Diabetic_Retinopathy_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.layers import Dense, Activation, Flatten\n",
    "from keras.layers import merge, Input\n",
    "from keras.models import Model\n",
    "from keras.utils import np_utils\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading base model, which is a VGG - 16 model pretrained on imagenet weights.\n",
    "# Freezing all layers except last 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0314 02:35:30.835151  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0314 02:35:30.850111  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0314 02:35:30.853103  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0314 02:35:30.872094  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0314 02:35:32.611677  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0314 02:35:32.611677  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Loading base model\n",
    "base_model = VGG16(weights = 'imagenet', \n",
    "                   include_top = True, \n",
    "                   input_shape = (224,224,3))\n",
    "# Freezing layers of VGG16\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.input_layer.InputLayer object at 0x000001CCAB0735C0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB356DA20> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCAB0736D8> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x000001CCB37C2B70> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB37C2D68> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB3799E10> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x000001CCB3824710> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB38244E0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB3850438> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB3850F98> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x000001CCB388CB70> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB388C2B0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB492C080> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB4949DA0> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x000001CCB495EDA0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB495E6A0> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB4996470> False\n",
      "<keras.layers.convolutional.Conv2D object at 0x000001CCB49AFB38> False\n",
      "<keras.layers.pooling.MaxPooling2D object at 0x000001CCB49C2F28> False\n",
      "<keras.layers.core.Flatten object at 0x000001CCB49C2A58> False\n",
      "<keras.layers.core.Dense object at 0x000001CCB49C27F0> True\n",
      "<keras.layers.core.Dense object at 0x000001CCB4A17A90> True\n",
      "<keras.layers.core.Dense object at 0x000001CCB4A35128> True\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 123,642,856\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Freezing the layers except the last 3 layers\n",
    "for layer in base_model.layers[:-3]:\n",
    "    layer.trainable = False\n",
    "# Checking the trainable status of the individual layers\n",
    "for layer in base_model.layers:\n",
    "    print(layer, layer.trainable)\n",
    "base_model.summary()\n",
    "# Check the last 3 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "`pydot` failed to call GraphViz.Please install GraphViz (https://www.graphviz.org/) and ensure that its executables are in the $PATH.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1914\u001b[0m                 \u001b[0marguments\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marguments\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1915\u001b[1;33m                 \u001b[0mworking_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtmp_dir\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1916\u001b[0m             )\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcall_graphviz\u001b[1;34m(program, arguments, working_dir, **kwargs)\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[0mstdout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m     )\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors, text)\u001b[0m\n\u001b[0;32m    774\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 775\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    776\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m   1177\u001b[0m                                          \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcwd\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1178\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m   1179\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[1;31m# to check the pydot/graphviz installation.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1921\u001b[0m                     prog=prog)\n\u001b[1;32m-> 1922\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1923\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] \"dot\" not found in path.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-57c9d61dea35>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m            \u001b[0mto_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'base_model_plot.png'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m            \u001b[0mshow_shapes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m            show_layer_names = True)\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mplot_model\u001b[1;34m(model, to_file, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m    130\u001b[0m             \u001b[1;34m'LR'\u001b[0m \u001b[0mcreates\u001b[0m \u001b[0ma\u001b[0m \u001b[0mhorizontal\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m     \"\"\"\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_to_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_layer_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextension\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mextension\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mmodel_to_dot\u001b[1;34m(model, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m     \u001b[0m_check_pydot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[0mdot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'rankdir'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         raise OSError(\n\u001b[1;32m---> 29\u001b[1;33m             \u001b[1;34m'`pydot` failed to call GraphViz.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m             \u001b[1;34m'Please install GraphViz (https://www.graphviz.org/) '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m             'and ensure that its executables are in the $PATH.')\n",
      "\u001b[1;31mOSError\u001b[0m: `pydot` failed to call GraphViz.Please install GraphViz (https://www.graphviz.org/) and ensure that its executables are in the $PATH."
     ]
    }
   ],
   "source": [
    "# GraphViz not installed\n",
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(base_model,\n",
    "           to_file = 'base_model_plot.png', \n",
    "           show_shapes = True,\n",
    "           show_layer_names = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "`pydot` failed to call GraphViz.Please install GraphViz (https://www.graphviz.org/) and ensure that its executables are in the $PATH.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1914\u001b[0m                 \u001b[0marguments\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marguments\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1915\u001b[1;33m                 \u001b[0mworking_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtmp_dir\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1916\u001b[0m             )\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcall_graphviz\u001b[1;34m(program, arguments, working_dir, **kwargs)\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[0mstdout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m     )\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors, text)\u001b[0m\n\u001b[0;32m    774\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 775\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    776\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m   1177\u001b[0m                                          \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcwd\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1178\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m   1179\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[1;31m# to check the pydot/graphviz installation.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1921\u001b[0m                     prog=prog)\n\u001b[1;32m-> 1922\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1923\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] \"dot\" not found in path.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-05a58a113bb3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvis_utils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmodel_to_dot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mSVG\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_to_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'dot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mmodel_to_dot\u001b[1;34m(model, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m     \u001b[0m_check_pydot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[0mdot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'rankdir'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         raise OSError(\n\u001b[1;32m---> 29\u001b[1;33m             \u001b[1;34m'`pydot` failed to call GraphViz.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m             \u001b[1;34m'Please install GraphViz (https://www.graphviz.org/) '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m             'and ensure that its executables are in the $PATH.')\n",
      "\u001b[1;31mOSError\u001b[0m: `pydot` failed to call GraphViz.Please install GraphViz (https://www.graphviz.org/) and ensure that its executables are in the $PATH."
     ]
    }
   ],
   "source": [
    "# GraphViz not installed\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(base_model).create(prog = 'dot', format = 'svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "BATCH_SIZE = 32 \n",
    "CLASSES = 5 # No. of output classes\n",
    "EPOCH = 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding layers to the VGG16 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0314 02:35:45.749571  6544 deprecation.py:506] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 1000)              138357544 \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              1025024   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 5)                 5125      \n",
      "=================================================================\n",
      "Total params: 139,387,693\n",
      "Trainable params: 124,673,005\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "\n",
    "# Creating the model\n",
    "model = models.Sequential()\n",
    "# Adding the base VGG16 model\n",
    "model.add(base_model)\n",
    "# Adding new layers\n",
    "model.add(layers.Dense(1024, activation = 'relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(CLASSES, activation = 'softmax', name = 'output'))\n",
    "# Checking the final model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15_left</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15_right</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16_left</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16_right</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17_right</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23_left</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23_right</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       image  level\n",
       "0    10_left      0\n",
       "1   10_right      0\n",
       "2    13_left      0\n",
       "3   13_right      0\n",
       "4    15_left      1\n",
       "5   15_right      2\n",
       "6    16_left      4\n",
       "7   16_right      4\n",
       "8    17_left      0\n",
       "9   17_right      1\n",
       "10   19_left      0\n",
       "11  19_right      0\n",
       "12   20_left      0\n",
       "13  20_right      0\n",
       "14   21_left      0\n",
       "15  21_right      0\n",
       "16   22_left      0\n",
       "17  22_right      0\n",
       "18   23_left      0\n",
       "19  23_right      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the labels CSV file\n",
    "import pandas as pd\n",
    "train_labels = pd.read_csv('D:/diabetic-retinopathy-detection/trainLabels.csv/trainLabels.csv')\n",
    "train_labels.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ccb5454320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Class distributions\n",
    "import seaborn as sns\n",
    "sns.countplot('level', data = train_labels)\n",
    "# Highly imbalanced dataset LOL !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of severity '0': 25810\n",
      "Counts of severity '1': 2443 \n",
      "Counts of severity '2': 5292\n",
      "Counts of severity '3': 873 \n",
      "Counts of severity '4': 708 \n"
     ]
    }
   ],
   "source": [
    "print(\"Counts of severity '0': {}\".format(sum(train_labels.level==0)))\n",
    "print(\"Counts of severity '1': {} \".format(sum(train_labels.level==1)))\n",
    "print(\"Counts of severity '2': {}\".format(sum(train_labels.level==2)))\n",
    "print(\"Counts of severity '3': {} \".format(sum(train_labels.level==3)))\n",
    "print(\"Counts of severity '4': {} \".format(sum(train_labels.level==4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       image  level\n",
      "0    10_left      0\n",
      "1   10_right      0\n",
      "2    13_left      0\n",
      "3   13_right      0\n",
      "4    15_left      1\n",
      "5   15_right      2\n",
      "6    16_left      4\n",
      "7   16_right      4\n",
      "8    17_left      0\n",
      "9   17_right      1\n",
      "10   19_left      0\n",
      "11  19_right      0\n",
      "12   20_left      0\n",
      "13  20_right      0\n",
      "14   21_left      0\n",
      "15  21_right      0\n",
      "16   22_left      0\n",
      "17  22_right      0\n",
      "18   23_left      0\n",
      "19  23_right      0\n",
      ".........\n",
      "(35126, 2)\n"
     ]
    }
   ],
   "source": [
    "print(train_labels.head(20))\n",
    "print('.........')\n",
    "print(train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35126\n",
      "10_left\n",
      "10_right\n",
      "13_left\n",
      "13_right\n",
      "15_left\n"
     ]
    }
   ],
   "source": [
    "# Testing\n",
    "print(train_labels.shape[0])\n",
    "for i in range(0,5):\n",
    "    print(train_labels['image'][i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input image dimensions\n",
    "img_rows, img_cols = 224, 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"from PIL import Image\\nfrom tqdm import tqdm\\ntrain_image = []\\nfor i in tqdm(range(train_labels.shape[0])):\\n    img = image.load_img('D:/diabetic-retinopathy-detection/train.zip/train'+train_labels['image'][i]+'.jpeg',target_size=(img_rows,img_cols,3))\\n    img = image.img_to_array(img)\\n    img = img/255\\n    train_image.append(img)\\nX = np.array(train_image)\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8 gb RAM is too puny for this block to run...LOL !\n",
    "'''from PIL import Image\n",
    "from tqdm import tqdm\n",
    "train_image = []\n",
    "for i in tqdm(range(train_labels.shape[0])):\n",
    "    img = image.load_img('D:/diabetic-retinopathy-detection/train.zip/train'+train_labels['image'][i]+'.jpeg',target_size=(img_rows,img_cols,3))\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255\n",
    "    train_image.append(img)\n",
    "X = np.array(train_image)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                         | 0/1000 [00:00<?, ?it/s]\n",
      "  0%|                                                                 | 1/1000 [00:00<05:03,  3.29it/s]\n",
      "  0%|▏                                                                | 2/1000 [00:00<04:25,  3.75it/s]\n",
      "  0%|▎                                                                | 4/1000 [00:00<03:31,  4.71it/s]\n",
      "  0%|▎                                                                | 5/1000 [00:00<03:24,  4.86it/s]\n",
      "  1%|▍                                                                | 6/1000 [00:01<03:17,  5.03it/s]\n",
      "  1%|▍                                                                | 7/1000 [00:01<03:10,  5.22it/s]\n",
      "  1%|▌                                                                | 8/1000 [00:01<02:56,  5.63it/s]\n",
      "  1%|▌                                                                | 9/1000 [00:01<02:45,  5.99it/s]\n",
      "  1%|▋                                                               | 10/1000 [00:01<02:34,  6.41it/s]\n",
      "  1%|▋                                                               | 11/1000 [00:01<02:35,  6.36it/s]\n",
      "  1%|▊                                                               | 12/1000 [00:01<02:23,  6.89it/s]\n",
      "  1%|▊                                                               | 13/1000 [00:02<02:23,  6.89it/s]\n",
      "  1%|▉                                                               | 14/1000 [00:02<02:20,  7.01it/s]\n",
      "  2%|▉                                                               | 15/1000 [00:02<02:44,  5.98it/s]\n",
      "  2%|█                                                               | 16/1000 [00:02<02:49,  5.80it/s]\n",
      "  2%|█▏                                                              | 18/1000 [00:02<02:20,  7.00it/s]\n",
      "  2%|█▏                                                              | 19/1000 [00:02<02:24,  6.81it/s]\n",
      "  2%|█▎                                                              | 20/1000 [00:03<02:20,  6.99it/s]\n",
      "  2%|█▎                                                              | 21/1000 [00:03<02:16,  7.15it/s]\n",
      "  2%|█▍                                                              | 22/1000 [00:03<02:19,  6.99it/s]\n",
      "  2%|█▌                                                              | 24/1000 [00:03<01:59,  8.17it/s]\n",
      "  2%|█▌                                                              | 25/1000 [00:03<02:16,  7.15it/s]\n",
      "  3%|█▋                                                              | 26/1000 [00:03<02:29,  6.52it/s]\n",
      "  3%|█▋                                                              | 27/1000 [00:04<02:42,  5.99it/s]\n",
      "  3%|█▊                                                              | 28/1000 [00:04<02:43,  5.95it/s]\n",
      "  3%|█▉                                                              | 30/1000 [00:04<02:11,  7.36it/s]\n",
      "  3%|█▉                                                              | 31/1000 [00:04<02:13,  7.28it/s]\n",
      "  3%|██                                                              | 32/1000 [00:04<02:15,  7.17it/s]\n",
      "  3%|██                                                              | 33/1000 [00:04<02:14,  7.17it/s]\n",
      "  3%|██▏                                                             | 34/1000 [00:04<02:09,  7.48it/s]\n",
      "  4%|██▏                                                             | 35/1000 [00:05<02:13,  7.24it/s]\n",
      "  4%|██▎                                                             | 36/1000 [00:05<02:12,  7.29it/s]\n",
      "  4%|██▎                                                             | 37/1000 [00:05<02:15,  7.09it/s]\n",
      "  4%|██▍                                                             | 38/1000 [00:05<02:13,  7.18it/s]\n",
      "  4%|██▌                                                             | 40/1000 [00:05<01:54,  8.36it/s]\n",
      "  4%|██▌                                                             | 41/1000 [00:05<02:29,  6.43it/s]\n",
      "  4%|██▋                                                             | 42/1000 [00:05<02:35,  6.16it/s]\n",
      "  4%|██▊                                                             | 43/1000 [00:06<02:46,  5.76it/s]\n",
      "  4%|██▊                                                             | 44/1000 [00:06<02:50,  5.62it/s]\n",
      "  4%|██▉                                                             | 45/1000 [00:06<02:55,  5.45it/s]\n",
      "  5%|██▉                                                             | 46/1000 [00:06<02:56,  5.42it/s]\n",
      "  5%|███                                                             | 47/1000 [00:06<02:40,  5.95it/s]\n",
      "  5%|███                                                             | 48/1000 [00:07<02:24,  6.57it/s]\n",
      "  5%|███▏                                                            | 49/1000 [00:07<02:20,  6.78it/s]\n",
      "  5%|███▏                                                            | 50/1000 [00:07<02:14,  7.04it/s]\n",
      "  5%|███▎                                                            | 51/1000 [00:07<02:02,  7.72it/s]\n",
      "  5%|███▍                                                            | 53/1000 [00:07<01:46,  8.85it/s]\n",
      "  6%|███▌                                                            | 55/1000 [00:07<01:46,  8.84it/s]\n",
      "  6%|███▌                                                            | 56/1000 [00:07<01:50,  8.56it/s]\n",
      "  6%|███▋                                                            | 57/1000 [00:08<01:54,  8.26it/s]\n",
      "  6%|███▋                                                            | 58/1000 [00:08<02:03,  7.63it/s]\n",
      "  6%|███▊                                                            | 60/1000 [00:08<01:46,  8.84it/s]\n",
      "  6%|███▉                                                            | 61/1000 [00:08<01:57,  8.01it/s]\n",
      "  6%|███▉                                                            | 62/1000 [00:08<01:55,  8.16it/s]\n",
      "  6%|████                                                            | 64/1000 [00:08<01:40,  9.33it/s]\n",
      "  7%|████▏                                                           | 66/1000 [00:08<01:33,  9.99it/s]\n",
      "  7%|████▎                                                           | 68/1000 [00:09<01:54,  8.12it/s]\n",
      "  7%|████▍                                                           | 69/1000 [00:09<01:55,  8.05it/s]\n",
      "  7%|████▌                                                           | 71/1000 [00:09<01:36,  9.62it/s]\n",
      "  7%|████▋                                                           | 73/1000 [00:09<01:28, 10.49it/s]\n",
      "  8%|████▊                                                           | 75/1000 [00:09<01:38,  9.44it/s]\n",
      "  8%|████▉                                                           | 77/1000 [00:10<01:51,  8.25it/s]\n",
      "  8%|████▉                                                           | 78/1000 [00:10<02:09,  7.12it/s]\n",
      "  8%|█████                                                           | 79/1000 [00:10<02:08,  7.19it/s]\n",
      "  8%|█████                                                           | 80/1000 [00:10<02:02,  7.54it/s]\n",
      "  8%|█████▏                                                          | 81/1000 [00:10<02:23,  6.39it/s]\n",
      "  8%|█████▏                                                          | 82/1000 [00:11<02:32,  6.02it/s]\n",
      "  8%|█████▎                                                          | 83/1000 [00:11<02:26,  6.28it/s]\n",
      "  8%|█████▍                                                          | 84/1000 [00:11<02:20,  6.51it/s]\n",
      "  9%|█████▌                                                          | 86/1000 [00:11<02:00,  7.61it/s]\n",
      "  9%|█████▋                                                          | 88/1000 [00:11<01:45,  8.61it/s]\n",
      "  9%|█████▋                                                          | 89/1000 [00:11<01:50,  8.24it/s]\n",
      "  9%|█████▊                                                          | 91/1000 [00:11<01:39,  9.17it/s]\n",
      "  9%|█████▉                                                          | 93/1000 [00:12<01:37,  9.32it/s]\n",
      "  9%|██████                                                          | 94/1000 [00:12<01:38,  9.18it/s]\n",
      " 10%|██████                                                          | 95/1000 [00:12<01:52,  8.04it/s]\n",
      " 10%|██████▏                                                         | 96/1000 [00:12<01:56,  7.77it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|██████▎                                                         | 98/1000 [00:12<01:41,  8.87it/s]\n",
      " 10%|██████▎                                                         | 99/1000 [00:12<01:53,  7.91it/s]\n",
      " 10%|██████▎                                                        | 100/1000 [00:13<02:00,  7.50it/s]\n",
      " 10%|██████▎                                                        | 101/1000 [00:13<01:54,  7.82it/s]\n",
      " 10%|██████▍                                                        | 102/1000 [00:13<01:47,  8.33it/s]\n",
      " 10%|██████▍                                                        | 103/1000 [00:13<02:09,  6.95it/s]\n",
      " 10%|██████▌                                                        | 104/1000 [00:13<02:19,  6.42it/s]\n",
      " 11%|██████▋                                                        | 106/1000 [00:13<01:59,  7.49it/s]\n",
      " 11%|██████▋                                                        | 107/1000 [00:13<01:53,  7.90it/s]\n",
      " 11%|██████▊                                                        | 109/1000 [00:14<01:53,  7.87it/s]\n",
      " 11%|██████▉                                                        | 110/1000 [00:14<01:56,  7.67it/s]\n",
      " 11%|██████▉                                                        | 111/1000 [00:14<01:47,  8.23it/s]\n",
      " 11%|███████                                                        | 113/1000 [00:14<01:43,  8.55it/s]\n",
      " 11%|███████▏                                                       | 114/1000 [00:14<01:49,  8.10it/s]\n",
      " 12%|███████▏                                                       | 115/1000 [00:14<02:16,  6.50it/s]\n",
      " 12%|███████▎                                                       | 116/1000 [00:15<02:42,  5.46it/s]\n",
      " 12%|███████▎                                                       | 117/1000 [00:15<02:42,  5.43it/s]\n",
      " 12%|███████▍                                                       | 118/1000 [00:15<02:37,  5.58it/s]\n",
      " 12%|███████▍                                                       | 119/1000 [00:15<02:34,  5.70it/s]\n",
      " 12%|███████▌                                                       | 120/1000 [00:15<02:28,  5.94it/s]\n",
      " 12%|███████▌                                                       | 121/1000 [00:15<02:10,  6.73it/s]\n",
      " 12%|███████▋                                                       | 123/1000 [00:16<02:14,  6.50it/s]\n",
      " 12%|███████▊                                                       | 124/1000 [00:16<02:41,  5.43it/s]\n",
      " 12%|███████▉                                                       | 125/1000 [00:16<02:32,  5.73it/s]\n",
      " 13%|███████▉                                                       | 126/1000 [00:16<02:33,  5.70it/s]\n",
      " 13%|████████                                                       | 127/1000 [00:17<02:38,  5.51it/s]\n",
      " 13%|████████                                                       | 128/1000 [00:17<02:39,  5.48it/s]\n",
      " 13%|████████▏                                                      | 129/1000 [00:17<02:27,  5.92it/s]\n",
      " 13%|████████▏                                                      | 130/1000 [00:17<02:17,  6.35it/s]\n",
      " 13%|████████▎                                                      | 131/1000 [00:17<02:18,  6.26it/s]\n",
      " 13%|████████▎                                                      | 132/1000 [00:17<02:04,  6.97it/s]\n",
      " 13%|████████▍                                                      | 133/1000 [00:18<02:33,  5.64it/s]\n",
      " 13%|████████▍                                                      | 134/1000 [00:18<02:52,  5.03it/s]\n",
      " 14%|████████▌                                                      | 135/1000 [00:18<02:28,  5.84it/s]\n",
      " 14%|████████▋                                                      | 137/1000 [00:18<02:19,  6.19it/s]\n",
      " 14%|████████▋                                                      | 138/1000 [00:18<02:19,  6.16it/s]\n",
      " 14%|████████▊                                                      | 139/1000 [00:18<02:04,  6.93it/s]\n",
      " 14%|████████▉                                                      | 141/1000 [00:19<01:50,  7.78it/s]\n",
      " 14%|█████████                                                      | 143/1000 [00:19<01:59,  7.18it/s]\n",
      " 14%|█████████                                                      | 144/1000 [00:19<02:19,  6.15it/s]\n",
      " 14%|█████████▏                                                     | 145/1000 [00:19<02:39,  5.36it/s]\n",
      " 15%|█████████▏                                                     | 146/1000 [00:20<02:45,  5.17it/s]\n",
      " 15%|█████████▎                                                     | 147/1000 [00:20<03:01,  4.69it/s]\n",
      " 15%|█████████▎                                                     | 148/1000 [00:20<03:09,  4.49it/s]\n",
      " 15%|█████████▍                                                     | 149/1000 [00:20<03:01,  4.68it/s]\n",
      " 15%|█████████▍                                                     | 150/1000 [00:21<02:51,  4.97it/s]\n",
      " 15%|█████████▌                                                     | 151/1000 [00:21<02:25,  5.83it/s]\n",
      " 15%|█████████▋                                                     | 153/1000 [00:21<02:21,  5.99it/s]\n",
      " 15%|█████████▋                                                     | 154/1000 [00:21<02:29,  5.66it/s]\n",
      " 16%|█████████▊                                                     | 155/1000 [00:21<02:15,  6.26it/s]\n",
      " 16%|█████████▊                                                     | 156/1000 [00:21<02:01,  6.93it/s]\n",
      " 16%|█████████▉                                                     | 157/1000 [00:22<02:14,  6.29it/s]\n",
      " 16%|█████████▉                                                     | 158/1000 [00:22<02:20,  5.99it/s]\n",
      " 16%|██████████                                                     | 159/1000 [00:22<02:43,  5.14it/s]\n",
      " 16%|██████████                                                     | 160/1000 [00:22<02:51,  4.90it/s]\n",
      " 16%|██████████▏                                                    | 161/1000 [00:22<02:46,  5.03it/s]\n",
      " 16%|██████████▏                                                    | 162/1000 [00:23<02:41,  5.18it/s]\n",
      " 16%|██████████▎                                                    | 163/1000 [00:23<02:42,  5.15it/s]\n",
      " 16%|██████████▎                                                    | 164/1000 [00:23<02:39,  5.23it/s]\n",
      " 16%|██████████▍                                                    | 165/1000 [00:23<02:45,  5.05it/s]\n",
      " 17%|██████████▍                                                    | 166/1000 [00:23<02:48,  4.96it/s]\n",
      " 17%|██████████▌                                                    | 168/1000 [00:24<02:16,  6.08it/s]\n",
      " 17%|██████████▋                                                    | 169/1000 [00:24<02:19,  5.96it/s]\n",
      " 17%|██████████▋                                                    | 170/1000 [00:24<02:20,  5.91it/s]\n",
      " 17%|██████████▊                                                    | 171/1000 [00:24<02:03,  6.72it/s]\n",
      " 17%|██████████▉                                                    | 173/1000 [00:24<01:43,  8.01it/s]\n",
      " 18%|███████████                                                    | 176/1000 [00:24<01:21, 10.12it/s]\n",
      " 18%|███████████▏                                                   | 178/1000 [00:24<01:17, 10.63it/s]\n",
      " 18%|███████████▎                                                   | 180/1000 [00:25<01:16, 10.71it/s]\n",
      " 18%|███████████▍                                                   | 182/1000 [00:25<01:32,  8.87it/s]\n",
      " 18%|███████████▌                                                   | 184/1000 [00:25<01:48,  7.52it/s]\n",
      " 18%|███████████▋                                                   | 185/1000 [00:25<01:51,  7.33it/s]\n",
      " 19%|███████████▋                                                   | 186/1000 [00:26<01:44,  7.82it/s]\n",
      " 19%|███████████▊                                                   | 188/1000 [00:26<01:32,  8.79it/s]\n",
      " 19%|███████████▉                                                   | 189/1000 [00:26<01:45,  7.68it/s]\n",
      " 19%|███████████▉                                                   | 190/1000 [00:26<01:50,  7.35it/s]\n",
      " 19%|████████████                                                   | 192/1000 [00:26<01:38,  8.22it/s]\n",
      " 19%|████████████▏                                                  | 193/1000 [00:26<02:13,  6.04it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|████████████▏                                                  | 194/1000 [00:27<02:35,  5.17it/s]\n",
      " 20%|████████████▎                                                  | 195/1000 [00:27<02:37,  5.10it/s]\n",
      " 20%|████████████▎                                                  | 196/1000 [00:27<02:28,  5.40it/s]\n",
      " 20%|████████████▍                                                  | 197/1000 [00:27<02:49,  4.75it/s]\n",
      " 20%|████████████▍                                                  | 198/1000 [00:28<02:56,  4.54it/s]\n",
      " 20%|████████████▌                                                  | 199/1000 [00:28<03:08,  4.25it/s]\n",
      " 20%|████████████▌                                                  | 200/1000 [00:28<03:11,  4.18it/s]\n",
      " 20%|████████████▋                                                  | 201/1000 [00:28<02:59,  4.45it/s]\n",
      " 20%|████████████▋                                                  | 202/1000 [00:28<02:48,  4.73it/s]\n",
      " 20%|████████████▊                                                  | 203/1000 [00:29<03:07,  4.24it/s]\n",
      " 20%|████████████▊                                                  | 204/1000 [00:29<03:15,  4.06it/s]\n",
      " 20%|████████████▉                                                  | 205/1000 [00:29<03:02,  4.35it/s]\n",
      " 21%|████████████▉                                                  | 206/1000 [00:29<02:53,  4.57it/s]\n",
      " 21%|█████████████                                                  | 207/1000 [00:30<02:39,  4.98it/s]\n",
      " 21%|█████████████                                                  | 208/1000 [00:30<02:22,  5.55it/s]\n",
      " 21%|█████████████▏                                                 | 209/1000 [00:30<02:41,  4.89it/s]\n",
      " 21%|█████████████▏                                                 | 210/1000 [00:30<02:51,  4.61it/s]\n",
      " 21%|█████████████▎                                                 | 211/1000 [00:31<03:02,  4.32it/s]\n",
      " 21%|█████████████▎                                                 | 212/1000 [00:31<03:07,  4.20it/s]\n",
      " 21%|█████████████▍                                                 | 213/1000 [00:31<02:54,  4.50it/s]\n",
      " 21%|█████████████▍                                                 | 214/1000 [00:31<02:38,  4.95it/s]\n",
      " 22%|█████████████▌                                                 | 215/1000 [00:31<03:00,  4.35it/s]\n",
      " 22%|█████████████▌                                                 | 216/1000 [00:32<02:59,  4.37it/s]\n",
      " 22%|█████████████▋                                                 | 217/1000 [00:32<02:38,  4.95it/s]\n",
      " 22%|█████████████▋                                                 | 218/1000 [00:32<02:26,  5.33it/s]\n",
      " 22%|█████████████▊                                                 | 219/1000 [00:32<02:31,  5.14it/s]\n",
      " 22%|█████████████▊                                                 | 220/1000 [00:32<02:16,  5.73it/s]\n",
      " 22%|█████████████▉                                                 | 221/1000 [00:33<02:42,  4.78it/s]\n",
      " 22%|█████████████▉                                                 | 222/1000 [00:33<02:55,  4.43it/s]\n",
      " 22%|██████████████                                                 | 223/1000 [00:33<03:08,  4.13it/s]\n",
      " 22%|██████████████                                                 | 224/1000 [00:33<03:18,  3.91it/s]\n",
      " 22%|██████████████▏                                                | 225/1000 [00:34<03:36,  3.58it/s]\n",
      " 23%|██████████████▏                                                | 226/1000 [00:34<03:33,  3.63it/s]\n",
      " 23%|██████████████▎                                                | 227/1000 [00:34<03:26,  3.75it/s]\n",
      " 23%|██████████████▎                                                | 228/1000 [00:34<03:17,  3.91it/s]\n",
      " 23%|██████████████▍                                                | 229/1000 [00:35<03:40,  3.49it/s]\n",
      " 23%|██████████████▍                                                | 230/1000 [00:35<03:42,  3.46it/s]\n",
      " 23%|██████████████▌                                                | 231/1000 [00:35<03:44,  3.42it/s]\n",
      " 23%|██████████████▌                                                | 232/1000 [00:36<03:30,  3.64it/s]\n",
      " 23%|██████████████▋                                                | 233/1000 [00:36<03:11,  4.01it/s]\n",
      " 23%|██████████████▋                                                | 234/1000 [00:36<02:58,  4.28it/s]\n",
      " 24%|██████████████▊                                                | 235/1000 [00:36<03:00,  4.23it/s]\n",
      " 24%|██████████████▊                                                | 236/1000 [00:37<03:04,  4.14it/s]\n",
      " 24%|██████████████▉                                                | 237/1000 [00:37<02:56,  4.33it/s]\n",
      " 24%|██████████████▉                                                | 238/1000 [00:37<02:57,  4.29it/s]\n",
      " 24%|███████████████                                                | 239/1000 [00:37<03:12,  3.96it/s]\n",
      " 24%|███████████████                                                | 240/1000 [00:37<03:01,  4.18it/s]\n",
      " 24%|███████████████▏                                               | 241/1000 [00:38<02:52,  4.40it/s]\n",
      " 24%|███████████████▏                                               | 242/1000 [00:38<02:46,  4.55it/s]\n",
      " 24%|███████████████▎                                               | 243/1000 [00:38<03:01,  4.18it/s]\n",
      " 24%|███████████████▎                                               | 244/1000 [00:38<03:10,  3.97it/s]\n",
      " 24%|███████████████▍                                               | 245/1000 [00:39<03:03,  4.11it/s]\n",
      " 25%|███████████████▍                                               | 246/1000 [00:39<02:53,  4.36it/s]\n",
      " 25%|███████████████▌                                               | 247/1000 [00:39<03:03,  4.10it/s]\n",
      " 25%|███████████████▌                                               | 248/1000 [00:39<03:12,  3.91it/s]\n",
      " 25%|███████████████▋                                               | 249/1000 [00:40<03:14,  3.86it/s]\n",
      " 25%|███████████████▊                                               | 250/1000 [00:40<03:10,  3.94it/s]\n",
      " 25%|███████████████▊                                               | 251/1000 [00:40<03:10,  3.93it/s]\n",
      " 25%|███████████████▉                                               | 252/1000 [00:40<03:03,  4.08it/s]\n",
      " 25%|███████████████▉                                               | 253/1000 [00:41<02:49,  4.41it/s]\n",
      " 25%|████████████████                                               | 254/1000 [00:41<02:35,  4.79it/s]\n",
      " 26%|████████████████                                               | 255/1000 [00:41<02:30,  4.95it/s]\n",
      " 26%|████████████████▏                                              | 256/1000 [00:41<02:23,  5.18it/s]\n",
      " 26%|████████████████▏                                              | 257/1000 [00:41<02:22,  5.22it/s]\n",
      " 26%|████████████████▎                                              | 258/1000 [00:41<02:21,  5.24it/s]\n",
      " 26%|████████████████▎                                              | 259/1000 [00:42<02:02,  6.07it/s]\n",
      " 26%|████████████████▍                                              | 261/1000 [00:42<01:53,  6.50it/s]\n",
      " 26%|████████████████▌                                              | 262/1000 [00:42<01:59,  6.18it/s]\n",
      " 26%|████████████████▌                                              | 263/1000 [00:42<02:21,  5.20it/s]\n",
      " 26%|████████████████▋                                              | 264/1000 [00:43<02:40,  4.58it/s]\n",
      " 26%|████████████████▋                                              | 265/1000 [00:43<02:50,  4.31it/s]\n",
      " 27%|████████████████▊                                              | 266/1000 [00:43<03:04,  3.99it/s]\n",
      " 27%|████████████████▊                                              | 267/1000 [00:43<02:44,  4.45it/s]\n",
      " 27%|████████████████▉                                              | 268/1000 [00:43<02:33,  4.78it/s]\n",
      " 27%|████████████████▉                                              | 269/1000 [00:44<02:50,  4.28it/s]\n",
      " 27%|█████████████████                                              | 270/1000 [00:44<03:01,  4.03it/s]\n",
      " 27%|█████████████████                                              | 271/1000 [00:44<02:36,  4.66it/s]\n",
      " 27%|█████████████████▏                                             | 272/1000 [00:44<02:14,  5.42it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|█████████████████▏                                             | 273/1000 [00:44<01:59,  6.10it/s]\n",
      " 27%|█████████████████▎                                             | 274/1000 [00:45<01:46,  6.79it/s]\n",
      " 28%|█████████████████▎                                             | 275/1000 [00:45<02:06,  5.75it/s]\n",
      " 28%|█████████████████▍                                             | 276/1000 [00:45<02:06,  5.73it/s]\n",
      " 28%|█████████████████▍                                             | 277/1000 [00:45<02:28,  4.85it/s]\n",
      " 28%|█████████████████▌                                             | 278/1000 [00:45<02:43,  4.42it/s]\n",
      " 28%|█████████████████▌                                             | 279/1000 [00:46<02:58,  4.03it/s]\n",
      " 28%|█████████████████▋                                             | 280/1000 [00:46<03:02,  3.94it/s]\n",
      " 28%|█████████████████▋                                             | 281/1000 [00:46<02:32,  4.71it/s]\n",
      " 28%|█████████████████▊                                             | 283/1000 [00:46<02:14,  5.35it/s]\n",
      " 28%|█████████████████▉                                             | 284/1000 [00:47<02:08,  5.58it/s]\n",
      " 28%|█████████████████▉                                             | 285/1000 [00:47<02:12,  5.40it/s]\n",
      " 29%|██████████████████                                             | 286/1000 [00:47<02:08,  5.55it/s]\n",
      " 29%|██████████████████                                             | 287/1000 [00:47<02:15,  5.26it/s]\n",
      " 29%|██████████████████▏                                            | 289/1000 [00:47<02:10,  5.46it/s]\n",
      " 29%|██████████████████▎                                            | 290/1000 [00:48<02:13,  5.32it/s]\n",
      " 29%|██████████████████▎                                            | 291/1000 [00:48<02:04,  5.68it/s]\n",
      " 29%|██████████████████▍                                            | 292/1000 [00:48<01:59,  5.94it/s]\n",
      " 29%|██████████████████▌                                            | 294/1000 [00:48<01:41,  6.96it/s]\n",
      " 30%|██████████████████▌                                            | 295/1000 [00:48<01:38,  7.17it/s]\n",
      " 30%|██████████████████▋                                            | 296/1000 [00:48<01:35,  7.40it/s]\n",
      " 30%|██████████████████▋                                            | 297/1000 [00:49<01:47,  6.54it/s]\n",
      " 30%|██████████████████▊                                            | 298/1000 [00:49<01:54,  6.14it/s]\n",
      " 30%|██████████████████▊                                            | 299/1000 [00:49<02:11,  5.32it/s]\n",
      " 30%|██████████████████▉                                            | 300/1000 [00:49<02:23,  4.86it/s]\n",
      " 30%|██████████████████▉                                            | 301/1000 [00:49<02:16,  5.13it/s]\n",
      " 30%|███████████████████                                            | 302/1000 [00:50<02:09,  5.38it/s]\n",
      " 30%|███████████████████                                            | 303/1000 [00:50<02:34,  4.52it/s]\n",
      " 30%|███████████████████▏                                           | 304/1000 [00:50<02:33,  4.52it/s]\n",
      " 30%|███████████████████▏                                           | 305/1000 [00:50<02:43,  4.26it/s]\n",
      " 31%|███████████████████▎                                           | 306/1000 [00:51<02:51,  4.05it/s]\n",
      " 31%|███████████████████▎                                           | 307/1000 [00:51<02:42,  4.27it/s]\n",
      " 31%|███████████████████▍                                           | 308/1000 [00:51<02:31,  4.58it/s]\n",
      " 31%|███████████████████▍                                           | 309/1000 [00:51<02:34,  4.47it/s]\n",
      " 31%|███████████████████▌                                           | 310/1000 [00:52<02:28,  4.64it/s]\n",
      " 31%|███████████████████▌                                           | 311/1000 [00:52<02:39,  4.31it/s]\n",
      " 31%|███████████████████▋                                           | 312/1000 [00:52<02:42,  4.22it/s]\n",
      " 31%|███████████████████▋                                           | 313/1000 [00:52<02:23,  4.80it/s]\n",
      " 32%|███████████████████▊                                           | 315/1000 [00:52<02:08,  5.33it/s]\n",
      " 32%|███████████████████▉                                           | 316/1000 [00:53<02:06,  5.39it/s]\n",
      " 32%|███████████████████▉                                           | 317/1000 [00:53<02:06,  5.40it/s]\n",
      " 32%|████████████████████                                           | 318/1000 [00:53<02:02,  5.56it/s]\n",
      " 32%|████████████████████                                           | 319/1000 [00:53<01:48,  6.29it/s]\n",
      " 32%|████████████████████▏                                          | 321/1000 [00:53<01:45,  6.42it/s]\n",
      " 32%|████████████████████▎                                          | 322/1000 [00:54<01:48,  6.24it/s]\n",
      " 32%|████████████████████▎                                          | 323/1000 [00:54<01:52,  6.03it/s]\n",
      " 32%|████████████████████▍                                          | 324/1000 [00:54<01:47,  6.27it/s]\n",
      " 32%|████████████████████▍                                          | 325/1000 [00:54<01:44,  6.46it/s]\n",
      " 33%|████████████████████▌                                          | 326/1000 [00:54<01:41,  6.67it/s]\n",
      " 33%|████████████████████▌                                          | 327/1000 [00:54<01:47,  6.28it/s]\n",
      " 33%|████████████████████▋                                          | 328/1000 [00:55<01:47,  6.28it/s]\n",
      " 33%|████████████████████▋                                          | 329/1000 [00:55<01:35,  7.05it/s]\n",
      " 33%|████████████████████▊                                          | 330/1000 [00:55<01:26,  7.72it/s]\n",
      " 33%|████████████████████▊                                          | 331/1000 [00:55<01:36,  6.91it/s]\n",
      " 33%|████████████████████▉                                          | 332/1000 [00:55<01:37,  6.82it/s]\n",
      " 33%|████████████████████▉                                          | 333/1000 [00:55<01:48,  6.12it/s]\n",
      " 33%|█████████████████████                                          | 334/1000 [00:55<01:51,  5.99it/s]\n",
      " 34%|█████████████████████                                          | 335/1000 [00:56<01:57,  5.66it/s]\n",
      " 34%|█████████████████████▏                                         | 336/1000 [00:56<01:58,  5.61it/s]\n",
      " 34%|█████████████████████▏                                         | 337/1000 [00:56<01:43,  6.41it/s]\n",
      " 34%|█████████████████████▎                                         | 339/1000 [00:56<01:35,  6.89it/s]\n",
      " 34%|█████████████████████▍                                         | 340/1000 [00:56<01:39,  6.63it/s]\n",
      " 34%|█████████████████████▍                                         | 341/1000 [00:56<01:40,  6.57it/s]\n",
      " 34%|█████████████████████▌                                         | 342/1000 [00:57<01:38,  6.67it/s]\n",
      " 34%|█████████████████████▌                                         | 343/1000 [00:57<01:48,  6.03it/s]\n",
      " 34%|█████████████████████▋                                         | 344/1000 [00:57<01:52,  5.82it/s]\n",
      " 34%|█████████████████████▋                                         | 345/1000 [00:57<02:14,  4.89it/s]\n",
      " 35%|█████████████████████▊                                         | 346/1000 [00:58<02:26,  4.47it/s]\n",
      " 35%|█████████████████████▊                                         | 347/1000 [00:58<02:17,  4.77it/s]\n",
      " 35%|█████████████████████▉                                         | 348/1000 [00:58<02:12,  4.93it/s]\n",
      " 35%|█████████████████████▉                                         | 349/1000 [00:58<02:27,  4.41it/s]\n",
      " 35%|██████████████████████                                         | 350/1000 [00:58<02:33,  4.24it/s]\n",
      " 35%|██████████████████████                                         | 351/1000 [00:59<02:40,  4.03it/s]\n",
      " 35%|██████████████████████▏                                        | 352/1000 [00:59<02:40,  4.03it/s]\n",
      " 35%|██████████████████████▏                                        | 353/1000 [00:59<02:41,  4.02it/s]\n",
      " 35%|██████████████████████▎                                        | 354/1000 [00:59<02:38,  4.06it/s]\n",
      " 36%|██████████████████████▎                                        | 355/1000 [01:00<02:19,  4.61it/s]\n",
      " 36%|██████████████████████▍                                        | 356/1000 [01:00<02:02,  5.25it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|██████████████████████▍                                        | 357/1000 [01:00<02:01,  5.31it/s]\n",
      " 36%|██████████████████████▌                                        | 358/1000 [01:00<01:58,  5.40it/s]\n",
      " 36%|██████████████████████▌                                        | 359/1000 [01:00<01:52,  5.71it/s]\n",
      " 36%|██████████████████████▋                                        | 360/1000 [01:00<01:44,  6.10it/s]\n",
      " 36%|██████████████████████▋                                        | 361/1000 [01:01<02:03,  5.19it/s]\n",
      " 36%|██████████████████████▊                                        | 362/1000 [01:01<02:11,  4.86it/s]\n",
      " 36%|██████████████████████▊                                        | 363/1000 [01:01<01:57,  5.44it/s]\n",
      " 36%|██████████████████████▉                                        | 364/1000 [01:01<01:43,  6.12it/s]\n",
      " 36%|██████████████████████▉                                        | 365/1000 [01:01<01:33,  6.76it/s]\n",
      " 37%|███████████████████████                                        | 367/1000 [01:02<01:38,  6.44it/s]\n",
      " 37%|███████████████████████▏                                       | 368/1000 [01:02<01:48,  5.81it/s]\n",
      " 37%|███████████████████████▏                                       | 369/1000 [01:02<01:50,  5.71it/s]\n",
      " 37%|███████████████████████▎                                       | 370/1000 [01:02<01:51,  5.66it/s]\n",
      " 37%|███████████████████████▎                                       | 371/1000 [01:02<01:59,  5.26it/s]\n",
      " 37%|███████████████████████▍                                       | 372/1000 [01:03<01:57,  5.36it/s]\n",
      " 37%|███████████████████████▍                                       | 373/1000 [01:03<01:56,  5.37it/s]\n",
      " 37%|███████████████████████▌                                       | 374/1000 [01:03<01:52,  5.56it/s]\n",
      " 38%|███████████████████████▋                                       | 375/1000 [01:03<01:37,  6.41it/s]\n",
      " 38%|███████████████████████▊                                       | 377/1000 [01:03<01:25,  7.29it/s]\n",
      " 38%|███████████████████████▉                                       | 379/1000 [01:03<01:20,  7.68it/s]\n",
      " 38%|███████████████████████▉                                       | 380/1000 [01:04<01:16,  8.10it/s]\n",
      " 38%|████████████████████████                                       | 381/1000 [01:04<01:25,  7.20it/s]\n",
      " 38%|████████████████████████                                       | 382/1000 [01:04<01:30,  6.82it/s]\n",
      " 38%|████████████████████████▏                                      | 383/1000 [01:04<01:38,  6.29it/s]\n",
      " 38%|████████████████████████▏                                      | 384/1000 [01:04<01:40,  6.11it/s]\n",
      " 38%|████████████████████████▎                                      | 385/1000 [01:04<01:45,  5.82it/s]\n",
      " 39%|████████████████████████▎                                      | 386/1000 [01:05<01:47,  5.72it/s]\n",
      " 39%|████████████████████████▍                                      | 387/1000 [01:05<01:34,  6.50it/s]\n",
      " 39%|████████████████████████▌                                      | 389/1000 [01:05<01:33,  6.56it/s]\n",
      " 39%|████████████████████████▌                                      | 390/1000 [01:05<01:37,  6.24it/s]\n",
      " 39%|████████████████████████▋                                      | 391/1000 [01:05<01:53,  5.36it/s]\n",
      " 39%|████████████████████████▋                                      | 392/1000 [01:06<01:58,  5.14it/s]\n",
      " 39%|████████████████████████▊                                      | 393/1000 [01:06<01:55,  5.26it/s]\n",
      " 39%|████████████████████████▊                                      | 394/1000 [01:06<01:50,  5.49it/s]\n",
      " 40%|████████████████████████▉                                      | 396/1000 [01:06<01:33,  6.47it/s]\n",
      " 40%|█████████████████████████                                      | 397/1000 [01:06<01:32,  6.51it/s]\n",
      " 40%|█████████████████████████                                      | 398/1000 [01:06<01:31,  6.55it/s]\n",
      " 40%|█████████████████████████▏                                     | 399/1000 [01:07<01:40,  6.00it/s]\n",
      " 40%|█████████████████████████▏                                     | 400/1000 [01:07<01:41,  5.91it/s]\n",
      " 40%|█████████████████████████▎                                     | 401/1000 [01:07<01:42,  5.82it/s]\n",
      " 40%|█████████████████████████▎                                     | 402/1000 [01:07<01:39,  5.99it/s]\n",
      " 40%|█████████████████████████▍                                     | 403/1000 [01:07<01:50,  5.39it/s]\n",
      " 40%|█████████████████████████▍                                     | 404/1000 [01:08<01:47,  5.56it/s]\n",
      " 40%|█████████████████████████▌                                     | 405/1000 [01:08<01:59,  4.99it/s]\n",
      " 41%|█████████████████████████▌                                     | 406/1000 [01:08<02:07,  4.66it/s]\n",
      " 41%|█████████████████████████▋                                     | 407/1000 [01:08<01:53,  5.24it/s]\n",
      " 41%|█████████████████████████▋                                     | 408/1000 [01:08<01:42,  5.78it/s]\n",
      " 41%|█████████████████████████▊                                     | 409/1000 [01:09<01:53,  5.21it/s]\n",
      " 41%|█████████████████████████▊                                     | 410/1000 [01:09<02:02,  4.83it/s]\n",
      " 41%|█████████████████████████▉                                     | 411/1000 [01:09<02:00,  4.87it/s]\n",
      " 41%|█████████████████████████▉                                     | 412/1000 [01:09<01:56,  5.06it/s]\n",
      " 41%|██████████████████████████                                     | 413/1000 [01:09<02:02,  4.79it/s]\n",
      " 41%|██████████████████████████                                     | 414/1000 [01:10<01:58,  4.96it/s]\n",
      " 42%|██████████████████████████▏                                    | 415/1000 [01:10<02:05,  4.66it/s]\n",
      " 42%|██████████████████████████▏                                    | 416/1000 [01:10<02:12,  4.40it/s]\n",
      " 42%|██████████████████████████▎                                    | 417/1000 [01:10<02:06,  4.62it/s]\n",
      " 42%|██████████████████████████▎                                    | 418/1000 [01:11<01:59,  4.88it/s]\n",
      " 42%|██████████████████████████▍                                    | 419/1000 [01:11<01:55,  5.02it/s]\n",
      " 42%|██████████████████████████▍                                    | 420/1000 [01:11<01:52,  5.17it/s]\n",
      " 42%|██████████████████████████▌                                    | 422/1000 [01:11<01:33,  6.21it/s]\n",
      " 42%|██████████████████████████▋                                    | 423/1000 [01:11<01:36,  6.01it/s]\n",
      " 42%|██████████████████████████▋                                    | 424/1000 [01:11<01:35,  6.05it/s]\n",
      " 43%|██████████████████████████▊                                    | 426/1000 [01:12<01:20,  7.13it/s]\n",
      " 43%|██████████████████████████▉                                    | 427/1000 [01:12<01:45,  5.43it/s]\n",
      " 43%|██████████████████████████▉                                    | 428/1000 [01:12<01:52,  5.09it/s]\n",
      " 43%|███████████████████████████                                    | 429/1000 [01:12<01:41,  5.62it/s]\n",
      " 43%|███████████████████████████                                    | 430/1000 [01:12<01:32,  6.16it/s]\n",
      " 43%|███████████████████████████▏                                   | 431/1000 [01:13<01:48,  5.27it/s]\n",
      " 43%|███████████████████████████▏                                   | 432/1000 [01:13<01:55,  4.91it/s]\n",
      " 43%|███████████████████████████▎                                   | 433/1000 [01:13<01:48,  5.25it/s]\n",
      " 43%|███████████████████████████▎                                   | 434/1000 [01:13<01:38,  5.74it/s]\n",
      " 44%|███████████████████████████▍                                   | 435/1000 [01:13<01:26,  6.51it/s]\n",
      " 44%|███████████████████████████▌                                   | 437/1000 [01:14<01:28,  6.36it/s]\n",
      " 44%|███████████████████████████▌                                   | 438/1000 [01:14<01:35,  5.87it/s]\n",
      " 44%|███████████████████████████▋                                   | 439/1000 [01:14<01:37,  5.78it/s]\n",
      " 44%|███████████████████████████▋                                   | 440/1000 [01:14<01:36,  5.80it/s]\n",
      " 44%|███████████████████████████▊                                   | 441/1000 [01:14<01:32,  6.06it/s]\n",
      " 44%|███████████████████████████▊                                   | 442/1000 [01:14<01:28,  6.30it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|███████████████████████████▉                                   | 443/1000 [01:15<01:26,  6.41it/s]\n",
      " 44%|███████████████████████████▉                                   | 444/1000 [01:15<01:29,  6.22it/s]\n",
      " 45%|████████████████████████████                                   | 446/1000 [01:15<01:17,  7.17it/s]\n",
      " 45%|████████████████████████████▏                                  | 447/1000 [01:15<01:26,  6.37it/s]\n",
      " 45%|████████████████████████████▏                                  | 448/1000 [01:15<01:30,  6.09it/s]\n",
      " 45%|████████████████████████████▎                                  | 449/1000 [01:15<01:30,  6.06it/s]\n",
      " 45%|████████████████████████████▎                                  | 450/1000 [01:16<01:25,  6.45it/s]\n",
      " 45%|████████████████████████████▍                                  | 451/1000 [01:16<01:28,  6.20it/s]\n",
      " 45%|████████████████████████████▍                                  | 452/1000 [01:16<01:28,  6.16it/s]\n",
      " 45%|████████████████████████████▌                                  | 453/1000 [01:16<01:32,  5.94it/s]\n",
      " 45%|████████████████████████████▌                                  | 454/1000 [01:16<01:30,  6.01it/s]\n",
      " 46%|████████████████████████████▋                                  | 455/1000 [01:16<01:32,  5.90it/s]\n",
      " 46%|████████████████████████████▋                                  | 456/1000 [01:17<01:34,  5.78it/s]\n",
      " 46%|████████████████████████████▊                                  | 457/1000 [01:17<01:37,  5.59it/s]\n",
      " 46%|████████████████████████████▊                                  | 458/1000 [01:17<01:38,  5.51it/s]\n",
      " 46%|████████████████████████████▉                                  | 459/1000 [01:17<02:10,  4.14it/s]\n",
      " 46%|████████████████████████████▉                                  | 460/1000 [01:18<02:03,  4.37it/s]\n",
      " 46%|█████████████████████████████                                  | 461/1000 [01:18<02:04,  4.33it/s]\n",
      " 46%|█████████████████████████████                                  | 462/1000 [01:18<02:04,  4.31it/s]\n",
      " 46%|█████████████████████████████▏                                 | 463/1000 [01:18<01:54,  4.71it/s]\n",
      " 46%|█████████████████████████████▏                                 | 464/1000 [01:18<01:47,  4.97it/s]\n",
      " 46%|█████████████████████████████▎                                 | 465/1000 [01:18<01:31,  5.83it/s]\n",
      " 47%|█████████████████████████████▍                                 | 467/1000 [01:19<01:24,  6.34it/s]\n",
      " 47%|█████████████████████████████▍                                 | 468/1000 [01:19<01:21,  6.55it/s]\n",
      " 47%|█████████████████████████████▌                                 | 469/1000 [01:19<01:29,  5.94it/s]\n",
      " 47%|█████████████████████████████▌                                 | 470/1000 [01:19<01:31,  5.82it/s]\n",
      " 47%|█████████████████████████████▋                                 | 471/1000 [01:19<01:30,  5.84it/s]\n",
      " 47%|█████████████████████████████▋                                 | 472/1000 [01:20<01:26,  6.13it/s]\n",
      " 47%|█████████████████████████████▊                                 | 473/1000 [01:20<01:24,  6.27it/s]\n",
      " 47%|█████████████████████████████▊                                 | 474/1000 [01:20<01:20,  6.50it/s]\n",
      " 48%|█████████████████████████████▉                                 | 475/1000 [01:20<01:25,  6.11it/s]\n",
      " 48%|█████████████████████████████▉                                 | 476/1000 [01:20<01:26,  6.05it/s]\n",
      " 48%|██████████████████████████████                                 | 477/1000 [01:20<01:16,  6.84it/s]\n",
      " 48%|██████████████████████████████▏                                | 479/1000 [01:21<01:13,  7.06it/s]\n",
      " 48%|██████████████████████████████▏                                | 480/1000 [01:21<01:17,  6.71it/s]\n",
      " 48%|██████████████████████████████▎                                | 481/1000 [01:21<01:21,  6.39it/s]\n",
      " 48%|██████████████████████████████▎                                | 482/1000 [01:21<01:23,  6.20it/s]\n",
      " 48%|██████████████████████████████▍                                | 483/1000 [01:21<01:40,  5.14it/s]\n",
      " 48%|██████████████████████████████▍                                | 484/1000 [01:22<01:43,  4.97it/s]\n",
      " 48%|██████████████████████████████▌                                | 485/1000 [01:22<01:50,  4.65it/s]\n",
      " 49%|██████████████████████████████▌                                | 486/1000 [01:22<01:50,  4.64it/s]\n",
      " 49%|██████████████████████████████▋                                | 487/1000 [01:22<01:59,  4.31it/s]\n",
      " 49%|██████████████████████████████▋                                | 488/1000 [01:23<02:01,  4.20it/s]\n",
      " 49%|██████████████████████████████▊                                | 489/1000 [01:23<01:49,  4.67it/s]\n",
      " 49%|██████████████████████████████▊                                | 490/1000 [01:23<01:44,  4.87it/s]\n",
      " 49%|██████████████████████████████▉                                | 491/1000 [01:23<01:31,  5.57it/s]\n",
      " 49%|███████████████████████████████                                | 493/1000 [01:23<01:15,  6.71it/s]\n",
      " 49%|███████████████████████████████                                | 494/1000 [01:23<01:08,  7.37it/s]\n",
      " 50%|███████████████████████████████▏                               | 495/1000 [01:24<01:28,  5.71it/s]\n",
      " 50%|███████████████████████████████▏                               | 496/1000 [01:24<01:39,  5.04it/s]\n",
      " 50%|███████████████████████████████▎                               | 497/1000 [01:24<01:38,  5.11it/s]\n",
      " 50%|███████████████████████████████▎                               | 498/1000 [01:24<01:37,  5.13it/s]\n",
      " 50%|███████████████████████████████▍                               | 499/1000 [01:24<01:44,  4.77it/s]\n",
      " 50%|███████████████████████████████▌                               | 500/1000 [01:25<01:49,  4.55it/s]\n",
      " 50%|███████████████████████████████▌                               | 501/1000 [01:25<01:41,  4.91it/s]\n",
      " 50%|███████████████████████████████▋                               | 502/1000 [01:25<01:35,  5.20it/s]\n",
      " 50%|███████████████████████████████▋                               | 503/1000 [01:25<01:36,  5.14it/s]\n",
      " 50%|███████████████████████████████▊                               | 504/1000 [01:25<01:34,  5.25it/s]\n",
      " 50%|███████████████████████████████▊                               | 505/1000 [01:26<01:40,  4.95it/s]\n",
      " 51%|███████████████████████████████▉                               | 506/1000 [01:26<01:40,  4.91it/s]\n",
      " 51%|███████████████████████████████▉                               | 507/1000 [01:26<01:25,  5.76it/s]\n",
      " 51%|████████████████████████████████                               | 509/1000 [01:26<01:20,  6.14it/s]\n",
      " 51%|████████████████████████████████▏                              | 510/1000 [01:26<01:21,  6.03it/s]\n",
      " 51%|████████████████████████████████▎                              | 512/1000 [01:27<01:08,  7.08it/s]\n",
      " 51%|████████████████████████████████▎                              | 513/1000 [01:27<01:27,  5.59it/s]\n",
      " 51%|████████████████████████████████▍                              | 514/1000 [01:27<01:39,  4.88it/s]\n",
      " 52%|████████████████████████████████▍                              | 515/1000 [01:27<01:27,  5.52it/s]\n",
      " 52%|████████████████████████████████▌                              | 517/1000 [01:27<01:16,  6.33it/s]\n",
      " 52%|████████████████████████████████▋                              | 519/1000 [01:28<01:11,  6.72it/s]\n",
      " 52%|████████████████████████████████▊                              | 520/1000 [01:28<01:10,  6.78it/s]\n",
      " 52%|████████████████████████████████▊                              | 521/1000 [01:28<01:12,  6.64it/s]\n",
      " 52%|████████████████████████████████▉                              | 522/1000 [01:28<01:05,  7.32it/s]\n",
      " 52%|████████████████████████████████▉                              | 523/1000 [01:28<01:15,  6.33it/s]\n",
      " 52%|█████████████████████████████████                              | 524/1000 [01:28<01:20,  5.95it/s]\n",
      " 52%|█████████████████████████████████                              | 525/1000 [01:29<01:23,  5.70it/s]\n",
      " 53%|█████████████████████████████████▏                             | 526/1000 [01:29<01:21,  5.80it/s]\n",
      " 53%|█████████████████████████████████▏                             | 527/1000 [01:29<01:29,  5.27it/s]\n",
      " 53%|█████████████████████████████████▎                             | 528/1000 [01:29<01:26,  5.45it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████████████████████████████████▎                             | 529/1000 [01:29<01:27,  5.36it/s]\n",
      " 53%|█████████████████████████████████▍                             | 530/1000 [01:30<01:23,  5.64it/s]\n",
      " 53%|█████████████████████████████████▍                             | 531/1000 [01:30<01:20,  5.84it/s]\n",
      " 53%|█████████████████████████████████▌                             | 532/1000 [01:30<01:15,  6.18it/s]\n",
      " 53%|█████████████████████████████████▌                             | 533/1000 [01:30<01:31,  5.11it/s]\n",
      " 53%|█████████████████████████████████▋                             | 534/1000 [01:30<01:37,  4.80it/s]\n",
      " 54%|█████████████████████████████████▋                             | 535/1000 [01:31<01:44,  4.44it/s]\n",
      " 54%|█████████████████████████████████▊                             | 536/1000 [01:31<01:45,  4.38it/s]\n",
      " 54%|█████████████████████████████████▊                             | 537/1000 [01:31<01:41,  4.54it/s]\n",
      " 54%|█████████████████████████████████▉                             | 538/1000 [01:31<01:37,  4.72it/s]\n",
      " 54%|█████████████████████████████████▉                             | 539/1000 [01:31<01:25,  5.40it/s]\n",
      " 54%|██████████████████████████████████                             | 541/1000 [01:32<01:11,  6.38it/s]\n",
      " 54%|██████████████████████████████████▏                            | 543/1000 [01:32<01:08,  6.69it/s]\n",
      " 55%|██████████████████████████████████▎                            | 545/1000 [01:32<01:05,  6.98it/s]\n",
      " 55%|██████████████████████████████████▍                            | 546/1000 [01:32<01:08,  6.63it/s]\n",
      " 55%|██████████████████████████████████▍                            | 547/1000 [01:32<01:05,  6.88it/s]\n",
      " 55%|██████████████████████████████████▌                            | 548/1000 [01:33<01:01,  7.31it/s]\n",
      " 55%|██████████████████████████████████▌                            | 549/1000 [01:33<01:18,  5.78it/s]\n",
      " 55%|██████████████████████████████████▋                            | 550/1000 [01:33<01:26,  5.21it/s]\n",
      " 55%|██████████████████████████████████▋                            | 551/1000 [01:33<01:19,  5.66it/s]\n",
      " 55%|██████████████████████████████████▊                            | 552/1000 [01:33<01:13,  6.13it/s]\n",
      " 55%|██████████████████████████████████▊                            | 553/1000 [01:33<01:17,  5.79it/s]\n",
      " 55%|██████████████████████████████████▉                            | 554/1000 [01:34<01:16,  5.86it/s]\n",
      " 56%|███████████████████████████████████                            | 556/1000 [01:34<01:03,  7.01it/s]\n",
      " 56%|███████████████████████████████████                            | 557/1000 [01:34<01:10,  6.30it/s]\n",
      " 56%|███████████████████████████████████▏                           | 558/1000 [01:34<01:07,  6.56it/s]\n",
      " 56%|███████████████████████████████████▏                           | 559/1000 [01:34<01:21,  5.41it/s]\n",
      " 56%|███████████████████████████████████▎                           | 560/1000 [01:35<01:27,  5.02it/s]\n",
      " 56%|███████████████████████████████████▎                           | 561/1000 [01:35<01:36,  4.57it/s]\n",
      " 56%|███████████████████████████████████▍                           | 562/1000 [01:35<01:36,  4.53it/s]\n",
      " 56%|███████████████████████████████████▍                           | 563/1000 [01:35<01:45,  4.15it/s]\n",
      " 56%|███████████████████████████████████▌                           | 564/1000 [01:36<01:43,  4.23it/s]\n",
      " 56%|███████████████████████████████████▌                           | 565/1000 [01:36<01:33,  4.64it/s]\n",
      " 57%|███████████████████████████████████▋                           | 566/1000 [01:36<01:28,  4.93it/s]\n",
      " 57%|███████████████████████████████████▋                           | 567/1000 [01:36<01:28,  4.91it/s]\n",
      " 57%|███████████████████████████████████▊                           | 568/1000 [01:36<01:23,  5.17it/s]\n",
      " 57%|███████████████████████████████████▊                           | 569/1000 [01:37<01:20,  5.38it/s]\n",
      " 57%|███████████████████████████████████▉                           | 570/1000 [01:37<01:15,  5.69it/s]\n",
      " 57%|███████████████████████████████████▉                           | 571/1000 [01:37<01:14,  5.80it/s]\n",
      " 57%|████████████████████████████████████                           | 572/1000 [01:37<01:11,  5.99it/s]\n",
      " 57%|████████████████████████████████████▏                          | 574/1000 [01:37<01:00,  7.05it/s]\n",
      " 57%|████████████████████████████████████▏                          | 575/1000 [01:37<01:17,  5.52it/s]\n",
      " 58%|████████████████████████████████████▎                          | 576/1000 [01:38<01:21,  5.19it/s]\n",
      " 58%|████████████████████████████████████▎                          | 577/1000 [01:38<01:20,  5.27it/s]\n",
      " 58%|████████████████████████████████████▍                          | 578/1000 [01:38<01:21,  5.17it/s]\n",
      " 58%|████████████████████████████████████▍                          | 579/1000 [01:38<01:20,  5.25it/s]\n",
      " 58%|████████████████████████████████████▌                          | 580/1000 [01:38<01:13,  5.70it/s]\n",
      " 58%|████████████████████████████████████▌                          | 581/1000 [01:39<01:15,  5.53it/s]\n",
      " 58%|████████████████████████████████████▋                          | 582/1000 [01:39<01:16,  5.49it/s]\n",
      " 58%|████████████████████████████████████▋                          | 583/1000 [01:39<01:29,  4.65it/s]\n",
      " 58%|████████████████████████████████████▊                          | 584/1000 [01:39<01:24,  4.94it/s]\n",
      " 58%|████████████████████████████████████▊                          | 585/1000 [01:39<01:21,  5.09it/s]\n",
      " 59%|████████████████████████████████████▉                          | 586/1000 [01:40<01:16,  5.41it/s]\n",
      " 59%|████████████████████████████████████▉                          | 587/1000 [01:40<01:13,  5.63it/s]\n",
      " 59%|█████████████████████████████████████                          | 588/1000 [01:40<01:12,  5.68it/s]\n",
      " 59%|█████████████████████████████████████                          | 589/1000 [01:40<01:20,  5.12it/s]\n",
      " 59%|█████████████████████████████████████▏                         | 590/1000 [01:40<01:25,  4.80it/s]\n",
      " 59%|█████████████████████████████████████▏                         | 591/1000 [01:41<01:35,  4.30it/s]\n",
      " 59%|█████████████████████████████████████▎                         | 592/1000 [01:41<01:35,  4.27it/s]\n",
      " 59%|█████████████████████████████████████▍                         | 594/1000 [01:41<01:17,  5.27it/s]\n",
      " 60%|█████████████████████████████████████▍                         | 595/1000 [01:41<01:23,  4.82it/s]\n",
      " 60%|█████████████████████████████████████▌                         | 596/1000 [01:42<01:26,  4.66it/s]\n",
      " 60%|█████████████████████████████████████▋                         | 598/1000 [01:42<01:11,  5.65it/s]\n",
      " 60%|█████████████████████████████████████▋                         | 599/1000 [01:42<01:19,  5.08it/s]\n",
      " 60%|█████████████████████████████████████▊                         | 600/1000 [01:42<01:13,  5.41it/s]\n",
      " 60%|█████████████████████████████████████▊                         | 601/1000 [01:42<01:04,  6.22it/s]\n",
      " 60%|█████████████████████████████████████▉                         | 603/1000 [01:42<01:00,  6.58it/s]\n",
      " 60%|██████████████████████████████████████                         | 604/1000 [01:43<00:59,  6.68it/s]\n",
      " 61%|██████████████████████████████████████▏                        | 606/1000 [01:43<00:47,  8.34it/s]\n",
      " 61%|██████████████████████████████████████▎                        | 608/1000 [01:43<00:44,  8.79it/s]\n",
      " 61%|██████████████████████████████████████▍                        | 610/1000 [01:43<00:42,  9.16it/s]\n",
      " 61%|██████████████████████████████████████▌                        | 612/1000 [01:44<00:56,  6.82it/s]\n",
      " 61%|██████████████████████████████████████▌                        | 613/1000 [01:44<01:02,  6.22it/s]\n",
      " 61%|██████████████████████████████████████▋                        | 614/1000 [01:44<00:59,  6.49it/s]\n",
      " 62%|██████████████████████████████████████▊                        | 616/1000 [01:44<00:51,  7.50it/s]\n",
      " 62%|██████████████████████████████████████▉                        | 618/1000 [01:44<00:46,  8.25it/s]\n",
      " 62%|██████████████████████████████████████▉                        | 619/1000 [01:44<00:45,  8.34it/s]\n",
      " 62%|███████████████████████████████████████                        | 621/1000 [01:45<00:40,  9.30it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|███████████████████████████████████████▏                       | 623/1000 [01:45<00:41,  9.12it/s]\n",
      " 62%|███████████████████████████████████████▎                       | 624/1000 [01:45<00:49,  7.62it/s]\n",
      " 63%|███████████████████████████████████████▍                       | 626/1000 [01:45<00:44,  8.33it/s]\n",
      " 63%|███████████████████████████████████████▌                       | 627/1000 [01:45<00:43,  8.55it/s]\n",
      " 63%|███████████████████████████████████████▋                       | 629/1000 [01:45<00:42,  8.73it/s]\n",
      " 63%|███████████████████████████████████████▋                       | 630/1000 [01:46<00:44,  8.28it/s]\n",
      " 63%|███████████████████████████████████████▊                       | 631/1000 [01:46<00:44,  8.25it/s]\n",
      " 63%|███████████████████████████████████████▊                       | 632/1000 [01:46<00:44,  8.33it/s]\n",
      " 63%|███████████████████████████████████████▉                       | 633/1000 [01:46<00:41,  8.75it/s]\n",
      " 64%|████████████████████████████████████████                       | 635/1000 [01:46<00:38,  9.38it/s]\n",
      " 64%|████████████████████████████████████████▏                      | 637/1000 [01:46<00:42,  8.57it/s]\n",
      " 64%|████████████████████████████████████████▏                      | 638/1000 [01:47<00:49,  7.38it/s]\n",
      " 64%|████████████████████████████████████████▎                      | 639/1000 [01:47<00:45,  7.97it/s]\n",
      " 64%|████████████████████████████████████████▍                      | 641/1000 [01:47<00:45,  7.82it/s]\n",
      " 64%|████████████████████████████████████████▍                      | 642/1000 [01:47<00:50,  7.16it/s]\n",
      " 64%|████████████████████████████████████████▌                      | 643/1000 [01:47<00:52,  6.81it/s]\n",
      " 64%|████████████████████████████████████████▌                      | 644/1000 [01:47<00:55,  6.44it/s]\n",
      " 64%|████████████████████████████████████████▋                      | 645/1000 [01:48<00:55,  6.36it/s]\n",
      " 65%|████████████████████████████████████████▋                      | 646/1000 [01:48<00:56,  6.25it/s]\n",
      " 65%|████████████████████████████████████████▊                      | 647/1000 [01:48<00:57,  6.12it/s]\n",
      " 65%|████████████████████████████████████████▊                      | 648/1000 [01:48<00:58,  5.99it/s]\n",
      " 65%|████████████████████████████████████████▉                      | 649/1000 [01:48<01:02,  5.63it/s]\n",
      " 65%|████████████████████████████████████████▉                      | 650/1000 [01:49<01:02,  5.57it/s]\n",
      " 65%|█████████████████████████████████████████                      | 651/1000 [01:49<00:54,  6.42it/s]\n",
      " 65%|█████████████████████████████████████████▏                     | 653/1000 [01:49<00:46,  7.40it/s]\n",
      " 66%|█████████████████████████████████████████▎                     | 655/1000 [01:49<00:42,  8.06it/s]\n",
      " 66%|█████████████████████████████████████████▍                     | 657/1000 [01:49<00:43,  7.93it/s]\n",
      " 66%|█████████████████████████████████████████▍                     | 658/1000 [01:49<00:46,  7.37it/s]\n",
      " 66%|█████████████████████████████████████████▌                     | 659/1000 [01:50<00:50,  6.74it/s]\n",
      " 66%|█████████████████████████████████████████▌                     | 660/1000 [01:50<00:49,  6.93it/s]\n",
      " 66%|█████████████████████████████████████████▋                     | 661/1000 [01:50<00:52,  6.43it/s]\n",
      " 66%|█████████████████████████████████████████▋                     | 662/1000 [01:50<00:54,  6.26it/s]\n",
      " 66%|█████████████████████████████████████████▊                     | 663/1000 [01:50<00:52,  6.37it/s]\n",
      " 66%|█████████████████████████████████████████▊                     | 664/1000 [01:50<00:48,  6.88it/s]\n",
      " 66%|█████████████████████████████████████████▉                     | 665/1000 [01:51<00:59,  5.59it/s]\n",
      " 67%|█████████████████████████████████████████▉                     | 666/1000 [01:51<01:05,  5.10it/s]\n",
      " 67%|██████████████████████████████████████████                     | 667/1000 [01:51<01:02,  5.29it/s]\n",
      " 67%|██████████████████████████████████████████                     | 668/1000 [01:51<00:58,  5.64it/s]\n",
      " 67%|██████████████████████████████████████████▏                    | 669/1000 [01:51<00:53,  6.21it/s]\n",
      " 67%|██████████████████████████████████████████▎                    | 671/1000 [01:52<00:49,  6.71it/s]\n",
      " 67%|██████████████████████████████████████████▎                    | 672/1000 [01:52<00:46,  6.99it/s]\n",
      " 67%|██████████████████████████████████████████▍                    | 674/1000 [01:52<00:41,  7.88it/s]\n",
      " 68%|██████████████████████████████████████████▌                    | 675/1000 [01:52<00:56,  5.75it/s]\n",
      " 68%|██████████████████████████████████████████▌                    | 676/1000 [01:52<01:01,  5.24it/s]\n",
      " 68%|██████████████████████████████████████████▋                    | 677/1000 [01:53<01:00,  5.30it/s]\n",
      " 68%|██████████████████████████████████████████▋                    | 678/1000 [01:53<00:59,  5.40it/s]\n",
      " 68%|██████████████████████████████████████████▊                    | 679/1000 [01:53<01:00,  5.28it/s]\n",
      " 68%|██████████████████████████████████████████▊                    | 680/1000 [01:53<00:58,  5.47it/s]\n",
      " 68%|██████████████████████████████████████████▉                    | 681/1000 [01:53<01:01,  5.21it/s]\n",
      " 68%|██████████████████████████████████████████▉                    | 682/1000 [01:53<00:57,  5.54it/s]\n",
      " 68%|███████████████████████████████████████████                    | 684/1000 [01:54<00:46,  6.73it/s]\n",
      " 68%|███████████████████████████████████████████▏                   | 685/1000 [01:54<00:52,  5.96it/s]\n",
      " 69%|███████████████████████████████████████████▏                   | 686/1000 [01:54<00:55,  5.62it/s]\n",
      " 69%|███████████████████████████████████████████▎                   | 687/1000 [01:54<00:55,  5.63it/s]\n",
      " 69%|███████████████████████████████████████████▎                   | 688/1000 [01:54<00:52,  5.89it/s]\n",
      " 69%|███████████████████████████████████████████▍                   | 689/1000 [01:55<00:54,  5.69it/s]\n",
      " 69%|███████████████████████████████████████████▍                   | 690/1000 [01:55<00:54,  5.65it/s]\n",
      " 69%|███████████████████████████████████████████▌                   | 691/1000 [01:55<00:55,  5.54it/s]\n",
      " 69%|███████████████████████████████████████████▌                   | 692/1000 [01:55<00:54,  5.65it/s]\n",
      " 69%|███████████████████████████████████████████▋                   | 693/1000 [01:55<01:03,  4.87it/s]\n",
      " 69%|███████████████████████████████████████████▋                   | 694/1000 [01:56<01:03,  4.79it/s]\n",
      " 70%|███████████████████████████████████████████▊                   | 695/1000 [01:56<01:07,  4.51it/s]\n",
      " 70%|███████████████████████████████████████████▊                   | 696/1000 [01:56<01:07,  4.49it/s]\n",
      " 70%|███████████████████████████████████████████▉                   | 697/1000 [01:56<01:09,  4.37it/s]\n",
      " 70%|███████████████████████████████████████████▉                   | 698/1000 [01:57<01:10,  4.26it/s]\n",
      " 70%|████████████████████████████████████████████                   | 699/1000 [01:57<01:04,  4.70it/s]\n",
      " 70%|████████████████████████████████████████████                   | 700/1000 [01:57<00:57,  5.17it/s]\n",
      " 70%|████████████████████████████████████████████▏                  | 701/1000 [01:57<00:57,  5.24it/s]\n",
      " 70%|████████████████████████████████████████████▏                  | 702/1000 [01:57<00:55,  5.41it/s]\n",
      " 70%|████████████████████████████████████████████▍                  | 705/1000 [01:57<00:45,  6.53it/s]\n",
      " 71%|████████████████████████████████████████████▍                  | 706/1000 [01:58<00:43,  6.79it/s]\n",
      " 71%|████████████████████████████████████████████▌                  | 707/1000 [01:58<00:45,  6.40it/s]\n",
      " 71%|████████████████████████████████████████████▌                  | 708/1000 [01:58<00:45,  6.38it/s]\n",
      " 71%|████████████████████████████████████████████▋                  | 709/1000 [01:58<00:47,  6.11it/s]\n",
      " 71%|████████████████████████████████████████████▋                  | 710/1000 [01:58<00:49,  5.86it/s]\n",
      " 71%|████████████████████████████████████████████▊                  | 711/1000 [01:59<00:57,  5.03it/s]\n",
      " 71%|████████████████████████████████████████████▊                  | 712/1000 [01:59<00:59,  4.80it/s]\n",
      " 71%|████████████████████████████████████████████▉                  | 713/1000 [01:59<00:58,  4.92it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|████████████████████████████████████████████▉                  | 714/1000 [01:59<00:56,  5.08it/s]\n",
      " 72%|█████████████████████████████████████████████                  | 715/1000 [01:59<00:48,  5.90it/s]\n",
      " 72%|█████████████████████████████████████████████▏                 | 717/1000 [01:59<00:41,  6.85it/s]\n",
      " 72%|█████████████████████████████████████████████▎                 | 719/1000 [02:00<00:39,  7.12it/s]\n",
      " 72%|█████████████████████████████████████████████▎                 | 720/1000 [02:00<00:40,  6.91it/s]\n",
      " 72%|█████████████████████████████████████████████▍                 | 721/1000 [02:00<00:37,  7.54it/s]\n",
      " 72%|█████████████████████████████████████████████▌                 | 723/1000 [02:00<00:36,  7.61it/s]\n",
      " 72%|█████████████████████████████████████████████▌                 | 724/1000 [02:00<00:41,  6.59it/s]\n",
      " 72%|█████████████████████████████████████████████▋                 | 725/1000 [02:01<00:49,  5.51it/s]\n",
      " 73%|█████████████████████████████████████████████▋                 | 726/1000 [02:01<00:54,  5.05it/s]\n",
      " 73%|█████████████████████████████████████████████▊                 | 727/1000 [02:01<00:47,  5.76it/s]\n",
      " 73%|█████████████████████████████████████████████▊                 | 728/1000 [02:01<00:42,  6.43it/s]\n",
      " 73%|█████████████████████████████████████████████▉                 | 729/1000 [02:01<00:40,  6.63it/s]\n",
      " 73%|█████████████████████████████████████████████▉                 | 730/1000 [02:01<00:40,  6.65it/s]\n",
      " 73%|██████████████████████████████████████████████                 | 731/1000 [02:02<00:42,  6.26it/s]\n",
      " 73%|██████████████████████████████████████████████                 | 732/1000 [02:02<00:41,  6.39it/s]\n",
      " 73%|██████████████████████████████████████████████▏                | 733/1000 [02:02<00:50,  5.32it/s]\n",
      " 73%|██████████████████████████████████████████████▏                | 734/1000 [02:02<00:52,  5.04it/s]\n",
      " 74%|██████████████████████████████████████████████▎                | 735/1000 [02:02<00:53,  4.93it/s]\n",
      " 74%|██████████████████████████████████████████████▎                | 736/1000 [02:03<00:52,  5.07it/s]\n",
      " 74%|██████████████████████████████████████████████▍                | 737/1000 [02:03<00:57,  4.55it/s]\n",
      " 74%|██████████████████████████████████████████████▍                | 738/1000 [02:03<00:59,  4.39it/s]\n",
      " 74%|██████████████████████████████████████████████▌                | 739/1000 [02:03<01:01,  4.21it/s]\n",
      " 74%|██████████████████████████████████████████████▌                | 740/1000 [02:04<00:59,  4.38it/s]\n",
      " 74%|██████████████████████████████████████████████▋                | 741/1000 [02:04<00:58,  4.39it/s]\n",
      " 74%|██████████████████████████████████████████████▋                | 742/1000 [02:04<00:58,  4.43it/s]\n",
      " 74%|██████████████████████████████████████████████▊                | 743/1000 [02:04<01:01,  4.20it/s]\n",
      " 74%|██████████████████████████████████████████████▊                | 744/1000 [02:05<01:00,  4.20it/s]\n",
      " 74%|██████████████████████████████████████████████▉                | 745/1000 [02:05<00:54,  4.66it/s]\n",
      " 75%|██████████████████████████████████████████████▉                | 746/1000 [02:05<00:49,  5.13it/s]\n",
      " 75%|███████████████████████████████████████████████                | 747/1000 [02:05<00:47,  5.36it/s]\n",
      " 75%|███████████████████████████████████████████████                | 748/1000 [02:05<00:44,  5.62it/s]\n",
      " 75%|███████████████████████████████████████████████▎               | 750/1000 [02:05<00:38,  6.52it/s]\n",
      " 75%|███████████████████████████████████████████████▎               | 751/1000 [02:06<00:40,  6.11it/s]\n",
      " 75%|███████████████████████████████████████████████▍               | 752/1000 [02:06<00:39,  6.28it/s]\n",
      " 75%|███████████████████████████████████████████████▍               | 753/1000 [02:06<00:40,  6.03it/s]\n",
      " 75%|███████████████████████████████████████████████▌               | 754/1000 [02:06<00:41,  5.98it/s]\n",
      " 76%|███████████████████████████████████████████████▌               | 755/1000 [02:06<00:36,  6.74it/s]\n",
      " 76%|███████████████████████████████████████████████▋               | 757/1000 [02:07<00:37,  6.50it/s]\n",
      " 76%|███████████████████████████████████████████████▊               | 758/1000 [02:07<00:42,  5.68it/s]\n",
      " 76%|███████████████████████████████████████████████▊               | 759/1000 [02:07<00:41,  5.76it/s]\n",
      " 76%|███████████████████████████████████████████████▉               | 760/1000 [02:07<00:38,  6.17it/s]\n",
      " 76%|███████████████████████████████████████████████▉               | 761/1000 [02:07<00:46,  5.09it/s]\n",
      " 76%|████████████████████████████████████████████████               | 762/1000 [02:08<00:48,  4.92it/s]\n",
      " 76%|████████████████████████████████████████████████               | 763/1000 [02:08<00:45,  5.21it/s]\n",
      " 76%|████████████████████████████████████████████████▏              | 764/1000 [02:08<00:42,  5.60it/s]\n",
      " 76%|████████████████████████████████████████████████▏              | 765/1000 [02:08<00:36,  6.38it/s]\n",
      " 77%|████████████████████████████████████████████████▎              | 767/1000 [02:08<00:32,  7.22it/s]\n",
      " 77%|████████████████████████████████████████████████▍              | 769/1000 [02:08<00:30,  7.68it/s]\n",
      " 77%|████████████████████████████████████████████████▌              | 771/1000 [02:09<00:29,  7.65it/s]\n",
      " 77%|████████████████████████████████████████████████▋              | 772/1000 [02:09<00:32,  6.92it/s]\n",
      " 77%|████████████████████████████████████████████████▋              | 773/1000 [02:09<00:31,  7.15it/s]\n",
      " 77%|████████████████████████████████████████████████▊              | 774/1000 [02:09<00:31,  7.21it/s]\n",
      " 78%|████████████████████████████████████████████████▊              | 775/1000 [02:09<00:39,  5.69it/s]\n",
      " 78%|████████████████████████████████████████████████▉              | 776/1000 [02:10<00:43,  5.20it/s]\n",
      " 78%|█████████████████████████████████████████████████              | 778/1000 [02:10<00:34,  6.37it/s]\n",
      " 78%|█████████████████████████████████████████████████              | 779/1000 [02:10<00:40,  5.40it/s]\n",
      " 78%|█████████████████████████████████████████████████▏             | 780/1000 [02:10<00:44,  4.97it/s]\n",
      " 78%|█████████████████████████████████████████████████▏             | 781/1000 [02:10<00:42,  5.15it/s]\n",
      " 78%|█████████████████████████████████████████████████▎             | 782/1000 [02:11<00:41,  5.29it/s]\n",
      " 78%|█████████████████████████████████████████████████▎             | 783/1000 [02:11<00:38,  5.65it/s]\n",
      " 78%|█████████████████████████████████████████████████▍             | 784/1000 [02:11<00:35,  6.09it/s]\n",
      " 78%|█████████████████████████████████████████████████▍             | 785/1000 [02:11<00:41,  5.23it/s]\n",
      " 79%|█████████████████████████████████████████████████▌             | 786/1000 [02:11<00:44,  4.76it/s]\n",
      " 79%|█████████████████████████████████████████████████▌             | 787/1000 [02:12<00:43,  4.90it/s]\n",
      " 79%|█████████████████████████████████████████████████▋             | 788/1000 [02:12<00:39,  5.39it/s]\n",
      " 79%|█████████████████████████████████████████████████▋             | 789/1000 [02:12<00:43,  4.90it/s]\n",
      " 79%|█████████████████████████████████████████████████▊             | 790/1000 [02:12<00:44,  4.69it/s]\n",
      " 79%|█████████████████████████████████████████████████▉             | 792/1000 [02:12<00:36,  5.64it/s]\n",
      " 79%|█████████████████████████████████████████████████▉             | 793/1000 [02:13<00:37,  5.58it/s]\n",
      " 79%|██████████████████████████████████████████████████             | 794/1000 [02:13<00:37,  5.54it/s]\n",
      " 80%|██████████████████████████████████████████████████▏            | 796/1000 [02:13<00:30,  6.60it/s]\n",
      " 80%|██████████████████████████████████████████████████▏            | 797/1000 [02:13<00:27,  7.26it/s]\n",
      " 80%|██████████████████████████████████████████████████▎            | 799/1000 [02:13<00:29,  6.78it/s]\n",
      " 80%|██████████████████████████████████████████████████▍            | 800/1000 [02:14<00:33,  5.88it/s]\n",
      " 80%|██████████████████████████████████████████████████▍            | 801/1000 [02:14<00:38,  5.22it/s]\n",
      " 80%|██████████████████████████████████████████████████▌            | 802/1000 [02:14<00:40,  4.90it/s]\n",
      " 80%|██████████████████████████████████████████████████▌            | 803/1000 [02:14<00:39,  5.04it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|██████████████████████████████████████████████████▋            | 804/1000 [02:14<00:37,  5.30it/s]\n",
      " 80%|██████████████████████████████████████████████████▋            | 805/1000 [02:15<00:40,  4.84it/s]\n",
      " 81%|██████████████████████████████████████████████████▊            | 806/1000 [02:15<00:42,  4.61it/s]\n",
      " 81%|██████████████████████████████████████████████████▉            | 808/1000 [02:15<00:34,  5.53it/s]\n",
      " 81%|██████████████████████████████████████████████████▉            | 809/1000 [02:15<00:31,  5.97it/s]\n",
      " 81%|███████████████████████████████████████████████████            | 811/1000 [02:16<00:31,  5.98it/s]\n",
      " 81%|███████████████████████████████████████████████████▏           | 812/1000 [02:16<00:33,  5.57it/s]\n",
      " 81%|███████████████████████████████████████████████████▎           | 814/1000 [02:16<00:28,  6.55it/s]\n",
      " 82%|███████████████████████████████████████████████████▎           | 815/1000 [02:16<00:30,  6.11it/s]\n",
      " 82%|███████████████████████████████████████████████████▍           | 816/1000 [02:16<00:30,  6.13it/s]\n",
      " 82%|███████████████████████████████████████████████████▍           | 817/1000 [02:16<00:27,  6.77it/s]\n",
      " 82%|███████████████████████████████████████████████████▌           | 819/1000 [02:17<00:27,  6.48it/s]\n",
      " 82%|███████████████████████████████████████████████████▋           | 820/1000 [02:17<00:31,  5.74it/s]\n",
      " 82%|███████████████████████████████████████████████████▋           | 821/1000 [02:17<00:30,  5.95it/s]\n",
      " 82%|███████████████████████████████████████████████████▊           | 822/1000 [02:17<00:28,  6.33it/s]\n",
      " 82%|███████████████████████████████████████████████████▊           | 823/1000 [02:17<00:30,  5.77it/s]\n",
      " 82%|███████████████████████████████████████████████████▉           | 824/1000 [02:18<00:29,  5.92it/s]\n",
      " 82%|███████████████████████████████████████████████████▉           | 825/1000 [02:18<00:29,  5.86it/s]\n",
      " 83%|████████████████████████████████████████████████████           | 826/1000 [02:18<00:29,  5.90it/s]\n",
      " 83%|████████████████████████████████████████████████████           | 827/1000 [02:18<00:30,  5.76it/s]\n",
      " 83%|████████████████████████████████████████████████████▏          | 828/1000 [02:18<00:28,  6.03it/s]\n",
      " 83%|████████████████████████████████████████████████████▏          | 829/1000 [02:19<00:31,  5.35it/s]\n",
      " 83%|████████████████████████████████████████████████████▎          | 830/1000 [02:19<00:32,  5.23it/s]\n",
      " 83%|████████████████████████████████████████████████████▎          | 831/1000 [02:19<00:35,  4.80it/s]\n",
      " 83%|████████████████████████████████████████████████████▍          | 832/1000 [02:19<00:36,  4.63it/s]\n",
      " 83%|████████████████████████████████████████████████████▌          | 834/1000 [02:19<00:29,  5.65it/s]\n",
      " 84%|████████████████████████████████████████████████████▌          | 835/1000 [02:20<00:28,  5.85it/s]\n",
      " 84%|████████████████████████████████████████████████████▋          | 836/1000 [02:20<00:25,  6.50it/s]\n",
      " 84%|████████████████████████████████████████████████████▊          | 838/1000 [02:20<00:21,  7.47it/s]\n",
      " 84%|████████████████████████████████████████████████████▊          | 839/1000 [02:20<00:24,  6.70it/s]\n",
      " 84%|████████████████████████████████████████████████████▉          | 840/1000 [02:20<00:25,  6.39it/s]\n",
      " 84%|████████████████████████████████████████████████████▉          | 841/1000 [02:20<00:28,  5.62it/s]\n",
      " 84%|█████████████████████████████████████████████████████          | 842/1000 [02:21<00:31,  5.05it/s]\n",
      " 84%|█████████████████████████████████████████████████████          | 843/1000 [02:21<00:26,  5.83it/s]\n",
      " 84%|█████████████████████████████████████████████████████▏         | 845/1000 [02:21<00:26,  5.80it/s]\n",
      " 85%|█████████████████████████████████████████████████████▎         | 846/1000 [02:21<00:30,  5.03it/s]\n",
      " 85%|█████████████████████████████████████████████████████▎         | 847/1000 [02:21<00:26,  5.81it/s]\n",
      " 85%|█████████████████████████████████████████████████████▍         | 849/1000 [02:22<00:25,  6.03it/s]\n",
      " 85%|█████████████████████████████████████████████████████▌         | 850/1000 [02:22<00:27,  5.48it/s]\n",
      " 85%|█████████████████████████████████████████████████████▌         | 851/1000 [02:22<00:23,  6.26it/s]\n",
      " 85%|█████████████████████████████████████████████████████▋         | 853/1000 [02:22<00:22,  6.44it/s]\n",
      " 85%|█████████████████████████████████████████████████████▊         | 854/1000 [02:23<00:24,  6.03it/s]\n",
      " 86%|█████████████████████████████████████████████████████▊         | 855/1000 [02:23<00:27,  5.34it/s]\n",
      " 86%|█████████████████████████████████████████████████████▉         | 856/1000 [02:23<00:28,  5.07it/s]\n",
      " 86%|█████████████████████████████████████████████████████▉         | 857/1000 [02:23<00:25,  5.63it/s]\n",
      " 86%|██████████████████████████████████████████████████████         | 858/1000 [02:23<00:22,  6.27it/s]\n",
      " 86%|██████████████████████████████████████████████████████         | 859/1000 [02:23<00:23,  6.11it/s]\n",
      " 86%|██████████████████████████████████████████████████████▏        | 860/1000 [02:24<00:21,  6.67it/s]\n",
      " 86%|██████████████████████████████████████████████████████▏        | 861/1000 [02:24<00:21,  6.50it/s]\n",
      " 86%|██████████████████████████████████████████████████████▎        | 862/1000 [02:24<00:21,  6.32it/s]\n",
      " 86%|██████████████████████████████████████████████████████▎        | 863/1000 [02:24<00:22,  5.98it/s]\n",
      " 86%|██████████████████████████████████████████████████████▍        | 864/1000 [02:24<00:21,  6.20it/s]\n",
      " 86%|██████████████████████████████████████████████████████▍        | 865/1000 [02:24<00:21,  6.21it/s]\n",
      " 87%|██████████████████████████████████████████████████████▌        | 866/1000 [02:25<00:20,  6.56it/s]\n",
      " 87%|██████████████████████████████████████████████████████▌        | 867/1000 [02:25<00:21,  6.10it/s]\n",
      " 87%|██████████████████████████████████████████████████████▋        | 868/1000 [02:25<00:22,  6.00it/s]\n",
      " 87%|██████████████████████████████████████████████████████▋        | 869/1000 [02:25<00:21,  5.99it/s]\n",
      " 87%|██████████████████████████████████████████████████████▊        | 870/1000 [02:25<00:21,  6.17it/s]\n",
      " 87%|██████████████████████████████████████████████████████▉        | 873/1000 [02:26<00:18,  6.96it/s]\n",
      " 87%|███████████████████████████████████████████████████████        | 874/1000 [02:26<00:19,  6.40it/s]\n",
      " 88%|███████████████████████████████████████████████████████▏       | 875/1000 [02:26<00:20,  6.09it/s]\n",
      " 88%|███████████████████████████████████████████████████████▏       | 876/1000 [02:26<00:20,  6.20it/s]\n",
      " 88%|███████████████████████████████████████████████████████▎       | 877/1000 [02:26<00:17,  6.96it/s]\n",
      " 88%|███████████████████████████████████████████████████████▎       | 878/1000 [02:26<00:17,  7.04it/s]\n",
      " 88%|███████████████████████████████████████████████████████▍       | 879/1000 [02:27<00:24,  4.89it/s]\n",
      " 88%|███████████████████████████████████████████████████████▍       | 880/1000 [02:27<00:25,  4.67it/s]\n",
      " 88%|███████████████████████████████████████████████████████▌       | 881/1000 [02:27<00:26,  4.47it/s]\n",
      " 88%|███████████████████████████████████████████████████████▌       | 882/1000 [02:27<00:26,  4.43it/s]\n",
      " 88%|███████████████████████████████████████████████████████▋       | 884/1000 [02:28<00:21,  5.48it/s]\n",
      " 89%|███████████████████████████████████████████████████████▊       | 886/1000 [02:28<00:17,  6.67it/s]\n",
      " 89%|███████████████████████████████████████████████████████▉       | 888/1000 [02:28<00:14,  7.86it/s]\n",
      " 89%|████████████████████████████████████████████████████████       | 890/1000 [02:28<00:18,  6.02it/s]\n",
      " 89%|████████████████████████████████████████████████████████▏      | 891/1000 [02:29<00:20,  5.22it/s]\n",
      " 89%|████████████████████████████████████████████████████████▏      | 892/1000 [02:29<00:19,  5.68it/s]\n",
      " 89%|████████████████████████████████████████████████████████▎      | 893/1000 [02:29<00:18,  5.64it/s]\n",
      " 89%|████████████████████████████████████████████████████████▎      | 894/1000 [02:29<00:18,  5.73it/s]\n",
      " 90%|████████████████████████████████████████████████████████▍      | 895/1000 [02:29<00:19,  5.39it/s]\n",
      " 90%|████████████████████████████████████████████████████████▍      | 896/1000 [02:29<00:19,  5.45it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|████████████████████████████████████████████████████████▌      | 897/1000 [02:30<00:19,  5.19it/s]\n",
      " 90%|████████████████████████████████████████████████████████▌      | 898/1000 [02:30<00:20,  4.98it/s]\n",
      " 90%|████████████████████████████████████████████████████████▋      | 899/1000 [02:30<00:19,  5.10it/s]\n",
      " 90%|████████████████████████████████████████████████████████▋      | 900/1000 [02:30<00:18,  5.31it/s]\n",
      " 90%|████████████████████████████████████████████████████████▊      | 902/1000 [02:30<00:15,  6.28it/s]\n",
      " 90%|████████████████████████████████████████████████████████▉      | 903/1000 [02:31<00:14,  6.79it/s]\n",
      " 90%|█████████████████████████████████████████████████████████      | 905/1000 [02:31<00:13,  7.05it/s]\n",
      " 91%|█████████████████████████████████████████████████████████      | 906/1000 [02:31<00:13,  7.07it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▏     | 907/1000 [02:31<00:16,  5.49it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▏     | 908/1000 [02:31<00:18,  4.99it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▎     | 910/1000 [02:32<00:14,  6.01it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▍     | 911/1000 [02:32<00:14,  6.03it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▍     | 912/1000 [02:32<00:13,  6.35it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▌     | 913/1000 [02:32<00:14,  5.90it/s]\n",
      " 91%|█████████████████████████████████████████████████████████▌     | 914/1000 [02:32<00:14,  5.90it/s]\n",
      " 92%|█████████████████████████████████████████████████████████▋     | 915/1000 [02:32<00:14,  5.76it/s]\n",
      " 92%|█████████████████████████████████████████████████████████▋     | 916/1000 [02:33<00:14,  5.62it/s]\n",
      " 92%|█████████████████████████████████████████████████████████▊     | 918/1000 [02:33<00:12,  6.51it/s]\n",
      " 92%|█████████████████████████████████████████████████████████▉     | 919/1000 [02:33<00:11,  7.18it/s]\n",
      " 92%|██████████████████████████████████████████████████████████     | 921/1000 [02:33<00:11,  6.79it/s]\n",
      " 92%|██████████████████████████████████████████████████████████     | 922/1000 [02:34<00:13,  5.85it/s]\n",
      " 92%|██████████████████████████████████████████████████████████▏    | 923/1000 [02:34<00:13,  5.65it/s]\n",
      " 92%|██████████████████████████████████████████████████████████▏    | 924/1000 [02:34<00:13,  5.67it/s]\n",
      " 92%|██████████████████████████████████████████████████████████▎    | 925/1000 [02:34<00:14,  5.27it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▎    | 926/1000 [02:34<00:14,  5.20it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▍    | 927/1000 [02:35<00:15,  4.79it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▍    | 928/1000 [02:35<00:15,  4.62it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▌    | 929/1000 [02:35<00:14,  4.82it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▌    | 930/1000 [02:35<00:13,  5.06it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▋    | 931/1000 [02:35<00:14,  4.66it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▋    | 932/1000 [02:36<00:14,  4.74it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▊    | 933/1000 [02:36<00:14,  4.62it/s]\n",
      " 93%|██████████████████████████████████████████████████████████▊    | 934/1000 [02:36<00:14,  4.50it/s]\n",
      " 94%|██████████████████████████████████████████████████████████▉    | 936/1000 [02:36<00:11,  5.45it/s]\n",
      " 94%|███████████████████████████████████████████████████████████    | 937/1000 [02:36<00:10,  6.29it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▏   | 939/1000 [02:37<00:09,  6.53it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▏   | 940/1000 [02:37<00:09,  6.17it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▎   | 941/1000 [02:37<00:11,  5.18it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▎   | 942/1000 [02:37<00:11,  4.86it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▍   | 943/1000 [02:38<00:11,  4.99it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▍   | 944/1000 [02:38<00:10,  5.43it/s]\n",
      " 94%|███████████████████████████████████████████████████████████▌   | 945/1000 [02:38<00:10,  5.46it/s]\n",
      " 95%|███████████████████████████████████████████████████████████▌   | 946/1000 [02:38<00:09,  5.71it/s]\n",
      " 95%|███████████████████████████████████████████████████████████▋   | 947/1000 [02:38<00:09,  5.82it/s]\n",
      " 95%|███████████████████████████████████████████████████████████▋   | 948/1000 [02:38<00:08,  5.89it/s]\n",
      " 95%|███████████████████████████████████████████████████████████▊   | 949/1000 [02:38<00:07,  6.54it/s]\n",
      " 95%|███████████████████████████████████████████████████████████▉   | 951/1000 [02:39<00:07,  6.67it/s]\n",
      " 95%|███████████████████████████████████████████████████████████▉   | 952/1000 [02:39<00:07,  6.27it/s]\n",
      " 95%|████████████████████████████████████████████████████████████   | 953/1000 [02:39<00:07,  6.42it/s]\n",
      " 95%|████████████████████████████████████████████████████████████   | 954/1000 [02:39<00:06,  6.87it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▏  | 955/1000 [02:39<00:07,  6.22it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▏  | 956/1000 [02:40<00:07,  6.08it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▎  | 957/1000 [02:40<00:06,  6.27it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▎  | 958/1000 [02:40<00:06,  6.37it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▍  | 959/1000 [02:40<00:07,  5.42it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▍  | 960/1000 [02:40<00:07,  5.01it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▌  | 961/1000 [02:40<00:06,  5.74it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▋  | 963/1000 [02:41<00:05,  6.79it/s]\n",
      " 96%|████████████████████████████████████████████████████████████▊  | 965/1000 [02:41<00:05,  6.91it/s]\n",
      " 97%|████████████████████████████████████████████████████████████▊  | 966/1000 [02:41<00:05,  6.53it/s]\n",
      " 97%|████████████████████████████████████████████████████████████▉  | 967/1000 [02:41<00:05,  6.36it/s]\n",
      " 97%|████████████████████████████████████████████████████████████▉  | 968/1000 [02:41<00:05,  6.36it/s]\n",
      " 97%|█████████████████████████████████████████████████████████████  | 969/1000 [02:42<00:05,  5.80it/s]\n",
      " 97%|█████████████████████████████████████████████████████████████  | 970/1000 [02:42<00:05,  5.69it/s]\n",
      " 97%|█████████████████████████████████████████████████████████████▏ | 971/1000 [02:42<00:05,  5.08it/s]\n",
      " 97%|█████████████████████████████████████████████████████████████▏ | 972/1000 [02:42<00:05,  4.87it/s]\n",
      " 97%|█████████████████████████████████████████████████████████████▎ | 973/1000 [02:43<00:05,  4.52it/s]\n",
      " 97%|█████████████████████████████████████████████████████████████▎ | 974/1000 [02:43<00:05,  4.53it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▍ | 975/1000 [02:43<00:05,  4.66it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▍ | 976/1000 [02:43<00:04,  4.98it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▌ | 977/1000 [02:43<00:04,  4.98it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▌ | 978/1000 [02:43<00:04,  5.25it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▋ | 979/1000 [02:44<00:03,  5.34it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▋ | 980/1000 [02:44<00:03,  5.83it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▊ | 981/1000 [02:44<00:03,  5.57it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▊ | 982/1000 [02:44<00:03,  5.66it/s]\n",
      " 98%|█████████████████████████████████████████████████████████████▉ | 983/1000 [02:44<00:03,  5.46it/s]\n",
      " 98%|██████████████████████████████████████████████████████████████ | 985/1000 [02:45<00:02,  5.95it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|██████████████████████████████████████████████████████████████ | 986/1000 [02:45<00:02,  6.24it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████▏| 987/1000 [02:45<00:01,  6.81it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████▎| 989/1000 [02:45<00:01,  7.65it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████▍| 991/1000 [02:45<00:01,  7.34it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████▍| 992/1000 [02:46<00:01,  6.87it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████▌| 993/1000 [02:46<00:01,  6.43it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████▌| 994/1000 [02:46<00:00,  6.26it/s]\n",
      "100%|██████████████████████████████████████████████████████████████▋| 996/1000 [02:46<00:00,  7.24it/s]\n",
      "100%|██████████████████████████████████████████████████████████████▊| 997/1000 [02:46<00:00,  7.69it/s]\n",
      "100%|██████████████████████████████████████████████████████████████▉| 999/1000 [02:46<00:00,  7.63it/s]\n",
      "100%|██████████████████████████████████████████████████████████████| 1000/1000 [02:47<00:00,  6.75it/s]"
     ]
    }
   ],
   "source": [
    "# Trying to load 1000 images \n",
    "# Its still loading !!!!!!\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "train_image = []\n",
    "for i in tqdm(range(1000)):\n",
    "    img = image.load_img('D:/diabetic-retinopathy-detection/train.zip/train/'+train_labels['image'][i]+'.jpeg',target_size=(img_rows,img_cols,3))\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255\n",
    "    train_image.append(img)\n",
    "X = np.array(train_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######train_image = [0] #fuck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]]], dtype=float32), array([[[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]]], dtype=float32), array([[[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]]], dtype=float32), array([[[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.],\n",
       "         [0., 0., 0.]]], dtype=float32)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 224, 224, 3)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1000 - No. of images \n",
    "# 224, 224 - Resized dims\n",
    "# Channels\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cca5a0d518>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cca5a68ac8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvcuvbNuV5vUbY8w511oRsfc+r/vyvbav8/qRNs5SGhX5EHSREB1Ej2rQTRqUBBIdRIt/AGgiIUEbIdFBqZKqVTTooCqSSiAzoSiq8lW20/a1zzl774hYa845Bo0Z5/raaWfa6Xuur13nk0LaETtirRmx1hxzPL7xTYkIXuEVXuEVXkB/3gN4hVd4hU8WXhmFV3iFV/gBvDIKr/AKr/ADeGUUXuEVXuEH8MoovMIrvMIP4JVReIVXeIUfwEszCiLyb4nI/yMi/1RE/tOXdZ5XeIVX+GghL4OnICIG/BPg3wT+HPiHwN+JiD/8yE/2Cq/wCh8pXpan8BvAP42IfxYRG/DfA//OSzrXK7zCK3yESC/puG8Df/ah538O/OaPe7OI/HLQKkUQkcvfQICkhbwkkgrigVz+BeNvQwhAwgm5fDACERnPHUQgCCK4HD8gBCEIQE3BA1XF3S9DGcdBXpxvPI0eBIEmJQJUQQLiw8eVAIRWOynpGABjDHL5Yt5BDAQhXoyXQC/HieAy8EAikJAPxhIyPhMIHoGGgF6OLeOzcXlzBDiBxgeHGwNGCA/CQdPlG47hf/Ab+4fuqrj8AB1BGe/zyxheXLtQwUU43VWiHl986vtH+MW/S78TEa/9dW96WUZBfsRrP/CTisjvAL/zks7/8UD4YMIogpcZMScJRATl+qt87l99m1ycqTp7dVQV6x0L2GsiRaObYRsgG5oMxbhfN0jKUmBrHROjboHKSp4WTlujmFJC6NoRy2RxiIm9dnoLmneyJYyK2syxV7a1c7AdfT7jXsg0ihhrd7AAC6Qn8ODu6cqDR3lMpFBSDnC4ryvajXTIzKG0toIaSQ2nIylzOq/kgCkXvG1c6cLWV5Ik7qJSpaJaOHUhPd+YrgzHMNmIMI5JqE1BVkIXtDkFcKlsvZGj8N3379HJmJZCFsHFcXWsByVlTn4xKlVQEU66oaqwGScdRtgR1ugkFarObE8m7rbEH/yDrxPf+T06GykV2rrRCSQ6Es7F9v6i4U9+kje9rPDhz4FPf+j5O8DXP/yGiPhvIuJvR8TffkljePm4mDkTRZNREiRJmCTUhbd//R3SHOxQlgCzDC5oQEHp3SEZqSuKU6aMt6A1Zy7KPk1oGNaDvlV2k5ItIU2YIpHVUMBqITbhvHbMz5xbRRIsWbEUNJlwFVKemQ8Tz47vc/7eRrDRknD2M2ZGCsFcmMuESUeA1pxsTi5QNGMiRFPIgjSn940lzywmLLmwSGIS4fVpj7YgIthbovcNC2guiCUkGU0LuQiTZc7bBtpomnBVegWiUlsm1kqw0lPgDhlFXEA7ZVeYU6IH9HAcBS3Qge6EGCkD2shqTCjanB1Cjkr2ICE0D5I76b7xYILP/+ZbyM3nUC30XsEUVb14OT9qzfvlwcsyCv8Q+IKIfE5ECvDvAf/TSzrXx4vLLyYGYoqI4JqRXAgVRDrNK5JfZ793DqszdWcqidQh0SkikJRZjakltr5BDqiOKmQLcCFihdbxBCozGsKiO7IoERVZGxHBvE9cZThMBQu4KUa5uMPSKyqZiMCig2ZkWVBhGKh+QnXmWFf2u5lMgtbJORPi1LuGhNG2hmbH6ZSk7HNmNykqE+fzmeiGe0NdWMiECo+vdjxIhksi6KgkJHXEhCwzWld0EzYN7KZgoZyr4rFiJTBJXO8LU+rMDtIr0R2xxBYdUHJWXIXQjokSIRdvZYRTOYLeOyJKDsCDNCvQMElICJMJkyWSOtocP1ae3DQ+99tfRtLb43pLEDFCD/0lL+S/lK8XEQ34u8DfB/4I+B8i4g9exrk+dnwQBA2DoKqYGcrIGYQ3Qt/g0Ze/zNJgUUUDFnFMOskhtk6ORpNKzZXdnEhqLDox5RHRmWVMKs2dyY1dagzn/MTMymSFsoxz19PGZIpp57DLqGQkZbIsTNMOY0VCKYwI4erqwFRmtHZMd6gmXpsPrL5SFkWtQ848erynN2GaCkvO6OqXiTRum7oqzTutJFzBenDuJ07hRHQ0Ga6CEVzvJsTBstK602rFRVF1YhPmLRAzVjuDT5QKWSC2M70GQcaYQIXF4f67d6gHhkCrqCpNlaKBEvQeVAkaHUl9XDEJ8iXcE8lkUeYME46Kj2iwdqa6YcfOG0+MN/+1L6P2Ju6O2UjChKVxzT9kHUTGIvFJxU9jyF5WToGI+HvA33tZx/+54WIUJEC1ICJICtwyHmesFR589ou89vZDZl/ZJ+d43jh1mHpDklGyY5HZUmLyIFJHUZo1pHUOk1G3TouZ/ZTZaNCMoFJKpvWVgpEjs0kjDCrO7BmvmafaidqYJLDzhudMUidypt6dmZbE8+1MCiEtQZZKC2NbM6GVuRi9ViRPpKXhWyVpoqtQEO5to0hhK4AI2hpO0LuTpj1KYKFjhQ5F5oV2d6IWZ42VrIWWMskb3hNcd+6OG7EY193YShDdqFGRJhSbWRbhtJ2YreDNUSlwpWgXWjbEnRaBulFMyDQOXuhWuT87qhWZlLsaSO9oFSggkZAezJYuOYbOed0IZlyOvPPZK27f/wr3/+SWFnfjFrACfh6JZHmRaFX8R5T3xQCU6D/fJMRPwzz45Jq2TzicoEknzFBNaGtES+xe+xqf/tI1O9ko0lnXSooxAZgyIkKPTBXloGBJiC1orkQLihXa2mkeKMa2nZlCEe2gAd6BifDGpg0NyFkgEmvO3GrlapkpQIkR07vD1jd8M/KcaV2xqfDWm6/Rj3V4PGbsDkopM92MyAn0xG63IzyR9zO7pIhC6onqnYQwe+M6z+RsSE7I1sADt07QSbOStWMHZUnC1BJJFUmds488wLQ4VOEqCT1nSmuIKfs8Mc1Gscb96UzRBQl4dn9Gc+dml8A6rTlmxt6MrIp7Y6tGN/AozDmRNOObYbWRSVgxRKG1M1MWjBGKZTUm3yM07G6lHO/54tcekt78WwgzJgJ+JkRw/X5uwdU/qDx9UA3hMhn951+2+GnSIK+Mwk+IF6ECMCoEaZQZ1QJapTtM5TO8/pWHzB5MtSG9IRJkKqaKBSzZMDOyVPp65rR2RIKigtqIcZdcyKJkCeakuHZEncbG6op22IUhrTJpouG4NKKt7KeCr2cMw62jUyYlyByI3Km1MwnsrxeefusvwINwx3KitY5NypSM/aEwyfAcVBp93YiS6HRsds5r0GtD3Ige9OqEKHnqzJqYTAkXUnS0nlDvOBCWWTdFeyeZY5OTRDGE4/1KMhCdUAJ3J3dFCmh2olUiQVshxwjZti4sKUAqIsEcY3JKdLAO0aiu1EtpdM4F0UC0QQjLrECQBSYas0K2jaUHgiO9U6Tzud98TN5/btwLoaimYSDSCCMkvu8NRP+wEfhkJCV/mqL/L59R0A9xBT5iODESjKGIDA/hBcXC0g1vfO1dHuwSVs/sFIoYoYGooyWz9srmfbiboXhKFDEMQ4MLz6Bf4ldF1MASBUUlk2NPkU5LFU+J6InQYEZIZaJJpp+C49rp6iiCibJowaZRDl1ScDresd2ubNXQs1C3zpQbc1LmUEIDXzttM+J0plUjp+GR2H5mmQ9YD8SEZck0nJISiwkzO5I2FgwL8NbponRJrBF0E0I34pxYpGPN2XpD5oLfBa2dUWmod6TXy82cKFFIJSPdkZQpjwp0Z84XfkEFXRvn2EiRQRPWhMDY58RkjWSGhjORkTBSBEQiiTPJ8IL6Wpl8hARdQNaK3554PAmf/q0voPlTl8XBRyWCDipEOFw8hx9w1X14az90k47HSwve/zL+pQ4fJH46V+knRcjgIigGSRBTLA+ykLPj5gu/xqMnmQVhkgymeN8oKHlKuHf2qZDC2E0JS8Lkwm4yukKLxtYqaulS8nKmJFi9ZL1VseSoFvZpTzdBTciamczo542kwiadPBXoSiRlmsFLQ7xyun1KvavkmvDbimE8uLlhPcOff/Oe3gMHdAVcsLlRdtf0c6VvFa9KqZ0uG0Uz2TLuMOVM0SA5ZFsRCU7HlR5G18L55Ny+4DIYzKUgO6NtE30zOBt5DuzxjNxn6gYlV+YyjaoJncAglNO54b2Ss5KXhAO1NcwMzzZ4Ed7IqV2um9MCwocRqAFVAsuJFzyonRkiQRKlJAg2UhIOdXAvyhpo7bz1emH3+a8CB5DhGVlkLnSxv7wYqXxgKH7gHjUHdcQ/RqvwU+CXzihExEuJ4cYF9xFHqpFs1K8llN3rX+TNd3fMNcg4kzlST0RAFqGHYCK4CaFO7Y3qnbM4G21UHBRyTmy6EuIUNbbmozQYwRzDRfUeVO8IjqfBQ+jhpAt5SLOSrLPMSpbO8U6op43b75zRdY/cC1ITj26uefCpG9bTc+S8UlC29yu3d89odJIqiy1c7SAvM6XMTEXpc+HB4YruG32r9B4IjWKjshCeAGVNChP02mjWCBJdnPXcQJyEs8zGfhZ0J4QZcxH2jwr+3JGnE7KueFRSM5JWgs7xuWMmTBp4H16WSAY1smbOTS4TNlFpJOkojSUrbsFuMsIcBaaSUM3ULpgkpoCEkCWQ3rBJ0FrR1piODVlX3vtbhenNryIxowQkx0RIKX3gEcgHFY4fvTpFyFi8vH3k9+lHgV86o/Dy4IQVxBKaEyojs27T27z11TeYJMjhl5jWsEhMluixoQ4Zo9PHxFXINo96uwuWxmWIgCILmhUxyNZBBjH36CAUQhR1Q7tiYpz7Sk7zcEhFBgnJB226e4a45+47oFOmPb/l0WsHHr05MR0CS04nmOcd4YX72yOnb3eO3zpzf3+Pr8+5r8+hnoh2op1PTMeN5M5hPpCLsghkNwB67zQ1Tm2jWQyCkRXKPjHPw1jkAh5jEh6PJyQg9WE8vQs9nJt3DhATdCXVgByYGcdtJU+Jq5sD3jIlBc2cWRXpGzVWsjRCfPAKSIRmugm1O1ur3PeguyM4GsokzhaBXBaSQpBMmCKYo32QKzjXjVw3dinx3m+9gZbP0BWIRIgRfYRng+A07pjo/qMXKI+fyp3/uPHKKPwk0MFCNIwkBY1GtMC54tGX3mU/B1mDXRlJu8VHqVJjlLwWFKFTuiMxchLWg2lWTJ27Y+AueB+rSCbRpCN5priRUyJl6KlxSMI8KftDokzCeoJtO2MTzDlxSAnLRvQzp9tnnO9nln3Qnm+U1/fsrjNzMZIYkwsPPvUG+3li2Sf27zyhV+jd2d7vnJ7P3P0L4b4a3/1OhdhYso4EamqUyKQLazJJQmbjuHa8JOZcBiFLnN4z3QwmRXNhKsrmG5oLNk2cTLGU8d4B59gq02uO5ULqiWIFWub+/Y3YVuYdiFTuwxHfWDcndWXuI6xbJBGeURSNNn5XU5Ias2SKgJkg2i9kJOVMxaXiAq7GbAuLJ2YLEp1Fg211pucrN3vlya9/kRyPURy3/kElwom/Mqkn8vJyXh8VXhmFnwAiglsiJUWmC83Vlf2n3uP1d3bMLhw60AuFQORM1ooCeQua1LF6qNF01MK1GFEriA36claCRphTaUjMWNsIgzmBdecgmTQlRDtrB2mw7ArVM1DY+jZW67qy3ilFDsR2op+MB2/uubkuPD9unNuKlqATTNKJAvXZLUU33vnSY1LaYwHHu++xm52dbMxz4f0/rXz7609hW0ly5rCMqkEKo2vndB9Mu4L2McFNFMuZZEY9r+xCSAI0Y58zKQe1r6RWqW0jZ8UIUnO6w2YdXRL3T+/53vkWM2N3syMJaC5kb4gLD8o0Sr1meAvUMtXPFGuceofu9OpUg9o2hMT9uVK7IJYR6agqUySS6AghUqdaG/RzFSZTZh9hRV4r735hYffOVxAy5kqW4SW8oLz/OIRcmrY+wYbhlVH4IXz4YokxqhkBlhPkkb3GA919hje+8ISEM/WKRoN2Qhxq9EHqMZBiiBYWYTTsjL5Gat8u7n0npKMiSBjeOmGJokqShOIoxmzQpNP7qF60enH7c0GyYyWzaOJ8v+JrJdZEv11ZlsIbbx3YTTMmhsywehpdjqI0E5YZHr5xhZnRA67fLExvHLh+8w1YDtAL2/MGUumb8a1vwvm7oN5QjB4b/egsi5LTxX03x91Rc5xROo0OScGW4O50N7gNQM55rNDu3HanMXIk05TZ0srdqtiaUFV2NxNrdI5bIzOR0sRzHLeRKEyS2NjIcwYupUvL2GSYKCSn6MY0TeyXTJUT4kKJYLU0mrL6YEDGBjPClIIQMD8TdSOe31Fa5Z2vPaTv36EHbKkhnugEEf3HJhnxkfOKT3Bj8Cuj8EMIGW3Ao8w0mp3ClBSGCXivqOx59JVfIfkZ/dYRbQ1dG9aDJBvqo1Zv4ZSiiFe26Cy2YxIhi5IwpK9MWqi0sVJNiVmV2SYiO+jwTnIRLCuTDQ7B1bRwc5hGmVBhmiZOp5W745leV9bv7RBbsesdV9d7+gSRBcsGkijLTJ8WtlpJ6izXM3Ls8L17ICglsz8Ulr3y+qduuHn3Abu3dpTDI/omxHaHZOOb33gfnUZOZLraA04zIWVY0gGzjNNZ0kyUzmSKBPTq7PRAEh2EIgLRUaLd60ho0gUJpWHsbjKyCVqDFJ0Uif0ugzZCnd0EJckgk6VOxjCHFp25wFKULMaSdkyWcS9oc86bYyR6DfyS65HeB4MU2BfoPSgoU4fCIJ9lGnFceeO68NbX/hXMHiIYkQbn4kXD1F8ZRnxybcIro/DDkA9dUEVBjClnPGeaVtQL6dF7vPapNBKCs47Oxm3UqjcXpjTh3UALGkqYkPKESgx+fCn05JjO4BXZBiUYLquLdSYTpDgdwVsl+8ScRj4ilYT20fBjOSE9IR3a+cR6Umx/i2nh8aMr5oe7UXfH6VMefkcJCsH+MHPahK0q8+Mrrh88gG0l2ii39b5SJZDYePj4iv218fp7D0myo61n+pb51h9/C5dCk07Kozeh1e8TeRIg2ikFNg9oo29AiuACy1WiuYy8hBnYGckF045JsJRMpOAsjdbOhAtCZ9saLonZM/10Gp2p/cJZUKMGqBUsgt43PCrdV6J3rBiYo9ZoDmkpl7Au2DTIIXQfoV1sDUKxEEQc2QLCmL3jp3veeWeHPfw80hSXSktCvCC66aUaYXxATXgx414lGn+B8EIwBI0PWIxdFJMOTUnpCW//2htkUd5ME4/3C7s8Ouxqbdw971Q2PK04FZKzyyMEqbVCU8pwUsmLsZHI8yABeR8tzNupQ2RKGJEyagtpnlCMnJTwjcM8kUplJ8qyF3bLIP88erBAPfDk9UdkM9QbeZqYc2HKjWXKaBSQmYoy7wu5JuYH4Cj5DObOfhbm/YHz+cg0TazrSswZT/D6lx4y5QfkCLRm3v/Tb5El2PpG8pndPuGyYr0jLTHLju1szIuSF8MkCFO0ZnoLIgfSgN7wWMg6qigAgmNixLmxzDPGRinKbkokOTPllZL3eOskGUnS3ishTnLAlM0d9yC8Ihi0ClwMsQbBCr0hqsyqBOlSUbHBdEyd5JWkkMzIbrStk9Yg68rnf/szWPnc4EKokCRdRGv0En4qEoJcuiw/6XgpGo0/9SB+CuWlFzH/Rz7uQVL7fhyoCUuFpGm0DLdO7L/Ir/4b7zJp8NCN7BuzxWhWik7WsYrcr43UOlfLRJkSLmdqUzRl0gTb2pm74Q5l6kQoOQnJRu/Delspe2jdmZjYzQvezoiChYN3zrcb4UI/O70YvXcmg7WDrRvpqrBcJ66uMj4vuM8YmbWdICqugvaEqqAe6L4Qz87cb0fKasjBmHcLXQDr9A2ITgmh+kjMLZr51tdv8ePKVuHweuVwc03rYDqh0snWOG0TkZzeO7uSudvOyJRpJzhzJHnmdBpiKsKZsEqrymKGeFBF+O6fncA3bq73zA+MFk5IIwu0mmihVF9pBEJhtqB1574NLQivDZWJJnHp8mwQmZBRhuy903W8vtZGSsq2CqErM3sid8KFqp1zy9zWRleHaaEtE8808Ye/+7+zHf/4ciPBCyUW91F1+knu2e8rbv00FET5SY//v/0k+iW/CIbr48GHmthEhGyCqSLThQMfBz711beRvnFdoXiQQtA+WndzSdQeSG48uBaSQklgeoZQkjQKTupgAmUSTBu1DvILOImZ031jvk5EVXLMpKnRcxsrozfq0ZFNuL56wPWTJ+zefEg5GPvXH3FqsOyuKDdPSOw5f1c4/wWc/uTM3Z98m/P5KVoaZZpAnLwX5gV2V4XUJmLOzPPMvFeKGiUbZRnhwJKCKWXybMRJSeqc3XntjR3MmbLAs281bp8f2SVjmiFPxubKvAfxE/tZIDZMEnE3SEwWRrbCvhhz2ZBkhM4ISmS5GLwVyw1TcG+EbfQGc5+Is9Cb062jOZGmAmw4nU0H+Uu7suSCaiN6+6BhSaIx09nqGSuMEMKdpJneg6k5sxRcHBPFTcjeKFE55GDSkZtIW+XGgse/8UWSPBpScy9U3i7z9Kdj2f7Q5P7riI8/kkr9N8cvlFF4oU/40r2bUExnzEYmuXUhP36Xq0czN1aYFchBZjQ8pZRwb1jKiAnIjnkxom8ct4q5MU0Lqom1VYoaYUpJE2U/OA3rakTdKHuhbZ3d9UTZKRIHWu3Uc+f+WSASnNtMaGDWOeyNej7T7p5iSbl6ZLz2hYUnvzLz9nuPya+/DjLRV5h7xtcgwkgU7r/bOd8HTVbSQ6XMEykVVleWVqj3t2z3cLU3LCeKCn1t5OsJY+awZEpR3vj8DZnCzWHh/A1h2zaoZ0pkrpYJbc5hd0XqSkghJtBiiCZEg2A0lEkoYWDRKKr0rZNCRs6EQjks5IcT5/cbuzQo5qkY86zMbmg0+nYH3dE0QWok7czFkB6IdyRGo1WE4Jfrq3Nh6zb0LiQBzpJHeGFp9K608PF5TUwKqXVm6ZemqcHufOdTN6THv0LWiUTg6KXa9JOzbAcj9wdfe6Ff8XHhb3w2Efm0iPwDEfkjEfkDEfmPLq//5yLyL0TkH18e//ZHNdhhDF5ifVcFVNE8WnLNMtIg7IYnX36LJZwZxcWZOiNm14RL51CGTmCKgrQjVoznz4fuQE+JdQ0U55AnQgzpDWZlcaVk4XoZrccWE2VakBZ0b8OVXhs9OiYdV2X/OBE54z3Ybr+Dnves397YXy9EKO1pp94KmwviR/afnXntSzdUEvKdyvq97yDe2e2N3ZxoJ2N7eoY5MFHKIbg7PmU7bWTd6C3G92yDwBN+RqUTXkci9Lzy5hdvQCau3sp885/fEVsjGCu6mRD9RCkJ7SuzC0UFsw2vwrY6NieyZtSFUnZMlih5oXmn98aCcPNgj7nRXfHWKbNwWjvr1jjXlfVoLHYYLnjfkPvMehLWcyNMcC2YDbFYwzBJNDNwAdmoZyciUIWtG9MUiCdchiydyvAAeg9SmdBoaI/BSu2VJTpv/8bbRH4yujWyopJQ/fG8hZ8EH7cWw89ighrwn0TEl4HfAv5DEfnK5X//VUT8+uXxkQqtvEwvQS9Kv2JKsQW8Edp48PZXeHytpN4Q2pAbiwCD2jcKibZVTEcpa20VDaXkhSQTxZx5MrYu1AhMnKATtbP5iVo792uHqCTrqHTIjnWj9grnznZyyrLj+mZC48ypNrZ+x3afmK4C2SXmZKRDopaLyGoeY1RJGDP7h8aWhZ1d0Z42+vkWU5iWIeMmz+/RcqavG4fdNQ/Ywe2K1qDeBx7Gbsmk3Q4OO+rJkeyIKFWC5TNXnJ8FVzc7/uJPjxz7mZLzmExpGVyCxS6qzCv7aWIqiSkLOSYUZe7Kjk6ejcwIy+9vRx+IUUdT2G7Pfe88uz+To1M9c5gXpl3gl34RXCg7I5VOyYw+lV7JKGEdfPREWFyaprogosQ8IWIc7zuSJo79TJI0jokykVnKRIrKZEbOo3U+nR1bzzx5VNh/5vNYTER3QkfHJcKP5i18AvE3NgoR8Y2I+L3L37cM2bW3P6qBfdx4oZUQllBJbHGm947zJo8+V5DQIcBhUNuJnI0uypRnXB1NGTVhwni0u2KTyiaVu+M9cW7glckaWftwmd1IOXDdMZPZiUIkfA0mS2wVKo22wjzt2V0V5ivDt4afN5ZUaeHcnhqn7z7j0c2MlaDfN3J0fEqEGknBe2ftjreNJ289wq2RpwOHNnP+ZuPu298h6pFeK/f/opOPwbEL56uFqU7Y9xr0ys3jPdOjheSNdjwzPTpwvIWy25N6YlF47Vce0E6VeZ757h+fWNcja0yYOsfnlS0S3pzTWthqo1ghWyHNY/KUYpw2wVxp6kg4D2Zj/9roCQkNbq4S+7QwSWba71lK4FQUw7USOqpH3kc/ilrByoRpoqqSq7FF54zSIlHDMVWmFPh2P4RfYuNoQpqnwXi8eKjuUDSYNFEjmLfRrBYCsVbS8cRnfu1NbH4HGNqOKgk1Q39BFKA/kmBFRN4Fvgb8r5eX/q6I/B8i8t+JyMOP4hwvE2Ljooop2QpalIihA3D9uS+yzMrSO7tekU0oWbHjinrFouEo0Rvdh37ChrKTHbvDUEZu3pGumCTWc8dcBwdhrRiVyI20gzllyiHRNUhZELfB7a8bWi6iS7bgPSNdOH2rkufhFjeG8Ee2waRc10rcn6mhRF9JKWE6sd4fUduxe5KZP/MAX4zcJuR+ZWnGMhn18ICHn33C/tED5i88QW8m9ma07TQSkYuRI4hzZ7nZs547NTqbdjQJT959g35ulP3EN7/xPkZn7QVKIfVAyOx2E0ImZ0PNqe1IKsrmMGXlft2ICqdvn7i7fUbKnWZGztPoLJ2V833Dz2e0OYNEKqQ0o+Gsm1KSkafC2irSHAqYGWcdhDD1TszBrCOT10Muxwm8GLl1elNCM9GFooHFxnE9skZnDiFlRXBq36i1ot652Qe7z38BJA9eig0v9BdFBPpnNgoicgD+R+A/jojnwH8NvAf8OvAN4L/4MZ/7HRH5RyLyj37WMfws0CQjbFAd+YQkF7KKQn6L1z/O+wHmAAAgAElEQVQ3s0MpOSilEMkxVyKB+Oj2yxHQIaEkDba2otZJYWzHE6djJ/xMAGUpjH7HgtgQaG0IdQtqb6wnH8k1N6bJ2JcDy42ytZUtKpbPaKnc3R3pKVNvO/mQmBehbZW6ntlaYz/vKVlQX5lKgGzMS2KajOXxmJy6wvVjY//pK/S1x/jhgF0vPHiyJ6eNwwNhomE6E8cV+VbjfF9pJMSGUGqSM1LSmBBSyCmRr4Lrd1+nPr1DTzt8OyO+Duqx2Wi1noOSBd9WNBkzM2kH01RoDtOcySJc7W94/OR1rncLJeIifuuoJPaPdrRz4uSJ7XIt1+o0N1LWYXRFKZbw4TJRIkgeiA4j2erKWocWhachhNueb5RZhgBujHJ0ZyWNBpZRhch5bFoTnUMy5sgkgr6u+OnEZ790TZ4+h7fRFMaFe/FJFnd9gZ+JpyAiGfhd4O9HxH/5I/7/LvC7EfHVv+Y4PzeyxIuLZKLoPJPTDG2jkXj05d/mjXeNmz6EOJZuoyLRVnKHJScM4Z6NKYZMWymZta0ERmodU+f2WNEGD64XeigyJ1KshCbUR+Vin4Uagq/PkJo5nSrZEvW+E0mhbaiNjsEkDAUiAp8zT157QKWRzfGulEnJKTBL9JOhu054Zp4TFobvx74KcxG22yDPFZHE6ifohd3DhfWucn0zcfzeEVajayIdv8cdDb1+QN/qaNOWzratQ6uyzYjdk21UVJ7+6R3b+RnHp403f/UhejWhNhFb0OPI3ck5HBbuTxuK0yMzpc7t8yGp9uxbz6jHzqfeezSqBDIUmk/3sGVnZ8HTb5yZbsZ+FNvaYRry+Mdo7MJG1+KFp3DflV0qnKLRa7uocBv4hluh01lv71nP8OjJgrjQtpUgUYrD6rgYU8rcBbQWuDV6g+8Cm0CTRGSDh1f8f79/z7d//3/GY6XTwZ3QTmw/r7v9JfMUZNQH/1vgjz5sEETkrQ+97d8F/q+/6Tk+HvilUSaPG0Tq0CEsr/HoU0pag0WEfFFDaq2RUmK2sS9CRJBIeBZahdU3tBfkPDjz4pkELGUhNhmJMu9YlNHSy5G63vPNrz/l+Z/dc3w/cXdbSd2Iuw2pHT/DpAsSiaxChCG5UA479ssOdsLuKoEmVAJ1BQzaxnytsLYLsarRYuX07J52PuEulJsCMbFlBk2YIbx6mPZsd+fR3HQlzDfCvQs35SGaN1IOVBsmQrEDN/MN5aBITyQtIM7Dz93AuvCp997k2//8e8Q6Nmnz1NC8sCyF43GlWMH7kLbrDaYrQ1Pl0WffZP9wokil1opHsK0NWzolG9oq0zTRn1dQQ6dBpQ5pRGNsHkPHHbokDmXQylUc005dhwr1BvjpBJsjzXj4aEdvAh5MYiw5kb0g2dDSOUUjScWikhDcYF6htIRvlVZXtvsTr723I/afpjOqN0kN6fkvycP/tfhxzVUvCT+LHtS/Dvz7wP8pIv/48tp/BvwdEfl1BgPjj4H/4Gca4UcJlR+oFw9y5KglJ8toErw1RDKPvvAe+5zJ24aakX2o/QpgYpzYuBalWsb8ntYmkE5uQ2xEUicUeleyTRzPR+6qc2OFacqstfLs+YZtg2ufXMCE0EKSBSZnd32FXicWUyJ0aBRwxjsYK/ffdcQgTp0tK7lk0pLYtkbJhslQcihpIl0Zx2cndrs9gWM6OA9+d8/y+BH+/BaNPVfXmZ4UsTNz3nP69lP2S6GvK/PVQo4jdivYzcR2f6KTsFJpXsnTHg4dM4g6oxI8/tJrPPtn3+HR66/x9OvfY37rhnm/gATt3klWkGQk6xyPGykl+tp4+s1nFDvx+mtXNIU8gzTFLLM55Ai2yVhuhPv3E6jjzZHu2BxcM9HpSIJj7Yh2qlciLSScrgaT03wliXLWThJlUh87R8XMua1s0VjMaN6pAd0nArDYEEmc9bJ5jndcg50oTYXz2bl+LFz/6ns8+70/oft5tOCLox4Xvc+fXPpdPsatLP/GRiEi/hd+NGngE7vXg0T8wA8rZkOhiNGcgyniRi9PePR2IkVnzol6V5kncO3MFGrALitrODk9Z10zFishsKmy04SksVtUSIA7Vw8PHN8/cn/rPHv/7tL7kJEz2NXMvGTSDGYb2RLRgt5GwrFvQ2JNtnvutTLViW0T3OFK0yXzfSalCTsHV8vEel4phwJ+pJeJ7XalrUqNIzplVDOtbswPFuL8lJQyzVdub0+UZWhI9lS4+dRD1qdn5usDZas8+6OnpDlRc5Cl4BXW45HlemGOjE0BFXIJop2Zl0R+93We/vnXqffO1RsV9xkRB1sgNTw2OEHyGZOGkggffRf2GcHXDVEZXgsQLWhJkFMMDoHfU9KBFLB2wXsmacf9DLpDdEUi0RHatrEUoQskFSZPrDJUnntSsk9MVHRZ0TtBc8J17CeBbPQYycranTIrM8JmSvSKarCrmVvtiFT8tPHZ93b8/h++jR7/X6qMjqiQPrYV/mF7YLzY+fYHX/cAU+gfj1n45Gc9PkK8SJ+8UL9xd7ooOWeyCbFtRAgP3nmXglJaZ+ewzInaYNmM3odWn6xO3HWiL1gSPCWmkmFthFZGK88IPaY5I3KP1wbnirhikbAy8fiLex680cnLSkmdbBkLZz81ll0mjieqbeCB5MzcjVY63ca4tljxHkx5dA/KJOglky90kB1XZiyWuNoZh+UKasf7Rlahn45sq2BU5snYXz1gkon17kQ7rdBhfzWBMXasWpQ5G4dlx+4q0ansbnYsOdFZmecZoZLnQvNMpA29aeSb17l5bc9f/N932HrCmrDsRs+GqKLFWJ4k8i5xrCsPHlxz9WhHnB1pMLmQPUMMBuly2bOzhTMdHrDdNjQmsgGxDp6IHAbj1AqD+hFjbnkiWsdro46GF5oo7dnKlBRXY90aaSqDudJ1SPXL2D/CHHC56FOOZqdJZ/Yomjq6xfAe1s4+B9ef/QLCdFHXHt2t0uUvc27+ivLEx0lg+pfKKLyA6tjyDYOsmZTK2L4sAtITHn96TwrHslPHlqUkE1gSc1GmnLBJmW/GDRJ17JLUBVIbRse0E70NpY7YePb1FaWwvLbn+vE1D94qPLopHI9nvGdUx0YkJaD5mWff3Mhe2ZVplNO8cTyfkdnQ1UlZ2byjszMvRq2d4sL29DQ6Mb3Rzh3t9+QsaBuCp2qV6VDIKN474tvY0n5ruINSweCw3FC3M5Mp7XSL351QV5Y9LE8ecnp+ZDsHuzT2k2xWMGvkKTEdrmn99v9n7112LNuyNK1vjHlba+1tZu5+rhEZEZlRSRZQqAAhQEhISDQKiRa8QfV4Bh6BZ6BXbRoIJCQEqh4NhECiRAepBCJRZUTG5VzczPbea83LGDTmPieTrMwgI+JERGYEUzoyd5ebm51te805xxj///1oLhA2xJXTR8L1s87bj574/Ee3WSq5sywLGgTJ80E99k79ovHl5z/i6XElnTO5QNFIDPM12nRi2Pw+WUgZiiUUo7AQZSOMgfpB8IgyJdFN5ykfZDpXv3ovZJ3lAYfjBHYXRk3TOdkc7QrK1weJh4hIwqshdrC4TFt2M9QD4pBEaf1CPAbf//tvCct3iEzAq+J/OZ3J/Gs9xG9y/U5uCu6zplNXeoqTqiQTuHr61vfIGUp3ShNOURHatNi2/Z7Z4Ow2OY3NwWUnpYK3TirzzWRjjreuvfPy04qOxJtPTiwZllPFhjLalRQiFiajsY7OMQb9qtMqnSPWG1kMIbOVjfo6Ee6qShxOskRyZ41n6l4p5+U+Vo0ITmuFy/uDsoDa5BAUE5YkFDH8CMTRyFum3xr1dgXpXGyWDEOhrGce3jwxpCIaJ2T1y4pddvKaidpIElBdadedEA3rZ+I5IIx7rqTywR9+DP1K/xz8OIhi1Fo56iAkpuioRdLpzPm8IdHotSEUxjC6jGmmUqH3iiGoQsmKt4N1C6SsBLFJyRqCmM3JTYmsOjkUGRiiuEJrAxMj7YKeE6qVkpW8KN1heMDUQRTHiNqptRGSEsu8QVyvN3IIxC0hUgn3JC8diWGNp1JZvvuHDIt077Q/Z5j6i8v+koz7r/7qr4vg9ru3Kdxr06gBcyfJ/YcwAh4+4t33n1CLpJJxlGQDyUqQWV+qAWHmQoYQcesEP+F94GE2LVMwGpXnLyr1p0JMG+++f8blxq07rWeqgclC0czeZgebAZLgtCS0DEZ3hiQ0TBL0cRyoNnZzVMYEwwYYlwvH/oxnYAysQwidsmZO786kEtlfdoZEYsjEADkmgg1KHJQtk7uyPQnRE8flYImZ+nnFrLM8vaXXCyKRnAKXy3uiNranFWLAY6SHQetOOGXyUgix4q1Rj4G5cPRX3n76FuLG299b+Pz/fKE3YTs9wQi0o5JcUIP25edsTzPPQm1mMrQaQJwgMKFpGS0DH5DVaALWnRSNJSYsKs0n+ISYSTrt0DFG1ITeOzQj5MDxxcGQMG+MlibnoaYJbV0qmozqg44yXIg5QgWrwv7cOS8ZE4jeCQGSCYtMFkOyQDuM7/wrJ4jvJsk6JbyNv7aNx7mXvL8m9dPv3qbgM1xluKFpClgGY1plP/o2pyScvLFoI1ijmhJqI5lAc0wiQYy9T2iH4WiYpUdmUGVw9MCoAzuc9cNHtjeZ2Iw2lCjgoRIimFde/WChE3yegGPvXN4/35VysOXIFqHIVMY5yqMOxCeyXG3QUmR93FDifNOHwKhK3Q/qcXBcB8Qy05mD0aJR9xuJhDcj7Z3Xly8pshKlsfqMrHv8dIPaQeZUYFlX1ofHr+fsw2bjLOjcQMMpcFwadW+cTk/T9XneQBNb/pC97Xzw/XfU950SN8brZ4x64/yw4KK00Xj/4/csW2bN4U5/mlxKyZ0sgbwItXdkmRtIygGxQkwrt9cb4oOLd1o7GAKL650luRKGEKXjPjf6uC34reLAuhgrDjIwBi1WrGTwRBsdH4CUGTrT5s/vtl9ZzgULTlKh45S4saQAo5FHx/dK2DuPp43Tt/5oqlkxPAXk5xhLfgV8/XWs36lNQURQuTvXJFPSQoiCjIrIysd/8AHJJr03diEvEfc+8xbUiMHBG3sDtNF7R9xgTGttb5DuTL/22tnenNlOgzEu7K1PfHsIMAptXHEVkgBlJcXZ5/BurA+PpLLS3Li1ShuK+6RDl1KwNEsfkQnvKGVFxDiv6+xQSwPpM8h13MjbYOTZXA2x0+/iIKJit8boV2LvXP/0C6Ks8/u43Rj7K2ndGC8X6rXNiPpeCetC3CfDcMthAmLuJYmNgffBGjNaEm6D05sT6UER78Ql8cHvf0TMB+//2Rwz9rbju/PyJ1/y+//q7/Pm7cLjtrCWSAyKpMISAl4U6Z2n5TQlxDZLtcaNdZ0/MyHM2twLj0uhxzhvh32wDyd6wlNARyC4Y1UJ55XHmAnRaCJImLQk64GuStA4CdJ9vzepA/VmnM9nfByk0RneCa4TnSfGY1lJGqb60gZbP/i9v/8RGt9C16/zJ78iTP1/rm+YmfCz1u/UpvDVbqsiSIggd837cOL5u6znwRIG55Cx7lhTFIM40GBkXdAYOC2Bc3nAhxFVJyo9hYnb6sqlPc88yFSptd1BrIESA70ZQxtLLAxbyDHB2Dm8g3ZWUzQM/HoQxDGv9N7xMYix0mujv8Bx6wwTQsxkddSc23VeX20EkDSbYpfAfhjRIzE5sc+PhpA1sJwfYCyEFFm3grSfoKGhZSWkBbtMjGncIu3i0zfwprCtb6hf3mYACp2yJuwYvH3YUHGMG0UjKwFzRYcSS0YTnN6t9J754NsbMd7oe6C3g/X8jh/87z8kP2w0jGM3yrKSDDQnRhPMFhiVlBpaHEmgY+GcM30M3IUSIhsyyVRjuk4tCTE5vddJlY7gz538EFmTYTrTuYMow6Hj1HGD1hg+LfI1hqlKRKjS77LzlSGB5R7oE72RspJoOH2+FqPRu/H2bUGf7to+FZA5ofomhUnfBDr+d2pT+OoF69JmWnS67+y+8sF3v81KYJOIjErMQlInJsgD/FDqqGhv9MPu/vpEtcEYDbOG2YDSOF5B0oL2zLIsXNsNcaX6IAYhdUFjnHj31knuFIzWjM+vbSYahTghq7oSNdFV6YdRApA7Hg0dE0aqJHKOpFhn0nJrk59oSijGaYn4mA93CkbxwcLA6oV17cRl6jW0QVgeWfOJJQjDjJgbYwjehbI9cHrYaP3g/Hcf6aMirVNyIEc4pQlR5VY5joOyJEwaWXfyNi0APgyS8e1//dt8+ScXvvjRjvQL+3vhyx//iA8+XmYatWckJDwAOMM7pwylDFIpyJglGQ1SvCLeiJ6nv0B8TiWssngkhfu0xSDnBXE4njumgRIEhuFWEYdug4qBBoYnJEfqOKijkwa4BEKcm0eK0NpBN7gcDZUIRFqFmITFjeDGMgT6waLGh//Sd3E9fW29178sg/LPv2eDws+RE/FN3CZ+NzaFexK1j5kUnD2jGsl3OKstb3n8WDiJsN4ToIMYa3CkRZoIj1uYV3afp89wRZISPXFaEjEWYlK+/NOG9ZV32ww7setBkROocJoDbkp0Ymjs+xdcvdND5vllZ9WV7emMu5DTIBARHzONKQZiLnha0a6UWNC4IgL1eKbdKjlE1pBYVkXTZDuoVaw50RsArU5GYRKbBORmJGZXf1jFbztiV8w6m87XLBw39trY/YKY4RJoLxBflaPeGNdGQvB+47xGHj56QzwG/f2N8/k8exejYEmx5ozdEHc++Luf0q/K9fNBKgfnjz4gndYZ9R4cKXXG7p0fZ23vRu2D1hohyky60oCSZimWAoXBqkoOHcmTg7CMwKbwEACbdOl0KBomuSlqmBF1MdKZ6LYUlJgF1UlnDhFI0zjXXwcpK/utTq1CHJzyNidCaqRgjNbZvLBoQHQQjkp/vfHtbz2h20cwIhYCSPjZY0i7Sxl/jTLnv9Wbws97VZKgk88a0vzhuuFjsH76/XtXeoalujvRA3sdMx26NW51EMNEeZEN8cGgIdEZYtTjlZfPbqRQePdp5IjGMZwhEfODopFjCD0GWlf2BqUslCDYPgU5zz/9KalASBDU0HDQ6qBVpytogzh20jKdnEvKoGWWQclprdFapY8LbhGlomGbuHNJFM3kmBijcbsdaG+EkBjNeDgVQilTZtwD/XpF/CAgeIpI74zLBeuVOAbp4wfeff9TdLc5baDzsL1h7wfWD8KW5mhy7AjQx0FKmfNpoR6OqvPwrrA9zh5KrcbjUwGvxLLgQWmV+fD4je6dEjayOsNncO1+VxfGbZmxbSlgQab0+x4z76HNBBom2CZqw16deMqctwA7VJuxcmMYIsbtdkzF6aiYCQ85c3TH6sHed6r0yfEsmZAT4ybsXjGvDJ9xgVEmai7SyCHPyU8zts05f+/vINHRe+iM3XULf9n7+euT/1cQmvxXrb/Vm8Jfe/e8N2m+NqEEw6NNnYGeeft7ZxKNpM7QQbc5fy4iJHckB3o3GFPsksbMcQyT40P7YtBeDPGFp48XKkZrjai3CfaMkdHv9OXuhDwzDtZ0wl8VoWM38PSAS8Ir3FqmmqDLgORzypAVO8BESdo42Lndbmhw9p4YacEko/pI8MltuO47YTi9G1YH19srlIhb5nBot53tIdLawbLeyOtMSDKD6/sLvu9EG8Q7WsxlNuFCUl7qTubE6NB7pPb3vDs/0p53Uu6syyMlQ86Jh3UjjldurXN6s4EJMS9wE2IW1jBP4WhQYpimsbQSUsR85d3pjOdAt6lITCFC6ngSjtYIJRLNuY5GJ6A5E7zDAfiOuuEpc7waeIRkKJnDn0kl0OvcYAoLa4ygN45bZe+Dm8GaMiwFf99Zt4gxx6HamYfFCJTJiprsyTZmVmjUe2r4TJkOrfGdf/EtxgdEA1e/OzV/diPx10ld/1u9Kfw80k8JyhgdYkAlzRPQB/nxE5bFWDQQ3EghsATDvM761IE+yJG7IUYJQWhHp7cbt/bMcRyMkHj6lhDMCMNJodAl0OK8XYwg5BTYglCYIpXg997AwsxQPDvoMbMiAqxqZFmRYaQAASEs9wBVdfqxUyRxG4MSKt77bIQNg7jgGsjukAyTTufGrRvXS0OfNkxh18CxV5anM62tjGbkbeVUFsIh+KWR+k6xYxKRj05vO9sp8/SdDzA/qD/9HLjSrgdfvvyU82Mk14zbM7f3nXbdiasS5EwcQh1XUKf1nXFAuxwTEJNBU+R2u2GilHWG5ZY4eB43pHU0L4R83xgtk7vRfCLnh80xpdRB7x0LPglMrhwu1Nsrx9EI2723hCNyQlpDkmF1pzGVnbdLxbZCjoHDBy0Yx/OBvZnxd4w4E6XEkRSJoXPpN3KYZq0eOt12Vs8sQQjNpmjq2Hl4EJYPvoeLfq2bMbO/MfmSf6s3hZ9niQiIEihoFro41oWP/s7v8SiBHBLkiA3HxsDqRK0N76jOoBQPA9XO6JURF1DBrpn4dGI7C1Zht7tzUeZYbLghwbFjjupChIOZMN3doESCjK/JQfvR7266AwmREJxTykQCQTqHD55fb3gNhBB4vlzJMmf1JXe6GcM7x22nh46u/Z4/oIyuSI6YO8flBigpKBWdfYFxEALUBgQjb8rymEhRSAOiHrh1nEF/fqbtQvn+p+S00H5wsI9ItIV6NUZ0GIWyRdQb9ac/RdognJyUZpiKyODh0zOnrNSXRvbCcTTW84nIIFhHMBCbuZnuM20XJSyBCHQpiCdaVSxMRkJeEw1QjYycaObYZceugW07Q1JOJaMM1kUZEug+m5Al3O3j+kAQpXUmgm8X3OAkcfantNGH4DTEneQrIcyvixjbNvUZFowwGjlCVEOGs4rx5l/+EAtl/v/xZ/Svvwnrt39T0BlPbn0ab0h3c0s1JL9jeZgsw0UjMQjqPk/lHNjrDVOhy6TqeB9AplkgeeP9F52QEyl/tRFACZEYM0UEUUdDYQCizqGOm4I0zsUoScmu7C/T11+ikpcMOvA6T/w6OpfbDikRTAgWiWshrkpZZnLSaAMTpzehdyMuZZKgDOoBpoL3xhiNy94RIC6ZNhw0ENSo0rm5046DfDL6CAzd8NqRoMQ4XXq+Vx6WB27XV8qDE3wQv/MxHgfLDepxwVfDc2Z5t+H7PQxFI2lx3pQToRtPTwuxwXYeHN6IOtj3K9t2grazpgBHnzF5ZvTLC7FEemuE2Mnu5LgQo7LmgaTOkACh0GSi24dVxvOOX4xRA+UU6MXovU6Fohv1YJK5bapRxz0VbFsNqjHsoL4atVfyaVqYV71ni6oRY8bM6Fpn83YIXQ26EaLM8J7eSA6YUGon3Yzf//gJ0rdxmwRxkUkA+5sAd/3t3xRgNmnujSgJSkLpMljefkwCTkSSDOjTyCIpYr1S6ZOXiKPAQpjE5U25XTvmiYfz7GbDlBwHAfoBMh/qYLNZmJOiyp3CrHhX2u7oPZ+wbIW1TPyXkPByopmQQ+S0nIh+m+IhjFAbeYDERLynOyepIAGJgcvLjoty2Tsp3ilNktBQ2JaFkAejHaTgd5clEIT1w0+pV+X2esVCw8cVT4V+TKUmbiRdkbsQav/xF4TzivUb4Vsn9teKdOX2f79w/exLLrdnbEvIupLFiLKj0nn7sILdyDKQdSFVcIkzwi5PGraVSEjKsjrUTjpndrNJnyorANUO4ErrTDiLGllgPF+xm5H2Mje1xXh4M/NARWRupj5o3XACVXyyLsPAvM1Ub2aWw+hKCI2nh4JKAWw2blOcOaHqdBwbirvhSYhMB2UEmg2KKMs9JySlhJizLY3tW98FvaeT35OhfmG46/3w+ybWb/2m8BUwM8gcH0YNdHOEwpvvfUTROfZrh0Ht1LrT24H7HMfZ0anSuNkxhSbWqM837AIffpjpprjNsVUdyheXC4SV4cJymqe0xoBZRNNEupQQcBc0wc0a2/mR9c3Cda9EAhqmBDptkVGPu07fsWYsJRJTmOGsS2LNJzChdcWPMYNRc+SwTixCO5RRna8CTpI1yvrAUhI5CQvbFHM9D25/+kPCkyK6Tm1CE652xfPK9XJjPw7quBLVWEIiJLh89hPK6YEYTsQ/PDPGioVG+tJ5+eP3XJ5fiAncAmIrvc/XOIyVuCZyc6xXSofbj78gdKdsC9Gne7F4QbdE0MyS7hOiGOA+fkUy+VTQOKGu3PYpQV8XjthZHzdyLOiy8JAShEars+mcU0DDwHxMTLsHWp0ahegJqYroNFfVe1m3e5iIeL9hdzR8cLsLthJh+D2c2GYbuhqxOAQhtJlaTm/ELvze3/uIIE9TJyFy/+/PyOI/zxL3b8xe/U2AW/8vEfnf7sEv//P9z96JyH8vIv/0/vE3RnQ2ph3VNBDSTIAOw5H0EWWFNQweSiZGqG3nqEJ/CfTbYEhENaMW6L3z+lJpL9CugfzBaUqJg0yKs0WQRkmF6u3eb5ipRU6FaMQd6s3pfbrusKmw219fphw2MJOsW5xhJcMZXQiiSN5AI15vHLVxtIpoo9ZKE5/osWikEBkKyzlhI0Iwhg4ezmVGxksg1MbmQm+Gt5c5VYgwrzIVGZ0QnOWkJNm41QNJGzku5Lyw7xduV8O6oGlj9IP0dOa0nDh9ckLyG15b46EsnL5Urs8VXxKjJIREzhvr44pZJxIJLrz99MOZCB0Co0ZEI5oio+606hz7YCCUbSE4dK+EHMjDiSMwLlfCGOwC29NKEuW0JkwOVJy97Rz7YLXMGhbqmM1K3NliRiXT3diKMtQZ5oTVeLfkecXvDTEnMe4HQURCvDcIA2Z9mut06liyCH50VhHQhdCNlMNscN/7EB++UbR8GyPey4df3N/wTQ4nvqmbwr9/D375Cgr5nwL/2N3/CPjH99//Rpaq4mIztl0zYge4cv7oDwgB1rTR/Dbn7zFSslA24elxIRwdGZXXlyv2vrCeFsq7CGmhyDEfwJFYSp5MAEuUUgjVcWnYuItfjU8AACAASURBVMCIZC/sr5UhRjlnfHRu9Zin4YTBUXsjyqQixwVQpQ9niwFxnzkLhyMjU8IMTjk+NzRnxvtBNIg5sfvOEoVWQUYnrQtbXmh90p4lDdw66GQPDlmx/TaTmCNIWWlHxbuzH7P8ydY4Z+C2o2MaqWCnpsax33CB6E73iCyR/CaRP3xgj3C87pzf7zQ3bHRut53LqDxfLpy2N5Ql8fa7H6JpYK9Ktxe2JITeCEG4dGd7u7GeFoLBaDdad3JeqHXHRLgeTm/K861STmc0F0yFfe8soZCXMk/f0KgaaDoBMzEZOa2zaSgBc2F0JXqke2Z7OqEIpySEkDCviM8SJRhT8elT/BRIU5vQEqWseFi43RxKQe6BvtYbbkYcCnVHtXP+/ncQHbN8CPoL3RK+6fWr+g7+I+Af3X/9j4D/+Ff0dX7mEpF7tHxANE1hi/n8gX8iJBzawagDVYjmBHFKZNb6GfrNib6QPwwQZ+8gRcOncnXKpUfHrM7afTQ8D4yAxnUaX0w4nzP4jBcrpbAtCZKiz0JIGWIgx0SJAQYzuWgYplPxlnRgi6Nh0DE0Cx4iy5NT9xtu86GPIWOtItnR5MjeGD6l2KrKcWnslyvvX154/+WBHAduQhXButD2G/lxqjrTvUaNCdqtklJBdCNlaByE6sQl0sfgy88/YzBx6VIS8TGQnh5Y/ugtt3wivTSiKMvbDauGrIFDjLQqEoT+0pBgYCeSjNm860bJkV4Hvb8i2ihhpURIAqqZpkrMg350lofCGMfXh0AsSoo6Xz8B0RM5TiYmErAWOY7GbuABRJQRBKPPG5MENCXqiGic056ynNEYqUGnszYDMlijYdbJy5z+XC9fENOYJCZVlhg4pTRhvsDeB8kCn/7BE+IfTlHcXavg8s2UAb/o+iY2BQf+OxH5X0TkP7n/2Sfu/kOYSVLAx3/xk35duQ8igjHddkHHrJVP3yI9JlYxVJlgU9GZNC1Ks8Zrqxwvg3gqnB5navSQabnuwRlB6KFxZZ86BIcYI230mXJ+bZh1ujWCJqrP0y2lhHhFCSRR4ilx3gphKHVcCDaJzMlng9OrcTt2Xlrj9vJ6b1pCfb9zOieSKOenM+2lcdwG7YtBvTrj5UobgyrT/DR6o/ZjZirGzLiPYCmBcIrkUjifFyQGXr6oHD4YD9NRCBvptBJOsD4m6uH0mzGaweuVgSMlsb9eGaQ5YTEhp0QsmfRx5nbt7D/8HCOw5oLWQRvPXPdKPoWJjGcl6EBLwphN1RymPbteA60KL6+vkBdu1UEH4XaQ+uCj0xmJUE3IdteZRKUy6NbuitVG9cYY87p/2DETnFwZZpCUYJAsoj54eb3w+eWCMzCUJUb6qHSbiVE5Z0adHAlV0CB4T4zd2NiIS8KMGRTUKxFhLYr3Rq4gtfP0Tsjrp4BCGFPRbPobnUJ8E5vCv+vu/wbwHzLzJP+9v84nuft/7u7/5l+HQ/+LLve584ZYCHHmPyrC6ZNHTkNJcVqnNXRMIMfA8I4Ep73spK1Mqe5dxScm1L2xpEwbA3pikQw+KBrxYSy5kPNCPEWSBs65MPRK6o6MPmf1nsgaSNEoRJrvOJVUTtTjQquD3XeSO0OgM6gNLKxc9guhHazbm3krMZ1iqTdK3oTtA+XpozeExwXdAaZFWJmGJ5WIxkgSo6yOM6jXG4Zze3/htG4sSdFu9Ith3jj6MSXUddJ/yuMjOc8wm8uXV7ze6E1AV9ADyQHzyG4HfdywDusHCzF2gu3Te1AWpCfimujiSJqxeF/+4Mf0Y5ZyMgzdMkGUEJlhNutCzLNBqCERn06MffBaK3Ep5BAJIbBtGyKDPi93DFHaXcKuYaAxkUPG87Qv5xQmKEeF18tBesw8bhspFXLOqA0u+2D41JygjvoUSK0hMLoyunG0K/VmjBwYNilPIQSygkYoOFEm1NfGjQdV0rc/AYxEJsSMh5mu/ptav/Sm4O4/uH/8MfBfAv828KOv8h/uH3/8y36dX2SJCI6iOrvNZsYYhdO7iR9ffc6HoyfEjGHKlhfqzTB5Qy7QXWjdmPCvgI1A984alJHnQzlsdpU9GJdRZxR6nSyB0Tpb2SixkHwmC0UbjL5z2YXnS2OJgaGZ/VY5bydEB0uJSClYh6yB0zlyTjAGvH8PxEYoKykK+34l14yKoB7huBAx2ur0946NhKWAlkwNQAr0EqgtYnT8VKh1yrH3erBuG8M7nZ0tLLTRGTonCHtv7McLcUmQID2utNsg9kbcZmAKtwZJGPvgqIYH4+Xyyhd/emP/Pz6jH7B6nDzDsjA88PDdD+B6MOqCxkwoC9u2cVw62hr5vMABZh3agKHUEQlW6a8HsSgxlRnk2zt2vKChkFSQrSB9ZoGuJEL0eYvrnQ2/x8zPXI8UYIRpYb/ts03dDbqMr5OwTnmWdCEKJQcO79MBGzNRC+sS7rSuyIh52p00Ee9CuJPBQ4iM3bB645N/4QNUn6bV/b6LqYZfG37tL65falMQkZOIPHz1a+A/YIa//NfAP7z/tX8I/Fe/zNf5RdaMgZu1Y0oZEyfYtAY/nDILhqrAtc83vRsqjffvL4yunB8rwxuhdWR0spfJVYiOjEmBzpKms1AFSRmCMhCaNiiN5kK1zr7v01pNx0XRNSFSkJdKOpdJUKoH2Tq3WyNLoraB0wnZ2ZbAEufYDY/EU0b8YFsG5kpeEiEJZoNUjJsP6nUQLaAP92v2i7HvN4JPgpHu00LuulKqYNIZeZqGulZSyaQrvLT7Ztg6Q5Q6OmbOHoTRCm2/UX1gOulEYV3Jb7dpVIoLHAO5Xbm87sSHRIyJ5z/+EZd2wRXqFy/kosR45XZtUDvXly+gN3pvpHwPaX3ppGzkENEA6/LIWga9K+8+/oiwCrQbilNk3gKsQ0hhQnDCgRdljAaeicEJOfAyjHi/Ragk9guctkIPg5JtemJiZ0uF5D7NUe5EVXQIrQ02XVBX9lbp7YaVycGoNqBVQh0zqzQqZXS8GEOM7ILdjE8+XgnLt/A7TNjuj6X/hnaFX/am8AnwP4jIPwH+J+C/cff/FvjPgH8gIv8U+Af33/9al8ik8Ex9Ogxp7GOQ3nxC9Eq4R6SXRe6Cocrzl4OwFp4eM26KEJGy4ilQqfQ+phAq65QpMzcD1Ug15xh9UptM8S5onHWnqtJ80NUY3tjrQYrCtiZKmBqK5ZQnEWhVmg1EFB3OlgaWptGmu4EcLKkzauD1ZoRoHCpUc7QJx4sTiMSojOCENJkK4c2CX8BMuVwPRg6YGn2/0XIkin6tudj3iprip9NEuOV5vW/+StCF3htlBMqWSGkljEbonf76itWD9z/+AdZvUwWZAreXG0s8s3z0hqdPPuD86ae01xtRwUskDKe97qzvHliK8PonFWcCavHALPcbdkDMAWuC5oHfKtI71+tOSomSyr05a5NV4WMSlDF0KDRH13XGyjchS6IwWRI+Lx/sx0HJC2iiM6lNSpiyeJzBVHEmprMyyMKQiuYBXaFGhgurzySoow16LmSmWbPrRrAIe4NQAWWRwem73wPm9CHc3ZO/6Pv+l352fp3uq7/ym/hVZEneyTZlOaN56twZj3zn3/l7vFPloyUS1GhuyK2ypIwFSHGwSWJ0YUglpcThAx0R94kjq93ZlkwugveARqXVHUkZaRdyXCazULnjvpnOzB44F6HZ4Hg5WI9I+GCZ6ci0afVlJjebTDsybbCmgNhAY+D5J+/xLfGwLkQPHPtOyRsxOXkJDJxhOssZKjEthC5sa2CQwOscf4Yyk5l04H1nPT1BrVNFGQOyBKweAIT1TF4VOxqaEmYD77OxWh5OXJ+vhDQnNm3vcCq0W2BUZ9wurE8PxDywnElV2cWQ2rm9f6G5U04r0g/CduYH/+tP2NYZz/b2ex9OhFqM3C47xIyokYoyjsrtaDz/+JUulfXpgSCJW6vQJyw2mnFYB/zrkWxIkeM48KD0Dmhg7wcpyV1vYBwYdjAJ3pWZeZEU9gp3S/TkczCFR9EZXdFrJTytXNttZmbSZsCQTHhLToVo0IbxLJHPrgdHyBxLYC8b/+S/+B856h/PHuPd2PYNP5+/2izJv8lLZKK/RSMhxpmyNA708R3qkFVQNTRMU00kkuJAmf6Baz0YdwWjjSl2cm8T/oGiHie9Zx/sx4VxZ/c5jZA2qsb7SK3PMiaB3yXLwwKRzDt9wB4DwaC44xYIMtHf466wcx/kbJhWJMQZ745wEuW2Ny79RqiNSCcHZfSGd4e7a1BMiKosURECIRyz3EiC+202Kt1Z8za9ESFQMpjsRIy8JlJK2PWV/YsXRoh02+nP06qsubG/XkkbfPknn3H9bNBenNsXndoGYXWWDzfC16PQ98TDGK8XYs4seeH09ume5l2o+8GH/9q3ef78M55/+MLowKiEJRNPGU3XKSK7HBy9QXPePT0QcgACR6ukklhWoZmji6IEdEAuTsh3ZesMnZxio2EsRFrzyW8wJ5nNW4YLkicsV8agLIkSZfaorGPWsTDHn7fLPp2U1UAc64OshlpnzFkTOhqXdtAVch/kFHHbWcw5Z8gffp/45yz+P/PU/xU+ub+Vm4LLNAZqSGSNgOEeWD55IGZYglB9sLfK/txhEzxGXIwigkXDUKoLh3bGGPQqKI0enHYMhgKxs6yRHGfMeBnpLgGu5DjIymQ0HJ1ugofOGBWxK19cX1mkEBP0NM0z3WfGY5EwXZOzq4DdQLqQsxO2NK8eMsNfxhiYCSM0uk6dhFUIemUpK2I71dv9dIzEADIyuazEBPgkWQcFaY1+TDblaB3pwmgdTwmCoN6QHghbor/sdCu4DvpLZDu9YfsgkR+FdVPWh4wqU7z1wQpHo3525fT0DneZQowcWN9sHP3Ksk12wnYKxJYYDvvrCxIi/XbgVHwvpNNKiYVxzAi4JRXerA+I3vmUx450nZOHA7zd0HJ3Vmqauo8irOsJzYUcjJimMSxrprozPCMSiCEQVCg6pcuuwsgBUKo5RZzoc4S82KCcIqYdcyVEYXelKwSMIMI47B5Go/OWaZ1lzEChIIOH750RWe+5JFMi/1ct+RXSmH4rN4Ug96mCRg6dHV2VwnYKpDEIMhOHgkM8ZcIQsjWKQg+GtgVRZ7VIvHU0KyXPEA9hquGyKqNHpMVJULY799wC9RjEGLE4ewHqgDRaNc5RuX7uxKfCKMu8gvZO84YqJOsISoud663O2LOSMTGGQeiRLIU350dCWYgILU8TzqZ6H4k6Eyw30FAIDgQl75POJKlTjwPGPTLPhSZCCECMc0x469yeX2cD9jbj4EY7GA1MBrol+ufvkS6EpZM+PXMYxJPiKWC9QlrIq7IECHFjGYJwxbMTRoMCo+88vH03JyIJrDrp4w9JWfjRHz8zxqCFSj0E0UGvV7p2PEww7fNnX8yfB06KzDwP0VkuWqM8ngix0C0QckLuGyl+gHRCKugIJD1o/cpJFhZzojoS5veZUWg2HeijokROAj1GNk20Cst6Qoik0VkilKAUhDhmwnS/3ZAcWErG08GowqoR14NeB9yED79XQB5Q+yql+s9AbfLPQZ9/Ntvxl1m/fZuC3qO9iEgQYpDJ2c9vOKdIcaUHI5jRr8IijnhjH450Y3FlzaAqWLhhYQZ+WlOCKllPmDg6fE4vUgCfacgmB0EHmyZutSPWESKeIzlGSppNxGSdt6eVNbzQVWl94tUkKIdkejeyJJZlnlhdnJK4I8DijIFrg3OEoJkwBh4ihweOo9Mx4l1Wa2KQBPOdl9sxa9Wv5NhByDlSQ6N0YwwjCBzXMenT2wlGQINw+/IVPxLSjdtPDo7PGyaZFIWikS0I65sH2h6IAegH1ir9dnC9GWmdI7uXn3yJ3toM5CmZ2+02gTWHsYQFQuQP/q3v4JIIdfD82Qs+Mp4cLRtoZlQnZmhHp929H3giNiMZCEoaEE7bPCD6TlYjONg46AjDFLGMScOjsYQHRp34uB5hdMNGQzTjuZNPyywptMySICeiKxd7geazz9BBYkHIcxKUEsVh7Z0cAiXEmSFSI0kc7QclLag2vFXOJ4eHD5CvQn7c0XB/8O3/vQH4N2iA+uceoV/Jv/obXCLTQqphug0BRhfKw1s0zqagjrlx5AJLDuQY5i3BjWadvR1EBbWISyF7YVvyhJa6k4SJXQ9ztm04VjvJJvPA4ywBuI+ssmSSzybT648O9PzE0Q/u4kc8R0JO+JjyYVXIPsiq9H2QXNiroUNAISadnMhtnQnY55k3GcZtot5U8e7khwW5zs+TkSAF1nUljRtjr/Q+Q22iKk1tduWBoIm8pamuS5MsVE4LIRyUk/P46ULtzyzbwPfGoEGbuYmP24r3QV42Uhb0tND77K+0boyXg+W0MFho152xG4gim5CWQFyEPirxzSMswvt/dkF0oHEhnmeZtuRC38csd4rh+yBqYAwhLROYO2Ih+AyTUY1gkPogpEhZhOb2tdrUdDYYs87S80REs0xMfh9Um5SulAKEhoYMA5rIHLsOZhO6zB5GYEqVe+8oSj2ckhLeG9MnHSddKxciAx1Tvbp5YfvoW1iMCBNu+1WK3P+PY/sl1rxwzSaiofPEcmF7d5rcAOmIOLeLkYvjrSHDaEzyTdJI0siYniESlS6D5oMQJiHZxdAYKSIEhMWnRDonoY9GlQhB2eLU0YvO7Ih6qRCU05s5+78MnTWvV9re6G6s8TR18BLodeYJtN5xVV5uOx6N2pxhmTamU/D6xZWjGRIKM6eqIWGhPxtxy4BNGIwM6ssVMmgMFO3EZni9oUxxj0bBbee2H9SxI2NGrB23HfPIqCBWePzgjMSFvAbYGyaKjE4jkdeFer0QEGJSrF/xUohjcNl3zECWQIgLkhcogfp65bgORm0ctXL+3iPeBB/C559/jtVGbYLVg9F3Tovw+PgIScgPy+x3pMjl9ZhCJYycp4dDEZLNGr1kpbTISEpHCK4sruw/vpDLRO1dghGj4j6wpGi4p11LZOzKsCuGYGOOuWU4tqYpqhJDcaRGMOfoSjo5mmcilOKI7qQASxESRgSonTEO3v3hRyD3NG6ZY+1f/zP0W7aGT/SYiID6VKrpyvImsqqx3jvPYYkEmw0tu+PQtjwt0hZnSAyeGFEmPENXkGmKWc6B0Qw3wWSyQWFmF3Zd50Pid+SbACIEadCN7e1Ga063mfiUlsQY8yof4v/D3bvryJYlaXqfLVuXvd0jziUzqy/TBCmMQInEAHwCghIpDECNFPgGFMg3oEyAoEiBAFUKBCjyMSiMRmFIEODMdFVXZZ5zIsJ973Uxo2CeOYXqW1VXdjeyNpBCRsQJ93D3vdYys///fmH1sPS6hwRZcTQLuaw4lp4DF4mQmOMIKXAxfHWYBXRS8oXEiNp5RVNKx0nNhbwrcyY0GaIVZLLtO7WPOKKKsT+FVNv6RCikrVHLTs6KSkFSp+oGM8hDWgt+u7OmIKnjlrh8fMeywXyLyUxLlXy9Rq5CkYiye76wv1fuX17Znj9y/fgcoqda2K4X9j/9yPM3F/q3nbEOWva4QTO8fHdwefee/HSNkkaFmoS9bogmnrcLYkeYmjyoRiWHXH3qpLiE+vE4+fQyaR8adbsgQFqRE5nFKEA24TAjr/BQLBrgFBHcBTOnEjDejLEm1CaU6expsnsoIZWJ2yRbIqdQvKqEy3J5BBZ//aEh6Tn6UCmz/hHkzn9wi0IsBhlU0RRx7Xl/plRBEqgbrokqg2VHjBqpDxn0o/xYDj6RDMpCkrLOwVqRHykicWO50XucPAywMUlipFLJYzKXc20VgPvbjbsVtraQDUqL42N1oaZFN8VNsZygSTguVbmvCGn98quT1BTZK4dNEOH4diAfNu5f1oNBOcKGO0Gz07ZK9gkt4aky5slxQsoXkMZcQi1PSNlCZu0dG8pYMH0g9YlZBvMeirzRjSWLPixEPNkYPvHakLJR0oDRaclYt5McTX1UKlpD11CuGzqCTD9unXkf1JaxdPLp28+YGOd5YOcroxICJhPU4VghE0+ET+HTt7/g3eUdtu1ovURCtBolQ0oHdThNE7k4T0+K+UQ90rn2Cs8lUaZQLomtBcV6Hhb1vjr+qD9tTupUzpW43++oGy1XJsAy9n0HL5grwwXJ4KnQzZAUgrAmJYJf2KhemevGJsKeIog2L9Bj8v66kMufAZFOlvyvv0V/LNLSb14/6UXhN7uvoimoN55I7nGzCujlSpvB1jNdyJp4mo9AGI0XwTzgrEnwtFip0ntnnSGZFr2HQtLByaQ0aTXzvCnqBL3JBpwRFUaJbvccTuYOnxv1Wbm7c9wXiR2TG3117p4p1VlrIHagfeAKRf1BFIpaeNuM/ngsfIXAyh89qFwZCTQ1snTWuTCbLI3Q2mInmnb2S6LPezxWBeZJP87IIdBCKpOMU62yOMjbTnm6PNSb4MkoIsx5BLNgLlSMsSb38QopcbydeGmxiI1JLoEwm0DZGhQNnuXThlHRusPb4unSGEeYlpaXYGleNqQ7pJAmzzxxy3ifeG7o3vBNI8a+GVM8NB5pBxLl0ZCbQ2miSE8UOm7K508HKGxpojlOSe/fPWETnsqVLJmmlUtWnpqSPNLIF0Y/b+y5solixTEWJoNSjbUW57c3nvedbvG3iy/UG8lvYJPNKvkB2inu0UMoiuFcvvkGF8UfvMi/7vr1RuOPWWT8pBeF37zEH3mRySFXbIWAZ/v4jLtSpJBGwqaD7HQL2WpKTsoBNi0KvpymQk7QtkxaZzSE1Ogi9NWRvpA1YE4u10ItGdWCloyPCbOzPZKmjreJraAxR/hp4bxPfG6IT8o68B5lj1nwCmtKLOnM42TcC60GE3D1Fbi4ubh+fSHr4vpHF7qFym7ew8dxEqj4PqFKwTUjdsPVHrut4N3o03CP/ETxxJrh9V9p0mqhY6z+RmqBm5vngrLwmbh5NF7fzgOkMcjcj8X0Rb5stPoEmri9dVTg5kZ/u1MvjXHO6NCjOJPtUuj3G9iJZEETPH/YeX35JaPHyef2csN6xkvn+U8+RgDOmswhaKqsFGE2dwGhctKDp4GRSrgqy0OivicnnZ28C3/y9ceYMgis0clZIvR2BXwmPwjZOTmqsNVKyoU5OromaRdSymgO0ZRbpr5T3ubC1ZhqdBug8V6Q9Ydhwlac4saWMnp09Ox88+99r1eIH/rbwCsicQr+sQAtP+lF4Tc7si6QUHKuSHLc3lC/ct0SOwEdaaWEt54IHpVUme5Yj/rwdKelyjknSotTBZnk4ea7ZOG5VcQEl8wU5X50ukd/wcTQ0hDXqB/TJJ2V8o2QtQav0SdaEpYPNL9j8ohVz1Brjp1yGfZpoqmSrhFiezsPcl5s7YnzdTLTRGMgQX05kTUfb2ii+M79dCQpVsI6jDbQDdw5vJO80yqoBgV6zHv8XAE7nc7kko2UhdE7yOBcg94nthJ6Oms5oxunvWJJ8QxDnPHdSxilRFAbfPr0SsuKjUWuoZRUWZHH8XYwliFFEN1YC+pWSFJRLfE3jfCR6O4cr0ZLO/ndRsLJNkkSZiqVRF6JvE1IhbFCLdqHc5yDlJWMwxLatbCVyl98+8KaDmNxag7IjTi7Va6tIVnCbZkcdejHHU0ZTztaKrsWSmmUVPAjRSBVCt9NLoV5Omjm1gdTHE8ef/vK6PeO2GTY6DDgw1cNT4H7M4KG/bfdB/YjnhV+0ovCb14BaTVIjprDUmgXUstIFmouTD8x72QzbCX6HOFTqIVWlOqJKZ0gch9Y3hhjsLKxmXCOiHETjPu4sywxyKQlUTOnQMHPFVFvr78CcqFtgreo91suLBbz7Oi4U7TCtlgGYxrixvnq6LtCyY7aSStQn1qUOetEtFLKjqtiJowtc345sD7CFITRimB+Ms6JFuhmtN7BOpea2S+Kp0URYd8rW8sok2IGMtnblXlEb6NuiZY3Wq1ouiB7gtRIFsafTCVhKJBrxWWE2ep1sPpAjhNI1OcraxjbVrGjB7OibphMWJVSU7gW10LzYr2d5K1xnm9IVuweSd+SDJ+T4+1ASqaQKNcdW0HZ6tNosuMqrJJD+anO6YtzFs5bTICObqSmTHdclAC9n7Hr5sKyEWXa91OtlEhaQ2/SjbkG5+ud1V+49wU+oqEpEVw7DsG0k84ckxhXbBjnMnoeIBVB2ZawSSHLYr9UqN8g5qj8lpRm87918fjt76M/oCtCwhVSyE0RQy9XVJVr2/A5EEusKXTSDyOokBXCORaeE3kJ6hEFNkZHH861VUIHv8qEa2belaTBFx8YthbLQnlXijK/DGQR04oE2hcoHHOEkSZdeAGqLpgKSqDHjkz6iqA0M1G26EIPyLpRpVAuiqaJLTAblBaejFmNpBG4qh4Ny6SFfoSSs8/wQfhy1nzMNwT6nKyl0Sh9sCes31AXSBp6gtkpe8X6SZVCrhqlmns8XhfQwJ+lUvC1mNkR2/n4/IFxvHB9t7OGUyQcgWtMypbp94lu+RGrV1g+qblQtp1WC9J2ksNS5fLhPZ4KiUrad4ae3I7BEEjPlbQrkDltoJLJLhQNv+SwxaqLcy3GptBiZ9ciWImpj2wbwwEdQdBMhrswhzLXQRJh64UunS7Qe8duDm+fkbwxk8UgRxt7TZS1cfMvLDO63+N0NSfaPZrZyf4tOtAXuy8uP/tjjITx4zgff5frD2pRSEjcwLLIkuKI+3yhSTj/WrlQi5ITZJ0ISnsgtd0dn0YRGC6kVtiKsYsEEkyUMQZNM3xZXC6QWJHR6JO8UpQjWTASs2SKbuifbpzzld4752PkV1PcTKqdmT0aUUD/FOYfLrGzzDlhAeWkuMQR0yevXz6RSpx0dAw0BpAs6TTfQgtRt0in6qG63C7BY6jXSFfSosxUouY/b8ER1MDaJ3Gu1wupgKWTcUbKc6oFH5Pcrsgx0GPSLgVVKKWSP2SEA2JqyPmlc9Ener/z55//EO8NqQAAIABJREFUnPUupND26Y6IsxWhXS/gBVElpSgRStPoZ/SJsUhXp7GQeaDqnLMzSyx2NiYchf3dTvKFnTM69tqZy5CLgkzWMLIYMgozZUpTktTA9a3EcesoAiM+DzU5xcPIdphRLMq/ee6kJXReycnJrbDtjVEztWVqDp+KT2euO3MOFkZNEf9nUyjZeGqKlIFbTDNkxRRrdse3xIc/ewrRVXLM1j/owvAHtSi4EOo0CvhCRHm+Xqha2RASnTUGtWZEMqyDSSJbR1SoTbmdRikJP8+wMvsguWFEZoQ80oc6IJfCep2kUvHy2LVXoq2FcjJkkW3S0sZFP8S4bTgoYdM9B8/WMCZvrwPfFR8HJWdmCdy5lA1ZEt31lWmaWOcOriS1sHt7hJxqzozjjfXlxvlyw46EjYR/6ZyfbzCN2+fJOhbTHHVlvd5RSxiT+xho2hhHPMfsgnTBPx+sP5/4z2+sv7jD2wtUY7sI43ZEefVy43g5saX0bw8ozse8cfYX5F3j8uEbnr6+kv/JRzaE8+UNtLHOg7on2vYc0yJ1zjmYs7NdKsfLyTz1EVBTYHWyDHQYchU0L9rVybXgLuglM0piSkx/xhSmLNpeoW7IxZFOkJGy49M5gVwzXiu6Kds5H6e+wKxmamhO8sR8ctzukCvsFVnO6zJEFp4vLJnkorgMUs7MFWYoZDBscSntEUJzwUqL8SSh79hSItmg3E4+fH0Fb4/R0k/kpCAi//4j6+H7/76IyH8jIv+diPyrX/v6f/ZjPuG/7so5Y0AWKCkxrTOsoFUfwBGNtOQskQTtjnkG76x0xdfESGSE2SdZouE3VwR0ZDkoAs4CEixhFyfvjf72mFbkzOoDauH8zqkV0px4cpYd7KXSRFl9USRMQqYLo9Ced2qGUhrrvjjXYKkgWeg5R7SIViwL6Ro5Acd9UmvmdQ0ktRhTvkW0ul0V3mfyz5RxSRybMveMjU6+LeYvD7p10lY5NU5YaI/ae8D8xY357eT2r16wY2IJOjA9c/6rz6TvjE//+jusj1jgdLK3LVDzLTF+eXLLg3cfntmfrpRSGG9vyJocBvbLA1MnKeH1aErvPeziqZE8cTsG9dpolQe6bLAmLIHDO5wgpeJrcL68kLIHB2GuCIDNSsnC1t4xHDKGzMwlZ7Y9U8ikWtCcH3mh64FbV9q2MVYHrYxqkCv95Y7Ok/Zuw1ViEbNCeoB8tSjz8yLU6BVhUUtBU6ZIpuUShrRr9CpkQk6FfUtcy5XhnfIoca5P4Ok9a4WE+x8S5Pp3XhTc/f96ZD38M+A/Am4EoxHgf/z+e+7+f/wYT/S3eD7gzpIUYRtWyPVK0/TAak/mg5/YLoVuYXSS1Fh20Jcg0xGxIDbLwjTEMssNl4Slwpydsml8yFlogbUGr78aNHHq1Tm+PVmHgyvTE+7CLtF8mpbIOTNHYiuGzcW8L+bxhhElg6XClgoiwWWwM+LMlt84ftFpF2VafIBdKlVrNDr9iduCtAs5Ked5ctxjp9KUYCwuP9tZHwrtqZJviX47Y4LxwNfJ64317WfuU+iaaH/ygf2Pnti+Vvaf7Wx/XLn8048cLXozvCzEILen+MDvO6kWtu1Ce77QvSOrY0kREUa/kb6JbKD7v74F6i3DGgvVyjljpFmvO6Vl9HnD58nbL99i5Jtgz5eHViFUn2vF4lylBTsS0AyWBfNMJ8oQW4W2Q2HGFCKn8LB4aFTmcvDMoYv7/aTmAKVUV8YRJRw1U3PCPBiPSMc84uH6dNKeH3CUiQ+ifFgrTgMYK4GcIYpShFMmWZ1hAzVljQDrbG2RLh9jcxLgbxAx/djXj/VI/wnwL939//2Rft/vfLk7KhlN6VHXO/X5mZQBXxSCJZAIAEvWQSH0+uZQeajYLKM10yioKfhCCf+A2MJkQ0rEsanu7Knx7kPj3YfMWMZ4idX+wz9pLE6yJNY6GW6MceIaoqJWnOUprM1bDgOSGGKJ1AIMinfOOZAW2oG5JNBsPh4iqsS0AS3QZPMaI80p8SHMOd7emR4ZhoBbooxF2sDfF9QSl5sy/uIFfj64IZQ/fs/2vtL2wpQoY8gCraBk0lZ5+ubKx3/3GduU9OVG//kL7onblxf0rNwz3G8dqfwAItFcyWUH3pCvP/L11z/jy7ffPbDpHTOjj4WkQW6wtXe8f/8eN4Ka/ZAZH/1OvgrXNvBDMIRxD/FY0kVuF7ZUwZ25FqsvtO1QJ1o3Fg07TkyMkhMyB5KNLDBnx1Y0QQ+beM1Mg9LDtFW3aAi3rGASNOicqAQEV7KwbgeqShLBKRQejUTLJAuB0rDFkkFemXU6l71xkZBqyzHYEuzvf4aJYfw0DVH/BfC//tr//9ci8i9E5H/5B4uMS1F3W1ZYCRMo1wYoW46u8bHsEfB6wgjku+REFUFTkHttjbh5/dGBd2VxsKYyZWACYzmv335i2cnr4wZdVbl/6lxWYT0nxBZSNjQvLrVxzFBYsqA+QmF9DayfyOqspciarBzMg+VxMz7VSzTA1MhauX5IuOkDcw7mTvILa61olrZBqRfGGADUXULJl6KRlmwyinKewulv7B83+lcZeX7PfLdRLy2qozIDEJITYNgwZBy4Js7byfnyGZfC888u+NdfxZH+zz+hrycv48SzsT8Vkm8kreR6Uq87vd8odWP7o2c+/+oXXD58IHsltcKYk9aMMRZHX+izol8V6tbQfWd5J0ui1o01jG+/E/Zp0cvQyNEcHVJbnL4oSbi820n79lBJGjI66xE5l9aibRnRRdFwLnqKSZF7RA3OQ8nDmXUQnvAUdCUL3YLNhbjgKQhP0zJ9hg7CbOHamQnEDbOF5oRyBIWLFVDdBLY6moyqgBgM5fKz9sNUIqn9vcma/9Kt9Pv+AhGpwD8H/rfHl/4n4J8C/wz4N8D/8Nf8ux8tDEY0VlJJmYKALZIntsuVXTWoPXOgIhQTGpVcHCuEbbckkm6RNSBC0TjeCcpYibFCmKSykWyiVOqHhs0HYm2e9LdJ6XBvmU2doUZ2YY3K2SdCJA5dc2JZcBJ6ylgKRoKthZSMT0ct4uLH91mTD8hocuN+8rACC5oaJSXqbmEVLol6uXDeT7D5GPnFa5RW5pxngEhPgWo0b4wx8H4iOxznigmHBn/Qs5Fc6FOQmllkXm6vnDLxlsklyFDJOuXdhnxzZewXjDdSStzOTh8n4onz5szbjbLtSFkkXfj7d5xr0o8DscT+dOVSPtL0wno5WePgkpS3X32B5GTdWUxsQGuN8q5yezv4an9HsUopBe8H58s9jF8IY3WG9QCm5IprxruxeoyhvS+21sIdOiROAK54K+Qa7tD72w2rlTXCg7GrhtFKMiVvYBEqdGcx6eglcZ53ci1Ui9+n6IML2cFrfF57IPwmzlZ2NMM0Q10w63z8o/ckaSixGP1DXT/GI/2nwP/p7j8HcPefu/tydwP+ZyIH4i9dP2YYjHuIlgRwT5ECZJlcgpEnc6JJKA/HYJbIHKgykDmxPsBj9w4F3TV2BjqeznDm2cGQhaRHQzNn5m1SWNzNqZ5ZW8KTBiPhSJjciZchk3JB7hKp0GKRZTg7KpkxFk0hLYmmJDDmRA5FMHStaMj5iUlkFnRxzG6YCHYbsD2CTlphHAvKjljoNu5rsLxTktKPDi1xzMnNjANj1oKkFLqHuZBUGCac9zCFHevEe+gw6tbY1s5+qXz37Y1ozy1us0MfmDtZCutt4r7wkSA5+3Oj90iTuX3qfPr5X7D2RfsikbNxP/E2WTIpNcNeSfcb9elCfooxoOBs+xPtOTFl0JqhXz1z6ydJg22Yy2N3PYV6DShMrZVF5EQex8Ecyio5XFmiFBKzL6REMy9jETO4LlQS/j7MZKWEe/aYgzVOVFYs5hru2mINkpBTCafreYdWoBtWJQxOUgIzD7ALOsOToSmCY2p6JGvPxPW9kGTHdT00DL/vnfLbXT/GovBf8mulw/chMI/rPydyIP5eLxFHXcgCSRdiTtIt5s35CDGOAzYhF+4SNb7NiGi3nFmxojychoO0hJWUKgqciBakA2J07+DKMOF+v9POylDYP1TS8YYTo8LMBtmoNaGWsZbQtFMBl0WSQG4VIkvAmY9cgwVJmNvEXCgqIbx6NASlOMxFn8I5B/fPg7RKfLDVqU+VdT9JJRpi1ZS0FCtK3ivYJE9lnZ3z9WR+ObG1KKlgVRn9hSSDd61xrtD89zU514ySCufLF2Mcne6LjpCvjZcvkzHvlC2TL4njdaFbCqCJTMSEt59/R22F5JWtNdo1cG3temV9Xow3R6bz3C6cE/px8vRxoz4pOZd4L++dfd+pbeOyF2iJdzXTP99oW2gerCXW6xmNvQUlKaVFg9HlNQAmfg9lpAiXWiiaGY+GcN4E7Seuk7pvbCV294mQJWMlOJruKxK4xJBypzlYgtoaa+0kW1hO9HNBKo/Pq5IF7AyxXNKGWEybEoNpgzUPWquQP5IkTrB/10Tq3/X6fcNgLkSuw//+a1/+7x/R9P8C+I+B//b3eYzf5oqTQuj7I0gjoe2JXASsoW5gg07CywrhjQYBR1VpKZOykDR4CS6Ka+awO0MK0yszRXSZWuKpNHJdjHHnOT9xXwPXiQtsl8o4QYYyZ0dsMY9IUG5aI349Z86+OIfgPmLXU2ECuThCJadKdmGaYOpYUjRVSpqsEd112SrpdbDeZywZ5SnzejsDMCrweruTR4akzEH0BVaitJ38VKi1cn3aHmTqjl0IRkItiBaOFZHpSeLDnHPC5qDtC9XC/vV7xtsIzoIqH//sHfVyZc7FbdzZduV2P+nfvSFvwv7NO8q1cRwHy40vnz7zOox39ZmF8PTxKy7vNzzBcX8058bk+kdfIdP41f/9K2TcKVkpCrYWac9c68a/+de/YvcaUmVPiBvsG24xOkwpMh21FqxuIdJKmXGOwJ+tCXPRRJALjBdFViddClh6YPEWl7JxqkAqWC5MTqaFgQsvLIkTZzIhXYS3c0X/Qp3kEQjjybFxJ69MSok+zx/o232F4tTmQqvh+0d8xunjr70Sf+vI8nfxSv1ei4K739z9a3f//Gtf+6/c/T9w9//Q3f/590Gzf/9XeqgZw7TiCsUnJUfArFmmpMEFQ2sKTrJkWEKShZ2LvibdIjpMLIxRSe5ci8SRTiOzYIoj6+RjfeLb42B/l9kekWIkp+WG5DNqfstMjQ/Nse4YCcmhpN8qSI4eyNGdZIaZoB5UpCIpuI5LQRNv8x7qNiUwbbfBbQWsNKmzDErJqEBJC3mTYCi8vuDWsb7oPrgd9xinEbZevSZqK6gkVjc4Qx49LXIQF8GgcE+UtHG/TQavrNmhBLFq3O94yeimgKOWWKxgQV4Kb8cLt1/+ilwL6El9vrAm5OvOy6dfod8OXl9eQCqpbtTd2C5X6mWPfoJkOCcO1HYJdHttZHPy9Yl931FNnLdP1OfKvIVxKZeEkyhVIe+Mu7HXGDWXEs8110z+PpOBIFhd86BnDRn8A8OXVuJYN9Jaj0xQ59qeI5DHV6DyVkjIb9yZDj7DyekW0J3VJ54KIzW0RSmYxLHpXEomewDhnUFbTvvwhMlk+PwbVY1/VXTKr/+8y29/q//BKBpFnIn/cMRrl1CElTQ5GUiYpCmyUSj4KQyFG4sxHEmL55ZRTYzeKclIKC2lWGZNsLl4IlPo6G2jb8r+DHjm3heWw0fxylvkCaQ4PksqzARumbcR0JTVcugqlpO90DTGYLN3ls8IgiVhfjLvHf008fvkXA3VgteM7cp1z8zj5D4G5z1q3TkWEyf5Qb0IT+0Z3duDn7BQS2wSUmQ5BmkQRqJVKVQsJ0pR0j3YAMlhrBnZhgXKtuP2wNBb0JtdhOP1lbQmJGd6xy1zGzemGM8fPtLyA366f0S4se8b9VJJ33zF0x9f0cNIc5Ib1D0zvZOysz1v5Pc75MbP/58/Z6VFqhtbu8QOWQ7anz7xOh15M5YN8sdETYWzAyhfbvcHUknxVsk1cx6B1n+7d87bnaVGksw+Mp/6iWTl6II7eBpYBZeCeORuztmZdGQlNtnxPBkO+zV29WVHTDBWgGXniilFl/X4fhikWErZYpSecwo9xFBO6bz704+4yg826r/yMvAfkeH6B7EohPRYyDSGRVMmXWN375PIURBQydx8ckuJkcJNKA/4h7vwNmJsWIqG09DhXIt1S3g2CguY3L9bfHq9s10ccWWueywgunF3Jd8h05gqAWqxIwQptXJtNW7Yc7LIVCloiWwJX04S4VITA2Pe7qSzUC6N+b5w/fhMVUOYCHFMva/FzJVGobVMqhuSItJu1GdOy/QtwmXEKrc7DyZEjiNqa4zVmeuOW6fPF0SInsvFHmPZyZ4rqpl1vHC/v7HVRN4y2KL3gfcB5swVgbZNawBcfWd0I/tAtxw78Zr4ChVkzp33f/SO737xyvP2jJkhpjALSkxB9usz259+YB1vJGsRtOJOEyPlgg9IrmgWasskS6SlSI2IDPdFSY0vt5PjuLGWYGJkDedsS0RgUFf8NObnT2yPkqglQzO0vJO6I0YAetQfiVIpMjfOCT2jBY77YhLK0toaY5z0EvoYV+PiBW1hUGNNpA7W9GBrCGh9xP0NY3sP+YEX/Bu1Cn/Ft379538X8vMfxKKQ0KgL5aQAYoUNJavTSLCM7InsE+0LkZNEibpyxRFSUPJypE/mgjEBGRQynt64eMPc6W+TuRZejfVovHnKiEYcWEqBVJvq3G4Hw1Icy9fCZtz8YkHUyclYs5M9jqnZHCcI0P22SM8Fr5NikywHb2+vdHOGJVISVEBd0GkxcXHnfsYpQTJMnQw3jvvkEPAMlw+FpDNEMS20GqUUSt7ipHO5kFMmI2A7nhq+KrmAjUFiJ5dAua15oMm5aqFPp+UtAmh10jFSFRZ3vvrqA6dVjnHj2uLxMzPyFIFhg/bvPHPebtQj8/nbz8zjoLUrdSvc3r7l+Zsnrn/8Nck6r7/4TN5zqCU1ACeSGvmdMro/yg3DvVFqouSwTF8vlZqEVmKEaGYsUzQXpgk1Q+4H4ymTdyUlg9JA7vH+5VCnaipMhDvRjxgotjlTnEzFzGhegcQbr/QJxU9MnSWP3IkhzHUiJWzZpsFZSOr0lQK6MxPv9wvijiLo71AC/H7300/9SvIwQgmSSkRDSUHLjmumT8DXA4BC2KmXBlXXnaoTR7E+WRI1vq0ZnoSRIhRWEktOzm8NOwf5knn/ceN8M1JOZBI6laod7E5WASW6gUDTTG4bthyVGFsuBPNJ0p1zjuhk1wC02hJkU8QcJzESHKNi9ysphQy7984cC3dBGAFQtY6YIA42nKRhN66NIP+kxfk2WCsgL3NObsedbs5rv0GJ7At/1Kc2D9JjRx3LOV6OaOU3mDi2YPji9nZSW+b1/IL6Rr/d6ffJ6JC1sZaAvZHLxuwT9WAvtlYYpyHHoKgiHwvJPvFEpvcB3Dhe7ygVf7nz7k+/Yh3G7buTt9dPYd66HyBC71/QVPBScdMIzvGF+AXRjOpijYoSXIekLTwFGMMfPQdZIIW0VeYZJOVsYKtgKcpHp+NWGSucscsNceWcUQq+2cFImdMOTCpCpWyJoy/SAyNfPTPNsRwsZ334cpIbniK1Cgxdk9z8ocGJgNtfT4VJ+dcSYn6LZuNvfUv9KL/lH/0yknmgr3CkNEQnuxiSTuZbvBlrCUsSMhNbLuRaWZ7QtB4MgcWck6qFXSp7m9RUEJ/0T5BKgmtwAReKFuNcYaCZqdOn0NJGuQTdR3KJkBibUc8WAnNmi5Yfjyl3tn2HZBQyswtUQIWVEmsF+99fBukJjunh7UiFlZzpgqWw6rpeyCWiybxU1F+ZGfCKrUYfjtTMXAm3hc9QTU4rXC8N8QIFWCduidQy0xaHdc5zsD9vwVA44Hx7ZWhl9JjsOJnuyrCTfNkDuFoT5+2NMW8xCjTBxelnEJ73kqmipD1zvnzC1uR1uyJ2RxFevxtYUbRU2ofMdhGkNPwc3A9D9/CrZI1d1DzAuX4ORAnBVRuIOFUr/eUF3l+iR+ApFl1ZFFkRvFIulEumdMMkCFNDTpZPJDm5CbplJIONjqcU5cvqaBZKBXIJfUYCvDNXbEhCmKHOY3LIGQCXFZJ3JDQMSxItx5i8Ekf+raUI9OERFSf/tgwwi96EiCAuP5qO4ae9KKTvuYxhthFzMhLpxg9v/mV/h+bF2YWcnCQZ5WTaQkU4fTHGCsZJXjGC05gVz9m4f3tjvE5WmuQnpbVKThHTptpISWLxHpCzspLRTALG0geZ8CF0c3wYG45mKAS0006QY4LB/c3QFunIYv5Dr6SUFolNOzQFGTkMORIfVEuZlWGdnX5O3s6B9xtHb6xj0M3YtoInD3ryFqE1OTWWC1k6fS5sQUrKvRt9drg5SMbNmOfCOXi9HaSiPH34ClkLNDFcyGq0GlgxsfmIODJkuzC+3DhPp583zBzP4QQUT6z+yng9H7kRBro4/ZndhP3pCXrCfdDvznGeXH/2FTlnPv9/v+B4ecOfNhaDUho2Okkz5V2NE6FG4048QRaaRFiM1UevJFsg21yQUllvB1phqpKyU65XxBO1ZsbZaaakAWMMtlJJEjkf2iosY81HmcijOcnC14mTSDMSzpHFTCX4To86v58nXWEXiZ6CKQllrRH0cQtE/frNg8D3QTGPr/9Y/oif9KIgLo9VMqClCYkxWqlxo6yFmbG/a/j9hq0cJpWkCIN7f32w+p1kC+4ZsjL74vXzwfp0RoZgylyfr48d+ER8sdbg7Rihd19ngEFI4IVjLU7g4dRnT4kylVozqjnKGItmow+jqyOacV0BIc0PX+ecnFNYa7A9gffJdEP2CrmgMwW9WIy5OmcKklJJBUcZDrUpyzpr3FmHM7LQJ9zHZM4DGZGpYHMwfvVLbE0KznzAW8f9jLm/Fo57LBLLnOO4I6tTamLLysDAFD0NIUe+wpy4P2Lf14lbZo4IP1lHxzUUpGoDbvdwSzK5/vHO2/2GffcaSLfeWQ65ZXo+WGlgL4MliXe+UeQKa6K14VlYt5NhKbSWK9B4dhho2OF5BO2ilZQHyRPuyjVvZK2scVLTBb05Oo01EnvJnGnhY9KKMkmI5Xidb7EwfM9TbAqaK0ih5Cvqxskgp4pJYvpJwnnOJShdSYIbqTtjObn3mPY4FAkk2yMIEf0rKgRx/1ENUz/pRQGIoBYPDYFICERa3dimc80b6sYcQn5SfEzm/SSW2ESRjBI2ZMuT+zrhO8ffwpQzt075WHh+2rG1MOuwlCwJebxJopm1lPO2uHtguHKaNJmUvONp8ToFl8Stn5jaD5Zf3Z327GQD5I42/SGarntMR9oOaURn/4aGQMbCcn1MJ7lx2GTMGieWUhjZWa6UpLx1WCacA1QTvs5YINw4ppClINbwlFl8hKnheWgJ1RSmKzNSPpk4l7ozvWOvzhB5pCgp622AdZzKvA9GP2iXazAc90ppGykL7WljSOL+diKuHP3O2+tnvCj7JaC6Kx3kr9+hmrjWxLgvanKyPvP07h1tVWZX3DpjmwwOSmnstVDWybZd2JcGcLU0nEX/dEefJJgJMyzpe1XGHdqW0M9vrJcv3OzGuxQnvWMGsp15D7Fbz1DDr4IZrs6QQM71/ggYdg04zzJ8xWs3vbA0Tn4rZeqCTx4pYFVajC0HyLyzVaHulS1FtqgzEG3RGPZwhP5m7+DHNlD+pBcFd0cksh7GWjhRv+UikJWT/qDlKFkrnpyiwhihLJw4x+2NeZ/0P5/U6RxrcFNh/3iltErOG8sOUo5GUgyFgz1wbQ1fYV7an8Msk8w4zwqpPrIkFiKGzRNJzjycodBFcStYVzqRMlyygCSONYCEJcCc+wFzRT4AQNL4QCZVOoRhpgi3HgpEm3GiuNkLVb4fYDqnHbG7mcY4rgEtYT7guzvy3LEkjDOjh2P3ha/OOQbrhFIbo9/xAZYPGHDri9fX47GIwCydcyWOfiJ2p14EXR6Cn2SM+8FeMq0A68b97cC00PZMX511d9YhfPjqmVkzZSqXd5U1hbItckuk54aWzHqZ5Bnp4n11soaOwOyG50lLID7Z80ZLkCjR7GtKaRE2u2+Ft5c3LgbzXUXPzFmUOgcf9sxcJeLtJnTvke3hcTpjGWVBllAcLjeSTO5jMiTGnsMWuUDWYDJkDFPl2TPdengxbhEneCSheMLFMI3AWrdHhBwPHiZxMvj7vH7SiwLESiwasZ6S4siouT787GHIkTSDcrMl0MSTLY7PA+6d/JCUzi3T90z7mGkXRTXjVMwdsx0wNEXzZ4izl8ztPBgTRBbHimSgbgNyD0s0YCvjabJS59oy54zmGa4cJaE1xqluyjgnSSZ7biwL8ZQPKO8Vf+w0uURZMV6M2zjpaXE/YxGZQ7nfb48jeiN5pdPDwCOAh0/A1yu2EpyDcXskYz9d8BnYe3dnadB+am5cW0W0sJZz3g6Ot7cAptgir4VESCfDnFwKWidFM/OE1Y3+1jnfDF/OPCbj5Q2A+xuUvVIvOy9fBkkyl3cFSYN7v7O9ewotxct6mKkmhcTlZxtbFo7PB8uE56dGq+8oqQZ2bUVwMD1UrjYmct1I26TkioyDdYanAcm04YwZ0JOcDSyxWkx+guepzAR7yWw01lqo5yB+e2gS5pxo3kIPs8W/k5oDn4+HQUorfh+c54nfB/MASTWalz5oWkEDattyYfpk2cAsx1hSJBaHv2XIoPqXcut/p+unvSik6Cks91iVxyBRQMFTJ5mTSsDH0UWyRGmZkYWS9Ael4f7+Pe2afjCeyLQYZ+XAqNkcLIfWKqoNmYNjRJenFo1j3gOuWVJFRoSnTocpi5wETVfOFcGl8z5YacFMTFusHxBw0JNjPqhpMBfcxwFDWZZ4Y0YnXwtSlZohWSZvl4CDeGjoF8KYr6TlnEeMADsRTMMJohdsdro2ToUhC+YiScWv5kUgAAAgAElEQVQe47CJM8ZiSHAIigrzOJkY16dGH0bVHKEuAkucdcZr4m749IiYOwZ520lNEEqAWyDcqAnWGMx+oHkx7Y67sM5EMkGb4R+fuLaN67tnbp/vLBb5mknqjNtibQf9NLJ2TonGnF6fGCvyK8BgCtv7C9f0jGoDT0jaeH3pzFdDeguhliQkBXgnewGZzBmIuGwgTI414vW1OH9tWkgeNvV+vnE+VKonDzVoir8lpwDI9j2e1ixGuzTQx7TIHslkHDF5moNiKcJoHtmm65Ev+rcxG7+fSvydb6vf61//I17RYIzOq/hCNBPnhUiAvugF90p2Df2BxM6nklm3k+tHRQq0FrCLLStZOilF/NqUiJyX0lgl4ybc1wiuv4LNWFTGCsuyr4lIJqvhUuirQ9IgLEmme0w03MMbbzOUcIX6/7P3Nr+aZVl612+ttfc+5733RkRmdmVVuT9M2chYWIAQSOA5soTEAKZM8AAJIfgH4D+wxIwRQ5sJQwQDJpYlhISAEUjYqOVu22257O6urqrM+Lj3PWd/rMVgncguqlzqsioLOiVOKhSZb9zMvPHGeffZ+1nP83syt3CVhDx6ZhBiVjQCjSfEBMQos4DermmJM5exgPvReRkvoBkpdg3OAucmOffWtF/3KqwmzCG8PfKm9ei82RrL8oYb9/TJH+9PqEI/BHd4/zKZmjfk+5dnCoXzzMmGjkX0DG3dPyxkODE64YKVjwGhRfiE6cSY1LKzpFP39lWH5zwCKwusIHs6+Lw5L88n8Qd/SKyZrIueE4XanPXjHKv23tFQttK5VeOh1SRvz0mMzm0vmB5IC+qrHZUzaVTzpO+D2FvW4okQ+kD3Rfig7hubCCqBaKNtCaFS0hKzRqDRMjpdbuyqmWxdipYtdYWQNFWZYroz94buhXN5lhbFQmthuGD2RFhkka8vPFrGp39CTPyZnMPXrDF8YxeFj2+SmaWjTYQVzqIhxbASWb7hE1SoBiUWx3ph3W4cQxPouhZqBu7MkiJehFzmJthVeaiJebcAv7iLWN58NzMqk9125vGCu/L8/JIAWYM+YfXBHgWiMFlpIfaJ+uIdL+B6VX5ldHdGZjJCBW09nYr9ZPikr855nldzVaePQfiAVTjPyctMY5N5jviqdO6jZxvykUzTg0V7s2MKVl7xhx8W4+wc3XGHo09kM84Jcx35e5id2Z2lFbUty2htoiq4ORL5QTURRmRwrJ8nPmDMnMsfLx84j4EWeOnPjBkc90ET4+iZ9zjfvoAdnO8n1pRWlPn5jZDKww87ZQ7MO6//3Ce89kf6h2foZ2ZHxOjvspJGLKAG7eGJ27degRi9NtpMWI2VJ+Ileydvn73KpKdCNoXek4q1DOOqyysCx5nFMGulVh1KtDx2DApLnkGDJsm7mOeJtCwISoGgoXNQQ4hzAM6xuEA1uSAuhQcT7Bqpxpho+QT04z3yVU3JH1/+9WoM39hF4ePlfjUgXx0GlMaDFPa4Mug6UB+EOxTBu/C0FRznmE6oUkwZ6hRPEKc4oMJy5RwdfOEEsYKqIwGlIURbWHHQkkcNSU1BbguRyjwmEUZfzml3uidC1KpCMcSNBzNCHB2wCE6fnGPmfP1KJ64IdqsEoNOZboy1OF0hAh8LidyqCoqG8v7oWPRL86j0SFFyTodR8SW8eAadtpaeDS2BNsduN1iOrpkCmt8R06/IVO4Nq8Y5NCEqS+nj6nWUgSzLTMG2oSZsdee8H7x59ZpSHFPlfDlYKy4Arl/C48kqmsEoWXz44Qv9dBiL9bohJfB/9GN2LbSmDN7DB2GcPXmJPtg3ZcwX1sgn+/HlnWHOy3lwrkXslVJ22ALzQfnu66+EO5WJhSIrHaohjpwz+yKmYxj3D4N7H3iRfOj0xfOcWCEj5qsyJYtnrQirD+bKMhpwlkgK4pYPNfXAMDoTWUaZkWU/F3otBKxssK7d7v8L1zd+UZBIbJrHvLZsxiK4M6gXC8A968DPvnLuLI6ioOSTuR8w0+8vbJgGmxgzOqsnOciXUqyBbBQzqioxPk4kLGPAGBbKt64+SNsMs0A3I0YKoc0NmVAdaslM//Q8rqQzc6FFkQX3mZbmweBZR+oFlcxMLGGtBJMSBa0CJN9xhqNrMULpnnV0uFxBJOeYd9ZMpsQxBt2DKcoYaWB6ef+SarhPjufOejtZP7rTT3IRmovzJceh1oxWA+POZgkOGT5QaRzvBi8/OljjTlEY82DcB3MqtjWkJqMiYhFLWBLctPAy7hz9pG2JNRv9PXVX+NavUaux/uEPKLEgjCpCuBJmBOkf0K60vTF7nvVDHGsb1YxFx4E6c2Rb1dCWpb2yNAE5AkS2S60SeMlE68sMbg+NW92Zc+F1UarxtFeapvN0+kgz2MpwU7GgNvsKCb8iqc/mBScu7kqOL5dD2daV3Zi504vAnyoqkS7ZCwLzM58D+fqapH6hReECsP5ARP72T7z2mYj8TRH5nevnT6/XRUT+SxH53Qve+q99Ld/pz7lccvumCDOycFV2pVxecV+THWGIsLyyQnGDVZXGTglJEU4/AlryOtdkqnKWxJA5yTHwmMQVfd1twxeUlYwG0fzgx63x/PaF8J7OxJWilLlynwsrWXjy3FNcUouLNN2IajAzoemHMaezyOh2dRj9OluutPeqO106IxYrEmU2fTGqcr9grtPkipQbfjNqyxyGsyCUWjdaTcBrjr8mx5FV8+dw7LMd++wRPSf+45P55WS8e0bnRCJYPSilXhBTJ76YvPv+l/T3J/1lsH44WS8r3YlFGf7RXj05zpn9CwoqN/pytBi7bZR9w0JBdtYMfO+Mp9cwnf79tyiex4OHhpVciMdKTsZ0wUzxee3yVCiyUcuWwbXu+CfXNEiFx1dPRM3dGFZy5Dl6iq/u3J+dreV7HwZWSmYfRr94CQmFKVWhLxRnrMiy3ekUyhWBDpQbroJaZfWF62DbNkRHBq7IiJ5elXdmjwkV/mjWk8xl/NSn9Gv7TP2iO4W/DvzbP/Xafwb8rYj4C8Dfuv4Zktn4F64f/xEJcv2VXYJf3P6EtbrtyIyrcHNiYgwJcGfTiRTlfgyWp1pfL64ALR2QojlXVg/aDG4Y93NdN1yiyqu1jDavA9Fg2EzAiqwsZLkPtsfCXCnwFa9pLGp5bhy+iJIrfx/Qz8Eci5fo6Aq0WnoOfIAq4hNV53kO5hgcMVkhjHVPKtMYjHtyFOec9DHhPtgv27Us56UPIgYcweLAijKYSBmXuxGOlezJJsatVeJlYA9Z3S4a+M2YnxT6PtgeX+F34/jByfjR5P79O/0HJy9fvhCvO/Xznf0To72qnNsAEd5+8Y54C/IycQLzjKj31fOD650+nqF3tML9+RlrWbVnBFttyCcGD4/Yjzsbi+P9e/r7g36+YGZUu4JhZTCOFDk5c7IT5dJb+uLDmaPbYKBRuI+D6YbQ8DVydyWVYy7u714olrvKk0CnYxpZFgN4jK+syIoQ1qhVqXJlcubJXHfGcacWEOt5jFsTM8mCG7+AvJL25iyXXQmqeXhkRrIwI4KQn7U0/6QQ+ctev9CiEBH/E/Djn3r53wX+xvX3fwP4937i9f868vpfgU9+itv4tVwics1jFRdFZWNGcvfrNcgtaiyZrIvE1D3QN7BssEVhUjhJGMiYd1prjBX4rCy94rUWUDOGPWf/yuOuKwi9sbzgYZgEu30k785Efg9jHJeXIpz5fLC8sKYBghajrxORyoc5MMmx2FqLvT0ir2t+7YLzJVhrMktwnJOXtRhRGV2JWIxYHOvk+TzS39Ccc1QYG/e+cvcgyXcQrqPKEtYsjCkc86TqpHenijKmQn2kWrojrbQcIYbz+s2OvnJiE7ZvKfF6g289om8q5fZIK6+/Klqpj8H+uPF8vofHyrw5z+Pk/uMXtlKxqpfharFcOQ6nPL3mfBm01igOW01xz8wySv2dir1RHt9O3gwYxwdkLG6lsL++4f2OHVxN1IpXheYsn6y3AsekPj2AVvb9Des80gOwBosTqQ17uqVx7H6n3CqHCiOc4glDGWPRyga1Il7ZYjIm2X/pE12TWMoKQbThAdYqoEwFX2kZl1KpahgnNSLb0dvOXitz5L0ZDzk5K85XhbM/IzZ+jdcvoyl85yNq7fr529frvwH8o5/4uu9fr33t18eW6SQ5B6Bsmr0FKrmiMgXH2EWQEcgQdquIB8jJ6J7HglkZ6yBYqSVMYcVkjZl8RNnZ9wdOESaTKCnSqSq2Ik1NccdlUuwJOtgDOYpj4VMQbpfif3kCJJD1wO1W2duN+6EMF0SV5ffEuJerNKblONJ6lov4vNPmZM2D/hKskXmDsozwDBi9zGe632k1uN025HCOCHxF0p/c2LeC6MzFipLFNQrnFy9Um2hsSAzO4wMseCiF8wjinbA3MDaiQKwji213wTgos1NrZa7COk5uD5/kk7wG+8ONgfP8gwNGltpue2N7VVgE8zhxmayZwNh5BtPzaMCcfPrwhP/Wn6E9Vva3z2wvk+bOOl9YVXmQRp931vBMgtYbcRpxh1972hFNutZ9PXPGcx6vrCRmrwhTOi8//ECY0LbsvdgBu8pmuh8J3JmDSqLwetswyYUsQcEVK40q4GvAx1CTCzpIwdU7a52EBiPS5CRrXCBicmzrB2W7dgAlpztfV+X8z7t+FULjP+1w8zP7ml+29yGuEBQxrq69K4pcBb/GN+4TQ6hVsyloggyhaKOroJRc7Y0Ep3ohhlDLlh0S5UYNYQ9l3D/Qp19iT/ZQjg/j2tqmJ92XUr1yjpfMCszJ0+uaZ89jXgW1mea01RmibDq4nwetZWOxjmAth24Zpw1HvDKOe87iGbjqlYHIG0TNCe+XUg3Bxrg7jAFhPPvJ8/s7q2Xijy1dcRpOXws7a6Y0xyRkZ34Y7E8ViUGpsG03qhWK5sxfZVHLYPRIs04oUm9fRf1DKqFZ7Kr3E33Y8DhgKVEqt72wP2zMW2f98IVaN+xKjb56vDFeDpjBup/UugGKTKfMvI1GONUK69uPlO84T18G23GifTCe74llD0F1EKaUMPCgLeN5HBw6kKo8bq9QSrpWi9K2DYtGf574w0oatqbpKvF2SlNhj43Sksy0VpK967ijJe85kY9/EFlpF7Y+/iOyhKRXBkhlK4+staiSIrmpIpZcT2QlJsGMEoXpfqV2frVY519mUfjDj8eC6+cfXK9/H/itn/i63wT+yU//y19H70NE4FffIuTOwdQxUTSSyKR6WUQ1K9hLA4+TwBEt+cZ7jgN326kF1pwgngUytWC1J1NxZurR3VkR1KfJ2TvThJumDXjJ4MAoreJT+LJ3rCr7mz2ZAmuywjjJ/35YwUSTZESmK4vDiskcoOqUOlHJwlMXp4+BR08bbE8GhE44pjOO4MP5jJuzgoR+dKj7lsWznqRpLxtaknw9dFDqQjRQ7bgqbsGmKbatfk+QazhzQG7FCrIZtgY2z4SxENxZsDJ89UDyBp4/HEQERQXm4pgLE6W2xjtdvPuDLzm/7JxfDOTtybYbxznwNXh5/w4xR28GrfCgjUdrvGpge8V/rOxPg6fff5d1a7Io2xP1JWi3naYVVcdPp0XhxTq6F6wqL+eRtXsujPcH59tnXj7cUXV2NvwC1/o8UECaskiH54rUFFQrHif3ZTmmvhYCXZ0qjkiwcaP3kUYlekbZMcROZh/5vqgyfV5hu4T2NjWKVup++5iS/lq1g593/TKLwn8P/NXr7/8q8N/9xOv/wTWF+MvA218V0Tkk8wgfe/rwgNpQ1pVIA6mNQmRoRQoSWSdflqDRETViJuzC1zMixnKnSlKFuuf2tklAyRsgga6ACn4KMZ1jjasToVIwXj7kbNqi8iKL6DDuB0UaABpBnWnucYe1FmsGJoPBwIoQcuCxeH9cINhiaBg3NgpwPB+Jryc41kkZDt6xaZzvDqYJr7RiGH71N+CTOe7YPDiPjklHRThO8C70D+BF0droyxGxZEyo0lRpFpiQWPspnONI+Ix0xCqv9gf2x8a27QmQnSdbTYbi89mJFclZiMlWlVdPSn1948MX7zle7nwYg/UOHs7g6CcRwkMtPEZw80kpwsOmPD0Yj4+PtEfj5UfwyV/6LT5H8PuA+wfi5WS+HOyb0mg8lso475T9llmGmQ8RLkv1EsEeN+whuYviAiLYhFL2NMktAT4+dNq1G5iYVQrZUD4m1yTE6SO5G75ObmXPMFZRbrWCJGRlCclzEKNK9o+IFjSc6QFjcSsT1UaVNNB9XaPHn3f9oiPJ/wb4X4C/KCLfF5H/EPhrwF8Rkd8hux/+2vXl/wPw94HfJRui/pOv/bv+eF3b5yoKJMdOrhyETmBNGAdK8HIMVBZHnHx4HtRW6EvxNXMcF4FYdgZwldWqpoLsc8MFoqfTUYvRh6MlkG0wr0owMcdxagm0LcICtNKAKSdbbeic+HBuUjHdiTKZKy3OrRSEjeGVUMfYqK5sWhE/CHXe+smxXuiRZ9N7BPd7lqKeR9BPZ62BIpQBzxEM76g693c9zUj1RpRC2xvdK8fzC+sl6Oboq0ZteoWjSirhZWeMlRwFz1FvlYJb0LbHXMCeg+hOrEX0g3Um5althb0+Me+Lx12SyGQFV2Uux1qDuYjXG7JljuT0wf3dwe3HzisTTINPHp54uj3wuBtPt0KdQaUjy5HPHnn5v/4x+7nzGy8Djuec87OQNVGF/XSONpkvB0vW1ebtnKvT5aDKdVxYk70pcz1nFwTOJCGr3YVKSVr1PPDLwlzIHorKpIbBOIhQWkswcJSchFGE7sJgkrTZxvSBevI5qhlNM+egqtRIpsXH+Hr3BXJpaL/ChaH8Il8UEf/+z/mlf+uf8rUB/Ke/zDf1i14fZ7bdF8UKTqHoI80aroGR/YFL0xcfm2IjRxFrDcrVZeA4Swu2ckuvJcEmU5W50mR0iHAzZVHYLyI0XShW6c8LrRsIuN8ZqxAqbMDSRelBLY0hJ7eHigzhQ2Q7dczFcIeoiAWTRbPKXJNz5vl0TqfZBdrohhD4kUUzvTuHFWRO9qvItGrBPbeyOgSVjfsR3B4rxxxsu7GWcpdFmc6MRnkobBKZ9tOsZH8+B68+uaXdxwST9GGcYuz7jpwHowhtQHstxD3wUdAy6eOg7oUY0M8X6kNlUlh90vZKP3LioG2xPnhOMLyjughR/HXhdOPzD4tHG7TvbOzFcC34l8/EDKpthO6Ms2N/9ttUP+j/uHP7M490X7TWeO4Ob3/I+ylog7FOpO34eeQZfzM4QGwSM0CEGUppN/o8KWaYLF6OnrqTGj4WpSSKzkkCFu6Ase/gq6WvZWWycs6gOyg7HnfQwghHVxKxc6wusIyhiqpf8JWGrJFhCzG4RMZwQVRg/WqOEd9YR6OIfOX5FjFkpc1VrCPMHDGVi283lagN0ZwktGpEbDQ1djFCata1yUathlEwJUdEvii+aAvW9deUjSYwEKZXtlsQkYjvsgAzamSnYZsLF2eO9A283GHYQQlDo7DpA6oF88WMmccMH8xTqDdlxswimLUo58Iix4XNGh98ADsthBsJIV0zGMFXQRs0gS3aKs/nSdWCHGeOACUQ3TPDYJVlJwXhXJnN2Cxr8Wxm2jSsEFvh6dvfw/f9YkA84b/xbea5M3wg+o6tbnnjerAszWSG8UBj2zbgxr7vWNt4NRr21GgaDA9WlKzoo2JlsH2aIFX7w+dcRPozpdpFww7mmYCV7Wacf/Se2/feUJvw6es3PG47+77xan9C9wcmwu12Q3vW6JXHxJ55UbRt+AqaBSqZidgiwb8hja1qlgWtTonKnBOp6US1KGytsFWlhXErwJkTsRUDUcNH54g7DctMhVRuDcoaqAh1M0InHvOqlbvR1x0rjbIypJYCoyIXifxX1UL9jV0UfpJLly6za9XUki1KAaqVFYuXNWllXcxERzmBzjGc4YMWQR8Haw16nAntHDndsFaRloUwDybZHhyRjEERVAalVMbzcdWOKx4ZgJkB05zlaZH+cGRGQTosMz4cz7zMIycBJWjrluShmCzNjH7RoI+VCrhyZQs65xxsZWP1jnfn2Rd9wIosO+VCjPnS5AlGz2xGTEYtlFulRWOOZ+rtCZPOXAYGtdb0gNRU2NXIgJBP9k9+g8d/8d+hfCA5DD4xecXrb73m1jbkMI4OqhP3YMwsQlks7n6n3w/8fEHDkTF4P09mf8kdmqYtWDgwDh5bo2zC7dNCeU4mhk1Lc5NAOQf7GJifBJP9e9/GfviOh8gn9ibOKytUnyw5MId1TI7ndyAn0rLdannw4fnO/X7iKI8PiktnFWcvi3l23IPWGlDollpLWpkbZotxOIKz/KAv57a37A5J3g+2V15Hovg0XoiY9LFQFk1hdliSPRV4EHLQ7IE4T4j1VRmMRDpOga89CPXx+sYuCsBXXEYj8HOBNJRJKRWpLdn5GuxLEC3IaswIpO5wVc6r5KpfSkEUdBlmlX2zfGKG4DPLarsKspKxYGFUDUTJ1OLtxjpK1pu54DKZx0BmngdjDapl0Uz1yhoOD4VWJZ+qVAY5UWhaEVmXvbkQDMLuPI/JmsK0jNUSCytCJ9iGZSuyp3LdZbLcIQbdO46yWUOLUG1nHZOX54PTCy4nHtCudqiQmWfdgO6L6ZkDqPsbPvutv8IP/t7/jjO+QuGtP/p9Pvmz/0Y2ZuvK9mQx8BOVYPgz5+jIPJCSFfHneV6syk7ZjblO/O68f3eyJnzy8MQnu/D06RN2a9z+/Ld4/HCH0WlVLjxbcL5/xsZBMaFulfjQ4UOntsatQT2PtJqfC1lOWEFfgC0bqWtxtj1H01YL4srzh45iMNJBuG/KdGWdB7tmb0ctmaBdPgk3tgIbGxY71ZXVF1KcRxo9FuqRhrgIdoMyQCu0emMSSYJG6XOiNokp9NnpfeZOSeIywf3EA/Ga9H9MT35d1zd3UfCPfgHBTbLmjABZFCl5HqwQI6itZDaiajYGz0jclQT4IqykS7FVbM9phS9lq0ZBWdyZE8CRuJqcLB1zPpRSUqCMduIOjyLYFLxkp4DdICy3xcMXWoMicIvK8xjcamHNzEeIBGskeMNCMnUZly41ncXAR2fkDpctKg8qPL6+8dInQ3JnUSg0AdFJiOdIzBfzeXAfZ8JXWqU+GDKVD31yr47oxvSCa2WZsJV6efo3nr7157A3v87jD347E4mtcQPKcL4Yd9589jm1ZZS8qWHRqLVSouRCe3uCOXh8SPDsOuH+5YneJ+vFGC8dW8F+DL61KZ/cbmxTuE3FahA/PHjcKkXKFYjd0CWUZdw8ick+G/qugwQN5dWE9lB4bMq2PxDHgtcFk7Qrn92Zx6T3TqsJiBHJcbazKKVwvByoJ9FrDue2X1OCqWzFUkgOY64XWB+yTLYqzW4cpGW5acEcCguPyrzl1KNPR0TRkGRE1JqAFwqlf7QtahLL9acCUddG4eseUX5jFwVVBTVMChbzYiEKKhW1RXFNpNlYRDUsYHMnYnELsMjteWiuwMXya6c7vpQjjosREHRP4MqaQpjSrGDVCEnDUS2KyKBcuHAFXJXNC20Kdd0Iy5vjsSYpKtx5ns+IkfARza7LWAtZnSLtKgBZ6JZ+zbUllGOuI/0GppdguejHya1Vmm7Jk5DJQd7chcxsrDBkW4gsdCussghNwlRrBZZyf/5AxKK1jWMNaoEsWS6U7bu8yInql4gO+gK1hij0+5e8efUb2JRc3K5xXf+wiJWkpXMEVgv9+c66n4znF+yzSpdU5DuCe6HcGrddefj0kU0qTRVbyS4I7+xXX0SrSkilPKW+YWm7SNrRCJoqN8sg0q0WpN8Rn1/Zqt09hViFh+2WhidZWMuGMaUxxwLZrqLdRauV8dJppJfFwvFobDXtzNoeCVewCYM0v5FwFlNFpFLcKQJ22eMLDnMikgVFHoXeD7rC7cFokqExV/9jzPtPfHL//0Xhupwr9BQdV0MlM+dTUuldltHiEQLeOWPgAsMnzxHMZfmEtwrk17rkts4JLGoGZgoXU0BAJmdPT8Hojq3BubJ6zH3iJSPLp3uCQi1pzOIdWTVjxxdjL4AWjS1y7NdWpYnTVHBrnPMEoJWNzbesdvPCmsHT9obhixXCjlCsYWUlY9AGRrCiJaBEDUrL9F0RjIZFTZalGWUkyHWsYKwEo4YJfaUDc+CZGDRhf/qc48t/QATs9kST5D+ILO4//H32/fPEmLsTa+b8vXgKvOMdPg+ExXku/Ax0X5Sp2XOhQd0Krx+Ebz1VekzKvsOrCm/vtAJPf/Hb2N/+J4gJpWZkWplsbxohE5jEb37CXEfW3v2jHxP9mc/sxmMxXkvN3d1c9JcT2QU3oCS8Vi13VeqDURdKshvVJsuCRmES2IMxV6UUBalgaewaAT0iq4yjsRjcpyNl0E+hk+AUtcpDL5dRaWX7ueafCaqs86RqZXvaUdmzncodXYWPH1n5k0CNv8T1jV0UIMM5aG67Ihy33FrZSuXdfaKtUiW3Y4YiNPaazU09LhefTJop7tn0M2UxcFZaHVl9sVtcCDBJJqE6XQKzyowB9kRxpdTANOhjYFzwzZXJSVe5RMNkDvg1b1YLTs6L4VgoMqm0r3IdkzwPu2TMufvCObntWT3XAe+GSToOQyM5lXRiLrieTLpWdmIQ3MoOMvC6EO/UmIgL40PHR8HPha2FHoVxKDYLpd04v/hdvCtLB/rwyL49EUNosliiNCYrEg5jprTW6F2pdh3hjoWeHWueLAPNXUTMwHRB8+QQqmRFfCmUz5/gRy/wwdH2huBEWyXeDuyhsL95RAPqDB4/KcSC+fd/hIwgHhp2Ex7Pyo2eDsYFUQ1f5SrruSNlQ70kaNeNc+W5XhHOPvGLOfnHoBOlu2DqLO85AdPz8s6sS6dSmkjW1QnETL/CWuk/6MuREObKirvicL+PnEK1YLsEbaKxrk9qsli4N5MAACAASURBVCf8V+pq/MYuChLxlQd8iaKaH3kRZ9RrfDM2SiRWQwxWCCHO4YtaEoKxjomWQdDZLP/wbC2aZnGphtMM+pKEgmoAQfSZTIDZmcNg3tP3flGf26b0OLGo3EyoD4WYkgnE+0WfZqPHQFajae5a7vcPUArIwZgv2BI2gVKD4MBkEZw81Efm8IShilC3iTAp1VNQ9JJlIs3ZtOKx8LXoTGpJLkTxlmzFplnKUoRxy9IbZ0ErSGugOdoc5QZ9YnXn0YQSFfVFe3rg5o31cjItnZy+anrHfFA01fo5YXx5UG9KK4sigXge31SVN9vGoxoPr99Qbzfc51c9ivz5XyPeFOTf/B7+P/8ersr4UWf79mfcHhtWL4GXSjkXLYT4zUfs9Q27FfbffMWDVl7vBVXDJPMU1Qqt3pDlHGNmPFuDXW54AbdJtT2ZHfZExGLeFwyn2OIYSfP2c6CrJqXJLN2jphQtjPkhiVgVihd0j0vYThSgiuMkcbtEpdjG3jbGx3aokhyGqkn8+LqFxZ++vrGLQgTXLgHUB0tmlq36xh6KR6dc4SifM6vDxCkko3/Ojkqh7sYcO8INj6T/rIvUZAgrOuv6ILIJ5+GIVvZt4+wrx00WWUK7hK08ZWXcLGmLnYPWdkZf1DWTRlzyewkWN2lE5BOombO1pxw/6eWXuG4yX8qb8sB9SvYVatCqoFvubNwdxdnbln0kt8mayX28zzMXGoXNNLWAVtO8YxXrwpRkRpqfhDihXFvnQGu7RBgHm3icjBWYdqQkb1BaQbbF6Iv+IReoZo1yJvZ9/uBgfPmCPt04Pa287gnJGWNgFmy78ub1DRPl9/7gHbWUfK9Kovbk8RHZC/rrv44+K3VrPP6ZNynqtUZ5bOnlKIJ895FdBfWcAlgz5JMHHmc2MFlfV/VfRU9nMWlWcBbHPZugmIqe+XQPd87nD6wjjwCyJVZ/q/oVXCV7P506nRmT+/uDkEVYy98jkvpNr8z+QlVBbaEKYjv93YBilAfN6njNKUv3oIgSl0fBfzZf+LVe39hFAa5AFDkHD7/eNM3swPJC2xfVg1szLHKc5+aoTxqKLehzsV/i31xHPkEki2hNE/raIsdKL6dncEiyEShbgDUnHqRf4j6eWTHZLpisiDB90Pagt0kIvLwc/PjdW2R1+j0BHt3HhUqf1AiKGrdqX2kQmymHF5oVqm/ctspaQot0dGrZWbXiZGdmTGV/ekWlcbNG8YVYGnB2KQgDa3A/X5hbRaOhpWGmnPNIUrWOtOQyGeeJcFIevoXPbPpeY7HOXETKmzf05x8lNm1T/D5ZsyNNmO87/gjbZztFF0UzTiymieUvldZ2Zh+YCLUZ3/vet5M5WQy0sNTgYWPdKvqv//Os3/4B5fnk9V/8DvqwIdc0qH/xjPZAG4RnirYu5eFx4/HNI2jqJudcnGfnw5dveXl7Z3x50o+Zx5sC3mDWRTzkGHd5IuRKhec5QQ3WJGKxR0Ol0Uoeh0ZECp23DcdYCq4ZFFsrxV3kAbSxZEuex9tBFNjrvGDDOWlYZaWYSeo+oUnL+pnrT5AY/lls0d/oRQGyXBbJwFPYYHn+lhK00jKz4OkEe7B6PQ3z/NvlTGxXGCUGsu0ws2VqlyC8IraxTLBSaFJpsgOwY9xaehtELdn8pSQpWXfCOptUVBvnmoklp2Bdefv2C/7oi3fXE2fjQLhtGxpgfrJZUEMQdooCpjiVVgKroE+e1WWtIEV43SpLR1KHTNCy0YqyuTB4gbLYdMuCXS106fQ+cF8ZW34/stmankj4kt2PEjmqFQ3W6Nyff4d4+hazGogx1zNjHTQV6u1bPL/7xzw/px5QH5KWvV1agm6F2goLeHzaKTV7H7Zto8aiVZIH0Xb2xwe+++bpqz4EHrcLGz8zafqjE51C/UvfJTbNbtDbjdmD8kdvkX5ie3INzAyORT/fUiz47Dc/xyLYVAn1jEw/VmITvIBtORbsl6szZuLdawGJkSNJSfjL4Ulhdht4ZN5hhWJFsXbLarkx2GWjWKOqsUtFo1BkZc/EyMyKFOHp1Y6WHN+K5JHBXBnLs7j34wdb8rj1/zhG/Ambh38WDeIbuSiI/HGfnuIQyRLUFcnZH421K6dnC7FrbvkWA/FKbDurLIoJG8LhnV4D74Pp0KxyxmL5mQLfvFNDGHTOqzz1EE9453SIxH6NObNohcBP4YyTc97zximNKoVpk1Lhk9vrTEyuq4nYHUeQYhwYk8Gzn0iBPSkwSK1sLYm/pexsEmmJ10oVzV4JsbRvq+BrYPEqdY4NfBn93tnLzl73HMXOiZsQeyJrmnQsjMfHG02FyR1dgZVg/Ph3eXP7nKqKm9DniXIyMRh3jg9vKQZTs51ra9C/PNkfC1WcuRa1GGudV7OXs0bmO3of/NqbT6gPyj/84TNWChKObC15hb/2Cmk3fNWEnu0V/7QhDxvsxpLkJrQfH8RWEmsnDlHAn5Gzs71uFKnsgJAUrSZZKEyZmCj3sbisqFRrhHfOBWhWwNV645TOmhlge4nOfRrEhhVPjWRdfhk15NYy77BOjtFxTepVoPjonB9Ofv23PufpM8vuTo2cgFgwfGMG7ErG7Eld6Ksd8q/o4/sLBaL+dF4BXIUhMrFaiBHYrtn54M5mGxaBzyCYqEly78ag+gZyMjXLTtrw7Deoe5qd1iTEWJFJvljZpqxMli/KXunngVWwVZim2FRan/SqVKvc1LLNGWXFiXme0d+8/pS9ppJcTMAVE64zdqEZUCobgO+c48j8/rgj06BWypysYsyp6J49k7eiuFTGNZ5UGkUne7uhCu2mlNLwEcw42eqOFbAKZ88OjdHvSDF0LUInTTY6sC3l5Yvfx/7h/8baKhwHRY3uwdZO7v/k77A0QJPIXMS4v3+B1waW7yNkkclaTpu5w5sFxgGP1RjrhcfX3+Xzb2/pKLyehNIKUIkH0LYT0pDf+T4aTkwDfY18//eIaowS6K1SzHLROga+GXY0YFH9pGhQdeODD2J2WjHuvTDHM+qVbrBIOrYSsIQ+g02NLs/cyi0FP078bIy60eeR3hhRqkYakedCr/+XaupLx32xDggObk87VnZ++OVbtiKsqqhoFtT6oHIAzjnTI0Fo4svjGsd/tDt/zdc3cqcQEQmr85ReikNxT/hnd4asazcRCH5VeEtWgK1cdad0pqSwdGsbTmFrDYmrxWgBPYGmL2sxZ3L78atObjiP7UbMyqlp013V0rnoC5cDLCcjhUVMRbFsg64bm+1Ua9RiFPKp5nWwFWMuv6jRzuJEm/H0sOURoJRUsW+ViOBxUwpGqY90rWwPW4a7hmG3O3pLHYHIGXcrxm2v3LadtQZoPqlNFltzrGXzs5lQbM9xqBhLUpg7/+DvZPafTkQGvtaxuL+85TwmuizxbmOgFlTPIldzpe3BIEeR8ghFnIeHHdnSAPbq0ydut40na/nEjcSprdXBJzw8gRryndfwz30H/u5b5B+cxP/4f6KfPKB/8J6QoPyFzyEKhYo2Sbtyv4MNROBhK1iBQpbv9vuihINtiAXmQkWxNXGyBVqV9CpEFsm6d+YplAo+DoLBiCR+py4VyA7hDZ/OcR/0Hx+s9y+EdUopRIC2k80AFJOJurEbGCPj1M2YmvF/uQpm/zSTl/4/vyKygGWqsxyaKk7wgDJXegUQoxTlVGi2pbLuSc0tJOT1OA7GgnmJhYsk4MyVyvAnbSNkYV6YAWtVVJzT03ugfoWnwglZuaOQLAIJUbob1YTahKbG01agwPKDcxxMuaLTPbsX5gpU90SmG4gnREZYiE6qF3woD5titXGrifi6bSWLakgVu65HiMJte6BoevXdnekjOyeiJkexByGW5+eRqHezmu/xyh1NunyyC1M/vM1K+/Oek5o5vhJ63SSLWe6dW9WE4MQEmXhf6IA5FBlpL3eHmxSoO62lb6A0g0JizG8Neb/wxxtxuSdRg3/5N1mPBedE//K/gKty/OCH8PBA+/bnsIG1Gxqafz4G0Qsldqpd6j6OuLCZssgjj0+IknV37epiuB/9qhK4CodQlAql0iVQS7G7mlEBr4nR01G5n4t3P3zmfNfZrFBryXthV15//oiVHVwolnRvVc2HkCuUjaAgM6dpIkFR/VNNXvpTcVmkQBUqzMh5+LEysKIYHkfqBExgZT6+OEsqL/3EQziH5hkuHKk35sxxoashsegz/x9WMoRDDIQCnppGrCQ/I4E0xepEp3PEYs5OsZVmJ1+cfjCuWrEww6hUV8IibwCUh70CPT+YKyGfYgceglrivkUDtGCxOKWgtbIkI8U6N9pmWKvYyElJXB/0tpXLkANWBn0GpcB+y2CY7SmcrrUI70mtCrv4i1kNxzlYmjHo+pALrMdMO3YsxruD9qYQJaEv+1aJEDyyPbuoI6QoWmowY7FpnpFvbz7lfLnDeSbyzQT5/IlQgSJE2fE+QAX99An9/IGQHd124jzwfcvYsgi+BLlPSgiyHFt3Xu3K4+2GWYHeCdEskCEYL/NyDkIfLywRZKRnpYoSY+EzsJgZVMKz1CWSRrUk4asxjfDC+y9PxhcfKK0mfeq1sn32xKtXj7TWiHNQL+r1QnA1egwaXITu4Jgn95GuR1/6pwPH9nOKYP4LEfntq+zlvxWRT67XvycidxH5P64f/9Wv8pvPb8Zyzu/ZWDQjxashlgWiUTCd2SIlwIW+VF9srVGbsDdBQyk+cgYfTp4/J30VRpxESwAJbsko8A4iFEnCbtqsFekwVtBJfFqrOWeWCaHGsRK/1cTo3Wm2My2IcGoBijLcMXeaJQ6897QIPLQ9n74XjLSUgtSNWoRSLInD4ki5mrDPnjDS7REW9NU57g4VIoRt29msZYFtn2zN8sb3TB2Wmsj6qAtbeX4dqyebMNJCrutKUZox7/mk802YI+lVRY0elaI1x6xm3PbGdvMsfNHC7fbAZgpzsA6wmgGgCEA0vRTbDaGBNXQ4vJwpNqsjtuD3v4DXr2jf/QQtCdyJMxd2YiY5ewSrkmWzkeh+n4PTT2I5peV7MUh8mhWlbEnrXlrTgCXpbFWF6cJSx6Ritw2RLHG5vzs4fpAC7bY1SinUT2+UreRuUgWJxVQQ7WwlYELRxtOWkzJ3eCiZlZAJU3Ncu37FCwL8YjuFv87PFsH8TeBfioh/Bfi7wH/+E7/29yLiX71+/Mdfz7f586+s5gbEWQrRF97sOr87SzRr2VyoWgk3okD1xeqTl3vOjcMSRsqATSsqAvtG24Txf7P3NrGWpVea1rPW97P3OfdGREY6bZdxVanogmpBTVqi1EIMEBIDEEIwZYbEoMWAOUIggZgjZiAVghkSDFpi1AMYMOgBLQSiUNMluqnq7irb6Uw7MyPu39l7fz9rMVg7XabL3VVtO4s2YkuW7OsbN05EnPPtb6/vfZ/nRcgzn+fmk6KJWjLeLYS0Iox+oIVz2BaY9mYhW4WoydpwrnklJ8G6kavQEhRfGJ5ZNFOTIAKWLhw2SKcqLOeVKTH4qrKidQkYR4IiioxA2l/yFVUhzc6cFmg62+PNq8EStHYix85Tk5GUdAkngUhiTKc/HBRmRMfH2cyziXQnL3d0O+9m4izlSqlQLmDPM4JMWX6Ur1i9sGah4tSSSHPEwlaMcbSA7KqcXsqddMSihh24zdC2pXh8glg8OA7k1RIDNzXm80b64J58f+Gz/+ljPvsrv8vL734PuawMX8kzehiKcP/BG1YFPFPrAp4YkTVGMJZJ7Lo8x39qwYwYuLLgQ9jfT9r7G+N5YIezPTZe3h3sn74wb4NyV5BVWd4u5HulqpOWjGnC3OM41lJAgIPbypgHx66n+AVeenwf5yNQ3M5+wqU/3xnDn7go/CQRjLv/9+7+5ejzrxHE5v9XrggIpTA0S3xgxZWEM9yiO+Aeq2yPaPKKhOQ1xZtsCeUOcyimwLzRvcaHYcAozlC4rDUeHUQie6CZRePDigjigjrYTciWMR1oFmrNuBVyUWo2epsMyyws6DyY1hHiTecCeSZup9QlaVSmvUMJwQJadqof3KdMygWVdFa+hba3CFBrZUkSmXvLJAc1iTf56LQ24u+KgRw7+1NjuJGV8DpeKtutcy3RoPRSuaZ78rWSUifPGkyIGa7GXC4sI7G8KtQ8IVUwp6zCtIMK3NdCNie5oZ5olpCcsHHQXnaul0zqt1ikp0WD9dZCytNu2Dlg8+sKukC60A5Hv/cCT4P6y9/Cd7DnG6U3bt/7gi/+x9/n+Xe+C5shW8bev6c937iviYSTbZKmozm6Cb0nkEKaZ5tSE/sRx937Y+PhD9/x+N333G4bx3PHtzh2PX5wwEsjpwtZI9b+wdsropnXtSIWgJ28wlri1GxqvB/RmGFwO28AzAgqsUd79PaCeEFMztLUVzto/HkcSf5bwH/7Y//7HxeR/w14BP4Dd/+rP+kXichfIrRyP9NlZlEF9oKbMK2h0hle0WakYiH4LJk8B7jhuTD2A9WKe6dJZUkx+DqmoXWBOclFuT013tyvGJMxjJQumBmrFoQXDu5YS8LaQVaDllmuCx1D2o4VIaEsMng5HEkZSY4cB55XjgKJ6Fi0NmOLKcaaJkjBtKAXIDkpcslUETwlukzybMyiQZn2ULBLqpBAszLGpEhGJdPMqApLrhwGcw4kKeu8oHfxHN43o+pk9EHyTEfxsYc5erwwX2C5rMxxkD9YkQm13GPbjr++Y24vuDqSOpoqSYRlBWsTz1GjSLnSXXBvqKb44FHoDj3fsd527PYEXwjpn/g23gdSVmQ00IRrgW9c8HdK/eQR//gL5O1b+O4fkFri+hu/htPIH3+X49MH5ky8/5+/T/7oSvq1t+hFgtykhZc5GRoR5SSOpglaebl15t7pYzA2Yx4TE0cXJS/hD51LZozGVKFcQzeQX4NqdCWOo7MumTYa5IyMyewTavQdaB0p9SQpLeQL9H7QW8wwRC64dfoRaHivjo6Cz/H/zCr9nAlMP9OiICL/PjCA//r80veBX3X3z0XknwH+OxH5TXd//Ht/rbv/NvDb58/5qf9UIrFddwuqbm/gZBCBBW4WAFU7OrMkGEqeW6y6c5BrRh3GTKEiZ8DIdCuMw7jeLViHAydVhel0H3E6wAV1w8yRM3WYqqB08pwMoqRDXWl9o6hF56JkZFUowsViuz68UWuk3gaNnJaIEXfBMywGLhkbW6jlRVgkM70DsVA0elRtfeBkfFbED2Z1WIEDyqXCaGFJHkYtytBnlv0Oz2FKck2IDEqKQVrSwOnbrpiOMzUZJSJJK2Pf4iRHwO9WxkvIXEaGdCdcRbF6wRhohcMmo8VQuHvHXJECx8uN9nyhXFe0CpouEW5yRzgwCjpvyJeY+FdXsAyPT8gvr/hf/xx7c0f5duLlr/8hH/yFb3H9ja/z9PF7/A+eaM87/X9/z3Y36BfH2gxZbY8PfS5Gu8GwjXEY3G5sx0Fd7sjLSvGO3ytpuaBjBrzGonnqHvbu7BnzCKNlZgwkNWNjD/h/EbIJPjP1mvBhlLICFrOT/IqtvdCPEfmZkiPv4JGOnKZfKbQVfoZFQUT+TeBfBf7Fk+CMux/Acf73/1VEfh/4DeCnskD9qS4LwQgaMtY+A33lLtgx0erQQVdBRTE7OFJCdcIowQo8675DEnbEzmLJB7PEifAoC7nHdFzSJBPGocT5rH126GmDWlK4Kz1sx1kqY9/Ji+BeueZJn5Gd4DCGT6jhJkie8FNOYqpxTt+dehfUnm5C1gXXFK+nDGpe2I9nLGeu1yu9BXWpM9EyyTPFz9sMujGkhy49KzWdQ9p8oe9O1UQ5j74GShPhGvgasit5MZLmU5ajHI8HS0rkIsg1oC9JFa4e0pvZ6Xvi3Wysb7+BWGfOFKcWs7PboDWhXi4UdW7bwfc+ecef/9YbJCW8dJJVvHfELrD2k0UWenjdJ3z9DUMaOiaaC/L0wGW+4/NPH+D4CLfO/bcWyjcXnt4fPP3uO45H4/bJjozBQNDesOl0gdE701JE0O9eUbSQ1gyrUJfXmHfEB7Im9s251FDgzalghqkzx0RcSFoYMnGPOPmckyXXeP2rAha1cZ+oRYbFZCevmcMUo2OitKcOhHlLxYLh+JV9oH7KI0kR+ZeBfxf419z99mNf/7qchXMR+XOEefpv/zxe6E+8cgRGjRlKdBdm79x0x3Kk94Qaq+uA2Q+8LqQZrULyDIKRxBa23xrrdaGUPZiEnugkXsYzpa7nkeJAPDGt02diuLFh5ALkyXELSlNKhWu958RC05sgfsTk+hTeGkJZgnjkE7o3tgGcXARVpawR2Z0miNo5cGsRo82hErsrhTJjEGVnzXoRI42IEqs5eQZ6zsx+FKoaYyAez9RpzpDpYsgIyW12iSGXF54fe3gHuqC3RPI4aUkL5EWREu4KhiIPA/vsBT8S2SvlsjL6dkJRJmPO8HASj2vTJ00KPV94et74zuMWw9qh0Btya7hvuFkEBzD02EEVe/9I/ugj5LNn5Jc+iA7KH36fSqRYJQMSs4vL25WP/rmvcf2l12iJ7kASQUo+oazGelfJrxN3H11YXytvf+UN5etX1ssVk+AxzN5JLixVGHPn6OPUy9VzPqSsl+UE9CRMOmMGzu3LWVe2HZPA6w2Emf3MeCi9TVIOwZBOp489YEIWC8JX7ZL8E3cKpwjmXwA+EpHvAv8hcdqwAP/DWdL4a+dJwz8P/MciEqEA+Lfd/e+1Vf/8Los7tUf4mCQgvRM0hVB3V1EoKdj5lmDssQqbUuqA6fiolDGp9ws2YepCSR4Z+C68LheQQXLwXAKzLh5tRCmsPrApfOvtPZ/0jeaG6UJ1R7SyZPAaH7Zugkinpgvt5QUvNZR1OZEmdDGyFkx3NBfaLf4kS40cQ9ivMyLQxqDmeO5MOeO3jmqOwarGsZmYRspSjVrWOM3QwsKIib9lzBLUgzrCXeCSWKozfUAa+EjUpYQcZRmkUlA/uH9VSGsh18IcTtmMoaDryv2aoXfmc+PQyTITQzUWGZkhX6mZsiXGdnC5X6jaed8bXzwe/OpHH+J7Q3XB9h1NGewWujuCmCTd0L/zKf7mHv7md/FLx19d0W5crgnfGrNAuq7QB7kmTC+8+o0r1IP94UZuwiUb9EKWe0o2fEmUmjgehX48QRscxNCWFDSrOdtJTiqIbyGJSUonKFytNZJEeAsrWAl6dU+J5DtLumP4YMmJg0l3oTh4Gkw1JN9RVEMIvN8CN0diePvKPk5fXn/iovD3EcH8l3+f7/3LwF/+WV/Un/oyD4CgGOIVEaXvTwx+7byjwDCDHOfpjcGlLMwZeffpBW0duWv0nCnnHb8ZBM5i8mW/4uhGlYKMxjFLeBDOrZxoOCg+f9xxq2gf6ApjGrVcsFxJtuMMljzRXjj6zuXNhb41aikMG2ipHLcRPY1co8F5Faon9uGU3Bn7itXJWqEdzsDIUoJqfQkpyWECFHQ6lpTmG698xTByBjegVJ6m88YaOcd8JLWJLqBVTyJQJqsgi5LnREtiSOxYrsuV4/GgPjgbG3ontJJJtZNmRzHKRxdSWVmHwdOkHY3b0430eqFcFC+FvTXWmnl82Jj3HzLHjbVnxifPwVL4Youp1UcNvnZFlglrDYfjX/09ZDb8b32OfiDwW7+O7A7/x/dBNz7/ne/w9p/9dcaMHZQTxSmy8tRidzbLBBK6OguOxWSK/eFg7o5I1Ofvp2AaIOCsFfMIV6lCtYVBpnjAf5akdHdEO2ZCkcFICZVEZWJp5cYkkWlATjWCciJR+GqNfjzj2pjrgm+3wMDZV01SiOsXPtEIkE4RSpIM0iPBl/J5HiyY9wiKUBA3tDuXNaE+sKzIFBYZuEiUT0TpvWCeIMXxXRXjaFs8N+cRWjkLHJqrkFSZLuQyGYsgfiLXVcl2rv41jMk3m0gq0XEwpVlnySVOA/JkeJR+FkmojcgE6GQ2sNRxg7EbSVcqYbGSKriUiF2LIEkoOaHSWSmRyc8ZO5xrzTjGq+zkZQ2t2kXIr4M2lUURzUFktiBC5UswA+9rYlW4vTTSzejloN5BKoW6SByx2UDWhWMfvDw8MGzCh5m7b32N19+44+3lHnlv2KfP3G+DN1J59eLYH3zM9blzeXjAPv0C//Qddtzgw8zcb/B3P0een2Df8acn5ONPwSfS3jGzQV5i5/dLb0h3b+hbnHr0LYJmcwgjJ3YZmHR8BZkRBgsP6MQt6tGiC2kV3OIoutfMxJFsrGucXAgDiqAJlqL0dMpqLfoi4dsw8uVCmSdZWxM2IjjVZDBwkFOQLBaODQYzW/A/3On9IR4bJFqYX/X1C9ySjMHWl71zPBBietKQ2lm8cY+7+fQvjcoLl0vi6Hsk0dYBs0YIaoxIKWrGdeBqVF0wh35M1kuNKqMb0xs1BedPJQc2bDj9ZlyuS6QGY7MevzdBdA7oR6ZZ0I6LDbJcce8kSdFfeBiUonHSZMLL3vngldJVSfn0FSyF/WiYCjNruCTUSK4sFV62FBzFZUXtQETYRyeNhqUFbkpdM2YHLh5zheUV/fmZno2lgBdlSQlThTyhDXRZGZa5bs/0RWMXJhY2bzN6d9QK6ewZHulCcui3wa1/QVkquhbK5S2elf026HcXZrtBz5Syom9X7MO7GMJZw/O57F8VfrjBi2EvnfTwjL29Q8RI/9S3YWu4ONwV/Kp4s9DM1wRdmcmYR2dHubWOmTLEGa2RXFmvmX0Pijd30D5vaF6w3UE7KpXiCb8dJBXGyVh07Yx+qtxE0LqSujBap9YP8PmCl4VFnL03hhaWolx74lDIa7g1Ja+Mnpl9MF2xLFRZUOtxsiOCffl+/wqvX+idgvsfDV1EItloNIaFFjx7IN+dQpLEXb5EUzLHvCDlAShTJ1Msvi6G9UHKE5vKtht9NA4xZoqsguQYUs3ZWdzw2bi1DTSRq7DrweyTIoVCZ1j4IqYPWvPgGwAAIABJREFU1Iw+B8UOJDtyv2LSmePUEZrD3R5TZ5/kWrhfItG4DPAjcv9zhsquCKwCnc6iUHJmOuTFQSdpHyBLNBo1ka6Z9v4gy+A4dkYnlHXdmL0hHj0REaFI1KkzoD0H+XoaPNxgGn6BojMW3e2IX1smVhIHAVRZqqI5dhDX+zt8Gs0Ojnyj+421DEo9uJRYwFs+IvXXlf7xe/jc4A8e4PFA3x9QHP+/PiV9+o45NnTfAjhjGTcLBkN2rDgsF25fPAX6LcVdnevK/ukzopXiMdBbcybXeJxQVTQn7KlBqpg3hvaQ7XrcyamVKRlPAXGdFvxFcyfnSI1ODNVMn++DUnXc2G1Qc6YsirWB18rq+bSNh3NC7SChbH0wbeOwAbMF7s+jmg7wI37sV3D9Qi8KX1an3WMIl9xwM9psjL2SVc9hWZBrGjC90BtoudBVaGNgrowBcwtakOSEz4xKRIdzVvSScI/gTBtCNUHTykDpAm/KSppxxGUukFr83h7SDyOTNqFLQUqwB2WEl7DvRsornuIR6Kqv6PtBltjWrpcci9+6BjRmGJ4CF6+nTHbVjJuSzBgzjjbNRrx5Zpiob4dRPRgINp1ERrwjsoau/piUJNRyoUiIc9QmKTmXS2FZVtLjQe2NeS+sUkklwK6aDZmDScJVabcNL6Bu9D5JGq3QtKxwBObcAUtCf3He/90vsKeB/uBgfvKOp8cN//qCfaDoR1fs6ox9YH/jO8gnX2CvB+kbb+H1G0QrdgykCz4AV8qvvkZfFcYPT3pyqsxSmEfjk+OAbuhY0VIZGunGoXEqIzrJdxJchpxxmZSsZDMKyjgcbCdr0J6XVLHD8RkLm48zO5NjDbXZKLqiXjGLU5F6lu1cFyw5Wwv7mHti+Jet03vyonSNUzaRYH8AP9LIfRXXL/aiQBxbi0i8IUVBHW9CTzuHCJN4lpwmSO8wB7mWELBaQXMBH9QM9a5iRJhp2sa3X69cF6Vq4s4v2GOj5PgHPUYKSrQIZZ5bZ9GgQIvQ/RoEHYmFS/3A1VlPsUwfML1yGzslHbhGV8/M8GSU1fHbJIliZPqTQdfA0auhpqQaeQCTTjtV5d0V9RCLLOtrugglnfqzAki4MYc4g4Ms10CuJdDi1GuiOydGHNw7aGL74Ub/zhc4A3uTudSCLgUdob2fXkklWoVLlVDzWfRFlrKyjSiCdYyNzrYd9F2Qmbh99oy9SnBv6DcW+tfuuX7jFV2XAM+kif6ww/6E/PkPkb/4beT1Av/kBzA3vBTssePbDZlG76Hpu/tz36RvO74HKHbsB8fLpB3gi3Bki8VRQ6B7t4ZVS03xm7OIU0rhqhWOznJ3ZXio4VXWeHzNxuFnOzUHwq07qBrWG8tyh+kF12jSalakl3PepQFuJbEugmQi22EKJ/adCWIvCHG0LEQs+qvySML/BxaFsIDbjwAUPo22B6Bi2AypaEqYOPP0NChhCMozPrCpRBlqKYnZD1q/4XPl46eN3ju31OgM9CqMI4QgS4UsThdBs7LbiEry3GEkqm5Y77z0g6NN+tFYSvA1bTgiRwBgzEj1jtkDEjMYtKOTTNEV9oeD1I1X1ZDUSL6yi1FUmEO51sL9eqGqwghrMeoBnmkH3pRDF17fVS4JzAapKMMy0hPTWsxTpMcbLidGP7Ak56R+IT1GhXp+7UK6u1AXwZNFnr+Eg3HxDuPUmk0oS45i1RS6zig3TWJQlhdUFkwHL++ekTtl4gwyxybsYjzcNob3SKy2imHo63voAl2wW49HvVcX9mG8vNx4eGin9SuKcPI29H/2fmOURLpeeHjZ2PMRKUp1/FT12ZmGbcfkMCgnVHYMw1JF7wp924NtodFjMS14UrQ34s4Qd/I1ldADlEpyp6aBqNL7wRiTRMdTqAfmnIDhszJcyLni/cwyXAqWSvybnQKfHxUAv8LrF35R+HJb5f6lZ8/ZHm9hjfZ4RpyjIaIRdkEYw9BkSBk4HZ2CjcZ2xLNj1K1bTM1zoRxxjpez4jTGFnMCujF9kO1s+WWotbKUTGvx6LJqZpWD5E6bA3GjCJitqFQ8x+5AR6co4I1UMsMHMpVXbzJZ4ZDEMY/IXmiKP4PCfkzwzFRYaj4lpFG97amgclBb57bN+LtAuKREGg1KvN5UhKIXkjXUhVd3F7qFR4NPD3oDFkfoZP2SUyHUBDLjJKaqIiUAJG0MxvOB6OSQHemR8U85o2lQVHBJtBfh8uE9ulau91fmZnQdvNwa9rKTSuVmQvsbH+Ov9QSXhjQ3kWA3xhRs37k9GY+fPnGbnTEiN8KJvj/2nf6dz+mt8dQdmWHTTkm5LhcKmTE7Ix/Uu5VMYhdjycGVmByMLmSN1mgS45iDbBEnz6vCVJbz37/Tzg7DPJut8drX+wVJhVygUClqJDVUz5KZFvrtxu2lYxY3tlyiMTo15l/xpv//B43/wCv8e6cxJwVW3Y/OBDzHNjlpwVEYzpJncBZUEVaqrmF+ShVsMMZgyQUjU70GJDU5SzLMouK6XMJDebyE8v6wFgO+ISyy0NNOLgkXpY+dXMJXmHwwZtCB72tGjo2sJUQmdWEexn1aqUUp9UrKgQQ/no2ZYqs9bVCsUHOAS/ZhtOcb1g6mjyA6a+FSMtUido1EErA3ZZHJ3gbrkiMQZTt5xmyiDefYBttxcGkTvug8l0lahXq3IBZb3ABoC8c+GDMh5jSzaHBmJ2XhSNC8YPMCEh+ubpOnx8bYBse793zw9UsMU8M+SXoTLs5dlXfPjefuHJ89Ib98h0nhkBYm8ecRnMM+aO/f0Y5Bs87TdsDIkUfAmZ5Jv/ya5+/f8B3ePUw+f9yjLj56kK104NqoNaN7xnqc/IQ8yCJWfCLtbaaA2C7R9JxzMkfsDJVEH8Y+OirhFnExxpk8VRLWth/tyDyPsKTXxJiTScGncexQXxVmXugOW+9kT7iVE0P4jwZP4R/pyyecTGdsEgOesaElUWrFPFBcfTbG0EixWLS+nbirIMFeHG2GwakPhIgnuwbOjTERory0yhLUnXulHB0dOQZV3SPU0jLTIwAk4szh2EujkoO6rM7eBnopbPNg+iSpM/adlwltGr0XdL2gkkl3kKZTZuJuEZjGfgwGnTXH45N5+DCVRqGhTNJdxVosSJecYk7hznw+ghzllZwrenLHaqrURciH4HPiryxi1kvCZ8SnkzjugU73dA5m1ai10N0oopgn6rqibWLtYHscPN52/MUoJaOL8vobb2kzgqbuHn2CPbBw+8POQ9v4/Adf4J89c3td6SNmQCMlhnT2Y2PIwHpAVFw0dnC5044bXeJcf/n1D0Eq3ScPT1/QZ2PfOuTK5ZoYZiyXV/iJYZMy2ceID/FM5FIZrrRjY567urmD1gtICfGrOF07hpLD7IJpptY13pQTLDnuC4VCzgtZlJIWhExJlayOtEG69/N4M+ZCkwW3ETapr5jN+OX1C78ofHkFiyLkHN5vdPfgE2iJN7FoJAXreQTpYOrklPBp7K0jslAylJQpIiQK5o1jKk0zzZx1WfB5i+9ByfcrMwvPDy0wbFNRn4g3lMhS6Oqsrxb2lx2zFsDOZMzTUrTkEtoxKnkb5EdjbY/Mh2f2px3bgXc3ys04PjNMBJqx5NgirxflvsTi5QrmOdgDItRaqLWQSiKbMZtTKvjYoA38eKHMQXfjoqDvB+NoWAkAackL7o5mQYazmQFOzY4Q8JiUEpSEmLKdu63jcWPiNHa4z6y5wCJcLwtJlcMG6sb93YUlQSlhPppzMqdz2xqfffrAw4cLu0PXmLnM1mnTSDkzhtH3R0gLZckcu9FvgyaKW2KmxFTj+Gbhk99/z+1Ti5OooqzLDNajGJrCIjXN0JmoOeF9J6WE9JC/rss9Yg1bFb3oGT3uZxLRo6dximC7gzLRFGQtz8QOpKbYsUpHvNJ9Px/FGr01WFf2LYaVIOS64P2POKBGtHF/Ik/h58hY+MVfFM5jIQNogsvCHBs2M9sRmrY5nL1NyIneFNuC9ix7ojfDjk7OV7xMBomUOtM6Ko51pYwJ46B6nMk3avy+eoaWxFhfXZBjIAcYC1nDLCRWOG6DOR33VwwHfCBaOSSxrJmXp8boDm8q8yKUt4W+ZtIlUe5XXDv1GwtjKchl0B83FhPksfN8OCmv6BqAz6KJvNSYjI8bR94ZXaJ6m0ocB2o6AaSDvNyTlgtrMmQLmarcryhGlvA5eFd0GMuycNWK5MSwhF4SOgtgSL6Ql1doH4x9o5QFLZnrdQVPrPfXYCGMfvYCMp3O4+Nz/DzxwMoRmrbn5sgPG084LxOGJDY39jFoyRg2efruC2oLYxG2W0f2TksdT5mZGkMSc6nYN6+8TEOls6z3kW+ZmVxXdK04iZSFmhxdKxVYpTJnZ5Me0XQFTwHGPTYCLrvcB8syR0161YxKoqRCmw2fxpgbYhmtgitcawwku4Rpe/QNmQOtCzYnWRKGBQVMnTbixgGcfEb7iTuGn4E+8MeuX+hEI0TWfPgk54JgFFWmGWO84PMKBY5jclkrKUHWTrpfkTE4qlERRimYHVSJ/Po4sexzdnLu1JTpVGRASTH/nZJRMnhDWAJwerkgPpF9YBozCORGyktM/LfbCTl1at/52t09j+93JAvX+wvdZyxuo4clCKGW4A6oC1mfuaxX5p3w7oed671y/7Izu9JV0LUyDsjakEsCDwZAXgSlYJ7ZZOetLCSPrXtpij83+tiwvFI+DK1ASitpAcYa7MGUo3nqgkgcxXkPmIqngNZ0n5hcWO6MoYb0CSVRbEJ3lpKjhp2c7biheaUUME0UhJlgSiHJgT5Mbq/g8dP3wRG4K1wudywiaC/Mzw7yr9xz/M7HoJn+xc6WOq4rQ4L8pJfE48PBJ+82Hq9C+u47RjLq/RoLUdspRRmbQQqR0NiOALDOjowVmY0uRGXelEu6MuZO94KOjew50Hu+YV9CY0YU09wSulRuvZMJgne4QHOY0XejloyuF0wTfnS2o3MjovueKi/PB1O+rPlEe9X44y1J/zkeSvzC7xTiDXyy7qbTAfVOe0jMdKHPyKFrhZlBcwwWqZniBjlhXigl6tAzx6kFqhQXnELDsLZHqoyQfY5+o3vUgU07h3WGNUwqaXWO/UbWSFFmnL3v1OJogosukDPHw41O5CbasSOzkcy4zckqAjkxmzM0tubX+ysvbmCJUjrrpVA+XBmXhFeD/YX+9IS83ylfAM+N9aGzPHS2Hzyij0/c24RtIr1j/WCbnf0O9OsX5t0krxdImabGJFHvMstawAbH0Wl24G3Qbx3tgXV/edrp5vSjMa0xLTYiXoR9b/Fo0AezDzgabcSJSVkXxoxHAvEZWIw0aS8bZThHzezN+OHjjqUVTQsPOE2dsRwxeBvgdwu3zx8RC/aB1RW/Vm5z8oOXzrs5Ob75ik0S6cVwTczkzBzzm3JZKKVSl4XLmztUlbVULHdKKfGJtDhabbMhOVGWSrs19CL4MVHJ9DYhT1yEUoPs3ftEpKN1YofxMjr0DRisbwojx2zGZsB4zUMMfPikJ+XhfSRhReQrF8t+ef3C7xTgzCmkCC4lMwZOf77R+xUE0pLpBktynIlqoNsGk9QGCMzRwRR1Y1kKo53HjngwDpd4LGGs7D7Qcof7wRAoFBDFvNPGgRRluV7oPX62YohUXvrO/Vpoc+Jbw7VSqlJlkq4Lbo1bcxbJbG2SagZXFlVMBt1OsrB3jIVbd7TEnfGaK74Yl+kcw5l6Y/TEq/tEx8nrSlpX0uNznP/nRK4LWgrLRUkO3BWmJUoNjwFJg8MokJOylgIG7eUgXzWanSLcvV7YDOq6BHNBlDkcsYSK0OZERRgaz9br9Q4fnd47lxzS33YIWp1t79ResHuD4XjO3LrznS8e+fpl8uo+40fG3t7R9igo+ZvMODbSmwuW41h2v3WeWuKLrQX0Rox9LZQfPKMfvcayUPIdzI0xXqjlyrBn9n4OWctJUfaDJS3s1sgoroqPQR8H05U5OvMY+HREwW4Tk4l1De1ggjYFTYYn5c39gtTIOFhPSI0d5ZKd2+bcunGo4KKoCttnzxQ5j5pPdO1Xff3C7xSAHyuIGH5ao8fLAz0ZQytJnHSe5nxpl56yx0lFtTBQyx21CM311IYXsgrJMmr9dCAsQVjKHoBWUaoUtqNTmPH9i2KWYUCqTn/qtJ7Yt87WB9vz4HjemeWCJeFeY9D0/BQ4r1Jiip+946MjWWj7xHWlW+HogyEXbA0XRRlRo949+hCmF9Z1Zblk1roy1AlWVOZ4eiaLsuYSxaosaHHEBE2duTt9zmAgajyn2nFw7J2G8Hxs2DFZliX0duvC7I2+d7w3juMFcY2TAoHBGfs9diiO2uTu7p7ejJSFfEz6fiC9BCfCoTSjFOFyWamvXpEzdIen9weffPIJj+87Lx//kANlrpVJ5ekPNzZz9A3cfOf9y8F3vrjx3XePbNbJ1xWXhvzqlX0m8vvPSQPSkrCpjJcR1Ky6MjhwFey2oz1i59MHdkyGAUvQOlQDX//wuMUc51K5vn5FXq+gji6JchdOjbu3BbnP1PugZc8hzNnjmHMIuayYKocROEF3TAUzob17xkUQJ957fwbXT+t9+I9E5Hs/5nf4V37s//v3ROT3RORvisi/9FW98D/2Ov38y5wgGGO/MW8DlkggzjnP0EpBMyRJLFmxmfAaYMx8Ktymn3x9FcgBI6lSaOYxgGNhzBvqxkvvaE4cHuxChqAttpDeF1gy1hq48eZakCKUuzsc6NppSUm6UO8y5hV3ZzEhl0scHx4j5K/aEDVUCjZvjL6HsiwJ7Rj4mBQik9+3W2QqUgeDYoZbR0SjbFSNXgxLhdGFopNlucMlMQ7j5fZE74NxDNBKzZnawx41V+XZO0lg+CRdVvqMD1SqBR8T6REaSn5QloosKz48FsZjIyvMzZDilCXTjgMZirbJVYX6YaWbYWMHMhnBJZyKP3x65vPVefz8mZdPbvjrex4/fUdPBfvmB7x7d/C9dw+8NGffZhChz5BRSgnuYX4cQ8ClB2G5rgm9RIJTeom4eS6RhHXBRaiXiDXTJ+XuwvQGorzxBVmF5DB90Him5NDWCxVPld6NZMroZ/hKS+D55uCSVxiD0eaPKv+4nvMHpW/v8OFM/9PXpv9htPM/6fppvQ8A/+mP+R3+yvli/mng3wB+8/w1/9mXeLav+jKzH6UanfAHtJOrL5LoKgzCc+AkKlF8ygnKcJLGTkLFMR8cvgXajEEhMUZjTRb0nXGwrgv0CC+JRLvSpyBmWApt25ROzZAviVxjO54446plsGilj0HzBOOIYRygWnjZN2SeqPRZ8H3S2s5yXzieO3frHXM42qMZmdTYRzpjxAX2wvwyaZkMJAeTUZ2J0IfhNNZaScuFfU7MG/lSKFRkRolpf+logWPGcy3t7DoMpd86fetQo3KsUtm2dnImnXIpzL2TxCjLhWXNZFcGjuTEMYNxcXettHGw7sKxBLA05RodgwJliRj6vFxon00ehvHxu2duf+szPn964Dgac1Xk7WueH53jEA4x5FWKhCpQ1kL2zv1v/jr1/p6722R++sxSVnoT8v2FfElgB/10b9o8KGRUM6iQ0wwlfe+hw8O5fhSinVSUfjRW18jL9MZxHFQZLB6MBBsNI+F6kKbFI0QG1Qg+WevcjsaeJkMLLy8da59Hjkb/9Jv6n7Va/VN5H/4B178O/Dfufrj73wF+D/iLP8Pr+1NfkWyMQJJPY84X5rGfVmriLpsmjkazkDA+5dM67cQ2dcHJOVG0MtwYM4M4qQTHkRklqHm0ALPmekJcF1SMKacnYUyyyCn/CJ1YTnIy+RI6Y62sWmmjM6ygEoOsSY6f4dCPA0bnmNHhmFNw4pFGTWjSGDJZWOLoKoW1OqWDapPFBKOTZUc0TgH2Q8hLFL9Udrb2HM/dPdGPRh8jWnruLEuIUJJoVIat0GcAYKUoMw/cQz7Tbi+kNaEls64rvU3urpezqBUtR7+AbXDsg/78jGc4WoOHg/xGWGvhvlbq6iRR1pxYlgsXEapk0jdX9gYv22QskydPPHeH4jy6sy2JsSQgPBxSlaZKua/Uyx1eJrexc3t4Jr99DT+8sdYVvwnzcO7v35CXkMjmtCLFmf1guSpFy+n8MOjhDj2mYWmy7zcuZWWQKMmx3lhLDrCPhU4vXVdKOmPimWhKMuh0kp3V/ySIR0jsuBmJLd7ffwa6uC+vn2Wm8O+c2rj/SkTenl/7NvCdH/ue755f+2OXiPwlEflfROTnQno2O6nXFt19kcntNthOu5FIog89F4kSE2TNcUZcBBSyz1CBmaDqaAsY6vOIoE7vHT1bmW7KXV2pHqRdtcltjAj6HEFKTuJYzyTJrDkxZGXbIadJ9xHAWDYWCbmoJaLFaHv4C0smF6c7IEpO6fQORn7Cc9Sk5YBmLRqap/Wpj4gea3EqGUmXoAXNwaUK1YxaCl0E1wCKygfKth1RNBKQQZS8bhvb0bCj4xpuyTmdYc7oUaAaKRZeLHZd8+nGODq30VlSuBrX1wtjm6RqLG8qb772dfZ953hyWCuiGR+JIZNxOOtdRkjIMGZKcNyQUjleGus752l03n3/med72D+80B6e2caBnJFqJLb1Sx2hjzfAdu5+6Wu0WbC1cP/Lb3mzrLQvbkgN1d6SYFnmycCIu/T2OHnew1Y1Z0SXTZ3ZBrMpeb3jmOOU1Uog9jxkvp49/u5b/N2mLOT1nlkdTytZKltvbE1ASyRnET7/5F0kPZ0/k3jzl9dPuyj858CvA3+BcD38J+fXf9LDzE/807j7b7v7b7n7b/2Ur+GPXeIWjw6hfaW928Amh3vc+YRwFYwNwwKZnRZIQVjW08GY/KQz1lDNLeKBJs8SnfjTVt1Ho+fERQSzSZGQsNxSgFBMBlNv4S2QBKPz+iJ0h+wrPgdqBdxxS2TL5AxWlVG+RIRFpLh5EJ2HJJa7FS8J8xRn6h5+CU85evYGOa+suWBiuBeU0Kdf3ryh1kxKl+iEWDgz5j6QmVjXiD8zFZk7JGNJSr0oNjslOZIDoe+jRJszLUheUb2eO7bEkETJ6cSIKf3ouAklFXClPe08PD1xe+yMfuApM8Rx7bTnZ7JCe9rJRTnMyJuxfvAqPJMpc71LtMs9fZ9YupD+sbfMzZkKx3EgMyFALoW1XqMUVxIlreTV6b3z9H/+gPfbMxuFdSba3/6cNhyfinqliyOmkSvwFvmXx8axbdxeOt6E1o+Q1doklUyffgJZRhzFag9nhhk5XeLYcg7YJmtOZFd8OGVWttloDPYUJzXt8ziyNn5sl/AV26Hgp1wU3P1Td5/ubsB/wR89InwX+JUf+9ZfBj7+2V7iP9Trwq396Dx3Pj3SZKCqNJ0IlSaARgNwpYBYQElPTbsz4rnbJ3PAkEi/eRocc0a1uQszOV2FuR2MOXEEt0E6ASx2DDQXckpIikKQqmGq9PeQchiOTaCJMHVgTPYWO5vUEyk7qWREE6KNMQ5k7EiGbpNx2HnMupMKcZdOwmICOfR2/cVJusOIkxn3CZLBOpoFLRoW6BRt0pQK5AbJYkg2YZsJH4kpcOyT3gMC0nQwxJndaFtn9I1pQpsWJbBacHG2FwujtcT2O56RhbLeUVuhfK2yvF5hKM6KlhVVJZcVZVKSoteELZl+DMrzYHvZeETo185hLWLt3iPmvlTSIiAlTmRcyDWz1EvYlXpD7k5124vRdae/Xfjgw7cUlPZg2Diodu7Mjs7+PEkI9gLXWXm1wgdf/4ClKfZ0Y9wa3WLoLNrpM3yhSKKbQ82YdtJaYye7Fjg9kkOcfXSOMTja/03em+zYliXpeZ/ZavY+x/02EZFN9Y2oZijpAShooBeQBhI1E6CBCGimd+A7aKiRCAqiBnoDjQQJICQKoECwWA0ZWRmR0V3vztl7r8ZMAzs3MkqVLFZVZEaSyQUEENf9uvu5x/de25bZ/3+/UUpheqY9vPu6Qviujg7w1899+PVv/PE/Bd5PJv5X4G+JyCIiv0/kPvyf3+4l/qVfE8mjSsieogHYnxhj8OzhNMODwViSsphjaVJFKKWQ1ShJg5xkxurKmpY4KmRIJqwkmmYWGqKVszRKiSdSFcEtMXxDsiFLYTYh50wfF+bslJxxnZzeJPpRkVJ/KsDxGHNp1SiXcS495tXWJnlmckrgC0+POzoTJYFRKKd7JCfa6MzRw/8/Kq4Wkt+bYy/dEqqtB3NBXPE5uT4N3DrzGiWSz4UkjuhEVMN9OoR6y4c0TySJ90nTQFNCsoKmyNFwiwrGosrIErCVvR1ss+HLEjdDm+iHFefMxNnnQK49Iuq1IALjUE6pcP/BK97UO1LOLH3SKfifPpHKmeXX7zCMklZe5RPLWjktOTQG2SOCLWXIhVSUMoRXH57ZprH/+Mow4bodHPeV1/f3rP1A2xKEqgNKX1lZ+MGbhQ/vT5T7O/pp5avrI/WDM8vpxOpKvVyxrx6xI7Ge7sgFTmVhTTVyRt9j93MhM5juLBLSeRsx4h6EfHHfJrN/RdGC6jf8Dv8qgFv/BbkP/7GI/AfE0eBPgP8GwN3/kYj8T8D/S4C5/1v3n6cA8y9eLoZgcZ5EmbbRr5lyF6rEimMEem3PmcUGUzJjNiBoN1kqA6ObkzRSn1N2+nAkZ6x3nIy1jSuFrPGzBGVyAI7IwnF0SrWIJqtn1Cd7Dxdg8smxbySpXMpkJUUithplOm04Ogc1K0c/KDWjs7L3zkIi5RIRIq6kOcEM0YhtiwYbaI2wFaxFpWGRfkUxVhWGCHPsVFH6ybgeCzkduDWKVErK7MNZ60SHMw9DS46AU48Jh2yTYcrUnZIyI2VqFrbLxlIqyYVDBmtR2ubI2dCuzOMlYv3mjr59gyXHPIRi45Sxq2Ophwz5zWv65RL3Qqnoy2A1Z/tQGF82WsqU5UyS0Jy0EtFtk8ndqTCa0/3gbihf7JAiAAAgAElEQVRpCsdMnO5fgR/cfbiyvUyOxwcoJ8Y8sGVl/a1fCzbCjz8hvX1FuoPHzwv7vuMfZHwX9u2FZSlMBrzOQMWvTukLsxv7J+8odye4z4hOFjFEEzY7JEWopFQYZNI46Bx0NyjCrM71ZWL9CZH+NTvS7Lu5lX6uuQ+3v/93gL/zbV7UX2up/xTkqhPJCWzyfGm0H6y8Jkc2oyhajRWJRlHvFJRDxy0bcVDrmTE3Uj4jI2g+3Y00b5MhFcwTaxL2DmuCqxuIgrxi2mDKJE+lS2G1UAhuvpG9ICjlzcr2zljvE1OFgwMfzpIKUqBP43q5ku9P+AjASa2ZvQUDQAuIFlZNHHOQfAT/bwLrJBvMMUKuazPkxMfOWu+QZHgzmgV1qpAo62DfJidJNDP67LgK+AkYmELzyWIwzOmXDdFQVPajs5lQpjOTcLo7s11eIC+sp8SmHV2c3Comzg7UUtAxuVx38pLJrbOcznSblPvEfJ68+Y2PGA5qZ0wSCwvv/vAfBzp/fU3LV6Bzd3/Cxah3FerC5XIlp4p3A+s3t6uQ7xLlBYYMEjOOGM/O8cmF0+9WWocxnljPrxni8OH3eHp+IV0zdtqR66C/63gNgZy1wcwFtJFHoa5vGOsVPxwbis4dXhqmlXGn1DWxlEQvmSFCVoeU2MekD2dYaFaGwsOnL0DDXZhz/MxunaSfr+fh61vp5/8tfznLZwiYxJ1Cpt64Ce2rnd47uzqeR4wNHTQiWDmlgoiTDMiV6Y6NnTEm/dpuneaIdh9jRKSaC2kOmoHboA9DR+RRdptszeMoo0LNhYFz2IF6pSRliqDWWe7kNtKcVK0hoxVgCMng7vwGaQm5mavaaIy5U7UyeqDEBjPYDllZ8hKl5jwwnFoX5uxfTxBYQ1wUkXQENCWBm3HZDtCK5xxjh1wROX39dDKZzNaxemI9RVJVWVaYUDSYg0NhHEYS5e7VPXVRRJRFl8jJrIKocz4tyNaw29xfgZGF3UCSszWFM0zPHJeBpWBiXH/0BT/8nd8k/dYbxlcbd/cLr379FXcfrLx++yGlFDwry1Ij0dSEvN5hNkk+saNTTmeOlBjvdsiF9aMzab3Hu5EMCkp/eaLvB3s7AjMv4avRnILXsLU4IjlIPwIRB/T+gkwjp8T6gxN+tyAOix34sbM9XrAJJQUvQZHA0A/naMYxBpvtGJnnz17gm8ann3VqsF9M0/FXZlN4bxhxdzpBVXZ15uOXtJGYt4DWw51+DC4EDrzLRKSEC1Al3GkVlrxEMrMYbQTT0SkMP6IqWVZoR7gfEUaK4BlJBC/SEkZizGuwAHWQ1ZlqQKPtCcV5+nwL67cF8Wl2u3FgJiYdlck8jOaZNiv3pxNHc+7kxGwdF4U5QkeQjVwU10qdgWjTJbIDqmUkK8cY1LUgEhFkcw8tRZUSxKQBWguzR3ZGKoWhhjdHag4oTctsBn0YvUcVtdTEkhPrXaaNydx2jpcdIXG9XjlwDKOule2rg9kHdiqc7ypJjbRUZm94CsKQ5Mkc0c85JGGzsn/8GY8/eRe9mKszp3P6/krSE8O3GK/6RCzGmSqJ7EIeg64wrdPmC7rAev+aMQb1w4ykzP7ZwXHptyapsj9cqJYQ7yw5kfsIRatVqgaDUUpFc0G7RSOWjFiFHGlPdUnUDyPDwnIOHlQ32hEQH9OCa8BxuhSmZFKuXGdkZIo4/heoE39RzcdfmU0B+NpnLiPesESity+57u2G+U64ZbSsuMFMhSwZk0G6hbYmtUiDPpyehZKgYlCNbLAsC3UavjfMFnQK5ySIJlQLaoOaFrz0OJvNMNYkKjZDKGWaqFk4LcKrHwp2vQmqzCi+IrdxVholNin3G9sxuvgqg5Y751f3eBusOdH7IMmIgJm8YIsxjw0b0ZWXKpTk6KKMfkRwjC5RRV12vO83t2JHbnoOfdeZ1rDm1DUj22TNislkXc9Mh3K6ycrHwAhXaS2QloX712cuD+9YXVgROITnT9+RtSOnTPbJse2RtylL2LsP4bQ6ubzhul/oj50lndgfN9a3C/KB36Y9jaFKvTthfbD4KX73OXoupMrer8GQWCvM29dthZYrL9dnlpwwhOXDE3M487pzvyRmN9K54NmYkrHtCPaCTXISSMZ0w3xnMllrpuuO98ZT22AGeRmD7InVEsvpRH515pgDnQujJ7hOxta5XDtb35gKqRYuzxtj/yyS082+tWz5r34f/Yosvym+3J15o95OUYoZx8tkppC7TomzsVthyIKUYAq6FdYc0fGLFvISNJ3WhT4hj0zO0VEfY+BZOb8u7O63pKqE5kzJZ7bRotxvAdK4Hga+ROy8CAuG1iA/JVuod43jYmgShm54UpI4Qw7a3MmiKIMiiaQZmWDDOPYWFCYRki+YxChPJZ5cpiHkGhYmJmmKTiGnAIm4QBIJcZFm8kyhPyAIw3t7x/F8kFXY9w7SaNeDw8Hn7fjS4/iwEuIu8oSZaf1ge26gK94n+w522Xn19p7XH9yz1MzYJ8mDdzn6BuNg7Bt9FIaPGI+eavSCfvJC9kJ+8yHJNaoYAc2vGe68vLzgwLxMKMpuV3wU5jhQV1IKGnPPEj1OXRjPT9hhpNcrwybbVwf71hHVwNwdTh6NowvH1PBX4Mgtb2Ts8c/tfVKJjfTkBv1AJAYGXSYzR18VYHlzptxXsgpdnLF1jm3nhcS86RMuD8KwR1QJvNt3oE345vqV2RQARNI3YuQ6YAw2rl8dHH0yiPL+mI3mB25H9AyoQKTGDYm0qOETI8w7Swr1oORJ9sRIkUm5HY2TKs0bycMpOO1As3OXF86nSvKKLI7LNUaVIgxTMk7vguQeKcZyZb9KCIGsMIUbHjyzETHxas50aBD6/Gm4BEbd82CO9tOSnk7xE+aZdgz26UgSshrvE0XmtgVJagw6GVuFXAAG5sKrDz5E60KfAJMkFVuM1j1+fs2UsrD3HWpGUfwymNedMiBpY7UJx2DMC+XDe3YbtN5xd+oSIBJ/ucI1UHq1Knf3C1hCbSKayEUoL09c+8b60Ruuz43Cgucb7cKdUkKWvrfGbJ08AuJrfWJD2Y6JOvT5iJhz3AllVtIa0JfT9+7Rk7J/eWF/euLYjbLeobkyvaEnBYvUMDllZjfqkrGxB0uxG6UKtVZqroxxa3qPGAG7Oue7CJ/xrKT7BV0KQ4TnfWCXI9D/mnj65wfZ97iozfFp37jG5S+2Qdwa4d9m/UptCm4j3kR3cI0gTxX84Uu22W9IL8G8MEi0Pki6MGUn5dj98UTVwuoaSHKEqZPMIBO69FNylnxCZ+QoJs90C/iKOFRVNusc+yD7wWJCqZVkemtAOt7CqCUjkXLl/nTmenlmHMFv7OYgGUuJmirFwfJOuXk8SsrkFEDXOZ3rPr5+Gk2JSsMXC/TbDFDtPGKMOcZAR1zERQLYWtTJM3IydAppzhDf+ER8cNkaUxKtC+X2Xs3rYLZwQvYjc7zsjNZJC5TTwnq3hrjpXrg/vwZCDh65nY1F69dW7NZfWJaFlAr7ZY+mccnUWti/2vjgt3+d5fc+4vrlC2PrzCVHv6Mn1IypcUMtJYCrYwxKrRzbCK/COJhpBT2xWef+9StGm9ilh9FsrZgLUlf61VAdbMdDWL7zynzZbpkMAzuc071GEI8uuCVKEtIoyOzxoFEBv/WofLIuzuiQmXFEs0QAqQyrFddEMti2jcvDx9FA9p99e/7LWgnpW1qsf7U2BSIHIokzLFSKmLNdP+e4NjwrU40mSkIpZYmcv31SVNAs4MbWDg6Nr09ZEV/pXhALVl93YXjDU0PIVJGvN4ZSQyLN4agZoJxKRrqwlIrMwSKJ5S5jhG/Cj4Otde4/uufy+MLl0x6WXRWMidik1lNYtt2QEQrEvhsiM8w2IpgreVmwm16+75O+d3RqwGVOhWE9SFXuLFqDxlzBxoFK/Js74U6EqEYmiVfnVwyEkjIg7LOxrBqv8TDSMagnJddgN4x9Q2ZmuVupqdLngfUIb21TSSXTnp9Y1kTbdpblxN4mox2A8Xy5MtpkPl9pf/ynPPzkS9LpLUOhH522T/RuAUloPpGzBvw0R9BuqfBybNT7Sq4xDSpTGJfG8XL7L0N7PEglU9dA6H/0+x+S60p72tEc6PnejO7K2DfSq8Du+x7HrswM/JsrIpNUE54F18lag4i15OhToI5oxbXi+4gNpnUGwPmEvVp57sp4/gncjsHDY0N/f6f+S5uLQfP/VutXalOAn3IV3nf0LQnmO/tmvOwH+4izsJlFE+2YkW+IcleV5ZSRKqgWkgn0CANJ3tmPI7IePWHdEC1B6lVHCiGO6nAaM8jFa1hu24jfktpkLYr3gXeiV1BAUgmi82bcfVSRt8b+1cbLw4E3QYowj42cYC2ZIvGzVGNM1rAYByI3JFhijEoiUe8WJgtVczQSU8H3TtIIjBmz4TaC7tMFO7bArncLrLsptEG3DoRBKOdElcw4JvMavQ5ZhXMtEfJ6y7iMkFVlpIXeDTyAsbYP2lO8zq0PSo0D+LpWEgvb0ck5c76/4/J08PqjFf3NM31MxtGYL4FikzrYZ6e/XGnILVMyBGrbDskjBdsb7GPy0jfyslCzovmgawks3XCuzZEzHE8dPS9gCy+PG7Y7+ZSoxfFFGc04vV1IbLes0YJOYSSLnzcCwJMseiX4ZCTQlJkSo+CqmXRXsNl53CaXG3/Cs/LwZSf5423jtq+PAvKN7MhftOL5V25TiGbjTdGhKcJbmDx9/o6DaDx2E/oMleIxg8okObGPTmdQk7MyETXc4tigUiDdM22PCLnk0J3UJqNZzLKJm3ZW4ZwVJQUwg8RoR+Q3WkOlRukqRMdeB00z08CHcpcX7n94YlmN7d0jzz85aJ4Yc6f5pFnkCOi53JKiMvttRLjPzvHScDrTdvbdcNnQMUgKa63MLIx5u7I8cwt6B6Ky0mFkjQQo805rIxKVJHwZbW/MY4ujU1KsBKlYUmFdV451wlJi2tIa7Xim5ldMnUg3VAaiA5uJmgalrvGEHxtCJG7Xqjy+e6B99jn7V3D3wRu6OFMGoyvT4O63v3+Tag/MHVJMQnwezNmRGbyJJsrptJCzcnRhiHFsDX2VybYy+s6yFNY3CyYH1Im+WklHZX88aC8vMJSVBZVMe27k0x1iwlIc00HyxFyMkkNir8kwjRCYtGQGQtLIEcGM2Tr7Dn1cIrlLOpPJ48dPIceHSE2fv+Ad4GesX7lNAW7W5jnxOZAZEufx5RM6nZEzXTqXOThc0RJmpDnDMCWudDdGGqg4shSYyhSLBOaSgYgOlypMdVwFsXAybnuHqRSDp+tOa/3mEajoFI4dJhNNC8vtwDMb9L1DnVHdZGMMw2Uhvz1zepO4vrvy9Fnn6ccNbZ0i91gfDFcGkPIZUkLzii/Q5sBvG5uUwqyhtdi2DZHJMGfOA4DZtqBNzc6kRN5Dczrxvf1cMU30i0MHT4KmO9pTIMXUJrSGTAuT1Tbpe6ONzj6FoZlSoscy3MKvca5fbwy93456nJlSqd6RJoynzg++9wHyO2/ZPOHH5HoR9v2F4+GFZCk8CyXRLj3eD0mMvbPmhVwLfRyoQ2uN7IHvL7qQ00JaK2Ux3MIMVk8rl8fnQPClQX6rTOts14Nj65DDcp5k0rfGMZSkkduR7aDIimahZkVYWLPSbUNvmL33xrghThtCO3a6LWxzMvMduwvts08Z0lGP6ZB9A+/+Xa1fyU3BZzz33o8oJSnWHni+7ByS0VFvfQHBzBlSEY28PvdJ0oWksZnIME4ZGE6u5fb5SutG2A5uJKNemJaZCdaiXAcgEufL7Cwa46mlZFIZWJr0ZrT9CMJQNUwKU5zjiJh4ZGA3NPjrjxbWN5X7twv53PjyR19wWu7wFOrMogfTO6KdPI3sYYmWPsmaEApGR4sxKGg547YGibiV6K2kirQIM2k3KtVxgLdQ75kZuwo6I8GZGhbhIYmyLvThoYuYcHHoL8G2nNfG9vzCooKJk9XC7XgqN9qxcGwRknNMw+uZbdvpf/rEuy+e0TeFaY7Njj13qCvplVLk5kz1na6QdSF5pEjve3TvHTgul8hskI3jqQUvwsJmf20HaRhpidx4GcFgHLdRaXpbWc+vuOzG02df4DnYmFIyp9dKaxoJVamSLOhU3UPQNbMj+S54CD6CDaHKcoO19C4cPZKkrcDlYdLb58gMY5n7/M41CvCruCmo4BJUBcwYcw+WgW58/k/fsbfOxmRqYeuD5rdfshpLKXiJhqN4YLgsJTYdSCGETcDQyGzMOQbQokZKRzjePMZ16iFS8qQIieadNDK9O9ITdkwkZWSWgIu4hDMxLZSaODhAAk/v78EvteAySOs9H/3WWx4fv+LdxxvYgu17TFumYVLZp7KUynXz8DFImGrGNQCv3LInvSTyWoKxIANbwjnp6jiClEG+NWj11YnSDtpDZ6iiCVwTbRjX3TlE+OrxhaNF+vfMDiiS4RgaVcwemHwthXHA8rpyeEez064vUYkdB/1PHllPifo3PrqpNo13P37m+Y8+obXGD/793+Tx5cDnzigrGeH68sBlf0bkjKpyHOEnISlizuwnalWwsJVjG/7mA+ZlIKlhfefybMyxM0fwI2U6y53w5vsr5x98yMPnG/sefg1vk3rOmMbUyZOSDda8UIqhkqnqUCtSV6RWOsbzsfH4xRPvtp0nOi/FaenMH//DLzD7CksxPcPkO60Qvr6FvvOf+Ite5vBnSq7gJADsT5/xsjWGaAhHLHHYQERRX1ESiwW/MWmlpjO5Cqe0kIbcwK0J7YJnDbuxJ+aI83hXKGlGivXM+C0heh4dtxSbRYoKQIozOEiq9Bln9olzLsbRGiktjKGU3JkISAp0uydcCt0HpDPydvDytDPzfQSRHI4yWKpy2CSH2J7RnS6JQ0P41HC0ROZhy4PZwaWQT5H4ZFNJaaHk+3CF7k67XumWqW+MY4+g1DQU68ZIwmgzErDUMQ8UmVtikYQmaMeG3mVOpxO0QSkLfXfIC3MK6wevSZo5vjA++OEb9Nfu4mbScyDtHi/YkgOQW2JqcW1GHo4MWE535Jw5jhiNvr8G4qncWYpx3NKg1aEsFVYlr4XRClGyCV1H6DVGxtU4TPDeGZfG61/7iL0r1+cropmxXymakGSRipUTBxEdL+qwRvCMZ5hiFF0Yw5jmbH3Qh5LLicftmevnf8r7oCf3iIn7LolL79e/tpuC3JpeP3NZCEdiM4guupqT9xeOY9IsA8rUhNrCbA0Rp4/QvYtPtDbMw0F5lY5WcJMArbjh/SB5BjGKhhQ5mTJ8pU/wJZpfzSJvcM5JPsOYHZswbt34w52UEsce7II+A27SjgiJsRkmnfcXx5TIKLxuDcmT8/mE2qAdghEeAk1CQhBSwF0t7HTSbmBbcUbf6XPgbrTWmOL49eC4THStmCrNLogGZDafBSmJNQvYQiU8ClMmy3nBZmfbB8tdoU/HWkOz0FujDwlGQk3QD9qxk89LCMhEg3cpcaTTrPj2KZ+9e8BOJSY3OsM/8eNn6lpYf/1E2Svj2uKpbnH29xn9AveJltCsjHnbGOZgvx4Bm5nOZQwu14PX94X7HNoR+mQeO72NYHrSKFpZkiImmMC47CxvlP40omIoK7kkVAdt2+l2IHnSaSFR9o7pjNH2jWGchnA9nKs4VpRR4OUrY7ZPMDeSc1OV/nLWv7abwvt+wb9oiUORCI9NxLmtyzPPH79weJCDdh/sM6LKeu/RYPMV08IwSNIxg9oFmwHseJsWiqYoxX18Tfs91UKWRvXBpoHcSsTRxJjx5OmTtRY8rSQql31D5cCTUmu4ML2kaIiJ0GccYxIHuSgJp+RMtwRVI6xmCKcfnmgPj4jDsM7sGbdOt42W9SaCcSiZ1gM/P2qoMvt+kEZg03h1QpOhptEMnXdMg3Wm4DX2ncNnKBjt4NijzO3HwdHgfHrFsQneJ5ozfhwsK+QUuorRJrnekfOZPqF7/JzjuZOrUk4rT//gY9gLd7/1muXVK9b1nnY9mM8dc2e7XPno937ItT+QXVnefkhLMRael4DT5CXjI0xLtWamGVxGSNSJ6rEu4Sv48vmJ54fGK0tYWlnqPeMBmE5PhaM51kYE7haFdWDDqd97Q3scHJcX0uzhFK2R9YgvnO7uw3V6m0LMoSARinvtB/sxcEI6nVLl0392QX2QbpCVON5891UC/PVzH/7eNzIf/kRE/u/bx39PRLZvfO6//0W++L9waQSSqIeLUXKKc+fDp+yH0RVEZjQKRRnk6AFkQwtkBZeYq+ey0vuCpsruE2NgquScce106xyjoymUaT5gmgAhuy7pprLzyvTBlI5n4VTfsHNPm0brIWoabTJm+7pRmkpmWKIQLET3wWE72m8jT0m0A5a3ha8+6fhUEONwpeiJ9RS2ZtEFdcLhJwvrYaSUKDUcpCqZ1ILOhMas/eIbXSdTB1IzM58wL5jC8uE9yePIAEZS42gv1OSUc0GmUdYTWQqBvbVA2red/fIS+LeXMASd1ooz2f/ombuPFvy37knrGUMQm8yj8/BH77CzQFXW0weoLTRtVBe0QdEVS4l27Yxr43BndGfbNrQZvQxuWGokQd8bPiE1pwu8u2ykNaP3idYdlZXcDU2OIazrSj0lshbOS8Wtcf/RHX2bbI+T2Y3VhLqcwy4/BjlDqitJneYSNmxLyC7sfdDM8Oo8XBrtxx/jHsIyF74+RvxSbp2/xN/5H/j/5T64+3/xPvMB+PvA//KNT//hN/Ig/vbP76X+VVfAW4WBWGOfHRNj7F9wPZ7ZWqcdiWaTa2sM21EG03rIlS2zaA3OQmq4SrD7daJLQm5BnyMXsgQGbjRHJiyquDjLqswW5eCYQqIzzFED0c7eDpZVWa1wvwRZWVW/BrCKSOQppBM2/AZ7cbImUo14eFUF36Ek1tPOsYNkiWCXa8ztNR90m/TtCLGUOzMpPoicyqqMvpFlwOhx5p0ddWU2xbwgFk5NyYJYv00ZwrptPZFmYQxnbBNrR6DcSDHJkRwoe+9B214K+8NGuk+kNbNdruSZGJ/8mKcvr6yvX9NGp12OCL6VjO0blo3737ij7Rt9n6DOMY3lzQmKgV6hhm/DCZ5kTqG5KJaomqgpc0oFZuF+XeK4tVb0ulPvFs7rghblMhopz3iPR6O3K2yBq9tn5H2oN95+/47DGtfHK0eLSsB6I2s0CYfBsOg19H4wr43nvvFixpAa1c/joF9/FLb/GUfir3Mjfwm1/LfKfZA41P/nwN/9Ob+ub73eHy2GR1Qcc0AuqDf2L5RNDVPhscE+wT1xjESSjGBMjV+MZxhdOa8hhPJbnHpKGRuBNJ96Gyslp2ukAqkUIOAkm22oQkoSYA132sycX90z9oONwdYHJsFOtNvITHyy3lUGMzIqk4E4JQmYko9J0bDzLsl59dFrdOtYI3IhvCGuTAsIrC8r1kKHIZ6ZtrO4h1ZGYtwq0yKl3gQtdguHdcSCUO3u5POZsW8gMcnovYN01rogeSKWKbXiKWzb5h1sMEfItpNkvKSY0HRjPa88/pPPuHvzmvt/+y2eElIqWWEYPH/2zPd+99c5XpS3v/sbkZ9hjdPpFWVdeHk4aJdBSieqV6TGODFXp5Tlz/Se+mWnjcapAqrIgJk6thlaYtNln9Q02GdAYCiJdhhTNAJi6hqCM4yjd+7e3lHenEkyObbGvm20F2PbemxeZmRN2MxB1NoDZ9f1QOuZj//4S2DHMygRE/DeBKW/hIrh2+5DfxP4ibv/wTc+9vsi8n+JyP8mIn/zW37/v/66ncdCBKKILUQMh/DVjz+n78I8Z3o6eN4Hm1c0BWrdCdyWpwkTrE36hEMG2T0u6pTRFICMVZavQ0y7TWTs7GPGKBFYpWI2uI4AwRiOJMdnY6RB0cGihcRCn85CCI6SKK0NlmyQC7sYy6woiWs7eBmwDbBRcFOOY2C+8/TSSRmGZ0xCt3E5GnjD5GZyYsd6pjUQHUgezOnsZrRpIIXjAqNdcSZ9gHWh54CNHCKMNaqPY8YNI9pYLDHdSAizx9w+1RXbEzklSspfE4uzrBRdsNZZmFxL4+71m9jUxJmSuT68cPn4kR//6CfUxXFPPD9seHNyPsM0Kjuu0NpgzJ0lVU4lYCh9GK5Cn4O8KDYm7Zik9Y6xb8wxyOVEPQlpRKbmaIPeSxx3dtASuPzBoJzOjL6xaI3NRqPcf7Vm7GwsZ6He32Onyfl+iSasGf1qzG487oOjG09H6C0urXP5yZfhq/HbdTd/ylD4ZbQVvu2m8F/yZ6uET4Dfcff/EPjvgP9RRF7/rC/8eYfB/KwVvMZw/DkNRsiDpX3KpV0YfXKq99EtPgbbi3PdGr5lfBvMIyCq+eRIOcg9YwiWJBySPjirs8+Op4aSqNYwixK/z8GanIGQU5SvTkVSAFWGwZoDqnpYp3uMrC6HcUqRf5kQjl2Y3Vik8OSdOcJSXTKUZIwEx5gUTazfW7HLjvRErSHfdqBKgSF4EuyYlFNFTylw7XNlzIUhhTVXikcSzHKnIQSLYKnQ4b+EMEhShkOxc6VO4fruwCST3yzocC6XS7w+XZGHHbIhs2EvhlQomjDvzG3Hv3zkYHL327/G1TrYQU6VsW+cT6/oTxfo8Pp37pFhLMWoa6anTk7O3jbcI8U6e8GOznjZUN+pmrCjozYZm6G5gA+OtsUG7U7Kk3FM+sOFlAqTTr88s49OScLYdw7bSTevjI5Csw0pGS23QGNxVJV6OiGWOK2vgh6thfXViubM8bjx8uUjD61Ti0AuPD7syPVTSNz8OoZ/93qlP7P+2puCiGTgPwP+3vuP3eLivrz9/z8A/hD4d3/W1/8iwmD+3LL34JUoW7t3TGD2jafPGsdULuIMz2xumB4kdXq9Upf4FlUc34WxreQUU4c0BMmTklbkZquu7uScSfpejOSklPG00BQ64IMAACAASURBVBAuPuj9QNRpMyAs5jt+KDNlNn3fAQnJb9snSCOaeI1U44mzzrhwkirmApJJHptIm4PshXMVnl822hyRLZkyPjyyGHyQBeaLo62jObgRJYezsNmIQJLe8BaqOr/ldPY9RpSnfMe1bUzziEy/L6QaMuZt6wwZnN++xv3Efn1C7jPmEZJqZ0E72Ij36/rZwen0hvxbZ/ZjMlpC1rvIyagLX/3BZ+jZQZ3zBx+g4mytI28q+95ol8GdJnRAHpPZR0yckrA3MGsxVp1GrpAErCvJjVrOoYissCPs12gaz+bMGTdtG0ZKhXp6g6KkVWkywsw2nLk3ctZwSXKzbN9ndo/jAIviJPIiSBUOM44hPLeBkfj0440+H38KHeanpr5f1vo2lcJ/Avxjd//R+w+IyPffB8qKyL9F5D780bd7id9uuRuTSZpCIpgHnjcun3zBQ9vpZlzdmK4gZ1wXarlnSiWdoJyE/DpTtTNaY2zOHMq1b2GCaqEUTJ5ItjMIizRm9GFMi657IqG5cJjjcncLws34dFqbFFOyTnzCkgdpjSeHSCRldwydjtaAptqElIJ/YDaYHui4OZ31wztmm8yHFgahMZlq2JjBHRRhWGMS+gGm0IfSLpN5zPi33Mr/nCumRhEo5wjs3UfjVT6zvF7RG/x1fXVCx5X2btKuE7926BundaWimA+0nCKyby2MZPR9YxlPvLs+suR70n2BG8FotsmxG48/eYf7PafvV7Jktucn6E5/bOQeMvE2FCG0J/t+jeNiVcZo+DFxCyjKPHb2qdGraJ0pnURijkyulbXd0r7PhesBclNEmhlz25AlDGiLSJC/zVjWAiMEYkhBSwTjLusZp6A4pBPH7rRNmEKg6F6tXHfj8ic/QbCvc0tszJ+xIdzoS98RgekvM5L8u8D/Dvx7IvIjEfmvb5/6W/z5BuN/BPw/IvIPgf8Z+Nvu/pcNp/2FLCX6CkbYU8cwMoV5/Zj+MhlemKK4Jq59p89AukuOUSJEgMd6Xkh3gqQdZ1JG4bgM5gSZgCpZajx12kHJDkVAJt021io0m9ic+DxuhKjIKqg5ftk6E0UH5isyB5IXfHR299sRKLrefQ6SGktab2f5Su+D6gVyNEJf/eBEGwePP97JGl+XSmaOxnCFXDFzrMtNLDOgOHnN4fuXGKOKc6t+Ilw2GqaZ62xfP9nmLYzWTvfY6uQx2R4uiMa8fZNIjX4vKMtScEkhBvLXvPobP+AyO96MUo1CJDt9/n/8M6YfNL/w/X/nI85vXiE7MDrrqaC+k2cIhLx3mk9qzsx5IDNT14UhMSWq58qwQpnPHBrJ5MfeOa4XRruSawlGog/OuXLu0ZjmVHE9sJtfQ22S1FnP50gRN5g548kZ0oNiJXGdpZuycx6NcTl4PC48OjR3TBNfPUzG9s+jge3C9BFx93/u5rfbiPK7qR7+urkPuPt/9TM+9veJEeW/MssiuSvKaMI8FOM+5eFHD3zvgzNLqmy9U0rm5JniEd6hteI+kN7ZNZFzQkc0AF0HLy8bqZ/ZjkYp0AxmTWgKbmKbAzUji3K0SUqRhJw0LLFbPjjbEjoC8QCqtJDJakpYTognbHQaTs0J80GVRNbEGI0kEqX/EizEVQwrFfXBm9/5gJdPn9n3BU2DNgrJEkue9ObRc2iK1wXZD6jKHJPhE8kFyoH5Ss5CLZNQ3ZYIkJXMnE5ZU8i9c8K2QcqJ8sN75lcHrXeMiUtBlgRzUkqiB8uG4wH8I6cfIVwyH+ArT9d3PPzBA9fnr5gp8b3fvse98MWnT5zfZp5/smN7AzKHXFHLqEB2p5sga8aaR/6GOyk5e3NqTZS2YGMwxmB5c88hG+oLW9pIJfQUtVb67rjG+9tHYVnAOjQa9+vKGI2sRnMnaXzPpSrklcmB54JK8Lp8GtfRaC2cllOcmTI//qefwHzGJVy35qCizJvE6psraGLfzT3zr62i8a+8VKJpZhZnbBr7Z5/QJhHVtpQQv7QDupLTytEHZIG8UksKCEmauA7MBm/Ob6mnwen1Sjp1JCd0P8jPiafHgywaAaKmrCoUqxGCK0JXWPwMRSgVigQXobtxOp0o7uRuFA+knKoycKpn/Jjse6OkSh8Gs9L3ThHYjtsFZA5DSa8r/fFgkNCZIMEGLGVFLIxQre+kVQHDF8Wr0PYjeA8+6L1z2SfXbvTe6cMCPjtu+Luh2MVYl/y1Ki/fFbaXieQzfgO1SKpoPlGqcv3yAX94QdYTVpT95UJ/Np6vGy9/unP59EvyR6+5+/6Jcr+yD0G5sh2ZelrZ9yttDhYL1erx8oSYoGkym7PtL3QXpit9xmX+dOlsedC3SboLFauYkEqwJm0oaYHldcFu+aL7fg2u5x4mscUyYxvxZJf3RNZbDqgHsTuXgqRC16hOZwsX6MWhkaFWXoaxf/InYTTTGBO7hxblu8tU+9nr34xNYYYHAhvhmuudrAmf73j48plr63QPX/8uwrWPsLRaZiKci1MsoKY6Y2RU0srQgaSFlBKa7yh5cP4AyrqxamZuweHDnJ6UkTqeuJXQC8Ma1oR+3FDiw/EsXK9bQFck0WwHjD626A9oh7PgJZpaag51kG+mIlFF/RZsOjLlXBh18viTK9vYUTHOosCOVsFScCG8CX1EYK7NBPVEsyWUmX1GajcJTiviMHPFpIY2oxtrLWQ36ume3iDXRCmFfj0YqpGifMDuneu7C/pFJhcwllBnWufy+MS7jz/n6Z98ipwmY2vc/96HWFl5+fIZfRUw1u3liXp/hmPhsj9CG6znhalhhx5pIgYph7vy5dLZNUhcYgt3338TsfTtCHpS31nOoGOinpgcbOOKtYGWTKGixGnQ0kCXRCpRdaYcxOycMyftiLbIfNAYmWYq/bjSRue5BQDGyDx+vjHbJ8G6dEOm/VKbi99c/0ZsCg5fKwSnGmMKTYzEwbs/+ZStK7sYVy207kwWxmgsKXMmMS2s1UlDmZazYmIUnMSg94bNQaqJLnf0kinnhPYdaYqok1GKJ9wVmUbza4wmE5jKbaw1b4GtAU0VM6xnVCOp2MnYKKgtkSGpQioFv4CN8AbklOgmtCHMemUeyvl7p8g93FLo723gpPAfjJsYqgxKSpTTGk96FbIYaTpSz0wGWQQZRloKc+ykDNILyymStW0t9O1gzsHlmMFaICEJcr6npMn5dEf7fOd019juEzY2LpdHvvp45+GPH3n+wy8Yp0lvC69+5w5mItnA1Ng2ZWrHFGgDfbWBnNincxlGawHLYRRacsbRKOtCztD6RKrjhFLSJ+R0F96NfcP2wXO/cr00Tqc7ii9cn3fWnLC54TppTJRMGy0CfjJoXZgc9JQiqjCXCOTVE2ZGGz10CdtkKmxL5SLGx//oC8T/P/be3de2LEvz+o0xH2utvc99RT4qH5VdlS0ViFZTPKSSWsLEAqdxMKGFg9MGLeEg/oK22sDBwmgkHDwwMEAtHAzAoFVdBVlZmZEZGZkRkRH3ec5+rLXmYwyMuSMqu7oqKxKqs24pYjj33H33vdr3nLXmmnOM7/t9bUia+0jV+mWLgqr+2tgKX4hFAf5kzOMWUK0kS7gk9jcfs68r3ZSdyi7Kdi2MvMiR/NR0uBNpdbD63JFWh1ZBD8PsZGPm3duOpgMmhfQ4UKad/jDUiQ1ordLDzSu/DYafqA96kQSsK9aVFAd3oGZHUNQik3Y8dEIIkBI9DgxcCKOPEVSprRFcCQ4xTYh2zJXDO4H9ckboxDCNqYYYfTJ6jZgPzL37uDjHDS8wdYrtNy8HiAdSSIjPTEEhFGiORKOuhqdIOI78ieXxccBYSqOaI/Md9x9fiaWzz4EpH9nfdF78wUtOHz6HemH55h3MR5iVfDhCgtO108s2UGeaMXVaK7Se8OOEH2Yo4BKpXUeIqxhr7WznCxIghRmXgKqAdmrfkTwapdOj4zAnbU64m0ZY2+TENlE36HMeJjUHzyAhf5bYJrftf3KBMBPzAh4wFIJSt87pcuXN3tjohDRxeUjsn/x09FBsmMraXxge++vTL3xhFoVPpaPRC/ThJjQzlAuf/OzCw3YlxCGJ3cKYJVvvg7gUJlKcCaLjAtCExAM9KuY7EYeoJJsJ+zh3dxZ6UxY5oLNx/2q9AWWVOWdMhBCh1xHTfqqVyZ0smSXFwSNUI/tEoJOD4uQhZrIxIQllmGys9WEH3neaG1Uqlpy6CRsDde8kwjLht1yM3iuaEtIVYh//XvRxJhdGY0w76ELSwJQT6Ag7XS8XcgaOCVIYsBjGuNJahVJIy4wuEY2ZEpx9r6znE+Xje07bCTsurFfjxR/9BAuFb/zed2F5yv2L8yBDT0pfCyqJNHcOz54hjHSr5qMvtK/ruFFLQyYhpIIlJ3illUAyQafRS9+uZ4IDFawYaoG6FnhktBgQ39AYEAlsZWU5ZGoLlLDRt0KYR0Rcq46G+pnq1VxpLMMcFxykjMYjTt+Ma2tcNx2xgjHTNPDeD1/g/hGaxgL8mU36l4wcBwXg13O8+EItCiJCw4bl2BwNiSbO6aOf8PLnb9haZRW41s5WI7X1kTbtQAp4DkOkHA23gvdKRKiqqAlNz5Ajlm4UY2m0WElzpoXGdqpIUPa9QLfPLqSyN+aYRlNKnWJKtYBGoVnFvdNTRFtjax2zREy3tCLxQXs2IyUlxRkPcK5D8BNLH+ItcZ58NbHWQW+OMQwy0U3R5yZsl8ZWGWdkE6YUKdrwEIb6b6s4gTRNXC+V8vIK21BF9uogA5qrBEiBfTXCQegvx/RnfXEhSOfwO19ne1F4+fsf8eQ73wJPfPIH7xPudh5/LVLCyJFUiWzrlb5WPCSupWI9EEjs5x28DaKVVfbTjtVHBJw2BcKsbMHpW8WCjByJW6iOtYJG6KtBWLi+PlN9hiR4b+RJBnX6buwK0jye5OYVueWV9jrAOACHKdzm3kIM8xgdE9gqlPOVh3oZ9u4ceLVW1h+9hzYbknofnM63qb4wi8KnJT4gKljDTEhAqg88vKxsbUiSTQPVK8WVYJkigjRIU0BDRCRwiDKyGN3AAuCYZ0QCWYQoG6WNiLGqnSePj4hW6uvKfuo4w1GH7cx5bN2LMoQwdIr3gWsP4wKlFHoYUtoQArENjl8tQ+AUY8YJdBsmqJQSrfiYnqgQU2argV7nIdbqg5sgCtsWCZOiMjOheEs0hH4xsi8jT2E1zMf4UdYherJ5IO51zmzV8G7k40KfFtrpSgyM1OrYIUe4VE6t8vDBhdd//BHMxv2LF+RvThy+fSQ9uWP52jMkOduNQyHN0RCGAasXSikQI/FwQDSiLBRJeBOu+z3VHLu5GWNOWIwjJyNNFAG8QI5UF7ZbcIzT8d5RHXkhZk5aAtQxNapFcKmgE+lwoLUxtkQFA6opmuJt5Gy4T0MFul65roUNAU9sGvjkg51WP6QnBWvIjQ4mt5Cft6G+cIvC2DHctrpWb1HjV9h3NjOqKD0ErqWzlnrrQ4wtt5ozpYxap8SAacFiRDXQAiBjIuDurD1wN08jXdgVR0mPJtIjwaRgl43r68q+C9u608XGrmMvN/rzkAWjgldBw/A+jHlIo1gfAah0Nu/sdZCimoJbJFhDpdA00Oi0bad24fL6hHUwHSrJamFwJXpi10Z1xeaKqjM9m+kUpmVCH4+wEzEo4QrTRC/D2dndRvbjNLFfnNQrUz7Si7Ecj7Tzhp3GNpw0s3200peBt3v83QOPnhxIj4+4K3q3kB8deTwfMO9IM2LMtF2wtdJu0XhmI/Bmv2wsAsRhme6XIf+WVggW8dvh/1MBVzWFfR8df3acIaxqrWEh0OstIt6GPb3V4W41E0QqUZXpcBxbfgnknNGD42nAWJuOgJy+K+taOPVGU9iicqnOqx+8j9mV4IbfLJDeBx3sy+nDX0F92qixT48S7lQMc2F//R4P9ytbh6qdCmwOrY8mYY1Dq68yI4eJKUWyHgii9KmQVDCtpDgWgUUjFguiMJMGJ8GUMGWWJ4F4bBxzJO/QK9SXzvq60Tr0KLhPKIFStpErYBBuKcitr7gL1RUYgTO1RVQWbBUaIxWqxUCSPnogUwLvxDTjZRmOQlVqH6lKxVe6xuEVKEpwgdbJISFkjAUzvXXbHe/QSLAPxH3KM23bkX3HrI8w13VE0ntU2qnSS2V98cB1L9S98ehvLOS7x/DoMTEq6ZipW8DU6CFwOTWaNmrZ6GFnawVxZ7dC7RXRhdyvnNsJyQtpWujSsebsLeDsmBf2DrY22j4yQrs6fWugkaSN2J0UJ1L2EeeGYPsVrwumOxIDEhJRA5QV0Rv/UcdTXmNCPSLTAowx6L5ubGvjtDX2m4Lx4U2gvPkZQXxMnOyXIAX/CusLtShgN1HPzXwSvCN9nDfVCq9/8oZraXTNXB0u1bnfhwY/tECVgHkBxgKzy8Mw1NRhgIqi1AjB+wg82WEKg1NQSoXpiIeA+5HIHXIXSc8S6RBJjzo+NbQ4/Q0k25lC5NFyGOxGV86tjymF3aFqn/keojshNlQ68RCZJFOsM4lQTBEabd+ZjxGPK+c392xutK2hksF9LHLREYW1rHQz9lKx3ih9pe4FbpyCrSTWfcNFaTEjVamXEWqS5kScJ/IxMc3C6cUZwdnvT+ylAULIna//K1/l8OzAfJiY3IhpjPCsXjneLbTtRF0LpRptB66OaKPXxl2cqNZZywmXgGgamZhWyMEQCln78I5YJgZH5sTyKPJ4PhA0EkSZUuC8DyPYpWy07uyv31D3hrUFuJJ1Qd2ISbAug7MhIHmCeezWWinDSmqOyoSR2LYrr7YrK45JoqJ88McfYO0ev6HWftEE9TbVF2tRuJX4bUTZK73dLm7pbC9/RrlUigxtvBkUFzoGoRMR0jyckurOlO/QbKiOODCikboQJNwmTcM+3azD9Ii2rliPTFmwYFybYSNaCYlGSJn0jtPbA/W+UwtU6xRzQnSSO3sVXEda9gCW+ECiZXAias61bbQYaFUGs8Eci4G9GoenB3LOlGvBfHCvvXW8CXWreM9MMeBdiTGz7wXzCK1jmli3TkyB+OgOcSN6QNRZ5kTMkaQJ1US/Ov5mJbihm4wOvihNjMffOnD4ymNiWpCQKOeCd4dmw8167ZzPZ/ZtNGNNgLkjlmgVHuoDtMCcZoo6KTp+U6pKGE/epnEcI2ZH6g2Wsjv79ULsTlAGaTom1r0g/WYeq4X2cuOynWitsW/bbQQt1FiJ82B0dhmZDy6AZsIyoTmDRrbThWsZD5QukZYir15X9g/ex9hHyIv9gvL0Lasv5KLg8icdX/dP5+8g2yuef/KGdSvswO6RUzW2DbxFuiml+fDcx9FtDiqDFSABt4QH2NVJWZmIRI1EDUQg5zxALD64BgPu2vFaECaSCEkmlkd3TMfhzNteNrJkAqOJGQ4BTJAIKjImKeo3f0WjNWO1RlZBwqcA0HFGFhE2AumxsV+MrAN/Xy2w7gVMBh/SFD2EMUZsTpNOnZUuCktmK5W+joVpK+uwKa9QXzW21yvr+68xuRK+uiDTxPOfv+LSHULlyVcST77xNdIUsa2xPr+gwdm3hxFNVyHmiTyNacy6D7t5toDOM4djwmqmSOfh4Tx+bhKwOEaPe3F6S3iq9Ar7viMaR/JWNVxG2pUq0Crb5Ux3o3bBdxCfuZsmjoeNYx7TmbLvlMuF6Jly3SkmoM6cJ0xvJjQXWhtk7O2y8fL1mS6B3Ts7wic/ecD2DwiqQ4Vq7Z+LmH+b6gu5KGA384kNSpu10UR0dS4/+hnnvbIGQBo1wGnfaHsDGikAHolR0SWOPoOOxGkYjfaUEs0bbglsjASD7cScyMFwE4rAdIxUHwQfa8MdWH0AYfMSOb6j5AT1TWE/B3YLw4oRM0ETwSAmhWkkI5uNZKKDT7eG3M6VIb6qbnQBysZxWdDcebgfsekhGntciMsR00CJgctuTI8zh0cZ35x63zjfF+xS6Bv4qSLrlXZqXF8+MN910m8I/Wj4s4CFiTcfd1798M0IcvXA17/xdbaqeEqDfFUbx6z0agQ5YKXRZ6jlyhRvgbOudO30qFxfX7nsFbVCa+DR2HbG6M+VvVXElWl2Zr3D+o43gUnHAqY7EhOtdErrt7DhTirG4W5MifQJbMcr1/6EKF9lWTJfeTwWCr0U9t7GVClkJEc0ClMbzV2rsL/e2K4rK52SjPRo5n6vvP7Je3Rtg6xtt53NW1pfzEUBwIeNBRk6ANExFjR7ycsPzlhXLtHpBbrOlK5QA9USaMdk6OE9RYIZMTtRbk9tIBGRPKjMUStIoG+NrBNLhLuWR+PR6thlAHsfsXWncmbbndIW5q/OMF1p22vW153e4nAediUEIduElLEbqQ4xKiVHPGRyTBxiRlFyhGadOB24lMbdkwOdSpPAWo3YL+z7+pmcVmPifDGuu8EE+bFwfKTkJXBYKjwO5GdHDs8W4uMjGp/R7ztahdAD21l5+eMXXHsnzk5+1lmvEL1yeXHP+cXzkcWwN8JxRtoVWsMuZcBjDhPNwKrDlvDiiKwsKaMhk6wReyYddHxuG7sl6zvbtnE9X9AYRuDtqbO2AG3oNFaD5XAgmg2qlCRaG8Gxh3wg7InQhPB4QGWu3eEY2DMc4kxolXbLDI0aKDHSJbJfBj36oQtrEDaJdBIf/uiK7T8dnhG5NbzfvlPDZ/XFXRQYgTLqw6paGUm/QuP+J+/zcN24eqYGuNJ5sxuX2+ioe7rF00EISs+JEDpYJGQj+Tjvx96ZkpBlQuhoGE28vXQ0GbEJEmWErgpIyvSmZM303keD8tpZlgVbwMOZy8s3nF+sWNlRAl2MagMG4q60ANka0gxKw42bETcOjUYf+YTzskBRyqlRm0BakCxsreK9QWvDMYrStxEvb670aULTU3ClB0FCJISJ7XymeqRI4MVPLnzyvXfRYyDmwLf+9teJyyO2yxv0atTSmB4fRl8kV2y97dQEKCOoCTEOTxZCMiQ2ynrl2WGGWjnMwp0qWW7sg5iJ1qAbyyGTJZHc6d7Ya0dzhwA93Y6ModNqHSPdLlzWgorw9NHA4JfqWD8R4oJJZp5niBPHxwvp0Yw8ihymGdudrQda4wZ7qWy28abX0ZSOmY/3ysMPf4RTh5y93UDCb3F9HsjKd0TkfxWR74nI/y0i/9nt9XdE5H8RkR/cfn12e11E5L8SkR+KyD8TkX/7X/Z/4v9zmePW0CZ/El+PQnnO65+vtDpSfJrDbo2tDEbAiFWLaM5EmYkKqjMhD1KxBLslWE0j8i0MgYz2yw3J7hTr9FDZC6g6OczgI2xWUkDDgsQ+bhYLRDkwL3fMjzIhttEQux/6ickCniO0Bn14OiQ5FhLEiegJVWOKAVMl5AQIh69AXa/MBKhCqwE80DfHfdCUejOm4wFhHCP0+kDrK9oZ49k20O9pilx349X3H1jvnyOPnlDXylf+jW+geiCEQAG8D4yduuJpELTL9YRLorchI18OSnRhfb2hkihbJTRjq0oOUKtTeidmaJc2jEXWYIdtGz8r8uBDxjQMcGqdaNNnkmKzoShMuTMluN6v2BS5NgM5Mk0TMQ2fSbVbtkUYATpTXujRiDkwBUHDRGAkV9+vxqV1VgFJiRfvF/r1Q8CQHkaGhvunyYZvZX2enUID/nN3/9eAvwP8fRH5W8B/AfwTd/8d4J/cfg/w7zEwbL8D/KfAf/2X/qn/ksr99nSSm3hEFJKDV+5/8iEP6wXvkYduVCJ7UdY6wkGGmMlGopIO+6zEgSbrCpKE5h2lsfl6i36LUBpgt8g2J4ZxmY54uTGqqnXHpOK70KJBqJA6lZ0qgbtnz4hPJk72wPnlha0HpBsEY5LCLoVLM3Z16j68/6XpQNJJHs3F4uR05PjkKbv76J9oRRM0VSxG9hXCFDnvKxocTYbKgrLAXtnenOhSiHHi4++fePWDV7RUsXhHLye+/XvfIMlMbwURgzxYht43tvWC72HkWQRhs85Wr4Ql0Huid6eUMuL1umBLQhTu9zJ0EsBmwqqNrQfivAwOokPuw+bdrOMeqAyqdCmF7oGUZOyepFCvIzeynzZ678wJxEZidU8TIWUajgSjScFyQDWicUIfHWmewZxr3bhcNx7qEDlbnnh9LXz8vXcRzoMAHhU+FSm9xZuFz5P78JG7/1+3r0/A94BvA38X+Me3t/1j4D+4ff13gf/WR/3vwFMR+eZf+if/SyiRP8lhcHesVfyGYe/rBzx8svO6dTwqqyhnoFTDyk4Kg8qscTx5b0A1PApqkdCclAXrSgY23whByaqIGxlFZSDKogHS6X0YkwiR5mFMAVpEy4T0Efb6KV8QFg6Pj4R50Ir3MvwWXZRZMwedxm4mdYpV4q3huG3bwEvEyi6B0+lEfVhppgRR9q2xLAkRJx9ArDOFmW31IeWdZko7YS7EMLG9Ft77P9/ndPmE4zsLbWvMX3P+xu99F/UDddvZrjIcoghBRzpT74EuZUT6KWh3lkcz7gm60e5XrBZ6Hbun4IGtlhHe24yLK9etQXXykumlspUGeycdE5RbulTvRBXKPqLrvNi4MS0QVTnOCZXM3dMJobLvO0iFFOh7odY6vq8e0HmhMziLnhLdRnanlcB63jmdLrxpBtNCceejH19g/dHwnthgZKrpWzmG/MX6lXoKIvLbwL8F/B/Ab7j7RzAWDuDrt7d9G/jpL/y1n91ee+vqU/HIEC/1z5xoQRS88eLd96hbpZMoNkRMrUJtIy4dlGYynHx5gEWiGZoV8oFefNhnLZLjcBviHTcGjah1vFWqOQeZyChJJ+INydVDx8MYtTWu9O2K7Y63jp0vmCr5OBEmiLshZGoPbNYoVhFzSs+kICOKvQlTiGgX9iJYqTz6Sho27NKxUpnnA/jQ9oew0FDcOxYathmtdMQGqv7h3Qc++fHPkdmJeqRx5Zv/+js8/cZvsJ0qxQrdK9YKacnQnDU44TDjF/fwAAAAIABJREFUtaLzRG+DgVBbwUPicq7U9cr1akiITGkmYlyvRu1DQmz9Zg3PUJLzsBYutUMaC1npxi7rcB12yAbOmLRMWonzgnodzEwc3QVaGpOWlLAlYH1QtghOmAIeBG8dUoY8oXmkf/cOte48nFbub+yIPRrr7jz/ox9DK5+xEsQHY+IXS8Lb19b73J9IRO4Y/MV/4O4Pv+ytf8Zr/8LS+OvIffi89RlaW2QkK2lEabB9zPOPVtba2AS2JLyxznVbKaYjfZpCEqe1SI4R0sIUK5N0QkoojeMM+TaTDimiJNQaAWVJQwJ97XX48HWH3phVmFCktyGWkkgPnRSUGpUWE16GonGeM9dYuJ42xHwo+UJGiSSHytDZq3VMhb5wc2gqvU+UtXB5U5ijEuKAq5brFa77LUgXomZ6FOjG+mHhxbs/51zO5LsZEfjm7z7hN//W30T8QGuFNM1oGpj7EBx7bRwPmfx4xgTy3VMyIym7bU7Ix5GJOQd2oOOkOzhtZ9YWIRSiJoo2SoAwGd0johmvDaSwrX/CNkzdkTJyQvcGKWeSL7gF9vpAWcfP2aJyOb+EeYiRYk7kPINCigvTvBBvYiUPEWP4X1ozWoeyN651Z3248EY7a05c28y7P7jHtvcQdapz0yb0gYOHcefJoI2/bfW5FgURSYwF4b9z909zIz/+9Fhw+/WT2+s/A77zC3/9N4EP//S/+WvJffgVqvdO6J1uOkxNgzLC6cc/5OG0sTlUEXYV3nhkrY1WFfeJKrfQFW24bHQb2LCgFZVIqWNSUX2nOuS5IzmSMje0d2cWofUdMWFOmeBgHlBvw2nYjcWhCmA7SCeGShIdOZVTIszK1ZxiwloLJ6+0fkWRYQtOnWadthUIRkgRC4HlK3B8srBeG9e1U8yQGCjBcB+Is94r7Ww8f/c19+sJmyc0OGnpfPV3nhHtyHa6IrYT5kz1Ar5AKyQy+E6USHr2mPmY2W3lerqwFoE0CEXb/T5GiiehANPTR2gZDMTencKQSdt2Zt8da9uQIIeJrQpEpdYx+fGobKGireC1AEqczjQP0DNpGcRoJfDoeEec7KYXqcQbm9ISdCs06UgUjITKyPxEJtwDfa2cHq686hFkTKXuLxvbH//xcKzKaGLbDRj8z8kT3j7XNPD5pg8C/DfA99z9H/3CH/2PwN+7ff33gP/hF17/j29TiL8D3H96zHjbq4vfkOD9s+iuXp7zyU9OXKuwe6CSqG6sPqHNMM3EPg87swtTGjyDEIcEOEQhavjMmZl15C9qTDQ3ulQsDo2BpImYF3qLuIG2AiJsbWRMxjAjdJIEggw/RIERBRcTMkXseqFuo5kpalRVNh0pVF0jMiXidEd3sAREoRI5XVce7ocCsDeh7mAh43MGMZ6/d+bVT19QYiWTmNLKd/7Vd/jGd79NCBPrtbKXguaRWh010fcrmLI+vMbPI2TGBXo12BuEMWlwJoJCzErd4PTxA9EbYgpJKFtn99HdLzVCmJEMhrKuO7VWUkrsML4fe0fHzp/enTwnpO2U5qMxKpGcMykFogzfikbBdQBnTQM5hjEhiaAhESWhKRKnGW8Rl0i7bjycLnxSKpdJ2E0onnnvey+o5efEqCNzRG7+mpuCFADjrTRDwedAvAP/DvAfAX/waeQ88F8C/xD47285EO8D/+Htz/4n4N8Hfghcgf/kL/UT/0ss70ZgUIqadJaUada5Pv8Z2+UdyhTYtJO6ctrOZE88DpU+R6yNPoK0yhSEFWfOxlYLScdTCRl++6QJbGPTQEaIg+1Nt4j3QlAl0IZuoTh5SpTa2GwlhZkkRg1OCpm1G+ydeXH6JOwrcGmQlOCZJEqqnTUYyRIyD3aAueB7HWPFGNCpDVGWZ2QO5DlyPTfKqbC+OtG9sDx9zH5/5viNmWdPj5CWYTmujTQ5MR8wDNuh205s0NgJZI5fm2mPBkdiPxdEjZwDBWEOO2VVal053j3GQmP6+kLrO9WNOCf6tdJjJ6V+27oHog56tCeoewct+E2EVsxpkog4xTsxZaQ606FR20oriWlKlCsEBnxHGdMQSU6NQrNbypc7HUO9oTbjUenXxvmy8uq6cymNHUWy8MmrE+effn80Kw1AwfpbaXz68+rz5D78b/z5U9V/9894vwN////n5/orqcFOYGQwhBHB7rJi5QM+/uHXWf72b48UaHUuZWN25xg7KollnqDvqAa20Jh7ouuVSSa8d5Y4s1nBrKPYcPf1obkXURAnhE6tHY0DBugdYho7i46TfEY1srZKYGQvqAl5cdY+aMPLXYIaeNicZXKmIDz0zkyk0fA6+IEpBJo0em/gEU+CXQrXy5n2cOTV/jDcgtpJ80SSO67nC8++dcfx6R3CMGyRGyEFPCvtFi9ntVPPV6avHNleBBbbuHZjzo+x0hGvBJS670iYxxM8d3JLfPiD56gJd+mO3VfyPKEkOFTadkvkToEcldYHNaoPV9KIDw4JQqVXQbOx7o05RKrYAKf0TGNnSQN31/cVHutIp8apMBiUMrb7iuKqhJCJMVObI2bs58r5uvLQdy5WsePMQwn84J9+gNUXQ+3qjorQxZDAmHr8qevtbazPs1P4QpUZiDRim+ixYAwp6+WjP+SPrxd++9/8HZ49WXgyLbwoK+2ceKddQGZyXmjemWMcuZUeyERKqtRWUeI4m/qAd7iPZiTmtJsefsnQzbCQiL3AGNqhrVLkEa1fBoxpuqMEI7RBjMohUkND9oDHyl0dT8kqkSepU29+DTfBbZimYj6g4uNmrnA9d/SRc17PeOukg/P06wuPv/YY64G2P0V8J9Qx3gt5sCndEvVhG4KeUpkfHxB/zPlHn9AUjr/9G6QauZw2uu2IJvaywzLh+8YcE6Vs1BLQAI++udw0Fp1pirR6waKOkWKrhAASZkIDk0rSCVLCSqP0C70qUwzspZGT4d6JXXBptJ44pokeGPi0GeKS6WWEu6TlyOZDmNX6yiG/g8eE6YRboNRKPe98+MlzPlxXziTk0SNebZX/53/+Q+zhjyB0eh8XkzhYfztv/j+vvlwU/oxyhy4VbX044ERwMcr5Z3z04TPy/BvEqEw5c19W0jYxJ8jBUDEszIQ84ty7F/RmkXXAvNNMwSsHT2zdyIzEpJQ7VjtxfoRi9D5CQkwcC0KoDZsySZ1dhqYiJsXroB/FntCseBHebCeCz7yTA8UDqgPW2k0hd8RHr6Bule1F53S6R2IgnjqHJfLsbz4lySM8rNRLoPdKXMB7hCyEOLEXwy4dtZ1uOyFF4jyxv3TW0wvMlfk3n3A1IejOXhtNBbfCfJdhDaxdWddGWBLPP3jBMs8kWdjXSlhmSq9kiUQJdL+wa0RQrq/3kTxdjqNpGodFPcnIpNilIySCttsOQrFgJDVKUHrtzBrw6mBGyBNoAxUOCq9SoPZM94BLRly4bo2+Gc/vX3O/FYpmLCon77z3/RPl8r0BXvFxTDR37E85IUXG9fU215eLwp9VBlTHBFSF7oIkoK28ef/HPP3qY6bpKQ/SOWjiXCtHb4TqzIc8RoI64drRkLDmEEYqVLQD0SstJKoWUgck4OLsvRPjwl4LkY5q4k6c+2YEAi2uaJjoGtAywB+lQEfpzZmmQBKhJOd4FMp5pR9nemzQhCkFgipdZoIapw8K6/k1RZR8mAnbxtPfXJjDRJYJayu1GiEr4XAY0xEdPInrtmN1uAzFnDjNRFPW0wrrRo/K8beOSJ8oXihrvWHbjClN7JvT9gtxTuzWkauQnt5xd+dUq7g2tCp+C7vpZvgUaWcjHCOkTmlGdmfrY/Gew4Jrg2jQYI5hsBJl6EO6Oa0rUY2go/HrwfFpwtZOXBiCtHTzsKjSY0ZdRoCtd+r6wHV3zmZsUQYr4Xnh4Y++h5pjgLh/Rvd6y+//P7O+XBT+nBpS1DYWCDWyJyqO1Jc8/+HPuXuUBovvptI73Rdmz8zS8EVpISA10JONpluIxK1QveE0vBl4pFNJmklRqHsnBUebghjBCldXsgZMJ7QMe3WrnTgFunR0mQgykqUl7vQeETIhFx5Nkb1sZE1ERm/i9Koi13WkZlOJywEpzpyd4zefkCxg5uxbhdwJ4Y4WO8EVQsY2eLjsLG5IjsQ54i2i0Vh/fiXYzhY6T77zVfYmtFaw2qhmaAyEoKz7iOazqLAOmfjpYWVfC/2dA9qFGGdU6oCZSqDVAi0Tph0tjvoQO7sEsg69g8YKVekmo0HYDeuJHishzpTLifmw0HuFJPQ1Ms3zEBRlpQOtFUQjcVo4PH1CiEbbldIb++XMq+vKuVTWGNE5ca7KB//0A3p5PtKmxG47hTHm/hevq1/3lfyr19snp3rL6tNm0NYqMSrBG+uL9/jooys7MraP1Xi+G8/XyrmAtWlASKeRKagxYt7RvIxzvQhpnvCoJEl4bAQ3SDJGb2mwGItGUkrD4WdXDOhUMmDNCDoPCnEdCVSxR6qPXYeHyEO5sL5pCGG4Lk3Z3jxwCjsuhoWIBufZtxeevLPwaMnkJRCnSJwic5ggDgVeUkGvG2oXYg54croPonQIwuXdM2qDWjU9vuN6H1gfthFthxBkZGj45uADHW+lUrTRzbmeLuhNOCXS8X3HfHggrlsjaEJDJwXD6XQSQSI9NZqMEXLbfMTmqQ7iegfJHfowfi15Gvi6KY5AHO8QGrRKzBFCJsQjQZU4BdZro7ZId2PfCvenB+6vhTde2MW4uPPhT+65vvoe6gUXQfqICuxvoSjp89aXi8JfUIrcHI5CIxBCQu3C+Qc/5nJZubpSs7Ba43QxLq8a+2UYgMQyQRcO00IMCY1DIZiC4AGm2DBtJIFmcPBAd2dCCV5IEuk+7N1hWtDZmKNQcmOZBlRkoL2GHbqbYL4zBSOlhSdPHvPkKKy18+a18/xHr7n7yhOCwfL0wDvfecxXvvmIu7vHpMM8bsImkPN4+gq03eiXxna+DJbDbVu8qyD1CkV4+dMHlrvAtVSmby0D21Y+htzRvo6E6k3YSmHvhhgcXYaHwxXzPvgPS6K1mwDpqNS9kIKPEWrZaeaUOhFSYvKGKkiHJWWyO4QBU03BsWJUr2OUqBnxiM8jZDdKovplLLjHRMzHgWcPgmtHVIcAS5zusK2F7XriRam8vpwQzUh4xMuHwvM//CHa36DRiTZyPOVtVCT9CvXlovAXlJmNeDgRrKw0HA9Q9/f5+Mev2eoVJ1BC4CqVj+vK9b6wvRmKwRA7piMcReOChtGnyDLRQ0ZTBAkoRgmdiCHigzfoRpYRaJq7Ee1A08wUF9B8O/cDquMHGZQ5ZQoZD1CC8fIkrJ+ccV+Jd4lNNr72nQNPv/WE6BHNEyE2cGVvYxdAhctunM+N0gzNmRASaRYkLEQdeZbSncvzNzw+dM79RP7Nd9g3G2ndabyv6UzZ36AxEQ9jZxNU2EjINFGk0GpHF8HUkdSQYCPPIujI0dBxztfog9BcdtKU2ctKQAiEz9gYUQVD2bUQQiClALGiiyMdug7GQrYFCyO0xQI0FNM40p0kokzQGGzGy8bLS2GvnW2eKAE2L3zwz16j689wSQPbZoXuhvF2shc/b33ZU/gLashTjd4LEoVqgQREVfYP3+WTpwv5txYeT7A62LrzqgtP9k58yEyHBfONNGVaGedVjUK1TrBKc8WiD9u1KUu+TdzVaER6NEpv5CnCPhplKQ97NdwgLzJwa20ddOZeCvupj6Rqh8Nh5rJVpoPyztfu2OunZ/JIrZXJlYaS0hPa2mj9ytorUzzgBnu/4jKxTMCbQkltaC08cLwzTm827n77CWUbO4vpTrCaaHthK+uQSUuhX43QYSv7CMRNY1JiZpg3JGZKMUQj+WYc26WjUcADKQZUhiltv54I80IvhpaO6PAqiQuRSswZFx9ZlmnC4tCfTDkTRCh7H+PmMIJ0DKhqtGCE8AhQailctjOX68bL686bWmkxs0ng3R9fuPzs98FWuhrWZYBpe/slV9Nfj/pyUfiLyhzHR0RYT6g3egAlUu0VD+++z/2Thfzk5ue/mzm3il+Uu+PKZVdCdzZLiBXSLNATQYzdZ3JslO7MN9Kxt8hD7yO8lQ49U8s+2AAdEOW0+iAmM5gMkqBfG3baKXYeohlVxIQajULjUTbIR7ZiTGm4JiVBQLlszsKVGhKCUbqT8jJQ6aKEMFH3QjkFQk6Yb1jPcCmc98rdd55SLopRiHOkF8dGBA15OrKVRqFzmBJFGlkDVUbIjolxeeh4c/RpIDSFOdBKIQToLaJqbFLILRFTuHEwMlmda+9Y7oRbylK2SKER1EkiuM6j7+FjxGvTwKF5yMTQ0JC4emWOR5ROTI+ou9FbwWvj5etPeONwapWWJkrMfPTmysvf/z5qD5gq0YaB620VI/2q9eWi8DnLfYSvjkVC6DEi6rT9Iz764R35d3+LkCb8NlfffcSXhWOE2Icct0S2axskn9oJFjntFa/C6ob1NsZk3lktIr1SudxMU4r1joSBb8OFJE4hIL3SG6RDQnYdYSOtk58oTw8HYsrsrx+YyhVrd8hhwa1Stx1JjFQs6ywxIzpUlIgxxZlSCiINrTA9i3jpaAqcPnpgahCfjeg617FzaZvT42i4eXJ8ragYMcBWRrDOvhumSqNifeRPyiREBI6d1sYoL9aRCFtdacXIU8c+TXwKgd6UdDBqqXgaY9oQKz0EQoCy9yFImqYREbiM/M1AoPZOvpsYw96hDvXQKV3oKF4a59cveXmtXNXYVLAcuN9Wfv6Hz2n7eyiDUGUuwzj25aLwxapxTrSx5YSRSJ3GxVBe/ISP33+H6buBPM9s3YFGUedJ30AyHiZsFugrWgslOL5ViE5KPnIjaUzTgdZHJJnTUZsp1xXh5p2IgdlGKrU3JdhIOcYNNefuiWBTIPcZDyN6vbfK8uSO6+sL+vLM69eFPEcaDBLRDEimV0dItHIlpszpvHLIkVAPdHtFuwR67JTXRkiBPXVmGTbz7nGQq4MREzTRm4vUyUHZKAQL1NZBE+aGNaVlqKFzPGZKqsQ9IV5vnIHxfXRP5JyRKGxlHR6FkFGtSJiQmXE0Sw0PMxoG9SrnTHLBaISQR9ZCTPTU8NUI04LEiV4rMSnmRyITtTxwejjzequ8uWzUJzNVlb0bH/zRA/vPvwdScMJNutxR+Gvlb/hl9eWi8HnLHPCRu0gY0WE9jjN9X7n/8Q94cfe7TN9QiENbcLoWcli4OwCiw2orAcjIVgg6phBSO1lhv1GgJASknmlBCHTmu0ZiNAU9NrwLdxYwdcqmLJMQNKDTAa9lwD9qZQrjpmwx4ntnOUR6dSJGtEZXw/cO54BMyqVtwwE5RWS74tvOtilzXKm1Uu6VNimxNswK6VlCb9kH+34lyE3g0yMhOZHOJsJaIGik9Q2dEpfaCO74klEL8HgmpNuINW/4VQguJAmYd6IrrRmbQJBIUCekPjIzNeG1Mac4vscyALZJoLWKzoEsE006ISYQx0jkQ0BTZkdoMRC6I6ZsdaWcC5dtpwfhOgkeA5YCn3y4cf+j71PrGzSBmAytSHfsr/EI8k/Xl4vCr1BmN6RX74hE3BtFR/OL8gnPv/8u0/Jd/t/2ziXWkusqw9/aj6pzX3LHNkQmRMFBycACCSwURQJlCCQTwywjMkBiAhIZMDDKJFMiwQAJIYGIFBAiE0BkggRCSIwIGLAdJ8adTmPwK27bfbvv41TVfqzFYFc79zbduDuOc86N6pOOqs4+NfhLu86q/ViPH7m0z4nCWzphpx7VwAMHAQulzdV1ot9f4YY11RyWJyiOvbIPkqllBbaLrZQ8tfyC4hQpQtII0gq6igv0MbaEoquApycBUgqmCY17ZM1IVixV4kHA1UrWgKzADUoExt1ESB3dpV38JFSXKTaPYrpMJYAEcjpFc5vL5/1Id9BGNVpSc+9G2PWRExexNLWck3hMJ1QCJo40ZPoYQIyqxiQ7SCjYTkctrbJ1DAOu22caEx0RdQlnbeoioUUWlar0K4hhpFRQJy3CtQphFSip0u8ECsaJy0R28Xh88FS15tlZjd0dz6SBSTqi78iHhxydHHLttHCYB7L0mBPePEq8/NRlyvrb4CtUT6mlhWf/gIwQbrEYhfvE0VbL0ZaWPVRBvAdXqev/5o0rD7DzWOTB3Z6igZM00VWl84F+1bYiLc7JU1YHOE1Ui1jI1JywHAkSmGyNlF36GBjHNaItbXmUeQ7rIZhQA5Qx4VWYwrqFcDtPjPvIWAEHU6I/2AMxoleQwrQGNCB9z64Y0imrLrbturDTwrtXFV8Ut1KmrMQSGY4Txylz6eFdTk8LfedwFrEyoaLcrEbnmsu2oyOXgkforCWQ7VbSUty7Vog3R4e6Dul2CP4YHaBzkc4qRQAKAaGIsrfyCIEaobeA5IgguK6iokg1gs8Us5bRST3mAzuho7qEhh4JBtVRJ/Bd14LW/C5BjVOtnI4T1wZHoqAuYn1gmIyX/uMaevQi4ipibQ3mB5XFKNwHIjIvBLaV5hb7UsHC7OeuHL3+n/zPbqA++gB7+we4oXB9V+B4Ylc9u5oIxWFdR6BgWknSwoBjiIjVFsCrHhNlLCMhBEqpjCXhrBWOUYXYe3oMjZE6Fmp2ECteHQNKMAd1IOzvkuuIE89YHc5FYjR0zh+otHgGqw6YGFTxZsSukmn+ESYjSQteC13XIxpxklutxJTARbLPeGmejiE6cl0j6nAucJIyK+mZqHhrtTdj53BxD9YFywrWQUioZepkBB8pU+UkFHaCw/mOdU3s0FPMqD7Rq6OWFoLtQzMCBrhoFDOCda2ucPXgwVuPToHYGb3fZdKCTolsDh1HXjk+4niq3PDgouPIjJe+cZ3h1edA1lRxBOTtZ6HeQ+k359zbz8xFYDEK98HZTrXZs6+NGgo+uhYIxcjp1Su82f0k7/8o0DVnnlqNh6qR1hMHl3bwOrbU4xWUirM259ZSUKuMuc7zYkHFUWvBuZ22AxEEq5XjdSVEQ4AozVjVUTArWBJylzGLSFGqguscrsCQMzu9Q2vHmA0pRu2NOrXkLJaluVYXIZtRy5qTSXFOyQX8bsFRKDlDzqRiWIDePNmUxIhYS4VetTDmE8SvMAZIgo+eoRpSHVZK85wshaIFnyeoQtEBtYjrjFA6hmqkMlGDow4j+ECtoK55HXpriV7TmCleW1q7VUe2TJ4cvoNpMkKopDGhIVCGCTNHloreOOKtmyfcSJnBKRY8k3jeuHrK4dVvQLkBwRORlq7PrNWCnNPy38PT8149lt9zFqPwXSIGNq8x4ObS9tL87i1f5+bVyxx96Cewbp/3eThKI6bCaqiUmtnZiYQ4b8NJQqpnsoToCsqEViOJUr2iWlHnIGc6U6Zs5KDs7u0xrVt+BcQxTC0zcsRRpLAnl6A/Yj0VQojkMYMoUR05G8PNiXDg2ty/KJGuvQXNMZSC1LbbMjiPWmI8zXQhUqRw43hEU24p5laRciqMoRI7YapGH5Vh0lYJK7kW0bjyFJcoY1sDcF4ZhuO2BRgSXdfhzBhTwnvX8iENsDali46OijhPdgFqQjSwDhmtgexO6coOXaxki0QCEy3fpkcR17elgKykqmg1nILZhB0PHK4L14eBUZW850mu59uvjLzxzGXIb0FoW6FW9byT0j28/S/arsRWG4WW9HI7LezbGaC17bNjjorgomC9p0zXOTysrB5SjpzgUY5LYKgD6TTwQFLojS60N30IFU0Gum4egHnODFRbGJTg2SmOk6A4c5QxcDicghq7ThnU8C6jAusQ6DVyygmShFoUyhrnPM4gO5gmpUbBTYHeKioO9cY4tnAjfFvx74In1+aDkKpRJgOp+LiG6FCDYaoomQgM2aM1UHSEPPsm5EzsHHX09L4j1QnvPMdHp6xzS3nHgWNaDzitxC7g0ggqdEEwo7kYi+FrwvlWf7NMhRqErlNKEsJOYqgg5hlloE+emsB1geloYpSOPYR1rsQpkvQEhpFhnTmeRk5qYeo61HUcHmZee+pblOkKiLaQ6KrU26cB2/l4vitkG/50IvIGcAq8uWkt74KHudj64eLfw0XXD+/tPXzIzH7onS7aCqMAICJPbUu69++Gi64fLv49XHT9sB33sERJLiwsnGMxCgsLC+fYJqPwR5sW8C656Prh4t/DRdcPW3APW7OmsLCwsB1s00hhYWFhC9i4URCRXxSRF0Tkiog8uWk994qIvCgiXxORp29VzhaRB0Xk70Xkm/PxfZvWeRYR+aKIXBOR58603VHzXAv09+d+eVZEHt+c8re13kn/50XklbkfnhaRT5357bdn/S+IyC9sRvV3EJEPisg/isjzIvJ1EfnNuX27+uCWT/YmPoAHvgV8GOiAZ4DHNqnpPrS/CDx8W9sXgCfn8yeB39m0ztv0fQJ4HHjunTTT6oH+La1k4MeBr26p/s8Dv3WHax+bn6ceeHR+zvyG9T8CPD6fHwCXZ51b1QebHil8DLhiZlfNLAFfBp7YsKZ3wxPAl+bzLwG/tEEt/wcz+yfg+m3Nd9P8BPCn1vhn4JKIPPL9UXpn7qL/bjwBfNnMJjP7L1rB44+9Z+LuATN7zcz+fT4/Bp4HPsCW9cGmjcIHgJfOfH95brsIGPB3IvJvIvJrc9v7zew1aA8A8MMbU3fv3E3zReqb35iH1188M2Xbav0i8mPATwNfZcv6YNNG4U7VrC/KdsjPmtnjwCeBXxeRT2xa0PeYi9I3fwj8OPBTwGvA787tW6tfRPaBvwQ+a2ZH/9+ld2h7z+9h00bhZeCDZ77/KPDqhrTcF2b26ny8Bvw1bWj6+q3h3Xy8tjmF98zdNF+IvjGz182smpkCf8x3pghbqV9EIs0g/LmZ/dXcvFV9sGmj8K/AR0TkURHpgE8DX9mwpndERPZE5ODWOfDzwHM07Z+ZL/sM8DebUXhf3E3zV4BfmVfAPw7cvDXE3SZum2P/Mq0kkuWjAAAAyUlEQVQfoOn/tIj0IvIo8BHgX77f+s4iIgL8CfC8mf3emZ+2qw82uRp7ZoX1Mm11+HOb1nOPmj9MW9l+Bvj6Ld3AQ8A/AN+cjw9uWuttuv+CNsTOtLfQr95NM23o+gdzv3wN+Jkt1f9ns75naX+iR85c/7lZ/wvAJ7dA/8/Rhv/PAk/Pn09tWx8sHo0LCwvn2PT0YWFhYctYjMLCwsI5FqOwsLBwjsUoLCwsnGMxCgsLC+dYjMLCwsI5FqOwsLBwjsUoLCwsnON/AXu9/38sjWbLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cca5acc128>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvWmsdFt61/d71rSHqjO8w723R9uddmIScGQwwbKdSAiLQKxAsAQRhgxWEEZKIsVggYxDEogS45g4zWCGENkfgBiIFJIoVoAPJFKIpQx2ZMV2TJCNe+577zudc6pq772m58mHeo3cyKbbdt++3e3zk0rnnF27au86e9V/r/Ws5/8sMTPuueeee34G93afwD333PO5xb0o3HPPPZ/EvSjcc889n8S9KNxzzz2fxL0o3HPPPZ/EvSjcc889n8RbJgoi8ptF5P8TkZ8UkW97q45zzz33fGaRtyJPQUQ88PeB3wh8FPi/gG80s//3M36we+655zPKW9VT+HXAT5rZPzCzAvw14F95i451zz33fAYJb9H7vhv4yM/6+6PAV/18O4vIfVrlp0EQz3vecYG6CurQrlRnhA7OOQwwVUSEqopDsK44HIqhdFABBBUDAzEBkfPvYhiG4AAFOb8e5/jZPUoREAQB1AxngBfMDLPz84ogGE4caudz6CgOMBHMAIPgoHP+XZwBL8+H8/EEhzNDnJy3vDwWauCE15+fsP7Zuwaf5zw1s1c+1U5vlSjIz7Htk774IvLNwDe/Rcf/gkEAG+C/+fZ/iX9QMzPKs+PKMAw8qHBbBpopx/yCo3YuYmKgcXzSSCFStJK847YpyIDzytgary+FXjKDDpjL6LjnQc28oZVHu4mbW0F05XonrM7R10rrwlUMqB+wvrHbB14cGskJL7pj9J4UCiLC4AJbr1iYkbJSakXmCT9FhuMG3lHLTG0bVxeG7PbcPXnOeJlI6ZLRL1gVGoHWI5tsPLjyrEW4mAbYMj4JuTgu3jPyR77j73Kfsv8p+dCns9NbFVP4auCPmtlvevn3HwYwsz/+8+x/fzX/EQQwJ3zrt3w1Xzw+pG0LPkSC3HF7F7jYKU+OiRYah7wyVU/PAjSKFpZDZ75UHgwjz+46KVc2bRQf2KQx9QTeWJYGmzFOxrI2dnNkiY7dohgOpGMI1sE5EAfRB6wpq6tEcTAqukUijsPWmaLiQ4CgWFH6NFCDIZty5QTtUMSRtbJ1T2idV18duMnw4nbl8S4wOcchBZxbmPqOPhtxM/bjjvhAyE9W1gnaJrzjaqSFxI8fFv67v/zDgHLfoH5OftjMfu2n2umtEoXAOdD4dcDHOAcaf5eZ/fjPs//9NfxHeDwI3/mdX88bLzr2ZmVOjjou2FMoIdBSp60HvM4s1vCaeX4nRKtsGLvkWVohtoS4gNeVWgdyzoAj0MBFXKjcCoRDYT002uCYkhDjQHSRrZ5IPtKGyrQlVhNqAs3GrhWCTySviKvUNOFd441b49FVYrtTclCunaDOYXYe2jTpSJp5nDpvLp22eBJHLl59wCTGxw+dK2l0cWyh8jBMRBcpNIJVZIi07shsFDX8NHI1zJCV978a+JY/84Os9e2+gp+TvH2iACAiXw/8ScAD32dm/+k/Zt97UYCX3QP4q9/zm3j605U8DaxrxkInOU/ZKhwTxMxy9xzmS9yaIYzcHBaWU2W3D4y2UdpIYUM1sXeV2w2891hplKrUUogpIV1ZO/gEdYPjaWE3jNTceOXxxOY7ljtu8Jgb2Fdh6StdhDkNbNuKxIhvirOBHirOGlvpaHJcDJHbw8bkEgBdYHAw7wdiCxy2ldOWidHjveciDhx8pS6F6cEVQTthSGgtEJSyRXahYd5Re+NWHY/3gSlECA3cxCk23hf2fOuf/d8w7tXhZ/H2isIvhHtRODP4wPf8J7+NeveMN9KRR+sVPgdO0x089RwHz509hw7zac9yWPC68Xze08uByXvIjnXX2B8CTV5A22HSaFmIsmfzB4654zcYXGDxQlfPqCdimDBRjsuGN6W0ThwG0miMcaR2wAKzNEiOvp3wfaL6hd18gS/GJ2rDlc5FdEQf6A4O1kjdSD6gxbNJYzfDnRT2PXFqBT15QkpMO2HwgXyonGLlwZRYeicS4QHoXSAK9EnYi+eFdqQp77yc2LXKMXaSXBDcC5bXrvnOD/wQ1HtheMm9KHz+4Pg9v/uf4Su/7H3cHI7MQwN3hS4rrRXqunFsxhz23Kxv0HNEcyJvK6TI/tKTX5w4uk7wMylnKo21OZwCuRDnke1UqMlxZY4TRq6FScLLMwDTyoZQ1XCrslknSGGeLpmCcooD+66oeYRGQQl4GDzkQnCBpo1BAmEKPC+Z1BpGomwn/DDgvadpIPlClBmjcnfccD6CGVejUqaAXys1JG5uN97zYEcaB26L4dyGIgyTo2+eQzBes45ezPSlM+1AiVw5z23I7EPkyQePfO///FPUXt7Wq/w5wKclCvdpzm8nAYIb+Jt/6pv4Nf/E+/josyMXLrE8g+1wy2123B6V200J0wV1XcnrHj15Dq7jdkIcGsfDwoohRdhOK3mAYxX0ZGzLCe/gkDeaGkvvHJoi1YgS2Rq4XjnUztqUEISdOLyHKSS8zuTjxpMXmUdSmWIjWKM7wzVIg7CcGnNI5LKRXKHQsaJcuogYiHoKAW8QrBOcsoSB3FfwDvGww4h1YyBSD45int0I73l4xRt3hSdtYbBG2BJeHMdDIffK3JWbzVGXDeeUpQi9Nu5awTqs2fGeL3uNb/kDX805p+6eT8V9T+FtxMWZ7/2Pv4r1prPIiJnSlo1VBFTYWWY9Gpsz6rqRl4HDmHlvjORNOVhlXDsLgnfn4F9zytNT40FydPFsZcE3h4intYYbBI/QnCOVRiXiTZEUIK9ImnCxIU3Yto2ePHrTyb3gLBFpzPMIcyOlwCSJ7j2UQlGjZGN3EZBNUJfJBEwbrYN2RwwOEWN2nu4UlyKH2mjHyuQ9RRoPrye2VfEpkrXxaEp8+NlGHBymjYvoWGTgcVSKC9xuFR9gDAmNyiCenfdsY0S3wuxHwkXh4eUFf/BP/R+g+e2+9G8X98OHz0UEh+G4Co1/9w/9Bh6ocLg9YdHzqox86PYOTQP7vmF+T24nXjwrpNS5GCfKVrhrhtSORqXlDVcTuSrj5Gkdaq80FZIFei7EXllCYlBBh0DMGXOdpQtz8MQCjMLWldiMPghBjdvseO2ysbWI1kqujmVZiBJoFpDQ0VJ5OO3og+EXY3oUWXPGTmDJOGjE90oQpfWAhEpKidYK0QdO2hkscGuBBwm0RCxUHo4DbXYMMdJenMgJ8roQXYRhRHrGyYSbKzdHx4XreC9soxJ05rXrxKl2dqkhdUAHZSXyMAa+66//72wnAH2bW8NnnXtR+Fzl3/otX8NXfNUFrMqL50dm53mSHboVopwIwyWn00LaT9w+vSUXQaPnHSLc6ZHEyLNDhti5cjtOy4obAlI2hAReWHPDjUCu5OKYxKjmWGthvJjopTOZYt6hvWMp0RRS7syifKx2Hu09qglHBQnkrniDOXliDDy/3WhOsLVgzXAx4Lwxa2Kj0+1I73ui7yxiBOfRUgkeXAc3JqidbMbQwe+E1ZQpJMIohJgwr2iLtAmkGk8+8Zx3Xl+AN0QmBrfy5KA82iWKNabxkru+cjU7NgtcjxeU/AKLwjgkVI1hGvjR11/wAz/w997upvDZ5l4UPqdwgAZ+67/4bn7jV30VaXvGHYIdRpbxgNTKzalipXGXI2NroJkukSgd9Z4HVnm6bRxXo3foCrthRMhY7+jiuY2NJEJRgaoED6MTcA4xowTPUCvjEFi7EtVzcpWpGB0Dl9hqIbrIfvYsp0wYAzlXehPUFUYntDjz+NIT1XO3ZKYYGBNUH5kRTrnQCPTeSWNATxt9DOy8oznHelsJe4+oZzseGfd7Xjw/cZkcR68M3uOcYz8Emim1QZqM20Pn2Skzp4iLysTAi1y4urhANKPaseTx3RO8kFJkC5V5DNSlEC8uuHLKosrHDsLf+O9/mO55mWv9Bc+9KHwuMbHnz/35r0Ff7Hl6d0MOI7vbgrrKWs5eBW0nOBkxjHzw7inWA3TjZm2kcu7qbuYo+cguDvjBM3RlVcd+FPxWuWtKmCKnWgkS8GaEbmzScESGeB564DydjlcoUdjJjt5PTC5yuxYsCiLCRfJoESQKznvyseBTZHc1km+PTKNwqkIphQfzjsUKySJqQnQdnzxEoW9KVrgaEs6Um5LpPsCaCV6o1eO98OSwYdZ55cHEg2kACxCF2y1zCBE2JYxGX5TjduDRNHA4NdJeuRTPSSLVOrvLmbJuhNFRamdHwLnAbprpDtp1x1R519UD/th3/10K7W1uIZ8V7kXhcwaBv/J9v5tnP/XmufHjyceFdRb8tpCa43ktWBPSqnziSSHsNrDEi9bhtlJyJ7tGUyVV4Qbjugt30thJIDlhikKMERVla+d/qdMOIeGtYuJYa2NUww+BiyA08VCNYp3JO56eKvMkkKHtAjsEvMO1joqgejZKDXGEsNCXAZfOPqu+bkyzZ4iJkAZoFe2OoMptOREkEELAnCH7EX+CjFG6Ejts2ljXlYfzxGqQJiX5hKWEWkFqQFU49pXRHG+uG64qg8BmkXc/DtxlZXIDi5xTsFspMI9cJKF0kOBJLpO5ZgqVfjnx5deP+bf/xP+IfeHHGO5F4XOFv/gf/XZ6O7HKLeu6YyedJ3cZ8YG4VdZacS5wXA8YA8tpY/BwIQNv5BWh4/r5jkdRmALHQ+awrGCB1hopCn1rOM95/OzO43UdHJ2OrY5jb+yiUHDsfeBqNvCB6JSM4IqRG4y7ga4ZLEHZuEyJLMJE56Y4Hk4e8Q56wUdHbwLTxPFYGQajLBWLxuQS3Vd6Mx4MCeccDQU1botnf+WgwDwO3JXMzWFBmzFmR56Vi3HkaoDmRpp2hECuhWVr7AdPnyde3Dyj3YGfHfvg8CmgXWjSqHJ2/DmELhDGgeRArfBwuCAPsCNxujDmacd3fuDvvN1N5a3mXhTedhx8/5/5N3nx0TdZSkFqporHq2NpxjM5sS/gWqMvxqE2jtXx6n7k1DLDBjfVWPqGHCtuDGARo+CzsukG6llN0eV8x920oQreHN1BUmFTBSegHUWIpswpkqJxrIJTwYtyNQeaV1KIRG+AI0knuoRaoE7GdntkN04k75kVXlhnHEdaLWjtOCcEb0RxaOtINCI71nKE3Z5gDYchg+AtoM3Imhj3DcuV24OwLRnXYX51RtSYR0OKImPg1MC0UbPDRTiuynZYmNOAG+Aaz91grHVgItNjIRdHnEYCxh6leSHtZ7p6Hg6eO7cyzhOP0wO+/c/+bb6A3VSflii8Vdbpe4C/9Sd/Lz/6ob9H10j1hkbPcAi0tvGGbcxrJrsJoZ2TcWzi4ZWjHhdWWzjliAksW0Nk4LE2ujduTysXMYIE/Na4iANybRxbZW8DUo3alP1lZPCOtax0AmPwHHImmGddC21MjAJeO2Zw2gwNFS+OkBKhGd1GdkPDibLhmYcLWiiE6lhU2QHrCgTDRSE5z3bMLFGYTBjChL40QLU1k2JC2MBmcgUzQXrhdOxcivHYz3yYhYvkaGtmHj2uzbSwURbHzq3cDTBIYmhCuOi8cQelV6Zh5snWGZsxkbHYaVVwPjKsgTJW8pDoSyZ4Q8Q4us7lMHC6W3lz4gtZED5t7jMa3yoc/NTTm3MNgXlgXwSXA7lUXEg87HuwgcPNgfzCGNQITshL5fXe2I4OZ47cGw9Q6Edy7uStos2xSEcLqPNoNI6ndrY3i7C/SqTRCLnS8sbWHVaVUg2y0ek8vNzzrjny3mvH40t4NO8Yose7RC6dWgzvPas0DrmxOlhPK9VXRAQ/eMIukt4xsd9lLseAuAnXjeoczkGIiZozTTtLybjoGIbIpg5qxWkmpsaDOWI98Wx13PUjBGHtjcOpkiSSxkbWwAyoE6IN1Lyi0fBVudpPdOewnHGxkaLjpJW2OUqDKxFqz1iBLRdsTqx5RbSSgnI8rpQqxKr8hT/89fxy/1rcDx/eAj7wn/0O9BMHuh45iaEZljpwOWfaytml2DaiwPObTskLIQRi66wmnMwz1spWCx5BfiYwHiJrKcwDaAE6lGBIB+8FE8VrQFXRLnhnhEHoJoh1NjUQJYYZbRlfhDQ5sIaKh+YgKG88WRlH2LmIG5U5DmjrmAj7wXNXwdfCZRp4IZ05BiiFFgLRe6wXehCuh4G1e5Iq1sF859QaSSJNPK/uI1uuUI1hn3h63DANVNeoB0NcY8AxXEdKKcxzotbOSGSVc0bmsTfcmLg9najFeLifCFZ5Xg0fE601mjUmDbTgSQM4NcwFXBKqNXYkShR08rzbBXSAZzryfX/pB9/OZvRWcO99+GwTGHh3uMS2jelyYxv2SE2IT+yskl9U1qVxWgqlJ07PO8+fFZCAqeOmNTQrcdsoVujqKE0opdFMOZwKKRh560gQxCtbK2RpKEYqAWsVEUdMUMO5OIoPhqkQQsArBN2oRfEj4ITuAqUpzZRSCq9dBMjG822jdGhmFIwAmBmuNTR6blCcwbMl073HtoKqsnbjeKzcbIa2hgZHeelUnGPExU4IjSfPG6UU/KXjkDtx8lxfQ5gi04NIz4XbrZxnR2rksAyQHGuEwRk5Nmwa6L3yyjTg00jujeNSmRFy7UTxXDdPDx1Q2irkXsk5Y4dzABdtOAqXS6P7TjkZr87tXBXO/fL7ivzy+8RvIRHlP/gTv57dixPtEHDrka6Fup4wNqoFzDW2Wkj1hmVZma+VbA3pSnIDhY3JR0IVRnMk35DgMQFx/TzbgLCuldaNSzxT85RsdGe0bgzJcF6B8xRi3SoNYRJBzaPSmYKjm51rMFZh5845DJfjgAvgdxEvjrsTfPzJwvNnJ964O3FzKFSNRC+Mo2NwkccXe6r3zPsrog1IE2Y/4aqi1YFXquuk4GlN8W7gUZh49GoE8ax3CkHwa2erjqEUwpqZ55H9BM9uTiCNyVWSCDs5D20sTFyJEuKMJJCsBO/ZXV6AE2YH4h1tGljNk3CYyzgbERGmfSS1xu0GoXm26Fm7Z5oDx2XhD/3+r+GXYSr0L14UROS9IvK/iMhPiMiPi8i/93L7HxWRj4nIj7x8fP1n7nQ/d3E+8oH/8DcT3+jwqkN7O4/1a+UqQF6MJ8tzjseK75WPHBzmA7bBYJFTqUQBwXPbK9V7mnS2VdlyoakyBiiqDD5AEMx5injmkPBe6KHjR0etRi2GtYbTSq6dUjqnY2YcItEC5h1eAy5ATEJBcc6hqiwqYMbWOqVlVB3HBrd3jSe3K8+e3/HTH1/46Z9+wUef3HBcMzMGkhlGmIZz3Ug/JrRlDs82SoPDaWFMgb5Ulla4fVG4eJhoXjguJ8wH3MkQC1QT4n5kO3asCVNKxADzMGJuwifPtTgyAy4KuzgwTIl1tXNvJoz4aQAVqhmPh4hXz2gBH/o5cHo0ou1wwN3aCU0ZGtix4rhmfbLxXb/na4H92928Pqv8omMKIvJO4J1m9n+LyAXww8BvA/5V4Ghm//kv4L0+72MKf/m7vwF9sfB8g+wb1jPLqfKsONy2El7ecKQpbzyplFa52Dn6aqg3trWRTLAubJPH58xpaTzcj2y1ACOZTN0yF9NEtk6vjRAcg3py7+yHiLhG00h86RAsvZGBh1PkmJUpGcdqzM5xqJV5TEQSzQpNOyEKshm3t5VuyuWDPTVnDllpVUHPlZnFCkQh4s7DknOHgKurgUcPEmpQWzwXb+0V8Yl9MPKxIDHgE3QS6hrvut5zu1V6N3Jv7McB6YqFzpsrpMNGUBgejlzvBjIzudzSiedpTxpX48hxhWe3jSonvPeIj5gJLReuHwnPbjxNCpNBrwpjxOFxQVhUGaUxXs1cJDi1wDwW1IT3X89865/7P78QJiY+u3kKIvI/AN8DfC2/nETBB77/+/9lnn+osz73aAffjjxXJT1bwHme54KUhV4rN3eNhjAO56SjXATTQlRoCKEJmzN676h4Jm1Ug44nJs+mlVY9k3Scr4Se6P5cbr2XTDVhukjEomylMk4TFSW0jkhk7ZlxTNAdfV2x8Zxh6Zxj54yPP1tADRdHdvvGEAdy64TWqSLsfaAGobazFXurmb51rDn82YvFsWw8urrm8XXCpDH5gdIFlY71xhhHem3UqswPhG1zXE4eU6goooJTaEGIyXHzPBOz4mbHeDERemPeBRY8WxHmaJy2lZHEnYePfPCW3T4Rk+fKBZ5r5TGeu1YoIlj2SDoHIoM2TihjMuqxMez2zDNUV2k28DDAK+mK4f17fv93/E98nmdDf/ZEQUS+BPhfgV8F/AHgm4A74IeAbzWzF5/i9Z+XoiDAd/2W38D+q1+lry8oXTgeFnwFk8Zp86xLpbYT3ZQXTzLiOtcpUQk0hW0r5J6JJmCOZsZYGwuGmNC9EJrSvGLmGKxBOHsLam3EGM69EGc4kbOwBHe+O4tg3rGVxhw8YtDlvKaDNzhp5SIMbM2YpPHm00yxzrtf2+OcIzRhLSsWI6pKMGEaR7Q2WlBi94TROJbGZQwcjvD8djlnasZOKWB0UnS843pHmoTLIRGC42ZpECNzgFoNorIPga0LRTs1CEODi8sZp8abb6ygGYvGqw8fIA4W1xib4+awMM4XrDmz208cTysfez3zaFLiTjj2QK2Z0Q+4Tei+03WjAbspYeLZfEcXOZeemweCCRaVYJ55HMDB37fC3/ivf4zP4zjDZ2f2QUT2wH8LfIuZ3QF/Hng/8BXAJ4Dv/nle980i8kMi8kO/1HN4OzgvqeKJvybS2h23tZDzBmEgU8FlNFdWXfFUjoeNMAjX11eszbEcM/SM9ZVB5Fx4ZBBKW9B9JOFo2lAPeCFExwyU4GnVaK0xhvPlU6nkrtSmxNbP85UvbdLLsjG5gNBZSkdrw0zo3bjAUXtmTsJH39zIzvjiL3pMciNWGqTzXX2ycz3HIY44znULBhcQp2jxTHjWauwuE1/0rh2PriM0Y3CJ0UesBz5xs/DhDx35xJMTh5wZIqgWclG894TuOdyWc28iV1KHocN215C+8d4vucCAdGe8+fzA3Ysjux5eVosacYNhXui9It5x/VrkaYWbpREHz95dULtiO+GpnU1oU0hodUgBUaG6jviR1gIViGlm8MLSoPeFr5wuEL7wqzf9knoKIhKBHwD+tpn9Fz/H818C/ICZ/apP8T6ffz0FBx/4478DWe5wWWnbii+eY4iUvLL2yuF5p/dCr8qbz1be9c5L2ilzykdcS+TekJfz8g2gdpw6BumsPXIxOEqv56GDLpQSuL6cWPLGIJ7uPYM51tDP04QY20vL9BgdzRypNXowKokL79l6xocEdrZCr+tGrZ0miVeuPEii68aEYL5RdSBwLsS6YqTuMBfJznB0xuDJKL4KTR1pEHoQQm34MbHdbTxbK7l4KBv7ybF0mLrw3ncn6rhnJ1BaJZhnyQtpHhliIM4jJobmyiu7xKqBj33kFnUdQuCd+0hJIz1v9GkitxOtOmKM0Cp3pfPmmwuvXg0Mc+DmpjB6h4VO6w6RTm+eB0PihVaSdMRDdzAPO8QqIp40JSR6vKv4vuO7/toPfr5mPr61PQUREeB7gZ/42YLwMgD5M3wD8GO/2GN8LvOn/8K/geMpRE9Vow0PKBbQsJBPGb1Tui4Uq5RT4fG+s+UjS+lEwtng4x3OdyQXZu0MGOIUfMBLoeuCt0bXQpNEEePUN5opa+8spXBjGxHHWhXpwj4OxNEISRhio3oQ8UQzSt/AnX+KN57cLCzVMYwz73olYaIM3gguEncRCzOzgykJVfy5onIKqG4gShoCFkAaqAcZBoYoTHYupWalEYPwZe++5ovf5XnPKxeMcUTq+XP+1Mc2PvrTT3nx/MRS4bCccMkxqoEpMZ/wuTBk5emhEMbCF73vkoSDknlzq0Q5Mc6JyIaPiYsHkSTnKd6HrvO+13a88eJAaZ1clW7lnHMRHN57pmSIZpI2nAysxYgucdyO3Gydkwm9CS/uMq16nuWn/JHf+8+93c3vLeWX4n34WuBfB35URH7k5bZvB75RRL6Cs5Z+EPh9v6Qz/BzDA1/8dTPy7BklvwKHE3UQNB/JVRl0wuKJnYzcbYadPG6stLYj1U62xqYdtYC0glnApUjORpJEEMVLY0qO0pSujpVGEEcSCE3OFuZuaBTmrlQq197TEG7zia6RMBin3tlpBFGaGbMMrL0wxcTrTw5sqrx66bi4GOnlXLR1bf1shuqRKg0XAqU2Jmc0d3ZRDnKObZQm5/dFcBJo25HchHEUTpuy343sUuVUOjuUMikXFwNxJ7ApHDrdNw6boevGC1u4Po58tHUe7QPzdcJL5jpNDCj+FGij58G7Ik8/1BlOK29sE5evHkD3jGNgPCWOHAl6jo/Mc+SVR5ccbgvverzjWBa6nbM4W+skHSh6pIUIztjFAdcdyZ+XtIvZ0euBadqRSmNIV9ytja+9nvnBu+XzOLzw83Of5vwLRIj8lQ/8Vp7cnRjV6Kvn43dHBmvMNvJk3XDaaVp49uZCj8LYoLZO6Y2ujmUrpBTQUs+W3uqRwdExnPZzYdVeiBrBw6ZKE8/geJkKDSkaXZSQBXNCk4iXhmlDXKDTyR2SnQOQ0xSptQOO5883Qgi8eh0oQFBlU+V6P7OtFXFG9J5i59oJ3mDy8ezA7GDeUDMojTkFGByteKytpDgRkyMI9NIJO4cj0to5bJ/lnFQ0h4QLwgffuOF00HNRWW3Mj0ZscedFatWIwXj86sQcIw9em/DZsdSFwxq5fZbZeVhc5vGrI+oHdlcjxzcr3imZznJqXF0+4Cc/8pRp7OzmkaVUWj8vSuOa45ArWQohBIYQcc5BUXQSLkNi9Z0L97KG5aCYm7gcGt/5V38M/fwShXvr9GeaIPB3/qvfyf/z8YW7bWHIA5GFQ664IqxqhG3hzaOnY5TDDQfrDDrQayVGoXSl5UJRYd+VNQRUGyKC6w6/ZOrOiD7hi5BdpzXFFPZTYm3KFCNVK6LGYIHqFe3nAGBXh9m5R6NaYIznLEI7t97jIeOmwINpIq+xftFNAAAgAElEQVSFi3lHo1BrZQwzoAzSyQhEh+/nnsPSI70VgglpHEhOyV3w3p0rOUXDl06LDumBKTjW3kkehgjPujF6pefAxW4A64hrCAHM6Ao1N1YVDkvDlkLTgB8CmczgA++5drz2zitOGYYUeHYqlOeFqMqhZd7x7pH9fqLFPS9ORy4RTpsx7BzbsfLhNxcePIz4Jpg4llrZzQOHZxnnAWecvLBDiV7Y+cBzVQYXOKBM88BFaxSZ2E+O+Z0P+Y4/9bc+nyq53XsfPtN80zf8s/zEx2+opxOmG3f9yE1foQqLVk7beYWivLzg7sUNhmcYZrpmYleORTkcMqU0Ljw053DauYgDsQlj8vjkMfM0NZpvRA0MIgzec6gNvKC9ogq5VZp2MEPMod2RohC0nheDHc9l37WdnzcVNExcDRHrMISIuoaVxhhGxvHlgjB69kK4qlgxyiZMAhch4ANYUWoXBoFAJ4uD1iEFvBvYdKFFKL2cazAW4dp5gnPUdeO0ZXreQB2hOrw6jmUjzYmrnfCOBwPves817/unr4mDEiUQaudDT0/8yI+/QTkcEBqz6wyjchyhdOH1j608e35g6I19TBASYTwvnHs5jrxyfUFbQIfOxXQWrnpaudxFzCpL7sTiOW0d6Y6b7gl6rl25845YlJN2shZKyLTnz3j/P3X5djfLzzj3ovBp4oGv+3W/kmLCo2ngujtcVmjn4J/WRvLG8Xllq8bVwwkfFDsuSAPzSujnL3dwnlqNNTdM4FiNakbNRnFCcoLrQtdAo7NJojkoKKfTCdWGquIJSDyHhaIzcMbxVHC7EZwgJZG8Y0oDtRm3LzrvvIhYd+DOvZZSCi96ZwCkN5LzmHVSNMxeBtkTtNrJXZklYNZfzloouXVGOrUKWzlnBc5+RLvj8vIS9RVrndw6/eQYp0B0Ai5RauekGbQy+5maN4IkLqfI9V64SsKX/YoH/MovfUC6uqC0yFqMDz5tfPgjB46tgxOudonX3nkNPbG+gBc3J4amzF6ZfeTiMvBsWXl4MXBSYXSe49ZxwZ/Xp1w7W3d4p0StDAHMlP5ylieIg9JoFVIcIQWOKywn41/79V95bhy480Pcy+nq8zZ5OxrrL5F7Ufg0SAR+4L/8g+Rto9oFn1gz6xxRO69/MG9QmrEcVpYGV9OI2wQr5yxFRdhaw2j/cPyv3TOGgARh54xijZA8IkbNDutKMCN4Y6TjxLgGLuYdJuAxXDTWajin5+6vdbx3NDUi0CiUVmnWWHJn/zDResF5pbVCCNCa59Vxpul5RaXROUYvDDaSgmfR8xoS3hnRhAOKRJjHSBw8QQLVYBzOdSKPp05Mntg79XQg4UiDY82duPdsFdLgGIIhBg+ngPcBZxmxQHFGQTgeKjeLIkVoXfny98386l/xGvv9nroWni+Zm48eMe+YU+DiUeRL3n8NCp/48HPy3YK2ineRVIX3PZgZPbzj8cyHXj/gvedx8Li+I8vGMHpwwqbtbNJSoccEVek5o9XII6x5I+ZCqCdqrHzs9si/8/t+LZ7IBOckNBEQD3I2sn2+cR9T+JQ4vvT9kW/7pt/O1l4Qzbg5HpA+sh4zvZxAIs/WlXxXeRAHVldoq1Fyp/bGQYx9M3IH99LO3POGxEAC1CoZz5aNhDE6T42OUjrJQTPF9ZfOQDrOR4p2ognBe3AVcGCd3gMuCEgjaqRI4+55gRR4sPP03sEHRudoraECkwjV9Dx86YI6QTxsTQnOEcVhAmadOQROteLCwOAaOVdmcazeY4My6ogPQu9nI5X6Tl+MeT/ReqdX5XJwNOfptZKio3ePWsbHkRBGkEypSnCRaQQSxOKw0dM34/VT5SMffIN9TPSo/PO/+r00B+1FZd17nv3kDdsx88p79lw+nlmHgV07Z5sihU8cPFIyYXTUzTgeCm40jECyTnbnRWx1EPxNZj+NHFwneLBhgFLZTSMteK6mAd0K+4fXbC4zI0zJ6MPE4fbIl/+TX8Lv/La//ja34X/IfUzhM4JX/tg3fSMSF5pWbnvGx8DaO0THKvDmzZHl1HlwMZOlsNbG01K508bWN4atczytRCfnkupaMR/Y2jmJptrEzbOGbcqxdJ6WinRljufl4ERhlzzZOidrtNZYaqaaYaIYAVXoFgmDUOq5EnTvldffXGjSeXQRCSGgITAFoTVFVSmlnTMBxdGqYSHgveAlME+JXjPdGmKCeuFUMw0HrdG0E9NIHSZi9FzGGe0Z6jkpyDxcpenlsCZDfxlQDYEUhOv9iHOOpVROC5SiGAvSjBgcpTee3yo3d5khNbYlM07CWDKPridy8iTz/NCPvM7tiwOLU5ajcP1FV+wvB+5uGqcnC/PtieWUGUbPzda4nh1rBumCqTKMQvCJ5hzNlGhKcLCdMmE/nStVY9zlhpRzFHcD9s5TC7yeTxwOz7jyjhONYh5/tzE8eoW/+Jf+5tvdgn/B3PcU/jF4HL/r3/86/gURrDvy1sgBNFdujgUnypM3n+PTRAjQbyoFpa8rfZiwk7GR6ZtyvZ94fliJBuIqvg+IO5txnt46vvhxpBZFMe7yes5UNmMfPXFKCIqagHUMT/T+PDCJhjcPrVOAakoCTODmjQ1Lnot9JAZQ8xANMyFgUDspJcQC2RaGEAEjOWH0UFug9szSOqMfkADdlNlFfDC6dFxIUA0TGAehlED059mO51tmjgMpnus69N7JRbm+SIyjcHdqlAwx+nMh2SBspRKiB/OEaBxzZSJy3DKnmtnFHS55pn3AcuHppizPNoozvvQ9F4yTY1Ol+x23byxcOMWHzsX1QJfAMQu9KGtX7g4nrh9OlLKBDThnlE3PvZt+TjJzwXNcK5dD5CDKlCIUR08r+zj//+y9S6ht+57f9fn9/o8xxpxzvfbZZ59z6z7AVKoCRiQRDQE1YudKga+ISdCGEZQQUGyIDTt2bGgREG2oaFomQho2DKajKGmIog0jCCaoJUnVfdW957H3XmvNOccY/+fPxn9VKhSVS1m3Ludc7v3BZq692GvNuecc/9/4Pb4PkhNIyldezeTDwutj43LpnGbPDzTxn/zH/wtfIufKnwm3/sihgW/eHnl8t7PGgoaZ6/mKiDLbxuPmqNPMITeeV4OcKMGz1s6tdd6lRKwdFz2fnQcGQXRYrifq0EnIgbtToVsEq7S284HMbEuli3K9JqwLzjrLwSNdiKqkkgmzIxal9QLBITRiUh7LlbwH7j88IjXTGVJqk0AjIIBaB4ygjUuBg0aCg20f7QNibFYxUY7BIa7hNVCqI86dnpXJBXKp9NoIk1KzcjKll0T1whKUfa3oyWgWOIaIKJSS2TdBTLm9EfZrR6bANa0cFw86gFj7VvBOWbMx3RxZWqA5IQaHpsLd/cJthu+hPD9mfu07z/z81ycOp1tEhDffOPErv/aI3yvLjVJNmA4LzV+ZxLHuynXLvL674bKtlN3wNxP5KSN0tlY5euEQFJOOMmjYWzPuXKTTOGbPo5Tx2e6Fz23h4eBpVvjG4kkq0L+U97y/a/ysffgh8Zf+4r9AWjdk8ryeFuR6Jfadp/MTn22BrWY+0EbXIezRvCKXzENY6JLR7mkzNGcImS4eMMQHsMZeYDl2FgLXvdG1EuOB3UECvCg3t5Gb6JmmwLZmjEx3BarRmlFEKVRyMT75LPGDxwRZ+eA2IlaZXUC64M2RBIJ02ksLEqeASkB0DCNbsYFZjp1indDbqEgiOFWcgUrHULqzIemoDh8mRAJXg/ct86gOr54mHrTjXWSe4VIK+z4EV+5vFnCFKkPpSNOK60K9Gro18nnDzQ5vys3RM+k6sAFhQLZNjc8+K6xp5+feHLl7PdERfvVbO2nfmTSwrjs///MfoD7w2bcLh9BIeSWbI3h4/cEJtylla3R1HA6e2CuVgMUJ6LT6MvxtncnqUKpibGpwwh6FbIUzkdKMmK7kvUB2zNz9xCUE+FlS+KFhv/qOy7xQ3298thWeY+WpCXU78noSDt6z9Y49DYGS1hrTMkMx3l061RckL7Qs9FqZ+xgA7snxuBkf3AZmmUCFHhu+RK6ts5hx1JkZNyTZ/NjzH6dAXpXHz65ctszj5xufPF757J3x9FxZvPHVjxyv7hcO4pmiHypEDWo2onrEQMU4LZGiwl4S1qDTSSVRSqOlTmqRaYlE6QQBr4EmAzS0JkNTRUoj551GRlrlUCtpy9Qt8fYxsb3d2Wvls8cr18/bMH45HFgc9JLwk6PUnVZhq2BiHGbHJsIyeXLO6NGP1WgfSSZ12GonBcftg+B85JpW7m88H94faer5lW+dudpKCI5FG4cPThQvfPqdK4s0Qoa0gYXE8YPI9XHjw1NAiqOeM14csRdSj4gWJMy0upE7oMIUTqRSKVRcNmzLTNsz9Mo1d3wxqstcFuUncSf5s6Tw24V4/uv/6J/jmnY4F/ybE+XznZ6AtvLmrnBtjdxA28L1MFG3F8/GtpMj3BwmDuqQvhMd3MyKLJXnt0bWxv1doDfFnBA8+AZNjblXqim1rFws8VQquWXa1LheMurg9tUNt8eZw93EaVIejo5XR+Hh9Q2dCWcgUyO2jnohSSdOSq4NFw3nlK0ZmCLOceOFKUxMwXOYIpP33MZK7QXxjpwGDFnolDJk47z3mItYD4gp+JE0Dovj5rAQJsfrNwu/76M7vnq/cPrAoaFDLaSqpAIzMyd3ohhAZzKlo0w6QEJTPMG5oNIBZX1a0ToGsIvBZTesV4iRvTbuHhyvP1xoFf7W//UJTQtZKj//5sjDV+5Zu+Py6w16I4ph+3DKOtxHPv+8448TphPbtg4hHEmIKUolHo6DMeknFlfox4DkSifj45G3Fliz4eTAKpnb6vnWlZ9INuXPksJviZHYT3z/6ZlkMyk59suZGIQjEXc68L5vpJLxvVIeN5bnzMEVVo0cciB3wcxwLtCDcWhDI+Dp/c7hoROt4k3QYogZz1shVsNUMY3oNNaG3i0sOg7J+3eN++PEzY0DB27yTKIsoTPNigSDVOkl01W45k6PUMS9lLCdmyVA6lQTgjUmqywqdAJRGgXPEjpOjLM4clFCYYjAiqczOA9ZOk1gTZ3l4Dh4xTnD+ZmOcEkNVaWUxCXDlo1r8QgTosYkwu1pOF1VV7hT5W5aODrHOV9xXQkosV/ZF0dPHYcyhYDrilXhcDPhjspaFb8J0QXmbnz8ZuYbHz2Qs/I3/sZ7yvPG3pWvfTxzfDhRWuXyWWU/77w5jGz81W8c8ebxdR1IUDHUhHgQCIL0jmugRVAKbt3Ra8J7RedKS41SK1mhSKJcHGut/K9/8X/6Yi/m32X8LCn8NvFX/tN/ltsw093GGp94yhOocc5P1MeKPo0J9JqNPQhtifQ+I2vme21j7kaulbUPWfOkjatF1Dt2c2gMqEGWxrpvqESKGHMQ6J2tKKovOojO87h6DjcetxhaHUf8375zq3OIGuomQgg4F3AIsyole4Ia8xRZt4bWTlJlEQ/m2JuwNyE6QTQSpVERShyQXnHKpSlOPEHTqBBi4M5HgldiKJRu7K3QSmdvG1NcxrwiGh0lTmOFF6WxRIYv5iKUbIgaTjzJK9XB51ZwPo73tEGTmZAqKTh2q2w0Nss07Tw+7fhqxNk47wX6ir89ElzkK28Cty+Kzn/tr3/OJ9//Ps/PF772oEyvjlR20rXx7qmOlehzZ7ovrBvIrDBN7FvFN48w3icUklaeSuEclaJKS4ZrivhCXhtaGlogz44na/wfn737gq/k3138LCn8lvga8Ou/9o7v/+DMWjqzRHx6y5oyOXWsC2XZ2fdMbY3UMhfLpF45TpFbvSXlSvSeuRke4/mxseXEMgeiTBxwrDrWWxICk6uUUnm8JgQ4ijArtGJMYogbZCwpjo6wWecQwEfH2oGqTN3YciOq0a0yOSVGj7PO5IeicW/CokKzglGZJmEKA/Jce2EJHqmdqXa6VsiVOTa6CakH5hg4esY6E+UU51HKywHpwo3zPD9t3B6ORAk0hPWaOPkxy0g94wlI6zTrzHFiv24IiVwqQT1OHNLyQEhWQ6rRex8kLx9ITklVKKmwXjNWGh98eMLtC329os3wJvy9f9+HfPzBHZOf+dZ3Nn7wrTNWja99bWb++Aacsj9fiL0SRPkoBEoVfIeDwjwFZBZ66eM93WHuwgEhqCEVPqVy2Y2MoyVjEc/eHNYSuRb01U/mcu9nSeHvjOD49/7df4rNXZEbQezIvAuXVDgUZW4eR0MuE0s0FoODBg5VmOisOZHlSggTe87E4Pj8XaaJDo8BPGbDRm2qAbOGtE5qHbxjdo5LqVxFBxrQK99/LLyaAs063QlNO8IQc9UmHJWxPsPQOA6b2NhehDLAQh4bsmLWCSEg1fDq0dpRq2AFRdir4f1M6YZI4Dh5gujLoNGxp0G9rgjqjSyZKCChYjSeLo2PX000K6w5cRscClxzI9UyKiI6e1e2VqAMHwuaMtWx3egY2hxqsEsn3k/Dr4ExE3nwkbVsBD9htdNFqeeV2/tG1ok59AEtLztvXs883B8RUd5ed/6f7z3x/t2ZV4fAw+sD03zg3fvEa+3Y7Hj9ekboTAfHzeypzQgRrHfarITFUXPhGAKTDzy0wVidnKM05dKulP2JeC20c+WX/8U/yk/iEfvJe8U/xrgrAbHOw3zkaDOu7Ky98IGe+HTfWDHeXwtVCpe1cE6N3hzv6s6lDOmukjpSCjThuz84c38zcbzxTK1SvTEFYe8Z1StbbijCEjxODQ1DLj3qgB1H1eEv6QERgoy7Fl2QVim94Yl0dbjeiSjBeXSesZJJVui9A57TMUJN5G44N2EoTR2pC1kcHiGqsffM2jpBh7xac4JTJUqn9EKxTqMxiePoBqnJqUcqPNwGKsZinWWaKM24v71hDp1jEFpJKEK0YUSbymiZminNDz+L0KCqsGPcHBxqAwjknKOjJDPe3B6ZjpHoJ4IJG41rC7Q6/DKX6Ibxbjc+egh85WFmcgvpmvnVv/XEfrlye+/RSVjU88lj4tZHXt0G0t45dUPnmY/iYJ2KQK+GFWNWT0qJ4Cprc6x7QbxRQ6NejGl25Djhbx3feXdBfgIdpn4vhFt/TUT+zxfjl7/28r1XIvI/iMj/+/L48KO/1B9//Pn/8J/n0TueLoltSzQbIp1PreIPByZxOFX2tXDtARPjmjtqo5cvpRBd4G1ehzDqEglhWLUhAZ8K1h1zc4hOHOdAiMq+d9QcezaCU1pNSPRczhvHG8Vj9FJHRVGMOXi8Dkdp7UNHwag0B64r2hLiFOeU6B0iwwzGdU/fC3ts4BulVNQcUgc92MXArJ7bMNzSTDqtDXepYp3uI1aNIHDJmWyNirK3QgiBZgBKa4ZTY56M9XKmNFhi4GaZADgdhI6jWKOLQVRKbyTryIsUmzDWumk1nq6JIMYhTpwmx7pXrHZSELI1Wu9E15m0k3PjKWV0jvTbiePB8/rVxFe+OuPihIsH/uZ3L3z728989CCkBtkyzzWzb0P8tq2ZKJlLLWgwmnWOUyS5QJtmXHcEUU7mMDO8Fabs2EqHBH1dMQJC5Zf/jT/yhV3Pv9v4vUpj/7iZ/aG/A0L5bwN/1cx+AfirL3//8sYYMnN++4Q9Zyw11rVz3SupFpJ20rny+b5zfV6xrsx1x5eOto26J57LCijXlPFBebquZG2se0ItDbqxKWZjyp/WRmkD+ht9o2ID6ANgSsuJLoqTRq0KTlmC5xQ8a8rjYhRABxZhCRHNnUZB+kDRORRe9ACW4GjWIUSWAscpMvmAD43SKwbU2qnSuAkz256Jk0cItGr46DiIEiOoVw5honTDdQhNMd+ZJ4+lHZvAcqU0GZWB9+S90rvhgvKudESMZtD3Tl4ThxjItcBBWJYJSidYwbwQZk8XUGlcVhDvqb0i4jCEu3lmfX6p1FygboniMnWrND+UoqwKX/l4QVVZmuO7n1747ndX5NgJ6ljf7zwsxu1h4dOk7M+NVCI1GfM8Y73gJGOtkPaGlRfnb2f4qsjJUUqhsQ9OyOYpW+fybkfcHchPznzhx1Xb/DPAX3j5+i8wnKO+tCHm+S//i19ijw4UtFwJ84RzRk+NnjrS9wH2mzzJDBMht0YvHe89VIc5h3fC43vjdJy4j364NrlAS2PgF9SResYctA7WhRoDEaHWRipleDioHxuKLpQ6EsRWKs+9DIt6M3YBIxNEuSQhKjTv6MAhRq4tIw4OcWwmxA8KdIydLXfUGa0JKhNOgVaZp2Ft/2rypPYbyEclpTJMbfGkWujSuZ0i6sB6ZZoWUjXc7YFFPYRhMBOiw4mQu7GVSjNhGnPKoS2xTEQfmL1wP+mQlyv5ZRA6MasRRIdCtQuEOBb/1iq3bgjIpDRk7XqpsO1MS+T5sVGscr5kJETubmdaF77+tRN3H91ymg589lRwuSM+cO+EbWt89MHMwwcTn18zt7FhzRFN0dypWQgRulbUjQ2T2YB83x6E07yQ98AcYOuZ42nGxSO//Gf+ANhPjm7b70VSMOC/F5H/XUT+zMv3PjKz7wO8PL75rT/0ZfJ98CiffepxqWJmpMMdziD3RFs7pa5c9o3JlFA8S8/4HCgGcZJx99+hbBvPj4VjhLZ3dlPW1shNkNmPzUWriCle3VgbjrE85iGqIl7pnUEgwoji6G7MEjyGuIUmcNSh12AuUFvDx0ZqQmxGa8NV/i44rmbstXAtCTcNa/Ziw4mJ5gmxgYz/d/BK3TtrzlQ6p945SMVq48YpTjrNCoFp/I6XLYvzkVyuUBuW27BsQ0m5kXsj9z5mBjgOoSM600p7qWLGvn/fMoKjykBY3oWI0djpRG3EyZHsgpdOa8bk4qBwS2WOBs5hakxx2NV7jB887URz1JJI207Txnbd+OqbmY++ciRq5PqcCdY4XyvvP7+ih85XX594eDjwN3+wU1qh9ULxgeAqmGM53ZBK5W4KSHW4g+BzYa+Ndc/syXiqF2qCsu18+i7h3E8Oiun3Iin8w2b2DwC/BPxrIvLHfic/ZGZ/3sz+wd8Ja+vHHf/tn/snuH/bMTP2ORK3StxX2lPiiqNnYXETe8psFC7Nc+4Z6568e2xX5llICIc7R6BzWl70CioEHQxLXytrGd6RT6mz1/abfIIKtQvSX4byZhiw5sqtCzjxTBqRtuGD0kSJweilo7NiPeCjkntjjg7NjUxkRpnVE0JgNkXpBFGOThCXATg6T0PY2sApVPEDoegc2Q3egwVH70ZqRtOK2GBGqhhbToifOBwdzsDRCQ5mQPCIE6I4qiotg/bCHBRxyhSVyQY1uVjm4CY4Bnp0QOejOWBeqM2oOdKrcb+8DB5rYfIz12JoacPkZt8xE9aeYG+8Xc988nbl8bwTrp0tGevzmbtQefjQAcp3Pi8k5zmEgXZ8dZj46H7icFw4qlCqEYNwmA9gFa8OEYd3OoBjpRHFMUUlpfG6T4w1re1C1I1//1//x764C/z/Z/zIScHMfv3l8VPgLwN/BPjkN/wfXh4//VGf58cVwcN3SmFTeO7P6CWxL44nCqaNk1SoymNOIA1pBUcaLUHLOFdIvmBi5OfGUg0LjiYzvhs+GFvOzNHRonCaHLfec4pCax3oSIioCsFBsI7zoEHp1VhcoGFYrpxbJ2hkVhsUajOiDKfkiT5MXsXIVnDBI5LotdKsEbziJ+jqaB16bcxzxGsgh0bwEHSmm6JNuG4v7ULzBHVY68SgPMwBBxyWSO9QUKpCTxvP1454Jaf6mzoFKmxbZneGakWcYgwot/WKz0qVxjRNIErwwmHv9Fy57p33l4rtEKIRQ6NY55Iaj5cL3kdUhgVelop2G+2ICrIHmjT2i7Bk48452laIXXi6NC5JuPGO1/eBfc80LXzvvXHMylPKnI4TX//wnvMquOhpZSfnzGyQS6NqR02QatweIlsXwuTQuVPMMdWKU9CpU2vkcs7g5i/6cv8dxY+UFETk+OI4jYgcgW8yzF/+CvCnX/7Znwb+mx/leX4c4RiQ5n/nT/4i71bHJ/1KaCeu28q2bVQctxrJ10LrcDLHc240PdCzI60bC5GtQq9w1IibHdU6qVV6X9m9I/rRKnQVWulkG5LtQSEEP4ZlY4zAHMYk35nHWmH2jUQmmaBuVB+jNxekdYoI5xkuGOYh1THV903HPKAMGnRVgWLsRSmpcNSO6ejz1wROJ1ShAc4VjrMfUOqulG40Cmut5G6k3DBT1m1jq42UAXE4hIfZ2J93NI4qKbcByY7AkhuuDxBW8xCcMnvH6h1NBrR4njzdNZyH6DyvHHQb4C0y1A5BFC+dW+9xyBiqts5BPapQ1h1rnbd7YVo7J+94rsbnqREfjpQ41q2WEpIbdUu8ulk4nzu/78MDT0+dD7SS885XTpkYlUOB2CduCwR13M5KCEMGf14m3jU46MShg3eBXBpBKr5uXNcEt3d0y/zZf+kPfpGX/O84ftSR6EfAXx5mUXjgL5nZfyci/xvwX4nIvwJ8G/gTP+Lz/J7HuBA9v/j3/2E+eVq5y4ndO1ww9pzZzpXLnjETZi9cunCKkZwT2RJdlUtLWM04Fa77hZubI9aFpWR260juFOdJliF31CmuDZHWda/MR4/rhtdOqgUkcggTqWZaEwrKwQRMKEDeNjCPuk5cFmwrtOeGzh4TI6XKzeRIKrg8KoGSK06NQmfuG6kI1UeO0kmlczp6Yt3ZEYLP1O5I287DHDGzIRJblekwEbtQtSPeo70x0fC+MiYIgebgdDNmAaVBa51bHKebO6ytJBuckHbNvC8F7cpxGivV57aj5lCFcJh4deuoxwM91fFcwdFSpfSKRkd1sEye7boTKqRpEL1KNtxWeFhX7H7CodzNJw5BkeCRbVQcT9lIbuYQIq9n4ZO18b3zzjcehMfnwPEhglN+7r7w3XdXbo4VeZml5MtGV0eSjGvK1AolREKYkOvKwxR5rjA3JfqIz3kQV5cAACAASURBVBVvM39olmH6+yUQNvph8VOrvOQF/vNf/lNcns+EbWevkcu+0i2zVUgX43nf8K5wvQwjk+0yVoElNdbSCSYcg7B2oebGKQq9Ad7ozaA3XIjDsVmFLEaoYxi2l4JXpbUhytraaDuCGUUMETeGC6p0MZbgCSjXurGIUmQAlgiOko15HvZm029YFjnFtY5zHnEvaksBWjVuImwMzsTsZ/be8WUF8RSr3IQJVeXpso/ZhgnTLNRsTD5gajg3JN2iH9Lx5jqCol6ZnKeVCkF4oXPQcuf9mnh9WHgsGx/fHJkX4f3T2IKEIATxiGuID+xb4+1nF2jKfFJONwtBHXvJhCly3tbhGYmSEOZW6Op4+z7x+3/uxLtrYZ6EdTN8DNzPhcYEdcctCwcn0KDeNG7sxP/9g0fO7zd+8euv8NI57xWRhSIb339fOT+ufPBxJNjE03ljDkbwR9ys9F7ZssNR+M7jxpvbhVsnrM7zEMFCI/eJG6d8/PUDf/Y/+B+/KK+In2k0/rBoOOy5IOa5qCOHlcV16G7oFpI4qFAvDj8Jbu+0Ljw/V9ImLM6xNuPX18S+btwfAu9LRXoj5fGRNxt/tHfEBNkL2RyXlDAZCs8ER0DxXpE6BEy8OWIxqI09d9QGdbnQiU4JcUZ5QRoGxxKHnRy1MIdADJ5ZPeI9eMMpzNOYC8zqSaKoGL0I1+sG205qntQ6rjmuzdhS4TApGidyN0rqvHl9x3IY9mqW+xhG2mhdehMKncueKSa0KGiB817oDbYMD9PMVuDubkKtc7lmXr3oNPY+NCPd7ojWuDl6vv71O+Z7z/NW2R9XHt+9JzjDu84SJ7bnjVwTvu3srXM+Fz6elO+/uzCrZ/LK7RLwwfjsqWHW6NNEED+o48cJ0hHnI7//KzcsNnP/euH95lmCp9WEq443N5ESlXJtSC1E71mWaShte2HrynEW8I7DYSGlhMWOKWSnrNkNQp0Z3/10p8uX27n6pzQpKB9+ZLxPidUbDc91Ey5NIBlWPWsRtrrjfGffjMetcN4ydzeROI1h2TJ73tweOMye57xzr0rzDDkzILwQkqo6aq845+mWoHdqK3Rs0It7x7+oAYkazSqo4MQRvdH7bzoIWHfUWnEvJqnRwMnopw/Rcy0JodKsME0CXXDBj7s9SreKNwddiNGYZpAQXzwfBPxIXvPNzNqNaR7t0+wC789XshhOFD8NYVXzyrrumDSCyEikVtj3gkXBOcfeG+v7K+8uK8dbx4ObuabKMSqXUohOmYLHnOJPlVaEWgp04/Vt4OM3J+zguHQlbcr57cbsjPu7A64ZeWuUS+feR5I6TkvEOYUqVBvrnDd3C6XYUMemcXOayXsixEpn4tYrd/czU6t8/c1CWSu1bPgDzCivvGevUCdhlIMQnPF+K3gnXE24muGqYb1zigu3L7wT7Y15Cah1Yq78/FdfPCK+pPHlfWU/xhDgz/1bf4p4o+inV2JLTOKo+5XnWqAWpFRqU9ZUR/vg4I2OA9ktIz7iS2ctZTg9qScFh8qEiuEBK5CKIWKYDEFR1YDgkKbM6pj8TBChUGm8sBRfSvTrXrmbHdF5UKHsjWpjdWrRka2R60vfL0qTUa5nU4L3eMZWYVZwlgnBMU0BlQGikqpEE2bf6G60PtqNJMJ+Xlmcw0oneIWDogj5mjAznlJFqGxr4zhFnDi2Wkkm5K0wm7Lmim+Ny1NCDpEPXh9oudAUXh9nrs1Y1A+FZ0YCzZtgrXEwx54zpY+kdDvPvL49UTHOayGdG/k5sZ0HXfmDQ6BMhXkJTMEj2uitodY5HgOTwDxHdukcXioo0YDXCa0r52zcfDjz13/lU463kJYDUzxidYARbw+RzV4g1l4pPrD1gPcD+/FBNg6+ExEOauRSQIZBsAXHtnaSg90t/Jvf/KPEL7Ez7U9lUghh5vH5jFw6Z1+5FqHLTutuOBm1ghMj7YXnNTNHwYXIM0M8xGlgMaNrGQYgEqg101pjbQXRyCZCbpVpVoIIoByWSIid6A2JOoZOpeF8ozYjvfAMch6VwiqN1oXaRumrYZi1ZDNc6UzqiHHwK/wLNmCoLEC1UbLDWEP2Du+fBgTXe7AGIg1eZMpnU1SG6vLkwTNow2I6kl4bDlTTNHHJ4xfXJmNISh/ryVRfVJnG1uPYG817bmbH3cGRcxty6aWzduPGB/Y6tI6dNWaEOI15xt47k3NMYiziqGJIgPlgfPzxgXPNvK+FZXLE4NiioHEiusa2VpYoxOgYOb5xLYVoHpf8MPXZG/MiQ17eN+7fnDieZnJ1fPbuwt2NEins1zK2Jkvg5A1JFRrcaibVldIyrjXOWiB3nttGPCw8Jdh1jMr2vQ7zm1aJtfHpulO+xEfvy/vKfozxzW/+Aq16rqFwcp39uvLr505nqAtfd8daMw0huM6WKj1nRISWx0Wx5swhOuhgJaMEXC1YLYRqw2VZJkptpDLcofYt0VsgmQ3TFaBox0wGOo6Id4L34HpnwtNFmUNnYdjNVRFuF4dzjuCEckk4Ki546IJXfZlhdGocoCAtDVVPj+BE2Uqnh45UWHMjqKA6nrNYpzbPVo2mAJWoxrZXWqmklJhCRAxOU8RaJ+3GMikf3pwwgdZgr0b1C/Zc2TvEQ+B0jBw0oC8Wd3ttOC9MbnAnqsK6V5wMpagQApvB3iuBilqniad24eEUeTMd0FlfBF6FJTeaOeaj53Lp1FTQRXFqHA8zte0cJ8fpEKmlEMMwfWkY755WllaozvGD7185LY5LCRyWwHPZmRlaFXs2ojXOj4UtFdKWx83kutIFYi3E5nFWaLnxlDe8H+rb8zKxsvG2K//kH/sDX+wh+CHxU5kU/vg/9AuE2jmkjWaKTIFjK6SWeHrehnpvDyCChJnUIksveISkRpNCcJ5ahgaBRGWRwV5c4kKWMQcw33F9YAug44MMWrUI3XVqLX/7QKbWuZsatKGTkNvg+KuB05mi4KyyS+K6C60V9twwN3r8kDuoEJ2i4nARJvOoDYOTy6WyzJFrGpLvuRhldgRn7LnTBQoVkQBh1ButDdKW1s7klNZkOFqVjhqsqRFdJHr47LpxrhknSu1w8sLj+5VLt2GSU6DmRuqVbW9Iha5Kq8J5X7k+ZbaeEa00cbigQOXOj7u0zjM3MXKYPTl13l4SnaEU7T3jc/FCqR3f4eYgbBiTGVaNlIxpVtaXKmdSz7VVlmiEfXhoRgdWjffvBlDp5sMDLlWW4ihbR9VDLiQHWYQYZ5wYvTg0OmzrWAhsFIoGVoyTBKLOBGYsN9CFa3nPL35l+SKPwA+Nn7qkIAKtB7oznvsQ+OgFYjN686Tu6FIIJtyi9FQ5SiIzLN2RaagKuUBrA2O/7ZWmRm6eWjaa1YH/d53VKvMSedfAcEM41YQoHnwcUNge6L2y7mONl73neUvc+IEvOMQJL4pzjltmJtdobZjMHg5K6UJxHbOGqaBaB1Aq50GBRmlUxDriHTU3bqdAXiseoZCHO1JzrE8r8r5S9sL+BFyMlpXrVrDgMHlZbzrBuTFrceK5CRHRyLU0ztcL61a5pp3DjVJbolPZDZo0sg1dBvVjuDqFSFxmDtMRJ41SCttaycnI5kmXHZ8N5wplK9w/3BK9J9xEllB5FSNfnSKTM47OMAdXG9Dq9/vQvCAUSlaOUTHtVDGmpnz+WDFxGLCZcv96wUvk7afPOCvkQ6D3hvZM6JUQZqwKIoXeDW2CCyv5AltuL65TgqsbU6usPZHXK49tI5tyqZUbFuRpVG9fxvipSwr/6jf/ON9O3yf4CyeZWK+FluHcGtnBZDO+Vp7WzLXuLH70/k9N6B6kbxxE4bxz0kBXYXYDGdh6IjUd6MA948rgGVit3Pdh317zYA9e++hVnQ/03qmMu2NrjalsiAQO3rP3ymVPWK8Yw0NuMhuCBwGsTKh2qheCBHxXWjVmOodlInr4/Gnjzc3E4oRFlbInrikTLPHZZSftRn5fKNdGE2jRI1Hxt450Fyg6Zhvteef9Vgk9k6yRxbH1znPa+OTzC9v7THvcyH3hKRs3t0ese0QD1oxFDWXwBpK60T4ItK7I1FjXla6RTMeip0clCvhwwPvKWhdu7yeua+LmJhAcrBZ5XxrXPbNnowuIddx1Y1O4i0LOGTtXLCXebjtPl0J1gknn449vMFGen1dyy3x0u1A8fO8HiV47sxNsjhziwjQrqRbK3uhbwOfMdS+kUhGN9K4467ReyH0I1e5noVW4Y+LxeuXy7pFczjz6xB/++qsv+jj8tvFTlxT+4D994a5HUvasZeOweF6FRpxPtMtGy2fW3kEKLnn2dWM3xh2oemLzPLfCdepcrRNaw5uyTIJJwHvFSgc/sTmH4Cil4r1nAjCjA2oG3iitMKkNpeQqBKd8ulZujpHmhJlBtDFvXNMQGn3uiusebUq1lV6FiUBujdT3YfkmkZoLrUCcHE+ls6bO055Jq5EuhfWsuKpEF5BJcK8Cf8/XXnGYhY5H1XP7orUQj5EViBJYi6Kb4a8Z1kTZwAUledD7hTevHLenMMxbDoPwdTMPyvniPYpwVBmALadUqzy9G8Y1Ze9EcVip7JdEkYaXjBZFQ2N7Ssy2MQEuOz6Iw5eiu45Iw8sQpVldZK6d6CfqmjmfO9faaVfh88fMesls15V93YhRWaYZK8LlunNcJnLp5LLhZ2VX4ylfuW2Bm8NMoOKl8m4Vzm2j1syqiTXthOCQ0tAmrNvOHipXCm+3lboVHMJ5c9Tnxi/9o1//Io/C3zW+nPXLjykUh3xLcXujtjN7V86pMbeJlK7kXJnM8XxduTPlqh0zJQRPXROiUG1wCo4hUKwSD5HWhNoSTYQPg/FUBxgnrR23KCbCU8ocp4CLjklgLQ06TOJI0gnq2XtmkYneI9EprlW6M3KqyGHmfu7kljhGiD5wvQ6p9TBNlFaZtKNhmMus18zdDE9754NT4HEt7Bmcdbif8U6I954lOFwf68MMvLteiATi1HE9k/swufUewuJ5asbpGIbYiAyBl4eD0KiYBUQy9+o5e+N8rfiuBIWIQBPOPRGi492euJ1G2xRVCAdHKcbxoKznzDJHlkPj83eF2+jQELHrRq/KcloGSMxVrtfO3Y2wJuVumXm6bkBHRXn/LhOj4ebIzRQ4LBNrz7y9GJfPN9zNzM0CE8OL49KM1IVdKg3j8W1H/U6MB7o1Dg2+pspzvWGaOkUq5z5TuhvcDOustTIvDsk7fgo4US690a+VKThW57BVWB4y8zSBBMTKl8oe4qcnKQgsv/ACuFFBykSqytTPFEuw77xZ7nh3eR5w2N4RGg2PXQu1dUIU1IQ5Qq2VRZQtDdv1yXnojU/3QFTjGOHoIJVO944QBjW6A9YTM4b3jmSJkuEUR1l9XhOHg6OXTpXONE0DrLRWWnQUExan7MUz/GCHpftxdvgO15Y56kQxY8tKMDi/z5g6zBo3t5E3UdgLlKo8pURrA0YtXXHe0TD21LiNntwdjYbTzvvzzsPDRJGBdZgmx+Wy43zgoAeq7ewVHnOiN4d6o+4Vcw4NMj4Dr9TSOQWHibKtQ8Ha+6EWtT0Ly/1E2gt5hbvTRK6JSMOHiPP2YlZbCTKTZOX5eeA46vjkEAmU58xx8pxOC/MsHIJg0jguR4LfeN5nqhiffbpzummcbieC60RnHKPSF89n2xXZbwj5iiqoRr4RlXdTY5krHzwcuUrnk8fKHhr26Ci18nxOzPGIyzuTn5Ay4Oi1NSgVT+AQFwqZf+TVif/57fsv9mz8lvipaR+cwX/2L/8Sc3ZMTkmtoK5QynCUDsHxvD9yviai66ximEGxhJuEWQR9ISmVEGhe4TdMX0RofSd6h7eM0eimZBSZhiO0tPFmewTF0V1A1TEzcTt5YnDcR8f6Qsvde6XqMGBx3liisubEYZpJm9JrQvxIW6qKlcpqyuIc1zS4Gs/ngtXGBcFF5aNXEVRIXWkCpXZEHKcw4WNAxQjOs6EEFzmnirTKHJX1qXN6OFLwnK8bJoLrjZOLOBfYLbM1hwSPqiM6Y5LA6TAhIuRSOXol5U5vDnHw/vHM7CfcFOmqQxnq4Gi58f+R9y4htnVbntdvzNdaa+94nMf3uO9nppllpWlqIkopJiJFaYEJNhKrQLtiQ7DsVdOmCCJUx4Yggg0bgk0bSjUE24VWWaKZVWTdW7e+e7/HOXEiYu+91ppzjjFszH2Ta6YFknnNc/1ywuEQO06c2Dtir7nGHOP///1vpsKL25kiyovDQkkyCNZRWEoklZmUjBgjh5tIyYK2zvPjhW1VwlT44EXh5hZupgGADakQJXA8Tnzrq7d8/HpBUuTHn27sW0cvjabO5kDK5A0OBvkQuJsmfunjTMmR73+w8FIzv/Ji4Z84HPjNjw98/NGR25J4Nd2S44zsidaGh2Lqo2/SzbnURp4iMSU+f9z5S7/za79wyXJ/ZioFFXh4DtRFOLd9UIzPOxnjdH7i3Au6CnEWqE5zxc1RVaoqNQpLGzPvvBqSA4ZD0BG/RuJUO2ZCEqPVHffBKHSXkZikTr8CRpIbzTpzhN7h+bRiHrk7LJjBnEBdB7mZyO7KIQXqthNjJF0DYaYU2PvgFPSrWtAkEK4b1gXj5THjbmx7oqQRT3felUMIhOj0CKdLI4eKX+/i1YT7NLOr0aqTj0KMw5J9MyVCyghOLM66XzASMUcykWaNY3I6hlkYOgSPfPKwDRv6pMQ088GrA+dWWULGPTEfCk9vz7x8uSDqdJRAoGmlaSBHAyKXvY8Qma4kj+imrLsiJsSbhXmJTNNE7I1U4UznpkRe3ETebEYDiil7j3zzw4nHFHj49Ew4wJsLrGtlls7NobA/Vb73jVt++fXM2jrf/Mod608e2Wrn9u5AfD7xUcno05l4jKy3sJ8DFzWkLkSB6spNMx6qI8U5LxDPGzJNtHfrL9TRAf4MbQoxFrx1eo/MHfZ99Bie605OicXhTVPKlLho47x1iiuKsKtznzObVKINpZp0J+Dcp0R1oTqgRrSOxIUpdS5bhVw4CEOC7LBieDdiHGfsLsJlU1KMfHA3sbU+WI0uLD4ISwSBWpmXgrqx11EdzKGw7Y15mRA3pnnkOj4/1pG+lAMvjoXO0C+Yj5yEyRhRb+JoG7JcpoAx0RpY86EG7AOtdtkryyGzrR1yJuThAN1tBKNEKShGEGXtSuvj4pUoROuoB04PZw43C2kqaK/85OHC1JTDnDiHsVGuDxfO1tFNOb5YmEqk5I4SETE0R1Idd/69V6bpMBSJT40okXyMHEtCE/Rt5XA8IOZkMfagg0vpkaUIhnGYAzeHQkyZcxc+e3xHTok9gKXCB3nicjnzcg5MTByWWwDKtxIPJyHulVcffcR+qnxDVjhmLofAaTViSMNar0JchMvzxqkJt6VwVwejI6RA90IC+vu8OP7Q+jNzfPgb/8Ff5mat7HXlaV2RJRGJSMzUFjnvKzUL+9Zo2liKoKnwYpqZ40Szhqhxbg3XEY6i7jxtRu0KOsAlHgcafW/ONGcCwqpAHNzE2YVlmphSZuuVx/OZ4yFzmIy1Ks2cqURcheWQmCN4rQRJuARSUw4xkkOg0yEH9nXjbMq6dZ7e7bgYh2PkcDuzt3FMUO+jyRiE01YRVxLCcYrsP/MuyCkwxRG1vplRnytpSvQ6Nkjr4zk2EUyM7oFL66zd6Q6lBHKyq35j8Bkuz5VXtzN3h4E/O5TIh3eF+48PfPSVG17eLaRFOL6ceX0s5EPm6Xzhizcnnlbn/NRoGrCTcnNTeHfeQYTHtyee36zsfScuMpqmYlAhhMS6DfCsxERRkKYsKIsFvAplivhVLJZvYV4WULhNEx9FIafOV1LiawjTfICpIEGR6cgLMcLaaFclYypHPkqJr0+JDw+Foxsvp8xUAulZuHRHQiBoZVcfuHiD2h/5q7/9z7+vy+L/cf2xNwUR+ZVr1sNP/zyJyF8Tkf9IRP7Rzzz+l3+eT/iPsxJwuGtcbuMo//M95RSo/oxujtpGisJdzJACszg3QYjNOO2d1TrRAqTI8VCG6s4hxsgch1pxCaN3sBwmbgu4wLYrYmPc2LadIGBpoMyf90pKidubidYahEJ3w1SYUkS6UXsfKkCFEJXL2pDuXFTpdHZ1koGhTGpcToalwOEw5NHBjXVdSdHpFghkRPv1ZzKanhcbNujlKvmVMPwQPYyGoC4DlHquUNuwNwcdis1dhwqSHEkpczkpp01pVejmvL5deHrc+fDFzOF2Bo+UrKx9H/glNXrtmIwjVMrK/c0Nc1S+entkmoXHy858TASUnBM/+fTE46Pz409W6mW4GD/46JZSRp/CQiTF4SpN0ZmWTIrDM3ERxZNRgxOT4XvjuZ1GqK02ShB8rcyh8vrlgX/y9cJcMnpxVAS1SigLIToX72hVwrYT2858f8tUnJc3L/iNlxN3GdreKDg9BvamHMR5dXzBrh3fLrQLfNGMj+f2Pi+PP7L+2JuCu/+f16yH3wB+E7gwGI0A/9lPP+fu//3P44n+SZYKfP5kyINSHVo/8Vn5HA0jGqyqsD+OmHW60SpsbQBG27YzieFZCOYjETkVau80D1hIqCoahfsYsYtzqh29muB296vtOWDqVHOSGJd1OOqkOTklmoGq4ii92xVWCohwkxLmAXEgjl6GKyw5U80QCm0PzItQstPd0F2Jk3CYh3aC4JxM6T5cl93jsHaLMBdBWwNGvkQsAW2wdcVCZApOysptUiI+Gqlm5DhI1iOWwpmXOBKscuSsyuefXZgOMhq2bSXGgNXK3SETohJERsydOCEEQgvUZhynmWqNF0vh5TLxo88uvHmovHmz8faxctp28lw4vr6BUkgeidYoFglaWUpA+8ijwIb3YwpHppDGa7rsFMncJDiWxFOt7JtyPBTK7UR/hrl1jl95wV10Tm8vzGVizjOESCgzJQUO3/rm6GNsjSA706t70pL56O6WrxwOvLqfKbvStwtZoGflsV/oPaGpMAflsGa2Jr9Qzcaf1/HhXwX+vrv/4Of0//1c160kJjWeotJ3Jyjs5xl5bMRFCcBTrwSMpp2YI5t1WkjEkgkW6H1kPoYw0qZTCGQxnvedaIG+KXMZY7E5RWIYmQ1BGhWj58DFlCk6D2fh1e1MN6fK6CvkAHNO3Ba5ThugmVxHqMO/L+4QhCVMQ/TTlCwjzqxjpDkSckB7YGdIgns3Lt0ICFMEE2MH5iy0rogbTQMpRZY8Yu9Oz411q3x4t6BurK0jnqhaiGFkSZp1tF83J5Urk9FoMuhRmUR347jMVKCT2dedkBLaI011QF274668KIkmAWX/AxajeeRkoxq6WQrLXeb+PvPtr95yuJ/QoCQzNHQ8ZDQYSSYmc14UZ4qd0J37OZBs5ybnAbCZjalA8ImntxXbKhU4HIYPRY6RZTXm086hxKE47UM4FnIipcDLj18jk+NSyOqUz584fvwVYnC8HPi1jxa+WhbSknGUKIFombUZOQ1GZKDRY+bcd46S3+MV8n9fP69N4a8A/83PfPzvi8jfFpH/8hchMu6//o//DTwpT9U40FHZuDsKexD6M0gzZMlsQTjmhRVFzDhKGJJVHV6CHIW1r8Q0Eplb0wFGFaWmxkYmy2ggTeKkmEkxcJNGsIu68PCukaeJDUWzUHxCvGMu1C40MtYGy2/yhhnXaPkyphoIwSvBnCCwbkpPwmEZm0FU53jIHHPG2mhwWnW2NsCrwQPBnKYKXQlmuIwqqdVxPLhUId9knrdG0nFsCr0Co9LZfPQUqhntOgUxFxJK7YYinGol3BbO285T7Vya0kwwcbbe0SRcdmWRREJ42BuPn595eLfx8O7MeVMeHlbaU2O5nwh54OSXpWApENNoHs85gWemQ2YpypKUHBMyJXrKzClgNdBkxlpnkkizQmyVfqmojWSpV0smlvE6vnFf+LXvf4CflCkWOol5EqblwDwVwnQgvjoivTN9/QW7RzaF/ekLDtOBEoSy3PO1W0c2R0wIcUxqvEOnYyEivXDeL7Arf+Xf/rX3fZn8wfp5ZEkW4LeB//b60H8OfB/4DeDHwH/6j/m6P7UwmB+8WVkvnRu58NwjZjfU3TEPrG1lj4EPPRD3ymnfKe486VC5RUkDeuJO7UbOBVQ4i+IxEWlIzORWaFohgrfOEjM59fFvr0Gyx5C5v0uYXohxXJy7bURJuHWWeVygFYYRSOLIMkDYdEejEDoggV2d8wYqiRRtaAyCcCiRVrdxBIljgwrXeLki0GQYhi4NQioDue5j3v9cO3U3bg4Q29U5mQSrje4gZbxdxCBoIHQjY+xiiAxXYu6jqqm7c1OEpoEShkTcpoQYLLlQ2hBvPV46n7/ZuZx3bm8mXt0uLPMwjt1MiduDsIiTQ2QuQo4DdBP2TrBOLonLtuJVaJa5dOi6gzpFoXpD54BH5dyUmcpd8hGbd2AE+ijkOXF5N17zqyVgH33Iyz//bY6vFiac/bMHKqAhkZcDiYgcDkiH6bsfUWQmnDpVKzYL+M6xQt4aHxxmXtwsRM1EAdlHj+qxNqKN0epN/8VxTf48KoV/Hfhb7v4pgLt/6u7q7gb8F4wciD+y/tTCYMKBLUfycqToRAyB/dLYd0VzHknG1XinkchEdcWvI8K9Gto6ZzdCgOUQsN1xd+5kploleCGFgEVliH0FC+GqFUg0caYS6NY41RESG+Uab+5gKdEYd6i+GVzT2ZN16j5YB1mUkISQBlCEKER1UtBxJGjO3h0LAygrZPDANDlBdAigxFmrUbcG+yAynVYbydlhZDXa3phuZtQGy+CmjATsnDMWI8+1srkPZZ4ofU50hF2EIKOpSo68fdy4e5GxLizF2Telx0rpFbFKr41LcEJtOJXji4W7wzx6FSKoFwKRcCwcjqP/MKWE9uEMndPQflQC27lyKImYnZg6KTSSZjJDEfq2wtPW2VxYY+QSM00izxcld7TYzwAAIABJREFUWEdIvH4ZWYjYvLCeNr796oZ5XugxcfPnvsd0P7N9fiLnyPH+FSFCt4CEwPTqjhQLfOUFoSp+qiSDdNn54vPGMXS+8bJQdyPVSmI0hs07013CZWOSxpuHRwK/GLkQP49N4a/yM0eHn4bAXNe/yciBeG/rxi7Ex87l+cLDVAdlOK20tuPnC1MHEUHszLvaiF2GlJiIoah3io83IS1g7pgMUIr3gMSRa5hFkA5ah7X40namYSu64tlm5imS48Cx9+bk0CimRCLdFL82BP063nOtWBQ2ItGVqEbtG+d3K5sPRqNZJ+eRStSbYyZYgGmOPK9CSZm92djsZATG2DTApKVsLFMkWmfvTkXovdItUGH0V3TEu81ElhAwVQ6lIBbJInRToo60qJImzqeN5bZAG8wGJwwzmDKSqYmYBBJCTTN384GsI6ymMwRXIsLalCkKz+tO0sSpXVir07dG1YQLxCIsNxM5T1z2StiEQ5hJWehpMB9jFC67YetK6YrtO+KVVoxzG1F8l1VYe+WLt49s1Xh5e4d3JR5m3J0Xv/YdUlxgrTQR0os70hLJty/xMg+JdTPk5p64Ke3hC+rbzttt47sf3HITE/c3M9NNoU+R2GeiKvVkeAxIWkimhF8QnuufNAzmAPxF4L/7mYf/k2s0/d8G/hXgP/yTfI8/6fqtb9zgJiNzsB3owTgDeSpIDjy0hl7qGInFxp7grmSaDm2AhAImVBmbhEXFdIfIddSnSIDogVTyOM86hBCu9txRiZy7kqyjqogEAo2qhZwG+zCnwLZ3uioiguHkw8xedZTLMhEQmsAWAsucUJw0ZSgMAxURghFRAsZc4tAOSOYQIikVUgrcpvG8S5rQZqzdiCLXr41Ub4g3xAMuYCnQRDEVUnHWuhLFKD5Sp+clsSucTp1UElEGK+Cn8QElRFLQQa8GkKHUrOeVPCtTHsi5nCMxCDEN/NzDtjKliXQo0IVpWvDrUSkWYeqB7bJBNCa5Bs8EIwUDFdwSS4oci1BDxJOgJdO94Hsj5sC7tbEFZ9WGnkdFtrz+CjYvOAWZFl58+7v4B/dsP3z3B8CXcntLOB7IMUEWbu8T4TDCbtuj82xwh/C97yz4iwNySNzfRpaYqX1jSwmhk7URekU34Z/+7vT+LpSfWX+iTcHdL+7+2t0ff+axf8fd/yl3/3V3/+2fBs2+r/Vv/Xv/EjGslGnheX9i2hulD+lw3Soly9jp04RowGrjvG7kWIYNNw623iFGXDJbl0HREQMShQE52QmoDi99Ykwq+t4JOMucSKaUKbNE4ThHLj58DZuOht1uMOdxF90ZY8wiEeuOW0KtYiJQ01XLDwEn0LEGF4VGQ11YO5z2DjYGXUbDBZBKtoG3368z0ykHjiGj20iODjlxnGZyzsScMBcITo7CvERm4vhcHD83kcDp1Om7Md1GQhwNVYtDJ1DXiifDyFQCp+4IRtDx3NbdOO+VpYxsyBKcEAXJieNUqN0QYL5mSxpDFx6JTCWRp4hsyo4hJnQiSRj4tjgawZ6cuxKJ17Hv65zJeeJ5A+LgJ2pLXDxymwNbEaabA2FJLLd3mMDhl75La4qedi6fPnD8zlfJhwNyu5CXA3J4zXz/mskiS3REK185Fqbjh0zqvIhONWE2ZY2ZWAVRaDbx+drY4s5v/fnvvY9L5I+sL73M2dYJ7Y2Tnpi78zjD/i5ArYSYCRWeWZlWp5dC6Z2WZ4Ju41jRIU4B1R21wCHAZkq2QVHOMbCESK07PQWCg3vgkB1Lw/zU98o8z2y9jhj5NiLUU3F6d5YpD0xYSqiO0dfWGzfLTBl5J7gI+1oJapR5QgSQQIkJEwMb8fQyoiuIFtm8Ii2QpsKlKVOMVBculzacnm1EtO1ELI3qJjM4ha06HhxJw2twM0dEHVGYYqB2OO8byzxxM0Wyd2LIXBK0ZkQDGLQotYCGnaTOJKMHEVPnuTZuayCEiPp1yjFlQnXmaxxcpHFZh4uya0OC0z0QtdFt3NWqN2yP+G3GxbjEQrRKngu7GillqoHMwiSZdzocoOenE/dTIcSJMHfubjK//upAOtwiMuzaKSW6OC8/fE3aFV1X4lS4+egj9stOuMDWOuINPZ9pVOTuhocf/ZDvf+sV3c58OGV+0hvxbGxBsF15vMJ6owbmJZNV+Gzb3+u18tP1pZc5f74/cKqGWyCmiaSF0BspT9dfJhwphLSg2wXVURmYTewtcrY2ZMxBRoaDQTLDcCQO2XDT4SkAOOaMxsH7X0Kk6QCXdt8HQj04ZgpiuARCGGPPKUZCHHez7A5hzK3Nx2jyeVVOaoQ5ca4NcUbAjCn1onRzxEc3PVTD1UgKlzrSrZXRx9h1NDVVElzDZre9sW2d89Z43mG9KBeH8z7K4RKEd5fKpSpriOzuKM7NMnG7FOaSCMtEi0YKkEpAxFm3BhG0V0AGui2O/MftyUgpYSXTgmMuCInz1gfroWRSdBj7HalkppxpfdCdIxFrOmTKklhSoq2KdaetO7X56B8xVJ27dnQzsM4iirQBjc03E7tUfvyw827d+JWvf4zhpOXIdDyiAVKZiDGyfO8bWIP561/BwsR0c8vlciGWI5bH7yvPQrtszPGIh8ihvOJ18Wuql1N6w6JzqUPWrih5G7kd0n8xsO9f7kpBIh/2Gx7qp0zLzLu+0/tgHv54PXPwyDtXjE7Ujqszp0JrG2dWgs1jrt12shckJk5mo2FII/qg8OzuIGDdONt+FQMFQBHAbUiXRxMtEHGmaJhn8GGOmZIgtkEIuAvHGBBX1qZ4BQ/CiyWS0iA5dwzriqSApEiMgulQG8bgSJ5YWyfSOc5jc0oxEmRwKqUb2zV0pgUfI8bQsaueYtLR8PSobLtRpkytDe999DxGDj3reUPdxtk6CN1GeC8xMM0jnUokkdxpLmQMM+FwHFOX1ho5wd4bSWA5ZB7XxiFHsifclCzDnXnZG4eUuLRGtEiZMs0aeKS2yjQlMgN3t6Fc9sqLKdFMuJXx2gvO3JUTTi5leFPahNk+/v03v0mYJsKUkBRZDrcjlyLPvPzwq6yffs48z7iMKABxkBTJNvH0eMIt8e6Td8wRePfM6XHj3bpxkCGCetg72gNpBiqITHgxtu4Q31OY3B9aX9pNIQJfPxz4h+2ZdHeHNKV5IMXIpVXuQmS1jaCNatA8oFFY24hRz2RCEawHbsNENYE4FIRWO8RAC0bHUI/ENsxEe2jo1gdfsMzMqRFCZL00QgiQIIrQI6Q+3IxYHJmSKeJtNPOCC1uF7EIukRCMmBOtdtyNEOI404eRzvxQG5sJhwTfuEn8vXcb1Ybbcd03Sso0VUoaP5yuDcJQ2IkEVBRD6GZEhHNrpJAwFRSnbW1g4GMEnFYVV2EzR7vTsxJSgN4IOaN1pDB1U6yNKDlXuCSwrsQyNjgXYduV22WE1+xXIMnaKqpDNGQ4IWTM2lCBmnMzDW9HiQFxoUqihAgxIAJFR1ZE34zAdYM1I+XI8XhAu/JglWVKNDGmd0JyocdASRlxp+vOzJH5eEd8fUc4fkB69QKTQLm7p37xODQcP/iH1Idn6hfv2NfKKzNe/upXeF4OlCTw9I7f+9ETv/R65uzCm/UZ2cEDKI1eobnwugRKKdQrcfp9rS/t8UEl8Nf/2m9hcaGEcA10BZqOhuBeoQyHo+2NEoWw6nVqEMk5clkbVY0eZYS+OhSGnbkjBHW8QjZY7gJlduaYOcwJ7YK3invgvO7Ylcacc2aOwqyBJQViDKQIkjLJjEmEp/POZdtxhFwCHgNZAlttVIcYyrjdX01YJ4x5KUw+jg17TGQSxceeny2CBxqDJaCquE24j1i1tm70yPAzpDTalx7Y951NnMg4OgmOdEOuZqi9NswMySAh4GojE+I6UrysjdYaHiKYc1wyU4e5BFJwCgHUOeYA0rnUEaUX9k7wyB46Gh1cOO87IWWajOcCI6Ze++ilWN2pGJaE2hX1qyktBzQ5GkeFtTXhLSeqK2gY8uwVnEw3J04zcZrJy8Lx9gXlxQvCskCZUVtZliPZnfnlK/peWf/O72JfvCOujSVm7o6Ju3/me+wv70lZaOvGIc3M84GpC0tw7uaZ3uswcO0DIBtyh+j8zr/86+/vormuL22lIG4cs/B53bls0Mfgj9bPdB+W2W5CSZGAoC1wXCZOpsTuXHYlTYHU4EJkSkYQOG87xIQpaNtoDsdD5HS+UCRTpshTD0hUSJlqisRAUUPM2Wtlj4kYHcehjUDaOQU2F8x2KBmJgWUZpqi6duqShnouOWodQhp+iTA2QG0VS5HdlM+flXkK6F7x7sihIK7gQz6dfMLCSveJnDOXCrc5cdlH7uLuAxwTYyY0pRHQGMg+AmK2VSllhOcmAe3Gs1amCD5cXBADW6ukFEjilGu8GstMcKNdR68lFEIwmhpxGoTnrE6eM9EiniCYjs042rCop4y4YSKYdgJGmQ64G+28EsrMcRrRfLsPkdfmzk1IuDZsS8MnERkS6WXnpiryNhKnmVImcsmj8gmCzROCwFNj/9GnvHv+gg+nA/tnb/CbW9LXP0I/fTs29o+/hzWBfsY1ohX2HLhsGx9+ONG+yMxpBYl0H1yNGDNNOm+3ytfvHset+j22F760m0Ik8PkDTClzChcezpXFHBPD2kZxYVWheYQ+JM3P10qBBIWMyxjZmZ4xizRz5inTVZiK8dACt4eCRf+DyHapRupGiIZZJefMsizMGCEIrQ2Pf99H0ryLMU0DDGLA0SMxF5A6fA8m18gxpashOY4QWFVCTGzX5tTaIMdhfIpuGIanODQatqOeh+cBx3zlZkokMntfKQl27QiOiYzX7n1g3WMclmoX1r0DAckDeuI+xqXiSmqVanGkXFsYikcfgS9WjZydtUHfNg5LppRICBHxna0FypTYa2cqCSZAjb07i0aecbwr0xTHxYmiMg1bshmHNKFmqCglZbZt4xgPtF7p4pSUcXNIncMxYypcLjubBY65sV0UqvHx3YyYICOmG0nzqI5OJ2LbOf/wcy5vPsOenjj/7u+TP77l69/5C7z73d8nhUjTyuF4w/ObB6InQhZOSSkNXqXI528vfLAIfipYMGhXuMzemJYyAoVSJFmgv8dd4Uu7KYgYxT7laS8sJbNulczOUxVsuuGpP+Aa8d5YpROtMNtKk0C0QuuNlKCZsaTh2OveB2k5wd527pcJ9U7rI9DEV+NcMikKTYd82Hch5I0eEr12Shoz+JAdaiSGxpIzys6NJWoKnMS4tYSrE0NATSA0ljzALtGcirPXbfAUzFlc2KMAzpSAHGirUcXYWqQsCVFliU5ZZtidg2x4d47Hmae98YyS+9A1dCI9NEI/8tzfcZ8OSMpYv9BsGuPJa+ajO6QyxoB4pkWjWECCgDX2FjDb8e4EiZy3DtVIU7jqGSZa20jZsa6s67CO1668/HCBFpgPBx5PD7QeKCki2VAXSjai7tSYrx4VCLlw2c4DkRc6ZQvs3nl7MaYRwcWWEiXDV1/dID7x7I395HhKaOvE5zN6p6SbD2mffo7tC/P3v8H57/7vHG6/xv2v/hL94Rmhs3z4mlUiU4mc6yNa8tC8CNzc3BDqTg+Rw+E15d0Tl+nCMmd6H6XWzDQqJYTT2yeUocl4X+tL21NoUtj3QjkU9r2NDEU/MKdArBduZHSecRuCkwztOpeOMppqACkl9nVoC7TLEMVoR9v1bK4D63a5gKdAuFyI4kgP5K40WxEPV1ZCAoYNWzRTopJCZKMTY+aEjclE1T+Q++6tYUmIXTib09sQO2URog+J9pwCJ2tkhxLGc93XiEZH1YhpJDlnAY+J0I27JWCh0IhD5CMDbqvi7BXElYNnEmdu0g2XXkFW1EZUWsfBh1RbxDHveJmveZCG4qzN2dpgFLbu7C4gQ4qtyagpspsQs+EEoscBdZ3y8JB05d3ZsG6s9cwUI8crjcrUadrpm/DcwB8766WOcF5XPC88VeOmFrbHim0by+Lc32RsKhxS4sU8I6EQ7hPHaeIuGPm4UG4OpA9eEDZB37xlEiF9eE97rLz47ne4/9632J6ekOPM+YtH5uOB29cvKS/vmcs9N9ORRMK7Iiny7rIRl87JLryaIrfllr7tJBXWPirRCeEiznm6J7xnauOXtlL4qkXmubDvhmmgxYA8G9IU7wlxYfczh2A8N+dyqmwO2YaRKadK1UhXYZ4OVO8sdC4OOVzHgUsi1AASucuGBsfnmW7CFAWLTr6W7YdJ+GLreHdynLG6EuaCdSVdvQYxDvHSFApSFBqkSQhdoCQmF4orLkLtzhyFptBNySHSLDKnYZLaMZJnemgIEcNZJJN8AE32prgLuyu+GbM5WzdaGErEEIYduIRC7zshRVqLwz+hY1PYw+AmREb3nKpEGef/poJ3vbo0hVWV2CM1KyUK20XwXjkcZ/bdKYtyOTklCVGvXpMpkQ08OhOBsMyIG4/WWYAlGBYy2hrL/Zh0nNbGtAth6mgoeBBev4y0kjmWmdYaH5BGPyMNzqabcZgXbm6d+drP6ftOebFgL++xzbHdCMfC3cs7Tp99zmVtFCpyuGd9+8j9L32TNz/8BHPFaeP3GWCLypwmUq08Pl6QFNEvnklFsG4cpBPDzN6d4p1VJz6eA59s7+/a+dJWCr/5O9/kk+6YDkdaaYKUzpaNHjv7aUV2pcWMRiCNDj9SsRIQiUzuxKSs7USSxkXAbfQEYk6wGyZGEuXsBi7kCHOKtDAKwI7TxFkZYhwhgzS6JMyczhjzTW5MKWDROVtFPJAEUoSNQTxK3qkYIMQohFyY5oIkYQogVMyM6sOEVbqypPF9UwiY7KxuPFdl7zJQ8DYmKheGkUq00xy2aoCxXz+2PvotwQyNOgxJRLyNc3vvRggjuRuXUTmECGmgzQkBD05nEKRhiJi8Dzly24TlkJHJyIdCLxF0bKxBGfwH7ex1UK2tddoe2LaNXGBvo99xc7uwvDoSjwPQGpOwx8LMkdPTyqUJD+dGUUMDPJ0UPyvzbNzeJjwFjBHn10secuxDhGk0acvNgRdf+zrHl/c8fvIZj7/7u/TTM9qF6TiMVGleiHMiphnJBZoTn1aKdx7OZ9YwxFAhBO7iRLxKslsoZDG+/ee+896uG/gSbwp/8Z/7C5TVQBJTLnQ9DTjIJiQzLmVns86+GezOJJFFHAvjjd7U8ZLoDWSaUBeCVnoYJftSRtc/SWCrHcy5NKgEzvuGCwTGXVUkklU5xkSzhoWr/NfHxdqtUc3Rn1Equjv1qucvIuPzBlOM5Di8D67Gehl9hZ2Cy/g6+nA2doMoPohROug/m1X2pjxuio2seTZlNDE9UJuQo5CG5YEeApYCEhKJIXvGIlHCsDqTEDcOU8EMrDYsKIGfOjdHQ9JsODpdjFPrbKLEAN2d2h1rgf08JJmqDXrj5lBQGVi4as7lSsxeW4MYyKkPW3fOlOxkRmTctm0DTGNOTODGEFhNiWYdjwZi7Cu8W1ee+850nPjs3Y52cAwpw9hGHUTuuByG0On5wuef/IDlw5fk4z188Uz/Bz+gP3xOffMG3y7UywoitKr0T8+s2Ti/XjjeFSxBKxvntTF74N228exDIDYlp8bKv/id2/d56Xw5jw+JzOObz8iu1BY42UaME9KUki7ENUGaKMuOXZy5RLazs3kjxzKIzDGgOqCrWTd6v2oMSGx7G2O2OMaaaoFpMkqIeB/nwX7Ni8g5knxc1CEEbqfCWjuHkgfe+QpAcTeqBQQle0N1pEXjEJNTq5KSUDUxZQcSZkrOwxqd8pAyV4WGc5szN7lBCkx9VAGtwV59CKFUee5Djqx9pGdLhJIiWkca1l47JWbGS1L2mLCuA3DqQvLBcETh3HeSRCyFkYMh47U5YL2jtTIVYds6Ux4Kx2eBUjt5HseRlBKByH7aoBRMlVidaSlYa3hTSsksrTHfzOzV6F6JwGpDOOUSyB7Yr4zNujo308LJO4RMCPB8arTeeHpaIcLLVzP/09/6hA2jWyU1aK0RUsC7MB+OaLhFjmde/+q/wKtP/g/e/J3/hdPbL3jx69+n/fAn/Oh/+J95+avfp8eIPZzZfvBDystb5O6AfbrztTvhf/088UIj7zYn2CBv7TipBQjGto7gny8eV97nXPJLWSl0abx6d2F7DavtTHIFp7gidsMFQzpsu1PrkPtqFKayDCu0KV47yTvug21gjHi2tq3kZGBOc2har/FtYbgiQyB44BCFFIYhChmQFrNOt6sk2AwLY7oAg3MQI+Qc8Vgg+ND+A2IRKGSJhJjo3aiiiIz8wubQ3IgWxy80OrVWXt/fsK0N3Tt06LWRJODq7G0chZo52ke1AAEPQg9jE+tRaAGaOxISujVEAl0dNwZgRZ2qfbgRfXhMTMCaon00TgPCUI4F8tUj4O64KiklmoPESIrCpXfSMiGtIREkBs57H0cbnF4r6VBYW2ff6vg5psGrVNeByVOlbQM4E8KBGIQnG87RhHEsI8i2JcE8cpsT3/rowO1Uxs9AHV03fO2EbLTnE3X7YrwX3v4evhTuvvFNiAee//4/oNed43KDJid0BuT3ax+wffGG2Td+/8075DDx+Fz5TIw1DLoWgMtI0ApkYnbujoGJUcm8r/Wl3BQI8EAgvFVCdp7OHZWAtUBbN9YuJBO2rRLmQtVA8J0ebUTBRR+jRg+kEMkaEO+0GLHrBMHdmWIgxUIMyrYrJUeKOymOfIOSEubD87DVhrqRgpDCuNgv3VhrRXvAXa9kYyGGPsRRVWkOilJCw0xw39lhoNzUKTEhwN476gMVHvpgSn7y5kxIkZyFEsYxhi6YGSmP44iIMCaZzvOuVNOBZ7OA2KAtR4SqQ5e/X81Vuyu1Ka13dhv0p5aGHVrV6K5ggwWx+8iBGBOV0eDsCFvvmHTEBupuqyu5K9IH+SnrMF6RHfXBm0hT4rw2wt7pYpQ547VDGDmTm48jlgflkId3olqnWGdK4wilPtSENOdtV949OWzOd14tzHOBXmk/fqC+fYurY6eVfGnY81t4esB/8gkxZ2IwcnTq7vSnx+EVaRupd4JttPnIF3/3Uz58nXl+hO/c33Iso5qb4jhOHcuAzxiNIM4nq9Au1zfxe1r/r77zFcD6mYj8bz/z2CsR+R9F5Peuf7+8Pi4i8jdE5O9d4a3/7P9XT/4ft7LekEWp4Rapibsls4aKho2YoLhy9s59mseZ0ZxmiV4bZMGakCQRvdPUSB6ZJWJbIOXRkHQx1l1J0rCa0Ti8CCc6XSpYJwVhmgyzjsRIlIS2oeTbtDGnSEiJFScS2ftPtf1Dw29B0D4ummaBio1EqGtZ6WI8tw0wigRUIiklUl64eMAYF5haIKoMI5UoGjNNAy0IYHS5irYYjUYi43vJqCKajgvaQiQY1D5yHZo7W3dUne7x6i41kBHESxZERvDtJsPxaZoJzgjQtXDtNxglCDAT58FQyGZ0F3xKHGMiJIhSMAXM0DKqJjOYYyCY0iwSQ6C6kyYQFUJyTtVoKqga59YJZcJT4HaOlAxqGw9PK7fZefujTzn/8Cek+5lyu6BPG6xP2PMDfPGW7e0bvFbOP/khmUS6PdC0UdtK3zcEG2PfNJFuDtQgfPGPLrw0p0ejnoeYqaSIlshmStedFAemLalSRfn/g07hvwL+tT/02F8H/qa7/zLwN68fw2A2/vL1z7/LALn+qS6VSlomOg9oFLIvpPO4I/c8Y+mAxKHMszBsuHgbneZrw037oPGEYJy0cumDPRAZWvUBIRklsUVlCbC3xlEEsUCTQO/jbh8lDEciI1ym9kF6rrUSvJNc8AS3KeJqlFKwZqQ8pgzNR9aDiFw3FEPNCESS5Wu25CA4uQh3SYgIaxeqjCj5mp0kjZITUfXaJ3BaN7pGRJwpBzoj6MVEyN3BO4aw92tlYEOT0H04+7oMgxJR2dRZcYINv4grbK5ohNnHJMa8oYyq4yYm3EdV1a3jXYe60IxzHxDVrMbDug3kWwy0ZlxUWHJgGcBDdjU0jI0tCYQOVic0MwxuBLz7/8Xeu/valmVpXr8x5pxrrb33OffeiMhXZVd1dxVStdGAQDg44KCWMBDgIiRKAqdFezgI8Q+0hIvRJo0EOEgIDJBoB3BoB/EQBq/Kqq7MisyMyPs45+y911rzMQbGWBGdXQ9VFfmIqhBLCt0b+54TZ8e5Zz7GGN/3+0jupB7fGyTREUo1HnKmiPLhXaOv75FvvOH27sr6k2eoHb81xvMd2kBa492Pf8T13QfW3/09ymlhSKINGC9P1OuV+/3KvlWaN8ZHE/s88YO3n/PuvrKSaPMOXngYmYRgXgJIa4npkvFMgDq/oudPtSm4+/8AvPsDL/8rwN89fv93gX/1p17/jz2evw+8+QPcxl/4o1NlvTZmeQ1T55rupIfo6EqtlLrRtzu3vaJe2Y9TNM/CzQa1OGUekE/B/Fcl3NGd6pFL0G2AJEwGaz+uuSrsDpLKkSDlkUiVoBxThHlJsYhsMKeMWNwganNqjyBaa5WSMvsQbIQLcKix7QMrIOqUrBTxyG/A2H0EM7F3Pmwj3JeaGVtna0KtQusZhuNieKtcikYE/YDWw+0oSaOMMWezzvAlFvMIPiMjTt3WLUxiJqiHYKnvwjDjpqGsNI8bRkqJbkfgiSt1xHt+sUrde1CTRI5yBUyFexuYC9cW+oZ5yphv1FpJ4nx4unFdjekUkNxcFDTM6pY3nn1l78Gt2NvGM4OVwotVEonHSbnaQIvwdh8sAinD/XrDX25Y7ljvtPVGvd9oLx/YXp5Z373Qn5+pP3nLeLkyffwxsiqj7uzdUM2oeZSZw2iuXE4Tn75rvLxUWt/JaWEcMYXpyPVsJhRNqAh9pCjpvqLnZ9mOvv0Fas3dfygi3zpe/0vA93/q435wvPZLw7J9yz9mbRVmYVw3ehOuTy+YFLZ9pVqnUDBxege8Ui267WemF/S8AAAgAElEQVRJ+G48D0dTw6szXYJ9qAaqgXvPZMYwSpm5TB3VDD1yGnYqBadLNBp3A02dbQiq0ZJfbTBJYi6JZYSLcmhg55PE9bzowDVjVtERfoNCQjyQ5yawNkM9UpiGR3jtboYWxXbj6oNinXIAWJoNumVMhFZjqKhZojnYDU2CmbB7YyD0fcVVWFuNE0QzZo1JE+qJ4YM2nFQSqUSTTsypbkw5ox5J21kAyTiCJEE8xFp4wEYSmeIddcesUFRoNiI9WzrNEtagibK4w3TiNAnUHptw7xjOmyx0m9n3Sk+NZZ4plskYxQUpZ663lTkpD2a8upyRyehbx0VZ337gm7/51+k/eUftO2me8RZEpP2+krVgtzt2uzPnxMvLE5pe0NG4/+Qt/upE0RNj23m73tkS/N7o0bvqndsOg43pMnMCpExIreSSItX7ZjxcEuM84PbLWjH/6POL6Gb8UXvcH9Jt/iJzH9ZfX6ip8uO706rQbiv5lBkKH79+4CNtYZJhsFBYSqbmzCLhAmxHBrCIIOfMNIxSEtPpzNYMNWfIoGvjaQ277zDQmCCGLiFF2vNQY540FkobFC1YhsmiBzHa4DoUR8jJcIHVjc3CYNQIqpNrYNrr0KAtWZQ3JgFg6RLfdlfo2VGHPY1jkhBNOhB2c+q6IWr/UMot/SiLoPeAug6DQsY1yokolKIun1wYI247pk4ugongTSJ81gKZ1o2wQqcQBcmIxm3Yr0dIv8VCr0HcUkzjpjKdZ5yEzkKaZp5XWM8TWeP/M+GsrlQyvTtLiv7EroNVIC0FpplhMM2JLInburG1xupwb06aCylX7uvAfOLp3hi3J65vP6PWDd9Xnn/wffrLnVorfVvZPzyx3d5z+96nbI8Leq8UF/zxgf1ufPo77+l15f31Hes+eHox5mq0xxPzMF6XGfqJT7KgWhg01qKsrbL3xDVVruuV33z46tDOP8um8OMvyoLj18+O138A/NpPfdyvAp/+wU/+ReY+/I1/5i/xhke+jSJaOJ/PzFbIe+O6bnR/Q2+Nh7Jw75WUY0E2zSiVU144L4VJhIvBdRjeW2DEZdAQMKf4xKuTIoexCp8Qwk7s3jiJQI/8RBuZeQmIi1ooFNWcrkrKwhAwU/DAxbsrOYM4eDDHeIFwH2ahd2Nx5YLDMOYEy5x4s0z0BnfvPBYlqeJ90BJcd6d4QtLEGM7eokzoIzGIESOJ+LMB11rpu1FtYCMahWVAtS9coQ7dSW5471Tpx0Qj+I51dFJKEX3nztBBtwoqZOYgJImgmqgjswPugiclpxhxJskRUbdXlj0+15OyizH1YDBkAW/wOCXYoaTEPIwFZbRgaT6tOzocycrjaeHxMvF4WqCfOKmx946Y8rw7n3//dxm2s748kwTabcU+3KjPL7RasSasv/+WcjbW6439fiPPE9P5xLYa3//tH7J2ocnCw2vjbav8+P2VPgedW61ytVDDfjFpeZwfcd/4eH7gYb7wK7/6j/28l8Wf+vlZNoX/Cvit4/e/BfyXP/X6v3FMIf5Z4OmXTXT+J77xinXaoTnzeWI36F6Zy4WeEjV1mAJplrSwb41zCzWcykS3hrvT6LgnJAVhSYimlUpkKKoNvEUHP2fFpGMap7SZsh5mJNVESlFaZDyUgyokj5oyCYg16hjMEie1Af1QMTZzaosf/t6jMQlG70ZJiW8/TswCjymgKQOhkBkWQqIlC24JEbjVaHgOiVP85h5g1RFYdTiudSPgq+twrA2aGXUMVo8Jg7cA1EAoO1U13rfF+2rDwAfbQXIarl+6PrVk7IC6TCOMZc9jO5qjAV95uu4sWbC60irIktiHHEEwziSJD71xmTKZnZGNyUZ4XKyxISQR2hD2zfjG4zmSr/tAU2xa1M5mG5tlbjbobnz/7TOffvopb99+xlhXbk8f2NcP9P2FZIrXSv/9zxi2kz/+hDQ8nN4I+jjzsjc+fdp5//YDRRs/eQ++JUaHD7cXmjWmKchgk4J7SLQ32aht4HtF7Mby3fMvc8n8I8+fdiT5nwH/I/DXROQHIvJvAX8b+Bsi8n8T2Q9/+/jw/xr4HvD/EAlR//bP/V3/Cc/H3/0Op5Ywc3KvnNNgUufJd1IdJHfmEfW1eQ9cuhrpgK8OnOqDPKK9vYzEVBZEhOJxPS4lMYbgxzhtVKebM3ukPqsGGCkfGLK8LMwEz0FEOImyC1EWmOE9kUS4dZjFmUSwHg3FiaA2qytLTgwEySkyCDRIUFkLH3aYUzSuugRaTXxQ/UiLFsgaJqgyosGZVUBalCgu1D2CYqt5ECblHzYBs0vUJ4e+weX41YMNiUaLylwY3RmSMRT7ovsvUA+pcreGubNLYtHEdGDnS9bgGQy4u5HzBMPAlSKD1YhIvAGTZ6Q7KvmIyUuUAd4iMLdtkdMxL5nLJGzdsIOmbDUmMpdL3Fhynhhj8P6lcn/pPP/oczaDsRl9G9TrnXq70l6eePrdH5EuieGJbbszRsdTR/adb33yQAM+/dz57McfqG3wNCpb65hlTnPG2iDnEHFF9kaHJhQd6NqpY+Kvta9Op/CnajS6+7/2x/zRv/BHfKwDf+tneVM/67O8vODNWS8ruUYjLKczD+2ZZo0P3UgmFBP6PFOs03ZD1Mk91IvdnSJK9U4qDoNwL+aBWae1AJJoCn9BmaLW3S1xTs5wIVlm+GCI43tldJApk9xYbZBSCrsyKcQ0Mpgkc68NcgrPQ28RWkskS6sLuyS0dWwINym8ckDCSvxsCdfDIDVDromtG+5K650qzoJQdQQlSXOYtEZlNOiaYAzyF7xGg0on4XRLmDtJS6DMTZDWGKVEXuUwnGhaIhLqye6MNAJMq4XkAh7eCBFnjIGdot8goyNlPhiUwG7oY2aZJ5p3GguMCJdtxG1kUygtIXSu64Fu3zu2C7s7c85cxNjWiiShiLC58+b0gDNYe0Bin7aKi5FlwZrz9qkC3+fx8oqzFwaFXBuf/+/fw1V49a3vMrYXzAfLNz/iLsr7nzyzz4PyKjHtOz/4ceMqKy8otVZ8KbHhTxNsHT0lXINJ6Tnhe0LnmbbtbI/rV7Z+vpbeh1133jl8VzOfy4LyxHtTsivCTE538sg877cviTmuC71VCjEW1Or44ozVySnjDKqFE1I94uNyMoolblY5TzNbb2QvNIlMgplMs8GUQgUpk3NKiae2kzSz5MTeGh1nkhKgVKJ/kCXckHg03xAl66ATrMiUJebabkgOX4O4siPBScMoY0I91JE1GdnD4mxAQWhJ8RT9C+uCK7GwVfAjYLaLk+2LiYQxJ2XvIXcuYoyDszjE41aRE+5BYHZ3RAciGaQARhanSgppbzckQasWxKGUUVWWaSbbzobzdG2oZFSERTM+KpJDrDRPQhHYrEe5BDzXSHReGEjKCIpkmNXZW6KkQWKKyUJKFC3sY/D6ceHpuTFlZ1s7zY3b+8avl8ztvpO3zPV33qNZ+OjjVzAp2+2OPd/pl8L9sfAknaeXxumjmR+9d7bJ2LdGrZkmg/E82GXwybceuUnhtESI8GPdGHVQi4J10jnzbO0rWz9fS5nzqDCS8O5WaPuG5Ew+bNI3H/hYuJtCnmki2Mh0nLFHo6sMkOTUvVNymJtcogEnFLLGf99F6TmHnNlATKg+vuzWWwppcbeouUWEzTpKiUj3HoGzqoBX3OKarKSIdpfoc0DYkoeF1yFrXOnBQJ1tKElC4KQYngapZJZkPDwujClRCGIRgy9HnpqCZWnWo8loRylg0CVi6W0cPgXhONlbiIE8dBmdUGh6SuTkJIeikX2xuSOkmMbk6C2QQ4HoFqj4xcMJ2kajSQrQa6uRSTEcBZzwlHiq1CMWLs2FU1ampXAqieqD4k4uh218VqR3Shb2bfBhKF3BRqEnoZf40ddu5JPgKpxnpYrQVTlPmWko7z67sr03fvQ777hjLI8zLQ3e/eQ9UwnuZ68F64XRG74Z7586uwp9N169yrz+9hm0MJUzp2nm5WUnaecnn78w+pXRoSRIlsnFmDvMbfrK1s/XclPYV0HTFTD2pHgrzCq0sXEqhWQNTZUixwnCYGwDzbC7s5NI04yXwpQyYwxaDaOUeaW28AUUgWSDzTpoiHTimk1o7Mc4lvSg01iHYZoRGcxohMEgtJFoR+7icP+SvFOH0S1q91EHExFgOzzRRlzTh2TWOli7seNsLcREaTjDSjRD3TGBS4oyZPNBt7A1Q5CVDYkg3EOHocePRtYgV7cxmDRxmWdURsTGmWCS2X0gY+CeDpmzHanY0Z/xYexdAEGIFKdFJ1TCVCYZVNKRsxmlV0nGpNEMdetBe7JBtlBSDg+xVxtGNWM2ZxSl9bCjN1c8K+kwu02AqlBTp9aIst+2AMs2cx5OyuOpcN0Hv/+883LrJFHePVV+8A+e8eykc+b+oHy/DT7sxtunFy6PmS05a208dRiTcLWOZaWasA0l98a3X0943njfd2774P29Mi3Ktg60ZCafKd7YhvGig6H3r2DlxPO1LB+msXHhE67jPa8nuN7vXFUZnmm9slmEkJgnGJkswih3ek/03pmSUWxCPE67/MWJkJV8SNKzGVknbvvG2TOmx6QCyBJhrz4GFDCUPoykTmuNrAlTodhAk+CHAUpUsd5oHg1K1SAdOQlV5z4GRZXWjSJwLsrNB8OdaShVlJyMOSVQ5zYq+804I9wZzNNEea5wsB47Tu8dKYWJ6L7vPnBi0WFCPqTTQxNDlPXwQYwxGGSKjGhCZomtTAQjRXtxOLmEj4NDbyE2ICVEokwSi3JHcmJSJWNU6ywlkSyo0CbCVqNh6DJ4XKZDjBUo/t47rWROZrQUWorHc2Hbw3uxj8pSJlrfyGXmlAMT99EnZ0btnElsFqFcIkLbG99/cqYfC7rtTOdESme++2tn2qoMaYhsvP2RwceJ62vh3VvYNDbtnhZkqfSUeHVaaKPDML71eOZcYexGw6h7JQ+4ciPpDEdI8Clnuvw5N0T9RXtME6vcIWU+H0LJC/NoTN1AE6oXNBVmEcxHiAFEEHPmuTBNE9sYJO8UdXQkkM5F4tR3BpKFre94KTxLdIzDPbgjGuNMSpyw4gNJQfEZLlQbFA2BUNKCpsySEtUgayJLAD42dZQ4QW/DWFSR7HHCinCrDVpDJNHEOacIllGFx0nxNBg5fAJFE++vFZkUwfGD8bjkRG87To5sTRKIkETpMnihByxWFDmMTCIJQ0g4lRBrdQ6peBJmAgmnJfQGKsLimRKXC7JZ8CV7hOoIkA/wiYtRjeBEqjHNEaV3mUs4Ok0pZWZzyCVUppNm8ghOg/SwQq+r0kaUOZMK6xZlT73VKNdssK7OVo3uidlibJuy8HqOScx2u2KzgmTe/MqZT98b29gp2dnaxLc+nmm583QztjUCfbuB7Q1hxjOs684lw1Iy5znzyWkiv4bTIszzjE4z35wfeN52kjj32nm5bUfk3VfzfC03ha11pt0QF75ZYU/KbYA/RA2d8u1QLhounWyG1HQIdyJnUAfcHPYGlmGYxjVZlTkXDMc8kZsxpwCeaDJUjyE04G5xla1CtsYQZUa5pEJzJ6WCqKPduI/OcAtvXIpG4nywCZRgMwwJ8AtwnLiKa5Qsbp1qAUoZOOelcGZBNuP93dgHVDmQ719cy5PGGJBM9krvnaJx1RYJp+gkg6zhCjXrUbLglKRoMopKBNG6MEYLHsUXsXJobHzJaWkcGo8wLlVXGuDVkK7YdhiiqjFJUJuGC6/njE5CEucxF85zoiSYcVI2JgsEXZkzXRPTknh729DFmNQ4LYksiorhwznPJYJpXp1oNjhPCbVBzhNvHhJvzmc+fj1RXyofvz4zlcyb75z53g833r6/8vnWud0bdh18/6nxIQkf1s5936jFmTwOmkUGJ1VSyrQOD5cZHYlpjobpnDKvZzinxn1dEZvoW+f14xvSSSiUr2TtwNd0U7icMlou5JOyqmF2RxKUGjHmnibe7E6u0VisRLSYetwyDMVlcHIjAdrkCD1JwRagI+LooVuYJZEL9OE8ThesBlsxSUFx5sniqq2GSad7Ig2j18bAME0IhSJOz8qShGFx5fdhaAI8MdTAI+koJUFGpBjv3ljKQvJxNAeF51twDJoG5GWYxZU/KW5K9kTygLuMnOM9TBnX+Nrmg5RgkgmIpqNLbFJZoPlgeIxh1QN7VjQxawiiVaF4bADWnUWOndIS2xjRVBVBpol8isW8NdCUMA+hl5T4bi+BeKTYnfmU8L3FTWc4mxniR17CSdlaD2Dv3WmivG+wKuwdHi6JJINBofbGOYX5qMzGclm4i1D8zg/fd6ZsXDHefOfEc6tcFiXLxMu1ce9nHh7gXoTf/dHg/mx8f9+xF+FDgk8+ujAvimaJfpRvrFunzJ15NF6bIFMA67Zc0BxhQ9tdOPsLWfKXZepX8XwtNwWVwsvze/KHjjdlpMKSZrYGPs/kDu9FYFbKOBKk1ShSKH1QFUjwrnU0wVqiiTgvCXWlmIXNGuNKgE3pO26J57FGoImHGEdEYCiTD5opfQjb2GniDA0D09o6WQaugrTB3g0TZ9JCl7gZaA7YaC4RV2dH/uW5QeqZrVVElIvGKb0PYRELIVUKzkAWwC0amdmO3EVBPUhSPmLxpznKDQhsvWs0IUWcnOSQKeeQh4uiB02pe8BQReJ09hSp2EmiuSkY5oKPgMKeFBY6fQwKE6eTcCqZvQsqCVaDmsNbgpMeLtHs7YMJYW+F2+axQTZnvzZ6r6ge+Zejsd7vyN55OCf8aBBGezXR++GytAvJNpbuzI+PaI2/i29/48R1hcs5cyrCN7995le+8UC5XlnvxsswLFdubefj+URSZ2rOujsqJ2ZJPCwLHz285lQ6mykjTaznCjj7nJBspF4pi7MLfO/dFenGNX11eZJfy01hrC98cr7QslGnjd78CFvtjL6zDyXP4DkEObXHjGADmhUmtxAr5Rkdwitg1B21gUojlYwfPEZVZYwWUeWTRm3aYyz5kAwnY9LxMpOOUeKiho3YYHKamJOCHjJroIrhBmqdmRzjyXYAS1qnW2wKYNTUyOqcih5ciFAXWu9hzZYQSBVCaZhdyDkjQvAFgGY7JQlFOvlQKY5gpJA8ZNulFJJ+QU9ycgmwKynMXnE70GiQSlCmxBXNCVWJ8Fw0VJ5ZOR3YOtHMMiVybjwWZZIwnO17UJteWue2xeYzxqCYkkpsFN13VCL/sc1C743HcsTTjU73KXoRp9CLmClvSuFsDbowndORH9nwCg8Phd/7/pVcnFdL5vXjmTcn4RuvpgC53o0J4VsfzaynQfbQl5xOwkNR0pzCwt0G923luY24DbrRUxCerQxe8xiaGUksScn6SN0786R4Ez6sG9/Mf85lzn/RnvbwiuvojEfBNZRz+94Y4kxpAm+UkZGqjOJc0oRJXHnn0qgERHXJxrCd3WKkV4eweeK+e1zHcWYpzCUxTQv7FoKTHRje2WxiMPCUyWZwyJZdoh6vNhjWUOugie6KqpB6uCXFoVoji7G7Ryq2K9XGl7oHH3CK2ubQCyizhx7g7bZTLLLhc5IIUcFZ945boNhJinthCKhmxohRq3Do+RWmkqNuT8J03AScIFKLCCqQDgx93Gz0y4zOUC8qJyew9Bqy8IRDIpqmKNUz7k7JwsdvTjwsmW9948SlpEiOqvCQQ/OBN7AgQZ013FA+jsRmG+E1yRNnqWzEePicM7MJaVFC/7hCi/fktTO9urCOQl8bs574zjcuaMn8xncfmfKJV1nhPHN/GbzsMF0e+OjViSnFpCOJoxoI/onIF53P0QCua2xyU4W2Z25rpxFN0rkoq1aSZM4PEy0ntqp8/vz2K1s/X8tNoV/foyfgrsyemScjZWe1GF+JCHW/Uc05azgT8cFFwrUoJkHZkQTziTMCnnEzZIQdutfBqQR0tPfO6AlPGbfOnDT0+AiTF4pBTOJijt9GuP+WHMCVIQI2SBJcBCPFQCQlplxIpswpszdjLhqnlgRrcValZKGnyJpo3ahFIzeCuFp3BO/OTsTbpSSIOw1DulMyX24yWsK0lfCjPxC3m6LhYchJmDQ2B3CKODkrUwkfxnT8SJkZy6TMmtEkFPTLDRGg54CK5Bw+EB+B4885k12YFyEvIS57/fpCM+fl3ihpME2JpWS+vUR/Zd8MDLZuTCWRiT7MSwW6MZqxN8doSIeeElkmluTI3vnm6ULqlW8tznJKkCq/9lc+CjHWqzc8vLrw6vFCToNLNfZ95/UysVwmXl8uLAppUU6eyeaUJSTy7d6oWyWdE9Po7NIpi9G08lpTBOTImWmOvkKug3RySMpL/+ooK1/LTaEswhidMgZLa/jzgORc5AhS7QbLAwnh3iuahU7iqXWsh6GpTEqzQbPBmjz4eVkZ3gPaocraOuto1Kas9RaNPNEIeBV40ET3epiWnD7GoToUhh1BL81JIiR39tGpNugyyMDoDodzUSRIxa7C4sDohzQ48dwTYkFdSgp0ZR+DzUKhJxb9i5IV846kwLwVlNpb2KsPJWZC2C2k3j0RUmFzypSiz6ASSDdVVCPQRA4rtUmcvFmP7AjCAZpxPIcFfMnTMfoUNB00JjqpHHh7iQlQyYmMM2t8fi4gLXGvTs4z6oYdfMc5CaLGgxu5Kx3l3ncui+CHv2IpQtNMt0DGPeZgS3h3nu872ivv7zHJ+Ot/9Q3tPricJ2RS6m7cWsO2ysjK+QGyWExafHCaYrKQHhdOExQiFXspkYWx4myisDrWGq9UWNU4TcLaK3MxrhWu953pdOL1ZaKk/1/R+HN90uk7nEdh1c6zGWW5cPKJ2kcEkCrU7R7dfJQdRS0hh5W5ubOUHMnBJtQRXfqthYEaDbqRufNqTpTkpBy9gdORGbEALp3iQmsD17Dr6hfz/ALVCKk0Ej0Fg4VjZi5CydE0HAdmbJoPtWGyaABCdPD7jtA4lcxwC+ZDUhQLgCyOW7gup5xoZvgwujnLNJMMpkM2HeO7OK2SHbcHDYlzyKLj6zrRk9HhyKFayPqFa/JwcR6W6mBIRsO09R3BKBqBMilnTJQ6DNVEH04qivVBGx03yCLkrFQ25gn6ulLmCRkwTcq9R1bls0eSVPYI/Nmro5a4FPjQOtsGQ4zJOnXAkERaEtOcqZr4Bz/Y+MufnEkPhW5KeXPivsLmcF074+XOy+2Z6aMTooXTvFBcWA1Gdp7XHc0L7+93prkwcG4mjJedkwa300YCW7C90esh4gJSHvQ0eO1CmTvL40dfydqBr+mm8KN04Y5EnSvG+73GSaXOTOHFEjIHJPWL0Zz3ijJYR+dxKtx6x0bFNGrkzQXRCHfZjwbb41KgN0pxRu8Ri3aM/iQBPpAi5FlpozPlzD5ioW7V6MM5HZuUWTAORo6sA9XDtnx8/ftaUTe2Iaw1BEDuAVuR49TuwxleaN3II/BrhlImZWhMEV5qD4l20i/HjH5MOIocBGgRFgUTY5K4VUySSEe54h5chIzHBCIrnkElTjdJhD1aDTls4IqQPfFqXkhJyQnuGL01SnIWidtG74GDFy0UF+Zlog7nlJU1zdR7x1Kit41e4iZzSpmzJNIYTHNh7TunNDOVhLrx9mrc3g6sNkaL79+HreFtZ1YhnxIU5/5041d/9UzfJk6vT9imjP3OfFl4eTeY54lXnzzwcFm47j3Ky7PwUAqLKhfRIGftyrY29gFj3+Gs0dvQgXfnre9sJHyKZG25Dx6yMKeFNBxrmY/1qwuT/FpuCrk9ozZ4LQtXi7Skex0gE5LgnJ2SM55ywFUR0mmJxtU00WpImC1n6EHuUYm+gnVnrDCXiFRzUVznOE1NEEtfQktGirHeGB4W5B4p1psJc1LORSlFDnNT/GV4dbKGinGYICS6NcglLNHZiZgGJx0nMzmahKrBf5inTD+yG9TjRiIOdEFEUZwd51QS677DcXuRLJSUSYfRMh2jwH5oMlIqYfLCKAQLIKWYihR3kjSSRqZBNkeGkQUsySGWChNY8/h6p5Q5nQq4Ms2ZlCcsFbIot70iOVFrPaY7wrfOC7Unnu+D557Znxtbd5pAz4VEYW+VuRSGd/Z758Ot4Xvj9WtlLhNZY9O73zq9Aq7cn2589vngQQZ2emA5O701tq2j84zfG+d9ZXsezOdQTS658eaV0D1zu1b2GqXoPDlFEmVJFDFOZWI2od5v7D2I3H1vjB6p2uIak1eL8u1Ht0bqg8//EMDwl/d8PTeFz/9PkijPKpxTIefMI5l5itoTESYU6zVqbBFar8juTCYkVazvsRCyMqUpxm5H9/50HqgIddsZnvAWjMMp2vAkjVN63wZrjUnCkBzuvt655EzrBhp+iLFFM8/EKeWQEyNHcEmnVngskVs55cSchSHR5Nx6/JBX+6K+VkYPCfMXEfPudpizIi1bcfIwhoftycyoe0xOkofzEldMgv3AUXqAkTxKjJ6IVGliAYpEjyKlhFvDUwkJ9/E1XcL56ZJYJBymU1ZmD6t2mjIJ56KRYjUvSm9GM6FXYatOfbmTknB9XmnPOy8GW608P+/U68a1bry/OffbzvufNFprvD7B8nqmaKZcDlt8yrx+pbQmvFwbm0x8+ukH1lwYd6NpYVoeeHxQ8rrz+e//mL/y7TP5knh1nskygzmf/v4Lo+2sVZDcMXGeN0hz4t3THboyzPCeSVNBqOy9cUqNpcSmuVhnpjNpYhU4JSEtmU8/++p4Cn/ipvDHBMH8ByLyfxxhL/+FiLw5Xv+rIrKKyP9y/PN3fpFv/o97/s5/98SLOyc6MpRmG1uqrLdjNmzGVu+keQrusguzCExKK3FK5xSLbG2V3sJam0y5HYalUUE0pgDDlCZC05i7Dzfc4nM2i+73lw3DHMzCqUSTbtLwVSyzcsoT81IgdTTHbcOqMy2RBrUPo7aNGh09ksWprEMCaxag+WAlSqaTD3ehMBAW+hFo6wyBJP6yHrIAACAASURBVIY4LDn+PzIg2jkVCwtvis0MQskoR2IWCWY9Alg1yNYu4Dk2hjnPzDn8FcOipDAFFSNJ4OIUJVkDFU5E83RRSFnZj97PpMJUlNPkvMrKyM7pnPn2N0/MC3irrHcjdaWNSimJV2dhmhPnR+Pjjy5oLqHa1MF5Uvo0EZzqicvrGFc/P90ZrfPqlLDkLHOirpVR46ZXr8Zn18F3fu3CaMa17mFBnwrXrTFNna0L+xqN0/uts9jMaitz7czZjj7LOQRqcqY3IwlsaWYTRZeFWYSZzE9edp7fvv8qlg7wp7sp/Ef84SCYvwf84+7+TwL/F/Dv/dSf/ba7/1PHP3/z5/M2/2zPZz95yyxCJUEZdDqpGWXutGrs2dDziZMbS3FcNPwDfYAMUKMwQU4sOeEnyENYe+d1uaA5UdXBW7AI1FCEyZ3tOBVVlSSDV7mEyWopdIOTwAR4D8dkk8HDNNOqAqH2m+YT55TYNdBvmUzJQiFxZiHj2AjqdMsxmlzzIJGiSQioB3gkbMbRKHWdaGNwWqK/UFRIbgiBdhdVimTcZ+ywQU85mpAikclQNdgKzRpTCexayn70DDKmBZFA1WdzJEfoq1jHHCYLBYQCJuUwqGXco3eBF5oNEqHmtDZIU2Kt40sE3LgZj/PMR69f8eqiPL5JlNPCR1OUPl0EcqH3yqupkHwiTzPve6LXSt8hpYbWwSklPjxdMYOPPn5gbJVtreTpRN1vrPtOroPnVjEeeOlCt4gQfGmVOoL3UG3Q58SaI+xmP++onnlZhA/tFgE4Y8dG4mXO5JR4GJnJG3M+sX1oIYNH+aQLv//DPxiz8st7/sRN4Y8KgnH3/9bdv7Bx/X2C2Pzn5nm9dT7OwnU5sV4b9ISXiX3UiAlrxrINbkPoPWMYtYPPiadrg5QjCNY6vTl5HdQiqAlSdrwnZqCUmVmnIDuPQdWMaAtCsnc8Za69R9R8N5Yp89I6ZEVywkiYRSBpO3z/CafXQWs7du1Mh2KwD6epcTuu23gn54niEbqSXFjkmAv4IHk4+j7OCR2ODqFo41zCyXdWRcqBVXdBjlxLlRwMS4JZmb5omh56hdmVhTAoiaTIz0w5FjmD2TtZlJI6qsZpSmR38EIKcAJZlSkD7pwH5BKI9+FRfuV5YvJoiIoIVh2dC0LoQKZLoNZaazESRfEWoJjaIxOjpILLxI+2SmOn1o0TO7M6MjtVM6rC53tnXzOX2RjSKVqgVT589iniHbYI/n14nHi3vyCXHD6YO1xK4kFOpJyRduP2fCPfBclGuy5c9yvfmAonveASArFeB8t945QNS3F7tLohSxjeVjbeWuOD/MXWKfybwH/zU//+6yLyP4vIfy8i/9wf90m/yNyH91X5YVeW+51yOTFkMLZKYqI3j/qthz5AHMyEZZpIDg9LwYmQ2QYwQVOj3nZUczQLdVB7LNRuzpzjY7x1xCcGcvAUYCmRKPyYHbPBkhOtDuYCj6kgB2NgTgmVzsDJs7CcT1hyzlM0EHMJ78J8TA1yOWzBLlgSisHwRutR5pBiHPihd04Z6qihRExBS96HsddM1sCn5aKcD6BMkkSZlFdLZsk5Rq4KyyxMOcaUAXztqDVmiaSpS4pGbsrOUoKM1FtsJmPfKBK3luYeDsUkdHGSd2rvKJ3WdkrtMQbsjSGhEozP6cwd9iZM3mjSGMOxEYSlZl/wFRpb6lzbzqupUBHmKTOXwpwLH2nhk3OhlZl37zZEG9/9lQfenAtCpzdjSkrqyu1lI11gWeZI/16htcFud56eK8NWanemfOZSMpMqH56Ncf/AWTKvygTN6TLofQIdtBxlpVuiqjBSZqVT3FnKBUngfzgq5Zf2/Eybgoj8+0AH/pPjpR8Cf9nd/2ng3wH+UxF59Ud97i8y92F24ZLD1nz2wVQWelEmxnFdjayFImHgqXRuHqOzQJ175D0Oxe3ozCcY0sme6R7Coy9Q7EnCWm0HLyCENZ1LVlzCFLSNhI/Y/c3Aqn+JRB+mpOz4ppQphXW5G5cysw9HPOjFcUMYkQ41jgahCMXDq7AxMxgsJaMOkzonzWxDmJKyOmQtlC8OIRnkBKvnsF2bYxq5jtYil2DkmHCc8/Sl1mApykVCeZhSCvs34MeGoKZhSELJcb5H/6YO5pJ5M00R4Et4PNoIStI0TZwls3r0H86SI9bOBKkhbEolOI6rRS9F3Ugl+jFW1/CdSELuzptzxtT5ZImp0fPa6RpJWsOEp+uK1ZWUldoTp8sje6v4nrhcHrHivDxvjJY5n5SZCPt9eqm4zpxKYRtK750HPVEoDGu8XoydzH3fWa0hk3ExjciANFHdGSYhfDPH6cjW8UURqYfi9C8gZEVEfgv4l4B//SA44+67u789fv8/Ab8N/ObP443+WZ5G52RwOztbc9ZmJBK9JZDBMOcyIsSlSEwcsMGNTsqH5FaFPoycEnUfnKeMaMLFme0Iex1CUwcpTCUh3nCL2HtxpRHGKlKm+YEXJyLdukYZIERUvXlCinC9R3YiYnhylEFU/aGuFNWYNDAQb7Qe2ZB1GOqd3uF5D2R7cWPHwqikJUxUFnbjZDB5wQXKaIcoKuGjs3whVx7BW0xAlwjKVSGUhFmYS+FhnuObnkaYkyTSoOoeylA8JiWLCUtWth4bYfFBE6UmmA4RVLZwZVof9APBf99BsmDe6CPArCKOTJmHHNLp4s69VtJSmOaAzIwUqtHtZkiJG+LjOZSQdS5Ih/efdYbAb3z3Fd/+5iv62livxiSD636DmnlAOD8Ie4eaC7dRqcDNIsF7mRPF4L09sYhxl0SeJ9wiI+R5H9zazttmPD5e2HNnWsHHoItThnKrLXJEaky9Jo0e11f1/H/aFETkXwT+XeBfdvf7T73+TZGoQEXkN4jk6e/9PN7on+UZAs/JebMnPEm4G3Nj2D1CQjxRi3DbVvre2UZDGSRmnBjz0Zzb3nm5NzjAIHMKFd74Qp6bYkFX8wC75DmuCiio0+rA0qFzIEaTRY6k4RbTgjqOiDqHkzqY876FbbakFJoFnCkXxJTdOjlJ+O01M80JceWkR/Ash2LTBimP0NRbLGo0NPkFZdFgRjiQs5AlUc3YBbZjOiEicOghXpdCLukQSmkwHM1AB5qjDANjtI6OyMUoR0yEOKRpkIqyJKN7PWCs8ICRNDQbz/edPjwaj5LoLaTRxZycM0WE0YM/aXXl1qbwnoydXNJRmoSZ62Eq3Gvj/CDUtTIXxerOPM9MLjzdO/jON+bE5XKh1srLdUVtYHVHnjrPb1f0lZLOCWmN+rJTzgX3xK0+c62Nh5yoIszMSBvk1bDkfNiAPthpsAf5+v3bK1ureGlUEpMKu+8kKTxeQlOTysLj6fLnu3z4Y4Jg/kPgEfh7f2D0+M8D/5uI/K/Afw78TXf/5bdRXcncuJVBycr5MoHN6GmK7nQJ9HZTZXM7wksUq419M4Ybu3VkUpZC8P0GcYLZCKVgVppvccKKM+nEy7qF/v+Y7ScNzUE+8F9pDEg5eg5T4r55OA9R1tboxGmabfCyGdmMLvHj0exIsCaDhmVbNTNapQ+PqcA4aNRmLCL0OkMNJqUewZFdxmF8iuaedUWwoC4l5XRcW78oMTIBaR0jwLVFYBKYxL8MgpHhiOYAsh78BayTJDa2ogO3hFUjS4Y6gSxkg8uUkZGQXEBD0JNSYpLBNIdpah+BjD/NQibKhSkn5hMkn1BdWEdHhzI6zPPMGHBKE+rKdds5n5VpWvDVmDB++G5jEufyySOnJTH3xPVp5+lpkFR5Wjc+/dF7fB3RS0KxZLz9sLGIo9WZR2FVQ0ris71Th3DrN7QF/ds08cnDBW8jeJ10Zkvct7BX36rhKUo9gLMb2PL/svcuv7Zl2Z3WN+ZrrbX3Pue+IiMfdrqyqigV2J1q0a4eEh1Er/4DGvAP0IJONREtoFEq6NCgCrlDAwkJhAQSDYOEKWFsV9ku2/mIjMi4j3P2Y601H2PQGDvSKWxjlGlnpMJMKRS69564se+5e8815xjj933MWhk/9w/Nn6y/ENz654hg/vGf87W/Dvz6z/qifvaljIePGM9PSLuwritG4LoNsoE2WIvwKs1sY0CF2lZEEre+cTzMaGs/ftNf18oh+VMyRkEVshmimVF8XDlIR0IiSqIzSCLsBHzG0PzDIO6SxAZ76xwP0G9CnRolBmobEAIVY5LAj26d16fELH4VGRbo6B3FDgUXsySMboLG4EnAGP0IXwclT1irWFOMzpTynbkYqdFYRBlMHA/K861R7kNEWzfMAjV6WOqUM0MHQ4RFvjh9FfbamNIdCTeik5NadzzaGIgppcz3ANYgmPLitbsUlmnidqc+v/tw5TDNzBjEjhJhKFoisySCDPJIHErmtjYe5szWh6vne6ccCv16F+42oER0uM/y9XLgME3sDfJk/Na/uDCs8s1vL7w5Hjh/euH1t0986/Ca2+3C9z7f6GPw9QX6FHn/obI8FEozZhN2bXxrLvxgrbxKmToSL2MipSvTJXCKkX+xdhYblJKoSRhNUYscEWIOPLXhg2od9tFYt05C0HFGl9OX+un5Sk40AiyffEauPg7cNFJb44XPA9PozCnTu3Kru4NDk0NRBUXEcxIglBCRIvRuXPpOa4rZoCP0CGF4OlHUCEmINoi4bzGYbxhDA3V0BMXuXIJZClgmF6G1SN0EG0YwI4kXNvNwMvQI/u8SBLsPHC1BCURmFVJ0r2UWZ3iFFNE+mILA8EwF+Cg04uSk3v1NGIPTmvhiGlFgBB87DslzCPN9kMqGY9fHMIzgrUaEvXtrkGDsvRFL9NxCzKRUfAQ8+EaTkj+HThm0+hTlrTZ0wFwMDpEYvJioAmNtHEqmxMI+lNOSeHNwo7d2g3EjhExpRl4SE4nRFWmDl1NiioHpIbMP4+mycVk7t+tG6Mo0Tex748VJGLXTZeO2D0qClDItCY+nyPzmgA5hbYPb1kh0NjLFhOfqIJXLtrMxKLHQlwnZGmESghpxD4RhlCy8aytBBoOO7l5knEtgaCNHYamF/+Pd5ef+efnJ9ZXdFP6T//63WeNKTYnpGCjpQAvN++nNB41yglNMtHAn8WLMcUJqRyUjyavoovjvkT1sdOvGtbmD0kxcL6dCUE88dnHmYRYlpYFpB3F2QkyFYeKK+eHXEAudWFyuotqZNBEV747cU39mg30oZTgkZG9uex6i9PEFD+Gujx9OXG6mNG1kCVwNpnssWfvAQmQImDqjcqjCcFSbcIeqhMh2j1YngZwCk9x5lRKooiT1U1Ct1estEojDr2AiSgnGHEHovJwTD0FZMISJKRfMjK8fAq8eFwiwNa9lLBOUSQg50G6Vdd+5DuX9pry/NFI0DnMkzQsleSpTzTFspyUx7ufvEgrn26DtlTlFvv+2Q1R+5WuFelG+/a0j5dULQj4yLQt7G3zjo5c8LMJDKJw3yFvHRmVOgWI+YNWDUg6eXanNeOo3ni+wCczDdYLHObKZsYXObIE3Eb6WTuxkwgCiY/BdKxCxXrnNje//wZcHWIGv8Kbw4X3jyAtiMDZNjLoDPmAjSaFd2ZpLQyodjcKo8uPL9EGN2r0qnyKMIZ4QXJSpGHMQ9q0zOjyZQslwJ0PHOyVgu/sk/Q5fyFGIVFLwJ3vyLBOHWMAibTRmcfeiDmGZAxuucAsRltAZyS8kYfJWoIboOPkoaBsM6w49vd/vT2VyNHqMaPL5BYmBHJWXUZC7ryKGwHEq1Ma9duFpzZfLgTn46LRnLO4DRtzRbMl5DSUmGEqhc8Pj4zHm+/dXkZTpoticuWmkBRfYTFm4avYWXfVOyq1t3KqybpnHRSjHyK4Ombmsw8e+m9/rLdzbvNaRbixFed46x1m4jUg14yEZ8zzxoQ3evf3AnCHkwus3mWbwfN1p/cqHs/KQM0/bzqUqIxnTDNUiu0W2kTg8PvAwRT565V2gVAdvHjK/9mbikJS8Bz673GjDfOZlH7wQOEund/8HNaYyyMAaEufWKFOiSGHoxqe368/98/KT6yu7KaCNrj+ABifr5BKYLDCaURSkLIRSUTVkCHWEO7zV32T7MMIdQNplcKs7ZkLSgKqzB5dFCDTCrqzX248RZap+NF9KJAdnItIbTf1JuvVBCM4lHWY0aWDunDQtWHfICCGQgRF9rNkkEb4IH6mr7Rh4SLz7B36esivOuyIxcFFF6chQothd7hIJknkagxELJThezoJDYHK4MxEMrDe2EQiKh8JsOHQlBWZJLCEwRWituaC3LJQgHNNwE1cyTvPEg0QWInXtUBuLBo5z5rwLIRkDZVsHOiLbEKIklsmozQlFuQTevHrk8Zhoe2BtgxoGcutEq4QGRxEaBbk7HFLfiaMRRuB8vvGj7195XApv3kz88jceKEFRTe7JvAmLKTkHlilxOhX2Hnl884A0H0EOKUDbGSPy9u3K87bxFp9Yfd6PXEYC8dj6IsoISil+FQpa2DRRRqUUZfREkMjUK8fZW7LnoYT4gvElJiThq7wpJHg4fBuOHR0FsrDmmflQ0CWza6JtkJPP3s9tUDcHj0aJLnoFqhgPWybkjBlEdepO6EpJExYi85Q4hMh261z2ldaVTTtLSEicACXnfGcpeqsui2B9w+4i0TEGSGAED+Q0Ons1JPhwTCDeqUxGVaHkyLUJNTQfEArQOqzDmYhdDHbjKH7ctaEMS0x38ElXv24sNphDcOCoJYI4ZSgUOBwdMde1eYYjOaaNHLHeXAsXFLIQovEYDUYnxUbtcO1KMJf1ekIAhgWmOZKiUvfOMXVu2x1hlyKSlVPObvbuAsmHxaoq7/aVVqAsAQtw3YQqgSwTEibea6fXBksipk7IiZoi3/3RjR/8aKUN5XK78ne/9drTmo8veO439pCoN2VlJSYBOpfPN7amjPc3Xh4Ch3jgtjWmeWUjY/2BKgmtwnlr9P5MMaFI54M26ijQIroPrghXbuw5EuaJh4vxcoIUjBE6vUW2ljhMMz1s2Jc34Qx8RbVxAAwYvIV9YS8b/Qbx7irII7KxY6VgW0NxuImOytCZNZjPE8QIKpznxiSuTYsh0MeAFBl10Mx8fiFHDstgvRgxRDDheW/+IdHAKhtREkErAWEPgW4RWkZGB4XROyklrkMJKHV0sgVOZWYfHUjMUujSuO2DacnsozmWncgSI13AxM3UZ1GKGqiSgoNZnncjT+65MBFiiNzMXZkpJaJGrwFoYL11jknZ1+h36DhRyqANY5ozYkKOhX3rzPPMpo2SA8qMiTEHI+igmZIqpOgeyUKmm3E4RHSHGN3ItKSArorkBAUO4vq61iLLIUGvrBeIS6K15unTa+OTmCkPMB0PZJRZAJ14uq5oC1zbznoxzBq/+mvfpCKkx8BtV1oTHqLwKZ03ZqwpcJDE823nzeuZ6TBxGYX3b9+TQ+DDOZKnM9crvMyRtsOmSh+VlODcAn2Hrx8bWRKfhU7YjJdl4cNWsdGIx4z07Ni55sDaMSk9NB5qRyzdt9AvZ311NwULfOvjA8+frZRLQKwxQvRrwPAK/iRO1Y1qVB0cp5kGnIIz8wrKDQjqXohohpiBGbs1ovn9GzWaVSYmYtko80QwHJtGJrTAY2h3P6NCdGKSmaBWaRIoAXIArJFaBDwIVW8Gi2I4p3GMRohwa4NDi6QhiMBuDi11SrO3v1yFhwd26AyFUKANSMkHmQSPdh/ukfGJyCECGLUpyTIyD26aCcW7B1MHa4MdjzgPGRQVlph91NkEVZfUZgEd4taj5sNFqyrShFz0Dq711GSjUkJmXiK3dfC+KL1XHqZMrz56PefM+1unPnfeHGFLyeW2PbJ9doWSuYqSWoPJUWnXm3E4FL79sPD11wvv9sDH1bASWcTToA9zRKeJFzZ4fzXmBA8PkafnjbdSSdmvnlUM9sCLg/B8NXo/E+KRTZRM4CHC5+qzGjJH4nVQmKijMWrnJhF7WglpIoXBLXV0ROJtJaQDt1ffIPCDL3VO4at7fRDlv/gvf5fbRZCenIkQA8EiaxpkGy6NzZCnxBCvKEdR6paZSNyss5gDU7gblHoIYK4aW+4cAo13epIoczwyuncmojmmK02KpkzOEZKn/XIKPEweF36YHPm+ZGEukVePmbQETnNhhE5og7EO6u5dj737SaapK91S8Om4aMPHtc2nBV/k5Jh1dZwaMihBmBNEc+x7CIGXs/ssc04+nMUXPorIfBe0vEwQLSG1+4RkEJbsUtgpZ49gp0xtXpPIohwmQZJPNI6WuLbm05y1YWGwbjtT8bbd6MYSM8+10SrkMAhb44UEujqrUlL2jMZT5c1HM6eHA6UkXr08cjhmPvrowEhGCcZ+NN4+dS7PK7l4N+Pjbxz58LRzQLk0GHtn3ZX325kxBu8/v3HelA9PO984LTweCqXMHKqPWK+qZBOCZcatQoSeD3SMsTk4Z+uNy9ONx5K47t1hN1YR8n0q05Aps7edimdI9m5MDwfQzv/4v/6zL3VDgK/ypmDwm5+/5chKjMb0IvI0Kj0KxRIfiAyFKcx+Z9fBHDwlqFOlY3csyR0COqBtO9Y61RpznO5FOecAqkDr3TMOA7q5PLUPIVkhmyIBlpwIAbcym5BzZnQhlZlVBULmw74RAxwclEjIQp6FZUoeGLp2JDt5uUtD8CduQOg6yOqjw0qny52UHABJrF0cGIsgwwUyqpCjR6Vf5MRD9ui4FOgaPERUBOkus92kMwJAoMTAFLNP/bVGxMhmlHtCEjWGuWg1hUiQARa8MNq9yFeCB7Z0eDKz1cHoeg+iBULzmstWlefzlRffOrJj1KgEYFWhmYAIh6UgJfLpH22M7ii3N8vEt79xZA1wfJz4+NWBj149kKdAiDvJIjknMp2Ev9ZrgL1n2ui0ELBRMdnoWul9o6VIb8qoPsaeyuC6Dz50QWywteSR9Ti4qm+E3RrdoMlEngrb6lONhzmwN7geBtfbl8dm/GJ9dTeF++rJn+7reWMEI8XG0MZHc/Y3u+6MkCC4JFajIBrcLC3myrIAUYycCjo8xNL6YDQleNGfYIE+7tbo7EUzG15EvIyNKsa2ubLM7rkCBbR73/x49ziYGCozUQOjuS/CobPRqVGLcDw4F3BOkSkkmojbo8043tmTDGUfQtSMqvqfdZgPSN0JyzdrrOZTh4ZLZJsIWx/EGPznze4RB29r9tooMXHKgWbeY996Y68NHd5BkXtuoX2RAsUNVtfa2U2w6MQjiwO1PxHeTtkLma1udzir0npAos9hlBb46EXg6flK6katAaaEJRAz9j4IBn/4hzeWo6dI/9Y3D3znbxx5eDxRDhOxRD45X/n0utI2OJ8h55mtd+YUQAJTvQfigisBBsIHiTAi1iee1Lst+9hBOksUN4qPzlorXRJt7IyqqMysATYzokaaNBZ7Rod4W1n97+P5shO2L7nCeF9f+U0hlIOfAqYHTgPSSOSYvM9N90m9PpinQQEOFpmiTzEGbXTz3EAdleu2k+ZMij4yHO7jq63C1jsxCJYaYrgHQDyxmSWgzVXr+96cdxB8SjCGjJhx7YNpEsZQJjG2IfQcOC0+Cluif/1teG77mIUP5+p4+uDJTrPoxOog5OIZjJB8VJsROB4iybwwmWzcVXUBDcndlG24aSqCGgwiIYIOwxBCSZTi15Z9U1prbPf2WYqROReKmhOK76DXOWbScBKUI+gDo+8syZOk+2Xj/drY9kbtER0ZyYVL8zBXrZUlQV8Hh1IxzUxJSFNiGxGGEEVYQiJo5Hf++JnDIbG2znd+6YFXLwv5cCBHKA+FZZk4pgWpnXNtzFNgbB2aEY4zb8878zF58ElAh9ccZO/k8kAXR+RVC/dpzcDl1nhaG2YTfYNTgcMSqaLcnndEDendC5JNaL24n3NtjADrbRCDEfL/vyn8XNYpLbS1cd6uzClybSu6CVHGnR+Q76PAiSDChUEnkIleLDTIMpFDREpAzegiKJGqEHNh3IeQRHyTCObx33V4X7+pE5R783HlqgbdQHysOmjw/r+5YGSYkrM5qEXgpj6Z6EAUo43Bw+NMDHA9+8Y2go9q23AnAxa9mxD8/5OT0NdOTJ7SvKqj3/bR6baRQiR7j5FAJN0dD9u9K7E3o24bEhOtKio+WWkqRMHjkDiubbvbpl+E4HbplOghMUcDaT4/0Qcbwh69nbqUjJmSUwOUse6sl8bz3nn3+cphiaySCSNwLAvaBw+yUYBQK5et8rvfeybZ4No73/nmCzRHqizUi/LclbD7QJcUCA0+O3fMhOu+kecMV+Ht25VrNU5z5OXj5A7SIKSSiaHxvDaCOC+jqLFdFShMOdFQLgOkBCwmHkshBiH0SiJySo6fu/ZBbYMWJzpGLJkpKYt8yQMK9/WV3xT+l9/5TVoJPDwWIJCXA0wwzJ9kOVRC9jf6pspJPKI8xmAPRhaht502jFAFa91tSlF+PKLqacDg141uKIERFO1GsIBpJ2QoJbH3xnQ3Q3crPpEY/fjezQEwIfkduql7Jraq5DCxMWgtkmPnuhmvTpmJSm1GuncSbnUwhYBgVAMbBiZ0bT4KrFBiYpIIIxAF6khU9euI5EC0ztqBIEgK9NFQq35iaIPUYG2VFD1F+jhFXh0SiwSihR8nLFdVNAkq1Y/i5nTtgHAbg6TV+Y9E2p0RoUGwMTjNE5Yj+baj0Wc1qvou3Xvn4wkOU2DOyq6B3//0SknCrRl/5+OJ4zKzTJGA8qRXRIAIN0tsVWhJOLZODDfCFHk4JcpsfPNU+MbXCjlPvH3XCAT2qn56UuU4C9mM02EiJEED5AVCUMK2Y9FYjjMfqk+7vlk8at2AoMq5NSKFGxXNlVaN0d1M9Qeffnmm6Z9cX/1N4Tc/8DpFbP9iGjBwiIFkk6vLeqb3nRPicehoEJqbkkX8uJu8oKWl38nIGRWDJNT9ficXr+hbzm5LMIeMmAgJ4bZ50m9KmY75qF+b3gAAIABJREFUk1+c3hSG+FNanYCcxnAwYDDqUGYdDNlJqsylgi5EiVyacjhNjDbu2nuPeT9bJ4RIVhfTRoQ5TexmzCGwW3eXQ/aocRIAT2mG+/39kDwqXkJCQyCliAVhbw1J8BB9xmCrg3U1rk3pEYgdRJiKeLdFgTudqJqwG+wa6AZva+D91mhmHNPdZdFhjpHVjNSUx49OpALvL5V1HZyvOzkJTy3TLfGjDX7rj67EoVxW5V/59oGXHz2SHwIQ0CyMGlhEOMSJ87sra69QOy8eF97fIFvk7VPneTdufUdOhZ4TpXdudWXcOgfgsvlVrpfCtA0udfCQlForx3CkIbS1QVAokYtVGp2HYBzMeLquPOYAvXGkYHcGx959yvQ3vvvZl/hJ+ZP1ld8UYp/QOOhjJ+QDt15pIbLmC8M64RDZzahjUNq9cGZQu2vaTSCq487SMJLAUQLLELR1phTIye/k++iI+tFQJDJJZN8HuxpZoHfl0hpDYU5OXk7qmLWhuJOid56bov3evyYyYsSaOCQ2BpBGo2HBEeSvTpl96+xjQGrcYx40USZxvHtVJQePbgeLRHHtXQgw7qPNu0K1wKV7ClLtvjEQUEs85MCrg79lyjxRJuPNlHmxRMZQDhMky9gIrJfGeRvc9s7eDGkTqQ7qtbOvnTLgWOCUhFKVD9dGt0S1zJMauSUOBw+WlRyZj4XlCIcXM09759wGt2b89h98ILDT98Gv/Y2JXCbenjfadlfdWeDV6yMP3yiOPpPB6ylyWSvTHHhRFuzYEDqX1jk8zogNfvDpjc/2K0cBSiBGIwc/IdJdy4cFdMnUFni3PnPVDrEQtsatG2WaHXqbMlaUUQfZjGMwPrTODaONzpLzXbz7i/Fx/Gm9D/+hiHz/J/wO/+ZP/Nq/LyK/JyK/KyL/xl/VC///ugY74fTIMhW0bXwtDAgrH+vBR3ibY8l0yaw0DCHKxoiZORa2vrOaEfaOkEgiqA42NawECH6vTnjh6RQigcDaoZuSTFgZ3FJjx4ErapWhgb47Hn5oIkU/RguFEjIJuVf/x31sWtlCZa+J2O6cx2GMYYSU+HhJ3KoR8SxD3TvSIxUIRHYR2vAsf8doQci4kzLgjot66xQqYOzqT+5UMttQYmx0MS67EEzYRydGpw6tVum983wzelRyMeRUKNKJPfI4GfMJ8mkizsJ0CByWRGSixUQ/RXJJ2G1nq42+7uS8O7m6uu+z78reBvveSUn4cN353T/4wLHAw7Lwr/3dR0Y6kdQ4PRw4JFfaHU6RaQ7ULdPPjcUSl33w8evM87YTFuH6LtBL5u333qMLjD0jQSmWKA9HNGfe7sa7CutVeXx1YN2by24By/fMy96YdOM5CljkfLuy3qA14XZTwiTcmrAFeEyRQCSqMEflJhk/Vn3566f1PgD8xz/hd/hvAUTkV4F/APza/b/5T7/As32Z67QErjrQlND8QN0GT7pzkEDKMHJgGRCXha4Dk5kSBre2k2NxWewUaBEkR1owsgyK+kDoMG9h2VDemSHRceqNgEZx4nIN5Og1hK6Ja+9YDL4pSac2AYQQBzG6eNZQYskcsg8r2QjMcSAlkYP7HiVFahvcUmS2wO26s65GE+FwzAQdDKtMA5J0QjTG3jlIREIkDqUOWHdnOUqKlGjeLZgzl1GZYyCTfEoy+ZE8mj+JzdxfOdR/nO/I+LArPQnT40QKR/qqPG2NbVdaV6oO1v3GgnGyyJyE6SEyz5nHx4JNhUUEkiPjl8XbrohQysT3f7R60TIY3/6lzKFMLLFBybQKKRXO2+B6qeg+GJcdmYVmSjR4f+vkNLEZfHRa+OwHVw5zpu2Jt+vKuVWeLPD5+YrUitZGkhuvl8TztZLJPMyw9YZtoEnpGkhLoG+doMpxXohFqDaoKZHyRM7CTb3mJGpoqlza4FemL/1j8uP1U3kf/l/WvwX8V3eA678Efg/413+G1/eXsv7RP/mfeCgza+vctit5PpKS0O5H7UOM6KxYXckxeZtx7yiF3p1DkCQhXdmHg18HwhDFRFHtDDWqdNJQh5yYYChNvAh4Ki6hrdU5CTFGclDyvUMgulNXY6/G+WbEOwbdCUcT6y5gLm/Z2kbUQBKlVaMr7A1Oc2Q5CsWUdmt89u7m2nmESueUAlNeEBJ7bQxzrmEI3ma0KdCrIjVQu7Ht3tnoY3A1oeC8hZhnpjkyL67CMzOOs7su43D/5IpfodR2NFVG9uToaQkEDewGh1ToIoh1mkGIPttQJmFKkWW+X58YzCm5jVqV//P3PnCYhSaRv/O3XjA0ctsVS07Zvq5ndst89HoiLzOlOJDm7blRbaUN4SFmjMrlDOfR0euGhsK+V0IKTCTCtpGrEXJCiIQ6sabG7WmnzEqTQLtFmly5rp1W4fGwMEIg6M65D4fOmIEosUTippxy49Y7xESRTBqDX//N3/+yPyY/Xj/LJebfu2vj/nMR+cKb/UvAd3/ia753/7k/tf4qvQ//z/X5U+CmSloWXs1vkLVikohjAvEKsHXDSIzeCGKIDXrfkGzscvchFHFPYxCGCEOVNmCXxK7OH9QQGK2CBqQq0pWmTl9SVY4lYQQY6m284b9HlcxhTiCdY/ENJlrg6TzQprS+gxgpRqYYiEHZu6FWCSjI4LkPsiaWlwUpgmhDr3CpMMXIUx0EBiV5lyTFQZ4mpqDsuzERWHKm2f3UAMzmo9CnAZVwh6wOSoqYKaUUYg5Y9CLcbfjpIUYhYOSYWPedrUdmUZ52R6yV+3BQCIUahD1ksEQJwuUauG3KuQ5ynkhB2NUtUj86m9OMVPjV77yBWjhmD6/Voexb582LR84fnrjcPKz27snJyx8dE/0W2DZo3dg34fU8+OT7Z8IUeHgFwRLaB++er/ywNz7Uht7JUoOd860TrPLdi//dhpywOvP+ye3it70SNfCsTlsawzjfN73b+UbKCyuRiYnelWuAG+YzF78g66fdFP4z4G8Dfw93PfxH95//s6Yv/szm61+l9+FPLVFep84yjHfjGUqisZFi4xAmQmxebCOQJFO549ejE4e81S7sTZEkoJlIJJn36sWM+IWCvquHncSIWYi5IGLU1sghY8GBp8cipBgROqcpM4XBYCBx8isMEZKwHBO5BEp2U9J1929oRRkSmEK5o9XuR3mBuguHkjkej6TFwbDXs0/7VTOWpaAakDtWfXSfbxDxyPeIngDlrmm7tEHKXncwAnOKFNQHn3rDqnKMxlJgiZGgRm3+/QsIOUZeTTCyP/ldCX+3R9F9zHvs1KbQByEapfjbpmnjVoXRKret8vmPLpAGX3szc9ka1RpRBlOJnGLyOo8Krx68kNvMCLnzVAeX7n8Gwsp1r+y1cmnGZD4RSQzkoRQCkhNzTZSUMQ3U3jhNiTnObCkxRZhDYKs3Prmc6bESY3MZMd6twBrP9w9+kchUCmetvF8HPfiJMahxDPHHrtFfhPVTbQpm9qmZDTNT4B/xJ1eE7wHf/okv/WXgBz/bS/zLWdfpBVOaOIVIl86DHRD1QtlpXjBNpJScINyFIB4mGhZ98tECxxAQG1RzXmHTwS7+xFdV9O5wCMGn4FIUet3d+hQmN1L3TtPB2n2seUhAhyvYKnBIgVIy0YRinlzsphynyHkbhOKGK7HAGI1qEYmZPjxzsOLotirGtQ0sZ6aD39dVoa3G5byTgvD5tfG8V8rkpwYTobdBkoACdShY5BTcvRDF6x9tqEtqzdwglTzhGRBUBkOCZwZ2pddKSInzVTlfdz9BKGhKnLfqWDr172FOgqVMHXDbAipKN//13Qb/8rs7H79ZWAIsh8jjw8TDktmrC2O2Bsdo2FjRFHj9eIDaGd04Bd+gDg8HQpxIoXB8ND7/wRll5eNXC4LTqdatkawTHzPXvfJh33hRhc+fLrR7Rua2Nj7sG++rUvqAWvl4OdLaTkdYA9R7Abpsg9te6WOw953XFpGhZI0QBJbDn/04/ZLWT+t9+OZP/PDfBr7oTPw3wD8QkUlE/ibuffiNn+0l/iUsg3/yX//P9H6hyOAUH6mpYXF2gq4qol7oa8OIORPiFzMEFR2BrTZu5k+aWYyU71pzA7m3qqYc0GD0AHcYsYtYJKA41HQTWHKgxIGaYSZcht/LRYV93/1pqXcIag5gbrB+PAjPz/0urzXnPUiDPighcjOIzY/+RbKPU2tD1YNPS05MM7x6kWk2OFkk1MDt7PMXN4VSCin4vT4JTCIsh4neK0GEqwa26p7HoEZrnborg0AdyrWBmrCIa9g/b8p1cxcH4lSqFmGYezKT3qc8x2DbG9toLCUSMvTm5Oy4wB991pjnG2+fN37plxZ0wITdaxHGkMbaIM6FeFogFdIyEZcJkczTurHfdlBlv66oNT75TDkdIq++/jVSKshQZDJa9+Ba2pRcIkkKNQnfOMyErsR9I0Vj3Tr9/Y6ReLXMrGz0aiCRZEJtyrCBFJcNN/PBsZYGi3XWANLhv/vf/vmfc57+ctZfyFO4ex/+PvCRiHwP+A+Avy8ifw//o/wh8O8AmNlvicg/Bf4vXCf375r9YpyLLlbYSUyWSHHDtplub2l9oPGAhht1RIb6MJLeYagaDCXzqBmLA8OoIRB0uDBVIScgeWEwmWGqaPRWo5SEtMEIAxUjqY85zyGzmRJNUTFuojzE5JLVEO8FykjfQSRg5pIXQTk3ZWaQxCEwvSiFyEkGG8oSAr1u5LuZiuFa+RSMs0Zs3AufSySFQVuhNiWsg0uQe9GwE0SoEXTvTEVQBEyZYmRVKMM3hmxOXg4hEEdlqEtkY54JrSEyKBNY9fboywA3i6g1plLYWifECGJMwQWuVY0YoUngh5/7tGCcX/I3vxnpPRJS4KrKYQqoGqtlTofOXoVQdz+ZnYwyecH3+X2gTYJVoS+JqMZh23m/Nf72q8TtMgilcP6gTtjOmX2/kWpEi/Lh6YKVQtscMJNqY63iFK1oxAQ5Zt6jHNW4mhJtkOLEtTUWc9xfB0aeKDqQOtit0e963l+U9Zfqfbh//T8E/uHP8qL+SpZ1TrMhK3zehHwQWI/YMhiqZJl59+HiswnWsQgMRSxSbGcbipARG8TuJ4OhjSSJTV1WompkvSvk7xHa2Pw4nS0SkxEVggQkqTMX1BwKmyZqb8TE/e5u9BgoUbhpx5rxkCYej40Pl8p0KgQxqg2CRiqDhI8Zj+itymaD2DM5B2J0b0TUAWMwMPbdXZHLoVBrJ8/CWBvtolSMdEqcxJ/qmQzxTq0Oge1a2YJxLJG6KUuDp2Ycknc7milpVFpQ0lBsTETrmAyaQhQnQ1egkijq8Nyrdp5W8/brBNqUD59dWA6JFy82yuEFqSTQgcXM7TaoffDNjxa2NvP1V0Kzme39xvd+dCELvDgeueoz4XkQPlp4HJEfva2cBY4fP/J+FCw0XkzG55cVIXPdBosEzkM5jcxhzrxvnZKNp2tjmiPndafS+PbDgTOBMcDSzqYepR9ibKNziJFzbxSHhtPXK59G40VYiIuPp/8CHRR+QUaofg4ronz47pmbKqVEUq2QPFugYzAEHuYjN7y9tgTB7i27OQRCTo54s8gWBQ2RoYlxp2x64tFx6Yp3G0oB7p2GRqUPB6KqKmv1IpMN3LfQOjlETBM1CJYjdGgmZEksKVG1sRN5mDNPayWIccjuUpShCF68ktrpw7sJ2YwZ2KsPJIXgRbUsLnCZc+a6ut+SILx4XHj1OnOYvX329rwT73apvSqjC8+3nSkLD8UNTsMGzQJLCaQoLDEylcSH7vdmtUg243HJjOGwm5g9c77aIKeOSaINt3XFJBwLnKvxxz+88fA6kiM8Hh/RAc/XzmjdZa6nzEevF0+V6s77tTOa8fj1E9GUrpXP3r9He+Xr3zzy9K7zoTbmAVOJHEanPa+81523Tw0JhRhhdOECzFMG3TmT0JRYc2QE5cNNaR1eluiO8WHcto0pJq7394nj/0HDAIm0ZIgFLM3MoXC2zmef/PwFan/R+muzKQzgn/7vf4QNeMiRUgrWAlihEZliQUL3e/IwtgbLVIgSeO4dre5+XAJkE3odTGGQs1ufS3DGgoqr4kpwx2THCJJJ5pHfGFxA675EnHPQO6eSUAkO7NBBNCDBddxTls5j46UaL4+JUQPdXBorMbBiP+4WJOEONDE6lat1UnRuo5og0ZOd05S4bp2t37y1ZnYPg0VOc+Ll3Yp0eVZ++METhVEGD8Wjv5fmjIYyJUwC0139liafp3hdkuPdRehU9toIAfoQWq0EuRdUCYR8R8rnzMvi3/Pvf3Ijz8b+3Pn6q0ApiaUEZolonMiaOW9KHoEYjFcvZh5fTJQ5s7WOEL1uMUdezBPXW+SwCO8+3bn1K2MMrma0tsO10RtMMfgpL7jYd4zOU+sswU+Z100pMfH5+4t7LpfEJsZVBxv+99lGJYyVFBKNyL67qQqN9NjJsYMkkhm//en6C3VKgL9GmwL4sby8yEDgHAYhDkbeqKkSb66Jm0r0J2qKbAz6MEo+YcGYc6CaD/Qssfk1QcfdrOQTi0mCOxnFIaiHVDCqK957p/eCmtFaIwx/coSgnHtj7Zt3KLLzEdIILGIcYkPwvMUH4NI6X3sZeXduaBGqGotkNoVmPpWIdvahtCTUARoLQe4C1tS4acPaICC0Jnxxp1WBfXiQqpGYXydevfQN5sOHThuB3TJ7Nx5ypiMEyVirPG0bQV15d5oWFOFYPLuQo1OZA5FTNqYpU20wS+TSIk2FY07suvF77yqffF/55ovIbTV++VcemaaJy62xVyWwkxRC9HZvtshoStTEkECaC58+deIidMmMHZZTpI8z6945RGPITAnC41z4UBYeXjjaLaTiXaXrYAmZqnAi0JoS0sYkQl8bjMw0ZfYYEU18TeAYC+bNbmwUQoJM581mlHny+pEGKMYUKodp/jI/Dn/u+mu1KQB88sNnWttZdCJNhd6E2Y7oZGjJrhFvHcGxZlMw9u7hqT6g9kicEppOPvB0Tzp29bCU4DSmbsqIYDTnJQw/blq/Ek2JMXPTwJSd5TDlSNRAlkDv3qLs0u+470Bj3CWy7pJsFjiWzucfBgkh3MNPc4AsDg61WAgtEFWhN27DNXDXHY4SuWKU7BHkromSkuvyRKk1eIegFFYJPD4WDg+K7oPn641jgmsz5uyMBOtCtMTQjHRlG42YnMQsKjRLJAmEALtFzqMzMDQqaYGmO5+vg9/5wc762Y2Xr4za4DsfT4j6cV7uRciynJASKGGjRD/OP7wo5AJzDXRV5iKEGDlfNuaD0WKhj0gdcFWjx0HLiU+eK8vYua3KrMbzWBkKLw/Q+k6rxhQDH82BFBJbV97ujW47j4eAEDF87qCFzq0FXglsVqlbZ7TB7RSo540eIGknNJ/3+LT9QtTg/9T6a7cp/A+/8T0kZfK0M8bEMcJhhls3cu3Yrv501+Kmp1A4xEAJmdqVZXLUuzIchWbQFQqDMYy1G00yApgmWhNyiWgwJAbqtLCb0MyI1mma6drYbBCTXzmSTG6rjpEkzh/0p23gsUweWRqdPM3szRHqqu5reB6Ry2Z0Scw0SkqEKdNNodt9LsC5AocYQBJzKO6+TAkdHY2J09SptdOujdCVMIQlFb7+ZqJY53weHObI6MJpyTweoN5WXhQhBuUYA29K4BBhHUpM3qot0QhaeYiRoY29C8/XxveeI3/83asnMk8HrN349rcW9A6pRSJhChAKl6cP1L0TysQxFawY02wMU57WweX9zuWp8faHT77xjMTz04XbfqO9H+zbjRDVpTUx8PBRBuCDVfarcmFAmtE86LbzR0P55LwjOnMIkaerkKOPhC8WaHWwNreDT2bswPNwO1iJM2uPxNDZtNOWwrlHnrbGP/vnn3yJn4Q/f/212xS20XgxGv02e95gBNgb83yg5kjMG8SKREUwJAwkJ6x/YX9SUvVR6P1ORVaMBowwyCmgfUdRsmxkawxVZoOC3/eX6J7HNLmz8TEuyI63OIG9VUZ3DsKwQLRB68bogSGwdfW2pQ5enTJvP+xYTEzFdXBMRqzKrQmdQe8KPTDn5Pf+GJDRqTjYZB07531j23dCTIy9so7IPBeCCGqwj0EbyvtbYzo57Pbdh5WucL0NukTyIfNuH1TLyDAGiWHKYkpUpVlgj4EdH9qJOfP7n1z50Q935Hxhmt3o/K9+lPnmx2/8e/B/s/duv7Zt2XnXr7V+GWPOuda+nKpTVS47xImUiwhCFjzwgMIDQgoPPPFAgFekKFIe+F8QQuIBP0SCYAlQEFICJBF2RBKQZQiWk9iUL7HLdTl1zt57XeacY/Rbazz0eSInJHVxlX1OVZ0mbe2959pr7TnXGrOP3lv7vt8XZIbg9E5wo7Jx9+LE3bqwl85TvSBaiZLZOrx6mbHSWNPUBawOzTvbtXLM97x4oXgKk3LdnLgo/exI6ZR98Hg9Y0O5Xq/UPXGIeaZqaaDS+UZpDBqHY2ZZlScfXAKcxSdH04EQOUnggcajda6tMULCWoS9kVx4lScT89NYP7q5D//CMggZeZ3Q804MMycw28ZWd9aQGXS8Gp6EBeN6GaQ8dwhRldohjJn50EVJ2uiuWJnbcULAuhHDQtVBsI0RDmCCNaOLsYaINSMTuWonhBkpp+Y07WgKXAYkubEN+sDF8TanIW1MJNoCnI6Zp63xuZcRrbfQWSBHoQ/nGJWRoI2B2nRnVnGWAV2mEzPVSkrplg8ZCHLLjxiQl2nxbTa4tnmkWe8PlOfC89Y46WRJDs1sT326FWOglj6lwGEwhqBBsKF89c2FVmHsnfvXie5zl/VH3ltYFyWnheftSoqzV3PIQu3KKDtrXqayNDilDo6vDty/9wKvxt2rxIcfbkhx7u4OjF6Iyal1EOKRh8eGl41Fp9vVPE2Dm0e20QhjoDIXwoKTrTDWRNyMc50K1u3dzm6ORmdXZdRJ7dYBuRsfDWFQOOZMuVQOr9IkSo1IT4NehcMiuCwThPkprB+7nQLAf/4//zLJGjFngkVWaVg6cL+cKJIJKXI6ZropQzubd1Bh3yvRnQlPn+pGtYb1RPR5TOgIeYzJIdx3yl4xWxljOh7BiDqPJlszNjHwSIoT/Q4TuEqbises06SzpIhGxVwZ1kGcMAYhRn7iLmPFefdsbGYMJi0ZCQSFqzmjbfRwS7kKE4smMicnHiJJBGuNrc2jwugd8Wny2XufR5nk3MXEfQj0bkhSaE4P8FQH5zYo0vlwG3xzg29sgw9L512BD56E3/r6zld+8x22DU7RON0rJxv88S8d+Zk/8ZJjUsQiAyOmmcEpSXEJtD44LpkYApsJ1QPpxWlmSuwbI2WsGNv1wvFloOwVfJCWiA24S436vLE9DU73d9QmPLeCD8cuO2sAXRaSdqQV9j746NJ5vgyuXQkRpHS21ni1BLbhrHXGDG57pXVnv8UBqMmc/EShbBeuHpEwyO6sMTBq4m/+w69+22v0k6wfw50CBHfeq8obTYQ0CJ7x3uZc2Xc8Cs914knMD5zWhhkcTitbLfPca0ZzI0tiSGPeszutzZk7vbGuM1zUzPAqmAg5CzYqRWYgjLviUahl6htcnfu0AkYfg72D0mmdCV1JgWwzls6SM6xzroHTvfJ4GYSo3CVlt0CtHREnhGn1DjabntIHuwhqM10qtk5H8dtFm6PTbEJXu8ESItc+cxsmmF45ZZAl8PRu5/lxcHx54PmpQm00jKuWmWqlzrBIkAlD3UwofXCMgZ/4wjqTqbuzNyWvCv0GrmWmax+yYHvl7pBRd2IUDotSSycU4/D+HXttHGzjWx+diUF4d2m0vRNE6bVx2TopZo6r0o9pkpWL8ZPv3fNwvVJ0+kicznunF7zbC8M7SwqUYjz2yuu08CwDlcjh5GQiH4yG9pmEFYJOeO8aOLlSSuG0ztFx7hsjZ2TAZTSOOjUln859wo/pTmEAv/P1Z0QbQ3aKNQbGCAPiglVlWVZOsdNaYzFhuCOtzd1DCMQ4SUudSkIZVqjFSGasSyCfEkRBZeLWklRSFPZtMFyB2SdQMWwUcgTXQRJj6wP3yS5Q5rZddcqIZcC4JWOHcOMmjpn89KVXyvm58s23U5uf0twN0IwUF85lwmhLn1xIEQFzsibMB8QbS7E7dJkKPRc08k88IvEGl610ynDuXmYQ4+nNM0gjHDMShGLG3o3nCtYrrTXiaPzMn7zn3/iXX/FTf/QlhMgIMlmUbXDZB6Wl2QMJ4dZHmWnXr3LkGFfOxXl+aNPFmg7Y3jieXvDuPDM2zJ1rr6x3AXIHAnengNWJsVsOkV5n1sLlPIE0e2lEF67V+OhypdXBtXZaCFz3jVcSEXE+OG8Uq4gf2EvHzbjWecOwWrmUQtkbYzhnS3QPXIYROWDbYEThoM4//OjDaZ//lJa4f/LrlcgnwbaO/KV/91/lg/1CEKU2GGWf3XwJ1MuGREGq8dGlcrcIOiq7RKJNnHu3gYebqrENckzIzRqdw7zTqQA631DKLQrelK1XRGfseeuOqnC4SYStDzwqozvrMlHr8xsVcB0wYA3za2JOvzW4JAkrgYfrFZjiq8iNDBUDiUGWhMepTkw3wnEYThuCx2mA0uCIB8bNcTnDXAIiguyNsGZKd9bYeNgj7bLz4ph5dxn86fcPLKtytU7SyOP1jCyJgwTWmHjaBtC5Ww/svQE6FzANBIfCIAmIJsbWOJ7mIvH6/sjz3nj/5cK7h414UF68uGPYNE1Zq5gLS468vRTEjbTOwNrlLvPmTaEX5/Qi0tT43HrHxQsfvpsjy9qEUZ3nZoh36mC+/j4dsYsEvvawE+LgtCTSXeB6FqIYrgNtRksLpTTKMKoIqwz2zUlLoIvAMHpQ/u6vfv2T2iX80neDKvj0Lld/wCUY/9Xf+L+mKjE4tVwoo6OL4NFwnb2DXZXj6lwq7L7MZKcYGSlSmQEqh5hnlLvKBKIyONv2qwPPAAAgAElEQVRGd0Nc8DaoNonPQ0AS3K2ZU9ZpHopC64PdJ+V5yOxMp+DIEIIo597BCt6doPOcP9SoonBLYqrWudbCe8eFV/eRTOfhudLGzEAMutzYDs4SwHVqK0KA+wPYXolhLiLdZ2f8GONkJHQjm3EZ8NVv7bx9u/O1d5VyLnz5/QOfv1/43En4xuPOh7ux1c5zc8J64i7d4RrZ2iBGCCERdLo8I2OCWqKy+9Rc5JA55cj790f24eSYb/bxTpkeNL7w5SPvvcosqjw8V3brdK3kNFWm19oxIjuBN4+VV8AXv5SIKSBReCgbH7yrSFYeCrw99/nzlA25xc+lYFif6tI9OE2c+0PAk8zJxEEoPnmMd+uJeilkGaDOwSfmTrRP1akNmsIvfnILwnddP7aLAhi7w3JY6bqyHg+sS5p8hKq8CAf2AdEba868zMJmTvQEBca1cbKE5RkNH0KY4SoBgi4cEPBIE0dCwDSAKV0Vax1nshCSNsSVY4jUa6NY4LhGXmggyjznl9vxYqgyrE5dgsvcSdg8BqRVeK0LWZXhgjUhrsLnX68Mh8fqSIBNIehMvrrXzCF8LMASYg48dYgElhhY0syRiDHyUJXf+nDjfDbuF6ExXZ1/7P3EQWFdlHS/oMF4eK5cJHJ/DBP+YgW1PINYRKaK0wNZQFMgd+Npm/i24ylwEqXXgUsghMTuM0cy3q88P8Hp1co+Vh6e4Wodqzt7i7SWeHgMvHsqxNPCVhthUT56uPDbpXAtxlMvvJDE3QI5TLOa9cFxibAKud+x7cZz2Sk+GEm55omAFwuMm/uTbtS9oXqk987Xypl8WrAhs/nYOi0GNGXcFBvCglIn3vmTvvi/bf3YLgoOYHB8/YJY6zRJJeEwhMPRaLGSfSBhQj8KxikrT/s+R0y3O8fBAZQ1J1iU7FN4ZCTMp9R4t04ala6Ot05XZZARM1wzwzsWBvdLoLXCm9q5+GQdDu+kEJCuJE2oQmthBsiacPXKIQXCGDQ67ZZ/CUrpCazzak14aWytEwz6UNSVS5uYtCVERDOrKvWyUXqjNqd152zGP/7mmfO18vqYkDQ4Xxs//fmVz78+4iFTPFMIvFiVu5cngnfKtXMpUyPhrmyjcMwLas66Zqp0PAVG7bytG+tBMHe2qryVgUbnG9tGwojR8Bx59wyvXxkxJF4eEq3vfPTUSIeA3o5tFitBlTyg106tDenOmifFOXrk3eiUHki2oL0i5uQkeAGlcn9MrJoZRUlDOXYjjMFd6LQ6UK9oHJgI0h5RhKVnFjcuPncGqpHYDNPAQ7kiCmdrN8JS/yQv/e9YP7aLwsf1X/zXv4S/ULBIJN3GfjPQNcQZqZ5CJtjUvB81UMM0RWmKBBn06nibc27rjulOtzFRbYF55zCn36YGQcDM6XTOe8eZfgxT4UUK2LmxdQEiFjK7V0Rmg7Dreostc1IKHFKgjU4fwpDAi5hmAnaA4y3gVmRw99J5+9yIDA4hYENZFKIm9l4QjCKB6oHnmvn6Q+Vrb53reSMsQvTAw+POy1X46Z84sCyB593ZRsNt53Iu+BbJUXn98o6skXdvjT7D74gIv/M8GQ+1VpoutH773saM73PsWVG0DMwDh6RIMF4cV9YFVJzH66BH+N3feYD7A71UQoycvfN8HTxdKmt2xtLpYXA+C3d3k359DPNyP/fOeSuYVVwTd/HA86Px+HgmxM5jN97tThSj2M6VGYfXloW4Hokj8WgDulGq0YeAF97uO6kLIQhN4Mpg2yoHnWG9v/iVN5/Ydf691HcDWflZ4N8DvuXu/8rtsZ8D/tTtn7wCHtz9Z0Tkp4F/BPza7WP/h7v/xR/0k/5BlnnjhbzHt/av0hF2nLwpQYUnUUrr9NJJKZO8khOMM7QcWIPBcJZlgkIG8wwpXRkMmoaZ7ejCXco3AKhMb0MwUoi4DoJVuivDQUPgeNfZLo12uomIiASdIbYpLphUkOlQXPI0JblDaDtXEl2cjNBiorVBDAla4csn4bkpx+zkCE0gjUINBz54e6FU5xhs8h2SM/pALLKMwRe/GBjtnkWmBiISeXk0gmc06+y4VyN2Q0W5S061xvUiLKdAG4FXCzjzTWyl4mIUgRSV6IHSO3cCe1JKqXzhJBzvXlD6BK0u3WhRCQY1OdvbZyqdS1FOMbF8Tri8mSlY5yuoLbx9euCU4KdeJn73sUByUhAehmMdpFR6ShzytE0/jkmcWsN8PSILXjtLUDIGvXJFOLgzkqLNiUlpRVhF8UVovXBwJQ7laoWrKBI+PWDW71S/r9wHd//zH2c+AP898D/8ng//xu/Jg/hULwizjJ/9H/8Od8d7XuQF3QIWAs9jWn7fOxxZF6HpLdeRwXEd1NoofbC54qNM5PrH8NTgqGSsGc6YaDYGFuBifcJVbXIOvSk1TLs1Krh0hidenpyy9XkQGGHSl6MilNvkQ3ERHkufvD8ZSMocwuCUpltz6/utq28QlC6Jug0eS+ehw0dn+OCp8cE3r6wY96viIU8Ayz7IEb78Qnn1+kj2SIrCNgZq0IZR9kGJyvna2Rusybj2wVYrQSJ5UVYVHh4bH12vdIfNhO4TJ3cQ0EUnhl4iaw4UibhNy/NgobuQ1JBhbOscyz670STweHHuU6QDzeH50dhaJy1KKY19v7LaYJMpRF+yYgVkBCILozUOq5K0864PfEDswrY1enMsLLx52LgW6L3zKk5/yEQzR2qd6s69Od1hBOG6dywuQKRlQfXI+wK/+Gvf4NNEV/p29d2Ql/72bQfw/ysREeA/AP7tH+zT+sOtwmANxiPG4U5wCwQy9cEodJCF0QpNjGyBqvDiAE9b55icgpNyR3okIQw3cnS6OEmUgVPHbLodJFJGJ8c08WaxQxNCnlkIahHH6CFyzM5e5qKiNi+oNQZGMQ4BzJXDErDhM4eyGa7xJmWG48hYnGGz+QYlvbRG6rBZZVkSMRhLFp6L8RPvZVIOqArW51it54C0QdVIEMWkojnjo7OsCatOF2bobgscgpLz4LkOVhL3R58JzZp5emjsKtyd4D6tVGkcCdS9MCK04dwFo2fhFJWXr1euJbJVZ2jBS6SqoQh1KKYD7zCKsZwMs8b7ryPffFNZEjyXxukIKQgfXDuJyIsjXMU5Pz6zhkhXha2zEgjJWd3JKfDR48757JyyoGvlclG++Wwsa5k/t2zUbjdOp01q96hoFCiDEQzvARPDSbRP+Br/Xur77Sn8WeADd//K73nsj4nI/y0ivyAif/b7/Pp/aPWXf+E3EDfikhEdhDqIOaB6U+MFZQ23TrIpLom7Jd6iygMhZYjG0Kn4q63Pi7c7/ZYBEFVu3oI5Vdj7gB7wEKhtYuKDK8Ghic+wWh9TjRgziwV6EXLQucsYnVQmGEbEJw4uCL3PxOuQoN1oxpcx+O1vXbg7BEod+A0UY+a8WIQvvn8gxoi50weEmMnuhN1YdSoza22sUblsG73P9O3unUNw1ig0OoZTPSPqpCwMiax3Jw7HiCThpGMSnd6duV5haYOkEcRZc+BdK4gPLCyIRy7bhKwuQckB7g6BbW+MOvAxEe55TTz3SjdnqyDaOI/O83lG2X3h9YlX6cB7p5XShfLYad2pzAnCLpnzqJy7cDXjg8fKU3F+8mXi9XsRiQuYMWSgYU57Sp3hNu7OXjuxD4ZB6eOWgaG4DyTAz//OR5/05f091fc7G/mPgL/ye/7+DeBfcvc3IvKvA39VRP6Muz/9s58oIn8B+Avf5///A6tt21gPC3b1GS57EKwIsUfkuk8EW2lImIIW0XluH1WoQzjYFBXlWwdaMDRMFFnwionS+wxAiWUgS+AQjN6M0Y1DzLg4A6OJsDTHY2fJOmPKcqRGJ6sQQ6a7YxYYGllyYC+dFJ3ap5Iw4NSmdBm83QK9NV7dC1Thy184MlrntEb6ECRM7BzozWrsdOtEcSTC1geLO4dF0W50SYSkXMvg41mHwNRXqJDNuddIyplruyBjRTTw6oVQTUiWsG2jlJ12f0fpkAbsYxBCoqggvfFQE89l5/WLA1uFuEDVTDfnum0c1kzZGvnQOYRMPsHlsWHuDHW+tCZKUM7Fyavz5rzRHa43J6OqMszpNrg/vaLvG2+2wmUzTtl4FwPLqHgDvU10zDLGzsLCQ6ysJEwa67qyXS7knKf4TAd0OOpE9f0w1e97pyAiEfj3gZ/7+LFbXNyb259/CfgN4E/+8z7/DzUM5rusv/6//xaL7BzXBH6bCPRGuY8TuJoisk7X3hiD6wh4nN4GBvgwOjNW3lFa64gPiJE2pqglM6GqM/MhAMYaFlTmXcd95jeYTkCqBOVwzBSERQIaI9dawAanAMPqzCnQxF6MipBVCHlB18zD2cFnfyS0yMvjtA2HkLiWKaOOCL0au0AfToyRMGa6UzFHw8DGYG8wLKJp0MvgoMKSMjnPX0kgWSdlYe+dx62wxAMxdJ7rTg4R6QFNyudPK6/uE199mHmQ2xgT4mqDgEKEp+crx7zM55INA87PF657m4tQdXKOmCeaKu+eKz4GS1IeHpzHVvHUp09ln2rHshvrQUgqXG7pXVEqUq4zVvARchwshxUbhe0ciZqQoJTRaWOnNqVFJ3Thum3EqGyPDzQdXHtnBOdAIoTAr3wKGYzfqb6f48O/A/yqu//uxw+IyPsfB8qKyB9n5j785vf3FP/w6oN3Fw6nlWqDiw3uJBCSsoRIOsxAVhmO7dML4EzX4/2ycCmV5TAzm1sdrA4xHQjCBIIEx8TphJkngTNaR2Kg94rZQCWhqnQJ86Ifg71MQ5SXNkVLZcJfPUxQi0pA1BhWkBgQNxqBj552+t65O2W8D37ivYXjYXbAUxCizqNOGTaPMTg6fPInuyHJMYOX8Q4lcFwTYo2rONkzRZnNtTF3SMMaw4Q+ZL7eGElJOF9nXsTLPKclnn36Q15mdlFq7zw87TOrIkQOeS40ly1gKSAMnrsTwgIjcKnOaQHUSdKJ0jkF4VgKEWFZEs/NOJad3gTXTC+VWisyOuuitIvj1lnU6cMRO/D20tlqY1mN4ynRWiN1IWmH2rmLiSGB0hzTgASwMTikleyDp6CMrqxuYIHqglrnzVY/uQv691nfTRT9XwH+HvCnROR3ReQ/uX3oP+SfPjoA/FvAL4vI/wP8d8BfdPcfmqXSgV/53UdOS+Q+zRj7gzNHZx6xMDmPy90KKYFNqOqg00Vpw1l1ynhbTKSbSCWJctJM0nDLi1CCRiQIfTRagFaNwaCOThodc7kZoxyThXWZYaStFbI6q2QIka6dDnSEiLPVwUfnQg7G20sl1MGX31vn1ACZfQ4JqGSSJg5pZg40d6LajMpDUJ99D7ON7k7pjqaVJcHZjIMGQgiscaZi1TYwHUiUW3x9o9fBq9sR5T4uPPfGUeIEszwboQuvXyyUZrx9LjztjcCChsH7J0VI5KB87i5QSqHXSlAwpmU7LIpLZLtND0opuHUe3nX6aeXVews5pZlYJc4pr5yEGc5CotfAMU1h1W5w3ZQXy+RcHGPiGJQYJ9D2GBZ6n76GYYWt7/Th9DojB9GFlJTz6Ox7Iajz2w9X/Idj4PBP1Y+xIepfUAp/4c/9Gd5cCxSnjkrYnIvD2QZ0oTu0diGYzLyFkRhZ8GtjuHK4C7Q2kA6S4xxVyoSpJiLFKiHcmAceSQy27pPaxHzzLgp0geVG8/G5QDiBFzmgMqZRSg0dCgHe1cG+De5WoWzwU68yUQNVYFgnobQAUpywzhSjIp00Zu9B80rshWNQdldepkDzwI4gNrfyNpwwjJgmiaq64HREJiXK3Ynqc2Iy4oyLJ8y8zSAkdRZuyCqE5/2Gk+ud1iOvX0VCXvC98fJ1pkokJme/FswVesXGDY2XMsF2iIFLF4KD74NLmX0FPcHr45FvPG+8yiu9T//BczVOh8D1esUr9GJsY8CivDpMbufVZgix2sAFUle+9nyFKBzjDPS5AsfeOIXMroHdKzYEkcCrqPy1//dTkZj4e+szQ9Tvp8Tg5/76ryIe6Fk4pJWizh4GwQrHCPgg3LBmr9IRuSmB1ruMSadUiAYhyryoTBDzeZSQcZNBz4lE1jkZIA66TP3/xz+WFAMqBesT48ZNC1C7ce7zFuRDOIvztXcd2+aWP3Tny68CpvNNb+qYgAWhDWYIK8ZQSE2xLETPHMQ4xBlzd8rKcKVrJ+scq/U2GApdnCIymYOtzUXDQUxwn32VkybcjaRCYnAxJzDdkCVMY9gVpzJzIaMKaxTeXia0JN0tXJvz3l1idEVG5ojSeuYynHxKSDZqzGwDRJzrXvn6YyEvzvG+Yx552Dsvl8wYNic6A16ukbeXxuM5zqZyFl6dFg4+2FpFIyxEnusZd4MORSZ5ad/K7K+MxGFMn8hDH+zeUZ/xg9ELf/srX/80xUN+T/XZovDPlIvyqIP7kFjXyPBGuM/0mEnLPeaVbI2j+RQIFSeaMbRTbKLLW2k0TZRhRJlJ033sFAsoU+SS3dnDhKSICCfNHGJGZAqRXAMeYe9TYWdBQKFZwhkkXXg25+1e+Oh5x8Kg0PjSi8DhLmEp4dFmGK05rpPLsMTAmm60aXwi3sQIOhcUG1N3gTl17JStUnshB7g/JlI1SI7fgLVdjNyNOALpoLAmVlloHdBBGfBQbNKjO1xJvO2T1HxUJ+ugWqBq5O3eSGNn7J1QGzkGPnis1Nq52saHT/uNKdl597izj8rW4XFzPioVmhGicLbBIb9GUmbrynlMB+ZjGZCErTauj41lMSRFXoSFjoMcCA5P152rdw5x4XkMqlU+ajthzUhM9DYnLCMuVISwJqwHSm8gA9HMxflUMxO+Xf1wPus/yHIjmPLf/J+/wv0tFegkRz6vzsE7wwMjKo0ZGNy0TAKzRfItkSmlwX7dOMbIboaPgczxBGZGkvltP3pgOByCsLVKaTOezUlTNdgd9Zk1YSPQgfNeeCjGRw+V67myhJUsadKTX6xoTPNu3gaDiYdzlTnR8Ima3/qcMsBcGIIpGoWn50LzGUaLKFUSMc+E69CNdw87JKVWpbVBa53MhMlWaYx9sDTHvNBHJcVIMOXzdwtRlEUGWhtfvs+EIJQxA1wWG3z+FDkcEj0mPjwXLjYNYSEkbDPW5UhIkeveefniwLJmqJE+Bosah5H58GIEqTiJS7/MsJbst1QuZaHzcD3zrbeN49F4cYPLXAGG4DJ9DEtObLsR9k4wxdPKnSzkOljH3CH10LHRcFXaVrnPhoYjjMgv/sYHN3v0D2FDgU+7h/MTqoExrhBHRJNStzNnVXISVAZ5HNhoeG/EvNBrm2jvIWSbMfZ3pzAXC6AZLHpHzlBao3ulkUkM3CPF5WYcauytQ0zYuKnlUGqEfXdqqYhM6pOnxj5AWuFVUtI6jwQ+hCUEhjpjDKIrrrNTcQpwsRktNyQiHVIIDIVRbQJaRdm6I33MJqQ4viSsdliU0mdk3uKOhTAFCgom0/8gPmaY6jLdpUmFvTjVBpojYo3xICyrIKI8boKmaVk/nJQ37wp3S+ayd8ISYbuw3ie++ebMISWOLzNaK9kSj7qR2+xjfO3thc8fYHgml8E7n8DaogFvF8yFKpG3Z+PlUWgSeDd2FoeH0WCMaTpzJwBxhUs/cOiD683wJquwPQ96H5xihFHpMbDExGjz8yWnucj8ENdnO4VvU//lz/99MoMYDtPpJgGNAXU4ReF4PJDU0CgUIGugikBUSh9c9srFxhTJeOfaK7N/KCQUE6G7zVh521GLpDTlzzEodXTeXBvnp8HeL1gI8+4XnM+vCy8Pgc+dMi0Kg0hvzHBTh0SkN5nn/24khyKCiqBu5CisN1ybdGOXGWJTh1C9TWAM46bYM4aGOWnxzpIE16kIVHOkG7VNRd9w56o2A3Vqx0MjRSUvgVqnK3NZA8c10JuxritLyDw9D9aQuL/LSAi8uzaez5UhgQ8fZqe/euH8NHi6Di4UXlbh2pyvvTnzuZOzm3E6Go/W0RvWbdRBb1DduT5XXmvgLjrHFFANVAJ3NqiqVFVOKZJKQIqhtvEsho3KzqTjvHdQ3ITGQGJk2HRAdlVSzvzSb3w6sxy+l/psp/Btygd8ef0cX6sPpC5UrRzHyrNWrA6SJLZbFkPKgd4n4TkEZckRdaWVzh47a1qJ1qiiaBUsdsSEOjrXbQCBmCvaQIfyRmZ4SwiOm6MuvLyL2Md9CTeWMOGqHx9HUpy/9wYep8LRkRlJbzcNghkS0+wZyAyRWaJysU4mIsFYuhKGs9/Iz9I7TYUsQhCBS0NSIotxxVEf5HXh+VxY10wKyuXSWZdBq4mUjadqJHX2Pq3cT7vw3vHA2QrbU6OrsY1GSokPH89EnOfN6MugDcgBdgJBG0Lm+dr48DzIvbFKYm+FL55e8BDmEeJaL4wa8BwIGM/nSh6RvMKlBTQrKspz3RjmjDCIEnlbd45+4DqcA0K6dXmHQS87nhONwj2JoU72uRu8mrHmzP6JXa0/uPpsJPkdSmPgz/+bfwIbAa5n3oohBYZOUdIuTmxGGZ1IoNpAhk1l3g34WaqxRCOnSLOZA9CbUXsnhYio0SosqqQ8pcK1VswgJeFlDjRxVCM2ZlAZzFDVIEqUcUO8GikFAnNHMI2XMpuIPu/yH5udUorTrDUGV5vkIR1CvrEcn7ZBCpCTsKwrKlBrpZaZgQEOHiYcVg1rCloIHsGdtAjdM2uYasU+jCUpxZyXIbJEZxC4dig3kEwOSrULDxdhGTPZWTWiOM/WWU9KeRI+vyppidTeuBbjC58/AJPNoAjizsPTE2+LsWhkWRa+8bDxudeZ4oG1NS42QIXm02CFhin9jpmHcsVGIMT5sexCaYNnn+DaS3MWGYQ1k4DRjCUOfv53HrD+qZY0f1cjyc92Ct+hrA9+9/jMT9Z7LpKQagwq++5kMw5qSApUIue6oaqsQUmSJ+ZsOEs0nkogGZjv9H1wf4o3/YGRJXB3N0GsokbwyOZGisppWdlGnYvHgNU6w2fX3wGnE1OmDpu9BIPaG6clc6LTPbB3iLERZeowY4qMDrs0jnESl0YbdGPSnm5fK6ZJDhITSmssORE1sw8jpzHp1T7QDkM7IpHqhrvxfAm8OnSqKa07HgIM5y5lCELTedzy0Ql9Jm8fX2e8f5Hr9k1eHVfuXjTK3tGQOA2oQ/ncnfOwDVprHI9H3nsx6CbgaSpFe59Y+LCA7tQAT5crhwzP20b0QF8iuw3CCPRb4I7oVGQ+1DaPBObTVBY64hlLEGpGtNFsJx+OUAsWIho7z5f+aV8Qvuv6bKfwHUshGP/xn/vT1Cv0y35z4xX2AfjN4ahGqE4JSi0XRBK1dKqB+sSLiyhjzDf1YVk4xIlpH+70UVGJhKSoGc+XzhJnYvOUFTgeZiPzXAYk537N0BsmCcUQCeSbcMgMNDqZ2QvZ9s7hkHDmPD2HPNFuY1KFwph7jUMKU7psxm5wn0FSZvSJRQ/iXIdzCj4jqpmekJQCrfRJuLbZLG3V0SWRpMNQlmPE9jFdnuIc02xa7sNZY+Kj64aehaJl+jPGwFNi1I4sg26ToKQyeZNrVCoZa32G3CgsJuQcqX3wbm8894Ztnfu7iJFQNaI7xWcjuPdO1DQ5mB8bwm7vCacRLLME49xnA3cbjevmxGXu1gBer8L/+qvfun3Op7o+Ey/9YMrA4K/+ta8gYUHWSDXBY0I0ob7TqARXtmT00rExeYDjhkt3KoHBKcP93cpxTRzj+Ce5CzKMI5lr7zPg1SZQpQxDAlx7m4nTolxkUJlqvtIHDWHvlYHTb0zIUg1TwatS3CltZ10jpd30BWPKqq0PkMGyd+6WAL3SbUCfPosgUD3S9zlak2Fc937Di0Glo6rsrTJ6n1ZslZtrci5QXju1OZ6VWgd5GbjCsq4UU/Y6eHcZfPNbV/zSudYrKQn3LxZOL08cVjgdIjln7nUStJeYOOWV0SGlgAVhCWDNZsanVR4unbfnSr901lNi8UlO0hHYENScvTYacOlO1ITFiNjMAx0Djr6wywytDcMoW0EGVOxmHXeyGv/Lr30Lv/kofxTqs0Xhu6yNwa6CFaONwToC1+uFRmY02FplP3dKL6g5ncFdDCQCL9aVEFfQjLtPibImTMYtnLZjwbmLEdFEkIjhRJ+k4RAC1cBaZ9FETDBGnYEo6qQ4OQ9OwNB5A+8Dz9OroRK51ulGTBJwlOUW2CIdWla2Oheda20UDQTNBIFHq/ToDIerDNaU2XyfUm9LiMwdRKlGPiilO1EXaq3Tooxxf1hJBqegnEekIjyeL4Dz0buddW+4dEwbr7+0sq4r10unu7EcF0qc35Mi8DJlnmvlXak0zTDFoDSZisgzxrfOTq2Vl6sSE4QxuQYVuNCxZlxHIwgk6SQGvRVquYBmynXKpB8YyIC4zimNp2koC2Mgt2XA+jrVnD+kmoR/Xn22KHw35XNb+D/9rb/PyxcHjmtE1sDrV3eEoBxjZPRKirCmFVsir3PGUyCJUH0q6YoPohlJTwwGaQSKz4YekmgOcvNYiCpDbGJP+zQpmSb6aPTieIyTeuSZYfMNNAKYTkFSR+hjCo+eGxC43d1m6MvOAEsTJ+bGVqZTMYRM6wO0zHDXFtjr9C2cZCEgnOQAKB47tSsJ5Qq8OTdElQpogJeHwCknnh6vrNl5ah21Qd8ad0tgF+eUI8+jcndQNAQenvr0MEQDjFZn3kRz0F5xH5zWhRQy0Z3zXpA1UC6Vy1DevalQO+sLYVVnlcgQ5VqNPpzRDI9KdgExau1gzu4wSDR1ahQcY2mwU0nDOKxOtBmE2xdhEIgp8He/+rVbb+dHpz5bFL6XssRf/oV/QDomWAYxBF4eD8RFef+9E/GQOK4KLow49QQEI0bFbLIVzhrJoaOy0sU5hDDn3uasYSLd7wIcI4gGLDgSp1/Ch2HBWdNkL6RsFC3zTpUDqw1aNdKYMubkTqmdRQNLahMAAB+BSURBVEDqzIpo3VhcaTKmryIGEkqPkeuNnYCC+kJUIQ1YYqC1xlPbZ26mTOv3skOvxuXa5ht3GKPsWG8kIufu7AMsOP06szNDXHn9uZXnS+fxww7eeO/linsgyILGwGUfaAw4mctlOi8pjR4Xdh+YzB3AHhV6o14KpMGHT5U13xqkfbCb0KPQcUYK5DDt4a109jxYG+RwoC+BGDPJBbGpzQiW6epky1QVWk8sh5XT6hxk8jD+zm9+CP6j9xb60XtFf6DVoMF78XPoOIA0LmosS6IPIQDX3slpsOr03kfJOJGjgAzhaINLaxN1JtPyPFzp1hgiJIkMm/mQakYYc1vapRPzbO51v/kZhnKoE3aqA5orKUBFUAs8bJXDMbEpEJRFZ3jrpYPVyU0YpUETxMKEs4Spary0CiroQbA6U6sWmc25cxs0hzMGOn0Tw40Xh8ByPOFxsg/jAFkGhMglDobOBKxf/+qFugt97ByOkSoJSwGPztKchhEMLrUTU6Cg7DHiYfIa1Odx5VIacT3QET58nGPCJc9g2aSHuZA2Y/HZP9gqDIesgl2VHqBjnLyhvVDcMQJmwqadqxrNK6XZPB5ujdYhBuOa0y1K/kfn2PBxfbYo/D7qZ//m3+N0arxIr7hTQXWCNA5L4H4ZxKAMCzN/gYqZsddAykJXgyRcfUdtmpCGDLIEuhsmRsg6z/oCIyg+nFTSvCibUMfG5Wpca+R5GEkMMQdRRCNZIWXlxVFwVrJVbMCOYmIknTQiwoohM4ZdBr0nyt5JfSoidQjnZgyBPKB0wTVwCplLrbxcFjyChQQ+2GQ2MBNw8c4b7/MoEuBxL3zwRnh+FhTHvfBHv3hHkwOlNkrtXEtjpIgKbMVIoRMEzufLzOsMUxp9GZ1eG7EOHredhydnNVgPTpGIkBg+YbL6sYPTZ6N0kObuRYTrGPTa6CNxEYgY1QdjNMSETCCEAzlnKJ0eHA2dQ1J+/R//cHEXv5f6biArf0RE/jcR+Uci8g9E5D+9Pf6eiPwNEfnK7ffXt8dFRP4zEfl1EfllEfnX/qBfxCdR/+3f+i2OeaAOhwI5R7IEzBMwA2gPURims7GYQEiEIZxaIDZl1xnguvqCyuBOFoSE90FIEUpn75O5sFtHAoRoLMuCRmH4M9EjbR88D+PaGmBIFIYZ1Zy6b6Tw/7V3brGWZVd5/saYc6619jlV1Rcbg2MDMZEfYuUCFkGWgngMMS8mD5F4wkoi8UKkICUPTnjhNZHCQ5QIKRFIJkJBUQLCQgKCkJNwM8YYXyAt8N3YNG6b7rqcs/da8zJGHsYuXG13023s7nOqvX+pdPZZtUsau9beY8855hjfH0SoIhoAGAvHbFQwT2yjc0OVkhpJMl0LmpxDD6hJk8SmjpbostzbiNODUdnXzmYNnWamoTRvPH0w+lp5TUlsPXHn6QNsYBd36fXAYzcSjz+ycKjCNjpFBGOQXFjrypISbRj7Q+fOYWUuE6rKxYUhSaiHiAt17l4Yu2zcOE8xFt4d65V9X5GknC/CF7ZG71EgFq2IE/Y7ZSKlzH5b2ZmimshtoDKRRwBmh3YOl3epu4kJIBfe/ZCYuvxV9WJWCh34l+7+N4G3AD8sIm8C3gH8mru/Efi14+8AbyUwbG8kwKw/8TWP+jrIBj/zm09Q1BklM3t86HRKnM+ZnJxmzpyFIhpcxr7ieaanKKTlIx+xEVZjbTh5NDYr+AhUW90Gko00ASgiGR9wNsP5tGPejeiB8LCXv3doHDYLE5ih5DRwIClUc1prYVDbKto7IuHGZHp0T2qNfNaoRnT5GUyAamK1aONebdAPlZHKcfvjtDaOcxzKPHVuTIW7K9y7fYhv32a85ptu8trHb7DpYCMzvDO2yjqcw2Vj9UY+W7jXGg1hXjJ7T+wtjgDPymDtnS6DO4fBn99eubE4MlkUcZviPSz9aE7OE6t1bpSC1E7KMzeykvJgOAzvJBFuTMoiYFlxBKHTs+DzjJtheYkZkCH8/ke/8MqqKj6HXjApuPuT7v7+4+N7hAPU64C3Ae88Pu2dwPcfH78N+GkPvQd4VERe+zWP/BqoV+DRhE967HtPYOFJ4N5RjRVDvGEtUO/iuAnGBK6BUVNlGDQddAdplX0Dw7AEh+4MUYpObH2DnDg0Z+3OGE6eMzvJJAZiUUizo2nr1hOTFpYStYpLhD5iSOteG2w96gz7Q+NOjwLmnb1jfXC5bgyESo8PmUrUCiTGmtdNgmCN092p22DzgY/EvQZ1b3Qzbs6F3U2n+uDAYLSY3Rg4eRdA1ZIywsThULk8NJZd4t6+kWzQN9iG0VUYzWhVuDgYN5aZoins+IYiU0w52lBUhLuHyhBhHUYdmcutYtXYD2XKjjYIXOzE3gZ1bNQOs5SAwLQD2sMZuw7hVfOO2+OVv+P+il7h0RTmO4DfAb7R3Z+ESBzAa45Pex3wJw/8s88cr70i9XO/9DG+6fwWt3HmBZI4lmDeTUzEWG9JmSSZucQJQRdHrZELmIUvQ8pRRGwy0Cl+VxFmE2YM92MBDkitUfIMPuhdY9BJjZ4hF+HmjR2Pnk3M6ljrXFw2nrm7MUaYnmweiWpeCtUVl0SeClMaWM64p0hOOcEwhoSJzP7QcLOYvjSn64aIMzyz1s4dMXJS7lxU2Hf2Y8+rH1+4cauQyw6TaOpSVWqX6I1YnVYPzEvm5nkOA9cs3LusZIUyQdKOd+fuZWDr7q2d80XwBCmDpfDabBUWmTCEWSRcucbEmSrLzcyCsyZBGEdmpR4T9IGMUroyLfF/o+JMxxqDauZMOr/4kc/gr8DC4pfqRScFEblBWMT9yHP5ODz41Oe49mULLhH5IRF5n4i878XGcD1l/PS7n+CRc5CeuVGiky/XxMCRlBkGNjVMLPb2SVnV2ffOkuYgM3Wj22A9OIcNzueJUgotadjT59ivF93FJJ43POmR72gYQrJEzpmtVe7WTiqJOUtsIdzJI3GvBWi0pExvRlLoNrDRUFUOtYE6/Qgs7R6WbUPg5pRp0uPfTxPZE2kqAZLJMPXCxTOBaJtm5dFvfOTIcQyXp7U2fDRqLzRvNB+swN4VG87+csSxoAN9sG+JrcdAVdbEBBy2yjc8MqPimERHZD90NhVGEgaV4c5lKrhGH0gVITc4mzNjM7YuNCmxqnGlNkVEMXf2F4MkHXXoOnGeleqV3/r007i/MmYbXkgvKimISCESws+4+33fyM/d3xYcfz51vP4Z4Jsf+OevB76MYHkdfR/+SjruTf/3uz/G2XGaMXthFCcZ5Elo1uhb9AkMAWugnrHe6G1DgayJXhuTJEgaBUYCi67HhqDSlb1vTDkjEhRlO9KTF41vxiGCWZjGmCqrwZYIhyY1yjrodzcuLjoXvYW5TIEyZ2xECzFSMLPwrkDAM9kjaZWRovhojWpxFFlUWNixtsqhbzz6+MxF6vTauTgYh7Wxc7ABRmaMjX1tzFlZCZPWZ+rAh+ElOi6zKEtxZEBB2Ty2JjenzDYMHUIZGzaUaTpj1PCZFBK7SZlrpffOtlYO+4omwHowIiTQ9GaQ1Kk4ozZsDPIsuGSk9VihaeLDn7p9le+wl10v5vRBgJ8EnnD3H3/gr94FvP34+O3ALzxw/QePpxBvAe7c32a8kvUFlPd+/EnO8kQuiuZE3mXUnUdvnHOW44NfBHJWdqKIFNBY6lYTpmlhpMFZgSSBlk8ePpfb1pAlMWk+DknH0FIaTq+D1Zzk0XWnQLfoOVBVRhXW2jAVyq3CbpnCpLYJt+9V7twbfGENniIYdT1EV6EfP/QLtBQf1v0Y7JthzcjbYGuDpw+d2/f2GMZrXnVOI1O8MFYjjRghv2eV2cNKLWUjy8y9LmivWHLaqKwj0PT4QLRzuw6GKGYgdNbaICd67/QC68jhv+CBzrc62Iaxry3+P1OMoucp09wgQSmF3dYpLcxhN4PNOmk+0qmPMw1dEz0lPviZP2M8tAjWv5pecEpSRL4b+HXgw3yxU+PfEHWF/w58C/Bp4B+7+9PHJPIfCafqPfBP3P0v3SJc7ynJr0TK3/5738K3jka1GRsb+96hD6wpRv8LbPhhxDx/NmIUGWU9cg5yCisz6wJJ2anhovgwGh2VjHg4OUH4J0iOM/rizqLRiZ+G0ZNiHvvnIoIbeBbMQbwHPGQELboOGBke32WKGBXFuzKXQu2dRCfneJ0uIK7cPTR638AyN87CVUunxDoGj8yCjqPzlQpFE+toUWcZoMtC8sbdTfEEPjq9JZZiaNFjEVNpGo1Hz2wbj+0WlrB9QNRZhrCNoE0O0dhWWaLMGW+dfRvoJKTqLFPhsFWGKxcO50miKDkZbTSSFMZITFPQoT97z/jU519R24YXNSV5Gp3+miqDdP7pd7+JO5cHbq+XVF/Y5cZhwL4PziRxe22cFWGs7egopfRe0Zx4dIqiXZqUXgXLFp2GEt2SZ5Lx7LToRmYnSrNwuL6VNQaicDY3znJiNMeOhcpBoWQjG5gK93P8MhcuLjouxqywVYVdYlFhEmMpE26d5j0cl12pwzgcBjlnRu98w6sX7txtoEoSYTfBZsKNrDG3MDp5StSRcHVmS2zeaB5O2vtj70DtG1nnYEt0x+eYOq1bTD+eLTNnx+3ErIV9WzmbZu565xbO2hxU2LeN81K4PFTSfMYjDPZFGeuKpcTdQ+MxLZCjS7NK5xFmNm1MqvSl8NtPXDvfhq9Wp6RwlRLgrX/39bTs0ctfHU9gzSE5vlZGLuzvrmRN5Gmmjk5nJSeQVlj7YJkLwyMZND/WGFLsqbXE7k9MMDGyHm3kzLhHJnOBUcg5M9dGnjNDJ2pvLCiqivVKL2eIbcz5yDZITiJ6JEygaMFGTA5mLRzc8NGxXHlkukmjHlkLkH3BZGVYIXWjllglnMnEpR2wKkgOqtFEjr09A9GMW2e0BdWV5hNYZZqFM1P+HMMuNx492zG8Q17YbOPRVNgzYBOqVyaZuLTGzd0OqxvPdOUMR0uPU5XaqQlkFBaNRHtTo3N0qFNK4jc+8vmrffO8dDrxFK5SCpzdUuCM81JIu4JqDupQM/J0TtHCNC3U4rR+wEsieaK3xGZREV/NmCXhKUNSyhS1Ap8y+2bMujD0yDtwoR5PDs5LZxoTU5o414JMZ6yemY/O0C5RM3DJMCIxbbVDMloX6qjMU2I3K7k422iklLi7NXY5rNd2JXNZjSQ5gC1duegr5hPmnT47kxjbaqzjkiyKcHSq3hLDK16ExIQMAZuZ54aVCfHBvExknbkwUE/YHDyFrkqtG2uDp+7t2dfBphKj44kY5HJBUkHLoIqxugYaI00kUyjCZRs0G9xLYUqzdec3X+Hdii9Gp6TwEmkA/+PXP80tOX6LVg9qc8qkFDZta1sZ2jmThTxNTNYRnZAJpikcmOcUg06HutFGZ4x2rJx3buXE2jcmV5DoOTgfSnZnbRk5E9LI3K01XK3MOLQtnKlyoY5BV+g5aEuaBbE4echELSCgJA2dMtU7j9zK7IFZEqQzcnFqa9hQbAg5Z/aHDsdC5qEKnpRt74wGljNTG6TUWJtizcJ9ohtujYu7jm+VnAy8sfaOA9YbkwRKvfcAzoo15iVwcsaAkjDPTEBtF+y3Dd8aIkLpzjY6+x7mO7l13AdTVmzMFHHO8/R10YfwQjolhZdYP/++T3N+fs5uV0hJSAlulIK6ojKx5EKSRBEluzBn6D3QZiklCpklBSB1cSDPlFKidVqdrNElmYI9yj4ltgFJBj40rOHI4QOBMywzS+JQaxCMVVhcmcqO5ImEk7TQmUETewzthlhisw4DlhTHpn2rNHdMM+adnRqKU7KjJJpquCRZw893iAvzscBYZaL3HisEydx1YW8GuTNE8AG1T0wWO0uhxBGjO+4O08CbUi1I2KN1pA1GP9DaxmAKXJxOTCLsPZBtJWVmt6PHp6BFWJbCUpTf+uRplQCnpPCSy3De9etPoJOSkzGssSEc2gpWISfmqaEKPTuG8khODGDnAWhZPc7cmyr5OAcQx5gDlUpXpY2OOOTWo5AIpC2+MU0ajkBzCoOVwSaGdmUbnTEkXJ17dFVuNJzONjpnYuQyc1lXhglSNEAvEv6UO3WKCKML97LSBwyUO32PjwoYSRWrG5KU6hv79YBZAwV3ePrywHmOPoY0R9OUT4nSNyyDJ+gyaOKIOV0zthVSmcm5cLetNBEu62AMQ9IZFx59FJMK2zaYmPEe9ZgOYM4Gsbrxyrs/9gX86BL+9a5TUngZNHB+8b0fZ9lNzDoBzq2zcx47K+yyMqQc25gTw42miuZMnxOLKqIx0TiID8UY0e7bXGkYax+IOoZTLX6KJdqkuMwUB6+xLDZPmMGSCiNnsiuqdjSAcTQ52WM74e7sN9i2TtHgSd6+V4PvuDUmcZ6pPbwz3ZlrYztOZ3qbsG7UYdCFkhNrXdkkRVEUQcnc7Y2sxr5tpBwxJgrtUMPUdYQFnviAPtA5qLSDMPxtQ4KGjSIaoFdGZ86FXRa09yMjIprAtHGcOUmUIpAyv/2Jz7/ih5y+Ep2SwssgB9ThF37rU5RHF6YcVOfNMtOUGXQ2HyxZyFlJzbDe0BH7YDFHXMkUKIlNUli/G9RVKBO06mSP5fBgQN+4rI1lUvBgN6ZU6ClcocfaGa2hAnYkFXvK1G1AUkQTjqBT1CpUMzlN5KmQS+DouyQyiUtxvA/2Ht/yS04xEyE5RrwVdAySKGcpMetMNw+qtDtnZUZJgLAdDLOKa+LQGtZbJDQTahNajaSIT+zVoG+YhTOWSEJHzJZYjVrCnsDJH8xADPMeSbNWbpSZ3//kU1yHE7jrpFNSeJk0JI4pf/k3Ps584xZpyaSstNbYqTKVjCUh4yAxYTklJ5UJtWAfChWzFlRjH+xSQedYZeSstOEwhE0MnyayBMO9jYrScYnhq2mE1d2iQXgq2tibkHwgSaHWOCVI0L1SJBJJayNiKeU4LAWLhqluzlNAYnGqB7MgD2e4AINqsVUYdeNujwamDTB1mndQQVzRFLZ6We8ffypjSnFsmQRNxsEd18q5zvixD6LRcB8MT6yAStjGZY0htUkK2YWUJ0Sc8/Mdv/mJp/7ym/Z1qlNSeLnk91eoxi+95//FN3LOWFloaeEg4f4MIMsZcmyTHgYbRC2iC1uLxyKO5QYo3sPenhL1gDKAYXQ2uhEeFBJ+l0OhZUckyMSFzGZET0FtUUxUaC7IAMU5eEc8DGyHJ9YW9vDbMC5HNDBtGLoQmDhX0gSrdGY/FjhUMAxNM+YN74OxrRQUQfFqsWVBGS602ukK5oqaw0jk5FyuznmX2CaZ0RvMQNIFUMSdnQj3pNLMySM6RhVD53C/+sIl/J+PPnk6aXgenZqXrlCPIrzlzX+N9dCRqvTkuAEoh32DIsiomCfIRu1hp26eqN6QDrtUOAhRgJQYs+6eUV9JvqDTYO1OTcZjKfoKzorTCZfoKWXGaMxFuajKshhCNDglg+KJA1sMZW2GpgkVIxe4HJ3zUoINaRLIejNKKYxqyJJItYFPdCqNxKQNJ7G1wY2SWErhondcBG/KbnK2Aec6uDBDvcRcgxnVC5JWPAsYlEpAXcnYZFgbDCZmH9xtyjx7YOVSFBA9TXzos0/RNvt6LSGcmpeuu27j/PIHPst8s7CdZeqx+UjGRs/OlB2bCmWSqMzjNAKtpnqkObngFiPZWjKrKZoEdOFSK9Wd7rDowt0mMW48othYyNTRySlRexiq1G3gNdBlXZx1GjBN5JxZgTkPmg5AKXnm0AxtBpOzHr95R+tkVexg7JH44KqQ68BHjoSQJoZqGOWq0ruhaQs7e2K82Uc0KlkKLH3TTiEzycRCYZs1thwG0hKLTBR3iiZuLeF50RViakP4vY//GfXrNyG8aJ2SwlXL4Vff+1luPV7IJcxW6nKTngTxxJzD6ERTTFdWjYEp8Uxtxh3ZICXmFFBWK86IsSnOcA7ujORRkU/C7ghqLSielPn8jHVAt6BALZ7pvZIsk48+kmM4l62hKuy7MknBLTFqMBhGSrQ6WEZBUkJdERsckqOu7NcDB3d2JUhUhrOXwdYaWx3sh+C1k3sh6QyurB5MyrFVLqlMc8FIVEmMfv+UJVMT3MtGKxurKL5AKkGy8gQqjblM/P6nnjklgxep0/bhGulVWfjOv/UG7h32rFUoOZyHJo89Pr1Sc6auDR9GKkrrBHmpQKkCc/jC2VAkC94Hax/hHp2Cy5A9oRLV/+EJkUz2jZESQ2OfLt6QNGMCWRSXzv6isdvNJAs3bvNO7Z3dtOPQBmdFqEdWY2AhZnrbWKaMDo8TAP3iSco0BeCl9jid6d7JZUasMoiJTzR4FJIyY62ICGe7mT1OoSB2QBvUpKSSKb1hKVgTQuLDTz7Fer/94PQuO20fHjb9eXd+5QOf5JFHz2ERdpPj4lz6oEunFcV6Y5SEa8aSU5YEU0ZbgjlAJSklJMtx2EmZpokyh2VdbyBdGN3pImwMhq2MlKgmFJ9wMZyFXBZmDaem0ZRMAusMj/6Dvcd+vg5DpbPfYkuDZPBMt8HQTB8JlUDRdylMKjiddTVSdzazwM8tC7M3VDLNYzXR+oZ6PvpbJlxjK6DmtMsLTGZWWSBl+nCqe1CX0+B3P/0Ua4NXnIXTS6xTUrh2Mn75dz7GuOykqmQVzrRQCGCKi1JEOcuJRTPrZjQLr0Q5uhXFx9hJogE58fj2H6mgCdZ2OHpVCikJky6AsdC4rCt5ZJIP1vVAb0LrMEnCi9OO8Fkwpj7IWfHeUFcmidqBZtAe7tZJjKl3DpIYpuzSwE24oUIpTnOY1RguSGt0Ej5ihDsbNISR4sg1SWZWhWEkNXQR3CqqYWcPcJYTF9b4vU98fdGSvpbKVx3ASV8uBz746acA5Xu//fX0g7G5UnwwTYXb6x5SoZlzQzvVE0Udk3CcvrkkdlnZ1Ek202ksbgzJYWk3LaSkrL2iCHmn3GvpaMEexU5wppKR1kAGIgvqE6SOJKVVZ5eFVhtjkoDU2sC2jokyilBaYminTQlNCiRab9xKzqUIucfg1JQmmsWQ1yKFgwreHXGnpATmAZidKmtX5jyTiEJpo5MyDDGyD377U7c5lRK/Op1WCtdYgvErH/w0n7934HwCvHKRjLNppiShJKWR2ZUc6CRXzuZjZ2KHrSfWUaNAKULfOqSMpOh61ASUxF0z8hG3JjbAnFkSfd8ZFEQKF21lGMwpDGVHbzQPhNmcZ7pHd+E4GtGfT2f4FH4TOhwdWxjcDgt0nCl7CxCLlAmGkadCy84sAZu9T5MWE8ScwowsTu0HttHJKShUKcGfXq68/1PPnBLC10CnQuNDIlXlu/7O61hckREcwcO20XQmM+g1OhyNKNwlGRwqoE5xwY78hEkSJo3eFM9QTDjYhsp0hLB64NuHYyUz24b4WdQA6mDKQplmem+k7CTJTFk5tEo0UqUAxGrCxsbQAmxYmlCLuYo5wb3RURIpCyodtyBG+X1z2aN7luYY5R7euTkV9h2yG5skHkswdOI9n/jcKwmZ9lLqoSIvfR64BB5mg75X83DHDw//a3jY44eX9jV8q7t/wws96VokBQARed/DjHt/2OOHh/81POzxw/V4DaeawkknnfQsnZLCSSed9Cxdp6Twn686gK9SD3v88PC/hoc9frgGr+Ha1BROOumk66HrtFI46aSTroGuPCmIyD8UkT8SkY+KyDuuOp4XKxH5pIh8WEQ+cN85W0QeF5FfFZGPHH8+dtVxPigR+SkReUpE/uCBa88Z89EL9D8c78uHROTNVxf5X8T6XPH/mIh89ngfPiAi3/fA3/3rY/x/JCLfezVRf1Ei8s0i8m4ReUJE/lBE/sXx+vW6B35EZl/FHyABHwO+DZiADwJvusqYvoLYPwm8+kuu/TvgHcfH7wD+7VXH+SXxfQ/wZuAPXihm4PuAXyIocm8Bfueaxv9jwL96jue+6fh+moE3HN9n6Yrjfy3w5uPjm8AfH+O8VvfgqlcK3wV81N0/7u4V+FngbVcc01ejtwHvPD5+J/D9VxjLl8nd/y/w9Jdcfr6Y3wb8tIfeAzwqIq99eSJ9bj1P/M+ntwE/6+6bu38C+CjxfrsyufuT7v7+4+N7wBPA67hm9+Cqk8LrgD954PfPHK89DHLgf4nI74nIDx2vfaO7PwnxBgBec2XRvXg9X8wP073558fl9U89sGW71vGLyF8HvoNwb79W9+Cqk4I8x7WH5Tjk77v7m4G3Aj8sIt9z1QF9jfWw3JufAP4G8O3Ak8C/P16/tvGLyA3gfwI/4u53/7KnPse1l/w1XHVS+AzwzQ/8/nrgofD/dvc/Pf58Cvh5Ymn6ufvLu+PPh4Eh/nwxPxT3xt0/5+7D3Q34L3xxi3At4xeRQiSEn3H3nztevlb34KqTwu8CbxSRN4jIBPwA8K4rjukFJSLnInLz/mPgHwB/QMT+9uPT3g78wtVE+BXp+WJ+F/CDxwr4W4A795e410lfssf+R8R9gIj/B0RkFpE3AG8E3vtyx/egRESAnwSecPcff+Cvrtc9uMpq7AMV1j8mqsM/etXxvMiYv42obH8Q+MP7cQOvAn4N+Mjx5+NXHeuXxP3fiCV2I76F/tnzxUwsXf/T8b58GPjOaxr/fz3G9yHiQ/TaB57/o8f4/wh46zWI/7uJ5f+HgA8c/3zfdbsHp47Gk0466Vm66u3DSSeddM10SgonnXTSs3RKCieddNKzdEoKJ5100rN0SgonnXTSs3RKCieddNKzdEoKJ5100rN0SgonnXTSs/T/AR5txaYl+RjvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[765])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19_left\n",
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train_labels['image'][10])\n",
    "plt.imshow(X[10])\n",
    "print(train_labels['level'][10])\n",
    "# 19_left is actually 0....so its working !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, y) = (train_data[0],train_data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data,Label = shuffle(X,train_labels['level'][0:1000], random_state=2)\n",
    "train_data = [data,Label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_data.shape\n",
    "#train_data.type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_labels['level'][1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]],\n",
       "\n",
       "\n",
       "       [[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       "\n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         ...,\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]]], dtype=float32)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0] # Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37     0\n",
       "726    2\n",
       "846    2\n",
       "295    0\n",
       "924    0\n",
       "658    0\n",
       "682    0\n",
       "286    0\n",
       "880    0\n",
       "272    1\n",
       "137    0\n",
       "954    0\n",
       "515    0\n",
       "788    0\n",
       "860    0\n",
       "640    0\n",
       "737    0\n",
       "683    0\n",
       "412    0\n",
       "71     0\n",
       "213    0\n",
       "67     0\n",
       "231    0\n",
       "856    0\n",
       "197    0\n",
       "639    0\n",
       "376    0\n",
       "192    0\n",
       "99     0\n",
       "678    2\n",
       "      ..\n",
       "51     0\n",
       "875    0\n",
       "938    0\n",
       "772    2\n",
       "102    1\n",
       "195    0\n",
       "679    2\n",
       "805    0\n",
       "628    0\n",
       "124    0\n",
       "404    0\n",
       "730    0\n",
       "287    0\n",
       "831    0\n",
       "47     2\n",
       "725    0\n",
       "587    0\n",
       "607    0\n",
       "433    0\n",
       "674    2\n",
       "263    0\n",
       "360    1\n",
       "75     0\n",
       "466    0\n",
       "299    0\n",
       "534    0\n",
       "584    0\n",
       "493    0\n",
       "527    0\n",
       "168    0\n",
       "Name: level, Length: 1000, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[1] # Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, y) = (train_data[0],train_data[1]) # X = Images, y = Labels\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
    "# 80 - 20 split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 224, 224, 3)\n",
      "(800,)\n",
      "(200, 224, 224, 3)\n",
      "(200,)\n",
      "..........\n",
      "80/20 Split\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "print('..........')\n",
    "print('80/20 Split')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0])\n",
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "Y_train = np_utils.to_categorical(y_train, CLASSES)\n",
    "Y_test = np_utils.to_categorical(y_test, CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Augmentation\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "validationDataGenerator = ImageDataGenerator()\n",
    "# Augmenting training images\n",
    "trainDataGenerator = ImageDataGenerator(width_shift_range=0.1,\n",
    "                                        height_shift_range=0.1,\n",
    "                                        rotation_range=15,\n",
    "                                        zoom_range=0.1 )\n",
    "\n",
    "batchsize=8\n",
    "\n",
    "train_generator=trainDataGenerator.flow(X_train, \n",
    "                                        Y_train, \n",
    "                                        batch_size=batchsize) \n",
    "\n",
    "validation_generator=validationDataGenerator.flow(X_test, \n",
    "                                                  Y_test,\n",
    "                                                  batch_size=batchsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0314 04:33:43.806486  6544 deprecation_wrapper.py:119] From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss = 'categorical_crossentropy',\n",
    "             optimizer = 'adam',\n",
    "             metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-118-a2f40f59e47c>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-118-a2f40f59e47c>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    epochs = EPOCH,\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "'''history = model.fit_generator(train_generator, \n",
    "                             steps_per_epoch = int(len(X_train)/batchsize)),\n",
    "                             epochs = EPOCH,\n",
    "                             validation_data = validation_generator,\n",
    "                             validation_steps = int(len(X_test)/batchsize)'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPE = int(len(X_train)/batchsize) # Steps per epoch\n",
    "VS = int(len(X_test)/batchsize) # Validation steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - ETA: 13:30 - loss: 1.5571 - acc: 0.62 - ETA: 12:54 - loss: 1.5537 - acc: 0.56 - ETA: 11:12 - loss: 1.5221 - acc: 0.62 - ETA: 10:16 - loss: 1.5077 - acc: 0.65 - ETA: 9:41 - loss: 1.4943 - acc: 0.6750 - ETA: 9:15 - loss: 1.4668 - acc: 0.729 - ETA: 9:00 - loss: 1.4661 - acc: 0.696 - ETA: 8:46 - loss: 1.4767 - acc: 0.640 - ETA: 8:37 - loss: 1.4772 - acc: 0.625 - ETA: 8:26 - loss: 1.4694 - acc: 0.625 - ETA: 8:20 - loss: 1.4458 - acc: 0.647 - ETA: 8:10 - loss: 1.4194 - acc: 0.677 - ETA: 7:58 - loss: 1.4053 - acc: 0.682 - ETA: 7:50 - loss: 1.3868 - acc: 0.696 - ETA: 7:41 - loss: 1.3752 - acc: 0.700 - ETA: 7:32 - loss: 1.3556 - acc: 0.710 - ETA: 7:23 - loss: 1.3441 - acc: 0.713 - ETA: 7:15 - loss: 1.3361 - acc: 0.708 - ETA: 7:08 - loss: 1.3189 - acc: 0.717 - ETA: 7:01 - loss: 1.2999 - acc: 0.725 - ETA: 6:54 - loss: 1.2886 - acc: 0.726 - ETA: 6:47 - loss: 1.2709 - acc: 0.733 - ETA: 6:40 - loss: 1.2481 - acc: 0.744 - ETA: 6:34 - loss: 1.2391 - acc: 0.744 - ETA: 6:27 - loss: 1.2283 - acc: 0.745 - ETA: 6:22 - loss: 1.2102 - acc: 0.750 - ETA: 6:16 - loss: 1.1986 - acc: 0.750 - ETA: 6:10 - loss: 1.1883 - acc: 0.750 - ETA: 6:04 - loss: 1.1800 - acc: 0.750 - ETA: 5:58 - loss: 1.1716 - acc: 0.750 - ETA: 5:53 - loss: 1.1688 - acc: 0.746 - ETA: 5:49 - loss: 1.1548 - acc: 0.750 - ETA: 5:43 - loss: 1.1376 - acc: 0.753 - ETA: 5:38 - loss: 1.1156 - acc: 0.761 - ETA: 5:32 - loss: 1.1149 - acc: 0.757 - ETA: 5:26 - loss: 1.1068 - acc: 0.756 - ETA: 5:21 - loss: 1.1212 - acc: 0.746 - ETA: 5:16 - loss: 1.1124 - acc: 0.746 - ETA: 5:10 - loss: 1.1218 - acc: 0.740 - ETA: 5:05 - loss: 1.1164 - acc: 0.740 - ETA: 4:59 - loss: 1.1151 - acc: 0.737 - ETA: 4:54 - loss: 1.0951 - acc: 0.744 - ETA: 4:48 - loss: 1.0831 - acc: 0.747 - ETA: 4:43 - loss: 1.0645 - acc: 0.752 - ETA: 4:38 - loss: 1.0818 - acc: 0.741 - ETA: 4:33 - loss: 1.0862 - acc: 0.739 - ETA: 4:27 - loss: 1.0868 - acc: 0.736 - ETA: 4:22 - loss: 1.0781 - acc: 0.739 - ETA: 4:17 - loss: 1.0828 - acc: 0.737 - ETA: 4:11 - loss: 1.0945 - acc: 0.732 - ETA: 4:07 - loss: 1.0988 - acc: 0.730 - ETA: 4:01 - loss: 1.0872 - acc: 0.733 - ETA: 3:57 - loss: 1.0764 - acc: 0.735 - ETA: 3:51 - loss: 1.0817 - acc: 0.733 - ETA: 3:46 - loss: 1.0787 - acc: 0.734 - ETA: 3:41 - loss: 1.0687 - acc: 0.736 - ETA: 3:36 - loss: 1.0694 - acc: 0.734 - ETA: 3:31 - loss: 1.0602 - acc: 0.737 - ETA: 3:26 - loss: 1.0473 - acc: 0.741 - ETA: 3:21 - loss: 1.0422 - acc: 0.741 - ETA: 3:16 - loss: 1.0339 - acc: 0.743 - ETA: 3:11 - loss: 1.0255 - acc: 0.746 - ETA: 3:06 - loss: 1.0229 - acc: 0.746 - ETA: 3:00 - loss: 1.0325 - acc: 0.740 - ETA: 2:55 - loss: 1.0370 - acc: 0.736 - ETA: 2:50 - loss: 1.0289 - acc: 0.738 - ETA: 2:46 - loss: 1.0260 - acc: 0.738 - ETA: 2:41 - loss: 1.0187 - acc: 0.740 - ETA: 2:36 - loss: 1.0158 - acc: 0.740 - ETA: 2:31 - loss: 1.0203 - acc: 0.739 - ETA: 2:26 - loss: 1.0132 - acc: 0.741 - ETA: 2:20 - loss: 1.0256 - acc: 0.734 - ETA: 2:15 - loss: 1.0156 - acc: 0.738 - ETA: 2:10 - loss: 1.0134 - acc: 0.738 - ETA: 2:05 - loss: 1.0108 - acc: 0.738 - ETA: 2:00 - loss: 1.0105 - acc: 0.738 - ETA: 1:55 - loss: 1.0055 - acc: 0.740 - ETA: 1:50 - loss: 1.0032 - acc: 0.740 - ETA: 1:45 - loss: 0.9984 - acc: 0.742 - ETA: 1:40 - loss: 0.9937 - acc: 0.743 - ETA: 1:35 - loss: 0.9924 - acc: 0.743 - ETA: 1:30 - loss: 0.9902 - acc: 0.743 - ETA: 1:24 - loss: 0.9921 - acc: 0.742 - ETA: 1:19 - loss: 0.9951 - acc: 0.739 - ETA: 1:14 - loss: 0.9868 - acc: 0.742 - ETA: 1:09 - loss: 0.9787 - acc: 0.745 - ETA: 1:04 - loss: 0.9784 - acc: 0.744 - ETA: 59s - loss: 0.9850 - acc: 0.741 - ETA: 54s - loss: 0.9875 - acc: 0.74 - ETA: 49s - loss: 0.9796 - acc: 0.74 - ETA: 44s - loss: 0.9790 - acc: 0.74 - ETA: 39s - loss: 0.9783 - acc: 0.74 - ETA: 34s - loss: 0.9752 - acc: 0.74 - ETA: 29s - loss: 0.9744 - acc: 0.74 - ETA: 24s - loss: 0.9754 - acc: 0.74 - ETA: 19s - loss: 0.9756 - acc: 0.74 - ETA: 14s - loss: 0.9803 - acc: 0.73 - ETA: 9s - loss: 0.9800 - acc: 0.7398 - ETA: 4s - loss: 0.9773 - acc: 0.741 - 555s 6s/step - loss: 0.9702 - acc: 0.7438 - val_loss: 0.9596 - val_acc: 0.7050\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - ETA: 8:02 - loss: 1.0423 - acc: 0.625 - ETA: 7:54 - loss: 0.7975 - acc: 0.750 - ETA: 7:44 - loss: 0.9464 - acc: 0.708 - ETA: 7:37 - loss: 0.9235 - acc: 0.718 - ETA: 7:32 - loss: 0.9276 - acc: 0.725 - ETA: 7:27 - loss: 0.9300 - acc: 0.708 - ETA: 7:21 - loss: 0.9358 - acc: 0.714 - ETA: 7:17 - loss: 0.9661 - acc: 0.718 - ETA: 7:13 - loss: 1.0010 - acc: 0.708 - ETA: 7:08 - loss: 0.9704 - acc: 0.725 - ETA: 7:02 - loss: 0.9300 - acc: 0.738 - ETA: 6:57 - loss: 0.9164 - acc: 0.739 - ETA: 6:52 - loss: 0.9259 - acc: 0.730 - ETA: 6:46 - loss: 0.9341 - acc: 0.732 - ETA: 6:41 - loss: 0.9074 - acc: 0.741 - ETA: 6:36 - loss: 0.9289 - acc: 0.734 - ETA: 6:32 - loss: 0.9096 - acc: 0.742 - ETA: 6:27 - loss: 0.9179 - acc: 0.736 - ETA: 6:23 - loss: 0.9159 - acc: 0.736 - ETA: 6:19 - loss: 0.8996 - acc: 0.743 - ETA: 6:14 - loss: 0.9120 - acc: 0.738 - ETA: 6:09 - loss: 0.9083 - acc: 0.738 - ETA: 6:04 - loss: 0.9032 - acc: 0.739 - ETA: 5:59 - loss: 0.9100 - acc: 0.734 - ETA: 5:53 - loss: 0.8858 - acc: 0.745 - ETA: 5:47 - loss: 0.8797 - acc: 0.745 - ETA: 5:41 - loss: 0.8732 - acc: 0.745 - ETA: 5:35 - loss: 0.8669 - acc: 0.745 - ETA: 5:29 - loss: 0.8897 - acc: 0.732 - ETA: 5:23 - loss: 0.9053 - acc: 0.725 - ETA: 5:17 - loss: 0.8992 - acc: 0.725 - ETA: 5:12 - loss: 0.9070 - acc: 0.722 - ETA: 5:07 - loss: 0.9013 - acc: 0.723 - ETA: 5:01 - loss: 0.9027 - acc: 0.724 - ETA: 4:55 - loss: 0.9029 - acc: 0.721 - ETA: 4:51 - loss: 0.8974 - acc: 0.722 - ETA: 4:45 - loss: 0.8930 - acc: 0.726 - ETA: 4:40 - loss: 0.8975 - acc: 0.723 - ETA: 4:35 - loss: 0.8904 - acc: 0.727 - ETA: 4:29 - loss: 0.8865 - acc: 0.728 - ETA: 4:24 - loss: 0.8735 - acc: 0.734 - ETA: 4:19 - loss: 0.8834 - acc: 0.729 - ETA: 4:14 - loss: 0.8778 - acc: 0.732 - ETA: 4:11 - loss: 0.8734 - acc: 0.733 - ETA: 4:07 - loss: 0.8694 - acc: 0.733 - ETA: 4:03 - loss: 0.8707 - acc: 0.733 - ETA: 3:59 - loss: 0.8588 - acc: 0.739 - ETA: 3:55 - loss: 0.8600 - acc: 0.737 - ETA: 3:50 - loss: 0.8491 - acc: 0.742 - ETA: 3:46 - loss: 0.8486 - acc: 0.742 - ETA: 3:42 - loss: 0.8481 - acc: 0.740 - ETA: 3:38 - loss: 0.8471 - acc: 0.740 - ETA: 3:33 - loss: 0.8396 - acc: 0.742 - ETA: 3:29 - loss: 0.8558 - acc: 0.736 - ETA: 3:25 - loss: 0.8566 - acc: 0.736 - ETA: 3:20 - loss: 0.8537 - acc: 0.736 - ETA: 3:16 - loss: 0.8473 - acc: 0.739 - ETA: 3:12 - loss: 0.8378 - acc: 0.743 - ETA: 3:08 - loss: 0.8396 - acc: 0.743 - ETA: 3:03 - loss: 0.8365 - acc: 0.745 - ETA: 2:59 - loss: 0.8425 - acc: 0.741 - ETA: 2:54 - loss: 0.8410 - acc: 0.744 - ETA: 2:50 - loss: 0.8461 - acc: 0.744 - ETA: 2:45 - loss: 0.8370 - acc: 0.748 - ETA: 2:41 - loss: 0.8282 - acc: 0.751 - ETA: 2:36 - loss: 0.8390 - acc: 0.748 - ETA: 2:32 - loss: 0.8386 - acc: 0.748 - ETA: 2:27 - loss: 0.8556 - acc: 0.744 - ETA: 2:23 - loss: 0.8607 - acc: 0.740 - ETA: 2:18 - loss: 0.8615 - acc: 0.739 - ETA: 2:14 - loss: 0.8680 - acc: 0.737 - ETA: 2:09 - loss: 0.8598 - acc: 0.741 - ETA: 2:05 - loss: 0.8659 - acc: 0.739 - ETA: 2:00 - loss: 0.8634 - acc: 0.739 - ETA: 1:56 - loss: 0.8638 - acc: 0.740 - ETA: 1:51 - loss: 0.8601 - acc: 0.741 - ETA: 1:46 - loss: 0.8528 - acc: 0.745 - ETA: 1:42 - loss: 0.8500 - acc: 0.746 - ETA: 1:37 - loss: 0.8485 - acc: 0.748 - ETA: 1:33 - loss: 0.8605 - acc: 0.743 - ETA: 1:28 - loss: 0.8583 - acc: 0.743 - ETA: 1:23 - loss: 0.8561 - acc: 0.743 - ETA: 1:19 - loss: 0.8591 - acc: 0.742 - ETA: 1:14 - loss: 0.8595 - acc: 0.741 - ETA: 1:09 - loss: 0.8582 - acc: 0.742 - ETA: 1:05 - loss: 0.8613 - acc: 0.741 - ETA: 1:00 - loss: 0.8589 - acc: 0.741 - ETA: 56s - loss: 0.8545 - acc: 0.742 - ETA: 51s - loss: 0.8601 - acc: 0.74 - ETA: 46s - loss: 0.8584 - acc: 0.74 - ETA: 42s - loss: 0.8567 - acc: 0.74 - ETA: 37s - loss: 0.8653 - acc: 0.73 - ETA: 32s - loss: 0.8628 - acc: 0.73 - ETA: 28s - loss: 0.8614 - acc: 0.74 - ETA: 23s - loss: 0.8574 - acc: 0.74 - ETA: 18s - loss: 0.8598 - acc: 0.74 - ETA: 14s - loss: 0.8586 - acc: 0.74 - ETA: 9s - loss: 0.8570 - acc: 0.7449 - ETA: 4s - loss: 0.8586 - acc: 0.743 - 527s 5s/step - loss: 0.8594 - acc: 0.7438 - val_loss: 0.9553 - val_acc: 0.7150\n",
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - ETA: 8:00 - loss: 1.0938 - acc: 0.500 - ETA: 7:54 - loss: 0.7894 - acc: 0.687 - ETA: 7:48 - loss: 0.8036 - acc: 0.708 - ETA: 7:42 - loss: 0.8037 - acc: 0.718 - ETA: 7:36 - loss: 0.7410 - acc: 0.750 - ETA: 7:33 - loss: 0.6982 - acc: 0.770 - ETA: 7:27 - loss: 0.7214 - acc: 0.767 - ETA: 7:21 - loss: 0.7438 - acc: 0.750 - ETA: 7:16 - loss: 0.7635 - acc: 0.750 - ETA: 7:12 - loss: 0.8482 - acc: 0.712 - ETA: 7:07 - loss: 0.8250 - acc: 0.727 - ETA: 7:02 - loss: 0.9467 - acc: 0.687 - ETA: 6:57 - loss: 0.9252 - acc: 0.701 - ETA: 6:52 - loss: 0.9248 - acc: 0.696 - ETA: 6:47 - loss: 0.9371 - acc: 0.691 - ETA: 6:41 - loss: 0.8980 - acc: 0.710 - ETA: 6:37 - loss: 0.9164 - acc: 0.705 - ETA: 6:32 - loss: 0.9118 - acc: 0.708 - ETA: 6:28 - loss: 0.9135 - acc: 0.710 - ETA: 6:23 - loss: 0.9263 - acc: 0.700 - ETA: 6:18 - loss: 0.9061 - acc: 0.708 - ETA: 6:14 - loss: 0.8797 - acc: 0.721 - ETA: 6:09 - loss: 0.8640 - acc: 0.728 - ETA: 6:04 - loss: 0.8417 - acc: 0.739 - ETA: 6:00 - loss: 0.8412 - acc: 0.740 - ETA: 5:55 - loss: 0.8575 - acc: 0.735 - ETA: 5:50 - loss: 0.8640 - acc: 0.736 - ETA: 5:46 - loss: 0.8589 - acc: 0.736 - ETA: 5:41 - loss: 0.8543 - acc: 0.741 - ETA: 5:36 - loss: 0.8541 - acc: 0.741 - ETA: 5:32 - loss: 0.8740 - acc: 0.733 - ETA: 5:27 - loss: 0.8850 - acc: 0.726 - ETA: 5:23 - loss: 0.8976 - acc: 0.723 - ETA: 5:18 - loss: 0.8917 - acc: 0.724 - ETA: 5:13 - loss: 0.8843 - acc: 0.728 - ETA: 5:09 - loss: 0.8738 - acc: 0.732 - ETA: 5:04 - loss: 0.8691 - acc: 0.733 - ETA: 5:00 - loss: 0.8655 - acc: 0.736 - ETA: 4:56 - loss: 0.8513 - acc: 0.743 - ETA: 4:51 - loss: 0.8460 - acc: 0.746 - ETA: 4:46 - loss: 0.8624 - acc: 0.743 - ETA: 4:42 - loss: 0.8540 - acc: 0.747 - ETA: 4:37 - loss: 0.8456 - acc: 0.750 - ETA: 4:32 - loss: 0.8479 - acc: 0.747 - ETA: 4:27 - loss: 0.8460 - acc: 0.750 - ETA: 4:22 - loss: 0.8455 - acc: 0.750 - ETA: 4:17 - loss: 0.8530 - acc: 0.747 - ETA: 4:12 - loss: 0.8570 - acc: 0.744 - ETA: 4:08 - loss: 0.8497 - acc: 0.747 - ETA: 4:03 - loss: 0.8573 - acc: 0.745 - ETA: 3:58 - loss: 0.8608 - acc: 0.742 - ETA: 3:52 - loss: 0.8534 - acc: 0.745 - ETA: 3:47 - loss: 0.8611 - acc: 0.742 - ETA: 3:42 - loss: 0.8660 - acc: 0.738 - ETA: 3:37 - loss: 0.8647 - acc: 0.738 - ETA: 3:33 - loss: 0.8585 - acc: 0.741 - ETA: 3:28 - loss: 0.8517 - acc: 0.743 - ETA: 3:23 - loss: 0.8555 - acc: 0.743 - ETA: 3:19 - loss: 0.8532 - acc: 0.743 - ETA: 3:14 - loss: 0.8486 - acc: 0.745 - ETA: 3:09 - loss: 0.8502 - acc: 0.745 - ETA: 3:04 - loss: 0.8480 - acc: 0.746 - ETA: 2:59 - loss: 0.8425 - acc: 0.748 - ETA: 2:55 - loss: 0.8500 - acc: 0.744 - ETA: 2:50 - loss: 0.8477 - acc: 0.746 - ETA: 2:45 - loss: 0.8519 - acc: 0.744 - ETA: 2:40 - loss: 0.8464 - acc: 0.746 - ETA: 2:35 - loss: 0.8477 - acc: 0.746 - ETA: 2:30 - loss: 0.8424 - acc: 0.748 - ETA: 2:26 - loss: 0.8548 - acc: 0.742 - ETA: 2:21 - loss: 0.8525 - acc: 0.744 - ETA: 2:16 - loss: 0.8572 - acc: 0.743 - ETA: 2:11 - loss: 0.8612 - acc: 0.741 - ETA: 2:06 - loss: 0.8566 - acc: 0.743 - ETA: 2:01 - loss: 0.8577 - acc: 0.743 - ETA: 1:56 - loss: 0.8529 - acc: 0.745 - ETA: 1:52 - loss: 0.8508 - acc: 0.745 - ETA: 1:47 - loss: 0.8551 - acc: 0.742 - ETA: 1:42 - loss: 0.8507 - acc: 0.743 - ETA: 1:37 - loss: 0.8487 - acc: 0.743 - ETA: 1:32 - loss: 0.8499 - acc: 0.743 - ETA: 1:27 - loss: 0.8553 - acc: 0.742 - ETA: 1:22 - loss: 0.8604 - acc: 0.739 - ETA: 1:17 - loss: 0.8610 - acc: 0.739 - ETA: 1:13 - loss: 0.8661 - acc: 0.735 - ETA: 1:08 - loss: 0.8620 - acc: 0.736 - ETA: 1:03 - loss: 0.8622 - acc: 0.735 - ETA: 58s - loss: 0.8603 - acc: 0.735 - ETA: 53s - loss: 0.8603 - acc: 0.73 - ETA: 48s - loss: 0.8671 - acc: 0.73 - ETA: 43s - loss: 0.8696 - acc: 0.73 - ETA: 38s - loss: 0.8674 - acc: 0.73 - ETA: 34s - loss: 0.8669 - acc: 0.73 - ETA: 29s - loss: 0.8650 - acc: 0.73 - ETA: 24s - loss: 0.8663 - acc: 0.73 - ETA: 19s - loss: 0.8612 - acc: 0.73 - ETA: 14s - loss: 0.8679 - acc: 0.73 - ETA: 9s - loss: 0.8659 - acc: 0.7372 - ETA: 4s - loss: 0.8686 - acc: 0.736 - 543s 5s/step - loss: 0.8695 - acc: 0.7362 - val_loss: 0.9037 - val_acc: 0.7250\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - ETA: 7:00 - loss: 0.5094 - acc: 0.875 - ETA: 6:47 - loss: 0.6115 - acc: 0.812 - ETA: 6:41 - loss: 0.6375 - acc: 0.833 - ETA: 6:37 - loss: 0.6481 - acc: 0.812 - ETA: 6:32 - loss: 0.7169 - acc: 0.775 - ETA: 6:28 - loss: 0.7603 - acc: 0.770 - ETA: 6:23 - loss: 0.7210 - acc: 0.785 - ETA: 6:18 - loss: 0.7269 - acc: 0.796 - ETA: 6:13 - loss: 0.6829 - acc: 0.819 - ETA: 6:07 - loss: 0.7033 - acc: 0.812 - ETA: 6:03 - loss: 0.7399 - acc: 0.806 - ETA: 5:58 - loss: 0.7612 - acc: 0.791 - ETA: 5:54 - loss: 0.7239 - acc: 0.807 - ETA: 5:49 - loss: 0.7220 - acc: 0.812 - ETA: 5:45 - loss: 0.7781 - acc: 0.783 - ETA: 5:41 - loss: 0.7883 - acc: 0.781 - ETA: 5:37 - loss: 0.7579 - acc: 0.794 - ETA: 5:33 - loss: 0.7441 - acc: 0.798 - ETA: 5:28 - loss: 0.7716 - acc: 0.789 - ETA: 5:24 - loss: 0.7969 - acc: 0.781 - ETA: 5:20 - loss: 0.7972 - acc: 0.779 - ETA: 5:16 - loss: 0.8029 - acc: 0.778 - ETA: 5:14 - loss: 0.8115 - acc: 0.777 - ETA: 5:10 - loss: 0.7974 - acc: 0.781 - ETA: 5:06 - loss: 0.7933 - acc: 0.785 - ETA: 5:02 - loss: 0.7896 - acc: 0.783 - ETA: 4:58 - loss: 0.8162 - acc: 0.777 - ETA: 4:53 - loss: 0.7970 - acc: 0.785 - ETA: 4:49 - loss: 0.8097 - acc: 0.784 - ETA: 4:45 - loss: 0.7913 - acc: 0.791 - ETA: 4:41 - loss: 0.8102 - acc: 0.786 - ETA: 4:36 - loss: 0.7998 - acc: 0.789 - ETA: 4:33 - loss: 0.8201 - acc: 0.780 - ETA: 4:30 - loss: 0.8210 - acc: 0.779 - ETA: 4:28 - loss: 0.8467 - acc: 0.767 - ETA: 4:25 - loss: 0.8403 - acc: 0.770 - ETA: 4:22 - loss: 0.8405 - acc: 0.770 - ETA: 4:18 - loss: 0.8349 - acc: 0.773 - ETA: 4:13 - loss: 0.8258 - acc: 0.775 - ETA: 4:09 - loss: 0.8340 - acc: 0.771 - ETA: 4:04 - loss: 0.8318 - acc: 0.771 - ETA: 4:00 - loss: 0.8291 - acc: 0.770 - ETA: 3:56 - loss: 0.8214 - acc: 0.773 - ETA: 3:51 - loss: 0.8265 - acc: 0.769 - ETA: 3:47 - loss: 0.8147 - acc: 0.775 - ETA: 3:43 - loss: 0.8238 - acc: 0.771 - ETA: 3:39 - loss: 0.8265 - acc: 0.771 - ETA: 3:35 - loss: 0.8315 - acc: 0.768 - ETA: 3:30 - loss: 0.8414 - acc: 0.765 - ETA: 3:26 - loss: 0.8429 - acc: 0.765 - ETA: 3:22 - loss: 0.8356 - acc: 0.767 - ETA: 3:17 - loss: 0.8392 - acc: 0.764 - ETA: 3:13 - loss: 0.8327 - acc: 0.766 - ETA: 3:09 - loss: 0.8367 - acc: 0.763 - ETA: 3:05 - loss: 0.8474 - acc: 0.759 - ETA: 3:00 - loss: 0.8487 - acc: 0.758 - ETA: 2:56 - loss: 0.8429 - acc: 0.761 - ETA: 2:52 - loss: 0.8371 - acc: 0.762 - ETA: 2:48 - loss: 0.8435 - acc: 0.758 - ETA: 2:44 - loss: 0.8502 - acc: 0.756 - ETA: 2:40 - loss: 0.8510 - acc: 0.754 - ETA: 2:36 - loss: 0.8517 - acc: 0.754 - ETA: 2:31 - loss: 0.8429 - acc: 0.757 - ETA: 2:27 - loss: 0.8490 - acc: 0.753 - ETA: 2:23 - loss: 0.8499 - acc: 0.753 - ETA: 2:19 - loss: 0.8446 - acc: 0.755 - ETA: 2:15 - loss: 0.8489 - acc: 0.753 - ETA: 2:11 - loss: 0.8409 - acc: 0.757 - ETA: 2:06 - loss: 0.8391 - acc: 0.757 - ETA: 2:02 - loss: 0.8374 - acc: 0.758 - ETA: 1:58 - loss: 0.8379 - acc: 0.757 - ETA: 1:54 - loss: 0.8308 - acc: 0.760 - ETA: 1:50 - loss: 0.8361 - acc: 0.758 - ETA: 1:46 - loss: 0.8386 - acc: 0.756 - ETA: 1:42 - loss: 0.8365 - acc: 0.758 - ETA: 1:37 - loss: 0.8368 - acc: 0.758 - ETA: 1:33 - loss: 0.8325 - acc: 0.759 - ETA: 1:29 - loss: 0.8335 - acc: 0.758 - ETA: 1:25 - loss: 0.8323 - acc: 0.759 - ETA: 1:21 - loss: 0.8318 - acc: 0.759 - ETA: 1:17 - loss: 0.8303 - acc: 0.759 - ETA: 1:13 - loss: 0.8370 - acc: 0.757 - ETA: 1:09 - loss: 0.8344 - acc: 0.759 - ETA: 1:05 - loss: 0.8305 - acc: 0.760 - ETA: 1:01 - loss: 0.8264 - acc: 0.761 - ETA: 56s - loss: 0.8221 - acc: 0.763 - ETA: 52s - loss: 0.8268 - acc: 0.76 - ETA: 48s - loss: 0.8266 - acc: 0.76 - ETA: 44s - loss: 0.8292 - acc: 0.75 - ETA: 40s - loss: 0.8279 - acc: 0.75 - ETA: 36s - loss: 0.8272 - acc: 0.75 - ETA: 32s - loss: 0.8235 - acc: 0.76 - ETA: 28s - loss: 0.8197 - acc: 0.76 - ETA: 24s - loss: 0.8186 - acc: 0.76 - ETA: 20s - loss: 0.8152 - acc: 0.76 - ETA: 16s - loss: 0.8121 - acc: 0.76 - ETA: 12s - loss: 0.8109 - acc: 0.76 - ETA: 8s - loss: 0.8221 - acc: 0.7628 - ETA: 4s - loss: 0.8205 - acc: 0.763 - 453s 5s/step - loss: 0.8169 - acc: 0.7650 - val_loss: 1.0080 - val_acc: 0.6950\n",
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - ETA: 6:44 - loss: 1.2856 - acc: 0.500 - ETA: 6:30 - loss: 0.9930 - acc: 0.625 - ETA: 6:24 - loss: 0.9520 - acc: 0.666 - ETA: 6:19 - loss: 0.9107 - acc: 0.687 - ETA: 6:15 - loss: 0.8470 - acc: 0.725 - ETA: 6:12 - loss: 0.9375 - acc: 0.687 - ETA: 6:11 - loss: 0.9547 - acc: 0.678 - ETA: 6:06 - loss: 0.9500 - acc: 0.671 - ETA: 6:02 - loss: 1.0521 - acc: 0.652 - ETA: 5:57 - loss: 1.0180 - acc: 0.675 - ETA: 5:53 - loss: 1.0510 - acc: 0.659 - ETA: 5:49 - loss: 1.0405 - acc: 0.666 - ETA: 5:45 - loss: 1.0129 - acc: 0.673 - ETA: 5:40 - loss: 0.9906 - acc: 0.687 - ETA: 5:36 - loss: 0.9595 - acc: 0.700 - ETA: 5:32 - loss: 0.9309 - acc: 0.710 - ETA: 5:27 - loss: 0.9139 - acc: 0.720 - ETA: 5:24 - loss: 0.8979 - acc: 0.729 - ETA: 5:20 - loss: 0.8655 - acc: 0.743 - ETA: 5:16 - loss: 0.8563 - acc: 0.743 - ETA: 5:13 - loss: 0.8630 - acc: 0.738 - ETA: 5:08 - loss: 0.8512 - acc: 0.744 - ETA: 5:04 - loss: 0.8513 - acc: 0.744 - ETA: 5:00 - loss: 0.8454 - acc: 0.744 - ETA: 4:57 - loss: 0.8393 - acc: 0.750 - ETA: 4:53 - loss: 0.8310 - acc: 0.754 - ETA: 4:49 - loss: 0.8602 - acc: 0.740 - ETA: 4:46 - loss: 0.8541 - acc: 0.745 - ETA: 4:42 - loss: 0.8729 - acc: 0.741 - ETA: 4:38 - loss: 0.8680 - acc: 0.741 - ETA: 4:34 - loss: 0.8776 - acc: 0.737 - ETA: 4:30 - loss: 0.8788 - acc: 0.738 - ETA: 4:26 - loss: 0.8605 - acc: 0.746 - ETA: 4:22 - loss: 0.8555 - acc: 0.746 - ETA: 4:18 - loss: 0.8696 - acc: 0.739 - ETA: 4:14 - loss: 0.8680 - acc: 0.739 - ETA: 4:10 - loss: 0.8827 - acc: 0.736 - ETA: 4:06 - loss: 0.8902 - acc: 0.730 - ETA: 4:02 - loss: 0.8848 - acc: 0.730 - ETA: 3:58 - loss: 0.8781 - acc: 0.734 - ETA: 3:54 - loss: 0.8789 - acc: 0.731 - ETA: 3:50 - loss: 0.8804 - acc: 0.732 - ETA: 3:46 - loss: 0.8768 - acc: 0.735 - ETA: 3:42 - loss: 0.8634 - acc: 0.741 - ETA: 3:38 - loss: 0.8599 - acc: 0.741 - ETA: 3:34 - loss: 0.8746 - acc: 0.736 - ETA: 3:30 - loss: 0.8722 - acc: 0.736 - ETA: 3:26 - loss: 0.8960 - acc: 0.729 - ETA: 3:22 - loss: 0.8952 - acc: 0.729 - ETA: 3:18 - loss: 0.8833 - acc: 0.735 - ETA: 3:14 - loss: 0.8944 - acc: 0.730 - ETA: 3:10 - loss: 0.8887 - acc: 0.733 - ETA: 3:06 - loss: 0.8782 - acc: 0.738 - ETA: 3:02 - loss: 0.8786 - acc: 0.736 - ETA: 2:58 - loss: 0.8826 - acc: 0.736 - ETA: 2:54 - loss: 0.8964 - acc: 0.732 - ETA: 2:50 - loss: 0.8925 - acc: 0.732 - ETA: 2:46 - loss: 0.8977 - acc: 0.730 - ETA: 2:42 - loss: 0.8951 - acc: 0.733 - ETA: 2:38 - loss: 0.8857 - acc: 0.737 - ETA: 2:34 - loss: 0.8828 - acc: 0.737 - ETA: 2:30 - loss: 0.8879 - acc: 0.735 - ETA: 2:26 - loss: 0.8786 - acc: 0.740 - ETA: 2:22 - loss: 0.8761 - acc: 0.742 - ETA: 2:18 - loss: 0.8709 - acc: 0.744 - ETA: 2:14 - loss: 0.8654 - acc: 0.746 - ETA: 2:10 - loss: 0.8636 - acc: 0.746 - ETA: 2:06 - loss: 0.8644 - acc: 0.744 - ETA: 2:02 - loss: 0.8706 - acc: 0.742 - ETA: 1:58 - loss: 0.8754 - acc: 0.739 - ETA: 1:54 - loss: 0.8796 - acc: 0.735 - ETA: 1:50 - loss: 0.8773 - acc: 0.736 - ETA: 1:47 - loss: 0.8750 - acc: 0.736 - ETA: 1:43 - loss: 0.8876 - acc: 0.733 - ETA: 1:39 - loss: 0.8919 - acc: 0.733 - ETA: 1:35 - loss: 0.8893 - acc: 0.733 - ETA: 1:31 - loss: 0.8927 - acc: 0.733 - ETA: 1:27 - loss: 0.8883 - acc: 0.735 - ETA: 1:23 - loss: 0.8850 - acc: 0.737 - ETA: 1:19 - loss: 0.8807 - acc: 0.739 - ETA: 1:15 - loss: 0.8812 - acc: 0.737 - ETA: 1:11 - loss: 0.8792 - acc: 0.737 - ETA: 1:07 - loss: 0.8775 - acc: 0.738 - ETA: 1:03 - loss: 0.8877 - acc: 0.733 - ETA: 59s - loss: 0.8860 - acc: 0.733 - ETA: 55s - loss: 0.8835 - acc: 0.73 - ETA: 51s - loss: 0.8795 - acc: 0.73 - ETA: 47s - loss: 0.8734 - acc: 0.73 - ETA: 43s - loss: 0.8742 - acc: 0.73 - ETA: 39s - loss: 0.8702 - acc: 0.74 - ETA: 35s - loss: 0.8701 - acc: 0.74 - ETA: 31s - loss: 0.8675 - acc: 0.74 - ETA: 27s - loss: 0.8775 - acc: 0.73 - ETA: 23s - loss: 0.8772 - acc: 0.73 - ETA: 19s - loss: 0.8814 - acc: 0.73 - ETA: 15s - loss: 0.8775 - acc: 0.73 - ETA: 11s - loss: 0.8772 - acc: 0.73 - ETA: 7s - loss: 0.8822 - acc: 0.7347 - ETA: 3s - loss: 0.8860 - acc: 0.731 - 444s 4s/step - loss: 0.8837 - acc: 0.7325 - val_loss: 0.9670 - val_acc: 0.7050\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - ETA: 6:45 - loss: 0.5332 - acc: 0.875 - ETA: 6:34 - loss: 0.8495 - acc: 0.687 - ETA: 6:25 - loss: 0.9463 - acc: 0.666 - ETA: 6:22 - loss: 0.8773 - acc: 0.687 - ETA: 6:18 - loss: 0.8394 - acc: 0.700 - ETA: 6:12 - loss: 0.8119 - acc: 0.729 - ETA: 6:08 - loss: 0.9194 - acc: 0.678 - ETA: 6:04 - loss: 0.8941 - acc: 0.703 - ETA: 6:00 - loss: 0.9158 - acc: 0.694 - ETA: 5:56 - loss: 0.8607 - acc: 0.725 - ETA: 5:52 - loss: 0.8285 - acc: 0.738 - ETA: 5:48 - loss: 0.8289 - acc: 0.739 - ETA: 5:44 - loss: 0.8327 - acc: 0.740 - ETA: 5:40 - loss: 0.8990 - acc: 0.723 - ETA: 5:36 - loss: 0.9158 - acc: 0.716 - ETA: 5:32 - loss: 0.9225 - acc: 0.718 - ETA: 5:27 - loss: 0.9181 - acc: 0.720 - ETA: 5:24 - loss: 0.9182 - acc: 0.722 - ETA: 5:19 - loss: 0.9210 - acc: 0.717 - ETA: 5:16 - loss: 0.9015 - acc: 0.725 - ETA: 5:11 - loss: 0.9277 - acc: 0.720 - ETA: 5:08 - loss: 0.9228 - acc: 0.721 - ETA: 5:04 - loss: 0.9095 - acc: 0.728 - ETA: 5:00 - loss: 0.8920 - acc: 0.734 - ETA: 4:56 - loss: 0.8819 - acc: 0.740 - ETA: 4:52 - loss: 0.8603 - acc: 0.750 - ETA: 4:48 - loss: 0.8535 - acc: 0.754 - ETA: 4:44 - loss: 0.8776 - acc: 0.745 - ETA: 4:40 - loss: 0.8836 - acc: 0.741 - ETA: 4:36 - loss: 0.8918 - acc: 0.737 - ETA: 4:32 - loss: 0.8857 - acc: 0.737 - ETA: 4:28 - loss: 0.8793 - acc: 0.742 - ETA: 4:24 - loss: 0.8781 - acc: 0.742 - ETA: 4:20 - loss: 0.8775 - acc: 0.742 - ETA: 4:16 - loss: 0.8610 - acc: 0.750 - ETA: 4:12 - loss: 0.8798 - acc: 0.739 - ETA: 4:08 - loss: 0.8636 - acc: 0.746 - ETA: 4:04 - loss: 0.8620 - acc: 0.746 - ETA: 4:00 - loss: 0.8635 - acc: 0.746 - ETA: 3:56 - loss: 0.8694 - acc: 0.743 - ETA: 3:52 - loss: 0.8545 - acc: 0.750 - ETA: 3:48 - loss: 0.8676 - acc: 0.744 - ETA: 3:44 - loss: 0.8538 - acc: 0.750 - ETA: 3:40 - loss: 0.8479 - acc: 0.752 - ETA: 3:36 - loss: 0.8403 - acc: 0.755 - ETA: 3:33 - loss: 0.8521 - acc: 0.752 - ETA: 3:29 - loss: 0.8568 - acc: 0.750 - ETA: 3:25 - loss: 0.8486 - acc: 0.752 - ETA: 3:21 - loss: 0.8539 - acc: 0.750 - ETA: 3:17 - loss: 0.8464 - acc: 0.752 - ETA: 3:13 - loss: 0.8437 - acc: 0.754 - ETA: 3:09 - loss: 0.8579 - acc: 0.747 - ETA: 3:05 - loss: 0.8751 - acc: 0.740 - ETA: 3:01 - loss: 0.8695 - acc: 0.743 - ETA: 2:57 - loss: 0.8627 - acc: 0.745 - ETA: 2:53 - loss: 0.8594 - acc: 0.747 - ETA: 2:49 - loss: 0.8565 - acc: 0.747 - ETA: 2:45 - loss: 0.8548 - acc: 0.747 - ETA: 2:41 - loss: 0.8526 - acc: 0.747 - ETA: 2:37 - loss: 0.8612 - acc: 0.745 - ETA: 2:33 - loss: 0.8515 - acc: 0.750 - ETA: 2:29 - loss: 0.8464 - acc: 0.752 - ETA: 2:25 - loss: 0.8374 - acc: 0.756 - ETA: 2:22 - loss: 0.8381 - acc: 0.755 - ETA: 2:18 - loss: 0.8392 - acc: 0.753 - ETA: 2:14 - loss: 0.8409 - acc: 0.753 - ETA: 2:10 - loss: 0.8386 - acc: 0.755 - ETA: 2:06 - loss: 0.8340 - acc: 0.757 - ETA: 2:02 - loss: 0.8372 - acc: 0.757 - ETA: 1:58 - loss: 0.8384 - acc: 0.757 - ETA: 1:54 - loss: 0.8395 - acc: 0.755 - ETA: 1:50 - loss: 0.8426 - acc: 0.753 - ETA: 1:46 - loss: 0.8499 - acc: 0.750 - ETA: 1:42 - loss: 0.8503 - acc: 0.750 - ETA: 1:38 - loss: 0.8526 - acc: 0.750 - ETA: 1:34 - loss: 0.8507 - acc: 0.751 - ETA: 1:30 - loss: 0.8462 - acc: 0.753 - ETA: 1:26 - loss: 0.8441 - acc: 0.754 - ETA: 1:22 - loss: 0.8464 - acc: 0.753 - ETA: 1:18 - loss: 0.8456 - acc: 0.753 - ETA: 1:14 - loss: 0.8505 - acc: 0.750 - ETA: 1:10 - loss: 0.8501 - acc: 0.750 - ETA: 1:07 - loss: 0.8495 - acc: 0.750 - ETA: 1:03 - loss: 0.8524 - acc: 0.748 - ETA: 59s - loss: 0.8563 - acc: 0.747 - ETA: 55s - loss: 0.8519 - acc: 0.74 - ETA: 51s - loss: 0.8524 - acc: 0.74 - ETA: 47s - loss: 0.8488 - acc: 0.75 - ETA: 43s - loss: 0.8423 - acc: 0.75 - ETA: 39s - loss: 0.8546 - acc: 0.74 - ETA: 35s - loss: 0.8555 - acc: 0.74 - ETA: 31s - loss: 0.8599 - acc: 0.74 - ETA: 27s - loss: 0.8584 - acc: 0.74 - ETA: 23s - loss: 0.8603 - acc: 0.74 - ETA: 19s - loss: 0.8649 - acc: 0.74 - ETA: 15s - loss: 0.8612 - acc: 0.74 - ETA: 11s - loss: 0.8585 - acc: 0.74 - ETA: 7s - loss: 0.8574 - acc: 0.7487 - ETA: 3s - loss: 0.8562 - acc: 0.748 - 442s 4s/step - loss: 0.8586 - acc: 0.7475 - val_loss: 0.9270 - val_acc: 0.7200\n",
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - ETA: 6:43 - loss: 1.4312 - acc: 0.625 - ETA: 6:35 - loss: 1.3467 - acc: 0.625 - ETA: 6:26 - loss: 1.2259 - acc: 0.625 - ETA: 6:22 - loss: 1.1014 - acc: 0.656 - ETA: 6:17 - loss: 0.9852 - acc: 0.700 - ETA: 6:12 - loss: 0.9554 - acc: 0.708 - ETA: 6:07 - loss: 0.9437 - acc: 0.696 - ETA: 6:03 - loss: 0.9207 - acc: 0.703 - ETA: 6:00 - loss: 0.9218 - acc: 0.694 - ETA: 5:56 - loss: 0.8625 - acc: 0.725 - ETA: 5:51 - loss: 0.8339 - acc: 0.738 - ETA: 5:47 - loss: 0.8496 - acc: 0.739 - ETA: 5:43 - loss: 0.8455 - acc: 0.740 - ETA: 5:39 - loss: 0.8717 - acc: 0.741 - ETA: 5:35 - loss: 0.8710 - acc: 0.733 - ETA: 5:31 - loss: 0.8492 - acc: 0.742 - ETA: 5:27 - loss: 0.8408 - acc: 0.742 - ETA: 5:23 - loss: 0.8551 - acc: 0.736 - ETA: 5:20 - loss: 0.8261 - acc: 0.750 - ETA: 5:16 - loss: 0.8583 - acc: 0.737 - ETA: 5:12 - loss: 0.8317 - acc: 0.750 - ETA: 5:08 - loss: 0.8371 - acc: 0.750 - ETA: 5:04 - loss: 0.8148 - acc: 0.760 - ETA: 5:00 - loss: 0.8556 - acc: 0.739 - ETA: 4:57 - loss: 0.8719 - acc: 0.735 - ETA: 4:53 - loss: 0.8653 - acc: 0.735 - ETA: 4:49 - loss: 0.8650 - acc: 0.736 - ETA: 4:44 - loss: 0.8784 - acc: 0.732 - ETA: 4:41 - loss: 0.8836 - acc: 0.728 - ETA: 4:37 - loss: 0.8960 - acc: 0.720 - ETA: 4:33 - loss: 0.8871 - acc: 0.725 - ETA: 4:29 - loss: 0.9019 - acc: 0.718 - ETA: 4:25 - loss: 0.9035 - acc: 0.719 - ETA: 4:20 - loss: 0.9026 - acc: 0.716 - ETA: 4:16 - loss: 0.8908 - acc: 0.721 - ETA: 4:12 - loss: 0.8887 - acc: 0.722 - ETA: 4:08 - loss: 0.8938 - acc: 0.719 - ETA: 4:05 - loss: 0.8794 - acc: 0.727 - ETA: 4:03 - loss: 0.8755 - acc: 0.730 - ETA: 4:02 - loss: 0.8808 - acc: 0.731 - ETA: 4:00 - loss: 0.8845 - acc: 0.725 - ETA: 3:59 - loss: 0.8787 - acc: 0.729 - ETA: 3:56 - loss: 0.8750 - acc: 0.732 - ETA: 3:53 - loss: 0.8668 - acc: 0.735 - ETA: 3:50 - loss: 0.8801 - acc: 0.733 - ETA: 3:47 - loss: 0.8863 - acc: 0.731 - ETA: 3:43 - loss: 0.8808 - acc: 0.734 - ETA: 3:40 - loss: 0.8837 - acc: 0.731 - ETA: 3:36 - loss: 0.8756 - acc: 0.734 - ETA: 3:33 - loss: 0.8640 - acc: 0.740 - ETA: 3:29 - loss: 0.8614 - acc: 0.742 - ETA: 3:25 - loss: 0.8540 - acc: 0.745 - ETA: 3:21 - loss: 0.8595 - acc: 0.742 - ETA: 3:16 - loss: 0.8490 - acc: 0.747 - ETA: 3:11 - loss: 0.8509 - acc: 0.747 - ETA: 3:07 - loss: 0.8482 - acc: 0.747 - ETA: 3:03 - loss: 0.8485 - acc: 0.747 - ETA: 2:58 - loss: 0.8482 - acc: 0.747 - ETA: 2:54 - loss: 0.8440 - acc: 0.750 - ETA: 2:49 - loss: 0.8456 - acc: 0.750 - ETA: 2:45 - loss: 0.8428 - acc: 0.750 - ETA: 2:41 - loss: 0.8512 - acc: 0.748 - ETA: 2:36 - loss: 0.8455 - acc: 0.750 - ETA: 2:32 - loss: 0.8442 - acc: 0.750 - ETA: 2:27 - loss: 0.8458 - acc: 0.750 - ETA: 2:23 - loss: 0.8434 - acc: 0.750 - ETA: 2:19 - loss: 0.8380 - acc: 0.751 - ETA: 2:15 - loss: 0.8356 - acc: 0.751 - ETA: 2:10 - loss: 0.8361 - acc: 0.751 - ETA: 2:06 - loss: 0.8373 - acc: 0.750 - ETA: 2:02 - loss: 0.8417 - acc: 0.750 - ETA: 1:57 - loss: 0.8394 - acc: 0.750 - ETA: 1:53 - loss: 0.8452 - acc: 0.748 - ETA: 1:49 - loss: 0.8417 - acc: 0.750 - ETA: 1:44 - loss: 0.8386 - acc: 0.751 - ETA: 1:40 - loss: 0.8442 - acc: 0.750 - ETA: 1:36 - loss: 0.8422 - acc: 0.750 - ETA: 1:32 - loss: 0.8412 - acc: 0.750 - ETA: 1:27 - loss: 0.8534 - acc: 0.746 - ETA: 1:23 - loss: 0.8462 - acc: 0.750 - ETA: 1:19 - loss: 0.8442 - acc: 0.750 - ETA: 1:15 - loss: 0.8425 - acc: 0.750 - ETA: 1:10 - loss: 0.8384 - acc: 0.751 - ETA: 1:06 - loss: 0.8316 - acc: 0.754 - ETA: 1:02 - loss: 0.8318 - acc: 0.754 - ETA: 58s - loss: 0.8302 - acc: 0.754 - ETA: 54s - loss: 0.8263 - acc: 0.75 - ETA: 49s - loss: 0.8249 - acc: 0.75 - ETA: 45s - loss: 0.8258 - acc: 0.75 - ETA: 41s - loss: 0.8219 - acc: 0.75 - ETA: 37s - loss: 0.8204 - acc: 0.75 - ETA: 33s - loss: 0.8240 - acc: 0.75 - ETA: 29s - loss: 0.8257 - acc: 0.75 - ETA: 24s - loss: 0.8244 - acc: 0.75 - ETA: 20s - loss: 0.8269 - acc: 0.75 - ETA: 16s - loss: 0.8232 - acc: 0.75 - ETA: 12s - loss: 0.8174 - acc: 0.75 - ETA: 8s - loss: 0.8240 - acc: 0.7564 - ETA: 4s - loss: 0.8231 - acc: 0.757 - 461s 5s/step - loss: 0.8301 - acc: 0.7550 - val_loss: 0.9293 - val_acc: 0.7250\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - ETA: 6:46 - loss: 0.2512 - acc: 1.000 - ETA: 6:38 - loss: 0.6454 - acc: 0.812 - ETA: 6:36 - loss: 0.5163 - acc: 0.875 - ETA: 6:37 - loss: 0.5392 - acc: 0.875 - ETA: 6:33 - loss: 0.6502 - acc: 0.825 - ETA: 6:26 - loss: 0.7372 - acc: 0.791 - ETA: 6:20 - loss: 0.6705 - acc: 0.821 - ETA: 6:14 - loss: 0.6497 - acc: 0.828 - ETA: 6:10 - loss: 0.6549 - acc: 0.819 - ETA: 6:04 - loss: 0.6616 - acc: 0.812 - ETA: 6:00 - loss: 0.6741 - acc: 0.806 - ETA: 5:55 - loss: 0.6596 - acc: 0.812 - ETA: 5:51 - loss: 0.6779 - acc: 0.798 - ETA: 5:46 - loss: 0.6942 - acc: 0.785 - ETA: 5:41 - loss: 0.6911 - acc: 0.783 - ETA: 5:37 - loss: 0.6891 - acc: 0.781 - ETA: 5:33 - loss: 0.6917 - acc: 0.786 - ETA: 5:29 - loss: 0.6790 - acc: 0.791 - ETA: 5:25 - loss: 0.6927 - acc: 0.782 - ETA: 5:20 - loss: 0.6871 - acc: 0.787 - ETA: 5:16 - loss: 0.6861 - acc: 0.785 - ETA: 5:12 - loss: 0.6672 - acc: 0.795 - ETA: 5:08 - loss: 0.6667 - acc: 0.793 - ETA: 5:04 - loss: 0.6577 - acc: 0.796 - ETA: 5:00 - loss: 0.7145 - acc: 0.775 - ETA: 4:56 - loss: 0.7643 - acc: 0.764 - ETA: 4:52 - loss: 0.7806 - acc: 0.763 - ETA: 4:48 - loss: 0.7847 - acc: 0.758 - ETA: 4:44 - loss: 0.8086 - acc: 0.750 - ETA: 4:39 - loss: 0.8267 - acc: 0.745 - ETA: 4:35 - loss: 0.8366 - acc: 0.741 - ETA: 4:31 - loss: 0.8528 - acc: 0.738 - ETA: 4:27 - loss: 0.8764 - acc: 0.734 - ETA: 4:23 - loss: 0.8649 - acc: 0.739 - ETA: 4:19 - loss: 0.8653 - acc: 0.739 - ETA: 4:15 - loss: 0.8604 - acc: 0.743 - ETA: 4:11 - loss: 0.8786 - acc: 0.736 - ETA: 4:07 - loss: 0.8648 - acc: 0.743 - ETA: 4:03 - loss: 0.8819 - acc: 0.737 - ETA: 3:58 - loss: 0.8947 - acc: 0.731 - ETA: 3:54 - loss: 0.8949 - acc: 0.731 - ETA: 3:50 - loss: 0.8820 - acc: 0.738 - ETA: 3:46 - loss: 0.8892 - acc: 0.732 - ETA: 3:42 - loss: 0.8869 - acc: 0.735 - ETA: 3:38 - loss: 0.8950 - acc: 0.733 - ETA: 3:34 - loss: 0.8935 - acc: 0.733 - ETA: 3:30 - loss: 0.9007 - acc: 0.728 - ETA: 3:26 - loss: 0.9013 - acc: 0.729 - ETA: 3:22 - loss: 0.8975 - acc: 0.732 - ETA: 3:18 - loss: 0.8922 - acc: 0.735 - ETA: 3:14 - loss: 0.8887 - acc: 0.737 - ETA: 3:10 - loss: 0.8825 - acc: 0.740 - ETA: 3:06 - loss: 0.8891 - acc: 0.735 - ETA: 3:02 - loss: 0.8798 - acc: 0.740 - ETA: 2:58 - loss: 0.8706 - acc: 0.745 - ETA: 2:54 - loss: 0.8734 - acc: 0.743 - ETA: 2:50 - loss: 0.8845 - acc: 0.736 - ETA: 2:46 - loss: 0.8812 - acc: 0.739 - ETA: 2:42 - loss: 0.8838 - acc: 0.737 - ETA: 2:38 - loss: 0.8792 - acc: 0.739 - ETA: 2:35 - loss: 0.8853 - acc: 0.737 - ETA: 2:31 - loss: 0.8873 - acc: 0.735 - ETA: 2:27 - loss: 0.8887 - acc: 0.734 - ETA: 2:23 - loss: 0.8828 - acc: 0.736 - ETA: 2:19 - loss: 0.8872 - acc: 0.734 - ETA: 2:15 - loss: 0.8789 - acc: 0.738 - ETA: 2:11 - loss: 0.8830 - acc: 0.736 - ETA: 2:07 - loss: 0.8831 - acc: 0.737 - ETA: 2:03 - loss: 0.8858 - acc: 0.735 - ETA: 1:59 - loss: 0.8778 - acc: 0.739 - ETA: 1:55 - loss: 0.8918 - acc: 0.734 - ETA: 1:51 - loss: 0.8835 - acc: 0.737 - ETA: 1:47 - loss: 0.8826 - acc: 0.738 - ETA: 1:43 - loss: 0.8822 - acc: 0.738 - ETA: 1:39 - loss: 0.8773 - acc: 0.740 - ETA: 1:35 - loss: 0.8813 - acc: 0.738 - ETA: 1:31 - loss: 0.8795 - acc: 0.738 - ETA: 1:27 - loss: 0.8801 - acc: 0.738 - ETA: 1:23 - loss: 0.8843 - acc: 0.737 - ETA: 1:19 - loss: 0.8798 - acc: 0.739 - ETA: 1:15 - loss: 0.8784 - acc: 0.739 - ETA: 1:11 - loss: 0.8796 - acc: 0.739 - ETA: 1:07 - loss: 0.8803 - acc: 0.738 - ETA: 1:03 - loss: 0.8860 - acc: 0.735 - ETA: 59s - loss: 0.8913 - acc: 0.732 - ETA: 55s - loss: 0.8881 - acc: 0.73 - ETA: 51s - loss: 0.8868 - acc: 0.73 - ETA: 47s - loss: 0.8829 - acc: 0.73 - ETA: 43s - loss: 0.8838 - acc: 0.73 - ETA: 39s - loss: 0.8819 - acc: 0.73 - ETA: 35s - loss: 0.8758 - acc: 0.74 - ETA: 31s - loss: 0.8764 - acc: 0.73 - ETA: 27s - loss: 0.8755 - acc: 0.73 - ETA: 23s - loss: 0.8773 - acc: 0.73 - ETA: 19s - loss: 0.8714 - acc: 0.74 - ETA: 15s - loss: 0.8657 - acc: 0.74 - ETA: 11s - loss: 0.8679 - acc: 0.74 - ETA: 7s - loss: 0.8673 - acc: 0.7423 - ETA: 3s - loss: 0.8667 - acc: 0.742 - 446s 4s/step - loss: 0.8649 - acc: 0.7425 - val_loss: 0.9897 - val_acc: 0.6900\n",
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - ETA: 6:43 - loss: 0.2641 - acc: 1.000 - ETA: 6:39 - loss: 0.8744 - acc: 0.750 - ETA: 6:35 - loss: 0.7526 - acc: 0.791 - ETA: 6:31 - loss: 0.6269 - acc: 0.843 - ETA: 6:26 - loss: 0.6459 - acc: 0.850 - ETA: 6:22 - loss: 0.6664 - acc: 0.854 - ETA: 6:17 - loss: 0.6374 - acc: 0.857 - ETA: 6:12 - loss: 0.7068 - acc: 0.812 - ETA: 6:08 - loss: 0.7679 - acc: 0.791 - ETA: 6:04 - loss: 0.7145 - acc: 0.812 - ETA: 5:59 - loss: 0.7867 - acc: 0.784 - ETA: 5:54 - loss: 0.7817 - acc: 0.781 - ETA: 5:49 - loss: 0.8076 - acc: 0.769 - ETA: 5:45 - loss: 0.8055 - acc: 0.767 - ETA: 5:41 - loss: 0.7983 - acc: 0.775 - ETA: 5:36 - loss: 0.7636 - acc: 0.789 - ETA: 5:32 - loss: 0.8192 - acc: 0.772 - ETA: 5:27 - loss: 0.8306 - acc: 0.763 - ETA: 5:23 - loss: 0.8223 - acc: 0.763 - ETA: 5:19 - loss: 0.8508 - acc: 0.756 - ETA: 5:14 - loss: 0.8233 - acc: 0.767 - ETA: 5:10 - loss: 0.8476 - acc: 0.761 - ETA: 5:06 - loss: 0.8534 - acc: 0.760 - ETA: 5:02 - loss: 0.8731 - acc: 0.755 - ETA: 4:58 - loss: 0.8836 - acc: 0.750 - ETA: 4:53 - loss: 0.8759 - acc: 0.750 - ETA: 4:49 - loss: 0.8691 - acc: 0.754 - ETA: 4:45 - loss: 0.8551 - acc: 0.758 - ETA: 4:41 - loss: 0.8473 - acc: 0.762 - ETA: 4:37 - loss: 0.8585 - acc: 0.758 - ETA: 4:33 - loss: 0.8636 - acc: 0.754 - ETA: 4:29 - loss: 0.8455 - acc: 0.761 - ETA: 4:25 - loss: 0.8503 - acc: 0.757 - ETA: 4:21 - loss: 0.8404 - acc: 0.761 - ETA: 4:17 - loss: 0.8415 - acc: 0.760 - ETA: 4:13 - loss: 0.8559 - acc: 0.756 - ETA: 4:09 - loss: 0.8585 - acc: 0.756 - ETA: 4:05 - loss: 0.8515 - acc: 0.759 - ETA: 4:01 - loss: 0.8531 - acc: 0.759 - ETA: 3:57 - loss: 0.8503 - acc: 0.759 - ETA: 3:53 - loss: 0.8525 - acc: 0.759 - ETA: 3:49 - loss: 0.8572 - acc: 0.756 - ETA: 3:45 - loss: 0.8493 - acc: 0.758 - ETA: 3:41 - loss: 0.8490 - acc: 0.758 - ETA: 3:37 - loss: 0.8570 - acc: 0.755 - ETA: 3:33 - loss: 0.8589 - acc: 0.752 - ETA: 3:29 - loss: 0.8521 - acc: 0.755 - ETA: 3:25 - loss: 0.8673 - acc: 0.750 - ETA: 3:21 - loss: 0.8686 - acc: 0.750 - ETA: 3:17 - loss: 0.8798 - acc: 0.745 - ETA: 3:13 - loss: 0.8721 - acc: 0.747 - ETA: 3:09 - loss: 0.8779 - acc: 0.745 - ETA: 3:06 - loss: 0.8811 - acc: 0.742 - ETA: 3:02 - loss: 0.8745 - acc: 0.745 - ETA: 2:58 - loss: 0.8697 - acc: 0.747 - ETA: 2:54 - loss: 0.8601 - acc: 0.752 - ETA: 2:50 - loss: 0.8596 - acc: 0.752 - ETA: 2:46 - loss: 0.8606 - acc: 0.750 - ETA: 2:42 - loss: 0.8674 - acc: 0.747 - ETA: 2:38 - loss: 0.8653 - acc: 0.747 - ETA: 2:34 - loss: 0.8695 - acc: 0.745 - ETA: 2:30 - loss: 0.8635 - acc: 0.748 - ETA: 2:26 - loss: 0.8652 - acc: 0.746 - ETA: 2:22 - loss: 0.8668 - acc: 0.744 - ETA: 2:18 - loss: 0.8750 - acc: 0.742 - ETA: 2:14 - loss: 0.8667 - acc: 0.746 - ETA: 2:10 - loss: 0.8640 - acc: 0.746 - ETA: 2:06 - loss: 0.8607 - acc: 0.748 - ETA: 2:02 - loss: 0.8555 - acc: 0.750 - ETA: 1:59 - loss: 0.8538 - acc: 0.750 - ETA: 1:55 - loss: 0.8680 - acc: 0.744 - ETA: 1:51 - loss: 0.8769 - acc: 0.741 - ETA: 1:47 - loss: 0.8742 - acc: 0.741 - ETA: 1:43 - loss: 0.8699 - acc: 0.743 - ETA: 1:39 - loss: 0.8745 - acc: 0.743 - ETA: 1:35 - loss: 0.8722 - acc: 0.743 - ETA: 1:31 - loss: 0.8912 - acc: 0.737 - ETA: 1:27 - loss: 0.8859 - acc: 0.738 - ETA: 1:23 - loss: 0.8964 - acc: 0.735 - ETA: 1:19 - loss: 0.8994 - acc: 0.734 - ETA: 1:15 - loss: 0.9063 - acc: 0.731 - ETA: 1:11 - loss: 0.8995 - acc: 0.734 - ETA: 1:07 - loss: 0.8976 - acc: 0.734 - ETA: 1:03 - loss: 0.8952 - acc: 0.735 - ETA: 59s - loss: 0.8887 - acc: 0.738 - ETA: 55s - loss: 0.8823 - acc: 0.74 - ETA: 51s - loss: 0.8800 - acc: 0.74 - ETA: 47s - loss: 0.8817 - acc: 0.74 - ETA: 43s - loss: 0.8913 - acc: 0.73 - ETA: 39s - loss: 0.8947 - acc: 0.73 - ETA: 35s - loss: 0.8945 - acc: 0.73 - ETA: 31s - loss: 0.8927 - acc: 0.73 - ETA: 27s - loss: 0.8931 - acc: 0.73 - ETA: 23s - loss: 0.8904 - acc: 0.73 - ETA: 19s - loss: 0.8911 - acc: 0.73 - ETA: 15s - loss: 0.8953 - acc: 0.73 - ETA: 11s - loss: 0.8944 - acc: 0.73 - ETA: 7s - loss: 0.8887 - acc: 0.7398 - ETA: 3s - loss: 0.8871 - acc: 0.739 - 445s 4s/step - loss: 0.8937 - acc: 0.7375 - val_loss: 0.9118 - val_acc: 0.7250\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - ETA: 6:43 - loss: 1.2905 - acc: 0.375 - ETA: 6:37 - loss: 1.0333 - acc: 0.562 - ETA: 6:28 - loss: 0.9637 - acc: 0.625 - ETA: 6:23 - loss: 0.8507 - acc: 0.687 - ETA: 6:19 - loss: 0.7433 - acc: 0.750 - ETA: 6:14 - loss: 0.7387 - acc: 0.770 - ETA: 6:08 - loss: 0.8405 - acc: 0.732 - ETA: 6:06 - loss: 0.8498 - acc: 0.718 - ETA: 6:04 - loss: 0.8652 - acc: 0.722 - ETA: 6:00 - loss: 0.8108 - acc: 0.750 - ETA: 5:55 - loss: 0.8296 - acc: 0.750 - ETA: 5:50 - loss: 0.8309 - acc: 0.750 - ETA: 5:46 - loss: 0.8464 - acc: 0.740 - ETA: 5:42 - loss: 0.8244 - acc: 0.750 - ETA: 5:38 - loss: 0.8035 - acc: 0.758 - ETA: 5:34 - loss: 0.7865 - acc: 0.765 - ETA: 5:30 - loss: 0.7778 - acc: 0.772 - ETA: 5:25 - loss: 0.7872 - acc: 0.770 - ETA: 5:21 - loss: 0.7613 - acc: 0.782 - ETA: 5:17 - loss: 0.7365 - acc: 0.793 - ETA: 5:13 - loss: 0.7466 - acc: 0.791 - ETA: 5:08 - loss: 0.7341 - acc: 0.795 - ETA: 5:04 - loss: 0.7475 - acc: 0.788 - ETA: 5:00 - loss: 0.7509 - acc: 0.786 - ETA: 4:56 - loss: 0.7310 - acc: 0.795 - ETA: 4:53 - loss: 0.7740 - acc: 0.783 - ETA: 4:48 - loss: 0.8005 - acc: 0.768 - ETA: 4:44 - loss: 0.8150 - acc: 0.763 - ETA: 4:40 - loss: 0.8194 - acc: 0.758 - ETA: 4:36 - loss: 0.8257 - acc: 0.754 - ETA: 4:32 - loss: 0.8227 - acc: 0.754 - ETA: 4:28 - loss: 0.8348 - acc: 0.753 - ETA: 4:24 - loss: 0.8406 - acc: 0.753 - ETA: 4:20 - loss: 0.8475 - acc: 0.750 - ETA: 4:16 - loss: 0.8499 - acc: 0.750 - ETA: 4:12 - loss: 0.8451 - acc: 0.753 - ETA: 4:08 - loss: 0.8537 - acc: 0.746 - ETA: 4:04 - loss: 0.8560 - acc: 0.746 - ETA: 4:00 - loss: 0.8618 - acc: 0.746 - ETA: 3:56 - loss: 0.8588 - acc: 0.746 - ETA: 3:52 - loss: 0.8573 - acc: 0.747 - ETA: 3:48 - loss: 0.8519 - acc: 0.750 - ETA: 3:44 - loss: 0.8605 - acc: 0.747 - ETA: 3:40 - loss: 0.8525 - acc: 0.750 - ETA: 3:36 - loss: 0.8494 - acc: 0.750 - ETA: 3:33 - loss: 0.8428 - acc: 0.752 - ETA: 3:29 - loss: 0.8447 - acc: 0.752 - ETA: 3:24 - loss: 0.8498 - acc: 0.750 - ETA: 3:21 - loss: 0.8428 - acc: 0.752 - ETA: 3:17 - loss: 0.8403 - acc: 0.752 - ETA: 3:13 - loss: 0.8512 - acc: 0.750 - ETA: 3:09 - loss: 0.8446 - acc: 0.752 - ETA: 3:05 - loss: 0.8382 - acc: 0.754 - ETA: 3:01 - loss: 0.8321 - acc: 0.756 - ETA: 2:57 - loss: 0.8357 - acc: 0.754 - ETA: 2:53 - loss: 0.8323 - acc: 0.756 - ETA: 2:49 - loss: 0.8337 - acc: 0.756 - ETA: 2:45 - loss: 0.8488 - acc: 0.750 - ETA: 2:41 - loss: 0.8448 - acc: 0.752 - ETA: 2:37 - loss: 0.8390 - acc: 0.754 - ETA: 2:33 - loss: 0.8401 - acc: 0.752 - ETA: 2:29 - loss: 0.8361 - acc: 0.754 - ETA: 2:25 - loss: 0.8309 - acc: 0.756 - ETA: 2:22 - loss: 0.8259 - acc: 0.757 - ETA: 2:18 - loss: 0.8243 - acc: 0.757 - ETA: 2:14 - loss: 0.8271 - acc: 0.755 - ETA: 2:10 - loss: 0.8251 - acc: 0.755 - ETA: 2:06 - loss: 0.8199 - acc: 0.757 - ETA: 2:02 - loss: 0.8196 - acc: 0.757 - ETA: 1:58 - loss: 0.8175 - acc: 0.757 - ETA: 1:54 - loss: 0.8183 - acc: 0.755 - ETA: 1:50 - loss: 0.8163 - acc: 0.756 - ETA: 1:46 - loss: 0.8137 - acc: 0.758 - ETA: 1:42 - loss: 0.8190 - acc: 0.756 - ETA: 1:38 - loss: 0.8189 - acc: 0.756 - ETA: 1:34 - loss: 0.8254 - acc: 0.754 - ETA: 1:30 - loss: 0.8250 - acc: 0.754 - ETA: 1:26 - loss: 0.8307 - acc: 0.753 - ETA: 1:22 - loss: 0.8400 - acc: 0.750 - ETA: 1:18 - loss: 0.8414 - acc: 0.750 - ETA: 1:14 - loss: 0.8413 - acc: 0.750 - ETA: 1:10 - loss: 0.8415 - acc: 0.748 - ETA: 1:07 - loss: 0.8400 - acc: 0.750 - ETA: 1:03 - loss: 0.8397 - acc: 0.750 - ETA: 59s - loss: 0.8411 - acc: 0.750 - ETA: 55s - loss: 0.8390 - acc: 0.75 - ETA: 51s - loss: 0.8432 - acc: 0.75 - ETA: 47s - loss: 0.8394 - acc: 0.75 - ETA: 43s - loss: 0.8358 - acc: 0.75 - ETA: 39s - loss: 0.8438 - acc: 0.75 - ETA: 35s - loss: 0.8423 - acc: 0.75 - ETA: 31s - loss: 0.8544 - acc: 0.74 - ETA: 27s - loss: 0.8603 - acc: 0.74 - ETA: 23s - loss: 0.8627 - acc: 0.74 - ETA: 19s - loss: 0.8588 - acc: 0.74 - ETA: 15s - loss: 0.8612 - acc: 0.74 - ETA: 11s - loss: 0.8599 - acc: 0.74 - ETA: 7s - loss: 0.8585 - acc: 0.7449 - ETA: 3s - loss: 0.8571 - acc: 0.746 - 442s 4s/step - loss: 0.8549 - acc: 0.7475 - val_loss: 0.9467 - val_acc: 0.7100\n"
     ]
    }
   ],
   "source": [
    "history= model.fit_generator(train_generator, \n",
    "                             steps_per_epoch=SPE, \n",
    "                             epochs=10, \n",
    "                             validation_data=validation_generator, \n",
    "                             validation_steps=VS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start - 4:43 AM\n",
    "# End - 5:00 AM\n",
    "# 2 epochs ... val acc: 71 %\n",
    "# Runtime: Estimated 1hr for 1000 images out of 36000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "#plt.savefig('model_accuracy.png')\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "#plt.savefig('model_loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9466539764404297, 0.71]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "model.save('retinopathy_predict.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}