3-resnet50_rooms_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resnet50  : Residual Network on Custom Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Types of rooms found:  3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "dataset_path = os.listdir('rooms_dataset')\n",
    "\n",
    "room_types = os.listdir('rooms_dataset')\n",
    "#print (room_types)  #what kinds of rooms are in this dataset\n",
    "\n",
    "print(\"Types of rooms found: \", len(dataset_path))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "rooms = []\n",
    "\n",
    "for item in room_types:\n",
    " # Get all the file names\n",
    " all_rooms = os.listdir('rooms_dataset' + '/' +item)\n",
    " #print(all_shoes)\n",
    "\n",
    " # Add them to the list\n",
    " for room in all_rooms:\n",
    "    rooms.append((item, str('rooms_dataset' + '/' +item) + '/' + room))\n",
    "    #print(rooms[:1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "    \n",
    "# Build a dataframe        \n",
    "rooms_df = pd.DataFrame(data=rooms, columns=['room type', 'image'])\n",
    "#print(rooms_df.head())\n",
    "#print(rooms_df.tail())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of rooms in the dataset:  393\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Let's check how many samples for each category are present\n",
    "print(\"Total number of rooms in the dataset: \", len(rooms_df))\n",
    "\n",
    "room_count = rooms_df['room type'].value_counts()\n",
    "\n",
    "#print(\"rooms in each category: \")\n",
    "#print(room_count)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import cv2\n",
    "path = 'rooms_dataset/'\n",
    "\n",
    "\n",
    "im_size = 64\n",
    "\n",
    "images = []\n",
    "labels = []\n",
    "\n",
    "for i in room_types:\n",
    "    data_path = path + str(i)  # entered in 1st folder and then 2nd folder and then 3rd folder\n",
    "    filenames = [i for i in os.listdir(data_path) ]\n",
    "   # print(filenames)  # will get the names of all images\n",
    "    for f in filenames:\n",
    "        img = cv2.imread(data_path + '/' + f)  # reading that image as array\n",
    "        #print(img)  # will get the image as an array\n",
    "        img = cv2.resize(img, (im_size, im_size))\n",
    "        images.append(img)\n",
    "        labels.append(i)\n",
    "labels    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the image array to a numpy type\n",
    "images = np.array(images)\n",
    "images.shape   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = images.astype('float32') / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\preprocessing\\_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Lenovo\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\preprocessing\\_encoders.py:392: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(393, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder , OneHotEncoder\n",
    "y=rooms_df['room type'].values\n",
    "#print(y[:5])\n",
    "\n",
    "# for y\n",
    "y_labelencoder = LabelEncoder ()\n",
    "y = y_labelencoder.fit_transform (y)\n",
    "#print (y)\n",
    "\n",
    "y=y.reshape(-1,1)\n",
    "onehotencoder = OneHotEncoder(categorical_features=[0])  #Converted  scalar output into vector output where the correct class will be 1 and other will be 0\n",
    "Y= onehotencoder.fit_transform(y)\n",
    "Y.shape  #(393, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(373, 64, 64, 3)\n",
      "(373, 3)\n",
      "(20, 64, 64, 3)\n",
      "(20, 3)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "images, Y = shuffle(images, Y, random_state=1)\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(images, Y, test_size=0.05, random_state=415)\n",
    "\n",
    "#inpect the shape of the training and testing.\n",
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
    "from keras.models import Model, load_model\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "import pydot\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "\n",
    "from keras.initializers import glorot_uniform\n",
    "import scipy.misc\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "from keras.initializers import glorot_uniform\n",
    "from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identity Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x is input, y=F(x)\n",
    "# identity block simply means input should be equal to output. \n",
    "#  y = x + F(x)   the layers in a traditional network are learning the true output H(x)\n",
    "# F(x) = y - x   the layers in a residual network are learning the residual F(x)\n",
    "# Hence, the name: Residual Block.\n",
    "\n",
    "\n",
    "\n",
    "def identity_block(X, f, filters, stage, block):\n",
    "    \"\"\"\n",
    "   \n",
    "    Arguments:\n",
    "    X -- input of shape (m, height, width, channel)\n",
    "    f -- shape of the middle CONV's window for the main path\n",
    "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
    "    stage -- integer, used to name the layers, depending on their position in the network\n",
    "    block -- string/character, used to name the layers, depending on their position in the network\n",
    "    \n",
    "    Returns:\n",
    "    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)\n",
    "    \"\"\"\n",
    "    \n",
    "    # defining name basis\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "    # Retrieve Filters\n",
    "    F1, F2, F3 = filters\n",
    "    \n",
    "    # Saving the input value.we need this later to add to the output. \n",
    "    X_shortcut = X\n",
    "    \n",
    "    # First component of main path\n",
    "    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a')(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    \n",
    "    # Second component of main path (≈3 lines)\n",
    "    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    # Third component of main path (≈2 lines)\n",
    "    X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c')(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
    "\n",
    "    # Final step: Add shortcut value to main path, and pass it through a RELU activation \n",
    "    X = Add()([X, X_shortcut])\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def convolutional_block(X, f, filters, stage, block, s = 2):\n",
    "    \n",
    "    # defining name basis\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "    # Retrieve Filters\n",
    "    F1, F2, F3 = filters\n",
    "    \n",
    "    # Save the input value\n",
    "    X_shortcut = X\n",
    "\n",
    "\n",
    "    # First layer \n",
    "    X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a')(X) # 1,1 is filter size\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)  # normalization on channels\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "      \n",
    "    # Second layer  (f,f)=3*3 filter by default\n",
    "    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "\n",
    "    # Third layer\n",
    "    X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c')(X)\n",
    "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
    "\n",
    "\n",
    "    ##### SHORTCUT PATH #### \n",
    "    X_shortcut = Conv2D(filters = F3, kernel_size = (1, 1), strides = (s,s), padding = 'valid', name = conv_name_base + '1')(X_shortcut)\n",
    "    X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)\n",
    "\n",
    "    # Final step: Add shortcut value here, and pass it through a RELU activation \n",
    "    X = Add()([X, X_shortcut])\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Creating ResNet 50 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Each ResNet block is either 2 layer deep\n",
    "def ResNet50(input_shape=(64, 64, 3), classes=3):\n",
    "    \"\"\"\n",
    "    Implementation of the ResNet50 architecture:\n",
    "    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n",
    "    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # Define the input as a tensor with shape input_shape\n",
    "    X_input = Input(input_shape)\n",
    "\n",
    "    # Zero-Padding\n",
    "    X = ZeroPadding2D((3, 3))(X_input) #3,3 padding\n",
    "\n",
    "    # Stage 1\n",
    "    X = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(X) #64 filters of 7*7 \n",
    "    X = BatchNormalization(axis=3, name='bn_conv1')(X) #batchnorm applied on channels\n",
    "    X = Activation('relu')(X)\n",
    "    X = MaxPooling2D((3, 3), strides=(2, 2))(X) #window size is 3*3\n",
    "\n",
    "    # Stage 2\n",
    "    X = convolutional_block(X, f=3, filters=[64, 64, 256], stage=2, block='a', s=1)\n",
    "    # convolutional_block is a function defined above. Convolutional_block have 3 layers.\n",
    "    #filters=[64, 64, 256] first 64 is for 1st layer and 2nd 64 is for 2nd layer and 256 is for 3rd layer of convultional block   \n",
    "    # below are the conv layers from convolutional_block function\n",
    "    #X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a')(X)\n",
    "    #X = Conv2D(F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X)\n",
    "    #X = Conv2D(F3, (1, 1), strides = (s,s), name = conv_name_base + '2a')(X)\n",
    "   \n",
    "    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b') \n",
    "    #X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a')(X)\n",
    "    #X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X)\n",
    "    #X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c')(X)\n",
    "  \n",
    "    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')\n",
    "    #X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a')(X)\n",
    "    #X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b')(X)\n",
    "    #X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c')(X)\n",
    "\n",
    "\n",
    "    ### START CODE HERE ###\n",
    "\n",
    "    # Stage 3 \n",
    "    X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)\n",
    "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')\n",
    "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')\n",
    "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')\n",
    "\n",
    "    # Stage 4 \n",
    "    X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)\n",
    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')\n",
    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')\n",
    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')\n",
    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')\n",
    "    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')\n",
    "\n",
    "    # Stage 5 \n",
    "    X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)\n",
    "    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')\n",
    "    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')\n",
    "\n",
    "    # AVGPOOL \n",
    "    X = AveragePooling2D((2,2), name=\"avg_pool\")(X)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # output layer\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "    \n",
    "    \n",
    "    # Create model\n",
    "    model = Model(inputs = X_input, outputs = X, name='ResNet50')\n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:71: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:514: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4076: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:171: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:178: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:1811: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3900: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3904: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = ResNet50(input_shape = (64, 64, 3), classes = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\optimizers.py:757: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 64, 64, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, 70, 70, 3)    0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 32, 32, 64)   9472        zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 32, 32, 64)   256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 32, 32, 64)   0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 15, 15, 64)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 15, 15, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 15, 15, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 15, 15, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 15, 15, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 15, 15, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 15, 15, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 15, 15, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 15, 15, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 15, 15, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 15, 15, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 15, 15, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 15, 15, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 15, 15, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 15, 15, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 15, 15, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 15, 15, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 15, 15, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 15, 15, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 15, 15, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 15, 15, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 15, 15, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 15, 15, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 15, 15, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 15, 15, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 15, 15, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 15, 15, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 15, 15, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 15, 15, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 15, 15, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 15, 15, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 15, 15, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 15, 15, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 8, 8, 128)    32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 8, 8, 128)    512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 8, 8, 128)    0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 8, 8, 128)    147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 8, 8, 128)    512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 8, 8, 128)    0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 8, 8, 512)    66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 8, 8, 512)    131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 8, 8, 512)    2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 8, 8, 512)    2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 8, 8, 512)    0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 8, 8, 512)    0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 8, 8, 128)    65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 8, 8, 128)    512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 8, 8, 128)    0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 8, 8, 128)    147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 8, 8, 128)    512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 8, 8, 128)    0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 8, 8, 512)    66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 8, 8, 512)    2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 8, 8, 512)    0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 8, 8, 512)    0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 8, 8, 128)    65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 8, 8, 128)    512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 8, 8, 128)    0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 8, 8, 128)    147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 8, 8, 128)    512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 8, 8, 128)    0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 8, 8, 512)    66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 8, 8, 512)    2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 8, 8, 512)    0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 8, 8, 512)    0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 8, 8, 128)    65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 8, 8, 128)    512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 8, 8, 128)    0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 8, 8, 128)    147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 8, 8, 128)    512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 8, 8, 128)    0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 8, 8, 512)    66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 8, 8, 512)    2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 8, 8, 512)    0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 8, 8, 512)    0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 4, 4, 256)    131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 4, 4, 256)    1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 4, 4, 256)    0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 4, 4, 256)    590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 4, 4, 256)    1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 4, 4, 256)    0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 4, 4, 1024)   263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 4, 4, 1024)   525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 4, 4, 1024)   4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 4, 4, 1024)   4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 4, 4, 1024)   0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 4, 4, 1024)   0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 4, 4, 256)    262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 4, 4, 256)    1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 4, 4, 256)    0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 4, 4, 256)    590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 4, 4, 256)    1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 4, 4, 256)    0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 4, 4, 1024)   263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 4, 4, 1024)   4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 4, 4, 1024)   0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 4, 4, 1024)   0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 4, 4, 256)    262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 4, 4, 256)    1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 4, 4, 256)    0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 4, 4, 256)    590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 4, 4, 256)    1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 4, 4, 256)    0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 4, 4, 1024)   263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 4, 4, 1024)   4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 4, 4, 1024)   0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 4, 4, 1024)   0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 4, 4, 256)    262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 4, 4, 256)    1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 4, 4, 256)    0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 4, 4, 256)    590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 4, 4, 256)    1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 4, 4, 256)    0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 4, 4, 1024)   263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 4, 4, 1024)   4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 4, 4, 1024)   0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 4, 4, 1024)   0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 4, 4, 256)    262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 4, 4, 256)    1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 4, 4, 256)    0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 4, 4, 256)    590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 4, 4, 256)    1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 4, 4, 256)    0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 4, 4, 1024)   263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 4, 4, 1024)   4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 4, 4, 1024)   0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 4, 4, 1024)   0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 4, 4, 256)    262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 4, 4, 256)    1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 4, 4, 256)    0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 4, 4, 256)    590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 4, 4, 256)    1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 4, 4, 256)    0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 4, 4, 1024)   263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 4, 4, 1024)   4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 4, 4, 1024)   0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 4, 4, 1024)   0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 2, 2, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 2, 2, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 2, 2, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 2, 2, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 2, 2, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 2, 2, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 2, 2, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 2, 2, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 2, 2, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 2, 2, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 2, 2, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 2, 2, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 2, 2, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 2, 2, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 2, 2, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 2, 2, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 2, 2, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 2, 2, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 2, 2, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 2, 2, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 2, 2, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 2, 2, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 2, 2, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 2, 2, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 2, 2, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 2, 2, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 2, 2, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 2, 2, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 2, 2, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 2, 2, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 2, 2, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 2, 2, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "fc3 (Dense)                     (None, 3)            6147        flatten_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 23,593,859\n",
      "Trainable params: 23,540,739\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\lenovo\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Epoch 1/6\n",
      "373/373 [==============================] - 36s 96ms/step - loss: 2.6608 - acc: 0.3753\n",
      "Epoch 2/6\n",
      "373/373 [==============================] - 27s 71ms/step - loss: 1.9753 - acc: 0.4182\n",
      "Epoch 3/6\n",
      "373/373 [==============================] - 28s 76ms/step - loss: 1.5876 - acc: 0.4853\n",
      "Epoch 4/6\n",
      "373/373 [==============================] - 33s 88ms/step - loss: 1.3693 - acc: 0.6381\n",
      "Epoch 5/6\n",
      "373/373 [==============================] - 31s 84ms/step - loss: 1.0912 - acc: 0.7748\n",
      "Epoch 6/6\n",
      "373/373 [==============================] - 31s 83ms/step - loss: 1.0465 - acc: 0.7453\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1811ab84f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x, train_y, epochs = 6, batch_size = 32) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 2s 120ms/step\n",
      "Loss = 9.395952224731445\n",
      "Test Accuracy = 0.25\n"
     ]
    }
   ],
   "source": [
    "preds = model.evaluate(test_x, test_y)\n",
    "print (\"Loss = \" + str(preds[0]))\n",
    "print (\"Test Accuracy = \" + str(preds[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input image shape: (1, 64, 64, 3)\n",
      "[[1. 0. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mj1Jx5EUivY28bp5RN3xYK5/erZTQTDE/sAiWPj1+xNBFFNZhESFweOBeJZ7pLzz3yqqD3S9j+q2AG753jqoqMeImv52jQlEVfn2ZSVCDjYGSD64tPdrwUJkBFnPsgYsWBd8mtVFfI9+1ewzVaYUuPbW0UZWVB7+2vDTjWA1G1ShnwWWFxrX8rCPV5GZQn8VVM4RlzLoz1R1+mv78+8sAevh8XbkC8oYo9bbj2XiTgaoWtuni2FUpmFQb4rSZWhZYgCfUgb8FRlqydsrTFh1DrNvm126MbXfSzoK6fIV5bPpx/dAvg2PhXfhQaNw3WLkfBSwkpQXkm6y+DVjaWF4j4fXfMnlaWQPKMUzD3FI+W4DyRuLD51Jed4FCLd3+OIgFoKI+KvNxqba6L2MKdIOvGxd3Mx/drPkyAN70v+C+WfZFvtaSZX6lvqZKGnFfhhUvY95dzL4tjDa5DBuHKyBvlbKRB0zPm1OiyG0SCpcfluaRgqkbUwfuBUVQhl/f4t4E8TZx8dhqsrVMaNFJO4e/GvT2+kDfmxBaCNBSCUGWUuH48Fw3KTfbLKSnDbS789/OyNvSshjGuqwfKiZelmFQKFoqHyV81WVOuOxQvLip4vP3TfeqLC5NrET9qo1avnTBvdaof/MMmob/FgXYVN5tYL5UYZbu2stwnfT48lp7pBWOMBbSscikm3lu5mUZiHeloS2PlzWlNOUKyFvEl7ezPmj3H/UK8ZXnQLY6rKj5F7pvXrexzrLTFe9sbhvKoa0BBOF5phiOKIqAvXenSLRk/oWPgvQGaQ+UWy2NIYMOC7GDwS+YQxqrhJpl1wynZOlNgFoB2m3saJ3Ri3fn60FxgLoWAHSZM5rAV5pXGnXaSJZKZSNvShu4Nt+3pjWYD1kFYG3xrBr1LDe7LbprG1W2+g/6RelHXfuuli8u5t0FqKDenr565UlXXtvKuCsPzbAvqzy65ArIGxJ2Ic8um2zZS1RMMnaBdWgb86s/PKt34TdMAI0OvMBci2dWLSKmAtkFOKvnxIO1Nl5XKz/a/IiPsCXcxbjq7Ny7KgevwPJGKo1wWt20KDlk0da+UK6XNK0sMKoGIJfplfpIo5nOGmsM/NfSWtadFtcVmFcKvJntsIzreegCgOqxv9AQAykPlNFq3qQMF4JU5gt5aILgOsDWluZlymHZs4Vwyv8WVxXVRyEV2LcBbxdLXlfxN/20kYcuZXVlWrmMNBV08NAY3IabyGAydxZ4VJwD3ly1Yq0twbk0sqv7AEWe5+7oWg+Kxi/VWwQfCkZsg6NtRQT1nzfDYBD37c7YIJFBjSDWvbUhxfZiq95aA/wA2CvWHhSNKhJM4mmZRvdXDvs98GqF+yEQ1DpOqVAWmTdFqEHoy0WqzuiyE4K/VyhSc6tBGsr7RWsxvh5q9pPSlQNbLUHepzwo0M5O2ABZdX+l3VpsQRKrsJqg6v0pdrGUgrKv0lMa08vfSsFUjNXQ8l4Dk0ylj8sb1dAPrYyhi3EuJLw2uVq5r4cRgnDRRgnrWKkNagK/XQxZQ78EVSdhhh1zbzSR0lk9R3XyUn+7+Lw09QRFV7bTS8ivNpAHEpabodowAZRdxjNsa4tTEYsTEcMJG7UWm9fNKk603DWZqyVaALuOBidSHKEq5T9vI1cRp2z8FnN1G5Rq7b7sZbhhpIYW1Gan0kZfkepGArAUf2yAT1fl0pSoXy/ZcvKvVGCF3xLEAxVRlkdLaw4rplAkNVAv7tWnbIE1Bx1S6yMFJFB1IYj7OvGAg2BRbJAUbSQ33F1ty7oFX4+eNla5B9SUYL5MHGtfZKRhUKVyqaChiDLw22D7UnutgTKoqzzXpCTIZNiepMKptVll0M7CESGLjL0VNss69nVbjG1XmDZC9zVFVEtXULFazYXV2rcnXJUmr/mtRnVFq6w1ycXdpP7mMqz8Csib4jWjdaDsNXY45A/XcIfPa5OfNBhva0TtzxaYSw2sKM86rzWIVZUeKBzXgOudrkpzPRxpxL1M6gy7Oo+6a2jdLJemSWWZ27a4V7lfb6hcFkidiYsEW/MLsKGEjHp4Lc8cdquDV/UKzeusPGgri6xzMb3U3HTl0+XL1NIbels4zqGMumCg1Jcaev1bYh71d/W0Kp5l192tvl/G4rtMOG11u8xk49uoT2kXYw9HCc08LrRtqnmmpaa6hixzv65cAXmLeHt2SBLCipVgBUu4ttyUSxGLw6ykvm78MhMZTVtx6CPPc0wUlU9Fuo0RFXGobK4lEEiQ1waha8bt7ivAr6evSym1d6ymWaUdhBfd0nDT9BfG0YwvfLZ8Mk7LZGvDfQjughspuRHOakAq40FBtMRLhOKwM3UjqrXG1G5UtvB0Ie/1yb4293XC0LjwP1qvybJbLLMpi1doy4C0um/Lx7r9pc18shRwy/Ck1F1taagpMCq93mxDtbgCMG/md500XdY27uUKyDvEg7FxBuhSnPlES/AOn4ff9vQmhCbYLK2oAFjrjaMuapUoMWVnLRLcwso9WBOMmet2yBoT80yxE0wWAdXflitdStAtGvUSJtIaQwm+HbF3MO2u+7ZOtwwYyiF2W8cPw6EOWDVF7221tXh8IRcdtmD9Wi77kwrcl+TNJeMyJotFYFwWfglqYb5ksUV0gXhVVu11uGpCs21CcFn9NdvXcmbd9GuAHL+hK+BtlLZ2wipZvqKmNuINRsoS9LlKCTa6XkgQqN6tK1fnkS+RsPnWAbkYkhWF7z4kUT9CFaQE9tqEXxV4LWxf+aHN3TGqurFDxK9akcWAFjNQY9iLLyR80trxloln6PV81FPVZMVN982/LrdQDNaD8pFGGbaFEb5bnpe6AmkNp91n69MQkAwGtQYjEUIEGuFmYkwBJhGqkTO5rMXIu8uuK19tflbJ5ZVvrUIa7ys31X3XaGx1XOvKqrDLPq6UaapNBgT3q8rY56skNVT5bLuv3Aoipnwn1MNZR66AvFPcQVdWtWZf9g3PSLU9vnxmTI2Ve3OL9xtu2Q8bh5SMp95xNNTqRWNx5pxKkURREX7ppmLNoVavKwM8KgaRhI2tq9G2MP7CT1uHWQXMXVLLR5jmRZftnXNJfG0AuNARO9K5DqyJONOb/8h2nufEUezOb7cGmylicdc5gEEkRohY71tC64N4033wZCEcnz/PCxfbjVSV4EFPQ09SsFgJIMgEf1K7F/Eb2poEqF0pr6OouvK7ruJ6GlnV1pf9Nq8/TTqvTCsdYgsTihY2cKRig9Yquc1b/ZUVonXWvo44QF9uP3Zhuq8EgbozVyRz6yXDYaofEpejgaezvTVSuPRxPa2fTZwhg3ODiOUg3bz2CvVp466bDYCaGWXRbuomvSDL3LLTOI7J0rQY8QhRbPCTnYr70DcZDsiN4D6psTq9XXblRTdeOTRHheG1e6fNe8GRmNB58LGG4GHLvdC1W7RLVtVVsz4u43eZu1KRrZjf6Arj08a9zgToKrkC8g4xUkxoxjGxteRWsZFbLeI+/VZt6mkt8A5GUHa4APy8HTJkSqEdrtyKX7yzua0xforwq2WSVCgjlc0WadjH15U1SMJnzXjaCXg3K+tiOm1pW2UrVu0CjUo5ibjll36VSdM+HMeua1lriSOLFGayKIpJU4uJIrIsx0iEMW6UpVbBrFNBq7e+h3nypr224ivNfgVoFx7DgmgEqgWYU5KbSskG4dFuu15Mo/PYZStfB2S7AL7Lb6iYnWnLl48GRVC/9mbOZt9cBHQpwlWaX/qp6iHop60To7/gLwSJyPvACMiBTFXfEJED4B8DLwLvA39fVY8/TTy/UFE3EdVsLDXbLBVg+FUp1fc8qbmpPQvBpsm8feSEjXHRFgyU69StLTYQabgeOQCb0FMB7HIJMC9HBC22zHrhUFvK5tx9SjbeMjwuovrUw9BVAFOOaBrPwhUqfjV+c/mhN6e89o1XefToEUdHT/jaKy+j+ZyjkxNm0zm9/oC9vWtkueXk5Iyzs3M3ypK2Q4tbc8BlyvcyjNHnIbz3z1R1wfjTxU4lMLUUsEXI5StRwHaG81ncd9e3L0cPvr6b1BVfCLirlEgVjgThhe5DfVksRa2VBbX0rCufBSP/26p6GNz/CfC/q+p/LSJ/Utz/V59BPJeChk/DD7112dVfwZQ6NH04oekrubUCanbsEJy7cxW2t5DIq7XBxw6828VGHA6sgxxVbkQcuzIVk1wlrUP4lnSHDbEql44VI+Govyhq3+Wb7pfdt4F7WFdt1003VYaCzk1Rdp524llpjNvukxek1oXTSxKefeYWL959gbPTIw72BiSxkKc5k4tJwQINYiKSrw753l/8gE8eHoIpRnk4Bu/Ib6XAagXWKPQqD4vFG7K++vuwHDwDrW+GC8vFl4dZg/n6Oq2cCYhWX3SrJd5QHv9ZRRrca1knYTrqzhdHHcsUdhuz7mLbLr466DfjrRpxxcjb4qqPHArgLwtEF9j6uvJ5mFb+HvD7xfV/B/xLPiMg/0WJ77TGM7/GBqCQNVtraxXk2agqaG6LLfLFsKzAggi3qQix5fIzLRtJaCIwJfsDxVCsV1c3Ai+aTQnaTgrG7RtCCTDVbrfKpyl6g3HAIRRMsw6IfjLVd/wmYEoRRtWQw168mj2qI2VF4RQtWysW2OxgVbxV/G3A3GZm6VIm1WjL56VwC6go3nBV1hMCGoPmWCxI8RUoBM0tjx58zNdeeZmtG7tkdooxLg8bUYzkOdPpBUQJZxfnHB0eEpmIuQoqikGJbVYkLCIXUxy/4NJhPONtoSvtg5S2NdEN7Ql027QDcF8XW8LqDx+W4Xie7p9HAep77al1f8X0gTM1Ln6Epcp7/YiL8LqTTDSkHrY0GHc93ibAt5mFFhXJ4s7Z0n8R5mXI6KddtaLA/yoifyEif1w8u6WqnxQZ+AS4+Snj+IVLOfMuUm7+qTOI4kekthIlz/PynBQPIu5eapUa/rV9bcj7b0RXhlk2qGKHZ2vf6mgFbY1YRLoQ4K9RWtKzANDtAL401CVuXDms5xYA65DFHyksYjBRAhLzzs/e5cHHnyAqDDd26A22yHLLZDphns2YTM85OT3kpz/5ETbP3Ye1TYRKsXpFouLP1Y1INRci5chgfVk1slk/oE/pvz241qcLaW486677p09bGwloe9f2fFWYremU6jLUzc22uEo+LSP/jqp+LCI3gf9NRN5c12MB/H8McOfOnfX8PFUSLynFWCfUrk1w9gButdL8XqohXp0NenbfNMP49z7qtgYUuvFxeCVRHylUFMizyc5sUpwBchl71S9YwvLu6kTNZ6vYeNP9gs3TOai7D2y7JbMSBcnd6EMgz92JlFYNKGSZ5Yc/+inWvsLN557FGCVOEhhEpJMLLCnj6QX9QYwZz8nzDGN6DU4sBS8rDk3zk4wd5dCVp7Bs2u3IxYitke9mmZa275bn69y3hwmCQYOPkzv3hjbbeTPtTfNJV/zLTCnL0rqsrXRJs26a5h2RyjRU2dO7zT7ryKdi5Kr6cfH7CPgnwLeBhyLybJGgZ4FHHX7/oaq+oapv3LhxY80IL/H31BIEIEJUrAkOJzWj4k9ofHCicNf8zmfIoEpgot5AuhhAnS1LrZLz3JbrlT1DpXLZybKducY1onWn137R0pb0paOWS143n5Xv2uIQauYm/zudXXAxHmFtRhSFjDkis5BZ2NzaQ9Qwn6QcHT5mOj3D2gtm0xNsNmFve4OEnGFsiL1tvDZpUK3B9q1mVffuIgNt7yqGHx4PUNHDsF2ty0gvz/4rZVUnQaZ+L+3pWIxvMc9Nd8uIwbJ0d8W9Krz6+3BJbfVbr125FI49NZCLyKaIbPtr4A+BHwH/FPgHhbN/APzPTxvHZUVp/1jxJQOpwirYuM1zd4BWwKZtq5Zts+O6ymlONPq/OiPoaASlvyptIGgxQdTq61MOX5ayj+a75q1W/p19vXG/TvxL3lVl1e1q2cimC2ikKMvWTilNtzAcDshtynR2gUqOkpPbFBWl1xvw7d/+HYYb2xiNIcuw6ZQnj+9z9OQ+eXZBL7Ic7Ax5+c5tXnjmOge7G0RkGFGMFKAu4VxG2OGXlU09r8vy7DNXkY3KTOQ37FQbd0zZCNsVwur7znqhyOcC4w/JyXKgXPLY9AkAACAASURBVKW0256re1jrL7WwloD/ZUG8ma8SuF0iqrktrZ6tK5/GtHIL+CdFImPgf1DVfy4i3wP+JxH5L4APgf/kU8Tx1yJ+aVlU2D6jKMIGleHByQOxB/zw3u/otNYSFeF5lu7jCIHfrX5x9vTwufcTdgJrK9OMLc47t1m2yCa78qeK/zCFjwNvPlpVNh6IQ3MSTVNRHbSbcLtyFQt1wPeKsnnvh6efqRQBN4e77lWgeIt6OjjY4+T8mNPRIfvXrpPNUvI059rNm2xsbDCbTVEjXIxOmU9HjE4fg50w7A1J5wabGuYTi0Q98smIyKZEYpxhoTgq1h+FLct112eQ9dXLFNuKu0lKvLRN8q0nzqxSv/cToOukec2CqrUpgkL2D3FLTqUA1iUmnmXPL5v/y5fXpwByVX0XeL3l+RPgD5423KVxtnbacOF++WQpa1kqQvBBBV1ouSWgmkU23bSnQzG56VMavm+xNdaHlR3JW2BSxUBc2tboriEt5Lotdtcpi5FA4aZ7BUgz4PY5gSrcp0n4otSYdQdL6rKTl/eFdqill/qoyrs1UcI8nbC51eejt9/m5Pwxzz93B+KEWTrmw3sfsbO1zVYSMZmMSdM5WZ6iWYrRhOkYJpMzkmRIno4hHbOZuKV4oqBxj2luybEUJ727uRaKj4isKIcwv13uqvcO/Fb7rS+v62L6bfW9zFbu3/t+oVqlJ1xh0xz5ttm62647SgBY4Vak3KVNI84wT13pCcNeBfqr3C2TL8zOTp+t2gRdTXlqORnkFeinw4fKJLC4uqRi0V6aH1gOK8KIQRobh9qH/eF10MA73Ingtuo/hQZvSkk4tGLYGjRg8e+pN7SKdXcz8s9amsP0LuDuMq0007YS0Cp7W+VWwdutjbFs7ST88Cd/wdHxx7zy0quMZ5uMLna4deMWo7NjRpMxaE7UGyBJgmiPOEnIbcw8E3Kbsrc94M6X7rC1uU06zzm5mPP2+w84m6SAW5pYkpQ1i3bRfNeedylMK6ESdu+8u6p00EUgXRV3d1pC9yEw+jOEguW9HeGsA+xtcYcosRx4haADrMzX+uDuS3QRxC8L5l8YIIdiGalWHatkteI0uD8VMNJqTefTiqo/b8UdnBXax0tTQqOwTWNiVMR/Hq5IZsi2ZRGQmlKCE/VOVaqowp/VxR13T5XhYE3s4rvmOHR5J655X4gq8O87yRrisGY1E20C+6pOVrsvgLLOyKlGPb6TIWANxrhNQSdnj4n7GYfHHzH90ZhvfuM7bA33iJIe1kRkxpBlQjTYJhZhcpZhkk2evfE8uWb0+sK1/R4bGxFnJ2cokIjFZlOcvbqYBK2NdrrbTGvelgBRdd0FrhX4tYNid50sY+fhQKjJ9p2DavVK4PDSwN0OjnUTTJMEVESmcrYu217FvusjjLaSu5x8IYC8Zi+lMWhXv/VXSfPMnZESxWthwwLTbVx7GJUC1IESnOM4dvEWk6A2+HXf8HTQaq0lD84u927EdENvrUGV6qo5hK13BMdgbPmsq3G0P66XL43y9maUoDVX3VpDJdQWuoIGTDLIiuJGWG5Sr5hMK81atvhWqWKIMP7oV7VIsRvKwakpJ8lUIkDd59J8mBpTnSrYvhUcAmCp9VoKUPeJLkvDtQ8jQIxVIc1Tcp2QW8t03mcyGfP48DH3H9xna2sDNTHzDDZ6AyBilo0Ra9jcu4baOSaak2UXfPLRA0anY+a54fFoymQ2h3gT40eIQC7u663La5ImSq5k551l0rhXH3aL3/Cc9hD8mnG1AXq9nL2bxvKFIFz/ptx1u9Ch6/kvy8Bft5Cxpjw92+4G+8v8ritfCCD3IlBOAlV5dB1NRIjjZG3tpsFvAyLxq3cFtwszQrAS7O50dB1TLDmLKOxoqrjv+pm6aUIhigxWMyITFTuxK3t5zS31Bm+txajLuZqILMsQo1ibIZoTiYCJ3Sl6oogGp+eFDUE9/LhGb20GUeTWwtvcsVj17N97kQKotbATeiAEaw2IwYjbkGTF2dCjouQc+NsCqA02txgBYw2mJ1gsViwmMlixQExknW8TWbIoR2IlshGJ7WFkQI6iSYZlhhEhFkOcx1gRbC8iGm4xmY2JzQUiU4z2QAcoCUgOZC5XwYFIC61AXD7Lz+KIr1c/MHFl4nZyzoAehmtsb93m4yfvkOqEntlna+cGEvX5yVs/4Jkbz6LzGSZThr1tDBHpfEzcU6yZs7UhTM5PefjgHoePHpKYTaba58nUkg6GYKGXu7aVRhG5gV6QxKLRrGryn4loAOB+17qWheMVbKCpA1nPXNBGrwqlJa5tVzsiqX2ouryWioYV4ODZSK28HGfooDYB8VrXjHUZWddevq58oYAc8G2FcKjn5TLDdB+EVpflrxZhuRMQF+3a1lYgbPBryyMiY0qzi68IZ4IJomwM32rXUiwuq5kISn6CmAgTuaNrsyylFzsGGonBal4NIzRgODXmVIFsrUDx3aPecJoWFSgO+akxcr+73pl38uI8Dcel3UYoS9HpRYiSiHk6Ix7EGGPcZ+viyJ3bHfcxkZDrHLF9jHWfs1NJC7Ydo3lEFA2JRbD5nNikzCbn2NwpgX7UQ4mBYpekcZtNwPqTr8u+7dJf1HZ4X5SZ+2kbcQTlaUFtxN72PsOkh9GUg519tvrb9ONdsvkpn9z7mN2tTexkzEB6IELS3+T6jVsAzKZj+sawv7vPydGYs5GQRj2sJC4dahAbIyoYKc528dVZDv8leLScYVb1u87kZt2v1guvPlApHUJtPqEl/naW63G3kcagd5YUKzy0paye4DllYEFapSqfsmMtpmMx40GwS9yua15pK4s2hv9vLSN3EjRY/8TXlVUsii4xW6wdi7iC9jsy/ZK/0MSghYYPP/vmG13NVs7ihFx47yutbaVN2YQD5DQi9Hs9FKWXxGTZnEiiz4Q41Ff8aDmcrxJRmXpKHSUOxq0WayvEQ43zbNWdU5KpBc2QyJBnOXFiiE0CuRDHMamdYZIEISHKB8Ri0Cgji1KMUSLpE2uCtTGYiOl8xoPDd5H8gtsv3CW2OdYacjNAk4hcIrf6WWagOWhSln9Y7oudrxpVyQIe+A6mmKItqBgS6SHzhL5u8utffo3f/fbfws5j7t/7GR/d/wibW67feIZe0sMi9GJLpoaP7z1kM5mT5BckiUG1xySHuSZIZIlt6s7mkRgQElKiAr2U1WCxTNYxCfg8L/Nfzd+076NYBXpd1+3+payTxTDrz1fF4dPdpXDKug4V1pJ8LIb96VaiXEa+gEAeEE+vlEsSVYDsApN8yngKpa2qJTN39vGEJElcfP5rPb4R+xF6ANZV2nx66w2+zvgaCZfmSNGBS55nbGwMODnM3QFcgYJ5KglGx+WogjA53qTgEqNIBWQWd+aRhEYpdefUiGCzuVOKRsiLEQSqGOs3u0Tk6ZyoJ2RWEXr0TAqakEmMGksU51i9ILY9Bv1dhpu7vPnee+zu73Hj5nPEcY/5RU4cG4zpkxebV9yH1OYgiiUpM9TGfCp2XpmUqnprAoJiTI5iSOIeG70dIt1jd3uDb379Nzk/OWF7uM3OZsTWRsS16zcQFSbTOUl/g/4wYTy+YHp2RLKdMJ+dk2UTTs+mzNklNUpkLMZmgCU3Ce6jcRkRObnEZXV32bSbbP1p7OH1PHczagkb6Yo4usJdFoergYpRt9dd94ijawK0mcam37JNUPXxZfGuo7TCOJp+f2VMK2F5lyax4i+OnL3WQN0ethDGKpQvV7OC1o+qpXjuJy1NEZ5f6VBOZoYV5n9VFyqna+hZKQFTZlAETCRcu37A9lafi7NtPsFv6f4spBmOp+OCFZ8MwU06VlNNAri9TAYrxRyDuIlgMXCwt0dkhMOjxyCCVaFnIm4eXOf1197gnZ+9SzK0fPDxm6QWkjgBOUfNBrnskotBZYKJ5+T5lP72NQ5PjvjKa68ytXNmeg52xmDQR7NJebIkbpqU2E+pFpqpq6NUzwzVWdHhOvdq/sFPdBsDr7/+Oh8/PODe/ff4gz/4PQ72bnL//fvo/hbb25Zvvv4y/99fvslgsAmmT57OGA4MWTbl5PiQ9CxlGGVEkroRZZKRmznGZBg7QU1CJj0sEQk5UXFYV7lEL0g/BG2qAehtYNXldxU7l8LsVGfhnw3D77pvPl8GzF3SHgcstv3AZlQATRfIrmLly9xBd5lfRr5QQN4c3YhAhCfFEth46y5bteYqMC/rsZrkrCYnK2fuU2tSnunt3eR5HphbXNz+q0Luu5u2BviqjsFKYXOvOiNUkOlGBkkSk6VzTk+OQS2m6Ex5UQ5Iu9kgLDhHoALzTtkhtdBfzubtF5H4r9hYFaJi2/b+wT4v3rnL22+/zeb2Fs8+9xxvvvkWxgjPP3+bx48fcef55/jOb38bQ853v/fn/PTtd4hISCTi5o3rvPTiXZ67dZtMD9nePuGddz8kT2dI4o74NRi3zA9nI+/1hzx/9yvQO6S31efjD95mK57Q0zE9e4aRGMUyzy0m3kWzTaKoT4Y71Co0R3SyIzUgthxJeQYuIuR5XjQdg0qfpL/J5s42zyXPsLnTZ7BhULlgbo94fHLM6XnKn/7ff8rJ6YTf/3f/CBFLbjMuLs4ZnRwiOmEynWCNJZaMVGIySTHJwClHE2Ex2EjIJULzhF4UIVZq4LKsGXcx8nVsuMvfh31hPXC+LFDV/X8av11iynqufxWoelbtRPS0cdlvPZ3LGHaT2H2acvpCALnvcNY28bc6HRrColyzwWr9JrzVwNTgw/D2MlVbY+nW2pK5+o8y12zgVJXWZBH+XjUYXiwpAxG3uy+JYyIBpz9s1anR5R28LKRGREHcPp0uuxaJYjS3qDgwHw77fOMb3+BvfOtbbAyHbA4HXL9xwHBji5+//Ta3nnuGP/w7f0A2n5HPJ+xsDhiPTnj1117h5vUDbt54gYuzE2yeks1GDKOEw5P73HkmYiA7vPXTc7JsgzgxSDrCJJZIU/J5xEt3v8nhgwvuPv8C77z/5+zb++zPzuj3MrdiRwQTwThTcnuLObeZzfqYwQY1Rr1iaO2IgftqD/hlpTlx7Bhcnltis8V0Mmd0/oSdfWFrV/m//p9/ymtff5X7Dx/w6OPHTM5HXJwdkyQJjw8/YHf/FmqFfDYnm51jmKOaMc0sETH0NkjiAZlGoBFCHyMCVpDIoPTIbU5MHoBNuzkhrPJaVV8CJLrBsGLjnya8dVl4GacG11RLj9FwHL14rnrXCAzC8qhPeldsnaDfLAfzxRVRTyf/VptWmn3NBmzATyj69d5tNqmmeJwOq6OMq4hvAWypNv5kWUaoDdyQftFP6LetcuqMozu9jjBLaUZydnu/QsSrNClt3GH41eBXSsUiwXv3Wx9t+DNnbJ6DJCS9HrM041t/4zf4W9/5DvPphH6c8Bvf/CYnJ0+4d/8er7/6db71xm8wOj3m4uyUxFj6MifSjDif8qVnD8jSKcmWIc2UWC7I5ikb8THT2Qfc2s4xz+/ywUOLnZ+yv2npxafkjEmjazx+78fce5hy97nfIhr/jBvRE/rjIy7OR3z/3/wlN64f8OVXnuOZG7c4ncRMopvk0S4pEW5TTXvbqNVLAd6VJleMcSaWLM8wRkjTGZFR+rHl/PQjLJbhMOeT+5/wf/7LD9nbvIaQMJtO2dkekOUz/uz//Rck/W1m4wkvv3CHO88+Sz/pY5INzsZzctmkl+wSRwnORj/AZs5Wvh2nzNNzrPaIoj7YGeHRr2U9N4frre3oMpOLi34XyquMqWFD7gjmaeJ07dWUdMtTNw3BvJGWznCWmtVa7oPgntaM8rR5X1e+cEAeZtpI8QEeAXCasASsRmNdkEDVtsKnuObhJznjOCbPc1D3AYnmlvyKsdeDsY3NQOHv5fJesAX1m1Fcno2AGEXz5lC3aS7wY5UKrZ2d268ubxn2WetMRzjqn+eWmzdvcf3GDaLITWRmWYqdz5hdjNjdGPLi3RcYxBF5L+Y8m5HmM86yMYaUQSJk9hzVHhJZ0vQUq32wKYPeCT1zxDydEm8PEBkyOnzEnc0xUfYuY32M3bjLD99/n+nxBr3JNb72LOiJ5e2PHvDjd9/lPIPzR4ecjx/ym9/6BoNhn2l+TjS8zjRT4qJDNieXFsUW74IhN16JQ5JEnJ2ecXL8EddvbHFt7xr3P7nH2eGI/a1bnNsTTo6OGCR9jp98zMPZiLOzczZ3drGnp3znt36b6zu77A77ZKnBxj2G21vkZgdMD6vnxCKoJGAiEub05odskGGjbab5FplxMxZhfXWSlhbQbusf6967Z1Vb8ya5ms1c6yDX2iY77i9jyw+fX8ZG7tOoVLgSzIzhyWGNiZdxNJh7TaEsLm5ogv1lfteVLxSQqyp5XnyJR7W08UZGmM9Tt7a616OXJAUr9R9eCCu/0t6KlI2Q8n+q6tTwniCMRQVhC3OLRPWPLYsEezMD0A9NLSWDb820S4FIeQYciCEysQNT45ZIapGf+i6R9vCqgYxUjZSgbIp0RSYiy3MkduUZxzF/99//u7zw/AtMxheksym9KGY6PicxsLm3y3w2o9eLSGPDtYNd5pMLdD4mMQp2wtZGjzSbcTY54WL0iE9mT7i1t4eVB0RyhL04IppFHAz2SHqfsKeHDMxb6NYxcj3n+LzPk8Ocez+PIHvIT999xP0zy1n/BhOU7UgZcsKH9x5w984N4sEYm58jskWxmB23n1QL5h2CTllziOS4cz6cQcZaixF1E5LphFvXB4yTKT/6qz/l33z/n3M2snzla2/wO7/ztxmfP+EnP/pX/OVf/hmPHn7sPk1Oj9dffQ1rIR1fQL9HqhYTb5JnSjzYJmdApimRTDDGktmcRBI2GLFjHjKfnDCXHejfZix75L77+g1yWrQBALGthz2F8ukm4xpr75tmhUBpXsaUsi6TbXtepXfBWae4RQrSQIWgHLXqG76veNhGQwpY9MDicdtI6WnAfV35JQJy/9FGlwHrzSWAZ802U6zA3CqpdRtJjFV6GNRaIiMYLFk+rb5qL7YwwXjqLlUlVfBHMXfkqkzL05eJrMFYyslJq9U2fLcVPy5t5KHS8BJ+kMLfG2NqB275jyl7EQW1ikGIxK2HhhzE025DHPVAc0zkgTcuGrBQn5whuC7i1AhUqDaBKkqOMTGau4bq7M0Ga3OMWveh4B7cunmN8XiEqDM35DqhH4OmlsejEdvXD7iYnHNx/hhjEoh3mc4VI2NMfsTp4ykbO3skesyN/jEb8oDRe4949OjnvP43f52jh+9y8egRN178TQYbiqYnRP05/U1B9Zg7t57hJN3l/UcPyVPL41GPuN9nK57T66WYPGNut/jkaMz23iN2t49IzE1mdg+NBqi15DoiSVL3VR/t4b8BiY1BE4QcNWfEdorJe6QMSSMh1ofscURkR6iNef/BB8QiJPkEySf85K3vM8lTvvLi8zx48C7KKXHf0iPh9s3n2R1uEtkBGybBpDmZvSBjRh4bYtliYGBn2zAlZ5yOGHLBnlquZyfYxz/kbPSQmQzYuvMb5MkrXJgdsjwhVkOiAKaYLJVimWJamRrLxlUBlAb3GoB+q/mv9F+1mUXxJkV3Vzav5lDRkxafttqIcVHqaS+URZluW+wwDZc6FAq7BrQEZrXiXdHhhXryymfetwpuZ4Tz0EyplgFVp1J6U2f1W7gty89tzVXVMv2VOcErxC8kkENQdMXngOtZMZGQ55AMEgwJbmM3JEAvHuA1omPrpgxNSpRcrFgfi1Ehp2hz4tm682saNV1vk0WzKcA6CzYOhTa2ps3chVOtvw3NMlJE4sL1yqFikKqGKIrBgPFfYXbrd4KcVX22NtQobI3BuAMB9/HfdF6MDtyWk3makgz65GrJ84zZbMYPfvCXfPP1b5HOU5K4z2Q8Jdacs3TC0UVG72CP+x99yN6GIUuF0WiGyU4R3ie297ACF6cfs7FhiPiIeHyf8/tvkZ9ajn46ZBtDvz9mmCREyQ5Hj2ZsPXeN+dzy5MhwFh+gW7foDQ94+4c/4d23fsjBbsLBwS7P3LiBlV0e3Ztx/3iEsW/x6v4Wg61NrMRk2SY5A5IkwpI6hSY9N3Jj7kY9ahALWYQ760UiIpsz5AkH8j67sw/ZGmwwePE1/vTHOfePxlzf6AHHzO0Rb73/A0z/jPtPPiBNp9g85oUXnuPOszeR7IJ+P0Zyy3yWk2OJ+wNsdoI1wvbODnuxZc4nbPGInkxITg+JRo/ZSA8xZsz+7nUOj79Lf3SI7P0a88FtNNrCpO58Ges3Y4kNwKGkiVXjbYLrElkC6w2pN7iaq6aZoEhPqwJpUS4+3AY2l1lojDVa573CVLapDU8a8aaRWh8Odz5Xzxx+a6EX/B5nCEG81BS+j4engzZBvAxe2xPfIb9kQF6JJ5eh9tei0CbzjLOLCVmes7+zTdxLiETKIlRww6WgXYWGi2IxIWWgKKhj9C5a9830XJWZpszsvHYCYpnG2r0G4TUzoy3uKU0q4fc8w+dtZVK0r/KDvxRAr83GsEzKMyuqcHPr1iUbE5NnbqRhTESeW1S0OHnSEEcD0D55qkwm50wmc5Apw2eu8cyBO7Fvdp5Dr4eIJdLHXN8+Qo5+zNmH32X/5S8zHBwQbwwx9jHjw5+wszkin/bZ7Bs+/Nm7DOOMXj4jiyOOz59wPR0wSXv86J7lkcT09m/z3js/I5485qWDlJfubrmVNUlEf/cmz+zc5vG7P+TNH/wFzz2T8MJXNtBsDPFtUnuNPB8ylz7G9FCbIDLBkBJpSlQgxZwe83gDEWXIGXv6Ic/3PiKZP+Rn78zZ3ngBNrbJBtvce3LE5qBHjwnzPOXn751yMT3HXigv3nqFZ/f3GcQxqUyxoqjpYe2QYX+LSCAyZ+zuKJuDOdn4nL3BA/r2fS4evcOWteRMmWrGsy89R97fxH54iP3wEZskTG/scKIJRDHoDCMpsfizSTz7XBzK+3a26rr7vmrzdZNGuGMS2th9rSleIt5l/p9q0rBQZuWoIbQ7itQBX6qFBB6Y/Jk7qpXaECoF1Z23iry1l1P37tUu+SUGcj/54CbZ3AoRYZ4rD4+ecDq+YDadkpjn2TzYqw14HKNWrDq4NoXpWEr7lgNux7rjUktKeXqgxSDkRtG42LmYV9/rNLU/z6S9bbL4CcBZC3NMOEHaBHWoGHoI5LW6x+fL4r+q7oep1XBzRe2XDMAGJia3ltZEMfN5Shw7c4MiZLnbUh8nfeJkyHBzDzRia3ObUZozzkbQS7DG0E8EezFDZ8oov+DGM33G5+8zH/2YzfPvcvvaCcJ9RqdHJNEBMn+IyY/YHUbs3L7BZDzh7OEJz768T/rkbab5jD09Iz6fMezvcG37Oj/44Sfsnl/jhQ3DyzefY2fnljsi1m7RP7iLDG4R2YTs5gYvbR/z4U/fpTdVooNvsHFrk36yzzhXJI4REzsOVZwPI0QgMSlKSkwmA0QmDGRKlB9ydvgmWzbnZBTxe7/5mzzOhfvvfozt9cl1jJ4/ZjtR7DTBnM3ZiLe4Ntihl21i5kISp8QxiHWAYazFzkfcfqHHM7cvmE1TzLSHffKY+OQjro9HGKtcWOhvDpFok+lxSnw+YWcy4cFH7xDvfBWzuUcqc4ymiAqR9kBjlLhq73SDedj2VoFqNd+zCDSLhL8bYC8bb5u/rjAFz5QX3S6UQY38BKtf1OGIHzGXZxGVOC6VoiQc7bjf1atZhGrWq95vq7JbH8l/aYG87dyRKDKItezu7rJzsE86z9jdHJTYNMtyFEtmM3K1xEmMMe64WSlxtgJzq1KcoucME6IRojluOOSOChVjEWMxRgMgpjSllPZVWGiM1lp3GFQSL7DsNnZfy38LIwfPyBWk+BivRGBi/LnNq0ULdzkQlUPvLLeYKMLEPcQkKML+wQGHTx6TJD1yDK++9k3u3rlLmqaogfn8jF4/B2vZtAOm0zGT0QWiShSl9HoXpNMfkMubSO8h6WRKf37Aww8+5oWesiE5Ou8zn87J8zF/9YO/oJcqMhPyk7/C6JTNeE7+ICaN5tza3ufLe3By/gEXp2fsfuNLbO3eIdt4lsmFZXPzgPN5zmR8QTyD/eEu2y/e5snJlOH1bbJ4F0uPKEnImBAxwRgQnWHEYhiAxJj4nC1yekxAz+nLE95786e8vD/lzgv7fC2Gs/f/nN99YZvsmy/zL/71d+nHKa++eIs3Xr6LphH/x7/+MdHmLX784x9x4yu/RTzfpjfcdAeAARtJjMkviPWIuzcOYP4B6YWy3X8O6WX0h7vMDk9RCyYRehs7HB+f0I+GzGYX2GHE6fiM+OQJ0dZtpgrWRIgajDW1kWwbmK6z+qNrZYm3Na+zGqWrLa8Tr3e3Kp7FOOsrR5ZOPHrm3Qw3YOe1FV0esKXg32uMBtpXoxSr7GrPmmWzvm3llxbIocqQMYYkSRCBvhHMIGaeK4mJyh2H6SxjMpuAUeb5jKQX0ev3QCJyYFqUTSTFIgJxR6+6LRYwQOiJgu8Eha2c3KDWaWWfJg+mPn0esMuJjeCdMcXXgWwdZNcZBvoKrytm9+FnMQYxsYvRRG5dHJawzZVD3SK/lOl2qzbEeEYvGImIoh5JL+a1115na2ubb37zdf78u9/j7Z+9zb/3h3/IK698DUF4/PARG32D6hnb23B+NCIb5WAzotgymY+4sRsxPfsRm/wcM/uAKFHm4z6n946YjUf0EuXk4yfsDXeZm5i33zxC8zk7O5tcnKTk0xHEFvox0/OYVHK2d4TffvUVfvbI8mf/6iFP2GHz4OucXQw4vzjDRjGzdE4+yZleWKbzbaIk4trtawxufYXzwR55GhMzQ9Mjeok7N16YoRb8UV/5/D32JKefWXfCYm/MtTubbJkB58ef8Pz+NeLH32V0NOfbt19i82++wtnZh3xp44L48TtEGvNHb9zCHNxiI56yMD/D7AAAIABJREFUEY3YGA6RJGY0mwGWRGbc2BN69hx7fkx/OGZbJ5jJCTaOkN1tpg83mZ6NuP7sPmOTuvX3OUyyFHYiNntg8iOG+QlRNGBKj8xGEPXJ8xlxLOUsfrhiytrlG2aWrlbRGqy1jirbwuxy35WGNllGfFZJOC+16l0rqMr65qlV7soAWxTtpc1DhfxSA3m1rlMBdd8HFrBZTp5l2FyZzFNIElTd2da5zUjiPoPekOkMxumMcaqMcyW1zlxgEeIkQbM5SZRzsLnBdgwSCb3g4ESjEGlMrJCLJROpfbINqJh+OalUsfXy+FvqjaNpPmnKYmWq60Dq7HE2txyfnEJkyK071nZ5Obr/m6FaLbbcSwQG4l6f3/3df4ff+73fx1rL2emIV7/xGr/xxhscXDtgNpsx6PeAKbPpmCQ5p5cow8EYm2ZInBLFcw52U2LzEYx+xO3eCZPxBU8epty88zroOZyc0evNiGPD6CSjH28iqqRpSp6lSG9IJH3O53OsiTACcQT2yRmnoydk3GLv7q/z/QcXDL9qmc3PAcvk9IycOfPZHHRI79rLmH7KKE/obW5j8glDlO0EVEcMJSNRZXT6CY8P7/P881/m6PATzg+/x62tPfb6+2icYDPLdPSI0ekH7O/lbDyzzfmTn8D5jNnFI954/qvk6T4bKpwdTdjY6NO/sYkMUq790QscP8p4/PAjzudCjmVre8jeJjy7DxsyZTC0RBsbpCePOR+fMrj9dY6fzJlubxMlOfFWxHw2YTKdkWfCZG7Z7Fn6pw/YHv2UwdEcTYZoco3+8EVmqSCRm6T2O1PDdrWuWaNTak4qe4o27i8zWfeLkFYQh1qSSxcSXhQErfHr3BfXNevIcmVVpaUJ5t3pXCW/nEBezjuoOz62WP4nUn1+TWyxTja3pOoPEXKek6jHdJxzejFhc2+H0dGY44s5anqoGHr9HmaWk2dztoeWZEeJIykn9UxQqQLFtwXCyc7quvqifWH7zm3ZQYByyaK/L5dFtmW7Bdi9EnD90cU1Oh/x9ts/4tpBj48/+QRs35lX1rGslJMs/ms8TrENNzb4D/+j/5ivf/3XyWxOHEdIJAw3BiS9mPPRiO3tHdCMfgzYnPnkjNOTMzSbYfOcuJ8xSOYMe3MuHv4VO5Ofk518RH46Izu3nJhHRGbGl7+yCekxw2HMxbFlpjGT8YRbt3r0c2U6HkOmmH6PqU2Jiq3xpIoQY6IhL73yMm/df48Hj94imaRs210iiTGxITMW7Q/p9SIOR/fpX9sniiY8u5mQX8w42BrQJ4HxnPloxCfv/hXT4zc5n/0QOznmmpyRPurzyOzxpdfe4OR8zPxsBBcpWWI5e3TE8ekTIoGtQQ95/CYyP2cazdi7eYNJPmWcPqKnBuYJ06NHfPVLL/CTtx8wSTN2+gdMjo8Z25wbz24yO7rAHvYxE4WRchKdkg32sdtDsvQxJ+Mzji/mDDd32JYdjp+k7CQDtocZs6M3SeQjNrYGnOoeuvk6m7tvMNNbqGyAmYFkS+3El5sANY3WVJkefOstl+OxzEZcb/Pr2cgXWWy7SUZbbeQLikoa4BzYzVUrkC96oevzIXAXYYcToG0HbIVpWOyHtfFNezrXkF9OIKdikZERVAr7eGFjiIBB0VYiqg95qcF9RUaBgQMha2B/e8hw0Of0bMLW9oD93cTZHtlm2INhAhFK7O3HAv7Dum4WLADxxvDO28spfysAb+sszUYaDnmXVl4ZrmJzuBhPeOWVZ8mtkog7C3zVPKcX6+wxGIkRIqwVXnrpy7z26mtMZjMms7HbsZmmRMYwm84wUcyTw0fk8zn5/IJeNEfTjOlkBHrIsH9GNDkmPXpIPpyzLadsJyNOj0bMRpbxBKb6MTevG0yyyWRqmY0N81nG6N5jzg4zDMJzez3m4xzI6Q0NPclJTMSzNw+Y6jbHo4yoD5sK33juGWbjj8knM1I7Jc2UZGcTMX0Gg4QBKQdJxtnjd+mbnG0Ds9wS5WMm8wtiG5NmOaPjx/SyU3YZ85W7fWbzHc4Pc0aqHKWnnGXnbG9sMrvYJE9zPvrohJlN2RvGXIwOmU4esLkdMTU5mRFSydH8HBsb+v0b3H1ml7OjT/jq3RvkuWF3f0CaZtjRA0w2g/GMyShmd7iPPT9jLOds394nijKenE/pRYZn7z6DlQEn90+4+9JNsvmc6XyGHc/IejP6A6E/f0A6S4ntgOHBkKN8iDXRWvp93QlQh1lrrGpxIXS/X2Fi8fGH996u3AxnIfyFycP2/CyLe9n7pf5p74ZdSqVzUvmSYP5LA+R1nU5tT0tpvigmKQ1u2Z0/O0NsGEYx2ytK30TkKlzfjbBquLa1QT8xDBJ3YqApTvKjsI9Gpvh+VhGOEjl6brQylxQfjAi/GlT9UQK9f6elaqd1az+sbjyq7hwXEfe5OIozXZz9PSJLLVleKIY1ylrEhF8wA2A0Omc6m6HAPJ0zOjshn08RlDhOGA62OD46JRbDfDJiPjki4oR+ouxs5GzHj9nKH5LJPeajJ2xtD0mnY8YzyMSw88wGN756m+jsPseHR+y88CUe3bvP5EFGeqYkmjCbKE/GY3YTw8Y1IY8gThK2Nvp8cv+YB+dj9PoOaWLZyhXGU0yvx+l0iqRztreHzPNzer2c69v7RKMph+/9hGEkbG/sE/WuMUgSUkkZRzmD/iZJvMHdr/4aD9+7j+gE0hSdRcS5ZaMfcXL8Dps7+8TTHIn7jE6eEG1Z9vo9+tMIM55zY69HNLRMz5X52YT9vX10nqLnU6bTU8z2Hof3pjxz17r1631LjwnxRowdT0gEpnlOOpowP7OoZDx45wNevLnFcG+PzWcPOLXKOx9+wk4SIf2E09E5hydTNnsbTLKcKIUknZPIEcnFz4g3N9gY3GLMMyg9NGgb4fkkUtBPZRFswvYZ2oq72G7t3j0sn6103xL3us8W0rjEXfgOXw5rAudKkBXwu2y73K2aDF6cFF1PVgK5iPwj4D8AHqnqN4pnB8A/Bl4E3gf+vqoeF+/+f+beNMiS7Lrv+90ll7fV2lXd1dvM9PQMZgYz4IDYCBgAtyAAkZQoM0QJNiySJmUpFFLIdoRCkj/ZXxShCIcVDtthWVRIlmQHZW0UCBIrsQ0wWIgBMMBg9umZ3ru69rfndhd/yMz3XlVX9TKA7LnRr+u9zJs3b2be/N9z/ufcc/474HcpbYp/y3v/+bvpiHflwulaa6md46SvjXW1CiTwvoo86KpFD45ymyxtO6XdzwKySlQgEN7TDiT4HJ85dBjiq6XTUgsmrkCVPlXnpffOTxIs39JnX0oI9SpPKcE6u29wTqR2SiAW1JrFzKCornsynUuBM6VmUAlAiEr3KOcKgzMWLWO8LZdOKe3xlVtb7cYz0QImHa6lf4dwvkyFBgihuXlzi5dffo1HHnuYMFSEseb6zi6NuIHKU7wtKPI+iXFoGSCDiDw1SDlAujG9m1dpLHSJGrs04wQKy96NLj7qcPyhNRodi1ADEp/RWm6TjQyDbkG2a/GDchFOK9RELmc4NDTOhGTKsTS/SHe7y2hoyGwIBdzc2CTYG7PaCZFzAc14Ea0CrBEUhaERBozHGVuXr9JqL7G60GbU71KoHYoowhYBKEGW79IoRqwu5pxeOIvwW5jBEJ9YhE3J+xnKW4zXXLx0k3MnTzHsbhKNLdpICudZaTbBFqS9HKVC2vE86eaQ4daQxXmN8wY3TJibj+kOduglY1R8gpZu4YsC0y8QzmEzy9Z4xPLpU7DnSXZGNFZPULRSdjcGXF7fJmp0aEUtdraGXL9e4F35+goEamRphiHLSzE3d17DqTGJaxOufRATniWjiVfV+PcOK8t3TDnQps67CjMDiFmyZJbDneWUK+ll3+8juei6/uyYrMHqEPrjMDpiei5xoF9+H0DXK1onNG1Fkez3Rqm7cLSL5N0YR2cvcjKN1ffoIP/u/b46NYU8vaaalLq3cjcS+T8H/jfgX85s+3vAl7z3/0AI8feq339XCPEY8HHg7cBJ4ItCiIe99/Z2J/DekxQW610FxGUGGS0FkRTgLcKBExI/SfjrMb7yL/flg1NUcb6pQbbObSgmS+69F3gUxoIxFk9GQ4WlxDshaWbjpdRufnXcFJjccO+rrDfly1QG2Co/NUjLKsZ42bqsJADAz8Q3Z1YjKVUQhy+XLteUDarktWvF0Xu8U2XI0ywt75EQ+1qbHfD1BFX2o8z7qESArDjylWPHePSRhxCiTGtXFDlxq4k1niJNcXmK9TmDNGF7q8vxxQ5NkbK2JGlmfdZ3tlDHO+RDi+inhDJEq3kWVldQ8wHIbXy6h3eOIIjZvLyDSz0qFfhUkpuCPCgotODk2ZigHaGUZHNjD5kbtFcEKiAtHGurq+x2x2UsmNwzzgpskhCKkFazRSTmGY9HrJ1/nOHuDejEmL09inyPuL3C4soZujsD+luXmZ8fM9cekA536fe3MSOPzgKEFORjQzJOEdEAQ0xfCM6+/WGKYQ8yQ5YMGBaOZGSQoSbTgmR7h5ZucGxtFXxKRI5uSNqtNrYjWZu/n903tumtDzgWB6hUkI4sCEm0pGFRsv38OkUasLOxw/L5RXS/R/6yQ48Sbm6PEVFEMdA4mwIFS3MRQShBBpw8uYwOBly88jInVu7H+G2GYo3UKpyQ6GoVohGqyq/qUV7hKFeF7gfxGeSuR2olbc6qfjVgHfl+3+H3vZT92DnTr1ltHs/BavtAdf/cMa12iE3gXjxK9rMKB42jhwH0rXWmPP3dS+NwF0Duvf+aEOL+A5t/Dfi56vu/AL4K/N1q+//jvc+Ai0KIC8B7gW/d7hzWeQZpTpaXnzCOyjCtwHyrQSRV5SZWSshlcH8x45YoJkkaiqJAKYX3pRfEbFIIa+2Eu7bWYl0p9wdKEoUh1lpUHfTK17P3dJEOAPuolJmHXD2QydDy04VAB787V7n/zdyDiWR+YNsUyGv+3U+BHJCiTFB8l5bOsv9VAIRSEnAILN5btA4xzoBXmBxCr0jTEcpphuOEoBly6r6TXLnyNH2X8vZH2qzGQ0j2SPMeexeGLC2fIhlEZNYTr87TODPHML2JcwlhGNKIY7pbBaLnWaBBZj39cY4Rmt3MsHJmHrkyT3fvJqGyuNSgJYxxDGxGFGsKZ1lcPs5glJAOU1JbEAYBicuh8MyZBo1Q4wtHM16kP0y4dG0DT4bYuAYX38CrDuPBgM7SKs1gjpyYonBI60vvEGsRwtPdTgnbCtVs8+orl3jggSUoCjQBc6v3kfa6hDJDaoNqglKWKAqQXtK9NsQJj+tus7C2wM7ukGhhxMqxE/SSgtbx47jdgJ3eOnPLTazMGXY3uf98kytvDHnj8jXMfMHxsydodG4y3EsxHkQBUkgKW4aPUAgCpfAiYf3yG6xvZygc4+42ttFHBYJABljhUMaiXIgkJpMShMHI0iZRUwL7xuQ9cNsHBhpHQfbdtnMUNfFmj73XOrczCt/N/sPO8+PUuV15sxz5ce/9OoD3fl0IsVptPwV8e6betWrbbYtUkkazSWYFxls6cYM4Dhj1x4xTg9ES6Q14WxrhnENrTRiEKK0miY5nkzoYY6bUgp+Cf52lp+aunXOkeUYQBNW+mdlzqoUdAOP9nilQUTAHbn5d59AP04VFtyuH7q8mLvwMF3+b4/e14AXe1RHMBQiHUo6tzXVeePF5HnzwPKP+GFcIvPGEQqHCFs6F9IdDVk/N8/Cj72T78osMs4xOUEqdgU1I92DcPEZuIoS2tFdaJNk2WbZLno3QQZsbL3YZDBw2DxFJQO5hWFh2Uo/vKFrzbda3usgs5+RiRBhDZqGbCfLGInGzQ5Im2FRhrMYWAc4JEunRsaLQDitytIigcIxGBZs7fVR4DOkzsjxjkCR8/8WXefLd7+eNbRiMBCtyGTPepO0dPvIM9wYEOkBbQ9obIwsoMkueGwKlubG1x0BGkErCXLK4vMDC8Sbd3YuofI84nkPHEd3dcTkR7XQRShK2Jb2N64yHA2SWEQpNe3GBbjIgiAQ2LTCFR4QW7STXL92k1+0RtyTjkUA5sK4MoyAcKGS5IlQZVtfmyYqcM6fa9IYp/TxHiQHIMcKMkYFCK4cyHuXKELm5dhidloHi3K2G97vheQ//PaVbjuKFD/Lph53zbsD8bgyJB3nno+rejfHxdv07uO+oc9Xbb1f/XspP2th5GCod2ishxF8F/irAmbNnkbL0TNFKoYQgkII4jgikIAo1wpfxpKshAky56TJOOEgtUXXqtUCglZ7pRHmMrHNgViDtPAhcmYUFP+G0pfDlKly33xDhncNNM1rg/dSlUCm1D6wPuhrWD6rcv+9e3ALY098zA6X6I8V0UUcN5LfVbfe3TLmisz7GIoUlt5bPfPqP+MQnfrPMLUkZUGpvZwuJZq7dROqAQESEYYOFtVMM1BbShfitEWtLa5B12dt4kbUHThNKR7J7BasMsYCIENGzmJEgTzVzS8dwzZCbOzfYlZ48gJMrEcWwR5YMkdozKgSdRovt7TG7dpnW0qOMXZthMiJqROQChqMR7U6bS+uXaS42WVtdIjGehvdEImCQFox9SamFSIxWfO0b3+f6zi6nzz9Aq7XIXraNYo+Ok6RpjjOOOJC4wjEfK7Z7jqQYcfbBZZYWF7h67QbdQUZjRSBEiAqbjFOL6hvQMUm2R+ZSVCSYX10m1hlZmrMwv0SeFDSaAf3MkeQZG90BcSMgddCRAUk/x+Lo9cGrkNE4B++Ymw/pLAg2b6Z4KzGFY9CHQAsWFkK0dGxt7NHshMzPtdABLIZttvwGib1GpObIVAPjHEI5pDdoPEbkGJUhjAb0kcA1W+7WYHnU73rbvnbvAuCOOvaw890LaB4G5rPt7nt77jAhHHbdB/cf1tZRk+XdljcL5BtCiLVKGl8DNqvt14AzM/VOAzcOa8B7/3vA7wH89Lve7YV3dGKNFiFFllMoSRxotCxdA30FsiWdUEm0UlV8+uxNKykOVaXOmVJiM36gHpAgvMALjxblgqJaQt7HUXm/7wHNgu7BpMw1xWOtRes7z6qHcW+3Dh6/73sZY8VO74U8nCes+3Po7C6mS7i9dzhvEB7G4wGXLl3i7JlzSCVJrETFbczYIn3Ow+dOstndYJxssNtf59VXn+fUasEpPY9I94gDWO5oRHGF4ciSjSXjQUEUBAyKAmk0WaZZWFhgq5dy5cYmVsLig23SXsrm9phGIGl2NEUUcLFviUaei5uab1/s8a5OQDOz5OOMZPcaQWeBQCt++OIPeGX9CuF8h9Fel1963wf4hXe+m9QWrHd7PPvK87zzyccRPuQr3/oBl/c2EApssUlT5IjRJXZ6N4nbAUVhiIFGFDIY5RRjR0Mp4jgkFJY8GaCEJwoEW+vXsEPHifYaYRSRC0PQkmirGJoxGElneY68P8ArT57kDJOMDjH52NNoasb9lEAF4A1uFGDHntQXrC63ubmZEogAlwU4p9GhIs9TBt0EpQTWC1SggZjhoE/hPGlRYHwPJwsKMeD4/TFb+XUyzpK6kFyVSZwVBuELJGXAMCHU7diQe6Jcyt/g/V3QfbcZ+3dDQxz17tz+ndq//bD99yIVH9bPO/1+M23crrxZIP8U8FvAP6j+/uHM9t8XQvxDSmPnQ8B37tycQ1qDFIKoGZNmORpHKCVa1AbAOvxqWWrKwNf4NSPBTozv7DeBiGrvxP2q4hfLtmdG8QTEq/Yq6bWmZqZScJlI2VY33Fp7C4de97UGfO/9REq/VT288+CbncWFEMgqkXOZUb3u77SN6X2atufcfnOQECW2W5Pz5S99kUajQ5YZTpw+yc/+3IdxPue73/4Kr12G7z73dbq9bcb9PUKzR/9si+Un76NvJMIKAmOQIqO3ZcmG0N8pV2V6CUEsOXX2Pq5d2+DG1V16fUhy6AcZ0jj6gzKIXyQ7GNkkScdY43nutRFbtsEXv/0sgYCm9vR6PY6feYB8NMSQYoQnS3JyGxB3TrDZL/DFkM1hn+cvvorqBJw+fprr/S42jrBJTm9ri1PvWGDY3aPRKLMhZdaCyRGq7HSva8idobkUEIeSna1N0rRcc9COAvq7lt7NHaLmPNoErDQWiSKFs31GSYbtbrO40GbYy9je7FEUniLNSYYFy+2o9AZKPEnusDIh6RmipmS+ExBpg8ks6TAhnpdkRYbUATc3UlpzgmYsKIxma3tE3CzptSx1DPopzTlNEHiS3Ws4swDBg6i5RYwOMUqhLES4MvixDcoHdER5M6r+vZZ75b1vJzXfS3/vVPfHAdc7SeQ/Tr8OlrtxP/xXlIbNY0KIa8B/Twng/0YI8bvAFeA3qpO/IIT4N8CLgAH+hr+DxwqUEnYcyAm0BI1wkvVGMOPbXaYV33fsm5n5JsA6Ocdk7+T/ChNnaJJbee5ZQC7PKychaWcH2qHqmhDl/LSPVpkJ7jV58NPJADHl1b33mKJAK72fkzxCqtr3EsiSMppU8wLvLAJJno/JshQpAq6+8ToXT6wxHve5fOMCV3/wA0b5NQJZcGKxxW9+7JdZDjOKwU2gw+DKBiZV9G1Iv5/gjKAYKwZGsHjyBNFyi1dfv0SymzLnQ4z3kBdoQOp5NvsJb+ykrDy8yMaVPfqDHrmxZEIxvxaRyxznBDdv7GILST/b4KO/9F5yM2CQO7773KsIG9DtJ6yHfUJtMFqh44jnXnyDqHmcrZFBRSFxU3DhtSu8vqZ5cCkmsJY8c3gRUlhDntoyn2Zo2dv2zJ0OCRqSogejkQUHi21J6iW+KEgGYzKjyNG0ooyWLGiEmsIJRgl0B5bx2NKKI4rUE0pFrzsmjGBne4jWjvZCh7EY0hsZMjPAe8HCXJNL2wNG4Yj2fIMz5xZIkiEvX9hlYU6QFhlrqw0CJ4kbMM4G5ENH2A4QRYEfFKT9dVrHdonmx2S2QaY6WOlK90PfQDpFGWfG3WJwPzh27qW82eN+/HIb1eI/5lnvQlOZLUfRKocde6dyN14r/9kRu37xiPp/H/j7d92DqghRScu+og/KxqbeHUdQRodxSYdxzrfloDzT8+wjY6YD4mi+b5ZaYWK0mX1I5f5pPV/qnZPvs3VuuZbDL7tMaRdFxI140p4Q5bL7g2FLZ695ch7hqpteTzyqopEswhuU8kgh+MbXv0Cz2SLNU6QOcC5gMB6wEIVkzvLc889zdiXiyusvEfZT/CBBB5p+WkaGXOrMc//aGje7e2xdXsfnOaSevFsQoemEGgMIbzm+2iSbX+D1vV26o5y9gcdqgQgcxxodPvTB9/Pyixe4er1LOna4fh9HwP1nHsB4hUkE3/veD7lx4wrtliQIBDKIaLfmGQwGvPzaC7TnAnrDAUJIcqW41k944FSLIhlhsxRvFcaBMZ69cYoJQTRBdyQuVOimgL7AFYJeL8d5h9QB3d6IJPWMbcR8w5NpwYljTTJhcRmEqs18K8TmhjhsETYEV651y1R60lMYgUWj4ojrlxO8N6zOC2SesNtTjBJD1E1oa8Pq6hr3uYgfPrtOK3YUZsTxlZgl2cLYiHGSQJBhy8UZhHKI1i+gI8PC4jkG6hSF0DhrESjwIVCAyG95N948H14O8bsFpJ8k6IsKMO5Ger9XA+NR79Tt/t7u3Hdz7N2Ut9DKztr4JisDZMU51459fkKalPVvQz0ctt97jzFm38P1gHFlnBUlZOV6KKp/glmqfDa9W/0p252eq/Sc2S+J1/VqSXran8OuoZw4ZimZWa2gjs/uPSwtLbFy7BjNRhNrLWGgsfbWgXuQWikVAYvAUy4yKu95Cf+lK6KQHo9FBh6kx1pBqCIW5k6hbINxe0C3SPnfP/UUDdflF55c49EHH+D6sy/jdUgiPHa+wSMPP0AsGlx5+Qrd7hY5HpN7WoWgEYckBYzSjCCWrCyBVRnttTluPj/m2mAMkSaXGgFkieP40gn+1bOfY1ioMphVkYPTFCNPqAUnOx1a732SR9/xOAbHxuYmnVaLU8fOciW5RL/bQ8aWZhQiiGnMdbg6suywTGgGyHGBLHJyJxmnnqIQDDJP30IiAnbGKb1xwdqZZW5c7LO5Z1C5Zy4OkTH0+oadsUUda5FlnuFmj7ABjYZCesfZU4tsd1P2kj7N+UXWNxzHz2i80BS54frWHosLc4jIcOHVAnVWsbTYotmRfP/FLgmClk+I1WWe+KnTNOcCJAX9gSPPx9zYSDlxMmRuqUWvPyZuBjQbAZHNyTa/x7GFITYZkNkCGR7HerA+wMp48n6VAHyvfPhhv8W+bYe9kwfLT1qCv5f2bmd4vFOdo4D4/6u+w1sEyD0CW1kma9udp0p668E5ixASVQMsUyOjNbYCzMpH3FVAKCsXOzcDptVCIyi5bQ+keUEUaMJAI2Q4AeJZ6Xgf511/l1Pj6SwNU4P8LI9dfiRCOMS+hBS3LvGfMi/TF6JcHMSM2uuIowhrDQJPGGqcrXOeHl72D4wZ1dODQJZeOrVB2TsEgsLmBDrEWYeSEe1wleWF+1g5u8bVG1d4/cIPkUHG99d3CXXA8n0PkWyv4/MeOpKM8hEv//Bl8r4hd4LGfOn37AOwAYimYHEpJtKOcXdEoAS53yEMChodT+EdgQhQBNhM4PIAbx3GuJIWk9Cam+fcA/dx5Y03OH3iBB974jFyDLnwaC145aULJKljMM5pzHXIXcHZM2d57/t+kR/98HkMI56/OuZ0Y4Vzq4uMdzbJRilGeRLvSD0kHi7dTDg3HxG2WnSHY5pzDYwpGIwSNkalh0qjqen2HXkRkQ0zUmuJE0He8sSxZ2dvhBCQFZrh1oi3PXmCpZMNRNDghec32N3Yhcxx9twagi79nRGD8YiruxabwWCQY0LQS4Yr69dpLWjSoUUGDitAScf2ToqVTdZOH2dnbwfrIWhAEOZk3dcoioxgeRHUHMgWHo21ovRHn8UNP/PnEOn7bqV8u79tAAAgAElEQVTs2eP2Ux617WvqVHCntica7Uz9A93d1/ak776uIZga1mb7N7P/aEfeA2eb1cj3XcL0iAMC1WHXcstZ3uQk8JYA8rKUxscajmoghymYZ9ZV3ioS5wxpkmKNIYxCBB4lJaYCfSEk1liGwyFJmuKdo9lqMT83VxosdZnKTKig8vyooyf66jlNH1TNJk/BnBlAl1AF9rL+VobR+yqRtKibFTNt75fmnRClqi7lzKD1WAHG+8qoWoUuECAq0JVVDJZJNnDvDx0M5SCp1nb60sJZr1r13lbtlkY+6SWSCKwAVFnfSHQRcWb+LLtXd1kMTpEXY8Ti/Xx/a4tH5zwry02W/A6j3k1uXr7BQiciVZZoKcIHDjdwJENDHuQkRmAKQTa2hApkQ7PnQi4MBuw6aLdj0lG58lDRotlaQsuQSBXkFlTYIjeO+eU5znKGbz/9dR579BGE9TQamnGvz1eeeoqxyZlbahDHbTqt4/z8R/4Cj7ztXbzt4Z/hj//973P+8Uf53L//ffT7z+JUwPyxjLTbQwhDEHriIKc7TNntaVpxQJIMmIs8i/MKl8TsZRmlnVvhbEBvIPG5JsYSyxYjB+MiQaaCE4stnnziPC+9cB0916bvE0ZjQ/vUI/zo8vdJxwXN4XV02CDsBGx1CxaOCdqdkKZKiRYjmosWrCEMFMtn2wz6A7zzJBlEQnH5UsbSSkwYd9jd7eF8xFKnifcCOU4J5w1eaDITg2riUEhRroiuh/+sA8EsgN4pac3+cTcJZzfzVxz4fiuleBQAThJJz55jdiLYB5ZTqeiWULO3TB5TMPeTi79dOWRCugte/uB7OUur3guNclh5ywB5/TjrLPV1HG8pBEKVEdyMt+RFjjcOqhWeeVFgrMGY0s3Ne491nrwwk5WdSpXJJYwxGGuRSmGtJctysiyn0YgI9EyS4zuWW2fXWT5w1hBaL0SarTu53pkHe+QsPKMBSCnL4EdC4KwF79FKl14rUtapOKnpmdvN+lIIXNXu1ADMhFYqA2uVsWHKAGWl5D7oDXnmW98hLVJOnTiD8wYZepJxwGubG9jlU8wHSzz0jneS3HgB3btEPh6TBwUm0vRyWy4+wSM9BFJSeEiAgdc888qAXtFEBSUwSRnhrKY7zvnBixfZ7uYEUYs4CklHCaG0dGJD+0QHITIyMySMm/zBp/6Ibz/7fUYmQ0QhYWue/jAnbgUsLy2TZSP+5Auf5amnvsHV117lodPn+PSXLpCOMj7xl3+NN7qvc/L8KXzS56k/+Cz3n4g5jWQ87hOFMMhyBhmo5Saq4RkOc7q9ghTJ9sYuzVAQOMf2YMjASFToiUlpq5y5+Ed0ewUmWufYeQiXJDqQrI8cAyE4Nd9g2M0JghZyJSIdDLBFwtoJjV4QNOcUpp+TjjO8UTTbEQZHTIutzRRnFV95+ionT4acXo4Z72bsXbd0mgrb7OPpsrockhZjHCOiIMLbgPKp3IE+mQjSPykaZEonvlme+v+Pcqc+3Ov1/LjlLQHk3vvS6DNDTdTb621QXnCgFabwleQtUErinMXkOXoScRCKoiDPc1qtFgB5Xhpy+v0+YRiitUZKWWb28b6iGO5cJhJ5+ePQaznMyHFY2/tplZL/nuXHZ1VZSQ2+5bF25p4opbCm0mXENKLbUf0vL7dCbb9/X9VpgGqlrJxOJpSrcL1ztBpNbF6glES5iHZ0nN72Ls+9dJGf+en7iY4tsHr2MdLXtrHDMR5N34SMMkcoPX4MWmt6WY4LIRfQLxx6Ieb+Y6e5fnUT4SXjtEAHsDMa8KWvfxMddyahFkIF2iW0gpxcWE6eXmK7u84Xnvouzzz3HE4LciGZa3UorMTkBdvbW3z/e0/zwvMv0tvr0p5zXNu4xPVrryOtRwUNnrlU8NM/+1vMr6xy/doF3hh+kex6wgfe+zCDmwWpS/BS0Sssg8GIwGu0UPR6Bl94vJc4KyisoCg8IvA4K0HBOPPMtSJa84LmkkfqjFhLnBsyzg2vX8l5LQywOehgxONvWyUMDNKNaEQO1ciRoaOzoii2LYOioBAQRAFKScJ2zM5GD4lj80bCSiugcJ7MShYaHfr5HCFL5CZGK4cTOc4VWF9GIzrM0DY7DsWEhrgVgG533JHbZumSSjSeDMmZOgcnmDsZW99suZ3B8TCHidsZNu9kNP5J9RneIkAO0xWKs7TC7OpFVYGukgp0KV075xBAI44JtSYMA/K8IB2VXGQcRdhqqX4YBmW0RDx4V1ESGikrI6g1xFE44eiPKrMeJ2J22yH1aoCWFZ2yT5WqCJt9L0P1dRbMxYF9tQqYjMelKixECWz1wDjEa+V2g3HSvJhREWsDK7WBtZbSS9XaWYcrDEortNSYtKChDfNqwNyxhNPLe1y7/EPUgw065yTdq5Jir6AZtVicbzIcDWgEIRmanjSodgdjLe947F289LkXiRlwZm2FwgjWt9ZJzZDGQpv13RsIHxApiRYF951e5kMfeCfWZAxGCQ8+ep7f++f/mss3N1BNTX9Y0Ox0sBa2tvp4C+Mk57Of+RQL8zHWpoyzLo12RJIUSK8ZmxEvXXyVD/3Kf4rTDb749e+zm2TYUcbnvnqFj3zgAa6/8RqDQcaNXYsE3KhgLoY5JRiNQElLO9K0mzGDXY9QOXFLEkYBUjkyaQnDABOmhBq017Racywdg8tmyM4oxyIwhWXzheucaXgeWxLETRANRx44VKQxqWJro6DTCcnSBO/GYAXHjkf0dwvGmeG1Dcux0/MsnjqNaq8Q+xU6Z9/D0C/ghUOSlJN/9fiPZIgPgOib9US5ZZuo/psah+7Af9/+HD9JQ+PdtPVm6h7cBj8+mL81gPzAzH5QCi/pkv1SehiGKCXI07SyXzictUgJjTiqePL9i2dqqkVKiVaSMAyQqpwAJK6q76ru3Nqn/YbLoxF/avyoQfQQyX2Gy548xAPSsfc14VRbDCqOXEjG44RLly4xHI2QVfwYV5Gct8z6+wZ6CcoIsLPXVO+suMbZ7WX9+juoCchLvINAGVpBl8ceCjnbbpD3n+HYqiQOYjI9onO2iexEJLsKs9VFeMd4nGFDMM15LnYtOS2eXHs7f/aXz/DP/u//QGEso9QRRUAIhUuIGpqsn+G8J44Ff+dv/3WOn1jgB9/9Fp/54lN85M99jBu7XVwQkNtSe1AIXGGQXvPoEz9NHArWr7+ON3lpQHUSQ0EhHCLIEYHk+tXn+cKn/i9+/Td+m+0rF1GEuMDy7OtD7juXcer0KfqvXyQMBdpGGGlwmSHqSBpLIdu7CeM8w0tFe7GJdwVe5AwzEA1oNAuMErQ7mkxCoGOSccDK6cdIrj1HoSKMCjAiYSsdoh287YSk0JTxWCjjl3fmmwx6CTbLpiMsFAyswc43MXlANt+h1zjJA499kLgxTzNcZleskviYQBgC5ym57BxEGUn0bsDlsDoHt91eaLj12DdnQH1z4H07TXm2zR93Yribye8wSf5ez/mWAHIB1QrOKZZJUQZbLSvUlMvU39o5U2VrV0gpCAJduhBSxluZjS1YS/pa748lIUSp3mutUFJP6s72Qwj2RU2cLVKV2kOtTUyT2h70I3dVlMKZaxZiX1/qc91KrfgSON3U6OoqGuqpp76GFK5czFOHID2C5p9OGBOxfyIMAQgpKZMyz2oadX+mPGaF6ZOQvBJBFGQszI2I5JB2bGlGHbTswahPFjtkMMfmIGVrPcHIJnkgWTm/yDjo8I2vXiNpnOCxR97HpfUl2tEJnnzsQ3ztG8/hEDgpUEGAFxonJVY6YiF51zvezplTZ+iN+3zmay+Qyg5f/+7LDHJBEDeROKLAoL2kKHLyxLOwsMxf+Z3f4ZP/7l/z3LPP4ArBmVOrrG/fwJJgvSPUIcMs45lvf5PRzoj1CxeQLqCPI1FjnnrpNX7lQ6ucfLjBSdfk6mtDMilxqSWc0zQ7muapkFEGmxs5O/0BK/MhoVZImdNe1ARRSYOFrZBMKHpqgd2kzRe+8QL3P/Q4Dzz4CN989hkub1wiz+HYnMTE87gooxAGJQWBtQTe8uDxFrsbCYU1ZBKGPmDuvrcRL7+N0/e/k6X546SEuDBm4DKcFKRS4MmRNkGS452m1uMOgsibkbzvDYju1i711iwHJ61ZIe6wctgEcZRjwr2UtwSQAxOpXNTqnfCVd3MtKAq0ljhrsbZMKFECaDkURMX71hJzHcrWWls1Pw1i5f00pOxRU3I9vKQoOWhjpwtUa8D1zuMPmPGnfPfB1g5wjTP161L362BOT1FfF9PjpgG6yixGYnLzDl8QdKiUdaexIkV9sVMQlwIpSn95KRVCSJbmljl9epH1y0PiLKaVDWnZHZqjnLEQDE3BxnYT1Hlk6wSF26XXCvnelcuMjy3ztnd/GMwZBn6NbASP/dSf4eJ1z/MvPY9TEckgozXXJg4CZJjy8P338cEP/Dxf+eI3Ofe28/zg1St0Fjpc2R2S5AqUxpqCTnOeIkmJo4jMpXzjT79AFFt+9aN/hnYr4nOf+Swf+eiv8dWnv8LrV15nmI1IhKZAMt9q8NLrr7G7t0vmAlLZpG8KVoTiB691+dA72kTa0lmyUBQUwlMIQyEdaMtCR9FZaGOTOZqBxRcFvb0EOXYshAKJxY0zGitL9IIOz72wgZo/wSd+83fpNBaZW1jkf/mn/ythFJH6Bo3lc+xuvkjDGpT2FE6gbc5CLFldidndKQOJKR3R7IScevA0neVFTK4R4RyFkHhvseQokaC8I/QZofMUXlPI0vh9EGRuHUOH8y/3IsXX9YSoVi9MpKZqXz2tzGwvBYxbaZfZtg/2+V6l6rs57k6T1iHM0G37enB7Xe7Vk+UtA+Qlb3ynSpVqLyVuBsT9JEOPpFzUIvFO4Fzp2eG9mMRDqUFOyTKwlq0l1VvoECYDq/Z8UZW3CzOD9jDV8tCOz9QRFTjO/i7buvXIkmOf4Okh7UAZJ8NN+ixmJo5bqBUoE1Z4P6lXT0zTJUdTSbzWA6Y+PVN3ytK5XZGNHW9cGLObnKSzdozF4w+y9cpn0d1NMhfRsyeYW3s3uTqODVYZZ1tsDbbwi/M0gw0yGSKkwoqAQgY47fjon/t1Tp9/hOdfeokwjHnXe97D9575U65dvMjm9hbGC9ZOn+e5ly+jG4tc39hFakGRKwItiRttwqiBNY4iz9GxIsmGfPqzf8g3v/FlGkHEcDzk//wX/4z2XBNcGcZ1nBQMrWer3+e/+kv/BX/yma/x3edeBTTOh1y9mTDcNJBb7ltTNFVI51iboR6SmAJfSDCC3DjmWpI879EdZbQa8zTbixxbbBKpPr4YggqwhebFCxu89GrK3/4f/lvCzjIBEd/71neIheLYymk+8eu/wSMrAV/7o6vkJqXdCEBKtC7IXE6eQd96dJGjQ8Fo+wI/fPomyycf5ZF3/BJKa5IcIgXCGawvM29p55FeIogQGJjQi0fbWKY+2eyrc/D3nYB9Uq92f62guhau6phIk1Fb48MdKIq7lYQPvhez9Y4qPw7A/8cubwkgLyVntw/UpJjYxif321lbgvPkuJI3rjlrIQRSibItPzUy7lNjZpMiawdeHexOec6Z7/WznXDaM541UspJUovDpPEJQPsD0vrM71k65SiJXswC9AGRaBrGQEw1mtmjZwdVVad8eaaALaYyT3W9+ykmD5X2Md3uy1VKZGODQZKqkwzEfQzjk+zI67RbGWHjPijm2TBt4vYyiQ3ZTg2KRQJ9jIWwQTC2zIcWbYYULmJoDY6Ec48/zK99/D/n9MkzZNkYT8rlyy8gAsUnP/9J/tpf+eucPfcg584/zPiF5xkN+7SbHRCeIoftpI+QntJbNUSriMeffJjxsMvmxg1QFuMT+v0EIRXCekRukSmMipQiSXjyiYe5ePENdpMRxuQ05+ZoNDQvXR1y4dqYYpRz/ozkkYfOoGxBby+jt9tnad6RZkNC4VENzdrDj/Dq5RF/+sJV7rtP0QrbhCIi3dF86Wub/Nyf/fMcP/0oIyNZv3yVC6++wv2nTvJbf/1vEYgmX3360yyvnGK8uYdwFi8cg8QQhQLfCDBNjfUNlPFEoSNkQHrjeZ6zklOP/hzzxx6gSAuUjJEoPKaKtyJJUYDdB55HAfJBqXF229FS/NH0wWQsew499pbzHtKPo8596Pn2aaTTVc/3wmEf3LYf4Gup/CezwvNuy1sCyOtZueatyzsBByFJqjKgratcBqWUkxna4yld7xRSQBzHIATJeLzPl3s2KbKfOcctt9tP93jPPo5ciP3Jl2EqLR8O5lPQnwTaOkQ/remfWUCvvUXkvmBhM1J9Bbp+5v/JFDhDPx42GMv5Zb8toVZp6+fgZbXiVJSSfBkUrOqjpFychUPg0arBa69tc5Uube4nlgHnH/wgfux49eVnaSbrIDyOBrgmTbnCybbkwmvf55XrL/LQuXdy3/kn2N7bY3d8kytXr/HUN77JR37xYzz5xNvZ624SxFDYMZfXt/nKN77E3/yv/xu2e5t4DK+8/Aqddoud3W2a7RY6DNjZ22Xt5P2srpxj9dhZPvaxX8T7hE9/5t/yzae/yGDQRyHQyoFVBIWglRpEMeaP/90fEYaa5fmQXn+LuVBhspQeTayJyXJBHCoG6yk/Wr9AOwRpNONuwTsfX+DMqkDrjMJIgpU1Glpz+eIWz762xYmVeXZvrFMkjgfe8S4+8qsfp/AtjLR85/s/Ioya/PZ/+Tu89PIrfPqPvkT32ut89D0LvOeRkxSDDZzJaLdglHosIWsPvJ00azFKuhgxwuUJxgY09RxhPE+aSZTsYH0VoEEAqsBLRy5ytHOo2p5ea2zVZM8EnGbelImEPN1ej7epHH1gYph90w4IHLNCyr7jbhFebg0jUG+fjurbl4OTwuTiqr8T4eUQDnuCHdMNR0r23vtb9h/Vux/H0AlvESD3lKs36wFjjEWKkhOvsUgpiXUO68F5gXEebz3Geqwx4BxKK4Kg9KTICoOUZT5KV5NsQD00VBCgtKIwBudEZeysMwRV4Ez5DMo8nPvD15bUjcP7W1Nk1bPxRFqvpfpKmq+RUSBxtswTLYXCu2p1ppCIyjiqhAZvEF4hhUIh0fVEUiXJkDIoNRVR0T5OIPzUOAquWkgkJ5mThHDgBcYamkFAnuXVvREI76oX2SMo60khEP4Av4PDY8iFRskA4S2xAEmLXETkFp594Q28EDRUC58UCOHQSlGIDCsVN7czXn5jwPvf80GOLa1SeMvZE2ssmIh+r8feTp9/9I/+Z5z1/Ox/8n4kTcKoSaQafPVrX2XlZIellRXmF1s8/LbHGI3GXLu5yeljq7RaHTY3hix07udv/M2/g8VT5AWhXuQvfvyv4azkiccf4jvf/ibPfu+HDPsjnPVk0mI0XBn08d4TKsXxtTPkecZwlJAlGWme05prQhCRmZDxyFEMcpqhIuy0ceFSadQcXqGhJWl/wFN/8iqDrqUxdxKpOgxGIyhCPvorf57GQkRqusQ+4sb6JrK1xj/9l/+BH77wLI0A5joxz7zS5/5HH8MHAdpcoy1SpIYin+PcY79I6/gTDEVK4Ub43OGygBt7PYTuoLTCmtJrS3uPdeBcAEAoSluQF4oykzklgTaZ0CvtazL1z4RB9jMQOrX0zbxBNSE3fdcnmnb9RvoZYaKy8YgZW8+0zr4t3AqLfqafVa3bUR6zmvpM/2779xCQ9Qfq3VplIuVRq+c/aUH9LQLknqwoAVGrEvyQsgRZX2bvCZTG1TOhLEGpjK9SGQKVwnuwtsoa5NzEIFgDKjAxfpaTQwmW+wyJMzSH9x7np7k+S6+ZqeQslaKOoeL9rUG1agl8koD5AIVS+4EfxptDLdEI6ow+U82l3AtUwY6Y/Dd9AWrJWuB9rbkInC9tmK46XklVLn3201fDU4bRqo8/2njhQTi8VNjKNC0oY6M7wnJOcVX2Ji+AEO8cOQ4ZBTQXFwhGQ46ffogT9z3E9voGq8ttvIN2s0MjbpI1DFoptnd2+IM/+EPm2w0yl7O80CbXY77yxS8Qxw22NxMeevAd9Hb20Hh+9Ox30Togjuf4+Q9+AGkSjDdEOqDIMqJQUuSWz3/+izzx+Ns5/9DjfP6zX+H11y9CYGgvLdJsd+j3ehTGMRhlRDpgYT5mp9el1WignGPYHzAeFzQELHZafPjDH6SzNE8cWvT4Gln/OgpDmmxz5lSTl25scnx1iVEvIVQdoEkcrWCtJhAGXEGajnn5tQsgUxqxIpSCLIdzDz/OB3/p49y4+AwvfPszSDsE72gv3oeMjtE3AUkgcJUXlg6anJw7QWELwKK1qLy9RBmHnGr875OVa4m3Hg1TjbMEKbmPYjv4HuNLCm6ycKiypZRjdiYuyUScn0rddRu1RD67vZb892sGs32Y1UaPktqrpm5D9xyouE9Cv/ci9v2ZordglgO9F6PmUeUtAeSCMtOJcxYvSldCIUUJMLa8kcZNwdJai60WBBVFgVbTBMv1/nrVJkyNla4C91mvkOnvyog3Ab+qb0Ii5a1ZgqaRDvdHKxR1smfvsM5O4kNMHuYB0EaWx0yOrST2CYBKWX1qKqUc5FJVlIsvwRRfT1B10ozyJXEeEIrcWISUqLCBdzkmz1A6qFRnP/HmKR1fynPPjq9pn2dpMDG5sCkVNC2+ut76hbbeg1YIEWCl5O3veJKz5x/i+KunSdMMqWOEDDHO0dsdcOXSNYaDMWEYszC3wKDbJRmO0NIy7FnazZhiOGbryhbJGF4ePker1aSlBWEr4Pz5B3j3ez5ALEe0dIbOM3wuaQQhcSNEOsvXv/YNvvzlb7OwOM/iwgo6ijl14iRCSi5dvYIxBuUlynqMKuh02uXtxpFlGcJrWlET5QrGueFHr75OagzGJrTY430PNWm2DOOii4oVJ9baSGVYv7lDqFo4q2m3lsE1gTGvX3iB5557hkakML7U1gonWOws8Bd+4+NcurLDSy9u0xseY3XtHNubNzBZAx+2QIcI5SfjwDpTCjS6fLbGTD24QFC/BlNAqyXu2iYzM27rGkKAV1XNWToAEFNJ3eORtfQuYBKQutJM98nXM/TD7QyKs+VujIt3a3i9XXu34//vtq1D65QVf2Ic+lsCyF016ApT+oZLUbq4eVHJo0Jgva3io2QUeU6RFwDkeUYjjtCVuyEwAfJaEq/d+vI8RymF1nrCc5VShscrVbrVTUZ3FX3RuYkhFqBOvDxtt6w+AXcpQMoyyqFSKK0OceOrB3R1KsHULeQWwKc6XlXBuUqxREqBFB7hLRN1108HiEDiZbnNWIcOo1LrcWV8FqnshG6ZumgeUAsngD5VlA+WI4WJesLxU0D3gJflpLJ6fJUobvLqGxe5dPUaC60OnfkFjHWEVUqh1ZUTuGKT7l6P1ZVVHjr3AMeX5ok05OM+x1cX6TRjhr0xg36Os5K5+TZRA8Zpl8WlDnNz8whyrr/xIlleEMdtPBKhNO94+6NE8V/i6W9+h6vXbpBlOceWVxgOehjvmZufI08LOo0mve09AqXI8wxTZIRxiLNlGkKhywk/M47XL1/BSwhDcLFnve9ZWOywO8iJ42NYu8Og16XIM4z1PHD2DCdOLZG7nEZD86ff+jqQ45xFCkcYhpxcO8Vf/sRv8+Wnvs4PfvAMEs+Hf+b9PPbe9/Otp7/M/NJ99HMIm5UrnyiX2yMVUoK1Bu+nGmipnZYa3oTHrewcteQsJoNp1pgHTtQ1/aSNKb8iJu/OlM2sB1U9UOyEL6/PUQr4R3vL3M6tsC63uEhy9xPCYee42+932+7t6v0kDKNvCSD33jNOUrx3ZQRDYwiCALwree5qANaoqbSeqF1CQLPZRIpy5WYtGXrvybKsyp+p0VqXknz1qQewp1wU5CljYcyqQxLwUqI1WOcr90OFEKY691Qin+Xd6mxCZZfdvgEwlWQrSbr+IBHCg/AIWe2T1WwiKuPUDNU2pVhgduCWXa8mDcqFVSdPHEeHEVGjxaXLl3n2u9/CFBl5XvCu97yXk+0ORWGo3qeqb3LmGm9fyol3Wm8fNSQ8XogJBWad5czZUzz48Hme/9FzfPIPP8l4OObMqdO884mfKm0h1rE4v8wTjz/JIw8ZTq2dZDwY0ogCpMsw2YhksMd40GPUH9FqtAlVC4EmLzLGwxShFLtbPfZ2hhS54xX7KsPhEB1ESBUSxBFLq8f44M+8n1/48M/yP/5P/5CLFy/RjmGYGsIwoNFq02y22bh2k7gRoL0jCCViZMoY8FFIllu8zaoJF5SAKJTcd3aFhx9Y46HTy4hsRBB2yLo5aXKJvcEIpCKIQcUphd8ibjX51je/zdNPfx2BQWKxtuCh84/xy7/yq/zBp/4tr7zyGvML8xiv6dkWrnmGt7/vY7Sbi6RFhKlTFnoNPqB8vV2p9VFKzRMbCaKSlGeG0Ew5CJbTMe6QHKQbagm7lk+mwD3VyqgkcVnJ5uW4mEwYniPB8l5BcZ+UdFf1b73mu9l+L20eUnPSx5+Ed8tbAsgF5ZJ7IQSqivOtlKq4u0pV874USqXEFKbkZa3FWlmGqbWWoiiI43giHddUSt221pqiKCZL9b3zIFUJMrqM/FYbJ72AMsS3x1UxzUU1WdQvA8xKAGU56I5YS0G+itZYB+sSolwViXVl3lDvJx8lJIX3aKkwrpi0iyhzdIqKKkJIpK785euXg9KVs8jLoGFFYYlbc1x//RK73R7b27tcu3oF6Utj1u7uHidPnmT6Mk6vpXa1nPXEmU4iU+2i1mr2vXC1RC7lxMDrnSUKNL2dLT7/xy8xShI2r18nSRL2Njd4+P77WWh3SNOU/naPzvwc73n3+7h+5Qq9Xo+hd2jpCLBkSUGvO8CYlChuYXAUWYHSGkmEFwEIC0jCSGGtoSUjsiJHKI11nlF/yLVLlzl/7n5+6y/+Ov/4H/8ekcjo2TGIEGM1aSYIIkE7bmLTFHfB4TcAACAASURBVIkh0IpGMyBJc+JGQKczR3evD0haoeTBsyf4pZ97P8eXF9nd2kS3l8mN4MIbP2A8yoiCmLAZE4Sabn+Tz37+k1gHn/vMl8iKAnBI5Wg2GwwHPf7JP/k/2O3v0J6bJyclahzj+AMPst7PsLnE6VIAUErgjEVKBQQ4X2aMKpdTOLQO0DqYrJAuH1Nl4PfTZ15NwZPtE2h0vhIOSrqESRtlnXLFsZzQok4oaonce1dOHJ7KaE55jvrgA6B2O3/tg/VuV263/6BXymFeKndbjpqEjmxLMNWIDrTxZoD9LQHkUC7yqVdq4ks6pf7hnas5EKACkwoMrS3KmV6Xl1JzvRMAnbkxQRAQhiHOuQkYl1JotXwfppI+tVAqJpRLnufEcQTUKzAVzhn8TAxxQWlAlBVPUfa97I9WU1pGVaZBqv4555FCVp43AbqmZoQgL3KKPMNbx3AwZHPr/yXvzWMty67zvt8eznTHNw9Vr6aeBw7dbJItipIliqJI2aIkGkGsBJGSSJEUyEYAwwkCxwkQQAgSBwnyhxU7oYY4CmIxtqzZlilRFpuSLFGimlOLQ3UNXeN7r9787nSmvXf+2Pvcd6u6ml1sUUI72cCt9+7wTp17zt5rr/Wtb31rkxvXb3Dl6jUmkzFlMWE4mlAWBUVeUJYFNjTcqI1lNM5RcYJQEYTNksBQyPMi3IFpPV14dn8o5d7xtT4TRRFFUaB06EQUsPzh8ZDVpQUq61hZmGP/wPHQhYfRSlCUBZPJmPW1daI45nB/H6U0WinyUc64HFMVE1ydI1WMcJaD42OclbRaXeoanIy8AcMCHjIzosJKiRUOoUI3KKEwlWHr1i1Wl/v8lXc/jcxifvczn0VlLYRWTPIRCN+8I826jAeH9HuaM+fO8JWXr+JsjbUT5ucz9vaOWOjP8Z3vfZ5yfxeylBtXN1lc2+DyjetcvnqVp59+DJ3NcWfvkK++/FXanZSrV29z8eIViolFRwl1PSKOJWWZc2d7Gyss/X6L0hXoKGN14xRL66v82cUrfOyf/J+044TlxWU+/OEPc/bMGerakcQRSIWQkKTJNFlvrWMKb1hPDmjWQLMhN+so2OoA2wXPuSEXhLtvQyJGiIYiK8B6iMdOJ4fwi2mqY9TMsJk5dI8hbAy2MWaa35pdy1P6Mfczlt8Y3PlredazTss3Ch6Z/X++3mO9aQy5mIbnznuv1iFCjk8iQJ5omTSdgDybxCFDlSZ4HLBp6TY7AZqfTem+1posyzDuJOHpjPXzMPR4bqAG7yj7jWA8Ht212woBUqop9l5VFc745rxRHFHKkkh5w+xCdain30nKssLWHvcfj0Yc7B9wdHTEwcEBW5ubbN+5w/b2FkdHB+zc2WEwGHjdkCKnNnWAJu3Uw1cyCpuJL6jyiI0ga7dC6sk3lJChXBtgOByGhenCRjYtL6LBx+9l1TzIaDZLrTXGWmyAzaytkULy0EMPc+nKFZ566mmWV1bpdns8//zzbG/d4eb1G5i6xNSGIi/o9bqMRz3qqgJX+9MwmmIywqKnVFArJCgZ6JhhI27mlDGeshr01S2C2jmsFTgjEU6gVcJ4PGJ+bp5RbTEIWu0Ow4NjyqpmeW4RYSuUtEjgsUcf4/rVK7iqwEXQ67WRwrGztUm5v8Xx3j7oHn/yuZe4vn2TR5+4wNzSAqc2nuCTn/oTitxR1wWf+cyXOXv6HIOjTayboGJQEZw9c56qtAzGA3IzRgpNVUsWF9dBCO7sHnFwmBMvZty8cZuf+ehPo6VGqpis1aPT7ZNlEd1eh06nS6fdJssy0tQ/kjghiiKSNAl5k0bCgmnZfBNxeh0egVSBGGBtSLqH5L6xvsCYBveWUwNNgGR8L15/cMdre9uzua5mDQNTw+0dH/sqrf+ZI/GNhC3+bRhvCkMuGmMdDCYzjSVowqfgIja0Py+9KXDO4ILRaJJ2zQ1ubvgs1HK3YWLaCq6qDDpo2EoJlTE+fy8FbsaT8PoiHiNX+iRB2nj7zlqc8YajmEzY2dpmPB4zGAzY2dllb2+PnZ1d7tzZZm9vl9FwyNHREaPRyHvXZUUVdL4FHjaR8qRQqIFYhNBI5XFPf44ETWl3wkaQwYkyxl875fMPRMm0on80Hgcohxn3+v4TX8z8+3pjKkcsTyAWYyyRjhBCc/v2NkVZs7Syxktf/irvevfzpN0+45tbVHjYK400xWRClsacOnWKyWhE7gyVs9TG4GSMxdLvdFGx73sptcLZEFtJz5e3rkYIixYCoSVKe0MuZQwypTJe9/zd73kX/+B/+1lcu8VwlNNbWObSlRtkWrOwtsb83BILvS75+IjRZEKn3ePc6XPs72yRG0Or3aXVTZjkI97+tqcpjOK3/s1LfPnqLZ597q08+47H2Ds44lMv/AFf+fINlEohMKKuXtlkdX2VnYNXcBikkrzjHc/xLe95H1euvcI/+5X/h6fe/hyd/gZLK+exlWBwVCBci7rUKCymrKhtjo4s4zxn52AHZxtdeUEURTOQn56yueI4Jkm8UU/TlFYro9Pp0mq1yLKMVqs1/T2OE5TWKBUjpS/sd84hdLN+T3p8ucbZEJ762kAsX6vL0L1GvMl7eTtx0q9gVjfptcY30lN+s483hyHHtwH2T0LGCEIWXUzDPutmpG2nSUamkAYw3amZea61JoqiVzWtyCc5Ikxof8MVZVkzHo955do1dvd32b6zy+3bt3niySd4x3PPMRwOGA6OOTw85s6dLQ4P9zk4OPCP/X2ODg45OjxiZ3eXIi8YDAYYY6hrgzUnkI+nD9rQFCI01TAGJQQy1oE3b3FBR0VK7003k1ipKGxYktp4b2eK+1tfzIMzJ011g6cVRRrvx3v4ajyZ+FD1LjClyU08oAfuIMCeMykuEdg2Eqk8tt/qxAyOjnAI9g4HtLpzfPy3P8Hy6grDyYRP/cHv84d/+GmwgueefAynvPEZDIasrKywvLrKrVs3SZRkfmGRg/09qtJijGC+PcdBuU+kImp8D1clJUJabFUgMUTC4FyNFDFKapSOkVGEiBMGRcXb3vI4G+c2uHx7l43T59jaOcBUhtIInnrirWSRJB8ckEYRvTlLUUhaCxGPP3SB/eGYuNsnoSRyQ27v7TOYWPZGNctr53nq6WepqpzF+TXK4ishUhAYW6Okpddr8a1/5Vv4579xDakk1hl+6Zd+jT/8vc9SO8fW3i5J+yYf/r4PMJqA0Iq6EAiX4IyiriGSAi0kpq6wCqwQZEk3eMJM2VpShPoHBM4KyqJmnBc4/BysaxMcKRc6dvny/TTLaLW7pFmHTqdDv9+n3W6TJClJkpCmCSq0TtRKksQexpMiVAELh1ZyBiNvqpyDzulMYZBDcDwYeCjTeehHCJ9Lc85NG8W8lpH+/4Pxnh1vCkPucFjRYN9i6i0q6TPctsGam8/PQCUiJAAb+mEjbCWEoCzLu3b45u8aY661r2yz1rG1fYf/42d/jv29Pba27vDSn32Rg8N9alPirOXU6Q3OnjnL4cEhh0cH5HlOWRU46/nESuoQxXvvXQrtKy8dRDpCK8+0qW0VJqXDGA/NVFVBkiQBppGUdY2WmrIsQyu3EoEKXrX3bExdTRemDy4sQjicUMQqQlB6jx2HVCl54bHh2pb+pjsLCvKyoLQ1Lpyrcw4RenbefZPENLHlRwBiXMC/vO8flqbASUltBVHS4tbmNmvr61RlzpWrlzj/yKNk3UX2Dguef88HmOvA5z/7u1y/cZGykpRFTE8Z3vWOd2DCBjsYTVhePc3h8Ygyn9CdmyNOWlRFzWQy9gVhPrTDVBUOS23wuYgQqSitiZ3HxqWMiGMN0mBEySAf8wv//Jc5HI3JYs3mjZvcuL1DFGXURc3S3AKxsmwNDn2UYUErhVItaqPod+f9fYtjknSZwWTIMM+ZDAv+3b/xQaSMKE3Mb/zmJ3n5+i0mrkJqjXAaKSIunDvD2555gt/8ZEo5HlNVgkgnbN/ZRGqDjko2b1/m6uWLnDn3LOAoi4KqHPgchAQlokAEEESxwmCxpkZJD5v46l+JUD4J6RU9/RwxyjtKSZQQB+1762pwVZhzAiUkeVHiVEE9dIgopnSgC0OSGLJaEEW+CjqOBEkNSlhUqHmorEGpyEcDwsszK+lCdK1RKkUJTRRLdrau8S//xa8SSUknayOlRjjL0tICS0tLbGxshDXztZ2Nxtk7cU5mpTmavxV3PfeHvPez3+hNQUyPePfPBtr8OhwpHsCQCyF+Dvge4I5z7i3htf8W+FFgJ3zsv3LO/cvw3t8FfgQwwH/mnPv46/0fDijrKoRR3pgoIT3bRArvXYpAtW6YIAEjbzyHe+lSjaGfNd7NZ6ZYu7PYyhvWo6Njfv7/+r9pZy2EgNFoiBQQKR+O793Z4mh/765qUIWgttDO2tR1U6pfBxjHf68mPJRKUZuKKPbGtw7sAmMtaZpSVzVSeckAISRlbTBOeJhFCL+o8FS/SCs6rRbOWbKW94ayLOX2zZuUBRSTnLe85RHOP7yGkIoonuPmrT22d3cYjA84PjzCGS8HUJmavKqJAgTiXGDTILhfAd+JlzPrf/sCpMBShuDZCZ0xKCrS3jxzy2vcvPoyr1y7xue/8hW+/YMfwdk273n2Xbz04id417NPcLjzJWySkSwsowQUZeHpglIznpS0OpYLDz3Gn730RUaTkljHdPtzbA2PsUKgo4iqrgNvusmH+JyHhxhASIV1EucEde3vR1lN+OQLv8N4fExZFIxGE7a2DlFoKGtkbSlHQwqTowVBX9PXDIhwLGpDIjUb66cZj0YMR4bu/Bzf8W3LSFdga6jqFlde2aaWCpnWPmpwKfmk4LMv/il5PaadthkfHQCKNEnR2mIZYKkZDHf51V/+Bb73+zpceOxxxuNjnBsxGpbEUUzSmkOqBIsjryYkSUSiY+rKeMaJ9NRYh6OsSpIkxcs0WGQkfH1GVZPJiKoq/WeVRCiJltp/a+E3bIfPYek4QUcJQke+IEknIBVOaowSKC093BWgH6sSRJwgtWcx1bZASIuOMoyLSbIOQhpu3znkeJTTijX5uKIqDHe2b5LnI6SU/PAP/zAXLlx4FVPkxHDP2rDZ+TsD28+8f7eTcvL+yWcfzJDfj+v+NSOD2f/cTflBhJDlgceDeOT/GPgp4Ofvef1/cc79T/d8iaeAHwCeBk4BnxBCPOaazNprDcddN0RKibqnC483wCdG+SThaH2bM05gldnuQg3dj/D+FGfmhD8upWJldYmVlRWGx4MppjttFOFC1GAts3bMWYvW8fSGNUyWE+yuoVJ6LRQZ5Ad8okhgnfeSjLPB0Bt0FKGjhMcef4Jub47Tp9ZxpubTn/5Dbt++Dc6RJAk/9EM/xN7+ztQr7/e7/PZv/xZXLt4ABFpHzPX7lMZSVYL5+SU6/QVubV9nPBx73rjzVMUiL4jbLZwzgbXzdcwgAnTTZIaDTy5DRWqsIp597u30+n1eufQVCqvJOj1G42PSlsKSY5yjqiPe+83fRW00WbZMGschGnEIZ1Eq4ujwkHPnztHv97Gm8nzz+QXubN7GGEOWZYxHQ+I4osgnXrs+hOHCOZJIe48UGaAmQZ6XfP4Ln2Vv74iiGPNNzz9Pv7/IP/3FX+V4MME5ycpynyIfUBcjWklMWQevXimfVPXZFJSUbG1uU1UlnU6XST5BK8mlazeYW1zn1MZ5uv0ek8MRzhoiHYMRxGmCcIYvfuElevN9FvpLPtvoNMZYpOxiixrpHHU1ZH/3Fc6cP01djogjP4fm+33+y//87zIY54zznMPhIb/zrz/BtUuvEOkIYyzrp9aJk5i8rACHMTVFoGzmw5wszXBVTW2dpwErgUBT1cZrujhLO4uQElpZQpJEaOU1kZQSpJFGKTCmRCfaU3ulxEqFUjGxSnzSXUiE1EilcVahI0mkErAaHaUU+ZC93UPqCnJroLakKsFZiOOYKIpotVpTevFfJoTy4DzxB/v7b9R4XUPunPuUEOL8Ax7v+4CPOecK4KoQ4hLwbuAPX+8Poyjypfmi8QgJ3kPgdRs7Za5Yc+Jhg5hCEnfxtmc87yapM8s2aYyvUhKtI+oqYmlpkePDo3AeMvC+hc/IE7rVzJT3S6UoAyddiAaHNmHxBQaL0KRpwngyRktFVZUIITh7/mG+64Pfzfr6Gvl4wMd+4WNsbW2S5wWLy3P8w3/0D+n15yiLgnw44h/8FPz6r/861hoq4ymPWvtFhXOkUUyv10eq22glGQwGWCuwtUXJyOPU0tFq94njlCrP/bWwjslkQq/TxgbHQHydhpwgpuUIuujOwxlCKDbW13n54lfY2t5i89YtHnvy7Xzr+7+ZP37xj9g7uIaI3s766TXGR3eQao5WHIeND6raECcRIUGC0LC/v8f6+jqXL12k2+mgsURxwtHRMXO9DkI4hLC0Wr7B9s7ODsYaz592vlmJbzwSAYq5hQW+8wN/lcHxATjDqVOn6XTmaXVXuXnzJs4UtDRoW5Cm2ndrkhG1EwglqWyFwOPApjYQCVZW1zg6GlDWDnREa3GF7eMhrcmYjXNn2TnaohWn6CihzAlqniVzvQ62NJx77AI/+B/9+2xu7fOLv/jrDIdDRocDbJWzMKcZD26TyIJidEAaKeoyp9tuM7/QJ2m1QSkuJA/xhS9+nsHBAClgf3+fD37oA7zzne9kPJ4wmuTUxvJLv/yrXLp0hdpUbO3v803PvZvluQXG+YSj4ZDdo0NGh8dYI4i1IM9HtFSGKXJMVUFiSeMIqSJi7auNW3GMTBQuaBEhFSqKUfgqYyl9tC0D51wJgZSaJMpQQjAxluPjEVJEniorLVVVT71trTWtVut1YZX7jXsj9wc3qif6LV+Livgg//dfxPjzYOR/SwjxQ8BngL/jnDsATgN/NPOZm+G1rz2EN9pNssMzSpzHmxtv17mpAX4VLW7Gm4+iyBcNBepb4703FZ2NUZdSEmmNxbNU4jhhY2ODq5evUtcmtIWTNPRCh5+UJyGX535HUUS320Vrj2kfHx1RG4MC3v7Ms/wnP/pjrK2t86kXPsnP/MxHMdZSVyVzcwv86I//OFrFFJNj/uQzL3JwfExe1QyGI25vbpK120wmE4SAXn+eJE05OjxAKcHB4QFpGmMDH742FfPzC1TGoIWv4MO5IIrl08leH0YTRfE0XLTOG/KZW4GHRxpe/Bs06g7iSPPFl75AnKWcO7vOqfUVrly5TbfXZXfvJlkr4ubtL3Hxy5c4t7pBrNr+ENYFGqEOEBREUYKwjtFgwPzcPJFOiKMEgWVuYZGd7U3m+l20lAjpmTvOWeq6RMcRDRxAyKlIpUFqcJqiMLQ7814agJjDw5xTp85RFiWmHBJRUA1zIqewtQOhTpLQ4sQoKCW9JIDURFmHuN0nryp2jg+onGRhZY1Ot+c3JQsrSys8+8zz/Oqv/QoqFhhb0IpTrl9/mX/9yX/F937kB/j2938HWdJn89YtXvjdf4G0I7ZuX+L3P/Wb1OWAupgQByqtE/4crBBMxjlVaXEWnHBorWi3UqoyRwhHr9dlUpQkScLi8hLOVGxVFd/5vm/j7JmzFFWFihP+6E8/y+e/8BK2NtzZ3KQsKz784b9KVRlGRcXewTHXrl4BpIcitWJ9bZUozdCtLknWIo4TjPE1nbEQKOmbXDgEOk597YSOPHUSS10V1GXpqat1icK3diyrgjhWdDodWq3WXdTEBxlvlMXS7Bd3wzGvNt5fK/E6+9mvfxN5/fFGDfk/An4S7yv9JPA/Az/M/WPy+56tEOLHgB8D2DhzlkaoB4J8aqAGejttpkqHzZdv+m8aU1OWFW4mzGqqL++lJ81qkdd1jQsJMGstcRLxyMOP8MLvvkDT39NDIw2s4jh9+hTPPfccH//4x6c4bJpm/L2/91/z9NNP8Su/8it89H//KEp6hkycZrz3W78Vax2D4RErv7HO5q1b4Cx3dneZlJZ+N6Ld7bC0vEKaZgyHQwbDAV+9eJEnnnxyCgN1Op1QQbiPc3BwsM/GqfVpgqSqarJ2G6U0wglGoxFYQ11bZJAeEEIRJxlxlAB+I9JSMplMptzxZrNUIggjzXgv972PM79NdTeC15u1WjzzzNuZX5rj4ssv8eKLL3L2/NOoyJEmljQuKfI7KFVgsBjr8VelG7aOj7iaPAgOlPJSC3PzCwxHvmy/259j8/Ytzw4yFZ0sYTg8YjAc4rDoSFOWFSjpjZ1UCKmnDy20T5I7izEKKVOkKdEqwgqoixzpjNcBQmIAFdhCUkyVp4iCHPC4qEmyDgdHAx558kmeXJ3j8rUbdOfnORock2UZUliee+adfOi7v4fPff5z3Ny8RBqDcjlaOy5e+iLXbr6Ddz7/Tv7o37zEhz70Eea6HX7rNz+GNROuvfJljo+OkcJHpkkcI0MuxTq/DkajMUorrKkDRVZ79UMEdV1ijCHPJ+ActqqJlSJLY3AlzhqMjTDGkWVthLMcJQlL823e9pYnmeQ1VkTkleHwcMBoNOaffeyfEGnBB973LRyNhlROsX90yO6koCoLvvT5z/HoQ+c5e+YsKI2VMVZosk6HTrtFlsY4U5IXx5Tl2MspY5FKMK5GRJGmqkq63a6X8HitOSkEs/Dr1BDNROSzP7+ecb+K0nuf/3loj2/03OTrf+S+/9m2c844Txr9aTx8At4DPzPz0Q3g9msc46POuXc65965vLyMkk37tRBqcXLBPGVKoLWaUuxOqrt8kivLMmQwSuPxGOAuOOVeY9TAMEJCXZcoJThz9vRd1WJSqmnC2jk4d/48f//v//c8+tijwVPU5EVJVdf0+n0ef/xx2q0OaZqhVMT1GzfZ2z8EIZhfXKDVaqG1N5AHh4fs7e2BgCTOWFtdnVKrAK5euRLayoFQkv7cHJ1Oe2pcNzdv45wF4fyiqyuSNJ0WWhhTU5sqQE0OYw1OgFSaKEk9Tix8MnAyyad5iub4s1vy608qz5gRoRgE4SUOOr02Tz31OLduXeEPfu932N+7zalT8xTjI0aHe1TjQ7Zuvcyjj55BRz5h6kNuFxpWzHgvQYbAWovSmoWlJYqywlpIszZSacajEaau2d3ZYX9vnzzPQ1TmEFL5+ymUZ20ID6mFovMACymcUDgh2T88pjaOSOuT+oDAEJJCoqPIw9hhgxTBaSiLilarg4oirFBcePgxlpZP8cijT+GE5uDomMWlZb7nez7Mhz70PQg03/n+76KVZjhRUpkB7W6KkI7Pfv5PsaLi0ccf48UXX2Jt/TzLa6c4GhxRmzFxJNHa4/RJ2jqZ23iN/rqqfWGdkCilfbOVpquWg3ySM85LD4MhSZOULIuwrgrFeJLJuMDUnr1RFGMW5vtUZUFVl9R1hQ0aREkSk+djVlcW6bZjlvptVhb7PPf2t/DBD7yfp594jMtf/iKtyPHYuTXOri5yZm2Z82dPMz/X4ac/+r/yP/zkf8Mrl7/ErRuXGB7vY6qcuq5wGCqTAx5SXVxcnEKoDzoeBPZ4I+O1jjvLff/LGG/IkAsh1meefgR4Kfz+a8APCCESIcQF4FHgj1//iIFi2FRjMbUHENQGcW5afCPVSSLUGDu1OaPRiJ2dHXZ2dqbGfFaydrYyTCk1LZDQWmONY3V19aTK0zni+GSxShlxe/MOw3HBqdNniJLEi0E5x83bt7A4Tp05Q6vVgkBxOzwacGdnBycES8srtNptkjRBAGVRcP3GK0SR/z6rqyskcQzGEinF5UuXMKFC1QHdfo92uzPdwO7c2Q6Xzn9/6wxZq+OTeQFaqmvjS8idIUo0aZYgI3li3IQv0hmPJ4Gxctcdmd6P+96xxnMIN8wzh5ob1/D/4c72JrduvMKtW1dQsqQqj7hy8SvICnZvbrG3tcXm7WtYVwIhUgpJZdcohU03U2+EyrKk1WqRphm1deg4Jk4SBsMhWmsm4/FUQVPJiLqynh7aSECEiCvNWkRxghVNHUEolgkyygQjX1cGKTzU5hp+vBRIHeAZIQP9FXTkBdqOjgacO3+eOEkRaFwtkCj++l//d/hPf+In+K7v/msIGWOtYnF+Ba1iVKSQseZ97/8ulldOs729y1e/8mWWluYp64rD4yHj0iKThKIYoSOJVBrrIGu1QQhfayGgrmqqqgahsAjiOCGNU2b3aK8kWoXEu++qlaUpxtYICdY5hoMhzliUg3w8ZHl5ASGclzgI16eqDWVZMDg6Ym1tBVyNxKIRdNpdqrJmZ2cXa2rWFudQNke5gvm5LqdOrXHm1Dr58SHtRHDh7DpaWExV+DqAEN2VVYUJea+VlZW75uBf1Dgxwn85xnj2/5yVKXiQ8bqGXAjxC/hk5eNCiJtCiB8B/kchxBeFEF8A3gf8bQDn3J8B/xT4EvCvgL/5uowVwjp1Xm+cYLiahhJN8rORoa0qLyI1C6HUdT2tAGu32ywvL9PpdKYMllkN8lm83Bfq1NPXNjZO0263Q0MKFfRXREiMwXA4YnNzi7e//RmqqpHjdFy6dBnnYG1tjaXlFXQU4xAUZcW1GzdROqLTn2NhaQmlteftmopLF7/qKdhYzpzZoJ2lCAFaKG7fvEU+miCcZ9t0u13anbZfOlIwHA5xQcIWDNZYkiRF6RgQ4buVKOUQWJw1VHVBXZd+A0OEij/JZJLf3wt/jXVyV/jXPAgcv7AB1KZEKbh+4zKbm9coiwFVeczweAdRWdqyQ3lUs7e5y+7WFjiDswaJQglFI/fo/OTwEZKPI8gnEwSCOEkpgwhau92hLEukUCSxp9U561kXoBBCTyMQ50BISavTIcs8W6e2FqGCAiWGrJUSRTpsJl5DXQjtN28psEqgkwRP4TSehSF8xWRDLz2zcZaqMrjSsTK/TBolPPn008wvLVMaR20ldS14+KFHefZtz2KNIOsscOHCM6wsP0KWzLO1tYUQho1zp7h45TLPvut51jY2kLFs+LgYJEnWGMwmcAAAIABJREFUwU676LiA7xuk8KJwWke+KjMseSUl+aTAOV91WdeOVqvjN7sggV/XVVAl9RXUdZmztLjgdc2dACEx1tMRxpOCSZGzuDiPs15GQekYoWJ01mZzZwcrBHNzPeqqxDmLjHzUNTw6Znh4wNrKMtQlw6MDlJBIERHplChKqWpDUwS4tLR0FzPtL2K8nkd97/t/WZ73a43XNeTOuX/PObfunIuccxvOuZ91zv2gc+6tzrm3Oee+1zm3OfP5/84597Bz7nHn3G8+yEl471vc9XzqHrq7u9jP9sp0zmumNM0clFKkaUqapqGaU4dqMxU8bzWVtPXH9I/Gy11eXmF+ft4b/2koTjh2xPHxkFu3bvHII49OaX9KKm7dukVRlGRZyurq2lTAH+CV69dQkSaJE86eO0ujaCKl4JVXXqGqSnCwvrZGmiZoJXFYDg8O2NvZRUlvfHq9nk+qRhqcl+idTPJAO/UbkdIRcRJ7025rinziN0gMtSkxpiJtpWStFkpH4TwUeZ77hTFjNH0B1ut7PHdtwpxw96WU3Lx5g1u3fef3OBKkicKWOcpKHjv/JK5QjA7H3Lh2g8lo7DFcY32J/ZQPfC/O6c/TGEuSJFRVhbF2ymKoyoosa6FURCtre4MTVAClDK0OXIjKtBcO83MpGAol8Lc9UFWFxhgfldnAl7cCLw2rtLd4QqB0hBAy6PGMsdayubVFFMUoIbFlDThM7aUfjBMIGRPHGUrGPPfsO8HFPPzoM5w6/QQPX3iG59/5bayt+EKqhYUeKo74xAsvsHnnDrUxlHVN7QChSNLMN18J0VFVllOhLJyYFqX5++P16ofDEQEQo6ot/d58YB/5kec51niNnKooUMIx1+/4imOYRn5KRRweHuEgrB/jtVpURI1CqJjrNzZpdbq0u/57CO2ZLFGSsLvtq6AXFxaItWI0GCGcBKtQOkEIjbX+nOM4ptfrTefZX4QBfVDjfD9j/uc9n3uZdQ863hC08g0fgqm31XgUM4F7WCjeCHtlQf9JJX1IGylvnOMoJo6iYNx9tt5YA8Lh4W5fFKG08nob1k4XtVSSTqfF+fPnfAMKPaPl4CzO1Zi65uqV62xsnKHT6frzcI6drW2Oj47QOuLhRx6irn0xhQCuXr5MMcnBOc6fOTf12oSDG9euMR6PMc4yt7BAt9fzRRrAeDRme3t72nA6SRJanQ4qirFOUFaGSVEgQ2gPoKUgSRQW3xlmUtQ4J4mkQjpDXZY4E7jeUmLCZJlMckwd+PdCTBfz7DRyM9AX03vjHx5NaTxz/7uSgq3bNxgPjnjo7BmsMUg0raiHq6Hb7fPu578Frboc7g3ZvHmbSApfjdokEIUJTZ/9wzjv/RkHg+Ex62ur4CxaCtI0Io4VeVkQxRlKtoh0G2vFVAvHG3PPWDHOG+LKmqkxdtipKFRZ5r6sX3kEXQR83YVZao0JFYwJWsakic/RWAGlswyLnMLU1NbTUZXy6ppYhxIxtvZFU3kxwjjL3sGYhx55K9///X+D8ajgwvlHePyRJ5mM/Eaflzmnz5xB6zab2znDScykcNi6RuFoZ/67gk/olmXp9WhCMV0rS71wm/OYPlIzHE2QwldfWlMwN98Ly1FjkeRFgcOgNJRlRaQT2q0+SsQexhGOwvi1crB/QKJi5vp9v561QicZ1kFdGe7c3mJ+bo5WllLVFQbIWl26nXk2b21ja8fKyioABweHPtktNEqGPgLOwz2tVot2uz1lslk324GImXk7KwM38whQaTNzZ/+m0We302O5KXQ4NVTTRzPdX/3abLL13lqYmWXyDR1vCkMuEGjhE50NVzOYXc8dd9bLYhrPUcYGjsTMXXDW+uKa2mCNoSgLqqqkKHPyMqcyte8yZL3nZR2YoFsCnp7lHFy4cCHAN44o0l68SoBf3zUXL10iTiN6/QW8VoVlPByyt7eHkIqz588jpEMrAdawd+cOdZETScmF8+dppS2U0iih2N/ZZXB8TO0cSZYwN79AlrXA+oVz49atUP4OURyTtdpEcQrKKwoOh2M8hiyC5+TIUo3FL5TxxKCIcXWNwvq2cLX0CT2fXUYoL8/r9WlCph1OOFdhOOw9M3DWkDtUA9XgE4nOGpbm5ihGI2xtGA1znImIXIaSmlExZml9nQ9+9/fz1qfeyeBggKkKEIbaWbzUaiM7EG67dwN95WKek2Up7VYLZw1KWpI09l6qEURJByFTX9otLZGW2KBJg5QBDlFYoTBNYpvZ7+5Q0vmmEc5TOZ0/ASKpEM7rxWdpRqQ8K8YKQeUcpbU4rVk7fRrv20NtvUSEQOJqiXQK5wwqElS25uHHnuYH/4MfpZ31ENJizITxeMTS4ioORVmVWAff9r6/xrve/UF0vIZ1sS/GEZZYRWB9gY1DMiknvgRfgaGm3Un9+gqQE0IxGA5AWJS0WJvT67V94lZoQDLJiyleXpmaOG557RY8pbWyNbWrMbZmcHxEv9cnS1o+IgxGTAnBZDhgf2eb9eUltBKBZZOgdQJorl5+BeFgcXGJSZ6TFwVCKi9fHEVUdYnvi+vodrskiZflFYGF5I2wT7DbME+c8CqXrslrhJ/Nc6+CeZLvcE1SToqpUbecGHeHhyFPjPbJ741B9xCU7xfc5PGqqprKg3gUQPtN/QEhmwcdbwpD3tx4O5vgChdOCIWUOnhS0hfACIdxltrUGFNRm8p7H1N6YoOnSlToPC+dAAPOOGzlH84Qds0TfviTgfLX7KY+iWWnzRsuX75MFCk2NjamCdjxZMzt25tIqTh/4VwQpvLZ/N3dXQaDY6x1nFo/RbvdDjxux/HxMQcH+1OPcXFxiSzNpkqO127cCNxvRxLHtFstWq0siGn5vz8pRHI4i38/RBqTycTLADhLq92i3ekQxwlxkkyxXICqqijLavraazF9mrs1yyYieC0eWBFNwzBwoKTG+n7CvPc9344tmyZPgjhOKcsKKRRve+szPP/ubwpFXr5NWYPR303rOhFOa67R8tKyZxCp2Cd7gdF4hMOR5xOUEiFCC/mWoKo55QZbG+JAppIJEHI0CLABNmoWWDAKOEFRejlhIQVFkRNFMUVV0en1WFhYZG5uPnhod9NgmznnpX4rrLW0Wm063T5FUYbY1BuijdNnkI1nWhtwkueee54nn3yKNE190tE5Wq3WXXDDaDQOuR9f7dzKWj6BO0ONOzo6otmIrbX0el1fd2B8cng4HE7nQZHntDsd0jT1US5By9w6qrJiZ2eHhcUFojgUcNGQCAxHB/sc7u2wsXHK97AVMnRq8q3zrl69QpymrK2tMhiOKGtfBS2kP8ZgMPDUSmtZWlr6S6/mvHe8Jj4eNhIZmGngCx2b8/Xr0mBt/XXx3x9kvCkMOeFLnnSrt3cZi5OkGnivMRR8TPUw/WJzwmKdCXrlFqwLyTONRKOERjgVtJJloBeKmf9PcPbs2UBtChtKc7OkX4CeLaJ47LFHQmNlf243rl/HOcfK8grdbm/61Y6Pj9nbO8Ah6Pa6tNudKQe2qio2b2/ja6Ekp06t0263kcJ7tVcuX/Fl/wi0FGRpQitLcYG/fHw8CJtXMBbG0Wn3qCsvhlTkBSZ8N6mVN2ba3/Io0uBCVaG1lGVxlyxBA6Xcex/uXT93G/HgxSBwToHTnD/zML3WIhfOPMp73/NtU5zE09ZilIopS3++UmqM8XmHJpE8C/D44i//uzGG8XhMf24O6yRaJ2gd4xUsS+rae/e+q5QAwVQzXjS3NSRYm/vbGB4hGunfWXG2k5BdBCjGWkteTFDaw18ySD705xc4c+YcSsaYqgmnZzc/pkn4ZkgpKYsSrWOMCVKwzhvzqjYURUVZ1kipwqbrxeGiWIN0RFHoqCVAa8VwMKRRDKzrmla7Fb5nE+V69U8lfMctU9f0+x57tmGj8Z6xN/xFkTM310PqwBgTAmf8mjR1zeHhPvMLfcD5Fm6qyV05DnZ3qMqC5aVFLw8tfM9Uax3j4YidnS36/S5pmnF4eOSJC8rrswgpqKoy5KQ89bDxbk/aPb4xTPuBPydOyuLuR2Oe/o6Y3hfnmEKXs3k9pvmnb+x4cxjye0bjETe7mDEGYy3GmZBUC7i2FDTdpKaa3SHjLmVTun/vxffc64b33DRT9v8vzM35qjEBoXw/dB4KovgHBwfs7O7z8MPnA9QgkEpz+fIViqKiP9dnfX116smPRyOuXbuGANIkZnl5Ga29BG1dVVy6dAnwN3t5ZcX3I5U+Grm9tUVReFqeFIJup02n3UHicfbB4BjjTjY9U1t63T5NEtNLkwLCF04ZZ30PTecbMDuCuBg+sQWNtyimHvFrjalH1CRnhJdN9Ubcc7JN7bBGeIU/m6BFShOSOucVInEKLz+mPJYsfWOQJvl6txFkutEopZhMJoFCmCJURBy3ggHxcgSdTopPZjY69sKH9i6go84zbbxt82Gvz6U6bCj48pcvSL6G0NzvBI7aVkglSbKEtNUiSVu02h3OnXuIc+cvBP66vOuazT5mxdw8xCdC0ZvHsaWQ1JXXcC/ykqqsOTg4Is8LxqPca8jgc0VJEvuNLxiO4WiIv4B+U2q329NtUQhJnheUVeXxfufPodPphvvuN6miKKfYQlmWLC4u+CMIvxnXlcXWPvIZj4YsLsyHa+PVJX1SFe5s3qadxczN9b1jIaWvZRCSg4N99vfvcGp9lSjSjMc5QupAo3SUlf+eBI35xcXF6fWazdncZXy/hqF+Q5+jgW7ETM4mPOfu540DKJViNB6xtbXFzs4O+WQS8mPqxKh/A8ebQsaW6UT2QwoBM5opJ2FUsMzOBd1krypo8ZzXZjEI4aa0vUbNr2nHJoTF2vIkkUGjw+IXcKvV4tSpU9y4ee0u6qIPJ30Z/le+8lUuXHgoYF0+vLx58yZ5PqHXbbO6usrLL19CSkFV11y+fBlrLVEUc+bMWb7whS/48mJjufrKVb9LO8HCwgLtTocoTqiM5c7uHkfHx/S7GUopOu0uWZZ5j8nCYDDylZtCTBOgrXZ3ynnO8zx4WJaqKqmFpDQieKQn18Ra4xeLODHks2Nq3P2TV4W1Di91KV1QGAwvSqmQeKXKSGnf4SdwkJWMpu32/H3zDKKqqtE6Qtyn+4CU/ns25zeZTNCRh1RsldNq9cnSNpO6pqgm6MjPBa0iTN0YZXwpvlIkccRoOEJJBc6hlGfNCKCuqqmXI2UAXkWoPg4dlhzQn+9TFSVZq8O4KFg/e95r5FTNPuGwYTOZZSTMNjppnBWtNLigTlh6MSsc1LX1POomRxQMa7PJGVuDcOGc/LGKovRs0PB9Wq2WjyICz3ySTygrr+lta0Mciuqac2vE1PwaEFR1xdxcv8ki4BDUtV9fk9GIupiwtDSPVpKq9nUXxgmUsWxv3aLXzuj1uj5BiQrRl+HWjeuMxwPW11dDBfTIF1sZrwZqrMeYpfC03Lm5+Sks5buDnVTWzvz4xo8Gimv8m+b59M1mc3bEccILL7zA5csvIxykSUKWpl5aIMuYm5uj3e1OYaJmTtzv54OON4ch58TTkjP0p2Y0C1cKrxBonUVqRRQlXlir0Sh1XltcCl/ZqLX2i6CuSROPPUtF8MYtUsUYF6h7yuO3WdZiY2ODa9evIqWY4lsCQEBVlVy79grv+7b30mq3qYoxxhgODg7Y3z+k3+tw/vx5fv/3/8AviKrk+rUbwXhGnD61gUAQRzF5UXD79q1QfSdYWlr0/TqjiNIUHB8N2Ny6w9L8oxRFzuLKCucfepjBcESaxKRxRKvd870ui4ooSZnrzyOkwlhHkZdTkSKLo6oLIJpeU2stOjTsLYqCWfPtN9BgOE5e5LXbu4SQnPCZ5oIFxBcLSsY40VAVm25L4uTQAf5opHCn3IFZT5ZmEzrxaju9Hoe7OUpr2p0uk9EhUSSJYkU1qdAqxgnlk+bWoRUURe6bHkhI44y8KAARtLoddVWSajGF6mTIh9hA3RPSM5a0jphMSlpxgqkMC0sr1MaFz02d97scknsXaVOg1kQhvhKT4J37zaCuDUJKotjnc8pQQ6FiRauVIYQLjg04Z0NtQOh9a72DYo0/hhW+M5Tn3QvKoiRNPG3Xn5rEGl+lGuITrw8038c4GypuFVXleeqj4QjrDEuLCyEf4LsvWSso64LtzVv0em2SRPvpJJVXRJSazds3kbZkZWWRuqoYDscY5wXppPDwY2M94yQNsGWYV0JNo0Y3ta4nG/bs9b0399OsaTfzmdm5/0aHEL5Zy8uXLlHkBXk+xlQ1OItWCi0kcZKwuLzM+9//flrt9p/biMObxZA7R13dQ/AXTDnUni8eGkYELBUIOJvBWc/jresaYwlcbIUx/qdSUBuvnexcDcLrttR1iVBx8EJ8hj1SKQ8//BCf+r0XgrqgPPGepOcYf/WrF5mbW2R1ZZUrly8CgvF4wvXr13j0kbNceOj8dMeWQnD9xo3QEFdy/vxZhFCh0UTB9p1thqMRvW6PXq9P2mrT6fZ4/r3P8MRbnmZ3f5/PvPhZ3vL005w+fYb3fsu3srZ+iqosGQ6OaXfnwBm6/ZTDwYQPf+QjfOeHPsB/8Xf+NuPjvSnG7qT1Gtr4Sa4DTt8Yx9Fo/KoEjE8BvFrr/eT9RlTLLzQpmjZfzfDsgKb2Npjsu96/ZyKACBxl1BRaaYa1vjlG0yXJe5458/Pz7G5tkWUdIh17bZV8CCMHQuOspLYGqb3kqhCOSCuqspgWZHU6HcqyQmtFVXjKX8ir+01Gnqhgehw6RmcaFcUIWWCANGvR6fY8n9vJ0L7w/oty9lq74Ew0iVchPd7aFMOhBMPBgKPDQ5yMMKZmcDzEOkueV8z35mm3WzRG11rLcDgI1ZcWISXdTtdTOK1FaDgeDnyyMlQG93pdn8gMTburqmYymXjBq7qkNiWLC/O+0QpezKwq/Xc42NsHLAuLcxhTg9BeX1xI8nzCzvYmq0vzaO11apzSeOExx83r10lSzerqMseDIUVR4ymUBh1phoMCU9eoSNFutZibm5tes7ukpr/OcRJhvs5n7nP/XlNDRXjm1u7eHSaTgscffwxTlwyPB2Atx8dH1EXpu0Ip6SU17tlc3uh402Dk95LpZ/VUfOjpGQ1FUVFXhqqsySdeJ8Jjp75oJ0lSWu0+7c4CUdIjzbokaSsIJMVM8hrnyXghRD65eA3Ovra2ArhpskiFxIuXjtVsb28zGBzx8MMP+YYQUmCM5eqVVwI7Zd1j9EEHZm93l8ODPZyzbGxskGVZ0Hnx/NutzU3A0e70mJ9fIGu3+Zt/6yf48R//j+nPzbG5tY1QEQiJjjOipEVRW8rakrY6xFkbnaRYJF986UukWZvzD12gqEqvgohDWH+RXLiOcRyfTGa8h9q0BJsa7QeA8ZrFIKaOuDcmJw8THhYrPPXTd4V+9aSdRgHcLXh0v0QhhBLy0ZBOp43UvoBLRdrXF0TeUEQ6JopSj9uqZuF7nvvw+Ii6KjF1HXRuPMugaSPWbBh+02247f7MpdKkacZkXJCmLSaTksWlVU9ptC5ILhucp0a9yttq5noDqzSSESfyEz5h37B0xuMhRVlQlgVFWVAURZBzzZifn0drNYVv6rpiNBp5qQPrqKvKb9xChLoEqEpfXdlc83a7Q8Pe8vmHnLI6qXoWAtqdVsDTJcZ4lpREcnR06JuddFogQ1SsIgSKfJIzHByxNN9DSREofX5zL8qKzc3btDst5uc9Y6cq62kyOYo1VekjRVMbOp1uiBreiAzt/ZOc9yviufe12edfq6Kz8fB3d3d9t6UkIcta9Pt9FhYXOH/2LOcvnGPj9CnarRZxHN91jP/PGPLG+20qOZsLOG0M4Vz48n4ixXFMpCOyrE2WpXQ6bbrdHpH2zWRNbfnjT7/IT/3UP+bnf/6XKEtHmvVxRMRxhkMSRTFx7KscrQVj4OzZcx4vtXbmXIIIl3Xs7e2xu3uHRx99NLznK9w2t7bBwfLK8kmFm7WMRiOuXr0CCJaWVqeaLs5ZxuMxL1+6hFKaKPZNhqWUXLx4mT/69Od55OGHuLOzy2c/93mkTujPzaOjmPn5ReYXljkejpjkJYPRmP3DQyZVxe3tbRCCvCjI83zKfLW19/pM2Hx8KO9QUk6rRO8KQbl7gr2awdJkIwnGyoIzeMQ8PITBNQ/MPUbcTR+NjsrdrzleZfCnobT/ZTQZo2JNt9ulqmuSJCWK4qnIWrvd8W338MnDxghopbF1jTMWU9fkk3wq2VBXpY8A60YDeyZHExKSAONJzmA4Ik5SrBPML5ywMvz18RtYc9Kz13GWWwwe8y6r0ielTR2S0DbkOAzWObIsQQTYMJ9MpkVcvorTYEzt2yM6N00YN5tEt9vFOf9dwesSSSmJlMJUNfPzc9NrrqRiMBhQV154qygKXzGdxeB8uUxV1WDBGsfhwSG9fo80TXwqQXr9F+sc+3t7lPmEtdUVPNFAIHWE1BFHR8fsbG+zMD9HmiQMh0MvlSB8z1KwDMceMzdVzcryyvR6zebUvp5xL8vkfq/f7/P3NbGzrJaQPJVKcf3GdbJWFqBd45GF2tOjpYB2u838wvy0JWUzJ/88CdA3B7QCdxnse1/3P6F2LmCtkjjL+JNP/zGD4ZAzGxt02h2SJMI5uHl7l9/53T/g4sVLCODxxx7l27/jr9BuJ+RFSRrH5OWIJPFNeLWKgkiSQyrB2bNniOOm3VUoTFAJ1nqoZDwcsbW5wyOPPuYrLa3FIbl16yZFkdPrdVlbW2V7+w4Wv6iu37jJN71HkLQSlldW0dGXkVJQW8ONa9f9d8SxtraGA+IkQmvFYDjh1u1NllfWkEpMaWQOweHRMZ1W5vWdWy0cPjKQaJyRaKHI8zH9bgsECOcxYQ8RRQFjrJHC65d4b114DDP4F9MenAEjlgh8m0UXqjkJrrhfpB4Waf7G/xRN9hNCMo9pMrOxkSdzWEyP4MdJ4cV00s+InxnjdT26vR7DoyOiJCFJW0zGR0Q6ot1uMRxWKCWmUZKzFidqIJomOot8gtAxdW08w0ApTBWuhXPYBvef7l0SrPFQTF0TxQlpqwOEpiSVQQl/nYwz0ysJnqoptPbnbg1ZHJElGa1YMxkcY2xOUeYYp6hFgrE6FOcYtHIsL3R59/PvpN+R2GrA9u1tVDheXVhGecE3v/e9VBNDVYxxwtJbWCBJU8rjIdJKjo/GKJkipcWYiv5cz+dNQhw1mkyoTY11nnrYbreJ4zTcM+eT58bj14PBIYvzc8RRRFkWqMj3DrVas7N7h6oqWFld9olfB5GKAmNlh8ODfZ56dJkkjryWf+1lqY11VLXvx6rDxri0vHTCvOHVnvLJhttMqpMJ1syvJhfjmjvRTEsc9zOjdxnwBmsXM28Kn5No5sZkUnDnzh5pkiEAKbyWugoN5W3taxCWl7zwVwMRznrj92L6DzLeJIZc3HMj7nl3usq9NyClAFPxiY//Nj/z0z9Ht9MnimK6vb4vDkk7/Ic/8oP86Pt+mCeeeIhuN0Hhb2CaCoQzJDr1Whku8smrBsZBcHrjFP1+j6OjoxAmxlM8WAJ1UXD18iu8+/l3kGRtnyg0hls3bjEYDeh3Mx55+AKff/FzOOMwQnLxyivU+NZj66c3kCGpJp3j2pWrmFDNtnZ6nSiNqEyFlN5TTdIWk3xMVU1oZREL83McHh7TyTqYuibKgmaI8sk3JWKoJBJNkQ9B96mqnNJIJL7oxbrQMKPpWznO/eJDTJOlWoAKM13iK2ltHbjZyvdvlDQUzlD6Pb2fobcpktmZ7+e+Z+k09735m7sfnsmCa8QbgvGWJ3+jpMRWBls50qyFjCLM/0vdu8balqVnec+4zDnXfa99O/vcT526V3V1dbW7iWNsiMF0UCIMBAQkTjCJkEgCUYTgByT5jUL+IOVnkFACUaIAchQcLnIMBtM2xgYbu7uqq7ruda77vve6ztu45Mc31tr7nKpyd5Mgtad0aq9aa+2155pzjG984/3e7319hrFFsg4LNOVcGCjGgkoOVCrVxp0TLLawtK6l3+0RvKNJsIXTBmyGMpYYG9Jaj7YZNi+opieMxpu0ztEdbmHyHj6sjN9WMJNnRXV1LqKNyOg2vsVmihfv3mVvPCSUC2y15Fsf/QadwlFPjli2isHeS4RsRAyauiz5wa9+gd/2297gk/v7nBwf8uY3fp0XX97DBZFoQFl+8es/z+ODQ3p5hy+/9AJf+eEfYbB3haqpWRyf0y96LBeRNuSEWKGMZzzu4xNpwBGZLaWA6Z2jqUq2NreI0Uo3ZdA0bS3SsiEwnZzyyouvJ0VN4QP74NEqsL//kKACWzvbRGVA5VjbQWM4PjygXk64cfULqBiYTSZiHh2hyAtIvgQ++YdubW9dtOWnUXOZqqyUJBkCf13qRH4iQj9RHV2zcC77015Qb1dFmlXGraUGEGVRXytJhAsYbrYoOZvOeebmdVzbEl2Lji0rqotS0lm7glXcpV3fZbjttyxrBZ5cVT8bz0rFMCWZ87PP3cH7mtnsDGszTk6PCBFu3n0e7yNZpmmbJU1l6HWshAaVeMsmkyBtV12dibaopKHi9u07/Oqv/ipGGzJboFQtNChjiCry8b0P+d2/53eyvbPFg4cP0QaOTw7ZP9hnc+M5nn/uecE9oyDEH3/4AcvFnF63z93bN8lsssFSkY8//pjJdMJotMXVa9fpD0aEGMTtJYlDnZ6dcj45Z2tray0noIyGxLTBQFZYogps72xJcS7CfFmhdCaiTkS8g0xDL1NYFQlKJbs6seEy1q4HF1HhVapXqEC28j9VCi9d9IBC29RcteJbBymqGWPWHGWlnjSe+C5HxKefijwx6KMP1HXFYDDA5jkqtmSdjuiPK0VZLhFf1kvF3ZRcS7OLfGyRZZJRRpH+xdi07Ain30QLtKDAtzV10+K9o9vtMF22DIZDoYEqhXdC2bvA9D1GBSn7BiB4xsNrOT+JAAAgAElEQVQeX/nKlyhMxE1PePe9t9DlgmZ5io0wzETmwM+Pee7ZV/jv/vx/zmyx4ODxJ+wM+8wHXbQbs7ezy0svvkTTtmR5gQ+R61f3yPMMHQNtu+T+/XuUD/dp2pqdQRf0knJ+AqHGI5ruw6E0A6GkR2GxWEDKUpfLBc/cuC6WiMGnLL0lxshyMWexWLCzvcNKd00WK4FI79/7hI3RkNHGBj6y7pEwxvDg/n3apuHqtWs0bSuSASl4huhpalE7LbQRDfqtrbXL1xP9Dp8TQz5vCF0+QurS5VI+sVZ6ilL4iSt9CCVSxKkajThQtWtmW2ZyDvY/wNc1G6MBTV0lv1lN2zRrFVUfwzqQr4f1pcVo9T2+l+P7KpDDxRfQeoXPrl5Z4aiCswYPzzxzE2PB6IDzS9GHDpF7n7zLX/9f/if+wd+7wvVr1/i3f/CrvPLyyzz37HPsbA+JXpNZjTExYXkq0dMvGDM/8RN/nNt3nuX999/n0aPHlHWNr8rECoh88NEH9Acdrt+4xr0H98jynPlyzr3793npxRfYu3otteI76qbh8eNHzGdz+p0uzz13V9Qcs5ygWo6OTpjN5vQGm4xGPbq9boI/jGzZu128b8jygoji+Rde4KWXCv7+3/v7FHmO0pbX3/gCt+/e4eRkAi0EHChoG0cMCiHrKELrwJVEt8C1S7QyjMab7Oxs0x+NsEZTLhc431JXDm2tSKHqsNZTkYKBRdsMFLggwIG1K8hDJ8aE0P5BsozL0sTfy3EZk1dPPaeUoixLdneHGGPBZmR5gTYZwde0dYstOknGAERuVwp+WmuREWAV1AVmiNGxkrwViPtCFxvAR9H6Ljod0TOfNwwGg/UioxPrxPuANeJupKJHJU52DIFXXnyNzWGX/fsf0TeKXEXmiwkhtmgKqrIlV5qzswPe/8Yvcvfl19naucqJb3nnG7/BaGOTzV7B7//3fi8+KopuD5TGuZY7t25Ix6p3GBU4XXo+enSK9hEbPaE+Z3LyCcGJxaHWBcPh5lraIYTIZDoVvNyIOuZ4cyyd10R8gGVZEqJmNpvSttIsJF2xUnQOUSicB/uP2buyR5ZluFoEyrwXaObx40dEIlf29qiqmvl8QYgxFW4jVd0klpqmyAs2RhtJbdE8MTY+I4pc+ndhV7jeDV6AM6mJJ722yshJwTzVjETXKcluJRpr9BGtLUZHggtoZdDKMDk54bm7t7FKaNAxBpqmpSgKnPMslhUb4026Sfjr6YXne4VUVsf3SSB/elVNONblp9NF1WnF9N6xu7vD5uaIyfk5SgsWJW9UVItzTpVnenbIW9/4VwwGGwwGG9y8eZsf+NJX+MIXXmB3b5vN7Q0Gw17ScpZf9x5+9Md+J7/j3/nttI1jMlnw8Scf8s7b7/DJvXt89PEnIsrfKl56+VV+/uu/SEDhfOTtdz/gd3/t93L91h26gxHNZI62MJ0tWJYV1mTs7GwyGG1QTGeUk4bz6ZSj43Ou37xDXnQYj8epE9FQdAp6vT79/jbz2Zzr16+zd+UaBsNP/uR/Qts6Io696ztEHel1+8xOJhgTsAbmsxk4j4martVsjUaMNzbp917gj/3hP8ju3g22d6+KjvuVbTIDhY54VzMta86mM05OzlgsSubzksWypG1F/13IIymDCtC20u4ukFCCZDSXMvJ/jZHxFGPlaSxTa0W1LFFakXc6VG1FlhV0uj3KeQNJjng1YXQaXCHhtUZJgFZJZTKGgPcNSuUJbhMpW4JDo4k4YhC4ZLixQdN6cajqdFKBTjqAQ4wUnQ5tXWKsJteGol9gtOX6rZtc29vl5PCAk6ND3nt0nwLPol4SFHx47zE3rl7DEOllSzq24uTxO6hQ4cqars1xywl3nnkWbXNMpwcmE5f5zKCR7N8oYQpdtyMq/z73P3yfl+7e4up2jzz/o/zs1/8lv/7N98iyDt1uP6lfisRBXdeJ7iusl93dHYgRZQzeeZx3gGUymVAUuRT3YyBEjYkioTCfTjk9PebV578kBWJjJKwqRVPXPH5wnzzP2BxvM5lOcV7s9HwIZEazmM9ZdV9vjEYURS7NfyF8Zjb+RAxJ9+FySn6xH5RHa8Tv8thMuPr681N9R2uILhBSHU1b6UDWUZr+JpMJjx4+wlVzrmxv4Np6ZV+LtVYMr5cV1hYMNzbWMtif9x1+y0IrMtlXj4VO+EQBgFVLd0irNGxt7bC3t8dsNpPmBpsKWT45jKg+dSl45+T8mOOTIz788EO+/k+/Tr8/YPfKDjdvXeWVV1/m2Wfvcvfus+zsbNPtikKZsZl0DvZ7XLu+yw/90A+iVOT4ZMZiMWF7e5s/9If/CLt71/jwgw94+OgxZROZlZ7+eJvx9jVOJx/hCOyfnPL48JRn7z5Lp9dna3uHh48PUMpQ1S0PHz3mjR94nSzvsLO9g0JRFBlFkfHjv+/3sbk1IsRWrMcymbQ3bmzTNILD1k1N1Vb08oJic8xo0OWVl5/jtZee5bd95Q02xlv0+2PyokdhXMJCM5zKuP3MXdoQOD875/T4MaFesr05ZGPnCjdu3OTWrWeIWrBRn3RSggt4L5rndVlSVeX6PjRNTVWVeO9pmoq6rtf3+Ls5Lmfel5+TB09CK8RI09QoFJ1Ol2oxw2Q5nf6AcjkjN2a9LZCMSq+LVt57tM2xWUZmrQRx16Yi3kriWFr3STkCUbL3wlqKTpdFWdPp9MmyLCUSMqU2xmOeffZZ5tNznKto64roImenZ2xsbjOdzTmfzfn4/kOM8+jccG//iFG/A50RHz085ca1Xcq6YWhhNj8F3SWoMTYraJdzyukZw81ddIK+Ygy0dUOznFPOp5TllGk143ShmC40NkY++va3+MSfc7pc0MkBPEXRvTDjiCJrLBRMSZhC9IzH47QbVdLApjXRS6LQ63QYDgcQRTbaIEXoo6MDyuWC69evSQd269CZsIHqqubw8DFbmxv0Bn0ePt4HxDxdBZ+atkoUAk1tbm2JFg4XuPIqoH/eGFqPpaeohKvfk/GQpCBWr0epxK0HG5I8GAVkcV3oXVQVk/MJp2cnTM4nzBcLea93RC/vj9HTtI7FYk5ZVhRFF21N8mxVSavp4ryeGOff4/F9EshlW7tqvZVW+pUY1Kr4IEU4EJ5uDIbRcMydO8/xztvvIg0kqxsmF9O3rcjJJklUo4WO1fpAdXbO0cl9vvlm5B/8g78vxdLBiKtXr3Ljxg1eeOEF7tx5hmvXdtne3mQ8HjMcDslzy+bGiO3NEUrDF199ji+88iyNC5R15HSxQBmLtX3+i//qz3L/4QMOjo84PDrk4OScs9kcY3PuPv8iDw8OWNYyaT744AMUhk6u2dneom1qvvjqy/igqMqa4+ND9va2RC7Vt8RgaSrH+fmUsirZGA0oVIb2kSKz/Nk/86cZdDVWtTgn3ofBR1wrAbZtIjof8PxLLxN0wXI2BzTloiJTjg/e/4De0Ql7N59hOBqhbE7dOPK8I4VSpbFWMxwOGW8MyZPFmbXSej2fz5nNZhwdHfDWW29hjMElFyc+Y/I9XbVfEw+eoqCuX7/4TVxqNe92e5xGhS062KIgoOn0ejROaHmubVHWyvY9z3EBCiPGy1opXNvSNlWSaJQdnlUaYzXRGRHS0hbnGrZ3dqUzOMi22ViRdnUxoBPGvFjMRDPeGmx3iNWG3sYWR2dTPIGiM+Tmsy/TLudkwfPC6xlVNactHTposl7O1d1t2lCS6cjZrKQ7usLGxoiDyQGz+YzNK3uU5YzJfMGbb77FYjbBEiC0BF+zdBWmv4vNtymIHD58iIozFjFwenpMVVZsjnZFbkJKNiwWC+q6JsZI3TQQIxsbSatcG6pKzLpjFAXOwTCpIsaA1hnOe5yHk+NDTAxcubJHUye4Cg0RJufnTKfnvPTcHbK84PT0nLZ1iMBjSPejQRuFbx27u7uJLRTXGPnT5uqXx9JnwRVPj6HZ9JwYHN1uT2oKCfuGxO9P9M2yXLBcLFks5kynM+bzGWVZXjRUKYUxiTGmxMZxtpgzn8+p21a8EvICH4XC3OsKZ594QT/8VET8DjuOp4/vk0Ae1/ij6CfwBNpykX3JD21s2u4GXn3lNf7vn/674oyTqs3WFDR1K+9RSSQLj1YerVq08qloKr2OIUR8U3N8uM/x4QFvfvM3+H9+Rrbcg/6AjY0xm+Nt9vb2uHp1l71rV9m7eo0bN66zuSW4trEGZSzDfoYLmrp0vP76l3j11dc4nZzjdaSulrz1rffJtOb1L73B7Vs3qOqSqpLuxLfefBNrDXu7O6Aijx48pNPps1gsyIzm0YOH5HnOjBnj0TaLaUtdl3R7OU1dEVpFcA39wjDodWmbOT4kFUAAHK2raD3UreIHvvxFTNbFYyjyLc61otPtMjub4Zzn4NE+B4dnvPLaa2zt7GIUlOUcm+Voa1FRE2LEt575rGY6mzGdTJnN5ywXi7WNnlJa2rm1uWAJfIfsfLUp/k0Hc4JKghcOdX8wJCpNVnTJ8g79wYi6qdZY3WriX+5RiIhUqlbCtGibVpKCRDU1CryKuOjFpk8pVDQEVeCCjNfeYHgJHxfZ4LpuaFwrdL4ohV7vpEGncTXKGmJZU+QDCttFe8/OYCO15mtUUIBD+QVKt9RNy8mkZVIqdvpDNnb3OJ+c0XzwPpPpjOlkxmJ6LmqWWrOxMSD6AlspKDJ0rqmnE0zw0k+gLY8eHtI6x+bmhkhdBEGPq6pO5uJybQXe6ybaXqR1Hh/kvk9nUzbHY2mkcg0mzyhbhwuKg/3H9HpdxiOhNhptWNYVHVtwcnJMVS65du0qjXOcnJ8LPp/gnLpscbVICATgypXd1Ptwwbn+LMqePP40jVl0hSxt2xIjfOMb3+DjD9/FNSWdTsHW1ha7Ozt0e12apmG5XFJXNWUpmjQ+8fTDKsOPolHUti1lWSXTd9mdeueSAqQmy/NUHJV6l7EZnW6xbvT69HnGJ35+t8f3SSBfnbhaN98oJSJMFzDLhRZ1TNrjvm15443X0cZgkpa0D7LKNU0rk8oHUUQ0mpC6HFNFJxUwIAa/5k3HGIn+YmE5O6s4Oz3ho/ABqFVXocJmlqzI6XW7DIcjNjbHbO1sM9jcZDAasjPeZtQf0e316Q77eAUQMERUCOAd1/b2cL5NgyvwyUcf0bZ1KlK2/Pw/+ccoFFXVoo1sAyOipW50DlHR63epqoVkpcHQNhWZifi2JPgaFRzeN3gnzSbOt7TeUHQG3Ns/p9MbokzO40eP+dabb+KaBSq01OWCPM8puj1+7dd+jaAUv+8P/AGu3bzBfLbAty3lYk65XMggLitEs12UF0kB0jmfClSrofa9bx2fxMlXlLD0vJLylfOBbq9LRDxWbVawtb3Do4f3kkCZ4NoiyS2Fz06vT5Y6XNMH0taNGDVHnXR8PEU3o66CFMVtV7wtsz5N22CznOlkSuvuo21O1Trh6GeWxrVSLE5SD1Zbtsbb7F7Z42w+JxKpo8JEjVEZwQMh0oYo5hoRTMzJgiGEgtFun+P7h8waR4Nl//ic4A4wKtLJLJlqMblNevPSjdw1GdPlEhMt1kaKfhejuhw+PKaswflIf9hd3xfJVGcEHxAbwJp+vy8Ndr4lAmVVJ/1/wcGfvfVC0iWytM5xejaj6PZ5/OgBw9GA4XAozXYgC4AP7D/eh7blxrVrtG3L2dl5spALWG2oq3ZdiyBGtrd3kl/BRWb9vQS7lbxsnue8+eab/OzP/iy+rSgKKaTu74sbl1JJ7E3rdees1jpp27TUVc18sZCgXdW0bbteHLQxZEn7fuX3a9EEbYhao7RYUfZ63d/03H8LFzsv8CGtRVVtNbdWbt1admRCrrcKFSJaWZ577ln6vU4yQyapDFqatuH8/IzBoL/WbFFkqBjRQdw88DJITGoSEcxMEVYcVAUqxouW8CA64KK70VIullTLCWdnB3BPBilGMvkM2U5pYwlGY4scbTS5MQx7PWEzqOQjmuRyszxDKWkT997LuRtLUXRBefLCYLRGR4vCsDLFDUEgi8Yl8+rY4NtKgkLdEHxIC0ZN61qa1nPj1jO8+9E/Y7Eo6fdFvjRLDjJN48iyLtZoPvnwIx7u7zNdLukN+vzw7/gRqmohWYdvIfgElcR1e7kipgLj6n7kSUN7xWr5bsbE5xSCnnoqBg9KaJrWZlibEWJDRJEXBUqbNR6emZS5EfE+stHpYLMCUpIQo5gW94oeRmuMViyXS1THiFwyBm0LvvzVH2Iyr7l37z3wnta13N7dFRkFbWh9kIYfFdnZ3RV82EcMhm6RA5F33nmHW8/cZTAc0tZyvlqJ7C4FxKSFroIo6vno8N5QOweFhU4f3enhFi3K1+gM+h0x3q7qEpMJgwsX2dscM75+g4ODxzSzGmP7fHTvbSJdmmbK9s5YyAJeoJOz8/N0k4S+ube7hdYa56Tpq3Wit11VNdVyyd7e7noxnEymzJctymQcHeyzubUlchAoXKo5OOd4/OgReSdnZ2eLyWRKk4KhdKp66mpJCB5NoFN02NzcYsXD/tc5Lps1v/fee0ynU5qmwlq1ZlJlmXjeZllGkRfEGLn38BHLuZh0iJaTT7nESkVUo5QEbTHijlg0GCv9BykbR8nudTQaiarlZ434p8b7b1Fo5WJrtDJEhkQSSkwiiyYYhUnqbNIksMnW1phHjx6jjEEbUazzwXN8esJ8PqfX77ExGmFUJkULldrHtUfhcC7pnK+DduqsSsa1KhUuxG4KopeCyIoGtWrZNqv24aAxKuLKhpCoeouZRxmNAY69F9qeF+2WNuGRwj2+aKsW/C3pdBuIeFGHDCIDq7W8x1opnERricFjrfhMhhBR5Ik7DyTrNDB88PFjptMpVlsya+jkoiRZ5BlEUbs7fPxQzKG1XPf9R4/wbYt3LeDRWnY0YZ3Nic57iBFrDP3egOlUNNVFmhZEW/U7wyqJ/fXEVvPTRdAVRUzud17kdLod2kpU/qKDPM9wbZMyrRX/OMmgRlBGo0LyuHQNTdPQL4q1z6Zra2oulBiDUjz34qvsXb/NP/25v8dHH75HpyvZuQ6gMwkKzjma4Dg7OxP2SlUTWs+juqHxkePTM27evr0W4nIROlYKplFFggp4lcw2ImSdLsoolFGUdUlVtwRlKHpdrNdYHWhr4Sx3ukW6NtIzsbkxJs8zBsMBJ4spD/aPeLx/IvCQc2wMB8LWih6DZTqbpsVWTJw3NyWQxxBpfUtdi7HzcjnDuYYrV66knbBYB+ZFwaJcMJue8+LdV7FG4b3srFsXaH3F0dEB3V6Xra0tDk5OmS+XVGVDbjOyIqNtWplbUVraB4N++k4XXY8xXlYkj5f++2nobiWwVVUVjx8/pk1NYiGGpD4KjW9YVDIXe70+g36fs/MZ3nkRT1tLGaesEnEwW3EXrclkwddCaVYRSLvTFXNqPB6zLv49PeYv//ytCK2I9gWsJrhi1Tzy5OFDXEvZxgjeiynuCy+9zMPH+6gg2fVl2tTSiZPMbDqjKAq2tjdBR2mEcK1U+1VS1XOrVVtwdVB4J38zRIdCWpi1lvbsFRy05qmG1WQPuCgwA0qjYsAqUElpTuo9wrWOBExuk/+oYKjKmES0EElQVgNnVVGPCodfd6PWPgXRRiCaNk1CGeZNauhJ11o+hMxY8jzDe5G6rZsy7UDEWaiua7SFkNzhiZHJbI7WK0l4JYqSWoJGbgzG5mhtUTZnNBrjnKNqHVHJeHatS9DGk/oSq+3yE5NvzWB6knFAFAEwnXYPKy5wW1dkVpMXGU2lsHmHqinJMosKNSLkm9Q0Q8SkdvyhyWh8hVaBZjklhoZo5PtpY+lkXXy9xCoDSnZHb731LX7+F3+Ztjyjqh3L8pymjVSNF7qj0bTBM9gYEFyLc0Na19IrOow6K5XBBhtaaCu5x9rSxLQr9NK0lQW1bvF2weEbR240tC06Ckddq1ykapXHZhIti1TQDURC0WdZe8rTOW3l6fXH3H1xlx/LNMenM8bvwd7mLsZq2rbEh8BseU6MDkVGXVXs7ewQWo/F4n2gqiuyLDKfnxBixXhrQ2SCE/Gx6FhmJycsyiXXr2xSUFNGTTRd8Yr1NdPTQ0b9nMGgx9sffEhd1zjXYNSqwLhkZZQx3BiQF5bWu8tBI82JC8pJTKyiuDbzvThCKjTOpwvOJufJmzMlAjJr05ATSCuzMubXiszpvasFbtVOgUqKlWq1Owaj1aX+ASmAZtYQg6fTST0NShr20ifLv2ReQ4xrssd3e3xfBPIV7CkXaa0z96lDvqaYITvXEp3HWsuLL77Iz/3cz7FychH3EJG3lUMwc2l42JCgpyNVU2IzS1H0MDpjcj7BKEOIbcpeVxm3l7NScS1KdOHz+TS9KRVtkVV+XdwDgT1Y0SzDyld5ne090bEYV89d6DjIDyWLTAr8FynIqqa3HnaywKgoxQYu2CCrBoe1+p0KaSBLl5voD+qLLF6Ljke5rFIBU/DTLLdkVlx/IrKo2qyg6PYpen0++fZ7tF4CS9O0dIo8WaglChhS71Bp26lW3z/xei+761hr1xZ8q+xGBNZE7thaQ1PVdDsdFtMJeadDW2VkWUY5a8isRifow9gMTCYB1YtZNyFQlsvkCmVEFAqFzbs0ZYU2iqLo0usPsJmhbhsyK9rxzkcWy4qj0zNA+P8vv/oyV69fY29vF110eHxwgCZiIigf5DEQnUNMghU+3RcJIKJ+6KOMuaoskzYLZDYj2Iz+YINMBWaTU3LboS1bxpsjBoM+p6entFWF6lgq32JbgVxu37nBcy/c5Yd/+KsQNa4BrQvKtqSNjsWy4ke++joffXyP05MJDzLYGg8gNMTgcJVDJ3W5cjal1+kyGoyIweKDwjlN0JqHD/cplxXX965A9Gid0fqAyTpMJ6ecnx3zzI0rdDsFpycntGnXhIo476iber0D29rallzGxXXhmvg011tdwKGfs+OLwOHh4ZoOe6lUykU4lURlVVeJIazH7DqLTsFfJ9182e6ptbOKaMsLHm8TI6oql3SLglEq/PqUBH56yUnn+j3C5N8xkCulbgF/A7iK9Ln91Rjj/6iU2gL+JvAM8DHwR2OMZ+l3/hvgTyL1jf86xvgzv9nfiGs89IJL/jkns36vUiIRG2Pktde+kPBmu9YpUIlKdCGso7iw1hLM7PT0jCzvcv3qmP/hv//LfP2f/gJ/+2//TQyK1lXpvXYdVFeOQZ9XNX/yVC9nmOpT71sFdhAcfpUdrGGd1Wp9OVhfXLBLj2WwKy6KwZeFxlaignp9zvKCNFakYK5Wi05YD1i1Wjy0Sri7ONcrJf6YKGmccl7OweSWTq9PXnTp9vpsbm7x+hd7lIspi9mM+fScqiyFEeFF271JkNKaZaBkAfNOOgiDv5ASXinFyfW5gFpCEKeovNvn6o3bDAdDHj64R78QXZXBcEi9nElmGxWZzbhy9TpV8r9cCRaJvnYrO4Z00aLWdAYjlLa0VUkx2MCJMwLWWLxrMFlO61sm0xkKzdb2Dl/+yg9w6/YtWt+yotVam1EulnSsyN9amyXsXuQACEGYDlqL0/0l2KB1bg0VNU2LUoa9azfY2trh5GCfsS2YTU658cyLFHkutZZ8xGw+ZVme0waH1jmz+ZyqqvjWm9+kUxhZNIJm2BuQG0WvU9C1hj/4Yz+ENj+Ki4rZYin+nrXMh+X9IwY20tY1i7MjRt0OG/0BPoitn2sDQSuO9o/QxrC7e4W29QTDOok5Pz9jPpuzvf0KMUZOjo9FD0crnAq0TUha8To1/l2RsfEZweF7oelprXn8+PElNtXq8y4DNKmTU6mk+3JxH1bhfn0Waa7ES69ppcWuMSV4Z2dndLtdhoM+29vbFEVB0zYid3zptC8gxO8EPH728d1k5A748zHGX1NKDYFfVUr9LPCfAv8oxviXlVJ/EfiLwF9QSr0K/IfAF4DrwD9USr0YY/Sf9wdWF+471TG0IknNiqONTQHg1q1b9Pt9XCNFv5iYK5dX3NWFapom4W2BIu8QoqFtAsbkfO33/B5+6qf+tmQPrk5ZQkSh0QYM5glBm8vB+3Ix5bOq6p8q0kRxKYIVJnxxIy+9Jb14EczlU2RvopQsAqvh5Z8cZhePV1l4UAkekkkVLr92ebHRq6xYmD0hejSKqqySiNhKQ0UL/m4z8qKDLbrkRZfhcINet0c37xBGA7KbGtfWBC/TpK4bDg8Pmc/nPN5/zIcffMhkMmG5XEpBOMuICF2MFLSFq2tSYNfrxdRoRZOYMasLthpPUYnhSJZ3BCc3Bg8478U6DU2WpU7M4KQOQkDj0RpClKJVb9yFBOe1XmC4PM/wukuM4BYNt27f4dq1G+xdu4bzjo8//phev4uPkf5og26nS1PWxIhIJK/v26rIvtqtpSJaMJC2776RGkNdNxLMlCYEmExm1K1ne7zDaDRGAWVVcX66IC8K8sGmwGwxYMmYzVuaSjLJUNYY7bFaM6+X9HQgGkVQBrKCaHOK/gijBBu/treF95ErV7b58hdfolwueOOVm5wcnzLsKuZNILaOGBpQmqODRwx7HXr9nphSx1VTVuDocJ+2qdnb28MHmE4nEAPBR7wyeCc6LtYYvHeisRJWdnmfjgtPi059XmAPIXB4eIj3l0LRCo9Z/38ShNOayjdJRM7KHPyMz1tBgusdopFi7srzdTabMRwKPXVra4uV5lBYwUJcDuIXO/Pv9fiOgTzG+Bh4nB7PlFJvAzeAPwD8aHrbXwf+CfAX0vP/R4yxBj5SSr0P/FvAL33uH1nBAlwOvp8+Uh0Saw0hZIIYxMjVq9e4cuUKH3/4UTJLfvKGrmKoNpq6rhmNhmlbbmkbxWy65P33PuZrX/td9HtDluUEhbqAEZRhe2OL4XDIdDpluVyuA/rTxbiVZdeqe+yyJ9/l7EEEvGLCvbn0GZcHoga1YtAINn4hLMuF69pqAO6AEUAAACAASURBVKgLXYn1whFWGbtKLeYJylGJuaPUE7j15d8Vd6GYBLEUy8WStmnpdAtWzjLS+Sh0P60svV6f0WgEaZC7NuAIidKlqNuAznKuXLvOnW6XV7/4Or//D/ZZLpccHx/z4MEDHj58wMnRAaenpywX5RpW0UlwCS7YC6ksgbWWuq4pq2WqPUCe5zTLiqA0QQkzyAPGWro9MZ/2rk2dhIq2rci1IvpGuNy2Q+MjmU7aMVHwz6Z13Lx1i05RUNcVs+mca9euoZOLFcDGaCSGIzFQVxXGZvR7XVQyTPFJn9pYDVp0OZRKDAqtwYR10pLnIpWsrWG4sUFVVRzv79MpcnrdPpPplE5RyI7R5kTtWJQ13V6XvLeJaxynR+fUtcHVhth6MtUSVIvKlBTLjaPVnoBhdtZQu0jlAt3hmGeefR4bBILKrSPPWvqjyPbrd6mb2yxqT3U44fzsiLaFGFpOTva5srstGuXe4yO0TvTpDx4/IHrHzVu3OZ1MODk5lrmjDUZBaN0al86znJ2dnXRdn2owuRxCvovMvKoqDg4O1gH4coy4AC7Fo8AY6UC9mLef/ryVd8Lqc4RlpAnJFKRtW5TSyQwjsLW1daF2SHzq7/9/O74njFwp9QzwZeCXgb0U5IkxPlZKXUlvuwH880u/9iA99x2PdWZ6sXfh6UdaKZQx5LlIVsYY2N3dZW9vjw/f/yBd+HRLtFqvlpKpiyB+jAKRdAoh/4cA5+cT7j5zg729PT744DxxoZGWf21o6gY1ehJSWWkJr7Jxa624vGjNaDRiPB7z7rvvPqGrsLamSit56zzG2GTuzHoxcO3KeECwYu+kY1Chab0XkwQiYmwsOiE6qUkrdXFblRJNCJsn9ou1kAphVmtiYk2s8frVYoIUFHUaxEI/E0ODopMlOEjumtKy6G1sjtjd2SLPjDB7vGS4i/mS5XJJWVW0qV6V5Tm9Xk8Gt5ci0I2bN7l1+zZ5ZvFNzXQy4b333udnfuZnaNOkIq5qF6vAp/GhTecLRmmMlgU7y3PqpUJbS2ENzjtpyGkbbNaRNvQYCa5hOZuAbwFN21S0XlFkHWrv8cpdUMZSkWowHCV53IzeYMDRyTF1WTEY9qXY2bQoJd6YJrM03mOUotvp4pNH6mpc61Tk0kloaSW6FZX0RyikLb2TFyxmc4YbIwqEE6+NJdOIKUPbsjEer5tqYggMhkNOjk6xmWU06FNXC0xYolWFUg2ND1Su4cxXeC9zISqLsR16ozHP371Lp9vj7OiEyWzOyckBjx59xOnZGQ8f7jNbNMyWju7GLq9/9bcTVMFiMWUxnfL8refRxkrHpiK5fDmOD/bpdnK2t7c5Oj6hqkoUSvBkpFYhrfSRbkdw5eAvaj2fihtPBcOnM/NVAnBycsp8PhcqapZd1GniCl+XYC47JhGDu+BYXWDzIt9hKIpOMvmo0EZDiEQvvSrWSGwoEqdfK0O/3wdEGVR2tZ/eRazC378x1opSagD8FPBnY4zT34TP+VkvfOqslFJ/CvhTALdu316vwCT45PMMQLy/UEIIQRxPtNbcvXuXf/YLv5iej0/oVsvfk1Nbua8oBUVRoBeOpm345JN7tG3khRee5+23v4mxktFVlWzzXNoqrTDyVVBecZRXR54VKDTXr93gJ37iJ/hzf+7PYbRN40RyY2MMzz33HD/+4z/Ovfv3KRdLZvMF+/uPePfb7wpdzyQqGgrfSgAOJBd5IzibVgqjNYu6FnaGvsCNV8E3xIi2lrqVBqm2bcBYfBAzWGOsMHBCXBdx5ILJVn/FfUEpnGuZz2f0BsKFDkGjkELQ1d0tnr1zCw2cnR5zdnrC+fk589mcumnRxmBtTlZ0McbS6XTY3t6m1+sJu6gscc4JNHB2yvTslNOTUx48eCDX96nMaTXMQrLfs/ZiYmbWUJUNQQXyTpc4m+JdTQyBzGY0tbjrSKdpAB2ZT8+wKtLJDXW5YFF7XFDk3YEIXwVLiAqPUOw63S75YMyDB/fxwTPo9yGIKUWeZ7RNRZ4VdPOMyeSMxXIhNL4ip6yrBCV42qZBB09eFGgVJHDH1EdhNLmxtI0jzyx1XRFjwGpFd9jj/OwMA1TlkiLPmU+nGCU2buPhkLxjOZ+dUFbneF/hw4SqrMh1SYhLlu0CglBaW0SauCjE2abo9egUXX7pn/1z3n33Qw6Pzzg9mzCZnKG06M/3+kOq2vPM86/yo1/79zmclJxOSybHE6plzY0b12mahizvUi88VetpFwsmk3P6vR4bG2O++e33RT1QC0HAt0JkABFE6/cHdLvdzwkrn308HQRDCFhrOD4+TnWeC8hxXRpNWbJSojoagsz1lSF1XVfrAnyW5+uEzbk0Gld4eZQFdqUaubGxQdu2bG9tPmFT9/mB+t+g+qFSKkOC+P8WY/w/09MHSqlrKRu/Bhym5x8Aty79+k3g0adON8a/CvxVgK989asxxEjTNORZLgL5T78fniw8pC2oT9n2l7/8Zf7Xv/43ElZ1UTC8KHZG0fdOsIe1hqKbg5qi0HzyyUecnZ3xxhtv8Hd++qewNocmOb2j1wUSa23KFi50tlcc1VW2bYzl+PiE0UgML0ST4WIrpZVhvLXLj/8Hf4jxaETdtFgtzRc/+cf/BB+8/z7GaHxyQ+r0BnR6ff7Yf/Qf89yLr+AjnE/OqauS+WzCfHrO3/3p/4vFbCJsh/S9V+qJL7/yCjdu3qRuhANcNuLA/s5bb9IsF9ikUPiksuDKfVOnDSc0bcNkes7GuJtsxSy97pCNQQerHN/4tV/h9OSYNqnnCearsVmBVQXRtaAMKhOxpbfe/Canp5IlzWeiTVFVFU1dEb0TTZjW0el0Lm2HEw6+2p4mxyHvnPhQrnBLFN5L9tTrj8hN4PzsmOAcwXs0LtHdILoaFR34hl5nwCIEVGxYzs7QKmKUhmjJiy5NhG6ng8kyFouSGGE0GFLVSyDg2oqmmrO5OWZyPsFqTaFhY2eTqqyZHJdUrSczWs7ZtbQLJ+703kMQKqaxVrK5PKfb6cp1TLS2yfkZXSvb+qzIUUvNfD6n05UgrIn0ugWnZ8fQAdfMKXIFcU6IFW27wIcSFVusMVILUB2yvMOyLDnYf8CjB/ucnE4oK4fSOUoXhKjodMcopajrlvPzhqzT55m7L3N+tuT0ZAK2wwfvvg8ucPPaNYzWtM6v5QwW8ynnJ0fc3Nuh1+tzsH8ou5Iou1/n2pScyf0bb47J8/xJXPvT8ekzg9/q+RW9dX9/n6ZpLp5H4ElxB9PEIMV+m3ZfwmqKNHVLFcsUN+waF19ZA64O6VPw69fb1jEYDGjbll7y6HSrwjUXCMTlc18XTv//zsiVRMG/BrwdY/wrl176aeBPAH85/fw7l57/35VSfwUpdr4A/Mpv9jcki7yMDwt+++mkX7YskDjlRlEowQ9fe+21daFBG/2Esph8jxWLRKiIWWbIMiu65zFweHTA4dEhr3/pdbkwVjwejZYdQvBhHUyMMdR1/YQu8urwoufKcrkkywyDwYDJZPLEFsp7z0ef3Odn/uHX2RxvELxj2O9hjaKshB1gjcZqjVaWEDRg6PY3+MoP/hAuSjvwcjHHu4amWvBLv/LLTM5OMKtBrTQhKkbjMb/rx36M//LP/GnKqsUFL1oZbcN/9pM/yfvvvJPgE58gqctXm1RhJmmGeGbTcwhXGPQ6GG0IvmX/4Sd8/FFNjIHovdwnm5x6Ol20ySgbx2IxYzFdMJ/NUpBu163QK1aKJqZMdaX3YSjLkixLxhBpsVmNE20UoRWowjuZ7HmeC9zTisaMzTKCryiyDNPLRQo5OOnmtYqT4wNcvcTqSLfImM3mdPJMmlcW5/S7XZTXuNiybC1HR0fcfPYlQqjo93sslzOqakHblmRWMdwYc7B/QJZZdnf3GA+6PH50n6CD6GpvdHjr2+8xnc3RWSa65kXBsN+jk2d0OwVKi7Z4U9ecHR2L+Ycx+ODp5jnj4RaLxYLJbIEtOlCJWqHzgaau2T84EP55E1HBcH52ysnREcNsE5S0m1utMbpD03ge7x9w7/4DmqZmY7jB7Vu3eePLV3DRMFvUnE3mHJ9NmE3mHB0d47yn9Z7p+Sn/+Od/jvHWFXqjMZ3ekJ3NDi++eJujo30OD0cMxzvUTaRcNhweHNDUFXtXrlCWFadnZ3JXEzvLx4ug7Fy7LhB+p+Py/Lpc/Fx9VghBTJFTAia7N7VeREIKoRInTBqPIltct8368y8H8dVcfvL8JIFamZ6b9Ljb6T55btKMcTHXVgH9XxMz/24y8h8G/jjwTaXUr6fn/lskgP8tpdSfBO4BfySd0FtKqb8FfAthvPyZ34yxsjq0VnQ7xfr/n753igQVrB8rwaTSxblx/Tqb400mk8n6Wlwudq4Fk2yCF+hijSHPLXXZMJ2ec//+Q9544wtsb+9Q13OyzFLVFVpZnHeyY8hzsuzisj1x7dVFobEsSw4PD7lz5w6/8Ru/Lph7TPzUCGXdcOvOLQb9Pv/iX/xLMmOE4lZ0MMbQOpGs9c6jjSZGzcNH+9SN48HBYWo/V/i2pt8tGGyMcd5jsixtFVP2qhRHpycsGxFxWume9Pt9se8KAfTKJX7F+kiPVJSFIZlpaGXI85zhULDWZdkQg2QXRivyIifPMpxraZ1nNp9y+uAhi7IGLZLAuc3o9jt45zCN9AQslyVVWTKfL5JPqrBCVnzxJ+4lF4UihSywMZlcX+yITGK7BOq6pdfrMjk+JwRPP8+ZL5fCATea2fkpbbVEE8gzS1tXaXxoDJHgHPVS3F2qOUwazcuD/vqer4rLg36fthHFvqauhVnfOs5PTjh7vMC7mrJ29AYbzMqKzJi1JGs5ORffTy0MlbZtUVrT7w/pD4eMx5tsbGxgs5wHDx7QzQtmZYmPiumyZDzeRGe5yBUgrjdaBQiGehlwdQauoFkqmkoTlKHXGTGfLvj48DGLeUO/V/Dl177EoN8Xdyqb4aJi0Bty57ldTNGnTdc4+JbWOQ4Pjzk8PKJqWpokA2GzBds7mvNJwS//0i/w0XvfYnP7Kp3N62xsX8UAV6/ssrO9xWw24/jkJEGUF2bU6pLv2pUrF4bLn3V8anxc+v/LGPliseDw8HBNIb6QspVu6tWYkp4ES1UJQ8v71KQnrP/UUS3zMkZRqJBOdAUxLQxKsazrBAnJOW1uba4z+qYRSYbPC9r/plgrv8DnA1Q/9jm/85eAv/TdnkSMYrS6wp+F+nap/fby9wrSAo5WhCiVYe9Eoe3WzZssZrN1wUDYicL4UMkcGKQjNEbpvCoyw3LRUlYV3/72J3z1Kz/A7Zt3ePf9f4Ux4uQNgiPXdU2nmxNVQBtSr40EMpeyiuA9Ostpvef+g0d84Quv8s1v/Ct0dIktIvzh4Foe3LvH1/7dr/HX/tr/zOtffI3hcMDmeMQHrpYBrWVC6ig6D8enJ8zKBbooqGMkw+CjRuuCfm+EV3IuBKF5EUWTpl6WWDxYB74kLBsWPlBYQIlAk05URqUkGGqliCoSQ8Oq0cqHwPlkwmQ2w7U11lgGwyHWdCjLhtPJGdP5IWW9lKtmMjLbZTjoEh203rMol7RtS7WsmM/mLBfLlEkrcmvJ8xwTFL5xqAxCTJmRTibMKhVi03JjVItRDd6VuCAmD0ppMmtwTWS+nJPlhrzXA5/jUtHKWoMOAe8qce9RkBc5Js/RJiO2jbAntKZxAeUrvA90bBffVLQ+JCmTQH8wZLmY0ekOKTYsjx89ZF6W5Kn4vTna4HD/ASoodIwQAouyotYGhyY3ItAVokdnml5nCEHjnOLsZM7R4US+s9Hs7V1hc2sb71pGO2PKumU6X9AfjOh1u9Tlgsa1EBzL+ZRcWYxRNEQ6/SHFYExbz3hwsE+zmFPXFc89+wzDbobVChU9XkV8aAlRMT0/YTqbkfdGoC3dfgebG7q9Pi+/8gVe+WIu9Zsg+ijVck7blLx89yoPHh2ilGJZNrSxxNTHbPe7fOVLX+Sdt9/mfLJgaA2lltqPW0GQSiNNUprx5hYe0Mlc+jIbLKZ5cTlLXtM39cXOVGvDbHbGZDqTtvxw0WcSVjmmEmjHWIkLkiCkHWaQ4jFK6i8rt6CQ6KqQ+DQRrNEYpQnOMxqIjo41mo3UCOScWxdVPwtage+OgfP08X3R2RkRSGJVNHAks9+EPws1cYUdqdSNJ1+0aVq0UhRFwcsvv8zbb74lXFudJn2i5qlLuPuaKqgN3U7OREmF+oMP75PZgtdee5133v4VsuxCxEujpGHEaMlUjQaXNmcqGS4gVmGuFcW7b7z1Nr/7R38E17ZkucFHD1G40NPJGW/+xq/xzDN3qJuGo6Njtp67w7VrV4XLrM0aKpAQFrh372NOTo4ZbG2zWC5Bw972GF9XXN3dEXnS1IxgVjiJD5weHnK0/4Byecbk9BDTOlAZOlRSVFZKipsxrimNWl3UJVbFINcG5vOKojtgtLHJYlHyYP+YyekUfCArcsgsJh+gokzgajkluMj0bEJZlsyWCybnZ8kLVYTFMivBpiUSg+jMqMTkUVra1P2awSO7sZjEy/CCpc4XZepIDRgTIdZYHSisolzMyXKLyTLauqbTKdBEZtNTlvMpMbTiqGMNy7KU7jwttRafuk6JDqM9xAZfV6ioyToFy+WMwWDE0dERk7YhzwyLZc1guIFvW/JOwaODI6KLbG9uYkzOtJrjvONsNidqQ64V2nvZ6WmxOVNRmA5GG2yWYTPJHJyvKcs5G6NNTk7PBWLUht3dHTq54aP3j4iuwRgobIBmhvc1RtVsbY84n01p6iWjjR36u1eZnh5zsP+AI4AQGG6M6A+HdEdDjNKo4KmbBm1r8sKwXJZkoUPtak7OjzF5B2MLet0Og96QbqdPrzC88vIrtCGyXJaoJI0gDWSByWsv8ujRl1ksS2bTKQ8e3uP09JTz6ZTFomQyExEto+HKlR2sVVRlm5ptku9lKtAbY6T7clVDiRKctdHS2Z0kLo5PTijLGq0NLrh13SykcSVxKCY/3Avj5hhEcxwuIQEIMy54h/QBpD1sDBhlkgAf9Lpd2qYRRdT+AGPEfUnpFfT76Zh0Ucz/3o7vi0CulSiPyTY+fY1wufPqontK6Yi2hrgSluoUGKXxbcuLL78sE94INS9eap16unknhCDOMHnOinv3yf1PcL7l+vWrqetPHOaDk0q0a5vEBjEJP5Mzk4UmCUilIqtWmgcPHrK7uyuDT7E2LNBaso1/9A9/jkXVcv36DYajESFGhsMRIYBVq6ULXHCY6HDVEtVW6GrOVmEJriQPmsnsmBu7I9Hf0CpRCsXk1UfH+fkJ737rTcZ9i/E1yjvywjLqdwmuES4zcHkAiXFAMqbFAoZAoG0V81nDtx9+iHMBawuMUmR5pGmWzCeO2aKhrDyT2YLFsqRuanx04APLszmL2YzBoE9p5C/ZTJMXlqKTkefyz4QhJqys4iLKgbEKY0AbKfrpaIj0iBhOzxfpO9doU6NjRa5aPFBWDW1dY3sZnQzaumIyn1LOp6x0dYKXHdNF+3bSrF8PoJDYCBl1vaRpKlzbYrOc+bJkuDHm9PhQ2EDWslzM2ByPpSuydYz6A85mS4Kf47TFNS33P/yAABgiHaXIuyPIu9LMY7OkaxPF9NqCUgGTbaF0ZFbWzMsKFwIb/QH7Dx+wmJ7RLmcUOtA2S3y5YD4/Z76YEyJiTk3g/PSMwbWrHB2dc3Z0ynwy5eatm/y/1L1Xk2VXeqb3rLW2PT7PSVMuywEFb7rR6JnuJqeHdsQgpYsJKhRS6EIRulDod2j+jm6kCA29hmwy2E2y2QYeKAOUSW+O33YZXax9TmYBkIYcaibAHVEopKnMc7b51rfe7zVxHJLXFR/8/D3eefc79Dc2EKZCaE2RLz3/vr1Be7BB2urS6Q5wwlP1Vpx3Jy0mitEyIFCCbrdDWVZMp2OiKCaKFDd2r3Lr9g1/7aVAOes9wPOC0/MJf/4XP+Lf/9EfoxH86R/+X7z55pvcvfsCnW6/WRA8jVQID68GKgDcmrf9dbzvw8NDdF2vO3rXMIMuqzsvd/pfoQtzAc+u6olpvF+eZ8eJdUB0FEXkeU673SZNLzBy3EU37v/6ymTqH318Iwo5XGKXiCboVH0VzVm77EnhVYLWb9WFg2KZcfeFuwSRL5rWXPCyV3+vTvhq0BZFEXGYNMINw8HhAccnc+7cvgMi8KnnWKw1hKG/cMYYAhU2Bc7fDBKvzPP5gl5SreIWZaUZT2bESQtb57BmuziMrsjzjCjyPNkw9Ak8rXaHKEnBuYuBrfRxbsVySn5+zPWNNtQFYQBmOiU2GYPUEeGQ1ucKOixO4IMkyiWdSLLVSdF5ha4ctc7pJiGhsI18vTlPlxoCZ4XvplAoGeGcoZX2+NVf/Q2quiTPSs7Oxpyc7HN8/JST42cc7J8xmxSUZd3Y+joqk5NXS/LlAlE0g0kz98UxkARRQFAGRFVEmibEJiGKNGGYeBaTUN6My+IXF6NwKgAVYJsotizLCAKoqhJJSael0BlY5Rf++XLKskkoqoqMusq9xVMINDazxmq//TEGa6qGr6PWXZ/A0u+1SZLInzO8IGkxnzHodYmTBGc0URyTxD7z0zjvL/7k4IhhbwMlvfGUqWtODvdpt9rEQUQtFXk5oQ4WeMMkPzQWwkvAlJQNVVPT7x2yeeUOrU6f/kBRlQXaGK5sjRgfZeTTMZPjfZbzCVmVo50lbXXYHm7x05/+nJfvvUyVFwRxh6u7XcbpKcenJ9R1Tp5n5MWSjz/4gH/1w18lwhAnkuUywywMSbdFWxiq+Tl5uaTbGxDFCTJJMCLEigCa0HBT1iyzJUkceUuCZs5k6iU4i5QO6SyiLBiPJxyenPNs/5AymyJ0SYDgyaOHLCZjPv7gfYabOwy3trh69SrD4XBtNat15fF1dWE6hRO4FZpq4fjwCJodp1fwglh5BIkm0EbI9VB9VTMuD0e/LP65rOxc1TApJWVZra2oAfr9fuOHri9gE8fXFnMPIHxdgf//Pr4xhRwu1qKvmt649X+NMVjnKGt/8bTRRGFEnCS8+OILdHpeffl1HfjqQlhr10IeIRpVYG2ZL+Z8/niPN199FSECnPBSbakczvp0kLoyKBWxVl3ixe6ikdEbY3jjjdf43q/8kG5/wHsffOx9FVjxTIGG/uic9y+p6hqE8PzlZhBXlsUaG9auRusSp0vODp5we3uArXOcsmiTo5Rl0JIkgcTWNSLw6jLR7CoCBRvtlGE7oT9KqaqaeWkYDTo4XSOU97H+8nW4oCM23YmzTKdTdO1FTL1eQqczYPfOXawCYRz5dMnZ0QnH+/s8fPAp9x9+yqPHxxTT0wazVMRpY+AfhF7NpwKUCBAmwtUxTkU4KhAWFTgiGRNISSAChPOPn3QO5QxSGrRZUM4PSQINkSIv/fdEkUK0Q+rYIaUfwub5EmFy4nDlpeFtgCtrqaoCT5n0vzeUCm39dUJJummKkJYoUgShJJUJkzxnuLmFrgryoiBYGedLydnZCXHkYZFuv08YJ9S1pdSGLC85Px0zC+dUeU0UxmhhIRKEkSJJYlrtlDiMCFUEVjI9y7i6lbCzuYsIAhxeZVqXNTKIyIsCIQOGoy1uXN0GU7Molkxmc5ZZwYOHj7lx8w6oiMlygnDeM6Xd7mDGR2yOBkThJlWZ8/57n7A8O6bdTtFVxa3NHpubmyTtDjLwts+L2RH5bI8qjKmdpEKhRYgIE+JWF6M9zFHGITiDFI52GtFKY892OtzjcH+P8f4+88WS0oCMUg739wmDAO0n7n6oaB2T6YRFnvPs2TPvZDocsrOzw3A4XA8WVw3bSmwjpaAsCk5OTp6jMK4ow1JczgoW6wCKlUbErl1OWauLaerQ6t+sivOKxVZVFcPhBkVREMcxGxsbzy0Kz7u3XKpyX2Kx/GOOb0whf47C4yFbgItO0XlpirEW3biRSelX0LBJYdnc3KLf7zOdTtc/88vbpNWFqKoKJRUyECRJTF7lLLIFDx4+5Hf/q++zfeUah0dPCAOBoMY6jVQReVH7LZ4MfOfmNEoKlBAeEgkjwjjm5u3bnE+m9Dc2STtdymLJypPKYXEW6qpgOp1w+97LOOez/r717Xd5/xc/56MPP1wLekTDHKnLnJPDfZJ33mwWjYpYGKoqp5MGRIGkqB1gvRcKErBMp+c8/eIh28ld8rJmvlxgg5StQc+LUJSkITw0cwouCjiNkT7ei2S5nCOF36U44bt2RITRihDHaNjn1rUR4TsvUeTv4qTk448/5n/7d/+O0/EMp2JUEBAEkkD6TldJBQZaqoUwilBLMAUKi3MaJTShkCRRgJKOwaBLux2gpPdFuXvvJfqb11FmAXVNIEPCMMU4i5YVhctJWg5bCpIwoSx99x2GXR/aLSTGxJRlDdL7yAR1gAKWpcUZrwguigVpELN74yphGLBYZvQ3NpiMz9FVSRQnhEpS5jlpqwXyHCElSRxRVgW1NRBE5PMMVMMBlwoZB2gZYWyFNJoiKymLnIODfQIVEKqIOGwRq5St4RU6aZt00GUymzGbz6l1ja4qWpEvpFo7tABbQ5wM2WpvsyUld156G6kCJpMJ3eE2Wbbg5PiYPF+Sa8P9R0+4dnWHVhpz8/Z1Hj76gm+99TobGwO2N4dsjkYsFwtcnhOHEm3nBKYmDFKWpcYYgSXEhSmz+Rkqjr2MXYf0u13qqmJ8dMT94xMvGDsfey4/FqF84HleVpyeT6iswxgIk9C73ziB4oJWWFUV+/v7HBwcEEVexr+zs8NoNCJJkqar9UV2Pp8xmUx8IV3h4c5j2t63ZiUEuijSq9phjF3/nMt04wuI5vkdv3VuzRufTqcMh0P6/f76dwBrX/OvP/4zCoL+SxwXw4rLb+L5N+td5BpTqqba1I0k29WGVqvFndu3efjwIWHjjAg8V8xXJ78svdLPWUcUDXEkIAAAIABJREFUhli3oKwLnjx7hlCCu3fv8fTpI4T0fHNrNMY5ytrRUTEqSqlN5W1Y3SrFyMvgz88n5GXlLVPDkMHGkLPjAz/AXb2ZZoAym8/88MNYkrTNtes3SNK2Dzj2ey9f/Bu8/+z0DK1rhLXenMfWVFojZUCUxFS29MPeppPRtQEL+/v7vHh1g2W18MOeWBPHIUkcUWiNB2FX57+h/WDWcJfnmRtmszOMzgkC2XgtN/mW2lIXS06W5zycnTKfnFHWNZX22G97sM3pTPvYM+U8/uwK4lCwe31Ev9NmPplQFzlJrPnW26+zu3sFIQ3Wljib0+/HtFshQtbEERRFhtBwdvYem6OQWOSUQmFESG2EZ7qEUGRzhJQUizlxFBJHAoeiqkuU8mwjpQRpGmGtx9SrOscUFTUBBokUjqgJ7ojCkEApOt0Oi8WSJE2Z5DkIzzQS0g/XgjDCWE2ZLb1yUXj+cBiHKAVB6AjjsIF/EsqlQNmAQPrFuBUFaOODpZeLKSbM2du7zwfv/y1vvP0dqqygKgpqY/zuS4UErQF5NsdJgYwSnJOY0kcgemtmjZMR3dEmwyvb3H7pRYQzSP1DZudjnPXccFPXnB4fEA5vcOPWLkJYMgFLW5KEgsPzU5SAnZ2rHBwcUhlH3OriakdZF0ShQtcZRVUzLUqeZDnzycxTcKUiCmLSMGRZlQRRgJEBUsVMpxPG8yVWRIhIYQnQTqKNI7C+gTCmYaY0z1NR5Ozv7XGwv08cx2xtb3H9xnUGgyGBkpycHlOWRaOXaJwtWRVWgW0G7GEzSL1cpFeUSF9/LqiNl7Hzi/okmkjBVcyhptVq0Wq1WKMKK3z8K7XNf2zdZZ3EP/z4xhTyL5+c4LmT14QQ+P+7SOYBjPAp26oh4r/15lv8yZ/8yVcK9+XfA56/W5ZVA8v4ZA+H49n+AbO55u233+HP/uyPiGKJkFCV3m60qh1Xr9/ine+8y1//5R8zOfd8U3/6PaXp7PycqtaoMCKWkqvXrvPpR++TRoHHPpvtnDF2LRayFvK64umzPTYafre2GgQEkcRoQ60t59MZIm75IOk6wJQZVWWx0hKmCXqZIaxqWNCShj1FWRmWeUE3VGxvjajwykHwDoE+TdRdeKg4T9ukGc4gPF+7yObMZ+e0WwnT8wnT6YJitsDkS+q6QEhDmASgJE4KwiQhr/1wsdONPXtio8XGRoJwBUU25ltvtfnBv3wHhWU2mXB2csQbr99gMj6n1UnptLvsPTvFmHPiqEuSBLSSiJkoyKZT+t2QQU8gXEEUdMgyjWkw7zCO6G10EGWFbCdUpQ/TTVsJWhekaYs8LxBCUVY1WvuoQF1JrHAEAoqyJlAeL7da8/TxF+x2b7Jcas7OzmklCfPFkn6nxfn5KTtbm0wnEwTWW/I6R11qAhFTVRnW1lzZ7vPmyze5/+gB7TjBCEW718YV3uI3r3LCOF6HZzsFuBqplrTb3iM8z3PiMCTLC7qDtofUnMXJECOE95k3IIRCBiHWXfgCGV1T1gXgWRnWKoJ0A+Fg0NshkJKrL78N1vBsIQiDwIvo2ttUZUZ/dwtna06LJcHoGvliwTivyPKS+XJJUSwoqzlWe7vgQAYkUUKgIj8nqC2hCuh1e2R1QVUb//wdHqNRoCQgccLfx9b5IT5yRRcUDT1XrmuENYYyNzx58phnz54yHG7y4gv3ePbkGVVZNNWjaVTcBSyyqhErRaevHbbpzHmumK9EQKvashqOroWCVbWW4kspSdOUNE2fG8CuCGVfrn//uQVB/0WPr/qjwGr1kg70+o36iyHx5jrGWuIw5LvfeZevW+0uXwzwJ66uKtI4JQxCDy84x+HRMefjCa+/8QZSBWveowoCKidIo5Rvv/s92mnEJ5/8kvHZAU7iH/IanJTMFkvOxxNGO1dwxjLa2sG5SxevgSWEUCwWM4yzhHEMRjBbLLhy9RrWepl1WZY4DTTby8/3Dvj0i6e0WglRGJDELcJum2FP0N/c4nQ6awz4Jc6IRiHomMwW9De3SUWJRbBcZly7foMoTikrPNtnhRU2/3FO+Ki61e7AwenJMT/+0V/S67aQCEIV0oojOp0QEVyknxRVhdUVUmqePr3PvWsh1tQIo/lvf/9X6Q8ShsM2s9kxuIqz0/c5PztlNBqycyWi1Z7yxRef8Ad/9IDvvvsq7777Nn/5o78iz5cMN4Z0dkfc2r3Ow+J9bt2+wbPTY6L+GBnHqADSNKAslF9QnSLPK5IoJIok02nVGG0ZtKkQwlJVNTRyozzLoCoaJ0JPRVSBoixy2r3euqtstyLMcEhVlbQ7HabTCSoIGU8maGOQAsqqwhmotCVxjZGb1AhV8Ovfe5XvvnmN9z7+jCcHY8osQ8URTgREsaKoDTLooE2NswFKaLpd76q4t/eMrDAss5zr165icDx4cJ9Br0unnVAVWcPG8j7nvhG1a9tjAajGSXMVcrCS0VeNgYgQDq1rpHRoXRIECoVFAeG8QilBFCqqvOBo/4z5+IywgRmFMyRhgghFYy7nPD9fCAwWIywq8vYX7aRDIhT7J1OOj09YWRXTcL29tLr2g27Uc3VihVWv8OtVVKOUivPzMX9z+re8//6HX8vNds2Od/X5583tXKPOfD7B6nLD+WV83DlvyDYaDlksFgRBQJqma3TgeYrh+gV8zev6ZwytrFe1VSe+5iFe+h4cGOsl+M1DpzxHi7IukXHMndu3aMUJtTPrafMqcOKyzNY2NpNKej5yEErywnJ2ds7R8Qk7VzZpt7uNwjOkrAsEiqysWC5LRpsbXLl2nc8++UUjpPGPh/cadhwdH7NzbRerLBujEXGaYnTZGCJ5ihKBJMtysmXG1s4AUyqyvGB7Z4cgCNFaEwbh2gNCW8hqw7y2JHGbZa1ZLjS6qlGBJGq1sc4LpcLG7lVrizGwzEvy2pCkIUiQyhIlvuMRUmB1E3XVdOKyqebOygZrF37RrA3OWDZ6fcJmAEmosBEYFBiFRBKGGmEmpKokKQ/5n/6bb7O50aYsFhTllOrsiFr0ubm9yS/ee49PPrnPcpnzNycfMRh0+Fc/FLz91ne5fu0FyqLC6JDXX3+X2XTBaHObp0/P+XT5jNdevcfe8THDnXuoKEaFAidKqnJBHAjQAWUpUE6hbU2oPBYaRgGyFCwWUzY2RkhZY7Qj0wXGGhTefEpGAUpJhPNSf60Nw+GITqeDXlRNIIXGWp+xdGVzk6PDfb+gWeu7RhkTBxGIAJzF1gWunPLKS7dQssv337nH06Mznu2fsFiWzJY1f/+Lz2h3+yxzRxx1/MCvLmklI6aTkihe0B+OmC8zjo4OeO21V9h/GlAuJgzSDaSofep9cx1Fk6wlaYr6Oo9WevovBmOWSBmCkNS1blz7PBShFAgM2jhkFFNYi9U1kRHky5zaGlqtGKocWxYkaQcjE7SxGAdCNU7vxvuoCCWRocTWGhp482B/D11XDcPFv24l3FoUJ74GP1510atQlZVPTW1Mw0QJqBtixJeryarurAryqk58nRneZXbK/1shN83vbLfbnJ6eePV0YzFweTjqn+fnWSsXdZB/5tBK88bsmpfjcWEpPaRibJMoblaUH7eOBwNHksQI4eh0u1y/cZ0vnjy+tJXxqsRGG7jGm2tt0MYSBpIkUFTOspjPePTFPj/4/itsDDc4PS5xViJF2NDRLCfnZ9x7/RWu3bxLqGKEzjxrRSqMczhjOTk6RjQ82X6nS6/bZXLu5f7IpuBbqIqC6fmY7e2rvot2vrjKMARj0FZ7DC/wPht5XpCXNWlngBBeRVaXJVjLcLiNkCFrx0Ln/a4NjqfHZ5xllqyWdBOJtiHTrPAp88aHMlgPh6+xfOkaGiM0W1zPkrAWkijB1NrnZVqNcRIlI2gYFkJYOp2UfgL/9vf+NfPTz7m+3eLotKQqNO0kos4KpBZsDXYYfHebdnvAxnCHebYkaXmJ+vHxgjwvqWvBzvYNRhuauq4JkFy/foMgCrly9QqFkWSLmo3NlEBZgjDC2ZxAOpJOi4UtmZxPkGlMt9vCmpp2q0WoJLosiYKQs8k5Qvi0nrAZemId1nqGSxRIzqdjjo6O6VybMM/8ohyGAZNxTpwklJVXCWutCZQkLwrSJMDWNVI3ikXjON7/gr86+4jtzZBX33qHF6/0iOySveOc3/3t7/Pa3SE/+/kD9k4MWZ1iXIJLDFlW8fD+Az746COSNCXLS3Zv7iJMzheffcT25hDdjZuMSIcRjYlTQ401TQEHT+MV+CAW5SxKOnAVxkIkPd121Sxp7el5cRxjGrgmUhJbF+iqJpTK30tSkXbaWCswToAKfGassz60W4pm8KdRTRMhhGL/6Ihne88IA08FdbIpsv42xiIwzusrbANBeNpgRaPJa8Q6YKx/fUEgWS5zirK6zL9a7zhXVMVVQVZKUdeN9N7a54qpCtT639sGZ1+FeK/UpLqBYoIwpKpr+mHAcDTyMyZWv/SCC3a5WX1uNPWfcHwzCrkAoRSr5AzrbBMo+/y3KSkJ1sn1xqeXyJXXgcNi6PTbvPDiXR4/ftx4s3jY5TkvBuu9uI02VFoTxpJYSaTVVGXOBx99zK//1rcZ7WxzeHCAcoGHN6zGipyz81O0UwxH10niNtgSsyp42t+006kfHIWBJGwljDY2mJ6dgpRNiLQvGNIJlrMJUWPSZTQsl7kPQ3AVDo2IwNiawAp0WaNLTZK0EMrnhoZRBMZw9eru+iZ2wiEbW1tjLdOs5mDqzZIUNVWeUWYlVvt8SJ8VKbybJJ5vqxxYYZsMG4sVXgHrPVH89ttabwMbOU8JFMISxAIZOyJhiFuO3CxxofA87jRBBwEffvIxaRzR7vW4dn2H07NznCjYP/iMq9ev8fnjB5ycJty4cZNXXrnH48dPWCxqdm/c4OzkmH5HUBfnPHvyhDBps3VlRNruII0gVilxmOJwFPWcvMwIA9jcHLCYz5gtMqypGfT7VE4wm84YDUe0WylZVpHEMYEuIA4pTI1zAUEY+YDnMGQ0HFBVBVUFQioWiyVSSrIsIwoUSdpiWhYgHEnaAuetaluRwoqETMRcv7rDqHPKcJgiZYW0GbduXGOj3yOqzvi1b1/hznbE37x/ws8+nbOo/c6orqYcHc5YljXL5ZJ+f0AnlbQiuHPzepPvWZJu+EQfoYtLo46m+OFDSFaQrFtXtlX2atMZKl/ojRMIFflGy67i6CwKP1SPpMIEMdpqZILH3I1rIE/T5LI2amgcutaNu6dvxtJ2BykMW8Mu03nOMquonMXii3EUgLU1WB9gLKTwtODAt2YOL+pyzvrmzgpwXoG9zDLvl7Ia9azf66oj91CMdzX1yUvOXhTyNazS1BLrbPP7aP69928SEqzVhHFIpSufIpUktDptXxkaMZG164txEZPhYH0xfJH6R5fQb0Yhx283rJSeYnTJT1ys3/Qq4MAPCX1iis839B4t/qJ0uh1eeeUV/u8//Q8Nde+rg4XVRdDGYKwhsJY0bSGYYuqKn/z4x/wv/+t/z507d/n53/yUNEyJo5ja1NTWsJhNGI5GRKL2uYl7583A0yLw3iCnZ95zpNVqEUQh21d2ePTovu9wncU62dALDcfHR6jGG8Lit2aDfp+zkxypJMZpFP5GqMqK4yPvGGybrbsQAhkohqOR91FuZN7+gfIQktZebBSGIcv5mQ8lICBNUvImBMFHvzmP3/PVhRS8cf5iPvVbXuWDhqVs5OQq9vFqyqJUTRJrJtMnLCdPWYwP+N73foUvnhzSCiJ+67d/gyCQfPzB+ww2XvPfn6RoXTEdH5AqULrm/oe/5K233yZbnKF6HT5/8jEbmwPsLOPps8+5sn2N89MxWf4hr7019CrVwiGoMLVFuJA4TJnMp5i6ACHp9noUZUFeViRp2w+RJzOSJMWJCudqDBVO1ejaIlTsMeLIF7KT8SFXtm5RlBAE8ZpxlSQJp6enSAGtVqsRPtUss4xWEFIbR2Vr5osloyTg5u4NHjy8z89+8TFb3QEyTHjh1ZfQteTJ0QG1U7zzzi1Osvs8PZiDTWi1+3Q7Kd3RBqPRJr1uF+s8JmyNa9JtQk+vVQqrnfcKkdLfY/aiDVwX8OY5WXWErvlANveDFSvrhpV1cOM9gm1k6g2+bEKc8R4kFoO1ulE/epV1ZSowDbTYBKMHEu5s9/nBWy8hfu+3qY1jNl9ycHTK3v4heweHzGZzFsuc3Gr00hGkbTARQsdIFSJEjBBB84oFSI1xFUGoWM5nGFOt4Umad7+ON2ysKVb2tBf88SaG0T0f/rCGVValRPjwFSkldV0zGAxYLpeEYUir1aLT6Xwtw+WrRemfdnxDCjk4bXDSIVRAINW6g3DaF+1aa8IoRNKQ8puEe4SnD9HIuauq4vatW/5LrBa754cKq4torfFRX4kiTVKkmKGt4emzx3z+6Ak3b93y2yDVGEg13OzTkyPOTo65srXJtRu7HD/5DKkClJMgfVrNbD5hkWe0uj2EcGxfvY42lihQjdWspfG1Yj6dIpz3qI7bKWkUsL29xcnhXpOfCattoJSC8WTsMTln1x23c45ur4dQjZ2U8Zi3FIJae1vdsiz9whIECBNglGkUaM/f5P60Xiyil64SUkKZZ0Sh8q9H+GxFJSKUCLDCgTCEoaKqSlABYdpnp91hMq9553s/ZP/zR8yXJVubG9x75RWKquTKtR0O9vfY2RlRFAUHj47JFgW3X3gBnWuGgy2CNOHRwwdEUYvtK3ewJGwNNik+e8BsvM9P/vr/4NaLbzIcvYCra3RmPaMphDCMCAJBvpyj8ecrz3PMcuktHqwhz3PqWmOdxhY50loq7ai1QwqBNjW1DUnSGIQgSRPyzMvCNze32Hv2BCG8A2K32/b0UClp9wb4O1ehjCBpxSRRxPjsGGlLdq9tc3VjRJLG5MtjFjnc3b3FLDf87fsPmDz7ghuje2yPbrI12EaqABNLtDVgtLe3UAoCD/45Z6gqQ5LEBIH3PTfWd9BCCOQq0Fs0c6fVPKRZvMXqUw0uvbKf8DeG76KdtRhrsVY3BmsChMKJsGlnGnjE+uJntC/qzjUQTRRidM3O5ja2zDnZf+oXoUARScWL14e8cnsHGbyLNY68rDhbFpyMZ5yejplOFywXJVleo432vkTCu3hap3EYnK5YzCa+i19XhBW8cXG3O+eIoug5RspFiPtX+eOrurN+RrhQi7fbbc7PzwnDkF6vt8bOvw7zfn4A+k87vhmFvLm4sukOZNNGW2NxDSaexhGoC4GMadzJlFI4vHexajCye/fu+axAIC8Lf89eKkriEmxTVgVd2SdQK8c8zXIx49NPP+XVV19vipz0uFdVIDBU+ZL5fMag12F75xqVcSSB73hsUxR1XTGZzLhx8y6hlNy4eRcVxAgsSnrTn0rXoCLKIkfXFa12e73Cj4ZDdF2jQh/NtiJOCSE5Pz/3N18cEzTycacNm5ubvjDXdTPtbybwxqJrzWKxoNfr+UJuA1zkvCLu8iSdZusnxPpzl7NUBY4iXxKoZufUDKNovCuUCgiigG5/QK+/S5GN2ei0KbOcMl8yPy8JoxHvf/xzzsY5VblASsvdu7u0e1v83c9+wWw6oadiXr73Mru37nB0NqbUjps3XqTV2mTv2TN0ZYjUJk8+P+DqzjYisGR1RciSgycfsb+3pN/dJgoCtDUoV2GsJg4Vde1VskkkWcyXlMaCgbxYUBtAhIhKIJwilgHWePFVleWcnJ+TxD26nSFkAqMz8izj9PSMoihxuiYKQ/b29qiqEgH0hkOKfIlQIWVlqOqCuBMgneOVF2/zbP8E5QxHB49Je10Wcwt6yeFxgbCbfP/d27S6m2AUgVMYAYUwRLHHpb0TqA/tkCJomEpeqaqUAylwxvjg6IbGux5srw/PFFnBL34o57tu6Zp8WWiAXQ+nWWf8tVdNcpJUjTupxVi/czbGUtcGrWtvm+EgCCDPS65c2SZOUpZVTaUtstA4LCoMfKi3bAbTKiKOY65t9tkadpH3bhKGMcY47xmvHbPFkvFkyt7eAc8OTylM6H1+siVKNHj2evx20WHLS/j4StF5OXMXWMf8fZmxsvpbSumFZY23flmWdDqdtaLzH1YC/2nF/BtRyF0ztFTKd6v+c94ykiZ2STQUqqLyBvpFkWONIWwoPtY5bK0JgoAXX3qJNG1xPhn7PETp2SvPnaqm6yirEpyf3EdRRKlrjCl59OgLfu93fpuk1fbYX+DDg621FNmCs5MT7ty+xbXdW1gnqbVFNnmYxnohzvHRCRBQVTW9/pBW2qXOFzi9wvk873cxmzA5P6Pf7xEFAS/cucn5D77Hn/7JH/i9g4MVcdIYw/h8TJbnRPgtYaS8H8jGcEgYReiq9F0SFwydoihYzuZw7aKriOLIF/LmyV4PSZ8/TaySeETzPVmesRpNy8bPVyqHDAQiDAiTBCsizicW7AhdJUTqKiKoSdI53YHle73bzKenzKanIDSHZyUv3rvH698acnp6iFke09neYO/skB/91S94+ZUXWb73Hq9+6x2iMGI5X/DCCy9i8gkCg6kc7/3sQzq9Eb/5W79LL53wF3/+V5yenPGd77zFp598jBKSIJBMZxqlHP1uSF15qEtKSZ7laCMpSkE9n1MscoRKSPpdxpMTjC7oDq5TLAqSsE0RaIKgbjIgA69JaLc4PT5s3Ct9B2q0Jlsu2djcxAnH9HzO/c8+5fYwRxcxk/GcycEJlbSYHDa6tzkvNwm6XbZ7I6b5gtqVCGnRJvNcbCEbhwiLICSQCkvTTTZDN2MslfZ4tGxYNKsle13MxWrg6a/2ukBxQVNcXeuVwlhim0EpWO1Ws1PfUDWBE1WlqbXGaF/IjQElw2ZXGbGzs0maxtTW4USMwb/uMIqw2hAFIJpZVlXXFNpgp2cI4ej1e9gg8Apv5Z/drc2Il166jVT3mC4Uv/zwCX/2H35Eked+2Ap4nccaFFnf46sQ5VXnfLlLvsykW9eqS1DLqiu31vo61LDh0jRlMBh8pdZd0CD/44X7n53XihSCUAW+E0cgxMpRUHgq6Wr70rwxa30nWdc+QSVsblYrHLXRnou8s83J2WnjlXyBd60O2zjqWKuptSVSIXEYsCSn0vDo0ROQip0rVzk8eIJs7GGlszhdMxmfEScpO1evE6dt72uN9/QwxiJRjM/Gnt0BRGmLfrfP0WxGqAIsBqXA1BohHDdvXOPf/MYPabU7tNOYbD6l3UrxuZ0+I1EIf7MvsyVlURClCcZaSmMQziGVD37InB9oidW03DnKomA2neGM8bQ4IYnC0MdoaYMIG9onFx34cwKFBuryJv2LxuXPW6waBC4SuEjS6naI0zZlXWP8UkJVKk9btAY5qwkDRxS2aXU7dDduoZRfCFCC0ZVrlK6PHGzwyeMHqCDg9//H/47FfE6eLamW57Ri5ztfVXDz9ja/+Onf00pHvPvWr2KdYHpySiQqfufffAcbSu6//xGv3bvG+GzCaDjkvfeecHZ4QEt0uP/gMWEYsLO1iZSK8emEKOmz0Us4nJ7w0UdP+O4PXmOjC/3ukNwIymLC5PyUw/Oco5MTlssFt3ZvNK53MQgfvl0UOcNBn9ligTOabDklqyriOIIypSxL7j84QAYp24MRrV4P3dkCsUEuNoAWUdoBVyGcbvxlQgSCVHqKnhMCYz0FtfHWZOXnLRANs8NzyVfPlHGevKtWAeOuYXy41VJ+iTkGXA56WE3ntPFuljQF2BiDtj76zAeL1BhtqRpqJk6ia0scx2yOdkjCCGH9oNDbGXu1qxAWKQV5tiTLlujaDyPDQDHoxbz9ztv0+gNMVaHjGK01y/GE8WTK0dExj58849OHpzzZm3F6etZ01rIZMn5553lxT3vGSv2VOnHZzO8y3LLasawcEbXWDAYD8jz3zVUU+dDoL0n5L46LgevlheE/9fhGFHK/J79AsWhuQC7VkdUXo9gXHms9z3U1TKhrTVnmvjuSIW+++Sa/fP99gihEOJ4v5lIgkTjh/cONtgglaaUJZ+MxDnj65BnHR8e88tobPH32OWGgUGrlv1Cxv7eHEILR1g4boy2OD54QBhIrPO/YOMHp0SFUOYkEFSpuXNth/8kjoijE2hop8T/TGuoyJ00S6rJgXhUMNwakaUqWLZoppL8PjTMsFz7fsjfa8Dx4AKOJ08QLU8bniGYXI5ruQtea8WRMGISYIMAZf/56vd5FwW4Kv/dpaYp6w2pYdV21NiyLCu2cLyTKef523KLV7ZO2O2hjEcqilPVmX83PFkYgiKhqQ1VqlsJ4lksovbf6lR06/R7dPKWV3OParV8hz+YsihwRdukMaiazCVvbNzj/5H3++I/+kJdefoFbL77GZ+8/YjmT3H3pRWZnByzzY2SguXrtGu3Q0t/Y4IXdK9R1zVuv3eX+Z59y5coOv/bDf0mR50zHU54926Pb3SI3Fbd2hty+0eVXftjCBrDIx0gsmU7JilP2Dh5xNhNUlWV7a5t+v89nn37CfDYlifxifuXKVXTlAzi6gw1snbEscnCCJOwhk5jtwQ2CpEdgDI4EWfYxLoTAou2U4vAMbXJ0lRPKgDhIQCgqVaICn7rkVZf+irmGnmet/6PwDdDl0HLR7FJd42UulPT3ixQX2O+KgmcNDtl0tU3wSOPzbVmFczus1di6pK4LyrKkKHKq5r2Cp1wOBgMGgwGBECwXc8ASRZIaTS5d4+GjG665vyUDFeEsxHGb195+m87OjqcWtjc4fPSEzz9/ymeffcH9B5/z5Ok+Z+Mpcdpl69oNFrn3qEcov5NYBa+sD//RKod3BZ1cxrTXdiBw6fPi4muw5om3223G4zFJkpAkCa1Wa92tr39j8zxe5hH8/4GTfzMKOb7L9vzq5gaU67nyuju0XGBSxvhV0DWDCGP9ap8kCYEIeOONN9dqK7PyLl9vm5pfKvxNqKsaEbVMLqO+AAAgAElEQVS8ZF0YQDGdTDg7m/Lqa2/wh3/0f6LcauX0Todnp8fU2tuD7ly9zvH+U4/pIxpfC0O5nBJSsru9zaCT8i+/+xY/+cmPMMKAZO2+lmcLHtz/jLLIEcIXy16vQ7/fI8sXDa4pmiZCkOcF88WCKIywuMaTRZGmKd2uD3uNw2A98PRbasd8Pl936arZTrbbbYy1hJcoTyuoq/mISyNQhAxZFgVZpWm3Y6wMCYKIbn+DwWATrS2gkcJSm4xQlBBaCBzOSGztfee9BZIXqVRag5RMpgVn4xznInCJD1JWXcIYAqVxZkmndQOlDBubNSrcQkWCVmeDH/76XYrFkmf7XzA+fcpos0MYxZwfnXB2eELdq5FKUJUlvc0+G8Muy8WY8/Mxw+GI7e0hi8WUyml2t64zavc43T8hCENyUzJst0jThFqM2F/kuHzJYGOXBw++IE0SxuMpOzs7jM9PPb97OSfPM+qyxCIp8yX9bot2L+Tg8Cnd/g4yMZROkpkWkYDQhqi6hROGwpSIwIGtCZxAuRhXgdOgIkklHLqqPDQIDXtp5fnj2URKSSIhkGFAoCIfLWf881CbBrdG+LlGEHoYUuAX9BVXTwYeOlsFkDgfroFYsei8Q2Bdl2S5f89VWaHrEmcrqkoThQlbm1sEQURVLLChpJ3GOBxSaLAVptIYDFJ50oLv7A0VNVtbV7ly5RqPv9jnlx/cZ3/vkL2DY754fMje/jHTeQEywlhBr7fFaGcbKwRn4xmo0IuYVLAecIrVjsP517/Og3UX9rSrQzaNknXPC4Quf11rrwqOooiiKOj1erTbbaIoeu6Z+opVyJeK99d5Q/1Dj29MIX/uJa8ZFI1PtmO9NXSwhlu09v7gURQRhKFPeZESYyz37t3zAadcqLJWF0riE2ZWbmhFUdHvyGaLF/iIrfGETz97xkv37noVmpI+37PyHOSzs1PmiwXtUY9bd+7w3t//ZD1EVcpHX+lqTqxKbt8Y0IpD3nr9BaDCusrTFd3FsPbx4y+YjMe0Wql/fqRkOBxyeHTgIaeGNiakf99nZ2dEUURttC+6wi9+m1tbGK0RYfhcCXbWeWiFZsDT9ATtdtuzfrgYgF229Xmuc0AgpCIrKmrjKLUlbEXs3r5DvzdA14A1hDJBa0kaKqwJwJbUdUlZ5TjjBRpV6c2glAyJk5QoaqOrijhpIYPIGyPJEIuj1IZSCwLZQ1eaSMGV3X/BnddaPPzg7/jbH/+Eu7c3SaKSxfRzBDnOpoyG13EV7F5/lflsTGujjbKGo+ND9vef0e/3UIFkMp2wu3uTN956g5/+8pdsXb1OPc9RYZu//fuPePXte95Le1FilMbJNirqU5aamzdvkiQxSkAQCNI0ZTwec3pyxM7OtrcIiFok7T5FXXIyy5gW8Pjz+/yLt16m0x1RyTYySRDWc/VFBI4KEUictkgXQGWoy4zSVoRhEzgRBkCjRFxDB14XgK6xGoTzZm81Al8lFUEY+zCGJhbPIbFNJ7/q3OU6jmn9UAICa2jUw02Xqi1lWbLMFiyXc8oipypLrK38LiIIGQ1HCKGRQtJut0jiAK1LlPQunaUxCATaGMq8oK69b3eadtjZ2UUEMX/9dx/w4acPebx3yGKR4aT3XzFO4MKIylgGm0NG21s4pdDGklc12oJrFri1wGiF/TddYhiG6676y55PK48U23ivSHmBiQfNvyurkjj2IfBaexbRYDBYD1Cfq3NfU7y/zgvqH3t8Iwq5o4luohksCJruwdOWVvJZQaNME95bIQgCz19tTryPh1I449i5ss1wY4PJdOKLU7MgWOvFENZZDL6oV3WJEwahBFEUkNc1Vpd88vGHvPOt32XQHZLnc6QMsMKbS5XLOZPTY+7s7nDv1VdRUeSHTFKsPYydqzg8eEz9xl0yI9jaGnhFo/MDtpX5vLU+wWS+mNPudsBolJRsb454v/bxcp6t45pzYJiOx41rY0MFcxppHJujLS6PqnwcFmijmS/mHhsNQoyusFiSdnqpWjfb8wYHdUL4nfGqQABCKmoN82XF9d27vPjSS3TaKcJowkjSCiN0rdEiYDmfM5mcsJxPKYqMWlcgDFIogjAgSVqEQUAcKeJYEcUxMvSLYNg85P41Kc+80ZblImM2Oefk+Jgnjx9x9OwB45OHBEHOS/c26HUU7bjFg/unTCd/z2CwzZ27L7Cx0eJ0NqXXSWl3Brzx7Svk0wnT6ZST0zOWWcFkPKGqHeenOa+99CryGgyv3OH4/IROPyVtd3n4NMPYhK3tbY6OMxbLJc5ZAuUL4XyxQAWK0dY2VV3T7XZBhpRlThK3uHl7h8H2Nv/7T3/MbL7k3/7+/0Cvu4OuNMaWLMsZZVGSlbnHmDONKy3SWSQ1UpYE1KQS4jAiirz5WRLFRKHPCA0C1exmLYEucdZ3t3olPa9KjCs9iUD6TEsZ+K48jSKEDHBOUFuHQfg/1mEceG6ZtxowlaHMMxazMfPpOdlyRlWVVGXuxTFKoFRIEAq2NjcQSLCOWheA8da+dUVe+oSgqqqQStFqd+gPhggR8ne/+Iz7Dz7n5GRMWVmCMIGghbGWytVUuiJptbh244r3Vbcl1liWy4pae/78KsD8ss/MarDrXGPz4Z7njl88D01DaW0zqJWsEoVXMwZjDN1ul7LwuyOlFIPBYM0tf86iu/n5z3/m+Ub260gH/7HjG1HIweGkAClZmUxa2zzIUjTeCRpbG0IV+IBUIem0WgDrIYUgaAYXktHmJru7u0ymk8ZfwpO2HQ7t/PbT4QNfIaOyOWEoSJOE+bzAuiWPH33Izvb/zJWtm+x98TFGxZQyRogQl805f/Y5V37n13jn+9+lMxySn5yCEQgnUaFnstx/9Dn/uvoVpNF0Oj2ubl3j2f4xBJ7vKlbhsLMl49mC7kYFVuOqgmtb29iqRKRxI59XSDw7J5stGPV7zPOM6XxKVZVYC8PB0Nsso5qQatDWooQkyzMMFhGEnulhLEEsG/GQwznhaV+AcxorfX4qOO8ZjUA4gbWS3Zsv8vob7zT0xQKrM2bjU85PjxmfHrOcT6irvOH3q0aoIlGh8NdPqiYkWpAkKb3ewNsbOIOzmmI5ZbGYM58uOT+fsbd3yNHRMefjcyaTM8oyw1pNFMDW5hbtVsThueHhkylFfsxyvgBjsfUpWx8d8+23XmQyfkqrFfHki4e0koROp0WrlRBGEXuPT0naKUXh+OXffcpf//l7zGczVKjY3b1CECXc//yQpesh2lOqcIej85qqKLl5fZu6yijKgm6vj3WO0/NzsvmM3atXaCWK8+mUa6++wstvvMZ7H77H5vY2R4f7/MGf/DG//lv/NTdv36PUGlUOWC7niCCjygvQS0pT+OurHWWhMXWJrSusuXAzXBWLIFAEQUiSJLTTmEHiYYxep027lZLEUeOfj99llAV5kUGZoTNN5UAGEVHcIopaEEZYFeJUiHFgKk1VG2xZs5iMmY6PmE9PKbM5tS6w1tKKI7rdDba3ttnYGCKk9HF/VnvXxaqkqgrqqqTIMsqiIAgi+v0Rg+EOjoDP7j/hk08ecnhyhpABUoao0DcsWVFSW0PS6bA12mQwHBGGEbY23ghMKHSdU5VVU3gt0pkvFUfRDHwhDCTOGnDuucIrhGyM8wTO6MaDzIF0BE0R96iApd3qMB6PUSogSRJ6vd5FdfvS8LSpeOsdc9M3rW1KvrSO/IOOb0YhdxAIuZasepjOfySVRAlBpT3VMFTKnwzZJF03K2tZlqhmYLrMMvr9Pjdv3uSXv/zl2gzH4YvVZcI/EqqyoCxzwrBFnKTgJkgpePr0GVpLXnjhDk8ffICI1Jora6qSvaePOT874+hkjArjNX6vtUbXNcgAbaDbG9FJYsqi4ubdu+wdnSGE9UOdBtPM84Kj4xNu3b3rk72F5OrVqw2fVq0hD3/5DY8ff87k/Iy002Y46HFc5GSzGYEAJQVG6wZm8t2IMWYtCopi738hnSONI49/Ost6H91sOb1NlrrAs5qhllQWJWvOTx+TLedMxkdk03OKfAFWEwiDdI5QgmxUt0J4rxtbO3SpiRNJ2kro9zeoypr7H3/C+XjMydkZx8dHnJ8dM51MGY+n6NohREAYRTjhHU6HoxFxHCCcRdcVQnUQCmpn6AxGyHCJBNI4ocgyfvb+Y6LIMD7bp9PqsH80QTAnSf1cZbFYgBBU1pHVnsnU7rSoq4KP7z8BAqxqU8qc7//mD7j/4DMeffYEU+a8bwrS2J8fFcbU2pAvF5gy5xOlCJMuZ9Mlf/jH/55f++3f4jd/8ze4e+s2aaBIooCf/vgvONx/xq0XXkIEAVYbTOUXibqsMLr2VEbr2UtR3EZEiX90LtHhLntoV8ZRzHJOxr5AOetl8oEStOOIdqtFt9ti0OuxsXGNKFJICcJYdF1S5hnZYokUy4YEI4iT2Cug0wTTFUQqA50S0sd1PESXJC163R6tVgTC+wTVZUHZ/KnrmqLIybJF85xohAxodzr0h1s8ePT4/2Hv3WItTa77vt+q+i77cm59Hc6lZ4bDmRE5HIqS7EhMhCSO5CDOk5KHAE4CJ0CMKA8WjAB5if2UwBCQAImCvCSAgjzIgBlHlhPEkB3YshDHkiVKIiVKIkVSM7RIzfR09+nuc/pc9t7frarysKq+79v77NOXESmNAxVwus/e57vUV1/VqrX+a63/4qtfe5ej4wUeSz6Z0XUKk7RdRfCBcjbj6pUrzHd3sVkBwdDULZmoH0wMnJ6eDv4xY9fGaRxHnsXQ3bbt1ioDpbWcou7XKD5GLV2/LEulFZ4U0Ve12xeoSPd6bBuCWEZdfHpJ/pER5K7tILP94LkQyG3W4+LTslRuh1HzYQje13hpx6rSuNG8zHn99df1uA3Mawj2D1jRWnoaxD8nz7VqS+Mc58sV7/7BN3n1469QNTWzyQSbWdqqRSTjzgcfsFouuXHjBs+/+BJf/fa31ZzMYmFgD1/4wpf4ub/789y8fp3ZdELtAiGTCBlpkWZBeRoOD+9pBI7rMMFz9eZNvImJUn0ITwxtfHDIycOHLJZnBBPIDOztzrj14sfIrdEwyUQ6JuoMrquas7MF1/JdcI6mqhDvyY2M9APVFpCgloVPtVI1ykcMLBYnfPUrX+Ds+BqhW2r8r+QYCVqkAA1CCiHQdC3eg5icPCvI84LcZCxOF7z3h3f44IND7t2/z/2Hx9RVTeM6rdCTZ7Gox5RyWmg8cudpW2We3D3YA6+4a1EGsnJK27Xs7N/kxvUbHD18yNnpKcV8Fy9TTh4dUdWeUH6MVRCKvQOKzLJaLrh/eEY5OaCNccmhDJQ7OZLlZJkjn+jzdzIh+JKs2GEnsxSmxVPz4MH7nIcukkJltF2Hdy25sgWwcoZsfoApZlTn50yLktdefoXVo4cxf8Jz99u/z+33v00x2yWLvDvGqCbonfKahxjed940ZFbnRBI4Rkyfm5C+y/ISP9H5rPIkYBGa4Knqjnvnp7j3jgDIJiWTScnBfMbNq/vc2L/KwYFlYj3iGqrVGavVGcfnj3A2Y1oUHFzd5+rBLs1iRbVcYsVAUB+V61Y0zYqqWlFVWgavrhvOlwuOj4/Ii5wsyzi4ep3nX/o4j06X/P1/+P/wwZ37FOUOJp/QNB3LswVV02jhlKJg/+oe851dxGYxGgV8p5mb3vRit3fsD7LBXxDCmpo/4OObgtdEmLePNEkRPXF8k2wpS60d2rYtu3taaDl9txm18t1qTxTkInIL+JvAx1Do+qdDCP+jiPxXwH8K3I+H/vUQwj+I5/w14C8DDvirIYR/+IR79NmcKYHMAG3TKgey1RCgMGTHAopFpfTZoig4Pz/VFxgE13lefPGF3itdVRXWZqTdtR/cTlPqVytlApxMpmRFiWvh5Pycb/7Bt3jrM5+J5FuOLDNUrqOQjKOHDzk5OeXqc9f4xGuv8zu//EtMygwXOrouYLOCw3tH/PqXvsLNm88RQsfhoxPaoOXV8JEuNkaj/P43vsEP/Sv/MpNpyTQv+NhLtygmExUwDA7K4B2Pjh5w8ughL+3fohsJv+efu0mRmZ7hUWOIFYparrSSSi4fQ1yNq1dqMiJ4LSWsIy8BelIk1TT7ZG0B52ru3f1DPvHiDGtWvf9BghYsaJ2jRbPyslw3v7PFinv3H3L39j0++OAui/MldetBLFlRYmzGbGeHouvweBqvFeurumV1ckKeF8x3dnj+1ovYTGhdS56XZDbHSEawlrLcw4dAE0rK3eeQ8gAJgcLM2bFTqvocaWrEKEWCs0LXCrNru1oy0Hsks9SuxWZaLSjLDK7tKIuS0pZ0K8e1a89RB8vzL76MdCtms5zMqCPaZnnU6jy5gPEOU0ww5Q7z/et87of/VSaTOXc/OCS3BZnxON+Qi2BNRrM8ZeUcTd3SNuoUDsSIiZAc9zoHko/IRis1+ZLSmsryAkzW8+FYa8nzXLlBbMZ0PomCxhFMhiPw4PiMw3sPCG1FYQM7k5yrV3b52I1rHBx8jJ3pDBcCTQwvbFbntHWLYGi7luA8dV1RLc8iPHbOcrmgrpvIbRTY2bnC1WvXuH79BrXz/Mpv/A7vfPPbWgTFTlm1sKoWrOqapu0QaygnOW0QTs4rFrUjyzOKstRErCynyAs635HFIsdVtXoqbThxkHedWxPYEB2+YVAYNwstp1DFg4MDzs/PMUbZIQ8ODvrNYVwD4bvZnkYj74D/IoTwmyKyC3xJRH4h/u1/CCH8d+ODReQt4C8CnwZeAP6xiLwZtCz4pS23mZr4vYMBTK7xnd4NacPJfEwe5nFGVp7nyv8b1Fv66U+/TZ4rr3daAGNzZRjjwGq11PR6FM+tW83Y+9a33udH/9yfYz6f4UKr9UEFQnAcPXzI3bt3+NitF3jttdfVmy9e6xCajLauCUa4e3jMzRdexfuGa889zzvvvkNmDeJcrOOpSRW/8Ru/Tsi00tHezg6TLCdg6VxHbq2OStzNuq7h8N5drl27QojROp33zKYl00nB4nypsEbStUPAOxW4RZ6xWJxyfvKItqlV0w+adq0hmT4mj0Q4hMS2YfBO+1cvKzIEXzeIF+rGEwJaQX4ywfnAo5NT3nvvW9y7/5DjR6csVrqZWlNg84nSuwZNne68j9EOnrprWdYuVuspOdi/wrVr1yinE0JweLzGrmcZwRTk5ZzJVGExHyOcwmqFtw14h7cNuTF468lKpTBQCptAPpur/8VkSKaWoM0LXDAYsbhgQBxtZzDkeA/Hx6dcfe4Fnn/5E+SZkE93mEwsNpOoJQpFlrEzKbDBMZkU7O5fo9y5xme/7/vI8pL5zhWOH9zHtx1BWnxwkeOkwPjArMwx05K2aVkutarR6ckJh4eHzOc7FGWh4bdBs3vVyZn1v1ur9qs1kBmrEKXVQtciRiO9Ci1+nec5JhaEsMYi0wnMNEtx4R3Hh0u+8cEJxgizouRgZ5frV69w/eoe+zduUGaerltydvKQo/t3+eDoLg8P79MsVninTv2i2CHPLPv7B1y/+Ryt8/zeO+/y9W+8y9GioXNC5wJN21I3K7qYleqdYtutD1hbINJGjVgoypw8z5mUJTuzGTYzTMoJnQ80TbumYcNFga4UxPmaPEmCPMkWXTvby7qlRCDlH3+AtbYvCg2qRPWw7hOglW3RLM/SnijIQwh3gDvx9zMR+Rrw4mNO+THgb4cQauAPRORd4AeBX33MPeKgp+ofg9CGQeCOLaO042ndSg37SRg5qLb+xhuvs7+/z9HR0Si7athxvddUZjGR9KbryPOM+WzK0aMzsmLOO+98A2sd169f4/DhXYRIx9l66korev/AD/0QL738CuVkTucWOrBWtF4mgfPzJbWD5aplvruPzTJcV5Fbo+WrxChL3tlZnPglR8fHVGcLbJ5j2yaSNinuHYLTQrZHDyjyjLppqesWEzRqZjafc36+6GkJgtfitmmze/XVV3DVDZrlgrt3PmAymbBoWvXcR4xR/RQq1NU+MtGJavBdxtGDBYuTjoKcSTkhn5WcLRe899493r9zlzuH91ksVwQsRT4hL2bMdua03uFFaF2gazq80zjyumlo2joW1ob5dJ/rN26yu7uLiMEJ1J0WJTDBEoJgxWKzQvHifIrkc3KrtRJNLoizdL7R9G8yjC3wAcQKeGW+xBSIszEaSh3gaiFm0XkuSE5MsLIUGdz74Db3HhxT7hzw9tuf4s6d21y7eZP5zpysnOCcp8wzXF3xrXe/QZFb7h2d03KXT3zqsxShZFF3kGvOQ9NoRrD3StfatV0sRVizWlUslytOT09ZLpd0XUdT1+xfucLBwYFm5qbMyq6jilzoKTKiMMqpLkYoipKiLCmKkrwoyLIk/C2ZMeQ2jmeWIVkBNgOTE8oJ2VS1eu8dx4uOB49u03z9HbIssDPLuXn9gJvX97nx6vfw0huf4uzRCYuTBednZ1ovMwSyPKOuG774O+/wjXe+SRXDA1etUNUNXasae9NpqT4NP0Zj1p3HecXTjSg3E95rYQqnVLmTaYlzShg2hlQ2wwDHwn1IBvJrAnec0dk27QWIRkRo27bPCk1cK0U+YXdnn+AFa3La1jHmMEh7wmUkWh+2PRNGLiKvAt8P/Brww8BPiMh/BHwR1dqPUSH/hdFp7/N4wR9NwsR4OI6TH3t7R/8LmGDWoBWAPMv05XdK1lMUJa+//jpf+MIXej6FEDZiN0UDkrwL1KuKPC+ZTks1lX3No4d3qZYL3nrrU9z5f7Uwc2aF0Krwv/3+bYzNuHHzY1y5doOjhzXGapFYExwueGbzGf/6v/Ej/O5Xf4d3v/7bFHmujhuUu0K8x3cdzWLByckJL+zsYs0OpSk42L/KnfMFWGL8N70m8Eu/9M/wCC+89DI3b9xgZz6lFMuVa9d5ePQIIq9ylhfUTYN3nUZiZDkhy9nd32M2m1JOJpyuGsgGZ1BAiwU7HD6GIGrUiqWrGwgle/svcHzvPd5991vcvnOPB0cPaZwW2MiKCcVspjHKPrBsgla1j2XV6rajrluaRn8CgfnujHI+oZyUTPIZnsD5cqEJKy4jy3Mym2GzjMLkqITNCCYHkxFieKgHgmR4aTUJEfDB4Hyh+SxeBVKIxGtqLGoVHQSUAiqWEfEBvItc67rwrxxcYeUM33znG3z73a9xcnSXb35NiZfmO3t4ILcG11Q8enAfsRlVJ0i5x7/2b/7bfObtF3nltdc5efQwFnKfEEJL52NM9mLJarFgEX/qqqaqtHKRiZv+/cOG46OHPVFTURQaujedMZ1Mes2cyA3edC2ruuFkeYZzJ4jR8N1yMmE+nVIWWlDa5jliLcZmfUiijcLeWoszkFvIZzn5bB/vOlbO8c33j3j3W/cwBKyBaVkwm+RMipJV13L3g9vcfu897t075HxVY7KCzhnOVyvaTpkDFe0UnAsQNJpK/fBeCbdE1GiMwQ5BDN5p5IhIZChFWK6qNUx8jG8neZMEcxLO3q/Hj49rdIbR5/HfUg5L2gBSabednR1gVMSZlIQYo9g/vLy+tD21IBeRHeDvAv95COFURP5n4G+g6+RvAP898J+w3dV6oesi8uPAjwPcevllreDTwx9D8zEeJwTIzKiAKesvxlqjDjJEq6LkhrZ1fOYzn+GLX/wi49TavlPpu7h5VKuKvd09JnlOmWnI1B+8+zX+989/nkmRI8TwLmtocXRtw3vvvaf4e17w4ksv8/DhHUQycBVGAo6Wk5MjOt/xuc99js9++nXuffsdfv9rv4vJizipAr5zNKHlzu0PmM52KPIC8WjZq2Tyjfqf5Rl37h3y1a+/w6/95u+S5zk3r1/h1ksv0bhA5+K0CULouvi75/DePSUeixpb57wuXqOViRLWHYIWlPAxTDPxBBiU3OxrX/99/ubfWrA8PaKcaEJWPt2jAE0WCkJTq1nsnaeuGqq6onINVR0TooCu7VjVNT54WiCvarLFkkl2Tp4VTCYzJpMJuSvIvScUCu04B20bwAayEDShRbS6kUtRSb4j+A7v27hYLd5bTfDwyi3uvSa4SCzyS/B9IQKPx+OAjswYBMfO3lVmO3tc3blC5zqWJw85vd9QL1qC95w9eoQnUOY5rqlYnp4QTAH5HOks1WqBsYGsUF9E5z1iMrwTnGuom45VVbOsKqqqpuvUqrNGIl7rtHiJeEK0XmpgJSpcHoaBuc9mOcaWmKzos56LYkpuLDZqos45js/OGdCDgFidX3lmKQuNLS+LgiLPKAqDzS1iM8TkUUPO8T7HmALE0HUO1wbO6orDe/+cb/7+11menmjorPM0ztFVK6pWM0zFdzFBDvpcBkKM6LKAidV/lG/IxqOM12Iy3hmcEeiEgsBitUSJ0AbWwrGsSC3BTW2EUTcjVmBwVK6dt8GvslwuaduW2Uyzw2cxLHoMBQ/y5rvTnkqQi0iOCvG/FUL4P2Kn7o3+/r8APx8/vg/cGp3+EvDB5jVDCD8N/DTAn/mzfzYEkd6IJ5lUSf73sZcMxPgyaNXpBaR4co1EUUfD22+/3WdbbQrzED35SajVTa1RBKJRIM4EQlfxi7/wf3PrxRdiv0KcXKoF3LnzAcfHx1w/uMJLt17hN3/r18isJi9Z8XS+5ej4kG9+8x329w84OXyf0EXKAAGx0fvtNDnp8PCQYjqjLCbMiqk6qYzt+X4SV0zVtJycLZjO97CTHeq64e7DIz64d5+jk1McElnbdCJaa3Ftx/n5KU3bKRUtAd+25OWERMqfhkRCTMwyqGkePBI8hoAxnrPFGXZSsFfeoG1rMBmLxtF4T+s9i2WN87BaVXRtR4iUpo5A57TghfPKDKikB8JyUWl9TAksjZZQy7NTylK1nMl0xmymmbMaMmkIQTSqJlpqapA7FXjeEVxDcDXBtwQ6vFEN3EuHF4c3MfkiZuVqFp+JfQpgNebdBc9sOsNOJuxdvcnO/nXOlyvaahsS98IAACAASURBVEVezKPT0lGUJXXTYq3gjMV3TsPjPMxnk5jI9D4nJw8gdBD0vYsUNKLOXzEWY3OyqO0JQYWgtz381YU079U3EWSdQ5sQ6Ooak0FoWurlGScxDHHMI5InrXw2ZzafM51PdaMMnrZrOTtd8ajVjX9SFJR5RlZYsnyCZAU2L7FZ0WcLWxEyaxETaLuaDw4fchqxctfUMU7b0jQtrY8lGCXBHkKI3CymD4CIcGgQEI+NOSBZVN50vWtxbGssiFBVVS9AN0Mzo+xRFMCq+FNrwF10dEK02EIvNpJwTlDNfD7n8PCQEJTTfHd3lyzLLuDzI7m3hgZ8p9rTRK0I8L8CXwsh/NTo++cjfg7w7wJfib//PeDzIvJTqLPzDeDXn3SfZOqE/uFSKaoB1+5zskLoMa10LugLsTZqG1F7f+mll3rOA53ow+6amFxS2n4TkxYmZc58WlKtFnTecX5yzOyN1zS8KwRya2hEMAInjx7x4P59rh1c4eOfeB2wEOPAtc+Kpf/KP/slXn75VUJ1TllOAHXypTBIjR23nJ2ccHp8rFzYxUTLYkWB3L//iNutqorj42PK2a7WNDUZ8/mM/YMr3L93HxMit/vo3Dsf3OHu4SG7u3PF262nnM1xgRjeOWgNHqXZVZqNgMFDcDjf0jpYNpVWoQ+G1eqUVdPSdB1V29G5QNs5rRvZOVzbRQ75KHFJEJrik9YYmlVNUSgu7S24mB6GCJxD13lNEfeB6VSdX7mxUbAnzp2UJDP6CV4Ft6kJwRFwhND15GAYNLQSrZmKz/A+4NCalaoNWpoQmOYlbYDagSlmhHzG/OAmdXVObgAxGPFk1tJ5ixSeLASmWYkY4d1vfB3XtRw9uIv3rfKEexB08QcEscoq6b2yWmbW0DWi1Xi8w4eAlaGazfAjI6jAx0CBOmrbsc5knD8SX0CoK+rmjOrkPvcDBCNkRcFkvkMxnTKd7bCzt0eeFxAE03maZcXx8oi6cziJQjEzlGVGUWh1KhDOzysO792hXpwRmko3o66l6RxishjZNRRGl7hmUkZJAjMUPsrR4LUhK1Lw2EwTBIssx2SaH9E0bRyH0I9HL1XCUL7NZrYXypshgtof5VgJcfNMMG46J8FXy+WydzRfvXqVLMt6zHzzHQ0tbMEpxrAyW/9+WXsajfyHgb8E/K6IfDl+99eBf19Evi/e7lvAf6YdCV8VkZ8Ffg+NePkrT4pYSQNrIswA6XMqpBrx4aila+qrVibpuk65mYUoPHWgq05Z5557/jmu37jG8dFxNFNT/b7QO0JAS6YtVysa15FPSorpFMlyQoBFVeMJ7ExmVHWFEYsX8KHF1CtMteDa7pyPv/YKZWGADk+J81o30Dcdx3duc213ztnpKWeVw0uJ7TMqvWKy3uHrmqN7dzE2oyhKMqOaSfADwKEFHaCuK96//R77V69qmrVAzbIvdquCKfJtWw0x/OY//yaf//zPMt/ZZX93l92dOUcnKyVHgrgIdQbZEGI1GRWmPvI5G2tovHD36BFtW+NcoK47XKf0pl2ntANdF7PpRiYz0pPkkv5kjXKHZFmGNVpQ2opmyAYneHF0pqNta1aVIFawRUbrppQuIGhlGAkaSBknVby+xWPBG8Rn4EIcREEkVsphKGWmjlXBGD3OeBUoxhbkdoK1BW3b0HUV3rVkWcZkNqcsM2xIlYeWTCYlpRWO6lrhrTwny3POjo+5/a1vsTo7J7gQqZkDXVfRdi14hw0KEWXWInkeDdKAc+rgM4nV0DmNSQrDfIZYw9aEXuCnNRaiAyoVD441/aI/JL6bGCW2PFuwXCw59g96KzTPc0qbYWMoYwiBumvpYinBtksp6vqu27ahbRpC4gMKGhUW4qYrhhjSmg8BDskqFEjFsSS+j8wKuVV6B2PSAUJeFIg1ZEXBclkhXikFQhigkTFkElCWxTzPtQCG12lB3OCSouecQnPaD4nPZntYZTqd9nxPKWJl/8o+TddgrJCoLYLzEb8nxZChWeZxAfQ/0s9b1WifXpI/TdTKL7PdCPgHjznnJ4GffNpOpIFZJ7dyo/hYSJ7Q9PAhamomVrcmhBhlAZ135EVJ8I4bN69z8+YNDu8d9s5UvYRqDUlDDKjGXtU1850dJrNZbwFUTc354pyyLFgulngfsLlVx1nX8nc+/7+xPF/x/AvPsX91n5Oje2pNmMgD4wMP7t7hxo3rrOqKLgRMlmsNSa+0A64bvOJNtcTYnNVqqTVMoyCFYWK74GnrmgcPDlk1laYod06zYKsVRZbhOhe1mGGcm6bBdR7XBU5OF9x/8JBVXfcTXGJN0SR8DYNJClqANgTwnef4+Ex5agIaAxzhE++T5z/qVMnMkjTyPsYyx+os/QJSIaRVW7SQhzExJE5ShSTpnVTKZZ2TmVwx7uhLSbz2WhEmAhNB6QUUjomdCcnPMghBH1L2rMJIEaHVQm1WE5oAurbBCpRFxnQywTXgmorgOsrMUi01aqgoS8BAdMw3TcX56QlNVeGDixxD0MXyZIJyg+eZxZDTkXjEU5EQtXIIAxd2erebYW7jz2OtMIUHjL/TdTf6zjvEx9j0uPmGrmXV1H2UzGYhhjH962AVxepfYjQsNCRmTemT1ZICZvqIIeJ5gy/MyDAXU63apKnnRUGWT8jykvOzZZx/EpWSOPVGjs9eUck0ocj5IWJlnOwz3vxkTTbpXJnNZiwWC6zVREZ1dM4VLYgJef28H0ntJHuSb24Qr6m/6YSnx14+EpmdYydewr30++Hf9KsRiSFJQ5ZVl2LK46GdcxR5hg+BKwdX+PjHP85v/eZvkeclKXkltRCGLM8QAovFgoODAyaTUheQUby9qmvyUsvHZbnFtoZVtcJm8Ntf/k0+uPsBq1XF4vyh4smp96o00LSNhks5z3QyUSEWDL51iPLQ9gtBMBR5QdM5hQVCKiIdJ1WKXiGwODvHOS37Np/PVaP0KlCVUKxbw+q6ruPk5ISqbplMSowwaBV9huew0Y0nV+/9j/zU5+crEnNiH1aFDHSo8Vwxw1vWyjJREJu0gAdTVqLwNkYFeZYpVJYXJVleKFNiOdUqOTbH+aBUriYSq0WiIheTqEIYFuimfjNe3OnHo/zdEhTWEFSx8rHv1ipFQ6IGTppYh6cLjrpr+0UvIuRZRpeoJIzCf1W9ou0iLtvDXqG3PK01SMj6zc1IhAicRtt4p1bSOKSu7/9IA90U5Nv+7zew+LZCLA+YNP3UvPd0rusx47TpJhZP1TWSIznBOCoo04pOmcYmKWdpk4raeMLFdU5kw7FGyIw6X42JFpm1Co1Yg9hCs1i9UkCPxDdsvPX0bCk5ikAfrpnmxDhrc5uzE3StzmYzDg8PEVHWy/l8zmw27zeSYaw3Jt6GgL4Iuzx7+0gI8sSX0j9MVEB7URIS/aQKGtWaRHfyWI8woI5DESE3mjyTNPw333xza4ou6CAbM9y7qipSvHWeFzRNC0EjGz7+iTd4//YdykwnWWaFEDpcu8SEhkw6jVRxXqsAxUWBDwTX8fKtl+h84N79e9wrCxb1QnE435FlRu+FkmhlWRFXgKPyDtc1JIyzn3QI9XKl8eoiHB0dMS0nfajceHKkpAYfAvcP77G7f8CjRzEyAI3V7+k9kyIhoX8PA66tR4kYNCLMRhgnIjCkuP9YqICBRljxT22RI20Q4AyatjHE0nqqdWdFHmOfS4pyQpaVWKuV0/M8pyzyHr90ztG0TVycyULoRhvT+N2HNYGXLB8VKApjiEgMx4sc39E6UG3Q9tXXdbx0M1lVy15bNsZgQqxEL5rI1TQmboDRiozWpDVqYQo5Lmqp1kDIUgFfxXJd5/qqPJsa9/hZtmHD2zR0oIcbL9PsjTFkrAu4QdCZNaEXguCdZmIPGnB8Pknw3TDRbKIVEIkwqcRnt3qOTeynFmMteZFFIW4jHt1xdPyIxaKKbJQxvX70zjfx6T7+u27XNsT0HjfHb5wklMIdE3tpcnTOZjNMz+ti1tZgVOwHof4dEN7j9pEQ5IKSZnnvtQ4hPaKKiFLXegJidXG0neKlAGVZRq+zx4fEM2HxLpBby3K55Hs/871rWlKvWUZnxqBtahHVrusoignz+YyqOkbEcOfwkDc++T2ItfjgKQrLctklMUhTLZjNZng/5eSkVvpYMbjQKfeKgff+8Ft8/5/5l/jkJ9+ksIFf+eV/Qp4pJ0uazKBRGbu7+/ggNF1N6xpa10SLQ+lSJcJL3jmKXHrHaV1Vse7pILRCCNEZqs98dnrG2fkC79S/kCYmcSz6skAJzlnbACUKbIt3YDOjY53Z3rqRCOekkDEzgneyyC2j7wl9L/Fzygsw1uj3UVDmeUFRTsiLUn8vStXKs6KHb5KDLQRHllmCM7RNSzJlnXNwwTk4LKYe1gNcUH9MSP0WZdQzkinXvNNElN5pFk1rz3i8hoWfRdpkKwHfNXRttE5C6COBkk4cJGCtRDgiw1vTZwjqu1SrzruB5GlTeG8TyNsE+Np3aaRG56W1snndTRhlG6zijCc44oblIxWFkPxRxgwRUjbCJwlK7aEzY3o8PssyJApvgFXTUFXnLFeVltLzYGxOnuXYmPw2bmONO91DUAup67oLY7PtnLQOmqZR2tq6XksKunbt2lYr73HtO6GNw0dEkEekJL5I/U4zNYdkn1Qhp2larbzSNthYoxKIE10Ln9rIjJjKnH384x/XEmdp5+2xt3UNl7jg27alKLRckxEtn3V2vqDr1Jyqlotecwxew87Oz07wrqWNzle9h2r2TaOl0b70xd/gt778W7G8Whcja1JkSXpQoWka8jynbrqeU2K1WvT6hcoW5VkuYnxv3TnlXY4aw2ZLBEAC1KtVH7cfIu9LKmklBEIwIH60MMNIe5Ye+0vCL8+jM1oyrUVg0sRPb3fQ7rOR6bq2aOMiVkGu/Ngm0+SfPM/IcjWf86LE5gU2i0krow1aJPoy4ztRbdz190oGf7Ls6De8mEXoR4svmiKJrzvLylhNXaJzb0EXhjRwY0dwSow4AbUQFPvVEFIjgvgkKDQhSbmuURjNa5RNiA5JY4gROYMS4r0Hn/WQ2Lo2vCHMY5zBGnzkFZpRRCf0kRmBFMudEudiaGMI8buEGYMPNvY9nudt73wNQRN1vAt0nUDwGNFrJGe6yKiCPaN3P5oP1lqyWHjF+0BT1yxXKxarZV8LNGm5WoO26HlRNq3vsQIXUG4mH0K/VsZhipuRLuNz04Y1n89ZLBZ99MpkMolZyMPmt47Lr7cEYW62NWTiGdpHQpCPk3uAkWkivWYnojpLUeTk+V6/UNJLM0ZYVVofUrJhR8darl67yvPPP8/t2x/0k0RNb717wvxCFAJVVTGf72pgf+xXXdWslivmszlNrCJvxNA4xUS9d4iBpq77Sdk2zcDv7ZU5ENfx6PghAU+WmehIUQ1QnWEdk4nlzTffwAV4ePyQe4eB05NjrQXatljRRZVSg6czZVzzQozLBm8job6kMY6yInjl24iMeN5Lvzn0b0PSwvXRFzGGaJL2mLRoJeSyMuCZPVwyxj9jGT+FcYYwLi1LZpSDxcYMwiwHoxiojcI8y4vo1Cp6LHwgMVrXNpNw00XhB8fcmra6HWbY1JCSL8qQsL6I2+cF7bIZIA7fxyOM5vBImEQB6rqB/AoiJp2cYkEFHEEVDGtSdZrBYknPFNxQ+X1TOx5r6amSz9ZnW3tmP2jm6Vrex8IlgQTNJeG/uTHoj+k/O/F0xHDTMHi7JMIrqW6ACvIRY2OWITGlvmlbzs4XrOqKpm5oXIuLdAMiNgYqJHhvJPwuIqj9fEtO3SzLIKjzfyz4xxmcm/CSbtCqAE0mE46Pj/s+l2WpGZ2jjeXxAnmYt2PhP34vz9I+EoIcdMdN1tca01hIDwYIMfRuoO4MJvSwRL67p3HPYnHO0zYNVgyTyZTXXnuNe/cOdeLHXdgYEyuKaxPRcKXlcsm1a4HpdNo7XLum4fTRI64eHHD04DBq2wXOaRUbYwxvffptHtx/wPvv36ZtW3WmjBYpQR2y6sE3MeEhOorihpJllqpa8YVf+1Xm812ef/F5dudaYNoT2e18hApiht50NqNzLjIg5iBCW9cahhax2PG0EIHcquMsPfy64Bn+3+JVQPEv9eRnucH02XamF+Smh1hSfHDEkHuO8ijwY6p40q6N1cIIEuPDba6ad5YrQVKW51EbNxGDT9q0Hy2IJMhcFNhuEFThIgY8jMs6lBTipPMh4r1ogRL9UetH+oUf1jbEtcUYJbz3AYnYYYiVrkISlAzzI0SHsEQoTTcTM3LCjbbdEVw4vu8gyCN75aZGvtHPZAGswwp263itXd97QjBx8xzGzWeBrOloJcEruoaT4zYJcSGG14ZA23acni+ij8PhU3imREvGaIw+ayM9hAyqheO2ztpx3yWuowC9lZ5gsOTbWEuu2hjnFDNeR6UtCfK9vb243NM8pO/noHBchG7+fwOtSPSUmRix0pup6KObQRmKeGWIwm9wxmm1FMUt29ASkFiIIotC9i3+6T/9JbIso+s0/te7FF0SRn3RIhVd58gy1QKbuiV4x/HRES/fuoXvfCwSG5N6jKGqWxbnS2azOZPJRLHqPCdE89i5NPEDiGaghRiWpTfWf5zrwAROTo558PAB773/bU2NtpkCAgkuiBEmIjCZTdnd2+NsuWR5fo5YS1PVLM7PKXINoBNCHwVojFCWOV0nowWZ9MmxkDDxZ9DCifdUSyJQFHrtqGOPLKkBi7Qmi1aAOjjTIlZBqBVYkpVgs8TQpyRNJprY1uYq7CN+rjsi9LG6walJ329cvk8OShZfghA2Td6x5qzQUdI+pf+RWKlKnW7SW0Tpx49ggRS215vpETYxYpToK1okISTlJS3uTjf+oP33YchwlBGvkPce363jt+n7dN30bM63F7Tx7ZbINkE+CO2+v6OxGyfSJOK6HtaJse7J6utbvF4bicESlJkq7YA6uGPUrmrgIgQbN1HNWY5QnVH20DinDfE5Ik/5ZkuC2FrTsxL2iVMbCUGb0Mo4LX9nZ4eqqvpxSI5OjUvfjs1ftPou/n1bX5+2fSQEOQwPkgYsra2klaSpYI0KG+cVRsiyjGq5ZLlcogWItSiEc57ZdEqR5VhjePvTb/f3yrNMnX8RAhiJcURiWnnXKQf2bEZTP8Iaw4P7D/jkm2+SZRa8J7M5Su2qlAInZ+cc7O+xszPj/OwM32kW3hiJ1aZajHdeNy+1VxXvF6FpG5xz5HkWw/Vi1moMx0raRNM6uq7h7t071G3L/tUr7O/tEjpHU9U0dR3vFfrNDyA4H2kEBO8crdMIj2244uZ3IeKb4DFGF4WVxIsR6z/G0EERGwWy6aM9xPhek5V4LKIOaonfm0z5OxKHtmKlKaJFBgejlagRjzRx55Sf3aU43hHUkCCY0Zu4AKOEmDATVJD3GjaJPtnTxaowXav3UQvEDA51hLFWKIgqICpy+v5uYgCBwUJTYe402sVYZGylMsBVMMQ3DwIpYccDWdSAbwfCSBj3eO5ohupxDBZDf24cpXhti2jikfeaGRzD+ZxXf00XPHXjqOqKtkladipwHHrdIAAY5Uj3PtC0ye+gju7gffRFROUi6CgqFKT9VuvMx3mcxnq9pU0pL3KMMVRVvbZJjfNY0s+4UEfaeHZ3d1ks1E/Wti3T6ZQrV6/oMa67sIlsh+4umYMfEl75yAjylPwxcP8q4X0yy1NzfeJG1Lha9YqLBBbLhToddnaUxN51rKoWCfDcczeZz2cxxlg1W5ucUmlRRZzcOU3znc0Mk9mUcHwMwHK1RKwlLyasqhXOhwiTqEVQr5ZwsEeIsdxaMUUhCN91KqxENbvQA6rRxI6wkQ9xwRLAO4gxDUVeQKrkHc3t5Dxrq4qH9+5y9ugRZaGl2+rVUs1MI6SyeULUNIyQZ+rhDyEgdY3rVgMMJEkMjSEZ7XByViW82VoNSst7nNuOnFWaBSi9uapWQXKsWmsxojAJvRavmZ3GiGa1WsXQjYkZhcZijVZNEgRMJPtCCaV8VOUkCaeIWTofUJIBR4gZjUnbW29RA49YdYIBkhURCHqtLsI16Cbug8afpwzJAbNC52esearDGGtdhcHhSFrkMZwzUTwg0ttJPX9H8IDmGKRzCRreJwS6zsd7hoGvJF0rCYoo7AmDj6h/4yNt0ceJmqyQXth7j/cS2Su1cHJd11RVHYtIqNDreq1dr2ticlaIkEkyiAfBNXCohF5jToR5treWQC3hECPaIiUQ4RIhnja5JJRBlaOxArkpyNesnd56U2bHR8dHEV4Tsjxnb/9AeyG2D7JI6/uiQB6s437OpbGVNC+eDW756Ahyq6xp1qrHvHNdNGMHjVmLCatw7Jyj7VrV1KyhnBTM5pPIPezZmc8wxvSZjC+/cotr165y+/ZtBHXO4XSyD8rRwK64XK7Y2z9gMpn02HzbtqxWK2bzOYvlMpr6yp4mwOL8XCeANZjMElwM+YvpuMkclfgMkiR4XPipVFc/8ZNhHzcEXY8DuZUR9aKrXhagazVlOmoOKXoEEj6rz9e2HatVNdJ2M4yYmLq9sQzC+i+ajhyxTZMxm801eibFVEcs19jkcI5RKBFKCqxTiCatBwYTVkQosnQNHR4Nx1MNH+i1NFqHD12E33wssp2iQeLYxALXPgk9E/0sjCyOMGijyZkswWnUiAQEH38fabPOx6K90SncR5cMcdFIRNJIlubgk4m6Qz/gPhhcLE5CYGMTlv65CV45V1JETnw/3ukcsQYtQhyPT69Qs3cZNo0NAdNrjL3logpK13W0bUvbtjRtw3K1pKoqmial57t+buslI1FCgg0x/TzypD1lBG+to5ukRZjg1f64IAi2P8SHQain1aKc8ttE+WBhaiFwS9e2a+Ow6XTs4Y1o/bjGMSlLQvCxIItXTvcoyF3QlZnmURreTZgk7a1B4oc+imnU135yPF37SAjyaEni0zsN0LZuEAD9cUJdN4qpuTbppTHLi7gYhfPzcxaLRU8n6V1gNp2xs7MTwxqVQ8Uk8H2zPyGwWq1iPHmhTtGIAZ6cnHBwcMCdO3f6MlsplnSxWAyk/iI0nZaeSskuyIAlJgeNFXvhRac+JHw3CZ6x2bUW5eM9BLvm8Bv/Pl406RrL5XJrmFU6Ln5xYWwSdJA0jizTWpyZHZJjJEYM9aRBqH4oooxzieNmTEI0vn4S8CFq8INjNCbrRC05M0lzT+MQNakQIDiC63Cu0+rtKZ3cp4zPQQtOAiMa6cr7HWdciNcSYshecOBjyFrK/gweE1TrtkbDVaMMj6yFfqTRDdDIpuCI8LhqdABeEDMkl+iJ+o9mHK+n4Q/RQoPDrh/L9E5Hc6gvzBI0eqNrB+1ahXZLE2Ole6rXZIV4DSoYz5e0gQ1zOanbo3k9TPALc+vCXNv4HC75fbikbkJbXfRhWEN5rDyWMsLHEAoM+PjY1yNRadq5crBWvCPPcyaTiTo6U1jn6NnCFo18/DHNw7XP2w58QvtICHLvA03EhAMQTKCYFNStVuxJlqHzgfPFMhaNiI7ELON8saDrWk5OH0VHUKuFkq1ltVohGLKs4LXXXuMrX/lKjFbRGpzbmuJnFVVV9SW0fPRuHx0dcevWrbWFAvrS66bhfLHAe/Vst6Kx7j64uPFu7PRbNKOE5yYh1y+aUVjUpjBIsazJfOxT/TcE+Phzusd4Io+vvTnBUtO0f63mtFyu+MNvv6dJO4XWTcyLQvlP8ozMqnNQf9cNLeUHpMWiZcaGeydoRiRxUcsIdpFe6KYoFLxTskmImmqHd532MSj9qwkBi75v02uA66ZzP04JjkgCUxLPR9TMfUfoarCO4FtwLeJb8C24Dt+1+K4mOCXAUrqGQCpGq7fTyIvxfqk4NZGHJD0PvTBPlk5KgBrHvKf3mX6SA1Gr5TS9IO66rq8ilIR4clCOo1DSz5hzZPhdu5WE+DB2SVjSb7jOB2RDaD2DkvnUbT1yJzxWk03PlazW9U3wYgw4RFoQr9b1fD7jUYRaQdfDfD7vC0yYS24+xsaHn/W/Dx94JiEOHxFBboziYi4o9poyDVdVReFihRKrnmybF3S+5nSxwHvHgexyfHKK946HD485PT1lf2/OdDKJGaAtZTmlbVtefvWV3tOeYskva845jTyxlrIoaGqNUX/48CGvv/56X2JrPAG8c5ydnTGZTnth5L3vsXdgbeKMzfT0XWq9lub8Vu0jHZ8E+XgCjsmUNq+b2lgb3sYnsTmZ+3HpFLbIIs/GalXjw4oQBW8yCVPfrImCOcYP21Fae+K7KPICYw15lmstyTxjWpSxOo2N0SIJhUqRNNGfEjyibjd6AzsKT7W4BN9r9Kh5LuBjFFsEVAYLIARCF+EP0Zhx9d+oIFdNv4HgyKzgjGK0Gm8RtMhSvK7CC3HTYaRZS+g1Na1MMzgAgx++S5FO+rcB4uhiRZ1BOKe/Nf25PiXrRJoCGBygiZIi3TM+anzv2unBYZgyc2PKu6RNaAQdjIV8CAQXNS8GKOeyts2hd9ncG7cxGrMmhLlcjqcNKdHMXhaNc6FPotFkiaQrhR2KCEVecOXKQQ9bhtG9Un/W+xD/T8++qZ33f90+Npe1j4QgDyGwWC5ZrlYcHBzoyxDpszZDCFpN21iyomBVax3DLLPUbcejkxNOHj1isThjZ2fOjRvPYYzyprRdR/ArFosVr77yKjazIEP86GX9CSFQ1TWz+ZyiLAmnpwCcnZ31u/DZ2VkvOF3kewkhMJtNaeqa1WKpk0d0o+qi4E/fbWtjhwuoQEisb9smdzpuc1NJfxt/Tn8fayObIWvrXvvxfUZ9jBp1iqF3QbUwH9evJG0zBJzv1u4tcUMc93+zj0CPbUsP1WSRtCrrwwCzvCCLcehZjGwRQTVz4qKNdVxtFkmSNu6bYtetGbJLk2Oxcx3LSvFva8/IT06Yub94GAAAIABJREFUlDOyYtIL/bppqKuKpqnpWi2n10X6VhdDYlvXRTY8H+GJ0CfbOKeRHEPizaBhX/aOdJ5cTEMfz4fLvtPfk3WXGAgfr0WqgE5x6R5SfoAZCy+Jm5Tp4dFEJbF2zf7OT+7307Y1aE4vNpKP62vBWmXMrKqqd5xuWqpjCyRdw3uPNUNcfZqz5aRkf/9ggLXWZTObnza/Vufyh3/21D4Sghy05NiA26lTochzAlqCLS+KqKWvKCcTPvaxKW3bsFqtOD9fcHZ2xpUr++zv7/fRLynzsYsVSG69fEsJ+7vknJI+wWiMZaV+LJdL9vb2VABEDbpp9J7z+ZzT09Peaeecw4hwdnZGOSljJRRVxRPBPWnzCElj0TaeSGNhLCJriSObx62NX7Quxqnu4xjfdN2xpZCO2RSmfVsTAOtamxGDtdJvutva5vcpVnnbMZsZdSFoFIiqzp6WyFC3dkkzQBD0LjY0pjyNpa4s1YijU1m2WUGjDSTEaI0Yq6/skwbv0bBNMbTO9cI0QQZGQrQShsUpMoT7jZ99faPcXMgXj9dzhr9fJvgujDlbBB3r7zUktk7ZlCnr4y0isZbpWGOUUX82fE5RqD0NnPIkDXyzpduMIcLUfK8oXZxryTocw0n9NUfKzBp8iVq9ewd7PSwVguYRFHnO7u7u2jWe7QkunvdhxPpHRJBrJltmC1wXyKxWFNe1pF5m13nqrqFrGvLplKZpuH//kJPjI6wR9vZ2mUwmTMqSvMiVQ1mU96IoSlznefGll2Kq/p3erDQXpvYAN9R1TV3XffQM6MR5+PAhBwcH3L59e02DFlHSKi3yMBJQPhBGjqDerNo2EhtYHaNjN4X5Zkv435DKva6Jj7HoTSxwcyGlzW3zPcWbD1+NTEVGG8LWhXlBWxntZ2t/kLWN7nIvlxbPUBhERUZaYOPnBDY2rJGJuyFMUySR18traGnQDwGhdW1fhMLHiKOUNtTDFP2zCetiLOHiA8ySPm+MSt+n8VCLpO/WszOHe11sQR4jGOIG11+7fz9rq2H9BAb4Zfy9wgQXhed3u22uh/CY/SCE0FPXDslMF/0ksK78gB5/sL/P0cMH/TEqWwr29/f7c2V0jQ/TwsWJ+VTtIyLI6eNdU7p98J6iLGKEhwrIZRSqqxjmNykKsmtXybMMa4S8yPp41mWnnCRaDHUOKL71yU9+knv3DvE+0LUDcVVqY7MqRGFeliVFUVBHWODBgwe89tpr/WLKskwZGKNwXCzOaetGs0hbpcEde8XH2Nk2TXhN8ybhk5tOHdYmWvqc+tQ7S0cmZbIcxtlsYydiOn69pQIBaz2MgzUsXJHLN4W1tkXplxgyduGwsTZ4yeVcGov4rzBYdyLjDUFx3z5LIT0CY8giRrwk8SYGjNbJ1GK+gmZo6jO6sUaK0Kffx4cZ6gokK2D83BefyI81WNk8JowGJVoam2O2Ze1f6l6U9F9SLIbbPO5643PXjuvnwkXF6LvZxvP3cQI0zcmy1JoEQwz5MGe3KTdpzaQSksvlsr9mlmXs7Owo6+lI2Rrw8WcbhU0h/i8eRh4FSlmWKmjathecBJhMCvxUCxFnuXY5ywyLRU5dVxFKcWTGEKylKDJgggDTcgJotfQsy3jttdf4R//oH/d0qducDWOMsmmaPiIEVEAul0t2d3cpiqJ3zIpIT5PqOqe1CGN8rY28rmNmwiCydffehFaepV2EMsKFZ9rGDHf5BdmyIteFS8oIVDxUIatUQmyrerTl3ttdVOs33z6l06Y7ct4RImw2+p7Q92XYBIdnGeCHBLHF8QoxVjwExNDHoav26RFbAkOSmsg6KCKSCggP4ZprY9hL9xRiG4WqqPBfFwTD5009/7KhfawYeIzid7n8kEve1WM2jKdqvVrzzMduU2Ye14qi6AU5ETbZ1MjHlquI9EUk6qpaU4KKouDg4Epc+zGz9juwhf0LC6344FmtlpSl4uDT6YRUZSMvLM5raGJe5DinWnJVtxgjlNEh2raamjuZTDVFuxdcqaq34DrHm2+8SWY1LTxxem+qS+nFJmdU8L6no/Xe8+jRI+7evUtZlmvCMQmDalWxM5sTbEZLQ4Bei+shixCx05F2vaZpgx6DxHp/46VO/3uvSaPPqwRUZpAqUSg5rQKheGhPXhOvE8IF1CSZ2gM8MGwIKekhYaoJVklsdEhEOsZx+qkvMl6uj1t0cvHThcNTJvB2GIc05sj6s6az04a5JiSjMJeElbKGcRujdMkaVu6j1qw324ZPDxvptmcMl34KG5/6QUjvc0T2Nn6e0aMDQ8jrtuMu68vFqwx/79/98E+0Ri48zjO0S7bpC9bb+JljfyKLp+6Hwxa3ecXkr7LW0rmBfjj4Tcfm+jlp7ezszFkulUo6AA4opzP2r1wlZQr7FB0Ew7u69JnHi+eS9/EMitxHQpCLCLPZROGSSYkxhjwf75KibIatJnNYa8kko3MabiWAtTlNq4UERDRuPCstmVZ4ZTqd4Jzj9U+8zqScKPeDdzG7iguaeXq5LkZ4ZFnWf1/XNV/+8peZTqf9dzZSb9J5jBdc52kbTShq2zZO9pS52UHCFUdm3Jp2PhaoEhePuYifRzpF/U7Q4hvopEdEIwzS5A6hn4hpa+hN0z5kMfS3T2kymmDSjboW4qZAPx+JBTb6Ad3UyEMywf1ogm4IsjWzNnBRo982sV1/nbFGPNzw4hW2Cdc1kRV3jbEWnbB1F3yvzStyky6yJWyNBEqFOE4XN5ELbU1zT19teY4twmcstNcsgsdcqxfqYT3jdLjK5jlDpvLG/nKxj1u+u9jC6GebrTGaK8FvbMhCqlAVGI3H2tgOfpsUmbSqFlvHdNMKTtp4CJ7ZdMLh2WG/nRmrRbf39g40SGrNig7rr3HjXqo3pciu4d4hJKf/xfnypPaREOSgCQ55ntN1ypsyKctBmROtzF03Xfxdd+GmVdwyz3K6TjHwLFMypq5TuloplQskpb1/4o3X2dvb4/79+712fMFLPRKWKXZ3/PcUDaMbTt4LcnW0ZaxWq15bDwybwlpkBqMp+hh4ZdyeBLdsJg1dJiw28cAEuzwrlLPe920LcEOrHjkkx+eO+znu06Z4eNr+PWkBPM0C2Txm7HN41muN+70tdvlx1/qjOM6+W23rnPrQV5ON/x93ZBSSDJbixSmxHjoro00p5S+M8fFtwQX9/aIgTxxCbVQUEROL2ih1rVrDYchkHm+oYRvoFNjmI0nHj/9/2rbdpvhjbiFoinBqSv0Z1kjzRURDEotCif1tJGaKDHljoarJFB1VU9N0rVaGERAxXL9xkxdvvUTrul7D3dYfGF5kijnPsqx3Fo6F8ziCoHNDwYvEBpjukUIVU1tzfm4RbvGXCxNjU7Pb9rdtE3PbM46PvSCkLj37Oy9gnsZRul3Qrff/uyHE03fb7jN2tl32Mz7/o9QuE2B/ck0u+V0/J0up/zHRCrxkE1h/B5qXIqI01bC+mW6unXGY7nQ6HRX01r8XRcFkUvY0IJtY/dr1uHyNbWsfRph/JAQ5wM7OTh8TWuR5D0Mk6lKRVAMyxBJeAzQxFo7e698xwu7eHpPJhCBQTiY4NPrg05/+NIhcME+3vdTk9Ez482U7eM9NHb3cKdJlja1tNKnSfcb/b3rLx+2ylzru82ZV9csKKGxe82mOHZ+z2e+nnXBhA+3YFHbrVtF2Ifkn1bYJ8W3C/bKfdHxq257lab/7o7TxWH5YAf6d7lOkhhuBLHLJ/yZyOqYooSH7dHSxrQ5MEenT6Ou66Z9/cz2nz2OOmp0dTf4bv08tJLEfIdfhPtsf8CK08ng16dnbEwW5iExE5NdF5LdF5Ksi8l/H76+KyC+IyDvx/yujc/6aiLwrIt8QkX/raTqi/NsqwKu6xrmBpD4J586lOoyD0MmygWxfHXqQFzk2y2napn85y2pFEyGSt956C2AoOsy6eb85CdIGMzad0vfj2G3lVQn9tafTKfOd+VpccxJShIvCYE2gPQWmOp6Mm99vwhXbrvXkBflHm2zbNZAwGoc/fuH8NPfcdswm9PYs7aOl9T5du2zTSm0bLPQsWue4edS/kwJdx4JdQzKHz2KshnUmWkzW0fUeqNmilGlRmQ5/CcdSOm+8fkSEspywXC7XnlkjVg6ib2kzPPfZ2mUW4LO0p9HIa+BHQgifBb4P+Asi8jngvwR+MYTwBvCL8TMi8hbwF4FPA38B+J9ENhh2NjsdKWLT4CUq2jRuqZiAwida1zEEDciflBoCppCLasx5niu3R59K7kbaqvDJT30KGLIgt2kp45eWzk+VzMeTZIwtJ5w8yzLOzs44Ojri6OjowsQIYZ21Lm0Q42slrWKcRnzxOqG/bz+WI+1626JK2sZYII3J89M1xueNIaFNDfpZBFvSsrf/7cMJ9k3Nfdv1LtN0n6Txb27oH7Y97j5PIwC/W5bJ2jWf1jX5RxiHS+GpeO9EVTz4EQZl1vugxScyq9w20RoXe3nESbqGzm3VyKuq6tfbuKwbsNUKTrQOrkuF26XnCDo4OIAQviPvZNv8fZb2REEetJ3Hj3n8CcCPAT8Tv/8Z4N+Jv/8Y8LdDCHUI4Q+Ad4EffOxNBKqqoes83iuWnWUFKv8NzqFOUOKuK8pdkud5rwH3GLkIdVUTOkcWE02C81rcWAzL5ZIb169x7erVvlZg341LTM6xxjwmpBpPgnRu4mNILWFr4w1CRPlK0ncptny8UYwTiDb7cpkGnv4OPBYqGT/npuD/0/an7Y+7WZvh3VAOUdeYzudkpes6M3QusLd3EOWD6bNp15qM1+w65t00Tb/eLttUgT7nYzKZUDc1LhKbCbpOp9Mp+/t78Vj7J76GngojFxErIl8GDoFfCCH8GvBcCOEOQPz/Zjz8ReC90envx+8ec33DfD7vKWN1cJPneYxXq8faiGD77MvQp9M3sZxU2+rLcl2H7zrVan2gi5Emuzt7vP7aJ3pq2viMl2pgYwglCduxwEznj1PjvffRyz18DyNSrOjl3t/fZzqdAutEXpf1ZxveutnPdO5l8bFjDpbUpw9rFv9p+9P2R2kBFeDJ6lPoI7EU5j3v99Vr1/mhH/wcP/ETf5Uf+dE/H89+smO8t5bjutX48fWSeReOjUqWc475fE61qoZ1G/8+nU6ZTKYxGezDR319p9pThR+GEBzwfSJyAPyfIvL2Yw7f9jQXpIOI/Djw4wC3bt1iPpuOYl/VZFY2vbTrJoImPd/7QB2x9N40izGYmc2UCxkhj0V/BTT9H+H6tau8+uqrfOlLX7qQor+tJXgmz/M1+tv0suPzqMA2QnCDUOxrbMZjVcgarFXzcD6f8+ILL7BYLLh7926vcY8F+SY39JOggnTOJo95On+siY83nws4/Z8K9T9t3+UmRIw8qL8rkxxBia2uXb/O66+/zme/97O88sqrTHfmIHB4eIjzAWu1VqrO0/QDKf9A57UK2DzL6Yu6pKkdNJti3Voe0vKNMRRFwcOHDyPMEwheI1Z2d3e1VkLihNrgMfrjbs8URx5CeCQi/wTFvu+JyPMhhDsi8jyqrYNq4LdGp70EfLDlWj8N/DTA9//AD4RlVTGZTNREidBJhMHGZ+FSGrzzdK6jbVpc1/Xk7ynkz/uuH/jOdRCgyHMym1HkOZ95+zP8nZ/7ObKEOaeMvpBcJ3HziJNETSdisYMk6DYdkur0bJs2YreGtuvWEoYSXte50FMABDSqRowhjDaGba1ndtsi0NOGs4nlp4FMThljUtLlyNG6NtbDotgAmbb2aVsbbwoXcfqnvspT3+/i/Z/mmIv+gyf1Z/OyT4stP33TbN7h+mwfhvFtw9CXMLgF43vfPHjzdk8eqO2C6aJlOBaIa+cMy2mt8yFobzvnyfIMmxc8f+Mmb731Fm+88T3cunWL2WzWC9bOOyYTJcxL8z+NVli7EX0/knaf51qZKmVnb1q7m/Ckj9nckCzl0K//6XTK3t5+fw+lcyZWT9p43rS3PHmUL/n/6doTBbmI3ADaKMSnwJ8H/lvg7wH/MfDfxP//r3jK3wM+LyI/BbwAvAH8+uPu4Xygdo6pFYhZYy6oJzuEgc7U+RbvOy147LX6izWG1ml6vs0t9apGMsHYWLi3yMgkB2KZsc5Rtx2feuvTzKZzmk5J4l0XVEiPJ3+AWNkR12lNxLKYsDhf6kYDSDB0rSfPLQS0SK8J+J5KlTVBrtEvGXQK86xWWpRBMzRNv7ASrqdhljDf2SMAbdfgXBe7KQTnomM0EFCzUSvaqEchOO0DJkBwOvGNgDhMMLEIAyBaSUd5xaMXPhYTCMHFbNRR9MbGO3x6ro2nPe5pJ/Lm9bZIuP77p7lmOn9LJMEF8rDvcPTuZV3n4oYR1gRjLxpHPUrzSJ445I8blQubHSDBR6V3pAUDvq+CpM5Tg2Yxi7F4EoWxEILmiVib8dxzz/PirVu8+eabvP7GGzz33HOacFdrxFkXAnQajeYRHIG6a7WeKSNvaN87E9dtIHiv4cBtR1mUvaKToMRUvDn1OYUYJ0G+t7fHcrlUwY8leOVQym3BlStXSShBUiLHY+lHMmQ8zmHtE/TFzMP4r88uzJ9GI38e+BlJnkf42RDCz4vIrwI/KyJ/GfhD4N8DCCF8VUR+Fvg9oAP+SoRmLm3Be+rliirLIEZraMSJCilNcfe0bR0jVnQH7RoVaMZaskxr5wFghelsggh0QauKe6eVVKaTEuPh7bffJi8K6qZRoRhXQNImBogh9jFBJaMszvT9GJfeBlUkgp08z3urQUSQEKibut/J8yJntWwVw4t9SfKnLEtmOzs433Fy8qhnYdNjQzQfS02k8oG2G7jPy3JC29VRK9esMukTKbTocghqpqbrhT4QbHvc8WNkzlO0J2u+H/Z64/f1rL0cni+51b/7JvKaZkhUIlIqeqQ7SEJ6fQu9KGCH37a/p21tc5Se3OGN7VFEycUu7jwEH3AB8ijY9VkFY3OyLOd73vwk/8F/+Je0olYMDVQqjgG63NT4Qwg9bq6W9Ij7J91YQj9qIvQZnXVf1GSEicvgtxoHGXjv2dnZ4cGDB6O1rGt/OptpxErqU7xRek+py8k5mn5fH/Xx/6O/PMYH9rj2REEeQvgd4Pu3fP8Q+NFLzvlJ4CefthPeO9q6oqkLfIghP23TCz2liPW0XUvrVCP3zmFNRvCBPNcsK5tZZjtzfOiUdyUKn7woyCYleC0+JUY4uHLAtf+vvTOLsSS5zvMXEZl511p7X2dpNWcfLuJmkyBliZBo2bBlP3kD/GDAsA3DNvxgSBBgw/CT/WD4yQa8wQS8CBBkwaSeRG+QBBvcRFLkmOQsnJ6t1+ra7ppLRPjhRGTmraoe9sw02T3se4BC1c2b91ZGZOSJc/7zn3M2jzEajQChPh0MeixeYxNITJKkbvfUZn1ELnlbkceFEaPv0MLTtKYspF1XkiR0u10mkwmhqU0N3wB1g2eTZGRZxmQykcUTAqcARVli0hSMEQc7WD1FZamsI02kTroKD7tzHhO63VfWIh6iDxubod3u5H5hfz9OuVNw+yh/Q34FJXkXmu/t1tIdPhE/KMrJN8cba7wGAFG0ioWphW84BM28F/CnDTfIGqB+XV9iHTxsxiBrSNNJU7RRzOczut0uWacTGnJIEuBgMMD6xS5IRwUO27cnUpXV4p7VOrkBwZxzdDqdmrHS3JeAz7cUZ1uRR3x8NpstzEWSJHR7XQaDQXjGo6V9BwzliI2offwoksG7UeYPRK0VpRRZmqDwVGUurhsNLU/YH1DhUU7T70s/x163S5pmtc+iE01eiEWbZkb6KIYSsjV2VasxxXPPP8+V1660YAx3QGG1J7dhebR52w01inoBHEzbj5BK29oXBSznzGczBsMhWZYt9PFslwGoAtZuXVUze5rvQkoVeMVwZZU//sc/xXw254033mBvd5e9vV1MklLZiiTR4AQy8t62HvqQKRc3j5Z1eif2y/tdfvSDctD6bSmX+NkjtOTBh/JulXmdxxi9iQPxGtVS1bF3ZgOqxHfiWY39/rbQSWtkh84LmrK+HMQqbZRXe6MSJZckSZ08F9vwlWXObDatS2gkScJsNke36tfE+FG7bv6CQmttVu3aR3cakyIqSVcbUdH4QvnFjagergqYt2RmH0zld8Eo7Pf7pKF72QIO7pvgqfcN7HNond1h3bUV+j23yH8SohSUxVz6KzqHd1K0vygKCSQoRbfbZWW4QpolsqM7T1kWlLNcKiYqj/MhGKkUlbXC1W4FM6OSdM6jjeKpZ57iS1/6YriGw9aZUi3ryzdNjg8mx7S7z0iQc5E6GBfQwfrlzjl0kjCbz+kPBk3iTbAodKDbaC3893hdcWH62oqJ7iFMZ3P+6Dvf4dSpU5y7cJEnn36KP/j932M+m6G89IYkcPVjhnMZuq57IEuFnwtxTO379F5su+Y7jgqKHbT82i6qvL8IfbTjuG1X+Z0+AIcVbXMdjejF9xur4B39n/b/O3SdwULUUUmGNSumbZiP+vrCSWqRaeQjPtjS/y6ce1TgWanW1rAwn3F4B6xFFRll0h81TRJhcoXSyfX6jXuQ9SGLUon32DJytJaeq3E64zMVn48jLVUaaKWeyyOwoXprCR5EfF7qDaC1eNr3PsKms/mMY5vHmM1mwXiyIV4nPTqFsZJSVosQ0CLO3dyKO8m99HAfDEWOQicJ1kPlPK5yJGlKf9hD4Uk7GUmSYp0jLx3OTklNQppmeDyV9ThbARU6UADbKbxSo6Xpc2OMJAc9/9xzAV6QaonGJFRVGc5qXFfvhSkSa65ECEQpVcMiRVHULly322U0GtWLdj6fs7OzU78frQ4dgptFtBQIjY3Lqi62ZbTBeofWUq7TxNiBUnglEXPpaC8bnq0qUIpbt29z89ZtlIF5aARstMa7wKPVBqM05y9c4PFLl8myjJdffonXX7siesI5hNnl6wV/RyV01D29g9L/UZ9tFFMo51l/prE+VfSsFvDwcNfc4R6Od5Jo+cXrbTwr2eGcs+i6dDC8/WP59mM6eJ2HT6KxOr2nqSMSPntAW3lPvTZEVBN0C4rbKxaaO7WV+cHNNPSikjmNhoj3wibR0h0nTROMlp9YcK42YgIkUZYFWdahLAo00iw7LwuMkfiX1omARTp6tofrFh39WjYbH5gnbSv9kAQjR9ZB09Aletw2sGSkfnjTc1eqr1Z45+n3+2xvbwdIVQw/EGNsfWOj2QygLi8Q78vCpcTrOWSVNOe3jPfWPf9RvtSiPBCK3APKpJgkQyeQdbp0OikowzzPyUtL5XXovGVCmdqK2XgGCM5rtCYxCT6wTLRW9W7fgCli1VQODIpTp0+ysbHBZDJZsKqb63ItfLJxAWuLuLUTL1rli2ns1lp2dnbY3NyslXuSJILje09RluR5zmAwYGd7W1qVWemyrrXGWYm253lOf9CvU4QrV9SWQtxsnPeUVUk3TVBGkxhpdzcti3pd6OCxJEnKdDplNN5n89gxNjY2uPLqD1E+dAxX7o5uYFviYj049kP3OcxTmxMfGQSRTZAkyUJWajvQ2ijEaKFTb6SwWCL2sNJssvzi98a4xkFF28ZL5Rrjg9hsJG+nlN/W7ff+0Dprz2NkHXmvQrJJhLbCRqbAe43DhjrYsrFnWdbalBB4suU5xP/bpsHGMZrQaMUkps6VgKY0QzvWI0anPGPOOtCaLEvJ81xoflmXvCxJkxS8I5/nFEUZvEYjheq8QuujGzocdS/i/fNeMO3RaERSGzRHzD/UjWVixvdC5uWBz8T70WaWGWPI81wMA92wbJIkYW119RBLpZ74I9beYWilfaXt9w4q8bv3+h4IRa5NQrfXp9PtUpYluXXMJjnGKLKsizZCGcwnFZHgn2hNkiYSvNNKIJVghccdLlriEkzxGKVIwtpxwIlTpzl/4SIvvfiDBbhEa0WT+SvuUrT0osKJiUEHA54qYIXx/TbLpR28hebhilSn0WiEJEA5adOgtRQJC4tvnucMhoP6O0vfwvmCW5cohStL9KAfXGfZ6JwVrr1RwpbRQS/sjfZ58eWXyF5/Td4P7e+8tTjvpNvQATm4vFT8MhYV5aHPhYelHZNYVBLNxhhpYAehgIMeQWQkxLk92JS4LQetvPY9aBSbwTlVF1iKFpyux9jGHu7eYjp6czl4jrTJiLEKbQweX8OLPsSOBKc28tt5vIOqDN4EOvQaUWjlUTpp4JNwDWma1tU5a5gvNPxQumkKXRuOiua4U8E79GiT4LxUEzSJVO6Yzudsbm4yGY0Y7e6FwnVh4wiwi1YarcMm+jb3aeEeASjxXvf392u46Shl194ojRGcfj6fL9Rb8gsDpDYiiqJgOBwGWLeF09Nqtry+LmsXaor0kffzzneaO9Ujf7fyQChygL3xlE7p6PU6aO3wKmF/MiXfmYhdrDSphjQx0ptTaZxvAnHWB96o0cIPBcrS4pSSBJig1CsHyjmMUqyvrXHxwkVeeOEFYcKEJCKlkSeqlkZJReUT3bAodd10reodPSqCqqpqiykGPbXWZGnKPFCiPvnJT6KU4lvf/CZf/+rX6GWdeiPwrkQnhjLUbI/KzzqHDmtZXGGBYGxV1iEwHSwm5RvYwCOceesqskySHqazKUWeI23MLKnRbdLKoqhFVrM/oMSj3KnT+0GLNG6OUdGbWOzMudDPtV3c6OAVNQGqdvmEw6yHxQ2m/brtJVnrSFOJZcQGIvK9DYTxbpT4nebh4PFYX04MES0dnxIv3qo2mESotmnaEVwaxebmOtevXmU2HeNsiXJSR9DjcUq81QgrRBqs1q1OUoBOkmDti9KMGc9RqXvaDI2mT6kxJkALnrW1NX7+c5/jE5/4BP/qX/5Lrl+9htaQpgmVLZv8hAjjaFM3LD9qPg6K1ppbt24xm80aZssRuty35jgJOP54PG7NcRM3o6Xw4zpaWVlp2Gy/vDRtAAAgAElEQVRa15tckqU1KQFo9x6/wz1vMJOjMP8D5nw4t7UzvAMk78FQ5EqxsrbBdDrh2tYek9kcH/C1rNMNyjFBqYDFaRPG63A+Vm8L0EdVYYPi11rgGO88DhesDCXZnAqybofHHn+UqixJQ1NnWeANFhwed6C5GVVVkaYps9nskCUZudh10kFr4bSzxdrvO2vZ3t7m4x//OGmS8s1v/GFNwyyKsl6wRV4Q46+JMcEVbzDcAFiQ53PwFq0NGk9iopUljAjnQCU+1Kvx0irOST2KMq9ITavTyZ1gkgOvxAs6iGs2cQYVrRDVYJZ5npNlGWfPnuXy5Q9w+fJljh8/jrWWspTOTGVVUeQ548lEcM4QmC3LkqqyVLYMbj1cv36Dt956SzyQgBU3S0wt/I5/50XBqVOnOXnqpPCenaMqHTdv3mR7e5tBv48HnPU11PZug1RtOCPO0aFzwtoGTSfr0O326PS69Po9Op0evV6PNOuSZl1ef/119na3yXPBpafTMbay2LIAHxS5sXR7PXqDfsN2cp7K2fpa4r1qb89tz1N0kuD2iUlwzpOFYJ/Dc/GRR/jZj36UZ599lizNQCv6/b54d7jQ9zYENL2k33ulQElxLJmGo5V5+7VWmmtXr1HkYtBobXBVKNER4wsE5WulnWKn06kt7fr+K2k+o0LJjgX4Dk+322Vra6s2DiorBl4n67CxsRGU+9EY/YKyjobQHdeLakz6g8rbRyD47uSBUOR5XvKDV94EPEmWYXRCkmiMNmgl2K/yFm00XkNpS4zW6MSAt1SlRSuNMQqjpBGFd7aupKhClmeaaKmNgKTI+8ry6KXHQ7PiCGVU6PoGhYmmudFFUTAYDGrrDxaVgydmiGUo5thSXH5bOXwKzquA4wcroCzBeXa2buMqi6sqsjRtNgYtHgU4fFXiq4o00aRakejIXIkBG4+z0hYvwYBTKA/aa7zXJImmiJmgXqGV8M2LwqGTLufOn+KN165QlTMSoxCIPG6K1NmdMs62xPlxktXqbGjw7Or6Nt66kOEnG9rG5gaXP/ABnnn6Wc5fuBioaKq2/oZaPCwXNseY9KWNQQerXZsEk8hDlaUJX//a1/jCF/4DGI22gWVk48Mr3pInwA4hzuJR/PKf+RX+5t/6GyiTCAUUz2Q846tf/Rr/9t/8G6oy56UXXxLvoLZJCXfx7iRaibUSR6HCZiyvJYbjXEmkhs6mBZPxKGDW4rEkScrFRx/n8ctP8KEPfojpeMx8NqE4nnNyehJblsznU2azCWVZMssL0jRBqVg/JOqMJiFM7ISGQivWqmDZcf6kN6Y0GI41jJ588il+9mMf4+Kjj2CyQEZwjizroDspjlKgvABX4sCkWmqMO1k3voZIWnN1lKeCrOOrb17DlhXKa5Tz6JDJ2VjmXpg6SjD8aD1H71nX39mCYVH0u9IFKOt0a2OthluRTOlet8twOKzLbLsQh/BhPOouFK8YZe34na/vfVTccUN5Jyb5A6HIvffoJA3JKe2gmcc7Cyp0L1dgbVAmzmPzkixJRFlES8NXoMAkhiyV9zyKsqyYzQqslYCp1gblHc998IPoRLBIZyuMbhJs4kKPnnQ7MBfxRWgUufMhRVrpYP3IU1NVFSsrK5RlRX+YMMslQzU2g62qitdfe40333iD3Z0d+v0+4/GYqqzqrkNVqCdz+Wd+hvF4hPKe3e0dCO6gKFsDzklVRwfRfDfahAfZo5Spg6JxjB/72Cf4hV/4JS49dom/87f/JtOpoyhGZCatE1MaOOAI/FtJLGMyGYtHYhKx4IxYP1XYrIb9Lo9/4Gd46qknuXDxEVZW18TTKCts5USZK01VlZSFFY9DG1QicRQXV39QMBpPpaRuvcbjjRbP60Ci+lHItjA8PF4ptra3+f7LV+j2h6yurHDi2BonT5/hT/zC5/iP//k/MZ9ydNxJHX340PoOiqhtAbfnLl6o9wK9eQdKQ1lZer0BRSkZzGdOn+ZDH/ow6+vHsEox2h9RtpqndLpdXJJgUs1gZRDYUSYUl7NUVUVRFMGbqeokN2st6CbAKxRCUZCVdXSyELsqCjbW1/nghz/Exz76cS488ghKa0aTccB8E1AOrRK63Z54ZEqLQUXYOlRMSItq7+2VVTsmUpWW2ze3wAmby1XlQnNpQSZ8fdO11vR6EiuKcan2v3TOy/oK8QhXFAz6g3q+2qwmo1s1yGsvPP7Txe+90xjqsdQLMihxVb+k2ZXemdf3QChy1OITocJN0EajVChTa3N87tBKCY3PaLSReuS2sjgjVlknS9EaisqyPy+Yz3PKUrDqNE0xSqNsJdhcWTKbzSVVfzYjTZJQTTHOtWrHQ+obEbv/RCXcJBK5Gj+ODVtt2NnngSv+7LPP8n+/8pW69nLctK5fv8729jZ5UdDtho4kwRWOgZiyLOn1ejz99FNMpxOuXr3KdCqJFrayKBzeWzSSjpx1soXgolh8FahUrGQHa6vr/OW/+JfoD9bZunUbbRLyvKCbdbFlKVz2txEPGJ3wS3/yl1Ha8N0XvsOtW7fY39+nKHKG/R6XLj3BM08/zYWL5+kNOsQFPC9EQ0a+dFn6sBkqNEmgYIoXVRUWk4gF58Mm5ZXHBDaTrWywmCWYi4rsGVf/j3Ynl2j1OOeoyhJbVoxHI3Zub/PGa8JqmkxGNXb/niTizurAIj8gSiXSMFxpbGXRJmUym9HrDfjMZz7DmTNnmec5u/t7TGdTZtMZeT5nOpvgvcNWFVIXR2IJwrZpYggxDtBmccjlOfJiHrpyhTiQk03fOql3cvHRx/j85z/Pz33ucxTOMh6Pmc9mFEVJtzegLK14id6TOEXHdMDHqh5NAk581LVGvEkfLdL2ilrc6OrM5fmMW7duERlEzlbilS/OYo31G60ZDgd14LJR+r719U1ZjTzPOXvmDLdu3TgUOI/ztrGxUbN+7gyZvDO5m0D4j5IHQ5ETlHcSsOVQFyUvLM5aklD/N0u6aIRFUNqKJJX+np1uRicVhsft3T32J1OKSlgXJlQ7TJMEV5ZUZcn+3i67u3v4smKl0+HUqVNcfetNqrKQWi42PvxHS1mW9UMRO3JHipjRUvFQKYFOYs3zeJvW1tbkPa3xJkEFl288HrO1tUW/32cwGLC1tSWfC4sp0ux2dnY4dmwTa2UzmU6nlGUplrtSWOtByWbTH/Tr60yMYT6fkdW1aiRDr6wqXnnlVdY3TkjdZWXQSPsqrdVdGQalteyOJjz51DOcPHce5xz7+/vk8zkb62usr66GEggV87Ko6XJlXog+cy7AR8Hq1wbrxAU1JlpvEv/AezBOYAmFQFY2GJA2FgALfXm9ppJ8YGKBpahQvfeiIGJgVCnmsxnayObZ7XS5ceOaQAnvUZHXsKdvUw/VgjKPisEYgdWcdxg06+trfOazn2VtfZ3pPGc2mzPP50wmI8bjMdZWIQNR/HtrK6qqbBkJDb2yrdDj65hINugPACnzWjkP3lBax7NPPMWv/Pk/x6c/9WlWV9exRlOpULlznlPMc6bjKeP9EeP9Efl8jsbQ7w5RKvazlI3FOyfwmzLB4G9qFB0C62orVmbQGMPe3h7j8QiPNKCpZ7cNhRLWtvOkmbBMxuOxsJra800IJiPdxeK96XS7TMaTmrUUmWppmtLv96VCq5X1957WxIGY0nuVB0aRO+9C9qIl0QaUJN70+71gBRdUeUG/06PbTVkZ9kErxuMxV6/usLO322RNJglJ2qGTJlSlYzraZT6dMh7tU5U5a2urnNjcZG1llc2VIR/5yEd4/cqrpEkiUA7Ui6MOpLQewtj6Labxtvt21hlfSpEYgw2wSNZiVGgde3s27pbWmslkwsmTJ1ldXZVAi5HPxA3DWsuNGze4fPlyXdw+Mmi89zhbkhhDUVX0el0GwyGj0RjrKpSO7AWPCUoc78nnBVevXiVJu4z3J/R7PbaVYO/e+YXFfyfxXjGZFezuj8SiNobecI3BcJ1utwMmBMacwgULejoXV1cpSI0OVRrlx1ZeeOxIMobcBeG9xyA3tsJ6DU7wc41GG0WSmJp+55UEvLxzoSheUKJONrtojVVVRVWI8hMvqmQ+n6OCa/12lMa7kRrO0e0We4cRVW0SnPVYZ0nSjJWVVX7hc5+j0+0xmc2ZTCdMJjPG433K+QRnrXiQKsQTQgJTmphQIbOpl3+wucnBwPRsOsVG1pYyaCNzc/r0aRKTcePWFpiE3sqq6E6nyNIOqU5ZHa7iT54O6ymnyOdcfOxRtEkgJKCBD9/flHKWuAXNxhaghbjknLP1YWMSrl27xnw+r69bYgftWQyoc4AvBoNB3d7t4GZcBzgRT91ay3A4pAqGWRuK8V6U/draGlmaUlSC0d8LuRfWODwgitw5y2Q8Ik0Ssk5GlqVCMfSO2XSK1rC2ssL6oE+qDaPxnJdffIXbe/tS1CbCDx2hVnlbMhnNGO3tM59NSIxm2O+zsb7GyeObDIYDup2MXkeU/fPPP89v/eZvkqVJnc/ZZq0cthcWm0XHc5NEAnFKSZAxSVPm83mgtTUsgTQV7yBJkmC9C2Y9nU5ZWVnh4sWLfOc73yG6pDJHsllMp9Ma+1xbW+Pq1as19dF4jzLymeHqKs998EPs7Y/AWb79h9/grTffkBhAJYkcxhjwjvFoJBhonuO8j/tWwGvfvlaInOvFw0EokbM8p6jEMtobT8hSQy/r0Ol28CRoryirgqrM0UpRqIpuJyNJNDr8XwlMWmx4bRKD1h7rigC9GZRTKFJ0kgScXKoBixEXYQPqDFUfbHPnD1vZSWLw2gS6Y9NcYGGs0XJsTOy7QjKtixm5Dde9Ce76pnyq81RhnZw4eZJPferTJGnG/mjEeDphMplQFCV5Pkd5j7VFgBAU1odG5N6jjA6p8ZF+mtWGwEEufk2pDRtBQC0oq5zKeb7837/M7/3BH9AbrHD+/AWefOIJnn3maZ548imOnzhBmmV1EhpKobspq6sDTp87h/dIk5eqACSWoZSqN2MXPF9jQlVQ09RYEXqtBy0EBuscN27epKwKjFbBk2rFOGtpvI9ut0un01nEx2mUuAvzo7VmNBpx5swZRuNxfV5Tf0k45GtraxA2fxuVrwpQXvj7qAVxKGfhDqvmbmmYR8kDociVUgz7/foG57MZqpMx6HdZXV0nS1OuX7/Gmz+8wo3rN5nNcsHJtSbJhMmQlyW7e7tMJ1OKvKCT9dhYX+ORs+c5fmyTtdUh3U5GtyNB1UTruvLfY489SqfboQr4eLTcopKKdbmhuRmRGx6vXx4ISWRRWqy+mCEXXTSlNaurq/R6fUb5Dlo1CS0eGI1GZFnGxsaGuHbBSoSm/dR0OpVAqdFsbm7WcyjFsDyVc3il2d0f0V9Z55nnPsylS4+xv7/Pa6+9RmJS0A6UsEvKsmD79haT8RiloNftNFH0u1xPAsNAt9shLyzKJJSlpSzzuuLiuJowm80wJqMqy8Bn7glDyFt292bYqiTrpHQ7GVlm6HRTsjQjFguzrgrxBwnsJonBOkgDRUwrLbTMqKhawSMfoJUarw10A+89ynmcFes/MglKX9WwmUAdYTJanvxhJXK0mLApPP3003S7Xd588012dvfqvq1NolmC0obzF87z2c9+lqIsuX37NvM8ZzwekeclzllsVYItAmvC4Wm48FUhtbp9YGxB00Iw5j7EQF7bMo+dc0DV5R6SABNOxiMmkzE7W7f4f9/6Jl9UitW1Nc5euMDlJ57gA089xaOXHuP02TN0en2c9qQ9aYrunSPE4HFOAq7KgPea9fUNLlx4hNF4zGg8oqpko7GVbChehaCrkrV4/eaNVqDSIsHhg3cgeB1KMRwOcc7VNOHmrkVlKdms8b21tTWuvPrqwmYX789gMGBzc7PxqCJcBYFKyV1j5m931ru1zh8MRQ6SxKLh2MYG62tDnK3YH435xte+zu3tbcqiwJcSCEuShOlUAnHVvmVvNKIoSzqdDsePHefSxUdYX9tgbW2N4XBAlglGbLTCaKHnGS04nbWO8+fPsb6+znh/j6LIA62siS7XLnmQpmVU45rHyHiSSG0Ka8tDldy0Vmxubsr/2ttdSFfXSrG/v0+322U4GLC2tsatm7fqZI4Ir+zu7mKto9PJOH78eH1dLuK92uCtIusN+NmPfYKqrHjl1ddxXgeLWRpIxAQPhWMyGZHPpuR5IbVmvA0Ys6uDZW05yLpQSjGdTPC2Ik0SsJos6+GdYz6bkM/ngCc1iSQwAXNbUOQakyjhuiddOp0+eZlze3tEZWcYo8k6HbpZRr/fo9fr0u100DrFObA2BiwFWtDI/TKJlDluU9vq56w9lAAvucD0EUUuAVId8g+gCco1H3pnEiG5GzdusLGxUdcKiQHHMmxspbU8+fQzPP/B59kfjZhOp0ynsgH6qiJRkFeVJHcppHFDvH7nsGW10LwkxjgixNJuWdjmsjfB8HhvI55tpWm5NpIU4yT1XjnH3s5ttrZu8kff/iZZr0vSyVg/downn3qKJ55+inw8ZTDoU8ymQjf0FucF+jFJwnyco1XCI49eYjKXgnBFUTAajZhMJozHY/K8oCiknWNRFGxtbZEkpv4eKf7WXo+LMYfV1dU6X6FRvi3sXQk3Pc5Zr9dbyA2J82OMYTgcsrG+sbD232MI/J7KA6HIjTE8dvEsaZpy7epbfP3Fl3jrrTeYzaYkoS9fVVUoD1VRUhQFeS4NIQaDAcdPnODsufNsrG8wHA4ZrgxJ01SK/BixXsXCFuvFaXDeoiq5kWfPnufE8RPs7WzLgvYguGzshLIo8cEQOCWpa4UXhZS91FpT+WZskdmSpWJtnzp1ijdfuxIwumZhTKdTrLWcOH6c06dPc+P6dZJWUCU+9EVRcPLkcTY3NxfS/50T5eWUJi8rrt64ibOeYj6j0xsgfPMERQmB4eKcZmd3m9F4jzTJGPR7kuFqokW7GCw7ZDGI98t8OqbMZzhv0EkH56S+RrfbQZGJe+09thBry1mHSUzIzlVUzlFWYmV2uz1UJQlglfXcur1Pdf024Bn0+6yuDOkPegwGQ4b9PlVRNNRVLbEJrzT4qvaUopI6hEmqwAyPFrzy7bea5I8w1ngfVPuku5Stra26EJMyBu1NjZtba/noRz/G45c/wGg0Yn93j7IomE2nVEUZkmocVVUGDz62NUzkPlY2bLpSKMpZS+V9oNbGS1X0er3a6IiWes3oCBtOogQGklo/IGnODoV04zJKy5OkQSnHbLxPmqdcG+3z5iuv8Ltf+hLDwYDV1RX8oEtZzimKqRAOnAPrQBmUSpjNClBS+77T7UsnLO8k2auqwmY25fvf+x6z+RxUWDuKUNn04CyLsk6SJNB491vlMxbFaMlHKMuSwWAQ+h6ErbwVF+l0pP54r9dtUX0F7rlrt/XHLA+EIvfO8Qe//3+4dfMmZVGGIj4OX1XkucU6x3i8T5HnaKVZWVnhzJnTnDt7jmObx1hZWaE/GNLvD6QMa6KEwwzhwZRGUypWbFNNfY8ExfrGBufOn+eFF75DJ8uIHVrqXbxlyjV86gZeid16yrKqq64JHtjUb1DBsu52u1y69Djf+OpXiDVdorKfz+fs7+9z6fHHOX/+PN/+1rdra6m9sLa2tnjssUdZWVml2+0xnozl6rTGJCm+sNze3uHq1et0ewOwJUmakWQdsWacB+VAS1nSfD6TwKy1SLs4RWIUlV/Mjqzvl297KGLBFflULHgHzlbM5oWkkmeCw1dVGXjzmk6nR17MJW0bzTxk6pnEoI1mXuRo0tqLSdI+/V4qHlRZsnV7hLu5E6rxebLUsLK6wu3bWyQmoSpjDZyYzbt4/RFDr5GXGm4heBhgnaUoGwVaJ2jEGAJ3b5HFDVDWRSjApA1lZUlTxWAw4BOf+CTDtTVu3rpFkedMxmOKPMdWJd4KtdCWVUgQ0ySdDmVgp+A9VdXKxIwbhTF45UMd+qawWLTMF4qMqVCP3seMXAR7rySYmmiNR+rXlz60/dPS3jDR4CvJFegkCUmaMhvtgzYYI9h9J1sBPMokOGcY9LpUZSWQlpHcjspaqryU85RG6YTByirrm8d48cWX5NxQxdM5iw6U0ub+yl1xztMLDJPr16/Vhk6k4DZYvdQWmo6nXLx4kdu3b4eCZfJ+/Ey/32N9fZ0kluUwR20g91ceCEU+2t/n9R/+MLjEFeXckQerezQaY5KEjY0Nzp17hJMnjnPq1ClWV1YZDKTuQbfbCQwPKeVpJVqJwmKQtH6vBVrwyAJFwbxwlEqxc3ufE6fOgjMoDE5For6CA4GxRpFLeniaioJx3uIr6WIU05qNiYlB8pmYjHHx4kWyVKxUrRRGJ+SuBOvY29un2+1x9uxZtFEhQzIUucLhleHW9h66MyQdePprm+yNxpiQuWhthfGeYjrh+tW3OHXmAt5Kuj7eoZz07YyBGWsd83nOZDYjybpkvb40hq58eFACVQvJbrOhx6daWMhi9UqmZYZD0+1Q93V01lIVlko5sjRlOp+EFHHAxR6KAg0UhUOjsa4g0SYwUmJ7L0diDMOhKAXh6AvjaXdvynRuufjoB5hOxozHY0ajEQ5hLumArRhizXojpQmslUbeVYExKdZbKmvBNThzxM0lYBdqdLwNPfWg+Bjk9NLXx6QditKidcrGsZP8sT/2CbqdLuPphGI+ZzaRujdlWaCJjVUcXst2Yp3FV43VmKQpTT0aCXbihf1CLEVMQz9sF4Nq1vRiKQkXrE2lYiP0YNGqaFT4AL0FeMJZKQWBkyA2Hu9tyG8QJkxVVbjSAgmJga9/5ff5J/9oiyefe47HH7/EY49fCgozwyO5IEVeonXCq69eoShylJJnwQXo83DoUHjnvW4/FMvKQ6p99CwbJay8lNOoqorNzU2uXLmy4LVFZd7v91lZXZVYRij9EJ+B6M3Vc0xkJLnmtWrK/cqCCDqExk5qwKEQpXuHO8UDocil1duE2bhkNptSzAscirX1DZ55+hmOnzjF8ZMnWVkZ0u/36Pd7JEkquHdwqSNW67wHLVQ0X4FRitRoKq8ovCcvSvYLKOZTprOKvLRsrPR58snn8EjqLVqsdiUMqYCntZW54ObWStGpuNCdKynynF5/UFvR7WI8eZ6zs7PD4z9ziWPHNrlx7WoIVBmh23m4efMWJkk5deo0vV6PPJ+jPWgV+OTWsbW7S9obcu7YSR557DI3r19HuVKsbTyJAm8rpvsjzFlNhWTnpcaAL6Scko9jki7mXmt0kpB2ujK32oUaNYSNJNS79ixsbAFmlvTsvCBJpSQqSop9Vc6ivDATPFIbPWrAWJmuzOcLBchMqkm0cKmL+Ty4smqhuFac0+D7k6YJvf46l5/YCFaVoyxL9vf22d3ZZjLeZTzaZzaf1ZXwbFGI54dYjfP5FBsaC2dJl/k8p6pc6JLuQLmakRQTXRYg9wPKMb6Weh2OylrSrMvx46c4d/4Cp8+eYzgcYquC0WTCdDJlFkoqJ0bjnQljS1G6US5tCzMqXQl8+pqRE//WLZpcGw9vK6v4fTZQTtuKyegYPA75EM6jvdD2okaMxbCqlteoFaRG4cP/945QOVEjZTWmVMWUb33j//Ctb36dNOuSdbqcu3CRp555lssfeIJzFy9y4uRprHW8+OJLVGWBISYrtaMfjQTbgdWVNaz1jMeTOugrvTzrJP0Q/C1ZWVmhqqqabOBsVc+NMYaNzU3WN9brOYmJVnjpSbroth7w11qB0bYsrJwWfv9uYjDwgChyay3Xr1/HO8vGxgYXzl3gxMlTrG1ssnn8JL3ekG6/T5ZJcfvoJmqtg6XaDN4Yg9eqJvxniVhU+6MRpddcu7GHB/rdlLIUutd8VnDxkUdZX1tnOt07dH13mt52YkW8irIqWQktr4o8r5VVDObs7u7S6/U4cfIEt25cr6EVsYhgZ2cH5xyDwUDS+ou8XghJYrClKJZTp0+jjWF1dVWaWqS6vkitNaWzjMajoIClAmKSJNhKWBTNQ+drTyHrNskq8kDf3f2TIlWC3yvTIS9mAV8GbZDiRDYoBpPgfFP4SmsNzi5Q/ay1dTq3QBJN+dv23FtrSQKGXVWWCivYqW/40/1en8Ggj+YMVTHH4et09clE8NfBYMD29nagg5Zs7+2yvbXL9Ws36XQ6FPnk7ibiDhKDa888+yy/+Iuf59jJ08znOVevXefmzZuUxZw8wCm2Kuv1Yq0FH5pl6MViW5HOWLNqWn9H2KRdKpgwn20lHr8rfl52ZVdvDg33vF1aQC1AUwcpc/VvFTxfmjon8fsiHVAqJ4pFPZmMmM6mjCYTXvh/38ekGSdPn+bk6TNcfORRXn75lYDj/+j5VkqxsrIitObJOBhartatsplEiqnU/dnd3V2gnMZnOqblr62t1XMSht/+j7xbBXyvRN0LMvp7vgilbgETYOt+X8t9lOMsx78c/8MpD/PY4e3H/4j3/sSP+oIHQpEDKKW+7r3/6P2+jvsly/Evx/+wjv9hHjvcm/H/dLZHX8pSlrKUh0iWinwpS1nKUt7n8iAp8n99vy/gPsty/A+3PMzjf5jHDvdg/A8MRr6UpSxlKUt5d/IgWeRLWcpSlrKUdyH3XZErpT6vlPqBUuplpdSv3u/r+XGIUurfK6VuKqW+2zq2qZT6slLqpfB7o/Xer4X5+IFS6pfuz1XfO1FKXVBK/S+l1PeUUi8opf5uOP5QzIFSqquU+qpS6tth/P84HH8oxg+glDJKqW8qpX4nvH5oxg6glLqilPqOUupbSqmvh2P3bg7aKak/6R/AAK8AjwMZ8G3g6ft5TT+mcX4G+Ajw3daxfwb8avj7V4F/Gv5+OsxDB3gszI+532N4j+M/A3wk/L0CvBjG+VDMAZIxMgx/p8BXgE8+LOMPY/r7wH8Gfie8fmjGHsZ1BTh+4Ng9m4P7bZF/HHjZe/9D730B/AbwZ+/zNd1z8d7/HmkmLw4AAAKUSURBVLB94PCfBb4Q/v4C8Cut47/hvc+9968CLyPz9L4V7/017/0fhr9HwPeAczwkc+BFYseCNPx4HpLxK6XOA38K+Letww/F2H+E3LM5uN+K/BzwRuv1m+HYwyCnvPfXQBQdcDIc/6meE6XUo8CHEav0oZmDAC18C7gJfNl7/zCN/18A/wBo98x7WMYexQO/q5T6hlLqr4dj92wO7netlaMKyD3sNJqf2jlRSg2B3wL+nvd+v1186+CpRxx7X8+Bl6pTH1JKrQO/rZR69m1O/6kZv1LqTwM3vfffUEr93N185Ihj78uxH5BPee+vKqVOAl9WSn3/bc59x3Nwvy3yN4ELrdfngav36Vp+0nJDKXUGIPy+GY7/VM6JUipFlPh/8t7/13D4oZoDAO/9LvC/gc/zcIz/U8CfUUpdQaDTn1dK/UcejrHX4r2/Gn7fBH4bgUru2Rzcb0X+NeCyUuoxpVQG/AXgi/f5mn5S8kXgr4a//yrw31rH/4JSqqOUegy4DHz1PlzfPRMlpve/A77nvf/nrbceijlQSp0IljhKqR7wOeD7PATj997/mvf+vPf+UeT5/p/e+7/CQzD2KEqpgVJqJf4N/CLwXe7lHDwA0dxfRlgMrwC/fr+v58c0xv8CXANKZLf9a8Ax4H8AL4Xfm63zfz3Mxw+AP3m/r/8ejP/TiGv4R8C3ws8vPyxzADwPfDOM/7vAPwzHH4rxt8b0czSslYdm7Agr79vh54Wo5+7lHCwzO5eylKUs5X0u9xtaWcpSlrKUpbxHWSrypSxlKUt5n8tSkS9lKUtZyvtclop8KUtZylLe57JU5EtZylKW8j6XpSJfylKWspT3uSwV+VKWspSlvM9lqciXspSlLOV9Lv8fqCS8Q4xSs6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import imread\n",
    "img_path = 'test_room.jpg'\n",
    "img = image.load_img(img_path, target_size=(64, 64))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "print('Input image shape:', x.shape)\n",
    "my_image = imread(img_path)\n",
    "imshow(my_image)\n",
    "print(model.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}