Feature+visualization+using+Deconvnets+in+Keras.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This notebook describes the implementation of deconvnets(transpose convolution) in keras and further use it for the purpose of feature visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import sys\n",
    "import time\n",
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.layers import (\n",
    "        Input,\n",
    "        InputLayer,\n",
    "        Flatten,\n",
    "        Activation,\n",
    "        Dense)\n",
    "from keras.layers.convolutional import (\n",
    "        Convolution2D,\n",
    "        MaxPooling2D)\n",
    "from keras.activations import *\n",
    "from keras.models import Model, Sequential\n",
    "from keras.applications import vgg16, imagenet_utils\n",
    "import keras.backend as K\n",
    "import numpy\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here for each layer we have defined a class for each type of layer. We have defined methods for forward pass\n",
    "and backward pass. During backward pass we use deconvnet. Here the term deconvolution is used for decribing transpose convolution operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "class DConvolution2D(object):\n",
    "    \n",
    "    def __init__(self, layer):\n",
    "\n",
    "        self.layer = layer\n",
    "\n",
    "        weights = layer.get_weights()\n",
    "        W = weights[0]\n",
    "        b = weights[1]\n",
    "\n",
    "        \n",
    "        filters = W.shape[3]\n",
    "        up_row = W.shape[0]\n",
    "        up_col = W.shape[1]\n",
    "        input_img = keras.layers.Input(shape = layer.input_shape[1:])\n",
    "\n",
    "        output=keras.layers.Conv2D(filters,(up_row,up_col),kernel_initializer=tf.constant_initializer(W),\n",
    "                                   bias_initializer=tf.constant_initializer(b),padding='same')(input_img)\n",
    "        self.up_func = K.function([input_img, K.learning_phase()], [output])\n",
    "        # Deconv filter (exchange no of filters and depth of each filter)\n",
    "        W = np.transpose(W, (0,1,3,2))\n",
    "        # Reverse columns and rows\n",
    "        W = W[::-1, ::-1,:,:]\n",
    "        down_filters = W.shape[3]\n",
    "        down_row = W.shape[0]\n",
    "        down_col = W.shape[1]\n",
    "        b = np.zeros(down_filters)\n",
    "        input_d = keras.layers.Input(shape = layer.output_shape[1:])\n",
    "\n",
    "        output=keras.layers.Conv2D(down_filters,(down_row,down_col),kernel_initializer=tf.constant_initializer(W),\n",
    "                                   bias_initializer=tf.constant_initializer(b),padding='same')(input_d)\n",
    "        self.down_func = K.function([input_d, K.learning_phase()], [output])\n",
    "\n",
    "    def up(self, data, learning_phase = 0):\n",
    "        #Forward pass\n",
    "        self.up_data = self.up_func([data, learning_phase])\n",
    "        self.up_data=np.squeeze(self.up_data,axis=0)\n",
    "        self.up_data=numpy.expand_dims(self.up_data,axis=0)\n",
    "        #print(self.up_data.shape)\n",
    "        return self.up_data\n",
    "\n",
    "    def down(self, data, learning_phase = 0):\n",
    "        # Backward pass\n",
    "        self.down_data= self.down_func([data, learning_phase])\n",
    "        self.down_data=np.squeeze(self.down_data,axis=0)\n",
    "        self.down_data=numpy.expand_dims(self.down_data,axis=0)\n",
    "        #print(self.down_data.shape)\n",
    "        return self.down_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class DActivation(object):\n",
    "    def __init__(self, layer, linear = False):\n",
    "        \n",
    "        self.layer = layer\n",
    "        self.linear = linear\n",
    "        self.activation = layer.activation\n",
    "        input = K.placeholder(shape = layer.output_shape)\n",
    "\n",
    "        output = self.activation(input)\n",
    "        # According to the original paper, \n",
    "        # In forward pass and backward pass, do the same activation(relu)\n",
    "        # Up method\n",
    "        self.up_func = K.function(\n",
    "                [input, K.learning_phase()], [output])\n",
    "        # Down method\n",
    "        self.down_func = K.function(\n",
    "                [input, K.learning_phase()], [output])\n",
    "\n",
    "   \n",
    "    def up(self, data, learning_phase = 0):\n",
    "        \n",
    "        self.up_data = self.up_func([data, learning_phase])\n",
    "        self.up_data=np.squeeze(self.up_data,axis=0)\n",
    "        self.up_data=numpy.expand_dims(self.up_data,axis=0)\n",
    "        print(self.up_data.shape)\n",
    "        return self.up_data\n",
    "\n",
    "   \n",
    "    def down(self, data, learning_phase = 0):\n",
    "        \n",
    "        self.down_data = self.down_func([data, learning_phase])\n",
    "        self.down_data=np.squeeze(self.down_data,axis=0)\n",
    "        self.down_data=numpy.expand_dims(self.down_data,axis=0)\n",
    "        print(self.down_data.shape)\n",
    "        return self.down_data\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "class DInput(object):\n",
    "\n",
    "    def __init__(self, layer):\n",
    "        self.layer = layer\n",
    "    \n",
    "    # input and output of Inputl layer are the same\n",
    "    def up(self, data, learning_phase = 0):\n",
    " \n",
    "        self.up_data = data\n",
    "        return self.up_data\n",
    "    \n",
    "    def down(self, data, learning_phase = 0):\n",
    "\n",
    "        #data=np.squeeze(data,axis=0)\n",
    "        data=numpy.expand_dims(data,axis=0)\n",
    "        self.down_data = data\n",
    "        return self.down_data\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "class DDense(object):\n",
    "  \n",
    "    def __init__(self, layer):\n",
    "\n",
    "        self.layer = layer\n",
    "        weights = layer.get_weights()\n",
    "        W = weights[0]\n",
    "        b = weights[1]\n",
    "        \n",
    "        # Up method\n",
    "        input = Input(shape = layer.input_shape[1:])\n",
    "        output = keras.layers.Dense(layer.output_shape[1],\n",
    "                 kernel_initializer=tf.constant_initializer(W), bias_initializer=tf.constant_initializer(b))(input)\n",
    "        self.up_func = K.function([input, K.learning_phase()], [output])\n",
    "        \n",
    "        #Transpose W  for down method\n",
    "        W = W.transpose()\n",
    "        self.input_shape = layer.input_shape\n",
    "        self.output_shape = layer.output_shape\n",
    "        b = np.zeros(self.input_shape[1])\n",
    "        flipped_weights = [W, b]\n",
    "        input = Input(shape = self.output_shape[1:])\n",
    "        output = keras.layers.Dense(self.input_shape[1:],\n",
    "                 kernel_initializer=tf.constant_initializer(W), bias_initializer=tf.constant_initializer(b))(input)\n",
    "        self.down_func = K.function([input, K.learning_phase()], [output])\n",
    "    \n",
    "\n",
    "    def up(self, data, learning_phase = 0):\n",
    "      \n",
    "        \n",
    "        self.up_data = self.up_func([data, learning_phase])\n",
    "        self.up_data=np.squeeze(self.up_data,axis=0)\n",
    "        self.up_data=numpy.expand_dims(self.up_data,axis=0)\n",
    "        print(self.up_data.shape)\n",
    "        return self.up_data\n",
    "        \n",
    "    def down(self, data, learning_phase = 0):\n",
    "    \n",
    "        self.down_data = self.down_func([data, learning_phase])\n",
    "        self.down_data=np.squeeze(self.down_data,axis=0)\n",
    "        self.down_data=numpy.expand_dims(self.down_data,axis=0)\n",
    "        print(self.down_data.shape)\n",
    "        return self.down_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "class DFlatten(object):\n",
    "\n",
    "    def __init__(self, layer):\n",
    "   \n",
    "        self.layer = layer\n",
    "        self.shape = layer.input_shape[1:]\n",
    "        self.up_func = K.function(\n",
    "                [layer.input, K.learning_phase()], [layer.output])\n",
    "\n",
    "    # Flatten 2D input into 1D output\n",
    "    def up(self, data, learning_phase = 0):\n",
    "\n",
    "        \n",
    "        self.up_data = self.up_func([data, learning_phase])\n",
    "        self.up_data=np.squeeze(self.up_data,axis=0)\n",
    "        self.up_data=numpy.expand_dims(self.up_data,axis=0)\n",
    "        print(self.up_data.shape)\n",
    "        return self.up_data\n",
    "\n",
    "    # Reshape 1D input into 2D output\n",
    "    def down(self, data, learning_phase = 0):\n",
    "\n",
    "        new_shape = [data.shape[0]] + list(self.shape)\n",
    "        assert np.prod(self.shape) == np.prod(data.shape[1:])\n",
    "        self.down_data = np.reshape(data, new_shape)\n",
    "        return self.down_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "class DPooling(object):\n",
    "\n",
    "    def __init__(self, layer):\n",
    "\n",
    "        self.layer = layer\n",
    "        self.poolsize = layer.pool_size\n",
    "       \n",
    "    \n",
    "    def up(self, data, learning_phase = 0):\n",
    "\n",
    "        [self.up_data, self.switch] = \\\n",
    "                self.__max_pooling_with_switch(data, self.poolsize)\n",
    "        return self.up_data\n",
    "\n",
    "    def down(self, data, learning_phase = 0):\n",
    "      \n",
    "        self.down_data = self.__max_unpooling_with_switch(data, self.switch)\n",
    "        return self.down_data\n",
    "    \n",
    "    def __max_pooling_with_switch(self, input, poolsize):\n",
    "\n",
    "        switch = np.zeros(input.shape)\n",
    "        out_shape = list(input.shape)\n",
    "        row_poolsize = int(poolsize[0])\n",
    "        col_poolsize = int(poolsize[1])\n",
    "        print(row_poolsize)\n",
    "        print(col_poolsize)\n",
    "        out_shape[1] = math.floor(out_shape[1] / poolsize[0])\n",
    "        out_shape[2] = math.floor(out_shape[2] / poolsize[1])\n",
    "        print(out_shape)\n",
    "        pooled = np.zeros(out_shape)\n",
    "        \n",
    "        for sample in range(input.shape[0]):\n",
    "            for dim in range(input.shape[3]):\n",
    "                for row in range(out_shape[1]):\n",
    "                    for col in range(out_shape[2]):\n",
    "                        patch = input[sample, \n",
    "                                row * row_poolsize : (row + 1) * row_poolsize,\n",
    "                                col * col_poolsize : (col + 1) * col_poolsize,dim]\n",
    "                        max_value = patch.max()\n",
    "                        pooled[sample, row, col,dim] = max_value\n",
    "                        max_col_index = patch.argmax(axis = 1)\n",
    "                        max_cols = patch.max(axis = 1)\n",
    "                        max_row = max_cols.argmax()\n",
    "                        max_col = max_col_index[max_row]\n",
    "                        switch[sample, \n",
    "                                row * row_poolsize + max_row, \n",
    "                                col * col_poolsize + max_col,\n",
    "                              dim]  = 1\n",
    "        return [pooled, switch]\n",
    "    \n",
    "    # Compute unpooled output using pooled data and switch\n",
    "    def __max_unpooling_with_switch(self, input, switch):\n",
    "\n",
    "        print('switch '+str(switch.shape))\n",
    "        print('input  '+str(input.shape))\n",
    "        tile = np.ones((math.floor(switch.shape[1] / input.shape[1]), \n",
    "            math.floor( switch.shape[2] / input.shape[2])))\n",
    "        print('tile '+str(tile.shape))\n",
    "        tile=numpy.expand_dims(tile,axis=3)\n",
    "        input=numpy.squeeze(input,axis=0)\n",
    "        out = np.kron(input, tile)\n",
    "        print('out '+str(out.shape))\n",
    "        unpooled = out * switch\n",
    "        unpooled=numpy.expand_dims(unpooled,axis=0)\n",
    "        return unpooled\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize(model, data, layer_name, feature_to_visualize, visualize_mode):\n",
    "    deconv_layers = []\n",
    "    # Stack layers\n",
    "    for i in range(len(model.layers)):\n",
    "        if isinstance(model.layers[i], Convolution2D):\n",
    "            deconv_layers.append(DConvolution2D(model.layers[i]))\n",
    "            deconv_layers.append(\n",
    "                    DActivation(model.layers[i]))\n",
    "        elif isinstance(model.layers[i], MaxPooling2D):\n",
    "            deconv_layers.append(DPooling(model.layers[i]))\n",
    "        elif isinstance(model.layers[i], Dense):\n",
    "            deconv_layers.append(DDense(model.layers[i]))\n",
    "            deconv_layers.append(\n",
    "                    DActivation(model.layers[i]))\n",
    "        elif isinstance(model.layers[i], Activation):\n",
    "            deconv_layers.append(DActivation(model.alyers[i]))\n",
    "        elif isinstance(model.layers[i], Flatten):\n",
    "            deconv_layers.append(DFlatten(model.layers[i]))\n",
    "        elif isinstance(model.layers[i], InputLayer):\n",
    "            deconv_layers.append(DInput(model.layers[i]))\n",
    "        else:\n",
    "            print('Cannot handle this type of layer')\n",
    "            print(model.layers[i].get_config())\n",
    "            sys.exit()\n",
    "        if layer_name == model.layers[i].name:\n",
    "            break\n",
    "\n",
    "    # Forward pass\n",
    "    deconv_layers[0].up(data)\n",
    "    for i in range(1, len(deconv_layers)):\n",
    "        deconv_layers[i].up(deconv_layers[i - 1].up_data)\n",
    "\n",
    "    output = deconv_layers[-1].up_data\n",
    "    print(output.shape)\n",
    "    assert output.ndim == 2 or output.ndim == 4\n",
    "    if output.ndim == 2:\n",
    "        feature_map = output[:, feature_to_visualize]\n",
    "    else:\n",
    "        feature_map = output[:,:, :, feature_to_visualize]\n",
    "    if 'max' == visualize_mode:\n",
    "        max_activation = feature_map.max()\n",
    "        temp = feature_map == max_activation\n",
    "        feature_map = feature_map * temp\n",
    "    elif 'all' != visualize_mode:\n",
    "        print('Illegal visualize mode')\n",
    "        sys.exit()\n",
    "    output = np.zeros_like(output)\n",
    "    if 2 == output.ndim:\n",
    "        output[:, feature_to_visualize] = feature_map\n",
    "    else:\n",
    "        output[:,: , :, feature_to_visualize] = feature_map\n",
    "\n",
    "    \n",
    "    # Backward pass\n",
    "    deconv_layers[-1].down(output)\n",
    "    for i in range(len(deconv_layers) - 2, -1, -1):\n",
    "        deconv_layers[i].down(deconv_layers[i + 1].down_data)\n",
    "    deconv = deconv_layers[0].down_data\n",
    "    deconv = deconv.squeeze()\n",
    "\n",
    "    return deconv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = \"E:\\GitHub\\Transposed convolution Zeiler\\kangaroo.jpg\"\n",
    "layer_name = 'block3_conv3'\n",
    "feature_to_visualize = 127\n",
    "visualize_mode = 'all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = vgg16.VGG16(weights = 'imagenet', include_top = True)\n",
    "layer_dict = dict([(layer.name, layer) for layer in model.layers])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_8 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and preprocess\n",
    "img = Image.open(image_path)\n",
    "img = img.resize((224, 224),resample=PIL.Image.NEAREST)\n",
    "\n",
    "img_array = np.array(img)\n",
    "img_array = img_array[np.newaxis, :]\n",
    "img_array = img_array.astype(np.float)\n",
    "img_array = imagenet_utils.preprocess_input(img_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Original image\n",
    "plt.imshow(img_array[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IN\n",
      "act\n",
      "(1, 224, 224, 64)\n",
      "act\n",
      "(1, 224, 224, 64)\n",
      "2\n",
      "2\n",
      "[1, 112, 112, 64]\n",
      "act\n",
      "(1, 112, 112, 128)\n",
      "act\n",
      "(1, 112, 112, 128)\n",
      "2\n",
      "2\n",
      "[1, 56, 56, 128]\n",
      "act\n",
      "(1, 56, 56, 256)\n",
      "act\n",
      "(1, 56, 56, 256)\n",
      "act\n",
      "(1, 56, 56, 256)\n",
      "(1, 56, 56, 256)\n",
      "(1, 56, 56, 256)\n",
      "(1, 56, 56, 256)\n",
      "(1, 56, 56, 256)\n",
      "switch (1, 112, 112, 128)\n",
      "input  (1, 56, 56, 128)\n",
      "tile (2, 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:60: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out (112, 112, 128)\n",
      "(1, 112, 112, 128)\n",
      "(1, 112, 112, 128)\n",
      "switch (1, 224, 224, 64)\n",
      "input  (1, 112, 112, 64)\n",
      "tile (2, 2)\n",
      "out (224, 224, 64)\n",
      "(1, 224, 224, 64)\n",
      "(1, 224, 224, 64)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "deconv = visualize(model, img_array, \n",
    "        layer_name, feature_to_visualize, visualize_mode)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQWG0jJbRosr/BZILAY2ro4M+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#After deconvolution-\n",
    "plt.imshow(deconv)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOAAAADgCAIAAACVT/22AACr+ElEQVR4nNz9WZMkSZImiH3MLKKq\nZubucWVm3dXXXD07Q8DsAAtaAvYFBMIbnvCAf4XBHwHRAi94A4iAJRAwu7OzO4Tunp7pnq6uriMz\nKzIj/DAzVRVhZjyIiKq6R+QVGZkR1VKV4e5mamqiIix8fHzRv/pX/wpvaRDBHQDg7nAmLn8AREwA\nmyrcQWBmd4cDBK/XgEAOENHy1vswCORwOIgIBKLymE4OzSaBJUaDg5mYx+vTeHt88uwZoJrnbuhz\nVnMO/W7OqdvF6XQXuy6y3L28FWYnhBgcJkFyzkREHNKYXJ0CC/h4exu7zgPt9rvpfA4iLGxmEiIJ\nu1meEws74G5lfu5OaBvxHoyyjct0iOq+lnmCiIjMrPwOdwfcnJkBB4G/1dfeH95mQEQEIiZiRtlV\nFmL2+jYTEbxcX/4qnwCIiAkEFJp4w8m97eHwcmjcncDCzFyWFSAOwZmN2UM4j+nF57eAmPo8znma\n5zmri8newt67/WnKyQwSxnHOczZVIgKDmUDOQSSElDUl59CH0LtDQuQQd5dXJEHNzWAOYiFmIjGz\nMjd3L6ccG2r4fRl18mXmDncvxADQmxIo6HXkQ/XL6qickUCV3oiJGeV8L0TZWGhd35VTlSm+47Es\nmTuszdNQqcGArDrO85jzBFKJo8FDVCJVU3h2JMik8ZwlUXfKmojRdc4MZnUzV9WspppVTR0Ai/Q9\nx0Gdwm7f7ffJLJsjRAVlM3OYeUqqqoU24Sj/NNp89+u2jM1pIYDKAhaCrG+5u/lysZdrnMqjhTf9\n2i86pQR4oX2gfoeD3Kxw1Sq+65VlTl4osZ4hW2/9rjkBlf+ciMBE5AQAaubmjcWTZWMWjh2FOBwu\nDo8eXT5+NJ0sn293+10feuP9efY5zwfek4TY9UScVVWEGOamZgKom2V1JCd32Hg6zrd3HMhyr9M8\nn0YJgZnyPK/SsK4cg4roLFsLJji9++Urg+o/q5Av/KmefffKhtz9wYcAAG9KoK8d61esmkb9bocj\nrxNsh6gK9IXDe32bHt7wnYyqs6zSwt2yuTsRmBjumpKpWRcoduNpTMfx+NnLm+fPPd2Ot9eEbDIY\nj6pDEESj+Xi6Y9L+PJ1O+y7GLsQgrt71HRE7iEhihFDQrLEXd815FOh8e5Pc+sPe3TQrFwljICJm\nAsGK4kTknp3eIwqtArJQ4xfwdmYiYS/Mk4nBKNbIWybQBxPb8EpaZudV6jtAXn+pH2hn6b2ST8U+\nqgoJQE4gYik6NYlITlnBpM7qF8NuGvb55rbv9eIwxC4i9mry4uXN3eku+jyALodhGPr8+NFhv1dP\nni2nPEk+nUY4Qgiqpp448OMPH5/Pd57yftfPx3E6nXf7gVZtiMzVVBXtVFfN2IUqc6J3rSOtp4RW\nZlWW0qvd6YvdbGpF8BeOSsR4cxG/zGA13VcrbJ0Vrf+B67zcTJjd3cxoy/ypKqZ1lvSA7X/fg1Bk\nuqMqTF5FvpklNXNNSl0XY4xdQNfn80zAxX7XCfcREofQddztcgq4DMFtB4qHQ9cFYQoiULOUOHCQ\n0He7ORGBQiBO2WYl593hQKzT3XHoOs4mRFcXl6dpnKcpdhGAAjAXZpBb3VfiwlHfh1Nej7Rv/1yG\nuxX7pGigZqqqABF4sffeAgfd3muhVLr3A4CXwyFSLCQzg5ubGTfq3RBiI4h3LaBWAbCurDcryWDQ\nOY9JdzvSWWUnPYlE7h5fBE6W85jG3snTUZP0w0HigQQ65vHmVmOEueZs5jmbCWdFVg8hgEk6Ftjd\n9c2nv/wtcz7dXp9DnO5GTZ4S+ssLEOUxkQgMcGciCJOaNQgFVUtdH+HdjMZfNpYw4CAmApgaQyIu\nh7+BP+xmBdejtynim35B618VLkA54O5gJsKWcmnZc2+UutVG3wc24HDyypMcgJOQ9H3sOhkSO4nJ\nZx9/onwTLw5D37PO5/NdHzRTFs02g3LM6QwhM3O3aZqVTDqBU5ROmBKRMDOxpuzJQgx9N+TxxSnN\njz64kCDMePzRE0b3u09fgmPYd2ZK7gzKKcONhMyMRVhCga6IuS7lu17D7fdXE9kBwNwY7AUlY7Aw\nGcHBzA6YmZkR09sh0HY+aPlRNTaqWDzxyukXxMuJUPBYb4oAwatOsn3EauW9lal+s1Ghz8qT3N3N\nScBRwmFnFMRZLOwPjygG7OJwsbsY+un2RR9dPTHEVOxkx9upPwynNHpAd7nrdz05zddHz0bmSsxh\nKKY4E4kwuTPTkw+efvizD0+3eyE8++FHh93TP/s3fzHPykHcMwkJMciJQUKkSiLMwQwF5faH6/hO\nR4M6iZmDEAAlBpkb3AoYzsx5zu6ZANNi9b0lAgWwYsT3eGRV4qqBWd5uNgfDKmJP98w7IsBX9vnu\nzdHiPgJAcIK5uWZKU5qTIIgZgoTdIPtd1+3caDymGVOaz5Hjhz/8qTvdfvrp/tnV8OyRdXn2ROQC\nxnlWSw5XxzzO86ih60If3HyaRmLiLrx8+WK8uxb3eZ6H7ubu7nbYX8ShU4UIwREogkAMEiFm8io0\n4eaLG+xdj9XIqGewMC+uhjoBBFUr7MBUCaQ5szDeFgct3968lW1a7gvqSSLCTdBXwNbNndQqykxo\nrtKHlvy7tJSIuFKnmzncmFn6KH0Hw3Qa2TlShLtOKSW8+M2Lm0+e33z+myc/vFCfppvx+ac3IXe/\n/A9/9/P5Hzz7kx+ebq4/f/G7nMfL/eVAcbc/XFxdGYXxrDmfYxdD4PHuLp/nYbfb7/YpnyOF+XS6\nOV3Pvacpx84wz/N0EibLWtx1DgNALOSU5hyCNE30XTDRrd1cWRYVH6G7m6nOuYD1VFW+uvkcgkhg\nCUQ8TzMR3O3twkyLP2AxeQlUvQbuTUQutLiBTdEA3HfOLh+MRZletGPL6piIhQhCTPAuCIdO0XcX\nFxd8+bM/+smf/Bd/HC/k07/6zfH5aR8uKO73j6+O18cXd589+ehJv4s2Zj8nMuSss+XTORVvPhww\nlxj6w+729m4+3w272F9c9PvdfvfE5NpBgAURCWwACMRsBncwiJhCEGZe3SDvYMkefmmLuqjoWCHN\nhjOVdwwOczUHmROxmbEwvlscFFjMSXhzaKNxR9pax5vnWgW8vx9Cyt3XxSUA2cwsRuk4BAoEcnMi\nJ3Z3u759gXn0/o91h6OezzT+8Oc//2H0GLrbz1+cp+5wdcUdnca7Oamz+jRng5n2+x6WT9d3mtLl\n08ePnj158ekn491JaNcN3TzrePfy+vld7HsZCEKyRC8QSQiqugYzMF5R5N/5WPS7ouDBC8lSFU/g\nEktEqotKSm5vUwe9N1bCWtwxBPBWiBNxs9S9Oe19w4OXByufL9Lq+6bV5oZ1FKSRiZmFhANL9mya\nmZjMgYxMErrofvfi5vO/+c3J7n7157/o+93LR48//+zTi8vHKY06JrubeRc77mUv0nfcBcpK5iFK\nPk/pfGImsGabweh2Q4kRyWPOowIUu056VnMUxJuZiMEgW4J0Fh3p3Wvvm0HLtqPZKwtUQ0QMciZi\nQYHrqyv3WwP1X2diZayq5PLKOmunGkZAzAxrWmlxLlW+u+BX3/cokRiVGIhNFapgmBoTO2Bunmci\n7/r+6UeP9NMbPU9Zx/1hd7i4jEKPrg4/+umPxrvp+PnnL3/72e7RgSQ6YNmY1MxqfJdp7MP+6hD7\nMN7ezseTlK+UCI4SolFCYHVTUy5eOCeHw4hqBKP71sB8r4Z7QXSaU6FyqKI9GazoUWYOImYplPzG\n0UxfNZkHlNT00IVM3ayGh8Ld3M29BJMtrvvlFuUJ3L9vpGk96gCIhFHVfFPVrOpwEuYYiMWLPqg2\nncbzzV06TY+fPP3xz/8A4OP13fn27vlvPwHRsx//eNhdDN0hht6zpynplHVWy6Zz1qyx74eLvbsf\nr2/zPDMzqofdDJlEnZIjl5gwVTNVU1PVAiiabZfo3bPPdlRWVKdg3S1A0Mu0ian5GIpND4ebmdtb\nNpK+fDjV/xbR30Db8i/TEqvXVChvIP67QO3rDIvU9AKIeXV6MJjAZHAqLscQQuxSSufzUV3nPO/Y\nVdPx5bVc0nQ3/urjv3n20/Tog2fuPJ/PzgUdZiaGGwiass4z2NM8a86akrCELjrBTM2yGZk7kxRM\ndnG8OFGx4qutuejv78lYPNc17hIGZ2DR9FBRyHIVE7t5xXjwXRpJbWwkMy2QN8BC5FJU5Bqex3Wq\nqLE6iwaI+uFFZ/mel3+JXiQici+KXmByN3M2A0FEQJRz6q/2zy5+3F3sXf18e/TsEuPFsyexm9Wg\nTtOUbl/c7q/20gVICVFgFiYziQFkNQ5aQhEt5XxyEJgLnNhLeB0Akhpfy2jJC++ZbN96sBvetNhJ\n7ShZVedMHVAQl/yLEubxHRNoo8iiKVXHPd1LovA1gpqrLC9hOI1bYXWj3v/1exs1GaWp9dQyBUAg\nNzKFunnW5O795Y4V0zjJwO5GQqfzqR8OxOF0fbp85BL7bhhImIScKkmZquVEQizBqr+KiQjCgBGR\nBLE5Y3ULV9XYzKr3sPrbULn9ezE2Yo+a652JvCqeXh7ctOsjiBgtytahqgS8NVfn15rrEgNS6Q9o\nPiXnyp6KTuxkzf6sCknTS+pzfr/2fJ1IdUK0oEA3B7wIaQ9Fz3c4VOfxdMp5ikFiHC6fBGIOIZzO\nNy8+fnl4/Ki76Dg8mqcTMYjLg7iZmlvXd8UoKGpZ6DoK7AYQcsppnIgEZADAy8ltapI3QYn3RsBv\ndmn9teHf7oCbr1gEDK5Ji2dZsxJIhL9jAl1MHQcIwlL0/QqLmqkpF4UfcMvV8e1gfgg0tzi+d7j2\nK4hrWo05JoC8QMoAFe1JQnA3U5gbCZOImTPRMHTMGiJgfro9BwpEYlmZmQnSSejEVF3NTQEnhpmZ\nGjNZUhCFLhb8hUpyHHH5X6HxBy6crz/K3d7aOi1j609arGRsFHvmIKFFt2kRT7HvmYUlwOGm35+R\n1AS2N2WuYmFLsKgvEMnCCNpn3wO4Hqhq8OJ7qMPNC3EURM8A7mJkztmdnJg8q+fMgxw+OECy+2R6\nNpsJTE7ktoiWnGZ346KDlyNqBd5iZw/ExGwF6KhiHbh3Zt/9Er12PJR3i5XXHsPN3QAUPxqqT8nc\n3b4rmOkLhy+okeOeMkfEzCzMQg1boVVovS+D1um2M9ZgMVAJElMQUkqn44klSNcBYJHu0EvkeTyx\na9eHvo8hBmIKUZgAWIjCQkwsIszMzAVo2biA3UyxSCRvANziontD+vzOV3jxtSzJkPX1okMvbhoi\nd2jSnHMJTXT/Hqz4MjabidXt3qYJeOMLvl67RULfA96w5tXUXyrgQAxb8gTBwjEGndlTJqqB4kEY\nBCdLc57GiRgGmBlYWCTlSd26IFY1A7ja4g8GSLPWr1ucGy0woIE17y33fDCW5VsR0mY9wR1ckSgq\nmd1vPVjki8eSG1dmSPfeQktSaQ/hi5z/rtSjbz7W07LoUWtG/3qiCG5uEpg4ErmrlYVW1dAFVaRU\nCjSIOxPIQVnNyY1Is+ak7MooaaS0ANtwZ5H2+8aZSW9Em/c+9N0u8KLKPfDclGnQQg4rLu5wKyY8\n7HvjoEXXbGHp1Na9/QkihtkqbVY+9V5QJxqnqtH1qLieO8yt6qDtqSxnwIUJ7kxkVLA+hBgocKFi\nsOSspM7iTsxBACKRENySwmHmXLSglUw3B2Ez3mSFXvkI0YJRfOE1bzZeM7177KmpL1tNurxm5vZ9\n6aCLSeRNF2n6U53Sxoe0vlY++f3M8CuHbzAJYI1yIGYWYZGiRAOLlQ3LChCzADUUMqeUcyo+Uieo\nmppJCBwkpWSqEoKp5qxo0TTu4JJbvB7XdU2+xQFeQQlgo1ctPp/2x9scr97N27cvY50Egb4vDuob\nqV4Zo5nDiZnaC4Wh2pIs26bYmOn7MZZtdKoaS/0Fxd/IzMzEBM1qZp5zycQq1XKqdyQECTGJlmx2\nsIM9pQlAiJJYmCl0nTOVIhFUi1ahiZxvsRyrBtt8dSjb4Wi54M0dXUXEvWd/s29ePrjFG+j+CWlX\n+vq24/twdbYJ3Wc99fzeC8GvE7R7HgisvPS9odMq7QGUEJxFMsDdshmcpfICzcaBSggHB4G75uyq\nWkqSQOZ5VkvdLpqqSCAwExfAOKdspiWGqoDHVRB/0+k2Pb768LajWVnVE+krK9loFJuL3/KgrZ23\nPQxLOMn3GSzSnndDadv1qs6wsh+vWYz3hDrRtOjVzi6+OyJ2d6g6iq9SSvIakXAJyXOCEzkIRObk\nLsIGUjUGCwcGu7pn8yglz7MgqcTk+qaCZBsW5humVWysGv1QWDQWillJc4NIr3rqGyzY60YjhFdY\ndX33OxfxtP5cI5Er716ocMN9ChB2f7w/ZAnUXW2P5QX6saqEeIObJQQrOWvlGc1JQiDhUoYkW9eB\npbifnBlgMjd3ZFVXN0LgqgeWOLSCu2xxum/5FEXDW2MfC8JS9IdCLe1Lqotku5Pf+uvrjV5FDxdm\nWjzhBLwtHHQLBt3jjw9/rsd5UcjKZ1bz1A0tXOd1m9HCSN7NKCeqcZUa5NIwMSIYg0HCZAufZXe3\nOYEoxCq2claYWTYiYaIQAxbQyinE4IQ8J3ejZhstp/ob4273U2uaLbqlTAAt9WKrf7WHfK24e6uj\n4YkocesNx/O3R6D+Bb8/nMaq8dy7mO5dU0U98Dpp8r7Y9KsRvNgbzZqjEs2IJhyIqTrm1Tlwx33o\nYk4ZRA6jEIXZiqcPbm7EVAIVwDUTsn7hW+JdRR2pf6yA3uapthzne2AFK8Dzyhl48wK2X/mlr+AT\nG63mnq/93nXe7Lgv2o3N6f++x4rDVOfHorUB60beZ0AAgRgSmIVQCowKqvHP5EApRlYTspfwuXZE\nfY3YXr7wGw/3Ld9cLXjU9aZaNbiFE9zjGIuqvX2k7SvfHojagovrvOr49hx0hd+/4O2HBlqdANXz\nuwAOzeBYKbjG7KyRIkUKvWHMzrcdG9S2bXTRmot7rKH0Jb163bhiPsPg7r548EyViEHNXDUHExNx\nEC464Gpcr2fgrQlZX38prgezar7df9YHqP091HTdhGZZvfm+fPHnvj2Bvn7ZFl/xq3UqVzjet5e3\n62j7+XtjEU7v3nDyFlWNrbDwxfPYkFyYu2UrIGgzNqgoAMxcoiXIzFQZ7NW9WRgotySsVxSjbzo2\nun7djyU0AqUYIpe8leUD9RlhTaKVl9eZ+NZbXff6O9mW7zwv/lWMtuIyXzgeYkyvOsGX+7wzUl2C\nAqnkgFQHUrHu3G0h2oLfMIu7mZmqE5XqFS1UywwgEaZmuaPmvrXDWJUGevMHfuh/InoAclalpYms\nRbJVKOEeQPnwl43B//rZfbttegsEWmywexibL9J5neBmnpuQK28VSGhxXpQlKcE7tDCq9ktFQb4d\nS/mW4z4qgWYJuxW/Q5HX9Zol6aWoLtUUBzYeCiKAuK1K9UutmPz641tqe02ctUlvNAdbFhYbe6Ue\noY05uGxB/WgLncZ9OnyLrOMtEGg1XB+8uHK7V43Qe4Jr1S6bt2O5vgWY3f/gA7vzHY5VTC5/b2lo\n5VtWatMsMa73VfMCPgFU8lo1KwAJgRa/31t62sovXhVRD2ZOyzl5ve62+iO3wSX37rbqpa+KxG80\n3pqIJ+CB3F54yKvz8421ey9Cp8GfdXWIN8q3N267+ca3Nfs3GMtcqOaoLeZMrYm1cqgqZNo/sKxe\nw+eWYKVKvgABCt9wusbGViOkRid+46e/d5Rw30il5lZY8pkXQbeNcGrkSa0y8kaJK8/8SjrEt9ul\ntwMz+cLWfCsA2zoDWAmRHiITiznlvh46tDgRb6L//uXtVg9f+n6HP/juxnU22s6iAsHdDUQs5G46\nZ8/GtZASefWFooTUEy+NO9BCKe/ha2/2xA+Y3dYMb7J7c/f1wNzftOUqa9R8X519wI+/pe30to2k\nLVtb1edGmw9dvPdVAQNKezFfrANy1wYElI3eANd4eLN3NdqU2iGtjK/x1cZRCgIFkLlpTsTCYTnD\nlZWVNFGrAmSxj3xV6Onb7PkSkFvvukEgVlVso5MVk605mOAlDb/cqXzCVmTaa6gJ+UZoNs/fm8q7\nt2EkPQyjXfGX+q+X9JJ7bLAmhRHVGhnMxO7ubm5JLWcHYtcVByBWswnbrX+vxpIEsKyAmxVElLgS\nbuGjEoT6LnSRmXVbO69q816QUuZQVomAVjpks9RvJOIfqPEb7N65wAheXdBmDnZuED4Rae1qBypF\n1ETKG1aaYJW6WgUKLl/yWuv/G463wUF91feXpaOWHbsicE1Hqbr3ci4La9kIFglSTm5xcDzw9VXL\nEW+yQ9/taBEO7S/yFqHlpagLGYHUjJghDiJz16zEoMBEBoLOk82ZSVCK7RRNb8vnKj/+1s++0RTv\nM2VH7cAGuLuRm5OUQ2bF9eoFM3NjCmbuhlJ0oRR694oHt+P6rVj+W7LisaHOVYJgE03RbB1fbInC\nUlqN+gL91TAbpiBhpftFDSiP+u6cnV8x7qkeVJGj5moiIjcIs7rHEEp4Z1X0uPbdFUbpcyhBADBz\nMZeqh60sx7eqXddYwmrQLXr//TUtjSuZawxhYeLugtIXGKWEEoPNTYiZWc1KY7FVbV2m+i3s2bei\ngy6P2J65LSFtgKKNnlbew3LOqmAp1Fgi7dttC/up2GF9eCtelveJgzpaU2QvUpoc5KalmALUjM2y\npa6LWZWIpzkReYgDJJKQ6iwCkQBKxCyOrLkViPHSvqEg5lW9eRO/YjPa1zlvDYbmzavqYlW3wLaY\nvoVxlmPl7qZObqa6uvqpZWWiBk20ToJvvrJvzUh68LhFp9/yuleXtH5kVQ68KXLV4VfKaC28GG0d\n+P3wd25GE2aF1xFKOhELg9kdrAQ3Jomxo8gxdOZCFBjB1ISg5hwCG52PY19001mDWiltV0qvlEIj\nbwq8bFWP18ncptwu9A9ullDRNOBFk24KWiv3uEQLfJHu8X5Y8a/O4uEr3rwjy0EuhLe5pMoDolbh\nrLivrb65HG56QxbyXQ5qmwsiB3Nt6ws4Ben7zsY8nWfXrPOY0sj90F/sbj+7vX158+jZo1mTu6ra\n6eZu/8HTvu+nlJiFGWxWsgxC6XBVldpqVn1tDPwVe+WBZG9ib7G+WWrzTzQNi0WaaVFU6hJNXWZH\nLVzqvi37rXfp+6ws0jjlCpVRLQu2AZ2KYLH6L9zrYQVQCZdb9cHvY6zf0xB13Jtx4/DUPJhuXgpj\nqKlZdigxQmTyjJyR5nx7vPv0ec9ydbFHmuH27Cc/ODx7IkM/XF5cPn4UYpfmbE1bKEmdywrRhlF9\ncw/N5iGAZUfo3gOVuxvgVnIEmnLWVObiOUAJgCl1J1/VtzZza8VB32h8nzlJi0zf/rnK9Q10BgBF\neixK9z1gZGOXfWdjs6CLOrUdzRYquR6oGE0hHWpVBg0Et5wny+OZLHdR0EXq86OLXWCcXr6YVE34\nbp6m492zp1d8MYynKZ9zhtNgmaAOJid3cqs9UtpCNl/oNxgPcYaNGbPCfosJ0bbGixXrztwcSKXO\nrDmLFL/fEhK/BkqtO/3mhUu/VwJ97dic54pObz0c9wixpnb5+u56Jb0JQ3kwk23iyrrKaxxMYRQs\nBYXxJiaL1sxMbNDySTX1ApBZZf4SBCYKpz4MthOmoZPHH109218++ejZifXmJlz88IPpN5+dX5zI\nnKMQC3O1j5gMVFrIlXpGXtsnfDMJ2nSr5YkAgNzMzESkXNKMooaxez0MzLzEDBKRE7IbHMxcgmNK\n5+W2mm0dG+j4ZpvyPRLoF56hje5DK+y5cTv5PWIkYEHCX1WtvvGkGiaycgtvuheqDVe0q9bNsXSk\nKIZDwWK8IWRLpkeaZy1WBaELgbrY7XaJKHRd2O3u7j7/6//xz370xz958sE+jfiP//2/S31/xpiJ\nbm/uPv/tb3/8ox/xEEudVzCbZqYSbN9saXe555b7Gg+/aplNqawI6yKU1hsR2tdV9kdrO4OqbhZv\nl5sb+cZ4x+qvfivjuyPQV1eNlp8lWKaRwipDmVbLfV2sFsT74Cjed1R8Y/KsDLEsvy8Qa7tXpcXq\nDyzDKw5mtcSpO9ytqGxqBXYIMXRdB4exhz6om47pdHMXnKZpsi5cPnn0/Pr2L//7/4G6/E//l//Z\n8//02S/+7Z//7F/8i+PpxYuPd+K8v7j48c9/Mnq+OY2zZumimpkZzEtBPIlBtRWlX4jrqx52a27U\nCihWRTMHrmXjlwVvzRRFpJEuXNW8BF+DqBTqicXdsNj7vITGti2vK/qmTPQ7Dlh+7Yv++jO/Ibh2\nCiu0WDBAekCT32qs3lKHF1FJTcfYXNXYS1WwaJ36GhjpqAnsNdSDRURTmk+jUx/64NA0nkM3CIsb\nDZePn/zoR89+8NGTy8sPP3xyofGXf/urP/z5j/InSpYPF/vw6DGbpOOd5wwqyH2GGjnVNKYFcUXj\n919jrGvXpr4EodR/N0zU3VUNBBZpkHOp3uNVASAwu6nR0oIVzYm0QIdvg49+jyJ+601b+VZboc2v\n9ZoGRX0xpX/zsZzj4h3xtlOFV9Z9Im4ry9Xp5xRKgmVhRQQwHAYt6qeEACZXIzcznY9TOo9Tmvbs\nMe6HruOLQwzd3d14e3OOL08nx/MXL//d/+tfh5h+8uwn548/O/7txz/86OnF08cv/vo3d8+vfzv5\ny+sXfLWjxzsP4p4jsZWqJIVhm4GK7/trjfu60EKTtYaUu5XkDizr0S4zLyn+IOKFwL0KG9OcuTQ8\nqdnCTs6ORqjbbXpTzvJOjaR7wNv29a2JsmLg9y59M166/VBDrJsy20rJlYnZ0iO+9ZZCQaaVnKWY\nrmrVUYnW9ompSMj+sOt56PfRNM/zXFKPQhCZfHo5f/SDn/7z//m//Pi//e9+8T/8e/3Z/Mv/+J8+\n+dvPH//DP/iH//J/8vkvPvaT/cE/+5Orl5/dYZ72PAd3kUgi6ih9LIurwrdq+jf3XNTA6eLFk1UB\nKFXYmYjJalNNWz/BzCJuAJGI2CZ3ZfHoggBb+8x8y/HurfjXjI3can74Ndb2G41NfP69tu/3Yxgb\ntyZg4UmLjuEAsYPUGhStRqggCwmZWU6zZnUzKkEeQbo+OpSFYEYSJAbmwDFcf3Y9fnL6w3/yz//p\nf/6P/+4PfjQM+F/8V//r//J//3/4//zX/83/+//x//xs/3dpmuw8dUM37Iabu5EpSsc2g4i7IPN5\nWvWLxbBr8/3q1biXP7tgqi0atDUBa0yUlvdXXK9Cnl6sqAXfXEJXm0BCYzGbNV5m8U3O0ntJoG9v\nrDrDsnK+irva5gw1KMBB5oYm4KKwMOeUOTAFqYFY5kwcJVrWrLMwO0DMIQDODjAxwcktzZMnN1XA\ndrsoMeyGq/xBfzry8198kk70/OY63Zymf/dv4+Onpwv55OXvPhx/1kV5+eL5L/78L+Y0X+sU8Dhn\nsZQ4dhb7dE4cpDS8qxAXfQOnxRaCXvV9B0yrJlMZc9FBFTUJpeha9YAvV5hpu4PBqEr2eudFl3+V\nFr8Zp39vCfRVbPdb2EfelM7mt6P1DYDgpqVhx5wSE3ehA3kg0SnN44yO3ZSEY+gIRBRMCQZNLkJg\nsLCTe1Y3ByxrgpJp6nd9t9v1+74PuxcfvzyfbzQNod/dXZ/vbl8+fvzso2c/n47cXw7/5F/+58fj\nzT//L/9Fns7j8xcfPX5yd3u3O97lQ3e2pMx9iAJu5mJtd0lN//m2CPAi8NH8RRXpvG8OVpgLFRoF\nmGVL6s2phrfYUOx9JdBFprzZoEUioa63bS2xSqxMTIGZ+HweuYtxN3iIMAiF+faUTprmcX+1l0fD\nmPI8zfN5JkckIwM78jgLwwOI4Ko656LIap67fT/EftgNbp6m2Uf89q/+zufdcPjw2c8/+vCHP/zb\nX/yPd3eT7u3zjz/7YTf84OnTww+fWKAXv7smz7+7+ez685czwNENCjPNOQNETCKlsVz1hJt9g1VZ\n3FD3gnha1oYBaK3FmBjsBGZaInasosItQrLt02ohLGbWqll9u318fwn02417PiYA5UxTEYpcAMCm\nOzGc5ykJizCXgrJwoYyolJPudoN2cTqfc8qU0UkEPKsGMJg4iouZq7qqKzFDiMChj8SUk91+fp1y\nutw9Op/0sD9Ms3/+2QtC+Mt/82f7R92fPv2XkvLf/Hd/9R9vXn78yS+Ov30xTacPP/wgPrtUx5xn\nP1MmqzgCg0IrRkL18BV38NdcluYSe61oolWerJeuVLgqm4szqUWYrff48p14o/H3k0Cx0exL2Waz\nSnvw2vndvbIHcgCBuQMFzTO1to1EcMsEDRKDUBg6nTRGERZxZrCeJ+lg7KoWY2DhBVKUPkoIPhtL\nN3R9v7uK+5e7p1fMh4vHj588vro8XIhQv7uMl5JvbyzEw6PH82zgbsx+Vuf9obM+HnazJUsZWmEG\nU6tcrvDON3Bz3yO99SBvqbeagyg5R0u070aGoxhMX/wlbwmy/ntLoOsuLL5KJzUzteYcglOrkiDB\n1HXM5BSCkBNFJpbgPYE9A9lBZOQmZJYlBokRytQLQdVyiJ3OyY1CDDEMEvs0JR2zGhH4dDdy6A+P\nHs2Tv3j+W/fxgz/84O72+ne//pXFwx/95/906MKLj38ZgD6GPM6qqdtd5PHsym6k2QqN1si2wFwL\nPZB/bfZZRpXyjWk2F+Viijc5XSJa62/rEaAvhAZfWfxv6Dr6IlYbsN7rW7Pj92NUPat0CiynnMHM\nxFzynFgCKpbFbs5E+/1uHKfTy+u4ixwozzMJwr4L1E1pPH5yfTydu/2uP+wOV/uXnz6fZsd+fz4f\n+x1JJIaR2fnmLsQ+dtHc717eHa+Pu90+dL1n19kuLg9x4Nubz373d78cby/+8J/+4e6WjscXWc7j\n43A66d3tNdJ8OOxtMgNC7G9f3uVp7i/60IcYO4qIXcyqJSKz6tffcMcqaoHVLF9Q+Ua39R9aGWS1\nzl/DFLcq6Os9z1+XqL7oovA1rvn9GdvV8ApsVjCpdFsEgYiFzQylx1lWsF1cXkbBeVSJrGmc7o7k\nHiNmy4e4i07dpFD0u2G42M+/mog5dtFM85zMMB3P8TDAEfs+9n06nvI0pVn3h+iGPM9kHIbOKHUH\nfvqDS9WReOp2Pp4nzdP0OeY52/nMcI1B4pBSnk3B5AyOglbGloOQq2V1q03L3yBHaRHrmwyx+1u/\nhG8B22tfw6xpoee3O5YplW7Hb83D/a5HW6waQGsl5Leq/aaes5opMUG1lIgHwKZBk+okyOQOaL9j\nmHc9p2wUHepuKoZB6NCFQxeIw0XX5f3Fo/0hzeM0WZZ5Sbcl5n6/49iFPkzHKacUJIJNfYoHusDu\n+HKczreAddEEtN9TjDwjMPFwGA5XT65vjtl0uNx1hxhi0Jxyyq4ao7gZ1XZNwDai82vv4av02F6r\nKFMtIdzQqyb5F+n/yqJ/2Vd/Q9K6Pzn63trQfE/jPgRXosKw6KMGYhERYrac2SFEakZwz8nzFIWo\nk8PhglK+/fxmN/Tj6dz1Xby4dHXKdn7+8vziZTqdslq+O7783WefMeY0S9c93V8+/ujD0A/C4vlk\ngQw+TaOZUkAcAhhpHsHm0Ljr+t3gpmQZQfaX/Tza9XkS4/n2HHmAaRrPFBzs6rnEoHjxLJSYZacG\nUH5jH9urJLPlgxsJtASTfLmYfhua4XoP2v7p31+vzu9lbFz2tQt5WWY3NzViSBdDF4ks5QRVyzqf\nR4YxLOlkMUw3pz1Uj+df/9Xfpg9ubm/vDPqDP/7jqydXPPvL3/727nT75EcfZDfK/sOf/sg0OdNw\nebl/9kGQ4ZNff0JmeUohSBQGIJ0wU9hFzXmezizk6gjRWabz+XyaqAt+HE1pGudodro7uQJ9nE+n\nft/BFQTpogdBSa6wGndFXBGJRR/9FuN1FLYNLvvCz70d2ds27QE2COB7bUPznY/ykNXjTE6M6g5k\nB9xVYQwPrmbmVIp0RaEYsO+EZeh38/ULB0vognShG/q9n4/TJ7/8OM954D4njofHFx/+4DydNeVn\nH32UdHL4NKbr45nm8ZO//e3V4cAda1aJoUSngQFGDbCHUGA3PY95OuXzMXWI8wQoNHnPwuBdP1jH\n03lxORopw0pNMm7FZ5pk+BbpPvfW7R0PAvy1esS3ItAGS5B/hSLyvYwlEqko94aaxF0DSp0Y5AbN\nbpncJAq5U8eZ021OKjYonedJhn536PcfPomPL55+9HToh3R2Jx/vzqNqf7j89PmL25fXIKh003hk\nQFgo9Lthd3l1udsNJIBbZFYzNYPDVDWrucMsxMgiRix9j3HkYSe7nU2ZhCkg7KMHV5tFKAQ2ZSUw\nkcGKd7yikkvg1e//aLDW6ovajq9BoPcTqZe4lYLZLvEw755C7z9e7X9VwREnQgXqTZmIhUCupoig\nPuyeXl48vuxpf/H4ycX+ah+Hzq+unl5J1+Vz+o9//u/P17eXjx///J/8k5/883/013/574Mcnn7w\nwdXjJ+Px+u76xZzmpJ5YuQtJUyDJaSIgzbNm4xglRgoUqTM4CUN9HqdALCGKiIDTPMPUXCWQQ91V\nApuptcmzsGur87hEF/+9GOu+LUduozk8JNBXSe21VmK1lBcvGS0Xv8OVW9xzzRFcfrQT6uamyY04\nlJRLuKXZsuyiE15++mK+/t1ut0/XL37z/Ob6k+uf/IOfx34YX9xJkuCSp3R3ff3i4+cvPvnMFafP\n7z75D78eOjHJoRdVNedh6AHtYsSkzCwsTogxUgHWmWDGQqo6ns5dDKZ6Ph4hPI9nCcSRVJEtIbA4\nmxkDWo09N3d2LiBoxXobe/h9J9fXuZ1qyt6rHPQ1z7qe2PsxAdWG3CRavh8LVXMQmvVQPZwlBdJN\nLTvEQ9dRFIKH/TAe82/+4m9f/urlP/jTf+yJ7j59oWe7/vRG55ef/urjD3/w45/+039E8JcvX/6n\nf/uXYzpfPn5y97vbX/67v3p0ueufDh/+4Y8YyHPWrBwoDL2RBRYCqU7E7GYlu9/MjCDCMUoI4jEY\nEIhURE0MMPekykLunnMOQQpS6XBm4RDMM6GkQgGoEs3fAwH2VsaSWFv37VUCfe1jNuCNVq9Yu7ik\nT/nGEbXVIr7HRSvQy8I7awZDy7p0uDPBhZ1BTBx5dziAxSHGxGY/+ODnMR+unn00naY4DLv+wB1R\nwpim/tlOHkcy22GXlYZ+f7h6NIW7n/7jP+gjn/Mp9P3gwbLmedKcS+GGbJXxac4cAtqRMVWQkzCI\nQtcRc4xRc1bNEAkxcgyhj2kcyZRFCiBBpYdyddG2XD+8y657bzaaB/Qe29/q04sHq2CGK4E+SKp4\ngIe5ewteXS9rkSwLyTNKuuN38mhfPhaGXuZVu2gaHGpu5moEMFGIkYXN/Xw3jdOcJjvdnYnj1aNH\nH/z0p7urq5Susxn3cT6fgsT9syu+kEnH44trTUYUp2O6e3mrKV88PQxR8tHmOc3zRFyS3wtaCXWL\nMe5EAHJqOWhBiEhYijWuWdM838zJTM0tdDsn1smY4EYSotfiqrVQibKZmkgx54t76RuE270Po52n\n+xpji7JZ4/TalSuBbumKlh+OTcZJEyjNkKwdp1pbUtp8IbCNMvge6yg1T0h91IqAlnqOpQ8RpTln\nzaae5jwMB+p3bohM3EWb50DcX1zqcb7+9WcXTx9RpGRTUANbtwsh9OnllMcTBSbJyXMcQs7zfD6H\nPoYoBpjlUjHGYKUWbQ0QWGrT1TwnFhE4zLXru9Ia/nR7PF7fDZe7OMRuF0C2cM9a6FBab0Uv6/p1\nszrfv7ERudSqEDy44ks8SXUpqURWY/F41XdLYvUah42FdS3f3ij7LT7SV48Hu7WQqUvJ+RIz5KQk\nIoGdeHd1EXPWOZNYOp2Oz1/Ooz794MP5etYznj37ke9xe/p8Op9NjZlEJEow1t3lDoyUUwzRspoq\nIRYxZFaT1r2mMTWvQeULbmo5pUDSdV3s+mw5xOBO7CFaCJkkxtBFkGXNXtopW8kBMncnIzcHP3zY\n39NR0eviXn34Dm0JtLnOAGwI2s1dl0ypZiGVYbaNqmok3BKuv12t1Tcb2ySkBS+sOpswiXhJVRQh\n4kAkwpbNoGQOy4HZBZ7VciZmiZJJ4e7ZPJt5mu2Up1HnOYQLYjKGRPFsbkrkLOSlzn49s/d1IZR0\ncpHA5BAigqd5zKaq2dQCd2DmQA5zM3JnYg5SbAAmYQlmRoA11Oz3l0YXwqk8DvX3rSseD6KZFnfT\nUrSrKZnuZg3eqP0ziUpyakmKXvZgIWJaMlvejVa68naAqFCnOcyciFlC4DDN5+n2bJqIiQhg2j85\naAY5+se9xnyars83p3k+sUC60HW9cNhfwNXzNMd9B8CymSoAM9OcW+rn6jcA0KZR8ys0a5pmikEi\nE1MXO2LWpMMwEEvR80MXnX2eDV7s/3L0vRkLtHHCvwYHfK/Ha3Cxh8esBUd/KVBfCZyZhaxQHNfy\nBkzk7K7wpSSy+4Ijl294V/YlbbatFHgXIWZ2NU1aEroZYrPNx8kkm839YZCwp6QQwHQap27f9TGM\n6SalCWTEIkFIyOHD1V7dsqbgDsBcOfBwMUgMtkkhp1bLqb1Qz4ypW1JNahIcUHMiY1ABmGA1YF41\nu8NMqXmNvPb4KtUW7F4hud+vsZrWC2pZ8/CWDoIFNcL9YBFqcnHz+eLqZW+FpmCmpXYnqJb7MTda\n6ik/IMp3QaMLlynfXmoJmZllNTUOgYh0ztPtSE6Hy8M0Q7oQokwvR2eY6jydY3/J0Uwt9MxcwrqR\n5hmgEGPoJJ9n1Od1MJiFmEqR9tb2gIqJXeCNzZF1Ygl9JzGaI2clIwmkWS2fS8cMFlEzEibUyhDV\nGCgFb7bC4fd9bHjaA2dgWa1XApbbNYvlY2albAYTSkHA0mCs/J+IWZr1RAR/UBCQ7t38exsVGXNa\nSjG7EVPoI7PA2TRrTt0QZZDIIQ6B4ZamcDl4YHOWAFdjBjE3L5RaNhCpJjNFjXirWE/JdCpYh5uT\nLJAHUAtK+gbJIxKptj0LByn9u5gYGW73ep5U5WqB5JtWXQstFGJ9n8HQV3I/aKmj2cimmTxNIixJ\n+K8Jt6OVNMsacxAYudbcfje3rMbMJDCgKPTqLLwY85vxLpTPdiiZCVgb1pTqzGaWU9aUndzJ5nlU\nzXrWdBzvrl9ePLmQIUYP5qo5iwReUToDnJgdxkLMpCk5gUr9t6YOEVrdaKo1Yx6qV44G1GPxErnD\nzCSKBDHV4nxydxYu31s+6SUEu9XZo/eeOjd5I6uX6JWL1udbxX6LmQ7LBYtaupHxxMzMAjCLuppm\n1axpTgYPHkqxqDxnc8S+I1mhgk1yVsNE19t+h2OhhqIFojQialzH3MxcAgfpQ2AiN8sxBrgb8/7i\nMM8TQ5lZs1rKUYKbqRoHCTFSV9KUnYWJkOY5xMhETpvgczTQ8wGwV4iRCqQCYl40hMIjHW4llRQA\nFa3eaOkigXLY7F4ZxYJFv1s/J5UKlcDr9vZeHtNKqoA39w5VhlrdYxtiLHIwLB9s69kypwpnnA1J\ny9qIRGLqByk+u7LdzJI55Wyx753dVF11dYZvoITvx2ZqAI+DHM71oal6FNzc3KSUv2KwCOCmmKcU\n+uGHH310e3tjqhLieDxOxzFQjENH5E4ojnJLc5rmYTeUPnEcxExb2bB6ypd+gUvlj7rctXtLBhBC\nKPJahN2dhEvNPM2qpkxBopRCpK2sAuretJBJ2pyDdzlW7Gcdi+1Wp96aRaHifY0KgRKfQM2bW+Mm\nfL3pF1vxRAw4cT3ORQxlM4VEcVe4NWWdNGdOQpFrgC3ALC2zv5Lnd7M896cMYNHe2r4yMxGpGRdE\nzAGHZTX1GFiYAZAp0HX9Be7O5kjZkrqzjGMiCRSkmJhe1PGcWPbMPJsyk6kVrcnd6fX5F/W0oqnu\nyzQr42kTRlFsmQwtOX/jWi726n30Cl/kg/m+xwNFs7DV+sfCAOsjV7lf9dAN/LNI+/aQ25SPjVuy\n/Wi19FBzUs3znB2IFtyVSjldy4CziKmyAGvL7Mq7qs713S3NZqwypJSFWYIqihhmKgVXi7HXxQ5Z\nc9LhcOD9cPfi+Jvbj6knJZvmfHj69NHFo+uPPxfmrotKPud0TkezrG4pp77rcs618Wurf+3Vd27r\nIteZbaRXDfkgNHdHC6YrQF45M66qvGWeRKClosTa0Qu+EMO7o9FKTtvq1+upfN20mlQtH6O6WVgE\nAi1pAq9P+djqNFXgUykN2QVmiV1I8wQ3EMyMJXQx5JzRjCpqNU633/19jcqPSmMAALW1tbCq5my7\nw8Fny3PaXw7Xn724/fzm6oNn0u+uP/7s5u7mT/5n/1gud6ebm36/D6E/XZ+uLg5jns55RBc4dvur\nSzuP8zhS7IiIWdy1yl1ianXn7k2IllUsK8kbrrdopiXSyc29FB8tn93ETlTdvoIn7SP+6ve9g1Hj\nNO69UojNWs1lAG3CtftXASXK6w+0aF+DYHjz2nII731TobGq9RBIajn9woodMLemZGBppoPqRmpn\n6zWC7zsZ1E4kqruLi/JDzKpGwsPFwU1h+epi//jRxeXl4fLy4vHTxz/9+Y8ePz08/uAQB1g657ub\n27/7ze2vPomKvh+G3SEOOydR4tjtdDah0KiNWbjhbvV517oc2LK2Sl5t97C06yyfccC1VjQuXZ4b\nh634SRPxG3757qlztcE3L1VdppgDvpyu9vCNUBZNzNvvi9Stgx/cc7vOaNa+q5mqqZqp5qSayy3M\nTFVVTYs+r27mBcgvDMwrMPidm5mrutMoFCidYbh8OYs4EcdIzJpzDKzzSedzP0QmeJ73PXecBknB\nRrHzPhrnYzpdH29fgBH3g4JOd+frz65P18fzzRHZTc1V3QzmrtbMgJU1bub36gGtUszX+beiyaun\ntBoPVFteNo3T/TVf8Z6Mhc0tMy9+5OKbbdXEiXhBixYDvkmDVrUZoBIsQhtBUn9phbFBtfIWC1P5\n39a9RkCdQOMZXo5+UZjKJd8HCtK+gFCrypuZF+CTCG6kyYg4dl06z9Pp/Oijx+46jqc4XOQ5uciu\nD7e3n//ul387ck7pHAbsfnD42Z/+dHex5+Dn8Zxy7nZ9GCKf0zSnrArAzYqQcbgwk7ABxXy899Qr\nwNcmuvTnaLaTqcG8WrKLo+EeO9k6cZfb+ubvtzGWr/wWqsMDcKlQX7mvmTOVGKPN06wcEVja8paY\nhIf3bIjApi8mWCTEUDROoDYdK044KSW4irlfQDupda3YNwroK+6E72jUiZcHMXdyZil8H0Ka8+n6\n5u7l9bNnV7zr+svD1aMPjy9y1z368R/89Nef/MpTph6XTx5JDJ714smlMX/2+e/uTnO4ODz68On+\nySVuz9fPP5eui5aZOcYgIvN8VzCs4lDaLPeXTbasMHGFW4iolMjbxGRtK840Kt+K99fw5q8xvjOb\naoOI3dcXi8pCzgtZ0so977HIjbVERJu27Mt97+lMVKryeKFLc8CNyAFe8OPSoq+sXOuNsjlB/l0u\nyDpWxu5A8ek0aetqcAtddMvmiRgvr1+qXxwt6/n8m799fh4/nmY6S88h5JAvHj357OXxd3/+y4D+\nZ3/6Dy8e9XGfPYTzze35dMdTNgAxig/pPHInHKKZkxo2LQe+7qw32lT1tm9EPLBu3sIr25mvGD2t\nvfn8jeXV5oOLQbdObJnSdmPptYFqKyssc1t1mIJR+IJJN6yzPkJTerw9SLlVQIPWyuVbcKT2Eiv6\neTGTuCqotLgO7h3g5gBHExBbuv+Ox/YLvMYIEwxwD4HB5AInv/jw0eFil8YzAstuoG736NlP8PF4\n9+v58Q9+/OgRP7/55PHFTzD4LV6MxzmGQ7Z8Ph9j1086scPVJXRz1jTZfJoldALRWUUiCZZsgy9/\n5IXI0FhGWdsGHWM51CtC9UCe++Z/29e+xljSmWiR5K3ad3MB+vbrv8EoM/XlqdovKMEKsFzbLwHV\njwQvWPtDS6VsYmg2bzmtoNrrrcXDcpHf8irrXvEO2i73im59o+f69oOq+lInwiIhREua55kILJSz\nUhABqfl8GmMMw+Fi2F8OH+6e/8Wf/bt//eeX/3D34R8+/k9/85ef/vLF5ZPLX//m46Hbz6d5Ps8v\nPvndow8+kJ772M/q+4to5hyFu5DdfJ7mNEMoSl/6sH1DgbGYFYu8eYUwGiHeA0QWltfAA+D+XrxC\nXfSaG3jlXtQsTHfypnmsUOy9W7UXH7Jsul/OrEaFNMlcfM3MvFDtli7vcehKWAju7lnNqn/ZzTVn\nIhKWulDtPr4wxSZoak2Z1lGPWgesdwTNLZYJuburo2jBKN2sfTydzf2z27vp+vbRo0M62s2vPz2f\n/tZu9r/4i//02z/7q0fnww//6H/1sz/5g8jDbtf/6X/xnx1kf37+Isjupz/+yfXN7e3Nzf7ikCxd\n7C+nm+PjD57s+m6ezgTsLw8SAwmbqpnxA3/P1xv+4OcWcaEG72+QT6otlu9v0HqP++L4Hs+u3Hgx\nUEpGiptBYWo1LpE3bKl9hCtM27xcjSarqK0186u+Ym4rt2aOfSfBSiJGuXxltkuDRq/h8oXyQpl6\nQ5eYalNbotKTpXH7AhOsKsXG+qlJMwvU9Y7GfVlHmlWzldIiw2437DtTA9C78OXVn/zpH93dvPzd\n6e+eXj76F/+7/82f/B//ZXp5+r//N//Xyx8f7uw83aVnHz770Y+fPv///c1//X/6vzz56Cf/0//t\nf3V7PX9wcfjxH//Rzen25tOXv/nrv3b9eXfo5nkadrtu31dPVQNY8BoF7SufgO5TVX25/vn6D/l9\nUvyiezct1VfgvPkLqoJZTYeq+4IWd849I20R/e1WRRGlV7n7YnUDWHzAVWXkgptZOxuN/Jdn8aZ1\nBiaCkFEpeG71XSYQeNFPG8fllt+zLuI9ae6LhPq+R7PcAcBL1Bu8dNE0d0dOmpOCvNsPl48vLx89\nmk53j55ehn53Gp//4ld/2VM35ru9EiGfb178bjym8yfz7cvLH3S7p5Lz7Xj6HDZ89vGvk7sEvnry\neLcfHC5gN1dVz75sfJkS4Zt60R4qYfW1RVVdNxDVGr33/K9bmMXEWa5cNLHCqEpzj1rdFxU6hBBz\nJVxYu3RD2IV+aaNjYtXxFrNuS7Lublkta3mjaoZmax/lNZhhPdqhPq2joYYO56KJ+nKMltZsRLCG\nCzTTqmnDD9CQ73f4cn7r6Xc3zdkluFk6pukIV+t2/eFwYWf7D//fv5jGu2cfPobIn//rf/PL//P/\n7dHTp1N3zvrDbrebfncMl4g/uPron/3JBz/70DRGjxKjJ3v+/AVELp4+7a9+xjFM5zObFH7QWofQ\nq774upHfcGGaGlZt8yYrq7VQ7vsqwvpwYRY1E01Zq0ohqJnPXnXJKkfLd5u5E4TZvHFXX3VW3Idq\n6lf5yi9XNgws8wWVrCFftqrGezxU2uvfFWbSOZuZdLEEw4qIqeqcSYSE3MyykpQPuqk6QCKoWvBG\nYXq3dS7qshkAGHJKpipBJIqpu1nf7eLQh264e3H98V/9dn/ZXT0T6fu4v3r2YfjZn/6D+Czc3jxP\nL883v3rpH+Dl4/7ufKeaaA50hms4PD487frj+Xh3vPPSv8tMVUm41C9hYQKXinarWMU94rxnynz5\nA63WxiqgAazb+dU3aVosbSxqYBUxhJLEXEIp7s3QXN0Actfihy3C0725BlcMp1LYCgKU4pCv2NUo\nenNND6Yl1KZe13oxLvLQgaBqKStATJzV3TRIUMuqVooRq5qay2LHbzlD0WfbiX53tNmm0441s9ec\n4lDPJwk7cLo9pbNPd2l3+cHTHz2Zze2cb8bzi7vr+Pnvnj364HSefcxGnM2ff3xtNGue2aKfMV+n\nJz98+sHPPtpHOf72UxAk9syialSjDSr7cSuYERaY/b7kfmXeXyR77lPxhky/8E6v3veB7C1Mq6A8\nJTK1NG8ufbaZF9FPTk5OIqEUsQQZoWT7MNxai2i6f2Row9Q3D7WaZ4sWuGZoPej7d19b8cDMIUYn\n4hhMlVhCF83UKTmzM4OZSm8g1LCLdpuNk+O+3vBOBi06f/lTClTvRelhFldP0+w5QOLwqONhP+UT\nBwl9FwKNdzfzcX+6Pfqkw4eP49V+1rHv99IP+93Vga8+/bPfDHbYy5XbXZA+e+qGwdVMQULW0OIq\nC7dhZivzvD/fZcn8wVtva9y3FRZjH3AvILeVcAUJDHNXZRIzM1MiMrWsFqJpzsyVDVl2JlbfKoq1\n6VQzpjdkcM90brk3vs7Nl2saGlANLzRDHhSIGSUBhqBuUKhKSlnNmZFrVTZsQiGWR9+mZr/7sZiz\nVHMgUbAMYpQ4AYM5IXRd6Ifz9fn25R0PFHvqr/Yfdj+I+77vw3weHfzsj3/ukU+ffBJZmFh2nWj4\n9Fe/TZ/fjcdzeNTF2Fs2IknTPJ/n4WIHcfKq7DO3iAVq+7hatE1st44LWzPzOzjiCxBQ+KcTsZNL\nEHJzBYvAWc0YVJzXbiYcJEbLFjpcPno0nY8wVctZLWflKObOMDdvaQRVX16l6YMjt/1zY87S6tVZ\nleqV0xIBCCWEMc+Jo1jOxARTqMYu9MOQTMFcY8C86f5rqaZmj7178V5Hc04UHcmKe41B7h67wBy6\nLgaK0+kMWNcN0+mk00Sw080pRPOcwv7w+OmTc0qRoxBpnk63d5yBbBj107/51bN/8KP+w4vpOMvx\nPN+N43jeXR5Y4GYl+36BphcnSLMJFgt6k8BYzZS3zkVXm3Fx5XjJt4M7mbupZhBZ0vPxtN/tnd01\na5opRqKQ5lnNh2FO08RwM3UFiEPX61z83yQiZZGb2F5Ri1VPrv/cf7qN6b+GZlVKX+uFAAilKlBO\nykVrJZB6Hue464Uoq1tWmJUUSW9Wl9sCg73VVf22o3iwFyvTi/mic9Ksw37P4ufTLeMMMQ4Myueb\nl+L+6OkjjnJ5dTGfHs3m44uXn/7mkxeffPrkg8ehI4ndcNn/5J/9/IDudy8+vfzoyvdCt06EYdfn\nnAiwcoYZJdCwYM5EVAJrFnscKzq0pmtVGrqfdvDA8f3gMb+SIyyItXtt/biYSDXHgMk8s0GnPN6N\nu37HQsLoe4l9lD7evLiZ5xy7qHkizZoTcaTYxZ2YBCTPU+tnLowlH1pr48QHD3Nvcv6aokhfFE0U\n4MQocyOw5Hma8zyfziSUxklTznMiwIW55HWVvHi8NqP2nY+20eUsevUkuTrUqOyVzs4WhuhQcyV2\nYQpRnDxNM7sPIjLOcUoXHAYWdyf3aTzOfg7B4qPonRl5HDoSgjkIaZ699GkwLmhlhViI7rnmeXEb\nbvDD0sQNrfxqDXr48uCvr4EA+IZpNeOaATAzk0gPgmcKLBDvY9d3EWxCBiAEZ0EcQhj6bh8tGTJy\nshAlwWGTkDk5odj5BiaH15TeB7T42qO0wOvVRdCSc9Y9XEc4n855yqfbo5yl64Kb9iE++uBpv98p\nOREFkXIzq3HKUtQ6apbh+yPfy1hVcyJTs6wl8zgXJ2QgEgIsz7ML7x4dhGjWnMY5Z6RzCj3lcdrv\n+xA59tHZyXzWE/duQVno9uaFDD0zmSqB+v0gQZx8VXeWOIxSl7QtVG3HqNYEujUrHwvo3XSUZk98\nc79+FZiLa7SAPgSYlfIq2SllmMNSEqJ0HFNK0zgC6mkyU+lDGA5OHrqQNWmahCwOod91mNJ0utFc\nw9ZKYUgJoZxOVfUmPepcKi61CUja+GYbyrEK9Hv07QAhMHO/2zkRsfdDBzefsxOy5Tmn4m0XZvKC\nR2xCotpivE+jSrLmvatiVWIouA8RcQh1jQhO6IaBgDzOiJFj5BBhNM8TBQ4csqmwaFYwD4cdMyPz\nNE4YZyrpwkyhCw3EKPZkDaSi1anBBdJzh6VsWutDUQWqAZBtzChgAZjeSDFtcFIzG93NaFNER5yY\nhAmIxADtPHSBgwgxR4GrMZk7OSxlCNAUmNpYJ5tAHABXa8R8RU+KjrUQ4fLSg3HfHGwATOWm7RIC\ngQIIoY9DZMCGQx9JTi9u8pQChUzOzGrKzExkqsVZ0GifF+3pPaLUjYCg6hbjVfNuWT4FtjS1lDIz\nKxB3O+cwTokJkbvSzzidz+TsZsWqyarCIkGAViG1JoCt4Fvtz1DiwkpRkMVOJ9KsXjrREDsU5Ugx\nkTWQeu31uoAA32ws9ODmTRuvQrUI01Jm2szBFpj7oSeCaSLXKGymRshK7OqGOPTO7InMMiEw4LAQ\noppVx727Lf1tFwt8BUbv6ypfwdleEciEMJ1HNU+aAWVxlzAeT5qMQsiuJFDT6oPPmYlJpJH5PUzr\nPRzNO9fwX3cAJWTEK9VKCePiEDkEz0hzjn2ULhosBCntryFSatEAKLarW0tYtVYebIlSaEbQEh3n\nDY+TkovDIKZiS8HdmW1JOsZ9HPGNnrlM8sFrKzPNag6R6GaWkpmxgIPkNJMbYjB3J56Tn09zUjVY\nTiNScneWTrPO4xS7mpEoMZasQW8Iz1Ltx7fz+fI9ejA2NO3ugZiG/Y7TZDkLC4P7YecRIAnEIOYg\nw36A5jSOhWFUVPY1nPu9HJuI7KqbMouEEiSoru7IOc/TrSdLaR6udtLFeTwys7kzuKyYmbsqeFXn\nF/Nn+aKWArUkKpXyJnC4qSpQQG8WITiZu6N4QMisufx88YV+fafo+qxV7yRUVbhIDKvZkQwSIoOb\nCQuRSaBh10mQ462aoRsO2XQc5zkrd0Mf+PFHH4yn23R3nMcRRMQCcPFpOkrSuSzfiK+e7n2lZeOd\nrA4E37xDBPcgQUIXkyUockrq2YU5iJoBbGZqmlOqUBex05Kbt9Tlen9E/DKNhoWu8Vj19dJQsCyu\nq5Xgs0IIEnh3tQ9DRGFwWRs4Q3BnYoWBiGuxO2NvYeiVd1Z6Miv4a72ygHcQxK6zrE3HgJd6n0wU\npGn2ldkXbfZLkabXjKIcFKdlpdRyCzMm4sBM7iIEmedivVGIIXYRMHfnELvDhU8zzZAuDPuDB7p8\n/IjZElHoQr/fuQESYgxlWmlKMJipY0mJLo6hRTnxVxCJh0907zEbATsAc8ADgVzNk5K6uVk2BkOo\nZmG7wyxPiQluRqUSzuo09dd+5XswKsSIGs3iCxMt8ramCwO19A0hBCZmjgMJSsg2WgjtRlYTS3NK\nubt5adpu5iCvFWt9XWM31zkRcQiRmCUIC6d5zikFEfdaS6eoud4AJmoY2b0H+grgaTmE7m5wQCoQ\nS8yk5O5uqqaQICHO00hOImJmauak2cEkd6f5fBxNzZnSnMbbkTWPpzt2K9FCLMIxeDOd3VOpzUlc\nozPrZFYfwVeL2Q22T1hi7b0WlQ5unsdZ50TLLaU2Yy2AbuCST0sK2agWi5b1+zHu2U4FfqTSLhFw\na+qfFRZkADMvfogCWhbBvShzG2bRkA0iApkvai+Z5vF4juYkbALMKeecU2YRMIGZg5T6oytO6Y5t\nXmib89d+UKrxa/e8p84EIbJSHqYE+wpLjEQKJjONu8GIs5qXY2lKmnuiYBg4qCZNeT6NIE5zLiIo\nhujupfyhCNecqYYg3Zv8l5yuex4EbyhMXRKHB89K5lFCEQpq6gQw0CoB1bgrX8EtaqDJ78N4YDf6\ner6rREaD5wroUzMVK0hU7f5KHy1IbS0o26QYKmcujuhFSIE4Suw7iVI6SIUYF7HuZp5hZlI4qJWK\npg0Q3fKer0Gfiw74EDklhzvVlsuaczYnDixdABOcS+npw0U/TlnVWUQAnY3d1XSezqrJXVk4iDBH\nuBCzmgmLSSC4qdki0psUWJEnfCmtvAo4EcFaJXVQ8KLKtz0TERJuO1lFjZtZcYSg1bZ5LyH6rzce\nnO816uvV95ZdL39Qa/C9rFf1by9QfKkV6ubu6u5CYddxJ2o5pUwMMDmQU+77XjigOGPYNOcS1Cgi\n3GydokQ/TATBgwnWjarnqr1egvw5SAji7pqzZZRuDxJAwsRkOes46jiRQC4fpTFpVjNlzZrmR4+u\nVCnGWNT1rutAlFOexznEzuFZXVWFqagP1dnQ4mMKG//6OvSC/xJxBe0I7h7i0IU+6pjznOAgEibK\nKatmDhyisHB1uVbcaxEjqIFW96ME/v4MWn5sFZvtWGFGwE0NqNVXnFlEuOtCFwuE3MXAUWLfpyGd\n7446J5tzPk8SYzd0Q9hZaU3h7lrLnhVR96XscyMWllmXyn7mqkYxSN+ZlpSXDCDEGIdhSmrZUI2n\nME3nF59+dkp2+cEPY9/RNE6n4+Hq8fzyRbff6VGn0xSGErru85TgzEEkCrlKDGB3UyIUl5KZNVHS\n1ueVlV1sWFpWlQnNvmwXEeDBAV1CmckhRb57lVSFDJlcGwOvLrhNae038Hb8Po0vN03a+dyUrG/t\nu5xI3VyIYE6AZc2cNWWdkziEiboYui4EmTWbGYSJCtbPBKg/TB350lGd+QW7dTiEUQLsYmAhBQEW\n+6Hb7bOOllKUyFLc8pbVQ4jD4YDA83ROrtkt5VT6SJoai1AMRtaZxxAhHEJQz4sPoCjO1XyufPSL\nWL/762lmobi28o5gpmmezYyEiAREpmpuxLQ0iizOGLy+1uM3A+p+L8ar60o13KMt4eJOW5QugERE\nxAFXLZtlahVxbA5Bd5i7xMgiXYxGyQmlR0XJCG0NUUtw5mIWf4UJXyWaoX4v4Cg76Fkz3DXrfB49\n55RdnafzxHB2T2m0rFm1P+wt+3R3A6Z0OprmlCcSSjmXpvU5KxPSnE2T1rSNEtrsrVQibbSlr4TJ\nK+/3JeGzSuj1U+VuFWaqj1nayqAVUW91dHzVitZPf+nX/36PLZraXvImcJfiH63Bd3kXTiDN2j5J\nREQCEDEJu5upETtYhi4OO50mgMxdNUuoYU7uXiuNe4te+7pavhcAwVXL9L2kkBecywlWul9YnlK/\nc2YSoq4LkG46nY2oP/Tp7jzevmRhuMZe1DLIc5rL/mvK7lbOnuXkdb5NVpRJWKPL1YH3RWPBPRa8\ntCnTSwSJg4hCPaaLNlVsoCJrFpa5xt7+fabL14yGPy2Si5ZKqAsQUiMTUHBNFiFuaAGROcyMK35M\n2UwIDprmxDH6grFSMzGIwBWjxhJD8RXemaYFbxgYSvIwAAMzEXHX9Rw6Zxq66ObCCJ2QxXmagkh3\nGHo1shFwcx+GToIws1sp6E5wA5grvNpKYjgq+1zc39+AQlZlE01BIcDgXM12B93r1Vnt2NV3216t\nxNyE2utk4N+vUbGnxSxsEXBVCd8YAKgxY61GUC1mm1VrmKe6qrFQkWXlU2ZmauhKD7yWY1nibH2T\nIfK1FnlFPQssX+tzoCIGIGLinNL57og5ITDMbo/HGEMYQjodb29uu4tdeHSY5jHPc2BRzZpMxIPE\nIiNiDBJEgjigqlS8nav1UWkUaG6hLxTvDyln40JqaEhF6Wq+5/0S4L5qVe2/BmG8soF/L8fmkdF+\nbWwUwD2z0N2sghhV3AEFjovCRCAyg5lZyswsIRA5lbBah8PnaS64qmVzrmVmqjbZ2NPXmLKjlZ0p\nk2u6LmC1uIFwICAE6bru8Phivz/kT42FeQgkOPQRgdV9nue765soIXQxZpvOk2XTPLvnrmPN6q4c\nItW4QhRnri/RrdtF/HonC3UtqUHL7gY3owIcwYk4PLz1JhKGqmF0rxnT31cpv8ibKrVpASxQXmlt\nP9lhRAJ3VeXCsRxmymBXT3nqqG/qu4cY1EHMxAQ3cvM5sUiMAVa97QW6R6mOhKrfojnSv8bcGwbT\nvEiVzzARxN2JRQbpuu4w9MPF3kEeQzgMu8cHkO9Uz6eTCXWX+wOxQLq+Ew55zhIiLJPEw6PdNI7z\nOJcAF1dzJyr5wBs3R9V+2wEuk6qTWTp7PJx4y1xvLy0BteXjoaks3k7t6lypybOLHVBn8fdzLFyg\ngL4s7YDXgli0xPwyPMQAx5xHZglBzAzmxGIwzUrEEsgcBBeS0h/WvBQVg8MZzkFclYhgvOxcFfHm\nC7r8tfnopig+EZwBJ2EiwJwluGrO+XSekgMkZpQNN9e3ROi6XlyQfb/fXV5eWDYYdFIlxD64Sxx4\nd7V3mKYcYnSQZiVQcQhb7SUMtMyNKqi9zgVYjBtsyGdRl1Y4v5TvrooK1Y3YtKFBu9e6afeX5u8f\nngQAK4qJ6qPzdmIrmLQY6QQnlAxHhUOzxhDdHFb6ExKcQQEiYLiqmVadteb0tmNQIkHNnamEm7xO\njr1Wufqi4fUsAQyoaoVhs2U1hnhWUu9iIOmyuZIImFiQ7XxzguU8TWPk/dV+PI2eXBDdyTTknHzC\n8ZbH03meZkBApDmH0DWDfcspF/NxQzvu7tVRvnmYFr5QP9AOqKNEijXZ9fo2NN/LeG+0hYbDVT19\nKTwJNyotPIizqgSpFfozNKuQlKVPcyq+DTVXdwiXWlssUkxgiVLq/Df93n3RNRdMBosaCzTlqvz6\ntYTWFmOhghfWkxWChBCyKhjJss2TOWIIu36/2+9otjyfuk5unj/vd/0f/6N/dHNz/fknn+Xk5/NI\n5H3fsYCdokSLbm4l/cPc3GrfnFUrdJgZwZmlLe3iRKfXgU8NPFqelVrbIJQe1fcJ9DtnkVs2/35Q\nJ+qkiupvhqUBjDmcWEIMpm5At9vVHsldZAl9GJC17/spJVNnEc0KQgml9VlLj0MwCUu2jHsaPjGz\nt3ZIbqWhX2Ol9/CaL0P37hGvV0dCVUSEQMTssetFWAnEpJqnec5qFON0HO8+vZ4+v3v07GL3o6fj\n7fl8O15/eLx+eby7OffDMI8TEUvgPCfLOQ79xZNdSllzlhAIPJ8nkSCgnBLMiUGldGidBryJpQUx\nLc/j7qDV1N8gQ05ERblqj/89cdAFaH2/lNjlvFBJXmuO7xovDKiqquY5zzkjhnmcLKfLywtzy/N8\n/eLFpRv1XauTa7EPEsXg0zTXuv7ucMs5S4k8rwr+PWMItEIHVOfVRN4XNFdcH2HrPSm4p5mmLEG4\nxJ245XG0eRYRFg7ERByHgVw0j3qcct/ls3Zxf3w5/vrPfztr5oG7qyHIXWQJMZ6nNE+zkwxdl9Vy\nNumkxmILmDm5uYFAHNiqZGi6p61QchUTWPQlePNPoG1EU1XKESW8ph332x11yVfQZvHP0ubVd85M\nK9VgDSOi6okpXosAGEnkzkEQlpwSCYWSIEpksGk86zTuL/em2QERSKjhncju5hwYvOEM5matexW3\nLy2zWVaIFl70hdOm7R+LEm3mRhBmphI1xyX2tTRZdHdmU3SH3dMff8QMB11+8CH4NJ2sO1yGS0YQ\nB6WUKTCLsEZQ4DBkneYxi2Rn1pyZCMHdaw9my0XyOy8AEJYgBYYv7qeSEdpya8vMqw5dblXDouzL\nmsl+q9FOxrrkyyIvSGsLvfTtYtP2j8XW+24mic3Jbb4clBSJWjhFSGKIbJ46MmYXmY7pOl2bqh9c\nhmAC6ViEp/PJPV9e7VOaT6fR3edJQx9LYlApC0UlH6MKea8Op7KFdG8VNrP7CjNpq9Y1rlWTOBcm\nVkKtzYy0BFqg6ztNOs+TU07nc75Ooe8nnWbXEIIRl0cwVbeSv8o5GVMX4mCdMwcmxK6T0j2Lydxs\nUgdikScSmRlmlpJmBREzV2PTzEFSquMWbXyzFWXiC27k9rZE/AMqeiiUlnlsQKzFYChk0dJ72iXb\nz37nY1H6igCqNVQAVpx/d306T/LkMp/HrDPcYj/sdzthmcYzgQJHV8wuoeshsGROLhLmPAXqOAhQ\nis8UmQZUlxMxiWvJPfJ1p76+4b5OvY0KdjuLcBAzR0kSKOFRTMTk2U1NSzWLPEuADOJkKU+hJ+k7\nhJQzYDmEuIQfEOF8e7yN1wojJp2TmobABoMBpWwCEQHdbpfczMlKch0xkRW02M2Z2QAzJyKzWsy/\nsrJKAIVHlOI1/vas+FdTo7Cw7rJ2LTBgEe+L4x8N6dl+cuWm3+2o1nSbKhFxYCLK80zE+2HnL2ZM\n+uTJk3yQaTrz0AfA3e9ubqZp3A9DOs2nj+8sTT/8ox+RhHk+J/XIPE+Zo0YOLMzBLRu3L1zL2yxZ\nJeW0vrKIX47V00ZvW/8kiAgzZ01eyu0w194bla+aTmdTBSnHIENHRKoqQcBcLR7ATXPW2Ed14ygS\nOY1HjiEI59OUp6l7fJmtlMoJ5i5tMU09qQEu7mRKxT4zNVdmdnUzdZNm4C8aJ9CQ34q5M8jpLYr4\nlcqKluzV6mgtm1BOhXPBQNxrp5vG4hdWv/6+yv3FpH3L5v8SQ4cWkl44qasTI0okp/Pd8fjyGt6l\nnGL2NKnEIVqM3B12F7ef3n7yF393Ph/7Yf90eCbcx4AQ+67LXbcjER2TZ0dWjmHdjibG6nOt+tBD\n0/zLJr/+bEpUs5Z9IzUXI8ThJBwpMIGYEHkpQ1NOB7mRqcRAoPNpzCkTHVhEWA7CWZUjEbnl2XKK\nMVqyaZq7gS1nTTmEoDkREQdiMKtacgDNLQTixUNXhcl90bto4avW8rZ1UNosDEoBaXczlNwqIqg2\naMWk1HjyAukYBwGv9uuGI/iDnXjbo2q6Ze0sKxGYycxurm+O4ynsO/Oc73LWtBsOMO887h9f5pd5\nPJ+Pp7vDB1dPhw+Hi/3p9ny8uU6W+l1O8xQ4DhcHM3JDTsoV/Vm55uY4VnR7iT6pi/h1vJ3biIFW\nndRbYcd2Z6NG7ixUovaBmpVa2qsy03wep3EceC8xQAhKKafQd8KcUlbN0rGXiiWMlGbNaqoSAgjC\n3PWdwms8alnSEj8TY0qp2EYOV1NWK2knAKDLA7aEsM2zv0UCXahoXdGqT5hTaS7mcPMQxQmkkBA0\n5RLV6+4cSsa6E1NpXFn5sG/Qqbds8FM7x7Qksy8ZwObulodnF0+e/oT28TyOZvrk4sndr198/Ne/\n3V/caJ93z7qrp49+9kd/crg4nE93d7cvBQhD3/e9jml8eQyI5iYiEPHSKxFbv1EjSwKan5kb6t5O\nOr16au/xgTVPrhKku5cMiHYbXySFN+lWSxw4CiRZPs3EIcQQozPtn1xBMZ3HPKWUx+PL2zjE4WJA\nCJdPH+mczZ2JSzXxAuUSYTqdncmFc9ZAAncRZuZ5nADj/QBARCQKWNys1bCl+1u8eue/LYEuJx11\nCcnhixlW6dMshCgS0zSnOUkUClTiz/OYTJ2Jax5psRxBqsqM0plgw1Fou3FvY5TbeyHQ0uOknOC6\niNlMSA7dcT7PaQYwaRoeXT75wGycBxF2zJQx+M35s9vnnwXhy8cX0keWOB/n8TxFDkFIAivxPJ6p\nFub0Il3QQO2l5DUKQ2vpy/eVGi8RqG0lKsOtOEBTV7gWFFlOuMNR6/a0EvQNTvNGoW7lgDJJH6UP\nc9YYA3dyPp0CCce42+1CH0rj9a7v2THenUKIsesAKvViJ0vjeYp9J8xZjaJ4YUzm4+2RCP3Qmyoz\ng9jUNCVm8tI2ibkkcy5CszzhtyJQevCLN9nVXNsoURcu5GAwg8kZ1cVN7gQnGFyIhVnEVVEqAuiS\njrMAvKtH7S2Ojb+mOR/JmSSESObjadQAGrrj+cRORHz7/GYX9ruri999/uvz59dy7E+YUs67KHY+\nWZA5z5y70A2B5XC42O8PY5rSNFvOmnPoAjFT8TWvz0OVmCpv3eCfi1a/xgQ1RW21N5eHQVPqnVoh\nx/LTreTyFrSxYgblZLIwvEXsuUsQkSjqntyhXehEyN1lEHDToLPBmgB3IaOh35fggv6AeR7zeYR7\n2O+zzZYVjiBCTDD3bGiVBomaC7SajJsNpmrKfCsC3Wj45Qw6h0AipRRlqbjSDwMC5il7diYJMRKL\nWdaUkaDZdM7SRY4McEqTZZWw2FNeVRJaxNRbVkSLLKEqYR1WOdI8TvPdaT5Pw9Mr6WK0Lo/JzUwt\n+TjIgGhEcvnDZ1eHuN/vL/s47/pxHLPDUGqU9DlrSrNqKg7s2vfPLKdMRNIJU60pUs2a2gauVqUz\nc3cjZq5lEZrov9+jAWjEynXH6/FeOo2gAF0uIqXfAzFXtkoEcIV+mUpflzTO03lMUwJot99LCEyI\nUZxI3cfzdL69ISdhPh9P0+0oEp588EHOqvDDh4/0zk+3t0S18rJnBfHVk8cgSGBmdlD1DJfDV5Tm\nJiqJ0GIJCG9LxBORg0r1AQ4Eqm5mzZpTIqPpeGRnEmEiEYaLzuqE2HVuFmKQELgskHsx8KtAb4yk\nfR21jjcPoNc3nn7lJ9TOANe2EsokF4+uuosDQDH0Th6ED8NgcybQo588++jxz+TZxfF8uv78OgEY\nzw6nrkuzqk9knKZZs1IfKLJIcHQSpLQ096K3oai6IKlt/IoeuojxylvKWB3aK7qxMsxFDzW3WhAU\nltXUQghUBWhVaUrp3QViK2enZE9pyjnPIoEiAxQkWlZ1VcskokSh793JVQ9Xl+S4w/V0mvKUbq6v\nx3nmQxguL7p9n+dJRJKV2pIqIWTNeSpZoOxqlrV9KbkV25QLgy9hIq5K/IYcdNE4V3ZWXGnETCzF\nbBcRTSmpkboBRjafR0UOO/HGByQGzsIxqGqalZkldqqlqjQcFcKvrS99m8j6FjRRWnWdjWpXygJM\niQPvHl8o0d2LGwXSaczCw0HSeHajzHZx+eSs6fbl3XScOIgnBXnfEYkA5ASHZ00CDkE0OxjmbmYc\nRETKgbDK/Lg0ogWqlQwiDnJf5gEVKm0wTGM5WPDDqlYWJgRvse9gKiX0lisqPlruUmQ9MxGZmqvJ\nEDgSnLKp5USmalkiGdNwdRn6Qed0eHxFREHi3Yuby8urq2dXqjZxVncKzCZpmqfjyMPOuYoKM2UW\nkJupzqlEjQiFUpo5dEJElgthkRvoTUW8P/yjrY2ZUSlIpUoxIAYYAI/7IQ5dSlOxMLMlZ3Ni1Uk1\nRUS455RiF4m4JAc63Fs1pKZ1NTn4lkT8ypeX5yAQoJpzzrELqjZn1SlLH71w95zJDBnjdBpvxlvL\np9vp4tGTy8vddHut42hNRTStspuYVC3NM5m7uJm6gwPDvVa8KbU7HKpZHGReqY3ZmvLdAk0aHLbO\nvywP+WLOFzkvpdMmixAJF82PmB3OxFW3LUqNVcfNQs1OMLOSBuBmIQQowJAgWTVPs5nleT7e3pgq\nqWebz/NdKdp/vL1BZASCmWfM4xQ4SowwxNhnTW4oLi7NFmJgZiY2soKLNlWnTobfmi+eQMTUgnvL\nS8Qiwq4wmzkGJTcGERvMGcPVXlh8Vk5spsyh6UwwNWEmkVqozb3ms/JbIsxltMiNe/YegWPoRCRw\nNlXNxAhdCEHYTcCh7wiabtPpPE6BEzDmTLd3dh5jtY8RgqATkM+apY/GzhpITUSYKOVMLMJkyBSg\nZgYPMYJI1KAqJA5nCa2SGbnqQ2y/2kNUp9/KGZU6RRIKFAoHWTYCl+Rmy9nLtV7KRlNZ2IZtgYPE\nWtKM3TynFAObqsOZSIhtThzC0A2e3TSHEOIQ3SyPU9f3+93A+262nKepix09ITYej+f5xc3u0YG7\nIBJi3wuxZi0Ql2rtBl+N91bivkizt0KgXmQUSsFigpppSiFwCJ2q5mneHXZM3vcR7BJZWPohWlbq\nxbPkOYcuDLsBIEuqs7IEErIlX7JuzirPNi79tzC2+mw5DNIFUJHAJXNYyT2nDM2RiNWEpR8GOexE\nYp7m+e4c3LgTJjI4MYUYLActctxcRNI4Z1DoItw1p5L1EYKQ0aiJaCAGtMTle1ZzsrJ5CJznmeCx\ni7543LyqVWZmqqX/YogxZ9WcgjAKTmeeTmMIganLKad5DvueA2u2Ak6XVBZhQlNeqfTDJcDMcjIS\nUIX9CUjTLGoUQkqZyI2MRWwyzcoiqkpqIQSl1O2H/eHSJ+/63fF4F7soXYRTlFisMRTMGyhFfQsj\nL8mDHKRUA35zAqWG+5c/qoFNQEmjNtNpEqZ0dzq9vOkjm5G7wpDOc8r59PLa3feXF3nOx5d3+8Pl\n/vGVG/I8WTIM1PaAmUm1FERYwlzeioWE1bBYXqglO23BCllEmEuYhTuFrkO283RGYIjP063mKU9T\nNItdEGZTnccp5xT6lFUVavPZo/SxSzm5KtdUIbPk0+kYY0cimmZNk2n2ZOJQN81aCok5IYiUVN9a\nJwvVsvHFoip8kFmYjYiKZmAmIQCcAZEQSNI46pz6ww6BVXPpn7RAI6bFLSQgNjNmEKEEPhfrwgAW\nZjZNCWpmGrpoGZ4pj5kYGn06nec0x90wnc6U0cUBmXYXh7Dvp/lMwOn2bjye+mFweOgid6ya3dnd\n8zwzc0luriWChd+cQFeMuDrLKn26KQtiZE0zEnGeWNMuikcm5xCjaZ6nlC1JDJePrmxnOM+lOmCa\nczpOMGIq6aYKb1j6d4GCbvjm6r0oHMocDICqfqae50wgjl2az9OcPEDcaZ5788BUimuTuzBJ6ykV\nAgtzJpgQR+52XWSJfRAjt9wRMwupUQhDF7soygYggl3dTWsVMM219uXaT2gz0SLnufAIV8umuaSP\nmqmjIM1egkQ15TzPmjMHKREhtGBb5q4tKrACqFVoGZwArooptdoNJBxC7Ew9pyln74YYu67LfTbt\nJNCwYxJxqGpOk8Hm6dyHTuAgA6wUOPNk5sY11qq2hC8Qk5mBvwWB3qMYhxV9n1qMFZNZBnfccbZ0\nOt2RSJ5mnXOaZ2emns30+ccfB5fblzfD4aq72EkIDhKJcIZZRX6w7sg9r9/bG6tXBUBxZ5dKymZl\nmVq8HM1pntNEfUDANI95nkqThJL9FoLAaaaE0suy+WnAbDAKzOA8zVw6ME0pTdM8Ju47G9gjuyo7\nEbw0SAd5jMEIXNxaXt1yzEKtl0g5v0UaApXLFoflPGlBzQCoZRaOu4hYpDmkkHsuSdHszRbzJg4r\nyMfEzGAiFpiDKPRiqp4NDmZxdw4xDORk8zxLCOQcJYhImlKaZ5tVLQ+HXd93cDi7xIAAkVBwLnYG\nvCozpMjOhS+Yw+ybEyg1BK7BM0uMBUAOI6cQYog9YFcffShOGRL6zsxJNGv2MDz+wYe7J/vzzfUn\nv/jFeDrFrmcJwnG/v7BEln3OM5lxLFYAiUg9A82V/NZpdEv5RS0rnhkWLm7rULUi4ihd36mrZuPS\nqKMZnkSlrzWxBJB7tjSm5CYiKefpNLlxGqd+Pzz96ClIENL+SrSjs2emYO7CEkRgoGzkzEVIl1Cb\nzRlyq92PKgpvBncmKu7KGPtuGNyLozGELjtg5GHfBepBpKZLaPbi0OEYzXK9uW/sR5FKswV2YADq\nrprdRzLzGGO/21lKOc2li8R4PJf0OjOHueUU+0jMno2E49A7gdTcnanqLeZWiu2YaUrJzYlY34RA\nF/PEG5rJxeNPEqJaSakVc5uVlSJixHCwEJj58tGHhyecnS8/eCY7kv6iv7jSOV0+evLy+cvzeRrP\nL5m5vxgwGjGTYBpHd499VwGmt+pG2jzTAq7WZqQr7y7WrRkR1RQbIWNo9uI8ZKYQo6uae8o23p7n\nnHYxFB6ss4euu7x8CiM6ewdhE8s+3uXxxe35xe3TH/1geHyZp5Mz5VFh6GKEIM/TfHdmmZNp7GIR\nvMyltg0XN0y1GQupZUNtxkDuNE3zOCUmDgHqIIbBqp/J3NREZPGgas4OlxhB3GiTWYK7e86oVdEB\nd2gm8RjFhaZzsjyrqoiR9FE4q5XuWfM8EXPsB3N3A5nNc1JNDDKARMxUczZVZpIQCrcOIUgMOWUm\nEQnd0I/n8RsQaLGK7gWAea23bOZmHlhqcQxnN2IPeYbOZpmyQzVPc57PPiVMfqc+jXef7fcBhLiH\nIzDZPJ77XZdtUp8CCRMz0AoEfF+Nb7wihBUdNQd79RMYtHglzeE1UUPNQ+m0q8YSiWdiBZE7NFsa\nMzLmYxIKlpBds0FCZ8Zp9uNx4utjH3jUFIZoztk8JXMj4iAgZjEn5lKQbPXCNwupnSgvbK/EFAhK\nVwSDkaeUs6qAyWrwm5trtjL9gt25upshLH5/wCEhmhpRKpzP3WIMJB4EXaScjVjJEZhEAM8OIjII\nE4vnzEHC0KWUPXvsouUMLWedzKz0SJZSSK3YvGrqDqrleqqQ8G/CQReH40om1Lz+zAw3d1XLSU1z\nmiZ3u/vspTtpVnKoUgzdYbcPpJxEuEPYkbp5vv30djqfdrueIksf3LMIA5ZzKbLhtaxhBckWd9/b\nHyuOhVX1LQnyKN4yAERmbuoeapQFQGauWeFz1uylEDghm5aGV5YyM0rT6OGw311chBgkyMVHT7rD\nQZk5zQYzluDOgOYMMJWCxdnNaneHGtRSnO4LKtyAFLibmqpVv30IEgNAznCr7ilXM3Vzt6whsAQp\nXYVUlUw5RHe3rNNpduWiBoRddPeUkkQm5fl8RA61uG6QotelpAQq/jAQcQwQyarzPOusse/cjYQk\nBDg7zM2IwRRgxsKwFiWjBoeIgDmlbPp1CJQ2UESz7Kr+WaoElr4QtaaaEzlcmV1Y9Hw29X43kLAI\nCztx5pDH8103SL8LprNAptvj6ea2C48NpjnP8zmEwoxdQqDmkXH3bY3U70LWo7pjqgpDVMuBLNRa\nZH2NDC+AGpVOX+ZmYC9bTkQkFIcu9n3oh36/t1klUDd0oe+ypfk8muXQhSmfp2zOZJbJlUk0qc5J\nRHaHQ8op5VQFCbXgVSzFnqojiYCSZ+wOdm6N8FCAldh3blqUFlUlCbvdPqdceomaak1XsqXVKEsI\nIiHNaTqfS+8u1VxKe53vjjkE6iTsD91hn6acxwmqpausZjXXkomV5xlqlUnXiAEnIlcth62AeoDB\nwSGEGM1NUy6LWwLzvopACylS87Z4axlN7A0BArfqmAAxOBAzh06iyHyeFNp1om4wnY8vVElCZ9M4\nJ5fLHp67YYBQitz1pZArpXGWXW+BzVyCLLB8hQCBqm58R2OTHeUlVLHmI4JabCAKtsyl/EBpUaCo\nsWo8pdlhDjJyIjK38+luvj3Px2OMNJ/S6XjsugiBJZrmDJZuvzPLBAfDptnGREMngefiY+yiaUIJ\n4q4uYKLax3sxWNvSMLGwMKuqZ3XyEKPXXh+AOzNLiDmrqTMM7rHrJEqtx2Pu7v1+d/X0aTpPLz8d\n3UxiEDCYyQNJBxEnyo4YujxqVgQSMINlPk0AuiDubpqZSUJZMJTKdYuzuuBZDhSXEjFb87taVjMv\nUURfT8QvO9biZ4ipqGMtApGW+A+HmysRmcFVg3AQ8WxEQACxDXs203mcAndOZDqrT6A8zWePzKCC\nNzCtaeQOv0+Q3xl1vvrolTq96TPwZj+VPApGxagsJQsCwHLyALAU41qCFygqxhBjhNswxP3FPmvO\n0ywwCYE8R3JmEpTeiappSuM5z/MakbAB3FYT1aupY0Wyc2UVBncrzX+dgliqrclczZCVZs9KcOKi\nrqhX3ZuIYLCk8zgfzTRbFlAIwoGG3aCniSX0F3slz07CHXlCK1xR4vW5lWahJTy18Eiq57m6gyst\nlfA0riGp1nQYaPEwfRWBrvGx7ZR68+Vb42hFjSgLwExAxWOsTqWYbIBLlJxy0lE1qc5MJcXfdxdD\nt+8oBMTALFlnJ2guKAVqoZ/iz6jJ9Gt4x3c+igq/LCnxgsW6WUmYNHciiHDf98yMwJkdQYjYFfvd\n3rJy5zwMFxeH88vry2F/cXX52fPn0/HUX1w8/uDZ7e1dmmeHqRkH7i93TgA5M2KQpfDJAvFhgzwA\naIGOTMLqCnMGsQQCzC2GYKplCSUEJnbTluhRFRQqsf5c++cSURDiEE+R8zSdTUm4D8Pd85vT9eny\nyRMiz6dpvh5tVOHIgdw1pUzMkFIK3Uu0fFm5CoBU7I6IyK111JRSfhql72Y5e9Xj+hUR9Vtzdl2a\nFmrizi1mDIA7yj4V2i3t6kjY1Mbz6O5gxKFnNxC6oWMhCOc0E5xjIOasuZQoin1Hpc1PKeO1ODfq\nnPC9ctAHv3uND+ClVp07iDiKwwtWW4DGopNoyp7s+PLWJ/PgkeXjX/x62A2xj6e703ge48UBzOPp\nzCCO7HAOzIHMXU1L5HrpIAOgyfO68XXhF19nW/1FRdec1ZRFCt6JShukOVfllWDUoADzNE7ErKpE\nPgliDGDXOU/jCHggOd8dj7c34/ExouRxTnefOzBc7EpYj7nFPnplpEJEmrVOSk1CKBA2M5tqTolF\nSkoTKjJhQI0aAUBMX2XFLx4131hIqA4M1NNH7s7Cls3MhNmyOSxwxyKhc52zAyEGI+cgSAAh9IEj\nq2nOWZh0nt08q3FWlexw4WaoNm+S09bL9w5GjTEu7btZCmJYooiIwSGo6fF4hrvsBpAkdUua5nS6\nPd29uH108fSYTo7ALpZAHGUYDl3HfT+OaZ7Tbjd47YhFKWd3l0ilNKm35rP35rP5vXjiUePmqmPM\nzPOcvdby4IWYq83eHGZCpWofm3kakwQJMbhlNgvCV08fxb5zdZ1T7IYh7kLsuzhwlLiP4+0Eoi7E\n7HNREFhEc6rlRkpYA7ggr1S8ZIUWs8JcIoNKoAVxCaoqimnh6Mz4Ghy0LkDTUIDqcYRWG8zdTLiG\n0xOxayVns5w1myuIDFzWJU1ziIFKPkOpYsniXooXE0sNxW/qFahmIm9UjXc4aji6lXgfNXMmY5Cw\nBKYgKJr20Kc5ZTXpuwLNEMvV1ZM88dUHHxw/v57uzmZiJGHo1Xye5hi7fhjmeQJMWLQWeAoGL/K3\nbMV9EBrFSF8gsQo3FQFaAiOES/M7tHyiRZP1xna8kAIxuCq7IQiBuhDZ4Fk9WlLNmtP5lHLK7Ld3\nd/3QQ/10dwcm2QVnq9tTuWAAta7YKCaQEMgLkN/qNNGS6VGiHJkasgu00/RVHLQ8bRklMaABHFxj\nW7icGAnByGLsNFsI3A+D5pmYTFjNJQSvEboKl0bsLiHE2KnmYhjBC8lyYQRuJbCgKhu05ejf+yAi\n1BA2ZhEitpRK7g9AZtCsehpBQBenKdF+2D2+crbuhHB9Ol/fvfzsk4+ePD6+fPnik+dPfvZR6Hsz\nG0/jlM+qSaQq6yGEXAhUs6mVnWubvWg6vqyCA610UJUx1UFPzEEKjFB7cVQSdgmldzGZmlnWXJ2d\nYehCEAjN53k6j5rSNE+Hx1ckzGAOstsdht2la4a7kXc7Qy0CAaq1fV0kxBhzVvdcoBdmgbkpzK0L\nXQjiUUuCJAjVp1V1j+TuHMKSH/YFBNq08bIMVMMI3ErACy1562ARMyUIi8zTTDknVTBN0zzP527X\nK5DNRcRdVVVEYheLaNRsAMxr1CNJ8bNVNGQ5GBV0XSJz3+moEqp0XpMAhhHN0xyFI6B57vsulBrh\n/W66TYHpav/04sNePz0df/u72x8/ub377DzexEFc5PrFNRxx1w8yaE5MlN2JqOt7XzqwM4jYPbvD\n2V93OpsDaIPCVVbU8ua2TriFgxZKLrB/oWmJwkEAYhGJkboORCFECOuUcp6kQ+g6B5h4uNjHwyOH\nz9Mxz+dSDzXPSSJ7KbzvKMEMwsFd3WCz5ZSkFfA3V+bgQIm7IvLSlraAUMVL+QUE6q2aU3HNFtM/\nCFcvBpcUhZq8CQoxsMT55k5yzpY76eY5nU5j3O+mOaUpdcOuZISELpQGdqpmZqU1W/HcEDHIqSAe\n7kUpqQ6GZd3fJYHWmbrD3ErQpIM4CKl3Xbwa4svr24Fomsb90ydXP/nJX/+3f/n8P/yd//DHO+Lp\n85vLR7uf/enPhw/Dp7/+JD4drj97MaV0OFz2h91+6D//+LfV0CFwFFMr5RLNrcGdm5JVtvzur8qU\ndqSxvN5w4xbaUxkwuzsxsYiVsHZmUzM1IpKhZxDnzBLMTJOWXOo0zjrNBDELGQJyTerqTC7CFAML\nF/7FwqU8mJuaadcPWpKBNFVT3cFMKZlmkxCcXEr1dKDADryE178ylhNH1J7JVE1zoUk19XLQYhe6\nnkLH0gmHyHHohsurxyHEELp+2MPFUmk+GkpKXUEHC2JcGi6WjPmNvovWCht4xTj4/kc9F+tel4iN\nktHmLMIkgaUn6cEXoZNZcUpxRLjW+HG6vIuHO37xy48lhrOk1BldxZny3ek052ygm+P57ng+Hk+q\nSgRVNbWcctmkApgsGMZmJnhAhQ/Hw3Wr4Fj1P2GFIwk1PKqYpqbmjpx1PI9pTjnneRrnNIchUiSz\nLIG7vuv6PrCQIcZYuikApbiNWcnLJVhOUBW45QRXImMB2AFjJg5CLK5wRa2TwNS82XXyr+egDfJc\nqq81VVck9B0VaU5s2SwpgW3Os+b/f3Vv+iNZktyJ2eH+jojIzMq6+uZwOM3hHASXXGIhLLQgIAH6\nIOijpD9G/9wC+iAIArQgtVhSpHbY09M93XVX5RER73A3M30w9xeRdUz3dFcNm45GZ0VkvsPdze34\n2bV/fjGk5xAwGF6+eCGSp80mTwkATdRAJOUQuKi2zonNLOUiyrW0/T4mzKp21rpk/3LEevxkAxAV\nAGDmrm07iuvQtApd6FHYcrp8+vzi+h92w2Vche32ErmZGT7+6R+PoBePL15cXN3+cZuYIDan5/f2\n+30TeLU5aSKJZI/gpNL6Gx2B9/qaLtoqncIx7/SdOg5TsJpEV+SgGZEjOFiqJ1Vo3krIQ8nt1GBN\n0yKRZFqfhKZrRrV42t66d2e/3e0vtwCYYYY0DPs5zXO/alyHjG1joJIzojEFBA1NE5rQhGYaJxNF\nhG7VEeG02xMiGMzjPI8pzbkj4pahdppcgg3eJOKLjWi1wwhUJAC8ySwCmM37YbYxdg02IVLsm1Z1\nMgTIM6OBoU6paztozAEaUwUr6bCmRow3hLZD8VWjgCPeaW9iEn+QsZjNFWvEUtMHFIDmcZovh92Y\nTzmychJjas7P1+Hszi9++dfv6frR3/5T+s2zHruf/vKXD+nps18/fPjlo3/77//7F/3V8xfP8n68\n+Pqhna1DKHJYRIptqOatbxzNJsdcjw/KkWyv5Hj0y8MyHk+lIKn1A4KCqJSSY6Ym6hEbaZiHq93q\nbGVmnhY8j9O4HyRnZMpJzcBECBVAHBXwNsSenJSGGUDXq84bmqY55zxm0KbvIes8DP165e+02myG\nYQxN5ECSZyjupTKd1xPoIgzKEaz0qip5FGA2c5c0AgATE1Lbhtv3zyMSEjRduzlb73cjhdB2nNIs\neXZfCxJ6kpdkj7rwZIVaB+bgDsCb0OsPYBRlx40PAPDUdx2vd7tnlyzY37qlSFe7fe5DWIUt7qf0\npO/h2fBo+8Vn/+Vv/4+f/09/tluPw68fPP3HX/PVcHu10m4Pu/3+6wcbuNudd5qyzALI3LQc2NWe\nBcq+gTH5eAkZPQqDLD+PqHYB9gsXrt+LpyK1LaCZqomZgYiO23HYDv1mraKgmCVvL67ncYxNCDGk\nOSFbEwMHNlMv/j3Psz+AjIbdTnKKTVS1GIJByAm47yh24+XFPM33Prw/jfP+xXZztgoWMaCq5JSY\nyWMCjQzflNVZvF83PgMgYiAAA08hAcIYLCsgSM7jTk0zB9I5zeMWiFU0S45N6/FAnsnq2D4ZGZc1\nPTr1C6P8AZElwAIDHz45bzJAZMpJqAld24ezTboadnPGLs5manj14DFsH41Pf6v5ydXFPz/8/O8u\n2uHF88/n60cP/vPfjoFkBj6DO2f9+UlLAXfjnPcTtT1yMAMK7KLzTW/yzaOmNR5Y7UtXuwxbvFAI\n4HWyAnEMsW85RjPlGKgJRIQExEzs8VxaAQB1dFNFKQZAAmIFUIRMJKomABTnYTo57Ztuc/3omRli\n5PlqGod90/YKwIoARlBcPzUn7E0c9Jh5HRbISymaqSETBuYmQgAKIaeUJUVGZvTYFSXOiiX5ixDM\nVLIu+rnXs6uCpqJ3hyX8FzeMXhoLpFyRDfVyS4Gb2EW6c7JZn7SxG7d73MT+wzvD7Rb6dpCrr548\n/vDT0//wv/y3f/O//Y9/8Td//fmT3+b/9X9+8eBZ2sODB8+nAdrQ4ukJg1maZB4R7OzOrXa12W33\nmmXOc3FlCxa36ncay2UHoV9p1oNORKT0qivhEwqMcdUpmOYsahTI0GqOJHFgM/MovmVV3C8IZGDA\nqyaGDrsow5jnCQhyTtvnF5Dl+vlF09N+P4xp7jY9NUxZJCdHhTzYlUNgJn1zyscN6iyRgh4NgmSe\nA+DueI8wQMFA7aplNFDipplFTWYwnecUAokKIgCT56ARElBtHWCFQf9wRz0ulUSNiJjZssdOEhhK\nysO0RdOTzfrWe3euT3ggOP/pp7c++KQfd90f3RsH+4//8X//4sGXP/urv8wr+u1nv43xdNWudZC2\n3XAwbGIPCFPq1qsQG7CdeVHDojjqSyrm955UYUOlUTOAo4fenCvnpAaxbQQ8ZKcECHn+MQCagWYl\nJjHB6s0BACQCIjPrT9ahDchoOSrY6uzWerV59OXXu5wBLTRBRPvNOsYWlHBMeUqIhXOrg4xmbwpY\nroerKl3OPPzwEZOaSRaFbCJ+JB17mjNv97tpt2tP1hijGIQYxQyZmTA0MTRxniYRXUD4JcKlrNkP\nfNSAdnYPjRooqIglmeaJxdomjuPu0T//Kt9dPc/DZrN68Wh7/asvn375pD3tfv3//OODB79hO99u\nd4/++dHHP/l529qzL5/Ifre6FdvThtow5unZ0xcIOGz3bddiqAFcgL+3fH/dMKiWOwCYAnrFkVIK\nz1TJU+BRiZEbdvF9nIIvWVQ0zxnAm3k6W0VEDG2MbczJPTI0Xo+Ss+YsSULA9Xq9WXd337vf3+q2\n24skkyS5vNhpthAiE8WmNUZAzMNQugCJvJZAjwEVK7Zg+WGeDcyEMYQIrNnr2SAoipoBKbIiMQcz\nNSJkRuY8Z8jKEVQBFJBLFPCRCv89V/5dDzcrzB2wOQsiihpkNe+lzWY5bdabIPL8Ytt3zSrPp81m\ndzWPT+f08fq99/7so5+t99Ct7vyRNNf8WKXZxFt342UG5rCJM84tN7FDxoCAbddzoEMY7BH4/r3G\nUpb0CCAxM/K6QgWfXirhqHkNCy5nsoAKxIhSszYNCIkLoh4pICMZMbCkOQBiCEZkXtgOUkoDT5bG\nERFaarLNXrRcUk4G2ATi2o7Bid4n/8ajWX2eVZaZihr6PSGnNO0GAKNAXk4XIlMXITAQiiqaACAg\ngaFmzbNYVlUl9koPFaN7BSL5AY6ySA4oAnEIvoxG2jYNiRhjt25lntOLia8HQjlpVvHOvfQhA58/\neph2W9pv4cWjXX926+RuUu5nbZR7XKVwFqchWUpqlqeZAJs2monkjJVevo8OWobrm2glXMNVB1VV\nZc8IxVL+0XdEc04phVoZ0wsBOW/jwOgR0uzxySBZ8pQgG4UIAjmnPCVii13kljVl0XHdt5DT/sUs\nKVNkJGWOoY2IOA8ToDmdcyhhbFgqi9wwUYuHc7EeC8gLtUevlZYzmsUrzINnOSGKCiHGGCMHU0NV\nJPNwpxKYV+KAzJ1pJc4TDH8IkUrfMGosEFRNCwk8AhZZss3DiJL2+2G326cpd1lP1uveolrebM7y\nrM9+/VXAkbO9+M0D+giaEEOIl0+fP/z8y5NbdP7eXWdQTdemIedpZiaviOR10A9NBb/POPa60KE+\nQ4kdAQshAAACkdfFA0iehCGQpsnfpHiMSl0nIuKck6maaJ5TnnLbrYCo6bqw4WnaG0BKOU+TYmYK\nYmIKxAGM5iFnFW6igVIgDhGZc85eKBQM6BuLh1XTtSiaYDVwRNXIkCm0DTJ5WZEsqcR2q+Y5QxZq\nowO47i0VFefES0oRHpTdb9HO4gcw3E40AEkyjTMYhBDE8rwd0CQ2LTTNnY/f7842inD15MXTL55r\narrNKhC89/H9TS9tpHW7fnp1seKAkVZd3Jx0IZDkxMztadt0/bQd0Ew1e2GonPXID/Td3rskArpz\n26o33kvZI6KImCkTS84lxheYmN0cdPAGCTkwaAENTUyzELEpIFLTt4GiqTVtN44zAFBgSBjbRlWa\nJjYU05Qtm4iW2CfD0LbdyWYctjkpE4pIzrlUdkEAg7A4Ft8UK2TVJ1HmRCX3veofYGAEpKrodeUN\nUQFE0WDRZA4RflTDkAsOXx75w6dOrK0boeSXliQb8JkQI5EiKVnsWgpECllywwQhtJ01DbWNacTT\nk75twpP9Xle7ru3P7qz6LpBBy9EAvFAmR7IsjpNYOcKLB+O7mkpYRHx5Y1xCSJxPIwAHDiYCBsRc\nAlcYj3xPAHWnqm5gFIpWq14xmkkBXD91LBIQRZXbpu02ADN4NAjiOI6iYFkaEbHiSNcCpy3vaeGm\n5n1gZXi8FjUQ2rxsvJMsoCcTA4HX0QH2RAhP9mCqoZxWswvMzPuzuuf4BlG+DRP1nY5KKQhgplqS\nedAAjBhj3zBTaOKwSzBnJJQpY4LVZoWh58CTzBePHj778jfjnfPN6dnFk6djyucfftivmnnYqe4p\ncsrzcLX1inJUlX4rBaYPBPL7v/srxYIWTlQ8DgYIKjrLlFNCKBXCQI2JEZEMwGNSqwh1rz0ysqdt\nmGrOItnMoyiRKZgqIUnKJkoY0l6uH29RMazb7mSlNkJksbS93hEaAqY5qacj13g8cKC+0KQTbi3g\nUX089X0qK/UrF8gJSnddNXCQDEnJzCiwKXqc7GID4VHv6MNR/u7r/i82EADR7QPj4BYjM5NYCUO0\ncRqudygoCZCnbqUQsOubtm+yZmh5db4WSGpjaGC4nrLkza01IWsSZC+HhCVXtvimAeA7r9KNqxZ5\naEspexEA1CyqSiGEwF6lLM2JQ2AkEcEaMl+9ToqMhB6uTt68mTxPUJEppJz226u2D8QIhCCwfX49\nXY1NbK/S5SzzKPNqs+q6LqcZTWVOWdIh3x/BU9vD8tYeiFXyp8wqOR7ZT3SDqy5M2FfAwOM8PX4R\nKDCUwI+qmkNl0FpbadxsPPLDH8exQ0UOlG4ZpaSoiqoZE2Lg/uy0b/urZ1fDNqWcgciaFro4WoY2\nrO6dTeMkmImIWzItHSSQ1d2IZqamh+V5ewe4Oh3MzBCIEL0JNkcmgNg2HChNE+RSoRMRQQuWBOAG\nFEoWt0/mOYUQAVydJQMzwqZrZMoE0PedgQARGc67IcZw+73bV9M1RGggGqmhAnqjLHKAOYuICBIh\nIziBHgwULE1HF1Z5hAUtC2VHEz1q2lc4cA2NsxJgcqRlHmlRy7P+VY2DwxCqW6laDO5B8Y0s5TSZ\ncgAJKKTJElPUANgGzXmWec4zBlQQNwQsg4JCCdLGZTfetvmIALVLSp2HC4PirVLLKiaWRZgQA4EX\niHBUkghU0L1oJUOnFDMDQm+2CAAYCCNBgtDEJjaiiQNbBgoIBDNO3JOgMiKipXFCM6wwgjqOqSUc\nx42k5XAu6ukNOfxm5bAoo1a1UqihiiUsrQZ/IFG1G19Zrn+do1pLRS9ylcy1I6QSY5DmeR4mAGvX\nLRGGiGbablZm4lnbMUYTEVBmLtlYquoWbpHvx0Ws386LL15bqBUWXJsEQGQG0YARwZjYy01aabPK\nXr5ZVMCVGFHkErktIoQBEU0ttCF03bgfdhfXBLa73saIKGHYTdSGNKfr4arddKoZAIl5nkY04wos\nICqyF0vDqoMaeHAjFqkPRTU5kiq1IFFBKA6IMVbNsqqw1eYHWATi4mr/V0uOrxkuOUqfdAAwVdWc\nvW2FlxdFA1WNXUNIOYvKvL+efSWmeXLvYklsYAJkh+oc5yhAoNqR3Hk74yg2yjfeREVVPTxYRZMZ\ngJpmRDBRyRJj4+Ej6MH2CoqappkiU/BoPWtjYA7762sBNcKr58/arr93/97+4kUgslmn/XR25+4w\nD9Rw3LRpmz09MAS2LIDIIUA9NwV6LyXriRAZDFTMVcjCCapZczSlBdmt5tNBN8BqSBWfba1E5WLw\nwD7/NZlCv2Mcb7OV+rHOAbxaak2pQTBFNCQjNFAhRDNVEWcAHoNsqgi1JAzVZcQaBfeOZlCrFZVt\nUjNRNE/HMDAAVRAz0SMXvJcnKXmtIUavCMeRq9qGIYQYOM8TBuC+2e2HnFRnS2NShVmF+xhWbejb\nnPM8TexdHMy8hWmaU07ZfQFOMgVm8reseRWuH3oBrDKOdE3fluOzCIvR/7LH8oB4Hv7wra/1H34s\nkM/hmyphlvgad2EXWwQRGQ1KykBtWuuYjRkYlUzrIpz8Fm/H+f76CSAgogI49kdIxFqaUpOJeq1c\nC8bGzqiIKg2gETEzcmD1xDIkNEhz4sAcWEUoEDBng9Bt2nYz7QeQKMlMTAUkK1FwOY5MmL22WO32\nfOz5dqAeERHJrMRVmaiRErPVCBezxf0JdgPwfe3M3/a6vsON+o6j6jlHX9XuAstfACzVTA28Mt+S\nmnBQ7bHQyIJTOs56jJC8g+EM2w7BOt4FRqqH6dgOrsg8FgODiSDLvB+ROaukYUKgEFvi0HVrVbm+\numzbrm83OPKt/h5s8/b5C2oaYySK0ziN42BqYNi0HSIgs4nmlH1ZiGsuJxoABNMi0YEQEYiIEE1N\nS7enAnBWDorFC7EAR6/KoJcwYXj14/FKfQva+4FRZxkHk77Q1KL7eLUF950tJnm5yBxrLsVCiBlr\n1RCvWnMEUb/VaR85gsoPNU/7BgAVEY8e9HcmhKUD3YJWuyWhCkrzME77sd1sOEZTtGSB2pZayNBu\n1iEQEu62+6f//HB8tMO9nX5w5+Of/fFgQx6zqaSsYCaSJUsWCUhoWHqPlAR9racV3YqvoDyRN2fO\ncyo1+lUMjCuoCTVna9E+j1xni3n4rRftxgLeAFO+P7byztnuQpf1i5cSgACgduQAACyWUw0XKqIc\n0dDrtsOBFOCdGZQ3WSMtBobrwYsG7EzU/YBYImLcGeodlySE0LYQgqCF1RoVMdHT3zx58MUXn/7V\nn/Yf9JLnrqHzj+8/2n29H6/P7vVxQ1998Sin1K260Ia+68dhiCGaGgLlec5J1uuVkUnOUIMEDCx4\nRLyJt9O2qhx5J3F2Sjco2dn1uB9My4p9wXciid95yevwrW/pEF3W/fd8n2+67UFLB3jl1V/2R1R5\nWcAiD7gxPbxadQ7h8Zv+gcSFIZZi2IBAgRb2b6BQ3VcOm9pxvaPA3MWI2BBSCJqT5mxATROoj7Ok\nSSbKNO4uY9ue3Oq6OyHEnuJ0+ezr7Yun1DZsbDlHaFJO5O0fTHJKkrMBIBMqgVZ83YqIB0fDOHKI\nQbNIgpyzmiJ57BXUgk9YO6q/up7fbmkPIsy1r4Nue/iLWlL5Nev6bffPyt0X0VYZ3re8/tsMP14V\ndjuUl8GawESLalS4o4GVXn9O6CV0bQkFeXdI3PFSvLqyh6qrBgc91PA4b0ycy2pK7O2C0zynNCni\n6ux0dXp6fvt89dHp5lY3wrbFTqZ0dfGCImzOVyFYxswras5WZ/fugEAfehtyGuYQAxK2XQdozI7H\nKS3qJEAoOENFnlRVVZCRge0o1d9dEP7qNb7gaDOO5vq7KbWY9a8W8F5YcbGy7HU3/11joY8Kat28\n0ErXlt/pevidwwDIY1xq39bS8hpsUX0AHTIEVazNtYon3er5sJv3hIND590GzNy8tePqVcc0KCyo\nuv7rNc5HTU1V3HMwD5PXFkawgERNs1mf7K6unlxcUxNijlnk7OTW8+vH3cn63vt/fPHZw2m/v/tH\nH+bTFrseEgzPrgUTJmCjLnYKAgHUnOITEnKMht7CHgMiEbGIgomKIoBmISJqgoo6qiciR0YSAECB\n7gpEf7Sub6TPBbavPphFJN6EWouFfOQIAIOlo0XBGl/HCw/GplscWHW+BYI9vuQb9ZGjeKsjNaZ8\nKL64UhnSOzET1NQcf3lySkUlDuVEV/zoQCIHZemgxf+BRlXkjnbWWVFhQFBS6YrPoch3QlXlgEwU\n20YNpmG+fnKxvbrWpGfv382X88PPP5/X7cPPvmh+9gteffD3/+d/vrh+/u8/uQcn7bSfH/6Xz4ff\nvLh/7/7de3eQTHbjbJkbAkbwlo5IXvgXETlwcM+7mYHDYaUuilubuigjhGSH4LvCNl4jkb5pjasa\nd+NSt7agqpg3GMmyjkcU9upDFnorgmphXd9dpzuyvqvMhsqnsYZ4lXwJDl412BvNgB1RqgEwgFUr\nvaIgCzki3jiw745Gb4ijghe+tIHV/qgfEbAyhKKAlOkTAWIak84aNTQSbq3PkLiNq93jJ/nJDgQ6\nYJtzTnl1doan/frs9ja9gBlQEQ1lnlCzSTILyEbIAKVLNCKYqmYhZjAIRFxCiBG88IkZuP/fI5Ct\nUiSWxhJ1urgc/Zcm+fLKANRSFlWbwDpJKPGFR0El9aKaDlJiSpZ1q7t4Yzft6GGVxZbb2A2MZTkL\nr7zh7/jqZd5bjFxkQjZ0oBsJVImYiAQFCR2iOaRJVKp14eNdDF86cPb6BXw7Y5mEk13l58ty1B0o\nOicsTMErc0PVmM0shCCikiXEhjFM+4EY1WTcXWNOP/75px9+eI5gXX/ars5/8Tf/bqBsDQ1PtqD4\no1/+afcp9oh3T04uLl8MKe3yrFjQEA96RiKOwZ1MwdREsxOlA1/ETBzMoDauMskqOnMMJRDPC38x\nw9Hbv6wvHjhDWXbEhVKs/OeNLplK8SoiU9clKpPFg+F8w4J+nQnl2++6EQCYKQDVvalb8+o4ouab\n2wmHTSonAg2QAFXBsokkQgZCIFQzy4IKITISEQdiyvOMRE3fiYhvrVf+yZJVjTH63UvS+RE3ewkr\n+E4D4bWz8qkdJnTj5MMN/moVxi9aVekHpMpIgVgm6Zru9M4dS/r4+mskahpum66/156drQMLGuy2\n44MHn2/HgdbNi88vdJq4a6ghCHR1cWUX0/XlpYVoEdUTMZnJt5wwxMDMIhJMS110K/XTwAiTiKSM\nBBwDERODF3qEWh+l+Njr4r5h3DAHfPolnxUQwVviGRhrFhWNLddy1GAmzla9SHvhwfXBZQ9u8Ljl\nKWbmTvBjbOBQM+1Acnj8Xq/fx4XxAyGAr6OzQkYEClFBADHGmLaDDDOoUQwGRoHSnIgJ0MSyqDdn\nt4BsqgClD7uqoXiKwdsV8C9TONZcyGrgLujBmx9pYGgOf1ox4QAACFHGebi4jitB4mk/iKa26bNl\nk2ncjhcXz+fttqE47CZJCH24f+fWPF01TZtSunz8dBU7G0fAtBunsKK2XZtomiZNRoFLFwcAZzch\nhDCnCQiYSbxoKnGWDIgU2MDUSpKGLdDugq/AG6jzlS/xxr+PhQ0gIiGpShEwVcTcWCqkIu1v2L1w\noLNCaVj12KImVuWiBmlTVSaPt+ZllbiYR0dGEYDrJKYApCokAEhqNM0jMrV9DEEVNQC3oRvzHIwD\nUoiBGG0WRQFCLXG/SEgxRDEvHbOQA1Rl6h2OGjNRrbbfMY4W1tU+33VvLkMKXdMyUBqmpm9C10y7\nnWYJoTORnNv+5BRs6OMK24C5IQndqh+mbZ5zjBGYGYlBBUAMRHVO2QBiDISoZlwcVxZUZb/dIQHF\nEzUFMck67kZi4ja4xOSC01bgGV5LRUefv8EkLYoiIhi5miA5JWRCxhqq6shivXPBZ6x8fIOsvoEG\n+B4s/js1QGDkJYOvvIjdUCTq6xekisp5RO/5iEQhRCitsIGbhkBCCGQBLYOApRRW68snLwjRIqwi\nz/M8pRkD931vWSwpIplZTllL67zDEah87s0r951GsWygWsNYOmq96VGFguuHg2laUREvJ9Z0HSIB\n0urkBBuEgTQEWq3nYT9nOjm/n7YXani5vfqnv/v71Vn89C8+ZaLQNKiwu9ohobgakZKohK5tVh2F\nkNPsiaPuOQqI2LTRazGAmqdCZSZkjiGIOOBcwvUK5yxnXSvDe91Ub6ijC3EbEFTQGBd2iei6BAHV\nXJlCbYsF+fpl9MzxyjlxuaWrKu6gq+rkQTMAOP7DI9J/eU8LGyZA9S6tZkoRADTnpDmuewuoAFdP\nnzdj6lWpbc7W6y+z9Cen2jNFxkChbanl0DUypDQMkUJKeR5nA8BA5BFPThJU/CBvdxwZ74ePeDiX\nb9i6xfWAdUDpbyFmYrq73o67cTfsupNu3A7zlLrTpm1bDPnF5fbq0bOr4Wp15/z2B/dtmrseAuEw\nTrFpAGDajbFrutO1ojExKyEX4Z6mmRxQIiCiQIH607UZUCDIxoGb2JqYpCw5q7fTU7VqbqOXBUUw\nPZaocFiCmxb1jYU6At7UPI+npIBGDhwYEDSrmNdqLCq6e7GdxMvhPvIUlBv7uleIyTs9es3xondR\nabBZFOffJUgXjuPMQgGx8F0zsBwjQrBxnOY84LqJFHbXF7YbTs7WxjDO17t5d/vWR1vJ+0mayFkM\np4SGJJCzxCYgIhGrW6FUbMSiHR6VA3r74wj+OKbMYi0ta2Illcx9N3W71VQ5MDFRE5p1zyHQxJuz\nk/VmJdsZRPJ+O8WmjdQGmZ4/QVYOp7Sy8z+5jTKN485y6s9OQujA+xASm6Y0jSYCgAIQmiYNE6ox\nEzO1fRtK9hOCZEUzTTkJWM4yz4hKgT2o8eBdQDPvLlVaNNZWs0dLcPj3TZGFB/BcoeqCVkJXSiUy\nU++ZguA1SLJw5OWOhURfS19lmesRqPhzMatMwQ3wRdd7o42w6KS1UCWYiGAgQMs6AwCgtKvQrHiK\nKpaFpqxbpsCxbTruNz0gDUOOMYa2n+chMCG4cY8mCbIAu/3OgT0fyJl9Pb5vU86/PMvX3vnIgF/m\nj7V9mIH54QFX5BVEwBCNWgaGMY8pj0Cmc067F5Gb1SnO15fZUPJmutyD5IgQzJBANKPl0EciTvME\nkEHE3UVTFgIkYtHECAYKCMGpzUug+zlSFSTAgOBdwLw2Ppf8TyT2N9ey24f5um5TF+Q4n+7wZ0XS\ne/cTPmhHfokWXQ+97T2UUNTlfB9pU66OVrwEarz0simIqGZUbZ2qc1m9vjCNN+zo8lewNOalwLEJ\nTWRWyzQFCMScGZH51if3oEcBHfZXzdntwHF/ub916+7t9+4ZztPVFYrlOfdna+iafL0jpPVmPWVN\nWZhjcTZLDbXFG6/yVikVymLURTv8ckFD/UCXgktFvHuNRs2qosYAzMYiqmISiImxW3ebzTrEkIdp\nutrdev/WeDrBOMY1U4tp7yVYoqacp2QZ29h265WYgHETiEARGKdkzN1qlSXFyJqTmQVnid6J3Tt0\nU2Bm5iZyCPM01c4G4CldHGNNaMKXLKFFlboZC26VmyFUxmaqwAvkYYRoWEwG5gCoYIBA3ETV7IHA\nDiWWvChZBM8BDaq+nmrAuQcZtC503SUrSadH71j/WY+W9xz11yUmCoEJFMB74jISGU3bYdzN/N6t\n1ftnq/WJGt3j8NXDJ/PV9PzBszRdfvLnt66eXD19/Nurywdn908NbL3pI3MWwUCxjeM8pilRYFXv\n6GPE5NjOGzX77zOOsNGKgC4bV/8ED97dRQtARM+ZM0nTbgLDfrNmCiCABhxofbKeE+WU0pRYIM/5\n5PZmfXa2FsVNP2m63k+aspgYgIrO+93Vs6ucpV13KU8EnNMIwEkoDyIG2BAEzfMkSMHMVAwZoNqq\nICYixIwMauBIGBJL1pwFidBb2Zgt4NiRyLXaV9KO6cIPJph3HsEapWDeywuZ1bRYJMyqJqIG0MRG\nk2XJXmzXzKhW0K3qgYHnrh2bAUWd93aaRS+wIw/oSwz+wKYWHcw7wlYlWObZwyLnYQqIQgGypuu8\nk2l9dmZKTz979PD//vu//sXPhiGvb7Uf/eTT7bWOk+x3L54/uejPVx/8/NM07LePnw+Pn5BKd7IR\nwqSaUorSmCkhAt1ot/Wy4+O7j4NmebxJN/4EbUHWDn4+KJqncyhDxdJbEE11f3UlcwpM0z4RZ4p4\n/fRi2k0n3UbyfCWXu4vr7TCefPRePOvn3UgGEGJoGsZIDbZN1mwx9ldXV9N+367DarNC4e1vXwz7\n4f6PP1ydrbZz1jmVAqTlpBAhsQs2U5OkqkZUWJXXkXK/6HHgw0sq/UuhI5Ugjt05iJVtw1I8g6gU\nLAAEL6aj4j1F4ehIu7WOS3v61++IHYUX1WuOxHt5y+VFX6+ULTzVwAwJAzN1XUBulBCs6bo871ft\npgndxbPh8T98FT75+d3ubhjC3bO79+6efPnw2b1P7p/cO090efbevf3VxdXDJxjDan0yzWmYZjE1\nRDVFQA6sgh5zWeJv3uV4SXPAxZ61OnGrTADJu72rCAGFGJkDqKEIaI5tayKY8+rkFDba4MxKoqnB\ncL2bhueX61tn4XTVUDC11XoTubl+fA0Ubt1/fzvtYtfH9Rpg7m+vY+wohaadri6uwEyz5JQ05cAx\nQFYRrQzJCMgL1mma1bSSSCEjqCJwEaw3hfmRk9AOOioienEfEQEBq42XQxu1ZjnnLCqmZAiETDJP\naZ4BVVXRcadAJScVbIHlDmu6rD04WHOAS492e9GRb8ALL+8fYgHUEcCzUl2ZUTDvMWe2u75+dvGM\n757c+dH7n/7Fz6//8bMXF1cXl1ePvvw7Ob+9On/vVw8e/Pnf/LshbZ98/at5vOhWQSWf3L9NjFeP\nn01pjusudA2o5pRFQLKw55G9ZFO/i3GTQlW16kVWeSd4FjQxmwKIAAI3jAKSk5p0m1WM68263+22\nhjiO85wlrLoIQVDb87O7TVx/9N7q3lkik2sbd0Ps9mn74p/+r3/oT89/8m9+OU/54tELQzi9e04i\nX/6/n+VEZ7fv3Lpz3jCn3RApYBcCMQFgzrkiHQWGJEQ1JUJP/obC+QswWQ/YzQlXeV6+qb9ysesl\nVvwYFDcPgrmBrIjEXpDZDaW2aVQjEYoo+u2r8lqtpWLvHkdiHp/+8mxTOI64K/z821FAwVl0iY0i\nBM2iTKEJ/Wm/klUa91/99osf3f/g7s8/uvzq8W/+6z/tLsaf/Mkn8aS5L2cffHB3O8ThcdMnWytv\nFS3rNOe+XzWxoRAwkqTZNJMHsquqgePBvy/J/Z7DVxSPP7tI8egWl6Yq4ppGzik2TexazTrvJwQO\nTUjzsN8OYhZiO45znpV6wqah1CoSr1ark14YxmECg261atqWEty5f7c/PW9X7Thtnz74Ksm83/YR\n7dlvvjQNm1XfrEKaRtXMTAYQchaoZQio+gm8cJk7GCt6vKDrBxztVWaESzOeOvtFCVjCZ4rfsUAG\nUnzDOYvkru9zymkcA3vMjYpkRECihTTdMV55+mGBq5/p8CpwjPLXONL6/je26uYulRPmnccMSrgn\nMlMgBG1ibPoutHEdM5603HK81X74lz95/7/58x//1Z/NIn/5P/x3X3zx+PyzB/c/Ojmdg+4+YEnX\nv/76er+9/ckHEKGJ7e7qeh7n2LShDUQIhBwDeOxB0eFvKPFvaRzMQP9/VSpKXJDULHjzSucpUWSO\nDNhg4CyS5jTshqbvQtOOQ5ryyF1EpmmWrNS1HbVd3u+fPrvIeV6dbbiLaUjznDe3zkLfEMKP/s1P\n+vXZME1939z60x+d3trstpcB9McffwLAFuJ+HqdxJKAQQko5eIF8lyx1c6wwJtNq+B2MiANBHtCY\nIzNj4aw3RD8eJPJC6U5ShISMRKBmaE3bgFoGYEIDZEKpHqWF0s1KAPMhAG+pCn14l+VMQM0gMbgh\nOl8H4Cy631FISUVrwUQlZUhKqklSGqaUpyb2+7z//z77x6vffLm9fz8NV7gKl/L06cUX09XFr//T\ns9A0uh9kHC6/frxPw8mdM+kCKqFYE5sQIyEyewRJUYYKe4N3Adgf7YlVcVKOAxz2smhxRO4wJ/T2\nRZLNFE0xZ8sCIiiTBOagoEYGlrOpTGkWBhRFEURFTZqnGc3m/T7tBlBFtf1uj4ax6Yg0p0FU4mZt\nOe2vrjKQijv/mYKFUjeZ2MW75NLWyMrGlwokx7N7VTQeWUWvCE5EAAc1PRyViqFDaKZE7OxUVXRK\niae0H/M0W985YRUgq0ROg3e9L61LrVpcR2+ARfd054Mtu13RxZt0aXCMlB1EXt0vCuTPMVUR03EO\nQLHrOBA21jckSKrQnZ2E+++pUUp42p0++fzR0y8epovpyZMXt997b322ufdHH//pT396PW3D6fpi\ndy1JFZGbOM/zsN01FsAA1IsgVKwXv4US8t1GWYODw2yZODEREXg7RjPvLICRckp5zsQRiULfUmBk\nDF0kgqZvuYkdwDhMKc95FjHtT0+D9g7WtV0XIq/61Tzv+1UPaHlIIXCk5urpxf7ZC4AUA8k4N7El\nbsiKe7rp1g1aKGiK77ILNeJiSOJLuWzVcX3j+6OJ4xIDWj5DDeFAq14ZNMkCACHGnDNmMVEksizb\n5y/m7Y6YJcu03wOhuA7KWCr1V4veKpMvWUH+uCOOsCAMBwbvomDxHVSO/AojLUj18uolJhYRmCxD\nMOi7XlWSZOIwjWJN263OxmYYrraTYtzDr/7TZ8N2f/f8XnNy9uOf//zq8iqEFg2n/QTYbIeUh6lf\nryIzIYEZIRGTZjHvDwTv2OG5qPRuZBa7CMy8KJiyR/pKTmOax7E/3TCSEXWrTkwFNaVZJXAkDk1o\nQp4nzRlR+76nFU/j6JuY0xRDF5sQjBAsTyn2Xdt1s2ZmIIyrDYYIiDOCEWJoOqJ23O1TVhXZX+37\nky6UIrTVGvfCO1CcPfjSpEp43BuQmZdHkbtWcnU8TAtRXKkIhgAMoGix4dXZJqA1bbM6WY/DKKrl\nKKuJRzG7vaLgbRuglu06HInXscj68tWVXzjqEU1T9RYs8r8qf2Zg3sePvDMSC2adMyQbrve7qx3f\nPgVETu3uq+Hpf3388b3bm7vneS+XXz0MDemZ8ppizw/+/re2J5rw2fbFL/7DvwVsJE/TnKbnk0cj\nAAAHJqI8J6t23jssTVkU0LISZjWrA9GbBkrOsY1tvzKx68utpWRqSNquwixJc87TFtfRGw1Jmsf9\nAECxb0PENE7zfh82oW0aTXMaRtMMACCQs+y2++urHVpAiGmeN+en/Wl79fwhgIDhxfba8g5DXJ1s\ndpcXj3779cn5CVXQpqohBfX8hum94TfVmeM8GQ8k5GzJLTAAIwJi8rbjXdc2bWz6Jq6ioYjMhqKW\nAU1NiKB06CFynuYRzbU5y5EVZMc/qpEPVaEsVho62uq/sDrtG1PCw/VFZTYAU1Mx8VATsKyE1MTI\nQlGCXVu+VoAWOe6utzLPgTEy5N12vLoMTBzCPMp+myShCTVd33YdqBFAE6On4FS6WYIF3uU4sjTK\nmTUFrymipiIIhmix4/VpTwSaE+QMeWaTGCw2wCSqs+ZZ8gyqhNY20XKSaYScUTUGDkxYXdAGFpqG\nOMzDnJMQBVVQoDnlYZyyGDRNs1onAVGLfRdXbegairwE0dW9fNlsrBzp2Dp+ZbxkCS9ns9KGd1co\nOgASxhg5kMyW5slEYAITSdOcp3kax9A0gKii8zQjuefJm/4ioFnxEhRHD1QGUGvvHjDmxaazI7vV\nNY1SJFGXakrm3NT/sfjDq1hxHxcCoxLMliUAdUEl635GiCGsT0/OUVhGadrm3o/fp8a689XDzx5+\n/cUX3ekq9WxMm3bz/NnDy4vHm5OVKAABh4CIeZ4XQVt83y95i9/6wMo/yoKViYqoqJrKNE3zNJrq\nenPStg2a5Zyun12oyXqzWvVNCAQcBNUMmEPO2VTncWbmpm3mcZSUVDMHJAoKJqZGQMTeT8vYKGLK\now4qpgEZiNquv3x6NU1Dt+1Snte3T9abvhRlLJt0mMBChfbyP16ruh8ifv1/WGKUKgFgHSVkKzIg\nEBKYIrKCSlbkEHuiGAKHOSUHeMg7M6sZuKvabcqSC+UoXYVjltCpSmRHoqBEfCAAeMQdwEH5qGlP\nN8+e03JhMGrePdYYZssagIizTJZnmDh0/dmdU9GcdlPT8umde/vpStCuL66y4nt/8sfGcvrR2ekn\n5+P1dt23602fJSEYB4LSUqNIhxIncMhOfBdjgQDLJzPzkooIQKuVl4y0LNeXF6DY9qu2X/XM18+e\nT7vdZr1B4+l6XN++JcFyEmDMeW9iOWWMBASWxZA4OLzrdVSNkAipaRskomAsajoR0mrdEcI87C0J\nonRdCAFzxhAZS5+kmyR3DGt/y3HzBgc7tBAQAsCC8AEA5iwAqlmokCAkMVFAZKQwZ5vG3DQthQaJ\nVZKHfVBthVP9b7is9dGpwcNhWTbC6bsiaWKwdBwqIFRR/GoKWz2hWNi+V2gxI0CkjAaRmFmmObbE\nLcRGE8i8G3OaspGRDNNeV7i6d/vkzh2LdHXxTMmart2Pl8QaAqaUQRTEKirOaloKDOmhDtk7GRX2\nMNMCa5shomRJU+Imhshiwhza9QnHZp5E9mlzdtZtbqkCWhyf7549fnb7I7XIqhpjk+ZMFDhGCgFQ\nOFKIUSRLVhekpiopp5SIsF+tYxMILU9zHpQbYqSUhQOcnZ+qGaARYp7TZEbfNJvvN6w6eqo+CoDm\nDveiaZEZATAASbacVRXVEJANyIBUQdRUTEUXTXaxig4s76BwHpC8gzFUjIGqb6kAWAGhDncgAIRS\n0u0IiCjlvhy9QGAUUEXIOac0IWlsESmpzobCTYDAk0g2HEXjrdP2/DR5l7N5yuMIpoSgOaGat4n1\nBj5Hb4tVS3pnFLrwnxvS0bFeD8IkyTrPiZqGuk6AUrZxzopkFOdkyB2H3jSABjMG4NopuWLb0RUz\nRULvy+o2rmZxbV5zQhQESeNe51lTQlVQCZEAVfPs0Rimr28m+72GGxawaG+EhJ4vWpo+F8seEJGJ\n2NRbmAXMAgiADGCIQQSIkIhCbIwECYhLbt0ioA6M88D1rUBaTspOsFRq/IMBl7AUUD88tph25YWX\n8IIKV1SLGgG95YV/T1BztyHNY06zow0hdhiAqJlNMYQ55Wm/58CqkjU3faM555wX8QLuBSYyVTCF\nita9SxG/IDZF1vkppRCangEwJ0FDik2z6hGYgqzaVbvqA3GgyEC3z/tb779vkWZJ8ziAGbISkZm4\n7y3PcwZQkRADEqmImXEMfWQkUJV5zIQUAksMRGyGIUQzk5SL9mbqcOHbJ1C7UVClqHrVODFH5osR\nYKYikjMyc2Ti0kciz8nEKCCAqRoRABfH/SKMF1v9taACVgkNCEBEYKZkImIZkP0wlwA/q0gLuBuA\nKmJaUInFZ3EEShgCABNjREQRlZzUzMtkkzLU0t7MLHOSOcUmcmAEKP1AEDkElUM4tk9KS6eAdynf\nX1olLFJOVNyDIimL5BAphCYwT/tpnsamtd11ijFakul6P637uOryJAJqoCoZEYhRMogKAiyNRb0S\nWD0QHrNWtAsDUERuW2Q2VWIEQCm1IRTQKBB8Y6/ObzPJV62mY5zxhv2sVswmM+TysogeGqEIXshd\nTTIhMQMCiGSt6SULN1vyMl8PeTlX8P4+6sX3we2A8sbFYUOlNJrCDS9opY9CnQt66sdBzfPvtOL5\nruCGJlhkUoshiAqaofdiM2B/HJGpShbNEtta7rCukx8Rqo9+7aq+5eECxYoCSv50QIzMEQEsp2Si\nw25AACYbtjtB6rve0ry/nDtLs2WP7gVVjiHECGZevJcY0TsuWI2trd8AlKIyCqUckHr1CpdzVNel\ndOX63hz0jZKoID+13zwCaFHnynUVKyJG5PKeoICIIRC5GeQ+qEqNi+vjG16pYN1Q4+0QPH3KoVOP\nMUUCzzYqdU8r17XynDqtatbBsdRdnITguiIF5iaqiGUFBEuiqiFyabxJ3u3LoNK1Bx4cbLHDwbs5\nhXc67KBulap9YACKjAhY1kaEXJOJyA2iGTUWN0FVQ8uSBEoTJUAzy6X4XBE41UpeNJmCnJiVohpF\npzoU9ndNrHrynK99bw76Gox7MYkP71plcyWEA2cFK+LeKxaBgbMcIq07uBDIYSbfMBbc6XClmoKA\nJjVRySosyMjI5j21y4KZSYFFAJebYFluK6mtWCP2fIsL8alJEi+q7a0SEMgzQr2zvLnKi4CEN0DO\nqugWFeLdU+YybAntMUAsQRel1S+6DQmhaTiwqIbIgZkDdqsm58wBIwUzFCkdTnLKporMvvcq4o3q\nqqrkcUHsYE4FBwE88NTzeTwlzQzMuSyA2tvVQY9XvYh19446vTo8XnAHf7XCs8rqGKL3QPEsqWqR\nQAFaX690LkzuZvEFPQQsU03tNVEAjE17VOMTmQOi2yjg1uhBMcSFpx4Y8mKXOYMABAdock5+OREa\nMiKoGJT6xSQ5+5tAyR2AWjAWFz33D0mdN4bVeDuH7BWQUNUkS2gaM8gpI4ChpTmHEGSex/0IzEAe\nHVECnhARGYs5X4FqqHA1uFlWdvTIDr0Ba1pBTNzchXdgJDl7cd7jGaGVF3mPpqL0LBzRA0FUFQpm\n7va1h9kX1fCAdtoScHz8vDIzXLowAuhSQcRFzeIZZY6xnecEKiZigBTYzCR5CEsoTk5alnGx6v0x\nduOpZRIAYN49aDHP6iWe06N5SsTkuf9VM7I6F1yyev9AA5efdYUXq9D3AcgkS9amYzU1ASDMSc00\nhhYhSMqQASOGGExVVD262TPGiuBxK+PwFDVBM3slHNtb3S3wmpkVHQPeMoE6fzsSj8X0LsJ6YeoF\nS/fd90aR1ZQqvZMBgJABFlMTsKZpw2tCkODo/kWTrK57MD+4Ht+IiOQNpJ2XYzGoDSyrpxIwsZnW\nkiSVeS4c+nhtq33qtjkh1E40ha6rZ8CxhJccVeUOcNjBP/RYnopVt1/8wABgYB6l7STrf2jgZWRZ\nJIkkLke6mpjLLKq98MowvLl9Nw5/PaPHF75VAl24S9VtYJHNddPg+FfV0ePydlkF32a3meuleOP+\nr3/4zZ4v9QoDKwUQ/bVExdWj4nlHyxmqsu7wm5mByI2le4M5eHT2rNJr+eaGoYNAkeGGpXXjNq+b\n0Lscixm5fFFUv8rMTZGAEEWSFiMS3LpMOakpR0YmYiyQEBIEhsPRrIK8mDr2ysPLU298/zoZ8v8D\nUWl2GWPyW3gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0xA614662CC0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(deconv.shape)\n",
    "# postprocess and save image\n",
    "#deconv = np.transpose(deconv, ())\n",
    "deconv = deconv - deconv.min()\n",
    "deconv *= 1.0 / (deconv.max() + 1e-8)\n",
    "#deconv = deconv[:, :, ::-1]\n",
    "uint8_deconv = (deconv * 255).astype(np.uint8)\n",
    "img = Image.fromarray(uint8_deconv, 'RGB')\n",
    "img.save('...\\kangaroo{}_{}_{}.png'.format(layer_name, feature_to_visualize, visualize_mode))\n",
    "img"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}