5- Object recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://camo.githubusercontent.com/cc5541fa8bc6f822cb8d5b4261a23f20ec8321f4/68747470733a2f2f757365722d696d616765732e67697468756275736572636f6e74656e742e636f6d2f373036353430312f33393131393438362d34373138653338362d343665632d313165382d396663332d3532353061343965663537302e706e67\">\n",
    "\n",
    "---\n",
    "\n",
    "# 4- Object recognizer\n",
    "\n",
    "Import necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep network\n",
    "\n",
    "Let's create a deep network which will learn our emotions and then will try to predict them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/santiagobasulto/.virtualenvs/keras-wine-model/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n",
      "96116736/96112376 [==============================] - 156s 2us/step\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 8 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 1 138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 1 576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, None, None, 6 192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 6 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, None, None, 9 55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 4 144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 4 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, None, None, 1 0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 6 76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, None, None, 9 82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, None, None, 3 6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 6 192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, None, None, 9 288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, None, None, 3 96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 6 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 9 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 3 0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_6[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, None, None, 6 192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 6 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, None, None, 9 55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, None, None, 4 144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 4 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, None, None, 6 76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, None, None, 9 82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, None, None, 6 192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, None, None, 9 288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 6 0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 9 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_13[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, None, None, 6 192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 6 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, None, None, 9 55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, None, None, 4 144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 4 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, None, None, 6 76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, None, None, 9 82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, None, None, 6 192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, None, None, 9 288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, None, None, 6 192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 6 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 9 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 6 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_20[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, None, None, 6 192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 6 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, None, None, 9 55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, None, None, 9 82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, None, None, 3 1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, None, None, 9 288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 3 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 9 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, None, None, 1 114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, None, None, 1 114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, None, None, 1 384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, None, None, 1 384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, None, None, 1 172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, None, None, 1 172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, None, None, 1 576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, None, None, 1 576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_31[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, None, None, 1 179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, None, None, 1 179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, None, None, 1 480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, None, None, 1 480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, None, None, 1 215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, None, None, 1 215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, None, None, 1 576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, None, None, 1 576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_41[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, None, None, 1 179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, None, None, 1 179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, None, None, 1 480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, None, None, 1 480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, None, None, 1 215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, None, None, 1 215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, None, None, 1 576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, None, None, 1 576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_51[0][0]              \n",
      "                                                                 activation_54[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "                                                                 activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, None, None, 1 258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, None, None, 1 258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_61[0][0]              \n",
      "                                                                 activation_64[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "                                                                 activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, None, None, 1 258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, None, None, 1 576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, None, None, 1 0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, None, None, 3 552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, None, None, 1 331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, None, None, 3 960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, None, None, 1 576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, None, None, 3 0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, None, None, 1 0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, None, None, 4 1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, None, None, 4 0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, None, None, 3 1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, None, None, 3 1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, None, None, 3 1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, None, None, 3 960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, None, None, 3 0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, None, None, 3 0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, None, None, 1 576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, None, None, 1 0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_77[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, None, None, 4 1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, None, None, 4 0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, None, None, 3 1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, None, None, 3 1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, None, None, 3 1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, None, None, 3 960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, None, None, 3 0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, None, None, 3 0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, None, None, 1 576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, None, None, 1 0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,851,784\n",
      "Trainable params: 23,817,352\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import inception_v3\n",
    "\n",
    "model = inception_v3.InceptionV3(weights='imagenet')\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# we won't train that already trained model!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict using our model\n",
    "\n",
    "Finally test our model predictions live with our camera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10cde17b8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RE+V6o1CcrJdvCvZtgrqVM0Qy5yZppN1XeaXtm51zx4UQ+4CPCyEeC28655yYg+4iSI98/fXXhc9silBN7Hu7es08PWKqrC+IdIHJ2XlK7idusZ5GWKiotAPhF/Q568BFtbwIFuem9bfqvrE+nEUUnKBq6ySdV914UqfqTffm6Qp15d0U+blLVUMhsMYiCo7lcgOqMEOXljILAodHbl8H4WRlNl31PISA6QDdcqIbkyHE9NL9qYj4hjFvul5XFzaXYK7OPH5VyOScO178PyOE+CP87henhRAHnXMnhRAHgTNznn0v8F6A17zmVdtiNVMmULbHoZpEvXo94T3n3MSMG1wvi1oi/OwvV2qWyrPBFStFrc03tW4Zk1ftyIt3RjLC+qyKvowL8zUEsWvFOfgJW19iURGATayfUzpDTWHPnEVJibV5UadByHIJQ17UKYs+8oTE4mb61ULFbZt0mXAih/F90z41iwuyVoXjUhkhalAPfi2vNakG9bZ81ZBJCNEFpHNuvTj+ZuDn8GmQ/yHwb4v/H9qyMge4IK+3a7A6OU/9hJwVE4r2VNfL880MFPWJFk5CjJ1aZOekqEL0rRvTbrfJ85zRaMz6+jpCCNI0r9qklQ++LCe01rqanFprjJsMcPl/xBghBFrF/r1yEqktpSTLIIo8pfZimMAwnZ/bWet3v5ASlKzi3cJ+mF6jBUpMxCvflgb9VFJ8g8RknlCYgkM7HLlxk6QlAXIoxJRjVojp1GMTvUxOxeZNSw95MT4KITyn1EoVWZymnfZNOTHCsQ8XdsJkzRVi+8R5M7gazrQf+KOiYRr4H865PxNCPAD8nhDiHwPPAt+7ZU0NBGGuAcEJtl6XtDXMK2eMIZKTNL2umFxxknD+/Hm+8MX7eO655zh8+DBJ0vaDE+zSkSQtnLFVtHOJuFGsybLMnxeToBzM8XhMFCU456roaOEmetKszF+IiYH+BtPio5TSd1XNmDPDlV1WfXcoIoZiZppljEYjrDFs9If0ej0WFxe5+ZabWFxcxBS7ZGRpSqT1JEmlm0zisv6yDU0pxzZLghnugOGcQzhHHuQ9n7eEPvzWOlcu+6nUmb5qTlvn3NPAKxuunwfefrn1bYYEMx3kJDBr+ZrXmU3vmldOSklmPZW0wjsO/+TDH2YwGLB77x4efeR57rvvs8RxTK/TJo7jioO122201rRallbbI8fCwgJra2usrCxVyFIuoS4n3fkLF/1kzDKyzAScrNxBAvxOEQWCY4rJNa0DKKVQSlVKvMknolU4mUMOnZtREXXtn83yHGthnHsEGgwGuNwwHA6RUhNFEYcOHWJ5eZn+YMj6+jqHDx7gjjvuwFrLOE0RlOuxJpO0RKJ5xoMm3SaE5hXD0883OV1DThzWWdeXQqPElcIOiYCYY1V7Md+4CRUrHY1aa5555hmee+45br31Vj72sY8x3JC85tVv4NSpEzz77LMcfeYpsmxMnue0OwmdToduV7KwsIAQgna7TZaN2b17d7ECViBEVKUtFkKwurpKp91FSkmSJEgEsrgf6QQpddUusAjppsJ3QhG1RCZjDCafpvp1I4B/OJ9wIcAYR5pbOtZ65F7McAXSx1FCZg0XLqxy8vQZHvqbL3LXXXfx3HPPEUURR44cIYoiRsNhEY09QaZyycM8PW4zo0lZpj5uqkjeH5apI8y8OsL/s4aIK4MdgkzTEFqcoLlTRE223y5X2gpKsaTVavHggw9y7Phx9uzZgxN+S5X3fuBXeM97fow3vulO3v7Nb0BgCjEsQ2mPHJ1kudKVSmufUqLgOJZEJ1Ue8CwzBZfyFDSOYz/pZFxMMoEpxMYsywC/gNAYg8BOKfazfTahtNXanXDZtnPYwoggUBhnsUiyLEdqRZYa/x6Ts7bWZ3V9jaefOso9n/gUJ55/njd8wxv5kw9/hG5LkaYpzzzzDK9+9atpt1oemd1kPEudqGxrfSOBujEg5BJ1C15YpvyOUP8MoW69a7pW6ntXK+a9KBtEXy685jWvcn9x76eq880ozEx7A/2pRKiQ0pQd7Zyb8uUA1Q545bO+OoMRki898jDHjj3Hnt0rLC8v88gjX2Y8yuh2FX/20U/y8U/+BbkR7FpcQAlJ0m6R4w0RGsNolPr6HAzHI3JrSfOMcT5GpgqtJYONDYzJitZMEoU4B0r7/+X4ion7a3LsKt25eq68D4V6KabvNQ23mFjGi0L+n5ST92sNeQ6vuPPlvPnNb2b//v0cuv46Pv7xj/P617yWpaUl+v0+w9GAt771rXTbLbTyLgZrDAg109dSSgii40sdr7rH7GZl4f+pcS+SoigZVeMfbnMT1jmFTBi0kDhnsNZy4PAtn3OTaJ7Lgh3JmZqglLlDhdPD5hHc846LB6aQzjnvwDt16hTHjh1j7979vPpVd/LXf/3XLC4s86sffB+nzvTp9hJuPHwDSbxAqgRaK1ouw2UpLlKMXOyjIwCcYWNjo9Bb3NQkybIMVcbEVVbKQnm3E+uXLpT68DuttQg1S1zKHfhCkW5C9c3shLQOFXmut7i4TBRFpGnq66+QylP9zPjl5/fe+1cAPPLlL/Pud7+bt73t7ayvX+LLX/4yi4uLfOxjH+Pb/t47MLmtdp1wVftqJutwZayYHqOQ69Qlj1mxbIIc1jZHuNS5m5dwyrnkjS5XAzsSmZrEuzq3qo5ryBAiR3jdO0Sbt28Mj1Nj+dIjD7N//0FuvvkmPnvf/XzqU/fyOx/8XY7ceAuL3b2IRILoEkU9pLYsLWpu60X0ukvc98zzKCdRRZ3jdMhL9u/nicceRyA9AuvSROt8SIy1no1gK2QwpXEhisisRShVTUjnHFJ7ahoShDzPC99LSVQkxnpDgM+DICd1WAdYtIgYDlJueMnNnDx5klZLokWEFCAL87pAIIUjkhqLo93SJEnCG177Bu6//0Fe/+tv4PMPfa4SY5eWlvjoRz/KN73tbUg3m/Qk/Olg576Qe4USRdN6rtAyKaWsXBeeWFlqvLZRHXDOIYvNz14I2JHINEttp7nSZspqk3ioapHF9bIhEv7hH/4hr/9bbyRJEqIo4cKFS9z32c9x+PqbWVw5gNuQZC0NicaaFshVImHZ04Innn4SmyyjN/osLHRZ3+izstAhiQRpNqAVt5FSYLE412QGljjvTpu6d/r0aeI49iWmfDI28K2oyshR6mtaQOo8l8H6sKCKMxXvM0IgoxiBZHlpBZdPTMal2CQorWIWVbxTSkWWGQ7u248Sjne845185CMf5sYbb+T9738fcdziueeeY8+eXWghKx2pjJ2rdKawTVJWOe5Ckaw8ryPClJRSEMrQv+dE5vfbZYKATUS5PLtalWfHINNmH1JXUrc6DjtZC1lknmle4VpH0pffcTuPPvowILn5Jbfw0z/z8xw4fCNLe3azkTqWl/czyC+RSEUsNcNc0+8PGC61SXNDd0GyILqMs4yO1txw/RGOnzzG1935Co4+9bTnDc5RmbmdrRynEPp/CvHMwb7du2riWkO2U8Ck48pq1o67KCVI07QiRuEEzU3qyzpDkrQ5d+4c3VYXW2Y3dQ4dxMelo3FhUPF6jjOWbrvDpUvr7N+/n3a7zZ/8yd184ze+hZe97GWcPXOaRx59lLd+41t8UG2QoWh6MV+gtwaWP+cm766PXWN0R23cJ9zOVu6FSd2T5SE+wPaFMV7tjBwQW3zHVh867750zXpUCGWnR1HE2toaWmsuXbrEfffd531CWpN0e+ioxeKefVzKHFK3EMaweuEMIBlmgpOrQ/YfOsxSK+bggQMsLCwxHmekuUHqFotLK5w9f5HBaNi8VKKIPLfG77NqXY6xGcZmKC1QWhDFCh1JolgRxROfUulfKvtCCMF4PGQ0HHrxx00CXJ21mDxHIjxSOMfu3buJC71MCU2eGvLMm8ZPnz7N2uolkiRBFX9SSpyZbIPTafe4dOkSd999N2fPnqXX6/Hlxx/j0KFDDIfjaitQY7x1MDwuz+u/+vU6MdnKhF6ObdP5Zpbiq4GdgUxsbrFrQoRwIobULjRxNoWn1CEUH44dO0YribjuukMMh0O+9KVH2Lf/MHFnkfXMYUSLeM9edNTGmZxv/qa3cnj/dSzvup7WvpeymksO7tlFtLDC6iBHtpd4/OgZzqymPPbUcW592SuJ20s4q6qfcf6XO793kEH448xWyJVnFmeFd8IWyJZnE7N4OflKg4W1kGWG0TgDoUAorCsW7FHQLqGIkzZ79+7n/PnzaBUT64Q0zdmzay+j4ZCDBw7z4+/5CX74h3+YbJTxdbffwbe/8y6+867v5k1/680sLexCojl69CgCxYMPPshv//YHuOOOO3HOkSQJzz77LGmaVoiTZZl3KBfIEiLGNELlcxGtaQ40IVvl1yqW9oRjXlkJi6UvPg5w/s4j24GdIebVrDjzuMjUI4EBon7NOR+s6phwpVBkKEHKiSJ78eJFNjY2SNqaleVl3v72t/M7v/s/Gec5i7rF0sICY2KiGPI0wuoeF9eGHNi1r4jfM1hpWN8YItqOgYsZW8Uot3QXFolpoZSg08shHwOTVaGllc0EGxFETPtQyuslOOdwNOfUKyMgnA3WZAlXIVs5kcbjMcPhkCRusbq6yg//ox+lFbd46KGHGA6HPPKlR7nj5a9gPNwg1gkHDxzi5htuwUlHt9Xl8ccfx+WSjY0hr33tq+n1evzxhz5Er/dBrr/uCFmWcer0aY4cua5KRBl+i7UWAvO1KNpVT2pZ3i+v12MOPVJMHO7hs1N+J2ERtf2GS/P81SIS7BRkckUwf830W3ZMnRp5y5SpdIomsIKJeZoSoUyh3Ct/v5K9Levrl9AahJLoKGL//r38xb1/yW23vwabWXKZs/vAfjZyzZgBY2F5+KmHESYnNyl7d+/h/PmzxDqiL44jzAKaFokEMxyiojbrxhAdOERrDOPhiBiQukcmFYoxiUtxsguqRUbGeHgWswE2H2GjMdYkIAzObZCIJawbouKIwWCAVD6wtERMFUc+mUCAbNYKdBKRW4OWipZok+Adwov7lnjf+96HlNIbO7Sit2eFP/z4R+gUeucff/zD/NGffagamziOccrR667Q6y0yHKzRbiV87oHP0+20OX/hJO1ORG4EznrDgApy3EkpMcHYaulz2NkywNjmuEJ4csgi4YqaIo7VnLAOKYUPhNW6iqmsi/miREzhrafYiYP7q7048EWBkjo1WW4qOXjKWTkrFnorzfwEkkJMe9LH4zHdbhctFbnK2bdvH+fPniG6Q2DynNRucPb0GXor+705N4pxqcEJiVYtzpw9z57du+mvrbNklrDKYF2GVW1E0gXlWLAOnbYwDHA2w0nFOMtJVpZBdhiMLuHyFC0dNpNoobFuiBY5Nh9V64akdDg7QgiLzSYL6aIowtoyu9P08gohRGXoiJQG4/likiSoSLMxGLG0ssjCwpIXwwqC1lUSYXKSJKmo/fr6ehHr53/WWtI0ZZylHD58mI2NjaItliRJGI1GJHKSpjgcsxAxQu4VGh7K8l6UnRDUus9sKmwp8F+F7wuNUSElrp9fCewMZJqDGDMOxqkys+WmqhST3HPBkzQ55qSUjEYjer0e7W4CQK/XA/BZYE2OlgqTjRhfWiUfb5CNxp66GUVmLUrFXFrdoN1qkRYTPW4lZHKMa8WkVoBVxHGb/FyfSEgWleO2A7uQyjJGMu7uZjRYw4zHXFQJxmmQktwJhIyQTiKE3yHDWYfAkZkMpRVCSJx0COf3Zi39as532KRfrEHgV5VGKiaJFBSrfXME5y9e9JO2nHzSr+AdDAZF8OokOjs0wwOsra1x4MAB1tbWGI1GSOnDo8bjITouIhPk9P5W5fCHBLSsu8nxKoQrftP5z0O9S2sNYtrXWNXh86yFXRLUMTM1Lgt2jAEihO0YILZzbxZmTap1zlXWp6Xy+Gqdj4HzWj+9VgR5jhTCe9p1VMjlEq01GxsbtHstEtUiymFZxyxISUd1aUcL5DbD6Q4u7mGsYqHdYn87ZkVFdGUCY0m+kdEShlhY4kjhhCI3XszJrQPnCYIuAl/LXNtTyriYxMGV/VRS/eqaMygLFGut8jxHx4ooipDShxNpMcnVp/VE56lzhlCvcc49jLr2AAAgAElEQVTR6XQq83PV1zQbDerWuboxgYbn6ks4murZDF5IK14JO4MzbQFNZswmg0WTKbSEUMwLr3lvebnobmI9KsN7tPScyVvWLKsnn6MVxwxthhQKHSlUpMmzMcJZpDW8ZiFiYVcL5TyVHBrFOIoZGZ+s5bRqk2cJzizw+HNnuHnvCqv9AauZIEla6AXFaLyGyCwqiZDKMkq9qbrVapGNNuj2erRiyYW1S0TthNW1PlEU4ZRE4grnsPHL4gMuUiJBK4qxuSO71Ke3e4WBUJBo0izzPi4pEHZiLVVKzExsKaVP2jJMGY+9j0trzWAw4MgNh3n+2JNEWpGlKUaramVwtd6pAFusKSoNC/OssNOEb7J4svShhZbZag/d2nyon9c53NXAjkSmOutuclDWy9ePfbn5GV6FKLet9+/KsqyK0bLWIktPPMKvH3IZnahDlo4ZD0coKVFaIDFI51esJhLufMUd3KrP4DiNVAYtFEK1cfICTmic6bHaMoyt5HTa5bTazXBhhbjVpTPoc/HiSSQ5Wi2hjaMn+9y0KNizcoCLly5w/sIard17GRrDMBcIY9hYX6PTapGZiTMyyzLfZ0wEYiEE1gA4xjaj1+lyZM9eLg03sGmG0LpCPCFEpcRPHNsCa4sN2ISvJ89zbMHxy3Gqdj8vxs7kpnExYJ3TqAY9ecqaF3I556o1X3Wi6XXq+REP9XfXF19eKewMZKrJrzDt5S8V0/K+EH6CV483cJ3y+mZlwiXVWWaKhXmx9yMV2Xesy1loLxFFCav9AVooklaLwbDPgV372NjYII5buCwlH26wevo0X5ZjdJywvNxh77IkMX0SEZGaGCMlcauHs9ASipWVJdqdHolp0VUSNR6Rm5QcgUsNtx1scctuiU7XEEstBge7HLnjbTxx/DjRrgN85BOfYHU0ZpDnCGHZ6I9x0hMJKRU4670HthCN5ESZ37t/H5FzrJ4+hStM5U5pfAqB0oo2SXbi+3GCZB58jKBhgoR5nhfrwXzIkzHTSCOFmMqSVD4nA04RTvAZI8LUWPo8EU2IMM9wtZWEc6WwM5BpDtQ7s4SSSm7Gwv1z8522ochTOhE/85nP8Hff+W2kucUy8V1EWjLYWGfX8m42xgaJY7m7wOr5M+xa3uX9VUTgukgHAxKGaczJc4KNpMMN3TZiNEALh5ADOmKJjrK0xAYDo3weuNGIG5dbHIl6DEZDUjdErnQ4sCulpUYIJVgfj0kW9vPgQw9zsr/GHQt7OH3yFKrXZXFhgf5gSBzHBVGIyOx0/jkL2GJtlFKSp558mqM2RUaa1FlErBFOFim/yj7XlQUMwEk7ZdAAjwzlcvvyXadOnWgU0+oGBtwslyrrrFvjSkNFeX9CYC3O4U3/xaJJayeGkXAOlLk4fN26Nm9e5CSUQoj3CyHOCCEeDq7tEkJ8XAjxRPF/pbguhBC/LIR4UgjxRSHEa7bVCjEd6lEiUF3OLyEU3UK5d+rDAmddE2sHz5nK/HRRFNFqtXjkkUc4duzY1LODwQCAQX+N0TCl3e5ijCGWmksXL9JfXcVlGVop2lHMysIh0mSJdPEgnzupuf9ZwSUTY22KzgXCeH2krSSdXpdLKEaLS5wZDzjbP02m+lzf6nO4m9PSGTZK2NALrIsu67bNTXe8njtf/yZWlha55eabKjN0uXq3XN5hjRd1pVRoHXmdSghy6y2NBsdYOlLpIIkQxXJ6JaRf7VvPwwdFnjw1Q9HzfJJQRkrJpUuXJqJatWZrAvMIXF2cbxIL6+VLCJfohxEW9ciJq+VA82A7qPgbwLfWrs1Lgfx3gVuL348Av7KtVhQxdEqIqkFlp5YdK4vGSqy3QlHktiuyqzYNQD3DaZnJFEA678dRKDCwsLDAkSNHOHzwELsWdkEmwCkyB0NjGAvBwAoyZ7iwtk6uYy45Q96Jcd0262sDXrrnCC9JDsBwwHIGPTTxwgJKKRZSRSuL0a3dJG4DObyEXr/AXtPnpq6jFUlGqkO7tcyuaJELo5yhtZw9uYpLN7Dji2gWufUlr0XINgsLh1F0edUdr2V5YYmTp89wYXWNUea5bCRAlmuSrCPLDCazxCJBWc3G2gicRooYk4Ib5QiTI4HM5IgowkrJMMswGJx03vRe+IsEyouDBWKlY4MULUCS5QParbhYbq+wwmFd7kVzYf3q3mKcKH4CZiZ+kwVvGoH8jHDOW1adc+TO4mRtFa5jkoG3KF9adpsshFcKW4p5zrl7hRA31i7fBby1OP5N4NPA/1lc/y3nv/azQohlUeTQ205j5imA29GHHLMixWYUKES8cg1Or9fDOsHS4i52795Du92eeYdwjtFwA3KNdTlDk0Nb4HCc2OizpFu0VAclcxJpWTAZ17UWyE4eZdgak+aSljBok5PEinzcJ1I9ZG6JZUSWjRkJy+52C+ccveUFzHgDITRaWLKsT7fTI1MWaSNAcuHCBW+ClhLjIC/2MlIinDACZ0UVVtTUh9Y6rB2jlMZaE+iUk3FoEt2kVlNO3To32S7M04/q/5vGdtO5s0UT5hm2LheuVGealwL5MPB8UO5YcW1byLQZNA3i1LFoRqAmI0QoRpZxbEmSMBwOWVleYWV5N1EUeYW8HLySZ4qcJPJRAUqAcYL1/gCpE75w5jg3XncjbRlx62KPZJSxMLTIs32U00gt6D93BrnkkMqRRAqbKdZO9Nm762UMNsbkzsJoSK40Oo4YGYVIEsZWcmZtzC6nsA5yY7C5Ie726CwusJoOya0Pl0J6xJhEzZfWNjXTjyXCOOEtdb5f8kJK8NwcY2m1WqQmR8lokhZMAFIgrGCcpVhhZ0RyoJIc6uNiC4kjFNvLe3XjQf1+Pd9D3YdWgrBuwgXnwAuBSPACGCCcm58CeTMQc9IjF/dmO6Vm5izvh4k0wtxvTWmb6gMaPquUotvtVseZCRXVMq4LjPLJHmMlcVZgc4dQvp7UGs6uXUSSoNZOc71W7B74JCgjZXFDy6FombW0j4qh026RqIwkFpw/9ThL8W6kbqOjNkLB2ORE7R6ptIxGKSISfOXo8+Tti5zrDxBK8oUvfZ7+xiWfJRaBtICR2NwnufemY8BJyoUYvu8KMVoqnCsWDwrAGOI4Js/TKjxHyAjnvK6U5ZkvJ6rxxzlTmeNLQ4RSflOA0lc1bxzCWJapWJUm4jmjM5WbB3hDgpSTbEWleOeKPZ+2k8brapHqSpFpXgrk48D1Qbnrimsz4GrpkesdPKMDTZ6bQqwScVyhOxXrC6pEHRPw1FcIAcZWSFQiy3DoLWFRFCGEQEtF1GpjrENoVcn10vqZlBXvEdK/U+JQDtbPn6VFwtPRkFUpiNtt9klF10m0kQirWHEL2LGhfzIlXpRIN2ZftwVqhG11yBwIl5IkCbmOEDm0VYLsCYZmzLNHT3LswgXOb2zgyEnzMXHUIhtnqDIxpZxkaLUGer0FBoNhsDDPR0/4Y1ksRfAhSaVRxk9QnysvyzJsScyCbrWFRJDnedV3/nld9XGTLhISqaZr9fEPr5eIM+FEE8c7MKND1+sIiW3TtSuFK326TIEM0ymQ/xh4d2HVeyNwabv6Ugghm2/qdJiNbqhfm6dnhWWb5HMZOAHDUJjyWjuPaOUREd5y5gQ44U2u1mU4ci7JNYZZxmhsGKyPcFmGsznEGqs1SRTTTjqItMPgXMR4NSIfZGDGpMM10uEqxklGxpGTYFQPq7pYoVBJQtxtc/TYSfJxjklzBJrxOCVzYITECMhlEfRpACRra+tYU6YEtpX1TTgI5QpnBRaJtRDHLTqdDlmakxmLrXEZIUSV/1u4chL7yT1j1q6/p6ZfbcUV6jpTaJDw7/FroLSYLNOA6fVq9Vi+q9HvmmBLziSE+B28sWGPEOIY8DP4POJNKZA/ArwDeBIYAD90pQ1rolrF0aYfPc/o4K8XImDQuSXVDFetQmFkaqyj3P0ChJRFmihX7B1U+KVMirMxG0LwjDP0lGKPaoMTtK3hYjpGRwlORGAiJAqbj8kyh0ocSkI26iNamvXcsW5iWp0eWWEUWGy3eeub3sRXjp3lzPnTOGEQWqGlRAnJcHiRVivGjkDrmNFoRKvV9ohibLEyV5JlYyY5ZgROuGLjAIGKIpyQrK8NEDrIIOSKciEEE9Lmpsrr4H8l5yjeUiJfQCg3I5ilTlfmwJi8ctYQUbWhAWnqS+ahef+tq4HtWPPeNefWTArkwor3nqtt1AvtB5hXXxNFrKhU6fuS09zO6sLMjgNnUSLyyCm8V98KiJxPWzVWkovacsFalpVGA5YUJROvawnj3QIGhIgYj1LaUYSQAi0Fl/Icm0Q43eZMf0B3QaEi/76O0izu04yEYGOjTz5OcUKw2OuwZ3GBE88/hZCLXmTVmnTsxbAoikgzM5lgRfRC8YWFnuSRxlqfeDOzEz+Ra+jKJkmiLrLV+3de2TqCNCHaPCQKxf66WFjnqE3z4mrn3Y6MGt/OR9U7Zzssuqnzmth86Oeow0DnjFSOUd4krJzPNaFRCOfFI9PSoCOMUmw4y5n1EacuDbiQpvTdGDmEOJNk6+vsWexg8jHDsUHEPc6upQxSxd4Dt9NevpGjF3OeHSpO2BYPHV/lC8dWeezEGmcHCX0dIxd3kyzvpbO4i4XFFWIVc+dtt/H2r/8G9u/fXwTHtnn88cd5y1u+kde//vU8/PDDVVoumM6VobX2S9yLqIZRmvsdL6RA6iL/Q1BeSumRVKqK41cWQimwAm+hbBi/zcS8UNRustLV9aHwvMmi2FR3/d1fI8hUm+SFyVQ4NzmuOsAnC6xTps06oqKcgbm4tEB5yqVY7C7SbXVJWj2k1GAM7UgjzcQpbJwlyiWJ1WgrccaSmpTMZmR2jLMpkTPYLMfZFJnn5CbiaWF4UPZ5YLDK3wwcD6ytc2ZlD7u+93tov+mtXBpLcglr6+sceMfbWD1yHR96+HP82bNP8Lpv/RZOnDjBMxcvsJ7sou9WWM8SHr14mv6ZVca2hULQaS9y9tyAH/+hf8Ibbz7Cshtw9twJNgYZt9z8cv7nb/0Wf3P/vTz68Bf58O/9Hi+78QhxrwdKFMl7JnqNDCab/2+93ynzeceVM8RS0I40GkeiZJWqWUqJkhFS6uktPqXwZnRVuhim8zLMQ5ZQL2riamWZiTN2mlM2mc/DOqaPr85pu0OQab64VR6HYkT93mW/rXhmPB6SZRnG+Cw8Fy9eLEQcU1FE38kSCtNwCUIIP/FqOQ2MMUjrFXODIwUGQnJBOo6JjAcunOTNP/aDPBFb/uUv/FsynXDfhZP85eAsR77zW/h3v/5rfPoLD/HUYJ0TgwH//pf/M9/3v/4AUkecvXiB177htVx/3SFia6Edk62dhdSRjIf8x5/653zzt38Xuh3R7rV5zSuu44ff/V38o3e/i5/4qZ/hHIJLqeWf/rN/xQ/+4D/jwb/666nUV+U4TPastSDyyfdVeZM1Tigy48htsfmhE4hiXVeaZ5RzuMngMzuJN9d165a9+q9eX1hXmFasaY41GaKuFHZGoKvbmsVOi2OmUQ6ui2ybK5gSHzTgUwPfcMP1DAYD8mxEFMWAqJZA584ircAVO1D4bTXx+1JOtc2hK+TySCikBK1YG2wQa0l7ZRf/93/9T7S7XUy7S9bu8OV8hFwfoR/6HGpxiVNjv4tEf5jjlF8JO0rHxK2EP/7I3URZTm/XMqPhiLbUHH3iCf7rL/0Cv/Te9/N1r34p59aHIJc5fvYcN9yY8dGP/SF7FhRmY0gcjbj+pn08c+YUearxwcACUVkiSgvBdCxbKQ2U42RckY4LxzgdE6ex17MIxLxyLMp8HZcJdaI5T3wr31UaGMLFkk11zrP0Xi3sDM4UfNu8jyop/zxW3CRzz6NgJUyWYTv+6I/+gE9+8hOcP3uSR7/8NzhyTp85gVQCKUFpiY4mg+mMnaFsojTZSoUpJqdwIDNHQoQ2GpdJurqDSS1ax2w4y3okiZIejz72BMP1ARvjIan1pvKNi6ukoyGdOKHdbpO0WzipGKz3yawGF/OLP/+z/Ng/+xecudjHZEM24kUeeORJbt3XZfelr3Bk+DRvuGE/lg6DrMWXHvkihw8ts3ePTzeG8zF0M4MR9KOUk+PMGjLr818gFUJHIFRlrAkX/4VLZepjtR1zeBNHajJchEHR5dq0ukVvK0PG1cLOQCY3S4VmwVaiw3bFhHmGhZCtl+Lc4cOHeePXv4Hde5ZxzjAY9MGBcTmODOcyECZYKDdxVpY5E0ql3RiD1MovthO+fq0jpI4wEiIHOIvEc70cgbOKPLd0Yr8/U7p6kXPPPcd/+cX/wE//X/+S4fo6l86fY2Njg9TkyEijFha46YbDfObev+SWG25Drl1ivW8hXuT4pVXOPvM4N+3qsk+Oedk+OLSSEiU5P/Wvfo7/8K9/kZfd/FLyvFwKUYYSl50ngz7yERDOuWrnkNB/U14vJ/RoNKJcdJlnsytrtwt1Hac+dvXjJs5UL1f+r+th4f0rhZ2BTC8ANHXEPNNr+ZNSkqYpeZ7TW+hw//3345yrPPndXst78Utkt4W4EmxNUx9wb24O2iV8jLRTYDTkEoyYRHmUATBOS7LcsjEcIHAsLyxy640v4Stf+QqHrjvsjR2DEZGSKCUY9jfQecY7v/V/4cMfvYeN/iUiJcitrFa/Pn5+laf6Gew+yFJLctfrbuPRz3yU177mVXzqrz7LMJtOvDhj7awttZjpc6YNCOXkLMO6yv5xVkyV2y7UTd51q1todX0hRLevDWteEOc1t0iDXyE8bzKvziqrZXqqyaCMx2PiRHPhwgVe+tLbOHH8NDfeeAud9gJxu4PNTTHpfdCkkgJjcyrTl/DbkviElt7yFcnIx/EJSy6c/+9yxtkIKxxOOZQWWOGXSGgETipkkoCUJDqivz7i3T/wj/m13/4AJo7Ixik6N8TWYdMUJSx37l3hz+/+EAdvvZk4GiNaMeurF+kKyAcpItnNe//8S/zk73+SB06uka+tcv8nP843fMMrueP2IyyZzHNdssLQUNs9omHv4DLioepr4ftVuMlGaulojLBlMKqekQiaxlaIYtsZISA4NtZirK0SVNbHN/xf19Xqlrv6r2nOXA3sDAPEVcBmnVHv7AkS+ftS+u0yz547TZLE7Nq1i057hThq49Bo1cLk3kjhKygiHIrFeNWgBdESQghMliOkQiH9+posQ1mDFgJhxygVYckxLgcp0JlAGEk+HGJwnD19hp/52X/DL/zbf4fo9pBJG2Esy60eg0EfZTNOP3+cu/73f06/f46/E3XAKSJr0PkIuiv8P//6p/nRH/p+utfdjG7v54/+6jE++P5f450/+h5e+cbXomTOuowRYjTTN5P+KhzVdpJCAIqwoSLmzhQxkc45H9jqqDbCniQ1mR8ONs9I0HRef6a0RE5cHM2bM2wG86zEVwI7A5mCEJOw46Zzhs+y9q06rqlzJx1nvB6A13eWFhaJdUTS7uBTGHrDhFQCm/tspFoIxsI7NiWKUZrjCs4lnAHrc1WM5RhtLVpqhHFYLJlwYHMiobBp5le9OkeeCHAj8vEqbaX5/nf9IHv37uWnf/E/0Nu/gopjut1l1jLByWNn+dF3/QDHjn6BzyQjfvSf/xjrgxRQLHeXGA2GPPnYl/lv7/9v/Py/+TkO3nobsdScf+oov/G+3+abv+0uXvWaVzIYD7HkWLGBRFVZgUorqUeoQBKwAqUjbLGCVWldjJnzKcWExDjnY02lt+gNxiP6/T7Ly8vkuUEJgTUGHcVefAwWFlZEyb/YSwJikidCFMdSiJmkK+WvvstHiBhVgphAFC3nwVSuvqu0RewMZBKzIkAdCer+hO1QkToSucrQUdvbp4AKeYvkjVprHEx0ACXRchIjFkmFUZO4P1MqwFIhnLf4KVFspiWcF1+cv2+trZaFmyzHRIbv+I67+NLDf8Ozzz7LnSuHGWuBbnWQukN//RI3HDnM3R/9Ey6uPs+GHLC0sEirY9FRC5s7ep0uq/v3IaXkuuuOYAZ9Ot0Ov/qf/gs/8U/+Ka+74w76q+s4DFKCzVNEr1stnyh3jq/6zvlNnqWUPkefUgjrxdM8z5EoJFmxe0YMlPn1NCsrKzhzPV/4whdYWVkhyzwBmSRamQZv9WvWOsKxqotp4f0wnfblcKdJ2a8FzlTAZmbxze5vBnWLz4SjTZTjMiRmKgEH3hABPsG+cxYFPh1xHBXJ+ieZSa0AJwRKFfu0Ov/D5YgynEYIhHOk2Zh2u02n0+YDH/jv9Ho9pJQ89thjKKW44YYbEKnALfQwcY846vLYiftYXEo41T9Dpx1j+n1cnpNEMRZHHPuUxzfcdIS/+MvPeCOESjhw40v49H338Z0/8C6syTBusq3nk195gs/9zRfoLXR83vDCKKNVXIUN+YnpF317ym8RTiCiYqmFk7SiGJycovI33HADzz7zFHv27OHcmbPc/tKXkmUZWkdFhL2ussb6HIWTuLqm8as7Y52XQad8T00Wuu1YfDe7fzmwQ5BpvmIK5YBeuemyyQdFkJoq3CuoWlKAKDKbBtY6NQmPcSYv9jvC7wurFDYXZGayeZZzkww5nuv5BXhREjNKx0RRxIlTJ5Ha+0YefeIx2kmL8XiM0QnR7n20entZaS/RkpLTzx0lxmDXUnZnCQM5QCtFFEeMRiNAYqTj6eeepd3qYITmuePHOX7yBM6mmGxEmqaeIzpBOh7TW1xASkmr3WFtbY0kSQpRzRMU6wTCWTKbVX1VikfSee6cmhwKnUkKTafT5SUvuYVP3fNxhhsD9uzZzfr6Or1er9qjdwo5yrVntWlQF+VnJI3iuOSGIafazGHfJAWVyHk1sDOseQ2wFZe6kvqakKopiJKa+BCKFlpItBLEWlcJKq0r1gYJVU2U0udUUtu6JalcGVyWLc8vra+B9EkxR4MhSkUM1gcoBwvtVpFARhKhK26RpyMvorkcqRT9jXVGWVrsF6vIspzBep9+v0868tvIjEcjbO5FV5RknGf0lhZ9hlYHubUY5wNVhZTFNk9B1tRqFxJvxYuVxrmJSJznOd1ulziOuPmmm6b0mXIVM1DF9G1lSAp/dVfHdubGZqbzF8qit2OQqZl7NB/XZeWtoKlcOSCljlDuvldSqHJt70Q5n5jXMTk2z72BogiCzfO8WJ4gGWcGJwS6WIOTZSlaT6KfrQChfcSAk35fpqTT9qKiFFglcMZCmtOJW1wc9FHtCNmJcAsJaS9m1I593N/GCC0VNh/R67TBZWitaUUxYyy0NXkkSCNBFimiXQuoxS651rhYe1OLgyhOcAKk0uTW0O52qkhx6/IiLVqKxfgMUdbRjiO0E5Abb+LXuuLEpdshiSIOHTowCVwWRW47zMxY1OdBnSM1GR7C83JuhA7lEpoQJzRy1e9dCewYZGqCOsXazJy6WR1Nx2GdZQyeEKKy6JSDU4p/zjm0VD4lsgIhDVr6HNxK+GXrojAlx3GM1jFYyytfeSfOWFaWlsnTjEF/A5fmZIMR0jikcaydv4hJM8aDoU/kMhgjNFw6c4ZXvuxm2h1Jr9sh0RF3vOx2rLW02jHdpd10uwuI3JIPRpw79jydOOLSuXPESnH+7EluvOGI/8YoYXn3HiyCvIjQiFoJg/6QXmcBLRXD/pBOu8WeXbsZDYY+oUqegbU4m4EzPiVlnqKdYSFOWExiYh3hrMUZU+16nmVjlPIWwkhPiJEx+UR3ZZKzQ9jpsakjSxPU50Ndr6qPeygylhbA8PxrwzTut5uYf/cyfQdNljp/LRS1qKiYUqrKe+D3EHJIMdkdXBfWN+cc1vk9gqyzWOfAglSCSESkaQp4P4yzDmsMh/btp9dq87a3vJn773+QO++8k09/+tN8z/d8D/1+n7vvvptvf/c/5GMf/wTf/n3fz71/+RlOnTlNK9Z837vugmzA+snnee3Xfz037T9Aq5Xw8t0H0cbxwXv/iu/4+3dx7vmjHDq4B60193zqk/yDb/8u7r//Qb7/+76bo0ePst5Z4G3fdhcPfP5zvOIVX0ckFX/yh3/AP/jO78a2Eu655x5edvttLC4u8tBDn+fpp58Ga0haLVJnITdFUn6FyXOSOGKh3WHPYo8hsL56jjAweDQaVRxqkiLZ15Fn2RS3RxSO4DnDWzdnbzbeZfk6x6qXqdc/0a++hjlTCFtRnxKaxMM6dyp/ldhVIMr09ifFeXFdKYVCoPVEhJAKhPBOS6VlZSUzxviFcrlBCck73/H3+PznPseN1x3m9PET3Hjd9Zw/fYYzJ05yy4038cSjj3P4wEHOnT3LQqfL+VNn+NIjX+Tg/j3c9pIjPHvyGW67/Tb+9E8/wmA0ZGXvChtmQG4tJ46dZLHdJXaCJx5+mIN79/Ge9/wor33lq3nkyw+ztLzMW7/pG3nymafZf+gg7/v13+B//P7v8oY3fT2f+sy9/Mg/+ie84vaX85EP3c36hVWuP3iIUX8DjWDU38CMUxYXunQ7LZSUREh2Ly1j0zGnnj/O+oXzk/4ptrjJsoyFhQUi7TeR00WKsfF4fNnjWS8bns+7Pk9FqBPX0Lr7QuhNV5oe+WeFEMeFEF8ofu8I7v1L4dMjPy6E+JarbWDYQZt/tI8on4QMmer5Uob2fhFVlPNihlIKY0WRpzoht8Yr9lYSywjnDFI5tDIolfldxk2GFhZlUyJpEKS4PCs4m0YqR24zVCvm05/5DL//of+PJ599no/d+xdcHA54/Jmn+Mz9n+Vjn/4kTz//PMv79vDZB+5HaY2T8H/85L/g6257Ob/wy7/E+3/nv3NocZlff9/7uO62m/nzv76Puz/6CZ547hiHDu5jaDM+fv9neeipoywfuYkRgp/4if+NX/21XyFD8cUnvsIffORu2iuLfOWZJ1jZs8LKygqPPf4ES7v38LM/83N86lN/zsn103TzEacf+yIv3bfMoeUey92Ymxz1xlEAACAASURBVLtLXB93SIwhMynEmosb6wwsXNKSozZjfdQnzQVGWozLyTO/t5N0FiViZCyQzucZ9Bu1CYST1WYC5cbY84xEoaQxLbIpfCozxTxuVK8vrAOYMkDJq8Sn7Yh5vwH8Z+C3atf/o3Pu/w0vCCFeDnwf8HXAIeATQojb3FXsvnslelIdKrkcfDahQB8qOVK/7/c3qgKcA8olpcSaYIWoC+ql9Ce5Yl8kyFNTGCUyTpw4jlK62rvos5/9LCtLi5w5c4Z2t8uJ06d44POf4/bbb+dX3vur/O2//RbuueceUuOtYY8//jgLi8s4AZ/89KdoJy3yPGd9fZ0jt76MJx97BI3jo088TpqO+IY3vYkP/PZv8tZv+ibufeCzLCws0Oq2+fUP/Da3vOQmok6LSxt9Tq2f4sGHPs/zF1a585Zb2LtnP7//e7/HLUcOkQ432L/vAG7cZ7kTs9jtcXLtHA6H05DnDlvsjhEJSVZkO9JaY6y3ShJO7lrKYxoQZt64bXd8t/IhbQYVsr7YERCuOT3yPLgL+KBzbgw8I4R4EngDcN8Vt7C5TbMOvAZ5tyzS5EfwHScqkaTVavnd1nvLU/XmuaWTdDA6qyZBnudBTrjy3UWWIyGwEkxh/m112t6pmxu0ViwtLWKBxV0+KiBuxdxw042Ms5RbbruFU2dOe/+W8GE+i8tLpOOMpN2h0+t6A4dW7Ot2uHj2FO1EMx5ssHfvbnKTcvzkMd70t9+CcZalQ/sYDocYm/GS227FGMvqpYu0ohjZijly280caMe01wboyHHLrl1c1+txcbTKiTPHSQQMbZ/8/AYryz3662uM0gHS+M3JIh0jhXcY60iR57LaTgZUsZHA9C4Zzk2yQzUZl8Lx3cyY0HTepF/NswRPz4MXBq5GZ/px4Xe6eL8odsFgfnrkzcHNp0LzWH94f261Ndm6+i/LDi92DC/Ca5LEB7DiHNbZysPut2CJEE4Wuyv4COtyabcX77zPBJETxSpwHHrxUyiJcYXFsFiNGupspU9qPB77dxQIm41HdHttbJ6R52mVq2+UjmklGpePSSKBIyeOI4bDASrW5NYy3ujTVor+6kUi58iHA7QFjE8BlueWaPUMS4mllTgO7VpmMZcMpILFRZb2HUS0O2wIw9mL51jpdkmMZaEV04k1QhhUBCsry1VfAJUfKSuMDRQpymCyO+O8Md5qPEMk20xqmacSVO6H4v9259J24EqR6VeAm4FX4fOI/+LlViCE+BEhxINCiAfPnT8PNBsKwvPy/zwFs4lqhdessJjAv1HurqeUqtY1hc7FNE3RWhPHMUpIIqmmdDGfMriQ62WxmXQBUk2cwqFz2AlQRXS1cKCEohUnaOkRMEmSarFhJLzZfdRfIx1vkMQaXIYrQoLybIwSrmC4ljQdIWXxXQKGZ88zOHeexFi6SmGHY1aWlkmSCKEUew7s57ZbbkREjr4ZYTttZLvDufU+WrVJ18bYDcu6FMTtHv2L6yTi/+fu3WJmWdP7rt97qqru/k7rtPeefZjZM/bMeGaIPU6csRULgiBAAAkTJJARSsIVkTASSHABXHFjxA2g3IDkiIsgkCASSDEoQgohkANOxthx7Izn4Jk9e/ZhHb/1nfpUVe/h4eKt6q6ur7+11j4wWviRWt1dXV3d9b7v8z7n/2OwCHfv3OKN1+9SmESzusIqsFrnquSNIye3pum9fH02yRDl9UU3yqHUGa+FfkPad71xkuv4+p+088WQPhYzicgjEYkikoC/RFbl4CPCI4vIz4nIz929c+fa52MRv8/9OR7Yfd3mekopXQOQ7wEk73/wIYWxOG1yWs7AXsoIqB5jFSG0kHKg1qicAUHXtU4SqM42iiKgLdplRtwayNk934NfjjdCJarL69se0113cZInhfxQQnaMbO43t2rRZnd8jAgqJaxSXC3n6MKw9A1mUmIOCp5cnvPOg1PEHmJSyd/9g+/xzfs/4mK+4DMnd7h7dMTj80esVx50ycHJLYqDAy7bNe8/esDF2TmfuXWbQimqwqIk4bTpvHZ6UxpB3A2MDudn3zyOmeOmjbSXckOo5KHUGj424yn7HQ2fhkfvYzGTyvjiPf0ZoPf0/Trwy0qpUin1eXKfpm9+3D93k8483GX27TY36dUishlM3QVYow8cTmdMphXGakpX0Hv7MiMIvm1p6zVaCaVzFDbDWFlbYK2jcBOMcQSfEGUwxmVpZhy1D1jrsNZhtOtSgAxbABONoEEZBI2oDGiprc2wWKpHmO2YEIUxDmtKUCajbBuDMx1zb65qiL5mMb9kdjDpurwrbGFIKuGMJcSWyyeXvP/OB6SzllhOOCsttSjO1gt+8OQhlyU446hcQWkLxBgmRyfocsKi9sRg+Owbr6Nj7knbNg1G2R37tM802CdR9s3Tvjnfty56LWK3VGf/xjtWDfueTZtcTPXsfL4XoY8Lj/xPKqW+Tra83wX+QvfHv6WU+ivA7wMB+JWP48kb3/w+Q3Wfujc8lidvlKbSgdP35KylCS1t25JCzOXom48TMWWVyhhFaBOFyb1vtbagBWMLwqZlSYeCGgWlNCF0nkJtiQlcUSGhgwNLMePIiSYi3UuFkB9KCdIxUZKQe9R2kk9hEBTaOqxKxKBJKUsmyLJPi0JiRCfhF3/+G2hrOZ/P+dZ3v8fJ0TGVK3ny5CnNfMEk1dRW+NznfpLf/dE7NBhiMrxzek4McPzKmxyiuFMdIm3iVlHwzuP7GF2yDIn2asXRTHM0m9Iu1x088xrYup377hh5jq6n7+yLE+1T5cdzP34/lnhj2rc5K6X2NHn4ePRx4ZH/m2ec/6vAr36SPzWmfQy1zxt0Pc9qV+SPqW1brNJUVcV6lZ0PyiiQzBDDwF6SgFJF56HKqKfGWkLrM4prlzQqIeWkUKXIHY5y+YZSBlRnbymFoFE6I/H1PaK21EurXt8f3He+OWCbN6gkbTrnieSAc0qCJnF1ccl3/+B7YB1GK9bLJZQJv64xnboYdKIJDTF6gqic/WEKlNEoscwOJ+ikEUl4H7hz6y6XywVztcarnJPnXLYB+0yH4TyN22C+iDp1k5q/z456kfDJTer/+FqfhF6SdKItfVQVoPcgpaR2gndab6/RT2SMcVOpmfsOaUJo8aHN6k8KkGKX6ApRBKssoY04VRKD7iSSxaCp2xYRcpMwQ7aptEKpGUkajMkM5bQhRp1LGySBTDBdlrjpegsZG9Aim4ZjWkXAIiZj2wUfca7E2ozzF9umw27o8MoxuVwCg48JbRVGH7CoPXdfeY2Lq0uM01w+PWVtXZawKKIpmSnFt995F4WmtcLxyQkXiyXWFFRBcbZSVFWBUwlpG0qnsRKonGVxteQ7D6/QSeMFBE3d5JIMR8BqUGXZAXhqjEpYBJUiRtsOrCZLaq3sZq73eWKvq2LZ7swbaRg0XRhqNtI9FMPWM2Op+Ow19mL00qcT7bvpm84bnzu2qYZMumW6vqteHgqtTLfz6263V2jjQLu864uwXq9ZrVaZSQfqRQ/d7Nu6Q4bNzoE+m1qr7GIvywnOlRhTUlQTXFlh3QSjHcZYtDYE0YiyJNEkFLYsckHhbIIPLTEGpO9n01F/fwcHB0hM1G1Gqg0hURQFTeO7qtqc5oQoVDL4Vjpciny9i4ur3D2xKtFaczCbkZIwm81AmZyr6BPtqiGFwGK9yhtFSoQUCT2AChptiyy5B+v0em/am+d937w+a01sW9r0z5mJt4/d3/g0vXkvnWQa0j7DcyyOtypQDp7mz3Zz7zICq6B0TkAd5uD1v7M7Ub16lr1v1jqiJLSzhLhGJBcQxOBzPVNKYIqOERXWKmIMXczKgASMLro4kyUIBJ84ODgkhIRvQ3YmKMEau7Et+mRRIcdpApF61SASUboz6EV2ckRDCITlElcWhLYBpZkvF1nqKnDGklRCIvStS7U2FG4COhCicHR8zPv3P8R1DFyZEomBw6MjJrMpT06f0Lb5/iSBD4Gzi3NOTgqU6tq/dKCUi8WKv/sb39y4x0UEMSPVLOum3Zhfn/OhuvgstW7fZrpvTX0a9tE+ejmYSd2c+TuWKjftaM9SDbfXYINXN1YB973fgvznTAktGl0YUoqkmOhtMozdZJ/7mLA6Y3ArdHY+aJ0bNhuXcboFqmmXE6gtYvJ3Y2g6D54wnUw2G0NTr0h+QdvmgLE2dHltHTPpfF6K+b9I9/9t4bIK6xzKkHEcegguZTLAZspQXIdHB+gUuVrMma/W3Lp9l8ViQVN7Hqwe0hYF905OeP/DB6xSpE0R37m82xgosbQhS76zszMkBIxxXFxcIIVwWB1t2pUqGbm7Oyz3MeTbTRJpM08jr56IdHVS13v3ZtJ8jI6xL0wvh5o3uL99AzhkoHHN/5jGiYz74hTDOpbe+E8pbYK4IoJW2363GdQxNzRr2kCSbFOJyuD8vceKrhFzXmQWlMO6Ch8iohV1G7pO6IqQFEk7lCk5uH2H8vAIZUqisihTEKKiaTNkWGYGs42VSSJJrqeyLhcdJgCj0cZkxCBtMgMPCvHK0oHOqqePIbeIcQW2rDg9P+diseALP/llJtMZxliOjo43Y3SxWvDe4wc8XS64XC9ZNk3O5NAZCk13wW0RYblacHl5znvvvcs3vvENvv3tb/PGm2/R1O22AHMzf3ornUZroH89xt3rpdNwLWzmdOCtHdrf+Zq7Kt3zQioflV4OZhrRi9zYvs9fVIQPPUwppYyL0EXl+xQTxZYxY/Q73xkzdH9en0GhTNfjSDRKW5R1aOMwzqKVoSwnWOeyx84YlLbZu2ct2rjsSOiu3zQdzl4H+xu7xmNWb1XVpPr2mjFjS1Rlvp5SO5611WqF9xFlchxI6ayKBUkUswkJxaMnj0kpezpjjJRl1bnzE2vf4rtAaerwXBO7HfhSylW55+fnFMYSQsgNETrYaO/9NTX7o9JQ9RvTvl61Py56OdQ8ns8IQ9fmsyTSMKX+uoRK6G3fyW7gNWVZcnl5zit371HXNdPplJyoqQHBFpZUt9nGSIrG+21aTMg7vCRFjB2aq51QFg5JCltkNJ5VU3N8fIeYFKFTz2LK9liyBcZYnCScVtTrFdZaytKxWs6p10vu3bnD6cPcSACBmGK3AeScvqKcYlzB26+/xaKu8SEQ1nPW9ZKYwBiNthqCoLVDULQhkazwpa/+FJLgvfc+IEq295zJuH4SMzSXpmCxWpI0Oc7VSYaYAiEltAh9M+mr8wumkxJXWNq25itf+UredBQUA4/bZk6BTSbJaB2MbaShVqH2BFl3Ph+tp31az4u61l+EXirJNLzZfQOxj0nG3+tVgrEtNNzNhoPXNE3W60U4OzujaeruqonW18ToadsaVK6wlZR/I8ZI27Y7/2k6zbu4LUq0KVHaEZKiCZEkiqvFPN+HUmhbZMiwosQ4h5tMuXX3Hj5FirJiPr/ssjGylDg7O8v/d11v1D0taZPJsVqtWNVrfvjuu6AMjQ/UdUvw2QmSY2EKbQq0KdGm4PDoFuVsyuTwgIdPn2Cc7UBT7AbQJSWQEGEQe+8ldF8IaToIZCV08bL8WWhzyXv0TReLcpseTztzKDcvw37exxrB2AzY/jfoa5yyCtkn4O7iqt+0tj4JvVTMtM1Z23VZZkYYAmYM8O0Gu9lY/G90bcBoTepuN0lAiNTBg6t48ws/yffffY/HZxe89voxi6fvZMPeWSS1FM5hi7yjWrdN1PTe40MiRY21U7Q7wss0N362Bl1aTOEoqhlgkJhYXJ4hQTpEV0VI4MVwsWxYBKHWilhZDkrHBz96h+PpAfgu8zo1GJezxr2o3Nals4GcVWiJuNRy9eBHxMvH+LRG6ZyzVx4cgalYB0U4OmD6xj1EIrNqyuMPH3L35ASjhaZZ4pzBNzWaCKEmhBoxmlUINCSSyQ4PJZmpY6ppQ5Nb6aDwSRCVsFZjjWwdDjFhEhAzY+Ts7bTBO9+3qHc1ki1W/Fjr2CYU57hVSmEjtTNlBtu3Tj4teqmYaXij14NpW104D9Q2qq7UFmVoPFBDdUKPjFOtNVoJlSvwTcuXv/xlPnzvfU4fPQRgtVoBUBQFWlkmk8kGygvAmoJJOSWEuGnNcnR0RIpZmjRNS4y51OPw6ARXTNCuQrSmbT0HBwdUVYUrLXfu3cYaeOvOHV6dHTC/OsUa4eHD95hWlnq17FBiIfoE6Xr5fY/I2pdzSBsotGFSlKyWS4yzvPrqq3ztKz9FqFtMAl/XPD19QrPMMGAHsxl1s8JYfW0Oehts6AzqF+zR4SFKcthhMpngOiBLZPc6veNguHG+6JoYO6P64s7++vta14w9w8/yCH9Sejlspm6sn6e7Dr1xg6PArnsUtmUAw7qhTZe87ge10rQxEAj81Je+iCYynR7w9OxRvq5kyF5Jw4rblsJajg4Os8fNg7ETFnWL95G6WaF1gSi9+Q8hgVIaXUyoKk20JUVVYZyjbloqBUYLYXHB/ScPSOsVSpqNQ2O1WKNSJKUuQVOZnL5ke3B8CCER45rCldR1TWkKnHEcHhwRUkTEoyeWs9Ul8f6HvPraK7zzwe8CCWMVpw/XKONYzecURZEdDQQ0grUFpnNrh+i7ucqZBSJC4SxN03A0K7FKc/H0bDOf+7qzA4hWm965+zbPIXb4du5HGy5b1T6neV3HQBzbRMON9tN2UrwczCTjFJDrKR7XVLfNOdfrXPrPe2DHnWNYUGnjdXJGMZscZGeDRApbdHVD24I2rQ3OlaxWK5QSqskUrR0pKZBI8ImqnFK3WRIZlWubUrco2pDbd5qiJAoUR8eYyQS0xtmaFCJpseTy0UPwK1Ce1HrquqauV0jIybVZlTGbjIpExDoDXTf048Nb1HVLMa0yepLRnNdLJrMpt+6+wr3PvM56vWaN8Obbb/Odb/4DDi2kkKWZrYSoLN6rXHaihJAiOkGMaiOZUkqbXlXaZMjoia2y1IppB7M8j/uuAhSRnYW3nbfd+bu2TPa4z8dSbgfiemQfP+uanwZDvRzM9BzaN4j7zhkO1nhnG1NeGDmZtGmaXGhnDNIV6fUKcLbPctGb1gYfGkR6mwdKCoQWLxrZROh7xo+olBc/XRl6WVVY43CuoI4edzCjnS948ugJh7MDTh+dkiSQas9yvmK+uCSlRGEsqYvJlGXJ4dEMAyjd776wWCyyEwFFBIpZDvwum5q7kzscHB8QJHF4fMzl/CLfZ0qQEs4YYoiYwlCVjnUKaCVdfmFfQpE6cM4uAbfDgQDh8PAwS8aiYL5YXPOU7czT4PWuGradm5tsmpvWQi/Nhsy0uzZuXAqfGr1UzDRmiLGu27/Oz/0kaFLa3YF66tP+d36DmBU91at7CmdyEmQKnmIy6crSs4oYg6BNSRMaXDGlKBxlWRG8UBQTat+iXcH6aoWpCtrGIyrRNrnX0rQoUC7HmwyKwjmqoEnLFmYGc+uAo6MZ68tzvv973yH5BSHUpCbnAKYU87/uAsda527mbVyjlFBWRQb+NwZrHd633Lp3l0dPHuBkynq5QpSwPL/gdx884tatW9TLFauzOQUalSIaCDFCB1WW424QO6TWvOA7w3/gjesfxliurhbcvTtFRLi4uNhRu0RSBxWgcmb9DS7w4bwPVbItY+6uk95W3uRVDopD+/fb869rP8MA/ietZYKXiJmep7/uMsVQ1eszGbag8mNX6hbwULrzh+3s2fzu0MiGhA8BwYAYUBZtNVoUq2VLOZkyXy25uFxgixJXlSzqBleWJJWg82LV9ZpCBGUgoVg2LWqtqINnPVVQXxGDcPr0lBrBL2pIkZAUUXRegJKDprqrjVJGWNctzmpaPyemhCkcVVUxOTxEF4a7b7zG1FQUt29zdXXF3YMTvv/+d7k4X1JrhcVyUkxYtC0hxJwIGwNVldW10HaB6jhkKNnk8yGysSVFZYhn59wG/8E5Q4gthSl2QPXHc737+qb57tfF/jqnnoYVt8+jF1EBPyq9NMwEN2c1AF3cYH+cIDPFbrCuH9h+9zHGZATWDqqrXxjjQYzStWeR0KkMGlEOSRExiiQGbRXzqwXLep09c8HjU3bZS1I0MXSTqijKMgOXqES7binLiqfrJ6zXa8JZ4PT3LqhFSAhf/+pX+eb/+X8wKwti26KsyrBhKaKw9A3IDKZrc5OBX1pfZ/xzhEYl9MxSziqu4prm6TxDL69XKO8pdEFyFqNLCIrZ0W1aX+duf0BRTTOikc/l8XTFj3SJxP349s4ArWyu/tWOxXKNLSqapt5uZAr2dVwfzt349dgBcZOH9yZP35CpnicBt+vnD4vNdJPHZ+8NjoOx23M3KUHd615S2c5ojirHRaBTCWDjbbq2MynF1dUVogxJwMecf2ckA5f0AVvnHHXjUaZgOj3Ex0Q5qQih7a6pKaxmfjXHuZLHDx9xWQZC3VCuAjydY9BMX7vLb/6D38YdHrCs15RKKFzO0Ih1wChNikPVxBBktbGZIrn0IQTPoydPeHP2OgevvsrRyTEXj0+Zn55TqJzd0Hrh5NYJy6dX+VpJuhIKKMsy217aISl0G5AMfQMbydRvWso4jLW4Kqc9ffbzn98kteahvD6P+xbwTS7rm2yocSC3Pzb8znhuxxJyn133cenliDONJmr/oPbu8LzjjyPc++yqbfsTSNLFmVKu+1cpG8I92L50QIqkFq0LoMTaCqM9KdWkUOdyiSawWLWsxSLukBhLrJnltpyxwdtIQEjaoFyBdha0oaqmGwSkcN6gakVQBnM8I5k19en7VMsLZkRKwDiF2ArlXkNV90hKo3SN0jUheeoEKZSIlGAqoihC8iiVcwQffHjKqkjcvXuXxz96wEFj0LYkWM1x4Qjzc1IlOKM2LXGKoqLxLUVVEiRLulwh3NkkAkYSRrbMEBG02JwiRcIo4YN33sEZi1cKbSx4GTQ3SLnAZeSd3c7xthZpCB+2D0MipyFlkMv+9RgBaZ906tfG8FrwYwChVEq9RUZzfZW87H9NRP6iUuo28D8Cb5NxIP41ETlX+Z/9ReBfAFbAvykiv/283/kogbTxLnWTevgsr1BS1wWiiGyx1EQ27SIvL88z7l3IsL66qAhAmxSFKwhtQBcFlA7RmqapmZQVdV1jUJRFwXx5xdnpOavlGjczSBKCb4ltS4pLYmgw1rJeLAgJKlMxPb7NRZOwekJ7Juh1AvEok1GL+mRWpRTGWWxhEZWIkpgvr/jR7/w+S3fAdDqlXraUtgJrwGbc9LBaQwJpGpIyaJtYieSEXJvd3LkWPbvepUu8ltGYZRr0k+3APTdu9OfM7xbQM+1gCfYB4ue5tYevb7K9hw6N4fOnpeLBi0mmAPz7IvJV4BeAX1EZBvk/BP6GiHwR+Bvde4B/noxK9EXg3yJj7D2Hnu/6/rg09uzA/h1INupil00xGHBgk3uWUsa+U6rr54SiDR5UBjlRRlNUZcZPMBpbFvgQtoCMHdC/Fogh4Js1EgOmCxJbo7EadIrMLy8pphOwLmeidyAxRukuVzAnj2a1dndBaZ1d5W9/6SeR0mJmFSEliknFfL3KOYghI80apemKOIjRb9RjYzL6EbpfoJ3to9KOHTRkqH68MvDMnt61N2x8N+Hejb93k+Zy07EXoY+ykT+LnstMIvKglywiMge+TUZp/SXgL3en/WXgX+5e/xLw30qmvwecqF1osD10s4dm8D92bnq8K40H5KZzx9cc/2bGvcsYBnW9AgSts8GttSZ2ya5aWaIo2pjyYncFSusMD2ayg8AWDm0Vi/WKta+JktOg6kVNu2oxSWOSxoqBpFAh5Z5NIdG2GWb4wfvv8vbn3sT7NaI6NUgiZuNwycCaPgaa4BEtGJu7ddz7/FvUtya89vNf5UeyoJ5qzFGFOihYNzVOtveWgTFzzCm0DdF3ENCic2O2frzUoOS8s0GHY92PZdM03a6/xeZ4njYxnLfr2Q+7ToabpNJNXsOhJBrTcMP8JPSRbCal1NvAzwJ/H3hVRB50Hz0kq4HwcSGSB7TP0NzHKENjc7xz9cevfc51Juq/09cEZXyEuPntfA6b9J6Mp5ebPS+7Wp3eG6ZiQIKA1kQRGt+iDDShoZyWFIVFguCbhujTJk7inAMRog/ZgzaZAvD6rUN+92//76hwBcojJts3JmmSeGzXtoUkmx6zuRI4cH51yYOrM84l8I/947/Anc+9wcHdW0hpObx1lGNKEvEkkqFLFUqbQK4RcFptiv5g/8IdLtJh58AxvkZPfU7ds1CLxura0MYau7/HXsCxarjXPurO22drf1x6YWZSSh0A/xPw74nI1fAzyf/mI8lJNYRHPn16bdCGTLGvmGzMCOPd51k72eZ7sOmKsTkeOvVF97jiGQFHRDaqVkqJ5APJJ2II9DVRqWmITQb4Fx+IvmGxmHN2cUZKieVqReNr2jAnxDVJ1VRHjmQDthRmxxWmSNgSbr/+Gq5QLJ7c506RKFKDqLaTQx3Dd1Bg07JCd1np9XyNCoJOgnky5+qH95m/95DmcsHZw8c8efCIP/azf5RkDMkZks2qqCi62qts9EsMNE2D957kwyYPTiQ7EfZVf3vfDBKOewmT1cPx4t8XpB2+H6YjDVX1Z9nL/fzvtk3d3VyHTDT8zqdBL+QaV0o5MiP99yLyP3eHHymlPiMiDzo17nF3/IUgkkXk14BfA/ijP/szO1MzljT7aN8gjo8/L6rd7wGpZz4FkHZQPq21LFc1SYFvW5QugJjTg0RTVge5CnXdZOPf56ClKjSJyOXlZZZcyRNiSxt8xiE3mrbDkhCtSE5lfAajkBC4//4PKQ1olTPSNQqHRlQuWMQJVhQSPRaXEYFESAQUiSiRwkfW8QmT4yN++M59qlYoT0749m/9Du1yne0zo4mtR5EwxiIqIwplSZt79ZqYeylJ8ijpK5L6Uoi+ulYoy0mO0ZHTekJoKWxxzUU9lhzDY/veD5+f5e4eqgclmgAAIABJREFUevi06vNb2Hht6UIAQ2b+NGylnl6k2Zkig05+W0T+i8FHvw78+e71nwf+6uD4n1OZfgG4HKiDz6XhYN0kfW4a9H2T8yw9fZ+qYHspGFNWvXTfQd1wMDtBjKWspigsq7VnfrVgva65uLwiBSFGgRBz60qBsiiomwwLZpxDKUGbCm0qivIApacoW7GuBdElKTlET5gS0CkQNDRK4cWgkkMnRZJIkJrY4Y4rgVLb/DopdFSkNievqphYXlwRmsB6tWJ1fsmhrtBNQEIkNi1G602D5yiCLg1Hd29jqwJVOaIPORaVtur1uNxhrLKVZZnHmMR4T3uWarVPdRy+HqtsNzHqTTbzPi3m06IXUfN+EfizwD+ldjsF/mfAP6OU+gPgT3XvAf4a8A7wfTKo/7/9Uf/UXpXsBnXueczyvIEd03BB5OZnOWNbKbVJtcFoLruq2Wo6wZUT5vM5oLqWMLttYoqiyLluKWdVWFPlR1GhrSOhMdbhgwAmZ55rCLHNXkFX0NcFbVQdtVVb6rpmuVxBzJJRsrggiuLyas6qXmd8iLqmaRoWl1dYY/DeZ/U0xJzsG8IG/845x/Rglrut74HY2rdZxeTzuSi0VtecCM+b5yHtkxxDFe1ZjoSbNJt91/w0melF4JH/DjfmKPBP7zlfgF/5JH/qWQMFfZ3/rqqwT/fdHOuflQKJpLQtDCRlbO6UsiqVRBFVAG1RyqEkcXJ8hzYpUA5ZrFiu1kRJuELj29yndVaWSGw5mFYZOCSkjFykJ9jCEFKNwiMqYFx2vydX5vKNwwmL80tKo9G6RIJjFUFMiYsJaSOihGSyiidicHZCFI1vawqdcARSbFFaEVYRq3NXillSxFWLLhWHR7eITcP86VOsyQm+4XIOCKXS+OWa41snPLk4J4nwxlufpTyY8rS9T2qaLk8vbrEXkoCAiglXgFYHKEoUEWkFkiGZBHqymbdcEyY4rTY5f6pvxi3bDhU9o/Y9rkSGiawZ9K+HsN7Md/dIPbMohdId1LRWm5L54frof0NEfixtOF86ygN48+6yz54aOyeGn/W0g2yjtrU78/mcqCyLZUOTAroDWIySqKop3nuapmE5X1CWZY47WbPJkI5AUoqkNbbKtUbWVSQRptMpElsktaANZVEhTqHUIZISoa5RNiEpT1UiknzL8fExD88uc2MzlTKgSxKsyrGvFCQ3ArCKGCJJey6XpxxNJsRoKFyB9wFb6NzKhqymzC8uUSlB3fLO73+bL375S1yg8N2iSyGHDfrxy0D8+bnPtgc6yZljanFTUNjNAwONYsMckE1zBtd+dnhjR13sJOoYUCdjn0PfsGGc7/dp0suRTvQRKA/CzUG28fGxd298zthhMZzA3i7ocfWKokChNyqR9z4nhXal4s65nffDLnk9oIiyJmPbkbtwlE5jESyCVglXaVypEBLWdph5hcVVJRGhDTmb+/LynLuv3EFbRVlVHQ6eIyZNEovWFQFD4aoc52kDOiXq+RxJ2V5y2tDG7CFMnUPBWc3EOKT2uCCc3X9EU6+IvkEl2bFZpI85ETfAXz2GX9vWm2zxwuxmjXPD/I3t35se/Tkbz+ogAXdMvVrcVwa/yJr5uPT/G8m0dXXHazc/lD5jEpGN2FcjZrn5N/LnGVgSjo+PqYNQNxERhbKOCsvF1SWTSf7OZDLh4uKCvm1nigrTqykhULqCZR/IjA1VVUFqKbXOcMqzac65a9eIMhTGZbVFK1C5p5OOAacS6/WSpo7400dU1mKNwRUVbQxo4ygnh4BlqhJJAtNqwnJ+SYqeqDqkWmLOEtca6VBenclqVpCIpIQxjsunZ4QUKY0hhpzcq3ZCFQmVcnm/1nrTcK1PBFYq24pDdWo43vu8cv0c9ClFw8/7udYdSi574lH9bw2Zb9ud47o5cNPa+aj0UjPTrgTZjTuMB2SIl9afsxHpN1x/vNOFEBC9VTX6IC5kUJXlapHxEZZLmsZ3YPjZfmp8njxbODSKkCLtek1IUEwrmvUapzI++cxZYrtGa1ivGurlGUqlLEWMQRmHNIL3LcoYgvekkPAxEENAq+zBszFASiyamiBgnQM3IShDUZS06znJB3xoKF3ZsU8kKGGiM56fUnrbSlQpfGgyYq0IPrYYa7AhJwYbpUlKSKQ8qF2vJaUzUKf3fjAHeTxiDHs9cDLoSNHPxXjux/l6O86OGHfc3M8q8hse7//DsMRjWKrzSeilUvNuUr/2xQX26dQv+hi2Humv37t7xxBjwEZtOz8/RyKI5Mm5urpiVa8zfoLNTZmvFgtOL84JKe+E1mnq9RJnNcm3OAMSfZYAbY0mEGLuE+XXK1bzOdI0TKqK2SwjvxqbW3r21asAplMNg29QMUGCet0C0LaBdR0QrYlKETFEUyLlAWp6jDcFwTiw5c4uLiJ9jjY5bSkHb6vCkSRsxsQMtqc+A7w33vvRy2PcZfij9y7UXvrcNE/71sL2h68zX69yPs/rOKRPSyrByySZkmwCbtBtfMOb7Nh+bPc8K8A7PGcfPdPu6pgu766RqBTOOdq2zVJqvc6xFK02dkK/OLTtUIkkZ3Dnvy9UVYESIbWRqqpytoURqqpCS86cmBYFhcmBUK0tKXl8ijkxtldzJA9QDwqjtQKtMUohKdtnIUZMadGFRieNtiXldJI7tpcOjM6AMKkHze+KJzvp1Y/DcNdXucx2kxSc1cXxeGbvWTWb5oDvC4z3vrF/lnNA8uIYHR3Em1Tf6kc/8zrj3/2kTPXyMBPPtn1uqk0ZM8zw87Enr9fhxwhHO7tXxxCobSPnjLsQcyHgVUskUhUFxtlNOlJICVcWGFcQVCTq7GFSojFKaJo1d2/fYrmcY4oD2uAJSSgKjbEW5T23D6ZYctVwMpY2ZhguEy3NagXdok6SJagHRFLOkHA5Fqa74OrElQSTmB4e4JuQYZldgZnMuPXKbWxoCIsrklW56Vn0GSwypo4pe44V2thiN7ZV15isG39FP0a7wdN63eG3B49WqlPTR/2RyC1pNvPQSTnF/sWtVHZv936E4ToYquwbDcReR/Ddx1t/+CRTrm/exAqu7SgDLWFY65Jhu7o+SB2NByd1EzQc7N3BZyOBhBqJBSCs1ueUhaVerxDjuHVyxNnT93GuJMSEBWLX+6nocbdDIDQLgpsym81o6wsKPJdXK2azGc4UmLKEpiLElnkbUb6irGDWKN6avkdSBadJc1bOmFwVLGxLsJFDnzPVQ4x4NcfIce6aofNYaKWxQCBXFRfljNZHolZYV+BcSWE1rjygvHWb9dEx0zoyX5yjjdBcneNii06QlCGmBEZjpXN9i2xiTHkMNTElLIa+43ufTpSbDCScUYjaDbZLgqgAtth5QxrPUU8xRpx2uceWNZvNTxudiz917u/bq63DlqCwazuN18mn4Sp/eZhpQGPJs++mb1LthrTf67cvI3n3/L6/7PHxMZIUbetpU0tKLbWvCZIy1K8xrDvUV1RivV4jGsrZLGMzrBeIX9HEmpOTA+rlFQdVyXTmiFXALheUNJyun5KuIqYq+Vf+xDe4/6Mf8BvvtazEEq1FVmuMEhbiSVpQ1mCsQ8XcLjpKjmXppFg2DVaXGGNpY8S6Eq0VRTlFYsQ4R1kWWK2ZTae4QlBF9kCuTYm/mtMurzKscuGIRFS47rYezkd/PG9qu2CgN9lKNy3b/jOl1GYetiCiu+tidx47MEq3LUi0prhxTYzv4dOgl4KZxnr3vrSPIQ31+M35cp0Jx4w3vGbvKeq/0+9YdV1vJqMoCp48eYKz93h6uUCZknW94mCSvWltXSNtk3dCEm/cvYPWMK8DRmvKiaM1hkkx5fTigs9/9kvopuVsdYlTidP5BUYltCpQJnFiNP/r//UP+eIbd/njr8LysuGhLqkS+NCySJ6c6KaJsYDgcy1V15kipoQok5ldEtPJjMV6RTWZECWnN5UuZ74v5wu0gehKioMjKjfl9NEVk+o2LllE1UAi+BVuMGa7c7JH5ZbrMbqt92zXzt2/2V1PZB3O+z6befhZjBGFubEwcR/f7MOS+Dj0UjDTTbRv8IbPY3to+J3+sYNSo2UDjbw7GdKlALUYzcZz1baeJmbchrZtSTGglJBCZL1aQEyomCgmJYVzPL7/PtPplGJ6yPxqTmo1hfMUVcntg0NO77/L26/e5pb22DbwmZOSH374Picnb+HXj/iZt7/Abz82XD5Y8SfvtNyLkUUQLpKQ6paZb4gIIWqMmhHVMje8VlnNESUYIik02FRQX57hjGV9foabTLJDRRyhaZlUU4wS1GSGhJz1fnR8C5Mi3ggSLNZB/bTprt8DT3LN+M+YC7vMlJkod04cUva49Y6O6/M93Eh7B9B43sdu7nxudywplL0OCbYvttTT2Lv7cemlYabhTrVPhRtKkCE+3qZXaldCEGPYMAgIrW93XMr79eR8rG1bXGk3bt6iKNCiiT4Q2galC6L3tDF715JEkhKW9ZpIxFYlUUE9f4oWTVprDmdHSO2ZFCVVFWiXD3ltOuH08gl/4mtHvFZU/Ma5Yjo55K9+70e4suJgcsTVQeQXTioOP3jMsg08bQtS64mxQWmDikLUglb5f89mh7lVKJ62qWmlxmpHdXiEJbK6WLIKYE48U1ugjo4oyxJ/1dC2NdPCEZoFptCEWNOsVlitIGhQwzoi1Xko98RkRG9c4rCN/Q3B/jdzIPtd1j3D7isCHM7fGMqrt6N7BtxnDow1mn31UZ+EXo44k+xnnGunyTYuMaZ9g3LzjrQ/jpGvH3b0/E3KSkpIChD7xsxx53tt8IiCNnhu3TpGSWJalOio0Tjqdc4CmJYFh7rh9bsz/JPv88btKXWCIJanQWG1wjdrfuud94jLJX/kjWMmYY6PgTpCqxTJJJKsB4sxEUKL1YoUA1oioV7hl3NSvUSahsoaDqoSFRO+afFtzDBmKVEVJYW1KB2R5Am+7mJMULmqy8DIGIJD1TjKcOw+moo0xDUczsF43m5yDDzPVt63FobP4/M+Dbvp5WCmEQ1vcDwIvVQantt7aca70Hgidq/Vp6xsYyoi2TXcU2EdwbeUVUFhFZoAKhBTk5M6VUBUAB3xKbJqaurgmS8XOGNo1zVXZxc0iwZNQWiF+XyJXLzDz9w1/Kk//gUqk5gtrwiLS46kwPiWiU/I9A46RT5/y/DPfulVXikC0UYoLJEaY1dYlbPgtda0bUtd15s8QmsMGiE2DbFZEdZrDquSylnadU0TPPPlioPpjNC0nD5+SL1acjm/YN3U2RuWPDrmLhjaOKwrM3gl2W7b2jj62tz0pek9jR0Rw01x7GXrj43ncawGjtfJWMW/zlR6L3N+Gp48eFmYqcswHnZ4Gz7ysf1Z38PHvpSi5w3S2Naq69wtkG4xNM2aerVksbwippZIoPZrar8gpBZFxlsgeYgBI4mLi5p6nZ0Qh5OSsrAUxmLtAa0veX9pePh0TrJTXn/9de6tPqRUHh806uAuppgyPX/Irc++yW++8z5fPzrmq+qc1/UFr8uK13XJbVNwUFqcEgqX1Ru0QbQlmZJWDGoyxU4P8DicLbi6mBOawGKx4JVXXuHWvduIChwcH2BcBWrC7duvc3hyB09CaY0uMiJTLlsxOOew1ubO8egd82mI+90zT39siD40nsNe8t8kgW5S9fads29zzdktz/7Op6HqvRw2k1zvFJgP76+K7IFN8gDsqmvPCuTlk7af5QnM3tx+Ur3PrSP77xhjQOW0GiUO0RliS4lCIfjQYLSDGHHKcnx4xLrW1Ksly+USKw2uKGh9xBjJKl8omZ4cMElXfKFc8R/8q1/jv/uND/mtdeLs/g/QyvMvfu0e7z95yIet4xdaw7/xjZ/mtUePuf9wyaMLy1o7pPLEVZOzL9AorSjKQ0xZ4kOimpZU1YTZyV3uf/CAwhTM53NeeeMNlusFRgpC8OATZTXl6PA2ysDxnQpCILYrQlPjY04XUnS9dNEo1dkcOyryrnoMu/2xblLPh7aLSFdyvsfmeZZK1v/+PvVRKXOjzdw//+FhJraqQm/k9s+gugnrpBO5pSbdYMe0TYAd7oAA0rd7JLuy+ymQTtdXpl8MkhnDNygtrFXGj0uNEI0QZMWd28fUy3OapFFRqDaOi0ibarSukKg4vZiDy97BCQbfJlQM+KIGm5BwSTvRPD5fslreoa0MlXrML/705/jd3/guX/7pz3D5sKY4Ufzs268jf/sM//oK+2TJv/TaEYvbhzxYFjy5uM9ff9DyYHbI/fMa0Qk3mRC1RlEhE43C0rSJlNboFLEqoCaHNFqBdUgLUrcY7bCTCq/I4CpiOH7j89SLORenT1DhAokNOek6A7rkcU6IhM5BoTGY3CBbJULy9GqVpBolXc8qNEElLHqDrJs6YBi0pmtzv3FcDDfRbRa5hkEORr9eht69belLsTcJdp+6+EnppWAmYXfX2dk95HocSuscee/drP2AD+MFOd6wP0jXU0o5daa/bgaa7BZKV5ZwdHDAwXSCnTieNgsCgab2JOXy5GuLInU45JEYGyQIrW1ZKTiuLe3hMScWJnhwjhQtH56u+O6Dgtmh5s2TKWXd8Nmi5MP7T6gOXuV/+Vu/zVf+yBf4uV+4TakUwd7DNFccrZbcOrrDo+kt3l2fEdaBdlZQHJwwDwFvDlBqmmGZvUcZBymXfPjlGqXX3J6UzGaHhKalLF0uz085l1BUBxdWt1RVxZtvvsnFB9CsrxDJwDDDBavZzSjIj63NdM1+UeNzdwO4N6l0/bzfuE4GkqyXhn091c7/HUitfXHIT0IvAqjyllLqbyqlfl8p9S2l1L/bHf9PlFIfql1ciP47/5FS6vtKqe8qpf65j/KHxow0uOYLffdZO82zVYVOp4+ZkSAz2/HxIc4YUmhREvH1CpLHahBJm1odJYkYW5QEXBAKEpXJ7utkFFVKTEMkKE2IlicLz3tXLRdNYq0L0EIVG/x6xWJ9xQeXuTv60URhXEIZEB1JZUCcx+jEUVViEY5nU7RSTIqS0joUCWIgSm7TKaI2vXh7tSchBGFTzh0k7UgCYwyVK4g+ZMeDcWit2YVdy21N+/HcHXtNiruxoPH8Pote1NbdN7/7HFjD73xazDOmTwKPDPBfisjXu8dfA+g++2Xga8CfBv4r1Rf530B98zHYDsi4KLIvkegl0OYG9gTwxsbu8PX4/XgCNs4MIovVkoODA7LnLzJxljuTgqlOOGmYqsAEzwTPydQydaBSgxI4ioa3fMnbquSo9Vwsz3Ft4NWVcOgdaV3yowvHdz9Y8J05/OYPvsuf/ie+wYlTnFTCYvoKf+vv/IDkSxbrJSasMqzy5JDVxGGnFbcPpxB8Z88YlORMDItgUstkeogrptiipGkDs4MDkoI79+7iygmHR0e5o7yxXZKu7ZwLebqapqEoKqazY3wU2rbt1KWta3vYADqjjW0zIMYb202LfEj75uImxhlf8yYn1Y4kkuvX+DQ8efDJ4JFvol8C/gcRaUTkh2SUom+8wO8A3aDEtGGa4WDuc5/21OvFY8/Qswq+etVhyFRtW+NjIEgipERSWf0JkmhTxIjm9sERsxT4zNRwh5p7uqVslpjoc2b5THGHii+bY15rW+5Qc5gin40FX1y0/HSs+SoK/XAO54p33m85bwu+9a0foEPFkx89BlvwN//gEb/zMKDlTsZ0SBXKz9BrsK7kuBAmJmJVXvhrH6ijweuKRczoQxiNKypMUbJsW5R1eMn31UpEuSInnXYItHn8soOnqqabMRQRoqSN48EY0zGU3ZE8Q9d4/16pDOT/PM1hyAT993P8amgjDTa8oX082iyH511bZ2m7We9zcH1c+kg2k1LqbbbwyL8I/DtKqT8H/D9k6XVOZrS/N/jaR4JHVnLdkZBSQptd8bx9Ldd2n8H/HZ3bH++cEN21RfW7XC4vkJhIKWdAJMkqkJfEKrQUuqBdNxxMS0oJ3Lt9RFIwbwQXNPNGuFXApTR8q77kj80Oef3WEe8+OqOYWd54ZUbh57RFwTvrOWlt+fDdBZfBosUTlOX45DYn4jipJjxNjvRhzU+cVJQEtAhaBUITuWgSk+N7nH6w5MwbgrGoSUU5qXLWh4LkA22sUdZx6+4xB3dvc3z7Vu7qp22ugzIGo3JmSWhrQgxI29KG3Ggt+jXagE15YYfQklL2qlqV3eMbO3WwifXe0d6bppTa2L9j+2kzzzsubbB2v5QBduyf8boYn7uRWENc+97H9SnRJ4FH/q+BnwC+DjwA/vOP8sNqCI/89OmNIvhZ7/vdaTxwvRE6/t5YfegpJr/5jnUZZljaQGUdJIWPiYiiVYp1jKxj5KJuOFuvWbYRbStAs1wuESK2WXMyMZzcO0Hfvc0P24anzvCei/zfDz7ge48bfu+DM757OufbF2tOV8KqdczFMrkzI5nI/Xd/wJvFjDvTkm9/8D51CITWs/QNYaJIyvKDK8d3HrVctI6EIwZFatbo1RXH2pOaFaur803vKWUM04MTJCmcVjjJzbFTyJ3lJW7x1VMStLU0beDi4jFKcpl+6rAhNj2QJOyodNZaisLStrnqdxy8fVHJ1L9/xvrZK+mGrzeZK8/6vYGE+rGUras98Mgi8khEomSsp7/EVpV7YXhkEfk5Efm5O3dud+1adm2dfqB2EHFuYLJhyflNEul51xARVoslmg60HovTjpTIQT+tMsh9UTAXxVIXPFy1/PDRU+ooOa9PCwsEmxKxbvnfHn3I3z9f8UgcH1w2zH7y67xx5y1eObiLLkrOkicYxXKd8BxxetaS5JCTNz7Ho4s1y9On/MlvfI1i1qKSMAsT7LqkTQWXq0AdwLiK4MEYh9EF6yaymDeE5QKnNeI9Go1vI4hmvVxy/viU+uoyd99QOfywY3N2EqLxLev1HN80aElYpTdZFhnaa5uQGkLYoDT1kqpfn+NFPWaU61Jkm3w6ViH32rgjk2BoGtzkBn/ehv1R6WPDI6vdNjF/BvhH3etfB35ZKVUqpT5P7tP0zWf+Bh1AYIdFN2SiflJ62kDzioAIujO8ez1YCaQQSWF3R9w1iDtVJMTOPa5x2mRcBmUQnVjFFfPU5lofFUixJqAotWPS1kwTIAWF07zyym0erlqehCMum4pHS8O3zj3/cN7gveWgLVh5WCvD/cunLBePuOs0X5ndyf93lSAo3nt4yukpPF04TpPhe2cXPLrQ1Ot5bkBWVTxdXqGjYTZp+cWfmPH2LUvdLgjUJL/ApZogDcXhhOreXequL5QigkloAlNTMb9YoopDSltAiETf4tsV3ntCgjZEFqsVxhiOD45xZYFoQ9u2mOzuyHPBbuaJ1prZbJZ/01xvCzMO4Pa9shKSs/BTzzjCFp5rCzraX2ucMdFji+9gHw6uMzgxq/Iq/6YMVM0fh83UwyP/nlLqd7pj/zHwryulvk42Pd4F/kL3p76llPorwO+TPYG/IkNooT0kI6TNmyTL2HNz7Tov4C0annvTTkcXuwohkELuNm60xqBYSmBa2uxJV7ncO4YG2pbCRaIPGxgqkQRK90lStCnStjXJauoUadqGWVlQTGfUofNUxpbVfE6RHtNeLjmZ/QSxrUmzAtGC1kLSAWU1oW0orWE6KdGtEEOgWa/ATTJYpE7MplNSikyqArrdXmnh3r17GKO7kdeklJkuhRx788EDHepQ25IkgqQbyxXGtoq1dgP3BQNoY62uzfdw3vfNyb653Gcjj98P1cDhaTcxzieVTJ8EHvmvPeM7vwr86kf5I/tUgGGpRUo3B3aH3+lfD3ua3kRjVzqATpLLORJIG/LCij4X4sVA1Io6hi7dPxJiy9X6EjezLOolOI1NBXaSccoNhmIyQxNRvsG3a9LtO9Q+cjxx3Fq1zJc1RVGxSi2ljtT1gv/0z/48Ojq+/Y++Qzh6DfQEVa45uTchtA0hWU7KKSfTOTMD3ltU0qBaiDWqSVwuTplNShChclNu3b2HtRrf1AQf0GpK7Vuc7cBYMDinaZZLDgrNetWyWl8RQ4PSkdD6rUe1r2+SrRcVMtM45zZjn8+P27kQuVaqLp03aMhkN+Xq9fN2EzPts8+GHrt99vWn5Rp/KTIgxjS8cdhmefc0donm89JO3cw4wbI/t6ft9WVnq1CSjda2bZlMJkgMvPXa61yeX6AlEZoWa0tEgdGGQ3eCNoqqnFFOJlytPUEiTfTYaLl15w519NytJqhF5AuzA947fcrJdMpR5TCh5eT4NusmYHzg3kEFLvLWnYDWFe9/UDI7vMP9hx/w2ROHJI+xh9z/cIG4W6BTdqCYEqMVbQqkDmnVGUXwTQ7Alo4QAgWC04Y61qwWcyiKjCORFOIjTb0irK94dPUUUgDJTbNFMmhKrhfT7JMa/Vz082CMYe09ZdFheaM2WA2b8U4CRu31rF0PBO/SMBevP39flsN4XY1/Y4O+9ON0jf9/ScMb7RlhuJMk2R9k7WkYl/qoah7sen6UUtR1hvf94IMPeOWnjiF4fL3EWE0TIgLYJJzXLaCJxrHSJW56m9XVYyazEyYY6pVHTzRxuaL0nlerGYspFKXlYn7KvTsnPKg9LmmOiwmpecxJqWmD5Wpxzpuvv5ptFD2hdQYxFrxFz4744HzJe08vWQRLIx4RhVeag8MjggRcErTTlDZneGutqVsPpmU1X3A4m5CUoFPMAd+YoF6xOnuC+EW2ZCViVLeB6R5/fTgHW7TUvutHP4ZN03QSXBPYjekMXw8fItKhyZrNvA431l5lG2snw+vdRPsY6SaT4ePQy1GCcUMAbRiI7aXM0OM3NjY3lxvo6f37sS4+HMihYRwlEVIEYzFFSdM0tKslsampNKi6wWr3/3L3prGyZdd9328PZ6hTwx3f1K+b3SJbFCVOoixRg4fI8iQpQWTDNhQgMwwYDpKPCZIAQYIEyQcDNpIACQwrQSwrCmIrhB0bTpzEkBSWaa5AAAAgAElEQVRTkiOKoii22GSTTTa7+83vjjWceQ/5sM+pe269ut2v2ZTw4A0UqurUOaeq9t5rr7X/a63/omoMTVvx+OyI0hTkxWNWq7vUnHLt2o31PeNYY02NkYacllcf3uWsrni4WlArmE3GZFLy0edeROQl450p0TRFNjHKKFIJkzTCqYQlMUeNpdaOKLN87rVvsGginNMI3zKZpozSCU3pcB21s3cC6wU6SojiEUkyQuuY3d1dTGNxVYltG1zbhITCYomtVggXQqOEdxjTBgBEaoL8SJSMkF6iu31Yj/BZa9f7peGY9Y749Xj6J6MYhtpuiOBuLpJDDXiVMGzWj9o2Pzbnxvttz5Rm6ltPS3xJM7kLj/pwdbsQNLs2L4a0unChdTZpnrabk6GZPlgSBcYwimN8pGjqGqcialuyH2t+8OOfItURqbAYEfN7bz7ggY1JtKIuc2LhiaioXIWn5QEe44O2WLUl8/slO4c3KeanJEA6mZKvTnjz7gM++PJ3cTJfUFQ5050Z90+OEUhan5NZz+TWh1h965wsHpFIh6tqMp1hpafOV/jZKJhVKg6kl96zWhakaXCCK++QeBKVsjibI21NkZ8jXLWmz2q9J9IaISTGOTwS3dX09XmJNQ4dXQ7h6QNML/ajF/tX0Wu0IYrWj8eWuTCc7O9F62yO8zvNt+G8ej/tmRGmYdtmDlxlT28z/d4NdLhKvQ+1l3PBp6K8J9bBkdsiSbKMhfCIWLNqKh7M5+AtzfkpL334wyx8SPlWQkMkiExL7Cw+lrRO0zaWvK2xyhMB0+mYklBsTGnBJBtTL85Z5A3z0vDg5ISbhzeZL86JdEZZV5yezNkdpcyrUERAeYW33SKCIdIaIrUuGJ0kSaAhU4amDfx2WRJjHSipUUKQjhKWp+fr+ktCiLCdFBKtIlprsdahdUykk0GKgwbsRoiRfMcxGY7Btv6Hy/wRV12/DVF8t0yBqz77Tph6z5wwee8D+SEX+Stt24IYokPBn9DTSAXNdTkcZUjltbn/Gg7wpt/DW4fH47qq69KGYsu2MYy0JossZ4saJwXnjWV+vgQvmURjvvmV12kn15hS05QlUaQZ2ZrrEk5XOVkUU3tJ7QzeCVrpqRLNsVmRZQlpMuFwtMuP/7FPkuVfx4od7j96lSyJiVXKneOcB8uauwuFlZYzEsrIIYxDR6PgSxMNUvfkiwatNFoQIsm9Z2c2CYJgLbFWRAKEs7R1hRKANygpCDRNYl3fNk0zrHO0rV1n3u7u7lMUK4S87BztLYTe7PP+QlOJwThvap0LAbw8HzZdJVI+mX6xbS+9TUDeCSF8v+2ZEaa1X80DAzChByNa067NB7jgY+sHzfkQd9V78jeFZ5jxqQT0wxroG0NUuMeGRLXWBYqvGFzZsqMSWulYRjGLomSSKIxpcX6HPK8AS6E8+9duY3yCbxxCGiJXUzQLjtDEOqYxBpd6YuGJkxYxMZwkkmphIVGUcY16dB9VwQcSyf6NhluH1zk5Nuw9d8DnH3yJOBuTlxVH/jpVaVFtGiIPfIdsmeCMlgoEGfnS4mXLaCdib/+QRb5Ci1Bi1PmQKVyfr8giTZtIFgJEFOOdQGnNKB2RHsxYnB3TdMQsGtb8fc5YZtMZdWMRmIssaKe7sWnAjzCuc5q74G6wKiQCSjpEzzuckggRHLDDdJBNQdlE7LYBGcPKFpvCCDxxbX/f99OeGWEatm02c7/abUNthgDCcJMLl3nFt202w+b4osKDM7ZLVhRIodFxSuvBiWC+ZFmGbYpAlyVjpHEIDFJ6vKlAyOAvsS2VbVDeQ6RYVQVEMMrG3DyIGGUx2W5MhWIVC9584xE/8Ed/lNd/60u0U8XCQlE7rDH4eMbrdx+xWBaYvKFpx1TVkrIR4EI0gh/0Q/jPfq1RvPccHR1RG8vOzg5pEqGlRHjL/HyBNy2P85w4kszGM5qmoqgrlBIkTrCcL0L92yhGdAXNrLUURUEcx0SRQqoYL2t0JEEE14IxpkP/rg7tQgzMtsHxtm3XOVhXmYNXgQfDMd+kBBtev9ner3Z6NtA8rt4owmVUbigw/bFt8OnwGrg6UWxzD9WvaNKHz+I0oagrGunxSgekr3XErUWbmlkEs0hyLY1pV3PKYk6TnyOwwS/loTAGnaWoLEInsH8jI04tkWzYn2UIaTk8POT4wTE7u4ccL3O+edJw/3jB3u6Er995yGuPFiyKmmUlOGsiaFokip5ia+gaCBogpGkgBa412KalWC54/OgBkVKMIg3Oo30oLrA728E2LXVlMMYzGe/wgRdeIo5jrGvRnWkVRQlRFBPHSdhP6Rjj7BquHloUV03ezXHYDEjdBgo9DQixOZ/6RXibZhrOp+9Ue+aEaZvduykcm4ID2/OZntZ+HiJ9HbNwEDDncUgWbU3eNDipWBiLFZK29fi2QdiKWaLZS0bspSO0tuxNYppiyWo5R0cRVoKPNTpRpCNBay1ITaInfPWVr4FQjHdnnJ/OKZqak3ZBhcD5ESMp2EljHiwqPBHCQNsahO3+p7uA9ofOx34SORPMW4mgzFeYquLeW2+CM+zPptx+7jlu3rhBHMfoOEXHGdPZNSY7u5yczzHGBQJOIbqsIklTG4q86vqwo/nyjiRJ1tB429ZIrp7EV43LJmixuUC+2722vb/q+PD674RgPTPC1LcheAAX4Sn96wtSjcuCNLSRNzt9s8M2UcDhqmetpTEhTk1JGQJvdYTTmuWqoFCepYPceyrnqKqKpmmx1pHEMbcPD0m84btuP8enf+BTrFYrhI6w3jHJ4hD6UzmUTLn/aEEa77AqGpZFS2sEi6LmvKx5/OBNvvH2Q5bLJX/y0x/n4aPHaJVymI1JbItEdLlEHpwPfhx/EQjcdMKmdeAWr8silI5pG9qq5v7de9y9e5fT01PO5qecnJ0ymkzQyYjz+YqyalmuCs5W52RpsuYTNMYwne6ws7OHVjGtMQEuV9G6bGlvpvWvh3vb4Xj0fd43RVjAhAuFpbHBH4UNyOow2mFzzmy+32YKvtN52z5/r+2ZEya4/CeHpTDhAsXrBWFoEw+zO7e1bfZ3/33DczZXLosPCJyHum0obEjia5xjNJlivWCeV+Al+XxJvlrxO1/8Io8ePQrfqYOWWC3naAGmbmjKCh3FTHZ2McbSGmgbgY4yWgsf+77v4fT0nPPFEtlWOGtQMsbWDZNRhO9+p1aiA1dYpz54qdYLy0U0iUd6R6IjrGupqqBZmrbtohtC3872dhlPZ8RJwmSSEUUXvjshBEk8Wvd1X5i5MQ4vFdY86bvb1vdXaYFtGmM4nlddNzTVhyDCVfD5O43/+2nPFABxFcBgjEF24fx93dS+A9ZlKXuHrdiudfoW/CdPdvAQHTLe4WyoRm4jTTId0+YldZ4T2ZbaK1ZNy0jFrMqKLB0hdUxd1fQFrP/Ej/84X339dUazCcZaslRz+/AGz++P8Mpz59FjmO4SK8nR/ce42CPdhLgFLRMeHN3hhRs3iXahXRU8f7hD2QhEa2mpEHonBON6ULrfq1ja1iF0hBQaLzzWtdimJU1TjGkwbTAB8zwPMXtpvHY7CK0wxnHjuVvU5QIpDWU5x3tB0zQkOkYIxXKZU7cV3nuMt+jOf6biBNGlZNRdMezeSugXwGGaxHDM+9fb/D9rIRGX3RibWsV7j1Tq0vduE6ir2j83AMRm682Cvl6s88H/JJW6KIgmJa0xCCmp63p93VBjbd5zM7KiP66U6hyQQJd6oCRkWcponIUqgcYyMQaaOhD0Ny0qimm8IPeQTmfsjMd88EPfzZdefRXrAwe5U+G/nD46IragK8HBZJ/5sub4+IwsTnCmxfmGWBn2JpqzxmE9vHZ3wcOziv1JglcJrc8ovCZvHHVTIDoS/dbUawQssLsKQg6QRWuJ1jIUIuidqgJOz89YLhaUZUGSRFRVEJAqLyhWOflyQV0uKYoCpQKLq1CSOEnQKqaxBodHqoQkyRilY4xxKBlRluVgIl8OA9vMgn4njbO5V96EyoePzQTBbcK27bv+udRMfRsCCH0dWaG4FPM17JShs1bri1ixzQzd4b6qN5P8RnLbpVXSw8HBAcY064Hci2IKKZmXtoOIPc475ChmVeSMneXt5V1UFNFYRyOgLgtcbbl5fUYxX7I3u0FJC1gm4zEfuvVB2nSX/+83X6FcLShXFXJyjXv37pG7gpenB7i6xrQTTKOoYhBSkciItq1RKuqyXiU6UrTWo3QEXMQ09kJkjAEp1ppieT4nHScI4RmNo04gAwSeRBrbNnilqW2NIiQHZqMJAGWb411LlmUkSUbbtlgZigG0Xdo6XOxFvffgQ6DqevzEZRNw8/U2oelLfg6FpgdhhvOnX4iHv2PY3itC+G7tmRGmIaI2XIHWCJUXOBNyYbxzISfGd+8Hq1ZfbW44EH3ZmfV3EUJlsOEz0/mRkBohQRiBk6FomGolUiVUZYGQjlrHzJxH56dE6YRMRERJhGtLpFKctjU1IQocFaNFhMSzsyfIspTjxpHnJcvlI2R2QH52xve/NOWrZwsye4dW30LJQ5blijeUQLWOzz4653E5pnUOl8YIE4bN+BjrLbYqkR2jkDSeGE0sLLnRRDpBK0VezFFKoiTUZYPvMpGdEKzymjTL8A6ackU8TjFNwfn5GUJEIUuXGK9ipE4oy4q2bUijlGVpGI8yIhnRVqF6opISYy1RrKhykDICUeC8QZHSVXjmYg5f8O/1Y94vAr0wXBasHnbvBc/hvVibkX3rgaqgCeUlgbsKBHk/7ZkQpiGCt6lthlqqX8mG1/VtLXQb994Go6+F1YXQlaEw9uf3EK8HjPcYa2kaw/xswWy8yzSbBhTNW5yTpEJhjaBwDusVCIkVEiElqZZMYklb1SQ7E7zM2dvd4fG84vDmIacywjZLPjbZ49VlztIWNCR4Z8lEzJ1HZ8hsRjOvscJCpyG0IKzyUhBFmkhprA3ZrFprMilpmwIpJYmUmLYKZUK1xhNIU1rrwTqOHz/mxq1baK25Pz/FtSWuj1rwEaNsyrX9G2RxgncGKQUn52ek2YQ8zxnFntlsRmOWVEVF3VSXoro3/Uib49+N1hPj1I9rFEXbKkI9cY+h+d4L46a5d5XT9/0K1TMhTHA5lKOHv/tjQ6L+3kQYClgfZDm8x2ZxYHgyyHETqOhNIWvt+novBZVpKU1LYw1WRizqikgKtO0S85CMRMLpcoWZKlQtccJhJUjlibQkEgKdpJysCm4fjPHRDk2RU0rJV+6ds1PlTIRCCENtW7wcIVSKHGlq4ORojtNpWMS9R0pomhJrGhQh8FZKidIJ3guKskRJgfQe21hQiiSJqeuaqirWfRPJiCRNcFJxfnpKnKZIQMtQ7WK6t0/rYvZme0zSEamOcSako2dpxr1Hj9nbnzAaJdRNiJQIv61hkvYhQVzq70tjguvoty7cHVdRGHu2728uzYvBsR7J3EYJtm3f/PsOQAghUiHEbwkhviQCPfJ/3h3/LiHE50SgQf67Qoi4O55077/Rff7Su39HeN5Edt5pozgUjG3nvpM9vKmhNq/pPzcduYtxFuMAoTAIvAw+EI1HqUCbFUvBJEtxDNloHd67UMTZOsqqppWK45Mlp6uGvAkRBYtVw0RrstkOjXE4K2mMxUvFKq+YrwrKJhDhA+uia335Ha01KtLd3skhOyheeFDiAu2UhAj4NE2Dtopj0jghjRMipXHGrP1JMooRKFprSdIM33HfeSyx1oySBGct41EIsO1R1uFCeDnT+Ukf01Xjsu39ULNtmwdXtX6RvMot8jT3eNr2NGheDfyE9/6TBI68nxRC/AjwVwn0yC8DZ8Bf6s7/S8BZd/y/7s571zac4JsbyU1TrX+9ma48bJub16EZ19+n98MMtVjvbOwZSPOy4NHjY4q6oeqiHxrrUFIwSROizpTJlytmkynWtowVJMIgfQ2mpWkaiqphVTvOKk9lI946LUisIc6XiLrkI7cOeVTluNajZNZV7NPUTmFJUPGIpmloqhpvbMfn1NVDUoHX23mB1FHIMdJxYGBVkiQJafZxOkLHCXE8Io5H3Lz1PC/evs3NwwP2ZztkWcZ0OkVFEc6C8ZCXoShaVVV4ISiKnLIKDyEEt27domka6rpe911d12Hv1JXF7Pt7TW/tNwrM+VCxsX89pG3b5oQfjt+2RbXfJ28CE8M2jJjZnGPfbnsaemTvvV91b6Pu4YGfAD7THf/bwJ/tXv9M957u8z8h3kV/9vuWvg2h034AtmmQbUSTW37/pQEZCuUQ4VrzR/ThMdailGC1WpFXNY1xiDiGKEJpjdIaqyXGwapoKJwjb2siAQezjE9+5Hu4sTMDa1iVBaerAh+NiKIxtXVMU8n3Jp4/+dJzTP0KWx7jpUE7iPph8RK8oiqbLpTHobyjLwEaNKCkbS1eRDgkrXWgNF4ofMfB0DiHF5q6dSBDBcCDw5tIEdNWNaZpGaUx1w4O8d6TxCPyssRY0FGK95ayLjg9P6Oxlto6yrZhMhlTliUvvPAieV4GQkvniKKQPyWl7oTigq5tWxTEcEz71/3Yb362DTjYnCe9qd6jiL1wbhPK/vrvhHZ6WhJKJQLN12PgnwDfBM6978qSX6ZAvg3c6X6kAebAwZZ7rhldT09Pn0TvBivKtpVjuG8aHtsUmE1goT8OT4bc9+etV8bOZMnzPEwUK3Be4L0gx3LWGorWY0SEm+5wWtUkRIwSRbGYQ22IlCJORug0IY41pipxTcmNqOVHXtzh8etf5sVZwr3HRxDFNBisN0RpwqoskJFGxQq8Caia6FJGBAihkCIgdoHkRCKEwtluMdAxUZwhZIxHYqxHCs1kvEu+KhFCkUQxWkqWi5yyLInjlDgdMZ3tM53t07QeY5rgL5MKGSegFBZJ0xqiSPHWW2/hnOOtt+5Q5KEUaFlUXSgT674O/X6ZR344bsO4yr5tLoSbj81xH+5/nwCvtsydTWvl/bSnEiYfmFu/n8DO+mngI+/rW8M914yu+/v7T/yRS8Gng07q3/ftKhNwW0dvHu8HqvdfDTvYmWb9/aYNldydcxjnMU5SC0WjElQ8hWhMqSIqqYiEpmzakGRoYWe6i4w02WRMUxW0qyWJgRdGKWfze8Q7M26OZjwqJQ9LRzFOcUlMUZWh1ItrO2XpEL7F+xYvXCBCEZooiklGI6SKQCisg57sXicpeVVSNvWawy5Jwh5nMh7jvWecZR3R5JTd3b2L6vVCcXq+YFVUFHnOcplz58FDzvOKVRWIKr2QhHI1EXjJ888/HyaxDaaed5ej8YeTOAAK9olxgycplQdzZqtg9POgH8+ej2K4X9q8z3D+fKfae4qA8N6fA78K/CiwK4To0cAhBfKaHrn7fAc4ebd792ZWv1L0+xngku9omKvS/abLHSwUHolfFzHeltYegAHnLc5dJLRZ61EiRAeUrQnVyL3AC4eVHqPBRhIjFLQJTTSmSTOWrWXhDMsoYm96g5Oy4E4NZTzDtiuuXVP8ocTyL778XchiwXOyQtQ5rT6gbuBhKViqfR7mDZ4UbzwxnlhYYuVCgp0O4IdSEYkGbUoQIchUqgwVZagoIc7G2CSi0BGLkzMiGZFGKVk2IcsmQVvYlnGiiXGcLM9QSjJJE1xVI7VgXhWs6q6Ob13R1I5JNiVWmjt37oV9VNlQtx7TWJqyQelQ8T2YnFA0OXXb0FrWSFvYhw72t/5ylIrF40R4Hr42PjDHXt5juS5PSlyaxiIM8FqYhqbikNW3f/1O5t97bU+D5l0TQux2r0fAnyKUlflV4C90p/2bwD/oXv/D7j3d57/in+KXbmqSzTToq87/dlp/bR96cwkqFwP2UeG7qhwa0TkW1yVUuvwe0zRkaYo1hlhpGmORcYr3EEcpaRQTS0UcRyxWK0aRRiUpZes4PjlFRBG1MZR1tS5i3Jdtcc4xmUyQ6gLq75EzN6DYkrKDuaMYgCRJiKKI8XRCHMfEcQwyhBMpFXxUxjvyssA5F86JIrCOxfmcSAnSKGZnZ2e9r1wsl7RtQA7Pzs/XIIPznvF4TFEU1HV9aZKu+/SK/dFVe5VNK2J43bbH8Nx3MhHf6V7fifY0fqZbwN8WgUldAr/kvf9HQoivAH9HCPFfAl8k8JHTPf/PQohvAKeEwmfv2qy/sJWHKeZ9G3bI5gAJcZGlOWzv1En9Sqm1xhh3EXHdrVrStMGlIwRaahQRwnmk9YHuWAqU9ERSoKzlpRvPce/slPG1AygkUsaMZMK+8Ix0S1vOeeP0lP10hJjsc1qV3H/0iDcenFNUFrF7jbq1SBmEI44lnphitSSSitnuBCEUWTri/v27QbCMxbqWxWrJbGcPCyRpiOoejRJUq7ED8zhKEo6OjtifTTHWk2QjEiXAQZkXzLIR2e6MozzHxgKXZOzPDimKFVXbMJ8vmc1maB0RpwnLxZymiTFNhRCSxoXs22wypqoqoigK4ya2u1vXY7rhiN+2fx6ef9VeuJ87WutL/qVNc294z++kmfc09MivEGoybR5/gy1FzLz3FfAX3+sPEUKEOC3POgen74A+jgxAqifJ/Lfej6sdcevJNah5aowjji+iMNZRzkIhVYJDgVBIa/BS4LD42rCbTtgbTzk6PkYKz+n5GRpP5QryZsHujmN+520+/uHv4m6Scv9bD3DzEWVZYqMp562jdIpqXuC60B+c6LgSPI1tsc5RFBVN05DHOYeHhxwdP0bLQIEWJRFtlyTYlBU3rx1iTUuaTLDdomGtJV8uA5+GtUTOkiQpbWtIZyloi2kdVZ4zjWOsCtRgxkNjG567dohQj8M4KMkqz1mtVmgJtnXMdjKOHz3COFBCUxclSZLgvL2k/enH5fKc6Z4vvx82a+06r20oRJsgwlBoNnPfvPeXrJxNYX2/7ZmIGh+ia15ckKUM83G2nX/V+20hRWF1uhxaYo0bmEqhoLDyrOsVBdIQj05idBwhpCaNI5JIcTAbM4skwtXMF8d415LgcOWSxBoyHLvTmLPlESOl2BMxb3/zTeokYbEqMA6clzResbJQE2M6wkjRmXi2NUgfslvxEik0UmjysiJOMqSUjEYJWiuatqW2jjgLJtd0PEFLhXSeRGliGZiIbh0c4FuDFjAZj9nd3aWqW0ajUeDSw5NIjakvwoGEdZyfnBKpkOpubRDOvb09Dg4O0HHE46Mj0jQNE1TKLhKiDyw26z7fhrReNZbbxrnXWNtAiuE9N++/7XWvsb4T+yV4RsKJnli11EWk83BFCZ0YBmYdOzc09+hKiwzMBBhSQ4VHz5WttaY1TaiU3ocjOR9Ky3T+iThOMc0ZaRqjPUSmIlYRqsiJvcHINkRKOEumA+ljmx9zkE1ZnZ4TpZ7J9WvkTjLbv8HdvCAzfUq3pK4NVkQ4GeorGWOwQqB8B8BIgUTRtBbVx96JsP9pbU1dFaAVKp2hk4SirLHSs9O2ZFKuV5ZRkqBHCVoqptevh1ytvKKxlr3dXSrTotMRB5MpNRKiGucdUaRolaQ0LcJ5RnHE66+/ziiNeekDLwahiQLRTGuKzrxLqKr60th6H2rijrJOSIZaZWP525wPm3D55jlXzalNwewXzfU8E086+99PeyaECS5rp7UgbUHu+pYkyRNITL/aDNMv+utC513+zrquiWJNXddI4ZAyCb9BQNvR+dJpmczUxLZiFisaF3KdxvGYpasoWoNrGyIhKdqa8SQm9S1KKFKVcbcoWZ0seZSXNFKxm0bY1rAzmZKOFW8tFgHNxOOdp3UNSkfriOmmtWgZYZ1DeAHKY4zD9zlLtgI/Qso0oFtCkxc1Bwd7mKZFeMskzYiloC4rEq3BO7SSjLMMa1oq63BS0CwN09mMEeAVvP761zm4fh0tUx4cHWOcZbYzCexEVQ7eM5lMKITHFhfabLlcDnxMF2OzGZAcxqY7UVwmr9ymZa4CLfrxH6abvBfz7Q8EzfuDatuwfy+6R79fGnRQ7zfpg1yXy+XWTellSPzy8zrYM4ou9mlahVpKXThRZFsOE8WucLw8TXlxnHGYRmBqisWcalHg8oqpUOwkEVpaxkJyXqzYvXad1aJlUSlOnMSomFQlNFbSOkmeV5yfn6OVAG8RpkFi0dJTVSVlWWCt6er5tgjpsa4mjjV4E9A/Z5HCEylwpiFKYlpPIIDB4nwwmW1bE0edmapUoHSOY2LriQiT+PRsHmihnSdTgvzRQ3aU5/de+RKnZ0cY24R6TW3QxlVTY7zjbH6K1ord3V0mk8lamJRSSKGQkV5rmH58N90fVwEN28Zxc74M25Bw9Ko98+9Xe6aF6arVaNtKNZ1OgYtynFfZ40O/0zDkpBdMB6BkyBhFIRHEQIpDmxbdGtqyDACFFIzTEYmMyeJgQgmtsGVNDZyulsh4hPMSY8Xar+KFDHsjwipeVVVIQMThTQu+I5GUHusN3vdMRJYoUuTFqkvj7hyiPpCpWNv2PYhph5EkoV/quu6KX4fyMBKB7Mxa1ZmOILl//z5KCK4dHLA6PeWFF26zXC7JsjREQojgU6raBou/VNhMqI5TLy/DAnVFYbNNKHubxnlaAXo3iHubZbPt8/fbnhkzD56Mz4tUt6J1E93aQBB5CZAYoDgX2bb60srU91W4fSBoBFBxdIHadancSji8N6EOk4wR2rCX12RCUC3m3BcpSgqmQpF6zzSOyZ3jzLac5hXpeMxOXXL+6JxKJSwFxHVNJKGJPbY2GCVwSnBmGqyARCisMRhh8IQkSNEYxCiidS3KtzgtsRZ0LYlVROUFvm3IREqtAp3YTjamOFnRCIWYTjBlS+YVsVekkWY822W1mBPZlkxbytWSBybjcDblQBoyWh4fPWQUj8iPHjHLRlwfj7DjGOUS4lRhRyNKk9EaxzJfsCgXKJ+iVM3uZI95XvKC2uP8fEkkLcLIEGHf0SwL0ZkbXXOdB29zXQ+RKYFzIrlSTnYAACAASURBVEx2ubFIPlniMyDCXfmh9Wch/Ct89KTJOFy436+p98wI0+af6QVjGMrf+yR6LdK3fkN71f0u7vukphpGJwcNJRAioFHOGmItkLZGK8/OOOUDIqWug7MTBfPzh6Q64oaKkEenfDCb8rCyfO8nPsGvffF3Sa7t4bDEUYQpGpRTtKb7TuExLuxV/Po3WSSwShRpG2ivZJYim5CWbhSUwqGMxyaa1nicCm6F47NTUp0SxYpRpKibnIMkI8Ux1h57/hD/8AEv3LxB/vgx9fyU796Z0JxWjJKU/Tjm2siS13PSZkrkE9QIGuOZ7exzVDdcn05Z1jWladB6RF0qrIqQOkTay0FQawhOkLTuIvJgrW38AJ3r9kxCPgka9GhuvxfrF1WlthtVw7mxzWzcBkBsnv/ttmdGmDabEAJnL9M29SsPXO0l34ReL1C8i7yp4bm976LvaO8kkYywTY3Cg2nZn2ZkQIbnYLUgyRJaYrz3jPd2ECj0eMrXI42LPLvXb/Jrv/07ZLs75EWNGqXMixqNpPGuI7cMdW5bH4JWrbcIBNqHfdyo+69aa7RXCK2x3iGdxeKQSdQJoUYqjfCeNE3JVwVZDLsabmYjdnBMpGfmSyKX40Yts8VbTFPIru0zsnNap/BKoJTDtjVIybxZsqzmuFVB6x1vnTQk155HywSNR8oWPRohpjOqBhqzRPqQV5WOM5wAqRWGAJT0YwgB4BmKgpcC6bfD2EPejn5fq1S0Fqxt/qvhsU0TcLhvuyp59Nttz5Qwbe6PhujcVaVFtl23+dyr+Ku+M5gP/X1CJ1dtCLnRkaQ4L5BKY+ocEYcCZN5Lpkoz1RN8ayjymqX0fPXRXWq5g5hm1NYxFjGubNGJpq4aqkgTdXFmre/8at0Ah4rv4fWHGPGtaoHa2aGsanySEEmFdgLlPEYKIhcBCmMkygEiVLagrYiKJXvphHFdsqskU7NEmzl7qeC5vQnGloi4RIkI4xVxmtBUJUkmcQKyWrA/jphECXd9xQGSEkvTGoSOmMS7CJrgoHWGqmxoy4J8VXbBtmB7U7vr68tgwmWNYfEof9mieNL0kghxmcthOE82fUls/d7Lxe2+E+Zd354JYfJcdN6Q7wwucpZ6dWzdZWfdNjCif33R4RfQeK+peuBBy6ABjGk7s8/Q2Ia6rZCRppGwsiCVAi9Zqhh3XjAWCikbXjk+QkvJKM6Q4zG2MJzHDbV1ZOkIZQUeR2M8IooDK5HtKYwjvJLU5YpYaZyxxDrCW0c08vzo7Q/zpTtvwijDOIlAYZRCeEhqQZSm1K1HSNVVkrB4X4fyMFpjj5YoHM63TMaK/VlKFlucKUkyTWErGiPJ0oh8cYbWkspqRJoSRxG2rvFmyY485AXXcG/5iCqe4vQMLVMO9IimqVhELrBHJZo+9FmIkC0iHQgTIhFsF9GtIolzm1kAFwLWz4VtZv82tHbbvqc34dem5BbIfBsI8n7aM4Hm9V6HoQNtK1T+Dibd07TN8/pI9T7iQikFgzgy5xzOw7ysOa9qWpVQekHZOKxKyIWgymLqSczcVJR5wc3pHs7RmY8OqQVeSawA16OJ3R68aUMWrlbBZMRdLAanVc7jh/f53hdeojg7o+MEwHbgheYiRTx0kkd4S4wjlh7TFNh6gfQNSjhwDU1drPcgjXWoKCGJxwDs7e0RxRlGj1jZmNPS0ogR2WyftjLcSMfcTBS7CdCWKNMgref49Izlao7WF3WxoigKKfNKIXwoDL2JzF01od9tzIbXv5OVsnnsD6I9U5ppWDkBnoytC3Aw600oEFA4/6Q/4Wk6MJgXvVCFHJguD5S2DRHQjeuIrmSExSHKkvFoxGldY2KLrxviynI9mTCa7XA/X6K8QEiBdYYiJgSwdrC4Buoh8UvTMBtPQsS1t1RNiPBYRQ150fL6F+7ypz/9R/n8nTfJpQ3UD0ZQo2mrljiOcTL0WyIcY2+RtmFkLFnUsj9O2E0ibsxSxiONNVWXm2VoW0cWjzg/P8cKy0nZ8sbpktxqugB2lHdUFj58mLFrK5ZFTs2IupqTRxMKaWiWOa3JGWd7TMcZPTYwZAYyA5dF0BJPmu2b5v3muF4FfweEUOJdCO68hO4xNOWfFMDN+7+f9kwIU9+GG0Lv/ToCAi6gb9eRnKzrNXE5anzrOtb1UQjhCRt+T5e75Ptoi65ju4ri1SpHQODyVhIvPJUL6N6qbfFCYGvLQZoxRRD7mDPruIdHao30HqUlq7ZGew0eKgDr8VqAC780jlOk1IzHMfPVEr82bxWl1mSTGXce3MPUOcpKlJd4r6i1YoRAViU+coyEYFTV/MUf+2NkGlxTEz/+JvuRIpWCkbeB8jmJcc7jPBg8JycntCLiqDC8dZoHmF1JklHGMi/xXtDqmiKOWJY1B+MZEwHEMXfmc9qmxpLg4ohqtYK2DeV4CJpYRDFGXYxp3zaTP7nEo9cN28aBYThQ//lakOjDzjYDXZ8U2G3+x3fyUz1teyaESbC9NOawXeJp4MJEazsT7WkCFof7piFa1LZtqKc0GFzbGjAtzhCcotKjpaSQgsQIEhXSEFrXUguJkJqv3XlAGUdoCTf299CjiIcnp7i8xitNKbuENxeCQHEepKCoA4m+R2I6c0iLBGpLrjxvuwWirclahY0VhbJor0mdINGCBsO+1uyNItKzM1Isewd7ZOMJsTWhLq9zlHWL1DJkFceS3d1dosxx92zF3TcfI8c7iCZncXYUKgfGMdPdAxLXcPfxYxqbkLqGSLakuuJlLfjB52/z945bluUpSEfuA9+7B3QS0ziLFfqSttmMn7yobrG9Ssa2iX6Vhhqaj0Ninm17pf6a4V77/bRnQpjoymCGYFO/jjLu82CUUggn6Jk8h0CFfIfNY38ea9jV4T1IoREETjkI0QYgGWUZwoOQI1ZNoAU2NsK0LcY06Chh5hOMbLCxJLItN1SCwnMSQ51Jpkbw+vFD/vpf+++o65Zf/if/D7/2679C6UpSIalQRFp1+VOqCxTt0ry739L7sLwUqFbhC49RMSLSIeUcHTScglpqRlVLJC3tJOXLx4954doh7uSEmWhJZiMiAfmyQO3MkL5FqBqZ7tCcWop8wYO8wY4zHj044a/8J3+d5sXbaFVz/MZb3P38V3nlS/8Y0ThsKiijmIwUpKUS8M2zJbfiBMqGKMvQekRrTbdHFGih0E6ufUYQKrw7FwKQL7TI5XjKvm2Dv4fMQ8Eq6eaFlJeCnfE+VDoUoT6V8L1JSPedPV1av8i9c1rPu7VnAoBYm2pbIMwedds0EYZOuKvs3k24fPN94MUTeCe6fJkE12tJ1yUPBiYtBIH/Lo4EqRYk3hELhcHSCM/J/HydxvGpD3+YfHHCS8/f5O9+5hdBOLQMhQFGURzYSZ3j5rVDPvqR78G3hvz0nFmUsjg+5QPXbvKp7/0EpycnzBdzzs7O0CrmD//hP8onP/kplI55cPwYqTxv3rvLj//EH+fo/jH1Wc10ckgtU47ihFW8x9dXNd/ykvSTnyY/fJ57RjNJbuNPa+r6GNVmVJWkNTHFSrD/gQ/z8KTl4b2KeZ4Sf+jjJERYGxGpGVIk1FIg44S8Nrx+do4tS3aTlEQIYkkoJuDDHtjicOLCAbuNIKUft/7Rj+lmcl9/3jYn6+b9Nj8ftou5dhEk3VeKfD/tGdFMXCIs3Ib99+/c0P4lbGyBSyZgf4+LFAy5zpXq7oL3IqRmiGBC6ihoCycCEhcS3xRKQJylZEmCEJokkuRFyMIdZRk6ivBa4poy1JYVgqaoeOMbr3PnrbdDLpL0KFTw+jtLY0OG7w9/+gf5m3/zb/BnfuJP8/zt25w8PsK0H8Vay/71G/zYD/wwn/rk9zNfLjg6PeFwZ4/96Q4ffOFFpBC4smZpHLc++DJ/6i/8qzz//PN89h/9Kj/8sT/EB587RI9GfEo73vj6a9zXB7y+OOOTH/8jLIuY+/NXyJdfJynPKJ3DCkOua7QqSfNjImW5cXvC2/cfIGtoXM4LB7c4Lz1HecVKJxQukLq0ZYWeROyMM1QriKMIARgbSGqM7/PGLrKZ+yTPbeZbH/GwKUx9FP0wPWcTseurpaw1VWe99AgqIsQ8+kHS4lqg36e76ZnQTH27ylTbtIWHHTg8vvl4J7i1t9d7Nptwry6EyQdHsbUhhydWuhtIt874jHWInKidoWxabIiPwXsYjTNuPneLH/mxH6WmCSFDhJUw1oo0jlACbBMCU5977jnu3bvHcrlkOp12C4Hh87/zOf6Hv/VznJ+fcrC7QxJrTo4fMx2l1EXOwbUb/NLf+99omobpdMryfEmUpty6fpNJOuL+8Sm2aLn/+psU8yW2ctQ64zWR8tn7Sx5UKedNjfYC6R3jacxbr72COn9M9fBtHn/zKzz48udxwqBEzvW0ZV+0RMKxKEtKDzodoaIEhycZZWitO1Q2sDo5t31MnkaTDN9vAg/De7w3RO6y+2UbmvjttnfVTEKIFPgskHTnf8Z7/58JIX4e+BcIvHgA/5b3/ndF+Df/LfDTQNEd/513+55tnTo0B3pNo5RaczdsDs7webtzr+tAKRASvO3ADqmI04woSVA6BlrKDhSQUqLiqGPWgdI5YqWwjaH1gvmqwCtJomPqxlB7j1cR6AgZRyGQU3d0wQgEhkBUDG+++QZ/7l/+GX7+F3+en/3zP8uXX30FJxzf+ta32N3f4dOf/nSXwFjzype+iO7i3t5+7WvcvHmTz332n/Kzf+TH+cIvfYYf/PhHGY0zmvkD/sFn/ieeP5ixk95gfyfhgzf2+N0v/DY3shlfe+V3+W9eeY2PTPY4KGpylbKbZIhVzt7+Dn//F/5HhIFGOYS0aGvIveX5axNm1TE74xk3b93iJK+pneSL33yDZnITlcQczxes6oLnDid4D8Y4QOKN65IaQ4RJF5h30b+dFhqad5tEN8MYvc150pv72zgfhikZQZvZJxJP+9/x+y5MXNAjr4QQEfDrQoh/3H32H3jvP7Nx/k8B3909fhj4G93z1c1fjqXbNPP6LNj++PD9NpNwfduBEIZzw/E1uqMuHI1BcEVn/klWqxWuo5uaLxYkcYTSCQpNYR1ZnGKFQMQxzrTspSOK1SlilLBaFvwvv/gLHF67xou3n8dbGzbFKtjmtQlZqK+99hpxHHPz+g1+67c/R9u2LBZh73VyfMQyngdyEA8jFeHqlkQHLfngzh2KyLJYnvHCzWvYs2NG6pCXdsfsqIjbkeb6QcMkKdBJzCdeOiQV+7x99piP7N1gUtXo6YSsbFACPnDzJl+794A29Rgv8S4CB5WyzGTMjXgXEolJYnR9wgsCmhLUc7f5jWbEwiw5KQrKuuKGkCAUINAicJhfQs14ctL3j6F51wvPZiULuBwB3r/fPAYXvkulojVL7/AePVChpHwiWPq9tqchVPHANnrkq9rPAL/QXfebQohdIcQt7/2Dd/uuvjP6DhiuHldxiV+VdjxccS5WosuBs30LmbmaKEqCOSFVV/kumC8GQSQE1nm0D9UthPc0bUMkPLEUZK3h5cNDXj9/hKwhdyXV6WMiUyNVKHzmHdTe4qUI9V9NjTGGT//QD/Gb/+w3+Omf/Em++uqrfOpTn+L89JSz+Zxf/2e/wb/xr/3r+Lplf7bD+ckpWZbx9W9+g5/60R/jwe99g+cOdvno7Rsc3XvAH/8zHyc5X6GOHhHdjtm5luGNZ1QmPCwUP/SRj7P45X/K+PoujpJRfYarcmKt+MSN2zwqTzmtanxtibRkZzziuQymWnLepix8zPnRCdIJqnjKq5XicXVGbQtqKzHOBe4877HW4H0gzuvJrfr9Us9DfqFRuCRc/RhughTD8d3MHOj5J7YJVqBtjsLv2Zgf+B5B/gMIdBWhJ74AvAz89977zwkh/h3gvxJC/KfALwP/kfe+ZkCP3LWeOvnBxj3/MvCXAW4/d+tKM2/TVOujH7aRrGxbvYYpFq7bYarBYLmOYqyqKo6Pj3HOBbhaRSE3hpDnZIVE6ghpPF5J2i7tW3rDwXTMvlXIccxK7YCTlGXJaJxh/JS8cTxaLGhcAIZt51/SSczRg4d87ctf4V/5c3+es+Njvu+DHyQ2lucPbvI9H/owb3/rLX7t//0s/9JP/TSP7j/EGMM8z3np5Zd58JWv8tEXP8wsVhzffcAHDq/xYDEHKdm5fUDmUuZnLY2tmE5HCAO//bnf5NyckosJWRtzdOt53Mkd2mqFayyzSHN9N0YkEyJjuWlqjEh4LGO+fFaxNIq6HbNoc044pq4VQsWUTY3U484McwgRIvJNF+k+HJNegC600GWUrm/D4ObNsb1qr3UJUNjYU/cBzTCA3NeWyQXpy7fbnkqYfBDn7xeBjPLvCyE+BvzHwEMgBn4O+A+B/+Jpv9h7/3PddXzi4x/rUl/kJWaioWkAF1pm2GHrUovCdXVu+2tl8MAP7OV+4fHe4yzr0BOlFFVVcHLyCG9ByJTpdIdFk5M6gYwnTJIYJQznzhA5SLxnhGAnSnErw2PpOVssqb0llQ4Va85XS46LnFYrSuuJdEbkYmrpwFmEaJhkIz758vfxv//8L/L9P/hRvnFyQoXi+mzKgwcPQEZMd3f5X/+v/5tJmlAvc2bA6tEJs6jCHn6R29Mpe+Mxr37NkzjP937qY3ztW69CIdifTHB1y7nwnJYFtw4Oaa/t8YX7j/DjHfzxKfvJGCVjrpeeunTk11Jkm5MZw0JIRmLG59444lztUGiohKN2EHvNQZSwe7jPvXPHickDdbPyKO8ZMUbVHkGMtTlSStI0RSiF1hf5SUrptSl2AXur9R65H+vLDt6NJsTaxTI872LuBF+WGgiR936gi94/FveeoHHv/bkQ4leBn/Te/7XucC2E+FvAv9+9X9Mjd21InXzVnfv7X3nG5urTI3G9zwZxucDZZrGzYZq3X3/nha+qLGvyvMQ6x3w1Zzydcnq0CDBrrGk7FGiiE6wxaC1BShZVjZSSoi5oFDR4tIRYKqbJCGkFjRAwy7h3fIbXEco70kiROo2Y7fKF175M89wOXzy+g2o0tHBUVyihUF5gFxWRh6aqQSsWbYW6NuP5w4+wm2RY5/jK2RlOCm7u7/N/fP4VdoTn2t6Uu+WSZJyxaGqOnOHk7fscWcU4ndK0AucVbWnxs4S32hxlLdSe5dEK2oZkmtCePeCRUJjYo60lQzHNplTecNw0LI9OyeuGdJRivcHVFiugcDU+0zRYmqYhjuP1vgguwIfNsd/mcxpqrk3/4vC5nxPb9tdKheDjbWbjd6I9DZp3DWg7Qerpkf9qvw/q0Ls/C3y5u+QfAv+eEOLvEICH+bvvl57uTw3V/dBHIKXEP4W9+yQM6tf+iFBRr8LhWRU509kYa1vysiCyFuFlqHJnu3QC7ymaGiXAmxYjPE7K4P33wduOcYxVzO54zGlVoRU0WMYGIt1VFxQebSV7ThI7xzKviOIRZWsQKsIrjdUhmkBZS1tWPL+7i2ot34NgZlsmOxMit8J7wbhYMQNuzWZ4U6KEp1gtEUmE9YbWtIz0mFW5omkjRCSIRgmrPIdIIEREYywrJ9HxmBWS2rWcCwWtZWQdoyQO0QxasigbMplghSYSmqqqUC6glQ0OK0P9YWPMOhEzjJ/ermGGs2KLQG1+vu39JsL7TnPh3X7De2nvhx75VzpBE8DvAn+lO///JMDi3yBA4//2u36D4JJgbOY0bSJ2mytL2BeFgEe87LIt4QJv6Dej/QHVrWKBlF9KHQj6kZwvzzk9PebFF1/gld95lcdnJ9yYzCjKgpGWKASmqQJJvoqQzqIThTWeSGsmkSSTClofBNA62rwkxjMF0t0J0aqhwVFoQd42lI/OeXF2SL1qaJqSvG04KXPCvwqE/QAjIEERnS347udf5NxaSCLuHT1kfLhHU9Vcu3aTTGjmpmZlE9pIs6xr6jii0GPeePgat2/vMzMtY2mRcULqam6OExrTcr9YYkYTikhRlYZxvEspAwWZbyGeZMzbCuNaaARjEs6dZaQjWmMpVhVHj4548aUP4JyjKgt2J2PSNGV3dxfRAQ/DKujbglc3HbfboiK2aZYh8HDxWiGEWi+gw7nTz6fvhJZ6P/TIP3HF+R74d9/Tr+gQPMS2UJGLWrbA2sm6mW4+3Ett0uL21yndCdzguv+/vTMPluwqD/vvO+cuvbxtljebRoxGCwMoCCEpwDBCm12OESJOAk6cDaiiKilwqpwiVYkdquxK/rCD/4hdqYq3Ckk5izEQE6MoRbCNRGwICCwsYMaS0MgejWafNzNv7e67nZM/zr3dp+/r92Yl856qv6quvn3u7XPP9p3vO9+aFzmtVgtjoNtN6CUZF+cucO99B0nTHmfPnuWN+27nzInXKKxiJUucyLy7QqA0MxMTpGmONhZtM9pRhOSQFLkLBBOHTnJVZGwJI3Snx8z0DFa5PheNgn/yC/+Mxuw2TBAwqRpIL6Mz2QYlWHH9CJQiyAu0NcSFQRnDQrNJ1M3RhcUGkOQZoQ7I85xekdCRGMld5KI8y7BFSvaF3+HciVdo2i4zUYzSAVtmWrRsgU0y7t41y4tpwrcvdCBU9FYukksTCWLCKMJYISmsyxCCAZPRChVKFyx1lpjdu4Ozp4/z6MMHmT91ls75S2wJIianp5mYmuonGqjmyLcEFxkEiqxYtWodjNIhjQJ/Lbg1UKYFqs7WpZ2mqLWFFdcKG8OcSFysg8IMkl05IdxgIKswViIyCAjvCRj867qirh+UpXCZLcAhkxtcRRTFzMxscbG8VzoknYQHD76bn371E9yx/w727b+Nubk5er0uAFo0lLmFVjpdijwjwmADl/Iyw5Ja60TCNoM0pRFo2nFEpITAGLrdHl2TcvvevXzy4x8jQ9MVlyFjqtXk2EqP+WSJrNxJAwzbCJjRTWZQTIYxf2W2CYVhqj2BFAZVWGyeIRMtXkuWKaJJ5haWSI2liGJ0o0Gj1aCzvMzElkkIY2wnhbal10toKEWyssQbt8xyMrrEShhycmnZzUcA3bwgW+nSCiJyk2MlRxqWHXnqErXtnCY1OUGs+MDf+AmOvfwSr504yfZdO9h7y17iuDGkH6ynynSIBEGwWnlbIdxaiOTrjPpCCuvceCpxOcZFoQKcy41SiFrt1X2tsCGQSRgOdOF2k2IVqwcDJZwvLveRaRS/vFZZmqauvID5S4vs3LmTqDHFgQNvQURz991vYnGhw5ee/iMePvQg3/32czSMdVbISqGU0M1TUBYbaJbzgu7SAiKaWIRGEBAbYaLRoJckEAX0lKZbJORSMNOeIL90gSd+9BDfP3eBV7opaZbRLQr2z76BxeUl532bpcxOTtFUmjxLaDYigkizLCGx0cyLYiXvkuQZUTvGWE2nOU2hhfMxpAq6ocGYFZorXXZObyVpNDh76SJpnjGdZOyOJ0hCw9mVBW5rTWFUwNzcRQoVoFSGznOkCIhUjAqE0FqmrCJe6bJlosF80mWl02VucZ7FziIPPfoI58+d5p2PvIdXXzvOvffd3xcKVYiklCJUeoBUnt5JqYEPWzVfPltvrR0K5F8Jl/ARza5mH+vrBKv6Ut3rhQ2BTBXUReHDZ6KB+X0lPq/u+QM+6v/Vb13tSt47nWWzpTnR5tLiAu974if4n198ilv23MoXn3yKdx48hNbC5//HF3jk4CHOnTiBMgUqL4hCTaRcMJA8zxE0BS7ugaEABIWlOdkmSVO6uWXZ9JgyiiBqsGRSWiGI6dLoddlpG+jWBCpPOWMKJmamWUk7xCrGWEGKnOk4pIlCugVGp1w0lkQJAQWRUsRWcX5pkUuxZiUNSHJFoQzapExGmmakWVo4Q6fjhCVzpsGCQKdnOHtpnjSG4y/8AIkbFFGTIIO8gFCB2B5Fr0NuY+Jmg6Kb0m7EpNMRc6fPk5ZxKH75F/8N23ds5/svHiYvEg688S6AoVQvQRAQyEB5K+JyXtXNivzzUf1TzbHIcEohQbvzshd8qC4qr8Dayqby+hFqwxi61pVrVVldJL6WAm89Tflaz8Mg4dn09DQvvPACM1u28c53vZuJqSl23bKHuXNnyYqCbbPbQSsSZUnFokLPgtniorkC2moUmkhHREHYFwd304RumpAZiw01XZOTYlCxRiholxkmerZg0QxSgDbjBs0gQlP6bmlFKoZUGzoYUmNROsQoTVLkdPKMTEOuFUsaetqSYSgyg7WapbRgyQTMpwFLpoFG0ykKTnaWKaIW3Z5BBzFWB/SKDMRZE2ZiKZSh1dRMt2OmWg2koek1NUfPn+Fir8tyr0uoQh48dIhvfvObLkJsYdizY7bPslXChwEFGv7456i1JHg+21dfP1Ji0ChW8EYgzHqwIShTv3vlLiGihgL4A32K5Jsa+fx39VyVfa+Kseaqte7Qa4tVyAkgxtJuNHns4UdYXlyh10sIg5jOwgJP/v7v80uf+kWOHTvOkb88ys7mLJMzLS5dvFC6XTh2T/IcipwCw/YooB3EKAxdYzl3qcNC0kOFEZLCnHRohiHt1KKY4XyvwbkiQU02ObNwkVwJs21Nnme0wwAtmoQCMzFBonWZ7kXQxomo41bM0soyttUCrej0YnpFhglBqQmstbRaLSyKRhRRlNYZnU6HQDtLeBfZtmB6YhKlNSY3tBouqYHtdkmzjCgKWLQFc/MXnaVIUqBUAFFKajP+9gf/Fvff93aQjP/6X/4TRZqxe9deWq0Z4tLlwqc+KghQWpfnGIUSoUr2BgMpX8WR1JFrFQsoAuJ0joj0o/9WoaadE+HqLJSm9M24EvXKerAhkKmCwWCtpkhpmvbFqZWz4CgKNKpsIIRYbfPlT0gURRx9+SX2vWE/rxz9ATMz03S6S7ztnrfyjf/7LW7Zu4dTK6/Ru9Cj3WzSbMS02y7VZVEU/fxEPeIyiApkpqBjMtIm5BZUAdrkzHdTYrG8euKMe2DsugAAEZdJREFUo1x5Qb6knZt3UXDqQhelIM8ztHKu5oForDVkWeIcFmVYlaDDgCTPaDSbZIUhIKBIs/4YVBYmKlT9lDyig/6YqNK93BfiGGOcY2SoSLoucL+UAqOwrbl48QITjWk+8pGPMLt1CydPnuTUydcIRGFEePt9bxvKShKUUjrfLq/yc4LVUru1FLQ+jJp7/x4MTNMq3WK/z56O8vUhzbM1FwqvT9XkVvlcfUndWlIdGBZoDCR91TMasChVmfYP8jztmt3Bydde5fnnv8P22a10k4R733Y3v/7rv8ozz/wxn/vck0zOzGBti06qWElyIEerEOg6nZU6S4QLDGmtY7PaExMknQ4ajbUxWrdYlAzVDkhtRl5YdGGJbIg1OR0tLC0vYKp4fpkvaTJOmOhysVHGiYE8BQu91LFnYtYwlkm88bKrzTtLodjQtVaQuQgAzM5uYXbHVt785gMceNNdXFzo8bnP/A7/4O//PRYWLmFNThxqnnjfewGIGiGaknvQA3bOIcBq94s64owSQvhzW5/7UdDfVAu7qpwbxPptDGSqQV1B64u4fYVu3Q3D/W81dfLF5jCYlIpl9ENQBUqzbesMYaA4deokWZZw/uIlvvp/vsbDD/0Iv/lrv8SpU6c4evQox479JcvLyyRJl+3bt9PpLtNqtZBwmmYYuQwagaLdbmOARhTSiGKCpkZpiANNFIcEOmRhKSEOYgIVEIcRW3ft4fnnv8OBA3fRbrfLs1d1HrCIcpkHtbiF1ggj1w+rOPLCi1hrufXO20uKDHnaY3pygr233sK5c2ewtiCO40rCDzgqWlGDJEv7bHYjbJFllgvzK5w+M8epU6dZWFzmxZeOcuzVE7RaLW7bv49nnvkKt+/fz56dO3j0kYdoNxt9V/UojN28aB9xglWIU5e4+azeKGRaSzBRgZ8Qr2L3/TVVJZSrr7trgQ2CTPVFXg8DRZ9VqLtn1JOdDbE9HjUb5RJfn7ggCPoW4w89/ChfefoP6KZdvvHNZ9m3bx9nz5/j6Wf+mGPH/oJHHn2Yt779fnbvmgUMH/ib7+fIkcOkSZew2cLmZWZDkyNaIYFAYdBaKPLyEF7k2F7Bi4df5g+e/hJnLixw+vxFJicnecMtMf/ykz9Lq+Wypad5gVZOvybKloprF93V3yzy3PCehx7gM5/5LL/7e0+xY+d2AoEwcoKOf/0LPw+FoCSAQg0tLBfVtiArxzrLCqAg7V2gMJrvHz7MuXOLqDJo5q5dezjy4hECDO/9az/ORKtBoxHx4KGDLp66dayviPR9mIxUlGa1JUNdP1jnQOoSPL/MB/9eXaE/injVWf5rhQ2CTKtD4foUw0eMqtxHorrydngnWp213b+us4VusgVFwI/+2Ht56qkn+ckPfBCU5sv/+yt0jeHH3v9+tkxNYzEcP3Ge48eP8Z4HH+PUiXkKk9Gg009Vk2VZ6YRYUFgXDWc5yzBZTpGnRDqg3Z4mb4bsvnMfew7cwdzceRYWTpEmOd1ej7xIUeKywlcbiGNP3K7fDyFtLegAUZrGxARpmrJ4aZ4oiogjxZ899yJ5bvj615511CdJ6PWcy/vC0hLNZpPKdV88CmLynDwvsCi2TM8Qxg7BZ2amuOeeOzFJwT333MPuXTvcmFtDGEXAcCxEt7gdRaru+dyFz2VU8zYKgao1MrSCagg0SuJXvbPy1PaVtHWW8VpggyCTg8t1xpfuwWhR+ag6RllKAKtYCABrLIJ2oacQHn/8Cb7+9T9hfn6Rxx57hOOnX+XIkSN877mThGHIqRMn+PCHPsTxY68wf2mONE3RqYHSi7co/aOSJOl72Oa2R7PZxGJotRrknZztO6dc/lqlmGxvwyw3mJubY3KqzalTJ7HWJTDzx8F6/L9oJ1yI4iZWCadPnuOuO/Zx6MGDNOMGYRjw4LtfI45Dzp45RavVKmN/w8LCAoWln4e2Ek5UZ5tG2KBAMbNlhk7SY2ZmkizLWF66xPbZbbz5jrvZvn07vc4yYRAQhsPxDd2JrbSL1KqPsP5Z6HLI4yPaKKp0Oda+eqZCoKHowXL9LB5sGGQaoUhjNBUZSGXc4FTBKYfPTaMRbRRf7ksGRYRQW6wp7biMC6h48K++g2a7xdzcHC//YIo337bPiekNnD17lpWlZW57wz6+8Sdfc21Ugipj3AnKuW1bmN62lYXlJXZNzBJGAbpkv6ZntrKylLBt+w6SXoek0yUgYPeOW9mxcysH7noTWgVgg+FNoZTEVfHgrLUYnFv4wfsPMb+yWNo8GkIlvPHO27DG8I//0ccoChfZtvIX8lO2hGHYT0wW6BCTCyjbN/MJQ2dvmCRJiXhOmjk1MYn0wxNrrFRIoAGXXwo9zHVUSOufYas5qs+dkmBojkfN69A6MoLFBcrBDt7R6/WYnJx0m11RoILVbiDXAvLDVGJdKdzz1rvt/3ry8/1BtlQ6owFbFwSuLE3TIZ1DkiREUdRfEJXcapS2G4ZZv1FIi2SYQtASIcadTyAny1OcUj1yLAIFaZahNKR5ThQ49qEfZ6A0OBUU5BaNkCRdVBg4L10l5L0ukQoAhdIBVhS9Xo84DklzhdaC0lX0JO3iKRiDkgBjcxJSVCnqrQQRVMIUQEnsqFU5dkWWl3aNAuICfgY6758rq6An/lgVRYEpUuKmy/znqGKAVgG2AKUirJTZF8V67uGCwTohg9bYwkklqYUv9udi1NmoPy0yMFL22zd0n2F2bVhq6O6HYUiaphRFQbPZdP3z/One8tYHnrPWPrD+ih0NG4IyWWoL2hblOckNdBgOBAn19PMDQQR9XhyGg/tDudNVYmDjcgbZMq2JkoFgAxsSKMEYJ+VxmSsUSIwWQZQlM87o1gXhgEbgsrSHWhEFgs0LKCAszf4LcXU3Gi23MZQ7dtgYWEZX/W+3XVaKIBi4i0TxgCJJiVAimtA0V7E//oFbIdhwIM2SMOqPV3VeCZSLc1E/gw5T+BbWWgLdHL4XlkhB0H9vVJ5tA61d+DMq6eNgbirE0GpYFbIeN+Gfrao2+nM9SojgP1vd66WJo4hRSJpnYGz/XJfl1+e6viGQSVjN2tUHorqus3r1kF9OCpWtWqTuOi9ftjpN4yjJUr99/iTLcNxqvy2DM8BwIq1KqVsPnugje30RKbV6Z159Dhi9U0fl4b+S9Pl1VN9Dh/2awMZHLL/evvV9eb/qr2E4tEA1J76SvI4E4hnO1Vlzv72+O4U/1jBgUUf9z/+uNlZ/nn3oR6tSrwMBhKXKWKBXTeJA6KAYaCcH+iS3IB2bU12r8lM9aa3tZ50AQEx/MuuHVX93H8V6KOfJ6C2kEUrEWuYFGN49/ffU2Zz6xuEjdb2do84OPrWGgf571G6vlOojnL9p+b5gXi5nxFSx39VQOxUyxAlUrFUVFrneTmvduW7UxjnqrORfV2e7qt7KzMwXpdc3pqHNUnk+b6JQgaYwGcaaoc3tWmBDIJMPo8i1G3QZ+Zy/uPyFX8/1VJUPoFTne/dGUcf6wl2vzlH36nXV211PuFaBL+ka9b5RZ8J6Wf2/VZmPdEObjPcfJ8iQIa6hTgnqiF8Xg1evH0U5rLm8xcKoPtTHxV8rdSSq/99a63LZWcfmI/TVLKMo1tXChkGmCgGqBQbDrhfWU07Wyb3ju13aF8SgdEXVSgWvVhhToD3t96DeYd7bh2qiFPQjHYmMnjS/H6O+19t1RWQoCMioXXW9d9bbe3Vtq3EC5W9VQ9Tq/lq/R1EgkWH1w9DzjGYB6799trgah0p079dZUXk/DFxd8lt9u/h6lsIMm6f9f1PaipNv/ilw0lr7hIjsB34X2IaLqfcPrbWpiMTAfwbuBy4Af8dae+xy9fs6JH83H3zU0MBV9+ssSlUOA6Vg9R9/styA4uI0jOLp+2zdoE5/t62/6wrGb937/gLwn6+fc670HZdDPr/ePoG2lAng1mDNavXVKZNfdxUxaq32GTt8JvKfqSNPvQ31dVJdV9R9LS7CvTfHYtAqWHU2NHb1BnI1cDWo+DPAC97vTwG/Yq29E7gEfLQs/yhwqSz/lfK59WGNM8DgtgVbIBiwhUumZXKwRb981KD7O5sft3yobpwepo9oavBxngDOakFk4PJen/z6p16+1nPVwvAtpeuRTuv31/v471nrP/X6UMP3qjqGnhnRn/q76s/XfZJGtaP+3/p3vf7qnfWz9XrUeBVFtKv1i0VRUNjcrYXrgCtCJhHZC7wP+A/lbwEeA6o447+NC/cFLjzyb5fX/x34Ebns9r061LFPceoGrv55qH7gXgtxfBI+0vlQht8NLi64NwarqMWavandXw/5Rv33h/G5EqQcdb/u/Vqvb6179b6s9ex647be+W89itVv+zqsqr+2qk1yrVDbVwpXyub9KvDPgcny9zZg3lpbCearEMjghUe21uYislA+P+dXKF545D17dvfLXXZAd8bxdROVCEJMKYkJgqFcPL4xqy31VLZKZlYTLNTZxcE7SouFUrJYxSyonh8ohQesoqNaA+9OY+hPjtfX8sqUPRltV+azSwXDCD0kjpfhxaRr/JSp6pfKhs9Ft60orXPNcNn0bK2uuhBDA+KJuEex41UfbPmcY5d0P+TaUNtsyYqVm5coSxUxyGVOFJfo2RuPCjEqtr2PPKW8oygK55MkYKzLFFiU0Yiqc3PFwtl+O5znstuoy/fY6zszXfbfIvIEcM5a+9x1vakG1trfstY+YK19YOuWLX75Wu0YeqZazP45yOeV16qn1oahBVx/R/259eq8kveNglHUyV9Ea1Gw6r+XJfrrvGdUXVdDga8HruQddYruQ31O1pqj1UKR0dzA9Qof4Moo0yHgr4vI40ADmMLlX5oRkaCkTn4I5Co88gkRCYBpnCBiTTh85M+X7zhw70vX2IeNDNupUeTXCbye+7Xvmv/tY/TlPsAjwFPl9eeBnyqvfwP4eHn908BvlNc/hYsAe7l6//Rq2rFZPuN+ba7P9fbremjbvwA+ISJHcWeiT5flnwa2leWfAH72Ot4xhjFsGrjaLBhfBb5aXv8F8I4Rz/SAn7wBbRvDGDYVbJS4eb91sxvwQ4JxvzYXXFe/NoQ/0xjG8HqAjUKZxjCGTQ83HZlE5MdF5CUROSoim0pYISL/UUTOichhr2yriPyhiLxcfm8py0VE/l3Zz++JyH03r+Xrg4jcKiLPiMifi8gREfmZsnxT901EGiLyLRH5btmvf1WW7xeRZ8v2f1ZEorI8Ln8fLe/ftu4LbrIoUgOvALfjcuN+F3jLzRaRXkX7HwLuAw57Zb+MS5YNTpL5qfL6ceBLOEOOdwHP3uz2r9Ov3cB95fUk8APgLZu9b2X7JsrrEHi2bO/nGFbzfKy8/jjDap7Prlv/Te7cQeDL3u+fA37uZg/6VfbhthoyvQTsLq93Ay+V178J/N1Rz230D/BFXPrV103fgBbwHVyq2DkgKMv7axL4MnCwvA7K52StOm82m9e34yvBt/HbrLDTDnL4ngF2ltebsq8la/N23C6+6fsmIlpEngfOAX+I44yuyM4UqOxMR8LNRqbXNVi3pW1acamITAC/B/xTa+2if2+z9s1aW1hr78WZwL0DeNONqvtmI1Nlx1eBb+O3WeGsiOwGKL/PleWbqq8iEuIQ6b9Za79QFr8u+gZgrZ0HnsGxdTOlHSmMtjPlSuxMbzYyfRu4q5SmRLhD3pM3uU3XC08CHy6vP4w7b1TlHyolX+8CFjyWaUOBONPqTwMvWGv/rXdrU/dNRGZFZKa8buLOgS/gkOqD5WP1flX9/SDwdEmRR8MGOAg+jpMWvQJ88ma35yrb/hngNJDheO2P4njqrwAvA38EbC2fFeDfl/38PvDAzW7/Ov16EMfCfQ94vvw8vtn7BtwD/FnZr8PAz5fltwPfAo7iDLjjsrxR/j5a3r99vfrHFhBjGMMNgpvN5o1hDK8bGCPTGMZwg2CMTGMYww2CMTKNYQw3CMbINIYx3CAYI9MYxnCDYIxMYxjDDYIxMo1hDDcI/h/lLrp64FrE9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "\n",
    "image = Image.open('./data/phone.jpg')\n",
    "\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('n02992529', 'cellular_telephone', 0.7631687),\n",
       " ('n03584254', 'iPod', 0.12571256),\n",
       " ('n03908618', 'pencil_box', 0.015993062)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras import preprocessing\n",
    "\n",
    "img = preprocessing.image.load_img('./data/phone.jpg', target_size=(299, 299))\n",
    "x = preprocessing.image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = inception_v3.preprocess_input(x)\n",
    "predictions = model.predict(x)\n",
    "labels = inception_v3.decode_predictions(predictions, top=3)[0]\n",
    "\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import time \n",
    "\n",
    "# get the reference to the webcam\n",
    "camera = cv2.VideoCapture(0)\n",
    "camera_height = 500\n",
    "\n",
    "while(True):\n",
    "    # read a new frame\n",
    "    _, frame = camera.read()\n",
    "    \n",
    "    # flip the frameq\n",
    "    frame = cv2.flip(frame, 1)\n",
    "\n",
    "    # rescaling camera output\n",
    "    aspect = frame.shape[1] / float(frame.shape[0])\n",
    "    res = int(aspect * camera_height) # landscape orientation - wide image\n",
    "    frame = cv2.resize(frame, (res, camera_height))\n",
    "\n",
    "    # add rectangle\n",
    "    cv2.rectangle(frame, (300, 75), (650, 425), (240, 100, 0), 2)\n",
    "\n",
    "    # get ROI\n",
    "    roi = frame[75+2:425-2, 300+2:650-2]\n",
    "    \n",
    "    # parse BRG to RGB\n",
    "    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    # resize to 224*224\n",
    "    roi = cv2.resize(roi, (399, 399))\n",
    "    roi = inception_v3.preprocess_input(roi)\n",
    "\n",
    "    # predict!\n",
    "    roi2 = np.array([cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)])\n",
    "\n",
    "    predictions = model.predict(roi2)\n",
    "\n",
    "    labels = inception_v3.decode_predictions(predictions, top=3)[0]\n",
    "\n",
    "    # add text\n",
    "    label_1 = '{} - {}%'.format(labels[0][1], int(labels[0][2]*100))\n",
    "    cv2.putText(frame, label_1, (70, 170), \n",
    "                cv2.FONT_HERSHEY_SIMPLEX, 0.9, (20, 240, 150), 2)\n",
    "\n",
    "    # add text\n",
    "    label_2 = '{} - {}%'.format(labels[1][1], int(labels[1][2]*100))\n",
    "    cv2.putText(frame, label_2, (70, 200), \n",
    "                cv2.FONT_HERSHEY_SIMPLEX, 0.9, (20, 240, 240), 2)\n",
    "\n",
    "    # add text\n",
    "    label_3 = '{} - {}%'.format(labels[2][1], int(labels[2][2]*100))\n",
    "    cv2.putText(frame, label_3, (70, 230), \n",
    "                cv2.FONT_HERSHEY_SIMPLEX, 0.9, (20, 20, 240), 2)\n",
    "\n",
    "    # show the frame\n",
    "    cv2.imshow(\"Real Time object detection\", frame)\n",
    "\n",
    "    key = cv2.waitKey(1)\n",
    "\n",
    "    # quit camera if 'q' key is pressed\n",
    "    if key & 0xFF == ord(\"q\"):\n",
    "        break\n",
    "\n",
    "camera.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://camo.githubusercontent.com/53f6fb6ccefefd87dc93af20b8e7972573de9828/68747470733a2f2f757365722d696d616765732e67697468756275736572636f6e74656e742e636f6d2f373036353430312f33393131393531382d35396661353163652d343665632d313165382d383530332d3566383133363535386632622e706e67\">\n",
    "<a href=\"https://rmotr.com\" target=\"_blank\">rmotr.com</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}