src/Reuters_newswire_topics_classification_keras_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports\n",
    "\n",
    "`numpy`      package for array handling  \n",
    "`pandas`     import to create and modify dataframes     \n",
    "`matplotlib` package for data visulization  \n",
    "`seaborn`    build on matploblib, higher level graph functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data\n",
    "Here we are taking the inbuilt function of keras to load the data from the server  \n",
    "The dataset file in present in the [Link to dataset in amazon server](https://s3.amazonaws.com/text-datasets/reuters.npz)  \n",
    "The inbuilt code \n",
    "```python\n",
    "def load_data(path='reuters.npz', num_words=None, skip_top=0,\n",
    "              maxlen=None, test_split=0.2, seed=113,\n",
    "              start_char=1, oov_char=2, index_from=3, **kwargs):\n",
    "    \"\"\"Loads the Reuters newswire classification dataset.\n",
    "    # Arguments\n",
    "        path: where to cache the data (relative to `~/.keras/dataset`).\n",
    "        num_words: max number of words to include. Words are ranked\n",
    "            by how often they occur (in the training set) and only\n",
    "            the most frequent words are kept\n",
    "        skip_top: skip the top N most frequently occurring words\n",
    "            (which may not be informative).\n",
    "        maxlen: truncate sequences after this length.\n",
    "        test_split: Fraction of the dataset to be used as test data.\n",
    "        seed: random seed for sample shuffling.\n",
    "        start_char: The start of a sequence will be marked with this character.\n",
    "            Set to 1 because 0 is usually the padding character.\n",
    "        oov_char: words that were cut out because of the `num_words`\n",
    "            or `skip_top` limit will be replaced with this character.\n",
    "        index_from: index actual words with this index and higher.\n",
    "    # Returns\n",
    "        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n",
    "    Note that the 'out of vocabulary' character is only used for\n",
    "    words that were present in the training set but are not included\n",
    "    because they're not making the `num_words` cut here.\n",
    "    Words that were not seen in the training set but are in the test set\n",
    "    have simply been skipped.\n",
    "    \"\"\"\n",
    "    # Legacy support\n",
    "    if 'nb_words' in kwargs:\n",
    "        warnings.warn('The `nb_words` argument in `load_data` '\n",
    "                      'has been renamed `num_words`.')\n",
    "        num_words = kwargs.pop('nb_words')\n",
    "    if kwargs:\n",
    "        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))\n",
    "\n",
    "    path = get_file(path,\n",
    "                    origin='https://s3.amazonaws.com/text-datasets/reuters.npz',\n",
    "                    file_hash='87aedbeb0cb229e378797a632c1997b6')\n",
    "    with np.load(path) as f:\n",
    "        xs, labels = f['x'], f['y']\n",
    "\n",
    "    np.random.seed(seed)\n",
    "    indices = np.arange(len(xs))\n",
    "    np.random.shuffle(indices)\n",
    "    xs = xs[indices]\n",
    "    labels = labels[indices]\n",
    "\n",
    "    if start_char is not None:\n",
    "        xs = [[start_char] + [w + index_from for w in x] for x in xs]\n",
    "    elif index_from:\n",
    "        xs = [[w + index_from for w in x] for x in xs]\n",
    "\n",
    "    if maxlen:\n",
    "        xs, labels = _remove_long_seq(maxlen, xs, labels)\n",
    "\n",
    "    if not num_words:\n",
    "        num_words = max([max(x) for x in xs])\n",
    "\n",
    "    # by convention, use 2 as OOV word\n",
    "    # reserve 'index_from' (=3 by default) characters:\n",
    "    # 0 (padding), 1 (start), 2 (OOV)\n",
    "    if oov_char is not None:\n",
    "        xs = [[w if skip_top <= w < num_words else oov_char for w in x] for x in xs]\n",
    "    else:\n",
    "        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]\n",
    "\n",
    "    idx = int(len(xs) * (1 - test_split))\n",
    "    x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
    "    x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n",
    "\n",
    "    return (x_train, y_train), (x_test, y_test)\n",
    "\n",
    "\n",
    "def get_word_index(path='reuters_word_index.json'):\n",
    "    \"\"\"Retrieves the dictionary mapping words to word indices.\n",
    "    # Arguments\n",
    "        path: where to cache the data (relative to `~/.keras/dataset`).\n",
    "    # Returns\n",
    "        The word index dictionary.\n",
    "    \"\"\"\n",
    "    path = get_file(\n",
    "        path,\n",
    "        origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.json',\n",
    "        file_hash='4d44cc38712099c9e383dc6e5f11a921')\n",
    "    f = open(path)\n",
    "    data = json.load(f)\n",
    "    f.close()\n",
    "    return data\n",
    "```\n",
    "## Regarding Dataset\n",
    "This dataset contains of newswires from retuers of 46 different classes. The number of samples are 11,228.\n",
    "\n",
    "[official Link](https://archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Loading the data\n",
    "from keras.datasets import reuters\n",
    "(x_train, y_train), (x_test, y_test) = reuters.load_data(path=\"reuters.npz\",num_words=10000,skip_top=0,maxlen=None,test_split=0.2,seed=113,start_char=1,oov_char=2,index_from=3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understand the dataset\n",
    "Let see the size and shape of test and training tuples\n",
    "\n",
    "``On Execution``  \n",
    "There will be 8982 samples of word embeddings list in training set  \n",
    "There will be 2246 samples of word embeddings list in testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of training samples 8982\n",
      "The number of testing samples 2246\n",
      "The shape of training samples array (8982,)\n",
      "The shape of training samples labels (8982,)\n",
      "First element in x_train ,its type : <class 'list'> it's shape : (87,)\n"
     ]
    }
   ],
   "source": [
    "# Understanding the data\n",
    "print(\"The number of training samples\",len(x_train))\n",
    "print(\"The number of testing samples\",len(x_test))\n",
    "print(\"The shape of training samples array\",np.shape(x_train))\n",
    "print(\"The shape of training samples labels\", np.shape(y_train))\n",
    "print(\"First element in x_train ,its type :\",type(x_train[0]),\"it's shape :\",np.shape(x_train[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data\n",
    "Let us try to visualise the few samples of data so, we could get an idea of how the data looks like  \n",
    "`On Execution`  \n",
    "We can see first two newswires. \n",
    "> Note: on loading the dataset we load the word embedding, to convert into the actual newswires we take the help of word to index dictonary to convert the given words embedding to actual news writings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First Review \n",
      "\n",
      "the of of mln loss for plc said at only ended said commonwealth could 1 traders now april 0 a after said from 1985 and from foreign 000 april 0 prices its account year a but in this mln home an states earlier and rise and revs vs 000 its 16 vs 000 a but 3 psbr oils several and shareholders and dividend vs 000 its all 4 vs 000 1 mln agreed largely april 0 are 2 states will billion total and against 000 pct dlrs\n",
      "\n",
      "Its label : 3\n",
      "\n",
      "Second Review \n",
      "\n",
      "the termination payment airport takes 6 of geological 3 6 602 begin up said fully bank expects commodity total is giant a of this takes of series termination payment airport mln a for capital 1 pre 50 american east said in council takes of of france a but u any 4 s 1st losses pct dlrs\n",
      "\n",
      "Its label : 4\n"
     ]
    }
   ],
   "source": [
    "# Data Visulization\n",
    "\n",
    "# A dictionary mapping words to an integer index\n",
    "word_index = reuters.get_word_index()\n",
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()]) # Reversed dictionary\n",
    "\n",
    "def decode_review(text):\n",
    "    return ' '.join([reverse_word_index.get(i, '?') for i in text])\n",
    "\n",
    "## Viewing the review's\n",
    "print('\\nFirst Review \\n')\n",
    "print(decode_review(x_train[0]))\n",
    "print('\\nIts label :',y_train[0])\n",
    "\n",
    "print('\\nSecond Review \\n')\n",
    "print(decode_review(x_train[1]))\n",
    "print('\\nIts label :',y_train[1])\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the data\n",
    "\n",
    "## Padding\n",
    "Each newswire in the dataset would have a different size but, in a neural network model would like to have all its inputs of same size for that we all padding to the reviews.  \n",
    "We make sure all the embeddings have the same length of max lenght i.e. 256 (for this dataset).   \n",
    "For embeddings longer than 256 the enddings will be chopped off.   \n",
    "For embeddings shorter than 256 '0' (zeros) would be \n",
    "> Note: On adding 0 to the embeddings we are not creating a new word in the embedding. The dataset doesn't assign any word to the value 0 . 0 is some kind of unknown word which we use as padding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "##Varibles\n",
    "classes = 46\n",
    "\n",
    "## Padding\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "x_train_padded = pad_sequences(x_train,value=0,padding='post',maxlen=256)\n",
    "x_test_padded =  pad_sequences(x_test,value=0,padding='post',maxlen=256)\n",
    "\n",
    "## Creating sparse vector representation\n",
    "from keras.utils import to_categorical\n",
    "y_train_sparse = to_categorical(y_train,num_classes=classes)\n",
    "y_test_sparse = to_categorical(y_test,num_classes=classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training varibles\n",
    "These Training varbles are hyper parameters for neural network training.   \n",
    "`epochs` : each epoch is forward propagation + backward propagation over the whole dataset once is called one epoch.  \n",
    "`learning_rate` : the magnitude in which the weights are modified one the acquired loss.   \n",
    "`learning_rate_decay` : there can be high leanring rate at the beining of the training when the loss is high. Over a period of time the learning rate can reduce for fine training of network.  \n",
    "`batch_size` : the data is fed to the network in batches of 32 samples at each time. This batch feeding is done all over the whole dataset.  \n",
    "`vocab_size` : vocab size is the total number of words present in the dictonary. This size would represent the input size to the nural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training varibles\n",
    "learning_rate = 0.0005\n",
    "learning_rate_decay = 0.00001\n",
    "batch_size = 512\n",
    "epochs = 30\n",
    "\n",
    "# input shape is the vocabulary count used for the movie reviews (10,000 words)\n",
    "vocab_size = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Netowork Model\n",
    "`Line 6` : we are building a keras sequential model  \n",
    "`Line 17` : we are using stochastic gradient decent optimizer  \n",
    "`Line 19` : compiling the model to check if the model is build properly.  \n",
    "\n",
    "The loss function being used is `categorical_crossentropy` since its a multi class classification    \n",
    "`model.summary()` would just describe the layers in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, None, 512)         5120000   \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d_1 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 46)                11822     \n",
      "=================================================================\n",
      "Total params: 5,263,150\n",
      "Trainable params: 5,263,150\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Building the model\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding,GlobalAveragePooling1D,Dense,Dropout\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(vocab_size, 512))\n",
    "model.add(GlobalAveragePooling1D())\n",
    "model.add(Dense(256, activation = 'relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(46, activation = 'sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# optimizer\n",
    "optimizer = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=learning_rate_decay, amsgrad=False)\n",
    "\n",
    "# Model Compilation\n",
    "model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training \n",
    "Training is the process of feeding the data to neural network and modifiying the weights of the model using the the backpropagation algorithm. The backpropagation using loss the function acquires the loss over batch size of data and does a backpropagation to modify the weights in such a way the in the next epoch the loss would be less when compared to the current epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8982 samples, validate on 2246 samples\n",
      "Epoch 1/30\n",
      "8982/8982 [==============================] - 7s 740us/step - loss: 3.7463 - acc: 0.3317 - val_loss: 3.6337 - val_acc: 0.3785\n",
      "Epoch 2/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 3.4892 - acc: 0.3640 - val_loss: 3.2866 - val_acc: 0.3798\n",
      "Epoch 3/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 3.0467 - acc: 0.3511 - val_loss: 2.7812 - val_acc: 0.3882\n",
      "Epoch 4/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.6398 - acc: 0.3090 - val_loss: 2.5533 - val_acc: 0.3838\n",
      "Epoch 5/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.4621 - acc: 0.3210 - val_loss: 2.3713 - val_acc: 0.3629\n",
      "Epoch 6/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.3571 - acc: 0.3437 - val_loss: 2.3178 - val_acc: 0.3624\n",
      "Epoch 7/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.3186 - acc: 0.3415 - val_loss: 2.2884 - val_acc: 0.3758\n",
      "Epoch 8/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.2850 - acc: 0.3387 - val_loss: 2.2550 - val_acc: 0.3753\n",
      "Epoch 9/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.2488 - acc: 0.3466 - val_loss: 2.2203 - val_acc: 0.3776\n",
      "Epoch 10/30\n",
      "8982/8982 [==============================] - 2s 194us/step - loss: 2.2094 - acc: 0.3552 - val_loss: 2.1863 - val_acc: 0.3767\n",
      "Epoch 11/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.1702 - acc: 0.3595 - val_loss: 2.1478 - val_acc: 0.3771\n",
      "Epoch 12/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.1286 - acc: 0.3637 - val_loss: 2.1056 - val_acc: 0.3807\n",
      "Epoch 13/30\n",
      "8982/8982 [==============================] - 2s 194us/step - loss: 2.0775 - acc: 0.3736 - val_loss: 2.0431 - val_acc: 0.3891\n",
      "Epoch 14/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 2.0032 - acc: 0.3907 - val_loss: 1.9561 - val_acc: 0.4083\n",
      "Epoch 15/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.9277 - acc: 0.4158 - val_loss: 1.8757 - val_acc: 0.4297\n",
      "Epoch 16/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.8504 - acc: 0.4442 - val_loss: 1.8061 - val_acc: 0.4586\n",
      "Epoch 17/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.7785 - acc: 0.4837 - val_loss: 1.7350 - val_acc: 0.5432\n",
      "Epoch 18/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.6959 - acc: 0.5363 - val_loss: 1.6635 - val_acc: 0.5539\n",
      "Epoch 19/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.6160 - acc: 0.5661 - val_loss: 1.5940 - val_acc: 0.6051\n",
      "Epoch 20/30\n",
      "8982/8982 [==============================] - 2s 194us/step - loss: 1.5215 - acc: 0.6127 - val_loss: 1.5242 - val_acc: 0.6469\n",
      "Epoch 21/30\n",
      "8982/8982 [==============================] - 2s 194us/step - loss: 1.4473 - acc: 0.6434 - val_loss: 1.4583 - val_acc: 0.6558\n",
      "Epoch 22/30\n",
      "8982/8982 [==============================] - 2s 194us/step - loss: 1.3647 - acc: 0.6642 - val_loss: 1.3971 - val_acc: 0.6692\n",
      "Epoch 23/30\n",
      "8982/8982 [==============================] - 2s 195us/step - loss: 1.2895 - acc: 0.6837 - val_loss: 1.3458 - val_acc: 0.6745\n",
      "Epoch 24/30\n",
      "8982/8982 [==============================] - 2s 194us/step - loss: 1.2283 - acc: 0.6971 - val_loss: 1.3047 - val_acc: 0.6790\n",
      "Epoch 25/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.1700 - acc: 0.7099 - val_loss: 1.2711 - val_acc: 0.6817\n",
      "Epoch 26/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.1207 - acc: 0.7196 - val_loss: 1.2422 - val_acc: 0.6910\n",
      "Epoch 27/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.0774 - acc: 0.7286 - val_loss: 1.2149 - val_acc: 0.7004\n",
      "Epoch 28/30\n",
      "8982/8982 [==============================] - 2s 192us/step - loss: 1.0383 - acc: 0.7400 - val_loss: 1.1934 - val_acc: 0.7097\n",
      "Epoch 29/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 1.0001 - acc: 0.7475 - val_loss: 1.1736 - val_acc: 0.7146\n",
      "Epoch 30/30\n",
      "8982/8982 [==============================] - 2s 193us/step - loss: 0.9701 - acc: 0.7567 - val_loss: 1.1561 - val_acc: 0.7191\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "model_history = model.fit(x_train_padded,y_train_sparse,epochs=epochs,batch_size=batch_size,validation_data=(x_test_padded, y_test_sparse),verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "using the trained model we try to predict what are the catogories of newswires in the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Results\n",
    "y_pred = model.predict(x_test_padded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visulizing the results\n",
    "checking the results by visulizing them and creating a confusion matrix. The values of precession and accuracy can be obtained by the help of confusion matrix and f1 scores to compare this architecure with other architectures of neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted  1    3    4   8   9   10  11  13  16  18   19  20  21  25  30\n",
      "Actual                                                                  \n",
      "0           5    0    1   0   5   0   0   0   0   0    0   0   1   0   0\n",
      "1          88    3    8   0   1   0   3   0   1   1    0   0   0   0   0\n",
      "2          16    0    0   0   1   0   2   1   0   0    0   0   0   0   0\n",
      "3           5  763   35   1   0   0   1   1   2   0    4   0   1   0   0\n",
      "4           4   17  434   0   0   0   5   3   6   0    4   1   0   0   0\n",
      "5           5    0    0   0   0   0   0   0   0   0    0   0   0   0   0\n",
      "6           0    1    3   0   1   0   0   1   1   1    0   0   0   6   0\n",
      "7           0    1    0   1   0   0   0   0   0   1    0   0   0   0   0\n",
      "8           0    9    3  15   1   0   0   0   0   0    8   2   0   0   0\n",
      "9           8    0    2   1  12   0   1   1   0   0    0   0   0   0   0\n",
      "10         25    1    1   0   0   1   1   1   0   0    0   0   0   0   0\n",
      "11          7    2    6   0   0   0  59   1   0   0    8   0   0   0   0\n",
      "12          0    0    1   1   0   0   1   1   0   0    9   0   0   0   0\n",
      "13          3    1    3   0   0   0   0  17  13   0    0   0   0   0   0\n",
      "14          1    0    0   0   0   0   1   0   0   0    0   0   0   0   0\n",
      "15          6    0    0   0   0   0   0   2   1   0    0   0   0   0   0\n",
      "16          0   10    7   0   0   0   2   1  78   0    1   0   0   0   0\n",
      "17          0    1    4   0   0   0   0   0   7   0    0   0   0   0   0\n",
      "18          0    3    1   1   0   0   1   0   0  10    0   1   3   0   0\n",
      "19          2    7    5   4   0   0   8   1   0   0  100   3   3   0   0\n",
      "20          0    6   14   0   0   0   1   0   1   0   33  14   1   0   0\n",
      "21          0    1    0   1   0   0   5   0   0   0    1   2  17   0   0\n",
      "22          5    0    1   0   0   0   0   0   0   1    0   0   0   0   0\n",
      "23          2    3    2   0   1   0   0   3   0   0    1   0   0   0   0\n",
      "24         16    1    1   0   0   0   0   1   0   0    0   0   0   0   0\n",
      "25          1    6    7   1   1   0   0   1   4   0    2   0   1   7   0\n",
      "26          2    0    0   0   4   0   0   1   0   0    1   0   0   0   0\n",
      "27          1    0    1   0   0   0   1   1   0   0    0   0   0   0   0\n",
      "28          5    0    1   0   1   0   1   1   0   1    0   0   0   0   0\n",
      "29          0    1    0   1   0   0   0   0   0   1    0   0   1   0   0\n",
      "30          0    2    0   2   0   0   1   0   0   2    0   1   4   0   0\n",
      "31          2    4    2   0   0   0   0   2   3   0    0   0   0   0   0\n",
      "32          3    0    1   0   6   0   0   0   0   0    0   0   0   0   0\n",
      "33          0    0    0   0   0   0   0   0   5   0    0   0   0   0   0\n",
      "34          0    1    1   0   0   0   1   0   0   3    0   0   0   0   1\n",
      "35          0    0    0   2   0   0   1   0   0   1    0   1   1   0   0\n",
      "36          0    0    0   0   0   0  10   0   0   0    0   0   1   0   0\n",
      "37          0    1    0   0   0   0   0   0   0   1    0   0   0   0   0\n",
      "38          2    0    1   0   0   0   0   0   0   0    0   0   0   0   0\n",
      "39          0    0    2   0   0   0   1   1   1   0    0   0   0   0   0\n",
      "40          0    1    0   0   0   0   0   0   0   0    9   0   0   0   0\n",
      "41          0    0    5   0   0   0   0   0   3   0    0   0   0   0   0\n",
      "42          0    0    0   0   2   0   0   0   0   0    0   0   0   1   0\n",
      "43          0    1    0   0   0   0   0   0   0   3    0   0   1   0   1\n",
      "44          4    1    0   0   0   0   0   0   0   0    0   0   0   0   0\n",
      "45          0    0    0   0   0   0   0   0   0   0    0   0   0   1   0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2130c39ff98>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BF4CfA/c75+7d8v5mdhTwU+BoUmt9jjezI3Z2NIgaHSIiIiIiIiIZ5lz1WM/Cs27AEufcR9s7y09ab2C4c24DsNTMFpNaBuO9HT3A12KkIiIiIiIiIhIzZnalmRVtcblyJ3f/KTBsi+u/NrMPzOwJMzsgfVsT4OMt7lOSvm2H1OgQERERERERkSrhnBvonMvf4jJwe/czs1rAOcCz6Zv+DfyA1GEtZcCATXfd3tPsrAY1OkREREREREQk084EpjvnVgE451Y55ypd6pieQaQOT4HUDI5mWzyuKbBiZ79Ya3SIiIiIiIiIZFoy+DU6LmSLw1bMrLFzrix99Txgdvr7F4FnzOw+UouRHg5M3dkvVqNDRERERERERDLGzOoC3YFfbXHz383sBFKHpSzb9DPn3BwzGwnMBSqAfjs74wqAOVczT917TMP2NbPwvbDg8493facY2clqu7GTrKH7n8jWwtlrd3FAqIiIiFSZio2lsX6LsbFkVizfVtRqeqz3cdMaHSIiIiIiIiISG2p0iIiIiIiIiEhsaI0OERERERERkUxzwS9GGhnN6BARERERERGR2FCjQ0RERERERERiQ40OEREREREREYkNrdEhIiIiIiIikmnJSt8VxJZmdOzExb/6KaPeeoYX3voPf3/kDmrVrkW7TvmMfG0Iz73+FE+9+CjNmjf1XWaVq127Nu++M4b3i16juPgNbrvtet8lRe6aa66geMbrzJg+nqFPPUzt2rV9lxSZHgVdmDN7IvPnTuLGG/r5LidSgwYOYEXJTIpnvO67lIwIaWxDe50KaWxD229DyxvSthxSVggrb0hZIby8Eg9qdOzAIY0O5qIr+nBBj59zXueLSCQSnHlud279vxv5w9W3c363S3j5+UJ+9duf+y61ym3YsIHuBX04Kb87+fkF9CjoQru2J/ouKzJ5eY3o1+8XtO9wNq1PPJ2srCz69DnHd1mRSCQSPPTgX+nZqy/HHt+VCy44l1atDvddVmSeemokZ/e8yHcZGRHa2Ib0OhXa2Ia030JYeUPalkPKCmHlDSkrhJdX4kONjp3Izsqidp3aZGVlkVu3DqtXrsY5R7196wGw7371WL1qtecqo7F+/dcA5ORkk5OTg3POc0XRys7KJje3TnqscykrW+W7pEi0bdOaJUuWsXTpcsrLyxk5cjTn9Orhu6zIvD1pCms+/8J3GRkR2thCOK9ToY1tSPsthJU3pG05pKwQVt6QskJ4eSU+ql2jw8yqxRSJT1auZvC//8P46aN484MxfPXlet59ayq3/+4u/v2f+xg/40V6nX8mjz30lO9SI5FIJCiaVsiK0g8Y//pEpk6b4bukyKxYsZL7H3iUJYunsPyj6Xy59ivGj5/ou6xI5DVpxMclKzZfLyktIy+vkceKpKqEOLahvE6FOLYSTyFtyyFlhbDyhpQVwsubcS4Zz0s1UO0aHUD/Hf3AzK40syIzK1rzzSeRFrFf/X3pesap9GjzI047vie5devQ88dncMmvLuSqi37H6a3PYdTwMdx4x3WR1uFLMpkkv00BzVvk0ya/NUcffaTvkiKz//716dWzgCOO7MChzU+iXr1cfnbhj3yXFQkz2+a2uP4veGhCHNtQXqdCHFuJp5C25ZCyQlh5Q8oK4eWV+PDS6DCzD3ZwmQU03NHjnHMDnXP5zrn8A3MPibTG9qe2oXT5Cj7/7AsqKip5/eUJtG57HEce3ZJZ0+cAMG70eE7IPzbSOnxbu/ZL3pr4LgUFXXyXEplup3Vk2bKP+fTTNVRUVDBq1DjadzjJd1mRKC0po1nTvM3XmzZpHNvDdEIT8tjG/XUq5LGVeAlpWw4pK4SVN6SsEF5eiQ9fMzoaApcAvbZz+cxTTd9RVrqK4048hjq5qbNvtOuUz5KFS9ln33049LBmAJzcuS0fLlrmscpoNGhwIPXr7wdAnTp16HZaJxYsWOK5qugs/3gF7dq1Jje3DgBdu3Zk/vzFnquKxrSiYlq2bEHz5s3IycmhT5/evDSm0HdZUgVCG9uQXqdCG1uJr5C25ZCyQlh5Q8oK4eWV+Mj29LxjgH2cc8Vb/8DMJmS+nG3Nmj6H18a8wcjXhlBZWcn8WQt5dugoVq34hPuf+Bsu6fjyi6+49bd/8V1qlWvcuCFPPP4AWVkJLJHguedeYuzY8b7Lisy0aTN4/vmxTJ3yChUVFRQXz+Gxx/7ju6xIVFZWcu11tzD25WfISiQYPGQEc+cu9F1WZJ4e+k86n9qBBg0OZNmHRfS/416eHDzcd1mRCG1sQ3qdCm1sQ9pvIay8IW3LIWWFsPKGlBXCy5txyeqxnkUcWU09xuqYhu1rZuF7YcHnH/suIaO2dyxgXCVr6P4nsrVw9lrQXisiIpIZFRtLY/0WY+OHU2P5tqLWYW29j1t1XIxURERERERERGSvqNEhIiIiIiIiIrGhRoeIiIiIiIiIxIavxUhFREREREREguWcFiONimZ0iIiIiIiIiEhsqNEhIiIiIiIiIrFRYw9dWfJlme8SMiaW5xzaiZp6ymORkGmvFREREZHqosY2OkRERERERERqrKTW6IiKDl0RERERERERkdhQo0NEREREREREYkONDhERERERERGJDa3RISIiIiIiIpJpTmt0REUzOkREREREREQkNtToEBEREREREZHYUKNjJx555B4++uh9iooKv3P7VVddxsyZb/D++6/x17/e7Km6aPUo6MKc2ROZP3cSN97Qz3c5kQspb0hZBw0cwIqSmRTPeN13KRkR0tg2bZrH+MJnmfXBBGYWv8Fvfn2575IipW05vkLKCmHlDSkrhJU3pKwQXl6JB3PO+a5hr+TmHhp54aec0pb167/mscfuIz+/AIBTT+3ATTf9mvPO+zkbN27k4IMPYvXqzyKto7yyItLfv7VEIsG8OW9zxlkXUlJSxuT3xtL34quZN29RRuvIlJDyhpQVoFPHdqxbt54nn3yQE1p3811OpEIb20aNDqFxo0OYUTybffapx9Qpr/Dj838R27zaluO5LYeUFcLKG1JWCCtvSFnBf96KjaWWkSfyZMP8t2rmh/FdqP3Dzt7HTTM6duKdd6ayZs0X37ntyiv7cu+9/2Ljxo0AkTc5fGjbpjVLlixj6dLllJeXM3LkaM7p1cN3WZEJKW9IWQHenjSFNZ9/ses7xkBoY7ty5SfMKJ4NwLp165k/fxFN8hp5rio62pbjuS2HlBXCyhtSVggrb0hZIby8Eh/eGh1m9kMz62Zm+2x1+xm+atodLVu24JRT2jJx4igKC0dw0knH+S6pyuU1acTHJSs2Xy8pLSMvxh8gQsobUtbQhDy2hx7alBOOP4YpU2f4LkWqQEjbckhZIay8IWWFsPKGlBXCyyvx4aXRYWbXAKOB3wCzzaz3Fj++y0dNuys7O5sDDqjPqaeeyx//eBdPP/0v3yVVObNtZxrV1EOcdkdIeUPKGppQx7ZevbqMHDGI3/3+dr76ap3vcqQKhLQth5QVwsobUlYIK29IWSG8vBIf2Z6e95fASc65dWbWHHjOzJo75x4Edng8j5ldCVwJkJ19INnZ++zorpEpLS1j1KhXACgqmkkymaRBgwP59NM1Ga8lKqUlZTRrmrf5etMmjSkrW+WxomiFlDekrKEJcWyzs7N5dsQghg17gVGjxvkuR6pISNtySFkhrLwhZYWw8oaUFcLLK/Hh69CVLOfcOgDn3DKgC3Cmmd3HThodzrmBzrl851y+jyYHwEsvFdKly8lA6jCWWrVyYtXkAJhWVEzLli1o3rwZOTk59OnTm5fGFO76gTVUSHlDyhqaEMd20MABzJu/mAceHOi7FKlCIW3LIWWFsPKGlBXCyhtSVggvb8a5ZDwv1YCvGR0rzewE51wxQHpmR0/gCeBYTzVtY8iQh+jUqQMNGhzA4sWTufPO+xkyZCSPPnoPRUWFbNxYzhVXXO+7zCpXWVnJtdfdwtiXnyErkWDwkBHMnbvQd1mRCSlvSFkBnh76Tzqf2oEGDQ5k2YdF9L/jXp4cPNx3WZEIbWxPObkNF/c9nw9mzaVoWuoN16233s24V97wXFk0tC3Hc1sOKSuElTekrBBW3pCyQnh5JT68nF7WzJoCFc65ldv52SnOuXd29TsycXrZ6iLTp5cVERERERHxLfanl533Ziw/09Zu1dX7uHmZ0eGcK9nJz3bZ5BARERERERER2R5fh66IiIiIiIiIhCtZPdaziCNfi5GKiIiIiIiIiFQ5NTpEREREREREJDbU6BARERERERGR2NAaHSIiIiIiIiKZ5rRGR1Q0o0NEREREREREYqPGzuioqKzwXULGfLPibd8lZFTDFj18l5AxX2742ncJIiISKPNdQAY53wWIiEhGaUaHiIiIiIiIiMRGjZ3RISIiIiIiIlJjJbVGR1Q0o0NEREREREREYkONDhERERERERGJDTU6RERERERERCQ21OgQERERERERkdjQYqQiIiIiIiIiGeZcpe8SYkszOvbAooWTmTF9PEXTCpn83ljf5XxvSz8q4ceX9tt8adf9Rwwd8QIA/3l2ND1/egW9L/oVA/75OACz5i7YfN8fXXo14996x2f5e6127Vq89uZzTHz3Rd6dOpY//PEaAB5+5P+YMesN3nrnRd5650WOObaV50qr3qCBA1hRMpPiGa/7LiVjehR0Yc7sicyfO4kbb+jnu5xIhZQ1tG05pLxNm+YxvvBZZn0wgZnFb/CbX1/uu6RIhbTfAiQSCaZNfZVRLwzxXUrkQhrbkF6jIKyxhfDySjyYc853DXslp1aTjBe+aOFk2nc4k88++zyjz/v1ircjf47KykpOO/dihg26n5LSlQx8ajj/uqc/tWrV4rPPv+CgA/bnm2+/JSc7h+zsLFZ/uoYfX3o1b4z+D9nZWVVaS8MWPar0921PvXp1Wb/+a7KzsxlXOJybb/oLl11+IYXj3uTF0a9E/vybfLnh64w9F0Cnju1Yt249Tz75ICe07pbR5/YhkUgwb87bnHHWhZSUlDH5vbH0vfhq5s1b5Lu0KhdSVghvWw4pb6NGh9C40SHMKJ7NPvvUY+qUV/jx+b+I5bbse7+1jDzLd1137ZWceNJx7Lfvvpx73qUZe95Mv2n0PbaZFtJrVGhj6ztvxcZSHy9VGfPtzLE188P4LtQ5/izv4+ZtRoeZtTWzNunvjzKz35nZWb7qCd3komKaNWlMXqOGjBj1Mpf37UOtWrUAOOiA/QHIrVNnc1Njw8aNYN633722fn2qwZCTk012TjY1teG3p96eNIU1n3/hu4yMadumNUuWLGPp0uWUl5czcuRozukVfSPNh5CyQnjbckh5V678hBnFswFYt2498+cvokleI89VRSO0/bZJk8aceWY3nnhimO9SIhfa2Ib0GhXa2IaWV+LDS6PDzG4HHgL+bWZ/Ax4G9gH+YGZ/8lHT7nDOMW7sMKZMHscVl1/ku5wqNe71tzjr9M4ALFteyvszZ3PhL6/jsn43MGvegs33+2DOfHpf9CvOu+Qqbrvh11U+myNTEokEb73zIgs+nMyEN9/h/aKZAPzp9t/y9nsv8de//XFzo0dqrrwmjfi4ZMXm6yWlZeTF9ANTSFklHIce2pQTjj+GKVNn+C4lEqHttwMG9Ofmm/9CMpn0XUrkQhvbkIQ2tqHlzTiXjOelGvA1o+N84BTgVKAfcK5z7g6gB3DBjh5kZleaWZGZFSWT6zNT6RY6dzmXtu3OoGevvlx11WV07Ngu4zVEoby8nAmTplBwWicgdRjLl1+t45mB93N9vyv4/a1/2zzj4bijf8jo/zzK8Mce5LGhI9mwYaPP0vdaMpmk8ynncMwPO3HiScfRqtXh3Hn7vbQ7sQfdOv+Y/Q+sz7W/vdJ3mfI92XZmHcV19k5IWSUM9erVZeSIQfzu97fz1VfrfJcTiZD227POOp3Vn3zK9BmzfJeSESGNbWhCG9vQ8kp8+Gp0VDjnKp1zXwNLnHNfAjjnvgF22AJyzg10zuU75/ITiXqZqnWzsrJVAKxe/RmjRo+jTZsTMl5DFN6eXESrI35AgwMPAKDhIQ04vfMpmBnHHnUkZsbnX6z9zmN+0Px/yK1Th0UfLvNQcdX5cu1XvPP2FLp1P5VVq1YTpbBCAAAgAElEQVQDsHHjRp4Z+v84Mf84z9XJ91VaUkazpnmbrzdt0njzfhw3IWWV+MvOzubZEYMYNuwFRo0a57ucyIS03558cj49exawaOFk/vP0v+ja9RSGDH7Id1mRCWlsQxPa2IaWV+LDV6Njo5nVTX9/0qYbzaw+O2l0+FS3bi777FNv8/fdT+/MnDkLdvGommHsaxM4q3uXzddP69SBqe8XA7BseQnlFRUcsH99SlaspKIidQqkFStXsWx5CU0aN/RR8vdyUIMD2a/+vgDUqVObzl1PZuHCD2nY8ODN9zm7Z3fmzV3oq0SpItOKimnZsgXNmzcjJyeHPn1689KYQt9lRSKkrBJ/gwYOYN78xTzw4EDfpUQqpP32llvupsVh+Rx+RHsu6ns1b775Dpdedo3vsiIT0tiGJrSxDS2vxEe2p+c91Tm3AcC57xzEkwNkbgnuPdCw4cE892zqNKtZ2VkMHz6KwsIJfouqAt98+y3vTZvB7Tf+983Gj3oWcMtd93Nu3/8lJyebu265HjNj+gdzeHzoSLKzs0kkjFt+348D9q/vsfq907Dhwfzr0b+TlZUgkUgw6vlxFL7yJqPGPEWDBgdiZsz6YB7XX3eb71Kr3NND/0nnUzvQoMGBLPuwiP533MuTg4f7LisylZWVXHvdLYx9+RmyEgkGDxnB3Jg2sELKCuFtyyHlPeXkNlzc93w+mDWXommpN9O33no34155w3NlVS+0/TYkoY1tSK9RoY1taHkzLoA1i3zR6WVrgEycXrY6ycTpZauLTJ9eVkREZJOae+60PRfMm0aRmIn96WWnvxjLl6c6J57jfdy8nV5WRERERERERKSqqdEhIiIiIiIiIrHha40OERERERERkXA5rdERFc3oEBEREREREZHYUKNDRERERERERGJDjQ4RERERERERiQ01OkREREREREQkNrQYaQ2wX7OuvkvIqPmtDvddQsb8YPY83yWIVAnvJ0uXyJiFM7pJ53yXICIiIUlW+q4gtjSjQ0RERERERERiQ40OEREREREREYkNNTpEREREREREJDa0RoeIiIiIiIhIprmk7wpiSzM6RERERERERCQ21OgQERERERERkdhQo0NEREREREREYkONjj2QSCSYNvVVRr0wxHcpVe6RR+7ho4/ep6iocPNtQ4c+zOTJY5k8eSzz509i8uSxHiusIokEjZ95hIMf/AsAB912PY2HP0rjEQNp8PfbsNw637l73W6dOHT6eGq1OsJHtZHoUdCFObMnMn/uJG68oZ/vciIXUt6QsgIsWjiZGdPHUzStkMnvxeD1aQdq167Nu++M4f2i1ygufoPbbrved0mRq19/P4YPe5RZH0zgg5lv0q7dib5Likxo+22c30ttbdDAAawomUnxjNd9l5IRIW3LIWWF8PJmVDIZz0s1oEbHHrjmN1cwb/4i32VEYujQZ+nd+9Lv3Hbxxb+mffuzaN/+LEaNeoXRo1/xVF3V2ffC8yhfunzz9TUD/k3ZT39F2QVXUrnyE/a94NzNP7O6uex74XlsmDXPR6mRSCQSPPTgX+nZqy/HHt+VCy44l1atDvddVmRCyhtS1i2d3v0n5LcpoH2Hs3yXEpkNGzbQvaAPJ+V3Jz+/gB4FXWjXNr4f/AHuG9CfVwsncOxxXTgpv4D58xf7LikSIe63cX4vtbWnnhrJ2T0v8l1GRoS0LYeUFcLLK/FRbRodZvaU7xp2pkmTxpx5ZjeeeGKY71Ii8c47U1mz5osd/vzHPz6bkSNfzGBFVS/rkAbkdmrHulH//Z9ft/7rzd9b7drg3Obr+199GV8OGYHbsDGjdUapbZvWLFmyjKVLl1NeXs7IkaM5p1cP32VFJqS8IWUN0fr0a1VOTjY5OTm4LV6r4mbfffehY6d2PPlk6t/b8vJy1q790nNV0Qhtv437e6mtvT1pCms+3/F7qzgJaVsOKSuEl1fiw0ujw8xe3OryEvCjTdd91LQrAwb05+ab/0KymkzFyaRTTmnLqlWfsmTJMt+lfC8H/P5qvnhwEC753Q8IB/359zR97VlymjfjqxGjAMg5siVZDQ/hm7en+Cg1MnlNGvFxyYrN10tKy8jLa+SxomiFlDekrJs45xg3dhhTJo/jisvj/b+miUSCommFrCj9gPGvT2TqtBm+S4rMYS3+h09Xr+GxQfcxdcorPPLve6hbN9d3WZEIbb8N+b1U3IW0LYeUFcLLK/Hha0ZHU+BL4D5gQPry1Rbfb5eZXWlmRWZWlEyuz0ihAGeddTqrP/mU6TNmZew5q5M+fc7h2WerZf9pt+V2akdyzRdsnLftdNnP/nwvJT0uoHzpcuoWdAEzDrz+Kj6/75HMFxoxM9vmtjj/z3BIeUPKuknnLufStt0Z9OzVl6uuuoyOHdv5LikyyWSS/DYFNG+RT5v81hx99JG+S4pMVnY2rVsfw6MDh9K23Rms//rr2B4THtJ+G/p7qbgLaVsOKSuElzfjXDKel2rAV6MjH3gf+BOw1jk3AfjGOfeWc+6tHT3IOTfQOZfvnMtPJOplqFQ4+eR8evYsYNHCyfzn6X/RtespDBn8UMae36esrCx69z6D5557yXcp30vt448ht3MHmox5moP/9ifq5J/AQX/5w3/vkEyyvnACdbt1wurVJecHzWk0aABNxjxN7WNbcfADd8RiQdLSkjKaNc3bfL1pk8aUla3yWFG0QsobUtZNNuVbvfozRo0eR5s2J3iuKHpr137JWxPfpaCgi+9SIlNaWkZJSRnT0rNWnn/+ZU5ofaznqqIR0n4b8nupEIS0LYeUFcLLK/HhpdHhnEs65+4Hfg78ycweBrJ91LI7brnlblocls/hR7Tnor5X8+ab73DpZdf4LisjTjutIwsXLqG0dKXvUr6XLx5+nNIzL6S0Z19W3/xXvi0q5rNb7ia72X9fuHNPbU/50uW4desp6fZjSnv2pbRnXzbMmsfq625j47yFHhNUjWlFxbRs2YLmzZuRk5NDnz69eWlM4a4fWEOFlDekrAB16+ayzz71Nn/f/fTOzJmzwHNV0WjQ4EDq198PgDp16tDttE4sWLDEc1XRWbVqNSUlKzjiiMMAOK1rR+ZtZzZeHIS034b8XioEIW3LIWWF8PJKfHhtLjjnSoCfmNnZpA5lEU+GDHmITp060KDBASxePJk777yfIUNG8JOf9Krxi5DukBkH9b+RRL16YFC+8EM++9uDvquKVGVlJddedwtjX36GrESCwUNGMHduzW/g7EhIeUPKCtCw4cE89+zjAGRlZzF8+CgKCyf4LSoijRs35InHHyArK4ElEjz33EuMHTved1mR+u1vb2XI4H9Qq1Ytli79iCt+Gc9T6oa234bm6aH/pPOpHWjQ4ECWfVhE/zvu5cnBw32XFYmQtuWQskJ4eSU+rKYeY5VTq0nNLHwvZGdV28kukZgf0CmrfjA7PqeulbBtewSvxMX2js+Oq2QNfU+0t8IZWQhrZEXio2Jjaaxfqr59b1gsX57qdLjQ+7iF9QlaREREREREpDrQWagi42sxUhERERERERGRKqdGh4iIiIiIiIjEhhodIiIiIiIiIhIbWqNDREREREREJNO0RkdkNKNDRERERERERGJDMzpqgPLKCt8lZJROuSpS88Ty3GgCQE09Db3smkZWRETiSjM6RERERERERCQ2NKNDREREREREJMOcq/RdQmxpRoeIiIiIiIiIxIYaHSIiIiIiIiISG2p0iIiIiIiIiEhsaI0OERERERERkUxLJn1XEFua0SEiIiIiIiIisaFGxx5YtHAyM6aPp2haIZPfG+u7nEj1KOjCnNkTmT93Ejfe0M93OZELKW9IWSGsvCFlhbDyhpQVwsobUlYIK29IWSGsvCFlhfDySjyYc853DXslp1aTjBe+aOFk2nc4k88++zyjz5vpoIlEgnlz3uaMsy6kpKSMye+Npe/FVzNv3qIMV5IZIeUNKSuElTekrBBW3pCyQlh5Q8oKYeUNKSuElTekrOA/b8XGUsvIE3nyzYQnauaH8V3I7fIL7+OmGR2yjbZtWrNkyTKWLl1OeXk5I0eO5pxePXyXFZmQ8oaUFcLKG1JWCCtvSFkhrLwhZYWw8oaUFcLKG1JWCC+vxEe1aHSYWUcz+52ZFfiuZWecc4wbO4wpk8dxxeUX+S4nMnlNGvFxyYrN10tKy8jLa+SxomiFlDekrBBW3pCyQlh5Q8oKYeUNKSuElTekrBBW3pCyQnh5M84l43mpBrycdcXMpjrn2qa//yXQD3gBuN3MTnTO3e2jrl3p3OVcyspWcfDBB/HKuOHMX7CYSZOm+C6rypltO9Ooph7itDtCyhtSVggrb0hZIay8IWWFsPKGlBXCyhtSVggrb0hZIby8Eh++ZnTkbPH9lUB351x/oADY4VQJM7vSzIrMrCiZXB91jdsoK1sFwOrVnzFq9DjatDkh4zVkQmlJGc2a5m2+3rRJ483Z4yikvCFlhbDyhpQVwsobUlYIK29IWSGsvCFlhbDyhpQVwssr8eGr0ZEwswPM7CBSC6KuBnDOrQcqdvQg59xA51y+cy4/kaiXqVoBqFs3l332qbf5++6nd2bOnAUZrSFTphUV07JlC5o3b0ZOTg59+vTmpTGFvsuKTEh5Q8oKYeUNKSuElTekrBBW3pCyQlh5Q8oKYeUNKSuEl1fiw8uhK0B94H3AAGdmjZxzK81sn/Rt1U7Dhgfz3LOPA5CVncXw4aMoLJzgt6iIVFZWcu11tzD25WfISiQYPGQEc+cu9F1WZELKG1JWCCtvSFkhrLwhZYWw8oaUFcLKG1JWCCtvSFkhvLwZl6we61nEUbU6vayZ1QUaOueW7uq+Pk4v60swQUVERERERNJif3rZ1wfG8qNebrcrvY+brxkd2+Wc+xrYZZNDRERERERERGR7qsXpZUVEREREREREqkK1mtEhIiIiIiIiEgSnNTqiohkdIiIiIiIiIhIbanSIiIiIiIiISGyo0SEiIiIiIiIisaE1OkREREREREQyLak1OqJSYxsdsTzhsADg/aTLGaTtWERERGTvJSykd46QdHr3KLI7dOiKiIiIiIiIiMSGGh0iIiIiIiIiEhs19tAVERERERERkRrLaY2OqGhGh4iIiIiIiIjEhhodIiIiIiIiIhIbanSIiIiIiIiISGyo0SEiIiIiIiIisaFGx27qUdCFObMnMn/uJG68oZ/vciI1aOAAVpTMpHjG675LyYjatWvz7jtjeL/oNYqL3+C22673XVKktC3HV0hjC2HlDWlbDikrhJc3pP02pKwQVt769fdj+LBHmfXBBD6Y+Sbt2p3ou6RIhTS2GZdMxvNSDajRsRsSiQQPPfhXevbqy7HHd+WCC86lVavDfZcVmaeeGsnZPS/yXUbGbNiwge4FfTgpvzv5+QX0KOhCu7bx/AdL23J8hTa2oeUNaVsOKSuElTek/TakrBBe3vsG9OfVwgkce1wXTsovYP78xb5LikxoYyvx4aXRYWbtzGy/9Pe5ZtbfzF4ys/8zs/o+atqZtm1as2TJMpYuXU55eTkjR47mnF49fJcVmbcnTWHN51/4LiOj1q//GoCcnGxycnJwznmuKBraluMrtLENLW9I23JIWSGsvCHttyFlhbDy7rvvPnTs1I4nnxwGQHl5OWvXfum5quiENLYSL75mdDwBfJ3+/kGgPvB/6due9FTTDuU1acTHJSs2Xy8pLSMvr5HHiqSqJRIJiqYVsqL0A8a/PpGp02b4LikS2pbjK7SxDS2vSByEtN+GlBXCyntYi//h09VreGzQfUyd8gqP/Pse6tbN9V1WZEIaW4kXX42OhHOuIv19vnPuOufcJOdcf+CwHT3IzK40syIzK0om12em0tTzbnNbXP/HP1TJZJL8NgU0b5FPm/zWHH30kb5LioS25fgKbWxDyysSByHttyFlhbDyZmVn07r1MTw6cCht253B+q+/jvW6FSGNrRe+19LQGh1VbraZ/Tz9/UwzywcwsyOA8h09yDk30DmX75zLTyTqZaJOAEpLymjWNG/z9aZNGlNWtipjzy+Zs3btl7w18V0KCrr4LiUS2pbjK7SxDS2vSByEtN+GlBXCyltaWkZJSRnT0rN/n3/+ZU5ofaznqqIT0thKvPhqdFwBdDazJcBRwHtm9iEwKP2zamVaUTEtW7agefNm5OTk0KdPb14aU+i7LKkiDRocSP36+wFQp04dup3WiQULlniuKhraluMrtLENLa9IHIS034aUFcLKu2rVakpKVnDEEalJ6Kd17ci8eYs8VxWdkMZW4iXbx5M659YCl5nZvqQOVckGSpxz1bI9WFlZybXX3cLYl58hK5Fg8JARzJ270HdZkXl66D/pfGoHGjQ4kGUfFtH/jnt5cvBw32VFpnHjhjzx+ANkZSWwRILnnnuJsWPH+y4rEtqW47sthza2oeUNaVsOKSuElTek/TakrBBe3t/+9laGDP4HtWrVYunSj7jil9f7LikyoY2txIfV1GOssms1qZmFyy5teyRgfGkjFhEREdl7ie2sIRFnyRr62W1vVWwsjfUAfzPmvlgOaG7P33kfN1+HroiIiIiIiIiIVDk1OkREREREREQkNtToEBEREREREZHY8LIYqYiIiIiIiEjQkknfFcSWZnSIiIiIiIiISGyo0SEiIiIiIiIisaFGh4iIiIiIiIjEhtbokGonlieTFhEREZEql3R65ygi21KjQ0RERERERCTTnBYjjYoOXRERERERERGR2FCjQ0RERERERERiQ40OEREREREREYkNrdEhIiIiIiIikmlJrdERFc3oEBEREREREZHYUKNjN/Uo6MKc2ROZP3cSN97Qz3c5kRo0cAArSmZSPON136VkTCjjq7GN79hCWFkhrLwhZYWw8ob2uhzS2IaUFcLKG1JWCC+vxIMaHbshkUjw0IN/pWevvhx7fFcuuOBcWrU63HdZkXnqqZGc3fMi32VkTEjjq7GN79iGlBXCyhtSVggvb0ivyyGNbUhZIay8IWWF8PJKfKjRsRvatmnNkiXLWLp0OeXl5YwcOZpzevXwXVZk3p40hTWff+G7jIwJaXw1tvEd25CyQlh5Q8oK4eUN6XU5pLENKSuElTekrBBe3oxzyXheqgEvjQ4zu8bMmvl47r2R16QRH5es2Hy9pLSMvLxGHiuSqqTxja+QxjakrBBW3pCyQnh5QxLS2IaUFcLKG1JWCC+vxIevGR13AlPM7G0zu9rMDvZUx24xs21uc855qESioPGNr5DGNqSsEFbekLJCeHlDEtLYhpQVwsobUlYIL6/Eh69Gx4dAU1INj5OAuWb2ipldamb77uhBZnalmRWZWVEyuT5TtVJaUkazpnmbrzdt0piyslUZe36JlsY3vkIa25CyQlh5Q8oK4eUNSUhjG1JWCCtvSFkhvLwSH74aHc45l3TOFTrnLgfygH8BZ5BqguzoQQOdc/nOufxEol6mamVaUTEtW7agefNm5OTk0KdPb14aU5ix55doaXzjK6SxDSkrhJU3pKwQXt6QhDS2IWWFsPKGlBXCy5txyWQ8L9VAtqfn/c4cKOdcOfAi8KKZ5fopaccqKyu59rpbGPvyM2QlEgweMoK5cxf6LisyTw/9J51P7UCDBgey7MMi+t9xL08OHu67rMiENL4a2/iObUhZIay8IWWF8PKG9Loc0tiGlBXCyhtSVggvr8SH+TjGysyOcM59rz0ku1YTHRwmIiIiIiISUxUbS7ddJCRGvnnuL7H8TJt7/i3ex83LoSvft8khIiIiIiIiIrI9vtboEBERERERERGpcr7W6BAREREREREJVzVZuDOONKNDRERERERERGJDjQ4RERERERERiQ01OkREREREREQkNrRGh4iIiIiIiEimuVieXbZaUKOjBshKhDXxJhnQojzZWWHtguWVFb5LEBGRtISZ7xIyJqkPEyIiQQnrE7SIiIiIiIiIxJoaHSIiIiIiIiISG2HNmxcRERERERGpDgI6ZD/TNKNDRERERERERGJDjQ4RERERERERiQ01OkREREREREQkNrRGh4iIiIiIiEimaY2OyGhGx27qUdCFObMnMn/uJG68oZ/vciL3636/YPr745kxfTy/+fXlvsuJVO3atXn3nTG8X/QaxcVvcNtt1/suqUo98sg9fPTR+xQVFW6+7U9/uo4lS6YwefJYJk8eS48eXT1WGJ1BAwewomQmxTNe911KRoT2OhVS3pCyQlh5Q8oKcM01V1A843VmTB/P0Kcepnbt2r5LikTTpnmML3yWWR9MYGbxG7F/LwVhbcshZYXw8ko8qNGxGxKJBA89+Fd69urLscd35YILzqVVq8N9lxWZo446kl/84mec0rEn+W16cNZZ3Wj5g+a+y4rMhg0b6F7Qh5Pyu5OfX0CPgi60a3ui77KqzNChz9K796Xb3P6PfzxO+/Zn0b79Wbz66pseKoveU0+N5OyeF/kuIyNCe50KKW9IWSGsvCFlBcjLa0S/fr+gfYezaX3i6WRlZdGnzzm+y4pERUUFN9zYn2OP68IpHXtx1VWXxXpsQ9qWQ8oK4eWV+PDS6DCzWmZ2iZmdnr7+MzN72Mz6mVmOj5p2pm2b1ixZsoylS5dTXl7OyJGjOadXD99lReaHP2zJlKnT+eabb6msrGTi21Po3fsM32VFav36rwHIyckmJycH55zniqrOO+9MZc2aL3yX4cXbk6aw5vMwsof2OhVS3pCyQlh5Q8q6SXZWNrm5dcjKyiK3bi5lZat8lxSJlSs/YUbxbADWrVvP/PmLaJLXyHNV0QlpWw4pK4SXV+LD14yOJ4GzgWvNbCjwE2AK0AZ4zFNNO5TXpBEfl6zYfL2ktIy8GP9jNXfOAjp1bMeBB+5Pbm4dzujRlaZN83yXFalEIkHRtEJWlH7A+NcnMnXaDN8lRe5///cSpk59hUceuYf999/PdznyPYX2OhVS3pCyQlh5Q8oKsGLFSu5/4FGWLJ7C8o+m8+Xarxg/fqLvsiJ36KFNOeH4Y5gyNb7vLULalkPKCuHllfjw1eg41jl3AXAeUACc75wbCvwcaL2jB5nZlWZWZGZFyeT6DJUKZrbNbXH6H/+tzV+wmHsH/IuxLz/DSy89zaxZc6moqPRdVqSSyST5bQpo3iKfNvmtOfroI32XFKlBg57mqKNOpV27M1m58hPuvvtW3yXJ9xTa61RIeUPKCmHlDSkrwP7716dXzwKOOLIDhzY/iXr1cvnZhT/yXVak6tWry8gRg/jd72/nq6/W+S4nMiFtyyFlhfDyZpxLxvNSDfhqdCTMrBawL1AXqJ++vTaww0NXnHMDnXP5zrn8RKJeBspMKS0po9kWMxqaNmkc26mWmwwePIL2Hc7i9NPPZ83na1m8eKnvkjJi7doveWviuxQUdPFdSqQ++eRTkskkzjmeeGIY+fnH+y5JvqfQXqdCyhtSVggrb0hZAbqd1pFlyz7m00/XUFFRwahR42jf4STfZUUmOzubZ0cMYtiwFxg1apzvciIV0rYcUlYIL6/Eh69Gx+PAfKAY+BPwrJkNAqYBwz3VtEPTiopp2bIFzZs3Iycnhz59evPSmMJdP7AGO/jggwBo1iyPc3ufwYiRoz1XFJ0GDQ6kfv3UoRt16tSh22mdWLBgieeqotWo0SGbv+/duwdz5y7wWI1UhdBep0LKG1JWCCtvSFkBln+8gnbtWpObWweArl07Mn/+Ys9VRWfQwAHMm7+YBx4c6LuUyIW0LYeUFcLLK/GR7eNJnXP3m9mI9PcrzOwp4HRgkHNuqo+adqayspJrr7uFsS8/Q1YiweAhI5g7d6HvsiI1fPhADjpwf8rLK7j2ulv44ou1vkuKTOPGDXni8QfIykpgiQTPPfcSY8eO911WlRky5CE6depAgwYHsHjxZO68835OPbU9xx13FM45PvqohN/85o++y4zE00P/SedTO9CgwYEs+7CI/nfcy5ODq10vtUqE9joVUt6QskJYeUPKCjBt2gyef34sU6e8QkVFBcXFc3jssf/4LisSp5zchov7ns8Hs+ZSNC31ofDWW+9m3CtveK4sGiFtyyFlhfDySnxYTT3GKrtWk5pZ+F7ISoR1FuBksnoc15UJ2Vleeo3elFdW+C5BRETSEts59j6ukjX0/a5I6Co2lsb6heqbp26O5YtT7iV/8z5uYX2CFhEREREREZFYU6NDRERERERERGJDjQ4RERERERERiY2wFggQERERERERqQ60flBkNKNDRERERERERGJDjQ4RERERERERiQ01OkREREREREQkNmrsGh0hnfu9Mpn0XYJEpLyywncJGZWdyPJdQsZUJCt9lyAislNJHRsuIuKXPudFRjM6RERERERERCQ21OgQERERERERkdhQo0NEREREREREYkONDhERERERERGJjRq7GKmIiIiIiIhIjaXFSCOjGR0iIiIiIiIiEhtqdIiIiIiIiIhIbKjRsZuuueYKime8zozp4xn61MPUrl3bd0mR6lHQhTmzJzJ/7iRuvKGf73Ii1bRpHuMLn2XWBxOYWfwGv/n15b5LisyggQNYUTKT4hmv+y4lMo8+eg/Ll0/n/fdf23zbXXf9kZkz32DatFcZMWIg9evv57HC6IS034awLW8ptLwhbcshZYWw8oaUFcLKG1JWCC+vxIMaHbshL68R/fr9gvYdzqb1iaeTlZVFnz7n+C4rMolEgoce/Cs9e/Xl2OO7csEF59Kq1eG+y4pMRUUFN9zYn2OP68IpHXtx1VWXxTbvU0+N5OyeF/kuI1JDhz7LOedc8p3b3njjbU48sTtt2vRg0aKl3BDDf6RD229D2Ja3FFLekLblkLJCWHlDygph5Q0pK4SXN+NcMp6XasBbo8PMfmBmvzezB81sgJn9r5nV91XPrmRnZZObW4esrCxy6+ZSVrbKd0mRadumNUuWLGPp0uWUl5czcuRozh1aJ3gAACAASURBVOnVw3dZkVm58hNmFM8GYN269cyfv4gmeY08VxWNtydNYc3nX/guI1KTJk3l860yjh//NpWVlQBMnTqdpk3jN76h7bchbMtbCilvSNtySFkhrLwhZYWw8oaUFcLLK/HhpdFhZtcAjwB1gDZALtAMeM/MuvioaWdWrFjJ/Q88ypLFU1j+0XS+XPsV48dP9F1WZPKaNOLjkhWbr5eUlpEX0w/+Wzv00KaccPwxTJk6w3cpEpFLL72AV1+d4LuMKhfyfivxEtK2HFJWCCtvSFkhrLwhZYXw8kp8+JrR8UvgDOfcX4DTgaOcc38CzgDu39GDzOxKMysys6Jk5foMlQr771+fXj0LOOLIDhza/CTq1cvlZxf+KGPPn2lmts1tzjkPlWRWvXp1GTliEL/7/e189dU63+VIBG666ddUVFQwbNgLvkupcqHutxI/IW3LIWWFsPKGlBXCyhtSVggvr8RHtufnrgRqA/sCOOeWm1nOjh7gnBsIDASoVbtpxvawbqd1ZNmyj/n00zUAjBo1jvYdTuKZYc9nqoSMKi0po1nTvM3XmzZpHOtDdQCys7N5dsQghg17gVGjxvkuRyLQt+/5nHlmN84880LfpUQixP1W4imkbTmkrBBW3pCyQlh5Q8oK4eXNNJdU0ygqvmZ0PAZMM7OBwHvAwwBmdjCwxlNNO7T84xW0a9ea3Nw6AHTt2pH58xd7rio604qKadmyBc2bNyMnJ4c+fXrz0phC32VFatDAAcybv5gHHhzouxSJQPfunbn++qs4//zL+eabb32XE4kQ91uJp5C25ZCyQlh5Q8oKYeUNKSuEl1fiw8uMDufcg2Y2HmgF3Oecm5++fTVwqo+admbatBn8f/buPLyq6m77+HedJMwKIgoEqGgRa6tVNIAoIIMSVAb71kK12GpteZ46VNuqtX20FqvWVnGsWsGWUYE4gQwiBUVEBBIhMqtMQkKYBKlgqyFnvX8QKEUDqNl7Jft3f67rXCb7JDn37VrncLKyh+efn8L8eVPZvXs3hYVLefLJp0LHikxZWRnX33ArUyY/TUYqxfAR41i27N3QsSJzztltuXzAJSxavIyC/D0v3Lfddg8vTX0lcLLKN3rUo5zbuQONGjVk7eoCBt1xH8OGjw0dq1KNHPkInTp1oFGjo1i5ch533nk/N910DTVr1mDy5D3P2/nzF3Lddb8NnLRyWXveWpjL+7PU19JcttQVbPW11BVs9bXUFez1leRw1fUYqzgPXQktXU3HSORAmamM0BFisztdFjqCiIiISLW2+9Piz54kJEE+HvKLRP6iV2fgA4ccN+dcA/Yc6XEK4IEfA+8A44CWwFqgn/d+u9tzspiHgAuBj4ErvPcLDvbzg11eVkRERERERMSsdDqZt8PzEDDVe/8N4DRgOXALMMN7fyIwo/xzgAuAE8tvA4HHD/XDtdAhIiIiIiIiIrFwzh3JnlNW/A3Ae/+p9/5DoC8wovzLRgAXl3/cFxjp95gLNHDONT3YY2ihQ0RERERERETicgKwBRjmnFvonHvSOVcXaOy9LwEo/++x5V/fDFi/3/cXlW+rkBY6RERERERERKRSOOcGOucK9rsNPOBLMoEzgMe9922AXfznMJXP/ZGfs+2g5zcJctUVEREREREREUke7/0QYMhBvqQIKPLezyv//Fn2LHRscs419d6XlB+asnm/r2+x3/c3BzYcLIP26BARERERERGJm08n83ao2t5vBNY7504q39QdWAa8CPyofNuPgAnlH78I/NDtcRawY+8hLhWptnt01MqsETpCbD4u/SR0BJFKoUuuioiIiIgIcB3wlHOuBrAauJI9O2LkOeeuAtYB3yv/2insubTsSvZcXvbKQ/3warvQISIiIiIiIiLVj/e+EMj5nLu6f87XeuCaL/LzdeiKiIiIiIiIiCSG9ugQERERERERiVv6oBcOka9Ae3SIiIiIiIiISGJooUNEREREREREEkMLHSIiIiIiIiKSGDpHh4iIiIiIiEjc0unQCRJLe3RUoFmzpkya8hT5b01jXv5Ufnb1FQD85rfXs+K9Ocx+cxKz35xEj9wuQXNGYeiQwWwoepvChTNCR4mFtb65PbqwdMksViybzc03faGrNFVLVvpam8fqm2xWnrdgqyvY6mupK9jqa6kr2OsryaCFjgrsLtvN//32btqe2YPuXb/LTwdezknfaAXAo3/5Ox079KJjh15Me3lm2KARGDkyj4t6/SB0jNhY6ptKpXj4obvo1XsAp57Wlf79L+bkk08MHSsylvpamsegvklm6XlrqSvY6mupK9jqa6kr2OsryaGFjgps2riFtwuXArBz5y7eeWcl2dlNAqeKx+uz57Ft+4ehY8TGUt92bduwatVa1qxZR2lpKXl5E+jTOzd0rMhY6mtpHoP6Jpml562lrmCrr6WuYKuvpa5gr68khxY6DsPXvtaMb5/2LQryCwEY+D8/ZM68KTz6+J9o0ODIwOlEDl92syasL9qw7/Oi4pJEL+BZ6yuSBJaet5a6gq2+lrqCrb6WuoK9vrFLp5N5qwK00HEIdevWYdTTj3HLzX/go4928uSTT3HaKV0456yL2LhxM3f98f9CRxQ5bM65z2zz3gdIEg9rfUWSwNLz1lJXsNXXUlew1ddSV7DXV5IjyEKHc66+c+4e59wK59wH5bfl5dsaHOT7BjrnCpxzBZ/u/mfkOTMzMxn99GPkjXuRiS++DMCWzVtJp9N47xkxbCxn5nw78hwilaW4qIQWzbP3fd68WVNKSjYFTBQta31FksDS89ZSV7DV11JXsNXXUlew11eSI9QeHXnAdqCL9/5o7/3RQNfybc9U9E3e+yHe+xzvfU6NzOgPGXn08Xt4551VPPrI3/Zta9zkmH0f9+6Ty/Kl70aeQ6Sy5BcU0qrV8bRs2YKsrCz69evLxEnTQseKjLW+Iklg6XlrqSvY6mupK9jqa6kr2OsryZEZ6HFbeu//tP8G7/1G4E/OuR8HyvRfzuqQw6WX/T+WLFnB7DcnAXDH7+/jku/15tRvfxPvPeveL+L6nyfv0JXRox7l3M4daNSoIWtXFzDojvsYNnxs6FiRsdS3rKyM62+4lSmTnyYjlWL4iHEsW5bcxTpLfS3NY1DfJPe19Ly11BVs9bXUFWz1tdQV7PWV5HAhjrFyzk0DpgMjvPebyrc1Bq4Azvfen3eon3Fk3RPMHBz2ceknoSOIiIiIiIjEavenxZ89SUiCfPzg/yTyd9o6NzwRfNxCHbrSHzgaeM05t805tw2YCTQEvhcok4iIiIiIiIhUc0EOXfHebwd+XX77L865K4FhsYcSERERERERkWqvKl5edlDoACIiIiIiIiJSPQXZo8M5t6iiu4DGcWYRERERERERiV06HTpBYoW66kpjIJc9l5PdnwPmxB9HRERERERERJIg1ELHJKCe977wwDucczPjjyMiIiIiIiIiSRDqZKRXHeS+y+LMIiIiIiIiIiLJEWqPjq/s37s/DR1BIpJywS+7HJu0T+Sls8Wg7HoNQ0eIzYad20JHEKkU+vdWRCSwtF6bolIVr7oiIiIiIiIiIvKlaKFDRERERERERBJDCx0iIiIiIiIikhjV9hwdIiIiIiIiItWWT4dOkFjao0NEREREREREEkMLHSIiIiIiIiKSGFroEBEREREREZHE0ELHYWrd+gTy57+877Z1y3Kuu+6q0LEi0bx5NtOnPcPiRTN5u/AVrrs2mT33V7/+kYwd8wSLF81k0duv0r79GaEjRSa3RxeWLpnFimWzufmma0LHiZylvha6vlE4lWmzn+el155h0oyxAJz8rda88PJops1+nr8//Qj1jqgbOGXlGzpkMBuK3qZw4YzQUWJhYS7vZakrwM9//hMKF85g4YLpjBr5F2rWrBk6UmSsja2lvpa6gr2+kgzOex86w5dSo2bzYMFTqRRr1xTQsVNv1q0rjvzx0jGPUZMmx9K0ybEsLFxCvXp1mT9vKt+95McsX/5eLI+fci6Wx9nf3558gNlvzGfYsDFkZWVRp05tduz4Z+SPG/fYplIpli99nZ4XXkpRUQlz35zCgMuvjm1s42apb+iu2fUaxvI4bxROpVe377N924f7tk2cPoY7fzeYeXMK6PeDi2lxXHMG3/2XyDJs2Lktsp9dkU4d27Nz5y6GDXuI09t0j/3x4xR6LscpdNe4/73Nzm7Cq68+z2mndePf//43Tz/1OC9NfYVRo56J/LH17220LPW11BXC9939aXH8vxjE6OM/XVk9fxk/hDq/HhZ83LRHx5fQrVtHVq9+P5ZFjhA2btzMwsIlAOzcuYsVK96jWXaTwKmic8QR9ejYqT3Dho0BoLS0NJZFjhDatW3DqlVrWbNmHaWlpeTlTaBP79zQsSJjqa+lrgc64cSWzJtTAMDrM9/kwt7nBU5U+V6fPY9t2z889BcmgKW5bKnrXpkZmdSuXYuMjAxq16lNScmm0JEiYW1sLfW11BXs9ZXk0ELHl9Dve30YlzchdIxYHHdcc04/7RTmzV8YOkpkTjj+a2zdso0nh97P/HlT+evj91KnTu3QsSKR3awJ64s27Pu8qLiE7AQvYlnqa6Wr957Rzz3B5FfGcdmPLgHgneUrOf+CrgBc1DeXpgnsbYmVuQy2ugJs2LCRBx58glUr57Hu/QX8c8dHTJ8+K3SsSFgbW0t9LXUFe30lOarVQodzbqBzrsA5V5Au2xUkQ1ZWFr169eC55yYFefw41a1bh7xxQ/nljbfz0Uc7Q8eJTEZmJm3anMITQ0bRrn1Pdn38cWKPP3Sfs5tydT187XBY6mul63cv+CEXde3PD/v9jB9e9X3adTiTm677HT/6yfeZ/Mo46tWrQ2lpaeiY8hVYmctgqytAgwb16d2rB61P6sBxLc+kbt3aXHbp/wsdKxLWxtZSX0tdwV5fSY4qt9DhnHupovu890O89zne+5xURpiTzfXs2ZWFhYvZvHlrkMePS2ZmJs+MG8qYMS8wfnyFQ5IIxcUlFBWVkJ+/Z6+V55+fzOltTg2cKhrFRSW0aJ697/PmzZomdrdhsNXXStdNG7cA8MHWbbw8eQann3kKq95bw4Dv/g8XdevPhOde4v016wOnlK/CylwGW10BunfryNq169m6dRu7d+9m/PiXOKvDmaFjRcLa2Frqa6kr2OsbN59OJ/JWFQRZ6HDOnVHB7Uzg9BCZDlf/fn0ZNy75h60MHTKY5StW8uBDQ0JHidymTVsoKtpA69YnANCta8fEnlAqv6CQVq2Op2XLFmRlZdGvX18mTpoWOlZkLPW10LV2ndrUrVdn38edup7NO8tXcnSjPSdCdc7x818NZPTwvJAx5SuyMJf3stQVYN36DbRv34batWsB0LVrR1asWBk4VTSsja2lvpa6gr2+khyZgR43H3gN+LyzsTaIOcthq127Ft27d+bqa24JHSVS55zdlssHXMKixcsoyN/zQnbbbffw0tRXAieLzi9+cRsjhj9CjRo1WLPmfX7y01+FjhSJsrIyrr/hVqZMfpqMVIrhI8axbNm7oWNFxlJfC12POeZohox6EIDMzAzGPzuF12a8wY//5wf88KrvAzB10gzynhofMmYkRo96lHM7d6BRo4asXV3AoDvuY9jwsaFjRcLCXN7LUleA/PyFPP/8FObPm8ru3bspLFzKk08+FTpWJKyNraW+lrqCvb6SHEEuL+ucWwJ8x3v/mT+bO+fWe+9bHOpnhLy8bNziviRaaCEuLxuKtbGV5Irr8rJVQYjLy4pEQf/eikhVl/TLy+76448S+eJU9zcjgo9bqD06fk/Fh81cF2MOERERERERkfilE7nOUSUEWejw3j97kLuPii2IiIiIiIiIiCRKlbvqCjAodAARERERERERqZ6C7NHhnFtU0V1A4ziziIiIiIiIiEhyhDpHR2MgF9h+wHYHzIk/joiIiIiIiEiMfDp0gsQKtdAxCajnvS888A7n3Mz444iIiIiIiIhIEoQ6GelVB7nvsjiziIiIiIiIiEhyhNqj4yvT9dCTS2MrUv1s2LktdAQR+YKcc6EjxEfvLURETKmKV10REREREREREflSqu0eHSIiIiIiIiLVVlp7m0VFe3SIiIiIiIiISGJooUNEREREREREEkMLHSIiIiIiIiKSGDpHh4iIiIiIiEjc0unQCRJLe3SIiIiIiIiISGJooeMw5fbowtIls1ixbDY333RN6DiRs9R36JDBbCh6m8KFM0JHiZylrntZmsuWulqby9b6WprLlroCXHvNj1nw1nQWLpjOdddeFTpOpKyNraW+lrqCvb6SDFroOAypVIqHH7qLXr0HcOppXenf/2JOPvnE0LEiY63vyJF5XNTrB6FjxMJSV7A1ly11BXtz2VJfS3PZUleAb37zJH7848s4p2MvctrmcuGF3Wn19ZahY0XC2tha6mupK9jrK8mhhY7D0K5tG1atWsuaNesoLS0lL28CfXrnho4VGWt9X589j23bPwwdIxaWuoKtuWypK9iby5b6WprLlroCfOMbrZg3fwH/+te/KSsrY9br8+jbt2foWJGwNraW+lrqCvb6xi7tk3mrArTQcRiymzVhfdGGfZ8XFZeQnd0kYKJoWesryWVpLlvqKslmaS5b6gqwbOk7dOrYnoYNG1C7di165nalefPs0LEiYW1sLfW11BXs9ZXkCHLVFefckcBvgObAS977p/e77zHv/dUVfN9AYCCAy6hPKlU3jrg45z6zzfuqsVIVBWt9JbkszWVLXSXZLM1lS10BVryzkvsGP8aUyU+zc9fHLF68jN27y0LHioS1sbXU11JXsNdXkiPUHh3DAAc8B3zfOfecc65m+X1nVfRN3vsh3vsc731OXIscAMVFJbTY7y8OzZs1paRkU2yPHzdrfSW5LM1lS10l2SzNZUtd9xo+fBxndbiQ8867hG3bd7By5ZrQkSJhbWwt9bXUFez1leQItdDxde/9Ld778d77PsAC4BXn3NGB8hxUfkEhrVodT8uWLcjKyqJfv75MnDQtdKzIWOsryWVpLlvqKslmaS5b6rrXMcfseavXokU2F/ftybi8CYETRcPa2Frqa6kr2OsbO59O5q0KCHLoClDTOZfyfs//Be/9Xc65ImAWUC9QpgqVlZVx/Q23MmXy02SkUgwfMY5ly94NHSsy1vqOHvUo53buQKNGDVm7uoBBd9zHsOFjQ8eKhKWuYGsuW+oK9uaypb6W5rKlrnuNHTuEoxs2oLR0N9ffcCsffrgjdKRIWBtbS30tdQV7fSU5XIhjrJxzfwamee+nH7C9J/CI9/6Q1yzKrNFMB4eJiIiIfEkZKTvnpC9LV42/MIrIF7P70+LPniQkQXbd1i+Rv9PW/UNe8HELskeH9/7mCrZPdc7dHXceEREREREREUmGqriUPyh0ABERERERERGpnkJdXnZRRXcBjePMIiIiIiIiIhK7dCKPXKkSQp2MtDGQC2w/YLsD5sQfR0RERERERESSINRCxySgnve+8MA7nHMz448jIiIiIiIiIkkQ6mSkVx3kvsvizCIiIiIiIiIiyRFqj46vLPj1amJk7cgtja2IVGVZGdX2n84vJcRl6EPZnS4LHSFWuuSqiEhYXq/DkamKV10REREREREREflStNAhIiIiIiIiIomhhQ4RERERERERSQxbBxqLiIiIiIiIVAVpO+fBipv26BARERERERGRxNBCh4iIiIiIiIgkhhY6RERERERERCQxtNBxmGrWrMmcNybxVsE/KCx8hd/97lehI0Vm6JDBbCh6m8KFM0JHiYWlsQXI7dGFpUtmsWLZbG6+6ZrQcSJnqa+lrpDsvn/96728//5bFBRM27dt1Ki/MHfuFObOncKKFbOZO3dKwISV64kn7mXdugW89dY/9m27/fZfkZ//MvPmvcSkSaNp2rRxwITRSfI8/jyW+lrqCrb6WuoK9vrGKu2TeasCnPdVI8gXlVWjWezB69atw65dH5OZmclrM1/gl7+8nXnzF0T+uHEX7dSxPTt37mLYsIc4vU33mB8dXOyPaGdsU6kUy5e+Ts8LL6WoqIS5b05hwOVXs3z5ezEniYelvpa6Qti+WRnRn8f7nHPasWvXxzz55P3k5PT4zP333HMrO3b8kz/+8eHIs8TxPqFjx3bs3Pkxf/vbA5x55vkAHHFEPT76aCcAV199JSeffCLXXffbSHPsTpdF+vMPpOdtcvta6gq2+lrqCuH77v60OMSvBrHZedN3qucv44dQ794Xgo+b9uj4Anbt+hiArKxMsrKyYnnzF8Lrs+exbfuHoWPEysrYtmvbhlWr1rJmzTpKS0vJy5tAn965oWNFxlJfS10h+X3feGM+27ZV/Dr83e9eRF7eizEmitbs2fPZfsC/O3sXOWDPYnQSX5eTPo8PZKmvpa5gq6+lrmCvrySHFjq+gFQqRUH+NDYUL2L6jFnMz18YOpJUEitjm92sCeuLNuz7vKi4hOzsJgETRctSX0tdwV7f/Z1zTjs2bdrKqlVrQ0eJ3KBBN7Fy5Vy+//2LueOOwaHjVDpr89hSX0tdwVZfS13BXl9JDi10fAHpdJqctj1oeXwObXPa8K1vnRQ6klQSK2Pr3Gf3IkviX0n3stTXUlew13d//fr14ZlnkrM3x8Hcfvu9tGp1FmPHjudnP7sidJxKZ20eW+prqSvY6mupK9jrK8kRZKHDOdfEOfe4c+5R59zRzrnfO+cWO+fynHNND/J9A51zBc65gnR6V5yR/8uOHf/ktVlz6NGjS7AMEo2kj21xUQktmmfv+7x5s6aUlGwKmChalvpa6gr2+u6VkZFB3749efbZiaGjxGrcuPFcfPEFoWNUOmvz2FJfS13BVl9LXcFe39j5dDJvVUCoPTqGA8uA9cCrwL+Ai4DXgb9W9E3e+yHe+xzvfU4qVTeOnPs0atSQ+vWPBKBWrVp079aJd95ZFWsGiYalsc0vKKRVq+Np2bIFWVlZ9OvXl4mTph36G6spS30tdQV7fffq1q0j7767iuLijaGjRO7rX2+57+OLLjo/ka/L1uaxpb6WuoKtvpa6gr2+khzRnzr+8zX23j8C4Jy72nv/p/LtjzjnrgqU6aCaNm3M3//2IBkZKVwqxbPPTmTKlOmhY0Vi9KhHObdzBxo1asja1QUMuuM+hg0fGzpWZCyNbVlZGdffcCtTJj9NRirF8BHjWLbs3dCxImOpr6WukPy+I0Y8TKdOHWjU6ChWrpzLH/7wACNGjON73+udqJOQ7jVy5CP79Z3HnXfeT25uV1q3/jrpdJp164q57rrfhI5Z6ZI+jw9kqa+lrmCrr6WuYK+vJEeQy8s659723p9W/vGd3vtb97tvsff+1EP9jBCXlw3FTNFywa9FFCNrYyuSBHFcXrYqsXQsdtyXlxURkYNL/OVlb+ybyH9k6903Ifi4hXq3NsE5V897v/OARY5WwDuBMomIiIiIiIjEI53IdY4qIchCh/f+dxVsX+mcmxx3HhERERERERFJhqp4edlBoQOIiIiIiIiISPUUZI8O59yiiu4CGseZRURERERERESSI9hVV4BcYPsB2x0wJ/44IiIiIiIiIvHxOkdHZEItdEwC6nnvCw+8wzk3M/44IiIiIiIiIpIEoU5GetVB7rssziwiIiIiIiIikhyh9uj4yrSTT3JpbEWkKist2x06goiIiIgcRLVd6BARERERERGptnSOjshUxcvLioiIiIiIiIh8KVroEBEREREREZHE0EKHiIiIiIiIiCSGFjpEREREREREJDF0MlIRERERERGRuKXToRMklvboEBEREREREZHE0ELHYRo6ZDAbit6mcOGM0FFikdujC0uXzGLFstncfNM1oeNEzlJfS13BVl9LXfWanGyW+lrqCrb6WuoKtvpa6gr2+koyaKHjMI0cmcdFvX4QOkYsUqkUDz90F716D+DU07rSv//FnHzyiaFjRcZSX0tdwVZfS11Br8lJHltLfS11BVt9LXUFW30tdQV7fSU5tNBxmF6fPY9t2z8MHSMW7dq2YdWqtaxZs47S0lLy8ibQp3du6FiRsdTXUlew1ddSV9BrcpLH1lJfS13BVl9LXcFWX0tdwV7f2KV9Mm9VgBY65DOymzVhfdGGfZ8XFZeQnd0kYKJoWeprqSvY6mupqzXWxtZSX0tdwVZfS13BVl9LXcFeX0mOKrPQ4Zw79jC+ZqBzrsA5V5BO74ojlknOuc9s875qrMxFwVJfS13BVl9LXa2xNraW+lrqCrb6WuoKtvpa6gr2+kpyBLm8rHOu4YGbgPnOuTaA895v+7zv894PAYYAZNZopmdYRIqLSmjRPHvf582bNaWkZFPARNGy1NdSV7DV11JXa6yNraW+lrqCrb6WuoKtvpa6gr2+khyh9ujYCry1360AaAYsKP9YAsovKKRVq+Np2bIFWVlZ9OvXl4mTpoWOFRlLfS11BVt9LXW1xtrYWuprqSvY6mupK9jqa6kr2Osbu9Dn0kjwOTqC7NEB3AycB9zkvV8M4Jxb470/PlCeQxo96lHO7dyBRo0asnZ1AYPuuI9hw8eGjhWJsrIyrr/hVqZMfpqMVIrhI8axbNm7oWNFxlJfS13BVl9LXUGvyUkeW0t9LXUFW30tdQVbfS11BXt9JTlcqGOsnHPNgQeA9cDtwNve+xMO9/t16IqIiIiIiEhy7f60+LMnCUmQj/63ZyJ/pz3ir1ODj1uwk5F674u8998DXgX+AdQJlUVEREREREREkiHUoSv7eO8nOuemA18HcM5d6b0fFjiWiIiIiIiISGR0BZvoVInLy3rv/+W9X1L+6aCgYURERERERESk2gp1edlFFd0FNI4zi4iIiIiIiIgkR6hDVxoDucD2A7Y7YE78cUREREREREQkCUItdEwC6nnvCw+8wzk3M/44IiIiIiIiIpIEQRY6vPdXHeS+y+LMIiIiIKL7QwAAIABJREFUIiIiIhK7tE5GGpXgV135slIu+KV5Y5PW2XhFREREREREDkuVuOqKiIiIiIiIiEhl0EKHiIiIiIiIiCRGtT10RURERERERKTa0jk6IqM9OkREREREREQkMbTQISIiIiIiIiKJoYUOEREREREREUkMnaNDREREREREJGZe5+iIjPbo+ALq1z+SsWOeYPGimSx6+1Xatz8jdKTI5PbowtIls1ixbDY333RN6DiRs9TXUlew1ddSV7DTt3nzbKZPe4bFi2byduErXHftVaEjRWrokMFsKHqbwoUzQkeJhZV5vJelvpa6gq2+lrqCvb6SDM776rmKVKNm89iD/+3JB5j9xnyGDRtDVlYWderUZseOf0b+uOmYxyiVSrF86ev0vPBSiopKmPvmFAZcfjXLl78Xa464WOprqSvY6mupK9jq26TJsTRtciwLC5dQr15d5s+byncv+XEiuwJ06tienTt3MWzYQ5zepnvoOJGyNI/BVl9LXcFWX0tdIXzf3Z8Wu1geKJAdV55XPX8ZP4T6w6YHHzft0XGYjjiiHh07tWfYsDEAlJaWxrLIEUK7tm1YtWota9aso7S0lLy8CfTpnRs6VmQs9bXUFWz1tdQVbPXduHEzCwuXALBz5y5WrHiPZtlNAqeKzuuz57Ft+4ehY8TC0jwGW30tdQVbfS11BXt9JTm00HGYTjj+a2zdso0nh97P/HlT+evj91KnTu3QsSKR3awJ64s27Pu8qLiE7AS/qbbU11JXsNXXUlew13ev445rzumnncK8+QtDR5FKYG0eW+prqSvY6mupK9jrG7u0T+atCgiy0OGc67nfx/Wdc39zzi1yzj3tnGt8kO8b6JwrcM4VpMt2xRO2XEZmJm3anMITQ0bRrn1Pdn38cWKPUXPus3saVddDnA6Hpb6WuoKtvpa6gr2+AHXr1iFv3FB+eePtfPTRztBxpBJYm8eW+lrqCrb6WuoK9vpKcoTao+Pu/T4eDJQAvYF84ImKvsl7P8R7n+O9z0ll1I044n8rLi6hqKiE/Pw9f0V7/vnJnN7m1FgzxKW4qIQWzbP3fd68WVNKSjYFTBQtS30tdQVbfS11BXt9MzMzeWbcUMaMeYHx418KHUcqibV5bKmvpa5gq6+lrmCvryRHVTh0Jcd7f6v3/n3v/QNAy9CBPs+mTVsoKtpA69YnANCta8fEnnQov6CQVq2Op2XLFmRlZdGvX18mTpoWOlZkLPW11BVs9bXUFez1HTpkMMtXrOTBh4aEjiKVyNo8ttTXUlew1ddSV7DXV5IjM9DjHuuc+yXggCOdc87/Zx+oqrD48rl+8YvbGDH8EWrUqMGaNe/zk5/+KnSkSJSVlXH9DbcyZfLTZKRSDB8xjmXL3g0dKzKW+lrqCrb6WuoKtvqec3ZbLh9wCYsWL6Mgf8+by9tuu4eXpr4SOFk0Ro96lHM7d6BRo4asXV3AoDvuY9jwsaFjRcLSPAZbfS11BVt9LXUFe30lOYJcXtY5d/sBmx7z3m9xzjUB/uy9/+GhfkaIy8uGEvflZUVEREREREJL/OVlL++eyF/06o+aEXzcguzR4b0fVMH2jc65V+POIyIiIiIiIiLJUBUPE/ncRRARERERERERkUMJskeHc25RRXcBFV5eVkRERERERETkYEKdjLQxkAtsP2C7A+bEH0dEREREREQkPj6dyFN0VAmhFjomAfW894UH3uGcmxl/HBERERERERFJglAnI73qIPddFmcWEREREREREUmOUHt0fGWWLrmakaqK54yNTohLHodiaR6LSPXUqkF26AixWfnhhtARREREpBJU24UOERERERERkWpL5+iIjK1dBUREREREREQk0bTQISIiIiIiIiKJoYUOEREREREREUkMnaNDREREREREJG7p0AGSS3t0iIiIiIiIiEhiaKFDRERERERERBJDCx2HKbdHF5YumcWKZbO5+aZrQsepdE88cR/r1y1kwVvT92076qgGTJn8FEuXzGLK5Kdo0KB+wITRad36BPLnv7zvtnXLcq677qrQsSKT9Ll8IEt9LXVt3jyb6dOeYfGimbxd+ArXXZvc5yzA0CGD2VD0NoULZ4SOEonjv34cL7zy1L5bwapX+eHAS/nGKa0ZO+XvvPDKUzw7bQSntvlm6KiVztLzNunz+ECWxhZs9bXUFez1lWRw3lfPa/dm1mgWW/BUKsXypa/T88JLKSoqYe6bUxhw+dUsX/5eLI+fkYp+Papjx/bs3LmLv//tQc448zwA7r7rt2zb/iH33fcYN954NUc1qM//3frHyLOEnJOpVIq1awro2Kk369YVR/546Zi7hp7LcbPU11JXgCZNjqVpk2NZWLiEevXqMn/eVL57yY8T27dT+Wv0sGEPcXqb7rE+dqsG2bE+XiqV4rVFU+jf8wr+cP//MfyvY3j9lTl07n42P7n2h/zwO/8b2WOv/HBDZD/781h73oacx3GzNraW+lrqCuH77v602MXyQIF82L9r9fxl/BAajHs1+Lhpj47D0K5tG1atWsuaNesoLS0lL28CfXrnho5VqWbPnsf27R/+17bevXswevSzAIwe/Sx9+iSr8+fp1q0jq1e/H8siRwgW5vL+LPW11BVg48bNLCxcAsDOnbtYseI9mmU3CZwqOq/Pnse2A16jk6pD57asX1vEhqKNeO+pd0RdAI44sh6bN24JnK5yWXveWprH1sbWUl9LXcFe37j5tE/krSrQQsdhyG7WhPVF//krT1FxCdkJfkO917HHNmLjxs3Anl8qjjnm6MCJotfve30YlzchdIzIWJvLlvpa6nqg445rzumnncK8+QtDR5FKcOHFPZj8/MsA3H3r/dx0+895deEkbv799dx/16OB01Uuy8/bpLM2tpb6WuoK9vpKclSZhQ7nXJX9Ldq5z+55U10P+ZGKZWVl0atXD557blLoKJGxNpct9bXUdX9169Yhb9xQfnnj7Xz00c7QceQrysrKpFtuZ6ZO3HMOh0uv+C73/O5+urbpxR9ve4A7H7wtcMLKZfV5a4G1sbXU11JXsNdXkiPIQodz7h7nXKPyj3Occ6uBec65951z5x7k+wY65wqccwXp9K7Y8hYXldCi+X+OUW7erCklJZtie/xQNm/eSpMmxwJ7joffsuWDwImi1bNnVxYWLmbz5q2ho0TG2ly21NdS170yMzN5ZtxQxox5gfHjXwodRypBp+5ns2zxCj7Ysg2Ai/v3YtqkVwGY+uJ0vp2wk5FafN5aYW1sLfW11BXs9ZXkCLVHx0Xe+72/Td4L9PfetwLOBwZX9E3e+yHe+xzvfU4qVTeOnADkFxTSqtXxtGzZgqysLPr168vESdNie/xQJk36BwMGXALAgAGXMHFisjv379eXceOSe9gK2JvLlvpa6rrX0CGDWb5iJQ8+NCR0FKkkF30nl8nP/2febt64hXZnnwHAWZ3a8v7q9aGiRcLi89YKa2Nrqa+lrmCvb+zSCb1VAZmBHjfLOZfpvd8N1Pbe5wN47991ztUMlKlCZWVlXH/DrUyZ/DQZqRTDR4xj2bJ3Q8eqVCNH/oXOnc6iUaOGrFo5nz/cOZh773uUp596nCuv+D7r1xdz6WU/Cx0zMrVr16J7985cfc0toaNEysJc3p+lvpa6ApxzdlsuH3AJixYvoyB/zxuu2267h5emvhI4WTRGj3qUczt3oFGjhqxdXcCgO+5j2PCxoWNVqlq1a3LOue24/ca792277Vd38X93/oqMzAw++fen/O5Xdx/kJ1Q/1p63FubxXtbG1lJfS13BXl9JjiCXl3XOXQf0Bu4BOgMNgOeB7sAJ3vvLD/Uz4ry8bGhxXF62KrF03F/cl5cVEfmi4r68bEhxX15WREQOLumXl93+3S6J/GXgqOdmBh+3IHt0eO8fcc4tBn4GtC7P0RoYD/whRCYRERERERERqf5CHbqC934mMPPA7c65K4FhcecRERERERERiYtPJ3KHjiqhKh4TMSh0ABERERERERGpnoLs0eGcW1TRXUDjOLOIiIiIiIiISHKEOnSlMZALbD9guwPmxB9HRERERERERJIg1ELHJKCe977wwDucczPjjyMiIiIiIiISo3ToAMkV6qorVx3kvsvizCIiIiIiIiIiyRHsqity+MrStpb6gl90WURE9lm9oyR0BBEREZEvpCpedUVERERERERE5EvRQoeIiIiIiIiIJIYOXRERERERERGJmbd1hoJYaY8OEREREREREUkMLXSIiIiIiIiISGJooUNEREREREREEkPn6BARERERERGJm87RERnt0XGYcnt0YemSWaxYNpubb7omdJxIDR0ymA1Fb1O4cEboKLFJpVLkz3+Z8S+MCB0lcpbmMtjqa6kr2OprqWvr1ieQP//lfbetW5Zz3XVXhY4VGUtjC7b6WuoKtvpa6gr2+koyOO996AxfSmaNZrEFT6VSLF/6Oj0vvJSiohLmvjmFAZdfzfLl78UVIVadOrZn585dDBv2EKe36R7747vYHxFuuH4gZ5z5bY484ggu/s6PYnvcuJ991uaypb6WuoKtvqG7plyIV+Xyx06lWLumgI6derNuXXHkj5eO+T1R6LGNm6W+lrqCrb6WukL4vrs/LQ73j1AMPrjo3Or5y/ghHD35teDjpj06DkO7tm1YtWota9aso7S0lLy8CfTpnRs6VmRenz2Pbds/DB0jNs2aNeWCC7rz97+PCR0lctbmsqW+lrqCrb6Wuh6oW7eOrF79fiyLHCFYG1tLfS11BVt9LXUFe30lObTQcRiymzVhfdGGfZ8XFZeQnd0kYCKpTIMHD+I3v7mTdDr5B8lZm8uW+lrqCrb6Wup6oH7f68O4vAmhY0TG2tha6mupK9jqa6kr2OsbN59O5q0qqFYLHc65gc65AudcQTq9K87H/cy26nrIj/y3Cy88jy2bt7Jg4eLQUWJhbS5b6mupK9jqa6nr/rKysujVqwfPPTcpdJTIWBtbS30tdQVbfS11BXt9JTmCLHQ45xY45251zn39i3yf936I9z7He5+TStWNKt5nFBeV0KJ59r7PmzdrSknJptgeX6Jz9tk59OrVg/fenctTox+ja9dzGDH84dCxImNtLlvqa6kr2Oprqev+evbsysLCxWzevDV0lMhYG1tLfS11BVt9LXUFe30lOULt0XEU0AB41Tk33zn3C+dc9qG+KZT8gkJatTqeli1bkJWVRb9+fZk4aVroWFIJbr31Ho4/IYcTW5/FDwZczauvvsGPrvh56FiRsTaXLfW11BVs9bXUdX/9+/Vl3LjkHrYC9sbWUl9LXcFWX0tdwV5fSY7MQI+73Xt/I3Cjc64TcCmwwDm3HBjjvR8SKNfnKisr4/obbmXK5KfJSKUYPmIcy5a9GzpWZEaPepRzO3egUaOGrF1dwKA77mPY8LGhY0klsDaXLfW11BVs9bXUda/atWvRvXtnrr7mltBRImVtbC31tdQVbPW11BXs9Y1dFTmfRRIFubysc26B9/6MA7ZlAOcD/b33Vx7qZ8R5eVmJV/BrEcVIk1hEqrqQl5eNW9yXlxURkYNL+uVlt+Ym8/KyjV4Of3nZUHt0fGYZ0HtfBkwtv4mIiIiIiIiIfGFBztHhvf9+Rfc55w65N4eIiIiIiIiIyOepipeXHRQ6gIiIiIiIiIhUT0EOXXHOLaroLqBxnFlERERERERE4uZ1MtLIhDpHR2MgF9h+wHYHzIk/joiIiIiIiIgkQaiFjklAPe994YF3OOdmxh9HRERERERERJIgyEKH9/6qg9x3WZxZRERERERERCQ5Qu3RIV9A8IsQxyyRF5MWEamm0t7Oq3JGqiqeoz06ZWkdHJ5Ult472nmFkiTSOTqiY+tfdBERERERERFJNC10iIiIiIiIiEhiaKFDRERERERERBJD5+gQERERERERiZnO0REd7dEhIiIiIiIiIomhhQ4RERERERERSQwtdBym3B5dWLpkFiuWzebmm64JHSdSNWvWZM4bk3ir4B8UFr7C7373q9CRIjV0yGA2FL1N4cIZoaNEzlLXvSw9dy11tTaXLY0tJLvvE0/cx/p1C1nw1vR92446qgFTJj/F0iWzmDL5KRo0qB8wYbSSPLYHsvQ6Ze29o6V5DPb6SjJooeMwpFIpHn7oLnr1HsCpp3Wlf/+LOfnkE0PHiswnn3zC+T36cWbO+eTk9CC3RxfatzsjdKzIjByZx0W9fhA6RiwsdQVbz11LXcHWXLY2tknvO2rUM/Tuc/l/bbvpxqt55dU3+NYpnXnl1Te46carA6WLVtLH9kCWXqcsvXe0No+t9Y2dd8m8VQFa6DgM7dq2YdWqtaxZs47S0lLy8ibQp3du6FiR2rXrYwCysjLJysrCex84UXRenz2Pbds/DB0jFpa6gq3nrqWuYGsuWxvbpPedPXse2w+Yu71792D06GcBGD36Wfr0SU7f/SV9bA9k6XUK7Lx3tDaPrfWV5NBCx2HIbtaE9UUb9n1eVFxCdnaTgImil0qlKMifxobiRUyfMYv5+QtDRxL5wiw9dy11tcba2FrrC3DssY3YuHEzABs3buaYY44OnCgaFsfWEivvHa3NY2t9JTm00HEYnPvs7jdJXaXeK51Ok9O2By2Pz6FtThu+9a2TQkcS+cIsPXctdbXG2tha62uJxjbZrLx3tDaPrfWV5Aiy0OGcy3HOveqcG+2ca+Gc+4dzbodzLt851+Yg3zfQOVfgnCtIp3fFlre4qIQWzbP3fd68WVNKSjbF9vgh7djxT16bNYcePbqEjiLyhVl67lrqao21sbXWF2Dz5q00aXIsAE2aHMuWLR8EThQNi2NrUdLfO1qbx9b6SnKE2qPjMeDPwGRgDvCE974+cEv5fZ/Lez/Ee5/jvc9JperGkxTILyikVavjadmyBVlZWfTr15eJk6bF9vhxa9SoIfXrHwlArVq16N6tE++8sypwKpEvztJz11JXa6yNrbW+AJMm/YMBAy4BYMCAS5g4MZl9LY6tFZbeO1qbx9b6xs2nk3mrCkItdGR571/y3o8BvPf+WfZ8MAOoFShThcrKyrj+hluZMvlpliyaybPPTmTZsndDx4pM06aNmf6PZ1jw1j94883JTJ8xiylTph/6G6up0aMeZfasFzmp9ddZu7qAK6/4fuhIkbHUFWw9dy11BVtz2drYJr3vyJF/4bWZ42nd+gRWrZzPFVf05977HuW87p1YumQW53XvxL33Vfg3n2ot6WN7IEuvU5beO1qbx9b6SnK4EMdYOefeBG4H6gP3Add778c7584FBnvvcw71MzJrNDNzcFjVuEBPfMwMrIiIVCkZKVunLitLV5E/u0mls/TeUe8bk233p8WJns4bO3dJ5BRuMmtm8HHLDPS4/8ueQ1fSQC7wM+fccKAY+GmgTCIiIiIiIiJSzQVZ6PDev82eBY69ri+/4Zy7kj3n7RARERERERFJJJ8OvuNDUM65DKAAKPbe9yrf+eFcYEf5l1zhvS90ey7/8xBwIfBx+fYFB/vZVXEfzUGhA4iIiIiIiIhIpK4Hlh+w7Sbv/enlt8LybRcAJ5bfBgKPH+oHB9mjwzm3qKK7gMZxZhERERERERGR+DjnmgMXAXcBvzzEl/cFRvo9Jxid65xr4Jxr6r0vqegbQp2jozF7Dl3ZfsB2hw5bEREREREREUmyB4GbgSMO2H6Xc+53wAzgFu/9J0AzYP1+X1NUvq3KLXRMAurttyvKPs65mfHHEREREREREYmPT+jFr5xzA9lziMleQ7z3Q/a7vxew2Xv/lnOuy35f9xtgI1ADGAL8GriDz7+Y1EGvWBPqZKRXHeS+y+LMIiIiIiIiIiKVo3xRY8hBvuQcoI9z7kKgFnCkc260935A+f2fOOeGATeWf14EtNjv+5sDGw6WIdQeHfIF7DnJrCE+kZeTFnSt+yRLGXqdSus1KrHK0gn901oFvn308aEjxGbxB2tCRxARkXLe+9+wZ+8NyvfouNF7P2DveTfKr7JyMbCk/FteBK51zo0F2gM7DnZ+DtBCh4iIiIiIiIiE95Rz7hj2HKpSCPxv+fYp7Lm07Er2XF72ykP9IC10iIiIiIiIiMTMezt7xFbEez8TmFn+cbcKvsYD13yRn5v6qsFERERERERERKoKLXSIiIiIiIiISGJooUNEREREREREEkMLHSIiIiIiIiKSGDoZqYiIiIiIiEjMvK2rmsdKe3QcpubNs5k+7RkWL5rJ24WvcN21V4WOFJnWrU8gf/7L+25btyznuuuS2xfgvXfnsnDBdArypzH3zSmh40QulUqRP/9lxr8wInSUyOX26MLSJbNYsWw2N9/0hU7WXO1Y6grw85//hMKFM1i4YDqjRv6FmjVrho4UmaFDBrOh6G0KF84IHSUWluZyErvefv9vmL54Inmvjty37cgGR/DY2AcY/8YYHhv7AEfUP2LffTf94XomzBnLuBnD+caprUNErnQ1a9ZkzhuTeKvgHxQWvsLvfver0JEiZa1vEp+3B2OtrySDFjoO0+7du7np5kGc+u0unNOxNz/72RWcfPKJoWNF4t13V9O2XS5t2+XS/qwL+PjjfzFhwtTQsSJ33vnfI6dtD87qcGHoKJH7+XU/YfmK90LHiFwqleLhh+6iV+8BnHpaV/r3vzixz1tLXQGys5twzTU/5qwOF9HmjPPIyMigX78+oWNFZuTIPC7q9YPQMWJhaS4ntevEvClce9l//6J75bUDmD/7LS4+51Lmz36LK68dAMA53c7iaye0oO/Z3+fOm+7lN/fcGCJypfvkk084v0c/zsw5n5ycHuT26EL7dmeEjhUZS32T+rytiLW+khxa6DhMGzduZmHhEgB27tzFihXv0Sy7SeBU0evWrSOrV7/PunXFoaNIJWnWrCkXXNCdv/99TOgokWvXtg2rVq1lzZp1lJaWkpc3gT69c0PHioSlrntlZmRSu3YtMjIyqF2nNiUlm0JHiszrs+exbfuHoWPEwtJcTmrXBXPfZsf2f/7XtnNzOzEp7yUAJuW9RJeenQDo0rMTk57Z88eUxQuWcsSR9Wh07NHxBo7Irl0fA5CVlUlWVhbe+8CJomWlb1KftxWx1leSQwsdX8JxxzXn9NNOYd78haGjRK7f9/owLm9C6BiR897z0pQxzJv7Ej+5Ktl/NR08eBC/+c2dpNPJPygwu1kT1hdt2Pd5UXEJ2QldoLTUFWDDho088OATrFo5j3XvL+CfOz5i+vRZoWNJJbA0ly11PfqYo9i6+QMAtm7+gIaNjgLg2CaN2LRh876v21yymWOaNgqSsbKlUikK8qexoXgR02fMYn5+st83Wulr6XkL9vrGzaddIm9VQbVa6HDODXTOFTjnCtLpXUEy1K1bh7xxQ/nljbfz0Uc7g2SIS1ZWFr169eC55yaFjhK5c7tcTLv2PenVewA/+9kVdOzYPnSkSFx44Xls2byVBQsXh44SC+c++0Kb1L8wWeoK0KBBfXr36kHrkzpwXMszqVu3Npdd+v9Cx5JKYGkuW+paoc/5f0BC/hek02ly2vag5fE5tM1pw7e+dVLoSJGy0tfa89ZaX0mOIAsdzrl6zrk7nHNLnXM7nHNbnHNznXNXHOz7vPdDvPc53vucVKpuTGn/IzMzk2fGDWXMmBcYP/6l2B8/bj17dmVh4WI2b94aOkrk9u7yvmXLB4yf8BJt254eOFE0zj47h169evDeu3N5avRjdO16DiOGPxw6VmSKi0po0Tx73+fNmzVN7OENlroCdO/WkbVr17N16zZ2797N+PEvcVaHM0PHkkpgaS5b6vrBlu37DklpdOzRbNu6HYDNJVtonH3svq87tumxbNmYrPcdO3b8k9dmzaFHjy6ho8Qi6X0tPW/BXl9JjlB7dDwFrAZygUHAw8DlQFfn3N2BMh3S0CGDWb5iJQ8+NCR0lFj079eXceOSf9hKnTq1qVev7r6Pzz/vXJYufSdwqmjceus9HH9CDie2PosfDLiaV199gx9d8fPQsSKTX1BIq1bH07JlC7KysujXry8TJ00LHSsSlroCrFu/gfbt21C7di0AunbtyIoVKwOnkspgaS5b6jpr2mx69bsAgF79LuC1l18H4LWXZ9Prez0BOPWMb7Hzo537DnGpzho1akj9+kcCUKtWLbp368Q776wKnCo6lvpaet6Cvb6SHJmBHrel9354+cf3O+fyvfd/cM5dCSwDfhsoV4XOObstlw+4hEWLl1GQv+fJfdtt9/DS1FcCJ4tG7dq16N69M1dfc0voKJFr3PgYnn3mbwBkZGYwdux4pk2bGTaUVIqysjKuv+FWpkx+moxUiuEjxrFs2buhY0XCUleA/PyFPP/8FObPm8ru3bspLFzKk08+FTpWZEaPepRzO3egUaOGrF1dwKA77mPY8LGhY0XC0lxOate7H/s9Z559Og0aNuClt57nr/f9jWF/Gc2fnriDiy+9iI3Fm7h54G0AzJ7xJh27d2DCm+P497/+ze9/UWX/3vWFNG3amL//7UEyMlK4VIpnn53IlCnTQ8eKjKW+SX3eVsRa37jpKKDouBDHWDnn5gA3e+9nO+d6A9d673PL73vHe3/Ig/oyazQzMy1Sn3f8aoLpuL/k0sgml6XXqbReoyQhvn308aEjxGbxB2tCR5CI6BU52XZ/WpzoNxjrcroncgp/rWBG8HELtUfH/wJPOudaA0uAHwM4544BHg2USURERERERESquSALHd77RUC7z9m+xTn3UYBIIiIiIiIiIpIAofboOJhBwLDQIURERERERESi4tPBj/BIrCALHc65RRXdBTSOM4uIiIiIiIiIJEeoPToas+fSstsP2O6AOfHHEREREREREZEkCLXQMQmo570vPPAO59zM+OOIiIiIiIiISBKEOhnpVQe577I4s4iIiIiIiIhIclTFk5GKcRmpjNARYrM7XRY6gkilSPtEXgZeJNHW7ty6fXGrAAAgAElEQVQUOkJsLL23AL2/EKkudDLS6KRCBxARERERERERqSxa6BARERERERGRxNBCh4iIiIiIiIgkhs7RISIiIiIiIhIzneIsOtqjQ0REREREREQSQwsdIiIiIiIiIpIYWugQERERERERkcTQQsdhyu3RhaVLZrFi2Wxuvuma0HEiV7/+kYwd8wSLF81k0duv0r79GaEjVaonnriXdesW8NZb//jMfTfcMJB//3sdRx99VIBk0bM2ly31tdR16JDBbCh6m8KFM0JHiYXGNrmSPrY1a9bgH68+y6w5LzJn/hRu+e3PAXj40buZNedFXn9zIsNHPULdunUCJ/3qLL+30PM22az1jZNPu0TeqgItdByGVCrFww/dRa/eAzj1tK70738xJ598YuhYkbp/8CBenjaTU7/dhTNzerBixcrQkSrVqFHP0KfPDz+zvXnzpnTv3ol164oCpIqetblsqa+lrgAjR+ZxUa8fhI4RC41tclkY208++ZSLe/2Qzmf3ofPZfeh+Xmdy2p7O/91yN53P7kOnDr0pKtrAT/5nQOioX5nV9xag523Snrf7s9ZXkkMLHYehXds2rFq1ljVr1lFaWkpe3gT69M4NHSsyRxxRj46d2jNs2BgASktL2bHjn4FTVa7Zs+ezffuHn9n+5z/fzm9/ezc+oadAtjaXLfW11BXg9dnz2PY5z+Ek0tgml5Wx3bXrYwCysjLJzMrEe89HH+3cd3+tWrUSceUBq+8tQM/bJD5v97LWV5IjyEKHc66+c+4e59wK59wH5bfl5dsahMh0MNnNmrC+aMO+z4uKS8jObhIwUbROOP5rbN2yjSeH3s/8eVP56+P3UqdO7dCxInfRReezYcNGFi9eHjpKZKzNZUt9LXW1RmObXFbGNpVK8dobL/LO6rnMfPUN3ip4G4C/PH4PK1a9yYmtT2DoX0cGThkNC+8trLHyvN3LWl9JjlB7dOQB24Eu3vujvfdHA13Ltz1T0Tc55wY65wqccwXp9K6YooJznz3OKMmr8hmZmbRpcwpPDBlFu/Y92fXxx4k/Hq927Vr8+tfXcscdg0NHiZS1uWypr6Wu1mhsk8vK2KbTac49pw+nfKMTZ5z57X27vV/7s1v45onn8O47q/jOdy8KnLLyWXlvYY2V5+1e1vrGzXuXyFtVEGqho6X3/k/e+417N3jvN3rv/wR8raJv8t4P8d7neO9zUqm6sQQFKC4qoUXz7H2fN2/WlJKSTbE9ftyKi0soKiohP38hAM8/P5nT25waOFW0TjjhOFq2bEF+/lTeeecNmjVryty5U2jc+JjQ0SqVublsqK+lrtZobJPL2tj+c8dHvPH6PLqf33nftnQ6zQvPTaF33+TtCm/lvYU11p631vpKcoRa6HjfOXezc67x3g3OucbOuV8D6wNlqlB+QSGtWh1Py5YtyMrKol+/vkycNC10rMhs2rSFoqINtG59AgDdunZk+fL3AqeK1tKl7/C1r53BSSedw0knnUNxcQlnnXUhmzZtCR2tUlmby5b6WupqjcY2uSyM7dGNGnJk/SMAqFWrJud2PZv33lvD8Sf85+9aPS/synvvrgoVMTJW3ltYY+F5uz9rfSU5MgM9bn/gFuC18sUOD2wCXgT6BcpUobKyMq6/4VamTH6ajFSK4SPGsWzZu6FjReoXv7iNEcMfoUaNGqxZ8z4/+emvQkeqVCNHPkKnTh1o1OgoVq6cx5133s/w4eNCx4qctblsqa+lrgCjRz3KuZ070KhRQ9auLmDQHfcxbPjY0LEiobHV2FZnjRsfw2NP/JmMjBSpVIrxz7/EtKmvMmXaGI44oh7OOZYsXsGNv7g9dNSvzOp7C9DzNmnP2/1Z6yvJ4UIdY+Wc+wbQHJjrvd+53/ae3vuph/r+zBrNzBwclvqcY+OSLOXsXAxod7osdAQRETHqyJp1QkeIzceln4SOECu9v5Ck2P1pcaJ/EVr5zdxE/k7batnLwcct1FVXfg5MAK4Fljjn+u53990hMomIiIiIiIhI9Rfq0JWfAmd673c651oCzzrnWnrvHwKCr/6IiIiIiIiISPUUaqEjY+/hKt77tc65Lvx/9u49Tsu6zv/463Mz4wFQy0hhZliGIsstExI8H/AQoKG4m6GteCiL3TIPWfqo31KubbZuRYmtbUKGqCuCbEIquOQpROMwyYCcPCCoMwyoeQpsc2A+vz9mYEk5zOBc13fm+ryfj8c8nPu+55778/ZzzXXPfLiu69s87OiDBh0iIiIiIiIisptSXQxhnZn133KjZegxHOgBFHsdUxERERERERHJTKojOs4HNm17h7tvAs43s5vSlCQiIiIiIiKSjybXyQxZSTLocPe6nTz2WJ61iIiIiIiIiEhxpDqiQ9qgKdESwKk0uZZEE+lsIv17RKw9shTZm395K3UJkpHyLrF+xW/cvGnXXyQioaS6RoeIiIiIiMh7oiGHiGxPrHGviIiIiIiISAfgukZHZnREh4iIiIiIiIgUhgYdIiIiIiIiIlIYGnSIiIiIiIiISGHoGh0iIiIiIiIiOfMmXaMjKzqiQ0REREREREQKQ4OOVho6ZDDLls5h5fK5XHXlxanLydSE8WNZW7eY2kUPpi4lN1H6q94Wt7cQKyvAM0/PY9ETD1CzcDbzfj8zdTmZitbbSHkjZY30HlRVVcEDs+/iySWPsLj2IS752kWpS2p3v/jFj3j++T9QUzN763233fYfzJs3k3nzZrJy5VzmzSvmvjnSzy3EyyvFYO6euobdUrZHZW6Fl0olVix7lGGnfZ66ugbm/X4mo877KitWPJNXCbk67tgj2LBhIxMnjqP/gJNTl5O5SP1Vb4vb29RZUxx4+czT8zjyqFP54x9fy/V1837XTN3bvEXKGykrxHoP6tnzAHr1PIBFtUvp3r0bC+bfz2fP+mJuvS3vkv3Z6cccczgbN77FL3/5EwYOHPKux6+7bgxvvPEm//ZvN2RaR+PmTZl+/3eK9nObOu+mt+sLfW7HyoNO65x/jO/Cx56embxvOqKjFQ4fNIBVq9awevULNDY2MnXqDM44fWjqsjLz6Nz5vPra66nLyE2k/qq3xe1tpKzRROttpLyRskKs96B1615iUe1SADZs2MjKlc9QWdEzcVXt67HHFvDqqzvu52c/+xmmTv1NjhXlI9rPbbS8eXMv5kdHoEFHK1RU9uTFurVbb9fVN1BRsDeryNTf4orU20hZt3B3Zs2czPx5s/jSReemLicz0XobKW+krJH16VNF/0M/wfwFi1KXkptjjjmc9etfYdWqNalLaXfRfm6j5ZXi6HCrrpjZLHc/NXUd2zJ795E3nfWUH3k39be4IvU2UtYtThh8Jg0N6/ngBz/A/bPuZOVTzzJ37vzUZbW7aL2NlDdS1qi6devK1CkTuOKbV/OnP21IXU5uRo48g7vuKt7RHBDv5zZaXimOJIMOM/vUjh4C+u/keaOB0QDWZT9KpW4ZVPdu9XUN9K6q2Hq7qrIXDQ3rc3ltyZ76W1yRehsp6xZb8r388h+ZPmMWgwb1L+SgI1pvI+WNlDWisrIy7poygcmT72b69Fmpy8lNly5dGDFiGMccMzx1KZmI9nMbLa8UR6pTVxYCPwbGvuPjx8D7dvQkdx/v7gPdfWBeQw6AhTW19OvXl+rq3pSXlzNy5AjuuXf2rp8onYL6W1yRehspK0DXrnvTvXu3rZ9/+pQTWLbsqcRVZSNabyPljZQ1ognjx7Ji5bNcP2586lJyddJJx/L006uor1+XupRMRPu5jZZXiiPVqSsrgH9093ddrtfMXkxQz05t3ryZyy4fw8z77qBLqcQtk6awfPnTqcvKzO233cgJxx9Fjx77s+a5Gq753o+ZeMudqcvKTKT+qrfF7W2krAAHHvhBpt11MwBdyrpw553TmT37kbRFZSRabyPljZQVYr0HHXP0IM4bdRZLnlxOzcLmPwq/853rmHX/Q4kraz+TJt3AcccdRY8e7+fZZ+fxr//6UyZNmsLnPnd6IS9CukW0n9toefPmTckXJymsJMvLmtlZwJPu/q5/fjOzM919+q6+R57Ly4qIyM5FepvWm4+IdHR5LC/bUeS9vKzkq+jLyy7/8GcK+WvF3666L3nfkpy64u7TADOzk82s+zse/t8UNYmIiIiIiIhI55dk0GFmlwIzgEuApWY2YpuHf5CiJhERERERERHp/FId1/Zl4DB332Bm1cA0M6t293HEOgJaREREREREAmpy/emblVSDji7uvgHA3deY2WCahx190KBDRERERERERHZTquVl15lZ/y03WoYew4EewCGJahIRERERERGRTi7VoON84K8W13b3Te5+PnB8mpJEREREREREpLPb4akrZvY3u/MN3f2FVnxN3U4ee2x3XldERERERESks3BdoyMzO7tGxxpgd9b17bJ7pbRNpE2ikIsri0ihaD8lItJxNG7elLoEEZGkdjbo+B763VVEREREREREOpEdDjrc/V9yrENERERERERE5D1LtbysiIiIiIiISFiu8ycys1uDDjPrDryP7aza0pqLkYqIiIiIiIiIZKFNgw4zOwcYAxy8ky/L5WKkIiIiIiIiIiLv9K4jMnbEzM4E7qB5OHITzQufTAbuAhqBJ2i+gKmIiIiIiIiISBJtOaLjm8AK4DCgO/BPwK/c/SEz+wTwGFDb/iWKiIiIiIiIiLROq4/oAD4JTHL3/wWaWu7rAuDuS4HxwLfbt7yO5Zmn57HoiQeoWTibeb+fmbqcTA0dMphlS+ewcvlcrrry4tTlZGrC+LGsrVtM7aIHU5eSi0i9hVh5I2WFWHkjZYVYefUeVFyRskKsvJGyQry8eWpyK+RHR9CWQUcX4I8tn/+55b/7bfP4U8An2qOojuyUT3+OgYOGcORRp6UuJTOlUokbxl3L8NNHccihJ3L22Wdy8MEfSV1WZm69dSqfGX5u6jJyEa23kfJGygqx8kbKCvHy6j2omL2NlBVi5Y2UFeLlleJoy6CjDugD4O5/Bl4CBm7z+EeBje1XmqRy+KABrFq1htWrX6CxsZGpU2dwxulDU5eVmUfnzufV115PXUYuovU2Ut5IWSFW3khZIV5evQcVs7eRskKsvJGyQry8UhxtGXQ8Dpyyze3fAJeZ2XfN7F+Ai4FH2q+0jsfdmTVzMvPnzeJLFxX3X18qKnvyYt3arbfr6huoqOiZsCJpL9F6GylvpKwQK2+krBAvbySRehspK8TKGykrxMsrxdGWi5H+HPg7M9u75YiOfwYOB/6l5fFlNF+wdJfMbF+ar+dRBcxy9zu2eezn7v7VHTxvNDAaoNRlP0qlbm0o/707YfCZNDSs54Mf/AD3z7qTlU89y9y583OtIQ9m7z6vyt0TVCLtLVpvI+WNlBVi5Y2UFeLljSRSbyNlhVh5I2WFeHnz5h3kehZF1OojOtx9obv/v5YhB+7+srv3B/oDhwCHuvuLrfx2E2lenva/gXPM7L/NbM+Wx47cSQ3j3X2guw/Me8gB0NCwHoCXX/4j02fMYtCg/rnXkIf6ugZ6V1VsvV1V2WtrduncovU2Ut5IWSFW3khZIV7eSCL1NlJWiJU3UlaIl1eKoy2nrmyXuy9x92Xu3rTrr97qw+7+LXef7u5nAE8AD5nZB95rPVnp2nVvunfvtvXzT59yAsuWPZW4qmwsrKmlX7++VFf3pry8nJEjR3DPvbNTlyXtIFpvI+WNlBVi5Y2UFeLljSRSbyNlhVh5I2WFeHmlONpy6kp72tPMSluGI+5+rZnVAXOA7olq2qkDD/wg0+66GYAuZV24887pzJ79SNqiMrJ582Yuu3wMM++7gy6lErdMmsLy5U+nLiszt992IyccfxQ9euzPmudquOZ7P2biLXemLisT0XobKW+krBArb6SsEC+v3oOK2dtIWSFW3khZIV5eKQ5r7TlWZtYE7OqL3d13OTwxsx8Cs939gXfcPwz4mbvvcs2i8j0qw5wcFiaoiIiIiIhIi01v1xf6IhZP9B5RyD/1PvXijOR9a8sRHbfy7r+5y4APA0cAS4Da1nwjd7/KzD5mZicD8919Q8v995vZpW2oSURERERERERkq1YPOtz9wh09ZmZH07zc7Fda873M7BLga8AK4GYzu8zdZ7Q8fC0wq7V1iYiIiIiIiIhs0S7X6HD3x81sIvBD4PhWPGU0cJi7bzCzamCamVW7+ziaV2MREREREREREWmz9rwY6TO08ogOoMs2p6usMbPBNA87+qBBh4iIiIiIiBRck+tP36y85+VltzEY+HMrv3admfXfcqNl6DEc6AEc0o41iYiIiIiIiEggrT6iw8zO38FD+wOnAKcCv2zltzsf2LTtHe6+CTjfzG5qbU0iIiIiIiIiIttqy6krt9C86sr2jq/ZBNwMXNGab+TudTt57LE21CQiIiIiIiIislVbBh0nbuc+B14FVrv7xvYpqXUKueDwDkQ7cytSb0Wk89E+WURERKRja8vysr/LshARERERERGRKFwXI81Mqy9GambPmdkZO3l8uJk91z5liYiIiIiIiIi0XVtWXakGuu/k8W5An/dUjYiIiIiIiIjIe9Cey8seCLzVjt9PRERERERERKRNdnqNDjM7Hhi8zV1/b2b9tvOl+wPnALXtV5qIiIiIiIhIMTXpGh2Z2dXFSE8Erm753IG/b/nYnmeBr7dTXSIiIiIiIiIibbarQcf1wC00r6b3HHA5MOMdX+PABnd/td2rExERERERERFpg51eo8Pd33D35919Dc1Hd0xuub3txwsRhhxVVRU8MPsunlzyCItrH+KSr12UuqTM7Lnnnjz+2L38oea31NY+xHe/+43UJWUqUm8Bhg4ZzLKlc1i5fC5XXXlx6nIyNWH8WNbWLaZ20YOpS8lFpN5CnLzR9skQp7cQK6v2ycUWKW+krBAvrxSDuXvrvtBsf6DK3Zfs4PFPAi+6+2vtWN8Ole1R2brC20nPngfQq+cBLKpdSvfu3Vgw/34+e9YXWbHimcxfO8WZW926dWXjxrcoKyvjd4/czRVXXM38BU/k8tq5Npa0vc1bqVRixbJHGXba56mra2De72cy6ryvFjIrwHHHHsGGDRuZOHEc/QecnLqcTEXrbcq82idnK9K2HCkraJ9c5N5GyhspK6TPu+nt+kJfxGJexd/n/TabiyPX/jp539qy6soPaT6NZUcmAv/2nqrpwNate4lFtUsB2LBhIytXPkNlRc/EVWVn48bmBXTKy8soLy+ntQOxzihSbw8fNIBVq9awevULNDY2MnXqDM44fWjqsjLz6Nz5vPra66nLyEW03kbLG2mfHKm3kbKC9slF7m2kvJGyQry8UhxtGXScCNyzk8d/A5zSmm9kZj3N7D/N7EYz+4CZ/YuZPWlmU82sVxtqSqJPnyr6H/oJ5i9YlLqUzJRKJWoWzmZt/RIeeHAOCxYWN+u2it7bisqevFi3duvtuvoGKgo61IkmWm+j5Y20T47U20hZo4nW20h5I2WFeHmlONoy6KgAXtjJ43UtX9MatwDLgReBh4E/A58BHgV+saMnmdloM6sxs5qmpo2tfKn21a1bV6ZOmcAV37yaP/1pQ5Ia8tDU1MTAQUOo7juQQQMH8PGPfzR1SZmL0Fuzdx9FVuR/GY4kWm+j5Y20T47U20hZo4nW20h5I2WFeHmlONoy6NgI9NnJ432Av7Tyex3o7j9z9+uA97n7v7dc1PRnO3sNdx/v7gPdfWCp1K31lbeTsrIy7poygcmT72b69Fm5v34Kb7zxJr+b8zhDhgxOXUqmovS2vq6B3lX/N4+squxFQ8P6hBVJe4nW22h5t4iwT47U20hZo4nW20h5I2WFeHnz1uRWyI+OoC2DjvnABWa2zzsfaLnvfGDBbrzure94rEsbasrVhPFjWbHyWa4fNz51KZnq0WN/9ttvXwD22msvTj7pOJ56alXiqrIVpbcLa2rp168v1dW9KS8vZ+TIEdxz7+zUZUk7iNbbSHmj7ZMj9TZS1mii9TZS3khZIV5eKY6yNnztj4EHgMfN7BqgluaLsQ8ArgaqgC+18nvNMLPu7r7B3cdsudPM+gFPtaGm3Bxz9CDOG3UWS55cTs3C5h/u73znOmbd/1Diytpfr14H8qubr6dLlxJWKjFt2j3MnPlA6rIyE6m3mzdv5rLLxzDzvjvoUipxy6QpLF/+dOqyMnP7bTdywvFH0aPH/qx5roZrvvdjJt5yZ+qyMhGtt5HyRtsnR+ptpKygfXKRexspb6SsEC+vFEerl5cFMLN/BMYB5e94qBG43N13eH2N7XyvjwGVwHx337DN/cPc/f5dPT/v5WVT6hgH/+QnTGNFpFPSPllERCQfRV9e9vFeny3k2+zRDf+dvG9tOaIDd7/JzO4FRgL9aP597ylgmrvXm9me7r7L63SY2SXA14AVwM1mdpm7z2h5+AfALgcdIiIiIiIiIiLv1KZBB4C71wM/3fY+MzvMzP4ZOBv4QCu+zWjgMHffYGbVwDQzq3b3ccT7xzIREREREREJxjvIhTuLqM2Dji3MbH9gFHAR8AmaBxStPWGry5bTVdx9jZkNpnnY0QcNOkRERERERERkN7Vl1RUAzGyomU0BthzZsQdwDXCIu3+sld9mnZn133KjZegxHOgBHNLWmkREREREREREoJVHdJhZX+ALwAU0r67yMjAN+Afgn93912183fOBTdve4e6bgPPN7KY2fi8REREREREREWAXgw4z+weaT005gebBxH3AJS3/7Qucuzsv6u51O3nssd35niIiIiIiIiKdRVPqAgpsV0d03A48B1wO3OHur255wMySLoVT3mW3Ly/S6TRu3rTrLxIRkVwUch04ERERkQLZ1TU63gaqgRHAqWa2d+YViYiIiIiIiIjspl0NOnrSfDTHB4DbgPVmdrOZHY9WRxERERERERGRDman53+4++vAfwD/YWafovl6HecAF9J8QVIH9su4RhEREREREZFCcR07kJlWLy/r7k+4+8VABXAesKzloV+aWa2ZjTGzj2dRpIiIiIiIiIhIa7R60LGFu//F3e9w95OBDwPXAu8Hvgcsbuf6RERERERERERarc2Djm25+xp3/y7NFyw9Dfh1exQlIiIiIiIiIrI72mWNVnd34P6WDxERERERERHZiSatWZ+Z93RER9H94hc/4vnn/0BNzey/uv8rX7mQxYsf4g9/+C3XXvvtRNVlZ8L4saytW0ztogdTl5KLaHmHDhnMsqVzWLl8LlddeXHqcjIXKW+krBArr/ZTxRUpK8TKGykrxMobKSvEyyvFoEHHTtx2212MGHHBX913/PFHMXz4pxk0aBiHHfZprr9+fKLqsnPrrVP5zPBzU5eRm0h5S6USN4y7luGnj+KQQ0/k7LPP5OCDP5K6rMxEyhspK8TLq/1UMXsbKSvEyhspK8TKGykrxMsrxaFBx0489tgCXn319b+6b/ToUfz4xz/n7bffBuDll/+YorRMPTp3Pq++9vquv7AgIuU9fNAAVq1aw+rVL9DY2MjUqTM44/ShqcvKTKS8kbJCvLzaTxWzt5GyQqy8kbJCrLyRskK8vFIcHWbQYWYHpK6hNfr168sxxxzOnDnTmT17Cocd9snUJYm0WkVlT16sW7v1dl19AxUVPRNWlK1IeSNlhXh5I4nU20hZIVbeSFkhVt5IWSFeXimOdrkYaVuZ2f7vvAtYYGYDAHP3VxOU1SplZWW8//37cfzxZzJw4KHcfvvPOfjgY1OXJdIqZvau+5qvJVxMkfJGygrx8kYSqbeRskKsvJGyQqy8kbJCvLx5a+Ld/3+lfSQZdACvAM+/475K4AnAgQ9t70lmNhoYDVBWtj9lZd2zrHG76usbmD69eXGZmprFNDU10aPH/rzySoedzYhsVV/XQO+qiq23qyp70dCwPmFF2YqUN1JWiJc3kki9jZQVYuWNlBVi5Y2UFeLlleJIderKVcBTwBnu3tfd+wJ1LZ9vd8gB4O7j3X2guw9MMeQAuOee2QwefDTQfBrLHnuUa8ghncbCmlr69etLdXVvysvLGTlyBPfcO3vXT+ykIuWNlBXi5Y0kUm8jZYVYeSNlhVh5I2WFeHmlOJIc0eHuPzazO4GfmtmLwNU0H8nRoUyadAPHHXcUPXq8n2efnce//utPmTRpKjfd9CNqambz9tuNfOlL30hdZru7/bYbOeH4o+jRY3/WPFfDNd/7MRNvuTN1WZmJlHfz5s1cdvkYZt53B11KJW6ZNIXly59OXVZmIuWNlBXi5dV+qpi9jZQVYuWNlBVi5Y2UFeLlleKw1OdYmdnpwD8D1e7e6ivb7L13nw43GMlK4+ZNqUsQERERERHJ1aa36wt9EYsHDzy7kH/Tnrx+SvK+JVt1xcw+ZmYnAw8DJwKntNw/LFVNIiIiIiIiItK5JRl0mNmlwAzgEmApMMTdl7Y8/IMUNYmIiIiIiIhI55dq1ZUvA4e5+wYzqwammVm1u48DrbEjIiIiIiIiIrsn1aCji7tvAHD3NWY2mOZhRx806BAREREREZGCa0pdQIGlukbHOjPrv+VGy9BjONADOCRRTSIiIiIiIiLSyaUadJwPrNv2Dnff5O7nA8enKUlEREREREREOrskp664e91OHnssz1pEREREREREpDhSXaPjPWvcvCl1CbkpmS5bUlRNXsils0WkQCK9A2mPLNL5RPs9Wb87FouHepfNV6pTV0RERERERERE2p0GHSIiIiIiIiJSGBp0iIiIiIiIiEhhaNAhIiIiIiIiIoXRaS9GKiIiIiIiItJZNaUuoMB0RIeIiIiIiIiIFIYGHSIiIiIiIiJSGBp0tNKE8WNZW7eY2kUPpi4lF/vtty93Tr6JJ5c8wpLFD3PEEZ9KXVJmDjroQyxc8D9bP155eQWXXHJR6rIyEW07jpZ36JDBLFs6h5XL53LVlRenLidzkfJG25ZLpTuqvDMAACAASURBVBILF/wP0++elLqUzEXajquqKnhg9l08ueQRFtc+xCVfK+Z77RaRehtpHxXp98YtIm3LUhzm7qlr2C1le1TmWvhxxx7Bhg0bmThxHP0HnJznS1Myy/X1AG7+5U+Z+9gCJk6cTHl5OV277s0bb7yZex15K5VKrFldw7HHnc4LL9Rn/npNOf/8pdyOU4iUt1QqsWLZoww77fPU1TUw7/czGXXeV1mx4pnUpWUiWt6U23L+70Bw+WWj+dRhn2TfffbhzL+7ILfXzfs3omjbcc+eB9Cr5wEsql1K9+7dWDD/fj571hcLmTdab6P9nrz1tXP+vRHy/90x9ba86e36dA3OwcwDz+mcf4zvwmnr70zeNx3R0UqPzp3Pq6+9nrqMXOyzT3eOPe4IJk6cDEBjY2OIIQfASScdy3PPPZ/bm1XeIm3HECvv4YMGsGrVGlavfoHGxkamTp3BGacPTV1WZqLljbQtV1b24tRTT+ZXv5qcupTMRduO1617iUW1SwHYsGEjK1c+Q2VFz8RVZSNabyPto7ZV9N8bId62LMWhQYe8y4f6/g2vvPwqv5zwExbMv59f/OeP6Np179Rl5WLk585gytQZqcsQabOKyp68WLd26+26+gYqCvoHBMTLG8nYsdfw7W9/n6am4l+LPvJ23KdPFf0P/QTzFyxKXUomIvc2kgi/N2pbls4qyaDDzIZt8/l+ZnazmS0xszvM7MCdPG+0mdWYWU1T08Z8ig2oS1kZAwZ8gpvG38bhRwxj41tvhTgfr7y8nOHDh/Df/31v6lJE2sy2c+huZz01sTWi5Y3itNNO4eWXXuGJRU+mLiUXUbfjbt26MnXKBK745tX86U8bUpeTiai9jSTK743alqWzSnVExw+2+Xws0ACcDiwEbtrRk9x9vLsPdPeBpVK3jEuMq76+gbq6BhYubP5Xll//+j76DzgkcVXZGzbsRBbVPslLL72SuhSRNquva6B3VcXW21WVvWhoWJ+womxFyxvF0UcPZPjwITzz9Dz+6/afc+KJxzDplhtSl5WZiNtxWVkZd02ZwOTJdzN9+qzU5WQmYm+jifJ7o7blbDlWyI+OoCOcujLQ3ce4+/Pu/lOgOnVB0a1f/zJ1dWs56KAPAXDSiccW9uJZ2zp75AimTCn24YdSXAtraunXry/V1b0pLy9n5MgR3HPv7NRlZSZa3ijGjLmOvh8ayEcOOpJzR32Vhx9+jAsuvDR1WZmJuB1PGD+WFSuf5fpx41OXkqmIvY0myu+N2pals0o16DjAzK4ws28A+9pfHxPVEYYv73L7bTcyd85v+OhBH2bNczV84cJzUpeUqa9//TtMuuVn/KHmtxx66N/y7//+s9QlZWrvvffi5JOPL/S/LkG87ThS3s2bN3PZ5WOYed8dLF3yCNOm3cPy5U+nLisz0fJG2pYjibYdH3P0IM4bdRYnnng0NQtnU7NwNqcOOyl1WZmI1tto+6govzdCvG1ZiiPJ8rJmdvU77vq5u79sZj2BH7r7+bv6HnkvL5tSymWzJFt5LxEmItJWkd6BtEcW6Xyi/Z4c7XfHoi8ve9+Bny9kQz+zfnLyvpWleFF3v8bMPgZUAvPdfUPL/evM7I4UNYmIiIiIiIjkpSn5OKC4Uq26cgkwA7gEWGpmI7Z5+Afbf5aIiIiIiIiIyM4lOaIDGA0c5u4bzKwamGZm1e4+jlhHyYqIiIiIiIhIO0o16Oiyzekqa8xsMM3Djj5o0CEiIiIiIiIiuynVCifrzKz/lhstQ4/hQA/gkEQ1iYiIiIiIiEgnl+qIjvOBTdve4e6bgPPN7KY0JYmIiIiIiIjko0knM2Qm1aordTt57LE8axERERERERGR4kh1RIe0QbT1sqW4Is2s9VMrRaFtWUQ6Mv2eLCLbk+oaHSIiIiIiIiIi7U5HdIiIiIiIiIjkTMcjZUdHdIiIiIiIiIhIYWjQISIiIiIiIiKFoUGHiIiIiIiIiBSGrtEhIiIiIiIikrOm1AUUmI7oEBEREREREZHC0KCjlaqqKnhg9l08ueQRFtc+xCVfuyh1SZkaOmQwy5bOYeXyuVx15cWpy8lcpLyRsgKUSiUWLvgfpt89KXUpmYvW20h5J4wfy9q6xdQuejB1KbmI1NtIWbUdF1ukvJGyQry8UgwadLTSpk2buPKqazjkk4M55tjT+cpXLuTggz+SuqxMlEolbhh3LcNPH8Uhh57I2WefWdisECtvpKxbXHrJl1ix8pnUZWQuWm+j5b311ql8Zvi5qcvIRaTeRsoK2o6L3NtIeSNlhXh5pTg6zKDDzD6QuoadWbfuJRbVLgVgw4aNrFz5DJUVPRNXlY3DBw1g1ao1rF79Ao2NjUydOoMzTh+auqzMRMobKStAZWUvTj31ZH71q8mpS8lctN5Gy/vo3Pm8+trrqcvIRaTeRsoK2o6L3NtIeSNlhXh589ZkVsiPjiDJoMPMrjOzHi2fDzSz54D5Zva8mZ2Qoqa26NOniv6HfoL5CxalLiUTFZU9ebFu7dbbdfUNVBR0qAOx8kbKCjB27DV8+9vfp6mp+Jd6itbbaHkjidTbSFmjidbbSHkjZYV4eaU4Uh3R8Rl3f6Xl8x8BZ7t7P+DTwNgdPcnMRptZjZnVNDVtzKPOd+nWrStTp0zgim9ezZ/+tCFJDVmz7Uzh3D1BJfmIlDdS1tNOO4WXX3qFJxY9mbqUXETqLcTLG0mk3kbKGk203kbKGykrxMsrxZFqedlyMytz903A3u6+EMDdnzazPXf0JHcfD4wHKNujMvefsLKyMu6aMoHJk+9m+vRZeb98burrGuhdVbH1dlVlLxoa1iesKFuR8kbKevTRAxk+fAjDhp3EXnvtyb777sOkW27gggsvTV1aJiL1FuLljSRSbyNljSZabyPljZQV4uWV4kh1RMeNwEwzOwm438yuN7PjzewaoDZRTbs0YfxYVqx8luvHjU9dSqYW1tTSr19fqqt7U15ezsiRI7jn3tmpy8pMpLyRso4Zcx19PzSQjxx0JOeO+ioPP/xYYYccEKu3EC9vJJF6GylrNNF6GylvpKwQL68UR5IjOtz9Z2b2JPAV4KCWOg4CpgPfT1HTrhxz9CDOG3UWS55cTs3C5h/u73znOmbd/1Diytrf5s2buezyMcy87w66lErcMmkKy5c/nbqszETKGylrNNF6Gy3v7bfdyAnHH0WPHvuz5rkarvnej5l4y52py8pEpN5Gygrajovc20h5I2WFeHnzppOAsmOpzrEys48BlcB8d9+wzf3D3P3+XT0/xakrIvLedIxrMOdDOygRERGR92bT2/WF/vXxrl7nFvJXxs81/FfyvqVadeVSYAZwCbDUzEZs8/APUtQkIiIiIiIiIp1fqouRfhk4zN03mFk1MM3Mqt19HLH+0VdERERERERE2lGqQUeXLaeruPsaMxtM87CjDxp0iIiIiIiISME1pS6gwFKturLOzPpvudEy9BgO9AAOSVSTiIiIiIiIiHRyqQYd5wPrtr3D3Te5+/nA8WlKEhEREREREZHOLtXysnU7eeyxPGsRERERERERkeJIdY0OEQmokOtniYiIiIjshiZdnTIzqU5dERERERERERFpdxp0iIiIiIiIiEhhaNAhIiIiIiIiIoWha3SIiIiIiIiI5KwJXaQjKzqiQ0REREREREQKQ4MOERERERERESkMDTpEREREREREpDA06GilCePHsrZuMbWLHkxdSi6GDhnMsqVzWLl8LlddeXHqcjKl3hZbpLyRskb7uY2WN9K2HCkrxMobKSvEyhspK8TLK8WgQUcr3XrrVD4z/NzUZeSiVCpxw7hrGX76KA459ETOPvtMDj74I6nLyox6W9zeRsobKSvE+rmFWHkjbcuRskKsvJGyQqy8kbJCvLx584J+dAQadLTSo3Pn8+prr6cuIxeHDxrAqlVrWL36BRobG5k6dQZnnD40dVmZUW+L29tIeSNlhVg/txArb6RtOVJWiJU3UlaIlTdSVoiXV4pDgw55l4rKnrxYt3br7br6BioqeiasSNpLtN5GyhspqxRbpG05UlaIlTdSVoiVN1JWiJdXiiPJoMPMnjCzMWb24RSvLztn9u71nN07ykFI8l5E622kvJGySrFF2pYjZYVYeSNlhVh5I2WFeHmlOMoSve77gfcBD5vZOmAyMMXd1+7sSWY2GhgNYF32o1TqlnmhEdXXNdC7qmLr7arKXjQ0rE9YkbSXaL2NlDdSVim2SNtypKwQK2+krBArb6SsEC9v3prePUeSdpLq1JXX3P2b7v43wDeAjwBPmNnDLcOM7XL38e4+0N0HasiRnYU1tfTr15fq6t6Ul5czcuQI7rl3duqypB1E622kvJGySrFF2pYjZYVYeSNlhVh5I2WFeHmlOJJfo8PdH3X3rwKVwL8DRyUuabtuv+1G5s75DR896MOsea6GL1x4TuqSMrN582Yuu3wMM++7g6VLHmHatHtYvvzp1GVlRr0tbm8j5Y2UFWL93EKsvJG25UhZIVbeSFkhVt5IWSFeXikOS3GOlZnd6e7v6be0sj0qdXKYiIiIiIhIQW16u77QJ3fcWjmqkH/Tnl9/e/K+JblGh7ufY2Yfo/kojvnuvmHLY2Y2zN3vT1GXiIiIiIiISB6aUhdQYKlWXbkEmAFcAiw1sxHbPPyDFDWJiIiIiIiISOeX6hodo4HD3P1MYDDwHTO7rOWx5Ie5iIiIiIiIiEj7M7O9zGyBmS02s2Vmdk3L/X3NbL6ZPWNmU8xsj5b792y5/WzL49W7eo1Ug44uW05Xcfc1NA87TjWzn6BBh4iIiIiIiEhR/QU4yd0PBfoDw8zsSJoXJ/mpu38EeA24qOXrL6J55dZ+wE9bvm6nUg061plZ/y03WoYew4EewCGJahIRERERERHJhRf0Y5e5m225Tmd5y4cDJwHTWu6fBJzZ8vmIltu0PH6yme30AIlUg47zgXXb3uHum9z9fOD4NCWJiIiIiIiIyHthZqPNrGabj9Hb+ZouZlYLvAT8FlgFvO7um1q+pI7mxUto+e+L0Dw3AN4APrCzGlKtulK3k8cey7MWEREREREREWkf7j4eGL+Lr9kM9Dez9wF3Awdv78ta/ru9ozd2evBIkkFHe4h0IY9CLq4sIiIiIiIiobn762b2CHAk8D4zK2s5aqMKWNvyZXVAb6DOzMqA/YBXd/Z9U526IiIiIiIiIiLBmNkHW47kwMz2Bk4BVgAPA2e1fNkFwIyWz3/TcpuWxx9y92Ie0SEiIiIiIiLSWTVFOk3hr/UCJplZF5oPvpjq7vea2XLgTjP7PrAIuLnl628GbjOzZ2k+kuOcXb2ABh0iIiIiIiIikgt3XwIM2M79zwGHb+f+/wU+15bX0KkrIiIiIiIiIlIYGnSIiIiIiIiISGHo1BURERERERGRnDWlLqDAdESHiIiIiIiIiBSGBh1t8MzT81j0xAPULJzNvN/PTF1OpoYOGcyypXNYuXwuV115cepyMjVh/FjW1i2mdtGDqUvJhXpbXOptcUXLq225uCL1NlJWiJU3UlaIl1eKQYOONjrl059j4KAhHHnUaalLyUypVOKGcdcy/PRRHHLoiZx99pkcfPBHUpeVmVtvncpnhp+buoxcqLfFpd4WW6S82paLK1JvI2WFWHkjZYV4eaU4NOiQdzl80ABWrVrD6tUv0NjYyNSpMzjj9KGpy8rMo3Pn8+prr6cuIxfqbXGpt8UWKa+25eKK1NtIWSFW3khZIV7evDUV9KMjSDLoMLOBZvawmd1uZr3N7Ldm9oaZLTSzd62n21G4O7NmTmb+vFl86aLi/utLRWVPXqxbu/V2XX0DFRU9E1Yk7UW9LS71VopC23JxReptpKwQK2+krBAvrxRHqlVXfg5cDbwPeBz4urt/2sxObnnsqO09ycxGA6MBSl32o1TqllO5zU4YfCYNDev54Ac/wP2z7mTlU88yd+78XGvIg5m96z53T1CJtDf1trjUWykKbcvFFam3kbJCrLyRskK8vFIcqU5dKXf3We4+GXB3n0bzJw8Ce+3oSe4+3t0HuvvAvIccAA0N6wF4+eU/Mn3GLAYN6p97DXmor2ugd1XF1ttVlb22ZpfOTb0tLvVWikLbcnFF6m2krBArb6SsEC+vFEeqQcf/mtkQM/sc4GZ2JoCZnQBsTlTTTnXtujfdu3fb+vmnTzmBZcueSlxVNhbW1NKvX1+qq3tTXl7OyJEjuOfe2anLknag3haXeitFoW25uCL1NlJWiJU3UlaIlzdvbsX86AhSDTr+CfgG8EVgKHCimb1O82krlyaqaacOPPCD/O6R6fyh5rc8/vh9zJz1ILNnP5K6rExs3ryZyy4fw8z77mDpkkeYNu0eli9/OnVZmbn9thuZO+c3fPSgD7PmuRq+cOE5qUvKjHqr3hZFpN5CrLzaltXbIoiUFWLljZQV4uWV4rBU51iZ2cFABTDf3Tdsc/8wd79/V88v36MyzMlhYYKKiIiIiIi02PR2fQc5PiAbv+g9qpB/6v3Ti7cn71uqVVcuBe4GLgGWmtmIbR7+QYqaRERERERERKTzS7XqypeBge6+wcyqgWlmVu3u44Dk0x8RERERERER6ZxSDTq6bDldxd3XmNlgmocdfdCgQ0RERERERAquKXUBBZbqYqTrzGzr2qwtQ4/hQA/gkEQ1iYiIiIiIiEgnl2rQcT6wbts73H2Tu58PHJ+mJBERERERERHp7JKcuuLudTt57LE8axERERERERGR4kh1jQ4RERERERGRsHSNjux02kFHIRccFhEREREREZH3JNU1OkRERERERERE2p0GHSIiIiIiIiJSGJ321BURERERERGRzkqXY8iOjugQERERERERkcLQoENERERERERECkODDhEREREREREpDA06WmnokMEsWzqHlcvnctWVF6cuJ1MTxo9lbd1iahc9mLqU3ETpr3pb3N5CrKwQK2+krBArb7T9cqTeRsqq7bjYouXNU5MV86Mj0KCjFUqlEjeMu5bhp4/ikENP5Oyzz+Tggz+SuqzM3HrrVD4z/NzUZeQmUn/V2+L2NlJWiJU3UlaIlzfSfjlSbyNlBW3HRe5ttLxSHBp0tMLhgwawatUaVq9+gcbGRqZOncEZpw9NXVZmHp07n1dfez11GbmJ1F/1tri9jZQVYuWNlBXi5Y20X47U20hZQdtxkXsbLa8UhwYdrVBR2ZMX69ZuvV1X30BFRc+EFUl7Un+LK1JvI2WFWHkjZYV4eSOJ1NtIWaOJ1ttoeaU4kgw6zKy7mX3PzJaZ2Rtm9rKZzTOzC1PUsytm7z7RyF2rHheF+ltckXobKSvEyhspK8TLG0mk3kbKGk203kbLK8VRluh1/wu4GxgKjAS6AXcCY8zsIHf/f9t7kpmNBkYDWJf9KJW65VJsfV0Dvasqtt6uquxFQ8P6XF5bsqf+Flek3kbKCrHyRsoK8fJGEqm3kbJGE6230fLmrSl1AQWW6tSVane/xd3r3P0nwBnu/gzwBeDvd/Qkdx/v7gPdfWBeQw6AhTW19OvXl+rq3pSXlzNy5AjuuXd2bq8v2VJ/iytSbyNlhVh5I2WFeHkjidTbSFmjidbbaHmlOFINOjaa2bEAZnY68CqAuzcBHWRBmv+zefNmLrt8DDPvu4OlSx5h2rR7WL786dRlZeb2225k7pzf8NGDPsya52r4woXnpC4pU5H6q94Wt7eRskKsvJGyQry8kfbLkXobKStoOy5yb6PlleKwFOdYmdmhwATgIGApcJG7P2VmHwQ+7+437Op7lO1RqZPDRERERERECmrT2/Ud7h/B29NP/2ZUIf+m/foLtyfvW5JrdLj7YjO7AKgE5rn7hpb7XzYzjQhFRERERESk0HSNjuykWnXlUpovRvo1YKmZjdjm4R+kqElEREREREREOr9Uq658GRjo7hvMrBqYZmbV7j6ODniNDhERERERERHpHFINOrpsc7rKGjMbTPOwow8adIiIiIiIiIjIbkq16so6M+u/5UbL0GM40AM4JFFNIiIiIiIiIrnwgn50BKkGHecD67a9w903ufv5wPFpShIRERERERGRzi7Vqit1O3nssTxrEREREREREZHiSHWNDhERERERyUCkC951lMPkRaRj0aBDREREREREJGdNkaaSOUt1jQ4RERERERERkXanQYeIiIiIiIiIFIYGHSIiIiIiIiJSGBp0iIiIiIiIiEhh6GKkIiIiIiIiIjlrSl1AgemIDhEREREREREpDA06WmnokMEsWzqHlcvnctWVF6cuJ1NVVRU8MPsunlzyCItrH+KSr12UuqTMRemvelvc3kKsrBPGj2Vt3WJqFz2YupRcROotxMmrfXJxewvx9lMApVKJhQv+h+l3T0pdSqYibccQL68Ug7l76hp2S9kelbkVXiqVWLHsUYad9nnq6hqY9/uZjDrvq6xY8UxeJeSqZ88D6NXzABbVLqV7924smH8/nz3ri4XNG6m/6m1xexspK8Bxxx7Bhg0bmThxHP0HnJy6nExF622kvNonF7e3kHY/Zbm+2v+5/LLRfOqwT7LvPvtw5t9dkMtr5v2XTLTtOHXeTW/Xp9qcc3Fdn1Gd84/xXfjW87cn75uO6GiFwwcNYNWqNaxe/QKNjY1MnTqDM04fmrqszKxb9xKLapcCsGHDRlaufIbKip6Jq8pOpP6qt8XtbaSsAI/Onc+rr72euoxcROttpLzaJxe3txBrPwVQWdmLU089mV/9anLqUjIVbTuOljdvXtCPjkCDjlaoqOzJi3Vrt96uq2+gosC/iGyrT58q+h/6CeYvWJS6lMxE7a96WyyRskYTrbfR8m6hfbJ0dmPHXsO3v/19mpqKfXnFaNtxtLxSHEkGHWZWZmb/aGb3m9kSM1tsZrPM7J/MrHwnzxttZjVmVtPUtDHPet91X2c95actunXrytQpE7jim1fzpz9tSF1OZiL2V70tnkhZo4nW22h5Qftk6fxOO+0UXn7pFZ5Y9GTqUjIXbTuOlleKI9XysrcBrwP/AtS13FcFXADcDpy9vSe5+3hgPOR7jY76ugZ6V1VsvV1V2YuGhvV5vXwSZWVl3DVlApMn38306bNSl5OpaP1Vb4vZ20hZo4nW22h5tU8ubm8jOfrogQwfPoRhw05ir732ZN9992HSLTdwwYWXpi6t3UXbjqPlleJIderKp9z9K+4+z93rWj7muftXgAGJatqhhTW19OvXl+rq3pSXlzNy5AjuuXd26rIyNWH8WFasfJbrx41PXUrmovVXvS1mbyNljSZab6Pl1T65uL2NZMyY6+j7oYF85KAjOXfUV3n44ccKOeSAeNtxtLx5a8IL+dERpBp0vGZmnzOzra9vZiUzOxt4LVFNO7R582Yuu3wMM++7g6VLHmHatHtYvvzp1GVl5pijB3HeqLM48cSjqVk4m5qFszl12Empy8pMpP6qt8XtbaSsALffdiNz5/yGjx70YdY8V8MXLjwndUmZidbbSHm1Ty5ubyHWfiqSaNtxtLxSHEmWlzWzauDfgRNpPoUF4H3Aw8C33H31rr5HnqeuiIiIiIh0FsnXdcyR/iAotqIvL3ttn3MLuQn/8/P/lbxvSa7R4e5rzOwnwFhgFXAwcCSwvDVDDhERERERERGR7Uky6DCzq4FTW17/t8DhwO+Ab5nZAHe/NkVdIiIiIiIiInko9mLMaaVadeUsoD+wJ7AOqHL3N83sR8B8QIMOEREREREREWmzVBcj3eTum939LWCVu78J4O5/RoMtEREREREREdlNqQYdb5tZ15bPD9typ5nthwYdIiIiIiIiIrKbUp26cry7/wXA3bcdbJQDF6QpSUREREREREQ6u1SrrvxlB/e/ArySczkiIiIiIiIiuSrk2rIdRKojOqQNki9CnDP9wIuIdByR3oP0/iNFoW1ZRKJLdY0OEREREREREZF2p0GHiIiIiIiIiBSGTl0RERERERERyZmWG82OjugQERERERERkcLQoENERERERERECkODDhEREREREREpDF2jQ0RERERERCRnTZHWcM+ZjuhopaqqCh6YfRdPLnmExbUPccnXLkpdUuZKpRILF/wP0++elLqUTEXr7dAhg1m2dA4rl8/lqisvTl1O5iLljZQV4uSNto/ac889efyxe/lDzW+prX2I7373G6lLylSU7XiLSHkjZYVYeSNlhXh5pRjM3VPXsFvK9qjMtfCePQ+gV88DWFS7lO7du7Fg/v189qwvsmLFM5m/dqpB3+WXjeZTh32SfffZhzP/7oLcXjfvLTJlb/NWKpVYsexRhp32eerqGpj3+5mMOu+rhcwKsfJGygqx8qbeR6V4D+rWrSsbN75FWVkZv3vkbq644mrmL3gi89fN+/0n0nYMsfJGygqx8kbKCunzbnq7vtDHPHy3+tzO+cf4LnxvzX8l75uO6GildeteYlHtUgA2bNjIypXPUFnRM3FV2ams7MWpp57Mr341OXUpmYvU28MHDWDVqjWsXv0CjY2NTJ06gzNOH5q6rMxEyhspK8TKG2kftcXGjW8BUF5eRnl5OZ31H2V2JdJ2DLHyRsoKsfJGygrx8kpxdLhBh5mNT13DrvTpU0X/Qz/B/AWLUpeSmbFjr+Hb3/4+TU2xVncuem8rKnvyYt3arbfr6huoKPAfTJHyRsoK8fJuUfR91BalUomahbNZW7+EBx6cw4KFxcwbbTuOlDdSVoiVN1JWiJc3b014IT86giSDDjPbfwcfHwBO28nzRptZjZnVNDVtzLHi/9OtW1emTpnAFd+8mj/9aUOSGrJ22mmn8PJLr/DEoidTl5KrCL01e/dRZEX9l1KIlTdSVoiXF2Lso7Zoampi4KAhVPcdyKCBA/j4xz+auqRMRNuOI+WNlBVi5Y2UFeLlleJIterKy8Dz/PWpv95y+4AdPcndxwPjIf9rdACUlZVx15QJTJ58N9OnEHbHKwAAIABJREFUz8r75XNz9NEDGT58CMOGncRee+3Jvvvuw6RbbuCCCy9NXVpmovS2vq6B3lUVW29XVfaioWF9woqyFSlvpKwQL2+UfdQ7vfHGm/xuzuMMGTKYZcueSl1Ou4u2HUfKGykrxMobKSvEyyvFkerUleeAwe7ed5uPD7l7X6DD/uRMGD+WFSuf5fpxHf7smvdkzJjr6PuhgXzkoCM5d9RXefjhxwo95IA4vV1YU0u/fn2pru5NeXk5I0eO4J57Z6cuKzOR8kbKCvHyRtlHAfTosT/77bcvAHvttRcnn3QcTz21KnFV2Yi2HUfKGykrxMobKSvEyyvFkeqIjuuB9wMvbOexH+ZcS6scc/Qgzht1FkueXE7NwuYf7u985zpm3f9Q4srkvYrU282bN3PZ5WOYed8ddCmVuGXSFJYvfzp1WZmJlDdSVoiVN9I+CqBXrwP51c3X06VLCSuVmDbtHmbOfCB1WZmItB1DrLyRskKsvJGyQry8UhzJlpc1s8MBd/eFZva3wDBgpbvPbM3zU5y6kkrytXlyFqaxIiKdQKT3IL3/iIh0LEVfXvafq/+hkG891665I3nfkhzRYWZXA6cCZWb2W+AI4BHgW2Y2wN2vTVGXiIiIiIiIiHRuqU5dOQvoD+wJrAOq3P1NM/sRMB/QoENERERERERE2izVxUg3uftmd38LWOXubwK4+5+BpkQ1iYiIiIiIiEgnl+qIjrfNrGvLoOOwLXea2X5o0CEiIiIiIiIFpz98s5Nq0HG8u/8FwN237W85cEGakkRERERERESks0sy6Ngy5NjO/a8Ar+RcjoiIiIiIiIgURKojOt6z5OvV5KiQaw5JSCWL85PblGjpbpH2pi1ZREREOptOO+gQERERERER6aya9M8JmUm16oqIiIiIiIiISLvToENERERERERECkODDhEREREREREpDF2jQ0RERERERCRnukJHdnREh4iIiIiIiIgUhgYdIiIiIiIiIlIYGnS00p577snjj93LH2p+S23tQ3z3u99IXVKmhg4ZzLKlc1i5fC5XXXlx6nIyNWH8WNbWLaZ20YOpS8lFpN4C7Lffvtw5+SaeXPIISxY/zBFHfCp1SZmJ1ttIeSNlhVh5I2WFWHkjZYVYeSNlhXh5pRjMvXOeGVS+R2XuhXfr1pWNG9+irKyM3z1yN1dccTXzFzyR+evmHbRUKrFi2aMMO+3z1NU1MO/3Mxl13ldZseKZnCvJx3HHHsGGDRuZOHEc/QecnLqcTKXubcksl9fZ1s2//ClzH1vAxImTKS8vp2vXvXnjjTczf92mnPetqXubt0h5I2WFWHkjZYVYeSNlhVh5I2WF9Hk3vV2f/y+PObqq+vOd84/xXfjhmsnJ+6YjOtpg48a3ACgvL6O8vJzOOiTalcMHDWDVqjWsXv0CjY2NTJ06gzNOH5q6rMw8Onc+r772euoychGtt/vs051jjzuCiRMnA9DY2JjLkCOFaL2NlDdSVoiVN1JWiJU3UlaIlTdSVoiXN29NBf3oCDToaINSqUTNwtmsrV/CAw/OYcHCRalLykRFZU9erFu79XZdfQMVFT0TViTtJVpvP9T3b3jl5Vf55YSfsGD+/fziP39E1657py4rE9F6GylvpKwQK2+krBArb6SsECtvpKwQL68UR5JBh5l1MbN/NLN/NbNj3vHYmBQ1tUZTUxMDBw2huu9ABg0cwMc//tHUJWXCtnN6QVGPXokmWm+7lJUxYMAnuGn8bRx+xDA2vvVWYc8tjdbbSHkjZYVYeSNlhVh5I2WFWHkjZYV4eaU4Uh3RcRNwAvBH4AYz+8k2j/39jp5kZqPNrMbMapqaNmZd4w698cab/G7O4wwZMjhZDVmqr2ugd1XF1ttVlb1oaFifsCJpL9F6W1/fQF1dAwtbjr769a/vo/+AQxJXlY1wvQ2UN1JWiJU3UlaIlTdSVoiVN1JWiJdXiiPVoONwd/8Hd78eOALobma/NrM9gR1euMTdx7v7QHcfWCp1y61YgB499me//fYFYK+99uLkk47jqadW5VpDXhbW1NKvX1+qq3tTXl7OyJEjuOfe2anLknYQrbfr179MXd1aDjroQwCcdOKxhb1YWLTeRsobKSvEyhspK8TKGykrxMobKSvEy5u3JryQHx1BWaLX3WPLJ+6+CRhtZlcDDwHdE9W0U716Hcivbr6eLl1KWKnEtGn3MHPmA6nLysTmzZu57PIxzLzvDrqUStwyaQrLlz+duqzM3H7bjZxw/FH06LE/a56r4Zrv/ZiJt9yZuqxMROstwNe//h0m3fIz9thjD1avfp4vfbmYS0NH622kvJGyQqy8kbJCrLyRskKsvJGyQry8UhxJlpc1s9uB2939/nfc/yXgP929fFffI8XysqmECSqFl2J52VTyXl5WREREpGiKvrzsFdXnFPIXxp+suTN535KcuuLuo4BXzWwQgJn9rZldAaxtzZBDRERERERERGR7kpy60nKayqlAmZn9lubrdDwCfMvMBrj7tSnqEhEREREREclDIQ/n6CBSXaPjLKA/sCewDqhy9zfN7EfAfECDDhERERERERFps1Srrmxy983u/hawyt3fBHD3PwNNiWoSERERERERkU4u1aDjbTPr2vL5YVvuNLP90KBDRERERERERHZTqlNXjnf3vwC4+7aDjXLggjQliYiIiIiIiORD/8KfnSSDji1Dju3c/wrwSs7liIiIiIiIiEhBpDqi4z3TFWpFOp8m10+uSGdjqQvIkfZQIiIixZDqGh0iIiIiIv+fvfuPs7OsD7z/+Z5kAAmKxahhkqxhG3X9USUlCaLyQ7EJooBdMeoK1dYt+1K0sLaw9inVxa77uBUepbtsu0FNEMqPwK4gJLgRLIS0EjKaoPkFGJOFSQYUQSCBhWTmev7IJBuBQHRz7mvm/n7evuaVOYdMzvfjdWYmc+U+9y1J0j7nRockSZIkSWqNUfvSFUmSJEmSRqviiya7xiM6JEmSJElSa7jRIUmSJEmSWsONDkmSJEmS1Bqeo0OSJEmSpIYN1R6gxTyiYy/NnnUcq1ctYd2apZx7zpm1x+k6e9srUyvk6s3UCrl6M7UC3HvPHaz44c30LV/MHd9fVHucrsq2tpl6M7VCrt5MrZCvV+0QpYzOM72O3W9iY4N3Oh3Wrr6dE078MP39A9zx/UWcdvonWbv23qZGaJS97e3N1Aq5ejO1Qq7e2q3RyKP8qnvvuYO3HPVufvGLRxp93Kb/RlR7bZuWqTdTK+TqzdQK9Xu3P72pxrehxnxqygdH5w/jL+C/bLy6+rp5RMdemDljGuvXb2TDhvvYtm0bCxZcz8knza49VtfY297eTK2QqzdTK+TqzdSaTba1zdSbqRVy9WZqhXy9ao8qGx0RcWBEnBsR50TEARHxsYj4dkT8dUQcVGOm59M7cQL392/edbt/0wC9vRMqTtRd9ra3N1Mr5OrN1Aq5ejO17lRK4aZFV7Lsjpv41x//SO1xuibb2mbqzdQKuXoztUK+3qYNUVr5NhLUOhnpfOB+4EXAQmAtcAFwEvC3wOnP9UERcQZwBkCMOZhOZ1wTsxLx7CNvRutLfvaGve3tzdQKuXoztUKu3kytOx173PsYGHiQl7/8ZXznpqtYd/dPWLp0We2x9rlsa5upN1Mr5OrN1Ar5etUetTY6XlNKmRM7PnMGgHeVUkpE3A7ctacPKqXMBeZCs+fo2NQ/wORJvbtuT5p4KAMDDzb18I2zt729mVohV2+mVsjVm6l1p519P//5L7ju+puYMePwVm50ZFvbTL2ZWiFXb6ZWyNer9qh6jo6yYztw0fCvO2+PuC3C5X0rmTr1MKZMmUxPTw9z5pzCDTcurj1W19jb3t5MrZCrN1Mr5OrN1Apw4IEv4qCDxu16//fedSyrV99dearuyLa2mXoztUKu3kytkK9X7VHriI6+iDiolLKllPJHO++MiN8GHq800x4NDg5y1tnnsWjhFYzpdJh/6dWsWXNP7bG6xt729mZqhVy9mVohV2+mVoBXvvLlXHvN1wEYM3YMV111HYsX31p3qC7JtraZejO1Qq7eTK2Qr7dpI+5f+Fuk2uVlI2ImOw7iWB4RrwdOAO5mtyM8nk+TL12RJCmr6teHa5B/sZCkkaXtl5f9xJQ5rfzW87cbF1RftypHdETE54F3A2Mj4rvAkcCtwL8DDge+WGMuSZIkSZI0utV66cqp7NjQ2B94AJhUSnksIr4MLMONDkmSJEmS9BuodTLS7aWUwVLKE8D6UspjAKWUJ4GhSjNJkiRJkqRRrtYRHU9HxIHDGx1H7LwzIg7GjQ5JkiRJUssNeXaorqm10XFMKeUpgFLK7hsbPcBH64wkSZIkSZJGuyobHTs3OZ7j/oeAhxoeR5IkSZIktUStc3RIkiRJkiTtc7VeuiJJkkaBTK8ejtoDNCzT2krSSOTJKbvHIzokSZIkSVJruNEhSZIkSZJaw40OSZIkSZLUGp6jQ5IkSZKkhhXPltQ1HtEhSZIkSZJaw40OSZIkSZLUGm50SJIkSZKk1nCjYy/NnnUcq1ctYd2apZx7zpm1x+mqS+ZeyOb+u1i54pbaozQiW2+m5zLk6s3U6udtu2Xp3X///fmnf7yRH/R9l5Urv8fnPventUfquixrC7laIVdvptZs32+bNtTSt5HAjY690Ol0+JuLvsh7TzqN33nzO/jgB9/H61736tpjdc03v7mA97z3I7XHaEym3mzP5Uy9mVrBz9s2r22m3qeeeorfmzWHI6b/HtOnz2L2rOM4cubv1h6razKtbaZWyNWbqRVyfb9Vu7jRsRdmzpjG+vUb2bDhPrZt28aCBddz8kmza4/VNbcvXcbDj/yy9hiNydSb7bmcqTdTK/h52+a1zda7desTAPT0jKWnp4dS2nsG/kxrm6kVcvVmaoVc32/VLiNmoyMi7qk9w570TpzA/f2bd93u3zRAb++EihNJv5lsz+VMvZlas8m2ttl6O50OfcsXs3nTj7j5liXcuXxF7ZG6JtPaZmqFXL2ZWqXRbGyNB42Ix2HXRYNj+NcDd95fSnnJHj7uDOAMgBhzMJ3OuK7POvy4z7qvzf/iovbK9lzO1JupNZtsa5utd2hoiOkzZnHwwS/h2mu+zhve8FpWr7679lhdkWltM7VCrt5Mreq+gs+dbql1RMd84Drg1aWUF5dSXgzcN/z+c25yAJRS5pZSppdSpje1yQGwqX+AyZN6d92eNPFQBgYebOzxpX0l23M5U2+m1myyrW223p0effQxblvyT8yadVztUbom09pmaoVcvZlapdGsykZHKeXTwEXAlRHxJxHRgZG7nbW8byVTpx7GlCmT6enpYc6cU7jhxsW1x5J+bdmey5l6M7Vmk21tM/WOH38IBx+84993DjjgAI5/59Hcfff6ylN1T6a1zdQKuXoztUqjWZWXrgCUUn4QEe8CPgXcBhxQa5YXMjg4yFlnn8eihVcwptNh/qVXs2bNiD2lyP+1yy+7mGOPOYrx4w9h40/7OP8LFzBv/lW1x+qaTL3ZnsuZejO1gp+3bV7bTL2HHvpKvvH1rzJmTIfodLj22htYtOjm2mN1Taa1zdQKuXoztUKu77dql6j1mrKImMmO83Esj4ijgXcAfaWURXvz8WP3mzhijwCRJEmjz7Nfed9u/kVK0ki3/elNrf7S/IdT3t/KL8XzNv736utW62SknwfeDYyNiO8CM9lxVMdnI2JaKeWLNeaSJEmSJKkJQ7UHaLFaL105FTgc2B94AJhUSnksIr4MLAPc6JAkSZIkSb+2Wldd2V5KGSylPAGsL6U8BlBKeRI3tiRJkiRJ0m+o1kbH0xFx4PD7R+y8MyIOxo0OSZIkSZL0G6r10pVjSilPAZRSdt/Y6AE+WmckSZIkSZKaMVTpwiAZVNno2LnJ8Rz3PwQ81PA4kiRJkiSpJWod0SFJkjSi+O9qkiS1Q61zdEiSJEmSJO1zHtEhSZIkSVLDPJKwezyiQ5IkSZIktYYbHZIkSZIkqTXc6JAkSZIkSa3hOTokSZIkSWrYkGfp6BqP6JAkSZIkSa3hRsdemj3rOFavWsK6NUs595wza4/Tdfa206RJvdy8+Bp+/KNbuWvl9/j0pz5ee6Suy7K2kKv1krkXsrn/LlauuKX2KI3ItLaQq9fncntlWttMrZDreQz5etUObnTshU6nw99c9EXee9Jp/M6b38EHP/g+Xve6V9ceq2vsbW/v9u3bOefc8/mdNx3H295+Ep/4xMda2wq51jZTK8A3v7mA97z3I7XHaES2tc3W63PZtW2DTK3ZnsfZetUebnTshZkzprF+/UY2bLiPbdu2sWDB9Zx80uzaY3WNve3tfeCBn7Fi5SoAtmzZyrp19zKxd0Llqbon09pmagW4fekyHn7kl7XHaES2tc3W63PZtW2DTK3ZnsfZetUebnTshd6JE7i/f/Ou2/2bBuht8Q+H9ra7d6dXvWoSh7/5jSy7c0XtUbom09pmas0m29pm683EtVUbZHseZ+ttWmnp/0aCKhsdEfGm3d7viYjzIuLbEfEfI+LAGjM9n4h41n2ljIwF7AZ7290LMG7cgSy4+hI+82ef5/HHt9Qep2syrW2m1myyrW223kxcW7VBtudxtl61R60jOubv9v6XgKnAhcCLgL/b0wdFxBkR0RcRfUNDW7s74W429Q8weVLvrtuTJh7KwMCDjT1+0+xtd+/YsWO55upLuPLKb3HddTfVHqerMq1tptZssq1ttt5MXFu1QbbncbZetUetjY7dtwaPB/64lHIb8Bng8D19UCllbilleilleqczrtsz7rK8byVTpx7GlCmT6enpYc6cU7jhxsWNPX7T7G137yVzL2Ttup/w1Yvm1h6l6zKtbabWbLKtbbbeTFxbtUG253G2XrVHrY2OgyPiX0bE+4H9SynbAMqO46BG3LFQg4ODnHX2eSxaeAWrfnQr1157A2vW3FN7rK6xt729b3vrDE4/7VTe8Y630rd8MX3LF/PuE95Ze6yuybS2mVoBLr/sYpYu+Tavfc1vs/Gnffzhxz5Ue6Suyba22Xp9Lru2bZCpNdvzOFtv04Za+jYSRI3XWEXEvGfc9dlSyoMRMQH4+1LK8S/0Z4zdb+KI2xCRJEmSJO0b25/e9OyThLTIB1/1vlb+THv1/7qu+rqNrfGgpZQ/jIgjgaFSyvKIeH1EfARYtzebHJIkSZIkSc+lykZHRHweeDcwNiK+C8wEbgM+GxHTSilfrDGXJEmSJEka3apsdACnsuOko/sDDwCTSimPRcSXgWWAGx2SJEmSpNYaGnmnp2yNWicj3V5KGSylPAGsL6U8BlBKeZKRc/4SSZIkSZI0ytTa6Hg6Ig4cfv+InXdGxMG40SFJkiRJkn5DtV66ckwp5SmAUsruGxs9wEfrjCRJkiRJkka7WlddeWoP9z8EPNTwOJIkSZIkNap4jo6uqXVEhySpRcZ0ar0SsnmDQ77CUpIkaSTL8zdTSZIkSZLUem50SJIkSZKk1nCjQ5IkSZIktYbn6JAkSZIkqWGe9at7PKJDkiRJkiS1hhsdkiRJkiSpNdzokCRJkiRJreFGx16aPes4Vq9awro1Szn3nDNrj9N19rZXplbI1dv21v/23y7g/vtW8MMf3Lzrvt/6rZeyaOHfs3rVEhYt/Hte+tKDK07YPW1f22fK1JupFXL1Zmq9ZO6FbO6/i5Urbqk9SiMyrS3k621SKaWVbyOBGx17odPp8DcXfZH3nnQav/Pmd/DBD76P173u1bXH6hp729ubqRVy9WZoveyyazjp5NN/5b5z/uyTfO8f/pE3vPEYvvcP/8g5f/bJStN1T4a13V2m3kytkKs3UyvAN7+5gPe89yO1x2hEtrXN1qv2cKNjL8ycMY316zeyYcN9bNu2jQULrufkk2bXHqtr7G1vb6ZWyNWboXXp0mU88sgvf+W+k06axeWXXwvA5Zdfy8knt6sZcqzt7jL1ZmqFXL2ZWgFuX7qMh5/x9bmtsq1ttl61R5WNjoj4VESMH35/akQsiYhfRsSyiPidGjM9n96JE7i/f/Ou2/2bBujtnVBxou6yt729mVohV2+m1t294hXjeeCBnwHwwAM/4+Uvf1nlifa9bGubqTdTK+TqzdSaTba1zdar9qh1RMcnSikPDb9/EfCVUspLgX8H/N2ePigizoiIvojoGxra2sScOx/3WfeNlNcedYO97e3N1Aq5ejO1ZpNtbTP1ZmqFXL2ZWrPJtrbZeps2RGnl20hQa6Nj7G7vv6KU8i2AUsqtwIv39EGllLmllOmllOmdzrguj/h/bOofYPKk3l23J008lIGBBxt7/KbZ297eTK2QqzdT6+5+9rOHmDDhFQBMmPAKfv7zX1SeaN/LtraZejO1Qq7eTK3ZZFvbbL1qj1obHddGxPyI+OfAtyLi7Ij4ZxHxh8B9lWbao+V9K5k69TCmTJlMT08Pc+acwg03Lq49VtfY297eTK2QqzdT6+5uvPG7nHbaqQCcdtqp3HBD+5qzrW2m3kytkKs3U2s22dY2W6/aY+wL/5Z9r5TyFxHxMeBK4LeB/YEzgOuAEXfK5sHBQc46+zwWLbyCMZ0O8y+9mjVr7qk9VtfY297eTK2QqzdD6ze/+V845ui3MH78Iaz/yZ381X+4kC9fcDFX/P3f8ocf+xD337+JD/+rT9Qec5/LsLa7y9SbqRVy9WZqBbj8sos59pijGD/+EDb+tI/zv3AB8+ZfVXusrsi2ttl61R5R6zVWETETKKWU5RHxBuAEYG0pZdHefPzY/SaOjBf/SJIY08lzEa/BoaHaI0iSlML2pzc9+yQhLXLSP3tvK3+mveG+G6uvW5UjOiLi88C7gbER8V1gJnAb8NmImFZK+WKNuSRJkiRJ0uhWZaMDOBU4nB0vWXkAmFRKeSwivgwsA9zokCRJkiRJv7ZaxxpvL6UMllKeANaXUh4DKKU8CXhMsCRJkiRJ+o3U2uh4OiIOHH7/iJ13RsTBuNEhSZIkSVIrRcQ3IuJnEbFqt/v+fURsioiVw28n7vbf/jwifhIRd0fE7L15jFovXTmmlPIUQCll942NHuCjdUaSJEmSJKkZhVaei3RvzAf+C/DNZ9z/lVLKBbvfERGvBz4EvAHoBW6OiNeUUgaf7wGqHNGxc5PjOe5/qJTy46bnkSRJkiRJ3VdKWQI8vJe//RTgqlLKU6WUDcBP2HExk+eV53qAkiRJkiSpqyLijIjo2+3tjL380E9FxI+GX9ryW8P3TQTu3+339A/f97xqvXRFktQig0OeXkmSJElQSpkLzP01P+xvgb8CyvCvFwJ/BMRzPcQL/WFudEiSJEmS1LChvOfoeJZSyoM734+IS4Abh2/2A5N3+62TgM0v9Of50hVJkiRJklRNRBy6283fB3ZekeXbwIciYv+IOAx4NXDnC/15HtEhSZIkSZIaERFXAscB4yOiH/g8cFxEHM6Ol6VsBP4NQClldUQsANYA24EzX+iKKwBRyug8XGbsfhNH5+CSJEmSpBe0/elNz3V+htY48Z+d2MqfaRfdt6j6unlEhyRJkiRJDRutBx2MBp6jQ5IkSZIktYYbHZIkSZIkqTXc6NhLs2cdx+pVS1i3ZinnnnNm7XG66pK5F7K5/y5Wrril9iiNybS+mVohV2+mVsjVm6kVcvVm+56baW0ztUKu3kytkK9X7eDJSPdCp9Nh7erbOeHED9PfP8Ad31/Eaad/krVr721qhEYd/fYj2bJlK/PmXcTh046vPU7XZVrfTK2QqzdTK+TqzdQK+Xozfc/NtLaZWiFXb6ZWqN/b9pORzp787tH5w/gL+J/331R93TyiYy/MnDGN9es3smHDfWzbto0FC67n5JNm1x6ra25fuoyHH/ll7TEak2l9M7VCrt5MrZCrN1Mr5OvN9D0309pmaoVcvZlaIV+v2qPKRkdE/I+IOC0iDqrx+L+u3okTuL9/867b/ZsG6O2dUHEi7UuZ1jdTK+TqzdQKuXoztUK+3kwyrW2mVsjVm6kV8vWqPWod0XEk8D7gvohYEBG/HxH7vdAHRcQZEdEXEX1DQ1u7P+X/edxn3TdaX/KjZ8u0vplaIVdvplbI1ZupFfL1ZpJpbTO1Qq7eTK2Qr1ftUWuj42ellFOBVwE3AH8MbIqIeRExa08fVEqZW0qZXkqZ3umMa2pWNvUPMHlS767bkyYeysDAg409vror0/pmaoVcvZlaIVdvplbI15tJprXN1Aq5ejO1Qr5etUetjY4CUEp5vJRyWSnlROC1wDLgs5Vm2qPlfSuZOvUwpkyZTE9PD3PmnMINNy6uPZb2kUzrm6kVcvVmaoVcvZlaIV9vJpnWNlMr5OrN1Ar5eptWWvq/kWBspcfd8sw7SikPA383/DaiDA4OctbZ57Fo4RWM6XSYf+nVrFlzT+2xuubyyy7m2GOOYvz4Q9j40z7O/8IFzJt/Ve2xuibT+mZqhVy9mVohV2+mVsjXm+l7bqa1zdQKuXoztUK+XrVHtcvLRsRMoJRSlkfE64ETgHWllEV78/FNXl5WkiRJktSstl9edtbkE1r5M+3i+79Tfd2qHNEREZ8H3g2MjYjvsuPkpLcCn42IaaWUL9aYS5IkSZIkjW61XrpyKnA4sD/wADCplPJYRHyZHefpcKNDkiRJktRaQyPkfBZtVOtkpNtLKYOllCeA9aWUxwBKKU8CQ5VmkiRJkiRJo1ytjY6nI+LA4feP2HlnRByMGx2SJEmSJOk3VOulK8eUUp4CKKXsvrHRA3y0zkiSJEmSJGm0q7LRsXOT4znufwh4qOFxJEmSJElqVK0roGZQ66UrkiRJkiRJ+5wbHZIkSZIkqTXc6JAkSZIkSa1R62SkkiRJkiSlNYTn6OgWj+iQJEmSJEmt4UaHJEmSJElqDTc6JEmSJElSa7jRIUmSJEmSWsONjr00aVIvNy++hh+YiZYUAAAgAElEQVT/6FbuWvk9Pv2pj9ceqatmzzqO1auWsG7NUs4958za43TVJXMvZHP/XaxccUvtURqRaW0hV2+mVsjVm6kVcvVmaoVcvZlaIVdvplbI19uk0tL/jQRRysgY5Nc1dr+JjQ4+YcIrOHTCK1ixchUHHTSOO5d9h/ef+kesXXtvk2M0otPpsHb17Zxw4ofp7x/gju8v4rTTP9nKVoCj334kW7ZsZd68izh82vG1x+mqbGubqTdTK+TqzdQKuXoztUKu3kytkKs3UyvU793+9KZo5IEqOW7Su0bnD+Mv4Nb+m6uvm0d07KUHHvgZK1auAmDLlq2sW3cvE3snVJ6qO2bOmMb69RvZsOE+tm3bxoIF13PySbNrj9U1ty9dxsOP/LL2GI3ItraZejO1Qq7eTK2QqzdTK+TqzdQKuXoztUK+XrWHGx2/gVe9ahKHv/mNLLtzRe1RuqJ34gTu79+863b/pgF6W7qpk022tc3Um6kVcvVmaoVcvZlaIVdvplbI1ZupFfL1qj3G1njQiPjnwHnAZuBLwFeAo4C1wDmllI015tob48YdyIKrL+Ezf/Z5Hn98S+1xuiLi2UcajdaXOOlXZVvbTL2ZWiFXb6ZWyNWbqRVy9WZqhVy9mVohX2/Thvz/smtqHdExH1gObAHuANYB7wa+A3xjTx8UEWdERF9E9A0NbW1izl8xduxYrrn6Eq688ltcd91NjT9+Uzb1DzB5Uu+u25MmHsrAwIMVJ9K+km1tM/VmaoVcvZlaIVdvplbI1ZupFXL1ZmqFfL1qj1obHS8upfxtKeVLwEtKKReWUu4vpXwd+K09fVApZW4pZXopZXqnM665aYddMvdC1q77CV+9aG7jj92k5X0rmTr1MKZMmUxPTw9z5pzCDTcurj2W9oFsa5upN1Mr5OrN1Aq5ejO1Qq7eTK2QqzdTK+TrVXtUeekKMBQRrwFeChwYEdNLKX0RMRUYU2mm5/W2t87g9NNO5Uc/XkPf8h2f3H/5l1/ipu98r/Jk+97g4CBnnX0eixZewZhOh/mXXs2aNffUHqtrLr/sYo495ijGjz+EjT/t4/wvXMC8+VfVHqsrsq1tpt5MrZCrN1Mr5OrN1Aq5ejO1Qq7eTK2Qr1ftUeXyshFxPPBfgSHgj4F/C7wJOBj441LK9S/0ZzR9eVlJkiRJUnPafnnZoyce38qfaW/fdEv1datyREcp5ZaI+ANgqJSyPCIeYcc5OtaUUhbVmEmSJEmSJI1+ta668nl2bGyMjYjvAjOB24DPRsS0UsoXa8wlSZIkSZJGt1rn6DgVOBzYH3gAmFRKeSwivgwsA9zokCRJkiRJv7ZaGx3bSymDwBMRsb6U8hhAKeXJiBiqNJMkSZIkSY0YopWn6BgRal1e9umIOHD4/SN23hkRB7PjBKWSJEmSJEm/tlpHdBxTSnkKoJSy+8ZGD/DROiNJkiRJkqTRrtZVV57aw/0PAQ81PI4kSZIkSWqJWi9dkSRJkiRJ2udqvXRFkiRJkqS0PBlp93hEhyRJkiRJag03OiRJkiRJUmu40SFJkiRJklrDc3RIkiRJktSwUjxHR7d4RIckSZIkSWoNNzokSZIkSVJruNGxl2bPOo7Vq5awbs1Szj3nzNrjdF2m3kmTerl58TX8+Ee3ctfK7/HpT3289khdlWltIVdvptZL5l7I5v67WLniltqjNCJTb6ZWyPV5C7l6M7VCrt5MrZCvV+0Qo/V1QWP3m9jY4J1Oh7Wrb+eEEz9Mf/8Ad3x/Eaed/knWrr23qREala13woRXcOiEV7Bi5SoOOmgcdy77Du8/9Y9a2ZttbTP1ZmoFOPrtR7Jly1bmzbuIw6cdX3ucrsvUm6k12+dtpt5MrZCrN1Mr1O/d/vSmaOSBKpnZe+zo/GH8Bdy5+bbq6+YRHXth5oxprF+/kQ0b7mPbtm0sWHA9J580u/ZYXZOt94EHfsaKlasA2LJlK+vW3cvE3gmVp+qObGubqTdTK8DtS5fx8CO/rD1GYzL1ZmrN9nmbqTdTK+TqzdQK+XrVHlU2OiKiExF/FBELI+KuiPhBRFwVEcfVmOeF9E6cwP39m3fd7t80QG9LfxCGfL27e9WrJnH4m9/IsjtX1B6lK7KtbabeTK1SW2T7vM3Um6kVcvVmaoV8vWqPWpeX/Trwv4D/FzgVeAy4HTgvIn6nlPKfn+uDIuIM4AyAGHMwnc64RoaNePaRN6P1JT97I1vvTuPGHciCqy/hM3/2eR5/fEvtcboi29pm6s3UKrVFts/bTL2ZWiFXb6ZWyNer9qi10XFEKeUPh99fGhF3lFI+FxFLgJXAc250lFLmAnOh2XN0bOofYPKk3l23J008lIGBB5t6+MZl6wUYO3Ys11x9CVde+S2uu+6m2uN0Tba1zdSbqVVqi2yft5l6M7VCrt5MrZCvt2kFN426pdY5OrZFxG8DRMTvAk8DlFKegpG32sv7VjJ16mFMmTKZnp4e5sw5hRtuXFx7rK7J1gs7zvK/dt1P+OpFc2uP0lXZ1jZTb6ZWqS2yfd5m6s3UCrl6M7VCvl61R60jOs4B/iEi/jfQA3wIICJeDtxYaaY9Ghwc5Kyzz2PRwisY0+kw/9KrWbPmntpjdU223re9dQann3YqP/rxGvqW7/jC/Zd/+SVu+s73Kk+272Vb20y9mVoBLr/sYo495ijGjz+EjT/t4/wvXMC8+VfVHqtrMvVmas32eZupN1Mr5OrN1Ar5etUe1S4vGxFHAdtLKcsj4vXACcC6Usqivfn4Jl+6IkmSJElqVtsvLzuj95hW/ky7fPOS6utW5YiOiPg88G5gbER8F5gJ3AZ8NiKmlVK+WGMuSZIkSZI0utV66cqpwOHA/sADwKRSymMR8WVgGeBGhyRJkiSptbyCTffUOhnp9lLKYCnlCWB9KeUxgFLKk8BQpZkkSZIkSdIoV2uj4+mIOHD4/SN23hkRB+NGhyRJkiRJ+g3VeunKMcOXkqWUsvvGRg/w0TojSZIkSZKk0a7KRsfOTY7nuP8h4KGGx5EkSZIkqVFDeI6Obql1RIckSZIqGtOp9QrmOgaHfHW0JGWR6zucJEmS0nGTQ5JycaNDkiRJkiS1hi9dkSRJkiSpYaV4jo5u8YgOSZIkSZLUGm50SJIkSZKk1nCjQ5IkSZIktYbn6JAkSZIkqWFDeI6ObvGIjr00e9ZxrF61hHVrlnLuOWfWHqerLpl7IZv772Lliltqj9KYTOubqRVy9WZqhVy9mVohV2+mVoBPnflH/PAHN7Pihzfz6U99vPY4XZVtbTP1ZmqFfL1qhxitZ3odu9/ExgbvdDqsXX07J5z4Yfr7B7jj+4s47fRPsnbtvU2N0Kij334kW7ZsZd68izh82vG1x+m6TOubqRVy9WZqhVy9mVohV2/t1jGdZv+96/Wvfy2XX3Yxb3v7e3n66W3ceMNlfPrT/w8/Wb+x6489ODTU9cfYXe21bVqm3kytUL93+9ObopEHquTNE946On8YfwF3PfBP1dfNIzr2wswZ01i/fiMbNtzHtm3bWLDgek4+aXbtsbrm9qXLePiRX9YeozGZ1jdTK+TqzdQKuXoztUKu3kytAP/iX0xl2Z0/5Mkn/zeDg4MsuX0Zp5xyQu2xuiLb2mbqzdQK+XrVHm507IXeiRO4v3/zrtv9mwbo7Z1QcSLtS5nWN1Mr5OrN1Aq5ejO1Qq7eTK0Aa1bfzdFvP5JDDnkpL3rRAZww+x1MmtRbe6yuyLa2mXoztUK+XrVHlZORRsRY4OPA7wO9QAE2A9cDXy+lbKsx155EPPvIm9H6kh89W6b1zdQKuXoztUKu3kytkKs3UyvAurt/wgUX/lcWLbyCLVuf4Mc/XsP27YO1x+qKbGubqTdTK+TrbVrxZKRdU+uIjsuAw4F/D5wIvAc4H3gzcPmePigizoiIvojoGxra2sScAGzqH2Dybv/iMGnioQwMPNjY46u7Mq1vplbI1ZupFXL1ZmqFXL2ZWneaP/9q3nLUibzrXafy8COP8pOfbKg9UldkW9tMvZlaIV+v2qPWRsfvllI+UUq5o5TSP/x2RynlE8C0PX1QKWVuKWV6KWV6pzOusWGX961k6tTDmDJlMj09PcyZcwo33Li4scdXd2Va30ytkKs3Uyvk6s3UCrl6M7Xu9PKXvwyAyZN7ed8pJ3D1gusrT9Qd2dY2U2+mVsjXq/ao8tIV4JGI+ADw30spQwAR0QE+ADxSaaY9Ghwc5Kyzz2PRwisY0+kw/9KrWbPmntpjdc3ll13Mscccxfjxh7Dxp32c/4ULmDf/qtpjdU2m9c3UCrl6M7VCrt5MrZCrN1PrTlddNZeXHfJStm3bzllnn8cvf/lo7ZG6ItvaZurN1Ar5etUeVS4vGxFTgP8EvJMdGxsBHAz8A/DZUsoLHsfY5OVlJUmS2qbpy8vW1PTlZSXtG22/vOwbX/mWVv5Mu+rBO6qvW5UjOkopG4EPAkTEy9ix0fHVUsppNeaRJEmSJEntUOuqK99+jrvfufP+UsrJDY8kSZIkSZJaoNY5OiYBa4CvsePSsgHMAC6sNI8kSZIkSWqBWhsd04GzgL8AzimlrIyIJ0spt1WaR5IkSZKkxhRaeYqOEaHWOTqGgK9ExDXDvz5YaxZJkiRJktQeVTcXSin9wAci4j3AYzVnkSRJkiRJo9+IOIqilLIQWFh7DkmSJEmSNLqNiI0OaXfVL7rcIF+VJ2mk82tyew0ODdUeQZJSGyrZvvM0p1N7AEmSJEmSpH3FjQ5JkiRJktQabnRIkiRJkqTWcKNDkiRJkiS1hicjlSRJkiSpYSXdabCb4xEdkiRJkiSpNdzokCRJkiRJreFGx16aPes4Vq9awro1Szn3nDNrj9N12XrvvecOVvzwZvqWL+aO7y+qPU5XZVvbTL2ZWiFXb6bW/fffn3/6xxv5Qd93Wbnye3zuc39ae6SuyrS2kKs3Uyvk6s3UCvl61Q5Ryuh8XdDY/SY2Nnin02Ht6ts54cQP098/wB3fX8Rpp3+StWvvbWqERtXujUYe5Vfde88dvOWod/OLXzzS6OM2/dlXe22blqk3Uyvk6q3dWuNr8rhxB7J16xOMHTuW2279Fp/5zOdZducPu/64fk3urky9mVohV2+mVqjfu/3pTTW+DTXmNS+fPjp/GH8B9/y8r/q6eUTHXpg5Yxrr129kw4b72LZtGwsWXM/JJ82uPVbXZOvNJNvaZurN1Aq5ejO17rR16xMA9PSMpaenh9H6jzIvJNvaZurN1Aq5ejO1Qr5etYcbHXuhd+IE7u/fvOt2/6YBensnVJyou7L1ApRSuGnRlSy74yb+9cc/Unucrsm2tpl6M7VCrt5MrTt1Oh36li9m86YfcfMtS7hz+YraI3VFtrXN1JupFXL1ZmqFfL1qjxG30RERc5/nv50REX0R0Tc0tLXJmZ51X1v/dQny9QIce9z7mHnkCbz3pNP4xCc+xtvffmTtkboi29pm6s3UCrl6M7XuNDQ0xPQZs5hy2HRmTJ/GG97w2tojdUW2tc3Um6kVcvVmaoV8vWqPKhsdEXHIHt5eBpy4p48rpcwtpUwvpUzvdMY1Nu+m/gEmT+rddXvSxEMZGHiwscdvWrZeYFffz3/+C667/iZmzDi88kTdkW1tM/VmaoVcvZlan+nRRx/jtiX/xKxZx9UepSuyrW2m3kytkKs3Uyvk621aaen/RoJaR3T8HOgDfrDbW9/w2ysqzbRHy/tWMnXqYUyZMpmenh7mzDmFG25cXHusrsnWe+CBL+Kgg8btev/33nUsq1ffXXmq7si2tpl6M7VCrt5MrQDjxx/CwQe/BIADDjiA4995NHffvb7yVN2RbW0z9WZqhVy9mVohX6/aY2ylx/0pcHwp5b5n/oeIuL/CPM9rcHCQs84+j0ULr2BMp8P8S69mzZp7ao/VNdl6X/nKl3PtNV8HYMzYMVx11XUsXnxr3aG6JNvaZurN1Aq5ejO1Ahx66Cv5xte/ypgxHaLT4dprb2DRoptrj9UV2dY2U2+mVsjVm6kV8vWqPapcXjYizgSWllLueo7/9ulSyn9+oT+jycvLqlnVr0XUIJ/EkkY6vyZLkmpp++VlX/3yI1r5refen/+g+rpVOaKjlHLxM++LiG+WUv5gbzY5JEmSJEkazYY8sWvXVNnoiIhvP/Mu4B0R8VKAUsrJzU8lSZIkSZJGu1rn6JgMrAa+xo4jRQOYDlxYaR5JkiRJktQCta66cgQ7rrTyF8CjpZRbgSdLKbeVUm6rNJMkSZIkSRrlap2jYwj4SkRcM/zrg7VmkSRJkiRJ7VF1c6GU0g98ICLeAzxWcxZJkiRJkppSvN5X14yIoyhKKQuBhbXnkCRJkiRJo9uI2OiQdue+piSNHH5NliRJo02tk5FKkiRJkiTtcx7RIUmSJElSw3Zco0Pd4BEdkiRJkiSpNdzokCRJkiRJreFGhyRJkiRJag3P0SFJkiRJUsOGvLZZ13hEhyRJkiRJag03OvbS7FnHsXrVEtatWcq555xZe5yus7e9MrVOmtTLzYuv4cc/upW7Vn6PT3/q47VH6qpMawu5ejO1Qq7eTK2QqzdTK+TqvWTuhWzuv4uVK26pPUojMq2t2iNKGZ2Hy4zdb2Jjg3c6Hdauvp0TTvww/f0D3PH9RZx2+idZu/bepkZolL3t7c3UCjBhwis4dMIrWLFyFQcdNI47l32H95/6R63szba2mXoztUKu3kytkKs3Uyvk6z367UeyZctW5s27iMOnHV97nK6qvbbbn94UjTxQJa962ZtG5w/jL+B//eJH1dfNIzr2wswZ01i/fiMbNtzHtm3bWLDgek4+aXbtsbrG3vb2ZmoFeOCBn7Fi5SoAtmzZyrp19zKxd0Llqboj29pm6s3UCrl6M7VCrt5MrZCv9/aly3j4kV/WHqMR2da2aaWUVr6NBFU2OiJiTET8m4j4q4h42zP+23k1Zno+vRMncH//5l23+zcN0NvSH5bA3jb3Zmp9ple9ahKHv/mNLLtzRe1RuiLb2mbqzdQKuXoztUKu3kytkK83E9dWo1WtIzr+G3As8AvgbyLi/9vtv/3LPX1QRJwREX0R0Tc0tLXbM+7+uM+6b6TsVHWDve3tzdS6u3HjDmTB1ZfwmT/7PI8/vqX2OF2RbW0z9WZqhVy9mVohV2+mVsjXm4lrq9Gq1kbHzFLKvyqlfBU4EjgoIv5HROwP7PH1PKWUuaWU6aWU6Z3OuMaG3dQ/wORJvbtuT5p4KAMDDzb2+E2zt729mVp3Gjt2LNdcfQlXXvktrrvuptrjdE22tc3Um6kVcvVmaoVcvZlaIV9vJq6tRqtaGx377XynlLK9lHIGcBfwPeCgSjPt0fK+lUydehhTpkymp6eHOXNO4YYbF9ceq2vsbW9vptadLpl7IWvX/YSvXjS39ihdlW1tM/VmaoVcvZlaIVdvplbI15uJa6vRamylx+2LiBNKKd/ZeUcp5fyI2AT8baWZ9mhwcJCzzj6PRQuvYEynw/xLr2bNmntqj9U19ra3N1MrwNveOoPTTzuVH/14DX3Ld3xT/su//BI3fed7lSfb97KtbabeTK2QqzdTK+TqzdQK+Xovv+xijj3mKMaPP4SNP+3j/C9cwLz5V9UeqyuyrW3ThvBlQN0yYi4vGxHfLKX8wd7+/iYvLytJkiRJalbbLy876ZA3tvJn2v6HV1VftypHdETEt595F/COiHgpQCnl5OankiRJkiRJo12tl65MBlYDXwMKOzY6pgMXVppHkiRJkiS1QK2NjiOAs4C/AM4ppayMiCdLKbdVmkeSJEmSpMaMlNNItFGVjY5SyhDwlYi4ZvjXB2vNIkmSJEmS2qPq5kIppR/4QES8B3is5iySJEmSJGn0GxFHUZRSFgILa88hSZIkSZJGtxGx0SFJklRb9WvhNcxXhktSXUOeo6NrOrUHkCRJkiRJ2lfc6JAkSZIkSa3hRockSZIkSWoNz9EhSZIkSVLDimdL6hqP6JAkSZIkSa3hRockSZIkSWoNNzokSZIkSVJruNGxl2bPOo7Vq5awbs1Szj3nzNrjdNUlcy9kc/9drFxxS+1RGpNpfTO1Qq7eTK2QqzdTK+Tp3X///fmnf7yRH/R9l5Urv8fnPventUfquixrC7las/3d0bWVRr4oZXSeAGXsfhMbG7zT6bB29e2ccOKH6e8f4I7vL+K00z/J2rX3NjVCo45++5Fs2bKVefMu4vBpx9cep+syrW+mVsjVm6kVcvVmaoW6vdH1R3i2ceMOZOvWJxg7diy33fotPvOZz7Pszh828thN/w0w03M5Uyvk+ruja9us7U9vqvGluTGvPPhfjM4fxl/Ag4+uq75uHtGxF2bOmMb69RvZsOE+tm3bxoIF13PySbNrj9U1ty9dxsOP/LL2GI3JtL6ZWiFXb6ZWyNWbqRXy9W7d+gQAPT1j6enpYbT+A9TeyLS2mVoh198dXVtpdHCjYy/0TpzA/f2bd93u3zRAb++EihNpX8q0vplaIVdvplbI1ZupFfL1djod+pYvZvOmH3HzLUu4c/mK2iN1Taa1zdSajWsrjQ5VNjoi4sCIODcizomIAyLiYxHx7Yj464g4qMZMzyfi2UfetPlfXLLJtL6ZWiFXb6ZWyNWbqRXy9Q4NDTF9xiymHDadGdOn8YY3vLb2SF2TaW0ztWbj2kqjQ60jOuYDrwQOAxYC04EL2PHy2L/d0wdFxBkR0RcRfUNDW5uYE4BN/QNMntS76/akiYcyMPBgY4+v7sq0vplaIVdvplbI1ZupFfL17vToo49x25J/Ytas42qP0jWZ1jZTazaurfalIUor30aCWhsdryml/ClwJvAG4NOllCXAucCb9/RBpZS5pZTppZTpnc64hkaF5X0rmTr1MKZMmUxPTw9z5pzCDTcubuzx1V2Z1jdTK+TqzdQKuXoztUKu3vHjD+Hgg18CwAEHHMDx7zyau+9eX3mq7sm0tplas3FtpdFhbM0HL6WUiFhUho/3Gr49MraAdjM4OMhZZ5/HooVXMKbTYf6lV7NmzT21x+qayy+7mGOPOYrx4w9h40/7OP8LFzBv/lW1x+qaTOubqRVy9WZqhVy9mVohV++hh76Sb3z9q4wZ0yE6Ha699gYWLbq59lhdk2ltM7VCrr87urbtXVu1S5XLy0bE14CzSylbnnH/bwOXllLe/kJ/RpOXl5UkSe1X/Vp4DfMvUpJGurZfXvblB7+2lV+Kf/7o3dXXrcoRHaWUf/3M+yLim6WUP4iIo2vMJEmSJElSUzyRbfdU2eiIiG8/8y7gHRHx0uHbJzc8kiRJkiRJaoFa5+iYDKwGvsaOIyeDHVdeubDSPJIkSZIkqQVqXXXlCOAHwF8Aj5ZSbgWeLKXcVkq5rdJMkiRJkiRplKt1jo4h4CsRcc3wrw/WmkWSJEmSpKYNeY6Orql9edl+4AMR8R7gsZqzSJIkSZKk0W9EHEVRSlkILKw9hyRJkiRJGt1GxEbHb6L6hXkb5AFNkiR1n99vJUlqh1onI5UkSZIkSdrnRu0RHZIkSZIkjVbFk5F2jUd0SJIkSZKk1nCjQ5IkSZIktYYbHZIkSZIkqTU8R4ckSZIkSQ0b8npfXeMRHZIkSZIkqTXc6Pg1dDodlt/5P7nuW5fWHqWrLpl7IZv772Lliltqj9KY2bOOY/WqJaxbs5Rzzzmz9jhdlakVcvVmaoVcvZlaIVdvplbI1ZupFXL1ZmqFfL1qhxitl7Tp2W9i44OffdYZ/O4Rb+IlL34x7/v9jzb2uE2HHv32I9myZSvz5l3E4dOOb/jRm9fpdFi7+nZOOPHD9PcPcMf3F3Ha6Z9k7dp7a4+2z2VqhVy9mVohV2+mVsjVm6kVcvVmaoVcvZlaoX7v9qc3RSMPVMnBB/326Pxh/AU8umV99XXziI69NHHiobz73cfzjW9cWXuUrrt96TIefuSXtcdozMwZ01i/fiMbNtzHtm3bWLDgek4+aXbtsboiUyvk6s3UCrl6M7VCrt5MrZCrN1Mr5OrN1Ar5eptWSmnl20gwYjY6IuKe2jM8nwsvPJ8///P/wNDQUO1RtI/1TpzA/f2bd93u3zRAb++EihN1T6ZWyNWbqRVy9WZqhVy9mVohV2+mVsjVm6kV8vWqPapsdETE4xHx2PDb4xHxOPDbO+9/no87IyL6IqJvaGhrY/OeeOK7+PnPHuKHK37c2GOqORHPPrJqpOxE7muZWiFXb6ZWyNWbqRVy9WZqhVy9mVohV2+mVsjXq/aodUTHfOA64NWllBeXUl4M3Df8/kv29EGllLmllOmllOmdzrimZuWtb53Oe987i3vvuYO/v/y/8o53vI1L5/9NY4+v7trUP8DkSb27bk+aeCgDAw9WnKh7MrVCrt5MrZCrN1Mr5OrN1Aq5ejO1Qq7eTK2Qr1ftUWWjo5TyaeAi4MqI+JOI6ND8OTf32nnnfYnD/vl0Xv2at/CR0z7JP/zDP/LRj/1J7bG0jyzvW8nUqYcxZcpkenp6mDPnFG64cXHtsboiUyvk6s3UCrl6M7VCrt5MrZCrN1Mr5OrN1Ar5eps2VEor30aCsbUeuJTyg4h4F/Ap4DbggFqz6FddftnFHHvMUYwffwgbf9rH+V+4gHnzr6o9VtcMDg5y1tnnsWjhFYzpdJh/6dWsWTOiTxnzG8vUCrl6M7VCrt5MrZCrN1Mr5OrN1Aq5ejO1Qr5etceIuLxsRBwKrCqlvGxvP6bG5WVrSRMqSZIkScPafnnZgw48rJU/6m15YkP1datyREdEfPs57t5/5/2llJMbHkmSJEmSJLVArZeuTALWAF9jxwELAcwALqw0jyRJkiRJaoFaGx3TgbOAvwDOKaWsjIgnSym3VZpHkiRJkqTGFE9S0DVVNjpKKUPAVyLimuFfH6w1iyRJkiRJao+qmwullH7gAxHxHuCxmrNIkiRJkqTRb0QcRVFKWQgsrHSRydgAABFXSURBVD2HJEmSJEka3UbERockSZIkSZkMFc/R0S2jdqPDp4QkSZIkSXqmTu0BJEmSJEmS9hU3OiRJkiRJUmuM2peuSJIkSZI0WhXP0dE1HtEhSZIkSZJaw40OSZIkSZLUGm50SJIkSZKk1vAcHZIkSZIkNazgOTq6xSM69tLsWcexetUS1q1ZyrnnnFl7nK6zt70ytUKu3kytkKs3Uyvk6s3UCrl6M7VCrt5L5l7I5v67WLniltqjNCLT2qo9YrSe6XXsfhMbG7zT6bB29e2ccOKH6e8f4I7vL+K00z/J2rX3NjVCo+xtb2+mVsjVm6kVcvVmaoVcvZlaIVdvplbI13v0249ky5atzJt3EYdPO772OF1Ve223P70pGnmgSvY/YPLo/GH8BTz1v++vvm4e0bEXZs6Yxvr1G9mw4T62bdvGggXXc/JJs2uP1TX2trc3Uyvk6s3UCrl6M7VCrt5MrZCrN1Mr5Ou9fekyHn7kl7XHaES2tVV7VNnoiIg37fZ+T0ScFxHfjoj/GBEH1pjp+fROnMD9/Zt33e7fNEBv74SKE3WXve3tzdQKuXoztUKu3kytkKs3Uyvk6s3UCvl6M3FtNVrVOqJj/m7vfwmYClwIvAj4uz19UEScERF9EdE3NLS1uxP+6uM+677R+pKfvWFve3sztUKu3kytkKs3Uyvk6s3UCrl6M7VCvt5MXNvuKqW08m0kqHXVld0/Y44HZpRStkXEEuCuPX1QKWUuMBeaPUfHpv4BJk/q3XV70sRDGRh4sKmHb5y97e3N1Aq5ejO1Qq7eTK2QqzdTK+TqzdQK+XozcW01WtU6ouPgiPj9iHg/sH8pZRtA2bH9MzK2gHazvG8lU6cexpQpk+np6WHOnFO44cbFtcfqGnvb25upFXL1ZmqFXL2ZWiFXb6ZWyNWbqRXy9Wbi2mq0qnVExxLg5OH374iIV5ZSHoyICcBDlWbao8HBQc46+zwWLbyCMZ0O8y+9mjVr7qk9VtfY297eTK2QqzdTK+TqzdQKuXoztUKu3kytkK/38ssu5thjjmL8+EPY+NM+zv/CBcybf1Xtsboi29qqPUbM5WUj4pullD/Y29/f5EtXJEmSJEnNavvlZXta+jPtthGwblWO6IiIbz/H3e+MiJcClFJOfo7/LkmSJEmS9LxqvXRlMrAa+Bo7zskRwAx2XHlFkiRJkiTpN1LrZKRHAD8A/gJ4tJRyK/BkKeW2UsptlWaSJEmSJEmjXJUjOkopQ8BXIuKa4V8frDWLJEmSJElNa+UJOvZSRJwAXASMAb5WSvnSvvzzq24ulFL6gQ9ExHuAx2rOIkmSJEmSuisixgAXA78H9APLI+LbpZQ1++oxRsRRFKWUhcDC2nNIkiRJkqSumgn8pJTyU4CIuAo4BdhnGx21ztEhSZIkSZLymQjcv9vt/uH79pkRcUTHb6LWNZUj4oxSytwaj920TK1gb5tlaoVcvZlaIVdvplbI1ZupFXL1ZmqFXL2ZWptU62fabouIM4Azdrtr7jOeP8/VvU9PWeIRHb++M174t7RGplawt80ytUKu3kytkKs3Uyvk6s3UCrl6M7VCrt5Mrfq/VEqZW0qZvtvbMzfJ+oHJu92eBGzelzO40SFJkiRJkpqyHHh1RBwWEfsBHwK+vS8fYNS+dEWSJEmSJI0upZTtEfEp4H+y4/Ky3yilrN6Xj+FGx68v02vTMrWCvW2WqRVy9WZqhVy9mVohV2+mVsjVm6kVcvVmalUDSimLgEXd+vOjlH16zg9JkiRJkqRqPEeHJEmSJElqDTc69lJEfCMifhYRq2rP0m0RcUBE3BkRd0XE6og4v/ZM3RYRYyJiRUTcWHuWbouIfzu8rqsi4sqIOKD2TN0SEWcNd66OiLNrz7OvPdfXpYg4JCK+GxH3Dv/6WzVn3Jf20PuB4fUdiojpNefbl/bQ+lcR8aOIWBkRiyOit+aM+9KevsdGxKcj4u7hNf7rWvPtS3tY28Mj4o7hte2LiJk1Z9yX9tD75oj4fkT8OCJuiIiX1JxxX4mIyRHxDxGxdvg5e9bw/a38uvw8va37uvw8rf8+IjYNf+6ujIgTa8+6L+zpZ4HhE0cuG34uXz18EklpRHKjY+/NB06oPURDngLeWUp5M3A4cEJEvKXyTN12FrC29hDdFhETgT8BppdS3siOk/98qO5U3RERbwT+GJgJvBl4b0S8uu5U+9x8nv116bPALaWUVwO3DN9ui/k8u3cV8C+BJY1P013zeXbrl0spbyqlHA7cCHyu8am6Zz7P6I2IdwCnAG8qpbwBuKDCXN0wn2ev7V8D5w+v7eeGb7fFfJ7d+zXgs6WU3wG+BZzT9FBdsh3401LK64C3AGdGxOtp79flPfW28evynloBvlJKOXz4rWvnG2jYnn4W+E/s6H018Ajw8YozSs/LjY69VEpZAjxce44mlB22DN/sGX5r7clcImIS8B52/MUrg7HAiyJiLHAg+/ia1SPI64A7SilPlFK2A7cBv195pn1qD1+XTgEuHX7/UuB9jQ7VRc/VW0pZW0q5u9JIXbOH1sd2uzmOFn1d3sNz+RPAl0opTw3/np81PlgX7KG1ADuPajiYFn1d3kPva/k/PwR/F3h/o0N1SSlloJTyw+H3H2fHP6BMpKVfl/fU28avy8+ztq30PD8LvBO4dvj+1jyX1U5udOg5Db+UYyXwM+C7pZRltWfqoq8C5wJDtQfptlLKJnb8q+h9wADwaCllcd2pumYVcExEvCwiDgROBCZXnqkJryylDMCOv5gBr6g8j/ahiPhiRNwPfIR2HdHxXF4DHD18mPRtETGj9kBddDbw5eG1vQD488rzdNsq4OTh9z9AC782R8QUYBqwjARfl5/R22rP0fqp4ZcVfqMtL0uCZ/8sAPz/7d15jF1lGcfx70+2gAiVRYMB1KIYNGrcCIpKBcTEqKhBjaBoCFHcwqJA2MJikGAEwQ0UAQPUmhIJyz8KCsQVF5QCWqQCRatYS6AYwFKWxz/eM3I7zIwXmMulx+8nmZy5977nPc87054757nP+55bgJXdh0cAy+hxskdrPxMdmlJVPdyV0G4N7NhNA+idJO8A/llV1447lqdC9wa8J/BC4HnAM5N8aLxRjUZVLaaVWF4B/ABYRCs9ldZaVXVUVW0DzAc+Pe54Rmxd4Nm0MvFDgYVJMt6QRuYTwMHd7/Zg4OwxxzNq+9FK/68FngWsHnM8syrJxsD3gYMmVWL10v/TeKcY6xnAdrTpHXcAp4wxvFk1+VqAVin7mGZPbVTS8Ex0aEZVtRK4mv6uT7Iz8K4kS4HvAbsmuWC8IY3U7sBtVbWiqh4ELgLeMOaYRqaqzq6qV1fVm2ml00vGHdNTYHmSrQC6bS/K/fUY36Un5f4zWAZc1JVQ/5pWdbfFmGMalY/QzscAF9IuKnqrqm6qqj2q6jXAAtonxb2QZD3ahfD8qpr4nfb2vDzNeHtpqrFW1fIuIfAIcBY9/L87cC2wEzCnm/oMLQHSm2l26h8THXqMJFsmmdN9vyHt4vim8UY1GlV1RFVtXVUvoC3KeWVV9bLCofMXYKckG3WfjO5GjxdhTfKcbrstbWG0BeON6ClxKe2iiW57yRhj0SyatJjuu+jpeXnAxbT54CTZHlgfuHOsEY3O34Fduu93pedJ2YFz8zOAo4EzxxvR7OjeV88GFlfVqQMv9fK8PMN4e2e6sU4ksDrvoU3LWutNcy2wGLgK2Ktr1pt/y+qnVFlxNIwkC4B5tE+TlgPHVlUvS0uTvIK2wNA6tGTYwqo6YbxRjV6SecDnquod445llLpbhH2ANo3j98D+E4v99U2SnwKbAw8Ch1TVj8cc0qya6rxEuzhcCGxLS2y9r6p6sZDyNOO9C/gqsCWwEriuqt42rhhnyzRjfTttEcdHgNuBA7p1d9Z604z3fOAcWkn4atr5+cpxxThbphnrn4DTadN1VgGf7MuUymnGuzHwqa7JRcAR1YM/SJO8EfgpcAOPrvt1JG0th96dl2cY7wb07Lw8w1g/SDtHFbAU+PjEeixrs+muBZLMpVVAb0b7G/JDff0bUms/Ex2SJEmSJKk3nLoiSZIkSZJ6w0SHJEmSJEnqDRMdkiRJkiSpN0x0SJIkSZKk3jDRIUmSJEmSesNEhyRJTzNJPpqkutteT/vc00mSpUmuHncckiRJJjokSQKSzOsSCYNf9ya5NsmBSdYZd4xPVDe245LMGXcskiRJo2aiQ5KkNS0APgzsC3we2Ag4DThjnEEB5wMbAj95AvvOA44FTHRIkqTeW3fcAUiS9DTzu6q6YOJBkjOAxcD+SY6pquWTd0iyHrBOVa0aVVBV9TDw8Kj6lyRJ6gsrOiRJmkFV/Qv4JRBgbjcFpJK8LMmpSZYBq4CdJvZJsnuSy5OsTLIqyfVJDpiq/yT7J7kpyQNJ/pzkwO5Yk9tNuUZHkvWTHJbkuiT3J7knyW+TfLp7/Tu0ag6A2wam5Rw30MemSU7ujv9AkhVJFiSZO0Uc2yRZ2B3nX0kuS7Ld4/qhSpIkjZAVHZIkzSBJgBd1D+8ceGk+8G/gFKCAO7r2HwPOBK4BTgTuA94KnJFku6o6dKDvg4AvA4uAI2nTZA4F/jlkbOsDP6RNTbkcuICWdHk58F7ga8A3gU2A9wAHD4zh+q6PTYFfANsC5wB/ALYCPgn8Kslrq+r2ru0c2tSZbbox/hHYBbiKNq1GkiRp7Ex0SJK0po2SbEGrqtgK+AzwSuCaqlrS8h4ArAR2r6qHJp5IshXwFeB7VbX3QJ/fSHI6cEiSM6vqli5pcCJtWswbqur+ro9zgZuGjPUgWpLjpKo6cvCFJM8AqKpfJrmelui4uKqWTurjBGAusFNVLRrY/zvADcDxwEe7pw8DXgDsV1XnDoztNODAIWOWJEkaKaeuSJK0puOBFbSqikXAfsClwLsntTttMMnR2QvYADg7yRaDX8BltPfd3bq2e9AqOL4+keQAqKpltGqRYewD3E1LVqyhqh75Xzt31Sr70Ko0/jYp3vtoVSl7DOzybmA5cN6krk4eMl5JkqSRs6JDkqQ1fQu4kDYd5T7g5qq6a4p2N0/x3A7d9kcz9P/cbjux/sVU1Rt/HCJOgBcD1z2JRVC3BDanJTNWTNNmMGEyF/hNtzDqf1XVHUlWPsEYJEmSZpWJDkmS1rSkqmZKVEy4f4rnJua17Eu3ZscUbp3UtmboZxhT7T+sieP8iOGrMqY73uOJWZIkaWRMdEiSNHuWdNs7h0iW3NJtdwCunPTaDgznZmCHJBtU1QMztJsuObGCttbIJkMmd24Ftk+yzmBVR7c2yaZDxixJkjRSrtEhSdLsWQg8AByf5DF3Ielu47pB9/AK2l1bPpVko4E2WwN7T953GvOBZwNHT3GswQqLe7vtZoNtunU85gM7JtlrqgMkec7Aw0toU2/2ndTs8CHjlSRJGjkrOiRJmiVVtSzJJ4BvA4uTnA/cTlsL4+W0xTxfCiytqruTHAN8CfhFkvNoi5MeQKsMedUQhzwdeCdwdJLX0W4xuwp4GfASYPeu3TXd9uQk87s2N1bVjcBRwM7AwiQLu7argecDbweu5dG7rnyRloQ5K8lraLeinQe8njVvvStJkjQ2JjokSZpFVXVukpuBzwEfB+bQkgB/Ao4B/jHQ9pQk9wKHACcBf6UlPu4BzhniWKuT7AF8lpaA+AItibEEOHeg3c+THE5LopxFe/8/npbsuCfJzl0f7wf2BB4ClgE/oyVtJvq5O8mbgFNpVR0BrgbeAvz48fycJEmSRiVVT2YNM0mSJEmSpKcP1+iQJEmSJEm9YaJDkiRJkiT1hokOSZIkSZLUGyY6JEmSJElSb5jokCRJkiRJvWGiQ5IkSZIk9YaJDkmSJEmS1BsmOiRJkiRJUm+Y6JAkSZIkSb1hokOSJEmSJPXGfwDhkTQOIe8P1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visulizing the results\n",
    "y_pred = np.argmax(y_pred,axis=1)\n",
    "y_pred = pd.Series(y_pred, name='Predicted')\n",
    "y_test = pd.Series(y_test, name='Actual')\n",
    "df_confusion  = pd.crosstab(y_test,y_pred, rownames=['Actual'], colnames=['Predicted'])\n",
    "print(df_confusion)\n",
    "plt.figure(figsize = (20,20))\n",
    "plt.xlabel('xlabel', fontsize=18)\n",
    "plt.ylabel('ylabel', fontsize=18)\n",
    "plt.title('Confusion Matrix',fontsize=20)\n",
    "sns.heatmap(df_confusion, annot=True,fmt=\"d\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}