark7MyePZtJkyZx8uRJTp8+rdwYmpqaYmpqWqS/kJAQNmzYwMOHD2nQoAGQHxKhV+v7\n7rvvXtLIJP80zp7N/4EfNQo8PDxYt24d7dq14+2336Zfv34EBAQwaNAgypcvj7+/P3379iUvLw8f\nHx8lT6pbt26kp6czfvx4IP9Hvnv37ly+fBkhBD/88IOiblhcbtw/Hf05LFs2XyK9UaNGRSTS69at\nq+SBJSQkUK9evSLeIIBSpUphYmLCxo0b+eyzz4rs68CBA0qO4vPm79Ybc3d353//+x/37t3D1dWV\nIUOGEBAQgIWFBX5+fqSnpxMeHk5cXByJiYl8/fXXSojVokWLGDx4MAMGDGDkyJEGKpghISF06dKF\nsWPH8s0337Bp0yZln/p8HIB334VLlwSVK0fz3XffYWtrq6z38ccfGxyrvb298trX11d57eTkZLBe\n1apVFUM1NTUVPz8/g+V6Bc7p06crbXrj0tPTU2mLiYn5w3MXFBRErVq1iIiIYNy4cUqdNInkeaDP\nOx41yon69Z2KXeePPgf6hx9gOL/1c7zgtgXnvYODAw4ODkXaJZKnRRpxkleK/gbP2vrJ68THx9O4\ncWOEEMTHxzNy5EgqVaoE5NfR2rFjByNGjCArK4ucnBzKlCmjGIKZmZmEhIQQFxfHhQsXmD17NgCV\nK1dGp9Nx7do1hgwZwo8//vjCxyr556F/QgvQq9dl+vfvD0CNGjWIjY01WLdp06bF5qht3brV4Els\nccWCQ0NDuXjx4nM88tcH/TkcOnQoW7YYlkvQ07p1a8LCwlCr1ZQtW5ZVq1bRqVMnKlasyLVr11i2\nbBk3btzA29ub+/fvc+DAASDfY+Tt7U1gYCAff/wx1tbWREVF0b9/f5o3b063bt1ISEggIyODGzdu\n0LRpUwIDA4mMjGT69OmoVCpmzJihGEMLFizA1NSURo0aERwczJIlS1i9ejUlS5ZkxYoVCCHo0qUL\ntWvX5urVq4SEhBQJJ3wSVapUoXv37mRkZHDmzBkePXpEiRIllO+qpKQkEhISFE9eo0aNAIiIiCAm\nJsYgNLGw57agJzIhIYGqVaty//59oqOjFY/a22/DxYvLyM2tjq3t8wnVLFjPrkaNGnz11VfPpd/C\ndOvWjYEDB1KvXj1pwEmeO8+7ILxE8rKQ4ZSSV4r+Bq9gbVv9DW9sbCxOTk7MnDmTcePGAWBiYoKP\nj4+ybsuWLalcuTL29vY4OTkpOUf6Pt566y2qVatGp06dlBw6eJyT0qNHDyUMQiIpTMEQupCQEMWI\ne1qeVvGxe/fudO/e/VkP86VTMIdVo9EUq8y5ZcsWbG1t2bfPBlfXLZiazlMEL/bt20dubi6Ojo7K\nk+uEhAT8/f3Jzs6mbdu2nDt3jsOHDzNmzBjeeOMNVq9eTZs2bUhISOCDDz5Ap9Nx4sQJgoKC8PT0\nxNjYmEePHuHo6EiLFi1YsWIFAQEBREREcOTIEWJjYxUxmpkzZ6LT6di1a5eB9yggIEDxzBf0jC1c\nuFDJUbO0tGTbtm0GYd5/xtmz4O19jzlzNhMREUG7du04deoUNWvWVNaxtLSkbdu26HQ6dDqdombq\n4uLC+PHjn0r44MCBA8oDr+LycYYNG4ZarS7Wi/ksFBTNMTY2pkKFCgbLx48fT4cOHahTpw5RUVFM\nmzYNyPc6hIaGsn79ejp06ICNjQ1r164FoGvXrqjVamxsbEhNTQWgR48e7NixgxYtWjB48GAAg0Lq\n+tdbt27Fzs4Oe3t7Re3P0tKS7t2706pVK6Kjo5/LuCUSieR1QHriJK+Ux2EMDtSvnx9WMHDgQFJS\nUoiNjSU0NNRg/RMnTgCGYQhz5swp0q/eI6JSqYoVOkhMTHxuY5D8eyn4hHblypV/efvivG7FYWZm\n9pf7ftUMHDiQGTNm0LdvX8aOHct3331HlSpVcHBw4N1332X27Nns2bOHEiVKcPXqVZYsOcq8efPQ\narXKjbiewjLcoaGhnDx5kpCQEE6dOsXp06dp1qwZe/fuJTc3FxMTE9577z3CwsJITU3l448/xsTE\nBCEEp06dIjMzEw8PD4QQ1K1bl9zcXBYtWkRaWhpDhgzh9u3bihCBXpHzaT1jhcO8n4aAAFi6dAHb\ntu2kSZNSdOrUiSNHjlCzZk1SUlKA/OgAR0dHRSyhT58+uLq6olKp+Oijjxg5ciRLlizB29u7SI5N\nYGAgq1evpmbNmkydOpWQkBAlH0dfwkDPvHnz6NmzJxEREUXk0J83165dIzo6mi+++AIwlFN3dHSk\nc+fO9O7dm7S0ND766CP69u3L+vXrMTU1Ze3atWzYsIGxY8eiUqm4e/cuq1atonbt2kX2o39YMn/+\nfGJjY7lz5w4ffPABq1evply5cvzwww/cvHlTURWUSCSSfwPSiJO8UmQYg0TyzyQ0NJSYmBhKlCjB\nvXv3qFatGgC1atXi6tWr1K5dmxIl8n9i9Mv0FJbhLhj2B3DlimDnTnj33WE8enSF5s2bk5KSQk5O\nDpmZmTx69EgREwkODmb48OGkp6fz66+/cuvWLS5evMj333/P3r17Wbx4MW+//Tbff/895cuXx8TE\nhJs3b7J48WKDPJRTp07h6uqq1IDTe8b0nrl79+6Rnp6uHLs+zPtpyH9YNYVRo6YUKRlRu3ZtxRs5\nbNgwhg0bVuQ8Q76hpqdgjg3AyJEjDQzjJ+Xj6B98vSgFz8LcuXOHUqVKKeUHCsupv/nmm0yePJlN\nmzYpht68efOIiori9u3bdOvWDcifL1988QXjx49n/fr1wONoCv31SEtLIzExkY4dOwKPQ0zffPNN\nVCoVFStWNCh5IHl9iY2NJSoqihkzZhASEkJaWhpr167ljTfeoFKlSmzYsIFPP/0UOzs7tFotXl5e\n+Pv7c/LkSfz8/FCpVLi5uSmeX3Nzcxo2bEhCQgI3b97E399f2VaPnZ2dktumf63RaIiKiuLs2bN0\n7dqV06dP4+XlxbRp09i7dy9ff/01+/btU77nJJKXjQynlLyW1K5du4gXTiKRvFoKy9THxcXh7OzM\nr7/+aqDMaW5uzoULF5Sb5ps3bz6xz+LC/nbtgqNHIT4+30tZpkwZBg4ciBCC3r17U716dYyMjBQP\nzPXr18nNzaVJkybExMSQlZXFhx9+yKpVq7h37x6nTp3it99+Iz09nRIlSmBvb8/EiRNxcnLC19eX\n9evXc+3aNYPwxoKeMbU6vzYc5Hv5C4d5/xlPU/Pv38TZszBkyE1UqrJKmxCiiJz6zZs3+eyzzzhx\n4gSff/45x48f58iRI0RFRfH5558r2168eJG8vDzFGwqP85r1IZJVqlTBysqKqKgodDpdkd+PCxcu\nKLnUkn8WTypIr0fvmdZoNMTHx7N//342b96sLHd1dSU6OprmzZsrbX9UmF7/Wv//888/VzzwABkZ\nGaxcuZL4+HhpwEleKXL2SSQSieSp0OewXr4MR4+GsWfPHoyMjNixY4eBMmfJkiUZN24cDg4OGBsb\nM2XKlGJvkoBiw/46dYK8vHdYsmQwe/bsJDRUR1bWEd566y02bdqEh4eHIvdtZGTEkCFDOH/+PCdO\nnECtVtO+fXtu375NZGQkPj4+nD9/nsDAQDw8PKhQoQJnzpzh888/54MPPlD2qfcE/pFnLCUlBQcH\nh9fqAdPrqGoXEABBQX0oX/6iogK8YsUKGjVqhLu7u1I4WV/qICsrCy8vL+rUqcO5c+coXbo0b731\nFgBlypTh8uXLxMTE8Msvv2Bubk5sbCwJCQk4Oztz9+5dcnNziY2NpVy5cmg0GhITE7G2tmbBggUc\nPHgQJycnDhw4wMcff0xISAjffvst+/btIyQkhOXLl7N79268vb1JTEzk4cOHLF68GHNzc959911i\nYmLYsWMHBw8eZObMmXh7e5OUlETFihVZu3YtYWFhrFy5khIlSqBSqdi8ebPiJZY8O6GhoezZs4er\nV6/yv//9jw8++IAzZ86QmpqKh4cHZmZmSo6tXsX23Llz+Pr6cvv2bYNlxRlaY8eO5c0336Rz585M\nnjxZUawuaNwJIdi+fTs2NjZK2DNA586dmTRpkhKKLZG8MvQ1i/7qH+AI7AX2AI7FLB8FRANxwPgn\n9CEkEolE8mpJSEgQrVq1EjY2NmLYsGFi5cqV4p133hFqtVpYWFiIqKgoERMTI2rUqC1q1NCIOnUa\niqCgIJGWliZatmwp7OzsRI8ePURubq6IiYkR7dq1E05OTqJDhw7iypUrBv17e3uLjIwMpe/mzZsL\nT09PMWjQIHH+/HkhhBBqtVoIIYSfn5/o1i1KQJZ4440aok6dOqJ58+bizTffFA4ODqJUqVJi0aJF\nonbt2kKj0QhHR0dx48YNkZiYKJycnIRGoxE1a9YUiYmJwtHRUdy6dUskJyeLAQMGPNN5+jvb/pc4\nc0YIc3M7cebM4zaNRvNU28bExIhp06YJIYQICQkRQUFBIicnRwghxLBhw8ShQ4dETEyM+PDDD4UQ\nQvz4449ixowZynbx8fHCyspKhISEiOTkZFGlShUxd+5cERoaKoQQYuXKlaJ58+bi6NGjwsfHR9jZ\n2QkhhHjw4IEQQoiff/5Z6XvVqlXCx8dHuLu7i5ycHLFlyxbxxRdfCCGEWLx4sfjxxx/FypUrxVdf\nfSWEECIwMFCEhYX9nVMnEYZzYOXKleLrr78WgYGBQgghJk+eLGJjY8Vvv/0mHB0dhbOzs6hbt65I\nSUkRPXv2FMePHxdCCNGhQwchhBBHjhwRM2bMEEIYfq9ERUUJIYSwtrYWQghha2ur7F8/J9Rqtejb\nt6/Iy8tTth00aJA4cOCAaNWqlUhMTHyh50Hy3+J3m+gv2WJ/xxM36XdDzgj4EYgqtHyxECJApVK9\nAWwBiqpPSCQSieSV07BhQw4dOgSAs7MzDRo0YMKECQwePNhA1XXw4Hwxk9DQUHJycpQwJ2NjY7y9\nvZUwpzZt2hAYGMi2bdtYtmwZEydONOg/Ozub6Ohog3pnXl5eyn4Keup69oS0tADS0soRGbmD6Oho\ncnJyCA8P59SpU1haWjJixAiD8VSoUMGgGDjkF9uGfGGSZ/WkyTDvp6N+fTh6dCMFxCtZt27dX+5H\nCMHNmzeLeGHAsKxCeHh4sTl3kB+yOX/+fIMSDQMGDGDKlCl069aNY8eOAfnCRRs3bjSoJ+rh4YG/\nvz9ffPEFxsbGJCYmsnbtWiIiInj48KEy7wqXeJA8O2fPwoIFUKPG47bs7GxWr17NTz/9REZGBs7O\nzpibmyufaX0+aNeuXfH09KRu3bqkpaWRmZnJ0KFDlTzKgt8rs2bN4ttvv8XR0bHIMj1GRkZ89NFH\nSsF7/XoVK1ZkzZo1eHl5ERERoXiNJZKXzd8x4lRCiIcAKpUqr/BCIUSWSqXqDcwGhhVeLpFIJJLX\ng+vXrzNp0iQuX77MmTNn8PDwICcnByiaO1KwLTMzk4kTJ/7pDXbh/u/du0fFihUN+i5btizJycnU\nqVPHQICiRImrNGuWxMOHbZW2KVOmYG1tjaWl5fM/GZLnQsHyA8W9L8yTaoaWKlUKKysrlixZwpQp\nU5R2/dwpWFZh/fr1zJ071yAHs0+fPrRp00ZR94T8uebs7Ezfvn0JDQ0lMzOT0NDQIvVEQ0ND6d27\nN4GBgbi5uVG/fn28vLz46KOPgHyxm4K18l5ksfn/CgEBsHmzAz4+hkWwCxekL0hxBbb1HDx4UHld\nXPF5PQVrfOpfR0U99k3ot9XvC2Dv3r1PMSKJ5MXxp0acSqWqAqwGBKD6/X88UNBwKzYwWAixXqVS\nbQZ0FPXUARjU6NEnkEskEonkxaO/cb5/fz59+vShS5cuiiH2ZwghCAsLe6ob7Pnz/7z/oUOHMnz4\ncObOnWvgNZk7dy7btm1j8uTJStusWbMIDw9nzZo19OvX71mGLnkOREVFkZSUxMOHD2ndujXWhS2w\nv0DBfEsrq8ftxXlhIL8sxb59+3jrrbdYs2YNBw4coE6dOgY5d0CREg2lS5dGpVIp3jqVSoWZmRlV\nq1alU6dO1KxZkxIlSnD58mXWrVvHjh07CAsLY/r06cyePZvhw4fj6OhIXl6eYuwVLvEgeXYelx16\ntcchkbxoYmJiiImJ+Vt9qIp7yvpUG6pUEUAP8g27LUKIToWWVxVCXFOpVCZAnBCifTF9iGfdv0Qi\nkUj+Hh9+mH/j7O6+k9TUCVhaWpKSksLo0aPJyclRwiltbW0xNjY2kP3Ozc2ldevWDBo0iBo1apCR\nkcGnn36KEAJvb2/efvtt5QZ79+7dTJjwuP/NmzdTq1Ytg3DKgmi1WnQ6Hf7+/piamjJx4kQGDx6M\nn58f0dHR5Obm0q9fP1xcXFiwYIGB6pzk5ZGVlUW/fv0oWbIka9asKTYk7WnRP1AYNerPVTwLStC/\navSfhcK1DyUSieSv8Lva8l/7Ev2rSXT6P6Aj+cIme4GOv7c5AwN/f/0N+aIme4EeT+jjeeYESiQS\nieQvcOaMED4+wkCA4u9SUJRA8ufMnz9f2NraCmtra6HT6UTr1q2Fg4OD0Gq1IjMzUyQnJ4uqVasK\nrVYr2rZtK27duiWuXbsm3NzchEajETNnzlT6ql+/vlCr1aJ+/fri0aNHikCDEEI4OjqK7OxsRcDh\nyy+/FDY2NsLJyUlcvnxZJCcni/79+wsh8gUd9CI1/v7+QojHwg9BQUHCy8vLoE2n0z21cMnz4HWa\nYytXrhRBQUGv+jAkEsk/HJ5B2OSZ9VGFEJFCCJvf/yJ/b/tJCBH6++sPhRD2vy//4Vn3I5FIJM/K\ngwcPeP/999FoNLi5uTF69Gg0Gg3du3fnzp07xMbG0r59e5ydnbGxseHKlSt07tyZRo0a0bBhQ1xc\nXIiNjVWKxnp6ehIaGkpKSgpWVlZ06dKF1q1bc/LkSXJzc/Hw8ECj0dC/f3/y8vIICQmhYcOGqNVq\n3n//fQC8vLxISkoiNjZWKcSs0WgA8Pf3R6fTkZ2dTYMGDUhNTSUkJITg4GB+/fVXWrZsqQiEPA/+\na/XLnsSxY8do3bo1tra2DB8+HMgvOaDRaKhevTpJSUmEhIQo4YIhISHY2dkB+dcuLy+P06dP8847\n7wCPr3HB5cVd2+vXrxMREYGxsTGVK1dm1KhRZGVlkZycTHJyMkuWLMHJyYkSJUqQkZFBo0aNOH36\n9P+zd+5xOd7/H3/eRQeUlBAVKqccisix3PddyBY5bA0VYs4MsznMmWnYZmt2oolKcmbOo+4ODsOQ\nJsfoJIpMSULp/vz+aPelsH1tM9v8rufj4eG+r/u6r+vzua6r+/F5f97vz+tFy5YtuXbtGmZmZpw/\nf55u3brRtGlTcnJyMDQ0xNLSkjfffJMrV64QEBDAmTNnKCwspFevXpw5cwZvb2/i4uI4dOgQCxYs\nkDzZnvTHKo9CoaCwsJDIyMgK27RaLStXrvyb7syz6dq1678iCwdlvwlyFk5GRuafQDa5kJGReWUJ\nCQnBy8uL2NhYpk2bRnFxMbGxsfj7+/PNN98AZUqKP/zwAzNmzOC7775jz549zJgxg6lTp7Jv3z6g\nbLB6/PjxCiazDRs2ZNeuXaxZs4Zly5axbds2nJycJFNZneDB1KlTiYuL4/79+7/ZzicHzcHBwZIh\nMpRVTMyZM4ctW7bQtm3b3+3zswLTt956Cw8PD8kXLSAgQFqDnJOTIwUYUBaAlA8eoWy9ckJCghR4\n3rhxg/bt27Nr166nzv9vGmA/Lzp1zkOHDpGWlsatW7fQarXExsbi5eUl7ffgwQOSkpL46aef/qch\nsI7fu7eLFy9m2rRp2NnZ8fPPP/Pll1/i6OiIQqHAyMgIPT09bGxsePToEXp6euzatYsGDRrQpk0b\natSowdGjRzl58iRff/01w4YNo0uXLuzbt4/s7Gz8/PxYv349Z8+e5ZNPPqFVq1a8/fbb1KlTh59+\n+kkSn2ndunWFNYi/hRCCJUuW8P7771fYFhISwttvv/0Hr7iMjIyMzF9FDuJkZGReWS5cuECHDmXL\ncbOzs2nVqhUArVq14sqVKygUigpKijk5OcDTiozPki7XDdgbN25MdnY2qampTx0fYOnSpXTs2BFj\nY2O02qeEfJ8iJyeH1NRUXF0rqjGWlpY+txpj+cA0JCSE/v37ExMTg6mpKWfPnuWbb74hLi6O119/\n/SlJ9CeDjk2bNnHr1i3pvRCCRYsW8emnn+Lt7f1c7fm3c/PmTfz9/VGpVCQnJ1NUVES3PRZdAAAg\nAElEQVRpaSlQ8VnQSdM7OTlJ20Q5Q+CqVatK23VZ2dOnT0vbyt9bIQQnT14gLKwxM2aEcvDgQZRK\nJVqtloCAACZNmgSAqakp3bp149ChQ9jY2BASEkJWVhZ3797F2NiYr776ioYNG3LmzBkcHR2Bsgx0\nq1atcHNz4+7du5w6dYp69epRo0YNLC0teeedd0hOTgbg1KlTNPo1Favrq3i85EHi6tWraLVamjVr\nJm0rKiri1KlTeHh4PFPFVEZGRkbm70MO4mRkZF5ZHBwcOHr0KAD29vbSgDopKQkHB4cKg1WdkuKz\n2LhxI76+vk9lxwCOHTtG8+bNsbe3lzJ1uuNDWSbuxx9/xNzcXPq8/GC5/P9QpsY4e/bsCtsWLVqE\nvr4+69ate65+PxmYfv7555JYyM2bN9FqtahUKiIjI2nfvj2mpqakp6cDZWIVOoqKitixYwe+vr7S\ntpCQEA4dOkSXLl2eqy3/izt37vD999+/kGP9UVJSysRd5swpU8+MjY1FqVRSUFDwzGehvDR9eaKi\nohg+fHiFe7Zu3TopK6uj/L1VKBTk5TkQFjYJpVJJnz59iIyM5MyZM6xbt46tW7cCZc9GdHQ0HTt2\n5NatWxQXFzNw4EDq1q1LaWkpM2bM4OzZs+zYsYNdu3ahUqm4c+eOpNBoamoqlX5CWZCelpaGi4sL\nnTp1Yu7cuUydOhUoy+L27t2b5ORk+vTpw4wZM6Rn7sGDB0yfPr1Cv+/fvy+phv4VUZP/z5Qv3zY0\nNKyQRc/JyZFKwstn0p/MnKempnLz5k28vb1Rq9V8+OGH0mf9+vWja9euFSagZGRkXg3+ik+cjIyM\nzL+WlBS4cGEEqakBREREUKNGDWxsbFAqlVSvXp21a9dy6tSpp6TKn8WzpMsTEhLo3r07ABEREdSs\nWZPBgwejUqmoV68eU6ZMISIigo8//pi1a9diampKy1+100eNGsX9+/e5desWp0+fJjk5maioKBQK\nBb6+vlhZWVUYFCsUCtauXYuXlxeOjo7PVGMs77NVPjC1srLCy8uL3r17o9VqKS4u5sGDB8TGxrJ2\n7VrCwsIYOHAg/v7+bNy4kcuXL0vH/Oqrr4iKiqoQZI0cOZLCwkKCgoIqyP7/WfLy8ti+fTs+Pj5/\n+Vh/FJ2sfa9ePZg1ayqhoaGkp6fj4uKCo6MjR44c4eLFi9SvXx87O7sK0vQ6fssQ+FmZqSfv7Vdf\nTcPPbxh16ypo27aDFJzplBczMjIIDg6Wnps9e/aQk5NDYGAg9erVw9bWlg8//JD8/HyUSiU+Pj4M\nGzaMFStW8O2333L06FHu37/P9OnTWb16NbNmzcLAwICaNWtK6+DKk5mZ+dQ2nXKoLkttYmIildme\nO3dO2k8XVMj8Mfbs2UN4eDiPHj3CwcGBTZs2sXz5cnbt2kVISAjVq1enf//++Pr6Mn36dCmD+iSL\nFy9mzpw5uLq64u/vT35+PlCWxXdxceHNN98kKysLa2vrl9k9GRmZv5M/qoTyIv8hq1PKyMj8TYwb\nJwSU/f9b/FmVu/T0dBEQEPAXWleR+Ph4SQXwz6Lrb58+caJJkybCw8ND9OnTRxQVFYkBAwYItVot\n1Gq1uHz5sujWrZtQKpWiS5cu4vLlyxWOExgYKDIyMsSaNWvEqFGjhBBCzJ8/X8THx1e4Xn379hV7\n9+790+3Ny8sT3bp1E56ensLe3l4olUrxzjvviPT0dNGgQQOhVCpFs2bNhBBC7Nq1S1hYWAhTU1Ph\n6uoqqlevLqpVqybMzMxEq1atRFBQkHBxcRGmpqZi1KhRIiYmRjRr1kwolUrRqlUrYWZmJjp06CB8\nfX1FkyZNRJ06dYSJiYkYMWKaaNdunoiMLOvbrFmzhBBC1KpVS4SFhYn09HTRt29fERAQIJRKpVi5\ncuWf7u+f4Xmfs4cPH4o7d+5I7+/duyfu3bsn7t69K3r27CmEEGLevHli2bJlwtraWmi1WhEXFyes\nra3Fjz/+KNzd3UXnzp3Fvn37hBCPVSeFEKJt27ZCCCG+//570aVLF+Hm5iZOnTolhCi7L82aNROt\nW7cWs2fPFhqNRnTr1k188803onnz5mLSpElCqVSK6OhocfHiReHh4SHc3NxEaGioEKJMBXPQoEGi\nU6dOYsmSJS/moj0n+fn5Yvv27S/1nE+Snp4u4uLiJIXL8n9fFy9eFGPGjBFjxowRHTt2FEqlUrRr\n105oNBoxb9484eTkJJRKpbCyshJXrlwRPXv2FCqVSiiVStGmTRtx6dIlMXToUHHlyhUhhBAzZswQ\nx48f/ye7KyMj8zvwJ9Qp5UycjIzMSyc+Pp4hQ4ZgZ2dHTk4OkydP5qeffiI1NRULCwvWrVvH2rVr\nKS0tpUOHDvj7+7Ny5Up2795NdHQ0Dx8+ZP78+fTs2VNSEExPT8fHx4epU6eiUqn49tsYQO8/YRrb\nrl273yzlfF50/ezYES5e9K0gLhIVFVVh3/379//mcXRZliFDhjBkyBAASUUTyoRLAKnc78+yatUq\nRo8eTdu2bZk3bx6hoaH06dOHzMxMAgMDmTNnjpQFmjhxIkFBQQQEBODo6Ii+vr7kR6dUKlm4cCEn\nTpzA3d2d1NRUvvzySwBef/11Nm/ezLx58zA3NycvL48ePXpw6NAhzpw5w8qVi5k/fz7W1iDEYwEb\nCwsLqZ1VqlShcePGdOvWjYCAgL/U578LAwODCqW+VapUkV7v2bMHeFzuWLNmTXbs2EF8fDz16tVj\n/vz57NmzByMjI/r16yeZaeswNjZGCMFnn31GfHw8d+/eZezYsURGRpKdnc1nn32GsbEx0dHRqFQq\n0tLSKCoq4vr165w7d44BAwbg4eHBW2+9RVhYGPXq1eP1118nMDAQKDPKNjc3x83NjXfffZdKlV7O\nsOSfzADrSE9PJy4ujgYNGkjbRLksevPmzSkpKSE9PZ2FCxfSunVrPv74Y7755huqVatGjRo1yM/P\nJzMzk0ePHnH37l3MzMwwNzendu3aZGVl0a9fP4yMjMjLy+P9999n/vz5bNu2DRMTE9q1a8eyZcuI\nj49n9OjRWFlZAWVZ1a+//prIyEiEEDg4OBAeHo6bmxsHDx7k448/5vvvv6dq1aqsXr2akpISZs2a\nRUREBCqVipiYGA4ePEhcXBxz5879Jy6tjMz/C+QgTkZG5h9h8ODBLFiwgPDwcPLz83FwcGDlypV8\n++237N27VypJ06kyNmzYkBYtWjB37lyys7MZOXIkPXv2BB4PBN3d3Zk8eTIKhQIHB8GvY/nfpGvX\nrlJQ8keoX78+4eHhf6bbz8TY2BhjY+O/dAydXUB8PFy8+IIa9jdy+fJlvL29K5QdNm/eHCMjo6cG\n8kVFRbi5uXH+/Hlyc3N58OABCoWCLVu2kJyczNmzZyWBmfv371OtWjUePnzI3r17qV69OqtWreLu\n3bv06NEDV1dX9u7dK5WVhYaGolKpWLx4MXZ2dsyaNQs7OzsABgwYwOXLl9m5cycbN26kpKSE5s2b\nEx0dLZmOl59k+F/KoX+Uv/Kc3b9/n6FDh3Lz5k2qVKmCk5MTGzZsICMjg5EjR+Lr68vZs2cpLi7G\n2tqaFi1aUFRURMuWLUlLS8PU1JSmTZsCZWWgFy5cwMLCAmdnZ+7evUtoaCiJiYns2LGDKVOmEBcX\nx4IFC5g8eTIWFhZUqVKFo0ePUrNmTUaNGkVKSgoBAQEIITh37hxubm78/PPP3L59m/79+/Pw4UPy\n8vJ466238Pf3JzAwkFGjRkkTO1FRUUREREjG2vPnz8fNzQ19fX2io6NZuHAhABkZGVJA8Szy8/Px\n9fVFCEFaWhoqlYqWLVvi4+PDnDlzUCgUzJ8/n7Nnz7Jt2zYSExNp06YNc+bM4aeffmLHjh0YGxsT\nGhpKaWkp7du3x9HREXNzcyZMmMC0adOoXr06hYWFbNmyhZs3bzJs2DCMjIxo0aIF3377Lenp6fj7\n+/PwoR6pqXmYmDygXTtnxo8f/1R5t/hVOKdv3740atSI4uJimjRpQu/evZkyZQqNGzcGwNvbmxUr\nVmBiYkJ2djYbNmygTp06aLVaHj58SFFRkbRWdtmyZahUKvr16yddlxkzZjB48GBp4mTr1q3ExsaS\nnZ0tWawoFAqys7Mle4pjx46xePFipkyZ8kx7CnmdpIzM34ssbCIjI/OPIoTgwYMHREVFoVarCQ8P\nJy8vDyHEU6qMu3fvxsvLC39/f+7duycdQ6cUaWNjQ35+PkIIPD09cXNzY/fu3f9Iv/4p/k6J/yct\nBnbu3ImVlRUqlQpzc3MAvvvuOzp16oSrqyurV6+Wvtu4cWNUKhWNGzcmJQXi4h7Qs6cP/fv35/r1\n6wB88cUXDBs2jJUrV3LkyBEePHiAj08A9+4Z89prfYiOjsbCwgIrKysqV65MZmYmP/74I/fu3SM1\nNVVSlOzevTslJSWcOHGCAwcO0K1bN6ZNm0Z4eDgfffQRRUVFCCGIj4/n+vXrTJo0iePHj7Nnzx7M\nzMw4cuQIQUFBFBUV0bp1aywtLVm+fDkBAQEUFRWRlZXFihUrEEIwZswYunTpQtu2bSUBkdDQUMk7\nTLdt7NixjBo16m+5L8+ivL3GBx98R0FBECNHzsHOzo7PPvuM999/n1atWuHh4cHkyZOZNGkSa9eu\npWHDhrRu3ZojR45Ia/GMjY1p2bIlrVu3JiYmhpEjRwJw6dIlzMzMyMnJkdZSLlmyhIYNG9K8eXNs\nbGwk9dPGjRuzfv16Pv/8czw9PTl48CBOTk5YWFgghCAzM5M7d+5w584dAHbu3ImDg4OU4dNlFJ+H\n3wsedBngVatW4e7uTmxsLJmZmYwcORKNRsOBAweYP38+77zzDrGxsbRq1QqNRkOzZs04ePAgBw8e\nZNGiRQQFBQFlz5pGo5EsRZ5Uhn3SvuKXX37h008/5YsvvsDG5kNu334Tb+8UsrKyePToEb6+vkRH\nR7Nt2zaMjY2pUqUKBw4coEGDBnzxxRc4OTnh4eFB69atAejcuTO2trZUr16dKVOmoNFoCA4OJj8/\nn7t371KpUiUMDAwoKiqSrsG7775L27Ztpd/Vhw8fPjVx8u6779K0adMKIj468RTdc/G89hQyMjJ/\nD3IQJyMj89LQqQFmZVXcrlAoCAwMRKPRcOTIEWmGWKfKqCsHXLhwIdu2bZP828pTUlLC1atXsbS0\nBCAmJoZt27a9dCPi/zLx8fE0aNAAtVqNo6MjISEhjBw5Ek9PT9566y20Wi1CCLp06UJJSQmjRo2i\nS5cuTJ06lUePHtGhQweysrI4cuQIXl5ejB8/HrVazYoVK7C2tqa4uJgbN27w/vsaLlw4T2ZmBpcv\nX2b//v20b98eCwsLTE1NWbx4sSSacfGigoKCz8nISJUyJTk5OTx69Iji4mLu3LmDnp6e1DYDAwPy\n8vJQKBSSGElERASzZ8+mbt26GBoacvfuXU6fPk3v3r0RQkh2DtbW1pw/fx43Nze6d+/OmTNnAKhW\nrRrR0dFER0dTUFDA8OHDyc/PZ+bMmeTm5rJt2zY6d+7MtWvXcHNzY8yYMezatYvp06dz//59XF1d\n2bp1q2R3oVKp8PPzo3PnzixduvRvuZfl7TWioqz46ivQxUGDBg2idu3aZGRkcPPmTZYsWcLMmTP5\n8MMPMTMzQ6FQVLDcABg3bhyJiYmo1Wr27NnD7t27SUpKYv/+/YwbN45Hjx6RnZ1NSkoKtWrV4urV\nq3zyyScUFhby008/sWDBAgICAvDz85Ouq76+PoMHDyYxMZHevXszadIkBg8eLLX/yYkdKLPsUKlU\nFUSGIiIiUCqVjBgx4n9aHVy+fJnmzZs/lQG+ceMGhoaGGBsbPzMITE9Pl0qenZycJIuSJ8/3pDLs\nk/YVhYWFUhv69xe4upaVQtvZ2VFYWPjUeXW/ma1aqRg1apSUzfw9dEq7p0+fZtKkSVIQqmPZsmWc\nPHmS8+fPc/v2bc6fP4+NjQ3wuJzT0tKS7t27s379eul7lpaWbNy4UVL5/V/2FP+rnTIyMn8NOYiT\nkZF5aejUAH+dtJaoUaMG58+fx8PDA5VKJQ3ydKqMK1as4PTp03Tq1Inu3bszbty4Ct/38fHB3d1d\n8tZSKBT4+fkxfPhwhg4d+jK69sowePBgNBqNFICUz4YcO3aMkJAQbt26xalTp+jevTv6+vqoVCpc\nXFw4evQoUVFR9OrVS1Ls1Gg0qFQqLl++jEajoUWLFly7No+6dQ1p3bodSUlJ1K5dm2PHjmFkZISh\noSEffPABTk5OREVF0bQp+Pn1olYtN5Yti8LFxQVnZ2csLS2pX78+zs7OTJo0CXt7e06dOoWtrS3x\n8fE4ODgwYcIEzM3NOXnyJDY2Nty6dYuaNWtSpUoVCgoK2LRpEw4ODly6dAkDAwM+/fRTLC0tadWq\nFf3790ehUNCzZ0+uXbuGvr4+7733Hk5OTixZsoSUlBTMzMy4dOkSLi4uHD58mFq1aqFUKqUy3cWL\nF3Pp0iVSUlL44YcfpGwNlJUAHz58mJ07d/Lo0aMXeg9TUuDnnx34/vujv97TXxg3DmbM6CApm+7f\nv58BAwZw4sQJPv/8cz744AOCgoIQQpCQkMCxY8dwdHRk06ZNCCHw8fHB2dmZmJgY+vbty40bN9i6\ndSumpqbMnTuXevXqYWVlxYYNG7h69SoBAQE0a9YMlUpFu3btaNy4MT/88ANhYWG0adMGKPNfjIqK\nok2bNnz88cf06NEDMzMzhBA0btz4mRM7U6dOJTY2VlqvqXtm4+LiEEJIWd3fui6JidYcOHAWeBxk\nnD17Fnt7e4qKiigqKqoQxOn2adCggaQMefr0acmi5EnKr2lzdHTks88q2ldA2WTB2bNnsbaG118H\ne3st2dnZ+Pj4PJVF1/1m5uf7kZubS/v27St8Xr6ty5cvx8PDg8uXL/P6668zf/58Zs+eja+vLzdu\n3JD2mzJlCmq1GisrK5KTkyWfQ93xSkpKmDFjBosXL67QR4VCgb29PU5OTs9lT/HkWlwZGZkXi7wm\nTkZG5qWhE9+YOLErjRqVrUXTDcbefvvtCvvqsggAcXFxAHz99dfPPO6OHTsqDGZkufO/Tvky1337\n9pGf/wCFQsUbb4xk3751BAUFkZeXh52dHZcvXyY5ORm1Wk1qaipHjhxh+fLlbNmyBSgLGMzMzDA0\nNERPT4+qVfVwcBA8elTK/fv3pXNaWlqi0Wi4ceMGb7/9Nl9++SUmJoLq1eHGDcGZM73IyvqCM2eu\nYGgoqF7dhNTUVIqLi7l27Ro3btzA0tISU1NTrly5QklJCQ0aNGDQoEFcvXqVxYsXM3r0aLp27YqR\nkRHnz5/H0NCQ1atX06FDB2bMmMGJEyfIyspi7969QJlHoL6+PlqtltDQUG7dusWCBQuoUqUKo0eP\nZuvWrSQmJnL69GlycnLQarVUr15d6pNWq6V+/fq4urpSp04d1qxZA1QsAc7Ly5MyyC+C4GA4fHgE\n168H8MMP4Zibm7NlyxYyMh7v4+LiwqJFi6R1pbqyuSetMx48ePDUAP3atWuSzYGBgQHjxo3DxsaG\nn3/+GXjsKXf79u2nslouLi5YWlri7u7OmTNnpH1MTEyYMGECYWFhKBQK+vTpw+jRo/Hw8ECr1fLR\nRx9VOE75427bto3Lly9TpUoV6tat+7vX5dixYaSn92PLFiPS09Nxd3fntdde45133sHT0xM9Pb0K\ngZTuPLVr16Zr16506dIFY2NjVq9eTWlp6VP9e3JN28GDB5k6dSqrVq0iIyMDhUIhZRwNDQ3Jz89n\n7969kgfck+h+M9u3v0aTJv4AFcRCdEJEBw8eZMKECVIZL1QUJ9Ixd+7cp8RGygsd6X47o6OjgbLn\nVLcuMyEhASgrmX2S37OnkJGR+Zv4o3KWL/IfssWAjIzMX0SlUonS0tJ/uhn/eS5dKrMnGDeuTOJ8\nzZo1YunSpSI4OFgIobMw2CeaNx8lZs6cKVQqlbCwsBAajUb069dPtGvXTgghhJGRkTAyMhKGhobC\n2NhYKJVKUblyZWFgYCDc3NyEoaGhMDIyEi1atBCVK1cWnTt3Ft7e3sLd3V0YGRmJtm3bio4dO4qG\nDRuK119/XRgYGAhHx9bCyKimsLdvJipXNhFgLKytPUXlypWFsbGx6NSpkzA0NBSJiYmiTZs2wtjY\nWBgbG4uqVauKGjVqiDVr1oj69euLGjVqCGNjY9G7d28RHx8vunXrJj07KpVKCCHE2rVrRceOHYWT\nk5Pw9vYW/fv3FxkZGaJLly7C1tZWhISEiJSUFFG7dm2h1WpFkyZNxAcffCCEKLMmKCgoEG+88YZ4\n4403hBBCWFpaihs3bgi1Wi3q1q0rCgoKhFKpFKWlpaK4uFi4ubn9Lfdy3Liy//8IL9o6499G+euS\nnp4uAgMDX+jx/6hlSVxc3HNbiwwZMkSkpqb+5uc6m4J/E+np6f90E2Rk/jPwJywG5HJKGRmZ/zQa\njQY9Pfmn7K8SHAzbt8OxY4+3lS9zPXFCRf/+phQVxbJ7925p/VlCQgI+Pj6Ulpbi6+uLtbU1K1as\nwMvLi8GDB9O1a1emT5+Ol5cX+fn5VK1alT59+vDzzz9jamrKoUOH2LRpE/Hx8Wg0Gjp06MCRI0ew\ntLRky5YtFBYWYmZmTHp6Mo6ODpw9W4ClpSPu7o1ISUnhjTfe4PDhw4wfP56qVavSunVr6tSpg7Oz\ns2TOvXTpUh48eMCAAQNo27YtVapUITMzEz8/P1avXk1UVBSWlpZs3ryZM2eyadPmCG++OZ6cnBwC\nAgLw9fUlOTmZ4uJiVq5cyeTJkzEyMkKhUODh4cGGDRto164dLVq0wMTEBBMTE5YsWSJJsjdt2hSt\nVkvTpk0lBUtdCfDEv8EDQ6dU+utyJZlf+S9flzVr1khCJM9iyJAhFbJw/wbq16//TzdBRuaVRi6n\nlJGRkZH5tWyrKxMn/n6Zq7t7bamsKjMzk3nz5jF37lxJkAIgLCyMXr16cfLkSfr160eDBg1Yv349\nJiYmnD17lh07dtC2bVvpO2vWrGHz5s08ePBAkkw3MDDA0NAQACMjIzIyMnB0dKRRIzh8OIrx48cD\nZaVgKpWKgoICpk2bxrVr1ygsLKRy5cpUq1aNkpISAgICyM/PB6CwsJCoqCg8PT0ZMGAApaWlrFmz\nhn379lFcXMykST25du09nJzOs3r1SgoKCmjXrh1Hjx5l165dJCYm4u7uLpWbubq64uLigoeHB7Nm\nzQIel7gpFAqaNGmCs7Mz0dHRDB8+XBKveLIE+N/Ai7bO+DdTv3596T69KP6oZcmftTiRkZGRAVnY\nREZGRkaG589SlBfnqFevHp988on0XqekpxM1tLe35+zZs5iampKUlISDgwOVK1fm888/l9TxMjMz\nCQ8PZ9++fRUsCXTcuXOHSpUq0aBBA0nwJi8vDwcHBwC6detGbGwsU6dOJTw8nCZNmtCiRQvi4+MZ\nMWIE27dvZ+XKlezbtw+NRkNJSQkAnTp1Yvr06cyePZs2bdqgUCgwNDSkUyd7Bgw4S7VqZyQZ9ycV\nB6FsvZhara6wPujAgQOoVCr69+/PgwcPpO3iV6+vzp07U7Vq1WeqGsrIyMjIyPwR5CBORkZGRua5\nqVWrlvRaX19f8oeDx0p6Bw6UZaFGjhxJVFQUKpUKAwMD2rVrB5Sp46lUKurWrYutrS21a9emW7du\nFQQlbt++jbe3Nz179mTOnDnUqlVLEpaYOXMmM2bMAMoEGDw8PPjqq69wcvIhN3c0Fy5cRq1Ws27d\nOry8vLh9+zY1atSgSpUqXL16lQ0bNhAbG8uiRYuYP38+Bw8e5Nq1awCMGTOIq1dH0auXh9QWXcCl\nUxyExyqe06ZNk/br3r07sbGxtG7dmlOnTlW4blFRUQwfPhwhxCtXAhwfH0+HDh3w8vKic+fOREVF\nSQbRQ4YMITw8nD179tCpUyfc3Nwk1cL4+HiaNWuGWq1GpVKxadMmVCoVZmZmqNVq1q9fT2RkJJ07\nd6Zr165SEN+wYUP69OmDi4sLsbGx/1i/ZWRkZP5JFP/kbKBCoRDybKSMjIzMq0FKSlkgN3HiX193\npFtP9rzEx8fj7e1PYWFTqlW7REnJDYyNjRFCUFxcTOPGjbGwsMDGxoatW7diZ2dH165dqV27Ntu3\nbyc1NZUxY8bwxhtv4OrqipOTE23atGHgwIH07dsXc3NzrKys6Ny5M1ZWVnz66adkZmbSpEkTpk2b\nhrGxMcOGDaNfv35kZmZSWlrK2bNnmTlzJh999BH6+vo0atSI69evk1FeJvIVID4+ns2bN7N8+XKp\n5LSkpARvb2/efvtt3nvvPV577TVq1qxJaWkp7u7uJCQkcOjQITIyMhg8eDBqtVpSRtR9rtVqcXNz\n4/Dhw2RnZzNq1Ch27NghBcm3b9/G399fUhKVkZGR+a/ya4XGH6qxf3WmAmVkZF4Z7t+/z1tvvYVK\npeK1117D1dUVpVKJh4cHd+/eJSMjg4CAAKBsADlnzhygbOAPZWuSAgMD+eKLLyrM7MfFxdG7d2+U\nSiWdO3eWZLHlmf0Xw4sUjvgz68VsbGpSo8Z13nijP87OzmRnZ9O6dWtOnz6NjY0N0dHRnDt3jqys\nLE6fPs2MGTM4deoU69ev59atW4wdOxYbGxt69OjB8ePHSUtL486dO4wfPx5HR0fee+89KleuTGxs\nrJQVql27NkOHDmXHjh107NiRtWvXUlJSQkJCAnfu3GHIkCHk5eXRu3dvIiMjX7kATkf5ktOoqCi0\nWi1ffPEFEyZMAKBmzZqMHj0aT09P0tLSyM3N5eHDh1SqVLY0/1kTurm5uZIJtZWVFQUFBUCZ+bpC\nocDCwkIqj5WRkZH5/4YcxMnIyPzrCAkJwcvLi9jYWFatWkVCQgJxcXF4enpW8DR6EoVCQWFhIZGR\nkSgUCt555x1iY2Np1aoVGo0GpVLJxo0biYuLY/z48WzatAko80Latm0b+/fvZ8HzRd4AACAASURB\nVOnSpS+rmy+EJwPejh07UqdOHdRqNefPn39mgDpp0iRUKhV9+vTh7t27xMfH06BBA9RqNc2aNZP8\n0FxcXHB3d6dfv35otVoAevbsiUqlws7OjoSEBOLj46XSOR2BgYGkpqZWCLBVKhUA8+fPl9amNW7c\nmMzMTMLCwggNDeXcuXO0adOGEydOSOIpf4Q+fXri7t6Ihg3NMDAwYM+ePSgUCsaOHcu9e/fIzc2l\nQYMGmJqaAlCnTh1yc3Oxs7MDygKF0NBQcnNzUalUJCcnS2vb+vTpw/Dhw1mxYgXGxsaSF5wQgpCQ\nEEkAJjc3F1tbWyk4qV27trTfq0hKCnz+Ofzyy+OS07Fjx7Jx40beeustDAwMALh37x7ffvstGo2G\nNm3aUKdOHc6fPy8FaeXRXStLS0uuXr2KVqvl+vXrFfz3ANLS0qhZs+bf3EMZGRmZfydyECcjI/Ov\n48KFC5LZt7m5ObNnz0atVrN27Vru3bsHlIlIqNVqJk+eLH1PCMGSJUt4//33f/PYy5Ytw8PDg08+\n+UQ61n95Zv/JgHfDhg10794djUZDs2bNngpQT5w4QXFxMbGxsfj7+/PNN98Aj9d46daamZubc/z4\ncRISErC0tCQxMREoC3RiY2MZOnTob7bpWVm0J7cFBwdLA3wou3dz5sxhy5Ytkgz/86ATU8nKKluj\n5+vry9ChQxFCsHDhQnbv3i2V21laWpKeni7d49u3b2Nubs6VK1cA+OWXX8jOzmbmzJnExsaiVCol\nE/DQ0FC+/fZbRo0aRWFhoXQ9ioqKOHXqFB4eHgghnnmOVxmdNcW6dZvw9PRky5Yt2NvbY2dnR69e\nvaT9Nm7cSJcuXejWrRvjxo0jISFBEnuBis+H7rWenh6+vr7UqVOHFi1aSIboFy5coGfPngwZMoR5\n8+a9vM7KyMjI/IuQgzgZGZl/FSkp8PPPDnz//VEANmzYIIlBTJo0SdpPF6h89tln0jbdrH2zZs0q\nZD50r8+cOUNiYiIxMTEsXrz4qXP/F2f2ywe8VlZWT2V8TExMpAC1uLiY1NRUWrVqBUCrVq24cuVK\nhQG07vsFBQWMHTsWlUpFdHS0FPDqgpPy54mIiECpVDJw4EBKSkr+Z9YpJyeH1NRUXF1dpW0zZ86k\ntLT0d72wnoVOTGXz5rLB/6BBg7C1tUWhUNCxY0cpaICyz9977z26du2Km5sbx48f58MPP2T48OEo\nlUrJ327WrFn07duX8+fPAzBnzhymTp2KpaUlCoWCb775hsmTJ1NaWsr9+/f54IMPpOM/6xy6z14V\n3n//fTp16oSdnR3t2sXQuPEq/Px8qVevHn379sXLywtbW1tUKhWrV6/mzp07hIeHk5ycjFarxcjI\niIkTJ2JmZkaHDh2IjIxEo9FI5dDz5s1DrVYDcPjwYVxdXbGzs8Pb2xsoewZ3795NQkICTZs2lcqr\nS0tL8ff3R6lUMnjwYAoKClCpVDRs2BBnZ2eGDh3K1atX6datG+7u7pKyamBgIP369aNr165S+aeM\njIzMvx3ZJ05G5h8kPj6emJgYxo0bR+/evfHz82PLli2UlpYyduxYBg0ahEqlIiYmBj09vQpiD40b\nN6ZevXpcu3aN5ORkPD09pRI0T09P9u3bh0ql4uDBg4wbN45Hjx5RUlJCYmIieXl52NnZsWHDBiZN\nmsT169epV68e4eHhREREsGbNGipVqoRCoWD79u1UqVLlpV2T4GA4fHgE168HsH9/BAqFgnv37uHl\n5YVCoWDgwIG/+d0HDx4wffp0bt++/cyZfTs7O3755Rc8PT0xMzOTghndzP69e/dYuXLl39vBF4yD\ngwNHjx6lWbNm/PLLLwDPDGDT0tKwtLTE3t6emJgYAEn2/8mgSwhBREQELVu2ZMWKFcycOROA0tJS\niouLn2rD4MGDWbBgAePHj5fWiumO+eT/AJ9++im7du2Sgh+ARYsWsXfvXtatW8egQYOeu/86r+yJ\nE7vSqNFjz63fKsf09fXF19e3wra4uLgK70+fPl3h/ccffyy91vl66b5z7tw56TOdMMezzvGiPcn+\nSW7cuEFsbCxLlizB2ho6dFBw//41EhMT8fDwYOfOnVhZWbFy5Ury8vKoVq0aEyZMwN3dXbpGBgYG\nREREoKenh0qlws/PD4VCgVarJSQkRDrXL7/8QkxMDEVFRVIJMDy7PHXz5s20bNmStWvXsnTpUmJi\nYoiNjWXBggV06dIFtVrN+PHjCQoKol27dvj4+ODn5weUTSK4uLjw5ptvkpWVhbW19Uu4kjIyMjJ/\nHjmIk5H5hxFCsGjRIj755BM+/fRTNmzYIMmpPzkQ1AUjJSUltG3blnXr1uHu7o6BgQGOjo7SALp5\n8+bSmpyTJ0/y8OFDvvvuO6BscBsdHc2CBQvYvHkzTk5OREZG8sknn0geYN7e3kyZMoUvv/ySrVu3\n4u/vz507d4iLi8PHx+dvvR5lg3ITJk7c/rsCGTpT4vKGuToPLxMTkwqDZt2AvmrVqtIgsjzNmjX7\nTyrcpaTAhQsjSE0NIDw8HHNzc5YtW1YhgH0yQG3atClGRkYolUqqV6/O2rVrn5LDVygUdO3alaFD\nh/LDDz9w584devTogbW1NXp6ejRt2pSHDx9y4MAB7t27x9WrV0lJSeHMmTP06tVLEhRp0qQJKSkp\nfP311xQUFBAUFIRCoSA7O5uJEycSGxuLs7MzZmZmLF68mDNnzuDl5YWjoyPOzs7PdQ10YioyL4+7\nd+9iaGiIEAKFQoGNjQ0ZGRlMmDABIUSF7LBO8ORJDA0NK5i5Q8X1hYsWLQKQzNGrVKkiTSa1bt0a\nT09PHj16xIwZM2jevDkHDhzgyJEjfPXVVwA4OzuTlJQkHVfHlStXaNmyJVD2O5mWllahnY0aNSI7\nO1sO4mRkZP71yEGcjMw/TEhICHXr1uWLL75g1qxZ1KlTBwBbW1tyc3MB8PAo86zSBSlpaWnY2tpW\nOI6fnx9r165FoVBIyo25ublS2aGOJwc0Tk5OQFlp3YkTJ7C2tq6gNKcbCOXl5bF9+/a/PYiTB+XP\nT3AwhISYMG7c9grXTBfgwrMD1ODg4ArvywfCQ4YMkbY/GdxlZ2cTHh7Oo0ePyMvLw8bGBl9fX8aP\nH8/gwYPZt28fAFqtlqysLExNTXnw4AFGRkZ8/fXXhIWFcezYMXbv3l0hC3Po0CG+++47jIyMnsqK\nyfx7SEmBJUtuo1A8FhgRQrBx40Y+/fRTKQOuyw737NmTgoICqlWrhp6e3jMzxHfu3EFfXx94vL5w\n1KhRfPjhh0DZhMKNG2V2EbpgDyAmJobbt28zfPhwli9fTrdu3VAqlSQmJuLl5UViYiJNmjR5qg8O\nDg4kJibSsWNHkpOTeeeddyq0KSkp6XfX1MrIyMj8W5DXxMnI/EPoVN3eeGMkSqWSoKAgzM3NycrK\norS0lMzMTMlYWaPREBsbKwV4+/fvx9PTE3g8GHJzc+PHH3/k1KlTkjCEpaUle/fuZcyYMZLKXnns\n7e0lgYakpCQaNWqEEKKCubGtrS3du3dnxIgRHDx4EJVKxTvvvENMTAydO3emS5cuUpDYunVrfH19\ncXV1JTIyEiiT79cpSjo4OKDRaCgoKMDHx6eCMMn8+fNxdnbGzc2Nd999FyhTNNRqtZJ6oW4bPFZA\n/OWXX7CwsJCOodFo0Gg0uLi4kJWV9Vz34s8oIf4bmDgRxo17XFL4MtA9H1euXHlqbZ0QgiFDhtC8\neXNJATIsLAxPT08iIiKwt7cHykrpDA0NqVy5sjQwz83NRa1Wo1QqOXHixMvrkMxzExwMq1YNIC7u\nGGq1mrCwMIYPH469vX0FEZO+ffuSnp6OSqWiX79+0jrK8hniJ83cgafWFwIEBQXRq1cvfHx8CAoK\nkj7z8/Nj+PDhkoiNbj3k4cOHUalUnD17VppwKn/eqVOnMnv2bNzd3XF3d5d+U0eMGIGbmxteXl6/\nmT2UkZGR+TchZ+JkZP4hdKpubdvCTz8to1+/fqjVagYNGoRWq2XcuHHo6+vrDCClgUhSUhILFy7E\n0dGRRYsWkZyczP79++nevTtdunShWrVq0jkUCgWurq5MnjyZt99+m6+++opDhw5Jn/ft25fBgwej\nUqmoV68eU6ZMISIigi+//JJ169ZhY2ODsbExo0ePpm3btsybN4/Q0FD69OnDyJEjOXfuHFqtlh49\neqBWq5+5zkVnpqynpycN7FeuXImfn5+UxdGJPyxbtkwa+Ona/yRPblu0aBHm5ubS+5KSEj799FNi\nY2Ol872qPE/W8kUFqDojbxsbqFnz8QRA06ZNSUpKYujQoVy6dInQ0FCWLl1KQkICzs7OhIWFkZCQ\nQFpaGh999FGFY5bPwlhaWqLRaDhx4gQRERF/SKFS5uUwcSJs3fqA+PjzNGoEq1atYsqUKdy7d4+Y\nmBi+++47Dh48yNixYzEyMkKj0TBq1Ci8vLwQQrB7926aNm1K3bp1uXbtGhYWFhw5coQdO3bg5uZG\nzZo1uX37NvXr1yctLY0+ffpw9epVPv74Y6Kjoxk7dixpaWk4Ojoyd+5cmjZtKrVNl33etWvXU+3W\nBYmA5Bf4JCEhIZLVhIyMjMx/ATmIk5H5hyjLnnRl4sSyMratW7cCPKWOVr4UUufLNWrUKBYsWACU\nDV50ZZbXr1+Xtuv2B3jzzTd58803SU9P5/Lly6xevRook2TXZczKM2HCBIYNGwbAmDFj8Pb2rlAK\n1bx5c6Kjo6U1LXp6ZUl9XYYFHq9zqVy5MpaWlqxevZpevXpJWRyd0pwuiwPw7rvvoq+vL5X2/S9+\n/vlnTE1NK3hNDRkyhIEDB77yAdzLRqcC6eEBgwYpGDlyJH5+fqxcuRJnZ2fatWvH3r17MTIyYtOm\nTfTu3ZtNmzZRp04dunXrhrW1NZUrVwYeZ2Fu374tiYbk5eUxaNAg7t69K9kcyPy7aNQITp/ezK8F\nAvTq1YtevXpRtWpVevbsiUKh4OTJkxQXF/Pdd9+xY8eOCgInhoaG1KpVC41Gg7u7O1CW2f3ss8+I\nj4/n7t27jB07lsjISMka4/bt2/j7+7N3714yMjKYPXt2hXLhF8GrpBwqIyPz/wc5iJOR+Yf4s2u/\nOnbsSOvWraX3b7zxBlAmAHDz5s2n1soB5Ofn4+vrixCCtLQ0VCoVLVu2xMLCgujoaB4+fMj8+fPp\n2bMnp06dYs+ePaxatQp//6kcPlyJAwf6YWZmTNWqVQFYsWIFAG3atJFKH3Xy4IGBgUyaNIni4mIC\nAwMB2LNnD5MnT2b//v1MmTKFgIAA+vXrR3JyMklJSRw9epTevXvj5OREamoqoaGhtG/fvkJpp47y\n75cvX86uXbuIj4+XtpXPBOkGijJ/nccqkEMkwZkdO3ZU2Kd8xuPHH38EHk9OlMfc3PypjElycvIL\nbO1/gz+rTvvjjz8ybdo0tFotgwYN4ocffiAkJIRatWoxceJEhg0bhpmZGUqlkgYNGnDjxg3OnTtH\nYGAgs2fPljJOH3zwAbGxsVJ266uvvmLw4MFUrVoVfX19tm/fzu3bt5k1axYRERHEx8cTHR1NaWmp\n9L3S0lLs7e1JT0/HzMyswhrc3xM40f195ubmcuHCBak8XLffy/RufJWUQ2VkZP7/IK+Jk5H5j2Fg\nYFAhy6RTbatWrRp79ux55ndWrVrF6NGjWbVqFe7u7sTGxpKZmcmwYcM4ePAg33//PV9//TVCCI4d\nO8b58+c5fPgwx4+358yZFAoKjDExMeHYsWO4ublRvXp1tm/fjlarZcaMGcydOxeFQsHt27eJjo4m\nMDCQiRMnSr5ZxsbGDBgwgJMnT6JQKBgxYgQ3b97Ew8MDAwMDqlcvE0pISkqiUqVK9OzZk4iICAD6\n9OlDcHAwy5cvp1evXiQnJ6PRaFAoyrJBxsbG0ky6QqHAyMiIzZs3M23atOdeEyfzv9FNOvyeYujz\nImc+HlNenVaj0bBhwwYSEhL45ptvePToUYV9ddftgw8+YOfOnRw6dIi+ffsycOBA1q9fT2lpKefO\nnZPEigIDAyuspdWdT0dQUBDr16+vYA5/8OBB4uLi8PT0lNayPtmGoKAgPvpoPaamKo4cySU+Pp72\n7dtTq1YtaQ3u/fv3sbe35+jRMr/HgoICSktLn5qUqVWrFi1btiQmJgaNRvNUlu2/6N0oIyMj8zKQ\nM3EyMv8PuHz58jNLIqOiotBoNJJBc25uLra2tpI9wQcf1GH//vvExZ3AwCADtVrNxo0bGTRoEIsW\nLaKwsJAvv/wSlUrF3LlzMTc3x9XVldmzZ6Ovry8Z8rq5uRESEsLnn3/OokWLMDU1lQZuenp6qNVq\n5s6dS4sWLQgJCSE3NxetVktsbOxTfQkPDycrK6vC7Llu5v9ZmSCZfx//VSGZv4M/o06r1WqliQ8r\nKyt8fHzo1asXjRs3pkePHgA8fPhQ+jsuj7+/P/r6+gwbNkwqmdbx8OFDZs+ezcmTJ7lx4wbTpk0D\n4MCBA6jVavLz86Uy6NBQSEm5gFLZlebNDQkKCuLdd9+V1uCOGDGCiIgIduzYgUqlQl9fn127dj0z\ngB83bhwqlUqytpg3b95/2rtRRkZG5mUgB3EyMq84KSmQmGjNgQNn6dXLRQrkzp49S1paGkePHqVS\npUr06NEDS0tL0tPTKSkpoXLlylhY3MbV1ZwqVa7x8GEphYWFkmJmTEwMpaWlqNVqfHx8KoivPFkG\n+Szp8PLo9l24cCE//vgjlSpVonv37s/sj87sV0bmv0x5dVoDg8IK6rRWVlZPqdMqFIoK68jy8/Mx\nMzOT1Fmtra356KOPWL9+PQDnz5+X1oqW/1uMjIzE1tYWlUr1VBC3f/9+hBBoNJoKhtvdu3cnPDxc\nKv8EGDYMjh9vyc6d4VJ29sk1uFCmTlqe8gG87rWPj89T1iXPssaoX7/+C18PJyMjI/NfRQ7iZGRe\ncYKD4dixYaSn92PLFiPS09Nxd3fntddeo27dunTv3p1mzZoBZaVS7733Hl27dkVfX5+ZM2eyYMEC\nBg4ciFar5fPPP5fUBNVqNY8ePcLf3x8oGzR26tQJjUbD1atXAUhJSSE3N/eZ0uGANHDTlUh26tRJ\nas9vldzJgiUyzyI+Pp5p06ZRvXp1CgsL2bx5M5MmTeLWrVs0adKEr7/+GgB3d3f09fVJSkri1q1b\nhIaGEhoayqNHjxgzZgyBgYHS2rHDhw/zxRdfMHjwYLZu3UpiYiJt2rRhzpw5/PTTT+zYsQNjY2NC\nQ0MpLS3F29ub+vXrk5OTQ1hYGM2bN//N9v5ZdVqABQsW4O3tjZ6entTeQYMG8cknn2BlZcWlS5dY\nunSpVF5d/ruTJk2icuXKDBw48Kk2ubi4sGjRInr27AnwzH10NGwIrq4vprxWRkZGRuZPoJsx/yf+\nlZ1eRkbm7+TSJSHGjSv7Pz09XQQGBr60cwcGBoqMjIz/uV9YWJgICwt7CS2SeVWJi4sT48ePF0II\nsXPnTjF//nyxYcMGIYQQ06ZNE8nJyUIIIfr37y+EEEKlUonS0lJx//59IYQQDx8+FB07dhRCCDF0\n6FBx6tQpoVarRWlpqXQONzc3IYQQOTk5olevXkIIIY4fPy7GjBkj0tPTpW1nzpwRw4YN+932lv+7\nfBGEhoaK8PDwP/SduLg4MXv2bJGTkyNcXV1FcHCwcHd3F507dxaRkZFCCCG2b98uOnfuLDp16iS2\nb98u5s2bJwYMGCCEEGLevHkiICBACCHEt99+K9zc3IRarZZ+Z5ydnUX9+vWFSqUSN2/eFIMGDRJK\npVL4+flJ19XZ2VkolUphZWUlMjIyRGZmpvD09BRubm7i448/FkKU3Y/27dsLlUoldu7c+WIumIyM\njMy/iF9joj8UR8mZOBmZV5zyKpgZGS/33IsWLZJKwn4PuURS5kWgUzZs3Lgxe/bsYd++fXz77bcU\nFhbSo0cP7OzsJOsL8WuJ4bFjx/jkk0+4d+8e+fn50rFee+01pk+fLtlnlCc9PR1HR0cAnJycuHLl\nCgqFAjMzM+n8OTk5xMfHM2TIEOzs7MjJyWHKlCl8/vnn2NracuPGDcLCwnBwcGTkyFGkpqZiYWFB\nVFQUenp6+Pr6kpuby8WLFzl06BDVqlVj2LBhFBYW4uDggFarJTExkby8POzs7CRhoZCQEGrWrMnm\nzZs5ePCgdH7xa5nkggULcHNzw83NDT8/P15//XVcXFwoLS1l9uzZeHl5ERwcjKOjIytXruTKlSuk\npaVx7do1unTpQqVKlcjJyeHGjRusWrUKFxcXbt26xd69e0lISODChQssX76c0NBQqfxywYIFbN68\nGScnJyIjI/n444/ZsmULb775Jq1btyY0NFQq7VyyZAlBQUG0a9cOHx8f/Pz8AFi3bp3s4yYjIyNT\nDlmdUkbm/xH169d/qXLaVlZWUvnl72FqaiqXSb5g4uPjsbGxQQghvY6JiaFz58506dJFEoPRWUPo\nXm/atAmVSoWZmRlqtZo5c+ZQs2ZN3NzccHJyonbt2ixYsAAjIyNq1KhBo0aNiI2NRaVSodVqCQsL\nk54x3bF1n128eJEmTZoAZcqJqampFT6fP3++JLTTuHFjMjMzuXnzJt7e3iiVSt5++22GDRtG69at\nadCgAWq1mpMn8xk/HrKyHgdmx48fx8rKiunTp6PRaDh69CgdOnQgJiYGpVIp9VcIwYwZM1izZg0x\nMTEVVBB37NhBREQEFy9erLA/QIMGDSRLhNOnT+Pg4FBhHeixY8ekIG/w4MFoNBqmT58OQMOGDdm9\nezdr1qxh2bJl7Ny5EwcHB6Kjo1GpVNI6MJ2wj5eXFwCLFy9mypQpxMXFsXDhQkJDQwkODpaOv3//\nfk6cOEFCQgKWlpYkJiZWOL+VlRX379+X+hIcHEzlypXZtm0btWrVYsyYMTRo0IANGzawdOlSXF1d\nCQ8PR09Pj3v37gEwYsQIhg4diq2tLX5+flhbWwOQmppKUlISarWasWPHVjiPjitXrtCqVSugLPC9\nfPkygKTAqbt+V65coWXLlkCZ+FJaWpqsZiojIyPzDORMnIyMjMwrSs2aNdmxYwfx8fHUrVuXhQsX\notFo0Gq19OjRA7VaXWF/hUIhiVK4u7uj0WiIi4vjs88+49133yU6OpoNGzYwdepUtm3bxu7duwkM\nDGTp0qXPHGiXt36AskBEl6161n46goODMTAwkL4zZcoUVCoV2dnZWFlZkZCQQHR0NAsWLGD8+DIT\n8mvX4Pz5Tfz444+YmJiwbt06hg0bRnBwMFC2jmzcuHHUrVuXyMhIkpKSiIiIwNvbm9dee41GjRrx\n4MEDqT0WFhasW7eOwMBA9u3bh4mJidTO2rVr07VrV7p06YKxsTGrV6+mtLSUhIQESZAnIiKCCxcu\nSH3SBXjls3XZ2dlcuHCBqKgo9u3bx4MHDxgzZgwApaWlFb574cIFFi1aBJRNjpQ/JsDdu3eZNm0a\nly5dIjMzU8pghYeHExcXV0Gp8vTpHMLCUrG1bYazcyP09fWJj4/HxMSErKwszp8/z9GjRwkICODW\nrVtcu3aNSpUqUVxcTOXKlTE2NsbDw4OTJ08SHx/PhAkTsLS0lCYGdEFfeezt7UlMTMTLy4ukpCQc\nHBwkA/Dyz4GDgwOJiYl07NiR5ORk3nnnnadsCWRkZGRk5CBORkZG5pWlZ8+eREVF4ejoKAVFukGz\nrkwwNzcXtVr9mwPl/Px8hBCMHj1aMnvfs2cP6enpBAQEIIR4ZslheYQQ7N69m86dO5NRrqbXz88P\nIyMjkpKSpG05OTmkpqbi6ur6XMGLzoS8Y0e4eNGXBQsWSJ9FRUVJrzMyMujataukbpiQkEBcXBxz\n5syRRHd0lM9WHz58WHpdXlnx/fff5/3333/m8VNSYOFCMDNbz/LlwcyfP58LFy6wYsUKbG1tcXNz\n49atWzg6OtK4cWMKCwtxdHQkPT2djIwMkpOTSUxMRKvVkpSUxNtvv03Lli0ZNWoUV69epVKlSpIX\n4uXLl9m4cSMKhYJhw4aRk5NDaWkpX375JePGjWPw4MGEhIRQpUoVateuzXvvvceHHy4kL88Mw/9j\n78zjakzfP/4+FQola7KUrYghGcbSek5EJMyCZCszmUH4mkEoxDD2ZSzztQwtiDGmETMYOsnOLCZr\n9rK2CNmT0/37o+95dCozZn62Gff79erlnGe9n/t5yn0913V/PqWSMTPLY+vWrZQpU4by5cvz1ltv\nYWJiwpgxYxg9ejRr1qxhwIABZGdn06lTJ+U5GjNmDB988AEuLi6sX78eBwcHJcvZs2dPPv74Y4M+\n7datG3379kWtVlO9enVGjBiBh4cHM2fOBJ4E8qNGjSIgIIBHjx7h6+tL1apVZSZOIpFIikEGcRKJ\nRPIvQy9fX62aMb6+vri4uLB9+3aEENy7d89gUFwwg1KwtFIIwZkzEBFhSfnyVZg2bTKurq707NmT\nyZMn07BhQ7Zu3Yqbmxu2trakp6cXCQQLfo+JiWH16tWsXr1aWbZmzRpq165tkBGcPXs2mzdvZuzY\nsUpm5sCBA6jVakVOvyD6OZ+JiVCg8rEINWrUYN68ecr3Vq1a0bRp02fs0b/G/Pn52UEXFzAzMyMu\nLo7Dhw9Tvnx5bty4QZUqVahSpQp37tyhS5cuDB48mEuXLlGyZEm+/fZboqKiyMrKok2bNhw/fpwx\nY8bg7OxMYGAgdevWxdTUlOjoaKZPn86cOXPo0qULb7/9Np06dcLKygobGxtyc3NJTk4mOjoayC/j\nrFatGgAVKxrRpUsst2+P4dKl4zx+/BgrKyuWLVuGp6cn6enpBAUFodFoKFGiBPPnz+c///kPoaGh\nlCtXjp07dwJQtmxZsrKy6NGjB//5z38YNGiQQT+4u7vj7u4OgLGxscG9B9i9e7fyuWDgvGPHDoPt\nXmYJuEQikfxTkEGcRPKG8+DBA/r3709GRgZmZmbcvHmTCxcu0LBhQxYtj+HfBwAAIABJREFUWkTH\njh1xdHTk0qVLzJo1C7VazfDhw0lKSqJcuXJER0fz22+/sWPHDiZPnky/fv3QaDQkJiYaCC9s2LCB\n0aNHFxFwcHJywtLSklOnTnHgwAESEhLQ6XRFPKz+TTx69IgPP/yQkSNHsnnzZsaMGfNcj/9Evl7F\nokW9gPxMx4QJE2jXrh1GRkZMnjxZWa6n8Of582HjRhV167Zg2bJlLF++nCtXrtCpUyeWLVtGnTp1\nuHPnDitXruSTTz6ha9euXLlyBSEEsbGxSobOyMiIoUOHolKpDEosi8v+de/eHWtra2W70aNHExgY\nyKRJk2jUqBEL9So9hSgYMBSHsbExFSpUUL6XLFlSySo9Dwp6mD3JDjbl+PFAYmJiaNOmDTdv3sTM\nzAwjIyNMTEx4/PgxKpUKe3t7xfje39+fc+fOUb16dZKTk1m4cCH79u3DwcGBMmXKoFKpyM7O5s6d\nO7i7u5OSkoJOp8PR0ZHOnTszbNgwGjRowNKlSzE3N+fUqVMMGTKEpUuXotFomDBhAlqtlpUrqwIr\n8ff3JyMjg1q1aqFWq4H8ctGNGzcaXF/BjKQ+6F+5cuVz6z/Js/Msdhp+fn506tQJnU7H48ePKV26\nNN9//z3r1q3D1dWV3bt3s2LFCnbv3s3KlStZsmQJ0dHRCCGYM2cOQghGjRpFXl4e/v7+vPfeewQG\nBnL//n00Gg2hoaEEBASQnZ1NVlYWTZo0YcGCBa+6aySSNwoZxEkkbzjLli2jQ4cOBAQEcO3aNXJz\ncwkNDVUGpJaWlsTGxnLjxg169+6Nubk5jx49IiEhgW+//ZavvvqKli1bolKpOHToEIcPH8bT07OI\nOl1cXBz16tVj6dKl/Pe//2XLli106tSpiDod8FrOgZk8eTKHDh3iwYMHfPvtt8XO7XpWSpYsiY2N\nDZ9++inLly9/jq3MJz+IcGfYMHflHqxfvx5fX190Op1i8P7+++/z3XffoVarqVatGjqdjhkzZjBq\n1CiMjY0JDs5TjlOvnmDgwIEYGxuTlZVF69atGTBgABEREQQHB1OiRAnWrVtH6dKli7RHbxANTwKA\ngtkV/bIJEyYoywqu37Ztm8Hx/ixge50wNn6SDd28eTNHjhxR5pi1b9/eYNvc3FwuXbpEXFwcPXr0\nYNeuXfTo0YOYmBju37+Pt7c3c+bMAYqfd6afT9agQQOSkpLo379/sabfFSpU4MqVK1StWpWLFy9i\nZWX1Wv7OSZ5OixYtWLBgAZs3b2bZsmW89957dO/enZCQEI4fP05MTAxt27alZ8+e6HQ6IiIi2Lp1\nK5D/AuXu3busXr0aW1tbrl+/znfffceePXvIy8sjKyuL7t27s2nTJsqVK8fVq1eZNm0a48eP5513\n3qF3796Kkuu4ceN4++23+eCDD7h8+bIidiORSF48Up1SInnDSU5OplWrVkD+nKPCgzm9oEPFihV5\n9OgR58+fV1TmmjRposir5+XlsWDBAoKDg596npiYGDQaDVFRUdy4cQMwVKfTM3PmTNzd3RkyZMhz\nv97CJCYm0qpVK9q3b4+zszO7du2iefPmuLi4KPN6vvvuO3744Qfu3r1L9erVsbS05NKlS7Rr1w43\nNzdmzZoFGJYj6rMaT1NmDAwMZO3atTg5OT33a9KXGOqNmIUQTJkyhVmzZvH++++zZcsWbty4gUql\nUhQZFyxYwN69e9m8eTO5ubn/K2UUynEKqyjq75+Pjw/bt2/H19eX77777rlfyz8RfTnlt9/mD5h7\n9eqFjY0Npqam9OzZk3bt2jF48GCDfbp06YKbmxvDhw+nSpUqqFQq6tatq/xONWnSBAsLCzQaDR4e\nHmzfvr3IeT/66CNiYmJQq9WULFmS+vXr89FHH9G7d2/gSaY1PDwcPz8/3N3dGTRokGIqLvnnUNBO\nIy0tjXnz5qHRaNBqtWRkZADQpk0bQkJCCAsLo1mzZso9FkIwffp0ZU7n+fPnldJiIyMjKleuTF5e\nHuXKlQOgWrVqJCcnExISglqt5uTJk2RmZhq0w87OjmvXrr28DpBIJDITJ5G8yZw5A0eO1GPjxgM4\nODiQlZUFGAZU+s8XLlygcuXK1K1bV8ms6FXmhBB88803zJ49WxncF8bOzo6AgACGDh0KwP3797lx\n44aBOp2eUaNGERgYiI+PDzdv3lQGCi+Kgm+1d+3axS+//AJA+/btycrKYvfu3Sxfvpy33nqL4OBg\nEhMTWb9+fRE/q+JKE/9ImfHzzz83KPF7USxbtoxq1arx5ZdfYmtry+jRo7ly5Qo9evRQttG3rWbN\nmoqYSdu2bXn8+DEhISFFVBRHjx5NXl6ewWCyoEDJm4y+nHLYMHfs7J5kDAsKoxQmLi7O4PnRZyed\nnZ356aefAJg4cWKR/fr166d8trCwIC4uzmD9oUOHihyzSZMmRdqiXyd5vXky39XQTqNDhw74+vqS\nl5fHo0ePSE9PJyEhgSlTppCXl8fq1au5evUq1apV49KlS+Tl5eHg4ADkz5f87bffgPy/99nZ2Qgh\nuHXrFpaWlmRlZWFvb09gYCBNmjRRXvLoEUKQlJRkIPQjkUhePDITJ5G8wcyfD3v3fsTSpRtRq9UE\nBQUBhnOjkpOT8fb2pl+/fkycOJG3334bU1NTPDw8WLVqlZKtqlOnDp07d37qubp27cqJEyfw9PRE\nrVaTlJREly5dGDBgQJFzLl26lJ49e1KtWrUXHsCB4Vvt8+fP07t3b9RqNceOHePevXsGpWuNGzfm\n6tWrnD9/voifVW5uLvfu3SM3N1fZvqAyo17dEeDIkSNYWFgopW7PmzNnUPzTgoKC8PDwYOrUqdjY\n2HDr1i2lvLIg+nK+ypUrA/llkLGxsSxZsgR7e3sCAgLQarXs27ePd99912DfQ4cOKd5obzqFM6F/\nxtPmB0okhdHPd12zZj1t27Zlw4YNfPbZZ8TExODp6Um7du24cuUKI0aMYP78+ZiamlKqVCm+/PJL\npbLh4cOHim8h5Isb+fj44OzsjJubGykpKUyePBkfHx/c3NyIi4tj7NixhIWFodFo8PHx4f79+0B+\n9tfV1ZUOHTq8lL/VEomkAHqDzVfxk396iUTyqjh9WojBg/P/fRqurq4vr0EvEf21r1q1U4SGhgoh\nhIiKihIVKlQQmzZtEkII0atXL5GamirmzZsnZs+eLYQQ4sMPPxQnTpwQQ4YMEfv27RNCCNG5c2dx\n7do1sXbtWtGmTRvRtWtXoVarhRBCeHh4CD8/P5GXlyc8PDyEEEL0799faDQacf/+fWXZ82bwYCFA\niK5dd4qwsDAhhBDdunUTW7ZsEUuWLBEfffSRsq2Hh4fw8fERrVq1Ehs2bBBCCKFWq0XPnj2Fr6+v\niI2NFQsWLBC2traifPnyokWLFmL//v0iIiJCNG3aVGg0GtGvX78Xch1/h507dwpbW1uhVquFg4OD\nWL58uejVq5fw8PAQ/v7+QqfTiYiICNGqVSshhBARERHCxcVFCCFEdHS0aNOmjXBzcxNHjhwRQghl\nXf/+/cW5c+dESkqK6N+/v8G6gp89PDyETqcTycnJwt7e3mDfqKgo0bx5c5Gbm/tyOkPyr+L06fzf\n6cGDw151U5RnWiKR/P/5X0z0l+IoWU4pkbzB6DMGbyL6eUuFTaIjIiIIDQ3l66+/VjzNPvzwQ957\n7z02bdqEp6cnDg4OBn5WnTt3pmrVqvTo0UMpUdTL5j9NmTEoKAgzM7OnzkVKSEhg8eLF3Lp1iwED\nBtCzZ8+/dH3FlfTp56xNmDCBPn36GGz/tHI+gN9++40JEyZQr149zMzMGDJkCMOHD+fevXs8fPiQ\nLVu2cOrUKcLCwhSFUk9PT9zd3WnZsiUNGzakQoUKjB8/nsDAQExNTXnrrbf473//C+QrUmZmZnLq\n1Cn27NnD7t27FYVSDw8PJk2ahBDCQJFvw4YNVK1a9anX37dvXyZNmkRUVBQZGRk4OjqyevVqZs2a\nxbfffgvkZySSkpL4+eeflXmdX331FXv37uXatWsMHDiwSL/o+Tvm5tnZ2URERHDw4ME/9db7J6AX\nzRk8eDC+vr74+/uzYcMGdDod7733HtevX+fBgwds3rwZa2trFi5cSOPGjaldu3YRxdsffviBkSNH\nYmpqio+PD5MmTTJQUdyzZw8rVqxQlgHK54EDB5KcnMzDhw9ZvHgxe/fuJTY2lsOHD9OsWTPGjx/P\nzz//TFxcHGZmZqxYsQKdToePjw+2trakpaURGRlJo0aNXnGP/jl2djB8OBTQCnplyHmUEsmrRQZx\nEonkD/mjeTz/ZP7IJLq4slC9spuemjVrFvGzKog+CHpWZcbCVg+XL1+mevXq3Lt3j7fffhugWGuH\nwoHNqVOnFEXQpCRX7Ox2ExkZiUqlwt3dndDQUC5duoQQgoSEBMaPH8/hw4cRQvDee+/h6OhooBIJ\n+X5eU6ZMwcfHh9mzZ5OWlkaLFi1o3rw5v/32G66urpQtW5aUlBTKlStHbGwsP//8M4mJiXh5eVG3\nbl1iY2MZOnQoAwcOZN++fZw4cYIzZ87QqlUr1Go1S5YswcPDg549e1K1alUuXLhAWFgY169f57PP\nPmPmzJlFFPnCwsL+9D4LIXj06BHNmjUD8ueD/fLLL9SoUYPevXszbtw4unTpwpEjR8jMzFTU9ayt\nrbl9+/afHr84s3TxFHPzjh07EhIS8q8I4PSIAqI5s2fPZt26dVSpUgV3d3fs7e2JjY0lNTWVu3fv\nKkFxYcVbtVrNtWvXmDt3LmZmZsrvVWEVxcLogwh92eD+/ftZuXIlCxcuZOjQobi5uaHVaklPT2fO\nnDns3r2bn3/+malTpzJ69Ghq165NXFwcx44dY86cOXz99dcvte/+Lq+LOqv075NIXi3/nv9JJBKJ\n5C+gz0K+LorYequHhIQEQkND8fLyYsuWLXz55ZdMnTqVX375RbF26N27N1999RWQL8qybds2xowZ\nw7JlywyO+bRsUVRUFFu2bFGWNW7cmO+//x4nJ6ciARxQ7JzA8uXL069fPwYPHoyJiQnz58+ncePG\nxMTE4OfnR0hICLm5uVy/fh2AOXPmsGrVKhITE9m6dSunT59m+vTpVKhQAZ1Ox9ixY2nTpg1r167F\n09OTgQMHUqtWLby9vRX1z8KKfMWhF34oqK9TsmRJfv/9dyBfjMfOzg4hBJaWlrRv3x4/Pz8gf27Q\n5cuXycvL4+rVq4o6X+EAreB3vVl6QkKCQTtiYmIYMGCAwbZxcXFER0dz6o9cyZ8DDx48oEePHnh4\neFCmTBlmz54NwOHDhxk+fDiJiYnUqlULjUaDg4MDK1asQAhBUFAQbdu2pUePHuTl5REZGYlaraZd\nu3Z4eXlx//59IiMjadCgAR4eHoSHh7Ns2TLWr1/P2LFj0Wq1HD58GCMjI06fPs2kSZNo1aoVzZs3\nx9ramt27d6NWq0lNTSUnJwedTsfPP/+MRqMhOjoaW1tbvvjiC7755hvc3d05e/asgYoiPAma9eqv\nABEREbRt25aRI0cqc7UKkpKSoszXdHR0VBR19VnSP3qeJBKJ5HVFBnESieSNxt3d3SAL96ooaPUA\nTwKHJk2aKEIqxVk7PEtgUxAhBF988YWBsMGRI0cICgpShG306MVRypdvpCgkHjp0CCcnJ6V9hw4d\nwsLCAiEEx48fJyMjg9q1awPg4ODA7du3lWBy7ty59OzZkw4dOlC/fn3Mzc0pX748jRo1IjMzE3Nz\ncyBfKXPRokW0b9++WKXUPxJR0Qs/HDz4ZFmFChU4duyYIqjz3nvvAfkBbXBwMGXLlgXyS18HDRqE\ni4sLfn5+TJkyBYCTJ0/i6+uLVqtl4MCBfPjhh8THx5OZmVlsoPy0EtqKFSuyZs0aAgMDuXPnzp/e\nq7+L/oXAzp07OXv2LD/88AMAq1atUtQs+/bti1arVYzmC1tI/Pjjj4ChhcSGDRsA6NdvFG+9tZNb\nt3IICgrigw8+wNvbG2dnZ+bMmYNOpyMnJ4dKlSrxyy+/YGlpydWrV2nTpg0JCQmYmpqSmprKmDFj\ncHJyQqvVcu7cOaU81tXVlcTERLKzs7l165aioghFg+bbt28TFRXF1q1bWblyZbHPS61atTh27BgA\nv//+u6Koq19/8OBBKcojkUj+cchySolEInnFFLZ6qFevHnv27EEIwcGDB3FycnqqtUNxgY1+WcEB\nrZ6DBw8SHBys+MNBflD42WefERgYqAz44cm8wUGDfMnMXI1araZRo0aYmZmxevV6IiL2U79+WaUE\nsXz58nzyySecPXuWatWqcfLkSSXAg3zLhtGjR3Pjxg1u3LjBsmXLWLJkCbm5uVy+fJnk5GRq1apF\n5cqVcXd3x9bWVpE+B1i//sncxTVr1hTblwWNzuGJBH9BM/mCy/Xoy4b9/f3x9/c3WKfPJhYkMDCQ\nBw8eGJQb6z//WQnt3r17i2378yI5OVnxa7S2tqZu3bocP36co0eP4uTkRGJiorKt/t4UtpD45JNP\nAIpYSFSpUoUZM2Zw69Yyqlc3Qwih+M1ZWlqSnp6Ou7s71atX56effsLX15f169fj5+fHyJEjcXd3\nJzMzk6FDh/Lzzz9jb29PnTp1SE9Pp0OHDpw5c4ajR48q7dMH3HoKB80WFhZYWVnRrl07atasiYmJ\nSZFtrayscHd3x8XFBTMzM1auXIlOp2PXrl14eXkBEB0d/Vz6XiKRSF4af1UJ5Xn+INUpJRLJv5j7\n9++L7t27Cw8PD9GxY0cxceJE0apVK9G0aVMRHh4uhBAiLCxMVK+uFuAlbG07CXd3d1G9enVRp04d\nYWlpKXx9fUXTpk2Fu7u7qFGjhnBxcRFt27YVDg4OolWrVqJUqVKiVatWwsvLS/Ts2VPs3LlT2Nvb\ni86dO4uKFSuKzp07CycnJ9G+fXuRkpIiGjVqJHQ6ndi5c6fSBr0C6axZs0RISIjS/qepl+7cuVM0\nbx4mQIh69boripbh4eEiLi5OdO7cWXh4eIjhw4cry+Pj45X9AwICxNq1a4UQQpQrV04IIcTRo0dF\ny5YtRdWqVcWUKVOEEPmqkV9//bVyTr3K5uvA1atXxePHj191M4pw+rQQzs6zxRdfrBBCCHH9+nWh\n1WqFs7OzmDZtmhDCsC/1fRwbGyvmz5+vHOfu3bsG/T958mSxefNmERERIaZO/VoMHiyEn9/H4pdf\nfhGOjo7i/v374tGjR09VZX348KF48OCBEEIIW1tbERsbK4YNGyaSkpJEQECAOHXqlMjNzRX9+/cX\nrVq1Enl5eaJjx47ixo0bL6SfUlJSRJ8+fV7IsSUSieSvglSnlEgkktcHfVlbQEAA165dY9myZaxb\nt46UlBR27tzJkSNHuH79OgkJWkaM2IqT01kqVcrD3Nxc2cfa2pqHDx9iamrKF198gb29Pc2bNyc4\nOJhPP/2UmJgYcnNzGT9+PKGhobi7uxc750qj0WBra6uUlRUUR9BnkD799FODff5IvbRly/yfYcPW\nKX5o48ePB4oKw+iX6ymYlTp9+jQAb731Fnv37iU7O1sxQC+cLXudsLa2ftVNKBa99+PVq33Yti2K\nChUq8O2333Lx4sUiGcaCdO3alY8//hhPT0/y8vL44osvAFiwYAGrV6+mZs2ahIaGEhkZSVTUTKyt\nV2FhYUHjxo1p06YNXl5eBmWPhUtKs7Oz6dGjB+J/ZtKurq60bt2aoKAgbty4wZAhQxTVUBMTE1xd\nXenRo4f0HpNIJJKnoBKv0GBUpVKJV3l+iUQieZEMGjSI4OBgZXA7duxYhgwZwpkzZ0hMTKRBgwZM\nmDABa2trHj9+jLe3N5cvX2bIkCHKPjk5OYSGhvLrr7+Snp7O6NGjWblyJb/99hvlypXDxMSEhw8f\nUrt2bQ4fPkzLli05deoUy5Ytw93dna5duxIXF4enpyd79+7FyMhIkWYPCAggLCyMcuXKYW9vT1ZW\nFuHh4bi6ugIwcuRINm7cqJRLSp6NgkqjpUuX5p133lHKFLt168b48eMZP348e/bswczMjJiYGCws\nLBg5ciR79+4lLS2N5cuX07x5c/r06cOdO3dwdHRk7ty5hIeHExsbi7m5OS1atGDOnDkGEvshIYuZ\nM2cvubmxJCfnS+yPHDmS0aNHY2lpSYkSJVi5ciU2NjYEBASQkpJCWloaY8aMUdRL9aWFkZGRitXD\ny0L/TNapU+elnVMikUheNSqVCiHEX/LtkMImEonklVBQIa9hw4Z8/fXXBAQEoFarcXBwICoqCp1O\nh7+/P2q1mt69e3P79m3UajW1a9emadOm9O/fn8jISCWzEx4ejlarJTExUZGf79evH1FRUaSmptKn\nTx8ePnyIWq0uouT4PNELgpQrV48DBw4A+fOqzp8/T5UqVZTtbt++Tbly5YiJiSEnJ4cSJUrwww8/\n0LFjR1auXElWVhadOnVi165daLVaLC0t2bBhAxEREVhYWFC6dGmEEDg6OjJ06FDKli3LwoULadmy\nJR999BFBQUGULFmS0qVLk5ycTF5eHomJiVy6dAlAEa/w8/MjNzcXyM+S5ebm0q9fP5o0aSIDuL9B\nQaXR5cuXo1KpWLduHfPnzwdQMrBarZbg4GAlaEpLSyMhIYH+/fsDsHTpUvz9/dFqteTm5nLo0CEg\nX+lz165dXLhwAciX2E9MTGTevHnEx69k9+6hHDiQQJMmTdBqtZw+fRoTExN27drFokWL+PLLL4H8\nQUNCQoIibvI6IL3HJBKJ5NmQQZxEInll6BXy9EqJhQeVsbGxODo6kpCQQNOmTdm2bRsJCQkEBAQw\nZ84cIiIinnpslUrFoUOHOHz4sMHyxYsXExgYyEcfffTCrksvCJKV9REbN25ErVZjZWVVRHjBzs6O\nkiVL0rx5cx49eoSNjQ1Hjx6lcePGDB8+nKCgICpWrEhycjKenp6cOHECyFdLzMzMxNjYmLt373Li\nxAkmT57MjRs3GDp0KKmpqTRr1ozMzEwDAZN27doxfPhw7t69q/TRwYMHOXPmDGXKlFG28/f3p1y5\ncqxcufKF9dG/mYJKo/pyWBMTEwMRkYSEBDQaDVOnTlX86O7evUupUqWU+Q7nzp0rokgKMGLECJo3\nb66ogP6ZxH7r1q25efMmGo2GQYMG8fDhQwAlcC9YEbN9+3bUajWdOnWiS5cuLzULB/kvEf4pWTi9\nlYNaraZjx460bt2aqlWrotFoOHnypJLRXrFiBQEBAUB+dtPJyYnWrVsrfavfTqvVotFoANi4cSMu\nLi44OzszY8YM1Go11tbWtG7d2kBZViKRvLnIIE4ikbxy9IPIx48fK9+LG8SePXvWYHs9+kFOZGSk\nsiwvL48FCxYoKn2Qb9g9d+5cevTo8UKvZ9gwGDwYRo405/vvvychIQGdTsfMmTMBqFbNnYyM8Vy+\nnD8nrHLlyhw+fBgrKyt69erF3bt3qV69Ohs2bKB06dJMmzYNlUrF9OnTDXyudDodFhYWuLu7s2jR\nIqpXr87jx4+5e/cuFy9exMLCgoyMDKVdO3bsYN68eYqkvhCCsWPHMm/ePGWbe/fukZOTg6mp6b/W\n6P1F8awZWHt7e7y9vdFqtezatYuhQ4dy48YNxZdOT7169ZSXEHpFUsjPxP3666+cPHmSixcvPpPE\n/jvvvINWq0Wr1TJ9+nTA0P9Pj5eXFwkJCajV6iLedxJDCmZcv/76a9atW4eXlxdarRYHBwcDs/KC\nGcb58+ezbt065btKpSIvL0+pDhD/swHZuXMne/fupV+/fiQkJODt7c3q1auZNm3aS79WiUTy+iGD\nOIlE8lLRD3QvXy66Tp8hgvyBzdMGsYUZNWoUCQkJBkIY33zzDd27d6dkyZLKsg4dOjBy5EiDwO5F\noBcE0Qt+FEafqfv2WwgKCsLDw4OpU6cyduxYIiIiiI+PN8igeXl54enpSf369blxA7ZsqY25eXlG\njRrFsWPHePDgASNGjMDIyIgVK1ZQsWJF3n//fcaPH89vv/2mZGfy8vIM2nHv3j2aNm1K06ZNlWVl\nypRh+vTpqFQqRo4cyeXibpSkWJ41A9u0aVMsLCzQaDR4eHiwfft2/Pz8OHjwoPIyYtWqVQQFBRET\nE4NaraZkyZK0aNECyBegUavVVKtWDRsbG0Vif/LkyQbBgv5zlSpV8PT0xMPDAw8PD6Kjo/nss89w\nc3Mrcg179uyhV69e7N69u9j1bwrFlXs3btyYTp060bx5c44fP87JkyfZsGEDarWakSNHotPpOHfu\nHA0aNECj0XDx4kU6depEdnY2W7du5YMPPiAnJ4cff/yRbt26sWnTJiXwdnFxUe6Xn58ft2/fxsTE\nBCMjI7Kzs7l37x6rVq3i4sWLBiXkHh4e7Nq1yyAr2KlTJ8aOHWuQGUxOTn6V3SmRSF4AUp1SIpG8\nVPQD3StXoHHjJ8tDQkIIDQ012LZbt2706dMHtVpN9erVFfXEp82bKbi8Tp06dO7c2SA7p1KpGDp0\nKMHBwSxZsoSBAwc+xyt7dvK9zKB1azh1Kj+z8u677+Lt7U3Hjh2xs7NTSt706n5jxowhMTGRgwfh\nl1860qJFEKtWrSIqKorhw4dTvnx54uPjqVu3Lh9//DE6nQ5LS0tMTU3p2bMnJiYmvPvuu2RlZWFq\nagpA2bJliYqK4saNG3h7ewNga2vLoEGDqFy5shQ1+Yvo7+uwYebY2X1fZH1BRdCJEycarJszZw4n\nT55Uvms0GszNzYmLizPYbsKECUyYMMFgWWxsbLHtKZhJLc7MXU/Blx/nz58vdpt/Iz169GDZsmWM\nHDmSJUuWFFnft29fJk2aRFRUFLm5udSuXZu4uDiOHTvGxIlzOHr0MbVrm5GQsJnw8HB+/PFHhBCM\nGjWKwMBAatWqhRCCDz/8kEOHDlGhQgV27NjBlClT+OSTT5gwYQLLli0jJyeHu3fvYm5uzqNHj7h0\n6RLZ2dnk5uZSv359Fi9eTOPGjTE3Nzdo3/r16xUPw8JKuNbW1qQw0whdAAAgAElEQVSmphIWFkZU\nVNRL6U+JRPJykeqUEonkpXLmTH4gN2zY0zNVL5KkpCQAHB0d/1/HGTBgAAsWLCAgIMCgNOpF8yz9\n17FjRy5cuIAQAltbWywtLbl+/boyILSysmL9+vU0a9ZMUa/s1KkT8fHxBuqVO3bsYMKECahUKiZN\nmoRGoyEyMpJ58+ZhamqKg4MDK1asYMaMGWzatAkzMzNWrFiBTqejZcuWNGzYUJG4l/wxGRkZBiWX\nhb9Lnj+LFi3i22+/Zc6cOTg5ORmsS0xMJD4+nkmTJhEZGcnjx49JTEwkKiqKR48eYWfXlYsXW2Nl\ntQUHh1Lk5ubSunVr9u3bx4ABAwgMDKRq1aoEBgbSuHFjtm/fjomJCSdOnGDq1KmEh4dz4sQJxUKh\nUaNGaDQaxo0bx7Rp09i2bRvXrl3j+PHjuLm5UbNmTXbs2MHSpUtJTU3lzp07HDx4EDs7O9zd3Vm3\nbp2BEi4ggziJ5B+EVKeUSCSvPX9Wavii+f333/n999//38cZMGAA3t7etG/f/jm06tl5lv778ccf\nGTNmDKNGjcLHx4f33nuP+Ph4LCwsOH78OD/99BNjx45lxIgRTJs2jdKlSxdbhjd58mS0Wi3bt283\nyBwVnNOTnp7Onj172L17N1OmTGHq1KkAytygFxXAHTlyhObNm+Pi4sLAgQOJjIxUytg+//xzTp8+\nTdu2bXFzc1MEWtRqtVJSqg9U1Wo15cuXR61WG8wNfNkUDtgKq5h26dIFjUbD8OHDCQ8Pp2nTporY\nxfnz58nIyMDHx0e5fsiX63/33Xdxd3cnODiYR48eGZRItm3blsePHyv9plarlfWrVq3C2dkZd3d3\njh49CuSrv7Zu3RonJycmTZoEPBHl0Ku/Qv4cVVdXV7y8vLh8+TKpqamUKVOG7OxsUlJSMDY2Ji8v\nj6SkJNzd3XFxcWHNmjVFjqcXA1myZAlubm54enpy8eJFg3MlJiYqPoTFtUW/TI/+GQgPD8fBwYGf\nfvqJHj16cPHiReCPy731HDx4kHbtGtGhQ0P8/TuTkJBAly5deOeddwyynWlpaZQqVQorKytWrFhB\nXl4eS5YsISQkhG+++YarV69SqVIlTpw4QdmyZfHy8mLkyJHUr1+fRo0asW/fPpo0aUJ4eDhdunTB\nxcUFGxsbID8A/fTTTxFCKKXn+nmYWVlZT2+8RCL51yCDOIlE8lqSmJhIzZo1EUIonw8cOKAM+rZt\n2wbkC55Ur14dCwsLOnToQJcuXfDy8uLq1atYWlpia2tLzZo1qVevHsbGxnz88cd8+eWXlC9fnkqV\nKtGyZUtsbW1ZsWIFjRs3pn79+jRr1gxjY2OWL1+Oi4sLZcuWxc3NDVNTU86ePYunpycajYbvvvuO\ngQMHYmdnR4UKFahWrRpvvfUW5ubm7N27lyVLlmBpaUnr1q1p3bo1VapUoXTp0tSrV0+ZV/Oi0IvD\nnDx5knnz5qHRaNBqtWRkZODo6EjHjh1JT0+nefPmyvaenp6o1WrS0tKU45QqVQozMzOMjPL/u8jJ\nyTFQWkxNTaVhw4YABqIrL5oGDRrwyy+/sGfPHlJSUrh+/TqjRo1Cq9USGhpKWFgYkZGR7Nq1q9hA\nUqVS4erqSkJCAk5OTmi1WoYPH/7C2/13KM5qYO7cuYrYBcC0adMYP348Wq2W5ORkbt26BcC4ceNI\nTEwkLS2N9PR0GjZsyNGjRzl69CiNGjXCxMSEKlWqoNVqFSGTvLw8vvrqK/bu3cvatWsZN26c0paC\nVgkF0QcTTwvq7ezsiIyMJDo6WskWTZgwgXXr1rFr1y6++uorRdhIj0ql4vr162zZsqWIPcIfoW9L\ncRRePm/ePIN5swXnqxbeJzExES8vLyZPnsyUKZ+xeXNX0tKOoVarSUpK4r333iv2nKGhoXh4eFCp\nUiUaNWpEp06d6NSpk2J5oj+Hvmy6cHubNWtW5GWRu7s7TZs2Vdr20UdP5mE+rWxWIpH8u5Bz4iQS\nyWtLpUqViIuLIzExkerVqyvzTkxNTXn33Xdp3749t27dYsaMGXTv3h0bGxuWLl3KgAEDePfdd+ne\nvTs1atRg0aJFCCG4du0azZo1o0GDBnz++ed89NFHzJs3j44dO5KXl0daWhplypQhLi5OKa8qUaIE\nzZs3Z+fOnZQuXZrjx49z/vx5TE1NCQ4OxsjIiLCwME6cOEFcXByVKlVSpOK3bNmCk5MTixYtYsWK\nFdSsWZOsrCzi4+M5duwYc+bM4euvv36ufaYvt6xZEypVyldC7NChA76+vuTl5fHo0SOuX7/Onj17\nqFy5Mnv27MHFxQXIlzhXqVQG2ZrCkvUnT57E29tbyWjVqlVLydT8/vvv1KtXTwkgXyQZGRmEhIRw\n5coVTp8+jb+/v0EQcObMGfr06YMQghs3bvDgwQMAPD09AQwCVfjjgf+r5ty5c/j4+AD5gfKOHTuU\n/i1oWxASEoIQgtu3b5OZmQmglOvZ2dmRlpZG7969WbVqFSqVSslWFb5X169fV+ZCWltbKxYIhQN4\ngKNHj6LRaHjw4AF2dnakpKQUG9Q3btyYpKQkLC0tqVKlCkIIbt68SdWqVQGwsbEhIyODO3fuoNPp\nePToEZA/Ry8pKUmR3tcHgNu3b0ej0XDr1i2lbwq3BSAzMxONRoNKpWLRokUG15mWlsaFCxd45513\nlGVP5jW6Y2eXP3+xb9++pKamKuWUBVm9erXB94LzC/V8/vnnSvshP7DWB8aJiYm0atUKS0tLevfu\nTcOGDfnkk0+YNGkSc+fOZcSIEYSHh+Pq6oq/vz8ffPABGzduxMTEBGdnZ0xMTOjWrRvm5ubs3LmT\nt99+mwsXLnDu3DkqV65MdnY2Go1GMYsHcHNzw9jYmKSkJK5fv87kyZNxdXXF1dWVRo0asWHDBoYO\nHUpKSgrlypWjadOmzJgxg8DAQO7fv49GoyE0NJTU1FQ8PDyoVasW6enpnDhxQpq1SyQvEZmJk0gk\nry3e3t7ExMRgaWlJiRIlOHLkCJ07d6Zt27bKhP7c3FzefvttSpQogaWlJenp6ZQuXZr09HTFq8vY\n2JiMjAx69uzJ7du3uXPnDjt27ODatWt4e3tz584ddu3ahZmZGVevXjV4k/348WP2799P+fLlefjw\nIceOHcPY2JiSJUty7NgxrKysmDt3LuvWrSM5OZmkpCTq1q1LQEAA27ZtY8+ePQwePJgHDx4ghMDC\nwgLID64KBxLPA30mYfv2/Lf4AwcOJCYmBk9PT9q1a8eVK1cYOnQoM2bMYO7cuYwePZr79+/r6/EN\njjV+/Hjatm2Ll5cX48eP55tvviE9PZ2aNWsq21SpUkXJjo4bN07JJLzIgOjMGfDxmYubW08SEhLw\n8PAoso29vT1r164lISGBffv2YWZmBqBknKysrJRtX+e52WfOwJEj9diy5Y9VWu3t7Zk3bx4JCQkc\nOHBA8ZCD/OvT7+fi4sL+/fv57bfflCxsYSpVqsTly5fJy8vj6tWrivVBYasEgMaNG6PValm7di0A\ntWvXLjaoh3whkV69ein7VqhQgStXrqDT6bh48SJWVlb0798fDw8Phg4dChS1R5gxYwbwpFxXH5gU\n1xaAypUro9VqCQ8P57vvvjNo++zZswkLCzO4/8+73PtZfg9atGjB1q1bGTt2LLa2tkpp6ZYtW5QA\nNTc3l2nTppGQkEC1atVYvHhxkUxpkyZNiI+PJyAggAsXLhhkcB89esTPP/8M5P/O6r03CzJ//nxK\nlixJuXLlivhxFs70ZmdnA/kluwkJCUowDq/375NE8m9CZuIkEslri7GxMb6+vri4uLB9+3YcHR3Z\nsGEDZmZmJCXdY8gQMDEx5fDhw1hbW3Pv3j2aNGmCkZERmZmZTJo0icDAQB4/foyVlRXR0dFoNBo8\nPT1ZsmQJtra2bNu2DScnJy5evIiNjQ0PHjwgMTGR0qVLI4QgLS0NGxsbzpw5Q9myZfnuu++oU6cO\n8fHxLF68mL59+xISEkJqaio//PADFStW5Ndff2XHjh3MnDmTjIwM4uPjefDgAf3791cGOAcPHlQy\nFs+TJ5mEfspANCYmxmAb/SARYO/evUB+cKNHr2ro6empZK70dO/eHUApQYV8i4dRo0YZbPcixRTm\nz4ekpPaMHTuKLVu+JjU1tUi5WXh4OH369CE3Nxdzc3M2btyoBKr60jU9r2sGDvKvdd++j7h6tTdx\ncUtp2rQpLVu2VNbr2z5mzBiCgoK4c+cOJUqUYP369UB+mV1OTg49evRQsnL6EuHCx9BjZGTEoEGD\ncHFxoUSJEixcuJB58+ZRo0YNA6uE4vYtGNSbmZmxcuVKdDodKpUKLy8vg/3Cw8Px8/MjLy+PQYMG\nYWxszPDhwxk+fDgXL15k4sSJBvYIAD179lRKSAtT3H28efMmvXr14v79+0ydOpUdO3Yo67p37461\ntfUz3X9bW9u/9Uzr5+v9Efr7Ym9vz48//sixY8c4fvw49vb2Sn/369cPPz8/LCwsDF6kFMyUHjly\nBI1Gw5UrV9iyZQsbNmwwyOCePXuWt956S1GnLUhaWhrnz583yEoWDMYKZnqzs7PJyMhACFHkeQDo\n3bs3xsbGBAYGvnSzeInkjUJf9vIqfvJPL5FIJE84fVqIwYOFWLVqpwgLC1OWu7q6iv379wu1Wi3c\n3d1Fo0YDBQhhaVlfVKlSRVhYWIgPPvhA9OrVS2zadET06XNBlCljLmxsbESNGjVEjRo1hJGRkTA1\nNRW1a9cW5ubmwsrKSvTu3VuYmZmJ6dOni6pVqwpTU1OhVquFkZGRqFixoihXrpwwNjYWrVq1EiVK\nlBATJkwQ3t7eokyZMkIIISpXrizq1q0rqlatKszMzMTbb78typYtK/bs2SOWLFkiLC0thZubm/jq\nq6/EBx98IKpVqybatWsn2rVrJ9LS0l5VN/+j0T8jp08/+z47d+4Utra2Qq1WCwcHB7F8+XLx1ltv\niY4dO4q3335bHDt2TDx+/Fj06tVLeHh4CH9/f6HT6URERISoX7++cHd3F927dxdCCLF9+3bRpk0b\n4ezsLOLj44UQQhw7dkzUrl1btG7dWvTp00cIIYSLi4tyfv1nDw8PodPpRHJysrC3txdCCNG/f39x\n7tw5ERUVJZo3by5yc3P/X9eqR3/cwvTr10+kpqb+9QNKnjs7d+4UoaGhQgghoqOjxcKFC8WECROE\ns7OzOHDggBBCiIkTJ4offvhBuLu7i127dgmdTifatGkjdDqduHLlivD19RVCPHnGDhw4IIYMGSJm\nzZol1qxZI4QQYtCgQeLQoUNi06ZNYtmyZUKIJ8/ixIkTRbNmzcTVq1dF//79lWdj4sSJyvM9bNgw\nkZSUJIQQIicnR+Tm5orvv/9eREVFKccS4skzl5uba/D8SySSP+Z/MdFfiqNkJk4ikbxW6MsBwZ2F\nC92V5frskD5jpJ/7deBAZX75xdDIdsgQiI6GwYNvs3BhviKdfr5XZGQkKpWKvn37KttHR0cDsHXr\nVuLj45U383oxEP0b+IL76MnIyHjqtTg7OxuUZnp7e0vJ7+eAvuTtr/JHvl9z5szB29sbR0dHVq9e\nzaxZsxRBFL3vl6+vL/BEtTMvL4/27duj0Wi4du0ao0ePxtvbu4jfITzJEun/nTZtGpaWlsr67Oxs\nIiIiOHjwoCIi8/+51oLnKsjdu3fJyMhQVA4lrwb936/WrfP93vbv34+5uTlr1qzh0qVLxMTEKBlX\nlUqFqakp69evx9fXl/Xr1zN48GAlU6qf63fs2DE0Gg05OTnMmjWLRo0a0bt3b5Yuzc/g2tjY8P77\n71OtWjVWr15NUlISUVFRqFSqYrOSBT8XzvROnjyZGTNm8OOPPxbZdvjw4ZQoUQI/P7+X0ZUSyRuL\nDOIkEslrxZNywD/eTj+4dXMrOlAtfIyCZXR/xNq1aw220c+t6dq167M1XvKPQPyvTEwfRNnb23Pt\n2jXOnz9PkyZNgPz5Rb/++ivVqlVjxowZLFu2DBsbG0XQpVSpUgBKwHX+/HlsbW0NStD0ohoFlwkh\n+OGHH3B2diY1NVVZ3rFjR0JCQgwCuP8v+nLXgpQtW1YZeL+uJCYm0q9fP+rUqYMQgvnz5xMcHIxO\np2PQoEH06tWLuLg4Zs6ciUqlYv78+Tg5OSm2EampqYSGhiovZ15H9C+rrlzJL+vUWzYAXLlyhd69\neyvfC5Zk7t+/H4BevXoZzC+E/NLRwhQ0i09NTcXd3V15ibRr1y527txpcPyCz0zB5VZWVmzcuNHg\n2PpSbHjyck1v5yGRSF48UthEIpG8VvxVYQF9hu6PjqHVapXBcb9+/YrNqAHMnDmTNm3aKHPeFixY\nAEBwcDDff/89QgiCgoJQq9V4eHhw586dYj224uLicHV1xc3Njd9++42xY8fSunVrWrZsyeXLl/nq\nq68ICwsDIDIykqioKOXYbdu2pUePHuTl5REZGYlaraZdu3Z4eXkVUYqU/DnP6vvVqFEj6taty+HD\nRQVERo0axf79+6lQoYKy/v79+wb3Y/fu3Tg7OxsEbHpRDb10v56YmBgGDBhgsG1cXBzR0dGcOnXq\n/33N/wb69u2LVqulatWqfPrppwZWBI8ePWLu3LkkJiayadMmZs2aZbDvs7ywedUMGwaDB8P77xdd\nFxkZaRDEPS9q1qxp4IXYqlWr19ZWQyKR/DkyiJNIJJL/kZ6eTkJCAv379wfyM3iHDh1SBu6bNm3C\n2tqahIQEYmNjKVWqVBGPLSGEwQBz9uzZTJ06lbVr1yqKeg0bNiwyyNy0aRP16tVjx44dqNVqJVvi\n4+PD9u3b8fX1LaKuJ/lzntX367PPPqNr164cO1bU92vmzJlKyWTjxo2LqHZGRkaybds2unTpgp+f\nH9u3byc9Pb3YQMLIyIihQ4caiKuoVCoqVqzImjVrCAwM5M6dOy++Y15zoqKicHNzIz09ndu3b1O1\nalWMjIywsbEhLS2N5ORk2rZtS9euXRWvNb3FwJ+V8RU2iu/atatSFj1s2DCSkpKA/AytWq3G3t4e\nyFdiPH/+PFlZWVSsWJEzZ84ohuutW7cmJCSEI0eOFDEwV6vV+Pv74+zsrKhr6l80+fu7G2ThACIi\nIgzURZ8XRkZGVKhQQflesmRJRS1XIpH885DllBKJRPI/7ty5Q6lSpZQ3+Xl5eSxYsIDg4GCEECQn\nJyu2BXpFucJkZmYqA8ynbWdhYUFKSgqQLx1esmRJkpOTiYmJYevWrTx8+JBPPvnEYH97e3tlcCl5\ndp6H71dh76/Cqp2TJk1i7dq1ihdYYGAgOTk5Blli/ef4+Hhlmb4ErWAJW8EStTeNgvPE9PMXp0+f\nzueff86VK1eoWrUqFy9exNramsaNG7Nt2zZUKhX37t0D8ktgtVotqampSqa7OPRG8QDt27end+/e\nrF27lsGDB3PixAkcHR3Jzc2lefPmrFmzxsA3EWDq1KlUqFABOzs7EhISCAwMJCwsjNq1a9O1a1fW\nrVunqHTq1VwXLFhAhQoVcHV1ZcSIEcWqOkokEslfQf4VkUgkbzxnzsD06TdQqcopy4QQfPPNN8ye\nPZsbN26gUqmoV68eBw4cwNvbm9u3b1OmTJkinkhVqlQpdoCpPyagGIl7enpy9+5dBg8ejL29PQEB\nAYo/1r179xRhDYBDhw4p+0menf+PMMizMnz4cAPZ9lmzZhmIlkiejYLzxH7/PZo9e/ZgZGTEjz/+\nqFgRDB48mBIlSjB48GDUajUqlQp3d3cmTpz4zOcpbBTfokULhgwZgr29vWJVceHChWLFX44cOYK5\nubmBV6J4orhdrIE5PJl/WbNmTW7evEnlypX/Vh9JJBKJHhnESSSSN5758+Hrr3tSvvwlNBoNKSkp\nrFy5EgcHBzp37kxkZCQA3bp1Y+PGjajVaoyNjdm8eXOxJXNPG2AW3FafAdL/27VrVz7++GM8PT3J\ny8vjiy++APLf4K9evZqaNWsWq3oo+Xv8Xd+v4ihcklawZE3y7BhmTS8YrCs897VLly506dKl2G3+\n6N6eOQMffDCXQYN6EhTkg7+/P6VLl6ZGjRp88cUXipjRTz/9pGTTC76oWbBgAZs3byYxMbHY4+sN\nzPVZw4Km8rm5uVy+fFkGcBKJ5LmgKvwW+aWeXKUSr/L8EolEAvkDO3d3NxITdyliKHp7gVdJZGQk\nOp1OGuZKJM+JIUNg0aKfqFRpNC4utUhNTeX7778nOTmZWbNm8dNPP5GUlISXlxcNGzYE4Pfff2ft\n2rWsW7eO9u3b06NHD4O/D4GBgYSGhlKnTh2OHDnCkCFDlKyhn58farWasmXLcv36dT777DNlrqVE\nIpHo+Z+K9l9SZJJBnEQikZBfYlWlSpWnfn8VyCBOInm+6OfdDRtmqIC7cuVKTExM6NOnD4mJicTH\nxyuCI3/kE/ksFPSplEgkkuKQQZxEIpFIJBLJX8Tb25sNGzZw9uxZAgICKFmyJE2aNKFNmzasWLEC\nY2NjTExMiI2NZdKkSYqRdqdOnYiIiCAkJITq1auzY8cOfv31V1asWMHgwYPJyclBp9MRHByMr68v\nbdu2pWXLlnTr1o2QkBDc3NyIj4+nbt263L59m/v379O0aVOOHj2KnZ2dotLZoEEDcnJyKFOmDA8e\nPODs2bM0b96c27dvY2FhQV5eHhqNhtDQUAICAggLC2Pv3r18+eWX9O3bl++++47Dhw/TrFkzxo8f\nz88//0xcXBxmZmasWLECnU6Hj48Ptra2pKWlERkZSaNGjV71bZFI3hj+ThAnLQYkEolEIpG80WzZ\nsoXSpUvToEEDfv31V/bv309KSgrXr1/H19cXrVaLr68vsbGxfP755yxfvpxRo0YRFBSElZUVKpWK\nHj164OzsjLW1NSdOnCAjIwN/f3/+85//4OLiwuzZs2nQoAE9e/Zk2bJlODg4oFKpuHjxIg8fPiQr\nK4vWrVuzZs0a6taty+zZs1m+fDkqlYp169bRpk0bypUrx+zZs3F0dKRfv35Uq1aNunXrotVqSU5O\n5tatW6hUKrKzs4mIiODgwYMEBweTkJCgqHc6ODiwe/dudu/ezZQpU5g6dSoAtWvXZvPmzURERDBn\nzpxXfEckEsmfIYM4iUQikUgkEvLLqHv37o1arebYsWNUrlzZwObj2rVrlChRgo8//pjt27fTrVs3\nIF/85Ny5c2i1WjIzM/H29iYsLIwtW7bw/fffc+/ePVJSUsjKyuKLL77g5s2bVKxYESEErVq1Urzo\nCpKWlsbJkydp0KABkK9seffuXVQqFTVq1ODatWvcvHmTH374AbVazcmTJ8nMzEQIQceOHfH19cXI\nqOgwLyUlRZnv5+joyLlz51CpVIqCpr29PWlpaS+kfyUSyfNDBnESiUQikUjeeM6cAR+fubi59SQh\nIQEPDw/giTrloUOHaNiwITk5OURFReHp6cmaNWs4cwYOHIApU+awb98+nJyc8PDwQAhBhw4dCAgI\nAPIzXa6urkRGRhIbG4uxsTGQ7zOYkpJCRkaGgRLm7NmzGTFiBFlZWQBcvHiRKlWqIITgypUrNGrU\nCDMzM/r27UtCQgIHDhxQTMLj4uKIjo7m1KlTyvH0x65VqxbHjh0D8kVb6tWrZ2CTcPDgQSXIk0gk\nry9yTpxEIpFIJJI3Hr1yZcWKo3B1rU1qairDhw9n7ty5VKhQgZo1axIREcHIkSPx9PREo9Hg6elJ\nvXrriYgYi41NOrVr3+Pw4cP897//Zf78+Vy+fJm6desyYMAAatSoQe/evWnQoAHm5ubcuXOHnJwc\nLl26hIWFBVevXkUIwbhx41i1ahV+fn6MHj2ahg0bUq5cOXJycnj8+DEmJiacO3eOFi1aULJkSYyM\njLh//z4lSpTgm2++4T//+Q+hoaE8fvyYgIAAtm7dirm5OW5ubooNw8yZM9m4cSNmZmasXLkSnU6H\nm5sb9evXByA6OtrAHkEikbxYpLCJRCKRSCQSyd+gOOXKZ1GIfZri5bOQmppKWFiYooC5a9cudu7c\nyfjx44tsq29L3bp1DdQznweF2yGRSF4ufyeIk2bfEolEIpFI3njs7GDhwpe3H+TPc5s3b57yvVWr\nVjRt2vTvHUwikbxRyEycRCKRSCQSiUQikbwiZCZOIpG8cdy+fZs+ffpw584dmjRpQvny5YmNjaV8\n+fIkJyezd+9eypYtS2BgIPfv3zfwUsrOziYrK4smTZqwYMECwsPD2bFjBzk5OUycOJG7d+/y1Vdf\nKf5KQUFB6HQ6Fi9ejImJCQsXLqRx48bUrl0bR0dHLl26xKxZs1Cr1a+6WyQSiUQikfyLkeqUEonk\nH83SpUvx9/dHq9WSm5vLoUOHmDt3LgkJCXh7ewMwbdo0xo8fb+ClBDBu3DgSExNJS0vj8uXLjB49\nmt27d7Nx40YWL15M9+7dDfyVunfvzuLFi9m7dy9r165l3LhxAFhaWhIbG8tPP/3EjBkzXllfSCQS\niUQieTOQmTiJRPKP5ty5c/j4+AD5nkc7duxQpLL1/yYnJxMSEoIQgtu3b5OZmQmg+D/Z2dlx7do1\nDh06xNKlS3n8+DF5eXlFzpWZmUnNmjUBsLa25vb/sXff8TXd/wPHXydBUXtr7ZFaIWpX1r0JoSJG\nayeItlRT40utVkKilNqCtvykElvwRWtLbgZFvjVLjWgISuyZGJH7+f2R3tNciVbVCu/n45FHbs49\n43NO5Drv8/l83u8bNwDIly8fmqZRtGhRUlNTn+4JCyGEEOKVJ0GcECLbio+HAweqsGHDXqpVq8b+\n/fupUqVKpvXs7Ozo1asXtWvXJjU1FU37c9i5UooDBw4wZMgQPvroI3bs2EGOHDlo3ry51ToAxYsX\n5/Tp05jNZpKSkihYsKDVcU6cOEGxYsWe0tkKIYQQQqSTIE4IkW1Nnw4//fQRZ896s3btHBwcHGjU\nqJH+viVYGzFiBL179+bmzZvkzJmT8PBwAD766CPu3r1L55hCNNUAACAASURBVM6dKVy4MO+88w7N\nmzenevXqVoGe5bWNjQ1+fn44OjqSM2dOZs2aBaT39LVs2ZLk5GTmzJnzrE5fiCxNmzaNlStXkpaW\nxtixYxk6dCivv/46tra2rF69mitXrlCjRg3Onj3L1atXqVy5MqmpqRw/fpxPPvmEe/fu4evri6+v\nL2vWrGHixIkopWjTpg0bNmzgyJEjVKhQARcXF/LkyYOTkxNOTk7UrFmTlStX0r9/f06ePEnBggVx\ncHDg66+/zjQnNTExEVdXVypUqMD58+f59ddf8fX1xd/fn0qVKj3vSyiEEC8+pdRz+0o/vBAvv6io\nKFWmTBllNpv114GBgcrR0VE1aNBArV+/Xl+vWrVqymAwKIPBoJRSatasWeqdd95RTZo0UT4+Pkop\npRwcHFSHDh1UgwYN1MKFC5/beT1vx44p5eeX/v2f6tmzp/rtt9+eSDucnJyeyH6EeBJu3ryplFIq\nLi5ODR8+XN2+fVsppdS4cePUihUr1MmTJ1WdOnXU9OnTVVBQkKpZs6ZKS0tTHTt2VGfOnFFKKfXu\nu+8qs9msGjVqpFJTU5VSSiUlJSmllPL19VUJCQlKKaVGjx6tIiIi1MSJE1XNmjVVYmKiUkqpwMBA\nFRERoZRS6j//+Y/atWuXUkqpbt26qWvXrqmTJ0+qwMBApZTSP+t69uypjh8//tSvjxBCvGj+iIn+\nURwliU2EeEaKFSvG2rVrWbNmDW+++SZDhw7Vk2hYenQgvdcoMjJS/3nVqlWYTCaWLFmiL8uVKxcL\nFixg+/btzJ49+5mex4vEUp/pnxbYBax62sSzN2/ePBwdHXF2diYiIgInJyf9PcvrtWvX4uTkhLOz\nM3v37gWgYsWKtG3blnr16mEymYD0xDJGoxGj0Uhqaiq9e/fG3d2dTp06cePGDQwGAxUrVsTBwYGe\nPXsSGhpKSEgIAK6ursTExBAdHU1AQADnz5+nUaNG/Pjjj8/4ijw5lmysXbp0oWXLlvj7+2M0Glm4\ncCHJyckA2Nvbs3//fq5du0aJEiVQShEfH4+Pjw8Gg4EzZ85w+vRpypUrR44c6YN2SpYsCZDxQSwA\nSUlJJCQk0LBhQ31Zxvctc1INBgO//vorFy5c4O7du/p+M/L29sbR0VH//QghhMiaDKcU4hlp2bIl\nS5YsoUaNGuTKlYv169czZ84cUlNT9RuerG5sBg0aRLVq1ShVqpQ+3ytXrly89tprAOTJk+fZnshL\n4kneJMbExDyxfb0qWrduzQcffEBycjItW7bMNHxVKcXUqVOJjo7m5s2bfPLJJyxatEjPBHrlyhW8\nvb0xGAx69lBID/yqVKnCnDlz+Pbbb4mNjcVkMhEUFISjoyNGo5HQ0FAAwsPDuXTpkn5cpRRjx45l\n8uTJODo6PtsL8oTEx8OECVcYMSKEoKAzlCtXjkGDBhEZGcncuXOt1u3UqRNFixZl7969aJqGnZ0d\nM2bMoESJEiQnJ5M3b15OnjxJamoqOXPm5MqVKxQpUiTTMSdPnsyPP/7I559/nmWbMs5JvXfvHjY2\nNqxbt05PEpQx4Fu0aBHlypXDYDDQq1evJ3hlhBDi5SI9cUI8RfHx8OmncOYM2Nra4uXlRc+ePVFK\nMWbMGP773/+yceNG/Sbm8OHDmW5sihcvTvPmzVm6dGmm/V+/fh1bW9tnd0LiiYqOjqZChQoYjUZq\n1KjB3LlzrXqRzGYzoaGhGAwGmjVrRvPmzUlJSeHChQt4enpiNBr58ssvAfD19aVx48Y0btyYqVOn\nAjBx4kScnJxo3rw5Z86cAWDWrFm8/fbb1KhRg5CQEKteqcDAQL1Xyt/fH4AePXoQFhZGYmIiPj4+\n3LlzB4PBkCkg+KdKlChB27Ztad26NUePHuXixYsYjUa9xt7Fixc5cuQI7u7utGvXjjt37gBZZwJ9\nsNdnyZIlGI1GwsLCuHLlSqZ1AFJSUli7di0dO3bUl82dO5dt27Zl2wAO0ueJzpv3Ja6urrz//vss\nWrSIbdu20aJFC1atWqWvp2kazZs3p169evqywMBAfHx8MBqNdO3aFU3T+Oyzz3BxccHJyYm4uDh9\n24w6duxI6dKls5xHCumjCyy9ga1bt2bPnj18/fXXeHl5ZVp34MCBdOrUiS5dujzZCyPEEzBr1iza\ntGmDm5sbR44ced7NEa846YkT4imaPh1mzYLffwd7e42uXbsC6TctTZo0oVmzZtSoUQNN04iJiWHd\nunX069dPXyc1NZURI0awYsUKrl+/ru/3ypUreHp6cuXKFalLls11796doKAgwsLCuHbtmlUv0vr1\n6wHw9PRk8ODBzJw5k5UrV7J3714CAgJo2LAh3t7eet27xYsXU7x4cfr27cv58+eJjY0lNjaW//3v\nf4wbN47Zs2dz7tw5lixZwq5du7h///5D26VpGnFxcezduxc3Nzd9+ezZs+nVqxc+Pj7/6ryTk5NZ\nvXo1d+/epWPHjly9elXvTXN2dqZEiRLY29uzadMmNE3ThwFaPCwTaNWqVfH19aV///5AerCWlVmz\nZrFkyRLWrFmjL+vduze3bt1i3LhxD+1VetENGAAwhQED/hxmnFVAFBYWpr+2XPe33nqLTZs2Wa3X\nsWNHq0AXrHuxR40aleXygIAA/XXJkiWtrjPA9u3bMx3/+++//6tTE+JvRUdHM2zYMAoWLMitW7f4\n6quvGDRoELlz56ZWrVp8++23DB06lJ07d5KWlsb//d//kTdvXjw9PSlfvjxJSUmEhoZSsmRJPDw8\neP311ylWrBgrVqxg3759zJkzh+LFi5M3b14qVarEjRs38PHx4ebNm9SpU4e+ffvSp08fPfmPs7Mz\ncXFxREREYGNjg8FgwGQy6Ul8ChYsiJ2dHZcvXyYwMFAfSj5kyBDWrFlDmTJlnvMVFS8yCeLEC8PP\nz49evXpRr149pkyZot9kFC5cmKNHj7Jt2zaOHj3K6NGj0TQNT09PRo4cqX/wKaUYOnQoa9asITo6\nmtmzZ5MjRw5mzpyJvb09hQoVol69ely9epXw8HDOnDlDREQEQUFBODk5ERsbS2hoKJqm0b179ydy\nTuk3VDBggAtVq7royx82/G7z5s36a8uNzdatW4H0eT+Wa1KkSJFsPWdHZKaU4s6dOyxZsoSNGzdy\n584d+vbtC/xZz87Ozo59+/Y9tO5d165duXnzJn369OHkyZPUqFEDSK+f99tvvwFw8uRJypUrx86d\nOwEoWLAgu3fvBrCqcWc2mwkODtYfKgBs3LiRqKgofV+PIz4+/eFGrlzT2L17M6+99hrjxo1j0KBB\nmdb18/PDYDCgaRouLi6MHj3aKhOopTcwY09O27Zt6du3L25ubpjNZr766isaN26cqffIxcUFBwcH\n1q5da7V8ypQptG/fno0bN9KiRYvHPs/nxTJPVIhXVYMGDQgODubHH38kJiaGn3/+GQAPDw8uX75M\nQEAA+fLlIzw8nJiYGFq0aEHFihVZu3YtBw8eZMqUKcydO5e4uDhsbW3p06cPe/fuZdu2bYwdOxZP\nT08mT57MkiVLuHjxIt26daNjx474+flx/fp1TCYTvXr1wt/fn4oVK2I0GvW2Pfg5NHbsWKshyqmp\nqUyePBmTyUSBAgWezQUT2ZYEceKF4e3tzaJFi6hXrx4bN27Uh4sZjUZ9boTBYKBly5YA1KtXj5Ej\nRwLpH3yTJk3CZDKRL18+Zs+ezfbt2zl37hx9+vRh7dq11K5dm4iICIKDgzlx4gQ5c+bUj/20klw8\nrRuq7JSUo0uXLixYsIDu3buzePFiIL0XZPPmzdy6dYtZs2ZRrVq1R9pXQEAAPj4+BAQEWCV6yW4s\ngUyTJtbLNU2z6kVKTk5mxYoV+vtxcXHUrVuXs2fPZln3bvHixVSqVImGDRvSuXNnfvnlFwD27dtH\nlSpVSEtLIyUlxWoeZfPmzQkJCaFZs2acP39er4+3fPlyJk+erA9HBGjRogUNGjSgX79+fPfdd491\n7pbeaT+/L4iO/kJfnvHBhuV1mzZtaNOmjdX21atXZ8OGDVbLMm6raRrffvttpuNm7Bnq0aMHPXr0\nyLTcxSX9QUvGYYdCiOwl40OvVatW4e3tze+//86xY8dITk6maNGivPvuu/z22296z3OhQoX0bZKS\nkrhx4wbDhg3j2LFjnDp1im7dulmNBrC3t2f37t2cPn0aT09PIP1h2fHjx2nQoIFV8p8CBQpw8uRJ\nKlSogNls1vdx4MABChQooE+hgPTPpi5dukgAJx6JzIkTL4wmTZrw888/c+jQIezs7MiVK5f+nuXD\n8Pjx47Rv3x6j0cjJkyf193v06EGtWrUoUKAAFy9e1D8US5cuzY0bN4D0D0yj0cjMmTOpXLnyszux\npyA7JdLo0KEDbm5u+vC7PXv2MGfOHD1xwr1796hfvz6Ojo58/PHHAFnOAwsNDeWHH37g448/JjU1\n9S8zGFres8zjetFYApkM8RmQfvNx+PBh3NzcMBgMehAWHByMm5sbx48fp1WrVlZzjDw9PfUhg127\ndsXFxQUPDw9KlCiBi4sLjo6OfPHFF4wYMYK+ffuyd+9ejEYjEyZMYMGCBeTNm5e1a9eyZcsW3n//\nfb0tlSpVonXr1lbt0zSN/v37kytXrscO4gYMAD+/P3uphRDi38o4/9xyv7Br1y7WrFlD586dMZlM\nuLq6AunTEdavX8+8efOYPn06YL1NjRo1WLBgAfb29phMJjp37gxAzZo19dEylgdqlStX1v/f2b9/\nv558LKOBAwfi7e1N27ZtuXr1qr48ODiYYcOGWc3XDQkJYe/evdnq/3jx/EhPnHghWHomatc20KdP\nHyZPnszGjRut1lFKMWrUKIKCgqhVq5ZV8oGQkBC+/vprYmNjadq0KadPn8ZsNpOUlETBggWB9Cdn\nkZGR7Nq1iylTpvD+++/rH54PJj14GUVHR9OjRw8qVapEUlISgwcPJjIykrNnz/Lmm28SFhaGjY0N\ndevWpVChQhw9epSdO3diMplIS0ujV69euLq6EhQUhFKKrVu3MmbMGH0Iqo+PD3369CEhIYGiRYuy\nZMkSFixYQFpaGtHR0bi6uvL6668TFxdHgQIFuH//PsePH2fnzp3kyZOH2NhYSpYsScGCBbGxsSFH\njhxs2bKFRo0a8Z///Ac7OzsSExOZP38+H330ERcuXHhoBkMLpdQL2WuZ1TBbS8/Qhx9+aLXu0aNH\n6devn1WmvqzmGGU1n2jo0KEMHTpU//n48eMkJCTo1yTjMB/IulfK0i74cx5VcHDwI5xl1v5t77Tc\n3AghHpRx/vnhw+Hs2LGD/PnzM3/+fEaOHMm8efNITEwE4NNPP+Xs2bPcvXtXTwIVExOjj0JYsGAB\nSUlJ+Pr6smnTJq5fv46HhwdeXl4sWrQIg8FAzZo1adGiBe+88w7e3t7MmTMHBwcHGjRoAFiPlnF1\ndeWnn34C/vzM1TSN3r17kydPHn1dTdPInTs3K1asoHXr1oSHh8ucOPGXJIgTLwTLB3C3bt24eHEp\njRo1sgriNE1D0zS8vLzo0aMHlStX5vz58/p7uXPnJjw8HC8vL8LDw/Hz88PR0ZGcOXPqNdgOHjyI\n0Wjk7t27TJo0iXv37rF8+XL279/P4cOH8fLy4syZM5QoUeKJzYl70WRMonHhwgXq1KnDokWLmDhx\nIitXrqRDhw7UrVuXkJCQTOm9M6Zj37dvH9HR0Zw/f57AwECaNm3KuHHjSE5OJigoiLt37+rzCsxm\nMzt27CApKUkfnlKqVCnWr19Pt27dSEpKYs+ePZQsWZLbt2+zePFi8uTJw/nz55k9ezYXLlzg+vXr\naJpGpUqVGDRoEBcuXACsMxgqpfS5Bb/88gtGo5Hbt29T9XGKyD1lz2ve0tKlS61uLrLKeCqEENmN\n5cFYkyZw9GhHgoKC9PceHFFgGdZvkZiYiIuLi1Wyn5IlS7Jnz55Mx1m+fLnVzwUKFMg0rxYeXsLG\nMtc94/uWZRkfou3YsSPL7YXISII48UKwfAA3avQ7b73lDWSd9axSpUpWPQOQ9Qdf165d9UyQFhmH\nMVgcPXo007IHeyeyu6zmXymluHfvHm+//TaQPpbfktzCkrEwY+9kSkoKJpPJKkudpaZW586dqVy5\nMvb29ixevJiBAwdSs2ZNrl69ysCBA0lLS8NkMpEnTxFsbTtSq9YmihXLC8CtW7dYsWKFnj6+RYsW\nHDlyhNatW7N3717Gjh1LgwYNeOONN1BKkTNnTu7evcuXX37JyJEjH5rB0FI3LDExUU+Vn109+O/9\n3yhRosRf/vyq+Oabb7h48SIBAQF07tyZrVu3kpSURI4cOXj//ff57rvvyJ07N1WrVqVatWrcv3+f\nmJgYFi5cyDfffEOOHDkIDg7m8uXLjB8/nrVr1+Lu7k5kZCStW7dmyJAhbNu2DScnJ4xGo56JLjY2\nNste7YiICPz8/PDy8sLf31+fYyOEeDSWB2PR0ZDFf+tCvJRkTpx4IVg+gCMiQvH29s5ynejoaAIC\nAjh//jyNGjVixowZ+pwfy5M1g8GgTxy2zIuC9MnKBoMBOzs7IL2mVkJCApcvX6Zo0aLEx8djMBgo\nXbo0165dY/jw4Rw7dgx3d3ecnZ31oWoGg4Fu3brRtGnTbJPa/2Hzr3LlysW+ffuAP8fyX716VS8i\nntGsWbMYPHiwVWBnydb11ltvcfz4ccLDw0lJSeHNN9/kp59+omjRouTNm5dZs2ZRq1YtfvllL9u3\nz+PSpfJs3ryZzp07c+LECa5du0bOnDnRNI01a9Zw6tQpUlNT2bx5M0lJSZhMJtatWwek1wA7d+4c\nX3/9Nffu3QOgUaNG5MuXj/z58/Puu+/q5wOwbNkyfvrpJ2bMmIHBYKBQoUIYjUaioqIy1VBLTEzE\n3t4eT09P6tevz6FDh57470K8GPr27UtcXByjR4+mYcOGfPbZZ2zYsIErV67oNejOnTtHjx499Kfk\nZrOZb775hu3bt7N06VJGjhyJwWCgU6dOeo/m8uXLadeunVVJhqz8VZFxCeCEeHwuLi5WvXCPonz5\n8la9cEJkFxLEiRfK/PnzqVix4kPft9zsTJo0icjISJYtW0ZMTAzffPNNpppXlmFjqamp1K9fH5PJ\nRKlSpazWGTduHEWKFKFq1aqYTCZatmzJypUrGT9+PP7+/oSGhhITE2OVITA4OJjt27fzww8//GWd\nrReFJZFEhpwVQHqZgoMHD2IwGNi/fz/t27fHy8uLDz74ALAe029Jx37lisa0aXDxIrz22mtcv36d\nESNG8O2337Jx40YMBgNnz57FYDBw69YtUlJScHFxoUuXLtSqVYfGjX147bUD5MyZk+TkZFJSUhgy\nZAilS5emfv36TJgwgQIFCmBra0vu3Lnp0KEDM2bMAOC9997j3r17HD16lK+++kqvEVa+fHlWrVrF\nunXr9HlcderU4datW2zatAlnZ2f69++PyWTSe+iqV6+u11AbO3Ys48aNA6BixYr8+OOPzJ8/nylT\npjztX414xjI+CDpx4gQTJ05k9erVrFy5knHjxrF8+XIOHz7MnDlzSEhIYNWqVYSEhJCamkrdunU5\nduwY7du3p2TJkiQmJhIQEECHDh3o378/u3fvZvDgwdy8eZO3336bmTNn0qNHD5o0acKyZcuA9KyX\nK1euZO3atfz+++988cUX9O7dm1mzZmX7IuNCCCGeLRlOKbKVuXPn8sYbbzBjxgxGjhypB2XlypXT\na2VZnoInJSUB6UWBy5Url2lfBw4cIH/+/FbpfTOmBY6Pj8fHxwelFFeuXNEzAFpSEZctW5arV69S\nvHjxp3S2T8af869c/vj6c4jeg/PeYmNj9deWIawZ07GnpQXw3/+mXyc/Pz+CgoJo3749+/bt4913\n36Vq1ap6QBwVFcXIkSOxsbFhx44dlCpViLi48dy6dYvXX3+d06dPU758efz8/Dh06BDJyckMHjyY\n8ePH6/MhR48eTbVq1Zg3bx558+alQIEC+uRzy1y3hIQEHB0d9TmSkN6+CRMmMGTIkExzGIAsa6hp\nmpYpzbR4uZw5Axs2KE6eTK/NZDabadq0KWPHjqVYsWIsW7aMYsWKsWPHDuLi4vQyJLa2tvz888+4\nurpSrFgxNm/eTL58+QCYMGECnTp1IioqiqCgIKZOncqSJUsYPnw4VapU4dNPP8XNzY2zZ8/yyy+/\ncPPmTbZs2cLWrVv58ssvuX79OoMGDcLV1TVbFxkXQgjxbEkQJ154Ged09e7dm1u3buk9aGfOnKF0\n6dKcOnVKn98TGRmJpmk4OzsD6QW03d3dAet5XpZioNHR0Vke187OjhkzZlCiRAk9Hb5FamoqZ86c\neeEDuCctq6yKq1atIjExkfLly1ut6+rqyrZt24iPh0mTkgkO9qVcubt07NiRq1evMmnSJAYOHMjd\nu3dp1qwZBw4cANKDs9jYWFxdXa3qxz1s/tuhQ4eoVq2aVdBlyU5avXp1q9+55XWFChU4ePAg8GcN\ntYwBvCXNtHi5rFgBP/88l8OHczJx4ucsXbqUAwcOcOjQIapWrcobb7zB2bNnyZcvH9999x3ly5fn\n66+/5ty5c3Tp0oWrV68SFhbGrl27GDhwIF988QVpaWkULVoUgM6dOzN06FAuXLiQKSvqN998w7vv\nvsuuXbtwcHAgV65caJpGwYIFMZvN2b7IuBBCiGdLgjjxwsuYOtjeHv1mx2g00rVrV8xmM35+ftja\n2qJpmlVa+f379zNmzBhq1KjB2LFjOXjwoB4EPJjeF6yHEAYGBuLj40Nqair58+fXU7q3adOGS5cu\n8dlnnz3bC/ECeFhWxQcDuIymT4c5c6bx44+bqVnzNcaNG8egQYNo2LAh//nPf/joo49YsGABa9eu\nxWAwcPjwYasC1hn5+flhMBjQNA0XFxdee+014uLiMBqNXL16lWvXrhEUFMSdO3cYPny4PsfJwvK6\nZMmS+nzKPHny8P3335OWlpYpzbR4OVgeBDk6wpkzvalV6xZXr16lSJEiBAcHU7p0ac6dO8eECRMI\nDAykRo0adOjQAQ8PD1JTU1m5ciVlypRhxYoVfPHFF3h4eKCU4sMPPyQoKIjQ0FDS0tJIS0ujfv36\nODk5kZCQQFpaGgA2NjY0btyY2rVr6w+bypUrh7OzMydOnKBhw4aAFBkXQgjx6LTnWR9L0zT1KtTn\nEv+O5QZswID0IOKfiI6OJiIiQp/obJm8/LglBAwGg97TJx7Nv/n9/Z1evXrh7++vz6M0Go16Iop/\nypLJUia4v3w+/TT9QVDbttHY20fow4ANBgPh4eGYzWauXLnCr7/+itFoZO3atdjY2BAeHk5aWhrV\nqlXDz8+PMmXKcP36db788ks9q2TGIG7Xrl1s3ryZihUrkpSURMmSJZk/fz6tWrXiwIEDxMbG6vPy\nihcvTv369UlOTmbOnDlWvc5CCCFeLX90Qvyjm0sJ4sRL7d69e9y5c4cCBQoA6PPaMg6N/CeMRiNb\nt27FxkZyAr0Irly5QqFChfTfx4ULFx47bb4EcS+vp/kgISOj0UhERIRVMfWHPVRwdnaWwuVCCCEA\nCeKEEEKI5+bBhwh/9VBBgjghhBAWzzSI0zTNDQgCFDBKKRWRxTr5gXXAfKVUpvL1EsQJIYQQQggh\nXmWPE8T9m8QmwwE30mvN/QBYBXGapr0OTAN+/BfHEEIIIYQQQgiRwb+Z2KMppe4opVIAs9UbmpYb\nmA58AZwHJAuEEEIIIYQQQjwBf9sTp2laCWAR6cMmtT++78I6cHswGKwI1AYWA6X/2M//lFIHHtz/\n6NGj9deurq64urr+k/YLIYQQQgghRLYRFRVFVFTUv9rHv5kTtxFoR3pgt0Yp1ewh6/UAbGVOnBBC\nCCGEEEJYe9Zz4iYBW/94PeqPBngAJZVSkqNbCCGEEEIIIZ6Cx54Tp5TaqpRq+sfX1j+WbXowgFNK\nhWbVCyfEy8pSzPf8+fM0atSIGTNm4OLigqOjI4sXLwbSi4bPmTMHSK8lFRISQmJiIqVKlcJoNFK3\nbl0CAgIACAwMpEmTJtStW1cvWh4QEIDBYKBVq1bcuHEj0zGDg4MxGAyULl2aJk2aMHz4cA4cOJBl\nO4xGI/Xr1yc8PBwAJycnAHx9fUlISCAxMRFfX1+r9yIjIzEajfo+zGYzX331Fa1atXoWl1gIIYQQ\n4pUmFYuFeAqUUowdO5ZJkyYRGRnJsmXLiImJ4ZtvvuH+/fsA7Nixg+PHj3P9+nV9u+bNmxMZGcm0\nadOs9rds2TKmT58OwIEDB7h06RImk4l+/foRFhZm6YZn7NixTJ48mX79+mEymWjZsiWLFy9m/Pjx\nBAQEZGqHpmls3bqV77//nv379wPohYozsizTNA2z2czcuXOt3jt16hQ//fQT69ate7IXUgghhBBC\nZCJBnBBPwdy5c9m2bRtOTk5cvXqVUqVKYWNjQ7ly5bhw4QIADRs2ZODAgXTv3v0v93X37l1y5MiB\nZf7okSNHMJlMGI1Gxo0bx82bN1FK6cd0dHTUt1VK6ds92I6LFy+ilMLNzY127drRoEGDvz0vy3E+\n/PBDq2VNmjSxWiaEEEIIIZ4eCeKEeELi4+HTT+HMGejduzeurq6MGzeOIkWKcObMGdLS0jh16hQl\nS5YEoGvXrnh4eFCoUCF9H1kl+klISKBEiRL6z3Z2drRs2ZLIyEhiYmLo168fYH3MrBQpUoTff/9d\nb4dlnxERERw+fJhJkyZlakPGIBAgJSWFPXv24ObmZrU8JiaGwMBAPUAVQgghhBBPz79JbCKEyGD6\ndJg1C37/HeztYcqUKbRv3x6j0UjXrl0xm834+flha2uLpmnkz5+ffv36ERoaqu/jwaGM06ZNo0yZ\nMuTI8eefqoODA6tXr8ZoNGI2mxk4cCCFCxcG/jzmxo0badGihdX+AgMD6dKli1U7IH1O3v379/H2\n9gbg8OHDeHl5sX//fk6fPg1AfHw8Fy9e5Pbt23z+T0MqdwAAIABJREFU+edWbdU0jcqVKxMcHIy3\ntzcbNmzQ9y2EEEIIIZ68xy4x8EQOLiUGxEskPj49kBswAKpWfd6tebJ69erF6NGjKVeu3PNuihBC\nCCHES+VxSgxIECeE+Fvnzp2jRIkS0sMmhBBCCPGESRAnhBBCCCGEENnI4wRxkthECCGEeEKio6Op\nUKECRqORGjVqMG/ePOzt7WnVqhX169fn0KFDKKXo3bs37u7udOrUCbPZDEDHjh0xGAy88cYbJCQk\ncOHCBTw9PXF1deXDDz985O02bNhAo0aNaNy4MV9++SXHjh3LVDfy2LFjuLu74+zszPfff6+3vXr1\n6hiNxkx1IIUQQrxYJIgTQgghnqDu3bsTGRnJ8OHDAahYsSLr1q1j/vz5TJkyhR9++IEqVaqwdetW\nDAYDGzZsAMBsNmMymWjRogUA48ePZ/DgwURFRTFmzJhH3s5gMLBr1y527tzJf//7X+zs7DLVjfT3\n9yc0NJSYmBhWrFiht33EiBFERkbqP1tqUAohhHixSBAnhBBCPAWW4MdSRsTOzo5z585x5MgRlixZ\ngtFoJCwsjCtXrgCQlpZmte2RI0do3LgxAKVLl/7b7SzHO378uJ4Z9+TJk1b7tKwTHx+Pj48PBoOB\nM2fOcPv2bb0m5YPn4O7ujpOTE+vWrXvSl0gIIcRjkhIDQgghxL9kyU7bpMnD19m1axc1a9bEzs4O\nX19f+vfvD6TXXzx48CA1a9YE/gzGqlSpws6dOzEYDFy+fPlvt7NsO2rUKIKCgqhVqxZNmzbNsi12\ndnbMmDGDEiVKkJycTJ48eTh8+DBvv/12pnUjIiK4cuUKH3zwAa1atXqs6yOEEOLJkiBOCCGE+Jce\nrBMJf9ZSjI6Opnnz5gAsWLCAEiVK0LdvX9zc3DCbzYwbN46ePXuSN29etm/fztGjR6lQoQLDhw/H\n19eXoKAgatasycyZM/n444//drs2bdrQo0cPKleuzIULF/Q2Plg30sfHh9TUVPLnz8/gwYNZt24d\n/fr1szovTdPo1q0bKSkp9OzZ8+leRCGEEI9MslMKIYQQ/9LD6kQmJibi7+9PWFjYX27v7OxMTEwM\nAKdOnWL06NGEhIT87XEfdzshhBAvDslOKYTI5FGy5aWlpdGtWzcMBgPe3t6PnPUO0p/oOzg44OTk\nxKBBgwBYu3YtTk5OODs7s3fvXgCcnJyA9JtaX1/f53AlhHh6qlaFmTOtA7h/ImNykTfffJNJkyY9\n1e2epoiICObOnUtwcDA7dux43s0RQoiXkgRxQrwC/i5b3n//+1/q1KmDyWSiTp06rFy5Evj7rHcW\nU6ZMISYmhhMnTqCUYurUqURHR/PDDz/oN5UZh3JlfC3Ey6x8+fJ/2wsHUKJECf21ra0tRYoUeaT9\nP+52T5OTkxObNm3ip59+0hOzCCGEeLIkiBPiFfKwbHkJCQnUrl0bgDp16nD8+HHg0bPeDRo0iPr1\n61OxYkUuXrzIkSNHcHd3p127dty5c+dZnZ4Q2Y6fnx+7d+8G0h+GODg44ODgYNUDHhoaisFgoFmz\nZjRv3pyUlJRH7hXPqvbb7Nmzadq0Ke+88w7du3cH/uwpnzhxIo6Ojnh4eHD27FkSExPx8fEB/qwZ\nFx0dTVBQkNV2ISEh9OrVCwA3NzdWrFhB7969cXNzexaXUQghXjkSxAnxEouPh2nT4I9M5JlYsuVV\nrlxZH/a4f/9+qlSp8pdZ7yIjI6levbr+3pQpU9i9ezeHDx/GxsYGe3t7IiIiiIyM1HshLIFgxjTn\n4vHdvXuXIUOG0KpVK1q2bPm8myMek7e3N4sWLQJg48aNeHp6MmXKFKsecABPT0+2bNmCl5cXq1at\neqRecYsHa7+tWrUKk8nEkiVL9GWapnHu3DmioqLYtm0bQUFBjB8/Xn8v4/eMNE3j1q1b+jlYlpnN\nZubMmfNvL48QQoiHkCBOiJfY9OmwejXs2pX+84PZ8saMGcNnn31G27ZtOXjwIAaDgf379/Pee+/R\nvn171q1bh8FgYNOmTSxcuFDPetexY0fOnz+vH2fw4MEYDAZKly5NsWLF8PPzw2AwYDAYmDhxIgCH\nDx/Gy8uL9957jwULFnDx4kWio6MpW7YsERERNG3aFEdHR/1m0/KE3/I6PDwcg8FAoUKFMBqNLF26\nlD59+uDs7EyjRo3Ys2cPhw8fxsnJifPnz+u9Bl27dmXhwoWEhobqCR8CAwOJjIwkOjoaf39/AHr0\n6EFYWJje83Dnzh0MBgNz5859rGv/uHMRH7XXZdasWezevZvr169Tt27dx2qjeP6aNGnCzz//zKFD\nh7CzsyNXrlz6exkfdhQuXBj4s/f8UXrFgSxrvw0aNIhq1arRpUsXfdmFCxfw9PTE/o/UmnXr1uW3\n33772/YrpZgwYQJDhgyxWjZ37lw+/PDDf3AlhBBC/BMSxAnxEhswAPz8XFi8OH3oU/fu3XF3d8fF\nxYXNmzezefNmSpYsia2tLYsWLcJkMrFw4UJsbGwoVaoUe/fuxWQysXPnThITE+nevTu7d+9m+fLl\nxMfHAzBq1Ch9PUuQ1KZNG6KiojCZTIwePRqAS5cusXbtWqZMmULBggVZs2YNa9as4Y033mDMmDFE\nRkayZcsWff2MNE2jQ4cOmEwmateuTWRkJJ07d2b69OnExMQwbdo0QkJCqF69OoGBgXz77bdomkZs\nbCzly5fH29v7oddI0zTi4uL0nkiL2bNn06tXLz766KPHvv7/ZC6ig4ODnqTiUXpdYmNj+eGHH9i2\nbRu//PILp06deux2iucjPh4+/RRq1zbQp08ffHx8/raXOi4ujho1ajxSr/iVK1c4fPgwZcuWBf4M\nCosXL07z5s1ZunSpvl3x4sVZvnw5+/btA2DPnj1U/SNLy1/1op8+fRqz2WzVhpSUFPbs2YObm1u2\n6HU/cOAA9evXx9HRkY8//hjInNQpNDSUatWq4erqSqdOnQDYunXrQx8+ffLJJ/Tp04dLly5Rr149\nnJ2dad++PWlpabz77rtUr16datWqWfW2CiHEPyF14oR4iVky5j2Op5n1rlu3bmzZsoWaNWvqPQ+v\nvfYaADY26c+WLl68iNFo/MubwNDQUMLDw7lz5w52dnZA+rydjz/+mPPnz9OhQwdiY2P19b/++msW\nLFhAYmKifrNlNpsJDg62qo+1ceNGoqKiHqkn4lE8ylzE2rVr8/PPP1OmTBmrXpf9+/dz/PhxAgIC\nuHbtmt7rkpycrO/X3t6es2fPUq5cuSfSXvFsWGrLdevWjYsXl9KoUSM2btyov59x+GJwcDCLFi2i\nbNmyjBw5kkuXLtGzZ08qVaqUqVe8cOHCVKhQgYMHD1rVftM0jdTUVEaMGMGKFSu4fv261bEqV65M\nnTp1eOedd8ifPz/ff/89qampREdH4+XlxcGDB2nbti2XL1/mypUrBAQEcOfOHYYPH86VDGO2b9++\nzeeff57pHF5U1apV4+effwbAw8ODS5cu6UmdLPP8AIYOHUqvXr3w8vIC0B8+mc1mPDw89DmHu3fv\n5t69e/zf//0faWlpxMXFYWtrS58+fdi3bx/r168nLCyM+/fvW+1fCCH+CQnihHjFPO1seQ9jqaPV\npAkULFiQxo0b4+joyJYtW1BKkZycbHXDV7x48SyHVloClxs3bhAaGqrP//nqq68AmDdvHt7e3kRF\nRTF69GhGjhxJeHg48OdNWGBgoL6/5cuXM3nyZKub0BYtWtCgQQP69evHd99999jn+TAPzkVs0aIF\n+/fvp2rVqqSkpOjnGBcXR926dfVel1q1auHo6AhArVq12Lx5M+3bt+fgwYNWw9lE9jBgQPr3Ro1+\n56230nuLR40apb9v6dmOjY2lX79+Vjf8PXr0oEePHlb7GzVqlNX2AJs3b9ZfW/6etm7dCqQ/VLB8\nFlhqzU2YMCFTO7Pq5bUELElJSQDkz59fb++vv/6a6ZgvsgsXLjB8+HB+//13jh07RkpKSqakTpD+\nEGju3LmUK1dOL8OS1cOn5s2b6+d98+ZNhg0bxrFjxzh16hTdunXLtF8hhHgcMpxSCPFMWHodVqxI\nfzrftWtXypUrh6ZpjBo1Sp8DZrkJfVhJAsvr/PnzU6pUKZo1a8aYMWMAOHv2LMuXL2fkyJEAODo6\nUqZMGVauXPnQ/VWqVInWrVtbtVXTNPr370+uXLn+cRCX8TwfPN6jzEUEmDlzJm5ubhw/fpxWrVrh\n5eVFz549reYiDh48mOnTp+Pi4kK7du303juRfVh6yiMiQv9yyO+L6Pvvv3/eTXgi4uPB03Mqzs6d\nMZlMuLq6cuPGDWrUqJFp3aFDh7Jjxw6KFCmiD79OSUkhJSVFX6d48eJs2LCBvn37cvv2bRYsWIC9\nvT0mk4nOnTs/s/MSQrwCLGPcn8dX+uHFqyQqKkqVL19eGQwGlTdvXuXq6qrc3d1Vs2bN1K1bt5SX\nl5c6f/68Ukqp/v37q7179yo3Nzfl6uqqWrZsqVJSUlTPnj3Vb7/9ppRSytXVVSml1OjRo1VERIS6\nd++eqlq1qkpMTFTz589X8+bNU4cOHVJ169ZV//vf/57beQuljh1Tys8v/fvL7GHnefLkSeXj4/O3\n21v+3Qohnj4/P6VgkypatI5q166dqlu3rsqVK5dycHBQrq6uqnTp0mr06NFq/vz56q233lIGg0G1\nadNG3b17V23dulU1adJENW3aVEVGRiqllHJyclJKKbV8+XLVrVs3tX//flW3bl3l6empnJycVHR0\ntFJK/s6FENb+iIn+URwlwynFM9e9e3eCgoIICwvj4sWLDB48mJkzZ7Jq1Sq6dOnC0qVL8fPz49df\nf8XBwYHVq1eTL18+Pv3000zDeh6cbzF9+vRM2d0CAgJYuXKlnq1NPB//Zn5edvKqnKcQL4P0Ia3N\nGTCgOX/kccHZ2VkfXnrq1ClGjx7NqFGjMg1fdXNzy1QHz7Jdhw4d6NChA5CeJOZBD+5LCCH+KRlO\nKZ4bpZRVAoekpCTatGnD2rVr2bJlCx4eHkD6EDkXFxd++eUXKlasSIECBfTkDqmpqfr+kpKSSEhI\noGHDhvqyL774grS0NAngXnGWUgZKKf31zp07cXFxwdHRkU2bNnHu3DmaNm1Kamoqa9as0ZNHZMws\nZyl0HBAQAKCXLchYEDkgICBTIeT27dszf/78R5qL2KNHD0l2IMQzYnnoYgng4OkmdRJCiCdFgjjx\nTFhSaZ85k/X7lrTZefLkoUyZMnz11Vd069aNO3fuUK5cOaKjo2nWrBkbNmygT58+jBo1ilatWlll\nD5w8eTL+/v5WE8bHjh2Lra0tixcvftqnKF5wxYoVY+3ataxZs4Y333yTwMBA1q9fT3R0NLNnz6Z0\n6dJ88skn/Oc//2HevHkEBgZSuHBh4uLiiImJoXjx4pnKEGSkaRqnTp1iw4YNVstXrVqlJycRQlh7\nlHqKSil69+6Nu7s7nTp10pOKPKwMgMFgoGLFinotyMaNG+Ph4UHTpk1JSkr6y/qQkF7T0vKgxtXV\nlSJFihASEqI/XKlYsaKeMKZKlSpERkZmKlNw48YNvR0ODg707NlTr8VnNBr1eo+JiYlUrFgRg8Gg\nz8Pz9fUlISHh2f0ShBDZkgRx4pnIKtkDpKfNzpjAAaBr16689tprlC5dmuvXr9OyZUtcXV2JiorC\n0dGRGjVqEBsby7p166hWrZq+r44dO1K6dOlMCSwWLlzInDlz9PpH4tXUsmVLlixZQqFChciZMycH\nDhygdevWuLu7c+nSJSA91fvmzZvx9fXF1taWmzdv8sknn2AwGNi6dSvJyckAhIWFYTQarTL5KaX4\n6quv9JpwkF5/qnfv3vTu3fvZnqwQ2cjf1VP84YcfqFKlClu3bsVgMOgPSixlADLWWhs6dCgmkwlf\nX199WYMGDdi0aRMjRoxg7ty5wMPrQz5I0zRu3brFokWL9GUlS5Zk9erVbN26lQIFCgB/linYtm0b\nJ06cIDU1VW/HlClTmD9/PuPHjycgIIDIyEiOHDmil3jw9fXFZDJRqlQp/RgZH0YKIURWZE6ceCYs\nqbQHDHChalUXffmDabMBfv/9d/2JaMmSJTGZTA/dryWNc1ZpuTPOOYiKivpX7RfZU8Z0/7a2tnh5\neellDerUqcPKlSuJi4vDx8cHo9HIuXPncHV1xdfXl4kTJ1K1alUaNGjAlClTKFmyJAMHDuT+/ft0\n796d2NhYhgwZwrlz5zAYDKSmppI7d24WLlxIsWLFKFCgABUrViQ1NZXq1auzZMkSPDw8CA0NZdq0\naeTOnZvq1asTEhKCk5OTVT07IV5FlsDlwXqKR44cYcmSJWzcuJE7d+7Qt29fgCzLADy4L8Bq2P6G\nDRvQNC3L+pBbtmzBaDRy7do1PD099f1MmDCBIUOGsHz5cgBy5sxJ8eLF+f777/XMtg+WKUhOTqZo\n0aJW7Thy5AjDhw9HKcX169e5cOFCenKCHJlvxby9vbG1taVXr14yvFoIkSXpiRPPRFbzDh5m+fLl\neqp1If6NvyprEBAQQKtWrRgwYADFixdnwYIFXL9+nRMnTvDll1/i7OzMgQMHCAkJoXXr1hQuXJhp\n06bp+7b0+K5fv54CBQpQsGBBjh49SpUqVejZsycDBw7k1KlTxMXFMWLECD799NMM7ZrOsmXLnvn1\nEOJFEh8P06ZBhhKNViz1FO3s7PD19SUyMpKffvqJ9957j4MHD1KzZs1HOo4lkLIM21dKsXz5cjp2\n7GiVCMtS323q1Kn6stOnT2M2m6levbrVPj/44ANatWqFra0tSimmTp1K585/linIip2dHdOmTcNk\nMrFz504qVqzI4cOHKVu2rFU7ARYtWkRUVNRLU8pBCPHkSU+ceG4elp3rwTlFQjyuh/UAWzLIWebM\n9OjRgy5dulCtWjVu3LihB1ynTp1i8uTJ/PLLLyQkJODs7EyhQoUICgrCYDBQrFgxevbsyeLFi/Ve\n4UKFCunzbXLnzk3r1q1RSunF0+/evUuOHDmsbtguXryI0WjEbDYzadIk6tev/9SvjRDP2/TpsHo1\nWP65P1hPEWDBggWUKFGCvn374ubmhtlsZty4cfTs2ZO8efOyfft2jh49Svny5alUqZK+74zD6sPD\nw9mxYwf58+dn8eLFxMXF6fUhQ0ND/7KNd+7cYfjw4VzJEGlqmka5cuXo2rUrQUFBaJqGh4cHQ4cO\nZd68eSQmJmbZjhEjRtC7d29u3rxJzpw5GTNmDF9//TXr16/PtO7AgQPJmTMnXbp0+aeXVQjxqvin\nNQme5BdSJ04I8ZxY6rktXBil/P39lVJKjR8/XuXLl0+dOXNG3b9/Xzk6Oqr79++rzz77TB37o/Cb\no6OjUiq9RmGXLl2U2WzW6xValqelpSmllF7bUCmlbt26pZRSauDAgerUqVPq5MmTytfX12qf//vf\n/1T//v2fwdkL8fxlVVPxUespWuqxKaVUYmKi/rf0oKioP/++hRDiRYXUiRNCiEdjGWr5+++wb98C\ntm3bho2NDevXr6dLly6YzWY++eQTtm7dyvz589m9ezdKKQ4ePMj+/fuxsbGhf//+aJqWKZmOhb+/\nP61atUIpxVtvvYXRaOT8+fOULVvW6mn91atX6dq1Kzdv3mTEiBHP9DoI8bz8m5qKUgZACPGq09Rz\nzICkaZp6nscXQry6LElPBgz467maoaGhpKWl6ckFgoKCcHV1xdnZ+Rm1VAghhBAvM03TUEppf79m\nhm0kiBNCiIdLSUkBIG/evADcuHGD3LlzWyVEEEIIIYR4XBLECSGEEEIIIUQ28jhBnJQYEEIIIYQQ\nQohsRII4IYQQQgghhMhGJIgTQgghhBBCiGxEgjghhBBCCCGEyEYkiBNCCCGEEEKIbESCOCGEEEII\nIYTIRiSIE0IIIYQQQohsRII4IYQQQgghhMhGJIgTQgghhBBCiGxEgjghhBBCCCGEyEYkiBNCCCGE\nEEKIbESCOCGEEEIIIYTIRiSIE0IIIYQQQohsRII4IYQQQgghhMhGJIgTQgghhBBCiGxEgjghhBBC\nCCGEyEYkiBNCCCGEEEKIbESCOCGEEEKIx3T79m06deqEwWCgVatWDBw4EIPBQNu2bbl58ybR0dFU\nqFABo9GIwWBAKcWxY8dwd3fH2dmZkJAQli9fjsFgoFChQhiNRpYsWYLBYMBsNgPg5OQEgK+vLwkJ\nCVy+fJmiRYsCEBgYSGRkJJGRkdSrV48zZ848t2shhHh2JIgTQgghhHhMc+fOpUWLFphMJoYNG8a9\ne/cwmUx4e3vzzTffANC9e3ciIyMpXbo0t2/fxt/fn9DQUGJiYli5ciUdO3bEZDJRu3ZtIiMj6dKl\nC5qm6cfI+Bpg7NixFClSRP85NTWV8ePHYzKZKFOmzLM5cSHEcyVBnBBCCCHEYzpy5AiNGzcG4Ny5\nc9SuXRuA2rVr89tvvwEQFhaGs7MzSUlJAMTHx+Pj44PBYODMmTPcvn0bAKWUvl+lFG5ubhgMBn07\ngAMHDlCgQAHKli2rL+vRowc1a9akQIECT/dkhRAvDAnihBBCCCEeU5UqVdi5cycAlStXZt++fQDs\n37+fKlWqAOk9cTExMXh4eLBhwwbs7OxYunQpJpOJ7du3kydPniz3HRkZiclkolSpUvqy4OBghg0b\nZhXwhYSEsHfvXmJiYp7WaQohXjA5nncDhBBCCCGyo/h4OHLkIxISfFiwYAGFCxembNmyuLq6UrBg\nQRYuXMiePXtYsGAB27Ztw8bGho8++ohatWrh4+NDamoq+fPnZ82aNQCZhlAqpTIt6927N3ny5NGX\na5pG7ty5WbFiBa1btyY8PFyGVArxCtAyPsl55gfXNPU8jy+EEEII8bg+/RRmzQI/P5g589kf38/P\nj169elGvXj2mTJlCWFgYe/bswcbGBoPBgMlkwtfXl+vXr3P58mVq165NcHAwO3fuZNiwYaSlpeHv\n74+HhweHDh2idevWlCpViipVqhAWFoaTkxOxsbFERkby5Zdf0rdvX2bPns3evXt5++236d27N3nz\n5mXixIlomsb06dOpW7cugYGBbNy4kTt37tCuXTsCAgL0fQkhMvvjoY3292v+SYZTCiGEEEI8hgED\n0gO4AQOez/G9vb1ZtGgRABs3biRfvnz6exl78L744guio6NJSkri9OnTjB49mvXr1xMdHc3s2bOB\n9Pl8w4YNY+nSpVb7MJvNzJ07F4AOHTpYJWDp1KkTU6dOJTo6mh9++IFJkybp2y5btozp06c/1fMX\n4lUmQZwQQgghxGOoWjW9B65q1edz/CZNmvDzzz9z6NAhqlatSrFixThx4gRms1kvTwBQuHDhP9pb\nlXPnznHgwAFat26Nu7s7ly5dAiAhIYHy5ctnSq4yd+5cPvzwwyyPf/HiRY4cOYK7uzvt2rXjzp07\nANy9e5ccOXJY7euXX37BYDDQrFkzTpw48cSvhRCvGgnihBBCCCGymfj49OGctWsb6NOnD927d2fg\nwIH4+PjQtm1brl69arW+UooDBw5QtWpVHBwcWLduHSaTic2bNwMQGxtL06ZNrQKvlJQU9uzZg5ub\nW6bgDqB48eLY29sTERFBZGQkYWFhQHpAWKJECavj29vbYzKZ6NOnD6tXr34q10SIV4kkNhFCCCGE\nyGamT0+fj9etWzcuXlxKo0aNAPjpp58AMBqN+rofffQRd+/epVOnThQuXBh/f39atWqFUoq33nqL\nJk2asHHjRtq0acPt27c5efIk58+f5/bt23z++edA5qQrlu9+fn4YDAY0TcPFxYVChQpRpkwZcuSw\nvsU8duwYXbt25caNG0ydOvWpXhshXgWS2EQIIYQQIpuJj08P5Bo1MnHy5Db8/f2zXM/X1xd/f38q\nVar00H0FBQXh6OioB369evVi9OjRlCtX7qm0XQhh7XESm0hPnBBCCCFENmOZj9ezZyijRo166HoZ\ne9AeZuDAgeTOnVv/edKkSRQqVOiJtFMI8XRIT5wQQgghhBBCPCdSYkAIIYQQQgghXnISxAkhhBBC\nCCFENiJBnBBCCCGEEEJkIxLECSGEEEIIIUQ2IkGcEEIIIYQQQmQjEsQJIYQQQgghRDYiQZwQQggh\nhBBCZCMSxAkhhBBCCCFENiJBnBBCCCGEEEJkIxLECSGEEEIIIUQ2IkGcEEIIIYQQQmQjEsQJIYQQ\nQgghRDYiQZwQQgghhBBCZCMSxAkhhBBCCCFENiJBnBBCCCGEEEJkIxLECSGEEEIIIUQ2IkGcEEII\nIYQQQmQjEsQJIYQQQgghRDYiQZwQQgghhBBCZCMSxAkhhBBCCCFENiJBnBBCCCGEEEJkIxLECSGE\nEEIIIUQ2IkGcEEIIIYQQQmQjEsQJIYQQQgghRDYiQZwQQgghhBBCZCMSxAkhhBBCCCFENiJBnBBC\nCCGEEEJkIxLECSGEEEIIIUQ2IkGcEEIIIYQQQmQjEsQJIYQQQgghRDYiQZwQQgghhBBCZCMSxAkh\nhBBCCCFENiJBnBBCCCGEEEJkIxLECSGEEEIIIUQ2IkGcEEIIIYQQQmQjEsQJIYQQQgghRDYiQZwQ\nQgghhBBCZCMSxAkhhBBCCCFENiJBnBBCCCGEEEJkIxLECSGEEEIIIUQ28thBnKZpbpqmbdc0bZum\naW5ZvD9K07R9mqZF/n979x8jdX3ncfz5Bl3CndcDztLcNeZYAxtcD39QSyMosANdQWy46EVMsPQw\njQlBim38Qa7HtpX0ONFDKNqY5hbLosIZ+0M5XHqF2R8IBnNXfxxULpxN6dlSm4ZFm+Iv2M/9seOC\nys4u7O6M3+H5SDY7zPc73+973vnynX3N5zvziYht/StTkiRJkgRwTj8euwyYQVcQ3ALsOMU6X0sp\n5fuxD0mSJEnSSfoT4iKl9DZARHSeYvkx4N6IeAv4l5TSU6fayDe/+c3u29OnT2f69On9KEmSJEmS\nPr5aW1tpbW3t1zYipVR8hYjRwGNAAqLwew+52DjwAAAPyUlEQVTwuZRSfWGdHSmlj1xSWVh2HtCW\nUvrMKZal3vYvSZIkSZUqIkgpxek8pteRuJTS74DPn2Jn2yJiOF3B7iMjcRExMqXUAbwLvHU6RUmS\nJEmSTq0/l1PeD2wv3P4GQERcA3wqpdQELI+IzxaW/3M/9iNJkiRJKuj1cspB3bmXU0qSJEk6i53J\n5ZTOEydJkiRJGWKIkyRJkqQMMcRJkiRJUoYY4iRJkiQpQwxxkiRJkpQhhjhJkiRJyhBDnCRJkiRl\niCFOkiRJkjLEECdJkiRJGWKIkyRJkqQMMcRJkiRJUoYY4iRJkiQpQwxx6pO2tjbGjBlDLpejtraW\nxsZG5s+fT11dHTfffDOdnZ1s2LCB8ePHM336dObNmwfAt771La6++momTZpEc3MzAHV1dcyfP58p\nU6awatWqcj4tSZIkKXMMceqzBQsWkM/nWbZsGR0dHVx66aW0tLRw2WWX8eSTTwJw11130drayltv\nvQXA3Xffzc6dO3nqqaf47ne/272tdevWsWvXLrZs2cKxY8fK8nwkSZKkLDLE6bSllHj33Xe55JJL\nALjkkkt49dVXAVi1ahVXXnklw4cPJ6XE1q1bmTVrFjfffDN//OMfu7cxYsQIAC644AI6OjpK/yQk\nSZKkjDLEqagDB+C22+C11z54f1VVFS+++CIAL730EmPHjgW6RuKee+45Ro0axc9+9jNWrFjBj370\nI7Zt2/aRbb/33nu89tprfPKTnxz05yFJkiRVCkOcilq7Fh56CApXS3YbNWoUe/fupa6ujpdeeokb\nbrgBgPvuu49cLsehQ4eYMGECkydPpr6+nsWLF3/g8XPnzmXq1KksXbq0VE9FkiRJqgiRUirfziNS\nOfev3h040BXkli6FceMGZpt1dXXk83kiYmA2KEmSJGVURJBSOq0/jM8ZrGJUGcaNgwcfHNhtFg5U\nQ5wkSZJ0BhyJkyRJkqQyOZOROD8TJ0mSJEkZYoiTJEmSpAwxxEmSJElShhjiJEmSJClDDHGSJEmS\nlCGGOEmSJEnKEEOcJEl9dOeddzJ58mQuvPBCduzYwfLlywH40pe+RFNTEwcPHmTChAlcd911XHHF\nFezbt4+UErfeeiszZ85k3rx5HD9+nA0bNrB+/Xp+/vOfM3HiRHbu3EldXR0jR46krq6ONWvWsGPH\nDqZMmcJVV11FPp8H4PLLL+fGG29k0qRJPPbYY+VshSSpjJzsW5KkPnr99ddpaWnh3nvvBbrm9nn+\n+ed54YUXmDFjBgDV1dU8/fTT7N27l9WrVzN37lzGjh3L9773PR5++GGam5vfnxOIhoYGfvCDH1Bd\nXU1LSwu5XI4dO3YQEUybNo18Pk9nZyfXXHMNuVyOqqoqNm7cyJAhQ5g+fTrz588vZzskSWXiSJwk\nSX30hz/8gWHDhpFSIiLo7Oxk3bp1LFmypHudESNGAFBTU8OhQ4fYv38/mzZtIpfL0dTUREdHBykl\nvv71r3P8+HGqq6u7H5tSIqXU/e9hw4YxfPhwhgzpermuqqpi2LBhnHvuuQwfPrxEz1qS9HFjiJMk\nqRcHDsCXv3yYiD/vvi+lxBNPPMGNN95IVVXVB+4H2LNnDxdffDE1NTUsXLiQfD7P7t27uf766wH4\n9re/zdChQ3n88cd73O/Ro0c5evToR+5/4403GDp06EA9PUlSxng5pSRJvVi7Fhobb2LkyP8jl8vx\ny1/+kkceeYSLLrqIL3zhC2zYsKF73fb2durr6wHYuHEjo0ePZtGiRcyYMYPOzk5WrlwJdF2K+eij\njzJr1ixqa2u57LLLiIju7TQ0NDBz5kyGDBnCihUrADh8+DDXXXcdhw8fZtWqVSXsgCTp4yROvmyj\n5DuPSOXcvyRJfXHgAEybNpW2tnbGjeu6L5fLdX/hyPsOHjzI8uXLaWpqGpQ6rr76anbu3Dko25Yk\nlUfhc9LR+5onOBInSVIvxo2DF198ktGjT9y3efPmktdx8kidJOns5UicJEmSJJXJmYzE+cUmkiRJ\nkpQhhjhJkiRJyhBDnCRJkiRliCFOkiRJkjLEECdJkiRJGWKIkyRJkqQMMcRJkiRJUoYY4iRJkiQp\nQwxxkiRJkpQhhjhJkiRJyhBDnCRJkiRliCFOkiRJkjLEECdJkiRJGWKIkyRJkqQMMcRJkiRJUoYY\n4iRJkiQpQwxxkiRJ0iBqa2tjzJgx5HI5amtraWxsZMKECcyZM4crrriCffv2kVLi1ltvZebMmcyb\nN4/jx49z1113MXXqVKZMmcIrr7zCwYMH+eIXvwhAQ0MD99xzDwBbt26ltraWiRMn0tDQ0L28rq6O\nOXPm8Oabb9LW1ta9bMOGDTQ2NnLLLbdw+eWXd9d25MiR8jRIp80QJ0mSJA2yBQsWkM/nWbZsGQDV\n1dVs3bqV73//+6xevZotW7YwduxYtm/fTl1dHc3NzTQ0NNDe3s7tt99Oe3s7ABHBr371K5qbm7u3\nfejQIR544AHWrFkDwMsvv8zvf/97WlpaWLJkCU1NTQCklLofExGsX7+eNWvWdNc2YsSIUrVD/XRO\nuQuQJEmSzhbvB6n3A1NNTQ2HDh1i//79bNq0iW3btvH222+zaNEizjvvPK699lpeffVVfvKTn3Q/\nfuXKlSxbtox9+/YB8Itf/IKrrrqK119/HYD9+/fT0tJCLpfj2LFjzJ49G4CNGzeya9cufvvb33LH\nHXeU+qlrADkSJ0mSJA2CAwfgttvgtdd6XmfPnj1cfPHF1NTUsHDhQvL5PLt37+b666/n8OHDPPPM\nMzQ2NrJ27dru9Wtrazn//PO7t7Fv3z7Gjx8PdIW8mpoaZs+eTT6fp729nSVLlgAnRgPvvvvuwXvS\nKglH4iRJkqRBsHYtPPQQ/PrXMGFC130RAXR9Tq6+vh7oGiEbPXo0ixYtYsaMGXR2drJy5Uq+853v\n8Jvf/IZ33nmHBx54AICqqioWL17Mzp07AVi5ciXPP/88uVyOjo4Ojhw5wooVK/jxj39MLpejs7OT\n22+/nZEjR5a+ARo0cfK1sSXfeUQq5/4lSZKkwXLgQFeQW7oUxo07cf/BgwdZvnx592fV+uOWW25h\n+fLlVFdXA5DL5cjn8/3erkonIkgpxek8xpE4SZIkaRCMGwcPPji4+7j//vs/8IUkmzdvHtwd6mPB\nkThJkiRJKpMzGYnzi00kSZIkKUMMcZIkSZKUIYY4SZIkScoQQ5wkSZIkZYghTpIkSZIyxBAnSZIk\nSRliiJMkSZKkDDHESZIkSVKGGOIkSZIkKUMMcZIkSZKUIYY4SZIkScoQQ5wkSZIkZYghTpIkSZIy\nxBAnSZIkSRliiJMkSZKkDDHESZIkSVKGGOIkSZIkKUMMcZIkSZKUIYY4SZIkScoQQ5wkSZIkZYgh\nTpIkSZIyxBAnSZIkSRliiJMkSZKkDDHESZIkSVKGGOIkSZIkKUMMcZIkSZKUIYY4SZIkScoQQ5wk\nSZIkZYghTpIkSZIyxBAnSZIkSRliiJMkSZKkDDHESZIkSVKGGOIkSZIkKUMMcZIkSZKUIYY4SZIk\nScoQQ5wkSZIkZYghTpIkSZIyxBAnSZIkSRliiJMkSZKkDDHESZIkSVKGGOIkSZIkKUMMcZIkSZKU\nIYY4SZIkScoQQ5wkSZIkZYghTpIkSZIyxBB3lmltbS13CWcde1569rz07Hnp2fPSs+elZ89Lz55n\nwxmHuIiYERG7IuLZiJhxiuXnRcT6iGiNiK/2r0wNFP9jlp49Lz17Xnr2vPTseenZ89Kz56Vnz7Ph\nnH48dhkwg64guAXY8aHl/wSsTyk92499SJIkSZJO0p/LKSOl9HZK6SjQeYrl04CvRER7RCzsx34k\nSZIkSQWRUiq+QsRo4DEgAVH4vQf4XEqpvrDOjpTSjA89ri2lNC0ihgLPArmU0lsfWqf4ziVJkiSp\nwqWU4nTW7/VyypTS74DPf/j+iNgWEcPpCnanGol7JyI+kVJ6MyLePNU6p1usJEmSJJ3t+vOZuPuB\n7YXb3wCIiGuAT6WUmoB7gOaIeA9oSim9069KJUmSJEm9X04pSZIkSfr4cJ44SZIkScoQQ5wkSZIk\nZUjJQ1wfJgm/MSJejoh7Sl1bJYmIoRHxaGGy9UcL3xLa6zKdud76GhHNEdEREb55MkB6Oc7HRcTO\niGiJiH/zOB8YvfR8VERsj4gdhZ+/LGetlaIv5+yIuCkiDpSjvkrUh/P5kYjIF35uKledlaQPPb+2\nMG1Ve0R8olx1VpJezuczC6+f+YjYHRGN5ay1UvTS8/Mi4qlCv9sj4oJi2yrHH5PvTxJeD/zDhxem\nlJ4Abit1URVoFrAvpTQd2Atc28dlOnNF+5pSmg28UIa6Klmxnv+arqlN6oDXgYmlL68iFev5EaC+\nMOVMG3Bp6curSEXPLRHxd8D5wKHSl1axenudfDmllCv8bC55dZWpx55HxKeBL9N1Tp+aUnqzPCVW\nnB57nlLanlKqSynlgCeBreUpseIUO7fkgOdSSpOBdcANxTZUjhDX2yTh0DVtgfpnPCcCw38BF/Vx\nmc5cX/rqsT2weux5SuloSum9iAjgQuB/y1BfJSrW886UUmdEtACfBX5ahvoqUY89j4i5wKdTSg/i\n+WUg9XY+H1t4p3xjYbol9V+xns8GPgE8ExE/jIi/KHVxFarXv1si4s+Aa1JKPyxlYRWsWM/bgFkR\nsYKuAa8nim1o0EJcRIyOiJ9GxH+c9HsFHwxuXlY2uE7u9Ye/hrTYMp253vpqrwdebz2/D1ifUuoo\nUT1ng6I9L4x+/jvw96Uq6CzQU8/rgbmF4Pw3EbG6tGVVtB6P85TSX6WUpgL/CSwsaVWVraeejwTW\nppTqgQ3AHSWtqrL19hp6J7CmRLWcLXrq+Xi6gty/0vWm3ORiGxm0EJVS+l1K6fMppfqTfi8HOiNi\neET8CT2PxIHvKPbXfk5cPvYZ4JU+LtOZ60tfPa4HVo89j4hzI+Jh4FnfQRxQxXo+IiLOLfwz0b+5\nSHVCjz1PKS0uXNJXB/x3Sulr5SiwAhU7zv80It4/to8CzoM7MIq9hv4PMKFw+w3gvRLWVcmK/t0S\nEZ8CJqWUmktdWAUr1vMrgYMppYN0vVlxZbENlWMk7P1Jwn8K3Atdk4RHxILC7TlAA/C3EeE7LWdu\nG13vyrYCl9A18fqmnpaVpcLKU6znRMQjdA2bPxYRY8pRYAU6Vc8fLyy7ia5rzb9S+GD2gjLVWGmK\nHedjgNaIyAPXAE1lqbDyFD23aFAU6/lfA88WjvOZwMbylFhxeux5SulpoKbQ86/iyNBA6e3c8o/A\nP5ejsApWrOcbgXmFKysa6OU4d7JvSZIkScoQP5MmSZIkSRliiJMkSZKkDDHESZIkSVKGGOIkSZIk\nKUMMcZIkSZKUIYY4SZIkScoQQ5wkSZIkZcj/A2fSawIUEY9GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4dac5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(repres[:,0], repres[:,1], s = 5, lw = 0)\n",
    "\n",
    "for i, txt in enumerate(x_plot_label):\n",
    "    plt.annotate(txt, (repres[i,0],repres[i,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Конечно, добавив ещё каких-нибудь выходов в модель (например, место работы) мы могли бы ещё улучшить модель, но радость в том, что она даёт интуитивно понятные результаты уже сейчас!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На этом я прощаюсь с вами. В следующий раз будем переводить в линейноые пространства слова (word2vec!).\n",
    "\n",
    "До встречи!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}