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@yaju
Created December 30, 2019 07:35
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled3.ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "kjTxAiY0Pr6v",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "a5b3f0da-dcc2-4e8e-f6c5-b1b73a8aa312"
},
"source": [
"%tensorflow_version 2.x\n",
"import tensorflow as tf\n",
"print(tf.__version__)"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 2.x selected.\n",
"2.1.0-rc1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "R2SQv1lJP5xB",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"outputId": "32504f1f-3f05-4589-98fa-3ba2dba77d12"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import math\n",
"import random\n",
"%matplotlib inline\n",
"random.seed(0)\n",
"# 乱数の係数\n",
"random_factor = 0.05\n",
"# サイクルあたりのステップ数\n",
"steps_per_cycle = 80\n",
"# 生成するサイクル数\n",
"number_of_cycles = 50\n",
"\n",
"df = pd.DataFrame(np.arange(steps_per_cycle * number_of_cycles + 1), columns=[\"t\"])\n",
"df[\"sin_t\"] = df.t.apply(lambda x: math.sin(x * (2 * math.pi / steps_per_cycle)+ random.uniform(-1.0, +1.0) * random_factor))\n",
"df[[\"sin_t\"]].head(steps_per_cycle * 2).plot()"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fcd3a60f978>"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
},
{
"output_type": "display_data",
"data": {
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8RESG9gJaB7yoPv6TdhaQLyIHgS3Az5VSrk0ExxuZGRfmkvncL5TZJPzyM/NYnhXDn3fp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\nSY6C9obcWQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "beB5LnamP_Cn",
"colab_type": "code",
"colab": {}
},
"source": [
"def _load_data(data, n_prev = 100): \n",
" \"\"\"\n",
" data should be pd.DataFrame()\n",
" \"\"\"\n",
"\n",
" docX, docY = [], []\n",
" for i in range(len(data)-n_prev):\n",
" docX.append(data.iloc[i:i+n_prev].values)\n",
" docY.append(data.iloc[i+n_prev].values)\n",
" alsX = np.array(docX)\n",
" alsY = np.array(docY)\n",
"\n",
" return alsX, alsY\n",
"\n",
"def train_test_split(df, test_size=0.1, n_prev = 100): \n",
" \"\"\"\n",
" This just splits data to training and testing parts\n",
" \"\"\"\n",
" ntrn = round(len(df) * (1 - test_size))\n",
" ntrn = int(ntrn)\n",
" X_train, y_train = _load_data(df.iloc[0:ntrn], n_prev)\n",
" X_test, y_test = _load_data(df.iloc[ntrn:], n_prev)\n",
"\n",
" return (X_train, y_train), (X_test, y_test)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Quc3R9_ZQBaU",
"colab_type": "code",
"colab": {}
},
"source": [
"length_of_sequences = 100\n",
"(X_train, y_train), (X_test, y_test) = train_test_split(df[[\"sin_t\"]], n_prev =length_of_sequences)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VS6ABHEcQFKR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 561
},
"outputId": "20eea6a5-dc3b-4370-f671-5b5ceba4a861"
},
"source": [
"from tensorflow.keras.models import Sequential \n",
"from tensorflow.keras.layers import Dense, Activation \n",
" \n",
"\n",
"in_out_neurons = 1\n",
"hidden_neurons = 300\n",
"\n",
"model = Sequential() \n",
"model.add(tf.keras.layers.LSTM(hidden_neurons, batch_input_shape=(None, length_of_sequences, in_out_neurons), return_sequences=False)) \n",
"model.add(Dense(in_out_neurons)) \n",
"model.add(Activation(\"linear\")) \n",
"model.compile(loss=\"mean_squared_error\", optimizer=\"rmsprop\")\n",
"model.fit(X_train, y_train, batch_size=600, epochs=15, validation_split=0.05) "
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 3325 samples, validate on 176 samples\n",
"Epoch 1/15\n",
"3325/3325 [==============================] - 20s 6ms/sample - loss: 0.1861 - val_loss: 0.1321\n",
"Epoch 2/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0350 - val_loss: 0.0030\n",
"Epoch 3/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0164 - val_loss: 0.0018\n",
"Epoch 4/15\n",
"3325/3325 [==============================] - 18s 5ms/sample - loss: 0.0092 - val_loss: 0.0092\n",
"Epoch 5/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0106 - val_loss: 0.0042\n",
"Epoch 6/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0083 - val_loss: 0.0063\n",
"Epoch 7/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0077 - val_loss: 0.0059\n",
"Epoch 8/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0075 - val_loss: 0.0047\n",
"Epoch 9/15\n",
"3325/3325 [==============================] - 19s 6ms/sample - loss: 0.0084 - val_loss: 0.0039\n",
"Epoch 10/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0045 - val_loss: 0.0054\n",
"Epoch 11/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0148 - val_loss: 8.5234e-04\n",
"Epoch 12/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0045 - val_loss: 0.0129\n",
"Epoch 13/15\n",
"3325/3325 [==============================] - 19s 6ms/sample - loss: 0.0075 - val_loss: 0.0060\n",
"Epoch 14/15\n",
"3325/3325 [==============================] - 19s 6ms/sample - loss: 0.0055 - val_loss: 0.0081\n",
"Epoch 15/15\n",
"3325/3325 [==============================] - 18s 6ms/sample - loss: 0.0062 - val_loss: 0.0094\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fccb0cc1da0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wnUiJcgId5R-",
"colab_type": "code",
"colab": {}
},
"source": [
"predicted = model.predict(X_test) "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "l5d07C_nd_wV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337
},
"outputId": "118e62df-c21e-4648-b72d-1a1e13e559fd"
},
"source": [
"dataf = pd.DataFrame(predicted[:200])\n",
"dataf.columns = [\"predict\"]\n",
"dataf[\"input\"] = y_test[:200]\n",
"dataf.plot(figsize=(15, 5))"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fcca95661d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
},
{
"output_type": "display_data",
"data": {
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Db4OvG5l7tkswDbyd+b8/YriUW3TDUwghqq7TGfk8OXMvUS4XeDzne21j1w6v\n6h3LPHR7BwqyYPMX173UqI4LY9oHMnt3MtuOX6jwaEIIM3M+DqtjS5lm7M7bA1pjbVnNb0VrN4b2\nr2prjbdPuGaausGg8NlDjTEoCq/8fhCTSaZYimtV87+ev4l6Ce77BhLWwrT7IDe99CUrCwNfDGxM\nRl4xHyyVJ8tCVCcFxSU8OWMvFsZ8Jtl+h2LtoE2pNFTNPX5uW60G0GQI7PoRdv0EJtM1L7/YLQR/\nd3vGLYiRjcKFqObSV35CnmpDfrPHaepbQ+845qH9K+AXCSvfhC/DYNV/4OIJAHxc7XjrvnB2JV7k\n562JOgcV5kYKub9rPhIGzYTUWPi5B1w6WfpSqJczT3UKYv6+FDYdS9MvoxCiwqiqypt/xBBzOpM/\ngpZiffEY9P8RnLz0jmZeur+nTTNd9ipM6wPpCaUv2Vlb8FH/CE5dzOOrNcd0DCmE0FPRhSRcEv5k\nsWU3nr63Gm3X8m8srWHkUhixWJshtv17+Lap1ruhpJgBzevQLawWn648Snxqtt5phRmRQu5GQu+F\nYQshNw1+7lX6VATgmc7BBHo68OYfMeQVGXUMKYSoCNO2JbFg32nejbShzok50OYpCOqidyzz4+AO\nQxdAvwlw7hD80A62flu6x1zbIHcGt6rL5M0niE7J0DmsEEIPR+b/D5MKdXqPxdHGUu845kVRtCJu\n4HR46ZC2pc3B32DeaBSTkY/6N8LRxpKX5x6kuMT07+cT1YIUcjfj1xZGLgNjAfxyH1xKAsDWyoKP\n+0eQcimfL1fJk2UhqrIdJ9J5f+kRuoXVYri6CCystSnY4sYURdvO5ZmdWrG7+r+wYExpA6lx94Th\n6WTD2HnRciMiRDWTmJRE/TN/sNelO5HNm+gdx7w5e0OP97Wu6kcWwYIxeNpb8L/7GxJzOpMJ64/r\nnVCYCSnk/olXQxj+JxTlaGvmMrT2r60C3BjS2peftyZyMFmeLAtRFZ3PKuCZX/fh527P1/fWQjn4\nGzR5BBxr6h3N/DnXhodnQbd3tQZSc4dDcQEudla8368hceeymbQx4d/PI4SoElSTiTNzXsBKMVL/\nwbf0jlN5tH0aenwAsX/AH09wT4Oa3N/Em+/WHScmJVPvdMIMSCH3b2pHwPCFkJ+prfvIPA3A6/eE\nUtPJltfnR1NklCfLQlQlJpPKK78fJLfIyKShzXHcPxlMRmj3nN7RKg9FgagXoffncHQZ/PYwFOXS\no4EX9zaqzbdrj3P8fI7eKYUQFWDfwm+IzN/AoXrP4ubXQO84lUu75y53VZ8Hfz7Lu/c1wMPRhpfn\nHqCgWJpHVXdSyN0K76Yw7I5VBKsAACAASURBVA9tj6Tp/cBYhLOtFe/1a0DcuWxm7jj57+cQQlQa\nU7clsTn+Av+5N5x6LibY8zOE9QX3arzf0Z1qNQb6fQ+JG2Hmg1CQyTt9G2BnbcG4+dHSTluIKi7r\n5AEaRH/IAaumNHr4Xb3jVE5RL0HUy3BwFi4XD/LJgAjiz+fwxaqjeicTOpNC7lbVaQ59x0N6vHZD\nAnQPr0VUsAffrosnM7/4X04ghKgMjpzN4pPlcXQLq8mQ1r6wZyoUZmmjS+LONB0CA36GlN2w7n94\nOtnw3z7h7Dl5iZk75UGYEFVWYQ4Fs4aTpdpj//AUDBayZcsda/8yWDvBrkl0DPFkSGtfJm9JZO/J\ni3onEzqSQu521L9H+yM6/CcAiqLwRu9QMvOL+V4WngpR6RUUl/DC7P242FvxyYMRKCVFsOMHCOio\njcyLO9fgAQi7T1szV1LMg818aF/Pg89WHuVibpHe6YQQ5eDC3OfxKDjF6rAPCAmSGQ13xcZJeygW\nuxCyU3mzdxi1nW15c8EhaR5VjUkhdzssbSCkJ8QthRJt64EG3i70b1qHqduSSL6Yp3NAIcTd+Hh5\nHMdSc/j8oca4O9rAwdmQc05G48pKo4cg7wKc2ICiKLzVJ5y8ohK+Wi0dgIWoaowH5uCRMJ+plgN5\noP9gveNUDS3HgKkY9v6Cg40l7/VryNHUbH7afOLfv1dUSVLI3a7wvpB/EU5uLT30as8QFOBzmass\nRKW16Vgav2xLYnRkAB1DPMFYCNu+Ba8ICOysd7yqIbg72LpC9FwA6tVyYkhrX37deZJjssmtEFWH\nqpK96kNiTP74PvAO9tayZ1yZ8AiG4G7aum1jEd3Ca9GrgRffrInnVLoMJlRHUsjdruBuYGkHRxaX\nHqrtYsdj7QP488AZ2ehWiEoop9DIGwtiCPJ0YGyv+mAsgrkjIP04dH5T68Ao7p6lNYT302Y1FOUC\n8GK3EBxtLHl/yWFUVRqfCFEVnI9eTY28JHbVepjuDb31jlO1tHpCmylyZBEA7/RtgJWFgf9bGCPX\n0GpICrnbZe0A9bpphZzprznJT3YMwt3Bmv8tPSJ/SEJUMp8sj+NMZj6fDmiMrcEE80fDseVa6/z6\n9+gdr2qJGAjFuXB0OQBuDta80C2EzfEX2HA0TedwQoi7paoqp1aO55LqRK9BT+odp+oJ7gZugbDr\nRwC8XGx5rWd9NsdfYNHBMzqHExVNCrk7EdZPexqSsrv0kJOtFS92q8fOxIusPXJex3BCiNux40Q6\nM3acZFS7AJrXdYY/ntAe1PT8SGudL8qWbztw9oGY30sPDWvjR4CHA+8vPSyL9oWo5DbsiaZJ7hZO\n+fXHx6OG3nGqHoNBWyuXvBPOHABgaBs/Gtdx4f0lh8nMky7q1YkUcncipAcYrEqHta94uJUvgZ4O\nfLT8CEa5GRHC7OUXlTBufjS+bva82iMYFj6tdVXs/h60fVrveFWTwQANH4TjayA3HQBrSwP/1zuM\nE2m5si+nEJVYTqGR4ysmYKmYCL/vBb3jVF1Nh4CVQ+monIVB4cP+jbiYW8Rnq+J0DicqkhRyd8LW\nBYI6w+FFcNU0SisLA+N6hZKQlsvs3ck6BhRC3IovVx8lKT2Pjx9shP2xRRA9Gzr/H0TKDUi5ihgI\nJiMcXlh6qGtYTaKCPfh6Tbw8URaikvp25WHuM64i06cjVp6y3UC5sXWBxg9DzDzITgW0LurD2vgx\na+cpDp/J0jmgqChSyN2psL6QeQrOHrjmcPfwWrQKcOPrNcfIKTTqFE4I8W8OJGcwZUsiQ1r70i7I\nAw7+Bi51of2reker+mo1BM/Qa6ZXKorCm73DyCoo5oeNCTqGE0LcidgzmSTvXICXcgmXDk/pHafq\na/uM9vnPp0t7NrzcvT4udla8syhW+jVUE1LI3anQe0Gx0EblrqIoCv/XO4wLOUVMkpsRIcySqqq8\nvSgWTycbxt0Tqj3RTFinjRQZ5LJY7hRF21Pu1HbIOFV6ONzbmfub+DB1ayJnM/N1DCiEuB0mk8p/\nFh5ipNUaTM51oF4PvSNVfe5B0OtDbZr6jgkAuNhb8VrPUHYlXZTGJ9WE3LHcKXs38I/S1sldOgn7\nf4U/noIJbWicv5O+jb35afMJzmUW6J1UCPE3S6LPcjA5g1d71MfJ1goOzQPVBBEP6x2t+mg0QPt8\n1agcwMvdQ1BV+Hp1vA6hhBB3Yv6+FDKTD9OaGAwtRoHBQu9I1UOLRyG0D6x5F07vBWBQy7o09HHm\no2Vx5MrMsCpPCrm7Ed5X22fqmwhtaPvYCu3p8oFfea1nfUwm+EI2CRfCrBQaS/hkRRxhtZ3p36yO\ndvDgbPBuCp4h+oarTmr4g18UbP4KTm4rPVzXzZ6hbfz4fW8y8bJJuBDmqcQI0++Hie0xTu+P3dJn\nGO84FdVgBc2G652u+lAU6DseHGvBvNFQkIWFQeHdvg04l1XAhPXH9U4oypkUcnej0UBtY8Z7PoOn\ntsNrCdDgATixkbquNoyM9GfevhRZdCqEGZm2LYmUS/n8594wLAwKnD8C56JlNE4PD/4ETl4w80FI\nWF96+NkuwThYW/LpSnkQJoRZSt4BJ9aDpQ2p587Q1BRLmOk4SrNh4FhT73TVi70bDJgCGcmw9GVQ\nVZr7udG/qQ+TNyeSdCFX74SiHEkhdzdsnaH3p9D6cagVrq2tCeoMBRlw5gDPdArGxc6Kz1ZKK1gh\nzMGl3CLGrztO5/qeRAZ7aAcPztbWuzZ8UN9w1ZGzN4xaBjUCYNYgOLYS0DYJf7JTEKsPp7In6aLO\nIYUQ14lbBhbWHO85g44Zb/Fd44UY/nse+nyld7LqybcNdHpDm6oeMw+AcfeEYmWh8MHSIzqHE+VJ\nCrmyFtBR+3xiHS72VoxpH8j6o2kcTM7QN5cQgm/XxZNbaOSN3mHaAZNJ+w9fcDdw9NQ3XHXlWBNG\nLtEehs0eAof/BGBUpD81nWz4eHmcdF8TwpyoKhxdihrYibdXnsTBxpLXetbXO5Vo/7K2RGD1f6Eo\nl5rOtjzdOZg1R1LZeSJd73SinEghV9YcPcGrESRsAGBEO39c7a0Yv04W7guhp8QLuczYfpJBLX0J\nqeWkHUzaDFmnofEgfcNVd/ZuMPxP7SZkwROQdxF7a0te7BbCnpOXWH/0vN4JhRBXnD8Ml5KIdYxk\n6/F0XukRgpuDtd6phMECen0M2Wdh6zcAjI4MoLaLLR8uO4LJJA/EqiIp5MpDUBdI3gmFOTjaWPJo\nZABrjpzn0OlMvZMJUW19tjIOG0sDL3Wv99fBg7PBxhnq99YvmNDYumjTsoz5cGAWAA+1qEOdGnZ8\nvSZeRuWEMBdxy1BReONwXUK9nHikla/eicQVvm20ZQJbv4GMZOysLXilR30OpmSyOFq2I6iKpJAr\nD4GdwVQMJ7cCMCLSHydbSxmVE0InceeyWBZzjtFRAdR0stUOFuVp24eE9wUrO30DCo1XQ/BtC3um\ngMmElYWB57oEE52SKaNyQpiLuCWkOIQTk2nL+/c3xNJCbiXNSrd3tM9r3wXggaY+hNd25tMVRyko\nLtEtligf8tdXHnzbgqVtaRc2Z1srRkcGsDI2lSNnpYOlEBXt27XxONlY8mhUwF8HY36HohzpVmlu\nWj4GF09oHfGA/s3qUNdNRuWEMAuZKXD2AL9mNmJ4Wz9a+rvpnUj8nasvtHtO+29c8i4sDApv9g7j\ndEY+07cn6Z1OlDEp5MqDla1WzJ34q5326MgAnGxkVE6IinZlNG5UpD+u9pfXcRxbBcteBZ8W4Bep\nb0BxrbD7wN4Ddk8B0EblOtcjOiWTdXEyKieEnoqPLAXgoF07xvYK1TmNuKnIF8HRC1a8ASYTUfU8\n6FTfk+/WHScjr0jvdKIMSSFXXoI6Q1ocZGlzkl3srRgZ6c+ymHMcPSeb3ApRUa6Mxo2+Mhp3bBXM\nGQI1w2HoPG3bEGE+LG20DYWPLdf2RQIeaOaDr5u9jMoJobPTO+aTYKrN4wPuwdHGUu844mZsHKHb\n23B6D0TPAeCNe8LIKTQyfp1sEl6VyB1MeQnqon0+saH00OjIABysLWRUTogKcmU0buSV0bj41X8V\nccMXgl0NvSOKG2kxSmtxvvcXQBuVe7ZLMDGnM1l7REblhNDDsZPJ+FzaQ5JnJzrXl02/zV7Ew1Cn\nFSx9Bc4epL6XEwNb1GX69iROpssm4VVFmRRyiqL0UhTlqKIoxxVFGXeD10cqipKmKMqByx+PlcXP\nNWs1G4CDJySsKz1Uw8GaEe38WRpzluPnZVROiPL27dp4rXNsVAAcX6vtU1YzTIo4c+fqCyG9YN80\nMGrTgB5oenlUbu0xGZUTooKVmFQWzZuOlVJCy57D9I4jboXBAINmaP+tmzUIMk/zcvcQLA0GPl15\nVO90oozcdSGnKIoFMAG4BwgHBiuKEn6Dt85RVbXJ5Y/Jd/tzzZ7BAIGdtBE5k6n08GPtA7GzsuA7\nGdoWolxdszbOUAB/PAnuwTBMirhKoeVjkJumdRaF0g6Wh05nsUZG5YSoUL9sSyI0YxMFNu44B7fV\nO464VU5eMGQuFObArEHUtClmTIdAlkafZd+pS3qnE2WgLEbkWgHHVVU9oapqETAb6FcG5638Ajtr\nNyLnY0sPuTlYM6yNH4sOnuFEWo6O4YSo2q4Zjdv0GeSeh37jtc2nhfkL6gI1/EubnoA2Kufnbs/X\na2RUToiKknIpj29XHaKrZTQ2DfrIuuLKplYDGPiLtpH7vNE8EeWLh6MNHy49ItfRKqAs/hp9gOSr\nvk65fOzvHlQUJVpRlHmKotS90YkURXlcUZQ9iqLsSUtLK4NoOgvqrH2O+R2K/pqP/Fj7QKwtDXy3\nXkblhCgPCWk5LD90jhHt/HDNT4YdP0CToeDTXO9o4lYZDNDiUTi1DRa/CPkZWFoYeK5LPWLPZLH6\ncKreCYWo8lRV5T8LD/EAG7BT81DC+uodSdyJ4G5w7+cQvwqHje/xcvcQ9py8xMpYuY5WdhX1WGUx\n4K+qagSwGph2ozepqvqjqqotVFVt4enpWUHRypGzN3hFwNZv4KM68H1bWPgMnmc3MrS1H38eOEPS\nBVlwKkRZm7z5BFYWBkZFBsDKN7VOiF3f0juWuF2tn4Q2z2hr5Sa0gtiF3N+4Nv7u0sFSiIqwOPos\nMUeP84b1HPBvD8Fd9Y4k7lSL0dBkCOyezMBGLtSr6cgnK+IoLjH9+/cKs1UWhdxp4OoRtjqXj5VS\nVTVdVdXCy19OBqrPY/ERi2HwHOjwGjj7QNximP8oj0f5YmlQ+H6DjMoJUZbOZxcwf+9pBjSvg8fZ\nzXBshfb351RL72jidllaQ68PYcw6cKwFv4/Acu4QXmrvxeGzWaySUTkhyk1GXhHvLY7lc5e5WJsK\n4N4vQVH0jiXuRpMhUFKI5Yk1vNE7lMQLuczaeUrvVOIulEUhtxuopyhKgKIo1sDDwKKr36AoSu2r\nvuwLHCmDn1s52LlC/V7Q+U1tz6o+X0FhFjVzjzG4lS8L9p0m+WKe3imFqDKmbUui2GRiTLu6sPIN\ncAuENk/pHUvcDe+mMGY9dH8fji2nT/FK/N3t+UZG5YQoNx8uO0Jo/n46F65HiXoRPEP0jiTulm8b\nraP64UV0rl+TdkHufL3mGDmFRr2TiTt014WcqqpG4FlgJVqBNldV1VhFUd5TFOXKZOrnFUWJVRTl\nIPA8MPJuf26l5RelfU7awpMdgzAoMionRFnJKTQyY/tJejXwIuDELLhwDHp+qE2tFJWbhSVEPg+e\nYVgkbuC5LvU4fDZL1ngIUQ62JVxg4Z5EvnGaATUCoP0rekcSZcFgAaF9IH41irGA13uFcimvmGnb\nkvROJu5QmayRU1V1maqqIaqqBqmq+r/Lx95SVXXR5f/9hqqqDVRVbayqamdVVePK4udWSk61wCME\nkrbg5WLLw63q8vueFM5m5uudTIhKb/auU2QVGHndJxrWvANBXbX9yETVEdQFTm6jXwNXAjwc+GZt\nPCaTjMoJUVYKikt4c0EM45xW4F5wCu79Aqzs9I4lykp4PyjOheNraVzXla6hNflx0wmyC4r1Tibu\ngPSQ1YN/FJzaDiVGxrQPxKSqTNt2Uu9UQlRqxSUmftl8nK/dF+K/8UWo0wL6/yRrOqqaoC7aGo+U\nHTzXJZgjZ7NYdfic3qmEqDLGr4uHiwmMLJkPDR+UBidVjX+Utpfq5T06X+hWj8z8Yn7ZmqRvLnFH\npJDTg38UFGbBuWjqutnTq6EXs3aeJFfmKAtxx1bsPcY7+R9yf+5caD5K2/jbwV3vWKKs+bUDC2tI\nWE/fxt4EeDgwYX2CrJUTogwcOZvFpI0n+LjWegwGC21quqhaLKyg/r1wdAUYi4io40q3sJr8tPkE\nWTIqV+lIIaeHq9bJATwaFUBWgZH5+1J0DCVE5aUWZtNwxUA6WxzEdM/ncN/XWsdDUfVY24NvW0hY\nh6WFgTHtA4k5ncn2hHS9kwlRqZWYVN5YEIO3bRGtc9dBowfByUvvWKI8hPeFwkxI3AjAi91CyCow\nyqhcJSSFnB6uWicH0My3Bk3quvLzlkRZ6yHEHUha9iUBppNsa/kdhtZj9I4jyltwVzh/GLLO0r+Z\nDx6ONkzcdELvVEJUajO2J3EgOYPxDY6hFOdBi0f1jiTKS2AnsHGGwwsBaOjjQvfwWkzefILMfBmV\nq0ykkNOLfxSc3AYlRhRF4bH2ASSl57E27rzeyYSoXPIzqBn9I5uUFrTuOVjvNKIiBHXRPp9Yj62V\nBaMi/dl0LI3DZ7L0zSVEJXUmI5/PVh6lQz0PIs7N17b88GmmdyxRXixtIKQnxC2DEm1Zzwtd65FV\nYGTq1kSdw4nbIYWcXvyjoCgbzh0EoFcDL3xc7Zi8WZ4qC3E70lZ/iYOaw7lmr2BtKZe0aqFmA3Co\nCQnrABja2g8Hawt+3JSgczAhKqd3F8diUuHzVjkoaXEyGlcdhPWF/ItwUpsd1tDHhR7htZiyJVFG\n5SoRuevRy9/WyVlaGBjZzp+diRc5dDpTx2BCVCK56Tgd+ImVamt6duuudxpRUQwGCOqsFXImEy72\nVgxu5cvi6LMkX8zTO50QlcqmY2msjE3l2S7B1Dz6K9i4aN0qRdUW3A2s7OHwotJDL3SrR3aBkRnb\nk3SLJW6PFHJ6+ds6OYBBreriYG3BlC0yrC3ErchZ/wXWJfkkNHgBFzsrveOIihTUBfLS4Vw0AKOj\nAlBArp9C3IYio4l3F8fi527PY80ctZv6JoO1pkKiarO214q5uCWl0ysbeLvQub4nU7cmkV9UonNA\ncSukkNOTfxSc3F76B+Rsa8XAlnVZfPAM5zILdA4nhJnLTsVm32QWmdpxX7fOeqcRFS3w8v/nl6dX\nerva0a+JD3N2J3Mpt0jHYEJUHtO2JZGQlstbfcKxiZ4FpmJoMVrvWKKiNBkCOamwb1rpoac6BZOe\nW8Tve5N1DCZulRRyevrbOjmAUe0CKFFVZu06pWMwIcxf0cYvUEqKiQ5+irpu8vS42nGqBbUalRZy\nAI93CCS/uIQZO07qGEyIyuF8dgHfrI2nc31Putb3gL1Twb89eNbXO5qoKCE9wbcdbPgICrMBaOlf\ng+Z+NZi08QTFJSadA4p/I4Wcnv62Tg7A192eTiGezN51Sv6AhLiZzNNY7P2Z+SUduL9re73TCL0E\ndYZTO6AoF4D6Xk50Ca3JL9tkWpAQ/+aT5UcpNJbw3z7hcHwtZJyS0bjqRlGgxweQmwZbv7l8SOGp\njkGczshnafRZnQOKfyOFnJ5usE4OYFhbP85nF7L6cKpOwYQwbyUHfsNCLWar9wgi6rjqHUfoJaiL\nNhUsaWvpoSc7BnExt4h5Mi1IiJvad+oS8/el8GhUIIGejrDnZ60TbGgfvaOJilanudbcZtt3kHUG\ngC6hNalfy4kfNiTI/sZmTgo5vZWuk/ur1WvHkJr4uNoxY7tMDxLiRrL3L+CAKYj7u0TqHUXoybct\nWDnAyjdKi7mW/jVo6uvKT5sTMcqsBiGuo6oq7y4+TE0nG57tEgwZyRC/EpoNB0trveMJPXR9C9QS\nWP8/AAwGhSc7BXI0NZv1R2V/Y3MmhZze6t+rrZPbN730kIVBYUgbX7afSOf4+WwdwwlhftRLSbhm\nxLLLNopOITX1jiP0ZGULg2dBSRH80hv+fAYl/xJPdAji1MU8lh86p3dCIczOythUDiZn8GqP+jja\nWMLeX0BVofkIvaMJvdTwh1aPw/5f4dwhAPpEeOPjascPG2R/TnMmhZzegrtet9AUYGCLulhbGJi5\nQ5qeCHG1pM1zAPBuOwiDQdE5jdBdYCd4eidEvggHZ8N3LehusYdADwcmbUpAVWVakBBXlJhUvlx9\nlEBPB/o38wFjkfYgOaQnuPrqHU/oqcOrYOsCq98CwMrCwOMdAtlz8hK7Ei/qHE7cjBRyelMU6PG+\nttB023elhz0cbbinkRfz96aQV2TUMaAQ5sUYu5Cj+NMtqo3eUYS5sLaH7u/CE5vAyRuLP5/m6XZe\nHDqdxbaEdL3TCWE2Fh08zbHUHF7uHoKlhUHbQyz3PLR4VO9oQm92NaDDa5CwVlvygzaoUMPeiilb\nTugcTtyMFHLmoE4LCL8fto2H7L8anAxr40d2oZE/D5zRMZwQ5uN4Qjz1Cg9zya8XtlYWescR5qZW\nA+jzFRRk0k9dg6eTDRM3yrQgIQCKS0x8tTqesNrO9G5YWzu452dtJC64q77hhHloMUpbd3xwFgB2\n1hYMbuXL6sOpJF/M0zmcuBEp5MxF17egpBA2flx6qLlfDUK9nJix/aRMDxICiFk9E4CwLkN1TiLM\nVt2W4NsOq10TebStD5vjL3DodKbeqYTQ3dw9yZy6mMdrPUO0aelpxyBpMzQfBQZ5MCYAawcI7wux\nf0JxAQBD2/ihKAozZX9OsySFnLlwD9L2b9k7Tbu4ou3lMaytH4fPZrHvVIbOAYXQ1/msArzOrCLN\nxg8Xv0Z6xxHmLPIFyExmuPN+HG0s+XGTTAsS1VtBcQnfro2nma8rnetfbhK152cwWEHTYfqGE+Yl\nYiAUZsKxFQB4u9rRq6EXv+06JUt9zJAUcuakw1iwsoe170JRHlxKor/nWe6xiWH2lsN6pxNCV3M2\n7qeVcgTrRvfrHUWYu3o9wKM+9ru/55FWdVkac1amBYlqbeaOk6RmFfJaz1AURdHuMQ7O0kZfHD31\njifMSUBHcKoN0XNKD41q509WgZGF+2Wpj7mRQs6cOHpC1Ava4uMPa8M3jbGb3pMflI8IipvIucwC\nvRMKoYvcQiPpexdioai4NH9Q7zjC3BkMEPk8pMbwRN2TGBSYsiVR71RC6CK30Mj3GxJoX8+DtkHu\n2sHYBVCQKU1OxPUMFtBoAMSvglytWVRzvxo09HHml22JstTHzEghZ27aPgud3oSub0O/CfDIXApr\nNaGdcogZO5L0TieELn7fk0ynku0UOtYFrwi944jKoNFD4OiF+4GJ9Gviw+zdp7iYW6R3KiEq3G+7\ntH/3X+wWoh0wFsH2CeAZBn7t9A0nzFPEw2AyagU/2lKfke0COJaaI52AzYwUcubGyg46vQ7tX4am\nQyGkJzahvWhoSGLRjiPkF5XonVCICmUsMTFnyyEiLWKxibhf27JDiH9jaQNtnoITG3g+LJeCYhPT\ntyfpnUqIClVoLOHHTSdoG+hOc78a2sF178H5w9D1v3I9FTfm1RBqNrhmemWfiNq4O1gzdWuSfrnE\ndaSQqwwCOmDARP3CGBYeOK13GiEq1MrYVMIyt2CFEcL66h1HVCYtRoG1E75xk+kWVpNp25LkYZio\nVubtTeF8diHPdA7WDhxfq2111OJRCL1X33DCvDUeBCm7IV3bwsXWyoJHWvuyNi6VU+my5thcSCFX\nGdRpgWppRx+neH7eIvOTRfWhqiq/bjjI6zbzUD1CwKeF3pFEZWLrAi1GQuxCnmtmzaW8YubuSdY7\nlRAVwlhiYuLGBBrXdSUy2B1y0uCPJ8EzFHr+T+94wtw1eghQIHpu6aGhbfywUBSmbU/SK5X4Gynk\nKgNLGxTf1nS2jiP+fA5bjl/QO5EQFWJ30iUeSvsWTy6h3D9Ra2IhxO1o/RQoBiKSZ9LM15WfNp/A\nWGLSO5UQ5W5J9FmSL+bzTKcgFIA/n9EanAz4WVvGIcQ/cfaGgA7a9MrLAwi1nG3p1dCLeXtTKCiW\n2Q3mQO6KKouADrhkHyPYoYCfpfuaqCb2L5vCAxZbKYl6Feo01zuOqIxcfKDRQyj7Z/JcGzdSLuWz\n7NA5vVMJUa5MJpUJ649Tv5YT3cJqwc5JEL8SenwAtRroHU9UFo0fhkuJkLyr9NAjrX3JzC9mafRZ\nHYOJK6SQqyz8OwDwUr3zrD+aRkJajs6BhChfSYnHePj8l5x1bIhVp7F6xxGVWbvnoDiPjlmLCPR0\nYNLGBJmiLqq01UdSiT+fw9OdgzBkn4HVb0FIL2g1Ru9oojIJuw+sHWHXpNJDbQPdCfRwYNauUzoG\nE1dIIVdZeDcFaye62MRhbWFg6lYZlRNVmMmEcf6TWFGCzaDJYGGpdyJRmdUKh3o9MOz6kacjvYk9\nkyVT1EWVpaoq368/jq+bPfc2qg1bvwbVBL0/ky6V4vbYOGnF/6EFkHYM0LYieKS1L3tPXuLI2Syd\nA4r/Z+++o6Oq0z+Ov+/MpJBKKklILyT0Fnpv0ouCCIKAoNjrrmXXsrv2uq5lFRVEUEBBlCIC0juB\nhBZKqIEUICGV9DJzf38MP8tagSTfmeR5ncMhzp2TfDzHjPe53+/3eaSQsxdGE4R1p1HGDka2DeLr\nfZlcLq9SnUqIWlG8/X2ii5NYG/Ig3iHNVccR9UGPh6A0h9HaVvzdnfhwyxnViYSoFbtO53Iwo5C7\n+0RhKsmCpHnQ7lZoHKo6mrBH3e63nqnc9sYPL43rGIyjycDCBFmVU00KOXsS0QtyT3JHW2dKK80s\nTcpQnUiImldVjmHbkOKu+QAAIABJREFUG2y3tKLNqIdUpxH1RVgPaNoRh93vMaNHKNtP5XA4s1B1\nKiFq3Oztqfi6OXJTh6aw423rYOdej6qOJeyVqy90ugOSl/wwiqCxiyMjWgfyzf5MSiqqFQds2KSQ\nsycR1nNyzSsO0j60MfN3ncNikXMeon6pPLQUl6p89gRNIcrfXXUcUV9oGnR/EPJTua3xYdycTHy4\nVVblRP1yKruYjSnZTO4ahnN5DiTNhbYTwStcdTRhz7o/AEYn2PbmDy/d2iWU4opqVh48rzCYkELO\nnjRpDc6NIXULU7uFk5pTwjY55yHqE12naMt7nLQ0pecN41SnEfVN85HgFYHL3veY1DmEVYfOy2Bb\nUa/M3ZGKo8nA5K5hsPMdMFfJapy4fm7+ED8dDn4BedYHYB3DvIht4s4C2V6plBRy9sRggPCekLqV\nYa0D8XVzYv7Os6pTCVFjzGl78Ll8lPUeY+gU4a06jqhvDEbrk+XMJO4OTsVo0Ji9XVblRP2QX1LJ\n0n0Z3NiuKb5chr1zoM148IlSHU3UBz0eBIMJtv0bsDY9mdQ1lOTMQg5lFCgO13BJIWdvIvpAQRqO\nRWnc2jmEjcez5YmyqDey1r/DZd2FiP7T0aS7mqgN7W8D7yi8tv6Dse2asDgxndziCtWphLhuC/ek\nUV5lYXrPCNj1LpgroNdfVMcS9YV7AHScBgcXQf45AMa0b0ojB6M0PVFICjl7E9HL+nfqNm7tEoZR\n0/hs91mlkYSoEZcv4J++mjUOAxjYNlJ1GlFfmRxh8EuQe5K/NN5KeZWFebvOqU4lxHWprLYwf9dZ\nesX4Elt1DPbMhlZjwTdGdTRRn/R4CDQDbHwBAA9nB0a1DWLFwfPSSV0RKeTsjV8cuPrBqXUEeDoz\nuFUAX+5Np6zSrDqZENfl/Mb3MegWjF3uxGSUjyZRi5oNhqgB+CW+xZhmTszfdZbSSum8JuzXquTz\nhBUd4O3Kf8KcQdZ28X2eVB1L1DeeTaHnI5C8GA4sAmBS11BKK80s35+pOFzDJHdL9kbTrB2ojq6A\nrKNM7RbO5fJqlh2QXyBhx6orcU3+jO1ae4b07qE6jajvNA2GvAyVxTzV6GsKSqtYvDdddSohrole\nkE70qltY7PQ8XsWn4IYX4OFD4ButOpqoj/o8AeG9YNWjkJ1Cm+DGtGrqwYKENHRdOqnXNSnk7FHP\nR8DJAzY8R6dwL+IC3Jm386z8Agm7lbPnSzzN+WQ1n4qrk0l1HNEQ+MVC55n4nVjE2KB8Pt6WSrXZ\nojqVEFft4tp/06wqhaTmT6A9dNDa0MfRVXUsUV8ZjDB2tvW/sSVTobKESV3CSLlYxL40aXpS16SQ\ns0cu3tDzYTixGi1tF9O6h5NysYi9Z/NVJxPimpRvf58zeiB9hoxXHUU0JH2fAOfGPGWcT2ZBKauS\nL6hOJMRVqzi9nUNaLC1vegIcXVTHEQ2Be4C1mLt0HFb9hVFtAnFzMrEgQc4b1zUp5OxVl7vBPRDW\n/YPRbYPwcDYxT0YRCDt0+dRugkuPcihoPP6echMi6lAjL+j/NN6XEpjklcKsLWdkZ4OwK2kXLhJS\ncZKq4G44OxhVxxENSWRf6zbLg4twPfoFY9oH8e2hCxSUVqpO1qBIIWevHF2g75OQsYdGqWu5pVMI\na45c5GJhuepkQlyV89//hyK9Ea2G3a06imiIOkyFRl7c6X2AYxcus+1kjupEQvxpW9d/i1HTad51\nsOoooiHq87h1LNaqvzA9PI/KagtL90nPhrokhZw9azcZfGJg/b+4rXMwFl2XZW1hV8rzzxOVvY7d\nHoOJDglSHUc0REYTxAwmLG8HQe4mPtx6WnUiIf6UwrIqyk5txYwRr2Y9VccRDZHBCOPmgnsAketn\nMqBpNQsSzsnOhjokhZw9M5pgwLOQc5zQ9OX0j/Vn0Z40KqplFIGwDye/excHqvHpf7/qKKIhix2C\nVpbHE60us+NULskZhaoTCfGHvtybRnv9GBVN2snZOKGOqw9M/AIqi3m9+lUyL+Wz+0ye6lQNhhRy\n9q75SGgaD1teZUrXEHKKK1mdfFF1KiH+kKWqgsBTi0hy6ED7dvGq44iGLGoAGBwY6ngAdycTs2RV\nTti4arOFL3acoJ3hDC7RvVTHEQ1dkxZw08d4FR7lLeePWSi7w+qMFHL2TtOg+/1QmE4v41EifV35\nVJqeCDtwdOPn+Or5VHW8E03TVMcRDZmzB4T3xPHUWiZ1DWN18gXO5ZaoTiXEb1pz5CJNipIxUQ1h\nMntT2IC4YWgDnmEYOwg7NotLRRWqEzUIUsjVB82GgrMnhkOLuK1bGAfSCziYLrM8hG1zSPyYDC2A\njgNl5ICwAbFDIfckd7YwYzIYmL0tVXUiIX7T7G2pDHY9ha4ZILSL6jhCWPV8lKKY0TxqWMzy7ftU\np2kQpJCrDxycodU4OLaSsS09cHE0Mn+XLGsL23Vi/zZiq46RHj0ZB5MMABc2oNkQAHwyNzK6XRBf\nJWVIG21hk5LO5XMgvYDB7mfQAlqDs6fqSEJYaRruve7DoOmkJG6istqiOlG9VyOFnKZpQzRNO65p\n2ilN0578letOmqZ9eeV6gqZp4TXxc8VPtLsVqsvwOP0tYzsEs/LQeXKLZVlb2Ka8Te9SihOtR9yn\nOooQVl5h4N8Sjq/h9h4RlFWZWbQnXXUqIX5h7o5UvJ0h4HKybKsUtiewDRbNRETFMdYckZ4Nte26\nCzlN04zAf4GhQAtgoqZpLf7nbTOAfF3Xo4G3gFev9+eK/9G0I/g2g4PW7ZWV1RaWJGWoTiXEL2Rm\nZtC+cCMp/sNx8/RWHUeIH8UOhbRdtGhcTfcoH+btPEuVWZ4oC9txqaiCtUcu8kDsZbTqcgjrrjqS\nED/n0AgtoCVdHVP5dIdsUa9tNbEi1xk4pev6GV3XK4EvgNH/857RwLwrX38FDNCku0HN0jRoOxHS\ndtHMlE3nCG8WJJzDYpFZHsK2JK1bhJNWReiAmaqjCPFzsUNBN8Op9czoGcHFy+WsPixPlIXtWJKU\nTpVZZ5TXWesLoVLICdujNY2ntXaGA2l5HMqQng21qSYKuabAT/efZFx57Vffo+t6NVAI+NTAzxY/\n1XYCaAY4uIjJXcNIzytjy8lLqlMJ8YPiimpcU9eSb/LDt1lX1XGE+LmgDuDqD8e/o1+sPxG+rszZ\nnirDbYVNsFh0Fiak0S3SB5+cRPBrbp3hJYStCY7H0VxCK8eL0km9ltlUsxNN02ZqmpaoaVripUtS\ngFw1jyCI7AcHv2BIC3983RxZsFuangjbsXT3CbpzkOqYodZVZCFsicEAzQbDqQ0YLFXc3iOcg+kF\n7EvLV51MCLaevERGfhmTOgVBWoJsqxS2q6l1NuyM8Dy+PXhBRhHUopoo5DKBkJ/8c/CV1371PZqm\nmQBPIPd/v5Gu6x/puh6v63q8n59fDURrgNrdCoXpOKbv4JZOIWxMySYjv1R1KiEwW3RSdqykkVaJ\nX/yNquMI8etih0HFZTi3g7EdgvFwNjFnu5zzEOotTEjDx9WRwT6XoLIIwqXRibBRPtHg5Ek/t3NU\nmi0s2pOmOlG9VROF3F4gRtO0CE3THIEJwIr/ec8KYOqVr8cBG3XZq1I74oaDkwccWMjEzqEA8gsk\nbML6Y1m0K91JlckNwnqqjiPEr4vsCyZnOLQYVycTE7uEsubwRdLz5IGYUOdCYRkbUrIZ3ykEh/Tt\n1hflfJywVQYDNO2AR94h+jTz4/Pd56RxVC257kLuypm3+4G1wDFgsa7rRzRNe07TtFFX3jYH8NE0\n7RTwKPCLEQWihjg0gpY3wrEVBBsL6B/nz5d702WWh1Duk22nuMG0D2PsYDA5qo4jxK9zdIFOd8DB\nRZCRyNRu4WiaxvxdZ1UnEw3Y8p3JTNDW8VDaQ7DuWeuoDI9A1bGE+G1NO0LWUaZ3bkL2lW6roubV\nyBk5Xde/03W9ma7rUbquv3jltWd1XV9x5etyXddv1nU9Wtf1zrqun6mJnyt+Q7f7rE1PFtzMlI7e\n5BRXyi+QUOpwZiHmcwl4cRlD8+Gq4wjx+/o+Ce4B8O0jBHk4Mqx1IF/sSae4olp1MtEAWVY9xh0J\nQ3jR4ROcK/Oh31Mw+SvVsYT4fcHxoJvp5ZpBsFcjFibI7rDaYFPNTkQN8YuF8fPhUgq9kh4lwsvE\n59L0RCg0Z3sqwxz3oxscIHqQ6jhC/D4ndxjyMlw8BHvnML1HOEUV1SxJlAHhoo5lJmHY+xGrzF3Y\nOWg53JcAfR63NjcTwpZdaXhiOJ/ExM6h7DydS2pOieJQ9Y8UcvVV9AAY+Q5a6mZmec4nITWXE1lF\nqlOJBijrcjkrD2Yy2nk/WkRvcPZQHUmIP9ZijLUL8Mbnae9VSYfQxszdcRazzOYUdWnbvykxuPEf\n53vp3LW3dPsV9sPNDxqHQmYiN8cHYzJo0rOhFkghV5+1nwR9/07sxW/5q+NSOeMhlPhs1zkiyMCn\nIgPihqmOI8Sfo2kw7A2oLofvn2ZGz0jS8kpZfyxLdTLRUFw6Dinf8knlQEZ2jsVklFs2YWeaxkNG\nEv7uzgxq0YSvkjKoqDarTlWvyKdCfdfncWh/G/cbvqYw6RsKy6pUJxINSHmVmQUJ57i3SYr1hVgp\n5IQd8Y2GHg9D8mKGuJ6gaeNGfCKjCERd2fE2VQYn5luGcuuVLtRC2JXgeLicAUUXmdg5lLySStYe\nkYdhNUkKufpO02DEW5T5tOTvhrks252iOpFoQL7el0l+aRWDjEkQ1EHOdQj70+tR8ArH+P3fmdYt\njITUPA5nFqpOJeq7gnT0Q1+yxNKf+BYxBHg6q04kxNW7ck6OjER6RvsS6u3CwgTp2VCTpJBrCIwO\nNBrzNk20fJx3vCZnPESd0HWdT3akMiCgFLecg7KtUtgnh0bQ5wnIOsytvqdwcTTKqpyofbveQ9fh\nvfKh3NYtTHUaIa5NYBswmKxNewwaEzqHsPtMHqcvFatOVm9IIddQhHQiPfxmxlZ9y97dW1WnEfVd\nWT7H18ziqfxn+LjwbusHefPRqlMJcW1ajQP3QFyT3md8fAgrD50n+3K56lSivirJhaR5bHbsSyO/\nMLpF+qhOJMS1cWgETVpCZiIAN3cMsTY9kVEENUYKuQYkaNwrXNbc8Nn8JFhkQLioJYcWw+sxxCU8\nSazxAnrXe2HmFvBrpjqZENfG5Ahd7oYzm5nZrJhqi878XbI9SNSShFlQXcaLlwdzW9cwNOlUKexZ\n03jI3A8WM37uTtzQsglf7cugvEqantQEKeQaEAc3H/bFPkpM5VGyt36sOo6or3a+Q4VnJKMqnufr\nXqswDn4eAlqpTiXE9ek4DRzdCDoym4HNm7Ag4ZzciIiaV1EEez7ksHsvzptCualjsOpEQlyf4Hio\nLIIVD8C6Z3mi0QpuqljBpsTDqpPVC1LINTDtR9zDHr05bttesG7fEKIm5aXCxWTWOw/kuDGGW7uG\nq04kRM1o1Bg6TIXDS7m7nRP5pVUs25+pOpWobxLnQnkh/8wfzJj2TfFwdlCdSIjrE9kPfGPh+GrY\nPYuwQ//hWYfPsGx9Q3WyekEKuQbGx92ZLdF/w6G6hPKt/1EdR9Q3x1YA8GZ6LDd1CMbb1VFxICFq\nUNd7AOhwfhHNAz2Yu+Msui7No0QNqa6AXf8l06szidWR3NZVmpyIesAjEO7fA0+kwjPZ8EwuGT49\naFGSwKGMAtXp7J4Ucg3Q0P592W1pTlnyStVRRH1zdDlZbs05U+3LjJ7hqtMIUbMah0Crm9D2zWNm\nZ2+OZxWx87TsbBA15OAiKL7Iv8uGEx/mRYsgD9WJhKh5RhO+HUYSYcji283bVaexe1LINUCtmnpy\nyqsnXqWpVF06rTqOqC8KMyAziSWlHejTzI9of3fViYSoed0fgMpiRlR9j4+rI3N3yCgCUQMsZtjx\nNkXerVhaEC0jB0S95hx3AwDVx9eRU1yhOI19k0KugYrrNQ6Ao1uWKE4i6o1j1hXer8o6MKNnhOIw\nQtSSwLYQ0QeHvR8ypVMAG1KyOZtTojqVsHdHl0HeGT43jcXH1YkhrQJUJxKi9vhEUekZTg8OyiiC\n6ySFXAPVpWM8aYZgqlPWyBkPUSP0o8tJNYbj4BdDrxhf1XGEqD09HoKiC9zuthuTQePTnWdVJxL2\nTNdh+1tUeUXzRnoMt3QKwclkVJ1KiFrlGHsDPU1HWbz7JFVmGYl1raSQa6AMBo3y8IG0qkpm+xHZ\nGiSuU1EWpO1mWXlHpveMkLlHon6L6g9B7fFI+i+jWvuzJDGdy+VVqlMJe3VqA1xMZoP3RCwYuLVL\nqOpEQtS+6IE46RWElRxi7ZGLqtPYLSnkGrCI7mNx0qrZs+Eb1VGEvUtZiYbODqee3Ni+qeo0QtQu\nTYNef4H8VB4KOEJJpZkliRmqUwl7tf3f6O5B/PNsCwbE+RPs5aI6kRC1L7wnutGJkS5HmCe7Gq6Z\nFHINmENENypMbgRf2iotYMV1KTv4DactgXTr0h1nB9kSJBqA2OHgF0fo0Vl0CvXk052pmC2yTV1c\npbQEOLeDI+FTuFiic1u3cNWJhKgbjq5oYd25wTGZvWfzOZxZqDqRXZJCriEzOmCIGcgA434+2nJK\ndRphr0pyccrYxVq9i9yEiIbDYICej0L2UZ6IOkt6XhkbjmWpTiXszfa3oJEXr2R1IczHhV7Rcr5Y\nNCAxg/AqTSXKIU9W5a6RFHINnEPcMHy1QtKP7OJcrnReE1evNHkFBsyURQ/H38NZdRwh6k6rseAV\nTsdzn9DU05m5O86qTiTsSdZROLGaSy1uZ3taGZO7hGEwyPli0YBEDwTggbCzLD94nrySSsWB7I8U\ncg1d9EB0NAYa9zN7mzQ9EVcvd88XpFn8GDzgBtVRhKhbRhP0eBjtfBJPxmWx60wuxy5cVp1K2Ivt\nb4GDKx+WD8TJZGBcx2DViYSoW77NwDOUgQ7JVFZbWLRHRhFcLSnkGjpXH7SQztzoepjFienkymBG\ncRWqs08SkrebBI8baBXcWHUcIepeu1vBPZAh+Qto5GCUAeHiz8k/C4eXUtluCguTixjZNggvV0fV\nqYSoW5oG0QNwy9xJr0gPFuw+R7WMIrgqUsgJaDaY4PLjeFbnMm/XOdVphB1JX/sOlboRv753q44i\nhBomJ+j+AA5p23kq6jTLDpyXB2Lij+18FzQD3ziPobTSzG1dw1QnEkKN6IFQWcSDMXmcLyxn3VE5\na3w1pJAT0GwIAPcFn2b+rrOUVlarzSPsQ0Ux/me+YoupB706tFadRgh14mdAUHtuzXyJEHM6CxNk\ne5D4HcXZsP9zLG0n8P6+MjqGedE2RHY0iAYqsg8YTHSsTCTYqxGfStOTqyKFnAD/FuAZwhinRApK\nq1i8N111ImEH0jd/gqteSmWHOzDKAX3RkDk4w/jPMDg48ZnrOyzddYzKatkeJH7D7vehuoJdAZM5\nl1vK9B4RqhMJoY6TO0QPxLBvHne1dyEhNU/OGl8FKeSEdY9yx2l4Zm5lYuBFPt6WKnuUxe/TdUxJ\nszlMFH0HDFOdRgj1GofAzZ8SaD7PkxXv8N2hTNWJhC0qL4S9c6DFaN47CEGezgxu2UR1KiHUuuFF\nqC7nltz3cXYwyCiCqyCFnLDqcje4+vGYw2IyC0pZlXxBdSJhw3KSvyew8hxnIifh6uygOo4QtiGi\nN/qg5xhi3EvR+tfQdRkQLv7HgYVQcZkzcTPZdSaXqd3DMRnlVkw0cL7R0PsxHI8v58moNJYdyCRf\nRhH8KfLpIayc3KD3Y3hn7+Zmr1N8uOWM3ISI35S38V1ydQ86DpuhOooQNsXQ7T5SA4cxqeQzTuxe\nrTqOsDWn1oNvLB8cd6ORg5EJnUJVJxLCNvR4CPzimJTzDlpVKV8myjGfP0MKOfGjjtPAM4QnHZdw\n9EIh20/lqE4kbFBp1mmiC7azx3sUTX3lgL4QP6NpNJn8IdmaN9qWl1WnEbbEXAXndlEW3IPlB84z\nrmMwni6yo0EIAEyOMOI/OBRn8JrPKj7bdQ6zRRYU/ogUcuJHJifo+yQ+hYcZ73qQD7ecUZ1I2KDU\n1W9j0TWaDrpPdRQhbJKLqwfHwqfQrPwQGcmbVccRtiJzH1SVsKE8lkqzhWk9wlUnEsK2hHWDjtMY\nXrqMxoXHWH9MRhH8ESnkxM+1mQC+zXjS6St2nsrmcGah6kTChlhyUwk7u4QE5+60adFCdRwhbFbr\nUQ9QoLtSuO511VGErUjdCsBbJ/3pF+tHlJ+b4kBC2KCB/0Rz8eE95/dZv3mz6jQ2Two58XNGE/R7\nCu/SM0xw2sVHW2VVTlxRWULJ/Fuo1jXKez+jOo0QNs3X24d9AeNpeXk7uWcPqY4jbMHZreR7xHG6\nxIkZPSNVpxHCNjXyQhv7MU1MJbyYfS853z4H1dL45LdIISd+qfkoCGzLE05L2ZScSnpeqepEQjVd\nh+X341p4kn85Pkqfrp1VJxLC5kWPeJQy3ZHzq15RHUWoVlWOnpbAhvJY4gLc6RHtozqRELYrsi8V\nM3exRu+Kb+Kb8HE/OL9fdSqbJIWc+CWDAYa8ikdlFk8YFzJne6rqREK1HW/Dka95rWo8cT1vlHbZ\nQvwJoSGh7G48nLhLayi5dE51HKFSxh40cwXfFcdwd58oNE1TnUgIm+blF8j2Ni9zr/kxLMWX4JMh\nUHRRdSybI3dj4teFdUPrei+TjetI3fsdeTLPo+E6tQE2/IsDHv2YZxgj7bKFuApNBv8FTdc5s/I1\n1VGESqnbMGMg3b09I9oEqk4jhF2Y2j2c76ra802Lt6G6HE5vVB3J5kghJ35b/6ep9IzkRcMsvth2\nVHUaoULxJfhqOtU+sUzNncrN8SHSLluIq9CiRWt2ufQlKu0rKotyVccRihSlbCTZEsGk3i1lR4MQ\nf1LLIE86hXvx9mEHdFc/OL1JdSSbI58m4rc5uuA4dhZBWi7+CS9QVmlWnUjUtaS5UF7A5yH/5LLF\nkdt7RKhOJITdcer7KC6Uc3rFq6qjCBUqimmUvZ99xjbcIjsahLgqU7uHk5ZfwUWfrnBms/XMvviB\nFHLi94V2IavlHYzT17Fj7WLVaURdMldB4ieYIwfw7iEjA+L8ifB1VZ1KCLvTqXNPNjr0odnJj7Gc\n2606jqhj6Yc2YcKMV4sBNHI0qo4jhF0Z3DKAAA9nVhTFQkk2ZB1RHcmmSCEn/lDA6OfIMIbQet/T\nVJcXq44j6sqxlVB0gR3eN5FbUsn0nrIaJ8S10DSNisGvk2HxpeKLaVCapzqSqEMnd6+iUjfSd9BI\n1VGEsDsORgOTu4byyYVw6wtyTu5npJATf0hzdOFC93/RRM9h/4YvVMcRdWXPx+he4bx0IojmgR50\ni5R22UJcqxs6NONlt8cxlV1CX3aPbA9qIDLyS/G9lMAF91Z4NW6sOo4Qdmly1zCKHf244BgGZ+Sc\n3E9JISf+lI59RpOnNaZ0/1IsFrkBqfcuJkPaTs6ETyAlu4wZPSOkXbYQ18Fo0Bg0YCgvVt2KdmIN\n7H5fdSRRBz7ffIiWWipeLQeojiKE3Wrs4sjU7uGsKY3DcnYHVJWrjmQzpJATf4rBZKIwfCidqxL5\n/sAZ1XFEbdvzMZga8UpWPE08nBjVNkh1IiHs3qh2QWzwGMMux+7o6/4BGUmqI4laVFhWRfr+9Rg1\nHY/mUsgJcT1m9IwgQWuHwVwB6XLW+P9JISf+tNBet9JIqyRp/ZeyKlefleXDocXkRY9h3ZlKpveI\nwNEkHxVCXC8Ho4F7+8Vw1+VpVDRqAsvvlS2W9dji3acZrG/HYnSC4E6q4whh13zcnIjqPJhK3UjB\n4e9Vx7EZcncm/jRjeA/KHX1oW7SZ9ceyVMcRtWX/51BdxkdlA3BzMjGxi7TLFqKm3NShKa6evnxi\nHA+XUiA9QXUkUQuqj69l0NabGGXchaH9ZDA5qY4khN2b2rclB2hG8dF1qqPYDCnkxJ9nMOLYehQD\njAf4aMNhdHmSXP9YzLB3NuVBXfjohAuTuoTi4SwDwIWoKU4mI3f1juS97FaYTS6w/zPVkURNyjkF\nn4/DtGg8FrOZAz1nwfA3VacSol7wd3emOKgnweUnyMxIUx3HJkghJ66KoeWNNKIC34vb2Hz8kuo4\noqYdXw35Z1nhNBKjQZMB4ELUggmdQ3Fx82SbYy84/A1UyFiXeqE4G2YPQE9P4BOXGdzl/h5t+k8A\naRQlRI1p2+dGALZ+v1RxEtsghZy4OmE90F18GeecyDsbT8qqXH1SXQHrnsHsHc1zp8IZ3a4pAZ7O\nqlMJUe84Oxi5u08k7+R3g6oSOLpMdSRRE1Y/AVWlHB66lOfyBjClVzMMBinihKhJPs26UmZ0w3R2\nM5kFZarjKCeFnLg6RhNa85H00ZI4mpbNjlO5qhOJmrLrPcg7w4rAhymuMjCzd6TqRELUW5O7hnHR\nvTUZxmD0fbK90u6dWAtHvobej/FesoHGLg6M7dBUdSoh6h+DET28Nz20ZD7cfEp1GuWuq5DTNM1b\n07R1mqadvPK312+8z6xp2oErf1Zcz88UNqDlGBzMZdzodpR3Np5UnUbUhMJM2PoG5mbDeT4lgP5x\n/jRr4q46lRD1lrODkYdviOWz8l5o6bshRz5L7VZFMaz6C/jFca75nXx/NItJXUJxcTSpTiZEveQS\nN5AgLZc9iQlkXW7YM+Wud0XuSWCDrusxwIYr//xrynRdb3flz6jr/JlCtbCe4OLDTJ9D7EnNY/cZ\nWZWze+ueAd3Ciib3kldSKatxQtSBsR2C2e81BDMGLLIqZ782vgCFGTDyHebuPo/JoDGlW7jqVELU\nXzGD0NEYpu3gwy0Ne7bx9RZyo4F5V76eB4y5zu8n7IHRBHEjiMjfTlNXeFdW5ezb2e1weCnmbg/y\nZmIlHUIb0yXCW3UqIeo9o0FjxtCubDS3pyJpAZirVUcSVysjCRJmQacZFPi2Z3FiOiPbBNHEQ84X\nC1FrGoeixQx6mJ8NAAAgAElEQVRimtNmluw5zaWiCtWJlLneQq6JrusXrnx9EWjyG+9z1jQtUdO0\n3ZqmSbFXH7Qcg1ZZzMvRx9hxKpekc/mqE4lrYa6G7x4HzxC+dR9PRn4Z9/aNRpMua0LUiRtaNGGv\n1zAaVeRQmbJWdRxxNcxVsPIhcA+EAf/gs13nKK00M7OP7GgQotZ1uhOP6jz6WXYze1vDXZX7w0JO\n07T1mqYd/pU/o3/6Pt3avvC3WhiG6boeD9wK/EfTtKjf+FkzrxR8iZcuSWt7mxbRB8J60uvMm3R0\nyZJVOXuVNBeyj2C54QXe3X6euAB3+sf5q04lRIOhaRr9R07mku7J+U0fq44jrsbmVyArGYa/QbnR\nlU93nqVfrB9xAR6qkwlR/0UPBK9wHvLYwme7z5FXUqk6kRJ/WMjpuj5Q1/VWv/JnOZClaVogwJW/\ns3/je2Re+fsMsBlo/xvv+0jX9Xhd1+P9/Pyu8V9J1AmDEcbNQXN05WPnd0k4ns6hjALVqcTVqK6A\nbW9CaHfW6V04lV3MPX2jpF22EHWsa0wAezwGE5yzhaK0Q6rjiD8jLQG2/xvaTYa44SxJyiC3pJK7\n+vzqc2ohRE0zGCB+BlFlyYRWpfLJ9lTViZS43q2VK4CpV76eCiz/3zdomualaZrTla99gR7A0ev8\nucIWuAfA2Nl4labyqvM83lkvq3J25cACKLqA3udx3t98mlBvF4a3DlSdSogGKWrM3yjQ3Sj68i45\nK2frKorgm5ngGQxDXqbabOHjrWdoFyLni4WoU+0ng8mZp/y28+nOsxSWVqlOVOeut5B7BRikadpJ\nYOCVf0bTtHhN02ZfeU9zIFHTtIPAJuAVXdelkKsvIvui9XmCUWzB6+Rijp6/rDqR+DPMVbD9LWga\nz05LKw5mFHJXn0hMRhktKYQKcVGRrAp+hKCSoxRsfEt1HPF71vwNCtLgxo/A2YM1Ry6SllfK3X0i\n5XyxEHXJxRtaj6NH6QYMFYXM3dnwVuWu665N1/VcXdcH6Loec2ULZt6V1xN1Xb/jytc7dV1vret6\n2yt/z6mJ4MKG9Hmc6rDePG+ayzdr1qhOI/6M5CXWG5Hej/Hfzafxd3dibIdg1amEaNAGjbub7y2d\ncN35qsyVs1Upq2D/Z9DjYQjrhq7rzNpymkhfVwa1CFCdToiGp9OdGKrL+HvQfj7ZnkpRecNalZPH\n7+L6GYyYbp5DtaMnU8/9jdOpp1UnEr/HYraejWvSmv3Ondl5Opc7e0Xi7GBUnUyIBi3Iy4WT8f+i\nxOJIyeK7rL+rwnaU5MCKByGgDfT9GwA7T+dyOPMyM3tHYpTzxULUvaB2ENyJG6tXU1Reyfxd51Qn\nqlNSyIma4eaPZcIXeFOE6cuJUFmiOpH4LUeXQe4p6P1X3tt0Gs9GDkzsEqo6lRACuO2GzrxpmI5r\ndhL6no9UxxE/dWABlObAjbPA5AjArC2n8XN3Ykz7porDCdGAdboTp8upLPT+hOgtD2CeOwLe7wab\nX1WdrNZJISdqjEdUJ1bFvkBw2QlKFk6Tp8m2yGKBrW+CbyyHPHqzISWbO3tF4OZkUp1MCAF4ODsQ\nM2gGG83tsKz7F+Q3rKfLNu3IMghqD01aAnAwvYBtJ3OY3iNCdjQIoVLLMeDbjE7VicRYznCpoMja\nmXvnO1BVpjpdrZJCTtSofqOm8qI+Ddez38Pav6uOI/7XiTWQfQR6/YX/bDhNYxcHpnYPV51KCPET\nE7uE8aH7/VSbzVg2vqg6jgDrmeLz+6DFjyN0395wksYuDkzuKjsahFDK5AT378X093SeDZ3PiJKn\nqRj8OlQWw8nvVaerVVLIiRrl6+YEnWYy2zwMEmZBgmwNshnmatj0EniFc6DxADamZHNnr0jcnR1U\nJxNC/ISD0cD04b34tHoQWvJiyJJGz8odXWH9+0ohdzC9QD5DhbBBD/SPJqe4koVZoeDqB4eXqo5U\nq6SQEzXurj6RvK5P5oRbJ9j8knV5W6i3+7+QlQyDnuPtjWdo7OLAlG5hqlMJIX7FDS2akBQ8jWKc\nqVz3nOo44uhya5MT70jgx9U4+QwVwrZ0ifShc4Q3s7afozpuNJxYa539WE9JISdqXBMPZ27pFMZL\nBQOhLB+OrVQdSeSlwqaXIXY4+117sen4JXmSLIQN0zSNv4zpxuzqETieWg3pe1VHargKMyBjj6zG\nCWEnHhoQQ9blCtYbe0B1ORxfrTpSrZFCTtSKu/tEsVNvRb5jIOybrzpOw6br8O3DYDDB8Df4z4ZT\neMnZOCFsXmyAO2Ud7yJH96B49bOq4zRc//8wssUYQFbjhLB13aN86BjmxT/3u2FxD6rX2yulkBO1\nIqhxI26OD2NuaU9I3WJdERJqHPwCzmyGgf9gX0Ejtpy4xJ29I6VTpRB24L7B7fjEMBa38zvRT29S\nHadhOroc/FuCb7SsxglhBzRN429D47hYVMUBj/5wagOU5qmOVSukkBO15qGBMaw09MOCAfZ/rjpO\nw1SSY+0eGtIF4mfw1roT1tW4buGqkwkh/gRPFwfCbrifDN2Xgm+fsa6wi7pz+QKk7ba2N0dW44Sw\nF/Hh3gxtFcDL6S3AUgUp36qOVCukkBO1xt/dmZG9OrHJ3JbKpM+sXRNF3Vr7d+sh35HvsCs1n20n\nc7i7TxSushonhN0Y1yWKJW6T8cpPpuLwctVxGpZjKwEdWoxmf1q+rMYJYUeeGBLH/upwch2b1tvt\nlVLIiVo1s3ck3zkMwrE0C72ez/KwOZeOw6EvoceD6H6xvLY2hQAPZzkbJ4SdMRo0eo+7nzOWAPLX\nvCyrcnXp6HLwi0P3bcbz3x7F181JPkOFsBPhvq7c1i2cRaWd0FO3QnG26kg1Tgo5UavcnEy0HzCe\nS7onOVtnq47TsOz5CIxO0PVe1h/LZn9aAQ8NjMHZwag6mRDiKnWM8COx6RQCSlLI3Fd/O7DZlOJs\nOLcDWoxmxcHz7Esr4PHBsXK+WAg78mD/GDaYeqLpFuuDmXpGCjlR627pGsU6xwF4n99EdUGm6jgN\nQ3khHFgErcZibuTD62tTiPB15eaOwaqTCSGu0YBbHiAbL/LWvoouq3K178BCQKc8ZiSvrE6hZZAH\nY+UzVAi74uXqyND+/TluCebyngX1bkeDFHKi1jkYDQT3n4kRC0e++1B1nIbhwCKoKoEuM1l+IJMT\nWcU8OqgZJqP8ygthr3wae5AZN4PWlQfYtHGN6jj12/HVsOE5iBrArGOOXCgs5x8jW2I0aKqTCSGu\n0pRu4axyGopHzn4su95XHadGyV2dqBO9unYl2aENASc+p+xCiuo49ZvFYt1WGdyZSv+2/HvdCVoG\neTC8daDqZEKI69R29EMUa27o296ioLRSdZz66dxOWDINAttycciHzNp6huGtA+kc4a06mRDiGjg7\nGGk24hHWmDvBumfg7HbVkWqMFHKiTmiahsOgZ3HUKzB93Mc6JLyeLW/bjNMbIe80dJ7Joj1pZOSX\n8djgWAzyJFkIu2do5EFZu+n00/cwd7k0kKpxF5Nh4QTwDIFJX/HKhgwsOjw5NE51MiHEdRjeJohF\ngU9yTm+CZfE0KKwfR32kkBN1Jq7zIF6P/IREcxSseACWTIWyfNWx6p89H4JbE4qihvHuxlN0ifCm\nTzM/1amEEDXEb+BDmI2OND3yEfvS5DO0xuSlwudjwckNbvuGfbkGlh04z529IgjxdlGdTghxHTRN\n47HRnZhZ+TBV5SXWe9DqCtWxrpsUcqJO3TOyF9PMT7HS/y5IWQUf9KyX7WCVyT0NJ9dBx9t5b2sa\nOcUV/G1YczRNVuOEqDdcfdHb38aNph28tXQT1WaL6kT2z1wFX04GcyVM/hqzRzDPLj+Mv7sT9/aN\nVp1OCFEDWjX1JD6+G49WzISMvbDmSdWRrpsUcqJOhXi7ML1nFA+k9eHk8CVQdB52vqM6Vv2xdzYY\njKRF3sIn21MZ1zGYdiGNVacSQtQwx54PYtR0BuYuZN6uc6rj2L/d70PWYRj1LvjHsXBPGoczL/P0\niBa4yrgBIeqNv94Qy1aH7qxyHw+Jn0DyV6ojXRcp5ESdu7dvFL5ujvx9rzN663Gwdw6U5KiOZf/y\nUmH/Amgxhn9tysXRaODxIbGqUwkhaoNXGFrH27nNtJ7136/kYmG56kT2K/8cbHoZYodD85HkFFfw\n+poUukf5MLKNNIkSoj7xcXPioQExPHhpJIU+7eHbRyD/rOpY10wKOVHn3J0deHRQLHvP5rO1yTSo\nKoNd/1Udy36V5MDqJ+C9TmCpIrHpZDakZPPAgBj83Z1VpxNC1BJt4D+wuAXynPYhL63YrzqOfdJ1\nWPUX0Aww7DUAXlmdQlmVmedGt5Rt6ULUQ1O6hRPm58FdZfego8PSO8FcrTrWNZFCTihxS6cQ4gLc\neXpHBeYWY6zt8kvzVMeyL1XlsOV1eLsd7PkY2k+i8t5EHt+pEe7jwu09wlUnFELUJmcPTKPeJkbL\nIOr4R2w+LueNr9qRb+DUOuj/NHgGk3g2j6+SMpjRM5Jof3fV6YQQtcDRZODZES3YnefGmvAnIWMP\nbHlVdaxrIoWcUMJo0Hh6eAvS88r43HE8VBZDwizVsexHeSF8diNsegEi+8C9u2Hk28w/XMGZSyU8\nM6IFTiaj6pRCiNrW7AbMrcZzn2k5n36zivIqs+pE9qOswNrsILAtdJ5JtdnC08sOE+TpzIMDpMGJ\nEPVZ31h/RrQJ5KEjURTFjYdtb8DZHapjXTUp5IQyPWN8GdMuiBf2QnHEUNg9y/o/VvH7irPh0+HW\nJ0hj58CEBeDXjJziCt5ef5I+zfzoH+evOqUQoo4Yh76C7uTJI6Xv8P7GFNVx7MeG56DkEox8G4wm\n5u86R8rFIp4d2QIXR2lwIkR99+zIFjibDDxQMAHdKxy+nml3u8OkkBNK/X9HsH8WDoeKQusWS/Hb\nCtLgkyHWMQMTv4TW43649Mba45RVmXlmRAs51yFEQ+Lqg8PIN2lrOEPV9vc4lV2kOpHtS99j7VjX\n+S4Iak9abimvrz1O31g/BrcMUJ1OCFEH/N2deXJoczafLWd9i5egJBu+vM2u5stJISeU8nVz4qlh\nzfnqvDcZ/n2sTU9OrrfOQju+GlK+gwq5KQHg0gmYMxhKc+C2ZRAz8IdLyRmFfJmYzrTu4UT7uykM\nKYRQouWNVEQP4WHjEj74cgUWi646ke0yV8HKh8EjCPo/hcWi89hXBzEZNF66sbU8CBOiAZnQKYT4\nMC8e22mkaMjbcG47LL/f2gjJDkghJ5Qb1zGY7lE+/DVrMJQXwIKxsGAcLJoAX0yEeaOs/+NtyCxm\nWDodLFUw7TsI7fLDJV3X+dfKI3i7OPLgwBiFIYUQymgaTmPew+LkwcxLL/FVwknViWzXrv9C9hEY\n9jo4uTN/11kSUvN4ZkQLgho3Up1OCFGHDAaNl29qTUlFNc+eaQEDnoXkxbDxBdXR/hQp5IRymmZ9\nCrrfHMnzoXNg+lq4YwPcuQlG/AfO74NNL6mOqdbBRXAxGYa8AgGtfnZpxcHzJJ7L57HBsXg4OygK\nKIRQzs0P53EfEWvIwLz2abKLZLbcL+Sfhc2vQNwIiBvO2ZwSXl1j3VJ5c3yw6nRCCAVimrhzd58o\nvtmfyWa/ydBhqrX5SdKnqqP9ISnkhE0I93XlwQExzDnRiLVF4RAcD007QPzt0GEKbH8LUreqjqlG\nRTFseB6axkOrsT+7VFpZzcvfpdCqqQc3x4coCiiEsBVazEAK2s5kImv5etFs1XFsy//PjDMYYeir\nP26pNGq8clMb2VIpRAN2X79oYvzdeHxpMvn9XoHogfDto3Bqvepov0sKOWEzZvaOpEWgB099k0xu\n8U8Omg55BXyi4eu77K6bUI3Y+Q4UX4QhL8P/3Gh8sPk0Fy+X88+RLTEa5CZECAGNR77AJddm3Jz5\nCjv2H1Ydx3Yc+cZ6U3ZlZtzcnWfZezaff4xsSYCns+p0QgiFnB2MvHVLO/JKKnl6ZQr6uLng3wKW\nTIcc292qLoWcsBkORgP/vqUtl8uqeXrZYfT/P2jq6Arj5ljbRK94wG4OoNaIwkzY8Q60vAlCOv/s\nUnpeKR9uPcPodkHEh3srCiiEsDkmJzxvm4+rVkGjFTMpvWi7NyF1pjTvysy4dtB5JscuXOa1NSkM\niPNnbIemqtMJIWxAq6aePDKoGasOXWBFShFMXAhGB2vPBhsdjyWFnLApcQEePDKoGasPX2T5gfM/\nXghsCwP/CSnfQtJcVfHq3obnQLdY/91/Qtd1/v5NMiaDxpND45REE0LYLseA5lzo8SJtLUdxmRUP\nn46A5K+gqgGem7NY4Os7oSwfRr1DSZXOfQv34dnIgVfHyZZKIcSP7uodSccwL55edpjz+MEtn1nP\n1i6dYW08Z2OkkBM2Z+aVX6Jnlx/mYuFPbjq63gtRA2D1E5C+V13AupKZBIe+gG73glfYzy59uTed\nbSdz+Nuw5gR6Spc1IcQvRQyayX9aL+P1qvGU55y13oj8u3nDO2+87Q3rlsohr6AHtOHpZYc5m1PC\n2xPa4+vmpDqdEMKGmIwG/j2+LWaLzl+XHMQS0g2GXfkMWf8P1fF+QQo5YXOMBo03b25LlVnniaWH\nftxiaTDA2NnW2T9fToLL53//G9mzimLrIVtXP+j56M8uZRaU8cKqY3SL9GFS51BFAYUQ9uCeUT1Z\n4TmRoZa3qZj4Nbj5w8IJ1oHYDcHpTdaux21ugfjpLE5M55v9mTw8sBndonxUpxNC2KAwH1eeGdGC\nnadzmbM91dp4r9OdsPNdm+tkKYWcsEnhvq78bVgcW05c4rPd53684OINExZBZQl8MQmqytSFrC1V\n5fDFrXDxEIx8G5w9frik6zp/+zoZi67z2rg2GKTBiRDid7g4mnh1bBtS88p541QgTFkO7k3g83Fw\n4aDqeLWrMNO6CukXByPeIiWriGeXH6FntC/39YtWnU4IYcMmdAphcMsmvLImhd1ncq0N5yL7wsqH\nYNl9UFGkOiIghZywYZO7hNE/zp/nVh5l+8mcHy80aQE3fWSdL7fyofrV/MRcBV/dDqlbYPT7EDf8\nZ5eXJGaw9cQlnhwaR4i3i6KQQgh70j3Kl0ldQpmzPZV9+U4wZYX1AdFnN8Kl46rj1Y7KElgyDaor\n4JbPKNaduG/BPjwaOfDWLe2ky68Q4ndpmsYbN7clzNuF+xfu40JxNUz6Cno/BgcXwqyekJagOqYU\ncsJ2GQwab09oR5SfG/csSOJU9k+efsQNh35PwaEvre356wOLGZbdA8e/s+7HbjfxZ5cvFJbx/LdH\n6RLhzeQuYb/xTYQQ4peeHBpHgIczj391iHLXIOvKnMEE80ZZD/LXB7oOabth+f3wRixk7IFR72Lx\njuaRLw9wNreUtye0w89dzsUJIf6Yu7MDH97WkbJKM/d8vo8K3WAdX3L7auvnzdwhsPlVpQsKUsgJ\nm+bu7MCcafE4mYzc/unen8+X6/0YtBgD656FxE/UhawJFgt8+wgkL7F2qOx8588uV5ktPPzFAaot\nsqVSCHH13J0deGVsG05lF/Pyd8fAJ8pazFWVWT977H1nQ+pWeC8ePhkMh7+GFqNhxjpodRNvrjvO\nuqNZPDO8Od2jfFUnFULYkZgm7rxxc1sOpBfwr5VHrS+GdoW7t0OrcbD5JevZOUWkkBM2L9jLhdlT\n48m+XMHMz5Ior7rS/lXT4MYPIWaw9UYk4UO1Qa+VuRqW3wf75kGvv0DPR37xlue/PUpCah4v3dSK\nMB9XBSGFEPaudzM/ZvaOZN6uc6w8eB78m0O/v8HpjXBynep4164sH5beYR3VMvq/8NcTMOa/ENKZ\n5Qcy+e+m00zsHMLU7uGqkwoh7NDQ1oHc3SeKhQlpfLEnzfqis4f1HvT/FxSOrVSSTQo5YRfahTTm\n3+PbkXQun0cXH6DKbLFecHCGWz6HuBGw+nGlT0WuSXWF9UzcwYXWraL9n/nFWxbtSWP+rnPM7B3J\nje2DFYQUQtQXjw2OpWOYF08uPcSZS8XQ6Q7wiYa1f7ee0bVH3z8NJTlw8zxoPxmc3AA4lFHA418d\nonO4N/8a1UrmxQkhrtlfb2hGrxhfnl52mE3Hs60vGgxw4yxo2hGW3mkdG1XHpJATdmN4m0CeHt6c\n75Ivcu+CfT+uzJkc4eZPoeWN1v+hb31Dac4/rbIUFk2EYytg8MvQ53HrKuNPJJ7N49nlh+ndzI8n\nhsjgbyHE9XEwGnjv1vY4mgzWz1GLAW54AXJP2ucW9dObYP/n0ONBCGzzw8tZl8uZOT8JXzcnPpjc\nAUeT3O4IIa6dyWjg/UkdiA1w597P97E/Ld96waERTFwEbn7We7qC9DrNJZ9swq7c0SuS50a3ZN3R\nLO6Yl0hpZbX1gtEBbpptnRW08Xn4agaUFagN+3sqiuHzsdYtTaPetQ79/h/nC8q4+/Mkgr1ceHdC\ne+myJoSoEYGejXjrlnakXCziH8uPQLMhENEHNr9s3aZoLypLrJ2LvaOgzxM/vFxUXsW0uXu5XF7F\n7Knx+MjQbyFEDXB3duDT2zvj7+HE9E/3ciq72HrBzR9uXWIdH7VwfJ3ef0ohJ+zOlG7hvHFzW3ae\nzmHKnD1cLr+yHchogjEfQL+n4cg31taw53aqDftrdN3anTJ9N4ybAx2m/OItBaWVzJiXSHmVhY+n\ndMTTxUFBUCFEfdU31p/7+0XzZWI6i/amw+CXoLwQtrymOtqft/FFKDhnfRjm0AiAymoLd3+exMms\nIj6Y3JHmgR5/8E2EEOLP83N3Yv70zhgNBqbMSeBC4ZV5xv5xMH4e5Jy0PqivozlzUsgJuzSuYzDv\nTuzAgfQCJn60m4z8UusFgxH6PAYzvre21v50OGx4Dqor1Qb+qW1vWrdTDnoOWo39xeWC0komzU7g\n9KViPpjcgWh/dwUhhRD13cMDY+jdzI+nlx1mQ76f9aHSno+sNyK2LiMJEj6A+OkQ3gMAi0Xn8a8O\nsuNULq+MbUOfZn6KQwoh6qMwH1c+vb0Tl8uruW3Onh+Luah+1qM+5/fDgvHWXQO1TNNttOVwfHy8\nnpiYqDqGsHGbjmfz4ML9GI0a/7mlHX1j/X+8WFEMa56wnp/wiYEhr0DMQHVhAU6shYW3QOtxcNPH\nvzgT9/9F3MnsYj6eEi83IkKIWlVSUc3Ej3dzIquILydF0fbr/tYLYd0hoheE94KA1taHZLYg9zTs\n/gAOLADnxnBfgrV7HPDy6mN8uOUMjw2O5b5+0YqDCiHqu4QzucyYl4i7s4l50zvTrMmVB++Hl1o7\n6Yb3hFsX/7Bj4Fppmpak63r8r16TQk7Yu9ScEu75PInjWUU80D+GhwbE/Pw82YnvYc2TkHfaOqpg\nyMvWGUp1LecUfNwfvEJh+vfg6PKzyz8UcVnFfDSl48+LUiGEqCU5xRWM+2AnBWVVrBzjRMi5r+Hs\ndsg9ZX2DZwgMfhGaj/rFw6daV1EMhenWAm7/53BijfVMdOubreNarnyWz9pymldWpzC5ayjPj5YO\nlUKIunH0/GWmzd1DeZWZ2VM70TnC23rh4Bfwzd0QPdDaXd3B+Zp/hhRyot4rqzTz9LLDLN2XQa8Y\nX14b14ZAz588AamuhIRZ1vMf1eXWYeK9/1o3T5l1HfLPWlfiSnNg5mZoHPqzt5wvKOPO+YmczCrm\nwykd6SdFnBCiDqXllnLTBztwMhlZek93Ajyd4fJ5SN0GO9+BrMMQ2ReGvgZ+sbUb5mIyrPoLXDoO\n5T9pGuDiC51mQPwMcG8CgK7rvPH9cf676TTD2wTyjjSGEkLUsfS8UqbO3UNGfhnvTGjHkFaB1gtJ\nn1obMvnGWs/yhna5pu8vhZxoEHRd54u96fxzxRFMBo1Hb4hlarcwTMafHAUtyoLvn4LkJRDZD8bO\nBlffmg+Tl2odsJu2C9J2Q9F50IwwZRlE9P7ZW7efzOHBL/ZTWW3h3VvbSxEnhFDicGYht3y4iwBP\nZxbN7Iq/+5UnyOZqSJpr7QhcWWKdPRc7FJrG/zCzrcYcX23tOuzsAXHDwTPYuiLoGQKBbX/2VPv/\n2rvz8KrqO4/j7y9ZIEADCWFLIICAAgqCrHWpWEWFcQQXOmJLUTtan9F2rDMdt9Ghzjguo33qtI/V\nWrG0g1hti6hFRx3cLYssyr6HkJiEJSEJWchN8p0/ztEGTMKa3HvN5/U8eXLvLyfnfu/zfX7n3u85\nv9/v1NU79y1cy7ylucwY15f/mDZcRZyIREVJRQ03zF3O6l37uWXiIG67aHDw/XPLW/DqbVCaB+Nu\nhAvvg/bHtvZBixVyZjYdmA0MBca5e6OVl5ldCjwOJAC/dveHjrRvFXJyvHL3VXLvwrW8u3kPZ2Sl\n8sC04ZzZt+tfN3CHlb+FRT+Gjt1g+rOQPeHEX7iuFja/FtyLadvioK1L32Df2ROCwrHBkM76eueJ\nd7by2JubGdyjM7/8zmgGdj/JX4pERI7B0u37uP43y+ndpQPzb5xAj9QGw4EO7IH/mw2rnwOvD05O\n9ToDss+G0bOgx9Djf2F3+Msv4I17g4JtxvOQ2rvJzWtq6/mnFz/hlU8+4+bzB3LHpadpOKWIRFVV\nTR33LVzLiyvyGNs/jcevGUVm15RgiPjif4elT0FqFpz3IzhtCqRmHtV+W7KQGwrUA08B/9xYIWdm\nCcBmYBKQBywHZrj7+ub2rUJOToS78+c1BfzklfXsPXCQaSOz+IeJAxncs8FZkIJP4YXvwv7c4D5u\ngyZBn7FfmrvWzIsES1/nr4T8FcHk1vKCoJOeNQvO/DtI69/ovxaUVnHPgrUs3ribqSMzefDK4XRM\nTjzxNy4icoKW7SjmumeX0atLB54/vJiD4DYFecuD0Qa5S4LHtQdh2OXBsPVew4/tBcsL4e0HghNs\nw6bCtCebPQ6XVka4df5K3t+ylzsnD+Hm86Mw51lEpAkvrcrnngVrSExox6PTz2TSsGAoOLuWwau3\nQ9Ga4OOXWnkAAAyjSURBVHnvkUFBd8pE6DmsySt1LT600szeoelC7uvAbHe/JHx+F4C7P9jcPlXI\nyclQVh3hF4u38ru/7KS6to7JZ/TilgsGcXpml2CD6lJ45TZY/1JwhrldEmSdBVmjISU9GN7TPjVY\ncahiT1ColRcGl8gL10BVcbCfhOTghrpjboDBFwf3tGtEaVWEJ9/dxpwPduAO/3rZUGZO6KczySIS\nU5bnFHPdnGX0TA2GWfY8vJhrqGIfLHkiuHXBwbLgi0n2BEhMgcT2wfHz8PnIB3YHX2rylgeLmUBQ\nBE68G9o1fWekbXsOcOPcj9lVUskD04bzrbF9T8K7FRE5uXbsreAH81eyNr+Mq0f34e4pQ0nvlBxc\nBNi7GTYtCoaS71oGhLVY2oBglEPGqUFRl9wZkjtjo66NaiF3NXCpu/99+HwmMN7db21k25uAmwCy\ns7NH79y584RjEwEorqhhzgc7mPtRDuUHaxk/IJ0rRmUxeXhvuqQkBQVd7hLY+SHkfBhM7K+t/vKO\nLAG+1iv46TEMMkcFhV+P0yExucnXr47UMW9pLj9fvIX9lRGuGJXF7ZNOpW/6UV79ExFpZR/nFDNr\nzjK6dW7PUzOP4ubaVfuDYm7JE1BVcuQXSM0KRkH0HR8s0917RLObv71pNz+cv4rkhHY8OXM0Y/un\nH8O7ERFpXQdr6/jZW1t4+r3tdO6QyF2ThzB9dF/aNZzLe2BPcEKraG1wgaBoHZTsCC4uhOwnZcdf\nyJnZW0CvRv50j7svDLd5h5NQyDWkK3LSEkqrIvzPkp38cUUe2/dWkJzQjguGdOdvRmRy3qAM0jo1\nKMZqa4Kzy9WlEKmCTt2DhVGOYaXLLUXlPLcslz+tzKe0KsK5gzK4c/IQzsjq0gLvTkTk5FqVW8L3\nf7eCsuoID181gqkjs478T/X1EKkMhlvWVkGkGrzu0G3apzY7B+7Q3Tm/en87j7y+kSG9Unl61hiy\nup7YfZlERFrL5qJy7lmwhuU5JYzpl8bsy09v/nuge/C9s+YAHCzHMgZpaKVIQ+7OmvxSFqzK55VP\nCth74CBmMKJPV74xOIOzB2YwLDM1uFp3DGrr6tlYWM7HOcX8eU0By3NKSEowLjm9F98e34+vD+zW\nQu9IRKRl7C6v5tZ5q1iWU8z15/Tn7ilDSUpoevjjyZS7r5If/+ETlu4oZsrwXjw6/UzNJxaRuFNf\n7/xhZR4PLtpASWWEi4b24IcXDmZEn65H/N9oz5FLJFjs5EIgn2Cxk2vdfV1z+1QhJ62ltq6eT/JK\neW/zHt7fsofVu/ZTH3aLzC4dGNI7lcE9O9OtUzKpHZJITUmic/tEKmtqKamMUFxRQ0lFDRsKy1id\nu5+KmuDM84CMTlwzti9Xje5DRuf2UXyHIiInJlJXz38u2sCzH+Ywpl8aD145/NDFo06y+npn3rJc\nHly0gXZm3HfZMKaP6aP5xCIS18qqI/zmwxye+WAHpVURLjitOzefP5Cx/dMPHXLZQEuuWnkF8HOg\nO7AfWO3ul5hZJsFtBqaE200BfkZw+4E57v7AkfatQk6ipbQywsrcEjYWlrOxsIyNBeVs23OA2vqm\n+0pKUgIDMjoxpn8ao/ulMaZ/uob+iMhXzsLV+dz70loqauqYOaEft100mK4dm54ffDxW7CzhsTc2\n8dG2fZw3OIOHrhqh46mIfKWUV0eY+1EOv/5gB/srI2R26cDfjszk8jMzGdY79ZCTVrohuMgJcncq\nauooq4pQVh3hQHUtKckJpHdKJq1jMh2Sjn7enIhIPCuuqOGnb27iuaW5pKYk8aOLTmX6mD4nNOSx\ntq6eN9YX8fT721mVu5/UDoncMXkI147L1lU4EfnKqjhYy5vri1i4Op/3t+yltt7JTu/I8D5dGNY7\nlWGZqXxzSE8VciIiInLybCws4/5X1vPRtn2kJCXwzaE9uGx4byae1oOU5COf3KqqqWNZTjEfbNnD\na2sLySupIju9I987dwBXj+5Dp/aaCycibUdxRQ2L1hTw3uY9bCgsY1dxFQA7H75MhZyIiIicXO7O\n0h3FvPrpZ7y2ppB9FTV0TE5gaO9U+qalkJ3ekb7pHUlObEdJRQ3FlRFKKmrYsruclTv3U1NXT3JC\nO8YNSOc7E7KZNKwXCU3MExERaUvKqiNsLChn/CndVMiJiIhIy6mtq2fZjmJeX1fIlqID5BZXUlBa\nRcPpxWbQJSWJrK4pnDMog3MGZTCuf/pRXcETEWmLmpsjp3ELIiIicsISE9px9qAMzh6U8UVbpK6e\nz/ZXEalz0jsl0yUlSVfcREROEhVyIiIi0iKSEtrRr1unaIchIvKV1Dp39BQREREREZGTRoWciIiI\niIhInFEhJyIiIiIiEmdUyImIiIiIiMQZFXIiIiIiIiJxRoWciIiIiIhInFEhJyIiIiIiEmdUyImI\niIiIiMQZFXIiIiIiIiJxRoWciIiIiIhInDF3j3YMjTKzcmBTtOOQI8oA9kY7CGmWchT7lKPYpxzF\nPuUo9ilH8UF5ii393L17Y39IbO1IjsEmdx8T7SCkeWb2sfIU25Sj2KccxT7lKPYpR7FPOYoPylP8\n0NBKERERERGROKNCTkREREREJM7EciH3q2gHIEdFeYp9ylHsU45in3IU+5Sj2KccxQflKU7E7GIn\nIiIiIiIi0rhYviInIiIiIiIijYjJQs7MLjWzTWa21czujHY8AmbW18zeNrP1ZrbOzP4xbJ9tZvlm\ntjr8mRLtWNsyM8sxszVhLj4O29LN7E0z2xL+Tot2nG2VmZ3WoK+sNrMyM7tN/Sj6zGyOme02s7UN\n2hrtOxb47/Az6lMzOyt6kbcdTeTov8xsY5iHBWbWNWzvb2ZVDfrUk9GLvO1oIkdNHt/M7K6wH20y\ns0uiE3Xb0kSOft8gPzlmtjpsVz+KcTE3tNLMEoDNwCQgD1gOzHD39VENrI0zs95Ab3dfaWZfA1YA\n04BvAQfc/dGoBihAUMgBY9x9b4O2R4Bid38oPDGS5u53RCtGCYTHunxgPHA96kdRZWbfAA4Av3X3\nM8K2RvtO+EX0B8AUgvw97u7joxV7W9FEji4GFrt7rZk9DBDmqD/w6ufbSetoIkezaeT4ZmbDgPnA\nOCATeAs41d3rWjXoNqaxHB3298eAUne/X/0o9sXiFblxwFZ33+7uNcDzwNQox9TmuXuBu68MH5cD\nG4Cs6EYlR2kqMDd8PJegAJfouxDY5u47ox2IgLu/BxQf1txU35lK8CXI3X0J0DU82SUtqLEcufsb\n7l4bPl0C9Gn1wOQLTfSjpkwFnnf3g+6+A9hK8B1QWlBzOTIzIzhBP79Vg5LjFouFXBawq8HzPFQw\nxJTwDM0oYGnYdGs4rGWOhu1FnQNvmNkKM7spbOvp7gXh40KgZ3RCk8Ncw6EflupHsaepvqPPqdh0\nA/Bag+cDzGyVmb1rZudFKygBGj++qR/FnvOAInff0qBN/SiGxWIhJzHMzDoDfwRuc/cy4JfAQGAk\nUAA8FsXwBM5197OAycAt4RCKL3gwljq2xlO3QWaWDFwOvBg2qR/FOPWd2GZm9wC1wLywqQDIdvdR\nwO3Ac2aWGq342jgd3+LHDA49wah+FONisZDLB/o2eN4nbJMoM7MkgiJunrv/CcDdi9y9zt3rgafR\nsIiocvf88PduYAFBPoo+H/YV/t4dvQglNBlY6e5FoH4Uw5rqO/qciiFmdh1wGfDtsOAmHK63L3y8\nAtgGnBq1INuwZo5v6kcxxMwSgSuB33/epn4U+2KxkFsODDazAeFZ62uAl6McU5sXjpt+Btjg7j9t\n0N5wXsgVwNrD/1dah5l1Cheiwcw6ARcT5ONlYFa42SxgYXQilAYOOeupfhSzmuo7LwPfDVevnECw\nMEBBYzuQlmVmlwL/Alzu7pUN2ruHCwphZqcAg4Ht0YmybWvm+PYycI2ZtTezAQQ5Wtba8ckXLgI2\nunve5w3qR7EvMdoBHC5ceepW4H+BBGCOu6+LclgC5wAzgTWfL0sL3A3MMLORBEOOcoDvRyc8IZi/\nsyCouUkEnnP3181sOfCCmX0P2EkwkVmiJCyyJ3FoX3lE/Si6zGw+MBHIMLM84N+Ah2i87ywiWLFy\nK1BJsOqotLAmcnQX0B54Mzz2LXH3m4FvAPebWQSoB25296NdhEOOUxM5mtjY8c3d15nZC8B6gmGx\nt2jFypbXWI7c/Rm+PG8b1I9iXszdfkBERERERESaF4tDK0VERERERKQZKuRERERERETijAo5ERER\nERGROKNCTkREREREJM6okBMREREREYkzKuRERERERETijAo5ERERERGROKNCTkREREREJM78P+/S\nxOzd+s9sAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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