CNN_keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "np.random.seed(1337)  # for reproducibility\n",
    "\n",
    "# from keras.datasets import mnist\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (720, 128, 128, 1)\n",
      "720 train samples\n",
      "48 validate samples\n",
      "240 test samples\n"
     ]
    }
   ],
   "source": [
    "batch_size = 40\n",
    "#nb_classes = 10\n",
    "nb_epoch = 100\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 128, 128\n",
    "# size of pooling area for max pooling\n",
    "pool_size = (2, 2)\n",
    "# convolution kernel size\n",
    "kernel_size = (3, 3)\n",
    "\n",
    "# Training data, shuffled and split\n",
    "WB = np.array(sio.loadmat('sandstone_data.mat')['Data'])\n",
    "Y_data = np.array(sio.loadmat('sandstone_data.mat')['L'])\n",
    "# Y_data = (Y_data-min(Y_data))/(max(Y_data)-min(Y_data))\n",
    "X_data = np.reshape(WB,(768,1,img_rows,img_cols))\n",
    "X_train = X_data[:720]\n",
    "X_val = X_data[720:]\n",
    "\n",
    "WB1 = sio.loadmat('test_sandstone_data.mat')['Data']\n",
    "X_test = np.reshape(WB1, (240, 1, img_rows, img_cols))\n",
    "Y_test = sio.loadmat('test_sandstone_data.mat')['L']\n",
    "# Y_test = (Y_test-min(Y_test))/(max(Y_test) - min(Y_test))\n",
    "\n",
    "\n",
    "#normalize label all together to be consistent\n",
    "label_norm = np.concatenate((Y_data, Y_test), axis=0)\n",
    "label_norm = (label_norm-min(label_norm))/(max(label_norm)-min(label_norm))\n",
    "Y_train = label_norm[:720]\n",
    "Y_val = label_norm[720:-240]\n",
    "Y_test = label_norm[-240:]\n",
    "\n",
    "\n",
    "if K.image_dim_ordering() == 'th':\n",
    "    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "    X_val = X_val.reshape(X_val.shape[0], 1, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "    X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "    \n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_val = X_val.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_val.shape[0], 'validate samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CNN\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Convolution2D(24, (3, 3), padding='same', input_shape=input_shape))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Convolution2D(24, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=pool_size))\n",
    "\n",
    "model.add(Convolution2D(48, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=pool_size))\n",
    "\n",
    "model.add(Convolution2D(96, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Convolution2D(96, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=pool_size))\n",
    "# model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Convolution2D(128, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Convolution2D(128, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=pool_size))\n",
    "# model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1000))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(100))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 720 samples, validate on 48 samples\n",
      "Epoch 1/20\n",
      "720/720 [==============================] - 65s - loss: 0.0034 - mean_absolute_error: 0.0416 - val_loss: 0.0012 - val_mean_absolute_error: 0.0306\n",
      "Epoch 2/20\n",
      "720/720 [==============================] - 57s - loss: 0.0032 - mean_absolute_error: 0.0410 - val_loss: 2.4148e-04 - val_mean_absolute_error: 0.0117\n",
      "Epoch 3/20\n",
      "720/720 [==============================] - 62s - loss: 0.0032 - mean_absolute_error: 0.0407 - val_loss: 2.7244e-04 - val_mean_absolute_error: 0.0127\n",
      "Epoch 4/20\n",
      "720/720 [==============================] - 69s - loss: 0.0027 - mean_absolute_error: 0.0385 - val_loss: 4.1002e-04 - val_mean_absolute_error: 0.0173\n",
      "Epoch 5/20\n",
      "720/720 [==============================] - 83s - loss: 0.0036 - mean_absolute_error: 0.0433 - val_loss: 0.0035 - val_mean_absolute_error: 0.0533\n",
      "Epoch 6/20\n",
      "720/720 [==============================] - 69s - loss: 0.0028 - mean_absolute_error: 0.0384 - val_loss: 0.0014 - val_mean_absolute_error: 0.0330\n",
      "Epoch 7/20\n",
      "720/720 [==============================] - 58s - loss: 0.0029 - mean_absolute_error: 0.0399 - val_loss: 0.0011 - val_mean_absolute_error: 0.0281\n",
      "Epoch 8/20\n",
      "720/720 [==============================] - 57s - loss: 0.0033 - mean_absolute_error: 0.0414 - val_loss: 0.0014 - val_mean_absolute_error: 0.0323\n",
      "Epoch 9/20\n",
      "720/720 [==============================] - 57s - loss: 0.0030 - mean_absolute_error: 0.0393 - val_loss: 0.0014 - val_mean_absolute_error: 0.0335\n",
      "Epoch 10/20\n",
      "720/720 [==============================] - 57s - loss: 0.0032 - mean_absolute_error: 0.0404 - val_loss: 2.1426e-04 - val_mean_absolute_error: 0.0116\n",
      "Epoch 11/20\n",
      "720/720 [==============================] - 57s - loss: 0.0031 - mean_absolute_error: 0.0386 - val_loss: 5.6650e-04 - val_mean_absolute_error: 0.0192\n",
      "Epoch 12/20\n",
      "720/720 [==============================] - 57s - loss: 0.0027 - mean_absolute_error: 0.0383 - val_loss: 8.8499e-04 - val_mean_absolute_error: 0.0268\n",
      "Epoch 13/20\n",
      "720/720 [==============================] - 57s - loss: 0.0034 - mean_absolute_error: 0.0424 - val_loss: 8.5161e-04 - val_mean_absolute_error: 0.0235\n",
      "Epoch 14/20\n",
      "720/720 [==============================] - 57s - loss: 0.0030 - mean_absolute_error: 0.0398 - val_loss: 0.0014 - val_mean_absolute_error: 0.0342\n",
      "Epoch 15/20\n",
      "720/720 [==============================] - 58s - loss: 0.0031 - mean_absolute_error: 0.0398 - val_loss: 4.2204e-04 - val_mean_absolute_error: 0.0161\n",
      "Epoch 16/20\n",
      "720/720 [==============================] - 57s - loss: 0.0022 - mean_absolute_error: 0.0336 - val_loss: 5.1784e-04 - val_mean_absolute_error: 0.0186\n",
      "Epoch 17/20\n",
      " 40/720 [>.............................] - ETA: 63s - loss: 0.0019 - mean_absolute_error: 0.0323"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    861\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1428\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1430\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1077\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1078\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1079\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1080\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2267\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2268\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2269\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    998\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1137\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1140\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1120\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping\n",
    "early_stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')\n",
    "model.fit(X_train, Y_train, batch_size=40, epochs=20, verbose=1, validation_data=(X_val, Y_val), \n",
    "          callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training mse: 0.000626916978571\n",
      "testing mse: 0.002560406593\n"
     ]
    },
    {
     "data": {
      "image/png": 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TAiwkmUzbbtePjVprZdqjwAisSUJadRQQh8HQk/67Jo7XinOsHE8cmUmlC6cpLyqVCpDk\nYL34xS/m4x//+Lr7m80mb3nLWzh8+DAXXHABv/3bv02zOT7FUsZmf9/tiM69sk+jBK5hAMTB+FVJ\nMgyW2z6SiPA88FeZjpYIW2YBpo2eIC1RwTYOVu6sazDcY67wOoEVmiqCk8SJ+QUAjpxcGfJIesCv\nQVrQemVj6QBDv3ne857HN77xDR588EEAVldXuf/++9tiav/+/dRqtZGvGrgRI7DGhEBPnFwjsEaT\nKOhtUnHP5+Cv3pLfeAyjRWMRPvfLSShpDsjiIwDYplreGrrAxaJMY5siF7kTZERSrwJLfCOwJpWo\nleTXReNUoCsNDwRYeXx449glHDhwgBtvvJHXvva1XH311Vx33XXce++9zM3N8Yu/+ItcddVVvPSl\nL+U5z3nOsIfaE7mGCBr6R9Bq4gHOaVRoMgyAyO/NwXrwK3DHx+HH/xgss84x8aSTxlY+q7j1kw9T\nAZzYHB/a6M96gWku5sSQBzP5RFkHK+pVYK3lYMWhcRsnCi2ex+p7XSewjg9vHBPMoUOHuPPOO9vX\nf+RHfoRbb71103bvfOc7eec737np9htvvDHP4fUFM7MbEwJ98vLEN2VDR5Eo6G1S0VxOQhBayztv\naxh/0gparWouT19/Iim+ejolsieWjIOV5AUZFytP4jMIEcxWx42NgzVZ6CIXEo2Ru75OYJkQQcPp\nsaPAUkq9rZvbDPkS+ok7YhGPXzWe3UCvIYKNpeSyvpDPeAyjRfqbzcnB8k89DIBrQgTXSB0smU6u\nj9MEbwyJ/DUHX3oVWOGawDJVBCcLpcXzWAqs2QuNg2U4bbpxsH52i9t+rs/jMOzAuvAL0wtr9EiL\nXHTrLqYHcCOwdgdRKrDycbBYOgqAi1n9b5M6WGiBNW59eMaMOHNeCv3eCv6ooEEsSVn3eJwm4oYd\nsXSrBBmnheH0/Hzw6Umz4QlknBr2Dop+fyYdc7CUUq8laQx8sVLqrzN3TQNmVjhgsquDPYdfGPJH\n9ykj8sHpok1mW2DN5zcmw+iQc4igVzuWXBqBtYYOv12UqeS6mbjnynqB1aD7Iu1gRQ1qlJihbhys\nCcMOx9jBOng5PPClZLGmODPcMfWRYrHI/Pw8+/btQ/XQr26SERHm5+cpFot9e87tilx8E3gc2A/8\nQeb2KvC9vo3A0BVRkBVYxsEaOdKTR9jsTWA1zFrFriDdP3RFrX5TaSRhLMXTaPI6sTRXaOJRR58w\nx2kFfRzJnKN6dbCcsMEKZWaoj9dE3LAjTqTnK9EY/f7S8/OBy5PL6uMTJbDOP/98jh07xsmTJ4c9\nlJGiWCxy/vnn9+35OgosEXkEeAS4rm+vZjhtYuNgjTZtgdXFdyNiHKzdRpxjiGCrSjlcZkXKzKh6\nsi86Xv9fZ9xorVCVEiF2ct1M3HNFMgIr8Hs7R9lRgyUpgUoa0homB1cLrLELEfSmYO7C5PrKY3Dg\nsuGOqY+4rsvFF1887GFMPN0UuXiVUuoBpdSyUmpFKVVVSo1Rx7jJIM6WADc5WCNHmAqrbkq1B421\nkDGTg7U7yFNg6fyrH8i5AISZnkK7maiRiM62wDI5WPmSOfZFvTpYcYsqZcDkYA2c1VPw6C25Pb0b\npw7WGH2vzWUozsJMckw1vbAMp0M3RS7eBfy4iMyKyIyITIvI5HilY4IEmRVB42CNHFGgc1+6+W6y\nJWCNg7U70JMLyaOK4AaB5bfMAgxAWF+mSolI6UCNcQpRGkfCbIhgb/ugGzepSiKwJDJ5hAPlH34P\nPvqqfJ47jvFEnxNHycGqPZk0f+9EcykRWNPnJNdNJUHDadCNwHpCRO7JfSSGbcmWvTUr1KOHLfrk\n0avAMjlYu4JGK9kvpFXrfx+7xUcAeBgtsJq9uQeTStxYpipligWdE2kcrFyxwiZNSUpbrMsZ7gIv\nbtKwUoE1QhPx3cBjh8Gv5rNwGzaSHnQwWgLrk2+AL/x65/uby/juNMdqMZT3mV5YhtNiuyIXKYeV\nUp8E/gpo/wJF5C9yG5VhE9n4dr+52tUXZxgQIjjoJqZdCayl9r/x6rzp9r0LaPktSug+dkEdvErf\nntufP0IoBZrlc6DVu3swsbRWqLKHUqEIdcYrRGkMUVGLFSoUWSIKepisi1CQJi17CmIjsAZK2IIT\ndyb/N1dg6kB/nz+7GDxKCxzVHRyp5jL3rpR5+/+8jb+dOdc4WIbTopu53QzJ6eklwCv13yvyHJRh\nC6K1E1ZgQoBGi+zErZscLO1gPS57iVdNiOBuIA4zYU99zsMK5o9wTPYzU0nKkQdNc3wAUK0VqlKm\nXEqrCI7QBG8CsaImKzrML+4lBysKsIkJ3GT/NSGCA+SJuzItJHIIXw5W1/6Po/4//+nSqm0fnt9c\nZj4qce+JFcIpI7AMp8eORoiI/PwgBmLYgWyIYNOECI4U2QlBDwLrETmLA/VTOQ3KMErEYWZVvlWF\n6bP79+SLj3BMDjA7MwULEJgQYgCcoEqVEhUtsKIwSMtdGHLAijKFKnoJNwuS/TV0Z6A1Zg5WFMIX\nfhWe/29hz0XDHk3vHP/u2v/Z0PV+kTkWqVFa4Nhpkau5zGJUQgTmrb2cVT08mHEZJopuqgg+TSn1\nv5VSd+rrVyulfiv/oRnWEbWoSTJRMCFAI8Y6gdV9DtaR+Cys5mL/c3IMI0e0zsHq70qxWzvGo3KA\nPTPTQO89iCaSKMQJ61SlTKVUAiAMjDOSJ07cajtY0ksOlq6KG3vJ/jtWoZxLj8BtfwYPfmXYIzk9\n8hZYQTZEcEQcrLCVuHaNxa3HFMfQWuFUmBw3Hg32JG5Xj3mFBkM3IYIfBn4TCABE5HvAT+c5KMNm\nrLDFil4djMZUYMWxcP8TOZSpHjaZ5N24i4OwNJIcrKNyFpaE+ZTuNowU63r79PP7bizhBSsckwPs\nm02KuxqBRVvEVikzVU4mSuvCNA19x479toMlp+FgKU+HCI5SMYSdSBfX8givGwTHb4cp7abn8B7E\nX2usPjIOVrvZu0BjafP9fg0k5mSYLGjfU9fCf6e8LYNhA90IrLKIbGySMEZHwMlART4rkiTGj6vA\n+sdbD3PfH7+ax+e3KY86jmQcrKC1c3hWUF+iIR5PyJ7kBlOqfeKJopwEli7R/oR1kOnp8T4+9BU9\nWWzaFTwvabocGYGVK07coqUK+GL3JLDET/J07GIFX+zxcrDS99kcQ4EVNODJu1k6+58m13N4D0Fj\nLQdLjYqDlRWSW517tZO3rN3Y7ywllyYPqw/8w+/DF//D5tvv/2Jy34TRjcA6pZR6KiS1NpVSrwZM\n17UBY8Xj72C5R7/OK+1vs3riwWEPpb9kJgTdFCDxawusUGaRZMXWlGqffGSdg1XrvGGvaIFVL5+H\n46UFBsbz+NBX9GQx8qaxnERgmRDBfHFin8gu4uP2JLD8ZjIJd4sVQpzRKue9E+niWh7hdXlz4vsg\nEZ+rXgqA5PAeWo1kMakl7ug4WBlXbTuBtSIVLjk4xfdWUoFlpr1nxJP3wNd+b+tw2jv/Ar72++Cv\nbr5vjOlGYP0S8EHg6Uqpx4B/A7w511EZNmHFPnUpEorVVRjaKBI1k4Nt0JiwkLiMwOqm/0u4usSK\nVFgUHXpQNwJr0olzdrCCmQtxPV3MoZcS2ZOKXqWWwgyWk9RyWhemOQju/xJ8+R2Dfc0h4koL7AI+\nTk89lfxGVmBZY+VgNRpJxML8whgWK9L5V584eQhIGnP3m6CRiJkVyigZFQerS4FFmX926QFOyN7k\ndtML68z48jtA4q2PDVELJIJjk1VMZEeBJSIPici/AA4ATxeR54vIkdxHZliHHfn4ODTxkDFdoVZ6\nYhk2+riCPwpkQgTD1s4CK24srXewjMCaeLKT+7ifoThLR6lTpDyzH6+gc43GdAGmr+jPWBVmsOzE\nwRq4wLrvC3Drnw72NYeIKz64iYPVk8DSDpZXmibEHisHa6manMvqK2N4DD/+XcLyQe6qz1CTIkF9\ni3ykMyRoaoElZdSofK/rHKwthLFenFmRMj986X5WKdGyKyZE8Ez4wVfhgS8ROyXCrc5Pafj2ozcP\ndlw507FMu1Lq9SLyv5RSv7LhdgBE5N05j82QwYp9AuXRwkW6KQU+grQFVmuybOBsf50o2Fn8quYy\nK1Jm1Z5NbjA5WBNPtvR00Fim0K8nXnqEx+QAZ82WcLTA6qmC26SiJ0lWaRbb1TlYg+6vFPlJAQcR\n0OfNiSUKcYhQTjEJB+vhs04jGgrl8QsRTCMWbH8MozKOf5fj5cthQVGlTCUHByvUAqtKmSkZke+1\nyxysFSpcsLfEU/ZXONXaz3nGwTo94gi+9FswdyFfD67gmtVvMbdxm7TP69FvD3p0ubKdg1XRl9Md\n/gwDxBEfsQs08brrtTSCKN10MA0VnBQkkzzfTYNNy19hmQr79x0gxjI5WLuAtHlqQzyC1f5NZOKl\nYzwa7+PAdKHtYPVUwW1S0Q6WW57Dsl1gCFUEQx32MkYhb6eNPidZXilxsHoQWGlfR684RYg9Ork6\nXRD5yW/NCccsKqNVhZP38d3oEABVKRE3+i+w4tYqsShWpYgaGYGVdbC2OPe2c7DKzJRcrj5/lkfD\nuckKEbzjk1B7ckCv9XF44k7CH/lPHKlaidO9gXpdL7o/esvolPPvAx0Floh8UF/+zlZ/gxviLuHx\nO9Y15duIEwfgFGiKhxpTgeWEibCSfib5jwDZ5PluwrPcYIUaFc7bO8WKmjYO1i4gdbCWmCLsYw5i\n6Ddo4HFwuoDbFljb7IP3fB5u+XDfXn9kaSWTJLcy13awBh4imK7KBhPm2G+F3ueUUyTAwYp7EFit\nNESwQoQ9OqFkXZA6WF4wZouGj38PEL64eA5PPVBhhQqSQxXBuLVKnQIhNtao5GDpEMG6VdnWwapS\nZrbkcvX5c9weXIic+P7oFzMJGvCpN8Likc7bNJbgL2+AOz4xmDF9631wzjXcNfci6rGNy+bj8JOL\net/zq/Dk3YMZ1wDoptHwR5RSc5nre5RSuyewfBD4dfjwi+D2j3XcxBEf5RTwccdWYLlhciKVCQsR\nDDNFBeKdvhsRCmGNwJ1mb8VlSaZMDtYuQLSLsSRTfc3BikOfAIezZooUCgViUch2RS7u+Djc/MG+\nvf6oEjdWaIlLpVLBclIHa8ACK3XMuggbHnv0cU/cIoFyUVH3LmqUCqxymoM1IhPxLkgjFgrRmC0a\n6gIXtzQv4qVXnk1VSqhW/8WD+HUaFIiUgzUqwlmnKpxQZ3UUWC2rTMHzKDg211wwy5ejaxPh/8CX\nBzzYHjl1P9z9WXjoHzpvkxZZ8gewz556IBFM17yOWx9ZxMfFI0zCpjNYccB98fnJlQkKE+ymiuDV\nItLOfhSRReBZ+Q1pFxLUdWfxzkmmrgTgFGniocY0BMhLBdaEleJcV7VtJwfLX8UmIvJmmSt7zMuU\ncbB2ARIHRKKSVgt9bOgZhz4hDgdnCniOTQsXFW2zD0ZBT+Fb40pQX2KFErMlF8dNMt4GL7D097Ab\nBFZ63HOKBMrD6mEfi3XkRrFYJlIOSsYnRDDW5+JCXIc4HvJoemDhBzTcOU4xy4uvOIsq5XzyyPxV\n6lJA2c7oOFitKg3xOCmzHQTWEg2rwlwpWZi54pxZvqeexqqzB+772wEPtkdS8bRd+F8qrIKde3ae\nMXd/Nrm8/JUcPrJIS5LPdOM5yIlbPCTn0CqdtesElqWU2pNeUUrtZZviGIbTID0Rd1r1E8HFR2xP\nn7zG08EqxMkPWk1YyEyYye3YsQCJDjGQ4gxzZZf5eIp4kA6Wvwp/cQNUnxjcaxqQKCDEoSYlVD9X\nDqMAX2wOThdxbGvnCm6RvytygsL6MlUpM1N0sHUOlgyjyAVMXG+XLUmPe26RULnYPYQIil+nIR7l\ngjt2IYKxXlyzkCS8aVxoVVmRMk85kPR6qkoZJ48wx6BOnQLKdrFGJAcrbFapUeLJqHOIYE1NMVtO\nQotLns0lZ81yuPDcxMEa5eNnVwJLH4+2SUnpG3d/Fs5/DjJzLrceWaCVSocN8yRbAnxcHpu5ZqIq\nCXYjsP4A+JZS6j8rpd4JfBN4V77D2mWkE6JOE6M4xEIQp5AIrHg8HaxSW2AN4Ic9QCKdgxWLWgsL\n6oQWWKqUjPLqAAAgAElEQVQ0x1zJY1GmkdUBOlgn7oTvfXKiDmJjQRQRYlGj1N+V4jggVg57yomI\n8NkhPCsKOi/kTBBxY7mdQ5HmYGUrOQ6E9Hi+Cxws0e/RckqEltdTDpYEdRp4lFybSNlYY1TkIs6e\ns0c9PydD3KqxEHhc95R9VDyHKmXcHAp1WGESImiNkIMV1pepSZGT0RTSQWBVKbcdLIDnHtrDJ1eu\nSnI7H/nGAEfbI22Btc0CarpN3selhYfhxPfgip/g4VOrzK/6eIWkV+PGeZIjPi1xucu+HJYfheVj\n+Y5tQHTTB+t/Aq8CngBOAK8SkY/mPbBJ5btHF/HDDaEE6UG60wprqvZtj9DysMd0glSWRFjZ4WQJ\nrFgLrFWKO1d4bCZhoE55jj1ll0WmUc3FTTHJuZFOAnZBmNgoIXFAiE1NSjhh/xwNKw5wvWK7fYa/\nQ/5Lo9VsLwhMMtJcpipJiKCrQwRl0FUE09/YhC0obYXfSiZryi0SKRe7lzA/v0GDAiXP1iGCozER\n74p1Aqv/RSLyYrW6xIoUue6p+7AsRcuu4Eiwc4h7j1hhgyZFxHKxGQ0HK2pWWaXEgkwnrWM2Hhea\nyyxJsjiT8qPPOIevBlcSWYXRDhNMxdPqyc7baAcryrvY2D1/nVzq8ECACw8kwXDRhv3MiQN8HP6x\n9dTkhgkJE+wosJRSM/pyL4mwukn/ndC3GXrkyZUGX/nQr/P337lr/R3phKjT5Dw9ADhFQquIM4YO\nVhzFlNGrnBMWIhjp76dGacfk7qieHGi8yh7myh6LMoUVtQY3CUvzf4ZRKOVr74LbPz741x0BVBQQ\n4FClhBv1U2CFeJ7Xvh7gbpv/8uRidV1RlqFw6kH4r5fC0qO5vYRqVamSlFl2HItQLGTQzsgucrDC\nll4880pEltfTOUqFDRpSoOTaxGMWIriuJUIfcyvzplFbpi4FnveUfQAEru680+f34IQNfKsI1uhU\nEYybVVYpsph2G9rYJqW5zGJUYq68JrCec2gv09Oz3FV8Ftz7hcEtiPZKFw5WS/c7OzG/mO9Y7v4s\nnPNM2HOIW44ssKfscmBP0vszaK0/Jrr4+Lh8ef4g4lYmJsJmOwfrJn15G3A485deN/RI8+TD/Jrz\nKWYf2VCJRh+k404hgm0Hq6BPXuO3Al1v1vFUcoB1osmacKTfW01KiVjahvpKcjAvzuxjruyykB7k\nB1XoInWwhlEo5TsfhXs/P/jXHQEkDomwaFll3LjVtzh+mxDXW2tbvFOBASsOkjK5w5wgPHk3rD65\nfSnhM8QOVqjqVWjXVoTY7UqOA6Ndpn3yHaxACyxLC6xeHCwrrNNUBRzbSqrNjUiuTleMqYOFXyN0\nKuyfSo4dUSqw+vwenKiBb5WIlYM9Kt9rq0pVEgcL2HTuleYy81GJ2YzAsizFjz7jHD5ZvRqWj8IT\nGxbJR4W2wOrsYDVXk+8412rOS4/CY7fBFT8BwOEjCzz70F6UjiYIWhscLAlo4bLQjAnOuRaOfiu/\nsQ2Q7QTW7+nLy0XkKZm/i0XkKYMY3KSRNiWUDfZoGl5x7GSHKoL6RK3cApFdwJXxc7Dq1bXVEnfi\nBNZaiOBOAqtRTQRWZWYfe8peUqYdBleqve1gDWEfaiyOdoJwjqg4JMCBgv6+W33Iw4ojbGKw1xys\nJP+l83drSZLPOdRS2Ok+mGOYqhPU2jlYrm0R4AxeYIW7J0Qw0ucw2ysSW17St7FLrLCBr3SlRzU6\nuTpdkTnex2OUg+WFdXyn3L4eF1IHq7/vwY0bhHYJUQ4Wo1FlUfk1VimtOVhZgSWSOFhxibmSt+5x\nr7j6HL4UPBNBwX1fGOCIe6Bdgr3asYhFoPsw5los7Z7PJZdX/ARPVpscma/z3EN7sVKBlXX1RfAI\nsd0kP+vxPc8BtwyDzpnNge0E1h/py28OYiC7gTQ0Z2OluZbuZL9R1aekvTaUWyK2CjhbdMIedRrV\nNfHoxZM14UjLP9ekuGN54lYtEZrTc3sTB6vDKlputHOwBiywQj856O/S3C8VB4RiowozyQ39EFip\nYLDXVloDtX14VttZGOb30N4H8xM8TtSkqQqUPRvHtpL+SoM+YUe7KETQT96jUygTW96WzUQ7YUdN\nfJVMruKxc7B8fLGBtWP7OODFdUK7snZDelzqs4PlxU1Cp4RYDs6IfK92uMqqFJkX/Z6z516/hpKY\nFamsCxEEuPbCPdgzZ/NQ4elw/98NcMQ9kD2vrG5dSTBqJN+xE+U4D3vgS3Dgctj3VG7T+VfPPrQH\ny0l+52F2rqvPRXtmksXHfzjrZ+FffQns8S9Wvt07CJRSHwLOV0q9Z+OdIvLL+Q1rMklzdTa6B0Eq\noDpMegK/QQGw3AKhXcQTP1lp0Ynt40CrlkyqGuLhxeNZZr4T0nawSthx515mAOHqIqtSYM90haJr\nU3f0Qb4xoJNzc0gOli7usVsdLHSIoFWcgRp9EljJfifW2kQgsjysuPOEvh2mE7WAcsftcqWZs4MV\nhVjEKKeAUgrXUrqB7ZAcrF1Qpj1KBZZXomV7uD0sAjpRk9BKFppiZY+VwFJRi3lmOYcFgvoSpWEP\nqBtEKEiTyF0TWKqY5Mb0NQcrfR27hFiCRX+dycNHFrjk4BRzZW/njTM44SpVStTsLQSWXvxZ2VBF\nEJIwwR+7+hxuvvk8Ll74blcluAdNo5bZB2tPwp5Dm7aJmklxCydHB2tp/nEeDffyqb+6k3seX6Ho\nWlx57iw336cFlp85R2nDoVJO2mo88OQYtTvYge32kVcAfw80SPKuNv4ZeiStNrdxcpuGV6gOK8+p\ns2W5BcQpJGFBY5QIDGuJlafUHgrbTABHkns+Bw9/vePdcabIxU79X6L6MitU2FtJTgpxMUkyHpiD\nNawQwVRA7lIHizgkxMEq6ZN6P3phpccAe73A2i5Hc01gDVHo5l3JUj+vrcNRkhBBe+DvOd5FRS5S\ngeUWy4hdwCHquvGuEzcJ7GRaKNZ4hQiqyKcmJVriEK5uv7g2MgR1bOKkmIDGLs8l//QzzDFsYREj\nbjlxsPoosFphxGs//G3+9BtHentgFOLGLUK7gltJz72Z8PxUYEllXRXBlB+7+hwWpIJqLI5kY+mw\nvkxDtODs0AtL9OKem+NCd6O6zJGq4vPfO853ji7yosvPwnOsdtP30M/MP/T8SZwil541zQNP5Fzd\ncIBs52D9moi8XSl1oYh8ZGAjmmDSE+7GSnPpzmZ3crDSCk1OEXH0+kTQWDexGnUCLbCW7b3siR4Z\n8mh65Kv/D8xdBBf/8JZ3SxQQi6IuBeydVm6bS6xImUM6/MAuzxEvK6xB5WANy8HS7y8IWozPXts/\nVBwQKBun1M8QQb2vrRNY24cQO2mp5CEK3UZ1gRJQqzeYyuMF9HtzdP8rx1aEYg92USqO1pyYXZCD\nFacCyyshaU5g1AJrZ0/Hi5uEOv8iGqViCF1gxT4+DiuUUY0xycHS5bnFW/v1OVpgSXOZvsXF6P0+\ndssgES5h3yJvji81CSLhxHKPixe6B2HkVpibLrMaTFHp4GDNljefqZ51wRzfKO5BhXHyXKnzNyr4\nVY7I2VyujnasJCjaUc8zkqgodWbn9vLdX3kJcSztr9zy9O/cz4YI6nmx7XHpwSm+fPc2PbzGjO0c\nrB9VSXOVnx7UYCadSOdgqQ2T2zht0thh5TnUO6PllcDRFcOGUaTgDAjrycS+5u2nSHN0y5xuRVDf\ntqy5hD4BNr7ycHdwsOzWMquqQsFJ4vZnKkVW1dTk52BpB+vk0uTY/72g4pAYG1cLLOlHroN2ZFQm\nRDC2vG2L4KR5EAPphRXHSWPrDawsJfv6E3ntC/pzcfSkPS1yMVAHK3N8lg7J5pNEHDSJROEViojV\n2znK02FkAKLs3ATW8aUGv/O5uwij/jkPKkrKS1elTDwuVQRT9zwtbAEUKslxKar3USSmobFuBSy9\nli/9+eyPLdZ5kXUb4dLx3h6oxWXsTbO34rHMzNYhglLeMvRQKcWBg+ckVwa1KNoDyq9yRM5KrnTo\nhaX091+Q/OZhJWm0Q1AtS7X7NDq64m22Ynba6kA5BS45OMX8qs98bbzmt53YTmD9HbAIXK2UWlFK\nVbOXAxrfRJGGkm10sFLh1VlgpfHtBXD1imA4pLCToAErj/f8sFiHprWKB3GIx0sgBs1tV/wl8pMq\nZfbOBUicoErTXjux7Sl7LKvpzb048mJYfbC0wFKDzoMZEVQcESoHr5KseLb6MZFJ90lnbSIQ217S\nMLQDji4+sC4GPi/u+Wv4wPWw8PC6m5WuVCZ5HQNSB6uwJrBC7MHue5ljfLQLBJYEDVp4FD0b3NTB\n6k7EF6SF6OR3sdy+5+qk3PntL/PGW3+Soye2Dp06HVIHq0oZNSZVBEWfA1RhzcGaLhWoSgm/3scw\nR+1gKS8jsPq0yPHEkyf4sPtunj//6d4eqCMHVGGKfZUCCzLVIQersikHK0WVk2a5A8ub7gHbr7Eo\nUyyrmY4hguv6kOYRvhyFFPHXhaC2x6fnr1Hm/BO2m5QXuPSsZG704JOTESbYUWCJyK+JyBzwNyIy\nIyLT2csBjnFiSKvNbSyjHOuy7XaHCUA7hNArofSq7NAEyjf/GD70gp4fFuvSoFHlIADSjxyUQRE2\nt/28RTeRxS0mcebbVCsrhFV8d01gzZU9FuLBOVipc7LOoh/E62oB2Wkfn3QsSRysQiUJxWnVznwi\nk5YdV5kQwdgu4HUSWCLtPIhgEM2GH789uayuX5CxdJhOnJeLpif2nl4ttXWRi4E2sA3X3luUZ7+Z\nUSFs0cRNnHk7dbC6OMZEAQ4RsQ59T/ol5SOwphbv5mLrCfz5o317Tjv28fGoSgnlj8e6s99Izr12\nce08NF10qVIi7KODlYaiKa+yVoinT7/B6LE7sJSwp/VYbw/U8w6rMM3+KY8noylkC4HVsCqUPXvL\np7DKewEIV0fPwXLCGjXKnJKZjiGCdt4Cy09DUKc33eXoEME4c/5pF3lzilx6MBH9D0y6wEoRkZ9Q\nSl2klPoXAEqpklJq8ydn2JE4SnOtthZYndyPdgKxV8TSJ6KwNaRV0aVHkvKfvVrLfpVIFJQPJFcb\nY/QDCpvbh9RFfjKB06uw221bimuE3tr6xFzZ5VQ8hQwo3CDWeQLzy4MN1fOryUlsnPIr+omKAyLl\nUJ7Wnez7MJFJ2z4oO+tgFfHoIFziKOmBxYYk47x48t7kcsO+7eqJqORW5CIRmJ52sAAinMEKrGx/\npF0gsCRo0sKj4FiQHgfDLr5f7XK0V7ut/HKw0gl/q9q/Y60d+4jtUaWM7Y9H+HNa0dctZQWWk4Q5\n9jGPbE3IlcHSYqVPC2zFk98H4KzoBNLLXEQ7WHZphr0Vj/l4ClndLLCs4mw7rG0jTiURWM2VU6cx\n8hyJI9yowaoUORHNElW3drCcMDN3DPp/bAp1GXgKmzNs06iCbIhgOwXGLXDObJGKZ0++g5WilPpF\n4DPAB/VN5wN/1c2TK6VeppS6Tyn1oFLqNzps8wKl1O1KqbuUUl/rduDjiGgHa2OlubTxsN1h5Tl1\nGxyv1E4S9IclsBqLSRx1j5MV5ddYVWVUITmR+vXxOBkhoh2sbSYLUYCPg7WTuxjHlKWOFNYSY/eU\nXRZkwypaXsQRdpC6B4N1sIJa8v62C1+bZCyJiJXNjA7FCRtnvv+nbR+UkwllsXUPoq0mHRlBEw7C\nwTx5T3K5IZTGC/U+2M0E/DRIT95WJnQyUgMOEcy8t92Qg6XCJk1xKbo2SucJd3WMST8bd62KoJ1T\niKDSk0l/tX+hXbYEKKfAilRwg/GYFKbhyWk+KKQOVrmvfbB8nXdtF6bWCvH0qcH5vpW7ATifJ1ip\n9/C7TivoVWbYN1VggelNIYJNVWKqXOzwBOBNJ9UHW6MmsPR7q1HiFLPE1a0dLDeqU5VMsbQ+06xv\nDkFtv3bbwVo7NgTaQLDcIkopLjlremJKtXdTyv+XgOuBFQAReQA4uNODlFI28D7g5cAVwGuVUlds\n2GYOeD/w4yJyJfCankY/ZqT9kjY6WGkuQqcCCenO6BZLSaELIGgOS2Dp0KYeQxRtv0qdUvtH16qP\nRzhF+j5r9W0+7yhpImu3BdbWEwvxq9jEqNKawJoreUmz4UE4WJnKdRvzAPMmrOkQQXapgyUhkXKY\nLjqsUiTqdaX46Ldh8ci6m0I/FViFtRudAjay9QJIRmBEfQwR9GuLPHbzX264sQ6LulpoVmCJUIj0\nqmlOAisM0pCTNYEVKgc1SPc08/uSXVCmnWjNwbL0/hh0IeJFO1iWl/Rk63c573Xo1wr7mGeUOlh1\nq4wbjofACvS5N80HhdTBKqH62AerVdf9lopTqD7nYF3UegCAGdVgfr77qnOBdlcK5Vn2VTwWZRor\nbKwJ/eYSq1Zl295axZn9yXPVBlSYqlt0aF6VEidlFqu+dZELN64nIYSQS4++1CG1ipsD3byC7r2Y\ndbDSNkT6uHHpwSnun5BS7d0IrJbIWuyaUsoBuvFknws8KCIP6cd/AviJDdu8DvgLETkKICL9yz4d\nQSQNEdwYCqh3tk6r++mKrOuV2jGswbAcrHbD2N4mR06wStMq4+gfnd+HFfyBoIuJ+K1tJklxkoOV\nfjedBFazmkw07TRJliREcFWKyUE+774a2ZPngAVWmoO1Wx0sW0Ji5TBTcqlJqd2LpGs+/fPw9Xev\nuynUAsXKdry3dbGArSb1UT4C684v/Rnn/e3PMX8kUzHw1H20TxPZAi5+DYtkP88rRDANf7TsNeEZ\nqwGHCOpjdkvcXVGm3QqbOgfLwkp73XThYPmNNE9HT7wsJ7dFGCstG97HMDhXfCLLo2VPUYjr2+bf\njgqpyChuFFiU2/mRfXkd3dDWK02DzsGK+iCwWrVFLuBxHnQvBaB24sGuH9vQk//i1Bz7przEwYK1\nY1Rzmeo2BS4ApitlqlIiGrUcLH1OaVoVTsksdlhvV01sE8cU4wanSL77PArwpA6pvZXA8jxiUesK\nHEVBWiU7OXe96lnn8SsvflpvoZ8jSjcC62tKqX8PlJRSLwY+DXyui8edBzyauX5M35blacAepdQ/\nKKVuU0q9casnUkrdoJQ6rJQ6fPLk1qp8HEhDBDc2Ak13trTC10ZSB8srFLH1CkDQHNKqaLoa3WMV\nOjdapWlXsItJiGA/QqQGQjs/rvOJQekiF3YhrfC49eR1eTEJKfAqWYHl0UCvluVdGTIT/qEG3AdJ\n6f3G3bUOlg4RLDrUKKF6EVgiSd7jhol6u+1DxsFSenKbNidf9zTZVcM+FphIV8Tn7/0/azeevG/t\n/6yDla20lpfA0u/NyoRORspZ60s1CPR7W6LSnthPMipq4ePh2Fa7EFO43aKUxteTcHudgxXnUj7a\n1rkn0uifg+VIQGx5BI4Oh+qjA5QXkf7MS5UNIYJSxgn6d14O2wJrCqUXgfpx3Fl88DAAR89+CQD+\nyYe6fqyvJ//l6Vn2TRVYFC0C6vPJPrdwhIV4asseWCkzJYclmUJGrYqgPqdUpuc4hW4cvbrBs9DH\nolOSCKw8cuEDnf6RDUFN8RwbH2e9g9VOgUmOG//0kv289rkXdsyBGye6EVi/AZwEvg/8a+ALwG/1\n6fUd4IeAHwNeCvxHpdTTNm4kIh8SkWeLyLMPHDjQp5ceAnr1xt3gYKXhWjbxlitgErQIxKZQ8HB0\niOBAyixvxWmGCHrRKoG95mClB9+RJ+xCYMVJkQvL2z4Ha3U5EVjF6TWBtafs0kBPkPMOJdIn/5oU\nsQbsYDm6NLdN3Lc4/HEidbCSiUwJq5cqmq2VJORvgyCJAp3TmQmFS/vkbeW4BsHaPhz30cFqC7ej\nt6zd+OQ9YHuw96nrw1+zOR459aVqt71wM0UulI01BAdrWSqoaMAtEYaAHbUIVLIfpg5WNyGCaRhZ\nuvBGn6vNrRujXsBSfcwzciRALI8grQw7FgKrSkM8psprCzNThcTB8sJq38RtWj2zUFrLwYrCM//N\nN47eBoB1xf+V3LAhdHo7gvoKLXGYqVTYV9Hh+ZAIrKPfgie+z9/E1zFX6hwiOFN0k4WTQbVW6Ra9\n71nFGaJyEsa4qVS7Pu/kKrC0Q+pWthJYFj7uunNZery23cKm7cedbqoIxsBHgN8Bfhu4Ubrz7h4D\nLshcP1/fluUY8EURWRWRU8D/Aa7p4rnHE71TbRRY6ybkW018wyY+DgXHwtEO1lB6q4SttaozPa4+\nF+NVAmeqXbko2mhdjypaYG36zjKoOCRUTrvCo3Rw9+oryQG5PLO3fdts2aWZOlh5r3Rr9+CUzGIN\n2MHygsyq8YBfexSwJCS2HDzHomGVsXvJ10gFyoacpahdzGFttTV1D7YKIc4Wtoj72eZBP9fc/HfW\nbjt5L+y7FCoHBu9g+amzl6muyIAdLP2ZLDHVnthPMla8JrDSiVLURaXK1MFydPGjdjnvHMS3E+tk\n+j6WU3cJiG2PKK0MOw7Nhls1ahSZKqyFFtuWomlPJRUc+9QjMXXKiuU1B6sfAst6/HYek3085WlX\nsSDTuCvdl90PG1VqlJgtJQVZmm7q9MzDN9+LlPbysdb1zG4TIjhTclmSKexWH3uG9YM0KqIwjT19\ndvL/JoGVzN9SgRXkILAi/Rsolmc33ec5Fi1cVGYfizc4WJNEN1UEXwA8QFKw4v3A/Uqpf9bFc98K\nXKqUulgp5QE/Dfz1hm0+CzxfKeUopcrAPwHu6WH8Y4Xs4GABW7ofEvm0dHy7W0wF1hBWRbOhFT0e\nhItxg9Cp4JWSUIqoOSali7Wr5BJ2XNmz4oBQudjt/LitJ1RpeeCpuTUXdq7k0ZABOVj6wHeK2U2V\nLHMl9PGiOkuiV6l3ocCyJQSVTDJ8u4wb9rD/pwJrw+eWVuHLVstrFxjYYh8MM65VX3tQ6ePXwdYj\na2N98h44cBlS3rNeYLWyYao5OVjh5hXR2HIHHCK45mA5USOXkLdRwolahJbuO6ZFftRFDlaozwNO\n6mDZ/S3nncWLkt9E38LgRPAIwS4gBS2wxsDBwq+xKqV1AgsgcHWYY59Eovir1KVAuei2e/VFfShs\nM710N3fJxZw7V+S4dTal1WNdPzZuVlmV4pqA0iXXOfotuO8LNJ758zQpMLdNiGDFs1liCtcfTYFl\nlWYp7DknuW1jLyy9zbKdRNEEOUQSxVq0FbZwsBxLbXKw0sU+J02xmCC6CRH8A+AlIvLPReSfkYTy\n/bedHiQiIfBW4IskoulTInKXUupNSqk36W3uAf4O+B5wC/A/ROTOTs857qR5Lxv71KxzE7aYfKqw\nia+bOKZVWOJhhAg2swKrtwNlWerE3hSFcuJgyZg4WOsmCR2EgYqT8K80JMnvUOExWE0+v9m5/e3b\nPMdC3LRkas4Olj75n5IBCyy93zwperUwp4n1KGNJRKwraQXO1FolvW5odBBYWiRlhUQaphps4R5k\n8x+iPq1SA+tWI3n0lmSVdOkRHnUv4rP3NogyIYKxXqSJRaFy2gfXQk4yDpayh+JgrVBBIcNrDD8g\n7HhNYKUr0d0sAob6WOkVk8m9svpbzjuLqx2stE3AGaN/j7FTgGLqYJ1BAY1bPgzHDvdhYNtj+TVW\nKW5qpBv1OcxR/Dp1ClQ8p12I54wdrFaVucZRHvEuxbEt5t1zmOul2XCryqp2sAAKlb3EWPCdj4BT\n5InLkjIA2wkspRR1e4ZCMGJiOtPja3rfWUkxiQ4OVlBK5iB59FOVVpVQLErlzWXalVIEOOtywNs1\nBrzdKbBcEWlnLIvI/UDnvS+DiHxBRJ4mIk8Vkf+ib/uAiHwgs81/FZErROQqEfnDXt/AWBGnIYLr\nT/RWnHWwtjgpaQfLtRWFUkU/1RAEVmYlulMY3FbEUUSFJnjTlIpFWuK2mz6OOn7WaesgsCwJCJXT\nzo/bqsAAQKQnl9Nze9bdbnt69TZ3B0s3GZaZwQosPcE+2RZYu9DBIiLWDlboVCjG9e5djbRPy8Yc\nLH3dzoQI2lqsR1s6WJneTH0ska4inxUpE4hN46Fvtgtc3FI7yJNRZV0OVlO7uItM5Sa028LTWe9g\nDbLJtWRysICJryToSovI0iGC+jjYTR+sSJ8HvHLqYCX7ch490opaYBWjPgmscK3Rt1XU4VCn6/7c\n+efwhV+F2/6sP2PbBitcpanKm4oIxIU+hzkGdRpSoOTZ7WbocXiGv8HHv4eFcGr6cgBWiuexL3yi\n6+qNKkjCI2e0wNo7XaKqppKcv2tey4JKvsftQgQBGs4spaiaf+XfXtACyytNce6eaRaYprn0+Ppt\ndA6WlJMomlyaoGsBX9ngkKYEyl1nKrSLvBV2YYggcFgp9T90Q+AXKKX+B5D/MssEkobEbOtgbXFi\nUWGLABelFMWCRyhWuznxQMmECAY9CLz66gqWEihOU3Jt6hTaTR+3ZfERuPEVa/10hsC6fmMdTvpW\nHBArp21xh53y45rLSQU5e/3B2yvpEsUDyMFqStJQ0tkmp6zvaGF+UpeG3Y0Cy5EQtIMVe9NJqfJu\nv+9OIYJbOVhpBbct9sFsaXbpQy5E+zWjFiuUuUsuIjjy7bbA+trifpakgh0124sHfi05hizITG6N\nf2O9+GN7a5+LKGegAivNd1sSvYo74QLLiX0i3SKg7WB1cY5KJ3ieLn6U9kvqZ5XLlIIk4yn24h5v\nR/p7dArYpTMIEVw+Bp//t/rx+Ud2OGGdlr2FW9AOc+xPGXsVJA5W2bPb3+sZhwg+fjsAjQPPSC6n\nLkgKJ610FyZoBzWaqoJtJeJyb7vQhYLr3spyIxnfdn2wAAJvNjmG9+mz6gdxc4VVKVApFjl3NumF\n5S+tDxFMqzd7U3O0xM1FYCm/Ro0SZW9rgeXjbWkqeLuxyAXwZuBu4Jf13536NkOPpBMKT0VEmZWc\ndW7CFkUurNhvJxAXXZsmHjKMxOmMgxX2kANWryYHIVWYoeTZrFJEdeNg/d1vwpGvw8P/Z+dtc2Jd\ntdJ0sNIAACAASURBVMYOlfcsCYmU255YdPps3KBGg80ntjQ8Jm8HK6wvU6VMCyfJAxxUXkgqsLSD\nFeUweRp1LCIkbbZZSENxulxJTx2sDZOTVCRlBZa9TXjWeoHVvwUaK/bx8bgtvozyyTvgxPcR2+PL\nT5RZaveZSfaBoL5IU1xWKWLlJbDS6oqZEEGxBiyw9Ge9zIDc6SHj6X5QsCawuimkEusQpeIGB6sf\n/ZI2UiQZT0X6JGLS92d7OLq3Yc8NxOMY/vJNiQMzc37bYcgTN1zFt8qb7yj218GywjpNVcC1rXYh\nnjMNEYwe+y6Py17mDpwPQDhzEQDSZSVBN1zFt9fe+76pArfGT0Oe+TOw/xKW6sn4tuuDBRB6Ohpj\nhEq1h/VkAbdSsDl3rsQpmSXekIPl65YalelZGni5RBJZQY06RTxna3kRbnKwfJri4rn2ltuPMx0F\nllLqgFLqChFpici7ReRVIvIq4MvA5uw1w45kV2yzZZSt2GdVFzrYanJuZUrgFh2bFm7fKv30RFZg\ndQiD2/JhetXaLk1TcCzqUsAKd1jRffArcN/fJP8vdN/not+si1HuMGGw44BYubiFzuFZAFbUxFeb\nV8YK5cEIrKC+xIqUk+anMLhcKL3fpDlYYR9LhI8LDlG7QprVFlhdTqY65WDp6042RDAV+Vt8xtnV\n4347WGIX+L66DCduwl1/QXPmKTRCtVbYRO8DUX2ZFSq0cPMTWO3G7OsbDdsMrj1AehzfFSGCcYRD\nSOwk+55bSJtd7/w7l6BOU1zKhWQfTt39vjtYIpS1gzUt9b40MU0X35RToFQq0hCPcLXHwgffem+y\niPjy34d9TxmIg+XFdQJns8CyS1o09CkHyw4btFSyL6Q5WPEZnnPix27nzvhizt+TnGvtfYcAaDzR\n3RzBi+qEbqV9fV/F49f9X2TlpUl2SiqwdgoRjIr6s6qPjsCKGivUpMR00eG8uRInmcOpr+8b6+sS\n6lMze6hT2Loh/RniBDUaagsBn47T8rBkfYigr4u4TRrbvaP3Avu3uH0v8Ef5DGeyWSewMqFnjgRU\nSXbILQVW7BMqnZTpWklZ72GECGaKXIQ9vH5Ln3Sc0gxKKZqquH3zzdCHv/0N2PsUmLsQFn5w2kM+\nU9YJrA6hbbYExJaDm5bQ7/DZ2HEL39ocZ1wsDyaMKGwsU6WUVPGBgYl00Q7Mkq5c5A+jAuYwiSMs\nBKxkhc7S4UTS7USmnYO1fsKa5lHZmfK27jZFcLKVA6WPYZp27BNaLgv7npncUH2c494hAFbUegdL\nmstUpUQgdo4hglp4ZvpgMeAcrPQYsJQ6WMNoqzEo9HFE7ETQFlyPSFRXLqkEDRokeTrAWjnvPgus\nsFXHUkJVShRUQLNx5t+HrxcZlVtgupj0kQp7bWL8j/8NLnkxPOv14E0PxMEqxnVCp7Lpdrd8hnlk\nG3CiBr6VCCHVDwcr9HEWH+RuuagtsEr7LiAQm+bJ7uYIhbhO5KwVX9g3lSx4LqzqxuCNZHwzOwgs\nSjqPeoQcrLi5QpUSlYLDTMlh2Zqj2Dq1LlIl0iGCMzNzNKSAymHO4YR1mqpzwYpAediZY78KW7Rw\nOjpe48x27+gSEdkUmyUiXweuzm9Ik4vVwcFyYp+qdBZYdtQi1M5HwbHwxV1f2n1ASCZZvZcy8f5q\nEjbh6QN4S5VwoswPW4TgyM1QPZFcv+WDMP8AvOz3YP9lfXGwfnCyRt3vfYIVZSaqnUL/kgpxLl5R\nh8Z0ElhRs+1EZilVkgO+5DwJk8YyVSm396VB5UL5tYUkb7C0L3nZ3eZg6aapqYNll/TvoNvVbv27\n8zdUBkzbPjiZMu3t8Kwt9sF1IVt9/O6t2CdSHnvOPsTjJMnTd4fnct5cieL0vnXvgWbiYEXKxd6m\nefcZkTp7mdBJsQbrYMVBi1hU+7g+0Q6W3q9E52B5rp0s4nSTb6PzdEpuKrB0kYsuixZ0S1OHRs1b\nyf5YW54/4+dMHSzLKTBVSBqIR/UeQgRFEjFzztWgFBSmug8bPl1EKEqT2N1c4S0tqx32yZVxogaB\nFlhpIZ4zcs6DOgphRcqcP5f8rvbPVHhM9hPPH9n58XFMSRqIt/be91aSY8R8TYf01n1mik47R6sT\nVkUf10ZIYEmrSk2X31dK4RcPJKkAmX0qbFZpicv+2Ska5COw3GiVlr2dg+XiZHOwohYtvF0nsKa3\nua+rKoKG9WQFVrZPjSM+VdICCZtXni0J2vHtSilayltfGnlABKuLrIgOg+tFYOmTm1fRE0urlPSG\n0Tx86xdwb3wJ/MFl8P9dBn//X+DSl8DTXgr7ngrzD51RvlAcxTz6x6/ka5/7nz0/Nvs+t/puIHEg\nY8ttlxntJLCcuEW4hYPl6hysvJtHq9YKK5QpFvXq0oD2oaA2zxJTVMqpw7fLBFYaFqNzsLxyMpFp\n1noUWBvCclMHy3XXDsdpmOpW+S/ZEMF+LtA44hNaBS45MMUt0aUAfGN5P8+6cA61YSJi+VWqUkI5\nXo4hgj6hWHje2ucitpskww+o6lfsN/BxaDCgHnfDJH1vOkTQcyx8nHZ1sO2wggZNkjwdyIQI9rmK\nYHM1mWSuuMkCQNr0/UxIF9yUW6BSsKlSRnop0x4FIBHoBvV4U+0y2rkR1LEQxNvsYE2Viom73ItI\n3AYvbhBq0d2uIngmv3m9cBLgcvZs8rz7pwsclYPYK10UwkrdwULGwaok45rPOFg7FbgAcPRxLVo9\nc6HeN/waNcrt/mYypfttrq6FCca6yfRcyaVJASuHXH4vqhPYm/ev9hg2OliRjy8Onr27BNaDSqkf\n3XijUurlwPCSYsYYFa+tygWZ0DNXAmqSCqzNk14nXiuBC4nFavU6QQr9M865iVYX2oUKuinB236c\nTvwtTSWPDawibkZgLT32AAD/cM4vwFNeAOddm8SkQxImGKxu7kjeA9XaCi9Q32H21Hd7fqxkRFXQ\nQVTahIhyKHo2TXE7fjZupldMloJXJBbV7gmTF7ZfpSplilroDKo3T1ibZ1kqTKUCy99lRS7Sk4mV\n9l5JfgfNWncTmdQ53uj4SOTji43rrCUHpwJLtthX4+zqcR/z75w4KXBw6VlT3Bwn5ZO/uXoO1164\nBy91sHQemRusJL2hbC83B0tCn4D1J2zRJfLzaGC7FbHOK7C81MGaYIGVhgjqkEzPtrSDlTm+nLwP\n5jeHcVlhY11eqpUWuehziKCvm582igeTy2o/BFby/iynyHTRYUXKqF7yl9LJbdoHsTCVf4hgmuPl\nbXawkjDHEmHfBFaTSOd6OWkO1hmFCCafd7FUarsdB6YKPCoHKdW6qCKoP1uruFZCYP9U6mD5iAiP\nLze37YGV4k3pcPfq6Agsy69So8RUMfms3ZmzkzsyhS6kWaUuSQn1llXEivp/XCrGdYLtHCzbw5Gs\nwGoRKHdT24BJYOs6ign/BvgbpdT/Ddymb3s2cB3wirwHNolYsoWDJYJDwEo7B2vzpNeJfWI3K7AK\nlKMe3Yc//1dJhaZX/2nvA9fE9UVOMsdTebwnFyJuJquHRS2wfLuMlylyIdWkV8Nvnnwx//iLL11v\nz+99anK58AOYPuu0xr26lBQIP51V+zjIhghu/XhHQsRKGkG3cDvmx7nSYtXeLLBKBSep6JNzgrMT\nVKlSTpykFQYmsKS+yBJTTFf0Pr7rHCy9sKInGaVp/TvoZiIj0s7BcjYKkiggwGmv/kNGYG3hTsb6\ncw/E7muIYOLgelxycIq3Rv+cqQuv4ejD+7n2oj3cf2KFFi4F7WC5QZWmfSnTVoTd5zCwNlt8Lml1\nOqIAnPzLAf//7L15kCxbXh72nS23Wnq79751NpgZxEgzWDMsBgd2aLHDsgOQMZKFIYiwHLbDkiHs\ncCBkORx2KLxIDuTAsgXYCgGWrcVCCIkZYwRGAskjg4AZidHADO/xlpl337tL3+6uPfef/zjnZGZl\nZVZlVVf1e6/f/CI67r3dt6uyqjJPnu/3fb/vIztX4A+AELpJdFvLnGuswmCNoJbPsZ/6bsA7Ar7j\nx5d+VaYzjCoD8UzuKZC2VtFc34OS4GlgDMTbmlE0lG2GCkdvWN9AAB5t0Qi09xY7K+j09XuZpcVa\nse+iaAIGgLmrAqWBpzCmHnrbOiG2lEshMrk8g0XXuebN+TTolezIka/wGu7BSy613NJr91+LZldw\noc22bJ309HE9mUb44V98Cf/45Qt8z+/7wMZDGQQ+xhQAsycNvsBvTolkiomRCAKAf/IM8DIQj95A\nsXuMZ5iakOlz5kLuK3TbFhE8WiwZidQr585STAzP4nIu/JZVK4NFRC8A+DCAXwTwXvP1iwA+YsKG\nv1RbVpUWLaRneQqBHHO0GyRISpBXNuYpd7YOio3ufwaL139zh6Mui4VXeExa5rdNDhcZgNUzG8tU\nBnDz8vfF9CHOaYg3phl++eVaR+jsy/SfDd3PrjUf68dkOwCK6utsm8GSSEFCwVMCEZxWaYxDEXKx\nKhEMHIEF3MKy+CCVJXDyBSLeg7Jd9Rua42PhJS6pj6P+LZUIEgG/+fH2mRPDXFv5U9DX11Ay79Dt\njmdgubaxVZQsS2XzBCkElCgbEu4agGVntuZwwfYIsBTFyIWD95z1AK7wl169C1dyfOiZIc4GLq6o\nV7BwbjZFLPsm+PcwbBLLIsT1oWnDHt4Ug0Wp3jQU9uPvAAaLqRJgRaTAqk3A8RulWUulVDZHVLEM\n51ZK1sawjl7bKbYjCU3A6vBZ/e/Z9WdnrGRcKA99V2JCAUSyxYa1AFjm9VtWKT7cHFZk1hzuNQEs\nzWBtJXNsqzTWzqnmtXFpg4avse6Y++qgX27eOWe48p7T/7haLxOcjjWoVn4JwlwpMPAkfvLT9/Fn\nf+Zz+Kavehb/cQeANfQVrqiHfHZ9JnQvRQSVzjSDZQDW4I62sp88Ltk9ZizU+65Ewn3t+rrPSiNI\nZI0zfrZy7uh7malqDNFtq7WiR2PR/qNE9J+arx8honeYBdj+SlCKmLScJ7UMllk0IqNZzRs2nwp6\nA2Mr4e7WF0Y+eYDZ1e4yOwAQ0RWuqK8D6rbZJMcTxCThmtmfTPg69NFsFp3wIc7ZCXqOwMf/6evF\nr0Vphh9/ken8oGsYXURGDsK3BKUAShkHWgCWcYgj7sBVemPRNtvkUBnGWS2bbZZtEd68dZlB19QZ\ngFk29IYYLBldYYQ+js2N8Vo32bdiPfpN4P/8TuCFn23+eb48g2U3CEmLnf9SmU3pQzKuVRWZMbIY\nMSRUBUi4SphzcPWzte/7DN5eHfwUxci5nqN5z1mALCd8+LkjOJLjrOfikgZIphdAGsGhGIkaGoB1\nKAYrNgxWhQm3jMChWLNaURIhJomgbzayt9jkwq5b3EjdtERQLmXdYH7eaODgZjOEvLJhNkyHzTJb\nqV/6IeCvffvWM7mpAVjOid6MZ/PrM1i2USQcDwNXB7irZAsVQm12rZgNOqCSwUamSL8FYG0rc2wr\nw9iSlQhaBivf/fqzzsVH/eXN+7yngQQ2ZGHNjSTbzoLbOus5eOl8hn/h/Wf4/j/0EfANBheAfq8u\nMVgy/npTK1mAU4YpaRdBALhz71lEJLF4UgIsnswwIw+BK5EKD2rfAMvIMGkdgyUcKCwDLOuSfdvq\n9k2VvYVLUIIZs138ZYAVK73gNQIsSgoLXADIuLsdgxVN4dMC/fwaCycRVDLGFfqIOg4w2+LxBFPm\nFxrbTAV64NwAkSA6x0jewb/8oafw0595gDjVg+j/3U9/Dt/7t34DM/+5a1m1x1MDsHZhbCpgqdF+\n3W4ihIInBWLIVimiixgkWxgsckGH3ISZrmTuHhWd5psCWE4ywhX1cTzQi252Q897Y2U78y0bE8sc\nWZna0HeRkEDSIjldKjO79BAGYC05AaZIa7NGjuAmJ2/1sa01+5z2G/KrkCA3srsP3NPr2Mfeo4/3\nrO9ghJ4eBjf2z7kzQM7VquRxX5UlSEg0SwRviMFCpmewBkEPGTFk4e2VCNpGATcmP4wxpEyVLGk8\n1wCzATi4tUymjXlJ0URv4rY0g8gMwPLP3gUAoG3t1Jse09yrpWNMLsgwAl3nG9NlBqvo+h9wDiuc\n6WtQNQCsoadBIt8Hg2YNmxzLYFkXwd0BVhTq96swaTKVDN6t/7IBYC0MwPJqAOv99wb4yPNH+OHv\n/Bhc2S3sdugpjKgH9lZxETTNi4j7xbr37ImPB3SK7Op+8d9kMsMMHnwlkEp/SUm0z+Mgp90jj4Sr\nAZZpknAT83Eb60sA6wZLUFJIAXPLhpjNeGoW1xWDBCI4SEAVBivjLlS+xSbVDDl6iHeXqkQTcMow\nIh0Sii1mwHi8HDxHcjkbZpCcY6ru4Ju+6lmMFgn+4QuP8bOffYAf+0evAAAeyOeuxWCls90BFksj\nTMlaXzf8vt1EcN0xj+C0AyyKizDOavlKmFT1QzJYVhoyBDfH0CWn5tqVxnCyOWZiCLewsb9dDNbU\nOJI9etLczUzNPAmzDJYnEUO25qUtlemQPrIMVoUVYHmMhARkpePKOUOMlhiHrGSwdmJzW8qhBDDr\n0/vv6XXsd7/bACzDYNH8sgD55B6BuII8EIPFci3Pc9pmsG6iUs0uHvcczOEhjW4xwDLmPLySO5ZA\ngdlzrGhArG7c/XyOrCInKqVkLZ+TXbNmj5t/3lKZAXeDs2d0ZMQesp7sfK50PEjBEVrntK6Pba7/\nf/zaAn/8r3wK/8Hf+Jz+/gEZrMTMolkn02ppBsuH3IaFaykyAJgb2aNUlsHa/fqrujZWKzi6o906\nbcxLS9m5OzuqYOuHv/Oj+Fv/4Tdg4HXf5A99iSv0IaLrA/W9lFWoVK6luwMXD3AKOXuj+J7MFghZ\nAMEZcunDqSiJ9lFkr3G3XSJIwtW5kIbNlCbm4zbWVgCLMXbCGPtSBtaOJShDaPTmBZCyDkwG8a9s\nevNUn4wVaVkuXJ1v0LHCy7KDQQ06+E5lOjUj9BBDgbaQCMp0hrAKsOwMUDwFshRH+RVC7y6+8QN3\nceQr/MgnX8b3/s1fx+96bojf+ewQL6R3r2XVnptcD7HFe2aLp2FhQNI4O2QlR9IBY0y74TSyBylc\nlpSWvJXyzQzWQWVE5qYvguMimHYbq/2dy5w3sTqCMBuwJgvxt3M9Pj8HADy6aO5mZkbuZK2KS5e1\nDuejAViFRLAKEAyDVc9saQNYlkmL2H4ZrCoz+w1ffoa7Axdf+75TAMBpz8EV9cDDiwJgMf8IJBQk\nDgSwjMlFVerDihmsGwobNtkuJ4GDEM5yYPkNFhHhE7/+OqL0cBlgqWmUCbdc4xPmQGQ1gBVPlm3y\nsxQuImRqVSKYtQHhAmCdb3WMZACu2xtiwnrge5DBUcFg6XM/lqZrH3bcdBuA9mf+n1fwSy89KZqv\nh5zBio2BhRscrfys7+qwZCedXHvTbV0bmTknhFn76BoNDguweA1g3em7CEltVNXYmdfe8GTp+1Jw\nyC0twoe+wiX1oeL9GIJcu8z5XG1WBI7EOTuDH5YugiqbIREmykP4Wkm0x3nc2AQZc68dYDFrMmSu\nZR1U/w4FWIyxX2CMDRljpwA+BeAvMsb+h8Mf2u0rSUkx0Gs363ZRYI6WkqywJPaGUgkTzYUHZwuw\nMD0v55rC0XY3pvIX9U0jUkeISW51Uap0WsyY6W9YBmsGzB5DIEcSPAVHcvxrH34an3zxCbKc8D9/\n+0fxu999jE9Pz65n1W7kINsagwAAz8IiLLTR2MO8D9bAQFvorz5PZLq8TRJBXwmE5IAdIJOiKLO5\nVUEJdNpMO/ZaBmBl7hGUoxfWa1n1vgUrNfMceUuOWZpaO2fNYGmWSZYd/nVlJYImHqFqTMLyGAmT\nK/a2MWtmUSnVtu4ZV3sDWHmaQDAqJMzf8P47+JX//Pfj1OTL3Ok7uMJAb0TMGsL9I4A7UEj32j21\nxfIEKVt2YSsCbG9o/s9mu5wECgtyCgblpus335jgP/qrn8bf/9x2jM82lUUlk2Mr5apkSeeVe07V\nTdEACVKlnMjO6qBtjUh2Y7CsQ6sXDDBjPYh4HwyWvsaUed2pfR1dwZtZ77/xQ+/CL/+p34fTU7Px\nr8sfX/s14PzFax8vAGQL6+i7CrCk4Fjwvp6NvKYpS2SeR7j6Xi/24CJYuDbW7qF3By5iSCTR+vtZ\nasy2BsPjtf+vS/UdiRH6cNMxkN9cgHlrGeYor0nzJs5dDOJH5bx7tkBsFETWgGSfjd3IyDDFGjdH\nq3awDUbrQnsbqwtsPyKiMYBvBfCXiejrAPz+wx7W7SxBaXlymxtFahYF5fq6q13fGNkNQWVRIelC\nYTcGazra8UZrNsoyODEyuO6bczefI64ALGYWXYpnyMea1qe+tmD/to89D86A//ZbP4z33unhw88d\n4fOJCczbcQ6LGxpf7gSwovUMlt2omkUjZQ54g3wzXuhFjKlg5WeBowNJDwmwbBaZ0zuBcPW51Mlk\n4bplzhvyTyCVlSbeLomgfW/RIvHMzNyBlQgCQNom46vX/AlyMJwb984qg8XyFBlWZwYSpkr2YOlA\nNLNDewRYsWFmWIv1+YlhsEQeIx3pa10GxyB+OMmeBljLcp9DBdi2HoOxHj4xEsGDyn/X1KOJXqen\n0eGYu8JNr8JgZazidFsNY63KBA3ooYqcyLoIZi3npz3ftgVYSOaISSDwfSx4D2obt7+Wsky8BViZ\n3dx2lAhmhlU7Oz6GFBzcWqfXwfjf+WPA3/+vtziwvLVxYSNTggaABZSz4Limk2Bkgp2layWCZgN9\njeu9MBVx6wyWg5gUwg05knk4Rkocg377fFDX4pxhIY/AQNd+r/ZSdvapZr+/8J7Sjn2LS+PwlyKz\nGVV2L9LSGNylYsMSigaXyqLsvcLc/yQlSzmvt6m6ACzJGHsGwB8G8IkDH8+tLoUUiQVYZjMdm5uT\ncj3EkCubTyu/WNrASA8SeefFKh2VGtxwtCsLpDfKzuBMd9+32Ki42Xw5F8HospPFBIsL7XDDjX3u\nx95zin/6X/4r+Oav0v/+Xc8d4WUygXk7WrUL01GU28yt2d/NI4zJfmbNrABQyr9S7kI0PE9kbqZM\ntc9giQPORIVT/fn5gxLopFtY7e9cdgg4OIV0jEzklkkEi3mOlk6gnTljspplJ8G6XL/zC4yppyWk\nwJJ5BW8AEkA7yEeuAVbO9xfyG9uucQvAUoIjVCb361xfv07/BGSZij3KU2zxPEFWZ7AMe5je0Pwf\nN1bxx76WCNIeNzHb1MVMv94wOVyX3boIKqeUP2dVG/55M8DKzXVTzWQS0q4RzefnwwvdLKMtARaL\nZ1jAhSs5QjHQMrjrlgVYpmFFrunad2SwbINLmGiFwtmvbnIxe7zdJv7//hPAX/sjjT/KowkW5KDv\nN1+vqWOAV1eZY0slpukkTKi6FLoRdB0XQTtWIWr30Lt9FxFUuRa1VTTFnPkQW8oB2ypW5r16Kxhd\nmOuKu8vMUdIze6fx/YIZtZJcdoAQ9HRhTFQaZvyKMvcK66QtKX5HM1h/GsDfBfAiEf0KY+zLALxw\n2MO6nSWRIJWmW2cWZ3uSOW6ACM6K+1fSNNhp2ayOG3KaPNTWzQDi8a4zWHrB9QZnesZjCzYooDky\nWXYppdHnxosp5sZC1Dl5pvh5ddj0g08N8FjcQ8bEzkYXKtGL/S6uZSKLMIeLjFgjMLChuUWHnDuN\nTFliGSxnlcHyHYEFORAHSFW3ZQFW7+ikCKM9qC28LSNxk8EplFTIiRVudrelLMBiLQDLsiZVBiuB\n6mQ0kUzPcUF9JDYTfsnkIl0BEoABWA3vMctipBB7zaBKjLNXU+PAFnl6k5VfvAIA8PunpUzkIAAr\nRlYLriyYkZsCWMZ6uO9JLMh904KGL2YxPsBeQxgfjsGyG1/lVRisalZjVSJYYWeso121282tGUJL\n88EaFcXj7RqFLJ1jDu1kG8s+vGwPRg5pjIgkHKWvQWZlUR0ZrNQ4S0qj6JC+2bBXWT4ife/dBqA/\n/hxw8XLzzyIdNGuzkuqVOfY1XI+VscZSqqdlj1IKHVFznRksc57JOsAauIihNkreWTzFnK3ef3c+\nHtdIDd9KAKsuzTONa4zfKGWEBmBxsxehPbpWJubcbzJRsWXJgsQ2ZihZiiG6TbURYBHRjxPRR4jo\nj5l/v0RE/+bhD+32laK01L2azboFUJ7nI4ZckQjGZgPDKx3iYo6nIwMh5g/x26QvtHR2PZMLNTgz\nc0bdWQifFsicEmBZXXYSTpCMXkdODEEFYFVLCY4PPnOCR/zpnSWCXmq6KjuYXEiKkHG9gDcCrHSZ\nnWiz0E8MgyWc1Y2oIzki5kJsIbvctpLZJebk4qjfgzQAa6sssx3L5oSowRlcJTRQuCkntxsqa2vc\nJpu1M2e8wmC1gaB6JZNzXGIA3zgwVgEJp1WmBjAgv+FcZ3kpERRdJYJptHb20Uq2eAuDBQAIzGzJ\n5SvIiCEYDAvG92AMFq8zWMY84Ybm/zTAchA4AnO4b1rQcH7+In7O/RO4++iTh3uOeIGcGJzK2pYx\np1xvlxisEnxEs8pMnik7g5W3zOpY6Ws8etj487bi6QIRM51zNUBAewC8xorfNexMsbltcEtsKmt8\nYoGp7wdIwZcZrHgKULadLX04am2+6qBZH55q3vrZZsh1AVY+NzO/FmAJpuXM15hXyiu2+NW609cz\nWOmGhqFIpgj5qsnUrpV5byWAZU2sloGNPNYZYcnVF4tziBzDYJl9WLzHCIl8zYyfLQuw0lg7GCos\nxxDdpupicvGjjLEfqX/dxMHdtpLIAKkdb+wCaBks1w8Q0aoDnR3c5BX5BVOWgejW1XIXj/Ea3cGM\nXOQ7AqxsfomQFPq9AVLWrfsO6M1lwKIypR6lFCJdTJCPH+AJhjgZtAfTffi5IV5I74F2ZLCCTF/0\nuwAslWuHtBiy0fUti5cBVi6cxudJQmtb29xBS/kBQv8qlc1HmMDHSaAKKc9NMFjx9AIpcQT9H7K5\noAAAIABJREFUEygTQHrbZrCEAVi8ZYYuKwBWyaosmQCsqXz2BJfUx9HAXD8dGKyMqWZDlyxFCoWc\nO5DoCDR++X8B/sLXtW6MCmevhsaBLR5oR0E1/gImCDD0nYMyWIISZKzOYBmG+YYYLJHHyLgq5L8H\nNbBZdxxXrwIA1OJwJheUhIig4KryXMyFUyoGZufIYYxYKuAjmpm50Mqm0EoE0XJtWJl3NtmOwRLp\nHBGzs1JD9GkfDJaWgboGrBTZUh1NA+wMlmMA1sBXmJFXWl0DhXJkKwY0HLVeVyKZYV7JpKwXs2D3\nmjlh+eIKEUkEgZnB4gwJxLVy6AqA5S6DpCNfIYZajbiplUymSHj7PmPr8kzj6K0QNhxPdbC5v7y/\nCE6fRUYM4ZMvFsCdWet8MzMZzffHYOXRBBEpeG77/UAYgJxEYeGSTe9UBgt67ur/Ml8/D2AI4M2x\nRHo7FxEcloJxpU0irESwxmCx2sJoZ7CqGSP273FH699eco6JOsMlBmCL3RaDZHqBEXo46SlkWwCs\nmUmOZxXqugBY4Qx8+gAP6QQnQfsF9uHnjvDb2T3Qk9/eyXUsyPXp6nTdVFZKUWQAVoMBCYDEDt6a\nDRxxp1GKaLtr9ZuDrUz42r3pQOxOHo4xoQAngQNXScQkboTBSibnuEIfxz0HSnJzk71dAMvmxsiW\nTXRec5oEYK6hzZ81Dy9wSQOcDW1OXvneNQEJAMiE2zhvyPIYKRPIxRYZVJMHWuY5vt/4Y9sg4g3u\nmLacoTapccPHGFOAoa8KgHUIyZ6ewVpeT6xE8KZcBIVxxuq5EiFciDcJYPGZYXoOOANGyQIR1BIr\nknO3XAfnT/CI3THHUW4dknnpbGpLbAikFWQk2fPt3HBltkBk2IvcHSJAdC3LcKA0MrF5a5633UxL\nFi8QkoJnZJEDT2IKH+miCrAMO7LN57eGwRLJDDFrZ3FEsB8GixZXGCPAkbmvS84Ng7X7e25t8Z1a\nM4dzhpQ7K3uneqlsXszA76N470z/5S3AYGXhGBP46NWkn2fDHh7jGMnl/eLa40aSK83sYxzubztP\n0bTxOKrFTFRNGoflefpOBVhE9BOVr78CbXbx1Yc/tNtVtotNQiFmpYOY7bo4nr8czmjKSgirg52W\nzbLOdGsrjdHPxkj8uxihD7Hj8Go6e4Ir6uM4cJBW9fUbajExAKsyfOn4ZrMYTaDmj/CIjnHWb7/A\nrNEFT+bbW7UTYWD6AWqHuRNFMZj0dLhyE4NlZ7AKBqs5o8zKQYTTvMBnhezzMBsxFo4xRoDjQMFV\nwmSZHd7kIp1dYEQ9HAcOlGBI0NHc4W1UTqrPr7YZOisRFHIZYHWZg1LRFS4xwNmxaUpUPjOep8ib\nGKwWkM/zBCmkaQJ0BFj2Btgy01E4yK1hsPzhWfH3CQIMPVVI9g5htCIoRcZrDFYhEbwZgCXzGBnX\nEsFDz1euK2dh1ssDZuxRGiKEA1eWjpYkHCgkuiE2f4JX83v6BxV2xg7Eu71K822D25wy9x0VbqfE\nUNkcsZWH+RpEzMfX2xizLFrKW+t7CnNyOytL8niOEA58R79vA08zWPZ9AVCaTXT9/PLcAKzm81xl\nM0SifQ5JWjnvdZ3xwhHG1NPNFGgGK72mRJAKU5FVgNhFVePmsyXHyuuWlT++JQDWfIwZrc7W3R24\neEAnoPHrBTPKrLOjaQiki/0BLBZNMSMPPWfV3daWzTHTAMu6ZL9DJYIN9QEA9/Z9ILe9LFCCdJCg\nnGHK7ICw4yNhamUuw9q4i4pEUBQMVoeb9lR3MGP/HmZ8CCfeDWDl8yuM0MOxr5YHmDdUaBisqjbY\nd11EpJBHM/jRI5yzU/iq/YL84FMDvMbNsOaWc1h5OIFEroeRt2WwshQSGZjyEZNstNW2HXg7X0PS\ng4t4hWnLzWdVHQRfehwT/ncogMXjMaYI0HclXMkRQd1I4C/NL3GJAY4D3enVM1i3i8FyDUMqWySe\nTRLBTtdQPIfKQ8zFEH3fdP0qrKOgpBFg5bwZ5GtJoQJx1V0iaM+RyzaAZRhcp70rfjwYYE76Bjqm\nHo58Vcxs2d/fZ0lKSht4U/b6vKkZLJvt4kqOBVzIA85Xris/1kxPm3x1H8XSUMuCqgyWcMFBQJ6C\nZud4OTNRG5UZLJvJ5PfLXCILsNrc5ux57cWXy6HFG8rJQ6RC3zftrNR8fD1plwZY5XnW9yTmcDtv\nWClZYAEXnrIAS2IGH3nVpt1I9SiedlNvxBMAhLyFwVLZHMkagNUPfMzJRXpN2RuPRhijh4FbZv+l\nENdqruVppO/jcnWvsBQL0FDTMMFdukDee2rn569X33cxogD5fMe59j1WFpb392ppgHUGMX2AxFxv\nVkFkG93JBnv7bYolU8zgI1jDYNkZujQOS1XQGgXE27m6zGBNGGNj+yeAjwP4vsMf2u2qpHCbc5ZM\nInIDvKTr6+/XFonM/rwy2GnzRpIuw4mG8aH+U5jLI3jpbgCLhZcYUR8ngdPaIW+q0OjsC306AE8J\nzOAC4QhBcomJutOqCQe00YV/9m79j/Hrrf+vqWZjvcF4gmMolm0nC7GbEuU1sotA2REv0uWLjIfa\n52g6kNJtvrmRtADrMJ1mlUwQij4YY/AUN6Ydh9/08fASV9TDSaDA2O1ksLxcf2ZOC8Cy5xyvyCBy\nrjZfQ0bOm3mnxYxT1SmLNzA1gJ0DbGawMiZBwoHTMeT34YXpzrYwWLllsNa4CJ72XVxBM7cTFsBT\nvIw1OIBMVVCKvGZyYWd7bkQimOcQyJALF4wxZMLX85UHCFXeVIPEAqwD2sSn0QqDVUh+kjmwuMRD\nnOqA+gp4oGiCkBR6fnnuKCGQEm9msIjgIsaIAnDkWzEHTr4ogIVlaRaTa4KILEZSkehqOaiDNOo4\nL5VoiaCvqgyWW4QiAygYLEb5isNwYxnmiVMGNBiFuPlirUzuKHAwRoBkej1WRiUTzHmvYPcAaAaL\nrhEXkBpTkQaDjk2qmkeP3sCQLYCT9+7+/LUa+gqXNEA6ffNnsHIjEex7NYlgz8EDOoG/eIBkbmbR\n/aH5UwOsdI8h6CLRLpXrGub2XpGlUXFOs3ewRHBARMPKnx8kop+4iYO7TZUaMwQIpZmq3EoEbVih\n30hzZ4md3Sk35laOk27KfQCQT3QGFh88jUgdIUh3S7Dn0QhX1MNxYIbkOzJYcTHIXHYptbOWB3fy\nBXAQ5u7djY/zrme1yyBtKV2Yj/QG41LoQftOrJ8te0OTLhKmGjXelsEqhrMtwKqBF7J2/C0MFtRh\nGSwnmyIxVvmuFNq2/wYYLBHrTuaRbzbUkLdrBivPEdB6gFVKBMubX6cmhekis+C0BAgVQCIpBfHV\nTiEJV8uzalUFWPogNgPdq4m++SbnzQYz5frUDrDu9ByMSJ97kQH53DAVxbq4x5JIVnJVrP13fhMM\nlmme2ffZMic37SSYZDmOc71RFgcEWDxdGJOLynbCnmOTh2AgPKEBJvCL7CtAA6wJ/KWue+k21/A5\nmfXqPpn7xRZZWB6FyKVee5VhzMLJ9UCEteK3NXC1JX9XiSCSBUK4FYmgZrDQwGDp/9/hcav3xwbF\nhZfPixykpjryFUbUQza/nsmFk4wRiuWw2RwC7DoB51mMGLKYeVt6bO6sdUYdv/6iPq47X7b789dq\n6ElcoYdsV2fmfVY0wZRWZ588JXAp78DNpkjHOuhdBfpz8cyfedeGQIcS6Qwh8yF4e8Pcqh3yeFE6\nP77TJIKMsY+u+7rJg7wNVZ3VSZgDsTKD5TW6fxUSQneVwUo7LOSLC834OMfPIHVP0KPpTjpoFY+0\nRNAArK7zTInRkzsVnb3vCMzJRW/yiv4/wWbF6Ze/6zkAwOXFdsPNCyMDmSo9BxJ1AKVF2Q2RMuC3\nAWDZDa8wG7gyoyyq/T+zCfdabm4HBlh+NkNqMk5cxY0r4uEBFk9DhOTgyGjx047mDm+biqdaCgXA\noeb3MzdARqhy009dGCwjPZGDOxCGIc1WJIKrDBZJFw5Wz1VuTDG2sUjn1oznvFmamxXWye0SwdO+\ng0sLsKS+qVuJYHaAc7BJIigKk4sbOPfSZYCVy8Ne2211OYtxD3qjLA84A8Yyy2BVthN2w2TMUS5p\nsDJfxOIpZrVNoSjc5hokgkZRcJ/MTN8WAMulEJkBWG5fN9ui2TUZrFrQd8/VEsHOG9Z0gRBOIa0c\nehJTeGBJCbDyeQUEdskrqgKs+rVFBB8haA3AOvYVxgiQX9NF0MumiOUywEqZALvGDJZlsBy5um3N\nuGqMprC1eKQB1uCZ9+/+/LUa+gpXNAC9BWawWDTBtNassDX3tCySP3kBGTH4hrnyPQ8pceR7NMBR\n6QwhX581ZoO58yQqXLLX5Si+natdKAn8uTU/IwC/d8/HcqurKhFMKzMSlIVIicNRTuNchpUQqoq9\nt/172oGNCS/uwyeG4PRZ5N6J3gwuroDe2cbfLSpL4GRzjNFH35XIhatnOIiANdI+AKCZBkR+/6T4\nnq8EHsJDEOqueNZBF/0Vz99FTAJXF+c47X7kiCZ6kxq6d4Go23tWlGWhZDO7CACZ2aRKswFmBmBR\nssDSO1MA6ZbFp0hVP0CnOY3gIAZZgCWFdv26gVkonkXIhFvcFFMm4dwmgGVmSi6ojz5bLxGsmlzk\nwt0IsGh+AQbAH94tWOslgIVsBUgAmsGSyLVESJRLvJYUBiDrZtgFYBmmXY5eabzeqWgAtQOss56L\nl2DkKMoCLOsiuGeARQQHafkaTYkiwPYG2FO7sTXZLrn0gRjm2t5i3b1mPZlGeC+zAOtwcmCeRUjg\nLMu8LaNvJN1PMMQUQTF3Bej8uCl8vKcyEK8ERwjRKG+z7+vr2wKsLIWLpMigtPeidHY9EMHzGEnF\nrbLvaYkgdVzDWRpiQU5NIuhDVCzZk+kFitZql43wOoAVz/T931kGPtU6DhQeU+96JhdE8PMpEmc5\nkymHALtOwHkWISbZCLCWXCubDsmEnJ8+/4Hdn79WQ0/hIXpg4Wt7e8xdSyRTTKkZYKXB00AIyIsX\nMYNXzEf5jm4I0DYZaxtKm8msB1g2KDpPQiRxCB+VEYtbVq0Ai4h+z00eyG0vu5HgUptEeLm50SRR\nIa/QDNbyImEZEqciwVFe9xysdPQATzDA2SDAudGe0/wJ2DYAy3SzEudI30TtAHOWlDfSljq+/w9w\nn85w8sx7i+95SjNYzHT++aA5ZLhap30XY/SQL7Zb+G2ifBLcA8ZAsgXAsiCJKw8pUxD56u9a22zL\nMNhZmSQOUX1nyHRfPb+5e8gPyWAZWY61ynelnsGqZ64domQeAbLcfGdQ4LdoBiueX8EB8IhOcMq+\nuAJqgJLBknJ5BqtJxletxfgxAgDByVMVBqsECE1Mjf5BRaYqKgHfeYJcqMLxsgvAsuHXKp1pRq13\nZ/m1JeUMaVudBApXhsHKzOZOHApgWeajpukvJJY3ce5ZaZY9BnXY+cq2Gl1dwGfGda9h7dpXiSxC\nzGubdsvkG4B1aSSCWUUiKJIZFrVMJlG4zTV8TollsPQ5mIwfoeHsb/g9E7BqAFZgXC23vZfUS2ed\nlddX35V4nbqHSvN0gRBeYXLRdyVm8PS1Vhx6BWB1yMKixVXZ2KtJBPNoqiVL7nqJ4IvogcfXyE1L\nFlBIkTvLYbMp5LUYLG6CnRslgi35k7bU+At4giOc9doDcLetoS/xeepD7ujMvM8SybRxBgsAaPgc\ncAG445dwhUHBGAeOwAIusMc8TDebI27J+rQlnZLBKnIUbynA6mJy8V1NXzdxcLepSomgi4y7BVNF\ndnBTCp1fU1skrBGBBVVAyYJsCtYDAEwf4DGd4KzvQgT6xhRPtpPZ2WHizNWLExUbtA2bo3iG5y9/\nCf+f+ucRuOWt0JUcc5iOPDGoo80M1sBTmJAPRNvdFLOZPnbq6+fYBmDZnDHu+Mj4qgEJUA7N2w2c\nZbDimjMPS3S3sskBCQCEvekdYBNm59a4CZF0Jdeh1oeehcozSKRgFQvvjEnw63Qx32JlrZ6fMDNj\n2PD5kenGFzJSABAOFNZbpc8vtUHN8dndwuSm6g4mkYFEQ49MljewahXmD2J1nqutRB4jIvMcDUYX\n1jrZWQOwpOAIpQb3uVlDeANg3EsZ0Ei1GSwpb3AGq2Y9bDf2Nw2w5hdldlnbfOA+SuQR0lrumF0H\n85Hu7j+hIWbkLdm0q3TaKCfKIMCazBDMuTZzn0JODPOrB52Or5DsmYiM/tEJcmKga26MdQ5dhcEy\nEkHWEWCJmrRSzyb7Or7BnEPZkkRw8/kTV1m5WgNtYeahbQ5SUx37DsYUQMXXAJ/mfSWvxmAxAdbi\nDtmlmDEVaTLEIm7W0xZnyd78NTwST+/83E019BRG6EEmk60cLfdeaQyZR5rBclbvB/JYj1fwPMGc\nXASGMQ4cPTPI9jWfSQSXFkjF+qwxafaylIbI4s05im/n6mLT/jWVr28E8F8B+OYDHtOtrLQiEcx4\npduSGgZLcm0eUdt8Wittt2JyYed48g6dBzF/hMd0hLO+AznQnbvZVUt3Kp43OzOZ7+U2uVzYDvmG\nDdqLPw+HYrx8Z5kMZYwVoY9PcISTwfqOB6BvXmP0wOPJxv9bLVpcagmmCTuturBtqmRhQ559pMxp\nzC2yGzZlaG/LYNXNNFims2J4y/And7YLqdymFsYRSvU0CGCMmZmyAzNY6WrEQMZX5wzfzmVjCCZS\nNy8a9eyFRLAygyXctRsCAIjG5xhTgHvHAzjKAizz3hFBIgN4A4NsNvb1IHJBKXLmVBz8Nl8LIo/x\nEpmIhAar9lIiuP4GGbvm3PM0wBL117OvsqycXOY2CvvvKoP12Z8EXvu1/T4/UDSeLFPI7MzLIWew\n5hfA3/guoDJwH19q9ihiLlw6MMDiyx1oO2NHY22yNGZ9TOGDVeaIVDZH3LAZSyEbDVgSc7/rD4a4\nwADx6GGn44uNexozACtwFWbwwK6Z9aTyGDlfnsFawOlsKCKyEAlzC8DAGENqHf7M+8TCK1wY9hcd\npFxRxdGu3oBdTPTrFTXgU62BJzFBoLP9dgQNZBQvzD9Z+n7GJFjX/L2GYnm8ZIu/VC3uvbZO49cx\n8p7b+bmbaugrjKivlThbNn73WuZcmcJHz11t4J4cDYsZ2Oqclo2Q6NoQ2HwcWoK6zkQFAByzV6I0\nKuKL+JocxbdzdXER/O7K178H4KMA9pfW9g6pLCkH3XUYrf43M7riNoCFJEJCAq5TYYBMB6ALg+WG\nj/EIxzgJHHgDvQlcjFoA1s/9F8D//q2r3w+XF0yS3QBW9hsf1xf2e75+5Wcx1xfUQzrGabDZolNw\nhjkLoOLtXBBZqM05eoG+6LOtGCwbouprW+0G2Qpldshffz7WgrQ+68XTBWLW/jqlAc3ZHvXQtqZj\nvfi7QSmPSFoYub1WYgFWCaAzpsCvcZN9q1VkAFbo6WsrasjAoUIiuMxgAVgr00un57ikPp4aeHCU\nREwCZFkn85hNLoK2G5iEy+egMK6DzFy/SYcMKkkRXqDnkIOtZbDcNQwWAJBpzggT8moNP/aexWY3\n5jWJoCxmsCrX8M/8KeCXfnC/zw+U1sPSNl3Me7PHYfKVuv8p4Df+DvDyLxTfyoxr2JX73EEBlsoj\nZHWApSzAuo8ZC3Dv5AhT8iGSskHmZjPEDZbhaQvTkZiGwfFwgCc0RDbpJmMLDcDiRiXAGMOE9cC3\nvJfUS1CMvHKeBY5ASG4hq91UMg+RiOWNZW7ZTrNp5uEVHpiZs6yDnXbVXj2pNWBtZIr027dvnDNE\naqhBw5bNTFuROQZRcQ4GNIN1nbWfZ8uujUuPbZu+TU3DLMHd/DEWvXft/NxNNXAlRiZ+4k0NGzZz\nwJHoQTbIJ+/2ddgwAMzJQ+CU2WQRc/cX4WDO2VythweuEohIgtK4MHGTt9TkYpeg4RmA9+37QG57\n2Y0ElxZg6Y0VyyJEzIEUHCSc1ZDaLEIMueTQ5Lna/WVjRzTPEMQXmMgzCM7gH2u3vmTaIhG8+oL+\nqpdZPKRJLmdyzWJWHHcC/Nbfxc/nH8UHnzlZ+bEdhHxIJzjpdVLSY8H7UOl2mQ08GmGCfjEjkm6h\nN04jm13laxvYBo13YWBgJFzWTa0uReRpiAhrANY22WZb1nymb5RBr9zMZBUny4OVmTsr5ssA5Fyu\ntdO90ZpfAL/2v10rnyg1dsapr68tu5mrlg1NlRUXQXSZg1pc4BID3Btqk5AE+qZU/T1qyA+xjkxx\nbUOv7csrIb8dJIJOHmNMAR7SCfKLBqv2TDeAHLX+Go76untMQy3TkYdmsFZmsMyGvwqwokknVmDX\nY7Agg7s3IBG0HfTzF4pvsZlmeEb+u+AdEGDJPF5hsOw5yCdv4BJDfNndHqbwIStzRF6+QNYAsLQZ\nQgPAMrLr06MhntAQfN4NYMXGWIO55cZvzvqQOwIIW4qW4wCU4IjZNgArQsZrAMsaUBgwJeMxXicT\nMdIhwLhqr57UHHPjuXH0DdoZLADIlPn5jk6Cc5M96fSW7/s5kzqfa8equzYuVaGqWV1PoievQiBH\nfvyenZ+78XgMGAWw83u1lzKy26ZrCbBhw/ocmsJbYrki5u0vhNycs+SsB1iOmQFHGiGLbYP6HQqw\nGGMfZ4z9lPn6BIDPA/jJwx/a7So7a8ClY4I+LcAqae/GfBpjglHtTPiOdoHbGBQ7fwKBDAuTMzU4\nOkVCAtm0JbchHOmv+obTLB5OX3fSWIsV+VK98v9CxCP8bPYxfPCpVc13aowPHtEJTnvdQuYi2Yeb\nbndTVPEIU94vgE+2E8AKWsNb7YbXSrjagBzPIsSsfZBTGQYrPQDAChf6Md2KwUbKmyWPe61k1YI1\nZ+rwz9u1PvuTwMe/B3jSbEHepZK53tjSQAOHeNHw+WUJEhJQ1e6iWLVdr5cMLzDlQ3hKlADLgmID\nUplY3XBw837XN1iCtOugla6lHRgsRTEiOHiVnkL8ePV9YkbivC4oHADO73wdfk/055CfahcvKxGk\nvTNYZl2tASzlKOTEyrWVSHdcOxgHbFt2XbaNKOmZDcchJYJ2tqkCsNT8EWIoLLx78BEhyw8TdKwo\nLhkEU4Xpz+IJzvM+3nenhyn5kHmojWCyFC4ipA3d7hQtDFZoGawhnmAIGXbLH0oMwJKViIyF6EMl\n1wNYklbz1hLha0ORDk0blYdIa+8b7OY01hI9J53gDcNgJYvNx1udK6vfg+xa5fjrjR5y14CGHSWU\nNl/M7dcAFgT4NWawtKlIM8BitnnVsCe6uv9b+vfP9peBZSuzRh5vJoNlG2lOM8C603fxhgFY81oI\ncMK9/UU42IaFu4HBMi7GLIsKBkvcUoC1zqbd1vdX/p4CeJWI3nxfyrdZFWYIygEJF45xEONZVKTB\nU5Xmtvr9LFzRHbuS4wJO42KyVFPdwbQ5U8c9B1fogeYt+R/hWG/ckkVpGw4gnT2BBOANlxmsPAnb\nEfrnPoGEu/gkvgr/093VCz8TBmChm0QQABI5gB9ux2C56RhjMUC/weZ6U9kblHR9hC3BsJQlSIlD\nmRBZ6TRLBEUeIuHtAMtzPQ1+o/13ue2snqpYxOuw6JuZwVoCWNyBeKtIBO0G4upV4M5u+Sh5ONZG\nLUPDDjedn1mCDHypScKKOakF/JaZczcZIVR6/skRuutXMlhWCtcEsAxDVdtgKSQgoUqDiQ7ziAox\nhOPh1fgpfPTyn638nBlnr0111vfwMj2DoclDKxms/YLtNA4hUc4/2VImX6lgsOIZADqIbC+NIyiU\nmwbh2JnZ+U6SkS6VL8bgAOjJC4WLnBc+xpU4BakAASKESbYSRHr9J86hkCCvSd2qG6bzfIBnjjw8\nEVb+VgKFpm53xmTjRjyprGOX6hR+vHo+NlVimB9ZMXeIRA+D7BpOebDX0/J5lksfPCXdfFwne8oS\nzarU3zcLxqMJEE/AkReb46QDg8XCEabkoc/ClXlj+/tef4OTnn8CTLAzwIqNRNAbLjsV57zFvKRj\niTxeMhVZ/qF5HxsUAbOHujHUe/rLd37utsq9Ex3B8GY6CdomUYt7392Bi4fmHIp4sNQMS4Snmx77\nKMNgMbfdRAXQDNalyeEsXGhvKcDqMoP1i5WvT34JXO1WtlMrlAuSLlyTI6XT4M0Atl2sqzR3Fq/M\n7jDGsIALsakjOtEAKzc5U8e+gysagLV1W+yCWltYo8kTjCjAcU+DIrtBS9o2aHkOfO6n8Rnva/DM\nnVO4Dc55dphXSwQ7AixnoKUuTRkpLeWlY0RyWFzAWRfnRVOW7VJuABJOs612liCBhBKs+L9NzyOy\nCMkaBktbpjoHAVjWzl9V7HmzG2CwLLBjqgrs5OaA3ZsqO3A/+uLOD0HhGFP4GA71pqWRwcpTJJCQ\noryxWYCVrgm+DrIRUmMOoWUVleF/+2dDR1cUIH/5sSVSEFdFBlW6SZ6XZ3CQot/v41W6Byc8L26i\nxeuoNIjW1VlfP6cNnFaOncHar0QwTZoZLCn4sv23ldV0mGvZ+hgK62H9OTiGOW4E33uqB4/0Wp89\nfrFgT3rJBSbqDFABXJZgER1g5tI0UepAw0oyAW3RfnfglrMZ0aR4/5sAVs6a85Ls2qhcH7F7Cj+f\ndgpLT81nrCqzR7Eaws+u8XkUeWvLa3oBmDbJQc3PbaPRFrcGFPG0UI48xjFS4sXrWFciHuMx6bWo\n3uSzFvn+BoDFzZzkrqAhMc69/eFyYmXO5LWaa4KSDgzW6vmQnr+MiCROn96vRBAAyHvrMFi8BWCd\n9R28YdJDY7H8f1Lh781hNAtXpbhNpcPEtYuxnSneZJL0dq1WgMUYmzDGxm1fN3mQt6HsRoJLt7BR\npjQ0g5tmcWiYbeJZhLShQxwyHyLdIG+Z6iFnYeYePMUxYn3IqGUxMMOSdYCVTC8xoh6OzeaolCC1\nALzXPw1MXsfPpB/DB59qvtjsMO9Y3VmWTq2p3AYXRt1PvyCfIlbDIgiVtgBY1hFOeT2d6Qj/AAAg\nAElEQVSQcLRrW91ZKYs1wDIzcvZ56lJE2eC0VS3PEQjh7jVV3ZYFOtWQ41y4UAc2uUgKm/vydefc\ngdxgT15UPDustt1uWJrmDjsWj8aYIMBwoM/NNFq9JilLkUJA8QqDZZoUcVuTJEsQ0EJ3k2EAFsli\nbbCBuXWmBkCzHNa6DgpVbH6zTRJBs1np9fq4z4zF8eUrS//Fhsxuqjt9/ZwFwLLd/X0DLPOauKox\nWMLkKxUMlv7sR+P9u39ZpsXK5KypwCEBlpV/yXQGTLRz31H2BKF7p5AOhbPrSeIaywCsvAYUZGXD\n9ARD3Om7yC2YiqblteeuzgNpt7lVpsOez8Lxkfsmj222OXIkM7JrpwKwMjWAT9eQhxZulTUGq6sl\nv70P1Vgu6VdmsMymfUQ9zOEWr2NdyWSCcxyZQ1y+vsm8573++hksac0pdmSw8sUV5uRi2F9Wrlx3\nBktSjKzJNRWlsU9TA1WMXsVrdBdPH693t9uluG9A5Js5g2XONdECbJTgmDl6TCSrAaxsjwArtmvQ\nGpdKWwkUWBYXxIN6pzFYRDQgoiGA/xHAnwTwHIDnAXwfgB+4mcO7PVXYeTtOAbDiaAFBFV1xgzsf\nz2MkDYtKyDx9M11TyUjfaN1jHeTLGMOMD+HEDYtBlpYd/drCSosLXKGPYyPlswCr1fL8pb8HAPjx\nyYca568AIHH0Ij73N4cMF7WtNjzP0aMZUucI0rHZYd31xiUw8SvzcbWbVpYggSjCDy3AymvvjdOk\nt69UoAQW5Gx1fF2LzGO6fk0iaFjUQ1VsNgS8wmCRUJBdu5g//b3AX/23DnFouuz5frU7g8XjCWYI\n4Pf0ed60CWJ5ghRiicESm+agiuwyfZ24QiCpMFhJwdQ0SAQtwKpuNvJMh4NzVTj4bWRzzeaZSw/h\nwHR/a1btPGten+r1e3/HPfzpb/md+MhzevOnHImUeAEU91XWep7J5WtNCW7eP3PumSaN3NeAd6Ws\n9NKy5oF3OPmvLaoE+OL8BWQ54Ywukfj3is52cgC2rpCp14JCZSWa4cIwWFQYOEwKQCgaMplyNEsE\nLcBy3ACsrzeMmG2W+dkcrKqLau4M0Md89/wic4+mmsQPXQPjzaaYavk/ytf3OIomJYPkn2ABFxRv\n/vycZILHpNeMrHYMFE2wIAf9YP1m1h1o0JDvChoWVxihVzRTiudnArxrc62hmmbebNmGVZ21AwB/\n+kW8zp/evzwWQNALEMJ5kyWCZlbcaweQcaD3WalaBli58PbmMFrMOgbrJYIAEJuYGJvrqJz1LrRv\n1+pCHXwzEf0gEU2IaExEPwTgWw59YLet7EZCKLecvwjnSw5MrCHLgWdRo3NOxIONw4nR5esYk4+j\no/LGspDH8NIGgFJlheqLxeIKI+oVbn/WMa8+RF/Ug88gGrwblzTAV7QArN8+/np8R/yf4bL/wbWv\noVrMDud2BVjxROvcvWM4Rbhd97mjPAmRkIDnuqUrWe33WSER1JdSaaG//P8UrTpGVcsvUtUPsAkr\nAFbZ4SLp6g13Q97M3p7WzrBVmDNw1Z3BuvqCZkPz3buea8saA1yDwZLJFHPegxPo9zZr+vxyzWDJ\nSgZaMSfVcg0lMz0nSVUGCxLMAPwCmDW4CFp2aGnGquI6aBskG+V51m5ceeB2QLxm1c7zZoa9Xp4S\n+K6vf2+RA1cCnsNIBHmNWZCcLUkEyXz2Lu0fYKV1gGXkv4cwsLHF4kmRdYPz38LVeIxjNkPee6qw\nJ4/nhwNYdaBRnam4xAB3+24ZcBtNCstw3tDtbrPztvMajhfAGWrZezbdDLAsMPEqJj/kHYODkIc7\ninFa3CrRlcGywFQub3hd4/CXhqVE0BucYkYeaNO9Ic/h5jOcG4lg/R7E4ilm8Bol+9Xy+sfIiSGe\ntsxqbygejTGmAANvGdDk/LoMVrJki7/0nFZV09D0PYru48LZopG7RR37DkbU269E8LVfA77/K7TL\nbZeKLcBql+Ylw3fjN/N34zXvK5a+n6tA5zFuMXbR+hzWpXKDiQoApMzExKQRIpJwGwKSb0N1AVgz\nxth3MMYEY4wzxr4D2qr9S7VFUVoBWBWJnaiEFVqARZXOk8iTUkJYqVgEUNn6BTcbvYHHdIyzyoxT\n5Byhl41XmYslgLUMYHh0hRH6OPb148hiA9eyOXnwGTzuaeD0waebAZbruPhk/uGlY9tU3Oidbfdz\nU+VmkwrvuKSgtzC5QLJACAeeEqUNbG1DyPIYKQSE2Ti6BkzUHR4VxcjWMViOQAgHdICOOqUhIpLw\nqotYYWt7OPvm1Lh+VbvZxB29oHdhzsKRZgxrsrR9lZ1JuA7AUukEEQ/gmc0jNUgELcCqDhcXLHAL\nizQfaemTZbCsiyAzgNiatfAGBss6WVZz8qz7IBMK0tnsYAiUEk+mPNy5cw9X6K8wWCJPkHZgsOp1\nKIBVvC81BksYgMUMwLLhsy5Fu7MYG47BggzfkVjARX4IS3hTIp7gt+lZTMlD9vgFjB/fBwDw4dOF\ndKiLzffW1eAUCtQB1hFOAgfct/NFE0QGYKkGy/CsZSNeAiwf/rEGWLOLNzYeIsUzzMiF75bXim3W\nzSe7bYyLe3TtPLNhxl0ZLKaWO/dBECAhoeVWptE5OL5jmm8bzp9oBA4qZrBWcjKTBUK2WYp1FLiY\nwkcy3Y2VEfEIM94rmim26JozWIoSUJtEsG0ufHGJXj7FLNhvBpatk0DhkvrI53tksF77FT3eMepo\nd5BYhrYdYB0dHeEPxH8Grw4/tvyDgnG9fmM3W9gYgM0xuSlTBmDFiKEKBdBtqy6v6t8G8IcBPATw\nCMAfMt/7Um1RFmBJ5SzNMEmKC9qby9VFQjSEOAJAIgK4+fqLgqaP8IhOcNYvfz91j7WDYf2CqoKq\nGsBS8QhX1MNxYBksK0Fq2KBFE+DiJbwkvgyO4HjPafPgZeDoLlpXgwugDC4MJ906O4uJtvHlwQkc\nO4O1DaBIQkRQ8CQvJAgrQ7RZirRixuk5DhISK7NeDkUrcwrV8pTAgvaYql6tRGdweZXOJTWwpa31\nmb8JdOgU16twYazS/zZstwtzZkH/+W9t/dxd6vJSb65o8sbOs0BuNtPxAYGVoK5ek8wArGrxDXNQ\n4Vifu7Kn5Tp6MFgW4dBJYtmlhhksd5VFTZMygFcUDn7rAVZsADJXPt59GuCV/B6S8+UsLJFHjQ2g\nTaVdEeXeGVS7Jona+8IY09epkZ5Fs8qGaM/5VHZja5s6PUdf24eYr7Qlkgkm5ONlehqLNz6H2cXr\nAADn+BkI10ZAHI7B4jWgUJ2pyL1TcM4gLcCKJkgWFmCtdruJyUYGi+IFYhLwHAe9M81IzC4fbDxE\nFs8whwvfKa9BYQHWeDeWJo4tsKwBeRu4vQkM2ftDTRo18BVm8JAsJkiNWcTx2T3M4YJvOk/NfXvh\naPe+OoOlw+7bm3y2jgPNyqTz3cCnSiYIeZN5iYTAjs0MIjhoZ7BEW9PXNOeS4bt3e94NddxzMEIP\n6Wy386ixJvra7dr8zKI5UuLw14S92xnYwKmxl8o2BK6/NuXhBFPy0PM23w9SpiByPYMVQ8KR71CA\nRUSvENG3ENEd8/UHieiVGzi221VmI6uUVwKscG5o72WJYHW2qc05J5MB3Hz9ZpyFV7hCD3f65QlP\nnhnKrNPPFanEkvaaCG46xowPNJODcoC5cYbj4WcBAJ+Kn8eX3+s3JosDKLIYumZgAYAyuRrRrNvC\nvzA3T9k7husoxCTAtsndyaKCwWKiObeH5fGSi5qreGNGmYsYuWzvHgaOxALO/lLVq8eYhgjhLHUU\nWVcGKxwDP/HvAv/k/9j6ea0eXlW04UUHsguws+fk489v/dxdiifGVhYEjHczR/WyGRI5QOC5CEkB\nDawuyxPkNYAlNsQGRKaJoAalE1fKnIKBsTOdTQyWYx67Os9XziapgoGmZP1nEEc2KNrFu04DfIGe\nQv5kGWDJPEbWsulZV0qwG2WwACCr5CvFs0oTac/Mkj0GVTBYWiJ4EPmvKZVOMUGAl+hZsIsXEV3q\nTZp/8mwxm9FkwHLdskwOqw2pu45ERLrxxPrakKIAU9EEqZETub1mk4tGgGXWMU8JnJ6cISKFZPRw\n4zGyZI4FuUv5P8qYSTQFg3cpK49fYUrdrgyWubZqAGvoKUzhI1uMEU3OEZPA3ZMTLMjdfG8wACv3\nz5ATW2nyiQ1ZjLaOA4Uxgp1nsNxsglA2GB3wazBYRSxF8/HbJt6KNf25Ztz56ft2e94NdWrAaL5H\niSCNDcDq2GxNwinmcNH326Xadwf6fevVpXjO/kLQ82iCGbxVENdQKXMg8hgs0zmK7jsVYDHGnmeM\n/SRj7JH5+gnG2PNdHpwx9q8yxj7PGHuRMfYn1/y/r2GMpYyxb9vm4N9OZfNXpOMW3b40CeFUGCzW\nYB6h8riRwcpkT1uWrymezDCDv8RgIbCuNzWAVZEIJlUAE08hKEOsygWzccbD1oPPAAB+YfQ0vqLF\nQRDQrnkAcNIxAwvQ2nBAuxp2qdAwWE7/DFJwRHBWTCrWFUsXiEhpgGVmOuLaEC3LE2SVzbMrNcBa\nYrqM3XV9oLladk6DX0ey9+hzwA98ZIVt4ukCcd3prXBx2/B+2IW3g1tXvfJCG15uIljBYG3YWBMd\nnMGSyQyvkXEj29HowqcZMtWHJ7keRG9ksDKkbPnGZnOg2gBWOtPnrtsvs2R0PlBNItgAJIr3u3Iu\nJVFpX66MRDDf8BmULpA+3nMW4FV6Cmp6f2kmTlDSnk2zpoTJpbKAcV+VtzBYAJAyWTxfsqhIovcc\nNkxJhJwYHLfc1IRw986UVctNZ8idAV7Fswjmr4MuXwUADO48D2WGzvOqyUWeA3/vv+kuQ2op6yRb\nBwo2tw0A1FAbUlgwlSzGSM1AfJNlODHRPKtjFAWu5Lg39HCOIbLpo43HyNM55swvZNxA6ewYdwjv\nbaqksOJvBlhN60C1LNMtatbaA09iRh7yaKrde9HDnaGHBfPBN8nHDcASvRPEkCtNPpEt1jrZ2jry\nFcbU29lF0M+mSNTqaABxCYGOM1jRdFlGbu/bDa6pQDkXXgdY84cv6mO6t/+QYUBLBEfUA9uji+DV\nA33tPhl1mw9MwylCuOivMfGwDFbd6KMwwNkHux1NMSUfQYd5qpw7EHkCpBFiemdLBH8UwE8BeNZ8\nfdx8b20xxgSAvwDgDwD4EIBvZ4x9qOX//VkAP9v9sN+GlZUugrySU6PDCvXJLwqAVW66JMUrGSMA\nQE4PLuK1w4kqnWLBfPQqHQXe0xu2eFLbMFcW0yUAYxYOm8cDAMqzQ/INm8MHv47cO8E/Gfda56+A\nksHaZgarAFgdZ7BiwwJYV6QEcisGi6URYuZAcFZIQZJaV5DlKbIKg8UYQ2xSyosynah1AMuVHCFc\niOukqj/4jA7NvVhmGXgWIapnqdkb1Yb3ww7mP3y4edahXtaF0a2aXBRujBs21mlYyLkOxWCpbIbP\n50abv8scVhLCQYrcGUAKrlnChi4zowRZLdO9CPttmcHKTJMjOC4BVqFbB5AVsQ+rXUu3kMOWAKqa\nDyVNBhVtkEUmYbkJfNdpgEsa6I1vVG5KFcWtsp11pSV7qpgpA6AZnr/9x3eSo9rKCyn26rVWZbCy\nKsDaM4OVJ1b2oj/zwBGYkwt2gPlKW14+RSr7mA/fBwbCyeNfQUYMR3efhWOG3/Mqg3X1KvAP/nvg\n09sz09WyMlJRkwi6xpQlJoHBQCsPBoGPOblIZiNQOMaCHPT81c+J2piONNJSZyVwd+DiCQ3BOjR+\nRDpHVGNurJW0ZdK2LWtOU3erlGbebVMocGLWVeHWAZaWCCKaIJtfYkw9nAQOEu5tnLkmc692BydG\nRbG8tss8QrLGaMnWsa8wQg882gFgGeferMF+n7jQURGbanEFfP8Hgd/6mfJ7aYupiCl7vdfnzuLH\nL+Gchrhzdtb0a9eu40BLBMUu71VL8am+185m3UBPFs0xp/UAq2Cw3JpU3TQEovn110BrotKFwcq4\nA0mxdslmcmVe77ZUF4B1l4h+lIhS8/VjAO52+L2vBfAiEb1ERDGAv45m98HvBvAT0PNdt7eyGAkJ\nKCHLnJpoqjXJFmA1dGEUSglhtchqZ9usW4ngZHOQ6i8N17sD3bGfXy1vYjKjiX9MR0ir3RhDfZNX\nAViunTdpZrCmJ18JgLU6CAK7zWANAx9T8oobyabKjC7aNx3UmDnLwGdD8SwsmB+bs1F3feN5gowt\nLygxc5aAXBG4K9s10owxJNyDyK7BYNlFvtY95Vm4EnJsj2WTq+LMZOdML7e/PClZICUOr5KJYwNg\n28wdirLyQOFqBmvfdvLm+niRnkUGvhvAskDDbNYi5jZ2mVmeImfLS62Vj7XNQdHiClPyMAzKDVjG\nVMFgpbE1zVm9flxHaXlWpYNt329elQhuAteRzR3y0HdlGQpbA1hNDHuXSiHBqllsDz+rpaivfnKn\nxwOqM1gNrD8r7b+zSsc227N0Ts8VKLhKf+ZWIsgPMV8JAGkERQkSNYC8p13Cnhl9ChfsCEopOAWD\nVXmdtqH2+qev+dT2HFkGCtr1UuECQ9wd6vNt6EnM4CFdjEHRBFP4q5IlADlTEA0MllUUuJLDUwIj\nfgwVPtl4jDJdIGY1hs28J7vOpWUtDJYNlY43OEYWzQt32Vp74ElMyQeLpyBjd34cKCTC3+wabJoy\nveGZZg+zVYDVhcEa+gpjCiCTHdi9eAIOArkNTnJcdWOwJm9oVrlqbmRfS4tEsMyfrK1pV6/gi3QP\nzxwdxgb8tOfgivqQ2Xx5jnd+Abz2q9s/IBGCUN9r046xDnk0wwIugrUMlr5P1Nkle/7tyuRWiydT\nzMjvZIefCQeSEh1U38GF9u1aXQDWE8bYdxoXQcEY+04Am1c1nZtV1d28Zr5XFGPsOQD/BoAfWvdA\njLF/nzH2q4yxX338ePfu5ptamXab45wVJhEwXdRcWgbLdrXLhVS2OOfYtOy8LdskWYAjB2rhc+6R\nBljhePl9tMYR9+kMtKh0YwzAYsYuGgBc5TRqvJGlwMPfwOve+wGgNQMLQDHPddrrfnH1XYkxguXM\nlzWVLy5NB1UfRwydvdC1eBYh5sufTV2CwGmZwQKABM7S89gub32guV4p96CuEfr36Fx/plejZQAq\n8vJ12OIN51pT2Y2CjLfv0FVnJsqDsflPGySCVh747D+n/z7ZPMy+VaUhBDKMqI8HdILUSKq2KduU\nYK4FWF4zwKIMWV0i6No5qLYcLC0PqlodZ1zr1oGqK+DqhqOQZ1UAlgUeTLpwHYWU+EaZpgVl0jRU\nPMNELAOskoHfttKK5BFAKdW7hpTOyh6F0wywmGFGqmvIPjYX1aI0QgRZzBUERiLIr8NOryvzeeTO\nAKfv/kr9nNkEl1wz954Nr63OgBmHOrr/qWs1L6yMtBosDJTB2Jc0KORJQ09hQnq+CLEeiG/quhMT\njRtxlkW6iWG63Qt1Aj/ZLBeX+QJRzWDI6zXIJreo4tqoMaU2zHiTJb9939QKg6VBKE9m4OElrkjn\nT6Yi2BgIGxpHxMHRGSKoFbWGQxGyem5XQ3lKYM57cJPt2b2i+VlpyBbFpWawNp1v9jGqzLJ9LQ2S\naKCcwcprskhn8hq+SHfx9NFhgmyPA832AViOt/lHfx74sX99e4fS8AqK9GttjPxoqmSOBZy1zNGz\nRz6UYHhqWGdy9+cw6kbnuES/E4OVcweKYp2juIPE/O1SXQDWH4V2EXwA4A0A3wbg39nT8/8AgO8j\norVnIRH9r0T01UT01XfvdiHP3nrFcp2XBKCwSSZzQbICYJmudqUL4yAuHd+q5eobRNQmlzPMFqsF\nOfaGGmAl02WMHM+uMCMXFzQEW3IU1McoelWAJY3Gu7Y5fPICkEX4bP4e9F2J547bAcVH332Cf/GD\nd9eCsHoNPIkJBWBd6XjTARwae/nEZi90LFlxSOMFu7j8+5rBqgEs7ix15i1IqVvy1isVHlQe7bzh\nGV1ouczFaPn9kVm40rkUhZPl+pu2PXY32V5jTrEFWOUyww3jkmyyy7cb4Oe/Rv95vmeZoNlYTeHh\nNbqL8PErWz+ENVGx9tMxa2YgOaXIawBLbciiEtEII+ovbUB1Z98wMMbkoonBUoKtzAGm1hRDOoVF\n+iaJYGZNSgzbNhhagFVuvNY5e22qlKnlGSy7obqGZM8ad6gGBiuvMFiswvxHs/0CLKShZrCMa6fg\nDFHLubGXMp8HuQO8/7mncJ+0HGoiNcAq7JsrwPXSrBVs9giwQ/U7lG3Q1IGCI/Qs6hMTMgwYdgY+\n8mgCHs80g9UEsFokgiILlwyFIvcMg/Ri43qpssWKNM6GDu8MsCyDVWua+Z6PjNhGZsyypqoWDttz\nJObwINI5ZDzGCD2cBAqZ9HUg7JoNezy9QE4MR8YApC7/7gqwACBSQzj5YmuXz8hYu1vH32oRN5/1\nplxDu/+omvTEzZJMW8q1EsFa/mQyxpQPMPQ2syq7lKcEFsLIIavKmstXdINr2wDicSnFzzbcm4uK\ntURwHbA56Tn4uf/kX8I3fdWzS9+3519y3Yy+cIzjxRfxOXpPJ8MK4g4UJeB5vJML7dulurgIvkpE\n30xEd4nonnER7KKnuQ+gGj7wvPletb4awF9njL0CDdx+kDH2Bzse+9uqWJYgMZssS2czu1Exi4as\nO4tlCXzEyNVqQreldsO2zYHtMjvLAGY46OkuYg1gpfMrTBBgjAA8LjdQZKxaVb90M7NOeSvW1sbg\n4pOTZ/CVzwzW6mrfdRrgL//Rr8XA685gDTztbsSjbhsiFo0wppIF0DMs3RksUZFUFBLBujMTJSub\n54wpiAYGaxPAyoSnWccdndVyc2Oq39y1NGT5xlqGRa/vqttObNAUTr2p0oWxua/MAHaVCBoQ/Q8X\n79X/frxnowtz7Xm9Y9ynO2Cj7SWCczOrqMxmIuFeo4yH5ynymozUbgjaZHoqHmHCeksunDlXkKTP\njTxplwiWc4DleVRI56SCkt0yqOwgvjIzdEMzD1adX3IoBjpu2uqVosZg2Y7tHhgs5TYxWAKcDNCM\np3hChtnet315FiEmubTRSIUHeSiAZZsR7hC/4+kBXsq1hfnc0c00rlwkJMAq76uNAQAAvP6pnZ+6\nAOHe8tomBccn8m/AJ/KvLwDW0FeYkQ9EE4hkijn8Rntm4gqiIYy83u1Og3s6U2+Dg5uTh0jFMgD0\nDehszK3rUHZ2si5FDTyFBdyN7IOVjTve8nFxzhDzACqdaXDA+vCVQN4hwFjfw32c9b3VOWCYqJA1\nMvWl1+cY0LBlELPN75O9ZgYLQBH23VoWlFRea1LkrTUDLM91kBFbMfaQ2QLCCZbGJPZduZVDVs9D\n27ToGhZsa1I2O/KOkmKWzrGAt9Fc4r13elA1M4mCcb2uTPrhPwMAvCi/vNN7nQsXConJeb29EsHW\nT4Qx9ufX/SIRfc+Gx/4VAB9gjL0PGlj9EdTys4io8M5kjP0YgE8Q0d/e8Lhvz8qTIi+p6PaZTSQr\nANZyQGg+uwAHkLgn+P/Ze9MY2ZL0OuxExN1vLpW1vLWne3p6elYOe2ZMESOS0lCyTQoUKUqAARuw\nSNoibVOAYAkiQMiwIcMEDAuEBP6xaUGUwEWAbNBjWRIpSpRlUiIpc5sZsmfh7DO9vKXqvVdVud89\nwj8i4ubd82a+eg336/6Ah+mpJTMr896ION853znVMlzNYLUsgLpDW5EIHnhSM1y98Xkww0J4mAsf\nZkEaEC3P4QBwFfMFNEuQAACnn4FgFn7t/AB/7qMNNq2PWZZBsYIPo6d0wYhmmJONVXxKLLAdwEtR\ns57balclgjxFxsq3UUotWAUgl6ouKdsiERSGA0SQG0tLp66rNPDkFe22KepZaqzF1rZaWgfu84Xs\nFO+yUaURQmHhqNBZ0+YaaUv+U15qY//JT2b4Dn8E8vCL/Z+3T6n74+joEGd3r8ENfltKXFn/Tmes\nspRMdZhImQOD1zuWVKTIUG6SaIDVZjJiJXME9Frpa5xZYLE8eGogYTQALABISFkOywuugxajmMEo\nAbCm4ura0ADLGcq/M1pO4QFyjo2kzQx7j+LULDPKWiL4OHbmuclFC4OlmBGaLPFAHOCILJBcsUQQ\nWVyawQKAmHpwsqVkIOgVO2apZgFxRjgZ2vh14xlAfA6xu7l+QmKDFAxYYjWvwwVB9vqnYb7/+/Z6\nap07ZFn1vMOfIf8RgjTDf1FgsF6FCxovYaQCIW1RL1DWmJfEeIiEFO6j4U3ggbS1Jt5h7ed1OSJA\nWpEIuraJtbD3dpDkLRJB7QZbXYOrlcVrhMKEa9UPl4nhw85WIOCIjCEIIRBGAWDZze68Yj3FXPgY\neyYSYsKqqDVsxL3lvNwaAQEk2PH7G0QEatTAGjR8HjnA2mLVro21wmV+QE1bbPF1WYwhglVm5TmH\nJeJcBvekSrgHQIIyW5UDrEcA3t37sfjsbs569AVYNA2wxlEvaV619Mxg9rgM1v2XAQCvmv3+VsFs\nMHCYPEDWtg48BdW10v8ogO8AcA/AJwF8qvKvs4QQKYC/AuBXAXwBwC8KIT5PCPlRQsiPPu4Lf7MV\nKQAsHQSqD5Hanj0PCFWHrtVUDjvShgVOhzaW8lyKpQ/1FYnggWviEgPQsNz1E+EMc8Vg2ckil11E\n8wtEwoDvbx4nZ7CqB7TTzyI+fC+mEfCBm1cPsAAgYANYab+Os5XMsGKb151SG2wHBssshDznLkVV\n61uRgFc6MBm1YRQ2tyRstjKuVt5d3HMYnqo5KV6RWJk8RsbaJILbGCw1jA2+s20vyWTAcbGTT4zu\n/Ke81KHxInXw0HlnP6v2y1eBL/5KvxeX3x8jZKNnQJEB8yrB3l363rN92cHMJZ6VoiIDp2XgZmmZ\ncAvActI5gkqWjKAmDMXA6INEk5kDIOcAix1sLSmkplXIoOruJOtDpK0aQlpWFTUV7foAACAASURB\nVCpgmZvctFgnb6usMoM1nco16cHFY4R2phESwWCZzeYJ2v7byFZ4KCRgzK6YwSJZLF0EC93iqXlN\nzhWtrt7LKVFNNsMdgxCCYCgtqTN/A7ACJTvTla0vkQmCL4hnsfj67+793PoQaDp1gKXZKT2DNXRM\nLOCCJUuY6QoRqyszAHWdNzBYjMfICvPIdCyZuuCiw2peCNgi3DBAqmyDYg0bZG+ApRhhqwFg9QiV\n5nFQk0/rygwfDBkoBFJLsSOWNrXqeL1qDx+7ppTDFxkszmEjgejJYOWmVjtK3CLF6jvDBoClm1db\n1p2lYsHuPtiwrG22+LpsUwWXF9dTxYBpEPGkiuXRN+pMxbMCwOpjV7Cp4Fxey6mgvc8BLF0jEFYp\nSLtv2apRz8PHbDLdfxkzdojA7jfCI9SeYWcrpD2y2d6s1QWwbgL4ewC+G8APADAB/FMhxM8LIX6+\nz4MLIX5FCPEeIcQLQoj/UX3t7woh/m7Dz/5nQohP7P4nvDmKFqhQK5cIygOa7spYFR3xaipDFNng\nGNXSLkht+QU654U55UOaZzFMMYQZlQ8xLJpjITxEbCgPm2ohT1fnmGGAib+5CSxG6xpvIYDTz+Kh\n/x4AwAduPRmAFbMB7LTfYmCnC4QlgGWBif6ackPE4Er+pOfmqi5FVGQQtA6wimYVqc6Daujylp/w\n8QCWoYJzRWVzt1DX3ptdYdGFKh0UqtlpW4qlIWJiliQDWtKWbQm51c2HBTz8YXCtn1X77/8M8H/8\nUL8ZNsVgUXsI+1gS6WJHJ8F0LQ8f+jCRMhd2wyB60wyWZTB5D7WwSF62QGSW7yFOLSmJEiIPGjZa\nwE1CrFIzQb/fzLCVRboBbJlH1ADKUrEMjsosShTAivWMQEf8QFdl1Czdj8uF/MxXy/2sswFAZDES\nGDBZnWktMlhWusJDKIC15xxOW5EsRgKzJO+8tG7I/3jM3KmmitWh1lAAmF2XazAZ3tz8DLHBCgYs\nIphiDh8v83fBffiZvec+eRzKrMCGw51lUFgGzedffIthDQdGuoTNV4hbAZbRaOddlTpbExnHuX7U\n8Z6mISgEuFFeewkhCOCCPibAMivmHr5lIMB2ZkzEawSwywZA+rGtzfuSqXgU0gNgUSWJH7umksNv\n7q08l2uLTF1XPkO1Y1MtWco9wh81sF5qnxRbGCwdsl7MEstn3hriFwAdC1BR1eQh2E8WYBl6Pl3P\nYC0fANoFc0eAFV3cwSMxwhoOsE1Gr58/C6WLYMO1tK0c18c3+HUc3f/1nX+3VPdfxivWu+HZPV+D\nGhVw+BoZe3olgq0ASwhxrsDQn4I0tTgA8EeEkB94w17dU1TSDEFuNLbSq7NYAgXNYFmmgUSwfJEI\nZ7LbaY/qXQFLbaZp0HwYiVR33fDKhzRCCObsEG5cvvFZssASXm1h5etLzISPScHtz1BD8qUO2eI+\nsD7Hl8k7QUm3g+DjVGwO4fBVrwOBly0QFVgATi2YO5hcWCLOHR61S1ZWzRZBAl4BWIExkpI6VVpm\nx5wtC71muPacQdHMXg1giRiiArDyqIAtEsHiLIHePPtWkz28biakPRks2EN8cnUiO/9bZi0QLSRg\n6WGSwBWAM7whDm69AABYnH2961dqpWeRPCWdE3oQvVJMpBCVGSxpAmDU5xgBIAlgIUZiVqyO9UaU\nJTmDZTS45QHSaIUWwJtmxZkpHyOBWfp+84MESASDo57DGxyAC5I3b2J1XbcNnm+rrGDaARQMBx5D\nIiiyBAlYY3Alpyx/PpuvsYSPSJhb5Vy7Fs3i2lzB0lYAa584gC0VK6MjLVV13v2d+InkB7B69jvz\nn4moA1qcAQtnmAkfD4YfhJstgMtv7PXc0inUbAQKFqM4Gdh5g4UQgpj5sLI1nGyN1GhZD5kJClEz\nQzB4OXPNO5KmxNFlB8BSa4FomGMOiQO251qrAZZVZbBsKRHc2iRLQyURrL9veRwCALjyM9V5RV17\ng5EssCAeBrah5PCbNTZXKvRshphV0NCzsmAKLggG4zqDRRSLn25prnH1nKQBYLUx9pZB6w0rDXK3\nNTYfs3J3Vb0/FZUQOwIsPruHMzFRM+49ARYPEFGn1NDpW55t4Bey78LRxR/uH9mQBMDDL+Fr7F29\nQoaBzZ7hijX4W9nkghDyUQB/FcBfBPAv0EMe+HbVi/BN2KhlGoiEAVPlTGi5lpxt2rjzJXNJlTvj\na7XHy21mWxgsvelaXp1JWppHGCRl9yUrXSIyBhA6IFB3roJLTOFj7JZvgpSUh+i1wcXvh8/ghZNB\n44Z7FZWaA9nd3LYxcg5XrDYSC2zC7XqVELAR5xlkeXZQBRgYIt24I6mKrAlGYp47PumB5qrTVq30\nIWBPBsvW0sni76u/oxpynAOsLV2yIlhb7ZiFJUMEy9cNs/oxWCKcYSFcfP9H3oFXqOxUbzW60K91\nGxADEOoGhDvGM++UuvGLu1/d+nvF4uEMgbAw9JW013Bl+HelpESwfOCmlMj8jybArw4YWSVLJp+f\nyCII3u4iCEgGyyiwadqtkGmAS4yyg19DiTQq2ewPXVO6wCmApQ9t+wIsXmGwcmnrY5hcEDX/VB3m\nBgChAZ0QcHgAYQ2xgg1x1QxWw3UfeE+OwdKMoq0A1keeP8EviO/Bu25ulA8xdWAWDFhYJOVk19/3\nMQDA/Gu/t+eTB3n4b7Vsk+YGF7pScwBDJLAQIzNbZmNaZnVMEZeY+KPxCBdiAD7rcEHMAVZ97Q2p\nK/OL9ii9R1ft6X3LQCAckG1reLJGCAtuw/tG7E1zknny8E7V3FVXZpuZzBFQmXuZUQussNdFyoab\nbNuD9GOpGapktX0tLZYIpljCxdhrWBNUgyhLuxksodbvkqQ11g2iZoCoz05Fa/pMNU5oA7i+yhr7\nHubCBW8CWD2CsItFl6e4Lw4RCgukD8BKYzCR1WYM+5ZrMXwi+zgS5gK/+/f2egyc/REgMnyeP49h\njwwsADnQpxB7u9C+GaoVYBFCfoIQ8ikAfx3AvwXwLUKIHxZC/NEb9uqeopJuKQpgKQtbM9XmB/Ji\nkzrijfQuVTenP6kzWK4CTm02s1qX7/j1wL/APpYa94LRhZ0tkZhDwFE/rwAWjaaSwfLKB8SYlK3I\n8eALAIBfuzjB+5/Q/BUAcA2YtrkbrR+BQiCxN520jNow+wIs9RlozbrtNAMsJtLa4TlzDmGA5/r1\nrCfAoo/JYLlcbaLF389iGfxY2ZhMR8tRtyzihYPCerZbBh3LQiRVcw0laWsL2M2fdj3HAi6eO/Jw\n+90fll87+8KW16r+7h5zA5v7Y4T33j7BqZggerRbFhaJ5ljAw9BW0hfDlRK+yowBQwpB6htPAqM5\n+FpnFFWzZAoMlma+zBaAFVNP2iyr4pWZraqEqLHSEBHMfIZupGZoRFQGWLSn7KhaosJgaTBPHwNg\nQc8/NbrTKfvvZA0KDuIMlBTnahksxmOk1exC5wBLeE8EYGXBDJEw4atQ6ndfG+Dl//678M3PbK6f\nhLowC9eDEUuHug9+5GMIhYkHX/rt/Z48jRAKE07D+33s23jnUXnNy4wNqGpyxwWwAViV+8gSEXhh\nPTkaWDgThyDLs9aXJ3RciV1/rvgKAJZVAViuKRks1pCHVyyShghbJIK0YGJh+HL/yvOK2kytIPfw\n2JDgrJiZB/SPCskfayCBXbjcDWAhnGMOr9EdOGewtqz9OiaGFRoCmQIbbYy9oYy3ik3fSJnXUHu/\n9alvTTwTMzHYRN+o+auVsJGtdmOwnOAMD3CIiPQEWIqly+h+f6NvGQiZjz84/B7gc58AlnvkzN7/\nQwDAv57exHtv9FMukYK0ne8ZVP9mqC4G67+DlAW+BOB/AvBpQshnCCGfJYR85g15dU9REZEiUwdx\naaNswVGzRBpg5QyWXiRW51gIF6NBvdPnuzbWor37mqrFxfHrYCf1FCOmN6YkhCkSZNZok3elFjkz\nnmOGAcZuBURUZjywPodgNr40o09s/goAhFNh2NpKdZEifzOHwJkNs+8MVlqWVJhKZlA1JTCQ5nri\nvDzZOc6WEiAL1UU13W0A6zEYLJ7BFerAW9zc9WNVhputimNlW4nCa4kXu3XjiiYhuvQBn29hsNL1\nFAshB7b/9Me+BZEw8dqX/7D7CTUD0kPWogGW7Y9x6Ft4QK+B7mjVTqIlFvA2g+pWs5UyExkErR+k\npNNfO4OFapaMvs7SCDxLkAoKy2juGMbMg803r4PnkkJ5H6cwtkoESRbJcG4VtzB0DCyFmzc30hxg\n7c9gGYX7kajPj6aPw2AlSIQBoyEiglNDGk2oNdNwRwgew0murSiPa9l4vsVwSo6fCMDi4RxzuBgU\nsn6q+VIpc0tBtVa6QEAH+OAzx/gSeSdIH3nQp35edqtLDxy2Mlj/61/8KP6H7/+m8mstyN+E3XIY\nU40EUQRYQsBEAl5g4g99C6diAmvdHkKuZ5RpwxxOwjxY+4Y/ZxFiwWr3n7RZ3x4qTdMAoWhmsJhb\nMJRS852GMmpoDcXOUrh8jUTZq0u1xub9SwJ1b/UEWMPhCIlgO8vCjXiGJfHBGu4/YigGK+neg5ma\nTS/GGmS5RLB9D00qMSyataMtrotXVRPfwgw+Us32ze4gJhZeETeQLHYALGkEL73E3DxBQuzyCEZb\nqf2Z79nkYpTg4++5hr89/ZNSXvmpn9v9QU4/g9Qa41V+hJfe0WDP31BF1YN4KzJYAJ4H8KcBfK/6\n933qn/7vt2uHYrzsNhcTEy6Xi57Ov9I6YqIOQyQ4x6WogxtAmlWsYLfOm2ThAmthY+DWDz/GWMpV\nxEJtTIWgSqMyg2UnM6zZsKbvTasWy8ElEsUuPUkGi2iGLepmsMRMAqxssAFYglkwGyRcjZU7pCkD\nEjUfJyqLnol003FVxYYSYK0u5fsrtFlAQxe1WFQxXNscqBqr8H6wQlc20fMlFQbLsU3EgtUYuVol\ngZThCIJ0uRvAYqLuXpibhWzpYvJghgU8HHgWvv3F63iN3sbi9c9vea39GawsnJfuj6V7C4Pw/pbf\nKhdL5giIu5kxUZtcNbSRIavJSAEgrXRcdcWqE8qq1tO665fFQG7m0LyEJ8yFU2As8pktdR1k1Moz\nodqKpCHigtRt6EiJIFGzo0msAdZ+JhcSYG0YLM28sn0PvYCMwyBGYxaLoIoxU3EGpjfGGvbeczht\nxXgCXmGwXIvhLj8GZq9f6XMBgAilQdGgQ55TNWCx0wUiYwhGCS7HH8SN9Zcgsg7pVhpB/NJfxfp3\nf670ZZJGrTNYRwO7tneRoulSy8FXMx3a+VL+nwQMvGQzbhsMF+wIbth+iI3XmsGqP1fC3BLLu1Ol\nkXRIbfi7E+puzTwjaYSg4rCqy3Q375GrzCIs5QYcBy1yVrX+Z0WAVXSy1XPAW/YgXWNPggYtP+1b\nRjzHusV2m7CGz7WhdEyMWXgP9cxbVZJZrJRYpfU0UZ+90fNv3rcmnoWp8PPZMczv4QGOcS5GtbzR\nzlrI/Sf2riMmVnlmsq3UWaFq4rJL/fmP3MLvLU5wefNPAJ/8B80uj7/908A3frP5Ae6/jDP/vQAI\nPtITYNG3OsBSAcOt/97IF/k0FBNJqauZwIIDtWgoNsFSTjhEdWHM6BJTMoJtNOjbDYo1nFYXJB4t\nsILTuOm6h3I4OJyqA6UCU8QZ5x2zZHUJZAlsvkZcHbaHypQqSoyCS6zUwvqkLNoBgLrytWTr7oU/\nVHandHw7/5pgNiz0Y7B0YKE+NMvPxigDEiFgIIOoHKaskWQI18oFUiQBuCC5uUlbmbZOVd9jJqQg\nmWQFaUGuva90uGyDIYbZahOui6QBVsLFHB74eje5iMUjZJWAY51PxJvMHQolD40uxq5kUNbDd+Eo\n7GaYxA4MVhYusISb3x9i9AyOs4dIt3RXi2UlSwR0c3DTblVh5RDERFpzmgRkx7VpDipcyE05d6fS\nj683oiwBshQJGIwGtzwA4OYAjigALLVp6ve/apHeVDSLEGNzbcscOi93q9RzEdTaD2AJVrbjZqlq\nOD0GwKKFOIz6N5U7nQKI1BkiIu5jMWZNZYqynTgAnAxtvJYdQkyvHmCRWF7LQ6cdYGWsbMDiZkvE\nyqXSfu6PwUeI177SLkzh0zsgEPjy62W2iGYhYliNjEXjay0AHeq07BOKwUqLa0RabnjpWpon8NOL\nVutvzfg0GQxlhle6R3aqBiv+/KUypyTHbCojC6QRTcP7ZinVSSRMDIfqM1IjAWlbXpFuKqkGpFBB\nrrp004f1nME6cC3MhbcBDT3LShcIWQtwzmewutd+7RJcBL+6SWl2rDWSwSqCSrWebDOXesyaKDBK\n1AyWmN/FHT7BBYag6x2aknN5HuODm0ipBaNPpEyiZwz3l0H++++7Dt9i+CfW90qQ94Vfqv/Qv/1b\nwKcbzMOzBDj7PL6A53Fr7ODaqN9eUDyP9M1mezPWFScevl1tJWd1NhtgsTOcAywd4Ku6MFY0xZLV\nwQ2gbGaJC5q0dbSWWAqnJBvRNTyWpgFLbW+rDufMPYCjtNfR8iIHXkWjCF280iFDOMOl8HE8sGuD\nzVdZ2ikr2qIND85fRyIYhse38q8Jw4aJrOZO1VTa+a8MsMzyzIze1Cs2o/bBdfkaZxsJpjQL6B4A\nNdTmV2VA+pQosDbFA2qscqyqGVy2IecAxRadN01DRMTCpRjsbNNeHUoHNmyt2LLJ6vmmAzX7F/s3\ncCwuwLN6AKmu+VzJRnswWCJaYCmc/FBqHz8Pk2Q4vdvfTc3KlqUsH81A6q65LgMZ0DCDlRKzNCeh\nK142h3XmsoosAng3g8XNASyk+ayWlh0b2mSEmOUGSUPRLEJaMWsImQ9DzY6misEy9p3BolYJYGlp\nq/kYAItkcT7rWn8+EwxpbgDE3BEi6mydl9m1mIhrXdnnDn3cE8cg4WUuUbyqovFiK4PFTQ8O1L2e\nhLAQI7VkQ+y5D307AODO5/9d6+8vzr6mfre8NtEsqjmFdpXhbvaSakajLkL1Qbxwfep1qnKthc41\n6TjYMoeVqGaH2RA2m5k+nAbXzz5F0lCZqdQBUtaSh1csxiMkpPkwquemZ/BxoBhA25OvP23LK9JN\nUuU6yJlVmjfW+5nRF2B5JubwQHbMwXKzhZzlbijNTPIuk4ssgcVlQ9IqfDa5qUgHwJJjC8X8SfXZ\nP+EcrIlvYiZ8MJ1DOb2Lu3yCSzEEi3ZoSi7k7BYb3+6f2akl/B3SyW3lWgzf/cEb+KlXn5Mz53cr\nPnacyzPivEHh8fCLQBbjt1a38eFn+7FXQFlWvm9Q/Zuh3gZYb1AZIikBrKKNr87SIIQgIRv7czed\nYt0CsAAgIm7JaadYNC536It17fgIK2EjupQ3dLzSB7oDjHwPK2FLaYDqyPDqsD1QcylCcImzxH2i\n81dAEWB1H/bT6R2cYYLro8KGrDslW1gbAIhCrVlXBiSKwSrZwPJmgOUr10ftAok0QAgTdkOoZLFM\nV2+iu3fU46Vc3M/FsJTBlWiAVQ3aNJkyVOkGOjQLkFAHczKAsctmq9wLqx1nzaBsA1gsWWAp3PyA\nIQY34JIY02m75EJLzNIezlckWpYYXkPJOoN5fxBpZ2vEhaF9LUWpynjaJIJZpeOqK11egAsCd1gF\nWMogJImBLEEK1gqwNsGk6rWow6qVSwTNrZlwjMdIKkxMwnzYimniei5iTwYLzJQB1lyH/8rDgtWQ\nJda3KK9bpG++acIAR7TUDpIjJMx9LEDXVHKtL7+G54483BUqG2jHQOutz5fItd7rCBrlhgtXRNI5\nVkdwWHItvfmuD0m5cIdL51JFGBiV/YbxsHaNdL7WgvyNuS3NQy0lS+oMFqnM+yW+bGZh0TyHlYY6\nULx+6BemDxvJ1uDbxlIS3UYpqo5r6IgSMbIQactwv6MiWGQ8inxvPddBJIxWUysRqKaBkvgLZsvG\njrq3dgVYY8/EXPigW6T41fL4qh4voYqq9Svrmv1UjNkjjOXrV5+NNkWq5o4VK60Ye2Rq77MawPVV\nlmSwBrDiGcAz5QR4hAsxlNEpW/Y6XcmlXBec43cgo04/BkupNppMXHap7//IbcxDjsgY1ufb4yUA\n0bxu3Zes928ubuKlZ/oDrOKeQd6KEsG362qLIi35/RdtfK2CdCzFJr/Gz2YIrfaLNqIuzDaAlayw\nggu/IZfg5tjBQ3GAbC67fsFCHkhNf6I6Vz7S9WW+2BF3UnsMzsqZUmJ9gXuRg/fffDL5V7psbR+7\n3mZycQ+n4hDXi5S1oQ0Wth/gklAeuphifgxKagxWDhIqC8TBaISlcMCVC6R0jGrW2xfLdOTm12XF\n21aBApwPxKR0QM2195VDsJ3P+3W/FywLERMbKzqGGe8AsHIXxqpEUL5X2wCWmSwxh4eRAlhsLJnI\n2YN2iZUGltvANwCQZIlVwRhAzz3EO8wcuHyFrNCt1fMNUVD4/LSMtCFMMaPNLBJfX0onrsr8pGaw\nkiQA5QkSGK3SLC3F0nlfQt2rpjK54KQHwMrCmtQtMQew1eyotvg3rD3lKbkronptanbw8QDWJm+w\n/nzy67GSYFruCClzt8q5di1TJHm8g65njzzc0wDriuewzHSJkPmNh/3ND/mgREjTGt0oUdc8YQbO\n2A0481dafz15JJndKtvHsrjGcnaVVXC1tbwWpkOZIRTlukmeuVa+1sRQKRTmzVbtQjnlWg1uurl1\ne4/cvNprVGHSTZXpeZiOtdUQEVLWDBYGvodIGJjCzxl8HdLcltkWqnBe3YAUlWainuu1esrlBpaB\nOXyweAeAlaXwEGyiXiqlgTPvmsFS1+Z9oZpL2mY/iaSpj9keSltdT7VNu+0+WYDlWQxLMpDznZev\ngIgUp+IQF1DvQ88srPX5HayFjcnkGBmzt7KgAPK542oDddf69heOcORbuORufb5d///F/XrT4P7L\nSA0P3xA38eGe81dAJc/srchgabfAtn9v5It8Gsqo2HkXDy5W4YCS64iTEK4IkNp1cJP/LPNg8uYF\nlyVLhMRt1HhfGzp4iAPQVRlgecPDjfZ6vWGwmF8PDeTUglHQeItgigvuP9H5KwDw/QESwbbOYFnr\nU5yKQ1wbFW5e1b2PewSL6g1dy74IUblFhe5bpuaxSOXwfOCbuBBDELWw0lRmxXQegAB4toVImPnG\nsEtpyeQDcQBTbBZmzYZVtfeNjFxD6U5rYIzgpBVQ+43flKn1TaUOYlWAZSmTC9H1vGkEQ8QIqJ8P\nzzsTeZBaPWo/nOqDebraDrBYssRCbBoQpjqAJS3B3bXiGTwE4NbmkKgZrLQIkFWWD2lwEayxwLoC\nGe5dnanJLdbjCMjS9lkjAFQ5kYVrFWaeJTJ7T81zcmaVLNKbyhAx0soMXWYOpayKZ3mjgnV0lbsq\nl9FpSbT6/EzIGbN9ioqk9cCv5+C0iYg1GCNlHuyrBFicy857pekyckwsHWW4c8VOgna6QtQy96JL\nKIfLOFxC6MZZQZlwaT+Dg7AD+KmAZLNia27wCOkOMxTOoAiwWg5kWiJYWCM2UudK3MRYvqe8Sb4E\nQKweIRAWLLdhX1KOhm2sUFd1MqVaxthhVmTxsCaf1jV0TKzhYCZ8HKj8Sc82sIa9mTOtVKAAlm5A\noignxiaL0driZKuLUoKQDWCnOwAsfRB3mhkspmewuuZcFXtyJtS5RwEIoRxNu1QgvLKe6lw9x3uy\nAIsQkht84exzAIB74gihqa7vngArubyLUzHBjbErZaZ9ImWURLCveUlbGYzie7/5Ju5HFtLq2UrP\nd6dhPWPy/ss4c18ECMWHnmlXW1WLFc68ZE+TpDdDdbXUtVvgv1T//lP171fUv7drhzJEWupia4CV\nCQLb2mzGKbUkwFLzLplTBze6EubBbpG3mNm6NB9SLMugmLJD2KFkWPTh3B9NMHaV9jqaIVPdP3PQ\nxGAVLM/TCDRdYyZ8fPAJSwQHjnx9WdDBYAkBPzzDOTsuJYtrBiAOt3fIc2BSBb9FlyIlYalS3EPb\nwAVGMEIFsBQLtK10hso+LoKpYl4ekcPSXEEaN0tDCCEyy2yLU5HBIyTUQWwdwM8K73kaAf/wLwC/\n/b80/6I6fJMqwFJujJ3ATtuAF4JIBydybjCatoSKprG04AZ6mXEY6RoRdXMGSMcZ9AZYyomOF6ym\ntcQzK5qUKIlLk8lFRs1mkBNOMUMdYGmJTRqHIDzuBFg6NydcqkytLEYKltuXC1K2SG98DF43a8ht\ntqN57uzVNXjeVSXTDgA2DxAJ9TftaZ1OeVpyay2V7qCrbBrXHyMzyuYPj13qQNs0uO0ePYMMFCgY\nXcRf/FdY/v3vy0PJdy4hYPMVEmPLAUtlTkWrJWIlodUhtgAQDJ/Dzew+RMvrsBYSFFZd9wwR16IY\nusr1R+BC3XOD5r2C5hLBIoOlHSvL65g7uYFEMIQXzaCVBo/wSIzhNkjlNcsbdmRLtRXNYiQt1xkx\nm+Ma8lKOiFlLOOzQMXAqDvGAXsvz3HyLIRB2HmVQLf2ZOlpWXGWwEp3F2P8gHpsj2NmyU+pYrEgx\nw3oOrFq5xLmreRJUGSz1HqbSVMRmHTJYWp47E/EKkTDhOk+eIeG2+pvPpNPtzDiBe6DicHoCLCzu\n4Uwc4sbYBmc2LNFfIsiugKX7cx++jTn3MK/K8IuM1qLSyDj/Kr7Cb+E914els9a2Mgugat+g+jdD\nbXURBPAfCiF+XAjxWfXvbwD4rjfuJT4dZaDsJKY3pRgmrILVa6qHz/VN6R21PmZm+rBbXJCsbI2E\ntXerIvsYg0QCrGQ1RSYIhuODkvY6nMvXYA8aXoN25BMiXxSnGOCdR092oHTomFgIrzsHK5zCFBHW\n9vXSl/U8VdIDwOQD/FZRvmmWwpUT7ShoVKyICcGCjWGpAVeaRSVTk7ZyLYYA9n4AS0km1/axnH1S\nByXNhhlW/VpI23KYCmXyEClzkNoTeCLYzCtcvipn0NrkTi1D6SajSLYxeN2LdAAAIABJREFUZzo2\nwNocwMYn75B/z6zFSr14IN+WkQbASleICw0Ie6CGw9tyZiol9FB5QQ6j5TdpkYFUDJY+3BeLk4pR\njCoWzTAVg1pYpx4MTpMYhKftUjhsZl2CpXwvSZaUZkY4s7YDLBHXQyD13xstNs5e+wZ5FhmsNIaB\nFI+guqD7RBVAubU2zLsBG/MEsb5EJgg8fwhh+pJx6jknsbX0fGcDwHrH0RAPyVGJwXr1//kZDO78\nxu6BrrriJSgE0hZjAV00l68u8nW92Dgjh8/DJyEenTUDFT+QjQ27It80ebRTUOjItbGCoyISmoE5\n1XlJWdEFr1nqfDR08AAHSKbNc23G+hHOMWrMm8rfk1W/e770u7xDGpnn4bUwo0kzu69r5Jj4wfhv\n4GedH8y/5lkGVnBAWkYCkqW8pv2hun/MMsDSId67yOUSayybqD1jDJYzFS/hN6tuNHDmnc01eZY4\n1XJa/dyKwWoKENfFmV1e05I1AjRnjV156TEKBbDI+DbYQM719gVY1voUp5jg+siBMJxejsdaLWFc\nQdbXR589wIr4+TxfXsX9tMgUR0tg/QifWR3gIzsYXADl+3jfHMU3Q/WZwSKEkG8v/J9v6/l7b1eh\nTKSlQfdMHS4imCUbdk1zx3OZ7UH849bHzAxfhss2dJhsvkba0dVMvGvwxBqI1xDBFEu4OPAdDG0D\nC3gw4nluF+2P6wCrJO9RtHFijWt5WVddI8fAHF53DpbS48f+jdKXNZuiA1K7SgMsPRcF1F3fcptq\no77RrowJvFR1ivsyWBZDICyIPXJ5smCGQFgbeYZ6jKzh79CVEKvRZKFYpojAmQ2uNxAtEbhQrmIt\nw+X6eav0vwRYrPtAm+eyFQDWwQRL4YAsW0JFCwdyFm2fo7J4uQHhKoAlon6HrUAxQ9Stz5RkcV0i\nWM1KAwDOmlkkM55hDh9Du1kimMWRtCPvAFhaEpUzchX7ck7LFumNj6E++2IRzdhFC3AlA7V6Ds7X\nSq0hWRLlAPlcqM98z2wqJpIOBkt9PbxU83dmYQ7nipz99OGxoSv73JGH17NDcM1gCYGTi08CAFY7\nmKuUSrG9mdl9wKLq0J8Ey1yxUHSptK+/CAB49NoX67+chBinshlXbegZDU6hXTV0DCzhYgWnFoas\nS0uui2HkmsFilXm/I9/GmZhAtEgEregcj8SosbuujS/i9e4Ai/EEWQMrDWze61YWdgvA8m2Ghzgo\nz6sZFCFs0BbQlgVTLOBh7MnrTu91uUtsEiAVFM4ObE5sq32/TQZeKS1TtFoAFuvBYGnVzEMir009\nj0y0LX4XwKLWRlUDAEmANexO85erKuptGKwIFoaTa7CGJ/JrfQCWEPCih7igR7KxZjgyY3OL43Ga\nu2Q+fmObEILIGEhjjmKFRQaroCBRzdWvxkc7zV8BZbOSqsrlaao+p+EfBvDThJBXCCGvAPhpAH/p\nib6qp7BMJCVdPi8yWIVFI6MmDB4jmMlFzRy2Ayxhecppp3JYzVLYIipJrKpFhordWZ6Bh9ISe+KZ\nufbaShdIVxeYCxfjQf0AlVtrplHedRINboNXXZrBYj0AFtED0Kp010QHpHaVZn7MwuExpSZooauq\nc1qaXHAiawI/nUqTAx7VZlmayjMNhLA3Ice7VCiDeXM7Yp3wrrX3TTkwxATbkhZv8QgZc0FU6K3Q\nm8W5Algtw+U6g6RqD28yIme/uizC1YJeBC+EEJyTQ1jrls1eHcjnwoWxbTA7S2CJuHR/+Go2pC/A\nCtX9SQvziY4n3+PiIHqe99VwGOO0nFWjy07nWNNBbX4yB1hJCMITZB0SQX04S9YbBqsEyJjV+NzF\nMkUCXtn8tPNbGsxyoxLL3Y/B2pgZRDlAfiQ0g7WfRJBVZl1Lz6cO7jS4xBIywkJnl+0L6GqlGAPS\n0HR59tDDHXGMTAOs86/iIJMHSi3l3LnU9dpmLKCLFTL2EiUnc4ebg/DkmfcCAJb3G5wEFeP2QBzk\n2Y3ySaVT6C42y0PHwFK4WIhmh1sAoBp4F8wQ2lzwjgcWTsUhWEvjxQzP8UiMMXLrz2Xk4b27SwSZ\naGew9HvdOkubz6c23zcGo/AsholXfvyYOnlWXK2CGeZCBrMDmyDXNN4ArAB2YyB0W6Wekri1WOBX\nK1SuufaweaxhY3LR3lyLFUiLXBV1EugZUslgdeWtCWZtVDUAaLJGKOyt5lJXUYZuVly+glMc4vah\nB28sAZY2u+qs9TkMkWDtyL87B99FQJ0lwCd/tgS60miFRDA4WzI2+1ZsDOFkFYAVtTBYlzIO93Vx\ngpd2BFjFwGj2Fp3BAgAIIT4lhHgJwEsAXhJCfFgI8ekn/9KeohJC5i8V5hl0ZziqLBoZldKdfLEa\nnbQ/rJaFVId0VTdWmO1dDT0cvL64CxrNsYCfU+mxOYKTLcFX55iJQW6VXSxSBFiK1WjTXl9lOSbF\ngvgwkvaDsJhJuQib3C59XUsENfPUVXqA3yoxWOUh2rSDwYrtQ7nYx0sFsLZLBB2LIoDVKgPprEgG\n8+qBXj24rbX3TcPN+bxfR9mIwA0Xpi+7meuZZFZxIW2b+fxeI4OaD6VXFk9CCFIYID0kgkbFxnlm\nHMGJHjb/jjqQn4pDOZjdNTeg56cKAIsZJtbCBukJsJJzlbU+fkf+NUfLbwrgIMlBeP0eEk0yPSHg\npAsERv3AnAOsNAYVaasUDtjMlOncHFJhsGTIL+/skFqIa7NEhgJu4eIybwRYe0o8dGMijaMc4GgG\ni+/hpAk0W6Rvnk9+3YinWAoXQ9ss2NlfEcBS13VTV/a5Ix/3xBHY4h7AMyy/9Ov596Id3CtL1cD2\nNpUOW03CFbL1JUJhYjDYXP83nn0vUkGRPfpa/Zen8lr/Mr8NB/HmmtHzPTt0oEeuiRWcLQyWlpIV\nAJaWbFfkqEcDyWDZYUPjhXO4yQXm7KCRwTLU/Zr0lAUXSzJY3QArClpY0Ty7qP1QPHLM3EFQV0zd\n1hBuEikGS+3V2s4+VswfSUNEMNtjHRqK+7sBrFSpbtyD643fZ6qh0sVgxatLBMLKQZrOFOxybdSV\ngxJ9D6YBQmJvNZe6inLyMQqBu9khbh94mAw9zISHaN6yZxVLNSrTgVTdaLdMUQRY3/gN4Jf/GvDq\n/5t/KY1WCGDDt6+GpUvNgWy8FZq8QjU813DLDJYyvrkwb+DFa7u5R5uFxiuz3sISQULIdULIPwDw\nvwshZoSQDxBCfvgNeG1PTymZUNHkQh9cqosGVwArWUiA5Y7bGSzdfeXVQ2He1Wy/6L0jCT5mD+6A\nJXMEdGPzm5ojUHCwxT3M4Nc6aUAl9FQBLMNvnxe7qiKEIKQ+rLR9UwzPXwcXBN5hG8DazmBtmJ8N\nMJE2sAWApSQs1GhgJ1z1XqweyTmmPgyWZSAQFugW6/SmYvECc/hwffmZ6xwvvUBbDcPNEsx3AB3O\nYSOBMJycSV1fys0iffRVAHK+rOYsBCDObe4bpIkwQbqyZ9SCri2Hda2sY4ySlm6gOqCfikMwkXUz\nILoBYZUZ3jVxQXqaK2SX8hozCiDes00l8dxcX3pQvxFgUUvKQCqvjSFD3JAlo0M2sziUB7yOA4er\nGLmsCLCKDJY+HLZ9DpzLoOIKO2GpjJ5wOQPJIoTChL3njEPeZU/i/DM5VzNYcbifZM8QSaOhCLD5\nDKx4ihVcOCYFs/c/ZDdVHlDe0HR57sjDPXEMKlJgeYb1l38j/1682j432FgKYFFnG4OlDFgi6SI4\ng49RYcbPsm2c0muwZq/Ufje7kF/7CmQzIXfd05btO7gIDh0Dn+Lvwaf5i/Barhu9nhaZjqxhJhYA\nDlwTDzCRsqZqozGcgokModXMqFjKaTPb41qT84nNAEuD2WoeXl5qfaiy+8X6m9/3AfyXf/Jd5V9j\nbinjsFg0XmEJF76Sw2lWIImU2VAaSHXEDsWUyoUv+gGsbCn3huGkGWDRPJahfe3PVpfS4EfNkiU6\nj7LLtVGXvucU8GdZgLglzPmqyxseIBXyOH0fh7g9cXE8tHEhhvlZrrOUeYRW3RCrwfFYq3bWm8fj\n0epK58wy7YZYmLtK1lPEguEVcb0sxZ2+igg2btx6tpNZbCrbMqXZFd7iDBaAnwPwqwC03urLAP7a\nk3pBT2Npt62iRFDLKpKKzEC688Xgy0eYCQ8Hfvt8A1VysKiqIVeHFWK1SwTHypVtdX4XZrJEWLD5\n5WqOx1vdwVT4OPAbFjbVLcqSzQHbGj55gAUAkTGQ7kZt37+8g4cY49pBGWDqzTmXTXSUPiQ7BYDF\niVXKDtI27ayhg0+1Ocn6AqaIkfU4hLgmQwgLVB9c7nwK+Oc/1svFiSULrIiXa7H1NSGSQEkI6s+f\nUbsxhymvgpRFA/1QdeOyR1+VM19A4xyWtrk3Gma/UmIAHcxZqkxTtPGErsi9hgm/aHw/RIHBki+0\ngxHQB7HK/REQD0bS77CVXb6Ghxjj+mQDhGxDMZAlgKW6qU1sk9Eg01N/e277W/pxleOmGCzewWD5\n3gCpoBDqb6VVSWE+Q9nC5iqQX50T0Z9JtJoCadQrfqCtNAjJkjCftQjUYbi2pvUsGYfRfPDVkkQ3\nnSOkHgghudHBPk5yTZWotYU2dGWvDW08pEqRMH0d/v3fxuf5cwA2JjW7FlfyNuZ1WyRbnna4XAHh\nDHPhY1RxqTy3bmMU1E1r1g++gVgwTN1nAQCRfq+0occOByTbYPhJ/BB+kv5IY4QIUJjVKUgEcya+\nsp5QSrC0FNNSdThT0qzEaW5S2orl1U2IXUq6JzZfZ1ZPgEU6GKzv+dBNfHMluDVlXs3FURdN10io\nm9+Lupmomb++TrbFcg+uIRUU4WWLc2u11o9wKQY4HDWfWXQGIu8AWDyYYiZ8jMcaYKn1q8O1Ma+K\ncyJLQ8Q9GptXURPfxgzycz8Vh7h94OLIt3GBEfhyO4PFz6UixDyU64G+NrQSBMBm31pv5jV5tMJa\n2Ds5+HVV3pQvjGAkq0ss4OE+nyC53Jjg8ItX8Lo4xod2CBjWZTEVE4Pm89PTUn0A1rEQ4hcBcAAQ\nQqQAuifv3q5S5W5zDQxWtSuTd7XXj3Ahhjnl31RUacjDSvdTz5Ewt53BOrl2C6mgiGensLMFkoIL\nFVEAy4/OMCeD2rA9sHF+ScI1RHAJLkhJ0/8kKzGGcHjQmpXDZ3dxXxzi+qh84+oBad6DwRJJILvz\nhQHZjFkwCoBEd1hZA4NF1YBrNH8gzQJ6LPSMEsTEBstCSdH/4x8Bfv/vlxbUtrKSBULqg1p6c1dM\nTCJDjpu091Vb21rpg4DlwlOWs/HyHEgjWMt7+CR/j/y5RX0Dzt2NGg4RKQzQDmCn51HcCsDK/Btw\nEEM0gCe9Ed+HAlhBF8CS90e16x9RF0bbjEOl6PwO7opj3BwXhnUJQQS7JPFM1SGRNFwjYFZdpqea\nFbxB8mXmACsCFQmyjgPHQGXpFAFWca0RRjeDpSWyVZMSR30myXoOkkXSEXLPIsZGIqgP7YkjGxNJ\n2+F0SxlINwY8ldIddFuEiKg8BGqZWKuca8fSjAFrkM0RQjaS0m/8Bvz4EX5N/HsA5EzbPqXtuU13\nC8BSewGPVqDRXDJYlb1l5T+H6+ndWgMjOX8F98QxfLW+B0q2la8POw6pDx2zVR4IANTQEsHN+t6k\nKNAVu20AS8oGhd8ss7e9zXuyaxkiAW+5ziwtPQybH1fk6+pu5jDccFsz21gWIC3YvrMcYCnA39PJ\ntlgnYx/nGCG6bHFurRRdn+MCo5xFq31ffa6iy6Y9nGIOD5OxvNaylvWrqXLWWDWNTB4geYMA1qFv\nYSbk3ntfHOGZiYvjgVXKw+yq+LVP4kwcYHgiFRFU7ZtJEWBpVUawOQ+IZI0ADtyrMvLQYx4FBitd\ny/m+MzEp7fXx+St4nR/jA3tE81gGRaQUGFXjmqep+gCsFSHkCIAAAELIxwDsqWd4a1baFEirNqUq\ng6UPB+b6DJcYYuy1Lyo66yZalbuvGnBpl6Smunbg4RwjiMUpXL5CVgBYVN1kBAIBGzV2qLW8J4lD\nxMsLzOHhcPDGLGaptu9uMbpgy/s4E4e4Piq/Hj1YmfUxkUjrwIRXJHUbiWC9A2Oq2bn15SksESPr\neQhJqCMB1m/+nXzOCfH2DqudLREaw8KshWIx0wAhzMZBX2nV3Q6wtFEFMV2MR2NEwkC2vJBp9RD4\nXf5+AIBoMLpI1UHTaBi+zYgJ0gGwktUUa2FjVDFX0cYsi4d1K2nNePRhsEQOsMr3R8Q8WFm/w5a7\nvodTnNQaIBGxywxW2i4RzFmktMAi5YYx9WaFqTZdnkSgouxKWi3HpFjCAdFsdsXWXc8/ibSZwYrU\nxl4FWIPBGFwQZMFUDp7veGgrFlMd7TSN824/9+R9k+4R/ipnXdMOieDmtcbKYVWvocmejFm1cgar\npStrH0sWCC//bwCAV44/DgDg4X4MWqyYr6qctlr60C/iFcx4hiX82pqQTZ7HEGuspuWOO529hjvi\nGOOxfI5Y7Tfaoa6LiWmqkWu0GlwARbe5IoOl5v0aHCszNbeCqpPgSv4dbNgMsDzXRSQMiD0cJE2R\nQLQwWBq4pS0mFxp4NUVndFVm+LL52gBQjCxAZhTnWspyeCPbHWycDG08FAe9JYJmdIE5PWhltPu4\nCNJojpnwcXioJc4q54nHyLasNRvnRLmmGTxE0pI1dtV14JmYQd5jD8kxTgY2jgc2LsUQRtQjguHe\nH+Az/IX8zMLyYPDNNXT6UF7PFw83ihESSyv6q5rBojqQuwCwRCBN0E7FoYyeUe8vnb2G18U1vP/m\n7gDLNihiBbD2jvl4E1QfgPVjAP4ZgBcIIf8OwC8A+K+f6Kt6ykqbIRQlgrnErnIY0MyWG5zhUgwb\n2aP8IdTNEFfmBzTgMr32C982GC7IIYzVGTwRQNibDqhR2Kwjs/kxSJ4pFSJZnGMqBjj09z9s7VLc\nUgfjlrwjZ32K++IQJ8PyIUcHooo+ACuRQ8FOxULfKEi6Mr3QNBym3LFifGb35aGvp5VxSh346SXw\nWz8Frg8OPYwXHL5CYgxziWCuXe+ScRXDohtKs2DE9DDxbUwxgAjOcwfB3+PvAwCsz+sZNLrj3BRs\nmZFuBitdT7GAWzNXsdS80+JhXcaUqNd6oSVYHQxWrNgSwysDrJgNYGU9zA44xzA+w9y+UXtfQ+KA\nFQbRdXOFNgGsqtNX4XUTr35g1gwWshiGSDsZLEIIAuKCqpmyKuOlu71J3Ayw9cZeZSeGroklXIhw\nIQfPHwdgGdoVMcoZKzaU903a0v3vLH0gb2ILUZ6VTFUGmp2zDTsesv/N3wL+6V+pfVl/lm1d2evH\nJ7LTffE1PBAHOHnxW2Xwbk9zlWolqym4ILmpSVs5rvx7RbyCmS4QML927drXXgAAPHj1C+WvL+/g\ndXENRxMJ+rWzW5qH/+7OYA2cLgZLg/9CaGwSKpvxBmZwpKYXKky6lmZZ4+aZIM80sIazl2OliaSV\nKbVd7SLYfE3pe4vtGm/QYf9u8xDc2Dyebibm1yOPkOwoEbw2tPFAHICu+gEsN7nAymgH+oa+/zoM\njox4hhl83JiMkAiWZ0IyHrfa4uvSxh7aJdjm4U4RAo9TRQYrHdwEpQRj18QlGcGOp90y/3AGZ/Y1\nvMzfhRsaYNlaIrj5rNdLed4pmmaQNJASQfNqJIKmCh/XzLh8whkWwtuoQxb3gWAKK5njHrmGF052\nz+CyGEWsQuWNPYPq3wzVy0UQwMcBfBuA/wrAB4UQLz/pF/Y0lZYIFgef9WJQc5dTi7YfP8SSjVt1\n6sAmdyet2MxGisEyt+jyl+YRDsLXwcCBglyqmGORNsyCAIUh2jBAtr7EFP4bBrDyrKcmBitaws6W\nmJonNVmcdqBq69oXi2SSwTLZ5v0XrJyzoWcEDLO+8A9HE0TCBJ9KtoW3WPJWK2WOlO1ZHv7J8Y8C\nANaLLQ5jaQxbRMjMQd6RTzWD1aG950zZhLcs/lo2RW0PI9fEVAxAg8s8A+uu/TwuxADr8zrg4cp0\noknSkxKzE2DxYI6F8GrskKtMS9YXDYyZ+nv58KZ6jPauYX5/uOVDaWr4cHgPgLV6CFMkCLxbtW/l\nDKT+W1S3tpHBUgAjLgAsoV439eqD+ZbJZMc9jcC2MFgAEBE3t3VmIinNbOUOfi25OlqaQiub38gx\nsYALEc3VoW3LXERH6YM0T+LcZMIaSYC1j/FAfnBrYRaKACtTDquWp90Wd3y+V34L+Pq/qX05TTTA\naj40SKMLKYP8Xf4+fOu7jrCEC7Ing5UFc2k573avvY4tXTKRrOGkC4QNLpXj29KqfX7vS5svJgHc\n+Bz3yQkmk0P1JflZ6QF8uiMT8/EXj/Ed7243b2JaIsgLTIdueJn1I8tgdIClcGuzoNH0FFwQ+Ere\nXC3XYjK8d1eLfiGkAUwLwPIcF6mgpbiGYmn5dFM2YWfp97kBEFoiKjNYOdutwsAzGRi/S10bOngo\nDmAGPVzwAAzSKaIWQxH5mhRw5u0MlpXMscAAxwMbAWzweLN+tc286cpVNQr4WyIsvSdPsg48K5/B\nMg7kfDulBJF5IFUiXSzpvT8EALwsXsCtA/l683nxYuSHXqMKextJ1ghgX5lE0FLN9agQG0GiBRbw\nEDnqPprfzx0Es9E7OrPJ2opSgljtHW9pgEUI+RqAHxFCfF4I8TkhREII+eU34LU9NZUPuhcBluoM\n82oHWC0SpoixNrtlH7bffDjQ4aKO3w2wQucYx6nclFhBw+8Ucizasq20tWaWBEBwidkbyGARp05j\n56V0+KFzo/YtSwOsHgwWSSPEFeZHsLLrmw7CbBrSnPg2zjEEnSs5W0+JYKYkDfw/+An88h35uOtt\nGTkKaHJ7CFsPs+vDTxa2yrgEs0EhWudw4kB1Dy0PjBIs6BBmPEX68Gu4FAN8+MXncSYOkU3rgCdT\nm5zdcIjIiFEyC2n6exbwajbFwxM5v5JM64yZNkkwJnJzCzuCW7WsqmqikZk+XNHjsKUCFrPR7dq3\nEurAzBpMLhpYFaLnMAuxAelKvm7DbwBYBkUCAyJNOvOedEXUg6HmwShPSwG82gU0jZqbDXFLsOvA\nMbAQHki0AM3as4D61GYNiXIpkD8+RCSM/WzaNcDaMoMFAJliwR1PGx20HIDaOs/BVJooVL6faWls\ni/Xws0c+7iqA9Tv8A/im22Ms4YH0NFepls4w7FI6AIBjMKxhgyYruIrtrtaNZ98HLgjShwWrdpXZ\ntXBuwVTMjGYXdXe9DUy21V//rvfix//M+1q/b+QM1maNEEqybRv1g+TxwMapmCCbldeFeHaGCwxx\nOGwGMpZBEaj3ZKdS11kbg+XZBtawc/alWrkBUAO731WkLVKAZ7ARA4VYFh3kytXaYooIKd3R5MJi\nmLEJvPgC4Lz7h3mGoVjkM5RNxdT91zqDxTM42RKxMYRnyesVOcCK62elStGic6IQsEXUu7H5uDVy\nDFxghJWwMTzcMKapo9bxrjmsezL16I7z3lx1o82hNEsMIB8VYOFmb2PZ+krDlK2BYrCWGxBnxDIC\n5vjm8wDUSIACWJ5ivfepBGoc5q0MsAAkAP4UIeRnCcmv8PrJ4u1qrSyXCW0WiNwyvOouV5jnibcA\nLEfJiHjlcJApgGVvAVjcuybZK5Rlgf6ocLhrybbSHZYkjkBDafv7RgEsnZoumgDWXG6y2eBm7Vta\n68t7MFg0C+vyJ2bL90ttELnWuwlgeSYuxAjWUoGPnnMKvz/4OH5x+IP4/cmfxWsrlUu2zeFMvQ/c\nGpeG2QGAZWGrNKRktd9QutOqc13WbAwrniE8+zJeFdfxne89wamYgC7rQ9AiCcEFgd0wg8VpN4NF\nogUWwq0xWEeTCRbCrc9aQAKsSJiYHJ4gEwTRsn1D0+G7TkVCy80BPNEjhFodOo3Jc7XvpdSBUbBS\n1jNYjNUPwMTUMr3Nc8aLcySCwW2QfGnnJZH1Y7Bi5uWSRybSksSG6vmnpPmz14dAVrluTUaxJi5Y\nsgDrY53cURu3uBhZtAAXBAejMQLYuSvkTqUBVoNFOrD5m4GNw6rreeCCtAO6T/wl4P/6y/WvB5fS\nZbPyOvV8p9Eim3vuUFq1A8BX3JdwbehgTfu7V9YqnGMp3E7JHSADvgPYsMNHoOCNyoTxaID75Bhs\n+vXNF1UGVuTfhq3ZvlwiqB0Tr/YQm0sEC40fnePUNEt65Fs4ExOks/K6kC4e4FyMcNyxL4XEBduV\nwVLrftVhU5dvq8D4FoDF92Sw9DXLq9JD9fpF4V7N9zrFUJs8zrM3d6nQOQFF1g0QAGB9AQoB0RHV\nYlhboiHUPhabQ7gWQyCsnF2UpiLda01uvBWHQBaDgUMYO7KEexYhBL9o/QX85/GP4/ak8JxeD4B1\n99O4T2/i1q2NIkJ/fsVIGR0hYsabhitLAwTCvjKbds8fIRUUyXrzHGa6xBw+nn1egqnV+etYnskm\nzPGz79n7uRIirdpta/895P/v1QdgrYUQ/zGALwD4TULIs1CGF29Xv8oBllEHWFVLYVIAWInTTrcD\ngOv56nBQ1u9nwQKJYPC97sWFjDYsj10AWCPfxVIoLXCDVAkoZvIEMOMZpmKAI/8NsttUs0nJ6Rfq\n31OGC/Sg3gOwTUtmL/TImWJZVJfWVayt9RA2azjQHXjSQcgL1KbfE2A9ct+Ff+T8J/ilz55K2QuA\nZFtGjmKwiDuGq7rMG+16hLRNe18Mi26ofBhbHQQicww3nYFefh3fEDfw0jsOcGkcwwnrEhKRBlLS\n02Afy6kJJtplIiyRkoShU154J56FB5iAruuhojxeYQ0btyYe5vCRLNslglm4QCSM2v0h7AEckuTM\nZFutzr4BAHBPGgCW4cLim/eTZ3X2WtdG0rK5HlOVA1N1eAM2DBayWJk5dDc0UmMDsGQ+1GYTzh38\nWthcbe/cxE6E1IeRrGDwdqvqPqUbEzyNICL5+R0Nbdm53vXQi4Jpk+VQAAAgAElEQVT0t4VZYMWv\nKztizzawgtNsdCAE8PVfB84+V/+elumsyte+XuureU26bk9c/N/iW/F/Zn8C/i1pEhPuEA9QLRLL\ne8XfYtNMCEEIB54K6m5yqQSAh+YtDFavbb6gABY/eC43b9BscZozMVcLsFgOsDZrBMmkoqBJMn80\nsHGKCUjFRZCsHuKRGON42L4vhcQF6zN3WayOexoAPAUO2q7hLHdE3I3B0gZGUTWzTa31OcOFjVpD\nNxMtEeXqiF0qU6Yz28KGo7n8Pm1xbAQAgzE5b9gmEVQAK7XG8EyGAE5uGGSKpDV+QZduBmVxuGl8\n7GjA8jiV+Dfxe+L9uD3ZPCcdqPejwwlY3Ps0PpU+j/fd2NyTena5CLCo+pucZHMeMHiIhDqdoyS7\n1MizsIAHrmeYeQYrWyFkPt71zG0EwsL8wWuY3fsqFsLFC+/Yn2tJiYkYRmPT5GmpPn8ZAQAhxE8C\n+G8B/CsAzzzJF/W0VRrXA2n1bEPV6rUIsPgWgOU7pjwcVDpaPFpgBQdDp3tBsg82HROnkGF14JmY\nQ9kYD5o7UjnASgJY6QJLOrg6q9AtZYxv4FP8RZDP/+Pa97iSiTiT+nyMbSpr0D4MFm+QVFQAiR7C\nNhvkQJZBMaejPK+rr9OWZzEswgT/4rOnGCmr2uqMXbUyJXmj7giObSEUZj4IbWQRkhZpCNkCsPQB\nylTD2Il1gCGfwQ1O8Rpu4J1HPmLnGgbpRb0r2WASoosTs1MiaCYLhGxQCy+klOCSTmAHdYAFBbBu\nT1zMhA++bgdYPJT3R7XrT9She7VFkhk9egVz4eL4uD7bkTEHlti8n1kqDxNNYdS0weSCB5eYiXII\nrC6LSYBF0hgM2xms1PDhKEaOIS1JbDY2zls++4b5moh5MNMlmIh3lh0VS0trRRqDx2us4eBkYCMQ\n+wGsRMsx2ySCxfVXOUh6loGgjW2Y35NAqno4SqON0UClM50zBnYzu2EyilfG34IfS/4yvknlx0TM\n7+1eWS2q5DvDLQwWIB0uh7G6d5xmdcPCexYncUFqN30NsTDgTm7B8TU7Ltc0LV/a1Q1vW+UzrQWW\nm6btUuejgYUzIQ2bipEHRvAI5xjhqIPBkpLe3a61/NDbMtNkK+lhHsRcKR6tEQkTrr1b516HRUer\nSkNVu70WPoeNoZO8v21E4Dva6QMAGSi52xaANT+X4NYcNc+7AYDBKBKw9hks5aDKnQMYTMk31XvY\nZYuvS0uO0zjIIwTEFV+bXTVRkvZnDjZ7vTWUbLVYtcyxLR+CzO7gD7Ln8d4bG9muVWmUAsjnad1s\nIVU0QsDMrtYpcegYWAg3z9fT5juJMcS7rw9xKiaIL+4gOX8Fd8QJPnCrWyXVVRJgmXvNcL1Zqs9f\n9jf1fwgh/jWA7wbwPz+xV/QUlu5iF+289QFHVA4oRYBFvHa6HZCH8VWTC1K0wBLbZSP+8ab74I83\nxhYHrpk74tjDFgZLdcjoSktOdg+b27eGjol/ln0bzEdfAB6UWazo4nVciAGOJvXXYzEJsEgPBsvg\nDcAkz9lQGnz1v4bZvPCvjc172ldG45oMX3u4wvkqxvf9sRfl020JwgwVW8PcAymtKBwYTR61hhyT\nShhltfK5JtU5zZwJDGQgEFj6z8IyKPjwppzjqm7AemaiYSidUxNGB4NlZyskRrMz0cI8xiBu2Kzi\nNQJh4/aBhxl8iA4XQREtsBQuhpUDjgZY4RZTET59XWVg1T9TYbiwRcG0QrOcDfIWLWkpyfQCKbdt\nOjBbBkUkTCCL1JB99wGNmwO4GmCJsn25ZtOztJmty93HGiSeiTGAna1giHjrHFhXsZzBikESGZh5\nPLDVrNDuAEuDRdJyP7KCGY12YPUshpWwm40ONHMVVABW0UClcnDSofJWC8ACgOcO5f30QXU4iQ2/\nMzi9q4xkiSXczlwpXRF1ZDMEAPGaMwvT8TsxxgLpUv5cevEq7ohjXBt7cBWDpeWbmV5jrprBMvWs\nTgFgZVGrY+Wxb+NVcR1UpDnjBgB2fIFzMcKB134wL8po+5ZmnNuuM0IIIuKApi0SwSRAAGtnWZfp\nNWe2BUryzAozXbbJEAlTWukLIfMD95hHMsdSai8awuRLr+FSrv/OuANgUYIMDKRNIqjXbDX3HVMH\nTL2H0rWxu5mzsaaP8r2LmruxhI9TE3WdFRksRzlYxotHzb+k5q8+w1/A+wsMlp5dFgUTIqN4nQaX\nSgaZXalT4tAxMYcPEimWTKljMmuAm2MHD8kR6OI+rOXreGRc77y3tlVKLERvVYBFCNFTqHcJIR/V\n/wAcAXjb5GKHyiWChVkdbdEqKhlKRctbOmh3WgJkp2wNB7QiLyHxCkvhbs1GmCjTAAAYjDZgbuTK\nmwwAvHHza9AdMmMtF9bM3r+TsWsNbQP/PPsYBKHAZz9R+l5yeRen4gjXhvVFh1KCGCZIh02sLpOH\nNfnTxpRAbrD6ANA20B5aewAsJfUZ2Ab+7EvPYCVsiCrAmr4O/Mv/JmeNIgWwLH8C15TDwVpaYYio\ndQHW11ocNQNO3T2zlTOhKMhF6dG75eMrKSaflY0uSD6U3gywWhmsLIEtwtyEoFpr+wSj9KJuPpCs\nSwwWjdpBEomXWDU0IJg6dAdbJJnG4g7uVUKGdXHDhYMNYNIsZxODpZssWYHBIuEUM+HX5JGADKJO\nYYAql8K2vKf8ua2BHH7PUhiVmS3d7OEtM1i6S9/EYKXmEC5fweQxssdgsEx7w2ARxWCNXRMh2g+n\nXZWHqrZ0uo3CWmuoeSKTUYTEaTY6OP2s/N9knXfEAfQCWF2g49kj+Z5+UAV0JsYQbh/3yoYy0yXW\nxIfJth9SYurKZggAo2229ljOWTx6/YsAgEx1qq+PbHi2KcFoDrCUQ92OUrdtZarPqQiwumZJjwYW\nvsyVqOaBfN1IQtjZCmvrqMaEFytlHqyW8N62SlQuFLeaZZYAkFAbrKWRJ5JAZSzudrA0Feua1GJZ\nlPmBs2lK2UZBraFeR9vMWFc5Ewmw4lk3wIpm8j3xD+vmUroMSpCCQRSD1YulGCyqDLdi6sDIAoBn\nMpB9C4OlQ+2zJMxBKNnVCv8xauJZIASlxtvo4AiJYAinDaoLALj7KXBQ/BHeiRevFz4/BbB4QcJt\nZZL5BCCZc9UU4lc4ZzZSJkZUOzTn890yD3XtXIMbnuEgOkXov6PjkbZXRi3EwoDVY+16s1bXX/Zj\n6n//TsO/v/2EX9dTVVkeSLtZIHJrysqiUQRh1qgbYBFCZO5OpftKkyXWxGl0XCrW0c3NDTKabACW\nYzKsiI9EMAwHzcBJy8aMtTpguM0d0SdRQ8fAI4xxef2PA5/7ROmwTRb3cF8c4tqoeTNOYIK0mDrk\nxTkO04eYGuVunLbW1w5rG4lg88aVFiSefdPKdVfzuz5wHUe+LTOHqkHDX/lV4Hd+Grj3B/L1rOTG\nZA0OwChBBBtQB1RLtB+C9bVWDDMslgZYWq5QnMcb3JTsmn8sr6HZg9dKv0vSsObCqEtQEwZaGCwl\nSeAtACt1r0nQUAkSpqm0q702tLGADzNuB0k0XmIJp9aA0KxGtAVgecF9nJKTmsshAMD0YCDLwW+m\n5kia5vT0+18MvjaiKaYYtEq+EmLmzoDbGCyomQwRLWAghSj8vG4KtJlc5DlmTSYl1gAOItgi3Gtw\nPn8NBQaLplLi6ZgUEXXAWuRVXZU2xGEUq7j+2gWDE8k2NDxfcfaqKBMsAaxyZ1rPgdktTRcA+J5v\nuok//+FbeEZ1ujNz0M+9sqHsdImI9QM4aSFo1vCb1+vhLTm0Lj79D4Hf+imwy6/ijjjBjZEDk1EE\ncDYHu3yW6GoZLEPvWwUpGeXtjpWexfCaofayh0rRoIBvYnfL7FPDKzHOfYq89jsAgOnhS60/k1C3\nFNdQ/maAQFi1GJFt5ehYloqplZ7JMgsAyzIoIhhAFkFomdke80iHhxMshYPgsu4UW6x08RBcEIw6\nAJZsENH2kHnFYGkH1UQbBmlJ/jaAZW0aVjp4nu3o1Pg49We+6QZ+6I+/s8TIHA1tXGLYzmDd/TTu\nm8/h+vFR6XpwbAuxYEARYPEAd5RBDoKLXKWSXSHA8i0Dc3j/H3vvHS/bdZd3f9eu08vpt+jqNulK\nliVZsi1suUpysIixDcYVE2IbQ5JPsHkhwYaEEBNCSSC89DgJDnl5CSV+gcQxGAyOaZZljOWmZqSr\nW6RbT58zdbf1/rHXmtkzZ8o+7ar4Pp/P+ehqZs7MnJm99/o96/f8ngfLVzWHio/Qzs1RfoHp8DI5\nWpjTh3f0WvdnXs5Ho7uwvh4JlpTyu9V/7xryc/eVe4vPfmgpmZmQFOiCe3BXSc9mhVL02aWPQlvk\num307nMHDTpi8kmXyeapkceTVjeIUqNlFlmjQGWEcYWWwDgqI8McYiu9V9C7+2f2vw5WT8O5L3Tv\nsxsXuSinuonog/CEgzGJYG1cwMVj2e0f4OxZW6uLniqinRHBpkHCstZMqQXXdquvv3U/edekLrMY\ngyGk2j3xQhxHFyrHn6yyHW8Lt1ugOnK09t5IhEUPg7az18eGnsdblQWuORDvGFcXYqOH2mI/wRon\n6ZGmM1oiqP+2EUP4UuVcyQEnQTNo0SYuAttWEccfPbdmBg1aZDdtQOjcOL85hmB1NsiGG9SHhAzH\nT6LOa7XLr0n4MIKlSU6YmIOy/Vo8gzXE5AIgEFbPBn5S8Kae2WjVYoKV6Mhqcjeqg6V3Toe5QKLI\nb5mNiUXPONh6Myn0MIMWHZFBCIE3ENbcB68Jf/cnQ7OAgiFKgSSSeXVOwqK/Y2SHvp68+GA3DLNv\n1moCwepIG3dM8fzy62b4+bff1j1+IqdAlk7XnTQ1Ag9bevj28M2ITQ9PzGq4xeEEa+baG2hIl32P\n/Rb82YcgCrgvuok5dT1tJbp9vWNkl2ewTDMuLhMdLGvYTKyCEIJsocqqNdfrYCmCFebGb1KGVo6M\nbE+2IU/AfPKzPB7tR44xdPDNfjfRvvcbxB2srUoEdQRHMDBzrQPhrb4OlkkHB4JO1xFUbEMiOFfM\ncFlWCNbGd7BoLLJKgani6NcQIu7AjzK50M51diGuJQIzix21ey63EzZzes6JbTxFQrdqhb8T3HXD\nHB96w019t83kXVZksRt63Qcp4fwDfCk62icPhHiTu43TN8eXjZqc0wSruTLUPXKnMAxB28xjB6rm\n0AZaambTrh7odsFL+7Zv0Q7w+cJd/Jr4th09xzMd4ySCbxr3cyXf5LMd2sknWWR1JSQDhZfe1V2j\nQDk3uaXvGVnsgQFpO2jSMdMteutGlbrYfBH6X7k380P+e6nmhxdxrusQSoHbjlvf1hh71t3GwWoW\nxzL4ZPTi+KKrZYIXHyTjrXBBTjFbGNHBSkOwVmKb4rVMfwu8S0j8HsHqSAtnxEIp8r3F3UpZhNx2\nqMIdR6Z42fGZeNBXZDfJl7QkUKqAwrC1Hg+6q+MlWaC6eEQjJILadWnUDJb0mrSlTVbZqGZKcUFx\nWi5wfC5ezOcX9uNJk/byU32/awxzYdTPO7aDFV/QjexwgmUo58vmwI6qGbbwVAHm2aV4EHhEhpEV\nNIaeH26XYI0xFVmP/85OYbh7kh401xKVntPk5o5Ud2ZAm4xEIU5Qp0aB/AjDmEDY3aH8UWYO3edX\nkqLmxjq2DPoImZbLRSNmsDS5Hir/UruZJtGOOli2pQtpDzNs4hnKNdPM9mWJAfFGyid/BH7uRvit\nt8KXf2fT8+kZrGFkFnr5SgDZRITFUKMDrwkrJ/lCpGyIW5s7WJ40Nw2vy8CL7cS3Ujyr2b++4PQw\ngKXHNj/2wlfgF2+DJz/f7fYGI+YVB5EkWNnCcIK1b3qKt7r/kfdO/Tryh8/xkVd8ho9HL2VeKQKS\n3T7pt+hIi8yQoPWdwDI3F+JW5BGOOd6nCy5nrWs3dbBEYfRMEIC083HBmLZjGoU45/+Gv4lOjHU/\nC83MSOmh8Fu0txEOm9UmI+3+9UBLBt1s7ziwTYEvLUTodfPKtmOnP1d0WaQy0eTCaC2zSonShLnv\nEAMxgmB5G8t40iSn/s7QzOJEbdDXKGsCwcqoufagg68J1i7LV7eKmaLDqiwiBuc4Ic6Sai7z2fYh\nbljo3ySJlShOz/E4iJ1jn5LxGhw1ehJB7N3d4OiYRdxAkXjVwTJVNE5xruecu3DtiR29jmsZQ2e0\nn0sY99e9fszPN+/9W3vuYFggrVmY5f7oRpbLN/c9Vs9lrMpiqgFCz8xtKkbcsIGfUjbSyszSNjcv\n0GvF43wqeiGV7PD34JrxDlmmE+/gZktXroOVdy1eed0MH3u0jrzu78GDvwd/9R/gP7+aulXlfvfO\nkYOTvnAwogkzWIpgtUv9NtxdUwLd8Qk9AsyRGn+r2NvhTLuTds+N8/yPf/TS7vtvGTnMgRm7py7E\n3ZvOk7FEULbX2SBLQQ26e0YmlqeEfixXG9HB0h0Uf8QMFn4rzs5RF8FcOf57zsh5js3Gx8yBap7L\nVIkGO0pRm2CUra5p9wU2J6Gzzazc8BkRtxoTm8bik32322ELXxXogVPGJBza5QBwwsZQWVUmr0Nn\nxxAslYFFabiRqrZK7jTj70xbTZtDBuJ1VlK3i9ReRyBpW8Xh3TEgFHbvfJ8gEbRUJlq7vo4jAqS1\nWSIoR3SwpFrYM0M6WEamR34nDZ6PQzc4OfTV96dMV3RhpdFcgQ+/Aj77q3D01YAYWvBF3TiM4e9J\nfweRFOQLvb9hKKFbfAQhIz4TxTvSQT3RqVIE64xcINgYmK0IO3S2aj2sdoc7yc7pQ78Pv/oSqA88\n//kH4uvTb721OyA/Sk47CE2wajLX3YwZhGkI/uE33sGfnXf5k8c2uLThkXfMrmqgIzJdiaoM2vEa\nsMtFkqWkZMmOni07hMboDceZvMNJDsLi38VOgopgWcX5kb8DILvhvSldHC8/jOnV+JvoRg5NjS5s\nw4G4hiRE2KEtnaEOq+OQc22a0t0UKRCqjpaT6x0HcSfYie3tVYeLbTjqzRUzLMoyVmuEC56C3V6h\nZpRHXre673VMBytorFIjT1nVPZEyDOrOIU0iWInrqa9k78mu3tOBqbzDCgWs9hCCpc7fL0fHuGHf\n5g1FTzgYmmCp71wTrM7G4lB7/t2AZxVwoyZEUXc9dlSMz8z+w93H7b92+xlYEJuOPZfnr2C8RPDd\nY37ecyXf5LMdXTOEBMFy3Qxv9/4Vq1Mv6Hus3tVeobgpaHUYAisXnwwJuFGTIKUu9+Cr30P5zndv\nur2cjUMdR+2yuXYceqodmLIT5sV2G6+9aYFzay3OHngdNC7Dp/4N3PD3+ZH9v0ajfN3I3wuEjTmB\nYPlLJ/GkSWF2AsGK/DiXaAScBMFytjls6xm5TR1K2VIXvZWvQdBBdGpsyFx39zAwMnHBqK1qRxAs\nLVMNR3SwtJRFF4v56iwbMsu5zPHucZGxTZaNaaxGv4QkdmEc/rrSdOLA5iHDzi1lke6MIFj56Zhg\ndQY6WHbY6haQUrlQDc5pabhhk2AIwcoW498b59oY6ZDh6UND7zdUEdNWRY0ck5WmZ/d6BCt+v549\neng+EnbvfB/RqdHQM2WezgRLdrD0a4/q5vptfGmScTcXNWayuzih6BkHbTtP6OGELXzVVQzNLE5y\nLmb1FHRqfPrmn+LHcx+kYRRZX94sWdKOiOZIiWD8edXJUEhEWIRWrv/1AC7G81f3KYLVXOsVmEE9\nDoM+K+c2SX9Mv0EbF2sLuTSG6mC1NhLSw9XTcSG63r+R0CVcwoCPvguAyE1HsCK1010jRyk7+rr1\nptsOcHyuwM/8ydc4v9bqk1t7ZrbX7VNRDFvq1qVALCUz+2zabTk+KHe64PCQvz+Wk62cwq/Fn5Nb\nGU+w6BKslC6OZ+4D4IvG87oukMMgrYFjuM+woxUXz1vMLso7Fk3c3kyVQqA6VNl8P5nwhY0Ret0Z\n27RzwEmUshYrokqmM55gZf1VGtbkOexQmCM7WGFTR1TEx2Zk5zCJ8JVluDHheue6LpEUyKBNqD4T\n92nuYLmWSd2s4HpDYkPOPUAobB6VmztYQDzDrK/P6vjsuFN0pI1XW+pGRYhdtqIPnVLc1e3U8Brx\n+3ZVx3vhwBEANkSxa0ayXTiW8Zx2EIR0Nu0IIV4nhPiAEOJH9c9ev7HnErQMp49gqQNr8ADTRVfc\nwZpMsEIrRyYpRZCSjGwR2ul2bnJ3fCf5e35w0+0HKtk+u9FBOKaBR/z+mtKlUkq3yO8WXnPjPKYh\n+L36TXDrt8O3fQTe8v9wspHpylmGITQcrAkEq3PpMZ6Uc+yf6v8MTUvbmvckgsEYgpWrTBPI+Pu1\ntzmn4Jl53AGCZSoDB0MGcPlhjE6tL5g31q53uhavozK4tIQsGmFpLoIWHdzurmS1mOO1nX/HF/e9\nre9xDWeWXKd/p31sCK3uvAxxc2xv9F/QBzFVnaImswTr/R0zR7YJ1IyBoR3Shv1dUURGtgiG2Pfm\nFMEazJVLorV4Ck+aFGeHOyiZakHXsp3u5sqQOT3L7Ulaku/XHxN5EBg2rjrfjQkdLEcZOXQHrBMS\nK93BGhmqHMTF8zCi4CRCyUdlAaWBbYo41yv0cGSLUH1/oZXDlZ3uXMzK5ViW+fOfb/PfP3eGi0Ge\npcubh+57bq3DjzudVzfoIBmaA9dQgEsP0jGyfFUeBaC93ju+27Ul1sizJMuIgRmsQuciF5mZuJOf\nhKmkqZ16ooOlpYcbA526+iXITsE7/wfI+PMRI+YVB6FtukflrGlYpsE//8YTnFxs8GePXOojWL6R\nw1aflQjatOXWZ4nSIMDqK8QnEyyXL3bi+UwWH6G9doGGdKmWJ8SHKIIVttMSrM9w2Zhjev/RscWh\ntLJk8OJj2G/F0tZP/gigQuy34b6Zc02aQzLiImVJnsn1HwdaDq8D481tbPIJIWg6M2TCxvCsOIV8\nsNbnmjsKASZizPxtLRGyLtUmcdCI5x/FhM0c1zaVc6LXtWlPdvWeLrTtKtmw1pM6apz/Iheyx3Hd\nDAcqm9doX7jdDlagZaD5MisU4466+j6MXXZKjBKSZa+xRkfaFPLK1Ky8jwhBu7AzB0GAlxyd5lXX\nj55jfC5gIsESQnwYeBvwPuLQ4bcA1479pavoh7ZqdnoL/2zR5f13H+fvPa9/h03vLK/IdB2s0C6Q\nod2bN/Fb8WyEs7PW+D9/7Ql++7tfMvJ+wxDxxQxYI8/UmDDHvUA17/CSo1N8/OFV5Lf8Ktz8ZhCC\nS7U280Ms2jUCw8EaId3oYuUUp+UC+wdyjixXmxKoHJTIIxCjCVYln2GVApEUuGNyccYhsPK4A/Il\ny9vgiUi5NV34Cpa/wQa5rlQnMDM4URtPDzePGoKtHMSTJvbaE0PvNoI2ncQcVTXncEHMcHShXw7q\n5+apBv2Fph11Rlt4m9qee/P30FYdrExpxBB+0WFRVhD1RAcjinCkR6Q6WIZySJOtIbuGatdPDiNY\nGZeWdDabiiTgLZ/hopxiX3n4oqaloFqWo0M1h2Wl6YK/R7Di9xuNCIEFlSGm5ZUTCJZ2yvPqqkBJ\nPD5pMDEMIujQGeEC6Sbml0Y59qWBaQg8LETYISM7XTes7tC2KiTrSzGZev8b7+SRf3MvaxQx25u/\n22EbWUlYag6uLrPkEqQgsnOxtX7S6ODig5w2jyBMh5rM4W/0TC6CxgrrssAKxVj6k5j1K7UvcFGM\nn/sZhN11r+xtCKwtxn+zP7CRUFt8ilOdAk9mb4S3/gYnOUC9eCTV6+hjviaH56wl8dqb5nnBNRWC\nSPZtWIVWtkvwRRiTcNvcWicmDULMPinZuFlSgMPTOR4NVLj85Ufxa5dZliWmCxPmFFUx2Rk3d6kh\nJfLMfdwXnOC2Q+PJhNBzMUEr7no1FuG+X4KvfBQrbPc5OqaFo1wcjQE5Y+Q1CKRBPtt/TQqEHbsv\nKrKR1mhpEEFWqVMaI6zGw4CSrOFnJs9hR2M6WKK9zrrM9+oe9X51Jtso8xoNx4pVNTLo9CJGck9v\nBwtgMX9d3BFSplRArN44/yUe4hgnFopDu5m+4WCqWqXdiI/PfKnCmiwgmytIdX203N2VQUpXK0DW\nCZrrccdbb8iYNkZxgdlrdiYPBPiHdx7mJ7715skPfBYjTQfrTinldwKrUsofA14K7PzT/TqCdhF0\nEhlXQgh+4BtPcLDaf9HTBKtmlFLZuEo7F5+8eldLSx12SLAKrjXSiU8jUARrXV55ggVw700LPLHY\n4PHL8d/shxFL9c74DpZwR2cwAUiJu3GaM3J+UwdPz8zonXIRBmM7WNWcw4osqcDdyUGgwxDYBTID\nFs5usMFD8jAb5ODCl7H9Om0j3y2GQyVP8Vp6CHb49+g6LmfkAu7ayaH3x9kzve81Y5v88jtu5z0v\nGyjoivvJ0SZIzJDYsjOyINLmDOGQ7omvisz8CJezqZzDJarYzcTOvraNVkWNo1yoOvUhundFnuSQ\nuRUhBE2y4+VC609xTs6yMCQDC3qmELqo0dKgYTlYulvdJVhKIigyo3fdw8T3MUkyk1ERC0FDfQ6J\nDlbvtUd0sMIO3ggXyL7u4ohjKw20q5itBqp1nov+HvX36qmMnbmFaxBCUDfLQyU32kxoFMGylTtd\nS2T7Cpou2dZGB1LCpYf4aniQW68psyoLRM3eBkLUXGGNAkuyhCH9nvNl4FHyF7lobI1gOWpDwEvE\nAzRX4w2E2sCsYWftAk/5JT78FyeRx1/Da72f7TprTn6hnkQw74y/Hgkh+OC9cRTmfOJYD8yerbkI\n4mNkK926tOiTkoWBmiUdfV2/5WCFJhkauQOw+AiyvsgyZaZHmB1pGGpGR9t6j8XySURjkc+GN3Db\noUmdMX0Mt+Dk/4k3la55CXzsfRSClW0RLCEELSOLGQzMi3ktmrhkB8KmAxEX6IGKFdmu4YPMq03g\nwW6qhjJwkBMcGyGewRJyeA6W6fUX85qkRs34XDcm5Hi5llLk4B0AACAASURBVKFyLjvIToNQCrK7\n7HC5HVyuqDGQs5/t3bj0GHgb/FXzECeGyAMhzlLTTpRttQFQKldYlQVEa7UrDTV3ec6sK/1r14ha\n69Rkrt/V9lv/E9z1L3f1NZ+rSEOw9PZ5UwixH/CBlFf0q4AewRq2iz0I28nyq8Eb+Gvn5emeXBMp\nvaulCkiRUpe/E/hCESwKTD8NBOu1Ny0gBPzxgxeRUvIjf/AgkYSbD45e/CLTwZZjJIL1y9hhi7Ms\nsDBAMPV8nC7kROSP7WBVczYrskRrB4PgkVPAwe+TF2TCDdZlngfDw4Tnv4QbbvQZlUgVdut33aOG\nLzJZx+QJuY9sbXgHy4zaeAOFwOtu2beJXDhT8VzU4vnTvdvGSXoUMfD9zeYaQWudtrQpF4cXA5Zp\nsG5OkW0nZgK6drXx3+kW453U5rrqOlx+BD79k/GuoZb/jViUmiK7yVQkCad+jvNMs3+IpAN6WTR6\n8dMSQXsIGXKUk6i+PugOlsiONoxJhguPCtTVyOkMO5XhlCRkjh2TDTmig2UE7e75vel5i73za1LR\nMwkBFq6y1JfqOBUDxgNy4yI1mWOqEv89LatCxt8s/4wmzGDZyp2uY/SfD90ZBn0NXTsLnXW+2DnI\nNxyZZpUiIpGDJVprrMoCy1J9vlomWDuHQcRGZv8WPgFw1PcUtnoEy2nHz+mvDUhhW4ssUuGjX3iK\np1ZbBJHsmttMhDo/WubwHfNBvPTYND/7llv5jm/oCVYiOxvbmgPGwAbMbiIuxNVGmB70H0Pmr5sr\n4FoG5+xr4fKjmM1FlmSZmQkdLG2C4KUhWGc+A8Dno8kdLH3N9Vp1ePxTcO1L4a2/AdkKjhxtOT8J\nbZHrWWhr+E1aZDZJFkPDwYx8QqVk2O4csHZuHeUk6NXi28UY23qNSJgYIzpYtreuQtbj41koNUCk\nNoiMEXmTGo6pwpXDDtKPg8uzEzYSrgTcyj7OsK+fYCmDi/s7h7lxBMEKDberttHZjNVytds595VT\nrbPLJNJUBEu217oGWn2S4qOvgtmrPZY0SFP1fVwIUQF+BngAOA389l6+qeccAjWH4UxejFzH5N8H\nb+dSPqUFpqutW1UXR2l1jcyVIFjx37MuC6nkjLuNuVKG2w9V+cSDF/mJP3yE3/3bJ4fKLpMIDXc8\nwVIOgrXsNZsC8LrGAFoiKAPCcRLBnMMyJeXEt705Bdkl0Krol5JcVKcmCjwoDyMuPUgmbOAlsnC6\nw8GqGzRKGnKgkuUJuZ984+zQDB4rbOOnKAQKKmx45eLp7m023khzDd3B8jubv4eotc4GuZHulQBN\ne4Z8kOhg6MJYdSJy5XgntbMRL8y1T/4U/MW/I/r8R0CFNhsj5lbaRg5rcIdYI/TJdha5IGapjpiP\ntLryxJXu70RSYA3pYDqOHc/oafKsZrDM/PgNAg0xYcMmXygRSYGh5HQi0UXT80+jJIJGNDrHrFAo\nE0mh3sP2TS4gzvXKBEqepYhVl2Ap4iyaiyzKcncTx3MrFML1TTb82hFxFMGKg07NzRb9SmKjZ1l0\nwPAj0SFecE2FdYqYnd7xZnVWWadA09IEVhGstTgLLihubT5B59eFrZ5MLeurKIZkUSslOW+JFVEh\nCCN+4VOxjfskuZ+GoQrWTkpbd4A3v/Ag1yTc8qSdjzO7oggj8vBHRDHsFIEwEdoER3d4x5B5yzS4\naX+JR4IDsPwYbvsyS7LE9IgcRw1bOW3645xDNc7cx4ZZpVE4zP4RHWwNQx3D3uW/i63jj90DxXl4\n628QYNG0J3TARqBtFnAHrk+G3+yTcmuEhoMlva5cbrtzwJlqvJ/urw/Pwqorwxm7OJlghcKKu76D\nkBI3qFM3Cl3ljg4J9hXBMid0y4UQqoPlgd+kjdPNlXw6MVd0+VxwPfLs/T0Z8rkHCKw8T8j9Qx0E\nQROs+PrsKXXI9NQ0a7KA7a3hq81CO7u7HSxLrT+d+lrPQGuMKc5VjMZEgiWl/HEp5ZqU8veIZ69u\nkFL+q71/a88hRD6+NLGHZOEMQttWjiswk+gummr3U8+wWFeAYAVqh7tllbbsiLRbuPemBR6+UOPX\n/voU77rzMN//98bvrEzsYCmC5ZcPb7rLUS5MuoNlRB7hiF1+gFLG4r9Er+ff+P9ga7bNCYjBjBy/\niUVIrjjFg9FhjLCDRUCQDBtVUqtQhaOa7vBuS8Y2WctdiykDWDuz6f54jmpyh2J632EA6lrOFPpY\nRCMJljYhCIZZhLdr1GRuLGH3MlOxTEl3o3QhrohksVQllCJemDt13JN/QigF0Z/9GP6lrwFgZoef\nHx0j15WsbULtPAYRzcy+kbKoTHmWSAqEMimQUYCPiT3EjtY2lFGMOp5ka42WdMjnRy+YW+pgORYN\nMtgdJT1MSgQTDn7DYIadkcVzMWtTR8277UAiCPE1RBMsXZTq3X89qG63llgW1W7hFWSqsc3/gJRT\nZ47ZQ5wPoedO51v93VH9uh21OcXFB5EIHpWHOD4XEynX63XMHL9G3ShildRGjjakUARLVIc7TI6C\nJsKRPsfDgHyo4goaCYLVqcXBwtk5XnfLfn7/gdj8I20HS68VnpXOFGMYunJKv6lmifamgxXRkwj6\n6jgYOUuqcMvBCp/dmIXQI+uvUTMrE7OmbDWnGLZSmFycuY8vcCO3HZqaKIvUZjfi0Y8DEBx5dXzH\nNXfwfTP/hU+W3zz59YagYxVww/73agRNOmLzeRgpQ6dIXR+d7PYkgsXpBUIpaK6cG3p/Q4UQZyqT\npbGRMIdLBL06BmHfsakJVthIN4MF8fVEBB2EH8smt7vu7iZeeLjK5+WJOAtrWWXbnX+AC7kTCMMc\nKREMzQy2jNeGQB2fuWKJplkmE9QIWxt40iQ7LAx+B3CUBLxTX8H04vnucaY4VzEaaUwuTCHEG4QQ\n7wf+KfBdQogf2Pu39hxCGNt5DyuyBqEvCKWUHSHtBNeux4tzW7WSzRFBrbsJ7RI3zlZ6r3Hv8xdw\nLIM33XaAH/3m501c+KTpjsxgAmDlCQIM7OnNPi6Drm9GFBCJ0Qu4EIInMyf4ZPTibV/odSfS00PY\nqstRqs7wuHm0+7goSbBUgRqp7J5xw81ySlnaDwk1taM2YQqXuNkDh+P3qK3TlXvhKEmPLvS7BOuR\nj8PvvBN+5Rs4uvQpNkR+rKQyyqmdUk1iVAdLF5DVvEuNPGFzlejRP8KVbT4YfA9R4GP+Wbw3ZI2w\nmO2Y+W70wCYoy2xvRMgwwHQpzzLFrpxGhAEh5lA3PsNQXSS9S1lfZn2CYYxMGFVMmsEyDEGTDI4f\nXxOShhTaBVSM7GB5I4vngmtRIz6mdkywsOJQaMBQnaQuEVCEJ+MtU09YQEdZNUzfXCYJ3cGyx7yn\nj/Iavlh4Zd9t2pika3Rw6UFW3QMEZo5rpnJ4ToVs0JuzcqMmvlNGFPRxGJ9nwcppQinIjbDwH4V8\nJiasQndR1N8VSYHbSZjHqBmYIDvLP37VUSLVwEtLsEzVDQ+c7V+vdXfRb9cxI2/bUrdJiDsd8XW6\no2zGJx1rtxws81W/d2523MmmCxnlMheOcQ4F4oDx9bN8un3d5PkrevNO7uN/TJSf52W/fomf+2S8\nuXNGzmJt03AitItkowFX2bC1ScoNvQ6WVDEcdsosxkHMlvIsU8Yb0cHylMNmrrow8bmixPfah0sP\nAeAnjk19XmqzImuCRBDAJzb2MIJmnwPu04kXXlvlQfN58f+c/WysWLj4VT7XOcwLD1VHkpfIzOCo\nzWDtIpjNV2k5FQwi2LhIC3fXu3QZ1VH3m+uxgZbMpe6SX0U/0lR9/xt4FzANFBM/V5EWoYc/JpA2\nCa2jTmPRDj2CpTXkWqubdPraKwRKQhS4e/9ao3DNVI77f/ge/sNbb03VResSrCEZTADR8knOyRn2\nTW0+xO0uwfJAShb8s6yZ4xfxSi7OE9vuhV4TZd2ZlMoIwcxVsGav70pDZELy1u0AKHtba0QHCyB7\nIJaihotf23SfIzvdbKlxsDMFahQQG2qHc4KkRxODwFOP+/Ofiucbpo/z1zNv4+et7xr7eRkFtVOq\nCJav5LG6QK/kHNZlHtlao/a3v8N5OcX9hW/kF8Jvw1ByLmfEBkRg5TflynWxcir+b/XwyPeWd0xW\nqGBq2Vjkjw2j9rEQ6vPy6ysTDWNkoqA17cnXiLbIxhbB9GdxdcndCIJlRZ0+Q42++0wjNgOhN5e4\nXYSGjaN2afX3p69p2nig6C/TdHrnmaGG6aP6AMFSf4s9Ror9K+IdPF59Rd9teki806jH14Wzn+Ux\n6zqOzORjWWGmSla24uNanX+RW8Et9hOs9uJpLjDN/JBrxzjknZiwCiVf1cf1KblA3lvuyYq0XLAw\nz037y7z6RPz6hZTFjyzMxp2I7A5GqFXR227U4mNkTwlW3JHUOU6TO1hlTsr9SOJzLY2rnZvNx93D\nSTbt6vr4cHTtxPkrAEcRLKt5iYeyL+LShscvffpxvnBmhZYXpjKwGobILeLi9c3kWomQ7iSk6WJL\nH+m3CKUYGhqeBrNFl0VZRm4MJ1jBxiKRFJSnJmSOoWewBtbex/4UfvPbWDZn+Fr+ju7NWvom9JqX\nYjMnEA5G6CkH3J1dm3YLrmWy78hNrIgynL0fLj8EocenNw5y1w2ju36R5XYJluzU482bfJ5AufyZ\nG+do4U7s0m4VhVyOpnQJGqs4QZ2Wmd80LnEV6ZDmUzsopXyTlPJfSyl/TP+keXIhxL1CiK8JIR4X\nQvzQkPvfKYT4ihDiq0KI+4QQt275L3gWQEQePukIkyZYaWeadM6DpySCOpTvShCsSO1wS3fygrOX\nmMpvwclKO1ENsQgHCJZOciaaH2pi4CiJQhR04NwDzISLPFwcb0ZSzTnbXkyhFxbbVhk5OifKzFU5\nvlDhaxyOH5joyOhd5lB1sOwx7lEH9x1gSZZonn90032O7IyU+Q1iyZoj14w7WNpBT1jDF3RDfQeh\n34nnaFZOwa3vgLf/d36r9F2cy9809rWscryQd9SOqteMiyO9a1zJ2ayTJ1s/S/GpP+cPozv5qTff\nyn/y7uVC5lj8txVGECw7T3YEwZIrp/CliTMzOqVCCMG6WcXVJhxRGGe/jDg+fWEjVKBq1FxljcJ4\nR85EB2vUrFESbSNLXneIBjpeARZGONxR04o8wjG5Q21lFLFjgpWYYdTfnz5e/XYd/BY52cTL9BzK\n7GL87+b6wNB96NGRFrY1+nxzLJOi239t1YWc16rFO8yNRT4RvJDjc/HtUhfqzZWuEQnZKuVSgZrM\ndQlRtHom3pyZMJ8zCNOI3SsNJXmU6vkekYcwCbsubZpgmaWYIH3/a67n0FSOY7PpZjCM0j7u8n6O\nU1OvnPzgUe9VkeB2cwNbjj9GdoIoMYPlt+MOzKQC++hMAdPJseIok5EUpgs516ZBBjkpaFjJPy+K\nOW4+MHltTc7F/MblY7zmxjn2l7P8s//xZdZbwfbXBL2R1unNjDlRa+hGWGS62Pjgx4HxmW0W4nMl\nl0VZwRhh0y7ri6xQZLo4mcBt6mB9/iPwW2+DqaN8f+k/EBR65F/XNlYnXvvsFFEngWFjSA8rbG0r\na2yv8IrrZ/lccD3BqfvgXGxw8WV5lLvHECzMDC5xnRJ1NmiQoZhxiNzYBMlpnKcpXXK7bORRysQb\nPrK5ghO18K2r/ZTtIg3B+oQQ4hu3+sRCCBP4FeCbgOcB7xBCPG/gYaeAV0kpbwZ+HPjPW32dZwNE\n6Me5HingWiZTeYcjM+na+XonXmt0A0WwtEXzXiJU8yAi//QSrK1A6oIg2Oxgh5QYq3EG1rDgP8c2\n6ch4Zqb2xd/Dlybu87957OtVVQdru7BVCKkecm2pPB47X+XEQoEv+rEcKZmq3g2UVMWgPcY96vhc\ngSfkPoLLf9d/h5Rx9kxKgrWR2U/FiwlPR7lWGc4IgpWcwapfBr+BrB7hw39xkk8+fGnk0K9GRhGs\nxkrssOa1+gmWbRo0jAL7Nh7ElCEn51/Ly4/PcHyhynsb/4RfDt6IUxouZwntAjlaQ+/rXH6Mc3KG\nhcr4grbpTJPz46JYROPPfT8p02uvUZsUeZCwqp40gwXQMXMUUB2+AUIWk7vhHSxbdiYQLLVDP6Gr\nMAlRYobRUnNxjipOg1YjPj6AKNcrRNxyXDg31wYKvtDDxxobAPvjb7yJd7/8cN9tOocqaNfh4Y8h\nrQz/X+15HFMEyyjEBEs2l7rnlJmfZqbgsiRLhPWYEFkbT/LUGAv/cWgZOSw1+9dRMy2PRIrIq85B\nRzkKusp04NZrKvzlB+6aGKehkbENzsp5irkdZJd1bc1rKophrwhWrxD3lcTNmFBgG4bg+QfKnCQ2\nGTGKk2eCco5JE7dnlDMK608SYDK1cChVx8BVx3CE4FP+8/jAvTfwM2+5hdPLTZbqHbLONiXjXYe3\nRCRG1O5myPXBdLAJEMrwYbukbjrvskgFt7009H6jtZw6tzMSJgaqg7V8Ev7wB+DY3fDuT3C6U+4b\njXDVzJiWOKeRCIbCwQw91dXb3dmkneAV183yt9EJrNoZ+Non2DBKyNIhrp8fM29rZ7AJIQoRXp0G\nWfKuiczFBMttxhLB/C53sIoZmw2Zw1KKlL757qvYEtKc5fcDfyCEaAkhakKIDSFECssd7gAel1I+\nIaX0gN8B3ph8gJTyPimltme6Hzi4lTf/bIGIAvwxbnNJmIbgLz9wF++4I52O38nr4iDepQ6VVjd3\nBQiWXlzt/Ghb6WccxgWsNlew/A3OyHkOVjdfnF3LoKNkVeKRj/HZ6HnccePRzc+TwAuuqaTa8RyF\njPp+fTUf0lbOeG6hyvXzRb4SxR0Zke/t8Gvtuql2vsfZuB6bK3Ay2o+7PpCFFXqYRMgRXahBdPIH\nmY8uEYVRN39rlK2u7ryEgdc1FfnwVyU//YlHed3N+/j333bL2NfKT8UFZmdNzaSoY99JWK+3zXhR\nOBnt49jNL0MIwVtedA0P+fv52eBtFEYN7ToFMnhDXRWjlVOckfPsK4//TDx3hnK4ClIiooBgzJye\nr8JAAczOOuvkxzufJTpYaVxJfSOPSTysM6yDpbtn+K0umQGwpUc0ZgfYU0505jYDtDXChGmHJlba\n2S1o14nU3JFIOJTl1DB9p5aw6ie2xPexukZBw/BNN+/jhoV+Ap/Ja0K3AY98jPrBV1GXGY7NqgJP\ndcxa64tESnbrFqeZKTisUMKvXYbAI9O6zFNydssdLIgJq63iAXQG1hPG4fhOFardXj1PR9qUq5Pz\nhoYhqwrsnQys600Mv1mPj5EUM5rbQXIGK1ASwTRzS7ccLPNAO96AccuTJWs5x6QhM4gJBCtaPcsF\nOc2t106WHUKPYH01OsJLn3+C6+eL3HlshnfdeRjofRdbhZ4dbW70XC3dqE005Dot1WaM5ddo46aa\nAR8G0xDU7WlySblqAnZnhZpZSSfRF1ZsqgS9ud9X/xC4BWptv+/YdNR1QJvg2CkIVmA4mNLDjtoE\ne3RsbgfHZvOczqlQ3cf/lC+FR7nrxrnxyhv1nUq/hfAbtMhgmQZmPj4GDRnsiURQd7BcpUiJhmRG\nXkU6pDnjfo44XDgnpSxJKYtSyjRTsgeAZEriU+q2Ufgu4BPD7hBCfI8Q4m+FEH+7uLg47CHPaIjI\nGxtIO4iCa6Wa1wLI5rULkgpQ7dRpSJdiShfCnUCqHXSnkG7ReSZAqI6MDl/sw2o8Y3NajpAIKmOA\n2fUHKTaf5K+dl3VlRKPwvXdfx0fe9eJtv19XDZwGSgLqqYU1U5zmhoUS/yu6k3/g/RBR9Vj3d/Qu\ns6WspZ0xEsFSxmbRvYacv9rNSwK6RhWT5h40ZOUQedFhZekCvupgmfbwgkjPAoV+p0uwfvekxQ++\n9gS/9I7bJi4YU+UiNZkjUPkrfku5zSVkOdp45WPhndytbPu/9bYD2GZ8Xo2aW9EBxEFrYA9JSux1\nFUA9IgNLI8jNxnMSnQ2IxodRh8LG0LND3jprMk81P6YATnQMzAkmFwBBYmd78PHJ1+Z//hP4tXu6\n9znS63V7hz6vIkMjupRpkexguaqT5Obi5w47dZqrcddGy+IAiuVpAmkQbPTvqAvVwdpqIemqQq54\n/jOwcYHHZ14D0D23M6pjVl+9TLO2pG6bYTrvsixLsaRPZWAtWwvbkux0zAJOGB/HXu0SnjSJZmID\nmrAWEyx//QKLlJlN2bEahO5g7GRgXSsm/FaNDF63iN9tSGFiKLe5QNmMj5sl1bjlYIX/17ubD/nf\nSa6ShmBZNMkgghFzlwrtxVM8Gc3wgmvS2atni2UCafDn0a28757j3ds/eO8NvPL6WV50eHubkray\n0G7WegQrQ7sb0t0HvT77G3TYWT3Qycz0y1UTyHorNKx0n4s0rO73qk2DKB8kiiS1lt/XBctlXDrS\n6pp6pJEIxs6JfkywnkEdLCEEc9ffQYv4fHkgPDJeHggIJYnttJuYfoOWiP8eK1Fv7YlEMBt3sPKt\n+LojR0SaXMVkpFmJngQelHIgdGQXIYS4i5hgfXDY/VLK/yylfJGU8kWzs5N11c80xIG0e2Nzmc1k\n8aXZ05B7GzTIpg+f3AF0Bytb3t6O6tMCRbB8b8iCqor9Vffg0IuWNgbYv/EVQgStY9+05y5FOTVL\nF6ouTdCMB37z5SnmSy65TIa/im6hmNz5U4Qq46/FTmSZ8QuTV1HkbPnx7m1SWfuSkmA5M4cBWD3/\nOL52/RpREGmJYKQIVoDB7bfcwj+963iqz3M677CkC1t6DmBurrcQeE684H+hdDdHldx2Ku90M9KK\nI84Pochps77ef0drFVt1NyfJd7U0ya9dRETBWIlgIGzMyIPQx4latMwi7pgZoqQTYJoZrNDuvVdz\nIDcr0EYCF78KD/1BPGeiXCrtCcVzqBzprB12sKJkB0sRq4ybJZAGYadJcyXeRc0mHMqmChlWKXS7\nSRpCdbDSbk5pZPIxwZq98H/AsLnfejFCxDM9AHnVMWutLdJcX1K3zTJTdFmWRYzWcndGp50ft4c4\nGr6VJ6MKyXBjkWXKzKr4g5a2x964xKKsTAzPHYW8OuYrO5AI6k2MSB0n7BHBSkoEQyURTNvBOscs\n/y28l5ni5PdmGoKWyGD64wmWWI/ln4Pdz1HIFcp8u/cveeL69/b9TtYx+Y333MFrb5rsuDcMjpLj\ndztYUpKVna5zbB/UWuf4Nbwd5pWFWqI7JGw4H6zRttONCUTCwuwSrKeQpsMyZepeQCTpy1vKOVaX\nkAC4KTpYkXJOdGWbMKX64krhZScWeCCMyfaj4jh3HhtfN+nNzU6rgRk08RRhzBarcX4i7ImLoGsZ\n1EUOU5nMiBGOu1cxGWkI1hPAnwshflgI8QP6J8XvnQOSiYsH1W19EELcAvwa8EYp5fLg/c8FGJE/\nNpB2Jyhk4iFdnQkkOnUaMnNFAvZ0BytfefYQLD2Hogen+7DyBBECWR1tYqDNSv4mvJHbbjg+8nG7\nhWLOpSFdpPp+o9YaDelSzucQQnQzNJK70roIygbrarh5/LFnz8VOgnKpN4fltXR4bzo74cJ8TNLq\nl57odrCsEbNfVsIsxF96nKeiWY7vSz/HN11wWKKM2YqL3dBrEkrRlwfypZk38P3eP+HETbf3kbbv\nu+d63nXnYWYKI8JoM3Ex1Kqv9t+hyPdG9uDkbJ1y3G3ZWDqHkP5YK//QcGKSowrWwBm/E5wkWNaE\noGGAqI9g9f/NgbAxIh/+/Kd7Ny7HUlFX+sgxEhvd6bNTdBXGvj9FsDrSJp+N318+Y3fnYjw1j1So\n9jpY1bzDqizGuTJ9TxZ3sLaKXCZDR9qYkQ/H7uaR1TiEW3/PpSklSdxYpFNbIpAGlcoU0/m4OLTb\nK7B6On4L5a2FDGsEVqFnrtJYZFmWOHZglprM0tZdvOYil2WF2RTEYRgOT+f46TfdzL3P315xDz3j\nAanIrUzhMrodREZPShaqYHc7hQveoaleht6kkGGNjshijYpmAAg6ZNqXOccMR2fTzUaXMjZvffPb\n+Rff8qJUj0+LXCm+Tnrq+hR6LQwhh16nhSK/mWDnBMsoqmNmkGCFPkW5QZBJVwNIw4w7YUBj8TRn\n/Cov/In/wx0/8WdAv3w165hdgtWRNk4KWWVMsHxc2XnmEaxjM3xO3kgoBe61L544E6djCbx2Ezts\n4KmA9ErOZZV4jW/tQdaXEIK22VODmFcJ1raR5ps5BXwKcNiaTfvngeuEEEeEEA7wduBjyQcIIQ4B\nvw/8Aynl3w15jucETLl3Hayca1Iny9TaV6G5guHXaRq5K5L/sJa9lqfkDOXys2cGS7fdA2+IycXK\nEyyKGeaqoy8ovvoePxG9mJcf33timXctGmR7rlGtNWrkuzt918/Hp2KyY6kDJXOySRubzJiOCMD0\nNdfjSZNGwkmwrQiWmVICNnMwJpv+0umu/HKUfEwX+pHv4S+e5Iyc53hKJzSIdzbXRBmnrYq8ToMm\nGfIJkhlVruUPoldwz4AM48RCkQ+94aaR8wLaFr/TGJAIaov26fEzdwAZ1W2pL59HROHYzZVQqMJe\nmSfIzPjFzEh0DCxnctEknV5BaA1IBCPDZsE7C49+nPaJb41vXH4cGYU4IhiZYwZwqXo7fx3e1DVh\n2S50cHIDl7zaCMipwkp6TcKNS6zKAlPl3vGRd0zWKGG2+wmWEXoE29jIyrvK6ABoHn8d951c7us6\nzFRKbMgsYWOZsLHCOnlmihlmCrFE0CCCC18mxMCd3h7BipwCGTpxSHdriWVZ4sRCkUVZIVISQbe9\nyBLl1MRhEEII3n7HoR2pG7KqS2y04nNP7DAHbRSk6EnJdAfLHjNLqiGE4JaD8TGZttPXMbJY4ySC\n608hkDRz+7dkFPHmFx5MbUCSFvlSvNb6SsnQUtcp4Wwmfvq7yYYb+Dt01HMq8TVNz0R2oWTlUS7d\nmIA07C5x9lfOcl5O896XH+EddxzijS/Yz8uv662p/h9kQQAAIABJREFUOcekKRXBmmBeoxFZDq7s\n4OB3Z5ieKajmHT638A7e4v1rXvj8GyY+XptEddpNnLDVDUgv52zWZLzu+0ZmT2o9L+EcaOXTyT+v\nYjPGXmmVE2BRSvnPt/rEUspACPG9wJ8AJvBfpZQPCSH+sbr/w8CPEudr/ao6SAIp5e5u+TwDYETB\nthb+NHBMg18O38RP1P8rfPjlzLRCTokr01F6eP71fP/fPZ/7RnQDnokwrNEESy6f5NQIi3aNQNgg\n4bGpVzO3y4vnMORsk8XEELborLMu88ypnb5eBysxy5LrXRzbOFTN8RfgY/MVzsp5KhceRZexuoNl\njHEgTKJSnWZN5jHWzxLOxUWmNaIgshUxkEEba+00p+VLeMWEWbZBNOwpsn5MCKXXUFKJ3jl2y8Ey\n180Vtjzr4HQJ1lrf7XLlJALIzR8b8lv9KE7HMrH26gUMGRCOm8EyHMxgtZuvJLLjO3nGFjtYwk0s\nlAOELBQ2+WgDMmXet/Y2Psz/xFh6DL/Tiic2xjhIrs2/hH/5pSpfTiFTHAfdwWqS6XaMso7JsnTB\nb4C/xqIsM50oloUQNMwSrtfvIiginyBlHEYSGctklQwl0eInnzjKanOD77vnuu791ZzDJVlANJeJ\nRMSaLDBTiIfLG2ZcfETnHuCCnGK+sr2B8O4geWcDt7PMunE9t1VzPCWrlBqXIPTJBWvU7ZktSyB3\nE1pOaShyu1cEa9Wa40XRX8Clh5FqHtRNeS26+UCZv3psaWSXehC+mcWJhjuHAt1ZIWOMsuFKoaA6\nWIFylW02NijQy5BLQnewslGdYIfnaW469h9rLJ/r210PNi5hASKFJT7Es3W6g2XVz3Oe6/meVx1l\nrrj5OLJNg7bqvHnYlFLMVkrD7brAypTqiyuJbzhxiF861+EXT0z+vLQk1m83yUXNrlNkJWuzor4F\nf486yL5dBOV/5OSuEqztYmzVL6UMhRAv2+6TSyn/CPijgds+nPj3e4H3bvf5ny2IJYJ7YzohhOCP\nrNdw5MRL+EeX/y1T/hN8zUrnQLhT6FmRsbbSzzDo3J4gOYMlJdz3i4hzf8uXwtePNTGoiRKfC2/g\nxusn70DtBgxD0BQ5HDVjZ3k1aiLfLbK++Zb9LG50ukQLIOtmCKSBJSK8FGn2x+cKPCD3c8dqz0lQ\nh/eabjpJjBCCy+YCmcZThEoiOMq9UOfZWK1FnGCDc2KBQ1NbWww7zhSFZg1CH/wmTelSSUj33nT7\nQd50+9ZNSW018zZocuEtnmRZTnHN3OSd2urMQmzCULuEIYOxEsHIsLGkn7D/Hk8Ik1brVoqiSSQK\nL3OQYKkcuydveA9/en/Ek84csxceJWw3cegZwgzD627eRxTJvpmJbUHJjFvSZVp3sGyTp8iQ8ZvY\n/hrnZJmjA3NDLbtCNugXPRiRn9qtte/3DMEqJc6Z1/KbX67xvruPc/PBXmfOsQzWjRJuaxXDiFim\nwBElQ/Oz09ABcelBzslj27JoB5CZXr5Rzl+lYU8xU3B5gArPa57tZm213adXjp3PuLSljdHSBGtv\nCrxPT7+dO2t/SPkP/xnSfSEw3qwniXe/7AjH5wpUU65LvpnD8UYTrGj1DAaQn5vcvd5rlAtxCGzU\nVq6yAxmASWjyaxIRDAki3gpmp6ZoSJf2yvk+glVfuUQFsEspCZZhxQQr9Mm1L3NR3MnMmI6sDgv2\nsLoGRWOfPxldsUMDnr3AP3rlUV553QwHq5PXOx23EnQaZGWrO09byTmclsr5dI8IVpiwZs8UrxKs\n7SLNavQlIcTHgI8CXS9TKeXv79m7eo7BkAGhSLc4bAcF1+Lji/Pc++Y/5sHf/hc8KE7w0j17tR6+\n5bb9TBd2FqR7pWGogqDbwYoi+OSPwP2/wvrRb+bnHn4zvzjEol3j3+V+gCeWWvzsdVfOObFt5Miq\njBw72KBl9oq/qbzDP/vGE32Pz7kWTVxKtPBSEPuZgsM58wDF5pdie3LTmjhHNQxr7j72dc6wrnac\nR+Vv6Q5WoRYTunbx2i0nxQfZWWgCzWWE36SFy/5dcFNydezBAMHyF5/grJznyMzkz2OmmGWZEtQv\nYUQB3piiX88MyNYqAnAL6QhWJAWOPblbY2YSHayBx7fMIjVR4kOXXoFjejwh91Fd7JmUjOtOHJ7J\n875El2e7kN0OlkvGjo8ByzTijmTQirs55uFNx4fnVCjU1+PNEbWBICKfcJtS7B+yP8C5OpyYL/K9\nd2+erWyaJQreGrYR0TRLPYlpfiYmWJG/bYt2AEN1GoP1Cziyg+dOkXVM1o0qWe+BbhZWmJ+c7bSX\nyNgGK2SwlUOpsUcdrI5T4cP2d/LBs7/C4Vw8up2ZYNajMVt0t7S5Epg5MmM6WBsXnyAvDeYOHEn9\nnHuFnGNymSxCSca9Zmx+ZGY2d7CSM5fhDi3LF8oZFmUFp9YvEWyuXqQCuOWUx6UmWBsXMIhoZfeN\ntXf3RAakygtMIYVLGvMIe2uqiCuBvGulVlWYar41aDfI0kF2CZbNqiJY0R4RLOnGNUZLOhRzz7xO\n4LMFaaqaDLAM3A28Xv2MT1e9ij6Y0u/Le9lt/NO7j/PY5Q3u+eUv8IH1t/Bg+VV79lpJHJ8r8u6X\nPf2LzlagXc/04DQfex/c/yvwDf+Y+2//GTxsDlRGX1BazjQ1o8wdR64cweqYOewg3ttwgw06E5LV\nM7ZJW82UpCFYQgiapaNYMoC1M0C8awbpO1gArdx+ZsNLXUlPJjv8d3V+U3EjJljW7GTZ3Sbo3K/6\nZYygRQs3lUZ/ErLKFl/HHmhY66c4Hc1zZGbyop11TJapYjaXEDIgGkewzJhgeXWVb1Yaf1zp7l9a\nO3I7QbCcgWL4D6rv4U3hT/KpU22+7zXXcZp95DZOTcwx201IlevVEf2zBJ6RwQya5P1lGvbmgiTM\nTGESQSJw1Yz8bUux6+4CNaPMz77l1qEujh27QjZYw/FrdKzefJZZ6BWWOyFY3dm/y7GTZ5hV2Vvu\nDE7U7pqPGMXJ1uN7CSEEbTJk/FjSmnZGc6u4fr7Ih2svZXXqBZSbZ2hLe6JZz3YR2jksAgiGh263\nFk9zkSmOzj/9O/lCCJoij6EIVkeFrNtDOlhJ8jsuNDwN9pUzXKaCUJlsGt5qLJ/Mppw9lIYdf9br\nT8XvqzjeddNXxNBPu3GS+DvFDg14nm50DYSayvtNqRHKWZtV1UeM9qiDjOqo18j1hT9fxdYwcYWW\nUr57yM97rsSbe67AlEFf3stu453fcC1/+YN38fY7rqETROyfEIT69QxLLTqh34az98OXfhNe9n/B\nvT/NubUOAPsro4ukqbzDHUemrogNvoZn5nGUw1g2rONNSFZ3LaM7tJ92uFnMXB//QzkJ9mR+6QlW\nVDpEBq+bAO9khxNVRxXuxcZpIiko79u6G6Ohgmej+iJm0KSzQwmMhiZYspMgWJ06mc4yTzE8gHoY\nNqwqmc4ShgyQYySC0nCwpU+7Fi+i+fIkgqW+V0ysFJIZK2FdPziD5TtlHvemmCk4vOdlR2jkD2NH\nbaRyTDT2avFOQskUOwOZNZ6RIeevkpFtOpnN8iOZU6RLFx+AIf1tX2ffdPtB/vXrn9cnDUwiyFQp\nhDVyYY3A7RXaOiML4Ck5w8I2r73aqStYVFEJhfh5vWxM4OSFLwNglfdt/uUrjLbIkAtiYmumsE7f\nDr77FUe55WCV7175dkJM2jhjA6R3gu6sjo46Gbx/7Qzn5Ew3ePrpRsvIY/oqtkOHrGc3rwlm4vzd\nqaPeVN5hmQp2ayDce+1J1mWOStrwa8OMg89VrIFVHT/O4KvrQtrZSpHoYA2bS3s2Qa+9USN2yzVU\nNEbGNtkw4uv60PyzXYChrkcbMrejYPKvd0y8YgkhDgoh/kAIcVn9/J4QYuvDDV/HsKTflcLsFeZK\nGf7tt9zMZ3/4Hv7V65+3p6/1bIYOq5ReO7anzs3Aqz4AQnB+rUXGNsbOlP3fb3sBv/ztt1+ptwtA\nYOXJhA2IInKyQeiMz2IRQtDRBCulPW/hQHzMNM8/AkConABHzVENgzUdD4Fn1h6nIy0yzvBj3rZs\nIimwIo/zTHN4YevdQEcFz7ZWL2CGLTyxO2SgkMvSkTaynSBYKoC6Ubg2dYht05km769gymCsi2Bk\nOtgEePUVNmSWqeL4z7tHsCxsY/J7cRIEyx7oSOmO3/e88ihZx8Sci0m2efmh+L9XoIOlbee9AYLl\nG1kqfrxbHuQ2EyxTdTDDRBaWEW1fKfD+e67jO196eOT9UWaKPE3yskmY6RmRTBVzrCi5zrK9sO2N\nF1sPkqtOlakIlizEHavwfEywctXtW6zvFjpGhoJUsz87zEEbBccy+KV33M6j8jC/6H8LX5LXj5WS\n7QRaeoXXGHp/tnGOy8b8M2bWuGMVcJRk3GvH7zmTGyIRTJy/4yIX0kAIQdOZJucNZM+tP8V5OcO+\nMZuSfVDnp78Ub+LkZscbh4RKApfahbnPZfXZLW3TrpnhRkxqjYQawbPV9WKPjDxMdT3aINcX/nwV\nW0OaauHXie3V96uf/61uu4qUsGRAZFyZjsds0b2i3ZVnG2x10S1e/Cw88Wl42feBsrg9t9biQCU7\nVus9U3Cv+EIb2gWysgXeBgYS6Uy2xtbDwWHKDtahg/u5LCs0zsXFdaSKja10sHJqCLzaPE1njD28\nY5vdvKIz0TzHt+ggCJCtxoVna+0idtjqSkl2CtcyqJNFJHeztUV7Nb0c1s/MUI5WMWQ4/tw3HWx8\ngsYy6+QnHlva2CLATFVwZvK9Y8W2+t/HVN5htujyHS+Ji5zKNTcCEF58MH6tKzEkrobSB4e1AzMb\nSwABhswdWUUloVvrOQnupVJAJMxHjFyPYE3nHVakmtsrbn/f0VXfk1Bk3lW22KbaSBAXv8KqLDBd\n3p5L4W4i2W00tzCjuVUcms7xk2+6mV8Iv433Gz+8Z6+jr/9DCVboU/KXaOUPXJHokzTwrQJuqHIR\n2zpkffNxYSUJ1hjDmrTwsnPkojr4vXk1t3Gey8Zs+i6Huha2L59kRRaYnx4/j6Q7b0FKkzCRNAEa\nMpf2bIKrCJZoxgRLy4gh7qgDiD0ikbYKtN6Q2Z0bGX0dIw3BmpVS/rqUMlA//w1IZxlzFQCYBHve\nwbqKdNAdrIUn/yjuXr34uwAII8np5eZYi/anC5FTiHM9lFSA7ORZAC0NDFJq74/PFngsOoBY/Fr8\nmspl0cmmX6Qq+2OpXyVaoYMz0vXJMgSeIlhnmePYFjKwuq9VnaEjLbz1S9hRe8dD3BrxjEMW0+8R\nrGhZ7bbOp5cyhrlZbAJK0TqMkQhiOrj4yNYa6zIFwXJ6Haw0yBTiwt2TFmKg4/XBe2/gj97/iq69\n/eEjx2hKF3tRdbD2sHjuokuw+l8rSEiarNLmuSO3FJOu5lpv6N6SXtf2fbdhFXpLXtLpcaboskyJ\nUArMyvYysABcJU3N1E4DkFedKre6P37NzlocMvwMiMQIEgRrVNbdbuENt+7nXXce5oZ947v2O0Gk\nO5LNpc131s5hECEqV8aZNw1Cu0g2islgqGZls5MI1i7IfaXe6Kj3NjWK3iXq7ha6qmZ8rYlWTnFe\nznBgguQ60gQr5XndlxM4Ygb42QK9uWmpzDk7QRjXctcSSINGbvwM23bhFuJzokbu6ob9DpCGYC0L\nIb5DCGGqn+8gNr24ipSw5FWC9UyBnZS03Pk+GtLlv33mFHf97J/zyIUatx0an0P0tEBpr4PVWLcu\nUhAsLbmKUhKPA5Usp8TB2HhCSvDbBNIgm01PXPbNzXTlUh2ckTu+QggCRRDWMtdsy4VyWhe2G5dx\novamAn0naIkcZtAjWK1Lj7Msi+zfl95gwCjGxUhVro0t+rWtsNVaTkew9AxhSjOHnOqMDCNkeddi\nttgrSG7aX+WUXGCqdTp+rSvQwRJW/NkMzogkZwt0NyeJXDX+fDu13kyIJYOu9fxuI1PqzZi4xd6/\np/Mul2SVc3KG+cr2d8xz+ZikOWGdmsxRLcUFc6kyQ0fGn9GiLPd9X08X/MS5lib8d6f40Btu4ne/\n5yV79vxeISbGwfLpTffVL8abK9nZw3v2+ltF5JbIy3gDTCqCpfPJkrAzvXNqN/LKrHJ8Hsq62tTo\nbFCINvAKWyjy1bXQrsUhw+MiUQAiJYFL6w6aNPZwnuUdLG0S5XqxY6eTUCO0Skd5fucj1Eonhv7u\nTpHLlwikQdvIb9nh9yp6SPPJvQd4K3ARuAC8GXj3Xr6p5xosgj3bWb2KrcF1bHxp0naqNF/wLv7+\nL/4VH/rfDzNbdPmP77y9L2D0mQIdFttaigmWnSJZPVDEKm1nxzAE64VjuFET1p9CKuvzrZCfnGNx\nQcSFrz9B0qEL/rByOPXzJzGdd1mSZURjEVe2uzudu4G2kcUKejlp/tLjyqI9/Y6okzAjGOciqGcG\n3PYSNVGYuFuoO1hByg5Wvqjka+O6aArlnM1F+yCWCgK1r4ALl1AEM7L7P1tNsCIput2cJMqlKTxp\nEmz0ug7mHm5kaUIHkEsYkcwWHX4meCvf679/2xlYAIWsTZ34816SpS7Rnim5LBIXVotUnhEEK0iQ\nX+dKdDlhT+V5QfEAkRQEy6c23bd07jEAqge2bsSzZ8iUyNIh9D2kr82INpOJ5AaJ2IVZnUwl7qbW\nl2IHwEg5CIrqFjq3qoOV6yxyXs5MPmc0wUq5cdJHsJ7lHSzXcfClST6IHTuTMtBK1qGNS26Pukul\nrM1fRTfzNfvGPXn+rxdM/HaklGeAN1yB9/KchU3QH4B3FU8bHMvgL6JbyF33Rj7zmYucWW7ykX/4\nIu658em1Px4HoSxTW4unKQLOhKwk6BGstB0sgHDmBJwFFr+G8Fu0cahu0fp81V4A/wm8CeYa2k7b\nndseoa3mbB6UJfY3L2AS9ZzAdgGemacYrHb/31o7w2l5hJdsgWBlpnoES46ZwdKuVzl/mbb1/ImF\npCY9ae3ILcuiId3UNsft0hFY/QwATuZKdLDi6+Lg96f/f4XiUOOPasFhlSIy4SJoSb9r+77bKCYI\nVqHS+/d03uVJOc+TzPPOnRAsx4oHymmyTIlrC/HnMlvIcFlWOCiWWKb6jBg4T25mPNuLWIBMJst5\npplaOb3pvsalU0RScPDaZw7BMjIx4a7XVlQGoEN2iOGNk1Br7EYgdHEm7lRtLMVhw2sXn2AKyM6M\nN6pIQiSuhRvu/ETTID1jFKUkWGaCYA0z/ng2wTAETRyKUezYmZynreTi60DO2ZsM0mLG5tv9D3LD\n9NM/8/lsxshVWgjxo2N+T0opf3wP3s9zD1JiE3Rb41fx9MK1DN7r/yDvtA7x0b88xZtuP/CMJlcA\nlrLg9VfiDpbWR4+Ddl+SW+jsZPffBGehc/FhCNq0cbYsD2hkD4A/uYOlOzDVg9dv6fk1LNNgw5qi\n1FEzY7tMsFwvtpon6JBvX+C8eCnzxfQFdGm6J5uRxuhFUHdwbOnjpzAv0SHNYcoOFkBLZFN3vKy5\n60FxS3sLGWjbRddWeXBYW/3/oiwzPWTuaCrvcEYWybRWurdZezjrWpnuddFK0z2CVc7amIYgjOS2\nLdoB8q7JBZkFAcuyzAtUB2u26PIlGZ/vTWf6GWG0kOw2uleAhO81co7Jk9Ec0+tnNt0Xrp7hElUO\nzjz9GVgaVk4RrPUV8Bu0yTDsW3ASHWixC+fy1Nx+Iilor54HYP1CTLDKC0dTP4dIbICkkhaqYy0t\nwTISURSZIXNpzzZ0hMM0McHKFXvrQ3mPCZY2trhq0b4zjKueGkN+AL4L+OAev6/nDqIg/u8e7axe\nxdagral/+2/O4lgGP/RNNzzN72gyLJVJocMZcxPCaCExw7IF96iDBw6yJEs0nnwII2jRSWnxnoRf\nip3UJuVvBcLisqxwZP9mh7i0aNnVOISV3ZHAaARWnozKHWPtLAJJs3Dtlmyip2bm8KRa/MZ0j5K5\nLZE7mWA5dlxopJ3BAmiLLGEKiSDA1DU9SciVKJ6N/5+9O4+TrK7v/f/61N779Mz07AMz7MsACogg\nbqgIejUar14litFcQ1A05nfVoAlxySV5GL0Sl+BCxAsaFbdEcLkaUWMUjTIgsiMDDDAbs8/0Wuv3\n98c5p7u6p6v6VPeprjrd7+fjMY/uPl1d9e2eU+ecz/l8vp+vPwfLMpPvNgd3rve6PpZ3H3lx1ZFO\ncpBeUmMTAVaaEq5Jc7B6e3oYcVnKzli2dGIOViJhLPODoTVzyGClkglG/KUGDieXjN/ZX9adYbfz\nLu6LHW3SW8p/rxVcklwm/pUZ3dkUT7oBkgePDLAyg9vYl1pFskkt4mcjKBEfPryfZHF0vGPsVNl0\nirzzjhPJCDJYq/u7vXmvh73lE/L7nqDokqxa00AGKzlx3LIlM3fdDBrtVEJWAAVzVMdcmo4ay4TE\nSZ6J37uzuyrA8jPZHbOYvxxGjx9YqYPg3NQMsJxzHwv+AdcBHXhzr24Cwt+yWKy2bYZKGcr+6vAK\nsNpCsFhlxcFfvOh4VjSQlWiVYC2j7MhOb05K78wZrCBz5RqY3Hz8ym62uLW4PQ/6a0s1HmBZ/wZg\n5vbwJcuw1a3kuIHZ32Us5iYCzSju0AbG2+IDPPR9ANyyxkqElnXn2OvPnXHJOiWCVW2FXYjmJZl0\nkrxLNxZgJTpDryNz1PFnjH+erbFQdJSCrl9T2w0Hi2ruY8m0d1HNjOFUH9mCNz+BUsGbO9ak46yZ\ncch6OEz3Eeu7BRm2uczBAhhNeH+DscxECXA6mWAw7e3nla42ybT7bc3zZGbVoKbdHDPQxRNuBZnR\n3ZNakAP0FXYx0rmmRSObXs6vYBgdPOAdp2sssp5JJcj7C/QmI5hPuaw7yx63BBv2ugi6Q0+yi6Ws\nWRq+FK86g5VbNnNglvTnloUuEfQ7J46QbVp2Zz4F7elHXJau3MS5YkmHt72rSXOwenLKYEWhbv2P\nmS01s6uBu/HKCc90zl3pnNtd7+cWve13wOdfCLd+AFcKAqz43+lbCMyMbCrBcSu6+eNnbWj1cELJ\n+neuevJPMUgHfZ0zBz5ByZw1UCJ49LIuHnFr6Tq8hWR5jEKNO6P1BN22yjO0h/9i7g1cn/qj8VKH\n2ShXLUCbyEQXYLlMNx3kYcdvcT+5mh9VziKx9qyGniOTSnDA/EC4zhysZFWAleiceW5dNpWgQKqh\nBXULyc7QJYUrVqxgnx8YzkcDg7Elx3FX5RiG+ycvjp70A+ah9NKamcOx9BI6Sl75DA9+F4Cd3ac2\nbazDyT6GEkfeEFjenaE7mxq/6ztb+YR3MVnMTs5Qj+S8wMp6Vx/xM60Q3MwYI022wTma7ejoZV3s\nTPjB68EnxrfnC3kGKnup9M2+/X4zdPR4x5X80EFS5VGKNQIs71jh7ZOpCG5AJRPG4dRSMqNe587s\n0Hb2JFaMV4WEYf5xq+CS9K2YOYMVjDvsHPagscco2QWxbxb8G5Uj5CZlUfubXCKYTiboyaXaZnHt\nuKo3B+ujwKvwslenOeeGaj1Wpti22fv4y09R7j/O+yMrwGobf/eHp3H6ur4ZJ9i2i2Bya9aNsdsN\nMBBionswqdkaaLWdTibY13kMufytLCvs4FCi8YVT+1YfC8ycwbq365l09s/t5JCoWpsoEWFLXpfx\nL6K/9kbKuaX85fCfctUs1uoaTPVDibrzL6vXbQkWd6wnk0wwTKp+Z8IpRjvXUs6HW1nDzNibWU9v\nfoh0qvl3L13XCl5ZuJq/65s8HyPprwEzmqldDlvILKEzP+hVCmz+AtvcAE8ubV477+HOdRRKg0ds\nP31dXyRzowqpLihDpWv5pO3397+Atx+qsGH57OYrRi2R9d4LBTINlc22q2TCcEuOhkHgwFYY8Fpf\nb3v8UY61Ctnl4RcYnw9dvd6NmOLIQTrKY5RqlP9lUwn2jQdY0dwsGcksp6v4W8C74bctd1pDPx+U\nCO5yS1nbP3PQlwoWug+5nmPaz2CNkW2L+YpzVfKrSEZt8v/x2RuW8p6LTuTcY2aeLjBbN7z5HNYv\njf8cy1aqd5Z+F5AHrgL+umpnNbwmF81b+S/mCk/eyVhiCZml68j+4C+9jQqw2sarz2o8cGil6trr\nQetifYiynKDkqtHuUaWlJ8BOWFrazUOZxjtnrRpYzja3nNF0/WDh6lduIpeeW4Cb6pkomYriDm3A\n/AtId+hJvrDhHzmwr5cTVzVeyjiSWQ4l6jZeSFRlsLI9M58sEwmjSIpKyJI/gDMuvx5cJfTjGTie\nwo5HmY/ikODu99Q7sYlO/y59Z+1FTMu5pSQHK94Nra0/56vl15JKNW/OwKbLb8QFc2qrvOeiaOZx\nFlPdkAfrnjzXqq+3j29XzuNDbdCiHSayxYUZGtnESfeqY/0Aa2Ie1o5H7+VYYNm69lq6o7vPC7DK\no4fIVEYppaYvHTUziv67OFi0dq6KHQP0jh2EcpH+yl4KXY2VTwbr3u1gOetmWGQYIB0sdJ8Kt68F\nXVZnU33RjoK5zPnE5L9VJpXgigua29nyrKPbcE3QmKk3ByvhnOtwzvU453qr/vUouKpv7InNbC5u\n4J3ld47PCbCQBwiRqbo7c4w470A7nAiXSTH/IijR4GKx2TUTpVqlkHcNq63syfK6wvv58co31X3c\nprV9HLdibl2eqhegTUW5qKTfVOTf+y/h7x9cwdsvOI5T1zR+yCvmvEyEJWsHxNUlgh19y2s+btLz\nkqZSp+xwqlSum1RH+PGf+N/fT9drrw/9+LlIJ70bd52Zyb9Ppf9Y/qTwbh4deEHtH+70A9KffwyX\nSPH10vPIppo37yLZ1U+qp3mNJsppbx9O90y+YA7WvmqHNbCgqqspC+ectmrNUYy6DPm9j4xvy2/9\nDQBrTz6vVcOaVjAHy40eIuvGxjvGTifqAIvuFaQpkd9xLykqJJY0Vj6Z9K+HtrtlrJlhkWGAjL+v\nhS0RDDon5muUTcZNeTzAmp/15iRa8aiRipOMaPdeAAAgAElEQVTCMN2HH+Uet5Ef7uzi20e9F4BS\ntn3avEq8dGdTDOOXPiTDBRJB96VkgwHWmrVHccD5E4sbWEMrkEomWH308Ry9pvnzRbqXTlyIZjqi\nC7AOrHke7ylexhU7L+aKC47lXS8+YVblJq7L75BYJ4NVvW5L95JwAdaddgqP5Zq4AOTSY+Cklzbv\n+aus7M1hBmunXGx1ZpL8pHImS7pr/78mglK6h3/IY8svYA9LeN4JbdJpbxaGc6vIuzSZ/slZgfYL\nsPwSwRnKgOPkxFW9POkGGHnq0fFtvft+x/bkepIhSnfnVTLNGBnIHybrxuquARisf5eJqGFNyl9A\nfdf9vwAg58+5Dc0PsA6kVh5xU2U6md4VfKF0MU8sfXaop8/kvOPpTF1s46Li3+Qs1gmipX0pwIra\nrntIUGFs2Wm86OQVvOu+Y3jW2CfZs/I5rR6ZxFQ2lWDIX+mkkAqX9Tm04hlcX3oJIwNPa+i1jl/Z\ny8POmw9T785oPd+4/Fn82fOOndXPNmJZbyf7/WAwG2GAtXRJP98oP5/Lnn8i737xibOu5bcgE1Gn\ns10qWNfKGUuWzNzkAuDv0u/g1iWvndWY2s2xA93cedWFbFo7uUV9V9bLRC2bpkV7IFNVUnnN/vM5\n75hlnLE+vjeyHljxEl5Y+Ci9UwLtY5Z3k0oY6/vb4y52Jucdg0oLqETwhJU9POFWeHOwgHyxxLH5\nB9jXf3prB1bDsHVh+UE6yM8QYGWoOIusYU3nUi/4H3nMy+41sgYWQNIv4R3rDHcDriOb5m9Lb2S4\nN9z5JONXBBQTCyMgCZpFlVLxX9B7MVKAFbHStjsB6Nh4Nv/7lZvoyqTYwXLSTZwbIAubmY1Pci2l\nw5V6ZTp7+d+lS8l0NHZgPmagiy0VL8BqZJHiVljWlWWf8zvedUYXYD33hAG+9+fP5j0XzT64gokS\nRqvbRdC743qYLvq7w2UMM6kEmVT8J3AH+qfpVNWR9v5m062BFcj2edmq/bn1fG/oOP7sefFePaQz\nl2ObW3FEUPnCk1fwiytfMOc28FHJ+gu4lhZIlgC8DOquxEo6h7eBczz6+/tZZodJrH9Gq4c2rbFE\nF+QP00H+iCUOqpUTGfKkyYXIFoXRs9w7N/Tu+x0AA+sbe8+VOldSdsbQknAZ+GBuZthOhdlMkjGX\nnvXNwXYTVJGUFWDFkgKsiB165Hb2uD5OOPZ4Vvd18L6XegeS4I6syGyMJbwDbCnEYrQwsQBhrsE5\nKZ2ZFLtzGwCoNLBIcSss686w1w+wOjrnNp+rWjJhnLpm7p3hgru9dTNYWe9vfIgu+jvDZQTOPKr/\niIzPQnPMQBevfNoazj+udtlk59I17HfdfGL4xZy4qi/W5YEAXZnpg0oza5vgCiDb5b3Xyk1a1LkV\nEgljrPsospURGNnP3ge9ErgVp7Rn5Uk+1U22cJCslcbn206nZBlGycy5oVBg2aqjAFhdfIIDroeB\n/nBZ90C5bwNPz19HcfWZoR7f35mhvzPN0cvCBRiZpLf2V2mBBFjOPwdXIlyGROaP0ioRS+y6i7sq\nx3Dm0d6B55Jz1rOuv4NzNjZ2IBKpVkh0QgXIhctgnbKml6cftYTjVzYeeBT6T4A9QJtnsLqzKQ5Y\nL2MuTUe2/S72ulYfz6dKr2TNiufWfEzQVnjIuietc1LPJy95eiTja2e5dJKPv67+77mkp4dz8p+m\nRIp/fN4xsW/LvKI3SzppDLT54ucd/sLnM611FzfJpRtgGDi4Fbbdzgg5Bo45Y4afao1iqpv+0Z2Q\ngETdDJY3X6snogWhly9dyojL0ml59iYH6G/wPZdKJjhM1xFzLmvJpZPc+TcXhn5vp5IJPlx+PUuW\nnsUFDY2sPQVVJC4TYRMnmTfKYEWpMEzf8FYezxzPil7vJGlmPPeEgQWx4r20TsEvEbBcuDkmq/s6\n+Le3nT+rifGpNZsouiTFjuatsREFM+PJzLE85lY1bUX7uThqWTc35t7AivW15w+k/TlYo8noMnCL\nRX9XhhIp1i7p4GWnN9Yuuh296sy1fPcdz6EvxDp3rdTRkSPvUuMT8BeK3jVe2+vBnVsYOHQPT+RO\nxOpkn1upnO5hpR0AIJmtffG9P7WCHW4gsjUf06kk+807Bw1lay+hUEtQ8nfU0vBzwhq9cXJz4kXs\n7Tll5gfGgPnrJNbLUkr7UoAVJb/BRXFVe971kvgKJrkmO5s/iX/12qO5uPBhHlt5cdNfa66+3/c6\nXlb4+6ataD8XPbk0m6+6kOccX7t0Le13eSyEnFsnE5Z3Z1jVm+PPX3hcbBYNryebSs5qvbX51plJ\nsocljKQXVlXGyqO8BYafevR3HFt+lOGB9s0Uu2wPfTYCVC3GO42v91/G5XZVpK99OOXdeMt3r53h\nkUc6dU0vn7v0LC44aUWkY6q2qjfH6pAZsrbnr2Np2fY/LsiR2u+2b9zs/B2s9gKqQ4/8hj6g75j2\nnBgr8VVOeyfRdFfzL2qOW9HNI24t2Vx7lyoBLO3O4SxJNuQk6HaT9udgFTPx7X7XKtlUkl+97wWx\nLw2Mm3QywetLH+Q5K47jxa0eTISOW7eKva6Xjoe/R9rKdGw8t9VDqi07cUMmXaeDaiqdHb9Ij8pY\n1ltA3RpcAwu8bNRFpzae+WrEv73tfHKZeJ4PpjK/CVIypwArjhbGXtgipfu+A597LpVfXgvA4GOb\n2e2WcNIJJ7Z4ZLLQuIx3gM10N/9C/MRVPfR1hJ9Y3ErLurN0ZlKxvcjOZL1yq1Kuvcsx21Vc/9/j\nrtSzlu6+Nlsfao5W9mbZYStZW9wKwLrT2rPBBUCiY6LJTb01ADOpROSLb5c6vYx8bvnRkT5vVPo6\n001dcHw+mR8cB4t7S7wogzUHPy6fQbl8Di/997/ClfNkd/+Oe9wxPHu1yn0kWpWMt0/lept/Id6b\nS/Pbv7mQRMimC630mrPWccLK+E4AzqSSvKHy1zx343N4UasHIxLSTZedy5LO9pyfNFtmxuHcGhh7\nmJ22gtUD61o9pJpSndUBVu2L7+edMMC6/ojL5bpXwh7oW7Ux2ueVIySz3v9dulPXlHGkAGsOLjr9\nKD624+MUbns3r/zxhxgA9vacH3rNBpGwtq66iKsePcDrljZeljEbcQiuAJ55zDKeeUx8sz9mxvsu\n/58ctaw9FpEVCWN9A00K4qTYexSMwc6eTYRbCrc10l0TlQzZOktUvO6coyJ/7aNOOYexx29g7bGn\nRf7cMlmwQHRHlwKsOFIkMEf/66JT+O1ZH+ZrpecDUFp3TmsHJAtSqncF/1K+sO07jEnjNq3tozen\n/1eRVssu9xbOLa0+u8UjqS/bNVGemeua3/KxVWe/ktxfP0G6e2E1OWlHJz3zIratfznHnKrryjhS\ngDVHZsYH/uB0fr3pA1yc/zDLTm//zmsSP+cft5yXnb66rRYcFRFZSFad8iwKLsWqp1/U6qHU1dk7\nEWClc/NcIm1Wd/F0iU66bxXr/ue/YHVa8Uv7Mudcq8fQkLPPPttt3ry51cM4QrFc4WcP7eGCk1aE\nXjBURERE2ocrFbBU+y1cXq305B2krn+B98WVj0OHupCKNIuZ3eGcazitrTlYEUknE7zolJWtHoaI\niIjMUrsHVwCp6vUQtQitSFtSiaCIiIhIXPjrYBVJqVxPpE0pwBIRERGJi5wXYOVNc3JF2pUCLBER\nEZG4SGUpWoZyKuI1rkQkMpqDJSIiIhIj6c4++nJ9Mz9QRFpCGSwRERGROMn2QloZLJF2pQyWiIiI\nSJzkeiGZbfUoRKQGBVgiIiIicXLOn0FCl3Ai7UrvThEREZE4edolrR6BiNShOVgiIiIiIiIRaWqA\nZWYXm9lDZrbFzN47zffNzD7pf/9uMzuzmeMRERERERFppqYFWGaWBK4FXgKcAlxiZqdMedhLgOP9\nf5cBn2nWeERERERERJqtmRmsc4AtzrlHnXMF4CbgFVMe8wrgi87zX8ASM1vdxDGJiIiIiIg0TTMD\nrLXAk1Vfb/O3NfoYzOwyM9tsZpv37NkT+UBFRERERESiEIsmF86565xzZzvnzh4YGGj1cERERERE\nRKbVzABrO7C+6ut1/rZGHyMiIiIiIhILzQywbgeON7ONZpYBXgfcMuUxtwBv9LsJngsccs7tbOKY\nREREREREmqZpCw0750pm9nbgh0AS+IJz7j4zu9z//meB7wMvBbYAI8CbZ3reO+64Y6+ZPd6scc/C\ncmBvqwchC5b2L2km7V/STNq/pJm0f0kzBfvX0bP5YXPORTucRcbMNjvnzm71OGRh0v4lzaT9S5pJ\n+5c0k/Yvaaa57l+xaHIhIiIiIiISBwqwREREREREIqIAa+6ua/UAZEHT/iXNpP1Lmkn7lzST9i9p\npjntX5qDJSIiIiIiEhFlsERERERERCKiAEtERERERCQiCrDmwMwuNrOHzGyLmb231eOR+DOzrWZ2\nj5ndZWab/W1LzexHZvaw/7G/1eOUeDCzL5jZbjO7t2pbzf3JzN7nH88eMrOLWjNqiYMa+9YHzWy7\nf/y6y8xeWvU97VsSmpmtN7Ofmtn9Znafmb3T367jl8xZnf0rsmOY5mDNkpklgd8DFwLbgNuBS5xz\n97d0YBJrZrYVONs5t7dq20eA/c65D/uBfL9z7spWjVHiw8yeCwwBX3TObfK3Tbs/mdkpwFeBc4A1\nwK3ACc65couGL22sxr71QWDIOfd/pjxW+5Y0xMxWA6udc3eaWQ9wB/BK4E3o+CVzVGf/+h9EdAxT\nBmv2zgG2OOcedc4VgJuAV7R4TLIwvQK40f/8RryDgMiMnHP/CeyfsrnW/vQK4CbnXN459xiwBe84\nJ3KEGvtWLdq3pCHOuZ3OuTv9zweBB4C16PglEaizf9XS8P6lAGv21gJPVn29jfr/OSJhOOBWM7vD\nzC7zt610zu30P98FrGzN0GSBqLU/6ZgmUXiHmd3tlxAG5Vvat2TWzGwD8HTg1+j4JRGbsn9BRMcw\nBVgi7eXZzrmnAS8BrvDLcMY5r6ZXdb0SCe1PErHPAMcATwN2Ah9r7XAk7sysG/gW8BfOucPV39Px\nS+Zqmv0rsmOYAqzZ2w6sr/p6nb9NZNacc9v9j7uBf8NLQT/l1wsHdcO7WzdCWQBq7U86psmcOOee\ncs6VnXMV4J+ZKKHRviUNM7M03sXvl51z/+pv1vFLIjHd/hXlMUwB1uzdDhxvZhvNLAO8DrilxWOS\nGDOzLn+yJWbWBbwYuBdvv/pj/2F/DNzcmhHKAlFrf7oFeJ2ZZc1sI3A88JsWjE9iKrjw9f0h3vEL\ntG9Jg8zMgOuBB5xz11R9S8cvmbNa+1eUx7BUtENePJxzJTN7O/BDIAl8wTl3X4uHJfG2Evg3731P\nCviKc+4HZnY78HUz+5/A43hdbkRmZGZfBZ4PLDezbcAHgA8zzf7knLvPzL4O3A+UgCvUgUtqqbFv\nPd/MnoZXtrUV+DPQviWzcj5wKXCPmd3lb/srdPySaNTavy6J6himNu0iIiIiIiIRUYmgiIiIiIhI\nRBRgiYiIiIiIREQBloiIiIiISEQUYImIiIiIiEREAZaIiIiIiEhEFGCJiEjkzGyo1WMAMLP/MLPN\nVV+fbWb/EdFzv8nM/imK5xIRkYVDAZaIiCx0K8zsJa0exFRmlmz1GEREJHoKsEREpGnM7Plm9jMz\nu9nMHjWzD5vZ683sN2Z2j5kd6z/u5Wb2azP7rZndamYr/e0DZvYjM7vPzD5vZo+b2XL/e2/wn+cu\nM/tcnYDlo8BfTzO2SRkoM/uumT3f/3zIzD7qv+6tZnaOnw171Mz+oOpp1vvbHzazD1Q917Rj85/3\nY2b2O+C8Of1xRUSkLSnAEhGRZjsDuBw4GbgUOME5dw7weeAd/mN+AZzrnHs6cBPwl/72DwA/cc6d\nCnwTOArAzE4GXguc75x7GlAGXl/j9X8FFMzsggbG3FX1uoPA1cCFwB8Cf1v1uHOA/w6cDrzGL0Gs\nN7Yu4NfOuTOcc79oYDwiIhITqVYPQEREFrzbnXM7AczsEeDf/e33AEHQsw74mpmtBjLAY/72Z+MF\nNTjnfmBmB/ztLwTOAm43M4AOYHedMVwNXAVcGXLMBeAHVePMO+eKZnYPsKHqcT9yzu3zf7d/9cdb\nqjO2MvCtkGMQEZEYUoAlIiLNlq/6vFL1dYWJ89CngGucc7f4ZXofnOE5DbjROfe+MANwzv3EzK4G\nzq3aXGJyJUeu6vOic85NHbNzrmJm1edOx2RuhrGNOefKYcYsIiLxpBJBERFpB33Adv/zP67afhvw\nPwDM7MVAv7/9x8CrzWyF/72lZnb0DK9xNROlhwBbgaeZWcLM1uOV+zXqQv+1O4BX+uOdzdhERGSB\nUAZLRETawQeBb/glgD8BNvrbPwR81cwuxZtLtQsYdM7tNbOrgH83swRQBK4AHq/1As6575vZnqpN\nt+GVIt4PPADcOYtx/wav5G8d8C/Ouc0AjY5NREQWDpuogBAREWkvZpYFys65kpmdB3zGbxwhIiLS\nlpTBEhGRdnYU8HU/E1QA/rTF4xEREalLGSwREREREZGIqMmFiIiIiIhIRBRgiYiIiIiIREQBloiI\niIiISEQUYImIiIiIiEREAZaIiIiIiEhEFGCJiIiIiIhERAGWiIiIiIhIRBRgiYiIiIiIREQBloiI\niIiISEQUYImIiIiIiEREAZaIiLQ1M8ua2ZCZrWn1WKJiZrvM7NmtHoeIiERPAZaIiMyKH/QE/ypm\nNlr19evn8Lz/ZWZvCL52zuWdc93OuR3RjHzSa33YzIr+mA+a2S/M7OyoXyfEGD4/n68pIiLNowBL\nRERmxQ96up1z3cATwMurtn251eNrwI3+7zAA/BfwtRaPR0REYkwBloiINIWZJc3sb8zsUTPba2Zf\nNrMl/ve6zOwmM9vvZ45+bWb9ZvYx4BnA5/2s0sfMLGdmzszW+T97k5l93Mx+aGaDZnabmR1d9br/\nzcwe9p/341MzYrU454rAV4BjzKyn6vn+0Mzu9p/v52Z2StX3/sbMdprZYTN7wMyeUzXGq6oed7GZ\nbZnmb/RK4H8Bf+z/vr9p/C8tIiLtRAGWiIg0y7uBFwPPBtYBReAf/e+9BUgBa4HlwNuBgnPuXcDt\nwFv8TNi7ajz3HwHvA5YCO4EPAZjZarwM1P+Hl5HaAZwVZrBmlgUuBXYBQ/62c4FPA28GlgFfAr5t\nZikzO8Pf/jSgD/hvwLYwrxVwzn0buAY/i+acO6eRnxcRkfajAEtERJrlcuC9zrkdzrkxvCDotWZm\neMHWAHCsc67knLvdOTfcwHN/3Tl3Z1XW6Wn+9pcDtzvnvut/7/8AB2Z4rkvN7CAwArweeI1zzvnf\n+zPgn5xzdzjnys6564AsXtBWAjqAU4Ckc+5R59xjDfwOIiKyACnAEhGRyPlB1Hrg+35p3UHgt3jn\nnWXA9cDPgG+a2TYz+3szSzbwEruqPh8Buv3P1wBPBt9wzlWA7TM815ecc0uA1cAjwBlV3zsa+Kvg\nd/B/jwFgrXPuPuC9wN8Bu/0SyJUN/A4iIrIAKcASEZHI+Rmg7cALnHNLqv7lnHN7/c6A73fOnQQ8\nF3gN8Lrgx+fw0jvxyhEBMLMEXhlimDHvxstY/b2ZLfc3Pwm8f8rv0Omc+1f/Z250zj0LOAbIAVf7\nPzcMdFY9/ap6Lx1mfCIiEg8KsEREpFk+C3zYzNYDmNkKM3u5//mLzOwUPwA6jFduV/F/7im8gGU2\nbgGeaWYvNbMUXgOJ/rA/7Jy7G/g5EMz9ug54h5mdbZ5uM/sDM+v0x/88f+7WqP8v+B3uAl5mZkvM\nbC3wjjov+xSw0c/6iYhIzCnAEhGRZvkIcCvwEzMbBH4JnOl/by1wMzAI3At8n4n26P8IvNHMDpjZ\nRxp5QefcTuAS4JPAXrxs1j1AvoGn+ShwhZktdc7dBvw58DngIPB7vAYbDm/+1cf819mJV6b4N/5z\nfAHYgte+/rvAV+u83k142a79ZvbLBsYpIiJtyCbm8YqIiCwsfhZrF94aXb9q9XhERGThUwZLREQW\nFDN7iZn1mVkO+ABeE4w7WjwsERFZJBRgiYjIQvNc4DFgN/BC4A+dc4XWDklERBYLlQiKiIiIiIhE\nRBksERERERGRiKRaPYBGLV++3G3YsKHVwxARERERkQXsjjvu2OucG2j052IXYG3YsIHNmze3ehgi\nIiIiIrKAmdnjs/k5lQiKiIiIiIhERAGWiIiIiIhIRBRgiYiIiIiIRCR2c7BERERERKRxxWKRbdu2\nMTY21uqhtJVcLse6detIp9ORPJ8CLBERERGRRWDbtm309PSwYcMGzKzVw2kLzjn27dvHtm3b2Lhx\nYyTP2bQSQTP7gpntNrN7a3zfzOyTZrbFzO42szObNRYRERERkcVubGyMZcuWKbiqYmYsW7Ys0qxe\nM+dg3QBcXOf7LwGO9/9dBnymiWMREREREVn0FFwdKeq/SdMCLOfcfwL76zzkFcAXnee/gCVmtrpZ\n4xERkYgM7Ya7v9HqUYiIiLSlVnYRXAs8WfX1Nn/bEczsMjPbbGab9+zZMy+DExGRGu7+OvzrWyA/\n2OqRiIjIItfd3Q3Ajh07ePWrX133sR//+McZGRlp+phi0abdOXedc+5s59zZAwMDrR6OLGbffht8\n/z2tHoVIa5X8OvWiulCJiEj0yuVywz+zZs0avvnNb9Z9zGIIsLYD66u+XudvE2lfu++H3Q+0ehQi\nrVUpeR/L+daOQ0REYmfr1q2cdNJJvP71r+fkk0/m1a9+NSMjI2zYsIErr7ySM888k2984xs88sgj\nXHzxxZx11lk85znP4cEHHwTgscce47zzzuO0007jqquumvS8mzZtArwA7d3vfjebNm3i9NNP51Of\n+hSf/OQn2bFjBxdccAEXXHBBU3/HVrZpvwV4u5ndBDwTOOSc29nC8YjMrFyCcqHVoxBprfEAS+8F\nEZG4+tB37uP+HYcjfc5T1vTygZefOuPjHnroIa6//nrOP/98/uRP/oRPf/rTACxbtow777wTgBe+\n8IV89rOf5fjjj+fXv/41b3vb2/jJT37CO9/5Tt761rfyxje+kWuvvXba57/uuuvYunUrd911F6lU\niv3797N06VKuueYafvrTn7J8+fLofulpNC3AMrOvAs8HlpvZNuADQBrAOfdZ4PvAS4EtwAjw5maN\nRSQylaIuKkXKRe9jSe8FERFp3Pr16zn//PMBeMMb3sAnP/lJAF772tcCMDQ0xC9/+Ute85rXjP9M\nPu9VTdx2221861vfAuDSSy/lyiuvPOL5b731Vi6//HJSKS/UWbp0afN+mWk0LcByzl0yw/cdcEWz\nXl+kKcpFSGh9blnkVCIoIhJ7YTJNzTK1LXrwdVdXFwCVSoUlS5Zw1113hfr5dhOLJhcibaNShJIu\nKmWRCwIsZbBERGQWnnjiCX71q18B8JWvfIVnP/vZk77f29vLxo0b+cY3vCVBnHP87ne/A+D888/n\npptuAuDLX/7ytM9/4YUX8rnPfY5SyTtf7d/vrRzV09PD4GDzO+AqwBJphOZgiUyUCOq9ICIis3Di\niSdy7bXXcvLJJ3PgwAHe+ta3HvGYL3/5y1x//fWcccYZnHrqqdx8880AfOITn+Daa6/ltNNOY/v2\n6fvjveUtb+Goo47i9NNP54wzzuArX/kKAJdddhkXX3xx05tcmFepFx9nn32227x5c6uHIYvVR4+D\nRBrepU6Csojd8g6484tw6b/BsS9o9WhERCSkBx54gJNPPrmlY9i6dSsve9nLuPfee1s6jqmm+9uY\n2R3OubMbfS5NJhFpRLkIMbspIRK5ir8+iUoERUREjqASQZFGVFQiKE2UH/LKUNvdeImg5iOKiEhj\nNmzY0HbZq6gpwBJpRFlt2qWJPvMs+PVnWj2KmVXUpl1ERKQWlQiKNCK4sBRphsM7vH/tTgsNi4iI\n1KQMlkhYlQo4/18wB0UkKs55AXwlDiWCWgdLRESkFgVYImFVZ690516i5irexzgEWCoRFBERqUkB\nlkhY5aoAS4sNS9TGG0fEoAy1ogyWiIi0xpve9Ca++c1vtnoYdSnAEglrUgYrBhfBEi/B/hWH8tOy\n5mCJiMjcOeeoVCqtHkbkFGCJhFXdPlsXlhK1ICukEkEREVnAtm7dyoknnsgb3/hGNm3axJe+9CXO\nO+88zjzzTF7zmtcwNDQEwN/+7d/yjGc8g02bNnHZZZfhYrQOqboIioSlOVjSTEEAH4dOlSoRFBGJ\nv//3Xth1T7TPueo0eMmHZ3zYww8/zI033shxxx3Hq171Km699Va6urr4h3/4B6655hre//738/a3\nv533v//9AFx66aV897vf5eUvf3m0420SBVgiYZUVYEkTjZcIxiCDVVYGS0REZu/oo4/m3HPP5bvf\n/S73338/559/PgCFQoHzzjsPgJ/+9Kd85CMfYWRkhP3793PqqacqwBJZcCoqEZQmGi8RjMEcLGWw\nRETiL0SmqVm6uroAbw7WhRdeyFe/+tVJ3x8bG+Ntb3sbmzdvZv369Xzwgx9kbGysFUOdFc3BEglL\nAZbMlnPwo/fD3i21HxPLLoJ6H4iIyOyde+653HbbbWzZ4p0fh4eH+f3vfz8eTC1fvpyhoaG27xo4\nlQIskbDK6iIoszS8B277BDz4ndqPCTJXKhEUkYWiUobf/ks8MvPSEgMDA9xwww1ccsklnH766Zx3\n3nk8+OCDLFmyhD/90z9l06ZNXHTRRTzjGc9o9VAbohJBkbDU5EJmq+B1RGLscO3HxGkOlkoERSSM\nJ/4Lbr4C+jfChvNbPRppExs2bODee+8d//oFL3gBt99++xGPu/rqq7n66quP2H7DDTc0c3iRUAZL\nJKzqNu26cy+NKIx4H/N1AqxyDAOseu+DsUPwi4/DAlzfRERCKvlzZkqjrR2HyDybMcAys3eG2Say\n4CmDJbNVGPY+jh2q/Zg4lgjWy2A9/NSkOLgAACAASURBVCO49QOw54H5GZOItJ/xbHcMjmsiEQqT\nwfrjaba9KeJxiLQ/tWmX2SoGAdZCKREM0ZAjeI/kh5o/HhFpT5UYre+3iMRpwd75EvXfpOYcLDO7\nBPgjYKOZ3VL1rR5gf6SjEImDippcyCw1UiIYhzu9QbatVCeDFQRYhcHmj0dE2tP4cU03JdtFLpdj\n3759LFu2DDNr9XDagnOOffv2kcvlInvOek0ufgnsBJYDH6vaPgjcHdkIROKi+sJXk/ulEYUwGazS\n5I/tLEyJYPAYZbBEFq+5lgju+T30Hw2pbHRjWuTWrVvHtm3b2LNnT6uH0lZyuRzr1q2L7PlqBljO\nuceBx4HzIns1kTjTHCyZrWKYOVgxCrAqIdq0j2ewhps/HhFpT3NZM294L3zmWfCya+DMN0Y7rkUs\nnU6zcePGVg9jwQvT5OJVZvawmR0ys8NmNmhmdW7DiixQWgdLZitMiWBc5ipUKuD8zoD1LpqC90hB\nGSyRRWsux7Xd93s/N6xMi8RPmHWwPgK83DmnVlCyuFVnFpTBkkYEWZz8oBegJKa5tzXepr3NF+QM\n+z4YLxHUHCyRRWsuc0t3P+h9LKrFu8RPmC6CTym4EkFdBGX2ghJBXO0sVly6CFbfiQ7V5EIlgiKL\n1lxKBPcowJL4CpPB2mxmXwO+DYyfTZ1z/9q0UYm0o0kXlgqwpAHVQUb+MHQsOfIxQeaq3ctPKyGb\nvVRUIiiy6M2lRHA8wBqJbjwi8yRMgNULjAAvrtrmAAVYsrgogyWzVai6QKjVSbAckwxWUOqTzM7Q\n5CIIsJTBElm0xjNYcwmwlMGS+JkxwHLOvXk+BiLS9jQHS2arWBVk1OokWInLHCx/nJmu+l0Rxxca\n1hwskUVrfA5WgwHW0B4Y2ed9rgyWxFCYLoInmNmPzexe/+vTzeyq5g9N5sVj/wn339zqUcRDcIJI\npNq/jEvaS2EYLOl9XnMOVly6CPrjzHSDK9cOCNVFUESC40Ojx7UgewXKYEkshWly8c/A+4AigHPu\nbuB1zRyUzKNffRp++vetHkU8BCeIdJcWGpbGFEage6X3ec0SwZisgxUETplO72OtRhcqERSRyiwz\nWEGA1b9BAZbEUpgAq9M595sp29r8CkBCK+eVfg8ruADOdCqDJY0pDEHvau/zmiWCVQGWc/MzrtkI\nxpn2A6xaNxvGSwSVwRJZtGZbIrjnQcj2wtJjdI0isRQmwNprZsfiNbbAzF4N7GzqqGT+lIuTJ+BL\nbZUiYJDKaQ6WNKY4Aj1+gJWfYQ4WtPc8LD/AKqe7vK9rNboYb9OuOVgii9Zs27TveQgGTvJu5CiD\nJTEUJsC6AvgccJKZbQf+AnhrmCc3s4vN7CEz22Jm753m+31m9h0z+52Z3Wdmaqgx38oFHbzCKhch\nmYZkRgHWXG29Dbbf2epRzJ/CCHT0e533ZioRhPYuE/TvRP/8cf/GTI33wmjey2w5lQiKLF6znVu6\n+wEYONEPsHQTWOInTBfBR4EXmVkXkHDOhbodaWZJ4FrgQmAbcLuZ3eKcu7/qYVcA9zvnXm5mA8BD\nZvZl55yuXudLyS8RdA7MWj2a9lYpQSINqYzWwZqrH74PulfB67/e6pHMj8Kw13Uv1zdzieDUz9uN\nf6F0uJyBJDUDrENDw3QALj+Ejiwii9R4BquBY9rwXhjZCytO9jJZugksMVQzwDKzNzjn/sXM/teU\n7QA4566Z4bnPAbb4ARpmdhPwCqA6wHJAj3lP2g3sR/O75le5CDgojUG6o9WjaW/lIiRTymBFIT/o\nZXQWi+Kwdyc211uni2Bx+s/bjV++OOKy3tc1m1x475FEOT+R/RWRxWV8DlYD58w9D3kfB06Eg08q\nwJJYqlci6BfY01Pj30zWAk9Wfb3N31btn4CTgR3APcA7nXOVqU9kZpeZ2WYz27xnz54QLy2hBQc9\nHcBmVil6GSwFWHNXGGnsjmaclQreXdxMlzdpe6aFhqG952D54xwh5389fYBl1f+/atUusjjNpkRw\nzwPex4GTvRu/KhGUGKqZwXLOfc7/+KEmvv5FwF3AC4BjgR+Z2c+dc5OuQJxz1wHXAZx99tlt3F4r\nhoKLo8IwdC5t7VjaXblIgST5ktGTbOMMQxwUR9o7SxOlILiYsUSwKqiKQYngCH4Gq0Z3MKtU3YTI\nDy2ujKWIeMZLBBsJsB6CTA/0rvEy/5WSsuASO2EWGr7RzJZUfd1vZl8I8dzbgfVVX6/zt1V7M/Cv\nzrMFeAw4KcRzS1SCg54yWDOrlDg45nh0f1EZrLDyg0eWkDnnBR2LpdV9cPc101W3RNBV71Pt/Lfx\nM1Mjzs9g1SgRtOoAWhkskUXJ+QGWa+SYFjS4MJuYuqAslsRMmC6CpzvnDgZfOOcOAE8P8XO3A8eb\n2UYzy+AtTnzLlMc8AbwQwMxWAicCj4YZuEQkuDgqqtPXjMpFiiTJu5QCrLC++Aq4dUoSvJQHV1lE\nGSz/vZXurFsi6GLT5MIb2/BMJYKVovdeAS02LLJIHRzyAqOhkQZu4u55CFb499rHAyzdBJZ4CRNg\nJcxsvLbDzJYSrvtgCXg78EPgAeDrzrn7zOxyM7vcf9j/Bp5lZvcAPwaudM7tbfSXkDlQBiu8ShBg\nJRVghXVoGxx6YvK24E7kYpmD5QcXX797HwddZ80MVqUUkzlYU0sEa3TUtEqJg3R7X+S1FpbIYlQq\neseLStjOu6MHYHg3LD/R+zpY0FwZLImZGQMl4GPAr8zsG4ABrwb+LsyTO+e+D3x/yrbPVn2+A3hx\n6NFK9MabXOjgNaNyiaJLka8owAqtOObNv6kWZDMWSwbLf2/9232HOPoE45nFkWnnE1TKMeki6I9z\nNOgiWCODlagUOei6WWkHVSIoslj5x7LQJYIj+72PwcLsymBJTM2YwXLOfRF4FfAUsAt4lXPuS80e\nmMwD56qaXCjAmpGfwRpzyfaeI9NOSmNHloeNB1iLK4M14rIcqvgXC9OUCbq4LDTsZ9cmSgSnfy8k\nKsWJDJZKBBtz4HH44V+3dyZTJAz/uGZhz5lBhj/rN6sez2ApwJJ4qRlgmVmv/3EpXmD1Ff/fLn+b\nxF31RZwOXjMrFym6BGOVVO21f2RCpeIF8FMvroP5fousRHCEHPvLfoCVP7KT4KQ7vG0dYPkZLOqv\ng5VwJQ46lQjOysP/Dr/6Jzj4eKtHIjI3QTY+bFZ+bGqApSYXEk/1SgS/ArwMuANvQeCA+V8f08Rx\nyXyoLnNTk4uZVUoUXJJRZbDCGc+OTi0R9E+U7VwGF6WqDNbeop/1maZV++Qugm0cYAXrYM1QIph0\nxYkASxmsxpTGvI+jB+s/TqTVCsNeh9Ra/CyshT3eBzdjos5g/eB9sPwEOPvNc3sekZDqlQh+2P94\nsnPumKp/G51zCq5a4etvhLu/Ed3zVd95VgZrRq5cpOCSjGkOVjjBPjU1wBpvcrFIAiz/9x0hy56i\nH5TEukRwShfBGpPXk5USg3RQJqE5WI0q+gHWmAIsaWNbfgz/sBGG9tR8SBBYzT7AiiiDdf8t8Oh/\nzO05RBpQL8D6hP/xl/MxEAnh4R/Bk7+O7vmqL3CVfp+RKxcpOa+LoFOANbNS1SLW1YKL7XYOIqJU\nVSK4q+AHWNN0EoxPiaA3thmbXFCiQNrrNji10YnUV/JvTtRalFqkHTx1n/f+Pzx1idMq4xmskMe0\n8QCr1/sYVZOL4vDiuaknbaFeiWDRzK4D1pnZJ6d+0zn3580blkyrXIg2c1J9YaQmFzNylRJFkhRc\nGnNl78SRSLZ6WO0ruEgsjXklb8lgTaRFlsEqDFMhQZ40O8f8zoHTlQhOWgerjf825Slt2qf7f3SO\ntPOawgy5DroLQ9g8DjH2ijEtESwVvGOijouLw+Au72ONpSdgInOVCJ3BCuZg+eXFUbVpLwzXvBkk\n0gz1AqyXAS8CLsKbhyWtVKl4d46jvCidlMFSieBMXKlIiS6KwdumXIBER2sH1c4mlaAOQ7LP/3yR\nzcEqjlBM5ABj+2gGMky/2HC5xJhLk7Nie3eP8wPBPGnKJElO1+TCf0zRpRhxWcpjg6HWBBHfeAYr\nZgHWF/8A1pwJF/99q0ci82HID7DqZFrNeceyhGsgg5XMQsq/gRNFBqtcjP4GtcgM6p3z3uOcu9LM\njnLO3ThvI5LpBRejkWaw1OSiIZUiJZIU8O/OlgsTB385UvUJsTAMub6Jz8G7CHcObIHnNgrDjPmB\n+MGKP29puju+5SJjZMhRbPMSQe9YVCJFydIkp7sr7B9biqQYooPS6GEFWI0Yn4MVoxJB52Dn76Bz\nWatHIvNl8Cnv43Q3jHwTGawGAqxg/hVEE2AF55zFUjUhbaHeHKyXmpkBr5uvwUgd/gXLvsMRtjtW\nk4uGuLJX8jSRwdLBuq6gExpMnoNTPSernTM1USkMM+Y3hCiTpJLumv7CuVJijIz3eTvvW34zjhIJ\nSpaavsmFP/4iKYZdjkpeN3AaEmSw4lQiOHbQy05Xv+9lYRsKUyIYZLAaaHJRHWAl05BIz61EMPhZ\nLa8i86hegPUD4ABwupkdNrPB6o/zND4J+Bcsew9GOFl8oZQI1uhiFjm/yUUBfx6Nyg3qq77Qqu4i\nV32iXAxlgsURRoKOe0Ap3TP9Hd9KkTHnB1htncEqUSaBI0GR9PTvA//YUibpdRvUOliNCS4E45TB\nOuQ1Onjiqf3z83p7HoLN/3d+XkumFyaD5ZcGJp1fsTCTqQEWePOwlMGSmKkZYDnn3uOcWwJ8zznX\n65zrqf44j2MUGL+ICd3qtIHnBOK7Ts3YYfjIRnjgO81/rYqfwXJ+Bkt3w+orVgdYw9N/vhhOeIUh\nhl2G5d3enIJiumfahYYnZbDaObPnl8oCdQIsb1s6m/UCLLVpb0wxfnOw3KFtAJTnq2HSXV+G771r\nfl5LjpQfgoJ/46ROBiuYe2W4cMe1/OBEB8FAumNuGazxAEvn7CPc+UU4oAXNm6FeBgsA59wrzOxo\nM3sRgJl1mFnPTD8n0XL+xXzoTjxh+AebPOn4ZrBG9nkXb1tubf5rlUuUSKlEMKxSjQBrUgarjTM1\nUSmMMFTJcvQyrxvWWLK7Tomgnx1t58xeuUTZD7AKpKa/0eCPP5frYNh1kNAcz8bEcKHhwT1PAJCq\nzNNFbCkPrtzei3IvZENPTXxeJ9OaaLA7qssf5pHDxpv/72+44P/8B6e8/weMkIkog6Wqk0lKBbjl\nHXD311o9kgVpxgDLzP4U+CbwOX/TOuDbzRyUHKlYbEaA5T3XIdeFi+sFUHAhsr35jS5tvMlFVRdB\nqa1WiWC9DNb+R2Hvw80d13wrjnC4khkPsEatc9qSGquUyBOPEsEgg5UnPf1dYf//NednsFKlmB5f\nWqUYv3Wwhnd7d8FTlXk6LgaBvbISrRG0aIf6c7BcVdYqxE3J0uhh7t5b4bG9w5y0qoeRQpkRN8cA\na7Etbh9WcI6Oet7kd/4C/t+V0T5nDM0YYAFXAOcDhwGccw8DK5o5KDlSqeCdtEJPFA31pN6J6ZDr\nwhVimsEKDgxP3d/0tbzMXwerWN1FUGoLE2BNvWHwg/fBd97Z3HHNM1cYYrCSYX1/J2YwZN3TXpBY\nbOZgFSk6P4PlkjWaXHjbOjo6GHY5UuWx9i57bDfBeydGJYLFA08CkHbzFPAEx1+VardG0OAi01M/\ng+VKlJx/qRkmwMkPMuQ6+Ls/PI3PvOEsMskEebJzLBH0zz/aVyYbv0kR8bXMoz+F7XdG+5wxFCbA\nyjvnxv/6ZpYCQsxUlCiVCt4JNxllgOW/qQ7RNfdF/FolmOfjyrDr7qa+lPl37tXkIqRac7Cq97Wp\nJ9z8UN27oXHk8iOMuBz9nWl6c2kG6Zj2gsQq5aougm0cYJWLlPxTR96l6s/ByuQYS/gLhcZ1nmcr\nFKu6CIZpDFBLfgi++kfzMsciMeg1uUi7+c5g6TjcEkGDi+XH121ykXBlRoNFyUNU4CQKgwzRwcpe\n72d6cinGLDvHEkFlsKY1nsGK8D1UKcOhbTreEy7A+pmZ/RXQYWYXAt8A5qGjgFQbD7AaKb/4z4/C\nb/659vf9g81h19n07E/TlKoOuts2N+91nCPhilPmYOnEXletOViFOnOwyoWFd5exOMQIWfo60/R3\npjlY6fAuSKZcONukJhftG2C5Smn8PTBWM8Dyxp9IZqikgwBLjS5CK1XdOJrLhcreh+Ch78FjP4tm\nXHVkR7yMRma+AqygNHChHS/iYnAnJDPQf3Tdm2JJV738xAz7RilPslJg0HWwotfrvNqdS3mZ/Uia\nXOicPUkzblIM7vTOXzrehwqw3gvsAe4B/gz4PnBVMwclRyr5c7CSYVdDB7jnW/Dgd2t/3z9BHaJr\ncqASJ9Un12bOw/LLm4ouScEpwAqj7JedFkhNKREcmpjHNjWQqBQX1gVTpUKiNMYoWfo60izpzHCg\nnPN/z8l17+ZK4yWClTbOYFVK3nIFCfMCLDfd/5f/3kik0rh0t7ctrxNuaMUxSPkLrM6lTDC4sDy8\nY+5jqsc5+gq7AchQmFvWLazgrruyEq0x9BR0r/IWkK+VwXKOJGVGXcj1/fzlHPLJLnqy3jmiJ5fy\n52DNYZ5QUV0EpxWcg6K8ljnoNbuJbVVUhMJ0EawANwIfAj4I3ODcfBw9pVrZD7BSjZQIFobrZ6aq\nmlwkyvl4zpEIygaWn9jkAMv7W5W00HBopcIoeZdmyHVMvrgujnCYLu/zqX/DcnFhBa7+SWbY5ejr\n8DJYe0vBhfPkMsGEm+giWGnjv4ErFymToLcjTWGGAItUGpf1Ayzd0QyvNAo9q7zP59JJMDj+NzvA\nGtlPhgJ7XS9JKvNzbCzXaHKx83dw89uhUmn+GCJ0eKxIvhSjc/DgLuhZ6bVUr5XB8q8pJkoEZ7hx\n5D9PMteLmQHQk00zXImoi6CrxPM6p1makcEKAiyVCIbqIvh84GHgWuDTwO/N7LlNHpdMUS56b4CG\nMliFwfoHJf9NdRi/hCeOdxyCA8SG8+Hg4zC8tzmv418wFNVFMLRyYYQx0gy7HJWqi2tXGOFQxd/n\nptbklwvRdzRqJf8kM0qW3lya/s4Mewr+xcaUu76JqhLBcqmNM1hlr0SwJ5eiwPQBVqXk/b8mUlkS\nCrAaU6l474MgwJpLJ8Hgbz64c+7jOrSt5g27sr8G1lbnj3k+KiKCDNbU/W/Lj+G3X4rdXM5Xf+aX\nfOrHW1o9jPCGnoLulZDr9Y7Z0y7X4B3HRsOWCPoZrHRn3/im7lyKwUp6jiWCVT+7kCok5mp8DlaE\nf5PqDNYiD2bDlAh+DHixc+55zrnnAhcB/9jcYclUZf9kkmokwMoP1T8oVXURBOI5Dys4kR99vvex\nWZ1r/BNFiRRl85tcRDkxdLacg6E9rR7FtCqFUcbIMEyO8qh/oVepYMXhqgzW1DlYxfb4u0bFL00J\nMlh9nWmeCgKsKReA5srjDVRcG2dHXblAiSQ92TQF0rhp/r+CkuZEKk0y5y+bqDua4QQXPeMBVhQl\nghEEWP/8AvjVP037rYO7tgKwPbHa2zAfF7HlGnffg69jdgNs+4FRdhyMUan+4C5vH836wdB0ZYL+\nDbSxIIMVskQw2z0RYPXkUgyV57hWZ/XNnZjtF001XiIY4fkmCLAgnjftIxQmwEo75x4KvnDO/R6C\nNmoya6UC3PAyePL2UA8fLxEk5BuhVPAObnV28EopyGD5F7txfDMEJ/KjzgVLwPYmNbooT5QIZrLB\nyaINDtSP/hSuOan5JUCzUCmOkXdpL8Aa806cQUB82NXKYBUXVp28f9NihCy9HV4GazzAqs5MVCok\nqFBySQouOX5DpR25cokSCXo7UhTc9Otglfz3ZTKVJdXhB1gLdQ5WcWz8wjASwUVP99xLBEeHvYve\n8qHtcxuTc17GokYm7PCuxwAY6jrae/h8nEtKNZpcNGNeSZNVKo6RYpnRYkzu+BfHvMA/mIMF02cM\n/RuTYyHnYDk/SOvoWTq+rSebYrDsZ7BmOzulXufaxWz8ZkQTMliw6G+qhQmwNpvZ583s+f6/zwNN\nbNe2SIzsha0/hyd/HerhQTCUJmQGK7hjUy/AKuYpuQTDLuc/NkZ3zwLBmDuXwcDJzZuH5QcCFUuR\nzvh/r3Y4ge97xDuJDe1u9UiO4IIMlstRCQ60fsBxqOYcrIL3+yyU0gL/9y4lO8ilk/R3phkMSnIn\nBVgTJahlkrg2bnLhyl43zd5c2iuXnSYYDEqaE+kMmc5eb2MhwiCkndz6AfiX/x7d8wXHtAhKBA8e\nPABAcmz/3JoEBEFLjQum/P5tFF2SRP9R3sPG5jHAmnocLsUvgzVWKuMc8QmwgjWwevwSQZh+Py1P\nKRGcoU372LB3M6Gnr398W08uzaGSvzrQbDOjkxa3X0A38OaqCRksd/CJiXXPFGDN6K3A/cCf+//u\n9bfJXMxwwpqqEtwRJuQkzeCOap2gqVzKUyTFSJC+j3MGK5WDtWd6AVYzerAEB6BEikQqZEek+TDq\nXUC1Y3DsSnnypBkhV7U/evv7RAZrmi6CsHDq5P3f1zLePKQlnRkGg9+9+o6v/3cok6REsq27CFIp\nUiJJb0eaIilsmgvZSjBnNJUl0+FdgJXGFmiAdXiHNz8pKuMZrJXexzmUCJars4ZzmYcVHF9qnK/c\noW08RT9L/AvjQn4ejke12rQ3Y22fJhvK+4FIISYBVrAGVs9qr8kF1M1gjYYsERw85J3PliyZyGB1\nB10EYdbXKG5SgBWf/aLpamWBZ8tfA+sx55cKK8CanpkNmNkpzrm8c+4a59yrnHOvAn4E9M7fEBem\nil/yd9A/oMz4+OqTRZgDRPW6DzUu1srFAgVSE/XR7RBgOQdbfxE+SCqNQjILZrD2LC/gOPBY9OMK\nAoFkikQ6yGC1QRAwst/72A7/d1MVgzlYWSwYn79f1u0iCO3xt42C//smc97v29+Z8RYahslzFqpK\nUL0Aq30vAly5RNEl/SYXaZhmbb6JOVgZOrq900VhOF5NB0IrF6O9wRE8V6bTm98yhxJBN08BVnp4\nJ3sTA2Ry3r5dnJcMVo3yplpzs9rYSN7vthe3DFZ3dQarzhyskCWCo4Pevt7fv3x8W08uNRGgzfJ9\nNul90A43RsPa83v4p3MmAtqoRV1OO7gLqxT5vVvrfa0Aq6ZPAcun2b4U+ERzhrN4HDjs3c3duSdc\n1ztXrDqJhAqwJrfFnvY5S3kKpBhx/sGrHZpc3PUVuOG/wbZwc9Mo5SEIeNae5X3c1oQyweCgnEyT\nzrTRHKzRNg6wSnnyLs2Iy5EoTi4RrJnBKi+wDJb/+6b8Rg9LOtPTXyz4WWkvwEq0dYkg5RJlkvTm\n0uRJkSgfue5RcEMolcnS25lh2GUpjCzUACsfbedL/7m2Dzl/jaHZlwi66mP6XOZpzlBx0Z1/iqHs\nChIZL8Aq5SM8Ht1/M/zwr4/cPp7BmloiGARY8bmQHi7EM4P18GgXV3zL73wYJoM1U4ng0EGKLslA\n/8Q9/J5cemIdrVkGWJX8EGXntX2P1bll513eYuFhr4caFXWbdn/+1cNunfe1AqyajnPO/efUjc65\nnwOnN29Ii0PRL6FIhLwwrlSfLMKcOKonXdc4KFVKeQpUX/C1+CK9UoFf+A0qw15UFEdxKT/AWnas\n9/HwHCd0Tzs2729uiTSptN/jpR1O4OMZrPYrEbRSkMHqIFny963ilAxW9QnXuYkDfZxOgvX4v2+m\nwysR7O/KUCZJxZKT77xXrbNWjkGJYNEvESy4NIY7IlAO5mCl0hlvHRs6KI8t0CYXZX/R6KhKk/33\n8l/e/DDFTO+cSgStMMQu589nmVMGa3IGehLnWFreQ6FzNcmMd+OkFGWJ4P03w11fPnJ7rQzW+EVj\nfI4hI4UYZrAsyX886fj5k/7/w3QZrClzsKbrOFqtMHKIITpY0dcxvq0nO/cqG1cY4SD+chHtcN4O\nK8he73u4Oc8fdQbLD7Aeqqz3nnahloWHVC/A6qnzPXURnKPSeIAVLsKvPjBNu7DnVNUnwhqv4UoF\nii41MQG11RfpD31v4kAS8o5wMT/C9iHH/7tnJ6Q7vU6CUXb0CvgnCkumyaYzlEkogzUDK3tzsIZd\nllQl7/0Np2awqgOJShnwL1Lb4W8bBf99mO30Dqf9nd6hs2zpyUFk1TprJZKtbdN+8El45Ke1v1/2\n5mAF62ABRwTE4xmsdIbejjRDLrdwT7blgreAaVT/Z/6xb6SSZjTZM6cSQSuOsMv1e1UKc2nV7jfI\nGB468sZX4fBuMpSgbx2pbBMyWEO7p7/hMmMGKz7HkGF/DtZYXAKswV3QvYInDowxNF3TnkCQwfKr\nZGbqjloePcwwHXRnU+PbvBLBOV6jFIY56IIAKz6B9/jfdG+T1kcr1XgPzdZ4BssrESws1JtqIdUL\nsLaY2UunbjSzlwCPNm9Ii0Op4B0oxu/sz/wD45+Wi2ECrOoSwekPSs5vchEc/Fp6ke6cl71K+5mN\nkBmMwtgoo5U037xjmzcPK9PdnAVNgwxWKk0unfTmnrTDCbyNM1iJ8hhjZLwmF+D9v4zPwZqmTXv1\n5wtlsWE/oMx1eif3jnSSTDJByTKT95+gyYVLUnIt7iL468/C195Q+/uu7JcIpijWWHTb+RcxyXSW\n3o4UI+Rwzbjx0Q7Gs64RvQf99/IYGYasa04lgoniMCMuxy7XT/Hg7BtxBG3X3TTH1r3bvcuB7NJ1\npPwMVjnKcvPhvUdmCCuViaxpzTlY8clUBBmskdiUCHprYD15YIQKCYqprholgsE6WP4C6sWZFxrO\nJ7smberOzf0aJVEaqepc2wbn7bDG5iuDFVHQefBxDiWXst95JZ7F0QV6zA+pXoD1F8DHzewGM3uH\n/+9GvPlX75yf4S1cpYK3Y6dCBliu6qBQKoR4M+RnDrAqJa/JRVuUCG79udcB8Jy3eF+HDLAqxVHG\nSPPzh/d6nZiyPc1Zb8c/WSeSJDYgtgAAIABJREFUabLphHdh2Q5dqto4g5UoF8i7NMWkH0wVhsez\nqcHi1pMyNdUnvnb420bAFYYZc2l6O70g08xY0pmmaKnJQaR/sei1aU8cOTdtPhWGvH+13oNlv0Qw\nlx5fGHnqRUulVKTijHQqTU/OWwttwdbjB/vwXNqgV/P/7nnSHKx0zqlEMFkaYZgcu9xSSgdnXzod\nZKQy5SPPJcEiwz0rN4xnsCqF6G74FA8/5WUIq98T1ReEU/bT4NwapzLjIIM1WizjmtEFN2pDT0H3\nKp7c7+0XhVR3jSYXfmbOD7BKMwRYyeKgF6xV6cml55bBqpRJlsfGzzlxCrzH3/t7mxVgRT8H66nE\nivHO1CUFWNNzzj0MnAb8DNjg//sZcLq/2LDMQdk/AaXLjQdYxTABVvWdxloXNn4GqxzMYWplk4tf\n/KPXkeisN///7L1ptGTXdR72nXvPHavqDf1ez2g0AAEgBZIAKVKiFq15sCyLER3HkhVZ9tKyhthc\nkTMoiRQlTBxnyXEcU9EQSfQgg6JGkyK1SDEUSZESFUqkJAIkQIIAATQa6EYDPbyxXlXd+d6dH+ec\nO9V0a3g9GXutt7q7ul7VrVv3nrO//X372+LfDSsqFAuWJEozfOrpa5LBOoSbWjFYumCwYvDZF6Vn\nPg78yT9f3jGlSVHdvgkZLD0NEMKEbktpRjQoJIKymlh1xxyTQN3CEfk9DGBjxSlU1euuiQhmFUTW\nXARvqERQbbpjmBOWJUhIR0fNwSr/jgxKIsTQYRqC6eqTAy2+TeUiy2aw5OsEZGI7dReSCPLUgwcb\nV3AEbIEeLDXXyqRwaEyIt30BALB56h5wSzJYywKbaQI9lE675YJERV5bXYeVgVQQXgcWPE2Aj/wP\nwN6FhV5GMVdEQJhkyziyw43eFVD7OC7tyWtVawPh+DlYOYM1pXBmJH2kRrU7pW0tWASWv5P3YN1C\nwDu/9/3dQq2yzDgEieCL2VFk3EZG7Pbtu20YE+dgSYv2h4noJ+XPvyei20S7c2MjkxuQkTXblMuz\nZpIlSQSRxojAseZaCGE2Wrw++NhL+JF3L9nR5vLjwHN/DHz924up8E0XwdhHSCJ5/diXrwJW+1B7\nsDRuwuY6YuKzV8Ke+iDwV/92ecdUrmzfjAArCyXAkhtm1B+ag5Um4xisJS4z3UvA59+zvNcDxIb0\nyX828lr7xJNXESbSFTDow4eF1RLAWnMNhMRrJheqx48jgX5jGSx17sck9owSJNDhmDoSNo7BihCD\nw9Q1tEwxa09v2G96y4X67MsCFfJ1Api4GtsCcM2ZFBqphwFZuErrMLyrQlo3R1R6qmr7RLp3CRHp\nOHbiNAwJsGhJDFZ352Voqi9zHKiqnRsm/x1H1yFV2XkW+Kt/DTz+uwu9jHIRBG6BPqw0BrxteOZm\nDgY9zZ3IYGW6I391/HVMRLDSAZg1DLCCRVwEZYG56MG6hdQR5SLXYbBYcq2nZZyTLAV1L+G5+Aju\n3mhjABvZYaiJbqFoMmj4lTiEyOTiP0pyMTJmBljTTS6QRoiIY901RYWpweL16Scv4vwzT0x//1ni\nc78mDCre+MMAl5WqpglFEiKAideeXsGffOUaMqN9OBJByWBp3IBtaAjnYbDiYLnVs3JF62aTCGYZ\nOMVIdQvMUgyW6MGKS7LUyrynSRLBL/0e8Kl/Md+xfPG9wId+YrkStZc/D3z6ncDzVaPVF3c9/Oh7\nHsF7HxH9LknQg0fWEIMVEK9eC/n1ZQqAld7AJKsJgwXRS8Z0s/o7KlIJsLgGTWOIdBe8IVt/y0U+\nWmDJDBZMXA7k+Z2zD8vMAmhWG1foCHRKAG9nrteJg9K9U7uPtP5lbGub4JzDciTAWlLB5/NPFmIZ\nKq9xExgsTc5laySlXzQGW+LPlxYbDeKFKSxE0JDd/H1YfWHRvs3Wi4fgTuzBygwJsJLxRcmuH6MN\nH5pTHbOqawyaJeV9CwCs7rjZizdzBPvA5v3i74fQh6UK/SyL5y6+5CFnYL2QbuLuzRY8WMjC27So\n1jBeAVg3KFJZOTCpKYNVLAppg40ja2DTzmQStOYaQjM7LUkfbOPt59+O3zPegSRdkowhOBDJ82v/\nNuCsiaHBQGMgwlIfIQz8p2+4A/0wwU5sHo7JhTz/OjfhGDoi4shmBUtJIJKnZWns/TLAuskYLHl9\np5oF7igGS0gEfViizwhAmpRdBCdIBJ/84PwsVHIIPRnqGqslm/ueuE6+cEHImrJwAA92hcFabxnw\ns5qLoJRdcc5lD9bNIBEcw2BJgGVwBlL3a+37ojRGDA5DF99zrLdg3rYA6/AYrIu+BFjzyASJYGY+\nMqOFgXVUPNabbxZWhcGqXfNucBld4xgAwDIFO0tLYqCfPPdccQzRaIlgfR3WFcBa1vcxKcoAa4F1\nfRAl+ID5v+Kf8A/c/FbtcgbW5WwNAGAbGg5ojBmLYuYlwMomSNGuHoRow4fhrg79n7L/n6uQOMRg\nLbgPdC8Bl7+42Gs0jaALnHgQ0IxDYbAqUt5F9xzpIHiJNnH30RY8skC3a99tw5gJYDHG1hljr8zA\nWkKQvLDtEZr2UcGykotgg0Qx9XvYVQvKuOQ7ixDBwLprCpeeSYvXwcvAw9+Ne5Lz2GA9RMmSNoEv\nvVcwbG/6h+LfOgfqM4ImBJMM1nd89TG0LY6LA/2QGCwlESx6sBq5OZYjCYabtSeFvw+c++T4/7+Z\nGSyZYGW6BaPcgxUP4JEFUw5rzsZKBOuzbYLRG/hLjwKPPDzlWJTOfIkJl+pXrG0gSurz2IsiIabI\ng0d1iaAJL9Nzpz0AFQAfg99giaA8rikSQUPXwAwJAOpVYWmEYehiuGdmtGBmSywu3ExxCD1YMTgy\naNhOJlhgT4vYhwZCyl2k7ZPisTmHDVdcAWsFrLV4G75zHABgcU3KzRe/1+I0w8uXit4m1QcmDqgE\nsGrvpRisJoXIhWOwLf70tvMEc57wwhT3sMs4w67d/MOG+1cAAC+EonD21SdX0M3siXOwmCGu40kA\n69p+Dw6LYLWHAZZpK4A1xz2merBkPjTpGBrFx34GeO/fX+w1mobfBVqbwJG7gZ3lW7VXzGgWLUDm\nAOuoZLBssNu177ZhTAVYjLFPMcZWGGNHAHwewL9ljP1ckxdnjP0NxtjTjLFzjLGfHvOcb2GMPcYY\n+zJj7E9nO/xbN6icnDdJjksJTJPKXBr0sE1ioUrHAA4mq8zrLRMDMpGNqzbsvwg8/N2gg5fxR+nX\nAADCYAnJBBHwyLtFhebU1xSPc6vxza6lIUIysOoY+NZXH8O5LkCjpAqLRp4AW7ANDRH0qUMTh2LW\nRP+x3wZ+6++M7ylTDJZz5OZlsHQb3JWSj6iPLBxgQBbW2w4yYjWTiwkAK/ZFYle3L3/03cDH/+fJ\nx5InwMsEWIPqnzKUG9j57QG6XgxEAwxqDNaaI6r8WTwsEeSGhZRusIugOk9jGCxNMViaBm2cpDcV\nM/ZMLraYzGiJXpqbrRCwjFi2i2AszGE6Fi/mxanvYrANfPwdzWRO8tpMuQt9VcylmRdgZRWAVf0O\nO9RDbAq5mABYxlLutUcv7MGN94q3DUYng3UgxRXAmrUANk8oBgtYSCbohwEcFqGF8ObvwZKf+bmB\ng2MdCydWbOymzkiJYCqvU26aiEmvSsJrsbMr9rNWe33o/1qOiYg16xMfClkQ2JcSwYUNWF7+Qs7i\nHWpkqTAOsVeBjfsOhcGqFCcWlU52BcB6iYREcAAb7BUGa2qsEtEBgL8N4D1E9GYA3zHtlxhjOoBf\nBvDdAB4A8J8zxh6oPWcNwK8A+F4ieg2A75vx+G/dKG9ADS5CLYuRkqwGT5slAYDCPnYkwKro5yuv\nKWza110DPixk44ZDfuaXgN4VnPsbv4XPZq8Rh7wMgHXpEeDqlwR7xVjx+CwASxopOKaO73rNcezE\nJijsL79SnifABizJYM1cCVMgqGmlKOgKxmsceFIM1urpmxZgkW7BlgAr8Q+QBH14sLDRNhHX3fIm\nuQiq+6W+iQddCbwmbA7qfC9TMhSPAVil6vNjl/ahxd6QyYVwETRyJ1EAOaDihoEEOtiNBFjpZImg\nRolgpzgrAFY9cZLFG1NKBKH68G7Hpue8cLI8BiuAgftPdIq+EcUmPv67wGd+Ebj65emvIxNLMly4\nR04gBQPmdBKsmFaUrvksTdGGD5LGBLahIyATbAkA64+/cg3HteJ+j8vArnS9UV0iSOL/FmYqmsRg\nC7CltH2RPizJ/rgIbv4eLHm+XzjIcOaIizXXwHZii++ktsYq10DTsIQ76oTc5aAr9rP26jDA6tgG\nAlhz9mCJ66YrGaxGPezjwtsVTE3iH77rstrr7DVg815g9/xwgXHBqNw7i0on9y9iYGwghIm7Nlrw\nyIbWdM7rbRpNABZnjJ0E8P0APjzDa38dgHNEdJ6IIgC/C+Bttef8IIAPENFFACCiazO8/i0dlQu7\nAcBiWSxmyaDBsD4AFPbQhwOfzLEAi2VxYXJBZrVKWY7+FWDtLJ7S7xfVSQDxMpoXH31Y2Kq/7u9U\nHs40s3EFlKchImbC1DV8471H0ScbGiXLt2ItSbhsQ0dExuzOO7MyWCphG7ep+LuAxoHWsZuPGZAb\nLXEbtiuSr9DrIZU9SRstseGOlwjWzm08AWABo+Up+esepkSwChgUgwUAj13ch5548GHDNfX88TXX\nQAijymDJjdMwpMkF3TiA1R/Ie3uURDDLwEBISUkExzBYWSyMMCSDxcyS0cntFFkKkEyIlwTgKfbh\nk4n7j3fypDAHuxc/K/5sUhlWzzHbOL7WwRatzT0LqwywyoqIQb8LjVHu/so1hgDLAVifeOoqXt0p\nXicZw2Bl5fciAidprHBdGKxtYOUUcPJB4KXPz/86ch1zWHjz92ApgLWf4My6g1XHxFYspcK19TmT\nPbamJaTP2YRCWF8CrFE9WB2LNzbiGoqayUWTAvXYuFLqvZrTMKZxqPVXMVhZDOwvNg6gHlRhsBYs\nSOxfxI5xHOuugTXXwAAW9GUVnW7RaAKw/hmAj0GApc8xxu4B0ISrPA3gxdK/L8nHynE/gHUpQ3yU\nMfYPRr0QY+zHGWOPMMYe2draGvWUWy5YuVrQIOnQshieBFhNzBVYPEAfNnyYSMcALD1TJhcmPNig\ncQDL2wXcDVzYHuR2qfE4tqtp+HvAE+8HXvd9YjiwjI9/+Qou9TJ0+w0SiCwDpwipZoExhhWHw2dq\nqO1yEznFtHDDhM3FoOF65XRqJDMyWNMYL29XyANN96ZlsBi30XZtBGQg9nugcACfLGy2TTnvqbSo\nl5ps0/rnyWVrtV4UtQlNGsaqwNoyQXc8pgdLAqwTKzYee3EPPPWRchesxNCut0xE9etHfnbDNJFC\nB2vQl3lYEYfi3GcjAZY4zhg6uMagG3KGXm1zZqlgx5XJBRzREI/ui7itopwwLimZyCIxeuKOdQex\nKdfGYF+4fF34jPj3TACrhRMrNq7QOqK9OQFW6bNFXpFEez2RFDNbsNSMMcTMgJYuBrCe3x7g/NYA\ndzsDZJoo6iVlFq3CYFXnySlb95lNiOaJwZbokTn9RuDyY3MzDCxSDNYtIBGU5/7FgwRnjrhYdw3s\np8LEol7oKhisEYqFWgwO5HpTs2kHgI7Np/eJjwupNoh4W8xmWgR4X76OAEvtdc4asHmf+PsifVjR\nYFjZU5kttyDAGuxgF6s4vmLD0DWEzAZ/hcGaHET0PiJ6kIjeLv99noj+syW9PwfwRgDfA+C7ALyD\nMXb/iGP4N0T0JiJ609GjR5f01jc22IwMlkYxBqQA1vQbQYsHGJAtHNvGvL6WCRnPqiMnpY9bvLwd\nwD2CC7tePjCw0nA8T7zwZ+LmfugH8oeiJMPPfuQpRDAQNnl9CVIz6WTGGEOmKuVLnoWVpUrCZRaD\nhuftwWoKhtT3MS5x83cB94iwuL/ZGCy1cBs2VmwDA9hiqnvUxyCXCNYqmqWkaahBfRzAyhmsCQDr\nUBis0S6Cg1AkR2+5dwOPXdwTLm7cqTxn3TUQkVEFJVISaHATCTRhm3uDQs/kHKHB3vB/yu+LNAOM\nMejGaIkgyxLE4OCaAJY7m2/GPrVAn/u1wzvwGxHlz70kBiuNxPD0js1xbG0FEbPEdb79dNF3WSsg\nvbzv46//33+Kl/dLa4VMLDWrhZOrNq7QBnAw57DhOMCeZNNiv1hbw564RnSnYB0iZkFfUG70x18R\nYpaj7ACBKww6knCYweqRU+1nLr3vzD2y88RgC2gdBU6/SazBW1+Z62V0uV+5CG5+kwt5zQfEcWZd\nSATV4Pj6+qwYLMuSEsHydxIcAO95G7AjnCKDgQJYVZt2QAAsj+ZjsJRVuNteQYQ53H/Lcfnx4u9N\nAdbOc8BfvGv29wpqDBYwfx9W2Af+1auAp/6g8jBLI/hqxtiiDFYS4CDhOLEq8tRQc2Dcrs6xDaOJ\nycXDjLF/X/9p8NovAThT+vcd8rFyXALwMSIaENE2gP8PwENND/5WjjKDRQ36ErSSRLAJwNJj0Vzv\nk4V0DNukZQkSzUDL0uGTBTYRYG3gws4AkRwsWtns5gkFgNrH84fe89kXcGHHk03SDRZBudhm3C4e\nOySAlchzrhsGHFNHBH2OOVjLZrD2BINlODcfgyWPhxk2OjbHgOTQwVhI5o60pFV7BWCVGaxg5OsN\nSQHVhj7JxjpnsA5fIuhFCSyu4Y1n1zHwfehIQUar8pw110QIo1pkURJBUzB7jG5ckqVLcJeOAliq\nN4wJyaM+RiLIshgJeM7cue0Ofjv9duArHwb2XjiU415aXH4c2Hq62XMPgcFKI1HIapkcp9cdkbz6\n+wV7BQxdd09f6eGZq3185Upxf6ghn8zu4MSqYLD4YD6AxRIPuySYhTgo3jvoi/vOcNfyx2JmQcsW\nA1iPv7iP02sOzGAbcVsIXyqsttw/+3CqvSNJGWBdJ4lg6yhwWpo0vfTIXC+jJxJgsfDm78FKI8kq\nMtxxREgEeySLSGEVYCkGy5ISwQqDtXMOOP+pPOmPPfm7IxistmUII645ComqINBWAGtRiaACO2UX\n30nxpfcBH/2p2RUUam+z14DWBuCszz8Ly98Dot7Q2svSUNxDwOI9WEmIg1jHiRWRj8WaK5xj/yOO\nJhLBDwP4f+XPJwGsAGiiv/ocgPsYY3czxkwAPwDgQ7XnfBDANzDGOGPMBfBmAE81PfhbOVgWIiFx\n+pNgOhjQsxgBEzfC1I0jTcCzEANyBDM1isEiAqcIxAy0TDH4VRuVIBDlAOuFHQ9rHVFdShcFWCX5\nCgDsDSL84iefxT2bLUQwmt3s8jyQXgAs3VYzl5YrEVR9b4Zhwea6OMZZWYZZe7AUyJjUg5UzWIe4\nkCXR7KYh8rMy7qAjGaws7ENLfHhSIig23LKxxXiAleUAq7SBE5UYrAk21ofBYKmNvrbhD6IEbYvj\n9WfWcBcTdsZQM1xkdGyOCEZuJw2gNAaAI2M3ViJoKJOAkRJBcZyZzsVzrdESQS2LkTGe/3vFNvCe\n5DsBpgF/+W8O4aiXGB/+b4A/+l+aPfcQGKws9hGQgZbFcXrNQZdcUc2+8BnALM2UK0VfSlN7QXE/\nqcSS222cWLVxlY7ATHpzDdxmSYB9tJESQ+oXa2skWQejXQJYmrkwg7Xvxzja4sBgG3HnDgBAFg3L\nmfrkVJUEZYC1aEV+WsSB6DlqbQJH7hGJ8JxGF6a0s3ZwK/RgRUhlofXMupAI9qDcLms9WHJ9t00L\nMdWKkmpNv/wYsoyQ+fJ3x0oEzfFGXBMi8sWw982OM5+0X0XYFwzSV32b+HdTBkt9zlnBYbkHC5BO\ngnNKBHP1TPUYWBoW4HhBF0FKAhwkGo5LgJVwV/RD3kqDnZccTSSC7y/9/BaE2cWbGvxeAuC/hOjf\negrAe4noy4yxf8QY+0fyOU8B+CiALwL4KwD/joiemP/j3DqhpRH2ICuC/nQwoFOMQJM3wrQFQoKL\nAYREcGTynaVgIGSKwYIpdPP1RDroAlmC0FrHVi/EsSNiI00WddBRN7qcj/ELn3wW/TDBO976gGSw\nGmyOChCWGCxdTYFfsluZqsQZhgnb0BATB5vZ5EIxUs0Sse19scDG0Rjw5O2KqpbhHJ5EMBoA73yV\nGAY9S8jPykwHKw4X/YNRHzz14cHGZttCQlVNfjkhqs+2oVEmF1G/MBiYIBHMm92vw6DhQZDg72t/\niAc+/LfwceunAAC91tnKc0xdQwReA1jSREU3kYELo5YbEbLwAgBaOAK0KomgTLC4Ke+9EQxWWgZY\nDscVbKB79/eIgdGTTEludATd5nOnKsYsy7NpVxLB0+sOdjMXqbcnANY93yyeM8Zc5cAv7icFsAy7\nA9fkODA2xX/MIRPUkgA+iV7dtKQOSD3BcpqtwvktYSb4ggxW14twyg4BSpGtCCFMWgZYZQYrG/Md\nJIec2HlyBlbrqHDBPf3GuYwuiAhmKr5PFyGC6AY6iDaJNETCOHSN4eSqjTXXRE+NE6ibXKSKwbKG\n5/up/fDlx7DnRXBI7mGjGCybI4A13ohrQsS+kKUf7ViIYMwPsK4+AYDkPciaA6xZWwNUyDXoHR97\nSdzfm/fNz2Cp+6K2X+llBmvB/ZHiACEZuUQw4aof/j9eq/aZBg3LuA/AsSZPJKKPENH9RPRVRPSz\n8rF3EdG7Ss/5v4joASJ6LRH9/BzHc/PHhc8MNb/qaZhr2hsxWJQg1uSwvmmJfRlgjZP+yddINROu\nyeGRBY3SYdmbXES2M7HondoU1ZRsXNLfMPYleHjrux7F2/6fP8Nv/sUF/MDX3YnXnl5FRLxZk7Ra\nEIwCYOUORNGSe7CSGDHpsAw978GaCWClSbG5NFzIIgm8A28EWCQqMViOeO3DqBRtPyveZ/f8bL8n\nP6Nm2oLBIhta1APPQoTMQsfmwi2vdF+U3TGrDnsxdCggVUp8y3+fIBHcOejJQ1oiyzdm0LA7uIj/\nOv41sNjHb7Z/BN8Q/jxe2vyGynMYY8g0U1T3skw8KL87xg1kmg52owBWySSARyNAhrqGNSERzAFW\nncGiGAkrrOlPrYlN/MmzPyTuzcd+a8kHvsSI/eYFmvI9tywWOfGFRDBnsFrClr33MnDPtwD6sCpB\nMVgHFQZLfAbuiLU7aZ0Q/9GbfRaWngrQ58HKpYcAkEhZl9MpASzNzmdRzRtdP8ZpQ67hq4LBovL5\nlQWvHjlg42bpLSp5mhZqBlZL9oWffiNw7cnpCWWaVAqZYZKhLcEFZxmiZa5ThxFphIgMnFqzwXVt\nMoMle7Ac20ICPvp+2XseXzx3EW3mg8AKmX8pVmwOHyZojkJiGvSlsZJg0ea271cGF6feIAqbTQFW\nOi/A2kfGdPzGF7bxZ+e2gY17gf7V+YpTYxgsLQsLcLxo/pCKkTlKIpj3Hr8CsMYHY6zHGDtQfwL4\nAwA/dfiHdpvE7vPAw98NPPOHlYd1KhisNGjGYKW6JWZhTavMyQuajPZ46V+q5HVCIhhA9lLUFzCp\nM74ciZvw9LEjAEqSrTlja28PPpnoOBbWXBPf8+BJ/OR33g/L0BDCbAZe5DFoJYBltRSDtVyAlSZR\nbjttGYKBmMmIoFJZbXbu1Pc2csONBiKZcI7kLOChsFiqqTaecZGU3w03XXQsjgFs2KHYkDLuwtA1\n4SpVOoeqzy0ivWofW7rWyB8DsCYwDqoJfmHny3LEo3uwtFACve/833DxgR/DJTpWmYGlItGq5hBK\nSqNzA8S4KHbciJDrgkcWzKQvbMjLob4v6exmmmp0RDWZ1bIEmVYwWHdtCinw49lXAWe+HviLXx1+\n7ZslokFzifGk4dhzBpODhtsSYB2gBT2Q/XBn3yJk1WPMVcoSwVQW7ixHnHttVZhFzDMkVc9CBDAx\nIBtUem9S85tKACvVLRi0IIPlxzipi+Nn63eK94qHGaweHLAyW1ZeZw9bmjQQDNbTfQtJmgmARVnV\nCKEeWQb84huAz/274mXCBB1WrHHJMkagHGYkEQLiuPOI2HdWHAN92AIcjWGwbMtCDL26Z5bW9ac+\n/2ls8kCwV+WZmDLaliGLxfOYXPQxgI1NyWDNbeZw+XHA3QQ6JwF3Yw4Ga8b9J+gi4h0ADI+/uF84\nCe4+N9vrACUGqzxLLoFO6XJ6sIigpSFCGLlEMFO9x68ArPFBRB0iWin9eT8Rvf96HNztEN0d0Yfx\nwqVLlcd5FqFHDmLS841wUnBKAF0MKJ2qLZcVRs3uIGTWaDYodwMz4Vo6PAWw6hS8XEQuBOImPHt8\nA0BNDz8tRtD6FHnwYeKd3/8Qfv0ffh1+4QfegI226G8KwaHN0IPFjMKlzW4JBivxlytBypIYMXRY\nXDBYQuI1C8Aa3Yg9KVQfw0iTEuUmphgs4HAGH24/Pddrqx4qbjrQNIZIc+BG8phNAbAEg1Xqu5LO\ngQM4VaenUtIUeyWmqsxaTZAIqk09WZB1rcQYiSBTwN7q4PVnhJx2ZQTAIglQcidMeU9r3AQx/cZJ\nBOV5v0ayp6Y+d0wCQZI9WI5pICJ9qGdOpxhZicFasQ1sti08v90HvvZHxTyXReYGHWbEfvOkoAKw\nlnN9sTRAQBJgrUsGCwDsNbzIz2IvMYdUD4NI9WCV7qdwgAFZcCzxPXRW5Hc6RyGGpwEiZsGDDVZa\nC1h4gJh0tFqFrCtbEGBlGaHrxzimiaKJsXICIXFQpUgl/t4nF9qY+XlsQRZtakgG68fefxHvfeSS\nmIUFTB4C3b8CdC8C14pWcy9K0UFxTulmB1hpiCDTcWZdACzb0GEbBkK9NbYHy7WlRLDCYBWfObjw\nCO5fA9gIB0FA9mDBBJvjHqNoAB8WjrYt2YO1AMA6+ZAAgDMBrCm91OPC30egCzbv8Uv74j3l4zOH\nKqiXC6V5kUIWaBcpEMl7sCwRzM2dbrfZhzPEWIDFGPuaST/X8yBv5djeE0nl7n61wq5nEQKY8GE1\nWlA5xYBuNpOmqQvaaiGR/5lJAAAgAElEQVTWbPB0FIMlXoN0U7AJmmSB6ouAXESeG5jYaJlYWxEL\nIDVdLB77HeBf3jO8KMQevNoAVgAwdDZsADAu5GKrlwBWSyYRwWDZAKtgsGwuJIL6TACrLG9pBk71\nTDwvCUc8XzkYHTqD9cxcr51IUMgt8d0kupv39pDZhqEz4TJXOodKujGAXQOkpWGjZWe7hgyWupbS\npQKs0RJBPSoA1pvOrsPkWl7pLUemS2tc+ZnVwGWdG0hvJIMlz/U1yGS8ft/mEkGRtNuGmAmX1Aou\ngsGqAst7Nlt4fnsArAlGonGf0/WMLBWJR2MGq9RDuCSJoJ4Ktqhtcxzr2OgzmaicfQt+9iNP41po\noN+rnrt+mKANr8JgUdiHBxstU4DhI6ti7Z6n0MCzEOAOBrDBSkkaCw/QhwvTKNbxVLNg0vzgph8l\nyAjYgPiMfOW47MstrjElIfZgVQtdpefM3CM7a0iAtU2rePTCXrMEWEmt/cKBbhAl6LASwFqw4v9z\nH38aP/neCSzagpHEIbxMx5nSurbuGvC11tA9nc+P5Iac7zfMYMXGCu7PnsPZdjqy/woQACsYp8aZ\nEkyOrDnaEfMH2TxMTRICW08BJx9ClGRI7PXmLoILMFh9JgDWF1/sIlugpynwxXtv75b2z9KoAwCL\nMb7yvks1E+uuWPeZ+QqDxSf83zsn/B8B+LYlH8ttGZHsn6kP8TUoAmkmBrChN9D7G0igcbFATDWA\nkMkBM9tIdQdGNiJBVze9TIKI2+JbHZIICoD11L6JOzcsmJa4yanhQjf4s19BK/ERdq/CcgqnKS32\n4JOFozWAxRhDwkzodYBFJIwWHvhegFetoTWrAFirrg2PrMowzGVElgrbaYtrOTjQkIqETNOnv8Ac\nDJYhmceRhiJlBksl44fhJKgkgjMukgrMGPK7SY0WoC45s5AIlpue05IzmFM+RyV2JB0FqtzNiUmN\nngOsQ3IRzDJAE7UqrpJPawXHVmx89qe/DeuuOfTrpFtAjGJjkgBL0w1A49CSGwWwxHnfItnLWGcG\nZYLEpPxPsLkGeM36WKd4CGDdvdnCJ79yFTBlL9CsstPrEeo6j71m93YpWcsiHw1WgqmhZwFCGHAN\nHZrGhItYDFxaeQM++ukr+C9MG1RjsNrdZ/G49WP4pwf/GsAbxPGEfQRUFLGOrgmA1R8MsIbZwqAQ\nsBz4sQWt9L1pUQ8D5mK99NyM2zCQNF8ba9H1xDW2RvuAxmF3jmAPZmXdTOMAMXGEMKvFOAmqPLIO\nf5bcYAupbsODJRgGbgHcmTyTTwGsUnI+CFOslBisRRPSxy91cWnv8OYPBb6PCGIQtopV18Sg72K9\nxnhTmiAmHVzXkTI+EmA9a70GD8bnsWl8NYDRAKstXQT1LJr5utJiHz5WcEfLwnadRWsa154Ue9XJ\nB/HPP/IU3nIxxV83DpnBCvbRk/PFemGCFwcMZ4G5CqkDTwz5iYPStSWPq7cMiaC8N03byUdzwJK9\ndDfbjM7rGGMZLCL61gk/r4CrhqGcnOrNmQZFIG7BI6tK244JjgSMSwZrGrtTmn+ScVsArLo7oJII\nSrBCXFYbRgEs3cQze4S7NlqwHMWWNEhWrzyB1rZoDN3dq1Z7WOLBhwVTH74EU80cZrCuPQV84EeB\np0u9bHmfT7HQr7lyqK233Oo4SYmgyTUwxkoSr4aLdTw7g2WSeN5IOeZIBmvJACtLi8nxMy6SaSSs\npm1DJOKZUVQ7ud2GoWtIqSoRHM9glT7XKIC1fnYiG6KYxkX7BitRBr2lc8OTgsECgI22JZLkWqj7\nLu/BUgyWYQIah36DJYJbSiJYP6/KlEQX17+YCceHXB85JRWbdgC4+2gL2/0IvUwCzsOQtC4a5Wuk\nSaIrv7+Y9OUwpLIvItOK64a1hPvfL58/Bq4xDMgaOra29yJ0Rljzns8fY9EAHmw4EmBtrgvQ3B/M\n2J9KBItCgNvwmQ2eFN+bEffgseqcN1KurhVJX9R4rexKJ8ROsge0joJzLufGlQotcSgG0oOLpFvu\nb+o7GMCuSgcPIwbb8I0jABie2+rjIIgFGK7LassxAmB5UYI28/OCxKIMlh+nCOJsodeYFGEYIAav\nMFhrjjS6GMFgpdBgciaZ+dK6FgtTiz/q34U7cRXawaWxDNaKbYiRM/L3Zgk9GSDSnfndf4Gir+7k\nQ/j8xT284Nsgb6fZ+JIFGKwuuTjSEp/7S1uy6DaH5E4ZPGnlY5DH1c8ZrAXuF3lvOk5xTejWKxLB\nJiYX/2DUz/U4uNshEsVO1TZfgyKA20JyMW1BJRIVQd0YduIZFfKC5lYHZLjQkQ3fPKpaoaRKCqSM\nAFjkbODlgwBnN9wCzDRhYb7wm/lfQ6+6qetJgFCzi2pHKTLNHHahUr0tpeqgkrpUAJZjok92o762\nWYLSGAnpsLi8ZfQZp5/PymARFfOIRm0ovqT6yz1Yy64U7V8oPt/MDJaowluGOF9kFM5Q3BESwbg2\nUDcHWGSPnC+0S21oZfMSeS307ZMTq8a6Oo/LYrCIxD1mSxBSOjdWqhis0YlCcVBVFjZLE2TEYOg6\nMsaFa2J58/7MLwHPfmI5xz8plESQJksEFYPlGDoiMpDWrmkdcVGEkHG3NLq42JP3/M248ZaLXY0A\nlliLe3AWdlYFkJ9/Khn3XDrxnfjH8X+L33lpEz/yjXeLPqhaUU6TSb0ykgGkNApWzmCdWGsjJh3+\nYMZ1Io2hIQNxG5HmVCTnRtKHr1cBVj42owy63/fDwId+otHb7UsGqxXv5g59EcyKtCuNA0TgiMgA\nA+XXpZKq9siBRofPYPW5uE+IgCcudQXAmiR9HSURDFN04CNxjwMAtGSxdTyMU4SHyICncYCIOE6u\nFtfoestAl5whcElpghgcXNOQsVrfcuwh0218LrpL/Hvn3Nh10+IaIjamjWFK8DRAqrtyPIZRtfVv\nGpe/CFgryFbP4ty1PrbStmDjmphpjTKYaBL+PnYzF19z5zraFsdjV+S5mwOAq1xJL7eLKIAle7Cy\neHEGi1sFwNLkTNJsySNzbqVoYtP+taWfbwTwTwF87yEe020VaihjvTnTQALNsOHBrkguRr+IvLFU\nD9Y06YNMXAy3A+Jj+nOULbQCCnlD4rCLYGStgQi4a6MFMIaAjOnNpkkI+uJ/wCXp6K+YPBV66iNm\n1shfTTVL9JyVE0z1fqUFTbnCGXadwXJAS3YRpDRGIk0uACnxApozWOXz1WSDSMLcLrvODgAoMVjr\nJYC1ZAZLyQPt1ZkX9SzyEMCELXszmFUkYabdyU0uKj1YcYSElIvkMIO1RWvQo9IGHnQRai4++nw8\nManhMtGiZc0pSiMhy2yLhEjdb1GSwSFP9DPqw8YWlcgBlmQpJUNq6Fohf6FSFfozv1RxHju0UAyW\nFJFVet6AwkWQqx4swWBRbXPWKUWmD/dgAcDz6qu6GaUjFQarQWKQKrtwdzk9WApg8WJNO3ZkDX+Y\nvgl3bbTwo99wj2BnaueOx2K9c+MicddiDx7ZcCWL3LEFEzQzkyvfK+MOYt2FkRW/b6V9RHrVVnsk\ng7VzDth7odHbKQbLjnZygBUzs2J8lEkGK1aiTHndqkSyD2e2Htl5YrCFfbYGR65xjzcBWDujGawO\n85DJ9WQep7xy+HEKPzo8gMXSCDF4fl0BwKpjYi+1h0wuFIPFdYaEGWBZlcHyYeIF497isTEAizEG\nmrOQaGYeUu7C5JrsYZ/juth5Fti8H5d7EbwozR2gGxldzC0R7GIrcbDZNvHgHat45OU5gRoKZrfS\nj58bxUgZf7w4g8V4kdNxW44hajDn9XaNJi6CP1H6+TEAXwNgeFDBKzEyMmlgUQckJiLohiXmA00F\nWNIcQDPkIjV5gaCwh4wYTKcNZo5JvuVrMi4Alm6NeZ63jYEupCV3bgiwFjKzItcYGU//IZi/i4fj\n75QvWwU8PPURac6o3wTpBjRk1aGE8QiAFSiAVSTvq66BPhywJVfHKY0Ryx4sdYwAmuuWK3KZBr9T\n2kRGJm7+LmCt4OmtAP/k/U8P/c5SYku+7snXz/zaFPsIycgBllbaOC23IzZc6JUNN5MgNoRRkfeo\nz3+N1mAkvQJ4B10MtDauRhbI3x8t1yAqAFYTWWuTkGDzr7Z45d+DMEEHHmLeYHnkVQY0S4WJCtcZ\nSNmblxOBJJh9FtkcoaqYoSUSW79XSyBUYUarAazyNa0Y9xqDdeeGC8aAZ/eV1OUmBFjlY5oFYMFd\njougvNZZicG6c0Osb//9d70a664Bj2zoNZaDJ+Ia7CRF4q4nA3iwcomgbYh7a+ZBq2rt4g4S7sAs\nJWlONkBUv95HAaxgvzFjue+Lc2oGO0BbFuiYMQywiAtGAsi/hyQqZE/6oTNY29ihFdx5xMVdG66w\n0p4EsIjEPcw0ca3I73ogXQRZR9joq+9y3vgO/6P40ex9C73GpGBpjKikTgCEycVO4oDq8sgsyQtH\npPHKd5JFHg5SA1/7wL2F8c0YF0EA8xUSswwmhSBDAKwIejMDrXrsXwTWz+LZqyL/2CUFsBoYXaie\n+Vn20NgH0hBbkYX1lomHzqzhy1cGongxR26j1BvGCAbrQPZgJYvsj/IzltctQ87fi5esJrqVYp5B\nwwMAdy/7QG7bkMlXxf0mTcCRQTcdeLCGNsuhKIGhhPGpC0Qa9OHBQtsxS04utfdQFucy0dOs8T1Y\n+0wsenfJjT6ENd1G/Qu/Ad85iY9nb8qPqRxm6iPWRwOsVFcbdNnoQB5XqUIWS12xVQJYYuaSA23Z\n8qNMMVjilmGzSgTLi1cTJmVaz5a3Czjr+MRTV/H5y3MOMpwW28+ICvLK6ZmTYYqFE5otz5duF0mY\n3erA0DQ5F6XkepaGiMAR1voMlSPhFtagU1q6Fro4QAtdagmp4SiWLUtyJrBp79vUkO9zVQ7fzgFW\nJObZJMZ0gMV49RqnNEEKXfQkKoBVKTAEwN7zhz47SjEAVucIYtIR9GoJhJII6iWJIIzq8M6cca8C\nLIvruGPdwfmdQCThN6PJRXn9ayJtURJBcqcXnZqEqgSXnFHf+uBJPPzDX4u/+boT4LqGUHNgpLWe\n3kQc62q2jzQT17ue+vCZDVPegxbXhtz4GoVaVwwHKW/BQJwnVG42QGxUk2J1bVf6joNu4zVEMFgE\nzdsqGCzNglaad5UlgsHKAZa8j4oerENmsIiAwRauZh1sdkQC/PilfcBeGQ+w+teAeAA6+mrxb5mc\ne36AFgvzOWX6gkD9G+LP4q3aZxGnh9OHxbIIIXilf3rNlRLBoFstdKUxUujgGpPz/Yo1rd/vwctM\nfMurjooiHjBRWs3mcctNfLH+m61cIjjTeBVArLndl4C1O3HumrjP9uiQGSwpzd6jFjZaJh66Yw1x\nSki4O5dEUI3RUH3d5ePyyUZGbGiW4UwhX6syk9SyxQiPBnNeb9do0oP1B4yxD8mfDwN4GsDvH/6h\n3SYhk4iy9lU58HHLwWBENbIeefKim0gwncGK/R4GsNGxODSzmUSQTwBYW2kbHZvn9puimjhhk+5e\nAs59Eo9t/M18SvgQwKIA6RiAlfc3jXKSKzFYqdywzZKLIGMMse4uXAUcijTJbdoBgPEZTS4qFsMN\nEpxyEjTq+f4u4B7BV670ENCYIdGLxraQRcB0Z0+GEx8hCgaLO0US5rRWoWkMKXilT4ISIT2JyMhn\ngAEFkL5WN17w99ElFwfSaWlkH1bpGlpKAgzk53lbOe3lDFaKNnxk5pT+KwDMqAJ0wZDq4LpWMD8K\nYBEJpjSNxL11iKFkt2urK+iiNUIiKAEWL2zahbNp6V7Nihl79bh7sy1mYY0YlntTRPkemsHkogcH\n2jKuLzU83Sx6GWxDx7e++ljer5pyF2bmC/dK9ZxUrK9HWRf9UHxHRk0lYBs6AmqgPhhzTMxwCqvo\neABkGVz4yGpJsVJNqPsWcSDWs4brU9eLscEDYUYgGawhZ9lEFGMiZYSsZgbGhTPaoRrFhAdAGuHl\nuIWjbQsP3bGGy90AntYeD7AkA/2h7VPi37IPK5WGTPrqaQAYPVZlhrAogI0IQXw4xRg9i5Ayo2Le\ns+aYosiQJdXB8JnYNw1dQ6YZFYCVShn5RssCTr1BHvz4tVOz5jBzkjmCZrbAGEPaQAE0FL0rYk1b\nPYNz1/poWxy7M0kE5zC5kNdQl1pYd818pmIAe659nnKAFRVFOnlckWZKo6LFAVaZwXJNDh/WKwBr\nSvwrCMv2dwL4PwB8ExH99KEe1W0UTN4MegmQhEExhNWDPVSNrEcsL3ymm0i16fOXsuAAfXLQtjm4\nZHeyevUwZ8VEcq6eV7l50wTw9/Fi4OKrjrbzDT5mViUBHoon3g+A8IHsm+G05NysGqNkZiFSPjwf\nCCj3N41gsEoShDQKEJAB16q6lcW8NfWczhxZMWgYQGEV39jkQnznIRmN5tCkpdlobBSY9XYB5wi+\ncvlgbnelqbH9jJgeb7izy7mSsNKDZbrFxtluy+ZXxmsMViER1GkEg5UDLHkNBF3spaVBrKMSm7Sa\nlC0lZOKdW5nLa1sxWNQEYNVkVOqzGzoDdHmNqXNTPu7d58a/6BMfAL7ykeafY0Qok4D1lQ4OyEXq\n1QBWWrKTh3QRJKPqzKX+rg9PAblns4Xntwagea6p6xEVgDWbRLDMsMwdSbE3jH1L1S8rix5pRnAy\n8fdN1sWB7GEyUw+xVnL10hgiZsx8H1AO+hzxvQHiHoh60EBD17ti36KgYJrFA80ZrLts+dyWAFip\nZolZXOqYkhAhDESkGCxlCFQMK+c4RBfBwTYA4GLYxmbbwkMyAb4SWcMsjgoJsD4TSgGQZLBSuZ5p\n7WMgMJiZD2riTjcmzCyAzaJDcxLUshgpq7LTa65RDKwtywSlOZSQCBrgZdAbiR6slqUDp6YzWEUR\neBaAJe5hTSooMsaHR8BMi/2L4s81YXDxwKkVRKYcTHBYDJYsFh5AuAieWLVxfMVCPzPnkwiWi7Rq\njZPHZVoOIvAFGSzxu7pR9GC5po4B7FdMLiYFEf1p6efPiehwS6i3WeiSSeGlJDkK5IZlWIg0R+hi\nJyyoSlfOuCkqR1MAVi4RtIwcOIW1HigFXtQNYVkOUmLVTTDYB0A471m491ghe4qZNTmZePaPgOOv\nxae2WnjD3ccQkw4q32RZBgshMj4mieBVA4DK30uLdxYJlqQ+rDg12rCy5SZvLE2QEM8ZLK02i2tq\nyOPvotXIzjn0SwBr1Hv4u0jtNZzfFlPqASwXYA12RIV181WCbUjDmeRpLAkQkAlb6vRNVwDtgAys\ntAS4SLWaba9sno5gVIoIymK2bh1OwT52UgddxWCNmoVVOndTZa1NQzFYqDNYCdrwpzsIAtDNukQw\nziu9YDUGqyQZyrYnAKzP/CLwl786wwcZDgWwNlZXcIAWyK+B1qwGsGQPVqUYUjLlqcfdmy0MolQU\nV25GieDMPVjisx6QK5xPswWTWnkP69bo4hMAZEZ1gOcgEr1/ALCJLnp+DKQJDIoQ14pYMcyZ7wNV\n4GCGC5jt/L1jT95v9mrl+boEh+r3cmY56jeytd73YtxpynMvLerTurNsEiGC6EkWT5BunLGPmHRk\nul1N5pcdcsjw5aSNox0Lrzm1Aq4xvOgZ4r4dtRbvnkcGHU9kEmBJBosUALVXEOsOHIQIk/muoyTN\n4CA8ZAZreMbdmmvmapWK0UWW5r2ldQaL5CzMtsWBM28Gvvo/Ac7+tbHva4wqAk+JWLInueGCZs7e\nm9d9URzv2hk8e62Pe4+14bTXkUKbkcGaBWAVDJayaX/9mTXsJeZ8zH85l1JrnDwu23GlzHv+/VHl\nNFpJ2uyYOjyyQeFNuM5fpxgLsBhjPcbYwbif63mQt3KomSFGCZAoGY5m2Ih0Rxg6TJBtxCWAlTE+\nfYEI+xjAQcvS80Up8GrJgqpES4DlWgZ8WFWmSy4eLwZOBWAlmlmpJlbfuwdc/AsMznwLtnoh3njX\nBjxYOZMnPpD8uzENYJWtuodNLrI4qLAkKshsi7kt6RI32CxGynToUhah5yYFTedgie/3gNxGEkEF\niFNioxMibw/71EGaETJoSDRzuRLB7WfEn5v3F3O2ZljYWSps2pXDltMSoMODhRVbMBvE9OqGK4dS\nRqhWGZUUdEsBGgmyKeiiSy0c5Bv7CICVlgHWcnuwcomgPO+DMEWb+YDdAGDVGFBKk7zSmzM/Ixis\n/UtPTTgub/oQ8imh2NW1lQ4O0IIe1gGWSNoUwLINXTpzDfdgsTEACwACWDeEwfqffv9L+P0vTKgR\nzigRJHm+D1T1fkGZoGKLDGs8g1X01ZaAPRO/Z7MYXm+vYLfqAIvNDrAimSDpllO4gUYD+D3BbjK7\n2oOlDQEseQ1R2ojx7/oxThtynZcSwUy38rEVAIBUmFxwSxUqCgYrAofGTWG0clghAdYOrWCzbcE2\ndLzqRAfPHci9aBSbvnseV7RjBfOtDBIUILFWkOguHARzuwAGSQaHHS7A0ihGVpP/rrkGPIyQqsve\nZUMbZrBY4iOAiZbFRRHv7/4msHkvxkWhspkCVD76M8BTHwYA+H1xbk1HqSaM2Xvz9i8AALb14+j6\nMe471saxVRs9bXU6wCIqMVgzrHe+YrAKgPXQmTXsxgaSOSR3FWMbuTYoN1HbaSEGz9eyeSK3gTfL\nEkHBYN2U4ziuU0waNNwhohUAvwDgpwGcBnAHgJ8C8PPX5/Bu/VB66nJzYRQWmvZYn568KuqWcUsO\n4Z1u0z4gGx3LgOUIYBTWAJaSUmiyF6Rl6UIvOwJg7aKD+yoAyxoPsJ7/NJDFeLrzdQCA151ehVd3\n9ZMLDRmjq7RsFIOVSwRLTJx0qqszWExNEI+W517DsgQZK95HM2adg1UwWE1skkNffN59tCvyUvGe\nCRB2cTUpzl+i2ctlsLalg+DmfaIHC5hpg9CkRNBSAKstkgoPNlYdkZxnjEMrzcGqMFhI82ReVcci\nW1SzEXSBLIUWHuAAbsFgjUpqSpvGpMRyECb4vUcvNZPm5BJBNQern7/GCjzozuq43yyOJZ8nJyWC\nWSERZDUXwTLj6V15ZuxrBn4f/cFim5lymzItF4HegRHXBw3LwowsMFhcNI5X+hrUPcHHAyyx8V7/\nyuaHv3gZH3zs5fFPmNHkQs3/yqv3CwIsVXwzS8Y99SjWt+K666C4RsLu1fzcZrz6OolmQs9mO8ZY\nrkXccqFZBYOlAJbuVq93NQtHsaGV+7LBd77vxzjJ5dpdkgiWARZLhUTQUAmdvLeVdJAZFjjSxRnF\ncSEB1jatYrMj9quHzqzhyX2ZUo1Yi+Lt5/BsfBQ9TRZgJIPFlCrDXkHKHbgshD8nOAriFC5CGCxF\nEB6ORJJThFQfBliBkqqX7gEme7C4zgCdg5dAr5YG8GGhZQ5LiUeF5YhrmabtQ5//deDRdwMAvIEE\nWFKins3DYO1fBFpH8eyeOPZ7j7VxfMUWToLTAFYqDFsAzM1grUuA9ZpTq/BhD80UbRJsBIMVy/uz\n5bqIaHjUxiyh+i3L0mbXFDnlTTmO4zpFkx6s7yWiXyGiHhEdENGvAnjbYR/Y7RLK0tYsAZIy2k9y\ngDV+M1cSQU1KBKc172rxAAPYaNsclqusMquvr0Cbkgi6JodPZrUhUS4ee9SpMVjW8CBgFec+AZht\nfCa6DwDwwKkVBMyuDk9Um6w5BmCpRskRw2bLAIsSwWC5tQVaVxXVJWp/GcXIWPE+ivmbBWBlYOiT\n02ghi+TsiH1qD2vG5ZDhi74Ni2tCpsWWvJBtPyuc3lbPlGakNU+ItSyUJhdiiWkrgEUWOnYZYJUl\ngsIKf2gIbySAtNXZEI8H3ZzFOqAWDmiCRFB+PwfkTNTef/SJK/jv3vc4Luw0OIfyPO+hI2Qi8rx4\nYYw2/EYAi8vrJ9e9p0UzOFPuexJghoF4/YQ0GN0Xxr5m6PWwd7BYUUGZBHDLRmyswEpq95CSCEqT\nC9U4XnE2zRms4cTp1JoDk2s4SI0bIhEM4hTPXp2wLkQeACauwQaV1zQKkBETgBFYuMgRBgpgjZcI\nlkEOAPTDFB3mYWAKx720d6UAWLUiVqJZM/egJJLB4lYLunzvNOghGoj7jbtrlecriWCqGKzyfdlg\njTrwYxxlXQAMcMU9T9yuAawIEXhhcJQUACuCAT5rj+ysIXuw9tDB0bZ4r9ffsYZrkXzfOsAiAnbO\n4wU6jrfcfwoDspAOBMDSVCHQWkHGXbQwP8DyIwGwgIJ5XGoQwaDhIeJrjokw74crJfNZggSaZOZN\n6Mhy0KsnAXySPVgNQhWLk0mfi0hcYy89ChDlDJYtJeqkG/IYZji/+y9WHATvO9bBsY6FrawFmgaw\nRhWJm4RUYwS8jZYsIG+2TQzmBCzluZKx3E8UKHKcFiIYoAXulWLfKEsEebMxRLdxNAFYA8bY32OM\n6YwxjTH29yCs2l+JBqGYKwvFBa4YLN2whe0mMFEuk5TYJkGzT67AaPEAfbLRtjhsuSipm6r+mrps\nts8ZrHCYwerpq7hjvdioU92GSSNAAhFw7o+Au78ZX7zs4Z7NFtoWR6g5uVQSKAw3NHO0nfVEBqus\n746lDK3GYCnHuthfnpJVy5IKwFLnbRYXwZBkla9BlVvJa7poDbOFsvL53MDEq050sOYaCJm1ZAbr\nGWDjPqRguNCTblEzLOx6GiCEmVv5rrRs+GQiKNlGk1YtFjBpJFL//rPIRwAD7ZUjAIDU369U+HKH\nwpEmF5JhgAt9Qt/gvjQGUA5sE0MBKrKEq5P8d+j3oTHK+80mhZJS5FX+rNSDVTO5UD2b5+kkjkSX\nQWOuOZNCcFosoVTyVctykFqrcLNetW9GHpNWAk+pVnV4U6yOphcNzyp0jeGuDRd7sXHdJYJEhDDJ\n8NK+j8G47zn2hCTW6jQDWNL5MqDh6v08EUm2SFXrR4WuJKi1+WvByl3imHpbY4tY8wCsfKC75YLn\niogeYgmwzBrAUps7cNEAACAASURBVFXsnHktS3cbfOddP8YGugJcyeuMdAsWymy06MGy1JB5lRzG\nYp3VzcMGWFsI+QpicGx2CgnXwTjDHW8XRtLDlnEa3/rqY9hDB+GBYMHUkGjYqyBjQYlgGMFiYn2I\n/UNI0+T9X3cINblWFEbjOoPFhXlPPn5CHJ+eBog1SzinNghbGmaFgwn7ehqJAe3+LrD3Qi61d6Sx\nUg4MZ7ku9i/mAKttcRxfsXB8xcZO1kE2mAawRhh1NYmgi1BzsOK6ubnYumvCI2suwMJKSo5AMmCq\nt7ndUhLB+RmsJBw253ENfWgM0f/+4Sfx9t96dO73udWiyZX9gwC+H8BVANcAfJ987JVoEDZJnWsJ\nkKQ5nWoXtreTJILKoYVbyBoALJ568GCjY/O89yWpAay84mCKhVJZambl45BVuvWN43nvESBodmNU\nIrfznFiM7v12fPnlA7zmtKjk1wGWorjz2Vu1yGcpjOrBigd59YmlQobm1HqwDJng9g9GMBpzBqOk\n0tjLc4lgs0WJZL9YgGY2ybGs0nXRGQZYUrv/1D7Hq0900LG56GkZB7CCA+ADPw589peBgwnyqFIk\n157GM+kJfNO//BO84w/lgNsZGCw9C5EwK98cOjbHADbikm20GDxZlggKBkurMZgUC63+xtoKItIR\n9vfyBOYALu47uYY+3DE27dLljdzxslYAvUDcU2MT73KoxBa26DuQiXjqja7ojwouAVaaA6xCSqPl\nNu3imCJ5716x74GBBJcvnht+QSI4CEfflzNEJhNUy9BB9pqQWZW+9yyRyVFJ/kdazZQkHi8RBIRM\ncCfi1106UjYOUNXooYg90Rva0EY+iwWTEizJyVOxDvYEgGVIkJNKhr4fRGgjAB0R/StscK0EsKpF\nrHRS/+yYUMUew3ZhyGJG5Pdykwurs149PikRzKXQFYA1GbTGaYZ+mGAt28v7rwCAuJT8yb5aLYsQ\nkQHLrs1MlMBLMcQLWU/vvQD8i7OCza/HYAsDYx0ag7AZB3DnEbfoxasBrGxHmNOsnL4fJ1dt7FMb\ncV8k52qGGawVkOEuJBFUygegAMZLDcUUjuivzFnXmkQwhSb2Ab3at8yzoJh52SBajoMeOUj62+Of\nVF5TXnoUscw1XKmgyGadX5llwuRi9QyevSoMLhhjOLZiY486oGkAq+KEPNscrAEr5IGABFiYPtZn\nVLA0hC+LQDnAkgWQTsuVRkXz7x3qXjet4vt0TH1oDNH2+cfAXnpk7ve51aKJi+ALRPQ2ItqUP3+L\niF64Dsd2ywcRwZEMlsmSvPJcSAQdZHnD8viNJ80lgpZgsGrNu597YRfPb8sNNctgph585sDiGlpu\nCxmxKnBCMVtLbUQtk8MnC1TpwdqFBxt3Ht+oHg8fw2Cd+wQAoHvqm/DSvo/XnpJMkubAyEo9AnIT\nGOeUpRJsKrmnVXpLpEyQJQEimDkjosKSlS6vV7OYXiC0LAFpRdVeJchJww08lY6HIRmNmswVk+jz\nleGkWTJYF3wbrz6xgo5tCKv2cQnrpb8CvvgfgI/9DPBzDwDvfquY7TEmBoM+tP2L+MiVDu452ipc\nCmcAWDyLkJaqnI6hw4NdGS6daVwYvEjJCMtixMSHHfakI+HxVQcHaInKuWoCphbuO9YRVu0jGCxV\nlTuAO17WCuDAF/eU1ySxiT1kYAhhoE8WSJ6XVFZLWQMXQdMwkRLLCx35nDVdK2asyWpxPk/o+GsA\nAOeffnzEMUlzhAUBFklW2DI0aI5ybSwS5FSuG5pRFBsy3azMMyvY8aqMSMXdm21cC/jQ6IbDjjDO\ncA97Gavo45mrY6SUsS9YH7PdDGAlIWLoCNlyGKxEspWOO35YtWrYDwdd+ecBNEbQNu5GRgzc286P\nndUA1pBZRIPIcoDVzsFd7PeRSYWA064BLEcCrJzBKt2XU0C1sphfTXaAzsniP3i110rLIiSMwzBr\nDFYirl9DJnpRtMD3sf2suPZ3Rjh3DrbR1dZwpGXmxUfH1BFxeb5rxZ6Xzn8ZAHD2vgdxrGNjj9og\nKRE0kz5iZoqChNmCg3BuBisqFVLj4BAAVj6CYRTAkrlMGWBRglTNKtNL7FGWwqBovJPwiOjYHLvU\nAfW3xj+pDGJe/kLeGtHuSImgNuP8ysE1cbxrd+LcVj9vlTjWsbCLDvRwb3Kf39wM1j56aOFIq1hD\nHVOXQ8Ynu06PCi0LsQdx7JEy0JJjbtZa1sIAK40CxKTDtArVgsW1oTFEP7T/LvxX/mJOt7dSNBk0\nfAdj7PcZY9fkz/sZY3dcj4O71SOMUzgI82GIynpbVa255YD49P6Wol/KRKabQwxW9J7vw5O//T+K\nf0j6OOZisF5LJt9prZqVxhFSYjDMwuTCq7Eg6WAbu9Su9F8BAOk2TIxYoM59Ati4D1/yxIb7mlOr\n8lhcmCXb9NivWqfWQyXYcWlzLM+FUv03QmIwvNA7bZEY+v0xAx/nCI0SECsDLFMeY3OAFZApGrAb\nACyV1ETG6giAJYDjPlp49ckOVmwOj8zxFTK1If3g+4CvfzvwwqeBi38x9r0P9q5BY4Sve/A1+I0f\neXPRg9V0g8hScIqRliRijDF8jr0Oz9qvyx8jVmVqmJTJ6bX+NibZvxMrtpzNVEgEYa/gxKqN7qiZ\nTSgNHiVXFCbGaO+pfxU/xX8HfpPG8GgAjyxwTcOA7LxvMVPHZE2XCFqmjghGfnzqs/OyyYU8VgWw\nWmceAgDsXhx2ElR9CcasDdz1SEOE4LC4DkMmzmG/OK+ZZBB4CTyRboKXGaxSz+iouGezhT6ZQi62\nwLyfWSNIUvy68X/iHcZvjmewooGUCLarhjpjIpMSQcXaLAyw5PfotsYzWHZLrG+RdyAPWSTzRnsT\nXdaBEWwPzf/Jj1ezZwdYEihZjgvLUYqIHsjvIiYdrVb1PSzDQkosH25a6cGaIhFUUt1WeA1YGQZY\n6jX1LBaGT0a1XzPvzZLAKwoXYLDU/ZyMWFcHW9iFcBAsh66KEmFVxnbl+SeREsMbHnwQx1cs7KMN\nLRD3lZ32EejiHDKzhRaCuRmsuDSOJT2MHqzcwGZY/uu2RjNYyhyq6C1NCvn3TADLwC5WJhtLlPfA\nlx7N1+aVFdkXq4BhUzmcnIE1cE5jqxfmZl/HJYPFKButnFAhP6entXOH0EYhHXKPtGrn2XDBQDMz\n5XoWokuqXUQqLmJR9F1zDURUc4KdMbI4QAQOq1TsZowh1h0YaQAQgYhwNr0Ah5Y8r/MmjiYSwYcB\nfAjAKfnzB/KxV2JK9AYeDJZin4mb25dOfqrPwbRssZEDEwGWYpt0wwJp5hCDdX92Dvd3/7zyOsqe\nt2NzKWOqbmwUB4jBYcp+j5bF4cOEFhcLtLd/DbvUwX3HqhV54has+iYd+yJxv/c78JfP74Ax4LWn\nRaKZ6i7sknOVqqCMBViSwVK6XnG8pZtSJj6alKHVo7UiNrmJWu0ZQ6cxDFZDgJVJZiCEMbEXKH9+\n5CEiHcxqwURUTURVczs5OYM1yCYwWINr4s87vx54849XXmNURLIQYMmKZGEL3RBgyc2rLv/4Bfcn\n8KnNkrpYr7rlaVmMmBmFll9Nh5d2vsdXbPTggoKDPPnR3HUcbVvoUgvxYBhgKZDeR7UZvh53734a\n/5j/AfjuCPld/eMFfXiwceaIiwHswja35AY2LSwu7OhzCVNW2LQrAwnVdJwXR9buhM8cJFvDx+j1\nxT1hLjhcVTBYYn6Z1RE9bwd7RcVYMVgViaBuwUCcX6NKIshGJGEAcOaIC49ssIa23cuKIE6xxvp4\ni/YEnrkyZm2IfQGwGkoESQIsda/MlESNiEQaurTt0ewfANiyn0Ql04mU6pntNexra7DDnXy4J7eG\n125zVoAV+wjIgGMacF0XEelIgz5Y2EUPDtpO9VgtU0cAszgXQVeYwQBTjU26fgwdKeyoymCpNUEV\nG3SKhCNcXoxRAEuYXKhKetxg5uDYUPfzqLEagy1cS1dwtFO9xt1WGzEzhtj04Nqz2NKP4dj6Kjba\nFvbRgRFJgJUNcuZLM104LJzbYr1sZpUeRo+jArIjiieOZDcrPViUIpWFSVYebaKujRkAVtvi2KEO\ndH8CwJL3rO+eAl5+DAgPEJIBRzErs0oEJcB6IRVrYYXBInlvKbv9USH3sGtJa2YGazdzcMStrQPm\n9HxxVOhphH0JsBK5v6u1fs0xh0dtzBgkmeP6yJxYd6EhBZIQvd0tHGP7o9VPt2k0AVhHiehhIkrk\nz7sBHD3k47otwpcJ/kCXCb9kbhSDZZhyIwcmSgRVEsYNE9ANmEgqCbdDIc6mL4h+E7mxZoa4mSyu\nyf6cGoMlBzUqeZ1r6ng6O4NW/6IYMgsg6W1jl1aGGSzuwGBptdn+wp8DSQC699vxwcdexl/7qk2s\nuWIxS41W3osGFNIFJTepR2EAUNoc5QBJADnA4llYYUlUdGRPQOQtj8HSKQHpxWJn1k0KpoQwahAM\n1iSpmgrRd2QJ5y5kxUwkIL9W2p1VHGmZ6Ngc/cwYX9UabIkKsNUBzGqD/KhQ2n1dOgLlvXJNm2tV\ndbL23fzYN92Dv/u1Z4oHak3PqtrJanPQ1Eyt45LBQtDNExiztY7NjokDuKARLoJKXtujqi16PTSZ\nTKX1wbojIg768MjC2Q0XHll5MssU49FAImgbGkKYRZVf9mAZmpYbSCSx+M7VNWbYLvqtO7EevIid\nfnWT8iSbwZEtNP+NJaHoweI6HOnaOOgWPQ+qB4uXE6xa0pLGwyCsHBttcy7Z6aIRxGII6ym2i/6V\nMQObYw/nuxme3qNGJheURoiIw5GM06KyLGXo0rLGW1e3JMhRSoBMXrOmu4oDfR1uvFsUsZy6+sAS\n6oMZmEOKvLzXVSgdbFDYB4t6OKDWkM222HPMItEO9rEDNdJgCsDyYmzgQDADnRP540wyWGE4ALIM\nnBIJsJScXLKmaYSQjLwXpKnCYGQoQ6U6g5UmgLeLy0l7iME60jLQZ+0KwIrTDJ3Bi/DbdwIQRi+h\nsQor6SGKYrTJQ2KINUOz23AXkAiWWatsEXnk2DcYP0Q8Z11La6xGwwxWlkTTZ2GOCCERXAEPJgAa\nuQd+3LsPSHwc7X4JHrPzXuC8L3RGgPWUJ65fVWxuWRy+Ia/pSYyavC730ZqJdSJ/HzupU+nBAkoO\nojMaXXCK0GXV3k3Vb7vmGnLUxgIAS4I1q9aukaiWgGiAg0tPAAAsRM3GodwG0QRg7TDGfki6COqM\nsR8C0GB89SuhAJZviuqHakDN5AJkWE6RvDZlsNTCJhPuLM3gIoCJBP2XnshnP5EEWIwx4TBX2ySE\nnW1B6bZMjk9nDwr6+flPid/1d7CHDu7arPZKqc2uAoBefgwA8Lj2AC7uenjb608V7yWdkdSmrm5w\n2xmdiBq5C1VpoU587EDZr4vPqGcRUm24SbazKgBWukQXQR0pUDK5UBr/tGEPluptCf9/9t482Lbs\nrA/7rbX2Xns4wx3e2HO3GtCAQEQSGGQbLOJgrBBjl7ErrlgUxjbGA7ErJB6InTiQyuSKHeIyxlA4\nsRNXAbbjGFzG2MJAADlYEsKiJSFaLanVw+vXb7j3nmHPe6/8sda39tr77H3Oue++7ugJfVWvut99\n556zzx7W+r7v9/t+PyW1ct4umdgyQQppkbLO4lysUYPjDdd1AjwLfSxrf7x7vrql/WQY26ugJ8VJ\nYV4rwvMiWMbbqYdgvfern8A3fGmbNKkeFY43JSrmtwUWzVtUuZ7BmgdYIgYvFkB2ihoc8fQAV6Yh\nztQEbGAGi4qTBTY3fzdIyavZ456pshVSBHjy0qTj58RLGlbfXWARgkWJIWsqlBDwPWZNfGsqWAoy\nn40hLj+NJ9hNfPD5LlqXJc76MURp2jdInc3jmB5q37F00SY0TVVquXiXCuJ1aTd1z2OvH0exbGXN\nX8cCKy8yeEzPSzy5/vCwoEmZ4NVM4EYqzoVgxYYml6YXmytTphEz3VJgzUIfCULUmVnrTSHAwgOs\nvWPMqruobIG1iWAB2J8iBQAGQQ4lRyy1WI0qVhDFEmsWdwSQAH1va7XU1BzfGV5uzJzWjjXkLC1x\nnZn7bdbuIZxo41lqk+NGBHYdtr6OTY6K+RbZsvOL9xJjCFZ6F4DCC/lkA8E6jKX2RHPWopN1gSfY\nKygPnrQ/a8JjcCikyzuYsQSV2a+9YIoIBZL83qi+jWNN0p+7vi9BSOEAOj01BZbaoAjqe5nWtbLM\n7X7GR6xahmIWeriLOYLi7miDgDyyfqF8EwDg0eTjyJmzD4ku/XxnnH4WiC/h1+82CDyOR47agpBN\ndF63vcAy/pdqqhGiPZtfKjvDQk1wqVdg+eG9IVheU6D0Znrul4pwQ+s7iHwtLnVeA2b3eI1Kch/B\nqr0238he/hgAIETRERz6fI59Cqxvh1YRfAXADQDfAuCPvJYH9fkSmSmwylAnwiTBS11rGUbgcneB\npWpCsILuoCiANF2DM73YnH3qQ+1ws6PQV7AQvOwXWDoxsAhWIPAR9QZk3gx47mcBAEFxiio8QuD1\nfCpM1zB3u7X5AhAB/skzJwg8jm98q5NIywm4wxumBzwYGeT2gk10iFUZbinDozabl9/kUEMI1mRi\naCz3z2hYqKpFXABIIwO8r8iFKvUMVoYBr5CBYFWKggVg/ia1rc6WWKsQbzIiIvPIMxTBkXto/Sow\nMSa9XgAwsd13jVQuDYJlF/V9KQ7mOqsRipiN3sAxN15j7VyFPkfczNodRD4WKoZfLoHsDCvEOJ6F\nFsESxWaB5c5gue/ZD1nqZ1XtM3eTrZCAEKwQzDxzVm55nxksjyNXfltgqQo1BDzeilzUZddoOIhi\nzB9+Ix5jt/DBT73aeb88aQtDNURp2jOYncHiODjSRIV85RRYtSMnT9FLWioS0BlBsA5jH6kyv/M6\nKgnS+gsAX80/PjyHVSRYNxJndbjXDJYy5thzU2Dl6cW+j6o0RXCb+eo00EUOJdP2ng3nSOUxDppT\nVNkahRIIw26Tg5pj5yrCyxSpkggJwVIhVJHAL5dI+easWOjre5vUUlV6ihtKJ6NqR2J4mhS4xkzz\nwEGwyJi7yJLWVFi0CFZV0lpRoOTSije9JgiWoYTdqie4PO3e40exj9NegXX37l0csRXU4RP2Zyw+\nNh9xGzMkaKReM0Q4BWfqnhUAG+f3movQI8eCkMKBtf0wDpErT18jEy6CRTOZZZHb557L885gzfRe\nnA83wqg5+FnvKZyqCTxUKBzlWnYvCNbh4/jYjQW++Nq000zwp2ZP3QvBMnvoPs9dU4PnCyww2UCw\n/PjeCixfFWB+pNFn87uqLpBDIpICVd/L8LxRFVocqYdgWR++Yg1269cBACErkRf3zrJ4kGIfFcHn\nlVK/Ryl1RSl11agIfvb1OLgHPUiOXMVUYJkN0STlQRghCnysVbBVUcvScoJgY0gzdeaMqhd/1VIE\nWdh2LkseQtS9AstQW6jAkoKDc4Hn5+8APvVzQJUjUgm86SYb1PLhXZ+NfAUVTPGTH7mB3/nma9ZM\nFmiVrCrquOZrNIohjIa7V76krmSbKIo6w6vKdEFNQqFViDYRLM4ZEhZBFfexwEI9SBEkOtTOMBzl\nAvsN2fIqQ84Cq6jomiuuFmdYI8Sbr+tNWasIBsDYhrq+1UoeM6bn/raJqlhjUX19ojDUBsD7Lur0\n3QauTSdEV+RCe4354LaoNMhtk6Nk2i8lF1PIaokmPcWpinFpInFpomew/DrdUIeiAivhVGANn/eg\nNs/NHveMKhKsVagLLATglTlf5r8XQrAEg7AIVmlOD0ngxvCufBF8ViO99enO++UOgnWRrj2vtTKn\nJziOjvW6RYaogF6LSnidAquhuQCT9BCaYL2IeuEL3vozvY4IlusF+Fv4x/HsUIFVJlg0Eota7nds\nplE1NXNRRXbB71NmKFgA3kOF3NCiNmG71lOyGcyRh5cQIYNavYoUAeKeR6Cy8437Fx7MGLpriqCH\nNbQXj6xWyMRmgRV4AjlkK+aTnuFVdYhGMUtdHIuztHIKrHYGyxZSRWrXBSUkpJRoFLNUYNEUqJm0\n9155EZl2K3LRew9THCQINimCscRJE0E5VOPV7ecBAN5RS4/2pvrZyhe3MWMplFkzPMMWqLN7Q0Ib\nByG8SKNlNKgZ5g8VWHrGuFtg1VYcigqsqiwsKjhm1TIUgjOUgUGN1sNS7TQ//J63P42PMm1b4CrX\ntmj7nvv22Qto5o/hV184xdsf76plhodmT90DwTolf7R9aILmeT5TExzH3QIrMBY0zTkFTHxVQASh\nyRP09WEmJ4mlQMP9CyFYROPvI1iKCqwyQXDS2h2kr4VH2+dgjBZYjLH/dduf1/MgH9SgIWRuklvy\nNiIIPQgihEa+2oX2+0EUQd8PbYFFA/B50v5ecPsZi0x4ToFViRBe3Vtsq9yIXOhbgDGGWAo8O/lK\n4OwF1M//MgAgOhgqsEihqSs8kbEYd9dFhx4ItGhaarrsqlgjQYB4hAYTSK1C1S+wbhOClS+1HD3K\nQQQLAFIWg205p+eKpoFA06ogAQgDiUrxvSmCejGTrdP5TgQrQ+kgWLlzrlerM6xViDde19d4Hmof\nHlZnw5Kxq1vAxLmOcrrdFsB8lm82+2mgPdL2RhuoS7eLX99DsIQq0fCW3kPPiVdnqLj+WeHPIJsM\n1fIWFirG8UTL9Jdy2GyYrk/lmedhYINTSiGqe8nqtijXSBHg8eMJEoTW401WK+Q8ArjY8QZA4HPk\n8NuOsKrQQIAxZkUuKtNYUaZwDqMYOH4aADBdPd95v8JJyIoL+N/wutCS0QAmocRCxXbGBwBUrX1t\nfNEWAHVgnkszA9cMCGH0gxLJ+4pgvfzhrUURNSmSy1+OR9ltvPrZT2y8RpUJlrXESSV14b+rEKlL\nFPAwn1OBdcHvU2nz1W0xC32sEYAZxFoULYJVRfo5984+gzVCxD0kjHs9E+A9glcZcgTwBdfmoSoE\nLxME9Qq52GQh+IIhgwQ36xErFjjDBCkkimR7gXWaFnjUOwMY76xZNA9a5mnrLeQFCHzdqLBKu0rb\nQ2z4zN1LWIpg71yZv2eQgxRB/cy086D5nRcBANGlVnw5mOvvli1uYYYELNTPEDUjz5tAU7gIIbsH\nv6RdQQ0rMYBgzSMfOWQHfeOOOJRbYBGVzz9HgQW0zeqxoobWwctHh5g89VX680TbyGW7KII/9z8A\nP/UXzIcp4PSzuOtfR1LUeMcT3QLr6OBIsxC2Flj6c85wjvWOLEgQ47iHkIamwErX55svlyjAvLC7\nj1e5Loo8YcziL1Bg0XttFFiEuK0wX7Vzr8Vv9gILwHcC+G0AXgbwQQAf6v35QuwIUvSRZjG13Fcz\nSO55AqEvOlLPg+F4VjEXZgeQm+TnVXWIo+UnbILpcu9r0fWh0j/UnWgX0p0EHp4J3w4AWH3oRwEA\nB5euox+EMFRuIlescKfSNK7f8car3deb7ly+MgVWmejuqj9SYPlawtotsLwmw13M0CimNz7aZP1h\nlCTnMUR5vwosI8Ig2uMNfY4SHpo9O8GaeuUjGDBjHAqvzjTyaBKF3FmQVLbEGiGums19HjqUqz4F\noWm6CBag57C2FJ80WycNgjUNtUfaeVUE+ci1sUHn08wTClWhdgosSo500mQ62L4ppE4/i4Wa4Nh0\nkFUvyacgNKUm2t7A9crKBlNQsrr7nuFlggQBrh+EyHmkKStVYRLO/RKGwOMonALLnVUg/6jGUARV\nlWm6VyCBS7rAOkxf6LyfayR+IQSrKVAb+XzGGFZ82pEh1pQ40ZnBaoIudbcV5RkvFOz6dL8QrLoE\nfuR3AT/zfaMvofUqe+LdAAD50oBVQaFnH+9Wcr/jq3OUSuDQ+Oz07TDOG7zOdxZYoc+RIgQvW2pq\nDQ74MVSs9xp/8TwSFW4gWIQ85OcoBHmtUTUA8ARHxkKIao2wWaP0NtFaxhhKZvz+iiWYanCmdDOi\n3IHMnKUlHvVOgem1dn0A4PlOYWiV7AKDBPt2HSb/Pbr3qiGGQZnqJPp/fvNWBThSZ8z6yaBZYzMl\nNxCso4mPpYqhnEZPdfoSAGB6paUITg36sbh9A1OWgZHyqEF271li3U3gXwMEixBBPoBOR75A3tu3\nuao3KIJVkdsE2xvxwhwLToyaEQSLFF39cIK3fpV+zsO4vUe5q2Q4FM/9a+CXfxB48UN636wyPFdq\n1KxfYF2dh7iLGcrlFuNjZwYLwH57qLl3hhCsaKrvk/XqHPPldaWFsrwAmTMuwuscJZPgnKFhPsQF\nLD5YTeJI3ZLCjqosbmBe3sKnm2sAzrf+PMixrcB6CMAPAfhdAN4LwAfwT5VSf08p9fdej4N70IOK\npujwuvm7gWYNnArozXIXgkVolS8lmGgXKQAoDIL1b5s3ImhSNDc+ol8bHdjfb7wIfl8avC6QOzNY\ngFYSfAHXgcMnED37zwAAV6510Sig5U27nao6XeCVzMd7vuyhDeNfYWZ4MoNgsTJBogJEcrjTH9JC\nTclwXUGoCokKsEKEKjmzXUQ2ksSXXgy/uo/JG2DPPR2jLrD2oxrwWpvlWlrkjsLMa1LUIrRDwGXW\nJs36/IWWhjkLtcS+fmEvuc5OAVXjV+54rfTvDhNVMpuW5lhngacNdfdVLtpxbSi46CNY2mvMdqtJ\nPa/JUVHn0SCz3vJFLBDjsuGoi5hMcbudPdrsrfnvQGG7zErMYZQT9yjKRZ0gRYiJFFDGI6zJ14ia\nBKU3PFfYj8ATHe8RrirUttPbpQjqTqNE6OmufsZjXC5e7LyfW2AV2b0XWKLJUTnechmfwi+c+a66\nQuUg30BbYJGKY0PXc4QiCDgCN/erwMqXuunyzD8aHSSnAqu+/hVY8zkeOvlg9wVNA1alSBE4Ihzb\n7wfWlCjg4/JBjFKJbtPpHkLUGeodBRZjDBmPIQxC4VdLZHyi6b+mkRIkr2CNcGONbWeZ9r9H+n6D\nhYjgV2tEJV8o3wAAIABJREFUKrXiDP0oWKALLPM8LjBBooLOfToUZ0mJ6+y0M38FtPOgdd4WWBCB\nnmWEZ59zT5WoeQDf3Ht1v8i48RHgh36HTqKXLwMnnxk9lmyl7+e7Zz20wEGwNgqsWGKBiRbiMcGW\nLwMAZldaBGt+bFgttzXVV9B+7e8WIdoW3Cmw2I4m3r0EWaeIgeZJJAUyJS3iDmhqvW0cGXPyqiys\norKMzodgBXOi5Y0VWLR3TeE9+k4AwBMPtUgos6qTI+eGCqD3/ddWQfBXl3Ncn4d45LDLyLhqvLCK\nxRbjY5rBogJrH4qgaWgt1MQqMVNMjZ9Xep4Ci76rF+p5fGP8y+sclWErNOJiBRavcxRMdmdz4VBA\nX9KYzDPqKQDY2Wj5fInRAkspdUcp9YNKqXdDi1ocAvgYY+y9r9vRPeBBXajpJc0lt/zoukBhusSR\nL7BGALWNzlYX2iXb9zYQLOrYPON9KQBAfeYXAQDSEZBovAiB6i0odVfkAtAI1rqogKffDVnqTeXh\nhzc9pYUkBKtdLJaLEyyaEL/v33tk4/XUrS5NMcjKFBkLNgoxisA33X2i31XthrZAjDw52znnU3nT\njoP4hYJmhFyKYG+GZlfwWg+BRtF+XGy/yVGLEH5AdMw2MRHVGikL7fmzM1jAJgVhpcUQ/rdfXeNv\n/azxT9rh8WNnfogiaLzU9p4LIP+qXRTB3gwWUQQ9n+bbtPKkVEUr+W6oNLzOdYfPUCg8UnTKuup6\nyqDFvkEOhzbWRVZizoxRb7X7O3p1hsaLwRizHmFZssAUKaq9CyzemVPRswo6GbYzWIYiCKNA6QkO\nMIaFvIaDuptg1M71rC4w3C6awtIxASDz5lYABABUUxqKoKMiGOnObmVmtVpK85YCa0KUzfv0jBKd\na31Lz5AOBA39+9EUrxy9A28tn+kqCZp1JlUB1sqsKztoxswwAS5NAmSQ56LeDYUYsZ7oRylih5qa\n2FkoMdPJJIMyCFaXJWBN3M9RCIomR+motZY8wrS8Cw6FWg4LupQsgFfnLd1JTZAg2NpIBDSCdRUn\nnfkrwCmwipYiyP0AgWES0Fyzpwoo4W+oCwLQFNIf/np9TL/9u/XPthQyVCRtzDKZv+cswHFPhODI\nUAR53c4Z+ckrOMG8sx5eunQFleLwFprqaxtEpqGmxpCO534W+Owvjx4zKxNU0OeE98cC7kNQ02uo\nwAo90aE9A3pdIyo40QrrKrfz6OctsKJDjYDUq+GihtbBIIqB2TXg2pcBR0/Zf/d8oikO79tFtkIj\nQu3p+SFt9/qLt2K844mjVurdxLVZgLtqimYETdMfZGaw7oEiWMn5Ro40m+n9r0jOU2C1iG8pInhm\nndMqzIYBwqU2qd9HPv0ffhvw/r/Z+RGvC1Rs07uPkay8KbCe875YH/8XECwdjLG3A/izAP4wgJ/C\nF+iBewcVTcGB7ro0drgwR2kRLM1p364iqN3pfcGtPKqdLTEL1erSlyNXPsTdTyJVEtPIWQD9GAG6\nCwqrC5Sq24mOpcA6r/B+9eXtz2iQ0wkREI2hTSZUtsQK0QaMDgDSFFiFkcAWVdKVTu1F4ImOwhoV\nIykCrFSEKllYSXIxokLU+FOEfVrkPYYyHfHODJbkpsDar+vjNXpQvPUK2YVg6QKLKBQuguVVSWdw\ndxZ6yNQIgmVMhm/jAD/8C5/CjbPUIFhbZiFMYhBGNIPlG5T1fD5YfEeBZSmXtfZ181AD3GtNnHMt\nWsHRWMl34SCzC0xsghPNqMDqdptVZZBaU2ANJZaLrLIIltxVYDUN/CZDY74bbSDp+gwzlqCWuwUu\nAP3cF/CscpNW2yKKoGeO3dxbdYaCtYlcZe7tpmk3Q+V8r+oCFEGiWNn3knOEdXuvqLpEqURnBouZ\na0JGz6pqEfexiA3VZW/a6a5wE/df+/HBl9D664cTFI9+DZ7gr+Kzn/6N9gXm2UkQINlTRp41ulF1\nNNEKoRdVbvObXCd4O6ISsaV9h83KGtX68xb5WSPApIdgCTn+HIwekzMDCWh2QGB8DdWIoEvFA4gm\ncxCsGCmCnSqCZ2mJ4+bORoFFdOW6zCxlnvuGIqgM28FQoRoRWKGkjgjRSx/SzZxv/yngTd+kf7al\n4KPZtg37C5Mkh9F0Q6JeI1iGpWC+e5TexF1xqfO6awchTjHFNNFItIzNukaqwmNsgX/1XwHv+6uj\nx8yrBBlCFEyCX8SuYSQo76Dz60Yk9Vyp28QSqoIyM6ltcVPYBDsY8cIci6OjI6RKIjt7dfDfm0Kz\nY+LA7NV//F8D7/4v7b+3NMXh4nO1XOBfqN8Cdfg48OH/EwDw4cV0MK+5Og9xghlYus1o+B4QrLUu\nHmt3btrEgUGwinMgQMo2PEOUPITXUIGVo6aZfrGDOunGZ34JePEDnR/1mQ8UnmEvqZvPIFUSy5mm\nuF8U6X9QYpvIxfcyxj4E4D8D8PMA3qmU+qNKqY+9bkf3gIcyCWl4oLsuNNhJ3FeAEKztBRYc5a52\nUFQ/NJXZIK5euYpfV1qlaIUQ08BR8fNjxMihHAEEVneNhgHthfXB50/wJ98/RQOzcUSbC0s7QNw+\nJF691nNPA+pX0gxmEgIi6mxrgRX6unix0LY5bzl8rBChSc+sYMiozGswRYQUZX1xvwWSnSbqlj5G\ngVJ57SzYtmhqCFWhYtImCrvmZAKVQ3mxRbDchMir087g7jzajWDdVgdoFPDXfvoTO1UEKaEg1Idm\nsPYtsCjJFMH2RNEWrE1lF3Yl/LZbXeatl45BKkXc3o8LFePIUCiiA6PUuepudtrvzUdovgtRXNxY\nrlNMmbFOqHd8xyrVlgM+eYQZ+ut6iSlSKLlfwiDNDBavBwosk4gQRZBXOUq0m1fjTzBBiqWLvpT3\nB8HyVIHG2Sib8ACTpt3MVV2i6qkIRvFMG9+aAqupaWZ0/PpPpqYo26Eqt3eQVPnh48DH/9ng/U33\npQwnmL7x6wAAy0/8fPsC8zsZZItg7VCV5I0WuYilQIFg3Ituz/BVbu/1bVH7MYImhVIKcbO2yOls\nEuPMWBIkAxTBln2wP7rhqbxDW6xd0YDwYOhXUHGpDdUN3SlhM31Od3Twk2SNabPYLLDM89uUWceL\nSc8yGiaBlW8PIM337MzI0n41udIqfY4hWE3TUsz7zTCzL02mm8Xl0cTHghTjTIE1K29h4XcblZem\nAU7VFFeqV/T3mxoEy6wrbOw8FSvg7qeG/w2AMPYeJQsh9tmbzhlUmHgDBVboa4qgS00UqNFY6jM1\nhws7qxjF+zWkKK7OAtzBHMVIgaXMDKWdPfQkwNu1SvgDhbcTUmW4WYT4yBf/GQBAIQ+wQjxcYM0C\n3FFzyGy7imAFjhVMnrIPgrW8gRocLL688U+H0wiZ8lGfw2+PxMiYH6L2YkiDbHpNAcWpwOra/2x/\nw/XG+qrVOzdRTd8U0Kwu8Jx6GAcH+j7/TV9gAfjL0LTAtwH47wH8CmPsI4yxX2OMfeR1OboHPFi1\nRgkPXjRDqYTtXoim5b4GZgaLbZlvoS6p4KwzKApoTx4AePTaZXy0eRIAsFYRpmFLDWGma+kO67Km\n3KAIPnwYQQqO7/oPvxLs4bcD4WFL43LCM7xat1sbVOvRAX8aMiVfKq9OO5STfpDMr33YiV8fTLBU\nEZAvkadmUNMfHpJlwQxTpDhL751XTFEW+jhYjyJYwrPd+q1hNpzGC+GHRhVwB0QeIAf80BY5btIc\nNClqr/3eEymQ2QKrj2Bp+kIdXca3/9an8H/9yku4W/o7CnptGshM53EaCCQI9naPp+LR22EiyV2K\noKEJKi6d7nNurz0lncGk3ehKObeJ/syY4iZn/QJLo79BTAXW5nlPly2tMGp2fEeTpHGDXEkzE1Yk\nC0wdueVdITjreI9oKo1+Zj0SuTDIKfHbbcgpJsiwcO5tN7Ef687uE55Lx4QWD5lj3c7v1TWqHkXw\naCJxhoktsFCVqBWDlJtrB8V8pgVr0h2qcnsHJcrv+CP6Pv3ET228xDYOghgPfck7caYm8F5y6FaE\nYKlwbyNkbtZRjzPdNLvg3ItUOeDt9gZS/gSRSpGXFaZIUPlGUTTyrdrqeoAiaNHhcxThsslQO4h5\n46y5PBqmCNY8hK/aGSxvcoSch50ZoY3vpBSCzFC/ejNY0qybykGwhB9akQtUhS2ElAggQ1IidYoM\n+mw/1ig+MO51VqzAoZtzrI8EmftkNt1spkwDDyvWLbCOq1tIw2ud1wnOsBJzXGb6NXZdM2umGFMA\nLFNg9crofUnNy5JLjSDe56i2CNiQyIUtsIiVYBpHhGA1VWEVPcesWsbi6izAXTUbpQiqQs9QRv7w\nfLcnuwygfgQqR4oAf+3ltwLXvgw3/ccQ+hxveXjzPp8EHhbiWFt8jAmKVNr2Ihnbn4dicQN3+TGO\npps50jzUHnj1ORSSad6SeyEaL4JUNK/orPX7GjA3jV5fe/efpwqLhrkRygCZ0vvAs+oRHB/otemi\nYkAPSmwrsJ4C8PUAvsn8+Y/MH/r/L8SO4GWC3FT1GZN2oSYzRMAgWCrYuvEQzx9ouc/UgSHKxVMP\nXcVH1ZMAdOdy6kigczNLs3IGI3lTauqUkyj9pfe8Cb/8Pf8+/thvfwPYb/uzwFd9x+Dx+G43EQCa\nGlJlqLzhAiuYdL0b/DrtUNw2Xm+6kqyHYE0mM6wQgRVL5BmpEA0XaiKcYYIMp+sLmOeZKMhjxZGd\n9gXT12Sfjo+z8ZOKYLbFkFQphVDlgB9ZxMsWs0ohUCkavz3XjLHWV6h/H61fRQ2OcH4Zf+rdT+N4\nIvH+F7KtM3+sSpE7Cf008Dv+Gbu/rn6dt8NEkuYJm6q051EJH4H0UCihZ8EIwTKdx2DWFliNY+h7\nfDBHrjxky143sc5RKg9xrM/XUGJZrFqVvKjZ8R1JGts0GWRMBdYSM6TgIx39oaiYhDAFllCV7fTS\nDBb53/G6S79gwQwTlnWaBy4d6CI0NV+VaJyNUkVHiFiB5crcLw0ZDbdItZamnqBJDEWwLgzKNe7l\ndDQJ9pLt3jtoButLvhGYPwL82j/cfA3dv34Ez/NwS1yFv36l/XdzbVPItsDakcjwRptjM8ZQ8uDC\nwgJSFTvFYQBYcZX1aqGNak1hPws93Ia+B3MebjAKCB0+T4HV9xtUztrDo8PB36lFAF8Vdp6ERweo\nRLThx+hGVjY4qs3zO+8iWLRuqiJFYynIoZnXNUwCK34hrRF8R4SoWOvilQuN4gPjCJZj18A2fLD0\ndzicDSsoqtBR1axyHGGBIr628drMa9eKVuRCf8/RfIDu4REUy69TFDxExQN4r0GBRTNt/sC+G0lh\n7ELM+Wp0U4Zk2j1DGa4rjWClSmIajjdhhuLqPMRdNQcbk0YvE2RKbqhnUlh2wFCBVZfwUSFDgF98\n7hSfes8/wF+Rfx5ve/RwQ7yBIg8NyrQeRtRICj07j7H68gZeVUcb832AEbhh0U6qrRvEfuEyhPIi\nhLbAcmxuLEVwRz5Dx997bjxnnsuNWAq7lj7bPIorx6bAul/U8M/x2CZy8fy2P6/nQT6ooQssg1gg\nsLA/mSECsD5YYoviHatzVKwHs1PSbm7UNzxyFc8YBGuFEDMHwaI5ntTxzCI5ZndwM5Zeq1rzlm8G\nvr7lLrtBtLWm97DV/nCBFcdTNIrZpN5vMtRb5gy0AIAPRs7ilpIxxQoRvHJpYW9/RObVi+fgTGG5\nPB389/MEIVjcKbBIingvznLZ0twoUdjmVZTnOTzWgMkYgZmDssPzZQIOhaan3mXlbvsdstWrOGMH\nuH4YYx76+HO/84vx3Bk0YjrkmQXte1PALbA8JCoA39NXxdJIdkjwcivmULTnkftmWFrqzZxUAE1X\nfzI/0lL9AHjYJndXZiEWmKBcd683MxTBuH8enSiMVPNKHCDGju9oNjaiBgZGrCFbnWLG0o7B967Q\n3iMFoBQEGmvISYmIIjW+JkPl0C94OMMMCRZZt8AiJcnmXuWZzQyLcp5NErBIzswgd7NJETyKfSww\nseaqFjUU435gxxPd1b1valJUCIVz4K2/H/jk+4B1NwmzsyFmfm4tDhCUzsyeM+u5VqY5sEPNjRtr\nAUAXFRcRFsjLCiGKcdqzE3b2b7U0yGlrOn7LIFgl33wfWi+bfef06go+KjROQ0zJdp33J8MFViMC\nBCoHslM0YAimB2hEvFV46CwtB02GASCQEqUSUFXWKtnJ0LE7KOw+obwQ0uxRHYZBmbSNKNqrxgro\nrC2wRO+aqjJFpnxcng9fJ07FUnaK9K6esWpmm2q8VeCcOyvTrq+rN1aIEotgrMBqdPOy4iH85uLN\nxX6QdcbQDBaJXFhqYs/ehFCvpirQlJrKNxnxwhyLK9MAdzCDnw/PPTFjodFHbik8lx3RD5PPPHbt\nMqTg+DsfOMMv3PTxzic36YEUiuakRhA11DkKtUXld+g9l6/gpfpwsMACgIKHYOcosAjBEjKC8mOE\n0OJRAYpWJIx8zXaJdpnPPTnt7rN95gNFJI3GADSCde2SPpcXnVV9UGKnyMUX4t7DqxMUZmPKWWg3\nX+EMkkfGB0s0xWiyzpoSlRHF4L0ODCvWKJTAfBLjdvxFqBTHWnURLKL05eu2W0yd13sJad7PqisZ\nmkUzMn8Sh76GyEnhR3VniPrhCY4SxkcFsAufCCYovSlkvUZpEazhTY6GhleL+1BgkYml1+221Q7F\na2s4MqlBRKIV4wskFcJcxpZC0RDtixbWnkEjISpDFMFb6gDXD/Qi9+43XsWK5ktGummu7w2gu+IJ\nAog9h6apOyVHro39HGuo69z7QtqutKryDUXCgzjACjSP5RZYARYqRp12VQRhBGImhs4zZDxaG7+b\nVfgQJmr7dyTVTmm45dFE32fZiZZi9qL9EayGG2lc4wNmO72iSxEUTdHK1APwormmCCbtvSeqFEum\ni7t7LrAoQXUQLM9Ql9KFKVaaChV4h1p8FEucqQl4bp61pjReWdsQLIlEhaiy+4NgNSYh/rsfvA18\n+R/U5/Tj/7TzGtYzwM79A8SVU2CZ+zZDgJzvRxEUqkRj1LNqHm4k4+eJVZJCMLVTHAZo5Y/T9Slm\nSMFsgeVZimDlba6xNMtU7zsr1kOQgdYMFwDkaIFlXr++hTViHMQhGhlDbkFVTtMC18cKLK815i6J\nMi6Drt2B6b4zL2gtNfoIlims/vGHX8ZKhVivRvYHg2CdqOnGNS2z9aBEO4VH61K+wOKmUQk83Cyw\nmtBJ3AmN9yRqiOECqyrsWjFeYGUoeaQVaPvWLPchrELoAILFOUPJnCaDXdf08+E7FEGi8p23wJIe\nRyIOERYng//OqxQZAoT+cGrrFnkbYZ6JyXSG93zZdfzYB19A3ajB+Sv7eTODTK5uDv57U2bIlI/s\nHAUWli/jRnNo54s3DlNE4OewoCkLouyHYHICAdX6tJnCivUsU0bDNJy4Oy5QV/BQd2Z3KWLpWXrk\ns+oRPHTZnMsLzqo+KPGaFliMsW9kjH2CMfZJxthf3PK6r2SMVYyxb3ktj+f1DumIEZSsTVA1X1Xf\ndIRgARjdzFlTWgTL61EEUWpPHsYYrl46xIfUl+B5da0zg0WDhi6CJVTZUQs71/dy+fCA7QI2/nD3\nXgqaMzPJqcrR7JgzKJm0AgB2dk3GqP0ZZJO1iW44XKiFJvFN7gOCRWaVVNxS1MwpAre+QVtgkUx7\nuWXIPDGFMJeRRbDsuaYFLughWOGwDGyzuomb9QwPzfU9dmXmKqQNd29FT7VuGnhIEUA0uaV9bIum\n0B3ecISmQUGIYFM6yZHwbFdalZmdm+Gm83gQabQEACQpB0IjImeYgPVUBEnMZR4Fhna4ed4bU2Ct\nw4cwZSmKYnyToWsTGGrgxMjmNosbAABvJOEcipobGhXd55ZKozc7Uq/s0y9kPIfHGqzX7XrhNxkS\nYai497p5DVgf+FMjHrLUHWNWV6hUF8GaRz7OMIFH3j91K8ozFsexNLLd98cHq0j0df/rP/8yXo2+\nSCfSd57rvIbb59BYH8hDLahAQUmDjOGHRuhnB4IlmtImkI0XwrtAUpskppG0B4IlDFK6vnsTPqvt\nLJQvOE65GSTfUmDtXYSX7dpFwZy1J5wMJ5+KOuLLmzjDBIexD/gTbRcyIgV9lpS4yk50otYTV/IE\nN3O5uUWwfBkh9Int4DZjJMAYSgjrIQlA768yRlrU+J9++texRoh02fO4ojAF+y11oNc995/SNVIE\nuDwb3j8tqpedIb2jEazg+LGN1/GJebbgAU4BW4oIckgB101qe/c2RdBkqIQusGTfmuU+RFPlaBRD\nOOJxV5O4CeCwEvQ+4NviprRUvr7K5T5RBMd6VnEgX+J1hpwFG5LqFL4v0SjWFT8xQWsRkxO892ue\ntD9/++PjBZZ3YBoBWwqsHD4qeHqkYBdFsEjAsjPcVEe4NIJgVd5k72Yn0FL2hYys8W9ODTNz35E6\n9U7RLnPOO/cnicsMiPPEUue3BXy8zK7jyqF+Nu55j3rA4jUrsBhjAsDfAvC7AbwFwB9ijL1l5HX/\nI4B/+Vody/9f4TcpKoNgFTyEZzo7vqPUFfmiHYAcKbB4U6I2XVJhueUGDavWyIwi3+PHMf5w8T34\n3upbMXEgcpJCLRzlGdGUltpy3ggCTddg9JAYBIsFwwgWYwwJizSvvK4gUXUGpYeigw5ZkYvYqj8p\nA8mPFVjxlAz57h9F0PO6C14qJnrAdVc4MqkRzQJtoejkphD2ggmiIEClOJRZUK0SY9g91wH9vbdw\nNctXcQstghX6ohXIGBuUbvKOLPPEUAT1+++mCTallqQPRwaNKYgiWLmdWaF/j2T66TyR4fJB5Guh\nEwChU2D5giPlU4i8mzDxRiNY08DTtMMh7rcpytJYb5bpetxjJDEJGRXws5lObEWiN9ggPgeCJXpd\nzQ0Ey3SLVd6Zi5LUPFi139VvMhS+MfwdGeDeGbYAaa99MNNJYLkiBMugU858leAMmZhavyxmZrC8\nAUVRCqt8eU4uftMofO9Pfgyfvt29d8tkgbUKsCoU/sbPPDvo9caqTCc5RlWsDo8xx6ptGjiNnINY\nouDRdgSrabRKGkkdexdDDZK1ee5H1jQ3PFNQJXde0MfsIKdrXz8X9UCBFYRE794z+baoX/texIgo\nlGhtJ3qhTBGrljdw1sQ4jH3wINYKnCNzaqeGIlhNrmnT5F4URkChpSAbBAsGwaoIwYrs6zszJWUC\n+DH+7i99GjcXOVYqQp2NPOs5FViHG0Vzna+RKR+zYHj/nE1n+j7LzlCe6AJrcnmzwPLMs5Wy7jks\nRQS/yaD6haj7rNz99OBnBypD7UVoRACpis33uGAo8wwFI2t7xZ0mAz1XhGBR7lIX4FWKnAXa2++8\nxxAbyfsB/yltij3uIyd9LU7VDKyRRUYFVoy3P36Itz4yx5uuzzbMft0IjEJ0uXhl8N+pwAK0OulO\n5Gal3+emOsbRSIGlvBhyyyxjP1rlx8gyXVJjjsw3CqztjBxiHAQqa0cMnNnHfkRS4FRN8Ck8iuNp\n1NKff7MXWKQWOPZnj/f+KgCfVEp9SilVAPhRAN888LrvAvCPAYxMCT64ETQZarMxVaL1H3ClkAOf\nW47q9gKLPHJIHcmgKlVqC6zHjmKU8BBKvzPcTGZ+7rwDmbreS0jB9WJBG6WRMubhsKIUoCmSolq3\nXbgdBZb2UdHfkZJiEcZg5jO4GSoNwuENPprpTsn9oAgSgiV6CFbCZ4jqPShOJpnhMsKE1Oy2cJAz\nS3+M4QtmFmb9HpT8e71ZnyAaQLCUAktu4446wEMHbVecut9j3Xmv7hZY0uPtPMceCbEqUuTwdxZY\nRLl0RS6Y51uRE9SFleOnrv5B3CJY8fy483653zXFBUyyz/Sx5AYV6wc3yVQx1SbZ2XqYfgLAqt5F\nRqJ5Po2RKx+RUT/zJ/sXWHbA2Dz3RBGUntCqo7VOUHxVdLyRAmN7kBvURimlld78qf69exVacOSv\nKcK5HuKuzDwTU/WG0TAAFL7xy1IKaCqU8Ea7yIAuykoendto+MZZiub//dt4/69+tHvo6QJrRHj8\nOMaPfeAFTc3uSwnXacceQkVH2izXIJj0eh5MMA89pCzsqsy98AHtAWM/1AizmOYX8yKNSA7EP/nw\ni3jvj4wbxAJAalRex+ZK3SAEtTp9Sf+OQ5dNpX4u1MBMbCgD1Irtn+D0EGSgXT+WiMdFCmifWr6C\nUzXBYSQhrLDE8DU/S0tcwwnU9PrgvxdMasGXskWwrN1BU9gmFM0pV1R42TdIUHoxfvDnnsObrs+w\nRjgu9mOaLrdxsHFNm1LT0MbobYcTTVdW6RnUQlMRj44ubbwumuv5nbSnvluJGDEy5FVvRpaeFS8c\npQiGKkPjRVBehAAFyvp+F1haHCsYoeA1IoCnCrMOdGewyBdPVSWYkZO/l2BTM/eUbBZYXp2h2qpQ\n7NDPe5Eb814RTMAYww9/6zvxQ+9959ZjmU9jnKgpirNhBEuVGXJIs4/vocS7NAUWjnA8GXm2pPah\n27d4Jr9SP2wLLEKwuEGduJ3B2l5gZSb/0I0Ss4Y4s4/9iKXA91XvxXflfxJX5wHgBZoZcEExoAcl\ntrUPSC3wX5g//4n588/Nn13xCIAXnL+/aH5mgzH2CIDfB+Bvb3sjxth3MMY+yBj74K1bI8OEn2OR\nVzUiZHaT00OnRoFHlVaVKfA4ElpoRhJerkrrkm27QCbp9+oEuUl+HzvW/532Fv7IqPgVjky7p0o0\n7N4ogpqu4Vu1IGW6gF40PuCf80jD2mS2vEPCu+a+pRpQku2HE9up5WZxDUa6vZySgMV4srxvlFaa\ntnu+MjFD3OyPYHE/wiQKUSqxdQaiSMz3jaZgjJkhbiqw9MYve+c6jiLU6CVOxQqiznBbzS2CBbTK\nd2MD3l4PwQKcbvgeUu2qypCp3QjWYIElpJVeZlWGMuvSpmaBhxViFErgYNYt6Gs5R9hDFEVToGLS\nUokv2FehAAAgAElEQVSGNlZRLNCAo57oxK7YgnrmpsCaGKPcI0N1O6z0/egmurvCzjrRc28aHp5g\nqMGhTILiq6ItxtDe26W5F/KqQYgcXMZmTuVeZ7BMgeXQleJDncwoM9vGmmpD5AIAKnkAgQYoVhbB\n2vlxXgxvT+EUivTui/ir/t/Hwy91t6AmW2KpInz3N3wJJoGHm6nYKN5ET+6eGYrW6sT09qz/2wzz\nyEeCXpH2L/8y8K/+Svt3St5pfsEPIVGg6CfGAH7lU6/imU9+umMO3Q9Crv09ECz7/Btqqj9t77uT\n8Ak0imEVPrTxe6H0kEEOPgdDYRXInLkwzzAilire2GsoKNli61tYQCNYtD9QIgsAz95c4gOf0fTT\ns0QjWPxgc14JINp4bucoZRBauwPRFJZ2TcVgxXoIVrHCp88U1kWF//b3vlULmYzsuY0RbLmlTIHl\nJLOqSJFBjn73o9jHmYpRJSfgq5fxijrGpQHJ7dmRRj9y0WUjNF6ECDnSokfHpnvx6puB5cuDhWqI\n3BRYISJWIKt2U7rPE6rKNYLlDa/tSgRa3r6pHFaCfj4kzW3VhfYC3VIIbQt/pj3F1ACC5TcZqgFx\nFwophCmwNgsJYvhQEfLQQYTHL21/Fo9jiVvqAPXZFgRL+bg0CZCoPRCshZ7lvamORmf8WDBFhBxJ\n//4YCco1pIzts5sYHzFhnhXuU+N++96Ru6qvdP9Vm405itj38Cn1MJ5Vj+LqLAAYQw7Zsp8+z2On\niiCA/0Ap9eeVUr9m/vxFAN9wnz7/fwHwF5RSW91glVI/pJR6p1LqnVeubLpbfy7GOq8RswzKIDW1\nF9oBX4k2YdLyvrsoW60ghefA7ADgV62n1GNHrTGsG1FsBvwpoW5qcDStudw9RI5WkrgyZqFiy4B/\nwbWCVJ3vh2DVPIAwnUMShPDDiUUIPGPuF8bDCBakmVG4DzNYtZHL9vzu+cr9uVbK2pWsEJ1TRoiN\nlK3a4lVELu2UaBUssO9BQiUkfU8xiyRSFVhVSQAdk2G3wCJ629j95qt8Q+VRye33aCdKQrC20z+s\nYEtVAmbeiInAFkOoC0sRJEVCxhhSPsEZJrg8623QwRyxWneSId5oBCvwtAnmUPHhl0ukfGJFM2ie\nZ/ir6WszMwXWYexjjRCXlVG12tMHC9hEsECdXsG1x5o5JwGKbnfQfEaZ6kR1lVeIWAEexFYI4F6C\nElfmfNZ0dqgRDyO3zYxMe1/+m1TskJ6COYj7tmi8aFwtbSQSg0jzrPtcq3yJlUGw/vS7vwiv5gKn\np93mimiyTlLnmQJrfUoFln52/DDGPPK1Ma6bgJ98utuUcMyxAY1QhyixzDZn+N554x/gn8u/tDXh\nJeR6DJV3IzQUaJnqxM6dhVpMn8JX5j+AV2ZfuvF7rVjEfkU4JZ3cUQ6kBs0S0cZeQ0GiNEzVWKgJ\nDmMJ39CYl87c03/zkx/DH/jBf4Pvf9+zOE0LXGMn8EYKrMoUWERvlKQIxyR4U1pWgEWwmN9BsMps\njd84afAH3vEY3v74EdYIwcvhAqtITlEogVM11Y0Dd/i/SpEqiUkwXGQcxRILxKiSU4TJTdxixxuG\nzwAwv6QLhc0CK8YEOdKyd69QUnrtrfq/J12aYFnViJDrnMOPEKJo/evuV5iZ1sAbQbAoyS5Te86Y\nQeZ939NqwnWpkaYtSsLbIjrU5219sokaySZFtWW+W3q0tm4+o6VpQHt7PH8URxOpRWXWwwgWKk0R\nvDyTSNQIRd0Ng2DdZsd45HD4e4hgghgZ7u5pQVPT8xJGkOYZzHoUQWLnlMX2vaNjq0Fro53dHVYR\npLhi9muNRH8BwaJgjLHf6vzlXXv+3ksAXOLxo+ZnbrwTwI8yxj4D4FsA/ABj7Pfu8d6f87HOK0yQ\ngZmNqRGRHswEIFXZDgHDkTcfowiqytL5PPNAUAeGZFkB4LFjnYTONhAsY/RL0uBm0xlSfdk3Cubb\nhyQnVCUepwiWIoKsE+TUJdqxiDVCwld6Eawo8QhihGaA2Ev1AhGNGRUaOkq2Gk+W9w0SFOkjWDap\nzHZ8RkUzA5EVcGi2JDhkwkhzVaVzrmmBI4oQxTzSUrCVKxpgOnyJPO50W6MpFVjDycVQgcXsPboH\nRbDK9QzWSJeTgmba6qq0tgPc840ymE6OKvN5ruT7T0bfjO8rv3VDxlZEM21s6SRVetaQlAn9QZ+i\noFoiE1M710LI0FCUppkwN470kS+QQneL9ZvtX2ChX2DRrIJFsHSBJVXZERmg5kFj+PCrrEKEHFxO\nzHe8twLL9UuxhygEFphCGIVApurB4onk3JGdaZRrjwJL+RP4Q8P8WyIz6GJ/1g7FCmul7Sm+7V1P\nIkXY8f0DiD7kUF8N/TE9awusHBKTKMA89LFswvbaFGs9yO6iYta7Tb+nkDFCFFhm1cZxH6Qv4iF2\nF+t0PCkqjDdeMNY0coJYCZNcr4NUcAHAPPRxBweIBxBkzhkKyL3vkSIjY+022QviliI4ZujKHRSU\nRC5C0+hLnOvy/N01YinwN973G/ixX/o4piwDm28ibwCMeW6OpspRKIFAknqjRrAq8is0x1ozD7xp\nE+kyXWLdBPgTX/cGfR5EDG8Eka/Wp1gibtXfHFEBZmZMxxEs4wuXnmKSv4pTb7gpfHisEayqZ7nR\n+DEilg0UWOZYr3+5/m+PJphlKTzW6PlDKrCKrb3r80dVoFDeaIFlLR6qVhCJFOqk4EZ4RBdY26xa\ntsXskmYaJCebqJFUeYdOvfHvHkehvMEmVJW3s8/7xlEscQuHEAN0RfOmyKERrAxB22Aei+UNFCzA\n4dHl0fk0L5whRr63xyc1c2UY2+ZIaUSLSIWZntdyh0k97X8A2rWxp/TrhutHdnWm10lXUfvzPfYp\nlP4odOHzGVMI/QCAb9/j9z4A4IsZY08xxiSA/xjAT7gvUEo9pZR6Uin1JIB/BOBPKaX+7/N8gc/V\nWOUm6TGJvvIiBCrX8xIo2+QKQE2S5WOqbqoVuSAEi7iysmmVCh860JSJfleR+ST13YV0cQEES8ux\n6vehTnowHS+wKk/zhjNTIPRV8PrRcMPlhvYuypSPOPARGSpMmN1GpnxEI34XlOj65arjF3QvUVEx\n23evJ5PNdDtKRp4PXhBpyh/zx53fAUuLo0SrGDjX0bSLFs5CbWbYkX83c2qWs25iaubTqnR4fkz2\njEUBWPWhfSiCrNpvBouTOEBdWNsB7mm+esE8PW9hkn5XzOSV6VvwE827NlSWSD66dqS/hdJ+bzSD\nNbSxhvUShTez8sp1Ol5g1fnK2iIAGlHLXUrKOQosRgW72aiYg2BVEEBToa5K+KzudgdpTTHzQXat\nkTEK5hh9njMsxarXiVzyKTxT0PCmgmIDiTtRI7PTzszotuAy1sPS54jCzEv5RU/MpNAI1jTQ17rk\n0Qb90GuKToEVHuhOeLm8TW+OFBLz0NNqlY1sZ7BOjPWj2wTrUQS9IITPaiyTze9Es4FpMi6gUtrG\nym6KYEwIqqGmeg41lTwQx8xWCzbcaBiKKiPT8DbpDAzVL+WT0Tk75jAUFirGUezbonC90ue0qhvc\nOM3wbe96Et/znjfhoDL0/9lwgUVzuU2Zd1CUhkt4qrTNGOETgiXBnAKL1ykSBHjYIAOVNxn15aqS\nMyxV3NqAOOs1q7XnXDxWYE18LBCDpaeYV7exCq4Ovs6b6gL/+FKvAPMniAcpguZYrxsEq6ckSHsr\n/BjMjzRFsNws9i8SrM5RMm9cnIL2jSrV/oaAfT4YY1r8oy4gVba1ENoWx0eXUSiB/Kw3LlKXWi58\nDwRrSMyB9k6y4Ngnjibad05mI6MrVYYCPi5NJdK9EKwbuM2O8eTl8SJPxjNwpnC2HF9L3KBmrgwj\n2+Rokm6BRc3jaodAkmurYf+fmqP9/AjoNGCuzNrnUtzjHvWgxc4CSyn1IaXU2wC8DcDblFJfoZT6\nlT1+rwLwZwD8NICPA/hxpdRHGWPfyRj7zose+Od6JEkCyeq2wPIjhMiRF4U2kXWSmGYH/coVpCCH\nepKfJdUgQM9GPXoU4SDqFU5ms7Pu35bachEEq31IquQMufIxicYXhdqLETap7YDs6hIpTxpqRqVd\n3xEgkgKTA90pn1SnZnh05BaWExT+AR5ht/Di3YvxfRtDEfR7CBY3XXvV917qRWuKrK9TyYapavbz\nTKIV0SA7d861oQ/Gs36BpRGsOt+kCPrz7tD4fK6TsfUIuheqvGM2Czg+W3sgWKzOzQzW9uWFDHWb\nqrRKjcyTYIxpOlBT2OLUdxBPur/7KkukYpk5KoCiKdFwaU0w+52zplGYqDVKObfqfM2Yshj0tckQ\ndhKMjqGrPAeCRclIr8DyBNMFVl0iJ2UrtzsoqcDS98IyyRCwCiKcoITczzpgIMgvpS8TvuZzSGPI\nq9H0zcRSTrWwQrk+AVd7FljhFJHKoUYMrweP0RQosuw2B0S5xgqRLS4KsUk/lKrbNZ+a+bKKFBLL\nFIkKMA08zCMPSxWioTXz5DP6v50Cq/VuAwDfPCPr9WbjIqz0z9ItiHplDEHDLesoxXwyRaU4rihz\n7EHb3Jqb52OsADjPPUJFn6tsOI0CpEoi4+OJqIuCnmGCg0jaAiszCPHNZY6qUXjsOMZ3fO3T+P73\nmHVqNixyoSXAc6gqRwHPNnDaAqtVS9M/961QEgD4dYqCh/b3an+KoBlez5rsDEtE7bVwECxeaQRr\nCCEECMGKEa5egECDPLo2fJI8CUyv4/qjb+j+XMaI2RBF0Bzr9BoQX95AsIi+zOTErhd5dr4Zx53h\neHIOht8iWJXZN7lo78MaQisJN7lVmjxvXJ2HuIs5qr65rzk/aouPnBStgFI/CF06V4EVa4qgX6eD\nM82sLpArH5enARIEUDsMdtXyBl6qD/HEpfE1ICQEec/xB1Uaaf0gQGhYRizVBZZvnhVhrlu1xT4G\nABrnO+Yml6uJmjtw3jlnNg8gBKvkgVXU/nyPnbsgY+wagP8OwMNKqd9tpNa/Rin1I7t+Vym1IYih\nlPrBkdd+215H/IBEYrpJNFQIP0KEAidrY5PqUCiUR+pKwwWW51AEfU+gUMIOKYcq68jx/vU/+BU4\niHqX1ZOo4Miq287rvRdYFQsQGNGOOltiiWiUkw4AjTdBhAynKUkRb1/E7HxKnUOZznIsPQSeTuR8\nlMjZ9veoDp7E49mrePEkwVseHkfXdgVRBP2e94c/NSasyzvY1nMu8zVCtEVCtQNloAWLNveKB+B0\nrvMVasUwnXS/+zzUstcdX6G13oAmR92E5eCQJOzPMDQ1tzHzA6cg3kP1jdc5csQ7ESyaaVN1SxG0\nnTTmg9eFPReBQ1E6jHwcRP5GcU3iD9nqFBOT0whVohbSqjGy3sa6zCvMkKCRjyAw9NNmm/ltsUbK\nQ7h3U+XFQA2kLEbE95cdtk0WK3JhEnXeIlhZkiBGl3JFCJYw8yMZiSMEU6z5vRdYdt5Ndq99KmY4\nqIwEu6oGDcpDehYWd4yJ+e7utBdOwZnCYr3EfLaf+mJt5uPCulsE+9UKa0QWtakMJdkNqXLUor1y\ns4NjlErYbm5TrJEqiWngYx7qGSyrMkcFVm2oT1w4ypfmupkiJFlvruNxo++pzpB4/7sRHXaHABAA\nTEIPa4Q4YOY7OsgpUcTHCoCCB3vfIxZBdii6sRT4peZL8Wz85tHf406RvlBa5KIyojSEtLxwV7/3\no0f6tW+dmf1pNjyDRb5xRZkjR9vAaYQEyva5pfu3cSmCVQGhqo6yopITyLTQ85+id09nCyxVrOeX\n14AqUxBW59UZah6Aj9gQHMUSS8TgSqNH1WT4+wAA/vjPbHh+MalnbMZELm5mAteO37BRYBH9ngcT\nbWCOluJ5v0IjWOMFlm0ElS2CxRymTMl8sKaARL61ENoWV+cBPqPmiPu0PMpvthVYhqI/GSiwCF0K\n4v0LLF9wLI0tAtav2rWZgtVEEZTIEECV21Gn+uwGbjTX8dQWBCs0jYpkTwSL5sBC3wMz6reeoXxT\n05eeGVJMHgu3wCrWS0ygG8gRxv37YukhKwtcNV6cFQ/gfQHBsvG/Q6NQtEr8BoA/91od0OdL5KaL\nToO98GOdTJzqRcEdJG+CGRpwwHQV+uGpCsokX1IQxK0X0EhlaJwC6x1PHOGLrm520XMWtF04urkv\nUGCVXMIzoh0qW+r5hxFfEEBvZh5q1EYC2981SGplQ3Mti6skYilweHCASunbtsD24xdXnsaT7BW8\ncHJBBMssxlJ2Py8wJqzp2Z2tv09docAkKTUPWtPTgSCkkbyfah5YBUqVLbFGiHnUPZZZqNXBXApC\nvXwVJ2qKq4fdRf/ywQy58qzMtxtlVet5ol530TYK9hC54LWewRrj6dv3JKPhurBFLJkP11wjWKrc\nNC3+/e94FH/63U9vvB95g7lJrK+0ESyhYqLXOVtmJeZsDRXMEU7maBSzHjhDwcoEBeuem9rT93LG\ndyfGblgqHl1vk+RxzvR60FQorIqbO4NFBZb+PUpY/WhikL97LLCKVozFjdybIzYFDVf1IEUwMPNM\n+fIuuKospXlbkBLe2dn+c5JEi4x7apF+naAQsaWs1V68Ydbqq6KDYB3EEqeYgpt1t85WSBBgZiiC\nCUIwKn5dQQHLBGiVL4FWnMI1dAe0jP5U6Z8VWwosa77p71GcCq5VDgH9X95eE0LxhoQVAJzrHqEC\ny0WwJoGHP1b+F/h/Zt80+nvuPVT6c/iCY2qKaEJaXjTr8qNGnAnLG+YLDCM+DZnY1jkK1SJYtDeS\nmi0ljTWXEFRgDdiDMCpKi81rwosFlogxMwlpkbX3ktfkqMV4Ej+PfCzR7m/sYJjyCAA4eFTPTDnB\nAkMRHEGw3vt/PIPy8KkNLyxiN4hgAmH2jg5l/D4Er1tF48F/dxCs2iJY7etrg8yHKLYWQtsilh7O\n2BxeP18y54dtEdAKDEXQpY5SqHyNRjFE5xC5AIAsMBTP1abTEKcCa6pR363qeUqBLW/gpjraShGM\nLdV23wJLH4MvGGKDfgWlLrCIek+jJ9vUjQEATgO3MOMKNLsrBiiCQEsTJASrEtGFDNkfpNinwLqs\nlPpxAA1gqX/3WZrm8y9oIyXhB0qWkzPyH3DMPKWHBZ8PGucBPYqg6cCgLqCqApJVwB4dz5KFLb2L\n1H0uUGDVPGgd22n+YURRSh+4XjCUmQvqy4xvhGgLLJQJMkMRPJoGWJnEotoh0iGvPI1H2G28fPti\nQheWItgrsMK5LrDy1XaKICUpZPJZi6A1UR76POL70+ycaD3BUKyRINwoXmahj1QFHRnY/OwV3FYH\neOigm7BpukKIItmkNGQZyeh3f8en6+UiWKVjNuiEqDOULNjqgwRoNFabKJeW+00Flqb9FFBmoNzl\ncn/dl1zBd3ztZoFFMtAFzbkoBQ+lpcJWZkjejUVaYY4ECA8QBz5WCK0v1lDwKtlUvzLPX18NbFdY\nzrpVEWwTkQoeWFMhT0l4wklGuEDBIwRNgqJqkKUttUV/x/2Gn/tBm2sfwSrkASbGjkCoCmrAP292\ncIRGMRSru4aWubvAok7x4mx/pU9KoifKuXerXFPEvPb8194Eocrt/amU0tRXp7HlC44zzCAy/fzW\nhaYiT0MP80jfC6xM9HsQggW0zwCto+aeJS+6LO0mteuixhz6Z+XI3CPgPPd7UqfI/zDl3WRsZryp\n4pH51PPcI9SwkQ5tkVDCMR8ooIvC1YEurKZmboyS/hdP9Hs/fGiuyfKGptiOzDE2Xggf2lC4cGY8\nLdshpwLLCEFx3yI51h7EoabbOeABapcollioGLOZ8Q1yrqnfbJ8fEpyh9Nvv4B1tmgxvCxFMEbIS\nWdG7RuY7PHfa4Odvz4DFi531vnRkxvlrVWA15dbmSVtgpajLAYog8yCrbgPxXiLxjxCUvQJrDwsY\nKThK5W0wGQCNUiYIEG5h4gxFFVOBtakk2BZYEikC8GpLAZOdQdQZbqojPLWFIkg+dPmWec7uAeZa\n2IYxS/Wnhpk0OYlvC6wd60K5wpkyXp5mLetTc/tB6wXJzjcigK++UGBRrBljlwAoAGCMfTWAi0uz\nfZ5HkXbV3mgxIbTD5ahHvsApm1tKVz98VFYKWHA9KMrq0sp5U0d7W1SeY+q5RVZz36hFi6qwYoXV\nDoogCRCopTEI3gXDWwQrA6rMUASF8UGiWabtx8+On4ZgCtntYdf7faOdwep+3sQY3ZarYeSRoi4y\nLchhEpKaBxuJvhusTFCB24S7EQGkEfxg5RoZizaKl7mZwWLOYH+9uLkh0Q7oYdM1ws7AKoVNDnvd\nxcAgWNQlRdMAf/PtwL/9Oxvvob1Idt9bvpk1Uk2FpiIzZ9Plou5zqT1ndtENAUBG1CE336upwKFs\nh7tiwUZiuUxSTFkGER8i8gVWiFrUYiC8OtWUwM4HGzES73ydT+KsK/N5zHM6vUwYBGt4LqryJpgg\nxSIrW2XJaIqayXvuDpJMe9/otg4OMcVa2zuoepAieDQJsUSEKjnRDaE9ZrAiM/O2Wu6/nTCDNszV\nqpXjNwly5bn0L6Iq6echrxqEbJP6uuJz+IUu8FS+RqoCzAIP89BDokIwKKBMoE4+o9FNYAPBoqYA\nWUbkvQLrbJ1hzvR1rNItSRE1wPZAsABYcZVM9Assfe7H1mOaZdonaAYy6BRYXudzhkI49xA3z6VV\n1DUo5IsnKa7NA+2pVFfAZ/+NRnRGQolQr4O1nsGyTSZz/mHo05I8fYSEZ2h6dB8Ip8Ai1dB6YB2U\n1RIrFmNi9m+ahURdQqDeEAHqRyON96QSmB4Ni1yMBcmEbzTAyjUKePA8Hz/xWfP5TuFf2Xm5iVVd\nrfL7SxEUqkC9pXlC65QqM9S2AeE0jpgH39CNL1Jg5fIIcdVdN+j7czm+DnPOUDJvuMFZrJEiGG1M\njMZkBMFqaghVIVcSlycBUuyQJ3ck2m3TYSjM99tGN3aD1ZkW1gIArm1i5o2+BqFBsGgWq9khcsHL\nBLeUEckyuYBlPgTDxxxLgeOJhCRRGhFCnlPc6EGNfQqs74ZW/3uaMfZLAP4+gP/0NT2qz4OoTDeJ\nBhK5WfByo1glnE008AVOcAAkw1QzD1VHkEJD3AUyM0y9DRKnaLwIrEq10eV9QrDI4Z4XK6xUtJUi\nyIMughXG2xEs2wmrCzCiCPqe9kFiZvPYJTN//BQAgJ1csMCqh2XaD2ZTJCpAlWxHsJoi6QxFK+Gg\nfwPBqhQFtCkfoBckOteiXCMfmG+ZR3oGy1UHY8lt3MEcDx10k/PQF0gRtfMlTpBUdH9gNY4ilEqg\npAJrdRNYvATc/Gj3DeoKUXWGBd89U9P6PZW220kFliIEq9LXfkwOuvN+JEFLBZZpJCiPEKzNAitb\n6msn4kNwzpAghjfijQPoQfmmV2BRJ7wvt7wrBHUNzTnljnhEAwE0tbUo6HcHG3+CKcuwSMuO+lXt\nqG+eN2jezQ96nxUe6kI1OzMI1ua1OIx9nKkJVHqqi7A9EKy4pyq3T9DcmWQVMrPGEnLRuI0muhYm\nsc7KGhGKjcH6xJsjNHQZVaZIEWAW6hk/PTkJrSR48jyeVyZRtgVWF3Ul1KbooQar03Zdp2s9FIwa\nYP5+hXophpFTQrDGnpmah3sXWKpIUCiBKGgbJoIzRL4YlSkHuveQiM2MkRegBrdzoi+eJNa7Ee//\nfuDGvwO+9j8fPxYRQKIEM15MdgbT7GM8X2h1QUf8okWwqPho9x1ilyR9Y/GmgV8nKL2ZpUbaWSZC\njHahjKFe/26qY1yano8KR/PJVf9eMSIsf+irHkd5+KQ+9ld+w/5zTWtFOLXzgHV+fw1duRENGguS\nyC/ztKV9O3lGDQ++QbBEcO8FVhNfwkQlHVXYzCJ429+3Yv4gRRCl9jfbZ69xw5teQQ2+WWCZYyME\nK0OghXfGRH0sRfahcZVGwDImxlSA+8HrQgtrmcgRYGYaPsSqkeb5rnfYN4hyjTvQVPrG3J+0b4zN\njkZSWHogoPOZ4B73qAct9lIRBPB1AN4F4E8A+FKl1L97rQ/sQY/aLOg0F0IiAdVaox1uR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85w034aFzy4FfduAGUmKVfQrzHGbOPMmECEuf+yXlQ6akZ2RBSeY/IjY0Za0bhgme2zt8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\nEfG/AMmQ2C3kqWUdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12baf1610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[]\n",
    "num = 240\n",
    "for i in range(num):\n",
    "    x.append(i)\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1=fig.add_subplot(2,1,1)\n",
    "\n",
    "train_pred=model.predict(X_train)\n",
    "print('training mse:', mean_squared_error(Y_train, train_pred))\n",
    "\n",
    "ax1.plot(x,train_pred[0:num], label='predict')\n",
    "ax1.plot(x,Y_train[0:num],label='real')\n",
    "plt.legend()\n",
    "ax1.set_title('Training Result')\n",
    "ax1.set_xlabel('Image Number')\n",
    "ax1.set_ylabel('Normalized Modulus Coefficient')\n",
    "\n",
    "\n",
    "ax2=fig.add_subplot(2,1,2)\n",
    "\n",
    "test_pred=model.predict(X_test)\n",
    "print('testing mse:', mean_squared_error(Y_test, test_pred))\n",
    "\n",
    "ax2.plot(x,test_pred[0:num], label='predict')\n",
    "ax2.plot(x,Y_test[0:num],label='real')\n",
    "plt.legend()\n",
    "ax2.set_title('Testing Result')\n",
    "ax2.set_xlabel('Image Number')\n",
    "ax2.set_ylabel('Normalized Modulus Coefficient')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('original.png',dpi=400)\n",
    "# plt.savefig('prediction_result_2.png',dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "Unable to open file (Unable to open file: name = 'cnn_model_original_081217.h5', errno = 2, error message = 'no such file or directory', flags = 0, o_flags = 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-03a4101d7951>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CNN_model_original_081217.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m         \u001b[0;31m# instantiate model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mmodel_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model_config'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/h5py/_hl/files.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, **kwds)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m                 \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muserblock_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswmr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/hechaoli/anaconda/lib/python2.7/site-packages/h5py/_hl/files.pyc\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_RDWR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (/Users/ilan/minonda/conda-bld/h5py_1490026561416/work/h5py/_objects.c:2846)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (/Users/ilan/minonda/conda-bld/h5py_1490026561416/work/h5py/_objects.c:2804)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.open (/Users/ilan/minonda/conda-bld/h5py_1490026561416/work/h5py/h5f.c:2123)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: Unable to open file (Unable to open file: name = 'cnn_model_original_081217.h5', errno = 2, error message = 'no such file or directory', flags = 0, o_flags = 0)"
     ]
    }
   ],
   "source": [
    "#model = load_model('CNN_model_original_081217.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(Y_test == 0)[0]\n",
    "\n",
    "Y_test[132]\n",
    "\n",
    "\"\"\"\n",
    "se = (test_pred - Y_test)/Y_test\n",
    "count = 0\n",
    "for i in range(240):\n",
    "    if np.abs(se[i])>=0.3:\n",
    "        count += 1\n",
    "        \n",
    "count\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threshold = 0.3\n",
    "def find_image_nb(Input):\n",
    "    image_nb = []\n",
    "    for i in range(len(Input)):\n",
    "        if abs(Input[i]) >= threshold:\n",
    "            image_nb.append(i)\n",
    "            \n",
    "    return image_nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_nb = find_image_nb(se)\n",
    "diff_test = X_test[image_nb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.size(image_nb)\n",
    "image_nb[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8ebb37d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8ebac0150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vQY8r9bMnHjWHTql84xWZcodDDLNjaPbnow65GpkR32WU68nyJqfnOnlEbz2091h3faxm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\nGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8ebb50510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "\n",
    "'''\n",
    "ax1=fig.add_subplot(1,2,1)\n",
    "plt.axis('off')\n",
    "ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels([])\n",
    "plt.imshow(X_test[7].reshape(128,128))\n",
    "\n",
    "ax1=fig.add_subplot(1,2,2)\n",
    "plt.axis('off')\n",
    "ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels([])\n",
    "plt.imshow(diff_test[4].reshape(128,128))\n",
    "'''\n",
    "\n",
    "def plot(samples):\n",
    "    fig = plt.figure(figsize=(20, 20))\n",
    "    gs = gridspec.GridSpec(1, 10)\n",
    "    gs.update(wspace=0.05, hspace=0.0)\n",
    "\n",
    "    for i, sample in enumerate(samples):\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        plt.imshow(sample.reshape(128, 128), cmap='Greys_r')\n",
    "\n",
    "plot(X_test[image_nb[:10]])\n",
    "plot(diff_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110, 128, 128, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_test.shape\n",
    "# np.size(image_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_test = diff_test[:90]\n",
    "image_nb = image_nb[:90]\n",
    "sio.savemat('diff_fig.mat',{'img':diff_test, 'index':image_nb})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240/240 [==============================] - 0s     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0042388046470781164, 0.0042388046470781164]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test, batch_size=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_15 (Conv2D)           (None, 128, 128, 24)      888       \n",
      "_________________________________________________________________\n",
      "activation_24 (Activation)   (None, 128, 128, 24)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 64, 64, 24)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 64, 64, 48)        10416     \n",
      "_________________________________________________________________\n",
      "activation_25 (Activation)   (None, 64, 64, 48)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 32, 32, 48)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 32, 32, 96)        41568     \n",
      "_________________________________________________________________\n",
      "activation_26 (Activation)   (None, 32, 32, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 32, 32, 96)        83040     \n",
      "_________________________________________________________________\n",
      "activation_27 (Activation)   (None, 32, 32, 96)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 16, 16, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 16, 16, 128)       110720    \n",
      "_________________________________________________________________\n",
      "activation_28 (Activation)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "activation_29 (Activation)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1000)              8193000   \n",
      "_________________________________________________________________\n",
      "activation_30 (Activation)   (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 100)               100100    \n",
      "_________________________________________________________________\n",
      "activation_31 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 101       \n",
      "_________________________________________________________________\n",
      "activation_32 (Activation)   (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 8,687,417\n",
      "Trainable params: 8,687,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('CNN_original_model_081217.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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