Transfer_Learning_Resnet_20.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pvuc3C1Sw18l"
   },
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c_y0cnPwIjiP"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input,Lambda,Dense,Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zo4QaFfZw9z4"
   },
   "source": [
    "### Test and Train Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SF5Gc7I7KvDC"
   },
   "outputs": [],
   "source": [
    "IMAGE_SIZE = [224,224]\n",
    "\n",
    "train_path = '/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/train'\n",
    "test_path = '/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/test'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tmJqkgITxFYs"
   },
   "source": [
    "### Creating ResNet50 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FT-i8uSfMKwe"
   },
   "outputs": [],
   "source": [
    "resnet = ResNet50(input_shape = IMAGE_SIZE+[3],weights='imagenet',include_top=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2647,
     "status": "ok",
     "timestamp": 1691736386425,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "wWcGF7bDNLFt",
    "outputId": "2d9dacc7-f106-4874-d1cc-bbe8035f4e3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,534,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resnet.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EMc-qxulxd36"
   },
   "source": [
    "### Setup Trainable Layer to False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fOVCwIUVMl7P"
   },
   "outputs": [],
   "source": [
    "for layer in resnet.layers:\n",
    "  layer.trainable= False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ct2t0mymM6mU"
   },
   "outputs": [],
   "source": [
    "folder = glob('/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1691736396864,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "WTHuKLnrNxwd",
    "outputId": "2d14b882-5bcf-418a-b5e0-cd6d33badc55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/train/naruto',\n",
       " '/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/train/sasuke']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X-WnSrDLxsNM"
   },
   "source": [
    "### Inserting Input ans Output Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h1oB5vYwNCaB"
   },
   "outputs": [],
   "source": [
    "x = Flatten()(resnet.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qjkQEQv3OVgQ"
   },
   "outputs": [],
   "source": [
    "prediction = Dense(1,activation='sigmoid')(x)\n",
    "\n",
    "model = Model(inputs=resnet.input , outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 917,
     "status": "ok",
     "timestamp": 1691736423054,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "MjpA1eJvOnTL",
    "outputId": "1fcc21dc-a626-46ec-e58f-fa973c89012f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)            (None, 100352)       0           ['conv5_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 1)            100353      ['flatten_1[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,688,065\n",
      "Trainable params: 100,353\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lAt0BsKoPK8k"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy'],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yGCWdDRfx8f1"
   },
   "source": [
    "### Data Augumentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Yqjx6XfjPtTC"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    "    shear_range = 0.2,\n",
    "    zoom_range = 0.2,\n",
    "    horizontal_flip = True\n",
    ")\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1691736434311,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "gWP418U6Q_ES",
    "outputId": "304484c2-216e-44f8-cf65-1168f3ab723b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 70 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/train',\n",
    "                             target_size = (224,224),\n",
    "                             batch_size = 32,\n",
    "                             class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1691736434909,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "lBihOnmHRivK",
    "outputId": "cd4511a3-2a60-4d97-e4c9-34eb0d99dc3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 30 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/dataset/test',\n",
    "                             target_size = (224,224),\n",
    "                             batch_size = 32,\n",
    "                             class_mode='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2LEBfzpMyCSf"
   },
   "source": [
    "### Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 415810,
     "status": "ok",
     "timestamp": 1691736854031,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "BNO6vFOdScQH",
    "outputId": "6d72f2da-685f-4283-c9d6-0e3cdff6c078"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-61-929866611b01>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  r = model.fit_generator(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "3/3 [==============================] - 45s 15s/step - loss: 1.5652 - accuracy: 0.5857 - val_loss: 1.0396 - val_accuracy: 0.5000\n",
      "Epoch 2/100\n",
      "3/3 [==============================] - 3s 836ms/step - loss: 1.4840 - accuracy: 0.4714 - val_loss: 0.7822 - val_accuracy: 0.6333\n",
      "Epoch 3/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.7276 - accuracy: 0.6143 - val_loss: 0.6252 - val_accuracy: 0.6333\n",
      "Epoch 4/100\n",
      "3/3 [==============================] - 3s 826ms/step - loss: 0.6665 - accuracy: 0.6429 - val_loss: 0.6646 - val_accuracy: 0.7000\n",
      "Epoch 5/100\n",
      "3/3 [==============================] - 3s 843ms/step - loss: 0.6722 - accuracy: 0.6571 - val_loss: 0.4600 - val_accuracy: 0.7000\n",
      "Epoch 6/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.5905 - accuracy: 0.6286 - val_loss: 0.3685 - val_accuracy: 0.8333\n",
      "Epoch 7/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.5364 - accuracy: 0.6857 - val_loss: 0.3647 - val_accuracy: 0.8333\n",
      "Epoch 8/100\n",
      "3/3 [==============================] - 3s 764ms/step - loss: 0.5517 - accuracy: 0.7000 - val_loss: 0.3379 - val_accuracy: 0.8333\n",
      "Epoch 9/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.5409 - accuracy: 0.6857 - val_loss: 0.3316 - val_accuracy: 0.8667\n",
      "Epoch 10/100\n",
      "3/3 [==============================] - 2s 830ms/step - loss: 0.4597 - accuracy: 0.7857 - val_loss: 0.2930 - val_accuracy: 0.8667\n",
      "Epoch 11/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.3815 - accuracy: 0.8714 - val_loss: 0.3341 - val_accuracy: 0.8333\n",
      "Epoch 12/100\n",
      "3/3 [==============================] - 3s 953ms/step - loss: 0.4266 - accuracy: 0.7429 - val_loss: 0.3944 - val_accuracy: 0.7667\n",
      "Epoch 13/100\n",
      "3/3 [==============================] - 3s 840ms/step - loss: 0.4496 - accuracy: 0.7429 - val_loss: 0.3859 - val_accuracy: 0.7667\n",
      "Epoch 14/100\n",
      "3/3 [==============================] - 3s 963ms/step - loss: 0.4472 - accuracy: 0.7857 - val_loss: 0.3878 - val_accuracy: 0.7667\n",
      "Epoch 15/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.4275 - accuracy: 0.7857 - val_loss: 0.2525 - val_accuracy: 0.9333\n",
      "Epoch 16/100\n",
      "3/3 [==============================] - 3s 847ms/step - loss: 0.4045 - accuracy: 0.7429 - val_loss: 0.2183 - val_accuracy: 0.9333\n",
      "Epoch 17/100\n",
      "3/3 [==============================] - 3s 845ms/step - loss: 0.5448 - accuracy: 0.7429 - val_loss: 0.3096 - val_accuracy: 0.8333\n",
      "Epoch 18/100\n",
      "3/3 [==============================] - 3s 909ms/step - loss: 0.3850 - accuracy: 0.8429 - val_loss: 0.4863 - val_accuracy: 0.7333\n",
      "Epoch 19/100\n",
      "3/3 [==============================] - 3s 805ms/step - loss: 0.4211 - accuracy: 0.7857 - val_loss: 0.2721 - val_accuracy: 0.8667\n",
      "Epoch 20/100\n",
      "3/3 [==============================] - 3s 824ms/step - loss: 0.3388 - accuracy: 0.8429 - val_loss: 0.2031 - val_accuracy: 0.9667\n",
      "Epoch 21/100\n",
      "3/3 [==============================] - 3s 884ms/step - loss: 0.3092 - accuracy: 0.8857 - val_loss: 0.2053 - val_accuracy: 0.9333\n",
      "Epoch 22/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.3160 - accuracy: 0.8571 - val_loss: 0.2433 - val_accuracy: 0.9000\n",
      "Epoch 23/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.3154 - accuracy: 0.8857 - val_loss: 0.2057 - val_accuracy: 0.9333\n",
      "Epoch 24/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.3276 - accuracy: 0.8429 - val_loss: 0.1647 - val_accuracy: 0.9333\n",
      "Epoch 25/100\n",
      "3/3 [==============================] - 3s 835ms/step - loss: 0.3734 - accuracy: 0.8571 - val_loss: 0.2077 - val_accuracy: 0.9333\n",
      "Epoch 26/100\n",
      "3/3 [==============================] - 3s 2s/step - loss: 0.3189 - accuracy: 0.8857 - val_loss: 0.2425 - val_accuracy: 0.9000\n",
      "Epoch 27/100\n",
      "3/3 [==============================] - 3s 824ms/step - loss: 0.3187 - accuracy: 0.8714 - val_loss: 0.2603 - val_accuracy: 0.8667\n",
      "Epoch 28/100\n",
      "3/3 [==============================] - 3s 851ms/step - loss: 0.3134 - accuracy: 0.8286 - val_loss: 0.1497 - val_accuracy: 0.9333\n",
      "Epoch 29/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2651 - accuracy: 0.9000 - val_loss: 0.1457 - val_accuracy: 0.9333\n",
      "Epoch 30/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2277 - accuracy: 0.9143 - val_loss: 0.1458 - val_accuracy: 1.0000\n",
      "Epoch 31/100\n",
      "3/3 [==============================] - 3s 811ms/step - loss: 0.2904 - accuracy: 0.8714 - val_loss: 0.1898 - val_accuracy: 0.9333\n",
      "Epoch 32/100\n",
      "3/3 [==============================] - 3s 847ms/step - loss: 0.2952 - accuracy: 0.9000 - val_loss: 0.1374 - val_accuracy: 1.0000\n",
      "Epoch 33/100\n",
      "3/3 [==============================] - 3s 830ms/step - loss: 0.2298 - accuracy: 0.9143 - val_loss: 0.1348 - val_accuracy: 0.9667\n",
      "Epoch 34/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2111 - accuracy: 0.9429 - val_loss: 0.1360 - val_accuracy: 0.9667\n",
      "Epoch 35/100\n",
      "3/3 [==============================] - 3s 883ms/step - loss: 0.2162 - accuracy: 0.9000 - val_loss: 0.1315 - val_accuracy: 0.9667\n",
      "Epoch 36/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2492 - accuracy: 0.9000 - val_loss: 0.1448 - val_accuracy: 0.9667\n",
      "Epoch 37/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1921 - accuracy: 0.9286 - val_loss: 0.1372 - val_accuracy: 0.9667\n",
      "Epoch 38/100\n",
      "3/3 [==============================] - 3s 786ms/step - loss: 0.1885 - accuracy: 0.9286 - val_loss: 0.1330 - val_accuracy: 0.9667\n",
      "Epoch 39/100\n",
      "3/3 [==============================] - 3s 853ms/step - loss: 0.2117 - accuracy: 0.9286 - val_loss: 0.1862 - val_accuracy: 0.9333\n",
      "Epoch 40/100\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.2246 - accuracy: 0.9143 - val_loss: 0.1352 - val_accuracy: 0.9667\n",
      "Epoch 41/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2990 - accuracy: 0.8571 - val_loss: 0.1138 - val_accuracy: 0.9667\n",
      "Epoch 42/100\n",
      "3/3 [==============================] - 3s 843ms/step - loss: 0.1865 - accuracy: 0.9143 - val_loss: 0.1215 - val_accuracy: 0.9667\n",
      "Epoch 43/100\n",
      "3/3 [==============================] - 4s 2s/step - loss: 0.3412 - accuracy: 0.8286 - val_loss: 0.2019 - val_accuracy: 0.9333\n",
      "Epoch 44/100\n",
      "3/3 [==============================] - 3s 787ms/step - loss: 0.2311 - accuracy: 0.9143 - val_loss: 0.1950 - val_accuracy: 0.9000\n",
      "Epoch 45/100\n",
      "3/3 [==============================] - 3s 832ms/step - loss: 0.2301 - accuracy: 0.8857 - val_loss: 0.1379 - val_accuracy: 0.9667\n",
      "Epoch 46/100\n",
      "3/3 [==============================] - 3s 856ms/step - loss: 0.2191 - accuracy: 0.9143 - val_loss: 0.0999 - val_accuracy: 1.0000\n",
      "Epoch 47/100\n",
      "3/3 [==============================] - 2s 817ms/step - loss: 0.1779 - accuracy: 0.9571 - val_loss: 0.0994 - val_accuracy: 0.9667\n",
      "Epoch 48/100\n",
      "3/3 [==============================] - 4s 2s/step - loss: 0.1480 - accuracy: 0.9571 - val_loss: 0.0981 - val_accuracy: 1.0000\n",
      "Epoch 49/100\n",
      "3/3 [==============================] - 3s 808ms/step - loss: 0.1418 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9667\n",
      "Epoch 50/100\n",
      "3/3 [==============================] - 3s 807ms/step - loss: 0.1764 - accuracy: 0.9286 - val_loss: 0.1064 - val_accuracy: 0.9667\n",
      "Epoch 51/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1714 - accuracy: 0.9286 - val_loss: 0.1184 - val_accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1727 - accuracy: 0.9571 - val_loss: 0.1201 - val_accuracy: 0.9667\n",
      "Epoch 53/100\n",
      "3/3 [==============================] - 2s 750ms/step - loss: 0.1519 - accuracy: 0.9429 - val_loss: 0.1021 - val_accuracy: 1.0000\n",
      "Epoch 54/100\n",
      "3/3 [==============================] - 3s 838ms/step - loss: 0.1609 - accuracy: 0.9429 - val_loss: 0.0893 - val_accuracy: 0.9667\n",
      "Epoch 55/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1568 - accuracy: 0.9286 - val_loss: 0.0867 - val_accuracy: 0.9667\n",
      "Epoch 56/100\n",
      "3/3 [==============================] - 3s 2s/step - loss: 0.1467 - accuracy: 0.9571 - val_loss: 0.0868 - val_accuracy: 1.0000\n",
      "Epoch 57/100\n",
      "3/3 [==============================] - 3s 845ms/step - loss: 0.1609 - accuracy: 0.9571 - val_loss: 0.1507 - val_accuracy: 0.9333\n",
      "Epoch 58/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2054 - accuracy: 0.9000 - val_loss: 0.1340 - val_accuracy: 0.9667\n",
      "Epoch 59/100\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.1714 - accuracy: 0.9429 - val_loss: 0.1477 - val_accuracy: 0.9333\n",
      "Epoch 60/100\n",
      "3/3 [==============================] - 3s 847ms/step - loss: 0.1656 - accuracy: 0.9429 - val_loss: 0.0955 - val_accuracy: 1.0000\n",
      "Epoch 61/100\n",
      "3/3 [==============================] - 3s 916ms/step - loss: 0.1938 - accuracy: 0.9571 - val_loss: 0.0947 - val_accuracy: 0.9667\n",
      "Epoch 62/100\n",
      "3/3 [==============================] - 4s 2s/step - loss: 0.1725 - accuracy: 0.8857 - val_loss: 0.0770 - val_accuracy: 0.9667\n",
      "Epoch 63/100\n",
      "3/3 [==============================] - 3s 817ms/step - loss: 0.1690 - accuracy: 0.9429 - val_loss: 0.0792 - val_accuracy: 1.0000\n",
      "Epoch 64/100\n",
      "3/3 [==============================] - 3s 830ms/step - loss: 0.1422 - accuracy: 0.9571 - val_loss: 0.0719 - val_accuracy: 0.9667\n",
      "Epoch 65/100\n",
      "3/3 [==============================] - 3s 839ms/step - loss: 0.1395 - accuracy: 0.9429 - val_loss: 0.0697 - val_accuracy: 0.9667\n",
      "Epoch 66/100\n",
      "3/3 [==============================] - 3s 810ms/step - loss: 0.1250 - accuracy: 0.9714 - val_loss: 0.0687 - val_accuracy: 1.0000\n",
      "Epoch 67/100\n",
      "3/3 [==============================] - 3s 849ms/step - loss: 0.1543 - accuracy: 0.9429 - val_loss: 0.0611 - val_accuracy: 1.0000\n",
      "Epoch 68/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1212 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 1.0000\n",
      "Epoch 69/100\n",
      "3/3 [==============================] - 3s 2s/step - loss: 0.1232 - accuracy: 0.9571 - val_loss: 0.0627 - val_accuracy: 1.0000\n",
      "Epoch 70/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1397 - accuracy: 0.9714 - val_loss: 0.0589 - val_accuracy: 1.0000\n",
      "Epoch 71/100\n",
      "3/3 [==============================] - 2s 839ms/step - loss: 0.1095 - accuracy: 0.9857 - val_loss: 0.0872 - val_accuracy: 0.9667\n",
      "Epoch 72/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1198 - accuracy: 0.9714 - val_loss: 0.0890 - val_accuracy: 0.9667\n",
      "Epoch 73/100\n",
      "3/3 [==============================] - 3s 906ms/step - loss: 0.2037 - accuracy: 0.8857 - val_loss: 0.0587 - val_accuracy: 1.0000\n",
      "Epoch 74/100\n",
      "3/3 [==============================] - 3s 892ms/step - loss: 0.1556 - accuracy: 0.9429 - val_loss: 0.0923 - val_accuracy: 0.9667\n",
      "Epoch 75/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1293 - accuracy: 0.9571 - val_loss: 0.0750 - val_accuracy: 1.0000\n",
      "Epoch 76/100\n",
      "3/3 [==============================] - 3s 2s/step - loss: 0.1315 - accuracy: 0.9571 - val_loss: 0.1203 - val_accuracy: 0.9667\n",
      "Epoch 77/100\n",
      "3/3 [==============================] - 3s 838ms/step - loss: 0.1285 - accuracy: 0.9714 - val_loss: 0.0635 - val_accuracy: 1.0000\n",
      "Epoch 78/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.2332 - accuracy: 0.8857 - val_loss: 0.0709 - val_accuracy: 1.0000\n",
      "Epoch 79/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1877 - accuracy: 0.9000 - val_loss: 0.1790 - val_accuracy: 0.9333\n",
      "Epoch 80/100\n",
      "3/3 [==============================] - 3s 829ms/step - loss: 0.2001 - accuracy: 0.8857 - val_loss: 0.2633 - val_accuracy: 0.8667\n",
      "Epoch 81/100\n",
      "3/3 [==============================] - 2s 804ms/step - loss: 0.3063 - accuracy: 0.8286 - val_loss: 0.1842 - val_accuracy: 0.9333\n",
      "Epoch 82/100\n",
      "3/3 [==============================] - 2s 1s/step - loss: 0.3167 - accuracy: 0.8429 - val_loss: 0.0846 - val_accuracy: 0.9667\n",
      "Epoch 83/100\n",
      "3/3 [==============================] - 3s 975ms/step - loss: 0.2018 - accuracy: 0.9143 - val_loss: 0.1697 - val_accuracy: 0.9333\n",
      "Epoch 84/100\n",
      "3/3 [==============================] - 3s 845ms/step - loss: 0.1998 - accuracy: 0.9143 - val_loss: 0.1408 - val_accuracy: 0.9333\n",
      "Epoch 85/100\n",
      "3/3 [==============================] - 3s 791ms/step - loss: 0.1904 - accuracy: 0.8857 - val_loss: 0.0618 - val_accuracy: 1.0000\n",
      "Epoch 86/100\n",
      "3/3 [==============================] - 3s 2s/step - loss: 0.1495 - accuracy: 0.9429 - val_loss: 0.0590 - val_accuracy: 1.0000\n",
      "Epoch 87/100\n",
      "3/3 [==============================] - 5s 1s/step - loss: 0.1378 - accuracy: 0.9571 - val_loss: 0.0868 - val_accuracy: 0.9667\n",
      "Epoch 88/100\n",
      "3/3 [==============================] - 3s 818ms/step - loss: 0.0888 - accuracy: 0.9714 - val_loss: 0.0487 - val_accuracy: 1.0000\n",
      "Epoch 89/100\n",
      "3/3 [==============================] - 3s 835ms/step - loss: 0.1227 - accuracy: 0.9714 - val_loss: 0.0587 - val_accuracy: 1.0000\n",
      "Epoch 90/100\n",
      "3/3 [==============================] - 2s 858ms/step - loss: 0.1467 - accuracy: 0.9571 - val_loss: 0.0583 - val_accuracy: 0.9667\n",
      "Epoch 91/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1089 - accuracy: 0.9857 - val_loss: 0.0632 - val_accuracy: 1.0000\n",
      "Epoch 92/100\n",
      "3/3 [==============================] - 3s 997ms/step - loss: 0.1449 - accuracy: 0.9429 - val_loss: 0.0446 - val_accuracy: 1.0000\n",
      "Epoch 93/100\n",
      "3/3 [==============================] - 3s 884ms/step - loss: 0.0787 - accuracy: 1.0000 - val_loss: 0.0628 - val_accuracy: 0.9667\n",
      "Epoch 94/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.1083 - accuracy: 0.9857 - val_loss: 0.0948 - val_accuracy: 0.9667\n",
      "Epoch 95/100\n",
      "3/3 [==============================] - 3s 810ms/step - loss: 0.1725 - accuracy: 0.9429 - val_loss: 0.0438 - val_accuracy: 1.0000\n",
      "Epoch 96/100\n",
      "3/3 [==============================] - 4s 1s/step - loss: 0.1470 - accuracy: 0.9143 - val_loss: 0.1165 - val_accuracy: 0.9667\n",
      "Epoch 97/100\n",
      "3/3 [==============================] - 3s 844ms/step - loss: 0.1244 - accuracy: 0.9571 - val_loss: 0.0594 - val_accuracy: 1.0000\n",
      "Epoch 98/100\n",
      "3/3 [==============================] - 3s 823ms/step - loss: 0.1462 - accuracy: 0.9571 - val_loss: 0.0450 - val_accuracy: 1.0000\n",
      "Epoch 99/100\n",
      "3/3 [==============================] - 3s 1s/step - loss: 0.0802 - accuracy: 0.9857 - val_loss: 0.0962 - val_accuracy: 0.9667\n",
      "Epoch 100/100\n",
      "3/3 [==============================] - 3s 814ms/step - loss: 0.1050 - accuracy: 0.9714 - val_loss: 0.0415 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "r = model.fit_generator(\n",
    "    training_set,\n",
    "    validation_data = test_set,\n",
    "    epochs = 100,\n",
    "    steps_per_epoch = len(training_set),\n",
    "    validation_steps = len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 471
    },
    "executionInfo": {
     "elapsed": 1981,
     "status": "ok",
     "timestamp": 1691737446565,
     "user": {
      "displayName": "VRNM",
      "userId": "08595917627032908611"
     },
     "user_tz": -330
    },
    "id": "MTy2yycVTmKa",
    "outputId": "5c3e1d86-30d8-42ed-dbbb-28b55eaa3133"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KFdSvX199/7Vr1zB48GD4+PgAUGWyhYaG6jy1ihUrwtzcHNeuXVMv6ZTJZLh58yYmTJig3s7V1RWDBg3CoEGD0LJlS0yZMgVLly4FANjb2+Pzzz/H559/jk8//RRdunRBUlISnJ0Nv9qPwbUibESrCjj3OA77b0diYsfKKGZrYegpEREREREREdG7iEQaL800NF9fX/To0QOPHj3CgAED8tzn6emJAwcOwMvLCyKRCDNmzHiju6g2bGxs8M0332DKlClwdnZG2bJlsXjxYmRlZWHo0KEAgJkzZ+KTTz5BjRo1IJFIcPToUVSrVg0AsHz5cri5uaFevXoQi8X4448/ULJkSTg6Ouo8J31icK0Ia1TeGXXKOOBeZCp2/B2GiR0rG3pKRERERERERPQRaNeuHZydnfHkyRP0798/z33Lly/HkCFD0KxZMxQrVgzfffcd0tLSCnS8RYsWQalU4ssvv0R6ejoaNGiAU6dOwcnJCQBgbm6O6dOnIzQ0FFZWVmjZsiX27t0LALCzs8PixYvx7NkzmJiYoGHDhjh+/Li6RpyhiQRBEAw9ifykpaXBwcEBqampsLe3N/R0Pqij96MxZvddONuY49p37WBlbmLoKRERERERERERgJycHISEhKB8+fKwtLQ09HRIB+97DjWNRxWNEB+9U5caJVHGyQpJmVL8eSfS0NMhIiIiIiIiIqLXMLhWxJmaiDGkuapzqN/dKAPPhoiIiIiIiIiIXsfgmhGoWdoBAJCUWXgtb4mIiIiIiIiISHsMrhkBGwtVnbVMidzAMyEiIiIiIiIiotcxuGYEbMxVTV2zpAoDz4SIiIiIiIiIiF7H4JoRsLFQBdcypXIYQXNXIiIiIiIiIqL/DAbXjEDuslBBALJlzF4jIiIiIiIiIioqGFwzAlZmJhCJVP/PYN01IiIiIiIiIqIig8E1IyASiV7VXZMwc42IiIiIiIiIqKhgcM1I5C4NZeYaERERERERERUlHh4eWLlypaGnYTCmhp4AaUaVuSZhx1AiIiIiIiIiKpA2bdqgbt26eguI3bx5EzY2NnoZyxgxuGYk1B1DmblGRERERERERIVMEAQoFAqYmuYfOnJ1df0AMyq6uCzUSFibq5aFZkoZXCMiIiIiIiIi3QwePBiXLl3CqlWrIBKJIBKJEBoaiosXL0IkEuHEiRP45JNPYGFhgatXryIoKAje3t4oUaIEbG1t0bBhQ5w9ezbPmP9eFioSibBp0yb4+PjA2toanp6eOHz48HvntWPHDjRo0AB2dnYoWbIk+vfvj7i4uDzbPHr0CD169IC9vT3s7OzQsmVLBAUFqe///fffUaNGDVhYWMDNzQ1jxowp+A9MAwyuGQlmrhERERERERFRQa1atQpNmzbF8OHDERMTg5iYGLi7u6vvnzZtGhYtWoTAwEDUrl0bGRkZ6NatG86dO4e7d++iS5cu8PLyQnh4+HuPM2fOHPTt2xf3799Ht27d4Ovri6SkpHduL5PJMG/ePNy7dw8HDx5EaGgoBg8erL4/KioKrVq1goWFBc6fP4/bt29jyJAhkMtVcZJ169Zh9OjRGDFiBB48eIDDhw+jUqVKBfthaYjLQo3Eq+Aaa64RERERERERFUWCICBbnm2QY1uZWkEkEuW7nYODA8zNzWFtbY2SJUu+cf/cuXPRsWNH9ffOzs6oU6eO+vt58+bBz88Phw8ffm9m2ODBg/HFF18AABYsWIBffvkF//zzD7p06fLW7YcMGaL+f4UKFfDLL7+gYcOGyMjIgK2tLdasWQMHBwfs3bsXZmZmAIDKlSur9/npp5/w7bffYvz48erbGjZsmN+PQy8YXDMSNrnLQpm5RkRERERERFQkZcuz0Xh3Y4Mc+0b/G7A2sy7wOA0aNMjzfUZGBmbPno1jx44hJiYGcrkc2dnZ+Wau1a5dW/1/Gxsb2Nvbv7HM83W3b9/G7Nmzce/ePSQnJ0OpVAIAwsPDUb16dfj7+6Nly5bqwNrr4uLiEB0djfbt22vzUPWGy0KNhDpzjd1CiYiIiIiIiKiQ/Lvr5+TJk+Hn54cFCxbgypUr8Pf3R61atSCVSt87zr+DYCKRSB0w+7fMzEx07twZ9vb22LVrF27evAk/Pz8AUB/Hysrqncd6330fAjPXjAQz14iIiIiIiIiKNitTK9zof8Ngx9aUubk5FArNkneuXbuGwYMHw8fHB4Aqky00NFSXKb7T48ePkZiYiEWLFqnrv926dSvPNrVr18a2bdsgk8neCNzZ2dnBw8MD586dQ9u2bfU6N00wuGYkXmWuMbhGREREREREVBSJRCK9LM0sbB4eHrhx4wZCQ0Nha2sLZ2fnd27r6emJAwcOwMvLCyKRCDNmzHhnBpquypYtC3Nzc6xevRojR47Ew4cPMW/evDzbjBkzBqtXr0a/fv0wffp0ODg44Pr162jUqBGqVKmC2bNnY+TIkShevDi6du2K9PR0XLt2DWPHjtXrXN+Gy0KNhDW7hRIRERERERGRHkyePBkmJiaoXr06XF1d31s/bfny5XByckKzZs3g5eWFzp07o379+nqdj6urK7Zu3Yo//vgD1atXx6JFi7B06dI827i4uOD8+fPIyMhA69at8cknn2Djxo3qLLZBgwZh5cqVWLt2LWrUqIEePXrg2bNnep3nu4gEQRA+yJEKIC0tDQ4ODkhNTYW9vb2hp2MQfncjMfF/99DSsxh2DDVMcUQiIiIiIiIieiUnJwchISEoX748LC0tDT0d0sH7nkNN41HMXDMS1ubMXCMiIiIiIiIiKmq0Dq5dvnwZXl5eKFWqFEQiEQ4ePKjxvteuXYOpqSnq1q2r7WH/82zVy0LZLZSIiIiIiIiIqKjQOriWmZmJOnXqYM2aNVrtl5KSgoEDB6J9+/baHpIAWOd2C2VDAyIiIiIiIiKiIkPrbqFdu3ZF165dtT7QyJEj0b9/f5iYmGiV7UYqtmxoQERERERERERU5HyQmmtbtmxBcHAwZs2apdH2EokEaWlpeb7+69TdQqVcFkpEREREREREVFQUenDt2bNnmDZtGnbu3AlTU80S5RYuXAgHBwf1l7u7eyHPsuizfdnQQCpXQqZQGng2REREREREREQEFHJwTaFQoH///pgzZw4qV66s8X7Tp09Hamqq+isiIqIQZ2kcrF7WXAOALDY1ICIiIiIiIiIqErSuuaaN9PR03Lp1C3fv3sWYMWMAAEqlEoIgwNTUFKdPn0a7du3e2M/CwgIWFhaFOTWjY24qhrmJGFKFEhlSORyszQw9JSIiIiIiIiKi/7xCDa7Z29vjwYMHeW5bu3Ytzp8/j/3796N8+fKFefiPjo2FCaRZSmSxqQERERERERERUZGgdXAtIyMDz58/V38fEhICf39/ODs7o2zZspg+fTqioqKwfft2iMVi1KxZM8/+xYsXh6Wl5Ru3U/6szU2RnCVDBoNrRERERERERGRAHh4emDBhAiZMmGDoqRic1sG1W7duoW3bturvJ02aBAAYNGgQtm7dipiYGISHh+tvhqRm+7JjaBY7hhIRERERERERFQlaB9fatGkDQRDeef/WrVvfu//s2bMxe/ZsbQ9LAKwtVE0NmLlGRERERERERFQ0FGq3UNKvV5lrDK4RERERERERkfY2bNiAUqVKQalU5rnd29sbQ4YMAQAEBQXB29sbJUqUgK2tLRo2bIizZ89qdZybN2+iY8eOKFasGBwcHNC6dWvcuXMnzzYpKSn4+uuvUaJECXUJsaNHj6rvv3btGtq0aQNra2s4OTmhc+fOSE5O1vGRFx4G14yItXlu5hqXhRIREREREREVNYIgQJmVZZCv960yfN1nn32GxMREXLhwQX1bUlISTp48CV9fXwCqevvdunXDuXPncPfuXXTp0gVeXl5alQFLT0/HoEGDcPXqVVy/fh2enp7o1q0b0tPTAQBKpRJdu3bFtWvXsHPnTgQEBGDRokUwMVHFPvz9/dG+fXtUr14df//9N65evQovLy8oFEUvJlKo3UJJv2xyM9e4LJSIiIiIiIioyBGys/Gk/icGOXaVO7chsrbOdzsnJyd07doVu3fvRvv27QEA+/fvR7FixdQ19uvUqYM6deqo95k3bx78/Pxw+PBhjBkzRqP5tGvXLs/3GzZsgKOjIy5duoQePXrg7Nmz+OeffxAYGIjKlSsDACpUqKDefvHixWjQoAHWrl2rvq1GjRoaHftDY+aaEbExVwXXMhlcIyIiIiIiIiId+fr64s8//4REIgEA7Nq1C/369YNYrAoTZWRkYPLkyahWrRocHR1ha2uLwMBArTLXXrx4geHDh8PT0xMODg6wt7dHRkaGegx/f3+UKVNGHVj7t9zMNWPAzDUjkpu5lsluoURERERERERFjsjKClXu3DbYsTXl5eUFQRBw7NgxNGzYEFeuXMGKFSvU90+ePBlnzpzB0qVLUalSJVhZWeHTTz+FVCrV+BiDBg1CYmIiVq1ahXLlysHCwgJNmzZVj2GVz3zzu78oYXDNiNi8rLnGzDUiIiIiIiKiokckEmm0NNPQLC0t0bt3b+zatQvPnz9HlSpVUL9+ffX9165dw+DBg+Hj4wNAlckWGhqq1TGuXbuGtWvXolu3bgCAiIgIJCQkqO+vXbs2IiMj8fTp07dmr9WuXRvnzp3DnDlzdHiEHxaXhRoRZq4RERERERERkT74+vri2LFj+P3339WNDHJ5enriwIED8Pf3x71799C/f/83uovmx9PTEzt27EBgYCBu3LgBX1/fPNlorVu3RqtWrdCnTx+cOXMGISEhOHHiBE6ePAkAmD59Om7evIlRo0bh/v37ePz4MdatW5cnQFdUMLhmRGwsVJlrbGhARERERERERAXRrl07ODs748mTJ+jfv3+e+5YvXw4nJyc0a9YMXl5e6Ny5c57MNk1s3rwZycnJqF+/Pr788kuMGzcOxYsXz7PNn3/+iYYNG+KLL75A9erVMXXqVHU30MqVK+P06dO4d+8eGjVqhKZNm+LQoUMwNS16izBFgqa9Wg0oLS0NDg4OSE1Nhb29vaGnYzBH70djzO67aFzeGf/7uqmhp0NERERERET0n5aTk4OQkBCUL18elpaWhp4O6eB9z6Gm8ShmrhmR3G6hWVwWSkRERERERERUJDC4ZkSs2dCAiIiIiIiIiKhIYXDNiLxqaMDgGhERERERERFRUcDgmhFRB9ckXBZKRERERERERFQUMLhmRHK7hWZK5TCCPhRERERERERERB89BteMSG5DA0EAsmXMXiMiIiIiIiIqCpgAY7z08dwxuGZErMxMIBKp/s+loURERERERESGZWKiWmEmlUoNPBPSVVZWFgDAzMxM5zFM9TUZKnxisQjWZibIlCqQKZHD1c7C0FMiIiIiIiIi+s8yNTWFtbU14uPjYWZmBrGYOUzGQhAEZGVlIS4uDo6OjupAqS4YXDMyNhamquAaO4YSERERERERGZRIJIKbmxtCQkIQFhZm6OmQDhwdHVGyZMkCjcHgmpGxsTAF0iVcFkpERERERERUBJibm8PT05NLQ42QmZlZgTLWcjG4ZmRe7xhKRERERERERIYnFothaWlp6GmQgXAxsJGxftkxNFPC4BoRERERERERkaExuGZkbC1UwbUsLgslIiIiIiIiIjI4BteMjLW5alloBjPXiIiIiIiIiIgMjsE1I6POXGPNNSIiIiIiIiIig2Nwzcioa65JuSyUiIiIiIiIiMjQGFwzMra53UK5LJSIiIiIiIiIyOAYXDMy1ha53UKZuUZEREREREREZGgMrhkZG3NmrhERERERERERFRUMrhkZm9zMNTY0ICIiIiIiIiIyOAbXjIy6oQEz14iIiIiIiIiIDI7BNSNj+zJzLYvdQomIiIiIiIiIDI7BNSNj/bJbaAYz14iIiIiIiIiIDI7BNSPDzDUiIiIiIiIioqKDwTUjY23OzDUiIiIiIiIioqKCwTUjk5u5JpUrIVMoDTwbIiIiIiIiIqL/NgbXjExut1AAyJJwaSgRERERERERkSExuGZkzE3FMDdRPW2ZUi4NJSIiIiIiIiIyJAbXjFBux9BM1l0jIiIiIiIiIjIoBteMkM3LpaGZ7BhKRERERERERGRQDK4ZIRtmrhERERERERERFQkMrhkhm5cdQxlcIyIiIiIiIiIyLAbXjFDustAsLgslIiIiIiIiIjIoBteMkLW5alloBjPXiIiIiIiIiIgMisE1I2RrkZu5xuAaEREREREREZEhMbhmhKwtcjPXuCyUiIiIiIiIiMiQGFwzQrkNDbK4LJSIiIiIiIiIyKAYXDNCuQ0NMrkslIiIiIiIiIjIoBhcM0K5mWuZXBZKRERERERERGRQWgfXLl++DC8vL5QqVQoikQgHDx587/YHDhxAx44d4erqCnt7ezRt2hSnTp3Sdb4EwOZlt9BMLgslIiIiIiIiIjIorYNrmZmZqFOnDtasWaPR9pcvX0bHjh1x/Phx3L59G23btoWXlxfu3r2r9WRJRZ25xmWhREREREREREQGZartDl27dkXXrl013n7lypV5vl+wYAEOHTqEI0eOoF69etoengDYWORmrnFZKBERERERERGRIX3wmmtKpRLp6elwdnb+0If+aLChARERERERERFR0aB15lpBLV26FBkZGejbt+87t5FIJJBIJOrv09LSPsTUjMarhgYMrhERERERERERGdIHzVzbvXs35syZg3379qF48eLv3G7hwoVwcHBQf7m7u3/AWRZ9ucG1LC4LJSIiIiIiIiIyqA8WXNu7dy+GDRuGffv2oUOHDu/ddvr06UhNTVV/RUREfKBZGgd1t1CpHIIgGHg2RERERERERET/XR9kWeiePXswZMgQ7N27F927d893ewsLC1hYWHyAmRmn3Mw1pQDkyJSwehlsIyIiIiIiIiKiD0vr4FpGRgaeP3+u/j4kJAT+/v5wdnZG2bJlMX36dERFRWH79u0AVEtBBw0ahFWrVqFx48aIjY0FAFhZWcHBwUFPD+O/xcrsVTAtQyJncI2IiIiIiIiIyEC0XhZ669Yt1KtXD/Xq1QMATJo0CfXq1cPMmTMBADExMQgPD1dvv2HDBsjlcowePRpubm7qr/Hjx+vpIfz3iMUi9dLQLHYMJSIiIiIiIiIyGK0z19q0afPeOl9bt27N8/3Fixe1PQRpwNrCFJlSBTLYMZSIiIiIiIiIyGA+aLdQ0p9XmWvsGEpEREREREREZCgMrhmp3KYGmcxcIyIiIiIiIiIyGAbXjJSNeW5wjZlrRERERERERESGwuCakbKxUC0LzWRDAyIiIiIiIiIig2FwzUhZc1koEREREREREZHBMbhmpGxfLgtlQwMiIiIiIiIiIsNhcM1IWb9cFprBzDUiIiIiIiIiIoNhcM1I2b5cFprF4BoRERERERERkcEwuGakrF8uC81gt1AiIiIiIiIiIoNhcM1I2b5cFprFbqFERERERERERAbD4JqRepW5xuAaEREREREREZGhMLhmpGws2C2UiIiIiIiIiMjQGFwzUjYvl4VmMnONiIiIiIiIiMhgGFwzUrmZa5msuUZEREREREREZDAMrhkpm5c11zLZLZSIiIiIiIiIyGAYXDNS1uZcFkpEREREREREZGgMrhkp25fLQiVyJeQKpYFnQ0RERERERET038TgmpGyftnQAAAy2TGUiIiIiIiIiMggGFwzUhamJjAzEQEAstjUgIiIiIiIiIjIIBhcM2LW6qYGDK4RERERERERERkCg2tGLLfuGjuGEhEREREREREZBoNrRowdQ4mIiIiIiIiIDIvBNSNmk5u5xoYGREREREREREQGweCaEbOxYOYaEREREREREZEhMbhmxGxyGxqwWygRERERERERkUEwuGbE1MtCmblGRERERERERGQQDK4ZsVfLQllzjYiIiIiIiIjIEBhcM2LqZaHMXCMiIiIiIiIiMggG14wYu4USERERERERERkWg2tGzNqc3UKJiIiIiIiIiAyJwTUjlpu5lsVuoUREREREREREBsHgmhHLDa5lMHONiIiIiIiIiMggGFwzYjYvl4VmseYaEREREREREZFBMLhmxJi5RkRERERERERkWAyuGTEb85c11yTMXCMiIiIiIiIiMgQG14yYjcXLbqFsaEBEREREREREZBAMrhmx3GWhmRI5BEEw8GyIiIiIiIiIiP57GFwzYrnBNaUASORKA8+GiIiIiIiIiOi/h8E1I2ZtZqL+P5saEBERERERERF9eAyuGTGxWARrc1WAjU0NiIiIiIiIiIg+PAbXDCX6LrCuBfB7lwINY/2yYygz14iIiIiIiIiIPjxTQ0/gP8vMBnjxADC3BQQBEIl0GsbWwgQJGUAWO4YSEREREREREX1wzFwzFKdygEgMSDOAjDidh2HmGhERERERERGR4TC4ZiimFoBDGdX/k4J0Hsb2ZcfQLClrrhERERERERERfWgMrhmSc0XVv0nBOg9hbaFqaMDMNSIiIiIiIiKiD4/BNUNyrqD6N1H3zDWb3Mw1BteIiIiIiIiIiD44BtcMyaXgmWs25qrMtUwuCyUiIiIiIiIi+uAYXDMk9bJQ3TPXchsaZDJzjYiIiIiIiIjog9M6uHb58mV4eXmhVKlSEIlEOHjwYL77XLx4EfXr14eFhQUqVaqErVu36jDVj5B6WWgwIAg6DZHb0IDBNSIiIiIiIiKiD0/r4FpmZibq1KmDNWvWaLR9SEgIunfvjrZt28Lf3x8TJkzAsGHDcOrUKa0n+9Fx8gBEYkCWCWTE6TREbkMDLgslIiIiIiIiIvrwTLXdoWvXrujatavG2//2228oX748li1bBgCoVq0arl69ihUrVqBz587aHv7jYmoOOLgDKWGqpaF2JbQegplrRERERERERESGo3VwTVt///03OnTokOe2zp07Y8KECYV9aOPgXEEVXEsMAso103p3dc01Zq4RUREmKBQQpFKIraz0O65MBojFEJmY6HVcolzKnByILCwgEon0O252tsavh0xZJqxNrQ06B00JggAhJ0fv42o1B4UCUCohMjMz2By0oZRIILawMNjxi8RzJpdD/uKF3seVKKUwF5np/bUDUzOYlSiu3zG1ZEyvX22fX1NXV4jMzfU6h4+VIAiqn61Cs3NBsYMjTGxt9DsHbZ/f4sX1/v6szMyEIiVFwwmYquagwftCliwLVqZWRvP3V5vfBRMnJ4itrfU6B2l4OJQ5ObCsXFmv4xqLQg+uxcbGokSJvBlZJUqUQFpaGrKzs2H1ll8qiUQCiUSi/j4tLa2wp2k4LhWB4As6dwy1zV0Wysw1IirCoqdMRfr58yi3fRusatfWy5iCVIrQAV9CFhWFMqtWwrpBA72MS5Qr/fx5RE+ZCvNy5VBm3Tq9nUwnbtmKuGXLYNumNUovWfLeD9jbH23H0ltLMb7+eAytNVQvx1fm5CD6u2lIP3sWruPHw2X4ML2cOMgTEhAxajQkz5+j1KKFsO/USQ+z1U7WnTuIHDceZiVLotzuXRAX8RP0tBMnED31O9h37w63uXM+eEBBkZGBqAkTkXXjBkr88D2c+vX7oMcHAMmzZ4j4eiRk0dEf/NgFYdO6FcosXw6xjX4DFflRSqWI+f4HpJ04gWLffINio0fp5/WbnIzI0WOQExAAt/k/waF7dz3MFpAEBame38hIjfcxL1cO5Q8dhNjSUi9z+JjFzp2LlD17Nd5eZGEBt5/mwcHLSy/HlwSHIGLkSMjCwzXex7SUG9x/+01vAZi0U6cRPX06hKwsjfexadYMpVethImd3Tu3ORN2BlMuTUFp29Lw8fSBd0VvuFq7FmiugkyGmFmzkXrwIFyGDYPrxAl6ef0qUlMROXYcsv75R+N9xDY2KLVkCezatS3w8QEgJyAA4cNHQCQWo9yePTAvU1ov4xqTItktdOHChXBwcFB/ubu7G3pKhSe3qYGOHUPZLZSIirrM69eRdvw4hJwcvFi4CIKODVz+LXnvXuTcvw9FYiLChw5D+rlzehmXCABS9u9H5JixUGZmIicgAGFffAFJSEiBxhSUSrxYvARxP/8MyOXIOHsO4V8NgTw5+a3bnwk7gyW3lkCAgA33NyApJ6lAxwcARVoawocNQ/qpU4BCgfjly/FiwUIISmWBxpWGhyO0vy9y7t+HkJWFqPETkLxX8xM+fUg/fx7hXw2BIiEBOQ8fInn37g96fG0pMzNVP3uZDKkHDyLim1FQZGR+sOPL4+MRNnAgMq9ehSCTIXb2HMSv/lVv79GayLp9G6G+A1SBNVNTiCwt9fYlMxNDYgpITAGZmVivY0MkQualywgb/BXkSQV/XWpKkZGBiBFfI+3oUUChQMKvvyJ29hxVtmYByKKiENbfF9l37kDIyUH0t5ORtH1Hgeebdfcuwvr7qgJrGj6/EIshDQtD0rbtBT7+xy4nMBApe/8HAJr97pqbQ5BIED1lKhK3bC3w8bPv30dY//6qwJqmz6+pKeTRMQgb8CWybt0q8ByS9+xB1IQJELKyIDI31/h3LPOvvxA2cBDk8fFvHzcnGfP+ngeFoEB4ejhW3VmFjvs7Yuy5sbgQfgFypfbn3srMTESMGo3UAwcApRKJGzYg5ocfIcgLdh4vi41F2IABqsCaWMP3OjMzKDMzETl2LFL+PFCg4wOqz/phXw6EIjERJsWKQWxRtC9sFRaRUIC/oCKRCH5+fujVq9c7t2nVqhXq16+PlStXqm/bsmULJkyYgNTU1Lfu87bMNXd3d6SmpsLe3l7X6RZNT04Cez4HStQCvrmq9e63w5LRZ91fKOtsjctT9RN1JiLSF0GpRMinn0ISEKi+rfSqVbDvXLCMFkVaGoI6doIiNRXm5cpBGhYGiMUoOXsWnPr2Lei06T9MEAQkrl+P+JWrAAD2PXog58EDSMPCYOLkBPf1v+mUfSnIZIj+4QekHT4CAHAaMACpR45AmZoK84oVUXbTRpi5uam3vx9/H0NODYFEIYGFiQUkCgkG1xiMbxt8q/Njk714gYhhwyF59gxiW1s4+PggeYfqBNq+Wze4LVqoU6ZX9qNHiBjxNRSJiTBzd4d1/XpIPXQYAFBs9GgUGzNa/8vy/iVl/37EzJwFKJXq9wSxgwMqnT4FEweHQj22ruJ/XYOEX3+FqasrFBkZELKzYVmzJtzX/wZTF5dCPbY0LAzhw4ZDFhEBE2dn2HXupM5+cezbFyVnzSz05fbp588jauIkCBIJrOrVg/u6tTBxdNTL2Pue7MO86/NgZWoFQRCQo8hB38p98WOTH/Xyu5h9/z4ivh4JRXIyzMuVg/vmTTAvU0YPM383eXw8wr/+GpKAQIitreH42aeqAJggwK5jR5RaukSn5cU5T54iYvhwyOPiYFrKDTZNmqpO/gG4DB8O10kTdfqZpV+8iKgJEyHk5MCyTm24//YbTJ2c8t0v9cgRRE+ZCrGNDSqePlXorwVjJQgCIoYOReZff8O+e3eUXrY0/32USsT9vBhJ27YBAJyHDkHxb7+FSKx9vk3GlSuIHDde9b5Vq5bqfcvZOd/9FKmpiPhmFLLv3IHIwgKlly+DXfv2Wh9fEAQkrP4VCWvXAgAc+32OkjNmaPS+lRMQgPARX0ORkACzMmVQdtNGmHt45Nlm2pVpOBZ8DJ5Onviy2pfwe+6Hu3F31fcXsyqGnhV7wqeSDzwc8u77NvLkZER8PRI59+9DZGUFp759kbRzJ6BQwLZNG5ResVynZaKSoCCEDxsOeUwMTIsXh/umjRplBAoyGWJmzkKqnx8AwHXiRLiMGK7Taz3txAlETf0OkMlg3bgxyqz5FSa2tlqPU5SlpaXBwcEh33hUoWeuNW3aFOf+lU1w5swZNG3a9J37WFhYwN7ePs/XR8ulourfpGBAhzinDZeFElERlnb0qOpEwNYWjl+olhvFLV8GQSot0LgJ69erAmuVKqL8kcNw+LQPoFQiduYsxK9d+0EzL+jjISgUePHTfHVgzWXECJRashjl9uyGZc2aUCQnI2zwV8i4ot3FsNyr1WmHjwAmJnBbtBAlf/wBHjt3wLRkSUiDghDa7wtInj0DAESmR2Ls+bGQKCRoXaY1lrVWNYXa83gP4rPefpU9P5LgYIR+oTqGqasryu3aiZI/fI9SS5cCZmZIO34ckSNHap05lfn33wh/ebXaolo1eOzeBbdFi1Bs9GgAQMKaNYidNbvAmTXvIggCEn77DTE/zgCUSjj07o3yRw7DwtMTytRUJPy2vlCOW1CyuDgk/v47AKDE99NRbusWmDg6IufhQ4T27w+pFkvotJX96BFC+/tCFhEBM3d3eOzZDbdZs1By9ixAJELKvn2ImjABytcudOtb8h9/IHLMWAgSCWzbtEHZ3zfrLbAWmxmL5beXAwDG1RuHRa0WQQQR9j3dh+0B+smGsqpdG+V274JZqVKQhoUh9IsvkPP4sV7GfhtpWBhC+/tCEhAIExcXlN2+HSWmT0fplSshMjND+pkziBg6DAotS+lk3byJsAEDII+Lg4WnJzz27IHb/J/gOnEiACBx40adMmtSDvghcvQYCDk5sGndCuW2bNEosAYA9t27w7J6dSgzM5GwZq1Wx/0vybx6FZl//Q2RmZn6+cqPSCxG8WnfofiUyQCApM2/I2b6dFX9Wi2kHjqEiG9GQcjOhk2LFii3dYtGgTUAMHFwQNnfN8O2XTsIEgkix45D8r59Wh1fkMsRO2u2OrBWbOwYlJw1S+MLApbVq8Njz26YlS0LWWQkQvv7IvvBQ/X9VyKv4FjwMYhFYsxtNhc+nj7Y3nU7DnkfwuAag+Fs6YyE7AT8/vB3eB30wqATg3A46DCy5dlvPZ40MgphX/RHzv37MHFwQLktv6PE9Gkos3o1RBYWyLh48b1Z7O+Smxkqj4mBefny8NizW+OltiIzM7gtmA+XESMAAPErVuiUxZ60YyeiJn0LyGSw69IF7hs3fHSBNa0IWkpPTxfu3r0r3L17VwAgLF++XLh7964QFhYmCIIgTJs2Tfjyyy/V2wcHBwvW1tbClClThMDAQGHNmjWCiYmJcPLkSY2PmZqaKgAQUlNTtZ1u0SeTCMJsR0GYZS8IaTFa7x6emCmU++6oUOXH44UwOSIi3Smys4WnbdoKAVWqCvHrNwjy9AzhSbPmQkCVqkLitu06jyuJiBQCa9YSAqpUFdIvXhQEQRCUSqXwYsUKIaBKVSGgSlUhZs4cQSmX6+uh0H+AQiIRIsZPUP0OVa32xu+oIiNDCBsyVHV/jZpCyqFDGo0rS0oSgj/rKwRUqSoE1q0npF+6lOd+aXS08LxbdyGgSlXhccNGQtz1y0JPv55Cza01hc8OfyZkSjMFpVIpDDg2QKi5taYw//p8rR9blr+/8KRxEyGgSlXheecugiQiMs/96VevCo/r1RcCqlQVgn16C7L4eI3GTT12TAh4+VoMHTRYkKen57k/ac8eIaBqNSGgSlUhYswYQZGTo/Xc30cplwsxc+epX/cvli0XlEql6jFdvqz6mdesJUgiIvR6XH2I/nGG6ufdt696zjlBwcKztu2EgCpVhSctWgjZAQF6P27GtWvq5zqol48gi4vLc3/qyVPq99cQX19BrufP3kqlUohfu1b9nEV9/72glMn0Ov6Ys2OEmltrCv2P9RfkCtXfgW0Ptwk1t9YUam2tJZwJPaO340ljXwhBXj1Vr99PGggZ12+o7wtKDhKW/LNE6HOoj/Dj1R+Fuy/uqp9rbWQ9fKj+2/msQ0dBEhqa5/6MGzeEx580UD2nXj0FaewLjcZNPX1aCKxV+9VznZKS5/7k/fuFgOo1hIAqVYXwr0cKiqysfMdUKpVC/IYNr57fqd8JSqlU8web+5j+vq4ao3oNIScoWOv9P3ZKuVwI6uElBFSpKsQu+lmnMZIP+Kmf37DhwwVFZqZG+yVs2qx+fiMnTxGUEolOx1fKZEL0jz+qx4pbs0aj14ciO1sIHz1atV+16kLSnr3Co4RHwsDjA4UVt1Zo9RqTxccLwT69VX8r6tUX0q9eFTKkGUKHPzoINbfWFBb/s/it+0kVUuFs6FnhmzPfCLW31RZqbq0p1NxaU2iyq4lwKuRUnm2zHz8WnrZoKQRUqSo8bdtWyAkKynN/5u07wuNGjVV/n7t1F6TR0RrNPe3CBSGwTl313xFZUpLGj/vfErdte/WcTpwoKF57TlNyUoRJFyYJ/Y/1F3YF7BJSclTvE0qlUnix/PXP3XM/6s/dmsajtA6uXbhwQQDwxtegQYMEQRCEQYMGCa1bt35jn7p16wrm5uZChQoVhC1btmh1zI86uCYIgrCiliq4FnJV610TMyRCue+OCuW+OyrI5IpCmBwRkW5yP2A/bdNWUGRnC4IgCEl79qpOHBs30fmkLfLbyeqT+X9/iErcsfPVyfy48Xo/maePkzw9XQgdNFj1IbFmLSH12LG3bqeUSITIyVPUHyYTNv/+3nElEZHC885d1L/zWf7+bz9+crIQ8nk/IaBKVeFezerCoB+qC+32tRNiM2LV29yIviHU3FpTqLu9rhCVHqXxY0u/dEkIrFtP9QH8s76CLDHxrdtlPXgoPGnaTHUC37GTIHl50fRdErfvePVamzAhz4fx1xVWsCa/YKhSqRTCvvrq5cnCJL0cU19ynj4VAqpVFwKqVBUyb9/Oc9/7gjUFlXL06HuDobl0DdbkRymXCzFz5r41GKovJ4JPqF8nz5KevTq2UinM+3ueUHNrTaHBjgbC/bj7ejumPDVVCPUdoH7/OL1ljuB7zFd9wv36l5efl7DlwRYhIStBo7HzC4bmyg4MFJ60aKF6/bZtl29AKmnPXvXvYPjo0eq/0f+Wdu68EFi7jur1+3m/957AKxUKIWb+/FfP75IlBXp+w78eqZ4f5ZX8xx+q94hGjd8IimojT4Dms/cHaJQKhRC76Gf18xu7cJGgVBTs3FOpVAovVq7U+MKoPDVVCPH1VQXDatUWUk+dEv58+qdQf3t99Wts4/2NWs1Bnp4hhA5+9fd/24phQs2tNYXO+zsLmdL8A44xGTHC+nvrhS77uwg1t9YU6m2vJ9yOVb2vZ9y4ITxu0FD1+u3hJUhjY986Rs6zZ8LT1m1Un5lbtRZynj597zGT/zygU2D0fVKOvPb3YbDq78PDhIdCpz865XkPq7+9vvDd+cmC/4Th6uctft06vb+XFzWaxqMKVHPtQ9F0javR2uEDBJ0Hev4K1P9Sq10lcgWq/HgSAHB/difYWxpH23ki+rjJk5IQ1KkzlBkZKPXzIjh4ewNQpfIHe/eCNCgILsOGovjkyVqNm/3gIUI/+wwQiVD+z/2wrF79jW1yu+8JMhmsGzVS1X54Tzco+m+Tx8cjfMTXkASq6hiV+XU1bJo1e+f2glKJuMVLkLR1KwDAecgQFJ/8Zs2anCdPEDFsOOTx8TAt5YaymzbDokL5d46ryMrCha96oPS9GChFgMm00ag6aEyebYadGoYbsTfQx7MPZjebne9jSzl4EDE//AgoFLBp0QJlVq18b2dDaWioqg5XZKRq6dnGDW+8xgRBQPyKlUjcsAEA4OTrixI/fP/emj2Z//yDyFGjoczIgEXlynDfuLFAnVcVGRmIHDMWWdevA2ZmKP3zIth36/bGdjmBgQjp3QcQBHjs+5/eOhUXVPjXXyPz0mXYdeyIMqt/eeN+RVoaIkeNRtatWxCZmaHUkiWw79K5QMdM2r4DLxYsAADYde2CUj///N76ejmPHyN8+HAo4hNgVqoU3Ddteu/vb36UUqmqa/SpU4BIhBLTp8N5oHafefOTkpMC70PeSMpJwqg6o/BN3W/y3C9XyjH2/FhcjboKZ0tn7O6+G6VtC97NThAE3Iu8ibjvvof7nSgoAWzuLMb5T8zQqkwrdCjXATdjb+JU6Cn1sjFTkSnauLeBj6cPmpdqDhPxm8vZUo8dQ/S06ao6Rk2aoMyvq9+73EoaGYmIocNU9SEdHeG+Yf0bv/OCICBh7VokrP4VwMv6ejNnQGRq+s5xs+7cRcQ337yzPiSg6t4dPf17pB07BgAoPu07uAwerMmP750kz58juKc3oFSi3M4d7Aj+kjIrC0Gdu0AeH6+Xn3O2v7+qfmBqKszLl1c9v6Xzvi7+XTO0+JQpcBk6pEDHfV3Srl148dN8Vf3Azp1RavHPb9QP/HfN0OKrV2CF8jT8nqtqhlVxqoInyU8AAItbLUbX8l01Pr5SKkXMtGlIO34CALC1vRi9ftiEpqXeXcbq3xRKBSZfmoyz4WfhaOGIrVYjIZuxGIJUCqsGn8B9zZr31v+UxcQgfPhwSJ8HQWxvD/ff1sG6fv082wiCgMRNmxC/TLXs3cHbG24/zYPITD/n/xnXriFq7Dgos7KQXcENk7ySkWgth7udO/p49sHxkOMIjXuCCQeVaPBcgFIEPB/RAU1G/IgSNiX0MoeiqsjUXCMNFKBjqLmJGKZiVeFB1l0joqIiYe061Yl09Wqwf63du8jUFMUnqwqyJ23fAVlUlMZjCoKAuMWLAQAOPb3eGlgDAPuuXeG+cQPENjbI+ucfhH05ELK4uAI8GvpYqWol9Yck8GUdox3b3xtYA1Q1a0q8XrPm998RPW1anpo1mf/8g7ABX0IeH6+uY5RfYOL357vwbZc4XKgthlgAhIVrkPDbb3nqB46ppwq2HXx+EOFp4e8cSxAEJG7ejJhp0wGFAvY9veC+bu17A2sAYO7hAY89u2FRtSoUiYkI+3IgMq9ffzWuXI6YH39UB9ZcJ0xAiR9/yLcYtk2jRii3cwdMXItB8vSpqvNqsG6dV+Xx8Qj7ciCyrl+H2NoaZdf/9tbAGgBYVqumDuy/WLy4SNRizPz7b2ReugyYmqL4t5Peuo2JvT3cN2+CXccOEGQyRE2ciCQdO58KgoC4ZcvVgTUnX1+UXrYs38YVllWrwmPPHpiXKwdZdDTC+vdH9r17Os1BkZ6OiOEjVIE1MzOUXrZU74E1AFhyawmScpJQybEShtUa9sb9pmJTLG29FJWdKiMpJwmjz45GmlS7GmWvS8pJwrZH2+BzyAdfnh+KyR1jcaaeCGIAw08pcSjhC6xquwo9K/bEvObzcP6z85jVdBZqF6sNuSDH2fCzGH1uNDr92Qm/3PkFEekRr8bevgPR305+Vcdow/p86xiZlynzqj5kSgrCBg1GxpUr6vsFhQKxc+aoA2vFRn2DknNmvzewBgDW9evBY9fOt9aHBABFRiYiRo5UBdZMTVFqyeICB3wAwKJSJTh+9hkA4MXPiwvc0fhjkbhlC+Tx8TBzd4dT//4FHs+qbl2U270Lpm5ukIaEIPSL/sh58lR9vzIzExHfjMpTM1SfgTUAcPb1RekVy1X1A0+dQsTwEVCkp6vv/3fNUIsNS/F1wi/we+4HsUiM8fXHY5/XPnxZXfW+8uPVH/M0H8iP2NwcLj/Px9VmjgCAweeUqLjnL63+ZpiITbCg5QLUdKmJhn8nIWfaTxCkUti2b4+ymzbl21jHzM0NHjt3wqpePSjT0hD+1RCknz+vvl9QKvFi4UJ1YM1l2FC4LVqot8AaANg2b46SWzYix84CVsExmLUtB96WjbG3x14MrTUU/2u5CTtOVUKD5wKkpsDS3mL86HwRnf7shFFnR+Fs2FnIlNrV7/vYMHOtKPh7LXBqOlDdG+irfaHVOnNOIzVbhrOTWqNS8f9wAUEiKhIkISEI9uoJyOUou3ULbJo0yXO/IAgIHzQYWf/8A/ueXij9MmCWn/Tz5xE5ajREFhaoeOI4zEqVeu/2ebpBlS6Nsps3vdENiv67sh8+QsSIEVAkJcHM3V31+1G2rFZj5MkMa9kSZVauQMa1a4iePEXjq9UAcCLkBKZengoA+LHRD2h7IiZvZtj309WFmkedHYUrUVfQvUJ3LGq56I2x3ugG99VXKD5lslbd4BTp6YgcPQZZ//yjCoYs/hm2bdogauIkZFy8qOrMO2c2nF6e+Grqjcya9b/Bqk4dzfcPC0P40GHqzDr3DethVaPGe/eRxcYiqHMXCBIJyvy6GnYdOmg1Z316vXuy04ABKPnjD+/fXqFA7Nx5SPnf/wCogiHFxo7VuJubIJerusG97PzoOmE8XL7+WqtucPKkJESM+Bo5Dx9CZGWFMqtWwrZVK8331yAzVK6UY1fgLuTIc/BVza9gbqJ9x9prUdcw8uxIiCDCjm47UMf13b9XsZmx8D3mi7jsODRxa4K1HdbCTKzZCapCqcBf0X/B77kfLkRcgFypurBtaWKJTh6d0LuSD8ruv46EX9cAeHdm2LPkZzjw7ACOBh9FiiRFfXvjko3w/d1ykGzdAwBw6t9flRmqRedWZWYmIseNR+a1a6pg1/yfYNelC6InT0H6mTOqzMEZP8JZy8CMLCYG4cOGQxr0KrPGvFw51e/Ho0cQWVujzC+/wLZF8zz7haWF4WjwUdib22NAtQHa/f7Fx+N55y4QsrJQatlSOHTvrtWcPzav/zxKr1gO+66aZ2flRxYbi4jhwyF59hxiOzu4r10D84oVVR0uHzx45+s/Ii0Ch4IOITQtVONjudu5o2fFnijvkPeiU+b1G4gcPRrKzExYVK0K9w3rIY+JUWXWpaTA3MMD0fOG4bvny5AuTYezpTN+bvUzmripPmcqlApMvDgRFyIuwNHCEbu67UJZe83+rq++uxob7q3HF7es4HM2AwDg0KsX3ObN1TiAJQgCwlctRdZvqmY1/k1c0Xv9SVhYWGv6o4EyOzvP31q3uXPg0LOn3jND3yYiLQKTLk1CyvNA/PA/BUqkQP231tTZOU9mXfFfV+CKSwIOPDuA2y9uq8dwtnRGz4o9MajGIBSzKqb3ORqKpvEoBteKgqengN19gRK1gG+060AGAM0WnkN0ag4OjW6OOu6O+p8fURGjSEuDIi0d5mUKvpyD9C9y7DiknzkD29at4b7+t7duk/3wEUI//RQA4LF/P6xqvv8EWZDJENzTG9KQELgMH/7OjI9/k0ZEqE7Gw8Nh4uSEkrNn5xvoMEZiK0tY1qqlVQAlP4JMhuwHDyBI9X8V0qx0KZi7u+t9XE29vvTBono1lN2wAabFdPsQmHHpEiInTISQnQ1zDw9Iw8MBpRK2Hdqj9NKlEFtavnf/u3F3MezUMEiVUgysPhBTGk4BACRt344XCxYCAOy6dIFTP1W33dC0UMy7PhciiDCn2dw3lrWl7N+PtKNHARRs6c6/l/GZe3hAGhICkYUFSq9YDrt27XQa99/BmpIzZuQbKAcARVoqYmfP0SkYGrdiJRLXr4e5hwcqHDms85V+aXg4TIsXz/c5fZfUQ4cQ/d00iG1tUfHMaY26JwqCgIQ1a5HwqyrbyOHTPnDo4ZXPXgAgIGnrtgIFQ3P9O1hTYupUWGjQkU6Zk40XP81/bzA0MTsR313+DjdibwAAaherjWVtlqGkTUmN55cly4LPIR9EZ0ZjQLUB+K7Rd/nuE5gYiEEnByFbno3enr0xu+ns9wZ9ItMjcfD5QRx8fhAvsl6ob6/pUhM+nj7oWr4r7MxflR9I3rsXsXPnqd8LnAe8PVNPrpThbrw/rkZewaPER2j5QIm2D1SnZroEQ3MJUimif/gRaUdUy/jMy5dXvX4LuMxYkZKCiJHfINvfHyILC5i6uEAWHQ0TJyfV81urFgAgW56Ns2FnceDZAdx6cUu9/7Yu21C/RP13Df9W8WvXIuGX1TArXRoVThzPN+vyQxIEAdKQEJiXLZtvBqA+xMychZR9+2BZpzY89u7V6XfjfRSpqYgYNRrZt29DZG4OU1dXyKKi3rgYkiPPwZmwM/B77oebsTd1Pl794vXR27M3OpbrCGszVQDq9Qujpm5uUKSkQMjOhmWtWjg1pgHWhO4AANR2rY1lrd98r8iSZeGrU18hIDEAHvYe2NltJxws3v+570nSE/Q72g9yQY5lrZeh0e0MxMycqbpw1roVXL76CkD+P+u048eR8rLz6eGWFtjZXI7uFXtgYYuFWj1X/74wYl6hAqTBwapg+cIFcPDS5G+Adi5GXMT3V75HukwVtFxS83sU+3G9+sKI2M4O8hcvYFqiBNw3bsjTlTQ0NRR+z/1wOOgwErITYCoyxdnPzsLFykXv8zQUBteMScIz4NcGgJkN8H0UoOUbZYfll/A8LgO7hzdGs4ofT4SY6G0EQUCIT29Inj5FyZkz4dTvc0NPiV6Tdfs2wnwHAGIxKhw+BItKld65bdSUqUg7cgTWjRqh7Lat7/3gkbxnD2LnzIWJkxMqnj6lVQ01eUKC6mQ+IECrx2JsbJo1RelfVsPE9v1L/zQhT0xUXa1++DD/jXUhEqHE99/D+csBhTP+e0jDwhDs1ROCVArrpk1QZvX76xhp4vWaNYBmdYwAIDwtHL7HfZEiSUE793ZY3mZ5ntpLr9dc0srLbJXcJZG6EhQKvJi/AMkvlyS+qw6MtvIEa7SkSzBUkZGBoE6doUhKUmXs+PpqdUxBEJCwbp3qBL9cWZTdtEnr4LAyJwdBXbtBHhMD128nodjw4Vrt/3qwRhsFDYbm+newRhvvCob6x/nj24vfIi47DlamVjAVmyJdmg5HC0f83OpnNCv1/iXauX7+52fsDNyJUjal4Oftpz5Jz8+liEsYd2EclIIS4+uPf2MpqUQhwbmwczjw/ABuxNxQ3+5g4YAeFXrAp5IPqjhXeef4aadPq7NYtaEUAVbfT0SFL0dotd+//bs+pNjWFmV+/RU2TRoXaNw8mTUAzEqXhvumjTD38EBAYgAOPDuA4yHHkSFTZf+IRWIUsyqGuKw4dCrXCcvaLNPueK/XGJs6FS5DvirQ/PXl9QCIVd26cP9tHUwcHQvteJJnzxDs3UtVg273rgK/D7+LMicHUd9ORsa5cwCQp2ao+vkNPo50mWrZpggiNCvdTFU7UJR/hqVSUOJ6zHVciboCpaB6P7Mxs0HX8l3Ru1Jv1CxWE7LISPWFUQAwb9YYy31McDnpHwDAF1W/wJQGU2Bm8vYLJfFZ8eh/vD9iM2PxSYlPsKHjhndmxCqUCgw4PgAPEx+inXs7rGy7EiKRCOkXLiBq4iQIOTna/QBfZoY+aVMeo86OglyQ45s632BU3VFaDfPv+qbvygwtKIVSgTX+a7DxwUYAQB3XOljWehlK2JRQ1TcdPQZZN1Tvf+YVKqhq8r3jgphMKcPVyKsISQvBkJr6XTpsaAyuGRO5FJhfAhCUwLdPADvNr9YBgPeaa7gXkYKNAxugY/WPu5ggUdbduwj74tVShmJjx6DYqFF6v3pH2hMEAaH9+iHn3n049u0Lt7lz3ru9LCoKQV27QZBKUWbdWti1bfvW7RQZGQjq3AWKxESU+PFHOA/Q7sRYNUYmXsyfj+wH97Xe1xjIIiIhSCSwrFFDlb7vovvVQmlkJMKHDoUsLBxiGxuYumn3Nyk/gkwGWZjqA7PLiBFwnTjhg75+I8eNR/rp07Bu3FhVm09PWRCS4GC8WLgINk0aw3nIkHwfU0pOCgacGICwtDDUcKmB3zv//tagQObffyN+1S9QZLyqPyNVSNX1mcrYloGF6avCzyY2tig2ZgxsW7bQy+MSBAFJ27Yh8+o1lPhuKiw8PfUzrlSKuGXLkXFN84x9qxo1UWLGjzoFQ5N278aLufO0DtCrAozzkbx7j/o2k2LFVM0eqlXT+PgJGzYifvlymLq5oeKJ4zplv6WfO4eE39ZDmZ2l0fYmDo4o/u0kvZ2EC0olEn77DeknTkIQNAvyWZQvj5KzZuUJhgqCgN2Pd2PpzaWQC3KUdyiPFW1WwMLEApMuTkJgUiBEEGFMvTEYVmsYxKJ3Z+Tei7+HL49/CQEC1ndYj2alNQvI5doduBsL/1FliC5pvQRdPLrgSdIT9ZLN12uyNXVrit6evdG2bFtYmFi8a8g8sm7eRNyKlVCkpWq0fZgsDtsaZqJ2ryH4tsG3Wj2Wd0natQsZ5y+g+ORvtfqdfR9BLkf8ypWQhoXB+rsJOJF+HQeeHcDT5Fe1ukrbloZPJR94V/JGmjQNfQ73gYnIBCf7nNQqMxEAUv78EzE//AixvT0qnT5VqEEsTfw7wAjgnc0e9CXi65HIuHTpnY1Q9EmQyxG/6hdIgoNh8914nMy8Cb/nfnic9Fi9TSmbUujl2Qu9KvaCm632j/lF5gscDjoMv+d+eeoNVnKshD6efdDVsRmyF61CipMZvq3uj+icF7AytcLMpjPRo0KPfMd/mvwUA08MRKYsE14VvDC/xfy3/l3e9mgblt5aCjszOxzsdRDFrV8128m6exdxy5ZBkZKi0WMSW1nDZcRw2HfsCAD48+mfmP33bADA/Bbz0bNiT43GeV3yvn1IP3kSrhMnqjND9SUpJwnfXf4O12NUtVX7V+2PyQ0m5wlaKqVSxC1eAkVSIkrMmKFRxvXHiME1Y7OqDpAcCgw+DnhoF5Huv/E6/gpKxKp+deFdl8vk6OMWM3s2Uvb+D6ZubpDHxAAAHPt9jpIzZmhVk4T0L+3ECURNnASRtTUqnToJU1fXfPeJW7oUiZs2w7xiRVQ4dPCtmT5xK1ci8bf1MC9XDhWOHtFr8daPRfaDB4gY8TUUyck6Z9YAqu6K4SNG6K1D4NsIgoDE335D/CrVyYFDn95wmzPngyypybpzF2H9+wNiMcof9MuzrKGglIISFyIuICYjRqPtT4Wegn+8P9xs3LC7+26ta5NMuzINx4KPoVWZVljTfo0uU9ZYRHoE/OP80apMq3yX1xQFf0X/heCU4Lw3yhWoMWEDLKOTENO7GaIHvArmN3ZrDE+nN4OGSokE0VO/Uy+NdR0/DmknTkLy5AnENjYos2aNRllA8qQkBHXsBGVmZp7uydqKSIvAvYR76Fiuo8bBHX0SBAH+8f6Iy4pD81LNYWuufZAzS5aF2X/PxokQVVe+TuU6YW7zubAxU2Xc5shzsODGAnUHwNZlWmN+i/lv/b2TKWToe7Qvnqc8R8+KPTG/xXydHldu5pu52BwVHSsiMClQfV9Jm5LoVakXelXqpZfOovm5HHkZo8+Nhp25Hc5+elbjLDx9EAQB9+Lv4VHiI40KuQsQ8CD+Ac6Fn4NUqcrOMxebo3259ujt2RuNSjbKExgdemoo/on9B8NqDcP4+uO1m5tCoV614DxoEEpMn6bdg9MjeXIyIr8ZpV4aW/zbSUjc/LtqyVzJkii7cYPeLkLkyrx+HeGDvwJMTVHx6JFCrx+rFJS4GXsTB54dwNmws+rn10xshg5lO8DH0weN3Rq/N/CtzbFuv7iNP5/9ibNhZyFRSNTHauLWBH/H/A25Ug4Pew8sb7P8re/V73It6hpGnxsNhaDAqLqj8E2dvB2EI9Ij0PtQb+QocjC76Wz0qdynwI/n31beXonNDzfDVGyKDR03oGHJhno/Rq4XmS9wLvycOiPwfRSCAjsCduBFlipoObvpbHSr8PbmQMTgmvHZ4QMEnQd6rgbqD9Rq1+Hbb+FMwAvM96kJ38blCmmCRIanlErxrEVLKNPSUPb3zZCEhLxq3d2xI0otXfJG6276MJRSKYK794AsIgLFxo6B6+jRGu2nSEtTLddKSUHJ2bPfWOYri41FUJeuEHJyUHr1L+qrgfQmSUgIIoYNV9VHKVYMZTesf2dH1bfJvPGPqpBwRgYsKleG+8aNMCtRPP8ddZT8xx+InTVbVZOoTRuUXrEcYiurQjueIAgI+6I/sv394fjZp3CbN0+v4y+9uRTbArZptY+tmS22d92u1clCrrC0MHgf9FZ9QO66A3WL19V6DE2cDj2NGddmIEueVSgnVvokUUiw8MZC/Pnsz7fe3+CpElP/VEJqCoz/2gSJ9qosBgcLBxz0PpgnwPm2pg72Xbuqbh81Glk3b76sX7UY9l26vHdesfN+QvKuXbCoXg3l9+/XqjZijjwHZ8NVtatyaxt9VvkzzGw6U+MxCiohO0GVYfLMT120XF3A37M36hevr1H2aXBqMCZdmISg1CCYikwxqcGkdxa4P/DsAOZfnw+pUorStqWxos0KVHPJm3W1zn8d1t5bC2dLZxzyPgRHS0edHp9CqcCECxNwMfIiAFVX0bbubdHbszeaujXNs1S7sCkFJXr49UBEegRmNJmBvlX6FvoxE7MTcSToCA48P4CQVN26+FZxqgIfTx/0qNDjnQH4c+HnMOHCBDhaOOLMp2dgaapd9mbGlauIGD4cMDNDxePHDFK3821NHazr14csOlp1e3AwxA4OcF+3Dtb16+nlmNo2QimI2MxYHHp+CAefH0RkRqT6dk8nT/Tx7IPu5bvr/DrTRJo0DceDj+PAswN5gtwdynbAvObzdAro//H0D8z9ey4AYEGLBfCqqKpXJggCRpwZgesx19GoZCNs6rSpULLolYISUy9PxanQU7A3t8fObjvfaOSgD9EZ0fA97ouE7ASt9vOw98CKNitQyendZVyIwTXjc2wycHMj0GIi0GG2VrtO2HsXB/2j8UO3ahjeqkLhzI+oCEg7eQpREybAtGRJVDp3FiITE6SdPInoKVMhyGSwbtgQZdb8CpOP9X2iCEvcuhVxi36GqasrKp46CbG15lfbk7bvwIsFC2BSrBgqnjyZp2ZY9PTvkernB6v69VFu104u/82HLC4OEcNHvJZZ8+sb3VrfJu3kKURPmaJ6HTVogDJr13yQ11H6uXOImvQtBImk0GvW5L5/iKysUPHUSZgV11/g8H+P/4efbvwEAGhftj3MxfkvNTUzMcMXVb9AzWI1dT7urL9m4cCzA2js1hibOm3SeZy3kSllWHl7JbYHqLqYO1k4IVmSrL6/tG1peFfyhk8lH62XeBWGyPTIPEsK27i3gaXJv07eBQFdl/6Fks+T8LxJGVz5qh7uJ9xHVEYUunh0wZLWSwC8fB2N+BqSx49Vr6NfV8OmaVP1MEqJRNXs4fRpVX2dH394Zx23/Lonv0tgYiD+fPYnjoccR7r0VW0jAQJMRaY43Osw3O0LL7ggV8pxLeoaDjw7gEuRl6AQFAAAK1MruFq5Ijw9XL2th70HfDx90LNiz3dmYL4epHW1csXS1kvzLWwfkBiASRcnISojChYmFvih8Q/w8fQBADxPfo7Pjn4GuVKuXs5ZEFmyLKy/vx4uli7oUbEHnC2dCzReQewM2Imfb/6Mig4V4eftVyh/9+RKOf6K/kv1/EZcglxQdT21MrVCE7cmb7523sHFSvXzqu5cPd95KpQKdDvQDdGZ0ZjbbK76udRG+JChyPzrL9h17YIyK1ZovX9BSJ49Q/iw4eqi7mU3bcyToSZPTkbkyG+Qfe/ey1qHK2DX7u3lLrShSyMUbcgUMlyKvIQDzw7gWvQ1ddaTrZmtqg6aZ2/UcKnxwT9/BSYG4ljwMZS1L4vPKn9WoOMvv7UcWx5tgZnYDBs6bkCDkg3g98wPM/+aCQsTCxzoeUDjrqK6yJHnYNjpYbgXfw9lbMtgV/dden2PSZemY+CJgXie8hzudu6o6aLZ54pStqUwrNYwnYKW/zUMrhmbv9cCp6YD1XoCn+/Qatcf/B5g141wjG/viYkd9bfEhaioifhmFDIuXHijW2Se1t1VqsB9w4ZCzbihvBSpqXjeqTOUqalw+2keHF92AdWUIJUiyMsLsrBwFBs1Cq7jxgIAch4/RohPb0AQ4PG/veouVfR+b2TWvMy4eZek3bvxYt5PLzNAO6DU0qUfNAM06/ZtRHwzCsq0tEKrWSNIpQjq4QVZeDiKjR4N17Fj9Db2lcgrGHN+DJSCEmPqjsHXdb7W29j5ic6IRne/7pAr5djcaTMauTXSy7jxWfGYfGky7sTdAQB8VfMrjKs3Dk+Tn76zmHXvSr3R1r3tOwtMF6YrkVcw7co0pEnTVMXwW/78ztpb2ffvI7Tv54BIhPJ/7kdQCQH9j/WHUlBidbvVaKbwUBXSjop6Z4dLQLVMLfann5CyZy8AwOWbkXAdN+6NE8DIsWORfubse7sn50qVpOJ4yHH4PfPLk7Xxem2jOdfn4FrUtQItg3yf8LRwVde354cRlx2nvr22a230rtQbXcp3gbWpNe7F38OBZwdwMvQksuXZAAATkQlalWmFPp590Lx0c5iKTSFTyrDq9ip1VmeDEg2wpPUSjZdBp0pSMf3KdFyJugIA6OPZB981+g7DTg3D/YT7aOPeBr+0/eWjuvCSLk1Hhz86IEuehY2dNqKJm2YBWU1EpEfA75kfDgUdQlzWq+e3VrFaqq6nHl0L9UR768OtWHZ7Gao4VcEfXn9o/bzl+Vywdw+s6tYtnIn+S9adO4gY+c2rv1MbN7y1qLsyK0tVi+3SJcDEBG5z58KxT2+dj1vQRijvE5waDL9nqg6PSTlJ6ts/KfGJuoOnlWnhZZN/SEpBicmXJuNM2Bk4WDhgZZuVGHdhHNKl6Zj0ySR8VbPwm2Qk5STB95gvIjMiUde1LjZ13qSX5f0ypQyjz47G3zF/w9XKFbu77y4SF7w+NgyuGZunp4DdfYESNYFvtOueteB4IDZcDsbwluXxQ3fNlwARGRN5YiKetW4DyOWocOwoLCpWzHP/G7WiNm+CRXn9p13Tm178vBhJW7bAwtMT5Q/66VT7Lu3UaUSNH6/KKjp5EmYlihv0CnWqJBXmJuZG+8HyjcyaH354oxGEIAhIWL0aCWvXAQAcP/9c1eHSALULc54+RcTwEYVWsyZp+3a8WLAQJq7FUOnkSYhtCt5RFQCeJD3BwBMDkSXPQs+KPfFT858++En+/OvzsffJXtQrXg/bumwr8PFvxd7ClMtTkJCdABszG8xvPh/ty7XPs022PBtnw87C77mfeqkioMpu61GxBzqU7aDxSYOLlYvOJwIKpQK/3f8N6++thwABNV1qYnmb5fkW146a9C3Sjh+HddMmKPv771hxewW2PNqCBsmOmPY/BZTJyTArWxZlN218o8Pl617vIgoAjp99ipKzZkFkaoqYjBgk3bgK8eiZEMRiCNuWAeXfnmmWLEnG0eCjb9Qbal+2PXw8fdDErYl6Ce6jhEfod6wfxCIx/Hr6oYJjwVcsyBQynAo7lWfpKaB6Pr0qesGnks87lwxlyjJxMuQkDjw/gPvxrxrGuFq5wruSN+68uKMO0g6uMRjj64+HqVi7+opKQYkN9zdgrf9aCBBQzKoYErITYGtmi4PeB1HC5uNr5rXgxgLsebwHbdzbYHW71QUaS6aU4XToafg988ON2FddTx0tHNGjQg/09uyt09J0XaRKUtFxf0dky7Pxe+ffdao/9aEz2tPPn1d1jdQww1qQyRAzYyZSDx4EALhOnAiXEcN1mqc+GqG8LkeegxMhJ3Dg2QH4x/urby9mVQw9K/aETyUfeDh4FOgYRVW2PBtDTw3Fg4QH6tuqu1THrm67tH5P0lVwajAGHB+AdGk6unh0wc+tfi5QeQVBEDDn7zn489mfsDK1wtYuW1HdhbGAwsDgmrFJeA78+glgZg18Hw1o8Qa86uwzrDj7FF80KouFvfXbRYSoqMg9ObasVQvl/9j31m1e73Jo4uQE9/W/wap27Q880/8WaWQkgrt2gyCTwX3jBti2bKnTOIIgIKy/L7Lv3oXDp31g37mLQWqrCIKA/c/2Y+GNhShuXRzbu27P0znKmLyRWTPya7iOHw+RSARBLkfsnLlI+eMPAECxMWNQbLRhu+4WVs0aRVoagjp2giI1FSXnzoFTX/3UMIrLikP/Y/3xIusFGpZsiPUd1hskaysuKw7dDnSDRCHBug7r0KK0bl1CBUHA9oDtWHF7BRSCApUcK2FFmxX5nmjlZjoden4I8dnxOh27UclG8PH0QYeyHTSuw5SSk4JpV6bhWrTqguTnVT7H1IZTYW6S/5LcPO9bG9bDpFlDTF/WFYN2voClDLCsXl3VdbeYZtlVyf/bh9g5c1T1A9u1w7WvG2PB3SWYt12OytHAmboibOyqWdA6t1Nejwo93lnbaPz58TgfcR6dPTpjaeulGo37LoIgYNz5cep6YwXJRHye/Bx+z/1wJOhIniXENmY2+Kn5T+hQrkOB5vpX1F/47sp3SJGkAMAHq0lmCCGpIeh5sCdEEOFY72Nwt9Ptb6AgCJh4cSLOhZ8DoHp+m5ZqCh9PH7Rzb6fR60Xf5v49F388/QMdynbAirbaXzj7kLVYU/bvR8zMWVrXBhUEAfHLVyBx40YAgNOXX6LE9Gla1VuUJyUhqFNnKDMyUGrxz3DoqX2nydcpBSWGnR6mDqCbiEzQsnRL9PbsjRZlWsBM/PE3i0rIToDvMV9EZ0bDRGSCvT32oqpz1Q86h5uxNzHizAjIlXJ08eiCOc3m6Ny45PeHqotDIojwS7tf0Ma9jX4nS2oMrhkbuRSYXwIQlMCkx4C95ktiNl0Jxk/HAuFdtxRW9dNP8Uyioiakdx/kBASgxI8/vpGB8zp5YiIivh6JnIcPIbK2RplVq2DbUreTTcpfbgaITbOmcN+8uUDBmay7dxH2RX9AJIJZqVKQRUV90K5g2fJszL8+H4eCDqlvq+ZcDVu7bP2gHdv06d+ZNQ6f9kHJ6dMRNfU7ZJw7B4jFKDlzBpz69TPwTFUKo2bNiyVLkLT5d5hXqogKB9/ekVZbWbIsDD45GIFJgSjvUB47uu4waBfN3GYK1V2qY2/3vVq/DjOkGZj510ycCTsDAOhRoQdmNJmh1e+9XCnH1air8Hvmh4CkAI32EQQBcVlxEKD6KGpnbofu5bujt2fvN4rXv+5hwkNMujgJMZkxsDSxxMymM9VFqjX1KuO2ElyGDUPU9z9ApFDgvocIFdeuR4MK2l0oSD97VlU/UCrF4zIiXK4pwoiTSkjMRZg7sSTS7d4dXDMRmaBpqaboXak3aharme/z9zT5KT49/CkECPjD648CnRweCTqC769+D3OxOYbVHoZeFXvlm/mXH5lChgsRF3Dw+UFky7Mxs+lMvRXwjsmIwcJ/FsLFygUzmswock019Gnk2ZG4FnUNA6sPxJSGU3Qa43jwcXx35TuYic0wrNYw9KrUC6Vs31zO+CE9T34On8M+EIvEONH7hE7zKewu4oIgIHH9esSvXAUAcOjdG25zte9qnbRtG14sXAQAsO/WFW6LFkFsrllAM7cRimX16vDY/4dWgbm32R24Gwv/WQhrU2sMrz0c3hW94Wqdf1f3j01QShAW3liIruW7Fkp3UE0cDT6KGVdnQC7IUcGhAla0XYEKDtplIZ8OPY1vL30LAJjWaBp8q7373IgKjsE1Y7SqDpAcCgw+BnhoHgzY8084ph94gA7VSmDToAaFNz8yKoJMhvQLF6BMT9doe5GFJezatyvUbn26ynn6FCE9vQEzM3hevpRvMVdFRiaixo1D5l9/AaamKLVgfoGv+P1b1p27kIYE63VMAICJCWxbtoSpi4v+x9aAIiMTGefPQZDJ8t82NQ1xixerahcd+BOW1d59MqypyAkTkX7yJABAbG+PSqdPFVqB+9dFpEVg4sWJeJL8BGKRGF/V+Ap+z/2QlJOENu5tsLLNyg/aMU7fXs+sETs4QJmaCpG5OUotXQL7Tp0MPb083qxZMweOfXT7ACyNjEJwt24QpFK4r/8Ntq1bF3h+r3cWdLZ0xs5uO3XOKtGXpJwkdPmzC7Ll2VjZdiXal22f/04vPU9+jokXJyI0LRSmYlNMazgNfav0/WBZjNEZ0Tj0/BD8nvshJjNGfXs152ro7dkb3Sp0g7256rPf65mlMqUMZe3KYnmb5ajiXEXr475eKzJXWKOymNYmCu5O5bG/536t6+EEntuPzEkzYCN5dZs23ZO1MfXSVJwIPYE2ZdpgdXvdlg0mZifC+5A3UiWpGF9/PIbVGqbnWVJBXIm8glHnRsHOzA5nPzur9UWe5JxkeB/0RrIkGaPrjsbIOiMLaabaG3Z6GG7E3MBXNb/CpE8m5b/DvygyMhHUuTMUiYn5XnTVlqBU4sX8BUjetQsA4DJiBFwnTtD5PTH16DFET58OyGSwbtIEDl498t1HmZOjCspp2QjlXaIzotHrUC9ky7PxQ+Mf0K9q0big9l92N+4uJl+cjLjsOFibWmNO8zkaN2a5F38PQ08NhUQhgW81X0xr9GEuQv+XMbhmjHb0BoLOAT1XA/UHarzbIf8ojN/rj6YVXLBnhP6KnpJxS/7fPsTOmqXVPs5DhqDEVN2ujham3MwT2w7t4f7rrxrtI0iliP7+B6QdPQoAKD51KlyGFLxgqSAISFizFgkazkMXJq7FUHbDBr0Eq7QVOXEi0k+c1Gofh169UGrRQr0cXxoejqDuPQCZTG/PWX4uRlzE91e+R7osHc6WzljcajEauzWGf5w/hp4aCqlSigHVBuC7Rt8V+lwK0+uZNWJbW5RZuwY2jfRTAF/f3qhZM2ECXL4eofXJTdTkKUg7ehTWTZqg7Jbf9RIwWvTPIuwK3AULEwts7rwZdVyLRqONX+78go0PNqKSYyX82fNPjTJ6jgcfx+y/ZyNbno0S1iWwvM1y1HY1zFJ6hVKBGzE3cOD5AZwPPw+ZUhXgtzCxQIdyHeBd0RtHg4/icNBhAEA793b4qcVPsDO30/mYuV2OAcBp4JewmjQKPod7Iz47HsNqDcP4+uM1His2Mxa+x3xhEfYCc/abwDZVqlP3ZE2FpobC+5A3lIISu7rt0ul5m3JpCk6GnkRV56rY3X33f2JZmDFRCkr0PNgTYWlh+LHxj/i86uda7T/tyjQcCz6GSo6VsK/HPoMsW3+XC+EXMO7CONib2+PsZ2d1qm+avGcPYufMhYmTEyqePgUTO93fC942LkQilJg+Hc4DvyzwmBnXriFq7Dgos7K02k+TRij5EQQB35z7BteirqF+8frY0mXLR53xaUwSshMw9fJU9VLdAdUGYFKDSe99L45Mj4TvcV/Vxd8ybbCyrXFf/DUWDK4Zo2OTgZsbgeYTgI5zNN7tXOALDN12C3XKOODQGC5/I5WoSZOQdvwELCpXfmtHo9cpMzORdfMmTJyd4Xnpot7T6wtCUCjwvE1byOPjta6tISiViPt5MZK2qbqUOQ8ZguKTv9U5tV5QKBA7dx5S/vc/AIB1kyYFLi77b5LgYMjCw1XBjzVrYNP4wwU/1MsyxWJV7TQNAhFiezuU+O47vWbapR45ipzAQLhOGK/x8gldKJQKrPFfg40PVDVR6rjWwbLWy/IUxz4ZehJTLqkCzh8y7f7g84NYeXsl0qRpeh23UqQC7e4LSPZqjnZtB6NhyYZF9kO2qmbNciRu3AQAcBowACW+n67x6zf7wUOEfvaZuiukZfWCF/ndFbgLi/5RLfFZ2nopOnt0LvCY+pIqSUXXP7siXZYOU7EpRMj/9ZsbwGri1gQ/t/oZzpbOhT1NjaTkpOBo8FEceH4Az5Kf5blPLBJjfP3x+KrGVwUOlgpSKeLXrYN5GXc49PaBSCTCubBzmHBxglb1eDJlmRh4YiCeJj9FRYeK2FJ3MSTb/weHnl6F2s1wxrUZOPj8IJq6NcWGThu02vdixEWMPT8WYpEYu7vvRg2XNzuikuHlvueUdyiPQ96HNP6dz816E4vE2Nl1J2q5Fq2azAqlAt39uiMqIwqzms7Cp5W16zIOAIJcjuCe3pAGB7/RRV7neWVkIKhTZyiSklD8u+/g8tXgAo+ZKycgAImbf4cyM1Oj7cXWVij+7bcwK126QMfNXfptJjbD/p77tV5+SIVLrpRj9d3V+P3h7wCAesXrYWnrpW+t95sqScWXJ75ESGqI0ZctMTYMrhmj6+uAk9OAaj2Bz3dovNvfQYn4YuN1VHS1wblv2xTe/MioPGvXDvLoGI3SyQW5HM9at4EiMRFl1q2FXduC1TjSp4yr1xAxbBhMHBzgeeUyRFoGWwRBQNLvvyNuiaros4N3T7j99JPWAUSlRILoyVOQfuYMIBKh5KyZhVKnSpGWhshRo5F16xZEZmYotXQp7DsX/rK91xsKOH72KdzmzSv0YxpSUk4Spl6eihsxqs5p/av2x+QGk996ZX/Tg01YdWcVxCIxfmn7C1q7F3xp4btIFBIsvLEQfz77s9CO8boytmXg4+kD74reRbbj3us1a+y6dkGpn3/ON+gqCALCBw5C1s2bcPDuiVI//1zgeVyMuIjxF8ZDKSgxof4EDK01tMBj6tv2R9ux5NYSjbc3EZlgSM0hGF13dJG88i0IAh4lPsKfz/7EiZATsDSxxOJWi9HIrXAvOky6OAlnws6gmnM17O6++72d5ORKOcaeH4urUVfhYumCXd13obRtwU6GNRWVEYUefj0gV8q16ryYLk1Hr0O9EJcVh69qfIVJDQoelKDCkSHNQIf9HZApy8T6DuvRrHSzfPfJlGWi16FeiM2MxZfVv8TUhlM/wEy1t+3RNiy9tRSVHCvhQM8DOgXL08+fR+So0RBZWKDiieP5XkzOj7qWm4cHKhw5XKQuNusiKScJ3ge9kSJJwdh6YzGi9ghDT4ne4Vz4Ofx49UdkyDLgYumCJa2X5HlPlylk+ObsN7gRewMlrEtgd/fdRttwyxhpGo/6MH1nSTPOFVX/JmlXx8nWQvU0ZkkV+p4RGSnZizjIo2MAsRiWNfO/WikyNYVDjx5I2rYNqYcOF6ngWu6yMPvu3bUOrAGASCSCy9ChMHFxQcwPPyL10GHIk5NRZuVKjZfqfMiAl4m9Pdw3b1IH8qImTIBi5gw4ffFFoRwvV/rpM8i+exciKysUGzu2UI9laPfi7+Hbi9/iRdYLWJlaYXbT2ehWods7tx9acygi0iNw4NkBTLk8Bdu6bHtvsXVdRWVEYdLFSQhIDIAIIoyqOwq9KvXS+3FeZL3A4eeHcTzkOCIzIrH67mqs8V+D5qWao49nH7Ryb1Wkloc5DxoEE5diiJ4+HeknTiIiOQVlfl0NE1vbd+6TceEism7ehMjCAq7jNV/a9y4BiQGYenkqlIISfTz7YEjNIQUeszAMrDEQPSr2gFQh1Wh7azNrdT2zokgkEqFmsZqoWawmvm/0PQQIH6S74feNv8f1mOsITArEjoAd+Krm25enC4KARf8swtWoq7A0scTqdqs/WGANAErblkYfzz7435P/4de7v2Jrl60aBShW3l6JuKw4uNu545u633yAmZKubM1t0atSL+wK3IVdj3dpFFxbdWcVYjNjUdq2NMbUHfMBZqkbH08frPFfg+cpz3Ez9qZOQXPbtm1h3bAhsm7eRPyqVQW6kCKLjUXSlq0AoFrlYOSBNUBVxiBFkoLKTpXf+T5GRUP7su1RqUclTLw4Ec+Sn2H46eEYX388BtcYDACY8/cc3Ii9AWtTa6xpv4aBtSKqaK4F+a9yfpmmmxQMaJFQaG2hutqcIZEXxqzICGXf8wcAWHh6wsTWRqN9HHx6AQAyzp2D4rUCz4akyMhA+tmzAACHXt4FGsuxVy+4r10DkZUVMi9fQdjgryBPTs53P9mLOIQN+BJZt25BbGsL982bCj2TTGxhgdIrV8Cx3+eAICB2zlzE/7IahZVoLEiliFu2DADgMmQIzIp/nH+wBUHAnsd7MPjkYLzIegEPew/s7rb7vYE1QHWC/2OTH9HErQmy5dkYc24MYjNj9Tq3K5FX0PdIXwQkBsDRwhG/dfgNI+uMREmbknr/quNaBzOazsD5vucxv8V8fFLiEygFJa5EXcGEixPQ4Y8OWHZrGYJTC6Fhh44cenSH+2/rILa2Rtb16wgbOBDy+Pi3bivI5YhbqspUdR44sMCZDLGZsRhzbgyy5dlo4tYEPzT54YMV+9eFs6Wzxr8LRTmw9m9mJmYfJLAGAMWsimFKA9Vy8DX+axCeFv7W7XYE7MD/nvwPIoiwqOUigyy9G15rOMzF5rgTdwd/R/+d7/a3Ym9h39N9AIDZTWfrVOuKPqwvqqourl2OvIywtLD3busf54+9j/cCAGY1nVWkl4zZm9ujZ0VVs6ldgbt0GkMkEqH4VFVmXurhI8h+9Ejn+cSv+gWCRAKrBp/Atr3mTWGKqsuRl3Ei5ATEIjHmNptbpC6a0duVsy+HXd12wauCFxSCAstvL8fEixOx+u5qHAo6BLFIjKWtl+rUxIc+DAbXihLHsoDIBJBlAeman7jZmL/KXDOCVb70AWTfuwcAsKqjeaFty6pVYVGlCgSZDGlaFrQvLOmnTkPIyYF5hQqwrFXwkxbb1q1RbsvvMHFwQM79+wjr7wtZVNQ7t5cEhyDsiy8gefoUJq7FUG7njg9WAF5kYoKSs2ah2FjVVeeEtWsRO2s2BLn+g+jJe/8HWXg4TFyLFUoDgbC0MKy6swrfX/keSTlJeh8/P4Ig4GbsTUy4MAELbiyAXClHx3Idsaf7HlRyqqTRGGZiMyxvsxwVHSoiLjsOY86NQaZMs7op76MUlFjrvxajz41GmjQNNV1qYl+PfRplJxSUlakVelbsia1dtuJIryMYUnMIilkVQ1JOErY+2grvg94YfHKw3gOJfs/8MO/veZAoJPlv/Brb5s1Rdvt2mDg7QxIQiND+vpCGqU40BUHApgeb8MudXxD3v92QBgfDxMkJLiOGF2iu8VnxGH1uNOKz41HJsRKWt1nOE5T/iF6VeqGJWxNIFBLM/nv2G5+vzoWfw9JbqiDutw2+RftyhjkZL2FTQl3ofvXd91+EkSgkmPO3qqZvH88+hb68lvSjnH05tCzdEgDUgbO3kSqkmPXXLAgQ4F3RG01LNf1QU9RZbuDwYuRFRKZH6jSGVa2asO/RAxAExC1ZqtO5UM7jx+qVEiWmTi2UCyhPk59i1NlRGHpqqEZf486Pw4P4BzodK0Oagbl/zwUAfFntS9QoxpqKxsLK1ArzW8zHj41/hKnYFOfCz6lrA3/f6Hu0LNPSwDOk92FwrSgxNQcc3VX/TwrSeDebl5lrCqUAiVxZGDMjI5Pt/zK4pmUhZQdvVXZY6qFD+p6STnI/6Dh4e+vtg45V3boot2c3TEu5QRoSgtAv+iPnydM3tsu+fx9h/ftDFh0N83Ll4LFnDyyr5l/YWp9EIhFcR49GydmzAbEYKfv2IXLCBChzcvR2DEVaGhLWrAEAuI4ZC7GNZpmO+cmWZ+NI0BF8dfIr9PDrgU0PNuFI8BGMOz9O66CKruKz4rHpwSZ4HfTCkFNDcD7iPExEJpjSYAqWtV4GW/N3Lyt8GztzO6zpsAYuli54kvwEUy5NgVype7AzJScFo86Nwrp76yBAwOdVPse2rtvgZuum85i68nDwwMRPJuL0p6exqu0qtCnTBiYiE9x+cRszrs3Q24WbRwmPMPvv2dj3dB8OPdf+fcaqZg147N4FszJlIIuIQGh/X2Q/eoT7Cfex6s4q7Li1AeErFgMAio0eXaDucbdf3Ebfo33xNPkpXCxdsKb9mgJ1piTjIhKJMLPpTFiZWuFm7M08dRAfJTzCtMvTIEBA38p9MbC65h3eC8PQmkNhZWqFh4kPcTHi4ju3++3ebwhNC4WrlSvrrBmZ3GY6fs/93nlhZ8P9DQhODYaLpQumNCx6nd/fpqJjRTR1awqloMT/nvxP53FcJ0yAyMwMWdevI/PyZa32FQQBcYsXA4IA+27dYFVb/x2TBUHA/OvzcSXqCv6J/UejrwsRFzDw5ED87/H/tP4bvPLOSrzIeoEytmUwut5ovT8eKlwikQifV/0c27tsR0mbkgCAgdUHat0xmD48NjQoanb0BoLOAV6/AJ8M0mgXhVJAxe+PAwBu/9gBLrYWhTlDKuIEqRRPGjaCIJGgwvHjsKhQXuN95fHxeNa6DaBUouLJEzD38Ci0eeZHGhmFoA4dAJEIlc6fg5mbfgMOshcvEDFsOCTPnkFsZwf3tWtg3VBVODTjyhVEjhsPITsbljVrwn3Depg6G7aLXtrp04iePAWCVAqrBp/Afe1amOjh/TBu6VIkbtoM80oVUeHgQYhMdS/FKQgCApICcODpARwPOY4MWQYAVXe/ZqWa4X78faRJ09DFowt+bvVzoXSplCvluBJ5BQeeH8CVyCtQCKpalNam1uhaviv6Ve2nUfe/93kQ/wBDTg1BjiIHn1f5HD801n6Z4KOER5h0cRKiM6NhaWKJmU1nwquiV4HmpW9BKUH4/OjnkCgkmNd8XoHrv8mUMvQ72g9Pk1XB7IoOFeHn7adT4FweH4/wEV9DEhgIsbU1jo+ojd+tbuHzSwr0+UtArLMYyp0r0L6C9ku4BUHA9oDtWHF7BRSCApUcK2FFmxXwcPDQeiwyfrlF1+3M7HCw10HIlXL4HvdFQnYCmpdujl/b/frehgcfyqo7q7DpwSZUdqqMP7z+eOP99XHSY/Q72g8KQYGVbVeifVnjX/b2X6IUlPA+6I3QtFBMbzQd/av1z3P/0+Sn+PzI55ALcixrvQydPAq/EZK+XIq4hDHnx8DO3A5nPz2r81LWF0uWIGnz71p/nsm4cgURw0dAZGaGCieOw7xMGZ2O/z7Xoq5h5NmRsDCxwKymszTKgD4Vegpnw1WlUXpU6KEO9ufn9ovbGHxyMABgU6dNaOzWuEBzJ8NKl6bjecpz1HWtW6RLUnzsNI1HMXOtqHHRvqmBiVgEKzNV9lqmhE0N/utynjyFIJFA7OAAc49yWu1r6uoKmxbNAQCphw8XxvQ0lnZEdXzrJo31HlgDALMSJVBu5w5YNfgEyvR0hA8dhrQzZ5B66BAivhkFITsbNs2bo9y2rQYPrAGAfadOcN+0EWJbW2Tfuo0w3wGQvXhRoDFlUVFI2q7qTFx88mSdA2upklTsCtyFz458hn5H+2Hf033IkGWoiymf6nMK6zqsw8q2K2EqNsXJ0JP49e6vBZr7v4WlhWHF7RXouL8jxl0Yh4sRF6EQFKjrWhdzm83Fhb4XMLvZ7AIH1gCglmstLGq5CCKI8L8n/8POwJ0a7ysIAv54+ge+PPElojOjUdauLHZ221nkAmuAKqNgVN1RAIDFNxcjITuhQONtebgFT5OfwtHCEdam1ghKDcKN2Bs6jWXq6opyO7bDunFjKLOy0OGX6+h6U4net1W/wzvaABOufIvlt5drlV2YKcvEt5e+xdJbS6EQFOhWvht2ddvFwNp/2IBqA1DTpSbSZemY/ddsjD43GgnZCajsVBlLWy0tEoE1ABhcYzBszWzxNPkpToedznOfXCnHzGszoRAU6FiuIwNrRkgsEqsDanse74FSeLVSRaFUYPZfsyEX5Gjn3g4dy3U01DR10rJMS7jbuSNdmo6jwUd1HqfY11/DxMEB0udBSDlwQKN9BIUCcYtVHZadBgwolMCaIAhYfXc1AKBflX7wquiFLuW75Pu1vM1yTG4wGSYiExwNPgrf47751tyTKCSY/ddsAEBvz94MrH0E7MztUK94PQbWjAQz14qa6+uAk9OAal7A55qfsDX46QwSMqQ4Mb4lqrl95D8jeq+kHTvxYv582LRqibIbNmi9f+qxY4j+djLMSpdGxTOnIRJ/+Bi8IAgI7tIV0rAwuC1aCMdevQrtWMqcHERNnoyMs+cAsRhQqj6w2nt5odT8n3TqUFqYcp48QcSw4ZDHx8O0lBvKbtoEiwoVdBoraspUpB05AusmTVB2y+9a/+GOyojCqjurcC7sHKRKVXdCc7E52pdrj96evdGoZKM3sicOBx3GD1d/AADMbTYXPp4+Os0918WIi9j6aCtuv7itvs3Z0hk9K/aETyUfVHDU7WejidyMFhFE8K3mq9HV9pDUEJwJOwMAaOveFj+1+KlIF5WXK+Xof6w/ApMC0bFcRyxvs1yncYJTg/Hp4U8hU8qwoMUCPEh4gD2P96CNexusbrda5/kppVJcHt4bJW68KqVgWb8e9k+oi+2BqsBxw5INsbjVYhSzKvbesYJSgjDhwgSEpoXCVGyKqQ2nol+VfvxAS3iS9AT9jvaDXFAFal2tXLG7+271cp2iYt29dVjrvxYe9h7w8/ZTB/62PNyC5beXw97cHod6Hcr3tUBFU6YsEx3+6IAMWQbWdViHFqVbAAC2P9qOJbeWqLMrjbGL4I6AHVh8c7FWGc1JOUk4H34etua2aOveFhYmFkjavh0vFiyESbFiqHTqZL6lLlL270fMjzMgdnBApdOnYOLgoK+HpHY+/DzGXxgPK1MrnOxzEs6W2l2wvRV7C1MuT0FCdgJszWzxU4uf3hkg/+XOL9j4YCOKWRXDQe+DcLDQ/+Mh+i/SNB5VNC630SvOLzPXErXr0mZjYYqEDCky2TH0P0+XZgavs2vfHmJbW8iiopB169YHK+D/umx/f0jDwiCytoZ9x8K9Aiu2tESZlSsRO3ceUvapOqg5Dx6M4lOnGCSwmB/LKlVQbs8eRAwbBmloKML6+8J9/W9aP9/ZDx4i7cgRAEDxKZO1DiAoBSUmXJiAx0mPAQBVnKrAx9MHPSr0eO+HuZ4VeyIiPQK/3fsNc/+ei5I2JXUquixTyLDs9jJ1hzGxSIzmpZqjt2dvtC7TGmYmhV90fmD1gQhPC8e+p/u0yl4Ti8QYV28cvqr5VaEsjdUnU7Ep5jafi35H++FM2BmcCz+nddaLUlBizl9zIFPK0KJ0C/So0AM1i9XEnsd7cCniEiLSI+Bu567T/OQmwOwuafCWidDljupaYcnvvsOUOnVQu3gdzLw2Ezdjb6Lvkb5Y1mYZ6hWv99ZxToScwKy/ZiFbno0S1iWwrM0y1HHV7T2UPj5VnKtgSK0h2HB/A6xMrfBr+1+LXGANUBUu3xW4C6FpoTgWfAzelbwRnhaONf6qupqTG0xmYM2I2ZjZoFelXtgZuBO7AnehRekWiEyPxK/+qkzwSQ0mGWVgDVA1EFl9d7U6o7mJW5O3bqdQKvBX9F/we+6HCxEX1JnJ9ub26FGhB3w6e8FsZ1nIwsOR+PsWuL5sCvU2yqwsxK/6BQBQ7JuRhRJYUwpK9fMzoNoArQNrANCgZAPs67EPky9Nxp24O5hwYQK+qvkVxtUblydz9knSE2x5uAUA8EPjHxhYIzIABteKmteXhQoCoOEJr/XLjqGZUi4L/a/L9vcHoH0zg1xiS0vYd+2ClD/2I/XQIYME13IbKth37Ki3AvvvIzI1Rck5s2FVvx7E5uaw79at0I9ZEOZlSqPc7l2IGPmNquvp4K9QZtVK2LZqpdH+giAgbolqGYSDd09Y1dC+i9TZsLN4nPQYNmY22NRpE2q41NA4QDeqziiEp4XjeMhxTLo4CTu67tC4aycAxGbGYvKlybgXrwokf1n9SwysPvCDn+yKRCJMbzwdFR0r5rtUI5dYJEYnj07vDPIURVWdq2JwjcHY/HAz5l+fj4YlG2qVbbfvyT7cibsDK1MrzGgyAyKRCOUdyqN56ea4FnUNex/v1bn49snQk0iUJuNYLzcMHjAJpuYW6kBzZ4/O8HTyxMQLExGcGowhJ4fg2wbfwrear/p39d9B2sZujbG41WKdToDo4zayzkgUtyqO2q61Uc2lmqGn81a25rYYUnMIVtxegXX31qFr+a6Y8/ccSBQSNHFrUuC6iWR4X1T9ArsCd+Fq1FWEpIZgwY0FyJZno2HJhujj2cfQ09OZnbkdvCt6Y++TvdgVsOuN4FpkeiQOPj+Ig88P4kXWq5IY1V2qIzknGTGZMdj9eDd2P96NT1uXQt8dQOLmzXD8vC/Mir894Ji4ZQvk8fEwK1MGTv37v3WbgjoVegrPkp/BzswOg2poVkv7bVytXbGp8yasvL0S2wO2Y8vDLXiY8FCdlS1XyjHzr5mQC3J0KNsBHcp10OOjICJNcVloUaOQAT+VAAQFMCkQsC+l0W6f/fYXboYmY61vfXSr9eE7zVHRIE9IwLMWLQGRCJX/uaFzp7ysW7cQNuBLiG1s4Hn1CsRW+RdQ1RelRIJnLVtBmZaGslt+h03Tot9K3lCUmZmIHD8BmVevAiYmcJv/k0ZLaNPPX0DkqFEQmZuj4skTMCul2ftMLoVSgd6HeyM4NRgj64zE6Lrad6KSKqQYfno47sTdQSmbUtjVfZdGGRU3Ym5g6uWpSMpJgp2ZHRa0XIA27m20Pj5pJ0eeg8+OfIbQtFD08eyD2c1ma7RfbGYsvA96I0uehWmNpqk73gHAlcgrGHVuFOzM7HD2M+2LWAuCgH7H+iEgMQDj64/HsFrD3rpdliwLs/6ahZOhJwEAXTy6YE6zOUiTpuUJ0g6vNRyj646GidhEq3kQFSVZsix0O9ANiTmJaOzWGDdibsDK1Ap/9vxT5wxRKlrGnBuDS5GXUN6hPEJSQ2BhYoEDPQ+grH1ZQ0+tQEJSQ9DzYE+IIMIxn2MoblMc58PP48CzA7gec129nYOFA7wqeMHH0weVnSpDoVTgesx1HHh2AOcjzkOukGHeDgWqRAHPWpaD27x5aPB/9u46vMmze+D4N1Z3L/VixaV40Y29Y/hcmDHfYMZcmf3m+m5jvGPuzGHYhru7Fai7u6ZN8vvjaQodLVTSpi3nc129kibPcz8npUB7cp9zfIfUeQOwOjubmEsnYSorI+Ddd1rlTdVqYzWXL7mchKIE5g6cy90D7rbIun8n/M3zW5+nrLoMb3tv3hn/DgezDvLO3ndwtnFmyYwleDt4W+RaQgiFDDToqDQ6cKv5zzE39tzHnqF255qUhV7QzCWhtt26NjuxBmAfGYkuMBBjaSnFa9ZaKrxGKVm/AWNREVo/PxwssGsutzyXtJI0C0RWV3JxMgUVBRZft7GMJiNHy+Jw++BNXKZPA4OB9CefIvfzz885st1UXU3W228D4HHLLU1OrAGsiF9BXGEcLjYu3NT7pmbFb6Ox4YMJHxDiEkJaaRoPrHuA8uryhuM2mfj88Ofctfou8iry6Onek8VTF0tirY3Yae2YP3I+AL+d+o3dGbvPe47JZOLlHS9TVl3GAO8BXNfzujrPRwVEEeISQnFVMX/F/tXkmA5mH+RY7jFsNbbn3LHhoHPgzbFv8uSwJ9GqlIEa1y2/jmuXXcvB7IM465z574T/8sDgBySxJjo8B50Dd/a/E1DejACYM3COJNY6EfNgg/jCeADuG3hfh0+sAbU7mk2YeGTjI1z080U8vulxdqTvQIWKkf4jeWvsW6y7eh1PDHuCHu49ANCoNUQFRPHO+HdYe/VaHhv6OOunK1+PrlsSmf/dbKb+MZXPDn9GYWUhANkffoSprAy7Af1xvuyyVnk9y+KWkVCUgJutGzf2vtFi614aeik/Tv2RcNdwssuzuW3VbbUDEx4d8qgk1oSwIkmutUfNmBjqZCvJNdHyklAzlUqF64wZwOkSzbZivp7r9OmoNC37RTeuII7pf05n8u+T+fro1+dMOjWW0WRk4cGFTPl9CjOWzCClOKXFazZVkb6IB9c/yA0rbuDiP/7Dgqla9Ncq77pmvfU2Wa+/gclorPfcgl9/RR8Xh8bdHc+77mzytauMVXxy8BMAZved3aJm/G52bnx88ce42rpyOOcwT29+us4ENDPz631/3/sYTUZmdJ3Bd5O/I8hFfllsS0P8hnBNj2sAeGHbC1RUV5zz+JXxK9mUsgmdWseLo148K3GlVqm5PuJ6AL6P/r7Jfz/NpZxTwqfgbud+zmNVKmXoxBeTvsDH3of4wvg6SdoJwROadG0h2rOrelyFr4MvAH09+3JjL8v9Yi+sb6T/SMJdlWE9vTx6cXPvm60ckeXMilB2Nx/PO06Rvgg/Rz/uGXAPK69cyaf/+ZRJYZOw0TQ8aMrDzoOb+9zM+w+swjhuOGoT3LxBRVJxEh/s+4BbV91KyYljFPz6KwC+jz/eKkNrqgxVLDy4EIDb+96Oo86yLU7CXcP5ccqPTAqdRLWpGr1Rz3C/4VzerWVDooQQLSPJtfbIo2a6XV5Tdq4pv7RIz7ULW/mBlg0zOJPrjOkAlG7bRlVmVovXa4zq3FxKNm+uc/3myi3P5b6191GkL8JgMvD2nrd5ZOMjlFaVNnvNwspC5q6dy8cHPsaEibyKPOasnUORvqhFsTZFdF401y27jg3JGwCoMFSwNP4vbgz/h78mewKQ9/XXpD3+BCa9vs65hpJSsj9UGut6zZnTrN2NS2OWklycjIedBzdEtLxHSYhLCB9M+ACdWseapDW8v/f9Os+bp/StT16PTq1j/sj5vBz1MnZauxZfWzTdw5EP4+PgQ1JxEgsOLmjwuPyKfF7f9ToAd/W/i65uXes9bkbXGTjqHIkvjGd72vZGx5FRmlE7dbUp34eDfAaxeNpipoRP4cZeN/Lt5G8lSSs6HVuNLS+NeonRAaN5bcxrsiOzk1GpVDw9/GnGBIzh9bGv12lq39FFBUQxq9csJodNZuHEhay6YhVzBs4hwCmgSeuoVCq6P/0iaLUMiKnmbYebcbd1J6Yghv0vPAJGI86XTMQhMrJVXscfMX+QWpKKl70X10Zc2yrXMO/Kfm7Ec0wMnsjLUS/LdGshrEySa+2RR9N3rjnW7Fwr08vOtQuVqbqa8iNHgJbvXAOwCQ7GPjISjEaKljW9ZKs5ipYvh+pq7Pr1w7Zr/b+MN0ZFdQUPrH+A1JJUgpyDmBc5D61ay+rE1Vy37DpiCxqfuDY7lnuMa5ddy+bUzdhqbHlsyGP4OPgQVxjHvPXzqDJUNTvexloSs4QbV9xIcnEyAU4B/DT1J76b/B1Xdr8SB60D3w4o5MNpaqrVULRsGQdnX4e++HTiL/fzzzDk5mITEoL7tdc0+fp6g56Fh06/E9vUHlkNifSN5OWolwH48uiX/HxCmdq6NHZp7evt4tiFby/7lqt6XCU/PFqRk40Tz414DoBvjn7Dsdxj9R735u43ya/Mp5tbN27ve/s51zM3Wf8++vtGx/HziZ8xmAwM8R1CT4+ejX8BgJe9F6+PeZ0nhj2Bvbbt+kkK0ZZGBYzik4mfEOoaau1QRCsY7j+cBRMX1O5g6yzUKjVPDnuSN8a+QVRAVIsSwzYhIbhfr+yO7v7Ddp4e+iR9Eox47U8AjQbvefMsFHVdFdUV/O/g/wCll2dr/j+jUqm4puc1vDfhPfydpOe2ENYmybX2yLxzLbcpybWanWuVsnPtQlV58iSm8nLUzs7YhFvmhy3z7rHCP/+0SEnl+RT+WVMSOnNGs9cwmow8veVpDmUfwsXGhY8v/pjZfWfz1aSv8HXwJaEogeuXX8/K+JWNXvP3U79z04qbSC1JJdApkO8mf8fNfW5mwcULcNA6sDNjJy/teKnVvkaVhkpe3P4iz259lkpDJWMCxrB46mL6ePZhgPcAXhj1AuuvWc9Lo16ieMJg3rhKTYUObPceZ+3M0Xy0/lXiY/aS9+VXAHg/+ggqna7Jcfx68lcySjPwcfCx+DuxU8Kn1A5GeHXnqzy47kGe2fIMFYYKorpEKa/Xq+lTTYXljQ8az6TQSRhMBuZvm0+VsW5ieUvqFpbFLUOtUvPSqJfQac79vWYuDd2UsqlRU1crqiv45eQvAHUGJAghhBBn8rrvXtTOzlQeP87wgxXct0Upz9w9wgNtSOv0qfvl5C9klWfh5+jHVT2uapVrCCHaJ0mutUdn9lxroG/SvzlKz7VWV15dzpVLr2T2qtltkmhqqjJzv7X+/VGpLfNX22XSJFQ2NlSeiqHy+HGLrNmQipMnqTh2DHS6Fk1t+mDfB6xOXI1WreX9Ce8T5hoGwADvAfw87WdG+I+gvLqcxzc9zuu7Xj/njrOK6gqe3/o887fNR2/UMz5wPIunLSbCIwKAnh49eWvcW6hVav6M+ZPPDn/W7LgbklaSxi0rb+HXk7+iQsWcgXP46OKPcLV1rXOcg86By7tfzreTv+WleX+x55npFDuoCE2totfT37L54ZswVVRgP3gwzhObPqK9vLqcRYcXAXB3/7ux1dha5PWd6e7+dzO963QMJgPrktehQsW9A+7l44s/xs3OzeLXE8335LAncbV1JTovmq+Pfl37eGlVKS9tfwlQEl/9vPudd60QlxDGBIwB4Kfon857/Mr4lRRUFuDv6C8DLYQQQjRI6+6O1913AZD5wot4JxdTZgsLh+TxQ/QPFr9eWVVZ7c+C9/S/55z94YQQnU/nKdLvTNyCQaWB6nIoyQCX80/zczRPC5WyUAAK/1pGycaNjT7erlcvPG65GZW24b8Sy+OWczL/JAAn8k/UJljaC0sNMziTxsUF54kXU7RiJQV//olf794WW/vfzIMMnMaNRet+7ubkDfn15K98ceQLAF4a9RJD/YbWed7DzoOFExfy8YGPWXR4Ed8f/54jOUd4e9zb+Dn61Tk2uTiZRzY8wvG846hVauYOnMvt/W5HraqbuBwbOJanhj3F/+38P/67/78EOgdyWZhlJk9tSd3Ck5ufpLCyEFdbV94Yo5RJnE+4Wzjh171B6dA7iLvtVrpk5tElX0kIx944mtBmlFUujl5MTnkOAU4BrdYwV6VS8cLIFyjWF3M09ygvjHyBMYFjWuVaomU87T15fOjjPLPlGT458AkTgycS6hrKf/f9l/TSdAKcApg7cG6j15vVaxabUzfzR8wfzB00t8HmzyaTqfYXousirutUvYaEEEJYnvtNN5H3ww9Up6UDUHTNRIodNvDh/g+ZEDSBQOdAi13rh+gfyKvII8g5iOndWtY7WAjR8cjOtfZIo1MSbAC5jesNdXrn2oVdFmoymch65x3SHnuMomXLGv2R9dZbpDz0EMaK+qffmUym2sl0AJtTNrfVS2q08oM1wwwGtnyYwZnMU0OLli3HVNU6fcVMJhPFq/5Wrje9eT+MbEvbxis7XgHgvgH3Ma3rtHqP06g1PDD4AT686EOcdc4czD7ItcuuZWf6ztpjNqVs4tpl13I87zjutu4snLiQO/vfeVZizey6iOu4qfdNADy75Vn2Z+1v1mswM5qMfHLgE+5bcx+FlYX08ezDz1N/blRi7UyOXbsT8fMf2PZUelJt7aXisdxF7Mvc16R1SvQlfH7kcwDuGXDPecv8WkKn0fHfi/7LmqvWSGKtnZsWPo1RXUahN+p5YfsL7M/az4/RPwIwf+T8JvXkG9llJKEuoZRWlbIkpuEJxfuy9hGdF42dxo4ru1/Z4tcghBCic1Pb2uLzsNJfTevvz5h5bxDpG0l5dTkvbbdcS49ifTFfHvkSgHsH3ItO3Xo/Kwkh2id5y7e98uwK+fHKxNCw8/+C6WieFnoBl4WaqqpIf34+hX/8AYD7rFnogs7/bpSxuITcRYsoWbOWpDvuIGjBAjQuLnWO2Z2xm5iCmNrPt6Ru4c7+d1r2BbRAdV4eVYlJgFIWakmOUVFovLww5ORQsmULzhMmWHR9gKrERKpSU0GnwymqaQkkgFP5p3hkwyMYTAamhk/lngH3nPec8UHjWTx1MQ9veJgT+Se4a/Vd3D/ofsqry/n00KcA9Pfqzzvj3zlrV1t9Hol8hJTiFNYnr+eBdQ/w/eTvCXZpej+PgooCntzyJFtTtwJwdY+reXLYk80uLdD5+hDy/fcUb9nMMZZRlbmRB9c/2KT4vjv+HQWVBYS6hDI1fGqz4mgqGVrQ/qlUKp4f+TyXL7mcvZl7uXfNvZgwMaPrDEZ2GdmktdQqNTf0uoFXd77Kj9E/cl3EdfUms81vckwJn3JWabQQQghRH5epU1A7OmLbrStaewdeGPkCVy69ku3p21kau5QZ3Zrf69fs22PfUqQvItw1nMlhzW9vIoTouGTnWnvVxImhDuada/oLc+easbyclLn3K4k1jQb//3sFv+eexfPWW8/74X3/XII+W4TayYnyPXtJnHUjVZlZddY3/0I3LnAcAAezD1JYWdjmr7Mh5l1rNuHhaFwt+wunSqvFdaqSUDEPHLC0ki1KIslh8GDUjvWXgzUkpzyHOWvnUFJVQqRvJC+OerHRiZkglyC+m/wdM7rOwGgy8sG+D2oTa9f1vI6vJn3VqMQaKDviXh/zOr09e1NQWcCctXOa9D2SX5HPt8e+5Zpl17A1dSu2GlteiXqF50c+3+KeHRonR9wmTeKVi9+kj2cfCioLuG/tfRRUFJz33MLKwtqeWvcNvE/K8EQdAU4BPDj4QUDpt+Zp58ljQx9r1lrTu07HSedEQlEC29K2nfV8ekk665LWAXBDrxuaH7QQQogLikqlwvmiCdgEK28qhrqGcu/AewFlunVOeU6L1i+oKOCbY98AMGfgnBZNORVCdFySXGuvaieGNq4s1Mn2wt25Vp2fT9KtsynZuBGVrS2BH36I25VNKxdyHDaMkO+/Q+vtTeWpUyRefz2VcfEApBSnsCFlAwDzhswj3DUcg8nA9vTtln4pzVZ+wFwSOrBV1jdP7yxZtw5DoeWTiqVbtgDgOLppu9bKq8uZu3Yu6aXphLqE8sGED5qciLLT2vFy1MvMHzkfnVqHncaO18a8xjMjnmly+aODzoGPLvoIP0c/EooSeHD9g+gN+gaPN5qMbEvdxqMbH+XiXy7mzd1vkl6aTpBzEN9P/t4i76SeFd/FH+Hv6E9iUSIPbXjonPEBfH30a0qqSujm1o1LQy+1aDyic7iu53UM9hkMwDMjnmn2jjJHnSMzu80EqFOGb7b4xGIMJgPD/IbRw71Hs+MVQgghbulzCxEeERTpi3h91+stWuvLo19SWlVKhEcEE0OaPjRKCNE5SHKtvfJs4s61moEGZRfYQIOqtDQSZ91I+cGDqF1dCf7yS5wval7Zol3PnoT8+CM2oaHKujfcQPnBgyw+sRijyUhUlyjCXcNrp9ptSdliyZfSIqeHGVi235qZXUQEtj17YqqqomjlKouubdLrKd21CwCn0aMbfZ7BaOCpzU9xNPcobrZufHzxx83+pV6lUnFVj6tYecVKVl65skWlj94O3nx88cc46hzZm7mXF7a9cFY/j/SSdD458AmTfpvE3Wvu5u+Ev6kyVtHLoxdPD3+aX6b9Qk+Pns2O4Vy87L34+OKPcdI5sTdzL/O3zW+w30hueS7fHf8OgLmD5jbYc05c2DRqDZ9M/ISlM5dyScglLVrr+ojrUaFiS+oWEgoTah+vqK7g11O/ArJrTQghRMvp1DpeHPUiGpWGvxP+Zn3S+matk1Oeww/HlUE7cwfKz0pCXMjkb397Zd65lhcPRuN5D3eqKQstuYB2rlWcPEnC9Tegj4tD6+dH6Pff4TB4UIvWtAkMIOSH77Hr3x9DQQGJt97KiRU/Aad/oRsdqCSAtqRuwWg6/59NazMZDJQfPgyA/YCBrXYd15kzASj880+Lrlu2/wCmsjI0np61jfcb472977E2aS06tdIAvzn9zf7N19EXL3uvFq/Tw70H74x7B41Kw19xf7Hw0EL0Bj1/J/zN3avv5tLfLmXBwQWkl6bjbOPMdT2v4+epP/PztJ+5PuL6BiclWkp39+618S2LW8bCgwvrPe6LI19QXl1Ob8/eXBR0UavGJDo2B50DYa5hLV4n2CWYsYFjAWqHIwCsiF9BYWUhXRy7MD5wfIuvI4QQQvT27M3NfW4G4JUdr1CsL27yGp8f/pwKQwX9vfrX/v8lhLgwSXKtvXILBpUGqsuhOP28hzvUDDSoqDJiMFpm6k17VrZ3L4k33kR1ZiY23boS+uMP2HbrZpG1tR4ehHz5BY6jR2Mqr+D+n0q4/JQ7owOUpNpgn8HYa+3JrcglOi/aItdsicpTpzCVldU2am0trlOngFpN+YED6BMSGnXOqfxT/O/g/yirKmvwmNqS0KhRqNSN+yfp5xM/8/UxpQ/YK1GvMMinZUnV1hAVEMUzI54BYMGBBUz4eQKPbnyUbWnbMGFimN8wXh/zOuuuXsczI56hl2evNo1vVMAonh3xrBLfwQX8FftXneczSzNZfGIxAPcPul8GDIg2Y34j48+YPynRl9SZ1nx9xPXSy0YIIYTF3DfgPoKdg8kqz+K9ve81+jyTycTO9J21PyvNHTRXflYS4gInybX2SqMD9xDlft75+6452p5uMl7ayUtDi9etI+m22zEWFWE/aBCh332Hzt/fotdQOzoSuOBj9g9yQWuE63/NJv+LrwCw0dgwwn8EoOxeszZzvzW7/v1QaVrvl06tt3dtT7TCpUsbdc6zW5/lowMf8ebuNxs8pmSr8jVsbEloXGFcbW+M+wfdz+Tw9juR6eoeVzO7z2wAivRF+Dj4cGe/O1lx+Qo+v/RzpoRPwU5rZ7X4rupxFbf1vQ2A57c9z56MPbXPLTq8iEpDJYN8BhHVpekTXIVorpH+Iwl3DaesuowlsUvYk7mHk/knsdfac3n3y60dnhBCiE7ETmvHC6NeAOCXk7+wO2P3OY/PLsvms8OfMe3Padzxzx1UGasY4juk9ncDIcSFS5Jr7Vltaej5+67ZatVo1Mq7JWWVnXdiaP4vv5Ay935MlZU4jR9P8Befo3Fza5Vr7czZy+uXlrJypNIgP+utt8h8/Q1MRmPtLrbNKZtb5dpNcbrf2sBWv5ZbbWnoEkznKVeOyY/hWO4xAH479Ru70neddUx1Tg6Vx44D4Dhq1HmvbzQZeXHbi1QZq4gKiOLOfnc28RW0vYciH+LV0a+y4OIF/HPlPzww+AGCXIKsHVatBwc/yCUhl1BtrObB9Q8SXxhPakkqv536DZBda6LtqVQqbohQdq/9cPwHvjum9P2bGj612X0VhRBCiIYM9RvKld2VYWgvbHuBiuqKOs9XG6tZn7Se+9fezyW/XsIH+z4gsSgRB60DV3a/krfGvSU/Kwkh0J7/EGE1Hl2BNY2aGKpSqXC00VBUUW31vmtlu3eT/eFHmPTnnkLYVCaDgYqa3mKuV16B/4svotK23rfwD8d/wKRSUXnPdfiM8SPrzTfJ++orDEVFjHl6LgCHcg5RWFlo1V/4yg8qO9cc2iC55nTRRaidnKhKS6Ns9x4chw9r8NilccruNp1aR5Wxihe2v8Bv03/DXmtfe0zpdmXiqm2vXmi9zt/r7OcTP7Mvax/2WnueH/F8h/hBRq1SM63rNGuH0SC1Ss2ro18lszSTQzmHmLN2DhEeEVQbqxnhP4KhfkOtHaK4AE3rOo0P9n1AUnESScVJALUJNyGEEMLS5g2Zx6aUTSQVJ/HJwU94OPJhEgoT+CPmD5bGLiWnPKf22EE+g7i82+VcGnopDjoHK0YthGhPJLnWnjVxYqijrZaiimqrTwzN/uhjynadvUvJUjzvvhvvhx5s1cRKclEyG1M2AkqPH8/hoWi9PEl76mkKf/+doMsuo5tbN2IKYtietp1JYZNaLZZzMRQUoI+PB8Cuf/9Wv57azg6XyyZR8MuvFC5d0mByzWA0sDx2OQDzR87nw/0fklyczCcHPmHekHm1x5n7rTmNPn/ZYUZpRm0vjAcHP0gXpy4tfTmihp3Wjv9e9F9mrZhFcnEyycXJgNI/RAhrcNA5cEX3K2p7K47wH0E3d8v01RRCCCH+zcXGhWdGPMND6x/i66Nfsy9zHweyD9Q+72HnwfSu07m8++WEu4ZbL1AhRLslybX2rAlloXC675o1d64ZS0sp27cPAP//e8XiJZtaPz/s+/Sx6Jr1+fHEj5gwMTpgNKGuoQC4Tp9OxdFj5H39NVlvvcXoZ0cTUxDD5tTNVkuumXet2YSGonV3b5Nrus6cScEvv1K86m+Mzz6L2t7+rGN2pu8kqzwLV1tXLgu7DDdbN+aum8vXx77m0rBL6ePZB5PRSMnWbQA4Rp2735rJZOLlHS9TVl3GQO+BXNfzulZ5bRcyT3tPFly8gBtX3EhxVTHjAscxwHuAtcMSF7DrIq7jm2PfYMLErF6zrB2OEEKITu7i4Iv5T8h/+CfxHw5kH0CtUhPVJYoru1/J2KCx6NQ6a4cohGjHJLnWnp2ZXDMa4TyTFB1rJoZas+da6e7dUFWFLiAA1yuu6BBle/9WVlXGH6f+ADjrFzqve++h4I8/qDx5kgmHx/CVVhlqYDQZUavavoWhOblmP6DtkiD2gwejCwykKiWF4jVrcZ029axjzCWhk0InYaOxYVzQOC4LvYyVCSuZv3U+P079EcOJGAw5OagcHLAffO5pnyvjV7IpZRM6tY4XR70o0wJbSbhbOAsmLuDH6B+5f9D91g5HXOACnQN5dsSzZJRmMDZwrLXDEUIIcQF4dsSzONk4EeAUwPSu0/Fz9LN2SEKIDkIGGrRnbiGg1kJ1BRSnn/dw8841a04LLd2yVYll9OgOmVgDWBq7lJKqEkJdQhnVpW6TfY2bG1733AOA09d/4Wa0J68ij+O5x60R6ulhBoMGttk1VSoVrjNmAFC4ZMlZz5dWlbI2cS0AM7rOqH38iWFP4Grryon8E3x99GtKt9Z8rwwditrGpsHr5Vfk104Hvav/XYS7yVb81jTQZyBvjH2DQOdAa4ciBNf0vIYHBj9glTcvhBBCXHjc7dx5cdSL3NX/LkmsCSGaRH5abc80WnALVu7nnX+ogYNNTXLNmjvXanpoOTaih1Z7ZDQZ+SH6B0DptVbfL3TuN85CFxCAISuLO476ALA5te2nhpoMBsoPHgLaZlLomVxnTAegdNs2qjKz6jz3T8I/VBgqCHUJpa9X39rHPe09eWLoEwB8cuATcjasAZRE7Lm8uftN8ivz6ebWjdv73m7JlyGEEEIIIYQQQrSYJNfaO4+aoQaNmBjqZFtTFmqlnWv6lFT0CQmg0eA4YoRVYmipHWk7iC+Mx1HnyIxuM+o9Rm1jg/e8hwGIXJOMa4mJLalb2jJMACpjYzGWlqJycMC2W9s2+rYJDsY+MhKMRoqW/VXnub/ilM+nd51+1u7FqeFTieoShaqiEv1+JTF4rkTs5pTNLItbhlql5qVRL6HTSK8LIYQQQgghhBDtS7OSax9//DGhoaHY2dkxfPhwdp1nMuT7779Pz549sbe3JygoiIcffpiKiopmBXzBacLEUAcrDzQwl/nZDxiAxtnZKjG01PfR3wNwebfLcdQ5Nnicy+TJ2PXvj6ZCzzVbjBzKPkRBRUEbRamoLQnt1w+Vtu3bJ5p3rxX++ScmkwmAtJI0dmfsRoWKqeFn92JTqVQ8P/J5BqXaoDWY0Pu4YRMaWu/6pVWlvLTjJUDpfdfPu1/rvBAhhBBCCCGEEKIFmpxcW7x4MfPmzWP+/Pns27ePAQMGcOmll5KVlVXv8T/88ANPPvkk8+fP5/jx43z++ecsXryYp59+usXBXxCaMDG0dqCB3jploR29JDSxKJFNKZtQoeL6iOvPeaxKpcL38ccAuOigiS45RralbWuLMGtZY5jBmVwmTUJlY0PlqRgqjh0D4K9YZdfaML9h+Dv513teF6cu3FislItuCywjsyyz3uM+2PcBGaUZBDgFMHfg3FZ4BUIIIYQQQgghRMs1Obn27rvvcueddzJ79mx69+7NwoULcXBw4Isvvqj3+G3bthEVFcUNN9xAaGgo//nPf7j++uvPu9tN1GhCWaijFXeumaqrKd2xAwCn8/TQaq9+iv4JgDGBYwh2CT7v8Q5DhuA08WI0RrhxvbHN+66VH6hJrrVxvzUzjYsLzhMvBpTBBiaTqbYkdFrXaec8N+BYDgB7Qqt5ZccrtTvfzA5kHaj985g/cj4OOgdLhy+EEEIIIYQQQlhEk5Jrer2evXv3MnHixNMLqNVMnDiR7du313vOqFGj2Lt3b20yLS4ujhUrVjB58uQGr1NZWUlRUVGdjwuWR5hymx8PRuM5D3WsGWhQZoXkWvmhQxiLi9G4umLXp0+bX7+lSqtK+SPmDwBmRcxq9Hk+8x7BpFYTGWMiZ+sGjKZz/xlZiqGoCH2sknC1H2idnWtA7dTQomXLOZi+l8SiROy19lwSckmD51SlplIVHw8aNcfDdGxM2ciqhFW1z+sNeuZvm48JEzO6zmBkl5Gt/jqEEEIIIYQQQojmalJyLScnB4PBgK+vb53HfX19ycjIqPecG264gZdeeonRo0ej0+no2rUr48ePP2dZ6GuvvYarq2vtR1BQUFPC7FzcQkCtheoKKE4756Gnd661fVlo6Ral35rDqJGoNJo2v35LLYlZQmlVKWGuYU1K5tiGh+F23TUAzFxVwLHsI60VYh3mKaG64GC0Hh5tcs36OEZFofHywpCXx54liwCYGDzxnDvNSmp78w3kpqF3A/D6rtdre9Z9euhT4grj8LTz5LGhj7XuCxBCCCGEEEIIIVqo1aeFbtiwgVdffZUFCxawb98+fv/9d5YvX87LL7/c4DlPPfUUhYWFtR/JycmtHWb7pdEqCTY4b2mooxWnhZZsVfqtdcSSUKPJyA/RPwBwQ8QNZ024PB+fufejt9MQngEnfv68NUI8S22/NSvuWgNQabW4TlUGF9iuVnavnq8k1JyIdYwaxR397qCbWzfyKvJ4c/ebnMw/yeeHla/hU8OfwtXWtRWjF0IIIYQQQgghWq5JyTUvLy80Gg2ZmXUbkGdmZuLn51fvOc899xw33XQTd9xxB/369ePyyy/n1Vdf5bXXXsPYQJmjra0tLi4udT4uaI0camAuCy1t47JQQ0EBFYeVHVuOUR1vmMHW1K0kFiXipHNietfpTT5f6+FB/tUXARDw/QaMlZWWDvEstZNCrTTM4EyuM5XS0AEnqgjDm2F+wxo81lRdTWlNCbnT6NHoNDpeGPUCKlT8FfcX96+9n2pTNROCJvCfkP+0SfxCCCGEEEIIIURLaJtysI2NDZGRkaxdu5aZM2cCYDQaWbt2LXPn1j/Nr6ysDLW6bg5PU1M2+O8m5qIBnl0hZjXknXvnmkPNzrXSNp4WWrp9OxiN2Hbvhq6BJGt79n309wBc3v3yZjfOj7jnUU79uRqvfD2pX3xK0L33WzLEWvkV+fzvwEIm7d2BLfCJYR25G/Y2eLytxpYbe99IH8/W64NnFxFBToATXqkl3JzRDY264bLg8kOHT/fm66tMDB3gPYBZvWbx3fHvSCtNw0nnxDPDn2nyDkIhhBBCCCGEEMIampRcA5g3bx633HILQ4YMYdiwYbz//vuUlpYye/ZsAG6++WYCAgJ47bXXAJg2bRrvvvsugwYNYvjw4cTExPDcc88xbdq02iSbOA/zxNC8+HMe5mRrnZ1r5h5ajqM63q61tJI0tqZuRYWK63te3+x1/DyDWTS5C1ctTqNw0Rf4X3cjWnd3C0YKlYZKHlj3ANnH9zOj3EClFn427MSYeO4k1MaUjXw3+TvCXcMtGo9ZbnkuqyIquDEV+u7JPuexpVvr7813/6D7WZ+8ntSSVOYNmYevo29DSwghhBBCCCGEEO1Kk5Nr1157LdnZ2Tz//PNkZGQwcOBAVq1aVTvkICkpqc5OtWeffRaVSsWzzz5Lamoq3t7eTJs2jf/7v/+z3Kvo7DxrkmtZx855mIMVykJNJtPpHlodsN/asrhlAAzzG0aQS8sGZzhNnUL8hkWEZVaQs+AT/J5peGhHUxlNRp7d8iwHsg9wWaYtUEZVzxCeHjn7nOctjV3KoZxD3LfmPn6Y8gMedpYffrAyfiUbe5u4YR2oD0dTGR+PbVhYvceWbqnpzfev8mEHnQNfXvolMQUxjA7oeN9HQgghhBBCCCEuXE1OrgHMnTu3wTLQDRs21L2AVsv8+fOZP39+cy4lAAIGAyql51pxJjjXv6unduea3oDJZGqTsjp9bCzVGRmobG1xGDqk1a9nSSaTib9i/wLO34S/McYEjeWdiz7n+R+N5P/4Ix6zbsAmNLTF6wJ8tP8jViWsQqvSMsswFNhIyKhLGBpx7TnPuyT0EmYtn0VKSQoPrHuAz/7zGXZaO4vEZLY0dimFTiqKB3XDdV8MhUuX4vPgg2cdZygspPzwYaD+RKy/kz/+Tv4WjU0IIYQQQgghhGhtrT4tVFiAvTv49FbuJ+9o8DBzzzWD0URldf3DIiytpGYnksOQIajtLJu0aW2Hcg6RUJSAvdaeiSETW7zeAJ8BJHR3YV+4CqqryXr3PQtECX+c+oNFhxcBMH/UfJxPpgGNG2bgYefBxxM/xsXGhYPZB3lmyzMYTZb73jiVf4rjecfRqrUEX3szAEVLlmKqZ1iJuTefTbeuHbI3nxBCCCGEEEIIUR9JrnUUwSOU26SGk2vmaaHQdqWhpVu3KdfugFNCzbvWJgZPxFHn2OL1dGodI7uM5PuL1JhUKor/+YfSnbswVlQ0+2NHwiZe2/QiuioT90TcxlT3MVTGxABgP3Bgo+IKdw3n/Qnvo1Vr+SfxH/67778tfq1m5q/h2ICx+F06DbWTE1VpaZTt3nPWseZ+a05RUvYphBBCCCGEEKLzaFZZqLCCkFGw53NI2t7gIRq1CjudmooqI2V6A56tHJKxspKy3bsBcBzdsZJreoOelfErAcuUhJqNCRjD6sTV7B/mzuCdeSTdckuL1nMFvq797FNO8SkAusBAtF5ejV5nqN9QXhz1Is9seYbPj3xOkHMQV/a4skWxGYyG2p5107tOR21nh8tlkyj45VcKlyzBcfiw2mNNJhMltb35Otb3ihBCCCGEEEIIcS6yc62jMO9cSz8ElSUNHmbuu1bSBjvXyvbswVRRgdbXF9vu3Vv9epa0MWUjRfoifBx8GOY37PwnNFJUgJI4+t+wItTejU9+NZXrjBlNPmd61+ncM+AeAF7e8TLb0ra1KIad6TvJLs/G1daVsYFjlbhmzgSgeNUqjOXltcfq4+KoTk9HZWODw5CO1ZtPCCGEEEIIIYQ4F9m51lG4BoJrEBQmQ+oeCB9f72HKxFA9ZfrWT67VTgmNimqT4QmWtDR2KQDTwqehUWsstq6Pgw893XtyghOc/PxRJndpei+3CkM5c9bM4WjuUYKcg1j0n0W42brXPq/SqFHb2zcrvvsG3EdSURIr4lfwyIZH+Payb+nm3q1Zay2JXQLAZaGXodPoALAfPBhdYCBVKSkUr1mD6zRlV2Dpmb35mhm7EEIIIYQQQgjRHsnOtY6kEX3XHGyURFFppaHVwzH30HKMGtXq17KkvIo8tqQoyR5LloSajQkcA8DmjK1onByb9KFytOe5/a+yt+QYti5uvDdlIZ6egXWOaUlySqVS8XLUywz2GUxJVQlz1s4hpzynyeuU6EtYl7QOUHbEnbm+eVdd4Z9LTh+/9XQiVgghhBBCCCGE6EwkudaR1CbXGu67Zi4Lbe2BBlWZWVSePAkqFY6jOlZybWX8SqpN1fTx7ENXt64WX390gNKwf1vaNgzGpiU539/7PqsTV6NT6/hgwgeEuIRYPD4bjU3t2mmlady/9n7Kq8vPf+IZVieupsJQQZhrGH29+tZ5znWmklwr3b6dqsxMpTffLnNvPhlmIIQQQgghhBCic5HkWkcSPFK5Td4NhvqTZw7m5Jq+dXeumXet2fXti9bd/TxHty/mktAzd1xZ0gDvATjrnCmsLORwzuFGn/fziZ/58uiXALwU9RKRvpGtEh+Am50bCy5egJutG0dyj/DU5qcwmoyNPv/Mr+G/S4JtgoKwj4wEo5Giv/6ifO9epTefjw+2PTpWbz4hhBBCCCGEEOJ8JLnWkXj3AltXqCqFjEP1HuJkay4Lbd2da+YeWh1t8mNMfgzHco+hVWm5LOyyVrmGVq1lZBclEboldUujztmaupVXd74KwH0D72Nq+NRWie1MwS7BfDDhA3RqHWuT1vLe3vcadV5qSSp7MvegQtVgnK4zlMRlwZ9/np4S2gF78wkhhBBCCCGEEOcjAw06ErUagofDqX+UvmsBg886RBloAKWtONDAZDRSuk2ZNOnUwXpoLY1TdlyNCRyDu13r7bgbHTCafxL/4X+H/sdnhz877/EGk7LTcFr4NO7pf0+rxfVvg30H80rUKzyx+Qm+OvoV3x779rznmHe4DfMfhp+jX73HuFx2GZmv/B/6mFgKspWebtJvTQghhBBCCCFEZyQ71zqa8/Rda4ueaxVHj2EoKEDt6Ij9gAGtdh1LMxgNLI9dDrReSajZ+KDxeNh5KNc1Gc77ATAmYAwvjHqhzXd3TQ6fzMORD6NVaRsVqwkTKlTMipjV4JoaZ2ecJ14MgLGwUOnN18EGXwghhBBCCCGEEI0hO9c6GnPftaQdYDLBvxIxbTEttHSrUuroMHIEKp2u1a5jaTszdpJVnoWrrStjA8e26rXc7dxZfdVqCisLG3W8SqXCy96rVWM6l9v63sbVPa6morqiUcfbae1wtnE+5zGuM2ZQtGKlcnyfPh2uN58QQgghhBBCCNEYklzraLoMBo0NlGZBXhx41p126dgGO9dKa3poOXWwyY/mJvyTQidho7Fp9evZaGzwdvBu9etYirON83kTZk3hGBWFxssLQ05Oh+vNJ4QQQgghhBBCNJaUhXY0OjslwQbK7rV/cazZuVbWStNCDSUllB04oFyrA/XQKq0qZW3iWqD1S0KFQqXV4vPww9j17o3bVVdbOxwhhBBCCCGEEKJVSHKtIzpH3zVXB6VM83h6EVUGo8UvXbZzJ1RXowsJxiYoyOLrt5bViaupMFQQ6hJKP69+1g7nguF25RWE/f4bNoEB1g5FCCGEEEIIIYRoFZJc64jO7Lv2Lxf19MXD0Ya4nFK+2ppg8UuXbFH6rTlFdcyS0Oldp7f5wAAhhBBCCCGEEEJ0XpJc64iChim3uaegNKfOU64OOp6cFAHA+2tOklHYuAb1jVW6dRsAjh2o31paSRq7M3ajQsXU8KnWDkcIIYQQQgghhBCdiCTXOiIHD/DupdyvZ/faVZGBDA52o1Rv4JXlxyx2WX1SElVJSaDV4jBsmMXWbW3L4pYBMMxvGP5O/laORgghhBBCCCGEEJ2JJNc6qnP0XVOrVbw8sy9qFSw7lM7WmJyzjmkOc0mow6BBaJwcLbJmazOZTLUlodO6TrNyNEIIIYQQQgghhOhsJLnWUdX2XTs7uQbQp4srN48MBeD5JUfQV7d8uEHplq1AxyoJPZRziMSiROy19kwMmWjtcIQQQgghhBBCCNHJSHKtozLvXEs/CPrSeg95+JIeeDnZEptdyudb4lt0OVNVFWU7lBJUx9FRLVqrLf0V+xcAE4Mn4qjrGLvthBBCCCGEEEII0XForR2AaCa3YHDuAsVpkLoXwsaedYirvY6nJ0cw7+eD/HftKaYP7EKAm33t8yajkbI9ezCWlZ33clWJiRjLytB4eGDXq5dFX0pjZZdlk12eTYRHBGrV+fPCeoOelfErASkJFUIIIYQQQgghROuQ5FpHpVIpu9eO/q4MNagnuQZw+aAAftqVzK6EPF5ZdoxPboysfS7vyy/JeuvtJl3WcdQoVOq22/BYZaxiU/Imfo/5nS2pWzCajAQ4BTCj2wwu73Y5fo5+DZ67MWUjRfoifBx8GObXcQYwCCGEEEIIIYQQouOQ5FpHFjKqJrlWf981AJVKxUsz+zDlv1tYeSSDjSezGdfDm+q8PHIWfAKAbY8eqGxtz3s5tZ0dnrfNtlj45xJfGM8fp/5gSewS8iryah+319qTWpLKggML+OTAJ4wKGMUV3a5gQtAEdBpdnTVqBxmET0Oj1rRJ3EIIIYQQQgghhLiwSHKtIzP3XUveBYZq0NT/xxnh58Kto0L5fEs885cc4e+Hx5L/8QKMpaXY9e5N6K+/tOlutIaUVZXxT+I//H7qd/Zn7a993Mvei+ldp9fuVFuTtIbfT/3O7ozdbE3dytbUrbjbujOt6zSu6H4FXd26kleRx5YUZbqplIQKIYQQQgghhBCitahMJpPJ2kGcT1FREa6urhQWFuLi4mLtcNoPowHeCIXKIrh7E/gPaPDQ4ooqLn5nI1nFlTzX35FRr94P1dUEf/UVjiOGt13M/2IymTiSc4TfTv3GqoRVlFYpwxnUKjVjA8ZyeffLGRM4Bp1ad9a5SUVJ/BnzJ3/G/El2eXbt4/29+9PFsQurElbRx7MPP039qc1ejxBCCCGEEEIIITqHxuajZOdaR6bWQNAwiFmj9F07R3LN2U7HM1N68eBPB1B9tgCqq3EaP96qiTWAN3a/wffHv6/9PMg5iCu6X8H0rtPxcfA557nBLsE8MPgB7ht4H1tTt/L7qd/ZlLKJQ9mHOJR9CIDpXae3avxCCCGEEEIIIYS4sElyraMLHlGTXNsOw+8+56HTB3Rh2+9rGZl2GKNKhc+jj7RRkPXbnrad749/jwoVU8KncEX3KxjiOwSVStWkdbRqLeOCxjEuaBw55TksjV3KkpgltesKIYQQQgghhBBCtBZJrnV0wSOV28TtYDIpU0TP4dbDSpP/lSHDGVTlzMWtHV8DyqrKeHH7iwBcF3EdTw9/2iLretl7cVvf27it720WWU8IIYQQQgghhBDiXKzfxV60TJfBoNZBSQbkJ5zz0OKVK+H4Uapt7Pg+4j+88NdRKqoMjb6UwWi59nwfH/iY1JJU/Bz9eHDwgxZbVwghhBBCCCGEEKItyc61js7GAboMhJTdSt81j7B6DzPq9WS9+x4Annfcjl2pD8l55XyyIZaHL+lRe1yVwUhSXhlx2aXEZZcotznKbUF5Fe9dO5DpA7q0KOQjOUf47vh3ADw/4nkcdY4tWk8IIYQQQgghhBDCWiS51hkEj6hJrm2HgdfXe0j+9z9QlZKC1tsbvztu47nYQu77fh+fbIylpLKaxNxS4rJLScoro/ocO9Te+ecEU/r5o1E3rS+aWZWhiue3PY/RZGRK+BTGBI5p1jpCCCGEEEIIIYQQ7YEk1zqD4JGw7UNl51o9DAUF5CxcCID3Qw+idnDgsr723BySx0Vpi1i4bRo7jL1rj7fXaQj3diTc24lwL0fCvR0J8nBg9pe7ScwtY/WxTCb19WtWqF8c+YJT+adwt3XniaFPNGsNIYQQQgghhBBCiPZCkmudQdAI5TbnBJTmgqNnnadzFv4PY2Ehtj164DpzJgCqrOO8UPAsak0Bfh7O7B5xtZJM83bEz8Wu3omdN44I5uP1sSzaHNes5FpcQRz/O/Q/AJ4Y9gTudu5NXkMIIYQQQgghhBCiPZGBBp2Boyd49VTuJ++s85Q+OZn8778HwOexx1BpNJAXB9/ORF1ZAECEKombRoYS1c0Lf1f7sxJr1cZqNiZvpMJpCbZ2OexNzGdvYn6TQjSajMzfNp8qYxVjA8cyOWxy816rEEIIIYQQQgghRDsiO9c6i+ARys61pO0QcTpxlf3ee5iqqnAcNQqnMaOhKA2+mQklmUpCLucEFCRBRSHYudZZMqkoiT9i/mBJzBKyy7MBsA+zw5ByJZ9t9iMyJLLR4f0U/RMHsg/gqHPkuRHP1bszTgghhBBCCCGEEKKjkeRaZxE8EvZ9XafvWvnBgxStWAkqFT6PPwZlefDt5VCQCO5hcMtfsOgiKEqBrOMQPIKK6gpWJ67mj5g/2J2xu3Ytd1t3fB19ic6Lxj7we9ZnJxKb3Y2u3q71RVNHekk6H+z7AICHBj+En2Pz+rUJIYQQQgghhBBCtDeSXOssgmv6rqXth6pyTFo7Mt98CwDXmTOxCwuAr6dDdjQ4d4Gbl4CzL/j2gaIUjiWs4/e0NayIW0FxVTEAKlSMChjFFd2uYELQBFQqFR/u/5AvjnyBzmMLt/99O79c8QneDt4NhmUymXhpx0uUVZcx2Gcw1/S8ptW/FEIIIYQQQgghhBBtRZJrnYV7KDj5QUkGpO6lJKaC8r17UdnZ4X3fXfDj9ZC2D+w94OY/wT0EvUHPb462/N7Fj+j4H2uXCnAKYGa3mczsNvOsXWYPRz6MriqMhcdeIZcTXLX0at4Z/zZD/IbUG9by+OVsSd2CTq1j/qj5qFXS5k8IIYQQQgghhBCdhyTXOguVStm9duxPTHFbyXprPQAeN9+EbvOTkLAZbJzhpt/BuyfpJek8svERDhceBlsbdCaYGHYZl3e/nOH+w8+ZBJszfAZ/7jaQZvMJeWRyxz938NDgh7ilzy11eqnlVeTxxq43ALhnwD2Eu4a37tdACCGEEEIIIYQQoo3JNqLOJHgkAPlLV6FPTETj6Ymn32E4uRK0dnDDT9BlENtSt3HNsms4nHMYV50TT+Tmsy6jgDfHvM7ILiPPu7tMpVJxX9RwyhLmoC6NxGAy8M7ed3hk4yOU6Etqj3tj1xsUVBbQw70Hs/vObtWXLoQQQgghhBBCCGENsnOtAzAZjeR99TXVmZnnPrA0Gw67UJiQDqjwHu+H5uTvoNbCNd9gDBnFpwcXsuDAAkyY6O3Zm3fHvEXAfyPBoIfCJKW8tBGm9u/Cm6tcyEi6imsuGsHqzP+xOnE1p/JP8d7490grTWNF/ArUKjUvjXoJnVrX4q+DEEIIIYQQQgghRHsjybUOoGTdOrLefLORRzsBYONlh5tmNaCCy/9HYcgInlo7l82pmwG4qsdVPDnsSWw1tuAdARmHIONIo5NrNlo1t0aF8vrKaA4c7cXXN37NIxsfIaEogRtW3IC91h6Am3rdRB+vPk18xUIIIYQQQgghhBAdgyTXOoCSTUpCzD4yEofBg8998NHfoCAR19AsVGpg6rsc69KbecuuJbUkFVuNLc+OeJaZ3WaePse3r5JcyzwKvaY2Oq7rhwXz4dpTnMwsIS+vFz9P/ZknNj3B9vTtlFeXE+gUyJxBc5r+goUQQgghhBBCCCE6CEmutXMmk4nSLVsA8LrrTpzGjTv3CRurYf3/Kfcvns/vrm7834qb0Bv1BDoF8t6E94jwiKh7jm/NzrLMI02KzdVex7VDg/liazyfbY7nu57D+WTiJ3x66FP+SfyH+SPn1+5gE0IIIYQQQgghhOiMZKBBO6ePT6AqLQ2VTofD0KHnP6Hf1eAdQcX4J5mvKWT+tvnojXrGB45n8bTFZyfWAPz6KreZR5sc3+yoUDRqFVticjiaVohGreHegffyx4w/GOgzsMnrCSGEEEIIIYQQQnQkklxr50q3bgWUklC1g8P5T/AII/nmX7m5eD+/n/odtUrNA4Me4IOLPsDFxqX+c3xrkmt5caAvbVJ8QR4OXNbXD4DPN8c36VwhhBBCCCGEEEKIjk6Sa+2cuSTUaXRUo45PLk7mumXXcTzvOO627iycuJA7+9+JWnWOP2pHL3DyBUyQFd3kGO8aGw7A0oNppBeWN/l8IYQQQgghhBBCiI6qWcm1jz/+mNDQUOzs7Bg+fDi7du065/EFBQXMmTMHf39/bG1t6dGjBytWrGhWwBcSo15Pac3X1nH06Eadsyx2GUX6Irq7d+fnaT8zssvIxl2smX3XAPoHujEszINqo4mvtiU0+XwhhBBCCCGEEEKIjqrJybXFixczb9485s+fz759+xgwYACXXnopWVlZ9R6v1+u55JJLSEhI4Ndff+XEiRMsWrSIgICAFgff2ZXv24epvByNlxe2PXs26pydGTsBuCHiBvwc/Rp/sdrkWtP7rgHcOUbZvfbDziRKKqubtYYQQgghhBBCCCFER9Pk5Nq7777LnXfeyezZs+nduzcLFy7EwcGBL774ot7jv/jiC/Ly8vjzzz+JiooiNDSUcePGMWDAgBYH39mZ+605RY1CpVKd9/jy6nIOZh8EYLjf8KZdzLf5Qw0ALo7wIdzLkeKKahbvTm7WGkIIIYQQQgghhBAdTZOSa3q9nr179zJx4sTTC6jVTJw4ke3bt9d7ztKlSxk5ciRz5szB19eXvn378uqrr2IwGBq8TmVlJUVFRXU+LkQlW5TkWmNLQvdn7afaWI2/oz+BzoFNu1jtzrXDYDI17VxArVZx+5gwAL7YEk+1wdjkNYQQQgghhBBCCCE6miYl13JycjAYDPj6+tZ53NfXl4yMjHrPiYuL49dff8VgMLBixQqee+453nnnHV555ZUGr/Paa6/h6upa+xEUFNSUMDuF6pwcKo8fB8Bx1KhGnbMzXSkJHeY3rFE73erw6gFqLVQUQlFq086tceXgQDwcbUgtKGflkfq/H4QQQgghhBBCCCE6k1afFmo0GvHx8eHTTz8lMjKSa6+9lmeeeYaFCxc2eM5TTz1FYWFh7Udy8oVXZmguCbXr3Rutp2ejztmVrgw/GO7fxJJQAK2tkmCDZpeG2uk03DQiBIDPNsdhasYOOCGEEEIIIYQQQoiOpEnJNS8vLzQaDZmZmXUez8zMxM+v/ub5/v7+9OjRA41GU/tYr169yMjIQK/X13uOra0tLi4udT4uNCU1yTXHqKhGHV+kL+JY3jEAhvoNbd5FWzAx1OymkSHYatUcTClke2xus9cRQgghhBBCCCGE6AialFyzsbEhMjKStWvX1j5mNBpZu3YtI0eOrPecqKgoYmJiMBpP9+A6efIk/v7+2NjYNDPsjq+gooBfT/7KD8d/OOs5k9FI6dZtQOP7re3N2IvRZCTUJbRpU0LP1MKJoQBeTrZcPUTp9/bsn0co1zfcW08IIYQQQgghhBCio2tyWei8efNYtGgRX3/9NcePH+fee++ltLSU2bNnA3DzzTfz1FNP1R5/7733kpeXx4MPPsjJkydZvnw5r776KnPmzLHcq+iAjuUd48XtL/K/Q//DYKybgKqMjsaQm4vawQGHQQMbtd6uDKUkdJjfsOYH5dtPuW1Bcg3gsf9E4OtiS1xOKW+sim7RWkIIIYQQQgghhBDtWZOTa9deey1vv/02zz//PAMHDuTAgQOsWrWqdshBUlIS6enptccHBQXx999/s3v3bvr3788DDzzAgw8+yJNPPmm5V9EBDfUbiouNC3kVeezP2l/nOfOUUIfhw1E1cnffzoyaYQb+LUmu1excyzkFVRXNXsbVQccbV/YH4KttCWyLzWl+TEIIIYQQQgghhBDtmLY5J82dO5e5c+fW+9yGDRvOemzkyJHs2LGjOZfqtHRqHeODxrM0dilrktYwxG9I7XOlTey3lluey6n8U0AL+q0BOPuBvQeU50HOCfAf0Oylxvf04YbhwfywM4nHfjnEqofG4Gyna35sQgghhBBCCCGEEO1Qq08LFQ2bGDwRgDWJa2onaxpLSynbtw8Ap9GNS67tztwNQA/3HnjYeTQ/IJXKIn3XzJ6e3IsgD3tSC8p5ZdnxFq8nhBBCCCGEEEII0d5Ics2KRgWMwl5rT2ZZJkdylAmdpbt2QVUVusBAdCEhjVpnV7rSb224//CWB+XbV7m1QHLNyVbL21cNQKWCxXuSWRedef6ThBBCCCGEEEIIIToQSa5Zka3GlrGBYwFYk7QGgNKafmuOo6NQqVSNWsc8zGC4nyWSazU71zIOt3wtYHi4J7dHhQHwxG+HyS/VW2RdIYQQQgghhBBCiPZAkmtWNjGkbmloU/utZZRmkFiUiEalIdI3suUB1ZaFHoGaUtWWevTSnnTzcSK7uJLnl7Z8R5wQQgghhBBCCCFEeyHJNSsbEzAGG7UNScVJnDy+BX1CAmg0OI4Y0ajzzbvW+nj2wcnGqeUBeUeASg1luVCS1fL1ADudhnevGYBGreKvg2ksO5RmkXWFEEIIIYQQQgghrE2Sa1bmqHNkVMAoAI6v+BEA+4ED0Tg7N+r8nek7ARjmP8wyAdk4gEdX5X7mEcusCfQPdGPOhG4APPfnEbKKKyy2thBCCCGEEEIIIYS1SHKtHTBPDa3esQdo/JRQk8lUu3NtmJ+Fkmtg0YmhZ5o7oRt9uriQX1bFU78drp2Q2linMotZcyyTgjLp2yaEEEIIIYQQQoj2QWvtAASMDxqPjUlD2KlioPH91pKLk8kozUCn1jHQZ6DlAvLrC8f+tHhyzUar5t1rBjLtwy2sjc7il70pXDMk6Jzn6KuN/HMsg2+2J7IrPg8AtQoGB7szIcKHiyJ8iPBzbvTwByGEEEIIIYQQQghLkuRaO+Bq68q0yggcKg9S5WSHXZ8+jTpvZ4ZSEjrAewD2WnvLBeTbV7m1cHINoKefM/P+04PXV0bz0l/HGNXVk0B3h7OOSy8s58edSfy4O5ns4koANGoVQe72JOSWsScxnz2J+bz19wn8Xe0Y31NJtEV188TBRr6tW1NFlYEDyQUMD/OQpKYQQgghhBBCiAueZCHaifGpbgCc6GZHf42mUefsSq8pCbVUvzUzc1lodjQYqkCjs+jyd44JZ/WxTPYm5vP4r4f47vbhqNUqTCYT22Jz+XZ7IquPZ2IwKmWj3s62XD8smOuHBeHvak9KfhnrT2SzPjqLbbE5pBdW8OOuJH7clYSNVs2IcE8u6unNpX398He1YNJRAPDmqhN8sTWe16/ox3XDgq0djhBCCCGEEEIIYVWSXGsnAo5lUQ1sCihidEk6/k7+5zz+zH5rw/2GWzYY1yCwdYHKIsg5Bb69Lbq8Rq3inasHcNkHm9kWm8vCTbHY6zR8uyORuOzS2uOGh3lw08gQLu3jh05zuj1goLsDN40I4aYRIVRUGdgel8v66CzWRWeRkl/OppPZbDqZzburT7Ls/jEEe569M040j8lk4u+jGQCsP5ElyTUhhBBCCCGEEBc8Sa61A4aCAqqPRgNwKFTF2qS13Nj7xnOeE1MQQ15FHvZae/p59bNsQCqVsnstabsyMdTCyTWAUC9HnpocwfNLjvLmqhO1jzvaaLhicCA3jgihp9/5J6ba6TRM6OnDhJ4+vDjdRExWCeuis/hpdzLxOaV8viWOF2f0tXj8F6qkvDJSC8oB2JuYj8lkktJQIYQQQgghhBAXNJkW2g6Ubt8OJhPlQd7kuahYk7TmvOeYd60N9hmMzsJlm8AZE0OPWH7tGjcOD2FcD28Auvs48fKMPux8ZiIvz+zbqMTav6lUKrr7OnP3uK7830wlofbznhTyS2W6qKVsjcmtvZ9Toicxt8yK0QghhBBCCCGEENYnO9fagZItWwBwGzse+IN9mfvIKc/By96rwXN2pivDDCzeb82sNrlm+aEGZmq1ikU3DyEpr4yu3o4W3QE1sqsnfbq4cDStiO92JHL/xd0ttvaFbGtsTp3P9yTmE+rlaKVohBBCCCGEEEII65Oda1ZmMpko3bIVAJ8Jl9LXsy8mTKxLWtfgOdXGavZk7AFaod+aWStODD2TjVZNNx8ni5cWqlQq7hobDsDX2xOoqDJYdP0LkdFoYnussnNtcLAbAHsT86wYkRBCCCGEEEIIYX2SXLMyfWws1ZmZqGxtcRgSycUhFwOwNmltg+dE50VTXFWMs86ZCI+I1gnMp5dyW5wOpbnnPradmtzPny6uduSU6Plzf6q1w+nwojOKySvV42Cj4fbRSuJyT0K+laMSQgghhBBCCCGsS5JrVmYuCXUYMgS1nR0TgycCsCt9F4WVhfWeYy4JHeI3BI1a0zqB2TqDe6hyP6t1d6+1Fp1GzW2jwwBYtDkOo9Fk5Yg6tq0xSkno8DAPhod7AHAqq4SCMulpJ4QQQgghhBDiwiXJNSszl4Q6jh4NQKhrKN3culFtqmZjysZ6zzEPMxju30oloWZtVBramq4bFoyznZbY7FLWRWdZO5wOzdxvLaqbF15OtoTV9FrblyS714QQQgghhBBCXLgkuWZFxooKynbvBsBpdFTt45eEXALA6sTVZ51TZahiX+Y+AIb5tdIwA7Pa5FrrTQxtbU62Wm4YHgzAp5vjrBxNx6WvNrIrXumvNqqrMmgjMsQdkNJQIYQQQgghhBAXNkmuWVHZ3r2YKivR+vpi061b7eMXByt917albqOsqqzOOYdyDlFhqMDDzoNubt1oVW0wMbQtzB4VhlatYld8HgeSC6wdTod0MKWAMr0BD0cbIvycARhiTq4lSnJNCCGEEEIIIcSFS5JrVlRbEhoVVWdaZg/3HgQ7B6M36tmUuqnOObvSlZLQYX7DLD5h8yzm5FrWcTB23Gmbfq52TB/YBVB6r4mm23JKKQkd2dUTtVr5vhsSqiTXDiYXoK82Wi02IYQQQgghhBDCmiS5ZkWlNcMMziwJBVCpVEwMUQYbrE2sOzV0Z4YyzGCYfyuXhAK4h4HOAaorIDe29a/Xiu4co0y3XHk4neS8svMcLf5tW02/tdHdvGofC/dyws1BR2W1kaNp9Q/fEEIIIYQQQgghOjtJrllJVWYmladOgUqFw8iRZz1vnhq6KWUTlYZKAMqryzmYfRCA4X6tPMwAQK0Gn97K/Q7cdw2gl78LY3t4YzTB51virR1Oh1JaWc3+pAIAorqeTq6p1Soig5Xda3ulNFQIIYQQQgghxAVKkmtWoo+PR+3sjF3fvmjd3c96vq9XX3wdfCmrLmN72nYA9mftp9pYjZ+jH0HOQW0TaCfpuwZwV83utcW7kyko01s5mo5jV0Ie1UYTge72BHs61HkuMlSGGgghhBBCCCGEuLBJcs1KHEeMoMf2bQR+9GG9z59ZGmqeGtqm/dbMaieGdvzkWlQ3T3r5u1BeZeD7nUnWDqfD2BajlISeuWvNbEiIB6AMNTCZTG0alxBCCCGEEEII0R5Ics2KVFotOl/fBp83l4ZuSN5AlbGKXRlKcm24fxuUhJp1op1rKpWKu8aGAfDl1gQqqzvukIa2tCUmF4BR3TzPeq5/oCs6jYqckkqSpJedEEIIIYQQQogLkCTX2rFBPoPwsPOgSF/E+qT1HM1VElzD/NpgmIGZb03PtcIkqOj4Teun9u+Cv6sdOSWVLNmfZu1w2r3ckkqOpxcBMKqenWt2Og19A1yBBkpDdy2C9/p1+IEYQgghhBBCCCFEQyS51o5p1BouCr4IgPf2vofRZCTEJQQ/R7+2C8LeHVwClfuZx9ruuq1Ep1FzW5Sye+3TzXEYjVLKeC7b45RdaxF+zng729Z7zJCQmr5r9Q012P2Zkpg9tqTVYhRCCCGEEEIIIaxJkmvtnLk0NKUkBWjjXWtmtaWhHXtiqNl1w4JwttUSk1XChpNZ1g6nXdtqLgmtZ9eaWWRN37W9iXl1n6gshuwTyv2s460SnxBCCCGEEEIIYW2SXGvnhvkNw9nG+fTn/lZIrvl1nqEGAM52Oq4fHgzAp5virBxN+7bVPMygnn5rZpE1O9dOZpZQWFZ1+om0A0DNzkBJrgkhhBBCCCGE6KQkudbO6TQ6xgeOr/18qO/Qtg+ik+1cA7h1VChatYodcXkcSimwdjjtUnJeGUl5ZWjUKoaHN5xc83a2JdTTAYB9SWeUhqbtO30/5wQYqlsrVCGEEEIIIYQQwmokudYBTAqbBEBvz9542jec5Gg1vuada8fAaGz767eCLm72TBvQBYBFm+OtHE37tC1W2bU2MMgNJ1vtOY81l4buObM0NHXv6fsGPeTJLkEhhBBCCCGEEJ2PJNc6gLGBY3l/wvu8PfZt6wTg0RU0tlBVCgUJ1omhFdw5JhyAFYfTSc4rs3I07Y+531pU1/MndIeE1gw1OHNiaOp+5VZjo9xmdfyBGEIIIYQQQgghxL9Jcq2DuDj4YoJcgqxzcY0WfCKU+52k7xpA7y4ujOnuhcFo4outsnvtTCaTqXbn2qhuDQ8zMDNPDD2YUkCVwQgl2cqUUFTQ8zLlIOm7JoQQQgghhBCiE5Lkmmgcc2lo6r5zH9fBmHevLd6dTEGZ3srRtB8nMovJKdFjp1MzKNjtvMd39XbC1V5HRZWRo2lFp/uteXWHoOHKfdm5JoQQQgghhBCiE5Lkmmic8AnK7aGfwWiwbiwWNKa7F738XSjTG/hme6K1w2k3zCWhw8I8sdVqznu8Wq2qnRq6JyHvdBI2IBJ8ein3ZeeaEEIIIYQQQohOSJJronF6TQN7DyhKgVOrrR2NxahUKu4Zp+xe+3JrPGV660y0zCvVk5xXRlFFFSaTySoxnGlbjFIS2ph+a2bm5NrexPzTwwy6DAaf3sr9vFioqrBonEIIIYQQQgghhLWdewSgEGY6Oxh4A2z/CPZ+CT0nWTsii5nSz593/jlJUl4Zi3cnMzsqrM2uXVpZzTv/nOSrbfEYa3JqahW42OtwPePD/LmbvQ53BxsmRPjQzcepVWKqMhjZGa9M/YxqRL81s6GhNRNDE/Iw2exDBcrONSdfsHeH8nzIOQn+/VshaiGEEEIIIYQQwjokuSYaL/JWJbl26h8oTAHXQGtHZBFajZq7x4XzzB9HWLQpjlnDQ7DRtv6mztXHMpm/5AhphcpuLhutGn21EaMJCsqqKCiravDc11Ye56rIQB6a2IMubvYWjetQSgElldW4Oejo7e/S6PP6B7qi06iwLU1FVZ0Lah349QWVStm9lrhVKQ2V5JoQQgghhBBCiE5Ekmui8by6Q+gYSNgM+76BCU9bOyKLuXJwIO+vOUVaYQVLD6ZxVWTrJQ4zCit4YelRVh3NACDIw56XZ/RlfE8fKqoMFJZXnf4oq6r7eXkVcTmlbDqZzc97UvjzQBq3jgrl3nFdcXe0sUh85n5ro7p6olarGn2enU5D3wBXuqRsVx7w7QNaW+W+T6+a5JoMNRBCCCGEEEII0blIck00TeStp5NrYx8HTef4FrLTabh9dBivr4xm4cZYrhgU0KTEUmMYjCa+3Z7A2/+cpKSyGq1axZ1jw3ngou7Y22hq47DTafB1sTvnWvuS8nljZTQ74/P4dFMcP+5M4p7xXZkdFYqDTcv+TLbW9Fsb1bXxJaFmQ0Lc8UqLVT4JiDz9RO1QA0muCSGEEEIIIYToXGSggWiaXtPAwQuK0+HU39aOxqJmDQ/G2U5LTFYJq49nWnTto2mFXLFgKy/8dYySymoGBbux7IHRPDEpojax1hSDg9356a4RfDl7KL38XSiurOatv08w7q0NfLsjkSqDsVlxlusN7E8qAJrWb80sMsSDgWpzcm3w6SfMQw1kYqgQQgghhBBCiE5GkmuiabS2MGiWcn/Pl9aNxcKc7XTcPDIEgAUbYi0ytbO0spr/W36M6R9t5WBKIc52Wl6Z2Zff7hlFhF/j+5nVR6VSMaGnD8vvH80H1w0kyMOe7OJKnvvzCJe8u5G/DqZhNDbtNexOyENvMNLF1Y5QT4cmxxQZ5EJfVTwAxZ4DTj/hHaHcFiZDRVGT1xVCCCGEEEIIIdorSa6Jpht8i3IbswbyE60bi4XNjgrDVqvmYHIB22NzW7TW+hNZ/Oe9TSzaHI/BaGJKf3/WzhvHjSNCLFpyqlarmDEwgLXzxvPi9D54OdmQkFvG/T/uZ/rHW9gZ1/jXUVsS2s0LlarpMXpXJuKoqqTUZMue0jN2vjl4gLO/cj87usnrCiGEEEIIIYQQ7ZUk10TTeXaF8PGASem91ol4Odly7dAgAD7ZGNvsdVYdyWD2l7tJLSgn0N2eL2cP5eMbBuNznl5qLWGjVXPLqFA2PjaBhyf2wMlWy5HUIq79dAeP/3qQ/FL9edfYGqsk10Y3oyQUgNS9ABwxhbE36V871KTvmhBCCCGEEEKITkiSa6J5Imcrt/u/BUOVdWOxsDvHhKNRq9h8KofDKYVNPj86o4h5Px8A4KrIQP55eCwTevpYOMqGOdpqeXBidzY+Np7rhwUD8POeFC5+dyO/7U1psNy1oEzP0TQlITaqq2fzLp66D4ADxq7sScyr+9x5+q4tOZDKU78fpkxf3bxrCyGEEEIIIYQQViDJNdE8EVPA0QdKMuHESmtHY1FBHg5MH9AFgE82xjTp3LxSPXd8vYcyvYHR3bx4/Yp+LZ7e2VyeTra8dkU/frt3JD19nckr1fPILwe5YdFO4rJLzjp+e2wuJhN093Fq/g67mp1rh4xdOZBcUHewQm1y7eyda19tjefBnw7w464kvtneuUqNhRBCCCGEEEJ0bpJcE82j0cGgG5X7e76wbiyt4J5xXQFYeSSD2HoSUfWpMhi57/u9pOSXE+LpwEc3DEKrsf5fscgQj9rJpHY6Ndvjcpn0/mbeX3OSympD7XFbavqtNWdKKADVlZB5FIA4m55UVBk5lnZGaWhtWWjdnWufbY7jhb9OJ9y+2prQ7GmnQgghhBBCCCFEW2vWb/4ff/wxoaGh2NnZMXz4cHbt2tWo83766SdUKhUzZ85szmVFexN5C6CCuPWQF2ftaCyqp58zE3v5YDLBpxsb99peXnaMHXF5ONpoWHTzENwcbFo5ysbTadTcO74rqx8ex7ge3ugNRt5fc4rL3t/Mtpo+a9tqBjg0uyQ04wgYq8DBE/+QHgDsScw//bx3T0AFpdlQkg3Agg0xvLJcSbbdM64rXk62ZBRVsPxQevNiEEIIIYQQQggh2liTk2uLFy9m3rx5zJ8/n3379jFgwAAuvfRSsrKyznleQkICjz76KGPGjGl2sKKdcQ+Frhcp9/d+bdVQWsO945Xda7/vTyG9sPycx/6wUylnVKngg+sG0cPXuS1CbLIgDwe+mj2Uj24YhLezLXE5pdywaCf3fb+X+JxS1CoY0ex+a0pJKF0GExnqAcDeM/uu2Tgq3zMA2cf579pTvLnqBAAPT+zBk5dFcMvIEAA+2xLXYG84IYQQQgghhBCiPWlycu3dd9/lzjvvZPbs2fTu3ZuFCxfi4ODAF180XBpoMBiYNWsWL774IuHh4S0KWLQzQ25Tbg98D9Xnn0bZkUSGeDAszIMqg4nPNsc3eNyu+DyeX3IEgEf/05OJvX3bKsRmUalUTO3fhTXzxnHTiBBUKlhxOAOA/oFuuNjpmrdwmjLMgIBIhoS4A7AnIb9ukqym79q6TRt4d/VJAB67tCcPTuwOwKwRIdjp1BxJLWJH3L8GIgghhBBCCCGEEO1Qk5Jrer2evXv3MnHixNMLqNVMnDiR7du3N3jeSy+9hI+PD7fffnvzIxXtU49J4OyvlPpFL7N2NBZn3r32464k8kvPTh6m5Jdx73d7qTaamNrfn/tqju8IXO11vDyzL7/fO4oIP2Wn3SUtSQyad64FDGZAkBs6jYqs4kpS8k/v+jPV9F3LOLUfgKcnRzBnQrfa5z0cbbhycCCg9GITQgghhBBCCCHauyYl13JycjAYDPj61v0F3NfXl4yMjHrP2bJlC59//jmLFi1q9HUqKyspKiqq8yHaKY0WBt2k3N/7pXVjaQXje3jT29+FMr2Br7cn1HmuTF/NXd/sJbdUT58uLrx11QBUKpV1Am2BQcHu/HX/aJbdP5q7xzZzZ2lFEeScUu53GYydTkOfLq4A7KkpDTWZTPyZ6gJAD3UKz03tzV1jz05G3j46DJUK1kZnNXqYhBBCCCGEEEIIYS2tOsqwuLiYm266iUWLFuHl1fgJhK+99hqurq61H0FBQa0YpWixwTeDSg3xmyA31trRWJRKpardvfbVtgTK9NWAkih67JdDHEsvwsvJhk9vHoK9jeb0ifoy+OVW+P4a2PwOJGyFqnP3bbMmnUZN3wDX5k83TT8AmMA1GJy8Ac4qDX3xr2N8ctwWgP42adweFVrvUuHeTlwcoSTwP9/ScDmuEEIIIYQQQgjRHjTpN2kvLy80Gg2ZmZl1Hs/MzMTPz++s42NjY0lISGDatGlotVq0Wi3ffPMNS5cuRavVEhtbfyLmqaeeorCwsPYjOTm5KWGKtuYWBN0uUe53wt1rl/X1I8TTgYKyKn7cpXwvfrQuhuWH09FpVHxyYyQBbvZ1T9r0Fhz9A079DWtfgq8mw2tB8NlE+PsZOP5X7cTMTqG2JHRQ7UNDQk8n155bcoSvtiUQb/LHqNJiU10CRakNLnfHmDAAftubQl495bhCCCGEEEIIIUR7oW3KwTY2NkRGRrJ27VpmzpwJgNFoZO3atcydO/es4yMiIjh8+HCdx5599lmKi4v54IMPGtyRZmtri62tbVNCE9Y2ZLaSSDrwA1z0HGg7z5+fVqPm7rFdefqPw3y2OQ5/VzveqWnG//KMvgytmYxZK/sEbPtQuT/8XihOg6SdUJIBKbuVj+0fKc97hEPwSAgaDrbOoC+ByhLQl4K+uOa++bFi5fFqPUQ9CP2vbsOvwnmknh5mYBYZonxdTmQWcyKzGJUK/u/Kwah3dofs45B1HFwD611ueJgH/QJcOZxayHc7Enng4u6t/hKEEEIIIYQQQojmaFJyDWDevHnccsstDBkyhGHDhvH+++9TWlrK7NmzAbj55psJCAjgtddew87Ojr59+9Y5383NDeCsx0UH1+0ScAlQdiMd/wv6XWXtiCzqisEBvLfmJOmFFcz5QUkk3TIyhOuGBdc90GSC5Y+AsUoZ9jDpNVCplMfzEyB5JyTtUG6zjkNenPJx4PumBbThtfaZXOsyuPYhb2dbQjwdSMwtQ62Ct68ewBWDAyG+l5JcyzwK3S+pdzmVSsUdY8J48KcDfLM9gbvGhmOn09R7rBBCCCGEEEIIYU1NTq5de+21ZGdn8/zzz5ORkcHAgQNZtWpV7ZCDpKQk1OpWbeUm2iONVum9tuE12PNFp0uu2ek03DE6jNdWRmMywchwT56d2vvsAw//AgmbQWsPl72hJNZAufUIUz4GXKc8Vp4PybsheQek7AGjAWwcwdYJbGo+bP91q7WFX2ZDXizkJ4J7SNt9ERpSkgVFKYAKugys89RVgwP536Y4/u/yvswYGKA86NMbjv6uJBfPYXI/f15fGU16YQVLD6RxzVDpvSiEEEIIIYQQov1RmUwmk7WDOJ+ioiJcXV0pLCzExcXF2uGIhhSlwXt9wWSAObvAu6e1I7Ko4ooqLvtgM3Y6Db/cPRJ3R5u6B5QXwEdDoTRLKY0d+2jrBPL5pUpCbtoHEHlr61yjKU6sgh+vBe8ImLPzrKcNRhMa9RlTVI8vg8WzwK8/3LP5nEt/uimWV1dE08PXib8fGtshp7EKIYQQQgghhOiYGpuPki1mwnJcuiilkAB7v7JqKK3B2U7HxscmsOrBMWcn1gDW/5+SWPPsDqPub71Aul6k3Maua71rNIV5mMEZJaFnqpNYA/Dppdxmn1B2653DtUODcbTRcDKzhE2ncloaabtXpq/mgzWnOJBcYO1QhBBCCCGEEEI0kiTXhGUNUXrvceAHqCiybiytQKNWodXU89cmbT/s/ky5P+Wd1h3oYE6uxW08b3KqTaSZhxnUn1w7i3uoUjZrqIS8+HMe6mqv49qhSl+7zzbHtSDI9s9kMvHIzwd5b81JbvtqNzklldYOSQghhBBCCCFEIzS555oQ59T1InANhsIkeD0I7N3B2R+c/Rq+dfIFjc7akTef0QDL5oHJCP2uhvBxrXu9LoPA1hUqCiDtAARGnu+M1mMynd651tjkmlqjlAynH4CsY+DV7ZyHz44K5att8Ww+lUN0RhERfp2zNHzBhlhWHskAIK9Uz7N/HOGTGwdLKawQQgghhBBCtHOyc01YlloDE54GnaPyeXm+kkCJXadMxNz8Dqx4FBbfCJ9dDO/1gTfClF1f7b/9X/32fqXs3rJ1gf/8X+tfT6OF8LHKfWuXhuYnKH/GGhvwbcIEYJ+aYRDnGWoAEOThwGV9/QH4bPO5d7p1VOuiM3n7nxMA3DE6DK1axaqjGSw9mGblyIQQQgghhBBCnI/sXBOWN/B6ZSJmRQEUZ0BxegO3Nff1xbD8EYhdD9M/BAcPa7+CxivJgrUvKvcveg6cfdvmul0vguN/Kcm1cY+1zTXrYy4J9e3btFJYc9+1rGONOvyOMWEsP5zOkgOpPH5pT3xc7JoYaPsVm13Cgz8ewGSCG0cE8+zU3jjb6XhvzUmeX3KUEeGe+Hai1yuEEEIIIYQQnY0k10TrUKmUklB799OJlPoYjbBzIax+HqKXKb3LrlgEoVFtF2tLrH4eKgrBfwAMvb3trmvuu5ayCyqLwda57a59ptQm9lsza8LONYBBwe5EhrizNzGfb7Yn8uilnWMSbXFFFXd9s4fiymqGhrrz/NQ+ANw3oStrjmdyOLWQp34/zOe3DJHyUCGEEEIIIYRop6QsVFiXWg0j74M71oBHVyhKha+nwvpXwVBt7ejOLWELHPwRUMGU95SS2LbiHgruYWCsVuKwltrkWhP7vpkTrrkxUN24xv13jgkD4LudiZTr28EghxYyGk08vPggsdml+LnYsWBWJDZa5Z9knUbNO9cMwEajZl10Fr/sTbFytEIIIYQQQgghGiLJNdE+dBkId2+CgbOUwQAb31CSbAXJ1o6sftV6pZQVlAmp1hgqYN69Zq2+a4ZqZSgBQJcm7lxz6aIMZTAZIOdUo065pLcfwR4OFJRV8eu+jp9s+mDtKdYcz8RGq+Z/N0Xi7Vy3rLaHrzMPX9IDgJf/OkZaQbk1whRCCCGEEEIIcR6SXBPth60TzFwAV3wGNs6QtB0WRsGxJdaO7Gw7FkB2NDh4wcXPWycGayfXck5AVZnyZ+XVvWnnqlTg27TSUI1axW1RoQB8sSUeo7GDDsAA/j6awQdrlaTia5f3Y0CQW73H3TU2nEHBbhRXVvP4r4cwddShH0IIIYQQQgjRiUlyTbQ//a+GezYpu6EqCuHnm+Gvh0BfZu3IFAXJys46gP+8ovSVs4awMaDSKKWVBUltf31zSWiXgc0riW3iUAOAq4cE4WKnJT6nlLXRWU2/ZjtwKrOYeYsPADA7KpQrIwMbPFajVvHO1QOw06nZEpPDdzut8OcshBBCCCGEEOKcJLkm2iePcLjtb4h6SPl875ew6CLIPGrVsABY9aSyYyt4lDIV1VrsXCFwiHI/dn3bXz91r3LbZVDzzm/iUAMAR1stNwwPAWDR5rjmXdeKCsuruPObPZTqDYwM9+TpyecY9lEj3NuJxy+NAOC1FcdJym0nSWYh2sAdX+9m4rsbKa6osnYoQgghhBBCNEiSa6L90trAJS/CTX+Aow9kH4dPRsG7veG7K+Gf5+DAj5B2AKraqB/ViVXKVFO1Fqa+q5Q3WpM1S0PTmjnMwKwZO9cAbh0VilatYld8Hq8sO0a1wdi867cxg9HEgz/tJyG3jAA3ez66YRA6TeP+Cb51VCjDwzwo0xt49NeDHbokVojGyiyqYM3xLGKySlh9LNPa4QghhBBCCNEgSa6J9q/rRXDvNug5Wfm8KBVi1sC2/8Kf98Cn4+DVLvBhJCy+UZk0evQPKEq3bBxlebDyMeX+yDmnk0PWZE6uxW8EYxtO0KyqOL2LMKCJwwzMvGu+fgWJUFnS6NP8XO149NKeAHy2JZ5bvtxFXqm+eTG0oXf+OcGGE9nY6ZQBBp5Otuc/qYZareKtqwbgYKNhV3weX25LaL1AhWgndsXn1d5ffsjC/54LIYQQQghhQZJcEx2Dkzdc/yM8kaiUi059D4bdBSGjlZ5nJqPSe+z4X0o/tF9uhQ8GwKk1lrm+vgx+uFbpbeYWDGMft8y6LdVlsDJ1szz/9OTOtpBxGIzV4OgNrkHNW8PRE5x8lfvZ0U069Z5xXflk1mAcbDRsjcll+kdbOJZW1Lw42sCyQ2ks2BALwBtX9qdvgGuT1wj2dKgtI31zVTSx2Y1PSArREe1OOJ1c23wqh8JyKQ0VQgghhBDtkyTXRMdi7wbBI2DIbTD5LZi9HB6Ph0dOwk1/wqWvwqAbwTsCDJWweBYkbGnZNQ3V8OttkLIL7Nxg1q/KZNP2QKNVBhtA25aGmktCuwxuWWlsM0tDAS7r588f90UR4ulASn45V3yylaUH05ofi4WV6atZcTidOT/sY97PBwG4e2w4MwYGNHvNWcODGdPdi8pqI4/+chCDlIeKTsy8c02tAr3ByBopDRVCCCGEEO2UJNdEx6dSgbMvdJ2glGvO+Bju3gzdL4XqCmXHWfLu5q1tMsHyh+HkStDawQ2LwbunZeNvqdq+a2041MA8zKC5JaFmzRhqcKaefs4snTOacT28qagy8sCP+3l1xXGr9WErrazmr4Np3PvdXga/vJr7vt/H8kPp6KuNTOzly+OTIlq0vkql4o0r++Nsq2V/UgGfbup4Qx2EaIzC8ipOZBYDMKtmiMnyw1IaKoQQQggh2iettQMQolVobeCab+CHa5R+ZN9fCbcsA//+TVtnw+uw7xtQqeGqL5Rdc+2NObmWvAsqi8HWufWvmdrCYQZmLdi5ZubqoOOLW4fy9j8n+GRDLJ9uiuN4ehEfXj8INweblsXXCMUVVayLzmLF4XQ2nMimsvp0Yi/Yw4HJ/fyZ3M+PfgGuqCwwAKOLmz3PTevN478e4r3VJzGaTNho1KhUSvJNhZJvVqtUymMoj2vVKgLc7Qn1dKSLmz0atZWHcQhxDnsT8zCZIMzLkVtGhfDtjkQ2n8qmsLwKV3udtcMTQgghhBCiDkmuic5LZ6f0afv2CkjeAd/OhNkrG7/zbM8XsPF15f6UdyBiSquF2iIeYeAeCvkJkLAVek5q3etVFELuKeV+F+vuXDPTqFU8MSmCvl1cefSXg2w+lcO0j7bw6U1D6OXvcs5zqw1GEnJLOZZezPH0IgrLq9CqVWjUqppbdd3PNcotwK74fDadykZ/RkIt1NOcUPOnTxcXiyTU/u3qyED+PpLB2ugs3vr7RJPPt9GoCfZ0INTTkTAvB0K9HAmr+fB1tkMtiTdhZbvi8wEYGupONx9nevo6cyKzmNXHMrkqMtDK0QkhhLiQ6auN7EnIY+PJbI6mFXHv+K5EdfOydlhCCCuT5Jro3GwcYdbP8M0MSNsPX0+H21aCR/i5z4teDssfUe6Pe0Lp8daedb1ISQbGrmv95FrafuXWLUQZStAS5kRnSSaU5rZ4vSn9/enq48hd3+wlKa+MKxZs462r+zO1fxcAiiqqOJ5WxPH0Io6nF3M8o4gTGcV1dps1R7i3I1NqEmoRfs6tklA7k0ql4s2r+vPftacorqwGExhNJkyA0QSmmvsmkwmTSalurqw2kJxfTlJuGXqDkZisEmKyzh6KYKdTE+Hnwisz+zZr8IIQlmAeZjA01ANQ/m6fWF3M8kNpklwTQgjR5pLzyth4MpsNJ7LZFptDmd5Q+1xJZbUk14QQklwTFwA7V7jxd/hqilJ++PUMmL0C3BqYcpm0QxlgYDLC4Jth/FNtG29znJlca221JaEt3LUGSgmrW7AyhTX7ODiObvGSEX4uLJ0bxf0/7mfzqRzm/rCf73YkkpJfTkp+eb3nONho6OnnTG9/F7ydbTEaTVQbTRjq3BoxGMFgNNY+FurpyOR+/vTwdWr1hNq/eTrZ8uKMvk0+z2A0kVZQTnxOKQm5pcTnKB8JOaUk55dTUWXkQHIBd32zh+UPjMHdsfVLa4U4U0WVgUMpBcDp5Nrkfv68u/okW2JyKCyrwtVBSkOFEEK0nooqA7vi89hwIpuNJ7OIzS6t87yXkw2ju3mx5GAaB5ILSC8sx9/V3krRCiHaA0muiQuDg4cyTfSryZAbo+xkm71SGYRwpqxoZQBCdQX0uAymvNeyaZhtJXQMqDRKuWZBcsOJQ0s4uUq5DRxqmfV8eivJtazjENry5BqAm4MNX80expt/R/O/jXHsiMurfa6Lqx29/F3o3cWFXv7KR4iHwwVTCqlRqwjycCDIw4GxeNd5rspgJDG3jDu/2UN8TikP/3yAL24ZesF8bUT7cCC5gCqDCW9nW0I8HQDo5uNEhJ8z0RnF/HMsg6uHtOK/cUIIIS5YBWV6Hv/1EJtOZVNRdbqyQaNWMTjYjfE9fRjXw5ve/i6o1SpS8svZk5jPP0czuWVUqPUCF0JYnSTXxIXD2RduXgJfXAZ5sUoPtluXK4k3gKI0+O5KqChQEkdXfQGaDvJXxN5NGS6Qsgvi1is77lpD5lFI3glqLfS90jJr+vRSEnYtGGpQH41axVOX9WJCTx9OZBTTw9eZXv7ObTLkoKPSadR083FiwazBzPx4KxtOZLNgQwxzL+pu7dDEBWR3vJIMHxbqUWdH6JR+/kRnFLP8cLok14QQQrSK73cm8c+xTAD8XOwY18Ob8T29GdXNq96BOpP6+rEnMZ9VRzIkuSbEBU5t7QCEaFOugXDLUnD2V5I5316uNOgvL1ASa0Up4NkdbvgZbBysHW3TmKeGtmZp6J4vlduek8HZzzJrWmioQUNGhHtyy6hQRnb1lMRaI/Xyd+HlmpLTd1efZFtsjpUjEu1ddEYRX26Nx2A0tXitXbX91tzrPD65vz8AW07lUFCmb/F1hBBCiH/762AaAM9P7c32py7ijav6c1k//wYnVV/aR/l5eGd8LrkllW0WpxCi/ZHkmrjweITBzUvBwQvSD8D3V8NPNyjJNic/uOn307vZOhJzci1uAxgN5zy0WfSlcGixcn/IbMut69NLuc06pnTeF+3CNUODuDoyEKMJHvhxP5lFFdYOqZbBaCK1oJztsbn8vDuZzaeyrR3SBU1fbeT2r/bw4l/H+GN/aovWqjYY2ZdYMyk0rO6/w129nejl70K10VS7q0AIIYSwlJOZxURnFKPTqLhycGCj+ukGeTjQp4sLRhOsOS7/NwlxIesgNW9CWJh3D7j5T2XIQfJO5TFbF7jxV6XBfkcUEKm8hvJ8SD9omYEDZzryO1QWgXsYhI233Lqe3ZV+cRWFUJwOLl0st7ZokZdm9OVwaiHRGcXc/+N+frhjOFpN27wnU1hWRVJeGUl5ZSTn19zWfKQWlFNlqJuI/e3ekUSGdMCkeCfw274UUguUYSErDqe3aJrn8fRiSvUGnG21RPi5nPX8lH5+HE8vYvmhdK6R0lAhhBAWZN61Nq6HT5MG50zq48fRtCJWHcng2qEd9PcIIUSLyc41ceHy6wc3/gE2zqCxgeu+Vx7rqDRaCBur3G+N0tA9Xyi3kbeC2oL/dOjswLOrct/CfddEy9jbaFgwazBOtlp2xefx9j8nW/2aJpOJx345yICX/mHaR1uY88M+Xl8ZzQ87k9h8KoeE3DKqDCZ0GhWhng6E1jS8f3nZcUyy87HNVRmMfLw+pvbzzaeyKaqoavZ65pLQyFB3NPUM0pjcTykN3RqTQ36plIYKIYSwDJPJxNKa5Nq0Af5NOndSX6U0dGtMbov+DxRCdGySXBMXtsBIeGA/3L/vdGKqI+s6QbmNXW/ZddMPQto+UOtg4CzLrg1nlIa2Tt810Xzh3k68cWV/ABZujGVtK5c8/H00g1/2pgDg7WzL4GA3Zg7swv0XdePNq/rz010j2PrkRUS/fBkbHpvAz3ePxMFGw4HkgtofikXb+X1fCin55Xg52RDm5UiVwdSi7xHzMIOhofXvQgz3dqJ3bWloRrOvI4QQQpzpUEohibll2Os0XNLbt0nndvNxItzbEb3ByProrFaKUAjR3klyTQgnb3DrJOVF5r5ryTuhssRy65oHGfSerny9LK2VhxqIlpnS359bayZgPbz4AMl5Za1ynXK9gZeXKd8D91/Ujd3PTOT3+6J4/7pBPPKfnlwzJIgR4Z4EuNnX7mrycbHj3nHKzsc3V52goqoV+g2KelUZjHxUs2vt7rFdmVYzcGDF4eYlvUwmE7trdq4NC2u4xHdKzXWWN/M6QgghxL+Z36Cb2NsXB5umdU5SqVRMqhls8PdR+b9JiAuVJNeE6Ew8wsE9FIxVkLjVMmtWFsPhX5T7kRYcZHCm2uSalIW2V09P7sXAIDeKKqqZ88M+Kqstn8T636ZYUgvK6eJqx33juzX6vDvGhOPvakdqQTmfb4m3eFyifn/sTyU5rxxPRxtmjQjmspqSzY0nsymprG7yenE5peSW6rHRqukf6NrgcVIaKoQQwpIMRhPLDinJtekDmtf711wauj46W97oE+ICJck1ITqbcHNpqIX6rh3+BfQlyuCB0NGWWfPfapNr0WA0ts41RIvYaNV8PGswbg46DqUU8soyy+4yTM4r45MNsQA8PaUX9jaaRp9rb6Ph8Uk9AViwPobs4kqLxibOVn1Gr7W7xobjYKMlws+ZMC9H9NXNK4sxl4QODHTDVtvwn3+YlyN9urhgMJpkh4AQQogW252QR2ZRJS52Wsb28GrWGv0CXOniakd5lYFNJ2WKuRAXIkmuCdHZmEtDLdF3zWQ6XRI6ZDY0YiR5s3iEgcYWqsshX3YetVcBbva8d+1AAL7dkciSA6kWW/vVFceprDYyItyDKf2a1kgYYMaAAPoHulKqN/Du6tYfvHChW3IgjcTcMjwcbbhpZAiglMVcVvPO/coj6U1e0zzMYGiY+3mPNe9eW3646dcRQgghzmQuCZ3U1++cb+6ci0ql4tKa/wNXyRs/QlyQJLkmRGcTNhZUasg5AYUpLVsrdR9kHFISXwOut0x89VFrwLdm99rxpa13HdFiE3r6MHeCUrL51O+HickqbvGaW2NyWHkkA41axQvT+6BqRhJXrVbx3FTle2jx7iSiM4paHJeoX/UZvdbuHBNepzfNZX2VpNf66GzK9E0rDTX3W2tomMGZzAnYbbG55ElpqBBCiGaqMhhZWfNGzfQBAS1ay9x3bc2xTKoMUokhxIVGkmtCdDb2bhAQqdxv6e61vV8ot31mgsP5f+FtkWF3Kbdb3oPy/Na9lmiRhy/pwchwT8r0Bu79bh+lzeivZVZlMPLC0qMA3DQihAg/l2avNTTUg8n9/DCa4P+WH8dkMjV7LdGwvw6lEZ9TipuDrnbXmlnfABcC3e0przKw8UTjy2IyCitIzitHrYLIkPPvXAv1cqRvgJSGCiGEaJktp3LIL6vCy8mWkV09W7TWkFAPPB1tKKqoZkdcroUiFEJ0FJJcE6Izqi0NbUHftfICOPK7cn/IbS0O6bz6XwvevaCiELZ+0PrXu9BYMNGkUav44PqBeDvbciqrhDu+3kO5vnnNe7/ZnsiprBI8HG14eGKPFsf2xKQIbDRqNp/KYUMTkjuicQxGEx+uO71rzcm27kQ1lUpVW7K54kjjk17mktBe/i442+kadc6UfkrT6eWHpDRUCCHEORSmwoeRsOqps576q6YkdGp//9pJ5M2lUav4Tx9fAFY14f9AIUTnIMk1ITojc3ItbkPzBwQc+hmqypSEV9Bwi4XWILUGLn5eub9jIRTJL8wWkbQDvrsKXnSHg4sttqyPsx2Lbh6Ck62W7XG53PHN7iZPx8opqeT9mv5oj13aE1eHxiVVziXE05Fbo0IBeGX5MSnLsLBlh9KIyy7F1V7Hzf/atWZm7ru27nhmo78nzMMMGlMSamYuDd0el0tuiQyxEEII0YDtH0NuDOz4BHJO1T5cUWWo3f08rZlTQv9tUk17hL+PZmIwyg56IS4kklwTojMKiARbFyjPg4yDTT/fZIK9bTDI4N96XgZBI5TBBhvfaJtrdkYmk1IS/NVU+OJSiFkNmGD7Rxa9zMAgN76+bSiONhq2xuRy5zd7mpRge3NVNMWV1fQNcOGaIUEWi2vOhG54ONoQm13KT7uSLLbuhc5gNPHftcovJXeMDmtwh9nAIDe6uNpRqjew+VROo9Y291sbFtb45FqwpwP9AlxrSkMzG32eEEKIC0hFEez7puYTU53qiHXRWZTqDQS42TM42M0ilxsZ7omznZackkr2J0mbEyEuJJJcE6Iz0uggdIxyP3p5089P3gVZx0Brr5RrthWVCia+oNzf9w3kxLTdtTsDkwlOrITPLoZvZ0LCZlDrYOAsUGmU4RS5sRa9ZGSIB1/dNgwHGw2bT+Vw97d7qaw+f4LtQHIBP+9RBm68OL1Pi0sxzuRqr+Phid0BeG/NKQrLqyy29oVsxeF0YrNLcbHTckvN7sD6qFSq2nfuVzZimmdhWRUnMpXBGE3ZuQYwpb95amhak84TQghxgTjwA+iLwb7m/5dDi2urI5YeUP7vmDagS7OGKdXHRqtmYi8pDRXiQiTJNSE6q4jJyu2mt2HPF00713x83yuVAQltKWQkdL8UTAZY93LbXrujMhrgyG+wcDT8eB2k7gWtHQy/Bx48ADMXKFNkAY79afHLDw314Itbh2Kv07DxZDb3frfvnAk2o9HE/JohBlcMCiAyxPLDMq4fFkw3HyfySvUsWN8+krQxWSXc+c0elhxItXYoTWY0mvhwnbJr7fbR4bicpy/a5H5Kaejq45nnTbbuSczDZIIwL0e8nW2bFFdtaWislIYKIYT4F6MBdi5U7l/0LASPBIMediygqKKKdSeyAJhuoZJQs0trpoauOpohw5WEuIBIck2IzmrADTDkdsAEyx6Gze80rql9WR4c/UO5P2R2q4bYoInzAZWSCErd13bXNRot2vi/1RmqYP/38PEw+PU2yDwCNk4Q9RA8dBguewNcA5Vj+1yu3B79s1VCGRHuyee3DsFOp2ZddBZzvt+Hvrr+fme/7UvhYHIBjjYanrwsolXi0WrUPDO5FwBfbk0gKbesVa7TWHsT87lq4TZWH8vk0V8Ocqpmp1ZHsfJIBiczS3C209b2tDuXwcHu+DjbUlxRzbaYc09M252glM0MDT3/lNB/C/JwoH+gK0aT8kuMEEIIUevUP5AfD3auMOA65ecjgD1fsuHAKfTVRrr5ONHL39milx3Xwxs7nZqU/HKOphVZdG0hRPslyTUhOiu1Gqa8A2MfUz5f+xL88+z5k0cHfwJDJfj1U3q3WYNvn9PlqGtfbJtr5ifAguGwcAwUdYASs+Td8OFgWHKf0qTXzg3GP6Uk1S55EZx86h4fMbXVSkPNRnX14rObh2KrVbPmeBZzf9h31kCBoooq3lgVDcADF3fHx8WuVWIBGN/TmzHdvdAbjLy+6nirXed81hzLZNZnOygoq8JGo6bKYOKJ3w51mEbHxjN6rc2OCsPV/vyDJ9RqFZNqBhusOE9pqLnfWlNLQs3Mu9dkaqgQQog6dixQbiNvBRtH6P4f8OkN+mIqty8ClF1rlioJNbO30TC+h/Jz2N/yxo8QFwxJrgnRmalUyjb4S19VPt/+ESydC4bq+o83mU6XhEa24SCD+kx4SukXFrdBac7fmgqS4etpkHMSMg8r94vb8Q9DcRvgmxlQkASOPnDJS/DwERj/JDg0kKBw9GzV0lCz0d29WHTzEGy0av45lskDP+6vk2D775pT5JToCfdyZHZUWKvFAUrvr2em9EKtghWHM2qTOG3pp11J3PXtHiqqjEzo6c3Kh8bgZKtlX1IB32xPaPN4muPvoxmcyCzG2VbL7U34M7uspu/aP8cyG5zaWlFl4FBKAdC0YQZnmlyTXNsRl0uOlIYKIYQAyDgCbefyWgAAashJREFU8ZuUNxaH3qk8plZD1IMAjC/4DVv0FpsS+m/mN5hWSt81IS4YklwT4kIwcg7M/ET5AWP/d/DLLVBVcfZxiVsh9xToHKHf1W0f55ncQ2Ho7cr9NS+0XrlmUTp8M11JVHmEg2uQshPs6+lQktU612yJEyvh+2ugqhTCJ8AD+5UfFG0bUdLQyqWhZmN7ePO/myKx0ahZeSSDh346QLXBSExWMV9tSwDg+Wm9sdG2/n9BEX4uXDs0GIBXlh3D2Ea7xUwmE++vOcmTvx/GaIJrhgSy6OYhdPV24omaUti3/j5BSr51y1XPx2g08UHNrrVbo0JxdTj/rjWzYWEeeDraUFhexfbY+ktDDyQXUGUw4eNsS7CHQ7NiDPJwYIC5NFR+iRFCiA7NaDRR3cAbMk2y8xPltvd0cDtjInnfKymx88dbVcgDnnsI83Js+bXqMSHCB51GRUxWCTFZHasVhBCieSS5JsSFYuANcO23oLGF6GXww9VQ+a//7Pd8qdz2uwrsXNo+xn8b86jSQyz9QOvstirJUhJreXHgFgy3/AW3LAXnLpBzQtkdVnruflFt6vCv8NMspWw3YircsBhsnRp/fhuUhppN6OnDJzcORqdRsfxwOg//fJAX/zpGtdHExF4+jO/pc/5FLGTeJT1wtNFwMKWQpQdbv+S32mDk6T8O8/4aJSl1/0XdeOPK/mg1yn+5s4YFMyzUgzK9gaf/ONKumx2vPp5JdEYxTrZabh/dtJ2GGrWKS2vfua+/ZHN3fE1JaJhHi8pyzFNDv9uRyJdb4/lhZxK/7k1h6cE0Vh3JYH10FltjctiTkMehlAKiM4oorpApskII0Z6U6w1c/O5Gpvx3CwVl+uYvVJINh35R7o+4r+5zGh2/6GYAcKNxiTL0oBW42usY1dULgL+PZrbKNYQQ7Ysk14S4kERMgRt/VRJW8ZuU3Vnm5FFpDhxboty31iCDf3PyhpFzlftrX1Ya+FtKaa6SPMs5CS6BSmLNNVDZvXbrMnDyg6xjyjFlbV9OeJY9X8JvdyhTVPtfB1d/DdqmTVZsq9JQs4t7+fLxDYPRqlX8dTCNzadysNGoeW5q71a/9pm8nW25b0I3AOYvPcoXW+KpqGqdH6bL9Qbu+W4fP+5KRq2CV2b25ZH/9KyTOFKrVbx2ZT9stGo2nczm933tc3qoyXS619oto0Jwc7Bp8hqTzaWhRzPr3Ymwq6ZUd1gz+63VXqemNDQ6o5gX/zrG038c5tFfDvLAj/u557u9zP5qN7M+28lVC7cz/aOtTHp/M4NeWs2sz3bw+ZZ4EnJKW3R9IYQQLbc2OpP4nFJOZBYz94f9zd/BtvdL5Y3IgEgIHFrnqfTCct7MHka+yQnX8uTTP/u2AnNpqOyqFuLCIMk1IS40YWOVRJK9B6Ttgy8vg8JUOPA9GKugyyDlo70YNRccPCEvVilptYTyfPh2hpI8c/JTdqu5h55+3rOrkmBz9FF6sH07UznHWrb+F5Y9BJhg6B1Kia9G27y1+sxUblu5NNTsP338+KgmwQZw59gwQjxbpwTjXG4fHUZvfxcKy6t4adkxxr21nq+3JVBZbbkkW36pnlmf7WDN8UxstWo+uTGSG0eE1HtsV28nHry4OwAvLz/W7nqF6auNfLsjkaNpRTjaaLhjdHiz1hke7oG7g47cUn1tIs2s2mBkX6J5UmjLkmuB7g68eWV/rhgUwJT+/lzS25dxPbwZGe5JZIg7fQNc6OHrRKinA11c7XB30FFtNLE1JpeXlx1j/NsbuOidDfzf8mNsj81tsEecEEKI1rPkwOnd5VticnhtZXTTF6nWw+7PlPsj7jurf/Cyg+mUY8caZ2X3Glvfb7XWI5f09kWlgsOphe2+DYQQouVUpvZcj1KjqKgIV1dXCgsLcXFpB6VqQnQG2Sfg28uhKFXpM4YKCpNg2n8h8hZrR1fXjk9g1ZNKIuyB/WDTvN5MAFQUKbvR0vaBozfcugK8e9R/bFY0fDUFynKgy2C4+U9lnHtbMZlg/auw6U3l89EPw8XzWzZoojQX3u6u7IC7f5+SSGwDO+Jy2ZuYz+2jw7DTadrkmv+mrzby274UPloXQ2pBOQD+rnbMmdCNa4YEtagHXEp+GTd/sYu47FJc7XV8fssQhpwnYVRlMPL/7d13fE33/8DxV5KbJYuQYcTee8cepWiVKqWUUrRm1Wj77VT9deleqNXqsEvtFrVrhBArsXcIGbL3zb33/P74SEIJuTc3i/fz8biPu84993PJSe593/d4euZeTt5I4KmGZZn5fFOLn98aTCaNwMsxrD16nY0hN4hLUZmiYztV480etS3e75srj7P80FVeaFWJj/rUz7o9+Fo8vWbuwc1Jx9H3u2FnW7ADVC7dTGb76Ui2n47gwMUYDLf143Nz0tGxphdd6njTqaY3pVzMz9oTQgiRe/EpGTT/ZAsZRo3JXWvy7dazAHzdvxH9mlXI/Y6OLYfVo8CtrJqgbndnr9BeM/YQHBbPl0+Wp/+/PcCQCi+sgWqdrfhqsg2YG0DgpRimPlXX7PYKQoiiIbfxKMlcE+JR5VULRmyG0tUh/qoKrDm6Q/1+hb2yuzUfoXqiJYVD4FzL95OeBIufVYE1Z08Yui7nwBqAd22V1ZaZ5beonwrOFQSTCTa9nR1Y6/I+dP0g7xNcC7g0NFOrqqUZ37l6oQXWABx0tgxqWZHtr3fkoz718XV34kZ8Gu+tCaHzVztZGhhqUcbSyesJ9P1xHxejkinn4cTKMa0fGFgDsLez5YtnG2Jna8OG4zfYcrLge7JomkZIWDyf/HWSNp9tZ+C8/SwNDCUuJQMvN0dGd6ialWFnqSca3CqLORGO8bYAVmYmW/NKpQo8sAZQpYwLI9tVYfFLrTj8/uP8OLgp/ZpWwNPFgcQ0AxuO32Dy8mM0+3gLb68KLrBhGEII8SjaGHKDDKNGbV83JnatwYTHVDuHt1cHc+xqXO52ommwf5a63PLluwJrl24mExwWj52tDY81rQNNh6o79n5nnRdxDz3qqb+Bm6U0VIiHnoV1RUKIh0JJPxi+CRb3gxvHoNEg8xrkFxSdI3R+F1aPhj3fQrMXwbmUefvQp8DSgXD1gMo+G7oGfHLR+8unHgxdC7/1gmsHYXF/GPJn/v47mYyw/tXsMtgnv1JvEq2lXh+4uEOVhrZ/zXr7LSYcdXa80KoS/ZtVYPnBq8zaoTLZ3l4VzI87zzPhsRr0bVI+awABQFK6gSvRyYRGp3A5OoXQmGSuRKdwJTqFG/GpmDSo7evGr8Nb4uvhlOu11C/vwUvtqzB310XeWxOMf1VP3J1yP5HTUhejklh37Drrjl7n4m39xtycdDxZvyy9G5ejVdXSVgl6talWBjcnHVGJ6QRdiaVlFRV4vH2YQWFzd7LnyQZlebJBWYwmjaNX49h+OoJtpyI5HZ7I0sBQnO3tmPpUnTwNXhBCCHFvmSWhTzcuD8DkrjU5dSOBraciGb0wiHUT2uLt9oC/r6H71ftZnRM0u7t/8Lpbz9GuehlKuzpC6/GqhPTiTrh+JF/aonSv78uHG05y8EoMUYnpeLmZ2S9XCFFsWJS5NmvWLCpXroyTkxP+/v4EBgbmuO38+fNp3749pUqVolSpUnTt2vW+2wshCpjrrdLI/r+q7KiiqkF/8K4LafEqwGaOjDRY9jxc3g0ObjBkNZRtlPvHl22ognGOHnB1Pyx5DvT51ADdoIeVI1RgzcYW+syxbmANoHavApsaWpQ52dsxrE1l/v1fZ95/qi5lXB25GpPK/1Yep8s3u5i07AjP/LiXZh9tof60zfT8YQ9jFx/m802nWRp4lX0XogmLU4G19jXKsHx0a7MCa5kmd61J5dIliEhI5zNL+suYYcvJCHrP3MNjX+/iu63nuHgzGUedLT0blGXuC8049F5XPn+2IW2rl7FaNpmDzpbH6/oA8HewmhqqaRoHrTTMwNrsbG1oVqkUb3SvzaZJHfh+YGMAFuy9xPzdFwt3cUII8RAKj09j/yU1YKtXIzWgxtbWhm+fa0w1LxfCE9IYu+jwg/uk7v9RnTd8Dkrc+bdF0zTWHVMDhHo3KqduLFUJGjyrLu/5ziqv5b/Kl3SmYQUPNI1CyVAXQhQcs4Nry5cvZ8qUKUybNo3Dhw/TqFEjunfvTmRk5D2337lzJ4MGDWLHjh0EBATg5+dHt27dCAsrmtPRhHgkObpCvWeKZtZaJls71W8M4MBcSLh+/+0zGfTwx1CVqWXvorLOKjQz//nLNYEXVqvS2St7VBZcRqr5+7mfjDRYPliVa9raq4mgjQdZ9zmg0EpDiyoneztGtKvC7v915t0n61DaxYEr0SmsOXqdI6FxRCfrASjt4kCTiiXp07gcr3apwdf9G7FyTGsC3+3C7yNa4uFsWcaZk70dn/VrCMCSA6HsvxhttdeWKTw+jdELD/Hy74c4fk2VxHSs6cU3AxoRNPVxZg1uSvd6vjjq8qdsN3Nq6KaQcEwmjYs3k4lO1uOgs6VBhQLsY2iBpxuX550nVc+5T/8+zdqj8v5FCCGsacPx62gatKhcigqlsvvqujnZM39oc9ycdARdieWDdSfIsV147BU4vUFdbjX2rrtP3UjkQpT6QqlbPZ/sO9pOvLXBunz7wjFraugJKQ0V4mFm9kADf39/WrRowcyZMwEwmUz4+fkxYcIE3nrrrQc+3mg0UqpUKWbOnMnQoUNz9Zwy0EAIAaheGr88AaEBKpOt6VCVyZaWoM7TE267HK/O48PUpFGdMwxZCZXb5W0NVwPVIAh9ElTrAoOWgc4Kzc5NJvhzJJxYpdY6cBFU75r3/eYk6FdYPxF8G8KY3fn3PMVQit7AqsNhJKYZqFS6BBU9S1CpdAnc8rlc8+1VwSwNDKVy6RJsmtTBKv3pjCaNxQeu8MWmMySlG7CzteGl9lUY1b6qKokpIGkZRpp/vJWkdAN/jm3DuYhE3loVTMsqnvwxunWBrcNSmqbx0YZTLNh7CXs7G34d3pK21csU9rJEAboel8rUNSG0qOLJ6A5VpTxYFKpUvZFj1+JoWdkT20LoWWltmUMGPupTnxfuMWV755lIhv96EE0jx23Y/C4EzISqnVW1wX98tvE0c3Zd4In6vswe8p8vWRf3h3P/qFLSXt9Z50Xd5mJUEo99vQudrQ1BUx+3+Ms4IUThyG08yqyea3q9nqCgIN5+++2s22xtbenatSsBAQG52kdKSgoZGRl4ehatMhAhRDFgY6Oa+i/oDsEr1Ck37Bxh0JK8B9YA/FrC4JWwqC9c2AZrx8Mzc8E2j/NhdnysAmu2Onh+OVTtmPe13k/tXrBhSnZpaAFNDS0OSjjoGHKvN+757O0na7P9dASXo1P4dutZ3n6iTp72d+pGAm+vCuborUbQjf1KMr1vA+qULfgvqZzs7ehSx5u1R6+zKeRGVjbgA0tCd36uMkW964JvA3Xyqq36MBYgGxsb3utZh8jENDYcv8HohUEsH92KeuWKdtadsI4UvYGXfjvEyRsJbDsdyZXoFD7uU79QBnEIAfD+2hBWBF1jeNvKTOtVr7CXkycXopIIDotHZ2tDzwZl77lNp1re/K97bT7fdJr/W3eCmt6u+Fctnb1BeiIcXqgutxp31+NNJo31x1TFQ6/MktDbtZ2kgmtHl0Cnt8HN5+5t8qCqlys1fVw5G5HEsAWBlC/pjKujDlcnHa6OOtycdLg46rJuc3PUUbKEPVXKuMrvGSGKEbOCazdv3sRoNOLjc+cvHB8fH06fzl2fmDfffJNy5crRtWvOGRnp6emkp6dnXU9IKKDpfEKIoq9iK/Afq9L3Hd1UmaaTBzjdOnd0v+2yhzr3rQ/u93gzZalKrWHAQlj6HAT/AW6+0O0jy/d3ZBHs/lpd7vVD/gfWILs09OIOVRr6CA42KGrcnez5uE8DXv79ED/tvkSvhuWoX9784E1ahpHvt51j/r8XMZg0XB11vNG9FkNaVSrUN+lP1C/L2qPX+Ts4PCsWfd9hBuEhsPNTdfnSv9m32+pUgM2nfnbAzbfBXf11rM3W1oavBzQiOklPwMVoXvzlIKvGtsHPs8SDHyyKLZNJ47U/jnHyRgJuTjqS0g0sDQwlPlXPt881zrdSaiFyEpGQxuojqjz9l72XaV7Jk54N7x2UKg4yhwy0r1EGT5ecKwHGdKzKyRsJrD92nXGLD7NuQjvKl3RWdx5dqioWSlfPyvo3mTSOXI3lr+Ph/B18g/CENFwddTxW2/vunVdqAxVawrVAODBbfZFrZU83Ls+Xm89w9Gpc1pdeD+LhbE/LKp60rlqaNtVLU9Pb7aHIVBTiYVWg00I/++wzli1bxs6dO3Fyyrnp8/Tp0/m///u/AlyZEKJYeeIzdSpMNbpC75mwZgzs+0EF2FqPN38/F3ep8kyA9q9Dk8HWXef9POJTQ4uix+v60LNhWf46foP/rTzO2lfaYm+X+6zI3eeieHd1CKExKQB0r+fD//Wub9GgBWvrVMuLEg52hMWpXoW2NtC0YsmcH7Bvhjqv1FYNIAkPVqe0OIgIUafjy7K3dy+vGlN3/T+V5ZoPHHV2zB3ajAFzAjgdnsiwBYGsHNvmvh8IRfH23dazbAwJx8HOll9ebEFkYjoTlx3h7+BwElIPMfeFZrg4Fszb6YS0DPacu0nb6mWkrOwR9nvAZQwmDSd7W9IyTPxv5TFql3WjmlcR7pubAzVkQAXX+jQpf99tbWxs+KJfQy5GJXHiegKjfj/EyjFtcNbZwIE5AJhajuHI1Xj+Dr7B38E3uBGflvV4N0cdU5+qe++WCzY20G6SGn51cAG0m6K+qLWiUR2qUrecO9FJepLSMkhKN5CUbiQpPYOkNMOt67dOaQYiE9OJT81gy8mIrEEIni4OtKqqgm2tq5WmmperlKgLUYSY1XNNr9dTokQJVq5cSZ8+fbJuHzZsGHFxcaxduzbHx3711Vd8/PHHbN26lebNm9/3ee6Vuebn5yc914QQRc+eb2HrB+pyv5+zp07lRtQZ+Plx1Ruufj/o+1Pey0vNkRwNX9UAzQgTDhed0lBjhuqd51L6wds+hKIS03n8213EpWTg5+lM+ZLO+Lg74e3miLebE97u6tzH3RFvdydcHXVEJ6Xz8V+nsrIZfN2d+PDpenSr51vIr+ZO45cc5q/jamJo/fLubJjQ/t4bxl+D7xuByQAv74DyTdXtmqbuywy0Rdw6j72c/dheP0CzYfn6OsLj0+g3ex9hcak0qViSJS+1wtlBMpgeNuuPXWfC0iMAfPlsQ/o39wNUEHv0wiBS9EYa+ZXklxdb5HuANSQsnnGLDxMak4KniwOvdavJwBYVpWTsEZOqN9Lms23EpmQw6/mm/B5wmQOXYqjl48bq8W0o4VCgeRN5duxqHE/P2ouzvR2H3uuaq0D1tdgUes/cS0yynt6NyvFdkwhslz1Hqp0bT9nN4UJC9jHh6qjj8bo+PNmgLO1rlLl/L1OTCX5sBTfPwOMfZg86KCQGo4mQ6wkEXIhm34WbHLocS2rGndNSvdwcaVW1NJ1qetGnSXn5fSBEPsltzzWLBhq0bNmSGTPUN8omk4mKFSvyyiuv5DjQ4IsvvuCTTz5h8+bNtGrVypynA2SggRCiCNM02PS2KiOwtYfBK6Ba5wc/LikKfuoCcVfAzx+GrgP7Qsgu+r2Pyl7r8n7RyF5LiYEFPSAuFIauhYr+hb2iQrH+2HUmLz+KwfTgP9ElHOzQNEjNMGJjA8NaV+b17rVwvdeHFE2D5ChwvUdZTAHYcPw6ryxRwYr79grKbExduT28uOHBO06Lh/2zYed0VQ4+/gC452+Z1PnIRPrNDiA+NYOudbyZM6QZOjOyDEXRduxqHAPmBpBuMDGqQ1XeefLOHohHr8bx4i+BxKVkUN3blYUjW1LWw9nq69A0jaWBV/lg/Qn0BhN2tjYYb/1eqO3rxvu96tKmmgzXeFQsORDKO6uDqVDKmV1vdCY6OZ2eP+whKjGdvk3K8/WARsUqk+nD9SdZsPcSvRuV44dBTXL9uP0Xoxny0wEMJo1lTp/RiuPMMTzFZ4bncXGwo2tdH3o2KEuHml7mDQc6skj10nX1hUnHC7y/5/3oDSaOX4sj4EI0ARejCboSS7rBlHX/gOYV+Lxfw2L1/y9EcZHbeJTZ7wKnTJnC/Pnz+e233zh16hRjx44lOTmZ4cOHAzB06NA7Bh58/vnnTJ06lQULFlC5cmXCw8MJDw8nKSnJgpclhBBFjI0NdP8U6j0DpgxYPgRuHLv/YzJSVelB3BUoVRkGLimcwBqo0lBQpaGFzZgBK4apb40NqbDqZdWk+BHUq1E59rz5GEtfbsX3Axvz7pN1eLl9FXo3Kkerqp5U9XLJCp6l6I2kZhipU9ad1ePa8kHvevcOrAFs+z+VrRgwqwBfTbbOtbxx1Km3HjkOM0iNU9NsIfeZA04e0OENKNdU9d35+/U8r/VBqnu78fOw5jjqbNl6KpKpa0Mw8/tKUUSFx6fx8u+HSDeYeKy2N2/2qH3XNo39SrJyTGvKejhxPjKJZ2cHcCHKuu9tU/QGpvxxjHdWB6M3mOhS25sD73RhWq+6uDvpOB2eyPPzDzBmYRCh0SlWfW5R9GiaxoK9lwB4sU1l7Gxt8HZzYsagJtjawKojYSw7eLWQV5l7RpPG+uOqJPTpxub1xW1VtTTTetWlps1VWnEco2bDjZpDmPtCM4KmPs73A5vQrZ6v+VO3GwwAt3KQFA7Hlj14+wLkoLOleWVPJnSpwZKXW3FsWjeWjWrFuE7VsLWBPw5d45stZwt7mUI80szOXAOYOXMmX375JeHh4TRu3JgffvgBf3+VXdCpUycqV67Mr7/+CkDlypW5cuXKXfuYNm0aH3zwQa6eTzLXhBBFniEdFvWDy7vBxRtG/gOeVe7ezmSCP0fAidUqIDByK3jVLPj1Zrq9NPTVI+BZtXDWoWmwYZIKqji4qn+bhDBoPAT6FE4gqDhI0RuITEgnWW+glo/b/TOnLu6C33ury7Y6GPEPVGhWMAu9ze8Blzl4OZYvn2147w8+u79RQUDvujB2n3n908JDYF5HVU464Heo+7T1Fp6DzSfCGbsoCJMGk7rWYFLXQjyeRZ6lZRgZMDeA49fiqeHtyqpxbXBzyrm/WVhcKi/8fICLUcl4ujjw2/CWNKiQ9ymy5yMTGbf4MGcjkrC1gTe612Z0h6pZzcxjk/V8u/Usi/ZfwaSBg50tL7WvwrjO1XMOrotibdfZKIYtCMTVUUfA24/d8XM5e+cFPt90GgedLavGtrFoGE5B23v+JoN/OkDJEvYEvtMVB515OR+apnHt95fxu7QCY+3e2A1caJ2F7ZsJ/7yrhuaM3WudfeazpYGhvL0qGICPnq7HC60rF+6ChHjI5FtZaGGQ4JoQolhIi4dfeqo+UJ5VVfDC1evObbZ9qCaD2trDC6uhSg49pwpSVmnoNGg/pXDWsH82bHoLsIFBS1Vw7ZcnAa3AgiQPtdQ4mN0WEq6BU0k1FKBUFRizW03dLSoM6fBdA0iKgD6zofHz5u9j+8fw75cqyP1KIDiXsv46/2PR/iu8tyYEgA961eXFtvcIrBcATdM4G5GEi6MdZT2cpf+OmTRN49VlR1l/7DqlStizdnw7KpZ+8DTY6KR0XvzlIMFh8bg42DF/WPM8lWquPRrG26uCSdEb8XJzZMagJrSqeu8elGfCE/lwwwn2no8GVA+mN3vUpm+T8jJV8CEzdEEg/56NumdJvcmkMWphEFtPReDn6cyGV9rjUaJoD73438pj/HHoGs/7V+TTZxqYv4OUGPimDhjSYMRmNU3eGlJi4OtaYNTDmD1qGnUx8P3Wc3y79Sw2NjDr+aY82aD4TpAVoqjJt7JQIYQQOXDygCEroWRFiLkIS/pD+m1lQkcWqcAaQO8fikZgDW4rDV1dOM9/bgtsfkdd7vYR1HoCKrWBdpPVbesnQsKNwlnbw2LjmyqwVqqKygbzqAixl+Cv/C+fNMvxP1Rgza0c1DdjOMjtOrwBZWpCciRsfs+668vBkFaVeKVzdQA+WH+S99eGkGE0PeBR1pWqNzLi14N0/+5f2n2+g9pTN9L5q50MWxDI+2tD+Gn3Rf45Ec6Z8ERS9Ib77stk0kjVG4lJ1nM9LpULUUmE3zZ172E1c/t51h+7js7Whh8HN8tVYA2gtKsjS172p3XV0iTrjby44CCbQsLNfv50g5H31gQzcdlRUvRGWlctzV+vtssxsAZQy9eNRSP9mfdCMyqVLkFUYjqvrzjGMz/u5cDFaNINxhwfK4qPcxGJ/Hs2ChsbGN7m7uC9ra0NX/dvhJ+nM1djUnltxTFMuejZWVjSMoxsvHWMPN3IvJLQLCfXqsCaTwPVu9ZaSnhCzR7q8tGl1ttvPnu1S3UG+1dE02DSsqMEXIgu7CWJ+5DfzQ8nyVwTQghru3keFnSDlGio1gWeXw5X9sGivqpcrcMb8FjBfOjPlcIsDY08BT89DvpEaDIEes/MLgM06OHnrqqHXdXOMGRVwU5TfVicWKN62dnYqm/3/VpC6H745QnQTNB3PjQcUNirvDWpzR9unoXHP4K2r1q+r9ADsKA7oMELa3I3ZCSPNE1j1o7zfL3lLJoG/lU8+XFwU0q75n9D7MS0DEb+dojASzHobG2wsYEM4/3f3nm5OVLWwwm9wUS6wUTqrd59aRnGO5pk3653o3L8r0ctKpTKXdCpONkYfIOxiw8DML1vAwa1rGj2PtIyjExcdoTNJyKwtYEe9X2p7u1GNS8Xqnm5UtXLJcdpjldjUhi/5DDHr8UD8Ern6kx+vKZZ2YfpBiO/7r3MjO3nSUrPDqCWLGGfNW3Yy80RbzdHvG6dMicQl/VwKnaTJh8lb68KZmlgKN3q+jBvaPMctwsJi6fv7H3oDSbeeqI2YzoWkSng/7EpJJwxi4Io6+HE3jcfsyzL8rdecOlf6PpB9pdx1nJmIywdCC5eMOUU2FkhC1DT4OY59R7LLn+ONaNJY/ziw2w6EY6bo44/xrSmTln57FzUzPv3Ap9tPE3/Zn5M7VVXSvmLASkLFUKIwnTtkHrjl5ECtZ6EK3tV2Wj9ftDvZ/P6SBWE35+GizsLtjQ0+SbMf0wNdqjUVgVBdA53bhN1FuZ2UAMOenwOrcYUzNoeFonh8GMrSI1V02C7vJ99387PYeen4OCmykPv1SOwIGV+mHF0h8knwCmPf+//fgMC50HJSjAuABxcrLPOB9h6MoJJy4+SlG6gfEln5g1tRr1y+df/KDopnWG/BBISloCbo44Fw1vQtGIpbsSnEhqdQmhMCldiUrIvRyeTkHb/zLXbOehscdLZkphuQNPU9ZfaPVy9vULC4uk/J4DUDCMvtqnMB71zmGKbCwajiXdXh7D80L0by5fzcKKatyvVvFyzgm6xKRm8veo4CWkGSpaw59sBjelc2/KJvlGJ6Xy1+QxrjoblGCj9L0edLW90r8WItlWknLSIiU3W02r6NtINJpaPaoX/fTIZIXuiqJ2tDYtf8r9v5mNhGbc4iL+DwxndoSpv/2cSb64kRarSTc0EE4+p4VDWZMyAr2tDyk14/g+o2T3v+9zzHWydBn6tYMBv4Oab933eQ1qGkaE/BxJ4OQZvN0f+HNsGP8+H7wuR4upqTApdvtmF/tbvZj9PZ74Z0JgWOQ15EkWCBNeEEKKwnf1HBQu0W6nffv4wdF3hTQa9n6BfVfmlb0MVaMlvhnQV0AsNUG+KX9oOLjl8AAicr6Y/2jnC6F3gbcEb8UeRpsHi/nB+i/p/fWnbncFLkxF+fQpC90H5ZiqrzRrfzltqwRNqLW0nwuMf5n1/6Ykwq5Uqh239CnT/JO/7zKXzkYm8/HsQl24m42Rvy5fPNqKXpaVP93EjPpUhPx3gQlQypV0c+G1Ey1w1Mo9L0RMak0JkQjqO9rY42dvhbG+Hk70dTva2t122y8qcOnE9no83nCLgoio1KuPqyOvdatK/uV+x7u0WmZhGn5l7uR6fRvsaZfjlxRb3HwySC5qmEXAxmpPXE7gQlcSFyGQuRCURnay/7+Ma+ZXkx8FNKV/SOU/Pf/s64lIyiExMJyoxncjEtNsupxOZkJZ1OTPTrWNNL74e0IgyBZBxKXJn1o7zfLn5DPXKubNhQjtsHvDlnKZpvPbHMVYdCcPLzZG/Xm2Ht1vRed+RmJZBs4+3ojeY+OvVdpZ9+ZD5vqB8c3h5m/UXCbDxLTgwG+r2UcGwvMhIg2/rqWAdgKsP9P9VtcDIB/GpGQyYE8CZiESqlnFh5dg2eLo4PPiBIt+NX3yYv4JvUL+8O7HJGYTFpWJjA6M7VGPy4zVw1Jk54VYUCAmuCSFEUXBkMawdp8oARm4BF8ubXOergiwN1TRYOx6OLlZZSiO3gHft+2+fGSTyaaDeSOvkg98DHVoAGybfCkr+e+9/47irMKetyqpsNwW6Tiv4dQJcPahKgG3tYVIwuFupEfO5LbD4WVUS+9JWFUQsIPGpGby69Ai7zkYBMK5TNV7rVstqgajLN5MZ/NMBwuJSKevhxKKX/Knm5WqVfedE0zS2nork079PcelmMgC1fd14r2dd2tXI/e+22GQ9By/HEHgphtiUDHw9HCnr4UxZD6es85Il7B8YRMgLTdM4cCmGT/8+xfFr8VQt48Lq8W3xcM6/AHNssp6LN7ODbeqUzM3EdPo39+OtJ2qbPTHRGjRNY/GBUD7acJJ0g4kyro58+1wj2tfwevCDRb7SG0y0/2I7EQnpfDOgEX2bVsjV41L0BvrM2svZiCRaVfVk0Uj/PAeNrWVl0DVeX3GM6t6ubJncwbLjfEEP9eVc90+h9XjrLxJUS4q5HcDOAV4/m7fhOJnvBd3KgXNJiDyppnZ3+xj8x+RLNUN4fBp9f1RfHDT2K8mSl/2l9LuQHbgYzXPz9mNrA3+92p4KpZz5v/UnWRl0DYA6Zd357rnG1PItQoOmBCDBNSGEKDpiLqlvKR2KeFp+QZWG7v0etryvAh6DV0D1rg9+TGIEzG6t+thZK7PpYRZ9Aea0U2XJ3adD63E5b3tyLfwxFLCBoWuhascCW2aW5UPg1HpoPBj6/Gjdff/5MgT/Ad71YNTOu0uP85HRpPHF5tPM3XURgM61vPh+UBPcnfIWwDkdnsCQnwK5mZROlTIuLBzZskD7oOkNJhbuv8L3W89mlZh2qe3N20/Wobr33QG+yIQ0Ai/HcOCiCqidiUh84HM42dtS1sMZX3cnypZ0oqyHEzW83WhTvXSesnAiEtJYGXSNPw5d5Up0CgDuTjrWjG9L1XwOThZ1Z8ITmbD0MGcj1CCe0R2r8nq3WtgXkaDMo2jNkTAmLT+Kl5sje998zKzg64WoJHrP2EOy3si4TtX4X4/7fIlVgF74+QC7z93ktcdrMqFLDfN3EB8G39ZVlyefBI/y1l1gJk1TU7YjT8BT30LzEZbvZ24HCD+u+sO1HAXrJkDIn+r+Bv2h1/f50rrgfGQiz84JIC4lg861vJg3tLkcz4XEaNLoPXMPJ64n3DUhd1NIOO+sDiYmWY+DnS2vd6/JyHZVi3VW+MNGgmtCCCHMUxCloaf/hmXPAxo88QX4jzbjsX/deqwNDFtfdKatFjVGA/zSA64dhCod4IW1Dx4Ese5VOPwbuPqqaaI5lejmh+gLMKMZoMG4/dYv+02OhlktVGC283vQ8Q3r7j8X1h4N438rj5NuMFHVy4X5Q5tbnGV2ODSW4b8cJD41gzpl3fl9REu83AonkzM2Wc/3286xaP8VDCYNna0NQ1pVYmBLP06EJRB4KYbAyzFZWW63q+7tSssqnpQv6UxEQhrX49IIT0jlRlzaA8sn65R1p0ONMrSv4UXzyqVwsr9/GU2G0cS2U5H8cegqO89EkjlE0cXBjl6NyvFS+6r3DAo+ilL1Rj7+6ySLD4QCqlT1h4GNqVS6YHoWPkiK3sD3W8/h7mzPmI7VHuoPn5qm0XvmXoLD4i0ORG04fp1XlhwBYGKXGkx4rHqhZrBFJabj/+lWTBrseqOTZT9XAbPUhPGKrWHEJusv8nb7ZsA/70GFlvDSFsv2cWWfGiCkc4YpJ9U0Uk2DA3PUvk0G8K4Lzy2C0tYfQBF0JZbBP+0nLcNEv6YV+Kp/w3zNChb3tvxgKG/+GYybk46dr3e6a9hRZGIab/8ZzLbTkYAaivT1gEYP5QCh4kiCa0IIIcxze2nowKUqyOFW1no94sKD4efukJEMzUdCz6/NL4XIDAK5V4Cxe1V5hbjTri9hx8eq5HbsPijp9+DH6JNhXic1qbPmEzBoacEN3Vg/CYJ+gRrdYfAf+fMcx1fAqpdUec+YPeBVK3+e5z6Cr8UzeuEhrsen4eao44dBTcxuWr/3/E1e/v0QKXojTSuW5JcXW+JRohD75N1yISqJ6X+fYuupyHveb2MDdXzdaVnFE/8qnrSo4nnfnl5pGUYiEtK4EZ/GjfhUbsSncT0ulaNX4wgJS7hjW0edLf5VS9OhRhna1ShDLR+3rA+O5yOT+OPQVVYdvsbNpOyAXfNKpRjQwo+e9bxwOTgD7F3UtOK8DtF4iGwKucH/VqohC66OOj55pj5PN86nDKFcuhKdzOiFQZwOV5mPT9T35dvnGj8wuFpcHbwcQ/85ATjqbNn31mMWTx6evvFUVvZs04ol+X5gk0JrcP/r3kt8sP4kjf1KsmZ8W8t2Mr8LhB2CJ7+Cli9bd4H/lRgO39RRgxNeCYIy1c3fx/IX4NQ6aDoMev9w531X9sGKFyEpQv3NfmYu1H7SKku/3daTEYxeFITRpDG6Q1Ve7y4ZqQUpMS2Dzl/t5GaSnvd61uGl9vduvaJpGssOXuWjDSdJ0RtxddQxrVddnm1WQQKihUyCa0IIIcyXWRp6O2dPcC+v+mC5lQX3crfOy6secjb/fYN2jz8rhnRYMVw1l6/SEYb8aVnz/PQkmNseYi6qUop+P5m/j4fZ9SPwU1f1Tfgz86DRc7l/bHiwmt5q1BfMhxaApCjV5NmYDi/+BZXb5c/zaBosGQDn/lGDRYZvenA2Xz64mZTOuEWHCbwcg40NtK5ampo+brdOrtTwccux59fmE+FMWHIEvdFE+xplmPtCsyLXP2fv+Zt8+vcpzkYkUr+8R1YwrVklT6v1MotOSmfP+ZvsPneT3eeiiEhIv+N+bzdH2lUvw5WYFIKuxGbdXsbVkX7NytO/mV92ltqBebDxViajozs0Gwb+Y/NWZpYaq8q9zm2FpkPz5YNyQQmLS2XSsiMcvKz+Hfs1rcCHT9fDpRCmxO46G8WrS48Qn5pBaRcHEtMM6I0mWlbxZP7Q5vnaK6+wjFkYxKYT4Qxs4cdn/RrmaV9rj4bx3uoQEtNVsPSjPvV4pknu+rdZ0zM/7uVIaBzTetVleFsLJlTHXobvG6n3Ha+dAVfLp+rm2qJnVc/XDm/AY++Z99i4ULVezQRjA8Cn7t3bJIbDH8Pg6n51vf3r0PkdsLVu0DgzcwpUKXzXOj50q+dLx5peODsUvQC1yaTxz8kIypV0omGFkoW9nDzJDHBXLePCpkkdHljefSU6mSl/HMv6G9a9ng8f9alv1cEkkQlpxKdmUMNH+rvlhgTXhBBCmO9aEGz7QDW6T7wBhjTr7r90ddVYPi+Nga8dgp+7qQy7vj9Bw/7WW19+iToDx5fDmU0q265ck+yTZ1XrZIllpMLcjnDzDNR9Gvr/Zv5+98+GTW+pIQijdoBPvbyv6362fwL/fqEGDby0LX+z5eKuwo+tQJ8ET3wJ/qPy77nuQ28w8X/rT2SV3f2Xr7sTNXxc7wi4nY9I4u3VwRhNGj3q+fL9oMZFeqKYyaRhWwDlepqmcT4yiX9vBdr2X4wmLcOUdb+drQ2da3kxoLkfnWt735mpkRIDPzSBtDjVEzMpQt1uq4P6z0KbV8C3AbliNMCF7XBsiSp9N94K+NmXUJmS+VDqVVAMRhMztp9nxvZzmDSoUsaFr/o3pE5Zd5zt7fI9m0LTNH7ceYGv/jmDpkFjv5LMGdKMSzeTGfX7IRLTDdTyceO3ES3x9Sg6EzHz6mpMCh2/3IFJg38md6CmFT4AX41JYfLyoxy69YH96cbl+KhP/Tz3gMytK9HJdPxyJ7Y2sP+dLpYFCvZ8C1s/UC0Phq23+hrvKeRPWDkCPPxg4nHzvpj5Zyrs+0F9qThsXc7bGfSwZaoqFQWo9hj0+1mVkFrRwoDLfL/t3B2ZvE72tnSs6UX3er50qe1TJLKhL0Yl8dafwQRejsHezoa5LzTjsdo+Vtl30JUYpq07Qb+mFSwL8JrpSnQyj3/zL3qjiZ+HNadLndy9DqNJY86uC3y39SwZRg03Rx1v9KjFYP9KeSqHT8swMnvnBebsukCG0cSyUa1pWcW6P2cPIwmuCSGEyBtNU1kYCddVoO3288zLKdE5PPgef/hLVYKnZ1nng+bOz2Hnp+DoocpDc1P6WNASw9Wb8uPL1dSxnDh53BZsa6rOPSqYH2ja+BYcmK36po0LsOxN+e0ZXl51VIDN3tn8/eSGPlllraXGqkBgvT758zy3C5wPf78ODq6qv1sh/tycupFAcFg85yISORORxLmIRG7E3z+Y/WyzCnzWt0GRmfpX1KQbjARdjmXfhWjcnXX0aVweb/ccPsD//QYEzlODLkbvggs71Ifgy7f1m6zaGdpMUB9073U8Rp5SU4+P/5EdnAPwqa+2Dw9Wx/TIfyzL1C1C9l+MZvLyo3f8jDrobClVwp6Szg6ULGFPyRL2lCrhQMkSDrcu21O5tAstKntaFGxNSjfwxopjbAwJB2BQSz8+6F0vK7B86kYCwxYEEpmYTjkPJ34f2ZLq3vmfhZGiN/Dv2SiaVipl1UyS23204SQ/77lE+xplWDjS32r7NRhN/LjzAt9vO4fRpFG+pDPfDWxMi8r5/+F6xrZzfL3lbN5e05z2ajDAU99B8+FWXV+OMlLhq1qQHn+r32uH3D1On6xKStPiYdAyqPXEgx9z/A/V/sKQCh4VYdhaq09vN5o0gq7EsvlEOJtPhHMtNjXrPp2tDa2rlaZbPV+61/XJ+fdnPjEYTfy05xLfbjlLusGEjY16W+Kgs+Wnoc3pUDNvE4wPXIxmxK8HSdYb0dnasGVKR6qUyd9+kqN+P8Q/JyNoX6MMv49oafYXEieux/P2qmCOX4sHoGEFDz7uU9/sbD5N09h8IoKPNpwkLC77/7xhBQ/WjGtbIF+IFWcSXBNCCPHwur1pf5laUNFfZcM5lVTnzqVUhtjttzm6kfVOzZCmThlp2ZfvuJ6uJneVrKjKYHP7wTg9EU5tUNMpL+5UpSCgsmGqPw71+6myy+tH1Ck8ODvL5XYuXlC2sQpEupdXZWruFdS5qy/Y/acs6+JOVdILMHgl1Hjckn9VJSkKZreB5EjVG++pbyzf1/0cmAsb/welqsCEIKuXwNyTyQS/PgmhAVC1k/q3KkJBj4S0DM7dCrSdjUjibEQiZyMSiUvNYHibyrzZo7a8AbaGiJNqmq5mhKHr7pyQG3YYAmbCiTXqflDBsjYToF5flfkYvFJlqV0/kv24EqWhwQBo/DyUbQjx19RxlBYPHf4Hj71boC8xP8Qm65m6NoTNJ8LJMOb+44OfpzMDW1Tk2WYV8Mnlh/XMzLRzkUnY29nwf73r87x/xbu2uxabwtAFgVyMSsbD2Z6fhzWneT4FiiIS0vg94DKLD4QSl5JBGVcHZg9pZvXAVGJaBq2nbycp3cAvw1vQuZb1Sx8Ph8YyadlRQmNSsLWBVzpXZ0KXGvnWh0vTNLp+s4sLUcl8+WxD+je34IuNm+dhZjP19/S1swU7eCez36s5E60PLYANk6FUZZhwOPd/48JD1ATt2Evqd07/Xyxe9oNomsaJ6wn8cyKcTSfCsyYFZ+pQ04sZg5oUSNn1yesJvPnncYLDVBCpfY0yfPR0faZvPMXmExE46mz5ZXgL2lQrY9H+952/ycjfDpGaYcTBzha90UT3ej7MfaG5NV/GXc/5/E8HsLO1YdPE9haXYBpNGksOXOGLTWdITDdgYwMvtKrE691r5Srz9HxkEv+3/gS7z90EoJyHExO71uCjDadISjfw3XON6dOkcHtqFnUSXBNCCPFwi7movsXWJz14WwAbO/Wm/F7BrPs+zhbcyqlAW0k/de7hd+tyJVVSdmWfylA7/Zf6xjmTnz80HAB1n7n3BwGDHqJOqQ/014/A9cMqG8ZkeMB6yt4WdCsPJ1ZDQhg0HwFPfWve67uXC9th4TPqcouXVf8Xa5anGA0wo4nqR9Pza2jxkvX2/SBRZ1XfPkMa1HtGlRb/N1hZxBhN2kM9FbFAaRos7KMC0nV6qQl99xJ7RZVJH/5dDWEBFfROjQNThrpuq4OaPVRArfrjoHO4cx+Z5WQ2tjB8I1RslU8vqmBpmkay3khssp741AxiU/TEpmQQf+s8LiWDuBQ9MSl6gq7Ekpimfp/Z2drwWG1vBrX0o2NN7xx/prefjmDisqMkphnwdnNk9pBmNKuUcyuB2GQ9I347yJHQOBx1tswY1IRu9Xyt9npPXI/n5z2XWH/selZQ0UFni95gwt7Oho/71Oe5FncH/iy1YM8lPtxwkqpeLmyd3DHfAuqJaRl8sO4kfx6+BqiS2+/zaTLsievx9PxhDw46Ww6919WyUtRdX8COT6B6V9W3tSCF7ocF3VXW8+tn1Zdv96Npqg1B1GnoPh1ajzPv+cJDYE5b9btjQpDVs9dyculmMptPhLMpJJyjV+MA6FHPl9lDmuZbCXi6wcjM7eeZvfMCBpOGu5OOqU9lN/HXG0yMWRTE9tORONvb8duIlmaXMf57NoqXfz9EusFEx5pevNatJn1m7cWkwR+j86cs0mA08dSMPZwOT2RY60r839P187zPyMQ0PvnrFGuPXgfAy82R93rWoXejcvf8/0lKNzBj2zl+3nMJg0nDwc6WUR2qMq5zNUo46Ji14zxfbj5DOQ8ntr/e6aEdDmMNElwTQgjx8Iu9DOe2qA+8qbGqf1JqbPb1zFNOATUbW9A5q4moulsne2c1VTItHuKvqkwzc5SuDg2fgwbPWvaGOCMVIk7AjaOqT1hCGMSHqWEQCddzDrx5VlX9nR70pj+3dkyHXZ+py86loPO70Gy4dQJRwSvhz5Eq22fyifwrPc3J2c2wbLAKkjQcqDIRCiJzThS+03/DskGqr+D4A+D5gJ47qbFw6BfVCymz9NO3ocpgafCsGupyP6tGw/FlKhA/Zs8jN5E0VW/kr+AbLAsMzer1BVDWw4n+zf0Y0LwCFUqpyZUmk8bMHef5dutZNE1Ndf1xcNNclaal6o1MWHqYracisbWBj/s0uGemW26ZTBo7z0by0+5L7LuQ3f6gZWVPRravQptqpXnzz+P8HaxKVl9sU5n3etbJc8m20aTR+audhMak8HGf+gxpVSlP+8uN9ceu887qYBLTDLg42PFqlxq4OOpITDOQmJZxx3lC1nV1GQ1KuTjg6eJA6Vvnnq6Zlx0p7eJAKRcHVgZdZdH+UJ6o78vsIc0sW+isVurLqKd/hCaDrfuP8CCapno0xl5SEz0bDbz/9plfUDm4wpSTqv2DuTIHKVjrSzMzHQ6N5bm5AWQYNT7oVZcX86E/2eHQWN5ceZxzkepL0h71fPmwT727yq3TMoy8/Pshdp+7iYuDHQtf8qdpxdz17t1xOpLRi4LQG0x0qe3NrMFNcbK34+1VwSwNDKVRBQ9W50NZ5KL9V3hvTQgezvbsfL0TpVwcHvygXNp7/iZT14Rw8ab64qdt9dJ89HR9qnqpYT2aprH26HU+/fsUkYnq/W+X2t5MfaoulW8rg03LMNLl612ExaXyRvdajO9swTTcR4QE14QQQohMGanqQ7LJoIJpOsdbQbQHfHtuMqnyyLirEHdFBdvirqqMq/hb5xkpUKKM+qDdcIDqsZRfTb4z1xN/TZ0yA2+psaps7V6TyPLi4i414CDypLruXQ+e+ByqtLd8n5oG8zqqPnSd3oZOb1lnreY6tV5NaNOMaqrjU98XygRRUYAM6TDLX31AbjcFuk4z77GXdoObL/iakYGQFg+z20F8KDR6Hp6Zbf66HxLnIhJZdvAqqw5fIzZFZf/Z2ECHGmroxNqjYfxzUgUwX2hVialP1X3gVL3bGYwm3lsTwrKDVwGY2KUGk7rWMCvjJi3DyKrDYfy85yIXotQHVztbG55sUJaR7arQ2K9k1raapjFj+3m+2XIWUB9wZz3flJIlLP8QvflEOKMXBuHhbE/A248V2ETgsLhUJi87SuDlmHx9njlDmtKjflnzHxhxEma3Vl98vX5OtX0oaJm9Xqt2gqFr77/t4gFwbjO0HA1PfmHZ813eq9oY2DnCpGBws05Df3P8svcS/7f+JPZ2Nqwc04ZGt/3850WK3sDX/5xlwd5LaBqUcXXgw6fr82SDnH820jKMjPj1IPsuROPmpGPJS61oUOH+Qct/ToQzfslhMowa3ev5MGNQ06zfKZGJaXT+cifJeiPfD2zM042tVxYZn5pB5692EpOsz7fAZLrByNxdF5m54zx6gwkHO1vGdKxK59refPr3qawpz5VKl2Bar7o5DoRYcySMScuP4uqoY+cbnSjj6mj1tT4MJLgmhBBC5DdNU9lyDm5FvrTQYkYDBP0C2z9WrxXUNNJuH6sSWXNd3AW/91ZBzsknCrZvzn+F/Al/vqR647V4CZ78Kn8nlorCtec72DpN9S2ccEj1YSwIVwLUh2TNBP1/VeXIj7B0g5HNJyJYeiCUgIt3DsVxsLPl4z71GdDCsmEjmqbx7dZz/LDtHKCGIHz0dH00ICnNQFK6yrpKSjeQlJ6RfTnNQERCOmuOhhGTrLKV3Rx1DPKvyLA2lSlfMufs2k0h4Uz54ygpeiOVSpdg/tDmFk/3HDA3gMBLMYztVI03e9S2aB+WMpo0Fuy5xK6zUTg72OHmpMPdyR53Jx1uTva43XGuw93ZHhsgJllPdLKemFun6CQ9sSmZt6UTk6Qu1/RxY8WY1paVnm3/GP79Emo+Ac8vs/prz5XYy/B9I8AGJoeowUP3En0BZjRVlycctnyIk6bBz4+r3rLmfhlgJZqmMXbRYTadCKdCKWf+mtA+z9NEAy5E8+afxwmNSQGgb9PyTO1ZN1eZXSl6Ay8uOEjg5Rg8nO1Z+nIr6pa7d2xgY/ANJiw9gsGk0bNBWb4b2PiunoKZQzbKl3Rm22sdrVYWmTmQpLq3Kxsnts+3XoagppG+v/YEu85G3XG7s70drzxWnZHtqtz3dZlMGn1+3Mvxa/EM9q/IJ8/kckr2I0aCa0IIIYSwnpQY1e/m0AIVJNA5QZtXod1kcCiR8+PS4lU/ubDDEBak+tOlxqhebj2/Krj15+TYMlg9BtCg1Xjo/smjEWDLnAacnqim2umTVf/CnC7bO0OTIao5d3GUGKE+8OqToM8caDyoYJ9/20ew+ys1YGXsPtUvUXD5ZjLLDl5lZdA1HHW2zBrc9I7sMEst2n+F99eGYNLA3s7GrAEM5Us6M6JdFZ5r4YerY+6+NDkdnsBLvx3iWmwqro46vnuuMV3r5j7TKC3DyD8nI3h16RF0tjbsfrMzZT0KuFzeXCGrVCCp/ZQHltVrmmZ5zy5NgxnNIOaC6pHZsL9l+7GGX56EK3uhy/vQ/rV7b7PxTVVGXqMbDF6Rt+c7/Rcse15NRp8cUihl5fGpGTw1YzdXY1LpVteHuS80s/j/8o+DV3lr1XFMmmqq/0nfBmYP7EhKN/DCzwc4EhqHp4sDS19uRS3fO4PZ649dZ9LyoxhNGk83LsfX/Rvds2Q7VW+k81c7CU9I4+0najO6Y96n2V+ISqL7t/9iMGn8NqIlHfM44TQ3NE1jY0g4/7f+BBEJ6fRsWJZ3n6xDuft8KXC7wEsxDJgbgK0NbJ7UweLBCw8zCa4JIYQQwvrCQ1Sp6OXd6rp7eXj8QzUJ1ZAOESEqiJYZTIs+d/c+HD1gzG4olf/9hHIl6DdY/6q63G6K+uD0MAbYNE318ju5Dk6tg+jz5j3exk71E2z/GpQpZr1Z1o6HI4ugfDMYubXgS4CNGSoL5foRqNIRXlgjZci30TQNk4ZVB3dsPhHOpGVHSc0wZt3mbG+Hq5MON0cdrk46XB1vnW7d5l+1NN3q+ljUOy0mWc/YRUEcuBSDjQ280b0WYztWu2cgQtM0LkQls+tsFLvORnHgYjTpBjVdunejcvwwqInlL7wgnNkESwcCGnT7BNq8kn/PdeMYzO2gvtB543zBZZzey+GFsO4VKFMTxgfe/XciLQG+qQv6RBiyCqp3ydvzmUxqMMLNM+rvbNuJedufhYKvxdNv9j70RhNTn6rLyHbmlznO2XWBzzaeBqBvk/J82Kd+roPX/xWfmsELPx/g+LV4yrg6smxUK6p7q35jq49c47U/jmHSVFbcl882uu/vlZVB13h9xTHcnHTseqMznnnsjTbi14NsPx3JY7W9WfBiizztC1DDdc79A01eUP2B7yNVbyQiIe2Ovmq5NXrhITafiKBzLS9+Gd7S0tU+tCS4JoQQQoj8oWkqOLP5PdVLCsCjIiTeyJ6keLuSFVUvuvLNoHxTKNsYHF0LdMkPFDgf/n5dXe70DnR6s3DXYy0mE1wLvBVQW5/9/5XJvoQ6Obio5tsOLredbrseHgwXd6jH2Niq0sb2r1u/z19+CDsM8x8DNBVY87PCBx5L3DyvJtVmpOR/QEIAKsslLkWPm6M9Lo52eR448CAZRhMfrj/Jwv1XABUo+7xfQ5wd7EhMy2Dv+Wj+PRfFrjNRhMWl3vHYsh5OdKrlzVs9aue59C5fRZ2B+V1UAAlUif+4ffk30XLLNNj7HdTpDc8tzJ/nyK20BPiqppoK/tJ2qPCf4Qz7Z6svn8rUUgNTrPElzZHFsHacKmefdFz1jC0Evwdc5v21J9DZ2rBiTGua5HKggKZpTN94mnn/XgRgTMdqvNmjVp6nj8al6Hl+/gFO3kjA282RP0a3JvByDG/+eRxNg4Et/Pj0mQYPHFRgNGn0mrGHkzcSeLFNZT7oXc/iNe06G8WwBYHobG3YPLkD1bzy+D7n9h61LUfBk1/mbX/3celmMo9/swuDSWPRSH/a1XjAsJ5HjATXhBBCCJG/MlJh3wzY/Y36sAFquEP5W4G0ck3V5QdNVCwq9s2Ef95Vl7t+oEpeiyOjAa7sUQG10xuyp1yCCqRV76r65tXoZl6Z0bVD8O9XcHZj9m21n4IOb0C5xlZbvlVpGizoAVf3q6y7vvMKdz2HFsCGyaox+8vbwVf62zyMFh+4wrS1JzCYNOqUdcfNScfhK7EYTNkfuxzsbGlZxZOONb3oWMuLGt6ueQ445LvUWBVYi7kAFduoctDLu6Fyexi23voZv5oG3zdUw4OKSr/CP1+C4BWqT2fPr7NvNxlV+WrsJej5DbQYaZ3nM+jhh8ZqgFGvH6DZMOvs10yapvHKkiP8FXyD8iWd+evVdg8c3mEwmnh7VTArgq4B8M6TtRnVIe+ll5likvUMnBfA2YgkPF0csvolDmlVkQ9718/1BNC9528y+KcD6Gxt+Gdyh6ypm+bIMJp44vvdnI9MYkTbKrzfywpfPJ3aAMszJ+PawIhNULFV3vebg/9bf4Jf9l6mtq8bf73a3qqZxMWdBNeEEEIIUTASbqjMJq9aKkutqH9AvJ/dX8O2D9Xl7tOh9bjCXQ+oIGZ60v17omWkqMtxV+HsJtXXLpOjO9TsAXV7Q7Uu9++Rlxs3jqvm4qfWZd9Wo7sKshVWVlhOglfCnyNVUHFCELiXK9z1aBosHaQClF51YNTOB5b6iOLpwMVoxi4+nPWBH6BKGRcVTKvphX9VzwKbBmoVJiMs7g8XtoGHH7y8Q2Wv/dhGfbmSH4Gfa4fgpy5g76JKQvP6u8sazm+DRX3BuRS8diY7k+zMRlUq6+QBU06pjF9ryfzix7MavHLwgT3u8ktCWga9ZuzhSnQKXev4MH9ozv3X0jKMTFh6hC0nI7CztWF63wYMaG7ZoJL7iUpMZ+C8gKzpvsPbVub9p+qaHajOLOfsVteHeUObm/XYDKOJaetOsORAKKVK2LPz9c55zz41mVQ5dEQwlCgNKdGqHHn07nz7mxGbrKfjlztISDPwRb+GFg2WiU5K51psqtUmyxYVElwTQgghhLDEjk9h1+fqcs+vVYZCQTAaIOai6lsXcSL79N9Sztxw9oTaPVUpVdWO+VNKFHlKBSND/lRDLgCqdlI92Sq1K/yeYvoUmNlcZXw89p4K/hUFSVEwuzUkR4H/WHjis8JekcgnV2NS+HnPJap5u9KxhhcVSxeB4JCl/pkK+35QZaAjN0PZRur2zMCPo7sqhbRmAHvTO7B/FtR/Fp792Xr7zQuTEb6tD4nXYcBC9aUFwG+94dIuNein20fWfc70RPWcaXEw4HeVeVxIQsLi6fuj6r/2Xs86vNT+7nLghLQMXv7tEAcuxeCgs2XmoCZ0q+ebb2uKSEhj2toTNPIryZiOVS3KAD0XkUiP73djNGksH9UK/6q5m2R+MymdcYsPE3hJfaH1zYBG9G2awyRZc5xcC38MVdPox/wLP3eH5Ej1d+yx9/K+/xzM//cin/x9Cm83R3a83gkXM/ribQy+wXtrQtDZ2fDPpI5Fu7zdTBJcE0IIIYSwhKbB1g9Unx+ABv1V/zFbncoYsNWpvmOZ123ssi/rHG+dnNW3yzmd2+rUpL2sIFoIRJ0GQ1rO67LPoR/a7ScnD9Uwv1JbsCugrJjoC6o0+PgyMBnUbR5+UL+v+lDs26Bwshl3TIddn6l+gK8EqomnRcXZf2DJramH1mh8LkR+OrYcVo9Sl59doAbYZDIZ1bCOsCCo9SQMXGKd491kgm/rqSDWwCXqy4KiIrMPXK0nYdBS9UXDj63U34WJx1QGt7Vt/1hlDJdrorIGCzFDfOH+K0xdE4LO1oY/xrSm6W3916IS0xm2IJCTNxJwc9Qxf1hzWuUyUFXY3l0dzOIDoTSs4MGacW0fWFYafC2e0QsPcT0+DVdHHd8+15jHzZgSnCOTCWa3gahT0PFN6PxOdrDNVgejdoFv/bw/zz2kG4w8/s2/hMakMLFLDSY/XvOBj4lN1vP+uhOsP3YdgNq+bswe0owqFgxWKKokuCaEEEIIYSlNg01vw4HZBfu89iXAuw741AOf+urcq44qQSrsTLAHib0Ce7+H439kNzsHVcpS/1lo8CyUtl6/nfuKu6qy1gxp0P83qNenYJ7XHH+9Bgd/Uo3Ke/+ggrJ2DmBrD3aZJwf1YcrOQZ0cSli33EyIBwk7rPoWGtPVNOWu0+7eJuKkKmEzZcCzv6jAel5d2Qe/PKEy4t44X2iN/O8p8jT86K+OzdfOwPaPIOhXqNMLnluUP8+ZfFNlrxlSYeg6lZFcSDRNY8LSI2w4foNyHk789Wp7Srk4cDUmhRd+PsDl6BTKuDrw6/CW1C/vUWjrNFdUYjqdvtxBst7I9wMb83Tj8jluu+ZIGG/+eZx0g4kqZVyYP7QZ1T0drPNzGvInrByhJqtPOg7OJdXtywarPqrlmqjhPPn0Bdpfx28wfslhnO3t2PF6J3xjD6l+i3V63bXt5hPhvLs6hJtJ6djZ2jCuUzUmPFYDB10Rf79iJgmuCSGEEELkhabByTUQfV5lZ5iMoBlVdtYd12+7zZiueqQZ0iAjTX0QMtx+261zQ7rKbrg9iOZTD0pVKfpBtAfJSIVz/6h+Z2c3q3+TTGUbqUBb/b7gYYXSmZysHKE+oFRqBy9uKJp9APUpahLczbPmPa5WT+jwmhoaIsSDJEfDtYOqZNvcXk2J4TCvs8oeq9kDBi7N+fdTZqZoiTKqL1gJz7yt+6/X4eB8aPQ8PFPAX3LkxrxOcP0IdHxLfalgSIUX/4bKbfPvOTP/Tap2hqFr8u95ciExLYPeM/dy6WYyj9X25o3utRi2IJDIxHQqlHJm4Uj/Ypm5NGvHeb7cfIbyJZ3Z9lpHnOzv7G9nMJr4bONpftpzCYDOtbz47rlGeOz5CPbPgS5Toe1EyxdgMsKPreHmmbsnlyfcgFn+kB4P3T6GNhMsf5770DSNZ+cEEHQllol1k5l8eZwKnN+WQRqXoueDdSdYc1Rlq9XwduXrAY1oWKFkvqypsElwTQghhBBCFK60BDj9F4SshAs7VDAyU8U2UKMruHipzLysk6c6t7Rpc2bGCzYw+l8o29AqLyVfRJyEjf+DtHgwZqgPMEa96r9n1N+6npF93+2qdoYOr6sS4KIYPBR3M5luDR9JunMwSebAEls7qP649Rr3R52Bhc+ovoOuPtB6PDQbnrspwYZ0+PUpuBaosk9f2qrKznPcXq+CxZEnoeFA6DvX8nUbDfBNbdWXcPBKqPG45fvKLwfmwcY3VCmoZlLl76N35++xGHsZfmiqfo+O2lXoU5pPXI/nmR/3oTeYsLezIcOoUcvHjd9HtsTHvXgOaknVG3ns653ciE/jzR61GdspO9s6NlnPhKVH2HP+JgCvdK7O5E5+2K0ZnT3gx8ZWBVkrtbZsAcdXwKqXwKmkylr77zF3+HdYN0G1mBi3Dzzv7nlnDYdDYxny43b+cniHKrbh6kYXLxh3gG2hBt5aFUxUYjq2NjC6YzUmdqlxVyDyYSLBNSGEEEIIUXQk31SZgCGr4MreB2+vc74z6GbvDNx625r19lW78zKogELiDWj2IvT63qovoVBpGtw8B3u+hePLswOVfq1UkK16VwmyFTVnNsKOTyApUgXQMpIf/Bif+ipDpFSlvD33tUOw+FlVzpUZAAL1Yb3Fy+A/Bly97v1YTVMf4I8sVNu/vCN3Jd3XguDnruq58hIUu7gTfn9aHfevn1Ml0kVNcjR8XSs76P30j9BkcP4/758vQfAKqPcM9P81f57DaICAGSqY9/hH9w3GLjkQyjurgwFoVqkUC4a1KPaN7P8MusZrK47h5qhj5xudKO3qyKkbCYxaeIirMamUcLDjq/6NeLKqvZoQe+2gKtsv1wSuHlA9R8fsVj+/5jAaVLlx9Hl4bKr6vf5fmga/94ZL/0KVDqpEOJ9+7+/7ZiBtEjYSbVcGT88y2ESd5oj7YzwTqYY8VfNy4av+jWhS0czXWQxJcE0IIYQQQhRN8WFwYjWEB6sJeKmxkBKjzlNj78xws4STB7wSlHPwoLjL7G93ZFF22W3ZRmpSa+1exb+0uLgzGlQfrsyhKHexUUNJHF3vHFISdQZSbkKJ0moSpaUlhue3wvIXVJZc+WYqWHd+K+z5DqLPqW10TtB0KLR+5e5A3oG5KqPSxhYGr1CB29za/C4EzAT3CjB+Pzi6mb/+da/C4d/U+nrPMP/xBSWzB1aJMjD5hOXZtuYID4E5bdX/zSuHrN/HMuGGCuBd2aOuV2wNQ/7MsdejpmnM2XWRm0npvN6tFs4OxT97yWTS6DVzDyeuJzC0dSX8q5Tm9RXHSM0w4ufpzPyhzamti4TF/VQA0qmkOsbKNoQ57SH2EtTto4Kf5gS+ji6FNWNU9vak4zkfOzEX4cc2qhS59wx1nFhbyCpYORyTZsMg/Xv0bF6N54NHoMPE+IxXKd/2eaY8XvOhzla7nQTXhBBCCCFE8aNpkJ6YHWhLjVGBN6P+1ga3PqxkfWi5x/VyTaBM9QJcdCFJDId9M+DQAhVIAShTC9pPUb3tMhtem4yQnqDKdNPisy+n37qumcC7rgrQZTbPFpZJjFA9/zKDE/5joMmQ26b8uqoszHt96I4Pg2WD4MYx1Sy/59cqA9McwSth9WjVB7LaYypI5+iq7jOZVDBozzeqXxioaccN+kO7SWqYysVdqpRUM6qspbavmvf8+mQ16TD2ssqQ6/mVeY83ZsBXNdSx/8IaqNbZvMcXpKuBKnPpsfeg+YiCe95Fz8L5LarEt9d31tvvxZ0qsJYcpX5ObWzV74gqHeH55UVr4nI+23fhJs/PP4CtDZhuRUva1yjDjEFNKBkVpI7T1FgoWUkFH8vUUBuFBcHP3dTxZ07gy2hQQ3hiL0HXD6Dd5AcscAb8854aejD+ALiXtfi13iUuFGa3g/R49pZ7kcEXuwEwWbeCibrVGBxLoZsQCK7e1nvOIk6Ca0IIIYQQQjwKkqPVZNsD81Sza1D9cWzt1YdjfVLu91WqigqylWsMZRury3ltTv+ouLwXVg6HpAgVnOg9w/zJmfoUWDseTqxS11uOgu6f5q40cv8c2HSrAXr9Z6HPbNA53L2dpqmysj3fqIBKpppPwNX9KmjQ8Dl4Zq5lJWcXd6nSNYDhG6FSm9w/9txWlRHk4gVTTufbRMRi7fIe+LUn2DnCpGBw88nb/kxG+PdL2PkZoKnS5P6/qZ+DhX3U748a3dQk1KI0tTWfvfTbQbaeigRgVIeq/K97LXSnVsPqMerLnvLNYNDyuzOk93wLWz9Q079H7QKvmg9+siOL1HFfogxMPJYdEM+J0aBKsK8fgdpPwcDFlr3I/zIZ1c9WaABUaEH8wHU8/v0+opLSGdmqAu+EjcM2MkQ953OLHplWBBJcE0IIIYQQ4lGSFg8Hf4KAH1V54X/pnFTJrKO76qOUedlkgPDjKmPhXkpWzA60eddRZVBO7tn7cXRXzfgfVZqmynS3fagyvrzqwIDfc/ehOqf97f4Ktn+srlfpoIIdOQU5NU1tu/tWlljL0dDjs9yVB4cdVsGAU+vJ6ltYrokKiuUlU2ndBNV8vXR1GLM39yWTq8fCsSXQ4iWVuSfupmnw8+Oq11e7ySrTyVJJkbDq5ewga9Oh8MQX2f/3l/fCon6qBLH2U6rUsSj2wMsH1+NS+XLzGbrW8aFnA19V5r31A3Vn7aeg7/x7Dx8xmWDh0yqA7dtQDQO5X1DSmAEzmqrfv+ZMAQ0PUUNETAb1+6bu0+a+xLvt/Bx2fgoObqpvnGcVohLTSdUbqVi6hGrlMK+z6jXYdz40HJD35ywGJLgmhBBCCCHEo0ifAjeOZgfTMoNo98piul1KjCpJvHEUrh9Vl2Mv5e45HVzvDLY5uatSSJ2Tavatc1SZNjqHW+e3Tpn3uXiroE5es3AyJUXCjePq+crUUiVM+ZFlkRoHa8bBmb/U9YbPwVPf5tijyiyn/4JVo1TmUKkqMGgZeNe+cxujAf6aonqUgSpRbP+6+a/15jnY9wPEX4PeM8GjfN7WnhoHs/whKRzaTYGu0+7eRtNU/6jrR9TpxjEI3a8+uL/4t+U95x4FpzbA8sHqWJsccv9Jrjm5vAdWjlT/R/Yl1M9to4F3b3dhByx5TvV3rNcX+v30aAXTjQb4+3UI+kVdbzVOBcHu92+QcEOVR6fGqL6G3T/JedugX2H9RPU7cOIx86YFb/9YZR26eMMrgeYPUbhd6H41aVsz3T9wtutL2PGx+pkbZ+WS1CJKgmtCCCGEEEKIvEmNVUGqzKBbzMXb+rUlZA9UsBb38irIVr4plGuqLj+oD1xKTHaA5voRFRhMuHbnNk4e4FUbytQEr1rZlz38LB8AceMY/DFU9Rezc4AnPld9sKwZxIs4ofp6xYWqbJJ+P0GtHuq+jDT4c6Tqo2ZjCz2/gebDrffceXX6L1j2vOrr9vJ29X+QFUg7CtePZZcx3863oSqnk8EcOTOZ1GTJm2eh6/+pnnnmPHbPN2qSrWZSx0L/3+4O3N7u7GY1wMGUAY0Gqemoj8L/T3oirHhRDQTBRmWEthqTu8ee2aiOXYDBf0KNewwGMehV1lr8Veg+HVqPM299hnSY0079HDQeAn1mmff4TGnxqs9afCg0HAh95+a8rdEAP3VRx3CN7qof30NeHirBNSGEEEIIIUT+MqTfORwhPTE78KZPVsE3Q7rqUZR1nqY+VN5+X+xlNS2Te3w08ax2K9jWRAXcTAa4fjg7UBN7+R4Ls1FNxo0ZEHdFBRHuxb6EKl30qqWak7v53jqVVeeuPneXwWmaKnn8+w31GjwqwoDf1BrzQ3K0CuJd2aNeV9dpqoH+0ufVbXYO0O9nqNs7f54/L1a8qCYDY8M9/2/tHMG3vvq/LdtYnXvVll5ruZHZp8vVR2UalfBUk2adPXMuw02OhtWjbgWLUIGynl/nLtPy5Dr1/6kZVRD5qW8LJ6iSlqCO+dhL6jzmksq4LNsIWo+3Xo/I60dh3SuqFFLnDM/+DLV7mrePv16Hg/NVD8Gx++4eAnDwZ5V56uoLE49aVoodegAWdAc0y4aAaJoK0of8CaUqw+jdKvP4fiJPwdwO6nf307PU0JaHmATXhBBCCCGEEMVHeqLKBgvLDJwdziFwdg+e1W4F326dyjYERzd1X0YaRJ+Hm2dUAC/qjMr0uHlOZeI8iIuX+vCbGXhLjVXZYqAyN56Zk/9DH4wZsPF/ajIsqPKv1FiVzTZoierLVhQlRcKPrVUPQDsH8Kl3ZyDNu84j08PL6gx6+L4RJF6/+z57FxVoK+F5Z9Dt9AZICFPl2k9+pYIi5gTIgleqiaJo4D8WekzP/ePT4lUPt+RIlWlpY6dKK21s1Snr8m23J0ep4NntwbSU6Jyfw8FNZZa1Hm95iWTYYdj1BZzdqK67eKnsrPLNzN9XRirMfwwiT0L1rvD8iuyMv4w0lbWWEAZPfAn+oyxbL6hAf+A89QVBv5+hQvPc/78cXQJrxqoJxSM2q8fmxp7vYOs0VZo8LgA8Kli8/KJOgmtCCCGEEEKI4i0lRgXZwm4F264fVR8Cy98eSGtk2Qdpo0F9WM8MuiVch8QbkBiuJn4mhuccfLOxVf3N2k4u2PK4gz/B3/9T2UMuXjDkT/X6i7LECPXv6VX7wX3/hHnObVFBjpSbKuiUEqN+Nu6ndHVVBupb37LnPLIY1t4qX2w7SQ1UuFcgx5gBYUGqZ9vFHXDt0IPXllslSqs+hKUqg2cVdSwcWaiyzEAFfFqPh1Zjc9+P7loQ7PoMzv2jrtvYqqm7XaaqoS6WijgJ8zurjN3bSz8PzIONb6hS+AmHcz/0417SE1UQO/6quu5dVw2naPjc/QP/0RdgTnvISIYu70P713L/nCajypi7dhCqdoYXVj+05aESXBNCCCGEEEIIS5lMqiF54g0VIMoMvKXGQp1eUKl14azryj4IWaU+pHtWLZw1iKJJ01SGWEq0+jnNDLilRKuToxu0fDk7q9NSmeWMAJ3ehk5vqeeOvqACaRd2qGmZ+sQ7H+dZTfU61Ey3TkYVpMm8nnX51u3OpVTwLDOQlnm6V9miyaQGi+yYDpEn1G1OHmr6pv+YnF/z1UDY9Xl2qayNLTQYAB1eV6Xl1hA4Xw1FsHNQ00PL1IQfmqjfKT2/VtNx8yr6ghpucGKNmu4Kquy6Ti9oNgwqtbvziwCDHhZ0U1nCldvD0LXmD6q4eU71fDOkqTLh5iPy/jqKIAmuCSGEEEIIIYQQwvoCfoTNb6vLNbqpPlyZmVOZnD2hakeV2VStc94ywHLLZIJTa2HnZxB1+tY6SkGbV6HlKHB0VbeF7lfbXNyhrtvYqWmp7V+D0tWsuyZNUwMhzvwFpWuo59n+kRqoMiFITUy2ltQ4CF6hJghnZvKBClA2fQEaD1bl7Vumwd7v1L/NmL2WTwjO/Dmwd4Fx+1Tw8yEjwTUhhBBCCCGEEELkj91fw7YPs6/bOYCfP1R7TAXTfBsV3lRRk1EN09j5GUSfU7eVKK0CbFf2qsw6UGXmjQZB+yn5mwmaHA1z2qpstUy9vodmL+bfc14/ooavHF+RnUVoY6cCnhd2ABo8t0hlt1nKZIJfe0LovlsZcOseukmyElwTQgghhBBCCCFE/gn6DWIuQOUOUKkNOJQo7BXdyWRUgxh2fQYxF7Nvt9WpLK72Uwou2+rSv/Bbb0BTwwcmBBXMQA99sioXPfw7XN2ffXuz4dDru7zvP+YizG4LGSnwxBfgPzrv+yxCJLgmhBBCCCGEEEIIYTTA8eVwfJkqzWw3qWDKVP9r15ew4xPo/yvU61Pwzx95Wg1/yEiBbp9YLxia2VdO5wxj91q/tLYQSXBNCCGEEEIIIYQQoigxpFu3z1pRYDLBwj7gXg56TLdsgnMRldt4lK4A1ySEEEIIIYQQQgjx6HrYAmug+qw9/wfYOxX2SgrNw9VpTgghhBBCCCGEEEIUrEc4sAYSXBNCCCGEEEIIIYQQwmISXBNCCCGEEEIIIYQQwkISXBNCCCGEEEIIIYQQwkISXBNCCCGEEEIIIYQQwkISXBNCCCGEEEIIIYQQwkIWBddmzZpF5cqVcXJywt/fn8DAwPtuv2LFCmrXro2TkxMNGjTg77//tmixQgghhBBCCCGEEEIUJWYH15YvX86UKVOYNm0ahw8fplGjRnTv3p3IyMh7br9v3z4GDRrEyJEjOXLkCH369KFPnz6EhITkefFCCCGEEEIIIYQQQhQmG03TNHMe4O/vT4sWLZg5cyYAJpMJPz8/JkyYwFtvvXXX9s899xzJycls2LAh67ZWrVrRuHFj5syZk6vnTEhIwMPDg/j4eNzd3c1ZrhBCCCGEEEIIIYQQZsttPMqszDW9Xk9QUBBdu3bN3oGtLV27diUgIOCejwkICLhje4Du3bvnuD1Aeno6CQkJd5yEEEIIIYQQQgghhChqzAqu3bx5E6PRiI+Pzx23+/j4EB4efs/HhIeHm7U9wPTp0/Hw8Mg6+fn5mbNMIYQQQgghhBBCCCEKRJGcFvr2228THx+fdbp69WphL0kIIYQQQgghhBBCiLvozNm4TJky2NnZERERccftERER+Pr63vMxvr6+Zm0P4OjoiKOjozlLE0IIIYQQQgghhBCiwJmVuebg4ECzZs3Ytm1b1m0mk4lt27bRunXrez6mdevWd2wPsGXLlhy3F0IIIYQQQgghhBCiuDArcw1gypQpDBs2jObNm9OyZUu+++47kpOTGT58OABDhw6lfPnyTJ8+HYCJEyfSsWNHvv76a3r27MmyZcs4dOgQ8+bNs+4rEUIIIYQQQgghhBCigJkdXHvuueeIiori/fffJzw8nMaNG7Np06asoQWhoaHY2mYnxLVp04YlS5bw3nvv8c4771CjRg3WrFlD/fr1rfcqhBBCCCGEEEIIIYQoBDaapmmFvYgHSUhIwMPDg/j4eNzd3Qt7OUIIIYQQQgghhBDiIZfbeFSRnBYqhBBCCCGEEEIIIURxIME1IYQQQgghhBBCCCEsZHbPtcKQWbmakJBQyCsRQgghhBBCCCGEEI+CzDjUgzqqFYvgWmJiIgB+fn6FvBIhhBBCCCGEEEII8ShJTEzEw8Mjx/uLxUADk8nE9evXcXNzw8bGprCXYzUJCQn4+flx9epVGdQgRD6R40yI/CfHmRD5T44zIQqGHGtC5L/idJxpmkZiYiLlypXD1jbnzmrFInPN1taWChUqFPYy8o27u3uR/4ESoriT40yI/CfHmRD5T44zIQqGHGtC5L/icpzdL2Mtkww0EEIIIYQQQgghhBDCQhJcE0IIIYQQQgghhBDCQhJcK0SOjo5MmzYNR0fHwl6KEA8tOc6EyH9ynAmR/+Q4E6JgyLEmRP57GI+zYjHQQAghhBBCCCGEEEKIokgy14QQQgghhBBCCCGEsJAE14QQQgghhBBCCCGEsJAE14QQQgghhBBCCCGEsJAE14QQQgghhBBCCCGEsJAE1wrJrFmzqFy5Mk5OTvj7+xMYGFjYSxKi2Jo+fTotWrTAzc0Nb29v+vTpw5kzZ+7YJi0tjfHjx1O6dGlcXV3p168fERERhbRiIYq/zz77DBsbGyZNmpR1mxxnQlhHWFgYQ4YMoXTp0jg7O9OgQQMOHTqUdb+mabz//vuULVsWZ2dnunbtyrlz5wpxxUIUL0ajkalTp1KlShWcnZ2pVq0aH330EbfP+pPjTAjz/Pvvv/Tq1Yty5cphY2PDmjVr7rg/N8dUTEwMgwcPxt3dnZIlSzJy5EiSkpIK8FVYToJrhWD58uVMmTKFadOmcfjwYRo1akT37t2JjIws7KUJUSzt2rWL8ePHs3//frZs2UJGRgbdunUjOTk5a5vJkyezfv16VqxYwa5du7h+/Tp9+/YtxFULUXwdPHiQuXPn0rBhwztul+NMiLyLjY2lbdu22Nvbs3HjRk6ePMnXX39NqVKlsrb54osv+OGHH5gzZw4HDhzAxcWF7t27k5aWVogrF6L4+Pzzz5k9ezYzZ87k1KlTfP7553zxxRfMmDEjaxs5zoQwT3JyMo0aNWLWrFn3vD83x9TgwYM5ceIEW7ZsYcOGDfz777+MGjWqoF5C3miiwLVs2VIbP3581nWj0aiVK1dOmz59eiGuSoiHR2RkpAZou3bt0jRN0+Li4jR7e3ttxYoVWducOnVKA7SAgIDCWqYQxVJiYqJWo0YNbcuWLVrHjh21iRMnapomx5kQ1vLmm29q7dq1y/F+k8mk+fr6al9++WXWbXFxcZqjo6O2dOnSgliiEMVez549tREjRtxxW9++fbXBgwdrmibHmRB5BWirV6/Oup6bY+rkyZMaoB08eDBrm40bN2o2NjZaWFhYga3dUpK5VsD0ej1BQUF07do16zZbW1u6du1KQEBAIa5MiIdHfHw8AJ6engAEBQWRkZFxx3FXu3ZtKlasKMedEGYaP348PXv2vON4AjnOhLCWdevW0bx5c/r374+3tzdNmjRh/vz5WfdfunSJ8PDwO441Dw8P/P395VgTIpfatGnDtm3bOHv2LADHjh1jz549PPHEE4AcZ0JYW26OqYCAAEqWLEnz5s2ztunatSu2trYcOHCgwNdsLl1hL+BRc/PmTYxGIz4+Pnfc7uPjw+nTpwtpVUI8PEwmE5MmTaJt27bUr18fgPDwcBwcHChZsuQd2/r4+BAeHl4IqxSieFq2bBmHDx/m4MGDd90nx5kQ1nHx4kVmz57NlClTeOeddzh48CCvvvoqDg4ODBs2LOt4utd7STnWhMidt956i4SEBGrXro2dnR1Go5FPPvmEwYMHA8hxJoSV5eaYCg8Px9vb+477dTodnp6exeK4k+CaEOKhMn78eEJCQtizZ09hL0WIh8rVq1eZOHEiW7ZswcnJqbCXI8RDy2Qy0bx5cz799FMAmjRpQkhICHPmzGHYsGGFvDohHg5//PEHixcvZsmSJdSrV4+jR48yadIkypUrJ8eZEMIiUhZawMqUKYOdnd1d09MiIiLw9fUtpFUJ8XB45ZVX2LBhAzt27KBChQpZt/v6+qLX64mLi7tjeznuhMi9oKAgIiMjadq0KTqdDp1Ox65du/jhhx/Q6XT4+PjIcSaEFZQtW5a6devecVudOnUIDQ0FyDqe5L2kEJZ74403eOuttxg4cCANGjTghRdeYPLkyUyfPh2Q40wIa8vNMeXr63vXkEeDwUBMTEyxOO4kuFbAHBwcaNasGdu2bcu6zWQysW3bNlq3bl2IKxOi+NI0jVdeeYXVq1ezfft2qlSpcsf9zZo1w97e/o7j7syZM4SGhspxJ0QudenSheDgYI4ePZp1at68OYMHD866LMeZEHnXtm1bzpw5c8dtZ8+epVKlSgBUqVIFX1/fO461hIQEDhw4IMeaELmUkpKCre2dH4Xt7OwwmUyAHGdCWFtujqnWrVsTFxdHUFBQ1jbbt2/HZDLh7+9f4Gs2l5SFFoIpU6YwbNgwmjdvTsuWLfnuu+9ITk5m+PDhhb00IYql8ePHs2TJEtauXYubm1tWTb6HhwfOzs54eHgwcuRIpkyZgqenJ+7u7kyYMIHWrVvTqlWrQl69EMWDm5tbVh/DTC4uLpQuXTrrdjnOhMi7yZMn06ZNGz799FMGDBhAYGAg8+bNY968eQDY2NgwadIkPv74Y2rUqEGVKlWYOnUq5cqVo0+fPoW7eCGKiV69evHJJ59QsWJF6tWrx5EjR/jmm28YMWIEIMeZEJZISkri/PnzWdcvXbrE0aNH8fT0pGLFig88purUqUOPHj14+eWXmTNnDhkZGbzyyisMHDiQcuXKFdKrMkNhjyt9VM2YMUOrWLGi5uDgoLVs2VLbv39/YS9JiGILuOfpl19+ydomNTVVGzdunFaqVCmtRIkS2jPPPKPduHGj8BYtxEOgY8eO2sSJE7Ouy3EmhHWsX79eq1+/vubo6KjVrl1bmzdv3h33m0wmberUqZqPj4/m6OiodenSRTtz5kwhrVaI4ichIUGbOHGiVrFiRc3JyUmrWrWq9u6772rp6elZ28hxJoR5duzYcc/PZMOGDdM0LXfHVHR0tDZo0CDN1dVVc3d314YPH64lJiYWwqsxn42maVohxfWEEEIIIYQQQgghhCjWpOeaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAWkuCaEEIIIYQQQgghhBAW+n9bmMQpPFtGhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1250x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [12.50, 5.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(1, 1)\n",
    "\n",
    "plt.plot(r.history['loss'],label='train loss')\n",
    "plt.plot(r.history['val_loss'],label='val loss')\n",
    "\n",
    "plt.plot(r.history['accuracy'],label='train acc')\n",
    "plt.plot(r.history['val_accuracy'],label='val acc')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wt0-8mj1yY0z"
   },
   "source": [
    "### Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oAxRgYVvY4AL"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model.save('/content/drive/MyDrive/Colab Notebooks/Naruto vs Sasuke/model_resnet.h5')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyPQ4xrilIpEx9I74NqZLvvT",
   "gpuType": "T4",
   "mount_file_id": "1Z6Yum5SLTAKmhbt7KV1vA7Twz9JjXp1w",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}